From eb4e9aff9a153f6e8451ca11761728bdc3edd038 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ern=C5=91=20Horv=C3=A1th?= <horverno@gmail.com>
Date: Sat, 20 Oct 2018 15:20:34 +0200
Subject: [PATCH 0001/1080] First working copy of the LIDAR to 2D grid map
 example

---
 Mapping/lidar_to_grid_map/animation.gif       | Bin 0 -> 285406 bytes
 .../lidar_to_grid_map/grid_map_example.png    | Bin 0 -> 380419 bytes
 .../lidar_to_grid_map/lidar_to_grid_map.py    | 174 ++++++++++++
 .../lidar_to_grid_map_tutorial.ipynb          | 253 ++++++++++++++++++
 README.md                                     |   7 +
 5 files changed, 434 insertions(+)
 create mode 100644 Mapping/lidar_to_grid_map/animation.gif
 create mode 100644 Mapping/lidar_to_grid_map/grid_map_example.png
 create mode 100644 Mapping/lidar_to_grid_map/lidar_to_grid_map.py
 create mode 100644 Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb

diff --git a/Mapping/lidar_to_grid_map/animation.gif b/Mapping/lidar_to_grid_map/animation.gif
new file mode 100644
index 0000000000000000000000000000000000000000..210ff5817cf5f97c7fc2c855fcd47ae81c4269a7
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diff --git a/Mapping/lidar_to_grid_map/grid_map_example.png b/Mapping/lidar_to_grid_map/grid_map_example.png
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diff --git a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
new file mode 100644
index 0000000000..d350f8ec76
--- /dev/null
+++ b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
@@ -0,0 +1,174 @@
+"""
+
+LIDAR to 2D grid map example
+
+author: Erno Horvath, Csaba Hajdu based on Atsushi Sakai's scripts (@Atsushi_twi)
+
+"""
+
+import math
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+from math import cos, sin, radians, pi
+from scipy.ndimage.morphology import binary_closing, binary_fill_holes
+
+EXTEND_AREA = 1.0
+
+def file_read(f):
+    """
+    Reading LIDAR laser beams (angles and corresponding distance data)
+    """
+    measures = [line.split(",") for line in open(f)]
+    angles = []
+    distances = []
+    for measure in measures:
+        angles.append(float(measure[0]))
+        distances.append(float(measure[1]))
+    angles = np.array(angles)
+    distances = np.array(distances)
+    return angles, distances
+
+def bresenham(start, end):
+    """
+    Implementation of Bresenham's line drawing algorithm
+    See en.wikipedia.org/wiki/Bresenham's_line_algorithm
+    Bresenham's Line Algorithm
+    Produces a np.array from start and end (original from roguebasin.com)
+    >>> points1 = bresenham((4, 4), (6, 10))
+    >>> print(points1)
+    np.array([[4,4], [4,5], [5,6], [5,7], [5,8], [6,9], [6,10]])
+    """
+    # setup initial conditions
+    x1, y1 = start
+    x2, y2 = end
+    dx = x2 - x1
+    dy = y2 - y1
+    is_steep = abs(dy) > abs(dx) # determine how steep the line is
+    if is_steep: # rotate line
+        x1, y1 = y1, x1
+        x2, y2 = y2, x2
+    swapped = False # swap start and end points if necessary and store swap state
+    if x1 > x2:
+        x1, x2 = x2, x1
+        y1, y2 = y2, y1
+        swapped = True
+    dx = x2 - x1 # recalculate differentials
+    dy = y2 - y1 # recalculate differentials
+    error = int(dx / 2.0) # calculate error
+    ystep = 1 if y1 < y2 else -1
+    # iterate over bounding box generating points between start and end
+    y = y1
+    points = []
+    for x in range(x1, x2 + 1):
+        coord = [y, x] if is_steep else (x, y)
+        points.append(coord)   
+        error -= abs(dy)
+        if error < 0:
+            y += ystep
+            error += dx
+    if swapped: # reverse the list if the coordinates were swapped
+        points.reverse()
+    points = np.array(points)
+    return points
+
+def calc_grid_map_config(ox, oy, xyreso):
+    """
+    Calculates the size, and the maximum distances according to the the measurement center
+    """
+    minx = round(min(ox) - EXTEND_AREA / 2.0)
+    miny = round(min(oy) - EXTEND_AREA / 2.0)
+    maxx = round(max(ox) + EXTEND_AREA / 2.0)
+    maxy = round(max(oy) + EXTEND_AREA / 2.0)
+    xw = int(round((maxx - minx) / xyreso))
+    yw = int(round((maxy - miny) / xyreso))
+    print("The grid map is ", xw, "x", yw, ".")
+    return minx, miny, maxx, maxy, xw, yw
+
+def atan_zero_to_twopi(y, x):
+    angle = math.atan2(y, x)
+    if angle < 0.0:
+        angle += math.pi * 2.0
+    return angle
+
+def generate_ray_casting_grid_map(ox, oy, xyreso, yawreso, breshen = True):
+    """
+    The breshen boolean tells if it's computed with bresenham ray casting (True) or with flood fill (False)
+    """
+    minx, miny, maxx, maxy, xw, yw = calc_grid_map_config(ox, oy, xyreso)
+    pmap = np.ones((xw, yw))/2 # default 0.5 -- [[0.5 for i in range(yw)] for i in range(xw)] 
+    centix = int(round(-minx / xyreso)) # center x coordinate of the grid map
+    centiy = int(round(-miny / xyreso)) # center y coordinate of the grid map
+    #print(centix, centiy)
+    prev_ix, prev_iy = centix - 1, centiy
+    # occupancy grid computed with bresenham ray casting
+    if breshen:
+        for (x, y) in zip(ox, oy):
+            d = math.sqrt(x**2 + y**2)
+            angle = atan_zero_to_twopi(y, x)
+            angleid = int(math.floor(angle / yawreso))
+            ix = int(round((x - minx) / xyreso)) # x coordinte of the the occupied area
+            iy = int(round((y - miny) / xyreso)) # y coordinte of the the occupied area
+            laser_beams = bresenham((centix, centiy), (ix, iy)) # line form the lidar to the cooupied point
+            for laser_beam in laser_beams:
+                pmap[laser_beam[0]][laser_beam[1]] = 0.0 # free area 0.0
+            pmap[ix][iy] = 1.0     # occupied area 1.0
+            pmap[ix+1][iy] = 1.0   # extend the occupied area
+            pmap[ix][iy+1] = 1.0   # extend the occupied area
+            pmap[ix+1][iy+1] = 1.0 # extend the occupied area
+    # occupancy grid computed with with flood fill
+    else:
+        pmap = (np.ones((xw, yw), dtype=np.uint8)) * 5 # food fill does not work with float numbers such as 0.5; so 5 is the default and later it is divided by 10
+        for (x, y) in zip(ox, oy):        
+            ix = int(round((x - minx) / xyreso)) # x coordinte of the the occupied area
+            iy = int(round((y - miny) / xyreso)) # y coordinte of the the occupied area
+            free_area = bresenham((prev_ix, prev_iy), (ix, iy))
+            for fa in free_area:
+                pmap[fa[0]][fa[1]] = 0 # free area 0.0                  
+            prev_ix = ix
+            prev_iy = iy
+        cv2.floodFill(pmap, None, (centix, centiy), 0) # filling the free spaces with 0, stating from the center
+        pmap = np.array(pmap, dtype=np.float)
+        pmap /= 10
+        for (x, y) in zip(ox, oy):        
+            ix = int(round((x - minx) / xyreso))
+            iy = int(round((y - miny) / xyreso))
+            pmap[ix][iy] = 1.0     # occupied area 1.0
+            pmap[ix+1][iy] = 1.0   # extend the occupied area
+            pmap[ix][iy+1] = 1.0   # extend the occupied area
+            pmap[ix+1][iy+1] = 1.0 # extend the occupied area
+    return pmap, minx, maxx, miny, maxy, xyreso
+
+def main():
+    """
+    Example usage
+    """
+    print(__file__, "start")
+    xyreso = 0.02  # x-y grid resolution
+    yawreso = math.radians(3.1)  # yaw angle resolution [rad]
+    ang, dist = file_read("lidar01.csv")
+    ox = np.sin(ang) * dist
+    oy = np.cos(ang) * dist
+    pmap, minx, maxx, miny, maxy, xyreso = generate_ray_casting_grid_map(ox, oy, xyreso, yawreso, True)
+    xyres = np.array(pmap).shape
+    plt.figure(1, figsize=(10,4))
+    plt.subplot(122)
+    plt.imshow(pmap, cmap = "PiYG_r") # cmap = "binary" "PiYG_r" "PiYG_r" "bone" "bone_r" "RdYlGn_r"
+    plt.clim(-0.4, 1.4)
+    plt.gca().set_xticks(np.arange(-.5, xyres[1], 1), minor = True)
+    plt.gca().set_yticks(np.arange(-.5, xyres[0], 1), minor = True)
+    plt.grid(True, which="minor", color="w", linewidth = .6, alpha = 0.5)
+    plt.colorbar()
+    plt.subplot(121)
+    plt.plot([oy, np.zeros(np.size(oy))], [ox, np.zeros(np.size(oy))], "ro-")
+    plt.axis("equal")
+    plt.plot(0.0, 0.0, "ob")
+    plt.gca().set_aspect("equal", "box")
+    bottom, top = plt.ylim()  # return the current ylim
+    plt.ylim((top, bottom)) # rescale y axis, to match the grid orientation
+    plt.grid(True)
+    plt.show()
+        
+if __name__ == '__main__':
+    main()     
+        
diff --git a/Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb b/Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb
new file mode 100644
index 0000000000..6f8ef5ac95
--- /dev/null
+++ b/Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb
@@ -0,0 +1,253 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LIDAR to 2D grid map example\n",
+    "\n",
+    "This simple tutorial shows how to read LIDAR (range) measurements from a file and convert it to occupancy grid.\n",
+    "\n",
+    "Occupancy grid maps (_Hans Moravec, A.E. Elfes: High resolution maps from wide angle sonar, Proc. IEEE Int. Conf. Robotics Autom. (1985)_) are a popular, probabilistic approach to represent the environment. The grid is basically discrete representation of the environment, which shows if a grid cell is occupied or not. Here the map is represented as a `numpy array`, and numbers close to 1 means the cell is occupied (_marked with red on the next image_), numbers close to 0 means they are free (_marked with green_). The grid has the ability to represent unknown (unobserved) areas, which are close to 0.5.\n",
+    "\n",
+    "![Example](grid_map_example.png)\n",
+    "\n",
+    "\n",
+    "In order to construct the grid map from the measurement we need to discretise the values. But, first let's need to `import` some necessary packages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "import numpy as np\n",
+    "import cv2\n",
+    "import matplotlib.pyplot as plt\n",
+    "from math import cos, sin, radians, pi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The measurement file contains the distances and the corresponding angles in a `csv` (comma separated values) format. Let's write the `file_read` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def file_read(f):\n",
+    "    \"\"\"\n",
+    "    Reading LIDAR laser beams (angles and corresponding distance data)\n",
+    "    \"\"\"\n",
+    "    measures = [line.split(\",\") for line in open(f)]\n",
+    "    angles = []\n",
+    "    distances = []\n",
+    "    for measure in measures:\n",
+    "        angles.append(float(measure[0]))\n",
+    "        distances.append(float(measure[1]))\n",
+    "    angles = np.array(angles)\n",
+    "    distances = np.array(distances)\n",
+    "    return angles, distances"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the distances and the angles it is easy to determine the `x` and `y` coordinates with `sin` and `cos`. \n",
+    "In order to display it `matplotlib.pyplot` (`plt`) is used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ang, dist = file_read(\"lidar01.csv\")\n",
+    "ox = np.sin(ang) * dist\n",
+    "oy = np.cos(ang) * dist\n",
+    "plt.figure(figsize=(6,10))\n",
+    "plt.plot([oy, np.zeros(np.size(oy))], [ox, np.zeros(np.size(oy))], \"ro-\") # lines from 0,0 to the \n",
+    "plt.axis(\"equal\")\n",
+    "bottom, top = plt.ylim()  # return the current ylim\n",
+    "plt.ylim((top, bottom)) # rescale y axis, to match the grid orientation\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `lidar_to_grid_map.py` contains handy functions which can used to convert a 2D range measurement to a grid map. For example the `bresenham`  gives the a straight line between two points in a grid map. Let's see how this works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import lidar_to_grid_map as lg\n",
+    "map1 = np.ones((50, 50), dtype=np.uint8)\n",
+    "line = lg.bresenham((2, 2), (40, 30))\n",
+    "for l in line:\n",
+    "    map1[l[0]][l[1]] = 2\n",
+    "plt.imshow(map1)\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "line = lg.bresenham((2, 30), (40, 30))\n",
+    "for l in line:\n",
+    "    map1[l[0]][l[1]] = 2\n",
+    "line = lg.bresenham((2, 30), (2, 2))\n",
+    "for l in line:\n",
+    "    map1[l[0]][l[1]] = 2    \n",
+    "plt.imshow(map1)\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `cv2.floodFill` can be used to fill the connected area on a grid map."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cv2.floodFill(map1, None, (10, 20), 0)\n",
+    "plt.imshow(map1)\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The grid map is  150 x 100 .\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xyreso = 0.02  # x-y grid resolution\n",
+    "yawreso = math.radians(3.1)  # yaw angle resolution [rad]\n",
+    "ang, dist = file_read(\"lidar01.csv\")\n",
+    "ox = np.sin(ang) * dist\n",
+    "oy = np.cos(ang) * dist\n",
+    "pmap, minx, maxx, miny, maxy, xyreso = lg.generate_ray_casting_grid_map(ox, oy, xyreso, yawreso, False)\n",
+    "xyres = np.array(pmap).shape\n",
+    "plt.figure(figsize=(20,8))\n",
+    "plt.subplot(122)\n",
+    "plt.imshow(pmap, cmap = \"PiYG_r\") \n",
+    "plt.clim(-0.4, 1.4)\n",
+    "plt.gca().set_xticks(np.arange(-.5, xyres[1], 1), minor = True)\n",
+    "plt.gca().set_yticks(np.arange(-.5, xyres[0], 1), minor = True)\n",
+    "plt.grid(True, which=\"minor\", color=\"w\", linewidth = .6, alpha = 0.5)\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/README.md b/README.md
index 6f680e9a62..712e5d35c1 100644
--- a/README.md
+++ b/README.md
@@ -20,6 +20,7 @@ Python codes for robotics algorithm.
    * [Mapping](#mapping)
       * [Gaussian grid map](#gaussian-grid-map)
       * [Ray casting grid map](#ray-casting-grid-map)
+      * [Lidar to grid map](#lidar-to-grid-map)	  
       * [k-means object clustering](#k-means-object-clustering)
    * [SLAM](#slam)
       * [Iterative Closest Point (ICP) Matching](#iterative-closest-point-icp-matching)
@@ -177,6 +178,12 @@ This is a 2D ray casting grid mapping example.
 
 ![2](https://github.com/AtsushiSakai/PythonRobotics/raw/master/Mapping/raycasting_grid_map/animation.gif)
 
+## Lidar to grid map
+
+This example shows how to convert a 2D range measurement to a grid map.
+
+![2](Mapping/lidar_to_grid_map/animation.gif)
+
 ## k-means object clustering
 
 This is a 2D object clustering with k-means algorithm.

From 87df2b4c53e1cc699afd8143a18e57e7a44f84e4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ern=C5=91=20Horv=C3=A1th?= <horverno@gmail.com>
Date: Sat, 20 Oct 2018 15:29:31 +0200
Subject: [PATCH 0002/1080] Example lidar measurement file

---
 Mapping/lidar_to_grid_map/lidar01.csv | 68 +++++++++++++++++++++++++++
 1 file changed, 68 insertions(+)
 create mode 100644 Mapping/lidar_to_grid_map/lidar01.csv

diff --git a/Mapping/lidar_to_grid_map/lidar01.csv b/Mapping/lidar_to_grid_map/lidar01.csv
new file mode 100644
index 0000000000..da88c4c74e
--- /dev/null
+++ b/Mapping/lidar_to_grid_map/lidar01.csv
@@ -0,0 +1,68 @@
+0.008450416037156572,0.5335
0.046902201120156306,0.5345
0.08508127850753233,0.537
0.1979822644959155,0.2605
0.21189035697274505,0.2625
0.2587960806200922,0.26475
0.3043382657893199,0.2675
0.34660795861105775,0.27075
0.43632879047139106,0.59
0.4739624524675188,0.60025
0.5137777760286397,0.611
0.5492297764597742,0.6265
0.5895905154121426,0.64
0.6253152235389017,0.6565
0.6645851317087743,0.676
0.6997644244442851,0.6975
0.7785769484796541,0.3345
0.7772134100015329,0.74575
0.8652979956881222,0.3315
0.8996591653367609,0.31775
0.9397471965935056,0.31275
0.9847439663714841,0.31125
1.0283771976713423,0.31325
1.0641019057981014,0.31975
1.1009174447073562,0.3335
1.2012738766970301,0.92275
1.2397256617800307,0.95325
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1.8257744996762515,1.0025
1.8661352386286207,0.96075
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1.9465840088377355,0.8855
1.986944747790103,0.85725
2.025669240568728,0.832
2.065757271825472,0.80675
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2.1865667809869542,0.75625
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2.3510095214482956,0.59925
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+5.1863514328522085,0.69975
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+5.502419652080556,0.6835
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+6.176825783358991,0.5325
+6.215822983833238,0.53125
+6.252911230438118,0.53075
\ No newline at end of file

From 934d5f50b3c5b00a7aa8d33788f86e8bb1f3a1a7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ern=C5=91=20Horv=C3=A1th?= <horverno@gmail.com>
Date: Sat, 20 Oct 2018 15:38:09 +0200
Subject: [PATCH 0003/1080] Example lidar measurement file fix

---
 Mapping/lidar_to_grid_map/lidar01.csv | 88 ++++++++++++++++++++++++++-
 1 file changed, 87 insertions(+), 1 deletion(-)

diff --git a/Mapping/lidar_to_grid_map/lidar01.csv b/Mapping/lidar_to_grid_map/lidar01.csv
index da88c4c74e..1f6b3f5cd9 100644
--- a/Mapping/lidar_to_grid_map/lidar01.csv
+++ b/Mapping/lidar_to_grid_map/lidar01.csv
@@ -1,4 +1,90 @@
-0.008450416037156572,0.5335
0.046902201120156306,0.5345
0.08508127850753233,0.537
0.1979822644959155,0.2605
0.21189035697274505,0.2625
0.2587960806200922,0.26475
0.3043382657893199,0.2675
0.34660795861105775,0.27075
0.43632879047139106,0.59
0.4739624524675188,0.60025
0.5137777760286397,0.611
0.5492297764597742,0.6265
0.5895905154121426,0.64
0.6253152235389017,0.6565
0.6645851317087743,0.676
0.6997644244442851,0.6975
0.7785769484796541,0.3345
0.7772134100015329,0.74575
0.8652979956881222,0.3315
0.8996591653367609,0.31775
0.9397471965935056,0.31275
0.9847439663714841,0.31125
1.0283771976713423,0.31325
1.0641019057981014,0.31975
1.1009174447073562,0.3335
1.2012738766970301,0.92275
1.2397256617800307,0.95325
1.2779047391674068,0.9865
1.316629231946031,1.01775
1.3561718478115274,1.011
1.3948963405901518,1.0055
1.4330754179775278,1.00225
1.4731634492342724,0.99975
1.5113425266216485,0.9975
1.5517032655740168,1.001
1.5896096352657691,1.00275
1.6288795434356418,1.008
1.6684221593011381,1.0135
1.7066012366885142,1.022
1.7453257294671385,1.02875
1.7862318838107551,0.9935
1.8257744996762515,1.0025
1.8661352386286207,0.96075
1.9045870237116205,0.92125
1.9465840088377355,0.8855
1.986944747790103,0.85725
2.025669240568728,0.832
2.065757271825472,0.80675
2.1066634261690886,0.78875
2.1464787497302105,0.7705
2.1865667809869542,0.75625
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2.2683790896741876,0.68275
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3.0005992524249336,0.36575
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3.115409192282687,0.408
3.154679100452558,0.4265
3.184949654666836,0.447
3.2242195628367085,0.4715
3.2574899017028507,0.49875
3.2959416867858504,0.52875
3.3292120256519926,0.564
3.3665729799524957,0.6055
3.4031158111661277,0.6515
3.438022396206014,0.70675
3.4756560582021407,0.771
3.513562427893893,0.77075
3.5522869206725183,0.7785
3.621827383056667,0.79575
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+0.3043382657893199,0.2675
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+0.6253152235389017,0.6565
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+0.6997644244442851,0.6975
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+3.4756560582021407,0.771
+3.513562427893893,0.77075
+3.5522869206725183,0.7785
+3.621827383056667,0.79575
 3.65918833735717,0.8045
 3.697367414744546,0.81725
 3.7377281536969154,0.83325

From affcc3f1fded2e26c325f3ad3fd198e40d2cf26f Mon Sep 17 00:00:00 2001
From: horverno <horverno@gmail.com>
Date: Thu, 29 Nov 2018 14:07:23 +0100
Subject: [PATCH 0004/1080] Deleted scipy import

---
 Mapping/lidar_to_grid_map/lidar_to_grid_map.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
index d350f8ec76..cbe50e9770 100644
--- a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
+++ b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
@@ -11,7 +11,6 @@
 import cv2
 import matplotlib.pyplot as plt
 from math import cos, sin, radians, pi
-from scipy.ndimage.morphology import binary_closing, binary_fill_holes
 
 EXTEND_AREA = 1.0
 

From 2d90bd5a52cdd58b635e95ec5d5ef3d59a239caf Mon Sep 17 00:00:00 2001
From: Takayuki Murooka <takayuki5168@gmail.com>
Date: Fri, 25 Jan 2019 02:01:51 +0900
Subject: [PATCH 0005/1080] add n_joint_arm_3d & implement forward kinematics

---
 ArmNavigation/n_joint_arm_3d/NLinkArm.py      | 94 +++++++++++++++++++
 .../n_joint_arm_3d/random_joint_angles.py     | 20 ++++
 2 files changed, 114 insertions(+)
 create mode 100644 ArmNavigation/n_joint_arm_3d/NLinkArm.py
 create mode 100644 ArmNavigation/n_joint_arm_3d/random_joint_angles.py

diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm.py b/ArmNavigation/n_joint_arm_3d/NLinkArm.py
new file mode 100644
index 0000000000..d50f9d5c1b
--- /dev/null
+++ b/ArmNavigation/n_joint_arm_3d/NLinkArm.py
@@ -0,0 +1,94 @@
+import numpy as np
+import math
+from mpl_toolkits.mplot3d import Axes3D
+import matplotlib.pyplot as plt
+
+class Link:
+    def __init__(self, dh_params):
+        self.dh_params_ = dh_params
+
+    def calc_transformation_matrix(self):
+        theta = self.dh_params_[0]
+        alpha = self.dh_params_[1]
+        a = self.dh_params_[2]
+        d = self.dh_params_[3]
+
+        trans = np.array(
+            [[math.cos(theta), -math.sin(theta), 0, a],
+             [math.cos(alpha) * math.sin(theta), math.cos(alpha) * math.cos(theta), -math.sin(alpha), -d * math.sin(alpha)],
+             [math.sin(alpha) * math.sin(theta), math.sin(alpha) * math.cos(theta), math.cos(alpha), d * math.cos(alpha)],
+             [0, 0, 0, 1]])
+
+        return trans
+
+class NLinkArm:
+    def __init__(self, dh_params_list):
+        self.link_list = []
+        for i in range(len(dh_params_list)):
+            self.link_list.append(Link(dh_params_list[i]))
+
+        self.fig = plt.figure()
+        self.ax = Axes3D(self.fig)
+
+    def calc_transformation_matrix(self):
+        trans = np.array([[1, 0, 0, 0],
+                             [0, 1, 0, 0],
+                             [0, 0, 1, 0],
+                             [0, 0, 0, 1]])
+        for i in range(len(self.link_list)):
+            trans = np.dot(trans, self.link_list[i].calc_transformation_matrix())
+            
+        return trans
+    
+    def forward_kinematics(self):
+        trans = self.calc_transformation_matrix()
+
+        x = trans[0, 3]
+        y = trans[1, 3]
+        z = trans[2, 3]
+        alpha = math.atan2(trans[1, 2], trans[1, 3])
+        beta = math.atan2(trans[0, 2] * math.cos(alpha) + trans[1, 2] * math.sin(alpha), trans[2, 2])
+        gamma = math.atan2(-trans[0, 0] * math.sin(alpha) + trans[1, 0] * math.cos(alpha), -trans[0, 1] * math.sin(alpha) + trans[1, 1] * math.cos(alpha))
+
+        return [x, y, z, alpha, beta, gamma]
+
+    def set_joint_angles(self, joint_angle_list):
+        for i in range(len(self.link_list)):
+            self.link_list[i].dh_params_[0] = joint_angle_list[i]
+        
+    def plot(self):
+        x_list = []
+        y_list = []
+        z_list = []
+
+        trans = np.identity(4)
+        
+        x_list.append(trans[0, 3])
+        y_list.append(trans[1, 3])
+        z_list.append(trans[2, 3])
+        for i in range(len(self.link_list)):
+            trans = np.dot(trans, self.link_list[i].calc_transformation_matrix())
+            x_list.append(trans[0, 3])
+            y_list.append(trans[1, 3])
+            z_list.append(trans[2, 3])
+            
+        self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4, mew=0.5)
+        self.ax.plot([0], [0], [0], "o")
+        
+        self.ax.set_xlim(-1, 1)
+        self.ax.set_ylim(-1, 1)
+        self.ax.set_zlim(-1, 1)        
+        plt.show()
+        
+if __name__ == "__main__":
+    n_link_arm = NLinkArm([[0., -math.pi/2, .1, 0.],
+                           [math.pi/2, math.pi/2, 0., 0.],
+                           [0., -math.pi/2, 0., .4],
+                           [0., math.pi/2, 0., 0.],
+                           [0., -math.pi/2, 0., .321],
+                           [0., math.pi/2, 0., 0.],
+                           [0., 0., 0., 0.]])
+
+    print(n_link_arm.forward_kinematics())
+    n_link_arm.set_joint_angles([1, 1, 1, 1, 1, 1, 1])
+    n_link_arm.plot()
diff --git a/ArmNavigation/n_joint_arm_3d/random_joint_angles.py b/ArmNavigation/n_joint_arm_3d/random_joint_angles.py
new file mode 100644
index 0000000000..54be155ef1
--- /dev/null
+++ b/ArmNavigation/n_joint_arm_3d/random_joint_angles.py
@@ -0,0 +1,20 @@
+import math
+from NLinkArm import NLinkArm
+import random
+import time
+
+def random_val(min_val, max_val):
+    return min_val + random.random() * (max_val - min_val)
+
+for i in range(10):
+    n_link_arm = NLinkArm([[0., -math.pi/2, .1, 0.],
+                           [math.pi/2, math.pi/2, 0., 0.],
+                           [0., -math.pi/2, 0., .4],
+                           [0., math.pi/2, 0., 0.],
+                           [0., -math.pi/2, 0., .321],
+                           [0., math.pi/2, 0., 0.],
+                           [0., 0., 0., 0.]])
+
+    n_link_arm.set_joint_angles([random_val(-1, 1) for j in range(len(n_link_arm.link_list))])
+    n_link_arm.plot()
+

From c49f982923cb415e80291ae7d743fd42532713d8 Mon Sep 17 00:00:00 2001
From: Takayuki Murooka <takayuki5168@gmail.com>
Date: Fri, 25 Jan 2019 21:39:32 +0900
Subject: [PATCH 0006/1080] implement inverse kinematics

---
 ArmNavigation/n_joint_arm_3d/NLinkArm.py      | 65 ++++++++++++++++---
 ...angles.py => random_forward_kinematics.py} |  0
 .../random_inverse_kinematics.py              | 17 +++++
 3 files changed, 72 insertions(+), 10 deletions(-)
 rename ArmNavigation/n_joint_arm_3d/{random_joint_angles.py => random_forward_kinematics.py} (100%)
 create mode 100644 ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py

diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm.py b/ArmNavigation/n_joint_arm_3d/NLinkArm.py
index d50f9d5c1b..f6b9c28788 100644
--- a/ArmNavigation/n_joint_arm_3d/NLinkArm.py
+++ b/ArmNavigation/n_joint_arm_3d/NLinkArm.py
@@ -7,7 +7,7 @@ class Link:
     def __init__(self, dh_params):
         self.dh_params_ = dh_params
 
-    def calc_transformation_matrix(self):
+    def transformation_matrix(self):
         theta = self.dh_params_[0]
         alpha = self.dh_params_[1]
         a = self.dh_params_[2]
@@ -21,6 +21,14 @@ def calc_transformation_matrix(self):
 
         return trans
 
+    def basic_jacobian(self, trans_prev, ee_pose):
+        pos_prev = np.array([trans_prev[0, 3], trans_prev[1, 3], trans_prev[2, 3]])
+        z_axis_prev = np.array([trans_prev[0, 2], trans_prev[1, 2], trans_prev[2, 2]])
+
+        basic_jacobian = np.hstack((np.cross(z_axis_prev, ee_pose - pos_prev), z_axis_prev))
+        return basic_jacobian
+        
+
 class NLinkArm:
     def __init__(self, dh_params_list):
         self.link_list = []
@@ -30,18 +38,14 @@ def __init__(self, dh_params_list):
         self.fig = plt.figure()
         self.ax = Axes3D(self.fig)
 
-    def calc_transformation_matrix(self):
-        trans = np.array([[1, 0, 0, 0],
-                             [0, 1, 0, 0],
-                             [0, 0, 1, 0],
-                             [0, 0, 0, 1]])
+    def transformation_matrix(self):
+        trans = np.identity(4)
         for i in range(len(self.link_list)):
-            trans = np.dot(trans, self.link_list[i].calc_transformation_matrix())
-            
+            trans = np.dot(trans, self.link_list[i].transformation_matrix())
         return trans
     
     def forward_kinematics(self):
-        trans = self.calc_transformation_matrix()
+        trans = self.transformation_matrix()
 
         x = trans[0, 3]
         y = trans[1, 3]
@@ -52,9 +56,50 @@ def forward_kinematics(self):
 
         return [x, y, z, alpha, beta, gamma]
 
+    def basic_jacobian(self, ref_ee_pose):
+        basic_jacobian_mat = []
+        
+        trans = np.identity(4)        
+        for i in range(len(self.link_list)):
+            trans = np.dot(trans, self.link_list[i].transformation_matrix())
+            basic_jacobian_mat.append(self.link_list[i].basic_jacobian(trans, ref_ee_pose[0:3]))
+            
+        #print(np.array(basic_jacobian_mat).T)
+        return np.array(basic_jacobian_mat).T
+
+    def inverse_kinematics(self, ref_ee_pose):
+        ee_pose = self.forward_kinematics()
+        diff_pose = ee_pose - np.array(ref_ee_pose)
+        
+        for cnt in range(1000):
+            basic_jacobian_mat = self.basic_jacobian(ref_ee_pose)
+            alpha, beta, gamma = self.calc_euler_angle()
+            
+            K_zyz = np.array([[0, -math.sin(alpha), math.cos(alpha) * math.sin(beta)],
+                              [0, math.cos(alpha), math.sin(alpha) * math.sin(beta)],
+                              [1, 0, math.cos(beta)]])
+            K_alpha = np.identity(6)
+            K_alpha[3:, 3:] = K_zyz
+
+            theta_dot = np.dot(np.dot(np.linalg.pinv(basic_jacobian_mat), K_alpha), np.array(diff_pose))
+            self.update_joint_angles(theta_dot)
+
+    def calc_euler_angle(self):
+        trans = self.transformation_matrix()
+        
+        alpha = math.atan2(trans[1][2], trans[0][2])
+        beta = math.atan2(trans[0][2] * math.cos(alpha) + trans[1][2] * math.sin(alpha), trans[2][2])
+        gamma = math.atan2(-trans[0][0] * math.sin(alpha) + trans[1][0] * math.cos(alpha), -trans[0][1] * math.sin(alpha) + trans[1][1] * math.cos(alpha))
+        
+        return alpha, beta, gamma
+    
     def set_joint_angles(self, joint_angle_list):
         for i in range(len(self.link_list)):
             self.link_list[i].dh_params_[0] = joint_angle_list[i]
+
+    def update_joint_angles(self, diff_joint_angle_list):
+        for i in range(len(self.link_list)):
+            self.link_list[i].dh_params_[0] += diff_joint_angle_list[i]
         
     def plot(self):
         x_list = []
@@ -67,7 +112,7 @@ def plot(self):
         y_list.append(trans[1, 3])
         z_list.append(trans[2, 3])
         for i in range(len(self.link_list)):
-            trans = np.dot(trans, self.link_list[i].calc_transformation_matrix())
+            trans = np.dot(trans, self.link_list[i].transformation_matrix())
             x_list.append(trans[0, 3])
             y_list.append(trans[1, 3])
             z_list.append(trans[2, 3])
diff --git a/ArmNavigation/n_joint_arm_3d/random_joint_angles.py b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py
similarity index 100%
rename from ArmNavigation/n_joint_arm_3d/random_joint_angles.py
rename to ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py
diff --git a/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py
new file mode 100644
index 0000000000..a728647547
--- /dev/null
+++ b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py
@@ -0,0 +1,17 @@
+import math
+from NLinkArm import NLinkArm
+import random
+import time
+
+
+n_link_arm = NLinkArm([[0., -math.pi/2, .1, 0.],
+                       [math.pi/2, math.pi/2, 0., 0.],
+                       [0., -math.pi/2, 0., .4],
+                       [0., math.pi/2, 0., 0.],
+                       [0., -math.pi/2, 0., .321],
+                       [0., math.pi/2, 0., 0.],
+                       [0., 0., 0., 0.]])
+
+#n_link_arm.inverse_kinematics([-0.621, 0., 0., 0., 0., math.pi / 2])
+n_link_arm.inverse_kinematics([-0.5, 0., 0.1, 0., 0., math.pi / 2])
+n_link_arm.plot()

From 3504833748d7e5ebb0d9b8ca82b4ab854bd25572 Mon Sep 17 00:00:00 2001
From: Takayuki Murooka <takayuki5168@gmail.com>
Date: Sat, 26 Jan 2019 02:23:53 +0900
Subject: [PATCH 0007/1080] fix bug of NLinkArm.py and modify random exapmle
 scripts

---
 ArmNavigation/n_joint_arm_3d/NLinkArm.py      | 106 +++++++++++++++---
 .../random_forward_kinematics.py              |  16 ++-
 .../random_inverse_kinematics.py              |  32 ++++--
 3 files changed, 121 insertions(+), 33 deletions(-)

diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm.py b/ArmNavigation/n_joint_arm_3d/NLinkArm.py
index f6b9c28788..e1d7369731 100644
--- a/ArmNavigation/n_joint_arm_3d/NLinkArm.py
+++ b/ArmNavigation/n_joint_arm_3d/NLinkArm.py
@@ -13,11 +13,21 @@ def transformation_matrix(self):
         a = self.dh_params_[2]
         d = self.dh_params_[3]
 
+        '''
         trans = np.array(
             [[math.cos(theta), -math.sin(theta), 0, a],
              [math.cos(alpha) * math.sin(theta), math.cos(alpha) * math.cos(theta), -math.sin(alpha), -d * math.sin(alpha)],
              [math.sin(alpha) * math.sin(theta), math.sin(alpha) * math.cos(theta), math.cos(alpha), d * math.cos(alpha)],
              [0, 0, 0, 1]])
+        '''
+        st = math.sin(theta)
+        ct = math.cos(theta)
+        sa = math.sin(alpha)
+        ca = math.cos(alpha)
+        trans = np.array([[ct, -st * ca, st * sa, a * ct],
+                          [st, ct * ca, -ct * sa, a * st],
+                          [0, sa, ca, d],
+                          [0, 0, 0, 1]])
 
         return trans
 
@@ -35,44 +45,66 @@ def __init__(self, dh_params_list):
         for i in range(len(dh_params_list)):
             self.link_list.append(Link(dh_params_list[i]))
 
-        self.fig = plt.figure()
-        self.ax = Axes3D(self.fig)
-
     def transformation_matrix(self):
         trans = np.identity(4)
         for i in range(len(self.link_list)):
             trans = np.dot(trans, self.link_list[i].transformation_matrix())
         return trans
     
-    def forward_kinematics(self):
+    def forward_kinematics(self, plot=False):
         trans = self.transformation_matrix()
 
         x = trans[0, 3]
         y = trans[1, 3]
         z = trans[2, 3]
-        alpha = math.atan2(trans[1, 2], trans[1, 3])
-        beta = math.atan2(trans[0, 2] * math.cos(alpha) + trans[1, 2] * math.sin(alpha), trans[2, 2])
-        gamma = math.atan2(-trans[0, 0] * math.sin(alpha) + trans[1, 0] * math.cos(alpha), -trans[0, 1] * math.sin(alpha) + trans[1, 1] * math.cos(alpha))
+        alpha, beta, gamma = self.calc_euler_angle()
+
+        if plot:
+            self.fig = plt.figure()
+            self.ax = Axes3D(self.fig)
+            
+            x_list = []
+            y_list = []
+            z_list = []
+    
+            trans = np.identity(4)
+            
+            x_list.append(trans[0, 3])
+            y_list.append(trans[1, 3])
+            z_list.append(trans[2, 3])
+            for i in range(len(self.link_list)):
+                trans = np.dot(trans, self.link_list[i].transformation_matrix())
+                x_list.append(trans[0, 3])
+                y_list.append(trans[1, 3])
+                z_list.append(trans[2, 3])
+                
+            self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4, mew=0.5)
+            self.ax.plot([0], [0], [0], "o")
+            
+            self.ax.set_xlim(-1, 1)
+            self.ax.set_ylim(-1, 1)
+            self.ax.set_zlim(-1, 1)
+  
+            plt.show()
 
         return [x, y, z, alpha, beta, gamma]
 
-    def basic_jacobian(self, ref_ee_pose):
+    def basic_jacobian(self, ee_pose):
         basic_jacobian_mat = []
         
         trans = np.identity(4)        
         for i in range(len(self.link_list)):
+            basic_jacobian_mat.append(self.link_list[i].basic_jacobian(trans, ee_pose[0:3]))
             trans = np.dot(trans, self.link_list[i].transformation_matrix())
-            basic_jacobian_mat.append(self.link_list[i].basic_jacobian(trans, ref_ee_pose[0:3]))
             
-        #print(np.array(basic_jacobian_mat).T)
         return np.array(basic_jacobian_mat).T
 
-    def inverse_kinematics(self, ref_ee_pose):
-        ee_pose = self.forward_kinematics()
-        diff_pose = ee_pose - np.array(ref_ee_pose)
+    def inverse_kinematics(self, ref_ee_pose, plot=False):
+        for cnt in range(500):
+            ee_pose = self.forward_kinematics()
+            diff_pose = np.array(ref_ee_pose) - ee_pose
         
-        for cnt in range(1000):
-            basic_jacobian_mat = self.basic_jacobian(ref_ee_pose)
+            basic_jacobian_mat = self.basic_jacobian(ee_pose)
             alpha, beta, gamma = self.calc_euler_angle()
             
             K_zyz = np.array([[0, -math.sin(alpha), math.cos(alpha) * math.sin(beta)],
@@ -82,12 +114,45 @@ def inverse_kinematics(self, ref_ee_pose):
             K_alpha[3:, 3:] = K_zyz
 
             theta_dot = np.dot(np.dot(np.linalg.pinv(basic_jacobian_mat), K_alpha), np.array(diff_pose))
-            self.update_joint_angles(theta_dot)
-
+            self.update_joint_angles(theta_dot / 100.)
+            
+        if plot:
+            self.fig = plt.figure()
+            self.ax = Axes3D(self.fig)
+            
+            x_list = []
+            y_list = []
+            z_list = []
+    
+            trans = np.identity(4)
+            
+            x_list.append(trans[0, 3])
+            y_list.append(trans[1, 3])
+            z_list.append(trans[2, 3])
+            for i in range(len(self.link_list)):
+                trans = np.dot(trans, self.link_list[i].transformation_matrix())
+                x_list.append(trans[0, 3])
+                y_list.append(trans[1, 3])
+                z_list.append(trans[2, 3])
+                
+            self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4, mew=0.5)
+            self.ax.plot([0], [0], [0], "o")
+            
+            self.ax.set_xlim(-1, 1)
+            self.ax.set_ylim(-1, 1)
+            self.ax.set_zlim(-1, 1)
+  
+            self.ax.plot([ref_ee_pose[0]], [ref_ee_pose[1]], [ref_ee_pose[2]], "o")
+            plt.show()
+                
     def calc_euler_angle(self):
         trans = self.transformation_matrix()
         
         alpha = math.atan2(trans[1][2], trans[0][2])
+        if -math.pi / 2 <= alpha and alpha <= math.pi / 2:
+            alpha = math.atan2(trans[1][2], trans[0][2]) + math.pi
+        if -math.pi / 2 <= alpha and alpha <= math.pi / 2:
+            alpha = math.atan2(trans[1][2], trans[0][2]) - math.pi
         beta = math.atan2(trans[0][2] * math.cos(alpha) + trans[1][2] * math.sin(alpha), trans[2][2])
         gamma = math.atan2(-trans[0][0] * math.sin(alpha) + trans[1][0] * math.cos(alpha), -trans[0][1] * math.sin(alpha) + trans[1][1] * math.cos(alpha))
         
@@ -102,6 +167,9 @@ def update_joint_angles(self, diff_joint_angle_list):
             self.link_list[i].dh_params_[0] += diff_joint_angle_list[i]
         
     def plot(self):
+        self.fig = plt.figure()
+        self.ax = Axes3D(self.fig)
+        
         x_list = []
         y_list = []
         z_list = []
@@ -119,6 +187,10 @@ def plot(self):
             
         self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4, mew=0.5)
         self.ax.plot([0], [0], [0], "o")
+
+        self.ax.set_xlabel("x")
+        self.ax.set_ylabel("y")
+        self.ax.set_zlabel("z")        
         
         self.ax.set_xlim(-1, 1)
         self.ax.set_ylim(-1, 1)
diff --git a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py
index 54be155ef1..df037fb5ea 100644
--- a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py
+++ b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py
@@ -1,12 +1,15 @@
 import math
 from NLinkArm import NLinkArm
 import random
-import time
 
 def random_val(min_val, max_val):
     return min_val + random.random() * (max_val - min_val)
 
-for i in range(10):
+
+if __name__ == "__main__":
+    print("Start solving Forward Kinematics 10 times")
+
+    # init NLinkArm with Denavit-Hartenberg parameters of PR2    
     n_link_arm = NLinkArm([[0., -math.pi/2, .1, 0.],
                            [math.pi/2, math.pi/2, 0., 0.],
                            [0., -math.pi/2, 0., .4],
@@ -14,7 +17,10 @@ def random_val(min_val, max_val):
                            [0., -math.pi/2, 0., .321],
                            [0., math.pi/2, 0., 0.],
                            [0., 0., 0., 0.]])
-
-    n_link_arm.set_joint_angles([random_val(-1, 1) for j in range(len(n_link_arm.link_list))])
-    n_link_arm.plot()
+    
+    for i in range(10):
+        n_link_arm.set_joint_angles([random_val(-1, 1) for j in range(len(n_link_arm.link_list))])
+        
+        ee_pose = n_link_arm.forward_kinematics(plot=True)
+        print(ee_pose)
 
diff --git a/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py
index a728647547..dd51381678 100644
--- a/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py
+++ b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py
@@ -1,17 +1,27 @@
 import math
 from NLinkArm import NLinkArm
 import random
-import time
 
 
-n_link_arm = NLinkArm([[0., -math.pi/2, .1, 0.],
-                       [math.pi/2, math.pi/2, 0., 0.],
-                       [0., -math.pi/2, 0., .4],
-                       [0., math.pi/2, 0., 0.],
-                       [0., -math.pi/2, 0., .321],
-                       [0., math.pi/2, 0., 0.],
-                       [0., 0., 0., 0.]])
+def random_val(min_val, max_val):
+    return min_val + random.random() * (max_val - min_val)
 
-#n_link_arm.inverse_kinematics([-0.621, 0., 0., 0., 0., math.pi / 2])
-n_link_arm.inverse_kinematics([-0.5, 0., 0.1, 0., 0., math.pi / 2])
-n_link_arm.plot()
+if __name__ == "__main__":
+    print("Start solving Inverse Kinematics 10 times")
+
+    # init NLinkArm with Denavit-Hartenberg parameters of PR2        
+    n_link_arm = NLinkArm([[0., -math.pi/2, .1, 0.],
+                           [math.pi/2, math.pi/2, 0., 0.],
+                           [0., -math.pi/2, 0., .4],
+                           [0., math.pi/2, 0., 0.],
+                           [0., -math.pi/2, 0., .321],
+                           [0., math.pi/2, 0., 0.],
+                           [0., 0., 0., 0.]])
+
+    for i in range(10):
+        n_link_arm.inverse_kinematics([random_val(-0.5, 0.5),
+                                       random_val(-0.5, 0.5),
+                                       random_val(-0.5, 0.5),
+                                       random_val(-0.5, 0.5),
+                                       random_val(-0.5, 0.5),
+                                       random_val(-0.5, 0.5)], plot=True)

From b543623cada7f6f425df6463b44027fdc4ec211d Mon Sep 17 00:00:00 2001
From: Takayuki Murooka <takayuki5168@gmail.com>
Date: Sat, 26 Jan 2019 02:32:49 +0900
Subject: [PATCH 0008/1080] minor change

---
 ArmNavigation/n_joint_arm_3d/NLinkArm.py | 20 --------------------
 1 file changed, 20 deletions(-)

diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm.py b/ArmNavigation/n_joint_arm_3d/NLinkArm.py
index e1d7369731..19b1a32a5b 100644
--- a/ArmNavigation/n_joint_arm_3d/NLinkArm.py
+++ b/ArmNavigation/n_joint_arm_3d/NLinkArm.py
@@ -13,13 +13,6 @@ def transformation_matrix(self):
         a = self.dh_params_[2]
         d = self.dh_params_[3]
 
-        '''
-        trans = np.array(
-            [[math.cos(theta), -math.sin(theta), 0, a],
-             [math.cos(alpha) * math.sin(theta), math.cos(alpha) * math.cos(theta), -math.sin(alpha), -d * math.sin(alpha)],
-             [math.sin(alpha) * math.sin(theta), math.sin(alpha) * math.cos(theta), math.cos(alpha), d * math.cos(alpha)],
-             [0, 0, 0, 1]])
-        '''
         st = math.sin(theta)
         ct = math.cos(theta)
         sa = math.sin(alpha)
@@ -196,16 +189,3 @@ def plot(self):
         self.ax.set_ylim(-1, 1)
         self.ax.set_zlim(-1, 1)        
         plt.show()
-        
-if __name__ == "__main__":
-    n_link_arm = NLinkArm([[0., -math.pi/2, .1, 0.],
-                           [math.pi/2, math.pi/2, 0., 0.],
-                           [0., -math.pi/2, 0., .4],
-                           [0., math.pi/2, 0., 0.],
-                           [0., -math.pi/2, 0., .321],
-                           [0., math.pi/2, 0., 0.],
-                           [0., 0., 0., 0.]])
-
-    print(n_link_arm.forward_kinematics())
-    n_link_arm.set_joint_angles([1, 1, 1, 1, 1, 1, 1])
-    n_link_arm.plot()

From 33e7b67db0130f6ec5277fdf7e630738d13fb2dd Mon Sep 17 00:00:00 2001
From: Takayuki Murooka <takayuki5168@gmail.com>
Date: Sat, 26 Jan 2019 03:06:27 +0900
Subject: [PATCH 0009/1080] minor changes

---
 ArmNavigation/n_joint_arm_3d/NLinkArm.py      | 21 ++++++++++---------
 .../random_forward_kinematics.py              |  1 -
 2 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm.py b/ArmNavigation/n_joint_arm_3d/NLinkArm.py
index 19b1a32a5b..d0e32a05da 100644
--- a/ArmNavigation/n_joint_arm_3d/NLinkArm.py
+++ b/ArmNavigation/n_joint_arm_3d/NLinkArm.py
@@ -24,11 +24,11 @@ def transformation_matrix(self):
 
         return trans
 
-    def basic_jacobian(self, trans_prev, ee_pose):
+    def basic_jacobian(self, trans_prev, ee_pos):
         pos_prev = np.array([trans_prev[0, 3], trans_prev[1, 3], trans_prev[2, 3]])
         z_axis_prev = np.array([trans_prev[0, 2], trans_prev[1, 2], trans_prev[2, 2]])
 
-        basic_jacobian = np.hstack((np.cross(z_axis_prev, ee_pose - pos_prev), z_axis_prev))
+        basic_jacobian = np.hstack((np.cross(z_axis_prev, ee_pos - pos_prev), z_axis_prev))
         return basic_jacobian
         
 
@@ -50,7 +50,7 @@ def forward_kinematics(self, plot=False):
         x = trans[0, 3]
         y = trans[1, 3]
         z = trans[2, 3]
-        alpha, beta, gamma = self.calc_euler_angle()
+        alpha, beta, gamma = self.euler_angle()
 
         if plot:
             self.fig = plt.figure()
@@ -82,12 +82,13 @@ def forward_kinematics(self, plot=False):
 
         return [x, y, z, alpha, beta, gamma]
 
-    def basic_jacobian(self, ee_pose):
+    def basic_jacobian(self):
+        ee_pos = self.forward_kinematics()[0:3]
         basic_jacobian_mat = []
         
         trans = np.identity(4)        
         for i in range(len(self.link_list)):
-            basic_jacobian_mat.append(self.link_list[i].basic_jacobian(trans, ee_pose[0:3]))
+            basic_jacobian_mat.append(self.link_list[i].basic_jacobian(trans, ee_pos))
             trans = np.dot(trans, self.link_list[i].transformation_matrix())
             
         return np.array(basic_jacobian_mat).T
@@ -97,8 +98,8 @@ def inverse_kinematics(self, ref_ee_pose, plot=False):
             ee_pose = self.forward_kinematics()
             diff_pose = np.array(ref_ee_pose) - ee_pose
         
-            basic_jacobian_mat = self.basic_jacobian(ee_pose)
-            alpha, beta, gamma = self.calc_euler_angle()
+            basic_jacobian_mat = self.basic_jacobian()
+            alpha, beta, gamma = self.euler_angle()
             
             K_zyz = np.array([[0, -math.sin(alpha), math.cos(alpha) * math.sin(beta)],
                               [0, math.cos(alpha), math.sin(alpha) * math.sin(beta)],
@@ -138,13 +139,13 @@ def inverse_kinematics(self, ref_ee_pose, plot=False):
             self.ax.plot([ref_ee_pose[0]], [ref_ee_pose[1]], [ref_ee_pose[2]], "o")
             plt.show()
                 
-    def calc_euler_angle(self):
+    def euler_angle(self):
         trans = self.transformation_matrix()
         
         alpha = math.atan2(trans[1][2], trans[0][2])
-        if -math.pi / 2 <= alpha and alpha <= math.pi / 2:
+        if not (-math.pi / 2 <= alpha and alpha <= math.pi / 2):
             alpha = math.atan2(trans[1][2], trans[0][2]) + math.pi
-        if -math.pi / 2 <= alpha and alpha <= math.pi / 2:
+        if not (-math.pi / 2 <= alpha and alpha <= math.pi / 2):
             alpha = math.atan2(trans[1][2], trans[0][2]) - math.pi
         beta = math.atan2(trans[0][2] * math.cos(alpha) + trans[1][2] * math.sin(alpha), trans[2][2])
         gamma = math.atan2(-trans[0][0] * math.sin(alpha) + trans[1][0] * math.cos(alpha), -trans[0][1] * math.sin(alpha) + trans[1][1] * math.cos(alpha))
diff --git a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py
index df037fb5ea..e94795213c 100644
--- a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py
+++ b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py
@@ -22,5 +22,4 @@ def random_val(min_val, max_val):
         n_link_arm.set_joint_angles([random_val(-1, 1) for j in range(len(n_link_arm.link_list))])
         
         ee_pose = n_link_arm.forward_kinematics(plot=True)
-        print(ee_pose)
 

From 47b497019a0bcedc2cd257ea8bd48942f2b616d5 Mon Sep 17 00:00:00 2001
From: Takayuki Murooka <takayuki5168@gmail.com>
Date: Sat, 26 Jan 2019 15:28:59 +0900
Subject: [PATCH 0010/1080] add header comment in each scripts

---
 ArmNavigation/n_joint_arm_3d/NLinkArm.py                  | 4 ++++
 ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py | 7 ++++++-
 ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py | 5 +++++
 3 files changed, 15 insertions(+), 1 deletion(-)

diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm.py b/ArmNavigation/n_joint_arm_3d/NLinkArm.py
index d0e32a05da..da562d9f17 100644
--- a/ArmNavigation/n_joint_arm_3d/NLinkArm.py
+++ b/ArmNavigation/n_joint_arm_3d/NLinkArm.py
@@ -1,3 +1,7 @@
+"""
+Class of n-link arm in 3D
+Author: Takayuki Murooka (takayuki5168)
+"""
 import numpy as np
 import math
 from mpl_toolkits.mplot3d import Axes3D
diff --git a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py
index e94795213c..87907f803b 100644
--- a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py
+++ b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py
@@ -1,3 +1,7 @@
+"""
+Forward Kinematics for an n-link arm in 3D
+Author: Takayuki Murooka (takayuki5168)
+"""
 import math
 from NLinkArm import NLinkArm
 import random
@@ -17,7 +21,8 @@ def random_val(min_val, max_val):
                            [0., -math.pi/2, 0., .321],
                            [0., math.pi/2, 0., 0.],
                            [0., 0., 0., 0.]])
-    
+
+    # execute FK 10 times
     for i in range(10):
         n_link_arm.set_joint_angles([random_val(-1, 1) for j in range(len(n_link_arm.link_list))])
         
diff --git a/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py
index dd51381678..44be22709f 100644
--- a/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py
+++ b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py
@@ -1,3 +1,7 @@
+"""
+Inverse Kinematics for an n-link arm in 3D
+Author: Takayuki Murooka (takayuki5168)
+"""
 import math
 from NLinkArm import NLinkArm
 import random
@@ -18,6 +22,7 @@ def random_val(min_val, max_val):
                            [0., math.pi/2, 0., 0.],
                            [0., 0., 0., 0.]])
 
+    # execute IK 10 times
     for i in range(10):
         n_link_arm.inverse_kinematics([random_val(-0.5, 0.5),
                                        random_val(-0.5, 0.5),

From a382689a30aeb822039c01609565c4260cb9e4b6 Mon Sep 17 00:00:00 2001
From: zks <zks>
Date: Sun, 24 Mar 2019 10:03:37 +0800
Subject: [PATCH 0011/1080] hybrid a star

---
 PathPlanning/HybridAStar/a_star.py        | 241 +++++++++++
 PathPlanning/HybridAStar/car.py           |  92 +++++
 PathPlanning/HybridAStar/hybrid_a_star.py | 470 ++++++++++++++++++++++
 3 files changed, 803 insertions(+)
 create mode 100644 PathPlanning/HybridAStar/a_star.py
 create mode 100644 PathPlanning/HybridAStar/car.py
 create mode 100644 PathPlanning/HybridAStar/hybrid_a_star.py

diff --git a/PathPlanning/HybridAStar/a_star.py b/PathPlanning/HybridAStar/a_star.py
new file mode 100644
index 0000000000..f5753edcc1
--- /dev/null
+++ b/PathPlanning/HybridAStar/a_star.py
@@ -0,0 +1,241 @@
+"""
+
+A* grid based planning
+
+author: Atsushi Sakai(@Atsushi_twi)
+        Nikos Kanargias (nkana@tee.gr)
+
+See Wikipedia article (https://en.wikipedia.org/wiki/A*_search_algorithm)
+
+"""
+
+import matplotlib.pyplot as plt
+import math
+import heapq
+
+_round = round
+def round(x):
+    return int(_round(x))
+
+show_animation = False
+
+class Node:
+
+    def __init__(self, x, y, cost, pind):
+        self.x = x
+        self.y = y
+        self.cost = cost
+        self.pind = pind
+
+    def __str__(self):
+        return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind)
+
+
+def calc_final_path(ngoal, closedset, reso):
+    # generate final course
+    rx, ry = [ngoal.x * reso], [ngoal.y * reso]
+    pind = ngoal.pind
+    while pind != -1:
+        n = closedset[pind]
+        rx.append(n.x * reso)
+        ry.append(n.y * reso)
+        pind = n.pind
+
+    return rx, ry
+
+
+def dp_planning(sx, sy, gx, gy, ox, oy, reso, rr):
+    """
+    gx: goal x position [m]
+    gx: goal x position [m]
+    ox: x position list of Obstacles [m]
+    oy: y position list of Obstacles [m]
+    reso: grid resolution [m]
+    rr: robot radius[m]
+    """
+
+    nstart = Node(round(sx / reso), round(sy / reso), 0.0, -1)
+    ngoal = Node(round(gx / reso), round(gy / reso), 0.0, -1)
+    ox = [iox / reso for iox in ox]
+    oy = [ioy / reso for ioy in oy]
+
+    obmap, minx, miny, maxx, maxy, xw, yw = calc_obstacle_map(ox, oy, reso, rr)
+
+    motion = get_motion_model()
+
+    openset, closedset = dict(), dict()
+    openset[calc_index(ngoal, xw, minx, miny)] = ngoal
+    pq = []
+    pq.append((0, calc_index(ngoal, xw, minx, miny)))
+    
+
+    while 1:
+        if not pq:
+            break
+        cost, c_id = heapq.heappop(pq)
+        if openset.has_key(c_id):
+            current = openset[c_id]
+            closedset[c_id] = current
+            openset.pop(c_id)
+        else:
+            continue
+
+        # show graph
+        if show_animation:  # pragma: no cover
+            plt.plot(current.x * reso, current.y * reso, "xc")
+            if len(closedset.keys()) % 10 == 0:
+                plt.pause(0.001)
+
+        # Remove the item from the open set
+        
+
+        # expand search grid based on motion model
+        for i, _ in enumerate(motion):
+            node = Node(current.x + motion[i][0],
+                        current.y + motion[i][1],
+                        current.cost + motion[i][2], c_id)
+            n_id = calc_index(node, xw, minx, miny)
+
+            if n_id in closedset:
+                continue
+
+            if not verify_node(node, obmap, minx, miny, maxx, maxy):
+                continue
+
+            if n_id not in openset:
+                openset[n_id] = node  # Discover a new node
+                heapq.heappush(pq, (node.cost, calc_index(node, xw, minx, miny)))
+            else:
+                if openset[n_id].cost >= node.cost:
+                    # This path is the best until now. record it!
+                    openset[n_id] = node
+                    heapq.heappush(pq, (node.cost, calc_index(node, xw, minx, miny)))
+
+
+    rx, ry = calc_final_path(closedset[calc_index(nstart, xw, minx, miny)], closedset, reso)
+
+    return rx, ry, closedset
+
+
+def calc_heuristic(n1, n2):
+    w = 1.0  # weight of heuristic
+    d = w * math.sqrt((n1.x - n2.x)**2 + (n1.y - n2.y)**2)
+    return d
+
+
+def verify_node(node, obmap, minx, miny, maxx, maxy):
+
+    if node.x < minx:
+        return False
+    elif node.y < miny:
+        return False
+    elif node.x >= maxx:
+        return False
+    elif node.y >= maxy:
+        return False
+
+    if obmap[node.x][node.y]:
+        return False
+
+    return True
+
+
+def calc_obstacle_map(ox, oy, reso, vr):
+
+    minx = round(min(ox))
+    miny = round(min(oy))
+    maxx = round(max(ox))
+    maxy = round(max(oy))
+    #  print("minx:", minx)
+    #  print("miny:", miny)
+    #  print("maxx:", maxx)
+    #  print("maxy:", maxy)
+
+    xwidth = round(maxx - minx)
+    ywidth = round(maxy - miny)
+    #  print("xwidth:", xwidth)
+    #  print("ywidth:", ywidth)
+
+    # obstacle map generation
+    obmap = [[False for i in range(xwidth)] for i in range(ywidth)]
+    for ix in range(xwidth):
+        x = ix + minx
+        for iy in range(ywidth):
+            y = iy + miny
+            #  print(x, y)
+            for iox, ioy in zip(ox, oy):
+                d = math.sqrt((iox - x)**2 + (ioy - y)**2)
+                if d <= vr / reso:
+                    obmap[ix][iy] = True
+                    break
+
+    return obmap, minx, miny, maxx, maxy, xwidth, ywidth
+
+
+def calc_index(node, xwidth, xmin, ymin):
+    return (node.y - ymin) * xwidth + (node.x - xmin)
+
+
+def get_motion_model():
+    # dx, dy, cost
+    motion = [[1, 0, 1],
+              [0, 1, 1],
+              [-1, 0, 1],
+              [0, -1, 1],
+              [-1, -1, math.sqrt(2)],
+              [-1, 1, math.sqrt(2)],
+              [1, -1, math.sqrt(2)],
+              [1, 1, math.sqrt(2)]]
+
+    return motion
+
+
+def main():
+    print(__file__ + " start!!")
+
+    # start and goal position
+    sx = 10.0  # [m]
+    sy = 10.0  # [m]
+    gx = 50.0  # [m]
+    gy = 50.0  # [m]
+    grid_size = 2.0  # [m]
+    robot_size = 1.0  # [m]
+
+    ox, oy = [], []
+
+    for i in range(60):
+        ox.append(i)
+        oy.append(0.0)
+    for i in range(60):
+        ox.append(60.0)
+        oy.append(i)
+    for i in range(61):
+        ox.append(i)
+        oy.append(60.0)
+    for i in range(61):
+        ox.append(0.0)
+        oy.append(i)
+    for i in range(40):
+        ox.append(20.0)
+        oy.append(i)
+    for i in range(40):
+        ox.append(40.0)
+        oy.append(60.0 - i)
+
+    if show_animation:  # pragma: no cover
+        plt.plot(ox, oy, ".k")
+        plt.plot(sx, sy, "xr")
+        plt.plot(gx, gy, "xb")
+        plt.grid(True)
+        plt.axis("equal")
+
+    rx, ry, _ = dp_planning(sx, sy, gx, gy, ox, oy, grid_size, robot_size)
+
+    if show_animation:  # pragma: no cover
+        plt.plot(rx, ry, "-r")
+        plt.show()
+
+
+if __name__ == '__main__':
+    show_animation = True
+    main()
\ No newline at end of file
diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py
new file mode 100644
index 0000000000..e7d35e9973
--- /dev/null
+++ b/PathPlanning/HybridAStar/car.py
@@ -0,0 +1,92 @@
+
+import matplotlib.pyplot as plt
+from math import sqrt, cos, sin, tan, pi
+
+WB = 3. # rear to front wheel
+W = 2. # width of car
+LF = 3.3 # distance from rear to vehicle front end
+LB = 1.0 # distance from rear to vehicle back end
+MAX_STEER = 0.6 #[rad] maximum steering angle 
+
+WBUBBLE_DIST = (LF-LB)/2.0
+WBUBBLE_R = sqrt(((LF+LB)/2.0)**2+1)
+
+# vehicle rectangle verticles
+VRX = [LF, LF, -LB, -LB, LF]
+VRY = [W/2,-W/2,-W/2,W/2,W/2]
+
+
+def check_car_collision(xlist, ylist, yawlist, ox, oy, kdtree):
+    for x, y, yaw in zip(xlist, ylist, yawlist):
+        cx = x + WBUBBLE_DIST*cos(yaw)
+        cy = y + WBUBBLE_DIST*sin(yaw)
+
+        ids = kdtree.search_in_distance([cx, cy], WBUBBLE_R)
+
+        if len(ids) == 0:
+            continue
+        
+        if not rectangle_check(x, y, yaw,
+                [ox[i] for i in ids], [oy[i] for i in ids]):
+            return False # collision
+    
+    return True # no collision
+
+def rectangle_check(x, y, yaw, ox, oy):
+    # transform obstacles to base link frame
+    c, s = cos(-yaw), sin(-yaw)
+    for iox, ioy in zip(ox, oy):
+        tx = iox - x
+        ty = ioy - y
+        rx = c*tx - s*ty
+        ry = s*tx + c*ty
+
+        if not (rx > LF or rx < -LB or ry > W/2.0 or ry < -W/2.0):
+            # print (x, y, yaw, iox, ioy, c, s, rx, ry)
+            return False # no collision
+    
+    return True # collision
+
+
+def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
+    """Plot arrow."""
+    if not isinstance(x, float):
+        for (ix, iy, iyaw) in zip(x, y, yaw):
+            plot_arrow(ix, iy, iyaw)
+    else:
+        plt.arrow(x, y, length * cos(yaw), length * sin(yaw),
+                  fc=fc, ec=ec, head_width=width, head_length=width, alpha=0.4)
+        # plt.plot(x, y)
+
+def plot_car(x, y, yaw):
+    car_color = '-k'
+    c, s = cos(yaw), sin(yaw)
+    
+    car_outline_x, car_outline_y = [], []
+    for rx, ry in zip(VRX, VRY):
+        tx = c*rx-s*ry + x
+        ty = s*rx+c*ry + y
+        car_outline_x.append(tx)
+        car_outline_y.append(ty)
+    
+    arrow_x, arrow_y, arrow_yaw = c*1.5+x, s*1.5+y, yaw
+    plot_arrow(arrow_x, arrow_y, arrow_yaw)
+    
+    plt.plot(car_outline_x, car_outline_y, car_color)
+
+def pi_2_pi(angle):
+    return (angle + pi) % (2 * pi) - pi
+
+def move(x, y, yaw, distance, steer, L=WB):
+    x += distance * cos(yaw)
+    y += distance * sin(yaw)
+    yaw += pi_2_pi(distance * tan(steer) / L) # distance/2
+
+    return x, y, yaw
+
+if __name__ == '__main__':
+    x, y, yaw = 0., 0., 1.
+    plt.axis('equal')
+    plot_car(x, y, yaw)
+    plt.show()
+    
\ No newline at end of file
diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
new file mode 100644
index 0000000000..35af0c94e6
--- /dev/null
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -0,0 +1,470 @@
+"""
+
+Hybrid A* path planning
+
+author: Atsushi Sakai (@Atsushi_twi)
+
+"""
+
+import sys
+sys.path.append("../ReedsSheppPath/")
+
+import math
+import numpy as np
+import scipy.spatial
+import matplotlib.pyplot as plt
+import reeds_shepp_path_planning as rs
+import heapq
+from car import move, check_car_collision, MAX_STEER, WB, plot_car
+from a_star import dp_planning, calc_obstacle_map
+
+XY_GRID_RESOLUTION = 2.0 #[m]
+YAW_GRID_RESOLUTION = np.deg2rad(15.0) #[rad]
+GOAL_TYAW_TH = np.deg2rad(5.0) #[rad]
+MOTION_RESOLUTION = 0.1 #[m] path interporate resolution
+N_STEER = 20.0 # number of steer command
+EXTEND_AREA = 0.0  # [m]
+H_COST = 1.0
+MOTION_RESOLUTION = 0.1
+SKIP_COLLISION_CHECK = 4
+VR = 1.0 # robot radius
+
+SB_COST = 100.0 # switch back penalty cost
+BACK_COST = 5.0 # backward penalty cost
+STEER_CHANGE_COST = 5.0 # steer angle change penalty cost
+STEER_COST = 1.0 # steer angle change penalty cost
+# JACKKNIF_COST= 200.0 # Jackknif cost
+H_COST = 5.0 # Heuristic cost
+
+show_animation = True
+
+_round = round
+def round(x):
+    return int(_round(x))
+
+class Node:
+
+    def __init__(self, xind, yind, yawind, direction, xlist, ylist, yawlist, directions, steer=0.0, pind=None, cost=None):
+        self.xind = xind
+        self.yind = yind
+        self.yawind = yawind
+        self.direction = direction
+        self.xlist = xlist
+        self.ylist = ylist
+        self.yawlist = yawlist
+        self.directions = directions
+        self.steer = steer
+        self.pind = pind
+        self.cost = cost
+
+class Path:
+
+    def __init__(self, xlist, ylist, yawlist, directionlist, cost):
+        self.xlist = xlist
+        self.ylist = ylist
+        self.yawlist = yawlist
+        self.directionlist = directionlist
+        self.cost = cost
+
+
+class KDTree:
+    """
+    Nearest neighbor search class with KDTree
+    """
+
+    def __init__(self, data):
+        # store kd-tree
+        self.tree = scipy.spatial.cKDTree(data)
+
+    def search(self, inp, k=1):
+        """
+        Search NN
+        inp: input data, single frame or multi frame
+        """
+
+        if len(inp.shape) >= 2:  # multi input
+            index = []
+            dist = []
+
+            for i in inp.T:
+                idist, iindex = self.tree.query(i, k=k)
+                index.append(iindex)
+                dist.append(idist)
+
+            return index, dist
+        else:
+            dist, index = self.tree.query(inp, k=k)
+            return index, dist
+
+    def search_in_distance(self, inp, r):
+        """
+        find points with in a distance r
+        """
+
+        index = self.tree.query_ball_point(inp, r)
+        return index
+
+
+class Config:
+
+    def __init__(self, ox, oy, xyreso, yawreso):
+        min_x_m = min(ox) #- EXTEND_AREA
+        min_y_m = min(oy) #- EXTEND_AREA
+        max_x_m = max(ox) #+ EXTEND_AREA
+        max_y_m = max(oy) #+ EXTEND_AREA
+
+        ox.append(min_x_m)
+        oy.append(min_y_m)
+        ox.append(max_x_m)
+        oy.append(max_y_m)
+
+        self.minx = round(min_x_m / xyreso)
+        self.miny = round(min_y_m / xyreso)
+        self.maxx = round(max_x_m / xyreso)
+        self.maxy = round(max_y_m / xyreso)
+
+        self.xw = round(self.maxx - self.minx)
+        self.yw = round(self.maxy - self.miny)
+
+        self.minyaw = round(- math.pi / yawreso) - 1
+        self.maxyaw = round(math.pi / yawreso)
+        self.yaww = round(self.maxyaw - self.minyaw)
+
+
+def show_expansion_tree():
+    pass
+
+
+def calc_motion_inputs():
+    
+    for steer in np.linspace(-MAX_STEER, MAX_STEER, N_STEER):
+        for d in [1, -1]:
+            yield [steer, d]         
+
+
+def get_neighbors(current, config, ox, oy, kdtree):
+    # import ipdb; ipdb.set_trace()
+    for steer, d in calc_motion_inputs():
+        node = calc_next_node(current, steer, d, config, ox, oy, kdtree)
+        if node and verify_index(node, config):
+            yield node
+
+def calc_next_node(current, steer, direction, config, ox, oy, kdtree):
+    
+    x, y, yaw = current.xlist[-1], current.ylist[-1], current.yawlist[-1]
+
+    arc_l = XY_GRID_RESOLUTION * 1.5
+    xlist, ylist, yawlist = [], [], []
+    for dist in np.arange(0, arc_l, MOTION_RESOLUTION):
+        x, y, yaw = move(x, y, yaw, MOTION_RESOLUTION*direction, steer)
+        xlist.append(x)
+        ylist.append(y)
+        yawlist.append(yaw)
+    
+    # plt.plot(xlist, ylist)
+    if not check_car_collision(xlist, ylist, yawlist, ox, oy, kdtree):
+        # import ipdb; ipdb.set_trace()
+        return None
+
+    d = direction==1
+    xind = round(x/XY_GRID_RESOLUTION)
+    yind = round(y/XY_GRID_RESOLUTION)
+    yawind = round(yaw/YAW_GRID_RESOLUTION)
+    
+    addedcost = 0.0
+
+    if d != current.direction:
+        addedcost += SB_COST
+    
+    # steer penalty
+    addedcost += STEER_COST*abs(steer)
+
+    # steer change penalty
+    addedcost += STEER_CHANGE_COST*abs(current.steer-steer)
+
+    cost = current.cost + addedcost + arc_l   
+
+    node = Node(xind, yind, yawind, d, xlist,
+                ylist, yawlist, [d],
+                pind=calc_index(current, config),
+                cost=cost, steer=steer)
+
+    return node
+
+
+def is_same_grid(n1, n2):
+    if n1.xind==n2.xind and n1.yind==n2.yind and n1.yawind==n2.yawind:
+        return True
+    return False
+
+def analytic_expantion(current, goal, c, ox, oy, kdtree):
+
+    sx = current.xlist[-1]
+    sy = current.ylist[-1]
+    syaw = current.yawlist[-1]
+
+    gx = goal.xlist[-1]
+    gy = goal.ylist[-1]
+    gyaw = goal.yawlist[-1]
+
+    max_curvature = math.tan(MAX_STEER)/WB
+    paths = rs.calc_paths(sx,sy,syaw,gx, gy, gyaw,
+                            max_curvature, step_size=MOTION_RESOLUTION)
+
+    if not len(paths):
+        return None
+
+    best_path, best = None, None
+
+    for path in paths:
+        if check_car_collision(path.x, path.y, path.yaw, ox, oy, kdtree):
+            cost = calc_rs_path_cost(path)
+            if not best or best > cost:
+                best = cost
+                best_path = path
+  
+    return best_path  # no update
+
+
+def update_node_with_analystic_expantion(current, goal, 
+                                        c, ox, oy, kdtree):
+    apath = analytic_expantion(current, goal, c, ox,oy, kdtree)
+
+    if apath:
+        plt.plot(apath.x, apath.y)
+        fx = apath.x[1:]
+        fy = apath.y[1:]
+        fyaw = apath.yaw[1:]
+
+        fcost = current.cost + calc_rs_path_cost(apath)
+        fpind = calc_index(current, c)
+
+        fd = []
+        for d in apath.directions[1:]:
+            fd.append(d>=0)
+        
+        fsteer = 0.0
+        fpath = Node(current.xind, current.yind, current.yawind, 
+            current.direction, fx, fy, fyaw, fd, cost=fcost, pind=fpind, steer=fsteer)
+        return True, fpath
+
+    return False, None
+
+
+def calc_rs_path_cost(rspath):
+
+    cost = 0.0
+    for l in rspath.lengths:
+        if l >= 0: # forward
+            cost += l
+        else: # back
+            cost += abs(l) * BACK_COST
+
+    # swich back penalty
+    for i in range(len(rspath.lengths)-1):
+        if rspath.lengths[i] * rspath.lengths[i+1] < 0.0: # switch back
+            cost += SB_COST
+
+    # steer penalyty
+    for ctype in rspath.ctypes:
+        if ctype != "S": # curve
+            cost += STEER_COST*abs(MAX_STEER)
+
+    # ==steer change penalty
+    # calc steer profile
+    nctypes = len(rspath.ctypes)
+    ulist = [0.0]*nctypes
+    for i in range(nctypes):
+        if rspath.ctypes[i] == "R" :
+            ulist[i] = - MAX_STEER
+        elif rspath.ctypes[i] == "L":
+            ulist[i] = MAX_STEER
+ 
+    for i in range(len(rspath.ctypes)-1):
+        cost += STEER_CHANGE_COST*abs(ulist[i+1] - ulist[i])
+
+    return cost
+
+
+def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso):
+    """
+    start
+    goal
+    ox: x position list of Obstacles [m]
+    oy: y position list of Obstacles [m]
+    xyreso: grid resolution [m]
+    yawreso: yaw angle resolution [rad]
+    """
+
+    start[2], goal[2] = rs.pi_2_pi(start[2]), rs.pi_2_pi(goal[2])
+    tox, toy = ox[:], oy[:]
+
+    obkdtree = KDTree(np.vstack((tox, toy)).T)
+
+    config = Config(tox, toy, xyreso, yawreso)
+
+    nstart = Node(round(start[0] / xyreso), round(start[1] / xyreso), round(start[2] / yawreso),
+                  True, [start[0]], [start[1]], [start[2]], [True], cost=0)
+    ngoal = Node(round(goal[0] / xyreso), round(goal[1] / xyreso), round(goal[2] / yawreso),
+                 True, [goal[0]], [goal[1]], [goal[2]], [True])
+
+    openList, closedList = {}, {}
+    h = []
+
+    _, _, h_dp = dp_planning(nstart.xlist[-1], nstart.ylist[-1],
+            ngoal.xlist[-1], ngoal.ylist[-1], ox, oy, xyreso, VR)
+
+    pq = []
+    openList[calc_index(nstart, config)] = nstart
+    heapq.heappush(pq, (calc_cost(nstart, h_dp, ngoal, config), calc_index(nstart, config)))
+
+    while True:
+        if not openList:
+            print("Error: Cannot find path, No open set")
+            return [], [], []
+
+        cost, c_id = heapq.heappop(pq)
+        if openList.has_key(c_id):
+            current = openList.pop(c_id)
+            closedList[c_id] = current
+
+        if show_animation:  # pragma: no cover
+            plt.plot(current.xlist[-1], current.ylist[-1], "xc")
+            if len(closedList.keys()) % 10 == 0:
+                plt.pause(0.001)
+
+        isupdated, fpath = update_node_with_analystic_expantion(
+            current, ngoal, config, ox, oy, obkdtree)
+
+        if isupdated:
+            break
+        
+        for neighbor in get_neighbors(current, config, ox, oy, obkdtree):
+            neighbor_index = calc_index(neighbor, config)
+            if closedList.has_key(neighbor_index):
+                continue
+            if not openList.has_key(neighbor_index) \
+                or openList[neighbor_index].cost > neighbor.cost: # TODO huristic
+                heapq.heappush(pq, (calc_cost(neighbor, h_dp, ngoal, config), neighbor_index))
+                openList[neighbor_index] = neighbor
+        
+        #  print(current)
+
+    rx, ry, ryaw = [], [], []
+
+    path = get_final_path(closedList, fpath, nstart, config)
+    return path
+
+def calc_cost(n, h_dp, goal, c):
+    ind = (n.yind - c.miny) * c.xw + (n.xind - c.minx)
+    if not h_dp.has_key(ind):
+        return (n.cost + 999999999) # collision cost
+    return (n.cost + H_COST*h_dp[ind].cost)
+
+def get_final_path(closed, ngoal, nstart, config):
+    rx, ry, ryaw = list(reversed(ngoal.xlist)), list(reversed(ngoal.ylist)), list(reversed(ngoal.yawlist))
+    direction = list(reversed(ngoal.directions))
+    nid = ngoal.pind
+    finalcost = ngoal.cost
+
+    while nid:
+        n = closed[nid]
+        rx.extend(list(reversed(n.xlist)))
+        ry.extend(list(reversed(n.ylist)))
+        ryaw.extend(list(reversed(n.yawlist)))
+        direction.extend(list(reversed(n.directions)))
+       
+        nid = n.pind
+
+    rx = list(reversed(rx))
+    ry = list(reversed(ry))
+    ryaw = list(reversed(ryaw))
+    direction = list(reversed(direction))
+
+    # adjuct first direction
+    direction[0] = direction[1]
+
+    path = Path(rx, ry, ryaw, direction, finalcost)
+
+    return path
+
+
+def verify_index(node, c):
+    xind, yind = node.xind, node.yind
+    if xind>=c.minx and xind<=c.maxx and yind >=c.miny \
+        and yind<=c.maxy:
+        return True
+    
+    return False
+
+
+def calc_index(node, c):
+    ind = (node.yawind - c.minyaw) * c.xw * c.yw + \
+        (node.yind - c.miny) * c.xw + (node.xind - c.minx)
+
+    if ind <= 0:
+        print("Error(calc_index):", ind)
+
+    return ind
+
+
+def main():
+    print("Start Hybrid A* planning")
+
+    ox, oy = [], []
+
+    for i in range(60):
+        ox.append(i)
+        oy.append(0.0)
+    for i in range(60):
+        ox.append(60.0)
+        oy.append(i)
+    for i in range(61):
+        ox.append(i)
+        oy.append(60.0)
+    for i in range(61):
+        ox.append(0.0)
+        oy.append(i)
+    for i in range(40):
+        ox.append(20.0)
+        oy.append(i)
+    for i in range(40):
+        ox.append(40.0)
+        oy.append(60.0 - i)
+
+    # Set Initial parameters
+    start = [10.0, 10.0, np.deg2rad(90.0)]
+    goal = [50.0, 50.0, np.deg2rad(-90.0)]
+
+    plt.plot(ox, oy, ".k")
+    rs.plot_arrow(start[0], start[1], start[2], fc='g')
+    rs.plot_arrow(goal[0], goal[1], goal[2])
+
+    plt.grid(True)
+    plt.axis("equal")
+
+    path = hybrid_a_star_planning(
+        start, goal, ox, oy, XY_GRID_RESOLUTION, YAW_GRID_RESOLUTION)
+    
+    # plot_car(*start)
+    # plot_car(*goal)
+    x = path.xlist
+    y = path.ylist
+    yaw = path.yawlist
+    direction = path.directionlist
+
+    for ix, iy, iyaw in zip(x, y, yaw):
+        plt.cla()
+        plt.plot(ox, oy, ".k")
+        plt.plot(x, y, "-r", label="Hybrid A* path")
+        plt.grid(True)
+        plt.axis("equal")
+        plot_car(ix, iy, iyaw)
+        plt.pause(0.0001)
+
+    plt.show()
+    print(__file__ + " start!!")
+
+
+if __name__ == '__main__':
+    main()

From e102b5dd5f718b9eafffac2fa0198851bc353fd5 Mon Sep 17 00:00:00 2001
From: zks <zks>
Date: Sun, 24 Mar 2019 14:09:24 +0800
Subject: [PATCH 0012/1080] hybrid_a_star python 3.5

---
 PathPlanning/HybridAStar/a_star.py        |  8 ++---
 PathPlanning/HybridAStar/car.py           |  2 +-
 PathPlanning/HybridAStar/hybrid_a_star.py | 38 +++++++++++------------
 3 files changed, 24 insertions(+), 24 deletions(-)

diff --git a/PathPlanning/HybridAStar/a_star.py b/PathPlanning/HybridAStar/a_star.py
index f5753edcc1..ada80e1ca0 100644
--- a/PathPlanning/HybridAStar/a_star.py
+++ b/PathPlanning/HybridAStar/a_star.py
@@ -13,9 +13,9 @@
 import math
 import heapq
 
-_round = round
-def round(x):
-    return int(_round(x))
+# _round = round
+# def round(x):
+#     return int(_round(x))
 
 show_animation = False
 
@@ -73,7 +73,7 @@ def dp_planning(sx, sy, gx, gy, ox, oy, reso, rr):
         if not pq:
             break
         cost, c_id = heapq.heappop(pq)
-        if openset.has_key(c_id):
+        if c_id in openset:
             current = openset[c_id]
             closedset[c_id] = current
             openset.pop(c_id)
diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py
index e7d35e9973..2701d0da2c 100644
--- a/PathPlanning/HybridAStar/car.py
+++ b/PathPlanning/HybridAStar/car.py
@@ -23,7 +23,7 @@ def check_car_collision(xlist, ylist, yawlist, ox, oy, kdtree):
 
         ids = kdtree.search_in_distance([cx, cy], WBUBBLE_R)
 
-        if len(ids) == 0:
+        if not ids:
             continue
         
         if not rectangle_check(x, y, yaw,
diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
index 35af0c94e6..eb69de4c0f 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -16,7 +16,7 @@
 import reeds_shepp_path_planning as rs
 import heapq
 from car import move, check_car_collision, MAX_STEER, WB, plot_car
-from a_star import dp_planning, calc_obstacle_map
+from a_star import dp_planning #, calc_obstacle_map
 
 XY_GRID_RESOLUTION = 2.0 #[m]
 YAW_GRID_RESOLUTION = np.deg2rad(15.0) #[rad]
@@ -38,9 +38,9 @@
 
 show_animation = True
 
-_round = round
-def round(x):
-    return int(_round(x))
+# _round = round
+# def round(x):
+#     return int(_round(x))
 
 class Node:
 
@@ -92,9 +92,9 @@ def search(self, inp, k=1):
                 dist.append(idist)
 
             return index, dist
-        else:
-            dist, index = self.tree.query(inp, k=k)
-            return index, dist
+        
+        dist, index = self.tree.query(inp, k=k)
+        return index, dist
 
     def search_in_distance(self, inp, r):
         """
@@ -211,7 +211,7 @@ def analytic_expantion(current, goal, c, ox, oy, kdtree):
     paths = rs.calc_paths(sx,sy,syaw,gx, gy, gyaw,
                             max_curvature, step_size=MOTION_RESOLUTION)
 
-    if not len(paths):
+    if not paths:
         return None
 
     best_path, best = None, None
@@ -223,7 +223,7 @@ def analytic_expantion(current, goal, c, ox, oy, kdtree):
                 best = cost
                 best_path = path
   
-    return best_path  # no update
+    return best_path
 
 
 def update_node_with_analystic_expantion(current, goal, 
@@ -309,7 +309,6 @@ def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso):
                  True, [goal[0]], [goal[1]], [goal[2]], [True])
 
     openList, closedList = {}, {}
-    h = []
 
     _, _, h_dp = dp_planning(nstart.xlist[-1], nstart.ylist[-1],
             ngoal.xlist[-1], ngoal.ylist[-1], ox, oy, xyreso, VR)
@@ -324,9 +323,12 @@ def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso):
             return [], [], []
 
         cost, c_id = heapq.heappop(pq)
-        if openList.has_key(c_id):
+        # if openList.has_key(c_id): # python 2.7
+        if c_id in openList:
             current = openList.pop(c_id)
             closedList[c_id] = current
+        else:
+            continue
 
         if show_animation:  # pragma: no cover
             plt.plot(current.xlist[-1], current.ylist[-1], "xc")
@@ -341,23 +343,21 @@ def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso):
         
         for neighbor in get_neighbors(current, config, ox, oy, obkdtree):
             neighbor_index = calc_index(neighbor, config)
-            if closedList.has_key(neighbor_index):
+            # if closedList.has_key(neighbor_index):
+            if neighbor_index in closedList:
                 continue
-            if not openList.has_key(neighbor_index) \
-                or openList[neighbor_index].cost > neighbor.cost: # TODO huristic
+            if not neighbor in openList \
+                or openList[neighbor_index].cost > neighbor.cost:
                 heapq.heappush(pq, (calc_cost(neighbor, h_dp, ngoal, config), neighbor_index))
                 openList[neighbor_index] = neighbor
         
-        #  print(current)
-
-    rx, ry, ryaw = [], [], []
 
     path = get_final_path(closedList, fpath, nstart, config)
     return path
 
 def calc_cost(n, h_dp, goal, c):
     ind = (n.yind - c.miny) * c.xw + (n.xind - c.minx)
-    if not h_dp.has_key(ind):
+    if not ind in h_dp:
         return (n.cost + 999999999) # collision cost
     return (n.cost + H_COST*h_dp[ind].cost)
 
@@ -451,7 +451,7 @@ def main():
     x = path.xlist
     y = path.ylist
     yaw = path.yawlist
-    direction = path.directionlist
+    # direction = path.directionlist
 
     for ix, iy, iyaw in zip(x, y, yaw):
         plt.cla()

From d8367b56b77dfb2008823468e56db1a5f151d445 Mon Sep 17 00:00:00 2001
From: zks <zks>
Date: Sun, 24 Mar 2019 14:27:03 +0800
Subject: [PATCH 0013/1080] hybrid a star python 3.5

---
 PathPlanning/HybridAStar/hybrid_a_star.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
index eb69de4c0f..1c9a3988ee 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -3,6 +3,7 @@
 Hybrid A* path planning
 
 author: Atsushi Sakai (@Atsushi_twi)
+        Zheng Zh (@Zhengzh)
 
 """
 
@@ -143,7 +144,7 @@ def calc_motion_inputs():
 
 
 def get_neighbors(current, config, ox, oy, kdtree):
-    # import ipdb; ipdb.set_trace()
+
     for steer, d in calc_motion_inputs():
         node = calc_next_node(current, steer, d, config, ox, oy, kdtree)
         if node and verify_index(node, config):
@@ -163,7 +164,6 @@ def calc_next_node(current, steer, direction, config, ox, oy, kdtree):
     
     # plt.plot(xlist, ylist)
     if not check_car_collision(xlist, ylist, yawlist, ox, oy, kdtree):
-        # import ipdb; ipdb.set_trace()
         return None
 
     d = direction==1

From ca267c6eb6177df3105bdf599fb70ea3bb332a24 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 2 Apr 2019 21:07:42 +0900
Subject: [PATCH 0014/1080] Update users_comments.md

---
 users_comments.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index dc4b7d9cd2..d66b024d4b 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -154,6 +154,11 @@ Dear AtsushiSakai, <br>Hello! YOUR CODE IS SUPER SUPER HELPFUL AND GIVES ME CLEA
 
 Hi AtsushiSakai, <br> <br>Thanks for creating the pythonrobotics repo! It's an awesome repo that has been of great help to me. I've referenced your extended kalman filter algorithm while creating my own for localization using a 2D lidar, camera, and IMU. Our robot is destined to compete soon, so fingers crossed that all works out! Thanks again.
 
+---
+
+You rock!
+
+--Matt
 
 =========
 

From 74733643edb48fd0e7f8055bf8534ca415c41983 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 2 Apr 2019 22:18:16 +0900
Subject: [PATCH 0015/1080] code clean up

---
 PathPlanning/HybridAStar/a_star.py        |  22 ++-
 PathPlanning/HybridAStar/car.py           |  72 +++++----
 PathPlanning/HybridAStar/hybrid_a_star.py | 169 ++++++++++++----------
 3 files changed, 141 insertions(+), 122 deletions(-)

diff --git a/PathPlanning/HybridAStar/a_star.py b/PathPlanning/HybridAStar/a_star.py
index ada80e1ca0..7f26640f0c 100644
--- a/PathPlanning/HybridAStar/a_star.py
+++ b/PathPlanning/HybridAStar/a_star.py
@@ -2,23 +2,19 @@
 
 A* grid based planning
 
-author: Atsushi Sakai(@Atsushi_twi)
-        Nikos Kanargias (nkana@tee.gr)
+author: Nikos Kanargias (nkana@tee.gr)
 
 See Wikipedia article (https://en.wikipedia.org/wiki/A*_search_algorithm)
 
 """
 
-import matplotlib.pyplot as plt
 import math
 import heapq
-
-# _round = round
-# def round(x):
-#     return int(_round(x))
+import matplotlib.pyplot as plt
 
 show_animation = False
 
+
 class Node:
 
     def __init__(self, x, y, cost, pind):
@@ -67,7 +63,6 @@ def dp_planning(sx, sy, gx, gy, ox, oy, reso, rr):
     openset[calc_index(ngoal, xw, minx, miny)] = ngoal
     pq = []
     pq.append((0, calc_index(ngoal, xw, minx, miny)))
-    
 
     while 1:
         if not pq:
@@ -87,7 +82,6 @@ def dp_planning(sx, sy, gx, gy, ox, oy, reso, rr):
                 plt.pause(0.001)
 
         # Remove the item from the open set
-        
 
         # expand search grid based on motion model
         for i, _ in enumerate(motion):
@@ -104,15 +98,17 @@ def dp_planning(sx, sy, gx, gy, ox, oy, reso, rr):
 
             if n_id not in openset:
                 openset[n_id] = node  # Discover a new node
-                heapq.heappush(pq, (node.cost, calc_index(node, xw, minx, miny)))
+                heapq.heappush(
+                    pq, (node.cost, calc_index(node, xw, minx, miny)))
             else:
                 if openset[n_id].cost >= node.cost:
                     # This path is the best until now. record it!
                     openset[n_id] = node
-                    heapq.heappush(pq, (node.cost, calc_index(node, xw, minx, miny)))
-
+                    heapq.heappush(
+                        pq, (node.cost, calc_index(node, xw, minx, miny)))
 
-    rx, ry = calc_final_path(closedset[calc_index(nstart, xw, minx, miny)], closedset, reso)
+    rx, ry = calc_final_path(closedset[calc_index(
+        nstart, xw, minx, miny)], closedset, reso)
 
     return rx, ry, closedset
 
diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py
index 2701d0da2c..10b7a296df 100644
--- a/PathPlanning/HybridAStar/car.py
+++ b/PathPlanning/HybridAStar/car.py
@@ -1,36 +1,45 @@
+"""
+
+Car model for Hybrid A* path planning
+
+author: Zheng Zh (@Zhengzh)
+
+"""
+
 
 import matplotlib.pyplot as plt
 from math import sqrt, cos, sin, tan, pi
 
-WB = 3. # rear to front wheel
-W = 2. # width of car
-LF = 3.3 # distance from rear to vehicle front end
-LB = 1.0 # distance from rear to vehicle back end
-MAX_STEER = 0.6 #[rad] maximum steering angle 
+WB = 3.  # rear to front wheel
+W = 2.  # width of car
+LF = 3.3  # distance from rear to vehicle front end
+LB = 1.0  # distance from rear to vehicle back end
+MAX_STEER = 0.6  # [rad] maximum steering angle
 
-WBUBBLE_DIST = (LF-LB)/2.0
-WBUBBLE_R = sqrt(((LF+LB)/2.0)**2+1)
+WBUBBLE_DIST = (LF - LB) / 2.0
+WBUBBLE_R = sqrt(((LF + LB) / 2.0)**2 + 1)
 
 # vehicle rectangle verticles
 VRX = [LF, LF, -LB, -LB, LF]
-VRY = [W/2,-W/2,-W/2,W/2,W/2]
+VRY = [W / 2, -W / 2, -W / 2, W / 2, W / 2]
 
 
 def check_car_collision(xlist, ylist, yawlist, ox, oy, kdtree):
     for x, y, yaw in zip(xlist, ylist, yawlist):
-        cx = x + WBUBBLE_DIST*cos(yaw)
-        cy = y + WBUBBLE_DIST*sin(yaw)
+        cx = x + WBUBBLE_DIST * cos(yaw)
+        cy = y + WBUBBLE_DIST * sin(yaw)
 
         ids = kdtree.search_in_distance([cx, cy], WBUBBLE_R)
 
         if not ids:
             continue
-        
+
         if not rectangle_check(x, y, yaw,
-                [ox[i] for i in ids], [oy[i] for i in ids]):
-            return False # collision
-    
-    return True # no collision
+                               [ox[i] for i in ids], [oy[i] for i in ids]):
+            return False  # collision
+
+    return True  # no collision
+
 
 def rectangle_check(x, y, yaw, ox, oy):
     # transform obstacles to base link frame
@@ -38,14 +47,14 @@ def rectangle_check(x, y, yaw, ox, oy):
     for iox, ioy in zip(ox, oy):
         tx = iox - x
         ty = ioy - y
-        rx = c*tx - s*ty
-        ry = s*tx + c*ty
+        rx = c * tx - s * ty
+        ry = s * tx + c * ty
 
-        if not (rx > LF or rx < -LB or ry > W/2.0 or ry < -W/2.0):
+        if not (rx > LF or rx < -LB or ry > W / 2.0 or ry < -W / 2.0):
             # print (x, y, yaw, iox, ioy, c, s, rx, ry)
-            return False # no collision
-    
-    return True # collision
+            return False  # no collision
+
+    return True  # collision
 
 
 def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
@@ -58,35 +67,38 @@ def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
                   fc=fc, ec=ec, head_width=width, head_length=width, alpha=0.4)
         # plt.plot(x, y)
 
+
 def plot_car(x, y, yaw):
     car_color = '-k'
     c, s = cos(yaw), sin(yaw)
-    
+
     car_outline_x, car_outline_y = [], []
     for rx, ry in zip(VRX, VRY):
-        tx = c*rx-s*ry + x
-        ty = s*rx+c*ry + y
+        tx = c * rx - s * ry + x
+        ty = s * rx + c * ry + y
         car_outline_x.append(tx)
         car_outline_y.append(ty)
-    
-    arrow_x, arrow_y, arrow_yaw = c*1.5+x, s*1.5+y, yaw
+
+    arrow_x, arrow_y, arrow_yaw = c * 1.5 + x, s * 1.5 + y, yaw
     plot_arrow(arrow_x, arrow_y, arrow_yaw)
-    
+
     plt.plot(car_outline_x, car_outline_y, car_color)
 
+
 def pi_2_pi(angle):
     return (angle + pi) % (2 * pi) - pi
 
+
 def move(x, y, yaw, distance, steer, L=WB):
     x += distance * cos(yaw)
     y += distance * sin(yaw)
-    yaw += pi_2_pi(distance * tan(steer) / L) # distance/2
+    yaw += pi_2_pi(distance * tan(steer) / L)  # distance/2
 
     return x, y, yaw
 
+
 if __name__ == '__main__':
     x, y, yaw = 0., 0., 1.
     plt.axis('equal')
     plot_car(x, y, yaw)
-    plt.show()
-    
\ No newline at end of file
+    plt.show()
\ No newline at end of file
diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
index 1c9a3988ee..fb0e8a777f 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -2,40 +2,43 @@
 
 Hybrid A* path planning
 
-author: Atsushi Sakai (@Atsushi_twi)
-        Zheng Zh (@Zhengzh)
+author: Zheng Zh (@Zhengzh)
 
 """
 
+import heapq
+import matplotlib.pyplot as plt
+import scipy.spatial
+import numpy as np
+import math
+
 import sys
 sys.path.append("../ReedsSheppPath/")
-
-import math
-import numpy as np
-import scipy.spatial
-import matplotlib.pyplot as plt
-import reeds_shepp_path_planning as rs
-import heapq
-from car import move, check_car_collision, MAX_STEER, WB, plot_car
-from a_star import dp_planning #, calc_obstacle_map
-
-XY_GRID_RESOLUTION = 2.0 #[m]
-YAW_GRID_RESOLUTION = np.deg2rad(15.0) #[rad]
-GOAL_TYAW_TH = np.deg2rad(5.0) #[rad]
-MOTION_RESOLUTION = 0.1 #[m] path interporate resolution
-N_STEER = 20.0 # number of steer command
+try:
+    from a_star import dp_planning  # , calc_obstacle_map
+    import reeds_shepp_path_planning as rs
+    from car import move, check_car_collision, MAX_STEER, WB, plot_car
+except:
+    raise
+
+
+XY_GRID_RESOLUTION = 2.0  # [m]
+YAW_GRID_RESOLUTION = np.deg2rad(15.0)  # [rad]
+GOAL_TYAW_TH = np.deg2rad(5.0)  # [rad]
+MOTION_RESOLUTION = 0.1  # [m] path interporate resolution
+N_STEER = 20.0  # number of steer command
 EXTEND_AREA = 0.0  # [m]
 H_COST = 1.0
 MOTION_RESOLUTION = 0.1
 SKIP_COLLISION_CHECK = 4
-VR = 1.0 # robot radius
+VR = 1.0  # robot radius
 
-SB_COST = 100.0 # switch back penalty cost
-BACK_COST = 5.0 # backward penalty cost
-STEER_CHANGE_COST = 5.0 # steer angle change penalty cost
-STEER_COST = 1.0 # steer angle change penalty cost
+SB_COST = 100.0  # switch back penalty cost
+BACK_COST = 5.0  # backward penalty cost
+STEER_CHANGE_COST = 5.0  # steer angle change penalty cost
+STEER_COST = 1.0  # steer angle change penalty cost
 # JACKKNIF_COST= 200.0 # Jackknif cost
-H_COST = 5.0 # Heuristic cost
+H_COST = 5.0  # Heuristic cost
 
 show_animation = True
 
@@ -43,6 +46,7 @@
 # def round(x):
 #     return int(_round(x))
 
+
 class Node:
 
     def __init__(self, xind, yind, yawind, direction, xlist, ylist, yawlist, directions, steer=0.0, pind=None, cost=None):
@@ -58,6 +62,7 @@ def __init__(self, xind, yind, yawind, direction, xlist, ylist, yawlist, directi
         self.pind = pind
         self.cost = cost
 
+
 class Path:
 
     def __init__(self, xlist, ylist, yawlist, directionlist, cost):
@@ -93,7 +98,7 @@ def search(self, inp, k=1):
                 dist.append(idist)
 
             return index, dist
-        
+
         dist, index = self.tree.query(inp, k=k)
         return index, dist
 
@@ -109,10 +114,10 @@ def search_in_distance(self, inp, r):
 class Config:
 
     def __init__(self, ox, oy, xyreso, yawreso):
-        min_x_m = min(ox) #- EXTEND_AREA
-        min_y_m = min(oy) #- EXTEND_AREA
-        max_x_m = max(ox) #+ EXTEND_AREA
-        max_y_m = max(oy) #+ EXTEND_AREA
+        min_x_m = min(ox)  # - EXTEND_AREA
+        min_y_m = min(oy)  # - EXTEND_AREA
+        max_x_m = max(ox)  # + EXTEND_AREA
+        max_y_m = max(oy)  # + EXTEND_AREA
 
         ox.append(min_x_m)
         oy.append(min_y_m)
@@ -137,10 +142,10 @@ def show_expansion_tree():
 
 
 def calc_motion_inputs():
-    
+
     for steer in np.linspace(-MAX_STEER, MAX_STEER, N_STEER):
         for d in [1, -1]:
-            yield [steer, d]         
+            yield [steer, d]
 
 
 def get_neighbors(current, config, ox, oy, kdtree):
@@ -150,39 +155,40 @@ def get_neighbors(current, config, ox, oy, kdtree):
         if node and verify_index(node, config):
             yield node
 
+
 def calc_next_node(current, steer, direction, config, ox, oy, kdtree):
-    
+
     x, y, yaw = current.xlist[-1], current.ylist[-1], current.yawlist[-1]
 
     arc_l = XY_GRID_RESOLUTION * 1.5
     xlist, ylist, yawlist = [], [], []
     for dist in np.arange(0, arc_l, MOTION_RESOLUTION):
-        x, y, yaw = move(x, y, yaw, MOTION_RESOLUTION*direction, steer)
+        x, y, yaw = move(x, y, yaw, MOTION_RESOLUTION * direction, steer)
         xlist.append(x)
         ylist.append(y)
         yawlist.append(yaw)
-    
+
     # plt.plot(xlist, ylist)
     if not check_car_collision(xlist, ylist, yawlist, ox, oy, kdtree):
         return None
 
-    d = direction==1
-    xind = round(x/XY_GRID_RESOLUTION)
-    yind = round(y/XY_GRID_RESOLUTION)
-    yawind = round(yaw/YAW_GRID_RESOLUTION)
-    
+    d = direction == 1
+    xind = round(x / XY_GRID_RESOLUTION)
+    yind = round(y / XY_GRID_RESOLUTION)
+    yawind = round(yaw / YAW_GRID_RESOLUTION)
+
     addedcost = 0.0
 
     if d != current.direction:
         addedcost += SB_COST
-    
+
     # steer penalty
-    addedcost += STEER_COST*abs(steer)
+    addedcost += STEER_COST * abs(steer)
 
     # steer change penalty
-    addedcost += STEER_CHANGE_COST*abs(current.steer-steer)
+    addedcost += STEER_CHANGE_COST * abs(current.steer - steer)
 
-    cost = current.cost + addedcost + arc_l   
+    cost = current.cost + addedcost + arc_l
 
     node = Node(xind, yind, yawind, d, xlist,
                 ylist, yawlist, [d],
@@ -193,10 +199,11 @@ def calc_next_node(current, steer, direction, config, ox, oy, kdtree):
 
 
 def is_same_grid(n1, n2):
-    if n1.xind==n2.xind and n1.yind==n2.yind and n1.yawind==n2.yawind:
+    if n1.xind == n2.xind and n1.yind == n2.yind and n1.yawind == n2.yawind:
         return True
     return False
 
+
 def analytic_expantion(current, goal, c, ox, oy, kdtree):
 
     sx = current.xlist[-1]
@@ -207,9 +214,9 @@ def analytic_expantion(current, goal, c, ox, oy, kdtree):
     gy = goal.ylist[-1]
     gyaw = goal.yawlist[-1]
 
-    max_curvature = math.tan(MAX_STEER)/WB
-    paths = rs.calc_paths(sx,sy,syaw,gx, gy, gyaw,
-                            max_curvature, step_size=MOTION_RESOLUTION)
+    max_curvature = math.tan(MAX_STEER) / WB
+    paths = rs.calc_paths(sx, sy, syaw, gx, gy, gyaw,
+                          max_curvature, step_size=MOTION_RESOLUTION)
 
     if not paths:
         return None
@@ -222,13 +229,13 @@ def analytic_expantion(current, goal, c, ox, oy, kdtree):
             if not best or best > cost:
                 best = cost
                 best_path = path
-  
+
     return best_path
 
 
-def update_node_with_analystic_expantion(current, goal, 
-                                        c, ox, oy, kdtree):
-    apath = analytic_expantion(current, goal, c, ox,oy, kdtree)
+def update_node_with_analystic_expantion(current, goal,
+                                         c, ox, oy, kdtree):
+    apath = analytic_expantion(current, goal, c, ox, oy, kdtree)
 
     if apath:
         plt.plot(apath.x, apath.y)
@@ -241,11 +248,11 @@ def update_node_with_analystic_expantion(current, goal,
 
         fd = []
         for d in apath.directions[1:]:
-            fd.append(d>=0)
-        
+            fd.append(d >= 0)
+
         fsteer = 0.0
-        fpath = Node(current.xind, current.yind, current.yawind, 
-            current.direction, fx, fy, fyaw, fd, cost=fcost, pind=fpind, steer=fsteer)
+        fpath = Node(current.xind, current.yind, current.yawind,
+                     current.direction, fx, fy, fyaw, fd, cost=fcost, pind=fpind, steer=fsteer)
         return True, fpath
 
     return False, None
@@ -255,33 +262,33 @@ def calc_rs_path_cost(rspath):
 
     cost = 0.0
     for l in rspath.lengths:
-        if l >= 0: # forward
+        if l >= 0:  # forward
             cost += l
-        else: # back
+        else:  # back
             cost += abs(l) * BACK_COST
 
     # swich back penalty
-    for i in range(len(rspath.lengths)-1):
-        if rspath.lengths[i] * rspath.lengths[i+1] < 0.0: # switch back
+    for i in range(len(rspath.lengths) - 1):
+        if rspath.lengths[i] * rspath.lengths[i + 1] < 0.0:  # switch back
             cost += SB_COST
 
     # steer penalyty
     for ctype in rspath.ctypes:
-        if ctype != "S": # curve
-            cost += STEER_COST*abs(MAX_STEER)
+        if ctype != "S":  # curve
+            cost += STEER_COST * abs(MAX_STEER)
 
     # ==steer change penalty
     # calc steer profile
     nctypes = len(rspath.ctypes)
-    ulist = [0.0]*nctypes
+    ulist = [0.0] * nctypes
     for i in range(nctypes):
-        if rspath.ctypes[i] == "R" :
+        if rspath.ctypes[i] == "R":
             ulist[i] = - MAX_STEER
         elif rspath.ctypes[i] == "L":
             ulist[i] = MAX_STEER
- 
-    for i in range(len(rspath.ctypes)-1):
-        cost += STEER_CHANGE_COST*abs(ulist[i+1] - ulist[i])
+
+    for i in range(len(rspath.ctypes) - 1):
+        cost += STEER_CHANGE_COST * abs(ulist[i + 1] - ulist[i])
 
     return cost
 
@@ -311,11 +318,12 @@ def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso):
     openList, closedList = {}, {}
 
     _, _, h_dp = dp_planning(nstart.xlist[-1], nstart.ylist[-1],
-            ngoal.xlist[-1], ngoal.ylist[-1], ox, oy, xyreso, VR)
+                             ngoal.xlist[-1], ngoal.ylist[-1], ox, oy, xyreso, VR)
 
     pq = []
     openList[calc_index(nstart, config)] = nstart
-    heapq.heappush(pq, (calc_cost(nstart, h_dp, ngoal, config), calc_index(nstart, config)))
+    heapq.heappush(pq, (calc_cost(nstart, h_dp, ngoal, config),
+                        calc_index(nstart, config)))
 
     while True:
         if not openList:
@@ -340,29 +348,32 @@ def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso):
 
         if isupdated:
             break
-        
+
         for neighbor in get_neighbors(current, config, ox, oy, obkdtree):
             neighbor_index = calc_index(neighbor, config)
             # if closedList.has_key(neighbor_index):
             if neighbor_index in closedList:
                 continue
             if not neighbor in openList \
-                or openList[neighbor_index].cost > neighbor.cost:
-                heapq.heappush(pq, (calc_cost(neighbor, h_dp, ngoal, config), neighbor_index))
+                    or openList[neighbor_index].cost > neighbor.cost:
+                heapq.heappush(
+                    pq, (calc_cost(neighbor, h_dp, ngoal, config), neighbor_index))
                 openList[neighbor_index] = neighbor
-        
 
     path = get_final_path(closedList, fpath, nstart, config)
     return path
 
+
 def calc_cost(n, h_dp, goal, c):
     ind = (n.yind - c.miny) * c.xw + (n.xind - c.minx)
     if not ind in h_dp:
-        return (n.cost + 999999999) # collision cost
-    return (n.cost + H_COST*h_dp[ind].cost)
+        return (n.cost + 999999999)  # collision cost
+    return (n.cost + H_COST * h_dp[ind].cost)
+
 
 def get_final_path(closed, ngoal, nstart, config):
-    rx, ry, ryaw = list(reversed(ngoal.xlist)), list(reversed(ngoal.ylist)), list(reversed(ngoal.yawlist))
+    rx, ry, ryaw = list(reversed(ngoal.xlist)), list(
+        reversed(ngoal.ylist)), list(reversed(ngoal.yawlist))
     direction = list(reversed(ngoal.directions))
     nid = ngoal.pind
     finalcost = ngoal.cost
@@ -373,7 +384,7 @@ def get_final_path(closed, ngoal, nstart, config):
         ry.extend(list(reversed(n.ylist)))
         ryaw.extend(list(reversed(n.yawlist)))
         direction.extend(list(reversed(n.directions)))
-       
+
         nid = n.pind
 
     rx = list(reversed(rx))
@@ -391,10 +402,10 @@ def get_final_path(closed, ngoal, nstart, config):
 
 def verify_index(node, c):
     xind, yind = node.xind, node.yind
-    if xind>=c.minx and xind<=c.maxx and yind >=c.miny \
-        and yind<=c.maxy:
+    if xind >= c.minx and xind <= c.maxx and yind >= c.miny \
+            and yind <= c.maxy:
         return True
-    
+
     return False
 
 
@@ -445,7 +456,7 @@ def main():
 
     path = hybrid_a_star_planning(
         start, goal, ox, oy, XY_GRID_RESOLUTION, YAW_GRID_RESOLUTION)
-    
+
     # plot_car(*start)
     # plot_car(*goal)
     x = path.xlist

From 2d4e7c9281bc76362ed373f4edc5eda1d0d32ba7 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 3 Apr 2019 22:17:59 +0900
Subject: [PATCH 0016/1080] code clean up

---
 PathPlanning/HybridAStar/a_star.py        |  6 ---
 PathPlanning/HybridAStar/car.py           |  1 -
 PathPlanning/HybridAStar/hybrid_a_star.py | 53 ++++++++---------------
 3 files changed, 18 insertions(+), 42 deletions(-)

diff --git a/PathPlanning/HybridAStar/a_star.py b/PathPlanning/HybridAStar/a_star.py
index 7f26640f0c..2e0281bd34 100644
--- a/PathPlanning/HybridAStar/a_star.py
+++ b/PathPlanning/HybridAStar/a_star.py
@@ -142,15 +142,9 @@ def calc_obstacle_map(ox, oy, reso, vr):
     miny = round(min(oy))
     maxx = round(max(ox))
     maxy = round(max(oy))
-    #  print("minx:", minx)
-    #  print("miny:", miny)
-    #  print("maxx:", maxx)
-    #  print("maxy:", maxy)
 
     xwidth = round(maxx - minx)
     ywidth = round(maxy - miny)
-    #  print("xwidth:", xwidth)
-    #  print("ywidth:", ywidth)
 
     # obstacle map generation
     obmap = [[False for i in range(xwidth)] for i in range(ywidth)]
diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py
index 10b7a296df..13e50b2995 100644
--- a/PathPlanning/HybridAStar/car.py
+++ b/PathPlanning/HybridAStar/car.py
@@ -51,7 +51,6 @@ def rectangle_check(x, y, yaw, ox, oy):
         ry = s * tx + c * ty
 
         if not (rx > LF or rx < -LB or ry > W / 2.0 or ry < -W / 2.0):
-            # print (x, y, yaw, iox, ioy, c, s, rx, ry)
             return False  # no collision
 
     return True  # collision
diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
index fb0e8a777f..de23af8421 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -7,11 +7,10 @@
 """
 
 import heapq
-import matplotlib.pyplot as plt
 import scipy.spatial
 import numpy as np
 import math
-
+import matplotlib.pyplot as plt
 import sys
 sys.path.append("../ReedsSheppPath/")
 try:
@@ -24,32 +23,25 @@
 
 XY_GRID_RESOLUTION = 2.0  # [m]
 YAW_GRID_RESOLUTION = np.deg2rad(15.0)  # [rad]
-GOAL_TYAW_TH = np.deg2rad(5.0)  # [rad]
 MOTION_RESOLUTION = 0.1  # [m] path interporate resolution
 N_STEER = 20.0  # number of steer command
-EXTEND_AREA = 0.0  # [m]
 H_COST = 1.0
-MOTION_RESOLUTION = 0.1
-SKIP_COLLISION_CHECK = 4
 VR = 1.0  # robot radius
 
 SB_COST = 100.0  # switch back penalty cost
 BACK_COST = 5.0  # backward penalty cost
 STEER_CHANGE_COST = 5.0  # steer angle change penalty cost
 STEER_COST = 1.0  # steer angle change penalty cost
-# JACKKNIF_COST= 200.0 # Jackknif cost
 H_COST = 5.0  # Heuristic cost
 
 show_animation = True
 
-# _round = round
-# def round(x):
-#     return int(_round(x))
-
 
 class Node:
 
-    def __init__(self, xind, yind, yawind, direction, xlist, ylist, yawlist, directions, steer=0.0, pind=None, cost=None):
+    def __init__(self, xind, yind, yawind, direction,
+                 xlist, ylist, yawlist, directions,
+                 steer=0.0, pind=None, cost=None):
         self.xind = xind
         self.yind = yind
         self.yawind = yawind
@@ -114,10 +106,10 @@ def search_in_distance(self, inp, r):
 class Config:
 
     def __init__(self, ox, oy, xyreso, yawreso):
-        min_x_m = min(ox)  # - EXTEND_AREA
-        min_y_m = min(oy)  # - EXTEND_AREA
-        max_x_m = max(ox)  # + EXTEND_AREA
-        max_y_m = max(oy)  # + EXTEND_AREA
+        min_x_m = min(ox)
+        min_y_m = min(oy)
+        max_x_m = max(ox)
+        max_y_m = max(oy)
 
         ox.append(min_x_m)
         oy.append(min_y_m)
@@ -137,10 +129,6 @@ def __init__(self, ox, oy, xyreso, yawreso):
         self.yaww = round(self.maxyaw - self.minyaw)
 
 
-def show_expansion_tree():
-    pass
-
-
 def calc_motion_inputs():
 
     for steer in np.linspace(-MAX_STEER, MAX_STEER, N_STEER):
@@ -168,7 +156,6 @@ def calc_next_node(current, steer, direction, config, ox, oy, kdtree):
         ylist.append(y)
         yawlist.append(yaw)
 
-    # plt.plot(xlist, ylist)
     if not check_car_collision(xlist, ylist, yawlist, ox, oy, kdtree):
         return None
 
@@ -252,7 +239,8 @@ def update_node_with_analystic_expantion(current, goal,
 
         fsteer = 0.0
         fpath = Node(current.xind, current.yind, current.yawind,
-                     current.direction, fx, fy, fyaw, fd, cost=fcost, pind=fpind, steer=fsteer)
+                     current.direction, fx, fy, fyaw, fd,
+                     cost=fcost, pind=fpind, steer=fsteer)
         return True, fpath
 
     return False, None
@@ -331,7 +319,6 @@ def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso):
             return [], [], []
 
         cost, c_id = heapq.heappop(pq)
-        # if openList.has_key(c_id): # python 2.7
         if c_id in openList:
             current = openList.pop(c_id)
             closedList[c_id] = current
@@ -351,13 +338,13 @@ def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso):
 
         for neighbor in get_neighbors(current, config, ox, oy, obkdtree):
             neighbor_index = calc_index(neighbor, config)
-            # if closedList.has_key(neighbor_index):
             if neighbor_index in closedList:
                 continue
-            if not neighbor in openList \
+            if neighbor not in openList \
                     or openList[neighbor_index].cost > neighbor.cost:
                 heapq.heappush(
-                    pq, (calc_cost(neighbor, h_dp, ngoal, config), neighbor_index))
+                    pq, (calc_cost(neighbor, h_dp, ngoal, config),
+                         neighbor_index))
                 openList[neighbor_index] = neighbor
 
     path = get_final_path(closedList, fpath, nstart, config)
@@ -366,9 +353,9 @@ def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso):
 
 def calc_cost(n, h_dp, goal, c):
     ind = (n.yind - c.miny) * c.xw + (n.xind - c.minx)
-    if not ind in h_dp:
-        return (n.cost + 999999999)  # collision cost
-    return (n.cost + H_COST * h_dp[ind].cost)
+    if ind not in h_dp:
+        return n.cost + 999999999  # collision cost
+    return n.cost + H_COST * h_dp[ind].cost
 
 
 def get_final_path(closed, ngoal, nstart, config):
@@ -392,7 +379,7 @@ def get_final_path(closed, ngoal, nstart, config):
     ryaw = list(reversed(ryaw))
     direction = list(reversed(direction))
 
-    # adjuct first direction
+    # adjust first direction
     direction[0] = direction[1]
 
     path = Path(rx, ry, ryaw, direction, finalcost)
@@ -457,12 +444,9 @@ def main():
     path = hybrid_a_star_planning(
         start, goal, ox, oy, XY_GRID_RESOLUTION, YAW_GRID_RESOLUTION)
 
-    # plot_car(*start)
-    # plot_car(*goal)
     x = path.xlist
     y = path.ylist
     yaw = path.yawlist
-    # direction = path.directionlist
 
     for ix, iy, iyaw in zip(x, y, yaw):
         plt.cla()
@@ -473,8 +457,7 @@ def main():
         plot_car(ix, iy, iyaw)
         plt.pause(0.0001)
 
-    plt.show()
-    print(__file__ + " start!!")
+    print(__file__ + " done!!")
 
 
 if __name__ == '__main__':

From 1639032a219cb61b9948e19322de55c0417b2b40 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 4 Apr 2019 20:28:14 +0900
Subject: [PATCH 0017/1080] fix obstacle map bug

---
 PathPlanning/AStar/a_star.py       | 2 +-
 PathPlanning/Dijkstra/dijkstra.py  | 2 +-
 PathPlanning/HybridAStar/a_star.py | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py
index 8917630df8..4498c751be 100644
--- a/PathPlanning/AStar/a_star.py
+++ b/PathPlanning/AStar/a_star.py
@@ -149,7 +149,7 @@ def calc_obstacle_map(ox, oy, reso, vr):
     #  print("ywidth:", ywidth)
 
     # obstacle map generation
-    obmap = [[False for i in range(xwidth)] for i in range(ywidth)]
+    obmap = [[False for i in range(ywidth)] for i in range(xwidth)]
     for ix in range(xwidth):
         x = ix + minx
         for iy in range(ywidth):
diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py
index 889336ddc8..89eb632a4d 100644
--- a/PathPlanning/Dijkstra/dijkstra.py
+++ b/PathPlanning/Dijkstra/dijkstra.py
@@ -137,7 +137,7 @@ def calc_obstacle_map(ox, oy, reso, vr):
     #  print("ywidth:", ywidth)
 
     # obstacle map generation
-    obmap = [[False for i in range(xwidth)] for i in range(ywidth)]
+    obmap = [[False for i in range(ywidth)] for i in range(xwidth)]
     for ix in range(xwidth):
         x = ix + minx
         for iy in range(ywidth):
diff --git a/PathPlanning/HybridAStar/a_star.py b/PathPlanning/HybridAStar/a_star.py
index 2e0281bd34..c1c82edd0d 100644
--- a/PathPlanning/HybridAStar/a_star.py
+++ b/PathPlanning/HybridAStar/a_star.py
@@ -147,7 +147,7 @@ def calc_obstacle_map(ox, oy, reso, vr):
     ywidth = round(maxy - miny)
 
     # obstacle map generation
-    obmap = [[False for i in range(xwidth)] for i in range(ywidth)]
+    obmap = [[False for i in range(ywidth)] for i in range(xwidth)]
     for ix in range(xwidth):
         x = ix + minx
         for iy in range(ywidth):

From bf3c2651ec067c3107414362abb507be0c416899 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 8 Apr 2019 20:25:13 +0900
Subject: [PATCH 0018/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index d66b024d4b..b90ac1247a 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -160,6 +160,12 @@ You rock!
 
 --Matt
 
+---
+
+Dear AtsushiSakai, <br>You are the best. This is by far the best tutorial for learning and implementing robotics. <br>Thanks a lot for your time and efforts to do this!
+
+--Adi B
+
 =========
 
 # Citations

From ba3bd1729a373030765ac09725cc019ba3d44373 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 9 Apr 2019 20:38:03 +0900
Subject: [PATCH 0019/1080] Update users_comments.md

---
 users_comments.md | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index b90ac1247a..ff184f7b29 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -166,6 +166,18 @@ Dear AtsushiSakai, <br>You are the best. This is by far the best tutorial for le
 
 --Adi B
 
+---
+
+Dear Atushi, thank you for this amazing repository. It is a pleasure to have access to python algorithms without the burden of ROS. While I'm no longer working on such robotics projects, it's wonderful to know its available when I need it and it was amazing to see all the beautiful animations for each algorithm.
+
+--Shreeyak Sajjan
+
+---
+
+Dear AtsushiSakai <br>Thank you for your contributions!
+
+--Luo
+
 =========
 
 # Citations

From 3539400c8fc9582614074445dae9389883068ac6 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 10 Apr 2019 18:31:39 +0900
Subject: [PATCH 0020/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index ff184f7b29..ba19006049 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -178,6 +178,12 @@ Dear AtsushiSakai <br>Thank you for your contributions!
 
 --Luo
 
+---
+
+Dear AtsushiSakai, Your visualizations are awesome, and I am going to have this link bookmarked for future references. Thank you!
+
+--Srinivasa Varadhan Agaram Chakravarthy
+
 =========
 
 # Citations

From 4015e50cee59713e99ea1c4a15524fb000b0aefe Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 10 Apr 2019 18:35:07 +0900
Subject: [PATCH 0021/1080] Update users_comments.md

---
 users_comments.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index ba19006049..6030aeb30a 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -195,6 +195,8 @@ URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8516589&isnumber=85
 
 2. Peggy (Yuchun) Wang and Caitlin Hogan, "Path Planning with Dynamic Obstacle Avoidance for a Jumping-Enabled Robot", AA228/CS238 class report, Department of Computer Science, Stanford University, URL: https://web.stanford.edu/class/aa228/reports/2018/final113.pdf
 
+3. Hogan, Caitlin M. “Path Planning with Dynamic Obstacle Avoidance for a Jumping-Enabled Robot.” (2018)., URL: https://www.semanticscholar.org/paper/Path-Planning-with-Dynamic-Obstacle-Avoidance-for-a-Hogan/62d03bfc802d5a70686025b31a991bea423e5ae1
+
 # Others
 
 - Autonomous Vehicle Readings https://richardkelley.io/readings

From 7b92ed5a0dd45d6dbf3f2d039db9f1127f5fe2c5 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 10 Apr 2019 18:36:08 +0900
Subject: [PATCH 0022/1080] Update users_comments.md

---
 users_comments.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/users_comments.md b/users_comments.md
index 6030aeb30a..621e230e96 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -195,7 +195,6 @@ URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8516589&isnumber=85
 
 2. Peggy (Yuchun) Wang and Caitlin Hogan, "Path Planning with Dynamic Obstacle Avoidance for a Jumping-Enabled Robot", AA228/CS238 class report, Department of Computer Science, Stanford University, URL: https://web.stanford.edu/class/aa228/reports/2018/final113.pdf
 
-3. Hogan, Caitlin M. “Path Planning with Dynamic Obstacle Avoidance for a Jumping-Enabled Robot.” (2018)., URL: https://www.semanticscholar.org/paper/Path-Planning-with-Dynamic-Obstacle-Avoidance-for-a-Hogan/62d03bfc802d5a70686025b31a991bea423e5ae1
 
 # Others
 

From 1dd9bad012b0593f5afb1a64be55900fe7021c1c Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 10 Apr 2019 21:13:14 +0900
Subject: [PATCH 0023/1080] fix pure_pursuit distance bug

---
 PathTracking/pure_pursuit/pure_pursuit.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index 34ec527bb9..26c3d848bb 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -11,7 +11,7 @@
 
 k = 0.1  # look forward gain
 Lfc = 1.0  # look-ahead distance
-Kp = 1.0  # speed propotional gain
+Kp = 1.0  # speed proportional gain
 dt = 0.1  # [s]
 L = 2.9  # [m] wheel base of vehicle
 
@@ -84,9 +84,9 @@ def calc_target_index(state, cx, cy):
 
     # search look ahead target point index
     while Lf > L and (ind + 1) < len(cx):
-        dx = cx[ind + 1] - cx[ind]
-        dy = cy[ind + 1] - cy[ind]
-        L += math.sqrt(dx ** 2 + dy ** 2)
+        dx = cx[ind] - state.x
+        dy = cy[ind] - state.y
+        L = math.sqrt(dx ** 2 + dy ** 2)
         ind += 1
 
     return ind

From ac066ee6049adc6315a859127344ee5de1e48b30 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 12 Apr 2019 22:15:58 +0900
Subject: [PATCH 0024/1080] remove unused code in pure_pursuit control

---
 PathTracking/pure_pursuit/pure_pursuit.py | 20 ++++++++++++++++----
 1 file changed, 16 insertions(+), 4 deletions(-)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index 26c3d848bb..af953e3068 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -61,9 +61,6 @@ def pure_pursuit_control(state, cx, cy, pind):
 
     alpha = math.atan2(ty - state.y, tx - state.x) - state.yaw
 
-    if state.v < 0:  # back
-        alpha = math.pi - alpha
-
     Lf = k * state.v + Lfc
 
     delta = math.atan2(2.0 * L * math.sin(alpha) / Lf, 1.0)
@@ -92,6 +89,20 @@ def calc_target_index(state, cx, cy):
     return ind
 
 
+def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
+    """
+    Plot arrow
+    """
+
+    if not isinstance(x, float):
+        for (ix, iy, iyaw) in zip(x, y, yaw):
+            plot_arrow(ix, iy, iyaw)
+    else:
+        plt.arrow(x, y, length * math.cos(yaw), length * math.sin(yaw),
+                  fc=fc, ec=ec, head_width=width, head_length=width)
+        plt.plot(x, y)
+
+
 def main():
     #  target course
     cx = np.arange(0, 50, 0.1)
@@ -128,7 +139,8 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
-            plt.plot(cx, cy, ".r", label="course")
+            plot_arrow(state.x, state.y, state.yaw)
+            plt.plot(cx, cy, "-r", label="course")
             plt.plot(x, y, "-b", label="trajectory")
             plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
             plt.axis("equal")

From c0e27cca0da63dc24bf5b31671bf06c703f55b33 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 19 Apr 2019 18:43:05 +0900
Subject: [PATCH 0025/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 621e230e96..cba3f15c9e 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -184,6 +184,12 @@ Dear AtsushiSakai, Your visualizations are awesome, and I am going to have this
 
 --Srinivasa Varadhan Agaram Chakravarthy
 
+---
+
+Dear AtsushiSakai, <br>Thank you for this great resource! I very much appreciate all your hard work and effort.
+
+--Mridul Aanjaneya
+
 =========
 
 # Citations

From d8c70ddc9e2ced91ee1c916df9da9edb4287641a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 21 Apr 2019 18:35:31 +0900
Subject: [PATCH 0026/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index cba3f15c9e..0f38aef379 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -7,6 +7,12 @@ If you found other users, please make an issue to let me know.
 
 - [CSCI/ARTI 4530/6530: Introduction to Robotics (Fall 2018),  University of Georgia ](http://cobweb.cs.uga.edu/~ramviyas/csci_x530.html)
 
+# Stargazers location map
+
+You can check stargazer's locations of this project from:
+
+- [Stargazers location map](https://drive.google.com/open?id=1bBXS9IQmZ3Tfe1wNGvJbObRt9Htt4PGC&usp=sharing)
+
 # Individual users comments
 
 These are comments from user's using [![Say Thanks!](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/AtsushiSakai)

From d88b6467a544ee96b7d53d88c364ceff86f0c609 Mon Sep 17 00:00:00 2001
From: Andrew Tu <drewtu2@yahoo.com>
Date: Tue, 23 Apr 2019 16:37:01 -0400
Subject: [PATCH 0027/1080] added pynb file. started formatting and write up

---
 SLAM/EKFSLAM/animation.png     |  Bin 0 -> 113851 bytes
 SLAM/EKFSLAM/ekf_slam.ipynb    | 1502 ++++++++++++++++++++++++++++++++
 SLAM/FastSLAM1/FastSLAM1.ipynb |    2 +-
 3 files changed, 1503 insertions(+), 1 deletion(-)
 create mode 100644 SLAM/EKFSLAM/animation.png
 create mode 100644 SLAM/EKFSLAM/ekf_slam.ipynb

diff --git a/SLAM/EKFSLAM/animation.png b/SLAM/EKFSLAM/animation.png
new file mode 100644
index 0000000000000000000000000000000000000000..905ad102f2e6ef948b91a437590e47e3c77db599
GIT binary patch
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diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
new file mode 100644
index 0000000000..fe4f47806e
--- /dev/null
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -0,0 +1,1502 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# EKF SLAM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {
+      "image/png": {
+       "width": 600
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename=\"animation.png\",width=600)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation\n",
+    "\n",
+    "This is a simulation of EKF SLAM. \n",
+    "\n",
+    "- Black stars: landmarks\n",
+    "- Green crosses: estimates of landmark positions\n",
+    "- Black line: dead reckoning \n",
+    "- Blue line: ground truth\n",
+    "- Red line: EKF SLAM position estimation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "EKF SLAM models the SLAM problem in a single EKF where the modeled state is both the pose $(x, y, \\theta)$ and \n",
+    "an array of landmarks $[(x_1, y_1), (x_2, x_y), ... , (x_n, y_n)]$ for $n$ landmarks. The covariance between each of the positions and landmarks are also tracked. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\\begin{array}{cc}\n",
+    "X = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\\\ x_1 \\\\ y_1 \\\\ x_2 \\\\ y_2 \\\\ \\dots \\\\ x_n \\\\ y_n\\end{bmatrix}\n",
+    "&\n",
+    "P = \\begin{bmatrix} \n",
+    "\\sigma_{xx} & \\sigma_{xy} & \\sigma_{x\\theta} & \\sigma_{xx_1} & \\sigma_{xy_1} & \\sigma_{xx_2} & \\sigma_{xy_2} & \\dots & \\sigma_{xx_n} & \\sigma_{xy_n} \\\\\n",
+    "\\sigma_{yx} & \\sigma_{yy} & \\sigma_{y\\theta} & \\sigma_{yx_1} & \\sigma_{yy_1} & \\sigma_{yx_2} & \\sigma_{yy_2} & \\dots & \\sigma_{yx_n} & \\sigma_{yy_n} \\\\\n",
+    " &  &  &  & \\vdots &  &  &  &  &  \\\\\n",
+    "\\sigma_{x_nx} & \\sigma_{x_ny} & \\sigma_{x_n\\theta} & \\sigma_{x_nx_1} & \\sigma_{x_ny_1} & \\sigma_{x_nx_2} & \\sigma_{x_ny_2} & \\dots & \\sigma_{x_nx_n} & \\sigma_{x_ny_n}\n",
+    "\\end{bmatrix}\n",
+    "\\end{array}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A single estimate of the pose is tracked over time, while the confidence in the pose is tracked by the \n",
+    "covariance matrix $P$. $P$ is a symmetric square matrix whith each element in the matrix corresponding to the \n",
+    "covariance between two parts of the system. For example, $\\sigma_{xy}$ represents the covariance between the \n",
+    "belief of $x$ and $y$ and is equal to $\\sigma_{yx}$. \n",
+    "\n",
+    "The state can be represented more concisely as follows. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\\begin{array}{cc}\n",
+    "X = \\begin{bmatrix} x \\\\ m \\end{bmatrix}\n",
+    "&\n",
+    "P = \\begin{bmatrix} \n",
+    "\\Sigma_{xx} & \\Sigma_{xm}\\\\\n",
+    "\\Sigma_{mx} & \\Sigma_{mm}\\\\\n",
+    "\\end{bmatrix}\n",
+    "\\end{array}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here the state simplifies to a combination of pose ($x$) and map ($m$). The covariance matrix becomes easier to \n",
+    "understand and simply reads as the uncertainty of the robots pose ($\\Sigma_{xx}$), the uncertainty of the \n",
+    "map ($\\Sigma_{mm}$), and the uncertainty of the robots pose with respect to the map and vice versa \n",
+    "($\\Sigma_{xm}$, $\\Sigma_{mx}$).\n",
+    "\n",
+    "Take care to note the difference between $X$ (state) and $x$ (pose). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Extended Kalman Filter SLAM example\n",
+    "author: Atsushi Sakai (@Atsushi_twi)\n",
+    "notebook author: Andrew Tu (drewtu2)\n",
+    "\"\"\"\n",
+    "\n",
+    "import math\n",
+    "import numpy as np\n",
+    "%matplotlib notebook\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "# EKF state covariance\n",
+    "Cx = np.diag([0.5, 0.5, np.deg2rad(30.0)])**2\n",
+    "\n",
+    "#  Simulation parameter\n",
+    "Qsim = np.diag([0.2, np.deg2rad(1.0)])**2\n",
+    "Rsim = np.diag([1.0, np.deg2rad(10.0)])**2\n",
+    "\n",
+    "DT = 0.1  # time tick [s]\n",
+    "SIM_TIME = 50.0  # simulation time [s]\n",
+    "MAX_RANGE = 20.0  # maximum observation range\n",
+    "M_DIST_TH = 2.0  # Threshold of Mahalanobis distance for data association.\n",
+    "STATE_SIZE = 3  # State size [x,y,yaw]\n",
+    "LM_SIZE = 2  # LM state size [x,y]\n",
+    "\n",
+    "show_animation = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Algorithm Walkthrough\n",
+    "\n",
+    "At each time step, the following is done. \n",
+    "- predict the new state using the control functions\n",
+    "- update the belief in landmark positions based on the estimated state and measurements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ekf_slam(xEst, PEst, u, z):\n",
+    "    \"\"\"\n",
+    "    Performs an iteration of EKF SLAM from the available information. \n",
+    "    \n",
+    "    :param xEst: the belief in last position\n",
+    "    :param PEst: the uncertainty in last position\n",
+    "    :param u: the control function applied to the last position \n",
+    "    :param z: measurements at this step\n",
+    "    :returns: the next estimated position and associated covariance\n",
+    "    \"\"\"\n",
+    "    S = STATE_SIZE\n",
+    "\n",
+    "    # Predict\n",
+    "    xEst, PEst, G, Fx = predict(xEst, PEst, u, z)\n",
+    "    initP = np.eye(2)\n",
+    "\n",
+    "    # Update\n",
+    "    xEst, PEst = update(xEst, PEst, u, z, initP)\n",
+    "    \n",
+    "    xEst[2] = pi_2_pi(xEst[2])\n",
+    "\n",
+    "    return xEst, PEst\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1- Predict\n",
+    "**Predict State update:** The following equations describe the predicted motion model of the robot in case we provide only the control $(v,w)$, which are the linear and angular velocity repsectively. \n",
+    "\n",
+    "$\\begin{equation*}\n",
+    "F=\n",
+    "\\begin{bmatrix}\n",
+    "1 & 0 & 0 \\\\\n",
+    "0 & 1 & 0 \\\\\n",
+    "0 & 0 & 1 \n",
+    "\\end{bmatrix}\n",
+    "\\end{equation*}$\n",
+    "\n",
+    "$\\begin{equation*}\n",
+    "B=\n",
+    "\\begin{bmatrix}\n",
+    "\\Delta t cos(\\theta) & 0\\\\\n",
+    "\\Delta t sin(\\theta) & 0\\\\\n",
+    "0 & \\Delta t\n",
+    "\\end{bmatrix}\n",
+    "\\end{equation*}$\n",
+    "\n",
+    "$\\begin{equation*}\n",
+    "U=\n",
+    "\\begin{bmatrix}\n",
+    "v_t\\\\\n",
+    "w_t\\\\\n",
+    "\\end{bmatrix}\n",
+    "\\end{equation*}$\n",
+    "\n",
+    "$\\begin{equation*}\n",
+    "X = FX + BU \n",
+    "\\end{equation*}$\n",
+    "\n",
+    "\n",
+    "$\\begin{equation*}\n",
+    "\\begin{bmatrix}\n",
+    "x_{t+1} \\\\\n",
+    "y_{t+1} \\\\\n",
+    "\\theta_{t+1}\n",
+    "\\end{bmatrix}=\n",
+    "\\begin{bmatrix}\n",
+    "1 & 0 & 0 \\\\\n",
+    "0 & 1 & 0 \\\\\n",
+    "0 & 0 & 1 \n",
+    "\\end{bmatrix}\\begin{bmatrix}\n",
+    "x_{t} \\\\\n",
+    "y_{t} \\\\\n",
+    "\\theta_{t}\n",
+    "\\end{bmatrix}+\n",
+    "\\begin{bmatrix}\n",
+    "\\Delta t cos(\\theta) & 0\\\\\n",
+    "\\Delta t sin(\\theta) & 0\\\\\n",
+    "0 & \\Delta t\n",
+    "\\end{bmatrix}\n",
+    "\\begin{bmatrix}\n",
+    "v_{t} + \\sigma_v\\\\\n",
+    "w_{t} + \\sigma_w\\\\\n",
+    "\\end{bmatrix}\n",
+    "\\end{equation*}$\n",
+    "\n",
+    "Notice that while $U$ is only defined by $v_t$ and $w_t$, in the actual calcuations, a $+\\sigma_v$ and \n",
+    "$+\\sigma_w$ appear. These values represent the error bewteen the given control inputs and the actual control \n",
+    "inputs. \n",
+    "\n",
+    "As a result, the simulation is set up as the following. $R$ represents the process noise which is added to the \n",
+    "control inputs to simulate noise experienced in the real world. A set of truth values are computed from the raw \n",
+    "control values while the values dead reckoning values incorporate the error into the estimation. \n",
+    "\n",
+    "$\\begin{equation*}\n",
+    "R=\n",
+    "\\begin{bmatrix}\n",
+    "\\sigma_v\\\\\n",
+    "\\sigma_w\\\\\n",
+    "\\end{bmatrix}\n",
+    "\\end{equation*}$\n",
+    "\n",
+    "$\\begin{equation*}\n",
+    "X_{true} = FX + B(U)\n",
+    "\\end{equation*}$\n",
+    "\n",
+    "$\\begin{equation*}\n",
+    "X_{DR} = FX + B(U + R)\n",
+    "\\end{equation*}$\n",
+    "\n",
+    "The implementation of the motion model prediciton code is shown in `motion_model`. The `observation` function \n",
+    "shows how the simulation uses (or doesn't use) the process noise `Rsim` to the find the ground truth and dead reckoning estimtates of the pose.\n",
+    "\n",
+    "**Predict covariance:** Add the state covariance to the the current uncertainty of the EKF. At each time step, the uncertainty in the system grows by the covariance of the pose, $Cx$. \n",
+    "\n",
+    "$\n",
+    "P = G^TPG + Cx\n",
+    "$\n",
+    "\n",
+    "Notice this uncertainty is only growing with respect to the pose, not the landmarks. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(xEst, PEst, u, z):\n",
+    "    S = STATE_SIZE\n",
+    "    xEst[0:S] = motion_model(xEst[0:S], u)\n",
+    "    G, Fx = jacob_motion(xEst[0:S], u)\n",
+    "    # Fx is an an identity matrix of size (STATE_SIZE)\n",
+    "    # sigma = G*sigma*G.T + Noise\n",
+    "    PEst[0:S, 0:S] = G.T * PEst[0:S, 0:S] * G + Fx.T * Cx * Fx\n",
+    "    return xEst, PEst, G, Fx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def motion_model(x, u):\n",
+    "    \"\"\"\n",
+    "    Computes the motion model based on current state and input function. \n",
+    "    \n",
+    "    :param x: 3x3 pose estimation\n",
+    "    :param u: 2x1 control input [v; w]\n",
+    "    :returns: x' = Fx + Bu\n",
+    "    \"\"\"\n",
+    "    F = np.array([[1.0, 0, 0],\n",
+    "                  [0, 1.0, 0],\n",
+    "                  [0, 0, 1.0]])\n",
+    "\n",
+    "    B = np.array([[DT * math.cos(x[2, 0]), 0],\n",
+    "                  [DT * math.sin(x[2, 0]), 0],\n",
+    "                  [0.0, DT]])\n",
+    "\n",
+    "    x = (F @ x) + (B @ u)\n",
+    "    return x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2 - Update\n",
+    "In the update phase, the observations of nearby landmarks are used to correct the location estimate. \n",
+    "\n",
+    "For every landmark observed, it is associated to a particular landmark in the known map. If no landmark exists \n",
+    "in the position surrounding the landmark, it is taken as a NEW landmark. The distance threshold for how far a \n",
+    "landmark must be from the next known landmark before its considered to be a new landmark is set by `M_DIST_TH`.\n",
+    "\n",
+    "With an observation associated to the appropriate landmark, the **innovation** can be calculated. Innovation \n",
+    "($y$) is the difference between the observation and the observation that *should* have been made if the \n",
+    "observation were made from the pose predicted in the predict stage.\n",
+    "\n",
+    "$\n",
+    "y = z_t - h(X)\n",
+    "$\n",
+    "\n",
+    "With the innovation calculated, the question becomes which to trust more - the observations or the predictions? \n",
+    "To determine this, we calculate the Kalman Gain - a percent of how much of the innovation to add to the \n",
+    "prediction based on the uncertainty in the predict step and the update step. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update(xEst, PEst, u, z, initP):\n",
+    "    for iz in range(len(z[:, 0])):  # for each observation\n",
+    "        minid = search_correspond_LM_ID(xEst, PEst, z[iz, 0:2]) # associate to a known landmark\n",
+    "\n",
+    "        nLM = calc_n_LM(xEst) # number of landmarks we currently know about\n",
+    "        \n",
+    "        if minid == nLM: # Landmark is a NEW landmark\n",
+    "            print(\"New LM\")\n",
+    "            # Extend state and covariance matrix\n",
+    "            xAug = np.vstack((xEst, calc_LM_Pos(xEst, z[iz, :])))\n",
+    "            PAug = np.vstack((np.hstack((PEst, np.zeros((len(xEst), LM_SIZE)))),\n",
+    "                              np.hstack((np.zeros((LM_SIZE, len(xEst))), initP))))\n",
+    "            xEst = xAug\n",
+    "            PEst = PAug\n",
+    "        \n",
+    "        lm = get_LM_Pos_from_state(xEst, minid)\n",
+    "        y, S, H = calc_innovation(lm, xEst, PEst, z[iz, 0:2], minid)\n",
+    "\n",
+    "        K = (PEst @ H.T) @ np.linalg.inv(S) # Calcualte Kalman Gain\n",
+    "        xEst = xEst + (K @ y)\n",
+    "        PEst = (np.eye(len(xEst)) - (K @ H)) @ PEst\n",
+    "    \n",
+    "    return xEst, PEst\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_innovation(lm, xEst, PEst, z, LMid):\n",
+    "    delta = lm - xEst[0:2]\n",
+    "    q = (delta.T @ delta)[0, 0]\n",
+    "    zangle = math.atan2(delta[1, 0], delta[0, 0]) - xEst[2, 0]\n",
+    "    zp = np.array([[math.sqrt(q), pi_2_pi(zangle)]])\n",
+    "    y = (z - zp).T\n",
+    "    y[1] = pi_2_pi(y[1])\n",
+    "    H = jacobH(q, delta, xEst, LMid + 1)\n",
+    "    S = H @ PEst @ H.T + Cx[0:2, 0:2]\n",
+    "\n",
+    "    return y, S, H\n",
+    "\n",
+    "\n",
+    "def jacobH(q, delta, x, i):\n",
+    "    sq = math.sqrt(q)\n",
+    "    G = np.array([[-sq * delta[0, 0], - sq * delta[1, 0], 0, sq * delta[0, 0], sq * delta[1, 0]],\n",
+    "                  [delta[1, 0], - delta[0, 0], - 1.0, - delta[1, 0], delta[0, 0]]])\n",
+    "\n",
+    "    G = G / q\n",
+    "    nLM = calc_n_LM(x)\n",
+    "    F1 = np.hstack((np.eye(3), np.zeros((3, 2 * nLM))))\n",
+    "    F2 = np.hstack((np.zeros((2, 3)), np.zeros((2, 2 * (i - 1))),\n",
+    "                    np.eye(2), np.zeros((2, 2 * nLM - 2 * i))))\n",
+    "\n",
+    "    F = np.vstack((F1, F2))\n",
+    "\n",
+    "    H = G @ F\n",
+    "\n",
+    "    return H"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Observation Step\n",
+    "The observation hmere is use"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def observation(xTrue, xd, u, RFID):\n",
+    "    \"\"\"\n",
+    "    :param xTrue: the true pose of the system\n",
+    "    :param xd: the current noisy estimate of the system\n",
+    "    :param u: the current control input\n",
+    "    :param RFID: the true position of the landmarks\n",
+    "    :returns: Computes the true position, observations, dead reckoning (noisy) position, \n",
+    "    and noisy control function\n",
+    "    \"\"\"\n",
+    "    xTrue = motion_model(xTrue, u)\n",
+    "\n",
+    "    # add noise to gps x-y\n",
+    "    z = np.zeros((0, 3))\n",
+    "\n",
+    "    for i in range(len(RFID[:, 0])):\n",
+    "\n",
+    "        dx = RFID[i, 0] - xTrue[0, 0]\n",
+    "        dy = RFID[i, 1] - xTrue[1, 0]\n",
+    "        d = math.sqrt(dx**2 + dy**2)\n",
+    "        angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])\n",
+    "        if d <= MAX_RANGE:\n",
+    "            dn = d + np.random.randn() * Qsim[0, 0]  # add noise\n",
+    "            anglen = angle + np.random.randn() * Qsim[1, 1]  # add noise\n",
+    "            zi = np.array([dn, anglen, i])\n",
+    "            z = np.vstack((z, zi))\n",
+    "\n",
+    "    # add noise to input\n",
+    "    ud = np.array([[\n",
+    "        u[0, 0] + np.random.randn() * Rsim[0, 0],\n",
+    "        u[1, 0] + np.random.randn() * Rsim[1, 1]]]).T\n",
+    "\n",
+    "    xd = motion_model(xd, ud)\n",
+    "    return xTrue, z, xd, ud"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_n_LM(x):\n",
+    "    \"\"\"\n",
+    "    Calculates the number of landmarks currently tracked in the state\n",
+    "    \"\"\"\n",
+    "    n = int((len(x) - STATE_SIZE) / LM_SIZE)\n",
+    "    return n\n",
+    "\n",
+    "\n",
+    "def jacob_motion(x, u):\n",
+    "    \"\"\"\n",
+    "    Calculates the jacobian of motion model. \n",
+    "    \n",
+    "    G: Jacobian\n",
+    "    Fx: STATE_SIZE x (STATE_SIZE + 2 * num_landmarks) matrix where the left side is an identity matrix\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # [eye(3) [0 x y; 0 x y; 0 x y]]\n",
+    "    Fx = np.hstack((np.eye(STATE_SIZE), np.zeros(\n",
+    "        (STATE_SIZE, LM_SIZE * calc_n_LM(x)))))\n",
+    "\n",
+    "    jF = np.array([[0.0, 0.0, -DT * u[0] * math.sin(x[2, 0])],\n",
+    "                   [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])],\n",
+    "                   [0.0, 0.0, 0.0]])\n",
+    "\n",
+    "    G = np.eye(STATE_SIZE) + Fx.T * jF * Fx\n",
+    "    if calc_n_LM(x) > 0:\n",
+    "        print(Fx.shape)\n",
+    "    return G, Fx,\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_LM_Pos(x, z):\n",
+    "    \"\"\"\n",
+    "    Calcualtes the pose in the world coordinate frame of a landmark at the given measurement. \n",
+    "\n",
+    "    :param x: [x; y; theta]\n",
+    "    :param z: [range; bearing]\n",
+    "    :returns: [x; y] for given measurement\n",
+    "    \"\"\"\n",
+    "    zp = np.zeros((2, 1))\n",
+    "\n",
+    "    zp[0, 0] = x[0, 0] + z[0] * math.cos(x[2, 0] + z[1])\n",
+    "    zp[1, 0] = x[1, 0] + z[0] * math.sin(x[2, 0] + z[1])\n",
+    "    #zp[0, 0] = x[0, 0] + z[0, 0] * math.cos(x[2, 0] + z[0, 1])\n",
+    "    #zp[1, 0] = x[1, 0] + z[0, 0] * math.sin(x[2, 0] + z[0, 1])\n",
+    "\n",
+    "    return zp\n",
+    "\n",
+    "\n",
+    "def get_LM_Pos_from_state(x, ind):\n",
+    "\n",
+    "    lm = x[STATE_SIZE + LM_SIZE * ind: STATE_SIZE + LM_SIZE * (ind + 1), :]\n",
+    "\n",
+    "    return lm\n",
+    "\n",
+    "\n",
+    "def search_correspond_LM_ID(xAug, PAug, zi):\n",
+    "    \"\"\"\n",
+    "    Landmark association with Mahalanobis distance.\n",
+    "    \n",
+    "    If this landmark is at least M_DIST_TH units away from all known landmarks, \n",
+    "    it is a NEW landmark.\n",
+    "    \n",
+    "    :returns: landmark id\n",
+    "    \"\"\"\n",
+    "\n",
+    "    nLM = calc_n_LM(xAug)\n",
+    "\n",
+    "    mdist = []\n",
+    "\n",
+    "    for i in range(nLM):\n",
+    "        lm = get_LM_Pos_from_state(xAug, i)\n",
+    "        y, S, H = calc_innovation(lm, xAug, PAug, zi, i)\n",
+    "        mdist.append(y.T @ np.linalg.inv(S) @ y)\n",
+    "\n",
+    "    mdist.append(M_DIST_TH)  # new landmark\n",
+    "\n",
+    "    minid = mdist.index(min(mdist))\n",
+    "\n",
+    "    return minid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_input():\n",
+    "    v = 1.0  # [m/s]\n",
+    "    yawrate = 0.1  # [rad/s]\n",
+    "    u = np.array([[v, yawrate]]).T\n",
+    "    return u\n",
+    "def pi_2_pi(angle):\n",
+    "    return (angle + math.pi) % (2 * math.pi) - math.pi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def main():\n",
+    "    print(\" start!!\")\n",
+    "\n",
+    "    time = 0.0\n",
+    "\n",
+    "    # RFID positions [x, y]\n",
+    "    RFID = np.array([[10.0, -2.0],\n",
+    "                     [15.0, 10.0],\n",
+    "                     [3.0, 15.0],\n",
+    "                     [-5.0, 20.0]])\n",
+    "\n",
+    "    # State Vector [x y yaw v]'\n",
+    "    xEst = np.zeros((STATE_SIZE, 1))\n",
+    "    xTrue = np.zeros((STATE_SIZE, 1))\n",
+    "    PEst = np.eye(STATE_SIZE)\n",
+    "\n",
+    "    xDR = np.zeros((STATE_SIZE, 1))  # Dead reckoning\n",
+    "\n",
+    "    # history\n",
+    "    hxEst = xEst\n",
+    "    hxTrue = xTrue\n",
+    "    hxDR = xTrue\n",
+    "\n",
+    "    while SIM_TIME >= time:\n",
+    "        time += DT\n",
+    "        u = calc_input()\n",
+    "\n",
+    "        xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID)\n",
+    "\n",
+    "        xEst, PEst = ekf_slam(xEst, PEst, ud, z)\n",
+    "\n",
+    "        x_state = xEst[0:STATE_SIZE]\n",
+    "\n",
+    "        # store data history\n",
+    "        hxEst = np.hstack((hxEst, x_state))\n",
+    "        hxDR = np.hstack((hxDR, xDR))\n",
+    "        hxTrue = np.hstack((hxTrue, xTrue))\n",
+    "\n",
+    "        if show_animation:  # pragma: no cover\n",
+    "            plt.cla()\n",
+    "\n",
+    "            plt.plot(RFID[:, 0], RFID[:, 1], \"*k\")\n",
+    "            plt.plot(xEst[0], xEst[1], \".r\")\n",
+    "\n",
+    "            # plot landmark\n",
+    "            for i in range(calc_n_LM(xEst)):\n",
+    "                plt.plot(xEst[STATE_SIZE + i * 2],\n",
+    "                         xEst[STATE_SIZE + i * 2 + 1], \"xg\")\n",
+    "\n",
+    "            plt.plot(hxTrue[0, :],\n",
+    "                     hxTrue[1, :], \"-b\")\n",
+    "            plt.plot(hxDR[0, :],\n",
+    "                     hxDR[1, :], \"-k\")\n",
+    "            plt.plot(hxEst[0, :],\n",
+    "                     hxEst[1, :], \"-r\")\n",
+    "            plt.axis(\"equal\")\n",
+    "            plt.grid(True)\n",
+    "            plt.pause(0.001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " start!!\n",
+      "New LM\n",
+      "New LM\n",
+      "New LM\n"
+     ]
+    },
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        fig.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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MGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGA0ZW4AACcMSURBVGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYuB06IAEAAAAQ9P91OwKdoAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDv4EANAYAAaTBs3wAAAAASUVORK5CYII=\" width=\"703\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New LM\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib notebook\n",
+    "%matplotlib notebook\n",
+    "main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/SLAM/FastSLAM1/FastSLAM1.ipynb b/SLAM/FastSLAM1/FastSLAM1.ipynb
index 44ea968529..9ce48238f9 100644
--- a/SLAM/FastSLAM1/FastSLAM1.ipynb
+++ b/SLAM/FastSLAM1/FastSLAM1.ipynb
@@ -668,7 +668,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,

From d75a12608a87371d3162789767d1a3f31114b75e Mon Sep 17 00:00:00 2001
From: Andrew Tu <drewtu2@yahoo.com>
Date: Tue, 23 Apr 2019 23:40:19 -0400
Subject: [PATCH 0028/1080] finished first round writeup

---
 SLAM/EKFSLAM/ekf_slam.ipynb | 96 +++++++++++++++++++++++--------------
 1 file changed, 61 insertions(+), 35 deletions(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index fe4f47806e..23a4f85d3f 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -114,13 +114,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
     "\"\"\"\n",
     "Extended Kalman Filter SLAM example\n",
-    "author: Atsushi Sakai (@Atsushi_twi)\n",
+    "original author: Atsushi Sakai (@Atsushi_twi)\n",
     "notebook author: Andrew Tu (drewtu2)\n",
     "\"\"\"\n",
     "\n",
@@ -131,11 +131,11 @@
     "\n",
     "\n",
     "# EKF state covariance\n",
-    "Cx = np.diag([0.5, 0.5, np.deg2rad(30.0)])**2\n",
+    "Cx = np.diag([0.5, 0.5, np.deg2rad(30.0)])**2 # Change in covariance\n",
     "\n",
     "#  Simulation parameter\n",
-    "Qsim = np.diag([0.2, np.deg2rad(1.0)])**2\n",
-    "Rsim = np.diag([1.0, np.deg2rad(10.0)])**2\n",
+    "Qsim = np.diag([0.2, np.deg2rad(1.0)])**2  # Sensor Noise\n",
+    "Rsim = np.diag([1.0, np.deg2rad(10.0)])**2 # Process Noise\n",
     "\n",
     "DT = 0.1  # time tick [s]\n",
     "SIM_TIME = 50.0  # simulation time [s]\n",
@@ -182,8 +182,6 @@
     "\n",
     "    # Update\n",
     "    xEst, PEst = update(xEst, PEst, u, z, initP)\n",
-    "    \n",
-    "    xEst[2] = pi_2_pi(xEst[2])\n",
     "\n",
     "    return xEst, PEst\n"
    ]
@@ -351,12 +349,43 @@
     "\n",
     "With the innovation calculated, the question becomes which to trust more - the observations or the predictions? \n",
     "To determine this, we calculate the Kalman Gain - a percent of how much of the innovation to add to the \n",
-    "prediction based on the uncertainty in the predict step and the update step. "
+    "prediction based on the uncertainty in the predict step and the update step. \n",
+    "\n",
+    "$\n",
+    "K = \\bar{P_t}H_t^T(H_t\\bar{P_t}H_t^T + Q_t)^{-1}\n",
+    "$\n",
+    "In these equations, $H$ is the jacobian of the measurement function. The multiplications by $H^T$ and $H$ \n",
+    "represent the application of the delta to the measurement covariance. \n",
+    "Intuitively, this equation is applying the following from the single variate Kalman equation but in the \n",
+    "multivariate form, i.e. finding the ratio of the uncertianty of the process compared the measurment. \n",
+    "\n",
+    "$\n",
+    "K = \\frac{\\bar{P_t}}{\\bar{P_t} + Q_t}\n",
+    "$\n",
+    "\n",
+    "If $Q_t << \\bar{P_t}$, (i.e. the measurement covariance is low relative to the current estimate), then the \n",
+    "Kalman gain will be $~1$. This results in adding all of the innovation to the estimate -- and therefore \n",
+    "completely believing the measurement. \n",
+    "\n",
+    "However, if $Q_t >> \\bar{P_t}$ then the Kalman gain will go to 0, signaling a high trust in the process \n",
+    "and little trust in the measurement.\n",
+    "\n",
+    "The update is captured in the following. \n",
+    "\n",
+    "$\n",
+    "xUpdate = xEst + (K * y)\n",
+    "$\n",
+    "\n",
+    "Of course, the covariance must also be updated as well to account for the changing uncertainty. \n",
+    "\n",
+    "$\n",
+    "P_{t} = (I-K_tH_t)\\bar{P_t}\n",
+    "$"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -382,6 +411,7 @@
     "        xEst = xEst + (K @ y)\n",
     "        PEst = (np.eye(len(xEst)) - (K @ H)) @ PEst\n",
     "    \n",
+    "    xEst[2] = pi_2_pi(xEst[2])\n",
     "    return xEst, PEst\n"
    ]
   },
@@ -396,14 +426,16 @@
     "    q = (delta.T @ delta)[0, 0]\n",
     "    zangle = math.atan2(delta[1, 0], delta[0, 0]) - xEst[2, 0]\n",
     "    zp = np.array([[math.sqrt(q), pi_2_pi(zangle)]])\n",
-    "    y = (z - zp).T\n",
+    "    # zp is the expected measurement based on xEst and the expected landmark position\n",
+    "    \n",
+    "    y = (z - zp).T # y = innovation\n",
     "    y[1] = pi_2_pi(y[1])\n",
+    "    \n",
     "    H = jacobH(q, delta, xEst, LMid + 1)\n",
     "    S = H @ PEst @ H.T + Cx[0:2, 0:2]\n",
     "\n",
     "    return y, S, H\n",
     "\n",
-    "\n",
     "def jacobH(q, delta, x, i):\n",
     "    sq = math.sqrt(q)\n",
     "    G = np.array([[-sq * delta[0, 0], - sq * delta[1, 0], 0, sq * delta[0, 0], sq * delta[1, 0]],\n",
@@ -427,12 +459,18 @@
    "metadata": {},
    "source": [
     "## Observation Step\n",
-    "The observation hmere is use"
+    "The observation step described here is outside the main EKF SLAM process and is primarily used as a method of\n",
+    "driving the simulation. The observations funciton is in charge of calcualting how the poses of the robots change \n",
+    "and accumulate error over time, and the theoretical measuremnts that are expected as a result of each \n",
+    "measurement. \n",
+    "\n",
+    "Observations are based on the TRUE position of the robot. Error in dead reckoning and control functions are \n",
+    "passed along here as well. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -442,6 +480,7 @@
     "    :param xd: the current noisy estimate of the system\n",
     "    :param u: the current control input\n",
     "    :param RFID: the true position of the landmarks\n",
+    "    \n",
     "    :returns: Computes the true position, observations, dead reckoning (noisy) position, \n",
     "    and noisy control function\n",
     "    \"\"\"\n",
@@ -450,7 +489,7 @@
     "    # add noise to gps x-y\n",
     "    z = np.zeros((0, 3))\n",
     "\n",
-    "    for i in range(len(RFID[:, 0])):\n",
+    "    for i in range(len(RFID[:, 0])): # Test all beacons, only add the ones we can see (within MAX_RANGE)\n",
     "\n",
     "        dx = RFID[i, 0] - xTrue[0, 0]\n",
     "        dy = RFID[i, 1] - xTrue[1, 0]\n",
@@ -473,7 +512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -489,8 +528,8 @@
     "    \"\"\"\n",
     "    Calculates the jacobian of motion model. \n",
     "    \n",
-    "    G: Jacobian\n",
-    "    Fx: STATE_SIZE x (STATE_SIZE + 2 * num_landmarks) matrix where the left side is an identity matrix\n",
+    "    :param G: Jacobian\n",
+    "    :param Fx: STATE_SIZE x (STATE_SIZE + 2 * num_landmarks) matrix where the left side is an identity matrix\n",
     "    \"\"\"\n",
     "    \n",
     "    # [eye(3) [0 x y; 0 x y; 0 x y]]\n",
@@ -563,20 +602,14 @@
     "\n",
     "    minid = mdist.index(min(mdist))\n",
     "\n",
-    "    return minid"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "    return minid\n",
+    "\n",
     "def calc_input():\n",
     "    v = 1.0  # [m/s]\n",
     "    yawrate = 0.1  # [rad/s]\n",
     "    u = np.array([[v, yawrate]]).T\n",
     "    return u\n",
+    "\n",
     "def pi_2_pi(angle):\n",
     "    return (angle + math.pi) % (2 * math.pi) - math.pi"
    ]
@@ -649,7 +682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 20,
    "metadata": {
     "scrolled": false
    },
@@ -1447,7 +1480,7 @@
     {
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MGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGA0ZW4AACcMSURBVGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYuB06IAEAAAAQ9P91OwKdoAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDAgAEDBgwYMGDA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\" width=\"636\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1469,13 +1502,6 @@
     "%matplotlib notebook\n",
     "main()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From fd51500c37351e8d9e64923b7a873a159e954471 Mon Sep 17 00:00:00 2001
From: Andrew Tu <drewtu2@yahoo.com>
Date: Tue, 23 Apr 2019 23:42:17 -0400
Subject: [PATCH 0029/1080] math formatting

---
 SLAM/EKFSLAM/ekf_slam.ipynb | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index 23a4f85d3f..a9680b1327 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -62,6 +62,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "$\n",
     "\\begin{array}{cc}\n",
     "X = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\\\ x_1 \\\\ y_1 \\\\ x_2 \\\\ y_2 \\\\ \\dots \\\\ x_n \\\\ y_n\\end{bmatrix}\n",
     "&\n",
@@ -71,7 +72,8 @@
     " &  &  &  & \\vdots &  &  &  &  &  \\\\\n",
     "\\sigma_{x_nx} & \\sigma_{x_ny} & \\sigma_{x_n\\theta} & \\sigma_{x_nx_1} & \\sigma_{x_ny_1} & \\sigma_{x_nx_2} & \\sigma_{x_ny_2} & \\dots & \\sigma_{x_nx_n} & \\sigma_{x_ny_n}\n",
     "\\end{bmatrix}\n",
-    "\\end{array}"
+    "\\end{array}\n",
+    "$"
    ]
   },
   {
@@ -90,6 +92,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "$\n",
     "\\begin{array}{cc}\n",
     "X = \\begin{bmatrix} x \\\\ m \\end{bmatrix}\n",
     "&\n",
@@ -97,7 +100,8 @@
     "\\Sigma_{xx} & \\Sigma_{xm}\\\\\n",
     "\\Sigma_{mx} & \\Sigma_{mm}\\\\\n",
     "\\end{bmatrix}\n",
-    "\\end{array}"
+    "\\end{array}\n",
+    "$"
    ]
   },
   {

From 7699219a5bfc1393899001fcdcad03738b275192 Mon Sep 17 00:00:00 2001
From: Andrew Tu <drewtu2@yahoo.com>
Date: Tue, 23 Apr 2019 23:51:02 -0400
Subject: [PATCH 0030/1080] math formatting

---
 SLAM/EKFSLAM/ekf_slam.ipynb | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index a9680b1327..98ca229c4b 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -62,7 +62,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "$\n",
+    "$$\n",
     "\\begin{array}{cc}\n",
     "X = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\\\ x_1 \\\\ y_1 \\\\ x_2 \\\\ y_2 \\\\ \\dots \\\\ x_n \\\\ y_n\\end{bmatrix}\n",
     "&\n",
@@ -73,7 +73,7 @@
     "\\sigma_{x_nx} & \\sigma_{x_ny} & \\sigma_{x_n\\theta} & \\sigma_{x_nx_1} & \\sigma_{x_ny_1} & \\sigma_{x_nx_2} & \\sigma_{x_ny_2} & \\dots & \\sigma_{x_nx_n} & \\sigma_{x_ny_n}\n",
     "\\end{bmatrix}\n",
     "\\end{array}\n",
-    "$"
+    "$$"
    ]
   },
   {
@@ -92,7 +92,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "$\n",
+    "$$\n",
     "\\begin{array}{cc}\n",
     "X = \\begin{bmatrix} x \\\\ m \\end{bmatrix}\n",
     "&\n",
@@ -101,7 +101,7 @@
     "\\Sigma_{mx} & \\Sigma_{mm}\\\\\n",
     "\\end{bmatrix}\n",
     "\\end{array}\n",
-    "$"
+    "$$"
    ]
   },
   {

From d7d3f32899dda6eb896b6fa3787f1c5a706e94cd Mon Sep 17 00:00:00 2001
From: Andrew Tu <drewtu2@yahoo.com>
Date: Tue, 23 Apr 2019 23:52:59 -0400
Subject: [PATCH 0031/1080] testing different math representations for gitub
 rendering

---
 SLAM/EKFSLAM/ekf_slam.ipynb | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index 98ca229c4b..9b58493199 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -64,8 +64,8 @@
    "source": [
     "$$\n",
     "\\begin{array}{cc}\n",
-    "X = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\\\ x_1 \\\\ y_1 \\\\ x_2 \\\\ y_2 \\\\ \\dots \\\\ x_n \\\\ y_n\\end{bmatrix}\n",
-    "&\n",
+    "X = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\\\ x_1 \\\\ y_1 \\\\ x_2 \\\\ y_2 \\\\ \\dots \\\\ x_n \\\\ y_n\\end{bmatrix}\\\n",
+    "&\\\n",
     "P = \\begin{bmatrix} \n",
     "\\sigma_{xx} & \\sigma_{xy} & \\sigma_{x\\theta} & \\sigma_{xx_1} & \\sigma_{xy_1} & \\sigma_{xx_2} & \\sigma_{xy_2} & \\dots & \\sigma_{xx_n} & \\sigma_{xy_n} \\\\\n",
     "\\sigma_{yx} & \\sigma_{yy} & \\sigma_{y\\theta} & \\sigma_{yx_1} & \\sigma_{yy_1} & \\sigma_{yx_2} & \\sigma_{yy_2} & \\dots & \\sigma_{yx_n} & \\sigma_{yy_n} \\\\\n",
@@ -95,8 +95,7 @@
     "$$\n",
     "\\begin{array}{cc}\n",
     "X = \\begin{bmatrix} x \\\\ m \\end{bmatrix}\n",
-    "&\n",
-    "P = \\begin{bmatrix} \n",
+    "&P = \\begin{bmatrix} \n",
     "\\Sigma_{xx} & \\Sigma_{xm}\\\\\n",
     "\\Sigma_{mx} & \\Sigma_{mm}\\\\\n",
     "\\end{bmatrix}\n",

From af5665ce9f3d696260a87fbe11b0e03704f69a52 Mon Sep 17 00:00:00 2001
From: Andrew Tu <drewtu2@yahoo.com>
Date: Wed, 24 Apr 2019 00:10:04 -0400
Subject: [PATCH 0032/1080] testing different math representations for gitub
 rendering

---
 SLAM/EKFSLAM/ekf_slam.ipynb | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index 9b58493199..a225413caf 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -62,18 +62,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "$$\n",
+    "\n",
     "\\begin{array}{cc}\n",
     "X = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\\\ x_1 \\\\ y_1 \\\\ x_2 \\\\ y_2 \\\\ \\dots \\\\ x_n \\\\ y_n\\end{bmatrix}\\\n",
-    "&\\\n",
-    "P = \\begin{bmatrix} \n",
+    "& P = \\begin{bmatrix} \n",
     "\\sigma_{xx} & \\sigma_{xy} & \\sigma_{x\\theta} & \\sigma_{xx_1} & \\sigma_{xy_1} & \\sigma_{xx_2} & \\sigma_{xy_2} & \\dots & \\sigma_{xx_n} & \\sigma_{xy_n} \\\\\n",
     "\\sigma_{yx} & \\sigma_{yy} & \\sigma_{y\\theta} & \\sigma_{yx_1} & \\sigma_{yy_1} & \\sigma_{yx_2} & \\sigma_{yy_2} & \\dots & \\sigma_{yx_n} & \\sigma_{yy_n} \\\\\n",
     " &  &  &  & \\vdots &  &  &  &  &  \\\\\n",
     "\\sigma_{x_nx} & \\sigma_{x_ny} & \\sigma_{x_n\\theta} & \\sigma_{x_nx_1} & \\sigma_{x_ny_1} & \\sigma_{x_nx_2} & \\sigma_{x_ny_2} & \\dots & \\sigma_{x_nx_n} & \\sigma_{x_ny_n}\n",
     "\\end{bmatrix}\n",
-    "\\end{array}\n",
-    "$$"
+    "\\end{array}"
    ]
   },
   {
@@ -92,15 +90,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "$$\n",
     "\\begin{array}{cc}\n",
     "X = \\begin{bmatrix} x \\\\ m \\end{bmatrix}\n",
     "&P = \\begin{bmatrix} \n",
     "\\Sigma_{xx} & \\Sigma_{xm}\\\\\n",
     "\\Sigma_{mx} & \\Sigma_{mm}\\\\\n",
     "\\end{bmatrix}\n",
-    "\\end{array}\n",
-    "$$"
+    "\\end{array}"
    ]
   },
   {

From ca61cbbc302acbd56ebb803d7b02f17967418f02 Mon Sep 17 00:00:00 2001
From: Andrew Tu <drewtu2@yahoo.com>
Date: Wed, 24 Apr 2019 18:47:09 -0400
Subject: [PATCH 0033/1080] testing math

---
 SLAM/EKFSLAM/ekf_slam.ipynb | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index a225413caf..5f2ad6da6a 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -62,7 +62,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
+    "\\begin{equation}\n",
     "\\begin{array}{cc}\n",
     "X = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\\\ x_1 \\\\ y_1 \\\\ x_2 \\\\ y_2 \\\\ \\dots \\\\ x_n \\\\ y_n\\end{bmatrix}\\\n",
     "& P = \\begin{bmatrix} \n",
@@ -71,7 +71,8 @@
     " &  &  &  & \\vdots &  &  &  &  &  \\\\\n",
     "\\sigma_{x_nx} & \\sigma_{x_ny} & \\sigma_{x_n\\theta} & \\sigma_{x_nx_1} & \\sigma_{x_ny_1} & \\sigma_{x_nx_2} & \\sigma_{x_ny_2} & \\dots & \\sigma_{x_nx_n} & \\sigma_{x_ny_n}\n",
     "\\end{bmatrix}\n",
-    "\\end{array}"
+    "\\end{array}\n",
+    "\\end{equaiton}"
    ]
   },
   {

From 2ef4c241c1c53d8dfeead4edc641d147a9ae3c51 Mon Sep 17 00:00:00 2001
From: Andrew Tu <drewtu2@yahoo.com>
Date: Wed, 24 Apr 2019 18:50:48 -0400
Subject: [PATCH 0034/1080] testing math

---
 SLAM/EKFSLAM/ekf_slam.ipynb | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index 5f2ad6da6a..ea8419099c 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -63,16 +63,17 @@
    "metadata": {},
    "source": [
     "\\begin{equation}\n",
-    "\\begin{array}{cc}\n",
     "X = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\\\ x_1 \\\\ y_1 \\\\ x_2 \\\\ y_2 \\\\ \\dots \\\\ x_n \\\\ y_n\\end{bmatrix}\\\n",
-    "& P = \\begin{bmatrix} \n",
+    "\\end{equation}\n",
+    "\n",
+    "\\begin{equation}\n",
+    "P = \\begin{bmatrix} \n",
     "\\sigma_{xx} & \\sigma_{xy} & \\sigma_{x\\theta} & \\sigma_{xx_1} & \\sigma_{xy_1} & \\sigma_{xx_2} & \\sigma_{xy_2} & \\dots & \\sigma_{xx_n} & \\sigma_{xy_n} \\\\\n",
     "\\sigma_{yx} & \\sigma_{yy} & \\sigma_{y\\theta} & \\sigma_{yx_1} & \\sigma_{yy_1} & \\sigma_{yx_2} & \\sigma_{yy_2} & \\dots & \\sigma_{yx_n} & \\sigma_{yy_n} \\\\\n",
     " &  &  &  & \\vdots &  &  &  &  &  \\\\\n",
     "\\sigma_{x_nx} & \\sigma_{x_ny} & \\sigma_{x_n\\theta} & \\sigma_{x_nx_1} & \\sigma_{x_ny_1} & \\sigma_{x_nx_2} & \\sigma_{x_ny_2} & \\dots & \\sigma_{x_nx_n} & \\sigma_{x_ny_n}\n",
     "\\end{bmatrix}\n",
-    "\\end{array}\n",
-    "\\end{equaiton}"
+    "\\end{equation}"
    ]
   },
   {
@@ -91,13 +92,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\\begin{array}{cc}\n",
+    "\\begin{equation}\n",
     "X = \\begin{bmatrix} x \\\\ m \\end{bmatrix}\n",
-    "&P = \\begin{bmatrix} \n",
+    "\\end{equation}\n",
+    "\\begin{equation}\n",
+    "P = \\begin{bmatrix} \n",
     "\\Sigma_{xx} & \\Sigma_{xm}\\\\\n",
     "\\Sigma_{mx} & \\Sigma_{mm}\\\\\n",
     "\\end{bmatrix}\n",
-    "\\end{array}"
+    "\\end{equation}"
    ]
   },
   {

From b51fc3998c1b7ab204286207a1d46550269c10a6 Mon Sep 17 00:00:00 2001
From: Andrew Tu <drewtu2@yahoo.com>
Date: Wed, 24 Apr 2019 18:52:23 -0400
Subject: [PATCH 0035/1080] testing math

---
 SLAM/EKFSLAM/ekf_slam.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index ea8419099c..c9712ef6f6 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -63,7 +63,7 @@
    "metadata": {},
    "source": [
     "\\begin{equation}\n",
-    "X = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\\\ x_1 \\\\ y_1 \\\\ x_2 \\\\ y_2 \\\\ \\dots \\\\ x_n \\\\ y_n\\end{bmatrix}\\\n",
+    "X = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\\\ x_1 \\\\ y_1 \\\\ x_2 \\\\ y_2 \\\\ \\dots \\\\ x_n \\\\ y_n \\end{bmatrix}\n",
     "\\end{equation}\n",
     "\n",
     "\\begin{equation}\n",

From 04a423211c4fa3a1f0861e0a08b7c6dfca8f2f05 Mon Sep 17 00:00:00 2001
From: Andrew Tu <drewtu2@yahoo.com>
Date: Wed, 24 Apr 2019 19:06:00 -0400
Subject: [PATCH 0036/1080] small updates to pydoc

---
 SLAM/EKFSLAM/ekf_slam.ipynb | 29 +++++++++++++++++++++++------
 1 file changed, 23 insertions(+), 6 deletions(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index c9712ef6f6..d509444118 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -180,7 +180,7 @@
     "    S = STATE_SIZE\n",
     "\n",
     "    # Predict\n",
-    "    xEst, PEst, G, Fx = predict(xEst, PEst, u, z)\n",
+    "    xEst, PEst, G, Fx = predict(xEst, PEst, u)\n",
     "    initP = np.eye(2)\n",
     "\n",
     "    # Update\n",
@@ -291,11 +291,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def predict(xEst, PEst, u, z):\n",
+    "def predict(xEst, PEst, u):\n",
+    "    \"\"\"\n",
+    "    Performs the prediction step of EKF SLAM\n",
+    "    \n",
+    "    :param xEst: nx1 state vector\n",
+    "    :param PEst: nxn covariacne matrix\n",
+    "    :param u: 2x1 control vector\n",
+    "    :returns: predicted state vector, predicted covariance, jacobian of control vector, transition fx\n",
+    "    \"\"\"\n",
     "    S = STATE_SIZE\n",
     "    xEst[0:S] = motion_model(xEst[0:S], u)\n",
     "    G, Fx = jacob_motion(xEst[0:S], u)\n",
@@ -307,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -317,7 +325,7 @@
     "    \n",
     "    :param x: 3x3 pose estimation\n",
     "    :param u: 2x1 control input [v; w]\n",
-    "    :returns: x' = Fx + Bu\n",
+    "    :returns: the resutling state after the control function is applied\n",
     "    \"\"\"\n",
     "    F = np.array([[1.0, 0, 0],\n",
     "                  [0, 1.0, 0],\n",
@@ -393,6 +401,15 @@
    "outputs": [],
    "source": [
     "def update(xEst, PEst, u, z, initP):\n",
+    "    \"\"\"\n",
+    "    Performs the update step of EKF SLAM\n",
+    "    \n",
+    "    :param xEst:\n",
+    "    :param PEst:\n",
+    "    :param u:\n",
+    "    :param z:\n",
+    "    :param initP:\n",
+    "    \"\"\"\n",
     "    for iz in range(len(z[:, 0])):  # for each observation\n",
     "        minid = search_correspond_LM_ID(xEst, PEst, z[iz, 0:2]) # associate to a known landmark\n",
     "\n",

From 320f0c4d896869ceea0102438e8271cbb42e4a43 Mon Sep 17 00:00:00 2001
From: Alec Li <li.ale@husky.neu.edu>
Date: Thu, 25 Apr 2019 18:57:24 -0400
Subject: [PATCH 0037/1080] added partial documentation to ekf methods

---
 SLAM/EKFSLAM/ekf_slam.ipynb | 75 ++++++++++++++++++++++++++-----------
 1 file changed, 54 insertions(+), 21 deletions(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index d509444118..843366e465 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -173,9 +173,9 @@
     "    \n",
     "    :param xEst: the belief in last position\n",
     "    :param PEst: the uncertainty in last position\n",
-    "    :param u: the control function applied to the last position \n",
-    "    :param z: measurements at this step\n",
-    "    :returns: the next estimated position and associated covariance\n",
+    "    :param u:    the control function applied to the last position \n",
+    "    :param z:    measurements at this step\n",
+    "    :returns:    the next estimated position and associated covariance\n",
     "    \"\"\"\n",
     "    S = STATE_SIZE\n",
     "\n",
@@ -301,8 +301,8 @@
     "    \n",
     "    :param xEst: nx1 state vector\n",
     "    :param PEst: nxn covariacne matrix\n",
-    "    :param u: 2x1 control vector\n",
-    "    :returns: predicted state vector, predicted covariance, jacobian of control vector, transition fx\n",
+    "    :param u:    2x1 control vector\n",
+    "    :returns:    predicted state vector, predicted covariance, jacobian of control vector, transition fx\n",
     "    \"\"\"\n",
     "    S = STATE_SIZE\n",
     "    xEst[0:S] = motion_model(xEst[0:S], u)\n",
@@ -404,11 +404,12 @@
     "    \"\"\"\n",
     "    Performs the update step of EKF SLAM\n",
     "    \n",
-    "    :param xEst:\n",
-    "    :param PEst:\n",
-    "    :param u:\n",
-    "    :param z:\n",
-    "    :param initP:\n",
+    "    :param xEst:  nx1 the predicted pose of the system and the pose of the landmarks\n",
+    "    :param PEst:  nxn the predicted covariance\n",
+    "    :param u:     2x1 the control function \n",
+    "    :param z:     the measurements read at new position\n",
+    "    :param initP: 2x2 an identity matrix acting as the initial covariance\n",
+    "    :returns:     the updated state and covariance for the system\n",
     "    \"\"\"\n",
     "    for iz in range(len(z[:, 0])):  # for each observation\n",
     "        minid = search_correspond_LM_ID(xEst, PEst, z[iz, 0:2]) # associate to a known landmark\n",
@@ -427,7 +428,7 @@
     "        lm = get_LM_Pos_from_state(xEst, minid)\n",
     "        y, S, H = calc_innovation(lm, xEst, PEst, z[iz, 0:2], minid)\n",
     "\n",
-    "        K = (PEst @ H.T) @ np.linalg.inv(S) # Calcualte Kalman Gain\n",
+    "        K = (PEst @ H.T) @ np.linalg.inv(S) # Calculate Kalman Gain\n",
     "        xEst = xEst + (K @ y)\n",
     "        PEst = (np.eye(len(xEst)) - (K @ H)) @ PEst\n",
     "    \n",
@@ -442,6 +443,16 @@
    "outputs": [],
    "source": [
     "def calc_innovation(lm, xEst, PEst, z, LMid):\n",
+    "    \"\"\"\n",
+    "    Calculates the innovation based on expected position and landmark position\n",
+    "    \n",
+    "    :param lm:   landmark position\n",
+    "    :param xEst: estimated position/state\n",
+    "    :param PEst: estimated covariance\n",
+    "    :param z:    read measurements\n",
+    "    :param LMid: landmark id\n",
+    "    :returns:    returns the innovation y, xxxxxxxxxxxx, and the jacobian H\n",
+    "    \"\"\"\n",
     "    delta = lm - xEst[0:2]\n",
     "    q = (delta.T @ delta)[0, 0]\n",
     "    zangle = math.atan2(delta[1, 0], delta[0, 0]) - xEst[2, 0]\n",
@@ -457,6 +468,15 @@
     "    return y, S, H\n",
     "\n",
     "def jacobH(q, delta, x, i):\n",
+    "    \"\"\"\n",
+    "    Calculates the jacobian of the measurement function\n",
+    "    \n",
+    "    :param q: \n",
+    "    :param delta: the difference between a landmark position and the estimated system position\n",
+    "    :param x:     the state, including the estimated system position\n",
+    "    :param i:     landmark id + 1\n",
+    "    :returns:     the jacobian H\n",
+    "    \"\"\"\n",
     "    sq = math.sqrt(q)\n",
     "    G = np.array([[-sq * delta[0, 0], - sq * delta[1, 0], 0, sq * delta[0, 0], sq * delta[1, 0]],\n",
     "                  [delta[1, 0], - delta[0, 0], - 1.0, - delta[1, 0], delta[0, 0]]])\n",
@@ -497,12 +517,12 @@
     "def observation(xTrue, xd, u, RFID):\n",
     "    \"\"\"\n",
     "    :param xTrue: the true pose of the system\n",
-    "    :param xd: the current noisy estimate of the system\n",
-    "    :param u: the current control input\n",
-    "    :param RFID: the true position of the landmarks\n",
+    "    :param xd:    the current noisy estimate of the system\n",
+    "    :param u:     the current control input\n",
+    "    :param RFID:  the true position of the landmarks\n",
     "    \n",
-    "    :returns: Computes the true position, observations, dead reckoning (noisy) position, \n",
-    "    and noisy control function\n",
+    "    :returns:     Computes the true position, observations, dead reckoning (noisy) position, \n",
+    "                  and noisy control function\n",
     "    \"\"\"\n",
     "    xTrue = motion_model(xTrue, u)\n",
     "\n",
@@ -539,6 +559,8 @@
     "def calc_n_LM(x):\n",
     "    \"\"\"\n",
     "    Calculates the number of landmarks currently tracked in the state\n",
+    "    :param x: the state\n",
+    "    :returns: the number of landmarks n\n",
     "    \"\"\"\n",
     "    n = int((len(x) - STATE_SIZE) / LM_SIZE)\n",
     "    return n\n",
@@ -548,8 +570,10 @@
     "    \"\"\"\n",
     "    Calculates the jacobian of motion model. \n",
     "    \n",
-    "    :param G: Jacobian\n",
-    "    :param Fx: STATE_SIZE x (STATE_SIZE + 2 * num_landmarks) matrix where the left side is an identity matrix\n",
+    "    :param x: The state, including the estimated position of the system\n",
+    "    :param u: The control function\n",
+    "    :returns: G:  Jacobian\n",
+    "              Fx: STATE_SIZE x (STATE_SIZE + 2 * num_landmarks) matrix where the left side is an identity matrix\n",
     "    \"\"\"\n",
     "    \n",
     "    # [eye(3) [0 x y; 0 x y; 0 x y]]\n",
@@ -593,7 +617,13 @@
     "\n",
     "\n",
     "def get_LM_Pos_from_state(x, ind):\n",
-    "\n",
+    "    \"\"\"\n",
+    "    Returns the position of a given landmark\n",
+    "    \n",
+    "    :param x:   The state containing all landmark positions\n",
+    "    :param ind: landmark id\n",
+    "    :returns:   The position of the landmark\n",
+    "    \"\"\"\n",
     "    lm = x[STATE_SIZE + LM_SIZE * ind: STATE_SIZE + LM_SIZE * (ind + 1), :]\n",
     "\n",
     "    return lm\n",
@@ -606,7 +636,10 @@
     "    If this landmark is at least M_DIST_TH units away from all known landmarks, \n",
     "    it is a NEW landmark.\n",
     "    \n",
-    "    :returns: landmark id\n",
+    "    :param xAug: The estimated state\n",
+    "    :param PAug: The estimated covariance\n",
+    "    :param zi:   the read measurements of specific landmark\n",
+    "    :returns:    landmark id\n",
     "    \"\"\"\n",
     "\n",
     "    nLM = calc_n_LM(xAug)\n",
@@ -1540,7 +1573,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.7.2"
   }
  },
  "nbformat": 4,

From a8ce073d0307ba8c41a0ac4875d3d04e4a0d5b31 Mon Sep 17 00:00:00 2001
From: Alec Li <li.ale@husky.neu.edu>
Date: Fri, 26 Apr 2019 01:14:18 -0400
Subject: [PATCH 0038/1080] added additional variable explanations

---
 SLAM/EKFSLAM/ekf_slam.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index 843366e465..f7e42ab28b 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -323,7 +323,7 @@
     "    \"\"\"\n",
     "    Computes the motion model based on current state and input function. \n",
     "    \n",
-    "    :param x: 3x3 pose estimation\n",
+    "    :param x: 3x1 pose estimation\n",
     "    :param u: 2x1 control input [v; w]\n",
     "    :returns: the resutling state after the control function is applied\n",
     "    \"\"\"\n",
@@ -451,7 +451,7 @@
     "    :param PEst: estimated covariance\n",
     "    :param z:    read measurements\n",
     "    :param LMid: landmark id\n",
-    "    :returns:    returns the innovation y, xxxxxxxxxxxx, and the jacobian H\n",
+    "    :returns:    returns the innovation y, and the jacobian H, and S, used to calculate the Kalman Gain\n",
     "    \"\"\"\n",
     "    delta = lm - xEst[0:2]\n",
     "    q = (delta.T @ delta)[0, 0]\n",
@@ -471,7 +471,7 @@
     "    \"\"\"\n",
     "    Calculates the jacobian of the measurement function\n",
     "    \n",
-    "    :param q: \n",
+    "    :param q:     the range from the system pose to the landmark\n",
     "    :param delta: the difference between a landmark position and the estimated system position\n",
     "    :param x:     the state, including the estimated system position\n",
     "    :param i:     landmark id + 1\n",

From f3915ae87e38ba1d9d67d6a811e3e0663f3d9969 Mon Sep 17 00:00:00 2001
From: Andrew Tu <drewtu2@yahoo.com>
Date: Fri, 26 Apr 2019 12:43:45 -0400
Subject: [PATCH 0039/1080] updates

---
 SLAM/EKFSLAM/ekf_slam.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index 843366e465..c8caef82c1 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -1573,7 +1573,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.2"
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,

From ad4ede4380a9224f976ce9100b304800be95593a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 1 May 2019 08:47:13 +0900
Subject: [PATCH 0040/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 0f38aef379..8583a5a969 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -196,6 +196,12 @@ Dear AtsushiSakai, <br>Thank you for this great resource! I very much appreciate
 
 --Mridul Aanjaneya
 
+---
+
+Thank you for the python robotics sample code and visualizations!! This is my new favorite thing on the internet.
+
+--selamg@mit.edu
+
 =========
 
 # Citations

From 28befd5ed29d6cb06fe6a2538f3543b655411c89 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 4 May 2019 11:49:18 +0900
Subject: [PATCH 0041/1080] Update users_comments.md

---
 users_comments.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 8583a5a969..6efbd3b7e5 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -218,3 +218,5 @@ URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8516589&isnumber=85
 
 - Autonomous Vehicle Readings https://richardkelley.io/readings
 
+- 36 Amazing Python Open Source Projects (v.2019) – Mybridge for Professionals https://medium.mybridge.co/36-amazing-python-open-source-projects-v-2019-2fe058d79450
+

From 62ee69d5d10418686d511f04b735e8a8d1ccc235 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 9 May 2019 20:00:38 +0900
Subject: [PATCH 0042/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 6efbd3b7e5..a68b828f9e 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -202,6 +202,12 @@ Thank you for the python robotics sample code and visualizations!! This is my ne
 
 --selamg@mit.edu
 
+---
+
+Mr AtsushiSakai .. <br>Your work and teaching is light for me <br>thank you very much for giving time and effort to make it public for us
+
+--Karim Anass
+
 =========
 
 # Citations

From af474687d1066e608ff95b3dd4df3c4615b37bbe Mon Sep 17 00:00:00 2001
From: Kitsunow <kitsunow@users.noreply.github.com>
Date: Thu, 9 May 2019 13:39:43 +0200
Subject: [PATCH 0043/1080] Use rear axle instead of center

---
 PathTracking/pure_pursuit/pure_pursuit.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index af953e3068..9413c24afd 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -26,6 +26,8 @@ def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0):
         self.y = y
         self.yaw = yaw
         self.v = v
+        self.rear_x = self.x - ((L / 2) * math.cos(self.yaw))
+        self.rear_y = self.y - ((L / 2) * math.sin(self.yaw))
 
 
 def update(state, a, delta):
@@ -34,6 +36,8 @@ def update(state, a, delta):
     state.y = state.y + state.v * math.sin(state.yaw) * dt
     state.yaw = state.yaw + state.v / L * math.tan(delta) * dt
     state.v = state.v + a * dt
+    state.rear_x = state.x - ((L / 2) * math.cos(state.yaw))
+    state.rear_y = state.y - ((L / 2) * math.sin(state.yaw))
 
     return state
 
@@ -59,7 +63,7 @@ def pure_pursuit_control(state, cx, cy, pind):
         ty = cy[-1]
         ind = len(cx) - 1
 
-    alpha = math.atan2(ty - state.y, tx - state.x) - state.yaw
+    alpha = math.atan2(ty - state.rear_y, tx - state.rear_x) - state.yaw
 
     Lf = k * state.v + Lfc
 
@@ -71,8 +75,8 @@ def pure_pursuit_control(state, cx, cy, pind):
 def calc_target_index(state, cx, cy):
 
     # search nearest point index
-    dx = [state.x - icx for icx in cx]
-    dy = [state.y - icy for icy in cy]
+    dx = [state.rear_x - icx for icx in cx]
+    dy = [state.rear_y - icy for icy in cy]
     d = [abs(math.sqrt(idx ** 2 + idy ** 2)) for (idx, idy) in zip(dx, dy)]
     ind = d.index(min(d))
     L = 0.0
@@ -81,8 +85,8 @@ def calc_target_index(state, cx, cy):
 
     # search look ahead target point index
     while Lf > L and (ind + 1) < len(cx):
-        dx = cx[ind] - state.x
-        dy = cy[ind] - state.y
+        dx = cx[ind] - state.rear_x
+        dy = cy[ind] - state.rear_y
         L = math.sqrt(dx ** 2 + dy ** 2)
         ind += 1
 

From 1ef134767d234e081aa74e12ea954c41e5be9ff1 Mon Sep 17 00:00:00 2001
From: Kitsunow <kitsunow@users.noreply.github.com>
Date: Thu, 9 May 2019 13:52:32 +0200
Subject: [PATCH 0044/1080] Only calculate all distances between car and path
 points once

---
 PathTracking/pure_pursuit/pure_pursuit.py | 29 +++++++++++++++++++----
 1 file changed, 24 insertions(+), 5 deletions(-)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index 9413c24afd..4fbeadb6c3 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -9,6 +9,8 @@
 import math
 import matplotlib.pyplot as plt
 
+old_nearest_point_index = None
+
 k = 0.1  # look forward gain
 Lfc = 1.0  # look-ahead distance
 Kp = 1.0  # speed proportional gain
@@ -71,14 +73,31 @@ def pure_pursuit_control(state, cx, cy, pind):
 
     return delta, ind
 
+def calc_distance(state, point_x, point_y):
+    dx = state.rear_x - point_x
+    dy = state.rear_y - point_y
+    return math.sqrt(distance_x ** 2 + distance_y ** 2)
+
 
 def calc_target_index(state, cx, cy):
 
-    # search nearest point index
-    dx = [state.rear_x - icx for icx in cx]
-    dy = [state.rear_y - icy for icy in cy]
-    d = [abs(math.sqrt(idx ** 2 + idy ** 2)) for (idx, idy) in zip(dx, dy)]
-    ind = d.index(min(d))
+    if old_nearest_point_index is None:
+        # search nearest point index
+        dx = [state.rear_x - icx for icx in cx]
+        dy = [state.rear_y - icy for icy in cy]
+        d = [abs(math.sqrt(idx ** 2 + idy ** 2)) for (idx, idy) in zip(dx, dy)]
+        ind = d.index(min(d))
+    else:
+        index = old_nearest_point_index
+        distance_this_index = calc_distance(state, cx[index], cy[index])
+        while True:
+            index = index + 1 if (index + 1) < len(cx) else index
+            distance_next_index = calc_distance(state, cx[index], cy[index])
+            if distance_this_index < distance_next_index
+                break
+            distance_this_index = distance_next_index
+        old_nearest_point_index = index
+
     L = 0.0
 
     Lf = k * state.v + Lfc

From 4706e4be0ae62ffeb770cec18b0b29bf0799be42 Mon Sep 17 00:00:00 2001
From: Kitsunow <kitsunow@users.noreply.github.com>
Date: Thu, 9 May 2019 14:12:05 +0200
Subject: [PATCH 0045/1080] Fixed syntax

---
 PathTracking/pure_pursuit/pure_pursuit.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index 4fbeadb6c3..66483b13c7 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -93,7 +93,7 @@ def calc_target_index(state, cx, cy):
         while True:
             index = index + 1 if (index + 1) < len(cx) else index
             distance_next_index = calc_distance(state, cx[index], cy[index])
-            if distance_this_index < distance_next_index
+            if distance_this_index < distance_next_index:
                 break
             distance_this_index = distance_next_index
         old_nearest_point_index = index

From d5d026db0f17a7426fb317c0abb6776ca296d8d4 Mon Sep 17 00:00:00 2001
From: Kitsunow <kitsunow@users.noreply.github.com>
Date: Thu, 9 May 2019 14:22:15 +0200
Subject: [PATCH 0046/1080] Set old index

---
 PathTracking/pure_pursuit/pure_pursuit.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index 66483b13c7..73c86c42a9 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -87,6 +87,7 @@ def calc_target_index(state, cx, cy):
         dy = [state.rear_y - icy for icy in cy]
         d = [abs(math.sqrt(idx ** 2 + idy ** 2)) for (idx, idy) in zip(dx, dy)]
         ind = d.index(min(d))
+        old_nearest_point_index = ind
     else:
         index = old_nearest_point_index
         distance_this_index = calc_distance(state, cx[index], cy[index])

From 82c55d42c4331bcd3b82cd3a980b89c6236fce77 Mon Sep 17 00:00:00 2001
From: Kitsunow <kitsunow@users.noreply.github.com>
Date: Thu, 9 May 2019 14:49:56 +0200
Subject: [PATCH 0047/1080] Made old index global

---
 PathTracking/pure_pursuit/pure_pursuit.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index 73c86c42a9..fb3a90b85d 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -74,13 +74,16 @@ def pure_pursuit_control(state, cx, cy, pind):
     return delta, ind
 
 def calc_distance(state, point_x, point_y):
+
     dx = state.rear_x - point_x
     dy = state.rear_y - point_y
-    return math.sqrt(distance_x ** 2 + distance_y ** 2)
+    return math.sqrt(dx ** 2 + dy ** 2)
 
 
 def calc_target_index(state, cx, cy):
 
+    global old_nearest_point_index
+
     if old_nearest_point_index is None:
         # search nearest point index
         dx = [state.rear_x - icx for icx in cx]

From 54c872aa2b91e2dfa430a1dd5a628c5448e47d9b Mon Sep 17 00:00:00 2001
From: Kitsunow <kitsunow@users.noreply.github.com>
Date: Thu, 9 May 2019 15:03:42 +0200
Subject: [PATCH 0048/1080] Use correct name

---
 PathTracking/pure_pursuit/pure_pursuit.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index fb3a90b85d..758bf1b9da 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -92,7 +92,7 @@ def calc_target_index(state, cx, cy):
         ind = d.index(min(d))
         old_nearest_point_index = ind
     else:
-        index = old_nearest_point_index
+        ind = old_nearest_point_index
         distance_this_index = calc_distance(state, cx[index], cy[index])
         while True:
             index = index + 1 if (index + 1) < len(cx) else index
@@ -100,7 +100,7 @@ def calc_target_index(state, cx, cy):
             if distance_this_index < distance_next_index:
                 break
             distance_this_index = distance_next_index
-        old_nearest_point_index = index
+        old_nearest_point_index = ind
 
     L = 0.0
 

From 58c2a80e9728375ddce2c4e9231de6804f378b2c Mon Sep 17 00:00:00 2001
From: Kitsunow <kitsunow@users.noreply.github.com>
Date: Thu, 9 May 2019 15:16:07 +0200
Subject: [PATCH 0049/1080] Set index to ind

---
 PathTracking/pure_pursuit/pure_pursuit.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index 758bf1b9da..68cd70d535 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -93,10 +93,10 @@ def calc_target_index(state, cx, cy):
         old_nearest_point_index = ind
     else:
         ind = old_nearest_point_index
-        distance_this_index = calc_distance(state, cx[index], cy[index])
+        distance_this_index = calc_distance(state, cx[ind], cy[ind])
         while True:
-            index = index + 1 if (index + 1) < len(cx) else index
-            distance_next_index = calc_distance(state, cx[index], cy[index])
+            ind = ind + 1 if (ind + 1) < len(cx) else ind
+            distance_next_index = calc_distance(state, cx[ind], cy[ind])
             if distance_this_index < distance_next_index:
                 break
             distance_this_index = distance_next_index

From a5db2fe793fed754071489ec3a36e29a374c0141 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 11 May 2019 08:21:58 +0900
Subject: [PATCH 0050/1080] tuned parameter

---
 PathTracking/pure_pursuit/pure_pursuit.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index 68cd70d535..38a84affce 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -9,15 +9,15 @@
 import math
 import matplotlib.pyplot as plt
 
-old_nearest_point_index = None
 
 k = 0.1  # look forward gain
-Lfc = 1.0  # look-ahead distance
+Lfc = 2.0  # look-ahead distance
 Kp = 1.0  # speed proportional gain
 dt = 0.1  # [s]
 L = 2.9  # [m] wheel base of vehicle
 
 
+old_nearest_point_index = None
 show_animation = True
 
 

From e548494b3908a43ee3c22fed1187db6aecd1fe08 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 18 May 2019 18:13:10 +0900
Subject: [PATCH 0051/1080] Update users_comments.md

---
 users_comments.md | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index a68b828f9e..9702806567 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -208,6 +208,14 @@ Mr AtsushiSakai .. <br>Your work and teaching is light for me <br>thank you very
 
 --Karim Anass
 
+---
+
+Thank You
+
+--Anonymous
+
+
+
 =========
 
 # Citations

From f0749b615055ddd16fae24a3ff698f6362d77ef4 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 20 May 2019 20:01:24 +0900
Subject: [PATCH 0052/1080] Update users_comments.md

---
 users_comments.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 9702806567..a1d94010a4 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -214,6 +214,11 @@ Thank You
 
 --Anonymous
 
+---
+
+I've learned the robotics from the traditional way of solving problem through finding packages and reading papers. This amazing project is unbelievably helpful for new-comers to learn and get familiar with the algorithms. Gods know how many hours you've taken to sort all the materials and written everything into one single coherent project. I'm truly amazed and deepest appreciation.
+
+--Ewing Kang
 
 
 =========

From d25d15ccc7539e9ad98ca42373b53b2c7af2f15d Mon Sep 17 00:00:00 2001
From: AfroDisco <julien.vandenbergh@hotmail.com>
Date: Wed, 22 May 2019 14:19:29 +0200
Subject: [PATCH 0053/1080] Fixed the sampling function

When creating a simple square of obstacles, the sampled points are not inside of it, if it is centered around (0, 0)
---
 PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
index d42592a08e..65a560975c 100644
--- a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
+++ b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
@@ -249,8 +249,8 @@ def sample_points(sx, sy, gx, gy, rr, ox, oy, obkdtree):
     sample_x, sample_y = [], []
 
     while len(sample_x) <= N_SAMPLE:
-        tx = (random.random() - minx) * (maxx - minx)
-        ty = (random.random() - miny) * (maxy - miny)
+        tx = (random.random() * (maxx - minx)) + minx
+        ty = (random.random() * (maxy - miny)) + miny
 
         index, dist = obkdtree.search(np.array([tx, ty]).reshape(2, 1))
 

From e1d402dcd6fdfcb1b4b96a1391469b832b66165c Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 28 May 2019 21:52:37 +0900
Subject: [PATCH 0054/1080] Create FUNDING.yml

---
 .github/FUNDING.yml | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100644 .github/FUNDING.yml

diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml
new file mode 100644
index 0000000000..e9456378ef
--- /dev/null
+++ b/.github/FUNDING.yml
@@ -0,0 +1,3 @@
+# These are supported funding model platforms
+patreon: Atsushi Sakai
+custom: https://www.paypal.me/myenigmapay/

From 95c2e24801afa05d7d455b2800a2940187511807 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 28 May 2019 21:54:39 +0900
Subject: [PATCH 0055/1080] Update FUNDING.yml

---
 .github/FUNDING.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml
index e9456378ef..80f4984c3f 100644
--- a/.github/FUNDING.yml
+++ b/.github/FUNDING.yml
@@ -1,3 +1,3 @@
 # These are supported funding model platforms
-patreon: Atsushi Sakai
+patreon: myenigma
 custom: https://www.paypal.me/myenigmapay/

From 6ee01460f21bb405ed3b0f7822137ef3838909d7 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 29 May 2019 18:19:11 +0900
Subject: [PATCH 0056/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index a1d94010a4..3fef92a057 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -220,6 +220,12 @@ I've learned the robotics from the traditional way of solving problem through fi
 
 --Ewing Kang
 
+---
+
+Hey, I'm a student and I just recently got into robotics, and visited your repository multiple times. Today I was super happy to find the link to Patreon! I am impressed by didactic quality of the repo, keep up the good work!
+
+--Carolina Bianchi
+
 
 =========
 

From 268b3e8123e3272bd86ad60385ad38a55b475ae5 Mon Sep 17 00:00:00 2001
From: AfroDisco <julien.vandenbergh@hotmail.com>
Date: Wed, 29 May 2019 15:35:00 +0200
Subject: [PATCH 0057/1080] Code live lock when no path is found

Added a simple boolean variable to track the state of the algorithm and return something empty when no path is found.
Added and fixed (partially) the documentation
---
 .../probabilistic_road_map.py                  | 18 +++++++++++++++---
 1 file changed, 15 insertions(+), 3 deletions(-)

diff --git a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
index 65a560975c..351232d849 100644
--- a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
+++ b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
@@ -161,12 +161,18 @@ def generate_roadmap(sample_x, sample_y, rr, obkdtree):
 
 def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y):
     """
+    sx: start x position [m]
+    sy: start y position [m]
     gx: goal x position [m]
-    gx: goal x position [m]
+    gy: goal y position [m]
     ox: x position list of Obstacles [m]
     oy: y position list of Obstacles [m]
-    reso: grid resolution [m]
-    rr: robot radius[m]
+    rr: robot radius [m]
+    road_map: ??? [m]
+    sample_x: ??? [m]
+    sample_y: ??? [m]
+    
+    @return: Two lists of path coordinates ([x1, x2, ...], [y1, y2, ...]), empty list when no path was found
     """
 
     nstart = Node(sx, sy, 0.0, -1)
@@ -175,9 +181,12 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y):
     openset, closedset = dict(), dict()
     openset[len(road_map) - 2] = nstart
 
+    path_found = True
+    
     while True:
         if not openset:
             print("Cannot find path")
+            path_found = False
             break
 
         c_id = min(openset, key=lambda o: openset[o].cost)
@@ -217,6 +226,9 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y):
                     openset[n_id].pind = c_id
             else:
                 openset[n_id] = node
+    
+    if path_found is False:
+        return [], []
 
     # generate final course
     rx, ry = [ngoal.x], [ngoal.y]

From 40e5c2cbb02fd41561e5c6ac3617a1451802b48f Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 31 May 2019 10:42:36 +0900
Subject: [PATCH 0058/1080] first release pose_optimization_slam

---
 .gitignore                                    |   1 +
 SLAM/PoseOptimizationSLAM/README.md           |   9 +
 SLAM/PoseOptimizationSLAM/data_downloader.py  |  28 ++
 .../pose_optimization_slam.py                 | 290 ++++++++++++++++++
 4 files changed, 328 insertions(+)
 create mode 100644 SLAM/PoseOptimizationSLAM/README.md
 create mode 100644 SLAM/PoseOptimizationSLAM/data_downloader.py
 create mode 100644 SLAM/PoseOptimizationSLAM/pose_optimization_slam.py

diff --git a/.gitignore b/.gitignore
index e04b28e1a5..62e48c6762 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,6 @@
 *.csv
 *.gif
+*.g2o
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
diff --git a/SLAM/PoseOptimizationSLAM/README.md b/SLAM/PoseOptimizationSLAM/README.md
new file mode 100644
index 0000000000..2dbb8d68bb
--- /dev/null
+++ b/SLAM/PoseOptimizationSLAM/README.md
@@ -0,0 +1,9 @@
+# How to use
+
+1. Download data
+
+    python data_downloader.py
+
+2. run SLAM
+
+    python pose_optimization_slam.py
diff --git a/SLAM/PoseOptimizationSLAM/data_downloader.py b/SLAM/PoseOptimizationSLAM/data_downloader.py
new file mode 100644
index 0000000000..d1b8a2c23b
--- /dev/null
+++ b/SLAM/PoseOptimizationSLAM/data_downloader.py
@@ -0,0 +1,28 @@
+"""
+
+Data down loader for pose optimization
+
+author: Atsushi Sakai
+
+"""
+
+
+import subprocess
+def main():
+    print("start!!")
+
+    cmd = "wget https://www.dropbox.com/s/vcz8cag7bo0zlaj/input_INTEL_g2o.g2o?dl=0 -O intel.g2o -nc"
+    subprocess.call(cmd, shell=True)
+
+    cmd = "wget https://www.dropbox.com/s/d8fcn1jg1mebx8f/input_MITb_g2o.g2o?dl=0 -O mit_killian.g2o -nc"
+
+    subprocess.call(cmd, shell=True)
+    cmd = "wget https://www.dropbox.com/s/gmdzo74b3tzvbrw/input_M3500_g2o.g2o?dl=0 -O manhattan3500.g2o -nc"
+    subprocess.call(cmd, shell=True)
+
+    print("done!!")
+
+
+if __name__ == '__main__':
+    main()
+
diff --git a/SLAM/PoseOptimizationSLAM/pose_optimization_slam.py b/SLAM/PoseOptimizationSLAM/pose_optimization_slam.py
new file mode 100644
index 0000000000..764b0f5e73
--- /dev/null
+++ b/SLAM/PoseOptimizationSLAM/pose_optimization_slam.py
@@ -0,0 +1,290 @@
+"""
+
+2D (x, y, yaw) pose optimization SLAM
+
+author: Atsushi Sakai
+
+Ref:
+- [A Compact and Portable Implementation of Graph\-based SLAM](https://www.researchgate.net/publication/321287640_A_Compact_and_Portable_Implementation_of_Graph-based_SLAM)
+
+- [GitHub \- furo\-org/p2o: Single header 2D/3D graph\-based SLAM library](https://github.com/furo-org/p2o)
+
+"""
+
+import sys
+import time
+import math
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy import sparse
+from scipy.sparse import linalg
+
+
+class Optimizer2D:
+
+    def __init__(self):
+        self.verbose = False
+        self.animation = False
+        self.p_lambda = 0.0
+        self.init_w = 1e10
+        self.stop_thre = 1e-3
+        self.dim = 3  # state dimension
+
+    def optimize_path(self, nodes, consts, max_iter, min_iter):
+
+        graph_nodes = nodes[:]
+        prev_cost = sys.float_info.max
+
+        for i in range(max_iter):
+            start = time.time()
+            cost, graph_nodes = self.optimize_path_one_step(
+                graph_nodes, consts)
+            elapsed = time.time() - start
+            if self.verbose:
+                print("step ", i, " cost: ", cost, " time:", elapsed, "s")
+
+            # check convergence
+            if (i > min_iter) and (prev_cost - cost < self.stop_thre):
+                if self.verbose:
+                    print("converged:", prev_cost
+                          - cost, " < ", self.stop_thre)
+                    break
+            prev_cost = cost
+
+            if self.animation:
+                plt.cla()
+                plot_nodes(nodes, color="-b")
+                plot_nodes(graph_nodes)
+                plt.axis("equal")
+                plt.pause(1.0)
+
+        return graph_nodes
+
+    def optimize_path_one_step(self, graph_nodes, constraints):
+
+        indlist = [i for i in range(self.dim)]
+        numnodes = len(graph_nodes)
+        bf = np.zeros(numnodes * self.dim)
+        tripletList = TripletList()
+
+        for con in constraints:
+            ida = con.id1
+            idb = con.id2
+            assert 0 <= ida and ida < numnodes, "ida is invalid"
+            assert 0 <= idb and idb < numnodes, "idb is invalid"
+            r, Ja, Jb = self.calc_error(
+                graph_nodes[ida], graph_nodes[idb], con.t)
+
+            trJaInfo = Ja.transpose() @ con.info_mat
+            trJaInfoJa = trJaInfo @ Ja
+            trJbInfo = Jb.transpose() @ con.info_mat
+            trJbInfoJb = trJbInfo @ Jb
+            trJaInfoJb = trJaInfo @ Jb
+
+            for k in indlist:
+                for m in indlist:
+                    tripletList.push_back(
+                        ida * self.dim + k, ida * self.dim + m, trJaInfoJa[k, m])
+                    tripletList.push_back(
+                        idb * self.dim + k, idb * self.dim + m, trJbInfoJb[k, m])
+                    tripletList.push_back(
+                        ida * self.dim + k, idb * self.dim + m, trJaInfoJb[k, m])
+                    tripletList.push_back(
+                        idb * self.dim + k, ida * self.dim + m, trJaInfoJb[m, k])
+
+            bf[ida * self.dim: ida * self.dim + 3] += trJaInfo @ r
+            bf[idb * self.dim: idb * self.dim + 3] += trJbInfo @ r
+
+        for k in indlist:
+            tripletList.push_back(k, k, self.init_w)
+
+        for i in range(self.dim * numnodes):
+            tripletList.push_back(i, i, self.p_lambda)
+
+        mat = sparse.coo_matrix((tripletList.data, (tripletList.row, tripletList.col)),
+                                shape=(numnodes * self.dim, numnodes * self.dim))
+
+        x = linalg.spsolve(mat.tocsr(), -bf)
+
+        out_nodes = []
+        for i in range(len(graph_nodes)):
+            u_i = i * self.dim
+            pos = Pose2D(
+                graph_nodes[i].x + x[u_i],
+                graph_nodes[i].y + x[u_i + 1],
+                graph_nodes[i].theta + x[u_i + 2]
+            )
+            out_nodes.append(pos)
+
+        cost = self.calc_global_cost(out_nodes, constraints)
+
+        return cost, out_nodes
+
+    def calc_global_cost(self, nodes, constraints):
+        cost = 0.0
+        for c in constraints:
+            diff = self.error_func(nodes[c.id1], nodes[c.id2], c.t)
+            cost += diff.transpose() @ c.info_mat @ diff
+
+        return cost
+
+    def error_func(self, pa, pb, t):
+        ba = self.calc_constraint_pose(pb, pa)
+        error = np.array([ba.x - t.x,
+                          ba.y - t.y,
+                          self.pi2pi(ba.theta - t.theta)])
+        return error
+
+    def calc_constraint_pose(self, l, r):
+        diff = np.array([l.x - r.x, l.y - r.y, l.theta - r.theta])
+        v = self.rot_mat_2d(-r.theta) @ diff
+        v[2] = self.pi2pi(l.theta - r.theta)
+        return Pose2D(v[0], v[1], v[2])
+
+    def rot_mat_2d(self, theta):
+        return np.array([[math.cos(theta), -math.sin(theta), 0.0],
+                         [math.sin(theta), math.cos(theta), 0.0],
+                         [0.0, 0.0, 1.0]
+                         ])
+
+    def calc_error(self, pa, pb, t):
+        e0 = self.error_func(pa, pb, t)
+        dx = pb.x - pa.x
+        dy = pb.y - pa.y
+        dxdt = -math.sin(pa.theta) * dx + math.cos(pa.theta) * dy
+        dydt = -math.cos(pa.theta) * dx - math.sin(pa.theta) * dy
+        Ja = np.array([[-math.cos(pa.theta), -math.sin(pa.theta), dxdt],
+                       [math.sin(pa.theta), -math.cos(pa.theta), dydt],
+                       [0.0, 0.0, -1.0]
+                       ])
+        Jb = np.array([[math.cos(pa.theta), math.sin(pa.theta), 0.0],
+                       [-math.sin(pa.theta), math.cos(pa.theta), 0.0],
+                       [0.0, 0.0, 1.0]
+                       ])
+        return e0, Ja, Jb
+
+    def pi2pi(self, rad):
+
+        val = math.fmod(rad, 2.0 * math.pi)
+        if val > math.pi:
+            val -= 2.0 * math.pi
+        elif val < -math.pi:
+            val += 2.0 * math.pi
+
+        return val
+
+
+class TripletList:
+
+    def __init__(self):
+        self.row = []
+        self.col = []
+        self.data = []
+
+    def push_back(self, irow, icol, idata):
+        self.row.append(irow)
+        self.col.append(icol)
+        self.data.append(idata)
+
+
+class Pose2D:
+
+    def __init__(self, x, y, theta):
+        self.x = x
+        self.y = y
+        self.theta = theta
+
+
+class Constrant2D:
+
+    def __init__(self, id1, id2, t, info_mat):
+        self.id1 = id1
+        self.id2 = id2
+        self.t = t
+        self.info_mat = info_mat
+
+
+def plot_nodes(nodes, color ="-r", label = ""):
+    x, y = [], []
+    for n in nodes:
+        x.append(n.x)
+        y.append(n.y)
+    plt.plot(x, y, color, label=label)
+
+
+def load_data(fname):
+    nodes, consts = [], []
+
+    for line in open(fname):
+        sline = line.split()
+        tag = sline[0]
+
+        if tag == "VERTEX_SE2":
+            data_id = int(sline[1])
+            x = float(sline[2])
+            y = float(sline[3])
+            theta = float(sline[4])
+            nodes.append(Pose2D(x, y, theta))
+        elif tag == "EDGE_SE2":
+            id1 = int(sline[1])
+            id2 = int(sline[2])
+            x = float(sline[3])
+            y = float(sline[4])
+            th = float(sline[5])
+            c1 = float(sline[6])
+            c2 = float(sline[7])
+            c3 = float(sline[8])
+            c4 = float(sline[9])
+            c5 = float(sline[10])
+            c6 = float(sline[11])
+            t = Pose2D(x, y, th)
+            info_mat = np.array([[c1, c2, c3],
+                                 [c2, c4, c5],
+                                 [c3, c5, c6]
+                                 ])
+            consts.append(Constrant2D(id1, id2, t, info_mat))
+
+    print("n_nodes:", len(nodes))
+    print("n_consts:", len(consts))
+
+    return nodes, consts
+
+
+def main():
+    print("start!!")
+
+    fnames = ["intel.g2o",
+              "manhattan3500.g2o",
+              "mit_killian.g2o"
+              ]
+
+    max_iter = 20
+    min_iter = 3
+
+    # parameter setting
+    optimizer = Optimizer2D()
+    optimizer.p_lambda = 1e-6
+    optimizer.verbose = True
+    optimizer.animation = True
+
+    for f in fnames:
+        nodes, consts = load_data(f)
+
+        start = time.time()
+        final_nodes = optimizer.optimize_path(nodes, consts, max_iter, min_iter)
+        print("elapsed_time", time.time() - start, "sec")
+
+        # plotting
+        plt.cla()
+        plot_nodes(nodes, color="-b", label="before")
+        plot_nodes(final_nodes, label="after")
+        plt.axis("equal")
+        plt.grid(True)
+        plt.legend()
+        plt.pause(3.0)
+
+    print("done!!")
+
+
+if __name__ == '__main__':
+    main()

From d665f6d93f036b1d5290fb4d7fb75c9257d0c259 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 31 May 2019 10:44:56 +0900
Subject: [PATCH 0059/1080] Update README.md

---
 SLAM/PoseOptimizationSLAM/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/SLAM/PoseOptimizationSLAM/README.md b/SLAM/PoseOptimizationSLAM/README.md
index 2dbb8d68bb..612fa5186d 100644
--- a/SLAM/PoseOptimizationSLAM/README.md
+++ b/SLAM/PoseOptimizationSLAM/README.md
@@ -2,8 +2,8 @@
 
 1. Download data
 
-    python data_downloader.py
+>$ python data_downloader.py
 
 2. run SLAM
 
-    python pose_optimization_slam.py
+>$ python pose_optimization_slam.py

From b7896869926ec1d4365f0d7a28b4d93524243cfc Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 31 May 2019 10:53:45 +0900
Subject: [PATCH 0060/1080] update README

---
 README.md | 19 +++----------------
 1 file changed, 3 insertions(+), 16 deletions(-)

diff --git a/README.md b/README.md
index d0f840f4d3..9c3841f232 100644
--- a/README.md
+++ b/README.md
@@ -242,24 +242,11 @@ Ref:
 - [SLAM simulations by Tim Bailey](http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm)
 
 
-## Graph based SLAM
+## Pose Optimization SLAM
 
-This is a graph based SLAM example.
-
-The blue line is ground truth.
-
-The black line is dead reckoning.
-
-The red line is the estimated trajectory with Graph based SLAM.
-
-The black stars are landmarks for graph edge generation.
-
-![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/GraphBasedSLAM/animation.gif)
-
-Ref:
-
-- [A Tutorial on Graph-Based SLAM](http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf)
+This is a graph based Pose Optimization SLAM example.
 
+![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/PoseOptimizationSLAM/animation.gif)
 
 # Path Planning
 

From 9414678153e3a58d1e276562de2fd1ee2853012a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 31 May 2019 10:55:19 +0900
Subject: [PATCH 0061/1080] update README

---
 README.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 9c3841f232..c786620495 100644
--- a/README.md
+++ b/README.md
@@ -14,6 +14,7 @@ Python codes for robotics algorithm.
 
 
 
+
 # Table of Contents
    * [What is this?](#what-is-this)
    * [Requirements](#requirements)
@@ -31,7 +32,7 @@ Python codes for robotics algorithm.
    * [SLAM](#slam)
       * [Iterative Closest Point (ICP) Matching](#iterative-closest-point-icp-matching)
       * [FastSLAM 1.0](#fastslam-10)
-      * [Graph based SLAM](#graph-based-slam)
+      * [Pose Optimization SLAM](#pose-optimization-slam)
    * [Path Planning](#path-planning)
       * [Dynamic Window Approach](#dynamic-window-approach)
       * [Grid based search](#grid-based-search)
@@ -244,7 +245,7 @@ Ref:
 
 ## Pose Optimization SLAM
 
-This is a graph based Pose Optimization SLAM example.
+This is a graph based pose optimization SLAM example.
 
 ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/PoseOptimizationSLAM/animation.gif)
 
@@ -596,3 +597,4 @@ This is a list: [Users comments](https://github.com/AtsushiSakai/PythonRobotics/
 
 
 
+

From 0ceb95685b02f59457c7791d3e8964b3782d4dc2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E9=9A=BC=E7=94=B0=E9=A7=BF=E5=A4=A7?=
 <toshi@Toshi-Mac.local>
Date: Fri, 31 May 2019 14:23:51 +0900
Subject: [PATCH 0062/1080] fixed miss code

---
 Mapping/kmeans_clustering/kmeans_clustering.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Mapping/kmeans_clustering/kmeans_clustering.py b/Mapping/kmeans_clustering/kmeans_clustering.py
index 984b5565e1..8edfa11e3a 100644
--- a/Mapping/kmeans_clustering/kmeans_clustering.py
+++ b/Mapping/kmeans_clustering/kmeans_clustering.py
@@ -73,7 +73,7 @@ def update_clusters(clusters):
         mind = min(dlist)
         min_id = dlist.index(mind)
         clusters.labels[ip] = min_id
-        cost += min_id
+        cost += mind
 
     return clusters, cost
 

From f8fab6d3fa8409aa7465b5ded233fb9dda2c474f Mon Sep 17 00:00:00 2001
From: Hayata Toshihiro <koda123456789koda@yahoo.co.jp>
Date: Fri, 31 May 2019 14:23:51 +0900
Subject: [PATCH 0063/1080] fixed miss code

---
 Mapping/kmeans_clustering/kmeans_clustering.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Mapping/kmeans_clustering/kmeans_clustering.py b/Mapping/kmeans_clustering/kmeans_clustering.py
index 984b5565e1..8edfa11e3a 100644
--- a/Mapping/kmeans_clustering/kmeans_clustering.py
+++ b/Mapping/kmeans_clustering/kmeans_clustering.py
@@ -73,7 +73,7 @@ def update_clusters(clusters):
         mind = min(dlist)
         min_id = dlist.index(mind)
         clusters.labels[ip] = min_id
-        cost += min_id
+        cost += mind
 
     return clusters, cost
 

From 5d6927a7d9be841a759b6f5aa8a6d64ae535b552 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ern=C5=91=20Horv=C3=A1th?= <horverno@gmail.com>
Date: Fri, 31 May 2019 16:56:36 +0200
Subject: [PATCH 0064/1080] Remove unused variables and imports

---
 Mapping/lidar_to_grid_map/lidar_to_grid_map.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
index cbe50e9770..1e390ffe29 100644
--- a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
+++ b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
@@ -10,7 +10,6 @@
 import numpy as np
 import cv2
 import matplotlib.pyplot as plt
-from math import cos, sin, radians, pi
 
 EXTEND_AREA = 1.0
 
@@ -103,9 +102,7 @@ def generate_ray_casting_grid_map(ox, oy, xyreso, yawreso, breshen = True):
     # occupancy grid computed with bresenham ray casting
     if breshen:
         for (x, y) in zip(ox, oy):
-            d = math.sqrt(x**2 + y**2)
             angle = atan_zero_to_twopi(y, x)
-            angleid = int(math.floor(angle / yawreso))
             ix = int(round((x - minx) / xyreso)) # x coordinte of the the occupied area
             iy = int(round((y - miny) / xyreso)) # y coordinte of the the occupied area
             laser_beams = bresenham((centix, centiy), (ix, iy)) # line form the lidar to the cooupied point

From 193ff61c60185e29ede2495a551c114566584283 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 1 Jun 2019 15:18:53 +0900
Subject: [PATCH 0065/1080] fix coordinate bug and code clean up

---
 PathPlanning/AStar/a_star.py | 126 ++++++++++++++++++++---------------
 1 file changed, 72 insertions(+), 54 deletions(-)

diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py
index 4498c751be..e1c162667d 100644
--- a/PathPlanning/AStar/a_star.py
+++ b/PathPlanning/AStar/a_star.py
@@ -14,44 +14,36 @@
 
 show_animation = True
 
-
 class Node:
 
     def __init__(self, x, y, cost, pind):
-        self.x = x
-        self.y = y
+        self.x = x # index of grid
+        self.y = y # index of grid
         self.cost = cost
         self.pind = pind
 
     def __str__(self):
         return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind)
 
-
-def calc_final_path(ngoal, closedset, reso):
-    # generate final course
-    rx, ry = [ngoal.x * reso], [ngoal.y * reso]
-    pind = ngoal.pind
-    while pind != -1:
-        n = closedset[pind]
-        rx.append(n.x * reso)
-        ry.append(n.y * reso)
-        pind = n.pind
-
-    return rx, ry
-
-
 def a_star_planning(sx, sy, gx, gy, ox, oy, reso, rr):
     """
-    gx: goal x position [m]
-    gx: goal x position [m]
-    ox: x position list of Obstacles [m]
-    oy: y position list of Obstacles [m]
-    reso: grid resolution [m]
-    rr: robot radius[m]
+    A star path search
+
+    input:
+        sx: start x position [m]
+        sy: start y position [m]
+        gx: goal x position [m]
+        gx: goal x position [m]
+        ox: x position list of Obstacles [m]
+        oy: y position list of Obstacles [m]
+        reso: grid resolution [m]
+        rr: robot radius[m]
+
+    output:
+        rx: x position list of the final path
+        ry: y position list of the final path
     """
 
-    nstart = Node(round(sx / reso), round(sy / reso), 0.0, -1)
-    ngoal = Node(round(gx / reso), round(gy / reso), 0.0, -1)
     ox = [iox / reso for iox in ox]
     oy = [ioy / reso for ioy in oy]
 
@@ -59,6 +51,9 @@ def a_star_planning(sx, sy, gx, gy, ox, oy, reso, rr):
 
     motion = get_motion_model()
 
+    nstart = Node(calc_xyindex(sx, minx, reso), calc_xyindex(sy, minx, reso), 0.0, -1)
+    ngoal = Node(calc_xyindex(gx, minx, reso), calc_xyindex(gy, minx, reso), 0.0, -1)
+
     openset, closedset = dict(), dict()
     openset[calc_index(nstart, xw, minx, miny)] = nstart
 
@@ -69,7 +64,7 @@ def a_star_planning(sx, sy, gx, gy, ox, oy, reso, rr):
 
         # show graph
         if show_animation:  # pragma: no cover
-            plt.plot(current.x * reso, current.y * reso, "xc")
+            plt.plot(calc_position(current.x, minx, reso), calc_position(current.y, miny, reso), "xc")
             if len(closedset.keys()) % 10 == 0:
                 plt.pause(0.001)
 
@@ -81,6 +76,7 @@ def a_star_planning(sx, sy, gx, gy, ox, oy, reso, rr):
 
         # Remove the item from the open set
         del openset[c_id]
+
         # Add it to the closed set
         closedset[c_id] = current
 
@@ -94,7 +90,7 @@ def a_star_planning(sx, sy, gx, gy, ox, oy, reso, rr):
             if n_id in closedset:
                 continue
 
-            if not verify_node(node, obmap, minx, miny, maxx, maxy):
+            if not verify_node(node, obmap, minx, miny, maxx, maxy, reso):
                 continue
 
             if n_id not in openset:
@@ -104,26 +100,49 @@ def a_star_planning(sx, sy, gx, gy, ox, oy, reso, rr):
                     # This path is the best until now. record it!
                     openset[n_id] = node
 
-    rx, ry = calc_final_path(ngoal, closedset, reso)
+    rx, ry = calc_final_path(ngoal, closedset, reso, minx, miny)
 
     return rx, ry
 
 
+
+def calc_final_path(ngoal, closedset, reso, minx, miny):
+    # generate final course
+    rx, ry = [calc_position(ngoal.x, minx, reso)], [calc_position(ngoal.y, miny, reso)]
+    pind = ngoal.pind
+    while pind != -1:
+        n = closedset[pind]
+        rx.append(calc_position(n.x, minx, reso))
+        ry.append(calc_position(n.y, miny, reso))
+        pind = n.pind
+
+    return rx, ry
+
+
+
 def calc_heuristic(n1, n2):
     w = 1.0  # weight of heuristic
     d = w * math.sqrt((n1.x - n2.x)**2 + (n1.y - n2.y)**2)
     return d
 
+def calc_position(index, minp, reso):
+    return index*reso+minp
 
-def verify_node(node, obmap, minx, miny, maxx, maxy):
+def calc_xyindex(position, minp, reso):
+    return round((position - minp)/reso)
 
-    if node.x < minx:
+def verify_node(node, obmap, minx, miny, maxx, maxy, reso):
+
+    px = calc_position(node.x, minx, reso)
+    py = calc_position(node.y, miny, reso)
+
+    if px < minx:
         return False
-    elif node.y < miny:
+    elif py < miny:
         return False
-    elif node.x >= maxx:
+    elif px >= maxx:
         return False
-    elif node.y >= maxy:
+    elif py >= maxy:
         return False
 
     if obmap[node.x][node.y]:
@@ -132,21 +151,21 @@ def verify_node(node, obmap, minx, miny, maxx, maxy):
     return True
 
 
-def calc_obstacle_map(ox, oy, reso, vr):
+def calc_obstacle_map(ox, oy, reso, rr):
 
     minx = round(min(ox))
     miny = round(min(oy))
     maxx = round(max(ox))
     maxy = round(max(oy))
-    #  print("minx:", minx)
-    #  print("miny:", miny)
-    #  print("maxx:", maxx)
-    #  print("maxy:", maxy)
+    print("minx:", minx)
+    print("miny:", miny)
+    print("maxx:", maxx)
+    print("maxy:", maxy)
 
     xwidth = round(maxx - minx)
     ywidth = round(maxy - miny)
-    #  print("xwidth:", xwidth)
-    #  print("ywidth:", ywidth)
+    print("xwidth:", xwidth)
+    print("ywidth:", ywidth)
 
     # obstacle map generation
     obmap = [[False for i in range(ywidth)] for i in range(xwidth)]
@@ -154,10 +173,9 @@ def calc_obstacle_map(ox, oy, reso, vr):
         x = ix + minx
         for iy in range(ywidth):
             y = iy + miny
-            #  print(x, y)
             for iox, ioy in zip(ox, oy):
                 d = math.sqrt((iox - x)**2 + (ioy - y)**2)
-                if d <= vr / reso:
+                if d <= rr:
                     obmap[ix][iy] = True
                     break
 
@@ -191,37 +209,37 @@ def main():
     gx = 50.0  # [m]
     gy = 50.0  # [m]
     grid_size = 1.0  # [m]
-    robot_size = 1.0  # [m]
+    robot_radius = 1.0  # [m]
 
+    # set obstable positions
     ox, oy = [], []
-
-    for i in range(60):
+    for i in range(-10, 60):
         ox.append(i)
-        oy.append(0.0)
-    for i in range(60):
+        oy.append(-10.0)
+    for i in range(-10, 60):
         ox.append(60.0)
         oy.append(i)
-    for i in range(61):
+    for i in range(-10, 61):
         ox.append(i)
         oy.append(60.0)
-    for i in range(61):
-        ox.append(0.0)
+    for i in range(-10, 61):
+        ox.append(-10.0)
         oy.append(i)
-    for i in range(40):
+    for i in range(-10, 40):
         ox.append(20.0)
         oy.append(i)
-    for i in range(40):
+    for i in range(0, 40):
         ox.append(40.0)
         oy.append(60.0 - i)
 
     if show_animation:  # pragma: no cover
         plt.plot(ox, oy, ".k")
-        plt.plot(sx, sy, "xr")
+        plt.plot(sx, sy, "og")
         plt.plot(gx, gy, "xb")
         plt.grid(True)
         plt.axis("equal")
 
-    rx, ry = a_star_planning(sx, sy, gx, gy, ox, oy, grid_size, robot_size)
+    rx, ry = a_star_planning(sx, sy, gx, gy, ox, oy, grid_size, robot_radius)
 
     if show_animation:  # pragma: no cover
         plt.plot(rx, ry, "-r")

From 3073a02fe02db95779eddbeae60ed611896cc0dc Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 1 Jun 2019 16:44:29 +0900
Subject: [PATCH 0066/1080] fix bug and huge refactoring

---
 PathPlanning/AStar/a_star.py      | 366 ++++++++++++++--------------
 PathPlanning/Dijkstra/dijkstra.py | 385 ++++++++++++++++--------------
 2 files changed, 390 insertions(+), 361 deletions(-)

diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py
index e1c162667d..88f65a57d3 100644
--- a/PathPlanning/AStar/a_star.py
+++ b/PathPlanning/AStar/a_star.py
@@ -14,190 +14,191 @@
 
 show_animation = True
 
-class Node:
-
-    def __init__(self, x, y, cost, pind):
-        self.x = x # index of grid
-        self.y = y # index of grid
-        self.cost = cost
-        self.pind = pind
-
-    def __str__(self):
-        return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind)
-
-def a_star_planning(sx, sy, gx, gy, ox, oy, reso, rr):
-    """
-    A star path search
-
-    input:
-        sx: start x position [m]
-        sy: start y position [m]
-        gx: goal x position [m]
-        gx: goal x position [m]
+class AStarPlanner:
+
+    def __init__(self, ox, oy, reso, rr):
+        """
+        Intialize map for a star planning
+
         ox: x position list of Obstacles [m]
         oy: y position list of Obstacles [m]
         reso: grid resolution [m]
         rr: robot radius[m]
-
-    output:
-        rx: x position list of the final path
-        ry: y position list of the final path
-    """
-
-    ox = [iox / reso for iox in ox]
-    oy = [ioy / reso for ioy in oy]
-
-    obmap, minx, miny, maxx, maxy, xw, yw = calc_obstacle_map(ox, oy, reso, rr)
-
-    motion = get_motion_model()
-
-    nstart = Node(calc_xyindex(sx, minx, reso), calc_xyindex(sy, minx, reso), 0.0, -1)
-    ngoal = Node(calc_xyindex(gx, minx, reso), calc_xyindex(gy, minx, reso), 0.0, -1)
-
-    openset, closedset = dict(), dict()
-    openset[calc_index(nstart, xw, minx, miny)] = nstart
-
-    while 1:
-        c_id = min(
-            openset, key=lambda o: openset[o].cost + calc_heuristic(ngoal, openset[o]))
-        current = openset[c_id]
-
-        # show graph
-        if show_animation:  # pragma: no cover
-            plt.plot(calc_position(current.x, minx, reso), calc_position(current.y, miny, reso), "xc")
-            if len(closedset.keys()) % 10 == 0:
-                plt.pause(0.001)
-
-        if current.x == ngoal.x and current.y == ngoal.y:
-            print("Find goal")
-            ngoal.pind = current.pind
-            ngoal.cost = current.cost
-            break
-
-        # Remove the item from the open set
-        del openset[c_id]
-
-        # Add it to the closed set
-        closedset[c_id] = current
-
-        # expand search grid based on motion model
-        for i, _ in enumerate(motion):
-            node = Node(current.x + motion[i][0],
-                        current.y + motion[i][1],
-                        current.cost + motion[i][2], c_id)
-            n_id = calc_index(node, xw, minx, miny)
-
-            if n_id in closedset:
-                continue
-
-            if not verify_node(node, obmap, minx, miny, maxx, maxy, reso):
-                continue
-
-            if n_id not in openset:
-                openset[n_id] = node  # Discover a new node
-            else:
-                if openset[n_id].cost >= node.cost:
-                    # This path is the best until now. record it!
-                    openset[n_id] = node
-
-    rx, ry = calc_final_path(ngoal, closedset, reso, minx, miny)
-
-    return rx, ry
-
-
-
-def calc_final_path(ngoal, closedset, reso, minx, miny):
-    # generate final course
-    rx, ry = [calc_position(ngoal.x, minx, reso)], [calc_position(ngoal.y, miny, reso)]
-    pind = ngoal.pind
-    while pind != -1:
-        n = closedset[pind]
-        rx.append(calc_position(n.x, minx, reso))
-        ry.append(calc_position(n.y, miny, reso))
-        pind = n.pind
-
-    return rx, ry
-
-
-
-def calc_heuristic(n1, n2):
-    w = 1.0  # weight of heuristic
-    d = w * math.sqrt((n1.x - n2.x)**2 + (n1.y - n2.y)**2)
-    return d
-
-def calc_position(index, minp, reso):
-    return index*reso+minp
-
-def calc_xyindex(position, minp, reso):
-    return round((position - minp)/reso)
-
-def verify_node(node, obmap, minx, miny, maxx, maxy, reso):
-
-    px = calc_position(node.x, minx, reso)
-    py = calc_position(node.y, miny, reso)
-
-    if px < minx:
-        return False
-    elif py < miny:
-        return False
-    elif px >= maxx:
-        return False
-    elif py >= maxy:
-        return False
-
-    if obmap[node.x][node.y]:
-        return False
-
-    return True
-
-
-def calc_obstacle_map(ox, oy, reso, rr):
-
-    minx = round(min(ox))
-    miny = round(min(oy))
-    maxx = round(max(ox))
-    maxy = round(max(oy))
-    print("minx:", minx)
-    print("miny:", miny)
-    print("maxx:", maxx)
-    print("maxy:", maxy)
-
-    xwidth = round(maxx - minx)
-    ywidth = round(maxy - miny)
-    print("xwidth:", xwidth)
-    print("ywidth:", ywidth)
-
-    # obstacle map generation
-    obmap = [[False for i in range(ywidth)] for i in range(xwidth)]
-    for ix in range(xwidth):
-        x = ix + minx
-        for iy in range(ywidth):
-            y = iy + miny
-            for iox, ioy in zip(ox, oy):
-                d = math.sqrt((iox - x)**2 + (ioy - y)**2)
-                if d <= rr:
-                    obmap[ix][iy] = True
-                    break
-
-    return obmap, minx, miny, maxx, maxy, xwidth, ywidth
-
-
-def calc_index(node, xwidth, xmin, ymin):
-    return (node.y - ymin) * xwidth + (node.x - xmin)
-
-
-def get_motion_model():
-    # dx, dy, cost
-    motion = [[1, 0, 1],
-              [0, 1, 1],
-              [-1, 0, 1],
-              [0, -1, 1],
-              [-1, -1, math.sqrt(2)],
-              [-1, 1, math.sqrt(2)],
-              [1, -1, math.sqrt(2)],
-              [1, 1, math.sqrt(2)]]
-
-    return motion
+        """
+
+        self.reso = reso
+        self.rr = rr
+        self.calc_obstacle_map(ox, oy)
+        self.motion = self.get_motion_model()
+
+    class Node:
+        def __init__(self, x, y, cost, pind):
+            self.x = x  # index of grid
+            self.y = y  # index of grid
+            self.cost = cost
+            self.pind = pind
+
+        def __str__(self):
+            return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind)
+
+    def planning(self, sx, sy, gx, gy):
+        """
+        A star path search
+
+        input:
+            sx: start x position [m]
+            sy: start y position [m]
+            gx: goal x position [m]
+            gx: goal x position [m]
+
+        output:
+            rx: x position list of the final path
+            ry: y position list of the final path
+        """
+
+        nstart = self.Node(self.calc_xyindex(sx, self.minx),
+                           self.calc_xyindex(sy, self.miny), 0.0, -1)
+        ngoal = self.Node(self.calc_xyindex(gx, self.minx),
+                          self.calc_xyindex(gy, self.miny), 0.0, -1)
+
+        openset, closedset = dict(), dict()
+        openset[self.calc_index(nstart)] = nstart
+
+        while 1:
+            c_id = min(
+                openset, key=lambda o: openset[o].cost + self.calc_heuristic(ngoal, openset[o]))
+            current = openset[c_id]
+
+            # show graph
+            if show_animation:  # pragma: no cover
+                plt.plot(self.calc_position(current.x, self.minx),
+                         self.calc_position(current.y, self.miny), "xc")
+                if len(closedset.keys()) % 10 == 0:
+                    plt.pause(0.001)
+
+            if current.x == ngoal.x and current.y == ngoal.y:
+                print("Find goal")
+                ngoal.pind = current.pind
+                ngoal.cost = current.cost
+                break
+
+            # Remove the item from the open set
+            del openset[c_id]
+
+            # Add it to the closed set
+            closedset[c_id] = current
+
+            # expand search grid based on motion model
+            for i, _ in enumerate(self.motion):
+                node = self.Node(current.x + self.motion[i][0],
+                                 current.y + self.motion[i][1],
+                                 current.cost + self.motion[i][2], c_id)
+                n_id = self.calc_index(node)
+
+                if n_id in closedset:
+                    continue
+
+                if not self.verify_node(node):
+                    continue
+
+                if n_id not in openset:
+                    openset[n_id] = node  # Discover a new node
+                else:
+                    if openset[n_id].cost >= node.cost:
+                        # This path is the best until now. record it!
+                        openset[n_id] = node
+
+        rx, ry = self.calc_final_path(ngoal, closedset)
+
+        return rx, ry
+
+    def calc_final_path(self, ngoal, closedset):
+        # generate final course
+        rx, ry = [self.calc_position(ngoal.x, self.minx)], [
+            self.calc_position(ngoal.y, self.miny)]
+        pind = ngoal.pind
+        while pind != -1:
+            n = closedset[pind]
+            rx.append(self.calc_position(n.x, self.minx))
+            ry.append(self.calc_position(n.y, self.miny))
+            pind = n.pind
+
+        return rx, ry
+
+    def calc_heuristic(self, n1, n2):
+        w = 1.0  # weight of heuristic
+        d = w * math.sqrt((n1.x - n2.x)**2 + (n1.y - n2.y)**2)
+        return d
+
+    def calc_position(self, index, minp):
+        pos = index*self.reso+minp
+        return pos
+
+    def calc_xyindex(self, position, minp):
+        return round((position - minp)/self.reso)
+
+    def calc_index(self, node):
+        return (node.y - self.miny) * self.xwidth + (node.x - self.minx)
+
+    def verify_node(self, node):
+        px = self.calc_position(node.x, self.minx)
+        py = self.calc_position(node.y, self.miny)
+
+        if px < self.minx:
+            return False
+        elif py < self.miny:
+            return False
+        elif px >= self.maxx:
+            return False
+        elif py >= self.maxy:
+            return False
+
+        if self.obmap[node.x][node.y]:
+            return False
+
+        return True
+
+    def calc_obstacle_map(self, ox, oy):
+
+        self.minx = round(min(ox))
+        self.miny = round(min(oy))
+        self.maxx = round(max(ox))
+        self.maxy = round(max(oy))
+        print("minx:", self.minx)
+        print("miny:", self.miny)
+        print("maxx:", self.maxx)
+        print("maxy:", self.maxy)
+
+        self.xwidth = round((self.maxx - self.minx)/self.reso)
+        self.ywidth = round((self.maxy - self.miny)/self.reso)
+        print("xwidth:", self.xwidth)
+        print("ywidth:", self.ywidth)
+
+        # obstacle map generation
+        self.obmap = [[False for i in range(self.ywidth)]
+                      for i in range(self.xwidth)]
+        for ix in range(self.xwidth):
+            x = self.calc_position(ix, self.minx)
+            for iy in range(self.ywidth):
+                y = self.calc_position(iy, self.miny)
+                for iox, ioy in zip(ox, oy):
+                    d = math.sqrt((iox - x)**2 + (ioy - y)**2)
+                    if d <= self.rr:
+                        self.obmap[ix][iy] = True
+                        break
+
+    def get_motion_model(self):
+        # dx, dy, cost
+        motion = [[1, 0, 1],
+                  [0, 1, 1],
+                  [-1, 0, 1],
+                  [0, -1, 1],
+                  [-1, -1, math.sqrt(2)],
+                  [-1, 1, math.sqrt(2)],
+                  [1, -1, math.sqrt(2)],
+                  [1, 1, math.sqrt(2)]]
+
+        return motion
 
 
 def main():
@@ -208,7 +209,7 @@ def main():
     sy = 10.0  # [m]
     gx = 50.0  # [m]
     gy = 50.0  # [m]
-    grid_size = 1.0  # [m]
+    grid_size = 2.0  # [m]
     robot_radius = 1.0  # [m]
 
     # set obstable positions
@@ -239,7 +240,8 @@ def main():
         plt.grid(True)
         plt.axis("equal")
 
-    rx, ry = a_star_planning(sx, sy, gx, gy, ox, oy, grid_size, robot_radius)
+    a_star = AStarPlanner(ox, oy, grid_size, robot_radius)
+    rx, ry = a_star.planning(sx, sy, gx, gy)
 
     if show_animation:  # pragma: no cover
         plt.plot(rx, ry, "-r")
diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py
index 89eb632a4d..bb73ed501a 100644
--- a/PathPlanning/Dijkstra/dijkstra.py
+++ b/PathPlanning/Dijkstra/dijkstra.py
@@ -1,7 +1,9 @@
 """
-Dijkstra grid based planning
+
+Grid based Dijkstra planning
 
 author: Atsushi Sakai(@Atsushi_twi)
+
 """
 
 import matplotlib.pyplot as plt
@@ -9,210 +11,235 @@
 
 show_animation = True
 
-
-class Node:
-
-    def __init__(self, x, y, cost, pind):
-        self.x = x
-        self.y = y
-        self.cost = cost
-        self.pind = pind
-
-    def __str__(self):
-        return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind)
-
-
-def dijkstra_planning(sx, sy, gx, gy, ox, oy, reso, rr):
-    """
-    gx: goal x position [m]
-    gx: goal x position [m]
-    ox: x position list of Obstacles [m]
-    oy: y position list of Obstacles [m]
-    reso: grid resolution [m]
-    rr: robot radius[m]
-    """
-
-    nstart = Node(round(sx / reso), round(sy / reso), 0.0, -1)
-    ngoal = Node(round(gx / reso), round(gy / reso), 0.0, -1)
-    ox = [iox / reso for iox in ox]
-    oy = [ioy / reso for ioy in oy]
-
-    obmap, minx, miny, maxx, maxy, xw, yw = calc_obstacle_map(ox, oy, reso, rr)
-
-    motion = get_motion_model()
-
-    openset, closedset = dict(), dict()
-    openset[calc_index(nstart, xw, minx, miny)] = nstart
-
-    while 1:
-        c_id = min(openset, key=lambda o: openset[o].cost)
-        current = openset[c_id]
-        #  print("current", current)
-
-        # show graph
-        if show_animation:
-            plt.plot(current.x * reso, current.y * reso, "xc")
-            if len(closedset.keys()) % 10 == 0:
-                plt.pause(0.001)
-
-        if current.x == ngoal.x and current.y == ngoal.y:
-            print("Find goal")
-            ngoal.pind = current.pind
-            ngoal.cost = current.cost
-            break
-
-        # Remove the item from the open set
-        del openset[c_id]
-        # Add it to the closed set
-        closedset[c_id] = current
-
-        # expand search grid based on motion model
-        for i, _ in enumerate(motion):
-            node = Node(current.x + motion[i][0], current.y + motion[i][1],
-                        current.cost + motion[i][2], c_id)
-            n_id = calc_index(node, xw, minx, miny)
-
-            if not verify_node(node, obmap, minx, miny, maxx, maxy):
-                continue
-
-            if n_id in closedset:
-                continue
-            # Otherwise if it is already in the open set
-            if n_id in openset:
-                if openset[n_id].cost > node.cost:
-                    openset[n_id].cost = node.cost
-                    openset[n_id].pind = c_id
-            else:
-                openset[n_id] = node
-
-    rx, ry = calc_final_path(ngoal, closedset, reso)
-
-    return rx, ry
-
-
-def calc_final_path(ngoal, closedset, reso):
-    # generate final course
-    rx, ry = [ngoal.x * reso], [ngoal.y * reso]
-    pind = ngoal.pind
-    while pind != -1:
-        n = closedset[pind]
-        rx.append(n.x * reso)
-        ry.append(n.y * reso)
-        pind = n.pind
-
-    return rx, ry
-
-
-def verify_node(node, obmap, minx, miny, maxx, maxy):
-
-    if obmap[node.x][node.y]:
-        return False
-
-    if node.x < minx:
-        return False
-    elif node.y < miny:
-        return False
-    elif node.x > maxx:
-        return False
-    elif node.y > maxy:
-        return False
-
-    return True
-
-
-def calc_obstacle_map(ox, oy, reso, vr):
-
-    minx = round(min(ox))
-    miny = round(min(oy))
-    maxx = round(max(ox))
-    maxy = round(max(oy))
-    #  print("minx:", minx)
-    #  print("miny:", miny)
-    #  print("maxx:", maxx)
-    #  print("maxy:", maxy)
-
-    xwidth = round(maxx - minx)
-    ywidth = round(maxy - miny)
-    #  print("xwidth:", xwidth)
-    #  print("ywidth:", ywidth)
-
-    # obstacle map generation
-    obmap = [[False for i in range(ywidth)] for i in range(xwidth)]
-    for ix in range(xwidth):
-        x = ix + minx
-        for iy in range(ywidth):
-            y = iy + miny
-            #  print(x, y)
-            for iox, ioy in zip(ox, oy):
-                d = math.sqrt((iox - x)**2 + (ioy - y)**2)
-                if d <= vr / reso:
-                    obmap[ix][iy] = True
-                    break
-
-    return obmap, minx, miny, maxx, maxy, xwidth, ywidth
-
-
-def calc_index(node, xwidth, xmin, ymin):
-    return (node.y - ymin) * xwidth + (node.x - xmin)
-
-
-def get_motion_model():
-    # dx, dy, cost
-    motion = [[1, 0, 1],
-              [0, 1, 1],
-              [-1, 0, 1],
-              [0, -1, 1],
-              [-1, -1, math.sqrt(2)],
-              [-1, 1, math.sqrt(2)],
-              [1, -1, math.sqrt(2)],
-              [1, 1, math.sqrt(2)]]
-
-    return motion
+class Dijkstra:
+
+    def __init__(self, ox, oy, reso, rr):
+        """
+        Initialize map for a star planning
+
+        ox: x position list of Obstacles [m]
+        oy: y position list of Obstacles [m]
+        reso: grid resolution [m]
+        rr: robot radius[m]
+        """
+
+        self.reso = reso
+        self.rr = rr
+        self.calc_obstacle_map(ox, oy)
+        self.motion = self.get_motion_model()
+
+    class Node:
+        def __init__(self, x, y, cost, pind):
+            self.x = x  # index of grid
+            self.y = y  # index of grid
+            self.cost = cost
+            self.pind = pind
+
+        def __str__(self):
+            return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind)
+
+    def planning(self, sx, sy, gx, gy):
+        """
+        dijkstra path search
+
+        input:
+            sx: start x position [m]
+            sy: start y position [m]
+            gx: goal x position [m]
+            gx: goal x position [m]
+
+        output:
+            rx: x position list of the final path
+            ry: y position list of the final path
+        """
+
+        nstart = self.Node(self.calc_xyindex(sx, self.minx),
+                           self.calc_xyindex(sy, self.miny), 0.0, -1)
+        ngoal = self.Node(self.calc_xyindex(gx, self.minx),
+                          self.calc_xyindex(gy, self.miny), 0.0, -1)
+
+        openset, closedset = dict(), dict()
+        openset[self.calc_index(nstart)] = nstart
+
+        while 1:
+            c_id = min(openset, key=lambda o: openset[o].cost)
+            current = openset[c_id]
+
+            # show graph
+            if show_animation:  # pragma: no cover
+                plt.plot(self.calc_position(current.x, self.minx),
+                         self.calc_position(current.y, self.miny), "xc")
+                if len(closedset.keys()) % 10 == 0:
+                    plt.pause(0.001)
+
+            if current.x == ngoal.x and current.y == ngoal.y:
+                print("Find goal")
+                ngoal.pind = current.pind
+                ngoal.cost = current.cost
+                break
+
+            # Remove the item from the open set
+            del openset[c_id]
+
+            # Add it to the closed set
+            closedset[c_id] = current
+
+            # expand search grid based on motion model
+            for i, _ in enumerate(self.motion):
+                node = self.Node(current.x + self.motion[i][0],
+                                 current.y + self.motion[i][1],
+                                 current.cost + self.motion[i][2], c_id)
+                n_id = self.calc_index(node)
+
+                if n_id in closedset:
+                    continue
+
+                if not self.verify_node(node):
+                    continue
+
+                if n_id not in openset:
+                    openset[n_id] = node  # Discover a new node
+                else:
+                    if openset[n_id].cost >= node.cost:
+                        # This path is the best until now. record it!
+                        openset[n_id] = node
+
+        rx, ry = self.calc_final_path(ngoal, closedset)
+
+        return rx, ry
+
+    def calc_final_path(self, ngoal, closedset):
+        # generate final course
+        rx, ry = [self.calc_position(ngoal.x, self.minx)], [
+            self.calc_position(ngoal.y, self.miny)]
+        pind = ngoal.pind
+        while pind != -1:
+            n = closedset[pind]
+            rx.append(self.calc_position(n.x, self.minx))
+            ry.append(self.calc_position(n.y, self.miny))
+            pind = n.pind
+
+        return rx, ry
+
+    def calc_heuristic(self, n1, n2):
+        w = 1.0  # weight of heuristic
+        d = w * math.sqrt((n1.x - n2.x)**2 + (n1.y - n2.y)**2)
+        return d
+
+    def calc_position(self, index, minp):
+        pos = index*self.reso+minp
+        return pos
+
+    def calc_xyindex(self, position, minp):
+        return round((position - minp)/self.reso)
+
+    def calc_index(self, node):
+        return (node.y - self.miny) * self.xwidth + (node.x - self.minx)
+
+    def verify_node(self, node):
+        px = self.calc_position(node.x, self.minx)
+        py = self.calc_position(node.y, self.miny)
+
+        if px < self.minx:
+            return False
+        elif py < self.miny:
+            return False
+        elif px >= self.maxx:
+            return False
+        elif py >= self.maxy:
+            return False
+
+        if self.obmap[node.x][node.y]:
+            return False
+
+        return True
+
+    def calc_obstacle_map(self, ox, oy):
+
+        self.minx = round(min(ox))
+        self.miny = round(min(oy))
+        self.maxx = round(max(ox))
+        self.maxy = round(max(oy))
+        print("minx:", self.minx)
+        print("miny:", self.miny)
+        print("maxx:", self.maxx)
+        print("maxy:", self.maxy)
+
+        self.xwidth = round((self.maxx - self.minx)/self.reso)
+        self.ywidth = round((self.maxy - self.miny)/self.reso)
+        print("xwidth:", self.xwidth)
+        print("ywidth:", self.ywidth)
+
+        # obstacle map generation
+        self.obmap = [[False for i in range(self.ywidth)]
+                      for i in range(self.xwidth)]
+        for ix in range(self.xwidth):
+            x = self.calc_position(ix, self.minx)
+            for iy in range(self.ywidth):
+                y = self.calc_position(iy, self.miny)
+                for iox, ioy in zip(ox, oy):
+                    d = math.sqrt((iox - x)**2 + (ioy - y)**2)
+                    if d <= self.rr:
+                        self.obmap[ix][iy] = True
+                        break
+
+    def get_motion_model(self):
+        # dx, dy, cost
+        motion = [[1, 0, 1],
+                  [0, 1, 1],
+                  [-1, 0, 1],
+                  [0, -1, 1],
+                  [-1, -1, math.sqrt(2)],
+                  [-1, 1, math.sqrt(2)],
+                  [1, -1, math.sqrt(2)],
+                  [1, 1, math.sqrt(2)]]
+
+        return motion
 
 
 def main():
     print(__file__ + " start!!")
 
     # start and goal position
-    sx = 10.0  # [m]
-    sy = 10.0  # [m]
+    sx = -5.0  # [m]
+    sy = -5.0  # [m]
     gx = 50.0  # [m]
     gy = 50.0  # [m]
-    grid_size = 1.0  # [m]
-    robot_size = 1.0  # [m]
-
-    ox = []
-    oy = []
+    grid_size = 2.0  # [m]
+    robot_radius = 1.0  # [m]
 
-    for i in range(60):
+    # set obstacle positions
+    ox, oy = [], []
+    for i in range(-10, 60):
         ox.append(i)
-        oy.append(0.0)
-    for i in range(60):
+        oy.append(-10.0)
+    for i in range(-10, 60):
         ox.append(60.0)
         oy.append(i)
-    for i in range(61):
+    for i in range(-10, 61):
         ox.append(i)
         oy.append(60.0)
-    for i in range(61):
-        ox.append(0.0)
+    for i in range(-10, 61):
+        ox.append(-10.0)
         oy.append(i)
-    for i in range(40):
+    for i in range(-10, 40):
         ox.append(20.0)
         oy.append(i)
-    for i in range(40):
+    for i in range(0, 40):
         ox.append(40.0)
         oy.append(60.0 - i)
 
-    if show_animation:
+    if show_animation:  # pragma: no cover
         plt.plot(ox, oy, ".k")
-        plt.plot(sx, sy, "xr")
+        plt.plot(sx, sy, "og")
         plt.plot(gx, gy, "xb")
         plt.grid(True)
         plt.axis("equal")
 
-    rx, ry = dijkstra_planning(sx, sy, gx, gy, ox, oy, grid_size, robot_size)
+    dijkstra = Dijkstra(ox, oy, grid_size, robot_radius)
+    rx, ry = dijkstra.planning(sx, sy, gx, gy)
 
-    if show_animation:
+    if show_animation:  # pragma: no cover
         plt.plot(rx, ry, "-r")
         plt.show()
 

From 323ca861bc1762620803b9c92a752d1df287f1ae Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 1 Jun 2019 16:55:54 +0900
Subject: [PATCH 0067/1080] Update README.md

---
 README.md | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c786620495..2cad2844f0 100644
--- a/README.md
+++ b/README.md
@@ -545,7 +545,11 @@ MIT
 
 # Use-case
 
-See: [users\_comments](https://github.com/AtsushiSakai/PythonRobotics/blob/master/users_comments.md)
+If this project helps your robotics project, please let me know with [![Say Thanks!](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/AtsushiSakai).
+
+Your robot's video, which is using PythonRobotics, is very welcome!!
+
+This is a list of other user's comment and references:[users\_comments](https://github.com/AtsushiSakai/PythonRobotics/blob/master/users_comments.md)
 
 # Contribution
 

From 1896ee7df2028d142ddc4d03548653bc3180f71e Mon Sep 17 00:00:00 2001
From: KoenHan <sliver1998324@gmail.com>
Date: Sun, 2 Jun 2019 04:21:52 +0900
Subject: [PATCH 0068/1080] Changed the computation method of to_goal_cost

Signed-off-by: KoenHan <sliver1998324@gmail.com>
---
 .../dynamic_window_approach.py                | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index 3144d9a223..ff588f030e 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -31,8 +31,9 @@ def __init__(self):
         self.yawrate_reso = 0.1 * math.pi / 180.0  # [rad/s]
         self.dt = 0.1  # [s]
         self.predict_time = 3.0  # [s]
-        self.to_goal_cost_gain = 1.0
+        self.to_goal_cost_gain = 0.15
         self.speed_cost_gain = 1.0
+        self.ob_cost_gain = 1.0
         self.robot_radius = 1.0  # [m]
 
 
@@ -133,18 +134,16 @@ def calc_obstacle_cost(traj, ob, config):
             if minr >= r:
                 minr = r
 
-    return 1.0 / minr  # OK
+    return config.ob_cost_gain / minr  # OK
 
 
 def calc_to_goal_cost(traj, goal, config):
-    # calc to goal cost. It is 2D norm.
-
-    goal_magnitude = math.sqrt(goal[0]**2 + goal[1]**2)
-    traj_magnitude = math.sqrt(traj[-1, 0]**2 + traj[-1, 1]**2)
-    dot_product = (goal[0] * traj[-1, 0]) + (goal[1] * traj[-1, 1])
-    error = dot_product / (goal_magnitude * traj_magnitude)
-    error_angle = math.acos(error)
-    cost = config.to_goal_cost_gain * error_angle
+    # calc to goal cost.
+
+    dx = goal[0] - traj[-1, 0]
+    dy = goal[1] - traj[-1, 1]
+    error_angle = math.atan2(dy, dx)
+    cost = config.to_goal_cost_gain * abs(error_angle - traj[-1, 2])
 
     return cost
 

From a4f33de46ef2930eca67e86a007bde5f61657d06 Mon Sep 17 00:00:00 2001
From: KoenHan <sliver1998324@gmail.com>
Date: Sun, 2 Jun 2019 04:35:51 +0900
Subject: [PATCH 0069/1080] Changed the computation method of to_goal_cost

Signed-off-by: KoenHan <sliver1998324@gmail.com>
---
 PathPlanning/DynamicWindowApproach/dynamic_window_approach.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index ff588f030e..f8eefb746b 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -33,7 +33,6 @@ def __init__(self):
         self.predict_time = 3.0  # [s]
         self.to_goal_cost_gain = 0.15
         self.speed_cost_gain = 1.0
-        self.ob_cost_gain = 1.0
         self.robot_radius = 1.0  # [m]
 
 
@@ -134,7 +133,7 @@ def calc_obstacle_cost(traj, ob, config):
             if minr >= r:
                 minr = r
 
-    return config.ob_cost_gain / minr  # OK
+    return 1.0 / minr  # OK
 
 
 def calc_to_goal_cost(traj, goal, config):

From 56e0b7a8cccbfdb63ae054a9a375649c4d891ae8 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 2 Jun 2019 09:51:36 +0900
Subject: [PATCH 0070/1080] DWA code clean up

---
 .../dynamic_window_approach.py                | 101 ++++++++++--------
 1 file changed, 58 insertions(+), 43 deletions(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index f8eefb746b..fa1dcc62c4 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -10,15 +10,26 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-import sys
-sys.path.append("../../")
-
 
 show_animation = True
 
 
+def dwa_control(x, u, config, goal, ob):
+    """
+        Dynamic Window Approach control
+    """
+
+    dw = calc_dynamic_window(x, config)
+
+    u, traj = calc_final_input(x, u, dw, config, goal, ob)
+
+    return u, traj
+
+
 class Config():
-    # simulation parameters
+    """
+    simulation parameter class
+    """
 
     def __init__(self):
         # robot parameter
@@ -29,15 +40,19 @@ def __init__(self):
         self.max_dyawrate = 40.0 * math.pi / 180.0  # [rad/ss]
         self.v_reso = 0.01  # [m/s]
         self.yawrate_reso = 0.1 * math.pi / 180.0  # [rad/s]
-        self.dt = 0.1  # [s]
+        self.dt = 0.1  # [s] Time tick for motion prediction
         self.predict_time = 3.0  # [s]
         self.to_goal_cost_gain = 0.15
         self.speed_cost_gain = 1.0
-        self.robot_radius = 1.0  # [m]
+        self.obstacle_cost_gain = 1.0
+        self.robot_radius = 1.0  # [m] for collision check
+
 
 
 def motion(x, u, dt):
-    # motion model
+    """
+    motion model
+    """
 
     x[2] += u[1] * dt
     x[0] += u[0] * math.cos(x[2]) * dt
@@ -49,6 +64,9 @@ def motion(x, u, dt):
 
 
 def calc_dynamic_window(x, config):
+    """
+    calculation dynamic window based on current state x
+    """
 
     # Dynamic window from robot specification
     Vs = [config.min_speed, config.max_speed,
@@ -67,9 +85,12 @@ def calc_dynamic_window(x, config):
     return dw
 
 
-def calc_trajectory(xinit, v, y, config):
+def predict_trajectory(x_init, v, y, config):
+    """
+    predict trajectory with an input
+    """
 
-    x = np.array(xinit)
+    x = np.array(x_init)
     traj = np.array(x)
     time = 0
     while time <= config.predict_time:
@@ -81,42 +102,44 @@ def calc_trajectory(xinit, v, y, config):
 
 
 def calc_final_input(x, u, dw, config, goal, ob):
+    """
+    calculation final input with dinamic window
+    """
 
-    xinit = x[:]
-    min_cost = 10000.0
-    min_u = u
-    min_u[0] = 0.0
+    x_init = x[:]
+    min_cost = float("inf")
+    best_u = [0.0, 0.0]
     best_traj = np.array([x])
 
     # evalucate all trajectory with sampled input in dynamic window
     for v in np.arange(dw[0], dw[1], config.v_reso):
         for y in np.arange(dw[2], dw[3], config.yawrate_reso):
-            traj = calc_trajectory(xinit, v, y, config)
+
+            traj = predict_trajectory(x_init, v, y, config)
 
             # calc cost
-            to_goal_cost = calc_to_goal_cost(traj, goal, config)
+            to_goal_cost = config.to_goal_cost_gain * calc_to_goal_cost(traj, goal, config)
             speed_cost = config.speed_cost_gain * \
                 (config.max_speed - traj[-1, 3])
-            ob_cost = calc_obstacle_cost(traj, ob, config)
-            # print(ob_cost)
+            ob_cost = config.obstacle_cost_gain*calc_obstacle_cost(traj, ob, config)
 
             final_cost = to_goal_cost + speed_cost + ob_cost
 
-            #print (final_cost)
-
             # search minimum trajectory
             if min_cost >= final_cost:
                 min_cost = final_cost
-                min_u = [v, y]
+                best_u = [v, y]
                 best_traj = traj
 
-    return min_u, best_traj
+    return best_u, best_traj
 
 
 def calc_obstacle_cost(traj, ob, config):
-    # calc obstacle cost inf: collistion, 0:free
+    """
+        calc obstacle cost inf: collision
+    """
 
-    skip_n = 2
+    skip_n = 2 # for speed up
     minr = float("inf")
 
     for ii in range(0, len(traj[:, 1]), skip_n):
@@ -137,26 +160,18 @@ def calc_obstacle_cost(traj, ob, config):
 
 
 def calc_to_goal_cost(traj, goal, config):
-    # calc to goal cost.
+    """
+        calc to goal cost with angle difference
+    """
 
     dx = goal[0] - traj[-1, 0]
     dy = goal[1] - traj[-1, 1]
     error_angle = math.atan2(dy, dx)
-    cost = config.to_goal_cost_gain * abs(error_angle - traj[-1, 2])
+    cost = abs(error_angle - traj[-1, 2])
 
     return cost
 
 
-def dwa_control(x, u, config, goal, ob):
-    # Dynamic Window control
-
-    dw = calc_dynamic_window(x, config)
-
-    u, traj = calc_final_input(x, u, dw, config, goal, ob)
-
-    return u, traj
-
-
 def plot_arrow(x, y, yaw, length=0.5, width=0.1):  # pragma: no cover
     plt.arrow(x, y, length * math.cos(yaw), length * math.sin(yaw),
               head_length=width, head_width=width)
@@ -182,21 +197,20 @@ def main(gx=10, gy=10):
                    [12.0, 12.0]
                    ])
 
+    # input [forward speed, yawrate]
     u = np.array([0.0, 0.0])
     config = Config()
     traj = np.array(x)
 
-    for i in range(1000):
-        u, ltraj = dwa_control(x, u, config, goal, ob)
+    while True:
+        u, ptraj = dwa_control(x, u, config, goal, ob)
 
-        x = motion(x, u, config.dt)
+        x = motion(x, u, config.dt) # simulate robot
         traj = np.vstack((traj, x))  # store state history
 
-        # print(traj)
-
         if show_animation:
             plt.cla()
-            plt.plot(ltraj[:, 0], ltraj[:, 1], "-g")
+            plt.plot(ptraj[:, 0], ptraj[:, 1], "-g")
             plt.plot(x[0], x[1], "xr")
             plt.plot(goal[0], goal[1], "xb")
             plt.plot(ob[:, 0], ob[:, 1], "ok")
@@ -205,8 +219,9 @@ def main(gx=10, gy=10):
             plt.grid(True)
             plt.pause(0.0001)
 
-        # check goal
-        if math.sqrt((x[0] - goal[0])**2 + (x[1] - goal[1])**2) <= config.robot_radius:
+        # check reaching goal
+        dist_to_goal = math.sqrt((x[0] - goal[0])**2 + (x[1] - goal[1])**2)  
+        if dist_to_goal <= config.robot_radius:
             print("Goal!!")
             break
 

From a6aae7135b31c46e74429ee1c21299c83363f0af Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 2 Jun 2019 10:10:11 +0900
Subject: [PATCH 0071/1080] code clean up for EKF and UKF

---
 .../extended_kalman_filter.py                 | 30 +++++++++---------
 .../unscented_kalman_filter.py                | 31 +++++++++++--------
 2 files changed, 33 insertions(+), 28 deletions(-)

diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py
index 11760f0330..3f0589b8c6 100644
--- a/Localization/extended_kalman_filter/extended_kalman_filter.py
+++ b/Localization/extended_kalman_filter/extended_kalman_filter.py
@@ -6,17 +6,22 @@
 
 """
 
-import numpy as np
 import math
+import numpy as np
 import matplotlib.pyplot as plt
 
-# Estimation parameter of EKF
-Q = np.diag([0.1, 0.1, np.deg2rad(1.0), 1.0])**2  # predict state covariance
+# Covariance for EKF simulation
+Q = np.diag([
+    0.1, # variance of location on x-axis
+    0.1, # variance of location on y-axis
+    np.deg2rad(1.0), # variance of yaw angle
+    1.0 # variance of velocity
+    ])**2  # predict state covariance
 R = np.diag([1.0, 1.0])**2  # Observation x,y position covariance
 
 #  Simulation parameter
-Qsim = np.diag([1.0, np.deg2rad(30.0)])**2
-Rsim = np.diag([0.5, 0.5])**2
+INPUT_NOISE = np.diag([1.0, np.deg2rad(30.0)])**2
+GPS_NOISE = np.diag([0.5, 0.5])**2
 
 DT = 0.1  # time tick [s]
 SIM_TIME = 50.0  # simulation time [s]
@@ -27,7 +32,7 @@
 def calc_input():
     v = 1.0  # [m/s]
     yawrate = 0.1  # [rad/s]
-    u = np.array([[v, yawrate]]).T
+    u = np.array([[v], [yawrate]])
     return u
 
 
@@ -36,14 +41,10 @@ def observation(xTrue, xd, u):
     xTrue = motion_model(xTrue, u)
 
     # add noise to gps x-y
-    zx = xTrue[0, 0] + np.random.randn() * Rsim[0, 0]
-    zy = xTrue[1, 0] + np.random.randn() * Rsim[1, 1]
-    z = np.array([[zx, zy]]).T
+    z = observation_model(xTrue) + GPS_NOISE @ np.random.randn(2, 1)
 
     # add noise to input
-    ud1 = u[0, 0] + np.random.randn() * Qsim[0, 0]
-    ud2 = u[1, 0] + np.random.randn() * Qsim[1, 1]
-    ud = np.array([[ud1, ud2]]).T
+    ud = u + INPUT_NOISE @ np.random.randn(2, 1)
 
     xd = motion_model(xd, ud)
 
@@ -62,19 +63,18 @@ def motion_model(x, u):
                   [0.0, DT],
                   [1.0, 0.0]])
 
-    x = F@x + B@u
+    x = F @ x + B @ u
 
     return x
 
 
 def observation_model(x):
-    #  Observation Model
     H = np.array([
         [1, 0, 0, 0],
         [0, 1, 0, 0]
     ])
 
-    z = H@x
+    z = H @ x
 
     return z
 
diff --git a/Localization/unscented_kalman_filter/unscented_kalman_filter.py b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
index cb59a57ecf..78b44754ee 100644
--- a/Localization/unscented_kalman_filter/unscented_kalman_filter.py
+++ b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
@@ -11,13 +11,18 @@
 import math
 import matplotlib.pyplot as plt
 
-# Estimation parameter of UKF
-Q = np.diag([0.1, 0.1, np.deg2rad(1.0), 1.0])**2
-R = np.diag([1.0, np.deg2rad(40.0)])**2
+# Covariance for UKF simulation
+Q = np.diag([
+    0.1, # variance of location on x-axis
+    0.1, # variance of location on y-axis
+    np.deg2rad(1.0), # variance of yaw angle
+    1.0 # variance of velocity
+    ])**2  # predict state covariance
+R = np.diag([1.0, 1.0])**2  # Observation x,y position covariance
 
 #  Simulation parameter
-Qsim = np.diag([0.5, 0.5])**2
-Rsim = np.diag([1.0, np.deg2rad(30.0)])**2
+INPUT_NOISE = np.diag([1.0, np.deg2rad(30.0)])**2
+GPS_NOISE = np.diag([0.5, 0.5])**2
 
 DT = 0.1  # time tick [s]
 SIM_TIME = 50.0  # simulation time [s]
@@ -42,14 +47,10 @@ def observation(xTrue, xd, u):
     xTrue = motion_model(xTrue, u)
 
     # add noise to gps x-y
-    zx = xTrue[0, 0] + np.random.randn() * Qsim[0, 0]
-    zy = xTrue[1, 0] + np.random.randn() * Qsim[1, 1]
-    z = np.array([[zx, zy]]).T
+    z = observation_model(xTrue) + GPS_NOISE @ np.random.randn(2, 1)
 
     # add noise to input
-    ud1 = u[0] + np.random.randn() * Rsim[0, 0]
-    ud2 = u[1] + np.random.randn() * Rsim[1, 1]
-    ud = np.array([ud1, ud2])
+    ud = u + INPUT_NOISE @ np.random.randn(2, 1)
 
     xd = motion_model(xd, ud)
 
@@ -100,7 +101,9 @@ def generate_sigma_points(xEst, PEst, gamma):
 
 
 def predict_sigma_motion(sigma, u):
-    #  Sigma Points prediction with motion model
+    """
+        Sigma Points prediction with motion model
+    """
     for i in range(sigma.shape[1]):
         sigma[:, i:i + 1] = motion_model(sigma[:, i:i + 1], u)
 
@@ -108,7 +111,9 @@ def predict_sigma_motion(sigma, u):
 
 
 def predict_sigma_observation(sigma):
-    # Sigma Points prediction with observation model
+    """
+        Sigma Points prediction with observation model
+    """
     for i in range(sigma.shape[1]):
         sigma[0:2, i] = observation_model(sigma[:, i])
 

From a5aad31271f10adc59adbe50b0cf3a50dd28ab7b Mon Sep 17 00:00:00 2001
From: kyberszittya <kyberszittya@protonmail.ch>
Date: Mon, 3 Jun 2019 00:02:18 +0200
Subject: [PATCH 0072/1080] Getting rid of OpenCV2

---
 .../lidar_to_grid_map/lidar_to_grid_map.py    | 102 +++++++++++----
 .../lidar_to_grid_map_tutorial.ipynb          | 120 ++++++++++++++----
 2 files changed, 169 insertions(+), 53 deletions(-)

diff --git a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
index 1e390ffe29..29040d77bd 100644
--- a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
+++ b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
@@ -8,11 +8,12 @@
 
 import math
 import numpy as np
-import cv2
 import matplotlib.pyplot as plt
+from collections import deque
 
 EXTEND_AREA = 1.0
 
+
 def file_read(f):
     """
     Reading LIDAR laser beams (angles and corresponding distance data)
@@ -27,6 +28,7 @@ def file_read(f):
     distances = np.array(distances)
     return angles, distances
 
+
 def bresenham(start, end):
     """
     Implementation of Bresenham's line drawing algorithm
@@ -70,6 +72,7 @@ def bresenham(start, end):
     points = np.array(points)
     return points
 
+
 def calc_grid_map_config(ox, oy, xyreso):
     """
     Calculates the size, and the maximum distances according to the the measurement center
@@ -83,13 +86,72 @@ def calc_grid_map_config(ox, oy, xyreso):
     print("The grid map is ", xw, "x", yw, ".")
     return minx, miny, maxx, maxy, xw, yw
 
+
 def atan_zero_to_twopi(y, x):
     angle = math.atan2(y, x)
     if angle < 0.0:
         angle += math.pi * 2.0
     return angle
 
-def generate_ray_casting_grid_map(ox, oy, xyreso, yawreso, breshen = True):
+
+def init_floodfill(cpoint,  opoints, xypoints, mincoord, xyreso):
+    """
+    cpoint: center point
+    opoints: detected obstacles points (x,y)
+    xypoints: (x,y) point pairs
+    """
+    centix, centiy = cpoint
+    prev_ix, prev_iy = centix - 1, centiy
+    ox, oy = opoints
+    xw, yw = xypoints
+    minx, miny = mincoord
+    pmap = (np.ones((xw, yw))) * 0.5
+    for (x, y) in zip(ox, oy):
+        ix = int(round((x - minx) / xyreso))  # x coordinate of the the occupied area
+        iy = int(round((y - miny) / xyreso))  # y coordinate of the the occupied area
+        free_area = bresenham((prev_ix, prev_iy), (ix, iy))
+        for fa in free_area:
+            pmap[fa[0]][fa[1]] = 0  # free area 0.0
+        prev_ix = ix
+        prev_iy = iy
+    return pmap
+
+
+def flood_fill(cpoint, pmap):
+    """
+    cpoint: starting point (x,y) of fill
+    pmap: occupancy map generated from Bresenham ray-tracing
+    """
+    # Fill empty areas with queue method
+    sx, sy = pmap.shape
+    fringe = deque()
+    fringe.appendleft(cpoint)
+    while len(fringe)>0:
+        n = fringe.pop()
+        nx, ny = n
+        # West
+        if nx > 0:
+            if pmap[nx - 1, ny] == 0.5:
+                pmap[nx - 1, ny] = 0.0
+                fringe.appendleft((nx - 1, ny))
+        # East
+        if nx < sx - 1:
+            if pmap[nx + 1, ny] == 0.5:
+                pmap[nx + 1, ny] = 0.0
+                fringe.appendleft((nx + 1, ny))
+        # North
+        if ny > 0:
+            if pmap[nx, ny - 1] == 0.5:
+                pmap[nx, ny - 1] = 0.0
+                fringe.appendleft((nx, ny - 1))
+        # South
+        if ny < sy - 1:
+            if pmap[nx, ny + 1] == 0.5:
+                pmap[nx, ny + 1] = 0.0
+                fringe.appendleft((nx, ny + 1))
+
+
+def generate_ray_casting_grid_map(ox, oy, xyreso, breshen=True):
     """
     The breshen boolean tells if it's computed with bresenham ray casting (True) or with flood fill (False)
     """
@@ -97,14 +159,11 @@ def generate_ray_casting_grid_map(ox, oy, xyreso, yawreso, breshen = True):
     pmap = np.ones((xw, yw))/2 # default 0.5 -- [[0.5 for i in range(yw)] for i in range(xw)] 
     centix = int(round(-minx / xyreso)) # center x coordinate of the grid map
     centiy = int(round(-miny / xyreso)) # center y coordinate of the grid map
-    #print(centix, centiy)
-    prev_ix, prev_iy = centix - 1, centiy
     # occupancy grid computed with bresenham ray casting
     if breshen:
         for (x, y) in zip(ox, oy):
-            angle = atan_zero_to_twopi(y, x)
-            ix = int(round((x - minx) / xyreso)) # x coordinte of the the occupied area
-            iy = int(round((y - miny) / xyreso)) # y coordinte of the the occupied area
+            ix = int(round((x - minx) / xyreso)) # x coordinate of the the occupied area
+            iy = int(round((y - miny) / xyreso)) # y coordinate of the the occupied area
             laser_beams = bresenham((centix, centiy), (ix, iy)) # line form the lidar to the cooupied point
             for laser_beam in laser_beams:
                 pmap[laser_beam[0]][laser_beam[1]] = 0.0 # free area 0.0
@@ -114,19 +173,10 @@ def generate_ray_casting_grid_map(ox, oy, xyreso, yawreso, breshen = True):
             pmap[ix+1][iy+1] = 1.0 # extend the occupied area
     # occupancy grid computed with with flood fill
     else:
-        pmap = (np.ones((xw, yw), dtype=np.uint8)) * 5 # food fill does not work with float numbers such as 0.5; so 5 is the default and later it is divided by 10
-        for (x, y) in zip(ox, oy):        
-            ix = int(round((x - minx) / xyreso)) # x coordinte of the the occupied area
-            iy = int(round((y - miny) / xyreso)) # y coordinte of the the occupied area
-            free_area = bresenham((prev_ix, prev_iy), (ix, iy))
-            for fa in free_area:
-                pmap[fa[0]][fa[1]] = 0 # free area 0.0                  
-            prev_ix = ix
-            prev_iy = iy
-        cv2.floodFill(pmap, None, (centix, centiy), 0) # filling the free spaces with 0, stating from the center
+        pmap = init_floodfill((centix, centiy), (ox, oy), (xw, yw),  (minx, miny), xyreso)
+        flood_fill((centix, centiy), pmap)
         pmap = np.array(pmap, dtype=np.float)
-        pmap /= 10
-        for (x, y) in zip(ox, oy):        
+        for (x, y) in zip(ox, oy):
             ix = int(round((x - minx) / xyreso))
             iy = int(round((y - miny) / xyreso))
             pmap[ix][iy] = 1.0     # occupied area 1.0
@@ -135,25 +185,25 @@ def generate_ray_casting_grid_map(ox, oy, xyreso, yawreso, breshen = True):
             pmap[ix+1][iy+1] = 1.0 # extend the occupied area
     return pmap, minx, maxx, miny, maxy, xyreso
 
+
 def main():
     """
     Example usage
     """
     print(__file__, "start")
     xyreso = 0.02  # x-y grid resolution
-    yawreso = math.radians(3.1)  # yaw angle resolution [rad]
     ang, dist = file_read("lidar01.csv")
     ox = np.sin(ang) * dist
     oy = np.cos(ang) * dist
-    pmap, minx, maxx, miny, maxy, xyreso = generate_ray_casting_grid_map(ox, oy, xyreso, yawreso, True)
+    pmap, minx, maxx, miny, maxy, xyreso = generate_ray_casting_grid_map(ox, oy, xyreso, True)
     xyres = np.array(pmap).shape
     plt.figure(1, figsize=(10,4))
     plt.subplot(122)
-    plt.imshow(pmap, cmap = "PiYG_r") # cmap = "binary" "PiYG_r" "PiYG_r" "bone" "bone_r" "RdYlGn_r"
+    plt.imshow(pmap, cmap="PiYG_r") # cmap = "binary" "PiYG_r" "PiYG_r" "bone" "bone_r" "RdYlGn_r"
     plt.clim(-0.4, 1.4)
-    plt.gca().set_xticks(np.arange(-.5, xyres[1], 1), minor = True)
-    plt.gca().set_yticks(np.arange(-.5, xyres[0], 1), minor = True)
-    plt.grid(True, which="minor", color="w", linewidth = .6, alpha = 0.5)
+    plt.gca().set_xticks(np.arange(-.5, xyres[1], 1), minor=True)
+    plt.gca().set_yticks(np.arange(-.5, xyres[0], 1), minor=True)
+    plt.grid(True, which="minor", color="w", linewidth=0.6, alpha=0.5)
     plt.colorbar()
     plt.subplot(121)
     plt.plot([oy, np.zeros(np.size(oy))], [ox, np.zeros(np.size(oy))], "ro-")
@@ -164,7 +214,7 @@ def main():
     plt.ylim((top, bottom)) # rescale y axis, to match the grid orientation
     plt.grid(True)
     plt.show()
+
         
 if __name__ == '__main__':
     main()     
-        
diff --git a/Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb b/Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb
index 6f8ef5ac95..b9058ff5ce 100644
--- a/Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb
+++ b/Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb
@@ -18,13 +18,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "import math\n",
     "import numpy as np\n",
-    "import cv2\n",
     "import matplotlib.pyplot as plt\n",
     "from math import cos, sin, radians, pi"
    ]
@@ -38,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,17 +66,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x720 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -103,26 +104,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "import lidar_to_grid_map as lg\n",
-    "map1 = np.ones((50, 50), dtype=np.uint8)\n",
+    "map1 = np.ones((50, 50)) * 0.5\n",
     "line = lg.bresenham((2, 2), (40, 30))\n",
     "for l in line:\n",
-    "    map1[l[0]][l[1]] = 2\n",
+    "    map1[l[0]][l[1]] = 1\n",
     "plt.imshow(map1)\n",
     "plt.colorbar()\n",
     "plt.show()"
@@ -130,27 +133,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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SV0SUJS9ZR0RpDGRZm4goTnpcEVGWvPITEaUxOPO4IqI4mTkfEcVJjSsiimLnqWJEFCg9rogoi/HY2LCD6CiJKyJOlWVtIqJILZ8OcdqwA4iIdjHgcTfaupF0j6TDkp6Z4XNJ+oykA5L2SHptkxiTuCLiVK4XEmyydbcZWN/h87cAF9XbRuCuJhfNUDEipuhXcd72TkkXdGhyHfBF2wYel7Rc0irbhzpdd6CJ60WeO/J13/ffwErgyCDvPQclxQplxVtSrFBGvL8z1wu8yHOPfN33rWzYfJmk3RP2R2yP9HC71cBPJuyP1sfak7hsvwJA0m7bawd579kqKVYoK96SYoXy4p0t252Gdv2m6ULodlJqXBExTKPA+RP21wAHu52UxBURw7QNeG/9dPEK4Ofd6lswvOJ8L2PgYSspVigr3pJihfLiHTpJ9wLrgJWSRoHbgaUAtjcBDwPXAAeAXwE3NbquW/5OUkTEZBkqRkRxkrgiojgDTVyS1kv6fj29/7ZB3ruJ6V5PkLRC0qOS9tc/zx1mjCdJOl/SY5L2Sdor6Zb6eFvjXSbpCUnfqeP9WH38Qkm76ni/LOmMYcd6kqQlkp6S9FC939pYF5uBJS5JS4DPUU3xfzVwg6RXD+r+DW1m6usJtwHbbV8EbK/32+AE8EHbFwNXADfX/z3bGu8x4CrblwCXAuvrp0ifAD5dx/scsGGIMU52C7Bvwn6bY11UBtnjuhw4YPtZ278BtlJN928N2zuBo5MOXwdsqX/fArx9oEHNwPYh29+uf3+R6n+w1bQ3Xtv+Rb27tN4MXAXcVx9vTbyS1gBvBb5Q74uWxroYDTJxzTS1v+3OOzmvpP75yiHHM0X9LthlwC5aHG899HoaOAw8CvwQeN72ibpJm/5N3Al8CDj5JvHLaW+si84gE9espvZHZ5LOAu4H3m/7hWHH04ntMduXUs2Ovhy4eLpmg41qKklvAw7bfnLi4WmaDj3WxWqQE1BnNbW/BX568m11SauoegutIGkpVdL6ku0H6sOtjfck289L2kFVm1su6fS6J9OWfxNXAtdKugZYBpxD1QNrY6yL0iB7XN8CLqqfzJwBvJNqun/bbQNurH+/EXhwiLH8n7rmcjewz/anJnzU1nhfIWl5/ftLgDdS1eUeA66vm7UiXtsftr3G9gVU/07/3fa7aWGsi5btgW1UU/t/QFXb+PtB3rthfPdSLadxnKqHuIGqtrEd2F//XDHsOOtY/5RqqLIHeLrermlxvK8BnqrjfQb4aH38d4EnqF75+Cpw5rBjnRT3OuChEmJdTFte+YmI4mTmfEQUJ4krIoqTxBURxUniiojiJHFFRHGSuCKiOElcEVGc/wUBMZqj39GYDAAAAABJRU5ErkJggg==\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "line = lg.bresenham((2, 30), (40, 30))\n",
     "for l in line:\n",
-    "    map1[l[0]][l[1]] = 2\n",
+    "    map1[l[0]][l[1]] = 1\n",
     "line = lg.bresenham((2, 30), (2, 2))\n",
     "for l in line:\n",
-    "    map1[l[0]][l[1]] = 2    \n",
+    "    map1[l[0]][l[1]] = 1\n",
     "plt.imshow(map1)\n",
     "plt.colorbar()\n",
     "plt.show()"
@@ -160,27 +165,78 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `cv2.floodFill` can be used to fill the connected area on a grid map."
+    "To fill empty areas, a queue-based algorithm can be used that can be used on an initialized occupancy map. The center point is given: the algorithm checks for neighbour elements in each iteration, and stops expansion on obstacles and free boundaries."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import deque\n",
+    "def flood_fill(cpoint, pmap):\n",
+    "    \"\"\"\n",
+    "    cpoint: starting point (x,y) of fill\n",
+    "    pmap: occupancy map generated from Bresenham ray-tracing\n",
+    "    \"\"\"\n",
+    "    # Fill empty areas with queue method\n",
+    "    sx, sy = pmap.shape\n",
+    "    fringe = deque()\n",
+    "    fringe.appendleft(cpoint)\n",
+    "    while len(fringe)>0:\n",
+    "        n = fringe.pop()\n",
+    "        nx, ny = n\n",
+    "        # West\n",
+    "        if nx > 0:\n",
+    "            if pmap[nx - 1, ny] == 0.5:\n",
+    "                pmap[nx - 1, ny] = 0.0\n",
+    "                fringe.appendleft((nx - 1, ny))\n",
+    "        # East\n",
+    "        if nx < sx - 1:\n",
+    "            if pmap[nx + 1, ny] == 0.5:\n",
+    "                pmap[nx + 1, ny] = 0.0\n",
+    "                fringe.appendleft((nx + 1, ny))\n",
+    "        # North\n",
+    "        if ny > 0:\n",
+    "            if pmap[nx, ny - 1] == 0.5:\n",
+    "                pmap[nx, ny - 1] = 0.0\n",
+    "                fringe.appendleft((nx, ny - 1))\n",
+    "        # South\n",
+    "        if ny < sy - 1:\n",
+    "            if pmap[nx, ny + 1] == 0.5:\n",
+    "                pmap[nx, ny + 1] = 0.0\n",
+    "                fringe.appendleft((nx, ny + 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This algotihm will fill the area bounded by the yellow lines starting from a center point (e.g. (10, 20)) with zeros:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "cv2.floodFill(map1, None, (10, 20), 0)\n",
+    "flood_fill((10, 20), map1)\n",
+    "map_float = np.array(map1)/10.0\n",
     "plt.imshow(map1)\n",
     "plt.colorbar()\n",
     "plt.show()"
@@ -188,24 +244,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The grid map is  150 x 100 .\n"
+      "The grid map is  150 x 100 .\n",
+      "150 100\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x576 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -215,7 +281,7 @@
     "ang, dist = file_read(\"lidar01.csv\")\n",
     "ox = np.sin(ang) * dist\n",
     "oy = np.cos(ang) * dist\n",
-    "pmap, minx, maxx, miny, maxy, xyreso = lg.generate_ray_casting_grid_map(ox, oy, xyreso, yawreso, False)\n",
+    "pmap, minx, maxx, miny, maxy, xyreso = lg.generate_ray_casting_grid_map(ox, oy, xyreso, False)\n",
     "xyres = np.array(pmap).shape\n",
     "plt.figure(figsize=(20,8))\n",
     "plt.subplot(122)\n",
@@ -245,7 +311,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,

From ce1fa0fbf52cf8c845e4305f414ec2720df5adea Mon Sep 17 00:00:00 2001
From: kyberszittya <kyberszittya@protonmail.ch>
Date: Mon, 3 Jun 2019 08:29:24 +0200
Subject: [PATCH 0073/1080] Fixing queue length checking

---
 .../lidar_to_grid_map/lidar_to_grid_map.py    |  2 +-
 .../lidar_to_grid_map_tutorial.ipynb          | 29 +++++++++----------
 2 files changed, 15 insertions(+), 16 deletions(-)

diff --git a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
index 29040d77bd..60bf4db69c 100644
--- a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
+++ b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
@@ -126,7 +126,7 @@ def flood_fill(cpoint, pmap):
     sx, sy = pmap.shape
     fringe = deque()
     fringe.appendleft(cpoint)
-    while len(fringe)>0:
+    while fringe:
         n = fringe.pop()
         nx, ny = n
         # West
diff --git a/Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb b/Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb
index b9058ff5ce..9e8a00f009 100644
--- a/Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb
+++ b/Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -170,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -184,7 +184,7 @@
     "    sx, sy = pmap.shape\n",
     "    fringe = deque()\n",
     "    fringe.appendleft(cpoint)\n",
-    "    while len(fringe)>0:\n",
+    "    while fringe:\n",
     "        n = fringe.pop()\n",
     "        nx, ny = n\n",
     "        # West\n",
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -243,23 +243,22 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "Let's use this flood fill on real data:"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The grid map is  150 x 100 .\n",
-      "150 100\n"
+      "The grid map is  150 x 100 .\n"
      ]
     },
     {

From 57f451337a38052beeb2a0aa8873c67ac7cbac71 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 6 Jun 2019 20:41:23 +0900
Subject: [PATCH 0074/1080] Update users_comments.md

---
 users_comments.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 3fef92a057..732553a6e3 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -226,6 +226,11 @@ Hey, I'm a student and I just recently got into robotics, and visited your repos
 
 --Carolina Bianchi
 
+---
+
+Dear AtsushiSakai, thanks a lot for the PythonRobotics git repository. I'm a college student who is trying to make a mini autonomous robot for my final project submission, I still haven't started using your code but by the look of it I'm sure it will be of greater use. Will let you know how my project is coming up.
+
+--Pragdheesh
 
 =========
 

From 9f739c100e37f3ac605d2df483e9f8c137d53f3b Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 7 Jun 2019 14:54:40 +0900
Subject: [PATCH 0075/1080] Update users_comments.md

---
 users_comments.md | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 732553a6e3..cf91cd132a 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -13,6 +13,13 @@ You can check stargazer's locations of this project from:
 
 - [Stargazers location map](https://drive.google.com/open?id=1bBXS9IQmZ3Tfe1wNGvJbObRt9Htt4PGC&usp=sharing)
 
+
+# Related projects
+
+This is a list of related projects. 
+
+- [onlytailei/CppRobotics: cpp implementation of robotics algorithms including localization, mapping, SLAM, path planning and control](https://github.com/onlytailei/CppRobotics)
+
 # Individual users comments
 
 These are comments from user's using [![Say Thanks!](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/AtsushiSakai)

From f6ef266dc85da0e908dde24b3ee5710630ac348d Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 14 Jun 2019 18:08:55 +0900
Subject: [PATCH 0076/1080] Update users_comments.md

---
 users_comments.md | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/users_comments.md b/users_comments.md
index cf91cd132a..6bb8f8d1f8 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -239,7 +239,19 @@ Dear AtsushiSakai, thanks a lot for the PythonRobotics git repository. I'm a col
 
 --Pragdheesh
 
-=========
+---
+
+Hello AtsushiSakai, <br> <br>Thank you very much for sharing your work!
+
+--Uma K
+
+---
+
+Hey Atsushi <br>We are working on a multiagent simulation game and this project gave us really good insights. <br>In future, I would like to contribute to this project with our multiagent task allocation among robots and multi robot map merging( Which is a big hurdle as we found). <br>Thanks for what you are doing for open source. <br>
+
+--Mayank
+
+---
 
 # Citations
 

From 54b6052e9a3df09900f20c148fd1b469e6412c3f Mon Sep 17 00:00:00 2001
From: Bao Sheng <baosheng@isurro.com>
Date: Tue, 18 Jun 2019 15:40:25 +0800
Subject: [PATCH 0077/1080] Once find the path, stop RRT*

---
 PathPlanning/RRTstar/rrt_star.py | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/PathPlanning/RRTstar/rrt_star.py b/PathPlanning/RRTstar/rrt_star.py
index 3ca43b7c45..a2064a21a3 100644
--- a/PathPlanning/RRTstar/rrt_star.py
+++ b/PathPlanning/RRTstar/rrt_star.py
@@ -63,12 +63,14 @@ def Planning(self, animation=True):
             if animation and i % 5 == 0:
                 self.DrawGraph(rnd)
 
-        # generate coruse
-        lastIndex = self.get_best_last_index()
-        if lastIndex is None:
-            return None
-        path = self.gen_final_course(lastIndex)
-        return path
+            # generate coruse
+            lastIndex = self.get_best_last_index()
+            if lastIndex is None:
+                continue
+            path = self.gen_final_course(lastIndex)
+            return path
+
+        return None
 
     def choose_parent(self, newNode, nearinds):
         if not nearinds:

From 1ee4ceae0a7745c6614ed4ffe3ffca7647796d58 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 19 Jun 2019 22:25:54 +0900
Subject: [PATCH 0078/1080] add control flag for search until maxiter

---
 PathPlanning/RRTstar/rrt_star.py | 111 ++++++++++++++++---------------
 1 file changed, 58 insertions(+), 53 deletions(-)

diff --git a/PathPlanning/RRTstar/rrt_star.py b/PathPlanning/RRTstar/rrt_star.py
index a2064a21a3..169ead8256 100644
--- a/PathPlanning/RRTstar/rrt_star.py
+++ b/PathPlanning/RRTstar/rrt_star.py
@@ -1,3 +1,6 @@
+
+
+
 """
 Path Planning Sample Code with RRT*
 
@@ -14,6 +17,15 @@
 show_animation = True
 
 
+class Node():
+
+    def __init__(self, x, y):
+        self.x = x
+        self.y = y
+        self.cost = 0.0
+        self.parent = None
+
+
 class RRT():
     """
     Class for RRT Planning
@@ -39,11 +51,12 @@ def __init__(self, start, goal, obstacleList, randArea,
         self.maxIter = maxIter
         self.obstacleList = obstacleList
 
-    def Planning(self, animation=True):
+    def Planning(self, animation=True, search_until_maxiter=True):
         """
-        Pathplanning
+        rrt path planning
 
         animation: flag for animation on or off
+        search_until_maxiter: search until max iteration for path improving or not
         """
 
         self.nodeList = [self.start]
@@ -51,35 +64,39 @@ def Planning(self, animation=True):
             rnd = self.get_random_point()
             nind = self.GetNearestListIndex(self.nodeList, rnd)
 
-            newNode = self.steer(rnd, nind)
-            #  print(newNode.cost)
+            new_node = self.steer(rnd, nind)
 
-            if self.__CollisionCheck(newNode, self.obstacleList):
-                nearinds = self.find_near_nodes(newNode)
-                newNode = self.choose_parent(newNode, nearinds)
-                self.nodeList.append(newNode)
-                self.rewire(newNode, nearinds)
+            if self.__CollisionCheck(new_node, self.obstacleList):
+                nearinds = self.find_near_nodes(new_node)
+                new_node = self.choose_parent(new_node, nearinds)
+                self.nodeList.append(new_node)
+                self.rewire(new_node, nearinds)
 
             if animation and i % 5 == 0:
                 self.DrawGraph(rnd)
 
-            # generate coruse
-            lastIndex = self.get_best_last_index()
-            if lastIndex is None:
-                continue
-            path = self.gen_final_course(lastIndex)
-            return path
+            # generate course
+            if not search_until_maxiter:
+                lastIndex = self.get_best_last_index()
+                if lastIndex:
+                    return self.gen_final_course(lastIndex)
+
+        print("reached max iteration")
+
+        lastIndex = self.get_best_last_index()
+        if lastIndex:
+            return self.gen_final_course(lastIndex)
 
         return None
 
-    def choose_parent(self, newNode, nearinds):
+    def choose_parent(self, new_node, nearinds):
         if not nearinds:
-            return newNode
+            return new_node
 
         dlist = []
         for i in nearinds:
-            dx = newNode.x - self.nodeList[i].x
-            dy = newNode.y - self.nodeList[i].y
+            dx = new_node.x - self.nodeList[i].x
+            dy = new_node.y - self.nodeList[i].y
             d = math.sqrt(dx ** 2 + dy ** 2)
             theta = math.atan2(dy, dx)
             if self.check_collision_extend(self.nodeList[i], theta, d):
@@ -92,30 +109,30 @@ def choose_parent(self, newNode, nearinds):
 
         if mincost == float("inf"):
             print("mincost is inf")
-            return newNode
+            return new_node
 
-        newNode.cost = mincost
-        newNode.parent = minind
+        new_node.cost = mincost
+        new_node.parent = minind
 
-        return newNode
+        return new_node
 
     def steer(self, rnd, nind):
 
         # expand tree
-        nearestNode = self.nodeList[nind]
-        theta = math.atan2(rnd[1] - nearestNode.y, rnd[0] - nearestNode.x)
-        newNode = Node(rnd[0], rnd[1])
+        nearest_node = self.nodeList[nind]
+        theta = math.atan2(rnd[1] - nearest_node.y, rnd[0] - nearest_node.x)
+        new_node = Node(rnd[0], rnd[1])
         currentDistance = math.sqrt(
-            (rnd[1] - nearestNode.y) ** 2 + (rnd[0] - nearestNode.x) ** 2)
+            (rnd[1] - nearest_node.y) ** 2 + (rnd[0] - nearest_node.x) ** 2)
         # Find a point within expandDis of nind, and closest to rnd
         if currentDistance <= self.expandDis:
             pass
         else:
-            newNode.x = nearestNode.x + self.expandDis * math.cos(theta)
-            newNode.y = nearestNode.y + self.expandDis * math.sin(theta)
-        newNode.cost = float("inf")
-        newNode.parent = None
-        return newNode
+            new_node.x = nearest_node.x + self.expandDis * math.cos(theta)
+            new_node.y = nearest_node.y + self.expandDis * math.sin(theta)
+        new_node.cost = float("inf")
+        new_node.parent = None
+        return new_node
 
     def get_random_point(self):
 
@@ -155,25 +172,24 @@ def gen_final_course(self, goalind):
     def calc_dist_to_goal(self, x, y):
         return np.linalg.norm([x - self.end.x, y - self.end.y])
 
-    def find_near_nodes(self, newNode):
+    def find_near_nodes(self, new_node):
         nnode = len(self.nodeList)
         r = 50.0 * math.sqrt((math.log(nnode) / nnode))
-        #  r = self.expandDis * 5.0
-        dlist = [(node.x - newNode.x) ** 2 +
-                 (node.y - newNode.y) ** 2 for node in self.nodeList]
+        dlist = [(node.x - new_node.x) ** 2 +
+                 (node.y - new_node.y) ** 2 for node in self.nodeList]
         nearinds = [dlist.index(i) for i in dlist if i <= r ** 2]
         return nearinds
 
-    def rewire(self, newNode, nearinds):
+    def rewire(self, new_node, nearinds):
         nnode = len(self.nodeList)
         for i in nearinds:
             nearNode = self.nodeList[i]
 
-            dx = newNode.x - nearNode.x
-            dy = newNode.y - nearNode.y
+            dx = new_node.x - nearNode.x
+            dy = new_node.y - nearNode.y
             d = math.sqrt(dx ** 2 + dy ** 2)
 
-            scost = newNode.cost + d
+            scost = new_node.cost + d
 
             if nearNode.cost > scost:
                 theta = math.atan2(dy, dx)
@@ -232,18 +248,6 @@ def __CollisionCheck(self, node, obstacleList):
         return True  # safe
 
 
-class Node():
-    """
-    RRT Node
-    """
-
-    def __init__(self, x, y):
-        self.x = x
-        self.y = y
-        self.cost = 0.0
-        self.parent = None
-
-
 def main():
     print("Start " + __file__)
 
@@ -260,7 +264,7 @@ def main():
     # Set Initial parameters
     rrt = RRT(start=[0, 0], goal=[10, 10],
               randArea=[-2, 15], obstacleList=obstacleList)
-    path = rrt.Planning(animation=show_animation)
+    path = rrt.Planning(animation=show_animation, search_until_maxiter=False)
 
     if path is None:
         print("Cannot find path")
@@ -278,3 +282,4 @@ def main():
 
 if __name__ == '__main__':
     main()
+

From cb13e71e3a89b658eeb0cab5783b184f65777e70 Mon Sep 17 00:00:00 2001
From: Ryohei <rsasaki0109@gmail.com>
Date: Fri, 21 Jun 2019 02:38:09 +0900
Subject: [PATCH 0079/1080] Add Ensemble Kalman Filter(EnKF)

---
 .../ensemble_kalman_filter.py                 | 229 ++++++++++++++++++
 1 file changed, 229 insertions(+)
 create mode 100644 Localization/ensemble_kalman_filter/ensemble_kalman_filter.py

diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
new file mode 100644
index 0000000000..e24684ef3f
--- /dev/null
+++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
@@ -0,0 +1,229 @@
+"""
+
+Ensemble Kalman Filter localization sample
+
+author: Ryohei Sasaki
+
+"""
+
+import numpy as np
+import math
+import matplotlib.pyplot as plt
+
+#  Simulation parameter
+Qsim = np.diag([0.2, np.deg2rad(1.0)])**2
+Rsim = np.diag([1.0, np.deg2rad(30.0)])**2
+
+DT = 0.1  # time tick [s]
+SIM_TIME = 50.0  # simulation time [s]
+MAX_RANGE = 20.0  # maximum observation range
+
+# Ensemble Kalman filter parameter
+NP = 20  # Number of Particle
+
+show_animation = True
+
+
+def calc_input():
+    v = 1.0  # [m/s]
+    yawrate = 0.1  # [rad/s]
+    u = np.array([[v, yawrate]]).T
+    return u
+
+
+
+def observation(xTrue, xd, u, RFID):
+
+    xTrue = motion_model(xTrue, u)
+
+    z = np.zeros((0, 4))
+
+    for i in range(len(RFID[:, 0])):
+
+        dx = RFID[i, 0] - xTrue[0, 0]
+        dy = RFID[i, 1] - xTrue[1, 0]
+        d = math.sqrt(dx**2 + dy**2)
+        angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
+        if d <= MAX_RANGE:
+            dn = d + np.random.randn() * Qsim[0, 0]  # add noise
+            anglen = angle + np.random.randn() * Qsim[1, 1]  # add noise
+            zi = np.array([dn, anglen,RFID[i, 0], RFID[i, 1]])
+            z = np.vstack((z, zi))
+
+    # add noise to input
+    ud = np.array([[
+        u[0, 0] + np.random.randn() * Rsim[0, 0],
+        u[1, 0] + np.random.randn() * Rsim[1, 1]]]).T
+
+    xd = motion_model(xd, ud)
+    return xTrue, z, xd, ud
+
+
+def motion_model(x, u):
+    F = np.array([[1.0, 0, 0, 0],
+                  [0, 1.0, 0, 0],
+                  [0, 0, 1.0, 0],
+                  [0, 0, 0, 0]])
+
+    B = np.array([[DT * math.cos(x[2, 0]), 0],
+                  [DT * math.sin(x[2, 0]), 0],
+                  [0.0, DT],
+                  [1.0, 0.0]])
+    x = F.dot(x) + B.dot(u)
+
+    return x
+
+
+def calc_LM_Pos(x, landmarks):
+    landmarks_pos=np.zeros((2*landmarks.shape[0],1))
+    for (i,lm) in enumerate(landmarks):
+        landmarks_pos[2*i] = x[0, 0] + lm[0] * math.cos(x[2, 0] + lm[1]) + np.random.randn() * Qsim[0, 0]/np.sqrt(2)
+        landmarks_pos[2*i+1] = x[1, 0] + lm[0] * math.sin(x[2, 0] + lm[1]) + np.random.randn() * Qsim[0, 0]/np.sqrt(2)
+    return landmarks_pos
+
+def calc_covariance(xEst, px):
+    cov = np.zeros((3, 3))
+
+    for i in range(px.shape[1]):
+        dx = (px[:, i] - xEst)[0:3]
+        cov +=  dx.dot(dx.T)
+
+    return cov
+
+
+def enkf_localization(px, xEst, PEst, z, u):
+    """
+    Localization with Ensemble Kalman filter
+    """
+    pz = np.zeros((z.shape[0]*2, NP))  # Particle store of z
+    for ip in range(NP):
+        x = np.array([px[:, ip]]).T
+
+        #  Predict with random input sampling
+        ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0]
+        ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1]
+        ud = np.array([[ud1, ud2]]).T
+        x = motion_model(x, ud)
+        px[:, ip] = x[:, 0]
+        z_pos=calc_LM_Pos(x, z)
+        pz[:, ip] = z_pos[:, 0]
+
+    x_ave=np.mean(px, axis=1)
+    x_dif=px - np.tile(x_ave,(NP,1)).T
+
+    z_ave=np.mean(pz, axis=1)
+    z_dif=pz - np.tile(z_ave,(NP,1)).T
+
+    U = 1/(NP-1)* x_dif @ z_dif.T
+    V = 1/(NP-1)* z_dif @ z_dif.T 
+
+    K = U @ np.linalg.inv(V) # Kalman Gain
+
+    z_lm_pos = z[:,[2,3]].reshape(-1,)
+
+    px_hat=px + K @ (np.tile(z_lm_pos,(NP,1)).T- pz)
+
+    xEst=np.average(px_hat, axis=1).reshape(4,1)
+    pEst=calc_covariance(xEst, px_hat)
+
+    return xEst, PEst, px_hat
+
+
+def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
+    Pxy = PEst[0:2, 0:2]
+    eigval, eigvec = np.linalg.eig(Pxy)
+
+    if eigval[0] >= eigval[1]:
+        bigind = 0
+        smallind = 1
+    else:
+        bigind = 1
+        smallind = 0
+
+    t = np.arange(0, 2 * math.pi + 0.1, 0.1)
+
+    # eigval[bigind] or eiqval[smallind] were occassionally negative numbers extremely
+    # close to 0 (~10^-20), catch these cases and set the respective variable to 0
+    try:
+        a = math.sqrt(eigval[bigind])
+    except ValueError:
+        a = 0
+
+    try:
+        b = math.sqrt(eigval[smallind])
+    except ValueError:
+        b = 0
+
+    x = [a * math.cos(it) for it in t]
+    y = [b * math.sin(it) for it in t]
+    angle = math.atan2(eigvec[bigind, 1], eigvec[bigind, 0])
+    R = np.array([[math.cos(angle), math.sin(angle)],
+                  [-math.sin(angle), math.cos(angle)]])
+    fx = R.dot(np.array([[x, y]]))
+    px = np.array(fx[0, :] + xEst[0, 0]).flatten()
+    py = np.array(fx[1, :] + xEst[1, 0]).flatten()
+    plt.plot(px, py, "--r")
+
+
+def pi_2_pi(angle):
+    return (angle + math.pi) % (2 * math.pi) - math.pi
+
+def main():
+    print(__file__ + " start!!")
+
+    time = 0.0
+
+    # RFID positions [x, y]
+    RFID = np.array([[10.0, 0.0],
+                     [10.0, 10.0],
+                     [0.0, 15.0],
+                     [-5.0, 20.0]])
+    
+    # State Vector [x y yaw v]'
+    xEst = np.zeros((4, 1))
+    xTrue = np.zeros((4, 1))
+    PEst = np.eye(4)
+
+    px = np.zeros((4, NP))  # Particle store of x
+
+    xDR = np.zeros((4, 1))  # Dead reckoning
+
+    # history
+    hxEst = xEst
+    hxTrue = xTrue
+    hxDR = xTrue
+
+    while SIM_TIME >= time:
+        time += DT
+        u = calc_input()
+
+        xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID)
+
+        xEst, PEst, px = enkf_localization(px, xEst, PEst, z, ud)
+
+        # store data history
+        hxEst = np.hstack((hxEst, xEst))
+        hxDR = np.hstack((hxDR, xDR))
+        hxTrue = np.hstack((hxTrue, xTrue))
+
+        if show_animation:
+            plt.cla()
+
+            for i in range(len(z[:, 0])):
+                plt.plot([xTrue[0, 0], z[i, 2]], [xTrue[1, 0], z[i, 3]], "-k")
+            plt.plot(RFID[:, 0], RFID[:, 1], "*k")
+            plt.plot(px[0, :], px[1, :], ".r")
+            plt.plot(np.array(hxTrue[0, :]).flatten(),
+                     np.array(hxTrue[1, :]).flatten(), "-b")
+            plt.plot(np.array(hxDR[0, :]).flatten(),
+                     np.array(hxDR[1, :]).flatten(), "-k")
+            plt.plot(np.array(hxEst[0, :]).flatten(),
+                     np.array(hxEst[1, :]).flatten(), "-r")
+            #plot_covariance_ellipse(xEst, PEst)
+            plt.axis("equal")
+            plt.grid(True)
+            plt.pause(0.001)
+
+
+if __name__ == '__main__':
+    main()

From 0fabea14c45f1588212ef63668947e3b5bc9aa93 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 21 Jun 2019 08:13:50 +0900
Subject: [PATCH 0080/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 6bb8f8d1f8..9ba481d2b4 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -253,6 +253,12 @@ Hey Atsushi <br>We are working on a multiagent simulation game and this project
 
 ---
 
+Thanks a lot for this amazing set of very useful librarires!
+
+--Razvan Viorel Mihai
+
+---
+
 # Citations
 
 1. B. Blaga, M. Deac, R. W. Y. Al-doori, M. Negru and R. Dǎnescu, "Miniature Autonomous Vehicle Development on Raspberry Pi," 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, 2018, pp. 229-236.

From 4a8d359bd3e818ee636eaa133e4b966634606d5f Mon Sep 17 00:00:00 2001
From: Ryohei Sasaki <ryohei.sasaki@akane.waseda.jp>
Date: Fri, 21 Jun 2019 14:26:12 +0900
Subject: [PATCH 0081/1080] add header comments

---
 Localization/ensemble_kalman_filter/ensemble_kalman_filter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
index e24684ef3f..eeba9a6ab2 100644
--- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
+++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
@@ -2,7 +2,7 @@
 
 Ensemble Kalman Filter localization sample
 
-author: Ryohei Sasaki
+author: Ryohei Sasaki(rsasaki0109)
 
 """
 

From fdaf592cde637d6ab50c5c82ae2119e1d394de10 Mon Sep 17 00:00:00 2001
From: Ryohei Sasaki <ryohei.sasaki@akane.waseda.jp>
Date: Fri, 21 Jun 2019 14:32:50 +0900
Subject: [PATCH 0082/1080] Update header comments

---
 Localization/ensemble_kalman_filter/ensemble_kalman_filter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
index eeba9a6ab2..053bdf4cdb 100644
--- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
+++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
@@ -1,6 +1,6 @@
 """
 
-Ensemble Kalman Filter localization sample
+Ensemble Kalman Filter(EnKF) localization sample
 
 author: Ryohei Sasaki(rsasaki0109)
 

From 2fb7c720ef62fea712b4180625f8c59a4e851cb0 Mon Sep 17 00:00:00 2001
From: Ryohei Sasaki <ryohei.sasaki@akane.waseda.jp>
Date: Fri, 21 Jun 2019 18:09:47 +0900
Subject: [PATCH 0083/1080] add reference & correct misspelling

---
 .../ensemble_kalman_filter/ensemble_kalman_filter.py         | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
index 053bdf4cdb..38a3a0b363 100644
--- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
+++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
@@ -4,6 +4,9 @@
 
 author: Ryohei Sasaki(rsasaki0109)
 
+Ref:
+- [Ensemble Kalman filtering](https://rmets.onlinelibrary.wiley.com/doi/10.1256/qj.05.135)
+
 """
 
 import numpy as np
@@ -124,7 +127,7 @@ def enkf_localization(px, xEst, PEst, z, u):
     px_hat=px + K @ (np.tile(z_lm_pos,(NP,1)).T- pz)
 
     xEst=np.average(px_hat, axis=1).reshape(4,1)
-    pEst=calc_covariance(xEst, px_hat)
+    PEst=calc_covariance(xEst, px_hat)
 
     return xEst, PEst, px_hat
 

From 43a59ae09bf5d2b78dd02b21942a44f54579c2b7 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 21 Jun 2019 18:38:02 +0900
Subject: [PATCH 0084/1080] code clean up

---
 .../ensemble_kalman_filter.py                 | 47 +++++++++++--------
 1 file changed, 27 insertions(+), 20 deletions(-)

diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
index 38a3a0b363..5ca7713b7d 100644
--- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
+++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
@@ -1,3 +1,4 @@
+
 """
 
 Ensemble Kalman Filter(EnKF) localization sample
@@ -34,7 +35,6 @@ def calc_input():
     return u
 
 
-
 def observation(xTrue, xd, u, RFID):
 
     xTrue = motion_model(xTrue, u)
@@ -50,7 +50,7 @@ def observation(xTrue, xd, u, RFID):
         if d <= MAX_RANGE:
             dn = d + np.random.randn() * Qsim[0, 0]  # add noise
             anglen = angle + np.random.randn() * Qsim[1, 1]  # add noise
-            zi = np.array([dn, anglen,RFID[i, 0], RFID[i, 1]])
+            zi = np.array([dn, anglen, RFID[i, 0], RFID[i, 1]])
             z = np.vstack((z, zi))
 
     # add noise to input
@@ -78,18 +78,23 @@ def motion_model(x, u):
 
 
 def calc_LM_Pos(x, landmarks):
-    landmarks_pos=np.zeros((2*landmarks.shape[0],1))
-    for (i,lm) in enumerate(landmarks):
-        landmarks_pos[2*i] = x[0, 0] + lm[0] * math.cos(x[2, 0] + lm[1]) + np.random.randn() * Qsim[0, 0]/np.sqrt(2)
-        landmarks_pos[2*i+1] = x[1, 0] + lm[0] * math.sin(x[2, 0] + lm[1]) + np.random.randn() * Qsim[0, 0]/np.sqrt(2)
+    landmarks_pos = np.zeros((2*landmarks.shape[0], 1))
+    for (i, lm) in enumerate(landmarks):
+        landmarks_pos[2*i] = x[0, 0] + lm[0] * \
+            math.cos(x[2, 0] + lm[1]) + np.random.randn() * \
+            Qsim[0, 0]/np.sqrt(2)
+        landmarks_pos[2*i+1] = x[1, 0] + lm[0] * \
+            math.sin(x[2, 0] + lm[1]) + np.random.randn() * \
+            Qsim[0, 0]/np.sqrt(2)
     return landmarks_pos
 
+
 def calc_covariance(xEst, px):
     cov = np.zeros((3, 3))
 
     for i in range(px.shape[1]):
         dx = (px[:, i] - xEst)[0:3]
-        cov +=  dx.dot(dx.T)
+        cov += dx.dot(dx.T)
 
     return cov
 
@@ -108,26 +113,26 @@ def enkf_localization(px, xEst, PEst, z, u):
         ud = np.array([[ud1, ud2]]).T
         x = motion_model(x, ud)
         px[:, ip] = x[:, 0]
-        z_pos=calc_LM_Pos(x, z)
+        z_pos = calc_LM_Pos(x, z)
         pz[:, ip] = z_pos[:, 0]
 
-    x_ave=np.mean(px, axis=1)
-    x_dif=px - np.tile(x_ave,(NP,1)).T
+    x_ave = np.mean(px, axis=1)
+    x_dif = px - np.tile(x_ave, (NP, 1)).T
 
-    z_ave=np.mean(pz, axis=1)
-    z_dif=pz - np.tile(z_ave,(NP,1)).T
+    z_ave = np.mean(pz, axis=1)
+    z_dif = pz - np.tile(z_ave, (NP, 1)).T
 
-    U = 1/(NP-1)* x_dif @ z_dif.T
-    V = 1/(NP-1)* z_dif @ z_dif.T 
+    U = 1/(NP-1) * x_dif @ z_dif.T
+    V = 1/(NP-1) * z_dif @ z_dif.T
 
-    K = U @ np.linalg.inv(V) # Kalman Gain
+    K = U @ np.linalg.inv(V)  # Kalman Gain
 
-    z_lm_pos = z[:,[2,3]].reshape(-1,)
+    z_lm_pos = z[:, [2, 3]].reshape(-1,)
 
-    px_hat=px + K @ (np.tile(z_lm_pos,(NP,1)).T- pz)
+    px_hat = px + K @ (np.tile(z_lm_pos, (NP, 1)).T - pz)
 
-    xEst=np.average(px_hat, axis=1).reshape(4,1)
-    PEst=calc_covariance(xEst, px_hat)
+    xEst = np.average(px_hat, axis=1).reshape(4, 1)
+    PEst = calc_covariance(xEst, px_hat)
 
     return xEst, PEst, px_hat
 
@@ -171,6 +176,7 @@ def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
 def pi_2_pi(angle):
     return (angle + math.pi) % (2 * math.pi) - math.pi
 
+
 def main():
     print(__file__ + " start!!")
 
@@ -181,7 +187,7 @@ def main():
                      [10.0, 10.0],
                      [0.0, 15.0],
                      [-5.0, 20.0]])
-    
+
     # State Vector [x y yaw v]'
     xEst = np.zeros((4, 1))
     xTrue = np.zeros((4, 1))
@@ -230,3 +236,4 @@ def main():
 
 if __name__ == '__main__':
     main()
+

From f1a82fc91463492f07adb387ba043e1cec7640d5 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 25 Jun 2019 14:07:07 +0900
Subject: [PATCH 0085/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 9ba481d2b4..bdcc1ce94e 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -259,6 +259,12 @@ Thanks a lot for this amazing set of very useful librarires!
 
 ---
 
+Dear Atsushi Sakai, <br> <br>This is probably one of the best open-source robotics based Algorithms I have seen so far. Thank you for posting this knowledge with other engineers. It will definitely help soon to become engineers like myself.
+
+--Don
+
+---
+
 # Citations
 
 1. B. Blaga, M. Deac, R. W. Y. Al-doori, M. Negru and R. Dǎnescu, "Miniature Autonomous Vehicle Development on Raspberry Pi," 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, 2018, pp. 229-236.

From 5d6e04b57baa05b2005ef3aac489c8cafe0c9b9d Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 25 Jun 2019 15:18:29 +0900
Subject: [PATCH 0086/1080] Update users_comments.md

---
 users_comments.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index bdcc1ce94e..aa255393ea 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -265,6 +265,10 @@ Dear Atsushi Sakai, <br> <br>This is probably one of the best open-source roboti
 
 ---
 
+hanks frnd, for ur contibusion
+
+—
+
 # Citations
 
 1. B. Blaga, M. Deac, R. W. Y. Al-doori, M. Negru and R. Dǎnescu, "Miniature Autonomous Vehicle Development on Raspberry Pi," 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, 2018, pp. 229-236.

From 3b1c48692ddb9d4a5078bb5ba4db7bcfe26b4201 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 25 Jun 2019 15:31:17 +0900
Subject: [PATCH 0087/1080] Update .travis.yml

---
 .travis.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index 6cbe37a215..b6850e8bf6 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -30,8 +30,9 @@ install:
   - conda install scipy
   - conda install matplotlib
   - conda install pandas
+  - conda config --add channels oxfordcontrol
   - conda install -c conda-forge lapack
-  - conda install -c cvxgrp cvxpy
+  - conda install -c cvxgrp cvxpy=1.0.24
   - conda install coveralls
 
 script:

From bbe0f3a41c6eecf102e3a06d65d4ebaacd2b165e Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 25 Jun 2019 17:47:50 +0900
Subject: [PATCH 0088/1080] Update .travis.yml

---
 .travis.yml | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index b6850e8bf6..3be3da8d93 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -30,9 +30,10 @@ install:
   - conda install scipy
   - conda install matplotlib
   - conda install pandas
-  - conda config --add channels oxfordcontrol
-  - conda install -c conda-forge lapack
-  - conda install -c cvxgrp cvxpy=1.0.24
+  #- conda config --add channels oxfordcontrol
+  #- conda install -c conda-forge lapack
+  #- conda install -c cvxgrp cvxpy=1.0.24
+  - pip install cvxpy
   - conda install coveralls
 
 script:

From 92237d61b4b39e7b655851e3d7da3a56bc1971a0 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 25 Jun 2019 17:58:30 +0900
Subject: [PATCH 0089/1080] Update .travis.yml

---
 .travis.yml | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 3be3da8d93..dd2a9651dc 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -3,8 +3,8 @@ language: python
 matrix:
   include:
     - os: linux
-    #- os: osx
-      #python: "3.6-dev"
+    - os: osx
+      python: "3.6-dev"
 
 python:
   - 3.6
@@ -21,18 +21,12 @@ before_install:
     # Useful for debugging any issues with conda
     - conda info -a
     - conda install python==3.6.8
-    #- conda env create -f ./environment.yml
-    #- activate python_robotics
-
 
 install:
   - conda install numpy==1.15
   - conda install scipy
   - conda install matplotlib
   - conda install pandas
-  #- conda config --add channels oxfordcontrol
-  #- conda install -c conda-forge lapack
-  #- conda install -c cvxgrp cvxpy=1.0.24
   - pip install cvxpy
   - conda install coveralls
 

From 32574637b7a123b2b9db115a92dcfb252d64841a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 25 Jun 2019 18:02:43 +0900
Subject: [PATCH 0090/1080] Update .travis.yml

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index dd2a9651dc..a206ac29e1 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -4,7 +4,7 @@ matrix:
   include:
     - os: linux
     - os: osx
-      python: "3.6-dev"
+      python: "3.6"
 
 python:
   - 3.6

From 4b2c23a91e400add67f3ceeb43fb6fb4a220c03a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 25 Jun 2019 18:04:52 +0900
Subject: [PATCH 0091/1080] Update .travis.yml

---
 .travis.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index a206ac29e1..d12886ce2e 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -4,7 +4,6 @@ matrix:
   include:
     - os: linux
     - os: osx
-      python: "3.6"
 
 python:
   - 3.6

From df636de5949cfb1e6a89196e308633e0ebc07fd1 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 25 Jun 2019 18:07:14 +0900
Subject: [PATCH 0092/1080] Update .travis.yml

---
 .travis.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index d12886ce2e..eff3df24fa 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -3,7 +3,6 @@ language: python
 matrix:
   include:
     - os: linux
-    - os: osx
 
 python:
   - 3.6

From e62e5c7c214e75fc6a15b10093ff40fd48038e42 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 25 Jun 2019 21:57:56 +0900
Subject: [PATCH 0093/1080] try implementing

---
 PathPlanning/AStar/a_star.py | 89 +++++++++++++++++++++---------------
 1 file changed, 51 insertions(+), 38 deletions(-)

diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py
index 88f65a57d3..ee14a94f3a 100644
--- a/PathPlanning/AStar/a_star.py
+++ b/PathPlanning/AStar/a_star.py
@@ -1,6 +1,6 @@
 """
 
-A* grid based planning
+A* grid planning
 
 author: Atsushi Sakai(@Atsushi_twi)
         Nikos Kanargias (nkana@tee.gr)
@@ -14,11 +14,12 @@
 
 show_animation = True
 
+
 class AStarPlanner:
 
     def __init__(self, ox, oy, reso, rr):
         """
-        Intialize map for a star planning
+        Initialize grid map for a star planning
 
         ox: x position list of Obstacles [m]
         oy: y position list of Obstacles [m]
@@ -61,19 +62,19 @@ def planning(self, sx, sy, gx, gy):
         ngoal = self.Node(self.calc_xyindex(gx, self.minx),
                           self.calc_xyindex(gy, self.miny), 0.0, -1)
 
-        openset, closedset = dict(), dict()
-        openset[self.calc_index(nstart)] = nstart
+        open_set, closed_set = dict(), dict()
+        open_set[self.calc_grid_index(nstart)] = nstart
 
         while 1:
             c_id = min(
-                openset, key=lambda o: openset[o].cost + self.calc_heuristic(ngoal, openset[o]))
-            current = openset[c_id]
+                open_set, key=lambda o: open_set[o].cost + self.calc_heuristic(ngoal, open_set[o]))
+            current = open_set[c_id]
 
             # show graph
             if show_animation:  # pragma: no cover
-                plt.plot(self.calc_position(current.x, self.minx),
-                         self.calc_position(current.y, self.miny), "xc")
-                if len(closedset.keys()) % 10 == 0:
+                plt.plot(self.calc_grid_position(current.x, self.minx),
+                         self.calc_grid_position(current.y, self.miny), "xc")
+                if len(closed_set.keys()) % 10 == 0:
                     plt.pause(0.001)
 
             if current.x == ngoal.x and current.y == ngoal.y:
@@ -83,66 +84,76 @@ def planning(self, sx, sy, gx, gy):
                 break
 
             # Remove the item from the open set
-            del openset[c_id]
+            del open_set[c_id]
 
             # Add it to the closed set
-            closedset[c_id] = current
+            closed_set[c_id] = current
 
             # expand search grid based on motion model
             for i, _ in enumerate(self.motion):
                 node = self.Node(current.x + self.motion[i][0],
                                  current.y + self.motion[i][1],
                                  current.cost + self.motion[i][2], c_id)
-                n_id = self.calc_index(node)
+                n_id = self.calc_grid_index(node)
 
-                if n_id in closedset:
+                # If the node is in closed_set, do nothing
+                if n_id in closed_set:
                     continue
 
+                # If the node is not safe, do nothing
                 if not self.verify_node(node):
                     continue
 
-                if n_id not in openset:
-                    openset[n_id] = node  # Discover a new node
+                if n_id not in open_set:
+                    open_set[n_id] = node  # discovered a new node
                 else:
-                    if openset[n_id].cost >= node.cost:
-                        # This path is the best until now. record it!
-                        openset[n_id] = node
+                    if open_set[n_id].cost >= node.cost:
+                        # This path is the best until now. record it
+                        open_set[n_id] = node
 
-        rx, ry = self.calc_final_path(ngoal, closedset)
+        rx, ry = self.calc_final_path(ngoal, closed_set)
 
         return rx, ry
 
     def calc_final_path(self, ngoal, closedset):
         # generate final course
-        rx, ry = [self.calc_position(ngoal.x, self.minx)], [
-            self.calc_position(ngoal.y, self.miny)]
+        rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
+            self.calc_grid_position(ngoal.y, self.miny)]
         pind = ngoal.pind
         while pind != -1:
             n = closedset[pind]
-            rx.append(self.calc_position(n.x, self.minx))
-            ry.append(self.calc_position(n.y, self.miny))
+            rx.append(self.calc_grid_position(n.x, self.minx))
+            ry.append(self.calc_grid_position(n.y, self.miny))
             pind = n.pind
 
         return rx, ry
 
-    def calc_heuristic(self, n1, n2):
+    @staticmethod
+    def calc_heuristic(n1, n2):
         w = 1.0  # weight of heuristic
-        d = w * math.sqrt((n1.x - n2.x)**2 + (n1.y - n2.y)**2)
+        d = w * math.sqrt((n1.x - n2.x) ** 2 + (n1.y - n2.y) ** 2)
         return d
 
-    def calc_position(self, index, minp):
-        pos = index*self.reso+minp
+    def calc_grid_position(self, index, minp):
+        """
+        calc grid position
+
+        :param index:
+        :param minp:
+        :return:
+        """
+        pos = index * self.reso + minp
         return pos
 
-    def calc_xyindex(self, position, minp):
-        return round((position - minp)/self.reso)
+    def calc_xyindex(self, position, min_pos):
+        return round((position - min_pos) / self.reso)
 
-    def calc_index(self, node):
+    def calc_grid_index(self, node):
         return (node.y - self.miny) * self.xwidth + (node.x - self.minx)
 
     def verify_node(self, node):
-        px = self.calc_position(node.x, self.minx)
-        py = self.calc_position(node.y, self.miny)
+        px = self.calc_grid_position(node.x, self.minx)
+        py = self.calc_grid_position(node.y, self.miny)
 
         if px < self.minx:
             return False
@@ -153,6 +164,7 @@ def verify_node(self, node):
         elif py >= self.maxy:
             return False
 
+        # collision check
         if self.obmap[node.x][node.y]:
             return False
 
@@ -169,8 +181,8 @@ def calc_obstacle_map(self, ox, oy):
         print("maxx:", self.maxx)
         print("maxy:", self.maxy)
 
-        self.xwidth = round((self.maxx - self.minx)/self.reso)
-        self.ywidth = round((self.maxy - self.miny)/self.reso)
+        self.xwidth = round((self.maxx - self.minx) / self.reso)
+        self.ywidth = round((self.maxy - self.miny) / self.reso)
         print("xwidth:", self.xwidth)
         print("ywidth:", self.ywidth)
 
@@ -178,16 +190,17 @@ def calc_obstacle_map(self, ox, oy):
         self.obmap = [[False for i in range(self.ywidth)]
                       for i in range(self.xwidth)]
         for ix in range(self.xwidth):
-            x = self.calc_position(ix, self.minx)
+            x = self.calc_grid_position(ix, self.minx)
             for iy in range(self.ywidth):
-                y = self.calc_position(iy, self.miny)
+                y = self.calc_grid_position(iy, self.miny)
                 for iox, ioy in zip(ox, oy):
-                    d = math.sqrt((iox - x)**2 + (ioy - y)**2)
+                    d = math.sqrt((iox - x) ** 2 + (ioy - y) ** 2)
                     if d <= self.rr:
                         self.obmap[ix][iy] = True
                         break
 
-    def get_motion_model(self):
+    @staticmethod
+    def get_motion_model():
         # dx, dy, cost
         motion = [[1, 0, 1],
                   [0, 1, 1],

From 02c197d6991254c8c48b4f9817b86bc7c2e7c00c Mon Sep 17 00:00:00 2001
From: Zhao Zihe <2809075376@qq.com>
Date: Thu, 27 Jun 2019 17:36:37 +0800
Subject: [PATCH 0094/1080] patch the yaw=0 direction while expanding

---
 PathPlanning/HybridAStar/hybrid_a_star.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
index de23af8421..e82003ed25 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -131,7 +131,7 @@ def __init__(self, ox, oy, xyreso, yawreso):
 
 def calc_motion_inputs():
 
-    for steer in np.linspace(-MAX_STEER, MAX_STEER, N_STEER):
+    for steer in np.concatenate((np.linspace(-MAX_STEER, MAX_STEER, N_STEER),[0.0])):
         for d in [1, -1]:
             yield [steer, d]
 

From 9d81a98b57780a8e46fa4db3c5cd4b2b79b849a1 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 29 Jun 2019 21:11:39 +0900
Subject: [PATCH 0095/1080] add grid map lib and code clean up

---
 Mapping/grid_map_lib/__init__.py              |   0
 Mapping/grid_map_lib/grid_map_lib.py          | 238 ++++++++++++++++++
 .../lqr_speed_steer_control.py                |  17 +-
 tests/test_rrt_star.py                        |   6 +-
 4 files changed, 251 insertions(+), 10 deletions(-)
 create mode 100644 Mapping/grid_map_lib/__init__.py
 create mode 100644 Mapping/grid_map_lib/grid_map_lib.py

diff --git a/Mapping/grid_map_lib/__init__.py b/Mapping/grid_map_lib/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/Mapping/grid_map_lib/grid_map_lib.py b/Mapping/grid_map_lib/grid_map_lib.py
new file mode 100644
index 0000000000..ef94cb5e98
--- /dev/null
+++ b/Mapping/grid_map_lib/grid_map_lib.py
@@ -0,0 +1,238 @@
+"""
+
+Grid map library in python
+
+author: Atsushi Sakai
+
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+class GridMap:
+    """
+    GridMap class
+    """
+
+    def __init__(self, width, height, resolution,
+                 center_x, center_y, init_val=0.0):
+        """__init__
+
+        :param width: number of grid for width
+        :param height: number of grid for heigt
+        :param resolution: grid resolution [m]
+        :param center_x: center x position  [m]
+        :param center_y: center y position [m]
+        :param init_val: initial value for all grid
+        """
+        self.width = width
+        self.height = height
+        self.resolution = resolution
+        self.center_x = center_x
+        self.center_y = center_y
+
+        self.left_lower_x = self.center_x - \
+                            (self.width / 2.0) * self.resolution
+        self.left_lower_y = self.center_y - \
+                            (self.height / 2.0) * self.resolution
+
+        self.ndata = self.width * self.height
+        self.data = [init_val] * self.ndata
+
+    def get_value_from_xy_index(self, x_ind, y_ind):
+        """get_value_from_xy_index
+
+        when the index is out of grid map area, return None
+
+        :param x_ind: x index
+        :param y_ind: y index
+        """
+
+        grid_ind = self.calc_grid_index_from_xy_index(x_ind, y_ind)
+
+        if 0 <= grid_ind <= self.ndata:
+            return self.data[grid_ind]
+        else:
+            return None
+
+    def get_xy_index_from_xy_pos(self, x_pos, y_pos):
+        """get_xy_index_from_xy_pos
+
+        :param x_pos: x position [m]
+        :param y_pos: y position [m]
+        """
+        x_ind = self.calc_xy_index_from_position(
+            x_pos, self.left_lower_x, self.width)
+        y_ind = self.calc_xy_index_from_position(
+            y_pos, self.left_lower_y, self.height)
+
+        return x_ind, y_ind
+
+    def set_value_from_xy_pos(self, x_pos, y_pos, val):
+        """set_value_from_xy_pos
+
+        return bool flag, which means setting value is succeeded or not
+
+        :param x_pos: x position [m]
+        :param y_pos: y position [m]
+        :param val: grid value
+        """
+
+        x_ind, y_ind = self.get_xy_index_from_xy_pos(x_pos, y_pos)
+
+        if (not x_ind) or (not y_ind):
+            return False  # NG
+
+        flag = self.set_value_from_xy_index(x_ind, y_ind, val)
+
+        return flag
+
+    def set_value_from_xy_index(self, x_ind, y_ind, val):
+        """set_value_from_xy_index
+
+        return bool flag, which means setting value is succeeded or not
+
+        :param x_ind: x index
+        :param y_ind: y index
+        :param val: grid value
+        """
+
+        grid_ind = int(y_ind * self.width + x_ind)
+
+        if 0 <= grid_ind <= self.ndata:
+            self.data[grid_ind] = val
+            return True  # OK
+        else:
+            return False  # NG
+
+    def set_value_from_polygon(self, pol_x, pol_y, val, inside=True):
+        """set_value_from_polygon
+
+        Setting value inside or outside polygon
+
+        :param pol_x: x position list for a polygon
+        :param pol_y: y position list for a polygon
+        :param val: grid value
+        :param inside: setting data inside or outside
+        """
+
+        # making ring polygon
+        if (pol_x[0] != pol_x[-1]) or (pol_y[0] != pol_y[-1]):
+            pol_x.append(pol_x[0])
+            pol_y.append(pol_y[0])
+
+        # setting value for all grid
+        for x_ind in range(self.width):
+            for y_ind in range(self.height):
+                x_pos, y_pos = self.calc_grid_central_xy_position_from_xy_index(
+                    x_ind, y_ind)
+
+                flag = self.check_inside_polygon(x_pos, y_pos, pol_x, pol_y)
+
+                if flag is inside:
+                    self.set_value_from_xy_index(x_ind, y_ind, val)
+
+    def calc_grid_index_from_xy_index(self, x_ind, y_ind):
+        grid_ind = int(y_ind * self.width + x_ind)
+        return grid_ind
+
+    def calc_grid_central_xy_position_from_xy_index(self, x_ind, y_ind):
+        x_pos = self.calc_grid_central_xy_position_from_index(
+            x_ind, self.left_lower_x)
+        y_pos = self.calc_grid_central_xy_position_from_index(
+            y_ind, self.left_lower_y)
+
+        return x_pos, y_pos
+
+    def calc_grid_central_xy_position_from_index(self, index, lower_pos):
+        return lower_pos + index * self.resolution + self.resolution / 2.0
+
+    def calc_xy_index_from_position(self, pos, lower_pos, max_index):
+        ind = int(np.floor((pos - lower_pos) / self.resolution))
+        if 0 <= ind <= max_index:
+            return ind
+        else:
+            return None
+
+    def check_occupied_from_xy_index(self, xind, yind, occupied_val=1.0):
+
+        val = self.get_value_from_xy_index(xind, yind)
+
+        if val >= occupied_val:
+            return True
+        else:
+            return False
+
+    @staticmethod
+    def check_inside_polygon(iox, ioy, x, y):
+
+        npoint = len(x) - 1
+        inside = False
+        for i1 in range(npoint):
+            i2 = (i1 + 1) % (npoint + 1)
+
+            if x[i1] >= x[i2]:
+                min_x, max_x = x[i2], x[i1]
+            else:
+                min_x, max_x = x[i1], x[i2]
+            if not min_x < iox < max_x:
+                continue
+
+            if (y[i1] + (y[i2] - y[i1]) / (x[i2] - x[i1])
+                * (iox - x[i1]) - ioy) > 0.0:
+                inside = not inside
+
+        return inside
+
+    def plot_grid_map(self):
+
+        grid_data = np.reshape(np.array(self.data), (self.height, self.width))
+        fig, ax = plt.subplots()
+        heat_map = ax.pcolor(grid_data, cmap="Blues", vmin=0.0, vmax=1.0)
+        plt.axis("equal")
+        plt.show()
+
+        return heat_map
+
+
+def test_polygon_set():
+    ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0]
+    oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0]
+
+    grid_map = GridMap(600, 290, 0.7, 60.0, 30.5)
+
+    grid_map.set_value_from_polygon(ox, oy, 1.0, inside=False)
+
+    grid_map.plot_grid_map()
+
+    plt.axis("equal")
+    plt.grid(True)
+
+
+def test_position_set():
+    grid_map = GridMap(100, 120, 0.5, 10.0, -0.5)
+
+    grid_map.set_value_from_xy_pos(10.1, -1.1, 1.0)
+    grid_map.set_value_from_xy_pos(10.1, -0.1, 1.0)
+    grid_map.set_value_from_xy_pos(10.1, 1.1, 1.0)
+    grid_map.set_value_from_xy_pos(11.1, 0.1, 1.0)
+    grid_map.set_value_from_xy_pos(10.1, 0.1, 1.0)
+    grid_map.set_value_from_xy_pos(9.1, 0.1, 1.0)
+
+    grid_map.plot_grid_map()
+
+
+def main():
+    print("start!!")
+
+    test_position_set()
+    test_polygon_set()
+
+    plt.show()
+
+    print("done!!")
+
+
+if __name__ == '__main__':
+    main()
diff --git a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
index a39cfba9f6..ff8d890d0f 100644
--- a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
+++ b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
@@ -1,8 +1,3 @@
-import cubic_spline_planner
-import scipy.linalg as la
-import matplotlib.pyplot as plt
-import math
-import numpy as np
 """
 
 Path tracking simulation with LQR speed and steering control
@@ -10,8 +5,15 @@
 author Atsushi Sakai (@Atsushi_twi)
 
 """
+import math
 import sys
+
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy.linalg as la
+
 sys.path.append("../../PathPlanning/CubicSpline/")
+import cubic_spline_planner
 
 
 # LQR parameter
@@ -59,6 +61,7 @@ def solve_DARE(A, B, Q, R):
     solve a discrete time_Algebraic Riccati equation (DARE)
     """
     X = Q
+    Xn = Q
     maxiter = 150
     eps = 0.01
 
@@ -85,9 +88,9 @@ def dlqr(A, B, Q, R):
     # compute the LQR gain
     K = la.inv(B.T @ X @ B + R) @ (B.T @ X @ A)
 
-    eigVals, eigVecs = la.eig(A - B @ K)
+    eigs = la.eig(A - B @ K)
 
-    return K, X, eigVals
+    return K, X, eigs[0]
 
 
 def lqr_steering_control(state, cx, cy, cyaw, ck, pe, pth_e, sp):
diff --git a/tests/test_rrt_star.py b/tests/test_rrt_star.py
index 8926a2b0f8..2171402c9b 100644
--- a/tests/test_rrt_star.py
+++ b/tests/test_rrt_star.py
@@ -1,6 +1,7 @@
-from unittest import TestCase
-import sys
 import os
+import sys
+from unittest import TestCase
+
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
                 "/../PathPlanning/RRTstar/")
 
@@ -23,4 +24,3 @@ def test1(self):
 if __name__ == '__main__':  # pragma: no cover
     test = Test()
     test.test1()
-    print(aa)

From 474ffef84b796da377e59eb9750019c0c69c57c8 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 30 Jun 2019 12:37:09 +0900
Subject: [PATCH 0096/1080] first release grid based_sweep_planner.py

---
 Mapping/grid_map_lib/__init__.py              |   0
 Mapping/grid_map_lib/grid_map_lib.py          |   7 +-
 .../grid_based_sweep_planner.py               | 274 ++++++++++++++++++
 3 files changed, 278 insertions(+), 3 deletions(-)
 delete mode 100644 Mapping/grid_map_lib/__init__.py
 create mode 100644 PathPlanning/GridBasedSweepCCP/grid_based_sweep_planner.py

diff --git a/Mapping/grid_map_lib/__init__.py b/Mapping/grid_map_lib/__init__.py
deleted file mode 100644
index e69de29bb2..0000000000
diff --git a/Mapping/grid_map_lib/grid_map_lib.py b/Mapping/grid_map_lib/grid_map_lib.py
index ef94cb5e98..87b035c94c 100644
--- a/Mapping/grid_map_lib/grid_map_lib.py
+++ b/Mapping/grid_map_lib/grid_map_lib.py
@@ -185,13 +185,14 @@ def check_inside_polygon(iox, ioy, x, y):
 
         return inside
 
-    def plot_grid_map(self):
+    def plot_grid_map(self, ax=None):
 
         grid_data = np.reshape(np.array(self.data), (self.height, self.width))
-        fig, ax = plt.subplots()
+        if not ax:
+            fig, ax = plt.subplots()
         heat_map = ax.pcolor(grid_data, cmap="Blues", vmin=0.0, vmax=1.0)
         plt.axis("equal")
-        plt.show()
+        # plt.show()
 
         return heat_map
 
diff --git a/PathPlanning/GridBasedSweepCCP/grid_based_sweep_planner.py b/PathPlanning/GridBasedSweepCCP/grid_based_sweep_planner.py
new file mode 100644
index 0000000000..7d6f701def
--- /dev/null
+++ b/PathPlanning/GridBasedSweepCCP/grid_based_sweep_planner.py
@@ -0,0 +1,274 @@
+"""
+Grid based sweep planner
+
+author: Atsushi Sakai
+"""
+
+import math
+import os
+import sys
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+sys.path.append(os.path.relpath("../../Mapping/grid_map_lib/"))
+from grid_map_lib import GridMap
+
+
+class SweepSearcher:
+
+    def __init__(self, mdirection, sdirection, xinds_miny, miny):
+        self.moving_direction = mdirection  # +1 or -1
+        self.sweep_direction = sdirection  # +1 or -1
+        self.turing_window = []
+        self.update_turning_window()
+        self.xinds_miny = xinds_miny
+        self.miny = miny
+
+    def move_target_grid(self, cxind, cyind, gmap):
+
+        nxind = self.moving_direction + cxind
+        nyind = cyind
+
+        # found safe grid
+        if not gmap.check_occupied_from_xy_index(nxind, nyind, occupied_val=0.5):
+            return nxind, nyind
+        else:  # occupided
+            ncxind, ncyind = self.find_safe_turning_grid(cxind, cyind, gmap)
+            if not ncxind and not ncyind:
+                # moving backward
+                ncxind = -self.moving_direction + cxind
+                ncyind = cyind
+                if gmap.check_occupied_from_xy_index(ncxind, ncyind):
+                    # moved backward, but the grid is occupied by obstacle
+                    return None, None
+            else:
+                self.swap_moving_direction()
+            return ncxind, ncyind
+
+    def find_safe_turning_grid(self, cxind, cyind, gmap):
+
+        for (dxind, dyind) in self.turing_window:
+
+            nxind = dxind + cxind
+            nyind = dyind + cyind
+
+            # found safe grid
+            if not gmap.check_occupied_from_xy_index(nxind, nyind, occupied_val=0.5):
+                print(dxind, dyind)
+                return nxind, nyind
+
+        return None, None
+
+    def is_search_done(self, gmap):
+        for ix in self.xinds_miny:
+            if not gmap.check_occupied_from_xy_index(ix, self.miny, occupied_val=0.5):
+                return False
+
+        # all lower grid is occupied
+        return True
+
+    def update_turning_window(self):
+        self.turing_window = [
+            (self.moving_direction, 0.0),
+            (self.moving_direction, self.sweep_direction),
+            (0, self.sweep_direction),
+            (-self.moving_direction, self.sweep_direction),
+        ]
+
+    def swap_moving_direction(self):
+        self.moving_direction *= -1
+        self.update_turning_window()
+
+
+def find_sweep_direction_and_start_posi(ox, oy, start_posi):
+    # find sweep_direction
+    maxd_id = None
+    maxd = 0.0
+    vec = [0.0, 0.0]
+    for i in range(len(ox) - 1):
+        dx = ox[i + 1] - ox[i]
+        dy = oy[i + 1] - oy[i]
+        d = np.sqrt(dx ** 2 + dy ** 2)
+
+        if d > maxd:
+            maxd_id = i
+            maxd = d
+            vec = [dx, dy]
+
+    # find sweep start posi
+    d1 = np.sqrt((ox[maxd_id] - start_posi[0]) ** 2 +
+                 (oy[maxd_id] - start_posi[1]) ** 2)
+    d2 = np.sqrt((ox[maxd_id + 1] - start_posi[0]) ** 2 +
+                 (oy[maxd_id + 1] - start_posi[1]) ** 2)
+
+    if d2 >= d1:  # first point is near
+        sweep_start_posi = [ox[maxd_id], oy[maxd_id]]
+    else:
+        sweep_start_posi = [ox[maxd_id + 1], oy[maxd_id + 1]]
+        vec = [-1.0 * iv for iv in vec]  # reverse direction
+
+    return vec, sweep_start_posi
+
+
+def convert_grid_coordinate(ox, oy, sweep_vec, sweep_start_posi):
+    tx = [ix - sweep_start_posi[0] for ix in ox]
+    ty = [iy - sweep_start_posi[1] for iy in oy]
+
+    th = math.atan2(sweep_vec[1], sweep_vec[0])
+
+    c = np.cos(-th)
+    s = np.sin(-th)
+
+    rx = [ix * c - iy * s for (ix, iy) in zip(tx, ty)]
+    ry = [ix * s + iy * c for (ix, iy) in zip(tx, ty)]
+
+    return rx, ry
+
+
+def convert_global_coordinate(x, y, sweep_vec, sweep_start_posi):
+    th = math.atan2(sweep_vec[1], sweep_vec[0])
+    c = np.cos(th)
+    s = np.sin(th)
+
+    tx = [ix * c - iy * s for (ix, iy) in zip(x, y)]
+    ty = [ix * s + iy * c for (ix, iy) in zip(x, y)]
+
+    rx = [ix + sweep_start_posi[0] for ix in tx]
+    ry = [iy + sweep_start_posi[1] for iy in ty]
+
+    return rx, ry
+
+
+def search_free_lower_y_grid_index(grid_map):
+    miny = None
+    xinds = []
+
+    for iy in range(grid_map.height):
+        for ix in range(grid_map.width):
+            if not grid_map.check_occupied_from_xy_index(ix, iy):
+                miny = iy
+                xinds.append(ix)
+        if miny:
+            break
+
+    return xinds, miny
+
+
+def setup_grid_map(ox, oy, reso):
+    offset_grid = 10
+
+    width = math.ceil((max(ox) - min(ox)) / reso) + offset_grid
+    height = math.ceil((max(oy) - min(oy)) / reso) + offset_grid
+    center_x = np.mean(ox)
+    center_y = np.mean(oy)
+
+    grid_map = GridMap(width, height, reso, center_x, center_y)
+
+    grid_map.set_value_from_polygon(ox, oy, 1.0, inside=False)
+
+    # fill grid
+    # for i in range(len(ox) - 1):
+    #     grid_map.set_value_from_xy_pos(ox[i], oy[i], 1.0)
+    #
+    #     x, y = ox[i], oy[i]
+    #     th = math.atan2(oy[i + 1] - oy[i], ox[i + 1] - ox[i])
+    #     d = np.sqrt((x - ox[i + 1])**2 + (y - oy[i + 1])**2)
+    #
+    #     while d > reso:
+    #         x += np.cos(th) * reso
+    #         y += np.sin(th) * reso
+    #         d = np.sqrt((x - ox[i + 1])**2 + (y - oy[i + 1])**2)
+    #
+    #     grid_map.set_value_from_xy_pos(x, y, 1.0)
+
+    xinds, miny = search_free_lower_y_grid_index(grid_map)
+
+    # grid_map.plot_gridmap()
+
+    return grid_map, xinds, miny
+
+
+def sweep_path_search(sweep_searcher, gmap, start_posi):
+    sx, sy = start_posi[0], start_posi[1]
+    # print(sx, sy)
+
+    # search start grid
+    cxind, cyind = gmap.get_xy_index_from_xy_pos(sx, sy)
+    if gmap.check_occupied_from_xy_index(cxind, cyind):
+        cxind, cyind = sweep_searcher.find_safe_turning_grid(cxind, cyind, gmap)
+    gmap.set_value_from_xy_index(cxind, cyind, 0.5)
+
+    px, py = [], []
+
+    # fig, ax = plt.subplots()
+
+    while True:
+
+        cxind, cyind = sweep_searcher.move_target_grid(cxind, cyind, gmap)
+
+        if sweep_searcher.is_search_done(gmap) or (not cxind and not cyind):
+            print("Done")
+            break
+
+        x, y = gmap.calc_grid_central_xy_position_from_xy_index(
+            cxind, cyind)
+
+        px.append(x)
+        py.append(y)
+
+        gmap.set_value_from_xy_index(cxind, cyind, 0.5)
+
+        # gmap.plot_grid_map(ax=ax)
+        # plt.pause(0.1)
+
+    gmap.plot_grid_map()
+
+    return px, py
+
+
+def planning(ox, oy, reso, start_posi):
+    sweep_vec, sweep_start_posi = find_sweep_direction_and_start_posi(
+        ox, oy, start_posi)
+
+    rox, roy = convert_grid_coordinate(ox, oy, sweep_vec, sweep_start_posi)
+
+    moving_direction = 1
+    sweeping_direction = -1
+
+    gmap, xinds_miny, miny = setup_grid_map(rox, roy, reso)
+
+    sweep_searcher = SweepSearcher(moving_direction, sweeping_direction, xinds_miny, miny)
+
+    px, py = sweep_path_search(sweep_searcher, gmap, start_posi)
+
+    rx, ry = convert_global_coordinate(px, py, sweep_vec, sweep_start_posi)
+
+    return rx, ry
+
+
+def main():
+    print("start!!")
+
+    start_posi = [0.0, 0.0]
+
+    ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0, 0.0]
+    oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0, 0.0]
+    reso = 5.0
+
+    px, py = planning(ox, oy, reso, start_posi)
+
+    plt.subplots()
+
+    plt.plot(start_posi[0], start_posi[1], "or")
+    plt.plot(ox, oy, "-xb")
+    plt.plot(px, py, "-r")
+    plt.axis("equal")
+    plt.grid(True)
+    plt.show()
+
+    print("done!!")
+
+
+if __name__ == '__main__':
+    main()

From 927b60f42b3688122af9886a9ac9d0c526de13dd Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 1 Jul 2019 20:38:51 +0900
Subject: [PATCH 0097/1080] Update users_comments.md

---
 users_comments.md | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/users_comments.md b/users_comments.md
index aa255393ea..4a4f9fcb4f 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -267,7 +267,13 @@ Dear Atsushi Sakai, <br> <br>This is probably one of the best open-source roboti
 
 hanks frnd, for ur contibusion
 
-—
+—--
+
+Thank you for python robotics!!
+
+--Manuel Miguez
+
+---
 
 # Citations
 

From 7f12244c0118c3b69e6f3bc72669549101157265 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 2 Jul 2019 20:42:25 +0900
Subject: [PATCH 0098/1080] Update appveyor.yml

---
 appveyor.yml | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/appveyor.yml b/appveyor.yml
index 6cfe488378..7ad43f3842 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -7,8 +7,10 @@ environment:
 
 
   matrix:
-  - PYTHON_VERSION: 3.6
-    MINICONDA: "C:\\Miniconda36-x64"
+  
+  - PYTHON: "C:\\Python36-x64"
+    PYTHON_VERSION: "3.6.x" # currently 3.6.5
+    PYTHON_ARCH: "64"
 
 init:
   - "ECHO %MINICONDA% %PYTHON_VERSION% %PYTHON_ARCH%"

From 284ea2bd76657f5d0c1851c953ffdc457fc30d6c Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 2 Jul 2019 20:43:20 +0900
Subject: [PATCH 0099/1080] Update appveyor.yml

---
 appveyor.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/appveyor.yml b/appveyor.yml
index 7ad43f3842..e29584058e 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -5,8 +5,7 @@ environment:
     # See: http://stackoverflow.com/a/13751649/163740
     CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd"
 
-
-  matrix:
+matrix:
   
   - PYTHON: "C:\\Python36-x64"
     PYTHON_VERSION: "3.6.x" # currently 3.6.5

From 9c965e6447189868b5545d2e1e1a575f0434c8c3 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 2 Jul 2019 20:47:17 +0900
Subject: [PATCH 0100/1080] Update appveyor.yml

---
 appveyor.yml | 2 --
 1 file changed, 2 deletions(-)

diff --git a/appveyor.yml b/appveyor.yml
index e29584058e..9470b6c6fa 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -6,7 +6,6 @@ environment:
     CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd"
 
 matrix:
-  
   - PYTHON: "C:\\Python36-x64"
     PYTHON_VERSION: "3.6.x" # currently 3.6.5
     PYTHON_ARCH: "64"
@@ -14,7 +13,6 @@ matrix:
 init:
   - "ECHO %MINICONDA% %PYTHON_VERSION% %PYTHON_ARCH%"
 
-
 install:
   # If there is a newer build queued for the same PR, cancel this one.
   # The AppVeyor 'rollout builds' option is supposed to serve the same

From f2ee576a0e98b09c2f2f80e0beece79e61a17056 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 2 Jul 2019 20:50:27 +0900
Subject: [PATCH 0101/1080] Update appveyor.yml

---
 appveyor.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/appveyor.yml b/appveyor.yml
index 9470b6c6fa..cb3ebfee60 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -5,10 +5,10 @@ environment:
     # See: http://stackoverflow.com/a/13751649/163740
     CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd"
 
-matrix:
-  - PYTHON: "C:\\Python36-x64"
-    PYTHON_VERSION: "3.6.x" # currently 3.6.5
-    PYTHON_ARCH: "64"
+  matrix:
+    - PYTHON: "C:\\Python36-x64"
+      PYTHON_VERSION: "3.6.x" # currently 3.6.5
+      PYTHON_ARCH: "64"
 
 init:
   - "ECHO %MINICONDA% %PYTHON_VERSION% %PYTHON_ARCH%"

From babeb93497a5ffdbab575050b7d3917c99120eab Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 2 Jul 2019 20:55:45 +0900
Subject: [PATCH 0102/1080] Update appveyor.yml

---
 appveyor.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/appveyor.yml b/appveyor.yml
index cb3ebfee60..ac8e88405e 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -6,9 +6,9 @@ environment:
     CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd"
 
   matrix:
-    - PYTHON: "C:\\Python36-x64"
-      PYTHON_VERSION: "3.6.x" # currently 3.6.5
-      PYTHON_ARCH: "64"
+    - PYTHON_VERSION: 3.6
+      MINICONDA: C:\Miniconda3
+
 
 init:
   - "ECHO %MINICONDA% %PYTHON_VERSION% %PYTHON_ARCH%"

From 8496a9e31e71337f31996fa3e95156d861cf3fa0 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 2 Jul 2019 20:58:59 +0900
Subject: [PATCH 0103/1080] Update appveyor.yml

---
 appveyor.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/appveyor.yml b/appveyor.yml
index ac8e88405e..fbd7729847 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -7,7 +7,7 @@ environment:
 
   matrix:
     - PYTHON_VERSION: 3.6
-      MINICONDA: C:\Miniconda3
+      MINICONDA: C:\Miniconda4
 
 
 init:

From 3f60bc54a5017a707b262b15aba5df9236f0e9a7 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 2 Jul 2019 21:00:33 +0900
Subject: [PATCH 0104/1080] Update appveyor.yml

---
 appveyor.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/appveyor.yml b/appveyor.yml
index fbd7729847..0bcec0f3dd 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -6,8 +6,8 @@ environment:
     CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd"
 
   matrix:
-    - PYTHON_VERSION: 3.6
-      MINICONDA: C:\Miniconda4
+    - MINICONDA: C:\Miniconda36-x64
+
 
 
 init:

From 7471fab261840bc6124351cd17e9c6f58eb72851 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 2 Jul 2019 21:05:01 +0900
Subject: [PATCH 0105/1080] Update appveyor.yml

---
 appveyor.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/appveyor.yml b/appveyor.yml
index 0bcec0f3dd..673b8e2856 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -36,7 +36,7 @@ install:
   - conda config --set always_yes yes --set changeps1 no
   - conda update -q conda
   - conda info -a
-  - "python -m pip install --upgrade pip"
+  #- "python -m pip install --upgrade pip"
   #- conda install numpy==1.15
   #- conda install scipy
   #- conda install matplotlib

From 363e6e98c9517d8879e8dc499cd627dc768d3152 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 2 Jul 2019 21:09:08 +0900
Subject: [PATCH 0106/1080] Update environment.yml

---
 environment.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/environment.yml b/environment.yml
index 34d7edeec6..28ff9f1b4e 100644
--- a/environment.yml
+++ b/environment.yml
@@ -1,6 +1,6 @@
 name: python_robotics
 dependencies:
-- python==3.6
+- python
 - matplotlib
 - scipy
 - numpy==1.15

From ea36fa5fdf92345900f35fb3f0408eac70223917 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 2 Jul 2019 21:16:38 +0900
Subject: [PATCH 0107/1080] Update appveyor.yml

---
 appveyor.yml | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/appveyor.yml b/appveyor.yml
index 673b8e2856..61f8a0b364 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -36,15 +36,6 @@ install:
   - conda config --set always_yes yes --set changeps1 no
   - conda update -q conda
   - conda info -a
-  #- "python -m pip install --upgrade pip"
-  #- conda install numpy==1.15
-  #- conda install scipy
-  #- conda install matplotlib
-  #- conda install pandas
-  #- conda config --add channels conda-forge
-  #- conda install -c conda-forge lapack
-  #- conda install -c cvxgrp cvxpy
-  #- conda install coveralls
   - conda env create -f C:\\projects\pythonrobotics\environment.yml
   - activate python_robotics
 

From b06e42fe41a121bd41d86fc608c3495e395bd933 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 6 Jul 2019 10:32:00 +0900
Subject: [PATCH 0108/1080] first release grid based_sweep_planner.py

---
 Mapping/grid_map_lib/grid_map_lib.py          |  23 +++-
 PathPlanning/AStar/a_star.py                  |  17 ++-
 .../grid_based_sweep_planner.py               | 123 +++++++++---------
 3 files changed, 92 insertions(+), 71 deletions(-)

diff --git a/Mapping/grid_map_lib/grid_map_lib.py b/Mapping/grid_map_lib/grid_map_lib.py
index 87b035c94c..fd016a288f 100644
--- a/Mapping/grid_map_lib/grid_map_lib.py
+++ b/Mapping/grid_map_lib/grid_map_lib.py
@@ -98,9 +98,13 @@ def set_value_from_xy_index(self, x_ind, y_ind, val):
         :param val: grid value
         """
 
+        if (x_ind is None) or (y_ind is None):
+            print(x_ind, y_ind)
+            return False, False
+
         grid_ind = int(y_ind * self.width + x_ind)
 
-        if 0 <= grid_ind <= self.ndata:
+        if 0 <= grid_ind < self.ndata:
             self.data[grid_ind] = val
             return True  # OK
         else:
@@ -164,6 +168,23 @@ def check_occupied_from_xy_index(self, xind, yind, occupied_val=1.0):
         else:
             return False
 
+    def expand_grid(self):
+        xinds, yinds = [], []
+
+        for ix in range(self.width):
+            for iy in range(self.height):
+                if self.check_occupied_from_xy_index(ix, iy):
+                    xinds.append(ix)
+                    yinds.append(iy)
+
+        for (ix, iy) in zip(xinds, yinds):
+            self.set_value_from_xy_index(ix + 1, iy, val=1.0)
+            self.set_value_from_xy_index(ix, iy + 1, val=1.0)
+            self.set_value_from_xy_index(ix + 1, iy + 1, val=1.0)
+            self.set_value_from_xy_index(ix - 1, iy, val=1.0)
+            self.set_value_from_xy_index(ix, iy - 1, val=1.0)
+            self.set_value_from_xy_index(ix - 1, iy - 1, val=1.0)
+
     @staticmethod
     def check_inside_polygon(iox, ioy, x, y):
 
diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py
index ee14a94f3a..6b0392f515 100644
--- a/PathPlanning/AStar/a_star.py
+++ b/PathPlanning/AStar/a_star.py
@@ -9,9 +9,10 @@
 
 """
 
-import matplotlib.pyplot as plt
 import math
 
+import matplotlib.pyplot as plt
+
 show_animation = True
 
 
@@ -66,6 +67,10 @@ def planning(self, sx, sy, gx, gy):
         open_set[self.calc_grid_index(nstart)] = nstart
 
         while 1:
+            if len(open_set) == 0:
+                print("Open set is empty..")
+                break
+
             c_id = min(
                 open_set, key=lambda o: open_set[o].cost + self.calc_heuristic(ngoal, open_set[o]))
             current = open_set[c_id]
@@ -89,25 +94,25 @@ def planning(self, sx, sy, gx, gy):
             # Add it to the closed set
             closed_set[c_id] = current
 
-            # expand search grid based on motion model
+            # expand_grid search grid based on motion model
             for i, _ in enumerate(self.motion):
                 node = self.Node(current.x + self.motion[i][0],
                                  current.y + self.motion[i][1],
                                  current.cost + self.motion[i][2], c_id)
                 n_id = self.calc_grid_index(node)
 
-                # If the node is in closed_set, do nothing
-                if n_id in closed_set:
-                    continue
 
                 # If the node is not safe, do nothing
                 if not self.verify_node(node):
                     continue
 
+                if n_id in closed_set:
+                    continue
+
                 if n_id not in open_set:
                     open_set[n_id] = node  # discovered a new node
                 else:
-                    if open_set[n_id].cost >= node.cost:
+                    if open_set[n_id].cost > node.cost:
                         # This path is the best until now. record it
                         open_set[n_id] = node
 
diff --git a/PathPlanning/GridBasedSweepCCP/grid_based_sweep_planner.py b/PathPlanning/GridBasedSweepCCP/grid_based_sweep_planner.py
index 7d6f701def..0127d83f88 100644
--- a/PathPlanning/GridBasedSweepCCP/grid_based_sweep_planner.py
+++ b/PathPlanning/GridBasedSweepCCP/grid_based_sweep_planner.py
@@ -12,7 +12,12 @@
 import numpy as np
 
 sys.path.append(os.path.relpath("../../Mapping/grid_map_lib/"))
-from grid_map_lib import GridMap
+try:
+    from grid_map_lib import GridMap
+except ImportError:
+    exit()
+
+do_animation = True
 
 
 class SweepSearcher:
@@ -35,7 +40,7 @@ def move_target_grid(self, cxind, cyind, gmap):
             return nxind, nyind
         else:  # occupided
             ncxind, ncyind = self.find_safe_turning_grid(cxind, cyind, gmap)
-            if not ncxind and not ncyind:
+            if (ncxind is None) and (ncyind is None):
                 # moving backward
                 ncxind = -self.moving_direction + cxind
                 ncyind = cyind
@@ -43,6 +48,8 @@ def move_target_grid(self, cxind, cyind, gmap):
                     # moved backward, but the grid is occupied by obstacle
                     return None, None
             else:
+                while not gmap.check_occupied_from_xy_index(ncxind + self.moving_direction, ncyind, occupied_val=0.5):
+                    ncxind += self.moving_direction
                 self.swap_moving_direction()
             return ncxind, ncyind
 
@@ -55,7 +62,6 @@ def find_safe_turning_grid(self, cxind, cyind, gmap):
 
             # found safe grid
             if not gmap.check_occupied_from_xy_index(nxind, nyind, occupied_val=0.5):
-                print(dxind, dyind)
                 return nxind, nyind
 
         return None, None
@@ -81,34 +87,22 @@ def swap_moving_direction(self):
         self.update_turning_window()
 
 
-def find_sweep_direction_and_start_posi(ox, oy, start_posi):
+def find_sweep_direction_and_start_posi(ox, oy):
     # find sweep_direction
-    maxd_id = None
     maxd = 0.0
     vec = [0.0, 0.0]
+    sweep_start_pos = [0.0, 0.0]
     for i in range(len(ox) - 1):
         dx = ox[i + 1] - ox[i]
         dy = oy[i + 1] - oy[i]
         d = np.sqrt(dx ** 2 + dy ** 2)
 
         if d > maxd:
-            maxd_id = i
             maxd = d
             vec = [dx, dy]
+            sweep_start_pos = [ox[i], oy[i]]
 
-    # find sweep start posi
-    d1 = np.sqrt((ox[maxd_id] - start_posi[0]) ** 2 +
-                 (oy[maxd_id] - start_posi[1]) ** 2)
-    d2 = np.sqrt((ox[maxd_id + 1] - start_posi[0]) ** 2 +
-                 (oy[maxd_id + 1] - start_posi[1]) ** 2)
-
-    if d2 >= d1:  # first point is near
-        sweep_start_posi = [ox[maxd_id], oy[maxd_id]]
-    else:
-        sweep_start_posi = [ox[maxd_id + 1], oy[maxd_id + 1]]
-        vec = [-1.0 * iv for iv in vec]  # reverse direction
-
-    return vec, sweep_start_posi
+    return vec, sweep_start_pos
 
 
 def convert_grid_coordinate(ox, oy, sweep_vec, sweep_start_posi):
@@ -140,19 +134,26 @@ def convert_global_coordinate(x, y, sweep_vec, sweep_start_posi):
     return rx, ry
 
 
-def search_free_lower_y_grid_index(grid_map):
-    miny = None
+def search_free_grid_index_at_edge_y(grid_map, from_upper=False):
+    yind = None
     xinds = []
 
-    for iy in range(grid_map.height):
-        for ix in range(grid_map.width):
+    if from_upper:
+        xrange = range(grid_map.height)[::-1]
+        yrange = range(grid_map.width)[::-1]
+    else:
+        xrange = range(grid_map.height)
+        yrange = range(grid_map.width)
+
+    for iy in xrange:
+        for ix in yrange:
             if not grid_map.check_occupied_from_xy_index(ix, iy):
-                miny = iy
+                yind = iy
                 xinds.append(ix)
-        if miny:
+        if yind:
             break
 
-    return xinds, miny
+    return xinds, yind
 
 
 def setup_grid_map(ox, oy, reso):
@@ -167,47 +168,33 @@ def setup_grid_map(ox, oy, reso):
 
     grid_map.set_value_from_polygon(ox, oy, 1.0, inside=False)
 
-    # fill grid
-    # for i in range(len(ox) - 1):
-    #     grid_map.set_value_from_xy_pos(ox[i], oy[i], 1.0)
-    #
-    #     x, y = ox[i], oy[i]
-    #     th = math.atan2(oy[i + 1] - oy[i], ox[i + 1] - ox[i])
-    #     d = np.sqrt((x - ox[i + 1])**2 + (y - oy[i + 1])**2)
-    #
-    #     while d > reso:
-    #         x += np.cos(th) * reso
-    #         y += np.sin(th) * reso
-    #         d = np.sqrt((x - ox[i + 1])**2 + (y - oy[i + 1])**2)
-    #
-    #     grid_map.set_value_from_xy_pos(x, y, 1.0)
-
-    xinds, miny = search_free_lower_y_grid_index(grid_map)
-
-    # grid_map.plot_gridmap()
+    grid_map.expand_grid()
+
+    xinds, miny = search_free_grid_index_at_edge_y(grid_map)
 
     return grid_map, xinds, miny
 
 
-def sweep_path_search(sweep_searcher, gmap, start_posi):
-    sx, sy = start_posi[0], start_posi[1]
-    # print(sx, sy)
+def search_start_grid(grid_map):
+    xinds, y_ind = search_free_grid_index_at_edge_y(grid_map, from_upper=True)
+
+    return min(xinds), y_ind
 
-    # search start grid
-    cxind, cyind = gmap.get_xy_index_from_xy_pos(sx, sy)
-    if gmap.check_occupied_from_xy_index(cxind, cyind):
-        cxind, cyind = sweep_searcher.find_safe_turning_grid(cxind, cyind, gmap)
-    gmap.set_value_from_xy_index(cxind, cyind, 0.5)
 
+def sweep_path_search(sweep_searcher, gmap):
     px, py = [], []
 
-    # fig, ax = plt.subplots()
+    # search start grid
+    cxind, cyind = search_start_grid(gmap)
+    if not gmap.set_value_from_xy_index(cxind, cyind, 0.5):
+        print("Cannot find start grid")
+        return px, py
 
+    # fig, ax = plt.subplots()
     while True:
-
         cxind, cyind = sweep_searcher.move_target_grid(cxind, cyind, gmap)
 
-        if sweep_searcher.is_search_done(gmap) or (not cxind and not cyind):
+        if sweep_searcher.is_search_done(gmap) or (cxind is None or cyind is None):
             print("Done")
             break
 
@@ -220,16 +207,15 @@ def sweep_path_search(sweep_searcher, gmap, start_posi):
         gmap.set_value_from_xy_index(cxind, cyind, 0.5)
 
         # gmap.plot_grid_map(ax=ax)
-        # plt.pause(0.1)
+        # plt.pause(1.0)
 
     gmap.plot_grid_map()
 
     return px, py
 
 
-def planning(ox, oy, reso, start_posi):
-    sweep_vec, sweep_start_posi = find_sweep_direction_and_start_posi(
-        ox, oy, start_posi)
+def planning(ox, oy, reso):
+    sweep_vec, sweep_start_posi = find_sweep_direction_and_start_posi(ox, oy)
 
     rox, roy = convert_grid_coordinate(ox, oy, sweep_vec, sweep_start_posi)
 
@@ -240,7 +226,7 @@ def planning(ox, oy, reso, start_posi):
 
     sweep_searcher = SweepSearcher(moving_direction, sweeping_direction, xinds_miny, miny)
 
-    px, py = sweep_path_search(sweep_searcher, gmap, start_posi)
+    px, py = sweep_path_search(sweep_searcher, gmap)
 
     rx, ry = convert_global_coordinate(px, py, sweep_vec, sweep_start_posi)
 
@@ -250,17 +236,26 @@ def planning(ox, oy, reso, start_posi):
 def main():
     print("start!!")
 
-    start_posi = [0.0, 0.0]
-
     ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0, 0.0]
     oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0, 0.0]
     reso = 5.0
 
-    px, py = planning(ox, oy, reso, start_posi)
+    px, py = planning(ox, oy, reso)
 
     plt.subplots()
 
-    plt.plot(start_posi[0], start_posi[1], "or")
+    # animation
+    if do_animation:
+        for ipx, ipy in zip(px, py):
+            plt.cla()
+            plt.plot(ox, oy, "-xb")
+            plt.plot(px, py, "-r")
+            plt.plot(ipx, ipy, "or")
+            plt.axis("equal")
+            plt.grid(True)
+            plt.pause(0.1)
+
+    plt.cla()
     plt.plot(ox, oy, "-xb")
     plt.plot(px, py, "-r")
     plt.axis("equal")

From 90ee562b8922a56ca27c2a1f58a935895a46e7e7 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 6 Jul 2019 11:32:14 +0900
Subject: [PATCH 0109/1080] first release grid based_sweep_planner.py

---
 PathPlanning/GridBasedSweepCCP/grid_based_sweep_planner.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/GridBasedSweepCCP/grid_based_sweep_planner.py b/PathPlanning/GridBasedSweepCCP/grid_based_sweep_planner.py
index 0127d83f88..0c7230e86b 100644
--- a/PathPlanning/GridBasedSweepCCP/grid_based_sweep_planner.py
+++ b/PathPlanning/GridBasedSweepCCP/grid_based_sweep_planner.py
@@ -15,7 +15,7 @@
 try:
     from grid_map_lib import GridMap
 except ImportError:
-    exit()
+    raise
 
 do_animation = True
 

From 2671688f62d8547f623ff945ac194f0052eef41a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 6 Jul 2019 16:02:23 +0900
Subject: [PATCH 0110/1080] release grid based_sweep_coverage_path_planner.py

---
 ...grid_based_sweep_coverage_path_planner.py} | 126 ++++++++++++------
 ..._grid_based_sweep_coverage_path_planner.py |  92 +++++++++++++
 2 files changed, 174 insertions(+), 44 deletions(-)
 rename PathPlanning/GridBasedSweepCCP/{grid_based_sweep_planner.py => grid_based_sweep_coverage_path_planner.py} (67%)
 create mode 100644 PathPlanning/GridBasedSweepCCP/test_grid_based_sweep_coverage_path_planner.py

diff --git a/PathPlanning/GridBasedSweepCCP/grid_based_sweep_planner.py b/PathPlanning/GridBasedSweepCCP/grid_based_sweep_coverage_path_planner.py
similarity index 67%
rename from PathPlanning/GridBasedSweepCCP/grid_based_sweep_planner.py
rename to PathPlanning/GridBasedSweepCCP/grid_based_sweep_coverage_path_planner.py
index 0c7230e86b..aeba545e23 100644
--- a/PathPlanning/GridBasedSweepCCP/grid_based_sweep_planner.py
+++ b/PathPlanning/GridBasedSweepCCP/grid_based_sweep_coverage_path_planner.py
@@ -7,6 +7,7 @@
 import math
 import os
 import sys
+from enum import IntEnum
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -21,14 +22,21 @@
 
 
 class SweepSearcher:
+    class SweepDirection(IntEnum):
+        UP = 1
+        DOWN = -1
 
-    def __init__(self, mdirection, sdirection, xinds_miny, miny):
-        self.moving_direction = mdirection  # +1 or -1
-        self.sweep_direction = sdirection  # +1 or -1
+    class MovingDirection(IntEnum):
+        RIGHT = 1
+        LEFT = -1
+
+    def __init__(self, mdirection, sdirection, xinds_goaly, goaly):
+        self.moving_direction = mdirection
+        self.sweep_direction = sdirection
         self.turing_window = []
         self.update_turning_window()
-        self.xinds_miny = xinds_miny
-        self.miny = miny
+        self.xinds_goaly = xinds_goaly
+        self.goaly = goaly
 
     def move_target_grid(self, cxind, cyind, gmap):
 
@@ -48,6 +56,7 @@ def move_target_grid(self, cxind, cyind, gmap):
                     # moved backward, but the grid is occupied by obstacle
                     return None, None
             else:
+                # keep moving until end
                 while not gmap.check_occupied_from_xy_index(ncxind + self.moving_direction, ncyind, occupied_val=0.5):
                     ncxind += self.moving_direction
                 self.swap_moving_direction()
@@ -67,8 +76,8 @@ def find_safe_turning_grid(self, cxind, cyind, gmap):
         return None, None
 
     def is_search_done(self, gmap):
-        for ix in self.xinds_miny:
-            if not gmap.check_occupied_from_xy_index(ix, self.miny, occupied_val=0.5):
+        for ix in self.xinds_goaly:
+            if not gmap.check_occupied_from_xy_index(ix, self.goaly, occupied_val=0.5):
                 return False
 
         # all lower grid is occupied
@@ -86,10 +95,24 @@ def swap_moving_direction(self):
         self.moving_direction *= -1
         self.update_turning_window()
 
+    def search_start_grid(self, grid_map):
+        xinds = [], y_ind = 0
+        if self.sweep_direction == self.SweepDirection.DOWN:
+            xinds, y_ind = search_free_grid_index_at_edge_y(grid_map, from_upper=True)
+        elif self.sweep_direction == self.SweepDirection.UP:
+            xinds, y_ind = search_free_grid_index_at_edge_y(grid_map, from_upper=False)
+
+        if self.moving_direction == self.MovingDirection.RIGHT:
+            return min(xinds), y_ind
+        elif self.moving_direction == self.MovingDirection.LEFT:
+            return max(xinds), y_ind
+
+        raise ValueError("self.moving direction is invalid ")
+
 
 def find_sweep_direction_and_start_posi(ox, oy):
     # find sweep_direction
-    maxd = 0.0
+    max_dist = 0.0
     vec = [0.0, 0.0]
     sweep_start_pos = [0.0, 0.0]
     for i in range(len(ox) - 1):
@@ -97,8 +120,8 @@ def find_sweep_direction_and_start_posi(ox, oy):
         dy = oy[i + 1] - oy[i]
         d = np.sqrt(dx ** 2 + dy ** 2)
 
-        if d > maxd:
-            maxd = d
+        if d > max_dist:
+            max_dist = d
             vec = [dx, dy]
             sweep_start_pos = [ox[i], oy[i]]
 
@@ -156,9 +179,7 @@ def search_free_grid_index_at_edge_y(grid_map, from_upper=False):
     return xinds, yind
 
 
-def setup_grid_map(ox, oy, reso):
-    offset_grid = 10
-
+def setup_grid_map(ox, oy, reso, sweep_direction, offset_grid=10):
     width = math.ceil((max(ox) - min(ox)) / reso) + offset_grid
     height = math.ceil((max(oy) - min(oy)) / reso) + offset_grid
     center_x = np.mean(ox)
@@ -170,27 +191,28 @@ def setup_grid_map(ox, oy, reso):
 
     grid_map.expand_grid()
 
-    xinds, miny = search_free_grid_index_at_edge_y(grid_map)
-
-    return grid_map, xinds, miny
-
-
-def search_start_grid(grid_map):
-    xinds, y_ind = search_free_grid_index_at_edge_y(grid_map, from_upper=True)
-
-    return min(xinds), y_ind
+    xinds_goaly = [], goaly = 0
+    if sweep_direction == SweepSearcher.SweepDirection.UP:
+        xinds_goaly, goaly = search_free_grid_index_at_edge_y(grid_map, from_upper=True)
+    elif sweep_direction == SweepSearcher.SweepDirection.DOWN:
+        xinds_goaly, goaly = search_free_grid_index_at_edge_y(grid_map, from_upper=False)
 
+    return grid_map, xinds_goaly, goaly
 
-def sweep_path_search(sweep_searcher, gmap):
-    px, py = [], []
 
+def sweep_path_search(sweep_searcher, gmap, grid_search_animation=False):
     # search start grid
-    cxind, cyind = search_start_grid(gmap)
+    cxind, cyind = sweep_searcher.search_start_grid(gmap)
     if not gmap.set_value_from_xy_index(cxind, cyind, 0.5):
         print("Cannot find start grid")
-        return px, py
+        return [], []
+
+    x, y = gmap.calc_grid_central_xy_position_from_xy_index(cxind, cyind)
+    px, py = [x], [y]
+
+    if grid_search_animation:
+        fig, ax = plt.subplots()
 
-    # fig, ax = plt.subplots()
     while True:
         cxind, cyind = sweep_searcher.move_target_grid(cxind, cyind, gmap)
 
@@ -206,44 +228,39 @@ def sweep_path_search(sweep_searcher, gmap):
 
         gmap.set_value_from_xy_index(cxind, cyind, 0.5)
 
-        # gmap.plot_grid_map(ax=ax)
-        # plt.pause(1.0)
+        if grid_search_animation:
+            gmap.plot_grid_map(ax=ax)
+            plt.pause(1.0)
 
     gmap.plot_grid_map()
 
     return px, py
 
 
-def planning(ox, oy, reso):
+def planning(ox, oy, reso,
+             moving_direction=SweepSearcher.MovingDirection.RIGHT,
+             sweeping_direction=SweepSearcher.SweepDirection.UP,
+             ):
     sweep_vec, sweep_start_posi = find_sweep_direction_and_start_posi(ox, oy)
 
     rox, roy = convert_grid_coordinate(ox, oy, sweep_vec, sweep_start_posi)
 
-    moving_direction = 1
-    sweeping_direction = -1
+    gmap, xinds_goaly, goaly = setup_grid_map(rox, roy, reso, sweeping_direction)
 
-    gmap, xinds_miny, miny = setup_grid_map(rox, roy, reso)
-
-    sweep_searcher = SweepSearcher(moving_direction, sweeping_direction, xinds_miny, miny)
+    sweep_searcher = SweepSearcher(moving_direction, sweeping_direction, xinds_goaly, goaly)
 
     px, py = sweep_path_search(sweep_searcher, gmap)
 
     rx, ry = convert_global_coordinate(px, py, sweep_vec, sweep_start_posi)
 
-    return rx, ry
+    print("Path length:", len(rx))
 
+    return rx, ry
 
-def main():
-    print("start!!")
-
-    ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0, 0.0]
-    oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0, 0.0]
-    reso = 5.0
 
+def planning_animation(ox, oy, reso):
     px, py = planning(ox, oy, reso)
 
-    plt.subplots()
-
     # animation
     if do_animation:
         for ipx, ipy in zip(px, py):
@@ -260,6 +277,27 @@ def main():
     plt.plot(px, py, "-r")
     plt.axis("equal")
     plt.grid(True)
+    plt.pause(0.1)
+
+
+def main():
+    print("start!!")
+
+    ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0, 0.0]
+    oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0, 0.0]
+    reso = 5.0
+    planning_animation(ox, oy, reso)
+
+    ox = [0.0, 50.0, 50.0, 0.0, 0.0]
+    oy = [0.0, 0.0, 30.0, 30.0, 0.0]
+    reso = 1.3
+    planning_animation(ox, oy, reso)
+
+    ox = [0.0, 20.0, 50.0, 200.0, 130.0, 40.0, 0.0]
+    oy = [0.0, -80.0, 0.0, 30.0, 60.0, 80.0, 0.0]
+    reso = 5.0
+    planning_animation(ox, oy, reso)
+
     plt.show()
 
     print("done!!")
diff --git a/PathPlanning/GridBasedSweepCCP/test_grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCCP/test_grid_based_sweep_coverage_path_planner.py
new file mode 100644
index 0000000000..25f343c6d1
--- /dev/null
+++ b/PathPlanning/GridBasedSweepCCP/test_grid_based_sweep_coverage_path_planner.py
@@ -0,0 +1,92 @@
+from unittest import TestCase
+
+import grid_based_sweep_coverage_path_planner
+
+
+class TestPlanning(TestCase):
+
+    def test_planning1(self):
+        ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0, 0.0]
+        oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0, 0.0]
+        reso = 5.0
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
+                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT,
+                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN,
+                                                                 )
+        self.assertTrue(len(px) >= 5)
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
+                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.LEFT,
+                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN,
+                                                                 )
+        self.assertTrue(len(px) >= 5)
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
+                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT,
+                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.UP,
+                                                                 )
+        self.assertTrue(len(px) >= 5)
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
+                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT,
+                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN,
+                                                                 )
+        self.assertTrue(len(px) >= 5)
+
+    def test_planning2(self):
+        ox = [0.0, 50.0, 50.0, 0.0, 0.0]
+        oy = [0.0, 0.0, 30.0, 30.0, 0.0]
+        reso = 1.3
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
+                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT,
+                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN,
+                                                                 )
+        self.assertTrue(len(px) >= 5)
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
+                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.LEFT,
+                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN,
+                                                                 )
+        self.assertTrue(len(px) >= 5)
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
+                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT,
+                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.UP,
+                                                                 )
+        self.assertTrue(len(px) >= 5)
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
+                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT,
+                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN,
+                                                                 )
+        self.assertTrue(len(px) >= 5)
+
+    def test_planning3(self):
+        ox = [0.0, 20.0, 50.0, 200.0, 130.0, 40.0, 0.0]
+        oy = [0.0, -80.0, 0.0, 30.0, 60.0, 80.0, 0.0]
+        reso = 5.1
+        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
+                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT,
+                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN,
+                                                                 )
+        self.assertTrue(len(px) >= 5)
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
+                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.LEFT,
+                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN,
+                                                                 )
+        self.assertTrue(len(px) >= 5)
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
+                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT,
+                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.UP,
+                                                                 )
+        self.assertTrue(len(px) >= 5)
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
+                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT,
+                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN,
+                                                                 )
+        self.assertTrue(len(px) >= 5)

From 142016e827e94a0b7b932f44902463000902a833 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 6 Jul 2019 16:10:23 +0900
Subject: [PATCH 0111/1080] update README and change dir name

---
 .../grid_based_sweep_coverage_path_planner.py       |  0
 .../test_grid_based_sweep_coverage_path_planner.py  |  0
 README.md                                           | 13 +++++++++----
 3 files changed, 9 insertions(+), 4 deletions(-)
 rename PathPlanning/{GridBasedSweepCCP => GridBasedSweepCPP}/grid_based_sweep_coverage_path_planner.py (100%)
 rename PathPlanning/{GridBasedSweepCCP => GridBasedSweepCPP}/test_grid_based_sweep_coverage_path_planner.py (100%)

diff --git a/PathPlanning/GridBasedSweepCCP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
similarity index 100%
rename from PathPlanning/GridBasedSweepCCP/grid_based_sweep_coverage_path_planner.py
rename to PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
diff --git a/PathPlanning/GridBasedSweepCCP/test_grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/test_grid_based_sweep_coverage_path_planner.py
similarity index 100%
rename from PathPlanning/GridBasedSweepCCP/test_grid_based_sweep_coverage_path_planner.py
rename to PathPlanning/GridBasedSweepCPP/test_grid_based_sweep_coverage_path_planner.py
diff --git a/README.md b/README.md
index 0aa7220d40..e91f704f9c 100644
--- a/README.md
+++ b/README.md
@@ -12,9 +12,6 @@
 
 Python codes for robotics algorithm.
 
-
-
-
 # Table of Contents
    * [What is this?](#what-is-this)
    * [Requirements](#requirements)
@@ -27,7 +24,7 @@ Python codes for robotics algorithm.
    * [Mapping](#mapping)
       * [Gaussian grid map](#gaussian-grid-map)
       * [Ray casting grid map](#ray-casting-grid-map)
-      * [Lidar to grid map](#lidar-to-grid-map)	  
+      * [Lidar to grid map](#lidar-to-grid-map)
       * [k-means object clustering](#k-means-object-clustering)
       * [Rectangle fitting](#rectangle-fitting)
    * [SLAM](#slam)
@@ -40,6 +37,7 @@ Python codes for robotics algorithm.
          * [Dijkstra algorithm](#dijkstra-algorithm)
          * [A* algorithm](#a-algorithm)
          * [Potential Field algorithm](#potential-field-algorithm)
+         * [Potential Field algorithm](#potential-field-algorithm-1)
       * [State Lattice Planning](#state-lattice-planning)
          * [Biased polar sampling](#biased-polar-sampling)
          * [Lane sampling](#lane-sampling)
@@ -299,6 +297,12 @@ Ref:
 
 - [Robotic Motion Planning:Potential Functions](https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf)
 
+### Potential Field algorithm
+
+This is a 2D grid based coverage path planning simulation.
+
+![PotentialField](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/GridBasedSweepCPP/animation.gif)
+
 ## State Lattice Planning
 
 This script is a path planning code with state lattice planning.
@@ -609,3 +613,4 @@ This is a list: [Users comments](https://github.com/AtsushiSakai/PythonRobotics/
 
 
 
+

From efb4ee072dea723f5acece3a6e5c5af4a0bdc40c Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 6 Jul 2019 16:15:27 +0900
Subject: [PATCH 0112/1080] fix README

---
 README.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index e91f704f9c..bbadbc3ce6 100644
--- a/README.md
+++ b/README.md
@@ -12,6 +12,7 @@
 
 Python codes for robotics algorithm.
 
+
 # Table of Contents
    * [What is this?](#what-is-this)
    * [Requirements](#requirements)
@@ -37,7 +38,7 @@ Python codes for robotics algorithm.
          * [Dijkstra algorithm](#dijkstra-algorithm)
          * [A* algorithm](#a-algorithm)
          * [Potential Field algorithm](#potential-field-algorithm)
-         * [Potential Field algorithm](#potential-field-algorithm-1)
+         * [Grid based coverage path planning](#grid-based-coverage-path-planning)
       * [State Lattice Planning](#state-lattice-planning)
          * [Biased polar sampling](#biased-polar-sampling)
          * [Lane sampling](#lane-sampling)
@@ -297,7 +298,7 @@ Ref:
 
 - [Robotic Motion Planning:Potential Functions](https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf)
 
-### Potential Field algorithm
+### Grid based coverage path planning
 
 This is a 2D grid based coverage path planning simulation.
 
@@ -614,3 +615,4 @@ This is a list: [Users comments](https://github.com/AtsushiSakai/PythonRobotics/
 
 
 
+

From 6d5ca205aabc25a39739c25f3e2c36f9ef6aa563 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 6 Jul 2019 20:53:00 +0900
Subject: [PATCH 0113/1080] add shbang

---
 runtests.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/runtests.sh b/runtests.sh
index fbb87df518..52ef50f001 100755
--- a/runtests.sh
+++ b/runtests.sh
@@ -1,3 +1,4 @@
+#!/usr/bin/env bash
 echo "Run test suites! "
 #python -m unittest discover tests 
 #python -Wignore -m unittest discover tests #ignore warning

From 62db9d0b83a06b2006a21e3bb8944dffa58661d6 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 7 Jul 2019 19:41:04 +0900
Subject: [PATCH 0114/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 4a4f9fcb4f..a56ae4da5a 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -275,6 +275,12 @@ Thank you for python robotics!!
 
 ---
 
+Great job
+
+--Anonymous
+
+---
+
 # Citations
 
 1. B. Blaga, M. Deac, R. W. Y. Al-doori, M. Negru and R. Dǎnescu, "Miniature Autonomous Vehicle Development on Raspberry Pi," 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, 2018, pp. 229-236.

From 37b88b3deddb7962ac2671960ef94ff14ef20b5a Mon Sep 17 00:00:00 2001
From: Jumpei Arima <arijun0307@gmail.com>
Date: Mon, 8 Jul 2019 23:33:09 +0900
Subject: [PATCH 0115/1080] fixed path

---
 PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
index 489cf08709..aa03418f59 100644
--- a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
+++ b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
@@ -13,7 +13,7 @@
 import sys
 import os
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../PathPlanning/ReedsSheppPath/")
+                "/../ReedsSheppPath/")
 
 try:
     import reeds_shepp_path_planning

From 21f2eeabae7cac8548ac465959dd50f0735c7642 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 9 Jul 2019 09:47:08 +0900
Subject: [PATCH 0116/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index a56ae4da5a..a87de730b6 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -281,6 +281,12 @@ Great job
 
 ---
 
+Dear Atsushi Sakai <br>Thank you for the Python Robotics
+
+--Alex Liberzon
+
+---
+
 # Citations
 
 1. B. Blaga, M. Deac, R. W. Y. Al-doori, M. Negru and R. Dǎnescu, "Miniature Autonomous Vehicle Development on Raspberry Pi," 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, 2018, pp. 229-236.

From 3aa21316f1c483e68f9065289d2ec22adcac108a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 9 Jul 2019 19:43:10 +0900
Subject: [PATCH 0117/1080] Update users_comments.md

---
 users_comments.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index a87de730b6..021d0760b0 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -296,6 +296,8 @@ URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8516589&isnumber=85
 
 2. Peggy (Yuchun) Wang and Caitlin Hogan, "Path Planning with Dynamic Obstacle Avoidance for a Jumping-Enabled Robot", AA228/CS238 class report, Department of Computer Science, Stanford University, URL: https://web.stanford.edu/class/aa228/reports/2018/final113.pdf
 
+3. Welburn, E, Hakim Khalili, H, Gupta, A, Watson, S & Carrasco, J 2019, A Navigational System for Quadcopter Remote Inspection of Offshore Substations. in The Fifteenth International Conference on Autonomic and Autonomous Systems 2019. URL:https://www.research.manchester.ac.uk/portal/files/107169964/ICAS19_A_Navigational_System_for_Quadcopter_Remote_Inspection_of_Offshore_Substations.pdf
+
 
 # Others
 

From 14bc640d3f2438689e2e3b417c01bbc6c040153e Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 10 Jul 2019 19:36:20 +0900
Subject: [PATCH 0118/1080] release grid based_sweep_coverage_path_planner.py

---
 .../grid_based_sweep_coverage_path_planner.py            | 7 ++++---
 .../test_grid_based_sweep_coverage_path_planner.py       | 9 +++++++--
 2 files changed, 11 insertions(+), 5 deletions(-)
 rename {PathPlanning/GridBasedSweepCPP => tests}/test_grid_based_sweep_coverage_path_planner.py (97%)

diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
index aeba545e23..3d068559f8 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -39,7 +39,6 @@ def __init__(self, mdirection, sdirection, xinds_goaly, goaly):
         self.goaly = goaly
 
     def move_target_grid(self, cxind, cyind, gmap):
-
         nxind = self.moving_direction + cxind
         nyind = cyind
 
@@ -96,7 +95,8 @@ def swap_moving_direction(self):
         self.update_turning_window()
 
     def search_start_grid(self, grid_map):
-        xinds = [], y_ind = 0
+        xinds = []
+        y_ind = 0
         if self.sweep_direction == self.SweepDirection.DOWN:
             xinds, y_ind = search_free_grid_index_at_edge_y(grid_map, from_upper=True)
         elif self.sweep_direction == self.SweepDirection.UP:
@@ -191,7 +191,8 @@ def setup_grid_map(ox, oy, reso, sweep_direction, offset_grid=10):
 
     grid_map.expand_grid()
 
-    xinds_goaly = [], goaly = 0
+    xinds_goaly = []
+    goaly = 0
     if sweep_direction == SweepSearcher.SweepDirection.UP:
         xinds_goaly, goaly = search_free_grid_index_at_edge_y(grid_map, from_upper=True)
     elif sweep_direction == SweepSearcher.SweepDirection.DOWN:
diff --git a/PathPlanning/GridBasedSweepCPP/test_grid_based_sweep_coverage_path_planner.py b/tests/test_grid_based_sweep_coverage_path_planner.py
similarity index 97%
rename from PathPlanning/GridBasedSweepCPP/test_grid_based_sweep_coverage_path_planner.py
rename to tests/test_grid_based_sweep_coverage_path_planner.py
index 25f343c6d1..c4401ebd5f 100644
--- a/PathPlanning/GridBasedSweepCPP/test_grid_based_sweep_coverage_path_planner.py
+++ b/tests/test_grid_based_sweep_coverage_path_planner.py
@@ -1,7 +1,12 @@
+import os
+import sys
 from unittest import TestCase
 
-import grid_based_sweep_coverage_path_planner
-
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../PathPlanning/GridBasedSweepCPP")
+try:
+    import grid_based_sweep_coverage_path_planner
+except:
+    raise
 
 class TestPlanning(TestCase):
 

From 6201aa262ed3f94323a3e01dd28ce652967237b6 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 10 Jul 2019 19:48:20 +0900
Subject: [PATCH 0119/1080] release grid based_sweep_coverage_path_planner.py

---
 tests/test_grid_based_sweep_coverage_path_planner.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/test_grid_based_sweep_coverage_path_planner.py b/tests/test_grid_based_sweep_coverage_path_planner.py
index c4401ebd5f..3caaac8952 100644
--- a/tests/test_grid_based_sweep_coverage_path_planner.py
+++ b/tests/test_grid_based_sweep_coverage_path_planner.py
@@ -3,6 +3,7 @@
 from unittest import TestCase
 
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../PathPlanning/GridBasedSweepCPP")
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../Mapping/grid_map_lib")
 try:
     import grid_based_sweep_coverage_path_planner
 except:

From cfa97b497c23cfdb0ccffcf696cac2b315ff5128 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 10 Jul 2019 20:03:54 +0900
Subject: [PATCH 0120/1080] Update README.md

---
 README.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/README.md b/README.md
index bbadbc3ce6..551490461b 100644
--- a/README.md
+++ b/README.md
@@ -569,6 +569,12 @@ A small PR like bug fix is welcome.
 
 If your PR is merged multiple times, I will add your account to the author list.
 
+# Citing
+
+If you use this project's code for your academic work, we encourage you to cite [our papers](https://arxiv.org/abs/1808.10703) 
+
+If you use this project's code in industry, we'd love to hear from you as well; feel free to reach out to the developers directly.
+
 # Support
 
 If you or your company would like to support this project, please consider:

From 06dfc05d89395e69e182d36f892cb2c584efd33a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 11 Jul 2019 21:12:46 +0900
Subject: [PATCH 0121/1080] release grid based_sweep_coverage_path_planner.py

---
 .../closed_loop_rrt_star_car.py               | 25 +++++++++++--------
 .../ClosedLoopRRTStar/pure_pursuit.py         |  7 ++++--
 2 files changed, 19 insertions(+), 13 deletions(-)

diff --git a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
index 927fcd9448..2aa0ba61de 100644
--- a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
+++ b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
@@ -1,19 +1,21 @@
 """
-Path Planning Sample Code with Closed loop RRT for car like robot.
+Path planning Sample Code with Closed loop RRT for car like robot.
 
 author: AtsushiSakai(@Atsushi_twi)
 
 """
 
-import random
-import math
 import copy
+import math
+import os
+import random
+import sys
+
+import matplotlib.pyplot as plt
 import numpy as np
+
 import pure_pursuit
-import matplotlib.pyplot as plt
 
-import sys
-import os
 sys.path.append(os.path.dirname(
     os.path.abspath(__file__)) + "/../ReedsSheppPath/")
 
@@ -32,11 +34,11 @@
 
 class RRT():
     """
-    Class for RRT Planning
+    Class for RRT planning
     """
 
     def __init__(self, start, goal, obstacleList, randArea,
-                 maxIter=200):
+                 maxIter=50):
         """
         Setting Parameter
 
@@ -77,6 +79,7 @@ def Planning(self, animation=True):
         self.try_goal_path()
 
         for i in range(self.maxIter):
+            print("loop:", i)
             rnd = self.get_random_point()
             nind = self.GetNearestListIndex(self.nodeList, rnd)
 
@@ -113,7 +116,7 @@ def search_best_feasible_path(self, path_indexs):
 
         best_time = float("inf")
 
-        fx = None
+        fx, fy, fyaw, fv, ft, fa, fd = None, None, None, None, None, None, None
 
         # pure pursuit tracking
         for ind in path_indexs:
@@ -246,7 +249,7 @@ def get_random_point(self):
         return node
 
     def get_best_last_indexs(self):
-        #  print("get_best_last_index")
+        #  print("search_finish_node")
 
         YAWTH = np.deg2rad(1.0)
         XYTH = 0.5
@@ -387,7 +390,7 @@ def __init__(self, x, y, yaw):
         self.parent = None
 
 
-def main(gx=6.0, gy=7.0, gyaw=np.deg2rad(90.0), maxIter=500):
+def main(gx=6.0, gy=7.0, gyaw=np.deg2rad(90.0), maxIter=200):
     print("Start" + __file__)
     # ====Search Path with RRT====
     obstacleList = [
diff --git a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
index c90af1567d..0490727933 100644
--- a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
+++ b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
@@ -5,9 +5,11 @@
 author: Atsushi Sakai
 
 """
-import numpy as np
 import math
+
 import matplotlib.pyplot as plt
+import numpy as np
+
 import unicycle_model
 
 Kp = 2.0  # speed propotional gain
@@ -75,7 +77,7 @@ def calc_target_index(state, cx, cy):
 
     while Lf > L and (ind + 1) < len(cx):
         dx = cx[ind + 1] - cx[ind]
-        dy = cx[ind + 1] - cx[ind]
+        dy = cy[ind + 1] - cy[ind]
         L += math.sqrt(dx ** 2 + dy ** 2)
         ind += 1
 
@@ -153,6 +155,7 @@ def set_stop_point(target_speed, cx, cy, cyaw):
     forward = True
 
     d = []
+    is_back = False
 
     # Set stop point
     for i in range(len(cx) - 1):

From 3c6833210b9cd9254946989e73457ae6617d20ed Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 12 Jul 2019 20:46:32 +0900
Subject: [PATCH 0122/1080] release grid based_sweep_coverage_path_planner.py

---
 .../lqr_speed_steer_control.py                | 92 +++++++++++--------
 1 file changed, 56 insertions(+), 36 deletions(-)

diff --git a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
index ff8d890d0f..6dec12bd30 100644
--- a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
+++ b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
@@ -13,14 +13,17 @@
 import scipy.linalg as la
 
 sys.path.append("../../PathPlanning/CubicSpline/")
-import cubic_spline_planner
 
+try:
+    import cubic_spline_planner
+except ImportError:
+    raise
 
-# LQR parameter
-Q = np.eye(5)
-R = np.eye(2)
+# === Parameters =====
 
-# parameters
+# LQR parameter
+lqr_Q = np.eye(5)
+lqr_R = np.eye(2)
 dt = 0.1  # time tick[s]
 L = 0.5  # Wheel base of the vehicle [m]
 max_steer = np.deg2rad(45.0)  # maximum steering angle[rad]
@@ -56,23 +59,23 @@ def pi_2_pi(angle):
     return (angle + math.pi) % (2 * math.pi) - math.pi
 
 
-def solve_DARE(A, B, Q, R):
+def solve_dare(A, B, Q, R):
     """
     solve a discrete time_Algebraic Riccati equation (DARE)
     """
-    X = Q
-    Xn = Q
-    maxiter = 150
+    x = Q
+    x_next = Q
+    max_iter = 150
     eps = 0.01
 
-    for i in range(maxiter):
-        Xn = A.T @ X @ A - A.T @ X @ B @ \
-            la.inv(R + B.T @ X @ B) @ B.T @ X @ A + Q
-        if (abs(Xn - X)).max() < eps:
+    for i in range(max_iter):
+        x_next = A.T @ x @ A - A.T @ x @ B @ \
+                 la.inv(R + B.T @ x @ B) @ B.T @ x @ A + Q
+        if (abs(x_next - x)).max() < eps:
             break
-        X = Xn
+        x = x_next
 
-    return Xn
+    return x_next
 
 
 def dlqr(A, B, Q, R):
@@ -83,17 +86,17 @@ def dlqr(A, B, Q, R):
     """
 
     # first, try to solve the ricatti equation
-    X = solve_DARE(A, B, Q, R)
+    X = solve_dare(A, B, Q, R)
 
     # compute the LQR gain
     K = la.inv(B.T @ X @ B + R) @ (B.T @ X @ A)
 
-    eigs = la.eig(A - B @ K)
+    eig_result = la.eig(A - B @ K)
 
-    return K, X, eigs[0]
+    return K, X, eig_result[0]
 
 
-def lqr_steering_control(state, cx, cy, cyaw, ck, pe, pth_e, sp):
+def lqr_speed_steering_control(state, cx, cy, cyaw, ck, pe, pth_e, sp, Q, R):
     ind, e = calc_nearest_index(state, cx, cy, cyaw)
 
     tv = sp[ind]
@@ -102,6 +105,11 @@ def lqr_steering_control(state, cx, cy, cyaw, ck, pe, pth_e, sp):
     v = state.v
     th_e = pi_2_pi(state.yaw - cyaw[ind])
 
+    # A = [1.0, dt, 0.0, 0.0, 0.0
+    #      0.0, 0.0, v, 0.0, 0.0]
+    #      0.0, 0.0, 1.0, dt, 0.0]
+    #      0.0, 0.0, 0.0, 0.0, 0.0]
+    #      0.0, 0.0, 0.0, 0.0, 1.0]
     A = np.zeros((5, 5))
     A[0, 0] = 1.0
     A[0, 1] = dt
@@ -109,35 +117,47 @@ def lqr_steering_control(state, cx, cy, cyaw, ck, pe, pth_e, sp):
     A[2, 2] = 1.0
     A[2, 3] = dt
     A[4, 4] = 1.0
-    # print(A)
 
+    # B = [0.0, 0.0
+    #     0.0, 0.0
+    #     0.0, 0.0
+    #     v/L, 0.0
+    #     0.0, dt]
     B = np.zeros((5, 2))
     B[3, 0] = v / L
     B[4, 1] = dt
 
     K, _, _ = dlqr(A, B, Q, R)
 
+    # state vector
+    # x = [e, dot_e, th_e, dot_th_e, delta_v]
+    # e: lateral distance to the path
+    # dot_e: derivative of e
+    # th_e: angle difference to the path
+    # dot_th_e: derivative of th_e
+    # delta_v: difference between current speed and target speed
     x = np.zeros((5, 1))
-
     x[0, 0] = e
     x[1, 0] = (e - pe) / dt
     x[2, 0] = th_e
     x[3, 0] = (th_e - pth_e) / dt
     x[4, 0] = v - tv
 
+    # input vector
+    # u = [delta, accel]
+    # delta: steering angle
+    # accel: acceleration
     ustar = -K @ x
 
     # calc steering input
-
-    ff = math.atan2(L * k, 1)
-    fb = pi_2_pi(ustar[0, 0])
+    ff = math.atan2(L * k, 1)  # feedforward steering angle
+    fb = pi_2_pi(ustar[0, 0])  # feedback steering angle
+    delta = ff + fb
 
     # calc accel input
-    ai = ustar[1, 0]
-
-    delta = ff + fb
+    accel = ustar[1, 0]
 
-    return delta, ind, e, th_e, ai
+    return delta, ind, e, th_e, accel
 
 
 def calc_nearest_index(state, cx, cy, cyaw):
@@ -162,7 +182,7 @@ def calc_nearest_index(state, cx, cy, cyaw):
     return ind, mind
 
 
-def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
+def do_simulation(cx, cy, cyaw, ck, speed_profile, goal):
     T = 500.0  # max simulation time
     goal_dis = 0.3
     stop_speed = 0.05
@@ -179,8 +199,8 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
     e, e_th = 0.0, 0.0
 
     while T >= time:
-        dl, target_ind, e, e_th, ai = lqr_steering_control(
-            state, cx, cy, cyaw, ck, e, e_th, speed_profile)
+        dl, target_ind, e, e_th, ai = lqr_speed_steering_control(
+            state, cx, cy, cyaw, ck, e, e_th, speed_profile, lqr_Q, lqr_R)
 
         state = update(state, ai, dl)
 
@@ -216,13 +236,13 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
     return t, x, y, yaw, v
 
 
-def calc_speed_profile(cx, cy, cyaw, target_speed):
-    speed_profile = [target_speed] * len(cx)
+def calc_speed_profile(cyaw, target_speed):
+    speed_profile = [target_speed] * len(cyaw)
 
     direction = 1.0
 
     # Set stop point
-    for i in range(len(cx) - 1):
+    for i in range(len(cyaw) - 1):
         dyaw = abs(cyaw[i + 1] - cyaw[i])
         switch = math.pi / 4.0 <= dyaw < math.pi / 2.0
 
@@ -256,9 +276,9 @@ def main():
         ax, ay, ds=0.1)
     target_speed = 10.0 / 3.6  # simulation parameter km/h -> m/s
 
-    sp = calc_speed_profile(cx, cy, cyaw, target_speed)
+    sp = calc_speed_profile(cyaw, target_speed)
 
-    t, x, y, yaw, v = closed_loop_prediction(cx, cy, cyaw, ck, sp, goal)
+    t, x, y, yaw, v = do_simulation(cx, cy, cyaw, ck, sp, goal)
 
     if show_animation:  # pragma: no cover
         plt.close()

From 6c2f96f4e8f6c00ad499d9d01cec0b4b78aa79d7 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 13 Jul 2019 19:27:05 +0900
Subject: [PATCH 0123/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 021d0760b0..b7e88c92bb 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -287,6 +287,12 @@ Dear Atsushi Sakai <br>Thank you for the Python Robotics
 
 ---
 
+Thanks for your robotics repo!
+
+--Mithi
+
+---
+
 # Citations
 
 1. B. Blaga, M. Deac, R. W. Y. Al-doori, M. Negru and R. Dǎnescu, "Miniature Autonomous Vehicle Development on Raspberry Pi," 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, 2018, pp. 229-236.

From 80d0be2baf65f13d00a3b0e10754aa1a1222523c Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 15 Jul 2019 08:31:46 +0900
Subject: [PATCH 0124/1080] Fix RRT Star algorithm

---
 .gitignore                       |   2 +
 PathPlanning/RRTStar/rrt_star.py | 286 +++++++++++++++++++++++++++++++
 PathPlanning/RRTstar/rrt_star.py | 285 ------------------------------
 PathTracking/.gitignore          |   0
 tests/test_rrt_star.py           |   2 +-
 5 files changed, 289 insertions(+), 286 deletions(-)
 create mode 100644 PathPlanning/RRTStar/rrt_star.py
 delete mode 100644 PathPlanning/RRTstar/rrt_star.py
 delete mode 100644 PathTracking/.gitignore

diff --git a/.gitignore b/.gitignore
index 62e48c6762..272dc63b6d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -68,3 +68,5 @@ target/
 
 #Ipython Notebook
 .ipynb_checkpoints
+
+matplotrecorder/*
diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py
new file mode 100644
index 0000000000..509e2d0152
--- /dev/null
+++ b/PathPlanning/RRTStar/rrt_star.py
@@ -0,0 +1,286 @@
+"""
+Path planning Sample Code with RRT*
+
+author: Atsushi Sakai(@Atsushi_twi)
+
+"""
+
+import copy
+import math
+import random
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+show_animation = True
+
+
+class RRTStar:
+    """
+    Class for RRT planning
+    """
+
+    class Node:
+
+        def __init__(self, x, y):
+            self.x = x
+            self.y = y
+            self.cost = 0.0
+            self.parent = None
+
+    def __init__(self, start, goal, obstacle_list, rand_area,
+                 expand_dis=0.5,
+                 goal_sample_rate=20,
+                 max_iter=500,
+                 connect_circle_dist=50.0
+                 ):
+        """
+        Setting Parameter
+
+        start:Start Position [x,y]
+        goal:Goal Position [x,y]
+        obstacleList:obstacle Positions [[x,y,size],...]
+        randArea:Random Sampling Area [min,max]
+
+        """
+        self.connect_circle_dist = connect_circle_dist
+        self.start = self.Node(start[0], start[1])
+        self.end = self.Node(goal[0], goal[1])
+        self.min_rand = rand_area[0]
+        self.max_rand = rand_area[1]
+        self.expandDis = expand_dis
+        self.goalSampleRate = goal_sample_rate
+        self.maxIter = max_iter
+        self.obstacleList = obstacle_list
+        self.node_list = []
+
+    def planning(self, animation=True, search_until_maxiter=True):
+        """
+        rrt path planning
+
+        animation: flag for animation on or off
+        search_until_maxiter: search until max iteration for path improving or not
+        """
+
+        self.node_list = [self.start]
+        for i in range(self.maxIter):
+            rnd = self.get_random_point()
+            nearest_ind = self.get_nearest_list_index(self.node_list, rnd)
+
+            new_node = self.steer(rnd, self.node_list[nearest_ind])
+
+            if self.check_collision(new_node, self.obstacleList):
+                near_inds = self.find_near_nodes(new_node)
+                new_node = self.choose_parent(new_node, near_inds)
+                if new_node:
+                    self.node_list.append(new_node)
+                self.rewire(new_node, near_inds)
+
+            if animation and i % 5 == 0:
+                self.draw_graph(rnd)
+
+            if not search_until_maxiter:  # check reaching the goal
+                d, _ = self.calc_distance_and_angle(new_node, self.end)
+                if d <= self.expandDis:
+                    return self.gen_final_course(len(self.node_list) - 1)
+
+        print("reached max iteration")
+
+        last_index = self.search_best_goal_node()
+        if last_index:
+            return self.gen_final_course(last_index)
+
+        return None
+
+    def choose_parent(self, new_node, near_inds):
+        if not near_inds:
+            return None
+
+        # search nearest cost in near_inds
+        costs = []
+        for i in near_inds:
+            d, theta = self.calc_distance_and_angle(self.node_list[i], new_node)
+            if self.check_collision_extend(self.node_list[i], theta, d):
+                costs.append(self.node_list[i].cost + d)
+            else:
+                costs.append(float("inf"))  # the cost of collision node
+        min_cost = min(costs)
+
+        if min_cost == float("inf"):
+            print("There is no good path.(min_cost is inf)")
+            return None
+
+        new_node.cost = min_cost
+        min_ind = near_inds[costs.index(min_cost)]
+        new_node.parent = self.node_list[min_ind]
+
+        return new_node
+
+    def steer(self, rnd, nearest_node):
+        new_node = self.Node(rnd[0], rnd[1])
+        d, theta = self.calc_distance_and_angle(nearest_node, new_node)
+        if d > self.expandDis:
+            new_node.x = nearest_node.x + self.expandDis * math.cos(theta)
+            new_node.y = nearest_node.y + self.expandDis * math.sin(theta)
+        new_node.cost = float("inf")
+        return new_node
+
+    def get_random_point(self):
+
+        if random.randint(0, 100) > self.goalSampleRate:
+            rnd = [random.uniform(self.min_rand, self.max_rand),
+                   random.uniform(self.min_rand, self.max_rand)]
+        else:  # goal point sampling
+            rnd = [self.end.x, self.end.y]
+
+        return rnd
+
+    def search_best_goal_node(self):
+        dist_to_goal_list = [self.calc_dist_to_goal(n.x, n.y) for n in self.node_list]
+        goal_inds = [dist_to_goal_list.index(i) for i in dist_to_goal_list if i <= self.expandDis]
+
+        if not goal_inds:
+            return None
+
+        min_cost = min([self.node_list[i].cost for i in goal_inds])
+        for i in goal_inds:
+            if self.node_list[i].cost == min_cost:
+                return i
+
+        return None
+
+    def gen_final_course(self, goal_ind):
+        path = [[self.end.x, self.end.y]]
+        node = self.node_list[goal_ind]
+        while node.parent is not None:
+            path.append([node.x, node.y])
+            node = node.parent
+        path.append([node.x, node.y])
+
+        return path
+
+    def calc_dist_to_goal(self, x, y):
+        return np.linalg.norm([x - self.end.x, y - self.end.y])
+
+    def find_near_nodes(self, new_node):
+        nnode = len(self.node_list) + 1
+        r = self.connect_circle_dist * math.sqrt((math.log(nnode) / nnode))
+        dist_list = [(node.x - new_node.x) ** 2 +
+                     (node.y - new_node.y) ** 2 for node in self.node_list]
+        near_inds = [dist_list.index(i) for i in dist_list if i <= r ** 2]
+        return near_inds
+
+    def rewire(self, new_node, near_inds):
+        for i in near_inds:
+            near_node = self.node_list[i]
+            d, theta = self.calc_distance_and_angle(near_node, new_node)
+            new_cost = new_node.cost + d
+
+            if near_node.cost > new_cost:
+                if self.check_collision_extend(near_node, theta, d):
+                    near_node.parent = new_node
+                    near_node.cost = new_cost
+                    self.propagate_cost_to_leaves(new_node)
+
+    def propagate_cost_to_leaves(self, parent_node):
+        for node in self.node_list:
+            if node.parent == parent_node:
+                d, _ = self.calc_distance_and_angle(parent_node, node)
+                node.cost = parent_node.cost + d
+                self.propagate_cost_to_leaves(node)
+
+    def check_collision_extend(self, near_node, theta, d):
+
+        tmp_node = copy.deepcopy(near_node)
+
+        for i in range(int(d / self.expandDis)):
+            tmp_node.x += self.expandDis * math.cos(theta)
+            tmp_node.y += self.expandDis * math.sin(theta)
+            if not self.check_collision(tmp_node, self.obstacleList):
+                return False
+
+        return True
+
+    def draw_graph(self, rnd=None):
+        plt.clf()
+        if rnd is not None:
+            plt.plot(rnd[0], rnd[1], "^k")
+        for node in self.node_list:
+            if node.parent is not None:
+                plt.plot([node.x, node.parent.x],
+                         [node.y, node.parent.y],
+                         "-g")
+
+        for (ox, oy, size) in self.obstacleList:
+            plt.plot(ox, oy, "ok", ms=30 * size)
+
+        plt.plot(self.start.x, self.start.y, "xr")
+        plt.plot(self.end.x, self.end.y, "xr")
+        plt.axis([-2, 15, -2, 15])
+        plt.grid(True)
+        plt.pause(0.01)
+
+    @staticmethod
+    def get_nearest_list_index(node_list, rnd):
+        dlist = [(node.x - rnd[0]) ** 2 + (node.y - rnd[1])
+                 ** 2 for node in node_list]
+        minind = dlist.index(min(dlist))
+
+        return minind
+
+    @staticmethod
+    def check_collision(node, obstacleList):
+        for (ox, oy, size) in obstacleList:
+            dx = ox - node.x
+            dy = oy - node.y
+            d = dx * dx + dy * dy
+            if d <= size ** 2:
+                return False  # collision
+
+        return True  # safe
+
+    @staticmethod
+    def calc_distance_and_angle(from_node, to_node):
+        dx = to_node.x - from_node.x
+        dy = to_node.y - from_node.y
+        d = math.sqrt(dx ** 2 + dy ** 2)
+        theta = math.atan2(dy, dx)
+        return d, theta
+
+
+def main():
+    print("Start " + __file__)
+
+    # ====Search Path with RRT====
+    obstacle_list = [
+        (5, 5, 1),
+        (3, 6, 2),
+        (3, 8, 2),
+        (3, 10, 2),
+        (7, 5, 2),
+        (9, 5, 2)
+    ]  # [x,y,size(radius)]
+
+    # Set Initial parameters
+    rrt = RRTStar(start=[0, 0],
+                  goal=[10, 10],
+                  rand_area=[-2, 15],
+                  obstacle_list=obstacle_list)
+    path = rrt.planning(animation=show_animation, search_until_maxiter=False)
+
+    if path is None:
+        print("Cannot find path")
+    else:
+        print("found path!!")
+
+        # Draw final path
+        if show_animation:
+            rrt.draw_graph()
+            plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
+            plt.grid(True)
+            plt.pause(0.01)  # Need for Mac
+            plt.show()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/PathPlanning/RRTstar/rrt_star.py b/PathPlanning/RRTstar/rrt_star.py
deleted file mode 100644
index 169ead8256..0000000000
--- a/PathPlanning/RRTstar/rrt_star.py
+++ /dev/null
@@ -1,285 +0,0 @@
-
-
-
-"""
-Path Planning Sample Code with RRT*
-
-author: AtsushiSakai(@Atsushi_twi)
-
-"""
-
-import random
-import math
-import copy
-import numpy as np
-import matplotlib.pyplot as plt
-
-show_animation = True
-
-
-class Node():
-
-    def __init__(self, x, y):
-        self.x = x
-        self.y = y
-        self.cost = 0.0
-        self.parent = None
-
-
-class RRT():
-    """
-    Class for RRT Planning
-    """
-
-    def __init__(self, start, goal, obstacleList, randArea,
-                 expandDis=0.5, goalSampleRate=20, maxIter=500):
-        """
-        Setting Parameter
-
-        start:Start Position [x,y]
-        goal:Goal Position [x,y]
-        obstacleList:obstacle Positions [[x,y,size],...]
-        randArea:Ramdom Samping Area [min,max]
-
-        """
-        self.start = Node(start[0], start[1])
-        self.end = Node(goal[0], goal[1])
-        self.minrand = randArea[0]
-        self.maxrand = randArea[1]
-        self.expandDis = expandDis
-        self.goalSampleRate = goalSampleRate
-        self.maxIter = maxIter
-        self.obstacleList = obstacleList
-
-    def Planning(self, animation=True, search_until_maxiter=True):
-        """
-        rrt path planning
-
-        animation: flag for animation on or off
-        search_until_maxiter: search until max iteration for path improving or not
-        """
-
-        self.nodeList = [self.start]
-        for i in range(self.maxIter):
-            rnd = self.get_random_point()
-            nind = self.GetNearestListIndex(self.nodeList, rnd)
-
-            new_node = self.steer(rnd, nind)
-
-            if self.__CollisionCheck(new_node, self.obstacleList):
-                nearinds = self.find_near_nodes(new_node)
-                new_node = self.choose_parent(new_node, nearinds)
-                self.nodeList.append(new_node)
-                self.rewire(new_node, nearinds)
-
-            if animation and i % 5 == 0:
-                self.DrawGraph(rnd)
-
-            # generate course
-            if not search_until_maxiter:
-                lastIndex = self.get_best_last_index()
-                if lastIndex:
-                    return self.gen_final_course(lastIndex)
-
-        print("reached max iteration")
-
-        lastIndex = self.get_best_last_index()
-        if lastIndex:
-            return self.gen_final_course(lastIndex)
-
-        return None
-
-    def choose_parent(self, new_node, nearinds):
-        if not nearinds:
-            return new_node
-
-        dlist = []
-        for i in nearinds:
-            dx = new_node.x - self.nodeList[i].x
-            dy = new_node.y - self.nodeList[i].y
-            d = math.sqrt(dx ** 2 + dy ** 2)
-            theta = math.atan2(dy, dx)
-            if self.check_collision_extend(self.nodeList[i], theta, d):
-                dlist.append(self.nodeList[i].cost + d)
-            else:
-                dlist.append(float("inf"))
-
-        mincost = min(dlist)
-        minind = nearinds[dlist.index(mincost)]
-
-        if mincost == float("inf"):
-            print("mincost is inf")
-            return new_node
-
-        new_node.cost = mincost
-        new_node.parent = minind
-
-        return new_node
-
-    def steer(self, rnd, nind):
-
-        # expand tree
-        nearest_node = self.nodeList[nind]
-        theta = math.atan2(rnd[1] - nearest_node.y, rnd[0] - nearest_node.x)
-        new_node = Node(rnd[0], rnd[1])
-        currentDistance = math.sqrt(
-            (rnd[1] - nearest_node.y) ** 2 + (rnd[0] - nearest_node.x) ** 2)
-        # Find a point within expandDis of nind, and closest to rnd
-        if currentDistance <= self.expandDis:
-            pass
-        else:
-            new_node.x = nearest_node.x + self.expandDis * math.cos(theta)
-            new_node.y = nearest_node.y + self.expandDis * math.sin(theta)
-        new_node.cost = float("inf")
-        new_node.parent = None
-        return new_node
-
-    def get_random_point(self):
-
-        if random.randint(0, 100) > self.goalSampleRate:
-            rnd = [random.uniform(self.minrand, self.maxrand),
-                   random.uniform(self.minrand, self.maxrand)]
-        else:  # goal point sampling
-            rnd = [self.end.x, self.end.y]
-
-        return rnd
-
-    def get_best_last_index(self):
-
-        disglist = [self.calc_dist_to_goal(
-            node.x, node.y) for node in self.nodeList]
-        goalinds = [disglist.index(i) for i in disglist if i <= self.expandDis]
-
-        if not goalinds:
-            return None
-
-        mincost = min([self.nodeList[i].cost for i in goalinds])
-        for i in goalinds:
-            if self.nodeList[i].cost == mincost:
-                return i
-
-        return None
-
-    def gen_final_course(self, goalind):
-        path = [[self.end.x, self.end.y]]
-        while self.nodeList[goalind].parent is not None:
-            node = self.nodeList[goalind]
-            path.append([node.x, node.y])
-            goalind = node.parent
-        path.append([self.start.x, self.start.y])
-        return path
-
-    def calc_dist_to_goal(self, x, y):
-        return np.linalg.norm([x - self.end.x, y - self.end.y])
-
-    def find_near_nodes(self, new_node):
-        nnode = len(self.nodeList)
-        r = 50.0 * math.sqrt((math.log(nnode) / nnode))
-        dlist = [(node.x - new_node.x) ** 2 +
-                 (node.y - new_node.y) ** 2 for node in self.nodeList]
-        nearinds = [dlist.index(i) for i in dlist if i <= r ** 2]
-        return nearinds
-
-    def rewire(self, new_node, nearinds):
-        nnode = len(self.nodeList)
-        for i in nearinds:
-            nearNode = self.nodeList[i]
-
-            dx = new_node.x - nearNode.x
-            dy = new_node.y - nearNode.y
-            d = math.sqrt(dx ** 2 + dy ** 2)
-
-            scost = new_node.cost + d
-
-            if nearNode.cost > scost:
-                theta = math.atan2(dy, dx)
-                if self.check_collision_extend(nearNode, theta, d):
-                    nearNode.parent = nnode - 1
-                    nearNode.cost = scost
-
-    def check_collision_extend(self, nearNode, theta, d):
-
-        tmpNode = copy.deepcopy(nearNode)
-
-        for i in range(int(d / self.expandDis)):
-            tmpNode.x += self.expandDis * math.cos(theta)
-            tmpNode.y += self.expandDis * math.sin(theta)
-            if not self.__CollisionCheck(tmpNode, self.obstacleList):
-                return False
-
-        return True
-
-    def DrawGraph(self, rnd=None):
-        """
-        Draw Graph
-        """
-        plt.clf()
-        if rnd is not None:
-            plt.plot(rnd[0], rnd[1], "^k")
-        for node in self.nodeList:
-            if node.parent is not None:
-                plt.plot([node.x, self.nodeList[node.parent].x], [
-                         node.y, self.nodeList[node.parent].y], "-g")
-
-        for (ox, oy, size) in self.obstacleList:
-            plt.plot(ox, oy, "ok", ms=30 * size)
-
-        plt.plot(self.start.x, self.start.y, "xr")
-        plt.plot(self.end.x, self.end.y, "xr")
-        plt.axis([-2, 15, -2, 15])
-        plt.grid(True)
-        plt.pause(0.01)
-
-    def GetNearestListIndex(self, nodeList, rnd):
-        dlist = [(node.x - rnd[0]) ** 2 + (node.y - rnd[1])
-                 ** 2 for node in nodeList]
-        minind = dlist.index(min(dlist))
-
-        return minind
-
-    def __CollisionCheck(self, node, obstacleList):
-        for (ox, oy, size) in obstacleList:
-            dx = ox - node.x
-            dy = oy - node.y
-            d = dx * dx + dy * dy
-            if d <= size ** 2:
-                return False  # collision
-
-        return True  # safe
-
-
-def main():
-    print("Start " + __file__)
-
-    # ====Search Path with RRT====
-    obstacleList = [
-        (5, 5, 1),
-        (3, 6, 2),
-        (3, 8, 2),
-        (3, 10, 2),
-        (7, 5, 2),
-        (9, 5, 2)
-    ]  # [x,y,size(radius)]
-
-    # Set Initial parameters
-    rrt = RRT(start=[0, 0], goal=[10, 10],
-              randArea=[-2, 15], obstacleList=obstacleList)
-    path = rrt.Planning(animation=show_animation, search_until_maxiter=False)
-
-    if path is None:
-        print("Cannot find path")
-    else:
-        print("found path!!")
-
-        # Draw final path
-        if show_animation:
-            rrt.DrawGraph()
-            plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
-            plt.grid(True)
-            plt.pause(0.01)  # Need for Mac
-            plt.show()
-
-
-if __name__ == '__main__':
-    main()
-
diff --git a/PathTracking/.gitignore b/PathTracking/.gitignore
deleted file mode 100644
index e69de29bb2..0000000000
diff --git a/tests/test_rrt_star.py b/tests/test_rrt_star.py
index 2171402c9b..c5f8f870e0 100644
--- a/tests/test_rrt_star.py
+++ b/tests/test_rrt_star.py
@@ -3,7 +3,7 @@
 from unittest import TestCase
 
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../PathPlanning/RRTstar/")
+                "/../PathPlanning/RRTStar/")
 
 try:
     import rrt_star as m

From 29da707a9a95f0ee1c78d38d5f0b222f624d8308 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 15 Jul 2019 11:42:35 +0900
Subject: [PATCH 0125/1080] clean rrt codes

---
 PathPlanning/RRT/rrt.py                    | 198 +++++++++++++++++++++
 PathPlanning/RRT/rrt_with_pathsmoothing.py | 180 +++----------------
 PathPlanning/RRT/simple_rrt.py             | 173 ------------------
 PathPlanning/RRTStar/rrt_star.ipynb        |  74 ++++++++
 PathPlanning/RRTStar/rrt_star.py           | 130 +++-----------
 tests/test_rrt.py                          |   8 +-
 6 files changed, 323 insertions(+), 440 deletions(-)
 create mode 100644 PathPlanning/RRT/rrt.py
 delete mode 100644 PathPlanning/RRT/simple_rrt.py
 create mode 100644 PathPlanning/RRTStar/rrt_star.ipynb

diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py
new file mode 100644
index 0000000000..f257ef4d2a
--- /dev/null
+++ b/PathPlanning/RRT/rrt.py
@@ -0,0 +1,198 @@
+"""
+
+Path planning Sample Code with Randomized Rapidly-Exploring Random Trees (RRT)
+
+author: AtsushiSakai(@Atsushi_twi)
+
+"""
+
+import math
+import random
+
+import matplotlib.pyplot as plt
+
+show_animation = True
+
+
+class RRT:
+    """
+    Class for RRT planning
+    """
+
+    class Node():
+        """
+        RRT Node
+        """
+
+        def __init__(self, x, y):
+            self.x = x
+            self.y = y
+            self.parent = None
+
+    def __init__(self, start, goal, obstacle_list,
+                 rand_area, expand_dis=1.0, goal_sample_rate=5, max_iter=500):
+        """
+        Setting Parameter
+
+        start:Start Position [x,y]
+        goal:Goal Position [x,y]
+        obstacleList:obstacle Positions [[x,y,size],...]
+        randArea:Ramdom Samping Area [min,max]
+
+        """
+        self.start = self.Node(start[0], start[1])
+        self.end = self.Node(goal[0], goal[1])
+        self.min_rand = rand_area[0]
+        self.max_rand = rand_area[1]
+        self.expand_dis = expand_dis
+        self.goal_sample_rate = goal_sample_rate
+        self.max_iter = max_iter
+        self.obstacleList = obstacle_list
+        self.node_list = []
+
+    def planning(self, animation=True):
+        """
+        rrt path planning
+
+        animation: flag for animation on or off
+        """
+
+        self.node_list = [self.start]
+        for i in range(self.max_iter):
+            rnd = self.get_random_point()
+            nearest_ind = self.get_nearest_list_index(self.node_list, rnd)
+            nearest_node = self.node_list[nearest_ind]
+
+            new_node = self.steer(rnd, nearest_node)
+            new_node.parent = nearest_node
+
+            if not self.check_collision(new_node, self.obstacleList):
+                continue
+
+            self.node_list.append(new_node)
+            print("nNodelist:", len(self.node_list))
+
+            # check goal
+            if self.calc_dist_to_goal(new_node.x, new_node.y) <= self.expand_dis:
+                print("Goal!!")
+                return self.generate_final_course(len(self.node_list) - 1)
+
+            if animation and i % 5:
+                self.draw_graph(rnd)
+
+        return None  # cannot find path
+
+    def steer(self, rnd, nearest_node):
+        new_node = self.Node(rnd[0], rnd[1])
+        d, theta = self.calc_distance_and_angle(nearest_node, new_node)
+        if d > self.expand_dis:
+            new_node.x = nearest_node.x + self.expand_dis * math.cos(theta)
+            new_node.y = nearest_node.y + self.expand_dis * math.sin(theta)
+
+        return new_node
+
+    def generate_final_course(self, goal_ind):
+        path = [[self.end.x, self.end.y]]
+        node = self.node_list[goal_ind]
+        while node.parent is not None:
+            path.append([node.x, node.y])
+            node = node.parent
+        path.append([node.x, node.y])
+
+        return path
+
+    def calc_dist_to_goal(self, x, y):
+        dx = x - self.end.x
+        dy = y - self.end.y
+        return math.sqrt(dx ** 2 + dy ** 2)
+
+    def get_random_point(self):
+        if random.randint(0, 100) > self.goal_sample_rate:
+            rnd = [random.uniform(self.min_rand, self.max_rand),
+                   random.uniform(self.min_rand, self.max_rand)]
+        else:  # goal point sampling
+            rnd = [self.end.x, self.end.y]
+        return rnd
+
+    def draw_graph(self, rnd=None):
+        plt.clf()
+        if rnd is not None:
+            plt.plot(rnd[0], rnd[1], "^k")
+        for node in self.node_list:
+            if node.parent:
+                plt.plot([node.x, node.parent.x],
+                         [node.y, node.parent.y],
+                         "-g")
+
+        for (ox, oy, size) in self.obstacleList:
+            plt.plot(ox, oy, "ok", ms=30 * size)
+
+        plt.plot(self.start.x, self.start.y, "xr")
+        plt.plot(self.end.x, self.end.y, "xr")
+        plt.axis([-2, 15, -2, 15])
+        plt.grid(True)
+        plt.pause(0.01)
+
+    @staticmethod
+    def get_nearest_list_index(node_list, rnd):
+        dlist = [(node.x - rnd[0]) ** 2 + (node.y - rnd[1])
+                 ** 2 for node in node_list]
+        minind = dlist.index(min(dlist))
+
+        return minind
+
+    @staticmethod
+    def check_collision(node, obstacleList):
+        for (ox, oy, size) in obstacleList:
+            dx = ox - node.x
+            dy = oy - node.y
+            d = dx * dx + dy * dy
+            if d <= size ** 2:
+                return False  # collision
+
+        return True  # safe
+
+    @staticmethod
+    def calc_distance_and_angle(from_node, to_node):
+        dx = to_node.x - from_node.x
+        dy = to_node.y - from_node.y
+        d = math.sqrt(dx ** 2 + dy ** 2)
+        theta = math.atan2(dy, dx)
+        return d, theta
+
+
+def main(gx=5.0, gy=10.0):
+    print("start " + __file__)
+
+    # ====Search Path with RRT====
+    obstacleList = [
+        (5, 5, 1),
+        (3, 6, 2),
+        (3, 8, 2),
+        (3, 10, 2),
+        (7, 5, 2),
+        (9, 5, 2)
+    ]  # [x,y,size]
+    # Set Initial parameters
+    rrt = RRT(start=[0, 0],
+              goal=[gx, gy],
+              rand_area=[-2, 15],
+              obstacle_list=obstacleList)
+    path = rrt.planning(animation=show_animation)
+
+    if path is None:
+        print("Cannot find path")
+    else:
+        print("found path!!")
+
+        # Draw final path
+        if show_animation:
+            rrt.draw_graph()
+            plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
+            plt.grid(True)
+            plt.pause(0.01)  # Need for Mac
+            plt.show()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/PathPlanning/RRT/rrt_with_pathsmoothing.py b/PathPlanning/RRT/rrt_with_pathsmoothing.py
index 70dd55a327..2920f9b000 100644
--- a/PathPlanning/RRT/rrt_with_pathsmoothing.py
+++ b/PathPlanning/RRT/rrt_with_pathsmoothing.py
@@ -1,152 +1,22 @@
 """
-Path Planning Sample Code with Randamized Rapidly-Exploring Random Trees (RRT)
+
+Path planning Sample Code with RRT with path smoothing
 
 @author: AtsushiSakai(@Atsushi_twi)
 
 """
 
-import matplotlib.pyplot as plt
-import random
 import math
-import copy
-import numpy as np
-
-show_animation = True
-
-
-class RRT():
-    """
-    Class for RRT Planning
-    """
-
-    def __init__(self, start, goal, obstacleList, randArea, expandDis=1.0, goalSampleRate=5, maxIter=500):
-        """
-        Setting Parameter
-
-        start:Start Position [x,y]
-        goal:Goal Position [x,y]
-        obstacleList:obstacle Positions [[x,y,size],...]
-        randArea:Ramdom Samping Area [min,max]
-
-        """
-        self.start = Node(start[0], start[1])
-        self.end = Node(goal[0], goal[1])
-        self.minrand = randArea[0]
-        self.maxrand = randArea[1]
-        self.expandDis = expandDis
-        self.goalSampleRate = goalSampleRate
-        self.maxIter = maxIter
-        self.obstacleList = obstacleList
-
-    def Planning(self, animation=True):
-        """
-        Pathplanning
-
-        animation: flag for animation on or off
-        """
-
-        self.nodeList = [self.start]
-        while True:
-            # Random Sampling
-            if random.randint(0, 100) > self.goalSampleRate:
-                rnd = [random.uniform(self.minrand, self.maxrand), random.uniform(
-                    self.minrand, self.maxrand)]
-            else:
-                rnd = [self.end.x, self.end.y]
-
-            # Find nearest node
-            nind = self.GetNearestListIndex(self.nodeList, rnd)
-            # print(nind)
-
-            # expand tree
-            nearestNode = self.nodeList[nind]
-            theta = math.atan2(rnd[1] - nearestNode.y, rnd[0] - nearestNode.x)
-
-            newNode = copy.deepcopy(nearestNode)
-            newNode.x += self.expandDis * math.cos(theta)
-            newNode.y += self.expandDis * math.sin(theta)
-            newNode.parent = nind
-
-            if not self.__CollisionCheck(newNode, self.obstacleList):
-                continue
-
-            self.nodeList.append(newNode)
-
-            # check goal
-            dx = newNode.x - self.end.x
-            dy = newNode.y - self.end.y
-            d = math.sqrt(dx * dx + dy * dy)
-            if d <= self.expandDis:
-                print("Goal!!")
-                break
-
-            if animation:
-                self.DrawGraph(rnd)
-
-        path = [[self.end.x, self.end.y]]
-        lastIndex = len(self.nodeList) - 1
-        while self.nodeList[lastIndex].parent is not None:
-            node = self.nodeList[lastIndex]
-            path.append([node.x, node.y])
-            lastIndex = node.parent
-        path.append([self.start.x, self.start.y])
-
-        return path
-
-    def DrawGraph(self, rnd=None):
-        plt.clf()
-        if rnd is not None:
-            plt.plot(rnd[0], rnd[1], "^k")
-        for node in self.nodeList:
-            if node.parent is not None:
-                plt.plot([node.x, self.nodeList[node.parent].x], [
-                         node.y, self.nodeList[node.parent].y], "-g")
-        for (x, y, size) in self.obstacleList:
-            self.PlotCircle(x, y, size)
-
-        plt.plot(self.start.x, self.start.y, "xr")
-        plt.plot(self.end.x, self.end.y, "xr")
-        plt.axis([-2, 15, -2, 15])
-        plt.grid(True)
-        plt.pause(0.01)
-
-    def PlotCircle(self, x, y, size):
-        deg = list(range(0, 360, 5))
-        deg.append(0)
-        xl = [x + size * math.cos(np.deg2rad(d)) for d in deg]
-        yl = [y + size * math.sin(np.deg2rad(d)) for d in deg]
-        plt.plot(xl, yl, "-k")
-
-    def GetNearestListIndex(self, nodeList, rnd):
-        dlist = [(node.x - rnd[0]) ** 2 + (node.y - rnd[1])
-                 ** 2 for node in nodeList]
-        minind = dlist.index(min(dlist))
-        return minind
-
-    def __CollisionCheck(self, node, obstacleList):
-
-        for (ox, oy, size) in obstacleList:
-            dx = ox - node.x
-            dy = oy - node.y
-            d = math.sqrt(dx * dx + dy * dy)
-            if d <= size:
-                return False  # collision
-
-        return True  # safe
+import random
 
+import matplotlib.pyplot as plt
 
-class Node():
-    """
-    RRT Node
-    """
+from rrt import RRT
 
-    def __init__(self, x, y):
-        self.x = x
-        self.y = y
-        self.parent = None
+show_animation = True
 
 
-def GetPathLength(path):
+def get_path_length(path):
     le = 0
     for i in range(len(path) - 1):
         dx = path[i + 1][0] - path[i][0]
@@ -157,7 +27,7 @@ def GetPathLength(path):
     return le
 
 
-def GetTargetPoint(path, targetL):
+def get_target_point(path, targetL):
     le = 0
     ti = 0
     lastPairLen = 0
@@ -172,17 +42,14 @@ def GetTargetPoint(path, targetL):
             break
 
     partRatio = (le - targetL) / lastPairLen
-    #  print(partRatio)
-    #  print((ti,len(path),path[ti],path[ti+1]))
 
     x = path[ti][0] + (path[ti + 1][0] - path[ti][0]) * partRatio
     y = path[ti][1] + (path[ti + 1][1] - path[ti][1]) * partRatio
-    #  print((x,y))
 
     return [x, y, ti]
 
 
-def LineCollisionCheck(first, second, obstacleList):
+def line_collision_check(first, second, obstacleList):
     # Line Equation
 
     x1 = first[0]
@@ -199,7 +66,7 @@ def LineCollisionCheck(first, second, obstacleList):
 
     for (ox, oy, size) in obstacleList:
         d = abs(a * ox + b * oy + c) / (math.sqrt(a * a + b * b))
-        if d <= (size):
+        if d <= size:
             return False
 
     #  print("OK")
@@ -207,20 +74,15 @@ def LineCollisionCheck(first, second, obstacleList):
     return True  # OK
 
 
-def PathSmoothing(path, maxIter, obstacleList):
-    #  print("PathSmoothing")
-
-    le = GetPathLength(path)
+def path_smoothing(path, max_iter, obstacle_list):
+    le = get_path_length(path)
 
-    for i in range(maxIter):
+    for i in range(max_iter):
         # Sample two points
         pickPoints = [random.uniform(0, le), random.uniform(0, le)]
         pickPoints.sort()
-        #  print(pickPoints)
-        first = GetTargetPoint(path, pickPoints[0])
-        #  print(first)
-        second = GetTargetPoint(path, pickPoints[1])
-        #  print(second)
+        first = get_target_point(path, pickPoints[0])
+        second = get_target_point(path, pickPoints[1])
 
         if first[2] <= 0 or second[2] <= 0:
             continue
@@ -232,7 +94,7 @@ def PathSmoothing(path, maxIter, obstacleList):
             continue
 
         # collision check
-        if not LineCollisionCheck(first, second, obstacleList):
+        if not line_collision_check(first, second, obstacle_list):
             continue
 
         # Create New path
@@ -242,7 +104,7 @@ def PathSmoothing(path, maxIter, obstacleList):
         newPath.append([second[0], second[1]])
         newPath.extend(path[second[2] + 1:])
         path = newPath
-        le = GetPathLength(path)
+        le = get_path_length(path)
 
     return path
 
@@ -259,16 +121,16 @@ def main():
         (9, 5, 2)
     ]  # [x,y,size]
     rrt = RRT(start=[0, 0], goal=[5, 10],
-              randArea=[-2, 15], obstacleList=obstacleList)
-    path = rrt.Planning(animation=show_animation)
+              rand_area=[-2, 15], obstacle_list=obstacleList)
+    path = rrt.planning(animation=show_animation)
 
     # Path smoothing
     maxIter = 1000
-    smoothedPath = PathSmoothing(path, maxIter, obstacleList)
+    smoothedPath = path_smoothing(path, maxIter, obstacleList)
 
     # Draw final path
     if show_animation:
-        rrt.DrawGraph()
+        rrt.draw_graph()
         plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
 
         plt.plot([x for (x, y) in smoothedPath], [
diff --git a/PathPlanning/RRT/simple_rrt.py b/PathPlanning/RRT/simple_rrt.py
deleted file mode 100644
index 3a40c672c4..0000000000
--- a/PathPlanning/RRT/simple_rrt.py
+++ /dev/null
@@ -1,173 +0,0 @@
-"""
-Path Planning Sample Code with Randamized Rapidly-Exploring Random Trees (RRT)
-
-author: AtsushiSakai(@Atsushi_twi)
-
-"""
-
-import matplotlib.pyplot as plt
-import random
-import math
-import copy
-
-show_animation = True
-
-
-class RRT():
-    """
-    Class for RRT Planning
-    """
-
-    def __init__(self, start, goal, obstacleList,
-                 randArea, expandDis=1.0, goalSampleRate=5, maxIter=500):
-        """
-        Setting Parameter
-
-        start:Start Position [x,y]
-        goal:Goal Position [x,y]
-        obstacleList:obstacle Positions [[x,y,size],...]
-        randArea:Ramdom Samping Area [min,max]
-
-        """
-        self.start = Node(start[0], start[1])
-        self.end = Node(goal[0], goal[1])
-        self.minrand = randArea[0]
-        self.maxrand = randArea[1]
-        self.expandDis = expandDis
-        self.goalSampleRate = goalSampleRate
-        self.maxIter = maxIter
-        self.obstacleList = obstacleList
-
-    def Planning(self, animation=True):
-        """
-        Pathplanning
-
-        animation: flag for animation on or off
-        """
-
-        self.nodeList = [self.start]
-        while True:
-            # Random Sampling
-            if random.randint(0, 100) > self.goalSampleRate:
-                rnd = [random.uniform(self.minrand, self.maxrand), random.uniform(
-                    self.minrand, self.maxrand)]
-            else:
-                rnd = [self.end.x, self.end.y]
-
-            # Find nearest node
-            nind = self.GetNearestListIndex(self.nodeList, rnd)
-            # print(nind)
-
-            # expand tree
-            nearestNode = self.nodeList[nind]
-            theta = math.atan2(rnd[1] - nearestNode.y, rnd[0] - nearestNode.x)
-
-            newNode = copy.deepcopy(nearestNode)
-            newNode.x += self.expandDis * math.cos(theta)
-            newNode.y += self.expandDis * math.sin(theta)
-            newNode.parent = nind
-
-            if not self.__CollisionCheck(newNode, self.obstacleList):
-                continue
-
-            self.nodeList.append(newNode)
-            print("nNodelist:", len(self.nodeList))
-
-            # check goal
-            dx = newNode.x - self.end.x
-            dy = newNode.y - self.end.y
-            d = math.sqrt(dx * dx + dy * dy)
-            if d <= self.expandDis:
-                print("Goal!!")
-                break
-
-            if animation:
-                self.DrawGraph(rnd)
-
-        path = [[self.end.x, self.end.y]]
-        lastIndex = len(self.nodeList) - 1
-        while self.nodeList[lastIndex].parent is not None:
-            node = self.nodeList[lastIndex]
-            path.append([node.x, node.y])
-            lastIndex = node.parent
-        path.append([self.start.x, self.start.y])
-
-        return path
-
-    def DrawGraph(self, rnd=None):  # pragma: no cover
-        """
-        Draw Graph
-        """
-        plt.clf()
-        if rnd is not None:
-            plt.plot(rnd[0], rnd[1], "^k")
-        for node in self.nodeList:
-            if node.parent is not None:
-                plt.plot([node.x, self.nodeList[node.parent].x], [
-                         node.y, self.nodeList[node.parent].y], "-g")
-
-        for (ox, oy, size) in self.obstacleList:
-            plt.plot(ox, oy, "ok", ms=30 * size)
-
-        plt.plot(self.start.x, self.start.y, "xr")
-        plt.plot(self.end.x, self.end.y, "xr")
-        plt.axis([-2, 15, -2, 15])
-        plt.grid(True)
-        plt.pause(0.01)
-
-    def GetNearestListIndex(self, nodeList, rnd):
-        dlist = [(node.x - rnd[0]) ** 2 + (node.y - rnd[1])
-                 ** 2 for node in nodeList]
-        minind = dlist.index(min(dlist))
-        return minind
-
-    def __CollisionCheck(self, node, obstacleList):
-
-        for (ox, oy, size) in obstacleList:
-            dx = ox - node.x
-            dy = oy - node.y
-            d = math.sqrt(dx * dx + dy * dy)
-            if d <= size:
-                return False  # collision
-
-        return True  # safe
-
-
-class Node():
-    """
-    RRT Node
-    """
-
-    def __init__(self, x, y):
-        self.x = x
-        self.y = y
-        self.parent = None
-
-
-def main(gx=5.0, gy=10.0):
-    print("start " + __file__)
-
-    # ====Search Path with RRT====
-    obstacleList = [
-        (5, 5, 1),
-        (3, 6, 2),
-        (3, 8, 2),
-        (3, 10, 2),
-        (7, 5, 2),
-        (9, 5, 2)
-    ]  # [x,y,size]
-    # Set Initial parameters
-    rrt = RRT(start=[0, 0], goal=[gx, gy],
-              randArea=[-2, 15], obstacleList=obstacleList)
-    path = rrt.Planning(animation=show_animation)
-
-    # Draw final path
-    if show_animation:  # pragma: no cover
-        rrt.DrawGraph()
-        plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
-        plt.grid(True)
-        plt.show()
-
-
-if __name__ == '__main__':
-    main()
diff --git a/PathPlanning/RRTStar/rrt_star.ipynb b/PathPlanning/RRTStar/rrt_star.ipynb
new file mode 100644
index 0000000000..20efa46878
--- /dev/null
+++ b/PathPlanning/RRTStar/rrt_star.ipynb
@@ -0,0 +1,74 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {
+      "image/png": {
+       "width": 600
+      }
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename=\"Figure_1.png\",width=600)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![gif](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTstar/animation.gif)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Ref\n",
+    "\n",
+    "- [Sampling-based Algorithms for Optimal Motion Planning](https://arxiv.org/pdf/1105.1186.pdf)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py
index 509e2d0152..d789c50bf6 100644
--- a/PathPlanning/RRTStar/rrt_star.py
+++ b/PathPlanning/RRTStar/rrt_star.py
@@ -1,4 +1,5 @@
 """
+
 Path planning Sample Code with RRT*
 
 author: Atsushi Sakai(@Atsushi_twi)
@@ -7,21 +8,28 @@
 
 import copy
 import math
-import random
+import os
+import sys
 
 import matplotlib.pyplot as plt
-import numpy as np
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../RRT/")
+
+try:
+    from RRT.rrt import RRT
+except ImportError:
+    raise
 
 show_animation = True
 
 
-class RRTStar:
+class RRTStar(RRT):
     """
-    Class for RRT planning
+    Class for RRT Star planning
     """
 
     class Node:
-
         def __init__(self, x, y):
             self.x = x
             self.y = y
@@ -34,6 +42,8 @@ def __init__(self, start, goal, obstacle_list, rand_area,
                  max_iter=500,
                  connect_circle_dist=50.0
                  ):
+        super().__init__(start, goal, obstacle_list,
+                         rand_area, expand_dis, goal_sample_rate, max_iter)
         """
         Setting Parameter
 
@@ -44,29 +54,19 @@ def __init__(self, start, goal, obstacle_list, rand_area,
 
         """
         self.connect_circle_dist = connect_circle_dist
-        self.start = self.Node(start[0], start[1])
-        self.end = self.Node(goal[0], goal[1])
-        self.min_rand = rand_area[0]
-        self.max_rand = rand_area[1]
-        self.expandDis = expand_dis
-        self.goalSampleRate = goal_sample_rate
-        self.maxIter = max_iter
-        self.obstacleList = obstacle_list
-        self.node_list = []
 
     def planning(self, animation=True, search_until_maxiter=True):
         """
-        rrt path planning
+        rrt star path planning
 
         animation: flag for animation on or off
         search_until_maxiter: search until max iteration for path improving or not
         """
 
         self.node_list = [self.start]
-        for i in range(self.maxIter):
+        for i in range(self.max_iter):
             rnd = self.get_random_point()
             nearest_ind = self.get_nearest_list_index(self.node_list, rnd)
-
             new_node = self.steer(rnd, self.node_list[nearest_ind])
 
             if self.check_collision(new_node, self.obstacleList):
@@ -74,21 +74,21 @@ def planning(self, animation=True, search_until_maxiter=True):
                 new_node = self.choose_parent(new_node, near_inds)
                 if new_node:
                     self.node_list.append(new_node)
-                self.rewire(new_node, near_inds)
+                    self.rewire(new_node, near_inds)
 
             if animation and i % 5 == 0:
                 self.draw_graph(rnd)
 
-            if not search_until_maxiter:  # check reaching the goal
+            if not search_until_maxiter and new_node:  # check reaching the goal
                 d, _ = self.calc_distance_and_angle(new_node, self.end)
-                if d <= self.expandDis:
-                    return self.gen_final_course(len(self.node_list) - 1)
+                if d <= self.expand_dis:
+                    return self.generate_final_course(len(self.node_list) - 1)
 
         print("reached max iteration")
 
         last_index = self.search_best_goal_node()
         if last_index:
-            return self.gen_final_course(last_index)
+            return self.generate_final_course(last_index)
 
         return None
 
@@ -116,28 +116,9 @@ def choose_parent(self, new_node, near_inds):
 
         return new_node
 
-    def steer(self, rnd, nearest_node):
-        new_node = self.Node(rnd[0], rnd[1])
-        d, theta = self.calc_distance_and_angle(nearest_node, new_node)
-        if d > self.expandDis:
-            new_node.x = nearest_node.x + self.expandDis * math.cos(theta)
-            new_node.y = nearest_node.y + self.expandDis * math.sin(theta)
-        new_node.cost = float("inf")
-        return new_node
-
-    def get_random_point(self):
-
-        if random.randint(0, 100) > self.goalSampleRate:
-            rnd = [random.uniform(self.min_rand, self.max_rand),
-                   random.uniform(self.min_rand, self.max_rand)]
-        else:  # goal point sampling
-            rnd = [self.end.x, self.end.y]
-
-        return rnd
-
     def search_best_goal_node(self):
         dist_to_goal_list = [self.calc_dist_to_goal(n.x, n.y) for n in self.node_list]
-        goal_inds = [dist_to_goal_list.index(i) for i in dist_to_goal_list if i <= self.expandDis]
+        goal_inds = [dist_to_goal_list.index(i) for i in dist_to_goal_list if i <= self.expand_dis]
 
         if not goal_inds:
             return None
@@ -149,19 +130,6 @@ def search_best_goal_node(self):
 
         return None
 
-    def gen_final_course(self, goal_ind):
-        path = [[self.end.x, self.end.y]]
-        node = self.node_list[goal_ind]
-        while node.parent is not None:
-            path.append([node.x, node.y])
-            node = node.parent
-        path.append([node.x, node.y])
-
-        return path
-
-    def calc_dist_to_goal(self, x, y):
-        return np.linalg.norm([x - self.end.x, y - self.end.y])
-
     def find_near_nodes(self, new_node):
         nnode = len(self.node_list) + 1
         r = self.connect_circle_dist * math.sqrt((math.log(nnode) / nnode))
@@ -193,60 +161,14 @@ def check_collision_extend(self, near_node, theta, d):
 
         tmp_node = copy.deepcopy(near_node)
 
-        for i in range(int(d / self.expandDis)):
-            tmp_node.x += self.expandDis * math.cos(theta)
-            tmp_node.y += self.expandDis * math.sin(theta)
+        for i in range(int(d / self.expand_dis)):
+            tmp_node.x += self.expand_dis * math.cos(theta)
+            tmp_node.y += self.expand_dis * math.sin(theta)
             if not self.check_collision(tmp_node, self.obstacleList):
                 return False
 
         return True
 
-    def draw_graph(self, rnd=None):
-        plt.clf()
-        if rnd is not None:
-            plt.plot(rnd[0], rnd[1], "^k")
-        for node in self.node_list:
-            if node.parent is not None:
-                plt.plot([node.x, node.parent.x],
-                         [node.y, node.parent.y],
-                         "-g")
-
-        for (ox, oy, size) in self.obstacleList:
-            plt.plot(ox, oy, "ok", ms=30 * size)
-
-        plt.plot(self.start.x, self.start.y, "xr")
-        plt.plot(self.end.x, self.end.y, "xr")
-        plt.axis([-2, 15, -2, 15])
-        plt.grid(True)
-        plt.pause(0.01)
-
-    @staticmethod
-    def get_nearest_list_index(node_list, rnd):
-        dlist = [(node.x - rnd[0]) ** 2 + (node.y - rnd[1])
-                 ** 2 for node in node_list]
-        minind = dlist.index(min(dlist))
-
-        return minind
-
-    @staticmethod
-    def check_collision(node, obstacleList):
-        for (ox, oy, size) in obstacleList:
-            dx = ox - node.x
-            dy = oy - node.y
-            d = dx * dx + dy * dy
-            if d <= size ** 2:
-                return False  # collision
-
-        return True  # safe
-
-    @staticmethod
-    def calc_distance_and_angle(from_node, to_node):
-        dx = to_node.x - from_node.x
-        dy = to_node.y - from_node.y
-        d = math.sqrt(dx ** 2 + dy ** 2)
-        theta = math.atan2(dy, dx)
-        return d, theta
-
 
 def main():
     print("Start " + __file__)
diff --git a/tests/test_rrt.py b/tests/test_rrt.py
index b0c1504a32..6e8f3f300a 100644
--- a/tests/test_rrt.py
+++ b/tests/test_rrt.py
@@ -1,12 +1,12 @@
+import os
+import sys
 from unittest import TestCase
 
-import sys
-import os
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
 try:
-    from PathPlanning.RRT import simple_rrt as m
+    from PathPlanning.RRT import rrt as m
     from PathPlanning.RRT import rrt_with_pathsmoothing as m1
-except:
+except ImportError:
     raise
 
 

From 846ba843aa2281824cc91ff6f64d2fd1ce6698a9 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 15 Jul 2019 11:48:28 +0900
Subject: [PATCH 0126/1080] Delete figure_1.png

---
 PathPlanning/RRTstar/figure_1.png | Bin 119836 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 PathPlanning/RRTstar/figure_1.png

diff --git a/PathPlanning/RRTstar/figure_1.png b/PathPlanning/RRTstar/figure_1.png
deleted file mode 100644
index 72a4ebadf94b2bb803be5eb5513284b63c6c59a6..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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From c0ecfbffe6be3c295b5fff0365d57ef46529e319 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 15 Jul 2019 11:49:03 +0900
Subject: [PATCH 0127/1080] Delete rrt_star.ipynb

---
 PathPlanning/RRTstar/rrt_star.ipynb | 74 -----------------------------
 1 file changed, 74 deletions(-)
 delete mode 100644 PathPlanning/RRTstar/rrt_star.ipynb

diff --git a/PathPlanning/RRTstar/rrt_star.ipynb b/PathPlanning/RRTstar/rrt_star.ipynb
deleted file mode 100644
index 20efa46878..0000000000
--- a/PathPlanning/RRTstar/rrt_star.ipynb
+++ /dev/null
@@ -1,74 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Simulation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename=\"Figure_1.png\",width=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![gif](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTstar/animation.gif)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Ref\n",
-    "\n",
-    "- [Sampling-based Algorithms for Optimal Motion Planning](https://arxiv.org/pdf/1105.1186.pdf)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From b14a97f1536e04989f8f0a80d829ae135e59dbcc Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 15 Jul 2019 12:02:05 +0900
Subject: [PATCH 0128/1080] add module setting for travis

---
 PathPlanning/BezierPath/bezier_path.py |   6 +++---
 PathPlanning/RRT/__init__.py           |   0
 PathPlanning/RRTStar/Figure_1.png      | Bin 0 -> 119836 bytes
 PathPlanning/RRTStar/__init__.py       |   0
 4 files changed, 3 insertions(+), 3 deletions(-)
 create mode 100644 PathPlanning/RRT/__init__.py
 create mode 100644 PathPlanning/RRTStar/Figure_1.png
 create mode 100644 PathPlanning/RRTStar/__init__.py

diff --git a/PathPlanning/BezierPath/bezier_path.py b/PathPlanning/BezierPath/bezier_path.py
index fb6050d98a..516a681e3b 100644
--- a/PathPlanning/BezierPath/bezier_path.py
+++ b/PathPlanning/BezierPath/bezier_path.py
@@ -1,14 +1,14 @@
 """
 
-Path Planning with Bezier curve.
+Path planning with Bezier curve.
 
 author: Atsushi Sakai(@Atsushi_twi)
 
 """
 
-import scipy.special
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
+import scipy.special
 
 show_animation = True
 
diff --git a/PathPlanning/RRT/__init__.py b/PathPlanning/RRT/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/PathPlanning/RRTStar/Figure_1.png b/PathPlanning/RRTStar/Figure_1.png
new file mode 100644
index 0000000000000000000000000000000000000000..72a4ebadf94b2bb803be5eb5513284b63c6c59a6
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HcmV?d00001

diff --git a/PathPlanning/RRTStar/__init__.py b/PathPlanning/RRTStar/__init__.py
new file mode 100644
index 0000000000..e69de29bb2

From 33d62af0dd1ee45b8c35ad7e3368437b1771af6a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 15 Jul 2019 12:13:53 +0900
Subject: [PATCH 0129/1080] update import for travis

---
 PathPlanning/RRT/rrt_with_pathsmoothing.py | 9 ++++++++-
 PathPlanning/RRTStar/rrt_star.py           | 2 +-
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/RRT/rrt_with_pathsmoothing.py b/PathPlanning/RRT/rrt_with_pathsmoothing.py
index 2920f9b000..24b6bfeb6e 100644
--- a/PathPlanning/RRT/rrt_with_pathsmoothing.py
+++ b/PathPlanning/RRT/rrt_with_pathsmoothing.py
@@ -7,11 +7,18 @@
 """
 
 import math
+import os
 import random
+import sys
 
 import matplotlib.pyplot as plt
 
-from rrt import RRT
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+try:
+    from rrt import RRT
+except ImportError:
+    raise
 
 show_animation = True
 
diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py
index d789c50bf6..da24d2c86b 100644
--- a/PathPlanning/RRTStar/rrt_star.py
+++ b/PathPlanning/RRTStar/rrt_star.py
@@ -17,7 +17,7 @@
                 "/../RRT/")
 
 try:
-    from RRT.rrt import RRT
+    from rrt import RRT
 except ImportError:
     raise
 

From 9b8f2bd88a3d516d8deb473693661c1aea59fe68 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 18 Jul 2019 20:57:42 +0900
Subject: [PATCH 0130/1080] fix typo

---
 README.md | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 551490461b..967c4aca69 100644
--- a/README.md
+++ b/README.md
@@ -12,7 +12,6 @@
 
 Python codes for robotics algorithm.
 
-
 # Table of Contents
    * [What is this?](#what-is-this)
    * [Requirements](#requirements)
@@ -45,7 +44,7 @@ Python codes for robotics algorithm.
       * [Probabilistic Road-Map (PRM) planning](#probabilistic-road-map-prm-planning)
       * [Rapidly-Exploring Random Trees (RRT)](#rapidly-exploring-random-trees-rrt)
          * [RRT*](#rrt)
-         * [RRT* with reeds-sheep path](#rrt-with-reeds-sheep-path)
+         * [RRT* with reeds-shepp path](#rrt-with-reeds-shepp-path)
          * [LQR-RRT*](#lqr-rrt)
       * [Quintic polynomials planning](#quintic-polynomials-planning)
       * [Reeds Shepp planning](#reeds-shepp-planning)
@@ -69,6 +68,7 @@ Python codes for robotics algorithm.
    * [License](#license)
    * [Use-case](#use-case)
    * [Contribution](#contribution)
+   * [Citing](#citing)
    * [Support](#support)
    * [Authors](#authors)
 
@@ -360,11 +360,11 @@ Ref:
 
 - [Sampling-based Algorithms for Optimal Motion Planning](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.419.5503&rep=rep1&type=pdf)
 
-### RRT\* with reeds-sheep path
+### RRT\* with reeds-shepp path
 
 ![Robotics/animation.gif at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarReedsShepp/animation.gif))
 
-Path planning for a car robot with RRT\* and reeds sheep path planner.
+Path planning for a car robot with RRT\* and reeds shepp path planner.
 
 ### LQR-RRT\*
 
@@ -622,3 +622,4 @@ This is a list: [Users comments](https://github.com/AtsushiSakai/PythonRobotics/
 
 
 
+

From 8ff5df74637e40e6496c8ea3b74129cf73cd6d36 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 19 Jul 2019 20:35:55 +0900
Subject: [PATCH 0131/1080] start implementing.

---
 PathPlanning/RRT/rrt.py | 43 +++++++++++++++++++++++------------------
 1 file changed, 24 insertions(+), 19 deletions(-)

diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py
index f257ef4d2a..0d44703be2 100644
--- a/PathPlanning/RRT/rrt.py
+++ b/PathPlanning/RRT/rrt.py
@@ -47,7 +47,7 @@ def __init__(self, start, goal, obstacle_list,
         self.expand_dis = expand_dis
         self.goal_sample_rate = goal_sample_rate
         self.max_iter = max_iter
-        self.obstacleList = obstacle_list
+        self.obstacle_list = obstacle_list
         self.node_list = []
 
     def planning(self, animation=True):
@@ -59,14 +59,14 @@ def planning(self, animation=True):
 
         self.node_list = [self.start]
         for i in range(self.max_iter):
-            rnd = self.get_random_point()
-            nearest_ind = self.get_nearest_list_index(self.node_list, rnd)
+            rnd_node = self.get_random_node()
+            nearest_ind = self.get_nearest_list_index(self.node_list, rnd_node)
             nearest_node = self.node_list[nearest_ind]
 
-            new_node = self.steer(rnd, nearest_node)
+            new_node = self.steer(nearest_node, rnd_node)
             new_node.parent = nearest_node
 
-            if not self.check_collision(new_node, self.obstacleList):
+            if not self.check_collision(new_node, self.obstacle_list):
                 continue
 
             self.node_list.append(new_node)
@@ -78,16 +78,21 @@ def planning(self, animation=True):
                 return self.generate_final_course(len(self.node_list) - 1)
 
             if animation and i % 5:
-                self.draw_graph(rnd)
+                self.draw_graph(rnd_node)
 
         return None  # cannot find path
 
-    def steer(self, rnd, nearest_node):
-        new_node = self.Node(rnd[0], rnd[1])
-        d, theta = self.calc_distance_and_angle(nearest_node, new_node)
+    def steer(self, from_node, to_node):
+        d, theta = self.calc_distance_and_angle(from_node, to_node)
         if d > self.expand_dis:
-            new_node.x = nearest_node.x + self.expand_dis * math.cos(theta)
-            new_node.y = nearest_node.y + self.expand_dis * math.sin(theta)
+            x = from_node.x + self.expand_dis * math.cos(theta)
+            y = from_node.y + self.expand_dis * math.sin(theta)
+        else:
+            x = to_node.x
+            y = to_node.y
+
+        new_node = self.Node(x, y)
+        new_node.parent = from_node
 
         return new_node
 
@@ -106,25 +111,25 @@ def calc_dist_to_goal(self, x, y):
         dy = y - self.end.y
         return math.sqrt(dx ** 2 + dy ** 2)
 
-    def get_random_point(self):
+    def get_random_node(self):
         if random.randint(0, 100) > self.goal_sample_rate:
-            rnd = [random.uniform(self.min_rand, self.max_rand),
-                   random.uniform(self.min_rand, self.max_rand)]
+            rnd = self.Node(random.uniform(self.min_rand, self.max_rand),
+                            random.uniform(self.min_rand, self.max_rand))
         else:  # goal point sampling
-            rnd = [self.end.x, self.end.y]
+            rnd = self.Node(self.end.x, self.end.y)
         return rnd
 
     def draw_graph(self, rnd=None):
         plt.clf()
         if rnd is not None:
-            plt.plot(rnd[0], rnd[1], "^k")
+            plt.plot(rnd.x, rnd.y, "^k")
         for node in self.node_list:
             if node.parent:
                 plt.plot([node.x, node.parent.x],
                          [node.y, node.parent.y],
                          "-g")
 
-        for (ox, oy, size) in self.obstacleList:
+        for (ox, oy, size) in self.obstacle_list:
             plt.plot(ox, oy, "ok", ms=30 * size)
 
         plt.plot(self.start.x, self.start.y, "xr")
@@ -134,8 +139,8 @@ def draw_graph(self, rnd=None):
         plt.pause(0.01)
 
     @staticmethod
-    def get_nearest_list_index(node_list, rnd):
-        dlist = [(node.x - rnd[0]) ** 2 + (node.y - rnd[1])
+    def get_nearest_list_index(node_list, rnd_node):
+        dlist = [(node.x - rnd_node.x) ** 2 + (node.y - rnd_node.y)
                  ** 2 for node in node_list]
         minind = dlist.index(min(dlist))
 

From d7c570bf97c2cbc097494295322276c8877c4516 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 19 Jul 2019 20:39:41 +0900
Subject: [PATCH 0132/1080] remove duplicate script

---
 .../RRTStarDubins/dubins_path_planning.py     | 303 ------------------
 1 file changed, 303 deletions(-)
 delete mode 100644 PathPlanning/RRTStarDubins/dubins_path_planning.py

diff --git a/PathPlanning/RRTStarDubins/dubins_path_planning.py b/PathPlanning/RRTStarDubins/dubins_path_planning.py
deleted file mode 100644
index b29465209b..0000000000
--- a/PathPlanning/RRTStarDubins/dubins_path_planning.py
+++ /dev/null
@@ -1,303 +0,0 @@
-"""
-
-Dubins path planner sample code
-
-author Atsushi Sakai (@Atsushi_twi)
-
-"""
-import math
-import matplotlib.pyplot as plt
-import numpy as np
-
-
-def mod2pi(theta):
-    return theta - 2.0 * math.pi * math.floor(theta / 2.0 / math.pi)
-
-
-def pi_2_pi(angle):
-    return (angle + math.pi) % (2 * math.pi) - math.pi
-
-
-def LSL(alpha, beta, d):
-    sa = math.sin(alpha)
-    sb = math.sin(beta)
-    ca = math.cos(alpha)
-    cb = math.cos(beta)
-    c_ab = math.cos(alpha - beta)
-
-    tmp0 = d + sa - sb
-
-    mode = ["L", "S", "L"]
-    p_squared = 2 + (d * d) - (2 * c_ab) + (2 * d * (sa - sb))
-    if p_squared < 0:
-        return None, None, None, mode
-    tmp1 = math.atan2((cb - ca), tmp0)
-    t = mod2pi(-alpha + tmp1)
-    p = math.sqrt(p_squared)
-    q = mod2pi(beta - tmp1)
-    #  print(np.rad2deg(t), p, np.rad2deg(q))
-
-    return t, p, q, mode
-
-
-def RSR(alpha, beta, d):
-    sa = math.sin(alpha)
-    sb = math.sin(beta)
-    ca = math.cos(alpha)
-    cb = math.cos(beta)
-    c_ab = math.cos(alpha - beta)
-
-    tmp0 = d - sa + sb
-    mode = ["R", "S", "R"]
-    p_squared = 2 + (d * d) - (2 * c_ab) + (2 * d * (sb - sa))
-    if p_squared < 0:
-        return None, None, None, mode
-    tmp1 = math.atan2((ca - cb), tmp0)
-    t = mod2pi(alpha - tmp1)
-    p = math.sqrt(p_squared)
-    q = mod2pi(-beta + tmp1)
-
-    return t, p, q, mode
-
-
-def LSR(alpha, beta, d):
-    sa = math.sin(alpha)
-    sb = math.sin(beta)
-    ca = math.cos(alpha)
-    cb = math.cos(beta)
-    c_ab = math.cos(alpha - beta)
-
-    p_squared = -2 + (d * d) + (2 * c_ab) + (2 * d * (sa + sb))
-    mode = ["L", "S", "R"]
-    if p_squared < 0:
-        return None, None, None, mode
-    p = math.sqrt(p_squared)
-    tmp2 = math.atan2((-ca - cb), (d + sa + sb)) - math.atan2(-2.0, p)
-    t = mod2pi(-alpha + tmp2)
-    q = mod2pi(-mod2pi(beta) + tmp2)
-
-    return t, p, q, mode
-
-
-def RSL(alpha, beta, d):
-    sa = math.sin(alpha)
-    sb = math.sin(beta)
-    ca = math.cos(alpha)
-    cb = math.cos(beta)
-    c_ab = math.cos(alpha - beta)
-
-    p_squared = (d * d) - 2 + (2 * c_ab) - (2 * d * (sa + sb))
-    mode = ["R", "S", "L"]
-    if p_squared < 0:
-        return None, None, None, mode
-    p = math.sqrt(p_squared)
-    tmp2 = math.atan2((ca + cb), (d - sa - sb)) - math.atan2(2.0, p)
-    t = mod2pi(alpha - tmp2)
-    q = mod2pi(beta - tmp2)
-
-    return t, p, q, mode
-
-
-def RLR(alpha, beta, d):
-    sa = math.sin(alpha)
-    sb = math.sin(beta)
-    ca = math.cos(alpha)
-    cb = math.cos(beta)
-    c_ab = math.cos(alpha - beta)
-
-    mode = ["R", "L", "R"]
-    tmp_rlr = (6.0 - d * d + 2.0 * c_ab + 2.0 * d * (sa - sb)) / 8.0
-    if abs(tmp_rlr) > 1.0:
-        return None, None, None, mode
-
-    p = mod2pi(2 * math.pi - math.acos(tmp_rlr))
-    t = mod2pi(alpha - math.atan2(ca - cb, d - sa + sb) + mod2pi(p / 2.0))
-    q = mod2pi(alpha - beta - t + mod2pi(p))
-    return t, p, q, mode
-
-
-def LRL(alpha, beta, d):
-    sa = math.sin(alpha)
-    sb = math.sin(beta)
-    ca = math.cos(alpha)
-    cb = math.cos(beta)
-    c_ab = math.cos(alpha - beta)
-
-    mode = ["L", "R", "L"]
-    tmp_lrl = (6. - d * d + 2 * c_ab + 2 * d * (- sa + sb)) / 8.
-    if abs(tmp_lrl) > 1:
-        return None, None, None, mode
-    p = mod2pi(2 * math.pi - math.acos(tmp_lrl))
-    t = mod2pi(-alpha - math.atan2(ca - cb, d + sa - sb) + p / 2.)
-    q = mod2pi(mod2pi(beta) - alpha - t + mod2pi(p))
-
-    return t, p, q, mode
-
-
-def dubins_path_planning_from_origin(ex, ey, eyaw, c):
-    # nomalize
-    dx = ex
-    dy = ey
-    D = math.sqrt(dx ** 2.0 + dy ** 2.0)
-    d = D / c
-    #  print(dx, dy, D, d)
-
-    theta = mod2pi(math.atan2(dy, dx))
-    alpha = mod2pi(- theta)
-    beta = mod2pi(eyaw - theta)
-    #  print(theta, alpha, beta, d)
-
-    planners = [LSL, RSR, LSR, RSL, RLR, LRL]
-
-    bcost = float("inf")
-    bt, bp, bq, bmode = None, None, None, None
-
-    for planner in planners:
-        t, p, q, mode = planner(alpha, beta, d)
-        if t is None:
-            #  print("".join(mode) + " cannot generate path")
-            continue
-
-        cost = (abs(t) + abs(p) + abs(q))
-        if bcost > cost:
-            bt, bp, bq, bmode = t, p, q, mode
-            bcost = cost
-
-    #  print(bmode)
-    px, py, pyaw = generate_course([bt, bp, bq], bmode, c)
-
-    return px, py, pyaw, bmode, bcost
-
-
-def dubins_path_planning(sx, sy, syaw, ex, ey, eyaw, c):
-    """
-    Dubins path plannner
-
-    input:
-        sx x position of start point [m]
-        sy y position of start point [m]
-        syaw yaw angle of start point [rad]
-        ex x position of end point [m]
-        ey y position of end point [m]
-        eyaw yaw angle of end point [rad]
-        c curvature [1/m]
-
-    output:
-        px
-        py
-        pyaw
-        mode
-
-    """
-
-    ex = ex - sx
-    ey = ey - sy
-
-    lex = math.cos(syaw) * ex + math.sin(syaw) * ey
-    ley = - math.sin(syaw) * ex + math.cos(syaw) * ey
-    leyaw = eyaw - syaw
-
-    lpx, lpy, lpyaw, mode, clen = dubins_path_planning_from_origin(
-        lex, ley, leyaw, c)
-
-    px = [math.cos(-syaw) * x + math.sin(-syaw)
-          * y + sx for x, y in zip(lpx, lpy)]
-    py = [- math.sin(-syaw) * x + math.cos(-syaw)
-          * y + sy for x, y in zip(lpx, lpy)]
-    pyaw = [pi_2_pi(iyaw + syaw) for iyaw in lpyaw]
-    #  print(syaw)
-    #  pyaw = lpyaw
-
-    #  plt.plot(pyaw, "-r")
-    #  plt.plot(lpyaw, "-b")
-    #  plt.plot(eyaw, "*r")
-    #  plt.plot(syaw, "*b")
-    #  plt.show()
-
-    return px, py, pyaw, mode, clen
-
-
-def generate_course(length, mode, c):
-
-    px = [0.0]
-    py = [0.0]
-    pyaw = [0.0]
-
-    for m, l in zip(mode, length):
-        pd = 0.0
-        if m == "S":
-            d = 1.0 / c
-        else:  # turning couse
-            d = np.deg2rad(3.0)
-
-        while pd < abs(l - d):
-            #  print(pd, l)
-            px.append(px[-1] + d * c * math.cos(pyaw[-1]))
-            py.append(py[-1] + d * c * math.sin(pyaw[-1]))
-
-            if m == "L":  # left turn
-                pyaw.append(pyaw[-1] + d)
-            elif m == "S":  # Straight
-                pyaw.append(pyaw[-1])
-            elif m == "R":  # right turn
-                pyaw.append(pyaw[-1] - d)
-            pd += d
-
-        d = l - pd
-        px.append(px[-1] + d * c * math.cos(pyaw[-1]))
-        py.append(py[-1] + d * c * math.sin(pyaw[-1]))
-
-        if m == "L":  # left turn
-            pyaw.append(pyaw[-1] + d)
-        elif m == "S":  # Straight
-            pyaw.append(pyaw[-1])
-        elif m == "R":  # right turn
-            pyaw.append(pyaw[-1] - d)
-        pd += d
-
-    return px, py, pyaw
-
-
-def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):  # pragma: no cover
-    """
-    Plot arrow
-    """
-
-    if not isinstance(x, float):
-        for (ix, iy, iyaw) in zip(x, y, yaw):
-            plot_arrow(ix, iy, iyaw)
-    else:
-        plt.arrow(x, y, length * math.cos(yaw), length * math.sin(yaw),
-                  fc=fc, ec=ec, head_width=width, head_length=width)
-        plt.plot(x, y)
-
-
-if __name__ == '__main__':
-    print("Dubins path planner sample start!!")
-
-    start_x = 1.0  # [m]
-    start_y = 1.0  # [m]
-    start_yaw = np.deg2rad(45.0)  # [rad]
-
-    end_x = -3.0  # [m]
-    end_y = -3.0  # [m]
-    end_yaw = np.deg2rad(-45.0)  # [rad]
-
-    curvature = 1.0
-
-    px, py, pyaw, mode, clen = dubins_path_planning(start_x, start_y, start_yaw,
-                                                    end_x, end_y, end_yaw, curvature)
-
-    plt.plot(px, py, label="final course " + "".join(mode))
-
-    # plotting
-    plot_arrow(start_x, start_y, start_yaw)
-    plot_arrow(end_x, end_y, end_yaw)
-
-    #  for (ix, iy, iyaw) in zip(px, py, pyaw):
-    #  plot_arrow(ix, iy, iyaw, fc="b")
-
-    plt.legend()
-    plt.grid(True)
-    plt.axis("equal")
-    plt.show()

From 2bf23adb27727e17a68ce2718e849ec2ffc826ba Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 20 Jul 2019 12:01:03 +0900
Subject: [PATCH 0133/1080] code clean up again

---
 PathPlanning/RRT/rrt.py          | 47 +++++++++------
 PathPlanning/RRTStar/rrt_star.py | 99 ++++++++++++++++++--------------
 tests/test_rrt_star.py           |  2 +-
 3 files changed, 87 insertions(+), 61 deletions(-)

diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py
index 0d44703be2..225bfa5dca 100644
--- a/PathPlanning/RRT/rrt.py
+++ b/PathPlanning/RRT/rrt.py
@@ -27,17 +27,19 @@ class Node():
         def __init__(self, x, y):
             self.x = x
             self.y = y
+            self.path_x = []
+            self.path_y = []
             self.parent = None
 
-    def __init__(self, start, goal, obstacle_list,
-                 rand_area, expand_dis=1.0, goal_sample_rate=5, max_iter=500):
+    def __init__(self, start, goal, obstacle_list, rand_area,
+                 expand_dis=3.0, path_resolution=1.0, goal_sample_rate=5, max_iter=500):
         """
         Setting Parameter
 
         start:Start Position [x,y]
         goal:Goal Position [x,y]
         obstacleList:obstacle Positions [[x,y,size],...]
-        randArea:Ramdom Samping Area [min,max]
+        randArea:Random Sampling Area [min,max]
 
         """
         self.start = self.Node(start[0], start[1])
@@ -45,6 +47,7 @@ def __init__(self, start, goal, obstacle_list,
         self.min_rand = rand_area[0]
         self.max_rand = rand_area[1]
         self.expand_dis = expand_dis
+        self.path_resolution = path_resolution
         self.goal_sample_rate = goal_sample_rate
         self.max_iter = max_iter
         self.obstacle_list = obstacle_list
@@ -63,7 +66,7 @@ def planning(self, animation=True):
             nearest_ind = self.get_nearest_list_index(self.node_list, rnd_node)
             nearest_node = self.node_list[nearest_ind]
 
-            new_node = self.steer(nearest_node, rnd_node)
+            new_node = self.steer(nearest_node, rnd_node, self.expand_dis)
             new_node.parent = nearest_node
 
             if not self.check_collision(new_node, self.obstacle_list):
@@ -82,16 +85,25 @@ def planning(self, animation=True):
 
         return None  # cannot find path
 
-    def steer(self, from_node, to_node):
-        d, theta = self.calc_distance_and_angle(from_node, to_node)
-        if d > self.expand_dis:
-            x = from_node.x + self.expand_dis * math.cos(theta)
-            y = from_node.y + self.expand_dis * math.sin(theta)
-        else:
-            x = to_node.x
-            y = to_node.y
+    def steer(self, from_node, to_node, extend_length=float("inf")):
+
+        new_node = self.Node(from_node.x, from_node.y)
+        d, theta = self.calc_distance_and_angle(new_node, to_node)
+
+        new_node.path_x = [new_node.x]
+        new_node.path_y = [new_node.y]
+
+        if extend_length > d:
+            extend_length = d
+
+        n_expand = math.floor(extend_length / self.path_resolution)
+
+        for _ in range(n_expand):
+            new_node.x += self.path_resolution * math.cos(theta)
+            new_node.y += self.path_resolution * math.sin(theta)
+            new_node.path_x.append(new_node.x)
+            new_node.path_y.append(new_node.y)
 
-        new_node = self.Node(x, y)
         new_node.parent = from_node
 
         return new_node
@@ -149,10 +161,11 @@ def get_nearest_list_index(node_list, rnd_node):
     @staticmethod
     def check_collision(node, obstacleList):
         for (ox, oy, size) in obstacleList:
-            dx = ox - node.x
-            dy = oy - node.y
-            d = dx * dx + dy * dy
-            if d <= size ** 2:
+            dx_list = [ox - x for x in node.path_x]
+            dy_list = [oy - y for y in node.path_y]
+            d_list = [dx * dx + dy * dy for (dx, dy) in zip(dx_list, dy_list)]
+
+            if min(d_list) <= size ** 2:
                 return False  # collision
 
         return True  # safe
diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py
index da24d2c86b..446033d77f 100644
--- a/PathPlanning/RRTStar/rrt_star.py
+++ b/PathPlanning/RRTStar/rrt_star.py
@@ -6,7 +6,6 @@
 
 """
 
-import copy
 import math
 import os
 import sys
@@ -29,21 +28,20 @@ class RRTStar(RRT):
     Class for RRT Star planning
     """
 
-    class Node:
+    class Node(RRT.Node):
         def __init__(self, x, y):
-            self.x = x
-            self.y = y
+            super().__init__(x, y)
             self.cost = 0.0
-            self.parent = None
 
     def __init__(self, start, goal, obstacle_list, rand_area,
-                 expand_dis=0.5,
+                 expand_dis=3.0,
+                 path_resolution=1.0,
                  goal_sample_rate=20,
-                 max_iter=500,
+                 max_iter=300,
                  connect_circle_dist=50.0
                  ):
         super().__init__(start, goal, obstacle_list,
-                         rand_area, expand_dis, goal_sample_rate, max_iter)
+                         rand_area, expand_dis, path_resolution, goal_sample_rate, max_iter)
         """
         Setting Parameter
 
@@ -65,11 +63,12 @@ def planning(self, animation=True, search_until_maxiter=True):
 
         self.node_list = [self.start]
         for i in range(self.max_iter):
-            rnd = self.get_random_point()
+            print(i, len(self.node_list))
+            rnd = self.get_random_node()
             nearest_ind = self.get_nearest_list_index(self.node_list, rnd)
-            new_node = self.steer(rnd, self.node_list[nearest_ind])
+            new_node = self.steer(self.node_list[nearest_ind], rnd, self.expand_dis)
 
-            if self.check_collision(new_node, self.obstacleList):
+            if self.check_collision(new_node, self.obstacle_list):
                 near_inds = self.find_near_nodes(new_node)
                 new_node = self.choose_parent(new_node, near_inds)
                 if new_node:
@@ -79,7 +78,7 @@ def planning(self, animation=True, search_until_maxiter=True):
             if animation and i % 5 == 0:
                 self.draw_graph(rnd)
 
-            if not search_until_maxiter and new_node:  # check reaching the goal
+            if (not search_until_maxiter) and new_node:  # check reaching the goal
                 d, _ = self.calc_distance_and_angle(new_node, self.end)
                 if d <= self.expand_dis:
                     return self.generate_final_course(len(self.node_list) - 1)
@@ -92,6 +91,25 @@ def planning(self, animation=True, search_until_maxiter=True):
 
         return None
 
+    def connect_nodes(self, from_node, to_node):
+        new_node = self.Node(from_node.x, from_node.y)
+        d, theta = self.calc_distance_and_angle(new_node, to_node)
+
+        new_node.path_x = [new_node.x]
+        new_node.path_y = [new_node.y]
+
+        n_expand = math.floor(d / self.path_resolution)
+
+        for _ in range(n_expand):
+            new_node.x += self.path_resolution * math.cos(theta)
+            new_node.y += self.path_resolution * math.sin(theta)
+            new_node.path_x.append(new_node.x)
+            new_node.path_y.append(new_node.y)
+
+        new_node.parent = from_node
+
+        return new_node
+
     def choose_parent(self, new_node, near_inds):
         if not near_inds:
             return None
@@ -99,9 +117,10 @@ def choose_parent(self, new_node, near_inds):
         # search nearest cost in near_inds
         costs = []
         for i in near_inds:
-            d, theta = self.calc_distance_and_angle(self.node_list[i], new_node)
-            if self.check_collision_extend(self.node_list[i], theta, d):
-                costs.append(self.node_list[i].cost + d)
+            near_node = self.node_list[i]
+            t_node = self.steer(near_node, new_node)
+            if self.check_collision(t_node, self.obstacle_list):
+                costs.append(self.calc_new_cost(near_node, new_node))
             else:
                 costs.append(float("inf"))  # the cost of collision node
         min_cost = min(costs)
@@ -110,9 +129,10 @@ def choose_parent(self, new_node, near_inds):
             print("There is no good path.(min_cost is inf)")
             return None
 
-        new_node.cost = min_cost
         min_ind = near_inds[costs.index(min_cost)]
         new_node.parent = self.node_list[min_ind]
+        new_node = self.steer(new_node.parent, new_node)
+        new_node.cost = min_cost
 
         return new_node
 
@@ -141,34 +161,27 @@ def find_near_nodes(self, new_node):
     def rewire(self, new_node, near_inds):
         for i in near_inds:
             near_node = self.node_list[i]
-            d, theta = self.calc_distance_and_angle(near_node, new_node)
-            new_cost = new_node.cost + d
+            edge_node = self.steer(new_node, near_node)
+            edge_node.cost = self.calc_new_cost(near_node, new_node)
+
+            no_collision = self.check_collision(edge_node, self.obstacle_list)
+            improved_cost = near_node.cost > edge_node.cost
+
+            if no_collision and improved_cost:
+                near_node.parent = new_node
+                near_node.cost = edge_node.cost
+                self.propagate_cost_to_leaves(new_node)
 
-            if near_node.cost > new_cost:
-                if self.check_collision_extend(near_node, theta, d):
-                    near_node.parent = new_node
-                    near_node.cost = new_cost
-                    self.propagate_cost_to_leaves(new_node)
+    def calc_new_cost(self, from_node, to_node):
+        d, _ = self.calc_distance_and_angle(from_node, to_node)
+        return from_node.cost + d
 
     def propagate_cost_to_leaves(self, parent_node):
         for node in self.node_list:
             if node.parent == parent_node:
-                d, _ = self.calc_distance_and_angle(parent_node, node)
-                node.cost = parent_node.cost + d
+                node.cost = self.calc_new_cost(parent_node, node)
                 self.propagate_cost_to_leaves(node)
 
-    def check_collision_extend(self, near_node, theta, d):
-
-        tmp_node = copy.deepcopy(near_node)
-
-        for i in range(int(d / self.expand_dis)):
-            tmp_node.x += self.expand_dis * math.cos(theta)
-            tmp_node.y += self.expand_dis * math.sin(theta)
-            if not self.check_collision(tmp_node, self.obstacleList):
-                return False
-
-        return True
-
 
 def main():
     print("Start " + __file__)
@@ -184,11 +197,11 @@ def main():
     ]  # [x,y,size(radius)]
 
     # Set Initial parameters
-    rrt = RRTStar(start=[0, 0],
-                  goal=[10, 10],
-                  rand_area=[-2, 15],
-                  obstacle_list=obstacle_list)
-    path = rrt.planning(animation=show_animation, search_until_maxiter=False)
+    rrt_star = RRTStar(start=[0, 0],
+                       goal=[10, 10],
+                       rand_area=[-2, 15],
+                       obstacle_list=obstacle_list)
+    path = rrt_star.planning(animation=show_animation)
 
     if path is None:
         print("Cannot find path")
@@ -197,7 +210,7 @@ def main():
 
         # Draw final path
         if show_animation:
-            rrt.draw_graph()
+            rrt_star.draw_graph()
             plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
             plt.grid(True)
             plt.pause(0.01)  # Need for Mac
diff --git a/tests/test_rrt_star.py b/tests/test_rrt_star.py
index c5f8f870e0..514ed34829 100644
--- a/tests/test_rrt_star.py
+++ b/tests/test_rrt_star.py
@@ -7,7 +7,7 @@
 
 try:
     import rrt_star as m
-except:
+except ImportError:
     raise
 
 

From c66fccc71c681387ff61b59554694b25399ca790 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 24 Jul 2019 19:42:54 +0900
Subject: [PATCH 0134/1080] Update users_comments.md

---
 users_comments.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index b7e88c92bb..bf3c8455de 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -304,6 +304,9 @@ URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8516589&isnumber=85
 
 3. Welburn, E, Hakim Khalili, H, Gupta, A, Watson, S & Carrasco, J 2019, A Navigational System for Quadcopter Remote Inspection of Offshore Substations. in The Fifteenth International Conference on Autonomic and Autonomous Systems 2019. URL:https://www.research.manchester.ac.uk/portal/files/107169964/ICAS19_A_Navigational_System_for_Quadcopter_Remote_Inspection_of_Offshore_Substations.pdf
 
+4. E. Horváth, C. Hajdu, C. Radu and Á. Ballagi, "Range Sensor-based Occupancy Grid Mapping with Signatures," 2019 20th International Carpathian Control Conference (ICCC), Krakow-Wieliczka, Poland, 2019, pp. 1-5.
+URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8765684&isnumber=8765679
+
 
 # Others
 

From 446391e9e21df9a710b434c0617881cd962f05fb Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 26 Jul 2019 19:42:34 +0900
Subject: [PATCH 0135/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index bf3c8455de..9bbf87be67 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -293,6 +293,12 @@ Thanks for your robotics repo!
 
 ---
 
+You made such a nice work. Congratulations :)
+
+--ATroya
+
+---
+
 # Citations
 
 1. B. Blaga, M. Deac, R. W. Y. Al-doori, M. Negru and R. Dǎnescu, "Miniature Autonomous Vehicle Development on Raspberry Pi," 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, 2018, pp. 229-236.

From 4102b6ed150930c463ecb743bc7994b72ad45636 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 27 Jul 2019 07:32:17 +0900
Subject: [PATCH 0136/1080] Update README.md

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 967c4aca69..e8bf0d297d 100644
--- a/README.md
+++ b/README.md
@@ -491,6 +491,8 @@ Ref:
 
 - [notebook](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/model_predictive_speed_and_steer_control/Model_predictive_speed_and_steering_control.ipynb)
 
+- [Real\-time Model Predictive Control \(MPC\), ACADO, Python \| Work\-is\-Playing](http://grauonline.de/wordpress/?page_id=3244)
+
 ## Nonlinear Model predictive control with C-GMRES
 
 A motion planning and path tracking simulation with NMPC of C-GMRES 

From cc9bb11345975bae4a1b3946953ad746b43ca94d Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 27 Jul 2019 07:32:54 +0900
Subject: [PATCH 0137/1080] Update users_comments.md

---
 users_comments.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 9bbf87be67..087acd3a7c 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -320,3 +320,5 @@ URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8765684&isnumber=87
 
 - 36 Amazing Python Open Source Projects (v.2019) – Mybridge for Professionals https://medium.mybridge.co/36-amazing-python-open-source-projects-v-2019-2fe058d79450
 
+- Real-time Model Predictive Control (MPC), ACADO, Python | Work-is-Playing http://grauonline.de/wordpress/?page_id=3244
+

From d9508f6e725d1735bb80a9acebecd4e8ca2667ad Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 27 Jul 2019 15:08:25 +0900
Subject: [PATCH 0138/1080] fix rrt_star_dubins

---
 .../DubinsPath/dubins_path_planning.py        | 17 +++++----
 PathPlanning/RRT/rrt.py                       | 13 +++++--
 PathPlanning/RRTStar/rrt_star.py              | 38 +++++--------------
 3 files changed, 28 insertions(+), 40 deletions(-)

diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py
index dc7485ad9a..706b5a49af 100644
--- a/PathPlanning/DubinsPath/dubins_path_planning.py
+++ b/PathPlanning/DubinsPath/dubins_path_planning.py
@@ -6,8 +6,9 @@
 
 """
 import math
-import numpy as np
+
 import matplotlib.pyplot as plt
+import numpy as np
 
 show_animation = True
 
@@ -136,8 +137,8 @@ def LRL(alpha, beta, d):
     return t, p, q, mode
 
 
-def dubins_path_planning_from_origin(ex, ey, eyaw, c):
-    # nomalize
+def dubins_path_planning_from_origin(ex, ey, eyaw, c, D_ANGLE):
+    # normalize
     dx = ex
     dy = ey
     D = math.sqrt(dx ** 2.0 + dy ** 2.0)
@@ -165,12 +166,12 @@ def dubins_path_planning_from_origin(ex, ey, eyaw, c):
             bcost = cost
 
     #  print(bmode)
-    px, py, pyaw = generate_course([bt, bp, bq], bmode, c)
+    px, py, pyaw = generate_course([bt, bp, bq], bmode, c, D_ANGLE)
 
     return px, py, pyaw, bmode, bcost
 
 
-def dubins_path_planning(sx, sy, syaw, ex, ey, eyaw, c):
+def dubins_path_planning(sx, sy, syaw, ex, ey, eyaw, c, D_ANGLE=np.deg2rad(10.0)):
     """
     Dubins path plannner
 
@@ -199,7 +200,7 @@ def dubins_path_planning(sx, sy, syaw, ex, ey, eyaw, c):
     leyaw = eyaw - syaw
 
     lpx, lpy, lpyaw, mode, clen = dubins_path_planning_from_origin(
-        lex, ley, leyaw, c)
+        lex, ley, leyaw, c, D_ANGLE)
 
     px = [math.cos(-syaw) * x + math.sin(-syaw)
           * y + sx for x, y in zip(lpx, lpy)]
@@ -210,7 +211,7 @@ def dubins_path_planning(sx, sy, syaw, ex, ey, eyaw, c):
     return px, py, pyaw, mode, clen
 
 
-def generate_course(length, mode, c):
+def generate_course(length, mode, c, D_ANGLE):
 
     px = [0.0]
     py = [0.0]
@@ -221,7 +222,7 @@ def generate_course(length, mode, c):
         if m == "S":
             d = 1.0 * c
         else:  # turning couse
-            d = np.deg2rad(3.0)
+            d = D_ANGLE
 
         while pd < abs(l - d):
             #  print(pd, l)
diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py
index 225bfa5dca..768fe41d1e 100644
--- a/PathPlanning/RRT/rrt.py
+++ b/PathPlanning/RRT/rrt.py
@@ -19,7 +19,7 @@ class RRT:
     Class for RRT planning
     """
 
-    class Node():
+    class Node:
         """
         RRT Node
         """
@@ -104,6 +104,13 @@ def steer(self, from_node, to_node, extend_length=float("inf")):
             new_node.path_x.append(new_node.x)
             new_node.path_y.append(new_node.y)
 
+        d, _ = self.calc_distance_and_angle(new_node, to_node)
+        if d <= self.path_resolution:
+            new_node.x = to_node.x
+            new_node.y = to_node.y
+            new_node.path_x[-1] = to_node.x
+            new_node.path_y[-1] = to_node.y
+
         new_node.parent = from_node
 
         return new_node
@@ -137,9 +144,7 @@ def draw_graph(self, rnd=None):
             plt.plot(rnd.x, rnd.y, "^k")
         for node in self.node_list:
             if node.parent:
-                plt.plot([node.x, node.parent.x],
-                         [node.y, node.parent.y],
-                         "-g")
+                plt.plot(node.path_x, node.path_y, "-g")
 
         for (ox, oy, size) in self.obstacle_list:
             plt.plot(ox, oy, "ok", ms=30 * size)
diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py
index 446033d77f..19e6812686 100644
--- a/PathPlanning/RRTStar/rrt_star.py
+++ b/PathPlanning/RRTStar/rrt_star.py
@@ -53,7 +53,7 @@ def __init__(self, start, goal, obstacle_list, rand_area,
         """
         self.connect_circle_dist = connect_circle_dist
 
-    def planning(self, animation=True, search_until_maxiter=True):
+    def planning(self, animation=True, search_until_max_iter=True):
         """
         rrt star path planning
 
@@ -63,7 +63,7 @@ def planning(self, animation=True, search_until_maxiter=True):
 
         self.node_list = [self.start]
         for i in range(self.max_iter):
-            print(i, len(self.node_list))
+            print("Iter:", i, ", number of nodes:", len(self.node_list))
             rnd = self.get_random_node()
             nearest_ind = self.get_nearest_list_index(self.node_list, rnd)
             new_node = self.steer(self.node_list[nearest_ind], rnd, self.expand_dis)
@@ -78,10 +78,10 @@ def planning(self, animation=True, search_until_maxiter=True):
             if animation and i % 5 == 0:
                 self.draw_graph(rnd)
 
-            if (not search_until_maxiter) and new_node:  # check reaching the goal
-                d, _ = self.calc_distance_and_angle(new_node, self.end)
-                if d <= self.expand_dis:
-                    return self.generate_final_course(len(self.node_list) - 1)
+            if (not search_until_max_iter) and new_node:  # check reaching the goal
+                last_index = self.search_best_goal_node()
+                if last_index:
+                    return self.generate_final_course(last_index)
 
         print("reached max iteration")
 
@@ -91,25 +91,6 @@ def planning(self, animation=True, search_until_maxiter=True):
 
         return None
 
-    def connect_nodes(self, from_node, to_node):
-        new_node = self.Node(from_node.x, from_node.y)
-        d, theta = self.calc_distance_and_angle(new_node, to_node)
-
-        new_node.path_x = [new_node.x]
-        new_node.path_y = [new_node.y]
-
-        n_expand = math.floor(d / self.path_resolution)
-
-        for _ in range(n_expand):
-            new_node.x += self.path_resolution * math.cos(theta)
-            new_node.y += self.path_resolution * math.sin(theta)
-            new_node.path_x.append(new_node.x)
-            new_node.path_y.append(new_node.y)
-
-        new_node.parent = from_node
-
-        return new_node
-
     def choose_parent(self, new_node, near_inds):
         if not near_inds:
             return None
@@ -130,8 +111,8 @@ def choose_parent(self, new_node, near_inds):
             return None
 
         min_ind = near_inds[costs.index(min_cost)]
+        new_node = self.steer(self.node_list[min_ind], new_node)
         new_node.parent = self.node_list[min_ind]
-        new_node = self.steer(new_node.parent, new_node)
         new_node.cost = min_cost
 
         return new_node
@@ -162,14 +143,14 @@ def rewire(self, new_node, near_inds):
         for i in near_inds:
             near_node = self.node_list[i]
             edge_node = self.steer(new_node, near_node)
-            edge_node.cost = self.calc_new_cost(near_node, new_node)
+            edge_node.cost = self.calc_new_cost(new_node, near_node)
 
             no_collision = self.check_collision(edge_node, self.obstacle_list)
             improved_cost = near_node.cost > edge_node.cost
 
             if no_collision and improved_cost:
+                near_node = edge_node
                 near_node.parent = new_node
-                near_node.cost = edge_node.cost
                 self.propagate_cost_to_leaves(new_node)
 
     def calc_new_cost(self, from_node, to_node):
@@ -177,6 +158,7 @@ def calc_new_cost(self, from_node, to_node):
         return from_node.cost + d
 
     def propagate_cost_to_leaves(self, parent_node):
+
         for node in self.node_list:
             if node.parent == parent_node:
                 node.cost = self.calc_new_cost(parent_node, node)

From 67540275a6ca5abad9ff8466ce76e65c422d1724 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 27 Jul 2019 15:25:50 +0900
Subject: [PATCH 0139/1080] fix RRTDubins

---
 PathPlanning/RRT/rrt.py                       |  10 +-
 PathPlanning/RRTDubins/rrt_dubins.py          | 277 +++++++-------
 PathPlanning/RRTStarDubins/rrt_star_dubins.py | 347 ++++++++----------
 3 files changed, 288 insertions(+), 346 deletions(-)

diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py
index 768fe41d1e..541c2a9636 100644
--- a/PathPlanning/RRT/rrt.py
+++ b/PathPlanning/RRT/rrt.py
@@ -67,15 +67,13 @@ def planning(self, animation=True):
             nearest_node = self.node_list[nearest_ind]
 
             new_node = self.steer(nearest_node, rnd_node, self.expand_dis)
-            new_node.parent = nearest_node
 
-            if not self.check_collision(new_node, self.obstacle_list):
-                continue
+            if self.check_collision(new_node, self.obstacle_list):
+                self.node_list.append(new_node)
 
-            self.node_list.append(new_node)
-            print("nNodelist:", len(self.node_list))
+            if animation and i % 5 == 0:
+                self.draw_graph(rnd_node)
 
-            # check goal
             if self.calc_dist_to_goal(new_node.x, new_node.y) <= self.expand_dis:
                 print("Goal!!")
                 return self.generate_final_course(len(self.node_list) - 1)
diff --git a/PathPlanning/RRTDubins/rrt_dubins.py b/PathPlanning/RRTDubins/rrt_dubins.py
index 54752ef81e..e87e3aa0a6 100644
--- a/PathPlanning/RRTDubins/rrt_dubins.py
+++ b/PathPlanning/RRTDubins/rrt_dubins.py
@@ -1,204 +1,207 @@
 """
-Path Planning Sample Code with RRT for car like robot.
+Path planning Sample Code with RRT for car like robot.
 
 author: AtsushiSakai(@Atsushi_twi)
 
 """
-import matplotlib.pyplot as plt
-import numpy as np
 import copy
 import math
+import os
 import random
 import sys
-import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
                 "/../DubinsPath/")
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../RRT/")
 
 try:
+    from rrt import RRT
     import dubins_path_planning
-except:
+except ImportError:
     raise
 
 
 show_animation = True
 
 
-class RRT():
+class RRTDubins(RRT):
     """
-    Class for RRT Planning
+    Class for RRT planning with Dubins path
     """
 
-    def __init__(self, start, goal, obstacleList, randArea,
-                 goalSampleRate=10, maxIter=1000):
+    class Node(RRT.Node):
+        """
+        RRT Node
+        """
+
+        def __init__(self, x, y, yaw):
+            super().__init__(x, y)
+            self.cost = 0
+            self.yaw = yaw
+            self.path_yaw = []
+
+    def __init__(self, start, goal, obstacle_list, rand_area,
+                 goal_sample_rate=10,
+                 max_iter=200,
+                 ):
         """
         Setting Parameter
 
         start:Start Position [x,y]
         goal:Goal Position [x,y]
         obstacleList:obstacle Positions [[x,y,size],...]
-        randArea:Ramdom Samping Area [min,max]
+        randArea:Random Sampling Area [min,max]
 
         """
-        self.start = Node(start[0], start[1], start[2])
-        self.end = Node(goal[0], goal[1], goal[2])
-        self.minrand = randArea[0]
-        self.maxrand = randArea[1]
-        self.goalSampleRate = goalSampleRate
-        self.maxIter = maxIter
-        self.obstacleList = obstacleList
-
-    def Planning(self, animation=False):
+        self.start = self.Node(start[0], start[1], start[2])
+        self.end = self.Node(goal[0], goal[1], goal[2])
+        self.min_rand = rand_area[0]
+        self.max_rand = rand_area[1]
+        self.goal_sample_rate = goal_sample_rate
+        self.max_iter = max_iter
+        self.obstacle_list = obstacle_list
+
+        self.curvature = 1.0  # for dubins path
+        self.goal_yaw_th = np.deg2rad(1.0)
+        self.goal_xy_th = 0.5
+
+    def planning(self, animation=True, search_until_max_iter=True):
         """
-        Pathplanning
+        execute planning
 
         animation: flag for animation on or off
         """
 
-        self.nodeList = [self.start]
-        for i in range(self.maxIter):
-            rnd = self.get_random_point()
-            nind = self.GetNearestListIndex(self.nodeList, rnd)
-
-            newNode = self.steer(rnd, nind)
+        self.node_list = [self.start]
+        for i in range(self.max_iter):
+            print("Iter:", i, ", number of nodes:", len(self.node_list))
+            rnd = self.get_random_node()
+            nearest_ind = self.get_nearest_list_index(self.node_list, rnd)
+            new_node = self.steer(self.node_list[nearest_ind], rnd)
 
-            if self.__CollisionCheck(newNode, self.obstacleList):
-                self.nodeList.append(newNode)
+            if self.check_collision(new_node, self.obstacle_list):
+                self.node_list.append(new_node)
 
             if animation and i % 5 == 0:
-                self.DrawGraph(rnd=rnd)
+                self.plot_start_goal_arrow()
+                self.draw_graph(rnd)
 
-        # generate coruse
-        lastIndex = self.get_best_last_index()
-        #  print(lastIndex)
+            if (not search_until_max_iter) and new_node:  # check reaching the goal
+                last_index = self.search_best_goal_node()
+                if last_index:
+                    return self.generate_final_course(last_index)
 
-        if lastIndex is None:
-            return None
+        print("reached max iteration")
 
-        path = self.gen_final_course(lastIndex)
-        return path
+        last_index = self.search_best_goal_node()
+        if last_index:
+            return self.generate_final_course(last_index)
+        else:
+            print("Cannot find path")
 
-    def pi_2_pi(self, angle):
-        return (angle + math.pi) % (2 * math.pi) - math.pi
+        return None
+
+    def draw_graph(self, rnd=None):
+        plt.clf()
+        if rnd is not None:
+            plt.plot(rnd.x, rnd.y, "^k")
+        for node in self.node_list:
+            if node.parent:
+                plt.plot(node.path_x, node.path_y, "-g")
+
+        for (ox, oy, size) in self.obstacle_list:
+            plt.plot(ox, oy, "ok", ms=30 * size)
+
+        plt.plot(self.start.x, self.start.y, "xr")
+        plt.plot(self.end.x, self.end.y, "xr")
+        plt.axis([-2, 15, -2, 15])
+        plt.grid(True)
+        self.plot_start_goal_arrow()
+        plt.pause(0.01)
+
+    def plot_start_goal_arrow(self):
+        dubins_path_planning.plot_arrow(
+            self.start.x, self.start.y, self.start.yaw)
+        dubins_path_planning.plot_arrow(
+            self.end.x, self.end.y, self.end.yaw)
 
-    def steer(self, rnd, nind):
-        #  print(rnd)
-        curvature = 1.0
+    def steer(self, from_node, to_node):
 
-        nearestNode = self.nodeList[nind]
+        px, py, pyaw, mode, course_length = dubins_path_planning.dubins_path_planning(
+            from_node.x, from_node.y, from_node.yaw,
+            to_node.x, to_node.y, to_node.yaw, self.curvature)
 
-        px, py, pyaw, mode, clen = dubins_path_planning.dubins_path_planning(
-            nearestNode.x, nearestNode.y, nearestNode.yaw, rnd[0], rnd[1], rnd[2], curvature)
+        new_node = copy.deepcopy(from_node)
+        new_node.x = px[-1]
+        new_node.y = py[-1]
+        new_node.yaw = pyaw[-1]
 
-        newNode = copy.deepcopy(nearestNode)
-        newNode.x = px[-1]
-        newNode.y = py[-1]
-        newNode.yaw = pyaw[-1]
+        new_node.path_x = px
+        new_node.path_y = py
+        new_node.path_yaw = pyaw
+        new_node.cost += course_length
+        new_node.parent = from_node
 
-        newNode.path_x = px
-        newNode.path_y = py
-        newNode.path_yaw = pyaw
-        newNode.cost += clen
-        newNode.parent = nind
+        return new_node
 
-        return newNode
+    def calc_new_cost(self, from_node, to_node):
 
-    def get_random_point(self):
+        _, _, _, _, course_length = dubins_path_planning.dubins_path_planning(
+            from_node.x, from_node.y, from_node.yaw,
+            to_node.x, to_node.y, to_node.yaw, self.curvature)
 
-        if random.randint(0, 100) > self.goalSampleRate:
-            rnd = [random.uniform(self.minrand, self.maxrand),
-                   random.uniform(self.minrand, self.maxrand),
-                   random.uniform(-math.pi, math.pi)
-                   ]
+        return from_node.cost + course_length
+
+    def get_random_node(self):
+
+        if random.randint(0, 100) > self.goal_sample_rate:
+            rnd = self.Node(random.uniform(self.min_rand, self.max_rand),
+                            random.uniform(self.min_rand, self.max_rand),
+                            random.uniform(-math.pi, math.pi)
+                            )
         else:  # goal point sampling
-            rnd = [self.end.x, self.end.y, self.end.yaw]
+            rnd = self.Node(self.end.x, self.end.y, self.end.yaw)
 
         return rnd
 
-    def get_best_last_index(self):
-        #  print("get_best_last_index")
+    def search_best_goal_node(self):
+
+        goal_indexes = []
+        for (i, node) in enumerate(self.node_list):
+            if self.calc_dist_to_goal(node.x, node.y) <= self.goal_xy_th:
+                goal_indexes.append(i)
+
+        # angle check
+        final_goal_indexes = []
+        for i in goal_indexes:
+            if abs(self.node_list[i].yaw - self.end.yaw) <= self.goal_yaw_th:
+                final_goal_indexes.append(i)
 
-        disglist = [self.calc_dist_to_goal(
-            node.x, node.y) for node in self.nodeList]
-        goalinds = [disglist.index(i) for i in disglist if i <= 0.1]
-        #  print(goalinds)
+        if not final_goal_indexes:
+            return None
 
-        mincost = min([self.nodeList[i].cost for i in goalinds])
-        for i in goalinds:
-            if self.nodeList[i].cost == mincost:
+        min_cost = min([self.node_list[i].cost for i in final_goal_indexes])
+        for i in final_goal_indexes:
+            if self.node_list[i].cost == min_cost:
                 return i
 
         return None
 
-    def gen_final_course(self, goalind):
+    def generate_final_course(self, goal_index):
+        print("final")
         path = [[self.end.x, self.end.y]]
-        while self.nodeList[goalind].parent is not None:
-            node = self.nodeList[goalind]
+        node = self.node_list[goal_index]
+        while node.parent:
             for (ix, iy) in zip(reversed(node.path_x), reversed(node.path_y)):
                 path.append([ix, iy])
-            #  path.append([node.x, node.y])
-            goalind = node.parent
+            node = node.parent
         path.append([self.start.x, self.start.y])
         return path
 
-    def calc_dist_to_goal(self, x, y):
-        return np.linalg.norm([x - self.end.x, y - self.end.y])
-
-    def DrawGraph(self, rnd=None):  # pragma: no cover
-        plt.clf()
-        if rnd is not None:
-            plt.plot(rnd[0], rnd[1], "^k")
-        for node in self.nodeList:
-            if node.parent is not None:
-                plt.plot(node.path_x, node.path_y, "-g")
-
-        for (ox, oy, size) in self.obstacleList:
-            plt.plot(ox, oy, "ok", ms=30 * size)
-
-        dubins_path_planning.plot_arrow(
-            self.start.x, self.start.y, self.start.yaw)
-        dubins_path_planning.plot_arrow(
-            self.end.x, self.end.y, self.end.yaw)
-
-        plt.axis([-2, 15, -2, 15])
-        plt.grid(True)
-        plt.pause(0.01)
-
-    def GetNearestListIndex(self, nodeList, rnd):
-        dlist = [(node.x - rnd[0]) ** 2
-                 + (node.y - rnd[1]) ** 2
-                 + (node.yaw - rnd[2] ** 2) for node in nodeList]
-        minind = dlist.index(min(dlist))
-
-        return minind
-
-    def __CollisionCheck(self, node, obstacleList):
-
-        for (ox, oy, size) in obstacleList:
-            for (ix, iy) in zip(node.path_x, node.path_y):
-                dx = ox - ix
-                dy = oy - iy
-                d = dx * dx + dy * dy
-                if d <= size ** 2:
-                    return False  # collision
-
-        return True  # safe
-
-
-class Node():
-    """
-    RRT Node
-    """
-
-    def __init__(self, x, y, yaw):
-        self.x = x
-        self.y = y
-        self.yaw = yaw
-        self.path_x = []
-        self.path_y = []
-        self.path_yaw = []
-        self.cost = 0.0
-        self.parent = None
-
 
 def main():
     print("Start " + __file__)
@@ -216,12 +219,12 @@ def main():
     start = [0.0, 0.0, np.deg2rad(0.0)]
     goal = [10.0, 10.0, np.deg2rad(0.0)]
 
-    rrt = RRT(start, goal, randArea=[-2.0, 15.0], obstacleList=obstacleList)
-    path = rrt.Planning(animation=show_animation)
+    rrt_dubins = RRTDubins(start, goal, obstacleList, [-2.0, 15.0])
+    path = rrt_dubins.planning(animation=show_animation)
 
     # Draw final path
     if show_animation:  # pragma: no cover
-        rrt.DrawGraph()
+        rrt_dubins.draw_graph()
         plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
         plt.grid(True)
         plt.pause(0.001)
diff --git a/PathPlanning/RRTStarDubins/rrt_star_dubins.py b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
index 0494bbabef..b98c7dca1c 100644
--- a/PathPlanning/RRTStarDubins/rrt_star_dubins.py
+++ b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
@@ -1,271 +1,212 @@
 """
-Path Planning Sample Code with RRT and Dubins path
+Path planning Sample Code with RRT and Dubins path
 
 author: AtsushiSakai(@Atsushi_twi)
 
 """
 
-import random
-import math
 import copy
-import numpy as np
-import dubins_path_planning
+import math
+import os
+import random
+import sys
+
 import matplotlib.pyplot as plt
+import numpy as np
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../DubinsPath/")
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../RRTStar/")
+
+try:
+    import dubins_path_planning
+    from rrt_star import RRTStar
+except ImportError:
+    raise
 
 show_animation = True
 
 
-class RRT():
+class RRTStarDubins(RRTStar):
     """
-    Class for RRT Planning
+    Class for RRT star planning with Dubins path
     """
 
-    def __init__(self, start, goal, obstacleList, randArea,
-                 goalSampleRate=10, maxIter=100):
+    class Node(RRTStar.Node):
+        """
+        RRT Node
+        """
+
+        def __init__(self, x, y, yaw):
+            super().__init__(x, y)
+            self.yaw = yaw
+            self.path_yaw = []
+
+    def __init__(self, start, goal, obstacle_list, rand_area,
+                 goal_sample_rate=10,
+                 max_iter=200,
+                 connect_circle_dist=50.0
+                 ):
         """
         Setting Parameter
 
         start:Start Position [x,y]
         goal:Goal Position [x,y]
         obstacleList:obstacle Positions [[x,y,size],...]
-        randArea:Ramdom Samping Area [min,max]
+        randArea:Random Sampling Area [min,max]
 
         """
-        self.start = Node(start[0], start[1], start[2])
-        self.end = Node(goal[0], goal[1], goal[2])
-        self.minrand = randArea[0]
-        self.maxrand = randArea[1]
-        self.goalSampleRate = goalSampleRate
-        self.maxIter = maxIter
-        self.obstacleList = obstacleList
-
-    def Planning(self, animation=True):
+        self.start = self.Node(start[0], start[1], start[2])
+        self.end = self.Node(goal[0], goal[1], goal[2])
+        self.min_rand = rand_area[0]
+        self.max_rand = rand_area[1]
+        self.goal_sample_rate = goal_sample_rate
+        self.max_iter = max_iter
+        self.obstacle_list = obstacle_list
+        self.connect_circle_dist = connect_circle_dist
+
+        self.curvature = 1.0  # for dubins path
+        self.goal_yaw_th = np.deg2rad(1.0)
+        self.goal_xy_th = 0.5
+
+    def planning(self, animation=True, search_until_max_iter=True):
         """
-        Pathplanning
+        RRT Star planning
 
         animation: flag for animation on or off
         """
 
-        self.nodeList = [self.start]
-        for i in range(self.maxIter):
-            rnd = self.get_random_point()
-            nind = self.GetNearestListIndex(self.nodeList, rnd)
+        self.node_list = [self.start]
+        for i in range(self.max_iter):
+            print("Iter:", i, ", number of nodes:", len(self.node_list))
+            rnd = self.get_random_node()
+            nearest_ind = self.get_nearest_list_index(self.node_list, rnd)
+            new_node = self.steer(self.node_list[nearest_ind], rnd)
 
-            newNode = self.steer(rnd, nind)
-            #  print(newNode.cost)
-
-            if self.CollisionCheck(newNode, self.obstacleList):
-                nearinds = self.find_near_nodes(newNode)
-                newNode = self.choose_parent(newNode, nearinds)
-                self.nodeList.append(newNode)
-                self.rewire(newNode, nearinds)
+            if self.check_collision(new_node, self.obstacle_list):
+                near_indexes = self.find_near_nodes(new_node)
+                new_node = self.choose_parent(new_node, near_indexes)
+                if new_node:
+                    self.node_list.append(new_node)
+                    self.rewire(new_node, near_indexes)
 
             if animation and i % 5 == 0:
-                self.DrawGraph(rnd=rnd)
+                self.plot_start_goal_arrow()
+                self.draw_graph(rnd)
 
-        # generate coruse
-        lastIndex = self.get_best_last_index()
-        #  print(lastIndex)
+            if (not search_until_max_iter) and new_node:  # check reaching the goal
+                last_index = self.search_best_goal_node()
+                if last_index:
+                    return self.generate_final_course(last_index)
 
-        if lastIndex is None:
-            return None
+        print("reached max iteration")
 
-        path = self.gen_final_course(lastIndex)
-        return path
+        last_index = self.search_best_goal_node()
+        if last_index:
+            return self.generate_final_course(last_index)
+        else:
+            print("Cannot find path")
 
-    def choose_parent(self, newNode, nearinds):
-        if not nearinds:
-            return newNode
+        return None
 
-        dlist = []
-        for i in nearinds:
-            tNode = self.steer(newNode, i)
-            if self.CollisionCheck(tNode, self.obstacleList):
-                dlist.append(tNode.cost)
-            else:
-                dlist.append(float("inf"))
+    def draw_graph(self, rnd=None):
+        plt.clf()
+        if rnd is not None:
+            plt.plot(rnd.x, rnd.y, "^k")
+        for node in self.node_list:
+            if node.parent:
+                plt.plot(node.path_x, node.path_y, "-g")
 
-        mincost = min(dlist)
-        minind = nearinds[dlist.index(mincost)]
+        for (ox, oy, size) in self.obstacle_list:
+            plt.plot(ox, oy, "ok", ms=30 * size)
 
-        if mincost == float("inf"):
-            print("mincost is inf")
-            return newNode
+        plt.plot(self.start.x, self.start.y, "xr")
+        plt.plot(self.end.x, self.end.y, "xr")
+        plt.axis([-2, 15, -2, 15])
+        plt.grid(True)
+        self.plot_start_goal_arrow()
+        plt.pause(0.01)
 
-        newNode = self.steer(newNode, minind)
+    def plot_start_goal_arrow(self):
+        dubins_path_planning.plot_arrow(
+            self.start.x, self.start.y, self.start.yaw)
+        dubins_path_planning.plot_arrow(
+            self.end.x, self.end.y, self.end.yaw)
 
-        return newNode
+    def steer(self, from_node, to_node):
 
-    def pi_2_pi(self, angle):
-        return (angle + math.pi) % (2 * math.pi) - math.pi
+        px, py, pyaw, mode, course_length = dubins_path_planning.dubins_path_planning(
+            from_node.x, from_node.y, from_node.yaw,
+            to_node.x, to_node.y, to_node.yaw, self.curvature)
 
-    def steer(self, rnd, nind):
-        #  print(rnd)
-        curvature = 1.0
+        new_node = copy.deepcopy(from_node)
+        new_node.x = px[-1]
+        new_node.y = py[-1]
+        new_node.yaw = pyaw[-1]
 
-        nearestNode = self.nodeList[nind]
+        new_node.path_x = px
+        new_node.path_y = py
+        new_node.path_yaw = pyaw
+        new_node.cost += course_length
+        new_node.parent = from_node
 
-        px, py, pyaw, mode, clen = dubins_path_planning.dubins_path_planning(
-            nearestNode.x, nearestNode.y, nearestNode.yaw, rnd.x, rnd.y, rnd.yaw, curvature)
+        return new_node
 
-        newNode = copy.deepcopy(nearestNode)
-        newNode.x = px[-1]
-        newNode.y = py[-1]
-        newNode.yaw = pyaw[-1]
+    def calc_new_cost(self, from_node, to_node):
 
-        newNode.path_x = px
-        newNode.path_y = py
-        newNode.path_yaw = pyaw
-        newNode.cost += clen
-        newNode.parent = nind
+        _, _, _, _, course_length = dubins_path_planning.dubins_path_planning(
+            from_node.x, from_node.y, from_node.yaw,
+            to_node.x, to_node.y, to_node.yaw, self.curvature)
 
-        return newNode
+        return from_node.cost + course_length
 
-    def get_random_point(self):
+    def get_random_node(self):
 
-        if random.randint(0, 100) > self.goalSampleRate:
-            rnd = [random.uniform(self.minrand, self.maxrand),
-                   random.uniform(self.minrand, self.maxrand),
-                   random.uniform(-math.pi, math.pi)
-                   ]
+        if random.randint(0, 100) > self.goal_sample_rate:
+            rnd = self.Node(random.uniform(self.min_rand, self.max_rand),
+                            random.uniform(self.min_rand, self.max_rand),
+                            random.uniform(-math.pi, math.pi)
+                            )
         else:  # goal point sampling
-            rnd = [self.end.x, self.end.y, self.end.yaw]
-
-        node = Node(rnd[0], rnd[1], rnd[2])
+            rnd = self.Node(self.end.x, self.end.y, self.end.yaw)
 
-        return node
+        return rnd
 
-    def get_best_last_index(self):
-        #  print("get_best_last_index")
+    def search_best_goal_node(self):
 
-        YAWTH = np.deg2rad(1.0)
-        XYTH = 0.5
-
-        goalinds = []
-        for (i, node) in enumerate(self.nodeList):
-            if self.calc_dist_to_goal(node.x, node.y) <= XYTH:
-                goalinds.append(i)
+        goal_indexes = []
+        for (i, node) in enumerate(self.node_list):
+            if self.calc_dist_to_goal(node.x, node.y) <= self.goal_xy_th:
+                goal_indexes.append(i)
 
         # angle check
-        fgoalinds = []
-        for i in goalinds:
-            if abs(self.nodeList[i].yaw - self.end.yaw) <= YAWTH:
-                fgoalinds.append(i)
+        final_goal_indexes = []
+        for i in goal_indexes:
+            if abs(self.node_list[i].yaw - self.end.yaw) <= self.goal_yaw_th:
+                final_goal_indexes.append(i)
 
-        if not fgoalinds:
+        if not final_goal_indexes:
             return None
 
-        mincost = min([self.nodeList[i].cost for i in fgoalinds])
-        for i in fgoalinds:
-            if self.nodeList[i].cost == mincost:
+        min_cost = min([self.node_list[i].cost for i in final_goal_indexes])
+        for i in final_goal_indexes:
+            if self.node_list[i].cost == min_cost:
                 return i
 
         return None
 
-    def gen_final_course(self, goalind):
+    def generate_final_course(self, goal_index):
+        print("final")
         path = [[self.end.x, self.end.y]]
-        while self.nodeList[goalind].parent is not None:
-            node = self.nodeList[goalind]
+        node = self.node_list[goal_index]
+        while node.parent:
             for (ix, iy) in zip(reversed(node.path_x), reversed(node.path_y)):
                 path.append([ix, iy])
-            #  path.append([node.x, node.y])
-            goalind = node.parent
+            node = node.parent
         path.append([self.start.x, self.start.y])
         return path
 
-    def calc_dist_to_goal(self, x, y):
-        return np.linalg.norm([x - self.end.x, y - self.end.y])
-
-    def find_near_nodes(self, newNode):
-        nnode = len(self.nodeList)
-        r = 50.0 * math.sqrt((math.log(nnode) / nnode))
-        #  r = self.expandDis * 5.0
-        dlist = [(node.x - newNode.x) ** 2 +
-                 (node.y - newNode.y) ** 2 +
-                 (node.yaw - newNode.yaw) ** 2
-                 for node in self.nodeList]
-        nearinds = [dlist.index(i) for i in dlist if i <= r ** 2]
-        return nearinds
-
-    def rewire(self, newNode, nearinds):
-
-        nnode = len(self.nodeList)
-
-        for i in nearinds:
-            nearNode = self.nodeList[i]
-            tNode = self.steer(nearNode, nnode - 1)
-
-            obstacleOK = self.CollisionCheck(tNode, self.obstacleList)
-            imporveCost = nearNode.cost > tNode.cost
-
-            if obstacleOK and imporveCost:
-                #  print("rewire")
-                self.nodeList[i] = tNode
-
-    def DrawGraph(self, rnd=None):  # pragma: no cover
-        """
-        Draw Graph
-        """
-        plt.clf()
-        if rnd is not None:
-            plt.plot(rnd.x, rnd.y, "^k")
-        for node in self.nodeList:
-            if node.parent is not None:
-                plt.plot(node.path_x, node.path_y, "-g")
-                #  plt.plot([node.x, self.nodeList[node.parent].x], [
-                #  node.y, self.nodeList[node.parent].y], "-g")
-
-        for (ox, oy, size) in self.obstacleList:
-            plt.plot(ox, oy, "ok", ms=30 * size)
-
-        dubins_path_planning.plot_arrow(
-            self.start.x, self.start.y, self.start.yaw)
-        dubins_path_planning.plot_arrow(
-            self.end.x, self.end.y, self.end.yaw)
-
-        plt.axis([-2, 15, -2, 15])
-        plt.grid(True)
-        plt.pause(0.01)
-
-        #  plt.show()
-        #  input()
-
-    def GetNearestListIndex(self, nodeList, rnd):
-        dlist = [(node.x - rnd.x) ** 2 +
-                 (node.y - rnd.y) ** 2 +
-                 (node.yaw - rnd.yaw) ** 2 for node in nodeList]
-        minind = dlist.index(min(dlist))
-
-        return minind
-
-    def CollisionCheck(self, node, obstacleList):
-
-        for (ox, oy, size) in obstacleList:
-            for (ix, iy) in zip(node.path_x, node.path_y):
-                dx = ox - ix
-                dy = oy - iy
-                d = dx * dx + dy * dy
-                if d <= size ** 2:
-                    return False  # collision
-
-        return True  # safe
-
-
-class Node():
-    """
-    RRT Node
-    """
-
-    def __init__(self, x, y, yaw):
-        self.x = x
-        self.y = y
-        self.yaw = yaw
-        self.path_x = []
-        self.path_y = []
-        self.path_yaw = []
-        self.cost = 0.0
-        self.parent = None
-
 
 def main():
     print("Start rrt star with dubins planning")
@@ -284,12 +225,12 @@ def main():
     start = [0.0, 0.0, np.deg2rad(0.0)]
     goal = [10.0, 10.0, np.deg2rad(0.0)]
 
-    rrt = RRT(start, goal, randArea=[-2.0, 15.0], obstacleList=obstacleList)
-    path = rrt.Planning(animation=show_animation)
+    rrtstar_dubins = RRTStarDubins(start, goal, rand_area=[-2.0, 15.0], obstacle_list=obstacleList)
+    path = rrtstar_dubins.planning(animation=show_animation)
 
     # Draw final path
     if show_animation:  # pragma: no cover
-        rrt.DrawGraph()
+        rrtstar_dubins.draw_graph()
         plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
         plt.grid(True)
         plt.pause(0.001)

From 0937486803ba629c9bc8db99455ef930ea00a612 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 27 Jul 2019 15:56:16 +0900
Subject: [PATCH 0140/1080] fix rrt star reeds shepp rewire and test

---
 PathPlanning/RRTDubins/rrt_dubins.py          |   2 +-
 PathPlanning/RRTStar/rrt_star.py              |   4 +-
 .../RRTStarReedsShepp/rrt_star_reeds_shepp.py | 362 +++++++-----------
 .../reeds_shepp_path_planning.py              |   6 +-
 tests/test_dubins_path_planning.py            |   8 +-
 5 files changed, 158 insertions(+), 224 deletions(-)

diff --git a/PathPlanning/RRTDubins/rrt_dubins.py b/PathPlanning/RRTDubins/rrt_dubins.py
index e87e3aa0a6..78ce9611b3 100644
--- a/PathPlanning/RRTDubins/rrt_dubins.py
+++ b/PathPlanning/RRTDubins/rrt_dubins.py
@@ -1,5 +1,5 @@
 """
-Path planning Sample Code with RRT for car like robot.
+Path planning Sample Code with RRT with Dubins path
 
 author: AtsushiSakai(@Atsushi_twi)
 
diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py
index 19e6812686..c92f435080 100644
--- a/PathPlanning/RRTStar/rrt_star.py
+++ b/PathPlanning/RRTStar/rrt_star.py
@@ -100,7 +100,7 @@ def choose_parent(self, new_node, near_inds):
         for i in near_inds:
             near_node = self.node_list[i]
             t_node = self.steer(near_node, new_node)
-            if self.check_collision(t_node, self.obstacle_list):
+            if t_node and self.check_collision(t_node, self.obstacle_list):
                 costs.append(self.calc_new_cost(near_node, new_node))
             else:
                 costs.append(float("inf"))  # the cost of collision node
@@ -143,6 +143,8 @@ def rewire(self, new_node, near_inds):
         for i in near_inds:
             near_node = self.node_list[i]
             edge_node = self.steer(new_node, near_node)
+            if not edge_node:
+                continue
             edge_node.cost = self.calc_new_cost(new_node, near_node)
 
             no_collision = self.check_collision(edge_node, self.obstacle_list)
diff --git a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
index aa03418f59..9082117286 100644
--- a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
+++ b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
@@ -1,289 +1,219 @@
 """
 
-Path Planning Sample Code with RRT for car like robot.
+Path planning Sample Code with RRT with Reeds-Shepp path
 
 author: AtsushiSakai(@Atsushi_twi)
 
 """
-import matplotlib.pyplot as plt
-import numpy as np
 import copy
 import math
+import os
 import random
 import sys
-import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
                 "/../ReedsSheppPath/")
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../RRTStar/")
 
 try:
     import reeds_shepp_path_planning
-except:
+    from rrt_star import RRTStar
+except ImportError:
     raise
 
 show_animation = True
-STEP_SIZE = 0.1
-curvature = 1.0
 
 
-class RRT():
+class RRTStarReedsShepp(RRTStar):
     """
-    Class for RRT Planning
+    Class for RRT star planning with Reeds Shepp path
     """
 
-    def __init__(self, start, goal, obstacleList, randArea,
-                 goalSampleRate=10, maxIter=400):
+    class Node(RRTStar.Node):
+        """
+        RRT Node
+        """
+
+        def __init__(self, x, y, yaw):
+            super().__init__(x, y)
+            self.yaw = yaw
+            self.path_yaw = []
+
+    def __init__(self, start, goal, obstacle_list, rand_area,
+                 goal_sample_rate=10,
+                 max_iter=200,
+                 connect_circle_dist=50.0
+                 ):
         """
         Setting Parameter
 
         start:Start Position [x,y]
         goal:Goal Position [x,y]
         obstacleList:obstacle Positions [[x,y,size],...]
-        randArea:Ramdom Samping Area [min,max]
+        randArea:Random Sampling Area [min,max]
 
         """
-        self.start = Node(start[0], start[1], start[2])
-        self.end = Node(goal[0], goal[1], goal[2])
-        self.minrand = randArea[0]
-        self.maxrand = randArea[1]
-        self.goalSampleRate = goalSampleRate
-        self.maxIter = maxIter
-        self.obstacleList = obstacleList
-
-    def Planning(self, animation=True):
+        self.start = self.Node(start[0], start[1], start[2])
+        self.end = self.Node(goal[0], goal[1], goal[2])
+        self.min_rand = rand_area[0]
+        self.max_rand = rand_area[1]
+        self.goal_sample_rate = goal_sample_rate
+        self.max_iter = max_iter
+        self.obstacle_list = obstacle_list
+        self.connect_circle_dist = connect_circle_dist
+
+        self.curvature = 1.0
+        self.goal_yaw_th = np.deg2rad(1.0)
+        self.goal_xy_th = 0.5
+
+    def planning(self, animation=True, search_until_max_iter=True):
         """
-        Pathplanning
+        RRT Star planning
 
         animation: flag for animation on or off
         """
 
-        self.nodeList = [self.start]
-        for i in range(self.maxIter):
-            rnd = self.get_random_point()
-            nind = self.GetNearestListIndex(self.nodeList, rnd)
-
-            newNode = self.steer(rnd, nind)
-            if newNode is None:
-                continue
+        self.node_list = [self.start]
+        for i in range(self.max_iter):
+            print("Iter:", i, ", number of nodes:", len(self.node_list))
+            rnd = self.get_random_node()
+            nearest_ind = self.get_nearest_list_index(self.node_list, rnd)
+            new_node = self.steer(self.node_list[nearest_ind], rnd)
 
-            if self.CollisionCheck(newNode, self.obstacleList):
-                nearinds = self.find_near_nodes(newNode)
-                newNode = self.choose_parent(newNode, nearinds)
-                if newNode is None:
-                    continue
-                self.nodeList.append(newNode)
-                self.rewire(newNode, nearinds)
+            if self.check_collision(new_node, self.obstacle_list):
+                near_indexes = self.find_near_nodes(new_node)
+                new_node = self.choose_parent(new_node, near_indexes)
+                if new_node:
+                    self.node_list.append(new_node)
+                    self.rewire(new_node, near_indexes)
 
             if animation and i % 5 == 0:
-                self.DrawGraph(rnd=rnd)
-
-        # generate coruse
-        lastIndex = self.get_best_last_index()
-        if lastIndex is None:
-            return None
-        path = self.gen_final_course(lastIndex)
-        return path
-
-    def choose_parent(self, newNode, nearinds):
-        if not nearinds:
-            return newNode
+                self.plot_start_goal_arrow()
+                self.draw_graph(rnd)
 
-        dlist = []
-        for i in nearinds:
-            tNode = self.steer(newNode, i)
-            if tNode is None:
-                continue
+            if (not search_until_max_iter) and new_node:  # check reaching the goal
+                last_index = self.search_best_goal_node()
+                if last_index:
+                    return self.generate_final_course(last_index)
 
-            if self.CollisionCheck(tNode, self.obstacleList):
-                dlist.append(tNode.cost)
-            else:
-                dlist.append(float("inf"))
+        print("reached max iteration")
 
-        mincost = min(dlist)
-        minind = nearinds[dlist.index(mincost)]
+        last_index = self.search_best_goal_node()
+        if last_index:
+            return self.generate_final_course(last_index)
+        else:
+            print("Cannot find path")
 
-        if mincost == float("inf"):
-            print("mincost is inf")
-            return newNode
+        return None
 
-        newNode = self.steer(newNode, minind)
+    def draw_graph(self, rnd=None):
+        plt.clf()
+        if rnd is not None:
+            plt.plot(rnd.x, rnd.y, "^k")
+        for node in self.node_list:
+            if node.parent:
+                plt.plot(node.path_x, node.path_y, "-g")
 
-        return newNode
+        for (ox, oy, size) in self.obstacle_list:
+            plt.plot(ox, oy, "ok", ms=30 * size)
 
-    def pi_2_pi(self, angle):
-        return (angle + math.pi) % (2 * math.pi) - math.pi
+        plt.plot(self.start.x, self.start.y, "xr")
+        plt.plot(self.end.x, self.end.y, "xr")
+        plt.axis([-2, 15, -2, 15])
+        plt.grid(True)
+        self.plot_start_goal_arrow()
+        plt.pause(0.01)
 
-    def steer(self, rnd, nind):
+    def plot_start_goal_arrow(self):
+        reeds_shepp_path_planning.plot_arrow(
+            self.start.x, self.start.y, self.start.yaw)
+        reeds_shepp_path_planning.plot_arrow(
+            self.end.x, self.end.y, self.end.yaw)
 
-        nearestNode = self.nodeList[nind]
+    def steer(self, from_node, to_node):
 
-        px, py, pyaw, mode, clen = reeds_shepp_path_planning.reeds_shepp_path_planning(
-            nearestNode.x, nearestNode.y, nearestNode.yaw, rnd.x, rnd.y, rnd.yaw, curvature, STEP_SIZE)
+        px, py, pyaw, mode, course_lengths = reeds_shepp_path_planning.reeds_shepp_path_planning(
+            from_node.x, from_node.y, from_node.yaw,
+            to_node.x, to_node.y, to_node.yaw, self.curvature)
 
-        if px is None:
+        if not px:
             return None
 
-        newNode = copy.deepcopy(nearestNode)
-        newNode.x = px[-1]
-        newNode.y = py[-1]
-        newNode.yaw = pyaw[-1]
+        new_node = copy.deepcopy(from_node)
+        new_node.x = px[-1]
+        new_node.y = py[-1]
+        new_node.yaw = pyaw[-1]
 
-        newNode.path_x = px
-        newNode.path_y = py
-        newNode.path_yaw = pyaw
-        newNode.cost += sum([abs(c) for c in clen])
-        newNode.parent = nind
+        new_node.path_x = px
+        new_node.path_y = py
+        new_node.path_yaw = pyaw
+        new_node.cost += sum(course_lengths)
+        new_node.parent = from_node
 
-        return newNode
+        return new_node
 
-    def get_random_point(self):
+    def calc_new_cost(self, from_node, to_node):
 
-        if random.randint(0, 100) > self.goalSampleRate:
-            rnd = [random.uniform(self.minrand, self.maxrand),
-                   random.uniform(self.minrand, self.maxrand),
-                   random.uniform(-math.pi, math.pi)
-                   ]
-        else:  # goal point sampling
-            rnd = [self.end.x, self.end.y, self.end.yaw]
+        _, _, _, _, course_lengths = reeds_shepp_path_planning.reeds_shepp_path_planning(
+            from_node.x, from_node.y, from_node.yaw,
+            to_node.x, to_node.y, to_node.yaw, self.curvature)
+        if not course_lengths:
+            return float("inf")
+
+        return from_node.cost + sum(course_lengths)
 
-        node = Node(rnd[0], rnd[1], rnd[2])
+    def get_random_node(self):
 
-        return node
+        if random.randint(0, 100) > self.goal_sample_rate:
+            rnd = self.Node(random.uniform(self.min_rand, self.max_rand),
+                            random.uniform(self.min_rand, self.max_rand),
+                            random.uniform(-math.pi, math.pi)
+                            )
+        else:  # goal point sampling
+            rnd = self.Node(self.end.x, self.end.y, self.end.yaw)
 
-    def get_best_last_index(self):
-        #  print("get_best_last_index")
+        return rnd
 
-        YAWTH = np.deg2rad(3.0)
-        XYTH = 0.5
+    def search_best_goal_node(self):
 
-        goalinds = []
-        for (i, node) in enumerate(self.nodeList):
-            if self.calc_dist_to_goal(node.x, node.y) <= XYTH:
-                goalinds.append(i)
-        #  print("OK XY TH num is")
-        #  print(len(goalinds))
+        goal_indexes = []
+        for (i, node) in enumerate(self.node_list):
+            if self.calc_dist_to_goal(node.x, node.y) <= self.goal_xy_th:
+                goal_indexes.append(i)
 
         # angle check
-        fgoalinds = []
-        for i in goalinds:
-            if abs(self.nodeList[i].yaw - self.end.yaw) <= YAWTH:
-                fgoalinds.append(i)
-        #  print("OK YAW TH num is")
-        #  print(len(fgoalinds))
-
-        if not fgoalinds:
+        final_goal_indexes = []
+        for i in goal_indexes:
+            if abs(self.node_list[i].yaw - self.end.yaw) <= self.goal_yaw_th:
+                final_goal_indexes.append(i)
+
+        if not final_goal_indexes:
             return None
 
-        mincost = min([self.nodeList[i].cost for i in fgoalinds])
-        for i in fgoalinds:
-            if self.nodeList[i].cost == mincost:
+        min_cost = min([self.node_list[i].cost for i in final_goal_indexes])
+        for i in final_goal_indexes:
+            if self.node_list[i].cost == min_cost:
                 return i
 
         return None
 
-    def gen_final_course(self, goalind):
+    def generate_final_course(self, goal_index):
+        print("final")
         path = [[self.end.x, self.end.y]]
-        while self.nodeList[goalind].parent is not None:
-            node = self.nodeList[goalind]
+        node = self.node_list[goal_index]
+        while node.parent:
             for (ix, iy) in zip(reversed(node.path_x), reversed(node.path_y)):
                 path.append([ix, iy])
-            goalind = node.parent
+            node = node.parent
         path.append([self.start.x, self.start.y])
         return path
 
-    def calc_dist_to_goal(self, x, y):
-        return np.linalg.norm([x - self.end.x, y - self.end.y])
-
-    def find_near_nodes(self, newNode):
-        nnode = len(self.nodeList)
-        r = 50.0 * math.sqrt((math.log(nnode) / nnode))
-        #  r = self.expandDis * 5.0
-        dlist = [(node.x - newNode.x) ** 2 +
-                 (node.y - newNode.y) ** 2 +
-                 (node.yaw - newNode.yaw) ** 2
-                 for node in self.nodeList]
-        nearinds = [dlist.index(i) for i in dlist if i <= r ** 2]
-        return nearinds
-
-    def rewire(self, newNode, nearinds):
-
-        nnode = len(self.nodeList)
-
-        for i in nearinds:
-            nearNode = self.nodeList[i]
-            tNode = self.steer(nearNode, nnode - 1)
-            if tNode is None:
-                continue
-
-            obstacleOK = self.CollisionCheck(tNode, self.obstacleList)
-            imporveCost = nearNode.cost > tNode.cost
-
-            if obstacleOK and imporveCost:
-                #  print("rewire")
-                self.nodeList[i] = tNode
-
-    def DrawGraph(self, rnd=None):  # pragma: no cover
-        plt.clf()
-        if rnd is not None:
-            plt.plot(rnd.x, rnd.y, "^k")
-        for node in self.nodeList:
-            if node.parent is not None:
-                plt.plot(node.path_x, node.path_y, "-g")
-                #  plt.plot([node.x, self.nodeList[node.parent].x], [
-                #  node.y, self.nodeList[node.parent].y], "-g")
-
-        for (ox, oy, size) in self.obstacleList:
-            plt.plot(ox, oy, "ok", ms=30 * size)
-
-        reeds_shepp_path_planning.plot_arrow(
-            self.start.x, self.start.y, self.start.yaw)
-        reeds_shepp_path_planning.plot_arrow(
-            self.end.x, self.end.y, self.end.yaw)
-
-        plt.axis([-2, 15, -2, 15])
-        plt.grid(True)
-        plt.pause(0.01)
-
-        #  plt.show()
-        #  input()
-
-    def GetNearestListIndex(self, nodeList, rnd):
-        dlist = [(node.x - rnd.x) ** 2 +
-                 (node.y - rnd.y) ** 2 +
-                 (node.yaw - rnd.yaw) ** 2 for node in nodeList]
-        minind = dlist.index(min(dlist))
-
-        return minind
-
-    def CollisionCheck(self, node, obstacleList):
-
-        for (ox, oy, size) in obstacleList:
-            for (ix, iy) in zip(node.path_x, node.path_y):
-                dx = ox - ix
-                dy = oy - iy
-                d = dx * dx + dy * dy
-                if d <= size ** 2:
-                    return False  # collision
-
-        return True  # safe
-
-
-class Node():
-    """
-    RRT Node
-    """
-
-    def __init__(self, x, y, yaw):
-        self.x = x
-        self.y = y
-        self.yaw = yaw
-        self.path_x = []
-        self.path_y = []
-        self.path_yaw = []
-        self.cost = 0.0
-        self.parent = None
-
 
-def main(maxIter=200):
+def main():
     print("Start " + __file__)
 
     # ====Search Path with RRT====
@@ -303,14 +233,14 @@ def main(maxIter=200):
     start = [0.0, 0.0, np.deg2rad(0.0)]
     goal = [6.0, 7.0, np.deg2rad(90.0)]
 
-    rrt = RRT(start, goal, randArea=[-2.0, 15.0],
-              obstacleList=obstacleList,
-              maxIter=maxIter)
-    path = rrt.Planning(animation=show_animation)
+    rrt_star_reeds_shepp = RRTStarReedsShepp(start, goal,
+                                             obstacleList,
+                                             [-2.0, 15.0])
+    path = rrt_star_reeds_shepp.planning(animation=show_animation)
 
     # Draw final path
-    if show_animation:  # pragma: no cover
-        rrt.DrawGraph()
+    if path and show_animation:  # pragma: no cover
+        rrt_star_reeds_shepp.draw_graph()
         plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
         plt.grid(True)
         plt.pause(0.001)
diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
index da1b4f3494..54b5adbe6d 100644
--- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
+++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
@@ -5,10 +5,10 @@
 author Atsushi Sakai(@Atsushi_twi)
 
 """
-import numpy as np
 import math
-import matplotlib.pyplot as plt
 
+import matplotlib.pyplot as plt
+import numpy as np
 
 show_animation = True
 
@@ -353,7 +353,7 @@ def calc_paths(sx, sy, syaw, gx, gy, gyaw, maxc, step_size):
 
 
 def reeds_shepp_path_planning(sx, sy, syaw,
-                              gx, gy, gyaw, maxc, step_size):
+                              gx, gy, gyaw, maxc, step_size=0.2):
 
     paths = calc_paths(sx, sy, syaw, gx, gy, gyaw, maxc, step_size)
 
diff --git a/tests/test_dubins_path_planning.py b/tests/test_dubins_path_planning.py
index d2ddb8cac1..ad140b6c3a 100644
--- a/tests/test_dubins_path_planning.py
+++ b/tests/test_dubins_path_planning.py
@@ -1,7 +1,9 @@
 from unittest import TestCase
-from PathPlanning.DubinsPath import dubins_path_planning
+
 import numpy as np
 
+from PathPlanning.DubinsPath import dubins_path_planning
+
 
 class Test(TestCase):
 
@@ -19,8 +21,8 @@ def test1(self):
         px, py, pyaw, mode, clen = dubins_path_planning.dubins_path_planning(
             start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
 
-        assert(abs(px[-1] - end_x) <= 0.1)
-        assert(abs(py[-1] - end_y) <= 0.1)
+        assert (abs(px[-1] - end_x) <= 0.5)
+        assert (abs(py[-1] - end_y) <= 0.5)
         assert(abs(pyaw[-1] - end_yaw) <= 0.1)
 
     def test2(self):

From 8061e733375a389bb49802ad7f9c172e88aa42c8 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 27 Jul 2019 16:32:40 +0900
Subject: [PATCH 0141/1080] fix LQR rrt star

---
 PathPlanning/LQRPlanner/LQRplanner.py         |  11 +-
 PathPlanning/LQRRRTStar/lqr_rrt_star.py       | 337 +++++++-----------
 .../RRTStarReedsShepp/rrt_star_reeds_shepp.py |   4 +-
 tests/test_rrt_star_reeds_shepp.py            |   6 +-
 4 files changed, 138 insertions(+), 220 deletions(-)

diff --git a/PathPlanning/LQRPlanner/LQRplanner.py b/PathPlanning/LQRPlanner/LQRplanner.py
index fef1020e5b..e726609298 100644
--- a/PathPlanning/LQRPlanner/LQRplanner.py
+++ b/PathPlanning/LQRPlanner/LQRplanner.py
@@ -6,19 +6,20 @@
 
 """
 
+import math
+import random
+
 import matplotlib.pyplot as plt
 import numpy as np
 import scipy.linalg as la
-import math
-import random
 
-show_animation = True
+SHOW_ANIMATION = True
 
 MAX_TIME = 100.0  # Maximum simulation time
 DT = 0.1  # Time tick
 
 
-def LQRplanning(sx, sy, gx, gy):
+def LQRplanning(sx, sy, gx, gy, show_animation=SHOW_ANIMATION):
 
     rx, ry = [sx], [sy]
 
@@ -129,7 +130,7 @@ def main():
 
         rx, ry = LQRplanning(sx, sy, gx, gy)
 
-        if show_animation:  # pragma: no cover
+        if SHOW_ANIMATION:  # pragma: no cover
             plt.plot(sx, sy, "or")
             plt.plot(gx, gy, "ob")
             plt.plot(rx, ry, "-r")
diff --git a/PathPlanning/LQRRRTStar/lqr_rrt_star.py b/PathPlanning/LQRRRTStar/lqr_rrt_star.py
index 0fb25fe421..4c8d33aa26 100644
--- a/PathPlanning/LQRRRTStar/lqr_rrt_star.py
+++ b/PathPlanning/LQRRRTStar/lqr_rrt_star.py
@@ -5,283 +5,200 @@
 author: AtsushiSakai(@Atsushi_twi)
 
 """
-
-import matplotlib.pyplot as plt
-import numpy as np
 import copy
 import math
+import os
 import random
 import sys
-import os
+
+import matplotlib.pyplot as plt
+import numpy as np
+
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../LQRPlanner/")
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../RRTStar/")
 
 try:
     import LQRplanner
-except:
+    from rrt_star import RRTStar
+except ImportError:
     raise
 
-
 show_animation = True
 
-LQRplanner.show_animation = False
 
-STEP_SIZE = 0.05  # step size of local path
-XYTH = 0.5  # [m] acceptance xy distance in final paths
-
-
-class RRT():
+class LQRRRTStar(RRTStar):
     """
-    Class for RRT Planning
+    Class for RRT star planning with LQR planning
     """
 
-    def __init__(self, start, goal, obstacleList, randArea,
-                 goalSampleRate=10, maxIter=200):
+    def __init__(self, start, goal, obstacle_list, rand_area,
+                 goal_sample_rate=10,
+                 max_iter=200,
+                 connect_circle_dist=50.0,
+                 step_size=0.2
+                 ):
         """
         Setting Parameter
 
         start:Start Position [x,y]
         goal:Goal Position [x,y]
         obstacleList:obstacle Positions [[x,y,size],...]
-        randArea:Ramdom Samping Area [min,max]
+        randArea:Random Sampling Area [min,max]
 
         """
-        self.start = Node(start[0], start[1])
-        self.end = Node(goal[0], goal[1])
-        self.minrand = randArea[0]
-        self.maxrand = randArea[1]
-        self.goalSampleRate = goalSampleRate
-        self.maxIter = maxIter
-        self.obstacleList = obstacleList
-
-    def planning(self, animation=True):
+        self.start = self.Node(start[0], start[1])
+        self.end = self.Node(goal[0], goal[1])
+        self.min_rand = rand_area[0]
+        self.max_rand = rand_area[1]
+        self.goal_sample_rate = goal_sample_rate
+        self.max_iter = max_iter
+        self.obstacle_list = obstacle_list
+        self.connect_circle_dist = connect_circle_dist
+
+        self.curvature = 1.0
+        self.goal_xy_th = 0.5
+        self.step_size = step_size
+
+    def planning(self, animation=True, search_until_max_iter=True):
         """
-        Pathplanning
+        RRT Star planning
 
         animation: flag for animation on or off
         """
 
-        self.nodeList = [self.start]
-        for i in range(self.maxIter):
-            rnd = self.get_random_point()
-            nind = self.get_nearest_index(self.nodeList, rnd)
-
-            newNode = self.steer(rnd, nind)
-            if newNode is None:
-                continue
+        self.node_list = [self.start]
+        for i in range(self.max_iter):
+            print("Iter:", i, ", number of nodes:", len(self.node_list))
+            rnd = self.get_random_node()
+            nearest_ind = self.get_nearest_list_index(self.node_list, rnd)
+            new_node = self.steer(self.node_list[nearest_ind], rnd)
 
-            if self.check_collision(newNode, self.obstacleList):
-                nearinds = self.find_near_nodes(newNode)
-                newNode = self.choose_parent(newNode, nearinds)
-                if newNode is None:
-                    continue
-                self.nodeList.append(newNode)
-                self.rewire(newNode, nearinds)
+            if self.check_collision(new_node, self.obstacle_list):
+                near_indexes = self.find_near_nodes(new_node)
+                new_node = self.choose_parent(new_node, near_indexes)
+                if new_node:
+                    self.node_list.append(new_node)
+                    self.rewire(new_node, near_indexes)
 
             if animation and i % 5 == 0:
-                self.draw_graph(rnd=rnd)
+                self.draw_graph(rnd)
 
-        # generate coruse
-        lastIndex = self.get_best_last_index()
-        if lastIndex is None:
-            return None
-        path = self.gen_final_course(lastIndex)
-        return path
-
-    def choose_parent(self, newNode, nearinds):
-        if not nearinds:
-            return newNode
-
-        dlist = []
-        for i in nearinds:
-            tNode = self.steer(newNode, i)
-            if tNode is None:
-                continue
-
-            if self.check_collision(tNode, self.obstacleList):
-                dlist.append(tNode.cost)
-            else:
-                dlist.append(float("inf"))
-
-        mincost = min(dlist)
-        minind = nearinds[dlist.index(mincost)]
+            if (not search_until_max_iter) and new_node:  # check reaching the goal
+                last_index = self.search_best_goal_node()
+                if last_index:
+                    return self.generate_final_course(last_index)
 
-        if mincost == float("inf"):
-            print("mincost is inf")
-            return newNode
+        print("reached max iteration")
 
-        newNode = self.steer(newNode, minind)
+        last_index = self.search_best_goal_node()
+        if last_index:
+            return self.generate_final_course(last_index)
+        else:
+            print("Cannot find path")
 
-        return newNode
-
-    def pi_2_pi(self, angle):
-        return (angle + math.pi) % (2 * math.pi) - math.pi
-
-    def sample_path(self, wx, wy, step):
+        return None
 
-        px, py, clen = [], [], []
+    def draw_graph(self, rnd=None):
+        plt.clf()
+        if rnd is not None:
+            plt.plot(rnd.x, rnd.y, "^k")
+        for node in self.node_list:
+            if node.parent:
+                plt.plot(node.path_x, node.path_y, "-g")
 
-        for i in range(len(wx) - 1):
+        for (ox, oy, size) in self.obstacle_list:
+            plt.plot(ox, oy, "ok", ms=30 * size)
 
-            for t in np.arange(0.0, 1.0, step):
-                px.append(t * wx[i + 1] + (1.0 - t) * wx[i])
-                py.append(t * wy[i + 1] + (1.0 - t) * wy[i])
+        plt.plot(self.start.x, self.start.y, "xr")
+        plt.plot(self.end.x, self.end.y, "xr")
+        plt.axis([-2, 15, -2, 15])
+        plt.grid(True)
+        plt.pause(0.01)
 
-        dx = np.diff(px)
-        dy = np.diff(py)
+    def search_best_goal_node(self):
+        dist_to_goal_list = [self.calc_dist_to_goal(n.x, n.y) for n in self.node_list]
+        goal_inds = [dist_to_goal_list.index(i) for i in dist_to_goal_list if i <= self.goal_xy_th]
 
-        clen = [math.sqrt(idx**2 + idy**2) for (idx, idy) in zip(dx, dy)]
+        if not goal_inds:
+            return None
 
-        return px, py, clen
+        min_cost = min([self.node_list[i].cost for i in goal_inds])
+        for i in goal_inds:
+            if self.node_list[i].cost == min_cost:
+                return i
 
-    def steer(self, rnd, nind):
+        return None
 
-        nearestNode = self.nodeList[nind]
+    def calc_new_cost(self, from_node, to_node):
 
         wx, wy = LQRplanner.LQRplanning(
-            nearestNode.x, nearestNode.y, rnd.x, rnd.y)
-
-        px, py, clen = self.sample_path(wx, wy, STEP_SIZE)
-
-        if px is None:
-            return None
+            from_node.x, from_node.y, to_node.x, to_node.y, show_animation=False)
 
-        newNode = copy.deepcopy(nearestNode)
-        newNode.x = px[-1]
-        newNode.y = py[-1]
+        px, py, course_lengths = self.sample_path(wx, wy, self.step_size)
 
-        newNode.path_x = px
-        newNode.path_y = py
-        newNode.cost += sum([abs(c) for c in clen])
-        newNode.parent = nind
+        if not course_lengths:
+            return float("inf")
 
-        return newNode
+        return from_node.cost + sum(course_lengths)
 
-    def get_random_point(self):
+    def get_random_node(self):
 
-        if random.randint(0, 100) > self.goalSampleRate:
-            rnd = [random.uniform(self.minrand, self.maxrand),
-                   random.uniform(self.minrand, self.maxrand),
-                   random.uniform(-math.pi, math.pi)
-                   ]
+        if random.randint(0, 100) > self.goal_sample_rate:
+            rnd = self.Node(random.uniform(self.min_rand, self.max_rand),
+                            random.uniform(self.min_rand, self.max_rand)
+                            )
         else:  # goal point sampling
-            rnd = [self.end.x, self.end.y]
-
-        node = Node(rnd[0], rnd[1])
-
-        return node
-
-    def get_best_last_index(self):
-        #  print("get_best_last_index")
+            rnd = self.Node(self.end.x, self.end.y)
 
-        goalinds = []
-        for (i, node) in enumerate(self.nodeList):
-            if self.calc_dist_to_goal(node.x, node.y) <= XYTH:
-                goalinds.append(i)
-
-        if not goalinds:
-            return None
-
-        mincost = min([self.nodeList[i].cost for i in goalinds])
-        for i in goalinds:
-            if self.nodeList[i].cost == mincost:
-                return i
-
-        return None
+        return rnd
 
-    def gen_final_course(self, goalind):
+    def generate_final_course(self, goal_index):
+        print("final")
         path = [[self.end.x, self.end.y]]
-        while self.nodeList[goalind].parent is not None:
-            node = self.nodeList[goalind]
+        node = self.node_list[goal_index]
+        while node.parent:
             for (ix, iy) in zip(reversed(node.path_x), reversed(node.path_y)):
                 path.append([ix, iy])
-            goalind = node.parent
+            node = node.parent
         path.append([self.start.x, self.start.y])
         return path
 
-    def calc_dist_to_goal(self, x, y):
-        return np.linalg.norm([x - self.end.x, y - self.end.y])
-
-    def find_near_nodes(self, newNode):
-        nnode = len(self.nodeList)
-        r = 50.0 * math.sqrt((math.log(nnode) / nnode))
-        dlist = [(node.x - newNode.x) ** 2
-                 + (node.y - newNode.y) ** 2
-                 for node in self.nodeList]
-        nearinds = [dlist.index(i) for i in dlist if i <= r ** 2]
-        return nearinds
-
-    def rewire(self, newNode, nearinds):
-
-        nnode = len(self.nodeList)
-
-        for i in nearinds:
-            nearNode = self.nodeList[i]
-            tNode = self.steer(nearNode, nnode - 1)
-            if tNode is None:
-                continue
-
-            obstacleOK = self.check_collision(tNode, self.obstacleList)
-            imporveCost = nearNode.cost > tNode.cost
-
-            if obstacleOK and imporveCost:
-                #  print("rewire")
-                self.nodeList[i] = tNode
-
-    def draw_graph(self, rnd=None):
-        plt.clf()
-        if rnd is not None:
-            plt.plot(rnd.x, rnd.y, "^k")
-
-        for node in self.nodeList:
-            if node.parent is not None:
-                plt.plot(node.path_x, node.path_y, "-g")
+    def sample_path(self, wx, wy, step):
 
-        for (ox, oy, size) in self.obstacleList:
-            plt.plot(ox, oy, "ok", ms=30 * size)
+        px, py, clen = [], [], []
 
-        plt.plot(self.start.x, self.start.y, "or")
-        plt.plot(self.end.x, self.end.y, "or")
+        for i in range(len(wx) - 1):
 
-        plt.axis([-2, 15, -2, 15])
-        plt.grid(True)
-        plt.pause(0.01)
+            for t in np.arange(0.0, 1.0, step):
+                px.append(t * wx[i + 1] + (1.0 - t) * wx[i])
+                py.append(t * wy[i + 1] + (1.0 - t) * wy[i])
 
-    def get_nearest_index(self, nodeList, rnd):
-        dlist = [(node.x - rnd.x) ** 2
-                 + (node.y - rnd.y) ** 2
-                 for node in nodeList]
-        minind = dlist.index(min(dlist))
+        dx = np.diff(px)
+        dy = np.diff(py)
 
-        return minind
+        clen = [math.sqrt(idx ** 2 + idy ** 2) for (idx, idy) in zip(dx, dy)]
 
-    def check_collision(self, node, obstacleList):
+        return px, py, clen
 
-        px = np.array(node.path_x)
-        py = np.array(node.path_y)
+    def steer(self, from_node, to_node):
 
-        for (ox, oy, size) in obstacleList:
-            dx = ox - px
-            dy = oy - py
-            d = dx ** 2 + dy ** 2
-            dmin = min(d)
-            if dmin <= size ** 2:
-                return False  # collision
+        wx, wy = LQRplanner.LQRplanning(
+            from_node.x, from_node.y, to_node.x, to_node.y, show_animation=False)
 
-        return True  # safe
+        px, py, course_lens = self.sample_path(wx, wy, self.step_size)
 
+        if px is None:
+            return None
 
-class Node():
-    """
-    RRT Node
-    """
+        newNode = copy.deepcopy(from_node)
+        newNode.x = px[-1]
+        newNode.y = py[-1]
+        newNode.path_x = px
+        newNode.path_y = py
+        newNode.cost += sum([abs(c) for c in course_lens])
+        newNode.parent = from_node
 
-    def __init__(self, x, y):
-        self.x = x
-        self.y = y
-        self.path_x = []
-        self.path_y = []
-        self.cost = 0.0
-        self.parent = None
+        return newNode
 
 
 def main(maxIter=200):
@@ -301,14 +218,14 @@ def main(maxIter=200):
     start = [0.0, 0.0]
     goal = [6.0, 7.0]
 
-    rrt = RRT(start, goal, randArea=[-2.0, 15.0],
-              obstacleList=obstacleList,
-              maxIter=maxIter)
-    path = rrt.planning(animation=show_animation)
+    lqr_rrt_star = LQRRRTStar(start, goal,
+                              obstacleList,
+                              [-2.0, 15.0])
+    path = lqr_rrt_star.planning(animation=show_animation)
 
     # Draw final path
     if show_animation:  # pragma: no cover
-        rrt.draw_graph()
+        lqr_rrt_star.draw_graph()
         plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
         plt.grid(True)
         plt.pause(0.001)
diff --git a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
index 9082117286..ea31337cbc 100644
--- a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
+++ b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
@@ -213,7 +213,7 @@ def generate_final_course(self, goal_index):
         return path
 
 
-def main():
+def main(max_iter=200):
     print("Start " + __file__)
 
     # ====Search Path with RRT====
@@ -235,7 +235,7 @@ def main():
 
     rrt_star_reeds_shepp = RRTStarReedsShepp(start, goal,
                                              obstacleList,
-                                             [-2.0, 15.0])
+                                             [-2.0, 15.0], max_iter=max_iter)
     path = rrt_star_reeds_shepp.planning(animation=show_animation)
 
     # Draw final path
diff --git a/tests/test_rrt_star_reeds_shepp.py b/tests/test_rrt_star_reeds_shepp.py
index 4a1fa132fe..f7eeae3e6b 100644
--- a/tests/test_rrt_star_reeds_shepp.py
+++ b/tests/test_rrt_star_reeds_shepp.py
@@ -1,7 +1,7 @@
+import os
+import sys
 from unittest import TestCase
 
-import sys
-import os
 sys.path.append(os.path.dirname(os.path.abspath(__file__))
                 + "/../PathPlanning/RRTStarReedsShepp/")
 sys.path.append(os.path.dirname(os.path.abspath(__file__))
@@ -20,7 +20,7 @@ class Test(TestCase):
 
     def test1(self):
         m.show_animation = False
-        m.main(maxIter=5)
+        m.main(max_iter=5)
 
 
 if __name__ == '__main__':  # pragma: no cover

From 86b80d55c33d1c5b33880e67d71141393b78e681 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 28 Jul 2019 15:04:15 +0900
Subject: [PATCH 0142/1080] clean up lqr planner

---
 PathPlanning/LQRPlanner/LQRplanner.py   | 141 ++++++++++++------------
 PathPlanning/LQRRRTStar/lqr_rrt_star.py |   8 +-
 2 files changed, 76 insertions(+), 73 deletions(-)

diff --git a/PathPlanning/LQRPlanner/LQRplanner.py b/PathPlanning/LQRPlanner/LQRplanner.py
index e726609298..e2000a957c 100644
--- a/PathPlanning/LQRPlanner/LQRplanner.py
+++ b/PathPlanning/LQRPlanner/LQRplanner.py
@@ -15,105 +15,104 @@
 
 SHOW_ANIMATION = True
 
-MAX_TIME = 100.0  # Maximum simulation time
-DT = 0.1  # Time tick
 
+class LQRPlanner:
 
-def LQRplanning(sx, sy, gx, gy, show_animation=SHOW_ANIMATION):
+    def __init__(self):
+        self.MAX_TIME = 100.0  # Maximum simulation time
+        self.DT = 0.1  # Time tick
+        self.GOAL_DIST = 0.1
+        self.MAX_ITER = 150
+        self.EPS = 0.01
 
-    rx, ry = [sx], [sy]
+    def lqr_planning(self, sx, sy, gx, gy, show_animation=SHOW_ANIMATION):
 
-    x = np.array([sx - gx, sy - gy]).reshape(2, 1)  # State vector
+        rx, ry = [sx], [sy]
 
-    # Linear system model
-    A, B = get_system_model()
+        x = np.array([sx - gx, sy - gy]).reshape(2, 1)  # State vector
 
-    found_path = False
+        # Linear system model
+        A, B = self.get_system_model()
 
-    time = 0.0
-    while time <= MAX_TIME:
-        time += DT
+        found_path = False
 
-        u = LQR_control(A, B, x)
+        time = 0.0
+        while time <= self.MAX_TIME:
+            time += self.DT
 
-        x = A @ x + B @ u
+            u = self.lqr_control(A, B, x)
 
-        rx.append(x[0, 0] + gx)
-        ry.append(x[1, 0] + gy)
+            x = A @ x + B @ u
 
-        d = math.sqrt((gx - rx[-1])**2 + (gy - ry[-1])**2)
-        if d <= 0.1:
-            #  print("Goal!!")
-            found_path = True
-            break
+            rx.append(x[0, 0] + gx)
+            ry.append(x[1, 0] + gy)
 
-        # animation
-        if show_animation:  # pragma: no cover
-            plt.plot(sx, sy, "or")
-            plt.plot(gx, gy, "ob")
-            plt.plot(rx, ry, "-r")
-            plt.axis("equal")
-            plt.pause(1.0)
+            d = math.sqrt((gx - rx[-1]) ** 2 + (gy - ry[-1]) ** 2)
+            if d <= self.GOAL_DIST:
+                found_path = True
+                break
 
-    if not found_path:
-        print("Cannot found path")
-        return [], []
+            # animation
+            if show_animation:  # pragma: no cover
+                plt.plot(sx, sy, "or")
+                plt.plot(gx, gy, "ob")
+                plt.plot(rx, ry, "-r")
+                plt.axis("equal")
+                plt.pause(1.0)
 
-    return rx, ry
+        if not found_path:
+            print("Cannot found path")
+            return [], []
 
+        return rx, ry
 
-def solve_DARE(A, B, Q, R):
-    """
-    solve a discrete time_Algebraic Riccati equation (DARE)
-    """
-    X = Q
-    maxiter = 150
-    eps = 0.01
+    def solve_dare(self, A, B, Q, R):
+        """
+        solve a discrete time_Algebraic Riccati equation (DARE)
+        """
+        X, Xn = Q, Q
 
-    for i in range(maxiter):
-        Xn = A.T * X * A - A.T * X * B * \
-            la.inv(R + B.T * X * B) * B.T * X * A + Q
-        if (abs(Xn - X)).max() < eps:
-            break
-        X = Xn
+        for i in range(self.MAX_ITER):
+            Xn = A.T * X * A - A.T * X * B * \
+                 la.inv(R + B.T * X * B) * B.T * X * A + Q
+            if (abs(Xn - X)).max() < self.EPS:
+                break
+            X = Xn
 
-    return Xn
+        return Xn
 
+    def dlqr(self, A, B, Q, R):
+        """Solve the discrete time lqr controller.
+        x[k+1] = A x[k] + B u[k]
+        cost = sum x[k].T*Q*x[k] + u[k].T*R*u[k]
+        # ref Bertsekas, p.151
+        """
 
-def dlqr(A, B, Q, R):
-    """Solve the discrete time lqr controller.
-    x[k+1] = A x[k] + B u[k]
-    cost = sum x[k].T*Q*x[k] + u[k].T*R*u[k]
-    # ref Bertsekas, p.151
-    """
+        # first, try to solve the ricatti equation
+        X = self.solve_dare(A, B, Q, R)
 
-    # first, try to solve the ricatti equation
-    X = solve_DARE(A, B, Q, R)
+        # compute the LQR gain
+        K = la.inv(B.T @ X @ B + R) @ (B.T @ X @ A)
 
-    # compute the LQR gain
-    K = la.inv(B.T @ X @ B + R) @ (B.T @ X @ A)
+        eigValues = la.eigvals(A - B @ K)
 
-    eigVals, eigVecs = la.eig(A - B @ K)
+        return K, X, eigValues
 
-    return K, X, eigVals
+    def get_system_model(self):
 
+        A = np.array([[self.DT, 1.0],
+                      [0.0, self.DT]])
+        B = np.array([0.0, 1.0]).reshape(2, 1)
 
-def get_system_model():
+        return A, B
 
-    A = np.array([[DT, 1.0],
-                  [0.0, DT]])
-    B = np.array([0.0, 1.0]).reshape(2, 1)
+    def lqr_control(self, A, B, x):
 
-    return A, B
+        Kopt, X, ev = self.dlqr(A, B, np.eye(2), np.eye(1))
 
+        u = -Kopt @ x
 
-def LQR_control(A, B, x):
-
-    Kopt, X, ev = dlqr(A, B, np.eye(2), np.eye(1))
-
-    u = -Kopt @ x
-
-    return u
+        return u
 
 
 def main():
@@ -122,13 +121,15 @@ def main():
     ntest = 10  # number of goal
     area = 100.0  # sampling area
 
+    lqr_planner = LQRPlanner()
+
     for i in range(ntest):
         sx = 6.0
         sy = 6.0
         gx = random.uniform(-area, area)
         gy = random.uniform(-area, area)
 
-        rx, ry = LQRplanning(sx, sy, gx, gy)
+        rx, ry = lqr_planner.lqr_planning(sx, sy, gx, gy)
 
         if SHOW_ANIMATION:  # pragma: no cover
             plt.plot(sx, sy, "or")
diff --git a/PathPlanning/LQRRRTStar/lqr_rrt_star.py b/PathPlanning/LQRRRTStar/lqr_rrt_star.py
index 4c8d33aa26..c6cd760eda 100644
--- a/PathPlanning/LQRRRTStar/lqr_rrt_star.py
+++ b/PathPlanning/LQRRRTStar/lqr_rrt_star.py
@@ -18,7 +18,7 @@
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../RRTStar/")
 
 try:
-    import LQRplanner
+    from LQRplanner import LQRPlanner
     from rrt_star import RRTStar
 except ImportError:
     raise
@@ -59,6 +59,8 @@ def __init__(self, start, goal, obstacle_list, rand_area,
         self.goal_xy_th = 0.5
         self.step_size = step_size
 
+        self.lqr_planner = LQRPlanner()
+
     def planning(self, animation=True, search_until_max_iter=True):
         """
         RRT Star planning
@@ -131,7 +133,7 @@ def search_best_goal_node(self):
 
     def calc_new_cost(self, from_node, to_node):
 
-        wx, wy = LQRplanner.LQRplanning(
+        wx, wy = self.lqr_planner.lqr_planning(
             from_node.x, from_node.y, to_node.x, to_node.y, show_animation=False)
 
         px, py, course_lengths = self.sample_path(wx, wy, self.step_size)
@@ -182,7 +184,7 @@ def sample_path(self, wx, wy, step):
 
     def steer(self, from_node, to_node):
 
-        wx, wy = LQRplanner.LQRplanning(
+        wx, wy = self.lqr_planner.lqr_planning(
             from_node.x, from_node.y, to_node.x, to_node.y, show_animation=False)
 
         px, py, course_lens = self.sample_path(wx, wy, self.step_size)

From 5c15bc0357542ca8297fddba0dc9196c4eccdcae Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 28 Jul 2019 15:12:26 +0900
Subject: [PATCH 0143/1080] clean up quintic_polynomial_planner

---
 .../quintic_polynomials_planner.py            | 25 +++++++++++--------
 1 file changed, 14 insertions(+), 11 deletions(-)

diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
index e2411d2bcb..ecef7d454c 100644
--- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
+++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
@@ -1,19 +1,20 @@
 """
 
-Quinitc Polynomials Planner
+Quintic Polynomials Planner
 
 author: Atsushi Sakai (@Atsushi_twi)
 
 Ref:
 
-- [Local Path Planning And Motion Control For Agv In Positioning](http://ieeexplore.ieee.org/document/637936/)
+- [Local Path planning And Motion Control For Agv In Positioning](http://ieeexplore.ieee.org/document/637936/)
 
 """
 
-import numpy as np
-import matplotlib.pyplot as plt
 import math
 
+import matplotlib.pyplot as plt
+import numpy as np
+
 # parameter
 MAX_T = 100.0  # maximum time to the goal [s]
 MIN_T = 5.0  # minimum time to the goal[s]
@@ -21,7 +22,7 @@
 show_animation = True
 
 
-class quinic_polynomial:
+class QuinticPolynomial:
 
     def __init__(self, xs, vxs, axs, xe, vxe, axe, T):
 
@@ -72,9 +73,9 @@ def calc_third_derivative(self, t):
         return xt
 
 
-def quinic_polynomials_planner(sx, sy, syaw, sv, sa, gx, gy, gyaw, gv, ga, max_accel, max_jerk, dt):
+def quintic_polynomials_planner(sx, sy, syaw, sv, sa, gx, gy, gyaw, gv, ga, max_accel, max_jerk, dt):
     """
-    quinic polynomial planner
+    quintic polynomial planner
 
     input
         sx: start x position [m]
@@ -109,9 +110,11 @@ def quinic_polynomials_planner(sx, sy, syaw, sv, sa, gx, gy, gyaw, gv, ga, max_a
     axg = ga * math.cos(gyaw)
     ayg = ga * math.sin(gyaw)
 
+    time, rx, ry, ryaw, rv, ra, rj = [], [], [], [], [], [], []
+
     for T in np.arange(MIN_T, MAX_T, MIN_T):
-        xqp = quinic_polynomial(sx, vxs, axs, gx, vxg, axg, T)
-        yqp = quinic_polynomial(sy, vys, ays, gy, vyg, ayg, T)
+        xqp = QuinticPolynomial(sx, vxs, axs, gx, vxg, axg, T)
+        yqp = QuinticPolynomial(sy, vys, ays, gy, vyg, ayg, T)
 
         time, rx, ry, ryaw, rv, ra, rj = [], [], [], [], [], [], []
 
@@ -146,7 +149,7 @@ def quinic_polynomials_planner(sx, sy, syaw, sv, sa, gx, gy, gyaw, gv, ga, max_a
             break
 
     if show_animation:  # pragma: no cover
-        for i, _ in enumerate(rx):
+        for i, _ in enumerate(time):
             plt.cla()
             plt.grid(True)
             plt.axis("equal")
@@ -194,7 +197,7 @@ def main():
     max_jerk = 0.5  # max jerk [m/sss]
     dt = 0.1  # time tick [s]
 
-    time, x, y, yaw, v, a, j = quinic_polynomials_planner(
+    time, x, y, yaw, v, a, j = quintic_polynomials_planner(
         sx, sy, syaw, sv, sa, gx, gy, gyaw, gv, ga, max_accel, max_jerk, dt)
 
     if show_animation:  # pragma: no cover

From 0747db006f0b69803fb1ded2a1a0881e3871a8ca Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 28 Jul 2019 15:16:00 +0900
Subject: [PATCH 0144/1080] clean up model_predictive_trajectory gene

---
 .../model_predictive_trajectory_generator.py          | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/model_predictive_trajectory_generator.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/model_predictive_trajectory_generator.py
index ad3bb11f2a..3ba1dd9fc2 100644
--- a/PathPlanning/ModelPredictiveTrajectoryGenerator/model_predictive_trajectory_generator.py
+++ b/PathPlanning/ModelPredictiveTrajectoryGenerator/model_predictive_trajectory_generator.py
@@ -6,9 +6,11 @@
 
 """
 
-import numpy as np
-import matplotlib.pyplot as plt
 import math
+
+import matplotlib.pyplot as plt
+import numpy as np
+
 import motion_model
 
 # optimization parameter
@@ -37,7 +39,7 @@ def calc_diff(target, x, y, yaw):
     return d
 
 
-def calc_J(target, p, h, k0):
+def calc_j(target, p, h, k0):
     xp, yp, yawp = motion_model.generate_last_state(
         p[0, 0] + h[0], p[1, 0], p[2, 0], k0)
     dp = calc_diff(target, [xp], [yp], [yawp])
@@ -68,7 +70,6 @@ def calc_J(target, p, h, k0):
 
 
 def selection_learning_param(dp, p, k0, target):
-
     mincost = float("inf")
     mina = 1.0
     maxa = 2.0
@@ -110,7 +111,7 @@ def optimize_trajectory(target, k0, p):
             print("path is ok cost is:" + str(cost))
             break
 
-        J = calc_J(target, p, h, k0)
+        J = calc_j(target, p, h, k0)
         try:
             dp = - np.linalg.inv(J) @ dc
         except np.linalg.linalg.LinAlgError:

From 76977ac2564f1bae8d6dddae2c6fe454fddf232a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 28 Jul 2019 19:50:02 +0900
Subject: [PATCH 0145/1080] cleanup closed loop RRT star.

---
 .../closed_loop_rrt_star_car.py               | 325 +++---------------
 PathPlanning/LQRRRTStar/lqr_rrt_star.py       |   2 +-
 PathPlanning/RRT/rrt.py                       |   4 +-
 PathPlanning/RRTDubins/rrt_dubins.py          |   2 +-
 PathPlanning/RRTStar/rrt_star.py              |   2 +-
 PathPlanning/RRTStarDubins/rrt_star_dubins.py |   2 +-
 .../RRTStarReedsShepp/rrt_star_reeds_shepp.py |  50 +--
 7 files changed, 87 insertions(+), 300 deletions(-)

diff --git a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
index 2aa0ba61de..a07dc48af0 100644
--- a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
+++ b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
@@ -1,14 +1,13 @@
 """
+
 Path planning Sample Code with Closed loop RRT for car like robot.
 
 author: AtsushiSakai(@Atsushi_twi)
 
 """
 
-import copy
 import math
 import os
-import random
 import sys
 
 import matplotlib.pyplot as plt
@@ -18,92 +17,49 @@
 
 sys.path.append(os.path.dirname(
     os.path.abspath(__file__)) + "/../ReedsSheppPath/")
+sys.path.append(os.path.dirname(
+    os.path.abspath(__file__)) + "/../RRTStarReedsShepp/")
 
 try:
     import reeds_shepp_path_planning
     import unicycle_model
-except:
+    from rrt_star_reeds_shepp import RRTStarReedsShepp
+except ImportError:
     raise
 
 show_animation = True
 
 
-target_speed = 10.0 / 3.6
-STEP_SIZE = 0.1
-
-
-class RRT():
+class ClosedLoopRRTStar(RRTStarReedsShepp):
     """
-    Class for RRT planning
+    Class for Closed loop RRT star planning
     """
 
-    def __init__(self, start, goal, obstacleList, randArea,
-                 maxIter=50):
+    def __init__(self, start, goal, obstacle_list, rand_area,
+                 max_iter=200,
+                 connect_circle_dist=50.0
+                 ):
+        super().__init__(start, goal, obstacle_list, rand_area,
+                         max_iter=max_iter,
+                         connect_circle_dist=connect_circle_dist,
+                         )
+
+        self.target_speed = 10.0 / 3.6
+        self.yaw_th = np.deg2rad(3.0)
+        self.xy_th = 0.5
+        self.invalid_travel_ratio = 5.0
+
+    def planning(self, animation=True):
         """
-        Setting Parameter
-
-        start:Start Position [x,y]
-        goal:Goal Position [x,y]
-        obstacleList:obstacle Positions [[x,y,size],...]
-        randArea:Ramdom Samping Area [min,max]
-
-        """
-        self.start = Node(start[0], start[1], start[2])
-        self.end = Node(goal[0], goal[1], goal[2])
-        self.minrand = randArea[0]
-        self.maxrand = randArea[1]
-        self.obstacleList = obstacleList
-        self.maxIter = maxIter
-
-    def try_goal_path(self):
-
-        goal = Node(self.end.x, self.end.y, self.end.yaw)
-
-        newNode = self.steer(goal, len(self.nodeList) - 1)
-        if newNode is None:
-            return
-
-        if self.CollisionCheck(newNode, self.obstacleList):
-            #  print("goal path is OK")
-            self.nodeList.append(newNode)
-
-    def Planning(self, animation=True):
-        """
-        Pathplanning
+        do planning
 
         animation: flag for animation on or off
         """
-
-        self.nodeList = [self.start]
-
-        self.try_goal_path()
-
-        for i in range(self.maxIter):
-            print("loop:", i)
-            rnd = self.get_random_point()
-            nind = self.GetNearestListIndex(self.nodeList, rnd)
-
-            newNode = self.steer(rnd, nind)
-            #  print(newNode.cost)
-            if newNode is None:
-                continue
-
-            if self.CollisionCheck(newNode, self.obstacleList):
-                nearinds = self.find_near_nodes(newNode)
-                newNode = self.choose_parent(newNode, nearinds)
-                if newNode is None:
-                    continue
-
-                self.nodeList.append(newNode)
-                self.rewire(newNode, nearinds)
-
-                self.try_goal_path()
-
-            if animation and i % 5 == 0:
-                self.DrawGraph(rnd=rnd)
+        # planning with RRTStarReedsShepp
+        super().planning(animation=animation)
 
         # generate coruse
-        path_indexs = self.get_best_last_indexs()
+        path_indexs = self.get_goal_indexes()
 
         flag, x, y, yaw, v, t, a, d = self.search_best_feasible_path(
             path_indexs)
@@ -120,10 +76,9 @@ def search_best_feasible_path(self, path_indexs):
 
         # pure pursuit tracking
         for ind in path_indexs:
-            path = self.gen_final_course(ind)
+            path = self.generate_final_course(ind)
 
-            flag, x, y, yaw, v, t, a, d = self.check_tracking_path_is_feasible(
-                path)
+            flag, x, y, yaw, v, t, a, d = self.check_tracking_path_is_feasible(path)
 
             if flag and best_time >= t[-1]:
                 print("feasible path is found")
@@ -142,121 +97,46 @@ def search_best_feasible_path(self, path_indexs):
         return False, None, None, None, None, None, None, None
 
     def check_tracking_path_is_feasible(self, path):
-        #  print("check_tracking_path_is_feasible")
-        cx = np.array(path[:, 0])
-        cy = np.array(path[:, 1])
-        cyaw = np.array(path[:, 2])
+        cx = np.array([state[0] for state in path])[::-1]
+        cy = np.array([state[1] for state in path])[::-1]
+        cyaw = np.array([state[2] for state in path])[::-1]
 
         goal = [cx[-1], cy[-1], cyaw[-1]]
 
         cx, cy, cyaw = pure_pursuit.extend_path(cx, cy, cyaw)
 
         speed_profile = pure_pursuit.calc_speed_profile(
-            cx, cy, cyaw, target_speed)
+            cx, cy, cyaw, self.target_speed)
 
         t, x, y, yaw, v, a, d, find_goal = pure_pursuit.closed_loop_prediction(
             cx, cy, cyaw, speed_profile, goal)
-        yaw = [self.pi_2_pi(iyaw) for iyaw in yaw]
+        yaw = [reeds_shepp_path_planning.pi_2_pi(iyaw) for iyaw in yaw]
 
         if not find_goal:
             print("cannot reach goal")
 
-        if abs(yaw[-1] - goal[2]) >= math.pi / 4.0:
+        if abs(yaw[-1] - goal[2]) >= self.yaw_th * 10.0:
             print("final angle is bad")
             find_goal = False
 
         travel = sum([abs(iv) * unicycle_model.dt for iv in v])
-        #  print(travel)
         origin_travel = sum([math.sqrt(dx ** 2 + dy ** 2)
                              for (dx, dy) in zip(np.diff(cx), np.diff(cy))])
-        #  print(origin_travel)
 
-        if (travel / origin_travel) >= 5.0:
+        if (travel / origin_travel) >= self.invalid_travel_ratio:
             print("path is too long")
             find_goal = False
 
-        if not self.CollisionCheckWithXY(x, y, self.obstacleList):
+        if not self.collision_check_with_xy(x, y, self.obstacle_list):
             print("This path is collision")
             find_goal = False
 
         return find_goal, x, y, yaw, v, t, a, d
 
-    def choose_parent(self, newNode, nearinds):
-        if not nearinds:
-            return newNode
-
-        dlist = []
-        for i in nearinds:
-            tNode = self.steer(newNode, i)
-            if tNode is None:
-                continue
-
-            if self.CollisionCheck(tNode, self.obstacleList):
-                dlist.append(tNode.cost)
-            else:
-                dlist.append(float("inf"))
-
-        mincost = min(dlist)
-        minind = nearinds[dlist.index(mincost)]
-
-        if mincost == float("inf"):
-            print("mincost is inf")
-            return newNode
-
-        newNode = self.steer(newNode, minind)
-        if newNode is None:
-            return None
-
-        return newNode
-
-    def pi_2_pi(self, angle):
-        return (angle + math.pi) % (2 * math.pi) - math.pi
-
-    def steer(self, rnd, nind):
-        #  print(rnd)
-
-        nearestNode = self.nodeList[nind]
-
-        px, py, pyaw, mode, clen = reeds_shepp_path_planning.reeds_shepp_path_planning(
-            nearestNode.x, nearestNode.y, nearestNode.yaw,
-            rnd.x, rnd.y, rnd.yaw, unicycle_model.curvature_max, STEP_SIZE)
-
-        if px is None:
-            return None
-
-        newNode = copy.deepcopy(nearestNode)
-        newNode.x = px[-1]
-        newNode.y = py[-1]
-        newNode.yaw = pyaw[-1]
-
-        newNode.path_x = px
-        newNode.path_y = py
-        newNode.path_yaw = pyaw
-        newNode.cost += sum([abs(c) for c in clen])
-        newNode.parent = nind
-
-        return newNode
-
-    def get_random_point(self):
-
-        rnd = [random.uniform(self.minrand, self.maxrand),
-               random.uniform(self.minrand, self.maxrand),
-               random.uniform(-math.pi, math.pi)
-               ]
-
-        node = Node(rnd[0], rnd[1], rnd[2])
-
-        return node
-
-    def get_best_last_indexs(self):
-        #  print("search_finish_node")
-
-        YAWTH = np.deg2rad(1.0)
-        XYTH = 0.5
-
+    def get_goal_indexes(self):
         goalinds = []
-        for (i, node) in enumerate(self.nodeList):
-            if self.calc_dist_to_goal(node.x, node.y) <= XYTH:
+        for (i, node) in enumerate(self.node_list):
+            if self.calc_dist_to_goal(node.x, node.y) <= self.xy_th:
                 goalinds.append(i)
         print("OK XY TH num is")
         print(len(goalinds))
@@ -264,106 +144,17 @@ def get_best_last_indexs(self):
         # angle check
         fgoalinds = []
         for i in goalinds:
-            if abs(self.nodeList[i].yaw - self.end.yaw) <= YAWTH:
+            if abs(self.node_list[i].yaw - self.end.yaw) <= self.yaw_th:
                 fgoalinds.append(i)
         print("OK YAW TH num is")
         print(len(fgoalinds))
 
         return fgoalinds
 
-    def gen_final_course(self, goalind):
-        path = [[self.end.x, self.end.y, self.end.yaw]]
-        while self.nodeList[goalind].parent is not None:
-            node = self.nodeList[goalind]
-            path_x = reversed(node.path_x)
-            path_y = reversed(node.path_y)
-            path_yaw = reversed(node.path_yaw)
-            for (ix, iy, iyaw) in zip(path_x, path_y, path_yaw):
-                path.append([ix, iy, iyaw])
-            #  path.append([node.x, node.y])
-            goalind = node.parent
-        path.append([self.start.x, self.start.y, self.start.yaw])
-
-        path = np.array(path[::-1])
-        return path
-
-    def calc_dist_to_goal(self, x, y):
-        return np.linalg.norm([x - self.end.x, y - self.end.y])
-
-    def find_near_nodes(self, newNode):
-        nnode = len(self.nodeList)
-        r = 50.0 * math.sqrt((math.log(nnode) / nnode))
-        #  r = self.expandDis * 5.0
-        dlist = [(node.x - newNode.x) ** 2
-                 + (node.y - newNode.y) ** 2
-                 + (node.yaw - newNode.yaw) ** 2
-                 for node in self.nodeList]
-        nearinds = [dlist.index(i) for i in dlist if i <= r ** 2]
-        return nearinds
-
-    def rewire(self, newNode, nearinds):
-
-        nnode = len(self.nodeList)
-
-        for i in nearinds:
-            nearNode = self.nodeList[i]
-            tNode = self.steer(nearNode, nnode - 1)
-
-            if tNode is None:
-                continue
-
-            obstacleOK = self.CollisionCheck(tNode, self.obstacleList)
-            imporveCost = nearNode.cost > tNode.cost
-
-            if obstacleOK and imporveCost:
-                #  print("rewire")
-                self.nodeList[i] = tNode
-
-    def DrawGraph(self, rnd=None):  # pragma: no cover
-        """
-        Draw Graph
-        """
-        if rnd is not None:
-            plt.plot(rnd.x, rnd.y, "^k")
-        for node in self.nodeList:
-            if node.parent is not None:
-                plt.plot(node.path_x, node.path_y, "-g")
-
-        for (ox, oy, size) in self.obstacleList:
-            plt.plot(ox, oy, "ok", ms=30 * size)
-
-        reeds_shepp_path_planning.plot_arrow(
-            self.start.x, self.start.y, self.start.yaw)
-        reeds_shepp_path_planning.plot_arrow(
-            self.end.x, self.end.y, self.end.yaw)
-
-        plt.axis([-2, 15, -2, 15])
-        plt.grid(True)
-        plt.pause(0.01)
-
-    def GetNearestListIndex(self, nodeList, rnd):
-        dlist = [(node.x - rnd.x) ** 2
-                 + (node.y - rnd.y) ** 2
-                 + (node.yaw - rnd.yaw) ** 2 for node in nodeList]
-        minind = dlist.index(min(dlist))
+    @staticmethod
+    def collision_check_with_xy(x, y, obstacle_list):
 
-        return minind
-
-    def CollisionCheck(self, node, obstacleList):
-
-        for (ox, oy, size) in obstacleList:
-            for (ix, iy) in zip(node.path_x, node.path_y):
-                dx = ox - ix
-                dy = oy - iy
-                d = dx * dx + dy * dy
-                if d <= size ** 2:
-                    return False  # collision
-
-        return True  # safe
-
-    def CollisionCheckWithXY(self, x, y, obstacleList):
-
-        for (ox, oy, size) in obstacleList:
+        for (ox, oy, size) in obstacle_list:
             for (ix, iy) in zip(x, y):
                 dx = ox - ix
                 dy = oy - iy
@@ -374,26 +165,10 @@ def CollisionCheckWithXY(self, x, y, obstacleList):
         return True  # safe
 
 
-class Node():
-    """
-    RRT Node
-    """
-
-    def __init__(self, x, y, yaw):
-        self.x = x
-        self.y = y
-        self.yaw = yaw
-        self.path_x = []
-        self.path_y = []
-        self.path_yaw = []
-        self.cost = 0.0
-        self.parent = None
-
-
-def main(gx=6.0, gy=7.0, gyaw=np.deg2rad(90.0), maxIter=200):
+def main(gx=6.0, gy=7.0, gyaw=np.deg2rad(90.0), max_iter=100):
     print("Start" + __file__)
     # ====Search Path with RRT====
-    obstacleList = [
+    obstacle_list = [
         (5, 5, 1),
         (4, 6, 1),
         (4, 8, 1),
@@ -409,16 +184,18 @@ def main(gx=6.0, gy=7.0, gyaw=np.deg2rad(90.0), maxIter=200):
     start = [0.0, 0.0, np.deg2rad(0.0)]
     goal = [gx, gy, gyaw]
 
-    rrt = RRT(start, goal, randArea=[-2.0, 20.0],
-              obstacleList=obstacleList, maxIter=maxIter)
-    flag, x, y, yaw, v, t, a, d = rrt.Planning(animation=show_animation)
+    closed_loop_rrt_star = ClosedLoopRRTStar(start, goal,
+                                             obstacle_list,
+                                             [-2.0, 20.0],
+                                             max_iter=max_iter)
+    flag, x, y, yaw, v, t, a, d = closed_loop_rrt_star.planning(animation=show_animation)
 
     if not flag:
         print("cannot find feasible path")
 
     # Draw final path
     if show_animation:
-        rrt.DrawGraph()
+        closed_loop_rrt_star.draw_graph()
         plt.plot(x, y, '-r')
         plt.grid(True)
         plt.pause(0.001)
diff --git a/PathPlanning/LQRRRTStar/lqr_rrt_star.py b/PathPlanning/LQRRRTStar/lqr_rrt_star.py
index c6cd760eda..2e276b9f9b 100644
--- a/PathPlanning/LQRRRTStar/lqr_rrt_star.py
+++ b/PathPlanning/LQRRRTStar/lqr_rrt_star.py
@@ -72,7 +72,7 @@ def planning(self, animation=True, search_until_max_iter=True):
         for i in range(self.max_iter):
             print("Iter:", i, ", number of nodes:", len(self.node_list))
             rnd = self.get_random_node()
-            nearest_ind = self.get_nearest_list_index(self.node_list, rnd)
+            nearest_ind = self.get_nearest_node_index(self.node_list, rnd)
             new_node = self.steer(self.node_list[nearest_ind], rnd)
 
             if self.check_collision(new_node, self.obstacle_list):
diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py
index 541c2a9636..887cb1545f 100644
--- a/PathPlanning/RRT/rrt.py
+++ b/PathPlanning/RRT/rrt.py
@@ -63,7 +63,7 @@ def planning(self, animation=True):
         self.node_list = [self.start]
         for i in range(self.max_iter):
             rnd_node = self.get_random_node()
-            nearest_ind = self.get_nearest_list_index(self.node_list, rnd_node)
+            nearest_ind = self.get_nearest_node_index(self.node_list, rnd_node)
             nearest_node = self.node_list[nearest_ind]
 
             new_node = self.steer(nearest_node, rnd_node, self.expand_dis)
@@ -154,7 +154,7 @@ def draw_graph(self, rnd=None):
         plt.pause(0.01)
 
     @staticmethod
-    def get_nearest_list_index(node_list, rnd_node):
+    def get_nearest_node_index(node_list, rnd_node):
         dlist = [(node.x - rnd_node.x) ** 2 + (node.y - rnd_node.y)
                  ** 2 for node in node_list]
         minind = dlist.index(min(dlist))
diff --git a/PathPlanning/RRTDubins/rrt_dubins.py b/PathPlanning/RRTDubins/rrt_dubins.py
index 78ce9611b3..ed2b27ca0b 100644
--- a/PathPlanning/RRTDubins/rrt_dubins.py
+++ b/PathPlanning/RRTDubins/rrt_dubins.py
@@ -80,7 +80,7 @@ def planning(self, animation=True, search_until_max_iter=True):
         for i in range(self.max_iter):
             print("Iter:", i, ", number of nodes:", len(self.node_list))
             rnd = self.get_random_node()
-            nearest_ind = self.get_nearest_list_index(self.node_list, rnd)
+            nearest_ind = self.get_nearest_node_index(self.node_list, rnd)
             new_node = self.steer(self.node_list[nearest_ind], rnd)
 
             if self.check_collision(new_node, self.obstacle_list):
diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py
index c92f435080..eb37964d5c 100644
--- a/PathPlanning/RRTStar/rrt_star.py
+++ b/PathPlanning/RRTStar/rrt_star.py
@@ -65,7 +65,7 @@ def planning(self, animation=True, search_until_max_iter=True):
         for i in range(self.max_iter):
             print("Iter:", i, ", number of nodes:", len(self.node_list))
             rnd = self.get_random_node()
-            nearest_ind = self.get_nearest_list_index(self.node_list, rnd)
+            nearest_ind = self.get_nearest_node_index(self.node_list, rnd)
             new_node = self.steer(self.node_list[nearest_ind], rnd, self.expand_dis)
 
             if self.check_collision(new_node, self.obstacle_list):
diff --git a/PathPlanning/RRTStarDubins/rrt_star_dubins.py b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
index b98c7dca1c..4d7312933d 100644
--- a/PathPlanning/RRTStarDubins/rrt_star_dubins.py
+++ b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
@@ -81,7 +81,7 @@ def planning(self, animation=True, search_until_max_iter=True):
         for i in range(self.max_iter):
             print("Iter:", i, ", number of nodes:", len(self.node_list))
             rnd = self.get_random_node()
-            nearest_ind = self.get_nearest_list_index(self.node_list, rnd)
+            nearest_ind = self.get_nearest_node_index(self.node_list, rnd)
             new_node = self.steer(self.node_list[nearest_ind], rnd)
 
             if self.check_collision(new_node, self.obstacle_list):
diff --git a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
index ea31337cbc..5d78f98f31 100644
--- a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
+++ b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
@@ -44,7 +44,6 @@ def __init__(self, x, y, yaw):
             self.path_yaw = []
 
     def __init__(self, start, goal, obstacle_list, rand_area,
-                 goal_sample_rate=10,
                  max_iter=200,
                  connect_circle_dist=50.0
                  ):
@@ -61,7 +60,6 @@ def __init__(self, start, goal, obstacle_list, rand_area,
         self.end = self.Node(goal[0], goal[1], goal[2])
         self.min_rand = rand_area[0]
         self.max_rand = rand_area[1]
-        self.goal_sample_rate = goal_sample_rate
         self.max_iter = max_iter
         self.obstacle_list = obstacle_list
         self.connect_circle_dist = connect_circle_dist
@@ -72,7 +70,7 @@ def __init__(self, start, goal, obstacle_list, rand_area,
 
     def planning(self, animation=True, search_until_max_iter=True):
         """
-        RRT Star planning
+        planning
 
         animation: flag for animation on or off
         """
@@ -81,7 +79,7 @@ def planning(self, animation=True, search_until_max_iter=True):
         for i in range(self.max_iter):
             print("Iter:", i, ", number of nodes:", len(self.node_list))
             rnd = self.get_random_node()
-            nearest_ind = self.get_nearest_list_index(self.node_list, rnd)
+            nearest_ind = self.get_nearest_node_index(self.node_list, rnd)
             new_node = self.steer(self.node_list[nearest_ind], rnd)
 
             if self.check_collision(new_node, self.obstacle_list):
@@ -90,6 +88,7 @@ def planning(self, animation=True, search_until_max_iter=True):
                 if new_node:
                     self.node_list.append(new_node)
                     self.rewire(new_node, near_indexes)
+                    self.try_goal_path(new_node)
 
             if animation and i % 5 == 0:
                 self.plot_start_goal_arrow()
@@ -110,6 +109,17 @@ def planning(self, animation=True, search_until_max_iter=True):
 
         return None
 
+    def try_goal_path(self, node):
+
+        goal = self.Node(self.end.x, self.end.y, self.end.yaw)
+
+        new_node = self.steer(node, goal)
+        if new_node is None:
+            return
+
+        if self.check_collision(new_node, self.obstacle_list):
+            self.node_list.append(new_node)
+
     def draw_graph(self, rnd=None):
         plt.clf()
         if rnd is not None:
@@ -151,7 +161,7 @@ def steer(self, from_node, to_node):
         new_node.path_x = px
         new_node.path_y = py
         new_node.path_yaw = pyaw
-        new_node.cost += sum(course_lengths)
+        new_node.cost += sum([abs(l) for l in course_lengths])
         new_node.parent = from_node
 
         return new_node
@@ -164,17 +174,14 @@ def calc_new_cost(self, from_node, to_node):
         if not course_lengths:
             return float("inf")
 
-        return from_node.cost + sum(course_lengths)
+        return from_node.cost + sum([abs(l) for l in course_lengths])
 
     def get_random_node(self):
 
-        if random.randint(0, 100) > self.goal_sample_rate:
-            rnd = self.Node(random.uniform(self.min_rand, self.max_rand),
-                            random.uniform(self.min_rand, self.max_rand),
-                            random.uniform(-math.pi, math.pi)
-                            )
-        else:  # goal point sampling
-            rnd = self.Node(self.end.x, self.end.y, self.end.yaw)
+        rnd = self.Node(random.uniform(self.min_rand, self.max_rand),
+                        random.uniform(self.min_rand, self.max_rand),
+                        random.uniform(-math.pi, math.pi)
+                        )
 
         return rnd
 
@@ -184,6 +191,7 @@ def search_best_goal_node(self):
         for (i, node) in enumerate(self.node_list):
             if self.calc_dist_to_goal(node.x, node.y) <= self.goal_xy_th:
                 goal_indexes.append(i)
+        print("goal_indexes:", len(goal_indexes))
 
         # angle check
         final_goal_indexes = []
@@ -191,10 +199,13 @@ def search_best_goal_node(self):
             if abs(self.node_list[i].yaw - self.end.yaw) <= self.goal_yaw_th:
                 final_goal_indexes.append(i)
 
+        print("final_goal_indexes:", len(final_goal_indexes))
+
         if not final_goal_indexes:
             return None
 
         min_cost = min([self.node_list[i].cost for i in final_goal_indexes])
+        print("min_cost:", min_cost)
         for i in final_goal_indexes:
             if self.node_list[i].cost == min_cost:
                 return i
@@ -202,18 +213,17 @@ def search_best_goal_node(self):
         return None
 
     def generate_final_course(self, goal_index):
-        print("final")
-        path = [[self.end.x, self.end.y]]
+        path = [[self.end.x, self.end.y, self.end.yaw]]
         node = self.node_list[goal_index]
         while node.parent:
-            for (ix, iy) in zip(reversed(node.path_x), reversed(node.path_y)):
-                path.append([ix, iy])
+            for (ix, iy, iyaw) in zip(reversed(node.path_x), reversed(node.path_y), reversed(node.path_yaw)):
+                path.append([ix, iy, iyaw])
             node = node.parent
-        path.append([self.start.x, self.start.y])
+        path.append([self.start.x, self.start.y, self.start.yaw])
         return path
 
 
-def main(max_iter=200):
+def main(max_iter=100):
     print("Start " + __file__)
 
     # ====Search Path with RRT====
@@ -241,7 +251,7 @@ def main(max_iter=200):
     # Draw final path
     if path and show_animation:  # pragma: no cover
         rrt_star_reeds_shepp.draw_graph()
-        plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
+        plt.plot([x for (x, y, yaw) in path], [y for (x, y, yaw) in path], '-r')
         plt.grid(True)
         plt.pause(0.001)
         plt.show()

From 3e017c9170ded3625df5d57ff4a062a658130045 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 28 Jul 2019 20:04:12 +0900
Subject: [PATCH 0146/1080] fix closed RRT star test

---
 tests/test_closed_loop_rrt_star_car.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/tests/test_closed_loop_rrt_star_car.py b/tests/test_closed_loop_rrt_star_car.py
index f82d3f2561..18513ce665 100644
--- a/tests/test_closed_loop_rrt_star_car.py
+++ b/tests/test_closed_loop_rrt_star_car.py
@@ -1,6 +1,7 @@
-from unittest import TestCase
-import sys
 import os
+import sys
+from unittest import TestCase
+
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
                 "/../PathPlanning/ClosedLoopRRTStar/")
@@ -17,7 +18,7 @@ class Test(TestCase):
 
     def test1(self):
         m.show_animation = False
-        m.main(gx=1.0, gy=0.0, gyaw=0.0, maxIter=5)
+        m.main(gx=1.0, gy=0.0, gyaw=0.0, max_iter=5)
 
 
 if __name__ == '__main__':  # pragma: no cover

From 9a9ea3b3d7cc2f5e4cb10b384610964044f17583 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 28 Jul 2019 20:57:35 +0900
Subject: [PATCH 0147/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 087acd3a7c..61ffd07d3c 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -299,6 +299,12 @@ You made such a nice work. Congratulations :)
 
 ---
 
+thank you for python path finding repo
+
+--fengzongbao
+
+---
+
 # Citations
 
 1. B. Blaga, M. Deac, R. W. Y. Al-doori, M. Negru and R. Dǎnescu, "Miniature Autonomous Vehicle Development on Raspberry Pi," 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, 2018, pp. 229-236.

From 2b907725b89f50df02e72dea26899fd4a00bda20 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 29 Jul 2019 20:17:55 +0900
Subject: [PATCH 0148/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 61ffd07d3c..a68cbddbc1 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -305,6 +305,12 @@ thank you for python path finding repo
 
 ---
 
+Dear AtsushiSakai, <br> <br>Thank you so much for making the concept of robotics approachable for the general mass. Keep up the good work :)
+
+--Harsh Munshi
+
+---
+
 # Citations
 
 1. B. Blaga, M. Deac, R. W. Y. Al-doori, M. Negru and R. Dǎnescu, "Miniature Autonomous Vehicle Development on Raspberry Pi," 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, 2018, pp. 229-236.

From 2d9dcc1d99204700d774026dc5a472a119a71d0b Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 29 Jul 2019 20:54:59 +0900
Subject: [PATCH 0149/1080] Update users_comments.md

---
 users_comments.md | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index a68cbddbc1..72ac0e9c87 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -1,3 +1,19 @@
+# User's demos
+
+## WHILL MODEL CR with move to a pose control
+
+This is an electric wheelchair control demo by [Katsushun89](https://github.com/Katsushun89).
+
+[WHILL Model CR](https://whill.jp/model-cr) is the control target, [M5Stack](https://m5stack.com/) is used for the controller, and [toio](https://toio.io/) is used for the control input device.
+
+[move-to-a-pose-control](https://pythonrobotics.readthedocs.io/en/latest/modules/path_tracking.html#move-to-a-pose-control) is used for its control algorithm ([code link](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/move_to_pose/move_to_pose.py)).
+
+![1](https://github.com/AtsushiSakai/PythonRoboticsGifs/blob/master/Users/WHILL_Model_CR_with_move_to_a_pose/WHLL_Model_CR_with_move_to_a_pose.gif)
+
+Ref:
+
+- [toioと同じように動くWHILL Model CR (in Japanese)](https://qiita.com/KatsuShun89/items/68fde7544ecfe36096d2)
+
 
 # Educational users
 

From 763d1e3efdd72d9a0f1fb91a80af2e37655c182b Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 31 Jul 2019 17:41:59 +0900
Subject: [PATCH 0150/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 72ac0e9c87..49d4a3f7a3 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -327,6 +327,12 @@ Dear AtsushiSakai, <br> <br>Thank you so much for making the concept of robotics
 
 ---
 
+Benefit a lot for your GitHub repository of PythonRobotics. Thanks so much.
+
+--Haoran
+
+---
+
 # Citations
 
 1. B. Blaga, M. Deac, R. W. Y. Al-doori, M. Negru and R. Dǎnescu, "Miniature Autonomous Vehicle Development on Raspberry Pi," 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, 2018, pp. 229-236.

From 43c748a205ef7147102569d2971d1fa50ae10a56 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 1 Aug 2019 20:18:11 +0900
Subject: [PATCH 0151/1080] Update users_comments.md

---
 users_comments.md | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 49d4a3f7a3..1b6f192594 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -333,6 +333,19 @@ Benefit a lot for your GitHub repository of PythonRobotics. Thanks so much.
 
 ---
 
+Thanks!
+
+--Reinzor
+
+---
+
+Thanks for writing these algorithms. They are very helpful for learning robotics.
+
+--Arihant Lunawat
+
+---
+
+
 # Citations
 
 1. B. Blaga, M. Deac, R. W. Y. Al-doori, M. Negru and R. Dǎnescu, "Miniature Autonomous Vehicle Development on Raspberry Pi," 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, 2018, pp. 229-236.

From c9a2beab73d75ac9687765e73a8666f51ccf3429 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 2 Aug 2019 09:29:43 +0900
Subject: [PATCH 0152/1080] Update users_comments.md

---
 users_comments.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 1b6f192594..294f3b3b13 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -360,6 +360,8 @@ URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8516589&isnumber=85
 4. E. Horváth, C. Hajdu, C. Radu and Á. Ballagi, "Range Sensor-based Occupancy Grid Mapping with Signatures," 2019 20th International Carpathian Control Conference (ICCC), Krakow-Wieliczka, Poland, 2019, pp. 1-5.
 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8765684&isnumber=8765679
 
+5. Josie Hughes, Masaru Shimizu, and Arnoud Visser, "A Review of Robot Rescue Simulation Platforms for Robotics Education"
+URL: https://2019.robocup.org/downloads/program/HughesEtAl2019.pdf
 
 # Others
 

From 38373ac7de43573dad62cb4fbb3d206e62dc3b15 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 3 Aug 2019 13:45:20 +0900
Subject: [PATCH 0153/1080] fix batch_informed_rrtstar.py

---
 .../batch_informed_rrtstar.py                 | 250 +++++++++---------
 1 file changed, 127 insertions(+), 123 deletions(-)

diff --git a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
index 366ec5acc2..37ac75ada9 100644
--- a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
+++ b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
@@ -14,10 +14,11 @@
 Reference: https://arxiv.org/abs/1405.5848
 """
 
-import random
-import numpy as np
 import math
+import random
+
 import matplotlib.pyplot as plt
+import numpy as np
 
 show_animation = True
 
@@ -26,8 +27,14 @@ class RTree(object):
     # Class to represent the explicit tree created
     # while sampling through the state space
 
-    def __init__(self, start=[0, 0], lowerLimit=[0, 0], upperLimit=[10, 10], resolution=1):
+    def __init__(self, start=None, lowerLimit=None, upperLimit=None, resolution=1.0):
 
+        if upperLimit is None:
+            upperLimit = [10, 10]
+        if lowerLimit is None:
+            lowerLimit = [0, 0]
+        if start is None:
+            start = [0, 0]
         self.vertices = dict()
         self.edges = []
         self.start = start
@@ -42,20 +49,21 @@ def __init__(self, start=[0, 0], lowerLimit=[0, 0], upperLimit=[10, 10], resolut
             self.num_cells[idx] = np.ceil(
                 (upperLimit[idx] - lowerLimit[idx]) / resolution)
 
-        vertex_id = self.realWorldToNodeId(start)
+        vertex_id = self.real_world_to_node_id(start)
         self.vertices[vertex_id] = []
 
-    def getRootId(self):
+    @staticmethod
+    def get_root_id():
         # return the id of the root of the tree
         return 0
 
-    def addVertex(self, vertex):
+    def add_vertex(self, vertex):
         # add a vertex to the tree
-        vertex_id = self.realWorldToNodeId(vertex)
+        vertex_id = self.real_world_to_node_id(vertex)
         self.vertices[vertex_id] = []
         return vertex_id
 
-    def addEdge(self, v, x):
+    def add_edge(self, v, x):
         # create an edge between v and x vertices
         if (v, x) not in self.edges:
             self.edges.append((v, x))
@@ -63,7 +71,7 @@ def addEdge(self, v, x):
         self.vertices[v].append(x)
         self.vertices[x].append(v)
 
-    def realCoordsToGridCoord(self, real_coord):
+    def real_coords_to_grid_coord(self, real_coord):
         # convert real world coordinates to grid space
         # depends on the resolution of the grid
         # the output is the same as real world coords if the resolution
@@ -71,10 +79,10 @@ def realCoordsToGridCoord(self, real_coord):
         coord = [0] * self.dimension
         for i in range(len(coord)):
             start = self.lowerLimit[i]  # start of the grid space
-            coord[i] = np.around((real_coord[i] - start) / self.resolution)
+            coord[i] = int(np.around((real_coord[i] - start) / self.resolution))
         return coord
 
-    def gridCoordinateToNodeId(self, coord):
+    def grid_coordinate_to_node_id(self, coord):
         # This function maps a grid coordinate to a unique
         # node id
         nodeId = 0
@@ -85,12 +93,12 @@ def gridCoordinateToNodeId(self, coord):
             nodeId = nodeId + coord[i] * product
         return nodeId
 
-    def realWorldToNodeId(self, real_coord):
+    def real_world_to_node_id(self, real_coord):
         # first convert the given coordinates to grid space and then
         # convert the grid space coordinates to a unique node id
-        return self.gridCoordinateToNodeId(self.realCoordsToGridCoord(real_coord))
+        return self.grid_coordinate_to_node_id(self.real_coords_to_grid_coord(real_coord))
 
-    def gridCoordToRealWorldCoord(self, coord):
+    def grid_coord_to_real_world_coord(self, coord):
         # This function smaps a grid coordinate in discrete space
         # to a configuration in the full configuration space
         config = [0] * self.dimension
@@ -102,7 +110,7 @@ def gridCoordToRealWorldCoord(self, coord):
             config[i] = start + grid_step
         return config
 
-    def nodeIdToGridCoord(self, node_id):
+    def node_id_to_grid_coord(self, node_id):
         # This function maps a node id to the associated
         # grid coordinate
         coord = [0] * len(self.lowerLimit)
@@ -115,12 +123,10 @@ def nodeIdToGridCoord(self, node_id):
             node_id = node_id - (coord[i] * prod)
         return coord
 
-    def nodeIdToRealWorldCoord(self, nid):
-        # This function maps a node in discrete space to a configuraiton
+    def node_id_to_real_world_coord(self, nid):
+        # This function maps a node in discrete space to a configuration
         # in the full configuration space
-        return self.gridCoordToRealWorldCoord(self.nodeIdToGridCoord(nid))
-
-# Uses Batch Informed Trees to find a path from start to goal
+        return self.grid_coord_to_real_world_coord(self.node_id_to_grid_coord(nid))
 
 
 class BITStar(object):
@@ -135,6 +141,8 @@ def __init__(self, start, goal,
         self.maxrand = randArea[1]
         self.maxIter = maxIter
         self.obstacleList = obstacleList
+        self.startId = None
+        self.goalId = None
 
         self.vertex_queue = []
         self.edge_queue = []
@@ -154,8 +162,8 @@ def __init__(self, start, goal,
                           upperLimit=upperLimit, resolution=0.01)
 
     def setup_planning(self):
-        self.startId = self.tree.realWorldToNodeId(self.start)
-        self.goalId = self.tree.realWorldToNodeId(self.goal)
+        self.startId = self.tree.real_world_to_node_id(self.start)
+        self.goalId = self.tree.real_world_to_node_id(self.goal)
 
         # add goal to the samples
         self.samples[self.goalId] = self.goal
@@ -163,9 +171,9 @@ def setup_planning(self):
         self.f_scores[self.goalId] = 0
 
         # add the start id to the tree
-        self.tree.addVertex(self.start)
+        self.tree.add_vertex(self.start)
         self.g_scores[self.startId] = 0
-        self.f_scores[self.startId] = self.computeHeuristicCost(
+        self.f_scores[self.startId] = self.compute_heuristic_cost(
             self.startId, self.goalId)
 
         # max length we expect to find in our 'informed' sample space, starts as infinite
@@ -182,11 +190,11 @@ def setup_planning(self):
         # first column of idenity matrix transposed
         id1_t = np.array([1.0, 0.0, 0.0]).reshape(1, 3)
         M = np.dot(a1, id1_t)
-        U, S, Vh = np.linalg.svd(M, 1, 1)
+        U, S, Vh = np.linalg.svd(M, True, True)
         C = np.dot(np.dot(U, np.diag(
             [1.0, 1.0, np.linalg.det(U) * np.linalg.det(np.transpose(Vh))])), Vh)
 
-        self.samples.update(self.informedSample(
+        self.samples.update(self.informed_sample(
             200, cBest, cMin, xCenter, C))
 
         return etheta, cMin, xCenter, C, cBest
@@ -207,7 +215,7 @@ def setup_sample(self, iterations, foundGoal, cMin, xCenter, C, cBest):
                 else:
                     m = 100
                 cBest = self.g_scores[self.goalId]
-                self.samples.update(self.informedSample(
+                self.samples.update(self.informed_sample(
                     m, cBest, cMin, xCenter, C))
 
             # make the old vertices the new vertices
@@ -225,26 +233,25 @@ def plan(self, animation=True):
 
         foundGoal = False
         # run until done
-        while (iterations < self.maxIter):
+        while iterations < self.maxIter:
             cBest = self.setup_sample(iterations,
                                       foundGoal, cMin, xCenter, C, cBest)
             # expand the best vertices until an edge is better than the vertex
             # this is done because the vertex cost represents the lower bound
             # on the edge cost
-            while(self.bestVertexQueueValue() <= self.bestEdgeQueueValue()):
-                self.expandVertex(self.bestInVertexQueue())
+            while self.best_vertex_queue_value() <= self.best_edge_queue_value():
+                self.expand_vertex(self.best_in_vertex_queue())
 
             # add the best edge to the tree
-            bestEdge = self.bestInEdgeQueue()
+            bestEdge = self.best_in_edge_queue()
             self.edge_queue.remove(bestEdge)
 
             # Check if this can improve the current solution
-            estimatedCostOfVertex = self.g_scores[bestEdge[0]] + self.computeDistanceCost(
-                bestEdge[0], bestEdge[1]) + self.computeHeuristicCost(bestEdge[1], self.goalId)
-            estimatedCostOfEdge = self.computeDistanceCost(self.startId, bestEdge[0]) + self.computeHeuristicCost(
-                bestEdge[0], bestEdge[1]) + self.computeHeuristicCost(bestEdge[1], self.goalId)
-            actualCostOfEdge = self.g_scores[bestEdge[0]] + \
-                self.computeDistanceCost(bestEdge[0], bestEdge[1])
+            estimatedCostOfVertex = self.g_scores[bestEdge[0]] + self.compute_distance_cost(
+                bestEdge[0], bestEdge[1]) + self.compute_heuristic_cost(bestEdge[1], self.goalId)
+            estimatedCostOfEdge = self.compute_distance_cost(self.startId, bestEdge[0]) + self.compute_heuristic_cost(
+                bestEdge[0], bestEdge[1]) + self.compute_heuristic_cost(bestEdge[1], self.goalId)
+            actualCostOfEdge = self.g_scores[bestEdge[0]] + self.compute_distance_cost(bestEdge[0], bestEdge[1])
 
             f1 = estimatedCostOfVertex < self.g_scores[self.goalId]
             f2 = estimatedCostOfEdge < self.g_scores[self.goalId]
@@ -252,46 +259,44 @@ def plan(self, animation=True):
 
             if f1 and f2 and f3:
                 # connect this edge
-                firstCoord = self.tree.nodeIdToRealWorldCoord(
+                firstCoord = self.tree.node_id_to_real_world_coord(
                     bestEdge[0])
-                secondCoord = self.tree.nodeIdToRealWorldCoord(
+                secondCoord = self.tree.node_id_to_real_world_coord(
                     bestEdge[1])
                 path = self.connect(firstCoord, secondCoord)
-                lastEdge = self.tree.realWorldToNodeId(secondCoord)
+                lastEdge = self.tree.real_world_to_node_id(secondCoord)
                 if path is None or len(path) == 0:
                     continue
                 nextCoord = path[len(path) - 1, :]
-                nextCoordPathId = self.tree.realWorldToNodeId(
+                nextCoordPathId = self.tree.real_world_to_node_id(
                     nextCoord)
                 bestEdge = (bestEdge[0], nextCoordPathId)
-                if(bestEdge[1] in self.tree.vertices.keys()):
+                if bestEdge[1] in self.tree.vertices.keys():
                     continue
                 else:
                     try:
                         del self.samples[bestEdge[1]]
-                    except(KeyError):
+                    except KeyError:
                         pass
-                    eid = self.tree.addVertex(nextCoord)
+                    eid = self.tree.add_vertex(nextCoord)
                     self.vertex_queue.append(eid)
                 if eid == self.goalId or bestEdge[0] == self.goalId or bestEdge[1] == self.goalId:
                     print("Goal found")
                     foundGoal = True
 
-                self.tree.addEdge(bestEdge[0], bestEdge[1])
+                self.tree.add_edge(bestEdge[0], bestEdge[1])
 
-                g_score = self.computeDistanceCost(
+                g_score = self.compute_distance_cost(
                     bestEdge[0], bestEdge[1])
-                self.g_scores[bestEdge[1]] = g_score + \
-                    self.g_scores[bestEdge[0]]
-                self.f_scores[bestEdge[1]] = g_score + \
-                    self.computeHeuristicCost(bestEdge[1], self.goalId)
-                self.updateGraph()
+                self.g_scores[bestEdge[1]] = g_score + self.g_scores[bestEdge[0]]
+                self.f_scores[bestEdge[1]] = g_score + self.compute_heuristic_cost(bestEdge[1], self.goalId)
+                self.update_graph()
 
                 # visualize new edge
                 if animation:
-                    self.drawGraph(xCenter=xCenter, cBest=cBest,
-                                   cMin=cMin, etheta=etheta, samples=self.samples.values(),
-                                   start=firstCoord, end=secondCoord, tree=self.tree.edges)
+                    self.draw_graph(xCenter=xCenter, cBest=cBest,
+                                    cMin=cMin, etheta=etheta, samples=self.samples.values(),
+                                    start=firstCoord, end=secondCoord)
 
                 self.remove_queue(lastEdge, bestEdge)
 
@@ -306,14 +311,13 @@ def plan(self, animation=True):
         return self.find_final_path()
 
     def find_final_path(self):
-        plan = []
-        plan.append(self.goal)
+        plan = [self.goal]
         currId = self.goalId
-        while (currId != self.startId):
-            plan.append(self.tree.nodeIdToRealWorldCoord(currId))
+        while currId != self.startId:
+            plan.append(self.tree.node_id_to_real_world_coord(currId))
             try:
                 currId = self.nodes[currId]
-            except(KeyError):
+            except KeyError:
                 print("cannot find Path")
                 return []
 
@@ -324,29 +328,30 @@ def find_final_path(self):
 
     def remove_queue(self, lastEdge, bestEdge):
         for edge in self.edge_queue:
-            if(edge[1] == bestEdge[1]):
-                if self.g_scores[edge[1]] + self.computeDistanceCost(edge[1], bestEdge[1]) >= self.g_scores[self.goalId]:
-                    if(lastEdge, bestEdge[1]) in self.edge_queue:
+            if edge[1] == bestEdge[1]:
+                dist_cost = self.compute_distance_cost(edge[1], bestEdge[1])
+                if self.g_scores[edge[1]] + dist_cost >= self.g_scores[self.goalId]:
+                    if (lastEdge, bestEdge[1]) in self.edge_queue:
                         self.edge_queue.remove(
                             (lastEdge, bestEdge[1]))
 
     def connect(self, start, end):
         # A function which attempts to extend from a start coordinates
         # to goal coordinates
-        steps = int(self.computeDistanceCost(self.tree.realWorldToNodeId(
-            start), self.tree.realWorldToNodeId(end)) * 10)
+        steps = int(self.compute_distance_cost(self.tree.real_world_to_node_id(
+            start), self.tree.real_world_to_node_id(end)) * 10)
         x = np.linspace(start[0], end[0], num=steps)
         y = np.linspace(start[1], end[1], num=steps)
         for i in range(len(x)):
-            if(self._collisionCheck(x[i], y[i])):
-                if(i == 0):
+            if self._collision_check(x[i], y[i]):
+                if i == 0:
                     return None
                 # if collision, send path until collision
                 return np.vstack((x[0:i], y[0:i])).transpose()
 
         return np.vstack((x, y)).transpose()
 
-    def _collisionCheck(self, x, y):
+    def _collision_check(self, x, y):
         for (ox, oy, size) in self.obstacleList:
             dx = ox - x
             dy = oy - y
@@ -355,45 +360,44 @@ def _collisionCheck(self, x, y):
                 return True  # collision
         return False
 
-    # def prune(self, c):
-
-    def computeHeuristicCost(self, start_id, goal_id):
+    def compute_heuristic_cost(self, start_id, goal_id):
         # Using Manhattan distance as heuristic
-        start = np.array(self.tree.nodeIdToRealWorldCoord(start_id))
-        goal = np.array(self.tree.nodeIdToRealWorldCoord(goal_id))
+        start = np.array(self.tree.node_id_to_real_world_coord(start_id))
+        goal = np.array(self.tree.node_id_to_real_world_coord(goal_id))
 
         return np.linalg.norm(start - goal, 2)
 
-    def computeDistanceCost(self, vid, xid):
+    def compute_distance_cost(self, vid, xid):
         # L2 norm distance
-        start = np.array(self.tree.nodeIdToRealWorldCoord(vid))
-        stop = np.array(self.tree.nodeIdToRealWorldCoord(xid))
+        start = np.array(self.tree.node_id_to_real_world_coord(vid))
+        stop = np.array(self.tree.node_id_to_real_world_coord(xid))
 
         return np.linalg.norm(stop - start, 2)
 
     # Sample free space confined in the radius of ball R
-    def informedSample(self, m, cMax, cMin, xCenter, C):
+    def informed_sample(self, m, cMax, cMin, xCenter, C):
         samples = dict()
         print("g_Score goal id: ", self.g_scores[self.goalId])
         for i in range(m + 1):
             if cMax < float('inf'):
                 r = [cMax / 2.0,
-                     math.sqrt(cMax**2 - cMin**2) / 2.0,
-                     math.sqrt(cMax**2 - cMin**2) / 2.0]
+                     math.sqrt(cMax ** 2 - cMin ** 2) / 2.0,
+                     math.sqrt(cMax ** 2 - cMin ** 2) / 2.0]
                 L = np.diag(r)
-                xBall = self.sampleUnitBall()
+                xBall = self.sample_unit_ball()
                 rnd = np.dot(np.dot(C, L), xBall) + xCenter
                 rnd = [rnd[(0, 0)], rnd[(1, 0)]]
-                random_id = self.tree.realWorldToNodeId(rnd)
+                random_id = self.tree.real_world_to_node_id(rnd)
                 samples[random_id] = rnd
             else:
-                rnd = self.sampleFreeSpace()
-                random_id = self.tree.realWorldToNodeId(rnd)
+                rnd = self.sample_free_space()
+                random_id = self.tree.real_world_to_node_id(rnd)
                 samples[random_id] = rnd
         return samples
 
     # Sample point in a unit ball
-    def sampleUnitBall(self):
+    @staticmethod
+    def sample_unit_ball():
         a = random.random()
         b = random.random()
 
@@ -404,63 +408,64 @@ def sampleUnitBall(self):
                   b * math.sin(2 * math.pi * a / b))
         return np.array([[sample[0]], [sample[1]], [0]])
 
-    def sampleFreeSpace(self):
+    def sample_free_space(self):
         rnd = [random.uniform(self.minrand, self.maxrand),
                random.uniform(self.minrand, self.maxrand)]
 
         return rnd
 
-    def bestVertexQueueValue(self):
-        if(len(self.vertex_queue) == 0):
+    def best_vertex_queue_value(self):
+        if len(self.vertex_queue) == 0:
             return float('inf')
         values = [self.g_scores[v]
-                  + self.computeHeuristicCost(v, self.goalId) for v in self.vertex_queue]
+                  + self.compute_heuristic_cost(v, self.goalId) for v in self.vertex_queue]
         values.sort()
         return values[0]
 
-    def bestEdgeQueueValue(self):
-        if(len(self.edge_queue) == 0):
+    def best_edge_queue_value(self):
+        if len(self.edge_queue) == 0:
             return float('inf')
         # return the best value in the queue by score g_tau[v] + c(v,x) + h(x)
-        values = [self.g_scores[e[0]] + self.computeDistanceCost(e[0], e[1])
-                  + self.computeHeuristicCost(e[1], self.goalId) for e in self.edge_queue]
+        values = [self.g_scores[e[0]] + self.compute_distance_cost(e[0], e[1])
+                  + self.compute_heuristic_cost(e[1], self.goalId) for e in self.edge_queue]
         values.sort(reverse=True)
         return values[0]
 
-    def bestInVertexQueue(self):
+    def best_in_vertex_queue(self):
         # return the best value in the vertex queue
-        v_plus_vals = [(v, self.g_scores[v] + self.computeHeuristicCost(v, self.goalId))
+        v_plus_vals = [(v, self.g_scores[v] + self.compute_heuristic_cost(v, self.goalId))
                        for v in self.vertex_queue]
         v_plus_vals = sorted(v_plus_vals, key=lambda x: x[1])
         # print(v_plus_vals)
         return v_plus_vals[0][0]
 
-    def bestInEdgeQueue(self):
-        e_and_values = [(e[0], e[1], self.g_scores[e[0]] + self.computeDistanceCost(
-            e[0], e[1]) + self.computeHeuristicCost(e[1], self.goalId)) for e in self.edge_queue]
+    def best_in_edge_queue(self):
+        e_and_values = [(e[0], e[1], self.g_scores[e[0]] + self.compute_distance_cost(
+            e[0], e[1]) + self.compute_heuristic_cost(e[1], self.goalId)) for e in self.edge_queue]
         e_and_values = sorted(e_and_values, key=lambda x: x[2])
 
-        return (e_and_values[0][0], e_and_values[0][1])
+        return e_and_values[0][0], e_and_values[0][1]
 
-    def expandVertex(self, vid):
+    def expand_vertex(self, vid):
         self.vertex_queue.remove(vid)
 
         # get the coordinates for given vid
-        currCoord = np.array(self.tree.nodeIdToRealWorldCoord(vid))
+        currCoord = np.array(self.tree.node_id_to_real_world_coord(vid))
 
         # get the nearest value in vertex for every one in samples where difference is
         # less than the radius
         neigbors = []
         for sid, scoord in self.samples.items():
             scoord = np.array(scoord)
-            if(np.linalg.norm(scoord - currCoord, 2) <= self.r and sid != vid):
+            if np.linalg.norm(scoord - currCoord, 2) <= self.r and sid != vid:
                 neigbors.append((sid, scoord))
 
         # add an edge to the edge queue is the path might improve the solution
         for neighbor in neigbors:
             sid = neighbor[0]
-            estimated_f_score = self.computeDistanceCost(
-                self.startId, vid) + self.computeHeuristicCost(sid, self.goalId) + self.computeDistanceCost(vid, sid)
+            h_cost = self.compute_heuristic_cost(sid, self.goalId)
+            estimated_f_score = self.compute_distance_cost(
+                self.startId, vid) + h_cost + self.compute_distance_cost(vid, sid)
             if estimated_f_score < self.g_scores[self.goalId]:
                 self.edge_queue.append((vid, sid))
 
@@ -469,22 +474,22 @@ def expandVertex(self, vid):
 
     def add_vertex_to_edge_queue(self, vid, currCoord):
         if vid not in self.old_vertices:
-            neigbors = []
+            neighbors = []
             for v, edges in self.tree.vertices.items():
                 if v != vid and (v, vid) not in self.edge_queue and (vid, v) not in self.edge_queue:
-                    vcoord = self.tree.nodeIdToRealWorldCoord(v)
-                    if(np.linalg.norm(vcoord - currCoord, 2) <= self.r):
-                        neigbors.append((vid, vcoord))
+                    v_coord = self.tree.node_id_to_real_world_coord(v)
+                    if np.linalg.norm(currCoord - v_coord, 2) <= self.r:
+                        neighbors.append((vid, v_coord))
 
-            for neighbor in neigbors:
+            for neighbor in neighbors:
                 sid = neighbor[0]
-                estimated_f_score = self.computeDistanceCost(self.startId, vid) + \
-                    self.computeDistanceCost(
-                        vid, sid) + self.computeHeuristicCost(sid, self.goalId)
-                if estimated_f_score < self.g_scores[self.goalId] and (self.g_scores[vid] + self.computeDistanceCost(vid, sid)) < self.g_scores[sid]:
+                estimated_f_score = self.compute_distance_cost(self.startId, vid) + self.compute_distance_cost(
+                    vid, sid) + self.compute_heuristic_cost(sid, self.goalId)
+                if estimated_f_score < self.g_scores[self.goalId] and (
+                        self.g_scores[vid] + self.compute_distance_cost(vid, sid)) < self.g_scores[sid]:
                     self.edge_queue.append((vid, sid))
 
-    def updateGraph(self):
+    def update_graph(self):
         closedSet = []
         openSet = []
         currId = self.startId
@@ -498,22 +503,21 @@ def updateGraph(self):
             openSet.remove(currId)
 
             # Check if we're at the goal
-            if(currId == self.goalId):
-                self.nodes[self.goalId]
+            if currId == self.goalId:
                 break
 
-            if(currId not in closedSet):
+            if currId not in closedSet:
                 closedSet.append(currId)
 
             # find a non visited successor to the current node
             successors = self.tree.vertices[currId]
             for succesor in successors:
-                if(succesor in closedSet):
+                if succesor in closedSet:
                     continue
                 else:
                     # claculate tentative g score
                     g_score = self.g_scores[currId] + \
-                        self.computeDistanceCost(currId, succesor)
+                              self.compute_distance_cost(currId, succesor)
                     if succesor not in openSet:
                         # add the successor to open set
                         openSet.append(succesor)
@@ -522,14 +526,13 @@ def updateGraph(self):
 
                     # update g and f scores
                     self.g_scores[succesor] = g_score
-                    self.f_scores[succesor] = g_score + \
-                        self.computeHeuristicCost(succesor, self.goalId)
+                    self.f_scores[succesor] = g_score + self.compute_heuristic_cost(succesor, self.goalId)
 
                     # store the parent and child
                     self.nodes[succesor] = currId
 
-    def drawGraph(self, xCenter=None, cBest=None, cMin=None, etheta=None,
-                  samples=None, start=None, end=None, tree=None):
+    def draw_graph(self, xCenter=None, cBest=None, cMin=None, etheta=None,
+                   samples=None, start=None, end=None):
         plt.clf()
         for rnd in samples:
             if rnd is not None:
@@ -549,9 +552,10 @@ def drawGraph(self, xCenter=None, cBest=None, cMin=None, etheta=None,
         plt.grid(True)
         plt.pause(0.01)
 
-    def plot_ellipse(self, xCenter, cBest, cMin, etheta):  # pragma: no cover
+    @staticmethod
+    def plot_ellipse(xCenter, cBest, cMin, etheta):  # pragma: no cover
 
-        a = math.sqrt(cBest**2 - cMin**2) / 2.0
+        a = math.sqrt(cBest ** 2 - cMin ** 2) / 2.0
         b = cBest / 2.0
         angle = math.pi / 2.0 - etheta
         cx = xCenter[0]

From 097a289a88fe38dd6e27df8de956d75a17bba947 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 3 Aug 2019 14:04:10 +0900
Subject: [PATCH 0154/1080] code clean up for informed rrt star

---
 .../InformedRRTStar/informed_rrt_star.py      | 181 +++++++++---------
 1 file changed, 94 insertions(+), 87 deletions(-)

diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index 2a999fd0c5..4a32801da5 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -4,22 +4,22 @@
 author: Karan Chawla
         Atsushi Sakai(@Atsushi_twi)
 
-Reference: Informed RRT*: Optimal Sampling-based Path Planning Focused via
+Reference: Informed RRT*: Optimal Sampling-based Path planning Focused via
 Direct Sampling of an Admissible Ellipsoidal Heuristichttps://arxiv.org/pdf/1404.2334.pdf
 
 """
 
-
-import random
-import numpy as np
-import math
 import copy
+import math
+import random
+
 import matplotlib.pyplot as plt
+import numpy as np
 
 show_animation = True
 
 
-class InformedRRTStar():
+class InformedRRTStar:
 
     def __init__(self, start, goal,
                  obstacleList, randArea,
@@ -27,16 +27,17 @@ def __init__(self, start, goal,
 
         self.start = Node(start[0], start[1])
         self.goal = Node(goal[0], goal[1])
-        self.minrand = randArea[0]
-        self.maxrand = randArea[1]
-        self.expandDis = expandDis
-        self.goalSampleRate = goalSampleRate
-        self.maxIter = maxIter
-        self.obstacleList = obstacleList
+        self.min_rand = randArea[0]
+        self.max_rand = randArea[1]
+        self.expand_dis = expandDis
+        self.goal_sample_rate = goalSampleRate
+        self.max_iter = maxIter
+        self.obstacle_list = obstacleList
+        self.node_list = None
 
-    def InformedRRTStarSearch(self, animation=True):
+    def informed_rrt_star_search(self, animation=True):
 
-        self.nodeList = [self.start]
+        self.node_list = [self.start]
         # max length we expect to find in our 'informed' sample space, starts as infinite
         cBest = float('inf')
         pathLen = float('inf')
@@ -55,62 +56,62 @@ def InformedRRTStarSearch(self, animation=True):
         # first column of idenity matrix transposed
         id1_t = np.array([1.0, 0.0, 0.0]).reshape(1, 3)
         M = a1 @ id1_t
-        U, S, Vh = np.linalg.svd(M, 1, 1)
+        U, S, Vh = np.linalg.svd(M, True, True)
         C = np.dot(np.dot(U, np.diag(
             [1.0, 1.0, np.linalg.det(U) * np.linalg.det(np.transpose(Vh))])), Vh)
 
-        for i in range(self.maxIter):
+        for i in range(self.max_iter):
             # Sample space is defined by cBest
             # cMin is the minimum distance between the start point and the goal
             # xCenter is the midpoint between the start and the goal
             # cBest changes when a new path is found
 
             rnd = self.informed_sample(cBest, cMin, xCenter, C)
-            nind = self.getNearestListIndex(self.nodeList, rnd)
-            nearestNode = self.nodeList[nind]
+            nind = self.get_nearest_list_index(self.node_list, rnd)
+            nearestNode = self.node_list[nind]
             # steer
             theta = math.atan2(rnd[1] - nearestNode.y, rnd[0] - nearestNode.x)
-            newNode = self.getNewNode(theta, nind, nearestNode)
-            d = self.lineCost(nearestNode, newNode)
+            newNode = self.get_new_node(theta, nind, nearestNode)
+            d = self.line_cost(nearestNode, newNode)
 
-            isCollision = self.__CollisionCheck(newNode, self.obstacleList)
+            isCollision = self.collision_check(newNode, self.obstacle_list)
             isCollisionEx = self.check_collision_extend(nearestNode, theta, d)
 
             if isCollision and isCollisionEx:
-                nearInds = self.findNearNodes(newNode)
-                newNode = self.chooseParent(newNode, nearInds)
+                nearInds = self.find_near_nodes(newNode)
+                newNode = self.choose_parent(newNode, nearInds)
 
-                self.nodeList.append(newNode)
+                self.node_list.append(newNode)
                 self.rewire(newNode, nearInds)
 
-                if self.isNearGoal(newNode):
+                if self.is_near_goal(newNode):
                     solutionSet.add(newNode)
-                    lastIndex = len(self.nodeList) - 1
-                    tempPath = self.getFinalCourse(lastIndex)
-                    tempPathLen = self.getPathLen(tempPath)
+                    lastIndex = len(self.node_list) - 1
+                    tempPath = self.get_final_course(lastIndex)
+                    tempPathLen = self.get_path_len(tempPath)
                     if tempPathLen < pathLen:
                         path = tempPath
                         cBest = tempPathLen
 
             if animation:
-                self.drawGraph(xCenter=xCenter,
-                               cBest=cBest, cMin=cMin,
-                               etheta=etheta, rnd=rnd)
+                self.draw_graph(xCenter=xCenter,
+                                cBest=cBest, cMin=cMin,
+                                etheta=etheta, rnd=rnd)
 
         return path
 
-    def chooseParent(self, newNode, nearInds):
+    def choose_parent(self, newNode, nearInds):
         if len(nearInds) == 0:
             return newNode
 
         dList = []
         for i in nearInds:
-            dx = newNode.x - self.nodeList[i].x
-            dy = newNode.y - self.nodeList[i].y
+            dx = newNode.x - self.node_list[i].x
+            dy = newNode.y - self.node_list[i].y
             d = math.sqrt(dx ** 2 + dy ** 2)
             theta = math.atan2(dy, dx)
-            if self.check_collision_extend(self.nodeList[i], theta, d):
-                dList.append(self.nodeList[i].cost + d)
+            if self.check_collision_extend(self.node_list[i], theta, d):
+                dList.append(self.node_list[i].cost + d)
             else:
                 dList.append(float('inf'))
 
@@ -126,29 +127,30 @@ def chooseParent(self, newNode, nearInds):
 
         return newNode
 
-    def findNearNodes(self, newNode):
-        nnode = len(self.nodeList)
+    def find_near_nodes(self, newNode):
+        nnode = len(self.node_list)
         r = 50.0 * math.sqrt((math.log(nnode) / nnode))
         dlist = [(node.x - newNode.x) ** 2
-                 + (node.y - newNode.y) ** 2 for node in self.nodeList]
+                 + (node.y - newNode.y) ** 2 for node in self.node_list]
         nearinds = [dlist.index(i) for i in dlist if i <= r ** 2]
         return nearinds
 
     def informed_sample(self, cMax, cMin, xCenter, C):
         if cMax < float('inf'):
             r = [cMax / 2.0,
-                 math.sqrt(cMax**2 - cMin**2) / 2.0,
-                 math.sqrt(cMax**2 - cMin**2) / 2.0]
+                 math.sqrt(cMax ** 2 - cMin ** 2) / 2.0,
+                 math.sqrt(cMax ** 2 - cMin ** 2) / 2.0]
             L = np.diag(r)
-            xBall = self.sampleUnitBall()
+            xBall = self.sample_unit_ball()
             rnd = np.dot(np.dot(C, L), xBall) + xCenter
             rnd = [rnd[(0, 0)], rnd[(1, 0)]]
         else:
-            rnd = self.sampleFreeSpace()
+            rnd = self.sample_free_space()
 
         return rnd
 
-    def sampleUnitBall(self):
+    @staticmethod
+    def sample_unit_ball():
         a = random.random()
         b = random.random()
 
@@ -159,16 +161,17 @@ def sampleUnitBall(self):
                   b * math.sin(2 * math.pi * a / b))
         return np.array([[sample[0]], [sample[1]], [0]])
 
-    def sampleFreeSpace(self):
-        if random.randint(0, 100) > self.goalSampleRate:
-            rnd = [random.uniform(self.minrand, self.maxrand),
-                   random.uniform(self.minrand, self.maxrand)]
+    def sample_free_space(self):
+        if random.randint(0, 100) > self.goal_sample_rate:
+            rnd = [random.uniform(self.min_rand, self.max_rand),
+                   random.uniform(self.min_rand, self.max_rand)]
         else:
             rnd = [self.goal.x, self.goal.y]
 
         return rnd
 
-    def getPathLen(self, path):
+    @staticmethod
+    def get_path_len(path):
         pathLen = 0
         for i in range(1, len(path)):
             node1_x = path[i][0]
@@ -176,52 +179,55 @@ def getPathLen(self, path):
             node2_x = path[i - 1][0]
             node2_y = path[i - 1][1]
             pathLen += math.sqrt((node1_x - node2_x)
-                                 ** 2 + (node1_y - node2_y)**2)
+                                 ** 2 + (node1_y - node2_y) ** 2)
 
         return pathLen
 
-    def lineCost(self, node1, node2):
-        return math.sqrt((node1.x - node2.x)**2 + (node1.y - node2.y)**2)
+    @staticmethod
+    def line_cost(node1, node2):
+        return math.sqrt((node1.x - node2.x) ** 2 + (node1.y - node2.y) ** 2)
 
-    def getNearestListIndex(self, nodes, rnd):
-        dList = [(node.x - rnd[0])**2
-                 + (node.y - rnd[1])**2 for node in nodes]
+    @staticmethod
+    def get_nearest_list_index(nodes, rnd):
+        dList = [(node.x - rnd[0]) ** 2
+                 + (node.y - rnd[1]) ** 2 for node in nodes]
         minIndex = dList.index(min(dList))
         return minIndex
 
-    def __CollisionCheck(self, newNode, obstacleList):
+    @staticmethod
+    def collision_check(newNode, obstacleList):
         for (ox, oy, size) in obstacleList:
             dx = ox - newNode.x
             dy = oy - newNode.y
             d = dx * dx + dy * dy
-            if d <= 1.1 * size**2:
+            if d <= 1.1 * size ** 2:
                 return False  # collision
 
         return True  # safe
 
-    def getNewNode(self, theta, nind, nearestNode):
+    def get_new_node(self, theta, nind, nearestNode):
         newNode = copy.deepcopy(nearestNode)
 
-        newNode.x += self.expandDis * math.cos(theta)
-        newNode.y += self.expandDis * math.sin(theta)
+        newNode.x += self.expand_dis * math.cos(theta)
+        newNode.y += self.expand_dis * math.sin(theta)
 
-        newNode.cost += self.expandDis
+        newNode.cost += self.expand_dis
         newNode.parent = nind
         return newNode
 
-    def isNearGoal(self, node):
-        d = self.lineCost(node, self.goal)
-        if d < self.expandDis:
+    def is_near_goal(self, node):
+        d = self.line_cost(node, self.goal)
+        if d < self.expand_dis:
             return True
         return False
 
     def rewire(self, newNode, nearInds):
-        nnode = len(self.nodeList)
+        n_node = len(self.node_list)
         for i in nearInds:
-            nearNode = self.nodeList[i]
+            nearNode = self.node_list[i]
 
-            d = math.sqrt((nearNode.x - newNode.x)**2
-                          + (nearNode.y - newNode.y)**2)
+            d = math.sqrt((nearNode.x - newNode.x) ** 2
+                          + (nearNode.y - newNode.y) ** 2)
 
             scost = newNode.cost + d
 
@@ -229,30 +235,30 @@ def rewire(self, newNode, nearInds):
                 theta = math.atan2(newNode.y - nearNode.y,
                                    newNode.x - nearNode.x)
                 if self.check_collision_extend(nearNode, theta, d):
-                    nearNode.parent = nnode - 1
+                    nearNode.parent = n_node - 1
                     nearNode.cost = scost
 
     def check_collision_extend(self, nearNode, theta, d):
         tmpNode = copy.deepcopy(nearNode)
 
-        for i in range(int(d / self.expandDis)):
-            tmpNode.x += self.expandDis * math.cos(theta)
-            tmpNode.y += self.expandDis * math.sin(theta)
-            if not self.__CollisionCheck(tmpNode, self.obstacleList):
+        for i in range(int(d / self.expand_dis)):
+            tmpNode.x += self.expand_dis * math.cos(theta)
+            tmpNode.y += self.expand_dis * math.sin(theta)
+            if not self.collision_check(tmpNode, self.obstacle_list):
                 return False
 
         return True
 
-    def getFinalCourse(self, lastIndex):
+    def get_final_course(self, lastIndex):
         path = [[self.goal.x, self.goal.y]]
-        while self.nodeList[lastIndex].parent is not None:
-            node = self.nodeList[lastIndex]
+        while self.node_list[lastIndex].parent is not None:
+            node = self.node_list[lastIndex]
             path.append([node.x, node.y])
             lastIndex = node.parent
         path.append([self.start.x, self.start.y])
         return path
 
-    def drawGraph(self, xCenter=None, cBest=None, cMin=None, etheta=None, rnd=None):
+    def draw_graph(self, xCenter=None, cBest=None, cMin=None, etheta=None, rnd=None):
 
         plt.clf()
         if rnd is not None:
@@ -260,13 +266,13 @@ def drawGraph(self, xCenter=None, cBest=None, cMin=None, etheta=None, rnd=None):
             if cBest != float('inf'):
                 self.plot_ellipse(xCenter, cBest, cMin, etheta)
 
-        for node in self.nodeList:
+        for node in self.node_list:
             if node.parent is not None:
                 if node.x or node.y is not None:
-                    plt.plot([node.x, self.nodeList[node.parent].x], [
-                        node.y, self.nodeList[node.parent].y], "-g")
+                    plt.plot([node.x, self.node_list[node.parent].x], [
+                        node.y, self.node_list[node.parent].y], "-g")
 
-        for (ox, oy, size) in self.obstacleList:
+        for (ox, oy, size) in self.obstacle_list:
             plt.plot(ox, oy, "ok", ms=30 * size)
 
         plt.plot(self.start.x, self.start.y, "xr")
@@ -275,9 +281,10 @@ def drawGraph(self, xCenter=None, cBest=None, cMin=None, etheta=None, rnd=None):
         plt.grid(True)
         plt.pause(0.01)
 
-    def plot_ellipse(self, xCenter, cBest, cMin, etheta):  # pragma: no cover
+    @staticmethod
+    def plot_ellipse(xCenter, cBest, cMin, etheta):  # pragma: no cover
 
-        a = math.sqrt(cBest**2 - cMin**2) / 2.0
+        a = math.sqrt(cBest ** 2 - cMin ** 2) / 2.0
         b = cBest / 2.0
         angle = math.pi / 2.0 - etheta
         cx = xCenter[0]
@@ -295,7 +302,7 @@ def plot_ellipse(self, xCenter, cBest, cMin, etheta):  # pragma: no cover
         plt.plot(px, py, "--c")
 
 
-class Node():
+class Node:
 
     def __init__(self, x, y):
         self.x = x
@@ -320,12 +327,12 @@ def main():
     # Set params
     rrt = InformedRRTStar(start=[0, 0], goal=[5, 10],
                           randArea=[-2, 15], obstacleList=obstacleList)
-    path = rrt.InformedRRTStarSearch(animation=show_animation)
+    path = rrt.informed_rrt_star_search(animation=show_animation)
     print("Done!!")
 
     # Plot path
     if show_animation:
-        rrt.drawGraph()
+        rrt.draw_graph()
         plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
         plt.grid(True)
         plt.pause(0.01)
@@ -333,4 +340,4 @@ def main():
 
 
 if __name__ == '__main__':
-    main()
\ No newline at end of file
+    main()

From 98584912e9810add1c055e39db5a7ed1f7aea115 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 3 Aug 2019 14:17:03 +0900
Subject: [PATCH 0155/1080] add a test case for grid map lib

---
 ..._grid_based_sweep_coverage_path_planner.py |  2 +-
 tests/test_grid_map_lib.py                    | 38 +++++++++++++++++++
 2 files changed, 39 insertions(+), 1 deletion(-)
 create mode 100644 tests/test_grid_map_lib.py

diff --git a/tests/test_grid_based_sweep_coverage_path_planner.py b/tests/test_grid_based_sweep_coverage_path_planner.py
index 3caaac8952..3933356726 100644
--- a/tests/test_grid_based_sweep_coverage_path_planner.py
+++ b/tests/test_grid_based_sweep_coverage_path_planner.py
@@ -6,7 +6,7 @@
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../Mapping/grid_map_lib")
 try:
     import grid_based_sweep_coverage_path_planner
-except:
+except ImportError:
     raise
 
 class TestPlanning(TestCase):
diff --git a/tests/test_grid_map_lib.py b/tests/test_grid_map_lib.py
new file mode 100644
index 0000000000..63b3cc86b7
--- /dev/null
+++ b/tests/test_grid_map_lib.py
@@ -0,0 +1,38 @@
+import os
+import sys
+import unittest
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../Mapping/grid_map_lib")
+try:
+    from grid_map_lib import GridMap
+except ImportError:
+    raise
+
+
+class MyTestCase(unittest.TestCase):
+
+    def test_position_set(self):
+        grid_map = GridMap(100, 120, 0.5, 10.0, -0.5)
+
+        grid_map.set_value_from_xy_pos(10.1, -1.1, 1.0)
+        grid_map.set_value_from_xy_pos(10.1, -0.1, 1.0)
+        grid_map.set_value_from_xy_pos(10.1, 1.1, 1.0)
+        grid_map.set_value_from_xy_pos(11.1, 0.1, 1.0)
+        grid_map.set_value_from_xy_pos(10.1, 0.1, 1.0)
+        grid_map.set_value_from_xy_pos(9.1, 0.1, 1.0)
+
+        self.assertEqual(True, True)
+
+    def test_polygon_set(self):
+        ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0]
+        oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0]
+
+        grid_map = GridMap(600, 290, 0.7, 60.0, 30.5)
+
+        grid_map.set_value_from_polygon(ox, oy, 1.0, inside=False)
+
+        self.assertEqual(True, True)
+
+
+if __name__ == '__main__':
+    unittest.main()

From 625b00b5b88e470029b4fa5676b6a2e5c0999eec Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 3 Aug 2019 14:22:48 +0900
Subject: [PATCH 0156/1080] add no cover annotations

---
 .../grid_based_sweep_coverage_path_planner.py                | 4 ++--
 PathPlanning/RRTDubins/rrt_dubins.py                         | 5 ++---
 2 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
index 3d068559f8..5d691ffde3 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -259,7 +259,7 @@ def planning(ox, oy, reso,
     return rx, ry
 
 
-def planning_animation(ox, oy, reso):
+def planning_animation(ox, oy, reso)  # pragma: no cover:
     px, py = planning(ox, oy, reso)
 
     # animation
@@ -281,7 +281,7 @@ def planning_animation(ox, oy, reso):
     plt.pause(0.1)
 
 
-def main():
+def main()  # pragma: no cover:
     print("start!!")
 
     ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0, 0.0]
diff --git a/PathPlanning/RRTDubins/rrt_dubins.py b/PathPlanning/RRTDubins/rrt_dubins.py
index ed2b27ca0b..339acd2c3a 100644
--- a/PathPlanning/RRTDubins/rrt_dubins.py
+++ b/PathPlanning/RRTDubins/rrt_dubins.py
@@ -24,7 +24,6 @@
 except ImportError:
     raise
 
-
 show_animation = True
 
 
@@ -105,7 +104,7 @@ def planning(self, animation=True, search_until_max_iter=True):
 
         return None
 
-    def draw_graph(self, rnd=None):
+    def draw_graph(self, rnd=None):  # pragma: no cover
         plt.clf()
         if rnd is not None:
             plt.plot(rnd.x, rnd.y, "^k")
@@ -123,7 +122,7 @@ def draw_graph(self, rnd=None):
         self.plot_start_goal_arrow()
         plt.pause(0.01)
 
-    def plot_start_goal_arrow(self):
+    def plot_start_goal_arrow(self):  # pragma: no cover
         dubins_path_planning.plot_arrow(
             self.start.x, self.start.y, self.start.yaw)
         dubins_path_planning.plot_arrow(

From 76074568ced5118a8929cbe856492e156760d894 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 3 Aug 2019 14:33:58 +0900
Subject: [PATCH 0157/1080] fix test

---
 .../grid_based_sweep_coverage_path_planner.py                 | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
index 5d691ffde3..f22b058fa8 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -259,7 +259,7 @@ def planning(ox, oy, reso,
     return rx, ry
 
 
-def planning_animation(ox, oy, reso)  # pragma: no cover:
+def planning_animation(ox, oy, reso):  # pragma: no cover
     px, py = planning(ox, oy, reso)
 
     # animation
@@ -281,7 +281,7 @@ def planning_animation(ox, oy, reso)  # pragma: no cover:
     plt.pause(0.1)
 
 
-def main()  # pragma: no cover:
+def main():  # pragma: no cover
     print("start!!")
 
     ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0, 0.0]

From b94e13500fd9ca790fb4b488eba2428fe97b879c Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 3 Aug 2019 16:43:24 +0900
Subject: [PATCH 0158/1080] code clean up2

---
 .../histogram_filter/histogram_filter.py       | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py
index 1e43fee850..a70658e61c 100644
--- a/Localization/histogram_filter/histogram_filter.py
+++ b/Localization/histogram_filter/histogram_filter.py
@@ -11,12 +11,13 @@
 
 """
 
+import copy
 import math
-import numpy as np
+
 import matplotlib.pyplot as plt
-import copy
-from scipy.stats import norm
+import numpy as np
 from scipy.ndimage import gaussian_filter
+from scipy.stats import norm
 
 # Parameters
 EXTEND_AREA = 10.0  # [m] grid map extention length
@@ -41,7 +42,7 @@
 show_animation = True
 
 
-class grid_map():
+class GridMap:
 
     def __init__(self):
         self.data = None
@@ -117,7 +118,7 @@ def motion_model(x, u):
 
 def draw_heatmap(data, mx, my):
     maxp = max([max(igmap) for igmap in data])
-    plt.pcolor(mx, my, data, vmax=maxp, cmap=plt.cm.Blues)
+    plt.pcolor(mx, my, data, vmax=maxp, cmap=plt.cm.get_cmap("Blues"))
     plt.axis("equal")
 
 
@@ -157,8 +158,7 @@ def normalize_probability(gmap):
 
 
 def init_gmap(xyreso, minx, miny, maxx, maxy):
-
-    gmap = grid_map()
+    gmap = GridMap()
 
     gmap.xyreso = xyreso
     gmap.minx = minx
@@ -168,7 +168,7 @@ def init_gmap(xyreso, minx, miny, maxx, maxy):
     gmap.xw = int(round((gmap.maxx - gmap.minx) / gmap.xyreso))
     gmap.yw = int(round((gmap.maxy - gmap.miny) / gmap.xyreso))
 
-    gmap.data = [[1.0 for i in range(gmap.yw)] for i in range(gmap.xw)]
+    gmap.data = [[1.0 for _ in range(gmap.yw)] for _ in range(gmap.xw)]
     gmap = normalize_probability(gmap)
 
     return gmap
@@ -183,7 +183,7 @@ def map_shift(gmap, xshift, yshift):
             nix = ix + xshift
             niy = iy + yshift
 
-            if nix >= 0 and nix < gmap.xw and niy >= 0 and niy < gmap.yw:
+            if 0 <= nix < gmap.xw and 0 <= niy < gmap.yw:
                 gmap.data[ix + xshift][iy + yshift] = tgmap[ix][iy]
 
     return gmap

From ac547941aad6ecd340d99cbf76028f536b107179 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 3 Aug 2019 16:43:24 +0900
Subject: [PATCH 0159/1080] code clean up

---
 .../histogram_filter/histogram_filter.py      | 26 +++++++++----------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py
index 1e43fee850..f7696e6ba9 100644
--- a/Localization/histogram_filter/histogram_filter.py
+++ b/Localization/histogram_filter/histogram_filter.py
@@ -11,12 +11,13 @@
 
 """
 
+import copy
 import math
-import numpy as np
+
 import matplotlib.pyplot as plt
-import copy
-from scipy.stats import norm
+import numpy as np
 from scipy.ndimage import gaussian_filter
+from scipy.stats import norm
 
 # Parameters
 EXTEND_AREA = 10.0  # [m] grid map extention length
@@ -41,11 +42,11 @@
 show_animation = True
 
 
-class grid_map():
+class GridMap:
 
     def __init__(self):
         self.data = None
-        self.xyreso = None
+        self.xy_reso = None
         self.minx = None
         self.miny = None
         self.maxx = None
@@ -117,7 +118,7 @@ def motion_model(x, u):
 
 def draw_heatmap(data, mx, my):
     maxp = max([max(igmap) for igmap in data])
-    plt.pcolor(mx, my, data, vmax=maxp, cmap=plt.cm.Blues)
+    plt.pcolor(mx, my, data, vmax=maxp, cmap=plt.cm.get_cmap("Blues"))
     plt.axis("equal")
 
 
@@ -157,18 +158,17 @@ def normalize_probability(gmap):
 
 
 def init_gmap(xyreso, minx, miny, maxx, maxy):
+    gmap = GridMap()
 
-    gmap = grid_map()
-
-    gmap.xyreso = xyreso
+    gmap.xy_reso = xyreso
     gmap.minx = minx
     gmap.miny = miny
     gmap.maxx = maxx
     gmap.maxy = maxy
-    gmap.xw = int(round((gmap.maxx - gmap.minx) / gmap.xyreso))
-    gmap.yw = int(round((gmap.maxy - gmap.miny) / gmap.xyreso))
+    gmap.xw = int(round((gmap.maxx - gmap.minx) / gmap.xy_reso))
+    gmap.yw = int(round((gmap.maxy - gmap.miny) / gmap.xy_reso))
 
-    gmap.data = [[1.0 for i in range(gmap.yw)] for i in range(gmap.xw)]
+    gmap.data = [[1.0 for _ in range(gmap.yw)] for _ in range(gmap.xw)]
     gmap = normalize_probability(gmap)
 
     return gmap
@@ -183,7 +183,7 @@ def map_shift(gmap, xshift, yshift):
             nix = ix + xshift
             niy = iy + yshift
 
-            if nix >= 0 and nix < gmap.xw and niy >= 0 and niy < gmap.yw:
+            if 0 <= nix < gmap.xw and 0 <= niy < gmap.yw:
                 gmap.data[ix + xshift][iy + yshift] = tgmap[ix][iy]
 
     return gmap

From da2b1ac6c7b4521ec44dd3408e3b9e8971dcdfcf Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 3 Aug 2019 20:35:44 +0900
Subject: [PATCH 0160/1080] code cleanup

---
 .../histogram_filter/histogram_filter.py      | 109 +++++++++---------
 1 file changed, 53 insertions(+), 56 deletions(-)

diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py
index 5342d02b4f..9c1cb3e524 100644
--- a/Localization/histogram_filter/histogram_filter.py
+++ b/Localization/histogram_filter/histogram_filter.py
@@ -11,12 +11,13 @@
 
 """
 
+import copy
 import math
-import numpy as np
+
 import matplotlib.pyplot as plt
-import copy
-from scipy.stats import norm
+import numpy as np
 from scipy.ndimage import gaussian_filter
+from scipy.stats import norm
 
 # Parameters
 EXTEND_AREA = 10.0  # [m] grid map extended length
@@ -41,11 +42,11 @@
 show_animation = True
 
 
-class grid_map():
+class GridMap():
 
     def __init__(self):
         self.data = None
-        self.xyreso = None
+        self.xy_reso = None
         self.minx = None
         self.miny = None
         self.maxx = None
@@ -56,20 +57,19 @@ def __init__(self):
         self.dy = 0.0  # movement distance
 
 
-def histogram_filter_localization(gmap, u, z, yaw):
+def histogram_filter_localization(grid_map, u, z, yaw):
+    grid_map = motion_update(grid_map, u, yaw)
 
-    gmap = motion_update(gmap, u, yaw)
+    grid_map = observation_update(grid_map, z, RANGE_STD)
 
-    gmap = observation_update(gmap, z, RANGE_STD)
-
-    return gmap
+    return grid_map
 
 
 def calc_gaussian_observation_pdf(gmap, z, iz, ix, iy, std):
 
     # predicted range
-    x = ix * gmap.xyreso + gmap.minx
-    y = iy * gmap.xyreso + gmap.miny
+    x = ix * gmap.xy_reso + gmap.minx
+    y = iy * gmap.xy_reso + gmap.miny
     d = math.sqrt((x - z[iz, 1])**2 + (y - z[iz, 2])**2)
 
     # likelihood
@@ -93,8 +93,8 @@ def observation_update(gmap, z, std):
 
 def calc_input():
     v = 1.0  # [m/s]
-    yawrate = 0.1  # [rad/s]
-    u = np.array([v, yawrate]).reshape(2, 1)
+    yaw_rate = 0.1  # [rad/s]
+    u = np.array([v, yaw_rate]).reshape(2, 1)
     return u
 
 
@@ -115,7 +115,7 @@ def motion_model(x, u):
     return x
 
 
-def draw_heatmap(data, mx, my):
+def draw_heat_map(data, mx, my):
     maxp = max([max(igmap) for igmap in data])
     plt.pcolor(mx, my, data, vmax=maxp, cmap=plt.cm.get_cmap("Blues"))
     plt.axis("equal")
@@ -156,60 +156,57 @@ def normalize_probability(gmap):
     return gmap
 
 
-def init_gmap(xyreso, minx, miny, maxx, maxy):
+def init_gmap(xy_reso, minx, miny, maxx, maxy):
+    grid_map = GridMap()
 
-    gmap = grid_map()
+    grid_map.xy_reso = xy_reso
+    grid_map.minx = minx
+    grid_map.miny = miny
+    grid_map.maxx = maxx
+    grid_map.maxy = maxy
+    grid_map.xw = int(round((grid_map.maxx - grid_map.minx) / grid_map.xy_reso))
+    grid_map.yw = int(round((grid_map.maxy - grid_map.miny) / grid_map.xy_reso))
 
-    gmap.xy_reso = xyreso
-    gmap.minx = minx
-    gmap.miny = miny
-    gmap.maxx = maxx
-    gmap.maxy = maxy
-    gmap.xw = int(round((gmap.maxx - gmap.minx) / gmap.xy_reso))
-    gmap.yw = int(round((gmap.maxy - gmap.miny) / gmap.xy_reso))
+    grid_map.data = [[1.0 for _ in range(grid_map.yw)] for _ in range(grid_map.xw)]
+    grid_map = normalize_probability(grid_map)
 
-    gmap.data = [[1.0 for _ in range(gmap.yw)] for _ in range(gmap.xw)]
-    gmap = normalize_probability(gmap)
+    return grid_map
 
-    return gmap
 
+def map_shift(grid_map, x_shift, y_shift):
+    tgmap = copy.deepcopy(grid_map.data)
 
-def map_shift(gmap, xshift, yshift):
+    for ix in range(grid_map.xw):
+        for iy in range(grid_map.yw):
+            nix = ix + x_shift
+            niy = iy + y_shift
 
-    tgmap = copy.deepcopy(gmap.data)
+            if 0 <= nix < grid_map.xw and 0 <= niy < grid_map.yw:
+                grid_map.data[ix + x_shift][iy + y_shift] = tgmap[ix][iy]
 
-    for ix in range(gmap.xw):
-        for iy in range(gmap.yw):
-            nix = ix + xshift
-            niy = iy + yshift
+    return grid_map
 
-            if 0 <= nix < gmap.xw and 0 <= niy < gmap.yw:
-                gmap.data[ix + xshift][iy + yshift] = tgmap[ix][iy]
 
-    return gmap
+def motion_update(grid_map, u, yaw):
+    grid_map.dx += DT * math.cos(yaw) * u[0]
+    grid_map.dy += DT * math.sin(yaw) * u[0]
 
+    x_shift = grid_map.dx // grid_map.xy_reso
+    y_shift = grid_map.dy // grid_map.xy_reso
 
-def motion_update(gmap, u, yaw):
+    if abs(x_shift) >= 1.0 or abs(y_shift) >= 1.0:  # map should be shifted
+        grid_map = map_shift(grid_map, int(x_shift), int(y_shift))
+        grid_map.dx -= x_shift * grid_map.xy_reso
+        grid_map.dy -= y_shift * grid_map.xy_reso
 
-    gmap.dx += DT * math.cos(yaw) * u[0]
-    gmap.dy += DT * math.sin(yaw) * u[0]
+    grid_map.data = gaussian_filter(grid_map.data, sigma=MOTION_STD)
 
-    xshift = gmap.dx // gmap.xyreso
-    yshift = gmap.dy // gmap.xyreso
-
-    if abs(xshift) >= 1.0 or abs(yshift) >= 1.0:  # map should be shifted
-        gmap = map_shift(gmap, int(xshift), int(yshift))
-        gmap.dx -= xshift * gmap.xyreso
-        gmap.dy -= yshift * gmap.xyreso
-
-    gmap.data = gaussian_filter(gmap.data, sigma=MOTION_STD)
-
-    return gmap
+    return grid_map
 
 
 def calc_grid_index(gmap):
-    mx, my = np.mgrid[slice(gmap.minx - gmap.xyreso / 2.0, gmap.maxx + gmap.xyreso / 2.0, gmap.xyreso),
-                      slice(gmap.miny - gmap.xyreso / 2.0, gmap.maxy + gmap.xyreso / 2.0, gmap.xyreso)]
+    mx, my = np.mgrid[slice(gmap.minx - gmap.xy_reso / 2.0, gmap.maxx + gmap.xy_reso / 2.0, gmap.xy_reso),
+                      slice(gmap.miny - gmap.xy_reso / 2.0, gmap.maxy + gmap.xy_reso / 2.0, gmap.xy_reso)]
 
     return mx, my
 
@@ -226,8 +223,8 @@ def main():
     time = 0.0
 
     xTrue = np.zeros((4, 1))
-    gmap = init_gmap(XY_RESO, MINX, MINY, MAXX, MAXY)
-    mx, my = calc_grid_index(gmap)  # for grid map visualization
+    grid_map = init_gmap(XY_RESO, MINX, MINY, MAXX, MAXY)
+    mx, my = calc_grid_index(grid_map)  # for grid map visualization
 
     while SIM_TIME >= time:
         time += DT
@@ -238,11 +235,11 @@ def main():
         yaw = xTrue[2, 0]  # Orientation is known
         xTrue, z, ud = observation(xTrue, u, RFID)
 
-        gmap = histogram_filter_localization(gmap, u, z, yaw)
+        grid_map = histogram_filter_localization(grid_map, u, z, yaw)
 
         if show_animation:
             plt.cla()
-            draw_heatmap(gmap.data, mx, my)
+            draw_heat_map(grid_map.data, mx, my)
             plt.plot(xTrue[0, :], xTrue[1, :], "xr")
             plt.plot(RFID[:, 0], RFID[:, 1], ".k")
             for i in range(z.shape[0]):

From 44ac74b08f94ab59ff772dcbafbd8213927c994e Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 5 Aug 2019 20:11:06 +0900
Subject: [PATCH 0161/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 294f3b3b13..d0bd285842 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -345,6 +345,12 @@ Thanks for writing these algorithms. They are very helpful for learning robotics
 
 ---
 
+Dear AtsushiSakai, <br>Thank you for providing us this great repository for playing and look around:)!
+
+--Hsin-Wen
+
+
+
 
 # Citations
 

From e27f0cd70b503dd95e0437f32204b4c5774012d1 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 9 Aug 2019 22:37:09 +0900
Subject: [PATCH 0162/1080] Update users_comments.md

---
 users_comments.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index d0bd285842..e015c19d1f 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -349,7 +349,11 @@ Dear AtsushiSakai, <br>Thank you for providing us this great repository for play
 
 --Hsin-Wen
 
+---
+
+Thanks for PythonRobotics!
 
+--Nick V
 
 
 # Citations

From b762fd036e35f25a178de4add06706357fe1bed6 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 15 Aug 2019 09:00:42 +0900
Subject: [PATCH 0163/1080] Update users_comments.md

---
 users_comments.md | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index e015c19d1f..8e66ae9f11 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -355,6 +355,14 @@ Thanks for PythonRobotics!
 
 --Nick V
 
+---
+
+Dear AtsushiSakai, thank you so much for this material, it's so useful to me, i'm really glad with it =D
+
+--Juanda
+
+---
+
 
 # Citations
 

From a48bacd6cf6c55b304ebe7ea3c4632ed28cec819 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 22 Aug 2019 21:32:02 +0900
Subject: [PATCH 0164/1080] Update state_lattice_planner.py

---
 PathPlanning/StateLatticePlanner/state_lattice_planner.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/PathPlanning/StateLatticePlanner/state_lattice_planner.py b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
index 424609064d..c2819eb250 100644
--- a/PathPlanning/StateLatticePlanner/state_lattice_planner.py
+++ b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
@@ -4,6 +4,12 @@
 
 author: Atsushi Sakai (@Atsushi_twi)
 
+- lookuptable.csv is generated with this script: https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable_generator.py
+
+Ref:
+
+- State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.8210&rep=rep1&type=pdf
+
 """
 import sys
 import os

From 7864296494d936b35e80e6cef946fed2bcff066d Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 22 Aug 2019 21:53:35 +0900
Subject: [PATCH 0165/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 8e66ae9f11..772b5774ec 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -363,6 +363,12 @@ Dear AtsushiSakai, thank you so much for this material, it's so useful to me, i'
 
 ---
 
+Dear Atsushi Thanks for compiling such a vast resource for robotics motion planning.
+
+--Kartik Prakash
+
+---
+
 
 # Citations
 

From 4e76b94eabb2519e05a9c82ffd701699fc4f454b Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 26 Aug 2019 20:41:17 +0900
Subject: [PATCH 0166/1080] Update users_comments.md

---
 users_comments.md | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 772b5774ec..c0165bd2d7 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -369,6 +369,12 @@ Dear Atsushi Thanks for compiling such a vast resource for robotics motion plann
 
 ---
 
+Thanks for your job! <br>I have learned a lot from it!
+
+--ZhongyiShen
+
+---
+
 
 # Citations
 

From 21bd3b3ebc74c4a0313f3d35fdbe31da14a578f0 Mon Sep 17 00:00:00 2001
From: Ryohei Sasaki <ryohei.sasaki@akane.waseda.jp>
Date: Sat, 7 Sep 2019 12:34:53 +0900
Subject: [PATCH 0167/1080] Add PoseOptimizationSLAM3D

---
 SLAM/PoseOptimizationSLAM3D/README.md         |  21 +
 .../PoseOptimizationSLAM3D/data_downloader.py |  22 +
 .../pose_optimization_slam_3d.py              | 535 ++++++++++++++++++
 3 files changed, 578 insertions(+)
 create mode 100644 SLAM/PoseOptimizationSLAM3D/README.md
 create mode 100644 SLAM/PoseOptimizationSLAM3D/data_downloader.py
 create mode 100644 SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py

diff --git a/SLAM/PoseOptimizationSLAM3D/README.md b/SLAM/PoseOptimizationSLAM3D/README.md
new file mode 100644
index 0000000000..ac95c7e206
--- /dev/null
+++ b/SLAM/PoseOptimizationSLAM3D/README.md
@@ -0,0 +1,21 @@
+# PoseOptimizationSLAM3D
+3D (x, y, z, qw, qx, qy, qz) pose optimization SLAM
+
+## How to use 
+1. Download data  
+
+~~~
+python data_downloader.py
+~~~
+
+2. run SLAM 
+
+~~~
+python pose_optimization_slam_3d.py
+~~~
+
+## Reference 
+[A Compact and Portable Implementation of Graph\-based SLAM](https://www.researchgate.net/publication/321287640_A_Compact_and_Portable_Implementation_of_Graph-based_SLAM)  
+[GitHub \- furo\-org/p2o: Single header 2D/3D graph\-based SLAM library](https://github.com/furo-org/p2o)  
+[GitHub \- AtsushiSakai/PythonRobotics
+/SLAM/PoseOptimizationSLAM](https://github.com/AtsushiSakai/PythonRobotics/tree/master/SLAM/PoseOptimizationSLAM)
diff --git a/SLAM/PoseOptimizationSLAM3D/data_downloader.py b/SLAM/PoseOptimizationSLAM3D/data_downloader.py
new file mode 100644
index 0000000000..31bd3bb3a5
--- /dev/null
+++ b/SLAM/PoseOptimizationSLAM3D/data_downloader.py
@@ -0,0 +1,22 @@
+"""
+
+Data down loader for pose optimization
+
+author: Atsushi Sakai
+
+"""
+
+
+import subprocess
+def main():
+    print("start!!")
+
+    cmd = "wget https://www.dropbox.com/s/zu23p8d522qccor/parking-garage.g2o?dl=0 -O parking-garage.g2o -nc"
+    subprocess.call(cmd, shell=True)
+
+    print("done!!")
+
+
+if __name__ == '__main__':
+    main()
+
diff --git a/SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py b/SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py
new file mode 100644
index 0000000000..e5cf6ac62d
--- /dev/null
+++ b/SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py
@@ -0,0 +1,535 @@
+"""
+3D (x, y, z, qw, qx, qy, qz) pose optimization SLAM
+author: Ryohei Sasaki(@rsasaki0109)
+Ref:
+- [A Compact and Portable Implementation of Graph\-based SLAM](https://www.researchgate.net/publication/321287640_A_Compact_and_Portable_Implementation_of_Graph-based_SLAM)
+- [GitHub \- furo\-org/p2o: Single header 2D/3D graph\-based SLAM library](https://github.com/furo-org/p2o)
+- [GitHub \- AtsushiSakai/PythonRobotics
+/SLAM/PoseOptimizationSLAM](https://github.com/AtsushiSakai/PythonRobotics/blob/master/SLAM/PoseOptimizationSLAM/pose_optimization_slam.py)
+"""
+
+import sys
+import time
+import numpy as np
+from scipy import sparse
+from scipy.sparse import linalg
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+est_traj_fig = plt.figure()
+ax = est_traj_fig.add_subplot(111, projection='3d')
+
+def skew_symmetric(v):
+
+    return np.array(
+        [[0, -v[2], v[1]],
+         [v[2], 0, -v[0]],
+         [-v[1], v[0], 0]]
+    )
+
+def robust_coeff(squared_error, delta):
+
+    if (squared_error < 0): 
+        return 0
+    sqre = np.sqrt(squared_error)
+    if (sqre < delta):
+        return 1 # no effect
+    return delta / sqre # linear
+
+
+class Optimizer3D:
+
+    def __init__(self):
+        self.verbose = False
+        self.animation = False
+        self.p_lambda = 1e-7
+        self.init_w = 1e10
+        self.stop_thre = 1e-3
+        self.robust_delta = 1
+        self.dim = 6  # state dimension
+
+    def optimize_path(self, nodes, consts, max_iter, min_iter):
+
+        graph_nodes = nodes[:]
+        prev_cost = sys.float_info.max
+
+        est_traj_fig = plt.figure()
+        ax = est_traj_fig.add_subplot(111, projection='3d')
+
+        for i in range(max_iter):
+            start = time.time()
+            cost, graph_nodes = self.optimize_path_one_step(
+                graph_nodes, consts)
+            elapsed = time.time() - start
+            if self.verbose:
+                print("step ", i, " cost: ", cost, " time:", elapsed, "s")
+
+            # check convergence
+            if (i > min_iter) and (prev_cost - cost < self.stop_thre):
+                if self.verbose:
+                    print("converged:", prev_cost
+                          - cost, " < ", self.stop_thre)
+                    break
+            prev_cost = cost
+
+            if self.animation:
+                plt.cla()
+                plot_nodes(nodes, ax, color="-b")
+                plot_nodes(graph_nodes, ax)
+                plt.pause(1.0)
+
+        return graph_nodes
+
+    def optimize_path_one_step(self, graph_nodes, constraints):
+
+        indlist = [i for i in range(self.dim)]
+        numnodes = len(graph_nodes)
+        bf = np.zeros(numnodes * self.dim)
+        tripletList = TripletList()
+
+        for con in constraints:
+            ida = con.id1
+            idb = con.id2
+            assert 0 <= ida and ida < numnodes, "ida is invalid"
+            assert 0 <= idb and idb < numnodes, "idb is invalid"
+            pa = graph_nodes[ida]
+            pb = graph_nodes[idb]
+            r, Ja, Jb = self.calc_error(
+                pa, pb, con.t)
+ 
+            info_mat = con.info_mat * robust_coeff(r.reshape(self.dim,1).T @ con.info_mat @ r.reshape(self.dim,1), self.robust_delta)
+
+            trJaInfo = Ja.transpose() @ info_mat
+            trJaInfoJa = trJaInfo @ Ja
+            trJbInfo = Jb.transpose() @ info_mat
+            trJbInfoJb = trJbInfo @ Jb
+            trJaInfoJb = trJaInfo @ Jb
+
+            for k in indlist:
+                for m in indlist:
+                    tripletList.push_back(
+                        ida * self.dim + k, ida * self.dim + m, trJaInfoJa[k, m])
+                    tripletList.push_back(
+                        idb * self.dim + k, idb * self.dim + m, trJbInfoJb[k, m])
+                    tripletList.push_back(
+                        ida * self.dim + k, idb * self.dim + m, trJaInfoJb[k, m])
+                    tripletList.push_back(
+                        idb * self.dim + k, ida * self.dim + m, trJaInfoJb[m, k])
+                        
+            bf[ida * self.dim: (ida + 1) * self.dim ] += trJaInfo @ r
+            bf[idb * self.dim: (idb + 1) * self.dim ] += trJbInfo @ r
+        
+        for k in indlist:
+            tripletList.push_back(k, k, self.init_w)
+
+        for i in range(self.dim * numnodes):
+            tripletList.push_back(i, i, self.p_lambda)
+
+        mat = sparse.coo_matrix((tripletList.data, (tripletList.row, tripletList.col)),
+                                shape=(numnodes * self.dim, numnodes * self.dim))
+
+        x = linalg.spsolve(mat.tocsr(), -bf)
+
+        out_nodes = []
+
+        for i in range(len(graph_nodes)):
+
+            u_i = i * self.dim
+            
+            q_before = Quaternion(graph_nodes[i].qw, graph_nodes[i].qx, graph_nodes[i].qy, graph_nodes[i].qz)
+            rv_before = RotVec(quaternion = q_before)
+            rv_after = RotVec(ax = rv_before.ax + x[u_i + 3], ay = rv_before.ay + x[u_i + 4], az = rv_before.az + x[u_i + 5])
+            q_after = rv_after.toQuaternion()
+
+            pos = Pose3D(
+                graph_nodes[i].x  + x[u_i],
+                graph_nodes[i].y  + x[u_i + 1],
+                graph_nodes[i].z  + x[u_i + 2],
+                q_after.qw,
+                q_after.qx,
+                q_after.qy,
+                q_after.qz
+            )
+            out_nodes.append(pos)
+
+        cost = self.calc_global_cost(out_nodes, constraints)
+
+        return cost, out_nodes
+
+    def calc_global_cost(self, nodes, constraints):
+
+        cost = 0.0
+        for c in constraints:
+            diff = self.error_func(nodes[c.id1], nodes[c.id2], c.t)
+            info_mat = c.info_mat * robust_coeff(diff.reshape(self.dim,1).T @ c.info_mat @ diff.reshape(self.dim,1), self.robust_delta)
+            cost += diff.transpose() @ info_mat @ diff
+
+        return cost
+
+    def error_func(self, pa, pb, t):
+
+        ba = pb.ominus(pa)
+        q = t.rv().toQuaternion().conjugate().quat_mult(ba.rv().toQuaternion(), out = 'Quaternion')
+        drv = RotVec(quaternion = q)
+        error = np.array([ba.x - t.x,
+                          ba.y - t.y,
+                          ba.z - t.z,
+                          drv.ax[0],
+                          drv.ay[0],
+                          drv.az[0]])
+        return error
+
+    def dQuat_dRV(self, rv):
+
+        u1 = rv.ax; u2 = rv.ay; u3 = rv.az
+        v = np.sqrt(u1**2 + u2**2 + u3**2)
+        if v < 1e-6:
+            dqu = 0.25 * np.array(
+            [[ -u1, -u2, -u3],
+             [ 2.0, 0.0, 0.0],
+             [ 0.0, 2.0, 0.0],
+             [ 0.0, 0.0, 2.0]]
+            )
+            return dqu
+
+        vd = v*2
+        v2 = v**2
+        v3 = v**3
+
+        S = np.sin(v/2.0); C = np.cos(v/2.0)
+        dqu = np.array(
+            [[ -u1 * S/vd                       , -u2*S/vd                     , -u3*S/vd],
+             [ S/v + u1*u1*C/(2*v2) - u1*u1*S/v3, u1*u2*(C/(2*v2)-S/v3)        , u1*u3*(C/(2*v2)-S/v3)],
+             [ u1*u2*(C/(2*v2)-S/v3)            , S/v+u2*u2*C/(2*v2)-u2*u2*S/v3, u2*u3*(C/(2*v2)-S/v3)],
+             [ u1*u3*(C/(2*v2)-S/v3)            , u2*u3*(C/(2*v2)-S/v3)        , S/v+u3*u3*C/(2*v2)-u3*u3*S/v3]]
+            )
+
+        return dqu
+
+    def dR_dRV(self, rv):
+
+        q = rv.toQuaternion()
+        qw = q.qw;qx = q.qx; qy = q.qy;qz = q.qz
+        dRdqw = 2 * np.array(
+            [[ qw, -qz,  qy],
+             [ qz,  qw, -qx],
+             [-qy,  qx,  qw]]
+        )
+        dRdqx = 2 * np.array(
+            [[ qx,  qy,  qz],
+             [ qy, -qx, -qw],
+             [ qz,  qw, -qx]]
+        )
+        dRdqy = 2 * np.array(
+            [[-qy,  qx,  qw],
+             [ qx,  qy,  qz],
+             [-qw,  qz, -qy]]
+        )
+        dRdqz = 2 * np.array(
+            [[-qz, -qw,  qx],
+             [ qw, -qz, -qy],
+             [ qz,  qy,  qz]]
+        )
+        dqdu = self.dQuat_dRV(rv)
+        dux = dRdqw * dqdu[0,0] + dRdqx * dqdu[1, 0] + dRdqy * dqdu[2, 0] + dRdqz * dqdu[3, 0]
+        duy = dRdqw * dqdu[0,1] + dRdqx * dqdu[1, 1] + dRdqy * dqdu[2, 1] + dRdqz * dqdu[3, 1]
+        duz = dRdqw * dqdu[0,2] + dRdqx * dqdu[1, 2] + dRdqy * dqdu[2, 2] + dRdqz * dqdu[3, 2]
+        return dux, duy, duz
+
+    def dRV_dQuat(self, q):
+
+        if 1 - q.qw**2 < 1e-7:
+            ret = np.array(
+            [[ 0.0, 2.0, 0.0, 0.0],
+             [ 0.0, 0.0, 2.0, 0.0],
+             [ 0.0, 0.0, 0.0, 2.0]]
+            )
+            return ret
+        c = 1/(1 - q.qw**2)
+        d = np.arccos(q.qw)/(np.sqrt(1-q.qw**2))
+        ret = 2.0 * np.array(
+            [[ c*q.qx*(d*q.qw-1),   d, 0.0, 0.0],
+             [ c*q.qy*(d*q.qw-1), 0.0,   d, 0.0],
+             [ c*q.qz*(d*q.qw-1), 0.0, 0.0,   d]]
+            )
+        return ret
+
+    def QMat(self, q):
+
+        qw = q.qw; qx = q.qx; qy = q.qy; qz = q.qz
+        Q = np.array(
+            [[ qw, -qx, -qy, -qz],
+             [ qx,  qw, -qz,  qy],
+             [ qy,  qz,  qw, -qx],
+             [ qz, -qy,  qx,  qw]]
+            )
+        return Q
+
+    def QMatBar(self, q):
+
+        qw = q.qw; qx = q.qx; qy = q.qy; qz = q.qz
+        Q = np.array(
+            [[ qw, -qx, -qy, -qz],
+             [ qx,  qw,  qz, -qy],
+             [ qy, -qz,  qw,  qx],
+             [ qz,  qy, -qx,  qw]]
+            )
+        return Q
+
+    def calc_error(self, pa, pb, t):
+
+        e0 = self.error_func(pa, pb, t)
+        Ja = np.identity(6); Jb = np.identity(6) 
+
+        rva_inv = pa.rv().inverted()
+        rotPaInv = rva_inv.toRotationMatrix()
+
+        Ja[:3,:3] = -rotPaInv
+        Jb[:3,:3] = rotPaInv
+
+        dRux, dRuy, dRuz = self.dR_dRV(rva_inv)
+
+        cvec = np.array([[pb.x - pa.x],[pb.y - pa.y],[pb.z - pa.z]])
+
+        Ja[0:3,3:4] = -dRux @ cvec
+        Ja[0:3,4:5] = -dRuy @ cvec
+        Ja[0:3,5:6] = -dRuz @ cvec
+
+        # rotation part: qdiff = qc-1 * qa-1 * qb
+        qainv = rva_inv.toQuaternion()
+        qcinv = t.rv().inverted().toQuaternion()
+        qb = pb.rv().toQuaternion()
+        qinvca = qcinv.quat_mult(qainv, out = 'Quaternion')
+        qdiff  = qinvca.quat_mult(qb, out = 'Quaternion')
+
+        Ja[3:6,3:6] = -self.dRV_dQuat(qdiff) @ self.QMat(qcinv) @ self.QMatBar(qb) @ self.dQuat_dRV(rva_inv)
+        Jb[3:6,3:6] =  self.dRV_dQuat(qdiff) @ self.QMat(qcinv) @ self.QMat(qainv) @ self.dQuat_dRV(pb.rv())
+
+        return e0, Ja, Jb
+
+class Quaternion:
+
+    def __init__(self, qw, qx, qy, qz):
+        self.qw = qw; self.qx = qx; self.qy = qy; self.qz = qz  
+    
+    def conjugate(self):
+        return Quaternion(self.qw, -self.qx, -self.qy, -self.qz)
+    
+    def to_numpy(self):
+        return np.array([self.qw, self.qx, self.qy, self.qz]).reshape(4,1)
+
+    def quat_mult(self, q, out='np'):
+        v = np.array([self.qx, self.qy, self.qz]).reshape(3, 1)
+        sum_term = np.zeros([4,4])
+        sum_term[0,1:]   = -v[:,0]
+        sum_term[1:, 0]  = v[:,0]
+        sum_term[1:, 1:] = skew_symmetric(v)
+        sigma = self.qw * np.eye(4) + sum_term
+        q_new = sigma @ q.to_numpy()
+        if out == 'np':
+            return q_new
+        elif out == 'Quaternion':
+            q_obj = Quaternion(*q_new)
+            return q_obj
+    
+
+class RotVec:
+
+    def __init__(self, ax=0., ay=0., az=0., quaternion=None):
+        if quaternion is None:
+            self.ax = ax; self.ay = ay; self.az = az
+        else:
+            x = quaternion.qx; y = quaternion.qy; z = quaternion.qz
+            norm_im = np.sqrt(x**2 + y**2 + z**2)
+            if (norm_im < 1e-7):
+                self.ax = 2*x; self.ay = 2*y; self.az = 2*z
+            else:
+                th = 2 * np.arctan2(norm_im, quaternion.qw)
+                th = self.pi2pi(th)
+                self.ax = x / norm_im * th
+                self.ay = y / norm_im * th
+                self.az = z / norm_im * th
+
+    def inverted(self):
+
+        return RotVec(-self.ax, -self.ay, -self.az)
+
+    def toRotationMatrix(self):
+
+        q = self.toQuaternion()
+        q_vec =  np.array([q.qx, q.qy, q.qz]).reshape(3,1)
+        qw = q.qw
+        mat = (qw**2 - q_vec.T @ q_vec) * np.eye(3) + \
+               2 * q_vec @ q_vec.T - 2 * qw * skew_symmetric(q_vec.reshape(-1,))
+        return mat.T
+    
+    def toQuaternion(self):
+
+        v = np.sqrt(self.ax**2 + self.ay**2 + self.az**2)
+        if (v < 1e-6):
+            return Quaternion(1, 0, 0, 0)
+        else:
+            return Quaternion(np.cos(v/2), np.sin(v/2)*self.ax/v,
+                            np.sin(v/2)*self.ay/v, np.sin(v/2)*self.az/v)
+
+    def pi2pi(self, rad):
+
+        val = np.fmod(rad, 2.0 * np.pi)
+        if val > np.pi:
+            val -= 2.0 * np.pi
+        elif val < -np.pi:
+            val += 2.0 * np.pi
+
+        return val
+    
+
+class TripletList:
+
+    def __init__(self):
+        self.row = []
+        self.col = []
+        self.data = []
+
+    def push_back(self, irow, icol, idata):
+        self.row.append(irow)
+        self.col.append(icol)
+        self.data.append(idata)
+
+class Pose3D:
+
+    def __init__(self, x, y, z, qw, qx, qy, qz):
+        self.x = x; self.y = y; self.z = z
+        self.qw = qw; self.qx = qx; self.qy = qy; self.qz = qz
+    
+    def pos(self):
+        v = np.array([self.x, self.y, self.z]).reshape(3,1)
+        return v
+    
+    def rv(self):
+        q = Quaternion(self.qw, self.qx, self.qy, self.qz)
+        return RotVec(quaternion = q)
+
+    def ominus(self, base):
+        t = base.rv().toRotationMatrix().T @ (self.pos() - base.pos())
+        q = base.rv().toQuaternion().conjugate().quat_mult(self.rv().toQuaternion(), out = 'Quaternion')
+        return Pose3D(t[0][0], t[1][0], t[2][0], q.qw, q.qx, q.qy, q.qz)
+
+
+class Constrant3D:
+
+    def __init__(self, id1, id2, t, info_mat):
+        self.id1 = id1
+        self.id2 = id2
+        self.t = t
+        self.info_mat = info_mat
+
+def plot_nodes(nodes, ax, color ="-r", label = ""):
+
+    x, y, z = [], [], []
+    for n in nodes:
+        x.append(n.x); y.append(n.y); z.append(n.z)
+    ax.plot(x, y, z, color, label=label)
+
+def load_data(fname):
+
+    nodes, consts = [], []
+
+    for line in open(fname):
+        sline = line.split()
+        tag = sline[0]
+
+        if tag == "VERTEX_SE3:QUAT":
+            #data_id = int(sline[1]) # unused
+            x = float(sline[2])
+            y = float(sline[3])
+            z = float(sline[4])
+            qx = float(sline[5])
+            qy = float(sline[6])
+            qz = float(sline[7])
+            qw = float(sline[8])
+         
+            nodes.append(Pose3D(x, y, z, qw, qx, qy, qz))
+        elif tag == "EDGE_SE3:QUAT":
+            id1 = int(sline[1])
+            id2 = int(sline[2])
+            x = float(sline[3])
+            y = float(sline[4])
+            z = float(sline[5])
+            qx = float(sline[6])
+            qy = float(sline[7])
+            qz = float(sline[8])
+            qw = float(sline[9])
+            c1  = float(sline[10])
+            c2  = float(sline[11])
+            c3  = float(sline[12])
+            c4  = float(sline[13])
+            c5  = float(sline[14])
+            c6  = float(sline[15])
+            c7  = float(sline[16])
+            c8  = float(sline[17])
+            c9  = float(sline[18])
+            c10 = float(sline[19])
+            c11 = float(sline[20])
+            c12 = float(sline[21])
+            c13 = float(sline[22])
+            c14 = float(sline[23])
+            c15 = float(sline[24])
+            c16 = float(sline[25])
+            c17 = float(sline[26])
+            c18 = float(sline[27])
+            c19 = float(sline[28])
+            c20 = float(sline[29])
+            c21 = float(sline[30])
+            t = Pose3D(x, y, z, qw, qx, qy, qz)
+            info_mat = np.array([[c1,  c2,  c3,  c4,  c5,  c6],
+                                 [c2,  c7,  c8,  c9, c10, c11],
+                                 [c3,  c8, c12, c13, c14, c15],
+                                 [c4,  c9, c13, c16, c17, c18],
+                                 [c5, c10, c14, c17, c19, c20],
+                                 [c6, c11, c15, c18, c20, c21]
+                                 ])
+            consts.append(Constrant3D(id1, id2, t, info_mat))
+
+    print("n_nodes:", len(nodes))
+    print("n_consts:", len(consts))
+
+    return nodes, consts
+
+
+
+def main():
+    
+    print("start!!")
+
+    fnames = ["parking-garage.g2o"]
+
+    max_iter = 20
+    min_iter = 3
+
+    # parameter setting
+    optimizer = Optimizer3D()
+    optimizer.p_lambda = 1e-6
+    optimizer.verbose = True
+    optimizer.animation = True
+    
+    for f in fnames:
+        print(f)
+
+        nodes, consts = load_data(f)
+
+        start = time.time()
+        final_nodes = optimizer.optimize_path(nodes, consts, max_iter, min_iter)
+        print("elapsed_time", time.time() - start, "sec")
+
+        # plot
+        plt.cla()
+        est_traj_fig = plt.figure()
+        ax = est_traj_fig.add_subplot(111, projection='3d')
+        plot_nodes(nodes, ax, color="-b", label="before")
+        plot_nodes(final_nodes, ax, label="after")
+        plt.show()
+
+    print("done!!")
+
+if __name__ == '__main__':
+    main()

From 4beee9bb401be7e72ff733b52cf7a9a871654fa8 Mon Sep 17 00:00:00 2001
From: Ryohei Sasaki <ryohei.sasaki@akane.waseda.jp>
Date: Sat, 7 Sep 2019 13:01:47 +0900
Subject: [PATCH 0168/1080] Fix Trailing newlines

---
 SLAM/PoseOptimizationSLAM3D/data_downloader.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/SLAM/PoseOptimizationSLAM3D/data_downloader.py b/SLAM/PoseOptimizationSLAM3D/data_downloader.py
index 31bd3bb3a5..8dea252640 100644
--- a/SLAM/PoseOptimizationSLAM3D/data_downloader.py
+++ b/SLAM/PoseOptimizationSLAM3D/data_downloader.py
@@ -18,5 +18,4 @@ def main():
 
 
 if __name__ == '__main__':
-    main()
-
+    main()
\ No newline at end of file

From cc70f5c2667c4c775b96a4d1143ef0e0f2cb0f8b Mon Sep 17 00:00:00 2001
From: Ryohei Sasaki <ryohei.sasaki@akane.waseda.jp>
Date: Sat, 7 Sep 2019 14:32:38 +0900
Subject: [PATCH 0169/1080] Fix function dRV_dQuat  of class Optimizer3D

---
 pose_optimization_slam_3d.py | 541 +++++++++++++++++++++++++++++++++++
 1 file changed, 541 insertions(+)
 create mode 100644 pose_optimization_slam_3d.py

diff --git a/pose_optimization_slam_3d.py b/pose_optimization_slam_3d.py
new file mode 100644
index 0000000000..105201f8a2
--- /dev/null
+++ b/pose_optimization_slam_3d.py
@@ -0,0 +1,541 @@
+"""
+3D (x, y, z, qw, qx, qy, qz) pose optimization SLAM
+author: Ryohei Sasaki(@rsasaki0109)
+Ref:
+- [A Compact and Portable Implementation of Graph\-based SLAM](https://www.researchgate.net/publication/321287640_A_Compact_and_Portable_Implementation_of_Graph-based_SLAM)
+- [GitHub \- furo\-org/p2o: Single header 2D/3D graph\-based SLAM library](https://github.com/furo-org/p2o)
+- [GitHub \- AtsushiSakai/PythonRobotics
+/SLAM/PoseOptimizationSLAM](https://github.com/AtsushiSakai/PythonRobotics/blob/master/SLAM/PoseOptimizationSLAM/pose_optimization_slam.py)
+"""
+
+import sys
+import time
+import numpy as np
+from scipy import sparse
+from scipy.sparse import linalg
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+est_traj_fig = plt.figure()
+ax = est_traj_fig.add_subplot(111, projection='3d')
+
+def skew_symmetric(v):
+
+    return np.array(
+        [[0, -v[2], v[1]],
+         [v[2], 0, -v[0]],
+         [-v[1], v[0], 0]]
+    )
+
+def robust_coeff(squared_error, delta):
+
+    if (squared_error < 0): 
+        return 0
+    sqre = np.sqrt(squared_error)
+    if (sqre < delta):
+        return 1 # no effect
+    return delta / sqre # linear
+
+
+class Optimizer3D:
+
+    def __init__(self):
+        self.verbose = False
+        self.animation = False
+        self.p_lambda = 1e-7
+        self.init_w = 1e10
+        self.stop_thre = 1e-3
+        self.robust_delta = 1
+        self.dim = 6  # state dimension
+
+    def optimize_path(self, nodes, consts, max_iter, min_iter):
+
+        graph_nodes = nodes[:]
+        prev_cost = sys.float_info.max
+
+        est_traj_fig = plt.figure()
+        ax = est_traj_fig.add_subplot(111, projection='3d')
+
+        for i in range(max_iter):
+            start = time.time()
+            cost, graph_nodes = self.optimize_path_one_step(
+                graph_nodes, consts)
+            elapsed = time.time() - start
+            if self.verbose:
+                print("step ", i, " cost: ", cost, " time:", elapsed, "s")
+
+            # check convergence
+            if (i > min_iter) and (prev_cost - cost < self.stop_thre):
+                if self.verbose:
+                    print("converged:", prev_cost
+                          - cost, " < ", self.stop_thre)
+                    break
+            prev_cost = cost
+
+            if self.animation:
+                plt.cla()
+                plot_nodes(nodes, ax, color="-b")
+                plot_nodes(graph_nodes, ax)
+                plt.pause(1.0)
+
+        return graph_nodes
+
+    def optimize_path_one_step(self, graph_nodes, constraints):
+
+        indlist = [i for i in range(self.dim)]
+        numnodes = len(graph_nodes)
+        bf = np.zeros(numnodes * self.dim)
+        tripletList = TripletList()
+
+        for con in constraints:
+            ida = con.id1
+            idb = con.id2
+            assert 0 <= ida and ida < numnodes, "ida is invalid"
+            assert 0 <= idb and idb < numnodes, "idb is invalid"
+            pa = graph_nodes[ida]
+            pb = graph_nodes[idb]
+            r, Ja, Jb = self.calc_error(
+                pa, pb, con.t)
+ 
+            info_mat = con.info_mat * robust_coeff(r.reshape(self.dim,1).T @ con.info_mat @ r.reshape(self.dim,1), self.robust_delta)
+
+            trJaInfo = Ja.transpose() @ info_mat
+            trJaInfoJa = trJaInfo @ Ja
+            trJbInfo = Jb.transpose() @ info_mat
+            trJbInfoJb = trJbInfo @ Jb
+            trJaInfoJb = trJaInfo @ Jb
+
+            for k in indlist:
+                for m in indlist:
+                    tripletList.push_back(
+                        ida * self.dim + k, ida * self.dim + m, trJaInfoJa[k, m])
+                    tripletList.push_back(
+                        idb * self.dim + k, idb * self.dim + m, trJbInfoJb[k, m])
+                    tripletList.push_back(
+                        ida * self.dim + k, idb * self.dim + m, trJaInfoJb[k, m])
+                    tripletList.push_back(
+                        idb * self.dim + k, ida * self.dim + m, trJaInfoJb[m, k])
+                        
+            bf[ida * self.dim: (ida + 1) * self.dim ] += trJaInfo @ r
+            bf[idb * self.dim: (idb + 1) * self.dim ] += trJbInfo @ r
+        
+        for k in indlist:
+            tripletList.push_back(k, k, self.init_w)
+
+        for i in range(self.dim * numnodes):
+            tripletList.push_back(i, i, self.p_lambda)
+
+        mat = sparse.coo_matrix((tripletList.data, (tripletList.row, tripletList.col)),
+                                shape=(numnodes * self.dim, numnodes * self.dim))
+
+        x = linalg.spsolve(mat.tocsr(), -bf)
+
+        out_nodes = []
+
+        for i in range(len(graph_nodes)):
+
+            u_i = i * self.dim
+            
+            q_before = Quaternion(graph_nodes[i].qw, graph_nodes[i].qx, graph_nodes[i].qy, graph_nodes[i].qz)
+            rv_before = RotVec(quaternion = q_before)
+            rv_after = RotVec(ax = rv_before.ax + x[u_i + 3], ay = rv_before.ay + x[u_i + 4], az = rv_before.az + x[u_i + 5])
+            q_after = rv_after.toQuaternion()
+
+            pos = Pose3D(
+                graph_nodes[i].x  + x[u_i],
+                graph_nodes[i].y  + x[u_i + 1],
+                graph_nodes[i].z  + x[u_i + 2],
+                q_after.qw,
+                q_after.qx,
+                q_after.qy,
+                q_after.qz
+            )
+            out_nodes.append(pos)
+
+        cost = self.calc_global_cost(out_nodes, constraints)
+
+        return cost, out_nodes
+
+    def calc_global_cost(self, nodes, constraints):
+
+        cost = 0.0
+        for c in constraints:
+            diff = self.error_func(nodes[c.id1], nodes[c.id2], c.t)
+            info_mat = c.info_mat * robust_coeff(diff.reshape(self.dim,1).T @ c.info_mat @ diff.reshape(self.dim,1), self.robust_delta)
+            cost += diff.transpose() @ info_mat @ diff
+
+        return cost
+
+    def error_func(self, pa, pb, t):
+
+        ba = pb.ominus(pa)
+        q = t.rv().toQuaternion().conjugate().quat_mult(ba.rv().toQuaternion(), out = 'Quaternion')
+        drv = RotVec(quaternion = q)
+        error = np.array([ba.x - t.x,
+                          ba.y - t.y,
+                          ba.z - t.z,
+                          drv.ax[0],
+                          drv.ay[0],
+                          drv.az[0]])
+        return error
+
+    def dQuat_dRV(self, rv):
+
+        u1 = rv.ax; u2 = rv.ay; u3 = rv.az
+        v = np.sqrt(u1**2 + u2**2 + u3**2)
+        if v < 1e-6:
+            dqu = 0.25 * np.array(
+            [[ -u1, -u2, -u3],
+             [ 2.0, 0.0, 0.0],
+             [ 0.0, 2.0, 0.0],
+             [ 0.0, 0.0, 2.0]]
+            )
+            return dqu
+
+        vd = v*2
+        v2 = v**2
+        v3 = v**3
+
+        S = np.sin(v/2.0); C = np.cos(v/2.0)
+        dqu = np.array(
+            [[ -u1 * S/vd                       , -u2*S/vd                     , -u3*S/vd],
+             [ S/v + u1*u1*C/(2*v2) - u1*u1*S/v3, u1*u2*(C/(2*v2)-S/v3)        , u1*u3*(C/(2*v2)-S/v3)],
+             [ u1*u2*(C/(2*v2)-S/v3)            , S/v+u2*u2*C/(2*v2)-u2*u2*S/v3, u2*u3*(C/(2*v2)-S/v3)],
+             [ u1*u3*(C/(2*v2)-S/v3)            , u2*u3*(C/(2*v2)-S/v3)        , S/v+u3*u3*C/(2*v2)-u3*u3*S/v3]]
+            )
+
+        return dqu
+
+    def dR_dRV(self, rv):
+
+        q = rv.toQuaternion()
+        qw = q.qw;qx = q.qx; qy = q.qy;qz = q.qz
+        dRdqw = 2 * np.array(
+            [[ qw, -qz,  qy],
+             [ qz,  qw, -qx],
+             [-qy,  qx,  qw]]
+        )
+        dRdqx = 2 * np.array(
+            [[ qx,  qy,  qz],
+             [ qy, -qx, -qw],
+             [ qz,  qw, -qx]]
+        )
+        dRdqy = 2 * np.array(
+            [[-qy,  qx,  qw],
+             [ qx,  qy,  qz],
+             [-qw,  qz, -qy]]
+        )
+        dRdqz = 2 * np.array(
+            [[-qz, -qw,  qx],
+             [ qw, -qz, -qy],
+             [ qz,  qy,  qz]]
+        )
+        dqdu = self.dQuat_dRV(rv)
+        dux = dRdqw * dqdu[0,0] + dRdqx * dqdu[1, 0] + dRdqy * dqdu[2, 0] + dRdqz * dqdu[3, 0]
+        duy = dRdqw * dqdu[0,1] + dRdqx * dqdu[1, 1] + dRdqy * dqdu[2, 1] + dRdqz * dqdu[3, 1]
+        duz = dRdqw * dqdu[0,2] + dRdqx * dqdu[1, 2] + dRdqy * dqdu[2, 2] + dRdqz * dqdu[3, 2]
+        return dux, duy, duz
+    
+    def dRV_dQuat(self, q):
+        
+        qw = q.qw[0]
+        qx = q.qx[0]
+        qy = q.qy[0]
+        qz = q.qz[0]
+
+        if 1 - qw**2 < 1e-7:
+            ret = np.array(
+            [[ 0.0, 2.0, 0.0, 0.0],
+             [ 0.0, 0.0, 2.0, 0.0],
+             [ 0.0, 0.0, 0.0, 2.0]]
+            )
+            return ret
+        
+        c = 1/(1 - qw**2)
+        d = np.arccos(qw)/(np.sqrt(1-qw**2))
+        ret = 2.0 * np.array(
+            [[ c*qx*(d*qw-1),   d, 0.0, 0.0],
+             [ c*qy*(d*qw-1), 0.0,   d, 0.0],
+             [ c*qz*(d*qw-1), 0.0, 0.0,   d]]
+            )
+        return ret
+
+    def QMat(self, q):
+
+        qw = q.qw; qx = q.qx; qy = q.qy; qz = q.qz
+        Q = np.array(
+            [[ qw, -qx, -qy, -qz],
+             [ qx,  qw, -qz,  qy],
+             [ qy,  qz,  qw, -qx],
+             [ qz, -qy,  qx,  qw]]
+            )
+        return Q
+
+    def QMatBar(self, q):
+
+        qw = q.qw; qx = q.qx; qy = q.qy; qz = q.qz
+        Q = np.array(
+            [[ qw, -qx, -qy, -qz],
+             [ qx,  qw,  qz, -qy],
+             [ qy, -qz,  qw,  qx],
+             [ qz,  qy, -qx,  qw]]
+            )
+        return Q
+
+    def calc_error(self, pa, pb, t):
+
+        e0 = self.error_func(pa, pb, t)
+        Ja = np.identity(6); Jb = np.identity(6) 
+
+        rva_inv = pa.rv().inverted()
+        rotPaInv = rva_inv.toRotationMatrix()
+
+        Ja[:3,:3] = -rotPaInv
+        Jb[:3,:3] = rotPaInv
+
+        dRux, dRuy, dRuz = self.dR_dRV(rva_inv)
+
+        cvec = np.array([[pb.x - pa.x],[pb.y - pa.y],[pb.z - pa.z]])
+
+        Ja[0:3,3:4] = -dRux @ cvec
+        Ja[0:3,4:5] = -dRuy @ cvec
+        Ja[0:3,5:6] = -dRuz @ cvec
+
+        # rotation part: qdiff = qc-1 * qa-1 * qb
+        qainv = rva_inv.toQuaternion()
+        qcinv = t.rv().inverted().toQuaternion()
+        qb = pb.rv().toQuaternion()
+        qinvca = qcinv.quat_mult(qainv, out = 'Quaternion')
+        qdiff  = qinvca.quat_mult(qb, out = 'Quaternion')
+
+        Ja[3:6,3:6] = -self.dRV_dQuat(qdiff) @ self.QMat(qcinv) @ self.QMatBar(qb) @ self.dQuat_dRV(rva_inv)
+        Jb[3:6,3:6] =  self.dRV_dQuat(qdiff) @ self.QMat(qcinv) @ self.QMat(qainv) @ self.dQuat_dRV(pb.rv())
+
+        return e0, Ja, Jb
+
+class Quaternion:
+
+    def __init__(self, qw, qx, qy, qz):
+        self.qw = qw; self.qx = qx; self.qy = qy; self.qz = qz  
+    
+    def conjugate(self):
+        return Quaternion(self.qw, -self.qx, -self.qy, -self.qz)
+    
+    def to_numpy(self):
+        return np.array([self.qw, self.qx, self.qy, self.qz]).reshape(4,1)
+
+    def quat_mult(self, q, out='np'):
+        v = np.array([self.qx, self.qy, self.qz]).reshape(3, 1)
+        sum_term = np.zeros([4,4])
+        sum_term[0,1:]   = -v[:,0]
+        sum_term[1:, 0]  = v[:,0]
+        sum_term[1:, 1:] = skew_symmetric(v)
+        sigma = self.qw * np.eye(4) + sum_term
+        q_new = sigma @ q.to_numpy()
+        if out == 'np':
+            return q_new
+        elif out == 'Quaternion':
+            q_obj = Quaternion(*q_new)
+            return q_obj
+    
+
+class RotVec:
+
+    def __init__(self, ax=0., ay=0., az=0., quaternion=None):
+        if quaternion is None:
+            self.ax = ax; self.ay = ay; self.az = az
+        else:
+            x = quaternion.qx; y = quaternion.qy; z = quaternion.qz
+            norm_im = np.sqrt(x**2 + y**2 + z**2)
+            if (norm_im < 1e-7):
+                self.ax = 2*x; self.ay = 2*y; self.az = 2*z
+            else:
+                th = 2 * np.arctan2(norm_im, quaternion.qw)
+                th = self.pi2pi(th)
+                self.ax = x / norm_im * th
+                self.ay = y / norm_im * th
+                self.az = z / norm_im * th
+
+    def inverted(self):
+
+        return RotVec(-self.ax, -self.ay, -self.az)
+
+    def toRotationMatrix(self):
+
+        q = self.toQuaternion()
+        q_vec =  np.array([q.qx, q.qy, q.qz]).reshape(3,1)
+        qw = q.qw
+        mat = (qw**2 - q_vec.T @ q_vec) * np.eye(3) + \
+               2 * q_vec @ q_vec.T - 2 * qw * skew_symmetric(q_vec.reshape(-1,))
+        return mat.T
+    
+    def toQuaternion(self):
+
+        v = np.sqrt(self.ax**2 + self.ay**2 + self.az**2)
+        if (v < 1e-6):
+            return Quaternion(1, 0, 0, 0)
+        else:
+            return Quaternion(np.cos(v/2), np.sin(v/2)*self.ax/v,
+                            np.sin(v/2)*self.ay/v, np.sin(v/2)*self.az/v)
+
+    def pi2pi(self, rad):
+
+        val = np.fmod(rad, 2.0 * np.pi)
+        if val > np.pi:
+            val -= 2.0 * np.pi
+        elif val < -np.pi:
+            val += 2.0 * np.pi
+
+        return val
+    
+
+class TripletList:
+
+    def __init__(self):
+        self.row = []
+        self.col = []
+        self.data = []
+
+    def push_back(self, irow, icol, idata):
+        self.row.append(irow)
+        self.col.append(icol)
+        self.data.append(idata)
+
+class Pose3D:
+
+    def __init__(self, x, y, z, qw, qx, qy, qz):
+        self.x = x; self.y = y; self.z = z
+        self.qw = qw; self.qx = qx; self.qy = qy; self.qz = qz
+    
+    def pos(self):
+        v = np.array([self.x, self.y, self.z]).reshape(3,1)
+        return v
+    
+    def rv(self):
+        q = Quaternion(self.qw, self.qx, self.qy, self.qz)
+        return RotVec(quaternion = q)
+
+    def ominus(self, base):
+        t = base.rv().toRotationMatrix().T @ (self.pos() - base.pos())
+        q = base.rv().toQuaternion().conjugate().quat_mult(self.rv().toQuaternion(), out = 'Quaternion')
+        return Pose3D(t[0][0], t[1][0], t[2][0], q.qw, q.qx, q.qy, q.qz)
+
+
+class Constrant3D:
+
+    def __init__(self, id1, id2, t, info_mat):
+        self.id1 = id1
+        self.id2 = id2
+        self.t = t
+        self.info_mat = info_mat
+
+def plot_nodes(nodes, ax, color ="-r", label = ""):
+
+    x, y, z = [], [], []
+    for n in nodes:
+        x.append(n.x); y.append(n.y); z.append(n.z)
+    ax.plot(x, y, z, color, label=label)
+
+def load_data(fname):
+
+    nodes, consts = [], []
+
+    for line in open(fname):
+        sline = line.split()
+        tag = sline[0]
+
+        if tag == "VERTEX_SE3:QUAT":
+            #data_id = int(sline[1]) # unused
+            x = float(sline[2])
+            y = float(sline[3])
+            z = float(sline[4])
+            qx = float(sline[5])
+            qy = float(sline[6])
+            qz = float(sline[7])
+            qw = float(sline[8])
+         
+            nodes.append(Pose3D(x, y, z, qw, qx, qy, qz))
+        elif tag == "EDGE_SE3:QUAT":
+            id1 = int(sline[1])
+            id2 = int(sline[2])
+            x = float(sline[3])
+            y = float(sline[4])
+            z = float(sline[5])
+            qx = float(sline[6])
+            qy = float(sline[7])
+            qz = float(sline[8])
+            qw = float(sline[9])
+            c1  = float(sline[10])
+            c2  = float(sline[11])
+            c3  = float(sline[12])
+            c4  = float(sline[13])
+            c5  = float(sline[14])
+            c6  = float(sline[15])
+            c7  = float(sline[16])
+            c8  = float(sline[17])
+            c9  = float(sline[18])
+            c10 = float(sline[19])
+            c11 = float(sline[20])
+            c12 = float(sline[21])
+            c13 = float(sline[22])
+            c14 = float(sline[23])
+            c15 = float(sline[24])
+            c16 = float(sline[25])
+            c17 = float(sline[26])
+            c18 = float(sline[27])
+            c19 = float(sline[28])
+            c20 = float(sline[29])
+            c21 = float(sline[30])
+            t = Pose3D(x, y, z, qw, qx, qy, qz)
+            info_mat = np.array([[c1,  c2,  c3,  c4,  c5,  c6],
+                                 [c2,  c7,  c8,  c9, c10, c11],
+                                 [c3,  c8, c12, c13, c14, c15],
+                                 [c4,  c9, c13, c16, c17, c18],
+                                 [c5, c10, c14, c17, c19, c20],
+                                 [c6, c11, c15, c18, c20, c21]
+                                 ])
+            consts.append(Constrant3D(id1, id2, t, info_mat))
+
+    print("n_nodes:", len(nodes))
+    print("n_consts:", len(consts))
+
+    return nodes, consts
+
+
+
+def main():
+    
+    print("start!!")
+
+    fnames = ["parking-garage.g2o"]
+
+    max_iter = 20
+    min_iter = 3
+
+    # parameter setting
+    optimizer = Optimizer3D()
+    optimizer.p_lambda = 1e-6
+    optimizer.verbose = True
+    optimizer.animation = True
+    
+    for f in fnames:
+        print(f)
+
+        nodes, consts = load_data(f)
+
+        start = time.time()
+        final_nodes = optimizer.optimize_path(nodes, consts, max_iter, min_iter)
+        print("elapsed_time", time.time() - start, "sec")
+
+        # plot
+        plt.cla()
+        est_traj_fig = plt.figure()
+        ax = est_traj_fig.add_subplot(111, projection='3d')
+        plot_nodes(nodes, ax, color="-b", label="before")
+        plot_nodes(final_nodes, ax, label="after")
+        plt.show()
+
+    print("done!!")
+
+if __name__ == '__main__':
+    main()

From cb8af3c2e4db403ce524df2df8ade7dad28039a6 Mon Sep 17 00:00:00 2001
From: Ryohei Sasaki <ryohei.sasaki@akane.waseda.jp>
Date: Sat, 7 Sep 2019 14:34:09 +0900
Subject: [PATCH 0170/1080] Delete pose_optimization_slam_3d.py

---
 pose_optimization_slam_3d.py | 541 -----------------------------------
 1 file changed, 541 deletions(-)
 delete mode 100644 pose_optimization_slam_3d.py

diff --git a/pose_optimization_slam_3d.py b/pose_optimization_slam_3d.py
deleted file mode 100644
index 105201f8a2..0000000000
--- a/pose_optimization_slam_3d.py
+++ /dev/null
@@ -1,541 +0,0 @@
-"""
-3D (x, y, z, qw, qx, qy, qz) pose optimization SLAM
-author: Ryohei Sasaki(@rsasaki0109)
-Ref:
-- [A Compact and Portable Implementation of Graph\-based SLAM](https://www.researchgate.net/publication/321287640_A_Compact_and_Portable_Implementation_of_Graph-based_SLAM)
-- [GitHub \- furo\-org/p2o: Single header 2D/3D graph\-based SLAM library](https://github.com/furo-org/p2o)
-- [GitHub \- AtsushiSakai/PythonRobotics
-/SLAM/PoseOptimizationSLAM](https://github.com/AtsushiSakai/PythonRobotics/blob/master/SLAM/PoseOptimizationSLAM/pose_optimization_slam.py)
-"""
-
-import sys
-import time
-import numpy as np
-from scipy import sparse
-from scipy.sparse import linalg
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-
-est_traj_fig = plt.figure()
-ax = est_traj_fig.add_subplot(111, projection='3d')
-
-def skew_symmetric(v):
-
-    return np.array(
-        [[0, -v[2], v[1]],
-         [v[2], 0, -v[0]],
-         [-v[1], v[0], 0]]
-    )
-
-def robust_coeff(squared_error, delta):
-
-    if (squared_error < 0): 
-        return 0
-    sqre = np.sqrt(squared_error)
-    if (sqre < delta):
-        return 1 # no effect
-    return delta / sqre # linear
-
-
-class Optimizer3D:
-
-    def __init__(self):
-        self.verbose = False
-        self.animation = False
-        self.p_lambda = 1e-7
-        self.init_w = 1e10
-        self.stop_thre = 1e-3
-        self.robust_delta = 1
-        self.dim = 6  # state dimension
-
-    def optimize_path(self, nodes, consts, max_iter, min_iter):
-
-        graph_nodes = nodes[:]
-        prev_cost = sys.float_info.max
-
-        est_traj_fig = plt.figure()
-        ax = est_traj_fig.add_subplot(111, projection='3d')
-
-        for i in range(max_iter):
-            start = time.time()
-            cost, graph_nodes = self.optimize_path_one_step(
-                graph_nodes, consts)
-            elapsed = time.time() - start
-            if self.verbose:
-                print("step ", i, " cost: ", cost, " time:", elapsed, "s")
-
-            # check convergence
-            if (i > min_iter) and (prev_cost - cost < self.stop_thre):
-                if self.verbose:
-                    print("converged:", prev_cost
-                          - cost, " < ", self.stop_thre)
-                    break
-            prev_cost = cost
-
-            if self.animation:
-                plt.cla()
-                plot_nodes(nodes, ax, color="-b")
-                plot_nodes(graph_nodes, ax)
-                plt.pause(1.0)
-
-        return graph_nodes
-
-    def optimize_path_one_step(self, graph_nodes, constraints):
-
-        indlist = [i for i in range(self.dim)]
-        numnodes = len(graph_nodes)
-        bf = np.zeros(numnodes * self.dim)
-        tripletList = TripletList()
-
-        for con in constraints:
-            ida = con.id1
-            idb = con.id2
-            assert 0 <= ida and ida < numnodes, "ida is invalid"
-            assert 0 <= idb and idb < numnodes, "idb is invalid"
-            pa = graph_nodes[ida]
-            pb = graph_nodes[idb]
-            r, Ja, Jb = self.calc_error(
-                pa, pb, con.t)
- 
-            info_mat = con.info_mat * robust_coeff(r.reshape(self.dim,1).T @ con.info_mat @ r.reshape(self.dim,1), self.robust_delta)
-
-            trJaInfo = Ja.transpose() @ info_mat
-            trJaInfoJa = trJaInfo @ Ja
-            trJbInfo = Jb.transpose() @ info_mat
-            trJbInfoJb = trJbInfo @ Jb
-            trJaInfoJb = trJaInfo @ Jb
-
-            for k in indlist:
-                for m in indlist:
-                    tripletList.push_back(
-                        ida * self.dim + k, ida * self.dim + m, trJaInfoJa[k, m])
-                    tripletList.push_back(
-                        idb * self.dim + k, idb * self.dim + m, trJbInfoJb[k, m])
-                    tripletList.push_back(
-                        ida * self.dim + k, idb * self.dim + m, trJaInfoJb[k, m])
-                    tripletList.push_back(
-                        idb * self.dim + k, ida * self.dim + m, trJaInfoJb[m, k])
-                        
-            bf[ida * self.dim: (ida + 1) * self.dim ] += trJaInfo @ r
-            bf[idb * self.dim: (idb + 1) * self.dim ] += trJbInfo @ r
-        
-        for k in indlist:
-            tripletList.push_back(k, k, self.init_w)
-
-        for i in range(self.dim * numnodes):
-            tripletList.push_back(i, i, self.p_lambda)
-
-        mat = sparse.coo_matrix((tripletList.data, (tripletList.row, tripletList.col)),
-                                shape=(numnodes * self.dim, numnodes * self.dim))
-
-        x = linalg.spsolve(mat.tocsr(), -bf)
-
-        out_nodes = []
-
-        for i in range(len(graph_nodes)):
-
-            u_i = i * self.dim
-            
-            q_before = Quaternion(graph_nodes[i].qw, graph_nodes[i].qx, graph_nodes[i].qy, graph_nodes[i].qz)
-            rv_before = RotVec(quaternion = q_before)
-            rv_after = RotVec(ax = rv_before.ax + x[u_i + 3], ay = rv_before.ay + x[u_i + 4], az = rv_before.az + x[u_i + 5])
-            q_after = rv_after.toQuaternion()
-
-            pos = Pose3D(
-                graph_nodes[i].x  + x[u_i],
-                graph_nodes[i].y  + x[u_i + 1],
-                graph_nodes[i].z  + x[u_i + 2],
-                q_after.qw,
-                q_after.qx,
-                q_after.qy,
-                q_after.qz
-            )
-            out_nodes.append(pos)
-
-        cost = self.calc_global_cost(out_nodes, constraints)
-
-        return cost, out_nodes
-
-    def calc_global_cost(self, nodes, constraints):
-
-        cost = 0.0
-        for c in constraints:
-            diff = self.error_func(nodes[c.id1], nodes[c.id2], c.t)
-            info_mat = c.info_mat * robust_coeff(diff.reshape(self.dim,1).T @ c.info_mat @ diff.reshape(self.dim,1), self.robust_delta)
-            cost += diff.transpose() @ info_mat @ diff
-
-        return cost
-
-    def error_func(self, pa, pb, t):
-
-        ba = pb.ominus(pa)
-        q = t.rv().toQuaternion().conjugate().quat_mult(ba.rv().toQuaternion(), out = 'Quaternion')
-        drv = RotVec(quaternion = q)
-        error = np.array([ba.x - t.x,
-                          ba.y - t.y,
-                          ba.z - t.z,
-                          drv.ax[0],
-                          drv.ay[0],
-                          drv.az[0]])
-        return error
-
-    def dQuat_dRV(self, rv):
-
-        u1 = rv.ax; u2 = rv.ay; u3 = rv.az
-        v = np.sqrt(u1**2 + u2**2 + u3**2)
-        if v < 1e-6:
-            dqu = 0.25 * np.array(
-            [[ -u1, -u2, -u3],
-             [ 2.0, 0.0, 0.0],
-             [ 0.0, 2.0, 0.0],
-             [ 0.0, 0.0, 2.0]]
-            )
-            return dqu
-
-        vd = v*2
-        v2 = v**2
-        v3 = v**3
-
-        S = np.sin(v/2.0); C = np.cos(v/2.0)
-        dqu = np.array(
-            [[ -u1 * S/vd                       , -u2*S/vd                     , -u3*S/vd],
-             [ S/v + u1*u1*C/(2*v2) - u1*u1*S/v3, u1*u2*(C/(2*v2)-S/v3)        , u1*u3*(C/(2*v2)-S/v3)],
-             [ u1*u2*(C/(2*v2)-S/v3)            , S/v+u2*u2*C/(2*v2)-u2*u2*S/v3, u2*u3*(C/(2*v2)-S/v3)],
-             [ u1*u3*(C/(2*v2)-S/v3)            , u2*u3*(C/(2*v2)-S/v3)        , S/v+u3*u3*C/(2*v2)-u3*u3*S/v3]]
-            )
-
-        return dqu
-
-    def dR_dRV(self, rv):
-
-        q = rv.toQuaternion()
-        qw = q.qw;qx = q.qx; qy = q.qy;qz = q.qz
-        dRdqw = 2 * np.array(
-            [[ qw, -qz,  qy],
-             [ qz,  qw, -qx],
-             [-qy,  qx,  qw]]
-        )
-        dRdqx = 2 * np.array(
-            [[ qx,  qy,  qz],
-             [ qy, -qx, -qw],
-             [ qz,  qw, -qx]]
-        )
-        dRdqy = 2 * np.array(
-            [[-qy,  qx,  qw],
-             [ qx,  qy,  qz],
-             [-qw,  qz, -qy]]
-        )
-        dRdqz = 2 * np.array(
-            [[-qz, -qw,  qx],
-             [ qw, -qz, -qy],
-             [ qz,  qy,  qz]]
-        )
-        dqdu = self.dQuat_dRV(rv)
-        dux = dRdqw * dqdu[0,0] + dRdqx * dqdu[1, 0] + dRdqy * dqdu[2, 0] + dRdqz * dqdu[3, 0]
-        duy = dRdqw * dqdu[0,1] + dRdqx * dqdu[1, 1] + dRdqy * dqdu[2, 1] + dRdqz * dqdu[3, 1]
-        duz = dRdqw * dqdu[0,2] + dRdqx * dqdu[1, 2] + dRdqy * dqdu[2, 2] + dRdqz * dqdu[3, 2]
-        return dux, duy, duz
-    
-    def dRV_dQuat(self, q):
-        
-        qw = q.qw[0]
-        qx = q.qx[0]
-        qy = q.qy[0]
-        qz = q.qz[0]
-
-        if 1 - qw**2 < 1e-7:
-            ret = np.array(
-            [[ 0.0, 2.0, 0.0, 0.0],
-             [ 0.0, 0.0, 2.0, 0.0],
-             [ 0.0, 0.0, 0.0, 2.0]]
-            )
-            return ret
-        
-        c = 1/(1 - qw**2)
-        d = np.arccos(qw)/(np.sqrt(1-qw**2))
-        ret = 2.0 * np.array(
-            [[ c*qx*(d*qw-1),   d, 0.0, 0.0],
-             [ c*qy*(d*qw-1), 0.0,   d, 0.0],
-             [ c*qz*(d*qw-1), 0.0, 0.0,   d]]
-            )
-        return ret
-
-    def QMat(self, q):
-
-        qw = q.qw; qx = q.qx; qy = q.qy; qz = q.qz
-        Q = np.array(
-            [[ qw, -qx, -qy, -qz],
-             [ qx,  qw, -qz,  qy],
-             [ qy,  qz,  qw, -qx],
-             [ qz, -qy,  qx,  qw]]
-            )
-        return Q
-
-    def QMatBar(self, q):
-
-        qw = q.qw; qx = q.qx; qy = q.qy; qz = q.qz
-        Q = np.array(
-            [[ qw, -qx, -qy, -qz],
-             [ qx,  qw,  qz, -qy],
-             [ qy, -qz,  qw,  qx],
-             [ qz,  qy, -qx,  qw]]
-            )
-        return Q
-
-    def calc_error(self, pa, pb, t):
-
-        e0 = self.error_func(pa, pb, t)
-        Ja = np.identity(6); Jb = np.identity(6) 
-
-        rva_inv = pa.rv().inverted()
-        rotPaInv = rva_inv.toRotationMatrix()
-
-        Ja[:3,:3] = -rotPaInv
-        Jb[:3,:3] = rotPaInv
-
-        dRux, dRuy, dRuz = self.dR_dRV(rva_inv)
-
-        cvec = np.array([[pb.x - pa.x],[pb.y - pa.y],[pb.z - pa.z]])
-
-        Ja[0:3,3:4] = -dRux @ cvec
-        Ja[0:3,4:5] = -dRuy @ cvec
-        Ja[0:3,5:6] = -dRuz @ cvec
-
-        # rotation part: qdiff = qc-1 * qa-1 * qb
-        qainv = rva_inv.toQuaternion()
-        qcinv = t.rv().inverted().toQuaternion()
-        qb = pb.rv().toQuaternion()
-        qinvca = qcinv.quat_mult(qainv, out = 'Quaternion')
-        qdiff  = qinvca.quat_mult(qb, out = 'Quaternion')
-
-        Ja[3:6,3:6] = -self.dRV_dQuat(qdiff) @ self.QMat(qcinv) @ self.QMatBar(qb) @ self.dQuat_dRV(rva_inv)
-        Jb[3:6,3:6] =  self.dRV_dQuat(qdiff) @ self.QMat(qcinv) @ self.QMat(qainv) @ self.dQuat_dRV(pb.rv())
-
-        return e0, Ja, Jb
-
-class Quaternion:
-
-    def __init__(self, qw, qx, qy, qz):
-        self.qw = qw; self.qx = qx; self.qy = qy; self.qz = qz  
-    
-    def conjugate(self):
-        return Quaternion(self.qw, -self.qx, -self.qy, -self.qz)
-    
-    def to_numpy(self):
-        return np.array([self.qw, self.qx, self.qy, self.qz]).reshape(4,1)
-
-    def quat_mult(self, q, out='np'):
-        v = np.array([self.qx, self.qy, self.qz]).reshape(3, 1)
-        sum_term = np.zeros([4,4])
-        sum_term[0,1:]   = -v[:,0]
-        sum_term[1:, 0]  = v[:,0]
-        sum_term[1:, 1:] = skew_symmetric(v)
-        sigma = self.qw * np.eye(4) + sum_term
-        q_new = sigma @ q.to_numpy()
-        if out == 'np':
-            return q_new
-        elif out == 'Quaternion':
-            q_obj = Quaternion(*q_new)
-            return q_obj
-    
-
-class RotVec:
-
-    def __init__(self, ax=0., ay=0., az=0., quaternion=None):
-        if quaternion is None:
-            self.ax = ax; self.ay = ay; self.az = az
-        else:
-            x = quaternion.qx; y = quaternion.qy; z = quaternion.qz
-            norm_im = np.sqrt(x**2 + y**2 + z**2)
-            if (norm_im < 1e-7):
-                self.ax = 2*x; self.ay = 2*y; self.az = 2*z
-            else:
-                th = 2 * np.arctan2(norm_im, quaternion.qw)
-                th = self.pi2pi(th)
-                self.ax = x / norm_im * th
-                self.ay = y / norm_im * th
-                self.az = z / norm_im * th
-
-    def inverted(self):
-
-        return RotVec(-self.ax, -self.ay, -self.az)
-
-    def toRotationMatrix(self):
-
-        q = self.toQuaternion()
-        q_vec =  np.array([q.qx, q.qy, q.qz]).reshape(3,1)
-        qw = q.qw
-        mat = (qw**2 - q_vec.T @ q_vec) * np.eye(3) + \
-               2 * q_vec @ q_vec.T - 2 * qw * skew_symmetric(q_vec.reshape(-1,))
-        return mat.T
-    
-    def toQuaternion(self):
-
-        v = np.sqrt(self.ax**2 + self.ay**2 + self.az**2)
-        if (v < 1e-6):
-            return Quaternion(1, 0, 0, 0)
-        else:
-            return Quaternion(np.cos(v/2), np.sin(v/2)*self.ax/v,
-                            np.sin(v/2)*self.ay/v, np.sin(v/2)*self.az/v)
-
-    def pi2pi(self, rad):
-
-        val = np.fmod(rad, 2.0 * np.pi)
-        if val > np.pi:
-            val -= 2.0 * np.pi
-        elif val < -np.pi:
-            val += 2.0 * np.pi
-
-        return val
-    
-
-class TripletList:
-
-    def __init__(self):
-        self.row = []
-        self.col = []
-        self.data = []
-
-    def push_back(self, irow, icol, idata):
-        self.row.append(irow)
-        self.col.append(icol)
-        self.data.append(idata)
-
-class Pose3D:
-
-    def __init__(self, x, y, z, qw, qx, qy, qz):
-        self.x = x; self.y = y; self.z = z
-        self.qw = qw; self.qx = qx; self.qy = qy; self.qz = qz
-    
-    def pos(self):
-        v = np.array([self.x, self.y, self.z]).reshape(3,1)
-        return v
-    
-    def rv(self):
-        q = Quaternion(self.qw, self.qx, self.qy, self.qz)
-        return RotVec(quaternion = q)
-
-    def ominus(self, base):
-        t = base.rv().toRotationMatrix().T @ (self.pos() - base.pos())
-        q = base.rv().toQuaternion().conjugate().quat_mult(self.rv().toQuaternion(), out = 'Quaternion')
-        return Pose3D(t[0][0], t[1][0], t[2][0], q.qw, q.qx, q.qy, q.qz)
-
-
-class Constrant3D:
-
-    def __init__(self, id1, id2, t, info_mat):
-        self.id1 = id1
-        self.id2 = id2
-        self.t = t
-        self.info_mat = info_mat
-
-def plot_nodes(nodes, ax, color ="-r", label = ""):
-
-    x, y, z = [], [], []
-    for n in nodes:
-        x.append(n.x); y.append(n.y); z.append(n.z)
-    ax.plot(x, y, z, color, label=label)
-
-def load_data(fname):
-
-    nodes, consts = [], []
-
-    for line in open(fname):
-        sline = line.split()
-        tag = sline[0]
-
-        if tag == "VERTEX_SE3:QUAT":
-            #data_id = int(sline[1]) # unused
-            x = float(sline[2])
-            y = float(sline[3])
-            z = float(sline[4])
-            qx = float(sline[5])
-            qy = float(sline[6])
-            qz = float(sline[7])
-            qw = float(sline[8])
-         
-            nodes.append(Pose3D(x, y, z, qw, qx, qy, qz))
-        elif tag == "EDGE_SE3:QUAT":
-            id1 = int(sline[1])
-            id2 = int(sline[2])
-            x = float(sline[3])
-            y = float(sline[4])
-            z = float(sline[5])
-            qx = float(sline[6])
-            qy = float(sline[7])
-            qz = float(sline[8])
-            qw = float(sline[9])
-            c1  = float(sline[10])
-            c2  = float(sline[11])
-            c3  = float(sline[12])
-            c4  = float(sline[13])
-            c5  = float(sline[14])
-            c6  = float(sline[15])
-            c7  = float(sline[16])
-            c8  = float(sline[17])
-            c9  = float(sline[18])
-            c10 = float(sline[19])
-            c11 = float(sline[20])
-            c12 = float(sline[21])
-            c13 = float(sline[22])
-            c14 = float(sline[23])
-            c15 = float(sline[24])
-            c16 = float(sline[25])
-            c17 = float(sline[26])
-            c18 = float(sline[27])
-            c19 = float(sline[28])
-            c20 = float(sline[29])
-            c21 = float(sline[30])
-            t = Pose3D(x, y, z, qw, qx, qy, qz)
-            info_mat = np.array([[c1,  c2,  c3,  c4,  c5,  c6],
-                                 [c2,  c7,  c8,  c9, c10, c11],
-                                 [c3,  c8, c12, c13, c14, c15],
-                                 [c4,  c9, c13, c16, c17, c18],
-                                 [c5, c10, c14, c17, c19, c20],
-                                 [c6, c11, c15, c18, c20, c21]
-                                 ])
-            consts.append(Constrant3D(id1, id2, t, info_mat))
-
-    print("n_nodes:", len(nodes))
-    print("n_consts:", len(consts))
-
-    return nodes, consts
-
-
-
-def main():
-    
-    print("start!!")
-
-    fnames = ["parking-garage.g2o"]
-
-    max_iter = 20
-    min_iter = 3
-
-    # parameter setting
-    optimizer = Optimizer3D()
-    optimizer.p_lambda = 1e-6
-    optimizer.verbose = True
-    optimizer.animation = True
-    
-    for f in fnames:
-        print(f)
-
-        nodes, consts = load_data(f)
-
-        start = time.time()
-        final_nodes = optimizer.optimize_path(nodes, consts, max_iter, min_iter)
-        print("elapsed_time", time.time() - start, "sec")
-
-        # plot
-        plt.cla()
-        est_traj_fig = plt.figure()
-        ax = est_traj_fig.add_subplot(111, projection='3d')
-        plot_nodes(nodes, ax, color="-b", label="before")
-        plot_nodes(final_nodes, ax, label="after")
-        plt.show()
-
-    print("done!!")
-
-if __name__ == '__main__':
-    main()

From 01a5bcb419cbe3db2377e4b348e24dc76a79e200 Mon Sep 17 00:00:00 2001
From: Ryohei Sasaki <ryohei.sasaki@akane.waseda.jp>
Date: Sat, 7 Sep 2019 14:35:09 +0900
Subject: [PATCH 0171/1080] Fix function dRV_dQuat of class Optimizer3D

---
 .../pose_optimization_slam_3d.py              | 20 ++++++++++++-------
 1 file changed, 13 insertions(+), 7 deletions(-)

diff --git a/SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py b/SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py
index e5cf6ac62d..105201f8a2 100644
--- a/SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py
+++ b/SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py
@@ -235,22 +235,28 @@ def dR_dRV(self, rv):
         duy = dRdqw * dqdu[0,1] + dRdqx * dqdu[1, 1] + dRdqy * dqdu[2, 1] + dRdqz * dqdu[3, 1]
         duz = dRdqw * dqdu[0,2] + dRdqx * dqdu[1, 2] + dRdqy * dqdu[2, 2] + dRdqz * dqdu[3, 2]
         return dux, duy, duz
-
+    
     def dRV_dQuat(self, q):
+        
+        qw = q.qw[0]
+        qx = q.qx[0]
+        qy = q.qy[0]
+        qz = q.qz[0]
 
-        if 1 - q.qw**2 < 1e-7:
+        if 1 - qw**2 < 1e-7:
             ret = np.array(
             [[ 0.0, 2.0, 0.0, 0.0],
              [ 0.0, 0.0, 2.0, 0.0],
              [ 0.0, 0.0, 0.0, 2.0]]
             )
             return ret
-        c = 1/(1 - q.qw**2)
-        d = np.arccos(q.qw)/(np.sqrt(1-q.qw**2))
+        
+        c = 1/(1 - qw**2)
+        d = np.arccos(qw)/(np.sqrt(1-qw**2))
         ret = 2.0 * np.array(
-            [[ c*q.qx*(d*q.qw-1),   d, 0.0, 0.0],
-             [ c*q.qy*(d*q.qw-1), 0.0,   d, 0.0],
-             [ c*q.qz*(d*q.qw-1), 0.0, 0.0,   d]]
+            [[ c*qx*(d*qw-1),   d, 0.0, 0.0],
+             [ c*qy*(d*qw-1), 0.0,   d, 0.0],
+             [ c*qz*(d*qw-1), 0.0, 0.0,   d]]
             )
         return ret
 

From d147df5ba77b7898f1aa8b6f68a3ae2abe0c8dbe Mon Sep 17 00:00:00 2001
From: Ryohei Sasaki <ryohei.sasaki@akane.waseda.jp>
Date: Sat, 7 Sep 2019 15:02:36 +0900
Subject: [PATCH 0172/1080] Make 3D graph correctly scaled

---
 .../pose_optimization_slam_3d.py                       | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py b/SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py
index 105201f8a2..c9b72e88a8 100644
--- a/SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py
+++ b/SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py
@@ -434,6 +434,16 @@ def plot_nodes(nodes, ax, color ="-r", label = ""):
     x, y, z = [], [], []
     for n in nodes:
         x.append(n.x); y.append(n.y); z.append(n.z)
+    x = np.array(x)
+    y = np.array(y)
+    z = np.array(z)
+    max_range = np.array([x.max()-x.min(), y.max()-y.min(), z.max()-z.min()]).max() / 2.0
+    mid_x = (x.max()+x.min()) * 0.5
+    mid_y = (y.max()+y.min()) * 0.5
+    mid_z = (z.max()+z.min()) * 0.5
+    ax.set_xlim(mid_x - max_range, mid_x + max_range)
+    ax.set_ylim(mid_y - max_range, mid_y + max_range)
+    ax.set_zlim(mid_z - max_range, mid_z + max_range)    
     ax.plot(x, y, z, color, label=label)
 
 def load_data(fname):

From 7c1a1ddd66eca65c6e1b7eb3ddec555cd7c02066 Mon Sep 17 00:00:00 2001
From: Zhao Zihe <2809075376@qq.com>
Date: Sat, 7 Sep 2019 16:28:06 +0800
Subject: [PATCH 0173/1080] Update motion_model.py

fix the problem of the unsafe type in the `scipy.interpolate`
---
 .../ModelPredictiveTrajectoryGenerator/motion_model.py | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py
index bb66d1fb54..f6c78d3bc1 100644
--- a/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py
+++ b/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py
@@ -36,6 +36,11 @@ def generate_trajectory(s, km, kf, k0):
 
     n = s / ds
     time = s / v  # [s]
+    
+    if isinstance(time, type(np.array([]))): time = time[0]
+    if isinstance(km, type(np.array([]))): km = km[0]
+    if isinstance(kf, type(np.array([]))): kf = kf[0]
+    
     tk = np.array([0.0, time / 2.0, time])
     kk = np.array([k0, km, kf])
     t = np.arange(0.0, time, time / n)
@@ -62,6 +67,11 @@ def generate_last_state(s, km, kf, k0):
 
     n = s / ds
     time = s / v  # [s]
+    
+    if isinstance(time,  type(np.array([]))): time = time[0]
+    if isinstance(km, type(np.array([]))): km = km[0]
+    if isinstance(kf, type(np.array([]))): kf = kf[0]
+    
     tk = np.array([0.0, time / 2.0, time])
     kk = np.array([k0, km, kf])
     t = np.arange(0.0, time, time / n)

From ec2a275f6e546dd229f7c97a1baeb37f3cb07f6f Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 7 Sep 2019 22:58:05 +0900
Subject: [PATCH 0174/1080] release PoseOptimizationSLAM3D

---
 .../histogram_filter/histogram_filter.py      |  26 +-
 README.md                                     |  15 +-
 SLAM/PoseOptimizationSLAM3D/README.md         |  10 +-
 .../PoseOptimizationSLAM3D/data_downloader.py |   9 +-
 .../pose_optimization_slam_3d.py              | 390 ++++++++++--------
 5 files changed, 245 insertions(+), 205 deletions(-)

diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py
index 9c1cb3e524..bdfbb13fa7 100644
--- a/Localization/histogram_filter/histogram_filter.py
+++ b/Localization/histogram_filter/histogram_filter.py
@@ -38,7 +38,6 @@
 NOISE_RANGE = 2.0  # [m] 1σ range noise parameter
 NOISE_SPEED = 0.5  # [m/s] 1σ speed noise parameter
 
-
 show_animation = True
 
 
@@ -66,11 +65,10 @@ def histogram_filter_localization(grid_map, u, z, yaw):
 
 
 def calc_gaussian_observation_pdf(gmap, z, iz, ix, iy, std):
-
     # predicted range
     x = ix * gmap.xy_reso + gmap.minx
     y = iy * gmap.xy_reso + gmap.miny
-    d = math.sqrt((x - z[iz, 1])**2 + (y - z[iz, 2])**2)
+    d = math.sqrt((x - z[iz, 1]) ** 2 + (y - z[iz, 2]) ** 2)
 
     # likelihood
     pdf = (1.0 - norm.cdf(abs(d - z[iz, 0]), 0.0, std))
@@ -79,7 +77,6 @@ def calc_gaussian_observation_pdf(gmap, z, iz, ix, iy, std):
 
 
 def observation_update(gmap, z, std):
-
     for iz in range(z.shape[0]):
         for ix in range(gmap.xw):
             for iy in range(gmap.yw):
@@ -99,7 +96,6 @@ def calc_input():
 
 
 def motion_model(x, u):
-
     F = np.array([[1.0, 0, 0, 0],
                   [0, 1.0, 0, 0],
                   [0, 0, 1.0, 0],
@@ -122,7 +118,6 @@ def draw_heat_map(data, mx, my):
 
 
 def observation(xTrue, u, RFID):
-
     xTrue = motion_model(xTrue, u)
 
     z = np.zeros((0, 3))
@@ -131,7 +126,7 @@ def observation(xTrue, u, RFID):
 
         dx = xTrue[0, 0] - RFID[i, 0]
         dy = xTrue[1, 0] - RFID[i, 1]
-        d = math.sqrt(dx**2 + dy**2)
+        d = math.sqrt(dx ** 2 + dy ** 2)
         if d <= MAX_RANGE:
             # add noise to range observation
             dn = d + np.random.randn() * NOISE_RANGE
@@ -146,7 +141,6 @@ def observation(xTrue, u, RFID):
 
 
 def normalize_probability(gmap):
-
     sump = sum([sum(igmap) for igmap in gmap.data])
 
     for ix in range(gmap.xw):
@@ -214,11 +208,11 @@ def calc_grid_index(gmap):
 def main():
     print(__file__ + " start!!")
 
-    # RFID positions [x, y]
-    RFID = np.array([[10.0, 0.0],
-                     [10.0, 10.0],
-                     [0.0, 15.0],
-                     [-5.0, 20.0]])
+    # RF_ID positions [x, y]
+    RF_ID = np.array([[10.0, 0.0],
+                      [10.0, 10.0],
+                      [0.0, 15.0],
+                      [-5.0, 20.0]])
 
     time = 0.0
 
@@ -233,7 +227,7 @@ def main():
         u = calc_input()
 
         yaw = xTrue[2, 0]  # Orientation is known
-        xTrue, z, ud = observation(xTrue, u, RFID)
+        xTrue, z, ud = observation(xTrue, u, RF_ID)
 
         grid_map = histogram_filter_localization(grid_map, u, z, yaw)
 
@@ -241,10 +235,10 @@ def main():
             plt.cla()
             draw_heat_map(grid_map.data, mx, my)
             plt.plot(xTrue[0, :], xTrue[1, :], "xr")
-            plt.plot(RFID[:, 0], RFID[:, 1], ".k")
+            plt.plot(RF_ID[:, 0], RF_ID[:, 1], ".k")
             for i in range(z.shape[0]):
                 plt.plot([xTrue[0, :], z[i, 1]], [
-                         xTrue[1, :], z[i, 2]], "-k")
+                    xTrue[1, :], z[i, 2]], "-k")
             plt.title("Time[s]:" + str(time)[0: 4])
             plt.pause(0.1)
 
diff --git a/README.md b/README.md
index e8bf0d297d..d2614f0f9c 100644
--- a/README.md
+++ b/README.md
@@ -609,19 +609,6 @@ This is a list: [Users comments](https://github.com/AtsushiSakai/PythonRobotics/
 
 - [Alexis Paques](https://github.com/AlexisTM)
 
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+- [Ryohei Sasaki](https://github.com/rsasaki0109)
 
 
diff --git a/SLAM/PoseOptimizationSLAM3D/README.md b/SLAM/PoseOptimizationSLAM3D/README.md
index ac95c7e206..396a629735 100644
--- a/SLAM/PoseOptimizationSLAM3D/README.md
+++ b/SLAM/PoseOptimizationSLAM3D/README.md
@@ -15,7 +15,11 @@ python pose_optimization_slam_3d.py
 ~~~
 
 ## Reference 
-[A Compact and Portable Implementation of Graph\-based SLAM](https://www.researchgate.net/publication/321287640_A_Compact_and_Portable_Implementation_of_Graph-based_SLAM)  
-[GitHub \- furo\-org/p2o: Single header 2D/3D graph\-based SLAM library](https://github.com/furo-org/p2o)  
-[GitHub \- AtsushiSakai/PythonRobotics
+- [A Compact and Portable Implementation of Graph\-based SLAM](https://www.researchgate.net/publication/321287640_A_Compact_and_Portable_Implementation_of_Graph-based_SLAM)  
+
+- [GitHub \- furo\-org/p2o: Single header 2D/3D graph\-based SLAM library](https://github.com/furo-org/p2o)  
+
+- [GitHub \- AtsushiSakai/PythonRobotics
 /SLAM/PoseOptimizationSLAM](https://github.com/AtsushiSakai/PythonRobotics/tree/master/SLAM/PoseOptimizationSLAM)
+
+- [リー代数による3次元ポーズ調整\(Pose Adjustment\)\[PythonRobotics\]\[SLAM\] \- Qiita](https://qiita.com/saitosasaki/items/a0540cba994f08bf5e16#comment-3e6588e6b096cc2567d8)
diff --git a/SLAM/PoseOptimizationSLAM3D/data_downloader.py b/SLAM/PoseOptimizationSLAM3D/data_downloader.py
index 8dea252640..a0c04fd1ab 100644
--- a/SLAM/PoseOptimizationSLAM3D/data_downloader.py
+++ b/SLAM/PoseOptimizationSLAM3D/data_downloader.py
@@ -6,16 +6,21 @@
 
 """
 
-
 import subprocess
+
+
 def main():
     print("start!!")
 
     cmd = "wget https://www.dropbox.com/s/zu23p8d522qccor/parking-garage.g2o?dl=0 -O parking-garage.g2o -nc"
     subprocess.call(cmd, shell=True)
+    cmd = "wget http://www.furo.org/irie/datasets/torus3d_guess.g2o -nc"
+    subprocess.call(cmd, shell=True)
+    cmd = "wget http://www.furo.org/irie/datasets/sphere2200_guess.g2o -nc"
+    subprocess.call(cmd, shell=True)
 
     print("done!!")
 
 
 if __name__ == '__main__':
-    main()
\ No newline at end of file
+    main()
diff --git a/SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py b/SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py
index c9b72e88a8..0f39b22f3c 100644
--- a/SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py
+++ b/SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py
@@ -1,40 +1,45 @@
 """
 3D (x, y, z, qw, qx, qy, qz) pose optimization SLAM
+
 author: Ryohei Sasaki(@rsasaki0109)
+
 Ref:
+
 - [A Compact and Portable Implementation of Graph\-based SLAM](https://www.researchgate.net/publication/321287640_A_Compact_and_Portable_Implementation_of_Graph-based_SLAM)
+
 - [GitHub \- furo\-org/p2o: Single header 2D/3D graph\-based SLAM library](https://github.com/furo-org/p2o)
+
 - [GitHub \- AtsushiSakai/PythonRobotics
 /SLAM/PoseOptimizationSLAM](https://github.com/AtsushiSakai/PythonRobotics/blob/master/SLAM/PoseOptimizationSLAM/pose_optimization_slam.py)
+
+
 """
 
+import os
 import sys
 import time
+
+import matplotlib.pyplot as plt
 import numpy as np
 from scipy import sparse
 from scipy.sparse import linalg
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
 
-est_traj_fig = plt.figure()
-ax = est_traj_fig.add_subplot(111, projection='3d')
 
 def skew_symmetric(v):
-
     return np.array(
         [[0, -v[2], v[1]],
          [v[2], 0, -v[0]],
          [-v[1], v[0], 0]]
     )
 
-def robust_coeff(squared_error, delta):
 
-    if (squared_error < 0): 
+def robust_coeff(squared_error, delta):
+    if squared_error < 0:
         return 0
     sqre = np.sqrt(squared_error)
-    if (sqre < delta):
-        return 1 # no effect
-    return delta / sqre # linear
+    if sqre < delta:
+        return 1  # no effect
+    return delta / sqre  # linear
 
 
 class Optimizer3D:
@@ -90,14 +95,15 @@ def optimize_path_one_step(self, graph_nodes, constraints):
         for con in constraints:
             ida = con.id1
             idb = con.id2
-            assert 0 <= ida and ida < numnodes, "ida is invalid"
-            assert 0 <= idb and idb < numnodes, "idb is invalid"
+            assert 0 <= ida < numnodes, "ida is invalid"
+            assert 0 <= idb < numnodes, "idb is invalid"
             pa = graph_nodes[ida]
             pb = graph_nodes[idb]
             r, Ja, Jb = self.calc_error(
                 pa, pb, con.t)
- 
-            info_mat = con.info_mat * robust_coeff(r.reshape(self.dim,1).T @ con.info_mat @ r.reshape(self.dim,1), self.robust_delta)
+
+            info_mat = con.info_mat * robust_coeff(r.reshape(self.dim, 1).T @ con.info_mat @ r.reshape(self.dim, 1),
+                                                   self.robust_delta)
 
             trJaInfo = Ja.transpose() @ info_mat
             trJaInfoJa = trJaInfo @ Ja
@@ -115,10 +121,10 @@ def optimize_path_one_step(self, graph_nodes, constraints):
                         ida * self.dim + k, idb * self.dim + m, trJaInfoJb[k, m])
                     tripletList.push_back(
                         idb * self.dim + k, ida * self.dim + m, trJaInfoJb[m, k])
-                        
-            bf[ida * self.dim: (ida + 1) * self.dim ] += trJaInfo @ r
-            bf[idb * self.dim: (idb + 1) * self.dim ] += trJbInfo @ r
-        
+
+            bf[ida * self.dim: (ida + 1) * self.dim] += trJaInfo @ r
+            bf[idb * self.dim: (idb + 1) * self.dim] += trJbInfo @ r
+
         for k in indlist:
             tripletList.push_back(k, k, self.init_w)
 
@@ -133,18 +139,17 @@ def optimize_path_one_step(self, graph_nodes, constraints):
         out_nodes = []
 
         for i in range(len(graph_nodes)):
-
             u_i = i * self.dim
-            
+
             q_before = Quaternion(graph_nodes[i].qw, graph_nodes[i].qx, graph_nodes[i].qy, graph_nodes[i].qz)
-            rv_before = RotVec(quaternion = q_before)
-            rv_after = RotVec(ax = rv_before.ax + x[u_i + 3], ay = rv_before.ay + x[u_i + 4], az = rv_before.az + x[u_i + 5])
-            q_after = rv_after.toQuaternion()
+            rv_before = RotVec(quaternion=q_before)
+            rv_after = RotVec(ax=rv_before.ax + x[u_i + 3], ay=rv_before.ay + x[u_i + 4], az=rv_before.az + x[u_i + 5])
+            q_after = rv_after.to_quaternion()
 
             pos = Pose3D(
-                graph_nodes[i].x  + x[u_i],
-                graph_nodes[i].y  + x[u_i + 1],
-                graph_nodes[i].z  + x[u_i + 2],
+                graph_nodes[i].x + x[u_i],
+                graph_nodes[i].y + x[u_i + 1],
+                graph_nodes[i].z + x[u_i + 2],
                 q_after.qw,
                 q_after.qx,
                 q_after.qy,
@@ -161,16 +166,18 @@ def calc_global_cost(self, nodes, constraints):
         cost = 0.0
         for c in constraints:
             diff = self.error_func(nodes[c.id1], nodes[c.id2], c.t)
-            info_mat = c.info_mat * robust_coeff(diff.reshape(self.dim,1).T @ c.info_mat @ diff.reshape(self.dim,1), self.robust_delta)
+            info_mat = c.info_mat * robust_coeff(diff.reshape(self.dim, 1).T @ c.info_mat @ diff.reshape(self.dim, 1),
+                                                 self.robust_delta)
             cost += diff.transpose() @ info_mat @ diff
 
         return cost
 
-    def error_func(self, pa, pb, t):
+    @staticmethod
+    def error_func(pa, pb, t):
 
         ba = pb.ominus(pa)
-        q = t.rv().toQuaternion().conjugate().quat_mult(ba.rv().toQuaternion(), out = 'Quaternion')
-        drv = RotVec(quaternion = q)
+        q = t.rv().to_quaternion().conjugate().quat_mult(ba.rv().to_quaternion(), out='Quaternion')
+        drv = RotVec(quaternion=q)
         error = np.array([ba.x - t.x,
                           ba.y - t.y,
                           ba.z - t.z,
@@ -179,156 +186,177 @@ def error_func(self, pa, pb, t):
                           drv.az[0]])
         return error
 
-    def dQuat_dRV(self, rv):
-
-        u1 = rv.ax; u2 = rv.ay; u3 = rv.az
-        v = np.sqrt(u1**2 + u2**2 + u3**2)
+    @staticmethod
+    def dQuat_dRV(rv):
+        u1 = rv.ax
+        u2 = rv.ay
+        u3 = rv.az
+        v = np.sqrt(u1 ** 2 + u2 ** 2 + u3 ** 2)
         if v < 1e-6:
             dqu = 0.25 * np.array(
-            [[ -u1, -u2, -u3],
-             [ 2.0, 0.0, 0.0],
-             [ 0.0, 2.0, 0.0],
-             [ 0.0, 0.0, 2.0]]
+                [[-u1, -u2, -u3],
+                 [2.0, 0.0, 0.0],
+                 [0.0, 2.0, 0.0],
+                 [0.0, 0.0, 2.0]]
             )
             return dqu
 
-        vd = v*2
-        v2 = v**2
-        v3 = v**3
+        vd = v * 2
+        v2 = v ** 2
+        v3 = v ** 3
 
-        S = np.sin(v/2.0); C = np.cos(v/2.0)
+        S = np.sin(v / 2.0)
+        C = np.cos(v / 2.0)
         dqu = np.array(
-            [[ -u1 * S/vd                       , -u2*S/vd                     , -u3*S/vd],
-             [ S/v + u1*u1*C/(2*v2) - u1*u1*S/v3, u1*u2*(C/(2*v2)-S/v3)        , u1*u3*(C/(2*v2)-S/v3)],
-             [ u1*u2*(C/(2*v2)-S/v3)            , S/v+u2*u2*C/(2*v2)-u2*u2*S/v3, u2*u3*(C/(2*v2)-S/v3)],
-             [ u1*u3*(C/(2*v2)-S/v3)            , u2*u3*(C/(2*v2)-S/v3)        , S/v+u3*u3*C/(2*v2)-u3*u3*S/v3]]
-            )
+            [[-u1 * S / vd, -u2 * S / vd, -u3 * S / vd],
+             [S / v + u1 * u1 * C / (2 * v2) - u1 * u1 * S / v3, u1 * u2 * (C / (2 * v2) - S / v3),
+              u1 * u3 * (C / (2 * v2) - S / v3)],
+             [u1 * u2 * (C / (2 * v2) - S / v3), S / v + u2 * u2 * C / (2 * v2) - u2 * u2 * S / v3,
+              u2 * u3 * (C / (2 * v2) - S / v3)],
+             [u1 * u3 * (C / (2 * v2) - S / v3), u2 * u3 * (C / (2 * v2) - S / v3),
+              S / v + u3 * u3 * C / (2 * v2) - u3 * u3 * S / v3]]
+        )
 
         return dqu
 
     def dR_dRV(self, rv):
 
-        q = rv.toQuaternion()
-        qw = q.qw;qx = q.qx; qy = q.qy;qz = q.qz
+        q = rv.to_quaternion()
+        qw = q.qw
+        qx = q.qx
+        qy = q.qy
+        qz = q.qz
         dRdqw = 2 * np.array(
-            [[ qw, -qz,  qy],
-             [ qz,  qw, -qx],
-             [-qy,  qx,  qw]]
+            [[qw, -qz, qy],
+             [qz, qw, -qx],
+             [-qy, qx, qw]]
         )
         dRdqx = 2 * np.array(
-            [[ qx,  qy,  qz],
-             [ qy, -qx, -qw],
-             [ qz,  qw, -qx]]
+            [[qx, qy, qz],
+             [qy, -qx, -qw],
+             [qz, qw, -qx]]
         )
         dRdqy = 2 * np.array(
-            [[-qy,  qx,  qw],
-             [ qx,  qy,  qz],
-             [-qw,  qz, -qy]]
+            [[-qy, qx, qw],
+             [qx, qy, qz],
+             [-qw, qz, -qy]]
         )
         dRdqz = 2 * np.array(
-            [[-qz, -qw,  qx],
-             [ qw, -qz, -qy],
-             [ qz,  qy,  qz]]
+            [[-qz, -qw, qx],
+             [qw, -qz, -qy],
+             [qz, qy, qz]]
         )
         dqdu = self.dQuat_dRV(rv)
-        dux = dRdqw * dqdu[0,0] + dRdqx * dqdu[1, 0] + dRdqy * dqdu[2, 0] + dRdqz * dqdu[3, 0]
-        duy = dRdqw * dqdu[0,1] + dRdqx * dqdu[1, 1] + dRdqy * dqdu[2, 1] + dRdqz * dqdu[3, 1]
-        duz = dRdqw * dqdu[0,2] + dRdqx * dqdu[1, 2] + dRdqy * dqdu[2, 2] + dRdqz * dqdu[3, 2]
+        dux = dRdqw * dqdu[0, 0] + dRdqx * dqdu[1, 0] + dRdqy * dqdu[2, 0] + dRdqz * dqdu[3, 0]
+        duy = dRdqw * dqdu[0, 1] + dRdqx * dqdu[1, 1] + dRdqy * dqdu[2, 1] + dRdqz * dqdu[3, 1]
+        duz = dRdqw * dqdu[0, 2] + dRdqx * dqdu[1, 2] + dRdqy * dqdu[2, 2] + dRdqz * dqdu[3, 2]
         return dux, duy, duz
-    
-    def dRV_dQuat(self, q):
-        
+
+    @staticmethod
+    def dRV_dQuat(q):
+
         qw = q.qw[0]
         qx = q.qx[0]
         qy = q.qy[0]
         qz = q.qz[0]
 
-        if 1 - qw**2 < 1e-7:
+        if 1 - qw ** 2 < 1e-7:
             ret = np.array(
-            [[ 0.0, 2.0, 0.0, 0.0],
-             [ 0.0, 0.0, 2.0, 0.0],
-             [ 0.0, 0.0, 0.0, 2.0]]
+                [[0.0, 2.0, 0.0, 0.0],
+                 [0.0, 0.0, 2.0, 0.0],
+                 [0.0, 0.0, 0.0, 2.0]]
             )
             return ret
-        
-        c = 1/(1 - qw**2)
-        d = np.arccos(qw)/(np.sqrt(1-qw**2))
+
+        c = 1 / (1 - qw ** 2)
+        d = np.arccos(qw) / (np.sqrt(1 - qw ** 2))
         ret = 2.0 * np.array(
-            [[ c*qx*(d*qw-1),   d, 0.0, 0.0],
-             [ c*qy*(d*qw-1), 0.0,   d, 0.0],
-             [ c*qz*(d*qw-1), 0.0, 0.0,   d]]
-            )
+            [[c * qx * (d * qw - 1), d, 0.0, 0.0],
+             [c * qy * (d * qw - 1), 0.0, d, 0.0],
+             [c * qz * (d * qw - 1), 0.0, 0.0, d]]
+        )
         return ret
 
     def QMat(self, q):
 
-        qw = q.qw; qx = q.qx; qy = q.qy; qz = q.qz
+        qw = q.qw
+        qx = q.qx
+        qy = q.qy
+        qz = q.qz
         Q = np.array(
-            [[ qw, -qx, -qy, -qz],
-             [ qx,  qw, -qz,  qy],
-             [ qy,  qz,  qw, -qx],
-             [ qz, -qy,  qx,  qw]]
-            )
+            [[qw, -qx, -qy, -qz],
+             [qx, qw, -qz, qy],
+             [qy, qz, qw, -qx],
+             [qz, -qy, qx, qw]]
+        )
         return Q
 
     def QMatBar(self, q):
 
-        qw = q.qw; qx = q.qx; qy = q.qy; qz = q.qz
+        qw = q.qw
+        qx = q.qx
+        qy = q.qy
+        qz = q.qz
         Q = np.array(
-            [[ qw, -qx, -qy, -qz],
-             [ qx,  qw,  qz, -qy],
-             [ qy, -qz,  qw,  qx],
-             [ qz,  qy, -qx,  qw]]
-            )
+            [[qw, -qx, -qy, -qz],
+             [qx, qw, qz, -qy],
+             [qy, -qz, qw, qx],
+             [qz, qy, -qx, qw]]
+        )
         return Q
 
     def calc_error(self, pa, pb, t):
 
         e0 = self.error_func(pa, pb, t)
-        Ja = np.identity(6); Jb = np.identity(6) 
+        Ja = np.identity(6)
+        Jb = np.identity(6)
 
         rva_inv = pa.rv().inverted()
-        rotPaInv = rva_inv.toRotationMatrix()
+        rotPaInv = rva_inv.to_rotation_matrix()
 
-        Ja[:3,:3] = -rotPaInv
-        Jb[:3,:3] = rotPaInv
+        Ja[:3, :3] = -rotPaInv
+        Jb[:3, :3] = rotPaInv
 
         dRux, dRuy, dRuz = self.dR_dRV(rva_inv)
 
-        cvec = np.array([[pb.x - pa.x],[pb.y - pa.y],[pb.z - pa.z]])
+        cvec = np.array([[pb.x - pa.x], [pb.y - pa.y], [pb.z - pa.z]])
 
-        Ja[0:3,3:4] = -dRux @ cvec
-        Ja[0:3,4:5] = -dRuy @ cvec
-        Ja[0:3,5:6] = -dRuz @ cvec
+        Ja[0:3, 3:4] = -dRux @ cvec
+        Ja[0:3, 4:5] = -dRuy @ cvec
+        Ja[0:3, 5:6] = -dRuz @ cvec
 
         # rotation part: qdiff = qc-1 * qa-1 * qb
-        qainv = rva_inv.toQuaternion()
-        qcinv = t.rv().inverted().toQuaternion()
-        qb = pb.rv().toQuaternion()
-        qinvca = qcinv.quat_mult(qainv, out = 'Quaternion')
-        qdiff  = qinvca.quat_mult(qb, out = 'Quaternion')
+        qainv = rva_inv.to_quaternion()
+        qcinv = t.rv().inverted().to_quaternion()
+        qb = pb.rv().to_quaternion()
+        qinvca = qcinv.quat_mult(qainv, out='Quaternion')
+        qdiff = qinvca.quat_mult(qb, out='Quaternion')
 
-        Ja[3:6,3:6] = -self.dRV_dQuat(qdiff) @ self.QMat(qcinv) @ self.QMatBar(qb) @ self.dQuat_dRV(rva_inv)
-        Jb[3:6,3:6] =  self.dRV_dQuat(qdiff) @ self.QMat(qcinv) @ self.QMat(qainv) @ self.dQuat_dRV(pb.rv())
+        Ja[3:6, 3:6] = -self.dRV_dQuat(qdiff) @ self.QMat(qcinv) @ self.QMatBar(qb) @ self.dQuat_dRV(rva_inv)
+        Jb[3:6, 3:6] = self.dRV_dQuat(qdiff) @ self.QMat(qcinv) @ self.QMat(qainv) @ self.dQuat_dRV(pb.rv())
 
         return e0, Ja, Jb
 
+
 class Quaternion:
 
     def __init__(self, qw, qx, qy, qz):
-        self.qw = qw; self.qx = qx; self.qy = qy; self.qz = qz  
-    
+        self.qw = qw
+        self.qx = qx
+        self.qy = qy
+        self.qz = qz
+
     def conjugate(self):
         return Quaternion(self.qw, -self.qx, -self.qy, -self.qz)
-    
+
     def to_numpy(self):
-        return np.array([self.qw, self.qx, self.qy, self.qz]).reshape(4,1)
+        return np.array([self.qw, self.qx, self.qy, self.qz]).reshape(4, 1)
 
     def quat_mult(self, q, out='np'):
         v = np.array([self.qx, self.qy, self.qz]).reshape(3, 1)
-        sum_term = np.zeros([4,4])
-        sum_term[0,1:]   = -v[:,0]
-        sum_term[1:, 0]  = v[:,0]
+        sum_term = np.zeros([4, 4])
+        sum_term[0, 1:] = -v[:, 0]
+        sum_term[1:, 0] = v[:, 0]
         sum_term[1:, 1:] = skew_symmetric(v)
         sigma = self.qw * np.eye(4) + sum_term
         q_new = sigma @ q.to_numpy()
@@ -337,18 +365,24 @@ def quat_mult(self, q, out='np'):
         elif out == 'Quaternion':
             q_obj = Quaternion(*q_new)
             return q_obj
-    
+
 
 class RotVec:
 
     def __init__(self, ax=0., ay=0., az=0., quaternion=None):
         if quaternion is None:
-            self.ax = ax; self.ay = ay; self.az = az
+            self.ax = ax
+            self.ay = ay
+            self.az = az
         else:
-            x = quaternion.qx; y = quaternion.qy; z = quaternion.qz
-            norm_im = np.sqrt(x**2 + y**2 + z**2)
-            if (norm_im < 1e-7):
-                self.ax = 2*x; self.ay = 2*y; self.az = 2*z
+            x = quaternion.qx
+            y = quaternion.qy
+            z = quaternion.qz
+            norm_im = np.sqrt(x ** 2 + y ** 2 + z ** 2)
+            if norm_im < 1e-7:
+                self.ax = 2 * x
+                self.ay = 2 * y
+                self.az = 2 * z
             else:
                 th = 2 * np.arctan2(norm_im, quaternion.qw)
                 th = self.pi2pi(th)
@@ -360,25 +394,26 @@ def inverted(self):
 
         return RotVec(-self.ax, -self.ay, -self.az)
 
-    def toRotationMatrix(self):
+    def to_rotation_matrix(self):
 
-        q = self.toQuaternion()
-        q_vec =  np.array([q.qx, q.qy, q.qz]).reshape(3,1)
+        q = self.to_quaternion()
+        q_vec = np.array([q.qx, q.qy, q.qz]).reshape(3, 1)
         qw = q.qw
-        mat = (qw**2 - q_vec.T @ q_vec) * np.eye(3) + \
-               2 * q_vec @ q_vec.T - 2 * qw * skew_symmetric(q_vec.reshape(-1,))
+        mat = (qw ** 2 - q_vec.T @ q_vec) * np.eye(3) \
+              + 2 * q_vec @ q_vec.T - 2 * qw * skew_symmetric(q_vec.reshape(-1, ))
         return mat.T
-    
-    def toQuaternion(self):
 
-        v = np.sqrt(self.ax**2 + self.ay**2 + self.az**2)
+    def to_quaternion(self):
+
+        v = np.sqrt(self.ax ** 2 + self.ay ** 2 + self.az ** 2)
         if (v < 1e-6):
             return Quaternion(1, 0, 0, 0)
         else:
-            return Quaternion(np.cos(v/2), np.sin(v/2)*self.ax/v,
-                            np.sin(v/2)*self.ay/v, np.sin(v/2)*self.az/v)
+            return Quaternion(np.cos(v / 2), np.sin(v / 2) * self.ax / v,
+                              np.sin(v / 2) * self.ay / v, np.sin(v / 2) * self.az / v)
 
-    def pi2pi(self, rad):
+    @staticmethod
+    def pi2pi(rad):
 
         val = np.fmod(rad, 2.0 * np.pi)
         if val > np.pi:
@@ -387,7 +422,7 @@ def pi2pi(self, rad):
             val += 2.0 * np.pi
 
         return val
-    
+
 
 class TripletList:
 
@@ -401,27 +436,33 @@ def push_back(self, irow, icol, idata):
         self.col.append(icol)
         self.data.append(idata)
 
+
 class Pose3D:
 
     def __init__(self, x, y, z, qw, qx, qy, qz):
-        self.x = x; self.y = y; self.z = z
-        self.qw = qw; self.qx = qx; self.qy = qy; self.qz = qz
-    
+        self.x = x
+        self.y = y
+        self.z = z
+        self.qw = qw
+        self.qx = qx
+        self.qy = qy
+        self.qz = qz
+
     def pos(self):
-        v = np.array([self.x, self.y, self.z]).reshape(3,1)
+        v = np.array([self.x, self.y, self.z]).reshape(3, 1)
         return v
-    
+
     def rv(self):
         q = Quaternion(self.qw, self.qx, self.qy, self.qz)
-        return RotVec(quaternion = q)
+        return RotVec(quaternion=q)
 
     def ominus(self, base):
-        t = base.rv().toRotationMatrix().T @ (self.pos() - base.pos())
-        q = base.rv().toQuaternion().conjugate().quat_mult(self.rv().toQuaternion(), out = 'Quaternion')
+        t = base.rv().to_rotation_matrix().T @ (self.pos() - base.pos())
+        q = base.rv().to_quaternion().conjugate().quat_mult(self.rv().to_quaternion(), out='Quaternion')
         return Pose3D(t[0][0], t[1][0], t[2][0], q.qw, q.qx, q.qy, q.qz)
 
 
-class Constrant3D:
+class Constraint3D:
 
     def __init__(self, id1, id2, t, info_mat):
         self.id1 = id1
@@ -429,25 +470,28 @@ def __init__(self, id1, id2, t, info_mat):
         self.t = t
         self.info_mat = info_mat
 
-def plot_nodes(nodes, ax, color ="-r", label = ""):
 
+def plot_nodes(nodes, ax, color="-r", label=""):
     x, y, z = [], [], []
     for n in nodes:
-        x.append(n.x); y.append(n.y); z.append(n.z)
+        x.append(n.x)
+        y.append(n.y)
+        z.append(n.z)
     x = np.array(x)
     y = np.array(y)
     z = np.array(z)
-    max_range = np.array([x.max()-x.min(), y.max()-y.min(), z.max()-z.min()]).max() / 2.0
-    mid_x = (x.max()+x.min()) * 0.5
-    mid_y = (y.max()+y.min()) * 0.5
-    mid_z = (z.max()+z.min()) * 0.5
+    max_range = np.array([x.max() - x.min(), y.max() - y.min(), z.max() - z.min()]).max() / 2.0
+    mid_x = (x.max() + x.min()) * 0.5
+    mid_y = (y.max() + y.min()) * 0.5
+    mid_z = (z.max() + z.min()) * 0.5
     ax.set_xlim(mid_x - max_range, mid_x + max_range)
     ax.set_ylim(mid_y - max_range, mid_y + max_range)
-    ax.set_zlim(mid_z - max_range, mid_z + max_range)    
+    ax.set_zlim(mid_z - max_range, mid_z + max_range)
     ax.plot(x, y, z, color, label=label)
+    # ax.plot(x, y, color, label=label)
 
-def load_data(fname):
 
+def load_data(fname):
     nodes, consts = [], []
 
     for line in open(fname):
@@ -455,7 +499,7 @@ def load_data(fname):
         tag = sline[0]
 
         if tag == "VERTEX_SE3:QUAT":
-            #data_id = int(sline[1]) # unused
+            # data_id = int(sline[1]) # unused
             x = float(sline[2])
             y = float(sline[3])
             z = float(sline[4])
@@ -463,7 +507,7 @@ def load_data(fname):
             qy = float(sline[6])
             qz = float(sline[7])
             qw = float(sline[8])
-         
+
             nodes.append(Pose3D(x, y, z, qw, qx, qy, qz))
         elif tag == "EDGE_SE3:QUAT":
             id1 = int(sline[1])
@@ -475,15 +519,15 @@ def load_data(fname):
             qy = float(sline[7])
             qz = float(sline[8])
             qw = float(sline[9])
-            c1  = float(sline[10])
-            c2  = float(sline[11])
-            c3  = float(sline[12])
-            c4  = float(sline[13])
-            c5  = float(sline[14])
-            c6  = float(sline[15])
-            c7  = float(sline[16])
-            c8  = float(sline[17])
-            c9  = float(sline[18])
+            c1 = float(sline[10])
+            c2 = float(sline[11])
+            c3 = float(sline[12])
+            c4 = float(sline[13])
+            c5 = float(sline[14])
+            c6 = float(sline[15])
+            c7 = float(sline[16])
+            c8 = float(sline[17])
+            c9 = float(sline[18])
             c10 = float(sline[19])
             c11 = float(sline[20])
             c12 = float(sline[21])
@@ -497,14 +541,14 @@ def load_data(fname):
             c20 = float(sline[29])
             c21 = float(sline[30])
             t = Pose3D(x, y, z, qw, qx, qy, qz)
-            info_mat = np.array([[c1,  c2,  c3,  c4,  c5,  c6],
-                                 [c2,  c7,  c8,  c9, c10, c11],
-                                 [c3,  c8, c12, c13, c14, c15],
-                                 [c4,  c9, c13, c16, c17, c18],
+            info_mat = np.array([[c1, c2, c3, c4, c5, c6],
+                                 [c2, c7, c8, c9, c10, c11],
+                                 [c3, c8, c12, c13, c14, c15],
+                                 [c4, c9, c13, c16, c17, c18],
                                  [c5, c10, c14, c17, c19, c20],
                                  [c6, c11, c15, c18, c20, c21]
                                  ])
-            consts.append(Constrant3D(id1, id2, t, info_mat))
+            consts.append(Constraint3D(id1, id2, t, info_mat))
 
     print("n_nodes:", len(nodes))
     print("n_consts:", len(consts))
@@ -512,22 +556,26 @@ def load_data(fname):
     return nodes, consts
 
 
-
 def main():
-    
     print("start!!")
 
-    fnames = ["parking-garage.g2o"]
+    dir = os.path.dirname(os.path.abspath(__file__))
 
-    max_iter = 20
+    fnames = [
+        dir + "/sphere2200_guess.g2o",
+        dir + "/torus3d_guess.g2o",
+        dir + "/parking-garage.g2o",
+    ]
+
+    max_iter = 10
     min_iter = 3
 
     # parameter setting
     optimizer = Optimizer3D()
     optimizer.p_lambda = 1e-6
     optimizer.verbose = True
-    optimizer.animation = True
-    
+    optimizer.animation = False
+
     for f in fnames:
         print(f)
 
@@ -538,14 +586,16 @@ def main():
         print("elapsed_time", time.time() - start, "sec")
 
         # plot
-        plt.cla()
+        plt.close("all")
         est_traj_fig = plt.figure()
         ax = est_traj_fig.add_subplot(111, projection='3d')
         plot_nodes(nodes, ax, color="-b", label="before")
         plot_nodes(final_nodes, ax, label="after")
+        plt.legend()
         plt.show()
 
     print("done!!")
 
+
 if __name__ == '__main__':
     main()

From 52d836cff8103cda64100f3df3f1156e6217fa5f Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 Sep 2019 09:55:22 +0900
Subject: [PATCH 0175/1080] add PoseOptimizationSLAM3D gif and change dir name

---
 README.md                                         | 15 ++++++++++++---
 .../README.md                                     |  0
 .../data_downloader.py                            |  0
 .../pose_optimization_slam_2d.py}                 |  0
 4 files changed, 12 insertions(+), 3 deletions(-)
 rename SLAM/{PoseOptimizationSLAM => PoseOptimizationSLAM2D}/README.md (100%)
 rename SLAM/{PoseOptimizationSLAM => PoseOptimizationSLAM2D}/data_downloader.py (100%)
 rename SLAM/{PoseOptimizationSLAM/pose_optimization_slam.py => PoseOptimizationSLAM2D/pose_optimization_slam_2d.py} (100%)

diff --git a/README.md b/README.md
index d2614f0f9c..93ede20a7e 100644
--- a/README.md
+++ b/README.md
@@ -249,11 +249,20 @@ Ref:
 - [SLAM simulations by Tim Bailey](http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm)
 
 
-## Pose Optimization SLAM
+## Pose Optimization SLAM 2D
+
+This is a graph based 2D pose optimization SLAM example.
+
+![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/PoseOptimizationSLAM2D/animation.gif)
+
+
+## Pose Optimization SLAM 3D
+
+This is a graph based 3D pose optimization SLAM example.
+
+![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/PoseOptimizationSLAM3D/animation.gif)
 
-This is a graph based pose optimization SLAM example.
 
-![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/PoseOptimizationSLAM/animation.gif)
 
 # Path Planning
 
diff --git a/SLAM/PoseOptimizationSLAM/README.md b/SLAM/PoseOptimizationSLAM2D/README.md
similarity index 100%
rename from SLAM/PoseOptimizationSLAM/README.md
rename to SLAM/PoseOptimizationSLAM2D/README.md
diff --git a/SLAM/PoseOptimizationSLAM/data_downloader.py b/SLAM/PoseOptimizationSLAM2D/data_downloader.py
similarity index 100%
rename from SLAM/PoseOptimizationSLAM/data_downloader.py
rename to SLAM/PoseOptimizationSLAM2D/data_downloader.py
diff --git a/SLAM/PoseOptimizationSLAM/pose_optimization_slam.py b/SLAM/PoseOptimizationSLAM2D/pose_optimization_slam_2d.py
similarity index 100%
rename from SLAM/PoseOptimizationSLAM/pose_optimization_slam.py
rename to SLAM/PoseOptimizationSLAM2D/pose_optimization_slam_2d.py

From 4246172d261fa865cd5f07fea5dc01702dae91c0 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 Sep 2019 09:57:33 +0900
Subject: [PATCH 0176/1080] update TOC

---
 README.md | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 93ede20a7e..8e39d6982c 100644
--- a/README.md
+++ b/README.md
@@ -12,6 +12,7 @@
 
 Python codes for robotics algorithm.
 
+
 # Table of Contents
    * [What is this?](#what-is-this)
    * [Requirements](#requirements)
@@ -30,7 +31,8 @@ Python codes for robotics algorithm.
    * [SLAM](#slam)
       * [Iterative Closest Point (ICP) Matching](#iterative-closest-point-icp-matching)
       * [FastSLAM 1.0](#fastslam-10)
-      * [Pose Optimization SLAM](#pose-optimization-slam)
+      * [Pose Optimization SLAM 2D](#pose-optimization-slam-2d)
+      * [Pose Optimization SLAM 3D](#pose-optimization-slam-3d)
    * [Path Planning](#path-planning)
       * [Dynamic Window Approach](#dynamic-window-approach)
       * [Grid based search](#grid-based-search)
@@ -621,3 +623,4 @@ This is a list: [Users comments](https://github.com/AtsushiSakai/PythonRobotics/
 - [Ryohei Sasaki](https://github.com/rsasaki0109)
 
 
+

From 4264bde171840c1544da1f50eb71f7a346b89b5d Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 Sep 2019 16:10:58 +0900
Subject: [PATCH 0177/1080] once remove the scripts for solving refactoring

---
 .../pose_optimization_slam_2d.py              | 290 ---------
 .../pose_optimization_slam_3d.py              | 601 ------------------
 2 files changed, 891 deletions(-)
 delete mode 100644 SLAM/PoseOptimizationSLAM2D/pose_optimization_slam_2d.py
 delete mode 100644 SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py

diff --git a/SLAM/PoseOptimizationSLAM2D/pose_optimization_slam_2d.py b/SLAM/PoseOptimizationSLAM2D/pose_optimization_slam_2d.py
deleted file mode 100644
index 764b0f5e73..0000000000
--- a/SLAM/PoseOptimizationSLAM2D/pose_optimization_slam_2d.py
+++ /dev/null
@@ -1,290 +0,0 @@
-"""
-
-2D (x, y, yaw) pose optimization SLAM
-
-author: Atsushi Sakai
-
-Ref:
-- [A Compact and Portable Implementation of Graph\-based SLAM](https://www.researchgate.net/publication/321287640_A_Compact_and_Portable_Implementation_of_Graph-based_SLAM)
-
-- [GitHub \- furo\-org/p2o: Single header 2D/3D graph\-based SLAM library](https://github.com/furo-org/p2o)
-
-"""
-
-import sys
-import time
-import math
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy import sparse
-from scipy.sparse import linalg
-
-
-class Optimizer2D:
-
-    def __init__(self):
-        self.verbose = False
-        self.animation = False
-        self.p_lambda = 0.0
-        self.init_w = 1e10
-        self.stop_thre = 1e-3
-        self.dim = 3  # state dimension
-
-    def optimize_path(self, nodes, consts, max_iter, min_iter):
-
-        graph_nodes = nodes[:]
-        prev_cost = sys.float_info.max
-
-        for i in range(max_iter):
-            start = time.time()
-            cost, graph_nodes = self.optimize_path_one_step(
-                graph_nodes, consts)
-            elapsed = time.time() - start
-            if self.verbose:
-                print("step ", i, " cost: ", cost, " time:", elapsed, "s")
-
-            # check convergence
-            if (i > min_iter) and (prev_cost - cost < self.stop_thre):
-                if self.verbose:
-                    print("converged:", prev_cost
-                          - cost, " < ", self.stop_thre)
-                    break
-            prev_cost = cost
-
-            if self.animation:
-                plt.cla()
-                plot_nodes(nodes, color="-b")
-                plot_nodes(graph_nodes)
-                plt.axis("equal")
-                plt.pause(1.0)
-
-        return graph_nodes
-
-    def optimize_path_one_step(self, graph_nodes, constraints):
-
-        indlist = [i for i in range(self.dim)]
-        numnodes = len(graph_nodes)
-        bf = np.zeros(numnodes * self.dim)
-        tripletList = TripletList()
-
-        for con in constraints:
-            ida = con.id1
-            idb = con.id2
-            assert 0 <= ida and ida < numnodes, "ida is invalid"
-            assert 0 <= idb and idb < numnodes, "idb is invalid"
-            r, Ja, Jb = self.calc_error(
-                graph_nodes[ida], graph_nodes[idb], con.t)
-
-            trJaInfo = Ja.transpose() @ con.info_mat
-            trJaInfoJa = trJaInfo @ Ja
-            trJbInfo = Jb.transpose() @ con.info_mat
-            trJbInfoJb = trJbInfo @ Jb
-            trJaInfoJb = trJaInfo @ Jb
-
-            for k in indlist:
-                for m in indlist:
-                    tripletList.push_back(
-                        ida * self.dim + k, ida * self.dim + m, trJaInfoJa[k, m])
-                    tripletList.push_back(
-                        idb * self.dim + k, idb * self.dim + m, trJbInfoJb[k, m])
-                    tripletList.push_back(
-                        ida * self.dim + k, idb * self.dim + m, trJaInfoJb[k, m])
-                    tripletList.push_back(
-                        idb * self.dim + k, ida * self.dim + m, trJaInfoJb[m, k])
-
-            bf[ida * self.dim: ida * self.dim + 3] += trJaInfo @ r
-            bf[idb * self.dim: idb * self.dim + 3] += trJbInfo @ r
-
-        for k in indlist:
-            tripletList.push_back(k, k, self.init_w)
-
-        for i in range(self.dim * numnodes):
-            tripletList.push_back(i, i, self.p_lambda)
-
-        mat = sparse.coo_matrix((tripletList.data, (tripletList.row, tripletList.col)),
-                                shape=(numnodes * self.dim, numnodes * self.dim))
-
-        x = linalg.spsolve(mat.tocsr(), -bf)
-
-        out_nodes = []
-        for i in range(len(graph_nodes)):
-            u_i = i * self.dim
-            pos = Pose2D(
-                graph_nodes[i].x + x[u_i],
-                graph_nodes[i].y + x[u_i + 1],
-                graph_nodes[i].theta + x[u_i + 2]
-            )
-            out_nodes.append(pos)
-
-        cost = self.calc_global_cost(out_nodes, constraints)
-
-        return cost, out_nodes
-
-    def calc_global_cost(self, nodes, constraints):
-        cost = 0.0
-        for c in constraints:
-            diff = self.error_func(nodes[c.id1], nodes[c.id2], c.t)
-            cost += diff.transpose() @ c.info_mat @ diff
-
-        return cost
-
-    def error_func(self, pa, pb, t):
-        ba = self.calc_constraint_pose(pb, pa)
-        error = np.array([ba.x - t.x,
-                          ba.y - t.y,
-                          self.pi2pi(ba.theta - t.theta)])
-        return error
-
-    def calc_constraint_pose(self, l, r):
-        diff = np.array([l.x - r.x, l.y - r.y, l.theta - r.theta])
-        v = self.rot_mat_2d(-r.theta) @ diff
-        v[2] = self.pi2pi(l.theta - r.theta)
-        return Pose2D(v[0], v[1], v[2])
-
-    def rot_mat_2d(self, theta):
-        return np.array([[math.cos(theta), -math.sin(theta), 0.0],
-                         [math.sin(theta), math.cos(theta), 0.0],
-                         [0.0, 0.0, 1.0]
-                         ])
-
-    def calc_error(self, pa, pb, t):
-        e0 = self.error_func(pa, pb, t)
-        dx = pb.x - pa.x
-        dy = pb.y - pa.y
-        dxdt = -math.sin(pa.theta) * dx + math.cos(pa.theta) * dy
-        dydt = -math.cos(pa.theta) * dx - math.sin(pa.theta) * dy
-        Ja = np.array([[-math.cos(pa.theta), -math.sin(pa.theta), dxdt],
-                       [math.sin(pa.theta), -math.cos(pa.theta), dydt],
-                       [0.0, 0.0, -1.0]
-                       ])
-        Jb = np.array([[math.cos(pa.theta), math.sin(pa.theta), 0.0],
-                       [-math.sin(pa.theta), math.cos(pa.theta), 0.0],
-                       [0.0, 0.0, 1.0]
-                       ])
-        return e0, Ja, Jb
-
-    def pi2pi(self, rad):
-
-        val = math.fmod(rad, 2.0 * math.pi)
-        if val > math.pi:
-            val -= 2.0 * math.pi
-        elif val < -math.pi:
-            val += 2.0 * math.pi
-
-        return val
-
-
-class TripletList:
-
-    def __init__(self):
-        self.row = []
-        self.col = []
-        self.data = []
-
-    def push_back(self, irow, icol, idata):
-        self.row.append(irow)
-        self.col.append(icol)
-        self.data.append(idata)
-
-
-class Pose2D:
-
-    def __init__(self, x, y, theta):
-        self.x = x
-        self.y = y
-        self.theta = theta
-
-
-class Constrant2D:
-
-    def __init__(self, id1, id2, t, info_mat):
-        self.id1 = id1
-        self.id2 = id2
-        self.t = t
-        self.info_mat = info_mat
-
-
-def plot_nodes(nodes, color ="-r", label = ""):
-    x, y = [], []
-    for n in nodes:
-        x.append(n.x)
-        y.append(n.y)
-    plt.plot(x, y, color, label=label)
-
-
-def load_data(fname):
-    nodes, consts = [], []
-
-    for line in open(fname):
-        sline = line.split()
-        tag = sline[0]
-
-        if tag == "VERTEX_SE2":
-            data_id = int(sline[1])
-            x = float(sline[2])
-            y = float(sline[3])
-            theta = float(sline[4])
-            nodes.append(Pose2D(x, y, theta))
-        elif tag == "EDGE_SE2":
-            id1 = int(sline[1])
-            id2 = int(sline[2])
-            x = float(sline[3])
-            y = float(sline[4])
-            th = float(sline[5])
-            c1 = float(sline[6])
-            c2 = float(sline[7])
-            c3 = float(sline[8])
-            c4 = float(sline[9])
-            c5 = float(sline[10])
-            c6 = float(sline[11])
-            t = Pose2D(x, y, th)
-            info_mat = np.array([[c1, c2, c3],
-                                 [c2, c4, c5],
-                                 [c3, c5, c6]
-                                 ])
-            consts.append(Constrant2D(id1, id2, t, info_mat))
-
-    print("n_nodes:", len(nodes))
-    print("n_consts:", len(consts))
-
-    return nodes, consts
-
-
-def main():
-    print("start!!")
-
-    fnames = ["intel.g2o",
-              "manhattan3500.g2o",
-              "mit_killian.g2o"
-              ]
-
-    max_iter = 20
-    min_iter = 3
-
-    # parameter setting
-    optimizer = Optimizer2D()
-    optimizer.p_lambda = 1e-6
-    optimizer.verbose = True
-    optimizer.animation = True
-
-    for f in fnames:
-        nodes, consts = load_data(f)
-
-        start = time.time()
-        final_nodes = optimizer.optimize_path(nodes, consts, max_iter, min_iter)
-        print("elapsed_time", time.time() - start, "sec")
-
-        # plotting
-        plt.cla()
-        plot_nodes(nodes, color="-b", label="before")
-        plot_nodes(final_nodes, label="after")
-        plt.axis("equal")
-        plt.grid(True)
-        plt.legend()
-        plt.pause(3.0)
-
-    print("done!!")
-
-
-if __name__ == '__main__':
-    main()
diff --git a/SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py b/SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py
deleted file mode 100644
index 0f39b22f3c..0000000000
--- a/SLAM/PoseOptimizationSLAM3D/pose_optimization_slam_3d.py
+++ /dev/null
@@ -1,601 +0,0 @@
-"""
-3D (x, y, z, qw, qx, qy, qz) pose optimization SLAM
-
-author: Ryohei Sasaki(@rsasaki0109)
-
-Ref:
-
-- [A Compact and Portable Implementation of Graph\-based SLAM](https://www.researchgate.net/publication/321287640_A_Compact_and_Portable_Implementation_of_Graph-based_SLAM)
-
-- [GitHub \- furo\-org/p2o: Single header 2D/3D graph\-based SLAM library](https://github.com/furo-org/p2o)
-
-- [GitHub \- AtsushiSakai/PythonRobotics
-/SLAM/PoseOptimizationSLAM](https://github.com/AtsushiSakai/PythonRobotics/blob/master/SLAM/PoseOptimizationSLAM/pose_optimization_slam.py)
-
-
-"""
-
-import os
-import sys
-import time
-
-import matplotlib.pyplot as plt
-import numpy as np
-from scipy import sparse
-from scipy.sparse import linalg
-
-
-def skew_symmetric(v):
-    return np.array(
-        [[0, -v[2], v[1]],
-         [v[2], 0, -v[0]],
-         [-v[1], v[0], 0]]
-    )
-
-
-def robust_coeff(squared_error, delta):
-    if squared_error < 0:
-        return 0
-    sqre = np.sqrt(squared_error)
-    if sqre < delta:
-        return 1  # no effect
-    return delta / sqre  # linear
-
-
-class Optimizer3D:
-
-    def __init__(self):
-        self.verbose = False
-        self.animation = False
-        self.p_lambda = 1e-7
-        self.init_w = 1e10
-        self.stop_thre = 1e-3
-        self.robust_delta = 1
-        self.dim = 6  # state dimension
-
-    def optimize_path(self, nodes, consts, max_iter, min_iter):
-
-        graph_nodes = nodes[:]
-        prev_cost = sys.float_info.max
-
-        est_traj_fig = plt.figure()
-        ax = est_traj_fig.add_subplot(111, projection='3d')
-
-        for i in range(max_iter):
-            start = time.time()
-            cost, graph_nodes = self.optimize_path_one_step(
-                graph_nodes, consts)
-            elapsed = time.time() - start
-            if self.verbose:
-                print("step ", i, " cost: ", cost, " time:", elapsed, "s")
-
-            # check convergence
-            if (i > min_iter) and (prev_cost - cost < self.stop_thre):
-                if self.verbose:
-                    print("converged:", prev_cost
-                          - cost, " < ", self.stop_thre)
-                    break
-            prev_cost = cost
-
-            if self.animation:
-                plt.cla()
-                plot_nodes(nodes, ax, color="-b")
-                plot_nodes(graph_nodes, ax)
-                plt.pause(1.0)
-
-        return graph_nodes
-
-    def optimize_path_one_step(self, graph_nodes, constraints):
-
-        indlist = [i for i in range(self.dim)]
-        numnodes = len(graph_nodes)
-        bf = np.zeros(numnodes * self.dim)
-        tripletList = TripletList()
-
-        for con in constraints:
-            ida = con.id1
-            idb = con.id2
-            assert 0 <= ida < numnodes, "ida is invalid"
-            assert 0 <= idb < numnodes, "idb is invalid"
-            pa = graph_nodes[ida]
-            pb = graph_nodes[idb]
-            r, Ja, Jb = self.calc_error(
-                pa, pb, con.t)
-
-            info_mat = con.info_mat * robust_coeff(r.reshape(self.dim, 1).T @ con.info_mat @ r.reshape(self.dim, 1),
-                                                   self.robust_delta)
-
-            trJaInfo = Ja.transpose() @ info_mat
-            trJaInfoJa = trJaInfo @ Ja
-            trJbInfo = Jb.transpose() @ info_mat
-            trJbInfoJb = trJbInfo @ Jb
-            trJaInfoJb = trJaInfo @ Jb
-
-            for k in indlist:
-                for m in indlist:
-                    tripletList.push_back(
-                        ida * self.dim + k, ida * self.dim + m, trJaInfoJa[k, m])
-                    tripletList.push_back(
-                        idb * self.dim + k, idb * self.dim + m, trJbInfoJb[k, m])
-                    tripletList.push_back(
-                        ida * self.dim + k, idb * self.dim + m, trJaInfoJb[k, m])
-                    tripletList.push_back(
-                        idb * self.dim + k, ida * self.dim + m, trJaInfoJb[m, k])
-
-            bf[ida * self.dim: (ida + 1) * self.dim] += trJaInfo @ r
-            bf[idb * self.dim: (idb + 1) * self.dim] += trJbInfo @ r
-
-        for k in indlist:
-            tripletList.push_back(k, k, self.init_w)
-
-        for i in range(self.dim * numnodes):
-            tripletList.push_back(i, i, self.p_lambda)
-
-        mat = sparse.coo_matrix((tripletList.data, (tripletList.row, tripletList.col)),
-                                shape=(numnodes * self.dim, numnodes * self.dim))
-
-        x = linalg.spsolve(mat.tocsr(), -bf)
-
-        out_nodes = []
-
-        for i in range(len(graph_nodes)):
-            u_i = i * self.dim
-
-            q_before = Quaternion(graph_nodes[i].qw, graph_nodes[i].qx, graph_nodes[i].qy, graph_nodes[i].qz)
-            rv_before = RotVec(quaternion=q_before)
-            rv_after = RotVec(ax=rv_before.ax + x[u_i + 3], ay=rv_before.ay + x[u_i + 4], az=rv_before.az + x[u_i + 5])
-            q_after = rv_after.to_quaternion()
-
-            pos = Pose3D(
-                graph_nodes[i].x + x[u_i],
-                graph_nodes[i].y + x[u_i + 1],
-                graph_nodes[i].z + x[u_i + 2],
-                q_after.qw,
-                q_after.qx,
-                q_after.qy,
-                q_after.qz
-            )
-            out_nodes.append(pos)
-
-        cost = self.calc_global_cost(out_nodes, constraints)
-
-        return cost, out_nodes
-
-    def calc_global_cost(self, nodes, constraints):
-
-        cost = 0.0
-        for c in constraints:
-            diff = self.error_func(nodes[c.id1], nodes[c.id2], c.t)
-            info_mat = c.info_mat * robust_coeff(diff.reshape(self.dim, 1).T @ c.info_mat @ diff.reshape(self.dim, 1),
-                                                 self.robust_delta)
-            cost += diff.transpose() @ info_mat @ diff
-
-        return cost
-
-    @staticmethod
-    def error_func(pa, pb, t):
-
-        ba = pb.ominus(pa)
-        q = t.rv().to_quaternion().conjugate().quat_mult(ba.rv().to_quaternion(), out='Quaternion')
-        drv = RotVec(quaternion=q)
-        error = np.array([ba.x - t.x,
-                          ba.y - t.y,
-                          ba.z - t.z,
-                          drv.ax[0],
-                          drv.ay[0],
-                          drv.az[0]])
-        return error
-
-    @staticmethod
-    def dQuat_dRV(rv):
-        u1 = rv.ax
-        u2 = rv.ay
-        u3 = rv.az
-        v = np.sqrt(u1 ** 2 + u2 ** 2 + u3 ** 2)
-        if v < 1e-6:
-            dqu = 0.25 * np.array(
-                [[-u1, -u2, -u3],
-                 [2.0, 0.0, 0.0],
-                 [0.0, 2.0, 0.0],
-                 [0.0, 0.0, 2.0]]
-            )
-            return dqu
-
-        vd = v * 2
-        v2 = v ** 2
-        v3 = v ** 3
-
-        S = np.sin(v / 2.0)
-        C = np.cos(v / 2.0)
-        dqu = np.array(
-            [[-u1 * S / vd, -u2 * S / vd, -u3 * S / vd],
-             [S / v + u1 * u1 * C / (2 * v2) - u1 * u1 * S / v3, u1 * u2 * (C / (2 * v2) - S / v3),
-              u1 * u3 * (C / (2 * v2) - S / v3)],
-             [u1 * u2 * (C / (2 * v2) - S / v3), S / v + u2 * u2 * C / (2 * v2) - u2 * u2 * S / v3,
-              u2 * u3 * (C / (2 * v2) - S / v3)],
-             [u1 * u3 * (C / (2 * v2) - S / v3), u2 * u3 * (C / (2 * v2) - S / v3),
-              S / v + u3 * u3 * C / (2 * v2) - u3 * u3 * S / v3]]
-        )
-
-        return dqu
-
-    def dR_dRV(self, rv):
-
-        q = rv.to_quaternion()
-        qw = q.qw
-        qx = q.qx
-        qy = q.qy
-        qz = q.qz
-        dRdqw = 2 * np.array(
-            [[qw, -qz, qy],
-             [qz, qw, -qx],
-             [-qy, qx, qw]]
-        )
-        dRdqx = 2 * np.array(
-            [[qx, qy, qz],
-             [qy, -qx, -qw],
-             [qz, qw, -qx]]
-        )
-        dRdqy = 2 * np.array(
-            [[-qy, qx, qw],
-             [qx, qy, qz],
-             [-qw, qz, -qy]]
-        )
-        dRdqz = 2 * np.array(
-            [[-qz, -qw, qx],
-             [qw, -qz, -qy],
-             [qz, qy, qz]]
-        )
-        dqdu = self.dQuat_dRV(rv)
-        dux = dRdqw * dqdu[0, 0] + dRdqx * dqdu[1, 0] + dRdqy * dqdu[2, 0] + dRdqz * dqdu[3, 0]
-        duy = dRdqw * dqdu[0, 1] + dRdqx * dqdu[1, 1] + dRdqy * dqdu[2, 1] + dRdqz * dqdu[3, 1]
-        duz = dRdqw * dqdu[0, 2] + dRdqx * dqdu[1, 2] + dRdqy * dqdu[2, 2] + dRdqz * dqdu[3, 2]
-        return dux, duy, duz
-
-    @staticmethod
-    def dRV_dQuat(q):
-
-        qw = q.qw[0]
-        qx = q.qx[0]
-        qy = q.qy[0]
-        qz = q.qz[0]
-
-        if 1 - qw ** 2 < 1e-7:
-            ret = np.array(
-                [[0.0, 2.0, 0.0, 0.0],
-                 [0.0, 0.0, 2.0, 0.0],
-                 [0.0, 0.0, 0.0, 2.0]]
-            )
-            return ret
-
-        c = 1 / (1 - qw ** 2)
-        d = np.arccos(qw) / (np.sqrt(1 - qw ** 2))
-        ret = 2.0 * np.array(
-            [[c * qx * (d * qw - 1), d, 0.0, 0.0],
-             [c * qy * (d * qw - 1), 0.0, d, 0.0],
-             [c * qz * (d * qw - 1), 0.0, 0.0, d]]
-        )
-        return ret
-
-    def QMat(self, q):
-
-        qw = q.qw
-        qx = q.qx
-        qy = q.qy
-        qz = q.qz
-        Q = np.array(
-            [[qw, -qx, -qy, -qz],
-             [qx, qw, -qz, qy],
-             [qy, qz, qw, -qx],
-             [qz, -qy, qx, qw]]
-        )
-        return Q
-
-    def QMatBar(self, q):
-
-        qw = q.qw
-        qx = q.qx
-        qy = q.qy
-        qz = q.qz
-        Q = np.array(
-            [[qw, -qx, -qy, -qz],
-             [qx, qw, qz, -qy],
-             [qy, -qz, qw, qx],
-             [qz, qy, -qx, qw]]
-        )
-        return Q
-
-    def calc_error(self, pa, pb, t):
-
-        e0 = self.error_func(pa, pb, t)
-        Ja = np.identity(6)
-        Jb = np.identity(6)
-
-        rva_inv = pa.rv().inverted()
-        rotPaInv = rva_inv.to_rotation_matrix()
-
-        Ja[:3, :3] = -rotPaInv
-        Jb[:3, :3] = rotPaInv
-
-        dRux, dRuy, dRuz = self.dR_dRV(rva_inv)
-
-        cvec = np.array([[pb.x - pa.x], [pb.y - pa.y], [pb.z - pa.z]])
-
-        Ja[0:3, 3:4] = -dRux @ cvec
-        Ja[0:3, 4:5] = -dRuy @ cvec
-        Ja[0:3, 5:6] = -dRuz @ cvec
-
-        # rotation part: qdiff = qc-1 * qa-1 * qb
-        qainv = rva_inv.to_quaternion()
-        qcinv = t.rv().inverted().to_quaternion()
-        qb = pb.rv().to_quaternion()
-        qinvca = qcinv.quat_mult(qainv, out='Quaternion')
-        qdiff = qinvca.quat_mult(qb, out='Quaternion')
-
-        Ja[3:6, 3:6] = -self.dRV_dQuat(qdiff) @ self.QMat(qcinv) @ self.QMatBar(qb) @ self.dQuat_dRV(rva_inv)
-        Jb[3:6, 3:6] = self.dRV_dQuat(qdiff) @ self.QMat(qcinv) @ self.QMat(qainv) @ self.dQuat_dRV(pb.rv())
-
-        return e0, Ja, Jb
-
-
-class Quaternion:
-
-    def __init__(self, qw, qx, qy, qz):
-        self.qw = qw
-        self.qx = qx
-        self.qy = qy
-        self.qz = qz
-
-    def conjugate(self):
-        return Quaternion(self.qw, -self.qx, -self.qy, -self.qz)
-
-    def to_numpy(self):
-        return np.array([self.qw, self.qx, self.qy, self.qz]).reshape(4, 1)
-
-    def quat_mult(self, q, out='np'):
-        v = np.array([self.qx, self.qy, self.qz]).reshape(3, 1)
-        sum_term = np.zeros([4, 4])
-        sum_term[0, 1:] = -v[:, 0]
-        sum_term[1:, 0] = v[:, 0]
-        sum_term[1:, 1:] = skew_symmetric(v)
-        sigma = self.qw * np.eye(4) + sum_term
-        q_new = sigma @ q.to_numpy()
-        if out == 'np':
-            return q_new
-        elif out == 'Quaternion':
-            q_obj = Quaternion(*q_new)
-            return q_obj
-
-
-class RotVec:
-
-    def __init__(self, ax=0., ay=0., az=0., quaternion=None):
-        if quaternion is None:
-            self.ax = ax
-            self.ay = ay
-            self.az = az
-        else:
-            x = quaternion.qx
-            y = quaternion.qy
-            z = quaternion.qz
-            norm_im = np.sqrt(x ** 2 + y ** 2 + z ** 2)
-            if norm_im < 1e-7:
-                self.ax = 2 * x
-                self.ay = 2 * y
-                self.az = 2 * z
-            else:
-                th = 2 * np.arctan2(norm_im, quaternion.qw)
-                th = self.pi2pi(th)
-                self.ax = x / norm_im * th
-                self.ay = y / norm_im * th
-                self.az = z / norm_im * th
-
-    def inverted(self):
-
-        return RotVec(-self.ax, -self.ay, -self.az)
-
-    def to_rotation_matrix(self):
-
-        q = self.to_quaternion()
-        q_vec = np.array([q.qx, q.qy, q.qz]).reshape(3, 1)
-        qw = q.qw
-        mat = (qw ** 2 - q_vec.T @ q_vec) * np.eye(3) \
-              + 2 * q_vec @ q_vec.T - 2 * qw * skew_symmetric(q_vec.reshape(-1, ))
-        return mat.T
-
-    def to_quaternion(self):
-
-        v = np.sqrt(self.ax ** 2 + self.ay ** 2 + self.az ** 2)
-        if (v < 1e-6):
-            return Quaternion(1, 0, 0, 0)
-        else:
-            return Quaternion(np.cos(v / 2), np.sin(v / 2) * self.ax / v,
-                              np.sin(v / 2) * self.ay / v, np.sin(v / 2) * self.az / v)
-
-    @staticmethod
-    def pi2pi(rad):
-
-        val = np.fmod(rad, 2.0 * np.pi)
-        if val > np.pi:
-            val -= 2.0 * np.pi
-        elif val < -np.pi:
-            val += 2.0 * np.pi
-
-        return val
-
-
-class TripletList:
-
-    def __init__(self):
-        self.row = []
-        self.col = []
-        self.data = []
-
-    def push_back(self, irow, icol, idata):
-        self.row.append(irow)
-        self.col.append(icol)
-        self.data.append(idata)
-
-
-class Pose3D:
-
-    def __init__(self, x, y, z, qw, qx, qy, qz):
-        self.x = x
-        self.y = y
-        self.z = z
-        self.qw = qw
-        self.qx = qx
-        self.qy = qy
-        self.qz = qz
-
-    def pos(self):
-        v = np.array([self.x, self.y, self.z]).reshape(3, 1)
-        return v
-
-    def rv(self):
-        q = Quaternion(self.qw, self.qx, self.qy, self.qz)
-        return RotVec(quaternion=q)
-
-    def ominus(self, base):
-        t = base.rv().to_rotation_matrix().T @ (self.pos() - base.pos())
-        q = base.rv().to_quaternion().conjugate().quat_mult(self.rv().to_quaternion(), out='Quaternion')
-        return Pose3D(t[0][0], t[1][0], t[2][0], q.qw, q.qx, q.qy, q.qz)
-
-
-class Constraint3D:
-
-    def __init__(self, id1, id2, t, info_mat):
-        self.id1 = id1
-        self.id2 = id2
-        self.t = t
-        self.info_mat = info_mat
-
-
-def plot_nodes(nodes, ax, color="-r", label=""):
-    x, y, z = [], [], []
-    for n in nodes:
-        x.append(n.x)
-        y.append(n.y)
-        z.append(n.z)
-    x = np.array(x)
-    y = np.array(y)
-    z = np.array(z)
-    max_range = np.array([x.max() - x.min(), y.max() - y.min(), z.max() - z.min()]).max() / 2.0
-    mid_x = (x.max() + x.min()) * 0.5
-    mid_y = (y.max() + y.min()) * 0.5
-    mid_z = (z.max() + z.min()) * 0.5
-    ax.set_xlim(mid_x - max_range, mid_x + max_range)
-    ax.set_ylim(mid_y - max_range, mid_y + max_range)
-    ax.set_zlim(mid_z - max_range, mid_z + max_range)
-    ax.plot(x, y, z, color, label=label)
-    # ax.plot(x, y, color, label=label)
-
-
-def load_data(fname):
-    nodes, consts = [], []
-
-    for line in open(fname):
-        sline = line.split()
-        tag = sline[0]
-
-        if tag == "VERTEX_SE3:QUAT":
-            # data_id = int(sline[1]) # unused
-            x = float(sline[2])
-            y = float(sline[3])
-            z = float(sline[4])
-            qx = float(sline[5])
-            qy = float(sline[6])
-            qz = float(sline[7])
-            qw = float(sline[8])
-
-            nodes.append(Pose3D(x, y, z, qw, qx, qy, qz))
-        elif tag == "EDGE_SE3:QUAT":
-            id1 = int(sline[1])
-            id2 = int(sline[2])
-            x = float(sline[3])
-            y = float(sline[4])
-            z = float(sline[5])
-            qx = float(sline[6])
-            qy = float(sline[7])
-            qz = float(sline[8])
-            qw = float(sline[9])
-            c1 = float(sline[10])
-            c2 = float(sline[11])
-            c3 = float(sline[12])
-            c4 = float(sline[13])
-            c5 = float(sline[14])
-            c6 = float(sline[15])
-            c7 = float(sline[16])
-            c8 = float(sline[17])
-            c9 = float(sline[18])
-            c10 = float(sline[19])
-            c11 = float(sline[20])
-            c12 = float(sline[21])
-            c13 = float(sline[22])
-            c14 = float(sline[23])
-            c15 = float(sline[24])
-            c16 = float(sline[25])
-            c17 = float(sline[26])
-            c18 = float(sline[27])
-            c19 = float(sline[28])
-            c20 = float(sline[29])
-            c21 = float(sline[30])
-            t = Pose3D(x, y, z, qw, qx, qy, qz)
-            info_mat = np.array([[c1, c2, c3, c4, c5, c6],
-                                 [c2, c7, c8, c9, c10, c11],
-                                 [c3, c8, c12, c13, c14, c15],
-                                 [c4, c9, c13, c16, c17, c18],
-                                 [c5, c10, c14, c17, c19, c20],
-                                 [c6, c11, c15, c18, c20, c21]
-                                 ])
-            consts.append(Constraint3D(id1, id2, t, info_mat))
-
-    print("n_nodes:", len(nodes))
-    print("n_consts:", len(consts))
-
-    return nodes, consts
-
-
-def main():
-    print("start!!")
-
-    dir = os.path.dirname(os.path.abspath(__file__))
-
-    fnames = [
-        dir + "/sphere2200_guess.g2o",
-        dir + "/torus3d_guess.g2o",
-        dir + "/parking-garage.g2o",
-    ]
-
-    max_iter = 10
-    min_iter = 3
-
-    # parameter setting
-    optimizer = Optimizer3D()
-    optimizer.p_lambda = 1e-6
-    optimizer.verbose = True
-    optimizer.animation = False
-
-    for f in fnames:
-        print(f)
-
-        nodes, consts = load_data(f)
-
-        start = time.time()
-        final_nodes = optimizer.optimize_path(nodes, consts, max_iter, min_iter)
-        print("elapsed_time", time.time() - start, "sec")
-
-        # plot
-        plt.close("all")
-        est_traj_fig = plt.figure()
-        ax = est_traj_fig.add_subplot(111, projection='3d')
-        plot_nodes(nodes, ax, color="-b", label="before")
-        plot_nodes(final_nodes, ax, label="after")
-        plt.legend()
-        plt.show()
-
-    print("done!!")
-
-
-if __name__ == '__main__':
-    main()

From ae32cb332a91ac706c46e3a1e977f80d948b3e60 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 12 Sep 2019 20:26:48 +0900
Subject: [PATCH 0178/1080] fix early termination bug. fix #223

---
 PathPlanning/RRT/rrt.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py
index 887cb1545f..13ca367637 100644
--- a/PathPlanning/RRT/rrt.py
+++ b/PathPlanning/RRT/rrt.py
@@ -74,7 +74,7 @@ def planning(self, animation=True):
             if animation and i % 5 == 0:
                 self.draw_graph(rnd_node)
 
-            if self.calc_dist_to_goal(new_node.x, new_node.y) <= self.expand_dis:
+            if self.calc_dist_to_goal(self.node_list[-1].x, self.node_list[-1].y) <= self.expand_dis:
                 print("Goal!!")
                 return self.generate_final_course(len(self.node_list) - 1)
 

From 8eef2fd7bbda218d2c381c4a29a17c1f6f710591 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 12 Sep 2019 21:24:20 +0900
Subject: [PATCH 0179/1080] Update users_comments.md

---
 users_comments.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index c0165bd2d7..87170eb0c8 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -375,6 +375,11 @@ Thanks for your job! <br>I have learned a lot from it!
 
 ---
 
+Dear Atsushi Sakai, <br>Thank you so much for creating PythonRobotics and documenting it so well. I am a senior in high school trying to learn and better myself in these concepts.
+
+--Rohan Mathur
+
+
 
 # Citations
 

From 7bafe3d7a572aea4fa49cd0a4ffe667c288921da Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 22 Sep 2019 22:12:57 +0900
Subject: [PATCH 0180/1080] Update users_comments.md

---
 users_comments.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 87170eb0c8..b1a0b66f8e 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -398,6 +398,9 @@ URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8765684&isnumber=87
 5. Josie Hughes, Masaru Shimizu, and Arnoud Visser, "A Review of Robot Rescue Simulation Platforms for Robotics Education"
 URL: https://2019.robocup.org/downloads/program/HughesEtAl2019.pdf
 
+6. Hughes, Josie, Masaru Shimizu, and Arnoud Visser. "A review of robot rescue simulation platforms for robotics education." (2019).
+URL: https://www.semanticscholar.org/paper/A-Review-of-Robot-Rescue-Simulation-Platforms-for-Hughes-Shimizu/318a4bcb97a44661422ae1430d950efc408097da
+
 # Others
 
 - Autonomous Vehicle Readings https://richardkelley.io/readings

From a485b0137744a1295292a1caa7abad04da5472e0 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 22 Sep 2019 22:31:17 +0900
Subject: [PATCH 0181/1080] Update getting_started.rst

---
 docs/getting_started.rst | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/docs/getting_started.rst b/docs/getting_started.rst
index 2f60557f38..93ab70503a 100644
--- a/docs/getting_started.rst
+++ b/docs/getting_started.rst
@@ -3,6 +3,25 @@
 Getting Started
 ===============
 
+What is this?
+-------------
+
+This is an Open Source Software (OSS) project: PythonRobotics, which is a Python code collection of robotics algorithms.
+
+The focus of the project is on autonomous navigation, and the goal is for beginners in robotics to understand the basic ideas behind each algorithm.
+
+In this project, the algorithms which are practical and widely used in both academia and industry are selected.
+
+Each sample code is written in Python3 and only depends on some standard modules for readability and ease of use. 
+
+It includes intuitive animations to understand the behavior of the simulation.
+
+
+See this paper for more details:
+
+- PythonRobotics: a Python code collection of robotics algorithms: https://arxiv.org/abs/1808.10703
+
+
 Requirements
 -------------
 

From eefa6bf20a2a6d979ed3877b4c6d27bd29a31ef9 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 1 Oct 2019 20:10:42 +0900
Subject: [PATCH 0182/1080] try to check collision check bug

---
 PathPlanning/RRT/rrt.py | 24 ++++++++++++++++--------
 1 file changed, 16 insertions(+), 8 deletions(-)

diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py
index 13ca367637..5f7bab54fc 100644
--- a/PathPlanning/RRT/rrt.py
+++ b/PathPlanning/RRT/rrt.py
@@ -10,6 +10,7 @@
 import random
 
 import matplotlib.pyplot as plt
+import numpy as np
 
 show_animation = True
 
@@ -104,10 +105,8 @@ def steer(self, from_node, to_node, extend_length=float("inf")):
 
         d, _ = self.calc_distance_and_angle(new_node, to_node)
         if d <= self.path_resolution:
-            new_node.x = to_node.x
-            new_node.y = to_node.y
-            new_node.path_x[-1] = to_node.x
-            new_node.path_y[-1] = to_node.y
+            new_node.path_x.append(to_node.x)
+            new_node.path_y.append(to_node.y)
 
         new_node.parent = from_node
 
@@ -142,17 +141,26 @@ def draw_graph(self, rnd=None):
             plt.plot(rnd.x, rnd.y, "^k")
         for node in self.node_list:
             if node.parent:
-                plt.plot(node.path_x, node.path_y, "-g")
+                plt.plot(node.path_x, node.path_y, "-xg")
 
         for (ox, oy, size) in self.obstacle_list:
-            plt.plot(ox, oy, "ok", ms=30 * size)
+            self.plot_circle(ox, oy, size)
 
         plt.plot(self.start.x, self.start.y, "xr")
         plt.plot(self.end.x, self.end.y, "xr")
+        plt.axis("equal")
         plt.axis([-2, 15, -2, 15])
         plt.grid(True)
         plt.pause(0.01)
 
+    @staticmethod
+    def plot_circle(x, y, size, color="-b"):  # pragma: no cover
+        deg = list(range(0, 360, 5))
+        deg.append(0)
+        xl = [x + size * math.cos(np.deg2rad(d)) for d in deg]
+        yl = [y + size * math.sin(np.deg2rad(d)) for d in deg]
+        plt.plot(xl, yl, color)
+
     @staticmethod
     def get_nearest_node_index(node_list, rnd_node):
         dlist = [(node.x - rnd_node.x) ** 2 + (node.y - rnd_node.y)
@@ -182,7 +190,7 @@ def calc_distance_and_angle(from_node, to_node):
         return d, theta
 
 
-def main(gx=5.0, gy=10.0):
+def main(gx=6.0, gy=10.0):
     print("start " + __file__)
 
     # ====Search Path with RRT====
@@ -193,7 +201,7 @@ def main(gx=5.0, gy=10.0):
         (3, 10, 2),
         (7, 5, 2),
         (9, 5, 2)
-    ]  # [x,y,size]
+    ]  # [x, y, radius]
     # Set Initial parameters
     rrt = RRT(start=[0, 0],
               goal=[gx, gy],

From 97d6dbdd6cc91c7809d8607646e7af830068a9b1 Mon Sep 17 00:00:00 2001
From: Pavel <p_sumejko@yahoo.com>
Date: Tue, 1 Oct 2019 23:02:50 +0300
Subject: [PATCH 0183/1080] cvxpy link fixed

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 8e39d6982c..6577cf70b8 100644
--- a/README.md
+++ b/README.md
@@ -103,7 +103,7 @@ See this paper for more details:
 
 - pandas
 
-- [cvxpy](http://www.cvxpy.org/en/latest/) 
+- [cvxpy](https://www.cvxpy.org/index.html) 
 
 # Documentation
 

From 3cef87ea3fa28df6b1bab1bca9915aa427858453 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 2 Oct 2019 20:19:33 +0900
Subject: [PATCH 0184/1080] fixed rrt.py

---
 PathPlanning/RRT/rrt.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py
index 5f7bab54fc..3bcc622100 100644
--- a/PathPlanning/RRT/rrt.py
+++ b/PathPlanning/RRT/rrt.py
@@ -33,7 +33,7 @@ def __init__(self, x, y):
             self.parent = None
 
     def __init__(self, start, goal, obstacle_list, rand_area,
-                 expand_dis=3.0, path_resolution=1.0, goal_sample_rate=5, max_iter=500):
+                 expand_dis=3.0, path_resolution=0.5, goal_sample_rate=5, max_iter=500):
         """
         Setting Parameter
 
@@ -76,8 +76,9 @@ def planning(self, animation=True):
                 self.draw_graph(rnd_node)
 
             if self.calc_dist_to_goal(self.node_list[-1].x, self.node_list[-1].y) <= self.expand_dis:
-                print("Goal!!")
-                return self.generate_final_course(len(self.node_list) - 1)
+                final_node = self.steer(self.node_list[-1], self.end, self.expand_dis)
+                if self.check_collision(final_node, self.obstacle_list):
+                    return self.generate_final_course(len(self.node_list) - 1)
 
             if animation and i % 5:
                 self.draw_graph(rnd_node)
@@ -141,7 +142,7 @@ def draw_graph(self, rnd=None):
             plt.plot(rnd.x, rnd.y, "^k")
         for node in self.node_list:
             if node.parent:
-                plt.plot(node.path_x, node.path_y, "-xg")
+                plt.plot(node.path_x, node.path_y, "-g")
 
         for (ox, oy, size) in self.obstacle_list:
             self.plot_circle(ox, oy, size)
@@ -200,7 +201,8 @@ def main(gx=6.0, gy=10.0):
         (3, 8, 2),
         (3, 10, 2),
         (7, 5, 2),
-        (9, 5, 2)
+        (9, 5, 2),
+        (8, 10, 1)
     ]  # [x, y, radius]
     # Set Initial parameters
     rrt = RRT(start=[0, 0],

From a5c7096c7befd4c1af423b529d62dd440f105243 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 2 Oct 2019 20:28:59 +0900
Subject: [PATCH 0185/1080] fixed rrt_with_pathsmoothing.py

---
 PathPlanning/RRT/rrt_with_pathsmoothing.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/PathPlanning/RRT/rrt_with_pathsmoothing.py b/PathPlanning/RRT/rrt_with_pathsmoothing.py
index 24b6bfeb6e..9826dba072 100644
--- a/PathPlanning/RRT/rrt_with_pathsmoothing.py
+++ b/PathPlanning/RRT/rrt_with_pathsmoothing.py
@@ -76,8 +76,6 @@ def line_collision_check(first, second, obstacleList):
         if d <= size:
             return False
 
-    #  print("OK")
-
     return True  # OK
 
 
@@ -127,7 +125,7 @@ def main():
         (7, 5, 2),
         (9, 5, 2)
     ]  # [x,y,size]
-    rrt = RRT(start=[0, 0], goal=[5, 10],
+    rrt = RRT(start=[0, 0], goal=[6, 10],
               rand_area=[-2, 15], obstacle_list=obstacleList)
     path = rrt.planning(animation=show_animation)
 
@@ -141,7 +139,7 @@ def main():
         plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
 
         plt.plot([x for (x, y) in smoothedPath], [
-            y for (x, y) in smoothedPath], '-b')
+            y for (x, y) in smoothedPath], '-c')
 
         plt.grid(True)
         plt.pause(0.01)  # Need for Mac

From 295645f4169c4b7031f8cf0cadf3a643d242c50a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 2 Oct 2019 20:51:22 +0900
Subject: [PATCH 0186/1080] fixed rrt star.py

---
 PathPlanning/RRTStar/rrt_star.py | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py
index eb37964d5c..1c6d682800 100644
--- a/PathPlanning/RRTStar/rrt_star.py
+++ b/PathPlanning/RRTStar/rrt_star.py
@@ -52,6 +52,7 @@ def __init__(self, start, goal, obstacle_list, rand_area,
 
         """
         self.connect_circle_dist = connect_circle_dist
+        self.goal_node = self.Node(goal[0], goal[1])
 
     def planning(self, animation=True, search_until_max_iter=True):
         """
@@ -121,7 +122,14 @@ def search_best_goal_node(self):
         dist_to_goal_list = [self.calc_dist_to_goal(n.x, n.y) for n in self.node_list]
         goal_inds = [dist_to_goal_list.index(i) for i in dist_to_goal_list if i <= self.expand_dis]
 
-        if not goal_inds:
+        # safe_goal_inds = goal_inds
+        safe_goal_inds = []
+        for goal_ind in goal_inds:
+            t_node = self.steer(self.node_list[goal_ind], self.goal_node)
+            if self.check_collision(t_node, self.obstacle_list):
+                safe_goal_inds.append(goal_ind)
+
+        if not safe_goal_inds:
             return None
 
         min_cost = min([self.node_list[i].cost for i in goal_inds])
@@ -177,12 +185,13 @@ def main():
         (3, 8, 2),
         (3, 10, 2),
         (7, 5, 2),
-        (9, 5, 2)
+        (9, 5, 2),
+        (8, 10, 1),
     ]  # [x,y,size(radius)]
 
     # Set Initial parameters
     rrt_star = RRTStar(start=[0, 0],
-                       goal=[10, 10],
+                       goal=[6, 10],
                        rand_area=[-2, 15],
                        obstacle_list=obstacle_list)
     path = rrt_star.planning(animation=show_animation)

From 67141071c9e1b351ac6c3819c0d53b58d9582103 Mon Sep 17 00:00:00 2001
From: Naveen <Naveenal@users.noreply.github.com>
Date: Wed, 2 Oct 2019 17:48:51 -0700
Subject: [PATCH 0187/1080] convert 1D array to scalar for np.matmul operation

thrust variable is currently a 1D numpy array so the matmul function throws an error when executed, converting thrust to a scalar before the matmul operation solves the issue.
---
 .../drone_3d_trajectory_following.py                          | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/AerialNavigation/drone_3d_trajectory_following/drone_3d_trajectory_following.py b/AerialNavigation/drone_3d_trajectory_following/drone_3d_trajectory_following.py
index 438494a410..e00b3fff48 100644
--- a/AerialNavigation/drone_3d_trajectory_following/drone_3d_trajectory_following.py
+++ b/AerialNavigation/drone_3d_trajectory_following/drone_3d_trajectory_following.py
@@ -99,7 +99,7 @@ def quad_sim(x_c, y_c, z_c):
 
             R = rotation_matrix(roll, pitch, yaw)
             acc = (np.matmul(R, np.array(
-                [0, 0, thrust]).T) - np.array([0, 0, m * g]).T) / m
+                [0, 0, thrust.item()]).T) - np.array([0, 0, m * g]).T) / m
             x_acc = acc[0]
             y_acc = acc[1]
             z_acc = acc[2]
@@ -209,4 +209,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()

From d2da3abe25f56bf19f89d54d48c4f902f5922718 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 5 Oct 2019 18:00:18 +0900
Subject: [PATCH 0188/1080] fix safe index reference

---
 PathPlanning/RRTStar/rrt_star.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py
index 1c6d682800..09a421937a 100644
--- a/PathPlanning/RRTStar/rrt_star.py
+++ b/PathPlanning/RRTStar/rrt_star.py
@@ -59,7 +59,7 @@ def planning(self, animation=True, search_until_max_iter=True):
         rrt star path planning
 
         animation: flag for animation on or off
-        search_until_maxiter: search until max iteration for path improving or not
+        search_until_max_iter: search until max iteration for path improving or not
         """
 
         self.node_list = [self.start]
@@ -122,7 +122,6 @@ def search_best_goal_node(self):
         dist_to_goal_list = [self.calc_dist_to_goal(n.x, n.y) for n in self.node_list]
         goal_inds = [dist_to_goal_list.index(i) for i in dist_to_goal_list if i <= self.expand_dis]
 
-        # safe_goal_inds = goal_inds
         safe_goal_inds = []
         for goal_ind in goal_inds:
             t_node = self.steer(self.node_list[goal_ind], self.goal_node)
@@ -132,8 +131,8 @@ def search_best_goal_node(self):
         if not safe_goal_inds:
             return None
 
-        min_cost = min([self.node_list[i].cost for i in goal_inds])
-        for i in goal_inds:
+        min_cost = min([self.node_list[i].cost for i in safe_goal_inds])
+        for i in safe_goal_inds:
             if self.node_list[i].cost == min_cost:
                 return i
 
@@ -187,6 +186,7 @@ def main():
         (7, 5, 2),
         (9, 5, 2),
         (8, 10, 1),
+        (6, 12, 1),
     ]  # [x,y,size(radius)]
 
     # Set Initial parameters

From 45f90ca37f30e6b201ed5b9cb7b18a5f5fd6a3eb Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 6 Oct 2019 08:57:53 +0900
Subject: [PATCH 0189/1080] try to upgrade 3.7

---
 .travis.yml  | 4 ++--
 README.md    | 8 ++++----
 appveyor.yml | 4 +---
 3 files changed, 7 insertions(+), 9 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index eff3df24fa..042c69ee42 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -5,7 +5,7 @@ matrix:
     - os: linux
 
 python:
-  - 3.6
+  - 3.7
 
 before_install:
     - sudo apt-get update
@@ -18,7 +18,7 @@ before_install:
     - conda update -q conda
     # Useful for debugging any issues with conda
     - conda info -a
-    - conda install python==3.6.8
+#    - conda install python==3.6.8
 
 install:
   - conda install numpy==1.15
diff --git a/README.md b/README.md
index 6577cf70b8..1c455e928b 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-<img src="https://github.com/AtsushiSakai/PythonRobotics/raw/master/icon.png?raw=true" align="right" width="300"/>
+<img src="https://github.com/AtsushiSakai/PythonRobotics/raw/master/icon.png?raw=true" align="right" width="300" alt="header pic"/>
 
 # PythonRobotics
 [![Build Status](https://travis-ci.org/AtsushiSakai/PythonRobotics.svg?branch=master)](https://travis-ci.org/AtsushiSakai/PythonRobotics)
@@ -93,7 +93,7 @@ See this paper for more details:
 
 # Requirements
 
-- Python 3.6.x (2.7 is not supported)
+- Python 3.7.x (2.7 is not supported)
 
 - numpy
 
@@ -137,7 +137,7 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation
 
 ## Extended Kalman Filter localization
 
-<img src="https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif" width="640">
+<img src="https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif" width="640" alt="EKF pic">
 
 Documentation: [Notebook](https://github.com/AtsushiSakai/PythonRobotics/blob/master/Localization/extended_kalman_filter/extended_kalman_filter_localization.ipynb)
 
@@ -496,7 +496,7 @@ Ref:
 
 Path tracking simulation with iterative linear model predictive speed and steering control.
 
-<img src="https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/model_predictive_speed_and_steer_control/animation.gif" width="640">
+<img src="https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/model_predictive_speed_and_steer_control/animation.gif" width="640" alt="MPC pic">
 
 Ref:
 
diff --git a/appveyor.yml b/appveyor.yml
index 61f8a0b364..33d4d02970 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -6,9 +6,7 @@ environment:
     CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd"
 
   matrix:
-    - MINICONDA: C:\Miniconda36-x64
-
-
+    - MINICONDA: C:\Miniconda37-x64
 
 init:
   - "ECHO %MINICONDA% %PYTHON_VERSION% %PYTHON_ARCH%"

From 0e689bf0e88b51d79928e068afa272a95e693ba3 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 6 Oct 2019 09:12:29 +0900
Subject: [PATCH 0190/1080] removed pycharm markdown setting

---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 1c455e928b..d97d92adb3 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,6 @@
-<img src="https://github.com/AtsushiSakai/PythonRobotics/raw/master/icon.png?raw=true" align="right" width="300" alt="header pic"/>
+<div style="text-align:right">
+<img src="https://github.com/AtsushiSakai/PythonRobotics/raw/master/icon.png?raw=true" width="300" alt="header pic"/>
+</div>
 
 # PythonRobotics
 [![Build Status](https://travis-ci.org/AtsushiSakai/PythonRobotics.svg?branch=master)](https://travis-ci.org/AtsushiSakai/PythonRobotics)

From 16891e60d104824fadd908fdb41eb5eea4389d71 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 6 Oct 2019 09:18:35 +0900
Subject: [PATCH 0191/1080] revert it

---
 README.md | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/README.md b/README.md
index d97d92adb3..1c455e928b 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,4 @@
-<div style="text-align:right">
-<img src="https://github.com/AtsushiSakai/PythonRobotics/raw/master/icon.png?raw=true" width="300" alt="header pic"/>
-</div>
+<img src="https://github.com/AtsushiSakai/PythonRobotics/raw/master/icon.png?raw=true" align="right" width="300" alt="header pic"/>
 
 # PythonRobotics
 [![Build Status](https://travis-ci.org/AtsushiSakai/PythonRobotics.svg?branch=master)](https://travis-ci.org/AtsushiSakai/PythonRobotics)

From b942c430e6df8d854fe00f9a264efaca94cc90c9 Mon Sep 17 00:00:00 2001
From: sszwfy <476020374@qq.com>
Date: Sun, 6 Oct 2019 14:32:22 +0800
Subject: [PATCH 0192/1080] fix error for  goal cost angle

---
 PathPlanning/DynamicWindowApproach/dynamic_window_approach.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index fa1dcc62c4..a7d74332a8 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -167,7 +167,8 @@ def calc_to_goal_cost(traj, goal, config):
     dx = goal[0] - traj[-1, 0]
     dy = goal[1] - traj[-1, 1]
     error_angle = math.atan2(dy, dx)
-    cost = abs(error_angle - traj[-1, 2])
+    cost_angle = error_angle - traj[-1, 2]
+    cost = abs(math.atan2(math.sin(cost_angle), math.cos(cost_angle)))
 
     return cost
 

From 0c5bb95761c48bfe976395b350cce317e76ff838 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 9 Oct 2019 19:42:51 +0900
Subject: [PATCH 0193/1080] Update users_comments.md

---
 users_comments.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index b1a0b66f8e..2cfd7a4a9b 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -401,6 +401,9 @@ URL: https://2019.robocup.org/downloads/program/HughesEtAl2019.pdf
 6. Hughes, Josie, Masaru Shimizu, and Arnoud Visser. "A review of robot rescue simulation platforms for robotics education." (2019).
 URL: https://www.semanticscholar.org/paper/A-Review-of-Robot-Rescue-Simulation-Platforms-for-Hughes-Shimizu/318a4bcb97a44661422ae1430d950efc408097da
 
+7. Ghosh, Ritwika, et al. "CyPhyHouse: A Programming, Simulation, and Deployment Toolchain for Heterogeneous Distributed Coordination." arXiv preprint arXiv:1910.01557 (2019).
+URL: https://arxiv.org/abs/1910.01557
+
 # Others
 
 - Autonomous Vehicle Readings https://richardkelley.io/readings

From 415bf654f1992cf108eb761eb474b55892cef47d Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 11 Oct 2019 20:13:27 +0900
Subject: [PATCH 0194/1080] Fix #229

---
 .../particle_filter/particle_filter.py        | 24 +++++++++----------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py
index 8e93893e48..a3dd6bcdb5 100644
--- a/Localization/particle_filter/particle_filter.py
+++ b/Localization/particle_filter/particle_filter.py
@@ -6,17 +6,18 @@
 
 """
 
-import numpy as np
 import math
+
 import matplotlib.pyplot as plt
+import numpy as np
 
 # Estimation parameter of PF
-Q = np.diag([0.1])**2  # range error
-R = np.diag([1.0, np.deg2rad(40.0)])**2  # input error
+Q = np.diag([0.1]) ** 2  # range error
+R = np.diag([1.0, np.deg2rad(40.0)]) ** 2  # input error
 
 #  Simulation parameter
-Qsim = np.diag([0.2])**2
-Rsim = np.diag([1.0, np.deg2rad(30.0)])**2
+Qsim = np.diag([0.2]) ** 2
+Rsim = np.diag([1.0, np.deg2rad(30.0)]) ** 2
 
 DT = 0.1  # time tick [s]
 SIM_TIME = 50.0  # simulation time [s]
@@ -37,7 +38,6 @@ def calc_input():
 
 
 def observation(xTrue, xd, u, RFID):
-
     xTrue = motion_model(xTrue, u)
 
     # add noise to gps x-y
@@ -47,7 +47,7 @@ def observation(xTrue, xd, u, RFID):
 
         dx = xTrue[0, 0] - RFID[i, 0]
         dy = xTrue[1, 0] - RFID[i, 1]
-        d = math.sqrt(dx**2 + dy**2)
+        d = math.sqrt(dx ** 2 + dy ** 2)
         if d <= MAX_RANGE:
             dn = d + np.random.randn() * Qsim[0, 0]  # add noise
             zi = np.array([[dn, RFID[i, 0], RFID[i, 1]]])
@@ -64,7 +64,6 @@ def observation(xTrue, xd, u, RFID):
 
 
 def motion_model(x, u):
-
     F = np.array([[1.0, 0, 0, 0],
                   [0, 1.0, 0, 0],
                   [0, 0, 1.0, 0],
@@ -97,7 +96,7 @@ def calc_covariance(xEst, px, pw):
     return cov
 
 
-def pf_localization(px, pw, xEst, PEst, z, u):
+def pf_localization(px, pw, z, u):
     """
     Localization with Particle filter
     """
@@ -105,6 +104,7 @@ def pf_localization(px, pw, xEst, PEst, z, u):
     for ip in range(NP):
         x = np.array([px[:, ip]]).T
         w = pw[0, ip]
+
         #  Predict with random input sampling
         ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0]
         ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1]
@@ -115,8 +115,8 @@ def pf_localization(px, pw, xEst, PEst, z, u):
         for i in range(len(z[:, 0])):
             dx = x[0, 0] - z[i, 1]
             dy = x[1, 0] - z[i, 2]
-            prez = math.sqrt(dx**2 + dy**2)
-            dz = prez - z[i, 0]
+            pre_z = math.sqrt(dx ** 2 + dy ** 2)
+            dz = pre_z - z[i, 0]
             w = w * gauss_likelihood(dz, math.sqrt(Q[0, 0]))
 
         px[:, ip] = x[:, 0]
@@ -223,7 +223,7 @@ def main():
 
         xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID)
 
-        xEst, PEst, px, pw = pf_localization(px, pw, xEst, PEst, z, ud)
+        xEst, PEst, px, pw = pf_localization(px, pw, z, ud)
 
         # store data history
         hxEst = np.hstack((hxEst, xEst))

From 85bf664a9166d9cb7f9463a801931ceb3af5dec9 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 11 Oct 2019 20:49:07 +0900
Subject: [PATCH 0195/1080] fix PF problems

---
 .../particle_filter/particle_filter.py        | 94 +++++++++----------
 1 file changed, 47 insertions(+), 47 deletions(-)

diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py
index a3dd6bcdb5..6643116f5a 100644
--- a/Localization/particle_filter/particle_filter.py
+++ b/Localization/particle_filter/particle_filter.py
@@ -12,12 +12,12 @@
 import numpy as np
 
 # Estimation parameter of PF
-Q = np.diag([0.1]) ** 2  # range error
-R = np.diag([1.0, np.deg2rad(40.0)]) ** 2  # input error
+Q = np.diag([0.2]) ** 2  # range error
+R = np.diag([2.0, np.deg2rad(40.0)]) ** 2  # input error
 
 #  Simulation parameter
-Qsim = np.diag([0.2]) ** 2
-Rsim = np.diag([1.0, np.deg2rad(30.0)]) ** 2
+Q_sim = np.diag([0.2]) ** 2
+R_sim = np.diag([1.0, np.deg2rad(30.0)]) ** 2
 
 DT = 0.1  # time tick [s]
 SIM_TIME = 50.0  # simulation time [s]
@@ -32,30 +32,30 @@
 
 def calc_input():
     v = 1.0  # [m/s]
-    yawrate = 0.1  # [rad/s]
-    u = np.array([[v, yawrate]]).T
+    yaw_rate = 0.1  # [rad/s]
+    u = np.array([[v, yaw_rate]]).T
     return u
 
 
-def observation(xTrue, xd, u, RFID):
+def observation(xTrue, xd, u, RF_ID):
     xTrue = motion_model(xTrue, u)
 
     # add noise to gps x-y
     z = np.zeros((0, 3))
 
-    for i in range(len(RFID[:, 0])):
+    for i in range(len(RF_ID[:, 0])):
 
-        dx = xTrue[0, 0] - RFID[i, 0]
-        dy = xTrue[1, 0] - RFID[i, 1]
+        dx = xTrue[0, 0] - RF_ID[i, 0]
+        dy = xTrue[1, 0] - RF_ID[i, 1]
         d = math.sqrt(dx ** 2 + dy ** 2)
         if d <= MAX_RANGE:
-            dn = d + np.random.randn() * Qsim[0, 0]  # add noise
-            zi = np.array([[dn, RFID[i, 0], RFID[i, 1]]])
+            dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5  # add noise
+            zi = np.array([[dn, RF_ID[i, 0], RF_ID[i, 1]]])
             z = np.vstack((z, zi))
 
     # add noise to input
-    ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0]
-    ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1]
+    ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5
+    ud2 = u[1, 0] + np.random.randn() * R_sim[1, 1] ** 0.5
     ud = np.array([[ud1, ud2]]).T
 
     xd = motion_model(xd, ud)
@@ -92,6 +92,7 @@ def calc_covariance(xEst, px, pw):
     for i in range(px.shape[1]):
         dx = (px[:, i] - xEst)[0:3]
         cov += pw[0, i] * dx.dot(dx.T)
+    cov /= NP
 
     return cov
 
@@ -106,8 +107,8 @@ def pf_localization(px, pw, z, u):
         w = pw[0, ip]
 
         #  Predict with random input sampling
-        ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0]
-        ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1]
+        ud1 = u[0, 0] + np.random.randn() * R[0, 0] ** 0.5
+        ud2 = u[1, 0] + np.random.randn() * R[1, 1] ** 0.5
         ud = np.array([[ud1, ud2]]).T
         x = motion_model(x, ud)
 
@@ -127,30 +128,30 @@ def pf_localization(px, pw, z, u):
     xEst = px.dot(pw.T)
     PEst = calc_covariance(xEst, px, pw)
 
-    px, pw = resampling(px, pw)
+    px, pw = re_sampling(px, pw)
 
     return xEst, PEst, px, pw
 
 
-def resampling(px, pw):
+def re_sampling(px, pw):
     """
     low variance re-sampling
     """
 
-    Neff = 1.0 / (pw.dot(pw.T))[0, 0]  # Effective particle number
-    if Neff < NTh:
-        wcum = np.cumsum(pw)
+    N_eff = 1.0 / (pw.dot(pw.T))[0, 0]  # Effective particle number
+    if N_eff < NTh:
+        w_cum = np.cumsum(pw)
         base = np.cumsum(pw * 0.0 + 1 / NP) - 1 / NP
-        resampleid = base + np.random.rand(base.shape[0]) / NP
+        re_sample_id = base + np.random.rand(base.shape[0]) / NP
 
-        inds = []
+        indexes = []
         ind = 0
         for ip in range(NP):
-            while resampleid[ip] > wcum[ind]:
+            while re_sample_id[ip] > w_cum[ind]:
                 ind += 1
-            inds.append(ind)
+            indexes.append(ind)
 
-        px = px[:, inds]
+        px = px[:, indexes]
         pw = np.zeros((1, NP)) + 1.0 / NP  # init weight
 
     return px, pw
@@ -158,35 +159,35 @@ def resampling(px, pw):
 
 def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
     Pxy = PEst[0:2, 0:2]
-    eigval, eigvec = np.linalg.eig(Pxy)
+    eig_val, eig_vec = np.linalg.eig(Pxy)
 
-    if eigval[0] >= eigval[1]:
-        bigind = 0
-        smallind = 1
+    if eig_val[0] >= eig_val[1]:
+        big_ind = 0
+        small_ind = 1
     else:
-        bigind = 1
-        smallind = 0
+        big_ind = 1
+        small_ind = 0
 
     t = np.arange(0, 2 * math.pi + 0.1, 0.1)
 
-    # eigval[bigind] or eiqval[smallind] were occassionally negative numbers extremely
+    # eig_val[big_ind] or eiq_val[small_ind] were occasionally negative numbers extremely
     # close to 0 (~10^-20), catch these cases and set the respective variable to 0
     try:
-        a = math.sqrt(eigval[bigind])
+        a = math.sqrt(eig_val[big_ind])
     except ValueError:
         a = 0
 
     try:
-        b = math.sqrt(eigval[smallind])
+        b = math.sqrt(eig_val[small_ind])
     except ValueError:
         b = 0
 
     x = [a * math.cos(it) for it in t]
     y = [b * math.sin(it) for it in t]
-    angle = math.atan2(eigvec[bigind, 1], eigvec[bigind, 0])
-    R = np.array([[math.cos(angle), math.sin(angle)],
-                  [-math.sin(angle), math.cos(angle)]])
-    fx = R.dot(np.array([[x, y]]))
+    angle = math.atan2(eig_vec[big_ind, 1], eig_vec[big_ind, 0])
+    Rot = np.array([[math.cos(angle), -math.sin(angle)],
+                    [math.sin(angle), math.cos(angle)]])
+    fx = Rot.dot(np.array([[x, y]]))
     px = np.array(fx[0, :] + xEst[0, 0]).flatten()
     py = np.array(fx[1, :] + xEst[1, 0]).flatten()
     plt.plot(px, py, "--r")
@@ -197,16 +198,15 @@ def main():
 
     time = 0.0
 
-    # RFID positions [x, y]
-    RFID = np.array([[10.0, 0.0],
-                     [10.0, 10.0],
-                     [0.0, 15.0],
-                     [-5.0, 20.0]])
+    # RF_ID positions [x, y]
+    RFi_ID = np.array([[10.0, 0.0],
+                       [10.0, 10.0],
+                       [0.0, 15.0],
+                       [-5.0, 20.0]])
 
     # State Vector [x y yaw v]'
     xEst = np.zeros((4, 1))
     xTrue = np.zeros((4, 1))
-    PEst = np.eye(4)
 
     px = np.zeros((4, NP))  # Particle store
     pw = np.zeros((1, NP)) + 1.0 / NP  # Particle weight
@@ -221,7 +221,7 @@ def main():
         time += DT
         u = calc_input()
 
-        xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID)
+        xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFi_ID)
 
         xEst, PEst, px, pw = pf_localization(px, pw, z, ud)
 
@@ -235,7 +235,7 @@ def main():
 
             for i in range(len(z[:, 0])):
                 plt.plot([xTrue[0, 0], z[i, 1]], [xTrue[1, 0], z[i, 2]], "-k")
-            plt.plot(RFID[:, 0], RFID[:, 1], "*k")
+            plt.plot(RFi_ID[:, 0], RFi_ID[:, 1], "*k")
             plt.plot(px[0, :], px[1, :], ".r")
             plt.plot(np.array(hxTrue[0, :]).flatten(),
                      np.array(hxTrue[1, :]).flatten(), "-b")

From 46e0506cf57a96d67203a6f50e3dc7ab60f8c5f3 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 12 Oct 2019 14:01:54 +0900
Subject: [PATCH 0196/1080] Start fixing randn

---
 .../extended_kalman_filter.py                 | 34 +++++++++----------
 1 file changed, 16 insertions(+), 18 deletions(-)

diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py
index 3f0589b8c6..7150b497f9 100644
--- a/Localization/extended_kalman_filter/extended_kalman_filter.py
+++ b/Localization/extended_kalman_filter/extended_kalman_filter.py
@@ -7,21 +7,22 @@
 """
 
 import math
-import numpy as np
+
 import matplotlib.pyplot as plt
+import numpy as np
 
 # Covariance for EKF simulation
 Q = np.diag([
-    0.1, # variance of location on x-axis
-    0.1, # variance of location on y-axis
-    np.deg2rad(1.0), # variance of yaw angle
-    1.0 # variance of velocity
-    ])**2  # predict state covariance
-R = np.diag([1.0, 1.0])**2  # Observation x,y position covariance
+    0.1,  # variance of location on x-axis
+    0.1,  # variance of location on y-axis
+    np.deg2rad(1.0),  # variance of yaw angle
+    1.0  # variance of velocity
+]) ** 2  # predict state covariance
+R = np.diag([1.0, 1.0]) ** 2  # Observation x,y position covariance
 
 #  Simulation parameter
-INPUT_NOISE = np.diag([1.0, np.deg2rad(30.0)])**2
-GPS_NOISE = np.diag([0.5, 0.5])**2
+INPUT_NOISE = np.diag([1.0, np.deg2rad(30.0)]) ** 2
+GPS_NOISE = np.diag([0.5, 0.5]) ** 2
 
 DT = 0.1  # time tick [s]
 SIM_TIME = 50.0  # simulation time [s]
@@ -37,7 +38,6 @@ def calc_input():
 
 
 def observation(xTrue, xd, u):
-
     xTrue = motion_model(xTrue, u)
 
     # add noise to gps x-y
@@ -52,7 +52,6 @@ def observation(xTrue, xd, u):
 
 
 def motion_model(x, u):
-
     F = np.array([[1.0, 0, 0, 0],
                   [0, 1.0, 0, 0],
                   [0, 0, 1.0, 0],
@@ -116,20 +115,19 @@ def jacobH(x):
 
 
 def ekf_estimation(xEst, PEst, z, u):
-
     #  Predict
     xPred = motion_model(xEst, u)
     jF = jacobF(xPred, u)
-    PPred = jF@PEst@jF.T + Q
+    PPred = jF @ PEst @ jF.T + Q
 
     #  Update
     jH = jacobH(xPred)
     zPred = observation_model(xPred)
     y = z - zPred
-    S = jH@PPred@jH.T + R
-    K = PPred@jH.T@np.linalg.inv(S)
-    xEst = xPred + K@y
-    PEst = (np.eye(len(xEst)) - K@jH)@PPred
+    S = jH @ PPred @ jH.T + R
+    K = PPred @ jH.T @ np.linalg.inv(S)
+    xEst = xPred + K @ y
+    PEst = (np.eye(len(xEst)) - K @ jH) @ PPred
 
     return xEst, PEst
 
@@ -153,7 +151,7 @@ def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
     angle = math.atan2(eigvec[bigind, 1], eigvec[bigind, 0])
     R = np.array([[math.cos(angle), math.sin(angle)],
                   [-math.sin(angle), math.cos(angle)]])
-    fx = R@(np.array([x, y]))
+    fx = R @ (np.array([x, y]))
     px = np.array(fx[0, :] + xEst[0, 0]).flatten()
     py = np.array(fx[1, :] + xEst[1, 0]).flatten()
     plt.plot(px, py, "--r")

From 8fb4b8a5644904efc4dbbdefa1c6cd62c4cfe2da Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?=
 <20567087+goktug97@users.noreply.github.com>
Date: Sat, 12 Oct 2019 14:35:00 +0300
Subject: [PATCH 0197/1080] optimize obstacle cost calculation

The obstacle calculation was too slow to work with more obstacles,
it is vectorized to speed up the calculation.
---
 .../dynamic_window_approach.py                | 38 ++++++++-----------
 1 file changed, 15 insertions(+), 23 deletions(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index fa1dcc62c4..1896107220 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -47,8 +47,6 @@ def __init__(self):
         self.obstacle_cost_gain = 1.0
         self.robot_radius = 1.0  # [m] for collision check
 
-
-
 def motion(x, u, dt):
     """
     motion model
@@ -138,24 +136,14 @@ def calc_obstacle_cost(traj, ob, config):
     """
         calc obstacle cost inf: collision
     """
-
-    skip_n = 2 # for speed up
-    minr = float("inf")
-
-    for ii in range(0, len(traj[:, 1]), skip_n):
-        for i in range(len(ob[:, 0])):
-            ox = ob[i, 0]
-            oy = ob[i, 1]
-            dx = traj[ii, 0] - ox
-            dy = traj[ii, 1] - oy
-
-            r = math.sqrt(dx**2 + dy**2)
-            if r <= config.robot_radius:
-                return float("Inf")  # collision
-
-            if minr >= r:
-                minr = r
-
+    ox = ob[:, 0]
+    oy = ob[:, 1]
+    dx = traj[:, 0] - ox[:, None]
+    dy = traj[:, 1] - oy[:, None]
+    r = np.sqrt(np.square(dx) + np.square(dy))
+    if (r <= config.robot_radius).any():
+        return float("Inf")
+    minr = np.min(r)
     return 1.0 / minr  # OK
 
 
@@ -178,7 +166,7 @@ def plot_arrow(x, y, yaw, length=0.5, width=0.1):  # pragma: no cover
     plt.plot(x, y)
 
 
-def main(gx=10, gy=10):
+def main(gx=10.0, gy=10.0):
     print(__file__ + " start!!")
     # initial state [x(m), y(m), yaw(rad), v(m/s), omega(rad/s)]
     x = np.array([0.0, 0.0, math.pi / 8.0, 0.0, 0.0])
@@ -194,7 +182,12 @@ def main(gx=10, gy=10):
                    [5.0, 9.0],
                    [8.0, 9.0],
                    [7.0, 9.0],
-                   [12.0, 12.0]
+                   [8.0, 10.0],
+                   [9.0, 11.0],
+                   [12.0, 13.0],
+                   [12.0, 12.0],
+                   [15.0, 15.0],
+                   [13.0, 13.0]
                    ])
 
     # input [forward speed, yawrate]
@@ -204,7 +197,6 @@ def main(gx=10, gy=10):
 
     while True:
         u, ptraj = dwa_control(x, u, config, goal, ob)
-
         x = motion(x, u, config.dt) # simulate robot
         traj = np.vstack((traj, x))  # store state history
 

From bf3b4680ecd56c5478e247e4cfebc4539680e1dc Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 12 Oct 2019 20:48:04 +0900
Subject: [PATCH 0198/1080] fix randn usage and code clean up

---
 .../ensemble_kalman_filter.py                 | 95 +++++++++----------
 .../extended_kalman_filter.py                 | 14 +--
 .../unscented_kalman_filter.py                | 30 +++---
 SLAM/EKFSLAM/ekf_slam.py                      | 61 ++++++------
 SLAM/FastSLAM1/fast_slam1.py                  | 60 +++++-------
 SLAM/FastSLAM2/fast_slam2.py                  | 80 +++++++---------
 6 files changed, 152 insertions(+), 188 deletions(-)

diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
index 5ca7713b7d..293509157b 100644
--- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
+++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
@@ -1,4 +1,3 @@
-
 """
 
 Ensemble Kalman Filter(EnKF) localization sample
@@ -10,13 +9,14 @@
 
 """
 
-import numpy as np
 import math
+
 import matplotlib.pyplot as plt
+import numpy as np
 
 #  Simulation parameter
-Qsim = np.diag([0.2, np.deg2rad(1.0)])**2
-Rsim = np.diag([1.0, np.deg2rad(30.0)])**2
+Q_sim = np.diag([0.2, np.deg2rad(1.0)]) ** 2
+R_sim = np.diag([1.0, np.deg2rad(30.0)]) ** 2
 
 DT = 0.1  # time tick [s]
 SIM_TIME = 50.0  # simulation time [s]
@@ -30,13 +30,12 @@
 
 def calc_input():
     v = 1.0  # [m/s]
-    yawrate = 0.1  # [rad/s]
-    u = np.array([[v, yawrate]]).T
+    yaw_rate = 0.1  # [rad/s]
+    u = np.array([[v, yaw_rate]]).T
     return u
 
 
 def observation(xTrue, xd, u, RFID):
-
     xTrue = motion_model(xTrue, u)
 
     z = np.zeros((0, 4))
@@ -45,18 +44,18 @@ def observation(xTrue, xd, u, RFID):
 
         dx = RFID[i, 0] - xTrue[0, 0]
         dy = RFID[i, 1] - xTrue[1, 0]
-        d = math.sqrt(dx**2 + dy**2)
+        d = math.sqrt(dx ** 2 + dy ** 2)
         angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
         if d <= MAX_RANGE:
-            dn = d + np.random.randn() * Qsim[0, 0]  # add noise
-            anglen = angle + np.random.randn() * Qsim[1, 1]  # add noise
+            dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5  # add noise
+            anglen = angle + np.random.randn() * Q_sim[1, 1] ** 0.5  # add noise
             zi = np.array([dn, anglen, RFID[i, 0], RFID[i, 1]])
             z = np.vstack((z, zi))
 
     # add noise to input
     ud = np.array([[
-        u[0, 0] + np.random.randn() * Rsim[0, 0],
-        u[1, 0] + np.random.randn() * Rsim[1, 1]]]).T
+        u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5,
+        u[1, 0] + np.random.randn() * R_sim[1, 1] ** 0.5]]).T
 
     xd = motion_model(xd, ud)
     return xTrue, z, xd, ud
@@ -77,15 +76,13 @@ def motion_model(x, u):
     return x
 
 
-def calc_LM_Pos(x, landmarks):
-    landmarks_pos = np.zeros((2*landmarks.shape[0], 1))
+def observe_landmark_position(x, landmarks):
+    landmarks_pos = np.zeros((2 * landmarks.shape[0], 1))
     for (i, lm) in enumerate(landmarks):
-        landmarks_pos[2*i] = x[0, 0] + lm[0] * \
-            math.cos(x[2, 0] + lm[1]) + np.random.randn() * \
-            Qsim[0, 0]/np.sqrt(2)
-        landmarks_pos[2*i+1] = x[1, 0] + lm[0] * \
-            math.sin(x[2, 0] + lm[1]) + np.random.randn() * \
-            Qsim[0, 0]/np.sqrt(2)
+        landmarks_pos[2 * i] = x[0, 0] + lm[0] * math.cos(x[2, 0] + lm[1]) + np.random.randn() * Q_sim[
+            0, 0] ** 0.5 / np.sqrt(2)
+        landmarks_pos[2 * i + 1] = x[1, 0] + lm[0] * math.sin(x[2, 0] + lm[1]) + np.random.randn() * Q_sim[
+            0, 0] ** 0.5 / np.sqrt(2)
     return landmarks_pos
 
 
@@ -95,25 +92,26 @@ def calc_covariance(xEst, px):
     for i in range(px.shape[1]):
         dx = (px[:, i] - xEst)[0:3]
         cov += dx.dot(dx.T)
+    cov /= NP
 
     return cov
 
 
-def enkf_localization(px, xEst, PEst, z, u):
+def enkf_localization(px, z, u):
     """
     Localization with Ensemble Kalman filter
     """
-    pz = np.zeros((z.shape[0]*2, NP))  # Particle store of z
+    pz = np.zeros((z.shape[0] * 2, NP))  # Particle store of z
     for ip in range(NP):
         x = np.array([px[:, ip]]).T
 
         #  Predict with random input sampling
-        ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0]
-        ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1]
+        ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5
+        ud2 = u[1, 0] + np.random.randn() * R_sim[1, 1] ** 0.5
         ud = np.array([[ud1, ud2]]).T
         x = motion_model(x, ud)
         px[:, ip] = x[:, 0]
-        z_pos = calc_LM_Pos(x, z)
+        z_pos = observe_landmark_position(x, z)
         pz[:, ip] = z_pos[:, 0]
 
     x_ave = np.mean(px, axis=1)
@@ -122,12 +120,12 @@ def enkf_localization(px, xEst, PEst, z, u):
     z_ave = np.mean(pz, axis=1)
     z_dif = pz - np.tile(z_ave, (NP, 1)).T
 
-    U = 1/(NP-1) * x_dif @ z_dif.T
-    V = 1/(NP-1) * z_dif @ z_dif.T
+    U = 1 / (NP - 1) * x_dif @ z_dif.T
+    V = 1 / (NP - 1) * z_dif @ z_dif.T
 
     K = U @ np.linalg.inv(V)  # Kalman Gain
 
-    z_lm_pos = z[:, [2, 3]].reshape(-1,)
+    z_lm_pos = z[:, [2, 3]].reshape(-1, )
 
     px_hat = px + K @ (np.tile(z_lm_pos, (NP, 1)).T - pz)
 
@@ -139,32 +137,32 @@ def enkf_localization(px, xEst, PEst, z, u):
 
 def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
     Pxy = PEst[0:2, 0:2]
-    eigval, eigvec = np.linalg.eig(Pxy)
+    eig_val, eig_vec = np.linalg.eig(Pxy)
 
-    if eigval[0] >= eigval[1]:
-        bigind = 0
-        smallind = 1
+    if eig_val[0] >= eig_val[1]:
+        big_ind = 0
+        small_ind = 1
     else:
-        bigind = 1
-        smallind = 0
+        big_ind = 1
+        small_ind = 0
 
     t = np.arange(0, 2 * math.pi + 0.1, 0.1)
 
-    # eigval[bigind] or eiqval[smallind] were occassionally negative numbers extremely
+    # eig_val[big_ind] or eiq_val[small_ind] were occasionally negative numbers extremely
     # close to 0 (~10^-20), catch these cases and set the respective variable to 0
     try:
-        a = math.sqrt(eigval[bigind])
+        a = math.sqrt(eig_val[big_ind])
     except ValueError:
         a = 0
 
     try:
-        b = math.sqrt(eigval[smallind])
+        b = math.sqrt(eig_val[small_ind])
     except ValueError:
         b = 0
 
     x = [a * math.cos(it) for it in t]
     y = [b * math.sin(it) for it in t]
-    angle = math.atan2(eigvec[bigind, 1], eigvec[bigind, 0])
+    angle = math.atan2(eig_vec[big_ind, 1], eig_vec[big_ind, 0])
     R = np.array([[math.cos(angle), math.sin(angle)],
                   [-math.sin(angle), math.cos(angle)]])
     fx = R.dot(np.array([[x, y]]))
@@ -182,17 +180,15 @@ def main():
 
     time = 0.0
 
-    # RFID positions [x, y]
-    RFID = np.array([[10.0, 0.0],
-                     [10.0, 10.0],
-                     [0.0, 15.0],
-                     [-5.0, 20.0]])
+    # RF_ID positions [x, y]
+    RF_ID = np.array([[10.0, 0.0],
+                      [10.0, 10.0],
+                      [0.0, 15.0],
+                      [-5.0, 20.0]])
 
     # State Vector [x y yaw v]'
     xEst = np.zeros((4, 1))
     xTrue = np.zeros((4, 1))
-    PEst = np.eye(4)
-
     px = np.zeros((4, NP))  # Particle store of x
 
     xDR = np.zeros((4, 1))  # Dead reckoning
@@ -206,9 +202,9 @@ def main():
         time += DT
         u = calc_input()
 
-        xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID)
+        xTrue, z, xDR, ud = observation(xTrue, xDR, u, RF_ID)
 
-        xEst, PEst, px = enkf_localization(px, xEst, PEst, z, ud)
+        xEst, PEst, px = enkf_localization(px, z, ud)
 
         # store data history
         hxEst = np.hstack((hxEst, xEst))
@@ -220,7 +216,7 @@ def main():
 
             for i in range(len(z[:, 0])):
                 plt.plot([xTrue[0, 0], z[i, 2]], [xTrue[1, 0], z[i, 3]], "-k")
-            plt.plot(RFID[:, 0], RFID[:, 1], "*k")
+            plt.plot(RF_ID[:, 0], RF_ID[:, 1], "*k")
             plt.plot(px[0, :], px[1, :], ".r")
             plt.plot(np.array(hxTrue[0, :]).flatten(),
                      np.array(hxTrue[1, :]).flatten(), "-b")
@@ -228,7 +224,7 @@ def main():
                      np.array(hxDR[1, :]).flatten(), "-k")
             plt.plot(np.array(hxEst[0, :]).flatten(),
                      np.array(hxEst[1, :]).flatten(), "-r")
-            #plot_covariance_ellipse(xEst, PEst)
+            # plot_covariance_ellipse(xEst, PEst)
             plt.axis("equal")
             plt.grid(True)
             plt.pause(0.001)
@@ -236,4 +232,3 @@ def main():
 
 if __name__ == '__main__':
     main()
-
diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py
index 7150b497f9..d2d7178ae6 100644
--- a/Localization/extended_kalman_filter/extended_kalman_filter.py
+++ b/Localization/extended_kalman_filter/extended_kalman_filter.py
@@ -78,7 +78,7 @@ def observation_model(x):
     return z
 
 
-def jacobF(x, u):
+def jacob_f(x, u):
     """
     Jacobian of Motion Model
 
@@ -104,7 +104,7 @@ def jacobF(x, u):
     return jF
 
 
-def jacobH(x):
+def jacob_h():
     # Jacobian of Observation Model
     jH = np.array([
         [1, 0, 0, 0],
@@ -117,11 +117,11 @@ def jacobH(x):
 def ekf_estimation(xEst, PEst, z, u):
     #  Predict
     xPred = motion_model(xEst, u)
-    jF = jacobF(xPred, u)
+    jF = jacob_f(xPred, u)
     PPred = jF @ PEst @ jF.T + Q
 
     #  Update
-    jH = jacobH(xPred)
+    jH = jacob_h()
     zPred = observation_model(xPred)
     y = z - zPred
     S = jH @ PPred @ jH.T + R
@@ -149,9 +149,9 @@ def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
     x = [a * math.cos(it) for it in t]
     y = [b * math.sin(it) for it in t]
     angle = math.atan2(eigvec[bigind, 1], eigvec[bigind, 0])
-    R = np.array([[math.cos(angle), math.sin(angle)],
-                  [-math.sin(angle), math.cos(angle)]])
-    fx = R @ (np.array([x, y]))
+    rot = np.array([[math.cos(angle), math.sin(angle)],
+                    [-math.sin(angle), math.cos(angle)]])
+    fx = rot @ (np.array([x, y]))
     px = np.array(fx[0, :] + xEst[0, 0]).flatten()
     py = np.array(fx[1, :] + xEst[1, 0]).flatten()
     plt.plot(px, py, "--r")
diff --git a/Localization/unscented_kalman_filter/unscented_kalman_filter.py b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
index 78b44754ee..45d5b78132 100644
--- a/Localization/unscented_kalman_filter/unscented_kalman_filter.py
+++ b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
@@ -6,23 +6,24 @@
 
 """
 
-import numpy as np
-import scipy.linalg
 import math
+
 import matplotlib.pyplot as plt
+import numpy as np
+import scipy.linalg
 
 # Covariance for UKF simulation
 Q = np.diag([
-    0.1, # variance of location on x-axis
-    0.1, # variance of location on y-axis
-    np.deg2rad(1.0), # variance of yaw angle
-    1.0 # variance of velocity
-    ])**2  # predict state covariance
-R = np.diag([1.0, 1.0])**2  # Observation x,y position covariance
+    0.1,  # variance of location on x-axis
+    0.1,  # variance of location on y-axis
+    np.deg2rad(1.0),  # variance of yaw angle
+    1.0  # variance of velocity
+]) ** 2  # predict state covariance
+R = np.diag([1.0, 1.0]) ** 2  # Observation x,y position covariance
 
 #  Simulation parameter
-INPUT_NOISE = np.diag([1.0, np.deg2rad(30.0)])**2
-GPS_NOISE = np.diag([0.5, 0.5])**2
+INPUT_NOISE = np.diag([1.0, np.deg2rad(30.0)]) ** 2
+GPS_NOISE = np.diag([0.5, 0.5]) ** 2
 
 DT = 0.1  # time tick [s]
 SIM_TIME = 50.0  # simulation time [s]
@@ -43,7 +44,6 @@ def calc_input():
 
 
 def observation(xTrue, xd, u):
-
     xTrue = motion_model(xTrue, u)
 
     # add noise to gps x-y
@@ -58,7 +58,6 @@ def observation(xTrue, xd, u):
 
 
 def motion_model(x, u):
-
     F = np.array([[1.0, 0, 0, 0],
                   [0, 1.0, 0, 0],
                   [0, 0, 1.0, 0],
@@ -144,7 +143,6 @@ def calc_pxz(sigma, x, z_sigma, zb, wc):
 
 
 def ukf_estimation(xEst, PEst, z, u, wm, wc, gamma):
-
     #  Predict
     sigma = generate_sigma_points(xEst, PEst, gamma)
     sigma = predict_sigma_motion(sigma, u)
@@ -183,9 +181,9 @@ def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
     x = [a * math.cos(it) for it in t]
     y = [b * math.sin(it) for it in t]
     angle = math.atan2(eigvec[bigind, 1], eigvec[bigind, 0])
-    R = np.array([[math.cos(angle), math.sin(angle)],
-                  [-math.sin(angle), math.cos(angle)]])
-    fx = R @ np.array([x, y])
+    rot = np.array([[math.cos(angle), math.sin(angle)],
+                    [-math.sin(angle), math.cos(angle)]])
+    fx = rot @ np.array([x, y])
     px = np.array(fx[0, :] + xEst[0, 0]).flatten()
     py = np.array(fx[1, :] + xEst[1, 0]).flatten()
     plt.plot(px, py, "--r")
diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py
index de1b94ec8f..ae39c9e0ea 100644
--- a/SLAM/EKFSLAM/ekf_slam.py
+++ b/SLAM/EKFSLAM/ekf_slam.py
@@ -4,16 +4,16 @@
 """
 
 import math
-import numpy as np
-import matplotlib.pyplot as plt
 
+import matplotlib.pyplot as plt
+import numpy as np
 
 # EKF state covariance
-Cx = np.diag([0.5, 0.5, np.deg2rad(30.0)])**2
+Cx = np.diag([0.5, 0.5, np.deg2rad(30.0)]) ** 2
 
 #  Simulation parameter
-Qsim = np.diag([0.2, np.deg2rad(1.0)])**2
-Rsim = np.diag([1.0, np.deg2rad(10.0)])**2
+Q_sim = np.diag([0.2, np.deg2rad(1.0)]) ** 2
+R_sim = np.diag([1.0, np.deg2rad(10.0)]) ** 2
 
 DT = 0.1  # time tick [s]
 SIM_TIME = 50.0  # simulation time [s]
@@ -26,7 +26,6 @@
 
 
 def ekf_slam(xEst, PEst, u, z):
-
     # Predict
     S = STATE_SIZE
     xEst[0:S] = motion_model(xEst[0:S], u)
@@ -36,18 +35,18 @@ def ekf_slam(xEst, PEst, u, z):
 
     # Update
     for iz in range(len(z[:, 0])):  # for each observation
-        minid = search_correspond_LM_ID(xEst, PEst, z[iz, 0:2])
+        minid = search_correspond_landmark_id(xEst, PEst, z[iz, 0:2])
 
-        nLM = calc_n_LM(xEst)
+        nLM = calc_n_lm(xEst)
         if minid == nLM:
             print("New LM")
             # Extend state and covariance matrix
-            xAug = np.vstack((xEst, calc_LM_Pos(xEst, z[iz, :])))
+            xAug = np.vstack((xEst, calc_landmark_position(xEst, z[iz, :])))
             PAug = np.vstack((np.hstack((PEst, np.zeros((len(xEst), LM_SIZE)))),
                               np.hstack((np.zeros((LM_SIZE, len(xEst))), initP))))
             xEst = xAug
             PEst = PAug
-        lm = get_LM_Pos_from_state(xEst, minid)
+        lm = get_landmark_position_from_state(xEst, minid)
         y, S, H = calc_innovation(lm, xEst, PEst, z[iz, 0:2], minid)
 
         K = (PEst @ H.T) @ np.linalg.inv(S)
@@ -67,7 +66,6 @@ def calc_input():
 
 
 def observation(xTrue, xd, u, RFID):
-
     xTrue = motion_model(xTrue, u)
 
     # add noise to gps x-y
@@ -77,25 +75,24 @@ def observation(xTrue, xd, u, RFID):
 
         dx = RFID[i, 0] - xTrue[0, 0]
         dy = RFID[i, 1] - xTrue[1, 0]
-        d = math.sqrt(dx**2 + dy**2)
+        d = math.sqrt(dx ** 2 + dy ** 2)
         angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
         if d <= MAX_RANGE:
-            dn = d + np.random.randn() * Qsim[0, 0]  # add noise
-            anglen = angle + np.random.randn() * Qsim[1, 1]  # add noise
+            dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5  # add noise
+            anglen = angle + np.random.randn() * Q_sim[1, 1] ** 0.5  # add noise
             zi = np.array([dn, anglen, i])
             z = np.vstack((z, zi))
 
     # add noise to input
     ud = np.array([[
-        u[0, 0] + np.random.randn() * Rsim[0, 0],
-        u[1, 0] + np.random.randn() * Rsim[1, 1]]]).T
+        u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5,
+        u[1, 0] + np.random.randn() * R_sim[1, 1] ** 0.5]]).T
 
     xd = motion_model(xd, ud)
     return xTrue, z, xd, ud
 
 
 def motion_model(x, u):
-
     F = np.array([[1.0, 0, 0],
                   [0, 1.0, 0],
                   [0, 0, 1.0]])
@@ -108,15 +105,14 @@ def motion_model(x, u):
     return x
 
 
-def calc_n_LM(x):
+def calc_n_lm(x):
     n = int((len(x) - STATE_SIZE) / LM_SIZE)
     return n
 
 
 def jacob_motion(x, u):
-
     Fx = np.hstack((np.eye(STATE_SIZE), np.zeros(
-        (STATE_SIZE, LM_SIZE * calc_n_LM(x)))))
+        (STATE_SIZE, LM_SIZE * calc_n_lm(x)))))
 
     jF = np.array([[0.0, 0.0, -DT * u[0] * math.sin(x[2, 0])],
                    [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])],
@@ -127,35 +123,34 @@ def jacob_motion(x, u):
     return G, Fx,
 
 
-def calc_LM_Pos(x, z):
+def calc_landmark_position(x, z):
     zp = np.zeros((2, 1))
 
     zp[0, 0] = x[0, 0] + z[0] * math.cos(x[2, 0] + z[1])
     zp[1, 0] = x[1, 0] + z[0] * math.sin(x[2, 0] + z[1])
-    #zp[0, 0] = x[0, 0] + z[0, 0] * math.cos(x[2, 0] + z[0, 1])
-    #zp[1, 0] = x[1, 0] + z[0, 0] * math.sin(x[2, 0] + z[0, 1])
+    # zp[0, 0] = x[0, 0] + z[0, 0] * math.cos(x[2, 0] + z[0, 1])
+    # zp[1, 0] = x[1, 0] + z[0, 0] * math.sin(x[2, 0] + z[0, 1])
 
     return zp
 
 
-def get_LM_Pos_from_state(x, ind):
-
+def get_landmark_position_from_state(x, ind):
     lm = x[STATE_SIZE + LM_SIZE * ind: STATE_SIZE + LM_SIZE * (ind + 1), :]
 
     return lm
 
 
-def search_correspond_LM_ID(xAug, PAug, zi):
+def search_correspond_landmark_id(xAug, PAug, zi):
     """
     Landmark association with Mahalanobis distance
     """
 
-    nLM = calc_n_LM(xAug)
+    nLM = calc_n_lm(xAug)
 
     mdist = []
 
     for i in range(nLM):
-        lm = get_LM_Pos_from_state(xAug, i)
+        lm = get_landmark_position_from_state(xAug, i)
         y, S, H = calc_innovation(lm, xAug, PAug, zi, i)
         mdist.append(y.T @ np.linalg.inv(S) @ y)
 
@@ -173,19 +168,19 @@ def calc_innovation(lm, xEst, PEst, z, LMid):
     zp = np.array([[math.sqrt(q), pi_2_pi(zangle)]])
     y = (z - zp).T
     y[1] = pi_2_pi(y[1])
-    H = jacobH(q, delta, xEst, LMid + 1)
+    H = jacob_h(q, delta, xEst, LMid + 1)
     S = H @ PEst @ H.T + Cx[0:2, 0:2]
 
     return y, S, H
 
 
-def jacobH(q, delta, x, i):
+def jacob_h(q, delta, x, i):
     sq = math.sqrt(q)
     G = np.array([[-sq * delta[0, 0], - sq * delta[1, 0], 0, sq * delta[0, 0], sq * delta[1, 0]],
                   [delta[1, 0], - delta[0, 0], - 1.0, - delta[1, 0], delta[0, 0]]])
 
     G = G / q
-    nLM = calc_n_LM(x)
+    nLM = calc_n_lm(x)
     F1 = np.hstack((np.eye(3), np.zeros((3, 2 * nLM))))
     F2 = np.hstack((np.zeros((2, 3)), np.zeros((2, 2 * (i - 1))),
                     np.eye(2), np.zeros((2, 2 * nLM - 2 * i))))
@@ -246,7 +241,7 @@ def main():
             plt.plot(xEst[0], xEst[1], ".r")
 
             # plot landmark
-            for i in range(calc_n_LM(xEst)):
+            for i in range(calc_n_lm(xEst)):
                 plt.plot(xEst[STATE_SIZE + i * 2],
                          xEst[STATE_SIZE + i * 2 + 1], "xg")
 
@@ -262,4 +257,4 @@ def main():
 
 
 if __name__ == '__main__':
-    main()
\ No newline at end of file
+    main()
diff --git a/SLAM/FastSLAM1/fast_slam1.py b/SLAM/FastSLAM1/fast_slam1.py
index 757e9f95ad..c16407e89f 100644
--- a/SLAM/FastSLAM1/fast_slam1.py
+++ b/SLAM/FastSLAM1/fast_slam1.py
@@ -6,18 +6,18 @@
 
 """
 
-import numpy as np
 import math
-import matplotlib.pyplot as plt
 
+import matplotlib.pyplot as plt
+import numpy as np
 
 # Fast SLAM covariance
-Q = np.diag([3.0, np.deg2rad(10.0)])**2
-R = np.diag([1.0, np.deg2rad(20.0)])**2
+Q = np.diag([3.0, np.deg2rad(10.0)]) ** 2
+R = np.diag([1.0, np.deg2rad(20.0)]) ** 2
 
 #  Simulation parameter
-Qsim = np.diag([0.3, np.deg2rad(2.0)])**2
-Rsim = np.diag([0.5, np.deg2rad(10.0)])**2
+Qsim = np.diag([0.3, np.deg2rad(2.0)]) ** 2
+Rsim = np.diag([0.5, np.deg2rad(10.0)]) ** 2
 OFFSET_YAWRATE_NOISE = 0.01
 
 DT = 0.1  # time tick [s]
@@ -46,7 +46,6 @@ def __init__(self, N_LM):
 
 
 def fast_slam1(particles, u, z):
-
     particles = predict_particles(particles, u)
 
     particles = update_with_observation(particles, z)
@@ -57,7 +56,6 @@ def fast_slam1(particles, u, z):
 
 
 def normalize_weight(particles):
-
     sumw = sum([p.w for p in particles])
 
     try:
@@ -73,7 +71,6 @@ def normalize_weight(particles):
 
 
 def calc_final_state(particles):
-
     xEst = np.zeros((STATE_SIZE, 1))
 
     particles = normalize_weight(particles)
@@ -90,13 +87,12 @@ def calc_final_state(particles):
 
 
 def predict_particles(particles, u):
-
     for i in range(N_PARTICLE):
         px = np.zeros((STATE_SIZE, 1))
         px[0, 0] = particles[i].x
         px[1, 0] = particles[i].y
         px[2, 0] = particles[i].yaw
-        ud = u + (np.random.randn(1, 2) @ R).T  # add noise
+        ud = u + (np.random.randn(1, 2) @ R ** 0.5).T  # add noise
         px = motion_model(px, ud)
         particles[i].x = px[0, 0]
         particles[i].y = px[1, 0]
@@ -105,8 +101,7 @@ def predict_particles(particles, u):
     return particles
 
 
-def add_new_lm(particle, z, Q):
-
+def add_new_lm(particle, z, Q_cov):
     r = z[0]
     b = z[1]
     lm_id = int(z[2])
@@ -121,15 +116,15 @@ def add_new_lm(particle, z, Q):
     Gz = np.array([[c, -r * s],
                    [s, r * c]])
 
-    particle.lmP[2 * lm_id:2 * lm_id + 2] = Gz @ Q @ Gz.T
+    particle.lmP[2 * lm_id:2 * lm_id + 2] = Gz @ Q_cov @ Gz.T
 
     return particle
 
 
-def compute_jacobians(particle, xf, Pf, Q):
+def compute_jacobians(particle, xf, Pf, Q_cov):
     dx = xf[0, 0] - particle.x
     dy = xf[1, 0] - particle.y
-    d2 = dx**2 + dy**2
+    d2 = dx ** 2 + dy ** 2
     d = math.sqrt(d2)
 
     zp = np.array(
@@ -141,14 +136,14 @@ def compute_jacobians(particle, xf, Pf, Q):
     Hf = np.array([[dx / d, dy / d],
                    [-dy / d2, dx / d2]])
 
-    Sf = Hf @ Pf @ Hf.T + Q
+    Sf = Hf @ Pf @ Hf.T + Q_cov
 
     return zp, Hv, Hf, Sf
 
 
-def update_KF_with_cholesky(xf, Pf, v, Q, Hf):
+def update_kf_with_cholesky(xf, Pf, v, Q_cov, Hf):
     PHt = Pf @ Hf.T
-    S = Hf @ PHt + Q
+    S = Hf @ PHt + Q_cov
 
     S = (S + S.T) * 0.5
     SChol = np.linalg.cholesky(S).T
@@ -162,8 +157,7 @@ def update_KF_with_cholesky(xf, Pf, v, Q, Hf):
     return x, P
 
 
-def update_landmark(particle, z, Q):
-
+def update_landmark(particle, z, Q_cov):
     lm_id = int(z[2])
     xf = np.array(particle.lm[lm_id, :]).reshape(2, 1)
     Pf = np.array(particle.lmP[2 * lm_id:2 * lm_id + 2, :])
@@ -173,7 +167,7 @@ def update_landmark(particle, z, Q):
     dz = z[0:2].reshape(2, 1) - zp
     dz[1, 0] = pi_2_pi(dz[1, 0])
 
-    xf, Pf = update_KF_with_cholesky(xf, Pf, dz, Q, Hf)
+    xf, Pf = update_kf_with_cholesky(xf, Pf, dz, Q_cov, Hf)
 
     particle.lm[lm_id, :] = xf.T
     particle.lmP[2 * lm_id:2 * lm_id + 2, :] = Pf
@@ -181,11 +175,11 @@ def update_landmark(particle, z, Q):
     return particle
 
 
-def compute_weight(particle, z, Q):
+def compute_weight(particle, z, Q_cov):
     lm_id = int(z[2])
     xf = np.array(particle.lm[lm_id, :]).reshape(2, 1)
     Pf = np.array(particle.lmP[2 * lm_id:2 * lm_id + 2])
-    zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q)
+    zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q_cov)
 
     dx = z[0:2].reshape(2, 1) - zp
     dx[1, 0] = pi_2_pi(dx[1, 0])
@@ -205,7 +199,6 @@ def compute_weight(particle, z, Q):
 
 
 def update_with_observation(particles, z):
-
     for iz in range(len(z[0, :])):
 
         lmid = int(z[2, iz])
@@ -247,7 +240,7 @@ def resampling(particles):
         inds = []
         ind = 0
         for ip in range(N_PARTICLE):
-            while ((ind < wcum.shape[0] - 1) and (resampleid[ip] > wcum[ind])):
+            while (ind < wcum.shape[0] - 1) and (resampleid[ip] > wcum[ind]):
                 ind += 1
             inds.append(ind)
 
@@ -264,7 +257,6 @@ def resampling(particles):
 
 
 def calc_input(time):
-
     if time <= 3.0:  # wait at first
         v = 0.0
         yawrate = 0.0
@@ -278,7 +270,6 @@ def calc_input(time):
 
 
 def observation(xTrue, xd, u, RFID):
-
     # calc true state
     xTrue = motion_model(xTrue, u)
 
@@ -288,17 +279,17 @@ def observation(xTrue, xd, u, RFID):
 
         dx = RFID[i, 0] - xTrue[0, 0]
         dy = RFID[i, 1] - xTrue[1, 0]
-        d = math.sqrt(dx**2 + dy**2)
+        d = math.sqrt(dx ** 2 + dy ** 2)
         angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
         if d <= MAX_RANGE:
-            dn = d + np.random.randn() * Qsim[0, 0]  # add noise
-            anglen = angle + np.random.randn() * Qsim[1, 1]  # add noise
+            dn = d + np.random.randn() * Qsim[0, 0] ** 0.5  # add noise
+            anglen = angle + np.random.randn() * Qsim[1, 1] ** 0.5  # add noise
             zi = np.array([dn, pi_2_pi(anglen), i]).reshape(3, 1)
             z = np.hstack((z, zi))
 
     # add noise to input
-    ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0]
-    ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1] + OFFSET_YAWRATE_NOISE
+    ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0] ** 0.5
+    ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1] ** 0.5 + OFFSET_YAWRATE_NOISE
     ud = np.array([ud1, ud2]).reshape(2, 1)
 
     xd = motion_model(xd, ud)
@@ -307,7 +298,6 @@ def observation(xTrue, xd, u, RFID):
 
 
 def motion_model(x, u):
-
     F = np.array([[1.0, 0, 0],
                   [0, 1.0, 0],
                   [0, 0, 1.0]])
@@ -354,7 +344,7 @@ def main():
     hxTrue = xTrue
     hxDR = xTrue
 
-    particles = [Particle(N_LM) for i in range(N_PARTICLE)]
+    particles = [Particle(N_LM) for _ in range(N_PARTICLE)]
 
     while SIM_TIME >= time:
         time += DT
diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py
index 3d73a58e30..73624dbf1f 100644
--- a/SLAM/FastSLAM2/fast_slam2.py
+++ b/SLAM/FastSLAM2/fast_slam2.py
@@ -6,19 +6,19 @@
 
 """
 
-import numpy as np
 import math
-import matplotlib.pyplot as plt
 
+import matplotlib.pyplot as plt
+import numpy as np
 
 # Fast SLAM covariance
-Q = np.diag([3.0, np.deg2rad(10.0)])**2
-R = np.diag([1.0, np.deg2rad(20.0)])**2
+Q = np.diag([3.0, np.deg2rad(10.0)]) ** 2
+R = np.diag([1.0, np.deg2rad(20.0)]) ** 2
 
 #  Simulation parameter
-Qsim = np.diag([0.3, np.deg2rad(2.0)])**2
-Rsim = np.diag([0.5, np.deg2rad(10.0)])**2
-OFFSET_YAWRATE_NOISE = 0.01
+Q_sim = np.diag([0.3, np.deg2rad(2.0)]) ** 2
+R_sim = np.diag([0.5, np.deg2rad(10.0)]) ** 2
+OFFSET_YAW_RATE_NOISE = 0.01
 
 DT = 0.1  # time tick [s]
 SIM_TIME = 50.0  # simulation time [s]
@@ -47,7 +47,6 @@ def __init__(self, N_LM):
 
 
 def fast_slam2(particles, u, z):
-
     particles = predict_particles(particles, u)
 
     particles = update_with_observation(particles, z)
@@ -58,12 +57,11 @@ def fast_slam2(particles, u, z):
 
 
 def normalize_weight(particles):
-
-    sumw = sum([p.w for p in particles])
+    sum_w = sum([p.w for p in particles])
 
     try:
         for i in range(N_PARTICLE):
-            particles[i].w /= sumw
+            particles[i].w /= sum_w
     except ZeroDivisionError:
         for i in range(N_PARTICLE):
             particles[i].w = 1.0 / N_PARTICLE
@@ -74,7 +72,6 @@ def normalize_weight(particles):
 
 
 def calc_final_state(particles):
-
     xEst = np.zeros((STATE_SIZE, 1))
 
     particles = normalize_weight(particles)
@@ -85,19 +82,17 @@ def calc_final_state(particles):
         xEst[2, 0] += particles[i].w * particles[i].yaw
 
     xEst[2, 0] = pi_2_pi(xEst[2, 0])
-    #  print(xEst)
 
     return xEst
 
 
 def predict_particles(particles, u):
-
     for i in range(N_PARTICLE):
         px = np.zeros((STATE_SIZE, 1))
         px[0, 0] = particles[i].x
         px[1, 0] = particles[i].y
         px[2, 0] = particles[i].yaw
-        ud = u + (np.random.randn(1, 2) @ R).T  # add noise
+        ud = u + (np.random.randn(1, 2) @ R ** 0.5).T  # add noise
         px = motion_model(px, ud)
         particles[i].x = px[0, 0]
         particles[i].y = px[1, 0]
@@ -106,8 +101,7 @@ def predict_particles(particles, u):
     return particles
 
 
-def add_new_lm(particle, z, Q):
-
+def add_new_lm(particle, z, Q_cov):
     r = z[0]
     b = z[1]
     lm_id = int(z[2])
@@ -122,15 +116,15 @@ def add_new_lm(particle, z, Q):
     Gz = np.array([[c, -r * s],
                    [s, r * c]])
 
-    particle.lmP[2 * lm_id:2 * lm_id + 2] = Gz @ Q @ Gz.T
+    particle.lmP[2 * lm_id:2 * lm_id + 2] = Gz @ Q_cov @ Gz.T
 
     return particle
 
 
-def compute_jacobians(particle, xf, Pf, Q):
+def compute_jacobians(particle, xf, Pf, Q_cov):
     dx = xf[0, 0] - particle.x
     dy = xf[1, 0] - particle.y
-    d2 = dx**2 + dy**2
+    d2 = dx ** 2 + dy ** 2
     d = math.sqrt(d2)
 
     zp = np.array(
@@ -142,14 +136,14 @@ def compute_jacobians(particle, xf, Pf, Q):
     Hf = np.array([[dx / d, dy / d],
                    [-dy / d2, dx / d2]])
 
-    Sf = Hf @ Pf @ Hf.T + Q
+    Sf = Hf @ Pf @ Hf.T + Q_cov
 
     return zp, Hv, Hf, Sf
 
 
-def update_KF_with_cholesky(xf, Pf, v, Q, Hf):
+def update_kf_with_cholesky(xf, Pf, v, Q_cov, Hf):
     PHt = Pf @ Hf.T
-    S = Hf @ PHt + Q
+    S = Hf @ PHt + Q_cov
 
     S = (S + S.T) * 0.5
     SChol = np.linalg.cholesky(S).T
@@ -163,18 +157,17 @@ def update_KF_with_cholesky(xf, Pf, v, Q, Hf):
     return x, P
 
 
-def update_landmark(particle, z, Q):
-
+def update_landmark(particle, z, Q_cov):
     lm_id = int(z[2])
     xf = np.array(particle.lm[lm_id, :]).reshape(2, 1)
     Pf = np.array(particle.lmP[2 * lm_id:2 * lm_id + 2])
 
-    zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q)
+    zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q_cov)
 
     dz = z[0:2].reshape(2, 1) - zp
     dz[1, 0] = pi_2_pi(dz[1, 0])
 
-    xf, Pf = update_KF_with_cholesky(xf, Pf, dz, Q, Hf)
+    xf, Pf = update_kf_with_cholesky(xf, Pf, dz, Q, Hf)
 
     particle.lm[lm_id, :] = xf.T
     particle.lmP[2 * lm_id:2 * lm_id + 2, :] = Pf
@@ -182,12 +175,11 @@ def update_landmark(particle, z, Q):
     return particle
 
 
-def compute_weight(particle, z, Q):
-
+def compute_weight(particle, z, Q_cov):
     lm_id = int(z[2])
     xf = np.array(particle.lm[lm_id, :]).reshape(2, 1)
     Pf = np.array(particle.lmP[2 * lm_id:2 * lm_id + 2])
-    zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q)
+    zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q_cov)
 
     dz = z[0:2].reshape(2, 1) - zp
     dz[1, 0] = pi_2_pi(dz[1, 0])
@@ -205,15 +197,14 @@ def compute_weight(particle, z, Q):
     return w
 
 
-def proposal_sampling(particle, z, Q):
-
+def proposal_sampling(particle, z, Q_cov):
     lm_id = int(z[2])
     xf = particle.lm[lm_id, :].reshape(2, 1)
     Pf = particle.lmP[2 * lm_id:2 * lm_id + 2]
     # State
     x = np.array([particle.x, particle.y, particle.yaw]).reshape(3, 1)
     P = particle.P
-    zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q)
+    zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q_cov)
 
     Sfi = np.linalg.inv(Sf)
     dz = z[0:2].reshape(2, 1) - zp
@@ -232,7 +223,6 @@ def proposal_sampling(particle, z, Q):
 
 
 def update_with_observation(particles, z):
-
     for iz in range(len(z[0, :])):
         lmid = int(z[2, iz])
 
@@ -265,17 +255,16 @@ def resampling(particles):
     pw = np.array(pw)
 
     Neff = 1.0 / (pw @ pw.T)  # Effective particle number
-    #  print(Neff)
 
     if Neff < NTH:  # resampling
         wcum = np.cumsum(pw)
         base = np.cumsum(pw * 0.0 + 1 / N_PARTICLE) - 1 / N_PARTICLE
-        resampleid = base + np.random.rand(base.shape[0]) / N_PARTICLE
+        resamplei_id = base + np.random.rand(base.shape[0]) / N_PARTICLE
 
         inds = []
         ind = 0
         for ip in range(N_PARTICLE):
-            while ((ind < wcum.shape[0] - 1) and (resampleid[ip] > wcum[ind])):
+            while (ind < wcum.shape[0] - 1) and (resamplei_id[ip] > wcum[ind]):
                 ind += 1
             inds.append(ind)
 
@@ -292,7 +281,6 @@ def resampling(particles):
 
 
 def calc_input(time):
-
     if time <= 3.0:  # wait at first
         v = 0.0
         yawrate = 0.0
@@ -306,7 +294,6 @@ def calc_input(time):
 
 
 def observation(xTrue, xd, u, RFID):
-
     # calc true state
     xTrue = motion_model(xTrue, u)
 
@@ -317,17 +304,17 @@ def observation(xTrue, xd, u, RFID):
 
         dx = RFID[i, 0] - xTrue[0, 0]
         dy = RFID[i, 1] - xTrue[1, 0]
-        d = math.sqrt(dx**2 + dy**2)
+        d = math.sqrt(dx ** 2 + dy ** 2)
         angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
         if d <= MAX_RANGE:
-            dn = d + np.random.randn() * Qsim[0, 0]  # add noise
-            anglen = angle + np.random.randn() * Qsim[1, 1]  # add noise
+            dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5  # add noise
+            anglen = angle + np.random.randn() * Q_sim[1, 1] ** 0.5  # add noise
             zi = np.array([dn, pi_2_pi(anglen), i]).reshape(3, 1)
             z = np.hstack((z, zi))
 
     # add noise to input
-    ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0]
-    ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1] + OFFSET_YAWRATE_NOISE
+    ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5
+    ud2 = u[1, 0] + np.random.randn() * R_sim[1, 1] ** 0.5 + OFFSET_YAW_RATE_NOISE
     ud = np.array([ud1, ud2]).reshape(2, 1)
 
     xd = motion_model(xd, ud)
@@ -336,7 +323,6 @@ def observation(xTrue, xd, u, RFID):
 
 
 def motion_model(x, u):
-
     F = np.array([[1.0, 0, 0],
                   [0, 1.0, 0],
                   [0, 0, 1.0]])
@@ -383,7 +369,7 @@ def main():
     hxTrue = xTrue
     hxDR = xTrue
 
-    particles = [Particle(N_LM) for i in range(N_PARTICLE)]
+    particles = [Particle(N_LM) for _ in range(N_PARTICLE)]
 
     while SIM_TIME >= time:
         time += DT
@@ -409,7 +395,7 @@ def main():
             for iz in range(len(z[:, 0])):
                 lmid = int(z[2, iz])
                 plt.plot([xEst[0], RFID[lmid, 0]], [
-                         xEst[1], RFID[lmid, 1]], "-k")
+                    xEst[1], RFID[lmid, 1]], "-k")
 
             for i in range(N_PARTICLE):
                 plt.plot(particles[i].x, particles[i].y, ".r")

From 23c73567d6f20f539e860d03d0ce2e7bbe74cad5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?=
 <20567087+goktug97@users.noreply.github.com>
Date: Sat, 12 Oct 2019 15:08:20 +0300
Subject: [PATCH 0199/1080] Optimize the obstacle cost calculation.

The calculation was too slow to work with more obstacles.
The calculation function is vectorized to increase the speed.
---
 .../dynamic_window_approach.py                | 24 +++++++------------
 1 file changed, 8 insertions(+), 16 deletions(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index a7d74332a8..7b5eba1743 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -139,22 +139,14 @@ def calc_obstacle_cost(traj, ob, config):
         calc obstacle cost inf: collision
     """
 
-    skip_n = 2 # for speed up
-    minr = float("inf")
-
-    for ii in range(0, len(traj[:, 1]), skip_n):
-        for i in range(len(ob[:, 0])):
-            ox = ob[i, 0]
-            oy = ob[i, 1]
-            dx = traj[ii, 0] - ox
-            dy = traj[ii, 1] - oy
-
-            r = math.sqrt(dx**2 + dy**2)
-            if r <= config.robot_radius:
-                return float("Inf")  # collision
-
-            if minr >= r:
-                minr = r
+    ox = ob[:, 0]
+    oy = ob[:, 1]
+    dx = traj[:, 0] - ox[:, None]
+    dy = traj[:, 1] - oy[:, None]
+    r = np.sqrt(np.square(dx) + np.square(dy))
+    if (r <= config.robot_radius).any():
+        return float("Inf")
+    minr = np.min(r)
 
     return 1.0 / minr  # OK
 

From 87a5517c3a6f929c785c6b8a4c4926815e5d55ba Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 15 Oct 2019 21:56:17 +0900
Subject: [PATCH 0200/1080] code clean up for DWA sample.

---
 .../dynamic_window_approach.py                | 66 +++++++++----------
 1 file changed, 33 insertions(+), 33 deletions(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index f418372c46..fe906e0303 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -7,26 +7,26 @@
 """
 
 import math
-import numpy as np
-import matplotlib.pyplot as plt
 
+import matplotlib.pyplot as plt
+import numpy as np
 
 show_animation = True
 
 
-def dwa_control(x, u, config, goal, ob):
+def dwa_control(x, config, goal, ob):
     """
         Dynamic Window Approach control
     """
 
     dw = calc_dynamic_window(x, config)
 
-    u, traj = calc_final_input(x, u, dw, config, goal, ob)
+    u, trajectory = calc_final_input(x, dw, config, goal, ob)
 
-    return u, traj
+    return u, trajectory
 
 
-class Config():
+class Config:
     """
     simulation parameter class
     """
@@ -47,6 +47,7 @@ def __init__(self):
         self.obstacle_cost_gain = 1.0
         self.robot_radius = 1.0  # [m] for collision check
 
+
 def motion(x, u, dt):
     """
     motion model
@@ -99,27 +100,26 @@ def predict_trajectory(x_init, v, y, config):
     return traj
 
 
-def calc_final_input(x, u, dw, config, goal, ob):
+def calc_final_input(x, dw, config, goal, ob):
     """
-    calculation final input with dinamic window
+    calculation final input with dynamic window
     """
 
     x_init = x[:]
     min_cost = float("inf")
     best_u = [0.0, 0.0]
-    best_traj = np.array([x])
+    best_trajectory = np.array([x])
 
-    # evalucate all trajectory with sampled input in dynamic window
+    # evaluate all trajectory with sampled input in dynamic window
     for v in np.arange(dw[0], dw[1], config.v_reso):
         for y in np.arange(dw[2], dw[3], config.yawrate_reso):
 
-            traj = predict_trajectory(x_init, v, y, config)
+            trajectory = predict_trajectory(x_init, v, y, config)
 
             # calc cost
-            to_goal_cost = config.to_goal_cost_gain * calc_to_goal_cost(traj, goal, config)
-            speed_cost = config.speed_cost_gain * \
-                (config.max_speed - traj[-1, 3])
-            ob_cost = config.obstacle_cost_gain*calc_obstacle_cost(traj, ob, config)
+            to_goal_cost = config.to_goal_cost_gain * calc_to_goal_cost(trajectory, goal)
+            speed_cost = config.speed_cost_gain * (config.max_speed - trajectory[-1, 3])
+            ob_cost = config.obstacle_cost_gain * calc_obstacle_cost(trajectory, ob, config)
 
             final_cost = to_goal_cost + speed_cost + ob_cost
 
@@ -127,35 +127,35 @@ def calc_final_input(x, u, dw, config, goal, ob):
             if min_cost >= final_cost:
                 min_cost = final_cost
                 best_u = [v, y]
-                best_traj = traj
+                best_trajectory = trajectory
 
-    return best_u, best_traj
+    return best_u, best_trajectory
 
 
-def calc_obstacle_cost(traj, ob, config):
+def calc_obstacle_cost(trajectory, ob, config):
     """
         calc obstacle cost inf: collision
     """
     ox = ob[:, 0]
     oy = ob[:, 1]
-    dx = traj[:, 0] - ox[:, None]
-    dy = traj[:, 1] - oy[:, None]
+    dx = trajectory[:, 0] - ox[:, None]
+    dy = trajectory[:, 1] - oy[:, None]
     r = np.sqrt(np.square(dx) + np.square(dy))
     if (r <= config.robot_radius).any():
         return float("Inf")
-    minr = np.min(r)
-    return 1.0 / minr  # OK
+    min_r = np.min(r)
+    return 1.0 / min_r  # OK
 
 
-def calc_to_goal_cost(traj, goal, config):
+def calc_to_goal_cost(trajectory, goal):
     """
         calc to goal cost with angle difference
     """
 
-    dx = goal[0] - traj[-1, 0]
-    dy = goal[1] - traj[-1, 1]
+    dx = goal[0] - trajectory[-1, 0]
+    dy = goal[1] - trajectory[-1, 1]
     error_angle = math.atan2(dy, dx)
-    cost_angle = error_angle - traj[-1, 2]
+    cost_angle = error_angle - trajectory[-1, 2]
     cost = abs(math.atan2(math.sin(cost_angle), math.cos(cost_angle)))
 
     return cost
@@ -194,16 +194,16 @@ def main(gx=10.0, gy=10.0):
     # input [forward speed, yawrate]
     u = np.array([0.0, 0.0])
     config = Config()
-    traj = np.array(x)
+    trajectory = np.array(x)
 
     while True:
-        u, ptraj = dwa_control(x, u, config, goal, ob)
-        x = motion(x, u, config.dt) # simulate robot
-        traj = np.vstack((traj, x))  # store state history
+        u, predicted_trajectory = dwa_control(x, config, goal, ob)
+        x = motion(x, u, config.dt)  # simulate robot
+        trajectory = np.vstack((trajectory, x))  # store state history
 
         if show_animation:
             plt.cla()
-            plt.plot(ptraj[:, 0], ptraj[:, 1], "-g")
+            plt.plot(predicted_trajectory[:, 0], predicted_trajectory[:, 1], "-g")
             plt.plot(x[0], x[1], "xr")
             plt.plot(goal[0], goal[1], "xb")
             plt.plot(ob[:, 0], ob[:, 1], "ok")
@@ -213,14 +213,14 @@ def main(gx=10.0, gy=10.0):
             plt.pause(0.0001)
 
         # check reaching goal
-        dist_to_goal = math.sqrt((x[0] - goal[0])**2 + (x[1] - goal[1])**2)  
+        dist_to_goal = math.sqrt((x[0] - goal[0]) ** 2 + (x[1] - goal[1]) ** 2)
         if dist_to_goal <= config.robot_radius:
             print("Goal!!")
             break
 
     print("Done")
     if show_animation:
-        plt.plot(traj[:, 0], traj[:, 1], "-r")
+        plt.plot(trajectory[:, 0], trajectory[:, 1], "-r")
         plt.pause(0.0001)
 
     plt.show()

From 4cedb7aad4d2b4f6431536a8c749f1980cc8f84e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?=
 <20567087+goktug97@users.noreply.github.com>
Date: Tue, 15 Oct 2019 21:55:56 +0300
Subject: [PATCH 0201/1080] Add rectangle robot type and collision check

Add collision check for rectangle robots
Add new function to draw robot depending on the type
---
 .../dynamic_window_approach.py                | 56 ++++++++++++++++++-
 1 file changed, 53 insertions(+), 3 deletions(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index 7b0882ab0c..1fad0e91cf 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -45,8 +45,16 @@ def __init__(self):
         self.to_goal_cost_gain = 0.15
         self.speed_cost_gain = 1.0
         self.obstacle_cost_gain = 1.0
+        self.robot_type = 'circle' # circle or rectangle
+
+        # if robot_type == circle
+        # Also used to check if goal is reached in both types
         self.robot_radius = 1.0  # [m] for collision check
 
+        # if robot_type == rectangle
+        self.robot_width = 0.5 # [m] for collision check
+        self.robot_length = 1.2 # [m] for collision check
+
 
 def motion(x, u, dt):
     """
@@ -141,8 +149,25 @@ def calc_obstacle_cost(trajectory, ob, config):
     dx = trajectory[:, 0] - ox[:, None]
     dy = trajectory[:, 1] - oy[:, None]
     r = np.sqrt(np.square(dx) + np.square(dy))
-    if (r <= config.robot_radius).any():
-        return float("Inf")
+
+    if config.robot_type == 'rectangle':
+        yaw = trajectory[:, 2]
+        rot = np.array([[np.cos(yaw), -np.sin(yaw)], [np.sin(yaw), np.cos(yaw)]])
+        local_ob = ob[:, None] - trajectory[:, 0:2]
+        local_ob = local_ob.reshape(-1, local_ob.shape[-1])
+        local_ob = np.array([local_ob @ -x for x in rot.T])
+        local_ob = local_ob.reshape(-1, local_ob.shape[-1])
+        upper_check = local_ob[:, 0] <= config.robot_length/2
+        right_check = local_ob[:, 1] <= config.robot_width/2
+        bottom_check = local_ob[:, 0] >= -config.robot_length/2
+        left_check = local_ob[:, 1] >= -config.robot_width/2
+        if (np.logical_and(np.logical_and(upper_check, right_check),
+                           np.logical_and(bottom_check, left_check))).any():
+            return float("Inf")
+    elif config.robot_type == 'circle':
+        if (r <= config.robot_radius).any():
+            return float("Inf")
+
     min_r = np.min(r)
     return 1.0 / min_r  # OK
 
@@ -166,7 +191,30 @@ def plot_arrow(x, y, yaw, length=0.5, width=0.1):  # pragma: no cover
     plt.plot(x, y)
 
 
-def main(gx=10.0, gy=10.0):
+def plot_robot(x, y, yaw, config):  # pragma: no cover
+    if config.robot_type == 'rectangle':
+        outline = np.array([[-config.robot_length/2, config.robot_length/2,
+                             (config.robot_length/2), -config.robot_length/2,
+                             -config.robot_length/2],
+                            [config.robot_width / 2, config.robot_width / 2,
+                             - config.robot_width / 2, -config.robot_width / 2,
+                             config.robot_width / 2]])
+        Rot1 = np.array([[math.cos(yaw), math.sin(yaw)],
+                         [-math.sin(yaw), math.cos(yaw)]])
+        outline = (outline.T.dot(Rot1)).T
+        outline[0, :] += x
+        outline[1, :] += y
+        plt.plot(np.array(outline[0, :]).flatten(),
+                 np.array(outline[1, :]).flatten(), "-k")
+    elif config.robot_type == 'circle':
+        circle = plt.Circle((x, y), config.robot_radius, color="b")
+        plt.gcf().gca().add_artist(circle)
+        out_x, out_y = (np.array([x, y]) +
+                        np.array([np.cos(yaw), np.sin(yaw)]) * config.robot_radius)
+        plt.plot([x, out_x], [y, out_y], "-k")
+
+
+def main(gx=10.0, gy=10.0, robot_type='circle'):
     print(__file__ + " start!!")
     # initial state [x(m), y(m), yaw(rad), v(m/s), omega(rad/s)]
     x = np.array([0.0, 0.0, math.pi / 8.0, 0.0, 0.0])
@@ -193,6 +241,7 @@ def main(gx=10.0, gy=10.0):
     # input [forward speed, yawrate]
     u = np.array([0.0, 0.0])
     config = Config()
+    config.robot_type = robot_type
     trajectory = np.array(x)
 
     while True:
@@ -206,6 +255,7 @@ def main(gx=10.0, gy=10.0):
             plt.plot(x[0], x[1], "xr")
             plt.plot(goal[0], goal[1], "xb")
             plt.plot(ob[:, 0], ob[:, 1], "ok")
+            plot_robot(x[0], x[1], x[2], config)
             plot_arrow(x[0], x[1], x[2])
             plt.axis("equal")
             plt.grid(True)

From d98604158c56f67d47ad2f9481e6498ee469b415 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?=
 <20567087+goktug97@users.noreply.github.com>
Date: Tue, 15 Oct 2019 22:03:28 +0300
Subject: [PATCH 0202/1080] add test for the rectangle robot type

---
 tests/test_dynamic_window_approach.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/tests/test_dynamic_window_approach.py b/tests/test_dynamic_window_approach.py
index ab2e0a27ac..de371f4a2b 100644
--- a/tests/test_dynamic_window_approach.py
+++ b/tests/test_dynamic_window_approach.py
@@ -17,7 +17,12 @@ def test1(self):
         m.show_animation = False
         m.main(gx=1.0, gy=1.0)
 
+    def test2(self):
+        m.show_animation = False
+        m.main(gx=1.0, gy=1.0, robot_type='rectangle')
+
 
 if __name__ == '__main__':  # pragma: no cover
     test = Test()
     test.test1()
+    test.test2()

From bcdf088684b1b47ef9867c39263f624f677fe869 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?=
 <20567087+goktug97@users.noreply.github.com>
Date: Wed, 16 Oct 2019 19:19:11 +0300
Subject: [PATCH 0203/1080] Change robot_type comparison to enum class named
 RobotType

- Instead of using string based comparison for robot_type, use Enum class.
- Append authors
---
 .../dynamic_window_approach.py                | 22 +++++++++++--------
 1 file changed, 13 insertions(+), 9 deletions(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index 1fad0e91cf..0313b0f1ef 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -2,10 +2,11 @@
 
 Mobile robot motion planning sample with Dynamic Window Approach
 
-author: Atsushi Sakai (@Atsushi_twi)
+author: Atsushi Sakai (@Atsushi_twi), Goktug Karakasli
 
 """
 
+from enum import Enum
 import math
 
 import matplotlib.pyplot as plt
@@ -25,6 +26,9 @@ def dwa_control(x, config, goal, ob):
 
     return u, trajectory
 
+class RobotType(Enum):
+    circle = 0
+    rectangle = 1
 
 class Config:
     """
@@ -45,13 +49,13 @@ def __init__(self):
         self.to_goal_cost_gain = 0.15
         self.speed_cost_gain = 1.0
         self.obstacle_cost_gain = 1.0
-        self.robot_type = 'circle' # circle or rectangle
+        self.robot_type = RobotType.circle
 
-        # if robot_type == circle
+        # if robot_type == RobotType.circle
         # Also used to check if goal is reached in both types
         self.robot_radius = 1.0  # [m] for collision check
 
-        # if robot_type == rectangle
+        # if robot_type == RobotType.rectangle
         self.robot_width = 0.5 # [m] for collision check
         self.robot_length = 1.2 # [m] for collision check
 
@@ -150,7 +154,7 @@ def calc_obstacle_cost(trajectory, ob, config):
     dy = trajectory[:, 1] - oy[:, None]
     r = np.sqrt(np.square(dx) + np.square(dy))
 
-    if config.robot_type == 'rectangle':
+    if config.robot_type == RobotType.rectangle:
         yaw = trajectory[:, 2]
         rot = np.array([[np.cos(yaw), -np.sin(yaw)], [np.sin(yaw), np.cos(yaw)]])
         local_ob = ob[:, None] - trajectory[:, 0:2]
@@ -164,7 +168,7 @@ def calc_obstacle_cost(trajectory, ob, config):
         if (np.logical_and(np.logical_and(upper_check, right_check),
                            np.logical_and(bottom_check, left_check))).any():
             return float("Inf")
-    elif config.robot_type == 'circle':
+    elif config.robot_type == RobotType.circle:
         if (r <= config.robot_radius).any():
             return float("Inf")
 
@@ -192,7 +196,7 @@ def plot_arrow(x, y, yaw, length=0.5, width=0.1):  # pragma: no cover
 
 
 def plot_robot(x, y, yaw, config):  # pragma: no cover
-    if config.robot_type == 'rectangle':
+    if config.robot_type == RobotType.rectangle:
         outline = np.array([[-config.robot_length/2, config.robot_length/2,
                              (config.robot_length/2), -config.robot_length/2,
                              -config.robot_length/2],
@@ -206,7 +210,7 @@ def plot_robot(x, y, yaw, config):  # pragma: no cover
         outline[1, :] += y
         plt.plot(np.array(outline[0, :]).flatten(),
                  np.array(outline[1, :]).flatten(), "-k")
-    elif config.robot_type == 'circle':
+    elif config.robot_type == RobotType.circle:
         circle = plt.Circle((x, y), config.robot_radius, color="b")
         plt.gcf().gca().add_artist(circle)
         out_x, out_y = (np.array([x, y]) +
@@ -214,7 +218,7 @@ def plot_robot(x, y, yaw, config):  # pragma: no cover
         plt.plot([x, out_x], [y, out_y], "-k")
 
 
-def main(gx=10.0, gy=10.0, robot_type='circle'):
+def main(gx=10.0, gy=10.0, robot_type=RobotType.circle):
     print(__file__ + " start!!")
     # initial state [x(m), y(m), yaw(rad), v(m/s), omega(rad/s)]
     x = np.array([0.0, 0.0, math.pi / 8.0, 0.0, 0.0])

From 457b4086ef4e900f53a104a6baf36246ef57889e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?=
 <20567087+goktug97@users.noreply.github.com>
Date: Wed, 16 Oct 2019 19:24:37 +0300
Subject: [PATCH 0204/1080] Change string to RobotType enum

---
 tests/test_dynamic_window_approach.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_dynamic_window_approach.py b/tests/test_dynamic_window_approach.py
index de371f4a2b..890f000475 100644
--- a/tests/test_dynamic_window_approach.py
+++ b/tests/test_dynamic_window_approach.py
@@ -19,7 +19,7 @@ def test1(self):
 
     def test2(self):
         m.show_animation = False
-        m.main(gx=1.0, gy=1.0, robot_type='rectangle')
+        m.main(gx=1.0, gy=1.0, robot_type=m.RobotType.rectangle)
 
 
 if __name__ == '__main__':  # pragma: no cover

From 0ec56812be8e6811cc0a3b2486528b1fb2c64685 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?=
 <20567087+goktug97@users.noreply.github.com>
Date: Wed, 16 Oct 2019 20:17:46 +0300
Subject: [PATCH 0205/1080] Add type checking for robot_type

---
 .../DynamicWindowApproach/dynamic_window_approach.py     | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index 0313b0f1ef..267ca1676d 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -59,6 +59,15 @@ def __init__(self):
         self.robot_width = 0.5 # [m] for collision check
         self.robot_length = 1.2 # [m] for collision check
 
+    @property
+    def robot_type(self):
+        return self._robot_type
+
+    @robot_type.setter
+    def robot_type(self, value):
+        if not isinstance(value, RobotType):
+            raise TypeError("robot_type must be an instance of RobotType")
+        self._robot_type = value
 
 def motion(x, u, dt):
     """

From adb4e99ceb5628f103c486b9eb84ef35d1c4b3d0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?=
 <20567087+goktug97@users.noreply.github.com>
Date: Wed, 16 Oct 2019 21:03:18 +0300
Subject: [PATCH 0206/1080] Fix transpose error of the rotation matrix

---
 PathPlanning/DynamicWindowApproach/dynamic_window_approach.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index 267ca1676d..9216892aee 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -166,9 +166,10 @@ def calc_obstacle_cost(trajectory, ob, config):
     if config.robot_type == RobotType.rectangle:
         yaw = trajectory[:, 2]
         rot = np.array([[np.cos(yaw), -np.sin(yaw)], [np.sin(yaw), np.cos(yaw)]])
+        rot = np.transpose(rot, [2, 0, 1])
         local_ob = ob[:, None] - trajectory[:, 0:2]
         local_ob = local_ob.reshape(-1, local_ob.shape[-1])
-        local_ob = np.array([local_ob @ -x for x in rot.T])
+        local_ob = np.array([local_ob @ -x for x in rot])
         local_ob = local_ob.reshape(-1, local_ob.shape[-1])
         upper_check = local_ob[:, 0] <= config.robot_length/2
         right_check = local_ob[:, 1] <= config.robot_width/2

From c1ba9bab0a48100aa3f81286a2015c5112bb1f9f Mon Sep 17 00:00:00 2001
From: Francisco Moretti <franciscoemoretti@gmail.com>
Date: Thu, 17 Oct 2019 09:09:54 -0300
Subject: [PATCH 0207/1080] fix check segment intersection with obstacle

The previous method only worked because the step that checked collision
from a point of the line to obstacles was the same as the minimum
obstacle radius. If the obstacle radius is very small a great amount of
steps would be required.
Thus It's better to check the distance from the segment to the obstacles
directly and compare with obstacle radius
Now there is no need to have two check functions.
---
 .../InformedRRTStar/informed_rrt_star.py      | 54 ++++++++++---------
 1 file changed, 29 insertions(+), 25 deletions(-)

diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index 4a32801da5..b9769bea53 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -74,10 +74,9 @@ def informed_rrt_star_search(self, animation=True):
             newNode = self.get_new_node(theta, nind, nearestNode)
             d = self.line_cost(nearestNode, newNode)
 
-            isCollision = self.collision_check(newNode, self.obstacle_list)
-            isCollisionEx = self.check_collision_extend(nearestNode, theta, d)
+            isCollision = self.check_collision(nearestNode, theta, d)
 
-            if isCollision and isCollisionEx:
+            if isCollision:
                 nearInds = self.find_near_nodes(newNode)
                 newNode = self.choose_parent(newNode, nearInds)
 
@@ -110,7 +109,7 @@ def choose_parent(self, newNode, nearInds):
             dy = newNode.y - self.node_list[i].y
             d = math.sqrt(dx ** 2 + dy ** 2)
             theta = math.atan2(dy, dx)
-            if self.check_collision_extend(self.node_list[i], theta, d):
+            if self.check_collision(self.node_list[i], theta, d):
                 dList.append(self.node_list[i].cost + d)
             else:
                 dList.append(float('inf'))
@@ -194,17 +193,6 @@ def get_nearest_list_index(nodes, rnd):
         minIndex = dList.index(min(dList))
         return minIndex
 
-    @staticmethod
-    def collision_check(newNode, obstacleList):
-        for (ox, oy, size) in obstacleList:
-            dx = ox - newNode.x
-            dy = oy - newNode.y
-            d = dx * dx + dy * dy
-            if d <= 1.1 * size ** 2:
-                return False  # collision
-
-        return True  # safe
-
     def get_new_node(self, theta, nind, nearestNode):
         newNode = copy.deepcopy(nearestNode)
 
@@ -234,19 +222,35 @@ def rewire(self, newNode, nearInds):
             if nearNode.cost > scost:
                 theta = math.atan2(newNode.y - nearNode.y,
                                    newNode.x - nearNode.x)
-                if self.check_collision_extend(nearNode, theta, d):
+                if self.check_collision(nearNode, theta, d):
                     nearNode.parent = n_node - 1
                     nearNode.cost = scost
-
-    def check_collision_extend(self, nearNode, theta, d):
+                    
+    @staticmethod
+    def distance_squared_point_to_segment(v, w, p):
+        # Return minimum distance between line segment vw and point p
+        if (np.array_equal(v, w)):
+            return (p-v).dot(p-v) # v == w case
+        l2 = (w-v).dot(w-v) # i.e. |w-v|^2 -  avoid a sqrt
+        # Consider the line extending the segment, parameterized as v + t (w - v).
+        # We find projection of point p onto the line. 
+        # It falls where t = [(p-v) . (w-v)] / |w-v|^2
+        # We clamp t from [0,1] to handle points outside the segment vw.
+        t = max(0, min(1, (p - v).dot(w - v) / l2))
+        projection = v + t * (w - v) # Projection falls on the segment
+        return (p-projection).dot(p-projection)
+
+    def check_collision(self, nearNode, theta, d):
         tmpNode = copy.deepcopy(nearNode)
-
-        for i in range(int(d / self.expand_dis)):
-            tmpNode.x += self.expand_dis * math.cos(theta)
-            tmpNode.y += self.expand_dis * math.sin(theta)
-            if not self.collision_check(tmpNode, self.obstacle_list):
-                return False
-
+        endx = tmpNode.x + math.cos(theta)*d
+        endy = tmpNode.y + math.sin(theta)*d
+        for (ox, oy, size) in self.obstacle_list:
+            dd = self.distance_squared_point_to_segment(
+                np.array([tmpNode.x, tmpNode.y]),
+                np.array([endx, endy]),
+                np.array([ox, oy]))
+            if dd <= 1.1 * size**2:
+                return False  # collision
         return True
 
     def get_final_course(self, lastIndex):

From 35ea4b955cb776718e89440becb5aac8443e962d Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 17 Oct 2019 21:53:39 +0900
Subject: [PATCH 0208/1080] code clean up for DWA

---
 .../dynamic_window_approach.py                | 33 ++++++++++---------
 1 file changed, 18 insertions(+), 15 deletions(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index 9216892aee..c2a8674ae3 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -6,8 +6,8 @@
 
 """
 
-from enum import Enum
 import math
+from enum import Enum
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -17,7 +17,7 @@
 
 def dwa_control(x, config, goal, ob):
     """
-        Dynamic Window Approach control
+    Dynamic Window Approach control
     """
 
     dw = calc_dynamic_window(x, config)
@@ -26,10 +26,12 @@ def dwa_control(x, config, goal, ob):
 
     return u, trajectory
 
+
 class RobotType(Enum):
     circle = 0
     rectangle = 1
 
+
 class Config:
     """
     simulation parameter class
@@ -56,8 +58,8 @@ def __init__(self):
         self.robot_radius = 1.0  # [m] for collision check
 
         # if robot_type == RobotType.rectangle
-        self.robot_width = 0.5 # [m] for collision check
-        self.robot_length = 1.2 # [m] for collision check
+        self.robot_width = 0.5  # [m] for collision check
+        self.robot_length = 1.2  # [m] for collision check
 
     @property
     def robot_type(self):
@@ -69,6 +71,7 @@ def robot_type(self, value):
             raise TypeError("robot_type must be an instance of RobotType")
         self._robot_type = value
 
+
 def motion(x, u, dt):
     """
     motion model
@@ -98,7 +101,7 @@ def calc_dynamic_window(x, config):
           x[4] - config.max_dyawrate * config.dt,
           x[4] + config.max_dyawrate * config.dt]
 
-    #  [vmin,vmax, yawrate min, yawrate max]
+    #  [vmin,vmax, yaw_rate min, yaw_rate max]
     dw = [max(Vs[0], Vd[0]), min(Vs[1], Vd[1]),
           max(Vs[2], Vd[2]), min(Vs[3], Vd[3])]
 
@@ -171,10 +174,10 @@ def calc_obstacle_cost(trajectory, ob, config):
         local_ob = local_ob.reshape(-1, local_ob.shape[-1])
         local_ob = np.array([local_ob @ -x for x in rot])
         local_ob = local_ob.reshape(-1, local_ob.shape[-1])
-        upper_check = local_ob[:, 0] <= config.robot_length/2
-        right_check = local_ob[:, 1] <= config.robot_width/2
-        bottom_check = local_ob[:, 0] >= -config.robot_length/2
-        left_check = local_ob[:, 1] >= -config.robot_width/2
+        upper_check = local_ob[:, 0] <= config.robot_length / 2
+        right_check = local_ob[:, 1] <= config.robot_width / 2
+        bottom_check = local_ob[:, 0] >= -config.robot_length / 2
+        left_check = local_ob[:, 1] >= -config.robot_width / 2
         if (np.logical_and(np.logical_and(upper_check, right_check),
                            np.logical_and(bottom_check, left_check))).any():
             return float("Inf")
@@ -185,6 +188,7 @@ def calc_obstacle_cost(trajectory, ob, config):
     min_r = np.min(r)
     return 1.0 / min_r  # OK
 
+
 def calc_to_goal_cost(trajectory, goal):
     """
         calc to goal cost with angle difference
@@ -207,9 +211,9 @@ def plot_arrow(x, y, yaw, length=0.5, width=0.1):  # pragma: no cover
 
 def plot_robot(x, y, yaw, config):  # pragma: no cover
     if config.robot_type == RobotType.rectangle:
-        outline = np.array([[-config.robot_length/2, config.robot_length/2,
-                             (config.robot_length/2), -config.robot_length/2,
-                             -config.robot_length/2],
+        outline = np.array([[-config.robot_length / 2, config.robot_length / 2,
+                             (config.robot_length / 2), -config.robot_length / 2,
+                             -config.robot_length / 2],
                             [config.robot_width / 2, config.robot_width / 2,
                              - config.robot_width / 2, -config.robot_width / 2,
                              config.robot_width / 2]])
@@ -252,8 +256,7 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle):
                    [13.0, 13.0]
                    ])
 
-    # input [forward speed, yawrate]
-    u = np.array([0.0, 0.0])
+    # input [forward speed, yaw_rate]
     config = Config()
     config.robot_type = robot_type
     trajectory = np.array(x)
@@ -290,4 +293,4 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle):
 
 
 if __name__ == '__main__':
-    main()
+    main(robot_type=RobotType.rectangle)

From 71c124bdd3e15118004c06ed06f62cd7341c963c Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 17 Oct 2019 22:00:10 +0900
Subject: [PATCH 0209/1080] Update issue templates

---
 .github/ISSUE_TEMPLATE/bug_report.md | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)
 create mode 100644 .github/ISSUE_TEMPLATE/bug_report.md

diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 0000000000..a643b49571
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,21 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Desktop (please complete the following information):**
+ - Python version (This repo only supports Python 3.7.x or higher).
+ - Each library version

From afbcdfc0bdcd8a15b6b0503ae9027eb153d02afc Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 20 Oct 2019 11:27:37 +0900
Subject: [PATCH 0210/1080] update tests

---
 tests/test_dynamic_window_approach.py | 19 +++++++++----------
 1 file changed, 9 insertions(+), 10 deletions(-)

diff --git a/tests/test_dynamic_window_approach.py b/tests/test_dynamic_window_approach.py
index 890f000475..197dda2158 100644
--- a/tests/test_dynamic_window_approach.py
+++ b/tests/test_dynamic_window_approach.py
@@ -1,28 +1,27 @@
+import os
+import sys
 from unittest import TestCase
 
-import sys
-import os
 sys.path.append(os.path.dirname(__file__) + "/../")
 try:
     from PathPlanning.DynamicWindowApproach import dynamic_window_approach as m
-except:
+except ImportError:
     raise
 
 print(__file__)
 
 
-class Test(TestCase):
-
-    def test1(self):
+class TestDynamicWindowApproach(TestCase):
+    def test_main1(self):
         m.show_animation = False
         m.main(gx=1.0, gy=1.0)
 
-    def test2(self):
+    def test_main2(self):
         m.show_animation = False
         m.main(gx=1.0, gy=1.0, robot_type=m.RobotType.rectangle)
 
 
 if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
-    test.test2()
+    test = TestDynamicWindowApproach()
+    test.test_main1()
+    test.test_main2()

From 245838250bc1c2e23ee826026157aeb70d3116a5 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 20 Oct 2019 15:00:57 +0900
Subject: [PATCH 0211/1080] fix unittests.

---
 PathPlanning/LQRPlanner/LQRplanner.py                | 4 ++--
 tests/test_drone_3d_trajectory_following.py          | 8 +++++---
 tests/test_grid_based_sweep_coverage_path_planner.py | 3 +++
 tests/test_n_joint_arm_to_point_control.py           | 8 +++++---
 4 files changed, 15 insertions(+), 8 deletions(-)

diff --git a/PathPlanning/LQRPlanner/LQRplanner.py b/PathPlanning/LQRPlanner/LQRplanner.py
index e2000a957c..5a0b453f78 100644
--- a/PathPlanning/LQRPlanner/LQRplanner.py
+++ b/PathPlanning/LQRPlanner/LQRplanner.py
@@ -25,7 +25,7 @@ def __init__(self):
         self.MAX_ITER = 150
         self.EPS = 0.01
 
-    def lqr_planning(self, sx, sy, gx, gy, show_animation=SHOW_ANIMATION):
+    def lqr_planning(self, sx, sy, gx, gy, show_animation=True):
 
         rx, ry = [sx], [sy]
 
@@ -129,7 +129,7 @@ def main():
         gx = random.uniform(-area, area)
         gy = random.uniform(-area, area)
 
-        rx, ry = lqr_planner.lqr_planning(sx, sy, gx, gy)
+        rx, ry = lqr_planner.lqr_planning(sx, sy, gx, gy, show_animation=SHOW_ANIMATION)
 
         if SHOW_ANIMATION:  # pragma: no cover
             plt.plot(sx, sy, "or")
diff --git a/tests/test_drone_3d_trajectory_following.py b/tests/test_drone_3d_trajectory_following.py
index 194a329946..dfadcdaee4 100644
--- a/tests/test_drone_3d_trajectory_following.py
+++ b/tests/test_drone_3d_trajectory_following.py
@@ -1,9 +1,11 @@
+import os
+import sys
 from unittest import TestCase
 
-import sys
-sys.path.append("./AerialNavigation/drone_3d_trajectory_following/")
+sys.path.append(os.path.dirname(__file__) + "/../AerialNavigation/drone_3d_trajectory_following/")
+
+import drone_3d_trajectory_following as m
 
-from AerialNavigation.drone_3d_trajectory_following import drone_3d_trajectory_following as m
 print(__file__)
 
 
diff --git a/tests/test_grid_based_sweep_coverage_path_planner.py b/tests/test_grid_based_sweep_coverage_path_planner.py
index 3933356726..fa2d0a7a64 100644
--- a/tests/test_grid_based_sweep_coverage_path_planner.py
+++ b/tests/test_grid_based_sweep_coverage_path_planner.py
@@ -9,6 +9,9 @@
 except ImportError:
     raise
 
+grid_based_sweep_coverage_path_planner.do_animation = False
+
+
 class TestPlanning(TestCase):
 
     def test_planning1(self):
diff --git a/tests/test_n_joint_arm_to_point_control.py b/tests/test_n_joint_arm_to_point_control.py
index e39883559a..a935216475 100644
--- a/tests/test_n_joint_arm_to_point_control.py
+++ b/tests/test_n_joint_arm_to_point_control.py
@@ -1,9 +1,11 @@
+import os
+import sys
 from unittest import TestCase
 
-import sys
-sys.path.append("./ArmNavigation/n_joint_arm_to_point_control/")
+sys.path.append(os.path.dirname(__file__) + "/../ArmNavigation/n_joint_arm_to_point_control/")
+
+import n_joint_arm_to_point_control as m
 
-from ArmNavigation.n_joint_arm_to_point_control import n_joint_arm_to_point_control as m
 print(__file__)
 
 

From 692b7bd68411117204636f3534b4ea1eb661d872 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?=
 <20567087+goktug97@users.noreply.github.com>
Date: Wed, 23 Oct 2019 10:14:14 +0300
Subject: [PATCH 0212/1080] Fix wrong rotation direction

- The obstacle rotation for the rectangle obstacle calculation
is wrong. It should be positive instead of negative.
---
 PathPlanning/DynamicWindowApproach/dynamic_window_approach.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index c2a8674ae3..ebf254e110 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -172,7 +172,7 @@ def calc_obstacle_cost(trajectory, ob, config):
         rot = np.transpose(rot, [2, 0, 1])
         local_ob = ob[:, None] - trajectory[:, 0:2]
         local_ob = local_ob.reshape(-1, local_ob.shape[-1])
-        local_ob = np.array([local_ob @ -x for x in rot])
+        local_ob = np.array([local_ob @ x for x in rot])
         local_ob = local_ob.reshape(-1, local_ob.shape[-1])
         upper_check = local_ob[:, 0] <= config.robot_length / 2
         right_check = local_ob[:, 1] <= config.robot_width / 2

From 9f4102378a75b99ec3e8c2f9c465015a5ed2d093 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?=
 <20567087+goktug97@users.noreply.github.com>
Date: Wed, 23 Oct 2019 21:46:33 +0300
Subject: [PATCH 0213/1080] change resampling calculation to match with the
 book

---
 Localization/particle_filter/particle_filter.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py
index 6643116f5a..8859b51428 100644
--- a/Localization/particle_filter/particle_filter.py
+++ b/Localization/particle_filter/particle_filter.py
@@ -141,9 +141,8 @@ def re_sampling(px, pw):
     N_eff = 1.0 / (pw.dot(pw.T))[0, 0]  # Effective particle number
     if N_eff < NTh:
         w_cum = np.cumsum(pw)
-        base = np.cumsum(pw * 0.0 + 1 / NP) - 1 / NP
-        re_sample_id = base + np.random.rand(base.shape[0]) / NP
-
+        base = np.arange(0.0, 1.0, 1/NP)
+        re_sample_id = base + np.random.uniform(0, 1/NP)
         indexes = []
         ind = 0
         for ip in range(NP):

From 7a1784ad4798b4febd82a9a276fdae79a116a57b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?=
 <20567087+goktug97@users.noreply.github.com>
Date: Thu, 24 Oct 2019 19:43:22 +0300
Subject: [PATCH 0214/1080] condition check is moved

condition check is moved from the inside of the resampling function
to the outside of the function, this way the purpose of the
function is much clear. Instead "resampling" every iteration,
resample if the condition holds.
---
 .../particle_filter/particle_filter.py        | 31 +++++++++----------
 1 file changed, 15 insertions(+), 16 deletions(-)

diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py
index 8859b51428..0ebb5fc3d1 100644
--- a/Localization/particle_filter/particle_filter.py
+++ b/Localization/particle_filter/particle_filter.py
@@ -128,8 +128,9 @@ def pf_localization(px, pw, z, u):
     xEst = px.dot(pw.T)
     PEst = calc_covariance(xEst, px, pw)
 
-    px, pw = re_sampling(px, pw)
-
+    N_eff = 1.0 / (pw.dot(pw.T))[0, 0]  # Effective particle number
+    if N_eff < NTh:
+        px, pw = re_sampling(px, pw)
     return xEst, PEst, px, pw
 
 
@@ -138,20 +139,18 @@ def re_sampling(px, pw):
     low variance re-sampling
     """
 
-    N_eff = 1.0 / (pw.dot(pw.T))[0, 0]  # Effective particle number
-    if N_eff < NTh:
-        w_cum = np.cumsum(pw)
-        base = np.arange(0.0, 1.0, 1/NP)
-        re_sample_id = base + np.random.uniform(0, 1/NP)
-        indexes = []
-        ind = 0
-        for ip in range(NP):
-            while re_sample_id[ip] > w_cum[ind]:
-                ind += 1
-            indexes.append(ind)
-
-        px = px[:, indexes]
-        pw = np.zeros((1, NP)) + 1.0 / NP  # init weight
+    w_cum = np.cumsum(pw)
+    base = np.arange(0.0, 1.0, 1/NP)
+    re_sample_id = base + np.random.uniform(0, 1/NP)
+    indexes = []
+    ind = 0
+    for ip in range(NP):
+        while re_sample_id[ip] > w_cum[ind]:
+            ind += 1
+        indexes.append(ind)
+
+    px = px[:, indexes]
+    pw = np.zeros((1, NP)) + 1.0 / NP  # init weight
 
     return px, pw
 

From bf0652970abe99f42e9e175c55da0d8e5e38f540 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?=
 <20567087+goktug97@users.noreply.github.com>
Date: Fri, 25 Oct 2019 15:30:16 +0300
Subject: [PATCH 0215/1080] optimize nearest_neighbor_assosiation

---
 .../iterative_closest_point.py                | 22 ++++++-------------
 1 file changed, 7 insertions(+), 15 deletions(-)

diff --git a/SLAM/iterative_closest_point/iterative_closest_point.py b/SLAM/iterative_closest_point/iterative_closest_point.py
index 73651d8ec5..924619fdf1 100644
--- a/SLAM/iterative_closest_point/iterative_closest_point.py
+++ b/SLAM/iterative_closest_point/iterative_closest_point.py
@@ -39,7 +39,7 @@ def ICP_matching(ppoints, cpoints):
             plt.plot(cpoints[0, :], cpoints[1, :], ".b")
             plt.plot(0.0, 0.0, "xr")
             plt.axis("equal")
-            plt.pause(1.0)
+            plt.pause(0.1)
 
         inds, error = nearest_neighbor_assosiation(ppoints, cpoints)
         Rt, Tt = SVD_motion_estimation(ppoints[:, inds], cpoints)
@@ -93,18 +93,10 @@ def nearest_neighbor_assosiation(ppoints, cpoints):
     error = sum(d)
 
     # calc index with nearest neighbor assosiation
-    inds = []
-    for i in range(cpoints.shape[1]):
-        minid = -1
-        mind = float("inf")
-        for ii in range(ppoints.shape[1]):
-            d = np.linalg.norm(ppoints[:, ii] - cpoints[:, i])
-
-            if mind >= d:
-                mind = d
-                minid = ii
-
-        inds.append(minid)
+    d = np.linalg.norm(
+            np.repeat(cpoints, ppoints.shape[1], axis=1) - np.tile(ppoints, (1,
+                cpoints.shape[1])), axis=0)
+    inds = np.argmin(d.reshape(cpoints.shape[1], ppoints.shape[1]), axis=1)
 
     return inds, error
 
@@ -130,7 +122,7 @@ def main():
     print(__file__ + " start!!")
 
     # simulation parameters
-    nPoint = 10
+    nPoint = 1000
     fieldLength = 50.0
     motion = [0.5, 2.0, np.deg2rad(-10.0)]  # movement [x[m],y[m],yaw[deg]]
 
@@ -156,4 +148,4 @@ def main():
 
 
 if __name__ == '__main__':
-    main()
\ No newline at end of file
+    main()

From b2d65f277ec9d7a122baaec7d2bf4a4b77c5d6a8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?=
 <20567087+goktug97@users.noreply.github.com>
Date: Sat, 26 Oct 2019 09:11:23 +0300
Subject: [PATCH 0216/1080] change resampling function to match with the book

- changed resampling function so that it chooses random number once for the starting point.
- condition check is moved from the inside of the resampling function
to the outside of the function, this way the purpose of the
function is much clear. Instead "resampling" every iteration,
resample if the condition holds.
---
 .../particle_filter/particle_filter.py        | 32 +++++++++----------
 1 file changed, 15 insertions(+), 17 deletions(-)

diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py
index 6643116f5a..0ebb5fc3d1 100644
--- a/Localization/particle_filter/particle_filter.py
+++ b/Localization/particle_filter/particle_filter.py
@@ -128,8 +128,9 @@ def pf_localization(px, pw, z, u):
     xEst = px.dot(pw.T)
     PEst = calc_covariance(xEst, px, pw)
 
-    px, pw = re_sampling(px, pw)
-
+    N_eff = 1.0 / (pw.dot(pw.T))[0, 0]  # Effective particle number
+    if N_eff < NTh:
+        px, pw = re_sampling(px, pw)
     return xEst, PEst, px, pw
 
 
@@ -138,21 +139,18 @@ def re_sampling(px, pw):
     low variance re-sampling
     """
 
-    N_eff = 1.0 / (pw.dot(pw.T))[0, 0]  # Effective particle number
-    if N_eff < NTh:
-        w_cum = np.cumsum(pw)
-        base = np.cumsum(pw * 0.0 + 1 / NP) - 1 / NP
-        re_sample_id = base + np.random.rand(base.shape[0]) / NP
-
-        indexes = []
-        ind = 0
-        for ip in range(NP):
-            while re_sample_id[ip] > w_cum[ind]:
-                ind += 1
-            indexes.append(ind)
-
-        px = px[:, indexes]
-        pw = np.zeros((1, NP)) + 1.0 / NP  # init weight
+    w_cum = np.cumsum(pw)
+    base = np.arange(0.0, 1.0, 1/NP)
+    re_sample_id = base + np.random.uniform(0, 1/NP)
+    indexes = []
+    ind = 0
+    for ip in range(NP):
+        while re_sample_id[ip] > w_cum[ind]:
+            ind += 1
+        indexes.append(ind)
+
+    px = px[:, indexes]
+    pw = np.zeros((1, NP)) + 1.0 / NP  # init weight
 
     return px, pw
 

From 69772b4ad66cc27f7195659fff14e6c8006203b4 Mon Sep 17 00:00:00 2001
From: Mengge Jin <CH.Papaw@gmail.com>
Date: Sun, 27 Oct 2019 16:08:28 +0800
Subject: [PATCH 0217/1080] Remove PathLen in informed RRT* and instead with
 cBest

---
 PathPlanning/InformedRRTStar/informed_rrt_star.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index 4a32801da5..6047660fb4 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -40,7 +40,6 @@ def informed_rrt_star_search(self, animation=True):
         self.node_list = [self.start]
         # max length we expect to find in our 'informed' sample space, starts as infinite
         cBest = float('inf')
-        pathLen = float('inf')
         solutionSet = set()
         path = None
 
@@ -89,7 +88,7 @@ def informed_rrt_star_search(self, animation=True):
                     lastIndex = len(self.node_list) - 1
                     tempPath = self.get_final_course(lastIndex)
                     tempPathLen = self.get_path_len(tempPath)
-                    if tempPathLen < pathLen:
+                    if tempPathLen < cBest:
                         path = tempPath
                         cBest = tempPathLen
 

From 342e671b34f7cced14388b3b33fc4c4dd7dd119f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?=
 <20567087+goktug97@users.noreply.github.com>
Date: Fri, 25 Oct 2019 15:30:16 +0300
Subject: [PATCH 0218/1080] optimize nearest_neighbor_assosiation

- vectorize distance calculation
- append authors
---
 .../iterative_closest_point.py                | 24 +++++++------------
 1 file changed, 8 insertions(+), 16 deletions(-)

diff --git a/SLAM/iterative_closest_point/iterative_closest_point.py b/SLAM/iterative_closest_point/iterative_closest_point.py
index 73651d8ec5..ec81bb1233 100644
--- a/SLAM/iterative_closest_point/iterative_closest_point.py
+++ b/SLAM/iterative_closest_point/iterative_closest_point.py
@@ -1,6 +1,6 @@
 """
 Iterative Closest Point (ICP) SLAM example
-author: Atsushi Sakai (@Atsushi_twi)
+author: Atsushi Sakai (@Atsushi_twi), Göktuğ Karakaşlı
 """
 
 import math
@@ -39,7 +39,7 @@ def ICP_matching(ppoints, cpoints):
             plt.plot(cpoints[0, :], cpoints[1, :], ".b")
             plt.plot(0.0, 0.0, "xr")
             plt.axis("equal")
-            plt.pause(1.0)
+            plt.pause(0.1)
 
         inds, error = nearest_neighbor_assosiation(ppoints, cpoints)
         Rt, Tt = SVD_motion_estimation(ppoints[:, inds], cpoints)
@@ -93,18 +93,10 @@ def nearest_neighbor_assosiation(ppoints, cpoints):
     error = sum(d)
 
     # calc index with nearest neighbor assosiation
-    inds = []
-    for i in range(cpoints.shape[1]):
-        minid = -1
-        mind = float("inf")
-        for ii in range(ppoints.shape[1]):
-            d = np.linalg.norm(ppoints[:, ii] - cpoints[:, i])
-
-            if mind >= d:
-                mind = d
-                minid = ii
-
-        inds.append(minid)
+    d = np.linalg.norm(
+            np.repeat(cpoints, ppoints.shape[1], axis=1) - np.tile(ppoints, (1,
+                cpoints.shape[1])), axis=0)
+    inds = np.argmin(d.reshape(cpoints.shape[1], ppoints.shape[1]), axis=1)
 
     return inds, error
 
@@ -130,7 +122,7 @@ def main():
     print(__file__ + " start!!")
 
     # simulation parameters
-    nPoint = 10
+    nPoint = 1000
     fieldLength = 50.0
     motion = [0.5, 2.0, np.deg2rad(-10.0)]  # movement [x[m],y[m],yaw[deg]]
 
@@ -156,4 +148,4 @@ def main():
 
 
 if __name__ == '__main__':
-    main()
\ No newline at end of file
+    main()

From 97570de50c222c3362dfda765fd951f37cfb900f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?=
 <20567087+goktug97@users.noreply.github.com>
Date: Wed, 23 Oct 2019 10:14:14 +0300
Subject: [PATCH 0219/1080] Fix wrong rotation direction

- The obstacle rotation for the rectangle obstacle calculation
is wrong. It should be positive instead of negative.
---
 PathPlanning/DynamicWindowApproach/dynamic_window_approach.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index c2a8674ae3..c8a4434958 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -2,7 +2,7 @@
 
 Mobile robot motion planning sample with Dynamic Window Approach
 
-author: Atsushi Sakai (@Atsushi_twi), Goktug Karakasli
+author: Atsushi Sakai (@Atsushi_twi), Göktuğ Karakaşlı
 
 """
 
@@ -172,7 +172,7 @@ def calc_obstacle_cost(trajectory, ob, config):
         rot = np.transpose(rot, [2, 0, 1])
         local_ob = ob[:, None] - trajectory[:, 0:2]
         local_ob = local_ob.reshape(-1, local_ob.shape[-1])
-        local_ob = np.array([local_ob @ -x for x in rot])
+        local_ob = np.array([local_ob @ x for x in rot])
         local_ob = local_ob.reshape(-1, local_ob.shape[-1])
         upper_check = local_ob[:, 0] <= config.robot_length / 2
         right_check = local_ob[:, 1] <= config.robot_width / 2

From 49ce57d6f8de1265eb1e578dd0f069c6b7a56ca6 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 27 Oct 2019 17:59:08 +0900
Subject: [PATCH 0220/1080] Code cleanup.

---
 .../iterative_closest_point.py                | 57 +++++++++----------
 tests/test_LQR_planner.py                     |  4 +-
 2 files changed, 30 insertions(+), 31 deletions(-)

diff --git a/SLAM/iterative_closest_point/iterative_closest_point.py b/SLAM/iterative_closest_point/iterative_closest_point.py
index ec81bb1233..3512ef976b 100644
--- a/SLAM/iterative_closest_point/iterative_closest_point.py
+++ b/SLAM/iterative_closest_point/iterative_closest_point.py
@@ -4,22 +4,23 @@
 """
 
 import math
-import numpy as np
+
 import matplotlib.pyplot as plt
+import numpy as np
 
 #  ICP parameters
 EPS = 0.0001
-MAXITER = 100
+MAX_ITER = 100
 
 show_animation = True
 
 
-def ICP_matching(ppoints, cpoints):
+def icp_matching(previous_points, current_points):
     """
     Iterative Closest Point matching
     - input
-    ppoints: 2D points in the previous frame
-    cpoints: 2D points in the current frame
+    previous_points: 2D points in the previous frame
+    current_points: 2D points in the current frame
     - output
     R: Rotation matrix
     T: Translation vector
@@ -35,17 +36,17 @@ def ICP_matching(ppoints, cpoints):
 
         if show_animation:  # pragma: no cover
             plt.cla()
-            plt.plot(ppoints[0, :], ppoints[1, :], ".r")
-            plt.plot(cpoints[0, :], cpoints[1, :], ".b")
+            plt.plot(previous_points[0, :], previous_points[1, :], ".r")
+            plt.plot(current_points[0, :], current_points[1, :], ".b")
             plt.plot(0.0, 0.0, "xr")
             plt.axis("equal")
             plt.pause(0.1)
 
-        inds, error = nearest_neighbor_assosiation(ppoints, cpoints)
-        Rt, Tt = SVD_motion_estimation(ppoints[:, inds], cpoints)
+        indexes, error = nearest_neighbor_association(previous_points, current_points)
+        Rt, Tt = svd_motion_estimation(previous_points[:, indexes], current_points)
 
         # update current points
-        cpoints = (Rt @ cpoints) + Tt[:, np.newaxis]
+        current_points = (Rt @ current_points) + Tt[:, np.newaxis]
 
         H = update_homogeneous_matrix(H, Rt, Tt)
 
@@ -56,7 +57,7 @@ def ICP_matching(ppoints, cpoints):
         if dError <= EPS:
             print("Converge", error, dError, count)
             break
-        elif MAXITER <= count:
+        elif MAX_ITER <= count:
             print("Not Converge...", error, dError, count)
             break
 
@@ -85,31 +86,29 @@ def update_homogeneous_matrix(Hin, R, T):
         return Hin @ H
 
 
-def nearest_neighbor_assosiation(ppoints, cpoints):
+def nearest_neighbor_association(previous_points, current_points):
 
     # calc the sum of residual errors
-    dcpoints = ppoints - cpoints
-    d = np.linalg.norm(dcpoints, axis=0)
+    delta_points = previous_points - current_points
+    d = np.linalg.norm(delta_points, axis=0)
     error = sum(d)
 
     # calc index with nearest neighbor assosiation
-    d = np.linalg.norm(
-            np.repeat(cpoints, ppoints.shape[1], axis=1) - np.tile(ppoints, (1,
-                cpoints.shape[1])), axis=0)
-    inds = np.argmin(d.reshape(cpoints.shape[1], ppoints.shape[1]), axis=1)
-
-    return inds, error
+    d = np.linalg.norm(np.repeat(current_points, previous_points.shape[1], axis=1)
+                       - np.tile(previous_points, (1, current_points.shape[1])), axis=0)
+    indexes = np.argmin(d.reshape(current_points.shape[1], previous_points.shape[1]), axis=1)
 
+    return indexes, error
 
-def SVD_motion_estimation(ppoints, cpoints):
 
-    pm = np.mean(ppoints, axis=1)
-    cm = np.mean(cpoints, axis=1)
+def svd_motion_estimation(previous_points, current_points):
+    pm = np.mean(previous_points, axis=1)
+    cm = np.mean(current_points, axis=1)
 
-    pshift = ppoints - pm[:, np.newaxis]
-    cshift = cpoints - cm[:, np.newaxis]
+    p_shift = previous_points - pm[:, np.newaxis]
+    c_shift = current_points - cm[:, np.newaxis]
 
-    W = cshift @ pshift.T
+    W = c_shift @ p_shift.T
     u, s, vh = np.linalg.svd(W)
 
     R = (u @ vh).T
@@ -133,16 +132,16 @@ def main():
         # previous points
         px = (np.random.rand(nPoint) - 0.5) * fieldLength
         py = (np.random.rand(nPoint) - 0.5) * fieldLength
-        ppoints = np.vstack((px, py))
+        previous_points = np.vstack((px, py))
 
         # current points
         cx = [math.cos(motion[2]) * x - math.sin(motion[2]) * y + motion[0]
               for (x, y) in zip(px, py)]
         cy = [math.sin(motion[2]) * x + math.cos(motion[2]) * y + motion[1]
               for (x, y) in zip(px, py)]
-        cpoints = np.vstack((cx, cy))
+        current_points = np.vstack((cx, cy))
 
-        R, T = ICP_matching(ppoints, cpoints)
+        R, T = icp_matching(previous_points, current_points)
         print("R:", R)
         print("T:", T)
 
diff --git a/tests/test_LQR_planner.py b/tests/test_LQR_planner.py
index 5ece27f80b..2bcf828c38 100644
--- a/tests/test_LQR_planner.py
+++ b/tests/test_LQR_planner.py
@@ -1,6 +1,6 @@
+import sys
 from unittest import TestCase
 
-import sys
 sys.path.append("./PathPlanning/LQRPlanner")
 
 from PathPlanning.LQRPlanner import LQRplanner as m
@@ -11,5 +11,5 @@
 class Test(TestCase):
 
     def test1(self):
-        m.show_animation = False
+        m.SHOW_ANIMATION = False
         m.main()

From 3f14311cc54cb071b530a03b7a0245a61987c300 Mon Sep 17 00:00:00 2001
From: Francisco Moretti <franciscoemoretti@gmail.com>
Date: Sun, 3 Nov 2019 12:07:56 -0300
Subject: [PATCH 0221/1080] correct variable name

---
 PathPlanning/InformedRRTStar/informed_rrt_star.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index b9769bea53..98a239ab6e 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -74,9 +74,9 @@ def informed_rrt_star_search(self, animation=True):
             newNode = self.get_new_node(theta, nind, nearestNode)
             d = self.line_cost(nearestNode, newNode)
 
-            isCollision = self.check_collision(nearestNode, theta, d)
+            noCollision = self.check_collision(nearestNode, theta, d)
 
-            if isCollision:
+            if noCollision:
                 nearInds = self.find_near_nodes(newNode)
                 newNode = self.choose_parent(newNode, nearInds)
 

From 178dca3e343749b9e3ec3d1fda7a89f464532cda Mon Sep 17 00:00:00 2001
From: Francisco Moretti <franciscoemoretti@gmail.com>
Date: Sun, 3 Nov 2019 12:12:53 -0300
Subject: [PATCH 0222/1080] add collision check to node near to goal

---
 .../InformedRRTStar/informed_rrt_star.py      | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index 98a239ab6e..a8352bc03f 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -84,6 +84,7 @@ def informed_rrt_star_search(self, animation=True):
                 self.rewire(newNode, nearInds)
 
                 if self.is_near_goal(newNode):
+                    if self.check_segment_collision(newNode.x, newNode.y, self.goal.x , self.goal.y):
                     solutionSet.add(newNode)
                     lastIndex = len(self.node_list) - 1
                     tempPath = self.get_final_course(lastIndex)
@@ -240,19 +241,23 @@ def distance_squared_point_to_segment(v, w, p):
         projection = v + t * (w - v) # Projection falls on the segment
         return (p-projection).dot(p-projection)
 
-    def check_collision(self, nearNode, theta, d):
-        tmpNode = copy.deepcopy(nearNode)
-        endx = tmpNode.x + math.cos(theta)*d
-        endy = tmpNode.y + math.sin(theta)*d
+    def check_segment_collision(self, x1, y1, x2, y2):
         for (ox, oy, size) in self.obstacle_list:
             dd = self.distance_squared_point_to_segment(
-                np.array([tmpNode.x, tmpNode.y]),
-                np.array([endx, endy]),
+                np.array([x1, y1]),
+                np.array([x2, y2]),
                 np.array([ox, oy]))
-            if dd <= 1.1 * size**2:
+            if dd <= size**2:
                 return False  # collision
         return True
 
+
+    def check_collision(self, nearNode, theta, d):
+        tmpNode = copy.deepcopy(nearNode)
+        endx = tmpNode.x + math.cos(theta)*d
+        endy = tmpNode.y + math.sin(theta)*d
+        return self.check_segment_collision(tmpNode.x, tmpNode.y, endx, endy)
+
     def get_final_course(self, lastIndex):
         path = [[self.goal.x, self.goal.y]]
         while self.node_list[lastIndex].parent is not None:

From 3e5cad819247edd892ae7f8f0192170e8a4c31bd Mon Sep 17 00:00:00 2001
From: Francisco Moretti <franciscoemoretti@gmail.com>
Date: Sun, 3 Nov 2019 12:23:48 -0300
Subject: [PATCH 0223/1080] fix indentation

---
 PathPlanning/InformedRRTStar/informed_rrt_star.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index a8352bc03f..62a9406405 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -85,13 +85,13 @@ def informed_rrt_star_search(self, animation=True):
 
                 if self.is_near_goal(newNode):
                     if self.check_segment_collision(newNode.x, newNode.y, self.goal.x , self.goal.y):
-                    solutionSet.add(newNode)
-                    lastIndex = len(self.node_list) - 1
-                    tempPath = self.get_final_course(lastIndex)
-                    tempPathLen = self.get_path_len(tempPath)
-                    if tempPathLen < pathLen:
-                        path = tempPath
-                        cBest = tempPathLen
+                        solutionSet.add(newNode)
+                        lastIndex = len(self.node_list) - 1
+                        tempPath = self.get_final_course(lastIndex)
+                        tempPathLen = self.get_path_len(tempPath)
+                        if tempPathLen < pathLen:
+                            path = tempPath
+                            cBest = tempPathLen
 
             if animation:
                 self.draw_graph(xCenter=xCenter,

From 8b31c2bdf0cd1672f2282509e279540f76c2ef3a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 17 Nov 2019 10:25:24 +0900
Subject: [PATCH 0224/1080] Create greetings.yml

---
 .github/workflows/greetings.yml | 13 +++++++++++++
 1 file changed, 13 insertions(+)
 create mode 100644 .github/workflows/greetings.yml

diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml
new file mode 100644
index 0000000000..3da4a69458
--- /dev/null
+++ b/.github/workflows/greetings.yml
@@ -0,0 +1,13 @@
+name: Greetings
+
+on: [pull_request, issues]
+
+jobs:
+  greeting:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/first-interaction@v1
+      with:
+        repo-token: ${{ secrets.GITHUB_TOKEN }}
+        issue-message: 'Message that will be displayed on users'' This is first issue for you on this project'
+        pr-message: 'Message that will be displayed on users'' This is first pr for you on this project'

From 25ea5eb6c5a896350e6cd2c3ab2377ea2e9c1daa Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 17 Nov 2019 10:34:19 +0900
Subject: [PATCH 0225/1080] Create pythonpackage.yml

---
 .github/workflows/pythonpackage.yml | 33 +++++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)
 create mode 100644 .github/workflows/pythonpackage.yml

diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml
new file mode 100644
index 0000000000..901eb4c696
--- /dev/null
+++ b/.github/workflows/pythonpackage.yml
@@ -0,0 +1,33 @@
+name: Python package
+
+on: [push]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    strategy:
+      max-parallel: 4
+      matrix:
+        python-version: [3.5, 3.6, 3.7]
+
+    steps:
+    - uses: actions/checkout@v1
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v1
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+    - name: Lint with flake8
+      run: |
+        pip install flake8
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        runtests.sh

From 8877bc1a42d7853d949f00794385a16b488b3292 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 17 Nov 2019 10:41:28 +0900
Subject: [PATCH 0226/1080] Update pythonpackage.yml

---
 .github/workflows/pythonpackage.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml
index 901eb4c696..c49c61de54 100644
--- a/.github/workflows/pythonpackage.yml
+++ b/.github/workflows/pythonpackage.yml
@@ -28,6 +28,6 @@ jobs:
         flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
-    - name: Test with pytest
-      run: |
-        runtests.sh
+    - name: Test
+      run: runtests.sh
+      shell: bash

From 4d9ff51f27a11dbf615b5755781a3e1e961bb5a8 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 17 Nov 2019 12:00:00 +0900
Subject: [PATCH 0227/1080] Update pythonpackage.yml

---
 .github/workflows/pythonpackage.yml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml
index c49c61de54..b9f0adb822 100644
--- a/.github/workflows/pythonpackage.yml
+++ b/.github/workflows/pythonpackage.yml
@@ -28,6 +28,9 @@ jobs:
         flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test1
+      run: ls
+      shell: bash
     - name: Test
       run: runtests.sh
       shell: bash

From ecdff00fa2ee0d870b0c4b01fd2c92c52389ce21 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 17 Nov 2019 13:15:57 +0900
Subject: [PATCH 0228/1080] Update pythonpackage.yml

---
 .github/workflows/pythonpackage.yml | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml
index b9f0adb822..4d6e3d3b03 100644
--- a/.github/workflows/pythonpackage.yml
+++ b/.github/workflows/pythonpackage.yml
@@ -28,9 +28,5 @@ jobs:
         flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
-    - name: Test1
-      run: ls
-      shell: bash
     - name: Test
-      run: runtests.sh
-      shell: bash
+      run: bash runtests.sh

From 27b6e19ea6f9d4b4e08b23b5db305e79a624e6bf Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 17 Nov 2019 13:21:14 +0900
Subject: [PATCH 0229/1080] Update pythonpackage.yml

---
 .github/workflows/pythonpackage.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml
index 4d6e3d3b03..fbac68ac3b 100644
--- a/.github/workflows/pythonpackage.yml
+++ b/.github/workflows/pythonpackage.yml
@@ -28,5 +28,7 @@ jobs:
         flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: install coverage
+      run: pip insall coverage   
     - name: Test
       run: bash runtests.sh

From 157664857b9eb12181e1405c28b18928d7120a59 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 17 Nov 2019 13:23:30 +0900
Subject: [PATCH 0230/1080] Update pythonpackage.yml

---
 .github/workflows/pythonpackage.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml
index fbac68ac3b..fc70845789 100644
--- a/.github/workflows/pythonpackage.yml
+++ b/.github/workflows/pythonpackage.yml
@@ -29,6 +29,6 @@ jobs:
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: install coverage
-      run: pip insall coverage   
+      run: pip install coverage   
     - name: Test
       run: bash runtests.sh

From 3333b34ce215b0287073fd250aa0c2813ed36e48 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 17 Nov 2019 13:33:50 +0900
Subject: [PATCH 0231/1080] Update pythonpackage.yml

---
 .github/workflows/pythonpackage.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml
index fc70845789..a3203ff1ac 100644
--- a/.github/workflows/pythonpackage.yml
+++ b/.github/workflows/pythonpackage.yml
@@ -9,7 +9,7 @@ jobs:
     strategy:
       max-parallel: 4
       matrix:
-        python-version: [3.5, 3.6, 3.7]
+        python-version: [3.6, 3.7]
 
     steps:
     - uses: actions/checkout@v1

From 0067577cbe500054bafc5ce6dd00a3effc68f88e Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 17 Nov 2019 14:33:24 +0900
Subject: [PATCH 0232/1080] remove PoseOptimizationSLAM

---
 README.md                                     | 17 -----------
 SLAM/PoseOptimizationSLAM2D/README.md         |  9 ------
 .../PoseOptimizationSLAM2D/data_downloader.py | 28 -------------------
 SLAM/PoseOptimizationSLAM3D/README.md         | 25 -----------------
 .../PoseOptimizationSLAM3D/data_downloader.py | 26 -----------------
 5 files changed, 105 deletions(-)
 delete mode 100644 SLAM/PoseOptimizationSLAM2D/README.md
 delete mode 100644 SLAM/PoseOptimizationSLAM2D/data_downloader.py
 delete mode 100644 SLAM/PoseOptimizationSLAM3D/README.md
 delete mode 100644 SLAM/PoseOptimizationSLAM3D/data_downloader.py

diff --git a/README.md b/README.md
index 1c455e928b..e740049b90 100644
--- a/README.md
+++ b/README.md
@@ -31,8 +31,6 @@ Python codes for robotics algorithm.
    * [SLAM](#slam)
       * [Iterative Closest Point (ICP) Matching](#iterative-closest-point-icp-matching)
       * [FastSLAM 1.0](#fastslam-10)
-      * [Pose Optimization SLAM 2D](#pose-optimization-slam-2d)
-      * [Pose Optimization SLAM 3D](#pose-optimization-slam-3d)
    * [Path Planning](#path-planning)
       * [Dynamic Window Approach](#dynamic-window-approach)
       * [Grid based search](#grid-based-search)
@@ -251,21 +249,6 @@ Ref:
 - [SLAM simulations by Tim Bailey](http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm)
 
 
-## Pose Optimization SLAM 2D
-
-This is a graph based 2D pose optimization SLAM example.
-
-![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/PoseOptimizationSLAM2D/animation.gif)
-
-
-## Pose Optimization SLAM 3D
-
-This is a graph based 3D pose optimization SLAM example.
-
-![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/PoseOptimizationSLAM3D/animation.gif)
-
-
-
 # Path Planning
 
 ## Dynamic Window Approach
diff --git a/SLAM/PoseOptimizationSLAM2D/README.md b/SLAM/PoseOptimizationSLAM2D/README.md
deleted file mode 100644
index 612fa5186d..0000000000
--- a/SLAM/PoseOptimizationSLAM2D/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# How to use
-
-1. Download data
-
->$ python data_downloader.py
-
-2. run SLAM
-
->$ python pose_optimization_slam.py
diff --git a/SLAM/PoseOptimizationSLAM2D/data_downloader.py b/SLAM/PoseOptimizationSLAM2D/data_downloader.py
deleted file mode 100644
index d1b8a2c23b..0000000000
--- a/SLAM/PoseOptimizationSLAM2D/data_downloader.py
+++ /dev/null
@@ -1,28 +0,0 @@
-"""
-
-Data down loader for pose optimization
-
-author: Atsushi Sakai
-
-"""
-
-
-import subprocess
-def main():
-    print("start!!")
-
-    cmd = "wget https://www.dropbox.com/s/vcz8cag7bo0zlaj/input_INTEL_g2o.g2o?dl=0 -O intel.g2o -nc"
-    subprocess.call(cmd, shell=True)
-
-    cmd = "wget https://www.dropbox.com/s/d8fcn1jg1mebx8f/input_MITb_g2o.g2o?dl=0 -O mit_killian.g2o -nc"
-
-    subprocess.call(cmd, shell=True)
-    cmd = "wget https://www.dropbox.com/s/gmdzo74b3tzvbrw/input_M3500_g2o.g2o?dl=0 -O manhattan3500.g2o -nc"
-    subprocess.call(cmd, shell=True)
-
-    print("done!!")
-
-
-if __name__ == '__main__':
-    main()
-
diff --git a/SLAM/PoseOptimizationSLAM3D/README.md b/SLAM/PoseOptimizationSLAM3D/README.md
deleted file mode 100644
index 396a629735..0000000000
--- a/SLAM/PoseOptimizationSLAM3D/README.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# PoseOptimizationSLAM3D
-3D (x, y, z, qw, qx, qy, qz) pose optimization SLAM
-
-## How to use 
-1. Download data  
-
-~~~
-python data_downloader.py
-~~~
-
-2. run SLAM 
-
-~~~
-python pose_optimization_slam_3d.py
-~~~
-
-## Reference 
-- [A Compact and Portable Implementation of Graph\-based SLAM](https://www.researchgate.net/publication/321287640_A_Compact_and_Portable_Implementation_of_Graph-based_SLAM)  
-
-- [GitHub \- furo\-org/p2o: Single header 2D/3D graph\-based SLAM library](https://github.com/furo-org/p2o)  
-
-- [GitHub \- AtsushiSakai/PythonRobotics
-/SLAM/PoseOptimizationSLAM](https://github.com/AtsushiSakai/PythonRobotics/tree/master/SLAM/PoseOptimizationSLAM)
-
-- [リー代数による3次元ポーズ調整\(Pose Adjustment\)\[PythonRobotics\]\[SLAM\] \- Qiita](https://qiita.com/saitosasaki/items/a0540cba994f08bf5e16#comment-3e6588e6b096cc2567d8)
diff --git a/SLAM/PoseOptimizationSLAM3D/data_downloader.py b/SLAM/PoseOptimizationSLAM3D/data_downloader.py
deleted file mode 100644
index a0c04fd1ab..0000000000
--- a/SLAM/PoseOptimizationSLAM3D/data_downloader.py
+++ /dev/null
@@ -1,26 +0,0 @@
-"""
-
-Data down loader for pose optimization
-
-author: Atsushi Sakai
-
-"""
-
-import subprocess
-
-
-def main():
-    print("start!!")
-
-    cmd = "wget https://www.dropbox.com/s/zu23p8d522qccor/parking-garage.g2o?dl=0 -O parking-garage.g2o -nc"
-    subprocess.call(cmd, shell=True)
-    cmd = "wget http://www.furo.org/irie/datasets/torus3d_guess.g2o -nc"
-    subprocess.call(cmd, shell=True)
-    cmd = "wget http://www.furo.org/irie/datasets/sphere2200_guess.g2o -nc"
-    subprocess.call(cmd, shell=True)
-
-    print("done!!")
-
-
-if __name__ == '__main__':
-    main()

From 54991efb8f335faf42fa665dce9dfecdc2833810 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 25 Nov 2019 21:54:32 +0900
Subject: [PATCH 0233/1080] Update FUNDING.yml

---
 .github/FUNDING.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml
index 80f4984c3f..24f0926299 100644
--- a/.github/FUNDING.yml
+++ b/.github/FUNDING.yml
@@ -1,3 +1,4 @@
 # These are supported funding model platforms
+github:  AtsushiSakai
 patreon: myenigma
 custom: https://www.paypal.me/myenigmapay/

From 2fe00f8824842452345cbe180b4303e240c18a6c Mon Sep 17 00:00:00 2001
From: Michael Dobler <michael.dobler@outlook.com>
Date: Thu, 28 Nov 2019 23:35:55 +0100
Subject: [PATCH 0234/1080] Fix jacobian of observation

---
 SLAM/EKFSLAM/ekf_slam.ipynb | 2 +-
 SLAM/EKFSLAM/ekf_slam.py    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index 6509373d2b..f800939576 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -479,7 +479,7 @@
     "    \"\"\"\n",
     "    sq = math.sqrt(q)\n",
     "    G = np.array([[-sq * delta[0, 0], - sq * delta[1, 0], 0, sq * delta[0, 0], sq * delta[1, 0]],\n",
-    "                  [delta[1, 0], - delta[0, 0], - 1.0, - delta[1, 0], delta[0, 0]]])\n",
+    "                  [delta[1, 0], - delta[0, 0], - q, - delta[1, 0], delta[0, 0]]])\n",
     "\n",
     "    G = G / q\n",
     "    nLM = calc_n_LM(x)\n",
diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py
index ae39c9e0ea..84cf4d9701 100644
--- a/SLAM/EKFSLAM/ekf_slam.py
+++ b/SLAM/EKFSLAM/ekf_slam.py
@@ -177,7 +177,7 @@ def calc_innovation(lm, xEst, PEst, z, LMid):
 def jacob_h(q, delta, x, i):
     sq = math.sqrt(q)
     G = np.array([[-sq * delta[0, 0], - sq * delta[1, 0], 0, sq * delta[0, 0], sq * delta[1, 0]],
-                  [delta[1, 0], - delta[0, 0], - 1.0, - delta[1, 0], delta[0, 0]]])
+                  [delta[1, 0], - delta[0, 0], - q, - delta[1, 0], delta[0, 0]]])
 
     G = G / q
     nLM = calc_n_lm(x)

From 0b07425d2e8d97f61fb456d0969e1a64eeee33ba Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 30 Nov 2019 20:05:52 +0900
Subject: [PATCH 0235/1080] Code clen up

---
 SLAM/EKFSLAM/ekf_slam.py | 33 ++++++++++++++++-----------------
 1 file changed, 16 insertions(+), 17 deletions(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py
index 84cf4d9701..27e5035459 100644
--- a/SLAM/EKFSLAM/ekf_slam.py
+++ b/SLAM/EKFSLAM/ekf_slam.py
@@ -1,5 +1,6 @@
 """
 Extended Kalman Filter SLAM example
+
 author: Atsushi Sakai (@Atsushi_twi)
 """
 
@@ -35,10 +36,10 @@ def ekf_slam(xEst, PEst, u, z):
 
     # Update
     for iz in range(len(z[:, 0])):  # for each observation
-        minid = search_correspond_landmark_id(xEst, PEst, z[iz, 0:2])
+        min_id = search_correspond_landmark_id(xEst, PEst, z[iz, 0:2])
 
         nLM = calc_n_lm(xEst)
-        if minid == nLM:
+        if min_id == nLM:
             print("New LM")
             # Extend state and covariance matrix
             xAug = np.vstack((xEst, calc_landmark_position(xEst, z[iz, :])))
@@ -46,8 +47,8 @@ def ekf_slam(xEst, PEst, u, z):
                               np.hstack((np.zeros((LM_SIZE, len(xEst))), initP))))
             xEst = xAug
             PEst = PAug
-        lm = get_landmark_position_from_state(xEst, minid)
-        y, S, H = calc_innovation(lm, xEst, PEst, z[iz, 0:2], minid)
+        lm = get_landmark_position_from_state(xEst, min_id)
+        y, S, H = calc_innovation(lm, xEst, PEst, z[iz, 0:2], min_id)
 
         K = (PEst @ H.T) @ np.linalg.inv(S)
         xEst = xEst + (K @ y)
@@ -60,8 +61,8 @@ def ekf_slam(xEst, PEst, u, z):
 
 def calc_input():
     v = 1.0  # [m/s]
-    yawrate = 0.1  # [rad/s]
-    u = np.array([[v, yawrate]]).T
+    yaw_rate = 0.1  # [rad/s]
+    u = np.array([[v, yaw_rate]]).T
     return u
 
 
@@ -79,8 +80,8 @@ def observation(xTrue, xd, u, RFID):
         angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
         if d <= MAX_RANGE:
             dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5  # add noise
-            anglen = angle + np.random.randn() * Q_sim[1, 1] ** 0.5  # add noise
-            zi = np.array([dn, anglen, i])
+            angle_n = angle + np.random.randn() * Q_sim[1, 1] ** 0.5  # add noise
+            zi = np.array([dn, angle_n, i])
             z = np.vstack((z, zi))
 
     # add noise to input
@@ -128,8 +129,6 @@ def calc_landmark_position(x, z):
 
     zp[0, 0] = x[0, 0] + z[0] * math.cos(x[2, 0] + z[1])
     zp[1, 0] = x[1, 0] + z[0] * math.sin(x[2, 0] + z[1])
-    # zp[0, 0] = x[0, 0] + z[0, 0] * math.cos(x[2, 0] + z[0, 1])
-    # zp[1, 0] = x[1, 0] + z[0, 0] * math.sin(x[2, 0] + z[0, 1])
 
     return zp
 
@@ -147,25 +146,25 @@ def search_correspond_landmark_id(xAug, PAug, zi):
 
     nLM = calc_n_lm(xAug)
 
-    mdist = []
+    min_dist = []
 
     for i in range(nLM):
         lm = get_landmark_position_from_state(xAug, i)
         y, S, H = calc_innovation(lm, xAug, PAug, zi, i)
-        mdist.append(y.T @ np.linalg.inv(S) @ y)
+        min_dist.append(y.T @ np.linalg.inv(S) @ y)
 
-    mdist.append(M_DIST_TH)  # new landmark
+    min_dist.append(M_DIST_TH)  # new landmark
 
-    minid = mdist.index(min(mdist))
+    min_id = min_dist.index(min(min_dist))
 
-    return minid
+    return min_id
 
 
 def calc_innovation(lm, xEst, PEst, z, LMid):
     delta = lm - xEst[0:2]
     q = (delta.T @ delta)[0, 0]
-    zangle = math.atan2(delta[1, 0], delta[0, 0]) - xEst[2, 0]
-    zp = np.array([[math.sqrt(q), pi_2_pi(zangle)]])
+    z_angle = math.atan2(delta[1, 0], delta[0, 0]) - xEst[2, 0]
+    zp = np.array([[math.sqrt(q), pi_2_pi(z_angle)]])
     y = (z - zp).T
     y[1] = pi_2_pi(y[1])
     H = jacob_h(q, delta, xEst, LMid + 1)

From f26a91d5342c974b78e3cc3c820518c3400a08f7 Mon Sep 17 00:00:00 2001
From: harderthan <kheo1772@gmail.com>
Date: Tue, 3 Dec 2019 12:46:58 +0900
Subject: [PATCH 0236/1080] Update Kalmanfilter_basics.rst

Edited the misspelling
---
 docs/modules/Kalmanfilter_basics.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/modules/Kalmanfilter_basics.rst b/docs/modules/Kalmanfilter_basics.rst
index ce84fe0db4..7325dafdea 100644
--- a/docs/modules/Kalmanfilter_basics.rst
+++ b/docs/modules/Kalmanfilter_basics.rst
@@ -180,7 +180,7 @@ Central Limit Theorem
 ^^^^^^^^^^^^^^^^^^^^^
 
 According to this theorem, the average of n samples of random and
-independant variables tends to follow a normal distribution as we
+independent variables tends to follow a normal distribution as we
 increase the sample size.(Generally, for n>=30)
 
 .. code-block:: ipython3

From 338b7945b6e12511dc1ae19166c1f32c3e9d313e Mon Sep 17 00:00:00 2001
From: harderthan <kheo1772@gmail.com>
Date: Tue, 3 Dec 2019 12:49:52 +0900
Subject: [PATCH 0237/1080] Update Kalmanfilter_basics

Edited misspelling
---
 Localization/Kalmanfilter_basics.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Localization/Kalmanfilter_basics.ipynb b/Localization/Kalmanfilter_basics.ipynb
index 374287911b..b4913f9e7a 100644
--- a/Localization/Kalmanfilter_basics.ipynb
+++ b/Localization/Kalmanfilter_basics.ipynb
@@ -228,7 +228,7 @@
     "\n",
     "#### Central Limit Theorem\n",
     "\n",
-    "According to this theorem, the average of n samples of random and independant variables tends to follow a normal distribution as we increase the sample size.(Generally, for n>=30)"
+    "According to this theorem, the average of n samples of random and independent variables tends to follow a normal distribution as we increase the sample size.(Generally, for n>=30)"
    ]
   },
   {

From 99c784948d9cfc8145ae208d5301c6bd1144f401 Mon Sep 17 00:00:00 2001
From: Michael Dobler <michael.dobler@outlook.com>
Date: Fri, 6 Dec 2019 11:35:29 +0100
Subject: [PATCH 0238/1080] Use matrix multiplication instead of elementwise

---
 SLAM/EKFSLAM/ekf_slam.ipynb | 4 ++--
 SLAM/EKFSLAM/ekf_slam.py    | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index f800939576..ccf5e5bbd6 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -309,7 +309,7 @@
     "    G, Fx = jacob_motion(xEst[0:S], u)\n",
     "    # Fx is an an identity matrix of size (STATE_SIZE)\n",
     "    # sigma = G*sigma*G.T + Noise\n",
-    "    PEst[0:S, 0:S] = G.T * PEst[0:S, 0:S] * G + Fx.T * Cx * Fx\n",
+    "    PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx\n",
     "    return xEst, PEst, G, Fx"
    ]
   },
@@ -584,7 +584,7 @@
     "                   [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])],\n",
     "                   [0.0, 0.0, 0.0]])\n",
     "\n",
-    "    G = np.eye(STATE_SIZE) + Fx.T * jF * Fx\n",
+    "    G = np.eye(STATE_SIZE) + Fx.T @ jF @ Fx\n",
     "    if calc_n_LM(x) > 0:\n",
     "        print(Fx.shape)\n",
     "    return G, Fx,\n",
diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py
index 27e5035459..22ccd8d659 100644
--- a/SLAM/EKFSLAM/ekf_slam.py
+++ b/SLAM/EKFSLAM/ekf_slam.py
@@ -31,7 +31,7 @@ def ekf_slam(xEst, PEst, u, z):
     S = STATE_SIZE
     xEst[0:S] = motion_model(xEst[0:S], u)
     G, Fx = jacob_motion(xEst[0:S], u)
-    PEst[0:S, 0:S] = G.T * PEst[0:S, 0:S] * G + Fx.T * Cx * Fx
+    PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx
     initP = np.eye(2)
 
     # Update
@@ -119,7 +119,7 @@ def jacob_motion(x, u):
                    [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])],
                    [0.0, 0.0, 0.0]])
 
-    G = np.eye(STATE_SIZE) + Fx.T * jF * Fx
+    G = np.eye(STATE_SIZE) + Fx.T @ jF @ Fx
 
     return G, Fx,
 

From dacff1c97c21b915199493492e303a6e11031faa Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 7 Dec 2019 20:23:36 +0900
Subject: [PATCH 0239/1080] Fixed kmeans clustering bug #251

---
 .../kmeans_clustering/kmeans_clustering.py    | 174 +++++++-----------
 1 file changed, 71 insertions(+), 103 deletions(-)

diff --git a/Mapping/kmeans_clustering/kmeans_clustering.py b/Mapping/kmeans_clustering/kmeans_clustering.py
index 8edfa11e3a..bdaa45455e 100644
--- a/Mapping/kmeans_clustering/kmeans_clustering.py
+++ b/Mapping/kmeans_clustering/kmeans_clustering.py
@@ -6,97 +6,87 @@
 
 """
 
-import numpy as np
 import math
 import matplotlib.pyplot as plt
 import random
 
+# k means parameters
+MAX_LOOP = 10
+DCOST_TH = 0.1
 show_animation = True
 
 
-class Clusters:
-
-    def __init__(self, x, y, nlabel):
-        self.x = x
-        self.y = y
-        self.ndata = len(self.x)
-        self.nlabel = nlabel
-        self.labels = [random.randint(0, nlabel - 1)
-                       for _ in range(self.ndata)]
-        self.cx = [0.0 for _ in range(nlabel)]
-        self.cy = [0.0 for _ in range(nlabel)]
-
-
 def kmeans_clustering(rx, ry, nc):
-
     clusters = Clusters(rx, ry, nc)
-    clusters = calc_centroid(clusters)
+    clusters.calc_centroid()
 
-    MAX_LOOP = 10
-    DCOST_TH = 0.1
-    pcost = 100.0
+    pre_cost = float("inf")
     for loop in range(MAX_LOOP):
-        #  print("Loop:", loop)
-        clusters, cost = update_clusters(clusters)
-        clusters = calc_centroid(clusters)
+        print("loop:", loop)
+        cost = clusters.update_clusters()
+        clusters.calc_centroid()
 
-        dcost = abs(cost - pcost)
-        if dcost < DCOST_TH:
+        d_cost = abs(cost - pre_cost)
+        if d_cost < DCOST_TH:
             break
-        pcost = cost
+        pre_cost = cost
 
     return clusters
 
 
-def calc_centroid(clusters):
-
-    for ic in range(clusters.nlabel):
-        x, y = calc_labeled_points(ic, clusters)
-        ndata = len(x)
-        clusters.cx[ic] = sum(x) / ndata
-        clusters.cy[ic] = sum(y) / ndata
-
-    return clusters
-
-
-def update_clusters(clusters):
-    cost = 0.0
-
-    for ip in range(clusters.ndata):
-        px = clusters.x[ip]
-        py = clusters.y[ip]
-
-        dx = [icx - px for icx in clusters.cx]
-        dy = [icy - py for icy in clusters.cy]
-
-        dlist = [math.sqrt(idx**2 + idy**2) for (idx, idy) in zip(dx, dy)]
-        mind = min(dlist)
-        min_id = dlist.index(mind)
-        clusters.labels[ip] = min_id
-        cost += mind
-
-    return clusters, cost
-
-
-def calc_labeled_points(ic, clusters):
-
-    inds = np.array([i for i in range(clusters.ndata)
-                     if clusters.labels[i] == ic])
-    tx = np.array(clusters.x)
-    ty = np.array(clusters.y)
-
-    x = tx[inds]
-    y = ty[inds]
-
-    return x, y
-
-
-def calc_raw_data(cx, cy, npoints, rand_d):
+class Clusters:
 
+    def __init__(self, x, y, n_label):
+        self.x = x
+        self.y = y
+        self.n_data = len(self.x)
+        self.n_label = n_label
+        self.labels = [random.randint(0, n_label - 1)
+                       for _ in range(self.n_data)]
+        self.center_x = [0.0 for _ in range(n_label)]
+        self.center_y = [0.0 for _ in range(n_label)]
+
+    def plot_cluster(self):
+        for label in set(self.labels):
+            x, y = self._get_labeled_x_y(label)
+            plt.plot(x, y, ".")
+
+    def calc_centroid(self):
+        for label in set(self.labels):
+            x, y = self._get_labeled_x_y(label)
+            n_data = len(x)
+            self.center_x[label] = sum(x) / n_data
+            self.center_y[label] = sum(y) / n_data
+
+    def update_clusters(self):
+        cost = 0.0
+
+        for ip in range(self.n_data):
+            px = self.x[ip]
+            py = self.y[ip]
+
+            dx = [icx - px for icx in self.center_x]
+            dy = [icy - py for icy in self.center_y]
+
+            dist_list = [math.sqrt(idx ** 2 + idy ** 2) for (idx, idy) in zip(dx, dy)]
+            min_dist = min(dist_list)
+            min_id = dist_list.index(min_dist)
+            self.labels[ip] = min_id
+            cost += min_dist
+
+        return cost
+
+    def _get_labeled_x_y(self, target_label):
+        x = [self.x[i] for i, label in enumerate(self.labels) if label == target_label]
+        y = [self.y[i] for i, label in enumerate(self.labels) if label == target_label]
+        return x, y
+
+
+def calc_raw_data(cx, cy, n_points, rand_d):
     rx, ry = [], []
 
     for (icx, icy) in zip(cx, cy):
-        for _ in range(npoints):
+        for _ in range(n_points):
             rx.append(icx + rand_d * (random.random() - 0.5))
             ry.append(icy + rand_d * (random.random() - 0.5))
 
@@ -104,48 +94,28 @@ def calc_raw_data(cx, cy, npoints, rand_d):
 
 
 def update_positions(cx, cy):
-
+    # object moving parameters
     DX1 = 0.4
     DY1 = 0.5
-
-    cx[0] += DX1
-    cy[0] += DY1
-
     DX2 = -0.3
     DY2 = -0.5
 
+    cx[0] += DX1
+    cy[0] += DY1
     cx[1] += DX2
     cy[1] += DY2
 
     return cx, cy
 
 
-def calc_association(cx, cy, clusters):
-
-    inds = []
-
-    for ic, _ in enumerate(cx):
-        tcx = cx[ic]
-        tcy = cy[ic]
-
-        dx = [icx - tcx for icx in clusters.cx]
-        dy = [icy - tcy for icy in clusters.cy]
-
-        dlist = [math.sqrt(idx**2 + idy**2) for (idx, idy) in zip(dx, dy)]
-        min_id = dlist.index(min(dlist))
-        inds.append(min_id)
-
-    return inds
-
-
 def main():
     print(__file__ + " start!!")
 
     cx = [0.0, 8.0]
     cy = [0.0, 8.0]
-    npoints = 10
+    n_points = 10
     rand_d = 3.0
-    ncluster = 2
+    n_cluster = 2
     sim_time = 15.0
     dt = 1.0
     time = 0.0
@@ -154,20 +124,18 @@ def main():
         print("Time:", time)
         time += dt
 
-        # simulate objects
+        # objects moving simulation
         cx, cy = update_positions(cx, cy)
-        rx, ry = calc_raw_data(cx, cy, npoints, rand_d)
+        raw_x, raw_y = calc_raw_data(cx, cy, n_points, rand_d)
 
-        clusters = kmeans_clustering(rx, ry, ncluster)
+        clusters = kmeans_clustering(raw_x, raw_y, n_cluster)
 
         # for animation
         if show_animation:  # pragma: no cover
             plt.cla()
-            inds = calc_association(cx, cy, clusters)
-            for ic in inds:
-                x, y = calc_labeled_points(ic, clusters)
-                plt.plot(x, y, "x")
-            plt.plot(cx, cy, "o")
+            clusters.plot_cluster()
+
+            plt.plot(cx, cy, "or")
             plt.xlim(-2.0, 10.0)
             plt.ylim(-2.0, 10.0)
             plt.pause(dt)

From d019e416ba051a08db153c3177ef97a715031ed8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?=
 <20567087+goktug97@users.noreply.github.com>
Date: Sat, 7 Dec 2019 14:30:18 +0300
Subject: [PATCH 0240/1080] exit on key

---
 AerialNavigation/drone_3d_trajectory_following/Quadrotor.py | 6 ++++--
 .../rocket_powered_landing/rocket_powered_landing.py        | 2 ++
 .../arm_obstacle_navigation/arm_obstacle_navigation.py      | 4 +++-
 .../arm_obstacle_navigation/arm_obstacle_navigation_2.py    | 2 ++
 ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py      | 2 ++
 .../two_joint_arm_to_point_control.py                       | 2 ++
 Bipedal/bipedal_planner/bipedal_planner.py                  | 2 ++
 .../ensemble_kalman_filter/ensemble_kalman_filter.py        | 2 ++
 .../extended_kalman_filter/extended_kalman_filter.py        | 2 ++
 Localization/histogram_filter/histogram_filter.py           | 2 ++
 Localization/particle_filter/particle_filter.py             | 2 ++
 .../unscented_kalman_filter/unscented_kalman_filter.py      | 2 ++
 Mapping/circle_fitting/circle_fitting.py                    | 2 ++
 Mapping/gaussian_grid_map/gaussian_grid_map.py              | 2 ++
 Mapping/kmeans_clustering/kmeans_clustering.py              | 2 ++
 Mapping/raycasting_grid_map/raycasting_grid_map.py          | 2 ++
 Mapping/rectangle_fitting/rectangle_fitting.py              | 2 ++
 PathPlanning/AStar/a_star.py                                | 2 ++
 PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py | 2 ++
 PathPlanning/ClosedLoopRRTStar/pure_pursuit.py              | 2 ++
 PathPlanning/Dijkstra/dijkstra.py                           | 2 ++
 PathPlanning/DubinsPath/dubins_path_planning.py             | 2 ++
 .../DynamicWindowApproach/dynamic_window_approach.py        | 2 ++
 PathPlanning/Eta3SplinePath/eta3_spline_path.py             | 2 ++
 .../FrenetOptimalTrajectory/frenet_optimal_trajectory.py    | 2 ++
 .../grid_based_sweep_coverage_path_planner.py               | 4 ++++
 PathPlanning/HybridAStar/a_star.py                          | 4 +++-
 PathPlanning/HybridAStar/hybrid_a_star.py                   | 2 ++
 PathPlanning/InformedRRTStar/informed_rrt_star.py           | 3 ++-
 PathPlanning/LQRPlanner/LQRplanner.py                       | 2 ++
 PathPlanning/LQRRRTStar/lqr_rrt_star.py                     | 2 ++
 .../PotentialFieldPlanning/potential_field_planning.py      | 2 ++
 PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py | 2 ++
 .../quintic_polynomials_planner.py                          | 2 ++
 PathPlanning/RRT/rrt.py                                     | 2 ++
 PathPlanning/RRTDubins/rrt_dubins.py                        | 2 ++
 PathPlanning/RRTStarDubins/rrt_star_dubins.py               | 2 ++
 PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py      | 2 ++
 PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py    | 2 ++
 PathPlanning/VoronoiRoadMap/voronoi_road_map.py             | 2 ++
 PathTracking/cgmres_nmpc/cgmres_nmpc.py                     | 2 ++
 .../lqr_speed_steer_control/lqr_speed_steer_control.py      | 2 ++
 PathTracking/lqr_steer_control/lqr_steer_control.py         | 2 ++
 .../model_predictive_speed_and_steer_control.py             | 2 ++
 PathTracking/move_to_pose/move_to_pose.py                   | 3 +++
 PathTracking/pure_pursuit/pure_pursuit.py                   | 2 ++
 PathTracking/rear_wheel_feedback/rear_wheel_feedback.py     | 2 ++
 PathTracking/stanley_controller/stanley_controller.py       | 2 ++
 SLAM/EKFSLAM/ekf_slam.py                                    | 2 ++
 SLAM/FastSLAM1/fast_slam1.py                                | 2 ++
 SLAM/FastSLAM2/fast_slam2.py                                | 2 ++
 SLAM/GraphBasedSLAM/graph_based_slam.py                     | 5 +++--
 SLAM/iterative_closest_point/iterative_closest_point.py     | 2 ++
 53 files changed, 114 insertions(+), 7 deletions(-)

diff --git a/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py b/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py
index 9b38677cbe..89171da212 100644
--- a/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py
+++ b/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py
@@ -8,7 +8,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-
 class Quadrotor():
     def __init__(self, x=0, y=0, z=0, roll=0, pitch=0, yaw=0, size=0.25, show_animation=True):
         self.p1 = np.array([size / 2, 0, 0, 1]).T
@@ -24,6 +23,9 @@ def __init__(self, x=0, y=0, z=0, roll=0, pitch=0, yaw=0, size=0.25, show_animat
         if self.show_animation:
             plt.ion()
             fig = plt.figure()
+            fig.canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
+
             self.ax = fig.add_subplot(111, projection='3d')
 
         self.update_pose(x, y, z, roll, pitch, yaw)
@@ -81,4 +83,4 @@ def plot(self):  # pragma: no cover
         plt.ylim(-5, 5)
         self.ax.set_zlim(0, 10)
 
-        plt.pause(0.001)
\ No newline at end of file
+        plt.pause(0.001)
diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
index 9f1e16fc1d..c7ab998bec 100644
--- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
+++ b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
@@ -567,6 +567,8 @@ def plot_animation(X, U):  # pragma: no cover
 
     fig = plt.figure()
     ax = fig.gca(projection='3d')
+    rig.canvas.mpl_connect('key_release_event',
+            lambda event: [exit(0) if event.key == 'escape' else None])
 
     for k in range(K):
         plt.cla()
diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
index 2bcc33d0f7..b435e62d2b 100644
--- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
+++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
@@ -162,6 +162,8 @@ def astar_torus(grid, start_node, goal_node):
         for i in range(1, len(route)):
             grid[route[i]] = 6
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.imshow(grid, cmap=cmap, norm=norm, interpolation=None)
             plt.show()
             plt.pause(1e-2)
@@ -262,4 +264,4 @@ def plot(self, obstacles=[]):  # pragma: no cover
 
 
 if __name__ == '__main__':
-    main()
\ No newline at end of file
+    main()
diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
index b8c1ce89e9..5a21b806d3 100644
--- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
+++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
@@ -193,6 +193,8 @@ def astar_torus(grid, start_node, goal_node):
         for i in range(1, len(route)):
             grid[route[i]] = 6
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.imshow(grid, cmap=cmap, norm=norm, interpolation=None)
             plt.show()
             plt.pause(1e-2)
diff --git a/ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py b/ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py
index 8ed12c565b..576bd29a8f 100644
--- a/ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py
+++ b/ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py
@@ -51,6 +51,8 @@ def update_points(self):
 
     def plot(self):  # pragma: no cover
         plt.cla()
+        plt.gcf().canvas.mpl_connect('key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
 
         for i in range(self.n_links + 1):
             if i is not self.n_links:
diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
index e674ae8ac2..134583a95c 100644
--- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
+++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
@@ -114,6 +114,8 @@ def animation():
 def main():  # pragma: no cover
     fig = plt.figure()
     fig.canvas.mpl_connect("button_press_event", click)
+    fig.canvas.mpl_connect('key_release_event',
+            lambda event: [exit(0) if event.key == 'escape' else None])
     two_joint_arm()
 
 
diff --git a/Bipedal/bipedal_planner/bipedal_planner.py b/Bipedal/bipedal_planner/bipedal_planner.py
index 3cf2936ee4..afa68afb25 100644
--- a/Bipedal/bipedal_planner/bipedal_planner.py
+++ b/Bipedal/bipedal_planner/bipedal_planner.py
@@ -111,6 +111,8 @@ def walk(self, T_sup=0.8, z_c=0.8, a=10, b=1, plot=False):
                     if c > len(com_trajectory_for_plot):
                         # set up plotter
                         plt.cla()
+                        plt.gcf().canvas.mpl_connect('key_release_event',
+                                lambda event: [exit(0) if event.key == 'escape' else None])
                         ax.set_zlim(0, z_c * 2)
                         ax.set_aspect('equal', 'datalim')
 
diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
index 293509157b..6ac6144f36 100644
--- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
+++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
@@ -213,6 +213,8 @@ def main():
 
         if show_animation:
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
 
             for i in range(len(z[:, 0])):
                 plt.plot([xTrue[0, 0], z[i, 2]], [xTrue[1, 0], z[i, 3]], "-k")
diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py
index d2d7178ae6..029dac4bf8 100644
--- a/Localization/extended_kalman_filter/extended_kalman_filter.py
+++ b/Localization/extended_kalman_filter/extended_kalman_filter.py
@@ -191,6 +191,8 @@ def main():
 
         if show_animation:
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(hz[0, :], hz[1, :], ".g")
             plt.plot(hxTrue[0, :].flatten(),
                      hxTrue[1, :].flatten(), "-b")
diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py
index bdfbb13fa7..ac07c59fbd 100644
--- a/Localization/histogram_filter/histogram_filter.py
+++ b/Localization/histogram_filter/histogram_filter.py
@@ -233,6 +233,8 @@ def main():
 
         if show_animation:
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             draw_heat_map(grid_map.data, mx, my)
             plt.plot(xTrue[0, :], xTrue[1, :], "xr")
             plt.plot(RF_ID[:, 0], RF_ID[:, 1], ".k")
diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py
index 0ebb5fc3d1..4b9abd78bd 100644
--- a/Localization/particle_filter/particle_filter.py
+++ b/Localization/particle_filter/particle_filter.py
@@ -230,6 +230,8 @@ def main():
 
         if show_animation:
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
 
             for i in range(len(z[:, 0])):
                 plt.plot([xTrue[0, 0], z[i, 1]], [xTrue[1, 0], z[i, 2]], "-k")
diff --git a/Localization/unscented_kalman_filter/unscented_kalman_filter.py b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
index 45d5b78132..0005d12fe7 100644
--- a/Localization/unscented_kalman_filter/unscented_kalman_filter.py
+++ b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
@@ -240,6 +240,8 @@ def main():
 
         if show_animation:
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(hz[0, :], hz[1, :], ".g")
             plt.plot(np.array(hxTrue[0, :]).flatten(),
                      np.array(hxTrue[1, :]).flatten(), "-b")
diff --git a/Mapping/circle_fitting/circle_fitting.py b/Mapping/circle_fitting/circle_fitting.py
index dae9413d27..27ceac21f7 100644
--- a/Mapping/circle_fitting/circle_fitting.py
+++ b/Mapping/circle_fitting/circle_fitting.py
@@ -124,6 +124,8 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.axis("equal")
             plt.plot(0.0, 0.0, "*r")
             plot_circle(cx, cy, cr)
diff --git a/Mapping/gaussian_grid_map/gaussian_grid_map.py b/Mapping/gaussian_grid_map/gaussian_grid_map.py
index b520b63944..094b094fdd 100644
--- a/Mapping/gaussian_grid_map/gaussian_grid_map.py
+++ b/Mapping/gaussian_grid_map/gaussian_grid_map.py
@@ -73,6 +73,8 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             draw_heatmap(gmap, minx, maxx, miny, maxy, xyreso)
             plt.plot(ox, oy, "xr")
             plt.plot(0.0, 0.0, "ob")
diff --git a/Mapping/kmeans_clustering/kmeans_clustering.py b/Mapping/kmeans_clustering/kmeans_clustering.py
index 8edfa11e3a..7900e30457 100644
--- a/Mapping/kmeans_clustering/kmeans_clustering.py
+++ b/Mapping/kmeans_clustering/kmeans_clustering.py
@@ -163,6 +163,8 @@ def main():
         # for animation
         if show_animation:  # pragma: no cover
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             inds = calc_association(cx, cy, clusters)
             for ic in inds:
                 x, y = calc_labeled_points(ic, clusters)
diff --git a/Mapping/raycasting_grid_map/raycasting_grid_map.py b/Mapping/raycasting_grid_map/raycasting_grid_map.py
index 997687423e..cae92a9dcb 100644
--- a/Mapping/raycasting_grid_map/raycasting_grid_map.py
+++ b/Mapping/raycasting_grid_map/raycasting_grid_map.py
@@ -124,6 +124,8 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             draw_heatmap(pmap, minx, maxx, miny, maxy, xyreso)
             plt.plot(ox, oy, "xr")
             plt.plot(0.0, 0.0, "ob")
diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py
index e40fc2e4c7..77c66db250 100644
--- a/Mapping/rectangle_fitting/rectangle_fitting.py
+++ b/Mapping/rectangle_fitting/rectangle_fitting.py
@@ -242,6 +242,8 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.axis("equal")
             plt.plot(0.0, 0.0, "*r")
             v1.plot()
diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py
index 6b0392f515..e4f7dd4d97 100644
--- a/PathPlanning/AStar/a_star.py
+++ b/PathPlanning/AStar/a_star.py
@@ -79,6 +79,8 @@ def planning(self, sx, sy, gx, gy):
             if show_animation:  # pragma: no cover
                 plt.plot(self.calc_grid_position(current.x, self.minx),
                          self.calc_grid_position(current.y, self.miny), "xc")
+                plt.gcf().canvas.mpl_connect('key_release_event',
+                        lambda event: [exit(0) if event.key == 'escape' else None])
                 if len(closed_set.keys()) % 10 == 0:
                     plt.pause(0.001)
 
diff --git a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
index 37ac75ada9..46ff3caf5c 100644
--- a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
+++ b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
@@ -534,6 +534,8 @@ def update_graph(self):
     def draw_graph(self, xCenter=None, cBest=None, cMin=None, etheta=None,
                    samples=None, start=None, end=None):
         plt.clf()
+        plt.gcf().canvas.mpl_connect('key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
         for rnd in samples:
             if rnd is not None:
                 plt.plot(rnd[0], rnd[1], "^k")
diff --git a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
index 0490727933..d078338d67 100644
--- a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
+++ b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
@@ -135,6 +135,8 @@ def closed_loop_prediction(cx, cy, cyaw, speed_profile, goal):
 
         if target_ind % 1 == 0 and animation:  # pragma: no cover
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(cx, cy, "-r", label="course")
             plt.plot(x, y, "ob", label="trajectory")
             plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py
index bb73ed501a..8e719f50e2 100644
--- a/PathPlanning/Dijkstra/dijkstra.py
+++ b/PathPlanning/Dijkstra/dijkstra.py
@@ -69,6 +69,8 @@ def planning(self, sx, sy, gx, gy):
             if show_animation:  # pragma: no cover
                 plt.plot(self.calc_position(current.x, self.minx),
                          self.calc_position(current.y, self.miny), "xc")
+                plt.gcf().canvas.mpl_connect('key_release_event',
+                        lambda event: [exit(0) if event.key == 'escape' else None])
                 if len(closedset.keys()) % 10 == 0:
                     plt.pause(0.001)
 
diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py
index 706b5a49af..8690fbe8db 100644
--- a/PathPlanning/DubinsPath/dubins_path_planning.py
+++ b/PathPlanning/DubinsPath/dubins_path_planning.py
@@ -318,6 +318,8 @@ def test():
 
         if show_animation:
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(px, py, label="final course " + str(mode))
 
             #  plotting
diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index c8a4434958..c2cc358b13 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -268,6 +268,8 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle):
 
         if show_animation:
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(predicted_trajectory[:, 0], predicted_trajectory[:, 1], "-g")
             plt.plot(x[0], x[1], "xr")
             plt.plot(goal[0], goal[1], "xb")
diff --git a/PathPlanning/Eta3SplinePath/eta3_spline_path.py b/PathPlanning/Eta3SplinePath/eta3_spline_path.py
index 072b0bb46c..51ac612c28 100644
--- a/PathPlanning/Eta3SplinePath/eta3_spline_path.py
+++ b/PathPlanning/Eta3SplinePath/eta3_spline_path.py
@@ -213,6 +213,8 @@ def test1():
         if show_animation:
             # plot the path
             plt.plot(pos[0, :], pos[1, :])
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.pause(1.0)
 
     if show_animation:
diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
index 5c756a9dfe..1abda92646 100644
--- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
+++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
@@ -347,6 +347,8 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(tx, ty)
             plt.plot(ob[:, 0], ob[:, 1], "xk")
             plt.plot(path.x[1:], path.y[1:], "-or")
diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
index f22b058fa8..049373ece2 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -213,6 +213,8 @@ def sweep_path_search(sweep_searcher, gmap, grid_search_animation=False):
 
     if grid_search_animation:
         fig, ax = plt.subplots()
+        fig.canvas.mpl_connect('key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
 
     while True:
         cxind, cyind = sweep_searcher.move_target_grid(cxind, cyind, gmap)
@@ -266,6 +268,8 @@ def planning_animation(ox, oy, reso):  # pragma: no cover
     if do_animation:
         for ipx, ipy in zip(px, py):
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(ox, oy, "-xb")
             plt.plot(px, py, "-r")
             plt.plot(ipx, ipy, "or")
diff --git a/PathPlanning/HybridAStar/a_star.py b/PathPlanning/HybridAStar/a_star.py
index c1c82edd0d..918469f0d3 100644
--- a/PathPlanning/HybridAStar/a_star.py
+++ b/PathPlanning/HybridAStar/a_star.py
@@ -78,6 +78,8 @@ def dp_planning(sx, sy, gx, gy, ox, oy, reso, rr):
         # show graph
         if show_animation:  # pragma: no cover
             plt.plot(current.x * reso, current.y * reso, "xc")
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             if len(closedset.keys()) % 10 == 0:
                 plt.pause(0.001)
 
@@ -228,4 +230,4 @@ def main():
 
 if __name__ == '__main__':
     show_animation = True
-    main()
\ No newline at end of file
+    main()
diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
index e82003ed25..70ffcee865 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -327,6 +327,8 @@ def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso):
 
         if show_animation:  # pragma: no cover
             plt.plot(current.xlist[-1], current.ylist[-1], "xc")
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             if len(closedList.keys()) % 10 == 0:
                 plt.pause(0.001)
 
diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index 175749183d..ef15022aca 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -266,8 +266,9 @@ def get_final_course(self, lastIndex):
         return path
 
     def draw_graph(self, xCenter=None, cBest=None, cMin=None, etheta=None, rnd=None):
-
         plt.clf()
+        plt.gcf().canvas.mpl_connect('key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
         if rnd is not None:
             plt.plot(rnd[0], rnd[1], "^k")
             if cBest != float('inf'):
diff --git a/PathPlanning/LQRPlanner/LQRplanner.py b/PathPlanning/LQRPlanner/LQRplanner.py
index 5a0b453f78..d5cda0bc50 100644
--- a/PathPlanning/LQRPlanner/LQRplanner.py
+++ b/PathPlanning/LQRPlanner/LQRplanner.py
@@ -54,6 +54,8 @@ def lqr_planning(self, sx, sy, gx, gy, show_animation=True):
 
             # animation
             if show_animation:  # pragma: no cover
+                plt.gcf().canvas.mpl_connect('key_release_event',
+                        lambda event: [exit(0) if event.key == 'escape' else None])
                 plt.plot(sx, sy, "or")
                 plt.plot(gx, gy, "ob")
                 plt.plot(rx, ry, "-r")
diff --git a/PathPlanning/LQRRRTStar/lqr_rrt_star.py b/PathPlanning/LQRRRTStar/lqr_rrt_star.py
index 2e276b9f9b..c1f45445ff 100644
--- a/PathPlanning/LQRRRTStar/lqr_rrt_star.py
+++ b/PathPlanning/LQRRRTStar/lqr_rrt_star.py
@@ -102,6 +102,8 @@ def planning(self, animation=True, search_until_max_iter=True):
 
     def draw_graph(self, rnd=None):
         plt.clf()
+        plt.gcf().canvas.mpl_connect('key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
         if rnd is not None:
             plt.plot(rnd.x, rnd.y, "^k")
         for node in self.node_list:
diff --git a/PathPlanning/PotentialFieldPlanning/potential_field_planning.py b/PathPlanning/PotentialFieldPlanning/potential_field_planning.py
index 1e918fb158..642f1cbff9 100644
--- a/PathPlanning/PotentialFieldPlanning/potential_field_planning.py
+++ b/PathPlanning/PotentialFieldPlanning/potential_field_planning.py
@@ -98,6 +98,8 @@ def potential_field_planning(sx, sy, gx, gy, ox, oy, reso, rr):
 
     if show_animation:
         draw_heatmap(pmap)
+        plt.gcf().canvas.mpl_connect('key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
         plt.plot(ix, iy, "*k")
         plt.plot(gix, giy, "*m")
 
diff --git a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
index 351232d849..3fef2d0f7a 100644
--- a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
+++ b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
@@ -194,6 +194,8 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y):
 
         # show graph
         if show_animation and len(closedset.keys()) % 2 == 0:
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(current.x, current.y, "xg")
             plt.pause(0.001)
 
diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
index ecef7d454c..b8d533d4dc 100644
--- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
+++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
@@ -151,6 +151,8 @@ def quintic_polynomials_planner(sx, sy, syaw, sv, sa, gx, gy, gyaw, gv, ga, max_
     if show_animation:  # pragma: no cover
         for i, _ in enumerate(time):
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.grid(True)
             plt.axis("equal")
             plot_arrow(sx, sy, syaw)
diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py
index 3bcc622100..9d0bc36272 100644
--- a/PathPlanning/RRT/rrt.py
+++ b/PathPlanning/RRT/rrt.py
@@ -138,6 +138,8 @@ def get_random_node(self):
 
     def draw_graph(self, rnd=None):
         plt.clf()
+        plt.gcf().canvas.mpl_connect('key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
         if rnd is not None:
             plt.plot(rnd.x, rnd.y, "^k")
         for node in self.node_list:
diff --git a/PathPlanning/RRTDubins/rrt_dubins.py b/PathPlanning/RRTDubins/rrt_dubins.py
index 339acd2c3a..2a842aa42c 100644
--- a/PathPlanning/RRTDubins/rrt_dubins.py
+++ b/PathPlanning/RRTDubins/rrt_dubins.py
@@ -106,6 +106,8 @@ def planning(self, animation=True, search_until_max_iter=True):
 
     def draw_graph(self, rnd=None):  # pragma: no cover
         plt.clf()
+        plt.gcf().canvas.mpl_connect('key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
         if rnd is not None:
             plt.plot(rnd.x, rnd.y, "^k")
         for node in self.node_list:
diff --git a/PathPlanning/RRTStarDubins/rrt_star_dubins.py b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
index 4d7312933d..d4601c0f12 100644
--- a/PathPlanning/RRTStarDubins/rrt_star_dubins.py
+++ b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
@@ -112,6 +112,8 @@ def planning(self, animation=True, search_until_max_iter=True):
 
     def draw_graph(self, rnd=None):
         plt.clf()
+        plt.gcf().canvas.mpl_connect('key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
         if rnd is not None:
             plt.plot(rnd.x, rnd.y, "^k")
         for node in self.node_list:
diff --git a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
index 5d78f98f31..d37ae90634 100644
--- a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
+++ b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
@@ -122,6 +122,8 @@ def try_goal_path(self, node):
 
     def draw_graph(self, rnd=None):
         plt.clf()
+        plt.gcf().canvas.mpl_connect('key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
         if rnd is not None:
             plt.plot(rnd.x, rnd.y, "^k")
         for node in self.node_list:
diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
index 54b5adbe6d..b2f8d4a57d 100644
--- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
+++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
@@ -395,6 +395,8 @@ def test():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(px, py, label="final course " + str(mode))
 
             #  plotting
diff --git a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
index 3ac02993b7..86371b2051 100644
--- a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
+++ b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
@@ -185,6 +185,8 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y):
         # show graph
         if show_animation and len(closedset.keys()) % 2 == 0:  # pragma: no cover
             plt.plot(current.x, current.y, "xg")
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.pause(0.001)
 
         if c_id == (len(road_map) - 1):
diff --git a/PathTracking/cgmres_nmpc/cgmres_nmpc.py b/PathTracking/cgmres_nmpc/cgmres_nmpc.py
index 74a0f9cc57..1be91b2d20 100644
--- a/PathTracking/cgmres_nmpc/cgmres_nmpc.py
+++ b/PathTracking/cgmres_nmpc/cgmres_nmpc.py
@@ -546,6 +546,8 @@ def animation(plant, controller, dt):
             steer = math.atan2(controller.history_u_2[t] * WB / v, 1.0)
 
         plt.cla()
+        plt.gcf().canvas.mpl_connect('key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
         plt.plot(plant.history_x, plant.history_y, "-r", label="trajectory")
         plot_car(x, y, yaw, steer=steer)
         plt.axis("equal")
diff --git a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
index 6dec12bd30..0e22529623 100644
--- a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
+++ b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
@@ -224,6 +224,8 @@ def do_simulation(cx, cy, cyaw, ck, speed_profile, goal):
 
         if target_ind % 1 == 0 and show_animation:
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(cx, cy, "-r", label="course")
             plt.plot(x, y, "ob", label="trajectory")
             plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
diff --git a/PathTracking/lqr_steer_control/lqr_steer_control.py b/PathTracking/lqr_steer_control/lqr_steer_control.py
index f977cdc2a1..9cc36f231f 100644
--- a/PathTracking/lqr_steer_control/lqr_steer_control.py
+++ b/PathTracking/lqr_steer_control/lqr_steer_control.py
@@ -203,6 +203,8 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
 
         if target_ind % 1 == 0 and show_animation:
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(cx, cy, "-r", label="course")
             plt.plot(x, y, "ob", label="trajectory")
             plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
diff --git a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
index 0f5104e954..8bff2e107d 100644
--- a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
+++ b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
@@ -429,6 +429,8 @@ def do_simulation(cx, cy, cyaw, ck, sp, dl, initial_state):
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             if ox is not None:
                 plt.plot(ox, oy, "xr", label="MPC")
             plt.plot(cx, cy, "-r", label="course")
diff --git a/PathTracking/move_to_pose/move_to_pose.py b/PathTracking/move_to_pose/move_to_pose.py
index 0814ef62ef..838b780f48 100644
--- a/PathTracking/move_to_pose/move_to_pose.py
+++ b/PathTracking/move_to_pose/move_to_pose.py
@@ -93,6 +93,9 @@ def plot_vehicle(x, y, theta, x_traj, y_traj):  # pragma: no cover
 
     plt.plot(x_traj, y_traj, 'b--')
 
+    plt.gcf().canvas.mpl_connect('key_release_event',
+            lambda event: [exit(0) if event.key == 'escape' else None])
+
     plt.xlim(0, 20)
     plt.ylim(0, 20)
 
diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index 38a84affce..f9624956c4 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -166,6 +166,8 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plot_arrow(state.x, state.y, state.yaw)
             plt.plot(cx, cy, "-r", label="course")
             plt.plot(x, y, "-b", label="trajectory")
diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
index 285d9404e3..155eeec2ce 100644
--- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
+++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
@@ -149,6 +149,8 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
 
         if target_ind % 1 == 0 and show_animation:
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(cx, cy, "-r", label="course")
             plt.plot(x, y, "ob", label="trajectory")
             plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py
index addd5ad3a1..cbfa4c667e 100644
--- a/PathTracking/stanley_controller/stanley_controller.py
+++ b/PathTracking/stanley_controller/stanley_controller.py
@@ -186,6 +186,8 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(cx, cy, ".r", label="course")
             plt.plot(x, y, "-b", label="trajectory")
             plt.plot(cx[target_idx], cy[target_idx], "xg", label="target")
diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py
index 27e5035459..33a9b1ddb1 100644
--- a/SLAM/EKFSLAM/ekf_slam.py
+++ b/SLAM/EKFSLAM/ekf_slam.py
@@ -235,6 +235,8 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
 
             plt.plot(RFID[:, 0], RFID[:, 1], "*k")
             plt.plot(xEst[0], xEst[1], ".r")
diff --git a/SLAM/FastSLAM1/fast_slam1.py b/SLAM/FastSLAM1/fast_slam1.py
index c16407e89f..ef1282995b 100644
--- a/SLAM/FastSLAM1/fast_slam1.py
+++ b/SLAM/FastSLAM1/fast_slam1.py
@@ -365,6 +365,8 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(RFID[:, 0], RFID[:, 1], "*k")
 
             for i in range(N_PARTICLE):
diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py
index 73624dbf1f..f8958b8c73 100644
--- a/SLAM/FastSLAM2/fast_slam2.py
+++ b/SLAM/FastSLAM2/fast_slam2.py
@@ -390,6 +390,8 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(RFID[:, 0], RFID[:, 1], "*k")
 
             for iz in range(len(z[:, 0])):
diff --git a/SLAM/GraphBasedSLAM/graph_based_slam.py b/SLAM/GraphBasedSLAM/graph_based_slam.py
index b40ad6494e..e73e8d0d4a 100644
--- a/SLAM/GraphBasedSLAM/graph_based_slam.py
+++ b/SLAM/GraphBasedSLAM/graph_based_slam.py
@@ -303,7 +303,8 @@ def main():
 
             if show_animation:  # pragma: no cover
                 plt.cla()
-
+                plt.gcf().canvas.mpl_connect('key_release_event',
+                        lambda event: [exit(0) if event.key == 'escape' else None])
                 plt.plot(RFID[:, 0], RFID[:, 1], "*k")
 
                 plt.plot(hxTrue[0, :].flatten(),
@@ -319,4 +320,4 @@ def main():
 
 
 if __name__ == '__main__':
-    main()
\ No newline at end of file
+    main()
diff --git a/SLAM/iterative_closest_point/iterative_closest_point.py b/SLAM/iterative_closest_point/iterative_closest_point.py
index 3512ef976b..4a4d29eb5e 100644
--- a/SLAM/iterative_closest_point/iterative_closest_point.py
+++ b/SLAM/iterative_closest_point/iterative_closest_point.py
@@ -36,6 +36,8 @@ def icp_matching(previous_points, current_points):
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(previous_points[0, :], previous_points[1, :], ".r")
             plt.plot(current_points[0, :], current_points[1, :], ".b")
             plt.plot(0.0, 0.0, "xr")

From 8c701762b40e38318a4dd03485e9824504de458b Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <Gjacquenot@users.noreply.github.com>
Date: Sat, 7 Dec 2019 13:54:35 +0100
Subject: [PATCH 0241/1080] Fixed typo in stanley_controller.py

---
 PathTracking/stanley_controller/stanley_controller.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py
index addd5ad3a1..42a446dda3 100644
--- a/PathTracking/stanley_controller/stanley_controller.py
+++ b/PathTracking/stanley_controller/stanley_controller.py
@@ -21,7 +21,7 @@
 
 
 k = 0.5  # control gain
-Kp = 1.0  # speed propotional gain
+Kp = 1.0  # speed proportional gain
 dt = 0.1  # [s] time difference
 L = 2.9  # [m] Wheel base of vehicle
 max_steer = np.radians(30.0)  # [rad] max steering angle

From 22e92ed5583875fbc4e46fce044059f936b0be4e Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <Gjacquenot@users.noreply.github.com>
Date: Sat, 7 Dec 2019 14:40:28 +0100
Subject: [PATCH 0242/1080] Simplified calc_target_index function in
 stanley_controller.py

Used:

- np.hypot to replace np.sqrt(x**2 + y**2)
- np.argmin to find location of a minimum in a numpy array
---
 PathTracking/stanley_controller/stanley_controller.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py
index 42a446dda3..9b808dd963 100644
--- a/PathTracking/stanley_controller/stanley_controller.py
+++ b/PathTracking/stanley_controller/stanley_controller.py
@@ -127,20 +127,19 @@ def calc_target_index(state, cx, cy):
     :param cy: [float]
     :return: (int, float)
     """
-   # Calc front axle position
+    # Calc front axle position
     fx = state.x + L * np.cos(state.yaw)
     fy = state.y + L * np.sin(state.yaw)
 
     # Search nearest point index
     dx = [fx - icx for icx in cx]
     dy = [fy - icy for icy in cy]
-    d = [np.sqrt(idx ** 2 + idy ** 2) for (idx, idy) in zip(dx, dy)]
-    closest_error = min(d)
-    target_idx = d.index(closest_error)
+    d = np.hypot(dx, dy)
+    target_idx = np.argmin(d)
 
     # Project RMS error onto front axle vector
     front_axle_vec = [-np.cos(state.yaw + np.pi / 2),
-                      - np.sin(state.yaw + np.pi / 2)]
+                      -np.sin(state.yaw + np.pi / 2)]
     error_front_axle = np.dot([dx[target_idx], dy[target_idx]], front_axle_vec)
 
     return target_idx, error_front_axle

From 652c32fba9eadfeb1e2200b2f7e475de1acfc5bb Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:10:53 +0100
Subject: [PATCH 0243/1080] Replaced sqrt(x**2+y**2) with hypot in
 SLAM/GraphBasedSLAM/graph_based_slam.py

---
 SLAM/GraphBasedSLAM/graph_based_slam.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/SLAM/GraphBasedSLAM/graph_based_slam.py b/SLAM/GraphBasedSLAM/graph_based_slam.py
index b40ad6494e..41b9d52591 100644
--- a/SLAM/GraphBasedSLAM/graph_based_slam.py
+++ b/SLAM/GraphBasedSLAM/graph_based_slam.py
@@ -216,7 +216,7 @@ def observation(xTrue, xd, u, RFID):
 
         dx = RFID[i, 0] - xTrue[0, 0]
         dy = RFID[i, 1] - xTrue[1, 0]
-        d = math.sqrt(dx**2 + dy**2)
+        d = math.hypot(dx, dy)
         angle = pi_2_pi(math.atan2(dy, dx)) - xTrue[2, 0]
         phi = pi_2_pi(math.atan2(dy, dx))
         if d <= MAX_RANGE:

From 1d03241d5d6319fc1cdc42f6a11b560ee23dc0d2 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:11:28 +0100
Subject: [PATCH 0244/1080] Replaced sqrt(x**2+y**2) with hypot in
 SLAM/FastSLAM2/fast_slam2.py

---
 SLAM/FastSLAM2/fast_slam2.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py
index 73624dbf1f..b9df694830 100644
--- a/SLAM/FastSLAM2/fast_slam2.py
+++ b/SLAM/FastSLAM2/fast_slam2.py
@@ -304,7 +304,7 @@ def observation(xTrue, xd, u, RFID):
 
         dx = RFID[i, 0] - xTrue[0, 0]
         dy = RFID[i, 1] - xTrue[1, 0]
-        d = math.sqrt(dx ** 2 + dy ** 2)
+        d = math.hypot(dx, dy)
         angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
         if d <= MAX_RANGE:
             dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5  # add noise

From f1926bdd9e08733f7dce997fab3f0e6f4e97476f Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:11:57 +0100
Subject: [PATCH 0245/1080] Replaced sqrt(x**2+y**2) with hypot in
 SLAM/FastSLAM2/fast_slam1.py

---
 SLAM/FastSLAM1/fast_slam1.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/SLAM/FastSLAM1/fast_slam1.py b/SLAM/FastSLAM1/fast_slam1.py
index c16407e89f..298cc0363a 100644
--- a/SLAM/FastSLAM1/fast_slam1.py
+++ b/SLAM/FastSLAM1/fast_slam1.py
@@ -279,7 +279,7 @@ def observation(xTrue, xd, u, RFID):
 
         dx = RFID[i, 0] - xTrue[0, 0]
         dy = RFID[i, 1] - xTrue[1, 0]
-        d = math.sqrt(dx ** 2 + dy ** 2)
+        d = math.hypot(dx, dy)
         angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
         if d <= MAX_RANGE:
             dn = d + np.random.randn() * Qsim[0, 0] ** 0.5  # add noise

From 02f14e425efab343a26acbbf2feeb139cbe337b6 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:13:20 +0100
Subject: [PATCH 0246/1080] Replaced sqrt(x**2+y**2) with hypot in
 SLAM/EKFSLAM/ekf_slam.py

---
 SLAM/EKFSLAM/ekf_slam.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py
index 22ccd8d659..66234b57ec 100644
--- a/SLAM/EKFSLAM/ekf_slam.py
+++ b/SLAM/EKFSLAM/ekf_slam.py
@@ -76,7 +76,7 @@ def observation(xTrue, xd, u, RFID):
 
         dx = RFID[i, 0] - xTrue[0, 0]
         dy = RFID[i, 1] - xTrue[1, 0]
-        d = math.sqrt(dx ** 2 + dy ** 2)
+        d = math.hypot(dx, dy)
         angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
         if d <= MAX_RANGE:
             dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5  # add noise

From 41bcdf8573e4cd3b1773934f8b30d0363edda0f3 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:14:48 +0100
Subject: [PATCH 0247/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathTracking/rear_wheel_feedback/rear_wheel_feedback.py

---
 PathTracking/rear_wheel_feedback/rear_wheel_feedback.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
index 285d9404e3..bf4f31b39a 100644
--- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
+++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
@@ -136,7 +136,7 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
         # check goal
         dx = state.x - goal[0]
         dy = state.y - goal[1]
-        if math.sqrt(dx ** 2 + dy ** 2) <= goal_dis:
+        if math.hypot(dx, dy) <= goal_dis:
             print("Goal")
             goal_flag = True
             break

From e500f65d7ff6f8ddc82b5c9375fe8e423f8af387 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:16:12 +0100
Subject: [PATCH 0248/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathTracking/pure_pursuit/pure_pursuit.py

---
 PathTracking/pure_pursuit/pure_pursuit.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index 38a84affce..370cac96cc 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -77,7 +77,7 @@ def calc_distance(state, point_x, point_y):
 
     dx = state.rear_x - point_x
     dy = state.rear_y - point_y
-    return math.sqrt(dx ** 2 + dy ** 2)
+    return math.hypot(dx, dy)
 
 
 def calc_target_index(state, cx, cy):
@@ -88,7 +88,7 @@ def calc_target_index(state, cx, cy):
         # search nearest point index
         dx = [state.rear_x - icx for icx in cx]
         dy = [state.rear_y - icy for icy in cy]
-        d = [abs(math.sqrt(idx ** 2 + idy ** 2)) for (idx, idy) in zip(dx, dy)]
+        d = [idx ** 2 + idy ** 2 for (idx, idy) in zip(dx, dy)]
         ind = d.index(min(d))
         old_nearest_point_index = ind
     else:
@@ -110,7 +110,7 @@ def calc_target_index(state, cx, cy):
     while Lf > L and (ind + 1) < len(cx):
         dx = cx[ind] - state.rear_x
         dy = cy[ind] - state.rear_y
-        L = math.sqrt(dx ** 2 + dy ** 2)
+        L = math.hypot(dx, dy)
         ind += 1
 
     return ind

From 4ef4658f82c4baa4784b430e67847ed24aa8dd48 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:18:00 +0100
Subject: [PATCH 0249/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathTracking/move_to_pose/move_to_pose.py

---
 PathTracking/move_to_pose/move_to_pose.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathTracking/move_to_pose/move_to_pose.py b/PathTracking/move_to_pose/move_to_pose.py
index 0814ef62ef..e161b4616d 100644
--- a/PathTracking/move_to_pose/move_to_pose.py
+++ b/PathTracking/move_to_pose/move_to_pose.py
@@ -40,7 +40,7 @@ def move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal):
 
     x_traj, y_traj = [], []
 
-    rho = np.sqrt(x_diff**2 + y_diff**2)
+    rho = np.hypot(x_diff, y_diff)
     while rho > 0.001:
         x_traj.append(x)
         y_traj.append(y)
@@ -52,7 +52,7 @@ def move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal):
         # [-pi, pi] to prevent unstable behavior e.g. difference going
         # from 0 rad to 2*pi rad with slight turn
 
-        rho = np.sqrt(x_diff**2 + y_diff**2)
+        rho = np.hypot(x_diff, y_diff)
         alpha = (np.arctan2(y_diff, x_diff)
                  - theta + np.pi) % (2 * np.pi) - np.pi
         beta = (theta_goal - theta - alpha + np.pi) % (2 * np.pi) - np.pi

From 9f5b0a25cc4c0727087dddde445862e727f283f5 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:19:05 +0100
Subject: [PATCH 0250/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py

---
 .../model_predictive_speed_and_steer_control.py                 | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
index 0f5104e954..a16768e36b 100644
--- a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
+++ b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
@@ -348,7 +348,7 @@ def check_goal(state, goal, tind, nind):
     # check goal
     dx = state.x - goal[0]
     dy = state.y - goal[1]
-    d = math.sqrt(dx ** 2 + dy ** 2)
+    d = math.hypot(dx, dy)
 
     isgoal = (d <= GOAL_DIS)
 

From db78f07c0bc084748632bf36af713d249bf0647c Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:19:41 +0100
Subject: [PATCH 0251/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathTracking/lqr_steer_control/lqr_steer_control.py

---
 PathTracking/lqr_steer_control/lqr_steer_control.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathTracking/lqr_steer_control/lqr_steer_control.py b/PathTracking/lqr_steer_control/lqr_steer_control.py
index f977cdc2a1..b69577d3aa 100644
--- a/PathTracking/lqr_steer_control/lqr_steer_control.py
+++ b/PathTracking/lqr_steer_control/lqr_steer_control.py
@@ -191,7 +191,7 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
         # check goal
         dx = state.x - goal[0]
         dy = state.y - goal[1]
-        if math.sqrt(dx ** 2 + dy ** 2) <= goal_dis:
+        if math.hypot(dx, dy) <= goal_dis:
             print("Goal")
             break
 

From bc2b7c969cd37b572372c5cdaa36b680a02f7c56 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:20:12 +0100
Subject: [PATCH 0252/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py

---
 PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
index 6dec12bd30..c7c8e0dd97 100644
--- a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
+++ b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
@@ -212,7 +212,7 @@ def do_simulation(cx, cy, cyaw, ck, speed_profile, goal):
         # check goal
         dx = state.x - goal[0]
         dy = state.y - goal[1]
-        if math.sqrt(dx ** 2 + dy ** 2) <= goal_dis:
+        if math.hypot(dx, dy) <= goal_dis:
             print("Goal")
             break
 

From 26cff8e69fdbe7c11a55b83a3668e85e24aa952d Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:20:58 +0100
Subject: [PATCH 0253/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathPlanning/RRT/rrt.py

---
 PathPlanning/RRT/rrt.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py
index 3bcc622100..5b6f3252ae 100644
--- a/PathPlanning/RRT/rrt.py
+++ b/PathPlanning/RRT/rrt.py
@@ -126,7 +126,7 @@ def generate_final_course(self, goal_ind):
     def calc_dist_to_goal(self, x, y):
         dx = x - self.end.x
         dy = y - self.end.y
-        return math.sqrt(dx ** 2 + dy ** 2)
+        return math.hypot(dx, dy)
 
     def get_random_node(self):
         if random.randint(0, 100) > self.goal_sample_rate:
@@ -186,7 +186,7 @@ def check_collision(node, obstacleList):
     def calc_distance_and_angle(from_node, to_node):
         dx = to_node.x - from_node.x
         dy = to_node.y - from_node.y
-        d = math.sqrt(dx ** 2 + dy ** 2)
+        d = math.hypot(dx, dy)
         theta = math.atan2(dy, dx)
         return d, theta
 

From 5221a9df2a82d9a527930c59057cfaa398c6f403 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:21:29 +0100
Subject: [PATCH 0254/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathPlanning/InformedRRTStar/informed_rrt_star.py

---
 PathPlanning/InformedRRTStar/informed_rrt_star.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index 175749183d..065868f2d2 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -106,7 +106,7 @@ def choose_parent(self, newNode, nearInds):
         for i in nearInds:
             dx = newNode.x - self.node_list[i].x
             dy = newNode.y - self.node_list[i].y
-            d = math.sqrt(dx ** 2 + dy ** 2)
+            d = math.hypot(dx, dy)
             theta = math.atan2(dy, dx)
             if self.check_collision(self.node_list[i], theta, d):
                 dList.append(self.node_list[i].cost + d)
@@ -224,7 +224,7 @@ def rewire(self, newNode, nearInds):
                 if self.check_collision(nearNode, theta, d):
                     nearNode.parent = n_node - 1
                     nearNode.cost = scost
-                    
+
     @staticmethod
     def distance_squared_point_to_segment(v, w, p):
         # Return minimum distance between line segment vw and point p
@@ -232,7 +232,7 @@ def distance_squared_point_to_segment(v, w, p):
             return (p-v).dot(p-v) # v == w case
         l2 = (w-v).dot(w-v) # i.e. |w-v|^2 -  avoid a sqrt
         # Consider the line extending the segment, parameterized as v + t (w - v).
-        # We find projection of point p onto the line. 
+        # We find projection of point p onto the line.
         # It falls where t = [(p-v) . (w-v)] / |w-v|^2
         # We clamp t from [0,1] to handle points outside the segment vw.
         t = max(0, min(1, (p - v).dot(w - v) / l2))

From 06cbcc3ee55598f54b612c2f842cfb052f8b751e Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:22:14 +0100
Subject: [PATCH 0255/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py

---
 .../GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
index f22b058fa8..9d7e1616f3 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -118,7 +118,7 @@ def find_sweep_direction_and_start_posi(ox, oy):
     for i in range(len(ox) - 1):
         dx = ox[i + 1] - ox[i]
         dy = oy[i + 1] - oy[i]
-        d = np.sqrt(dx ** 2 + dy ** 2)
+        d = np.hypot(dx, dy)
 
         if d > max_dist:
             max_dist = d

From 1eb3ebbf26be7f000275f2a897d46a008613814e Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:22:47 +0100
Subject: [PATCH 0256/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathPlanning/DynamicWindowApproach/dynamic_window_approach.py

---
 PathPlanning/DynamicWindowApproach/dynamic_window_approach.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index c8a4434958..5c3d128dc4 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -164,7 +164,7 @@ def calc_obstacle_cost(trajectory, ob, config):
     oy = ob[:, 1]
     dx = trajectory[:, 0] - ox[:, None]
     dy = trajectory[:, 1] - oy[:, None]
-    r = np.sqrt(np.square(dx) + np.square(dy))
+    r = np.hypot(dx, dy)
 
     if config.robot_type == RobotType.rectangle:
         yaw = trajectory[:, 2]

From 00f8fd37d367aa6409b70e945356f1aa5abc4fde Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:24:27 +0100
Subject: [PATCH 0257/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathPlanning/ClosedLoopRRTStar/pure_pursuit.py

---
 PathPlanning/ClosedLoopRRTStar/pure_pursuit.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
index 0490727933..32faa51c11 100644
--- a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
+++ b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
@@ -68,17 +68,17 @@ def calc_target_index(state, cx, cy):
     dx = [state.x - icx for icx in cx]
     dy = [state.y - icy for icy in cy]
 
-    d = [abs(math.sqrt(idx ** 2 + idy ** 2)) for (idx, idy) in zip(dx, dy)]
+    d = np.hypot(dx, dy)
     mindis = min(d)
 
-    ind = d.index(mindis)
+    ind = np.argmin(d)
 
     L = 0.0
 
     while Lf > L and (ind + 1) < len(cx):
         dx = cx[ind + 1] - cx[ind]
         dy = cy[ind + 1] - cy[ind]
-        L += math.sqrt(dx ** 2 + dy ** 2)
+        L += math.hypot(dx, dy)
         ind += 1
 
     #  print(mindis)
@@ -121,7 +121,7 @@ def closed_loop_prediction(cx, cy, cyaw, speed_profile, goal):
         # check goal
         dx = state.x - goal[0]
         dy = state.y - goal[1]
-        if math.sqrt(dx ** 2 + dy ** 2) <= goal_dis:
+        if math.hypot(dx, dy) <= goal_dis:
             find_goal = True
             break
 

From 1fcfa72133522ce004b99ad079d9f7361ee8f981 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:30:04 +0100
Subject: [PATCH 0258/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py

---
 PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
index a07dc48af0..aea0080112 100644
--- a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
+++ b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
@@ -6,7 +6,6 @@
 
 """
 
-import math
 import os
 import sys
 
@@ -119,9 +118,8 @@ def check_tracking_path_is_feasible(self, path):
             print("final angle is bad")
             find_goal = False
 
-        travel = sum([abs(iv) * unicycle_model.dt for iv in v])
-        origin_travel = sum([math.sqrt(dx ** 2 + dy ** 2)
-                             for (dx, dy) in zip(np.diff(cx), np.diff(cy))])
+        travel = unicycle_model.dt * sum(np.abs(v))
+        origin_travel = sum(np.hypot(np.diff(cx), np.diff(cy)))
 
         if (travel / origin_travel) >= self.invalid_travel_ratio:
             print("path is too long")

From 0aaf6406d82f7067af460160f4d75495d59fa80f Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:30:39 +0100
Subject: [PATCH 0259/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathPlanning/BezierPath/bezier_path.py

---
 PathPlanning/BezierPath/bezier_path.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/BezierPath/bezier_path.py b/PathPlanning/BezierPath/bezier_path.py
index 516a681e3b..46faf1f310 100644
--- a/PathPlanning/BezierPath/bezier_path.py
+++ b/PathPlanning/BezierPath/bezier_path.py
@@ -26,7 +26,7 @@ def calc_4points_bezier_path(sx, sy, syaw, ex, ey, eyaw, offset):
     :param offset: (float)
     :return: (numpy array, numpy array)
     """
-    dist = np.sqrt((sx - ex) ** 2 + (sy - ey) ** 2) / offset
+    dist = np.hypot(sx - ex, sy - ey) / offset
     control_points = np.array(
         [[sx, sy],
          [sx + dist * np.cos(syaw), sy + dist * np.sin(syaw)],

From 72a28b4d4b850e3c2ac7e13ffacc2ea01736f0db Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:31:05 +0100
Subject: [PATCH 0260/1080] Replaced sqrt(x**2+y**2) with hypot in
 Mapping/rectangle_fitting/rectangle_fitting.py

---
 Mapping/rectangle_fitting/rectangle_fitting.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py
index e40fc2e4c7..2018f02514 100644
--- a/Mapping/rectangle_fitting/rectangle_fitting.py
+++ b/Mapping/rectangle_fitting/rectangle_fitting.py
@@ -162,7 +162,7 @@ def _adoptive_range_segmentation(self, ox, oy):
             C = set()
             R = self.R0 + self.Rd * np.linalg.norm([ox[i], oy[i]])
             for j, _ in enumerate(ox):
-                d = np.sqrt((ox[i] - ox[j])**2 + (oy[i] - oy[j])**2)
+                d = np.hypot(ox[i] - ox[j], oy[i] - oy[j])
                 if d <= R:
                     C.add(j)
             S.append(C)

From 2cb9e37792180fed4d73ce5db35a071ab10d2d17 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:32:02 +0100
Subject: [PATCH 0261/1080] Replaced sqrt(x**2+y**2) with hypot in
 Mapping/kmeans_clustering/kmeans_clustering.py

---
 Mapping/kmeans_clustering/kmeans_clustering.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Mapping/kmeans_clustering/kmeans_clustering.py b/Mapping/kmeans_clustering/kmeans_clustering.py
index bdaa45455e..c2518b80c0 100644
--- a/Mapping/kmeans_clustering/kmeans_clustering.py
+++ b/Mapping/kmeans_clustering/kmeans_clustering.py
@@ -68,7 +68,7 @@ def update_clusters(self):
             dx = [icx - px for icx in self.center_x]
             dy = [icy - py for icy in self.center_y]
 
-            dist_list = [math.sqrt(idx ** 2 + idy ** 2) for (idx, idy) in zip(dx, dy)]
+            dist_list = [math.hypot(idx, idy) for (idx, idy) in zip(dx, dy)]
             min_dist = min(dist_list)
             min_id = dist_list.index(min_dist)
             self.labels[ip] = min_id

From 4545084da430bd353239565715bb22dde1c1c96f Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:32:40 +0100
Subject: [PATCH 0262/1080] Replaced sqrt(x**2+y**2) with hypot in
 Localization/particle_filter/particle_filter.py

---
 Localization/particle_filter/particle_filter.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py
index 0ebb5fc3d1..d14b7c0697 100644
--- a/Localization/particle_filter/particle_filter.py
+++ b/Localization/particle_filter/particle_filter.py
@@ -47,7 +47,7 @@ def observation(xTrue, xd, u, RF_ID):
 
         dx = xTrue[0, 0] - RF_ID[i, 0]
         dy = xTrue[1, 0] - RF_ID[i, 1]
-        d = math.sqrt(dx ** 2 + dy ** 2)
+        d = math.hypot(dx, dy)
         if d <= MAX_RANGE:
             dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5  # add noise
             zi = np.array([[dn, RF_ID[i, 0], RF_ID[i, 1]]])
@@ -116,7 +116,7 @@ def pf_localization(px, pw, z, u):
         for i in range(len(z[:, 0])):
             dx = x[0, 0] - z[i, 1]
             dy = x[1, 0] - z[i, 2]
-            pre_z = math.sqrt(dx ** 2 + dy ** 2)
+            pre_z = math.hypot(dx, dy)
             dz = pre_z - z[i, 0]
             w = w * gauss_likelihood(dz, math.sqrt(Q[0, 0]))
 

From f66a10f42eca2a57cb8004f16403fd67e0788d58 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:33:21 +0100
Subject: [PATCH 0263/1080] Replaced sqrt(x**2+y**2) with hypot in
 Localization/histogram_filter/histogram_filter.py

---
 Localization/histogram_filter/histogram_filter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py
index bdfbb13fa7..8404646ca5 100644
--- a/Localization/histogram_filter/histogram_filter.py
+++ b/Localization/histogram_filter/histogram_filter.py
@@ -126,7 +126,7 @@ def observation(xTrue, u, RFID):
 
         dx = xTrue[0, 0] - RFID[i, 0]
         dy = xTrue[1, 0] - RFID[i, 1]
-        d = math.sqrt(dx ** 2 + dy ** 2)
+        d = math.hypot(dx, dy)
         if d <= MAX_RANGE:
             # add noise to range observation
             dn = d + np.random.randn() * NOISE_RANGE

From e289d1f9a9ea1a1284ab3f4e34cd89c5c21c72b8 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:33:52 +0100
Subject: [PATCH 0264/1080] Replaced sqrt(x**2+y**2) with hypot in
 Localization/ensemble_kalman_filter/ensemble_kalman_filter.py

---
 Localization/ensemble_kalman_filter/ensemble_kalman_filter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
index 293509157b..3e2af749e7 100644
--- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
+++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
@@ -44,7 +44,7 @@ def observation(xTrue, xd, u, RFID):
 
         dx = RFID[i, 0] - xTrue[0, 0]
         dy = RFID[i, 1] - xTrue[1, 0]
-        d = math.sqrt(dx ** 2 + dy ** 2)
+        d = math.hypot(dx, dy)
         angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
         if d <= MAX_RANGE:
             dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5  # add noise

From 84aead6149b885b44ad661b92cd8fa2ac6431a41 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:35:06 +0100
Subject: [PATCH 0265/1080] Replaced sqrt(x**2+y**2) with hypot in
 ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py

---
 .../two_joint_arm_to_point_control.py                           | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
index e674ae8ac2..0e91be0ff6 100644
--- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
+++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
@@ -55,7 +55,7 @@ def two_joint_arm(GOAL_TH=0.0, theta1=0.0, theta2=0.0):
         wrist = plot_arm(theta1, theta2, x, y)
 
         # check goal
-        d2goal = np.math.sqrt((wrist[0] - x)**2 + (wrist[1] - y)**2)
+        d2goal = np.hypot(wrist[0] - x, wrist[1] - y)
 
         if abs(d2goal) < GOAL_TH and x is not None:
             return theta1, theta2

From d979bb24d9cf6272a24bd000f5642cc1443c7b86 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:35:44 +0100
Subject: [PATCH 0266/1080] Replaced sqrt(x**2+y**2) with hypot in
 ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py

---
 .../n_joint_arm_to_point_control.py                             | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py b/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py
index b232ab33f3..3972474b8d 100644
--- a/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py
+++ b/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py
@@ -148,7 +148,7 @@ def jacobian_inverse(link_lengths, joint_angles):
 def distance_to_goal(current_pos, goal_pos):
     x_diff = goal_pos[0] - current_pos[0]
     y_diff = goal_pos[1] - current_pos[1]
-    return np.array([x_diff, y_diff]).T, np.math.sqrt(x_diff**2 + y_diff**2)
+    return np.array([x_diff, y_diff]).T, np.hypot(x_diff, y_diff)
 
 
 def ang_diff(theta1, theta2):

From d26eb04ae2c2f1047271e913977f27dfc129aad3 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:36:25 +0100
Subject: [PATCH 0267/1080] Removed useless zero in
 erialNavigation/rocket_powered_landing/rocket_powered_landing.py

---
 .../rocket_powered_landing/rocket_powered_landing.py          | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
index 9f1e16fc1d..dde76f19bd 100644
--- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
+++ b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
@@ -195,8 +195,8 @@ def B_func(self, x, u):
             [0, 0, 0],
             [0, 0, 0],
             [0, 0, 0],
-            [0, 0, 1.00000000000000],
-            [0, -1.00000000000000, 0]
+            [0, 0, 1.0],
+            [0, -1.0, 0]
         ])
 
     def euler_to_quat(self, a):

From e467f4fad1d055b947f565ac5d27e6dd21528621 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:53:07 +0100
Subject: [PATCH 0268/1080] Replaced sqrt(x**2+y**2) with hypot in
 Localization/histogram_filter/histogram_filter.py

---
 Localization/histogram_filter/histogram_filter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py
index 8404646ca5..db715a6d1e 100644
--- a/Localization/histogram_filter/histogram_filter.py
+++ b/Localization/histogram_filter/histogram_filter.py
@@ -68,7 +68,7 @@ def calc_gaussian_observation_pdf(gmap, z, iz, ix, iy, std):
     # predicted range
     x = ix * gmap.xy_reso + gmap.minx
     y = iy * gmap.xy_reso + gmap.miny
-    d = math.sqrt((x - z[iz, 1]) ** 2 + (y - z[iz, 2]) ** 2)
+    d = math.hypot(x - z[iz, 1], y - z[iz, 2])
 
     # likelihood
     pdf = (1.0 - norm.cdf(abs(d - z[iz, 0]), 0.0, std))

From ccf047135e558332f9e460e21f8263bfb3e2974a Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:53:07 +0100
Subject: [PATCH 0269/1080] Replaced sqrt(x**2+y**2) with hypot in
 Mapping/gaussian_grid_map/gaussian_grid_map.py

---
 Mapping/gaussian_grid_map/gaussian_grid_map.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Mapping/gaussian_grid_map/gaussian_grid_map.py b/Mapping/gaussian_grid_map/gaussian_grid_map.py
index b520b63944..9c1aa27320 100644
--- a/Mapping/gaussian_grid_map/gaussian_grid_map.py
+++ b/Mapping/gaussian_grid_map/gaussian_grid_map.py
@@ -31,7 +31,7 @@ def generate_gaussian_grid_map(ox, oy, xyreso, std):
             # Search minimum distance
             mindis = float("inf")
             for (iox, ioy) in zip(ox, oy):
-                d = math.sqrt((iox - x)**2 + (ioy - y)**2)
+                d = math.hypot(iox - x, ioy - y)
                 if mindis >= d:
                     mindis = d
 

From 1273934a7ea2723375422dc29081a014f649dbb8 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:53:07 +0100
Subject: [PATCH 0270/1080] Replaced sqrt(x**2+y**2) with hypot in
 Mapping/raycasting_grid_map/raycasting_grid_map.py

---
 Mapping/raycasting_grid_map/raycasting_grid_map.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Mapping/raycasting_grid_map/raycasting_grid_map.py b/Mapping/raycasting_grid_map/raycasting_grid_map.py
index 997687423e..a0d9060916 100644
--- a/Mapping/raycasting_grid_map/raycasting_grid_map.py
+++ b/Mapping/raycasting_grid_map/raycasting_grid_map.py
@@ -57,7 +57,7 @@ def precasting(minx, miny, xw, yw, xyreso, yawreso):
             px = ix * xyreso + minx
             py = iy * xyreso + miny
 
-            d = math.sqrt(px**2 + py**2)
+            d = math.hypot(px, py)
             angle = atan_zero_to_twopi(py, px)
             angleid = int(math.floor(angle / yawreso))
 
@@ -85,7 +85,7 @@ def generate_ray_casting_grid_map(ox, oy, xyreso, yawreso):
 
     for (x, y) in zip(ox, oy):
 
-        d = math.sqrt(x**2 + y**2)
+        d = math.hypot(x, y)
         angle = atan_zero_to_twopi(y, x)
         angleid = int(math.floor(angle / yawreso))
 

From d433748ef8ab6fba34f7f7882253e2c1f0c31611 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:53:08 +0100
Subject: [PATCH 0271/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathPlanning/AStar/a_star.py

---
 PathPlanning/AStar/a_star.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py
index 6b0392f515..061aafbeff 100644
--- a/PathPlanning/AStar/a_star.py
+++ b/PathPlanning/AStar/a_star.py
@@ -136,7 +136,7 @@ def calc_final_path(self, ngoal, closedset):
     @staticmethod
     def calc_heuristic(n1, n2):
         w = 1.0  # weight of heuristic
-        d = w * math.sqrt((n1.x - n2.x) ** 2 + (n1.y - n2.y) ** 2)
+        d = w * math.hypot(n1.x - n2.x, n1.y - n2.y)
         return d
 
     def calc_grid_position(self, index, minp):
@@ -199,7 +199,7 @@ def calc_obstacle_map(self, ox, oy):
             for iy in range(self.ywidth):
                 y = self.calc_grid_position(iy, self.miny)
                 for iox, ioy in zip(ox, oy):
-                    d = math.sqrt((iox - x) ** 2 + (ioy - y) ** 2)
+                    d = math.hypot(iox - x, ioy - y)
                     if d <= self.rr:
                         self.obmap[ix][iy] = True
                         break

From ed9b84dfc3310e6a45e19c05c38a485560da5c15 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:53:08 +0100
Subject: [PATCH 0272/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py

---
 PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
index 37ac75ada9..8e7af39f30 100644
--- a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
+++ b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
@@ -180,8 +180,8 @@ def setup_planning(self):
         cBest = self.g_scores[self.goalId]
 
         # Computing the sampling space
-        cMin = math.sqrt(pow(self.start[0] - self.goal[0], 2)
-                         + pow(self.start[1] - self.goal[1], 2)) / 1.5
+        cMin = math.hypot(self.start[0] - self.goal[0],
+                          self.start[1] - self.goal[1]) / 1.5
         xCenter = np.array([[(self.start[0] + self.goal[0]) / 2.0],
                             [(self.start[1] + self.goal[1]) / 2.0], [0]])
         a1 = np.array([[(self.goal[0] - self.start[0]) / cMin],

From cf538756e69713fe5d3d23a9c99a09ba5f62c35d Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:53:08 +0100
Subject: [PATCH 0273/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathPlanning/ClosedLoopRRTStar/pure_pursuit.py

---
 PathPlanning/ClosedLoopRRTStar/pure_pursuit.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
index 32faa51c11..be1d223815 100644
--- a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
+++ b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
@@ -161,7 +161,7 @@ def set_stop_point(target_speed, cx, cy, cyaw):
     for i in range(len(cx) - 1):
         dx = cx[i + 1] - cx[i]
         dy = cy[i + 1] - cy[i]
-        d.append(math.sqrt(dx ** 2.0 + dy ** 2.0))
+        d.append(math.hypot(dx, dy))
         iyaw = cyaw[i]
         move_direction = math.atan2(dy, dx)
         is_back = abs(move_direction - iyaw) >= math.pi / 2.0

From 4c73cada9b1fccbcf929a4520735c590f188a85c Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:53:08 +0100
Subject: [PATCH 0274/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathPlanning/CubicSpline/cubic_spline_planner.py

---
 PathPlanning/CubicSpline/cubic_spline_planner.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/PathPlanning/CubicSpline/cubic_spline_planner.py b/PathPlanning/CubicSpline/cubic_spline_planner.py
index 071b117185..239ce16738 100644
--- a/PathPlanning/CubicSpline/cubic_spline_planner.py
+++ b/PathPlanning/CubicSpline/cubic_spline_planner.py
@@ -140,8 +140,7 @@ def __init__(self, x, y):
     def __calc_s(self, x, y):
         dx = np.diff(x)
         dy = np.diff(y)
-        self.ds = [math.sqrt(idx ** 2 + idy ** 2)
-                   for (idx, idy) in zip(dx, dy)]
+        self.ds = np.hypot(dx, dy)
         s = [0]
         s.extend(np.cumsum(self.ds))
         return s

From 4c6fe30fb33bff9b85238f26e95e8ae7b9a973dd Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:53:08 +0100
Subject: [PATCH 0275/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathPlanning/Dijkstra/dijkstra.py

---
 PathPlanning/Dijkstra/dijkstra.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py
index bb73ed501a..5c06f53aed 100644
--- a/PathPlanning/Dijkstra/dijkstra.py
+++ b/PathPlanning/Dijkstra/dijkstra.py
@@ -123,7 +123,7 @@ def calc_final_path(self, ngoal, closedset):
 
     def calc_heuristic(self, n1, n2):
         w = 1.0  # weight of heuristic
-        d = w * math.sqrt((n1.x - n2.x)**2 + (n1.y - n2.y)**2)
+        d = w * math.hypot(n1.x - n2.x, n1.y - n2.y)
         return d
 
     def calc_position(self, index, minp):
@@ -178,7 +178,7 @@ def calc_obstacle_map(self, ox, oy):
             for iy in range(self.ywidth):
                 y = self.calc_position(iy, self.miny)
                 for iox, ioy in zip(ox, oy):
-                    d = math.sqrt((iox - x)**2 + (ioy - y)**2)
+                    d = math.hypot(iox - x, ioy - y)
                     if d <= self.rr:
                         self.obmap[ix][iy] = True
                         break

From 367c2c6dc635644b3fa37b5f921b3ebabb1e97c8 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:53:08 +0100
Subject: [PATCH 0276/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathPlanning/DubinsPath/dubins_path_planning.py

---
 PathPlanning/DubinsPath/dubins_path_planning.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py
index 706b5a49af..96c9c8e475 100644
--- a/PathPlanning/DubinsPath/dubins_path_planning.py
+++ b/PathPlanning/DubinsPath/dubins_path_planning.py
@@ -141,7 +141,7 @@ def dubins_path_planning_from_origin(ex, ey, eyaw, c, D_ANGLE):
     # normalize
     dx = ex
     dy = ey
-    D = math.sqrt(dx ** 2.0 + dy ** 2.0)
+    D = math.hypot(dx, dy)
     d = D * c
     #  print(dx, dy, D, d)
 

From 60387ee5f8b5f7898dbe73cc671f59503042a182 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:53:08 +0100
Subject: [PATCH 0277/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathPlanning/DynamicWindowApproach/dynamic_window_approach.py

---
 PathPlanning/DynamicWindowApproach/dynamic_window_approach.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index 5c3d128dc4..891e68cbe8 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -279,7 +279,7 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle):
             plt.pause(0.0001)
 
         # check reaching goal
-        dist_to_goal = math.sqrt((x[0] - goal[0]) ** 2 + (x[1] - goal[1]) ** 2)
+        dist_to_goal = math.hypot(x[0] - goal[0], x[1] - goal[1])
         if dist_to_goal <= config.robot_radius:
             print("Goal!!")
             break

From 077120201b41a8c4808a81170679aac7cd077acd Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:53:08 +0100
Subject: [PATCH 0278/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py

---
 .../FrenetOptimalTrajectory/frenet_optimal_trajectory.py        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
index 5c756a9dfe..f3ae2a439b 100644
--- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
+++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
@@ -230,7 +230,7 @@ def calc_global_paths(fplist, csp):
             dx = fp.x[i + 1] - fp.x[i]
             dy = fp.y[i + 1] - fp.y[i]
             fp.yaw.append(math.atan2(dy, dx))
-            fp.ds.append(math.sqrt(dx**2 + dy**2))
+            fp.ds.append(math.hypot(dx, dy))
 
         fp.yaw.append(fp.yaw[-1])
         fp.ds.append(fp.ds[-1])

From d20f1e336e9b2536eacea224d12c59858a9c74bd Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:53:08 +0100
Subject: [PATCH 0279/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py

---
 .../ProbabilisticRoadMap/probabilistic_road_map.py     | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
index 351232d849..8090e3364f 100644
--- a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
+++ b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
@@ -97,7 +97,7 @@ def is_collision(sx, sy, gx, gy, rr, okdtree):
     dx = gx - sx
     dy = gy - sy
     yaw = math.atan2(gy - sy, gx - sx)
-    d = math.sqrt(dx**2 + dy**2)
+    d = math.hypot(dx, dy)
 
     if d >= MAX_EDGE_LEN:
         return True
@@ -171,7 +171,7 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y):
     road_map: ??? [m]
     sample_x: ??? [m]
     sample_y: ??? [m]
-    
+
     @return: Two lists of path coordinates ([x1, x2, ...], [y1, y2, ...]), empty list when no path was found
     """
 
@@ -182,7 +182,7 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y):
     openset[len(road_map) - 2] = nstart
 
     path_found = True
-    
+
     while True:
         if not openset:
             print("Cannot find path")
@@ -213,7 +213,7 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y):
             n_id = road_map[c_id][i]
             dx = sample_x[n_id] - current.x
             dy = sample_y[n_id] - current.y
-            d = math.sqrt(dx**2 + dy**2)
+            d = math.hypot(dx, dy)
             node = Node(sample_x[n_id], sample_y[n_id],
                         current.cost + d, c_id)
 
@@ -226,7 +226,7 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y):
                     openset[n_id].pind = c_id
             else:
                 openset[n_id] = node
-    
+
     if path_found is False:
         return [], []
 

From c63a80b0ef148d5fb4bf75b1b0da41ba994c84ad Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 7 Dec 2019 22:53:08 +0100
Subject: [PATCH 0280/1080] Replaced sqrt(x**2+y**2) with hypot in
 PathPlanning/VoronoiRoadMap/voronoi_road_map.py

---
 PathPlanning/VoronoiRoadMap/voronoi_road_map.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
index 3ac02993b7..c73d650951 100644
--- a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
+++ b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
@@ -95,7 +95,7 @@ def is_collision(sx, sy, gx, gy, rr, okdtree):
     dx = gx - sx
     dy = gy - sy
     yaw = math.atan2(gy - sy, gx - sx)
-    d = math.sqrt(dx**2 + dy**2)
+    d = math.hypot(dx, dy)
 
     if d >= MAX_EDGE_LEN:
         return True
@@ -203,7 +203,7 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y):
             n_id = road_map[c_id][i]
             dx = sample_x[n_id] - current.x
             dy = sample_y[n_id] - current.y
-            d = math.sqrt(dx**2 + dy**2)
+            d = math.hypot(dx, dy)
             node = Node(sample_x[n_id], sample_y[n_id],
                         current.cost + d, c_id)
 

From bf86cc8096482745acaff354da1f8a56fff4a5d4 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 Dec 2019 10:25:08 +0900
Subject: [PATCH 0281/1080] fixed lgtm warning

---
 PathPlanning/HybridAStar/hybrid_a_star.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
index e82003ed25..ea6a466113 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -150,7 +150,7 @@ def calc_next_node(current, steer, direction, config, ox, oy, kdtree):
 
     arc_l = XY_GRID_RESOLUTION * 1.5
     xlist, ylist, yawlist = [], [], []
-    for dist in np.arange(0, arc_l, MOTION_RESOLUTION):
+    for _ in np.arange(0, arc_l, MOTION_RESOLUTION):
         x, y, yaw = move(x, y, yaw, MOTION_RESOLUTION * direction, steer)
         xlist.append(x)
         ylist.append(y)

From 25cffd70d21389b76760b44da2697cde35175472 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?=
 <20567087+goktug97@users.noreply.github.com>
Date: Sat, 14 Dec 2019 14:39:45 +0300
Subject: [PATCH 0282/1080] fix typo

---
 .../rocket_powered_landing/rocket_powered_landing.py            | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
index c7ab998bec..51e09b4e3c 100644
--- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
+++ b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
@@ -567,7 +567,7 @@ def plot_animation(X, U):  # pragma: no cover
 
     fig = plt.figure()
     ax = fig.gca(projection='3d')
-    rig.canvas.mpl_connect('key_release_event',
+    fig.canvas.mpl_connect('key_release_event',
             lambda event: [exit(0) if event.key == 'escape' else None])
 
     for k in range(K):

From 9ca7d8f14808f535b5e698bc57e5ab163b3dae4f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?G=C3=B6ktu=C4=9F=20Karaka=C5=9Fl=C4=B1?=
 <20567087+goktug97@users.noreply.github.com>
Date: Sat, 14 Dec 2019 14:44:01 +0300
Subject: [PATCH 0283/1080] add comment for stopping the simulation

---
 AerialNavigation/drone_3d_trajectory_following/Quadrotor.py     | 1 +
 .../rocket_powered_landing/rocket_powered_landing.py            | 1 +
 .../arm_obstacle_navigation/arm_obstacle_navigation.py          | 1 +
 .../arm_obstacle_navigation/arm_obstacle_navigation_2.py        | 1 +
 ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py          | 1 +
 .../two_joint_arm_to_point_control.py                           | 1 +
 Bipedal/bipedal_planner/bipedal_planner.py                      | 1 +
 Localization/ensemble_kalman_filter/ensemble_kalman_filter.py   | 1 +
 Localization/extended_kalman_filter/extended_kalman_filter.py   | 1 +
 Localization/histogram_filter/histogram_filter.py               | 1 +
 Localization/particle_filter/particle_filter.py                 | 1 +
 Localization/unscented_kalman_filter/unscented_kalman_filter.py | 1 +
 Mapping/circle_fitting/circle_fitting.py                        | 1 +
 Mapping/gaussian_grid_map/gaussian_grid_map.py                  | 1 +
 Mapping/kmeans_clustering/kmeans_clustering.py                  | 1 +
 Mapping/raycasting_grid_map/raycasting_grid_map.py              | 1 +
 Mapping/rectangle_fitting/rectangle_fitting.py                  | 1 +
 PathPlanning/AStar/a_star.py                                    | 1 +
 PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py     | 1 +
 PathPlanning/ClosedLoopRRTStar/pure_pursuit.py                  | 1 +
 PathPlanning/Dijkstra/dijkstra.py                               | 1 +
 PathPlanning/DubinsPath/dubins_path_planning.py                 | 1 +
 PathPlanning/DynamicWindowApproach/dynamic_window_approach.py   | 1 +
 PathPlanning/Eta3SplinePath/eta3_spline_path.py                 | 1 +
 .../FrenetOptimalTrajectory/frenet_optimal_trajectory.py        | 1 +
 .../GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py | 2 ++
 PathPlanning/HybridAStar/a_star.py                              | 1 +
 PathPlanning/HybridAStar/hybrid_a_star.py                       | 1 +
 PathPlanning/InformedRRTStar/informed_rrt_star.py               | 1 +
 PathPlanning/LQRPlanner/LQRplanner.py                           | 1 +
 PathPlanning/LQRRRTStar/lqr_rrt_star.py                         | 1 +
 PathPlanning/PotentialFieldPlanning/potential_field_planning.py | 1 +
 PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py     | 1 +
 .../QuinticPolynomialsPlanner/quintic_polynomials_planner.py    | 1 +
 PathPlanning/RRT/rrt.py                                         | 1 +
 PathPlanning/RRTDubins/rrt_dubins.py                            | 1 +
 PathPlanning/RRTStarDubins/rrt_star_dubins.py                   | 1 +
 PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py          | 1 +
 PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py        | 1 +
 PathPlanning/VoronoiRoadMap/voronoi_road_map.py                 | 1 +
 PathTracking/cgmres_nmpc/cgmres_nmpc.py                         | 1 +
 PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py | 1 +
 PathTracking/lqr_steer_control/lqr_steer_control.py             | 1 +
 .../model_predictive_speed_and_steer_control.py                 | 1 +
 PathTracking/move_to_pose/move_to_pose.py                       | 1 +
 PathTracking/pure_pursuit/pure_pursuit.py                       | 1 +
 PathTracking/rear_wheel_feedback/rear_wheel_feedback.py         | 1 +
 PathTracking/stanley_controller/stanley_controller.py           | 1 +
 SLAM/EKFSLAM/ekf_slam.py                                        | 1 +
 SLAM/FastSLAM1/fast_slam1.py                                    | 1 +
 SLAM/FastSLAM2/fast_slam2.py                                    | 1 +
 SLAM/GraphBasedSLAM/graph_based_slam.py                         | 1 +
 SLAM/iterative_closest_point/iterative_closest_point.py         | 1 +
 53 files changed, 54 insertions(+)

diff --git a/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py b/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py
index 89171da212..413a8625a5 100644
--- a/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py
+++ b/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py
@@ -23,6 +23,7 @@ def __init__(self, x=0, y=0, z=0, roll=0, pitch=0, yaw=0, size=0.25, show_animat
         if self.show_animation:
             plt.ion()
             fig = plt.figure()
+            # for stopping simulation with the esc key.
             fig.canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
 
diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
index 51e09b4e3c..ee0f5e26a5 100644
--- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
+++ b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
@@ -567,6 +567,7 @@ def plot_animation(X, U):  # pragma: no cover
 
     fig = plt.figure()
     ax = fig.gca(projection='3d')
+    # for stopping simulation with the esc key.
     fig.canvas.mpl_connect('key_release_event',
             lambda event: [exit(0) if event.key == 'escape' else None])
 
diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
index b435e62d2b..f3bd25577c 100644
--- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
+++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
@@ -162,6 +162,7 @@ def astar_torus(grid, start_node, goal_node):
         for i in range(1, len(route)):
             grid[route[i]] = 6
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.imshow(grid, cmap=cmap, norm=norm, interpolation=None)
diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
index 5a21b806d3..429cd4d211 100644
--- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
+++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
@@ -193,6 +193,7 @@ def astar_torus(grid, start_node, goal_node):
         for i in range(1, len(route)):
             grid[route[i]] = 6
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.imshow(grid, cmap=cmap, norm=norm, interpolation=None)
diff --git a/ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py b/ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py
index 576bd29a8f..854ade9038 100644
--- a/ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py
+++ b/ArmNavigation/n_joint_arm_to_point_control/NLinkArm.py
@@ -51,6 +51,7 @@ def update_points(self):
 
     def plot(self):  # pragma: no cover
         plt.cla()
+        # for stopping simulation with the esc key.
         plt.gcf().canvas.mpl_connect('key_release_event',
                 lambda event: [exit(0) if event.key == 'escape' else None])
 
diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
index 134583a95c..60a7cd5f0f 100644
--- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
+++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
@@ -114,6 +114,7 @@ def animation():
 def main():  # pragma: no cover
     fig = plt.figure()
     fig.canvas.mpl_connect("button_press_event", click)
+    # for stopping simulation with the esc key.
     fig.canvas.mpl_connect('key_release_event',
             lambda event: [exit(0) if event.key == 'escape' else None])
     two_joint_arm()
diff --git a/Bipedal/bipedal_planner/bipedal_planner.py b/Bipedal/bipedal_planner/bipedal_planner.py
index afa68afb25..6502ce5bed 100644
--- a/Bipedal/bipedal_planner/bipedal_planner.py
+++ b/Bipedal/bipedal_planner/bipedal_planner.py
@@ -111,6 +111,7 @@ def walk(self, T_sup=0.8, z_c=0.8, a=10, b=1, plot=False):
                     if c > len(com_trajectory_for_plot):
                         # set up plotter
                         plt.cla()
+                        # for stopping simulation with the esc key.
                         plt.gcf().canvas.mpl_connect('key_release_event',
                                 lambda event: [exit(0) if event.key == 'escape' else None])
                         ax.set_zlim(0, z_c * 2)
diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
index 6ac6144f36..244cfccd9c 100644
--- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
+++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
@@ -213,6 +213,7 @@ def main():
 
         if show_animation:
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
 
diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py
index 029dac4bf8..2b057e21de 100644
--- a/Localization/extended_kalman_filter/extended_kalman_filter.py
+++ b/Localization/extended_kalman_filter/extended_kalman_filter.py
@@ -191,6 +191,7 @@ def main():
 
         if show_animation:
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(hz[0, :], hz[1, :], ".g")
diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py
index ac07c59fbd..79302c8abb 100644
--- a/Localization/histogram_filter/histogram_filter.py
+++ b/Localization/histogram_filter/histogram_filter.py
@@ -233,6 +233,7 @@ def main():
 
         if show_animation:
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             draw_heat_map(grid_map.data, mx, my)
diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py
index 4b9abd78bd..3e901f8da0 100644
--- a/Localization/particle_filter/particle_filter.py
+++ b/Localization/particle_filter/particle_filter.py
@@ -230,6 +230,7 @@ def main():
 
         if show_animation:
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
 
diff --git a/Localization/unscented_kalman_filter/unscented_kalman_filter.py b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
index 0005d12fe7..7bf279ced0 100644
--- a/Localization/unscented_kalman_filter/unscented_kalman_filter.py
+++ b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
@@ -240,6 +240,7 @@ def main():
 
         if show_animation:
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(hz[0, :], hz[1, :], ".g")
diff --git a/Mapping/circle_fitting/circle_fitting.py b/Mapping/circle_fitting/circle_fitting.py
index 27ceac21f7..c331d56796 100644
--- a/Mapping/circle_fitting/circle_fitting.py
+++ b/Mapping/circle_fitting/circle_fitting.py
@@ -124,6 +124,7 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.axis("equal")
diff --git a/Mapping/gaussian_grid_map/gaussian_grid_map.py b/Mapping/gaussian_grid_map/gaussian_grid_map.py
index 094b094fdd..7893d6a5ee 100644
--- a/Mapping/gaussian_grid_map/gaussian_grid_map.py
+++ b/Mapping/gaussian_grid_map/gaussian_grid_map.py
@@ -73,6 +73,7 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             draw_heatmap(gmap, minx, maxx, miny, maxy, xyreso)
diff --git a/Mapping/kmeans_clustering/kmeans_clustering.py b/Mapping/kmeans_clustering/kmeans_clustering.py
index 5345813096..d658b84f48 100644
--- a/Mapping/kmeans_clustering/kmeans_clustering.py
+++ b/Mapping/kmeans_clustering/kmeans_clustering.py
@@ -133,6 +133,7 @@ def main():
         # for animation
         if show_animation:  # pragma: no cover
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             clusters.plot_cluster()
diff --git a/Mapping/raycasting_grid_map/raycasting_grid_map.py b/Mapping/raycasting_grid_map/raycasting_grid_map.py
index cae92a9dcb..372e31eccd 100644
--- a/Mapping/raycasting_grid_map/raycasting_grid_map.py
+++ b/Mapping/raycasting_grid_map/raycasting_grid_map.py
@@ -124,6 +124,7 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             draw_heatmap(pmap, minx, maxx, miny, maxy, xyreso)
diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py
index 77c66db250..1c4d412207 100644
--- a/Mapping/rectangle_fitting/rectangle_fitting.py
+++ b/Mapping/rectangle_fitting/rectangle_fitting.py
@@ -242,6 +242,7 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.axis("equal")
diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py
index e4f7dd4d97..d8ef09de85 100644
--- a/PathPlanning/AStar/a_star.py
+++ b/PathPlanning/AStar/a_star.py
@@ -79,6 +79,7 @@ def planning(self, sx, sy, gx, gy):
             if show_animation:  # pragma: no cover
                 plt.plot(self.calc_grid_position(current.x, self.minx),
                          self.calc_grid_position(current.y, self.miny), "xc")
+                # for stopping simulation with the esc key.
                 plt.gcf().canvas.mpl_connect('key_release_event',
                         lambda event: [exit(0) if event.key == 'escape' else None])
                 if len(closed_set.keys()) % 10 == 0:
diff --git a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
index 46ff3caf5c..e84f65d617 100644
--- a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
+++ b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
@@ -534,6 +534,7 @@ def update_graph(self):
     def draw_graph(self, xCenter=None, cBest=None, cMin=None, etheta=None,
                    samples=None, start=None, end=None):
         plt.clf()
+        # for stopping simulation with the esc key.
         plt.gcf().canvas.mpl_connect('key_release_event',
                 lambda event: [exit(0) if event.key == 'escape' else None])
         for rnd in samples:
diff --git a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
index d078338d67..5833f6e91e 100644
--- a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
+++ b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
@@ -135,6 +135,7 @@ def closed_loop_prediction(cx, cy, cyaw, speed_profile, goal):
 
         if target_ind % 1 == 0 and animation:  # pragma: no cover
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(cx, cy, "-r", label="course")
diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py
index 8e719f50e2..a8f6441328 100644
--- a/PathPlanning/Dijkstra/dijkstra.py
+++ b/PathPlanning/Dijkstra/dijkstra.py
@@ -69,6 +69,7 @@ def planning(self, sx, sy, gx, gy):
             if show_animation:  # pragma: no cover
                 plt.plot(self.calc_position(current.x, self.minx),
                          self.calc_position(current.y, self.miny), "xc")
+                # for stopping simulation with the esc key.
                 plt.gcf().canvas.mpl_connect('key_release_event',
                         lambda event: [exit(0) if event.key == 'escape' else None])
                 if len(closedset.keys()) % 10 == 0:
diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py
index 8690fbe8db..de5db2a5aa 100644
--- a/PathPlanning/DubinsPath/dubins_path_planning.py
+++ b/PathPlanning/DubinsPath/dubins_path_planning.py
@@ -318,6 +318,7 @@ def test():
 
         if show_animation:
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(px, py, label="final course " + str(mode))
diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index c2cc358b13..b0d020302f 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -268,6 +268,7 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle):
 
         if show_animation:
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(predicted_trajectory[:, 0], predicted_trajectory[:, 1], "-g")
diff --git a/PathPlanning/Eta3SplinePath/eta3_spline_path.py b/PathPlanning/Eta3SplinePath/eta3_spline_path.py
index 51ac612c28..414f6b4534 100644
--- a/PathPlanning/Eta3SplinePath/eta3_spline_path.py
+++ b/PathPlanning/Eta3SplinePath/eta3_spline_path.py
@@ -213,6 +213,7 @@ def test1():
         if show_animation:
             # plot the path
             plt.plot(pos[0, :], pos[1, :])
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.pause(1.0)
diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
index 1abda92646..861260e9c9 100644
--- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
+++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
@@ -347,6 +347,7 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(tx, ty)
diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
index 049373ece2..5c39a1ee81 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -213,6 +213,7 @@ def sweep_path_search(sweep_searcher, gmap, grid_search_animation=False):
 
     if grid_search_animation:
         fig, ax = plt.subplots()
+        # for stopping simulation with the esc key.
         fig.canvas.mpl_connect('key_release_event',
                 lambda event: [exit(0) if event.key == 'escape' else None])
 
@@ -268,6 +269,7 @@ def planning_animation(ox, oy, reso):  # pragma: no cover
     if do_animation:
         for ipx, ipy in zip(px, py):
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(ox, oy, "-xb")
diff --git a/PathPlanning/HybridAStar/a_star.py b/PathPlanning/HybridAStar/a_star.py
index 918469f0d3..a27293288c 100644
--- a/PathPlanning/HybridAStar/a_star.py
+++ b/PathPlanning/HybridAStar/a_star.py
@@ -78,6 +78,7 @@ def dp_planning(sx, sy, gx, gy, ox, oy, reso, rr):
         # show graph
         if show_animation:  # pragma: no cover
             plt.plot(current.x * reso, current.y * reso, "xc")
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             if len(closedset.keys()) % 10 == 0:
diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
index 70ffcee865..8a87be85a3 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -327,6 +327,7 @@ def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso):
 
         if show_animation:  # pragma: no cover
             plt.plot(current.xlist[-1], current.ylist[-1], "xc")
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             if len(closedList.keys()) % 10 == 0:
diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index ef15022aca..a77b1cea74 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -267,6 +267,7 @@ def get_final_course(self, lastIndex):
 
     def draw_graph(self, xCenter=None, cBest=None, cMin=None, etheta=None, rnd=None):
         plt.clf()
+        # for stopping simulation with the esc key.
         plt.gcf().canvas.mpl_connect('key_release_event',
                 lambda event: [exit(0) if event.key == 'escape' else None])
         if rnd is not None:
diff --git a/PathPlanning/LQRPlanner/LQRplanner.py b/PathPlanning/LQRPlanner/LQRplanner.py
index d5cda0bc50..ba01526a2c 100644
--- a/PathPlanning/LQRPlanner/LQRplanner.py
+++ b/PathPlanning/LQRPlanner/LQRplanner.py
@@ -54,6 +54,7 @@ def lqr_planning(self, sx, sy, gx, gy, show_animation=True):
 
             # animation
             if show_animation:  # pragma: no cover
+                # for stopping simulation with the esc key.
                 plt.gcf().canvas.mpl_connect('key_release_event',
                         lambda event: [exit(0) if event.key == 'escape' else None])
                 plt.plot(sx, sy, "or")
diff --git a/PathPlanning/LQRRRTStar/lqr_rrt_star.py b/PathPlanning/LQRRRTStar/lqr_rrt_star.py
index c1f45445ff..177131ac96 100644
--- a/PathPlanning/LQRRRTStar/lqr_rrt_star.py
+++ b/PathPlanning/LQRRRTStar/lqr_rrt_star.py
@@ -102,6 +102,7 @@ def planning(self, animation=True, search_until_max_iter=True):
 
     def draw_graph(self, rnd=None):
         plt.clf()
+        # for stopping simulation with the esc key.
         plt.gcf().canvas.mpl_connect('key_release_event',
                 lambda event: [exit(0) if event.key == 'escape' else None])
         if rnd is not None:
diff --git a/PathPlanning/PotentialFieldPlanning/potential_field_planning.py b/PathPlanning/PotentialFieldPlanning/potential_field_planning.py
index 642f1cbff9..31904fa45d 100644
--- a/PathPlanning/PotentialFieldPlanning/potential_field_planning.py
+++ b/PathPlanning/PotentialFieldPlanning/potential_field_planning.py
@@ -98,6 +98,7 @@ def potential_field_planning(sx, sy, gx, gy, ox, oy, reso, rr):
 
     if show_animation:
         draw_heatmap(pmap)
+        # for stopping simulation with the esc key.
         plt.gcf().canvas.mpl_connect('key_release_event',
                 lambda event: [exit(0) if event.key == 'escape' else None])
         plt.plot(ix, iy, "*k")
diff --git a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
index 3fef2d0f7a..0a8ea037d1 100644
--- a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
+++ b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
@@ -194,6 +194,7 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y):
 
         # show graph
         if show_animation and len(closedset.keys()) % 2 == 0:
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(current.x, current.y, "xg")
diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
index b8d533d4dc..406dbc1ee2 100644
--- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
+++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
@@ -151,6 +151,7 @@ def quintic_polynomials_planner(sx, sy, syaw, sv, sa, gx, gy, gyaw, gv, ga, max_
     if show_animation:  # pragma: no cover
         for i, _ in enumerate(time):
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.grid(True)
diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py
index 9d0bc36272..f8e9908287 100644
--- a/PathPlanning/RRT/rrt.py
+++ b/PathPlanning/RRT/rrt.py
@@ -138,6 +138,7 @@ def get_random_node(self):
 
     def draw_graph(self, rnd=None):
         plt.clf()
+        # for stopping simulation with the esc key.
         plt.gcf().canvas.mpl_connect('key_release_event',
                 lambda event: [exit(0) if event.key == 'escape' else None])
         if rnd is not None:
diff --git a/PathPlanning/RRTDubins/rrt_dubins.py b/PathPlanning/RRTDubins/rrt_dubins.py
index 2a842aa42c..4599904cc1 100644
--- a/PathPlanning/RRTDubins/rrt_dubins.py
+++ b/PathPlanning/RRTDubins/rrt_dubins.py
@@ -106,6 +106,7 @@ def planning(self, animation=True, search_until_max_iter=True):
 
     def draw_graph(self, rnd=None):  # pragma: no cover
         plt.clf()
+        # for stopping simulation with the esc key.
         plt.gcf().canvas.mpl_connect('key_release_event',
                 lambda event: [exit(0) if event.key == 'escape' else None])
         if rnd is not None:
diff --git a/PathPlanning/RRTStarDubins/rrt_star_dubins.py b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
index d4601c0f12..65fac54c78 100644
--- a/PathPlanning/RRTStarDubins/rrt_star_dubins.py
+++ b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
@@ -112,6 +112,7 @@ def planning(self, animation=True, search_until_max_iter=True):
 
     def draw_graph(self, rnd=None):
         plt.clf()
+        # for stopping simulation with the esc key.
         plt.gcf().canvas.mpl_connect('key_release_event',
                 lambda event: [exit(0) if event.key == 'escape' else None])
         if rnd is not None:
diff --git a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
index d37ae90634..6ea66dc729 100644
--- a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
+++ b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
@@ -122,6 +122,7 @@ def try_goal_path(self, node):
 
     def draw_graph(self, rnd=None):
         plt.clf()
+        # for stopping simulation with the esc key.
         plt.gcf().canvas.mpl_connect('key_release_event',
                 lambda event: [exit(0) if event.key == 'escape' else None])
         if rnd is not None:
diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
index b2f8d4a57d..fbf9495790 100644
--- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
+++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
@@ -395,6 +395,7 @@ def test():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(px, py, label="final course " + str(mode))
diff --git a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
index 86371b2051..7517669628 100644
--- a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
+++ b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
@@ -185,6 +185,7 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y):
         # show graph
         if show_animation and len(closedset.keys()) % 2 == 0:  # pragma: no cover
             plt.plot(current.x, current.y, "xg")
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.pause(0.001)
diff --git a/PathTracking/cgmres_nmpc/cgmres_nmpc.py b/PathTracking/cgmres_nmpc/cgmres_nmpc.py
index 1be91b2d20..98a12ab10c 100644
--- a/PathTracking/cgmres_nmpc/cgmres_nmpc.py
+++ b/PathTracking/cgmres_nmpc/cgmres_nmpc.py
@@ -546,6 +546,7 @@ def animation(plant, controller, dt):
             steer = math.atan2(controller.history_u_2[t] * WB / v, 1.0)
 
         plt.cla()
+        # for stopping simulation with the esc key.
         plt.gcf().canvas.mpl_connect('key_release_event',
                 lambda event: [exit(0) if event.key == 'escape' else None])
         plt.plot(plant.history_x, plant.history_y, "-r", label="trajectory")
diff --git a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
index 0e22529623..77409c7f42 100644
--- a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
+++ b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
@@ -224,6 +224,7 @@ def do_simulation(cx, cy, cyaw, ck, speed_profile, goal):
 
         if target_ind % 1 == 0 and show_animation:
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(cx, cy, "-r", label="course")
diff --git a/PathTracking/lqr_steer_control/lqr_steer_control.py b/PathTracking/lqr_steer_control/lqr_steer_control.py
index 9cc36f231f..fe440a1734 100644
--- a/PathTracking/lqr_steer_control/lqr_steer_control.py
+++ b/PathTracking/lqr_steer_control/lqr_steer_control.py
@@ -203,6 +203,7 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
 
         if target_ind % 1 == 0 and show_animation:
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(cx, cy, "-r", label="course")
diff --git a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
index 8bff2e107d..85aea2ed1b 100644
--- a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
+++ b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
@@ -429,6 +429,7 @@ def do_simulation(cx, cy, cyaw, ck, sp, dl, initial_state):
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             if ox is not None:
diff --git a/PathTracking/move_to_pose/move_to_pose.py b/PathTracking/move_to_pose/move_to_pose.py
index 838b780f48..685df1979d 100644
--- a/PathTracking/move_to_pose/move_to_pose.py
+++ b/PathTracking/move_to_pose/move_to_pose.py
@@ -93,6 +93,7 @@ def plot_vehicle(x, y, theta, x_traj, y_traj):  # pragma: no cover
 
     plt.plot(x_traj, y_traj, 'b--')
 
+    # for stopping simulation with the esc key.
     plt.gcf().canvas.mpl_connect('key_release_event',
             lambda event: [exit(0) if event.key == 'escape' else None])
 
diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index f9624956c4..473feea5da 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -166,6 +166,7 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plot_arrow(state.x, state.y, state.yaw)
diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
index 155eeec2ce..c6a3501368 100644
--- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
+++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
@@ -149,6 +149,7 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
 
         if target_ind % 1 == 0 and show_animation:
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(cx, cy, "-r", label="course")
diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py
index cbfa4c667e..604ed93f01 100644
--- a/PathTracking/stanley_controller/stanley_controller.py
+++ b/PathTracking/stanley_controller/stanley_controller.py
@@ -186,6 +186,7 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(cx, cy, ".r", label="course")
diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py
index b6698be228..4bcafef2fa 100644
--- a/SLAM/EKFSLAM/ekf_slam.py
+++ b/SLAM/EKFSLAM/ekf_slam.py
@@ -235,6 +235,7 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
 
diff --git a/SLAM/FastSLAM1/fast_slam1.py b/SLAM/FastSLAM1/fast_slam1.py
index ef1282995b..892010c53d 100644
--- a/SLAM/FastSLAM1/fast_slam1.py
+++ b/SLAM/FastSLAM1/fast_slam1.py
@@ -365,6 +365,7 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(RFID[:, 0], RFID[:, 1], "*k")
diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py
index f8958b8c73..25712fc0cb 100644
--- a/SLAM/FastSLAM2/fast_slam2.py
+++ b/SLAM/FastSLAM2/fast_slam2.py
@@ -390,6 +390,7 @@ def main():
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(RFID[:, 0], RFID[:, 1], "*k")
diff --git a/SLAM/GraphBasedSLAM/graph_based_slam.py b/SLAM/GraphBasedSLAM/graph_based_slam.py
index e73e8d0d4a..2a5acfab1f 100644
--- a/SLAM/GraphBasedSLAM/graph_based_slam.py
+++ b/SLAM/GraphBasedSLAM/graph_based_slam.py
@@ -303,6 +303,7 @@ def main():
 
             if show_animation:  # pragma: no cover
                 plt.cla()
+                # for stopping simulation with the esc key.
                 plt.gcf().canvas.mpl_connect('key_release_event',
                         lambda event: [exit(0) if event.key == 'escape' else None])
                 plt.plot(RFID[:, 0], RFID[:, 1], "*k")
diff --git a/SLAM/iterative_closest_point/iterative_closest_point.py b/SLAM/iterative_closest_point/iterative_closest_point.py
index 4a4d29eb5e..0b2802603c 100644
--- a/SLAM/iterative_closest_point/iterative_closest_point.py
+++ b/SLAM/iterative_closest_point/iterative_closest_point.py
@@ -36,6 +36,7 @@ def icp_matching(previous_points, current_points):
 
         if show_animation:  # pragma: no cover
             plt.cla()
+            # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(previous_points[0, :], previous_points[1, :], ".r")

From c09e20bc45b0c4408878b2d02cf17cc9d333dd16 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 14 Dec 2019 21:41:04 +0900
Subject: [PATCH 0284/1080] Update users_comments.md

---
 users_comments.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 2cfd7a4a9b..792f3a4d0d 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -404,6 +404,9 @@ URL: https://www.semanticscholar.org/paper/A-Review-of-Robot-Rescue-Simulation-P
 7. Ghosh, Ritwika, et al. "CyPhyHouse: A Programming, Simulation, and Deployment Toolchain for Heterogeneous Distributed Coordination." arXiv preprint arXiv:1910.01557 (2019).
 URL: https://arxiv.org/abs/1910.01557
 
+8. Hahn, Carsten, et al. "Dynamic Path Planning with Stable Growing Neural Gas." (2019).
+URL: https://pdfs.semanticscholar.org/5c06/f3cb9542a51e1bf1a32523c1bc7fea6cecc5.pdf
+
 # Others
 
 - Autonomous Vehicle Readings https://richardkelley.io/readings

From ca49613b53d8e45b169a3585eb7d0c8bf5057e64 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 23 Dec 2019 20:44:41 +0900
Subject: [PATCH 0285/1080] Update README.md

---
 README.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/README.md b/README.md
index e740049b90..f48e9c0ef3 100644
--- a/README.md
+++ b/README.md
@@ -605,5 +605,8 @@ This is a list: [Users comments](https://github.com/AtsushiSakai/PythonRobotics/
 
 - [Ryohei Sasaki](https://github.com/rsasaki0109)
 
+- [Göktuğ Karakaşlı](https://github.com/goktug97)
+
+
 
 

From 6132629a70b9b47ec9a00dcf338650e22368d0f6 Mon Sep 17 00:00:00 2001
From: YILUN CHEN <yilunchen@jhoma-e7450.int.uberatc.com>
Date: Mon, 30 Dec 2019 17:52:05 +0800
Subject: [PATCH 0286/1080] fix environment dependencies

---
 environment.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/environment.yml b/environment.yml
index 28ff9f1b4e..6866ed93ca 100644
--- a/environment.yml
+++ b/environment.yml
@@ -1,9 +1,11 @@
 name: python_robotics
 dependencies:
 - python
+- pip
 - matplotlib
 - scipy
 - numpy==1.15
 - pandas
+- coverage
 - pip:
     - cvxpy

From 164f82c0dc022e71625b53f8e899e7b361a50c17 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 3 Jan 2020 08:47:18 +0900
Subject: [PATCH 0287/1080] Delete greetings.yml

---
 .github/workflows/greetings.yml | 13 -------------
 1 file changed, 13 deletions(-)
 delete mode 100644 .github/workflows/greetings.yml

diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml
deleted file mode 100644
index 3da4a69458..0000000000
--- a/.github/workflows/greetings.yml
+++ /dev/null
@@ -1,13 +0,0 @@
-name: Greetings
-
-on: [pull_request, issues]
-
-jobs:
-  greeting:
-    runs-on: ubuntu-latest
-    steps:
-    - uses: actions/first-interaction@v1
-      with:
-        repo-token: ${{ secrets.GITHUB_TOKEN }}
-        issue-message: 'Message that will be displayed on users'' This is first issue for you on this project'
-        pr-message: 'Message that will be displayed on users'' This is first pr for you on this project'

From 0c708766e13a167e5afe4a96ea6d4527c74a3e56 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <Gjacquenot@users.noreply.github.com>
Date: Fri, 3 Jan 2020 14:01:47 +0100
Subject: [PATCH 0288/1080] :pencil: Removed unused class attribute in
 quintic_polynomials_planner.py

---
 .../quintic_polynomials_planner.py                       | 9 +--------
 1 file changed, 1 insertion(+), 8 deletions(-)

diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
index 406dbc1ee2..e7c744d1fe 100644
--- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
+++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
@@ -26,14 +26,7 @@ class QuinticPolynomial:
 
     def __init__(self, xs, vxs, axs, xe, vxe, axe, T):
 
-        # calc coefficient of quinic polynomial
-        self.xs = xs
-        self.vxs = vxs
-        self.axs = axs
-        self.xe = xe
-        self.vxe = vxe
-        self.axe = axe
-
+        # calc coefficient of quintic polynomial
         self.a0 = xs
         self.a1 = vxs
         self.a2 = axs / 2.0

From 48cdd0817365ac10fc4031a39110dabf06cf4b75 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <Gjacquenot@users.noreply.github.com>
Date: Fri, 3 Jan 2020 21:53:40 +0100
Subject: [PATCH 0289/1080] :pencil: Removed unused attribute in classes in
 frenet_optimal_trajectory.py

---
 .../frenet_optimal_trajectory.py                 | 16 ++--------------
 1 file changed, 2 insertions(+), 14 deletions(-)

diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
index 25fdd7dbcf..1b62ea0187 100644
--- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
+++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
@@ -49,13 +49,6 @@ class quintic_polynomial:
     def __init__(self, xs, vxs, axs, xe, vxe, axe, T):
 
         # calc coefficient of quintic polynomial
-        self.xs = xs
-        self.vxs = vxs
-        self.axs = axs
-        self.xe = xe
-        self.vxe = vxe
-        self.axe = axe
-
         self.a0 = xs
         self.a1 = vxs
         self.a2 = axs / 2.0
@@ -99,12 +92,7 @@ class quartic_polynomial:
 
     def __init__(self, xs, vxs, axs, vxe, axe, T):
 
-        # calc coefficient of quintic polynomial
-        self.xs = xs
-        self.vxs = vxs
-        self.axs = axs
-        self.vxe = vxe
-        self.axe = axe
+        # calc coefficient of quartic polynomial
 
         self.a0 = xs
         self.a1 = vxs
@@ -184,7 +172,7 @@ def calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0):
             fp.d_dd = [lat_qp.calc_second_derivative(t) for t in fp.t]
             fp.d_ddd = [lat_qp.calc_third_derivative(t) for t in fp.t]
 
-            # Loongitudinal motion planning (Velocity keeping)
+            # Longitudinal motion planning (Velocity keeping)
             for tv in np.arange(TARGET_SPEED - D_T_S * N_S_SAMPLE, TARGET_SPEED + D_T_S * N_S_SAMPLE, D_T_S):
                 tfp = copy.deepcopy(fp)
                 lon_qp = quartic_polynomial(s0, c_speed, 0.0, tv, 0.0, Ti)

From 1ac858b075b5db19ef4c6130ecd426d42a23eb77 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 4 Jan 2020 09:43:21 +0900
Subject: [PATCH 0290/1080] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index f48e9c0ef3..ec42812753 100644
--- a/README.md
+++ b/README.md
@@ -607,6 +607,6 @@ This is a list: [Users comments](https://github.com/AtsushiSakai/PythonRobotics/
 
 - [Göktuğ Karakaşlı](https://github.com/goktug97)
 
-
+- [Guillaume Jacquenot](https://github.com/Gjacquenot)
 
 

From 811bdc1d49ac5d04ebd05c18de906c0b3c7243dc Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <Gjacquenot@users.noreply.github.com>
Date: Fri, 3 Jan 2020 23:13:36 +0100
Subject: [PATCH 0291/1080] :hammer: Refactored pure_pursuit.py

- Added new method to state class
- Removed global variable old_nearest_point_index
- Created a class States that store the results
---
 PathTracking/pure_pursuit/pure_pursuit.py | 161 ++++++++++++----------
 1 file changed, 85 insertions(+), 76 deletions(-)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index dfe8a8e17e..49e32677f5 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -17,7 +17,6 @@
 L = 2.9  # [m] wheel base of vehicle
 
 
-old_nearest_point_index = None
 show_animation = True
 
 
@@ -31,89 +30,106 @@ def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0):
         self.rear_x = self.x - ((L / 2) * math.cos(self.yaw))
         self.rear_y = self.y - ((L / 2) * math.sin(self.yaw))
 
+    def update(self, a, delta):
 
-def update(state, a, delta):
+        self.x += self.v * math.cos(self.yaw) * dt
+        self.y += self.v * math.sin(self.yaw) * dt
+        self.yaw += self.v / L * math.tan(delta) * dt
+        self.v += a * dt
+        self.rear_x = self.x - ((L / 2) * math.cos(self.yaw))
+        self.rear_y = self.y - ((L / 2) * math.sin(self.yaw))
 
-    state.x = state.x + state.v * math.cos(state.yaw) * dt
-    state.y = state.y + state.v * math.sin(state.yaw) * dt
-    state.yaw = state.yaw + state.v / L * math.tan(delta) * dt
-    state.v = state.v + a * dt
-    state.rear_x = state.x - ((L / 2) * math.cos(state.yaw))
-    state.rear_y = state.y - ((L / 2) * math.sin(state.yaw))
+    def calc_distance(self, point_x, point_y):
 
-    return state
+        dx = self.rear_x - point_x
+        dy = self.rear_y - point_y
+        return math.hypot(dx, dy)
 
 
-def PIDControl(target, current):
-    a = Kp * (target - current)
+class States:
 
-    return a
+    def __init__(self):
+        self.x = []
+        self.y = []
+        self.yaw = []
+        self.v = []
+        self.t = []
 
+    def append(self, t , state):
+        self.x.append(state.x)
+        self.y.append(state.y)
+        self.yaw.append(state.yaw)
+        self.v.append(state.v)
+        self.t.append(t)
 
-def pure_pursuit_control(state, cx, cy, pind):
 
-    ind = calc_target_index(state, cx, cy)
+def PIDControl(target, current):
+    a = Kp * (target - current)
 
-    if pind >= ind:
-        ind = pind
+    return a
 
-    if ind < len(cx):
-        tx = cx[ind]
-        ty = cy[ind]
-    else:
-        tx = cx[-1]
-        ty = cy[-1]
-        ind = len(cx) - 1
 
-    alpha = math.atan2(ty - state.rear_y, tx - state.rear_x) - state.yaw
+class Trajectory:
+    def __init__(self, cx, cy):
+        self.cx = cx
+        self.cy = cy
+        self.old_nearest_point_index = None
 
-    Lf = k * state.v + Lfc
+    def __call__(self, state):
+        if self.old_nearest_point_index is None:
+            # search nearest point index
+            dx = [state.rear_x - icx for icx in self.cx]
+            dy = [state.rear_y - icy for icy in self.cy]
+            d = [idx ** 2 + idy ** 2 for (idx, idy) in zip(dx, dy)]
+            ind = d.index(min(d))
+            self.old_nearest_point_index = ind
+        else:
+            ind = self.old_nearest_point_index
+            distance_this_index = state.calc_distance(self.cx[ind], self.cy[ind])
+            while True:
+                ind = ind + 1 if (ind + 1) < len(self.cx) else ind
+                distance_next_index = state.calc_distance(self.cx[ind], self.cy[ind])
+                if distance_this_index < distance_next_index:
+                    break
+                distance_this_index = distance_next_index
+            self.old_nearest_point_index = ind
 
-    delta = math.atan2(2.0 * L * math.sin(alpha) / Lf, 1.0)
+        L = 0.0
 
-    return delta, ind
+        Lf = k * state.v + Lfc
 
-def calc_distance(state, point_x, point_y):
+        # search look ahead target point index
+        while Lf > L and (ind + 1) < len(self.cx):
+            dx = self.cx[ind] - state.rear_x
+            dy = self.cy[ind] - state.rear_y
+            L = math.hypot(dx, dy)
+            ind += 1
 
-    dx = state.rear_x - point_x
-    dy = state.rear_y - point_y
-    return math.hypot(dx, dy)
+        return ind
 
 
-def calc_target_index(state, cx, cy):
+def pure_pursuit_control(state, trajectory, pind):
 
-    global old_nearest_point_index
+    ind = trajectory(state)
+
+    if pind >= ind:
+        ind = pind
 
-    if old_nearest_point_index is None:
-        # search nearest point index
-        dx = [state.rear_x - icx for icx in cx]
-        dy = [state.rear_y - icy for icy in cy]
-        d = [idx ** 2 + idy ** 2 for (idx, idy) in zip(dx, dy)]
-        ind = d.index(min(d))
-        old_nearest_point_index = ind
+    if ind < len(trajectory.cx):
+        tx = trajectory.cx[ind]
+        ty = trajectory.cy[ind]
     else:
-        ind = old_nearest_point_index
-        distance_this_index = calc_distance(state, cx[ind], cy[ind])
-        while True:
-            ind = ind + 1 if (ind + 1) < len(cx) else ind
-            distance_next_index = calc_distance(state, cx[ind], cy[ind])
-            if distance_this_index < distance_next_index:
-                break
-            distance_this_index = distance_next_index
-        old_nearest_point_index = ind
-
-    L = 0.0
+        tx = trajectory.cx[-1]
+        ty = trajectory.cy[-1]
+        ind = len(trajectory.cx) - 1
+
+    alpha = math.atan2(ty - state.rear_y, tx - state.rear_x) - state.yaw
 
     Lf = k * state.v + Lfc
 
-    # search look ahead target point index
-    while Lf > L and (ind + 1) < len(cx):
-        dx = cx[ind] - state.rear_x
-        dy = cy[ind] - state.rear_y
-        L = math.hypot(dx, dy)
-        ind += 1
+    delta = math.atan2(2.0 * L * math.sin(alpha) / Lf, 1.0)
 
-    return ind
+    return delta, ind
 
 
 def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
@@ -144,25 +160,18 @@ def main():
 
     lastIndex = len(cx) - 1
     time = 0.0
-    x = [state.x]
-    y = [state.y]
-    yaw = [state.yaw]
-    v = [state.v]
-    t = [0.0]
-    target_ind = calc_target_index(state, cx, cy)
+    states = States()
+    states.append(time, state)
+    trajectory = Trajectory(cx, cy)
+    target_ind = trajectory(state)
 
     while T >= time and lastIndex > target_ind:
         ai = PIDControl(target_speed, state.v)
-        di, target_ind = pure_pursuit_control(state, cx, cy, target_ind)
-        state = update(state, ai, di)
-
-        time = time + dt
+        di, target_ind = pure_pursuit_control(state, trajectory, target_ind)
+        state.update(ai, di)
 
-        x.append(state.x)
-        y.append(state.y)
-        yaw.append(state.yaw)
-        v.append(state.v)
-        t.append(time)
+        time += dt
+        states.append(time, state)
 
         if show_animation:  # pragma: no cover
             plt.cla()
@@ -171,7 +180,7 @@ def main():
                     lambda event: [exit(0) if event.key == 'escape' else None])
             plot_arrow(state.x, state.y, state.yaw)
             plt.plot(cx, cy, "-r", label="course")
-            plt.plot(x, y, "-b", label="trajectory")
+            plt.plot(states.x, states.y, "-b", label="trajectory")
             plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
             plt.axis("equal")
             plt.grid(True)
@@ -184,7 +193,7 @@ def main():
     if show_animation:  # pragma: no cover
         plt.cla()
         plt.plot(cx, cy, ".r", label="course")
-        plt.plot(x, y, "-b", label="trajectory")
+        plt.plot(states.x, states.y, "-b", label="trajectory")
         plt.legend()
         plt.xlabel("x[m]")
         plt.ylabel("y[m]")
@@ -192,7 +201,7 @@ def main():
         plt.grid(True)
 
         plt.subplots(1)
-        plt.plot(t, [iv * 3.6 for iv in v], "-r")
+        plt.plot(states.t, [iv * 3.6 for iv in states.v], "-r")
         plt.xlabel("Time[s]")
         plt.ylabel("Speed[km/h]")
         plt.grid(True)

From f81140e65bcf4648c8aae4bdf14e48080bfbd706 Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 4 Jan 2020 09:45:05 +0100
Subject: [PATCH 0292/1080] :pencil: Renamed method name __call__ with
 search_target_index in Trajectory class

Thanks to @AtsushiSakai remarks

https://github.com/AtsushiSakai/PythonRobotics/pull/269#discussion_r363005681
---
 PathTracking/pure_pursuit/pure_pursuit.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index 49e32677f5..d7cf8fe9f8 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -75,7 +75,7 @@ def __init__(self, cx, cy):
         self.cy = cy
         self.old_nearest_point_index = None
 
-    def __call__(self, state):
+    def search_target_index(self, state):
         if self.old_nearest_point_index is None:
             # search nearest point index
             dx = [state.rear_x - icx for icx in self.cx]
@@ -110,7 +110,7 @@ def __call__(self, state):
 
 def pure_pursuit_control(state, trajectory, pind):
 
-    ind = trajectory(state)
+    ind = trajectory.search_target_index(state)
 
     if pind >= ind:
         ind = pind
@@ -163,7 +163,7 @@ def main():
     states = States()
     states.append(time, state)
     trajectory = Trajectory(cx, cy)
-    target_ind = trajectory(state)
+    target_ind = trajectory.search_target_index(state)
 
     while T >= time and lastIndex > target_ind:
         ai = PIDControl(target_speed, state.v)

From bfc874492390a803e803992324777376a6b12e8c Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 4 Jan 2020 09:50:41 +0100
Subject: [PATCH 0293/1080] :hammer: Refcatored modification in pure_pursuit.py

Thanks to @AtsushiSakai remark

https://github.com/AtsushiSakai/PythonRobotics/pull/269#discussion_r363005775
---
 PathTracking/pure_pursuit/pure_pursuit.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index d7cf8fe9f8..4e8b1bedef 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -80,8 +80,8 @@ def search_target_index(self, state):
             # search nearest point index
             dx = [state.rear_x - icx for icx in self.cx]
             dy = [state.rear_y - icy for icy in self.cy]
-            d = [idx ** 2 + idy ** 2 for (idx, idy) in zip(dx, dy)]
-            ind = d.index(min(d))
+            d = np.hypot(dx, dy)
+            ind = np.argmin(d)
             self.old_nearest_point_index = ind
         else:
             ind = self.old_nearest_point_index
@@ -100,9 +100,7 @@ def search_target_index(self, state):
 
         # search look ahead target point index
         while Lf > L and (ind + 1) < len(self.cx):
-            dx = self.cx[ind] - state.rear_x
-            dy = self.cy[ind] - state.rear_y
-            L = math.hypot(dx, dy)
+            L = state.calc_distance(self.cx[ind], self.cy[ind])
             ind += 1
 
         return ind

From 3bdf2555345b93c89068549fc1fd87ce7a7e92dc Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 4 Jan 2020 09:57:30 +0100
Subject: [PATCH 0294/1080] :hammer: Removed unnecessary parenthesis in
 pure_pursuit.py

---
 PathTracking/pure_pursuit/pure_pursuit.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index 4e8b1bedef..bfc3c7ccf9 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -136,7 +136,7 @@ def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
     """
 
     if not isinstance(x, float):
-        for (ix, iy, iyaw) in zip(x, y, yaw):
+        for ix, iy, iyaw in zip(x, y, yaw):
             plot_arrow(ix, iy, iyaw)
     else:
         plt.arrow(x, y, length * math.cos(yaw), length * math.sin(yaw),

From 08428bed153dfe49085e63becd6f399a713dd57c Mon Sep 17 00:00:00 2001
From: Guillaume Jacquenot <guillaume.jacquenot@gmail.com>
Date: Sat, 4 Jan 2020 09:57:47 +0100
Subject: [PATCH 0295/1080] :pencil: Added Gjacquenot as author as suggested by
 @AtsushiSakai

---
 PathTracking/pure_pursuit/pure_pursuit.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index bfc3c7ccf9..bcaf869726 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -3,6 +3,7 @@
 Path tracking simulation with pure pursuit steering control and PID speed control.
 
 author: Atsushi Sakai (@Atsushi_twi)
+        Guillaume Jacquenot (@Gjacquenot)
 
 """
 import numpy as np

From f19fdacb014af4af1395519746b2cd7f6c89b8cc Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 5 Jan 2020 13:47:32 +0900
Subject: [PATCH 0296/1080] change default mode for DWA

---
 PathPlanning/DynamicWindowApproach/dynamic_window_approach.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index 891e68cbe8..2dab2f301f 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -293,4 +293,4 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle):
 
 
 if __name__ == '__main__':
-    main(robot_type=RobotType.rectangle)
+    main(robot_type=RobotType.circle)

From 5ceb83783e7c2e561afac87f9bb899609fe7770e Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 5 Jan 2020 13:53:49 +0900
Subject: [PATCH 0297/1080] Add node valid check to fix #271

---
 PathPlanning/RRT/rrt.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py
index b590d7b1cb..4acea0c10e 100644
--- a/PathPlanning/RRT/rrt.py
+++ b/PathPlanning/RRT/rrt.py
@@ -140,7 +140,7 @@ def draw_graph(self, rnd=None):
         plt.clf()
         # for stopping simulation with the esc key.
         plt.gcf().canvas.mpl_connect('key_release_event',
-                lambda event: [exit(0) if event.key == 'escape' else None])
+                                     lambda event: [exit(0) if event.key == 'escape' else None])
         if rnd is not None:
             plt.plot(rnd.x, rnd.y, "^k")
         for node in self.node_list:
@@ -175,6 +175,10 @@ def get_nearest_node_index(node_list, rnd_node):
 
     @staticmethod
     def check_collision(node, obstacleList):
+
+        if node is None:
+            return False
+
         for (ox, oy, size) in obstacleList:
             dx_list = [ox - x for x in node.path_x]
             dy_list = [oy - y for y in node.path_y]

From fbeeefb52204ec50dc5686d36b78b83867e41080 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 5 Jan 2020 20:45:12 +0900
Subject: [PATCH 0298/1080] code cleanup for quintic_polynomials_planner.py

---
 .../quintic_polynomials_planner.py            | 25 +++++++++----------
 1 file changed, 12 insertions(+), 13 deletions(-)

diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
index e7c744d1fe..8600f1d7bd 100644
--- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
+++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
@@ -24,18 +24,17 @@
 
 class QuinticPolynomial:
 
-    def __init__(self, xs, vxs, axs, xe, vxe, axe, T):
-
+    def __init__(self, xs, vxs, axs, xe, vxe, axe, time):
         # calc coefficient of quintic polynomial
         self.a0 = xs
         self.a1 = vxs
         self.a2 = axs / 2.0
 
-        A = np.array([[T**3, T**4, T**5],
-                      [3 * T ** 2, 4 * T ** 3, 5 * T ** 4],
-                      [6 * T, 12 * T ** 2, 20 * T ** 3]])
-        b = np.array([xe - self.a0 - self.a1 * T - self.a2 * T**2,
-                      vxe - self.a1 - 2 * self.a2 * T,
+        A = np.array([[time ** 3, time ** 4, time ** 5],
+                      [3 * time ** 2, 4 * time ** 3, 5 * time ** 4],
+                      [6 * time, 12 * time ** 2, 20 * time ** 3]])
+        b = np.array([xe - self.a0 - self.a1 * time - self.a2 * time ** 2,
+                      vxe - self.a1 - 2 * self.a2 * time,
                       axe - 2 * self.a2])
         x = np.linalg.solve(A, b)
 
@@ -44,24 +43,24 @@ def __init__(self, xs, vxs, axs, xe, vxe, axe, T):
         self.a5 = x[2]
 
     def calc_point(self, t):
-        xt = self.a0 + self.a1 * t + self.a2 * t**2 + \
-            self.a3 * t**3 + self.a4 * t**4 + self.a5 * t**5
+        xt = self.a0 + self.a1 * t + self.a2 * t ** 2 + \
+             self.a3 * t ** 3 + self.a4 * t ** 4 + self.a5 * t ** 5
 
         return xt
 
     def calc_first_derivative(self, t):
         xt = self.a1 + 2 * self.a2 * t + \
-            3 * self.a3 * t**2 + 4 * self.a4 * t**3 + 5 * self.a5 * t**4
+             3 * self.a3 * t ** 2 + 4 * self.a4 * t ** 3 + 5 * self.a5 * t ** 4
 
         return xt
 
     def calc_second_derivative(self, t):
-        xt = 2 * self.a2 + 6 * self.a3 * t + 12 * self.a4 * t**2 + 20 * self.a5 * t**3
+        xt = 2 * self.a2 + 6 * self.a3 * t + 12 * self.a4 * t ** 2 + 20 * self.a5 * t ** 3
 
         return xt
 
     def calc_third_derivative(self, t):
-        xt = 6 * self.a3 + 24 * self.a4 * t + 60 * self.a5 * t**2
+        xt = 6 * self.a3 + 24 * self.a4 * t + 60 * self.a5 * t ** 2
 
         return xt
 
@@ -146,7 +145,7 @@ def quintic_polynomials_planner(sx, sy, syaw, sv, sa, gx, gy, gyaw, gv, ga, max_
             plt.cla()
             # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+                                         lambda event: [exit(0) if event.key == 'escape' else None])
             plt.grid(True)
             plt.axis("equal")
             plot_arrow(sx, sy, syaw)

From c50bba7c0489493dc144e9202aad65ab13abbcaa Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 5 Jan 2020 22:12:37 +0900
Subject: [PATCH 0299/1080] add jupyter docs

---
 .../quintic_polynomials_planner.ipynb         | 56 +++++++++++++++++++
 1 file changed, 56 insertions(+)
 create mode 100644 PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb

diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb
new file mode 100644
index 0000000000..bc829f2e13
--- /dev/null
+++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb
@@ -0,0 +1,56 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Quintic polynomials planner"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Quintic polynomials for one dimension robot motion\n",
+    "\n",
+    "$e^{i\\pi} + 1 = 0$\n",
+    "\n",
+    "\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "source": [],
+    "metadata": {
+     "collapsed": false
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file

From e4fb755471bc476d7fe21f17f1125bc311466baf Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 5 Jan 2020 22:24:46 +0900
Subject: [PATCH 0300/1080] update jupyter docs

---
 .../quintic_polynomials_planner.ipynb            | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb
index bc829f2e13..7b83a87b7e 100644
--- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb
+++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb
@@ -12,7 +12,21 @@
   {
    "cell_type": "markdown",
    "source": [
-    "## Quintic polynomials for one dimension robot motion\n",
+    "## Quintic polynomials for one dimensional robot motion\n",
+    "\n",
+    "We assume a dimensional robot motion $x(t)$ at time $t$ is formulated as a quintic polynomials based on time as follows:\n",
+    "\n",
+    "$x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4$\n",
+    "\n",
+    "a_0, a_1. a_2, a_3 are parameters of the quintic polynomial.\n",
+    "\n",
+    "So, when time is 0,\n",
+    "\n",
+    "$x(0) = a_0 = x_s$\n",
+    "\n",
+    "x_s is a start x position.\n",
+    "\n",
+    "\n",
     "\n",
     "$e^{i\\pi} + 1 = 0$\n",
     "\n",

From 763ca4fd15f4ae4bd272709bf5e187475ca4e4a3 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 5 Jan 2020 22:37:08 +0900
Subject: [PATCH 0301/1080] update jupyter docs

---
 .../quintic_polynomials_planner.ipynb                | 12 +++++++++---
 .../quintic_polynomials_planner.py                   |  1 +
 2 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb
index 7b83a87b7e..804b7fba13 100644
--- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb
+++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb
@@ -24,13 +24,19 @@
     "\n",
     "$x(0) = a_0 = x_s$\n",
     "\n",
-    "x_s is a start x position.\n",
+    "$x_s$ is a start x position.\n",
     "\n",
+    "Then, differentiating this equation with t, \n",
     "\n",
+    "$x'(t) = a_1+2a_2t+3a_3t^2+4a_4t^3$\n",
     "\n",
-    "$e^{i\\pi} + 1 = 0$\n",
+    "So, when time is 0,\n",
+    "\n",
+    "$x'(0) = a_1 = v_s$\n",
+    "\n",
+    "$v_s$ is a initial speed for x axis.\n",
     "\n",
-    "\n"
+    "=== TBD ==== \n"
    ],
    "metadata": {
     "collapsed": false
diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
index 8600f1d7bd..5538731dcd 100644
--- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
+++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
@@ -26,6 +26,7 @@ class QuinticPolynomial:
 
     def __init__(self, xs, vxs, axs, xe, vxe, axe, time):
         # calc coefficient of quintic polynomial
+        # See jupyter notebook document for derivation of this equation.
         self.a0 = xs
         self.a1 = vxs
         self.a2 = axs / 2.0

From 0de06534d22e22d84b4fe65ad973525e16e312c0 Mon Sep 17 00:00:00 2001
From: Yilun Chen <chenyilun12@gmail.com>
Date: Fri, 17 Jan 2020 19:17:28 -0500
Subject: [PATCH 0302/1080] cleanup in dynamic window approach

---
 .../DynamicWindowApproach/dynamic_window_approach.py      | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index 92fd08bb40..9e9d34d721 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -22,7 +22,7 @@ def dwa_control(x, config, goal, ob):
 
     dw = calc_dynamic_window(x, config)
 
-    u, trajectory = calc_final_input(x, dw, config, goal, ob)
+    u, trajectory = calc_control_and_trajectory(x, dw, config, goal, ob)
 
     return u, trajectory
 
@@ -77,9 +77,9 @@ def motion(x, u, dt):
     motion model
     """
 
-    x[2] += u[1] * dt
     x[0] += u[0] * math.cos(x[2]) * dt
     x[1] += u[0] * math.sin(x[2]) * dt
+    x[2] += u[1] * dt
     x[3] = u[0]
     x[4] = u[1]
 
@@ -101,7 +101,7 @@ def calc_dynamic_window(x, config):
           x[4] - config.max_dyawrate * config.dt,
           x[4] + config.max_dyawrate * config.dt]
 
-    #  [vmin,vmax, yaw_rate min, yaw_rate max]
+    #  [vmin, vmax, yaw_rate min, yaw_rate max]
     dw = [max(Vs[0], Vd[0]), min(Vs[1], Vd[1]),
           max(Vs[2], Vd[2]), min(Vs[3], Vd[3])]
 
@@ -124,7 +124,7 @@ def predict_trajectory(x_init, v, y, config):
     return traj
 
 
-def calc_final_input(x, dw, config, goal, ob):
+def calc_control_and_trajectory(x, dw, config, goal, ob):
     """
     calculation final input with dynamic window
     """

From dc612e6f2000a67b5542cc48303bf0b505350926 Mon Sep 17 00:00:00 2001
From: Yilun Chen <chenyilun12@gmail.com>
Date: Sun, 19 Jan 2020 11:25:52 -0500
Subject: [PATCH 0303/1080] minor fix in dijkstra.py

---
 .gitignore                        |  1 +
 PathPlanning/Dijkstra/dijkstra.py | 18 ++++++++----------
 2 files changed, 9 insertions(+), 10 deletions(-)

diff --git a/.gitignore b/.gitignore
index 272dc63b6d..c971b8f9c5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -70,3 +70,4 @@ target/
 .ipynb_checkpoints
 
 matplotrecorder/*
+.vscode/settings.json
diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py
index 62da6d761b..ee88b3f9f1 100644
--- a/PathPlanning/Dijkstra/dijkstra.py
+++ b/PathPlanning/Dijkstra/dijkstra.py
@@ -33,7 +33,7 @@ def __init__(self, x, y, cost, pind):
             self.x = x  # index of grid
             self.y = y  # index of grid
             self.cost = cost
-            self.pind = pind
+            self.pind = pind # index of previous Node
 
         def __str__(self):
             return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind)
@@ -88,10 +88,10 @@ def planning(self, sx, sy, gx, gy):
             closedset[c_id] = current
 
             # expand search grid based on motion model
-            for i, _ in enumerate(self.motion):
-                node = self.Node(current.x + self.motion[i][0],
-                                 current.y + self.motion[i][1],
-                                 current.cost + self.motion[i][2], c_id)
+            for move_x, move_y, move_cost in self.motion:
+                node = self.Node(current.x + move_x,
+                                 current.y + move_y,
+                                 current.cost + move_cost, c_id)
                 n_id = self.calc_index(node)
 
                 if n_id in closedset:
@@ -124,11 +124,6 @@ def calc_final_path(self, ngoal, closedset):
 
         return rx, ry
 
-    def calc_heuristic(self, n1, n2):
-        w = 1.0  # weight of heuristic
-        d = w * math.hypot(n1.x - n2.x, n1.y - n2.y)
-        return d
-
     def calc_position(self, index, minp):
         pos = index*self.reso+minp
         return pos
@@ -242,6 +237,9 @@ def main():
     dijkstra = Dijkstra(ox, oy, grid_size, robot_radius)
     rx, ry = dijkstra.planning(sx, sy, gx, gy)
 
+    print(rx)
+    print(ry)
+
     if show_animation:  # pragma: no cover
         plt.plot(rx, ry, "-r")
         plt.show()

From f261da52f0301041829dc20d8845372cf98a9a73 Mon Sep 17 00:00:00 2001
From: Yilun Chen <chenyilun12@gmail.com>
Date: Sun, 19 Jan 2020 11:29:05 -0500
Subject: [PATCH 0304/1080] remove print

---
 PathPlanning/Dijkstra/dijkstra.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py
index ee88b3f9f1..bb1fb3aa63 100644
--- a/PathPlanning/Dijkstra/dijkstra.py
+++ b/PathPlanning/Dijkstra/dijkstra.py
@@ -237,9 +237,6 @@ def main():
     dijkstra = Dijkstra(ox, oy, grid_size, robot_radius)
     rx, ry = dijkstra.planning(sx, sy, gx, gy)
 
-    print(rx)
-    print(ry)
-
     if show_animation:  # pragma: no cover
         plt.plot(rx, ry, "-r")
         plt.show()

From 1c081ed3e0ccfbc20214a505b2fbd925427e0fa1 Mon Sep 17 00:00:00 2001
From: Yilun Chen <chenyilun12@gmail.com>
Date: Sun, 19 Jan 2020 18:35:52 -0500
Subject: [PATCH 0305/1080] address the comment

---
 PathPlanning/DynamicWindowApproach/dynamic_window_approach.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index 9e9d34d721..b8395e5ff8 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -77,9 +77,9 @@ def motion(x, u, dt):
     motion model
     """
 
+    x[2] += u[1] * dt
     x[0] += u[0] * math.cos(x[2]) * dt
     x[1] += u[0] * math.sin(x[2]) * dt
-    x[2] += u[1] * dt
     x[3] = u[0]
     x[4] = u[1]
 

From cd65de271462b9cc50a130c708f1d2c0e005e953 Mon Sep 17 00:00:00 2001
From: Atsushi <asakai.amsl+github@gmail.com>
Date: Tue, 21 Jan 2020 10:58:02 +0900
Subject: [PATCH 0306/1080] add math descriptions

---
 .../quintic_polynomials_planner.ipynb         | 119 ++++++++++++++----
 1 file changed, 93 insertions(+), 26 deletions(-)

diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb
index 804b7fba13..f094ae2c15 100644
--- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb
+++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb
@@ -2,45 +2,112 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Quintic polynomials planner"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Quintic polynomials for one dimensional robot motion\n",
     "\n",
-    "We assume a dimensional robot motion $x(t)$ at time $t$ is formulated as a quintic polynomials based on time as follows:\n",
+    "We assume a one-dimensional robot motion $x(t)$ at time $t$ is formulated as a quintic polynomials based on time as follows:\n",
     "\n",
-    "$x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4$\n",
+    "$x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4+a_5t^5$ --(1)\n",
     "\n",
-    "a_0, a_1. a_2, a_3 are parameters of the quintic polynomial.\n",
+    "$a_0, a_1. a_2, a_3, a_4, a_5$ are parameters of the quintic polynomial.\n",
     "\n",
-    "So, when time is 0,\n",
+    "It is assumed that terminal states (start and end) are known as boundary conditions.\n",
+    "\n",
+    "Start position, velocity, and acceleration are $x_s, v_s, a_s$ respectively.\n",
     "\n",
-    "$x(0) = a_0 = x_s$\n",
+    "End position, velocity, and acceleration are $x_e, v_e, a_e$ respectively.\n",
     "\n",
-    "$x_s$ is a start x position.\n",
+    "So, when time is 0.\n",
     "\n",
-    "Then, differentiating this equation with t, \n",
+    "$x(0) = a_0 = x_s$ -- (2)\n",
     "\n",
-    "$x'(t) = a_1+2a_2t+3a_3t^2+4a_4t^3$\n",
+    "Then, differentiating the equation (1) with t, \n",
+    "\n",
+    "$x'(t) = a_1+2a_2t+3a_3t^2+4a_4t^3+5a_5t^4$ -- (3)\n",
     "\n",
     "So, when time is 0,\n",
     "\n",
-    "$x'(0) = a_1 = v_s$\n",
+    "$x'(0) = a_1 = v_s$ -- (4)\n",
     "\n",
-    "$v_s$ is a initial speed for x axis.\n",
+    "Then, differentiating the equation (3) with t again, \n",
     "\n",
-    "=== TBD ==== \n"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+    "$x''(t) = 2a_2+6a_3t+12a_4t^2$ -- (5)\n",
+    "\n",
+    "So, when time is 0,\n",
+    "\n",
+    "$x''(0) = 2a_2 = a_s$ -- (6)\n",
+    "\n",
+    "so, we can calculate $a_0$, $a_1$, $a_2$ with eq. (2), (4), (6) and boundary conditions.\n",
+    "\n",
+    "$a_3, a_4, a_5$ are still unknown in eq(1).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We assume that the end time for a maneuver is $T$, we can get these equations from eq (1), (3), (5):\n",
+    "\n",
+    "$x(T)=a_0+a_1T+a_2T^2+a_3T^3+a_4T^4+a_5T^5=x_e$ -- (7)\n",
+    "\n",
+    "$x'(T)=a_1+2a_2T+3a_3T^2+4a_4T^3+5a_5T^4=v_e$ -- (8)\n",
+    "\n",
+    "$x''(T)=2a_2+6a_3T+12a_4T^2+20a_5T^3=a_e$ -- (9)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From eq (7), (8), (9), we can calculate $a_3, a_4, a_5$ to solve the linear equations.\n",
+    "\n",
+    "$Ax=b$\n",
+    "\n",
+    "$\\begin{bmatrix} T^3 & T^4 & T^5 \\\\ 3T^2 & 4T^3 & 5T^4 \\\\ 6T & 12T^2 & 20T^3 \\end{bmatrix}\n",
+    "\\begin{bmatrix} a_3\\\\ a_4\\\\ a_5\\end{bmatrix}=\\begin{bmatrix} x_e-x_s-v_sT-0.5a_sT^2\\\\ v_e-v_s-a_sT\\\\ a_e-a_s\\end{bmatrix}$\n",
+    "\n",
+    "We can get all unknown parameters now"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Quintic polynomials for two dimensional robot motion (x-y)\n",
+    "\n",
+    "If you use two quintic polynomials along x axis and y axis, you can plan for two dimensional robot motion in x-y plane.\n",
+    "\n",
+    "$x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4+a_5t^5$ --(10)\n",
+    "\n",
+    "$y(t) = b_0+b_1t+b_2t^2+b_3t^3+b_4t^4+b_5t^5$ --(11)\n",
+    "\n",
+    "It is assumed that terminal states (start and end) are known as boundary conditions.\n",
+    "\n",
+    "Start position, orientation, velocity, and acceleration are $x_s, y_s, \\theta_s, v_s, a_s$ respectively.\n",
+    "\n",
+    "End position, orientation, velocity, and acceleration are $x_e, y_e. \\theta_e, v_e, a_e$ respectively.\n",
+    "\n",
+    "Each velocity and acceleration boundary condition can be calculated with each orientation.\n",
+    "\n",
+    "$v_{xs}=v_scos(\\theta_s), v_{ys}=v_ssin(\\theta_s)$\n",
+    "\n",
+    "$v_{xe}=v_ecos(\\theta_e), v_{ye}=v_esin(\\theta_e)$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -52,25 +119,25 @@
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
+   "pygments_lexer": "ipython3",
+   "version": "3.7.5"
   },
   "pycharm": {
    "stem_cell": {
     "cell_type": "raw",
-    "source": [],
     "metadata": {
      "collapsed": false
-    }
+    },
+    "source": []
    }
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
+ "nbformat_minor": 1
+}

From 33f4b944129b176c760879de7015f6183bb32950 Mon Sep 17 00:00:00 2001
From: Atsushi <asakai.amsl+github@gmail.com>
Date: Tue, 21 Jan 2020 11:12:15 +0900
Subject: [PATCH 0307/1080] keep DRY

---
 .../frenet_optimal_trajectory.py              | 58 +++++--------------
 1 file changed, 13 insertions(+), 45 deletions(-)

diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
index 1b62ea0187..b113bcbe32 100644
--- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
+++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
@@ -17,6 +17,17 @@
 import copy
 import math
 import cubic_spline_planner
+import sys
+import os
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../QuinticPolynomialsPlanner/")
+
+try:
+    from quintic_polynomials_planner import QuinticPolynomial
+except ImportError:
+    raise
+
 
 SIM_LOOP = 500
 
@@ -44,50 +55,6 @@
 show_animation = True
 
 
-class quintic_polynomial:
-
-    def __init__(self, xs, vxs, axs, xe, vxe, axe, T):
-
-        # calc coefficient of quintic polynomial
-        self.a0 = xs
-        self.a1 = vxs
-        self.a2 = axs / 2.0
-
-        A = np.array([[T**3, T**4, T**5],
-                      [3 * T ** 2, 4 * T ** 3, 5 * T ** 4],
-                      [6 * T, 12 * T ** 2, 20 * T ** 3]])
-        b = np.array([xe - self.a0 - self.a1 * T - self.a2 * T**2,
-                      vxe - self.a1 - 2 * self.a2 * T,
-                      axe - 2 * self.a2])
-        x = np.linalg.solve(A, b)
-
-        self.a3 = x[0]
-        self.a4 = x[1]
-        self.a5 = x[2]
-
-    def calc_point(self, t):
-        xt = self.a0 + self.a1 * t + self.a2 * t**2 + \
-            self.a3 * t**3 + self.a4 * t**4 + self.a5 * t**5
-
-        return xt
-
-    def calc_first_derivative(self, t):
-        xt = self.a1 + 2 * self.a2 * t + \
-            3 * self.a3 * t**2 + 4 * self.a4 * t**3 + 5 * self.a5 * t**4
-
-        return xt
-
-    def calc_second_derivative(self, t):
-        xt = 2 * self.a2 + 6 * self.a3 * t + 12 * self.a4 * t**2 + 20 * self.a5 * t**3
-
-        return xt
-
-    def calc_third_derivative(self, t):
-        xt = 6 * self.a3 + 24 * self.a4 * t + 60 * self.a5 * t**2
-
-        return xt
-
-
 class quartic_polynomial:
 
     def __init__(self, xs, vxs, axs, vxe, axe, T):
@@ -164,7 +131,8 @@ def calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0):
         for Ti in np.arange(MINT, MAXT, DT):
             fp = Frenet_path()
 
-            lat_qp = quintic_polynomial(c_d, c_d_d, c_d_dd, di, 0.0, 0.0, Ti)
+            # lat_qp = quintic_polynomial(c_d, c_d_d, c_d_dd, di, 0.0, 0.0, Ti)
+            lat_qp = QuinticPolynomial(c_d, c_d_d, c_d_dd, di, 0.0, 0.0, Ti)
 
             fp.t = [t for t in np.arange(0.0, Ti, DT)]
             fp.d = [lat_qp.calc_point(t) for t in fp.t]

From d4cb0f981d688d8df2c65ca846544a4d8494f9df Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 27 Jan 2020 17:39:10 +0900
Subject: [PATCH 0308/1080] fix cal_covariance func and clean up code and added
 jupyter doc

---
 .../particle_filter/particle_filter.ipynb     |  62 +++++++++++
 .../particle_filter/particle_filter.py        | 100 +++++++++---------
 2 files changed, 114 insertions(+), 48 deletions(-)
 create mode 100644 Localization/particle_filter/particle_filter.ipynb

diff --git a/Localization/particle_filter/particle_filter.ipynb b/Localization/particle_filter/particle_filter.ipynb
new file mode 100644
index 0000000000..46825b639d
--- /dev/null
+++ b/Localization/particle_filter/particle_filter.ipynb
@@ -0,0 +1,62 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Particle Filter\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## How to calculate covariance matrix from particles\n",
+    "\n",
+    "$\\Xi_{j,k}=\\frac{1}{1-\\sum^N_{i=1}(w^i)^2}\\sum^N_{i=1}w^i(x^i_j-\\mu_j)(x^i_k-\\mu_k)$\n",
+    "\n",
+    "Ref:\n",
+    "\n",
+    "- [Improving the particle filter in high dimensions using conjugate artificial process noise](https://www.visiondummy.com/2014/04/draw-error-ellipse-representing-covariance-matrix/)\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "source": [],
+    "metadata": {
+     "collapsed": false
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py
index bfef3ed929..6eca4eb53b 100644
--- a/Localization/particle_filter/particle_filter.py
+++ b/Localization/particle_filter/particle_filter.py
@@ -37,20 +37,20 @@ def calc_input():
     return u
 
 
-def observation(xTrue, xd, u, RF_ID):
-    xTrue = motion_model(xTrue, u)
+def observation(x_true, xd, u, rf_id):
+    x_true = motion_model(x_true, u)
 
     # add noise to gps x-y
     z = np.zeros((0, 3))
 
-    for i in range(len(RF_ID[:, 0])):
+    for i in range(len(rf_id[:, 0])):
 
-        dx = xTrue[0, 0] - RF_ID[i, 0]
-        dy = xTrue[1, 0] - RF_ID[i, 1]
+        dx = x_true[0, 0] - rf_id[i, 0]
+        dy = x_true[1, 0] - rf_id[i, 1]
         d = math.hypot(dx, dy)
         if d <= MAX_RANGE:
             dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5  # add noise
-            zi = np.array([[dn, RF_ID[i, 0], RF_ID[i, 1]]])
+            zi = np.array([[dn, rf_id[i, 0], rf_id[i, 1]]])
             z = np.vstack((z, zi))
 
     # add noise to input
@@ -60,7 +60,7 @@ def observation(xTrue, xd, u, RF_ID):
 
     xd = motion_model(xd, ud)
 
-    return xTrue, z, xd, ud
+    return x_true, z, xd, ud
 
 
 def motion_model(x, u):
@@ -86,13 +86,17 @@ def gauss_likelihood(x, sigma):
     return p
 
 
-def calc_covariance(xEst, px, pw):
+def calc_covariance(x_est, px, pw):
+    """
+    calculate covariance matrix
+    see ipynb doc
+    """
     cov = np.zeros((3, 3))
-
-    for i in range(px.shape[1]):
-        dx = (px[:, i] - xEst)[0:3]
-        cov += pw[0, i] * dx.dot(dx.T)
-    cov /= NP
+    n_particle = px.shape[1]
+    for i in range(n_particle):
+        dx = (px[:, i:i + 1] - x_est)[0:3]
+        cov += pw[0, i] * dx @ dx.T
+    cov *= 1.0 / (1.0 - pw @ pw.T)
 
     return cov
 
@@ -125,13 +129,13 @@ def pf_localization(px, pw, z, u):
 
     pw = pw / pw.sum()  # normalize
 
-    xEst = px.dot(pw.T)
-    PEst = calc_covariance(xEst, px, pw)
+    x_est = px.dot(pw.T)
+    p_est = calc_covariance(x_est, px, pw)
 
     N_eff = 1.0 / (pw.dot(pw.T))[0, 0]  # Effective particle number
     if N_eff < NTh:
         px, pw = re_sampling(px, pw)
-    return xEst, PEst, px, pw
+    return x_est, p_est, px, pw
 
 
 def re_sampling(px, pw):
@@ -140,8 +144,8 @@ def re_sampling(px, pw):
     """
 
     w_cum = np.cumsum(pw)
-    base = np.arange(0.0, 1.0, 1/NP)
-    re_sample_id = base + np.random.uniform(0, 1/NP)
+    base = np.arange(0.0, 1.0, 1 / NP)
+    re_sample_id = base + np.random.uniform(0, 1 / NP)
     indexes = []
     ind = 0
     for ip in range(NP):
@@ -155,9 +159,9 @@ def re_sampling(px, pw):
     return px, pw
 
 
-def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
-    Pxy = PEst[0:2, 0:2]
-    eig_val, eig_vec = np.linalg.eig(Pxy)
+def plot_covariance_ellipse(x_est, p_est):  # pragma: no cover
+    p_xy = p_est[0:2, 0:2]
+    eig_val, eig_vec = np.linalg.eig(p_xy)
 
     if eig_val[0] >= eig_val[1]:
         big_ind = 0
@@ -186,8 +190,8 @@ def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
     Rot = np.array([[math.cos(angle), -math.sin(angle)],
                     [math.sin(angle), math.cos(angle)]])
     fx = Rot.dot(np.array([[x, y]]))
-    px = np.array(fx[0, :] + xEst[0, 0]).flatten()
-    py = np.array(fx[1, :] + xEst[1, 0]).flatten()
+    px = np.array(fx[0, :] + x_est[0, 0]).flatten()
+    py = np.array(fx[1, :] + x_est[1, 0]).flatten()
     plt.plot(px, py, "--r")
 
 
@@ -197,54 +201,54 @@ def main():
     time = 0.0
 
     # RF_ID positions [x, y]
-    RFi_ID = np.array([[10.0, 0.0],
-                       [10.0, 10.0],
-                       [0.0, 15.0],
-                       [-5.0, 20.0]])
+    rf_id = np.array([[10.0, 0.0],
+                      [10.0, 10.0],
+                      [0.0, 15.0],
+                      [-5.0, 20.0]])
 
     # State Vector [x y yaw v]'
-    xEst = np.zeros((4, 1))
-    xTrue = np.zeros((4, 1))
+    x_est = np.zeros((4, 1))
+    x_true = np.zeros((4, 1))
 
     px = np.zeros((4, NP))  # Particle store
     pw = np.zeros((1, NP)) + 1.0 / NP  # Particle weight
-    xDR = np.zeros((4, 1))  # Dead reckoning
+    x_dr = np.zeros((4, 1))  # Dead reckoning
 
     # history
-    hxEst = xEst
-    hxTrue = xTrue
-    hxDR = xTrue
+    h_x_est = x_est
+    h_x_true = x_true
+    h_x_dr = x_true
 
     while SIM_TIME >= time:
         time += DT
         u = calc_input()
 
-        xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFi_ID)
+        x_true, z, x_dr, ud = observation(x_true, x_dr, u, rf_id)
 
-        xEst, PEst, px, pw = pf_localization(px, pw, z, ud)
+        x_est, PEst, px, pw = pf_localization(px, pw, z, ud)
 
         # store data history
-        hxEst = np.hstack((hxEst, xEst))
-        hxDR = np.hstack((hxDR, xDR))
-        hxTrue = np.hstack((hxTrue, xTrue))
+        h_x_est = np.hstack((h_x_est, x_est))
+        h_x_dr = np.hstack((h_x_dr, x_dr))
+        h_x_true = np.hstack((h_x_true, x_true))
 
         if show_animation:
             plt.cla()
             # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+                                         lambda event: [exit(0) if event.key == 'escape' else None])
 
             for i in range(len(z[:, 0])):
-                plt.plot([xTrue[0, 0], z[i, 1]], [xTrue[1, 0], z[i, 2]], "-k")
-            plt.plot(RFi_ID[:, 0], RFi_ID[:, 1], "*k")
+                plt.plot([x_true[0, 0], z[i, 1]], [x_true[1, 0], z[i, 2]], "-k")
+            plt.plot(rf_id[:, 0], rf_id[:, 1], "*k")
             plt.plot(px[0, :], px[1, :], ".r")
-            plt.plot(np.array(hxTrue[0, :]).flatten(),
-                     np.array(hxTrue[1, :]).flatten(), "-b")
-            plt.plot(np.array(hxDR[0, :]).flatten(),
-                     np.array(hxDR[1, :]).flatten(), "-k")
-            plt.plot(np.array(hxEst[0, :]).flatten(),
-                     np.array(hxEst[1, :]).flatten(), "-r")
-            plot_covariance_ellipse(xEst, PEst)
+            plt.plot(np.array(h_x_true[0, :]).flatten(),
+                     np.array(h_x_true[1, :]).flatten(), "-b")
+            plt.plot(np.array(h_x_dr[0, :]).flatten(),
+                     np.array(h_x_dr[1, :]).flatten(), "-k")
+            plt.plot(np.array(h_x_est[0, :]).flatten(),
+                     np.array(h_x_est[1, :]).flatten(), "-r")
+            plot_covariance_ellipse(x_est, PEst)
             plt.axis("equal")
             plt.grid(True)
             plt.pause(0.001)

From 123c4f777ccfc49a0a11a8b99c9a63013d46b9a7 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 27 Jan 2020 19:57:48 +0900
Subject: [PATCH 0309/1080] update ipynb doc.

---
 Localization/particle_filter/particle_filter.ipynb | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/Localization/particle_filter/particle_filter.ipynb b/Localization/particle_filter/particle_filter.ipynb
index 46825b639d..65f1e026df 100644
--- a/Localization/particle_filter/particle_filter.ipynb
+++ b/Localization/particle_filter/particle_filter.ipynb
@@ -9,7 +9,7 @@
     }
    },
    "source": [
-    "# Particle Filter\n",
+    "# Particle Filter Localization\n",
     "\n"
    ]
   },
@@ -18,11 +18,21 @@
    "source": [
     "## How to calculate covariance matrix from particles\n",
     "\n",
+    "The covariance matrix $\\Xi$ from particle information is calculated by the following equation: \n",
+    "\n",
     "$\\Xi_{j,k}=\\frac{1}{1-\\sum^N_{i=1}(w^i)^2}\\sum^N_{i=1}w^i(x^i_j-\\mu_j)(x^i_k-\\mu_k)$\n",
     "\n",
+    "- $\\Xi_{j,k}$ is covariance matrix element at row $i$ and column $k$.\n",
+    "\n",
+    "- $w^i$ is the weight of the $i$ th particle. \n",
+    "\n",
+    "- $x^i_j$ is the $j$ th state of the $i$ th particle. \n",
+    "\n",
+    "- $\\mu_j$ is the $j$ th mean state of particles.\n",
+    "\n",
     "Ref:\n",
     "\n",
-    "- [Improving the particle filter in high dimensions using conjugate artificial process noise](https://www.visiondummy.com/2014/04/draw-error-ellipse-representing-covariance-matrix/)\n"
+    "- [Improving the particle filter in high dimensions using conjugate artificial process noise](https://arxiv.org/pdf/1801.07000.pdf)\n"
    ],
    "metadata": {
     "collapsed": false

From 4e323212e51f32b2e11a94a4c35bd1ca9e1ef4ef Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 27 Jan 2020 20:25:40 +0900
Subject: [PATCH 0310/1080] start fix the bug.

---
 PathPlanning/DubinsPath/dubins_path_planning.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py
index 410e8db01a..ce32a4668b 100644
--- a/PathPlanning/DubinsPath/dubins_path_planning.py
+++ b/PathPlanning/DubinsPath/dubins_path_planning.py
@@ -212,7 +212,6 @@ def dubins_path_planning(sx, sy, syaw, ex, ey, eyaw, c, D_ANGLE=np.deg2rad(10.0)
 
 
 def generate_course(length, mode, c, D_ANGLE):
-
     px = [0.0]
     py = [0.0]
     pyaw = [0.0]
@@ -299,7 +298,6 @@ def main():
 
 
 def test():
-
     NTEST = 5
 
     for i in range(NTEST):
@@ -320,7 +318,7 @@ def test():
             plt.cla()
             # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+                                         lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(px, py, label="final course " + str(mode))
 
             #  plotting

From 18ac00dced6448f12241c8d9d42d0e06709cc17a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 28 Jan 2020 21:35:49 +0900
Subject: [PATCH 0311/1080] fix the dubins planning bug and code clean up

---
 .../DubinsPath/dubins_path_planning.py        | 208 +++++++++---------
 PathPlanning/RRTDubins/rrt_dubins.py          |   3 +
 PathPlanning/RRTStarDubins/rrt_star_dubins.py |   5 +-
 tests/test_dubins_path_planning.py            |  36 ++-
 4 files changed, 143 insertions(+), 109 deletions(-)

diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py
index ce32a4668b..b72801a6b3 100644
--- a/PathPlanning/DubinsPath/dubins_path_planning.py
+++ b/PathPlanning/DubinsPath/dubins_path_planning.py
@@ -21,7 +21,7 @@ def pi_2_pi(angle):
     return (angle + math.pi) % (2 * math.pi) - math.pi
 
 
-def LSL(alpha, beta, d):
+def left_straight_left(alpha, beta, d):
     sa = math.sin(alpha)
     sb = math.sin(beta)
     ca = math.cos(alpha)
@@ -38,12 +38,11 @@ def LSL(alpha, beta, d):
     t = mod2pi(-alpha + tmp1)
     p = math.sqrt(p_squared)
     q = mod2pi(beta - tmp1)
-    #  print(np.rad2deg(t), p, np.rad2deg(q))
 
     return t, p, q, mode
 
 
-def RSR(alpha, beta, d):
+def right_straight_right(alpha, beta, d):
     sa = math.sin(alpha)
     sb = math.sin(beta)
     ca = math.cos(alpha)
@@ -63,7 +62,7 @@ def RSR(alpha, beta, d):
     return t, p, q, mode
 
 
-def LSR(alpha, beta, d):
+def left_straight_right(alpha, beta, d):
     sa = math.sin(alpha)
     sb = math.sin(beta)
     ca = math.cos(alpha)
@@ -82,7 +81,7 @@ def LSR(alpha, beta, d):
     return t, p, q, mode
 
 
-def RSL(alpha, beta, d):
+def right_straight_left(alpha, beta, d):
     sa = math.sin(alpha)
     sb = math.sin(beta)
     ca = math.cos(alpha)
@@ -101,7 +100,7 @@ def RSL(alpha, beta, d):
     return t, p, q, mode
 
 
-def RLR(alpha, beta, d):
+def right_left_right(alpha, beta, d):
     sa = math.sin(alpha)
     sb = math.sin(beta)
     ca = math.cos(alpha)
@@ -119,7 +118,7 @@ def RLR(alpha, beta, d):
     return t, p, q, mode
 
 
-def LRL(alpha, beta, d):
+def left_right_left(alpha, beta, d):
     sa = math.sin(alpha)
     sb = math.sin(beta)
     ca = math.cos(alpha)
@@ -137,23 +136,21 @@ def LRL(alpha, beta, d):
     return t, p, q, mode
 
 
-def dubins_path_planning_from_origin(ex, ey, eyaw, c, D_ANGLE):
-    # normalize
-    dx = ex
-    dy = ey
+def dubins_path_planning_from_origin(end_x, end_y, end_yaw, curvature, step_size):
+    dx = end_x
+    dy = end_y
     D = math.hypot(dx, dy)
-    d = D * c
-    #  print(dx, dy, D, d)
+    d = D * curvature
 
     theta = mod2pi(math.atan2(dy, dx))
     alpha = mod2pi(- theta)
-    beta = mod2pi(eyaw - theta)
-    #  print(theta, alpha, beta, d)
+    beta = mod2pi(end_yaw - theta)
 
-    planners = [LSL, RSR, LSR, RSL, RLR, LRL]
+    planners = [left_straight_left, right_straight_right, left_straight_right, right_straight_left, right_left_right,
+                left_right_left]
 
-    bcost = float("inf")
-    bt, bp, bq, bmode = None, None, None, None
+    best_cost = float("inf")
+    bt, bp, bq, best_mode = None, None, None, None
 
     for planner in planners:
         t, p, q, mode = planner(alpha, beta, d)
@@ -161,17 +158,48 @@ def dubins_path_planning_from_origin(ex, ey, eyaw, c, D_ANGLE):
             continue
 
         cost = (abs(t) + abs(p) + abs(q))
-        if bcost > cost:
-            bt, bp, bq, bmode = t, p, q, mode
-            bcost = cost
-
-    #  print(bmode)
-    px, py, pyaw = generate_course([bt, bp, bq], bmode, c, D_ANGLE)
+        if best_cost > cost:
+            bt, bp, bq, best_mode = t, p, q, mode
+            best_cost = cost
+    lengths = [bt, bp, bq]
+
+    px, py, pyaw, directions = generate_local_course(
+        sum(lengths), lengths, best_mode, curvature, step_size)
+
+    return px, py, pyaw, best_mode, best_cost
+
+
+def interpolate(ind, length, mode, max_curvature, origin_x, origin_y, origin_yaw, path_x, path_y, path_yaw, directions):
+    if mode == "S":
+        path_x[ind] = origin_x + length / max_curvature * math.cos(origin_yaw)
+        path_y[ind] = origin_y + length / max_curvature * math.sin(origin_yaw)
+        path_yaw[ind] = origin_yaw
+    else:  # curve
+        ldx = math.sin(length) / max_curvature
+        ldy = 0.0
+        if mode == "L":  # left turn
+            ldy = (1.0 - math.cos(length)) / max_curvature
+        elif mode == "R":  # right turn
+            ldy = (1.0 - math.cos(length)) / -max_curvature
+        gdx = math.cos(-origin_yaw) * ldx + math.sin(-origin_yaw) * ldy
+        gdy = -math.sin(-origin_yaw) * ldx + math.cos(-origin_yaw) * ldy
+        path_x[ind] = origin_x + gdx
+        path_y[ind] = origin_y + gdy
+
+    if mode == "L":  # left turn
+        path_yaw[ind] = origin_yaw + length
+    elif mode == "R":  # right turn
+        path_yaw[ind] = origin_yaw - length
+
+    if length > 0.0:
+        directions[ind] = 1
+    else:
+        directions[ind] = -1
 
-    return px, py, pyaw, bmode, bcost
+    return path_x, path_y, path_yaw, directions
 
 
-def dubins_path_planning(sx, sy, syaw, ex, ey, eyaw, c, D_ANGLE=np.deg2rad(10.0)):
+def dubins_path_planning(sx, sy, syaw, ex, ey, eyaw, c, step_size=0.1):
     """
     Dubins path plannner
 
@@ -200,7 +228,7 @@ def dubins_path_planning(sx, sy, syaw, ex, ey, eyaw, c, D_ANGLE=np.deg2rad(10.0)
     leyaw = eyaw - syaw
 
     lpx, lpy, lpyaw, mode, clen = dubins_path_planning_from_origin(
-        lex, ley, leyaw, c, D_ANGLE)
+        lex, ley, leyaw, c, step_size)
 
     px = [math.cos(-syaw) * x + math.sin(-syaw)
           * y + sx for x, y in zip(lpx, lpy)]
@@ -211,44 +239,60 @@ def dubins_path_planning(sx, sy, syaw, ex, ey, eyaw, c, D_ANGLE=np.deg2rad(10.0)
     return px, py, pyaw, mode, clen
 
 
-def generate_course(length, mode, c, D_ANGLE):
-    px = [0.0]
-    py = [0.0]
-    pyaw = [0.0]
-
-    for m, l in zip(mode, length):
-        pd = 0.0
-        if m == "S":
-            d = 1.0 * c
-        else:  # turning couse
-            d = D_ANGLE
-
-        while pd < abs(l - d):
-            #  print(pd, l)
-            px.append(px[-1] + d / c * math.cos(pyaw[-1]))
-            py.append(py[-1] + d / c * math.sin(pyaw[-1]))
-
-            if m == "L":  # left turn
-                pyaw.append(pyaw[-1] + d)
-            elif m == "S":  # Straight
-                pyaw.append(pyaw[-1])
-            elif m == "R":  # right turn
-                pyaw.append(pyaw[-1] - d)
+def generate_local_course(total_length, lengths, mode, max_curvature, step_size):
+    n_point = math.trunc(total_length / step_size) + len(lengths) + 4
+
+    path_x = [0.0 for _ in range(n_point)]
+    path_y = [0.0 for _ in range(n_point)]
+    path_yaw = [0.0 for _ in range(n_point)]
+    directions = [0.0 for _ in range(n_point)]
+    ind = 1
+
+    if lengths[0] > 0.0:
+        directions[0] = 1
+    else:
+        directions[0] = -1
+
+    ll = 0.0
+
+    for (m, l, i) in zip(mode, lengths, range(len(mode))):
+        if l > 0.0:
+            d = step_size
+        else:
+            d = -step_size
+
+        # set origin state
+        origin_x, origin_y, origin_yaw = path_x[ind], path_y[ind], path_yaw[ind]
+
+        ind -= 1
+        if i >= 1 and (lengths[i - 1] * lengths[i]) > 0:
+            pd = - d - ll
+        else:
+            pd = d - ll
+
+        while abs(pd) <= abs(l):
+            ind += 1
+            path_x, path_y, path_yaw, directions = interpolate(
+                ind, pd, m, max_curvature, origin_x, origin_y, origin_yaw, path_x, path_y, path_yaw, directions)
             pd += d
 
-        d = l - pd
-        px.append(px[-1] + d / c * math.cos(pyaw[-1]))
-        py.append(py[-1] + d / c * math.sin(pyaw[-1]))
+        ll = l - pd - d  # calc remain length
+
+        ind += 1
+        path_x, path_y, path_yaw, directions = interpolate(
+            ind, l, m, max_curvature, origin_x, origin_y, origin_yaw, path_x, path_y, path_yaw, directions)
+
+    if len(path_x) <= 1:
+        return [], [], [], []
 
-        if m == "L":  # left turn
-            pyaw.append(pyaw[-1] + d)
-        elif m == "S":  # Straight
-            pyaw.append(pyaw[-1])
-        elif m == "R":  # right turn
-            pyaw.append(pyaw[-1] - d)
-        pd += d
+    # remove unused data
+    while len(path_x) >= 1 and path_x[-1] == 0.0:
+        path_x.pop()
+        path_y.pop()
+        path_yaw.pop()
+        directions.pop()
 
-    return px, py, pyaw
+    return path_x, path_y, path_yaw, directions
 
 
 def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):  # pragma: no cover
@@ -288,53 +332,11 @@ def main():
         plot_arrow(start_x, start_y, start_yaw)
         plot_arrow(end_x, end_y, end_yaw)
 
-        #  for (ix, iy, iyaw) in zip(px, py, pyaw):
-        #  plot_arrow(ix, iy, iyaw, fc="b")
-
         plt.legend()
         plt.grid(True)
         plt.axis("equal")
         plt.show()
 
 
-def test():
-    NTEST = 5
-
-    for i in range(NTEST):
-        start_x = (np.random.rand() - 0.5) * 10.0  # [m]
-        start_y = (np.random.rand() - 0.5) * 10.0  # [m]
-        start_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0)  # [rad]
-
-        end_x = (np.random.rand() - 0.5) * 10.0  # [m]
-        end_y = (np.random.rand() - 0.5) * 10.0  # [m]
-        end_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0)  # [rad]
-
-        curvature = 1.0 / (np.random.rand() * 5.0)
-
-        px, py, pyaw, mode, clen = dubins_path_planning(
-            start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
-
-        if show_animation:
-            plt.cla()
-            # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                                         lambda event: [exit(0) if event.key == 'escape' else None])
-            plt.plot(px, py, label="final course " + str(mode))
-
-            #  plotting
-            plot_arrow(start_x, start_y, start_yaw)
-            plot_arrow(end_x, end_y, end_yaw)
-
-            plt.legend()
-            plt.grid(True)
-            plt.axis("equal")
-            plt.xlim(-10, 10)
-            plt.ylim(-10, 10)
-            plt.pause(1.0)
-
-    print("Test done")
-
-
 if __name__ == '__main__':
-    test()
     main()
diff --git a/PathPlanning/RRTDubins/rrt_dubins.py b/PathPlanning/RRTDubins/rrt_dubins.py
index 4599904cc1..55cea0bde3 100644
--- a/PathPlanning/RRTDubins/rrt_dubins.py
+++ b/PathPlanning/RRTDubins/rrt_dubins.py
@@ -137,6 +137,9 @@ def steer(self, from_node, to_node):
             from_node.x, from_node.y, from_node.yaw,
             to_node.x, to_node.y, to_node.yaw, self.curvature)
 
+        if len(px) <= 1:  # cannot find a dubins path
+            return None
+
         new_node = copy.deepcopy(from_node)
         new_node.x = px[-1]
         new_node.y = py[-1]
diff --git a/PathPlanning/RRTStarDubins/rrt_star_dubins.py b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
index 65fac54c78..9cfd4e692a 100644
--- a/PathPlanning/RRTStarDubins/rrt_star_dubins.py
+++ b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
@@ -114,7 +114,7 @@ def draw_graph(self, rnd=None):
         plt.clf()
         # for stopping simulation with the esc key.
         plt.gcf().canvas.mpl_connect('key_release_event',
-                lambda event: [exit(0) if event.key == 'escape' else None])
+                                     lambda event: [exit(0) if event.key == 'escape' else None])
         if rnd is not None:
             plt.plot(rnd.x, rnd.y, "^k")
         for node in self.node_list:
@@ -143,6 +143,9 @@ def steer(self, from_node, to_node):
             from_node.x, from_node.y, from_node.yaw,
             to_node.x, to_node.y, to_node.yaw, self.curvature)
 
+        if len(px) <= 1:  # cannot find a dubins path
+            return None
+
         new_node = copy.deepcopy(from_node)
         new_node.x = px[-1]
         new_node.y = py[-1]
diff --git a/tests/test_dubins_path_planning.py b/tests/test_dubins_path_planning.py
index ad140b6c3a..72305530b9 100644
--- a/tests/test_dubins_path_planning.py
+++ b/tests/test_dubins_path_planning.py
@@ -7,6 +7,18 @@
 
 class Test(TestCase):
 
+    @staticmethod
+    def check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, end_y, end_yaw):
+        assert (abs(px[0] - start_x) <= 0.01)
+        assert (abs(py[0] - start_y) <= 0.01)
+        assert (abs(pyaw[0] - start_yaw) <= 0.01)
+        print("x", px[-1], end_x)
+        assert (abs(px[-1] - end_x) <= 0.01)
+        print("y", py[-1], end_y)
+        assert (abs(py[-1] - end_y) <= 0.01)
+        print("yaw", pyaw[-1], end_yaw)
+        assert (abs(pyaw[-1] - end_yaw) <= 0.01)
+
     def test1(self):
         start_x = 1.0  # [m]
         start_y = 1.0  # [m]
@@ -21,14 +33,28 @@ def test1(self):
         px, py, pyaw, mode, clen = dubins_path_planning.dubins_path_planning(
             start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
 
-        assert (abs(px[-1] - end_x) <= 0.5)
-        assert (abs(py[-1] - end_y) <= 0.5)
-        assert(abs(pyaw[-1] - end_yaw) <= 0.1)
+        self.check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, end_y, end_yaw)
 
     def test2(self):
         dubins_path_planning.show_animation = False
         dubins_path_planning.main()
 
     def test3(self):
-        dubins_path_planning.show_animation = False
-        dubins_path_planning.test()
+        N_TEST = 10
+
+        for i in range(N_TEST):
+            start_x = (np.random.rand() - 0.5) * 10.0  # [m]
+            start_y = (np.random.rand() - 0.5) * 10.0  # [m]
+            start_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0)  # [rad]
+
+            end_x = (np.random.rand() - 0.5) * 10.0  # [m]
+            end_y = (np.random.rand() - 0.5) * 10.0  # [m]
+            end_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0)  # [rad]
+
+            curvature = 1.0 / (np.random.rand() * 5.0)
+
+            px, py, pyaw, mode, clen = dubins_path_planning.dubins_path_planning(
+                start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
+
+            self.check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, end_y, end_yaw)
+

From 067304acdbac043cf302a2eb006ee6c767341826 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 28 Jan 2020 21:38:19 +0900
Subject: [PATCH 0312/1080] reformat codes

---
 tests/test_dubins_path_planning.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tests/test_dubins_path_planning.py b/tests/test_dubins_path_planning.py
index 72305530b9..f9349217a1 100644
--- a/tests/test_dubins_path_planning.py
+++ b/tests/test_dubins_path_planning.py
@@ -57,4 +57,3 @@ def test3(self):
                 start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
 
             self.check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, end_y, end_yaw)
-

From aa7d48fa5b84259c43ff58e36ec6a5b5a9197f38 Mon Sep 17 00:00:00 2001
From: Peteski <38994044+YAOS5@users.noreply.github.com>
Date: Wed, 29 Jan 2020 14:28:27 +1100
Subject: [PATCH 0313/1080] IMPROVEMENT: Fixed typo

---
 SLAM/FastSLAM1/FastSLAM1.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/SLAM/FastSLAM1/FastSLAM1.ipynb b/SLAM/FastSLAM1/FastSLAM1.ipynb
index 9ce48238f9..60faed6e88 100644
--- a/SLAM/FastSLAM1/FastSLAM1.ipynb
+++ b/SLAM/FastSLAM1/FastSLAM1.ipynb
@@ -93,7 +93,7 @@
    "metadata": {},
    "source": [
     "### 1- Predict\n",
-    "The following equations and code snippets we can see how the particles distribution evolves in case we provide only the control $(v,w)$, which are the linear and angular velocity repsectively. \n",
+    "The following equations and code snippets we can see how the particles distribution evolves in case we provide only the control $(v,w)$, which are the linear and angular velocity respectively. \n",
     "\n",
     "$\\begin{equation*}\n",
     "F=\n",
@@ -668,7 +668,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.7.5"
   }
  },
  "nbformat": 4,

From 29c02fae58bb16dc7ef963aa99a668079d2d8b03 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 29 Jan 2020 21:49:30 +0900
Subject: [PATCH 0314/1080] code clean up

---
 .../reeds_shepp_path_planning.py              | 183 +++++++-----------
 tests/test_reeds_shepp_path_planning.py       |  33 ++++
 2 files changed, 99 insertions(+), 117 deletions(-)

diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
index fbf9495790..9c72a0c9b2 100644
--- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
+++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
@@ -49,15 +49,15 @@ def mod2pi(x):
     return v
 
 
-def SLS(x, y, phi):
+def straight_left_straight(x, y, phi):
     phi = mod2pi(phi)
-    if y > 0.0 and phi > 0.0 and phi < math.pi * 0.99:
+    if y > 0.0 and 0.0 < phi < math.pi * 0.99:
         xd = - y / math.tan(phi) + x
         t = xd - math.tan(phi / 2.0)
         u = phi
         v = math.sqrt((x - xd) ** 2 + y ** 2) - math.tan(phi / 2.0)
         return True, t, u, v
-    elif y < 0.0 and phi > 0.0 and phi < math.pi * 0.99:
+    elif y < 0.0 < phi < math.pi * 0.99:
         xd = - y / math.tan(phi) + x
         t = xd - math.tan(phi / 2.0)
         u = phi
@@ -68,7 +68,6 @@ def SLS(x, y, phi):
 
 
 def set_path(paths, lengths, ctypes):
-
     path = Path()
     path.ctypes = ctypes
     path.lengths = lengths
@@ -89,12 +88,12 @@ def set_path(paths, lengths, ctypes):
     return paths
 
 
-def SCS(x, y, phi, paths):
-    flag, t, u, v = SLS(x, y, phi)
+def straight_curve_straight(x, y, phi, paths):
+    flag, t, u, v = straight_left_straight(x, y, phi)
     if flag:
         paths = set_path(paths, [t, u, v], ["S", "L", "S"])
 
-    flag, t, u, v = SLS(x, -y, -phi)
+    flag, t, u, v = straight_left_straight(x, -y, -phi)
     if flag:
         paths = set_path(paths, [t, u, v], ["S", "R", "S"])
 
@@ -107,7 +106,7 @@ def polar(x, y):
     return r, theta
 
 
-def LSL(x, y, phi):
+def left_straight_left(x, y, phi):
     u, t = polar(x - math.sin(phi), y - 1.0 + math.cos(phi))
     if t >= 0.0:
         v = mod2pi(phi - t)
@@ -117,7 +116,7 @@ def LSL(x, y, phi):
     return False, 0.0, 0.0, 0.0
 
 
-def LRL(x, y, phi):
+def left_right_left(x, y, phi):
     u1, t1 = polar(x - math.sin(phi), y - 1.0 + math.cos(phi))
 
     if u1 <= 4.0:
@@ -125,91 +124,89 @@ def LRL(x, y, phi):
         t = mod2pi(t1 + 0.5 * u + math.pi)
         v = mod2pi(phi - t + u)
 
-        if t >= 0.0 and u <= 0.0:
+        if t >= 0.0 >= u:
             return True, t, u, v
 
     return False, 0.0, 0.0, 0.0
 
 
-def CCC(x, y, phi, paths):
-
-    flag, t, u, v = LRL(x, y, phi)
+def curve_curve_curve(x, y, phi, paths):
+    flag, t, u, v = left_right_left(x, y, phi)
     if flag:
         paths = set_path(paths, [t, u, v], ["L", "R", "L"])
 
-    flag, t, u, v = LRL(-x, y, -phi)
+    flag, t, u, v = left_right_left(-x, y, -phi)
     if flag:
         paths = set_path(paths, [-t, -u, -v], ["L", "R", "L"])
 
-    flag, t, u, v = LRL(x, -y, -phi)
+    flag, t, u, v = left_right_left(x, -y, -phi)
     if flag:
         paths = set_path(paths, [t, u, v], ["R", "L", "R"])
 
-    flag, t, u, v = LRL(-x, -y, phi)
+    flag, t, u, v = left_right_left(-x, -y, phi)
     if flag:
         paths = set_path(paths, [-t, -u, -v], ["R", "L", "R"])
 
     # backwards
     xb = x * math.cos(phi) + y * math.sin(phi)
     yb = x * math.sin(phi) - y * math.cos(phi)
-    # println(xb, ",", yb,",",x,",",y)
 
-    flag, t, u, v = LRL(xb, yb, phi)
+    flag, t, u, v = left_right_left(xb, yb, phi)
     if flag:
         paths = set_path(paths, [v, u, t], ["L", "R", "L"])
 
-    flag, t, u, v = LRL(-xb, yb, -phi)
+    flag, t, u, v = left_right_left(-xb, yb, -phi)
     if flag:
         paths = set_path(paths, [-v, -u, -t], ["L", "R", "L"])
 
-    flag, t, u, v = LRL(xb, -yb, -phi)
+    flag, t, u, v = left_right_left(xb, -yb, -phi)
     if flag:
         paths = set_path(paths, [v, u, t], ["R", "L", "R"])
 
-    flag, t, u, v = LRL(-xb, -yb, phi)
+    flag, t, u, v = left_right_left(-xb, -yb, phi)
     if flag:
         paths = set_path(paths, [-v, -u, -t], ["R", "L", "R"])
 
     return paths
 
 
-def CSC(x, y, phi, paths):
-    flag, t, u, v = LSL(x, y, phi)
+def curve_straight_curve(x, y, phi, paths):
+    flag, t, u, v = left_straight_left(x, y, phi)
     if flag:
         paths = set_path(paths, [t, u, v], ["L", "S", "L"])
 
-    flag, t, u, v = LSL(-x, y, -phi)
+    flag, t, u, v = left_straight_left(-x, y, -phi)
     if flag:
         paths = set_path(paths, [-t, -u, -v], ["L", "S", "L"])
 
-    flag, t, u, v = LSL(x, -y, -phi)
+    flag, t, u, v = left_straight_left(x, -y, -phi)
     if flag:
         paths = set_path(paths, [t, u, v], ["R", "S", "R"])
 
-    flag, t, u, v = LSL(-x, -y, phi)
+    flag, t, u, v = left_straight_left(-x, -y, phi)
     if flag:
         paths = set_path(paths, [-t, -u, -v], ["R", "S", "R"])
 
-    flag, t, u, v = LSR(x, y, phi)
+    flag, t, u, v = left_straight_right(x, y, phi)
     if flag:
         paths = set_path(paths, [t, u, v], ["L", "S", "R"])
 
-    flag, t, u, v = LSR(-x, y, -phi)
+    flag, t, u, v = left_straight_right(-x, y, -phi)
     if flag:
         paths = set_path(paths, [-t, -u, -v], ["L", "S", "R"])
 
-    flag, t, u, v = LSR(x, -y, -phi)
+    flag, t, u, v = left_straight_right(x, -y, -phi)
     if flag:
         paths = set_path(paths, [t, u, v], ["R", "S", "L"])
 
-    flag, t, u, v = LSR(-x, -y, phi)
+    flag, t, u, v = left_straight_right(-x, -y, phi)
     if flag:
         paths = set_path(paths, [-t, -u, -v], ["R", "S", "L"])
 
     return paths
 
 
-def LSR(x, y, phi):
+def left_straight_right(x, y, phi):
     u1, t1 = polar(x + math.sin(phi), y - 1.0 - math.cos(phi))
     u1 = u1 ** 2
     if u1 >= 4.0:
@@ -224,60 +221,60 @@ def LSR(x, y, phi):
     return False, 0.0, 0.0, 0.0
 
 
-def generate_path(q0, q1, maxc):
+def generate_path(q0, q1, max_curvature):
     dx = q1[0] - q0[0]
     dy = q1[1] - q0[1]
     dth = q1[2] - q0[2]
     c = math.cos(q0[2])
     s = math.sin(q0[2])
-    x = (c * dx + s * dy) * maxc
-    y = (-s * dx + c * dy) * maxc
+    x = (c * dx + s * dy) * max_curvature
+    y = (-s * dx + c * dy) * max_curvature
 
     paths = []
-    paths = SCS(x, y, dth, paths)
-    paths = CSC(x, y, dth, paths)
-    paths = CCC(x, y, dth, paths)
+    paths = straight_curve_straight(x, y, dth, paths)
+    paths = curve_straight_curve(x, y, dth, paths)
+    paths = curve_curve_curve(x, y, dth, paths)
 
     return paths
 
 
-def interpolate(ind, l, m, maxc, ox, oy, oyaw, px, py, pyaw, directions):
-
-    if m == "S":
-        px[ind] = ox + l / maxc * math.cos(oyaw)
-        py[ind] = oy + l / maxc * math.sin(oyaw)
-        pyaw[ind] = oyaw
+def interpolate(ind, length, mode, max_curvature, origin_x, origin_y, origin_yaw, path_x, path_y, path_yaw, directions):
+    if mode == "S":
+        path_x[ind] = origin_x + length / max_curvature * math.cos(origin_yaw)
+        path_y[ind] = origin_y + length / max_curvature * math.sin(origin_yaw)
+        path_yaw[ind] = origin_yaw
     else:  # curve
-        ldx = math.sin(l) / maxc
-        if m == "L":  # left turn
-            ldy = (1.0 - math.cos(l)) / maxc
-        elif m == "R":  # right turn
-            ldy = (1.0 - math.cos(l)) / -maxc
-        gdx = math.cos(-oyaw) * ldx + math.sin(-oyaw) * ldy
-        gdy = -math.sin(-oyaw) * ldx + math.cos(-oyaw) * ldy
-        px[ind] = ox + gdx
-        py[ind] = oy + gdy
-
-    if m == "L":  # left turn
-        pyaw[ind] = oyaw + l
-    elif m == "R":  # right turn
-        pyaw[ind] = oyaw - l
-
-    if l > 0.0:
+        ldx = math.sin(length) / max_curvature
+        ldy = 0.0
+        if mode == "L":  # left turn
+            ldy = (1.0 - math.cos(length)) / max_curvature
+        elif mode == "R":  # right turn
+            ldy = (1.0 - math.cos(length)) / -max_curvature
+        gdx = math.cos(-origin_yaw) * ldx + math.sin(-origin_yaw) * ldy
+        gdy = -math.sin(-origin_yaw) * ldx + math.cos(-origin_yaw) * ldy
+        path_x[ind] = origin_x + gdx
+        path_y[ind] = origin_y + gdy
+
+    if mode == "L":  # left turn
+        path_yaw[ind] = origin_yaw + length
+    elif mode == "R":  # right turn
+        path_yaw[ind] = origin_yaw - length
+
+    if length > 0.0:
         directions[ind] = 1
     else:
         directions[ind] = -1
 
-    return px, py, pyaw, directions
+    return path_x, path_y, path_yaw, directions
 
 
-def generate_local_course(L, lengths, mode, maxc, step_size):
-    npoint = math.trunc(L / step_size) + len(lengths) + 4
+def generate_local_course(total_length, lengths, mode, max_curvature, step_size):
+    n_point = math.trunc(total_length / step_size) + len(lengths) + 4
 
-    px = [0.0 for i in range(npoint)]
-    py = [0.0 for i in range(npoint)]
-    pyaw = [0.0 for i in range(npoint)]
-    directions = [0.0 for i in range(npoint)]
+    px = [0.0 for _ in range(n_point)]
+    py = [0.0 for _ in range(n_point)]
+    pyaw = [0.0 for _ in range(n_point)]
+    directions = [0.0 for _ in range(n_point)]
     ind = 1
 
     if lengths[0] > 0.0:
@@ -305,14 +302,14 @@ def generate_local_course(L, lengths, mode, maxc, step_size):
         while abs(pd) <= abs(l):
             ind += 1
             px, py, pyaw, directions = interpolate(
-                ind, pd, m, maxc, ox, oy, oyaw, px, py, pyaw, directions)
+                ind, pd, m, max_curvature, ox, oy, oyaw, px, py, pyaw, directions)
             pd += d
 
         ll = l - pd - d  # calc remain length
 
         ind += 1
         px, py, pyaw, directions = interpolate(
-            ind, l, m, maxc, ox, oy, oyaw, px, py, pyaw, directions)
+            ind, l, m, max_curvature, ox, oy, oyaw, px, py, pyaw, directions)
 
     # remove unused data
     while px[-1] == 0.0:
@@ -344,22 +341,17 @@ def calc_paths(sx, sy, syaw, gx, gy, gyaw, maxc, step_size):
                   * iy + q0[1] for (ix, iy) in zip(x, y)]
         path.yaw = [pi_2_pi(iyaw + q0[2]) for iyaw in yaw]
         path.directions = directions
-        path.lengths = [l / maxc for l in path.lengths]
+        path.lengths = [length / maxc for length in path.lengths]
         path.L = path.L / maxc
 
-    #  print(paths)
-
     return paths
 
 
 def reeds_shepp_path_planning(sx, sy, syaw,
                               gx, gy, gyaw, maxc, step_size=0.2):
-
     paths = calc_paths(sx, sy, syaw, gx, gy, gyaw, maxc, step_size)
 
     if not paths:
-        #  print("No path")
-        #  print(sx, sy, syaw, gx, gy, gyaw)
         return None, None, None, None, None
 
     minL = float("Inf")
@@ -374,48 +366,6 @@ def reeds_shepp_path_planning(sx, sy, syaw,
     return bpath.x, bpath.y, bpath.yaw, bpath.ctypes, bpath.lengths
 
 
-def test():
-
-    NTEST = 5
-
-    for i in range(NTEST):
-        start_x = (np.random.rand() - 0.5) * 10.0  # [m]
-        start_y = (np.random.rand() - 0.5) * 10.0  # [m]
-        start_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0)  # [rad]
-
-        end_x = (np.random.rand() - 0.5) * 10.0  # [m]
-        end_y = (np.random.rand() - 0.5) * 10.0  # [m]
-        end_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0)  # [rad]
-
-        curvature = 1.0 / (np.random.rand() * 20.0)
-        step_size = 0.1
-
-        px, py, pyaw, mode, clen = reeds_shepp_path_planning(
-            start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature, step_size)
-
-        if show_animation:  # pragma: no cover
-            plt.cla()
-            # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
-            plt.plot(px, py, label="final course " + str(mode))
-
-            #  plotting
-            plot_arrow(start_x, start_y, start_yaw)
-            plot_arrow(end_x, end_y, end_yaw)
-
-            plt.legend()
-            plt.grid(True)
-            plt.axis("equal")
-            plt.xlim(-10, 10)
-            plt.ylim(-10, 10)
-            plt.pause(1.0)
-
-            #  plt.show()
-
-    print("Test done")
-
-
 def main():
     print("Reeds Shepp path planner sample start!!")
 
@@ -451,5 +401,4 @@ def main():
 
 
 if __name__ == '__main__':
-    test()
     main()
diff --git a/tests/test_reeds_shepp_path_planning.py b/tests/test_reeds_shepp_path_planning.py
index bc4a038cbb..2c3b38dc31 100644
--- a/tests/test_reeds_shepp_path_planning.py
+++ b/tests/test_reeds_shepp_path_planning.py
@@ -1,9 +1,42 @@
 from unittest import TestCase
 from PathPlanning.ReedsSheppPath import reeds_shepp_path_planning as m
 
+import numpy as np
+
 
 class Test(TestCase):
 
+    @staticmethod
+    def check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, end_y, end_yaw):
+        assert (abs(px[0] - start_x) <= 0.01)
+        assert (abs(py[0] - start_y) <= 0.01)
+        assert (abs(pyaw[0] - start_yaw) <= 0.01)
+        print("x", px[-1], end_x)
+        assert (abs(px[-1] - end_x) <= 0.01)
+        print("y", py[-1], end_y)
+        assert (abs(py[-1] - end_y) <= 0.01)
+        print("yaw", pyaw[-1], end_yaw)
+        assert (abs(pyaw[-1] - end_yaw) <= 0.01)
+
     def test1(self):
         m.show_animation = False
         m.main()
+
+    def test2(self):
+        N_TEST = 10
+
+        for i in range(N_TEST):
+            start_x = (np.random.rand() - 0.5) * 10.0  # [m]
+            start_y = (np.random.rand() - 0.5) * 10.0  # [m]
+            start_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0)  # [rad]
+
+            end_x = (np.random.rand() - 0.5) * 10.0  # [m]
+            end_y = (np.random.rand() - 0.5) * 10.0  # [m]
+            end_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0)  # [rad]
+
+            curvature = 1.0 / (np.random.rand() * 5.0)
+
+            px, py, pyaw, mode, clen = m.reeds_shepp_path_planning(
+                start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
+
+            self.check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, end_y, end_yaw)

From c6c0a61d495f234c1cff77f1f79a0676f6aa9f8a Mon Sep 17 00:00:00 2001
From: "Kenta Yonekura(a.k.a. yoneken)" <yoneken@ieee.org>
Date: Sat, 1 Feb 2020 22:59:52 +0900
Subject: [PATCH 0315/1080] [Kalman Basics] Align some subscripts on the text

---
 Localization/Kalmanfilter_basics.ipynb | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/Localization/Kalmanfilter_basics.ipynb b/Localization/Kalmanfilter_basics.ipynb
index 374287911b..bbc10ce5f1 100644
--- a/Localization/Kalmanfilter_basics.ipynb
+++ b/Localization/Kalmanfilter_basics.ipynb
@@ -354,14 +354,14 @@
     "\n",
     "Here for the numerator,  $P(Z \\mid X),P(X)$ both are gaussian.\n",
     "\n",
-    "$N(\\bar\\mu, \\bar\\sigma^1)$ and $N(\\bar\\mu, \\bar\\sigma^2)$ are their mean and variances.\n",
+    "$N(\\mu_1, \\sigma_1^2)$ and $N(\\mu_2, \\sigma_2^2)$ are their mean and variances.\n",
     "\n",
     "New mean is \n",
     "\n",
-    "$$\\mu_\\mathtt{new} = \\frac{\\sigma_z^2\\bar\\mu + \\bar\\sigma^2z}{\\bar\\sigma^2+\\sigma_z^2}$$\n",
+    "$$\\mu_\\mathtt{new} = \\frac{\\mu_1 \\sigma_2^2 + \\mu_2 \\sigma_1^2}{\\sigma_1^2+\\sigma_2^2}$$\n",
     "New variance is\n",
     "$$\n",
-    "\\sigma_\\mathtt{new} = \\frac{\\sigma_z^2\\bar\\sigma^2}{\\bar\\sigma^2+\\sigma_z^2}\n",
+    "\\sigma_\\mathtt{new} = \\frac{\\sigma_1^2\\sigma_2^2}{\\sigma_1^2+\\sigma_2^2}\n",
     "$$"
    ]
   },
@@ -759,4 +759,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file

From 74b723dc47798e9b5681828066e9e8ab87267bb7 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 2 Feb 2020 14:12:39 +0900
Subject: [PATCH 0316/1080] #fix 281 and doc clean up

---
 Localization/Kalmanfilter_basics.ipynb   | 13 ++++++++++---
 Localization/Kalmanfilter_basics_2.ipynb | 13 +++++++++++--
 2 files changed, 21 insertions(+), 5 deletions(-)

diff --git a/Localization/Kalmanfilter_basics.ipynb b/Localization/Kalmanfilter_basics.ipynb
index 86966c8d54..c501b117de 100644
--- a/Localization/Kalmanfilter_basics.ipynb
+++ b/Localization/Kalmanfilter_basics.ipynb
@@ -360,9 +360,7 @@
     "\n",
     "$$\\mu_\\mathtt{new} = \\frac{\\mu_1 \\sigma_2^2 + \\mu_2 \\sigma_1^2}{\\sigma_1^2+\\sigma_2^2}$$\n",
     "New variance is\n",
-    "$$\n",
-    "\\sigma_\\mathtt{new} = \\frac{\\sigma_1^2\\sigma_2^2}{\\sigma_1^2+\\sigma_2^2}\n",
-    "$$"
+    "$$\\sigma_\\mathtt{new} = \\frac{\\sigma_1^2\\sigma_2^2}{\\sigma_1^2+\\sigma_2^2}$$"
    ]
   },
   {
@@ -755,6 +753,15 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.6"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "source": [],
+    "metadata": {
+     "collapsed": false
+    }
+   }
   }
  },
  "nbformat": 4,
diff --git a/Localization/Kalmanfilter_basics_2.ipynb b/Localization/Kalmanfilter_basics_2.ipynb
index ac3101d518..43f34ec6f2 100644
--- a/Localization/Kalmanfilter_basics_2.ipynb
+++ b/Localization/Kalmanfilter_basics_2.ipynb
@@ -35,7 +35,7 @@
     "\n",
     "If $x_{t}$ is complete, then:\n",
     "\n",
-    "$$p(z_{t} | x-_{0:t},z_{1:t-1},u_{1:t})=p(z_{t} | x_{t})$$\n",
+    "$$p(z_{t} | x_{0:t},z_{1:t-1},u_{1:t})=p(z_{t} | x_{t})$$\n",
     "\n",
     "$x_{t}$ is **complete** means that the past states, controls or measurements carry no additional information to predict future.\n",
     "\n",
@@ -365,8 +365,17 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.6"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "source": [],
+    "metadata": {
+     "collapsed": false
+    }
+   }
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file

From c632666603c870fa26d895238e3bcbd4f8200e84 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 3 Feb 2020 20:41:35 +0900
Subject: [PATCH 0317/1080] #fix 281 and doc clean up

---
 README.md | 20 +++++---------------
 1 file changed, 5 insertions(+), 15 deletions(-)

diff --git a/README.md b/README.md
index ec42812753..ab62c9af3c 100644
--- a/README.md
+++ b/README.md
@@ -119,8 +119,6 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation
 
 > git clone https://github.com/AtsushiSakai/PythonRobotics.git
 
-> cd PythonRobotics/
-
 
 2. Install the required libraries. You can use environment.yml with conda command.
 
@@ -553,7 +551,7 @@ MIT
 
 # Use-case
 
-If this project helps your robotics project, please let me know with [![Say Thanks!](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/AtsushiSakai).
+If this project helps your robotics project, please let me know with creating an issue.
 
 Your robot's video, which is using PythonRobotics, is very welcome!!
 
@@ -561,7 +559,7 @@ This is a list of other user's comment and references:[users\_comments](https://
 
 # Contribution
 
-A small PR like bug fix is welcome.
+Any contribution is welcome!!
 
 If your PR is merged multiple times, I will add your account to the author list.
 
@@ -575,23 +573,15 @@ If you use this project's code in industry, we'd love to hear from you as well;
 
 If you or your company would like to support this project, please consider:
 
+- [Sponsor @AtsushiSakai on GitHub Sponsors](https://github.com/sponsors/AtsushiSakai)
+
 - [Become a backer or sponsor on Patreon](https://www.patreon.com/myenigma)
 
 - [One-time donation via PayPal](https://www.paypal.me/myenigmapay/)
 
-You can add your name or your company logo in README if you are a patron.
-
-E-mail consultant is also available.
-
- 　
-
-Your comment using [![Say Thanks!](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/AtsushiSakai) is also welcome. 
-
-This is a list: [Users comments](https://github.com/AtsushiSakai/PythonRobotics/blob/master/users_comments.md)
-
 # Authors
 
-- [Atsushi Sakai](https://github.com/AtsushiSakai/) ([@Atsushi_twi](https://twitter.com/Atsushi_twi))
+- [Atsushi Sakai](https://github.com/AtsushiSakai/)
 
 - [Daniel Ingram](https://github.com/daniel-s-ingram)
 

From a061e162d6ceaf83db57e3c48e7792e94f36ad3c Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 4 Feb 2020 20:41:51 +0900
Subject: [PATCH 0318/1080] start fixing.

---
 SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb b/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb
index 632bf57992..554ccd1a64 100644
--- a/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb
+++ b/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb
@@ -6,12 +6,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import copy\n",
     "import math\n",
     "import itertools\n",
     "import numpy as np \n",
-    "import cvxpy as cp\n",
     "import matplotlib.pyplot as plt\n",
     "from graph_based_slam import calc_rotational_matrix, calc_jacobian, cal_observation_sigma, \\\n",
     "                             calc_input, observation, motion_model, Edge, pi_2_pi\n",
@@ -650,8 +648,17 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.6.5"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "source": [],
+    "metadata": {
+     "collapsed": false
+    }
+   }
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file

From 994e0e354637d7bf540b1da8abaec312325b9f74 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 4 Feb 2020 21:13:12 +0900
Subject: [PATCH 0319/1080] fixing.

---
 SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb | 156 +++++++++++++-----------
 1 file changed, 87 insertions(+), 69 deletions(-)

diff --git a/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb b/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb
index 554ccd1a64..53364e3276 100644
--- a/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb
+++ b/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb
@@ -2,8 +2,12 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 11,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false
+    }
+   },
    "outputs": [],
    "source": [
     "import copy\n",
@@ -38,15 +42,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -55,23 +61,21 @@
     },
     {
      "name": "stdout",
-     "output_type": "stream",
      "text": [
       "The determinant of H:  0.0\n",
-      "The determinant of H after anchoring constraint:  18.75\n",
+      "The determinant of H after anchoring constraint:  18.750000000000007\n",
       "Running graphSLAM ...\n",
       "Odometry values after optimzation: \n",
-      " [[0.   ]\n",
-      " [0.947]\n",
-      " [1.939]]\n"
-     ]
+      " [[-0. ]\n",
+      " [ 0.9]\n",
+      " [ 1.9]]\n"
+     ],
+     "output_type": "stream"
     },
     {
      "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": "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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -138,8 +142,8 @@
     "        H[t2,t1] -= omegas[(t1,t2)]\n",
     "        H[t1,t2] -= omegas[(t1,t2)]\n",
     "\n",
-    "        b[t1] += measure_constraints[(t1,t2)]\n",
-    "        b[t2] -= measure_constraints[(t1,t2)]\n",
+    "        b[t1] += omegas[(t1,t2)] * measure_constraints[(t1,t2)]\n",
+    "        b[t2] -= omegas[(t1,t2)] * measure_constraints[(t1,t2)]\n",
     "\n",
     "    return H, b\n",
     "\n",
@@ -206,15 +210,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": 13,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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54/TP//zPVFZWHuie+au/+iu2bNnSaZ/+5MmTycvLY8qUKdx///1HbB89ejTXXXcdEydO5LrrrjvwULf8/HyWLFnCHXfcwZQpU5g6dSovvfTScbe1O3RGybLOJqnkh0MUhEOddgu5O62J5IEbvkGepCLSYxXj4X/9EP5nZfton4Y97X3+k69t/+Xfg5M/wLx585g3b96B5by8PNat6/y5XZFIhJUrVx6y7sYbbzxk+d577+Xee+89sNxxxTB16tQD7w3OJiWAXtAxSeXCM4awfNM7LH61jt3RGHkhO/DsoI7HO7QlncEl+cyZXsllE4bphq9ITxUPhsnXtX+kU0oAvai8KMK11aP5ZNUodkVb2bozyva97U8P7Xi8w+lDSxhSUqBx/iL9xIYNG/j0pz99yLqCggJ++9vf5qhF3acEkANmxtDSwkNe6CIi3ePufeoH0qRJk1i/fn2v15uJByrrJrCI9BuFhYXs2bMnay+E7y/cnT179lBYmN6PSF0BiEi/MWrUKOrr69m1a1eum5IVLS0t3T6pFxYWMmrUqLTqUwIQkX4jEokwduzYXDcja2pra9N6x+/xUheQiEhAZeqVkA+b2U4z29jFdjOzfzCzrWb2BzPr/FnHIiLSazJ1BfATYNZRtn8MGJf6zAN+lKF6RUSkhzKSANx9FfDeUYrMBn7q7V4BTjIzPdVMRCSHeusewEhg20HL9al1IiKSI31uFJCZzaO9m4iKigpqa2t7dJxoNNrjffujoMULwYs5aPFC8GLu7Xh7KwG8DYw+aHlUat0R3P0h4CGA6upqr6mp6VGFtbW19HTf/iho8ULwYg5avBC8mHs73t7qAnoK+ExqNNCHgH3unp2XXIqISLdk5ArAzBYBNcBgM6sHvg1EANz9n4BlwBXAVqAJ+Fwm6hURkZ7LSAJw97nH2O7AlzJRl4iIZIZmAouIBJQSgIhIQCkBiIgElBKAiEhAKQGIiASUEoCISEApAYiIBJQSgIhIQCkBiIgElBKAiEhAKQGIiASUEoCISEApAYiIBJQSgIhIQCkBiIgElBKAiEhAKQGIiARURhKAmc0ys9fNbKuZze9k+41mtsvM1qc+X8hEvSIi0nNpvxLSzPKAB4FLgXpgtZk95e6bDyv67+5+S7r1iYhIZmTiCmA6sNXd33T3GLAYmJ2B44qISBZlIgGMBLYdtFyfWne4a8zsD2a2xMxGZ6BeERFJg7l7egcw+yQwy92/kFr+NHDewd09ZjYIiLp7q5n9JfC/3f3iLo43D5gHUFFRUbV48eIetSsajVJSUtKjffujoMULwYs5aPFC8GLORLwzZsxY6+7V3Srs7ml9gPOBZw9a/ibwzaOUzwP2defYVVVV3lMvvPBCj/ftj4IWr3vwYg5avO7BizkT8QJrvJvn70x0Aa0GxpnZWDPLB+YATx1cwMyGH7R4FbAlA/WKiEga0h4F5O4JM7sFeJb2X/cPu/smM/su7ZnoKeCvzewqIAG8B9yYbr0iIpKetBMAgLsvA5Ydtu5bB33/Ju1dQyIi0kdoJrCISEApAYiIBJQSgIhIQCkBiIgElBKAiEhAKQGIiASUEoCISEApAYiIBFRGJoL1Be7OrsZWtu6Msn1fCxu3xdm9tp4R5YWcPrSEIaUFmDuYtX9ERAKu3yeAfc1xlm96h0Wv1rFnf4w8M+JtSZqa46zY/kcieSHaks6gknzurF/FlJeWE/7FUjjppFw3XUQkp/ptAkgmnWUbdrBwxRvEEkkKwiFKC8JY6td9XlsrpQPygfarg+T773PaP/w9myrGsO2t/VwxuZxQSFcCIhJc/TIBRFsT3LV0A+vq9lIUyaO8KHLU8mbGjc//jLKmBn78F7ewcfnrPP/aTu65ehIlBf3yP4GISNr63U3gaGuCWx5fx7q69ykrDJMfPnYIo979M1eu+jnLz7+SbWPOoqwwwrq697nl8XVEWxO90GoRkb6nXyWAZNK5a+kG3twVpawwcqC751huWvoArfmFPPrx/wO0XxGUFUZ4c1eUu5ZuIJlM761oIiL9Ub9KAMs27GBd3d7jOvlXbXqZ6s2vsHjWjewrHXhgfUcSWFf3Pss27MhWk0VE+qx+kwD2NcdZuOINiiJ53T7557Ul+MLSB3h7yCie+eg1R2w3M4oiYX644g32Nccz3WQRkT6t39wBXb7pHWKJ5FFv+JYl9zEttpbq+GoK4g0Mfm43o3bWcd//+TaJcOf75YdDNKSGkl5bPTpbzRcR6XMycgVgZrPM7HUz22pm8zvZXmBm/57a/lszG3M8x3d3Fr1aR8FRbvhWJt7iS9F/5KOxF0kQZu/+Yk5Z+Uf2jjuZCypfpjLxVpf75odDLH61ruOl9SIigZB2AjCzPOBB4GPAeGCumY0/rNhNwPvufjpwP/D3x1PHrsZW9uyPdZkAypL7uL7pUeIWYU9oMHHL56wXtpAXa2PDx6cTD+VzfdOjlCX3dbp/QTjE7miMXdHW42mWiEi/lokrgOnAVnd/091jwGJg9mFlZgOPpL4vAWZadzvyga07o+SZddn3Py22lghxmm0AAKU79nLK2reo+9Dp7B9aRrMNIEKcqfF1ne5vZuSFjK07o91tkohIv5eJBDAS2HbQcn1qXadl3D0B7AMGdbeC7ftaiLclu9xeHV9Ng5W1L7hz1jO/I1aUz9aZEw6UabAypsdWd3mMeFuS7XtbutskEZF+r8/dBDazecA8gIqKCmpra9m4LU5Tc5y8ts67aAriDezjZLA44eYYtLXx2oyzaQobxNtH98QchrCPxsbGTo/RFHM2bt7C4Mat2Qksy6LRKLW1tbluRq8KWsxBixeCF3Nvx5uJBPA2cPDwmVGpdZ2VqTezMFAO7OnsYO7+EPAQQHV1tdfU1LB7bT0rtv/xwLN9DtfaWEYxELcIRCKs/suZxGJx8iMfjPyJeIwY5ZSWlnZ6jLamGBPHn0FN1ahuhNz31NbWUlNTk+tm9KqgxRy0eCF4Mfd2vJnoAloNjDOzsWaWD8wBnjqszFPAZ1PfPwms9OMYcjOivJBIXtdNXRM5lzJv+GCFGRz2oLcyb+DV/HO7PEYkL8SIkwq72yQRkX4v7QSQ6tO/BXgW2AI84e6bzOy7ZnZVqti/AoPMbCvwVeCIoaJHc/rQEtqS3uUwzXX5VcSJUORNnW4v8ibiRFgfmdZVDLQlndOHlhxPs0RE+rWM3ANw92XAssPWfeug7y3AtT09/pDSAgaV5NPYkqAwknfE9oZQOY8NuIHrmx5lgO+mwcqIeXu3T5k3ECfCYwNuoCFU3unxWxNJBpfkM6SkoKdNFBHpd/rFoyDMjLnTK2lNdD0SqC48hgdLbqW2oIYwbQzhPcK0UVtQw4Mlt1IXHtPlvrFEkjnTK7v9iAkRkRNBnxsF1JXLJgzjx6veJJZIdvkI6IZQOasKZrCqYAaNjY1d3vA9WMfxLpswLNNNFhHp0/rFFQBAeVGEr8wcR3O8LWOPbHB3muMJvjxz3DFfKiMicqLpNwkA4IpJw5lWeRINLfG0k4C709ASZ1rlQK6YNDxDLRQR6T/6VQIIhYx7rp7EqUNK0koCHSf/U4eUcM/Vk/RuYBEJpH6VAABKCsI88KlpTKscSENLgthRbgx3JpZIHvjl/8CnpumdwCISWP0uAUB7EvjBdVO5/fIzSbrT0Byn5Sj3BtydlngbDc1xku7cfvlZ/OC6qTr5i0ig9dszYChkXDllBBeeMYTlm95h8at17I7GyAsZ8bYkTTGnrSlGJC9EW9IZXJLPnOmVXDZhmG74iojQjxNAh/KiCNdWj+aTVaPYFW1l684o2/e2sHHzFiaOP4MRJxVy+tAShpQUaJy/iMhB+n0C6GBmDC0tZGhp+/N8Bjdu7bcPdhMR6Q398h6AiIikTwlARCSglABERAJKCUBEJKCUAEREAkoJQEQkoJQAREQCSglARCSg0koAZnaymT1nZm+k/g7solybma1PfQ5/YbyIiORAulcA84EV7j4OWEHXL3tvdvepqc9VXZQREZFelG4CmA08kvr+CPAXaR5PRER6iaXzZi0z2+vuJ6W+G/B+x/Jh5RLAeiABLHD3XxzlmPOAeQAVFRVVixcv7lHbotEoJSUlPdq3PwpavBC8mIMWLwQv5kzEO2PGjLXuXt2twu5+1A/wPLCxk89sYO9hZd/v4hgjU39PBd4CTjtWve5OVVWV99QLL7zQ4337o6DF6x68mIMWr3vwYs5EvMAa78b51d2P/TRQd7+kq21m9q6ZDXf3HWY2HNjZxTHeTv1908xqgXOA/+lGfhIRkSxJ9x7AU8BnU98/Czx5eAEzG2hmBanvg4ELgM1p1isiImlKNwEsAC41szeAS1LLmFm1mf1LqszZwBoz+z3wAu33AJQARERyLK0Xwrj7HmBmJ+vXAF9IfX8JmJROPSIiknmaCSwiElBKACIiAaUEICISUEoAIiIBpQQgIhJQSgAiIgGlBCAiElBKACIiAaUEICISUEoAIiIBpQQgIhJQSgAiIgGlBCAiElBKACIiAaUEICISUEoAIiIBpQQgIhJQaSUAM7vWzDaZWdLMqo9SbpaZvW5mW81sfjp1iohIZqR7BbAR+ASwqqsCZpYHPAh8DBgPzDWz8WnWKyIiaUr3ncBbAMzsaMWmA1vd/c1U2cXAbEAvhhcRyaG0EkA3jQS2HbRcD5zXVWEzmwfMA6ioqKC2trZHlUaj0R7v2x8FLV4IXsxBixeCF3Nvx3vMBGBmzwPDOtl0p7s/mekGuftDwEMA1dXVXlNT06Pj1NbW0tN9+6OgxQvBizlo8ULwYu7teI+ZANz9kjTreBsYfdDyqNQ6ERHJod4YBroaGGdmY80sH5gDPNUL9YqIyFGkOwz0ajOrB84Hfmlmz6bWjzCzZQDungBuAZ4FtgBPuPum9JotIiLpSncU0FJgaSfrtwNXHLS8DFiWTl0iIpJZmgksIhJQSgAiIgGlBCAiElBKACIiAaUEICISUEoAIiIBpQQgIhJQSgAiIgGlBCAiElBKACIiAaUEICISUEoAIiIBpQQgIhJQSgAiIgGlBCAiElBKACIiAaUEICISUOm+EvJaM9tkZkkzqz5KubfMbIOZrTezNenUKSIimZHWKyGBjcAngB93o+wMd9+dZn0iIpIh6b4TeAuAmWWmNSIi0mt66x6AA8vNbK2ZzeulOkVE5CjM3Y9ewOx5YFgnm+509ydTZWqBr7t7p/37ZjbS3d82s6HAc8Ct7r6qi7LzgHkAFRUVVYsXL+5uLIeIRqOUlJT0aN/+KGjxQvBiDlq8ELyYMxHvjBkz1rp7l/dkD+HuaX+AWqC6m2W/Q3uyOGbZqqoq76kXXnihx/v2R0GL1z14MQctXvfgxZyJeIE13s1zd9a7gMys2MxKO74Dl9F+81hERHIo3WGgV5tZPXA+8Eszeza1foSZLUsVqwB+Y2a/B14Ffunu/5VOvSIikr50RwEtBZZ2sn47cEXq+5vAlHTqERGRzNNMYBGRgFICEBEJKCUAEZGAUgIQEQkoJQARkYBSAhARCSglABGRgFICEBEJKCUAEZGAUgIQEQkoJQARkYBSAhAR6QOi0Sh333030Wi01+pUAhAR6QNWrFhBbW0tK1eu7LU6lQBERPqApUuXHvK3NygBiIjkmLvzzDPPAPD00093vD0x65QARERybPPmzbS0tADQ3NzMli1beqVeJQARkRxbtmwZiUQCgGQyybJly46xR2ak+0rI+8zsNTP7g5ktNbOTuig3y8xeN7OtZjY/nTpFRE40TzzxBG2FbQy8eCDD/noYP/WfctOzN7FoyyJ2Ne3KWr3pXgE8B0x098nAH4FvHl7AzPKAB4GPAeOBuWY2Ps16RUT6jWuuuQYz6/Lz+t7XGf2l0Qz8yEA86TTuaGTF8yu4a/FdnPd/z6NoTNEh5a+55pqMtCutBODuy909kVp8BRjVSbHpwFZ3f9PdY8BiYHY69YqI9CcLFixg6tSpFBcXH7EtXB5m8HWD8TYn0ZDAE07Sk3gitdzmDL9+OOHyMMXFxZxzzjksWLAgI+3K5D2AzwO/6mT9SGDbQcv1qXUiIoEwbtw41qxZw913301RURGh0Aen3tKqUixsJFuTne6bbE0SCoeUuoVpAAAGp0lEQVQY9KFBfPe732XNmjWMGzcuI+2yYw03MrPngWGdbLrT3Z9MlbkTqAY+4Ycd0Mw+Ccxy9y+klj8NnOfut3RR3zxgHkBFRUXV4sWLjy+ilGg0SklJSY/27Y+CFi8EL+agxQsnZsz19fXcfffd1NfX09LSwpjbx+BJZ2DU+fA7eVzwbpjiBOwPw39XJHhlBDQPKuTU007l66d8/ZjHnzFjxlp3r+5OW8LHKuDulxxtu5ndCFwJzDz85J/yNjD6oOVRqXVd1fcQ8BBAdXW119TUHKuJnaqtraWn+/ZHQYsXghdz0OKFEzfmuXPnsmDBAu655x5CRSHG1LXxxS0FRJIQzYP38iGShMu2R/jYvkJWXz+Z1wbFMv7fIt1RQLOA24Gr3L2pi2KrgXFmNtbM8oE5wFPp1Csi0p/l5eUxceJE8vPzKX8/yRdfyycBvJ8P8TzA2v/uLQSLhLnw568zrLkg4+1I9x7AA0Ap8JyZrTezfwIwsxFmtgwgdZP4FuBZYAvwhLtvSrNeEZF+benSpTQ2NjLtlRj5brR00h/jSafR43gswce3Dc54G47ZBXQ07n56F+u3A1cctLwM6J2ZDSIifVzHox/cnQt2homOciwEePunoy/dgdZ4jFhJIWeu35nxdmgmsIhIL9u8eTPNzc0AlLjRtCtOOBKmqKSIcCRMyAwLQShigFMxYCR5TS0Zb0daVwAiInL8li1bRltbG6FQiCbgrJGnMXxsJX/e/Weai5rZ37yflv0txN+L09aQpMkaGFJZmfF2KAGIiPSyJ554gng8zpQpUzjvM5+h4Df/TTivgEHhQQwaNAiA/fv3s3btWhpiDTS+8w6lN92U8XaoC0hEpJcNGzaM++67jzVr1nDapz6F5eeT3L//kDLFxcVceOGFTBx3OpafT+mlRx2R3yO6AhAR6WVPP/30ge+hoUOp+Ju/4d3vfQ9raCBZUoJFIng8TrKxkdHDR1Dxjw8QGTo04+3QFYCISI4VTZzAyIX303L++eBO23t7wJ3yq69m5ML7KZo4ISv16gpARKQPiAwdSnPNRZxS8+1eq1NXACIiAaUEICISUEoAIiIBdczHQeeSme0C/tzD3QcDuzPYnL4uaPFC8GIOWrwQvJgzEe8p7j6kOwX7dAJIh5mt6e4zsU8EQYsXghdz0OKF4MXc2/GqC0hEJKCUAEREAupETgAP5boBvSxo8ULwYg5avBC8mHs13hP2HoCIiBzdiXwFICIiR3HCJQAzm2Vmr5vZVjObn+v2ZJuZjTazF8xss5ltMrMv57pNvcHM8szsd2b2TK7b0hvM7CQzW2Jmr5nZFjM7P9dtyiYzuy3173mjmS0ys8JctynTzOxhM9tpZhsPWneymT1nZm+k/g7MZhtOqARgZnnAg8DHgPHAXDMbn9tWZV0C+Jq7jwc+BHwpADEDfJn2d0wHxQ+B/3L3s4ApnMCxm9lI4K+BanefCOQBc3Lbqqz4CTDrsHXzgRXuPg5YkVrOmhMqAQDTga3u/qa7x4DFwOwctymr3H2Hu69LfW+k/cQwMretyi4zGwV8HPiXXLelN5hZOfBR4F8B3D3m7ntz26qsCwNFZhYGBgDbc9yejHP3VcB7h62eDTyS+v4I8BfZbMOJlgBGAtsOWq7nBD8ZHszMxgDnAL/NbUuybiFwO5DMdUN6yVhgF/BvqW6vfzGz4lw3Klvc/W3g/wF1wA5gn7svz22rek2Fu+9IfX8HqMhmZSdaAggsMysBfg58xd0bct2ebDGzK4Gd7r42123pRWFgGvAjdz8H2E+WuwZyKdXvPZv2xDcCKDazG3Lbqt7n7UM0szpM80RLAG8Dow9aHpVad0IzswjtJ//H3P0/c92eLLsAuMrM3qK9i+9iM3s0t03Kunqg3t07ruyW0J4QTlSXAH9y913uHgf+E/hwjtvUW941s+EAqb87s1nZiZYAVgPjzGysmeXTfuPoqRy3KavMzGjvG97i7j/IdXuyzd2/6e6j3H0M7f9/V7r7Cf3r0N3fAbaZ2ZmpVTOBzTlsUrbVAR8yswGpf98zOYFveh/mKeCzqe+fBZ7MZmUn1BvB3D1hZrcAz9I+cuBhd9+U42Zl2wXAp4ENZrY+te5v3H1ZDtskmXcr8Fjqh82bwOdy3J6scfffmtkSYB3to9x+xwk4I9jMFgE1wGAzqwe+DSwAnjCzm2h/EvJ1WW2DZgKLiATTidYFJCIi3aQEICISUEoAIiIBpQQgIhJQSgAiIgGlBCAiElBKACIiAaUEICISUP8fNOWgTlB0QBwAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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M8vGqCXxs1njqGlqpOdjA/iMdq2t2LpcweVQx5cX5GmcvMkhs27aNT37yk+/Yl5+fz+9+97t+alF6Cvx+YGaMKilgVInWwxHpLXcfUDdE06ZNY+vWrX1e76l0x+uhrYgMGgUFBRw+fDjwr/h0dw4fPkxBQe9uGnWHLyKDxvjx46mtrWUwvu862woKChg/fnyvvqPAF5FBIxqNMmnSpP5uxqClLh0RkYBQ4IuIBES23mn7kJkdNLPtaY6bmf2TmdWY2R/MLPVi7yIikjPZusN/GJjfxfEPAZXJn8XAD7JUr4iI9FBWAt/dNwBvdlFkAfBj7/AScJqZadlHEZE+1Fd9+OOAfSds1yb3vYuZLTazajOr1tArEZHsGXAPbd39QXevcveq8vLy/m6OiMiQ0VeB/xow4YTt8cl9IiLSR/oq8J8E/iI5Wud9wFF3z85beUVEpEeyMtPWzFYAc4CRZlYL/D0QBXD3fwZWA9cANUATcGs26hURkZ7LSuC7+43dHHfgs9moS0RETs2Ae2grIiK5ocAXEQkIBb6ISEAo8EVEAkKBLyISEAp8EZGAUOCLiASEAl9EJCAU+CIiAaHAFxEJCAW+iEhAKPBFRAJCgS8iEhAKfBGRgFDgi4gEhAJfRCQgFPgiIgGRlcA3s/lmttvMasxsaYrjt5hZnZltTf78ZTbqFRGRnsv4FYdmFga+D1wJ1AKbzOxJd995UtF/d/clmdYnIiKnJht3+LOBGnd/xd3bgMeBBVk4r4iIZFE2An8csO+E7drkvpPdYGZ/MLOVZjYh3cnMbLGZVZtZdV1dXRaaJyIi0HcPbZ8CJrr7BcBa4JF0Bd39QXevcveq8vLyPmqeiMjQl43Afw048Y59fHLfce5+2N1bk5s/AmZloV4REemFbAT+JqDSzCaZWR6wCHjyxAJmNuaEzeuAXVmoV0REeiHjUTruHjezJcAzQBh4yN13mNnXgWp3fxL4GzO7DogDbwK3ZFqviIj0jrl7f7chraqqKq+uru7vZoiIDBpmttndq1Id00xbEZGAUOCLiASEAl9EJCAU+CIiAaHAFxEJCAW+iEhAKPBFRAIi44lXA4W7U3eslZqDDew/2kKsPUE0HGJsWQGTRxVTXpKPuYNZx4+ISMAM+sA/2hxjzY7XWbFxL4cb2wibEWtPkHAnZEY0HKI94YwozuOe2g1Mf2ENkV+sgtNO6++mi4j0qUEb+ImEs3rbAZav20NbPEF+JERJfgRLcffu7iTeeouz/+kf2XHGRPa92sg1F5QRCulOX0SCY1AGfkNrnHtXbWPL3iMURsOUFUa7LG9m3PLsTyhtqueHH1nC9jW7efblg3zj+mkU5w/K/wQiIr026B7aNrTGWfLYFrbsfYvSggh5ke4vYfwbf+baDT9nzcXXsm/iuZQWRNmy9y2WPLaFhtZ4H7RaRKT/DarATySce1dt45W6BkoLoim7b1K5bdUDtOYV8NMP/xXQccdfWhDllboG7l21jURi4C4gJyKSLYMq8FdvO8CWvUd6FfazdrxI1c6XeHz+LRwtGX58f2fob9n7Fqu3HchVk0VEBoxBE/hHm2MsX7eHwmi4x2Efbo/zl6se4LXy8Tz9wRveddzMKIxG+O66PRxtjmW7ySIiA8qgeWK5ZsfrtMUTXT6gLU0cZWbbZqpimyjyRk574Q3GH9zLfX/198Qjqb+XFwlRnxza+fGqtO9WFxEZ9AbFHb67s2LjXvK7eEBbEX+VzzZ8jw+2PU+cCEebSjhz/R85Unk6l1S8SEX81bTfzYuEeHzjXgbyy2BERDKVlcA3s/lmttvMasxsaYrj+Wb278njvzOzib05f92xVg43tqUN/NLEUW5q+ikxi3I4NJKY5VH57HbCbe1s+/BsYqE8bmr6KaWJoym/nx8JcaihjbqG1pTHRUSGgowD38zCwPeBDwHnAzea2fknFbsNeMvdJwP3A//YmzpqDjYQNkvbdz+zbTNRYjTbMABKDhxhwqY/sfd9k2kcVUqzDSNKjBmxLemugXDIqDnY0JtmiYgMKtm4w58N1Lj7K+7eBjwOLDipzALgkeTnlcBc6+mTVzi+Nk46VbFN1Ftpx4Y75z79P8QKo9TMnXK8TL2VMrttU9pzxNoT7D/S0tMmiYgMOtkI/HHAvhO2a5P7UpZx9zhwFBiR6mRmttjMqs2suq6uDuD42jjpFHkjMToeykZaYoQSzp4rpxIvzDteJkaUYd6Y9hwJ9y5/qYiIDHYDbpSOuz8IPAhQVVXlANFwiFAXfxA0WhFRYsTII16Yx+8WXw4n/X6IEqPJitKeo3OhNRGRoSobCfcacOJ4xvHJfSnLmFkEKAMO97SCsWUFXYZxdfQiSr3+7R1mcNLCaKVez8a8i9KeIxoOMfa0gp42SURk0MlG4G8CKs1skpnlAYuAJ08q8yTwqeTnjwHrvRdjICePKqY94WmHTW7Jm0WMKIXelPJ4oTcRI8rW6MyUx92d9oQzeVRxT5skIjLoZBz4yT75JcAzwC7gCXffYWZfN7PrksX+FRhhZjXAXcC7hm52pbwknxHFebTGU/ex14fKeHTYzUQ9xojEIaLeBu5EvS25HePRYTdTHypL+f3WeIKRxXmUF+f3plkiIoOKDeTJRlVVVV5dXQ3Az6r38b31Nd3OtJ0R28Lstk0M80aarIiNeRexNTozbdgD1DfHWHLFZM20FZFBz8w2u3tVqmMD7qFtOldNGc0PN7xCWzyRdknk+lAZG/IvZ0P+5T0+b+f5rpoyOltNFREZkAbNsJSywiifn1tJc6w9a0sguDvNsTh3zK3s9iUqIiKD3aAJfIBrpo1hZsVp1LfEMg59d6e+JcbMiuFcM21MllooIjJwDarAD4WMb1w/jbPKizMK/c6wP6u8mG9cP03vthWRQBhUgQ9QnB/hgU/MZGbFcOpb4rSlGbmTTls8cfzO/oFPzNQ7bUUkMAZd4ENH6H9n4Qy+fPV7SLhT3xyjpYu+fXenJdZOfXOMhDtfvvpcvrNwhsJeRAJl0CZeKGRcO30sl55Tzpodr/P4xr0camgjHLLja+90LpfQnnBGFuexaHYFV00ZrQe0IhJIgzbwO5UVRvl41QQ+Nms8dQ2t1BxsYP+RjtU1O5dLmDyqmPLi/B6/GlFEZCga9IHfycwYVVLAqBKthyMiksqg7MMXEZHeU+CLiASEAl9EJCAU+CIiAaHAFxEJCAW+iEhAKPBFRAJCgS8iEhAZBb6ZnW5ma81sT/Lf4WnKtZvZ1uTPye+7FRGRPpDpHf5SYJ27VwLrSP+u2mZ3n5H8uS5NGRERyaFMA38B8Ejy8yPARzI8n4iI5EimgX+Gux9Ifn4dOCNNuQIzqzazl8ysy18KZrY4Wba6rq4uw+aJiEinbhdPM7NngVRv+L7nxA13dzNL9wqqM939NTM7C1hvZtvc/X9TFXT3B4EHAaqqqrLz8loREek+8N19XrpjZvaGmY1x9wNmNgY4mOYcryX/fcXMngMuBFIGvoiI5EamXTpPAp9Kfv4U8MuTC5jZcDPLT34eCVwC7MywXhER6aVMA38ZcKWZ7QHmJbcxsyoz+1GyzHlAtZn9HvgvYJm7K/BFRPpYRi9AcffDwNwU+6uBv0x+fgGYlkk9IiKSOc20FREJCAW+iEhAKPBFRAJCgS8iEhAKfBGRgFDgi4gEhAJfRCQgFPgiIgGhwBcRCQgFvohIQCjwRUQCQoEvIhIQCnwRkYBQ4IuIBIQCX0QkIBT4IiIBocAXEQmIjALfzD5uZjvMLGFmVV2Um29mu82sxsyWZlKniIicmkzv8LcDHwU2pCtgZmHg+8CHgPOBG83s/AzrFRGRXsr0nba7AMysq2KzgRp3fyVZ9nFgAaAXmYuI9KG+6MMfB+w7Ybs2uS8lM1tsZtVmVl1XV5fzxomIBEW3d/hm9iwwOsWhe9z9l9lukLs/CDwIUFVV5dk+v4hIUHUb+O4+L8M6XgMmnLA9PrlPRET6UF906WwCKs1skpnlAYuAJ/ugXhEROUGmwzKvN7Na4GLgV2b2THL/WDNbDeDucWAJ8AywC3jC3Xdk1mwREemtTEfprAJWpdi/H7jmhO3VwOpM6hIRkcxopq2ISEAo8EVEAkKBLyISEAp8EZGAUOCLiASEAl9EJCAU+CIiAaHAFxEJCAW+iEhAKPBFRAJCgS8iEhAKfBGRgFDgi4gEhAJfRCQgFPgiIgGhwBcRCQgFvohIQCjwRUQCItN32n7czHaYWcLMqroo96qZbTOzrWZWnUmdIiJyajJ6py2wHfgo8MMelL3c3Q9lWJ+IiJyiTF9ivgvAzLLTGhERyZm+6sN3YI2ZbTazxV0VNLPFZlZtZtV1dXV91DwRkaGv2zt8M3sWGJ3i0D3u/sse1vMBd3/NzEYBa83sZXffkKqguz8IPAhQVVXlPTy/iIh0o9vAd/d5mVbi7q8l/z1oZquA2UDKwBcRkdzIeZeOmRWZWUnnZ+AqOh72iohIH8p0WOb1ZlYLXAz8ysyeSe4fa2ark8XOAH5rZr8HNgK/cvf/zKReERHpvUxH6awCVqXYvx+4Jvn5FWB6JvWIiEjmNNNWRCQgFPgiIgGhwBcRCQgFvohIQCjwRUQCQoEvIhIQCnwRkYBQ4IuIBIQCX0QkIBT4IiIBocAXERkAGhoaWLhwIQ0NDTmrQ4EvIjIArFu3jp/97GesX78+Z3Uo8EVEBoBVq1a9499cUOCLiPQzd+fpp58G4KmnnsI9Ny/7U+CLiPSznTt30tLSAkBzczO7du3KST0KfBGRfrZ69Wri8TgAiUSC1atXd/ONU5PRC1BERCRzTzzxBO0F7Qy/ZDhlVWX82H/Mrmd2Ma9iHvPOnEf5sPKs1JPpKw7vM7OXzewPZrbKzE5LU26+me02sxozW5pJnSIig80NN9yAmaX92X1kNxM+O4HhHxiOJ5xjB46x7tl13Pv4vbz3/76XwomF7yh/ww03nFI7Mu3SWQtMdfcLgD8CXzm5gJmFge8DHwLOB240s/MzrFdEZNBYtmwZM2bMoKio6F3HImURRi4cibc78fo4HncSnsDjye12Z8xNY4iURSgqKuLCCy9k2bJlp9SOjALf3de4ezy5+RIwPkWx2UCNu7/i7m3A48CCTOoVERlMKisrqa6u5mtf+xqFhYWEQm9Hb8msEixiJFoTKb+baE0QioQY8b4RfP3rX6e6uprKyspTakc2+/A/Dfx7iv3jgH0nbNcC7013EjNbDCwGqKioyGLzRET6Tzgc5gtf+ALXXXcdCxcuZM+ePTQ2NlJWVUZ7Uzunt8D7Xw9zyRsRiuLQGIH/PiPOS+OgLVzI7M/M5q6P3JVRG7q9wzezZ81se4qfBSeUuQeIA49m1BrA3R909yp3ryovz86DChGRgaLzbv8rX/kKBQUFhApDnH0Y/nZrPlfuj5AA3syDBHDV/ihf21nCR8fNoD3SnnHd3d7hu/u8ro6b2S3AtcBcTz1b4DVgwgnb45P7REQCKRwOM3XqVPLy8ih7K8FnXs4jDjTkvV0mFoYjURgZjXDpz3dz5NZzM64301E684EvA9e5e1OaYpuASjObZGZ5wCLgyUzqFREZ7FatWsWxY8eY+VIbeW60pLj99oRzzGN4W5wP7xuZcZ2ZjtJ5ACgB1prZVjP7ZwAzG2tmqwGSD3WXAM8Au4An3H1HhvWKiAxanUspuDuXHIzQEHEsBGZgJ5YDWmNttBVGOGvrwYzrzeihrbtPTrN/P3DNCdurgdxMHRMRGWR27txJc3MzAMVuHKmLUTi+kILCAloaW2iPt+PmWNjAnTOGjSPc1JJxvZppKyLSx1avXk17ezuhUIgm4NxxZzNmUgWHmg9xKO8Qjc2NtDS2EHszRnt9giarpzwLoxYV+CIifeyJJ54gFosxffp03vsXf0H+b/+bSDifccXjGFc8DoDGxkY2b95MfVs9x15/nZLbbsu4Xi2eJiLSx0aPHs19991HdXU1Z3/iE1heHonGxneUKSoq4tJLL2Vq5WQsL4+SK7scMNkjlqt1l7OhqqrKq6ur+7sZIiI51bx9B29885t4WxuhkhIsGsVjMRLHjmF5eZzxt39L4dQpPTqXmW1296pUx9SlIyLSzwqnTmHc8vs5tvZZjqgMdRcAAAOuSURBVK1dS/ubhwkVl1B2/fWUXDmP6KhRWalHd/giIkNIV3f46sMXEQkIBb6ISEAo8EVEAmJA9+GbWR3w51P8+kjgUBabM9AF7XoheNcctOuF4F1zNq73THdPudTwgA78TJhZdboHF0NR0K4XgnfNQbteCN415/p61aUjIhIQCnwRkYAYyoH/YH83oI8F7XoheNcctOuF4F1zTq93yPbhi4jIOw3lO3wRETmBAl9EJCCGXOCb2Xwz221mNWa2tL/bk2tmNsHM/svMdprZDjO7o7/b1BfMLGxm/2NmT/d3W/qCmZ1mZivN7GUz22VmF/d3m3LJzO5M/v95u5mtMLOC/m5TtpnZQ2Z20My2n7DvdDNba2Z7kv8Oz2adQyrwzSwMfB/4EHA+cKOZnd+/rcq5OPAFdz8feB/w2QBcM8AddLwjOSi+C/ynu58LTGcIX7uZjQP+Bqhy96lAGFjUv63KiYeB+SftWwqsc/dKYF1yO2uGVOADs4Ead3/F3duAx4EF/dymnHL3A+6+Jfn5GB1BMK5/W5VbZjYe+DDwo/5uS18wszLgg8C/Arh7m7sf6d9W5VwEKDSzCDAM2N/P7ck6d98AvHnS7gXAI8nPjwAfyWadQy3wxwH7TtiuZYiH34nMbCJwIfC7/m1Jzi0Hvgwk+rshfWQSUAf8W7Ib60dmVtTfjcoVd38N+H/AXuAAcNTd1/Rvq/rMGe5+IPn5deCMbJ58qAV+YJlZMfBz4PPuXt/f7ckVM7sWOOjum/u7LX0oAswEfuDuFwKNZPlP/YEk2W+9gI5fdGOBIjO7uX9b1fe8Y8x8VsfND7XAfw2YcML2+OS+Ic3MonSE/aPu/h/93Z4cuwS4zsxepaPL7goz+2n/NinnaoFad+/8y20lHb8Ahqp5wJ/cvc7dY8B/AO/v5zb1lTfMbAxA8t+D2Tz5UAv8TUClmU0yszw6HvQ82c9tyikzMzr6dne5+3f6uz255u5fcffx7j6Rjv9917v7kL77c/fXgX1m9p7krrnAzn5sUq7tBd5nZsOS//+eyxB+SH2SJ4FPJT9/CvhlNk8+pN5p6+5xM1sCPEPHk/2H3H1HPzcr1y4BPglsM7OtyX1/6+6r+7FNkn2fAx5N3si8Atzaz+3JGXf/nZmtBLbQMQrtfxiCSyyY2QpgDjDSzGqBvweWAU+Y2W10LA2/MKt1amkFEZFgGGpdOiIikoYCX0QkIBT4IiIBocAXEQkIBb6ISEAo8EVEAkKBLyISEP8fCbs8AmbqSY8AAAAASUVORK5CYII=\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -291,8 +297,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 14,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false
+    }
+   },
    "outputs": [],
    "source": [
     "# Copy the data to have a consistent naming with the .py file\n",
@@ -312,18 +322,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
-     "output_type": "stream",
      "text": [
       "Node combinations: \n",
       " [(0, 1)]\n",
       "Node 0 observed landmark with ID 0.0\n",
       "Node 1 observed landmark with ID 0.0\n"
-     ]
+     ],
+     "output_type": "stream"
     }
    ],
    "source": [
@@ -354,12 +368,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
-     "output_type": "stream",
      "text": [
       "0.0 1.427649841628278 -2.0126109674819155 -3.524048014922737\n",
       "For nodes (0, 1)\n",
@@ -367,14 +384,13 @@
       " [[-0.02 ]\n",
       " [-0.084]\n",
       " [ 0.   ]]\n"
-     ]
+     ],
+     "output_type": "stream"
     },
     {
      "data": {
-      "image/png": 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tO9JUC+IawCpgnJmNMbMC4Erg6YPaPA1clfh8GfA7P4JbbkYNKiI/9whDPcIhn/Nzcxg1uOjI+hARyWJJHwEkzunfADwH5AIPuft6M7sdWO3uTwNLgEfMbDPwF9qKRLeNHVFCLO64e7cvBB8JdycWd8aOKAl82yIivVUg1wDcvRqoPmjeP+33uQm4vKfbHz6gkKElBexpilKUn9vzQDvRHI0zrKSA4em+ACwikkFZ8SSwmTFv+rE0R7t5J9ARaonGuXL6sSk5uhAR6a2yogAAnH/SyPZhG4K0b3iJ808aGeh2RUR6u6wpAIP65XPjueNobI0FNmSDu9PYGuVr54477EtlRET6mqwpAACzJx3N1GMHs7upNeki4O7sbmpl6rFDmD3p6IAiFBHJHllVAHJyjDsumcTxw0uSKgL7dv7HDy/hjksm6d3AIhJKWVUAAEoK87jvc1OZeuwQdjdFj/iaQEs03v7N/77PTdU7gUUktLKuAEBbEbjnilO46YITiLuzu7GVpi6uDbg7Ta0xdje2EnfnpgtO5J4rTtHOX0RCLWv3gDk5xsUnj2Lm+OEsW7+VpStr+TTSQm6OEY078Xjb8A75uTnE4s6wkgKunH4s5580Uhd8RUTI4gKwz6B++VxeMZrLppWxPdLM5m0RCv7Qj717G/jaeeMZNbiIsSNKGF5SqPv8RUT2k/UFYB8zY8SAIkYMKOLDkkLyo42cleaB3UREsklWXgMQEZHkqQCIiISUCoCISEipAIiIhJQKgIhISKkAiIiElAqAiEhIqQCIiIRUUgXAzI4ys+fN7N3E7yGdtIuZ2euJn4NfGC8iIhmQ7BHALcBydx8HLE9Md6TR3U9J/Hw2yT5FRCQAyRaAOcDPE59/DvxdktsTEZE0sWTerGVm9e4+OPHZgJ37pg9qFwVeB6LAne7+VBfbXAAsACgtLZ22dOnSI45ryI/uIRaLsfum/3vE66bb+8vb3mcw5tzkanEkEqGkpCSIkLJG2HIOW74QvpyDyPfss89e4+4V3Wl72MHgzOwFoKM3pt+6/4S7u5l1Vk2Oc/ePzOx44Hdmts7d/6ejhu6+GFgMUFFR4ZWVlYcL8RAfLnmI+vp6erJuuu1csxaAysqpSW2npqYmK/INUthyDlu+EL6c053vYQuAu5/X2TIz+7OZHe3un5jZ0cC2TrbxUeL3e2ZWA0wBOiwAIiKSHsleA3gauCrx+SrgVwc3MLMhZlaY+DwMOBPYkGS/IiKSpGQLwJ3A35rZu8B5iWnMrMLMHky0+Qyw2szeAH5P2zUAFQARkQxL6oUw7r4DOLeD+auB+YnPfwQmJdOPiIgET08Ci4iElAqAiEhIqQCIiISUCoCISEipAIiIhJQKgIhISKkAiIiElAqAiEhIqQCIiISUCoCISEipAIiIhJQKgIhISKkAiIiElAqAiEhIqQCIiISUCoCISEipAIiIhFRSBcDMLjez9WYWN7OKLtrNMrO3zWyzmd2STJ8iIhKMZI8A3gLmAis6a2BmucD9wIXABGCemU1Isl8REUlSsu8E3ghgZl01mw5sdvf3Em2XAnMAvRheRCSDkioA3XQMsGW/6TrgtM4am9kCYAFAaWkpNTU1R9zhkPp6YrFYj9ZNt/r6OEDSsUYikazIt6fMjP79+5Obm9s+b+DAgfzpT3/KYFTp1Vm+sViMvXv34u4ZiCq1+vrf9cHSne9hC4CZvQCM7GDRre7+q6ADcvfFwGKAiooKr6ysPOJtfLjkIerr6+nJuum2c81aACorpya1nZqamqzIt6fef/99BgwYwNChQ9uPOPfs2cOAAQMyHFn6dJSvu7Njxw727NnDmDFjMhRZ6vT1v+uDpTvfwxYAdz8vyT4+AkbvN12WmCfSbU1NTZSXlx/udGPomBlDhw5l+/btmQ5FslA6bgNdBYwzszFmVgBcCTydhn6lj9HOv2P6d5GeSvY20EvMrA6YAfzGzJ5LzB9lZtUA7h4FbgCeAzYCj7n7+uTCFukdPvjgAyZOnNijdTdt2sSMGTMoLCzkhz/8YcCRiRxesncBVQFVHcz/GJi933Q1UJ1MXyJ9zVFHHcW9997LU089lelQJKT0JLBIN91zzz1MnDiRiRMnsmjRovb50WiUz3/+83zmM5/hsssuo6GhAYBbbrmFCRMmMHnyZL75zW8esr0RI0Zw6qmnkp+f32mfH374IePGjePTTz8lHo8zc+ZMli1bFnxyEkrpuA1UJFjP3gJb19EvFoXcgP6ER06CC+/sdPGaNWt4+OGHee2113B3TjvtNM466yyGDBnC22+/zZIlSzjzzDO59tpr+dd//VeuueYaqqqq2LRpE2ZGfX19j8I67rjjuPnmm7n++us5+eSTmTBhAueff35PsxQ5gI4ARLrh5Zdf5pJLLqF///6UlJQwd+5cXnrpJQBGjx7NmWeeCcAXvvAFXn75ZQYNGkRRURHXXXcdTz75JMXFxT3ue/78+ezevZslS5boWoEESkcAkn0S39Qbe8lzAAffhWNm5OXlsXLlSpYvX84TTzzBfffdx+9+97sebb+hoYG6ujqg7UGh3pCz9A06AhDphpkzZ/LUU0/R0NDA3r17qaqqYubMmQDU1tbyyiuvAPCLX/yCv/7rvyYSibBr1y5mz57Nj3/8Y954440e933zzTfz+c9/nltvvZUvfelLgeQjAjoCEOmWqVOncvXVVzN9+nSg7bTMlClT+OCDDzjhhBO4//77ufbaa5kwYQLXX389u3btYs6cOTQ1NeHu3HPPPYdsc+vWrVRUVLB7925ycnJYtGgRGzZsYODAge1tXnzxRVatWsUf/vAHGhoaqK6u5uGHH+aaa65JW+7Sd6kAiHTTwoULWbhw4QHzysvL2bRp0yFti4uLWblyZZfbGzlyZPupnc6cddZZvPrqq+3TTz755BFELNI1nQISEQkpFQARkZBSARARCSkVABGRkFIBEBEJKRUAEZGQUgEQSUIyw0E/+uijTJ48mUmTJnHGGWck9bCYSE/oOQCRDBkzZgwvvvgiQ4YM4dlnn2XBggW89tprmQ5LQkRHACLdFPRw0GeccQZDhgwB4PTTT+/woTANBy2ppCMAyTp3rbyLTX/ZRCwWIzc3N5BtnnjUidw8/eZOl6d6OOglS5Zw4YUXHjJfw0FLKiX7SsjLzWy9mcXNrKKLdh+Y2Toze93MVifTp0gmpHI46N///vcsWbKEu+66q8PlGg5aUiXZI4C3gLnAA91oe7a7f5pkfyLt39T39IHhoN98803mz5/Ps88+y9ChQzvcvoaDllRJ6gjA3Te6+9tBBSPSW6ViOOja2lrmzp3LI488wvjx4zvtW8NBS6qk6xqAA8vMzIEH3H1xmvoVCUQqhoO+/fbb2bFjB1/5ylcAyMvLY/XqA8+QajhoSSVz964bmL0AjOxg0a3u/qtEmxrgm+7e4fl9MzvG3T8ysxHA88A/uvuKTtouABYAlJaWTlu6dGl3c2k35Ef3EIvF2H3T/z3iddPt/eVxAMacm9wNWZFIhJKSkiBC6pUGDRrE2LFjD5gX5EXgbNBVvps3b2bXrl1pjij1+vrf9cGCyPfss89e4+6dXpPd32GPANz9vKSiadvGR4nf28ysCpgOdFgAEkcHiwEqKiq8srLyiPv7cMlD1NfX05N1023nmrUAVFZOTWo7NTU1WZFvT23cuPGQc9+95RpAunSVb1FREVOmTElzRKnX1/+uD5bufFP+HICZ9TezAfs+A+fTdvFYREQyKNnbQC8xszpgBvAbM3suMX+UmVUnmpUCL5vZG8BK4Dfu/ttk+hURkeQldRHY3auAqg7mfwzMTnx+Dzg5mX5ERCR4GgpCRCSkVABEREJKBUAkCckMB71p0yZmzJhBYWGhhniQjNBgcCIZctRRR3Hvvffy1FNPZToUCSkdAYh0U9DDQY8YMYJTTz2V/Pz8Tvt86KGHuPHGG9unf/azn/H1r389wKwkzHQEIFln6w9+QPPGTURjMf4S0JPAhZ85kZHf/nany1M9HHRnrrjiCr7//e9z9913A/Dwww/zwAPdGXtR5PB0BCDSDakcDrorJSUlnHPOOTzzzDO88847tLa2MmnSpMDyknDTEYBknX3f1HvLUBDJDAfdHfPnz+cHP/gBxx9/vAaBk0DpCECkG1IxHHR3nXbaaWzZsoXHH3+cefPmBZKPCOgIQKRbUjEc9NatW6moqGD37t3k5OSwaNEiNmzYwMCBAw9pe8UVV7Bq1ar2dwiLBEEFQKSbFi5cyMKFCw+YV15ezqZNmw5pW1xczMqVK7vc3siRIzt8EXxHXn75Zb785S93P1iRbtApIJFerL6+nvHjx9OvX79QDYscRpFIhO9973tEIpG09akCINKLDR48mHfeeYfHH38806FIii1fvpyampoe3yzQEyoAIiK9QFVV1QG/00EFQEQkw9ydZ555BoBf//rXHO5VvUFRARARybANGzbQ1NQEQGNjIxs3bkxLvyoAIiIZVl1dTTQaBSAej1NdXX2YNYKR7Csh7zazTWb2pplVmdngTtrNMrO3zWyzmd2STJ8ivUkyw0E/+uijTJ48mUmTJnHGGWck9bCYZLfHHnuM5uZmAJqamnjsscfS0m+yRwDPAxPdfTLwDvCtgxuYWS5wP3AhMAGYZ2YTkuxXJOuNGTOGF198kXXr1nHbbbexYMGCTIckKXLppZdiZp3+vPnmmwe0f+ONN7psf+mllwYSV1IFwN2XuXs0MfkqUNZBs+nAZnd/z91bgKXAnGT6FcmEoIeDPuOMM9qf7D399NM7fChMw0H3DXfeeSennHIK/fv373B5S0sLAFNGj+LbF53N9+ecx7cvOpspo0cd0K5///5MmTKFO++8M5C4gnwS+FrgvzqYfwywZb/pOuC0APuVkHnpsXf4dEuEWCxGbkDDQQ8bXcLMK8Z3ujzVw0EvWbKECy+88JD5Gg66bxg3bhyrV69m0aJF3HbbbTQ3NxOPxw9oM2X0KC4/dRIFeW275aP6F3P5qW0jv77x0VYKCwu5/fbbufHGG8nJCeby7WELgJm9AIzsYNGt7v6rRJtbgSjwaLIBmdkCYAFAaWkpNTU1R7yNIfX1xGKxHq2bbvX1bX8EycYaiUSyIt+eGjRoEHv27AGgtaWVWCyGuxOLxQLZfmtLa/v2O/LCCy8we/bs9v9pL7roIp5//nlmz55NWVkZkydPZs+ePcydO5ef/vSnXHfddRQUFPDFL36RWbNmMWvWrE63v2LFCn72s5/x3HPPddhm5syZPP7444wdO5ampibKy8sPadfU1NQn//v3tb/radOmsXjxYr73ve9RV1fXfucPwIWTT2jf+e9TkJfH7JNPZE9hMd/97ncpKytjxYoVgcVz2ALg7ud1tdzMrgYuBs71jm9e/QgYvd90WWJeZ/0tBhYDVFRUeE8ef9/6xz9SV1eXFY/O71yzFoDKyqlJbaempiYr8u2pjRs3tg/9fM4XTgLSOxx0UVERhYWF7f0VFhZSVFRESUkJOTk57fOLi4vJz89nyJAhrF69un046CVLlnT4hOebb77JV7/6VZ599lnKy8s77Pv6669vHw56/vz5HeZcVFTElClTgku4l+irf9fz5s3jzjvv5I477mgvAkOK+3XYdnBxP95+++3AvvXvL6lTQGY2C7gJOMvdGzpptgoYZ2ZjaNvxXwl8Lpl+D2fkt7/Npj70rUEyb+bMmVx99dXccsstuDtVVVU88sgjwP8fDnrGjBkHDAfd0NDA7NmzOfPMMzn++OMP2WZtbS1z587lkUceYfz4zk8/7RsOes2aNaxbty5lOUr65ObmMnHiRAoKCtoLwM6GRk4ZMY3JQ86iOG8gDdHdvLnzRT72LSnZ+UPy1wDuAwqB5xMvxXjV3f+PmY0CHnT32e4eNbMbgOeAXOAhd1+fZL8iaZWK4aBvv/12duzYwVe+8hUA8vLyWL16dYf9azjovqeqquqAU3l7dw3j1AmzyMspAKB//iBOHTaL3+6sSVkMSRUAdx/byfyPgdn7TVcD6XmyQSRFgh4O+sEHH+TBBx/sVt8aDrpv2Tf0w/5nza+ceHn7zn+fvJwCphacgrsf8ua5IOhJ4AwbNrqEYaNLMh2G9FIaDrp0NsBTAAAEvElEQVRv2rBhA42Nje3TxcXFjBpY2mHbkSXDUjY0hF4Ik2Fd3Xoosm84aKDLu5Qku1RXVxOLxcjJyaGwsJB//ud/Jr+liFh98yFtP9mznZeqVzFhQvDPz+oIQEQkzR577DFaW1s5+eSTeeONN1i4cCEDLygnnnPgjZRNsRbuenFxyoaGUAGQrJGuIXKzjf5dss/IkSO5++67Wb16NePGjQOg/5QRbJvo5A4uBCB3cCGlV57EmddeQGlpx6eHkqVTQJIVioqK2LFjB0OHDk3JxbBs5e7s2LGDoqKiTIciR+DXv/51h/Mjo5yjPzf9gHnfmPYNvvGNb6QkDhUAyQplZWXU1dWxffv29nlNTU2h2vF1lm9RURFlZR0NwyXSNRUAyQr5+fmMGTPmgHk1NTV98unXzoQtX0k9XQMQEQkpFQARkZBSARARCSnrzbeQmdl24MMerj4M+DTAcHq7sOUL4cs5bPlC+HIOIt/j3H14dxr26gKQDDNb7e4VmY4jXcKWL4Qv57DlC+HLOd356hSQiEhIqQCIiIRUXy4AizMdQJqFLV8IX85hyxfCl3Na8+2z1wBERKRrffkIQEREutDnCoCZzTKzt81ss5ndkul4Us3MRpvZ781sg5mtN7OvZTqmdDCzXDP7k5k9k+lY0sHMBpvZE2a2ycw2mtmMTMeUSmb29cTf81tm9ksz63ODPpnZQ2a2zcze2m/eUWb2vJm9m/id0neA9qkCYGa5wP3AhcAEYJ6ZBf8Whd4lCnzD3ScApwP/EIKcAb4GpOY1Sb3TT4DfuvuJwMn04dzN7Bjgq0CFu0+k7V3iV2Y2qpT4d2DWQfNuAZa7+zhgeWI6ZfpUAQCmA5vd/T13bwGWAnMyHFNKufsn7r428XkPbTuGYzIbVWqZWRlwEdC9F+pmOTMbBPwNsATA3VvcvT6zUaVcHtDPzPKAYuDjDMcTOHdfAfzloNlzgJ8nPv8c+LtUxtDXCsAxwJb9puvo4zvD/ZlZOTAFeC2zkaTcIuAmIJ7pQNJkDLAdeDhx2utBM+uf6aBSxd0/An4I1AKfALvcfVlmo0qbUnf/JPF5K5CaN8Ek9LUCEFpmVgL8N3Cju+/OdDypYmYXA9vcfU2mY0mjPGAq8G/uPgXYS4pPDWRS4rz3HNoK3yigv5l9IbNRpZ+33aKZ0ts0+1oB+AgYvd90WWJen2Zm+bTt/B919yczHU+KnQl81sw+oO0U3zlm9p+ZDSnl6oA6d993ZPcEbQWhrzoPeN/dt7t7K/AkcEaGY0qXP5vZ0QCJ39tS2VlfKwCrgHFmNsbMCmi7cPR0hmNKKWt7P+ISYKO735PpeFLN3b/l7mXuXk7bf9/fuXuf/nbo7luBLWZ2QmLWucCGDIaUarXA6WZWnPj7Ppc+fNH7IE8DVyU+XwX8KpWd9ak3grl71MxuAJ6j7c6Bh9x9fYbDSrUzgb8H1pnZ64l533b36gzGJMH7R+DRxBeb94BrMhxPyrj7a2b2BLCWtrvc/kQffCLYzH4JVALDzKwO+C5wJ/CYmV1H20jIV6Q0Bj0JLCISTn3tFJCIiHSTCoCISEipAIiIhJQKgIhISKkAiIiElAqAiEhIqQCIiISUCoCISEj9Px2mMNw6PoAsAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
+      "image/png": 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IKHXYwD+tR04GNxcM5KYrB1BeUcXugxVk/joHd/jmdcPo3zObS/JzycvN0nX2IhJqHT7wTzMz8rtlk98tm09id8x+vq0nQhMRacc65Bi+iIi0nAJfRCQkFPgiIiGhwBcRCQkFvohISCjwRURCQoEvIhISCnwRkZBIKPDNrLeZvWpmu2LfezXRrs7MPox9nf28WxERaQOJHuHfB6xz96HAOpp+Vu0pd7889nVDE21ERCSJEg38GcCTsddPAn+e4PZERCRJEg38C9x9f+z1AeCCJtplm1mJmb1jZs3+UjCzebG2JeXl5QmWJyIip5138jQz+xXQ2BO+v9vwjbu7mTX1CKqL3X2fmX0OeM3Mtrj7fzfW0N2XAcsACgoKgnl4rYiInD/w3f26ptaZ2e/N7EJ3329mFwIHm9jGvtj3PWb2X8AVQKOBLyIiyZHokM4q4LbY69uAF89uYGa9zCwr9rovcA2wLcF+RUSkhRIN/MXAl8xsF3Bd7D1mVmBmj8fajABKzGwT8Dqw2N0V+CIibSyhB6C4+yFgUiPLS4C5sde/AUYn0o+IiCROd9qKiISEAl9EJCQU+CIiIaHAFxEJCQW+iEhIKPBFREJCgS8iEhIKfBGRkFDgi4iEhAJfRCQkFPgiIiGhwBcRCQkFvohISCjwRURCQoEvIhISCnwRkZBQ4IuIhERCgW9mN5vZVjOLmllBM+2mmNlOM9ttZvcl0qeIiLROokf4HwGzgDebamBmacBjwFRgJHCrmY1MsF8REWmhRJ9pux3AzJprVgjsdvc9sbbPATMAPchcRKQNJRT4cboI2NvgfRlwVVONzWweMA9g0KBBya1MOoyamhrKysqorKxMdSntTnZ2NgMGDCAjIyPVpUg7d97AN7NfAf0aWfVdd38x6ILcfRmwDKCgoMCD3r50TGVlZXTr1o3Bgwef7y/KUHF3Dh06RFlZGUOGDEl1OdLOnTfw3f26BPvYBwxs8H5AbJlI3CorKxX2jTAz+vTpQ3l5eapLkQ6gLS7LfA8YamZDzCwTuAVY1Qb9SiejsG+cPheJV6KXZc40szJgPPCymb0SW97fzIoB3L0WmA+8AmwHVrr71sTKFhGRlkoo8N29yN0HuHuWu1/g7tfHln/q7tMatCt292Hu/ifu/n8TLVqkvfj444+57LLLWvWzO3bsYPz48WRlZfGDH/wg4MpEztUWV+mISCN69+7No48+ygsvvJDqUiQkFPjS8ay5Dw5sCXab/UbD1MXNNnn44YdZsWIFAHPnzmXBggUA1NbW8tWvfpWNGzcyatQofvrTn9KlSxfuu+8+Vq1aRXp6OpMnTz7nKD4/P5/8/HxefvnlJvv85JNPuO6663j77bfp3bs31157LQ888ACTJ09OcIcljBT4InF4//33eeKJJ3j33Xdxd6666iquvfZaevXqxc6dO1m+fDnXXHMNc+bM4Yc//CF33HEHRUVF7NixAzPjyJEjrer34osv5t577+XOO++ksLCQkSNHKuyl1RT40vGc50g8GdavX8/MmTPp2rUrALNmzeKtt97ihhtuYODAgVxzzTUAfO1rX+PRRx9lwYIFZGdn8/Wvf53p06czffr0Vvc9d+5cfv7zn/OjH/2IDz/8MJD9kXDSbJkiCTr7skgzIz09nQ0bNnDTTTexevVqpkyZ0urtnzx5krKyMgAqKioSqlXCTYEvEoeJEyfywgsvcPLkSU6cOEFRURETJ04EoLS0lLfffhuAZ555hj/90z+loqKCo0ePMm3aNB555BE2bdrU6r7vvfdevvrVr7Jo0SK+8Y1vBLI/Ek4a0hGJw7hx47j99tspLCwE6odZrrjiCj7++GMuvfRSHnvsMebMmcPIkSO58847OXr0KDNmzKCyshJ35+GHHz5nmwcOHKCgoIBjx44RiURYsmQJ27Zto3v37mfavPHGG7z33nv8+te/Ji0tjV/84hc88cQT3HHHHW2279J5mHv7na6moKDAS0pKWvxzn/zFXwJw8VM/DbqkwBX980YAZn5rXIorad+2b9/OiBEjUl1Gu6XPR04zs/fdvdHnk2hIR0QkJBT4IiIhocAXEQkJBb6ISEgo8EVEQkKBLyISEgp8kQQkMj3y008/zZgxYxg9ejQTJkxI6OYskXjoxiuRFBkyZAhvvPEGvXr1Ys2aNcybN49333031WVJJ6bAlw7nexu+x44/7Ah0m8N7D+fewnubbRP09MgTJkw48/rqq68+M19OQ5oeWYKkwBeJQ7KnR16+fDlTp049Z7mmR5YgJRT4ZnYz8A/ACKDQ3RudB8HMPgaOA3VAbVO3/YrE43xH4smQzOmRX3/9dZYvX8769esbXa/pkSUoiZ60/QiYBbwZR9s/c/fLFfbS2SQyPfLmzZuZO3cuL774In369Gm0jaZHlqAk+hDz7e6+M6hiRNqrZEyPXFpayqxZs3jqqacYNmxYk31remQJSluN4Tuw1swc+Hd3X9ZUQzObB8wDGDRoUBuVJ9K8ZEyPvGjRIg4dOsRdd90FQHp6OmfPDqvpkSVI550e2cx+BfRrZNV33f3FWJv/Ar7dzBj+Re6+z8zygVeBv3b38w4DaXpkOU3T/zZPn4+c1tz0yOc9wnf36xItwN33xb4fNLMioJD4xv1FRCQgSb/T1sy6mlm306+BydSf7BURkTaUUOCb2UwzKwPGAy+b2Sux5f3NrDjW7AJgvZltAjYAL7v7fybSr4iItFxCJ23dvQgoamT5p8C02Os9wNhE+hERkcRp8jQRkZBQ4IuIhIQCXyQBiUyPvGPHDsaPH09WVtY5E6uJJIMmTxNJkd69e/Poo4/ywgsvpLoUCQkFvnQ4Bx56iKrtwU6PnDViOP3+9m+bbRP09Mj5+fnk5+fz8ssvN9nnihUr2Lx5M0uWLAHgxz/+Mdu2beORRx5JZHclpDSkIxKHhtMjv/POO/z4xz/mgw8+AGDnzp3cddddbN++ne7du/PDH/6QQ4cOUVRUxNatW9m8eTP3339/q/qdPXs2L730EjU1NQA88cQTzJkzJ7D9knDREb50OOc7Ek+GZE6P3Jzc3Fy++MUvsnr1akaMGEFNTQ2jR48ObL8kXHSEL5KgRKZHjsfcuXP5yU9+oknTJGEKfJE4JGN65HhdddVV7N27l2eeeYZbb701kP2RcNKQjkgckjE98oEDBygoKODYsWNEIhGWLFnCtm3b6N69+zltZ8+ezYcffkivXr2Svq/SeSnwReK0cOFCFi5c+EfLBg8ezI4d514x1KVLFzZs2NDs9vr169fog8sbs379eu6+++74i5UOp6Kigjlz5rBixQpyc3OT0oeGdETasSNHjjBs2DBycnKYNGlSqsuRJFq3bh0///nPee2115LWh47wRdqxnj178tvf/jbVZUgbKCoqOvP9hhtuSEofOsIXEUkxd2f16tUAvPTSS5zvSYStpcAXEUmxbdu2UVlZCcCpU6fYvn17UvpR4IuIpFhxcTG1tbUARKNRiouLz/MTraPAFxFJsZUrV1JVVQVAZWUlK1euTEo/iT7i8PtmtsPMNptZkZn1bKLdFDPbaWa7zey+RPoUaU8SmR756aefZsyYMYwePZoJEyYkdHOWtG833ngjZtbk1+bNm/+o/aZNm5ptf+ONN7aqjkSv0nkV+I6715rZ94DvAPc2bGBmacBjwJeAMuA9M1vl7tsS7FukQxsyZAhvvPEGvXr1Ys2aNcybN49333031WVJEixevJg9e/awa9cuTpw4cc766upqAK4Y2J+pYy6lV5ccDp88xZrNO/lg76dn2nXt2pVhw4axePHiVtWR6DNt1zZ4+w5wUyPNCoHdsWfbYmbPATMABb60ylsrf8tneysC3WbfgblMnD2s2TZBT488YcKEM6+vvvrqRm/C0vTIncPQoUMpKSlhyZIlPPDAA1RVVRGNRv+ozRUD+3Pz50eTmV4fy727duHmz9dPlLdp3wGysrJYtGgRCxYsIBJp3eBMkGP4c4A1jSy/CNjb4H1ZbFmjzGyemZWYWUl5eXmA5Ym0XrKnR16+fDlTp049Z7mmR+480tLS+Na3vsWmTZsYM2bMmZlXT5s65tIzYX9aZno6Xx47nLFjx7Jp0yYWLlzY6rCHOI7wzexXQL9GVn3X3V+MtfkuUAs83epKYtx9GbAMoKCgoFUXo2aNGJ5oGdKOne9IPBmSOT3y66+/zvLly1m/fv056zQ9cudz+mh/8eLFPPjgg2cux+zVJafR9j265FBS8mpCQX/aeQPf3a9rbr2Z3Q5MByZ543cL7AMGNng/ILYsaVIxX7qEV3PTI69bt47nn3+epUuXNnrL/ObNm5k7dy5r1qyhT58+jW5/7ty5PPTQQwwfPlzTI3cSaWlpXHbZZWRmZp4J/MMnT3F5/pWM6XUtXdK7c7L2GJsPv8GnvjeQsIfEr9KZAtwD3ODuJ5to9h4w1MyGmFkmcAuwKpF+RdpaMqZHLi0tZdasWTz11FMMG9b0Xy2aHrlzKioq4vjx42fenzjal8/3nULXjB6YGV0zevD5vlPISh8aWJ+JXqWzFMgCXo0d5bzj7v/bzPoDj7v7tNgVPPOBV4A0YIW7b02wX5E2lYzpkRctWsShQ4e46667AEhPT6ekpKTR/jU9cudyeiqFhoMit1x2M+mRzD9qlx7JZFzm5bj7OX9JtoYla86GIBQUFHhT/wN0Fm+trJ8YKxXj0h3J9u3bGTFiRKrLSJnp06dz9913NzljZtg/n45m69atFBYWcvJk/cBIly5d2D5/DZFGQj3qUSpuy2PkyJFxbdvM3nf3gsbW6U7bFJs4e5jCXpqk6ZE7p+LiYurq6ohEIuTk5PCP//iPZPTKbrTt/uPlgU21oOmRRdoxTY/cOa1cuZKamhrGjh3Lz372M4YOHcqJDw5y5Je78Jr/f31+ZV0133tjGWUlR/j2t7+dcL86wpcOoz0PP6aSPpeOp1+/fnz/+9+npKSEoUPrT8p2vSKfnrOGktYzC4C0nllccMsorplzPRdccEEg/WoMXzqE3/3ud3Tr1o0+ffoEcvKqs3B3Dh06xPHjxxkyZEiqy5F2oLkxfA3pSIcwYMAAysrK0N3X58rOzmbAgAGpLkM6AAW+dAgZGRk6ghVJkMbwRURCQoEvIhISCnwRkZBo11fpmFk58Ekrf7wv8FmA5bR3YdtfCN8+h21/IXz7HMT+XuzueY2taNeBnwgzK2nq0qTOKGz7C+Hb57DtL4Rvn5O9vxrSEREJCQW+iEhIdObAX5bqAtpY2PYXwrfPYdtfCN8+J3V/O+0YvoiI/LHOfIQvIiINKPBFREKi0wW+mU0xs51mttvM7kt1PclmZgPN7HUz22ZmW83sm6muqS2YWZqZfWBmq1NdS1sws55m9ryZ7TCz7WY2PtU1JZOZ3R379/yRmT1rZo0/HaQDM7MVZnbQzD5qsKy3mb1qZrti3wN9pmWnCnwzSwMeA6YCI4FbzSy+54J1XLXAt9x9JHA18Fch2GeAbwLbU11EG/oX4D/dfTgwlk6872Z2EfA3QIG7X0b9s7BvSW1VSfETYMpZy+4D1rn7UGBd7H1gOlXgA4XAbnff4+7VwHPAjBTXlFTuvt/dN8ZeH6c+CC5KbVXJZWYDgC8Dj6e6lrZgZj2A/wEsB3D3anc/ktqqki4dyDGzdKAL8GmK6wmcu78J/OGsxTOAJ2OvnwT+PMg+O1vgXwTsbfC+jE4efg2Z2WDgCuDd1FaSdEuAe4Do+Rp2EkOAcuCJ2DDW42bWNdVFJYu77wN+AJQC+4Gj7r42tVW1mQvcfX/s9QEgmEddxXS2wA8tM8sFfgEscPdjqa4nWcxsOnDQ3d9PdS1tKB0YB/ybu18BnCDgP/Xbk9i49Qzqf9H1B7qa2ddSW1Xb8/pr5gO9br6zBf4+YGCD9wNiyzo1M8ugPuyfdvdfprqeJLsGuMHMPqZ+yO6LZvYfqS0p6cqAMnc//Zfb89T/AuisrgN+5+7l7l4D/BKYkOKa2srvzexCgNj3g0FuvLMF/nvAUDMbYmaZ1J/oWZXimpLK6h/wuhzY7u4Pp7qeZHP377j7AHcfTP1/39fcvVMf/bn7AWCvmV0aWzQJ2JbCkpKtFLjazLrE/n1PohOfpD7LKuC22H6N71wAAACbSURBVOvbgBeD3HinesShu9ea2XzgFerP7K9w960pLivZrgH+AthiZh/Glv2tuxensCYJ3l8DT8cOZPYAd6S4nqRx93fN7HlgI/VXoX1AJ5xiwcyeBb4A9DWzMuDvgcXASjP7OvVTw88OtE9NrSAiEg6dbUhHRESaoMAXEQkJBb6ISEgo8EVEQkKBLyISEgp8EZGQUOCLiITE/wMEHQW7PAdp3QAAAABJRU5ErkJggg==\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -460,41 +476,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
-     "output_type": "stream",
      "text": [
-      "The determinant of H:  0.0\n"
-     ]
+      "The determinant of H:  0.0\n",
+      "The determinant of H after origin constraint:  716.1972439134893\n"
+     ],
+     "output_type": "stream"
     },
     {
      "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
+      "text/plain": "<Figure size 432x288 with 2 Axes>",
+      "image/png": "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\n"
      },
      "metadata": {
       "needs_background": "light"
      },
      "output_type": "display_data"
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The determinant of H after origin constraint:  716.1972439134918\n"
-     ]
-    },
     {
      "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
+      "text/plain": "<Figure size 432x288 with 2 Axes>",
+      "image/png": "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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -559,15 +569,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 18,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
-     "output_type": "stream",
      "text": [
       "dx: \n",
-      " [[ 0.   ]\n",
+      " [[-0.   ]\n",
       " [-0.   ]\n",
       " [ 0.   ]\n",
       " [ 0.02 ]\n",
@@ -582,13 +595,14 @@
       " [0.    1.428]\n",
       " [0.785 0.976]]\n",
       "SLAM: \n",
-      " [[ 0.     1.448]\n",
+      " [[-0.     1.448]\n",
       " [-0.     1.512]\n",
       " [ 0.785  0.976]]\n",
       "\n",
-      "graphSLAM localization error:  0.0107290727510571\n",
+      "graphSLAM localization error:  0.010729072751057656\n",
       "Odom localization error:  0.0004460978857535104\n"
-     ]
+     ],
+     "output_type": "stream"
     }
    ],
    "source": [
@@ -625,8 +639,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
+   "execution_count": 18,
+   "metadata": {
+    "pycharm": {
+     "is_executing": false
+    }
+   },
    "outputs": [],
    "source": []
   }

From 53b5b43a75b1593e8fcbd8f72cd11e7da6178d3b Mon Sep 17 00:00:00 2001
From: Marwan Taher <marokhaled99@gmail.com>
Date: Tue, 4 Feb 2020 15:38:57 +0000
Subject: [PATCH 0320/1080] Fixed typo in planning method docstring

---
 PathPlanning/AStar/a_star.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py
index b0c718c9a2..2229d63306 100644
--- a/PathPlanning/AStar/a_star.py
+++ b/PathPlanning/AStar/a_star.py
@@ -51,7 +51,7 @@ def planning(self, sx, sy, gx, gy):
             sx: start x position [m]
             sy: start y position [m]
             gx: goal x position [m]
-            gx: goal x position [m]
+            gy: goal y position [m]
 
         output:
             rx: x position list of the final path

From b3af4fcf507e9944265e6587c6c991a3b67f371d Mon Sep 17 00:00:00 2001
From: Peteski <38994044+YAOS5@users.noreply.github.com>
Date: Thu, 6 Feb 2020 17:31:34 +1100
Subject: [PATCH 0321/1080] IMPROVEMENT: Fixed typo

---
 SLAM/FastSLAM2/fast_slam2.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py
index 868812225a..4780b57982 100644
--- a/SLAM/FastSLAM2/fast_slam2.py
+++ b/SLAM/FastSLAM2/fast_slam2.py
@@ -25,7 +25,7 @@
 MAX_RANGE = 20.0  # maximum observation range
 M_DIST_TH = 2.0  # Threshold of Mahalanobis distance for data association.
 STATE_SIZE = 3  # State size [x,y,yaw]
-LM_SIZE = 2  # LM srate size [x,y]
+LM_SIZE = 2  # LM state size [x,y]
 N_PARTICLE = 100  # number of particle
 NTH = N_PARTICLE / 1.5  # Number of particle for re-sampling
 

From 03e8969e9d152cd3d0744c7478580fe7bb716b94 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 7 Feb 2020 20:32:12 +0900
Subject: [PATCH 0322/1080] inverted pendulum mpc control is added

---
 .../inverted_pendulum_mpc_control.py          | 171 ++++++++++++++++++
 1 file changed, 171 insertions(+)
 create mode 100644 InvertedPendulumCart/inverted_pendulum_mpc_control.py

diff --git a/InvertedPendulumCart/inverted_pendulum_mpc_control.py b/InvertedPendulumCart/inverted_pendulum_mpc_control.py
new file mode 100644
index 0000000000..d807a688c5
--- /dev/null
+++ b/InvertedPendulumCart/inverted_pendulum_mpc_control.py
@@ -0,0 +1,171 @@
+"""
+Inverted Pendulum MPC control
+author: Atsushi Sakai
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import math
+import time
+import cvxpy
+
+# Model parameters
+
+l_bar = 2.0  # length of bar
+M = 1.0  # [kg]
+m = 0.3  # [kg]
+g = 9.8  # [m/s^2]
+
+Q = np.diag([0.0, 1.0, 1.0, 0.0])
+R = np.diag([0.01])
+nx = 4  # number of state
+nu = 1  # number of input
+T = 30  # Horizon length
+delta_t = 0.1  # time tick
+
+animation = True
+
+
+def main():
+    x0 = np.array([
+        [0.0],
+        [0.0],
+        [0.3],
+        [0.0]
+    ])
+
+    x = np.copy(x0)
+
+    for i in range(50):
+
+        # calc control input
+        optimized_x, optimized_delta_x, optimized_theta, optimized_delta_theta, optimized_input = mpc_control(x)
+
+        # get input
+        u = optimized_input[0]
+
+        # simulate inverted pendulum cart
+        x = simulation(x, u)
+
+        if animation:
+            plt.clf()
+            px = float(x[0])
+            theta = float(x[2])
+            plot_cart(px, theta)
+            plt.xlim([-5.0, 2.0])
+            plt.pause(0.001)
+
+
+def simulation(x, u):
+    A, B = get_model_matrix()
+
+    x = np.dot(A, x) + np.dot(B, u)
+
+    return x
+
+
+def mpc_control(x0):
+    x = cvxpy.Variable((nx, T + 1))
+    u = cvxpy.Variable((nu, T))
+
+    A, B = get_model_matrix()
+
+    cost = 0.0
+    constr = []
+    for t in range(T):
+        cost += cvxpy.quad_form(x[:, t + 1], Q)
+        cost += cvxpy.quad_form(u[:, t], R)
+        constr += [x[:, t + 1] == A * x[:, t] + B * u[:, t]]
+
+    constr += [x[:, 0] == x0[:, 0]]
+    prob = cvxpy.Problem(cvxpy.Minimize(cost), constr)
+
+    start = time.time()
+    prob.solve(verbose=False)
+    elapsed_time = time.time() - start
+    print("calc time:{0} [sec]".format(elapsed_time))
+
+    if prob.status == cvxpy.OPTIMAL:
+        ox = get_nparray_from_matrix(x.value[0, :])
+        dx = get_nparray_from_matrix(x.value[1, :])
+        theta = get_nparray_from_matrix(x.value[2, :])
+        dtheta = get_nparray_from_matrix(x.value[3, :])
+
+        ou = get_nparray_from_matrix(u.value[0, :])
+
+    return ox, dx, theta, dtheta, ou
+
+
+def get_nparray_from_matrix(x):
+    """
+    get build-in list from matrix
+    """
+    return np.array(x).flatten()
+
+
+def get_model_matrix():
+    A = np.array([
+        [0.0, 1.0, 0.0, 0.0],
+        [0.0, 0.0, m * g / M, 0.0],
+        [0.0, 0.0, 0.0, 1.0],
+        [0.0, 0.0, g * (M + m) / (l_bar * M), 0.0]
+    ])
+    A = np.eye(nx) + delta_t * A
+
+    B = np.array([
+        [0.0],
+        [1.0 / M],
+        [0.0],
+        [1.0 / (l_bar * M)]
+    ])
+    B = delta_t * B
+
+    return A, B
+
+
+def flatten(a):
+    return np.array(a).flatten()
+
+
+def plot_cart(xt, theta):
+    cart_w = 1.0
+    cart_h = 0.5
+    radius = 0.1
+
+    cx = np.array([-cart_w / 2.0, cart_w / 2.0, cart_w /
+                    2.0, -cart_w / 2.0, -cart_w / 2.0])
+    cy = np.array([0.0, 0.0, cart_h, cart_h, 0.0])
+    cy += radius * 2.0
+
+    cx = cx + xt
+
+    bx = np.array([0.0, l_bar * math.sin(-theta)])
+    bx += xt
+    by = np.array([cart_h, l_bar * math.cos(-theta) + cart_h])
+    by += radius * 2.0
+
+    angles = np.arange(0.0, math.pi * 2.0, math.radians(3.0))
+    ox = [radius * math.cos(a) for a in angles]
+    oy = [radius * math.sin(a) for a in angles]
+
+    rwx = np.copy(ox) + cart_w / 4.0 + xt
+    rwy = np.copy(oy) + radius
+    lwx = np.copy(ox) - cart_w / 4.0 + xt
+    lwy = np.copy(oy) + radius
+
+    wx = np.copy(ox) + float(bx[0, -1])
+    wy = np.copy(oy) + float(by[0, -1])
+
+    plt.plot(flatten(cx), flatten(cy), "-b")
+    plt.plot(flatten(bx), flatten(by), "-k")
+    plt.plot(flatten(rwx), flatten(rwy), "-k")
+    plt.plot(flatten(lwx), flatten(lwy), "-k")
+    plt.plot(flatten(wx), flatten(wy), "-k")
+    plt.title("x:" + str(round(xt, 2)) + ",theta:" +
+              str(round(math.degrees(theta), 2)))
+
+    plt.axis("equal")
+
+
+if __name__ == '__main__':
+    main()

From bd5269b3d2f2e3e73739e51334868adac32e1665 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 7 Feb 2020 20:45:15 +0900
Subject: [PATCH 0323/1080] inverted pendulum mpc control is added

---
 .../inverted_pendulum_mpc_control.py              |  8 ++++----
 tests/test_inverted_pendulum_mpc_control.py       | 15 +++++++++++++++
 2 files changed, 19 insertions(+), 4 deletions(-)
 create mode 100644 tests/test_inverted_pendulum_mpc_control.py

diff --git a/InvertedPendulumCart/inverted_pendulum_mpc_control.py b/InvertedPendulumCart/inverted_pendulum_mpc_control.py
index d807a688c5..caf0c23e77 100644
--- a/InvertedPendulumCart/inverted_pendulum_mpc_control.py
+++ b/InvertedPendulumCart/inverted_pendulum_mpc_control.py
@@ -145,16 +145,16 @@ def plot_cart(xt, theta):
     by += radius * 2.0
 
     angles = np.arange(0.0, math.pi * 2.0, math.radians(3.0))
-    ox = [radius * math.cos(a) for a in angles]
-    oy = [radius * math.sin(a) for a in angles]
+    ox = np.array([radius * math.cos(a) for a in angles])
+    oy = np.array([radius * math.sin(a) for a in angles])
 
     rwx = np.copy(ox) + cart_w / 4.0 + xt
     rwy = np.copy(oy) + radius
     lwx = np.copy(ox) - cart_w / 4.0 + xt
     lwy = np.copy(oy) + radius
 
-    wx = np.copy(ox) + float(bx[0, -1])
-    wy = np.copy(oy) + float(by[0, -1])
+    wx = np.copy(ox) + bx[-1]
+    wy = np.copy(oy) + by[-1]
 
     plt.plot(flatten(cx), flatten(cy), "-b")
     plt.plot(flatten(bx), flatten(by), "-k")
diff --git a/tests/test_inverted_pendulum_mpc_control.py b/tests/test_inverted_pendulum_mpc_control.py
new file mode 100644
index 0000000000..bf6c8576e4
--- /dev/null
+++ b/tests/test_inverted_pendulum_mpc_control.py
@@ -0,0 +1,15 @@
+from unittest import TestCase
+
+import sys
+if 'cvxpy' in sys.modules:  # pragma: no cover
+    sys.path.append("./InvertedPendulumCart/inverted_pendulum_mpc_control/")
+
+    import inverted_pendulum_mpc_control as m
+
+    print(__file__)
+
+    class Test(TestCase):
+
+        def test1(self):
+            m.show_animation = False
+            m.main()

From cacb6db028332e9ef1b48757d6b7e53e6080324b Mon Sep 17 00:00:00 2001
From: Shilong <daishilong1236@gmail.com>
Date: Sun, 9 Feb 2020 16:24:21 -0800
Subject: [PATCH 0324/1080] fix RRT rewire

---
 PathPlanning/RRTStar/rrt_star.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py
index 09a421937a..7b46029791 100644
--- a/PathPlanning/RRTStar/rrt_star.py
+++ b/PathPlanning/RRTStar/rrt_star.py
@@ -34,7 +34,7 @@ def __init__(self, x, y):
             self.cost = 0.0
 
     def __init__(self, start, goal, obstacle_list, rand_area,
-                 expand_dis=3.0,
+                 expand_dis=30.0,
                  path_resolution=1.0,
                  goal_sample_rate=20,
                  max_iter=300,
@@ -141,6 +141,9 @@ def search_best_goal_node(self):
     def find_near_nodes(self, new_node):
         nnode = len(self.node_list) + 1
         r = self.connect_circle_dist * math.sqrt((math.log(nnode) / nnode))
+        # if expand_dist exists, search vertices in a range no more than expand_dist
+        if hasattr(self, 'expand_dis'): 
+            r = min(r, self.expand_dis)
         dist_list = [(node.x - new_node.x) ** 2 +
                      (node.y - new_node.y) ** 2 for node in self.node_list]
         near_inds = [dist_list.index(i) for i in dist_list if i <= r ** 2]
@@ -158,8 +161,7 @@ def rewire(self, new_node, near_inds):
             improved_cost = near_node.cost > edge_node.cost
 
             if no_collision and improved_cost:
-                near_node = edge_node
-                near_node.parent = new_node
+                self.node_list[i] = edge_node
                 self.propagate_cost_to_leaves(new_node)
 
     def calc_new_cost(self, from_node, to_node):

From 41d744c67f71de511d22179f2b8999ac41e789e6 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 22 Feb 2020 21:36:51 +0900
Subject: [PATCH 0325/1080] add error handling. But there is still bug.

---
 Mapping/grid_map_lib/grid_map_lib.py          |  4 +-
 .../grid_based_sweep_coverage_path_planner.py | 82 ++++++++++++-------
 2 files changed, 54 insertions(+), 32 deletions(-)

diff --git a/Mapping/grid_map_lib/grid_map_lib.py b/Mapping/grid_map_lib/grid_map_lib.py
index fd016a288f..4787238f07 100644
--- a/Mapping/grid_map_lib/grid_map_lib.py
+++ b/Mapping/grid_map_lib/grid_map_lib.py
@@ -51,7 +51,7 @@ def get_value_from_xy_index(self, x_ind, y_ind):
 
         grid_ind = self.calc_grid_index_from_xy_index(x_ind, y_ind)
 
-        if 0 <= grid_ind <= self.ndata:
+        if 0 <= grid_ind < self.ndata:
             return self.data[grid_ind]
         else:
             return None
@@ -163,7 +163,7 @@ def check_occupied_from_xy_index(self, xind, yind, occupied_val=1.0):
 
         val = self.get_value_from_xy_index(xind, yind)
 
-        if val >= occupied_val:
+        if val is None or val >= occupied_val:
             return True
         else:
             return False
diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
index 0d2239a9ae..c51e9393cb 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -30,22 +30,23 @@ class MovingDirection(IntEnum):
         RIGHT = 1
         LEFT = -1
 
-    def __init__(self, mdirection, sdirection, xinds_goaly, goaly):
-        self.moving_direction = mdirection
-        self.sweep_direction = sdirection
+    def __init__(self, moving_direction, sweep_direction, x_inds_goal_y, goal_y):
+        self.moving_direction = moving_direction
+        self.sweep_direction = sweep_direction
         self.turing_window = []
         self.update_turning_window()
-        self.xinds_goaly = xinds_goaly
-        self.goaly = goaly
+        self.xinds_goaly = x_inds_goal_y
+        self.goaly = goal_y
 
     def move_target_grid(self, cxind, cyind, gmap):
         nxind = self.moving_direction + cxind
         nyind = cyind
 
         # found safe grid
-        if not gmap.check_occupied_from_xy_index(nxind, nyind, occupied_val=0.5):
+        if not gmap.check_occupied_from_xy_index(nxind, nyind,
+                                                 occupied_val=0.5):
             return nxind, nyind
-        else:  # occupided
+        else:  # occupied
             ncxind, ncyind = self.find_safe_turning_grid(cxind, cyind, gmap)
             if (ncxind is None) and (ncyind is None):
                 # moving backward
@@ -56,27 +57,31 @@ def move_target_grid(self, cxind, cyind, gmap):
                     return None, None
             else:
                 # keep moving until end
-                while not gmap.check_occupied_from_xy_index(ncxind + self.moving_direction, ncyind, occupied_val=0.5):
+                while not gmap.check_occupied_from_xy_index(
+                        ncxind + self.moving_direction, ncyind,
+                        occupied_val=0.5):
                     ncxind += self.moving_direction
                 self.swap_moving_direction()
             return ncxind, ncyind
 
     def find_safe_turning_grid(self, cxind, cyind, gmap):
 
-        for (dxind, dyind) in self.turing_window:
+        for (d_x_ind, d_y_ind) in self.turing_window:
 
-            nxind = dxind + cxind
-            nyind = dyind + cyind
+            next_x_ind = d_x_ind + cxind
+            next_y_ind = d_y_ind + cyind
 
             # found safe grid
-            if not gmap.check_occupied_from_xy_index(nxind, nyind, occupied_val=0.5):
-                return nxind, nyind
+            if not gmap.check_occupied_from_xy_index(next_x_ind, next_y_ind,
+                                                     occupied_val=0.5):
+                return next_x_ind, next_y_ind
 
         return None, None
 
-    def is_search_done(self, gmap):
+    def is_search_done(self, grid_map):
         for ix in self.xinds_goaly:
-            if not gmap.check_occupied_from_xy_index(ix, self.goaly, occupied_val=0.5):
+            if not grid_map.check_occupied_from_xy_index(ix, self.goaly,
+                                                         occupied_val=0.5):
                 return False
 
         # all lower grid is occupied
@@ -95,22 +100,24 @@ def swap_moving_direction(self):
         self.update_turning_window()
 
     def search_start_grid(self, grid_map):
-        xinds = []
+        x_inds = []
         y_ind = 0
         if self.sweep_direction == self.SweepDirection.DOWN:
-            xinds, y_ind = search_free_grid_index_at_edge_y(grid_map, from_upper=True)
+            x_inds, y_ind = search_free_grid_index_at_edge_y(
+                grid_map, from_upper=True)
         elif self.sweep_direction == self.SweepDirection.UP:
-            xinds, y_ind = search_free_grid_index_at_edge_y(grid_map, from_upper=False)
+            x_inds, y_ind = search_free_grid_index_at_edge_y(
+                grid_map, from_upper=False)
 
         if self.moving_direction == self.MovingDirection.RIGHT:
-            return min(xinds), y_ind
+            return min(x_inds), y_ind
         elif self.moving_direction == self.MovingDirection.LEFT:
-            return max(xinds), y_ind
+            return max(x_inds), y_ind
 
         raise ValueError("self.moving direction is invalid ")
 
 
-def find_sweep_direction_and_start_posi(ox, oy):
+def find_sweep_direction_and_start_position(ox, oy):
     # find sweep_direction
     max_dist = 0.0
     vec = [0.0, 0.0]
@@ -194,9 +201,11 @@ def setup_grid_map(ox, oy, reso, sweep_direction, offset_grid=10):
     xinds_goaly = []
     goaly = 0
     if sweep_direction == SweepSearcher.SweepDirection.UP:
-        xinds_goaly, goaly = search_free_grid_index_at_edge_y(grid_map, from_upper=True)
+        xinds_goaly, goaly = search_free_grid_index_at_edge_y(grid_map,
+                                                              from_upper=True)
     elif sweep_direction == SweepSearcher.SweepDirection.DOWN:
-        xinds_goaly, goaly = search_free_grid_index_at_edge_y(grid_map, from_upper=False)
+        xinds_goaly, goaly = search_free_grid_index_at_edge_y(grid_map,
+                                                              from_upper=False)
 
     return grid_map, xinds_goaly, goaly
 
@@ -211,16 +220,19 @@ def sweep_path_search(sweep_searcher, gmap, grid_search_animation=False):
     x, y = gmap.calc_grid_central_xy_position_from_xy_index(cxind, cyind)
     px, py = [x], [y]
 
+    fig, ax = None, None
     if grid_search_animation:
         fig, ax = plt.subplots()
         # for stopping simulation with the esc key.
         fig.canvas.mpl_connect('key_release_event',
-                lambda event: [exit(0) if event.key == 'escape' else None])
+                               lambda event: [
+                                   exit(0) if event.key == 'escape' else None])
 
     while True:
         cxind, cyind = sweep_searcher.move_target_grid(cxind, cyind, gmap)
 
-        if sweep_searcher.is_search_done(gmap) or (cxind is None or cyind is None):
+        if sweep_searcher.is_search_done(gmap) or (
+                cxind is None or cyind is None):
             print("Done")
             break
 
@@ -245,13 +257,16 @@ def planning(ox, oy, reso,
              moving_direction=SweepSearcher.MovingDirection.RIGHT,
              sweeping_direction=SweepSearcher.SweepDirection.UP,
              ):
-    sweep_vec, sweep_start_posi = find_sweep_direction_and_start_posi(ox, oy)
+    sweep_vec, sweep_start_posi = find_sweep_direction_and_start_position(
+        ox, oy)
 
     rox, roy = convert_grid_coordinate(ox, oy, sweep_vec, sweep_start_posi)
 
-    gmap, xinds_goaly, goaly = setup_grid_map(rox, roy, reso, sweeping_direction)
+    gmap, xinds_goaly, goaly = setup_grid_map(rox, roy, reso,
+                                              sweeping_direction)
 
-    sweep_searcher = SweepSearcher(moving_direction, sweeping_direction, xinds_goaly, goaly)
+    sweep_searcher = SweepSearcher(moving_direction, sweeping_direction,
+                                   xinds_goaly, goaly)
 
     px, py = sweep_path_search(sweep_searcher, gmap)
 
@@ -270,8 +285,9 @@ def planning_animation(ox, oy, reso):  # pragma: no cover
         for ipx, ipy in zip(px, py):
             plt.cla()
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(ox, oy, "-xb")
             plt.plot(px, py, "-r")
             plt.plot(ipx, ipy, "or")
@@ -285,11 +301,17 @@ def planning_animation(ox, oy, reso):  # pragma: no cover
     plt.axis("equal")
     plt.grid(True)
     plt.pause(0.1)
+    plt.close()
 
 
 def main():  # pragma: no cover
     print("start!!")
 
+    ox = [0.0, 50.0, 50.0, 0.0, 0.0]
+    oy = [0.0, 0.0, 50.0, 50.0, 0.0]
+    reso = 0.4
+    planning_animation(ox, oy, reso)
+
     ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0, 0.0]
     oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0, 0.0]
     reso = 5.0

From 7c8a940ebfd44217ae325470c48b47d9ed35f0f3 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 22 Feb 2020 23:17:25 +0900
Subject: [PATCH 0326/1080] fixed the bug and code clean up.

---
 Mapping/grid_map_lib/grid_map_lib.py          | 21 +++++---
 .../grid_based_sweep_coverage_path_planner.py | 48 +++++++++----------
 2 files changed, 36 insertions(+), 33 deletions(-)

diff --git a/Mapping/grid_map_lib/grid_map_lib.py b/Mapping/grid_map_lib/grid_map_lib.py
index 4787238f07..10651cc673 100644
--- a/Mapping/grid_map_lib/grid_map_lib.py
+++ b/Mapping/grid_map_lib/grid_map_lib.py
@@ -32,10 +32,8 @@ def __init__(self, width, height, resolution,
         self.center_x = center_x
         self.center_y = center_y
 
-        self.left_lower_x = self.center_x - \
-                            (self.width / 2.0) * self.resolution
-        self.left_lower_y = self.center_y - \
-                            (self.height / 2.0) * self.resolution
+        self.left_lower_x = self.center_x - self.width / 2.0 * self.resolution
+        self.left_lower_y = self.center_y - self.height / 2.0 * self.resolution
 
         self.ndata = self.width * self.height
         self.data = [init_val] * self.ndata
@@ -99,7 +97,6 @@ def set_value_from_xy_index(self, x_ind, y_ind, val):
         """
 
         if (x_ind is None) or (y_ind is None):
-            print(x_ind, y_ind)
             return False, False
 
         grid_ind = int(y_ind * self.width + x_ind)
@@ -200,12 +197,22 @@ def check_inside_polygon(iox, ioy, x, y):
             if not min_x < iox < max_x:
                 continue
 
-            if (y[i1] + (y[i2] - y[i1]) / (x[i2] - x[i1])
-                * (iox - x[i1]) - ioy) > 0.0:
+            tmp1 = (y[i2] - y[i1]) / (x[i2] - x[i1])
+            if (y[i1] + tmp1 * (iox - x[i1]) - ioy) > 0.0:
                 inside = not inside
 
         return inside
 
+    def print_grid_map_info(self):
+        print("width:", self.width)
+        print("height:", self.height)
+        print("resolution:", self.resolution)
+        print("center_x:", self.center_x)
+        print("center_y:", self.center_y)
+        print("left_lower_x:", self.left_lower_x)
+        print("left_lower_y:", self.left_lower_y)
+        print("ndata:", self.ndata)
+
     def plot_grid_map(self, ax=None):
 
         grid_data = np.reshape(np.array(self.data), (self.height, self.width))
diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
index c51e9393cb..8d9ccd2e2f 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -30,7 +30,8 @@ class MovingDirection(IntEnum):
         RIGHT = 1
         LEFT = -1
 
-    def __init__(self, moving_direction, sweep_direction, x_inds_goal_y, goal_y):
+    def __init__(self,
+                 moving_direction, sweep_direction, x_inds_goal_y, goal_y):
         self.moving_direction = moving_direction
         self.sweep_direction = sweep_direction
         self.turing_window = []
@@ -88,6 +89,8 @@ def is_search_done(self, grid_map):
         return True
 
     def update_turning_window(self):
+        # turning window definition
+        # robot can move grid based on it.
         self.turing_window = [
             (self.moving_direction, 0.0),
             (self.moving_direction, self.sweep_direction),
@@ -189,35 +192,34 @@ def search_free_grid_index_at_edge_y(grid_map, from_upper=False):
 def setup_grid_map(ox, oy, reso, sweep_direction, offset_grid=10):
     width = math.ceil((max(ox) - min(ox)) / reso) + offset_grid
     height = math.ceil((max(oy) - min(oy)) / reso) + offset_grid
-    center_x = np.mean(ox)
-    center_y = np.mean(oy)
+    center_x = (np.max(ox)+np.min(ox))/2.0
+    center_y = (np.max(oy)+np.min(oy))/2.0
 
     grid_map = GridMap(width, height, reso, center_x, center_y)
-
+    grid_map.print_grid_map_info()
     grid_map.set_value_from_polygon(ox, oy, 1.0, inside=False)
-
     grid_map.expand_grid()
 
-    xinds_goaly = []
-    goaly = 0
+    x_inds_goal_y = []
+    goal_y = 0
     if sweep_direction == SweepSearcher.SweepDirection.UP:
-        xinds_goaly, goaly = search_free_grid_index_at_edge_y(grid_map,
+        x_inds_goal_y, goal_y = search_free_grid_index_at_edge_y(grid_map,
                                                               from_upper=True)
     elif sweep_direction == SweepSearcher.SweepDirection.DOWN:
-        xinds_goaly, goaly = search_free_grid_index_at_edge_y(grid_map,
+        x_inds_goal_y, goal_y = search_free_grid_index_at_edge_y(grid_map,
                                                               from_upper=False)
 
-    return grid_map, xinds_goaly, goaly
+    return grid_map, x_inds_goal_y, goal_y
 
 
-def sweep_path_search(sweep_searcher, gmap, grid_search_animation=False):
+def sweep_path_search(sweep_searcher, grid_map, grid_search_animation=False):
     # search start grid
-    cxind, cyind = sweep_searcher.search_start_grid(gmap)
-    if not gmap.set_value_from_xy_index(cxind, cyind, 0.5):
+    cxind, cyind = sweep_searcher.search_start_grid(grid_map)
+    if not grid_map.set_value_from_xy_index(cxind, cyind, 0.5):
         print("Cannot find start grid")
         return [], []
 
-    x, y = gmap.calc_grid_central_xy_position_from_xy_index(cxind, cyind)
+    x, y = grid_map.calc_grid_central_xy_position_from_xy_index(cxind, cyind)
     px, py = [x], [y]
 
     fig, ax = None, None
@@ -229,26 +231,26 @@ def sweep_path_search(sweep_searcher, gmap, grid_search_animation=False):
                                    exit(0) if event.key == 'escape' else None])
 
     while True:
-        cxind, cyind = sweep_searcher.move_target_grid(cxind, cyind, gmap)
+        cxind, cyind = sweep_searcher.move_target_grid(cxind, cyind, grid_map)
 
-        if sweep_searcher.is_search_done(gmap) or (
+        if sweep_searcher.is_search_done(grid_map) or (
                 cxind is None or cyind is None):
             print("Done")
             break
 
-        x, y = gmap.calc_grid_central_xy_position_from_xy_index(
+        x, y = grid_map.calc_grid_central_xy_position_from_xy_index(
             cxind, cyind)
 
         px.append(x)
         py.append(y)
 
-        gmap.set_value_from_xy_index(cxind, cyind, 0.5)
+        grid_map.set_value_from_xy_index(cxind, cyind, 0.5)
 
         if grid_search_animation:
-            gmap.plot_grid_map(ax=ax)
+            grid_map.plot_grid_map(ax=ax)
             plt.pause(1.0)
 
-    gmap.plot_grid_map()
+    grid_map.plot_grid_map()
 
     return px, py
 
@@ -307,11 +309,6 @@ def planning_animation(ox, oy, reso):  # pragma: no cover
 def main():  # pragma: no cover
     print("start!!")
 
-    ox = [0.0, 50.0, 50.0, 0.0, 0.0]
-    oy = [0.0, 0.0, 50.0, 50.0, 0.0]
-    reso = 0.4
-    planning_animation(ox, oy, reso)
-
     ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0, 0.0]
     oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0, 0.0]
     reso = 5.0
@@ -328,7 +325,6 @@ def main():  # pragma: no cover
     planning_animation(ox, oy, reso)
 
     plt.show()
-
     print("done!!")
 
 

From 065536a47b0c8e692638df288539eb301e3bb086 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 23 Feb 2020 20:35:09 +0900
Subject: [PATCH 0327/1080] start implementation

---
 PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py

diff --git a/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py b/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py
new file mode 100644
index 0000000000..e69de29bb2

From 6f0d9bfd5c6cd74bbd65891570568652c0160690 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 24 Feb 2020 16:15:17 +0900
Subject: [PATCH 0328/1080] clean up voronoi road map

---
 .../VoronoiRoadMap/dijkstra_search.py         | 106 +++++++
 PathPlanning/VoronoiRoadMap/kdtree.py         |  49 +++
 .../VoronoiRoadMap/voronoi_road_map.py        | 294 +++++-------------
 3 files changed, 239 insertions(+), 210 deletions(-)
 create mode 100644 PathPlanning/VoronoiRoadMap/dijkstra_search.py
 create mode 100644 PathPlanning/VoronoiRoadMap/kdtree.py

diff --git a/PathPlanning/VoronoiRoadMap/dijkstra_search.py b/PathPlanning/VoronoiRoadMap/dijkstra_search.py
new file mode 100644
index 0000000000..4d294ee5e5
--- /dev/null
+++ b/PathPlanning/VoronoiRoadMap/dijkstra_search.py
@@ -0,0 +1,106 @@
+"""
+
+Dijkstra Search library
+
+author: Atsushi Sakai (@Atsushi_twi)
+
+"""
+
+import matplotlib.pyplot as plt
+import math
+
+
+class DijkstraSearch:
+
+    class Node:
+        """
+        Node class for dijkstra search
+        """
+
+        def __init__(self, x, y, cost, parent):
+            self.x = x
+            self.y = y
+            self.cost = cost
+            self.parent = parent
+
+        def __str__(self):
+            return str(self.x) + "," + str(self.y) + "," + str(
+                self.cost) + "," + str(self.parent)
+
+    def __init__(self, show_animation):
+        self.show_animation = show_animation
+
+    def search(self, sx, sy, gx, gy, road_map, sample_x, sample_y):
+        """
+        gx: goal x position [m]
+        gx: goal x position [m]
+        ox: x position list of Obstacles [m]
+        oy: y position list of Obstacles [m]
+        reso: grid resolution [m]
+        rr: robot radius[m]
+        """
+
+        start_node = self.Node(sx, sy, 0.0, -1)
+        goal_node = self.Node(gx, gy, 0.0, -1)
+
+        open_set, close_set = dict(), dict()
+        open_set[len(road_map) - 2] = start_node
+
+        while True:
+            if not open_set:
+                print("Cannot find path")
+                break
+
+            c_id = min(open_set, key=lambda o: open_set[o].cost)
+            current = open_set[c_id]
+
+            # show graph
+            if self.show_animation and len(
+                    close_set.keys()) % 2 == 0:  # pragma: no cover
+                plt.plot(current.x, current.y, "xg")
+                # for stopping simulation with the esc key.
+                plt.gcf().canvas.mpl_connect(
+                    'key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
+                plt.pause(0.001)
+
+            if c_id == (len(road_map) - 1):
+                print("goal is found!")
+                goal_node.parent = current.parent
+                goal_node.cost = current.cost
+                break
+
+            # Remove the item from the open set
+            del open_set[c_id]
+            # Add it to the closed set
+            close_set[c_id] = current
+
+            # expand search grid based on motion model
+            for i in range(len(road_map[c_id])):
+                n_id = road_map[c_id][i]
+                dx = sample_x[n_id] - current.x
+                dy = sample_y[n_id] - current.y
+                d = math.hypot(dx, dy)
+                node = self.Node(sample_x[n_id], sample_y[n_id],
+                                 current.cost + d, c_id)
+
+                if n_id in close_set:
+                    continue
+                # Otherwise if it is already in the open set
+                if n_id in open_set:
+                    if open_set[n_id].cost > node.cost:
+                        open_set[n_id].cost = node.cost
+                        open_set[n_id].parent = c_id
+                else:
+                    open_set[n_id] = node
+
+        # generate final course
+        rx, ry = [goal_node.x], [goal_node.y]
+        parent = goal_node.parent
+        while parent != -1:
+            n = close_set[parent]
+            rx.append(n.x)
+            ry.append(n.y)
+            parent = n.parent
+
+        return rx, ry
diff --git a/PathPlanning/VoronoiRoadMap/kdtree.py b/PathPlanning/VoronoiRoadMap/kdtree.py
new file mode 100644
index 0000000000..d3dc01b1f6
--- /dev/null
+++ b/PathPlanning/VoronoiRoadMap/kdtree.py
@@ -0,0 +1,49 @@
+"""
+
+Kd tree Search library
+
+author: Atsushi Sakai (@Atsushi_twi)
+
+"""
+
+import scipy.spatial
+
+
+class KDTree:
+    """
+    Nearest neighbor search class with KDTree
+    """
+
+    def __init__(self, data):
+        # store kd-tree
+        self.tree = scipy.spatial.cKDTree(data)
+
+    def search(self, inp, k=1):
+        """
+        Search NN
+
+        inp: input data, single frame or multi frame
+
+        """
+
+        if len(inp.shape) >= 2:  # multi input
+            index = []
+            dist = []
+
+            for i in inp.T:
+                idist, iindex = self.tree.query(i, k=k)
+                index.append(iindex)
+                dist.append(idist)
+
+            return index, dist
+
+        dist, index = self.tree.query(inp, k=k)
+        return index, dist
+
+    def search_in_distance(self, inp, r):
+        """
+        find points with in a distance r
+        """
+
+        index = self.tree.query_ball_point(inp, r)
+        return index
diff --git a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
index 6db4bb26ce..dfac7258f8 100644
--- a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
+++ b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
@@ -10,252 +10,124 @@
 import numpy as np
 import scipy.spatial
 import matplotlib.pyplot as plt
-
-# parameter
-N_KNN = 10  # number of edge from one sampled point
-MAX_EDGE_LEN = 30.0  # [m] Maximum edge length
+from dijkstra_search import DijkstraSearch
+from kdtree import KDTree
 
 show_animation = True
 
 
-class Node:
-    """
-    Node class for dijkstra search
-    """
-
-    def __init__(self, x, y, cost, pind):
-        self.x = x
-        self.y = y
-        self.cost = cost
-        self.pind = pind
-
-    def __str__(self):
-        return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind)
-
-
-class KDTree:
-    """
-    Nearest neighbor search class with KDTree
-    """
-
-    def __init__(self, data):
-        # store kd-tree
-        self.tree = scipy.spatial.cKDTree(data)
-
-    def search(self, inp, k=1):
-        """
-        Search NN
-
-        inp: input data, single frame or multi frame
-
-        """
-
-        if len(inp.shape) >= 2:  # multi input
-            index = []
-            dist = []
-
-            for i in inp.T:
-                idist, iindex = self.tree.query(i, k=k)
-                index.append(iindex)
-                dist.append(idist)
-
-            return index, dist
-
-        dist, index = self.tree.query(inp, k=k)
-        return index, dist
-
-    def search_in_distance(self, inp, r):
-        """
-        find points with in a distance r
-        """
-
-        index = self.tree.query_ball_point(inp, r)
-        return index
-
+class VoronoiRoadMapPlanner:
 
-def VRM_planning(sx, sy, gx, gy, ox, oy, rr):
+    def __init__(self):
+        # parameter
+        self.N_KNN = 10  # number of edge from one sampled point
+        self.MAX_EDGE_LEN = 30.0  # [m] Maximum edge length
 
-    obkdtree = KDTree(np.vstack((ox, oy)).T)
+    def planning(self, sx, sy, gx, gy, ox, oy, rr):
+        obstacle_tree = KDTree(np.vstack((ox, oy)).T)
 
-    sample_x, sample_y = sample_points(sx, sy, gx, gy, rr, ox, oy, obkdtree)
-    if show_animation:  # pragma: no cover
-        plt.plot(sample_x, sample_y, ".b")
+        sample_x, sample_y = self.voronoi_sampling(sx, sy, gx, gy, ox, oy)
+        if show_animation:  # pragma: no cover
+            plt.plot(sample_x, sample_y, ".b")
 
-    road_map = generate_roadmap(sample_x, sample_y, rr, obkdtree)
+        road_map = self.generate_road_map(sample_x, sample_y, rr, obstacle_tree)
 
-    rx, ry = dijkstra_planning(
-        sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y)
+        rx, ry = DijkstraSearch(show_animation).search(sx, sy, gx, gy, road_map,
+                                                       sample_x, sample_y)
 
-    return rx, ry
+        return rx, ry
 
+    def is_collision(self, sx, sy, gx, gy, rr, obstacle_kdtree):
+        x = sx
+        y = sy
+        dx = gx - sx
+        dy = gy - sy
+        yaw = math.atan2(gy - sy, gx - sx)
+        d = math.hypot(dx, dy)
 
-def is_collision(sx, sy, gx, gy, rr, okdtree):
-    x = sx
-    y = sy
-    dx = gx - sx
-    dy = gy - sy
-    yaw = math.atan2(gy - sy, gx - sx)
-    d = math.hypot(dx, dy)
+        if d >= self.MAX_EDGE_LEN:
+            return True
 
-    if d >= MAX_EDGE_LEN:
-        return True
+        D = rr
+        n_step = round(d / D)
 
-    D = rr
-    nstep = round(d / D)
+        for i in range(n_step):
+            ids, dist = obstacle_kdtree.search(np.array([x, y]).reshape(2, 1))
+            if dist[0] <= rr:
+                return True  # collision
+            x += D * math.cos(yaw)
+            y += D * math.sin(yaw)
 
-    for i in range(nstep):
-        idxs, dist = okdtree.search(np.array([x, y]).reshape(2, 1))
+        # goal point check
+        ids, dist = obstacle_kdtree.search(np.array([gx, gy]).reshape(2, 1))
         if dist[0] <= rr:
             return True  # collision
-        x += D * math.cos(yaw)
-        y += D * math.sin(yaw)
-
-    # goal point check
-    idxs, dist = okdtree.search(np.array([gx, gy]).reshape(2, 1))
-    if dist[0] <= rr:
-        return True  # collision
-
-    return False  # OK
-
-
-def generate_roadmap(sample_x, sample_y, rr, obkdtree):
-    """
-    Road map generation
-
-    sample_x: [m] x positions of sampled points
-    sample_y: [m] y positions of sampled points
-    rr: Robot Radius[m]
-    obkdtree: KDTree object of obstacles
-    """
-
-    road_map = []
-    nsample = len(sample_x)
-    skdtree = KDTree(np.vstack((sample_x, sample_y)).T)
-
-    for (i, ix, iy) in zip(range(nsample), sample_x, sample_y):
-
-        index, dists = skdtree.search(
-            np.array([ix, iy]).reshape(2, 1), k=nsample)
-
-        inds = index[0]
-        edge_id = []
-        #  print(index)
-
-        for ii in range(1, len(inds)):
-            nx = sample_x[inds[ii]]
-            ny = sample_y[inds[ii]]
 
-            if not is_collision(ix, iy, nx, ny, rr, obkdtree):
-                edge_id.append(inds[ii])
+        return False  # OK
 
-            if len(edge_id) >= N_KNN:
-                break
-
-        road_map.append(edge_id)
-
-    #  plot_road_map(road_map, sample_x, sample_y)
-
-    return road_map
-
-
-def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y):
-    """
-    gx: goal x position [m]
-    gx: goal x position [m]
-    ox: x position list of Obstacles [m]
-    oy: y position list of Obstacles [m]
-    reso: grid resolution [m]
-    rr: robot radius[m]
-    """
-
-    nstart = Node(sx, sy, 0.0, -1)
-    ngoal = Node(gx, gy, 0.0, -1)
+    def generate_road_map(self, node_x, node_y, rr, obstacle_tree):
+        """
+        Road map generation
 
-    openset, closedset = dict(), dict()
-    openset[len(road_map) - 2] = nstart
+        sample_x: [m] x positions of sampled points
+        sample_y: [m] y positions of sampled points
+        rr: Robot Radius[m]
+        obstacle_tree: KDTree object of obstacles
+        """
 
-    while True:
-        if not openset:
-            print("Cannot find path")
-            break
+        road_map = []
+        n_sample = len(node_x)
+        node_tree = KDTree(np.vstack((node_x, node_y)).T)
 
-        c_id = min(openset, key=lambda o: openset[o].cost)
-        current = openset[c_id]
+        for (i, ix, iy) in zip(range(n_sample), node_x, node_y):
 
-        # show graph
-        if show_animation and len(closedset.keys()) % 2 == 0:  # pragma: no cover
-            plt.plot(current.x, current.y, "xg")
-            # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
-            plt.pause(0.001)
+            index, dists = node_tree.search(
+                np.array([ix, iy]).reshape(2, 1), k=n_sample)
 
-        if c_id == (len(road_map) - 1):
-            print("goal is found!")
-            ngoal.pind = current.pind
-            ngoal.cost = current.cost
-            break
+            inds = index[0]
+            edge_id = []
 
-        # Remove the item from the open set
-        del openset[c_id]
-        # Add it to the closed set
-        closedset[c_id] = current
+            for ii in range(1, len(inds)):
+                nx = node_x[inds[ii]]
+                ny = node_y[inds[ii]]
 
-        # expand search grid based on motion model
-        for i in range(len(road_map[c_id])):
-            n_id = road_map[c_id][i]
-            dx = sample_x[n_id] - current.x
-            dy = sample_y[n_id] - current.y
-            d = math.hypot(dx, dy)
-            node = Node(sample_x[n_id], sample_y[n_id],
-                        current.cost + d, c_id)
+                if not self.is_collision(ix, iy, nx, ny, rr, obstacle_tree):
+                    edge_id.append(inds[ii])
 
-            if n_id in closedset:
-                continue
-            # Otherwise if it is already in the open set
-            if n_id in openset:
-                if openset[n_id].cost > node.cost:
-                    openset[n_id].cost = node.cost
-                    openset[n_id].pind = c_id
-            else:
-                openset[n_id] = node
+                if len(edge_id) >= self.N_KNN:
+                    break
 
-    # generate final course
-    rx, ry = [ngoal.x], [ngoal.y]
-    pind = ngoal.pind
-    while pind != -1:
-        n = closedset[pind]
-        rx.append(n.x)
-        ry.append(n.y)
-        pind = n.pind
+            road_map.append(edge_id)
 
-    return rx, ry
+        #  plot_road_map(road_map, sample_x, sample_y)
 
+        return road_map
 
-def plot_road_map(road_map, sample_x, sample_y):  # pragma: no cover
+    @staticmethod
+    def plot_road_map(road_map, sample_x, sample_y):  # pragma: no cover
 
-    for i, _ in enumerate(road_map):
-        for ii in range(len(road_map[i])):
-            ind = road_map[i][ii]
+        for i, _ in enumerate(road_map):
+            for ii in range(len(road_map[i])):
+                ind = road_map[i][ii]
 
-            plt.plot([sample_x[i], sample_x[ind]],
-                     [sample_y[i], sample_y[ind]], "-k")
+                plt.plot([sample_x[i], sample_x[ind]],
+                         [sample_y[i], sample_y[ind]], "-k")
 
+    @staticmethod
+    def voronoi_sampling(sx, sy, gx, gy, ox, oy):
+        oxy = np.vstack((ox, oy)).T
 
-def sample_points(sx, sy, gx, gy, rr, ox, oy, obkdtree):
-    oxy = np.vstack((ox, oy)).T
+        # generate voronoi point
+        vor = scipy.spatial.Voronoi(oxy)
+        sample_x = [ix for [ix, _] in vor.vertices]
+        sample_y = [iy for [_, iy] in vor.vertices]
 
-    # generate voronoi point
-    vor = scipy.spatial.Voronoi(oxy)
-    sample_x = [ix for [ix, iy] in vor.vertices]
-    sample_y = [iy for [ix, iy] in vor.vertices]
+        sample_x.append(sx)
+        sample_y.append(sy)
+        sample_x.append(gx)
+        sample_y.append(gy)
 
-    sample_x.append(sx)
-    sample_y.append(sy)
-    sample_x.append(gx)
-    sample_y.append(gy)
-
-    return sample_x, sample_y
+        return sample_x, sample_y
 
 
 def main():
@@ -297,12 +169,14 @@ def main():
         plt.grid(True)
         plt.axis("equal")
 
-    rx, ry = VRM_planning(sx, sy, gx, gy, ox, oy, robot_size)
+    rx, ry = VoronoiRoadMapPlanner().planning(sx, sy, gx, gy, ox, oy,
+                                              robot_size)
 
     assert rx, 'Cannot found path'
 
     if show_animation:  # pragma: no cover
         plt.plot(rx, ry, "-r")
+        plt.pause(0.1)
         plt.show()
 
 

From ae85e66d07dedc37739a7f81910befce2e4065cc Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 24 Feb 2020 16:40:57 +0900
Subject: [PATCH 0329/1080] fix tests

---
 .../visibility_graph_planner.py               | 90 +++++++++++++++++++
 tests/test_voronoi_path_planner.py            |  6 ++
 2 files changed, 96 insertions(+)

diff --git a/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py b/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py
index e69de29bb2..06a4d59834 100644
--- a/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py
+++ b/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py
@@ -0,0 +1,90 @@
+"""
+
+Visibility Graph Planner
+
+author: Atsushi Sakai (@Atsushi_twi)
+
+"""
+
+import matplotlib.pyplot as plt
+
+show_animation = True
+
+class VisibilityGraphPlanner:
+
+    def __init__(self, robot_radius):
+        self.robot_radius = robot_radius
+
+    def planning(self, start_x, start_y, goal_x, goal_y, obstacles):
+        nodes = self.exstract_graph_node(start_x, start_y, goal_x, goal_y,
+                                         obstacles)
+
+        graph = self.generate_graph(nodes, obstacles)
+
+        rx, ry = dijkstra_search(graph)
+
+        return rx, ry
+
+    def exstract_graph_node(self, start_x, start_y, goal_x, goal_x, obstacles):
+        nodes = []
+
+        return nodes
+
+    def generate_graph(self, nodes, obstacles):
+
+        graph = []
+
+        return graph
+
+
+class ObstaclePolygon:
+
+    def __init__(self, x_list, y_list):
+        self.x_list = x_list
+        self.y_list = y_list
+
+        self.close_polygon()
+
+    def close_polygon(self):
+        is_x_same = self.x_list[0] == self.x_list[-1]
+        is_y_same = self.y_list[0] == self.y_list[-1]
+        if is_x_same and is_y_same:
+            return  # no need to close
+
+        self.x_list.append(self.x_list[0])
+        self.y_list.append(self.y_list[0])
+
+    def plot(self):
+        plt.plot(self.x_list, self.y_list, "-b")
+
+
+def main():
+    print(__file__ + " start!!")
+
+    # start and goal position
+    sx, sy = 10.0, 10.0  # [m]
+    gx, gy = 50.0, 50.0  # [m]
+    robot_radius = 5.0  # [m]
+
+    obstacles = [ObstaclePolygon(
+        [20.0, 30.0, 15.0],
+        [20.0, 20.0, 30.0],
+    ), ObstaclePolygon(
+        [30.0, 45.0, 50.0, 40.0],
+        [50.0, 40.0, 20.0, 40.0],
+    )]
+
+    rx, ry = VisibilityGraphPlanner(robot_radius).planning(sx, sy, gx, gy,
+                                                           obstacles)
+    assert rx, 'Cannot found path'
+    if show_animation:  # pragma: no cover
+        plt.plot(sx, sy, "or")
+        plt.plot(gx, gy, "ob")
+        [ob.plot() for ob in obstacles]
+        plt.plot(rx, ry, "-r")
+        plt.axis("equal")
+        plt.show()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tests/test_voronoi_path_planner.py b/tests/test_voronoi_path_planner.py
index 3e0ff961d8..5295769b40 100644
--- a/tests/test_voronoi_path_planner.py
+++ b/tests/test_voronoi_path_planner.py
@@ -1,6 +1,12 @@
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__))
+                + "/../PathPlanning/VoronoiRoadMap/")
+
 from unittest import TestCase
 from PathPlanning.VoronoiRoadMap import voronoi_road_map as m
 
+
 print(__file__)
 
 

From cfd83841d753956ae6957ca101d42cff5e5596a5 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 26 Feb 2020 20:37:45 +0900
Subject: [PATCH 0330/1080] for good measure

---
 .../visibility_graph_planner.py               | 126 ++++++++++++++++--
 .../VoronoiRoadMap/dijkstra_search.py         |   2 +-
 tests/test_rrt_star_dubins.py                 |   1 +
 3 files changed, 119 insertions(+), 10 deletions(-)

diff --git a/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py b/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py
index 06a4d59834..d00e8f43c6 100644
--- a/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py
+++ b/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py
@@ -6,36 +6,127 @@
 
 """
 
+import os
+import sys
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../VoronoiRoadMap/")
+
+import math
+import numpy as np
 import matplotlib.pyplot as plt
+from dijkstra_search import DijkstraSearch
 
 show_animation = True
 
+
 class VisibilityGraphPlanner:
 
     def __init__(self, robot_radius):
         self.robot_radius = robot_radius
 
     def planning(self, start_x, start_y, goal_x, goal_y, obstacles):
-        nodes = self.exstract_graph_node(start_x, start_y, goal_x, goal_y,
-                                         obstacles)
-
+        nodes = self.extract_graph_node(start_x, start_y, goal_x, goal_y,
+                                        obstacles)
         graph = self.generate_graph(nodes, obstacles)
 
-        rx, ry = dijkstra_search(graph)
+        # rx, ry = DijkstraSearch().search(graph)
+
+        rx, ry = [], []
 
         return rx, ry
 
-    def exstract_graph_node(self, start_x, start_y, goal_x, goal_x, obstacles):
-        nodes = []
+    def extract_graph_node(self, start_x, start_y, goal_x, goal_y, obstacles):
+
+        # add start and goal as nodes
+        nodes = [DijkstraSearch.Node(start_x, start_y),
+                 DijkstraSearch.Node(goal_x, goal_y, 0, None)]
+
+        # add vertexes in configuration space as nodes
+        for obstacle in obstacles:
+
+            cvx_list, cvy_list = self.calc_vertexes_in_configuration_space(
+                obstacle.x_list, obstacle.y_list)
+
+            for (vx, vy) in zip(cvx_list, cvy_list):
+                nodes.append(DijkstraSearch.Node(vx, vy))
+
+        for node in nodes:
+            plt.plot(node.x, node.y, "xr")
 
         return nodes
 
+    def calc_vertexes_in_configuration_space(self, x_list, y_list):
+        x_list=x_list[0:-1]
+        y_list=y_list[0:-1]
+        cvx_list, cvy_list = [], []
+
+        n_data=len(x_list)
+
+        for index in range(n_data):
+            offset_x, offset_y = self.calc_offset_xy(
+                x_list[index - 1], y_list[index - 1],
+                x_list[index], y_list[index],
+                x_list[(index + 1) % n_data], y_list[(index + 1) % n_data],
+            )
+            cvx_list.append(offset_x)
+            cvy_list.append(offset_y)
+
+        return cvx_list, cvy_list
+
     def generate_graph(self, nodes, obstacles):
 
         graph = []
 
+        for target_node in nodes:
+            for node in nodes:
+                for obstacle in obstacles:
+                    if not self.is_edge_valid(target_node, node, obstacle):
+                        print("bb")
+                        break
+                    print(target_node, node)
+                    print("aa")
+                    plt.plot([target_node.x, node.x],[target_node.y, node.y], "-r")
+
         return graph
 
+    def is_edge_valid(self, target_node, node, obstacle):
+
+        for i in range(len(obstacle.x_list)-1):
+            p1 = np.array([target_node.x, target_node.y])
+            p2 = np.array([node.x, node.y])
+            p3 = np.array([obstacle.x_list[i], obstacle.y_list[i]])
+            p4 = np.array([obstacle.y_list[i+1], obstacle.y_list[i+1]])
+
+            if is_seg_intersect(p1, p2, p3, p4):
+                return False
+
+        return True
+
+    def calc_offset_xy(self, px, py, x, y, nx, ny):
+        p_vec = math.atan2(y - py, x - px)
+        n_vec = math.atan2(ny - y, nx - x)
+        offset_vec = math.atan2(math.sin(p_vec) + math.sin(n_vec),
+                                math.cos(p_vec) + math.cos(
+                                    n_vec))+math.pi/2.0
+        offset_x = x + self.robot_radius * math.cos(offset_vec)
+        offset_y = y + self.robot_radius * math.sin(offset_vec)
+        return offset_x, offset_y
+
+
+def is_seg_intersect(a1, a2, b1, b2):
+
+    xdiff = [a1[0] - a2[0], b1[0] - b2[0]]
+    ydiff = [a1[1] - a2[1], b1[1] - b2[1]]
+
+    def det(a, b):
+        return a[0] * b[1] - a[1] * b[0]
+
+    div = det(xdiff, ydiff)
+    if div == 0:
+        return False
+    else:
+        return True
 
 class ObstaclePolygon:
 
@@ -44,6 +135,21 @@ def __init__(self, x_list, y_list):
         self.y_list = y_list
 
         self.close_polygon()
+        self.make_clockwise()
+
+    def make_clockwise(self):
+        if not self.is_clockwise():
+            self.x_list = list(reversed(self.x_list))
+            self.y_list = list(reversed(self.y_list))
+
+    def is_clockwise(self):
+        n_data = len(self.x_list)
+        eval_sum = sum([(self.x_list[i + 1] - self.x_list[i]) *
+                        (self.y_list[i + 1] + self.y_list[i])
+                        for i in range(n_data - 1)])
+        eval_sum += (self.x_list[0] - self.x_list[n_data - 1]) * \
+                    (self.y_list[0] + self.y_list[n_data - 1])
+        return eval_sum >= 0
 
     def close_polygon(self):
         is_x_same = self.x_list[0] == self.x_list[-1]
@@ -74,13 +180,15 @@ def main():
         [50.0, 40.0, 20.0, 40.0],
     )]
 
-    rx, ry = VisibilityGraphPlanner(robot_radius).planning(sx, sy, gx, gy,
-                                                           obstacles)
-    assert rx, 'Cannot found path'
     if show_animation:  # pragma: no cover
         plt.plot(sx, sy, "or")
         plt.plot(gx, gy, "ob")
         [ob.plot() for ob in obstacles]
+
+    rx, ry = VisibilityGraphPlanner(robot_radius).planning(sx, sy, gx, gy,
+                                                           obstacles)
+    # assert rx, 'Cannot found path'
+    if show_animation:  # pragma: no cover
         plt.plot(rx, ry, "-r")
         plt.axis("equal")
         plt.show()
diff --git a/PathPlanning/VoronoiRoadMap/dijkstra_search.py b/PathPlanning/VoronoiRoadMap/dijkstra_search.py
index 4d294ee5e5..7f248cd3cf 100644
--- a/PathPlanning/VoronoiRoadMap/dijkstra_search.py
+++ b/PathPlanning/VoronoiRoadMap/dijkstra_search.py
@@ -17,7 +17,7 @@ class Node:
         Node class for dijkstra search
         """
 
-        def __init__(self, x, y, cost, parent):
+        def __init__(self, x, y, cost=None, parent=None):
             self.x = x
             self.y = y
             self.cost = cost
diff --git a/tests/test_rrt_star_dubins.py b/tests/test_rrt_star_dubins.py
index 8ee3d38788..db1ca2bfa4 100644
--- a/tests/test_rrt_star_dubins.py
+++ b/tests/test_rrt_star_dubins.py
@@ -2,6 +2,7 @@
 
 import sys
 import os
+
 sys.path.append(os.path.dirname(os.path.abspath(__file__))
                 + "/../PathPlanning/RRTStarDubins/")
 

From 2e188f866b0de3e46cca5e3b9f807003be8bc305 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 26 Feb 2020 22:27:25 +0900
Subject: [PATCH 0331/1080] To fix #298

---
 PathTracking/pure_pursuit/pure_pursuit.py | 83 ++++++++++++-----------
 1 file changed, 43 insertions(+), 40 deletions(-)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index bcaf869726..ff995033a9 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -1,6 +1,6 @@
 """
 
-Path tracking simulation with pure pursuit steering control and PID speed control.
+Path tracking simulation with pure pursuit steering and PID speed control.
 
 author: Atsushi Sakai (@Atsushi_twi)
         Guillaume Jacquenot (@Gjacquenot)
@@ -10,13 +10,12 @@
 import math
 import matplotlib.pyplot as plt
 
-
+# Parameters
 k = 0.1  # look forward gain
-Lfc = 2.0  # look-ahead distance
+Lfc = 2.0  # [m] look-ahead distance
 Kp = 1.0  # speed proportional gain
-dt = 0.1  # [s]
-L = 2.9  # [m] wheel base of vehicle
-
+dt = 0.1  # [s] time tick
+WB = 2.9  # [m] wheel base of vehicle
 
 show_animation = True
 
@@ -28,20 +27,18 @@ def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0):
         self.y = y
         self.yaw = yaw
         self.v = v
-        self.rear_x = self.x - ((L / 2) * math.cos(self.yaw))
-        self.rear_y = self.y - ((L / 2) * math.sin(self.yaw))
+        self.rear_x = self.x - ((WB / 2) * math.cos(self.yaw))
+        self.rear_y = self.y - ((WB / 2) * math.sin(self.yaw))
 
     def update(self, a, delta):
-
         self.x += self.v * math.cos(self.yaw) * dt
         self.y += self.v * math.sin(self.yaw) * dt
-        self.yaw += self.v / L * math.tan(delta) * dt
+        self.yaw += self.v / WB * math.tan(delta) * dt
         self.v += a * dt
-        self.rear_x = self.x - ((L / 2) * math.cos(self.yaw))
-        self.rear_y = self.y - ((L / 2) * math.sin(self.yaw))
+        self.rear_x = self.x - ((WB / 2) * math.cos(self.yaw))
+        self.rear_y = self.y - ((WB / 2) * math.sin(self.yaw))
 
     def calc_distance(self, point_x, point_y):
-
         dx = self.rear_x - point_x
         dy = self.rear_y - point_y
         return math.hypot(dx, dy)
@@ -56,7 +53,7 @@ def __init__(self):
         self.v = []
         self.t = []
 
-    def append(self, t , state):
+    def append(self, t, state):
         self.x.append(state.x)
         self.y.append(state.y)
         self.yaw.append(state.yaw)
@@ -64,19 +61,22 @@ def append(self, t , state):
         self.t.append(t)
 
 
-def PIDControl(target, current):
+def proportional_control(target, current):
     a = Kp * (target - current)
 
     return a
 
 
-class Trajectory:
+class TargetCourse:
+
     def __init__(self, cx, cy):
         self.cx = cx
         self.cy = cy
         self.old_nearest_point_index = None
 
     def search_target_index(self, state):
+
+        # To speed up nearest point search, doing it at only first time.
         if self.old_nearest_point_index is None:
             # search nearest point index
             dx = [state.rear_x - icx for icx in self.cx]
@@ -86,30 +86,30 @@ def search_target_index(self, state):
             self.old_nearest_point_index = ind
         else:
             ind = self.old_nearest_point_index
-            distance_this_index = state.calc_distance(self.cx[ind], self.cy[ind])
+            distance_this_index = state.calc_distance(self.cx[ind],
+                                                      self.cy[ind])
             while True:
-                ind = ind + 1 if (ind + 1) < len(self.cx) else ind
-                distance_next_index = state.calc_distance(self.cx[ind], self.cy[ind])
+                distance_next_index = state.calc_distance(self.cx[ind + 1],
+                                                          self.cy[ind + 1])
                 if distance_this_index < distance_next_index:
                     break
+                ind = ind + 1 if (ind + 1) < len(self.cx) else ind
                 distance_this_index = distance_next_index
             self.old_nearest_point_index = ind
 
-        L = 0.0
-
-        Lf = k * state.v + Lfc
+        Lf = k * state.v + Lfc  # update look ahead distance
 
         # search look ahead target point index
-        while Lf > L and (ind + 1) < len(self.cx):
-            L = state.calc_distance(self.cx[ind], self.cy[ind])
+        while Lf > state.calc_distance(self.cx[ind], self.cy[ind]):
+            if (ind + 1) >= len(self.cx):
+                break  # not exceed goal
             ind += 1
 
-        return ind
+        return ind, Lf
 
 
-def pure_pursuit_control(state, trajectory, pind):
-
-    ind = trajectory.search_target_index(state)
+def pure_pursuit_steer_control(state, trajectory, pind):
+    ind, Lf = trajectory.search_target_index(state)
 
     if pind >= ind:
         ind = pind
@@ -117,16 +117,14 @@ def pure_pursuit_control(state, trajectory, pind):
     if ind < len(trajectory.cx):
         tx = trajectory.cx[ind]
         ty = trajectory.cy[ind]
-    else:
+    else:  # toward goal
         tx = trajectory.cx[-1]
         ty = trajectory.cy[-1]
         ind = len(trajectory.cx) - 1
 
     alpha = math.atan2(ty - state.rear_y, tx - state.rear_x) - state.yaw
 
-    Lf = k * state.v + Lfc
-
-    delta = math.atan2(2.0 * L * math.sin(alpha) / Lf, 1.0)
+    delta = math.atan2(2.0 * WB * math.sin(alpha) / Lf, 1.0)
 
     return delta, ind
 
@@ -147,7 +145,7 @@ def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
 
 def main():
     #  target course
-    cx = np.arange(0, 50, 0.1)
+    cx = np.arange(0, 50, 0.5)
     cy = [math.sin(ix / 5.0) * ix / 2.0 for ix in cx]
 
     target_speed = 10.0 / 3.6  # [m/s]
@@ -161,13 +159,17 @@ def main():
     time = 0.0
     states = States()
     states.append(time, state)
-    trajectory = Trajectory(cx, cy)
-    target_ind = trajectory.search_target_index(state)
+    target_course = TargetCourse(cx, cy)
+    target_ind, _ = target_course.search_target_index(state)
 
     while T >= time and lastIndex > target_ind:
-        ai = PIDControl(target_speed, state.v)
-        di, target_ind = pure_pursuit_control(state, trajectory, target_ind)
-        state.update(ai, di)
+
+        # Calc control input
+        ai = proportional_control(target_speed, state.v)
+        di, target_ind = pure_pursuit_steer_control(
+            state, target_course, target_ind)
+
+        state.update(ai, di)  # Control vehicle
 
         time += dt
         states.append(time, state)
@@ -175,8 +177,9 @@ def main():
         if show_animation:  # pragma: no cover
             plt.cla()
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
             plot_arrow(state.x, state.y, state.yaw)
             plt.plot(cx, cy, "-r", label="course")
             plt.plot(states.x, states.y, "-b", label="trajectory")

From ee423e8b94f8ef5b20faa18e510546ebfc05b4a2 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 29 Feb 2020 14:46:43 +0900
Subject: [PATCH 0332/1080] implement visibility_road_map planner

---
 PathPlanning/Dijkstra/dijkstra.py             | 157 ++++++++++--------
 PathPlanning/VisibilityRoadMap/__init__.py    |   0
 PathPlanning/VisibilityRoadMap/geometry.py    |  44 +++++
 .../visibility_road_map.py}                   | 135 ++++++++-------
 .../VoronoiRoadMap/dijkstra_search.py         |  93 +++++++----
 .../VoronoiRoadMap/voronoi_road_map.py        |  23 +--
 ...py => test_visibility_road_map_planner.py} |   0
 tests/test_voronoi_road_map_planner.py        |  17 ++
 8 files changed, 300 insertions(+), 169 deletions(-)
 create mode 100644 PathPlanning/VisibilityRoadMap/__init__.py
 create mode 100644 PathPlanning/VisibilityRoadMap/geometry.py
 rename PathPlanning/{VisibilityGraphPlanner/visibility_graph_planner.py => VisibilityRoadMap/visibility_road_map.py} (57%)
 rename tests/{test_voronoi_path_planner.py => test_visibility_road_map_planner.py} (100%)
 create mode 100644 tests/test_voronoi_road_map_planner.py

diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py
index bb1fb3aa63..ad3e3a85c8 100644
--- a/PathPlanning/Dijkstra/dijkstra.py
+++ b/PathPlanning/Dijkstra/dijkstra.py
@@ -11,32 +11,42 @@
 
 show_animation = True
 
+
 class Dijkstra:
 
-    def __init__(self, ox, oy, reso, rr):
+    def __init__(self, ox, oy, resolution, robot_radius):
         """
         Initialize map for a star planning
 
         ox: x position list of Obstacles [m]
         oy: y position list of Obstacles [m]
-        reso: grid resolution [m]
+        resolution: grid resolution [m]
         rr: robot radius[m]
         """
 
-        self.reso = reso
-        self.rr = rr
+        self.resolution = resolution
+        self.robot_radius = robot_radius
         self.calc_obstacle_map(ox, oy)
         self.motion = self.get_motion_model()
 
+        self.min_x = None
+        self.min_y = None
+        self.max_x = None
+        self.max_y = None
+        self.x_width = None
+        self.y_width = None
+        self.obstacle_map = None
+
     class Node:
-        def __init__(self, x, y, cost, pind):
+        def __init__(self, x, y, cost, parent):
             self.x = x  # index of grid
             self.y = y  # index of grid
             self.cost = cost
-            self.pind = pind # index of previous Node
+            self.parent = parent  # index of previous Node
 
         def __str__(self):
-            return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind)
+            return str(self.x) + "," + str(self.y) + "," + str(
+                self.cost) + "," + str(self.parent)
 
     def planning(self, sx, sy, gx, gy):
         """
@@ -53,39 +63,40 @@ def planning(self, sx, sy, gx, gy):
             ry: y position list of the final path
         """
 
-        nstart = self.Node(self.calc_xyindex(sx, self.minx),
-                           self.calc_xyindex(sy, self.miny), 0.0, -1)
-        ngoal = self.Node(self.calc_xyindex(gx, self.minx),
-                          self.calc_xyindex(gy, self.miny), 0.0, -1)
+        start_node = self.Node(self.calc_xy_index(sx, self.min_x),
+                               self.calc_xy_index(sy, self.min_y), 0.0, -1)
+        goal_node = self.Node(self.calc_xy_index(gx, self.min_x),
+                              self.calc_xy_index(gy, self.min_y), 0.0, -1)
 
-        openset, closedset = dict(), dict()
-        openset[self.calc_index(nstart)] = nstart
+        open_set, closed_set = dict(), dict()
+        open_set[self.calc_index(start_node)] = start_node
 
         while 1:
-            c_id = min(openset, key=lambda o: openset[o].cost)
-            current = openset[c_id]
+            c_id = min(open_set, key=lambda o: open_set[o].cost)
+            current = open_set[c_id]
 
             # show graph
             if show_animation:  # pragma: no cover
-                plt.plot(self.calc_position(current.x, self.minx),
-                         self.calc_position(current.y, self.miny), "xc")
+                plt.plot(self.calc_position(current.x, self.min_x),
+                         self.calc_position(current.y, self.min_y), "xc")
                 # for stopping simulation with the esc key.
-                plt.gcf().canvas.mpl_connect('key_release_event',
-                        lambda event: [exit(0) if event.key == 'escape' else None])
-                if len(closedset.keys()) % 10 == 0:
+                plt.gcf().canvas.mpl_connect(
+                    'key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
+                if len(closed_set.keys()) % 10 == 0:
                     plt.pause(0.001)
 
-            if current.x == ngoal.x and current.y == ngoal.y:
+            if current.x == goal_node.x and current.y == goal_node.y:
                 print("Find goal")
-                ngoal.pind = current.pind
-                ngoal.cost = current.cost
+                goal_node.parent = current.parent
+                goal_node.cost = current.cost
                 break
 
             # Remove the item from the open set
-            del openset[c_id]
+            del open_set[c_id]
 
             # Add it to the closed set
-            closedset[c_id] = current
+            closed_set[c_id] = current
 
             # expand search grid based on motion model
             for move_x, move_y, move_cost in self.motion:
@@ -94,94 +105,95 @@ def planning(self, sx, sy, gx, gy):
                                  current.cost + move_cost, c_id)
                 n_id = self.calc_index(node)
 
-                if n_id in closedset:
+                if n_id in closed_set:
                     continue
 
                 if not self.verify_node(node):
                     continue
 
-                if n_id not in openset:
-                    openset[n_id] = node  # Discover a new node
+                if n_id not in open_set:
+                    open_set[n_id] = node  # Discover a new node
                 else:
-                    if openset[n_id].cost >= node.cost:
+                    if open_set[n_id].cost >= node.cost:
                         # This path is the best until now. record it!
-                        openset[n_id] = node
+                        open_set[n_id] = node
 
-        rx, ry = self.calc_final_path(ngoal, closedset)
+        rx, ry = self.calc_final_path(goal_node, closed_set)
 
         return rx, ry
 
-    def calc_final_path(self, ngoal, closedset):
+    def calc_final_path(self, goal_node, closed_set):
         # generate final course
-        rx, ry = [self.calc_position(ngoal.x, self.minx)], [
-            self.calc_position(ngoal.y, self.miny)]
-        pind = ngoal.pind
-        while pind != -1:
-            n = closedset[pind]
-            rx.append(self.calc_position(n.x, self.minx))
-            ry.append(self.calc_position(n.y, self.miny))
-            pind = n.pind
+        rx, ry = [self.calc_position(goal_node.x, self.min_x)], [
+            self.calc_position(goal_node.y, self.min_y)]
+        parent = goal_node.parent
+        while parent != -1:
+            n = closed_set[parent]
+            rx.append(self.calc_position(n.x, self.min_x))
+            ry.append(self.calc_position(n.y, self.min_y))
+            parent = n.parent
 
         return rx, ry
 
     def calc_position(self, index, minp):
-        pos = index*self.reso+minp
+        pos = index * self.resolution + minp
         return pos
 
-    def calc_xyindex(self, position, minp):
-        return round((position - minp)/self.reso)
+    def calc_xy_index(self, position, minp):
+        return round((position - minp) / self.resolution)
 
     def calc_index(self, node):
-        return (node.y - self.miny) * self.xwidth + (node.x - self.minx)
+        return (node.y - self.min_y) * self.x_width + (node.x - self.min_x)
 
     def verify_node(self, node):
-        px = self.calc_position(node.x, self.minx)
-        py = self.calc_position(node.y, self.miny)
+        px = self.calc_position(node.x, self.min_x)
+        py = self.calc_position(node.y, self.min_y)
 
-        if px < self.minx:
+        if px < self.min_x:
             return False
-        elif py < self.miny:
+        elif py < self.min_y:
             return False
-        elif px >= self.maxx:
+        elif px >= self.max_x:
             return False
-        elif py >= self.maxy:
+        elif py >= self.max_y:
             return False
 
-        if self.obmap[node.x][node.y]:
+        if self.obstacle_map[node.x][node.y]:
             return False
 
         return True
 
     def calc_obstacle_map(self, ox, oy):
 
-        self.minx = round(min(ox))
-        self.miny = round(min(oy))
-        self.maxx = round(max(ox))
-        self.maxy = round(max(oy))
-        print("minx:", self.minx)
-        print("miny:", self.miny)
-        print("maxx:", self.maxx)
-        print("maxy:", self.maxy)
+        self.min_x = round(min(ox))
+        self.min_y = round(min(oy))
+        self.max_x = round(max(ox))
+        self.max_y = round(max(oy))
+        print("minx:", self.min_x)
+        print("miny:", self.min_y)
+        print("maxx:", self.max_x)
+        print("maxy:", self.max_y)
 
-        self.xwidth = round((self.maxx - self.minx)/self.reso)
-        self.ywidth = round((self.maxy - self.miny)/self.reso)
-        print("xwidth:", self.xwidth)
-        print("ywidth:", self.ywidth)
+        self.x_width = round((self.max_x - self.min_x) / self.resolution)
+        self.y_width = round((self.max_y - self.min_y) / self.resolution)
+        print("xwidth:", self.x_width)
+        print("ywidth:", self.y_width)
 
         # obstacle map generation
-        self.obmap = [[False for i in range(self.ywidth)]
-                      for i in range(self.xwidth)]
-        for ix in range(self.xwidth):
-            x = self.calc_position(ix, self.minx)
-            for iy in range(self.ywidth):
-                y = self.calc_position(iy, self.miny)
+        self.obstacle_map = [[False for _ in range(self.y_width)]
+                             for _ in range(self.x_width)]
+        for ix in range(self.x_width):
+            x = self.calc_position(ix, self.min_x)
+            for iy in range(self.y_width):
+                y = self.calc_position(iy, self.min_y)
                 for iox, ioy in zip(ox, oy):
                     d = math.hypot(iox - x, ioy - y)
-                    if d <= self.rr:
-                        self.obmap[ix][iy] = True
+                    if d <= self.robot_radius:
+                        self.obstacle_map[ix][iy] = True
                         break
 
-    def get_motion_model(self):
+    @staticmethod
+    def get_motion_model():
         # dx, dy, cost
         motion = [[1, 0, 1],
                   [0, 1, 1],
@@ -239,6 +251,7 @@ def main():
 
     if show_animation:  # pragma: no cover
         plt.plot(rx, ry, "-r")
+        plt.pause(0.01)
         plt.show()
 
 
diff --git a/PathPlanning/VisibilityRoadMap/__init__.py b/PathPlanning/VisibilityRoadMap/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/PathPlanning/VisibilityRoadMap/geometry.py b/PathPlanning/VisibilityRoadMap/geometry.py
new file mode 100644
index 0000000000..053a2bd70e
--- /dev/null
+++ b/PathPlanning/VisibilityRoadMap/geometry.py
@@ -0,0 +1,44 @@
+class Geometry:
+    class Point:
+        def __init__(self, x, y):
+            self.x = x
+            self.y = y
+
+    @staticmethod
+    def is_seg_intersect(p1, q1, p2, q2):
+
+        def on_segment(p, q, r):
+            if ((q.x <= max(p.x, r.x)) and (q.x >= min(p.x, r.x)) and
+                    (q.y <= max(p.y, r.y)) and (q.y >= min(p.y, r.y))):
+                return True
+            return False
+
+        def orientation(p, q, r):
+            val = (float(q.y - p.y) * (r.x - q.x)) - (
+                    float(q.x - p.x) * (r.y - q.y))
+            if val > 0:
+                return 1
+            elif val < 0:
+                return 2
+            else:
+                return 0
+
+        # Find the 4 orientations required for
+        # the general and special cases
+        o1 = orientation(p1, q1, p2)
+        o2 = orientation(p1, q1, q2)
+        o3 = orientation(p2, q2, p1)
+        o4 = orientation(p2, q2, q1)
+
+        if (o1 != o2) and (o3 != o4):
+            return True
+        if (o1 == 0) and on_segment(p1, p2, q1):
+            return True
+        if (o2 == 0) and on_segment(p1, q2, q1):
+            return True
+        if (o3 == 0) and on_segment(p2, p1, q2):
+            return True
+        if (o4 == 0) and on_segment(p2, q1, q2):
+            return True
+
+        return False
\ No newline at end of file
diff --git a/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py b/PathPlanning/VisibilityRoadMap/visibility_road_map.py
similarity index 57%
rename from PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py
rename to PathPlanning/VisibilityRoadMap/visibility_road_map.py
index d00e8f43c6..bf9b520a75 100644
--- a/PathPlanning/VisibilityGraphPlanner/visibility_graph_planner.py
+++ b/PathPlanning/VisibilityRoadMap/visibility_road_map.py
@@ -1,6 +1,6 @@
 """
 
-Visibility Graph Planner
+Visibility Road Map Planner
 
 author: Atsushi Sakai (@Atsushi_twi)
 
@@ -8,35 +8,47 @@
 
 import os
 import sys
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../VoronoiRoadMap/")
-
 import math
 import numpy as np
 import matplotlib.pyplot as plt
+
+from PathPlanning.VisibilityRoadMap.geometry import Geometry
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../VoronoiRoadMap/")
 from dijkstra_search import DijkstraSearch
 
 show_animation = True
 
 
-class VisibilityGraphPlanner:
+class VisibilityRoadMap:
 
-    def __init__(self, robot_radius):
+    def __init__(self, robot_radius, do_plot=False):
         self.robot_radius = robot_radius
+        self.do_plot = do_plot
 
     def planning(self, start_x, start_y, goal_x, goal_y, obstacles):
-        nodes = self.extract_graph_node(start_x, start_y, goal_x, goal_y,
-                                        obstacles)
-        graph = self.generate_graph(nodes, obstacles)
 
-        # rx, ry = DijkstraSearch().search(graph)
+        nodes = self.generate_graph_node(start_x, start_y, goal_x, goal_y,
+                                         obstacles)
+
+        road_map_info = self.generate_road_map_info(nodes, obstacles)
 
-        rx, ry = [], []
+        if show_animation:
+            self.plot_road_map(nodes, road_map_info)
+            plt.pause(1.0)
+
+        rx, ry = DijkstraSearch(show_animation).search(
+            start_x, start_y,
+            goal_x, goal_y,
+            [node.x for node in nodes],
+            [node.y for node in nodes],
+            road_map_info
+        )
 
         return rx, ry
 
-    def extract_graph_node(self, start_x, start_y, goal_x, goal_y, obstacles):
+    def generate_graph_node(self, start_x, start_y, goal_x, goal_y, obstacles):
 
         # add start and goal as nodes
         nodes = [DijkstraSearch.Node(start_x, start_y),
@@ -57,11 +69,11 @@ def extract_graph_node(self, start_x, start_y, goal_x, goal_y, obstacles):
         return nodes
 
     def calc_vertexes_in_configuration_space(self, x_list, y_list):
-        x_list=x_list[0:-1]
-        y_list=y_list[0:-1]
+        x_list = x_list[0:-1]
+        y_list = y_list[0:-1]
         cvx_list, cvy_list = [], []
 
-        n_data=len(x_list)
+        n_data = len(x_list)
 
         for index in range(n_data):
             offset_x, offset_y = self.calc_offset_xy(
@@ -74,31 +86,39 @@ def calc_vertexes_in_configuration_space(self, x_list, y_list):
 
         return cvx_list, cvy_list
 
-    def generate_graph(self, nodes, obstacles):
+    def generate_road_map_info(self, nodes, obstacles):
 
-        graph = []
+        road_map_info_list = []
 
         for target_node in nodes:
-            for node in nodes:
+            road_map_info = []
+            for node_id, node in enumerate(nodes):
+                if np.hypot(target_node.x - node.x,
+                            target_node.y - node.y) <= 0.1:
+                    continue
+
+                is_valid = True
                 for obstacle in obstacles:
                     if not self.is_edge_valid(target_node, node, obstacle):
-                        print("bb")
+                        is_valid = False
                         break
-                    print(target_node, node)
-                    print("aa")
-                    plt.plot([target_node.x, node.x],[target_node.y, node.y], "-r")
+                if is_valid:
+                    road_map_info.append(node_id)
+
+            road_map_info_list.append(road_map_info)
 
-        return graph
+        return road_map_info_list
 
-    def is_edge_valid(self, target_node, node, obstacle):
+    @staticmethod
+    def is_edge_valid(target_node, node, obstacle):
 
-        for i in range(len(obstacle.x_list)-1):
-            p1 = np.array([target_node.x, target_node.y])
-            p2 = np.array([node.x, node.y])
-            p3 = np.array([obstacle.x_list[i], obstacle.y_list[i]])
-            p4 = np.array([obstacle.y_list[i+1], obstacle.y_list[i+1]])
+        for i in range(len(obstacle.x_list) - 1):
+            p1 = Geometry.Point(target_node.x, target_node.y)
+            p2 = Geometry.Point(node.x, node.y)
+            p3 = Geometry.Point(obstacle.x_list[i], obstacle.y_list[i])
+            p4 = Geometry.Point(obstacle.x_list[i + 1], obstacle.y_list[i + 1])
 
-            if is_seg_intersect(p1, p2, p3, p4):
+            if Geometry.is_seg_intersect(p1, p2, p3, p4):
                 return False
 
         return True
@@ -108,25 +128,18 @@ def calc_offset_xy(self, px, py, x, y, nx, ny):
         n_vec = math.atan2(ny - y, nx - x)
         offset_vec = math.atan2(math.sin(p_vec) + math.sin(n_vec),
                                 math.cos(p_vec) + math.cos(
-                                    n_vec))+math.pi/2.0
+                                    n_vec)) + math.pi / 2.0
         offset_x = x + self.robot_radius * math.cos(offset_vec)
         offset_y = y + self.robot_radius * math.sin(offset_vec)
         return offset_x, offset_y
 
+    @staticmethod
+    def plot_road_map(nodes, road_map_info_list):
+        for i, node in enumerate(nodes):
+            for index in road_map_info_list[i]:
+                plt.plot([node.x, nodes[index].x],
+                         [node.y, nodes[index].y], "-b")
 
-def is_seg_intersect(a1, a2, b1, b2):
-
-    xdiff = [a1[0] - a2[0], b1[0] - b2[0]]
-    ydiff = [a1[1] - a2[1], b1[1] - b2[1]]
-
-    def det(a, b):
-        return a[0] * b[1] - a[1] * b[0]
-
-    div = det(xdiff, ydiff)
-    if div == 0:
-        return False
-    else:
-        return True
 
 class ObstaclePolygon:
 
@@ -161,7 +174,7 @@ def close_polygon(self):
         self.y_list.append(self.y_list[0])
 
     def plot(self):
-        plt.plot(self.x_list, self.y_list, "-b")
+        plt.plot(self.x_list, self.y_list, "-k")
 
 
 def main():
@@ -170,26 +183,36 @@ def main():
     # start and goal position
     sx, sy = 10.0, 10.0  # [m]
     gx, gy = 50.0, 50.0  # [m]
+
     robot_radius = 5.0  # [m]
 
-    obstacles = [ObstaclePolygon(
-        [20.0, 30.0, 15.0],
-        [20.0, 20.0, 30.0],
-    ), ObstaclePolygon(
-        [30.0, 45.0, 50.0, 40.0],
-        [50.0, 40.0, 20.0, 40.0],
-    )]
+    obstacles = [
+        ObstaclePolygon(
+            [20.0, 30.0, 15.0],
+            [20.0, 20.0, 30.0],
+        ),
+        ObstaclePolygon(
+            [40.0, 45.0, 50.0, 40.0],
+            [50.0, 40.0, 20.0, 40.0],
+        ),
+        ObstaclePolygon(
+            [20.0, 30.0, 30.0, 20.0],
+            [40.0, 45.0, 70.0, 50.0],
+        )
+    ]
 
     if show_animation:  # pragma: no cover
         plt.plot(sx, sy, "or")
         plt.plot(gx, gy, "ob")
         [ob.plot() for ob in obstacles]
+        plt.pause(1.0)
+
+    rx, ry = VisibilityRoadMap(robot_radius, do_plot=show_animation).planning(
+        sx, sy, gx, gy, obstacles)
 
-    rx, ry = VisibilityGraphPlanner(robot_radius).planning(sx, sy, gx, gy,
-                                                           obstacles)
-    # assert rx, 'Cannot found path'
     if show_animation:  # pragma: no cover
         plt.plot(rx, ry, "-r")
+        plt.pause(0.1)
         plt.axis("equal")
         plt.show()
 
diff --git a/PathPlanning/VoronoiRoadMap/dijkstra_search.py b/PathPlanning/VoronoiRoadMap/dijkstra_search.py
index 7f248cd3cf..f7b8fc48c1 100644
--- a/PathPlanning/VoronoiRoadMap/dijkstra_search.py
+++ b/PathPlanning/VoronoiRoadMap/dijkstra_search.py
@@ -8,20 +8,21 @@
 
 import matplotlib.pyplot as plt
 import math
+import numpy as np
 
 
 class DijkstraSearch:
-
     class Node:
         """
         Node class for dijkstra search
         """
 
-        def __init__(self, x, y, cost=None, parent=None):
+        def __init__(self, x, y, cost=None, parent=None, edge_ids=None):
             self.x = x
             self.y = y
             self.cost = cost
             self.parent = parent
+            self.edge_ids = edge_ids
 
         def __str__(self):
             return str(self.x) + "," + str(self.y) + "," + str(
@@ -30,71 +31,79 @@ def __str__(self):
     def __init__(self, show_animation):
         self.show_animation = show_animation
 
-    def search(self, sx, sy, gx, gy, road_map, sample_x, sample_y):
+    def search(self, sx, sy, gx, gy, node_x, node_y, edge_ids_list):
         """
+        Search shortest path
+
+        sx: start x positions [m]
+        sy: start y positions [m]
         gx: goal x position [m]
         gx: goal x position [m]
-        ox: x position list of Obstacles [m]
-        oy: y position list of Obstacles [m]
-        reso: grid resolution [m]
-        rr: robot radius[m]
+        node_x: node x position
+        node_y: node y position
+        edge_ids_list: edge_list each item includes a list of edge ids
         """
 
         start_node = self.Node(sx, sy, 0.0, -1)
         goal_node = self.Node(gx, gy, 0.0, -1)
+        current_node = None
 
         open_set, close_set = dict(), dict()
-        open_set[len(road_map) - 2] = start_node
+        open_set[self.find_id(node_x, node_y, start_node)] = start_node
 
         while True:
-            if not open_set:
+            if self.has_node_in_set(close_set, goal_node):
+                print("goal is found!")
+                goal_node.parent = current_node.parent
+                goal_node.cost = current_node.cost
+                break
+            elif not open_set:
                 print("Cannot find path")
                 break
 
-            c_id = min(open_set, key=lambda o: open_set[o].cost)
-            current = open_set[c_id]
+            current_id = min(open_set, key=lambda o: open_set[o].cost)
+            current_node = open_set[current_id]
 
             # show graph
             if self.show_animation and len(
                     close_set.keys()) % 2 == 0:  # pragma: no cover
-                plt.plot(current.x, current.y, "xg")
+                plt.plot(current_node.x, current_node.y, "xg")
                 # for stopping simulation with the esc key.
                 plt.gcf().canvas.mpl_connect(
                     'key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
-                plt.pause(0.001)
-
-            if c_id == (len(road_map) - 1):
-                print("goal is found!")
-                goal_node.parent = current.parent
-                goal_node.cost = current.cost
-                break
+                plt.pause(0.1)
 
             # Remove the item from the open set
-            del open_set[c_id]
+            del open_set[current_id]
             # Add it to the closed set
-            close_set[c_id] = current
+            close_set[current_id] = current_node
 
             # expand search grid based on motion model
-            for i in range(len(road_map[c_id])):
-                n_id = road_map[c_id][i]
-                dx = sample_x[n_id] - current.x
-                dy = sample_y[n_id] - current.y
+            for i in range(len(edge_ids_list[current_id])):
+                n_id = edge_ids_list[current_id][i]
+                dx = node_x[n_id] - current_node.x
+                dy = node_y[n_id] - current_node.y
                 d = math.hypot(dx, dy)
-                node = self.Node(sample_x[n_id], sample_y[n_id],
-                                 current.cost + d, c_id)
+                node = self.Node(node_x[n_id], node_y[n_id],
+                                 current_node.cost + d, current_id)
 
                 if n_id in close_set:
                     continue
                 # Otherwise if it is already in the open set
                 if n_id in open_set:
                     if open_set[n_id].cost > node.cost:
-                        open_set[n_id].cost = node.cost
-                        open_set[n_id].parent = c_id
+                        open_set[n_id] = node
                 else:
                     open_set[n_id] = node
 
         # generate final course
+        rx, ry = self.generate_final_path(close_set, goal_node)
+
+        return rx, ry
+
+    @staticmethod
+    def generate_final_path(close_set, goal_node):
         rx, ry = [goal_node.x], [goal_node.y]
         parent = goal_node.parent
         while parent != -1:
@@ -102,5 +111,29 @@ def search(self, sx, sy, gx, gy, road_map, sample_x, sample_y):
             rx.append(n.x)
             ry.append(n.y)
             parent = n.parent
-
+        rx, ry = rx[::-1], ry[::-1]  # reverse it
         return rx, ry
+
+    def has_node_in_set(self, target_set, node):
+        for key in target_set:
+            if self.is_same_node(target_set[key], node):
+                return True
+        return False
+
+    def find_id(self, node_x_list, node_y_list, target_node):
+        for i in range(len(node_x_list)):
+            if self.is_same_node_with_xy(node_x_list[i], node_y_list[i],
+                                         target_node):
+                return i
+
+    @staticmethod
+    def is_same_node_with_xy(node_x, node_y, node_b):
+        dist = np.hypot(node_x - node_b.x,
+                        node_y - node_b.y)
+        return dist <= 0.1
+
+    @staticmethod
+    def is_same_node(node_a, node_b):
+        dist = np.hypot(node_a.x - node_b.x,
+                        node_b.y - node_b.y)
+        return dist <= 0.1
diff --git a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
index dfac7258f8..d060c8e46c 100644
--- a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
+++ b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
@@ -23,21 +23,22 @@ def __init__(self):
         self.N_KNN = 10  # number of edge from one sampled point
         self.MAX_EDGE_LEN = 30.0  # [m] Maximum edge length
 
-    def planning(self, sx, sy, gx, gy, ox, oy, rr):
+    def planning(self, sx, sy, gx, gy, ox, oy, robot_radius):
         obstacle_tree = KDTree(np.vstack((ox, oy)).T)
 
         sample_x, sample_y = self.voronoi_sampling(sx, sy, gx, gy, ox, oy)
         if show_animation:  # pragma: no cover
             plt.plot(sample_x, sample_y, ".b")
 
-        road_map = self.generate_road_map(sample_x, sample_y, rr, obstacle_tree)
-
-        rx, ry = DijkstraSearch(show_animation).search(sx, sy, gx, gy, road_map,
-                                                       sample_x, sample_y)
+        road_map_info = self.generate_road_map_info(
+            sample_x, sample_y, robot_radius, obstacle_tree)
 
+        rx, ry = DijkstraSearch(show_animation).search(sx, sy, gx, gy,
+                                                       sample_x, sample_y,
+                                                       road_map_info)
         return rx, ry
 
-    def is_collision(self, sx, sy, gx, gy, rr, obstacle_kdtree):
+    def is_collision(self, sx, sy, gx, gy, rr, obstacle_kd_tree):
         x = sx
         y = sy
         dx = gx - sx
@@ -52,25 +53,25 @@ def is_collision(self, sx, sy, gx, gy, rr, obstacle_kdtree):
         n_step = round(d / D)
 
         for i in range(n_step):
-            ids, dist = obstacle_kdtree.search(np.array([x, y]).reshape(2, 1))
+            ids, dist = obstacle_kd_tree.search(np.array([x, y]).reshape(2, 1))
             if dist[0] <= rr:
                 return True  # collision
             x += D * math.cos(yaw)
             y += D * math.sin(yaw)
 
         # goal point check
-        ids, dist = obstacle_kdtree.search(np.array([gx, gy]).reshape(2, 1))
+        ids, dist = obstacle_kd_tree.search(np.array([gx, gy]).reshape(2, 1))
         if dist[0] <= rr:
             return True  # collision
 
         return False  # OK
 
-    def generate_road_map(self, node_x, node_y, rr, obstacle_tree):
+    def generate_road_map_info(self, node_x, node_y, rr, obstacle_tree):
         """
         Road map generation
 
-        sample_x: [m] x positions of sampled points
-        sample_y: [m] y positions of sampled points
+        node_x: [m] x positions of sampled points
+        node_y: [m] y positions of sampled points
         rr: Robot Radius[m]
         obstacle_tree: KDTree object of obstacles
         """
diff --git a/tests/test_voronoi_path_planner.py b/tests/test_visibility_road_map_planner.py
similarity index 100%
rename from tests/test_voronoi_path_planner.py
rename to tests/test_visibility_road_map_planner.py
diff --git a/tests/test_voronoi_road_map_planner.py b/tests/test_voronoi_road_map_planner.py
new file mode 100644
index 0000000000..a1f860a108
--- /dev/null
+++ b/tests/test_voronoi_road_map_planner.py
@@ -0,0 +1,17 @@
+import os
+import sys
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__))
+                + "/../PathPlanning/VisibilityRoadMap/")
+
+from unittest import TestCase
+from PathPlanning.VisibilityRoadMap import visibility_road_map as m
+
+print(__file__)
+
+
+class Test(TestCase):
+
+    def test1(self):
+        m.show_animation = False
+        m.main()

From ac1a903ca794ef3e943ce123f06d1bf5443c7a9f Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 29 Feb 2020 14:59:40 +0900
Subject: [PATCH 0333/1080] fixed CI

---
 PathPlanning/Dijkstra/dijkstra.py             | 28 +++++++++----------
 PathPlanning/VisibilityRoadMap/geometry.py    |  5 ++--
 .../VisibilityRoadMap/visibility_road_map.py  |  3 +-
 .../VoronoiRoadMap/dijkstra_search.py         |  2 +-
 4 files changed, 19 insertions(+), 19 deletions(-)

diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py
index ad3e3a85c8..0ece712ae7 100644
--- a/PathPlanning/Dijkstra/dijkstra.py
+++ b/PathPlanning/Dijkstra/dijkstra.py
@@ -24,11 +24,6 @@ def __init__(self, ox, oy, resolution, robot_radius):
         rr: robot radius[m]
         """
 
-        self.resolution = resolution
-        self.robot_radius = robot_radius
-        self.calc_obstacle_map(ox, oy)
-        self.motion = self.get_motion_model()
-
         self.min_x = None
         self.min_y = None
         self.max_x = None
@@ -37,6 +32,11 @@ def __init__(self, ox, oy, resolution, robot_radius):
         self.y_width = None
         self.obstacle_map = None
 
+        self.resolution = resolution
+        self.robot_radius = robot_radius
+        self.calc_obstacle_map(ox, oy)
+        self.motion = self.get_motion_model()
+
     class Node:
         def __init__(self, x, y, cost, parent):
             self.x = x  # index of grid
@@ -151,11 +151,11 @@ def verify_node(self, node):
 
         if px < self.min_x:
             return False
-        elif py < self.min_y:
+        if py < self.min_y:
             return False
-        elif px >= self.max_x:
+        if px >= self.max_x:
             return False
-        elif py >= self.max_y:
+        if py >= self.max_y:
             return False
 
         if self.obstacle_map[node.x][node.y]:
@@ -169,15 +169,15 @@ def calc_obstacle_map(self, ox, oy):
         self.min_y = round(min(oy))
         self.max_x = round(max(ox))
         self.max_y = round(max(oy))
-        print("minx:", self.min_x)
-        print("miny:", self.min_y)
-        print("maxx:", self.max_x)
-        print("maxy:", self.max_y)
+        print("min_x:", self.min_x)
+        print("min_y:", self.min_y)
+        print("max_x:", self.max_x)
+        print("max_y:", self.max_y)
 
         self.x_width = round((self.max_x - self.min_x) / self.resolution)
         self.y_width = round((self.max_y - self.min_y) / self.resolution)
-        print("xwidth:", self.x_width)
-        print("ywidth:", self.y_width)
+        print("x_width:", self.x_width)
+        print("y_width:", self.y_width)
 
         # obstacle map generation
         self.obstacle_map = [[False for _ in range(self.y_width)]
diff --git a/PathPlanning/VisibilityRoadMap/geometry.py b/PathPlanning/VisibilityRoadMap/geometry.py
index 053a2bd70e..c4d73837c9 100644
--- a/PathPlanning/VisibilityRoadMap/geometry.py
+++ b/PathPlanning/VisibilityRoadMap/geometry.py
@@ -18,10 +18,9 @@ def orientation(p, q, r):
                     float(q.x - p.x) * (r.y - q.y))
             if val > 0:
                 return 1
-            elif val < 0:
+            if val < 0:
                 return 2
-            else:
-                return 0
+            return 0
 
         # Find the 4 orientations required for
         # the general and special cases
diff --git a/PathPlanning/VisibilityRoadMap/visibility_road_map.py b/PathPlanning/VisibilityRoadMap/visibility_road_map.py
index bf9b520a75..fdd214f757 100644
--- a/PathPlanning/VisibilityRoadMap/visibility_road_map.py
+++ b/PathPlanning/VisibilityRoadMap/visibility_road_map.py
@@ -204,7 +204,8 @@ def main():
     if show_animation:  # pragma: no cover
         plt.plot(sx, sy, "or")
         plt.plot(gx, gy, "ob")
-        [ob.plot() for ob in obstacles]
+        for ob in obstacles:
+            ob.plot()
         plt.pause(1.0)
 
     rx, ry = VisibilityRoadMap(robot_radius, do_plot=show_animation).planning(
diff --git a/PathPlanning/VoronoiRoadMap/dijkstra_search.py b/PathPlanning/VoronoiRoadMap/dijkstra_search.py
index f7b8fc48c1..7bf5425ebf 100644
--- a/PathPlanning/VoronoiRoadMap/dijkstra_search.py
+++ b/PathPlanning/VoronoiRoadMap/dijkstra_search.py
@@ -121,7 +121,7 @@ def has_node_in_set(self, target_set, node):
         return False
 
     def find_id(self, node_x_list, node_y_list, target_node):
-        for i in range(len(node_x_list)):
+        for i, _ in enumerate(node_x_list):
             if self.is_same_node_with_xy(node_x_list[i], node_y_list[i],
                                          target_node):
                 return i

From 881308bc42764ee2652d4f17d5a99d0891fb8e93 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 29 Feb 2020 20:37:03 +0900
Subject: [PATCH 0334/1080] fixed CI

---
 PathPlanning/VisibilityRoadMap/visibility_road_map.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/PathPlanning/VisibilityRoadMap/visibility_road_map.py b/PathPlanning/VisibilityRoadMap/visibility_road_map.py
index fdd214f757..a677804440 100644
--- a/PathPlanning/VisibilityRoadMap/visibility_road_map.py
+++ b/PathPlanning/VisibilityRoadMap/visibility_road_map.py
@@ -34,7 +34,7 @@ def planning(self, start_x, start_y, goal_x, goal_y, obstacles):
 
         road_map_info = self.generate_road_map_info(nodes, obstacles)
 
-        if show_animation:
+        if self.do_plot:
             self.plot_road_map(nodes, road_map_info)
             plt.pause(1.0)
 
@@ -197,7 +197,7 @@ def main():
         ),
         ObstaclePolygon(
             [20.0, 30.0, 30.0, 20.0],
-            [40.0, 45.0, 70.0, 50.0],
+            [40.0, 45.0, 60.0, 50.0],
         )
     ]
 
@@ -206,6 +206,7 @@ def main():
         plt.plot(gx, gy, "ob")
         for ob in obstacles:
             ob.plot()
+        plt.axis("equal")
         plt.pause(1.0)
 
     rx, ry = VisibilityRoadMap(robot_radius, do_plot=show_animation).planning(
@@ -214,7 +215,6 @@ def main():
     if show_animation:  # pragma: no cover
         plt.plot(rx, ry, "-r")
         plt.pause(0.1)
-        plt.axis("equal")
         plt.show()
 
 

From 487a7e4141dc4e2a0ae887e7fec98251900362b7 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 29 Feb 2020 21:12:30 +0900
Subject: [PATCH 0335/1080] Update users_comments.md

---
 users_comments.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 792f3a4d0d..056a896e94 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -23,6 +23,8 @@ If you found other users, please make an issue to let me know.
 
 - [CSCI/ARTI 4530/6530: Introduction to Robotics (Fall 2018),  University of Georgia ](http://cobweb.cs.uga.edu/~ramviyas/csci_x530.html)
 
+- [CIT Modules & Programmes \- COMP9073 \- Automation with Python](https://courses.cit.ie/index.cfm/page/module/moduleId/14416)
+
 # Stargazers location map
 
 You can check stargazer's locations of this project from:
@@ -415,3 +417,4 @@ URL: https://pdfs.semanticscholar.org/5c06/f3cb9542a51e1bf1a32523c1bc7fea6cecc5.
 
 - Real-time Model Predictive Control (MPC), ACADO, Python | Work-is-Playing http://grauonline.de/wordpress/?page_id=3244
 
+- 💡 Greatest of GitHub - Python Robotics (Amazing!) - YouTube https://www.youtube.com/watch?v=f_pPaYx6Gu0&feature=emb_logo

From 80ebc55c2f8c986ef58aae28c43dcf0bbbaa85d1 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 1 Mar 2020 21:05:04 +0900
Subject: [PATCH 0336/1080] clean up codes

---
 PathPlanning/BSplinePath/bspline_path.py | 25 +++++++++++-------------
 1 file changed, 11 insertions(+), 14 deletions(-)

diff --git a/PathPlanning/BSplinePath/bspline_path.py b/PathPlanning/BSplinePath/bspline_path.py
index 0c453ad436..2c3d7ad68e 100644
--- a/PathPlanning/BSplinePath/bspline_path.py
+++ b/PathPlanning/BSplinePath/bspline_path.py
@@ -1,6 +1,6 @@
 """
 
-Path Plannting with B-Spline
+Path Planning with B-Spline
 
 author: Atsushi Sakai (@Atsushi_twi)
 
@@ -8,16 +8,13 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-import scipy.interpolate as si
+import scipy.interpolate as scipy_interpolate
 
-# parameter
-N = 3  # B Spline order
 
-
-def bspline_planning(x, y, sn):
+def b_spline_planning(x, y, sn, degree=3):
     t = range(len(x))
-    x_tup = si.splrep(t, x, k=N)
-    y_tup = si.splrep(t, y, k=N)
+    x_tup = scipy_interpolate.splrep(t, x, k=degree)
+    y_tup = scipy_interpolate.splrep(t, y, k=degree)
 
     x_list = list(x_tup)
     xl = x.tolist()
@@ -28,8 +25,8 @@ def bspline_planning(x, y, sn):
     y_list[1] = yl + [0.0, 0.0, 0.0, 0.0]
 
     ipl_t = np.linspace(0.0, len(x) - 1, sn)
-    rx = si.splev(ipl_t, x_list)
-    ry = si.splev(ipl_t, y_list)
+    rx = scipy_interpolate.splev(ipl_t, x_list)
+    ry = scipy_interpolate.splev(ipl_t, y_list)
 
     return rx, ry
 
@@ -37,14 +34,14 @@ def bspline_planning(x, y, sn):
 def main():
     print(__file__ + " start!!")
     # way points
-    x = np.array([-1.0, 3.0, 4.0, 2.0, 1.0])
-    y = np.array([0.0, -3.0, 1.0, 1.0, 3.0])
+    way_x = np.array([-1.0, 3.0, 4.0, 2.0, 1.0])
+    way_y = np.array([0.0, -3.0, 1.0, 1.0, 3.0])
     sn = 100  # sampling number
 
-    rx, ry = bspline_planning(x, y, sn)
+    rx, ry = b_spline_planning(way_x, way_y, sn)
 
     # show results
-    plt.plot(x, y, '-og', label="Waypoints")
+    plt.plot(way_x, way_y, '-og', label="way points")
     plt.plot(rx, ry, '-r', label="B-Spline path")
     plt.grid(True)
     plt.legend()

From 869650ffa93c444bafd8eea2a6f9868c0d53bb61 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 7 Mar 2020 21:57:26 +0900
Subject: [PATCH 0337/1080] Update users_comments.md

---
 users_comments.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/users_comments.md b/users_comments.md
index 056a896e94..44e9355ec1 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -409,6 +409,9 @@ URL: https://arxiv.org/abs/1910.01557
 8. Hahn, Carsten, et al. "Dynamic Path Planning with Stable Growing Neural Gas." (2019).
 URL: https://pdfs.semanticscholar.org/5c06/f3cb9542a51e1bf1a32523c1bc7fea6cecc5.pdf
 
+9. Brijen Thananjeyan, et al. "ABC-LMPC: Safe Sample-Based Learning MPC for Stochastic Nonlinear Dynamical Systems with Adjustable Boundary Conditions"
+URL: https://arxiv.org/pdf/2003.01410.pdf
+
 # Others
 
 - Autonomous Vehicle Readings https://richardkelley.io/readings

From 6cb903a8143064c8eaba811b7f894bd1539ee2ae Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 Mar 2020 22:27:07 +0900
Subject: [PATCH 0338/1080] add mypy setting and update bspline_path.py

---
 .github/workflows/mypycheck.yml          |  3 ++
 PathPlanning/BSplinePath/bspline_path.py | 57 ++++++++++++++++++------
 mypy.ini                                 |  2 +
 3 files changed, 49 insertions(+), 13 deletions(-)
 create mode 100644 .github/workflows/mypycheck.yml
 create mode 100644 mypy.ini

diff --git a/.github/workflows/mypycheck.yml b/.github/workflows/mypycheck.yml
new file mode 100644
index 0000000000..26e5566015
--- /dev/null
+++ b/.github/workflows/mypycheck.yml
@@ -0,0 +1,3 @@
+- name: Mypy Check
+  uses: jpetrucciani/mypy-check@0.761
+
diff --git a/PathPlanning/BSplinePath/bspline_path.py b/PathPlanning/BSplinePath/bspline_path.py
index 2c3d7ad68e..d4b24c7645 100644
--- a/PathPlanning/BSplinePath/bspline_path.py
+++ b/PathPlanning/BSplinePath/bspline_path.py
@@ -1,6 +1,6 @@
 """
 
-Path Planning with B-Spline
+Path Planner with B-Spline
 
 author: Atsushi Sakai (@Atsushi_twi)
 
@@ -11,38 +11,69 @@
 import scipy.interpolate as scipy_interpolate
 
 
-def b_spline_planning(x, y, sn, degree=3):
+def approximate_b_spline_path(x: list, y: list, n_path_points: int,
+                              degree: int = 3) -> tuple:
+    """
+    approximate points with a B-Spline path
+
+    :param x: x position list of approximated points
+    :param y: y position list of approximated points
+    :param n_path_points: number of path points
+    :param degree: (Optional) B Spline curve degree
+    :return: x and y position list of the result path
+    """
     t = range(len(x))
     x_tup = scipy_interpolate.splrep(t, x, k=degree)
     y_tup = scipy_interpolate.splrep(t, y, k=degree)
 
     x_list = list(x_tup)
-    xl = x.tolist()
-    x_list[1] = xl + [0.0, 0.0, 0.0, 0.0]
+    x_list[1] = x + [0.0, 0.0, 0.0, 0.0]
 
     y_list = list(y_tup)
-    yl = y.tolist()
-    y_list[1] = yl + [0.0, 0.0, 0.0, 0.0]
+    y_list[1] = y + [0.0, 0.0, 0.0, 0.0]
 
-    ipl_t = np.linspace(0.0, len(x) - 1, sn)
+    ipl_t = np.linspace(0.0, len(x) - 1, n_path_points)
     rx = scipy_interpolate.splev(ipl_t, x_list)
     ry = scipy_interpolate.splev(ipl_t, y_list)
 
     return rx, ry
 
 
+def interpolate_b_spline_path(x: list, y: list, n_path_points: int,
+                              degree: int = 3) -> tuple:
+    """
+    interpolate points with a B-Spline path
+
+    :param x: x positions of interpolated points
+    :param y: y positions of interpolated points
+    :param n_path_points: number of path points
+    :param degree: B-Spline degree
+    :return: x and y position list of the result path
+    """
+    ipl_t = np.linspace(0.0, len(x) - 1, len(x))
+    spl_i_x = scipy_interpolate.make_interp_spline(ipl_t, x, k=degree)
+    spl_i_y = scipy_interpolate.make_interp_spline(ipl_t, y, k=degree)
+
+    travel = np.linspace(0.0, len(x) - 1, n_path_points)
+    return spl_i_x(travel), spl_i_y(travel)
+
+
 def main():
     print(__file__ + " start!!")
     # way points
-    way_x = np.array([-1.0, 3.0, 4.0, 2.0, 1.0])
-    way_y = np.array([0.0, -3.0, 1.0, 1.0, 3.0])
-    sn = 100  # sampling number
+    way_point_x = [-1.0, 3.0, 4.0, 2.0, 1.0]
+    way_point_y = [0.0, -3.0, 1.0, 1.0, 3.0]
+    n_course_point = 100  # sampling number
 
-    rx, ry = b_spline_planning(way_x, way_y, sn)
+    rax, ray = approximate_b_spline_path(way_point_x, way_point_y,
+                                         n_course_point)
+    rix, riy = interpolate_b_spline_path(way_point_x, way_point_y,
+                                         n_course_point)
 
     # show results
-    plt.plot(way_x, way_y, '-og', label="way points")
-    plt.plot(rx, ry, '-r', label="B-Spline path")
+    plt.plot(way_point_x, way_point_y, '-og', label="way points")
+    plt.plot(rax, ray, '-r', label="Approximated B-Spline path")
+    plt.plot(rix, riy, '-b', label="Interpolated B-Spline path")
     plt.grid(True)
     plt.legend()
     plt.axis("equal")
diff --git a/mypy.ini b/mypy.ini
new file mode 100644
index 0000000000..1215375ed9
--- /dev/null
+++ b/mypy.ini
@@ -0,0 +1,2 @@
+[mypy]
+ignore_missing_imports = True
\ No newline at end of file

From ba13817862c503c79da0e847d1afd7555cb8015e Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 Mar 2020 22:29:03 +0900
Subject: [PATCH 0339/1080] fixed git hub action

---
 .github/workflows/mypycheck.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/mypycheck.yml b/.github/workflows/mypycheck.yml
index 26e5566015..649028674a 100644
--- a/.github/workflows/mypycheck.yml
+++ b/.github/workflows/mypycheck.yml
@@ -1,3 +1,3 @@
-- name: Mypy Check
-  uses: jpetrucciani/mypy-check@0.761
+name: Mypy Check
+uses: jpetrucciani/mypy-check@0.761
 

From 5f253ef56229b44cfb5e6ba5a317de5c042efad9 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 Mar 2020 22:31:17 +0900
Subject: [PATCH 0340/1080] fixed git hub action

---
 .github/workflows/mypycheck.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/mypycheck.yml b/.github/workflows/mypycheck.yml
index 649028674a..0e26c1e241 100644
--- a/.github/workflows/mypycheck.yml
+++ b/.github/workflows/mypycheck.yml
@@ -1,3 +1,4 @@
 name: Mypy Check
+on: [push]
 uses: jpetrucciani/mypy-check@0.761
 

From aad8d4face65a0adf571b01a54b32f307876dcb2 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 Mar 2020 22:34:00 +0900
Subject: [PATCH 0341/1080] fixed git hub action

---
 .github/workflows/mypycheck.yml     | 4 ----
 .github/workflows/pythonpackage.yml | 6 +++++-
 2 files changed, 5 insertions(+), 5 deletions(-)
 delete mode 100644 .github/workflows/mypycheck.yml

diff --git a/.github/workflows/mypycheck.yml b/.github/workflows/mypycheck.yml
deleted file mode 100644
index 0e26c1e241..0000000000
--- a/.github/workflows/mypycheck.yml
+++ /dev/null
@@ -1,4 +0,0 @@
-name: Mypy Check
-on: [push]
-uses: jpetrucciani/mypy-check@0.761
-
diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml
index a3203ff1ac..d03d5ea14c 100644
--- a/.github/workflows/pythonpackage.yml
+++ b/.github/workflows/pythonpackage.yml
@@ -29,6 +29,10 @@ jobs:
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: install coverage
-      run: pip install coverage   
+      run: pip install coverage
+    - name: Mypy Check
+      uses: jpetrucciani/mypy-check@0.761
     - name: Test
       run: bash runtests.sh
+
+

From d7dddc8772b04a6895902ea6e3f72be1ebf21a86 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 Mar 2020 22:51:47 +0900
Subject: [PATCH 0342/1080] mypy fail test

---
 PathPlanning/BSplinePath/bspline_path.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/BSplinePath/bspline_path.py b/PathPlanning/BSplinePath/bspline_path.py
index d4b24c7645..7c59e99bbb 100644
--- a/PathPlanning/BSplinePath/bspline_path.py
+++ b/PathPlanning/BSplinePath/bspline_path.py
@@ -11,7 +11,7 @@
 import scipy.interpolate as scipy_interpolate
 
 
-def approximate_b_spline_path(x: list, y: list, n_path_points: int,
+def approximate_b_spline_path(x: int, y: list, n_path_points: int,
                               degree: int = 3) -> tuple:
     """
     approximate points with a B-Spline path

From 0b9a0c88d6c5fc029bf9b0fb305e977c3b641d3c Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 Mar 2020 23:00:32 +0900
Subject: [PATCH 0343/1080] mypy fail test

---
 .github/workflows/pythonpackage.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml
index d03d5ea14c..af761ec7b3 100644
--- a/.github/workflows/pythonpackage.yml
+++ b/.github/workflows/pythonpackage.yml
@@ -32,6 +32,8 @@ jobs:
       run: pip install coverage
     - name: Mypy Check
       uses: jpetrucciani/mypy-check@0.761
+      with:
+        path: './AerialNavigation ./PathPlanning'
     - name: Test
       run: bash runtests.sh
 

From 8dc23fec2135f8c376d7aaa7696104d3deb40a8b Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 Mar 2020 23:15:13 +0900
Subject: [PATCH 0344/1080] mypy fail test

---
 .github/workflows/pythonpackage.yml | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml
index af761ec7b3..c81ce8930d 100644
--- a/.github/workflows/pythonpackage.yml
+++ b/.github/workflows/pythonpackage.yml
@@ -30,11 +30,12 @@ jobs:
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: install coverage
       run: pip install coverage
-    - name: Mypy Check
-      uses: jpetrucciani/mypy-check@0.761
-      with:
-        path: './AerialNavigation ./PathPlanning'
-    - name: Test
+    - name: install mypy
+      run: pip install mypy
+    - name: mypy check
+      run: |
+        - find . -name "*.py" | xargs mypy
+    - name: do all unit tests
       run: bash runtests.sh
 
 

From c36aa27d605628238ff4d05e66b2f59d91fc30b3 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 Mar 2020 23:20:10 +0900
Subject: [PATCH 0345/1080] mypy fail test

---
 .github/workflows/pythonpackage.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml
index c81ce8930d..1844e53820 100644
--- a/.github/workflows/pythonpackage.yml
+++ b/.github/workflows/pythonpackage.yml
@@ -34,7 +34,7 @@ jobs:
       run: pip install mypy
     - name: mypy check
       run: |
-        - find . -name "*.py" | xargs mypy
+        find . -name "*.py" | xargs mypy
     - name: do all unit tests
       run: bash runtests.sh
 

From 14ffc4a2d4e202c49c0ae385b3ba62144a1cf86c Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 9 Mar 2020 22:57:35 +0900
Subject: [PATCH 0346/1080] mypy fix test

---
 .../{NLinkArm.py => NLinkArm3d.py}            | 116 +++++----
 ArmNavigation/n_joint_arm_3d/__init__py.py    |   0
 .../random_forward_kinematics.py              |  21 +-
 .../random_inverse_kinematics.py              |  15 +-
 .../n_joint_arm_to_point_control/__init__.py  |   0
 PathPlanning/AStar/a_star.py                  |  12 +-
 PathPlanning/BSplinePath/bspline_path.py      |   2 +-
 PathPlanning/CubicSpline/__init__.py          |   0
 .../cubic_spline_planner.py                   | 239 ------------------
 .../frenet_optimal_trajectory.py              | 109 ++++----
 PathPlanning/HybridAStar/__init__.py          |   0
 .../{a_star.py => a_star_heuristic.py}        |   0
 PathPlanning/HybridAStar/hybrid_a_star.py     |   2 +-
 13 files changed, 150 insertions(+), 366 deletions(-)
 rename ArmNavigation/n_joint_arm_3d/{NLinkArm.py => NLinkArm3d.py} (75%)
 create mode 100644 ArmNavigation/n_joint_arm_3d/__init__py.py
 create mode 100644 ArmNavigation/n_joint_arm_to_point_control/__init__.py
 create mode 100644 PathPlanning/CubicSpline/__init__.py
 delete mode 100644 PathPlanning/FrenetOptimalTrajectory/cubic_spline_planner.py
 create mode 100644 PathPlanning/HybridAStar/__init__.py
 rename PathPlanning/HybridAStar/{a_star.py => a_star_heuristic.py} (100%)

diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm.py b/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py
similarity index 75%
rename from ArmNavigation/n_joint_arm_3d/NLinkArm.py
rename to ArmNavigation/n_joint_arm_3d/NLinkArm3d.py
index da562d9f17..b411cd7114 100644
--- a/ArmNavigation/n_joint_arm_3d/NLinkArm.py
+++ b/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py
@@ -7,6 +7,7 @@
 from mpl_toolkits.mplot3d import Axes3D
 import matplotlib.pyplot as plt
 
+
 class Link:
     def __init__(self, dh_params):
         self.dh_params_ = dh_params
@@ -28,16 +29,22 @@ def transformation_matrix(self):
 
         return trans
 
-    def basic_jacobian(self, trans_prev, ee_pos):
-        pos_prev = np.array([trans_prev[0, 3], trans_prev[1, 3], trans_prev[2, 3]])
-        z_axis_prev = np.array([trans_prev[0, 2], trans_prev[1, 2], trans_prev[2, 2]])
+    @staticmethod
+    def basic_jacobian(trans_prev, ee_pos):
+        pos_prev = np.array(
+            [trans_prev[0, 3], trans_prev[1, 3], trans_prev[2, 3]])
+        z_axis_prev = np.array(
+            [trans_prev[0, 2], trans_prev[1, 2], trans_prev[2, 2]])
 
-        basic_jacobian = np.hstack((np.cross(z_axis_prev, ee_pos - pos_prev), z_axis_prev))
+        basic_jacobian = np.hstack(
+            (np.cross(z_axis_prev, ee_pos - pos_prev), z_axis_prev))
         return basic_jacobian
-        
+
 
 class NLinkArm:
     def __init__(self, dh_params_list):
+        self.fig = plt.figure()
+        self.ax = Axes3D(self.fig)
         self.link_list = []
         for i in range(len(dh_params_list)):
             self.link_list.append(Link(dh_params_list[i]))
@@ -47,7 +54,7 @@ def transformation_matrix(self):
         for i in range(len(self.link_list)):
             trans = np.dot(trans, self.link_list[i].transformation_matrix())
         return trans
-    
+
     def forward_kinematics(self, plot=False):
         trans = self.transformation_matrix()
 
@@ -57,15 +64,12 @@ def forward_kinematics(self, plot=False):
         alpha, beta, gamma = self.euler_angle()
 
         if plot:
-            self.fig = plt.figure()
-            self.ax = Axes3D(self.fig)
-            
             x_list = []
             y_list = []
             z_list = []
-    
+
             trans = np.identity(4)
-            
+
             x_list.append(trans[0, 3])
             y_list.append(trans[1, 3])
             z_list.append(trans[2, 3])
@@ -74,14 +78,15 @@ def forward_kinematics(self, plot=False):
                 x_list.append(trans[0, 3])
                 y_list.append(trans[1, 3])
                 z_list.append(trans[2, 3])
-                
-            self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4, mew=0.5)
+
+            self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4,
+                         mew=0.5)
             self.ax.plot([0], [0], [0], "o")
-            
+
             self.ax.set_xlim(-1, 1)
             self.ax.set_ylim(-1, 1)
             self.ax.set_zlim(-1, 1)
-  
+
             plt.show()
 
         return [x, y, z, alpha, beta, gamma]
@@ -89,41 +94,45 @@ def forward_kinematics(self, plot=False):
     def basic_jacobian(self):
         ee_pos = self.forward_kinematics()[0:3]
         basic_jacobian_mat = []
-        
-        trans = np.identity(4)        
+
+        trans = np.identity(4)
         for i in range(len(self.link_list)):
-            basic_jacobian_mat.append(self.link_list[i].basic_jacobian(trans, ee_pos))
+            basic_jacobian_mat.append(
+                self.link_list[i].basic_jacobian(trans, ee_pos))
             trans = np.dot(trans, self.link_list[i].transformation_matrix())
-            
+
         return np.array(basic_jacobian_mat).T
 
     def inverse_kinematics(self, ref_ee_pose, plot=False):
         for cnt in range(500):
             ee_pose = self.forward_kinematics()
             diff_pose = np.array(ref_ee_pose) - ee_pose
-        
+
             basic_jacobian_mat = self.basic_jacobian()
             alpha, beta, gamma = self.euler_angle()
-            
-            K_zyz = np.array([[0, -math.sin(alpha), math.cos(alpha) * math.sin(beta)],
-                              [0, math.cos(alpha), math.sin(alpha) * math.sin(beta)],
-                              [1, 0, math.cos(beta)]])
+
+            K_zyz = np.array(
+                [[0, -math.sin(alpha), math.cos(alpha) * math.sin(beta)],
+                 [0, math.cos(alpha), math.sin(alpha) * math.sin(beta)],
+                 [1, 0, math.cos(beta)]])
             K_alpha = np.identity(6)
             K_alpha[3:, 3:] = K_zyz
 
-            theta_dot = np.dot(np.dot(np.linalg.pinv(basic_jacobian_mat), K_alpha), np.array(diff_pose))
+            theta_dot = np.dot(
+                np.dot(np.linalg.pinv(basic_jacobian_mat), K_alpha),
+                np.array(diff_pose))
             self.update_joint_angles(theta_dot / 100.)
-            
+
         if plot:
             self.fig = plt.figure()
             self.ax = Axes3D(self.fig)
-            
+
             x_list = []
             y_list = []
             z_list = []
-    
+
             trans = np.identity(4)
-            
+
             x_list.append(trans[0, 3])
             y_list.append(trans[1, 3])
             z_list.append(trans[2, 3])
@@ -132,30 +141,36 @@ def inverse_kinematics(self, ref_ee_pose, plot=False):
                 x_list.append(trans[0, 3])
                 y_list.append(trans[1, 3])
                 z_list.append(trans[2, 3])
-                
-            self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4, mew=0.5)
+
+            self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4,
+                         mew=0.5)
             self.ax.plot([0], [0], [0], "o")
-            
+
             self.ax.set_xlim(-1, 1)
             self.ax.set_ylim(-1, 1)
             self.ax.set_zlim(-1, 1)
-  
-            self.ax.plot([ref_ee_pose[0]], [ref_ee_pose[1]], [ref_ee_pose[2]], "o")
+
+            self.ax.plot([ref_ee_pose[0]], [ref_ee_pose[1]], [ref_ee_pose[2]],
+                         "o")
             plt.show()
-                
+
     def euler_angle(self):
         trans = self.transformation_matrix()
-        
+
         alpha = math.atan2(trans[1][2], trans[0][2])
-        if not (-math.pi / 2 <= alpha and alpha <= math.pi / 2):
+        if not (-math.pi / 2 <= alpha <= math.pi / 2):
             alpha = math.atan2(trans[1][2], trans[0][2]) + math.pi
-        if not (-math.pi / 2 <= alpha and alpha <= math.pi / 2):
+        if not (-math.pi / 2 <= alpha <= math.pi / 2):
             alpha = math.atan2(trans[1][2], trans[0][2]) - math.pi
-        beta = math.atan2(trans[0][2] * math.cos(alpha) + trans[1][2] * math.sin(alpha), trans[2][2])
-        gamma = math.atan2(-trans[0][0] * math.sin(alpha) + trans[1][0] * math.cos(alpha), -trans[0][1] * math.sin(alpha) + trans[1][1] * math.cos(alpha))
-        
+        beta = math.atan2(
+            trans[0][2] * math.cos(alpha) + trans[1][2] * math.sin(alpha),
+            trans[2][2])
+        gamma = math.atan2(
+            -trans[0][0] * math.sin(alpha) + trans[1][0] * math.cos(alpha),
+            -trans[0][1] * math.sin(alpha) + trans[1][1] * math.cos(alpha))
+
         return alpha, beta, gamma
-    
+
     def set_joint_angles(self, joint_angle_list):
         for i in range(len(self.link_list)):
             self.link_list[i].dh_params_[0] = joint_angle_list[i]
@@ -163,17 +178,17 @@ def set_joint_angles(self, joint_angle_list):
     def update_joint_angles(self, diff_joint_angle_list):
         for i in range(len(self.link_list)):
             self.link_list[i].dh_params_[0] += diff_joint_angle_list[i]
-        
+
     def plot(self):
         self.fig = plt.figure()
         self.ax = Axes3D(self.fig)
-        
+
         x_list = []
         y_list = []
         z_list = []
 
         trans = np.identity(4)
-        
+
         x_list.append(trans[0, 3])
         y_list.append(trans[1, 3])
         z_list.append(trans[2, 3])
@@ -182,15 +197,16 @@ def plot(self):
             x_list.append(trans[0, 3])
             y_list.append(trans[1, 3])
             z_list.append(trans[2, 3])
-            
-        self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4, mew=0.5)
+
+        self.ax.plot(x_list, y_list, z_list, "o-", color="#00aa00", ms=4,
+                     mew=0.5)
         self.ax.plot([0], [0], [0], "o")
 
         self.ax.set_xlabel("x")
         self.ax.set_ylabel("y")
-        self.ax.set_zlabel("z")        
-        
+        self.ax.set_zlabel("z")
+
         self.ax.set_xlim(-1, 1)
         self.ax.set_ylim(-1, 1)
-        self.ax.set_zlim(-1, 1)        
+        self.ax.set_zlim(-1, 1)
         plt.show()
diff --git a/ArmNavigation/n_joint_arm_3d/__init__py.py b/ArmNavigation/n_joint_arm_3d/__init__py.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py
index 87907f803b..b3426e53e8 100644
--- a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py
+++ b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py
@@ -3,9 +3,10 @@
 Author: Takayuki Murooka (takayuki5168)
 """
 import math
-from NLinkArm import NLinkArm
+from NLinkArm3d import NLinkArm
 import random
 
+
 def random_val(min_val, max_val):
     return min_val + random.random() * (max_val - min_val)
 
@@ -14,17 +15,17 @@ def random_val(min_val, max_val):
     print("Start solving Forward Kinematics 10 times")
 
     # init NLinkArm with Denavit-Hartenberg parameters of PR2    
-    n_link_arm = NLinkArm([[0., -math.pi/2, .1, 0.],
-                           [math.pi/2, math.pi/2, 0., 0.],
-                           [0., -math.pi/2, 0., .4],
-                           [0., math.pi/2, 0., 0.],
-                           [0., -math.pi/2, 0., .321],
-                           [0., math.pi/2, 0., 0.],
+    n_link_arm = NLinkArm([[0., -math.pi / 2, .1, 0.],
+                           [math.pi / 2, math.pi / 2, 0., 0.],
+                           [0., -math.pi / 2, 0., .4],
+                           [0., math.pi / 2, 0., 0.],
+                           [0., -math.pi / 2, 0., .321],
+                           [0., math.pi / 2, 0., 0.],
                            [0., 0., 0., 0.]])
 
     # execute FK 10 times
     for i in range(10):
-        n_link_arm.set_joint_angles([random_val(-1, 1) for j in range(len(n_link_arm.link_list))])
-        
-        ee_pose = n_link_arm.forward_kinematics(plot=True)
+        n_link_arm.set_joint_angles(
+            [random_val(-1, 1) for j in range(len(n_link_arm.link_list))])
 
+        ee_pose = n_link_arm.forward_kinematics(plot=True)
diff --git a/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py
index 44be22709f..a3838a6a38 100644
--- a/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py
+++ b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py
@@ -3,23 +3,24 @@
 Author: Takayuki Murooka (takayuki5168)
 """
 import math
-from NLinkArm import NLinkArm
+from NLinkArm3d import NLinkArm
 import random
 
 
 def random_val(min_val, max_val):
     return min_val + random.random() * (max_val - min_val)
 
+
 if __name__ == "__main__":
     print("Start solving Inverse Kinematics 10 times")
 
     # init NLinkArm with Denavit-Hartenberg parameters of PR2        
-    n_link_arm = NLinkArm([[0., -math.pi/2, .1, 0.],
-                           [math.pi/2, math.pi/2, 0., 0.],
-                           [0., -math.pi/2, 0., .4],
-                           [0., math.pi/2, 0., 0.],
-                           [0., -math.pi/2, 0., .321],
-                           [0., math.pi/2, 0., 0.],
+    n_link_arm = NLinkArm([[0., -math.pi / 2, .1, 0.],
+                           [math.pi / 2, math.pi / 2, 0., 0.],
+                           [0., -math.pi / 2, 0., .4],
+                           [0., math.pi / 2, 0., 0.],
+                           [0., -math.pi / 2, 0., .321],
+                           [0., math.pi / 2, 0., 0.],
                            [0., 0., 0., 0.]])
 
     # execute IK 10 times
diff --git a/ArmNavigation/n_joint_arm_to_point_control/__init__.py b/ArmNavigation/n_joint_arm_to_point_control/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py
index 2229d63306..55881f4e49 100644
--- a/PathPlanning/AStar/a_star.py
+++ b/PathPlanning/AStar/a_star.py
@@ -41,7 +41,8 @@ def __init__(self, x, y, cost, pind):
             self.pind = pind
 
         def __str__(self):
-            return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind)
+            return str(self.x) + "," + str(self.y) + "," + str(
+                self.cost) + "," + str(self.pind)
 
     def planning(self, sx, sy, gx, gy):
         """
@@ -72,7 +73,10 @@ def planning(self, sx, sy, gx, gy):
                 break
 
             c_id = min(
-                open_set, key=lambda o: open_set[o].cost + self.calc_heuristic(ngoal, open_set[o]))
+                open_set,
+                key=lambda o: open_set[o].cost + self.calc_heuristic(ngoal,
+                                                                     open_set[
+                                                                         o]))
             current = open_set[c_id]
 
             # show graph
@@ -81,7 +85,8 @@ def planning(self, sx, sy, gx, gy):
                          self.calc_grid_position(current.y, self.miny), "xc")
                 # for stopping simulation with the esc key.
                 plt.gcf().canvas.mpl_connect('key_release_event',
-                        lambda event: [exit(0) if event.key == 'escape' else None])
+                                             lambda event: [exit(
+                                                 0) if event.key == 'escape' else None])
                 if len(closed_set.keys()) % 10 == 0:
                     plt.pause(0.001)
 
@@ -104,7 +109,6 @@ def planning(self, sx, sy, gx, gy):
                                  current.cost + self.motion[i][2], c_id)
                 n_id = self.calc_grid_index(node)
 
-
                 # If the node is not safe, do nothing
                 if not self.verify_node(node):
                     continue
diff --git a/PathPlanning/BSplinePath/bspline_path.py b/PathPlanning/BSplinePath/bspline_path.py
index 7c59e99bbb..d4b24c7645 100644
--- a/PathPlanning/BSplinePath/bspline_path.py
+++ b/PathPlanning/BSplinePath/bspline_path.py
@@ -11,7 +11,7 @@
 import scipy.interpolate as scipy_interpolate
 
 
-def approximate_b_spline_path(x: int, y: list, n_path_points: int,
+def approximate_b_spline_path(x: list, y: list, n_path_points: int,
                               degree: int = 3) -> tuple:
     """
     approximate points with a B-Spline path
diff --git a/PathPlanning/CubicSpline/__init__.py b/PathPlanning/CubicSpline/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/PathPlanning/FrenetOptimalTrajectory/cubic_spline_planner.py b/PathPlanning/FrenetOptimalTrajectory/cubic_spline_planner.py
deleted file mode 100644
index 98ef8c4ecf..0000000000
--- a/PathPlanning/FrenetOptimalTrajectory/cubic_spline_planner.py
+++ /dev/null
@@ -1,239 +0,0 @@
-"""
-cubic spline planner
-
-Author: Atsushi Sakai
-
-"""
-import math
-import numpy as np
-import bisect
-
-
-class Spline:
-    """
-    Cubic Spline class
-    """
-
-    def __init__(self, x, y):
-        self.b, self.c, self.d, self.w = [], [], [], []
-
-        self.x = x
-        self.y = y
-
-        self.nx = len(x)  # dimension of x
-        h = np.diff(x)
-
-        # calc coefficient c
-        self.a = [iy for iy in y]
-
-        # calc coefficient c
-        A = self.__calc_A(h)
-        B = self.__calc_B(h)
-        self.c = np.linalg.solve(A, B)
-        #  print(self.c1)
-
-        # calc spline coefficient b and d
-        for i in range(self.nx - 1):
-            self.d.append((self.c[i + 1] - self.c[i]) / (3.0 * h[i]))
-            tb = (self.a[i + 1] - self.a[i]) / h[i] - h[i] * \
-                (self.c[i + 1] + 2.0 * self.c[i]) / 3.0
-            self.b.append(tb)
-
-    def calc(self, t):
-        """
-        Calc position
-
-        if t is outside of the input x, return None
-
-        """
-
-        if t < self.x[0]:
-            return None
-        elif t > self.x[-1]:
-            return None
-
-        i = self.__search_index(t)
-        dx = t - self.x[i]
-        result = self.a[i] + self.b[i] * dx + \
-            self.c[i] * dx ** 2.0 + self.d[i] * dx ** 3.0
-
-        return result
-
-    def calcd(self, t):
-        """
-        Calc first derivative
-
-        if t is outside of the input x, return None
-        """
-
-        if t < self.x[0]:
-            return None
-        elif t > self.x[-1]:
-            return None
-
-        i = self.__search_index(t)
-        dx = t - self.x[i]
-        result = self.b[i] + 2.0 * self.c[i] * dx + 3.0 * self.d[i] * dx ** 2.0
-        return result
-
-    def calcdd(self, t):
-        """
-        Calc second derivative
-        """
-
-        if t < self.x[0]:
-            return None
-        elif t > self.x[-1]:
-            return None
-
-        i = self.__search_index(t)
-        dx = t - self.x[i]
-        result = 2.0 * self.c[i] + 6.0 * self.d[i] * dx
-        return result
-
-    def __search_index(self, x):
-        """
-        search data segment index
-        """
-        return bisect.bisect(self.x, x) - 1
-
-    def __calc_A(self, h):
-        """
-        calc matrix A for spline coefficient c
-        """
-        A = np.zeros((self.nx, self.nx))
-        A[0, 0] = 1.0
-        for i in range(self.nx - 1):
-            if i != (self.nx - 2):
-                A[i + 1, i + 1] = 2.0 * (h[i] + h[i + 1])
-            A[i + 1, i] = h[i]
-            A[i, i + 1] = h[i]
-
-        A[0, 1] = 0.0
-        A[self.nx - 1, self.nx - 2] = 0.0
-        A[self.nx - 1, self.nx - 1] = 1.0
-        #  print(A)
-        return A
-
-    def __calc_B(self, h):
-        """
-        calc matrix B for spline coefficient c
-        """
-        B = np.zeros(self.nx)
-        for i in range(self.nx - 2):
-            B[i + 1] = 3.0 * (self.a[i + 2] - self.a[i + 1]) / \
-                h[i + 1] - 3.0 * (self.a[i + 1] - self.a[i]) / h[i]
-        #  print(B)
-        return B
-
-
-class Spline2D:
-    """
-    2D Cubic Spline class
-
-    """
-
-    def __init__(self, x, y):
-        self.s = self.__calc_s(x, y)
-        self.sx = Spline(self.s, x)
-        self.sy = Spline(self.s, y)
-
-    def __calc_s(self, x, y):
-        dx = np.diff(x)
-        dy = np.diff(y)
-        self.ds = [math.sqrt(idx ** 2 + idy ** 2)
-                   for (idx, idy) in zip(dx, dy)]
-        s = [0]
-        s.extend(np.cumsum(self.ds))
-        return s
-
-    def calc_position(self, s):
-        """
-        calc position
-        """
-        x = self.sx.calc(s)
-        y = self.sy.calc(s)
-
-        return x, y
-
-    def calc_curvature(self, s):
-        """
-        calc curvature
-        """
-        dx = self.sx.calcd(s)
-        ddx = self.sx.calcdd(s)
-        dy = self.sy.calcd(s)
-        ddy = self.sy.calcdd(s)
-        k = (ddy * dx - ddx * dy) / (dx ** 2 + dy ** 2)
-        return k
-
-    def calc_yaw(self, s):
-        """
-        calc yaw
-        """
-        dx = self.sx.calcd(s)
-        dy = self.sy.calcd(s)
-        yaw = math.atan2(dy, dx)
-        return yaw
-
-
-def calc_spline_course(x, y, ds=0.1):
-    sp = Spline2D(x, y)
-    s = list(np.arange(0, sp.s[-1], ds))
-
-    rx, ry, ryaw, rk = [], [], [], []
-    for i_s in s:
-        ix, iy = sp.calc_position(i_s)
-        rx.append(ix)
-        ry.append(iy)
-        ryaw.append(sp.calc_yaw(i_s))
-        rk.append(sp.calc_curvature(i_s))
-
-    return rx, ry, ryaw, rk, s
-
-
-def main():  # pragma: no cover
-    print("Spline 2D test")
-    import matplotlib.pyplot as plt
-    x = [-2.5, 0.0, 2.5, 5.0, 7.5, 3.0, -1.0]
-    y = [0.7, -6, 5, 6.5, 0.0, 5.0, -2.0]
-
-    sp = Spline2D(x, y)
-    s = np.arange(0, sp.s[-1], 0.1)
-
-    rx, ry, ryaw, rk = [], [], [], []
-    for i_s in s:
-        ix, iy = sp.calc_position(i_s)
-        rx.append(ix)
-        ry.append(iy)
-        ryaw.append(sp.calc_yaw(i_s))
-        rk.append(sp.calc_curvature(i_s))
-
-    plt.subplots(1)
-    plt.plot(x, y, "xb", label="input")
-    plt.plot(rx, ry, "-r", label="spline")
-    plt.grid(True)
-    plt.axis("equal")
-    plt.xlabel("x[m]")
-    plt.ylabel("y[m]")
-    plt.legend()
-
-    plt.subplots(1)
-    plt.plot(s, [np.rad2deg(iyaw) for iyaw in ryaw], "-r", label="yaw")
-    plt.grid(True)
-    plt.legend()
-    plt.xlabel("line length[m]")
-    plt.ylabel("yaw angle[deg]")
-
-    plt.subplots(1)
-    plt.plot(s, rk, "-r", label="curvature")
-    plt.grid(True)
-    plt.legend()
-    plt.xlabel("line length[m]")
-    plt.ylabel("curvature [1/m]")
-
-    plt.show()
-
-
-if __name__ == '__main__':  # pragma: no cover
-    main()
diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
index b113bcbe32..ad3da20835 100644
--- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
+++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
@@ -6,9 +6,11 @@
 
 Ref:
 
-- [Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame](https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf)
+- [Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame]
+(https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf)
 
-- [Optimal trajectory generation for dynamic street scenarios in a Frenet Frame](https://www.youtube.com/watch?v=Cj6tAQe7UCY)
+- [Optimal trajectory generation for dynamic street scenarios in a Frenet Frame]
+(https://www.youtube.com/watch?v=Cj6tAQe7UCY)
 
 """
 
@@ -16,19 +18,20 @@
 import matplotlib.pyplot as plt
 import copy
 import math
-import cubic_spline_planner
 import sys
 import os
 
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
                 "/../QuinticPolynomialsPlanner/")
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../CubicSpline/")
 
 try:
     from quintic_polynomials_planner import QuinticPolynomial
+    import cubic_spline_planner
 except ImportError:
     raise
 
-
 SIM_LOOP = 500
 
 # Parameter
@@ -38,36 +41,35 @@
 MAX_ROAD_WIDTH = 7.0  # maximum road width [m]
 D_ROAD_W = 1.0  # road width sampling length [m]
 DT = 0.2  # time tick [s]
-MAXT = 5.0  # max prediction time [m]
-MINT = 4.0  # min prediction time [m]
+MAX_T = 5.0  # max prediction time [m]
+MIN_T = 4.0  # min prediction time [m]
 TARGET_SPEED = 30.0 / 3.6  # target speed [m/s]
 D_T_S = 5.0 / 3.6  # target speed sampling length [m/s]
 N_S_SAMPLE = 1  # sampling number of target speed
 ROBOT_RADIUS = 2.0  # robot radius [m]
 
 # cost weights
-KJ = 0.1
-KT = 0.1
-KD = 1.0
-KLAT = 1.0
-KLON = 1.0
+K_J = 0.1
+K_T = 0.1
+K_D = 1.0
+K_LAT = 1.0
+K_LON = 1.0
 
 show_animation = True
 
 
-class quartic_polynomial:
-
-    def __init__(self, xs, vxs, axs, vxe, axe, T):
+class QuarticPolynomial:
 
+    def __init__(self, xs, vxs, axs, vxe, axe, time):
         # calc coefficient of quartic polynomial
 
         self.a0 = xs
         self.a1 = vxs
         self.a2 = axs / 2.0
 
-        A = np.array([[3 * T ** 2, 4 * T ** 3],
-                      [6 * T, 12 * T ** 2]])
-        b = np.array([vxe - self.a1 - 2 * self.a2 * T,
+        A = np.array([[3 * time ** 2, 4 * time ** 3],
+                      [6 * time, 12 * time ** 2]])
+        b = np.array([vxe - self.a1 - 2 * self.a2 * time,
                       axe - 2 * self.a2])
         x = np.linalg.solve(A, b)
 
@@ -75,19 +77,19 @@ def __init__(self, xs, vxs, axs, vxe, axe, T):
         self.a4 = x[1]
 
     def calc_point(self, t):
-        xt = self.a0 + self.a1 * t + self.a2 * t**2 + \
-            self.a3 * t**3 + self.a4 * t**4
+        xt = self.a0 + self.a1 * t + self.a2 * t ** 2 + \
+             self.a3 * t ** 3 + self.a4 * t ** 4
 
         return xt
 
     def calc_first_derivative(self, t):
         xt = self.a1 + 2 * self.a2 * t + \
-            3 * self.a3 * t**2 + 4 * self.a4 * t**3
+             3 * self.a3 * t ** 2 + 4 * self.a4 * t ** 3
 
         return xt
 
     def calc_second_derivative(self, t):
-        xt = 2 * self.a2 + 6 * self.a3 * t + 12 * self.a4 * t**2
+        xt = 2 * self.a2 + 6 * self.a3 * t + 12 * self.a4 * t ** 2
 
         return xt
 
@@ -97,7 +99,7 @@ def calc_third_derivative(self, t):
         return xt
 
 
-class Frenet_path:
+class FrenetPath:
 
     def __init__(self):
         self.t = []
@@ -121,15 +123,14 @@ def __init__(self):
 
 
 def calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0):
-
     frenet_paths = []
 
     # generate path to each offset goal
     for di in np.arange(-MAX_ROAD_WIDTH, MAX_ROAD_WIDTH, D_ROAD_W):
 
         # Lateral motion planning
-        for Ti in np.arange(MINT, MAXT, DT):
-            fp = Frenet_path()
+        for Ti in np.arange(MIN_T, MAX_T, DT):
+            fp = FrenetPath()
 
             # lat_qp = quintic_polynomial(c_d, c_d_d, c_d_dd, di, 0.0, 0.0, Ti)
             lat_qp = QuinticPolynomial(c_d, c_d_d, c_d_dd, di, 0.0, 0.0, Ti)
@@ -141,9 +142,10 @@ def calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0):
             fp.d_ddd = [lat_qp.calc_third_derivative(t) for t in fp.t]
 
             # Longitudinal motion planning (Velocity keeping)
-            for tv in np.arange(TARGET_SPEED - D_T_S * N_S_SAMPLE, TARGET_SPEED + D_T_S * N_S_SAMPLE, D_T_S):
+            for tv in np.arange(TARGET_SPEED - D_T_S * N_S_SAMPLE,
+                                TARGET_SPEED + D_T_S * N_S_SAMPLE, D_T_S):
                 tfp = copy.deepcopy(fp)
-                lon_qp = quartic_polynomial(s0, c_speed, 0.0, tv, 0.0, Ti)
+                lon_qp = QuarticPolynomial(s0, c_speed, 0.0, tv, 0.0, Ti)
 
                 tfp.s = [lon_qp.calc_point(t) for t in fp.t]
                 tfp.s_d = [lon_qp.calc_first_derivative(t) for t in fp.t]
@@ -154,11 +156,11 @@ def calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0):
                 Js = sum(np.power(tfp.s_ddd, 2))  # square of jerk
 
                 # square of diff from target speed
-                ds = (TARGET_SPEED - tfp.s_d[-1])**2
+                ds = (TARGET_SPEED - tfp.s_d[-1]) ** 2
 
-                tfp.cd = KJ * Jp + KT * Ti + KD * tfp.d[-1]**2
-                tfp.cv = KJ * Js + KT * Ti + KD * ds
-                tfp.cf = KLAT * tfp.cd + KLON * tfp.cv
+                tfp.cd = K_J * Jp + K_T * Ti + K_D * tfp.d[-1] ** 2
+                tfp.cv = K_J * Js + K_T * Ti + K_D * ds
+                tfp.cf = K_LAT * tfp.cd + K_LON * tfp.cv
 
                 frenet_paths.append(tfp)
 
@@ -166,7 +168,6 @@ def calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0):
 
 
 def calc_global_paths(fplist, csp):
-
     for fp in fplist:
 
         # calc global positions
@@ -174,10 +175,10 @@ def calc_global_paths(fplist, csp):
             ix, iy = csp.calc_position(fp.s[i])
             if ix is None:
                 break
-            iyaw = csp.calc_yaw(fp.s[i])
+            i_yaw = csp.calc_yaw(fp.s[i])
             di = fp.d[i]
-            fx = ix + di * math.cos(iyaw + math.pi / 2.0)
-            fy = iy + di * math.sin(iyaw + math.pi / 2.0)
+            fx = ix + di * math.cos(i_yaw + math.pi / 2.0)
+            fy = iy + di * math.sin(i_yaw + math.pi / 2.0)
             fp.x.append(fx)
             fp.y.append(fy)
 
@@ -199,12 +200,11 @@ def calc_global_paths(fplist, csp):
 
 
 def check_collision(fp, ob):
-
     for i in range(len(ob[:, 0])):
-        d = [((ix - ob[i, 0])**2 + (iy - ob[i, 1])**2)
+        d = [((ix - ob[i, 0]) ** 2 + (iy - ob[i, 1]) ** 2)
              for (ix, iy) in zip(fp.x, fp.y)]
 
-        collision = any([di <= ROBOT_RADIUS**2 for di in d])
+        collision = any([di <= ROBOT_RADIUS ** 2 for di in d])
 
         if collision:
             return False
@@ -213,38 +213,38 @@ def check_collision(fp, ob):
 
 
 def check_paths(fplist, ob):
-
-    okind = []
+    ok_ind = []
     for i, _ in enumerate(fplist):
         if any([v > MAX_SPEED for v in fplist[i].s_d]):  # Max speed check
             continue
-        elif any([abs(a) > MAX_ACCEL for a in fplist[i].s_dd]):  # Max accel check
+        elif any([abs(a) > MAX_ACCEL for a in
+                  fplist[i].s_dd]):  # Max accel check
             continue
-        elif any([abs(c) > MAX_CURVATURE for c in fplist[i].c]):  # Max curvature check
+        elif any([abs(c) > MAX_CURVATURE for c in
+                  fplist[i].c]):  # Max curvature check
             continue
         elif not check_collision(fplist[i], ob):
             continue
 
-        okind.append(i)
+        ok_ind.append(i)
 
-    return [fplist[i] for i in okind]
+    return [fplist[i] for i in ok_ind]
 
 
 def frenet_optimal_planning(csp, s0, c_speed, c_d, c_d_d, c_d_dd, ob):
-
     fplist = calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0)
     fplist = calc_global_paths(fplist, csp)
     fplist = check_paths(fplist, ob)
 
     # find minimum cost path
-    mincost = float("inf")
-    bestpath = None
+    min_cost = float("inf")
+    best_path = None
     for fp in fplist:
-        if mincost >= fp.cf:
-            mincost = fp.cf
-            bestpath = fp
+        if min_cost >= fp.cf:
+            min_cost = fp.cf
+            best_path = fp
 
-    return bestpath
+    return best_path
 
 
 def generate_target_course(x, y):
@@ -282,7 +282,7 @@ def main():
     c_speed = 10.0 / 3.6  # current speed [m/s]
     c_d = 2.0  # current lateral position [m]
     c_d_d = 0.0  # current lateral speed [m/s]
-    c_d_dd = 0.0  # current latral acceleration [m/s]
+    c_d_dd = 0.0  # current lateral acceleration [m/s]
     s0 = 0.0  # current course position
 
     area = 20.0  # animation area length [m]
@@ -304,8 +304,9 @@ def main():
         if show_animation:  # pragma: no cover
             plt.cla()
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(tx, ty)
             plt.plot(ob[:, 0], ob[:, 1], "xk")
             plt.plot(path.x[1:], path.y[1:], "-or")
diff --git a/PathPlanning/HybridAStar/__init__.py b/PathPlanning/HybridAStar/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/PathPlanning/HybridAStar/a_star.py b/PathPlanning/HybridAStar/a_star_heuristic.py
similarity index 100%
rename from PathPlanning/HybridAStar/a_star.py
rename to PathPlanning/HybridAStar/a_star_heuristic.py
diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
index 725976dc45..41e8b393c0 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -14,7 +14,7 @@
 import sys
 sys.path.append("../ReedsSheppPath/")
 try:
-    from a_star import dp_planning  # , calc_obstacle_map
+    from a_star_heuristic import dp_planning  # , calc_obstacle_map
     import reeds_shepp_path_planning as rs
     from car import move, check_car_collision, MAX_STEER, WB, plot_car
 except:

From ae2593b44425f805da85e245d925554cd8336700 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 9 Mar 2020 23:04:47 +0900
Subject: [PATCH 0347/1080] mypy fix test

---
 .github/workflows/pythonpackage.yml | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml
index 1844e53820..b3f1aa364c 100644
--- a/.github/workflows/pythonpackage.yml
+++ b/.github/workflows/pythonpackage.yml
@@ -9,7 +9,7 @@ jobs:
     strategy:
       max-parallel: 4
       matrix:
-        python-version: [3.6, 3.7]
+        python-version: [3.7, 3.8]
 
     steps:
     - uses: actions/checkout@v1
@@ -34,7 +34,15 @@ jobs:
       run: pip install mypy
     - name: mypy check
       run: |
-        find . -name "*.py" | xargs mypy
+        find AerialNavigation -name "*.py" | xargs mypy
+        find ArmNavigation -name "*.py" | xargs mypy
+        find Bipedal -name "*.py" | xargs mypy
+        find InvertedPendulumCart -name "*.py" | xargs mypy
+        find Localization -name "*.py" | xargs mypy
+        find Mapping -name "*.py" | xargs mypy
+        find PathPlanning -name "*.py" | xargs mypy
+        find PathTracking -name "*.py" | xargs mypy
+        find SLAM -name "*.py" | xargs mypy
     - name: do all unit tests
       run: bash runtests.sh
 

From 4107c424067fbbac8a855066e5cae03c9f8c4334 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 9 Mar 2020 23:06:48 +0900
Subject: [PATCH 0348/1080] mypy fix test

---
 .github/workflows/pythonpackage.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml
index b3f1aa364c..574c7be9d5 100644
--- a/.github/workflows/pythonpackage.yml
+++ b/.github/workflows/pythonpackage.yml
@@ -9,7 +9,7 @@ jobs:
     strategy:
       max-parallel: 4
       matrix:
-        python-version: [3.7, 3.8]
+        python-version: [3.7]
 
     steps:
     - uses: actions/checkout@v1

From 19630bc0ce9419a70c3ae301fed0f70553a2e5be Mon Sep 17 00:00:00 2001
From: Chachay <t.sasai0802@gmail.com>
Date: Fri, 20 Mar 2020 10:13:41 +0100
Subject: [PATCH 0349/1080] Integrate update function into state class

---
 .../rear_wheel_feedback.py                    | 32 ++++---------------
 1 file changed, 6 insertions(+), 26 deletions(-)

diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
index 758e9ad946..af9fb0680c 100644
--- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
+++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
@@ -16,7 +16,6 @@
 except:
     raise
 
-
 Kp = 1.0  # speed propotional gain
 # steering control parameter
 KTH = 1.0
@@ -26,34 +25,24 @@
 L = 2.9  # [m]
 
 show_animation = True
-#  show_animation = False
-
 
 class State:
-
     def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0):
         self.x = x
         self.y = y
         self.yaw = yaw
         self.v = v
 
-
-def update(state, a, delta):
-
-    state.x = state.x + state.v * math.cos(state.yaw) * dt
-    state.y = state.y + state.v * math.sin(state.yaw) * dt
-    state.yaw = state.yaw + state.v / L * math.tan(delta) * dt
-    state.v = state.v + a * dt
-
-    return state
-
+    def update(self, a, delta, dt):
+        self.x   = self.x + self.v * math.cos(self.yaw) * dt
+        self.y   = self.y + self.v * math.sin(self.yaw) * dt
+        self.yaw = self.yaw + self.v / L * math.tan(delta) * dt
+        self.v   = self.v + a * dt
 
 def PIDControl(target, current):
     a = Kp * (target - current)
-
     return a
 
-
 def pi_2_pi(angle):
     while(angle > math.pi):
         angle = angle - 2.0 * math.pi
@@ -63,7 +52,6 @@ def pi_2_pi(angle):
 
     return angle
 
-
 def rear_wheel_feedback_control(state, cx, cy, cyaw, ck, preind):
     ind, e = calc_nearest_index(state, cx, cy, cyaw)
 
@@ -78,7 +66,6 @@ def rear_wheel_feedback_control(state, cx, cy, cyaw, ck, preind):
         return 0.0, ind
 
     delta = math.atan2(L * omega / v, 1.0)
-    #  print(k, v, e, th_e, omega, delta)
 
     return delta, ind
 
@@ -104,9 +91,7 @@ def calc_nearest_index(state, cx, cy, cyaw):
 
     return ind, mind
 
-
 def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
-
     T = 500.0  # max simulation time
     goal_dis = 0.3
     stop_speed = 0.05
@@ -126,7 +111,7 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
         di, target_ind = rear_wheel_feedback_control(
             state, cx, cy, cyaw, ck, target_ind)
         ai = PIDControl(speed_profile[target_ind], state.v)
-        state = update(state, ai, di)
+        state.update(ai, di, dt)
 
         if abs(state.v) <= stop_speed:
             target_ind += 1
@@ -163,11 +148,8 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
 
     return t, x, y, yaw, v, goal_flag
 
-
 def calc_speed_profile(cx, cy, cyaw, target_speed):
-
     speed_profile = [target_speed] * len(cx)
-
     direction = 1.0
 
     # Set stop point
@@ -190,7 +172,6 @@ def calc_speed_profile(cx, cy, cyaw, target_speed):
 
     return speed_profile
 
-
 def main():
     print("rear wheel feedback tracking start!!")
     ax = [0.0, 6.0, 12.5, 5.0, 7.5, 3.0, -1.0]
@@ -237,6 +218,5 @@ def main():
 
         plt.show()
 
-
 if __name__ == '__main__':
     main()

From 74739078fdcc18cd12a2347efec0dac4ccca6dc2 Mon Sep 17 00:00:00 2001
From: Chachay <t.sasai0802@gmail.com>
Date: Fri, 20 Mar 2020 12:08:19 +0100
Subject: [PATCH 0350/1080] Replace Spline module to Scipy interpolate

---
 .../rear_wheel_feedback.py                    | 139 +++++++++++-------
 1 file changed, 84 insertions(+), 55 deletions(-)

diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
index af9fb0680c..5e6cedb377 100644
--- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
+++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
@@ -9,12 +9,9 @@
 import math
 import numpy as np
 import sys
-sys.path.append("../../PathPlanning/CubicSpline/")
 
-try:
-    import cubic_spline_planner
-except:
-    raise
+from scipy import interpolate
+from scipy import optimize
 
 Kp = 1.0  # speed propotional gain
 # steering control parameter
@@ -39,6 +36,61 @@ def update(self, a, delta, dt):
         self.yaw = self.yaw + self.v / L * math.tan(delta) * dt
         self.v   = self.v + a * dt
 
+class TrackSpline:
+    def __init__(self, x, y):
+        x, y = map(np.asarray, (x, y))
+        s = np.append([0],(np.cumsum(np.diff(x)**2) + np.cumsum(np.diff(y)**2))**0.5)
+
+        self.X = interpolate.CubicSpline(s, x)
+        self.Y = interpolate.CubicSpline(s, y)
+
+        self.dX = self.X.derivative(1)
+        self.ddX = self.X.derivative(2)
+
+        self.dY = self.Y.derivative(1)
+        self.ddY = self.Y.derivative(2)
+
+        self.length = s[-1]
+    
+    def yaw(self, s):
+        dx, dy = self.dX(s), self.dY(s)
+        return np.arctan2(dy, dx)
+    
+    def curvature(self, s):
+        dx, dy   = self.dX(s), self.dY(s)
+        ddx, ddy   = self.ddX(s), self.ddY(s)
+        return (ddy * dx - ddx * dy) / ((dx ** 2 + dy ** 2)**(3 / 2))
+    
+    def __findClosestPoint(self, s0, x, y):
+        def f(_s, *args):
+            _x, _y= self.X(_s), self.Y(_s)
+            return (_x - args[0])**2 + (_y - args[1])**2
+        
+        def jac(_s, *args):
+            _x, _y = self.X(_s), self.Y(_s)
+            _dx, _dy = self.dX(_s), self.dY(_s)
+            return 2*_dx*(_x - args[0])+2*_dy*(_y-args[1])
+
+        minimum = optimize.fmin_cg(f, s0, jac, args=(x, y), full_output=True, disp=False)
+        return minimum
+
+    def TrackError(self, x, y, s0):
+        ret = self.__findClosestPoint(s0, x, y)
+        
+        s = ret[0][0]
+        e = ret[1]
+
+        k   = self.curvature(s)
+        yaw = self.yaw(s)
+
+        dxl = self.X(s) - x
+        dyl = self.Y(s) - y
+        angle = pi_2_pi(yaw - math.atan2(dyl, dxl))
+        if angle < 0:
+            e*= -1
+
+        return e, k, yaw, s
+
 def PIDControl(target, current):
     a = Kp * (target - current)
     return a
@@ -52,46 +104,22 @@ def pi_2_pi(angle):
 
     return angle
 
-def rear_wheel_feedback_control(state, cx, cy, cyaw, ck, preind):
-    ind, e = calc_nearest_index(state, cx, cy, cyaw)
-
-    k = ck[ind]
+def rear_wheel_feedback_control(state, e, k, yaw_r):
     v = state.v
-    th_e = pi_2_pi(state.yaw - cyaw[ind])
+    th_e = pi_2_pi(state.yaw - yaw_r)
 
     omega = v * k * math.cos(th_e) / (1.0 - k * e) - \
         KTH * abs(v) * th_e - KE * v * math.sin(th_e) * e / th_e
 
     if th_e == 0.0 or omega == 0.0:
-        return 0.0, ind
+        return 0.0
 
     delta = math.atan2(L * omega / v, 1.0)
 
-    return delta, ind
-
-
-def calc_nearest_index(state, cx, cy, cyaw):
-    dx = [state.x - icx for icx in cx]
-    dy = [state.y - icy for icy in cy]
-
-    d = [idx ** 2 + idy ** 2 for (idx, idy) in zip(dx, dy)]
-
-    mind = min(d)
-
-    ind = d.index(mind)
+    return delta
 
-    mind = math.sqrt(mind)
 
-    dxl = cx[ind] - state.x
-    dyl = cy[ind] - state.y
-
-    angle = pi_2_pi(cyaw[ind] - math.atan2(dyl, dxl))
-    if angle < 0:
-        mind *= -1
-
-    return ind, mind
-
-def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
+def closed_loop_prediction(track, speed_profile, goal):
     T = 500.0  # max simulation time
     goal_dis = 0.3
     stop_speed = 0.05
@@ -105,17 +133,17 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
     v = [state.v]
     t = [0.0]
     goal_flag = False
-    target_ind = calc_nearest_index(state, cx, cy, cyaw)
+
+    s = np.arange(0, track.length, 0.1)
+    e, k, yaw_r, s0 = track.TrackError(state.x, state.y, 0.0)
 
     while T >= time:
-        di, target_ind = rear_wheel_feedback_control(
-            state, cx, cy, cyaw, ck, target_ind)
-        ai = PIDControl(speed_profile[target_ind], state.v)
+        e, k, yaw_r, s0 = track.TrackError(state.x, state.y, s0)
+        di = rear_wheel_feedback_control(state, e, k, yaw_r)
+        #ai = PIDControl(speed_profile[target_ind], state.v)
+        ai = PIDControl(speed_profile, state.v)
         state.update(ai, di, dt)
 
-        if abs(state.v) <= stop_speed:
-            target_ind += 1
-
         time = time + dt
 
         # check goal
@@ -132,23 +160,22 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
         v.append(state.v)
         t.append(time)
 
-        if target_ind % 1 == 0 and show_animation:
+        if show_animation:
             plt.cla()
             # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
-            plt.plot(cx, cy, "-r", label="course")
+            plt.plot(track.X(s), track.Y(s), "-r", label="course")
             plt.plot(x, y, "ob", label="trajectory")
-            plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
+            plt.plot(track.X(s0), track.Y(s0), "xg", label="target")
             plt.axis("equal")
             plt.grid(True)
-            plt.title("speed[km/h]:" + str(round(state.v * 3.6, 2)) +
-                      ",target index:" + str(target_ind))
+            plt.title("speed[km/h]:{:.2f}, target s-param:{:.2f}".format(round(state.v * 3.6, 2), s0))
             plt.pause(0.0001)
 
     return t, x, y, yaw, v, goal_flag
 
-def calc_speed_profile(cx, cy, cyaw, target_speed):
+def calc_speed_profile(track, target_speed, s):
     speed_profile = [target_speed] * len(cx)
     direction = 1.0
 
@@ -178,14 +205,16 @@ def main():
     ay = [0.0, 0.0, 5.0, 6.5, 3.0, 5.0, -2.0]
     goal = [ax[-1], ay[-1]]
 
-    cx, cy, cyaw, ck, s = cubic_spline_planner.calc_spline_course(
-        ax, ay, ds=0.1)
+    track = TrackSpline(ax, ay)
+    s = np.arange(0, track.length, 0.1)
+
     target_speed = 10.0 / 3.6
 
-    sp = calc_speed_profile(cx, cy, cyaw, target_speed)
+    # Note: disable backward direction temporary
+    #sp = calc_speed_profile(track, target_speed, s)
+    sp = target_speed 
 
-    t, x, y, yaw, v, goal_flag = closed_loop_prediction(
-        cx, cy, cyaw, ck, sp, goal)
+    t, x, y, yaw, v, goal_flag = closed_loop_prediction(track, sp, goal)
 
     # Test
     assert goal_flag, "Cannot goal"
@@ -194,7 +223,7 @@ def main():
         plt.close()
         plt.subplots(1)
         plt.plot(ax, ay, "xb", label="input")
-        plt.plot(cx, cy, "-r", label="spline")
+        plt.plot(track.X(s), track.Y(s), "-r", label="spline")
         plt.plot(x, y, "-g", label="tracking")
         plt.grid(True)
         plt.axis("equal")
@@ -203,14 +232,14 @@ def main():
         plt.legend()
 
         plt.subplots(1)
-        plt.plot(s, [np.rad2deg(iyaw) for iyaw in cyaw], "-r", label="yaw")
+        plt.plot(s, np.rad2deg(track.yaw(s)), "-r", label="yaw")
         plt.grid(True)
         plt.legend()
         plt.xlabel("line length[m]")
         plt.ylabel("yaw angle[deg]")
 
         plt.subplots(1)
-        plt.plot(s, ck, "-r", label="curvature")
+        plt.plot(s, track.curvature(s), "-r", label="curvature")
         plt.grid(True)
         plt.legend()
         plt.xlabel("line length[m]")

From a7c82ccb9e5cc18e6aaca3b3a8ff4de6fdcf1c21 Mon Sep 17 00:00:00 2001
From: Chachay <t.sasai0802@gmail.com>
Date: Mon, 23 Mar 2020 16:00:19 +0100
Subject: [PATCH 0351/1080] - remove unsued import and variable - rename
 functions - add target speed controller (as the substitute of calc speed
 profile)

---
 .../rear_wheel_feedback.py                    | 83 ++++++++-----------
 1 file changed, 34 insertions(+), 49 deletions(-)

diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
index 5e6cedb377..9b69f8eace 100644
--- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
+++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
@@ -8,7 +8,6 @@
 import matplotlib.pyplot as plt
 import math
 import numpy as np
-import sys
 
 from scipy import interpolate
 from scipy import optimize
@@ -24,11 +23,12 @@
 show_animation = True
 
 class State:
-    def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0):
+    def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0, direction=1):
         self.x = x
         self.y = y
         self.yaw = yaw
         self.v = v
+        self.direction = direction
 
     def update(self, a, delta, dt):
         self.x   = self.x + self.v * math.cos(self.yaw) * dt
@@ -52,36 +52,36 @@ def __init__(self, x, y):
 
         self.length = s[-1]
     
-    def yaw(self, s):
+    def calc_yaw(self, s):
         dx, dy = self.dX(s), self.dY(s)
         return np.arctan2(dy, dx)
     
-    def curvature(self, s):
+    def calc_curvature(self, s):
         dx, dy   = self.dX(s), self.dY(s)
         ddx, ddy   = self.ddX(s), self.ddY(s)
         return (ddy * dx - ddx * dy) / ((dx ** 2 + dy ** 2)**(3 / 2))
     
-    def __findClosestPoint(self, s0, x, y):
-        def f(_s, *args):
+    def __find_nearest_point(self, s0, x, y):
+        def calc_distance(_s, *args):
             _x, _y= self.X(_s), self.Y(_s)
             return (_x - args[0])**2 + (_y - args[1])**2
         
-        def jac(_s, *args):
+        def calc_distance_jacobian(_s, *args):
             _x, _y = self.X(_s), self.Y(_s)
             _dx, _dy = self.dX(_s), self.dY(_s)
             return 2*_dx*(_x - args[0])+2*_dy*(_y-args[1])
 
-        minimum = optimize.fmin_cg(f, s0, jac, args=(x, y), full_output=True, disp=False)
+        minimum = optimize.fmin_cg(calc_distance, s0, calc_distance_jacobian, args=(x, y), full_output=True, disp=False)
         return minimum
 
-    def TrackError(self, x, y, s0):
-        ret = self.__findClosestPoint(s0, x, y)
+    def calc_track_error(self, x, y, s0):
+        ret = self.__find_nearest_point(s0, x, y)
         
         s = ret[0][0]
         e = ret[1]
 
-        k   = self.curvature(s)
-        yaw = self.yaw(s)
+        k   = self.calc_curvature(s)
+        yaw = self.calc_yaw(s)
 
         dxl = self.X(s) - x
         dyl = self.Y(s) - y
@@ -91,7 +91,7 @@ def TrackError(self, x, y, s0):
 
         return e, k, yaw, s
 
-def PIDControl(target, current):
+def pid_control(target, current):
     a = Kp * (target - current)
     return a
 
@@ -119,10 +119,9 @@ def rear_wheel_feedback_control(state, e, k, yaw_r):
     return delta
 
 
-def closed_loop_prediction(track, speed_profile, goal):
+def simulate(track, goal):
     T = 500.0  # max simulation time
     goal_dis = 0.3
-    stop_speed = 0.05
 
     state = State(x=-0.0, y=-0.0, yaw=0.0, v=0.0)
 
@@ -135,13 +134,14 @@ def closed_loop_prediction(track, speed_profile, goal):
     goal_flag = False
 
     s = np.arange(0, track.length, 0.1)
-    e, k, yaw_r, s0 = track.TrackError(state.x, state.y, 0.0)
+    e, k, yaw_r, s0 = track.calc_track_error(state.x, state.y, 0.0)
 
     while T >= time:
-        e, k, yaw_r, s0 = track.TrackError(state.x, state.y, s0)
+        e, k, yaw_r, s0 = track.calc_track_error(state.x, state.y, s0)
         di = rear_wheel_feedback_control(state, e, k, yaw_r)
-        #ai = PIDControl(speed_profile[target_ind], state.v)
-        ai = PIDControl(speed_profile, state.v)
+
+        speed_r = calc_target_speed(state, yaw_r)
+        ai = pid_control(speed_r, state.v)
         state.update(ai, di, dt)
 
         time = time + dt
@@ -175,29 +175,20 @@ def closed_loop_prediction(track, speed_profile, goal):
 
     return t, x, y, yaw, v, goal_flag
 
-def calc_speed_profile(track, target_speed, s):
-    speed_profile = [target_speed] * len(cx)
-    direction = 1.0
-
-    # Set stop point
-    for i in range(len(cx) - 1):
-        dyaw = cyaw[i + 1] - cyaw[i]
-        switch = math.pi / 4.0 <= dyaw < math.pi / 2.0
-
-        if switch:
-            direction *= -1
-
-        if direction != 1.0:
-            speed_profile[i] = - target_speed
-        else:
-            speed_profile[i] = target_speed
-
-        if switch:
-            speed_profile[i] = 0.0
+def calc_target_speed(state, yaw_r):
+    target_speed = 10.0 / 3.6
 
-    speed_profile[-1] = 0.0
+    dyaw = yaw_r - state.yaw
+    switch = math.pi / 4.0 <= dyaw < math.pi / 2.0
 
-    return speed_profile
+    if switch:
+        state.direction *= -1
+        return 0.0
+    
+    if state.direction != 1:
+        return -target_speed
+    else:
+        return target_speed
 
 def main():
     print("rear wheel feedback tracking start!!")
@@ -208,13 +199,7 @@ def main():
     track = TrackSpline(ax, ay)
     s = np.arange(0, track.length, 0.1)
 
-    target_speed = 10.0 / 3.6
-
-    # Note: disable backward direction temporary
-    #sp = calc_speed_profile(track, target_speed, s)
-    sp = target_speed 
-
-    t, x, y, yaw, v, goal_flag = closed_loop_prediction(track, sp, goal)
+    t, x, y, yaw, v, goal_flag = simulate(track, goal)
 
     # Test
     assert goal_flag, "Cannot goal"
@@ -232,14 +217,14 @@ def main():
         plt.legend()
 
         plt.subplots(1)
-        plt.plot(s, np.rad2deg(track.yaw(s)), "-r", label="yaw")
+        plt.plot(s, np.rad2deg(track.calc_yaw(s)), "-r", label="yaw")
         plt.grid(True)
         plt.legend()
         plt.xlabel("line length[m]")
         plt.ylabel("yaw angle[deg]")
 
         plt.subplots(1)
-        plt.plot(s, track.curvature(s), "-r", label="curvature")
+        plt.plot(s, track.calc_curvature(s), "-r", label="curvature")
         plt.grid(True)
         plt.legend()
         plt.xlabel("line length[m]")

From 7005a664e1b65d6751d1cb323833acff7c7505ca Mon Sep 17 00:00:00 2001
From: Chachay <t.sasai0802@gmail.com>
Date: Mon, 23 Mar 2020 16:10:11 +0100
Subject: [PATCH 0352/1080] - Changed Class name (CubicSplinePath) - remove
 unnecessary else case

---
 PathTracking/rear_wheel_feedback/rear_wheel_feedback.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
index 9b69f8eace..8f1079b074 100644
--- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
+++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
@@ -36,7 +36,7 @@ def update(self, a, delta, dt):
         self.yaw = self.yaw + self.v / L * math.tan(delta) * dt
         self.v   = self.v + a * dt
 
-class TrackSpline:
+class CubicSplinePath:
     def __init__(self, x, y):
         x, y = map(np.asarray, (x, y))
         s = np.append([0],(np.cumsum(np.diff(x)**2) + np.cumsum(np.diff(y)**2))**0.5)
@@ -187,8 +187,8 @@ def calc_target_speed(state, yaw_r):
     
     if state.direction != 1:
         return -target_speed
-    else:
-        return target_speed
+
+    return target_speed
 
 def main():
     print("rear wheel feedback tracking start!!")
@@ -196,7 +196,7 @@ def main():
     ay = [0.0, 0.0, 5.0, 6.5, 3.0, 5.0, -2.0]
     goal = [ax[-1], ay[-1]]
 
-    track = TrackSpline(ax, ay)
+    track = CubicSplinePath(ax, ay)
     s = np.arange(0, track.length, 0.1)
 
     t, x, y, yaw, v, goal_flag = simulate(track, goal)

From e880b0efe448e7024f22bd3f70c26fe8ba7ef075 Mon Sep 17 00:00:00 2001
From: Michael Dobler <michael.dobler@outlook.com>
Date: Tue, 24 Mar 2020 12:59:49 +0100
Subject: [PATCH 0353/1080] Fix parameter of jacobian in EKF-SLAM

---
 SLAM/EKFSLAM/ekf_slam.ipynb | 2 +-
 SLAM/EKFSLAM/ekf_slam.py    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index ccf5e5bbd6..a64c145ad4 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -305,8 +305,8 @@
     "    :returns:    predicted state vector, predicted covariance, jacobian of control vector, transition fx\n",
     "    \"\"\"\n",
     "    S = STATE_SIZE\n",
-    "    xEst[0:S] = motion_model(xEst[0:S], u)\n",
     "    G, Fx = jacob_motion(xEst[0:S], u)\n",
+    "    xEst[0:S] = motion_model(xEst[0:S], u)\n",
     "    # Fx is an an identity matrix of size (STATE_SIZE)\n",
     "    # sigma = G*sigma*G.T + Noise\n",
     "    PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx\n",
diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py
index 3038e40a47..6b349d858b 100644
--- a/SLAM/EKFSLAM/ekf_slam.py
+++ b/SLAM/EKFSLAM/ekf_slam.py
@@ -29,8 +29,8 @@
 def ekf_slam(xEst, PEst, u, z):
     # Predict
     S = STATE_SIZE
-    xEst[0:S] = motion_model(xEst[0:S], u)
     G, Fx = jacob_motion(xEst[0:S], u)
+    xEst[0:S] = motion_model(xEst[0:S], u)
     PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx
     initP = np.eye(2)
 

From 6d178e1feb3740c4b7dc67136b9b701f22c6e6e6 Mon Sep 17 00:00:00 2001
From: Chachay <t.sasai0802@gmail.com>
Date: Tue, 24 Mar 2020 16:33:43 +0100
Subject: [PATCH 0354/1080] rename yaw_r to yaw_ref

---
 .../rear_wheel_feedback/rear_wheel_feedback.py     | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
index 8f1079b074..f6df94ac44 100644
--- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
+++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
@@ -104,9 +104,9 @@ def pi_2_pi(angle):
 
     return angle
 
-def rear_wheel_feedback_control(state, e, k, yaw_r):
+def rear_wheel_feedback_control(state, e, k, yaw_ref):
     v = state.v
-    th_e = pi_2_pi(state.yaw - yaw_r)
+    th_e = pi_2_pi(state.yaw - yaw_ref)
 
     omega = v * k * math.cos(th_e) / (1.0 - k * e) - \
         KTH * abs(v) * th_e - KE * v * math.sin(th_e) * e / th_e
@@ -134,14 +134,14 @@ def simulate(track, goal):
     goal_flag = False
 
     s = np.arange(0, track.length, 0.1)
-    e, k, yaw_r, s0 = track.calc_track_error(state.x, state.y, 0.0)
+    e, k, yaw_ref, s0 = track.calc_track_error(state.x, state.y, 0.0)
 
     while T >= time:
-        e, k, yaw_r, s0 = track.calc_track_error(state.x, state.y, s0)
-        di = rear_wheel_feedback_control(state, e, k, yaw_r)
+        e, k, yaw_ref, s0 = track.calc_track_error(state.x, state.y, s0)
+        di = rear_wheel_feedback_control(state, e, k, yaw_ref)
 
-        speed_r = calc_target_speed(state, yaw_r)
-        ai = pid_control(speed_r, state.v)
+        speed_ref = calc_target_speed(state, yaw_ref)
+        ai = pid_control(speed_ref, state.v)
         state.update(ai, di, dt)
 
         time = time + dt

From b61a8d8fb5d47c89c97ec8081942ce33f673538c Mon Sep 17 00:00:00 2001
From: Jeff Irion <jefflirion@users.noreply.github.com>
Date: Tue, 24 Mar 2020 10:56:11 -0700
Subject: [PATCH 0355/1080] Add Graph SLAM formulation PDF and SE(2) solver +
 example

---
 SLAM/GraphBasedSLAM/LaTeX/.gitignore          |    3 +
 SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib       |   30 +
 .../LaTeX/graphSLAM_formulation.tex           |  175 ++
 SLAM/GraphBasedSLAM/data/README.rst           |    6 +
 SLAM/GraphBasedSLAM/data/input_INTEL.g2o      | 2711 +++++++++++++++++
 .../graphSLAM_SE2_example.ipynb               |  332 ++
 SLAM/GraphBasedSLAM/graphSLAM_formulation.pdf |  Bin 0 -> 276368 bytes
 SLAM/GraphBasedSLAM/graphslam/__init__.py     |    9 +
 .../GraphBasedSLAM/graphslam/edge/__init__.py |    5 +
 .../graphslam/edge/edge_odometry.py           |  180 ++
 SLAM/GraphBasedSLAM/graphslam/graph.py        |  265 ++
 SLAM/GraphBasedSLAM/graphslam/load.py         |   66 +
 .../GraphBasedSLAM/graphslam/pose/__init__.py |    5 +
 SLAM/GraphBasedSLAM/graphslam/pose/se2.py     |  171 ++
 SLAM/GraphBasedSLAM/graphslam/util.py         |   78 +
 SLAM/GraphBasedSLAM/graphslam/vertex.py       |   67 +
 .../images/Graph_SLAM_optimization.gif        |  Bin 0 -> 114966 bytes
 17 files changed, 4103 insertions(+)
 create mode 100644 SLAM/GraphBasedSLAM/LaTeX/.gitignore
 create mode 100644 SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib
 create mode 100644 SLAM/GraphBasedSLAM/LaTeX/graphSLAM_formulation.tex
 create mode 100644 SLAM/GraphBasedSLAM/data/README.rst
 create mode 100644 SLAM/GraphBasedSLAM/data/input_INTEL.g2o
 create mode 100644 SLAM/GraphBasedSLAM/graphSLAM_SE2_example.ipynb
 create mode 100644 SLAM/GraphBasedSLAM/graphSLAM_formulation.pdf
 create mode 100644 SLAM/GraphBasedSLAM/graphslam/__init__.py
 create mode 100644 SLAM/GraphBasedSLAM/graphslam/edge/__init__.py
 create mode 100644 SLAM/GraphBasedSLAM/graphslam/edge/edge_odometry.py
 create mode 100644 SLAM/GraphBasedSLAM/graphslam/graph.py
 create mode 100644 SLAM/GraphBasedSLAM/graphslam/load.py
 create mode 100644 SLAM/GraphBasedSLAM/graphslam/pose/__init__.py
 create mode 100644 SLAM/GraphBasedSLAM/graphslam/pose/se2.py
 create mode 100644 SLAM/GraphBasedSLAM/graphslam/util.py
 create mode 100644 SLAM/GraphBasedSLAM/graphslam/vertex.py
 create mode 100644 SLAM/GraphBasedSLAM/images/Graph_SLAM_optimization.gif

diff --git a/SLAM/GraphBasedSLAM/LaTeX/.gitignore b/SLAM/GraphBasedSLAM/LaTeX/.gitignore
new file mode 100644
index 0000000000..b0b343402c
--- /dev/null
+++ b/SLAM/GraphBasedSLAM/LaTeX/.gitignore
@@ -0,0 +1,3 @@
+# Ignore LaTeX generated files
+graphSLAM_formulation.*
+!graphSLAM_formulation.tex
diff --git a/SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib b/SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib
new file mode 100644
index 0000000000..4d3b71ae50
--- /dev/null
+++ b/SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib
@@ -0,0 +1,30 @@
+@article{blanco2010tutorial,
+  title={A tutorial on ${SE}(3)$ transformation parameterizations and on-manifold optimization},
+  author={Blanco, Jose-Luis},
+  journal={University of Malaga, Tech. Rep},
+  volume={3},
+  year={2010},
+  publisher={Citeseer}
+}
+
+@article{grisetti2010tutorial,
+  title={A tutorial on graph-based {SLAM}},
+  author={Grisetti, Giorgio and Kummerle, Rainer and Stachniss, Cyrill and Burgard, Wolfram},
+  journal={IEEE Intelligent Transportation Systems Magazine},
+  volume={2},
+  number={4},
+  pages={31--43},
+  year={2010},
+  publisher={IEEE}
+}
+
+@article{thrun2006graph,
+  title={The graph {SLAM} algorithm with applications to large-scale mapping of urban structures},
+  author={Thrun, Sebastian and Montemerlo, Michael},
+  journal={The International Journal of Robotics Research},
+  volume={25},
+  number={5-6},
+  pages={403--429},
+  year={2006},
+  publisher={SAGE Publications}
+}
diff --git a/SLAM/GraphBasedSLAM/LaTeX/graphSLAM_formulation.tex b/SLAM/GraphBasedSLAM/LaTeX/graphSLAM_formulation.tex
new file mode 100644
index 0000000000..25f02cd3bd
--- /dev/null
+++ b/SLAM/GraphBasedSLAM/LaTeX/graphSLAM_formulation.tex
@@ -0,0 +1,175 @@
+\documentclass{article}
+
+\usepackage{amsfonts}
+\usepackage{amsmath,amssymb,amsfonts}
+\usepackage{textcomp}
+\usepackage{fullpage}
+\usepackage{setspace}
+\usepackage{float}
+\usepackage{cite}
+\usepackage{graphicx}
+\usepackage{caption}
+\usepackage{subcaption}
+\usepackage[pdfborder={0 0 0}, pdfpagemode=UseNone, pdfstartview=FitH]{hyperref}
+
+\DeclareMathOperator*{\argmax}{arg\,max}
+\DeclareMathOperator*{\argmin}{arg\,min}
+
+\def\keyterm{\textit}
+
+\newcommand{\transp}{{\scriptstyle{\mathsf{T}}}}
+
+
+
+\begin{document}
+
+\title{Graph SLAM Formulation}
+\author{Jeff Irion}
+\date{}
+
+\maketitle
+\vspace{3em}
+
+
+\section{Problem Formulation}
+
+Let a robot's trajectory through its environment be represented by a sequence of $N$ poses: $\mathbf{p}_1, \mathbf{p}_2, \ldots, \mathbf{p}_N$.  Each pose lies on a manifold: $\mathbf{p}_i \in \mathcal{M}$.  Simple examples of manifolds used in Graph SLAM include 1-D, 2-D, and 3-D space, i.e., $\mathbb{R}$, $\mathbb{R}^2$, and $\mathbb{R}^3$.  These environments are \keyterm{rectilinear}, meaning that there is no concept of orientation.  By contrast, in $SE(2)$ problem settings a robot's pose consists of its location in $\mathbb{R}^2$ and its orientation $\theta$.  Similarly, in $SE(3)$ a robot's pose consists of its location in $\mathbb{R}^3$ and its orientation, which can be represented via Euler angles, quaternions, or $SO(3)$ rotation matrices.  
+
+As the robot explores its environment, it collects a set of $M$ measurements $\mathcal{Z} = \{\mathbf{z}_j\}$.  Examples of such measurements include odometry, GPS, and IMU data.  Given a set of poses $\mathbf{p}_1, \ldots, \mathbf{p}_N$, we can compute the estimated measurement $\hat{\mathbf{z}}_j(\mathbf{p}_1, \ldots, \mathbf{p}_N)$.  We can then compute the \keyterm{residual} $\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j)$ for measurement $j$.  The formula for the residual depends on the type of measurement.  As an example, let $\mathbf{z}_1$ be an odometry measurement that was collected when the robot traveled from $\mathbf{p}_1$ to $\mathbf{p}_2$.  The expected measurement and the residual are computed as
+%
+\begin{align*}
+    \hat{\mathbf{z}}_1(\mathbf{p}_1, \mathbf{p}_2) &= \mathbf{p}_2 \ominus \mathbf{p}_1 \\
+    \mathbf{e}_1(\mathbf{z}_1, \hat{\mathbf{z}}_1) &= \mathbf{z}_1 \ominus \hat{\mathbf{z}}_1 = \mathbf{z}_1 \ominus (\mathbf{p}_2 \ominus \mathbf{p}_1),
+\end{align*}
+%
+where the $\ominus$ operator indicates inverse pose composition.  We model measurement $\mathbf{z}_j$ as having independent Gaussian noise with zero mean and covariance matrix $\Omega_j^{-1}$; we refer to $\Omega_j$ as the \keyterm{information matrix} for measurement $j$.  That is,
+\begin{equation}
+    p(\mathbf{z}_j \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) = \eta_j \exp \left( (-\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^\transp \Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j) \right), \label{eq:observation_probability}
+\end{equation}
+where $\eta_j$ is the normalization constant.
+
+The objective of Graph SLAM is to find the maximum likelihood set of poses given the measurements $\mathcal{Z} = \{\mathbf{z}_j\}$; in other words, we want to find 
+%
+\begin{equation*}
+    \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \ p(\mathbf{p}_1, \ldots, \mathbf{p}_N \ | \ \mathcal{Z}) 
+\end{equation*}
+%
+Using Bayes' rule, we can write this probability as
+%
+\begin{align}
+    p(\mathbf{p}_1, \ldots, \mathbf{p}_N \ | \ \mathcal{Z}) &= \frac{p( \mathcal{Z} \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) p(\mathbf{p}_1, \ldots, \mathbf{p}_N) }{ p(\mathcal{Z}) } \notag \\
+    &\propto p( \mathcal{Z} \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N), \label{eq:bayes}
+\end{align}
+%
+since $p(\mathcal{Z})$ is a constant (albeit, an unknown constant) and we assume that $p(\mathbf{p}_1, \ldots, \mathbf{p}_N)$ is uniformly distributed \cite{thrun2006graph}.  Therefore, we can use \eqref{eq:observation_probability} and \eqref{eq:bayes} to simplify the Graph SLAM optimization as follows:
+%
+\begin{align*}
+    \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \ p(\mathbf{p}_1, \ldots, \mathbf{p}_N \ | \ \mathcal{Z}) &= \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \ p( \mathcal{Z} \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) \\
+    &= \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \prod_{j=1}^M p(\mathbf{z}_j \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) \\
+    &= \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \prod_{j=1}^M \exp \left( -(\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^\transp \Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j) \right) \\
+    &= \argmin_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \sum_{j=1}^M (\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^\transp \Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j).
+\end{align*}
+%
+We define
+%
+\begin{equation*}
+    \chi^2 := \sum_{j=1}^M (\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^\transp \Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j),
+\end{equation*}
+%
+and this is what we seek to minimize.
+
+
+\section{Dimensionality and Pose Representation}
+
+Before proceeding further, it is helpful to discuss the dimensionality of the problem.  We have:
+\begin{itemize}
+  \item A set of $N$ poses $\mathbf{p}_1, \mathbf{p}_2, \ldots, \mathbf{p}_N$, where each pose lies on the manifold $\mathcal{M}$
+  \begin{itemize}
+    \item Each pose $\mathbf{p}_i$ is represented as a vector in (a subset of) $\mathbb{R}^d$.  For example:
+    \begin{itemize}
+      \item[$\circ$] An $SE(2)$ pose is typically represented as $(x, y, \theta)$, and thus $d = 3$.
+      \item[$\circ$] An $SE(3)$ pose is typically represented as $(x, y, z, q_x, q_y, q_z, q_w)$, where $(x, y, z)$ is a point in $\mathbb{R}^3$ and $(q_x, q_y, q_z, q_w)$ is a \keyterm{quaternion}, and so $d = 7$.  For more information about $SE(3)$ parameterizations and pose transformations, see \cite{blanco2010tutorial}.
+    \end{itemize}
+    \item We also need to be able to represent each pose compactly as a vector in (a subset of) $\mathbb{R}^c$.
+    \begin{itemize}
+      \item[$\circ$] Since an $SE(2)$ pose has three degrees of freedom, the $(x, y, \theta)$ representation is again sufficient and $c=3$.  
+      \item[$\circ$] An $SE(3)$ pose only has six degrees of freedom, and we can represent it compactly as $(x, y, z, q_x, q_y, q_z)$, and thus $c=6$.
+    \end{itemize}
+    \item We use the $\boxplus$ operator to indicate pose composition when one or both of the poses are represented compactly.  The output can be a pose in $\mathcal{M}$ or a vector in $\mathbb{R}^c$, as required by context.
+  \end{itemize}
+  \item A set of $M$ measurements $\mathcal{Z} = \{\mathbf{z}_1, \mathbf{z}_2, \ldots, \mathbf{z}_M\}$
+  \begin{itemize}
+    \item Each measurement's dimensionality can be unique, and we will use $\bullet$ to denote a ``wildcard'' variable.
+    \item Measurement $\mathbf{z}_j \in \mathbb{R}^\bullet$ has an associated information matrix $\Omega_j \in \mathbb{R}^{\bullet \times \bullet}$ and residual function $\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j) = \mathbf{e}_j(\mathbf{z}_j, \mathbf{p}_1, \ldots, \mathbf{p}_N) \in \mathbb{R}^\bullet$.
+    \item A measurement could, in theory, constrain anywhere from 1 pose to all $N$ poses.  In practice, each measurement usually constrains only 1 or 2 poses.  
+  \end{itemize}
+\end{itemize}
+
+
+\section{Graph SLAM Algorithm}
+
+The ``Graph'' in Graph SLAM refers to the fact that we view the problem as a graph.  The graph has a set $\mathcal{V}$ of $N$ vertices, where each vertex $v_i$ has an associated pose $\mathbf{p}_i$.  Similarly, the graph has a set $\mathcal{E}$ of $M$ edges, where each edge $e_j$ has an associated measurement $\mathbf{z}_j$.  In practice, the edges in this graph are either unary (i.e., a loop) or binary.  (Note: $e_j$ refers to the edge in the graph associated with measurement $\mathbf{z}_j$, whereas $\mathbf{e}_j$ refers to the residual function associated with $\mathbf{z}_j$.)  For more information about the Graph SLAM algorithm, see \cite{grisetti2010tutorial}.
+
+We want to optimize
+%
+\begin{equation*}
+    \chi^2 = \sum_{e_j \in \mathcal{E}} \mathbf{e}_j^\transp \Omega_j \mathbf{e}_j.
+\end{equation*}
+%
+Let $\mathbf{x}_i \in \mathbb{R}^c$ be the compact representation of pose $\mathbf{p}_i \in \mathcal{M}$, and let
+%
+\begin{equation*}
+    \mathbf{x} := \begin{bmatrix} \mathbf{x}_1 \\ \mathbf{x}_2 \\ \vdots \\ \mathbf{x}_N \end{bmatrix} \in \mathbb{R}^{cN}
+\end{equation*}
+%
+We will solve this optimization problem iteratively.  Let
+%
+\begin{equation}
+    \mathbf{x}^{k+1} := \mathbf{x}^k \boxplus \Delta \mathbf{x}^k = \begin{bmatrix} \mathbf{x}_1 \boxplus \Delta \mathbf{x}_1 \\ \mathbf{x}_2 \boxplus \Delta \mathbf{x}_2 \\ \vdots \\ \mathbf{x}_N \boxplus \Delta \mathbf{x}_2 \end{bmatrix} \label{eq:update}
+\end{equation}
+%
+The $\chi^2$ error at iteration $k+1$ is
+\begin{equation}
+    \chi_{k+1}^2 = \sum_{e_j \in \mathcal{E}} \underbrace{\left[ \mathbf{e}_j(\mathbf{x}^{k+1}) \right]^\transp}_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\mathbf{e}_j(\mathbf{x}^{k+1})}_{\bullet \times 1}.  \label{eq:chisq_at_kplusone}
+\end{equation}
+%
+We will linearize the residuals as:
+%
+\begin{align}
+    \mathbf{e}_j(\mathbf{x}^{k+1}) &= \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k) \notag \\
+    &\approx \mathbf{e}_j(\mathbf{x}^{k}) + \frac{\partial}{\partial \Delta \mathbf{x}^k} \left[ \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k) \right] \Delta \mathbf{x}^k \notag \\
+    &= \mathbf{e}_j(\mathbf{x}^{k}) + \left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right) \frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k} \Delta \mathbf{x}^k.  \label{eq:linearization}
+\end{align}
+%
+Plugging \eqref{eq:linearization} into \eqref{eq:chisq_at_kplusone}, we get:
+%
+\small
+\begin{align}
+    \chi_{k+1}^2 &\approx \ \ \ \ \ \sum_{e_j \in \mathcal{E}} \underbrace{[ \mathbf{e}_j(\mathbf{x}^k)]^\transp}_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\mathbf{e}_j(\mathbf{x}^k)}_{\bullet \times 1} \notag \\
+    &\hphantom{\approx} \ \ \ + \sum_{e_j \in \mathcal{E}} \underbrace{[ \mathbf{e}_j(\mathbf{x^k}) ]^\transp }_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN} \underbrace{\Delta \mathbf{x}^k}_{cN \times 1} \notag \\
+    &\hphantom{\approx} \ \ \ + \sum_{e_j \in \mathcal{E}} \underbrace{(\Delta \mathbf{x}^k)^\transp}_{1 \times cN} \underbrace{ \left( \frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k} \right)^\transp}_{cN \times dN} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)^\transp}_{dN \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN} \underbrace{\Delta \mathbf{x}^k}_{cN \times 1} \notag \\
+    &= \chi_k^2 + 2 \mathbf{b}^\transp \Delta \mathbf{x}^k + (\Delta \mathbf{x}^k)^\transp H \Delta \mathbf{x}^k,  \notag
+\end{align}
+\normalsize
+%
+where
+%
+\begin{align*}
+    \mathbf{b}^\transp &= \sum_{e_j \in \mathcal{E}} \underbrace{[ \mathbf{e}_j(\mathbf{x^k}) ]^\transp }_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN} \\
+    H &= \sum_{e_j \in \mathcal{E}} \underbrace{ \left( \frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k} \right)^\transp}_{cN \times dN} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)^\transp}_{dN \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN}.
+\end{align*}
+%
+Using this notation, we obtain the optimal update as
+%
+\begin{equation}
+    \Delta \mathbf{x}^k = -H^{-1} \mathbf{b}.  \label{eq:deltax}
+\end{equation}
+%
+We apply this update to the poses via \eqref{eq:update} and repeat until convergence.
+
+
+
+\bibliographystyle{acm}
+\bibliography{graphSLAM}{}
+
+\end{document}
diff --git a/SLAM/GraphBasedSLAM/data/README.rst b/SLAM/GraphBasedSLAM/data/README.rst
new file mode 100644
index 0000000000..c875cce73f
--- /dev/null
+++ b/SLAM/GraphBasedSLAM/data/README.rst
@@ -0,0 +1,6 @@
+Acknowledgments and References
+==============================
+
+Thanks to Luca Larlone for allowing inclusion of the `Intel dataset <https://lucacarlone.mit.edu/datasets/>`_ in this repo.
+
+1. Carlone, L. and Censi, A., 2014. `From angular manifolds to the integer lattice: Guaranteed orientation estimation with application to pose graph optimization <https://arxiv.org/pdf/1211.3063.pdf>`_. IEEE Transactions on Robotics, 30(2), pp.475-492.
diff --git a/SLAM/GraphBasedSLAM/data/input_INTEL.g2o b/SLAM/GraphBasedSLAM/data/input_INTEL.g2o
new file mode 100644
index 0000000000..16f3a2600c
--- /dev/null
+++ b/SLAM/GraphBasedSLAM/data/input_INTEL.g2o
@@ -0,0 +1,2711 @@
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+EDGE_SE2 172 305 -2.223813 -0.584373 0.139610 11.218103 -0.907468 0.000000 18.807912 0.000000 1924.985691
+EDGE_SE2 172 315 0.916741 1.192284 1.768200 29.887371 16.398688 0.000000 25.433291 0.000000 326.245913
+EDGE_SE2 172 328 0.389353 2.701274 -1.418880 11.306838 0.643966 0.000000 13.229838 0.000000 427.278816
+EDGE_SE2 172 337 0.634889 0.129966 2.220490 195.557030 -88.592401 0.000000 53.663494 0.000000 241.043874
+EDGE_SE2 172 342 1.382392 -0.108475 -0.255660 12.384043 -7.102031 0.000000 50.735268 0.000000 1585.608223
+EDGE_SE2 172 1010 2.747473 0.046909 -3.048210 11.123505 0.162102 0.000000 13.231218 0.000000 152.550608
+EDGE_SE2 172 1016 -1.097321 -0.382262 -3.001615 13.480215 -11.980076 0.000000 71.691915 0.000000 156.123881
+EDGE_SE2 178 342 -2.376956 -0.213431 -0.183040 11.578392 -1.671546 0.000000 17.090523 0.000000 1786.245513
+EDGE_SE2 178 367 -0.259854 -0.863160 1.012500 18.840526 -28.383170 0.000000 115.336891 0.000000 617.260137
+EDGE_SE2 178 374 1.732968 -1.057467 -0.499130 11.142356 0.640290 0.000000 24.232266 0.000000 1112.401122
+EDGE_SE2 178 1003 1.826983 -1.529516 2.351785 11.166956 -0.600028 0.000000 17.558198 0.000000 222.529516
+EDGE_SE2 178 1008 -0.403552 0.156306 2.716725 12.710306 -28.871300 0.000000 532.343545 0.000000 180.975206
+EDGE_SE2 178 1010 -1.026746 0.040588 -2.975590 14.592518 16.700937 0.000000 91.228510 0.000000 158.174631
+EDGE_SE2 183 367 -1.618063 -1.419954 1.686080 18.191945 4.896376 0.000000 14.496941 0.000000 346.499109
+EDGE_SE2 183 374 0.060720 -0.328723 0.174450 894.828841 7.251305 0.000000 11.170611 0.000000 1812.470490
+EDGE_SE2 183 382 0.411911 2.011657 2.779910 23.397894 1.979206 0.000000 11.429930 0.000000 174.975438
+EDGE_SE2 183 387 1.173883 2.095476 0.233105 14.481439 -3.100632 0.000000 13.963629 0.000000 1644.144968
+EDGE_SE2 183 1003 0.428660 -0.639029 3.025365 102.305125 77.922908 0.000000 77.694205 0.000000 154.287027
+EDGE_SE2 183 1008 -2.366308 -0.712782 -2.892880 11.121230 -0.230529 0.000000 16.363269 0.000000 164.967341
+EDGE_SE2 189 400 -1.948699 2.200613 -1.116370 11.144116 -0.119102 0.000000 11.540899 0.000000 558.157565
+EDGE_SE2 189 414 -0.254807 -1.783532 -1.508360 11.922188 3.913799 0.000000 29.996898 0.000000 397.338159
+EDGE_SE2 189 454 0.831360 -1.515476 1.318995 21.578630 -11.156350 0.000000 23.001623 0.000000 464.879365
+EDGE_SE2 189 994 1.332308 -0.340434 2.895765 11.111898 0.181350 0.000000 52.882967 0.000000 164.723074
+EDGE_SE2 195 437 -0.591701 -2.641206 -0.200180 13.648824 -0.051289 0.000000 11.112148 0.000000 1735.590395
+EDGE_SE2 195 467 -0.091210 1.365140 1.118420 21.523802 -18.224556 0.000000 43.008191 0.000000 557.077827
+EDGE_SE2 195 484 0.655807 2.573505 -2.009240 11.219291 -0.565783 0.000000 14.070164 0.000000 276.074540
+EDGE_SE2 195 494 0.855236 -0.512254 -0.301250 16.102937 20.540136 0.000000 95.628725 0.000000 1476.449250
+EDGE_SE2 195 988 1.415155 -0.856188 3.052380 16.022409 10.041613 0.000000 31.642140 0.000000 152.236812
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+EDGE_SE2 201 988 -2.053188 -0.978041 -2.963985 11.683303 -2.092334 0.000000 18.762142 0.000000 159.102109
+EDGE_SE2 203 500 -0.236863 -0.548090 0.348290 153.663942 -134.464803 0.000000 137.946777 0.000000 1375.223806
+EDGE_SE2 203 508 1.553867 1.875066 1.399800 12.536000 2.482853 0.000000 15.437455 0.000000 434.100125
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+EDGE_SE2 203 982 0.313263 -0.470658 -3.012275 253.866414 119.649456 0.000000 70.084044 0.000000 155.295388
+EDGE_SE2 209 508 -2.161765 2.058644 1.468780 11.180293 -0.053576 0.000000 11.152602 0.000000 410.180706
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+EDGE_SE2 209 528 1.736233 0.012599 -0.097130 11.350617 -2.286079 0.000000 32.931671 0.000000 2076.939411
+EDGE_SE2 209 976 0.433683 -0.153481 -3.063330 87.280240 171.295909 0.000000 396.336584 0.000000 151.417412
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+EDGE_SE2 220 532 -0.939792 0.407621 1.225630 94.950637 -5.379789 0.000000 11.456320 0.000000 504.700895
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+EDGE_SE2 220 575 -0.123656 2.505837 -2.951275 15.792523 -0.664538 0.000000 11.205444 0.000000 160.127317
+EDGE_SE2 220 959 2.587973 -0.016000 2.926965 11.274656 -0.773198 0.000000 14.766597 0.000000 162.116000
+EDGE_SE2 220 965 -1.062787 0.305497 3.105010 15.212918 16.523642 0.000000 77.674651 0.000000 148.358205
+EDGE_SE2 226 231 1.660221 0.126920 -0.049820 11.505996 -3.114430 0.000000 35.674411 0.000000 2268.351350
+EDGE_SE2 226 588 -0.273207 -0.444423 -0.016160 274.806053 -156.291042 0.000000 103.744249 0.000000 2421.117227
+EDGE_SE2 226 597 2.185242 1.674700 1.403760 12.078039 1.038206 0.000000 12.225850 0.000000 432.671013
+EDGE_SE2 226 953 2.006857 -0.437847 3.116305 11.557944 2.329386 0.000000 23.254435 0.000000 147.545120
+EDGE_SE2 226 959 -1.129223 -0.150509 2.830935 23.235807 -25.544568 0.000000 64.928953 0.000000 170.345385
+EDGE_SE2 923 1175 0.749610 0.273000 0.106670 19.536607 -34.047517 0.000000 148.697481 0.000000 2041.285424
+EDGE_SE2 929 1175 -2.005899 0.102421 -0.392910 12.648561 -4.320259 0.000000 23.251108 0.000000 1288.528106
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+EDGE_SE2 1029 1217 1.008685 -0.037889 -3.114265 11.476877 5.630362 0.000000 97.781127 0.000000 147.691473
+EDGE_SE2 1044 1217 2.098504 -0.838430 0.479890 15.976239 4.188481 0.000000 14.717054 0.000000 1141.513725
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+EDGE_SE2 1061 1210 1.269232 -1.441319 -2.909610 23.568115 6.644624 0.000000 14.655385 0.000000 163.558506
+EDGE_SE2 1075 1210 -1.662329 0.436026 -1.599840 32.701727 -4.997844 0.000000 12.268023 0.000000 369.868006
+EDGE_SE2 1084 1210 2.070374 1.896269 1.836205 12.355692 0.641825 0.000000 11.442098 0.000000 310.788312
+EDGE_SE2 1088 1210 0.609503 -1.485166 0.536770 38.204841 -4.021013 0.000000 11.707874 0.000000 1058.576530
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+EDGE_SE2 1098 1210 -0.378280 -0.172442 -2.937025 38.903093 -122.470929 0.000000 550.803768 0.000000 161.288572
+EDGE_SE2 1138 1188 0.583736 -1.006358 1.671805 21.764394 -23.563240 0.000000 63.228969 0.000000 350.211491
+EDGE_SE2 1147 1188 -2.232188 0.113982 2.992915 11.195782 -0.864255 0.000000 19.932721 0.000000 156.804966
+EDGE_SE2 1150 1188 -0.789302 2.834301 1.697150 11.120354 -0.063189 0.000000 11.543094 0.000000 343.660651
+EDGE_SE2 1158 1175 0.586855 -0.249767 2.829060 13.000858 20.975969 0.000000 243.941877 0.000000 170.512281
diff --git a/SLAM/GraphBasedSLAM/graphSLAM_SE2_example.ipynb b/SLAM/GraphBasedSLAM/graphSLAM_SE2_example.ipynb
new file mode 100644
index 0000000000..cd3d9fd5db
--- /dev/null
+++ b/SLAM/GraphBasedSLAM/graphSLAM_SE2_example.ipynb
@@ -0,0 +1,332 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from graphslam.graph import Graph\n",
+    "from graphslam.load import load_g2o_se2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction\n",
+    "\n",
+    "For a complete derivation of the Graph SLAM algorithm, please see [graphSLAM_formulation.pdf](./graphSLAM_formulation.pdf).  \n",
+    "\n",
+    "This notebook illustrates the iterative optimization of a real-world $SE(2)$ dataset.  The code can be found in the `graphslam` folder.  For simplicity, numerical differentiation is used in lieu of analytic Jacobians.  This code originated from the [python-graphslam](https://github.com/JeffLIrion/python-graphslam) repo, which is a full-featured Graph SLAM solver.  The dataset in this example is used with permission from Luca Carlone and was downloaded from his [website](https://lucacarlone.mit.edu/datasets/).  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of edges:    1483\n",
+      "Number of vertices: 1228\n"
+     ]
+    }
+   ],
+   "source": [
+    "g = load_g2o_se2(\"data/input_INTEL.g2o\")\n",
+    "\n",
+    "print(\"Number of edges:    {}\".format(len(g._edges)))\n",
+    "print(\"Number of vertices: {}\".format(len(g._vertices)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UhqCA2z4y3LCB6O23Wbbq358ng1lHUvIaH2zCk+qcNM3/TjiEX4ux9HIl5xTD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RpstvfdV/V9PP00ricqbH1fJyPiOuzxcKkiC4aLDrx8uh4uDpJJeniy8JHG8+35PCRVD2fMJiaa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\naDSaNoI2+BqNRtNG0AZfo9Fo2gj/D1KYU/J75gHUAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f58b45e2438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g.plot(title=r\"Original ($\\chi^2 = {:.0f}$)\".format(g.calc_chi2()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each edge in this dataset is a constraint that compares the measured $SE(2)$ transformation between two poses to the expected $SE(2)$ transformation between them, as computed using the current pose estimates.  These edges can be classified into two categories:\n",
+    "\n",
+    "1. Odometry edges constrain two consecutive vertices, and the measurement for the $SE(2)$ transformation comes directly from odometry data.\n",
+    "2. Scan-matching edges constrain two non-consecutive vertices.  These scan matches can be computed using, for example, 2-D LiDAR data or landmarks; the details of how these contstraints are determined is beyond the scope of this example.  This is often referred to as *loop closure* in the Graph SLAM literature.\n",
+    "\n",
+    "We can easily parse out the two different types of edges present in this dataset and plot them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def parse_edges(g):\n",
+    "    \"\"\"Split the graph `g` into two graphs, one with only odometry edges and the other with only scan-matching edges.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    g : graphslam.graph.Graph\n",
+    "        The input graph\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    g_odom : graphslam.graph.Graph\n",
+    "        A graph consisting of the vertices and odometry edges from `g`\n",
+    "    g_scan : graphslam.graph.Graph\n",
+    "        A graph consisting of the vertices and scan-matching edges from `g`\n",
+    "\n",
+    "    \"\"\"\n",
+    "    edges_odom = []\n",
+    "    edges_scan = []\n",
+    "    \n",
+    "    for e in g._edges:\n",
+    "        if abs(e.vertex_ids[1] - e.vertex_ids[0]) == 1:\n",
+    "            edges_odom.append(e)\n",
+    "        else:\n",
+    "            edges_scan.append(e)\n",
+    "\n",
+    "    g_odom = Graph(edges_odom, g._vertices)\n",
+    "    g_scan = Graph(edges_scan, g._vertices)\n",
+    "\n",
+    "    return g_odom, g_scan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of odometry edges:      1227\n",
+      "Number of scan-matching edges:  256\n",
+      "\n",
+      "χ^2 error from odometry edges:             0.232\n",
+      "χ^2 error from scan-matching edges:  7191686.151\n"
+     ]
+    }
+   ],
+   "source": [
+    "g_odom, g_scan = parse_edges(g)\n",
+    "\n",
+    "print(\"Number of odometry edges:      {:4d}\".format(len(g_odom._edges)))\n",
+    "print(\"Number of scan-matching edges: {:4d}\".format(len(g_scan._edges)))\n",
+    "\n",
+    "print(\"\\nχ^2 error from odometry edges:       {:11.3f}\".format(g_odom.calc_chi2()))\n",
+    "print(\"χ^2 error from scan-matching edges:  {:11.3f}\".format(g_scan.calc_chi2()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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yZfp370hhD5BKYf1hEiidfD04ZxyJwZUESmcPaMmkcsGJF7CoYjtevettcsoh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\nAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f587b80bc50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g_odom.plot(title=\"Odometry edges\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Jzn1WrGAcfP/+FC+rDMEgj9mtG7NgS0tFjjqKnHXffaw3q89br56lpVNdVFSI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\nFTs2RGNhsDdQwXa9ICp46ZiyZb7fH++ePWdmZ1MQUK/5nme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ILDMMDI1YvWlrqUU4hRsj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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f587b80b668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g_scan.plot(title=\"Scan-matching edges\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Optimization\n",
+    "\n",
+    "Initially, the pose estimates are consistent with the collected odometry measurements, and the odometry edges contribute almost zero towards the $\\chi^2$ error.  However, there are large discrepancies between the scan-matching constraints and the initial pose estimates.  This is not surprising, since small errors in odometry readings that are propagated over time can lead to large errors in the robot's trajectory.  What makes Graph SLAM effective is that it allows incorporation of multiple different data sources into a single optimization problem."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Iteration                chi^2        rel. change\n",
+      "---------                -----        -----------\n",
+      "        0         7191686.3825\n",
+      "        1       320031728.8624          43.500234\n",
+      "        2       125083004.3299          -0.609154\n",
+      "        3          338155.9074          -0.997297\n",
+      "        4             735.1344          -0.997826\n",
+      "        5             215.8405          -0.706393\n",
+      "        6             215.8405          -0.000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "g.optimize()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Graph_SLAM_optimization.gif](images/Graph_SLAM_optimization.gif)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+QwlrklvTY+kKDiCGkUoPMfvUmSSIMJAgXgdO5GmSgAF8bOzLPc553MVY0mm4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\nBL6Dg4NDK+H/A7aRY5b1tCw8AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f587c25a0b8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g.plot(title=\"Optimized\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "χ^2 error from odometry edges:       142.189\n",
+      "χ^2 error from scan-matching edges:   73.652\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\nχ^2 error from odometry edges:       {:7.3f}\".format(g_odom.calc_chi2()))\n",
+    "print(\"χ^2 error from scan-matching edges:  {:7.3f}\".format(g_scan.calc_chi2()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Wm4l+8IHIUUfpR+rEAbaeTZhm0Qyk3tCDryFDRP4T62Az4fXgG/y2Yl36ZnV1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\nRqPRVAj/D0Fj/txvoohWAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f587c00dba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g_odom.plot(title=\"Odometry edges\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kWku1JLBlLdUyAkd69hQ5q0dUPqFPXl1oqalpu0pZZQbtHWkLbO97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\nXvgUCWjQIb10hNcDHOIvddM6Jvn0CsVauqSGn8Gbb5K/qjT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CiH8A8APoJ4TY\nCOByANcCeFAIMQ0yffrU0rWwMDRznv72fj5VagWFQqEoE5RJR6FQKMoEJfAVCoWiTFACX6FQKMoE\nJfAVCoWiTFACX6FQKMoEJfAVCoWiTFACX6FQKMqE/wd2Tu+/2Hq4AwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f587c7e3cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g_scan.plot(title=\"Scan-matching edges\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/SLAM/GraphBasedSLAM/graphSLAM_formulation.pdf b/SLAM/GraphBasedSLAM/graphSLAM_formulation.pdf
new file mode 100644
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diff --git a/SLAM/GraphBasedSLAM/graphslam/__init__.py b/SLAM/GraphBasedSLAM/graphslam/__init__.py
new file mode 100644
index 0000000000..6aa2872e77
--- /dev/null
+++ b/SLAM/GraphBasedSLAM/graphslam/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) 2020 Jeff Irion and contributors
+#
+# This file originated from the `graphslam` package:
+#
+#   https://github.com/JeffLIrion/python-graphslam
+
+"""Graph SLAM solver in Python.
+
+"""
diff --git a/SLAM/GraphBasedSLAM/graphslam/edge/__init__.py b/SLAM/GraphBasedSLAM/graphslam/edge/__init__.py
new file mode 100644
index 0000000000..afb8549087
--- /dev/null
+++ b/SLAM/GraphBasedSLAM/graphslam/edge/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) 2020 Jeff Irion and contributors
+#
+# This file originated from the `graphslam` package:
+#
+#   https://github.com/JeffLIrion/python-graphslam
diff --git a/SLAM/GraphBasedSLAM/graphslam/edge/edge_odometry.py b/SLAM/GraphBasedSLAM/graphslam/edge/edge_odometry.py
new file mode 100644
index 0000000000..b247b97b43
--- /dev/null
+++ b/SLAM/GraphBasedSLAM/graphslam/edge/edge_odometry.py
@@ -0,0 +1,180 @@
+# Copyright (c) 2020 Jeff Irion and contributors
+#
+# This file originated from the `graphslam` package:
+#
+#   https://github.com/JeffLIrion/python-graphslam
+
+r"""A class for odometry edges.
+
+"""
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+#: The difference that will be used for numerical differentiation
+EPSILON = 1e-6
+
+
+class EdgeOdometry:
+    r"""A class for representing odometry edges in Graph SLAM.
+
+    Parameters
+    ----------
+    vertices : list[graphslam.vertex.Vertex]
+        A list of the vertices constrained by the edge
+    information : np.ndarray
+        The information matrix :math:`\Omega_j` associated with the edge
+    estimate : graphslam.pose.se2.PoseSE2
+        The expected measurement :math:`\mathbf{z}_j`
+
+    Attributes
+    ----------
+    vertices : list[graphslam.vertex.Vertex]
+        A list of the vertices constrained by the edge
+    information : np.ndarray
+        The information matrix :math:`\Omega_j` associated with the edge
+    estimate : PoseSE2
+        The expected measurement :math:`\mathbf{z}_j`
+
+    """
+    def __init__(self, vertex_ids, information, estimate, vertices=None):
+        self.vertex_ids = vertex_ids
+        self.information = information
+        self.estimate = estimate
+        self.vertices = vertices
+
+    def calc_error(self):
+        r"""Calculate the error for the edge: :math:`\mathbf{e}_j \in \mathbb{R}^\bullet`.
+
+        .. math::
+
+           \mathbf{e}_j = \mathbf{z}_j - (p_2 \ominus p_1)
+
+
+        Returns
+        -------
+        np.ndarray
+            The error for the edge
+
+        """
+        return (self.estimate - (self.vertices[1].pose - self.vertices[0].pose)).to_compact()
+
+    def calc_chi2(self):
+        r"""Calculate the :math:`\chi^2` error for the edge.
+
+        .. math::
+
+           \mathbf{e}_j^T \Omega_j \mathbf{e}_j
+
+
+        Returns
+        -------
+        float
+            The :math:`\chi^2` error for the edge
+
+        """
+        err = self.calc_error()
+
+        return np.dot(np.dot(np.transpose(err), self.information), err)
+
+    def calc_chi2_gradient_hessian(self):
+        r"""Calculate the edge's contributions to the graph's :math:`\chi^2` error, gradient (:math:`\mathbf{b}`), and Hessian (:math:`H`).
+
+        Returns
+        -------
+        float
+            The :math:`\chi^2` error for the edge
+        dict
+            The edge's contribution(s) to the gradient
+        dict
+            The edge's contribution(s) to the Hessian
+
+        """
+        chi2 = self.calc_chi2()
+
+        err = self.calc_error()
+
+        jacobians = self.calc_jacobians()
+
+        return chi2, {v.index: np.dot(np.dot(np.transpose(err), self.information), jacobian) for v, jacobian in zip(self.vertices, jacobians)}, {(self.vertices[i].index, self.vertices[j].index): np.dot(np.dot(np.transpose(jacobians[i]), self.information), jacobians[j]) for i in range(len(jacobians)) for j in range(i, len(jacobians))}
+
+    def calc_jacobians(self):
+        r"""Calculate the Jacobian of the edge's error with respect to each constrained pose.
+
+        .. math::
+
+           \frac{\partial}{\partial \Delta \mathbf{x}^k} \left[ \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k) \right]
+
+
+        Returns
+        -------
+        list[np.ndarray]
+            The Jacobian matrices for the edge with respect to each constrained pose
+
+        """
+        err = self.calc_error()
+
+        # The dimensionality of the compact pose representation
+        dim = len(self.vertices[0].pose.to_compact())
+
+        return [self._calc_jacobian(err, dim, i) for i in range(len(self.vertices))]
+
+    def _calc_jacobian(self, err, dim, vertex_index):
+        r"""Calculate the Jacobian of the edge with respect to the specified vertex's pose.
+
+        Parameters
+        ----------
+        err : np.ndarray
+            The current error for the edge (see :meth:`EdgeOdometry.calc_error`)
+        dim : int
+            The dimensionality of the compact pose representation
+        vertex_index : int
+            The index of the vertex (pose) for which we are computing the Jacobian
+
+        Returns
+        -------
+        np.ndarray
+            The Jacobian of the edge with respect to the specified vertex's pose
+
+        """
+        jacobian = np.zeros(err.shape + (dim,))
+        p0 = self.vertices[vertex_index].pose.copy()
+
+        for d in range(dim):
+            # update the pose
+            delta_pose = np.zeros(dim)
+            delta_pose[d] = EPSILON
+            self.vertices[vertex_index].pose += delta_pose
+
+            # compute the numerical derivative
+            jacobian[:, d] = (self.calc_error() - err) / EPSILON
+
+            # restore the pose
+            self.vertices[vertex_index].pose = p0.copy()
+
+        return jacobian
+
+    def to_g2o(self):
+        """Export the edge to the .g2o format.
+
+        Returns
+        -------
+        str
+            The edge in .g2o format
+
+        """
+        return "EDGE_SE2 {} {} {} {} {} ".format(self.vertex_ids[0], self.vertex_ids[1], self.estimate[0], self.estimate[1], self.estimate[2]) + " ".join([str(x) for x in self.information[np.triu_indices(3, 0)]]) + "\n"
+
+    def plot(self, color='b'):
+        """Plot the edge.
+
+        Parameters
+        ----------
+        color : str
+            The color that will be used to plot the edge
+
+        """
+        xy = np.array([v.pose.position for v in self.vertices])
+        plt.plot(xy[:, 0], xy[:, 1], color=color)
diff --git a/SLAM/GraphBasedSLAM/graphslam/graph.py b/SLAM/GraphBasedSLAM/graphslam/graph.py
new file mode 100644
index 0000000000..83c181dc79
--- /dev/null
+++ b/SLAM/GraphBasedSLAM/graphslam/graph.py
@@ -0,0 +1,265 @@
+# Copyright (c) 2020 Jeff Irion and contributors
+#
+# This file originated from the `graphslam` package:
+#
+#   https://github.com/JeffLIrion/python-graphslam
+
+r"""A ``Graph`` class that stores the edges and vertices required for Graph SLAM.
+
+"""
+
+
+from collections import defaultdict
+from functools import reduce
+import warnings
+
+import numpy as np
+from scipy.sparse import SparseEfficiencyWarning, lil_matrix
+from scipy.sparse.linalg import spsolve
+import matplotlib.pyplot as plt
+
+
+warnings.simplefilter("ignore", SparseEfficiencyWarning)
+warnings.filterwarnings("ignore", category=SparseEfficiencyWarning)
+
+
+# pylint: disable=too-few-public-methods
+class _Chi2GradientHessian:
+    r"""A class that is used to aggregate the :math:`\chi^2` error, gradient, and Hessian.
+
+    Parameters
+    ----------
+    dim : int
+        The compact dimensionality of the poses
+
+    Attributes
+    ----------
+    chi2 : float
+        The :math:`\chi^2` error
+    dim : int
+        The compact dimensionality of the poses
+    gradient : defaultdict
+        The contributions to the gradient vector
+    hessian : defaultdict
+        The contributions to the Hessian matrix
+
+    """
+    def __init__(self, dim):
+        self.chi2 = 0.
+        self.dim = dim
+        self.gradient = defaultdict(lambda: np.zeros(dim))
+        self.hessian = defaultdict(lambda: np.zeros((dim, dim)))
+
+    @staticmethod
+    def update(chi2_grad_hess, incoming):
+        r"""Update the :math:`\chi^2` error and the gradient and Hessian dictionaries.
+
+        Parameters
+        ----------
+        chi2_grad_hess : _Chi2GradientHessian
+            The ``_Chi2GradientHessian`` that will be updated
+        incoming : tuple
+            TODO
+
+        """
+        chi2_grad_hess.chi2 += incoming[0]
+
+        for idx, contrib in incoming[1].items():
+            chi2_grad_hess.gradient[idx] += contrib
+
+        for (idx1, idx2), contrib in incoming[2].items():
+            if idx1 <= idx2:
+                chi2_grad_hess.hessian[idx1, idx2] += contrib
+            else:
+                chi2_grad_hess.hessian[idx2, idx1] += np.transpose(contrib)
+
+        return chi2_grad_hess
+
+
+class Graph(object):
+    r"""A graph that will be optimized via Graph SLAM.
+
+    Parameters
+    ----------
+    edges : list[graphslam.edge.edge_odometry.EdgeOdometry]
+        A list of the vertices in the graph
+    vertices : list[graphslam.vertex.Vertex]
+        A list of the vertices in the graph
+
+    Attributes
+    ----------
+    _chi2 : float, None
+        The current :math:`\chi^2` error, or ``None`` if it has not yet been computed
+    _edges : list[graphslam.edge.edge_odometry.EdgeOdometry]
+        A list of the edges (i.e., constraints) in the graph
+    _gradient : numpy.ndarray, None
+        The gradient :math:`\mathbf{b}` of the :math:`\chi^2` error, or ``None`` if it has not yet been computed
+    _hessian : scipy.sparse.lil_matrix, None
+        The Hessian matrix :math:`H`, or ``None`` if it has not yet been computed
+    _vertices : list[graphslam.vertex.Vertex]
+        A list of the vertices in the graph
+
+    """
+    def __init__(self, edges, vertices):
+        # The vertices and edges lists
+        self._edges = edges
+        self._vertices = vertices
+
+        # The chi^2 error, gradient, and Hessian
+        self._chi2 = None
+        self._gradient = None
+        self._hessian = None
+
+        self._link_edges()
+
+    def _link_edges(self):
+        """Fill in the ``vertices`` attributes for the graph's edges.
+
+        """
+        index_id_dict = {i: v.id for i, v in enumerate(self._vertices)}
+        id_index_dict = {v_id: v_index for v_index, v_id in index_id_dict.items()}
+
+        # Fill in the vertices' `index` attribute
+        for v in self._vertices:
+            v.index = id_index_dict[v.id]
+
+        for e in self._edges:
+            e.vertices = [self._vertices[id_index_dict[v_id]] for v_id in e.vertex_ids]
+
+    def calc_chi2(self):
+        r"""Calculate the :math:`\chi^2` error for the ``Graph``.
+
+        Returns
+        -------
+        float
+            The :math:`\chi^2` error
+
+        """
+        self._chi2 = sum((e.calc_chi2() for e in self._edges))
+        return self._chi2
+
+    def _calc_chi2_gradient_hessian(self):
+        r"""Calculate the :math:`\chi^2` error, the gradient :math:`\mathbf{b}`, and the Hessian :math:`H`.
+
+        """
+        n = len(self._vertices)
+        dim = len(self._vertices[0].pose.to_compact())
+        chi2_gradient_hessian = reduce(_Chi2GradientHessian.update, (e.calc_chi2_gradient_hessian() for e in self._edges), _Chi2GradientHessian(dim))
+
+        self._chi2 = chi2_gradient_hessian.chi2
+
+        # Fill in the gradient vector
+        self._gradient = np.zeros(n * dim, dtype=np.float64)
+        for idx, contrib in chi2_gradient_hessian.gradient.items():
+            self._gradient[idx * dim: (idx + 1) * dim] += contrib
+
+        # Fill in the Hessian matrix
+        self._hessian = lil_matrix((n * dim, n * dim), dtype=np.float64)
+        for (row_idx, col_idx), contrib in chi2_gradient_hessian.hessian.items():
+            self._hessian[row_idx * dim: (row_idx + 1) * dim, col_idx * dim: (col_idx + 1) * dim] = contrib
+
+            if row_idx != col_idx:
+                self._hessian[col_idx * dim: (col_idx + 1) * dim, row_idx * dim: (row_idx + 1) * dim] = np.transpose(contrib)
+
+    def optimize(self, tol=1e-4, max_iter=20, fix_first_pose=True):
+        r"""Optimize the :math:`\chi^2` error for the ``Graph``.
+
+        Parameters
+        ----------
+        tol : float
+            If the relative decrease in the :math:`\chi^2` error between iterations is less than ``tol``, we will stop
+        max_iter : int
+            The maximum number of iterations
+        fix_first_pose : bool
+            If ``True``, we will fix the first pose
+
+        """
+        n = len(self._vertices)
+        dim = len(self._vertices[0].pose.to_compact())
+
+        # Previous iteration's chi^2 error
+        chi2_prev = -1.
+
+        # For displaying the optimization progress
+        print("\nIteration                chi^2        rel. change")
+        print("---------                -----        -----------")
+
+        for i in range(max_iter):
+            self._calc_chi2_gradient_hessian()
+
+            # Check for convergence (from the previous iteration); this avoids having to calculate chi^2 twice
+            if i > 0:
+                rel_diff = (chi2_prev - self._chi2) / (chi2_prev + np.finfo(float).eps)
+                print("{:9d} {:20.4f} {:18.6f}".format(i, self._chi2, -rel_diff))
+                if self._chi2 < chi2_prev and rel_diff < tol:
+                    return
+            else:
+                print("{:9d} {:20.4f}".format(i, self._chi2))
+
+            # Update the previous iteration's chi^2 error
+            chi2_prev = self._chi2
+
+            # Hold the first pose fixed
+            if fix_first_pose:
+                self._hessian[:dim, :] = 0.
+                self._hessian[:, :dim] = 0.
+                self._hessian[:dim, :dim] += np.eye(dim)
+                self._gradient[:dim] = 0.
+
+            # Solve for the updates
+            dx = spsolve(self._hessian, -self._gradient)  # pylint: disable=invalid-unary-operand-type
+
+            # Apply the updates
+            for v, dx_i in zip(self._vertices, np.split(dx, n)):
+                v.pose += dx_i
+
+        # If we reached the maximum number of iterations, print out the final iteration's results
+        self.calc_chi2()
+        rel_diff = (chi2_prev - self._chi2) / (chi2_prev + np.finfo(float).eps)
+        print("{:9d} {:20.4f} {:18.6f}".format(max_iter, self._chi2, -rel_diff))
+
+    def to_g2o(self, outfile):
+        """Save the graph in .g2o format.
+
+        Parameters
+        ----------
+        outfile : str
+            The path where the graph will be saved
+
+        """
+        with open(outfile, 'w') as f:
+            for v in self._vertices:
+                f.write(v.to_g2o())
+
+            for e in self._edges:
+                f.write(e.to_g2o())
+
+    def plot(self, vertex_color='r', vertex_marker='o', vertex_markersize=3, edge_color='b', title=None):
+        """Plot the graph.
+
+        Parameters
+        ----------
+        vertex_color : str
+            The color that will be used to plot the vertices
+        vertex_marker : str
+            The marker that will be used to plot the vertices
+        vertex_markersize : int
+            The size of the plotted vertices
+        edge_color : str
+            The color that will be used to plot the edges
+        title : str, None
+            The title that will be used for the plot
+
+        """
+        plt.figure()
+
+        for e in self._edges:
+            e.plot(edge_color)
+
+        for v in self._vertices:
+            v.plot(vertex_color, vertex_marker, vertex_markersize)
+
+        if title:
+            plt.title(title)
+
+        plt.show()
diff --git a/SLAM/GraphBasedSLAM/graphslam/load.py b/SLAM/GraphBasedSLAM/graphslam/load.py
new file mode 100644
index 0000000000..b0dd023d93
--- /dev/null
+++ b/SLAM/GraphBasedSLAM/graphslam/load.py
@@ -0,0 +1,66 @@
+# Copyright (c) 2020 Jeff Irion and contributors
+#
+# This file originated from the `graphslam` package:
+#
+#   https://github.com/JeffLIrion/python-graphslam
+
+"""Functions for loading graphs.
+
+"""
+
+
+import logging
+
+import numpy as np
+
+from .edge.edge_odometry import EdgeOdometry
+from .graph import Graph
+from .pose.se2 import PoseSE2
+from .util import upper_triangular_matrix_to_full_matrix
+from .vertex import Vertex
+
+
+_LOGGER = logging.getLogger(__name__)
+
+
+def load_g2o_se2(infile):
+    """Load an :math:`SE(2)` graph from a .g2o file.
+
+    Parameters
+    ----------
+    infile : str
+        The path to the .g2o file
+
+    Returns
+    -------
+    Graph
+        The loaded graph
+
+    """
+    edges = []
+    vertices = []
+
+    with open(infile) as f:
+        for line in f.readlines():
+            if line.startswith("VERTEX_SE2"):
+                numbers = line[10:].split()
+                arr = np.array([float(number) for number in numbers[1:]], dtype=np.float64)
+                p = PoseSE2(arr[:2], arr[2])
+                v = Vertex(int(numbers[0]), p)
+                vertices.append(v)
+                continue
+
+            if line.startswith("EDGE_SE2"):
+                numbers = line[9:].split()
+                arr = np.array([float(number) for number in numbers[2:]], dtype=np.float64)
+                vertex_ids = [int(numbers[0]), int(numbers[1])]
+                estimate = PoseSE2(arr[:2], arr[2])
+                information = upper_triangular_matrix_to_full_matrix(arr[3:], 3)
+                e = EdgeOdometry(vertex_ids, information, estimate)
+                edges.append(e)
+                continue
+
+            if line.strip():
+                _LOGGER.warning("Line not supported -- '%s'", line.rstrip())
+
+    return Graph(edges, vertices)
diff --git a/SLAM/GraphBasedSLAM/graphslam/pose/__init__.py b/SLAM/GraphBasedSLAM/graphslam/pose/__init__.py
new file mode 100644
index 0000000000..afb8549087
--- /dev/null
+++ b/SLAM/GraphBasedSLAM/graphslam/pose/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) 2020 Jeff Irion and contributors
+#
+# This file originated from the `graphslam` package:
+#
+#   https://github.com/JeffLIrion/python-graphslam
diff --git a/SLAM/GraphBasedSLAM/graphslam/pose/se2.py b/SLAM/GraphBasedSLAM/graphslam/pose/se2.py
new file mode 100644
index 0000000000..88518510e7
--- /dev/null
+++ b/SLAM/GraphBasedSLAM/graphslam/pose/se2.py
@@ -0,0 +1,171 @@
+# Copyright (c) 2020 Jeff Irion and contributors
+#
+# This file originated from the `graphslam` package:
+#
+#   https://github.com/JeffLIrion/python-graphslam
+
+r"""Representation of a pose in :math:`SE(2)`.
+
+"""
+
+import math
+
+import numpy as np
+
+from ..util import neg_pi_to_pi
+
+
+class PoseSE2(np.ndarray):
+    r"""A representation of a pose in :math:`SE(2)`.
+
+    Parameters
+    ----------
+    position : np.ndarray, list
+        The position in :math:`\mathbb{R}^2`
+    orientation : float
+        The angle of the pose (in radians)
+
+    """
+    def __new__(cls, position, orientation):
+        obj = np.array([position[0], position[1], neg_pi_to_pi(orientation)], dtype=np.float64).view(cls)
+        return obj
+
+    # pylint: disable=arguments-differ
+    def copy(self):
+        """Return a copy of the pose.
+
+        Returns
+        -------
+        PoseSE2
+            A copy of the pose
+
+        """
+        return PoseSE2(self[:2], self[2])
+
+    def to_array(self):
+        """Return the pose as a numpy array.
+
+        Returns
+        -------
+        np.ndarray
+            The pose as a numpy array
+
+        """
+        return np.array(self)
+
+    def to_compact(self):
+        """Return the pose as a compact numpy array.
+
+        Returns
+        -------
+        np.ndarray
+            The pose as a compact numpy array
+
+        """
+        return np.array(self)
+
+    def to_matrix(self):
+        """Return the pose as an :math:`SE(2)` matrix.
+
+        Returns
+        -------
+        np.ndarray
+            The pose as an :math:`SE(2)` matrix
+
+        """
+        return np.array([[np.cos(self[2]), -np.sin(self[2]), self[0]], [np.sin(self[2]), np.cos(self[2]), self[1]], [0., 0., 1.]], dtype=np.float64)
+
+    @classmethod
+    def from_matrix(cls, matrix):
+        """Return the pose as an :math:`SE(2)` matrix.
+
+        Parameters
+        ----------
+        matrix : np.ndarray
+            The :math:`SE(2)` matrix that will be converted to a `PoseSE2` instance
+
+        Returns
+        -------
+        PoseSE2
+            The matrix as a `PoseSE2` object
+
+        """
+        return cls([matrix[0, 2], matrix[1, 2]], math.atan2(matrix[1, 0], matrix[0, 0]))
+
+    # ======================================================================= #
+    #                                                                         #
+    #                                Properties                               #
+    #                                                                         #
+    # ======================================================================= #
+    @property
+    def position(self):
+        """Return the pose's position.
+
+        Returns
+        -------
+        np.ndarray
+            The position portion of the pose
+
+        """
+        return np.array(self[:2])
+
+    @property
+    def orientation(self):
+        """Return the pose's orientation.
+
+        Returns
+        -------
+        float
+            The angle of the pose
+
+        """
+        return self[2]
+
+    @property
+    def inverse(self):
+        """Return the pose's inverse.
+
+        Returns
+        -------
+        PoseSE2
+            The pose's inverse
+
+        """
+        return PoseSE2([-self[0] * np.cos(self[2]) - self[1] * np.sin(self[2]), self[0] * np.sin(self[2]) - self[1] * np.cos([self[2]])], -self[2])
+
+    # ======================================================================= #
+    #                                                                         #
+    #                              Magic Methods                              #
+    #                                                                         #
+    # ======================================================================= #
+    def __add__(self, other):
+        r"""Add poses (i.e., pose composition): :math:`p_1 \oplus p_2`.
+
+        Parameters
+        ----------
+        other : PoseSE2
+            The other pose
+
+        Returns
+        -------
+        PoseSE2
+            The result of pose composition
+
+        """
+        return PoseSE2([self[0] + other[0] * np.cos(self[2]) - other[1] * np.sin(self[2]), self[1] + other[0] * np.sin(self[2]) + other[1] * np.cos(self[2])], neg_pi_to_pi(self[2] + other[2]))
+
+    def __sub__(self, other):
+        r"""Subtract poses (i.e., inverse pose composition): :math:`p_1 \ominus p_2`.
+
+        Parameters
+        ----------
+        other : PoseSE2
+            The other pose
+
+        Returns
+        -------
+        PoseSE2
+            The result of inverse pose composition
+
+        """
+        return PoseSE2([(self[0] - other[0]) * np.cos(other[2]) + (self[1] - other[1]) * np.sin(other[2]), (other[0] - self[0]) * np.sin(other[2]) + (self[1] - other[1]) * np.cos(other[2])], neg_pi_to_pi(self[2] - other[2]))
diff --git a/SLAM/GraphBasedSLAM/graphslam/util.py b/SLAM/GraphBasedSLAM/graphslam/util.py
new file mode 100644
index 0000000000..070dc1aa06
--- /dev/null
+++ b/SLAM/GraphBasedSLAM/graphslam/util.py
@@ -0,0 +1,78 @@
+# Copyright (c) 2020 Jeff Irion and contributors
+#
+# This file originated from the `graphslam` package:
+#
+#   https://github.com/JeffLIrion/python-graphslam
+
+"""Utility functions used throughout the package.
+
+"""
+
+import numpy as np
+
+
+TWO_PI = 2 * np.pi
+
+
+def neg_pi_to_pi(angle):
+    r"""Normalize ``angle`` to be in :math:`[-\pi, \pi)`.
+
+    Parameters
+    ----------
+    angle : float
+        An angle (in radians)
+
+    Returns
+    -------
+    float
+        The angle normalized to :math:`[-\pi, \pi)`
+
+    """
+    return (angle + np.pi) % (TWO_PI) - np.pi
+
+
+def solve_for_edge_dimensionality(n):
+    r"""Solve for the dimensionality of an edge.
+
+    In a .g2o file, an edge is specified as ``<estimate> <information matrix>``, where only the upper triangular portion of the matrix is provided.
+
+    This solves the problem:
+
+    .. math::
+
+       d + \frac{d (d + 1)}{2} = n
+
+    Returns
+    -------
+    int
+        The dimensionality of the edge
+
+    """
+    return int(round(np.sqrt(2 * n + 2.25) - 1.5))
+
+
+def upper_triangular_matrix_to_full_matrix(arr, n):
+    """Given an upper triangular matrix, return the full matrix.
+
+    Parameters
+    ----------
+    arr : np.ndarray
+        The upper triangular portion of the matrix
+    n : int
+        The size of the matrix
+
+    Returns
+    -------
+    mat : np.ndarray
+        The full matrix
+
+    """
+    triu0 = np.triu_indices(n, 0)
+    triu1 = np.triu_indices(n, 1)
+    tril1 = np.tril_indices(n, -1)
+
+    mat = np.zeros((n, n), dtype=np.float64)
+    mat[triu0] = arr
+    mat[tril1] = mat[triu1]
+
+    return mat
diff --git a/SLAM/GraphBasedSLAM/graphslam/vertex.py b/SLAM/GraphBasedSLAM/graphslam/vertex.py
new file mode 100644
index 0000000000..6fb1f0d098
--- /dev/null
+++ b/SLAM/GraphBasedSLAM/graphslam/vertex.py
@@ -0,0 +1,67 @@
+# Copyright (c) 2020 Jeff Irion and contributors
+#
+# This file originated from the `graphslam` package:
+#
+#   https://github.com/JeffLIrion/python-graphslam
+
+"""A ``Vertex`` class.
+
+"""
+
+import matplotlib.pyplot as plt
+
+
+# pylint: disable=too-few-public-methods
+class Vertex:
+    """A class for representing a vertex in Graph SLAM.
+
+    Parameters
+    ----------
+    vertex_id : int
+        The vertex's unique ID
+    pose : graphslam.pose.se2.PoseSE2
+        The pose associated with the vertex
+    vertex_index : int, None
+        The vertex's index in the graph's ``vertices`` list
+
+    Attributes
+    ----------
+    id : int
+        The vertex's unique ID
+    index : int, None
+        The vertex's index in the graph's ``vertices`` list
+    pose : graphslam.pose.se2.PoseSE2
+        The pose associated with the vertex
+
+    """
+    def __init__(self, vertex_id, pose, vertex_index=None):
+        self.id = vertex_id
+        self.pose = pose
+        self.index = vertex_index
+
+    def to_g2o(self):
+        """Export the vertex to the .g2o format.
+
+        Returns
+        -------
+        str
+            The vertex in .g2o format
+
+        """
+        return "VERTEX_SE2 {} {} {} {}\n".format(self.id, self.pose[0], self.pose[1], self.pose[2])
+
+    def plot(self, color='r', marker='o', markersize=3):
+        """Plot the vertex.
+
+        Parameters
+        ----------
+        color : str
+            The color that will be used to plot the vertex
+        marker : str
+            The marker that will be used to plot the vertex
+        markersize : int
+            The size of the plotted vertex
+
+        """
+        x, y = self.pose.position
+        plt.plot(x, y, color=color, marker=marker, markersize=markersize)
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literal 0
HcmV?d00001


From 9b22d3fb88cde649a0f7b0d20b7d4a5f08cc3a99 Mon Sep 17 00:00:00 2001
From: Chachay <t.sasai0802@gmail.com>
Date: Tue, 24 Mar 2020 20:12:26 +0100
Subject: [PATCH 0356/1080] rename yaw_r to yaw_ref

---
 PathTracking/rear_wheel_feedback/rear_wheel_feedback.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
index f6df94ac44..4141cfbff3 100644
--- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
+++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
@@ -175,10 +175,10 @@ def simulate(track, goal):
 
     return t, x, y, yaw, v, goal_flag
 
-def calc_target_speed(state, yaw_r):
+def calc_target_speed(state, yaw_ref):
     target_speed = 10.0 / 3.6
 
-    dyaw = yaw_r - state.yaw
+    dyaw = yaw_ref - state.yaw
     switch = math.pi / 4.0 <= dyaw < math.pi / 2.0
 
     if switch:

From b6e89c6d8b15a4907b47ec8bfee145e36a54c783 Mon Sep 17 00:00:00 2001
From: Chachay <t.sasai0802@gmail.com>
Date: Wed, 25 Mar 2020 16:42:00 +0100
Subject: [PATCH 0357/1080] rename a variable

---
 .../rear_wheel_feedback.py                    | 24 +++++++++----------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
index 4141cfbff3..52b4a11a0b 100644
--- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
+++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
@@ -119,7 +119,7 @@ def rear_wheel_feedback_control(state, e, k, yaw_ref):
     return delta
 
 
-def simulate(track, goal):
+def simulate(path_ref, goal):
     T = 500.0  # max simulation time
     goal_dis = 0.3
 
@@ -133,11 +133,11 @@ def simulate(track, goal):
     t = [0.0]
     goal_flag = False
 
-    s = np.arange(0, track.length, 0.1)
-    e, k, yaw_ref, s0 = track.calc_track_error(state.x, state.y, 0.0)
+    s = np.arange(0, path_ref.length, 0.1)
+    e, k, yaw_ref, s0 = path_ref.calc_track_error(state.x, state.y, 0.0)
 
     while T >= time:
-        e, k, yaw_ref, s0 = track.calc_track_error(state.x, state.y, s0)
+        e, k, yaw_ref, s0 = path_ref.calc_track_error(state.x, state.y, s0)
         di = rear_wheel_feedback_control(state, e, k, yaw_ref)
 
         speed_ref = calc_target_speed(state, yaw_ref)
@@ -165,9 +165,9 @@ def simulate(track, goal):
             # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect('key_release_event',
                     lambda event: [exit(0) if event.key == 'escape' else None])
-            plt.plot(track.X(s), track.Y(s), "-r", label="course")
+            plt.plot(path_ref.X(s), path_ref.Y(s), "-r", label="course")
             plt.plot(x, y, "ob", label="trajectory")
-            plt.plot(track.X(s0), track.Y(s0), "xg", label="target")
+            plt.plot(path_ref.X(s0), path_ref.Y(s0), "xg", label="target")
             plt.axis("equal")
             plt.grid(True)
             plt.title("speed[km/h]:{:.2f}, target s-param:{:.2f}".format(round(state.v * 3.6, 2), s0))
@@ -196,10 +196,10 @@ def main():
     ay = [0.0, 0.0, 5.0, 6.5, 3.0, 5.0, -2.0]
     goal = [ax[-1], ay[-1]]
 
-    track = CubicSplinePath(ax, ay)
-    s = np.arange(0, track.length, 0.1)
+    reference_path = CubicSplinePath(ax, ay)
+    s = np.arange(0, reference_path.length, 0.1)
 
-    t, x, y, yaw, v, goal_flag = simulate(track, goal)
+    t, x, y, yaw, v, goal_flag = simulate(reference_path, goal)
 
     # Test
     assert goal_flag, "Cannot goal"
@@ -208,7 +208,7 @@ def main():
         plt.close()
         plt.subplots(1)
         plt.plot(ax, ay, "xb", label="input")
-        plt.plot(track.X(s), track.Y(s), "-r", label="spline")
+        plt.plot(reference_path.X(s), reference_path.Y(s), "-r", label="spline")
         plt.plot(x, y, "-g", label="tracking")
         plt.grid(True)
         plt.axis("equal")
@@ -217,14 +217,14 @@ def main():
         plt.legend()
 
         plt.subplots(1)
-        plt.plot(s, np.rad2deg(track.calc_yaw(s)), "-r", label="yaw")
+        plt.plot(s, np.rad2deg(reference_path.calc_yaw(s)), "-r", label="yaw")
         plt.grid(True)
         plt.legend()
         plt.xlabel("line length[m]")
         plt.ylabel("yaw angle[deg]")
 
         plt.subplots(1)
-        plt.plot(s, track.calc_curvature(s), "-r", label="curvature")
+        plt.plot(s, reference_path.calc_curvature(s), "-r", label="curvature")
         plt.grid(True)
         plt.legend()
         plt.xlabel("line length[m]")

From e3190328128bd51c4b25665d2268e2c9ac18ea4a Mon Sep 17 00:00:00 2001
From: Nguyen Dinh Tuan <ndinhtuan15@gmail.com>
Date: Thu, 9 Apr 2020 15:12:02 +0700
Subject: [PATCH 0358/1080] fix compute jacobian of F in ekf_estimation

---
 Localization/extended_kalman_filter/extended_kalman_filter.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py
index 2b057e21de..2d0d86bf77 100644
--- a/Localization/extended_kalman_filter/extended_kalman_filter.py
+++ b/Localization/extended_kalman_filter/extended_kalman_filter.py
@@ -117,7 +117,7 @@ def jacob_h():
 def ekf_estimation(xEst, PEst, z, u):
     #  Predict
     xPred = motion_model(xEst, u)
-    jF = jacob_f(xPred, u)
+    jF = jacob_f(xEst, u)
     PPred = jF @ PEst @ jF.T + Q
 
     #  Update
@@ -128,7 +128,6 @@ def ekf_estimation(xEst, PEst, z, u):
     K = PPred @ jH.T @ np.linalg.inv(S)
     xEst = xPred + K @ y
     PEst = (np.eye(len(xEst)) - K @ jH) @ PPred
-
     return xEst, PEst
 
 

From d0d4f1116e70987b8ab251a137ae3188bd464c9d Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Fri, 10 Apr 2020 15:07:21 +0200
Subject: [PATCH 0359/1080] add Depth-First Search

---
 PathPlanning/DepthFirstSearch/dfs.py | 273 +++++++++++++++++++++++++++
 1 file changed, 273 insertions(+)
 create mode 100644 PathPlanning/DepthFirstSearch/dfs.py

diff --git a/PathPlanning/DepthFirstSearch/dfs.py b/PathPlanning/DepthFirstSearch/dfs.py
new file mode 100644
index 0000000000..4ceb0e2bec
--- /dev/null
+++ b/PathPlanning/DepthFirstSearch/dfs.py
@@ -0,0 +1,273 @@
+"""
+
+Depth-First grid planning
+
+author: Erwin Lejeune (@spida_rwin)
+
+See Wikipedia article (https://en.wikipedia.org/wiki/depth-First_search)
+
+"""
+
+import math
+
+import matplotlib.pyplot as plt
+
+import random
+
+show_animation = True
+
+
+class DFSPlanner:
+
+    def __init__(self, ox, oy, reso, rr):
+        """
+        Initialize grid map for DFS planning
+
+        ox: x position list of Obstacles [m]
+        oy: y position list of Obstacles [m]
+        reso: grid resolution [m]
+        rr: robot radius[m]
+        """
+
+        self.reso = reso
+        self.rr = rr
+        self.calc_obstacle_map(ox, oy)
+        self.motion = self.get_motion_model()
+
+    class Node:
+        def __init__(self, x, y, cost, pind, parent):
+            self.x = x  # index of grid
+            self.y = y  # index of grid
+            self.cost = cost
+            self.pind = pind
+            self.parent = parent
+
+        def __str__(self):
+            return str(self.x) + "," + str(self.y) + "," + str(
+                self.cost) + "," + str(self.pind)
+
+    def planning(self, sx, sy, gx, gy):
+        """
+        Depth First search
+
+        input:
+            sx: start x position [m]
+            sy: start y position [m]
+            gx: goal x position [m]
+            gy: goal y position [m]
+
+        output:
+            rx: x position list of the final path
+            ry: y position list of the final path
+        """
+
+        nstart = self.Node(self.calc_xyindex(sx, self.minx),
+                           self.calc_xyindex(sy, self.miny), 0.0, -1, None)
+        ngoal = self.Node(self.calc_xyindex(gx, self.minx),
+                          self.calc_xyindex(gy, self.miny), 0.0, -1, None)
+
+        open_set, closed_set = dict(), dict()
+        open_set[self.calc_grid_index(nstart)] = nstart
+
+        while 1:
+            if len(open_set) == 0:
+                print("Open set is empty..")
+                break
+
+            c_id = max(
+                open_set,
+                key=lambda o: open_set[o].pind)
+
+            current = open_set[c_id]
+
+            # show graph
+            if show_animation:  # pragma: no cover
+                plt.plot(self.calc_grid_position(current.x, self.minx),
+                         self.calc_grid_position(current.y, self.miny), "xc")
+                # for stopping simulation with the esc key.
+                plt.gcf().canvas.mpl_connect('key_release_event',
+                                             lambda event: [exit(
+                                                 0) if event.key == 'escape' else None])
+                if len(closed_set.keys()) % 10 == 0:
+                    plt.pause(0.001)
+
+            if current.x == ngoal.x and current.y == ngoal.y:
+                print("Find goal")
+                ngoal.pind = current.pind
+                ngoal.cost = current.cost
+                closed_set[current.pind] = current
+                break
+
+            # Remove the item from the open set
+            del open_set[c_id]
+
+            # Add it to the closed set
+            closed_set[c_id] = current
+
+            # expand_grid search grid based on motion model
+            successors = [self.Node(current.x + self.motion[i][0],
+                                 current.y + self.motion[i][1],
+                                 current.cost + self.motion[i][2], c_id+1, current) for i, _ in enumerate(self.motion)]
+
+            random.shuffle(successors)
+            for node in successors:
+                n_id = self.calc_grid_index(node)
+
+                # If the node is not safe, do nothing
+                if not self.verify_node(node):
+                    continue
+
+                if n_id not in closed_set:
+                    open_set[n_id] = node
+
+        rx, ry = self.calc_final_path(ngoal, closed_set)
+        return rx, ry
+
+    def calc_final_path(self, ngoal, closedset):
+        # generate final course
+        rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
+            self.calc_grid_position(ngoal.y, self.miny)]
+        n = closedset[ngoal.pind]
+        while n.parent is not None:
+            rx.append(self.calc_grid_position(n.x, self.minx))
+            ry.append(self.calc_grid_position(n.y, self.miny))
+            n = n.parent
+
+        return rx, ry
+
+    @staticmethod
+    def calc_heuristic(n1, n2):
+        w = 1.0  # weight of heuristic
+        d = w * math.hypot(n1.x - n2.x, n1.y - n2.y)
+        return d
+
+    def calc_grid_position(self, index, minp):
+        """
+        calc grid position
+
+        :param index:
+        :param minp:
+        :return:
+        """
+        pos = index * self.reso + minp
+        return pos
+
+    def calc_xyindex(self, position, min_pos):
+        return round((position - min_pos) / self.reso)
+
+    def calc_grid_index(self, node):
+        return (node.y - self.miny) * self.xwidth + (node.x - self.minx)
+
+    def verify_node(self, node):
+        px = self.calc_grid_position(node.x, self.minx)
+        py = self.calc_grid_position(node.y, self.miny)
+
+        if px < self.minx:
+            return False
+        elif py < self.miny:
+            return False
+        elif px >= self.maxx:
+            return False
+        elif py >= self.maxy:
+            return False
+
+        # collision check
+        if self.obmap[node.x][node.y]:
+            return False
+
+        return True
+
+    def calc_obstacle_map(self, ox, oy):
+
+        self.minx = round(min(ox))
+        self.miny = round(min(oy))
+        self.maxx = round(max(ox))
+        self.maxy = round(max(oy))
+        print("minx:", self.minx)
+        print("miny:", self.miny)
+        print("maxx:", self.maxx)
+        print("maxy:", self.maxy)
+
+        self.xwidth = round((self.maxx - self.minx) / self.reso)
+        self.ywidth = round((self.maxy - self.miny) / self.reso)
+        print("xwidth:", self.xwidth)
+        print("ywidth:", self.ywidth)
+
+        # obstacle map generation
+        self.obmap = [[False for i in range(self.ywidth)]
+                      for i in range(self.xwidth)]
+        for ix in range(self.xwidth):
+            x = self.calc_grid_position(ix, self.minx)
+            for iy in range(self.ywidth):
+                y = self.calc_grid_position(iy, self.miny)
+                for iox, ioy in zip(ox, oy):
+                    d = math.hypot(iox - x, ioy - y)
+                    if d <= self.rr:
+                        self.obmap[ix][iy] = True
+                        break
+
+    @staticmethod
+    def get_motion_model():
+        # dx, dy, cost
+        motion = [[1, 0, 1],
+                  [0, 1, 1],
+                  [-1, 0, 1],
+                  [0, -1, 1],
+                  [-1, -1, math.sqrt(2)],
+                  [-1, 1, math.sqrt(2)],
+                  [1, -1, math.sqrt(2)],
+                  [1, 1, math.sqrt(2)]]
+
+        return motion
+
+
+def main():
+    print(__file__ + " start!!")
+
+    # start and goal position
+    sx = 10.0  # [m]
+    sy = 10.0  # [m]
+    gx = 50.0  # [m]
+    gy = 50.0  # [m]
+    grid_size = 2.0  # [m]
+    robot_radius = 1.0  # [m]
+
+    # set obstable positions
+    ox, oy = [], []
+    for i in range(-10, 60):
+        ox.append(i)
+        oy.append(-10.0)
+    for i in range(-10, 60):
+        ox.append(60.0)
+        oy.append(i)
+    for i in range(-10, 61):
+        ox.append(i)
+        oy.append(60.0)
+    for i in range(-10, 61):
+        ox.append(-10.0)
+        oy.append(i)
+    for i in range(-10, 40):
+        ox.append(20.0)
+        oy.append(i)
+    for i in range(0, 40):
+        ox.append(40.0)
+        oy.append(60.0 - i)
+
+    if show_animation:  # pragma: no cover
+        plt.plot(ox, oy, ".k")
+        plt.plot(sx, sy, "og")
+        plt.plot(gx, gy, "xb")
+        plt.grid(True)
+        plt.axis("equal")
+
+    DFS = DFSPlanner(ox, oy, grid_size, robot_radius)
+    rx, ry = DFS.planning(sx, sy, gx, gy)
+
+    if show_animation:  # pragma: no cover
+        plt.plot(rx, ry, "-r")
+        plt.pause(0.01)
+        plt.show()
+
+
+if __name__ == '__main__':
+    main()

From 39d12558e99b14e79bb5333b9ad54b24d2a8fca5 Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Fri, 10 Apr 2020 15:07:54 +0200
Subject: [PATCH 0360/1080] add Breadth-First Search

---
 PathPlanning/BreadthFirstSearch/bfs.py | 270 +++++++++++++++++++++++++
 1 file changed, 270 insertions(+)
 create mode 100644 PathPlanning/BreadthFirstSearch/bfs.py

diff --git a/PathPlanning/BreadthFirstSearch/bfs.py b/PathPlanning/BreadthFirstSearch/bfs.py
new file mode 100644
index 0000000000..d20c608006
--- /dev/null
+++ b/PathPlanning/BreadthFirstSearch/bfs.py
@@ -0,0 +1,270 @@
+"""
+
+Breadth-First grid planning
+
+author: Erwin Lejeune (@spida_rwin)
+
+See Wikipedia article (https://en.wikipedia.org/wiki/Breadth-First_search)
+
+"""
+
+import math
+
+import matplotlib.pyplot as plt
+
+import random
+
+show_animation = True
+
+
+class BFSPlanner:
+
+    def __init__(self, ox, oy, reso, rr):
+        """
+        Initialize grid map for bfs planning
+
+        ox: x position list of Obstacles [m]
+        oy: y position list of Obstacles [m]
+        reso: grid resolution [m]
+        rr: robot radius[m]
+        """
+
+        self.reso = reso
+        self.rr = rr
+        self.calc_obstacle_map(ox, oy)
+        self.motion = self.get_motion_model()
+
+    class Node:
+        def __init__(self, x, y, cost, pind, parent):
+            self.x = x  # index of grid
+            self.y = y  # index of grid
+            self.cost = cost
+            self.pind = pind
+            self.parent = parent
+
+        def __str__(self):
+            return str(self.x) + "," + str(self.y) + "," + str(
+                self.cost) + "," + str(self.pind)
+
+    def planning(self, sx, sy, gx, gy):
+        """
+        Breadth First search
+
+        input:
+            sx: start x position [m]
+            sy: start y position [m]
+            gx: goal x position [m]
+            gy: goal y position [m]
+
+        output:
+            rx: x position list of the final path
+            ry: y position list of the final path
+        """
+
+        nstart = self.Node(self.calc_xyindex(sx, self.minx),
+                           self.calc_xyindex(sy, self.miny), 0.0, -1, None)
+        ngoal = self.Node(self.calc_xyindex(gx, self.minx),
+                          self.calc_xyindex(gy, self.miny), 0.0, -1, None)
+
+        open_set, closed_set = dict(), dict()
+        open_set[self.calc_grid_index(nstart)] = nstart
+
+        while 1:
+            if len(open_set) == 0:
+                print("Open set is empty..")
+                break
+
+            c_id = min(
+                open_set,
+                key=lambda o: open_set[o].pind)
+
+            current = open_set[c_id]
+
+            # show graph
+            if show_animation:  # pragma: no cover
+                plt.plot(self.calc_grid_position(current.x, self.minx),
+                         self.calc_grid_position(current.y, self.miny), "xc")
+                # for stopping simulation with the esc key.
+                plt.gcf().canvas.mpl_connect('key_release_event',
+                                             lambda event: [exit(
+                                                 0) if event.key == 'escape' else None])
+                if len(closed_set.keys()) % 10 == 0:
+                    plt.pause(0.001)
+
+            if current.x == ngoal.x and current.y == ngoal.y:
+                print("Find goal")
+                ngoal.pind = current.pind
+                ngoal.cost = current.cost
+                break
+
+            # Remove the item from the open set
+            del open_set[c_id]
+
+            # expand_grid search grid based on motion model
+            successors = [self.Node(current.x + self.motion[i][0],
+                                 current.y + self.motion[i][1],
+                                 current.cost + self.motion[i][2], c_id+1, current) for i, _ in enumerate(self.motion)]
+
+            random.shuffle(successors)
+            for node in successors:
+                n_id = self.calc_grid_index(node)
+
+                # If the node is not safe, do nothing
+                if not self.verify_node(node):
+                    continue
+
+                if n_id not in closed_set:
+                    closed_set[n_id] = node
+                    open_set[n_id] = node
+
+        rx, ry = self.calc_final_path(ngoal, closed_set)
+        return rx, ry
+
+    def calc_final_path(self, ngoal, closedset):
+        # generate final course
+        rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
+            self.calc_grid_position(ngoal.y, self.miny)]
+        n = closedset[ngoal.pind]
+        while n.parent is not None:
+            rx.append(self.calc_grid_position(n.x, self.minx))
+            ry.append(self.calc_grid_position(n.y, self.miny))
+            n = n.parent
+
+        return rx, ry
+
+    @staticmethod
+    def calc_heuristic(n1, n2):
+        w = 1.0  # weight of heuristic
+        d = w * math.hypot(n1.x - n2.x, n1.y - n2.y)
+        return d
+
+    def calc_grid_position(self, index, minp):
+        """
+        calc grid position
+
+        :param index:
+        :param minp:
+        :return:
+        """
+        pos = index * self.reso + minp
+        return pos
+
+    def calc_xyindex(self, position, min_pos):
+        return round((position - min_pos) / self.reso)
+
+    def calc_grid_index(self, node):
+        return (node.y - self.miny) * self.xwidth + (node.x - self.minx)
+
+    def verify_node(self, node):
+        px = self.calc_grid_position(node.x, self.minx)
+        py = self.calc_grid_position(node.y, self.miny)
+
+        if px < self.minx:
+            return False
+        elif py < self.miny:
+            return False
+        elif px >= self.maxx:
+            return False
+        elif py >= self.maxy:
+            return False
+
+        # collision check
+        if self.obmap[node.x][node.y]:
+            return False
+
+        return True
+
+    def calc_obstacle_map(self, ox, oy):
+
+        self.minx = round(min(ox))
+        self.miny = round(min(oy))
+        self.maxx = round(max(ox))
+        self.maxy = round(max(oy))
+        print("minx:", self.minx)
+        print("miny:", self.miny)
+        print("maxx:", self.maxx)
+        print("maxy:", self.maxy)
+
+        self.xwidth = round((self.maxx - self.minx) / self.reso)
+        self.ywidth = round((self.maxy - self.miny) / self.reso)
+        print("xwidth:", self.xwidth)
+        print("ywidth:", self.ywidth)
+
+        # obstacle map generation
+        self.obmap = [[False for i in range(self.ywidth)]
+                      for i in range(self.xwidth)]
+        for ix in range(self.xwidth):
+            x = self.calc_grid_position(ix, self.minx)
+            for iy in range(self.ywidth):
+                y = self.calc_grid_position(iy, self.miny)
+                for iox, ioy in zip(ox, oy):
+                    d = math.hypot(iox - x, ioy - y)
+                    if d <= self.rr:
+                        self.obmap[ix][iy] = True
+                        break
+
+    @staticmethod
+    def get_motion_model():
+        # dx, dy, cost
+        motion = [[1, 0, 1],
+                  [0, 1, 1],
+                  [-1, 0, 1],
+                  [0, -1, 1],
+                  [-1, -1, math.sqrt(2)],
+                  [-1, 1, math.sqrt(2)],
+                  [1, -1, math.sqrt(2)],
+                  [1, 1, math.sqrt(2)]]
+
+        return motion
+
+
+def main():
+    print(__file__ + " start!!")
+
+    # start and goal position
+    sx = 10.0  # [m]
+    sy = 10.0  # [m]
+    gx = 50.0  # [m]
+    gy = 50.0  # [m]
+    grid_size = 2.0  # [m]
+    robot_radius = 1.0  # [m]
+
+    # set obstable positions
+    ox, oy = [], []
+    for i in range(-10, 60):
+        ox.append(i)
+        oy.append(-10.0)
+    for i in range(-10, 60):
+        ox.append(60.0)
+        oy.append(i)
+    for i in range(-10, 61):
+        ox.append(i)
+        oy.append(60.0)
+    for i in range(-10, 61):
+        ox.append(-10.0)
+        oy.append(i)
+    for i in range(-10, 40):
+        ox.append(20.0)
+        oy.append(i)
+    for i in range(0, 40):
+        ox.append(40.0)
+        oy.append(60.0 - i)
+
+    if show_animation:  # pragma: no cover
+        plt.plot(ox, oy, ".k")
+        plt.plot(sx, sy, "og")
+        plt.plot(gx, gy, "xb")
+        plt.grid(True)
+        plt.axis("equal")
+
+    bfs = BFSPlanner(ox, oy, grid_size, robot_radius)
+    rx, ry = bfs.planning(sx, sy, gx, gy)
+
+    if show_animation:  # pragma: no cover
+        plt.plot(rx, ry, "-r")
+        plt.pause(0.01)
+        plt.show()
+
+
+if __name__ == '__main__':
+    main()

From 89ef0a9a5287695c928db24a0fa7e15c51c02206 Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Fri, 10 Apr 2020 15:08:27 +0200
Subject: [PATCH 0361/1080] add bidirectional A*

---
 .../BidirectionalAStar/bidir_a_star.py        | 331 ++++++++++++++++++
 1 file changed, 331 insertions(+)
 create mode 100644 PathPlanning/BidirectionalAStar/bidir_a_star.py

diff --git a/PathPlanning/BidirectionalAStar/bidir_a_star.py b/PathPlanning/BidirectionalAStar/bidir_a_star.py
new file mode 100644
index 0000000000..3c709c64c1
--- /dev/null
+++ b/PathPlanning/BidirectionalAStar/bidir_a_star.py
@@ -0,0 +1,331 @@
+"""
+
+Bidirectional A* grid planning
+
+author: Erwin Lejeune (@spida_rwin)
+
+See Wikipedia article (https://en.wikipedia.org/wiki/Bidirectional_search)
+
+"""
+
+import math
+
+import matplotlib.pyplot as plt
+
+show_animation = True
+
+
+class BidirAStarPlanner:
+
+    def __init__(self, ox, oy, reso, rr):
+        """
+        Initialize grid map for a star planning
+
+        ox: x position list of Obstacles [m]
+        oy: y position list of Obstacles [m]
+        reso: grid resolution [m]
+        rr: robot radius[m]
+        """
+
+        self.reso = reso
+        self.rr = rr
+        self.calc_obstacle_map(ox, oy)
+        self.motion = self.get_motion_model()
+
+    class Node:
+        def __init__(self, x, y, cost, pind):
+            self.x = x  # index of grid
+            self.y = y  # index of grid
+            self.cost = cost
+            self.pind = pind
+
+        def __str__(self):
+            return str(self.x) + "," + str(self.y) + "," + str(
+                self.cost) + "," + str(self.pind)
+
+    def planning(self, sx, sy, gx, gy):
+        """
+        Bidirectional A star path search
+
+        input:
+            sx: start x position [m]
+            sy: start y position [m]
+            gx: goal x position [m]
+            gy: goal y position [m]
+
+        output:
+            rx: x position list of the final path
+            ry: y position list of the final path
+        """
+
+        nstart = self.Node(self.calc_xyindex(sx, self.minx),
+                           self.calc_xyindex(sy, self.miny), 0.0, -1)
+        ngoal = self.Node(self.calc_xyindex(gx, self.minx),
+                          self.calc_xyindex(gy, self.miny), 0.0, -1)
+
+        open_set_A, closed_set_A = dict(), dict()
+        open_set_B, closed_set_B = dict(), dict()
+        open_set_A[self.calc_grid_index(nstart)] = nstart
+        open_set_B[self.calc_grid_index(ngoal)] = ngoal
+
+        current_A = nstart
+        current_B = ngoal
+
+        while 1:
+            if len(open_set_A) == 0:
+                print("Open set A is empty..")
+                break
+            
+            if len(open_set_B) == 0:
+                print("Open set B is empty..")
+                break
+
+            c_id_A = min(
+                open_set_A,
+                key=lambda o: open_set_A[o].cost + self.calc_heuristic(current_B,
+                                                                     open_set_A[
+                                                                         o]))
+                                
+            current_A = open_set_A[c_id_A]
+
+            c_id_B = min(
+                open_set_B,
+                key=lambda o: open_set_B[o].cost + self.calc_heuristic(current_A,
+                                                                     open_set_B[
+                                                                         o]))
+
+            current_B = open_set_B[c_id_B]
+
+            # show graph
+            if show_animation:  # pragma: no cover
+                plt.plot(self.calc_grid_position(current_A.x, self.minx),
+                         self.calc_grid_position(current_A.y, self.miny), "xc")
+                plt.plot(self.calc_grid_position(current_B.x, self.minx),
+                         self.calc_grid_position(current_B.y, self.miny), "xc")
+                # for stopping simulation with the esc key.
+                plt.gcf().canvas.mpl_connect('key_release_event',
+                                             lambda event: [exit(
+                                                 0) if event.key == 'escape' else None])
+                if len(closed_set_A.keys()) % 10 == 0:
+                    plt.pause(0.001)
+
+            if current_A.x == current_B.x and current_A.y == current_B.y:
+                print("Found goal")
+                meetpointA = current_A
+                meetpointB = current_B
+                break
+
+            # Remove the item from the open set
+            del open_set_A[c_id_A]
+            del open_set_B[c_id_B]
+
+            # Add it to the closed set
+            closed_set_A[c_id_A] = current_A
+            closed_set_B[c_id_B] = current_B
+
+            # expand_grid search grid based on motion model
+            for i, _ in enumerate(self.motion):
+                node = self.Node(current_A.x + self.motion[i][0],
+                                 current_A.y + self.motion[i][1],
+                                 current_A.cost + self.motion[i][2], c_id_A)
+                n_id = self.calc_grid_index(node)
+
+                # If the node is not safe, do nothing
+                if not self.verify_node(node):
+                    continue
+
+                if n_id in closed_set_A:
+                    continue
+
+                if n_id not in open_set_A:
+                    open_set_A[n_id] = node  # discovered a new node
+                else:
+                    if open_set_A[n_id].cost > node.cost:
+                        # This path is the best until now. record it
+                        open_set_A[n_id] = node
+            
+            for i, _ in enumerate(self.motion):
+                node = self.Node(current_B.x + self.motion[i][0],
+                                 current_B.y + self.motion[i][1],
+                                 current_B.cost + self.motion[i][2], c_id_B)
+                n_id = self.calc_grid_index(node)
+
+                # If the node is not safe, do nothing
+                if not self.verify_node(node):
+                    continue
+
+                if n_id in closed_set_B:
+                    continue
+
+                if n_id not in open_set_B:
+                    open_set_B[n_id] = node  # discovered a new node
+                else:
+                    if open_set_B[n_id].cost > node.cost:
+                        # This path is the best until now. record it
+                        open_set_B[n_id] = node
+
+        rx, ry = self.calc_final_path_bidir(meetpointA, meetpointB, closed_set_A, closed_set_B)
+
+        return rx, ry
+
+    def calc_final_path_bidir(self, meetnode_A, meetnode_B, closed_set_A, closed_set_B):
+        rx_A, ry_A = self.calc_final_path(meetnode_A, closed_set_A)
+        rx_B, ry_B = self.calc_final_path(meetnode_B, closed_set_B)
+
+        rx_A.reverse()
+        ry_A.reverse()
+
+        rx = rx_A + rx_B
+        ry = ry_A + ry_B
+
+        return rx, ry
+
+    def calc_final_path(self, ngoal, closedset):
+        # generate final course
+        rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
+            self.calc_grid_position(ngoal.y, self.miny)]
+        pind = ngoal.pind
+        while pind != -1:
+            n = closedset[pind]
+            rx.append(self.calc_grid_position(n.x, self.minx))
+            ry.append(self.calc_grid_position(n.y, self.miny))
+            pind = n.pind
+
+        return rx, ry
+
+    @staticmethod
+    def calc_heuristic(n1, n2):
+        w = 1.0  # weight of heuristic
+        d = w * math.hypot(n1.x - n2.x, n1.y - n2.y)
+        return d
+
+    def calc_grid_position(self, index, minp):
+        """
+        calc grid position
+
+        :param index:
+        :param minp:
+        :return:
+        """
+        pos = index * self.reso + minp
+        return pos
+
+    def calc_xyindex(self, position, min_pos):
+        return round((position - min_pos) / self.reso)
+
+    def calc_grid_index(self, node):
+        return (node.y - self.miny) * self.xwidth + (node.x - self.minx)
+
+    def verify_node(self, node):
+        px = self.calc_grid_position(node.x, self.minx)
+        py = self.calc_grid_position(node.y, self.miny)
+
+        if px < self.minx:
+            return False
+        elif py < self.miny:
+            return False
+        elif px >= self.maxx:
+            return False
+        elif py >= self.maxy:
+            return False
+
+        # collision check
+        if self.obmap[node.x][node.y]:
+            return False
+
+        return True
+
+    def calc_obstacle_map(self, ox, oy):
+
+        self.minx = round(min(ox))
+        self.miny = round(min(oy))
+        self.maxx = round(max(ox))
+        self.maxy = round(max(oy))
+        print("minx:", self.minx)
+        print("miny:", self.miny)
+        print("maxx:", self.maxx)
+        print("maxy:", self.maxy)
+
+        self.xwidth = round((self.maxx - self.minx) / self.reso)
+        self.ywidth = round((self.maxy - self.miny) / self.reso)
+        print("xwidth:", self.xwidth)
+        print("ywidth:", self.ywidth)
+
+        # obstacle map generation
+        self.obmap = [[False for i in range(self.ywidth)]
+                      for i in range(self.xwidth)]
+        for ix in range(self.xwidth):
+            x = self.calc_grid_position(ix, self.minx)
+            for iy in range(self.ywidth):
+                y = self.calc_grid_position(iy, self.miny)
+                for iox, ioy in zip(ox, oy):
+                    d = math.hypot(iox - x, ioy - y)
+                    if d <= self.rr:
+                        self.obmap[ix][iy] = True
+                        break
+
+    @staticmethod
+    def get_motion_model():
+        # dx, dy, cost
+        motion = [[1, 0, 1],
+                  [0, 1, 1],
+                  [-1, 0, 1],
+                  [0, -1, 1],
+                  [-1, -1, math.sqrt(2)],
+                  [-1, 1, math.sqrt(2)],
+                  [1, -1, math.sqrt(2)],
+                  [1, 1, math.sqrt(2)]]
+
+        return motion
+
+
+def main():
+    print(__file__ + " start!!")
+
+    # start and goal position
+    sx = 10.0  # [m]
+    sy = 10.0  # [m]
+    gx = 50.0  # [m]
+    gy = 50.0  # [m]
+    grid_size = 2.0  # [m]
+    robot_radius = 1.0  # [m]
+
+    # set obstable positions
+    ox, oy = [], []
+    for i in range(-10, 60):
+        ox.append(i)
+        oy.append(-10.0)
+    for i in range(-10, 60):
+        ox.append(60.0)
+        oy.append(i)
+    for i in range(-10, 61):
+        ox.append(i)
+        oy.append(60.0)
+    for i in range(-10, 61):
+        ox.append(-10.0)
+        oy.append(i)
+    for i in range(-10, 40):
+        ox.append(20.0)
+        oy.append(i)
+    for i in range(0, 40):
+        ox.append(40.0)
+        oy.append(60.0 - i)
+
+    if show_animation:  # pragma: no cover
+        plt.plot(ox, oy, ".k")
+        plt.plot(sx, sy, "og")
+        plt.plot(gx, gy, "ob")
+        plt.grid(True)
+        plt.axis("equal")
+
+    bidir_a_star = BidirAStarPlanner(ox, oy, grid_size, robot_radius)
+    rx, ry = bidir_a_star.planning(sx, sy, gx, gy)
+
+    if show_animation:  # pragma: no cover
+        plt.plot(rx, ry, "-r")
+        plt.pause(.0001)
+        plt.show()
+
+
+if __name__ == '__main__':
+    main()

From 01596296c41b0a76775b64378734fa0bd25c5662 Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Mon, 13 Apr 2020 13:45:22 +0200
Subject: [PATCH 0362/1080] change breadth first search file name

---
 .../BreadthFirstSearch/{bfs.py => breadth_first_search.py}        | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename PathPlanning/BreadthFirstSearch/{bfs.py => breadth_first_search.py} (100%)

diff --git a/PathPlanning/BreadthFirstSearch/bfs.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
similarity index 100%
rename from PathPlanning/BreadthFirstSearch/bfs.py
rename to PathPlanning/BreadthFirstSearch/breadth_first_search.py

From f2c651ff5514109e5d26bce8dff234b798048d8e Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Mon, 13 Apr 2020 13:56:57 +0200
Subject: [PATCH 0363/1080] Changes following review

---
 .../breadth_first_search.py                   | 20 ++++++-------------
 1 file changed, 6 insertions(+), 14 deletions(-)

diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
index d20c608006..3f5c95de02 100644
--- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py
+++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
@@ -17,7 +17,7 @@
 show_animation = True
 
 
-class BFSPlanner:
+class BreadthFirstSearchPlanner:
 
     def __init__(self, ox, oy, reso, rr):
         """
@@ -48,7 +48,7 @@ def __str__(self):
 
     def planning(self, sx, sy, gx, gy):
         """
-        Breadth First search
+        Breadth First search based planning
 
         input:
             sx: start x position [m]
@@ -101,12 +101,10 @@ def planning(self, sx, sy, gx, gy):
             del open_set[c_id]
 
             # expand_grid search grid based on motion model
-            successors = [self.Node(current.x + self.motion[i][0],
+            for i, _ in enumerate(self.motion):
+                node = self.Node(current.x + self.motion[i][0],
                                  current.y + self.motion[i][1],
-                                 current.cost + self.motion[i][2], c_id+1, current) for i, _ in enumerate(self.motion)]
-
-            random.shuffle(successors)
-            for node in successors:
+                                 current.cost + self.motion[i][2], c_id+1, current)
                 n_id = self.calc_grid_index(node)
 
                 # If the node is not safe, do nothing
@@ -132,12 +130,6 @@ def calc_final_path(self, ngoal, closedset):
 
         return rx, ry
 
-    @staticmethod
-    def calc_heuristic(n1, n2):
-        w = 1.0  # weight of heuristic
-        d = w * math.hypot(n1.x - n2.x, n1.y - n2.y)
-        return d
-
     def calc_grid_position(self, index, minp):
         """
         calc grid position
@@ -257,7 +249,7 @@ def main():
         plt.grid(True)
         plt.axis("equal")
 
-    bfs = BFSPlanner(ox, oy, grid_size, robot_radius)
+    bfs = BreadthFirstSearchPlanner(ox, oy, grid_size, robot_radius)
     rx, ry = bfs.planning(sx, sy, gx, gy)
 
     if show_animation:  # pragma: no cover

From 68c4c78e089d9a41eeeb50dad50fc9481649138c Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Mon, 13 Apr 2020 14:02:07 +0200
Subject: [PATCH 0364/1080] Fix path not connecting to start node

---
 PathPlanning/BreadthFirstSearch/breadth_first_search.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
index 3f5c95de02..e67a478848 100644
--- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py
+++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
@@ -128,6 +128,9 @@ def calc_final_path(self, ngoal, closedset):
             ry.append(self.calc_grid_position(n.y, self.miny))
             n = n.parent
 
+        rx.append(self.calc_grid_position(n.x, self.minx))
+        ry.append(self.calc_grid_position(n.y, self.miny))
+
         return rx, ry
 
     def calc_grid_position(self, index, minp):

From 6f358adc5ea9c3a80ef6448e69006540f7f726e3 Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Mon, 13 Apr 2020 14:23:13 +0200
Subject: [PATCH 0365/1080] shorten motion expansion loop

---
 ...idir_a_star.py => bidirectional_a_star.py} | 70 ++++++++++---------
 1 file changed, 37 insertions(+), 33 deletions(-)
 rename PathPlanning/BidirectionalAStar/{bidir_a_star.py => bidirectional_a_star.py} (84%)

diff --git a/PathPlanning/BidirectionalAStar/bidir_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
similarity index 84%
rename from PathPlanning/BidirectionalAStar/bidir_a_star.py
rename to PathPlanning/BidirectionalAStar/bidirectional_a_star.py
index 3c709c64c1..37d14eab68 100644
--- a/PathPlanning/BidirectionalAStar/bidir_a_star.py
+++ b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
@@ -15,7 +15,7 @@
 show_animation = True
 
 
-class BidirAStarPlanner:
+class BidirectionalAStarPlanner:
 
     def __init__(self, ox, oy, reso, rr):
         """
@@ -125,44 +125,48 @@ def planning(self, sx, sy, gx, gy):
 
             # expand_grid search grid based on motion model
             for i, _ in enumerate(self.motion):
-                node = self.Node(current_A.x + self.motion[i][0],
+                continue_A = False
+                continue_B = False
+
+                child_node_A = self.Node(current_A.x + self.motion[i][0],
                                  current_A.y + self.motion[i][1],
                                  current_A.cost + self.motion[i][2], c_id_A)
-                n_id = self.calc_grid_index(node)
 
-                # If the node is not safe, do nothing
-                if not self.verify_node(node):
-                    continue
-
-                if n_id in closed_set_A:
-                    continue
-
-                if n_id not in open_set_A:
-                    open_set_A[n_id] = node  # discovered a new node
-                else:
-                    if open_set_A[n_id].cost > node.cost:
-                        # This path is the best until now. record it
-                        open_set_A[n_id] = node
-            
-            for i, _ in enumerate(self.motion):
-                node = self.Node(current_B.x + self.motion[i][0],
+                child_node_B = self.Node(current_B.x + self.motion[i][0],
                                  current_B.y + self.motion[i][1],
                                  current_B.cost + self.motion[i][2], c_id_B)
-                n_id = self.calc_grid_index(node)
 
-                # If the node is not safe, do nothing
-                if not self.verify_node(node):
-                    continue
-
-                if n_id in closed_set_B:
-                    continue
+                n_id_A = self.calc_grid_index(child_node_A)
+                n_id_B = self.calc_grid_index(child_node_B)
 
-                if n_id not in open_set_B:
-                    open_set_B[n_id] = node  # discovered a new node
-                else:
-                    if open_set_B[n_id].cost > node.cost:
-                        # This path is the best until now. record it
-                        open_set_B[n_id] = node
+                # If the node is not safe, do nothing
+                if not self.verify_node(child_node_A):
+                    continue_A = True
+                
+                if not self.verify_node(child_node_B):
+                    continue_B = True
+
+                if n_id_A in closed_set_A:
+                    continue_A = True
+                
+                if n_id_B in closed_set_B:
+                    continue_B = True
+
+                if not(continue_A):
+                    if n_id_A not in open_set_A:
+                        open_set_A[n_id_A] = child_node_A  # discovered a new node
+                    else:
+                        if open_set_A[n_id_A].cost > child_node_A.cost:
+                            # This path is the best until now. record it
+                            open_set_A[n_id_A] = child_node_A
+            
+                if not(continue_B):
+                    if n_id_B not in open_set_B:
+                        open_set_B[n_id_B] = child_node_B  # discovered a new node
+                    else:
+                        if open_set_B[n_id_B].cost > child_node_B.cost:
+                            # This path is the best until now. record it
+                            open_set_B[n_id_B] = child_node_B
 
         rx, ry = self.calc_final_path_bidir(meetpointA, meetpointB, closed_set_A, closed_set_B)
 
@@ -318,7 +322,7 @@ def main():
         plt.grid(True)
         plt.axis("equal")
 
-    bidir_a_star = BidirAStarPlanner(ox, oy, grid_size, robot_radius)
+    bidir_a_star = BidirectionalAStarPlanner(ox, oy, grid_size, robot_radius)
     rx, ry = bidir_a_star.planning(sx, sy, gx, gy)
 
     if show_animation:  # pragma: no cover

From 6d4ea8bd1da771ea1315a73f0759c5f732fe1b99 Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Mon, 13 Apr 2020 14:24:16 +0200
Subject: [PATCH 0366/1080] change file name

---
 PathPlanning/DepthFirstSearch/{dfs.py => depth_first_search.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename PathPlanning/DepthFirstSearch/{dfs.py => depth_first_search.py} (100%)

diff --git a/PathPlanning/DepthFirstSearch/dfs.py b/PathPlanning/DepthFirstSearch/depth_first_search.py
similarity index 100%
rename from PathPlanning/DepthFirstSearch/dfs.py
rename to PathPlanning/DepthFirstSearch/depth_first_search.py

From 18b73652f6767aef8a32da970f4f82387182d99b Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Mon, 13 Apr 2020 14:25:35 +0200
Subject: [PATCH 0367/1080] remove heuristic method

---
 PathPlanning/DepthFirstSearch/depth_first_search.py | 12 +++---------
 1 file changed, 3 insertions(+), 9 deletions(-)

diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py
index 4ceb0e2bec..466aa1646a 100644
--- a/PathPlanning/DepthFirstSearch/depth_first_search.py
+++ b/PathPlanning/DepthFirstSearch/depth_first_search.py
@@ -17,7 +17,7 @@
 show_animation = True
 
 
-class DFSPlanner:
+class DepthFirstSearchPlanner:
 
     def __init__(self, ox, oy, reso, rr):
         """
@@ -135,12 +135,6 @@ def calc_final_path(self, ngoal, closedset):
 
         return rx, ry
 
-    @staticmethod
-    def calc_heuristic(n1, n2):
-        w = 1.0  # weight of heuristic
-        d = w * math.hypot(n1.x - n2.x, n1.y - n2.y)
-        return d
-
     def calc_grid_position(self, index, minp):
         """
         calc grid position
@@ -260,8 +254,8 @@ def main():
         plt.grid(True)
         plt.axis("equal")
 
-    DFS = DFSPlanner(ox, oy, grid_size, robot_radius)
-    rx, ry = DFS.planning(sx, sy, gx, gy)
+    dfs = DepthFirstSearchPlanner(ox, oy, grid_size, robot_radius)
+    rx, ry = dfs.planning(sx, sy, gx, gy)
 
     if show_animation:  # pragma: no cover
         plt.plot(rx, ry, "-r")

From 952cff6b34e6abf9c52469952c20373c8d842f40 Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Mon, 13 Apr 2020 14:29:12 +0200
Subject: [PATCH 0368/1080] fix path not connecting to start node

---
 PathPlanning/DepthFirstSearch/depth_first_search.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py
index 466aa1646a..bb08080734 100644
--- a/PathPlanning/DepthFirstSearch/depth_first_search.py
+++ b/PathPlanning/DepthFirstSearch/depth_first_search.py
@@ -104,13 +104,13 @@ def planning(self, sx, sy, gx, gy):
             # Add it to the closed set
             closed_set[c_id] = current
 
+            random.shuffle(self.motion)
+
             # expand_grid search grid based on motion model
-            successors = [self.Node(current.x + self.motion[i][0],
+            for i, _ in enumerate(self.motion):
+                node = self.Node(current.x + self.motion[i][0],
                                  current.y + self.motion[i][1],
-                                 current.cost + self.motion[i][2], c_id+1, current) for i, _ in enumerate(self.motion)]
-
-            random.shuffle(successors)
-            for node in successors:
+                                 current.cost + self.motion[i][2], c_id+1, current)
                 n_id = self.calc_grid_index(node)
 
                 # If the node is not safe, do nothing
@@ -128,7 +128,7 @@ def calc_final_path(self, ngoal, closedset):
         rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
             self.calc_grid_position(ngoal.y, self.miny)]
         n = closedset[ngoal.pind]
-        while n.parent is not None:
+        while n is not None:
             rx.append(self.calc_grid_position(n.x, self.minx))
             ry.append(self.calc_grid_position(n.y, self.miny))
             n = n.parent

From 71daa7898d88f590ee4e65935e239115cb431fb5 Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Mon, 13 Apr 2020 14:34:47 +0200
Subject: [PATCH 0369/1080] easier path retrieving and shuffle children node

---
 PathPlanning/BreadthFirstSearch/breadth_first_search.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
index e67a478848..f4c0fcd0bb 100644
--- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py
+++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
@@ -100,6 +100,8 @@ def planning(self, sx, sy, gx, gy):
             # Remove the item from the open set
             del open_set[c_id]
 
+            random.shuffle(self.motion)
+
             # expand_grid search grid based on motion model
             for i, _ in enumerate(self.motion):
                 node = self.Node(current.x + self.motion[i][0],
@@ -123,14 +125,11 @@ def calc_final_path(self, ngoal, closedset):
         rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
             self.calc_grid_position(ngoal.y, self.miny)]
         n = closedset[ngoal.pind]
-        while n.parent is not None:
+        while n is not None:
             rx.append(self.calc_grid_position(n.x, self.minx))
             ry.append(self.calc_grid_position(n.y, self.miny))
             n = n.parent
 
-        rx.append(self.calc_grid_position(n.x, self.minx))
-        ry.append(self.calc_grid_position(n.y, self.miny))
-
         return rx, ry
 
     def calc_grid_position(self, index, minp):

From 81622428ef03cf27123cf9c79dede051cf8d1b89 Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Tue, 14 Apr 2020 15:27:33 +0200
Subject: [PATCH 0370/1080] not adding parent if node in closed set

---
 PathPlanning/BreadthFirstSearch/breadth_first_search.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
index f4c0fcd0bb..f25802434a 100644
--- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py
+++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
@@ -106,7 +106,7 @@ def planning(self, sx, sy, gx, gy):
             for i, _ in enumerate(self.motion):
                 node = self.Node(current.x + self.motion[i][0],
                                  current.y + self.motion[i][1],
-                                 current.cost + self.motion[i][2], c_id+1, current)
+                                 current.cost + self.motion[i][2], c_id+1, None)
                 n_id = self.calc_grid_index(node)
 
                 # If the node is not safe, do nothing
@@ -114,6 +114,7 @@ def planning(self, sx, sy, gx, gy):
                     continue
 
                 if n_id not in closed_set:
+                    node.parent = current
                     closed_set[n_id] = node
                     open_set[n_id] = node
 

From ab05ac600aeb9bc5d80947e5a2ba5694a07e0a66 Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Thu, 16 Apr 2020 16:00:11 +0200
Subject: [PATCH 0371/1080] Fix typo in url and variable names

---
 PathPlanning/DepthFirstSearch/depth_first_search.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py
index bb08080734..d2f41347c3 100644
--- a/PathPlanning/DepthFirstSearch/depth_first_search.py
+++ b/PathPlanning/DepthFirstSearch/depth_first_search.py
@@ -4,7 +4,7 @@
 
 author: Erwin Lejeune (@spida_rwin)
 
-See Wikipedia article (https://en.wikipedia.org/wiki/depth-First_search)
+See Wikipedia article (https://en.wikipedia.org/wiki/Depth-first_search)
 
 """
 
@@ -188,8 +188,8 @@ def calc_obstacle_map(self, ox, oy):
         print("ywidth:", self.ywidth)
 
         # obstacle map generation
-        self.obmap = [[False for i in range(self.ywidth)]
-                      for i in range(self.xwidth)]
+        self.obmap = [[False for _ in range(self.ywidth)]
+                      for _ in range(self.xwidth)]
         for ix in range(self.xwidth):
             x = self.calc_grid_position(ix, self.minx)
             for iy in range(self.ywidth):

From fa5ca3cd7c88736d28ec96868328637c3772b803 Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Thu, 16 Apr 2020 16:01:19 +0200
Subject: [PATCH 0372/1080] Fix typo in url, variable name

---
 PathPlanning/BreadthFirstSearch/breadth_first_search.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
index f25802434a..5a38035507 100644
--- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py
+++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
@@ -4,7 +4,7 @@
 
 author: Erwin Lejeune (@spida_rwin)
 
-See Wikipedia article (https://en.wikipedia.org/wiki/Breadth-First_search)
+See Wikipedia article (https://en.wikipedia.org/wiki/Breadth-first_search)
 
 """
 
@@ -186,8 +186,8 @@ def calc_obstacle_map(self, ox, oy):
         print("ywidth:", self.ywidth)
 
         # obstacle map generation
-        self.obmap = [[False for i in range(self.ywidth)]
-                      for i in range(self.xwidth)]
+        self.obmap = [[False for _ in range(self.ywidth)]
+                      for _ in range(self.xwidth)]
         for ix in range(self.xwidth):
             x = self.calc_grid_position(ix, self.minx)
             for iy in range(self.ywidth):

From 0400ce3ad962f9f3ec7fa27455ba106abd580bec Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Thu, 16 Apr 2020 16:11:18 +0200
Subject: [PATCH 0373/1080] Replace meaningless variable name

---
 PathPlanning/BidirectionalAStar/bidirectional_a_star.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
index 37d14eab68..4ffd34718b 100644
--- a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
+++ b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
@@ -256,8 +256,8 @@ def calc_obstacle_map(self, ox, oy):
         print("ywidth:", self.ywidth)
 
         # obstacle map generation
-        self.obmap = [[False for i in range(self.ywidth)]
-                      for i in range(self.xwidth)]
+        self.obmap = [[False for _ in range(self.ywidth)]
+                      for _ in range(self.xwidth)]
         for ix in range(self.xwidth):
             x = self.calc_grid_position(ix, self.minx)
             for iy in range(self.ywidth):

From 7993d7ca8b594fdaa9e8b8071f540cab079670e2 Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Thu, 16 Apr 2020 18:11:47 +0200
Subject: [PATCH 0374/1080] use pop instead of min+del

---
 .../breadth_first_search.py                   | 22 +++++++------------
 1 file changed, 8 insertions(+), 14 deletions(-)

diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
index 5a38035507..d9e2aa5bc3 100644
--- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py
+++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
@@ -74,11 +74,11 @@ def planning(self, sx, sy, gx, gy):
                 print("Open set is empty..")
                 break
 
-            c_id = min(
-                open_set,
-                key=lambda o: open_set[o].pind)
+            current = open_set.pop(list(open_set.keys())[0])
 
-            current = open_set[c_id]
+            c_id = self.calc_grid_index(current)
+
+            closed_set[c_id] = current
 
             # show graph
             if show_animation:  # pragma: no cover
@@ -97,25 +97,19 @@ def planning(self, sx, sy, gx, gy):
                 ngoal.cost = current.cost
                 break
 
-            # Remove the item from the open set
-            del open_set[c_id]
-
-            random.shuffle(self.motion)
-
             # expand_grid search grid based on motion model
             for i, _ in enumerate(self.motion):
                 node = self.Node(current.x + self.motion[i][0],
                                  current.y + self.motion[i][1],
-                                 current.cost + self.motion[i][2], c_id+1, None)
+                                 current.cost + self.motion[i][2], c_id, None)
                 n_id = self.calc_grid_index(node)
 
                 # If the node is not safe, do nothing
                 if not self.verify_node(node):
                     continue
 
-                if n_id not in closed_set:
+                if (n_id not in closed_set) and (n_id not in open_set):
                     node.parent = current
-                    closed_set[n_id] = node
                     open_set[n_id] = node
 
         rx, ry = self.calc_final_path(ngoal, closed_set)
@@ -186,8 +180,8 @@ def calc_obstacle_map(self, ox, oy):
         print("ywidth:", self.ywidth)
 
         # obstacle map generation
-        self.obmap = [[False for _ in range(self.ywidth)]
-                      for _ in range(self.xwidth)]
+        self.obmap = [[False for i in range(self.ywidth)]
+                      for i in range(self.xwidth)]
         for ix in range(self.xwidth):
             x = self.calc_grid_position(ix, self.minx)
             for iy in range(self.ywidth):

From 0dca0e328ddda954f149459073a944fc80a3452d Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Thu, 16 Apr 2020 18:25:00 +0200
Subject: [PATCH 0375/1080] Use pop instead of min/del

---
 .../DepthFirstSearch/depth_first_search.py    | 23 +++++--------------
 1 file changed, 6 insertions(+), 17 deletions(-)

diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py
index d2f41347c3..54bbc48fdd 100644
--- a/PathPlanning/DepthFirstSearch/depth_first_search.py
+++ b/PathPlanning/DepthFirstSearch/depth_first_search.py
@@ -74,11 +74,8 @@ def planning(self, sx, sy, gx, gy):
                 print("Open set is empty..")
                 break
 
-            c_id = max(
-                open_set,
-                key=lambda o: open_set[o].pind)
-
-            current = open_set[c_id]
+            current = open_set.pop(list(open_set.keys())[-1])
+            c_id = self.calc_grid_index(current)
 
             # show graph
             if show_animation:  # pragma: no cover
@@ -88,29 +85,19 @@ def planning(self, sx, sy, gx, gy):
                 plt.gcf().canvas.mpl_connect('key_release_event',
                                              lambda event: [exit(
                                                  0) if event.key == 'escape' else None])
-                if len(closed_set.keys()) % 10 == 0:
-                    plt.pause(0.001)
+                plt.pause(0.01)
 
             if current.x == ngoal.x and current.y == ngoal.y:
                 print("Find goal")
                 ngoal.pind = current.pind
                 ngoal.cost = current.cost
-                closed_set[current.pind] = current
                 break
 
-            # Remove the item from the open set
-            del open_set[c_id]
-
-            # Add it to the closed set
-            closed_set[c_id] = current
-
-            random.shuffle(self.motion)
-
             # expand_grid search grid based on motion model
             for i, _ in enumerate(self.motion):
                 node = self.Node(current.x + self.motion[i][0],
                                  current.y + self.motion[i][1],
-                                 current.cost + self.motion[i][2], c_id+1, current)
+                                 current.cost + self.motion[i][2], c_id, None)
                 n_id = self.calc_grid_index(node)
 
                 # If the node is not safe, do nothing
@@ -119,6 +106,8 @@ def planning(self, sx, sy, gx, gy):
 
                 if n_id not in closed_set:
                     open_set[n_id] = node
+                    closed_set[n_id] = node
+                    node.parent = current
 
         rx, ry = self.calc_final_path(ngoal, closed_set)
         return rx, ry

From b827b3e2eef46a393cacd1fa31f2cc1acafb84fe Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Thu, 16 Apr 2020 18:38:09 +0200
Subject: [PATCH 0376/1080] add plt pause in astar path display

---
 PathPlanning/AStar/a_star.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py
index 55881f4e49..cd5a365c90 100644
--- a/PathPlanning/AStar/a_star.py
+++ b/PathPlanning/AStar/a_star.py
@@ -271,6 +271,7 @@ def main():
     if show_animation:  # pragma: no cover
         plt.plot(rx, ry, "-r")
         plt.show()
+        plt.pause(0.001)
 
 
 if __name__ == '__main__':

From 6c977850c6e8e31fd136b265a515d84709bfe610 Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Thu, 16 Apr 2020 18:39:01 +0200
Subject: [PATCH 0377/1080] remove useless import

---
 PathPlanning/DepthFirstSearch/depth_first_search.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py
index 54bbc48fdd..d42aa5f100 100644
--- a/PathPlanning/DepthFirstSearch/depth_first_search.py
+++ b/PathPlanning/DepthFirstSearch/depth_first_search.py
@@ -12,8 +12,6 @@
 
 import matplotlib.pyplot as plt
 
-import random
-
 show_animation = True
 
 
@@ -21,7 +19,7 @@ class DepthFirstSearchPlanner:
 
     def __init__(self, ox, oy, reso, rr):
         """
-        Initialize grid map for DFS planning
+        Initialize grid map for Depth-First planning
 
         ox: x position list of Obstacles [m]
         oy: y position list of Obstacles [m]

From 97978ce51fcffeffd141bc4fae48e653b5339fc3 Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Thu, 16 Apr 2020 18:39:35 +0200
Subject: [PATCH 0378/1080] remove useless import

---
 PathPlanning/BreadthFirstSearch/breadth_first_search.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
index d9e2aa5bc3..b4a5c38a8b 100644
--- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py
+++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
@@ -12,8 +12,6 @@
 
 import matplotlib.pyplot as plt
 
-import random
-
 show_animation = True
 
 

From 5ba82c6ced66004d1bf8a8dc0b91672394c5af9b Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Sat, 18 Apr 2020 15:27:59 +0200
Subject: [PATCH 0379/1080] replace meaningless variable name with _

---
 PathPlanning/BreadthFirstSearch/breadth_first_search.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
index b4a5c38a8b..dbdeff47c8 100644
--- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py
+++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
@@ -178,8 +178,8 @@ def calc_obstacle_map(self, ox, oy):
         print("ywidth:", self.ywidth)
 
         # obstacle map generation
-        self.obmap = [[False for i in range(self.ywidth)]
-                      for i in range(self.xwidth)]
+        self.obmap = [[False for _ in range(self.ywidth)]
+                      for _ in range(self.xwidth)]
         for ix in range(self.xwidth):
             x = self.calc_grid_position(ix, self.minx)
             for iy in range(self.ywidth):

From b7f18b8416603237793effb70ed519d61c38b557 Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Sun, 19 Apr 2020 15:02:08 +0200
Subject: [PATCH 0380/1080] Improve readability to fetch lowest cost node

---
 .../bidirectional_a_star.py                   | 40 ++++++++++---------
 1 file changed, 22 insertions(+), 18 deletions(-)

diff --git a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
index 4ffd34718b..1924cf59b1 100644
--- a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
+++ b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
@@ -75,24 +75,20 @@ def planning(self, sx, sy, gx, gy):
             if len(open_set_A) == 0:
                 print("Open set A is empty..")
                 break
-            
+
             if len(open_set_B) == 0:
                 print("Open set B is empty..")
                 break
 
             c_id_A = min(
                 open_set_A,
-                key=lambda o: open_set_A[o].cost + self.calc_heuristic(current_B,
-                                                                     open_set_A[
-                                                                         o]))
-                                
+                key=lambda o: self.find_lowest_cost(open_set_A, o, current_B))
+
             current_A = open_set_A[c_id_A]
 
             c_id_B = min(
                 open_set_B,
-                key=lambda o: open_set_B[o].cost + self.calc_heuristic(current_A,
-                                                                     open_set_B[
-                                                                         o]))
+                key=lambda o: self.find_lowest_cost(open_set_B, o, current_A))
 
             current_B = open_set_B[c_id_B]
 
@@ -129,12 +125,12 @@ def planning(self, sx, sy, gx, gy):
                 continue_B = False
 
                 child_node_A = self.Node(current_A.x + self.motion[i][0],
-                                 current_A.y + self.motion[i][1],
-                                 current_A.cost + self.motion[i][2], c_id_A)
+                                         current_A.y + self.motion[i][1],
+                                         current_A.cost + self.motion[i][2], c_id_A)
 
                 child_node_B = self.Node(current_B.x + self.motion[i][0],
-                                 current_B.y + self.motion[i][1],
-                                 current_B.cost + self.motion[i][2], c_id_B)
+                                         current_B.y + self.motion[i][1],
+                                         current_B.cost + self.motion[i][2], c_id_B)
 
                 n_id_A = self.calc_grid_index(child_node_A)
                 n_id_B = self.calc_grid_index(child_node_B)
@@ -142,33 +138,36 @@ def planning(self, sx, sy, gx, gy):
                 # If the node is not safe, do nothing
                 if not self.verify_node(child_node_A):
                     continue_A = True
-                
+
                 if not self.verify_node(child_node_B):
                     continue_B = True
 
                 if n_id_A in closed_set_A:
                     continue_A = True
-                
+
                 if n_id_B in closed_set_B:
                     continue_B = True
 
                 if not(continue_A):
                     if n_id_A not in open_set_A:
-                        open_set_A[n_id_A] = child_node_A  # discovered a new node
+                        # discovered a new node
+                        open_set_A[n_id_A] = child_node_A
                     else:
                         if open_set_A[n_id_A].cost > child_node_A.cost:
                             # This path is the best until now. record it
                             open_set_A[n_id_A] = child_node_A
-            
+
                 if not(continue_B):
                     if n_id_B not in open_set_B:
-                        open_set_B[n_id_B] = child_node_B  # discovered a new node
+                        # discovered a new node
+                        open_set_B[n_id_B] = child_node_B
                     else:
                         if open_set_B[n_id_B].cost > child_node_B.cost:
                             # This path is the best until now. record it
                             open_set_B[n_id_B] = child_node_B
 
-        rx, ry = self.calc_final_path_bidir(meetpointA, meetpointB, closed_set_A, closed_set_B)
+        rx, ry = self.calc_final_path_bidir(
+            meetpointA, meetpointB, closed_set_A, closed_set_B)
 
         return rx, ry
 
@@ -203,6 +202,11 @@ def calc_heuristic(n1, n2):
         d = w * math.hypot(n1.x - n2.x, n1.y - n2.y)
         return d
 
+    def find_lowest_cost(self, open_set, lambda_, n1):
+        cost = open_set[lambda_].cost + \
+            self.calc_heuristic(n1, open_set[lambda_])
+        return cost
+
     def calc_grid_position(self, index, minp):
         """
         calc grid position

From 63a920223b034296edc3c4eae75ac4c0e80bb120 Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Sun, 19 Apr 2020 15:04:38 +0200
Subject: [PATCH 0381/1080] Fix pep8 indents

---
 .../BidirectionalAStar/bidirectional_a_star.py  | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
index 1924cf59b1..3839e4dff3 100644
--- a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
+++ b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
@@ -82,13 +82,13 @@ def planning(self, sx, sy, gx, gy):
 
             c_id_A = min(
                 open_set_A,
-                key=lambda o: self.find_lowest_cost(open_set_A, o, current_B))
+                key=lambda o: self.find_total_cost(open_set_A, o, current_B))
 
             current_A = open_set_A[c_id_A]
 
             c_id_B = min(
                 open_set_B,
-                key=lambda o: self.find_lowest_cost(open_set_B, o, current_A))
+                key=lambda o: self.find_total_cost(open_set_B, o, current_A))
 
             current_B = open_set_B[c_id_B]
 
@@ -148,7 +148,7 @@ def planning(self, sx, sy, gx, gy):
                 if n_id_B in closed_set_B:
                     continue_B = True
 
-                if not(continue_A):
+                if not continue_A:
                     if n_id_A not in open_set_A:
                         # discovered a new node
                         open_set_A[n_id_A] = child_node_A
@@ -157,7 +157,7 @@ def planning(self, sx, sy, gx, gy):
                             # This path is the best until now. record it
                             open_set_A[n_id_A] = child_node_A
 
-                if not(continue_B):
+                if not continue_B:
                     if n_id_B not in open_set_B:
                         # discovered a new node
                         open_set_B[n_id_B] = child_node_B
@@ -202,10 +202,11 @@ def calc_heuristic(n1, n2):
         d = w * math.hypot(n1.x - n2.x, n1.y - n2.y)
         return d
 
-    def find_lowest_cost(self, open_set, lambda_, n1):
-        cost = open_set[lambda_].cost + \
-            self.calc_heuristic(n1, open_set[lambda_])
-        return cost
+    def find_total_cost(self, open_set, lambda_, n1):
+        g_cost = open_set[lambda_].cost
+        h_cost = self.calc_heuristic(n1, open_set[lambda_])
+        f_cost = g_cost + h_cost
+        return f_cost
 
     def calc_grid_position(self, index, minp):
         """

From 38519b5df83141f6fc6bf452593fa748de2eae0f Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Fri, 24 Apr 2020 15:16:05 +0200
Subject: [PATCH 0382/1080] fix requests

---
 .../BidirectionalAStar/bidirectional_a_star.py       | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
index 3839e4dff3..ed1e58699f 100644
--- a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
+++ b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
@@ -126,11 +126,13 @@ def planning(self, sx, sy, gx, gy):
 
                 child_node_A = self.Node(current_A.x + self.motion[i][0],
                                          current_A.y + self.motion[i][1],
-                                         current_A.cost + self.motion[i][2], c_id_A)
+                                         current_A.cost + self.motion[i][2], 
+                                         c_id_A)
 
                 child_node_B = self.Node(current_B.x + self.motion[i][0],
                                          current_B.y + self.motion[i][1],
-                                         current_B.cost + self.motion[i][2], c_id_B)
+                                         current_B.cost + self.motion[i][2], 
+                                         c_id_B)
 
                 n_id_A = self.calc_grid_index(child_node_A)
                 n_id_B = self.calc_grid_index(child_node_B)
@@ -166,12 +168,12 @@ def planning(self, sx, sy, gx, gy):
                             # This path is the best until now. record it
                             open_set_B[n_id_B] = child_node_B
 
-        rx, ry = self.calc_final_path_bidir(
+        rx, ry = self.calc_final_bidirectional_path(
             meetpointA, meetpointB, closed_set_A, closed_set_B)
 
         return rx, ry
 
-    def calc_final_path_bidir(self, meetnode_A, meetnode_B, closed_set_A, closed_set_B):
+    def calc_final_bidirectional_path(self, meetnode_A, meetnode_B, closed_set_A, closed_set_B):
         rx_A, ry_A = self.calc_final_path(meetnode_A, closed_set_A)
         rx_B, ry_B = self.calc_final_path(meetnode_B, closed_set_B)
 
@@ -299,7 +301,7 @@ def main():
     grid_size = 2.0  # [m]
     robot_radius = 1.0  # [m]
 
-    # set obstable positions
+    # set obstacle positions
     ox, oy = [], []
     for i in range(-10, 60):
         ox.append(i)

From 18fe1960fb65ecfe1cee299b29ab4221cf03cc1c Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 28 Apr 2020 21:00:04 +0900
Subject: [PATCH 0383/1080] remove say thanks link

---
 README.md         | 1 -
 users_comments.md | 2 --
 2 files changed, 3 deletions(-)

diff --git a/README.md b/README.md
index ab62c9af3c..9a9bc9502a 100644
--- a/README.md
+++ b/README.md
@@ -8,7 +8,6 @@
 [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/AtsushiSakai/PythonRobotics.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/AtsushiSakai/PythonRobotics/context:python)
 [![CodeFactor](https://www.codefactor.io/repository/github/atsushisakai/pythonrobotics/badge/master)](https://www.codefactor.io/repository/github/atsushisakai/pythonrobotics/overview/master)
 [![tokei](https://tokei.rs/b1/github/AtsushiSakai/PythonRobotics)](https://github.com/AtsushiSakai/PythonRobotics)
-[![Say Thanks!](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/AtsushiSakai)
 
 Python codes for robotics algorithm.
 
diff --git a/users_comments.md b/users_comments.md
index 44e9355ec1..43ffc7cc36 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -40,8 +40,6 @@ This is a list of related projects.
 
 # Individual users comments
 
-These are comments from user's using [![Say Thanks!](https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg)](https://saythanks.io/to/AtsushiSakai)
-
 ---
 
 Dear AtsushiSakai, <br>thank you for building this cool repo for robotics. <br>I am still learning robotics and this will give me a good starting point. <br>I hope this note gets to you. <br> <br>

From e8a006f747f9fbf4821a9e14033d21aa8c5e53b7 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 3 May 2020 23:02:59 +0900
Subject: [PATCH 0384/1080] Add code style checker CI (#321)

Add code style checker CI
---
 .github/workflows/pythonpackage.yml | 12 +++++-------
 environment.yml                     |  2 +-
 rundiffstylecheck.sh                | 15 +++++++++++++++
 3 files changed, 21 insertions(+), 8 deletions(-)
 create mode 100644 rundiffstylecheck.sh

diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml
index 574c7be9d5..defc159687 100644
--- a/.github/workflows/pythonpackage.yml
+++ b/.github/workflows/pythonpackage.yml
@@ -21,17 +21,12 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install -r requirements.txt
-    - name: Lint with flake8
-      run: |
-        pip install flake8
-        # stop the build if there are Python syntax errors or undefined names
-        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: install coverage
       run: pip install coverage
     - name: install mypy
       run: pip install mypy
+    - name: install pycodestyle
+      run: pip install pycodestyle
     - name: mypy check
       run: |
         find AerialNavigation -name "*.py" | xargs mypy
@@ -43,7 +38,10 @@ jobs:
         find PathPlanning -name "*.py" | xargs mypy
         find PathTracking -name "*.py" | xargs mypy
         find SLAM -name "*.py" | xargs mypy
+    - name: do diff style check
+      run: bash rundiffstylecheck.sh
     - name: do all unit tests
       run: bash runtests.sh
 
 
+
diff --git a/environment.yml b/environment.yml
index 6866ed93ca..da1c3c0a6f 100644
--- a/environment.yml
+++ b/environment.yml
@@ -4,7 +4,7 @@ dependencies:
 - pip
 - matplotlib
 - scipy
-- numpy==1.15
+- numpy
 - pandas
 - coverage
 - pip:
diff --git a/rundiffstylecheck.sh b/rundiffstylecheck.sh
new file mode 100644
index 0000000000..627e4a1b63
--- /dev/null
+++ b/rundiffstylecheck.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+echo "$(basename $0) start!"
+VERSION=v0.1.3
+wget https://github.com/AtsushiSakai/DiffSentinel/archive/${VERSION}.zip
+unzip ${VERSION}.zip
+./DiffSentinel*/starter.sh HEAD origin/master
+check_result=$?
+rm -rf ${VERSION}.zip DiffSentinel*
+if [[ ${check_result} -ne 0 ]];
+then
+    echo "Error: Your changes contain pycodestyle errors."
+    exit 1
+fi
+echo "$(basename $0) done!"
+exit 0

From 614e18d246109566f3e071b63a57c5c87cb5164a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 4 May 2020 10:12:41 +0900
Subject: [PATCH 0385/1080] add test for PR

---
 .github/workflows/pythonpackage.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml
index 574c7be9d5..76196b6ed0 100644
--- a/.github/workflows/pythonpackage.yml
+++ b/.github/workflows/pythonpackage.yml
@@ -1,6 +1,6 @@
 name: Python package
 
-on: [push]
+on: [push, pull_request]
 
 jobs:
   build:

From ae19d07bc3fd2a79d5b6c45ff614ddb1ef84a1d3 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 4 May 2020 14:22:25 +0900
Subject: [PATCH 0386/1080] bump DiffSentinelVersion

---
 rundiffstylecheck.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rundiffstylecheck.sh b/rundiffstylecheck.sh
index 627e4a1b63..d170973751 100644
--- a/rundiffstylecheck.sh
+++ b/rundiffstylecheck.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 echo "$(basename $0) start!"
-VERSION=v0.1.3
+VERSION=v0.1.5
 wget https://github.com/AtsushiSakai/DiffSentinel/archive/${VERSION}.zip
 unzip ${VERSION}.zip
 ./DiffSentinel*/starter.sh HEAD origin/master

From ab49acb1acb2f60334febf6dc44dafa42ff6a836 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 4 May 2020 20:34:03 +0900
Subject: [PATCH 0387/1080] bump DiffSentinelVersion

---
 rundiffstylecheck.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/rundiffstylecheck.sh b/rundiffstylecheck.sh
index d170973751..8a19842c6e 100644
--- a/rundiffstylecheck.sh
+++ b/rundiffstylecheck.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 echo "$(basename $0) start!"
-VERSION=v0.1.5
+VERSION=v0.1.6
 wget https://github.com/AtsushiSakai/DiffSentinel/archive/${VERSION}.zip
 unzip ${VERSION}.zip
 ./DiffSentinel*/starter.sh HEAD origin/master

From 734f3aed20cfbbd634e0fe8f342655458ac3b2ff Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Tue, 5 May 2020 07:06:01 +0200
Subject: [PATCH 0388/1080] Greedy Best-First Search (#315)

---
 PathPlanning/AStar/a_star.py                  |   2 +-
 .../bidirectional_a_star.py                   |  78 ++---
 .../breadth_first_search.py                   |   7 +-
 .../DepthFirstSearch/depth_first_search.py    |   7 +-
 .../greedy_best_first_search.py               | 278 ++++++++++++++++++
 5 files changed, 326 insertions(+), 46 deletions(-)
 create mode 100644 PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py

diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py
index cd5a365c90..14c51ad9a1 100644
--- a/PathPlanning/AStar/a_star.py
+++ b/PathPlanning/AStar/a_star.py
@@ -237,7 +237,7 @@ def main():
     grid_size = 2.0  # [m]
     robot_radius = 1.0  # [m]
 
-    # set obstable positions
+    # set obstacle positions
     ox, oy = [], []
     for i in range(-10, 60):
         ox.append(i)
diff --git a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
index ed1e58699f..65d1fc31a7 100644
--- a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
+++ b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
@@ -100,8 +100,9 @@ def planning(self, sx, sy, gx, gy):
                          self.calc_grid_position(current_B.y, self.miny), "xc")
                 # for stopping simulation with the esc key.
                 plt.gcf().canvas.mpl_connect('key_release_event',
-                                             lambda event: [exit(
-                                                 0) if event.key == 'escape' else None])
+                                             lambda event:
+                                             [exit(0) if event.key == 'escape'
+                                              else None])
                 if len(closed_set_A.keys()) % 10 == 0:
                     plt.pause(0.001)
 
@@ -121,61 +122,50 @@ def planning(self, sx, sy, gx, gy):
 
             # expand_grid search grid based on motion model
             for i, _ in enumerate(self.motion):
-                continue_A = False
-                continue_B = False
 
-                child_node_A = self.Node(current_A.x + self.motion[i][0],
-                                         current_A.y + self.motion[i][1],
-                                         current_A.cost + self.motion[i][2], 
-                                         c_id_A)
+                c_nodes = [self.Node(current_A.x + self.motion[i][0],
+                                     current_A.y + self.motion[i][1],
+                                     current_A.cost + self.motion[i][2],
+                                     c_id_A),
+                           self.Node(current_B.x + self.motion[i][0],
+                                     current_B.y + self.motion[i][1],
+                                     current_B.cost + self.motion[i][2],
+                                     c_id_B)]
 
-                child_node_B = self.Node(current_B.x + self.motion[i][0],
-                                         current_B.y + self.motion[i][1],
-                                         current_B.cost + self.motion[i][2], 
-                                         c_id_B)
-
-                n_id_A = self.calc_grid_index(child_node_A)
-                n_id_B = self.calc_grid_index(child_node_B)
+                n_ids = [self.calc_grid_index(c_nodes[0]),
+                         self.calc_grid_index(c_nodes[1])]
 
                 # If the node is not safe, do nothing
-                if not self.verify_node(child_node_A):
-                    continue_A = True
-
-                if not self.verify_node(child_node_B):
-                    continue_B = True
-
-                if n_id_A in closed_set_A:
-                    continue_A = True
-
-                if n_id_B in closed_set_B:
-                    continue_B = True
+                continue_ = self.check_nodes_and_sets(c_nodes, closed_set_A,
+                                                      closed_set_B, n_ids)
 
-                if not continue_A:
-                    if n_id_A not in open_set_A:
+                if not continue_[0]:
+                    if n_ids[0] not in open_set_A:
                         # discovered a new node
-                        open_set_A[n_id_A] = child_node_A
+                        open_set_A[n_ids[0]] = c_nodes[0]
                     else:
-                        if open_set_A[n_id_A].cost > child_node_A.cost:
+                        if open_set_A[n_ids[0]].cost > c_nodes[0].cost:
                             # This path is the best until now. record it
-                            open_set_A[n_id_A] = child_node_A
+                            open_set_A[n_ids[0]] = c_nodes[0]
 
-                if not continue_B:
-                    if n_id_B not in open_set_B:
+                if not continue_[1]:
+                    if n_ids[1] not in open_set_B:
                         # discovered a new node
-                        open_set_B[n_id_B] = child_node_B
+                        open_set_B[n_ids[1]] = c_nodes[1]
                     else:
-                        if open_set_B[n_id_B].cost > child_node_B.cost:
+                        if open_set_B[n_ids[1]].cost > c_nodes[1].cost:
                             # This path is the best until now. record it
-                            open_set_B[n_id_B] = child_node_B
+                            open_set_B[n_ids[1]] = c_nodes[1]
 
         rx, ry = self.calc_final_bidirectional_path(
             meetpointA, meetpointB, closed_set_A, closed_set_B)
 
         return rx, ry
 
-    def calc_final_bidirectional_path(self, meetnode_A, meetnode_B, closed_set_A, closed_set_B):
-        rx_A, ry_A = self.calc_final_path(meetnode_A, closed_set_A)
-        rx_B, ry_B = self.calc_final_path(meetnode_B, closed_set_B)
+    # takes two sets and two meeting nodes and return the optimal path
+    def calc_final_bidirectional_path(self, n1, n2, setA, setB):
+        rx_A, ry_A = self.calc_final_path(n1, setA)
+        rx_B, ry_B = self.calc_final_path(n2, setB)
 
         rx_A.reverse()
         ry_A.reverse()
@@ -198,6 +188,16 @@ def calc_final_path(self, ngoal, closedset):
 
         return rx, ry
 
+    def check_nodes_and_sets(self, c_nodes, closedSet_A, closedSet_B, n_ids):
+        continue_ = [False, False]
+        if not self.verify_node(c_nodes[0]) or n_ids[0] in closedSet_A:
+            continue_[0] = True
+
+        if not self.verify_node(c_nodes[1]) or n_ids[1] in closedSet_B:
+            continue_[1] = True
+
+        return continue_
+
     @staticmethod
     def calc_heuristic(n1, n2):
         w = 1.0  # weight of heuristic
diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
index dbdeff47c8..198ddd2e3d 100644
--- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py
+++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
@@ -84,8 +84,9 @@ def planning(self, sx, sy, gx, gy):
                          self.calc_grid_position(current.y, self.miny), "xc")
                 # for stopping simulation with the esc key.
                 plt.gcf().canvas.mpl_connect('key_release_event',
-                                             lambda event: [exit(
-                                                 0) if event.key == 'escape' else None])
+                                             lambda event:
+                                             [exit(0) if event.key == 'escape'
+                                              else None])
                 if len(closed_set.keys()) % 10 == 0:
                     plt.pause(0.001)
 
@@ -216,7 +217,7 @@ def main():
     grid_size = 2.0  # [m]
     robot_radius = 1.0  # [m]
 
-    # set obstable positions
+    # set obstacle positions
     ox, oy = [], []
     for i in range(-10, 60):
         ox.append(i)
diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py
index d42aa5f100..229eb70c2d 100644
--- a/PathPlanning/DepthFirstSearch/depth_first_search.py
+++ b/PathPlanning/DepthFirstSearch/depth_first_search.py
@@ -81,8 +81,9 @@ def planning(self, sx, sy, gx, gy):
                          self.calc_grid_position(current.y, self.miny), "xc")
                 # for stopping simulation with the esc key.
                 plt.gcf().canvas.mpl_connect('key_release_event',
-                                             lambda event: [exit(
-                                                 0) if event.key == 'escape' else None])
+                                             lambda event:
+                                             [exit(0) if event.key == 'escape'
+                                              else None])
                 plt.pause(0.01)
 
             if current.x == ngoal.x and current.y == ngoal.y:
@@ -213,7 +214,7 @@ def main():
     grid_size = 2.0  # [m]
     robot_radius = 1.0  # [m]
 
-    # set obstable positions
+    # set obstacle positions
     ox, oy = [], []
     for i in range(-10, 60):
         ox.append(i)
diff --git a/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py b/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py
new file mode 100644
index 0000000000..c29e37e896
--- /dev/null
+++ b/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py
@@ -0,0 +1,278 @@
+"""
+
+Greedy Best-First grid planning
+
+author: Erwin Lejeune (@spida_rwin)
+
+See Wikipedia article (https://en.wikipedia.org/wiki/Best-first_search)
+
+"""
+
+import math
+
+import matplotlib.pyplot as plt
+
+show_animation = True
+
+
+class BestFirstSearchPlanner:
+
+    def __init__(self, ox, oy, reso, rr):
+        """
+        Initialize grid map for greedy best-first planning
+
+        ox: x position list of Obstacles [m]
+        oy: y position list of Obstacles [m]
+        reso: grid resolution [m]
+        rr: robot radius[m]
+        """
+
+        self.reso = reso
+        self.rr = rr
+        self.calc_obstacle_map(ox, oy)
+        self.motion = self.get_motion_model()
+
+    class Node:
+        def __init__(self, x, y, cost, pind, parent):
+            self.x = x  # index of grid
+            self.y = y  # index of grid
+            self.cost = cost
+            self.pind = pind
+            self.parent = parent
+
+        def __str__(self):
+            return str(self.x) + "," + str(self.y) + "," + str(
+                self.cost) + "," + str(self.pind)
+
+    def planning(self, sx, sy, gx, gy):
+        """
+        Greedy Best-First search
+
+        input:
+            sx: start x position [m]
+            sy: start y position [m]
+            gx: goal x position [m]
+            gy: goal y position [m]
+
+        output:
+            rx: x position list of the final path
+            ry: y position list of the final path
+        """
+
+        nstart = self.Node(self.calc_xyindex(sx, self.minx),
+                           self.calc_xyindex(sy, self.miny), 0.0, -1, None)
+        ngoal = self.Node(self.calc_xyindex(gx, self.minx),
+                          self.calc_xyindex(gy, self.miny), 0.0, -1, None)
+
+        open_set, closed_set = dict(), dict()
+        open_set[self.calc_grid_index(nstart)] = nstart
+
+        while 1:
+            if len(open_set) == 0:
+                print("Open set is empty..")
+                break
+
+            c_id = min(
+                open_set,
+                key=lambda o: self.calc_heuristic(ngoal, open_set[o]))
+
+            current = open_set[c_id]
+
+            # show graph
+            if show_animation:  # pragma: no cover
+                plt.plot(self.calc_grid_position(current.x, self.minx),
+                         self.calc_grid_position(current.y, self.miny), "xc")
+                # for stopping simulation with the esc key.
+                plt.gcf().canvas.mpl_connect('key_release_event',
+                                             lambda event:
+                                             [exit(0)
+                                              if event.key == 'escape'
+                                              else None])
+                if len(closed_set.keys()) % 10 == 0:
+                    plt.pause(0.001)
+
+            # Remove the item from the open set
+            del open_set[c_id]
+
+            # Add it to the closed set
+            closed_set[c_id] = current
+
+            if current.x == ngoal.x and current.y == ngoal.y:
+                print("Found goal")
+                ngoal.pind = current.pind
+                ngoal.cost = current.cost
+                break
+
+            # expand_grid search grid based on motion model
+            for i, _ in enumerate(self.motion):
+                node = self.Node(current.x + self.motion[i][0],
+                                 current.y + self.motion[i][1],
+                                 current.cost + self.motion[i][2],
+                                 c_id, current)
+
+                n_id = self.calc_grid_index(node)
+
+                # If the node is not safe, do nothing
+                if not self.verify_node(node):
+                    continue
+
+                if n_id in closed_set:
+                    continue
+
+                if n_id not in open_set:
+                    open_set[n_id] = node
+                else:
+                    if open_set[n_id].cost > node.cost:
+                        open_set[n_id] = node
+        closed_set[ngoal.pind] = current
+        rx, ry = self.calc_final_path(ngoal, closed_set)
+        return rx, ry
+
+    def calc_final_path(self, ngoal, closedset):
+        # generate final course
+        rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
+            self.calc_grid_position(ngoal.y, self.miny)]
+        n = closedset[ngoal.pind]
+        while n is not None:
+            rx.append(self.calc_grid_position(n.x, self.minx))
+            ry.append(self.calc_grid_position(n.y, self.miny))
+            n = n.parent
+
+        return rx, ry
+
+    @staticmethod
+    def calc_heuristic(n1, n2):
+        w = 1.0  # weight of heuristic
+        d = w * math.hypot(n1.x - n2.x, n1.y - n2.y)
+        return d
+
+    def calc_grid_position(self, index, minp):
+        """
+        calc grid position
+
+        :param index:
+        :param minp:
+        :return:
+        """
+        pos = index * self.reso + minp
+        return pos
+
+    def calc_xyindex(self, position, min_pos):
+        return round((position - min_pos) / self.reso)
+
+    def calc_grid_index(self, node):
+        return (node.y - self.miny) * self.xwidth + (node.x - self.minx)
+
+    def verify_node(self, node):
+        px = self.calc_grid_position(node.x, self.minx)
+        py = self.calc_grid_position(node.y, self.miny)
+
+        if px < self.minx:
+            return False
+        elif py < self.miny:
+            return False
+        elif px >= self.maxx:
+            return False
+        elif py >= self.maxy:
+            return False
+
+        # collision check
+        if self.obmap[node.x][node.y]:
+            return False
+
+        return True
+
+    def calc_obstacle_map(self, ox, oy):
+
+        self.minx = round(min(ox))
+        self.miny = round(min(oy))
+        self.maxx = round(max(ox))
+        self.maxy = round(max(oy))
+        print("minx:", self.minx)
+        print("miny:", self.miny)
+        print("maxx:", self.maxx)
+        print("maxy:", self.maxy)
+
+        self.xwidth = round((self.maxx - self.minx) / self.reso)
+        self.ywidth = round((self.maxy - self.miny) / self.reso)
+        print("xwidth:", self.xwidth)
+        print("ywidth:", self.ywidth)
+
+        # obstacle map generation
+        self.obmap = [[False for _ in range(self.ywidth)]
+                      for _ in range(self.xwidth)]
+        for ix in range(self.xwidth):
+            x = self.calc_grid_position(ix, self.minx)
+            for iy in range(self.ywidth):
+                y = self.calc_grid_position(iy, self.miny)
+                for iox, ioy in zip(ox, oy):
+                    d = math.hypot(iox - x, ioy - y)
+                    if d <= self.rr:
+                        self.obmap[ix][iy] = True
+                        break
+
+    @staticmethod
+    def get_motion_model():
+        # dx, dy, cost
+        motion = [[1, 0, 1],
+                  [0, 1, 1],
+                  [-1, 0, 1],
+                  [0, -1, 1],
+                  [-1, -1, math.sqrt(2)],
+                  [-1, 1, math.sqrt(2)],
+                  [1, -1, math.sqrt(2)],
+                  [1, 1, math.sqrt(2)]]
+
+        return motion
+
+
+def main():
+    print(__file__ + " start!!")
+
+    # start and goal position
+    sx = 10.0  # [m]
+    sy = 10.0  # [m]
+    gx = 50.0  # [m]
+    gy = 50.0  # [m]
+    grid_size = 2.0  # [m]
+    robot_radius = 1.0  # [m]
+
+    # set obstacle positions
+    ox, oy = [], []
+    for i in range(-10, 60):
+        ox.append(i)
+        oy.append(-10.0)
+    for i in range(-10, 60):
+        ox.append(60.0)
+        oy.append(i)
+    for i in range(-10, 61):
+        ox.append(i)
+        oy.append(60.0)
+    for i in range(-10, 61):
+        ox.append(-10.0)
+        oy.append(i)
+    for i in range(-10, 40):
+        ox.append(20.0)
+        oy.append(i)
+    for i in range(0, 40):
+        ox.append(40.0)
+        oy.append(60.0 - i)
+
+    if show_animation:  # pragma: no cover
+        plt.plot(ox, oy, ".k")
+        plt.plot(sx, sy, "og")
+        plt.plot(gx, gy, "xb")
+        plt.grid(True)
+        plt.axis("equal")
+
+    greedybestfirst = BestFirstSearchPlanner(ox, oy, grid_size, robot_radius)
+    rx, ry = greedybestfirst.planning(sx, sy, gx, gy)
+
+    if show_animation:  # pragma: no cover
+        plt.plot(rx, ry, "-r")
+        plt.pause(0.01)
+        plt.show()
+
+
+if __name__ == '__main__':
+    main()

From 86c511efc022c613979c949bacab5dd079aaf36a Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Tue, 5 May 2020 07:15:03 +0200
Subject: [PATCH 0389/1080] Bidirectional Breadth-First Search (#316)

---
 .../bidirectional_breadth_first_search.py     | 311 ++++++++++++++++++
 1 file changed, 311 insertions(+)
 create mode 100644 PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py

diff --git a/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py b/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py
new file mode 100644
index 0000000000..d29112eb65
--- /dev/null
+++ b/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py
@@ -0,0 +1,311 @@
+"""
+
+Bidirectional Breadth-First grid planning
+
+author: Erwin Lejeune (@spida_rwin)
+
+See Wikipedia article (https://en.wikipedia.org/wiki/Breadth-first_search)
+
+"""
+
+import math
+
+import matplotlib.pyplot as plt
+
+show_animation = True
+
+
+class BidirectionalBreadthFirstSearchPlanner:
+
+    def __init__(self, ox, oy, reso, rr):
+        """
+        Initialize grid map for bfs planning
+
+        ox: x position list of Obstacles [m]
+        oy: y position list of Obstacles [m]
+        reso: grid resolution [m]
+        rr: robot radius[m]
+        """
+
+        self.reso = reso
+        self.rr = rr
+        self.calc_obstacle_map(ox, oy)
+        self.motion = self.get_motion_model()
+
+    class Node:
+        def __init__(self, x, y, cost, pind, parent):
+            self.x = x  # index of grid
+            self.y = y  # index of grid
+            self.cost = cost
+            self.pind = pind
+            self.parent = parent
+
+        def __str__(self):
+            return str(self.x) + "," + str(self.y) + "," + str(
+                self.cost) + "," + str(self.pind)
+
+    def planning(self, sx, sy, gx, gy):
+        """
+        Bidirectional Breadth First search based planning
+
+        input:
+            sx: start x position [m]
+            sy: start y position [m]
+            gx: goal x position [m]
+            gy: goal y position [m]
+
+        output:
+            rx: x position list of the final path
+            ry: y position list of the final path
+        """
+
+        nstart = self.Node(self.calc_xyindex(sx, self.minx),
+                           self.calc_xyindex(sy, self.miny), 0.0, -1, None)
+        ngoal = self.Node(self.calc_xyindex(gx, self.minx),
+                          self.calc_xyindex(gy, self.miny), 0.0, -1, None)
+
+        open_set_A, closed_set_A = dict(), dict()
+        open_set_B, closed_set_B = dict(), dict()
+        open_set_B[self.calc_grid_index(ngoal)] = ngoal
+        open_set_A[self.calc_grid_index(nstart)] = nstart
+
+        while 1:
+            if len(open_set_A) == 0:
+                print("Open set A is empty..")
+                break
+
+            if len(open_set_B) == 0:
+                print("Open set B is empty")
+                break
+
+            current_A = open_set_A.pop(list(open_set_A.keys())[0])
+            current_B = open_set_B.pop(list(open_set_B.keys())[0])
+
+            c_id_A = self.calc_grid_index(current_A)
+            c_id_B = self.calc_grid_index(current_B)
+
+            closed_set_A[c_id_A] = current_A
+            closed_set_B[c_id_B] = current_B
+
+            # show graph
+            if show_animation:  # pragma: no cover
+                plt.plot(self.calc_grid_position(current_A.x, self.minx),
+                         self.calc_grid_position(current_A.y, self.miny), "xc")
+                plt.plot(self.calc_grid_position(current_B.x, self.minx),
+                         self.calc_grid_position(current_B.y, self.miny), "xc")
+                # for stopping simulation with the esc key.
+                plt.gcf().canvas.mpl_connect('key_release_event',
+                                             lambda event:
+                                             [exit(0) if
+                                              event.key == 'escape' else None])
+                if len(closed_set_A.keys()) % 10 == 0:
+                    plt.pause(0.001)
+
+            if c_id_A in closed_set_B:
+                print("Find goal")
+                meetpointA = closed_set_A[c_id_A]
+                meetpointB = closed_set_B[c_id_A]
+                break
+
+            elif c_id_B in closed_set_A:
+                print("Find goal")
+                meetpointA = closed_set_A[c_id_B]
+                meetpointB = closed_set_B[c_id_B]
+                break
+
+            # expand_grid search grid based on motion model
+            for i, _ in enumerate(self.motion):
+                breakA = False
+                breakB = False
+
+                node_A = self.Node(current_A.x + self.motion[i][0],
+                                   current_A.y + self.motion[i][1],
+                                   current_A.cost + self.motion[i][2],
+                                   c_id_A, None)
+                node_B = self.Node(current_B.x + self.motion[i][0],
+                                   current_B.y + self.motion[i][1],
+                                   current_B.cost + self.motion[i][2],
+                                   c_id_B, None)
+
+                n_id_A = self.calc_grid_index(node_A)
+                n_id_B = self.calc_grid_index(node_B)
+
+                # If the node is not safe, do nothing
+                if not self.verify_node(node_A):
+                    breakA = True
+
+                if not self.verify_node(node_B):
+                    breakB = True
+
+                if (n_id_A not in closed_set_A) and (n_id_A not in
+                                                     open_set_A) and (not
+                                                                      breakA):
+                    node_A.parent = current_A
+                    open_set_A[n_id_A] = node_A
+
+                if (n_id_B not in closed_set_B) and (n_id_B not in
+                                                     open_set_B) and (not
+                                                                      breakB):
+                    node_B.parent = current_B
+                    open_set_B[n_id_B] = node_B
+
+        rx, ry = self.calc_final_path_bidir(
+            meetpointA, meetpointB, closed_set_A, closed_set_B)
+        return rx, ry
+
+    # takes both set and meeting nodes and calculate optimal path
+    def calc_final_path_bidir(self, n1, n2, setA, setB):
+        rxA, ryA = self.calc_final_path(n1, setA)
+        rxB, ryB = self.calc_final_path(n2, setB)
+
+        rxA.reverse()
+        ryA.reverse()
+
+        rx = rxA + rxB
+        ry = ryA + ryB
+
+        return rx, ry
+
+    def calc_final_path(self, ngoal, closedset):
+        # generate final course
+        rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
+            self.calc_grid_position(ngoal.y, self.miny)]
+        n = closedset[ngoal.pind]
+        while n is not None:
+            rx.append(self.calc_grid_position(n.x, self.minx))
+            ry.append(self.calc_grid_position(n.y, self.miny))
+            n = n.parent
+
+        return rx, ry
+
+    def calc_grid_position(self, index, minp):
+        """
+        calc grid position
+
+        :param index:
+        :param minp:
+        :return:
+        """
+        pos = index * self.reso + minp
+        return pos
+
+    def calc_xyindex(self, position, min_pos):
+        return round((position - min_pos) / self.reso)
+
+    def calc_grid_index(self, node):
+        return (node.y - self.miny) * self.xwidth + (node.x - self.minx)
+
+    def verify_node(self, node):
+        px = self.calc_grid_position(node.x, self.minx)
+        py = self.calc_grid_position(node.y, self.miny)
+
+        if px < self.minx:
+            return False
+        elif py < self.miny:
+            return False
+        elif px >= self.maxx:
+            return False
+        elif py >= self.maxy:
+            return False
+
+        # collision check
+        if self.obmap[node.x][node.y]:
+            return False
+
+        return True
+
+    def calc_obstacle_map(self, ox, oy):
+
+        self.minx = round(min(ox))
+        self.miny = round(min(oy))
+        self.maxx = round(max(ox))
+        self.maxy = round(max(oy))
+        print("minx:", self.minx)
+        print("miny:", self.miny)
+        print("maxx:", self.maxx)
+        print("maxy:", self.maxy)
+
+        self.xwidth = round((self.maxx - self.minx) / self.reso)
+        self.ywidth = round((self.maxy - self.miny) / self.reso)
+        print("xwidth:", self.xwidth)
+        print("ywidth:", self.ywidth)
+
+        # obstacle map generation
+        self.obmap = [[False for _ in range(self.ywidth)]
+                      for _ in range(self.xwidth)]
+        for ix in range(self.xwidth):
+            x = self.calc_grid_position(ix, self.minx)
+            for iy in range(self.ywidth):
+                y = self.calc_grid_position(iy, self.miny)
+                for iox, ioy in zip(ox, oy):
+                    d = math.hypot(iox - x, ioy - y)
+                    if d <= self.rr:
+                        self.obmap[ix][iy] = True
+                        break
+
+    @staticmethod
+    def get_motion_model():
+        # dx, dy, cost
+        motion = [[1, 0, 1],
+                  [0, 1, 1],
+                  [-1, 0, 1],
+                  [0, -1, 1],
+                  [-1, -1, math.sqrt(2)],
+                  [-1, 1, math.sqrt(2)],
+                  [1, -1, math.sqrt(2)],
+                  [1, 1, math.sqrt(2)]]
+
+        return motion
+
+
+def main():
+    print(__file__ + " start!!")
+
+    # start and goal position
+    sx = 10.0  # [m]
+    sy = 10.0  # [m]
+    gx = 50.0  # [m]
+    gy = 50.0  # [m]
+    grid_size = 2.0  # [m]
+    robot_radius = 1.0  # [m]
+
+    # set obstacle positions
+    ox, oy = [], []
+    for i in range(-10, 60):
+        ox.append(i)
+        oy.append(-10.0)
+    for i in range(-10, 60):
+        ox.append(60.0)
+        oy.append(i)
+    for i in range(-10, 61):
+        ox.append(i)
+        oy.append(60.0)
+    for i in range(-10, 61):
+        ox.append(-10.0)
+        oy.append(i)
+    for i in range(-10, 40):
+        ox.append(20.0)
+        oy.append(i)
+    for i in range(0, 40):
+        ox.append(40.0)
+        oy.append(60.0 - i)
+
+    if show_animation:  # pragma: no cover
+        plt.plot(ox, oy, ".k")
+        plt.plot(sx, sy, "og")
+        plt.plot(gx, gy, "ob")
+        plt.grid(True)
+        plt.axis("equal")
+
+    bi_bfs = BidirectionalBreadthFirstSearchPlanner(
+        ox, oy, grid_size, robot_radius)
+    rx, ry = bi_bfs.planning(sx, sy, gx, gy)
+
+    if show_animation:  # pragma: no cover
+        plt.plot(rx, ry, "-r")
+        plt.pause(0.01)
+        plt.show()
+
+
+if __name__ == '__main__':
+    main()

From ada734f6622bf6a380266344c01601f97aedece3 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 5 May 2020 14:17:28 +0900
Subject: [PATCH 0390/1080] add an author

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 9a9bc9502a..aa9a47ca77 100644
--- a/README.md
+++ b/README.md
@@ -598,4 +598,6 @@ If you or your company would like to support this project, please consider:
 
 - [Guillaume Jacquenot](https://github.com/Gjacquenot)
 
+- [Erwin Lejeune](https://github.com/guilyx)
+
 

From 2b0020764b63b105ce90fc880d7e200203f44b2f Mon Sep 17 00:00:00 2001
From: Erwin Lejeune <erwin.lejeune15@gmail.com>
Date: Thu, 7 May 2020 13:06:08 +0200
Subject: [PATCH 0391/1080] Fix Hybrid A* (#327)

---
 PathPlanning/HybridAStar/hybrid_a_star.py | 47 ++++++++++++++---------
 tests/test_hybrid_a_star.py               | 22 +++++++++++
 2 files changed, 51 insertions(+), 18 deletions(-)
 create mode 100644 tests/test_hybrid_a_star.py

diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
index 41e8b393c0..14e4ac5aec 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -12,19 +12,22 @@
 import math
 import matplotlib.pyplot as plt
 import sys
-sys.path.append("../ReedsSheppPath/")
+import os
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__))
+                + "/../ReedsSheppPath")
 try:
     from a_star_heuristic import dp_planning  # , calc_obstacle_map
     import reeds_shepp_path_planning as rs
     from car import move, check_car_collision, MAX_STEER, WB, plot_car
-except:
+except Exception:
     raise
 
 
 XY_GRID_RESOLUTION = 2.0  # [m]
 YAW_GRID_RESOLUTION = np.deg2rad(15.0)  # [rad]
 MOTION_RESOLUTION = 0.1  # [m] path interporate resolution
-N_STEER = 20.0  # number of steer command
+N_STEER = 20  # number of steer command
 H_COST = 1.0
 VR = 1.0  # robot radius
 
@@ -131,7 +134,8 @@ def __init__(self, ox, oy, xyreso, yawreso):
 
 def calc_motion_inputs():
 
-    for steer in np.concatenate((np.linspace(-MAX_STEER, MAX_STEER, N_STEER),[0.0])):
+    for steer in np.concatenate((np.linspace(-MAX_STEER, MAX_STEER,
+                                             N_STEER), [0.0])):
         for d in [1, -1]:
             yield [steer, d]
 
@@ -225,7 +229,8 @@ def update_node_with_analystic_expantion(current, goal,
     apath = analytic_expantion(current, goal, c, ox, oy, kdtree)
 
     if apath:
-        plt.plot(apath.x, apath.y)
+        if show_animation:
+            plt.plot(apath.x, apath.y)
         fx = apath.x[1:]
         fy = apath.y[1:]
         fyaw = apath.yaw[1:]
@@ -337,6 +342,7 @@ def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso):
             current, ngoal, config, ox, oy, obkdtree)
 
         if isupdated:
+            print("path found")
             break
 
         for neighbor in get_neighbors(current, config, ox, oy, obkdtree):
@@ -437,12 +443,16 @@ def main():
     start = [10.0, 10.0, np.deg2rad(90.0)]
     goal = [50.0, 50.0, np.deg2rad(-90.0)]
 
-    plt.plot(ox, oy, ".k")
-    rs.plot_arrow(start[0], start[1], start[2], fc='g')
-    rs.plot_arrow(goal[0], goal[1], goal[2])
+    print("start : ", start)
+    print("goal : ", goal)
+
+    if show_animation:
+        plt.plot(ox, oy, ".k")
+        rs.plot_arrow(start[0], start[1], start[2], fc='g')
+        rs.plot_arrow(goal[0], goal[1], goal[2])
 
-    plt.grid(True)
-    plt.axis("equal")
+        plt.grid(True)
+        plt.axis("equal")
 
     path = hybrid_a_star_planning(
         start, goal, ox, oy, XY_GRID_RESOLUTION, YAW_GRID_RESOLUTION)
@@ -451,14 +461,15 @@ def main():
     y = path.ylist
     yaw = path.yawlist
 
-    for ix, iy, iyaw in zip(x, y, yaw):
-        plt.cla()
-        plt.plot(ox, oy, ".k")
-        plt.plot(x, y, "-r", label="Hybrid A* path")
-        plt.grid(True)
-        plt.axis("equal")
-        plot_car(ix, iy, iyaw)
-        plt.pause(0.0001)
+    if show_animation:
+        for ix, iy, iyaw in zip(x, y, yaw):
+            plt.cla()
+            plt.plot(ox, oy, ".k")
+            plt.plot(x, y, "-r", label="Hybrid A* path")
+            plt.grid(True)
+            plt.axis("equal")
+            plot_car(ix, iy, iyaw)
+            plt.pause(0.0001)
 
     print(__file__ + " done!!")
 
diff --git a/tests/test_hybrid_a_star.py b/tests/test_hybrid_a_star.py
new file mode 100644
index 0000000000..8cf5c30ad6
--- /dev/null
+++ b/tests/test_hybrid_a_star.py
@@ -0,0 +1,22 @@
+from unittest import TestCase
+import sys
+import os
+sys.path.append(os.path.dirname(__file__) + "/../")
+sys.path.append(os.path.dirname(os.path.abspath(__file__))
+                + "/../PathPlanning/HybridAStar")
+try:
+    from PathPlanning.HybridAStar import hybrid_a_star as m
+except Exception:
+    raise
+
+
+class Test(TestCase):
+
+    def test1(self):
+        m.show_animation = False
+        m.main()
+
+
+if __name__ == '__main__':  # pragma: no cover
+    test = Test()
+    test.test1()

From 3607d72b60cd500806e0f026ac8beb82850a01f9 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 8 May 2020 22:15:43 +0900
Subject: [PATCH 0392/1080] Update official Python version to 3.8 (#329)

---
 .../{pythonpackage.yml => Linux_CI.yml}       | 15 +++--
 .github/workflows/MacOS_CI.yml                | 58 +++++++++++++++++++
 .travis.yml                                   |  2 +-
 README.md                                     |  4 +-
 appveyor.yml                                  | 12 ++--
 5 files changed, 78 insertions(+), 13 deletions(-)
 rename .github/workflows/{pythonpackage.yml => Linux_CI.yml} (82%)
 create mode 100644 .github/workflows/MacOS_CI.yml

diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/Linux_CI.yml
similarity index 82%
rename from .github/workflows/pythonpackage.yml
rename to .github/workflows/Linux_CI.yml
index 7d1014ca57..293c5621e2 100644
--- a/.github/workflows/pythonpackage.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -1,4 +1,4 @@
-name: Python package
+name: Linux_CI
 
 on: [push, pull_request]
 
@@ -7,14 +7,17 @@ jobs:
 
     runs-on: ubuntu-latest
     strategy:
-      max-parallel: 4
       matrix:
-        python-version: [3.7]
+        python-version: ['3.8']
+
+    name: Python ${{ matrix.python-version }} CI
 
     steps:
-    - uses: actions/checkout@v1
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v1
+    - uses: actions/checkout@v2
+    - run: git fetch --prune --unshallow
+
+    - name: Setup python
+      uses: actions/setup-python@v2
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
new file mode 100644
index 0000000000..3a28347b18
--- /dev/null
+++ b/.github/workflows/MacOS_CI.yml
@@ -0,0 +1,58 @@
+# This is a basic workflow to help you get started with Actions
+
+name: MacOS_CI
+
+# Controls when the action will run. Triggers the workflow on push or pull request
+# events but only for the master branch
+on: [push, pull_request]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [ '3.8' ]
+    name: Python ${{ matrix.python-version }} CI
+    steps:
+      - uses: actions/checkout@v2
+      - run: git fetch --prune --unshallow
+
+      - name: Setup python
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+      - name: install coverage
+        run: pip install coverage
+
+      - name: install mypy
+        run: pip install mypy
+
+      - name: install pycodestyle
+        run: pip install pycodestyle
+
+      - name: mypy check
+        run: |
+          find AerialNavigation -name "*.py" | xargs mypy
+          find ArmNavigation -name "*.py" | xargs mypy
+          find Bipedal -name "*.py" | xargs mypy
+          find InvertedPendulumCart -name "*.py" | xargs mypy
+          find Localization -name "*.py" | xargs mypy
+          find Mapping -name "*.py" | xargs mypy
+          find PathPlanning -name "*.py" | xargs mypy
+          find PathTracking -name "*.py" | xargs mypy
+          find SLAM -name "*.py" | xargs mypy
+
+      - name: do diff style check
+        run: bash rundiffstylecheck.sh
+
+      - name: do all unit tests
+        run: bash runtests.sh
+
+
+
+
diff --git a/.travis.yml b/.travis.yml
index 042c69ee42..da82e6062d 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -5,7 +5,7 @@ matrix:
     - os: linux
 
 python:
-  - 3.7
+  - 3.8
 
 before_install:
     - sudo apt-get update
diff --git a/README.md b/README.md
index aa9a47ca77..4a64f53642 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,8 @@
 <img src="https://github.com/AtsushiSakai/PythonRobotics/raw/master/icon.png?raw=true" align="right" width="300" alt="header pic"/>
 
 # PythonRobotics
+![GitHub_Action_Linux_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/Linux_CI/badge.svg)
+![GitHub_Action_MacOS_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/MacOS_CI/badge.svg)
 [![Build Status](https://travis-ci.org/AtsushiSakai/PythonRobotics.svg?branch=master)](https://travis-ci.org/AtsushiSakai/PythonRobotics)
 [![Documentation Status](https://readthedocs.org/projects/pythonrobotics/badge/?version=latest)](https://pythonrobotics.readthedocs.io/en/latest/?badge=latest)
 [![Build status](https://ci.appveyor.com/api/projects/status/sb279kxuv1be391g?svg=true)](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics)
@@ -90,7 +92,7 @@ See this paper for more details:
 
 # Requirements
 
-- Python 3.7.x (2.7 is not supported)
+- Python 3.8.x
 
 - numpy
 
diff --git a/appveyor.yml b/appveyor.yml
index 33d4d02970..f34bcb2b28 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -1,3 +1,5 @@
+os: Visual Studio 2019
+
 environment:
   global:
     # SDK v7.0 MSVC Express 2008's SetEnv.cmd script will fail if the
@@ -6,7 +8,8 @@ environment:
     CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd"
 
   matrix:
-    - MINICONDA: C:\Miniconda37-x64
+    - MINICONDA: C:\Miniconda38-x64
+      PYTHON_VERSION: "3.8"
 
 init:
   - "ECHO %MINICONDA% %PYTHON_VERSION% %PYTHON_ARCH%"
@@ -24,13 +27,12 @@ install:
   - ECHO "Filesystem root:"
   - ps: "ls \"C:/\""
 
-  - ECHO "Installed SDKs:"
-  - ps: "ls \"C:/Program Files/Microsoft SDKs/Windows\""
-
   # Prepend newly installed Python to the PATH of this build (this cannot be
   # done from inside the powershell script as it would require to restart
   # the parent CMD process).
-  - "SET PATH=%MINICONDA%;%MINICONDA%\\Scripts;%PATH%"
+  - SET PATH=%MINICONDA%;%MINICONDA%\\Scripts;%PATH%
+  - SET PATH=%PYTHON%;%PYTHON%\Scripts;%PYTHON%\Library\bin;%PATH%
+  - SET PATH=%PATH%;C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin
   - conda config --set always_yes yes --set changeps1 no
   - conda update -q conda
   - conda info -a

From 03a92fc23eb97b0d58bea37c4f8cf22c3ef02fb3 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 28 May 2020 22:03:17 +0900
Subject: [PATCH 0393/1080] fix bipedal_planner and add its test (#332)

---
 Bipedal/bipedal_planner/__init__.py           |   0
 Bipedal/bipedal_planner/bipedal_planner.py    | 103 +++++++++++-------
 .../reeds_shepp_path_planning.py              |  22 ++--
 tests/test_bipedal_planner.py                 |  24 ++++
 4 files changed, 104 insertions(+), 45 deletions(-)
 create mode 100644 Bipedal/bipedal_planner/__init__.py
 create mode 100644 tests/test_bipedal_planner.py

diff --git a/Bipedal/bipedal_planner/__init__.py b/Bipedal/bipedal_planner/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/Bipedal/bipedal_planner/bipedal_planner.py b/Bipedal/bipedal_planner/bipedal_planner.py
index 6502ce5bed..60c03024bf 100644
--- a/Bipedal/bipedal_planner/bipedal_planner.py
+++ b/Bipedal/bipedal_planner/bipedal_planner.py
@@ -12,13 +12,17 @@
 
 class BipedalPlanner(object):
     def __init__(self):
+        self.act_p = []  # actual footstep positions
+        self.ref_p = []  # reference footstep positions
+        self.com_trajectory = []
         self.ref_footsteps = None
         self.g = 9.8
 
     def set_ref_footsteps(self, ref_footsteps):
         self.ref_footsteps = ref_footsteps
 
-    def inverted_pendulum(self, x, x_dot, px_star, y, y_dot, py_star, z_c, time_width):
+    def inverted_pendulum(self, x, x_dot, px_star, y, y_dot, py_star, z_c,
+                          time_width):
         time_split = 100
 
         for i in range(time_split):
@@ -37,23 +41,21 @@ def inverted_pendulum(self, x, x_dot, px_star, y, y_dot, py_star, z_c, time_widt
 
         return x, x_dot, y, y_dot
 
-    def walk(self, T_sup=0.8, z_c=0.8, a=10, b=1, plot=False):
+    def walk(self, t_sup=0.8, z_c=0.8, a=10, b=1, plot=False):
         if self.ref_footsteps is None:
             print("No footsteps")
             return
 
+        com_trajectory_for_plot, ax = None, None
+
         # set up plotter
         if plot:
             fig = plt.figure()
             ax = Axes3D(fig)
             com_trajectory_for_plot = []
 
-        self.com_trajectory = []
-        self.ref_p = []   # reference footstep positions
-        self.act_p = []   # actual footstep positions
-
-        px, py = 0.0, 0.0   # reference footstep position
-        px_star, py_star = px, py   # modified footstep position
+        px, py = 0.0, 0.0  # reference footstep position
+        px_star, py_star = px, py  # modified footstep position
         xi, xi_dot, yi, yi_dot = 0.0, 0.0, 0.01, 0.0
         time = 0.0
         n = 0
@@ -62,10 +64,10 @@ def walk(self, T_sup=0.8, z_c=0.8, a=10, b=1, plot=False):
         for i in range(len(self.ref_footsteps)):
             # simulate x, y and those of dot of inverted pendulum
             xi, xi_dot, yi, yi_dot = self.inverted_pendulum(
-                xi, xi_dot, px_star, yi, yi_dot, py_star, z_c, T_sup)
+                xi, xi_dot, px_star, yi, yi_dot, py_star, z_c, t_sup)
 
             # update time
-            time += T_sup
+            time += t_sup
             n += 1
 
             # calculate px, py, x_, y_, vx_, vy_
@@ -77,19 +79,22 @@ def walk(self, T_sup=0.8, z_c=0.8, a=10, b=1, plot=False):
                 f_x_next, f_y_next, f_theta_next = 0., 0., 0.
             else:
                 f_x_next, f_y_next, f_theta_next = self.ref_footsteps[n]
-            rotate_mat_next = np.array([[math.cos(f_theta_next), -math.sin(f_theta_next)],
-                                        [math.sin(f_theta_next), math.cos(f_theta_next)]])
-
-            T_c = math.sqrt(z_c / self.g)
-            C = math.cosh(T_sup / T_c)
-            S = math.sinh(T_sup / T_c)
-
-            px, py = list(np.array(
-                [px, py]) + np.dot(rotate_mat, np.array([f_x, -1 * math.pow(-1, n) * f_y])))
+            rotate_mat_next = np.array(
+                [[math.cos(f_theta_next), -math.sin(f_theta_next)],
+                 [math.sin(f_theta_next), math.cos(f_theta_next)]])
+
+            Tc = math.sqrt(z_c / self.g)
+            C = math.cosh(t_sup / Tc)
+            S = math.sinh(t_sup / Tc)
+
+            px, py = list(np.array([px, py])
+                          + np.dot(rotate_mat,
+                                   np.array([f_x, -1 * math.pow(-1, n) * f_y])
+                                   ))
             x_, y_ = list(np.dot(rotate_mat_next, np.array(
                 [f_x_next / 2., math.pow(-1, n) * f_y_next / 2.])))
             vx_, vy_ = list(np.dot(rotate_mat_next, np.array(
-                [(1 + C) / (T_c * S) * x_, (C - 1) / (T_c * S) * y_])))
+                [(1 + C) / (Tc * S) * x_, (C - 1) / (Tc * S) * y_])))
             self.ref_p.append([px, py, f_theta])
 
             # calculate reference COM
@@ -97,11 +102,11 @@ def walk(self, T_sup=0.8, z_c=0.8, a=10, b=1, plot=False):
             yd, yd_dot = py + y_, vy_
 
             # calculate modified footsteps
-            D = a * math.pow(C - 1, 2) + b * math.pow(S / T_c, 2)
-            px_star = -a * (C - 1) / D * (xd - C * xi - T_c * S * xi_dot) - \
-                b * S / (T_c * D) * (xd_dot - S / T_c * xi - C * xi_dot)
-            py_star = -a * (C - 1) / D * (yd - C * yi - T_c * S * yi_dot) - \
-                b * S / (T_c * D) * (yd_dot - S / T_c * yi - C * yi_dot)
+            D = a * math.pow(C - 1, 2) + b * math.pow(S / Tc, 2)
+            px_star = -a * (C - 1) / D * (xd - C * xi - Tc * S * xi_dot) \
+                      - b * S / (Tc * D) * (xd_dot - S / Tc * xi - C * xi_dot)
+            py_star = -a * (C - 1) / D * (yd - C * yi - Tc * S * yi_dot) \
+                      - b * S / (Tc * D) * (yd_dot - S / Tc * yi - C * yi_dot)
             self.act_p.append([px_star, py_star, f_theta])
 
             # plot
@@ -112,17 +117,22 @@ def walk(self, T_sup=0.8, z_c=0.8, a=10, b=1, plot=False):
                         # set up plotter
                         plt.cla()
                         # for stopping simulation with the esc key.
-                        plt.gcf().canvas.mpl_connect('key_release_event',
-                                lambda event: [exit(0) if event.key == 'escape' else None])
+                        plt.gcf().canvas.mpl_connect(
+                            'key_release_event',
+                            lambda event:
+                            [exit(0) if event.key == 'escape' else None])
                         ax.set_zlim(0, z_c * 2)
-                        ax.set_aspect('equal', 'datalim')
+                        ax.set_xlim(0, 1)
+                        ax.set_ylim(-0.5, 0.5)
 
                         # update com_trajectory_for_plot
                         com_trajectory_for_plot.append(self.com_trajectory[c])
 
                         # plot com
-                        ax.plot([p[0] for p in com_trajectory_for_plot], [p[1] for p in com_trajectory_for_plot], [
-                                0 for p in com_trajectory_for_plot], color="red")
+                        ax.plot([p[0] for p in com_trajectory_for_plot],
+                                [p[1] for p in com_trajectory_for_plot], [
+                                    0 for _ in com_trajectory_for_plot],
+                                color="red")
 
                         # plot inverted pendulum
                         ax.plot([px_star, com_trajectory_for_plot[-1][0]],
@@ -137,22 +147,39 @@ def walk(self, T_sup=0.8, z_c=0.8, a=10, b=1, plot=False):
                         foot_height = 0.04
                         for j in range(len(self.ref_p)):
                             angle = self.ref_p[j][2] + \
-                                math.atan2(foot_height, foot_width) - math.pi
+                                    math.atan2(foot_height,
+                                               foot_width) - math.pi
                             r = math.sqrt(
-                                math.pow(foot_width / 3., 2) + math.pow(foot_height / 2., 2))
-                            rec = pat.Rectangle(xy=(self.ref_p[j][0] + r * math.cos(angle), self.ref_p[j][1] + r * math.sin(angle)),
-                                                width=foot_width, height=foot_height, angle=self.ref_p[j][2] * 180 / math.pi, color="blue", fill=False, ls=":")
+                                math.pow(foot_width / 3., 2) + math.pow(
+                                    foot_height / 2., 2))
+                            rec = pat.Rectangle(xy=(
+                                self.ref_p[j][0] + r * math.cos(angle),
+                                self.ref_p[j][1] + r * math.sin(angle)),
+                                width=foot_width,
+                                height=foot_height,
+                                angle=self.ref_p[j][
+                                          2] * 180 / math.pi,
+                                color="blue", fill=False,
+                                ls=":")
                             ax.add_patch(rec)
                             art3d.pathpatch_2d_to_3d(rec, z=0, zdir="z")
 
                         # foot rectangle for self.act_p
                         for j in range(len(self.act_p)):
                             angle = self.act_p[j][2] + \
-                                math.atan2(foot_height, foot_width) - math.pi
+                                    math.atan2(foot_height,
+                                               foot_width) - math.pi
                             r = math.sqrt(
-                                math.pow(foot_width / 3., 2) + math.pow(foot_height / 2., 2))
-                            rec = pat.Rectangle(xy=(self.act_p[j][0] + r * math.cos(angle), self.act_p[j][1] + r * math.sin(angle)),
-                                                width=foot_width, height=foot_height, angle=self.act_p[j][2] * 180 / math.pi, color="blue", fill=False)
+                                math.pow(foot_width / 3., 2) + math.pow(
+                                    foot_height / 2., 2))
+                            rec = pat.Rectangle(xy=(
+                                self.act_p[j][0] + r * math.cos(angle),
+                                self.act_p[j][1] + r * math.sin(angle)),
+                                width=foot_width,
+                                height=foot_height,
+                                angle=self.act_p[j][
+                                          2] * 180 / math.pi,
+                                color="blue", fill=False)
                             ax.add_patch(rec)
                             art3d.pathpatch_2d_to_3d(rec, z=0, zdir="z")
 
diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
index 9c72a0c9b2..3bed9e2997 100644
--- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
+++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
@@ -369,13 +369,21 @@ def reeds_shepp_path_planning(sx, sy, syaw,
 def main():
     print("Reeds Shepp path planner sample start!!")
 
-    start_x = -1.0  # [m]
-    start_y = -4.0  # [m]
-    start_yaw = np.deg2rad(-20.0)  # [rad]
-
-    end_x = 5.0  # [m]
-    end_y = 5.0  # [m]
-    end_yaw = np.deg2rad(25.0)  # [rad]
+    # start_x = -1.0  # [m]
+    # start_y = -4.0  # [m]
+    # start_yaw = np.deg2rad(-20.0)  # [rad]
+    #
+    # end_x = 5.0  # [m]
+    # end_y = 5.0  # [m]
+    # end_yaw = np.deg2rad(25.0)  # [rad]
+
+    start_x = 0.0  # [m]
+    start_y = 0.0  # [m]
+    start_yaw = np.deg2rad(0.0)  # [rad]
+
+    end_x = 0.0  # [m]
+    end_y = 0.0  # [m]
+    end_yaw = np.deg2rad(0.0)  # [rad]
 
     curvature = 1.0
     step_size = 0.1
diff --git a/tests/test_bipedal_planner.py b/tests/test_bipedal_planner.py
new file mode 100644
index 0000000000..66049c68ec
--- /dev/null
+++ b/tests/test_bipedal_planner.py
@@ -0,0 +1,24 @@
+from unittest import TestCase
+
+import sys
+sys.path.append("./Bipedal/bipedal_planner/")
+try:
+    from Bipedal.bipedal_planner import bipedal_planner as m
+except Exception:
+    raise
+
+print(__file__)
+
+
+class Test(TestCase):
+
+    def test(self):
+        bipedal_planner = m.BipedalPlanner()
+
+        footsteps = [[0.0, 0.2, 0.0],
+                     [0.3, 0.2, 0.0],
+                     [0.3, 0.2, 0.2],
+                     [0.3, 0.2, 0.2],
+                     [0.0, 0.2, 0.2]]
+        bipedal_planner.set_ref_footsteps(footsteps)
+        bipedal_planner.walk(plot=False)

From 26b2c552efe7eec8f8f1505aabbf5591627878d8 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 5 Jun 2020 21:21:35 +0900
Subject: [PATCH 0394/1080] fix inaccurate covariance calculation to add a new
 landmark in FastSLAM1,2 (#334)

---
 SLAM/FastSLAM1/fast_slam1.py | 108 +++++++++++++++++++----------------
 SLAM/FastSLAM2/fast_slam2.py |  68 ++++++++++++----------
 2 files changed, 96 insertions(+), 80 deletions(-)

diff --git a/SLAM/FastSLAM1/fast_slam1.py b/SLAM/FastSLAM1/fast_slam1.py
index 2d7c003785..c4beaba257 100644
--- a/SLAM/FastSLAM1/fast_slam1.py
+++ b/SLAM/FastSLAM1/fast_slam1.py
@@ -16,16 +16,16 @@
 R = np.diag([1.0, np.deg2rad(20.0)]) ** 2
 
 #  Simulation parameter
-Qsim = np.diag([0.3, np.deg2rad(2.0)]) ** 2
-Rsim = np.diag([0.5, np.deg2rad(10.0)]) ** 2
-OFFSET_YAWRATE_NOISE = 0.01
+Q_sim = np.diag([0.3, np.deg2rad(2.0)]) ** 2
+R_sim = np.diag([0.5, np.deg2rad(10.0)]) ** 2
+OFFSET_YAW_RATE_NOISE = 0.01
 
 DT = 0.1  # time tick [s]
 SIM_TIME = 50.0  # simulation time [s]
 MAX_RANGE = 20.0  # maximum observation range
 M_DIST_TH = 2.0  # Threshold of Mahalanobis distance for data association.
 STATE_SIZE = 3  # State size [x,y,yaw]
-LM_SIZE = 2  # LM srate size [x,y]
+LM_SIZE = 2  # LM state size [x,y]
 N_PARTICLE = 100  # number of particle
 NTH = N_PARTICLE / 1.5  # Number of particle for re-sampling
 
@@ -34,15 +34,15 @@
 
 class Particle:
 
-    def __init__(self, N_LM):
+    def __init__(self, n_landmark):
         self.w = 1.0 / N_PARTICLE
         self.x = 0.0
         self.y = 0.0
         self.yaw = 0.0
         # landmark x-y positions
-        self.lm = np.zeros((N_LM, LM_SIZE))
+        self.lm = np.zeros((n_landmark, LM_SIZE))
         # landmark position covariance
-        self.lmP = np.zeros((N_LM * LM_SIZE, LM_SIZE))
+        self.lmP = np.zeros((n_landmark * LM_SIZE, LM_SIZE))
 
 
 def fast_slam1(particles, u, z):
@@ -56,11 +56,11 @@ def fast_slam1(particles, u, z):
 
 
 def normalize_weight(particles):
-    sumw = sum([p.w for p in particles])
+    sum_w = sum([p.w for p in particles])
 
     try:
         for i in range(N_PARTICLE):
-            particles[i].w /= sumw
+            particles[i].w /= sum_w
     except ZeroDivisionError:
         for i in range(N_PARTICLE):
             particles[i].w = 1.0 / N_PARTICLE
@@ -101,7 +101,7 @@ def predict_particles(particles, u):
     return particles
 
 
-def add_new_lm(particle, z, Q_cov):
+def add_new_landmark(particle, z, Q_cov):
     r = z[0]
     b = z[1]
     lm_id = int(z[2])
@@ -113,10 +113,14 @@ def add_new_lm(particle, z, Q_cov):
     particle.lm[lm_id, 1] = particle.y + r * s
 
     # covariance
-    Gz = np.array([[c, -r * s],
-                   [s, r * c]])
-
-    particle.lmP[2 * lm_id:2 * lm_id + 2] = Gz @ Q_cov @ Gz.T
+    dx = r * c
+    dy = r * s
+    d2 = dx**2 + dy**2
+    d = math.sqrt(d2)
+    Gz = np.array([[dx / d, dy / d],
+                   [-dy / d2, dx / d2]])
+    particle.lmP[2 * lm_id:2 * lm_id + 2] = np.linalg.inv(
+        Gz) @ Q_cov @ np.linalg.inv(Gz.T)
 
     return particle
 
@@ -146,10 +150,10 @@ def update_kf_with_cholesky(xf, Pf, v, Q_cov, Hf):
     S = Hf @ PHt + Q_cov
 
     S = (S + S.T) * 0.5
-    SChol = np.linalg.cholesky(S).T
-    SCholInv = np.linalg.inv(SChol)
-    W1 = PHt @ SCholInv
-    W = W1 @ SCholInv.T
+    s_chol = np.linalg.cholesky(S).T
+    s_chol_inv = np.linalg.inv(s_chol)
+    W1 = PHt @ s_chol_inv
+    W = W1 @ s_chol_inv.T
 
     x = xf + W @ v
     P = Pf - W1 @ W1.T
@@ -187,7 +191,7 @@ def compute_weight(particle, z, Q_cov):
     try:
         invS = np.linalg.inv(Sf)
     except np.linalg.linalg.LinAlgError:
-        print("singuler")
+        print("singular")
         return 1.0
 
     num = math.exp(-0.5 * dx.T @ invS @ dx)
@@ -201,12 +205,12 @@ def compute_weight(particle, z, Q_cov):
 def update_with_observation(particles, z):
     for iz in range(len(z[0, :])):
 
-        lmid = int(z[2, iz])
+        landmark_id = int(z[2, iz])
 
         for ip in range(N_PARTICLE):
             # new landmark
-            if abs(particles[ip].lm[lmid, 0]) <= 0.01:
-                particles[ip] = add_new_lm(particles[ip], z[:, iz], Q)
+            if abs(particles[ip].lm[landmark_id, 0]) <= 0.01:
+                particles[ip] = add_new_landmark(particles[ip], z[:, iz], Q)
             # known landmark
             else:
                 w = compute_weight(particles[ip], z[:, iz], Q)
@@ -229,28 +233,29 @@ def resampling(particles):
 
     pw = np.array(pw)
 
-    Neff = 1.0 / (pw @ pw.T)  # Effective particle number
-    # print(Neff)
+    n_eff = 1.0 / (pw @ pw.T)  # Effective particle number
+    # print(n_eff)
 
-    if Neff < NTH:  # resampling
-        wcum = np.cumsum(pw)
+    if n_eff < NTH:  # resampling
+        w_cum = np.cumsum(pw)
         base = np.cumsum(pw * 0.0 + 1 / N_PARTICLE) - 1 / N_PARTICLE
-        resampleid = base + np.random.rand(base.shape[0]) / N_PARTICLE
+        resample_id = base + np.random.rand(base.shape[0]) / N_PARTICLE
 
         inds = []
         ind = 0
         for ip in range(N_PARTICLE):
-            while (ind < wcum.shape[0] - 1) and (resampleid[ip] > wcum[ind]):
+            while (ind < w_cum.shape[0] - 1) \
+                    and (resample_id[ip] > w_cum[ind]):
                 ind += 1
             inds.append(ind)
 
-        tparticles = particles[:]
+        tmp_particles = particles[:]
         for i in range(len(inds)):
-            particles[i].x = tparticles[inds[i]].x
-            particles[i].y = tparticles[inds[i]].y
-            particles[i].yaw = tparticles[inds[i]].yaw
-            particles[i].lm = tparticles[inds[i]].lm[:, :]
-            particles[i].lmP = tparticles[inds[i]].lmP[:, :]
+            particles[i].x = tmp_particles[inds[i]].x
+            particles[i].y = tmp_particles[inds[i]].y
+            particles[i].yaw = tmp_particles[inds[i]].yaw
+            particles[i].lm = tmp_particles[inds[i]].lm[:, :]
+            particles[i].lmP = tmp_particles[inds[i]].lmP[:, :]
             particles[i].w = 1.0 / N_PARTICLE
 
     return particles
@@ -259,37 +264,39 @@ def resampling(particles):
 def calc_input(time):
     if time <= 3.0:  # wait at first
         v = 0.0
-        yawrate = 0.0
+        yaw_rate = 0.0
     else:
         v = 1.0  # [m/s]
-        yawrate = 0.1  # [rad/s]
+        yaw_rate = 0.1  # [rad/s]
 
-    u = np.array([v, yawrate]).reshape(2, 1)
+    u = np.array([v, yaw_rate]).reshape(2, 1)
 
     return u
 
 
-def observation(xTrue, xd, u, RFID):
+def observation(xTrue, xd, u, rfid):
     # calc true state
     xTrue = motion_model(xTrue, u)
 
     # add noise to range observation
     z = np.zeros((3, 0))
-    for i in range(len(RFID[:, 0])):
+    for i in range(len(rfid[:, 0])):
 
-        dx = RFID[i, 0] - xTrue[0, 0]
-        dy = RFID[i, 1] - xTrue[1, 0]
+        dx = rfid[i, 0] - xTrue[0, 0]
+        dy = rfid[i, 1] - xTrue[1, 0]
         d = math.hypot(dx, dy)
         angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
         if d <= MAX_RANGE:
-            dn = d + np.random.randn() * Qsim[0, 0] ** 0.5  # add noise
-            anglen = angle + np.random.randn() * Qsim[1, 1] ** 0.5  # add noise
-            zi = np.array([dn, pi_2_pi(anglen), i]).reshape(3, 1)
+            dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5  # add noise
+            angle_with_noize = angle + np.random.randn() * Q_sim[
+                1, 1] ** 0.5  # add noise
+            zi = np.array([dn, pi_2_pi(angle_with_noize), i]).reshape(3, 1)
             z = np.hstack((z, zi))
 
     # add noise to input
-    ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0] ** 0.5
-    ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1] ** 0.5 + OFFSET_YAWRATE_NOISE
+    ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5
+    ud2 = u[1, 0] + np.random.randn() * R_sim[
+        1, 1] ** 0.5 + OFFSET_YAW_RATE_NOISE
     ud = np.array([ud1, ud2]).reshape(2, 1)
 
     xd = motion_model(xd, ud)
@@ -332,7 +339,7 @@ def main():
                      [-5.0, 5.0],
                      [-10.0, 15.0]
                      ])
-    N_LM = RFID.shape[0]
+    n_landmark = RFID.shape[0]
 
     # State Vector [x y yaw v]'
     xEst = np.zeros((STATE_SIZE, 1))  # SLAM estimation
@@ -344,7 +351,7 @@ def main():
     hxTrue = xTrue
     hxDR = xTrue
 
-    particles = [Particle(N_LM) for _ in range(N_PARTICLE)]
+    particles = [Particle(n_landmark) for _ in range(N_PARTICLE)]
 
     while SIM_TIME >= time:
         time += DT
@@ -366,8 +373,9 @@ def main():
         if show_animation:  # pragma: no cover
             plt.cla()
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event', lambda event:
+                [exit(0) if event.key == 'escape' else None])
             plt.plot(RFID[:, 0], RFID[:, 1], "*k")
 
             for i in range(N_PARTICLE):
diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py
index 4780b57982..7cd708df81 100644
--- a/SLAM/FastSLAM2/fast_slam2.py
+++ b/SLAM/FastSLAM2/fast_slam2.py
@@ -113,10 +113,14 @@ def add_new_lm(particle, z, Q_cov):
     particle.lm[lm_id, 1] = particle.y + r * s
 
     # covariance
-    Gz = np.array([[c, -r * s],
-                   [s, r * c]])
-
-    particle.lmP[2 * lm_id:2 * lm_id + 2] = Gz @ Q_cov @ Gz.T
+    dx = r * c
+    dy = r * s
+    d2 = dx ** 2 + dy ** 2
+    d = math.sqrt(d2)
+    Gz = np.array([[dx / d, dy / d],
+                   [-dy / d2, dx / d2]])
+    particle.lmP[2 * lm_id:2 * lm_id + 2] = np.linalg.inv(
+        Gz) @ Q_cov @ np.linalg.inv(Gz.T)
 
     return particle
 
@@ -224,11 +228,11 @@ def proposal_sampling(particle, z, Q_cov):
 
 def update_with_observation(particles, z):
     for iz in range(len(z[0, :])):
-        lmid = int(z[2, iz])
+        landmark_id = int(z[2, iz])
 
         for ip in range(N_PARTICLE):
             # new landmark
-            if abs(particles[ip].lm[lmid, 0]) <= 0.01:
+            if abs(particles[ip].lm[landmark_id, 0]) <= 0.01:
                 particles[ip] = add_new_lm(particles[ip], z[:, iz], Q)
             # known landmark
             else:
@@ -254,27 +258,28 @@ def resampling(particles):
 
     pw = np.array(pw)
 
-    Neff = 1.0 / (pw @ pw.T)  # Effective particle number
+    n_eff = 1.0 / (pw @ pw.T)  # Effective particle number
 
-    if Neff < NTH:  # resampling
-        wcum = np.cumsum(pw)
+    if n_eff < NTH:  # resampling
+        w_cum = np.cumsum(pw)
         base = np.cumsum(pw * 0.0 + 1 / N_PARTICLE) - 1 / N_PARTICLE
-        resamplei_id = base + np.random.rand(base.shape[0]) / N_PARTICLE
+        resample_id = base + np.random.rand(base.shape[0]) / N_PARTICLE
 
         inds = []
         ind = 0
         for ip in range(N_PARTICLE):
-            while (ind < wcum.shape[0] - 1) and (resamplei_id[ip] > wcum[ind]):
+            while (ind < w_cum.shape[0] - 1) \
+                    and (resample_id[ip] > w_cum[ind]):
                 ind += 1
             inds.append(ind)
 
-        tparticles = particles[:]
+        tmp_particles = particles[:]
         for i in range(len(inds)):
-            particles[i].x = tparticles[inds[i]].x
-            particles[i].y = tparticles[inds[i]].y
-            particles[i].yaw = tparticles[inds[i]].yaw
-            particles[i].lm = tparticles[inds[i]].lm[:, :]
-            particles[i].lmP = tparticles[inds[i]].lmP[:, :]
+            particles[i].x = tmp_particles[inds[i]].x
+            particles[i].y = tmp_particles[inds[i]].y
+            particles[i].yaw = tmp_particles[inds[i]].yaw
+            particles[i].lm = tmp_particles[inds[i]].lm[:, :]
+            particles[i].lmP = tmp_particles[inds[i]].lmP[:, :]
             particles[i].w = 1.0 / N_PARTICLE
 
     return particles
@@ -283,12 +288,12 @@ def resampling(particles):
 def calc_input(time):
     if time <= 3.0:  # wait at first
         v = 0.0
-        yawrate = 0.0
+        yaw_rate = 0.0
     else:
         v = 1.0  # [m/s]
-        yawrate = 0.1  # [rad/s]
+        yaw_rate = 0.1  # [rad/s]
 
-    u = np.array([v, yawrate]).reshape(2, 1)
+    u = np.array([v, yaw_rate]).reshape(2, 1)
 
     return u
 
@@ -308,13 +313,15 @@ def observation(xTrue, xd, u, RFID):
         angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
         if d <= MAX_RANGE:
             dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5  # add noise
-            anglen = angle + np.random.randn() * Q_sim[1, 1] ** 0.5  # add noise
-            zi = np.array([dn, pi_2_pi(anglen), i]).reshape(3, 1)
+            angle_noise = np.random.randn() * Q_sim[1, 1] ** 0.5
+            angle_with_noise = angle + angle_noise  # add noise
+            zi = np.array([dn, pi_2_pi(angle_with_noise), i]).reshape(3, 1)
             z = np.hstack((z, zi))
 
     # add noise to input
     ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5
-    ud2 = u[1, 0] + np.random.randn() * R_sim[1, 1] ** 0.5 + OFFSET_YAW_RATE_NOISE
+    ud2 = u[1, 0] + np.random.randn() * R_sim[
+        1, 1] ** 0.5 + OFFSET_YAW_RATE_NOISE
     ud = np.array([ud1, ud2]).reshape(2, 1)
 
     xd = motion_model(xd, ud)
@@ -357,7 +364,7 @@ def main():
                      [-5.0, 5.0],
                      [-10.0, 15.0]
                      ])
-    N_LM = RFID.shape[0]
+    n_landmark = RFID.shape[0]
 
     # State Vector [x y yaw v]'
     xEst = np.zeros((STATE_SIZE, 1))  # SLAM estimation
@@ -369,7 +376,7 @@ def main():
     hxTrue = xTrue
     hxDR = xTrue
 
-    particles = [Particle(N_LM) for _ in range(N_PARTICLE)]
+    particles = [Particle(n_landmark) for _ in range(N_PARTICLE)]
 
     while SIM_TIME >= time:
         time += DT
@@ -391,14 +398,15 @@ def main():
         if show_animation:  # pragma: no cover
             plt.cla()
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(RFID[:, 0], RFID[:, 1], "*k")
 
             for iz in range(len(z[:, 0])):
-                lmid = int(z[2, iz])
-                plt.plot([xEst[0], RFID[lmid, 0]], [
-                    xEst[1], RFID[lmid, 1]], "-k")
+                landmark_id = int(z[2, iz])
+                plt.plot([xEst[0], RFID[landmark_id, 0]], [
+                    xEst[1], RFID[landmark_id, 1]], "-k")
 
             for i in range(N_PARTICLE):
                 plt.plot(particles[i].x, particles[i].y, ".r")

From d1bde5835f7f6063bcff5675e138d57644bc6d4a Mon Sep 17 00:00:00 2001
From: Justin Hutchings <jhutchings1@users.noreply.github.com>
Date: Fri, 5 Jun 2020 16:21:00 -0700
Subject: [PATCH 0395/1080] Add CodeQL security scanning (#333)

* Add CodeQL security scanning

* Add configuration for LGTM queries

* Remove excess whitespace
---
 .github/codeql/codeql-config.yml |  6 ++++
 .github/workflows/codeql.yml     | 54 ++++++++++++++++++++++++++++++++
 2 files changed, 60 insertions(+)
 create mode 100644 .github/codeql/codeql-config.yml
 create mode 100644 .github/workflows/codeql.yml

diff --git a/.github/codeql/codeql-config.yml b/.github/codeql/codeql-config.yml
new file mode 100644
index 0000000000..4542231f10
--- /dev/null
+++ b/.github/codeql/codeql-config.yml
@@ -0,0 +1,6 @@
+name: "Extended CodeQL Config"
+
+# This file adds additional queries to the default configuration to make it equivalent to what LGTM checks.
+queries:
+  - name: Security and quality queries
+    uses: security-and-quality
diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
new file mode 100644
index 0000000000..526b465eec
--- /dev/null
+++ b/.github/workflows/codeql.yml
@@ -0,0 +1,54 @@
+name: "Code scanning - action"
+
+on:
+  push:
+  pull_request:
+  schedule:
+    - cron: '0 19 * * 0'
+
+jobs:
+  CodeQL-Build:
+
+    # CodeQL runs on ubuntu-latest and windows-latest
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v2
+      with:
+        # We must fetch at least the immediate parents so that if this is
+        # a pull request then we can checkout the head.
+        fetch-depth: 2
+
+    # If this run was triggered by a pull request event, then checkout
+    # the head of the pull request instead of the merge commit.
+    - run: git checkout HEAD^2
+      if: ${{ github.event_name == 'pull_request' }}
+      
+    # Initializes the CodeQL tools for scanning.
+    - name: Initialize CodeQL
+      uses: github/codeql-action/init@v1
+      with:
+        config-file: ./.github/codeql/codeql-config.yml
+      # Override language selection by uncommenting this and choosing your languages
+      # with:
+      #   languages: go, javascript, csharp, python, cpp, java
+
+    # Autobuild attempts to build any compiled languages  (C/C++, C#, or Java).
+    # If this step fails, then you should remove it and run the build manually (see below)
+    - name: Autobuild
+      uses: github/codeql-action/autobuild@v1
+
+    # ℹ️ Command-line programs to run using the OS shell.
+    # 📚 https://git.io/JvXDl
+
+    # ✏️ If the Autobuild fails above, remove it and uncomment the following three lines
+    #    and modify them (or add more) to build your code if your project
+    #    uses a compiled language
+
+    #- run: |
+    #   make bootstrap
+    #   make release
+
+    - name: Perform CodeQL Analysis
+      uses: github/codeql-action/analyze@v1

From b8afdb10d8d8f57f5c6bde9b41ab64911d01ef8e Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 7 Jun 2020 20:28:29 +0900
Subject: [PATCH 0396/1080] Using scipy.spatial.rotation matrix (#335)

---
 .travis.yml                                   |   3 +-
 .../ensemble_kalman_filter.py                 |  40 ++-
 .../histogram_filter/histogram_filter.py      | 121 ++++---
 .../particle_filter/particle_filter.py        |  16 +-
 .../lidar_to_grid_map/lidar_to_grid_map.py    | 187 +++++-----
 .../rectangle_fitting/rectangle_fitting.py    |  74 ++--
 Mapping/rectangle_fitting/simulator.py        |  81 ++---
 PathPlanning/AStar/a_star.py                  | 130 +++----
 .../bidirectional_a_star.py                   |  22 +-
 .../bidirectional_breadth_first_search.py     |  20 +-
 .../breadth_first_search.py                   |  20 +-
 .../ClosedLoopRRTStar/pure_pursuit.py         |   6 +-
 .../DepthFirstSearch/depth_first_search.py    |  20 +-
 PathPlanning/Dijkstra/dijkstra.py             |   4 +-
 .../DubinsPath/dubins_path_planning.py        |  96 +++---
 .../dynamic_window_approach.py                |  36 +-
 .../greedy_best_first_search.py               |  20 +-
 .../grid_based_sweep_coverage_path_planner.py | 187 +++++-----
 PathPlanning/HybridAStar/a_star_heuristic.py  | 147 ++++----
 PathPlanning/HybridAStar/car.py               |  50 +--
 PathPlanning/HybridAStar/hybrid_a_star.py     | 322 +++++++++---------
 .../InformedRRTStar/informed_rrt_star.py      |  91 ++---
 .../probabilistic_road_map.py                 | 128 +++----
 .../quintic_polynomials_planner.py            |   6 +-
 .../VoronoiRoadMap/dijkstra_search.py         |   4 +-
 SLAM/GraphBasedSLAM/graph_based_slam.py       | 104 +++---
 26 files changed, 1003 insertions(+), 932 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index da82e6062d..99d239217b 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -18,10 +18,9 @@ before_install:
     - conda update -q conda
     # Useful for debugging any issues with conda
     - conda info -a
-#    - conda install python==3.6.8
 
 install:
-  - conda install numpy==1.15
+  - conda install numpy
   - conda install scipy
   - conda install matplotlib
   - conda install pandas
diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
index 58a998e2f3..1307b1ebd1 100644
--- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
+++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
@@ -5,7 +5,8 @@
 author: Ryohei Sasaki(rsasaki0109)
 
 Ref:
-- [Ensemble Kalman filtering](https://rmets.onlinelibrary.wiley.com/doi/10.1256/qj.05.135)
+Ensemble Kalman filtering
+(https://rmets.onlinelibrary.wiley.com/doi/10.1256/qj.05.135)
 
 """
 
@@ -13,6 +14,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+from scipy.spatial.transform import Rotation as Rot
 
 #  Simulation parameter
 Q_sim = np.diag([0.2, np.deg2rad(1.0)]) ** 2
@@ -48,8 +50,8 @@ def observation(xTrue, xd, u, RFID):
         angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
         if d <= MAX_RANGE:
             dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5  # add noise
-            anglen = angle + np.random.randn() * Q_sim[1, 1] ** 0.5  # add noise
-            zi = np.array([dn, anglen, RFID[i, 0], RFID[i, 1]])
+            angle_with_noise = angle + np.random.randn() * Q_sim[1, 1] ** 0.5
+            zi = np.array([dn, angle_with_noise, RFID[i, 0], RFID[i, 1]])
             z = np.vstack((z, zi))
 
     # add noise to input
@@ -79,10 +81,12 @@ def motion_model(x, u):
 def observe_landmark_position(x, landmarks):
     landmarks_pos = np.zeros((2 * landmarks.shape[0], 1))
     for (i, lm) in enumerate(landmarks):
-        landmarks_pos[2 * i] = x[0, 0] + lm[0] * math.cos(x[2, 0] + lm[1]) + np.random.randn() * Q_sim[
-            0, 0] ** 0.5 / np.sqrt(2)
-        landmarks_pos[2 * i + 1] = x[1, 0] + lm[0] * math.sin(x[2, 0] + lm[1]) + np.random.randn() * Q_sim[
-            0, 0] ** 0.5 / np.sqrt(2)
+        index = 2 * i
+        q = Q_sim[0, 0] ** 0.5
+        landmarks_pos[index] = x[0, 0] + lm[0] * math.cos(
+            x[2, 0] + lm[1]) + np.random.randn() * q / np.sqrt(2)
+        landmarks_pos[index + 1] = x[1, 0] + lm[0] * math.sin(
+            x[2, 0] + lm[1]) + np.random.randn() * q / np.sqrt(2)
     return landmarks_pos
 
 
@@ -148,8 +152,9 @@ def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
 
     t = np.arange(0, 2 * math.pi + 0.1, 0.1)
 
-    # eig_val[big_ind] or eiq_val[small_ind] were occasionally negative numbers extremely
-    # close to 0 (~10^-20), catch these cases and set the respective variable to 0
+    # eig_val[big_ind] or eiq_val[small_ind] were occasionally negative
+    # numbers extremely close to 0 (~10^-20), catch these cases and set
+    # the respective variable to 0
     try:
         a = math.sqrt(eig_val[big_ind])
     except ValueError:
@@ -163,11 +168,11 @@ def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
     x = [a * math.cos(it) for it in t]
     y = [b * math.sin(it) for it in t]
     angle = math.atan2(eig_vec[big_ind, 1], eig_vec[big_ind, 0])
-    R = np.array([[math.cos(angle), math.sin(angle)],
-                  [-math.sin(angle), math.cos(angle)]])
-    fx = R.dot(np.array([[x, y]]))
-    px = np.array(fx[0, :] + xEst[0, 0]).flatten()
-    py = np.array(fx[1, :] + xEst[1, 0]).flatten()
+    rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2]
+    fx = np.stack([x, y]).T @ rot
+
+    px = np.array(fx[:, 0] + xEst[0, 0]).flatten()
+    py = np.array(fx[:, 1] + xEst[1, 0]).flatten()
     plt.plot(px, py, "--r")
 
 
@@ -214,8 +219,9 @@ def main():
         if show_animation:
             plt.cla()
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
 
             for i in range(len(z[:, 0])):
                 plt.plot([xTrue[0, 0], z[i, 2]], [xTrue[1, 0], z[i, 3]], "-k")
@@ -227,7 +233,7 @@ def main():
                      np.array(hxDR[1, :]).flatten(), "-k")
             plt.plot(np.array(hxEst[0, :]).flatten(),
                      np.array(hxEst[1, :]).flatten(), "-r")
-            # plot_covariance_ellipse(xEst, PEst)
+            plot_covariance_ellipse(xEst, PEst)
             plt.axis("equal")
             plt.grid(True)
             plt.pause(0.001)
diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py
index 135b476cc4..7f453d69dd 100644
--- a/Localization/histogram_filter/histogram_filter.py
+++ b/Localization/histogram_filter/histogram_filter.py
@@ -28,11 +28,11 @@
 RANGE_STD = 3.0  # standard deviation for observation gaussian distribution
 
 # grid map param
-XY_RESO = 0.5  # xy grid resolution
-MINX = -15.0
-MINY = -5.0
-MAXX = 15.0
-MAXY = 25.0
+XY_RESOLUTION = 0.5  # xy grid resolution
+MIN_X = -15.0
+MIN_Y = -5.0
+MAX_X = 15.0
+MAX_Y = 25.0
 
 # simulation parameters
 NOISE_RANGE = 2.0  # [m] 1σ range noise parameter
@@ -41,17 +41,17 @@
 show_animation = True
 
 
-class GridMap():
+class GridMap:
 
     def __init__(self):
         self.data = None
-        self.xy_reso = None
-        self.minx = None
-        self.miny = None
-        self.maxx = None
-        self.maxx = None
-        self.xw = None
-        self.yw = None
+        self.xy_resolution = None
+        self.min_x = None
+        self.min_y = None
+        self.max_x = None
+        self.max_y = None
+        self.x_w = None
+        self.y_w = None
         self.dx = 0.0  # movement distance
         self.dy = 0.0  # movement distance
 
@@ -64,10 +64,10 @@ def histogram_filter_localization(grid_map, u, z, yaw):
     return grid_map
 
 
-def calc_gaussian_observation_pdf(gmap, z, iz, ix, iy, std):
+def calc_gaussian_observation_pdf(grid_map, z, iz, ix, iy, std):
     # predicted range
-    x = ix * gmap.xy_reso + gmap.minx
-    y = iy * gmap.xy_reso + gmap.miny
+    x = ix * grid_map.xy_resolution + grid_map.min_x
+    y = iy * grid_map.xy_resolution + grid_map.min_y
     d = math.hypot(x - z[iz, 1], y - z[iz, 2])
 
     # likelihood
@@ -76,16 +76,16 @@ def calc_gaussian_observation_pdf(gmap, z, iz, ix, iy, std):
     return pdf
 
 
-def observation_update(gmap, z, std):
+def observation_update(grid_map, z, std):
     for iz in range(z.shape[0]):
-        for ix in range(gmap.xw):
-            for iy in range(gmap.yw):
-                gmap.data[ix][iy] *= calc_gaussian_observation_pdf(
-                    gmap, z, iz, ix, iy, std)
+        for ix in range(grid_map.x_w):
+            for iy in range(grid_map.y_w):
+                grid_map.data[ix][iy] *= calc_gaussian_observation_pdf(
+                    grid_map, z, iz, ix, iy, std)
 
-    gmap = normalize_probability(gmap)
+    grid_map = normalize_probability(grid_map)
 
-    return gmap
+    return grid_map
 
 
 def calc_input():
@@ -112,8 +112,8 @@ def motion_model(x, u):
 
 
 def draw_heat_map(data, mx, my):
-    maxp = max([max(igmap) for igmap in data])
-    plt.pcolor(mx, my, data, vmax=maxp, cmap=plt.cm.get_cmap("Blues"))
+    max_value = max([max(i_data) for i_data in data])
+    plt.pcolor(mx, my, data, vmax=max_value, cmap=plt.cm.get_cmap("Blues"))
     plt.axis("equal")
 
 
@@ -140,43 +140,47 @@ def observation(xTrue, u, RFID):
     return xTrue, z, ud
 
 
-def normalize_probability(gmap):
-    sump = sum([sum(igmap) for igmap in gmap.data])
+def normalize_probability(grid_map):
+    sump = sum([sum(i_data) for i_data in grid_map.data])
 
-    for ix in range(gmap.xw):
-        for iy in range(gmap.yw):
-            gmap.data[ix][iy] /= sump
+    for ix in range(grid_map.x_w):
+        for iy in range(grid_map.y_w):
+            grid_map.data[ix][iy] /= sump
 
-    return gmap
+    return grid_map
 
 
-def init_gmap(xy_reso, minx, miny, maxx, maxy):
+def init_grid_map(xy_resolution, min_x, min_y, max_x, max_y):
     grid_map = GridMap()
 
-    grid_map.xy_reso = xy_reso
-    grid_map.minx = minx
-    grid_map.miny = miny
-    grid_map.maxx = maxx
-    grid_map.maxy = maxy
-    grid_map.xw = int(round((grid_map.maxx - grid_map.minx) / grid_map.xy_reso))
-    grid_map.yw = int(round((grid_map.maxy - grid_map.miny) / grid_map.xy_reso))
-
-    grid_map.data = [[1.0 for _ in range(grid_map.yw)] for _ in range(grid_map.xw)]
+    grid_map.xy_resolution = xy_resolution
+    grid_map.min_x = min_x
+    grid_map.min_y = min_y
+    grid_map.max_x = max_x
+    grid_map.max_y = max_y
+    grid_map.x_w = int(round((grid_map.max_x - grid_map.min_x)
+                             / grid_map.xy_resolution))
+    grid_map.y_w = int(round((grid_map.max_y - grid_map.min_y)
+                             / grid_map.xy_resolution))
+
+    grid_map.data = [[1.0 for _ in range(grid_map.y_w)]
+                     for _ in range(grid_map.x_w)]
     grid_map = normalize_probability(grid_map)
 
     return grid_map
 
 
 def map_shift(grid_map, x_shift, y_shift):
-    tgmap = copy.deepcopy(grid_map.data)
+    tmp_grid_map = copy.deepcopy(grid_map.data)
 
-    for ix in range(grid_map.xw):
-        for iy in range(grid_map.yw):
+    for ix in range(grid_map.x_w):
+        for iy in range(grid_map.y_w):
             nix = ix + x_shift
             niy = iy + y_shift
 
-            if 0 <= nix < grid_map.xw and 0 <= niy < grid_map.yw:
-                grid_map.data[ix + x_shift][iy + y_shift] = tgmap[ix][iy]
+            if 0 <= nix < grid_map.x_w and 0 <= niy < grid_map.y_w:
+                grid_map.data[ix + x_shift][iy + y_shift] =\
+                    tmp_grid_map[ix][iy]
 
     return grid_map
 
@@ -185,22 +189,26 @@ def motion_update(grid_map, u, yaw):
     grid_map.dx += DT * math.cos(yaw) * u[0]
     grid_map.dy += DT * math.sin(yaw) * u[0]
 
-    x_shift = grid_map.dx // grid_map.xy_reso
-    y_shift = grid_map.dy // grid_map.xy_reso
+    x_shift = grid_map.dx // grid_map.xy_resolution
+    y_shift = grid_map.dy // grid_map.xy_resolution
 
     if abs(x_shift) >= 1.0 or abs(y_shift) >= 1.0:  # map should be shifted
         grid_map = map_shift(grid_map, int(x_shift), int(y_shift))
-        grid_map.dx -= x_shift * grid_map.xy_reso
-        grid_map.dy -= y_shift * grid_map.xy_reso
+        grid_map.dx -= x_shift * grid_map.xy_resolution
+        grid_map.dy -= y_shift * grid_map.xy_resolution
 
     grid_map.data = gaussian_filter(grid_map.data, sigma=MOTION_STD)
 
     return grid_map
 
 
-def calc_grid_index(gmap):
-    mx, my = np.mgrid[slice(gmap.minx - gmap.xy_reso / 2.0, gmap.maxx + gmap.xy_reso / 2.0, gmap.xy_reso),
-                      slice(gmap.miny - gmap.xy_reso / 2.0, gmap.maxy + gmap.xy_reso / 2.0, gmap.xy_reso)]
+def calc_grid_index(grid_map):
+    mx, my = np.mgrid[slice(grid_map.min_x - grid_map.xy_resolution / 2.0,
+                            grid_map.max_x + grid_map.xy_resolution / 2.0,
+                            grid_map.xy_resolution),
+                      slice(grid_map.min_y - grid_map.xy_resolution / 2.0,
+                            grid_map.max_y + grid_map.xy_resolution / 2.0,
+                            grid_map.xy_resolution)]
 
     return mx, my
 
@@ -217,7 +225,7 @@ def main():
     time = 0.0
 
     xTrue = np.zeros((4, 1))
-    grid_map = init_gmap(XY_RESO, MINX, MINY, MAXX, MAXY)
+    grid_map = init_grid_map(XY_RESOLUTION, MIN_X, MIN_Y, MAX_X, MAX_Y)
     mx, my = calc_grid_index(grid_map)  # for grid map visualization
 
     while SIM_TIME >= time:
@@ -234,8 +242,9 @@ def main():
         if show_animation:
             plt.cla()
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
             draw_heat_map(grid_map.data, mx, my)
             plt.plot(xTrue[0, :], xTrue[1, :], "xr")
             plt.plot(RF_ID[:, 0], RF_ID[:, 1], ".k")
diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py
index 6eca4eb53b..89a43ea600 100644
--- a/Localization/particle_filter/particle_filter.py
+++ b/Localization/particle_filter/particle_filter.py
@@ -10,6 +10,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+from scipy.spatial.transform import Rotation as Rot
 
 # Estimation parameter of PF
 Q = np.diag([0.2]) ** 2  # range error
@@ -172,8 +173,9 @@ def plot_covariance_ellipse(x_est, p_est):  # pragma: no cover
 
     t = np.arange(0, 2 * math.pi + 0.1, 0.1)
 
-    # eig_val[big_ind] or eiq_val[small_ind] were occasionally negative numbers extremely
-    # close to 0 (~10^-20), catch these cases and set the respective variable to 0
+    # eig_val[big_ind] or eiq_val[small_ind] were occasionally negative
+    # numbers extremely close to 0 (~10^-20), catch these cases and set the
+    # respective variable to 0
     try:
         a = math.sqrt(eig_val[big_ind])
     except ValueError:
@@ -187,9 +189,8 @@ def plot_covariance_ellipse(x_est, p_est):  # pragma: no cover
     x = [a * math.cos(it) for it in t]
     y = [b * math.sin(it) for it in t]
     angle = math.atan2(eig_vec[big_ind, 1], eig_vec[big_ind, 0])
-    Rot = np.array([[math.cos(angle), -math.sin(angle)],
-                    [math.sin(angle), math.cos(angle)]])
-    fx = Rot.dot(np.array([[x, y]]))
+    rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2]
+    fx = rot.dot(np.array([[x, y]]))
     px = np.array(fx[0, :] + x_est[0, 0]).flatten()
     py = np.array(fx[1, :] + x_est[1, 0]).flatten()
     plt.plot(px, py, "--r")
@@ -235,8 +236,9 @@ def main():
         if show_animation:
             plt.cla()
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                                         lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
 
             for i in range(len(z[:, 0])):
                 plt.plot([x_true[0, 0], z[i, 1]], [x_true[1, 0], z[i, 2]], "-k")
diff --git a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
index 60bf4db69c..911a0bf0ea 100644
--- a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
+++ b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
@@ -2,15 +2,16 @@
 
 LIDAR to 2D grid map example
 
-author: Erno Horvath, Csaba Hajdu based on Atsushi Sakai's scripts (@Atsushi_twi)
+author: Erno Horvath, Csaba Hajdu based on Atsushi Sakai's scripts
 
 """
 
 import math
-import numpy as np
-import matplotlib.pyplot as plt
 from collections import deque
 
+import matplotlib.pyplot as plt
+import numpy as np
+
 EXTEND_AREA = 1.0
 
 
@@ -44,47 +45,49 @@ def bresenham(start, end):
     x2, y2 = end
     dx = x2 - x1
     dy = y2 - y1
-    is_steep = abs(dy) > abs(dx) # determine how steep the line is
-    if is_steep: # rotate line
+    is_steep = abs(dy) > abs(dx)  # determine how steep the line is
+    if is_steep:  # rotate line
         x1, y1 = y1, x1
         x2, y2 = y2, x2
-    swapped = False # swap start and end points if necessary and store swap state
+    # swap start and end points if necessary and store swap state
+    swapped = False
     if x1 > x2:
         x1, x2 = x2, x1
         y1, y2 = y2, y1
         swapped = True
-    dx = x2 - x1 # recalculate differentials
-    dy = y2 - y1 # recalculate differentials
-    error = int(dx / 2.0) # calculate error
-    ystep = 1 if y1 < y2 else -1
+    dx = x2 - x1  # recalculate differentials
+    dy = y2 - y1  # recalculate differentials
+    error = int(dx / 2.0)  # calculate error
+    y_step = 1 if y1 < y2 else -1
     # iterate over bounding box generating points between start and end
     y = y1
     points = []
     for x in range(x1, x2 + 1):
         coord = [y, x] if is_steep else (x, y)
-        points.append(coord)   
+        points.append(coord)
         error -= abs(dy)
         if error < 0:
-            y += ystep
+            y += y_step
             error += dx
-    if swapped: # reverse the list if the coordinates were swapped
+    if swapped:  # reverse the list if the coordinates were swapped
         points.reverse()
     points = np.array(points)
     return points
 
 
-def calc_grid_map_config(ox, oy, xyreso):
+def calc_grid_map_config(ox, oy, xy_resolution):
     """
-    Calculates the size, and the maximum distances according to the the measurement center
+    Calculates the size, and the maximum distances according to the the
+    measurement center
     """
-    minx = round(min(ox) - EXTEND_AREA / 2.0)
-    miny = round(min(oy) - EXTEND_AREA / 2.0)
-    maxx = round(max(ox) + EXTEND_AREA / 2.0)
-    maxy = round(max(oy) + EXTEND_AREA / 2.0)
-    xw = int(round((maxx - minx) / xyreso))
-    yw = int(round((maxy - miny) / xyreso))
+    min_x = round(min(ox) - EXTEND_AREA / 2.0)
+    min_y = round(min(oy) - EXTEND_AREA / 2.0)
+    max_x = round(max(ox) + EXTEND_AREA / 2.0)
+    max_y = round(max(oy) + EXTEND_AREA / 2.0)
+    xw = int(round((max_x - min_x) / xy_resolution))
+    yw = int(round((max_y - min_y) / xy_resolution))
     print("The grid map is ", xw, "x", yw, ".")
-    return minx, miny, maxx, maxy, xw, yw
+    return min_x, min_y, max_x, max_y, xw, yw
 
 
 def atan_zero_to_twopi(y, x):
@@ -94,96 +97,110 @@ def atan_zero_to_twopi(y, x):
     return angle
 
 
-def init_floodfill(cpoint,  opoints, xypoints, mincoord, xyreso):
+def init_flood_fill(center_point, obstacle_points, xy_points, min_coord,
+                    xy_resolution):
     """
-    cpoint: center point
-    opoints: detected obstacles points (x,y)
-    xypoints: (x,y) point pairs
+    center_point: center point
+    obstacle_points: detected obstacles points (x,y)
+    xy_points: (x,y) point pairs
     """
-    centix, centiy = cpoint
-    prev_ix, prev_iy = centix - 1, centiy
-    ox, oy = opoints
-    xw, yw = xypoints
-    minx, miny = mincoord
-    pmap = (np.ones((xw, yw))) * 0.5
+    center_x, center_y = center_point
+    prev_ix, prev_iy = center_x - 1, center_y
+    ox, oy = obstacle_points
+    xw, yw = xy_points
+    min_x, min_y = min_coord
+    occupancy_map = (np.ones((xw, yw))) * 0.5
     for (x, y) in zip(ox, oy):
-        ix = int(round((x - minx) / xyreso))  # x coordinate of the the occupied area
-        iy = int(round((y - miny) / xyreso))  # y coordinate of the the occupied area
+        # x coordinate of the the occupied area
+        ix = int(round((x - min_x) / xy_resolution))
+        # y coordinate of the the occupied area
+        iy = int(round((y - min_y) / xy_resolution))
         free_area = bresenham((prev_ix, prev_iy), (ix, iy))
         for fa in free_area:
-            pmap[fa[0]][fa[1]] = 0  # free area 0.0
+            occupancy_map[fa[0]][fa[1]] = 0  # free area 0.0
         prev_ix = ix
         prev_iy = iy
-    return pmap
+    return occupancy_map
 
 
-def flood_fill(cpoint, pmap):
+def flood_fill(center_point, occupancy_map):
     """
-    cpoint: starting point (x,y) of fill
-    pmap: occupancy map generated from Bresenham ray-tracing
+    center_point: starting point (x,y) of fill
+    occupancy_map: occupancy map generated from Bresenham ray-tracing
     """
     # Fill empty areas with queue method
-    sx, sy = pmap.shape
+    sx, sy = occupancy_map.shape
     fringe = deque()
-    fringe.appendleft(cpoint)
+    fringe.appendleft(center_point)
     while fringe:
         n = fringe.pop()
         nx, ny = n
         # West
         if nx > 0:
-            if pmap[nx - 1, ny] == 0.5:
-                pmap[nx - 1, ny] = 0.0
+            if occupancy_map[nx - 1, ny] == 0.5:
+                occupancy_map[nx - 1, ny] = 0.0
                 fringe.appendleft((nx - 1, ny))
         # East
         if nx < sx - 1:
-            if pmap[nx + 1, ny] == 0.5:
-                pmap[nx + 1, ny] = 0.0
+            if occupancy_map[nx + 1, ny] == 0.5:
+                occupancy_map[nx + 1, ny] = 0.0
                 fringe.appendleft((nx + 1, ny))
         # North
         if ny > 0:
-            if pmap[nx, ny - 1] == 0.5:
-                pmap[nx, ny - 1] = 0.0
+            if occupancy_map[nx, ny - 1] == 0.5:
+                occupancy_map[nx, ny - 1] = 0.0
                 fringe.appendleft((nx, ny - 1))
         # South
         if ny < sy - 1:
-            if pmap[nx, ny + 1] == 0.5:
-                pmap[nx, ny + 1] = 0.0
+            if occupancy_map[nx, ny + 1] == 0.5:
+                occupancy_map[nx, ny + 1] = 0.0
                 fringe.appendleft((nx, ny + 1))
 
 
-def generate_ray_casting_grid_map(ox, oy, xyreso, breshen=True):
+def generate_ray_casting_grid_map(ox, oy, xy_resolution, breshen=True):
     """
-    The breshen boolean tells if it's computed with bresenham ray casting (True) or with flood fill (False)
+    The breshen boolean tells if it's computed with bresenham ray casting
+    (True) or with flood fill (False)
     """
-    minx, miny, maxx, maxy, xw, yw = calc_grid_map_config(ox, oy, xyreso)
-    pmap = np.ones((xw, yw))/2 # default 0.5 -- [[0.5 for i in range(yw)] for i in range(xw)] 
-    centix = int(round(-minx / xyreso)) # center x coordinate of the grid map
-    centiy = int(round(-miny / xyreso)) # center y coordinate of the grid map
+    min_x, min_y, max_x, max_y, x_w, y_w = calc_grid_map_config(
+        ox, oy, xy_resolution)
+    # default 0.5 -- [[0.5 for i in range(y_w)] for i in range(x_w)]
+    occupancy_map = np.ones((x_w, y_w)) / 2
+    center_x = int(
+        round(-min_x / xy_resolution))  # center x coordinate of the grid map
+    center_y = int(
+        round(-min_y / xy_resolution))  # center y coordinate of the grid map
     # occupancy grid computed with bresenham ray casting
     if breshen:
         for (x, y) in zip(ox, oy):
-            ix = int(round((x - minx) / xyreso)) # x coordinate of the the occupied area
-            iy = int(round((y - miny) / xyreso)) # y coordinate of the the occupied area
-            laser_beams = bresenham((centix, centiy), (ix, iy)) # line form the lidar to the cooupied point
+            # x coordinate of the the occupied area
+            ix = int(round((x - min_x) / xy_resolution))
+            # y coordinate of the the occupied area
+            iy = int(round((y - min_y) / xy_resolution))
+            laser_beams = bresenham((center_x, center_y), (
+                ix, iy))  # line form the lidar to the occupied point
             for laser_beam in laser_beams:
-                pmap[laser_beam[0]][laser_beam[1]] = 0.0 # free area 0.0
-            pmap[ix][iy] = 1.0     # occupied area 1.0
-            pmap[ix+1][iy] = 1.0   # extend the occupied area
-            pmap[ix][iy+1] = 1.0   # extend the occupied area
-            pmap[ix+1][iy+1] = 1.0 # extend the occupied area
+                occupancy_map[laser_beam[0]][
+                    laser_beam[1]] = 0.0  # free area 0.0
+            occupancy_map[ix][iy] = 1.0  # occupied area 1.0
+            occupancy_map[ix + 1][iy] = 1.0  # extend the occupied area
+            occupancy_map[ix][iy + 1] = 1.0  # extend the occupied area
+            occupancy_map[ix + 1][iy + 1] = 1.0  # extend the occupied area
     # occupancy grid computed with with flood fill
     else:
-        pmap = init_floodfill((centix, centiy), (ox, oy), (xw, yw),  (minx, miny), xyreso)
-        flood_fill((centix, centiy), pmap)
-        pmap = np.array(pmap, dtype=np.float)
+        occupancy_map = init_flood_fill((center_x, center_y), (ox, oy),
+                                        (x_w, y_w),
+                                        (min_x, min_y), xy_resolution)
+        flood_fill((center_x, center_y), occupancy_map)
+        occupancy_map = np.array(occupancy_map, dtype=np.float)
         for (x, y) in zip(ox, oy):
-            ix = int(round((x - minx) / xyreso))
-            iy = int(round((y - miny) / xyreso))
-            pmap[ix][iy] = 1.0     # occupied area 1.0
-            pmap[ix+1][iy] = 1.0   # extend the occupied area
-            pmap[ix][iy+1] = 1.0   # extend the occupied area
-            pmap[ix+1][iy+1] = 1.0 # extend the occupied area
-    return pmap, minx, maxx, miny, maxy, xyreso
+            ix = int(round((x - min_x) / xy_resolution))
+            iy = int(round((y - min_y) / xy_resolution))
+            occupancy_map[ix][iy] = 1.0  # occupied area 1.0
+            occupancy_map[ix + 1][iy] = 1.0  # extend the occupied area
+            occupancy_map[ix][iy + 1] = 1.0  # extend the occupied area
+            occupancy_map[ix + 1][iy + 1] = 1.0  # extend the occupied area
+    return occupancy_map, min_x, max_x, min_y, max_y, xy_resolution
 
 
 def main():
@@ -191,18 +208,20 @@ def main():
     Example usage
     """
     print(__file__, "start")
-    xyreso = 0.02  # x-y grid resolution
+    xy_resolution = 0.02  # x-y grid resolution
     ang, dist = file_read("lidar01.csv")
     ox = np.sin(ang) * dist
     oy = np.cos(ang) * dist
-    pmap, minx, maxx, miny, maxy, xyreso = generate_ray_casting_grid_map(ox, oy, xyreso, True)
-    xyres = np.array(pmap).shape
-    plt.figure(1, figsize=(10,4))
+    occupancy_map, min_x, max_x, min_y, max_y, xy_resolution = \
+        generate_ray_casting_grid_map(ox, oy, xy_resolution, True)
+    xy_res = np.array(occupancy_map).shape
+    plt.figure(1, figsize=(10, 4))
     plt.subplot(122)
-    plt.imshow(pmap, cmap="PiYG_r") # cmap = "binary" "PiYG_r" "PiYG_r" "bone" "bone_r" "RdYlGn_r"
+    plt.imshow(occupancy_map, cmap="PiYG_r")
+    # cmap = "binary" "PiYG_r" "PiYG_r" "bone" "bone_r" "RdYlGn_r"
     plt.clim(-0.4, 1.4)
-    plt.gca().set_xticks(np.arange(-.5, xyres[1], 1), minor=True)
-    plt.gca().set_yticks(np.arange(-.5, xyres[0], 1), minor=True)
+    plt.gca().set_xticks(np.arange(-.5, xy_res[1], 1), minor=True)
+    plt.gca().set_yticks(np.arange(-.5, xy_res[0], 1), minor=True)
     plt.grid(True, which="minor", color="w", linewidth=0.6, alpha=0.5)
     plt.colorbar()
     plt.subplot(121)
@@ -210,11 +229,11 @@ def main():
     plt.axis("equal")
     plt.plot(0.0, 0.0, "ob")
     plt.gca().set_aspect("equal", "box")
-    bottom, top = plt.ylim()  # return the current ylim
-    plt.ylim((top, bottom)) # rescale y axis, to match the grid orientation
+    bottom, top = plt.ylim()  # return the current y-lim
+    plt.ylim((top, bottom))  # rescale y axis, to match the grid orientation
     plt.grid(True)
     plt.show()
 
-        
+
 if __name__ == '__main__':
-    main()     
+    main()
diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py
index 1a2a534093..ae41b3c53e 100644
--- a/Mapping/rectangle_fitting/rectangle_fitting.py
+++ b/Mapping/rectangle_fitting/rectangle_fitting.py
@@ -5,7 +5,10 @@
 author: Atsushi Sakai (@Atsushi_twi)
 
 Ref:
-- [Efficient L\-Shape Fitting for Vehicle Detection Using Laser Scanners \- The Robotics Institute Carnegie Mellon University](https://www.ri.cmu.edu/publications/efficient-l-shape-fitting-for-vehicle-detection-using-laser-scanners/)
+- Efficient L-Shape Fitting for Vehicle Detection Using Laser Scanners -
+The Robotics Institute Carnegie Mellon University
+https://www.ri.cmu.edu/publications/
+efficient-l-shape-fitting-for-vehicle-detection-using-laser-scanners/
 
 """
 
@@ -13,13 +16,14 @@
 import numpy as np
 import itertools
 from enum import Enum
+from scipy.spatial.transform import Rotation as Rot
 
 from simulator import VehicleSimulator, LidarSimulator
 
 show_animation = True
 
 
-class LShapeFitting():
+class LShapeFitting:
 
     class Criteria(Enum):
         AREA = 1
@@ -29,26 +33,27 @@ class Criteria(Enum):
     def __init__(self):
         # Parameters
         self.criteria = self.Criteria.VARIANCE
-        self.min_dist_of_closeness_crit = 0.01  # [m]
-        self.dtheta_deg_for_serarch = 1.0  # [deg]
+        self.min_dist_of_closeness_criteria = 0.01  # [m]
+        self.d_theta_deg_for_search = 1.0  # [deg]
         self.R0 = 3.0  # [m] range segmentation param
         self.Rd = 0.001  # [m] range segmentation param
 
     def fitting(self, ox, oy):
 
         # step1: Adaptive Range Segmentation
-        idsets = self._adoptive_range_segmentation(ox, oy)
+        id_sets = self._adoptive_range_segmentation(ox, oy)
 
         # step2 Rectangle search
         rects = []
-        for ids in idsets:  # for each cluster
+        for ids in id_sets:  # for each cluster
             cx = [ox[i] for i in range(len(ox)) if i in ids]
             cy = [oy[i] for i in range(len(oy)) if i in ids]
             rects.append(self._rectangle_search(cx, cy))
 
-        return rects, idsets
+        return rects, id_sets
 
-    def _calc_area_criterion(self, c1, c2):
+    @staticmethod
+    def _calc_area_criterion(c1, c2):
         c1_max = max(c1)
         c2_max = max(c2)
         c1_min = min(c1)
@@ -71,12 +76,13 @@ def _calc_closeness_criterion(self, c1, c2):
 
         beta = 0
         for i, _ in enumerate(D1):
-            d = max(min([D1[i], D2[i]]), self.min_dist_of_closeness_crit)
+            d = max(min([D1[i], D2[i]]), self.min_dist_of_closeness_criteria)
             beta += (1.0 / d)
 
         return beta
 
-    def _calc_variance_criterion(self, c1, c2):
+    @staticmethod
+    def _calc_variance_criterion(c1, c2):
         c1_max = max(c1)
         c2_max = max(c2)
         c1_min = min(c1)
@@ -110,17 +116,17 @@ def _rectangle_search(self, x, y):
 
         X = np.array([x, y]).T
 
-        dtheta = np.deg2rad(self.dtheta_deg_for_serarch)
-        minp = (-float('inf'), None)
-        for theta in np.arange(0.0, np.pi / 2.0 - dtheta, dtheta):
+        d_theta = np.deg2rad(self.d_theta_deg_for_search)
+        min_cost = (-float('inf'), None)
+        for theta in np.arange(0.0, np.pi / 2.0 - d_theta, d_theta):
 
-            e1 = np.array([np.cos(theta), np.sin(theta)])
-            e2 = np.array([-np.sin(theta), np.cos(theta)])
-
-            c1 = X @ e1.T
-            c2 = X @ e2.T
+            rot = Rot.from_euler('z', theta).as_matrix()[0:2, 0:2]
+            c = X @ rot
+            c1 = c[:, 0]
+            c2 = c[:, 1]
 
             # Select criteria
+            cost = 0.0
             if self.criteria == self.Criteria.AREA:
                 cost = self._calc_area_criterion(c1, c2)
             elif self.criteria == self.Criteria.CLOSENESS:
@@ -128,12 +134,12 @@ def _rectangle_search(self, x, y):
             elif self.criteria == self.Criteria.VARIANCE:
                 cost = self._calc_variance_criterion(c1, c2)
 
-            if minp[0] < cost:
-                minp = (cost, theta)
+            if min_cost[0] < cost:
+                min_cost = (cost, theta)
 
         # calc best rectangle
-        sin_s = np.sin(minp[1])
-        cos_s = np.cos(minp[1])
+        sin_s = np.sin(min_cost[1])
+        cos_s = np.cos(min_cost[1])
 
         c1_s = X @ np.array([cos_s, sin_s]).T
         c2_s = X @ np.array([-sin_s, cos_s]).T
@@ -181,7 +187,7 @@ def _adoptive_range_segmentation(self, ox, oy):
         return S
 
 
-class RectangleData():
+class RectangleData:
 
     def __init__(self):
         self.a = [None] * 4
@@ -207,7 +213,8 @@ def calc_rect_contour(self):
             [self.a[3], self.a[0]], [self.b[3], self.b[0]], [self.c[3], self.c[0]])
         self.rect_c_x[4], self.rect_c_y[4] = self.rect_c_x[0], self.rect_c_y[0]
 
-    def calc_cross_point(self, a, b, c):
+    @staticmethod
+    def calc_cross_point(a, b, c):
         x = (b[0] * -c[1] - b[1] * -c[0]) / (a[0] * b[1] - a[1] * b[0])
         y = (a[1] * -c[0] - a[0] * -c[1]) / (a[0] * b[1] - a[1] * b[0])
         return x, y
@@ -216,42 +223,43 @@ def calc_cross_point(self, a, b, c):
 def main():
 
     # simulation parameters
-    simtime = 30.0  # simulation time
+    sim_time = 30.0  # simulation time
     dt = 0.2  # time tick
 
-    angle_reso = np.deg2rad(3.0)  # sensor angle resolution
+    angle_resolution = np.deg2rad(3.0)  # sensor angle resolution
 
     v1 = VehicleSimulator(-10.0, 0.0, np.deg2rad(90.0),
                           0.0, 50.0 / 3.6, 3.0, 5.0)
     v2 = VehicleSimulator(20.0, 10.0, np.deg2rad(180.0),
                           0.0, 50.0 / 3.6, 4.0, 10.0)
 
-    lshapefitting = LShapeFitting()
+    l_shape_fitting = LShapeFitting()
     lidar_sim = LidarSimulator()
 
     time = 0.0
-    while time <= simtime:
+    while time <= sim_time:
         time += dt
 
         v1.update(dt, 0.1, 0.0)
         v2.update(dt, 0.1, -0.05)
 
-        ox, oy = lidar_sim.get_observation_points([v1, v2], angle_reso)
+        ox, oy = lidar_sim.get_observation_points([v1, v2], angle_resolution)
 
-        rects, idsets = lshapefitting.fitting(ox, oy)
+        rects, id_sets = l_shape_fitting.fitting(ox, oy)
 
         if show_animation:  # pragma: no cover
             plt.cla()
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
             plt.axis("equal")
             plt.plot(0.0, 0.0, "*r")
             v1.plot()
             v2.plot()
 
             # Plot range observation
-            for ids in idsets:
+            for ids in id_sets:
                 x = [ox[i] for i in range(len(ox)) if i in ids]
                 y = [oy[i] for i in range(len(ox)) if i in ids]
 
diff --git a/Mapping/rectangle_fitting/simulator.py b/Mapping/rectangle_fitting/simulator.py
index b21d4003a8..c7c3141fa9 100644
--- a/Mapping/rectangle_fitting/simulator.py
+++ b/Mapping/rectangle_fitting/simulator.py
@@ -10,15 +10,16 @@
 import matplotlib.pyplot as plt
 import math
 import random
+from scipy.spatial.transform import Rotation as Rot
 
 
-class VehicleSimulator():
+class VehicleSimulator:
 
-    def __init__(self, ix, iy, iyaw, iv, max_v, w, L):
-        self.x = ix
-        self.y = iy
-        self.yaw = iyaw
-        self.v = iv
+    def __init__(self, i_x, i_y, i_yaw, i_v, max_v, w, L):
+        self.x = i_x
+        self.y = i_y
+        self.yaw = i_yaw
+        self.v = i_v
         self.max_v = max_v
         self.W = w
         self.L = L
@@ -40,10 +41,10 @@ def plot(self):
         plt.plot(gx, gy, "--b")
 
     def calc_global_contour(self):
-        gx = [(ix * np.cos(self.yaw) + iy * np.sin(self.yaw)) +
-              self.x for (ix, iy) in zip(self.vc_x, self.vc_y)]
-        gy = [(ix * np.sin(self.yaw) - iy * np.cos(self.yaw)) +
-              self.y for (ix, iy) in zip(self.vc_x, self.vc_y)]
+        rot = Rot.from_euler('z', self.yaw).as_matrix()[0:2, 0:2]
+        gxy = np.stack([self.vc_x, self.vc_y]).T @ rot
+        gx = gxy[:, 0] + self.x
+        gy = gxy[:, 1] + self.y
 
         return gx, gy
 
@@ -67,69 +68,71 @@ def _calc_vehicle_contour(self):
         self.vc_x.append(self.L / 2.0)
         self.vc_y.append(self.W / 2.0)
 
-        self.vc_x, self.vc_y = self._interporate(self.vc_x, self.vc_y)
+        self.vc_x, self.vc_y = self._interpolate(self.vc_x, self.vc_y)
 
-    def _interporate(self, x, y):
+    @staticmethod
+    def _interpolate(x, y):
         rx, ry = [], []
-        dtheta = 0.05
+        d_theta = 0.05
         for i in range(len(x) - 1):
-            rx.extend([(1.0 - θ) * x[i] + θ * x[i + 1]
-                       for θ in np.arange(0.0, 1.0, dtheta)])
-            ry.extend([(1.0 - θ) * y[i] + θ * y[i + 1]
-                       for θ in np.arange(0.0, 1.0, dtheta)])
+            rx.extend([(1.0 - theta) * x[i] + theta * x[i + 1]
+                       for theta in np.arange(0.0, 1.0, d_theta)])
+            ry.extend([(1.0 - theta) * y[i] + theta * y[i + 1]
+                       for theta in np.arange(0.0, 1.0, d_theta)])
 
-        rx.extend([(1.0 - θ) * x[len(x) - 1] + θ * x[1]
-                   for θ in np.arange(0.0, 1.0, dtheta)])
-        ry.extend([(1.0 - θ) * y[len(y) - 1] + θ * y[1]
-                   for θ in np.arange(0.0, 1.0, dtheta)])
+        rx.extend([(1.0 - theta) * x[len(x) - 1] + theta * x[1]
+                   for theta in np.arange(0.0, 1.0, d_theta)])
+        ry.extend([(1.0 - theta) * y[len(y) - 1] + theta * y[1]
+                   for theta in np.arange(0.0, 1.0, d_theta)])
 
         return rx, ry
 
 
-class LidarSimulator():
+class LidarSimulator:
 
     def __init__(self):
         self.range_noise = 0.01
 
-    def get_observation_points(self, vlist, angle_reso):
+    def get_observation_points(self, v_list, angle_resolution):
         x, y, angle, r = [], [], [], []
 
         # store all points
-        for v in vlist:
+        for v in v_list:
 
             gx, gy = v.calc_global_contour()
 
             for vx, vy in zip(gx, gy):
-                vangle = math.atan2(vy, vx)
+                v_angle = math.atan2(vy, vx)
                 vr = np.hypot(vx, vy) * random.uniform(1.0 - self.range_noise,
                                                        1.0 + self.range_noise)
 
                 x.append(vx)
                 y.append(vy)
-                angle.append(vangle)
+                angle.append(v_angle)
                 r.append(vr)
 
         # ray casting filter
-        rx, ry = self.ray_casting_filter(x, y, angle, r, angle_reso)
+        rx, ry = self.ray_casting_filter(angle, r, angle_resolution)
 
         return rx, ry
 
-    def ray_casting_filter(self, xl, yl, thetal, rangel, angle_reso):
+    @staticmethod
+    def ray_casting_filter(theta_l, range_l, angle_resolution):
         rx, ry = [], []
-        rangedb = [float("inf") for _ in range(
-            int(np.floor((np.pi * 2.0) / angle_reso)) + 1)]
+        range_db = [float("inf") for _ in range(
+            int(np.floor((np.pi * 2.0) / angle_resolution)) + 1)]
 
-        for i in range(len(thetal)):
-            angleid = int(round(thetal[i] / angle_reso))
+        for i in range(len(theta_l)):
+            angle_id = int(round(theta_l[i] / angle_resolution))
 
-            if rangedb[angleid] > rangel[i]:
-                rangedb[angleid] = rangel[i]
+            if range_db[angle_id] > range_l[i]:
+                range_db[angle_id] = range_l[i]
 
-        for i in range(len(rangedb)):
-            t = i * angle_reso
-            if rangedb[i] != float("inf"):
-                rx.append(rangedb[i] * np.cos(t))
-                ry.append(rangedb[i] * np.sin(t))
+        for i in range(len(range_db)):
+            t = i * angle_resolution
+            if range_db[i] != float("inf"):
+                rx.append(range_db[i] * np.cos(t))
+                ry.append(range_db[i] * np.sin(t))
 
         return rx, ry
 
diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py
index 14c51ad9a1..eb2672a9ce 100644
--- a/PathPlanning/AStar/a_star.py
+++ b/PathPlanning/AStar/a_star.py
@@ -18,39 +18,43 @@
 
 class AStarPlanner:
 
-    def __init__(self, ox, oy, reso, rr):
+    def __init__(self, ox, oy, resolution, rr):
         """
         Initialize grid map for a star planning
 
         ox: x position list of Obstacles [m]
         oy: y position list of Obstacles [m]
-        reso: grid resolution [m]
+        resolution: grid resolution [m]
         rr: robot radius[m]
         """
 
-        self.reso = reso
+        self.resolution = resolution
         self.rr = rr
-        self.calc_obstacle_map(ox, oy)
+        self.min_x, self.min_y = 0, 0
+        self.max_x, self.max_y = 0, 0
+        self.obstacle_map = None
+        self.x_width, self.y_width = 0, 0
         self.motion = self.get_motion_model()
+        self.calc_obstacle_map(ox, oy)
 
     class Node:
-        def __init__(self, x, y, cost, pind):
+        def __init__(self, x, y, cost, parent_index):
             self.x = x  # index of grid
             self.y = y  # index of grid
             self.cost = cost
-            self.pind = pind
+            self.parent_index = parent_index
 
         def __str__(self):
             return str(self.x) + "," + str(self.y) + "," + str(
-                self.cost) + "," + str(self.pind)
+                self.cost) + "," + str(self.parent_index)
 
     def planning(self, sx, sy, gx, gy):
         """
         A star path search
 
         input:
-            sx: start x position [m]
-            sy: start y position [m]
+            s_x: start x position [m]
+            s_y: start y position [m]
             gx: goal x position [m]
             gy: goal y position [m]
 
@@ -59,13 +63,13 @@ def planning(self, sx, sy, gx, gy):
             ry: y position list of the final path
         """
 
-        nstart = self.Node(self.calc_xyindex(sx, self.minx),
-                           self.calc_xyindex(sy, self.miny), 0.0, -1)
-        ngoal = self.Node(self.calc_xyindex(gx, self.minx),
-                          self.calc_xyindex(gy, self.miny), 0.0, -1)
+        start_node = self.Node(self.calc_xy_index(sx, self.min_x),
+                               self.calc_xy_index(sy, self.min_y), 0.0, -1)
+        goal_node = self.Node(self.calc_xy_index(gx, self.min_x),
+                              self.calc_xy_index(gy, self.min_y), 0.0, -1)
 
         open_set, closed_set = dict(), dict()
-        open_set[self.calc_grid_index(nstart)] = nstart
+        open_set[self.calc_grid_index(start_node)] = start_node
 
         while 1:
             if len(open_set) == 0:
@@ -74,15 +78,15 @@ def planning(self, sx, sy, gx, gy):
 
             c_id = min(
                 open_set,
-                key=lambda o: open_set[o].cost + self.calc_heuristic(ngoal,
+                key=lambda o: open_set[o].cost + self.calc_heuristic(goal_node,
                                                                      open_set[
                                                                          o]))
             current = open_set[c_id]
 
             # show graph
             if show_animation:  # pragma: no cover
-                plt.plot(self.calc_grid_position(current.x, self.minx),
-                         self.calc_grid_position(current.y, self.miny), "xc")
+                plt.plot(self.calc_grid_position(current.x, self.min_x),
+                         self.calc_grid_position(current.y, self.min_y), "xc")
                 # for stopping simulation with the esc key.
                 plt.gcf().canvas.mpl_connect('key_release_event',
                                              lambda event: [exit(
@@ -90,10 +94,10 @@ def planning(self, sx, sy, gx, gy):
                 if len(closed_set.keys()) % 10 == 0:
                     plt.pause(0.001)
 
-            if current.x == ngoal.x and current.y == ngoal.y:
+            if current.x == goal_node.x and current.y == goal_node.y:
                 print("Find goal")
-                ngoal.pind = current.pind
-                ngoal.cost = current.cost
+                goal_node.parent_index = current.parent_index
+                goal_node.cost = current.cost
                 break
 
             # Remove the item from the open set
@@ -123,20 +127,20 @@ def planning(self, sx, sy, gx, gy):
                         # This path is the best until now. record it
                         open_set[n_id] = node
 
-        rx, ry = self.calc_final_path(ngoal, closed_set)
+        rx, ry = self.calc_final_path(goal_node, closed_set)
 
         return rx, ry
 
-    def calc_final_path(self, ngoal, closedset):
+    def calc_final_path(self, goal_node, closed_set):
         # generate final course
-        rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
-            self.calc_grid_position(ngoal.y, self.miny)]
-        pind = ngoal.pind
-        while pind != -1:
-            n = closedset[pind]
-            rx.append(self.calc_grid_position(n.x, self.minx))
-            ry.append(self.calc_grid_position(n.y, self.miny))
-            pind = n.pind
+        rx, ry = [self.calc_grid_position(goal_node.x, self.min_x)], [
+            self.calc_grid_position(goal_node.y, self.min_y)]
+        parent_index = goal_node.parent_index
+        while parent_index != -1:
+            n = closed_set[parent_index]
+            rx.append(self.calc_grid_position(n.x, self.min_x))
+            ry.append(self.calc_grid_position(n.y, self.min_y))
+            parent_index = n.parent_index
 
         return rx, ry
 
@@ -146,69 +150,69 @@ def calc_heuristic(n1, n2):
         d = w * math.hypot(n1.x - n2.x, n1.y - n2.y)
         return d
 
-    def calc_grid_position(self, index, minp):
+    def calc_grid_position(self, index, min_position):
         """
         calc grid position
 
         :param index:
-        :param minp:
+        :param min_position:
         :return:
         """
-        pos = index * self.reso + minp
+        pos = index * self.resolution + min_position
         return pos
 
-    def calc_xyindex(self, position, min_pos):
-        return round((position - min_pos) / self.reso)
+    def calc_xy_index(self, position, min_pos):
+        return round((position - min_pos) / self.resolution)
 
     def calc_grid_index(self, node):
-        return (node.y - self.miny) * self.xwidth + (node.x - self.minx)
+        return (node.y - self.min_y) * self.x_width + (node.x - self.min_x)
 
     def verify_node(self, node):
-        px = self.calc_grid_position(node.x, self.minx)
-        py = self.calc_grid_position(node.y, self.miny)
+        px = self.calc_grid_position(node.x, self.min_x)
+        py = self.calc_grid_position(node.y, self.min_y)
 
-        if px < self.minx:
+        if px < self.min_x:
             return False
-        elif py < self.miny:
+        elif py < self.min_y:
             return False
-        elif px >= self.maxx:
+        elif px >= self.max_x:
             return False
-        elif py >= self.maxy:
+        elif py >= self.max_y:
             return False
 
         # collision check
-        if self.obmap[node.x][node.y]:
+        if self.obstacle_map[node.x][node.y]:
             return False
 
         return True
 
     def calc_obstacle_map(self, ox, oy):
 
-        self.minx = round(min(ox))
-        self.miny = round(min(oy))
-        self.maxx = round(max(ox))
-        self.maxy = round(max(oy))
-        print("minx:", self.minx)
-        print("miny:", self.miny)
-        print("maxx:", self.maxx)
-        print("maxy:", self.maxy)
+        self.min_x = round(min(ox))
+        self.min_y = round(min(oy))
+        self.max_x = round(max(ox))
+        self.max_y = round(max(oy))
+        print("min_x:", self.min_x)
+        print("min_y:", self.min_y)
+        print("max_x:", self.max_x)
+        print("max_y:", self.max_y)
 
-        self.xwidth = round((self.maxx - self.minx) / self.reso)
-        self.ywidth = round((self.maxy - self.miny) / self.reso)
-        print("xwidth:", self.xwidth)
-        print("ywidth:", self.ywidth)
+        self.x_width = round((self.max_x - self.min_x) / self.resolution)
+        self.y_width = round((self.max_y - self.min_y) / self.resolution)
+        print("x_width:", self.x_width)
+        print("y_width:", self.y_width)
 
         # obstacle map generation
-        self.obmap = [[False for i in range(self.ywidth)]
-                      for i in range(self.xwidth)]
-        for ix in range(self.xwidth):
-            x = self.calc_grid_position(ix, self.minx)
-            for iy in range(self.ywidth):
-                y = self.calc_grid_position(iy, self.miny)
+        self.obstacle_map = [[False for _ in range(self.y_width)]
+                             for _ in range(self.x_width)]
+        for ix in range(self.x_width):
+            x = self.calc_grid_position(ix, self.min_x)
+            for iy in range(self.y_width):
+                y = self.calc_grid_position(iy, self.min_y)
                 for iox, ioy in zip(ox, oy):
                     d = math.hypot(iox - x, ioy - y)
                     if d <= self.rr:
-                        self.obmap[ix][iy] = True
+                        self.obstacle_map[ix][iy] = True
                         break
 
     @staticmethod
@@ -270,8 +274,8 @@ def main():
 
     if show_animation:  # pragma: no cover
         plt.plot(rx, ry, "-r")
-        plt.show()
         plt.pause(0.001)
+        plt.show()
 
 
 if __name__ == '__main__':
diff --git a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
index 65d1fc31a7..cf76ab67cd 100644
--- a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
+++ b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
@@ -23,7 +23,7 @@ def __init__(self, ox, oy, reso, rr):
 
         ox: x position list of Obstacles [m]
         oy: y position list of Obstacles [m]
-        reso: grid resolution [m]
+        resolution: grid resolution [m]
         rr: robot radius[m]
         """
 
@@ -48,8 +48,8 @@ def planning(self, sx, sy, gx, gy):
         Bidirectional A star path search
 
         input:
-            sx: start x position [m]
-            sy: start y position [m]
+            s_x: start x position [m]
+            s_y: start y position [m]
             gx: goal x position [m]
             gy: goal y position [m]
 
@@ -179,12 +179,12 @@ def calc_final_path(self, ngoal, closedset):
         # generate final course
         rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
             self.calc_grid_position(ngoal.y, self.miny)]
-        pind = ngoal.pind
+        pind = ngoal.parent_index
         while pind != -1:
             n = closedset[pind]
             rx.append(self.calc_grid_position(n.x, self.minx))
             ry.append(self.calc_grid_position(n.y, self.miny))
-            pind = n.pind
+            pind = n.parent_index
 
         return rx, ry
 
@@ -252,15 +252,15 @@ def calc_obstacle_map(self, ox, oy):
         self.miny = round(min(oy))
         self.maxx = round(max(ox))
         self.maxy = round(max(oy))
-        print("minx:", self.minx)
-        print("miny:", self.miny)
-        print("maxx:", self.maxx)
-        print("maxy:", self.maxy)
+        print("min_x:", self.minx)
+        print("min_y:", self.miny)
+        print("max_x:", self.maxx)
+        print("max_y:", self.maxy)
 
         self.xwidth = round((self.maxx - self.minx) / self.reso)
         self.ywidth = round((self.maxy - self.miny) / self.reso)
-        print("xwidth:", self.xwidth)
-        print("ywidth:", self.ywidth)
+        print("x_width:", self.xwidth)
+        print("y_width:", self.ywidth)
 
         # obstacle map generation
         self.obmap = [[False for _ in range(self.ywidth)]
diff --git a/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py b/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py
index d29112eb65..328898db85 100644
--- a/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py
+++ b/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py
@@ -23,7 +23,7 @@ def __init__(self, ox, oy, reso, rr):
 
         ox: x position list of Obstacles [m]
         oy: y position list of Obstacles [m]
-        reso: grid resolution [m]
+        resolution: grid resolution [m]
         rr: robot radius[m]
         """
 
@@ -49,8 +49,8 @@ def planning(self, sx, sy, gx, gy):
         Bidirectional Breadth First search based planning
 
         input:
-            sx: start x position [m]
-            sy: start y position [m]
+            s_x: start x position [m]
+            s_y: start y position [m]
             gx: goal x position [m]
             gy: goal y position [m]
 
@@ -170,7 +170,7 @@ def calc_final_path(self, ngoal, closedset):
         # generate final course
         rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
             self.calc_grid_position(ngoal.y, self.miny)]
-        n = closedset[ngoal.pind]
+        n = closedset[ngoal.parent_index]
         while n is not None:
             rx.append(self.calc_grid_position(n.x, self.minx))
             ry.append(self.calc_grid_position(n.y, self.miny))
@@ -220,15 +220,15 @@ def calc_obstacle_map(self, ox, oy):
         self.miny = round(min(oy))
         self.maxx = round(max(ox))
         self.maxy = round(max(oy))
-        print("minx:", self.minx)
-        print("miny:", self.miny)
-        print("maxx:", self.maxx)
-        print("maxy:", self.maxy)
+        print("min_x:", self.minx)
+        print("min_y:", self.miny)
+        print("max_x:", self.maxx)
+        print("max_y:", self.maxy)
 
         self.xwidth = round((self.maxx - self.minx) / self.reso)
         self.ywidth = round((self.maxy - self.miny) / self.reso)
-        print("xwidth:", self.xwidth)
-        print("ywidth:", self.ywidth)
+        print("x_width:", self.xwidth)
+        print("y_width:", self.ywidth)
 
         # obstacle map generation
         self.obmap = [[False for _ in range(self.ywidth)]
diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
index 198ddd2e3d..01f1a31d64 100644
--- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py
+++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
@@ -23,7 +23,7 @@ def __init__(self, ox, oy, reso, rr):
 
         ox: x position list of Obstacles [m]
         oy: y position list of Obstacles [m]
-        reso: grid resolution [m]
+        resolution: grid resolution [m]
         rr: robot radius[m]
         """
 
@@ -49,8 +49,8 @@ def planning(self, sx, sy, gx, gy):
         Breadth First search based planning
 
         input:
-            sx: start x position [m]
-            sy: start y position [m]
+            s_x: start x position [m]
+            s_y: start y position [m]
             gx: goal x position [m]
             gy: goal y position [m]
 
@@ -118,7 +118,7 @@ def calc_final_path(self, ngoal, closedset):
         # generate final course
         rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
             self.calc_grid_position(ngoal.y, self.miny)]
-        n = closedset[ngoal.pind]
+        n = closedset[ngoal.parent_index]
         while n is not None:
             rx.append(self.calc_grid_position(n.x, self.minx))
             ry.append(self.calc_grid_position(n.y, self.miny))
@@ -168,15 +168,15 @@ def calc_obstacle_map(self, ox, oy):
         self.miny = round(min(oy))
         self.maxx = round(max(ox))
         self.maxy = round(max(oy))
-        print("minx:", self.minx)
-        print("miny:", self.miny)
-        print("maxx:", self.maxx)
-        print("maxy:", self.maxy)
+        print("min_x:", self.minx)
+        print("min_y:", self.miny)
+        print("max_x:", self.maxx)
+        print("max_y:", self.maxy)
 
         self.xwidth = round((self.maxx - self.minx) / self.reso)
         self.ywidth = round((self.maxy - self.miny) / self.reso)
-        print("xwidth:", self.xwidth)
-        print("ywidth:", self.ywidth)
+        print("x_width:", self.xwidth)
+        print("y_width:", self.ywidth)
 
         # obstacle map generation
         self.obmap = [[False for _ in range(self.ywidth)]
diff --git a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
index a413a05d7f..f3984f87b0 100644
--- a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
+++ b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
@@ -40,7 +40,7 @@ def pure_pursuit_control(state, cx, cy, pind):
     if pind >= ind:
         ind = pind
 
-    #  print(pind, ind)
+    #  print(parent_index, ind)
     if ind < len(cx):
         tx = cx[ind]
         ty = cy[ind]
@@ -181,12 +181,12 @@ def set_stop_point(target_speed, cx, cy, cyaw):
             speed_profile[i] = 0.0
             forward = False
             #  plt.plot(cx[i], cy[i], "xb")
-            #  print(iyaw, move_direction, dx, dy)
+            #  print(i_yaw, move_direction, dx, dy)
         elif not is_back and not forward:
             speed_profile[i] = 0.0
             forward = True
             #  plt.plot(cx[i], cy[i], "xb")
-            #  print(iyaw, move_direction, dx, dy)
+            #  print(i_yaw, move_direction, dx, dy)
     speed_profile[0] = 0.0
     if is_back:
         speed_profile[-1] = -stop_speed
diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py
index 229eb70c2d..c8d2d5eaba 100644
--- a/PathPlanning/DepthFirstSearch/depth_first_search.py
+++ b/PathPlanning/DepthFirstSearch/depth_first_search.py
@@ -23,7 +23,7 @@ def __init__(self, ox, oy, reso, rr):
 
         ox: x position list of Obstacles [m]
         oy: y position list of Obstacles [m]
-        reso: grid resolution [m]
+        resolution: grid resolution [m]
         rr: robot radius[m]
         """
 
@@ -49,8 +49,8 @@ def planning(self, sx, sy, gx, gy):
         Depth First search
 
         input:
-            sx: start x position [m]
-            sy: start y position [m]
+            s_x: start x position [m]
+            s_y: start y position [m]
             gx: goal x position [m]
             gy: goal y position [m]
 
@@ -115,7 +115,7 @@ def calc_final_path(self, ngoal, closedset):
         # generate final course
         rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
             self.calc_grid_position(ngoal.y, self.miny)]
-        n = closedset[ngoal.pind]
+        n = closedset[ngoal.parent_index]
         while n is not None:
             rx.append(self.calc_grid_position(n.x, self.minx))
             ry.append(self.calc_grid_position(n.y, self.miny))
@@ -165,15 +165,15 @@ def calc_obstacle_map(self, ox, oy):
         self.miny = round(min(oy))
         self.maxx = round(max(ox))
         self.maxy = round(max(oy))
-        print("minx:", self.minx)
-        print("miny:", self.miny)
-        print("maxx:", self.maxx)
-        print("maxy:", self.maxy)
+        print("min_x:", self.minx)
+        print("min_y:", self.miny)
+        print("max_x:", self.maxx)
+        print("max_y:", self.maxy)
 
         self.xwidth = round((self.maxx - self.minx) / self.reso)
         self.ywidth = round((self.maxy - self.miny) / self.reso)
-        print("xwidth:", self.xwidth)
-        print("ywidth:", self.ywidth)
+        print("x_width:", self.xwidth)
+        print("y_width:", self.ywidth)
 
         # obstacle map generation
         self.obmap = [[False for _ in range(self.ywidth)]
diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py
index 0ece712ae7..b744bc6fc7 100644
--- a/PathPlanning/Dijkstra/dijkstra.py
+++ b/PathPlanning/Dijkstra/dijkstra.py
@@ -53,8 +53,8 @@ def planning(self, sx, sy, gx, gy):
         dijkstra path search
 
         input:
-            sx: start x position [m]
-            sy: start y position [m]
+            s_x: start x position [m]
+            s_y: start y position [m]
             gx: goal x position [m]
             gx: goal x position [m]
 
diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py
index b72801a6b3..9c13198aff 100644
--- a/PathPlanning/DubinsPath/dubins_path_planning.py
+++ b/PathPlanning/DubinsPath/dubins_path_planning.py
@@ -9,6 +9,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+from scipy.spatial.transform import Rotation as Rot
 
 show_animation = True
 
@@ -136,7 +137,8 @@ def left_right_left(alpha, beta, d):
     return t, p, q, mode
 
 
-def dubins_path_planning_from_origin(end_x, end_y, end_yaw, curvature, step_size):
+def dubins_path_planning_from_origin(end_x, end_y, end_yaw, curvature,
+                                     step_size):
     dx = end_x
     dy = end_y
     D = math.hypot(dx, dy)
@@ -146,7 +148,8 @@ def dubins_path_planning_from_origin(end_x, end_y, end_yaw, curvature, step_size
     alpha = mod2pi(- theta)
     beta = mod2pi(end_yaw - theta)
 
-    planners = [left_straight_left, right_straight_right, left_straight_right, right_straight_left, right_left_right,
+    planners = [left_straight_left, right_straight_right, left_straight_right,
+                right_straight_left, right_left_right,
                 left_right_left]
 
     best_cost = float("inf")
@@ -163,13 +166,14 @@ def dubins_path_planning_from_origin(end_x, end_y, end_yaw, curvature, step_size
             best_cost = cost
     lengths = [bt, bp, bq]
 
-    px, py, pyaw, directions = generate_local_course(
+    x_list, y_list, yaw_list, directions = generate_local_course(
         sum(lengths), lengths, best_mode, curvature, step_size)
 
-    return px, py, pyaw, best_mode, best_cost
+    return x_list, y_list, yaw_list, best_mode, best_cost
 
 
-def interpolate(ind, length, mode, max_curvature, origin_x, origin_y, origin_yaw, path_x, path_y, path_yaw, directions):
+def interpolate(ind, length, mode, max_curvature, origin_x, origin_y,
+                origin_yaw, path_x, path_y, path_yaw, directions):
     if mode == "S":
         path_x[ind] = origin_x + length / max_curvature * math.cos(origin_yaw)
         path_y[ind] = origin_y + length / max_curvature * math.sin(origin_yaw)
@@ -199,54 +203,49 @@ def interpolate(ind, length, mode, max_curvature, origin_x, origin_y, origin_yaw
     return path_x, path_y, path_yaw, directions
 
 
-def dubins_path_planning(sx, sy, syaw, ex, ey, eyaw, c, step_size=0.1):
+def dubins_path_planning(s_x, s_y, s_yaw, g_x, g_y, g_yaw, c, step_size=0.1):
     """
-    Dubins path plannner
+    Dubins path planner
 
     input:
-        sx x position of start point [m]
-        sy y position of start point [m]
-        syaw yaw angle of start point [rad]
-        ex x position of end point [m]
-        ey y position of end point [m]
-        eyaw yaw angle of end point [rad]
+        s_x x position of start point [m]
+        s_y y position of start point [m]
+        s_yaw yaw angle of start point [rad]
+        g_x x position of end point [m]
+        g_y y position of end point [m]
+        g_yaw yaw angle of end point [rad]
         c curvature [1/m]
 
-    output:
-        px
-        py
-        pyaw
-        mode
-
     """
 
-    ex = ex - sx
-    ey = ey - sy
+    g_x = g_x - s_x
+    g_y = g_y - s_y
 
-    lex = math.cos(syaw) * ex + math.sin(syaw) * ey
-    ley = - math.sin(syaw) * ex + math.cos(syaw) * ey
-    leyaw = eyaw - syaw
+    l_rot = Rot.from_euler('z', s_yaw).as_matrix()[0:2, 0:2]
+    le_xy = np.stack([g_x, g_y]).T @ l_rot
+    le_yaw = g_yaw - s_yaw
 
-    lpx, lpy, lpyaw, mode, clen = dubins_path_planning_from_origin(
-        lex, ley, leyaw, c, step_size)
+    lp_x, lp_y, lp_yaw, mode, lengths = dubins_path_planning_from_origin(
+        le_xy[0], le_xy[1], le_yaw, c, step_size)
 
-    px = [math.cos(-syaw) * x + math.sin(-syaw)
-          * y + sx for x, y in zip(lpx, lpy)]
-    py = [- math.sin(-syaw) * x + math.cos(-syaw)
-          * y + sy for x, y in zip(lpx, lpy)]
-    pyaw = [pi_2_pi(iyaw + syaw) for iyaw in lpyaw]
+    rot = Rot.from_euler('z', -s_yaw).as_matrix()[0:2, 0:2]
+    converted_xy = np.stack([lp_x, lp_y]).T @ rot
+    x_list = converted_xy[:, 0] + s_x
+    y_list = converted_xy[:, 1] + s_y
+    yaw_list = [pi_2_pi(i_yaw + s_yaw) for i_yaw in lp_yaw]
 
-    return px, py, pyaw, mode, clen
+    return x_list, y_list, yaw_list, mode, lengths
 
 
-def generate_local_course(total_length, lengths, mode, max_curvature, step_size):
+def generate_local_course(total_length, lengths, mode, max_curvature,
+                          step_size):
     n_point = math.trunc(total_length / step_size) + len(lengths) + 4
 
     path_x = [0.0 for _ in range(n_point)]
     path_y = [0.0 for _ in range(n_point)]
     path_yaw = [0.0 for _ in range(n_point)]
     directions = [0.0 for _ in range(n_point)]
-    ind = 1
+    index = 1
 
     if lengths[0] > 0.0:
         directions[0] = 1
@@ -262,25 +261,28 @@ def generate_local_course(total_length, lengths, mode, max_curvature, step_size)
             d = -step_size
 
         # set origin state
-        origin_x, origin_y, origin_yaw = path_x[ind], path_y[ind], path_yaw[ind]
+        origin_x, origin_y, origin_yaw = \
+            path_x[index], path_y[index], path_yaw[index]
 
-        ind -= 1
+        index -= 1
         if i >= 1 and (lengths[i - 1] * lengths[i]) > 0:
             pd = - d - ll
         else:
             pd = d - ll
 
         while abs(pd) <= abs(l):
-            ind += 1
+            index += 1
             path_x, path_y, path_yaw, directions = interpolate(
-                ind, pd, m, max_curvature, origin_x, origin_y, origin_yaw, path_x, path_y, path_yaw, directions)
+                index, pd, m, max_curvature, origin_x, origin_y, origin_yaw,
+                path_x, path_y, path_yaw, directions)
             pd += d
 
         ll = l - pd - d  # calc remain length
 
-        ind += 1
+        index += 1
         path_x, path_y, path_yaw, directions = interpolate(
-            ind, l, m, max_curvature, origin_x, origin_y, origin_yaw, path_x, path_y, path_yaw, directions)
+            index, l, m, max_curvature, origin_x, origin_y, origin_yaw,
+            path_x, path_y, path_yaw, directions)
 
     if len(path_x) <= 1:
         return [], [], [], []
@@ -295,14 +297,15 @@ def generate_local_course(total_length, lengths, mode, max_curvature, step_size)
     return path_x, path_y, path_yaw, directions
 
 
-def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):  # pragma: no cover
+def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r",
+               ec="k"):  # pragma: no cover
     """
     Plot arrow
     """
 
     if not isinstance(x, float):
-        for (ix, iy, iyaw) in zip(x, y, yaw):
-            plot_arrow(ix, iy, iyaw)
+        for (i_x, i_y, i_yaw) in zip(x, y, yaw):
+            plot_arrow(i_x, i_y, i_yaw)
     else:
         plt.arrow(x, y, length * math.cos(yaw), length * math.sin(yaw),
                   fc=fc, ec=ec, head_width=width, head_length=width)
@@ -322,11 +325,12 @@ def main():
 
     curvature = 1.0
 
-    px, py, pyaw, mode, clen = dubins_path_planning(start_x, start_y, start_yaw,
-                                                    end_x, end_y, end_yaw, curvature)
+    path_x, path_y, path_yaw, mode, path_length = dubins_path_planning(
+        start_x, start_y, start_yaw,
+        end_x, end_y, end_yaw, curvature)
 
     if show_animation:
-        plt.plot(px, py, label="final course " + "".join(mode))
+        plt.plot(path_x, path_y, label="final course " + "".join(mode))
 
         # plotting
         plot_arrow(start_x, start_y, start_yaw)
diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index b8395e5ff8..0df40410ab 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -41,11 +41,11 @@ def __init__(self):
         # robot parameter
         self.max_speed = 1.0  # [m/s]
         self.min_speed = -0.5  # [m/s]
-        self.max_yawrate = 40.0 * math.pi / 180.0  # [rad/s]
+        self.max_yaw_rate = 40.0 * math.pi / 180.0  # [rad/s]
         self.max_accel = 0.2  # [m/ss]
-        self.max_dyawrate = 40.0 * math.pi / 180.0  # [rad/ss]
-        self.v_reso = 0.01  # [m/s]
-        self.yawrate_reso = 0.1 * math.pi / 180.0  # [rad/s]
+        self.max_delta_yaw_rate = 40.0 * math.pi / 180.0  # [rad/ss]
+        self.v_resolution = 0.01  # [m/s]
+        self.yaw_rate_resolution = 0.1 * math.pi / 180.0  # [rad/s]
         self.dt = 0.1  # [s] Time tick for motion prediction
         self.predict_time = 3.0  # [s]
         self.to_goal_cost_gain = 0.15
@@ -93,15 +93,15 @@ def calc_dynamic_window(x, config):
 
     # Dynamic window from robot specification
     Vs = [config.min_speed, config.max_speed,
-          -config.max_yawrate, config.max_yawrate]
+          -config.max_yaw_rate, config.max_yaw_rate]
 
     # Dynamic window from motion model
     Vd = [x[3] - config.max_accel * config.dt,
           x[3] + config.max_accel * config.dt,
-          x[4] - config.max_dyawrate * config.dt,
-          x[4] + config.max_dyawrate * config.dt]
+          x[4] - config.max_delta_yaw_rate * config.dt,
+          x[4] + config.max_delta_yaw_rate * config.dt]
 
-    #  [vmin, vmax, yaw_rate min, yaw_rate max]
+    #  [v_min, v_max, yaw_rate_min, yaw_rate_max]
     dw = [max(Vs[0], Vd[0]), min(Vs[1], Vd[1]),
           max(Vs[2], Vd[2]), min(Vs[3], Vd[3])]
 
@@ -114,14 +114,14 @@ def predict_trajectory(x_init, v, y, config):
     """
 
     x = np.array(x_init)
-    traj = np.array(x)
+    trajectory = np.array(x)
     time = 0
     while time <= config.predict_time:
         x = motion(x, [v, y], config.dt)
-        traj = np.vstack((traj, x))
+        trajectory = np.vstack((trajectory, x))
         time += config.dt
 
-    return traj
+    return trajectory
 
 
 def calc_control_and_trajectory(x, dw, config, goal, ob):
@@ -135,8 +135,8 @@ def calc_control_and_trajectory(x, dw, config, goal, ob):
     best_trajectory = np.array([x])
 
     # evaluate all trajectory with sampled input in dynamic window
-    for v in np.arange(dw[0], dw[1], config.v_reso):
-        for y in np.arange(dw[2], dw[3], config.yawrate_reso):
+    for v in np.arange(dw[0], dw[1], config.v_resolution):
+        for y in np.arange(dw[2], dw[3], config.yaw_rate_resolution):
 
             trajectory = predict_trajectory(x_init, v, y, config)
 
@@ -182,7 +182,7 @@ def calc_obstacle_cost(trajectory, ob, config):
                            np.logical_and(bottom_check, left_check))).any():
             return float("Inf")
     elif config.robot_type == RobotType.circle:
-        if (r <= config.robot_radius).any():
+        if np.array(r <= config.robot_radius).any():
             return float("Inf")
 
     min_r = np.min(r)
@@ -269,8 +269,9 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle):
         if show_animation:
             plt.cla()
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(predicted_trajectory[:, 0], predicted_trajectory[:, 1], "-g")
             plt.plot(x[0], x[1], "xr")
             plt.plot(goal[0], goal[1], "xb")
@@ -296,4 +297,5 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle):
 
 
 if __name__ == '__main__':
-    main(robot_type=RobotType.circle)
+    main(robot_type=RobotType.rectangle)
+    # main(robot_type=RobotType.circle)
diff --git a/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py b/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py
index c29e37e896..7100b0e604 100644
--- a/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py
+++ b/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py
@@ -23,7 +23,7 @@ def __init__(self, ox, oy, reso, rr):
 
         ox: x position list of Obstacles [m]
         oy: y position list of Obstacles [m]
-        reso: grid resolution [m]
+        resolution: grid resolution [m]
         rr: robot radius[m]
         """
 
@@ -49,8 +49,8 @@ def planning(self, sx, sy, gx, gy):
         Greedy Best-First search
 
         input:
-            sx: start x position [m]
-            sy: start y position [m]
+            s_x: start x position [m]
+            s_y: start y position [m]
             gx: goal x position [m]
             gy: goal y position [m]
 
@@ -132,7 +132,7 @@ def calc_final_path(self, ngoal, closedset):
         # generate final course
         rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
             self.calc_grid_position(ngoal.y, self.miny)]
-        n = closedset[ngoal.pind]
+        n = closedset[ngoal.parent_index]
         while n is not None:
             rx.append(self.calc_grid_position(n.x, self.minx))
             ry.append(self.calc_grid_position(n.y, self.miny))
@@ -188,15 +188,15 @@ def calc_obstacle_map(self, ox, oy):
         self.miny = round(min(oy))
         self.maxx = round(max(ox))
         self.maxy = round(max(oy))
-        print("minx:", self.minx)
-        print("miny:", self.miny)
-        print("maxx:", self.maxx)
-        print("maxy:", self.maxy)
+        print("min_x:", self.minx)
+        print("min_y:", self.miny)
+        print("max_x:", self.maxx)
+        print("max_y:", self.maxy)
 
         self.xwidth = round((self.maxx - self.minx) / self.reso)
         self.ywidth = round((self.maxy - self.miny) / self.reso)
-        print("xwidth:", self.xwidth)
-        print("ywidth:", self.ywidth)
+        print("x_width:", self.xwidth)
+        print("y_width:", self.ywidth)
 
         # obstacle map generation
         self.obmap = [[False for _ in range(self.ywidth)]
diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
index 8d9ccd2e2f..09df21c4c8 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -11,6 +11,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+from scipy.spatial.transform import Rotation as Rot
 
 sys.path.append(os.path.relpath("../../Mapping/grid_map_lib/"))
 try:
@@ -36,52 +37,55 @@ def __init__(self,
         self.sweep_direction = sweep_direction
         self.turing_window = []
         self.update_turning_window()
-        self.xinds_goaly = x_inds_goal_y
-        self.goaly = goal_y
+        self.x_indexes_goal_y = x_inds_goal_y
+        self.goal_y = goal_y
 
-    def move_target_grid(self, cxind, cyind, gmap):
-        nxind = self.moving_direction + cxind
-        nyind = cyind
+    def move_target_grid(self, c_x_index, c_y_index, grid_map):
+        n_x_index = self.moving_direction + c_x_index
+        n_y_index = c_y_index
 
         # found safe grid
-        if not gmap.check_occupied_from_xy_index(nxind, nyind,
-                                                 occupied_val=0.5):
-            return nxind, nyind
+        if not grid_map.check_occupied_from_xy_index(n_x_index, n_y_index,
+                                                     occupied_val=0.5):
+            return n_x_index, n_y_index
         else:  # occupied
-            ncxind, ncyind = self.find_safe_turning_grid(cxind, cyind, gmap)
-            if (ncxind is None) and (ncyind is None):
+            next_c_x_index, next_c_y_index = self.find_safe_turning_grid(
+                c_x_index, c_y_index, grid_map)
+            if (next_c_x_index is None) and (next_c_y_index is None):
                 # moving backward
-                ncxind = -self.moving_direction + cxind
-                ncyind = cyind
-                if gmap.check_occupied_from_xy_index(ncxind, ncyind):
+                next_c_x_index = -self.moving_direction + c_x_index
+                next_c_y_index = c_y_index
+                if grid_map.check_occupied_from_xy_index(next_c_x_index,
+                                                         next_c_y_index):
                     # moved backward, but the grid is occupied by obstacle
                     return None, None
             else:
                 # keep moving until end
-                while not gmap.check_occupied_from_xy_index(
-                        ncxind + self.moving_direction, ncyind,
-                        occupied_val=0.5):
-                    ncxind += self.moving_direction
+                while not grid_map.check_occupied_from_xy_index(
+                        next_c_x_index + self.moving_direction,
+                        next_c_y_index, occupied_val=0.5):
+                    next_c_x_index += self.moving_direction
                 self.swap_moving_direction()
-            return ncxind, ncyind
+            return next_c_x_index, next_c_y_index
 
-    def find_safe_turning_grid(self, cxind, cyind, gmap):
+    def find_safe_turning_grid(self, c_x_index, c_y_index, grid_map):
 
         for (d_x_ind, d_y_ind) in self.turing_window:
 
-            next_x_ind = d_x_ind + cxind
-            next_y_ind = d_y_ind + cyind
+            next_x_ind = d_x_ind + c_x_index
+            next_y_ind = d_y_ind + c_y_index
 
             # found safe grid
-            if not gmap.check_occupied_from_xy_index(next_x_ind, next_y_ind,
-                                                     occupied_val=0.5):
+            if not grid_map.check_occupied_from_xy_index(next_x_ind,
+                                                         next_y_ind,
+                                                         occupied_val=0.5):
                 return next_x_ind, next_y_ind
 
         return None, None
 
     def is_search_done(self, grid_map):
-        for ix in self.xinds_goaly:
-            if not grid_map.check_occupied_from_xy_index(ix, self.goaly,
+        for ix in self.x_indexes_goal_y:
+            if not grid_map.check_occupied_from_xy_index(ix, self.goal_y,
                                                          occupied_val=0.5):
                 return False
 
@@ -138,64 +142,54 @@ def find_sweep_direction_and_start_position(ox, oy):
     return vec, sweep_start_pos
 
 
-def convert_grid_coordinate(ox, oy, sweep_vec, sweep_start_posi):
-    tx = [ix - sweep_start_posi[0] for ix in ox]
-    ty = [iy - sweep_start_posi[1] for iy in oy]
-
+def convert_grid_coordinate(ox, oy, sweep_vec, sweep_start_position):
+    tx = [ix - sweep_start_position[0] for ix in ox]
+    ty = [iy - sweep_start_position[1] for iy in oy]
     th = math.atan2(sweep_vec[1], sweep_vec[0])
+    rot = Rot.from_euler('z', th).as_matrix()[0:2, 0:2]
+    converted_xy = np.stack([tx, ty]).T @ rot
 
-    c = np.cos(-th)
-    s = np.sin(-th)
-
-    rx = [ix * c - iy * s for (ix, iy) in zip(tx, ty)]
-    ry = [ix * s + iy * c for (ix, iy) in zip(tx, ty)]
-
-    return rx, ry
+    return converted_xy[:, 0], converted_xy[:, 1]
 
 
-def convert_global_coordinate(x, y, sweep_vec, sweep_start_posi):
+def convert_global_coordinate(x, y, sweep_vec, sweep_start_position):
     th = math.atan2(sweep_vec[1], sweep_vec[0])
-    c = np.cos(th)
-    s = np.sin(th)
-
-    tx = [ix * c - iy * s for (ix, iy) in zip(x, y)]
-    ty = [ix * s + iy * c for (ix, iy) in zip(x, y)]
-
-    rx = [ix + sweep_start_posi[0] for ix in tx]
-    ry = [iy + sweep_start_posi[1] for iy in ty]
-
+    rot = Rot.from_euler('z', -th).as_matrix()[0:2, 0:2]
+    converted_xy = np.stack([x, y]).T @ rot
+    rx = [ix + sweep_start_position[0] for ix in converted_xy[:, 0]]
+    ry = [iy + sweep_start_position[1] for iy in converted_xy[:, 1]]
     return rx, ry
 
 
 def search_free_grid_index_at_edge_y(grid_map, from_upper=False):
-    yind = None
-    xinds = []
+    y_index = None
+    x_indexes = []
 
     if from_upper:
-        xrange = range(grid_map.height)[::-1]
-        yrange = range(grid_map.width)[::-1]
+        x_range = range(grid_map.height)[::-1]
+        y_range = range(grid_map.width)[::-1]
     else:
-        xrange = range(grid_map.height)
-        yrange = range(grid_map.width)
+        x_range = range(grid_map.height)
+        y_range = range(grid_map.width)
 
-    for iy in xrange:
-        for ix in yrange:
+    for iy in x_range:
+        for ix in y_range:
             if not grid_map.check_occupied_from_xy_index(ix, iy):
-                yind = iy
-                xinds.append(ix)
-        if yind:
+                y_index = iy
+                x_indexes.append(ix)
+        if y_index:
             break
 
-    return xinds, yind
+    return x_indexes, y_index
 
 
-def setup_grid_map(ox, oy, reso, sweep_direction, offset_grid=10):
-    width = math.ceil((max(ox) - min(ox)) / reso) + offset_grid
-    height = math.ceil((max(oy) - min(oy)) / reso) + offset_grid
-    center_x = (np.max(ox)+np.min(ox))/2.0
-    center_y = (np.max(oy)+np.min(oy))/2.0
+def setup_grid_map(ox, oy, resolution, sweep_direction, offset_grid=10):
+    width = math.ceil((max(ox) - min(ox)) / resolution) + offset_grid
+    height = math.ceil((max(oy) - min(oy)) / resolution) + offset_grid
+    center_x = (np.max(ox) + np.min(ox)) / 2.0
+    center_y = (np.max(oy) + np.min(oy)) / 2.0
 
-    grid_map = GridMap(width, height, reso, center_x, center_y)
+    grid_map = GridMap(width, height, resolution, center_x, center_y)
     grid_map.print_grid_map_info()
     grid_map.set_value_from_polygon(ox, oy, 1.0, inside=False)
     grid_map.expand_grid()
@@ -203,48 +197,51 @@ def setup_grid_map(ox, oy, reso, sweep_direction, offset_grid=10):
     x_inds_goal_y = []
     goal_y = 0
     if sweep_direction == SweepSearcher.SweepDirection.UP:
-        x_inds_goal_y, goal_y = search_free_grid_index_at_edge_y(grid_map,
-                                                              from_upper=True)
+        x_inds_goal_y, goal_y = search_free_grid_index_at_edge_y(
+            grid_map, from_upper=True)
     elif sweep_direction == SweepSearcher.SweepDirection.DOWN:
-        x_inds_goal_y, goal_y = search_free_grid_index_at_edge_y(grid_map,
-                                                              from_upper=False)
+        x_inds_goal_y, goal_y = search_free_grid_index_at_edge_y(
+            grid_map, from_upper=False)
 
     return grid_map, x_inds_goal_y, goal_y
 
 
 def sweep_path_search(sweep_searcher, grid_map, grid_search_animation=False):
     # search start grid
-    cxind, cyind = sweep_searcher.search_start_grid(grid_map)
-    if not grid_map.set_value_from_xy_index(cxind, cyind, 0.5):
+    c_x_index, c_y_index = sweep_searcher.search_start_grid(grid_map)
+    if not grid_map.set_value_from_xy_index(c_x_index, c_y_index, 0.5):
         print("Cannot find start grid")
         return [], []
 
-    x, y = grid_map.calc_grid_central_xy_position_from_xy_index(cxind, cyind)
+    x, y = grid_map.calc_grid_central_xy_position_from_xy_index(c_x_index,
+                                                                c_y_index)
     px, py = [x], [y]
 
     fig, ax = None, None
     if grid_search_animation:
         fig, ax = plt.subplots()
         # for stopping simulation with the esc key.
-        fig.canvas.mpl_connect('key_release_event',
-                               lambda event: [
-                                   exit(0) if event.key == 'escape' else None])
+        fig.canvas.mpl_connect(
+            'key_release_event',
+            lambda event: [exit(0) if event.key == 'escape' else None])
 
     while True:
-        cxind, cyind = sweep_searcher.move_target_grid(cxind, cyind, grid_map)
+        c_x_index, c_y_index = sweep_searcher.move_target_grid(c_x_index,
+                                                               c_y_index,
+                                                               grid_map)
 
         if sweep_searcher.is_search_done(grid_map) or (
-                cxind is None or cyind is None):
+                c_x_index is None or c_y_index is None):
             print("Done")
             break
 
         x, y = grid_map.calc_grid_central_xy_position_from_xy_index(
-            cxind, cyind)
+            c_x_index, c_y_index)
 
         px.append(x)
         py.append(y)
 
-        grid_map.set_value_from_xy_index(cxind, cyind, 0.5)
+        grid_map.set_value_from_xy_index(c_x_index, c_y_index, 0.5)
 
         if grid_search_animation:
             grid_map.plot_grid_map(ax=ax)
@@ -255,32 +252,34 @@ def sweep_path_search(sweep_searcher, grid_map, grid_search_animation=False):
     return px, py
 
 
-def planning(ox, oy, reso,
+def planning(ox, oy, resolution,
              moving_direction=SweepSearcher.MovingDirection.RIGHT,
              sweeping_direction=SweepSearcher.SweepDirection.UP,
              ):
-    sweep_vec, sweep_start_posi = find_sweep_direction_and_start_position(
+    sweep_vec, sweep_start_position = find_sweep_direction_and_start_position(
         ox, oy)
 
-    rox, roy = convert_grid_coordinate(ox, oy, sweep_vec, sweep_start_posi)
+    rox, roy = convert_grid_coordinate(ox, oy, sweep_vec,
+                                       sweep_start_position)
 
-    gmap, xinds_goaly, goaly = setup_grid_map(rox, roy, reso,
-                                              sweeping_direction)
+    grid_map, x_inds_goal_y, goal_y = setup_grid_map(rox, roy, resolution,
+                                                     sweeping_direction)
 
     sweep_searcher = SweepSearcher(moving_direction, sweeping_direction,
-                                   xinds_goaly, goaly)
+                                   x_inds_goal_y, goal_y)
 
-    px, py = sweep_path_search(sweep_searcher, gmap)
+    px, py = sweep_path_search(sweep_searcher, grid_map)
 
-    rx, ry = convert_global_coordinate(px, py, sweep_vec, sweep_start_posi)
+    rx, ry = convert_global_coordinate(px, py, sweep_vec,
+                                       sweep_start_position)
 
     print("Path length:", len(rx))
 
     return rx, ry
 
 
-def planning_animation(ox, oy, reso):  # pragma: no cover
-    px, py = planning(ox, oy, reso)
+def planning_animation(ox, oy, resolution):  # pragma: no cover
+    px, py = planning(ox, oy, resolution)
 
     # animation
     if do_animation:
@@ -311,18 +310,18 @@ def main():  # pragma: no cover
 
     ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0, 0.0]
     oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0, 0.0]
-    reso = 5.0
-    planning_animation(ox, oy, reso)
+    resolution = 5.0
+    planning_animation(ox, oy, resolution)
 
     ox = [0.0, 50.0, 50.0, 0.0, 0.0]
     oy = [0.0, 0.0, 30.0, 30.0, 0.0]
-    reso = 1.3
-    planning_animation(ox, oy, reso)
+    resolution = 1.3
+    planning_animation(ox, oy, resolution)
 
     ox = [0.0, 20.0, 50.0, 200.0, 130.0, 40.0, 0.0]
     oy = [0.0, -80.0, 0.0, 30.0, 60.0, 80.0, 0.0]
-    reso = 5.0
-    planning_animation(ox, oy, reso)
+    resolution = 5.0
+    planning_animation(ox, oy, resolution)
 
     plt.show()
     print("done!!")
diff --git a/PathPlanning/HybridAStar/a_star_heuristic.py b/PathPlanning/HybridAStar/a_star_heuristic.py
index a27293288c..36c3342462 100644
--- a/PathPlanning/HybridAStar/a_star_heuristic.py
+++ b/PathPlanning/HybridAStar/a_star_heuristic.py
@@ -8,8 +8,9 @@
 
 """
 
-import math
 import heapq
+import math
+
 import matplotlib.pyplot as plt
 
 show_animation = False
@@ -17,71 +18,73 @@
 
 class Node:
 
-    def __init__(self, x, y, cost, pind):
+    def __init__(self, x, y, cost, parent_index):
         self.x = x
         self.y = y
         self.cost = cost
-        self.pind = pind
+        self.parent_index = parent_index
 
     def __str__(self):
-        return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind)
+        return str(self.x) + "," + str(self.y) + "," + str(
+            self.cost) + "," + str(self.parent_index)
 
 
-def calc_final_path(ngoal, closedset, reso):
+def calc_final_path(goal_node, closed_node_set, resolution):
     # generate final course
-    rx, ry = [ngoal.x * reso], [ngoal.y * reso]
-    pind = ngoal.pind
-    while pind != -1:
-        n = closedset[pind]
-        rx.append(n.x * reso)
-        ry.append(n.y * reso)
-        pind = n.pind
+    rx, ry = [goal_node.x * resolution], [goal_node.y * resolution]
+    parent_index = goal_node.parent_index
+    while parent_index != -1:
+        n = closed_node_set[parent_index]
+        rx.append(n.x * resolution)
+        ry.append(n.y * resolution)
+        parent_index = n.parent_index
 
     return rx, ry
 
 
-def dp_planning(sx, sy, gx, gy, ox, oy, reso, rr):
+def dp_planning(sx, sy, gx, gy, ox, oy, resolution, rr):
     """
     gx: goal x position [m]
     gx: goal x position [m]
     ox: x position list of Obstacles [m]
     oy: y position list of Obstacles [m]
-    reso: grid resolution [m]
+    resolution: grid resolution [m]
     rr: robot radius[m]
     """
 
-    nstart = Node(round(sx / reso), round(sy / reso), 0.0, -1)
-    ngoal = Node(round(gx / reso), round(gy / reso), 0.0, -1)
-    ox = [iox / reso for iox in ox]
-    oy = [ioy / reso for ioy in oy]
+    start_node = Node(round(sx / resolution), round(sy / resolution), 0.0, -1)
+    goal_node = Node(round(gx / resolution), round(gy / resolution), 0.0, -1)
+    ox = [iox / resolution for iox in ox]
+    oy = [ioy / resolution for ioy in oy]
 
-    obmap, minx, miny, maxx, maxy, xw, yw = calc_obstacle_map(ox, oy, reso, rr)
+    obstacle_map, min_x, min_y, max_x, max_y, x_w, y_w = calc_obstacle_map(
+        ox, oy, resolution, rr)
 
     motion = get_motion_model()
 
-    openset, closedset = dict(), dict()
-    openset[calc_index(ngoal, xw, minx, miny)] = ngoal
-    pq = []
-    pq.append((0, calc_index(ngoal, xw, minx, miny)))
+    open_set, closed_set = dict(), dict()
+    open_set[calc_index(goal_node, x_w, min_x, min_y)] = goal_node
+    priority_queue = [(0, calc_index(goal_node, x_w, min_x, min_y))]
 
     while 1:
-        if not pq:
+        if not priority_queue:
             break
-        cost, c_id = heapq.heappop(pq)
-        if c_id in openset:
-            current = openset[c_id]
-            closedset[c_id] = current
-            openset.pop(c_id)
+        cost, c_id = heapq.heappop(priority_queue)
+        if c_id in open_set:
+            current = open_set[c_id]
+            closed_set[c_id] = current
+            open_set.pop(c_id)
         else:
             continue
 
         # show graph
         if show_animation:  # pragma: no cover
-            plt.plot(current.x * reso, current.y * reso, "xc")
+            plt.plot(current.x * resolution, current.y * resolution, "xc")
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
-            if len(closedset.keys()) % 10 == 0:
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
+            if len(closed_set.keys()) % 10 == 0:
                 plt.pause(0.001)
 
         # Remove the item from the open set
@@ -91,82 +94,82 @@ def dp_planning(sx, sy, gx, gy, ox, oy, reso, rr):
             node = Node(current.x + motion[i][0],
                         current.y + motion[i][1],
                         current.cost + motion[i][2], c_id)
-            n_id = calc_index(node, xw, minx, miny)
+            n_id = calc_index(node, x_w, min_x, min_y)
 
-            if n_id in closedset:
+            if n_id in closed_set:
                 continue
 
-            if not verify_node(node, obmap, minx, miny, maxx, maxy):
+            if not verify_node(node, obstacle_map, min_x, min_y, max_x, max_y):
                 continue
 
-            if n_id not in openset:
-                openset[n_id] = node  # Discover a new node
+            if n_id not in open_set:
+                open_set[n_id] = node  # Discover a new node
                 heapq.heappush(
-                    pq, (node.cost, calc_index(node, xw, minx, miny)))
+                    priority_queue,
+                    (node.cost, calc_index(node, x_w, min_x, min_y)))
             else:
-                if openset[n_id].cost >= node.cost:
+                if open_set[n_id].cost >= node.cost:
                     # This path is the best until now. record it!
-                    openset[n_id] = node
+                    open_set[n_id] = node
                     heapq.heappush(
-                        pq, (node.cost, calc_index(node, xw, minx, miny)))
+                        priority_queue,
+                        (node.cost, calc_index(node, x_w, min_x, min_y)))
 
-    rx, ry = calc_final_path(closedset[calc_index(
-        nstart, xw, minx, miny)], closedset, reso)
+    rx, ry = calc_final_path(closed_set[calc_index(
+        start_node, x_w, min_x, min_y)], closed_set, resolution)
 
-    return rx, ry, closedset
+    return rx, ry, closed_set
 
 
 def calc_heuristic(n1, n2):
     w = 1.0  # weight of heuristic
-    d = w * math.sqrt((n1.x - n2.x)**2 + (n1.y - n2.y)**2)
+    d = w * math.sqrt((n1.x - n2.x) ** 2 + (n1.y - n2.y) ** 2)
     return d
 
 
-def verify_node(node, obmap, minx, miny, maxx, maxy):
-
-    if node.x < minx:
+def verify_node(node, obstacle_map, min_x, min_y, max_x, max_y):
+    if node.x < min_x:
         return False
-    elif node.y < miny:
+    elif node.y < min_y:
         return False
-    elif node.x >= maxx:
+    elif node.x >= max_x:
         return False
-    elif node.y >= maxy:
+    elif node.y >= max_y:
         return False
 
-    if obmap[node.x][node.y]:
+    if obstacle_map[node.x][node.y]:
         return False
 
     return True
 
 
-def calc_obstacle_map(ox, oy, reso, vr):
-
-    minx = round(min(ox))
-    miny = round(min(oy))
-    maxx = round(max(ox))
-    maxy = round(max(oy))
+def calc_obstacle_map(ox, oy, resolution, vr):
+    min_x = round(min(ox))
+    min_y = round(min(oy))
+    max_x = round(max(ox))
+    max_y = round(max(oy))
 
-    xwidth = round(maxx - minx)
-    ywidth = round(maxy - miny)
+    x_width = round(max_x - min_x)
+    y_width = round(max_y - min_y)
 
     # obstacle map generation
-    obmap = [[False for i in range(ywidth)] for i in range(xwidth)]
-    for ix in range(xwidth):
-        x = ix + minx
-        for iy in range(ywidth):
-            y = iy + miny
+    obstacle_map = [[False for _ in range(y_width)] for _ in range(x_width)]
+    for ix in range(x_width):
+        x = ix + min_x
+        for iy in range(y_width):
+            y = iy + min_y
             #  print(x, y)
             for iox, ioy in zip(ox, oy):
-                d = math.sqrt((iox - x)**2 + (ioy - y)**2)
-                if d <= vr / reso:
-                    obmap[ix][iy] = True
+                d = math.sqrt((iox - x) ** 2 + (ioy - y) ** 2)
+                if d <= vr / resolution:
+                    obstacle_map[ix][iy] = True
                     break
 
-    return obmap, minx, miny, maxx, maxy, xwidth, ywidth
+    return obstacle_map, min_x, min_y, max_x, max_y, x_width, y_width
 
 
-def calc_index(node, xwidth, xmin, ymin):
-    return (node.y - ymin) * xwidth + (node.x - xmin)
+def calc_index(node, x_width, x_min, y_min):
+    return (node.y - y_min) * x_width + (node.x - x_min)
 
 
 def get_motion_model():
diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py
index 13e50b2995..9b956a0877 100644
--- a/PathPlanning/HybridAStar/car.py
+++ b/PathPlanning/HybridAStar/car.py
@@ -6,9 +6,11 @@
 
 """
 
+from math import sqrt, cos, sin, tan, pi
 
 import matplotlib.pyplot as plt
-from math import sqrt, cos, sin, tan, pi
+import numpy as np
+from scipy.spatial.transform import Rotation as Rot
 
 WB = 3.  # rear to front wheel
 W = 2.  # width of car
@@ -16,25 +18,25 @@
 LB = 1.0  # distance from rear to vehicle back end
 MAX_STEER = 0.6  # [rad] maximum steering angle
 
-WBUBBLE_DIST = (LF - LB) / 2.0
-WBUBBLE_R = sqrt(((LF + LB) / 2.0)**2 + 1)
+W_BUBBLE_DIST = (LF - LB) / 2.0
+W_BUBBLE_R = sqrt(((LF + LB) / 2.0) ** 2 + 1)
 
-# vehicle rectangle verticles
+# vehicle rectangle vertices
 VRX = [LF, LF, -LB, -LB, LF]
 VRY = [W / 2, -W / 2, -W / 2, W / 2, W / 2]
 
 
-def check_car_collision(xlist, ylist, yawlist, ox, oy, kdtree):
-    for x, y, yaw in zip(xlist, ylist, yawlist):
-        cx = x + WBUBBLE_DIST * cos(yaw)
-        cy = y + WBUBBLE_DIST * sin(yaw)
+def check_car_collision(x_list, y_list, yaw_list, ox, oy, kd_tree):
+    for i_x, i_y, i_yaw in zip(x_list, y_list, yaw_list):
+        cx = i_x + W_BUBBLE_DIST * cos(i_yaw)
+        cy = i_y + W_BUBBLE_DIST * sin(i_yaw)
 
-        ids = kdtree.search_in_distance([cx, cy], WBUBBLE_R)
+        ids = kd_tree.search_in_distance([cx, cy], W_BUBBLE_R)
 
         if not ids:
             continue
 
-        if not rectangle_check(x, y, yaw,
+        if not rectangle_check(i_x, i_y, i_yaw,
                                [ox[i] for i in ids], [oy[i] for i in ids]):
             return False  # collision
 
@@ -43,12 +45,12 @@ def check_car_collision(xlist, ylist, yawlist, ox, oy, kdtree):
 
 def rectangle_check(x, y, yaw, ox, oy):
     # transform obstacles to base link frame
-    c, s = cos(-yaw), sin(-yaw)
+    rot = Rot.from_euler('z', yaw).as_matrix()[0:2, 0:2]
     for iox, ioy in zip(ox, oy):
         tx = iox - x
         ty = ioy - y
-        rx = c * tx - s * ty
-        ry = s * tx + c * ty
+        converted_xy = np.stack([tx, ty]).T @ rot
+        rx, ry = converted_xy[0], converted_xy[1]
 
         if not (rx > LF or rx < -LB or ry > W / 2.0 or ry < -W / 2.0):
             return False  # no collision
@@ -59,24 +61,22 @@ def rectangle_check(x, y, yaw, ox, oy):
 def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
     """Plot arrow."""
     if not isinstance(x, float):
-        for (ix, iy, iyaw) in zip(x, y, yaw):
-            plot_arrow(ix, iy, iyaw)
+        for (i_x, i_y, i_yaw) in zip(x, y, yaw):
+            plot_arrow(i_x, i_y, i_yaw)
     else:
         plt.arrow(x, y, length * cos(yaw), length * sin(yaw),
                   fc=fc, ec=ec, head_width=width, head_length=width, alpha=0.4)
-        # plt.plot(x, y)
 
 
 def plot_car(x, y, yaw):
     car_color = '-k'
     c, s = cos(yaw), sin(yaw)
-
+    rot = Rot.from_euler('z', yaw).as_matrix()[0:2, 0:2]
     car_outline_x, car_outline_y = [], []
     for rx, ry in zip(VRX, VRY):
-        tx = c * rx - s * ry + x
-        ty = s * rx + c * ry + y
-        car_outline_x.append(tx)
-        car_outline_y.append(ty)
+        converted_xy = np.stack([rx, ry]).T @ rot
+        car_outline_x.append(converted_xy[0]+x)
+        car_outline_y.append(converted_xy[1]+y)
 
     arrow_x, arrow_y, arrow_yaw = c * 1.5 + x, s * 1.5 + y, yaw
     plot_arrow(arrow_x, arrow_y, arrow_yaw)
@@ -96,8 +96,12 @@ def move(x, y, yaw, distance, steer, L=WB):
     return x, y, yaw
 
 
-if __name__ == '__main__':
+def main():
     x, y, yaw = 0., 0., 1.
     plt.axis('equal')
     plot_car(x, y, yaw)
-    plt.show()
\ No newline at end of file
+    plt.show()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
index 14e4ac5aec..ff9b3fe43b 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -7,28 +7,27 @@
 """
 
 import heapq
-import scipy.spatial
-import numpy as np
 import math
-import matplotlib.pyplot as plt
-import sys
 import os
+import sys
+
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy.spatial
 
 sys.path.append(os.path.dirname(os.path.abspath(__file__))
                 + "/../ReedsSheppPath")
 try:
-    from a_star_heuristic import dp_planning  # , calc_obstacle_map
+    from a_star_heuristic import dp_planning
     import reeds_shepp_path_planning as rs
     from car import move, check_car_collision, MAX_STEER, WB, plot_car
 except Exception:
     raise
 
-
 XY_GRID_RESOLUTION = 2.0  # [m]
 YAW_GRID_RESOLUTION = np.deg2rad(15.0)  # [rad]
-MOTION_RESOLUTION = 0.1  # [m] path interporate resolution
+MOTION_RESOLUTION = 0.1  # [m] path interpolate resolution
 N_STEER = 20  # number of steer command
-H_COST = 1.0
 VR = 1.0  # robot radius
 
 SB_COST = 100.0  # switch back penalty cost
@@ -42,29 +41,29 @@
 
 class Node:
 
-    def __init__(self, xind, yind, yawind, direction,
-                 xlist, ylist, yawlist, directions,
-                 steer=0.0, pind=None, cost=None):
-        self.xind = xind
-        self.yind = yind
-        self.yawind = yawind
+    def __init__(self, x_ind, y_ind, yaw_ind, direction,
+                 x_list, y_list, yaw_list, directions,
+                 steer=0.0, parent_index=None, cost=None):
+        self.x_index = x_ind
+        self.y_index = y_ind
+        self.yaw_index = yaw_ind
         self.direction = direction
-        self.xlist = xlist
-        self.ylist = ylist
-        self.yawlist = yawlist
+        self.x_list = x_list
+        self.y_list = y_list
+        self.yaw_list = yaw_list
         self.directions = directions
         self.steer = steer
-        self.pind = pind
+        self.parent_index = parent_index
         self.cost = cost
 
 
 class Path:
 
-    def __init__(self, xlist, ylist, yawlist, directionlist, cost):
-        self.xlist = xlist
-        self.ylist = ylist
-        self.yawlist = yawlist
-        self.directionlist = directionlist
+    def __init__(self, x_list, y_list, yaw_list, direction_list, cost):
+        self.x_list = x_list
+        self.y_list = y_list
+        self.yaw_list = yaw_list
+        self.direction_list = direction_list
         self.cost = cost
 
 
@@ -88,9 +87,9 @@ def search(self, inp, k=1):
             dist = []
 
             for i in inp.T:
-                idist, iindex = self.tree.query(i, k=k)
-                index.append(iindex)
-                dist.append(idist)
+                i_dist, i_index = self.tree.query(i, k=k)
+                index.append(i_index)
+                dist.append(i_dist)
 
             return index, dist
 
@@ -108,7 +107,7 @@ def search_in_distance(self, inp, r):
 
 class Config:
 
-    def __init__(self, ox, oy, xyreso, yawreso):
+    def __init__(self, ox, oy, xy_resolution, yaw_resolution):
         min_x_m = min(ox)
         min_y_m = min(oy)
         max_x_m = max(ox)
@@ -119,94 +118,93 @@ def __init__(self, ox, oy, xyreso, yawreso):
         ox.append(max_x_m)
         oy.append(max_y_m)
 
-        self.minx = round(min_x_m / xyreso)
-        self.miny = round(min_y_m / xyreso)
-        self.maxx = round(max_x_m / xyreso)
-        self.maxy = round(max_y_m / xyreso)
+        self.min_x = round(min_x_m / xy_resolution)
+        self.min_y = round(min_y_m / xy_resolution)
+        self.max_x = round(max_x_m / xy_resolution)
+        self.max_y = round(max_y_m / xy_resolution)
 
-        self.xw = round(self.maxx - self.minx)
-        self.yw = round(self.maxy - self.miny)
+        self.x_w = round(self.max_x - self.min_x)
+        self.y_w = round(self.max_y - self.min_y)
 
-        self.minyaw = round(- math.pi / yawreso) - 1
-        self.maxyaw = round(math.pi / yawreso)
-        self.yaww = round(self.maxyaw - self.minyaw)
+        self.min_yaw = round(- math.pi / yaw_resolution) - 1
+        self.max_yaw = round(math.pi / yaw_resolution)
+        self.yaw_w = round(self.max_yaw - self.min_yaw)
 
 
 def calc_motion_inputs():
-
     for steer in np.concatenate((np.linspace(-MAX_STEER, MAX_STEER,
                                              N_STEER), [0.0])):
         for d in [1, -1]:
             yield [steer, d]
 
 
-def get_neighbors(current, config, ox, oy, kdtree):
-
+def get_neighbors(current, config, ox, oy, kd_tree):
     for steer, d in calc_motion_inputs():
-        node = calc_next_node(current, steer, d, config, ox, oy, kdtree)
+        node = calc_next_node(current, steer, d, config, ox, oy, kd_tree)
         if node and verify_index(node, config):
             yield node
 
 
-def calc_next_node(current, steer, direction, config, ox, oy, kdtree):
-
-    x, y, yaw = current.xlist[-1], current.ylist[-1], current.yawlist[-1]
+def calc_next_node(current, steer, direction, config, ox, oy, kd_tree):
+    x, y, yaw = current.x_list[-1], current.y_list[-1], current.yaw_list[-1]
 
     arc_l = XY_GRID_RESOLUTION * 1.5
-    xlist, ylist, yawlist = [], [], []
+    x_list, y_list, yaw_list = [], [], []
     for _ in np.arange(0, arc_l, MOTION_RESOLUTION):
         x, y, yaw = move(x, y, yaw, MOTION_RESOLUTION * direction, steer)
-        xlist.append(x)
-        ylist.append(y)
-        yawlist.append(yaw)
+        x_list.append(x)
+        y_list.append(y)
+        yaw_list.append(yaw)
 
-    if not check_car_collision(xlist, ylist, yawlist, ox, oy, kdtree):
+    if not check_car_collision(x_list, y_list, yaw_list, ox, oy, kd_tree):
         return None
 
     d = direction == 1
-    xind = round(x / XY_GRID_RESOLUTION)
-    yind = round(y / XY_GRID_RESOLUTION)
-    yawind = round(yaw / YAW_GRID_RESOLUTION)
+    x_ind = round(x / XY_GRID_RESOLUTION)
+    y_ind = round(y / XY_GRID_RESOLUTION)
+    yaw_ind = round(yaw / YAW_GRID_RESOLUTION)
 
-    addedcost = 0.0
+    added_cost = 0.0
 
     if d != current.direction:
-        addedcost += SB_COST
+        added_cost += SB_COST
 
     # steer penalty
-    addedcost += STEER_COST * abs(steer)
+    added_cost += STEER_COST * abs(steer)
 
     # steer change penalty
-    addedcost += STEER_CHANGE_COST * abs(current.steer - steer)
+    added_cost += STEER_CHANGE_COST * abs(current.steer - steer)
 
-    cost = current.cost + addedcost + arc_l
+    cost = current.cost + added_cost + arc_l
 
-    node = Node(xind, yind, yawind, d, xlist,
-                ylist, yawlist, [d],
-                pind=calc_index(current, config),
+    node = Node(x_ind, y_ind, yaw_ind, d, x_list,
+                y_list, yaw_list, [d],
+                parent_index=calc_index(current, config),
                 cost=cost, steer=steer)
 
     return node
 
 
 def is_same_grid(n1, n2):
-    if n1.xind == n2.xind and n1.yind == n2.yind and n1.yawind == n2.yawind:
+    if n1.x_index == n2.x_index \
+            and n1.y_index == n2.y_index \
+            and n1.yaw_index == n2.yaw_index:
         return True
     return False
 
 
-def analytic_expantion(current, goal, c, ox, oy, kdtree):
-
-    sx = current.xlist[-1]
-    sy = current.ylist[-1]
-    syaw = current.yawlist[-1]
+def analytic_expansion(current, goal, ox, oy, kd_tree):
+    start_x = current.x_list[-1]
+    start_y = current.y_list[-1]
+    start_yaw = current.yaw_list[-1]
 
-    gx = goal.xlist[-1]
-    gy = goal.ylist[-1]
-    gyaw = goal.yawlist[-1]
+    goal_x = goal.x_list[-1]
+    goal_y = goal.y_list[-1]
+    goal_yaw = goal.yaw_list[-1]
 
     max_curvature = math.tan(MAX_STEER) / WB
-    paths = rs.calc_paths(sx, sy, syaw, gx, gy, gyaw,
+    paths = rs.calc_paths(start_x, start_y, start_yaw,
+                          goal_x, goal_y, goal_yaw,
                           max_curvature, step_size=MOTION_RESOLUTION)
 
     if not paths:
@@ -215,7 +213,7 @@ def analytic_expantion(current, goal, c, ox, oy, kdtree):
     best_path, best = None, None
 
     for path in paths:
-        if check_car_collision(path.x, path.y, path.yaw, ox, oy, kdtree):
+        if check_car_collision(path.x, path.y, path.yaw, ox, oy, kd_tree):
             cost = calc_rs_path_cost(path)
             if not best or best > cost:
                 best = cost
@@ -224,99 +222,105 @@ def analytic_expantion(current, goal, c, ox, oy, kdtree):
     return best_path
 
 
-def update_node_with_analystic_expantion(current, goal,
-                                         c, ox, oy, kdtree):
-    apath = analytic_expantion(current, goal, c, ox, oy, kdtree)
+def update_node_with_analytic_expansion(current, goal,
+                                        c, ox, oy, kd_tree):
+    path = analytic_expansion(current, goal, ox, oy, kd_tree)
 
-    if apath:
+    if path:
         if show_animation:
-            plt.plot(apath.x, apath.y)
-        fx = apath.x[1:]
-        fy = apath.y[1:]
-        fyaw = apath.yaw[1:]
+            plt.plot(path.x, path.y)
+        f_x = path.x[1:]
+        f_y = path.y[1:]
+        f_yaw = path.yaw[1:]
 
-        fcost = current.cost + calc_rs_path_cost(apath)
-        fpind = calc_index(current, c)
+        f_cost = current.cost + calc_rs_path_cost(path)
+        f_parent_index = calc_index(current, c)
 
         fd = []
-        for d in apath.directions[1:]:
+        for d in path.directions[1:]:
             fd.append(d >= 0)
 
-        fsteer = 0.0
-        fpath = Node(current.xind, current.yind, current.yawind,
-                     current.direction, fx, fy, fyaw, fd,
-                     cost=fcost, pind=fpind, steer=fsteer)
-        return True, fpath
+        f_steer = 0.0
+        f_path = Node(current.x_index, current.y_index, current.yaw_index,
+                      current.direction, f_x, f_y, f_yaw, fd,
+                      cost=f_cost, parent_index=f_parent_index, steer=f_steer)
+        return True, f_path
 
     return False, None
 
 
-def calc_rs_path_cost(rspath):
-
+def calc_rs_path_cost(reed_shepp_path):
     cost = 0.0
-    for l in rspath.lengths:
-        if l >= 0:  # forward
-            cost += l
+    for length in reed_shepp_path.lengths:
+        if length >= 0:  # forward
+            cost += length
         else:  # back
-            cost += abs(l) * BACK_COST
+            cost += abs(length) * BACK_COST
 
-    # swich back penalty
-    for i in range(len(rspath.lengths) - 1):
-        if rspath.lengths[i] * rspath.lengths[i + 1] < 0.0:  # switch back
+    # switch back penalty
+    for i in range(len(reed_shepp_path.lengths) - 1):
+        # switch back
+        if reed_shepp_path.lengths[i] * reed_shepp_path.lengths[i + 1] < 0.0:
             cost += SB_COST
 
-    # steer penalyty
-    for ctype in rspath.ctypes:
-        if ctype != "S":  # curve
+    # steer penalty
+    for course_type in reed_shepp_path.ctypes:
+        if course_type != "S":  # curve
             cost += STEER_COST * abs(MAX_STEER)
 
     # ==steer change penalty
     # calc steer profile
-    nctypes = len(rspath.ctypes)
-    ulist = [0.0] * nctypes
-    for i in range(nctypes):
-        if rspath.ctypes[i] == "R":
-            ulist[i] = - MAX_STEER
-        elif rspath.ctypes[i] == "L":
-            ulist[i] = MAX_STEER
+    n_ctypes = len(reed_shepp_path.ctypes)
+    u_list = [0.0] * n_ctypes
+    for i in range(n_ctypes):
+        if reed_shepp_path.ctypes[i] == "R":
+            u_list[i] = - MAX_STEER
+        elif reed_shepp_path.ctypes[i] == "L":
+            u_list[i] = MAX_STEER
 
-    for i in range(len(rspath.ctypes) - 1):
-        cost += STEER_CHANGE_COST * abs(ulist[i + 1] - ulist[i])
+    for i in range(len(reed_shepp_path.ctypes) - 1):
+        cost += STEER_CHANGE_COST * abs(u_list[i + 1] - u_list[i])
 
     return cost
 
 
-def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso):
+def hybrid_a_star_planning(start, goal, ox, oy, xy_resolution, yaw_resolution):
     """
-    start
-    goal
+    start: start node
+    goal: goal node
     ox: x position list of Obstacles [m]
     oy: y position list of Obstacles [m]
-    xyreso: grid resolution [m]
-    yawreso: yaw angle resolution [rad]
+    xy_resolution: grid resolution [m]
+    yaw_resolution: yaw angle resolution [rad]
     """
 
     start[2], goal[2] = rs.pi_2_pi(start[2]), rs.pi_2_pi(goal[2])
     tox, toy = ox[:], oy[:]
 
-    obkdtree = KDTree(np.vstack((tox, toy)).T)
+    obstacle_kd_tree = KDTree(np.vstack((tox, toy)).T)
 
-    config = Config(tox, toy, xyreso, yawreso)
+    config = Config(tox, toy, xy_resolution, yaw_resolution)
 
-    nstart = Node(round(start[0] / xyreso), round(start[1] / xyreso), round(start[2] / yawreso),
-                  True, [start[0]], [start[1]], [start[2]], [True], cost=0)
-    ngoal = Node(round(goal[0] / xyreso), round(goal[1] / xyreso), round(goal[2] / yawreso),
-                 True, [goal[0]], [goal[1]], [goal[2]], [True])
+    start_node = Node(round(start[0] / xy_resolution),
+                      round(start[1] / xy_resolution),
+                      round(start[2] / yaw_resolution), True,
+                      [start[0]], [start[1]], [start[2]], [True], cost=0)
+    goal_node = Node(round(goal[0] / xy_resolution),
+                     round(goal[1] / xy_resolution),
+                     round(goal[2] / yaw_resolution), True,
+                     [goal[0]], [goal[1]], [goal[2]], [True])
 
     openList, closedList = {}, {}
 
-    _, _, h_dp = dp_planning(nstart.xlist[-1], nstart.ylist[-1],
-                             ngoal.xlist[-1], ngoal.ylist[-1], ox, oy, xyreso, VR)
+    _, _, h_dp = dp_planning(start_node.x_list[-1], start_node.y_list[-1],
+                             goal_node.x_list[-1], goal_node.y_list[-1],
+                             ox, oy, xy_resolution, VR)
 
     pq = []
-    openList[calc_index(nstart, config)] = nstart
-    heapq.heappush(pq, (calc_cost(nstart, h_dp, ngoal, config),
-                        calc_index(nstart, config)))
+    openList[calc_index(start_node, config)] = start_node
+    heapq.heappush(pq, (calc_cost(start_node, h_dp, config),
+                        calc_index(start_node, config)))
+    final_path = None
 
     while True:
         if not openList:
@@ -331,83 +335,85 @@ def hybrid_a_star_planning(start, goal, ox, oy, xyreso, yawreso):
             continue
 
         if show_animation:  # pragma: no cover
-            plt.plot(current.xlist[-1], current.ylist[-1], "xc")
+            plt.plot(current.x_list[-1], current.y_list[-1], "xc")
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
             if len(closedList.keys()) % 10 == 0:
                 plt.pause(0.001)
 
-        isupdated, fpath = update_node_with_analystic_expantion(
-            current, ngoal, config, ox, oy, obkdtree)
+        is_updated, final_path = update_node_with_analytic_expansion(
+            current, goal_node, config, ox, oy, obstacle_kd_tree)
 
-        if isupdated:
+        if is_updated:
             print("path found")
             break
 
-        for neighbor in get_neighbors(current, config, ox, oy, obkdtree):
+        for neighbor in get_neighbors(current, config, ox, oy,
+                                      obstacle_kd_tree):
             neighbor_index = calc_index(neighbor, config)
             if neighbor_index in closedList:
                 continue
             if neighbor not in openList \
                     or openList[neighbor_index].cost > neighbor.cost:
                 heapq.heappush(
-                    pq, (calc_cost(neighbor, h_dp, ngoal, config),
+                    pq, (calc_cost(neighbor, h_dp, config),
                          neighbor_index))
                 openList[neighbor_index] = neighbor
 
-    path = get_final_path(closedList, fpath, nstart, config)
+    path = get_final_path(closedList, final_path)
     return path
 
 
-def calc_cost(n, h_dp, goal, c):
-    ind = (n.yind - c.miny) * c.xw + (n.xind - c.minx)
+def calc_cost(n, h_dp, c):
+    ind = (n.y_index - c.min_y) * c.x_w + (n.x_index - c.min_x)
     if ind not in h_dp:
         return n.cost + 999999999  # collision cost
     return n.cost + H_COST * h_dp[ind].cost
 
 
-def get_final_path(closed, ngoal, nstart, config):
-    rx, ry, ryaw = list(reversed(ngoal.xlist)), list(
-        reversed(ngoal.ylist)), list(reversed(ngoal.yawlist))
-    direction = list(reversed(ngoal.directions))
-    nid = ngoal.pind
-    finalcost = ngoal.cost
+def get_final_path(closed, goal_node):
+    reversed_x, reversed_y, reversed_yaw = \
+        list(reversed(goal_node.x_list)), list(reversed(goal_node.y_list)), \
+        list(reversed(goal_node.yaw_list))
+    direction = list(reversed(goal_node.directions))
+    nid = goal_node.parent_index
+    final_cost = goal_node.cost
 
     while nid:
         n = closed[nid]
-        rx.extend(list(reversed(n.xlist)))
-        ry.extend(list(reversed(n.ylist)))
-        ryaw.extend(list(reversed(n.yawlist)))
+        reversed_x.extend(list(reversed(n.x_list)))
+        reversed_y.extend(list(reversed(n.y_list)))
+        reversed_yaw.extend(list(reversed(n.yaw_list)))
         direction.extend(list(reversed(n.directions)))
 
-        nid = n.pind
+        nid = n.parent_index
 
-    rx = list(reversed(rx))
-    ry = list(reversed(ry))
-    ryaw = list(reversed(ryaw))
+    reversed_x = list(reversed(reversed_x))
+    reversed_y = list(reversed(reversed_y))
+    reversed_yaw = list(reversed(reversed_yaw))
     direction = list(reversed(direction))
 
     # adjust first direction
     direction[0] = direction[1]
 
-    path = Path(rx, ry, ryaw, direction, finalcost)
+    path = Path(reversed_x, reversed_y, reversed_yaw, direction, final_cost)
 
     return path
 
 
 def verify_index(node, c):
-    xind, yind = node.xind, node.yind
-    if xind >= c.minx and xind <= c.maxx and yind >= c.miny \
-            and yind <= c.maxy:
+    x_ind, y_ind = node.x_index, node.y_index
+    if c.min_x <= x_ind <= c.max_x and c.min_y <= y_ind <= c.max_y:
         return True
 
     return False
 
 
 def calc_index(node, c):
-    ind = (node.yawind - c.minyaw) * c.xw * c.yw + \
-        (node.yind - c.miny) * c.xw + (node.xind - c.minx)
+    ind = (node.yaw_index - c.min_yaw) * c.x_w * c.y_w + \
+          (node.y_index - c.min_y) * c.x_w + (node.x_index - c.min_x)
 
     if ind <= 0:
         print("Error(calc_index):", ind)
@@ -457,18 +463,18 @@ def main():
     path = hybrid_a_star_planning(
         start, goal, ox, oy, XY_GRID_RESOLUTION, YAW_GRID_RESOLUTION)
 
-    x = path.xlist
-    y = path.ylist
-    yaw = path.yawlist
+    x = path.x_list
+    y = path.y_list
+    yaw = path.yaw_list
 
     if show_animation:
-        for ix, iy, iyaw in zip(x, y, yaw):
+        for i_x, i_y, i_yaw in zip(x, y, yaw):
             plt.cla()
             plt.plot(ox, oy, ".k")
             plt.plot(x, y, "-r", label="Hybrid A* path")
             plt.grid(True)
             plt.axis("equal")
-            plot_car(ix, iy, iyaw)
+            plot_car(i_x, i_y, i_yaw)
             plt.pause(0.0001)
 
     print(__file__ + " done!!")
diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index 912e56fa4b..baf7dcce91 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -5,7 +5,8 @@
         Atsushi Sakai(@Atsushi_twi)
 
 Reference: Informed RRT*: Optimal Sampling-based Path planning Focused via
-Direct Sampling of an Admissible Ellipsoidal Heuristichttps://arxiv.org/pdf/1404.2334.pdf
+Direct Sampling of an Admissible Ellipsoidal Heuristic
+https://arxiv.org/pdf/1404.2334.pdf
 
 """
 
@@ -14,6 +15,7 @@
 import random
 
 import matplotlib.pyplot as plt
+from scipy.spatial.transform import Rotation as Rot
 import numpy as np
 
 show_animation = True
@@ -38,7 +40,8 @@ def __init__(self, start, goal,
     def informed_rrt_star_search(self, animation=True):
 
         self.node_list = [self.start]
-        # max length we expect to find in our 'informed' sample space, starts as infinite
+        # max length we expect to find in our 'informed' sample space,
+        # starts as infinite
         cBest = float('inf')
         solutionSet = set()
         path = None
@@ -51,13 +54,14 @@ def informed_rrt_star_search(self, animation=True):
         a1 = np.array([[(self.goal.x - self.start.x) / cMin],
                        [(self.goal.y - self.start.y) / cMin], [0]])
 
-        etheta = math.atan2(a1[1], a1[0])
-        # first column of idenity matrix transposed
+        e_theta = math.atan2(a1[1], a1[0])
+        # first column of identity matrix transposed
         id1_t = np.array([1.0, 0.0, 0.0]).reshape(1, 3)
         M = a1 @ id1_t
         U, S, Vh = np.linalg.svd(M, True, True)
         C = np.dot(np.dot(U, np.diag(
-            [1.0, 1.0, np.linalg.det(U) * np.linalg.det(np.transpose(Vh))])), Vh)
+            [1.0, 1.0, np.linalg.det(U) * np.linalg.det(np.transpose(Vh))])),
+                   Vh)
 
         for i in range(self.max_iter):
             # Sample space is defined by cBest
@@ -66,11 +70,11 @@ def informed_rrt_star_search(self, animation=True):
             # cBest changes when a new path is found
 
             rnd = self.informed_sample(cBest, cMin, xCenter, C)
-            nind = self.get_nearest_list_index(self.node_list, rnd)
-            nearestNode = self.node_list[nind]
+            n_ind = self.get_nearest_list_index(self.node_list, rnd)
+            nearestNode = self.node_list[n_ind]
             # steer
             theta = math.atan2(rnd[1] - nearestNode.y, rnd[0] - nearestNode.x)
-            newNode = self.get_new_node(theta, nind, nearestNode)
+            newNode = self.get_new_node(theta, n_ind, nearestNode)
             d = self.line_cost(nearestNode, newNode)
 
             noCollision = self.check_collision(nearestNode, theta, d)
@@ -83,7 +87,8 @@ def informed_rrt_star_search(self, animation=True):
                 self.rewire(newNode, nearInds)
 
                 if self.is_near_goal(newNode):
-                    if self.check_segment_collision(newNode.x, newNode.y, self.goal.x , self.goal.y):
+                    if self.check_segment_collision(newNode.x, newNode.y,
+                                                    self.goal.x, self.goal.y):
                         solutionSet.add(newNode)
                         lastIndex = len(self.node_list) - 1
                         tempPath = self.get_final_course(lastIndex)
@@ -94,7 +99,7 @@ def informed_rrt_star_search(self, animation=True):
             if animation:
                 self.draw_graph(xCenter=xCenter,
                                 cBest=cBest, cMin=cMin,
-                                etheta=etheta, rnd=rnd)
+                                e_theta=e_theta, rnd=rnd)
 
         return path
 
@@ -117,7 +122,7 @@ def choose_parent(self, newNode, nearInds):
         minInd = nearInds[dList.index(minCost)]
 
         if minCost == float('inf'):
-            print("mincost is inf")
+            print("min cost is inf")
             return newNode
 
         newNode.cost = minCost
@@ -126,12 +131,12 @@ def choose_parent(self, newNode, nearInds):
         return newNode
 
     def find_near_nodes(self, newNode):
-        nnode = len(self.node_list)
-        r = 50.0 * math.sqrt((math.log(nnode) / nnode))
-        dlist = [(node.x - newNode.x) ** 2
-                 + (node.y - newNode.y) ** 2 for node in self.node_list]
-        nearinds = [dlist.index(i) for i in dlist if i <= r ** 2]
-        return nearinds
+        n_node = len(self.node_list)
+        r = 50.0 * math.sqrt((math.log(n_node) / n_node))
+        d_list = [(node.x - newNode.x) ** 2 + (node.y - newNode.y) ** 2
+                  for node in self.node_list]
+        near_inds = [d_list.index(i) for i in d_list if i <= r ** 2]
+        return near_inds
 
     def informed_sample(self, cMax, cMin, xCenter, C):
         if cMax < float('inf'):
@@ -192,14 +197,14 @@ def get_nearest_list_index(nodes, rnd):
         minIndex = dList.index(min(dList))
         return minIndex
 
-    def get_new_node(self, theta, nind, nearestNode):
+    def get_new_node(self, theta, n_ind, nearestNode):
         newNode = copy.deepcopy(nearestNode)
 
         newNode.x += self.expand_dis * math.cos(theta)
         newNode.y += self.expand_dis * math.sin(theta)
 
         newNode.cost += self.expand_dis
-        newNode.parent = nind
+        newNode.parent = n_ind
         return newNode
 
     def is_near_goal(self, node):
@@ -216,28 +221,29 @@ def rewire(self, newNode, nearInds):
             d = math.sqrt((nearNode.x - newNode.x) ** 2
                           + (nearNode.y - newNode.y) ** 2)
 
-            scost = newNode.cost + d
+            s_cost = newNode.cost + d
 
-            if nearNode.cost > scost:
+            if nearNode.cost > s_cost:
                 theta = math.atan2(newNode.y - nearNode.y,
                                    newNode.x - nearNode.x)
                 if self.check_collision(nearNode, theta, d):
                     nearNode.parent = n_node - 1
-                    nearNode.cost = scost
+                    nearNode.cost = s_cost
 
     @staticmethod
     def distance_squared_point_to_segment(v, w, p):
         # Return minimum distance between line segment vw and point p
-        if (np.array_equal(v, w)):
-            return (p-v).dot(p-v) # v == w case
-        l2 = (w-v).dot(w-v) # i.e. |w-v|^2 -  avoid a sqrt
-        # Consider the line extending the segment, parameterized as v + t (w - v).
+        if np.array_equal(v, w):
+            return (p - v).dot(p - v)  # v == w case
+        l2 = (w - v).dot(w - v)  # i.e. |w-v|^2 -  avoid a sqrt
+        # Consider the line extending the segment,
+        # parameterized as v + t (w - v).
         # We find projection of point p onto the line.
         # It falls where t = [(p-v) . (w-v)] / |w-v|^2
         # We clamp t from [0,1] to handle points outside the segment vw.
         t = max(0, min(1, (p - v).dot(w - v) / l2))
-        projection = v + t * (w - v) # Projection falls on the segment
-        return (p-projection).dot(p-projection)
+        projection = v + t * (w - v)  # Projection falls on the segment
+        return (p - projection).dot(p - projection)
 
     def check_segment_collision(self, x1, y1, x2, y2):
         for (ox, oy, size) in self.obstacle_list:
@@ -245,16 +251,15 @@ def check_segment_collision(self, x1, y1, x2, y2):
                 np.array([x1, y1]),
                 np.array([x2, y2]),
                 np.array([ox, oy]))
-            if dd <= size**2:
+            if dd <= size ** 2:
                 return False  # collision
         return True
 
-
     def check_collision(self, nearNode, theta, d):
         tmpNode = copy.deepcopy(nearNode)
-        endx = tmpNode.x + math.cos(theta)*d
-        endy = tmpNode.y + math.sin(theta)*d
-        return self.check_segment_collision(tmpNode.x, tmpNode.y, endx, endy)
+        end_x = tmpNode.x + math.cos(theta) * d
+        end_y = tmpNode.y + math.sin(theta) * d
+        return self.check_segment_collision(tmpNode.x, tmpNode.y, end_x, end_y)
 
     def get_final_course(self, lastIndex):
         path = [[self.goal.x, self.goal.y]]
@@ -265,15 +270,17 @@ def get_final_course(self, lastIndex):
         path.append([self.start.x, self.start.y])
         return path
 
-    def draw_graph(self, xCenter=None, cBest=None, cMin=None, etheta=None, rnd=None):
+    def draw_graph(self, xCenter=None, cBest=None, cMin=None, e_theta=None,
+                   rnd=None):
         plt.clf()
         # for stopping simulation with the esc key.
-        plt.gcf().canvas.mpl_connect('key_release_event',
-                lambda event: [exit(0) if event.key == 'escape' else None])
+        plt.gcf().canvas.mpl_connect(
+            'key_release_event',
+            lambda event: [exit(0) if event.key == 'escape' else None])
         if rnd is not None:
             plt.plot(rnd[0], rnd[1], "^k")
             if cBest != float('inf'):
-                self.plot_ellipse(xCenter, cBest, cMin, etheta)
+                self.plot_ellipse(xCenter, cBest, cMin, e_theta)
 
         for node in self.node_list:
             if node.parent is not None:
@@ -291,20 +298,18 @@ def draw_graph(self, xCenter=None, cBest=None, cMin=None, etheta=None, rnd=None)
         plt.pause(0.01)
 
     @staticmethod
-    def plot_ellipse(xCenter, cBest, cMin, etheta):  # pragma: no cover
+    def plot_ellipse(xCenter, cBest, cMin, e_theta):  # pragma: no cover
 
         a = math.sqrt(cBest ** 2 - cMin ** 2) / 2.0
         b = cBest / 2.0
-        angle = math.pi / 2.0 - etheta
+        angle = math.pi / 2.0 - e_theta
         cx = xCenter[0]
         cy = xCenter[1]
-
         t = np.arange(0, 2 * math.pi + 0.1, 0.1)
         x = [a * math.cos(it) for it in t]
         y = [b * math.sin(it) for it in t]
-        R = np.array([[math.cos(angle), math.sin(angle)],
-                      [-math.sin(angle), math.cos(angle)]])
-        fx = R @ np.array([x, y])
+        rot = Rot.from_euler('z', -angle).as_matrix()[0:2, 0:2]
+        fx = rot @ np.array([x, y])
         px = np.array(fx[0, :] + cx).flatten()
         py = np.array(fx[1, :] + cy).flatten()
         plt.plot(cx, cy, "xc")
diff --git a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
index 7715174e16..790db074c0 100644
--- a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
+++ b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
@@ -1,6 +1,6 @@
 """
 
-Probablistic Road Map (PRM) Planner
+Probabilistic Road Map (PRM) Planner
 
 author: Atsushi Sakai (@Atsushi_twi)
 
@@ -25,14 +25,15 @@ class Node:
     Node class for dijkstra search
     """
 
-    def __init__(self, x, y, cost, pind):
+    def __init__(self, x, y, cost, parent_index):
         self.x = x
         self.y = y
         self.cost = cost
-        self.pind = pind
+        self.parent_index = parent_index
 
     def __str__(self):
-        return str(self.x) + "," + str(self.y) + "," + str(self.cost) + "," + str(self.pind)
+        return str(self.x) + "," + str(self.y) + "," +\
+               str(self.cost) + "," + str(self.parent_index)
 
 
 class KDTree:
@@ -57,9 +58,9 @@ def search(self, inp, k=1):
             dist = []
 
             for i in inp.T:
-                idist, iindex = self.tree.query(i, k=k)
-                index.append(iindex)
-                dist.append(idist)
+                i_dist, i_index = self.tree.query(i, k=k)
+                index.append(i_index)
+                dist.append(i_dist)
 
             return index, dist
 
@@ -75,23 +76,24 @@ def search_in_distance(self, inp, r):
         return index
 
 
-def PRM_planning(sx, sy, gx, gy, ox, oy, rr):
+def prm_planning(sx, sy, gx, gy, ox, oy, rr):
 
-    obkdtree = KDTree(np.vstack((ox, oy)).T)
+    obstacle_kd_tree = KDTree(np.vstack((ox, oy)).T)
 
-    sample_x, sample_y = sample_points(sx, sy, gx, gy, rr, ox, oy, obkdtree)
+    sample_x, sample_y = sample_points(sx, sy, gx, gy,
+                                       rr, ox, oy, obstacle_kd_tree)
     if show_animation:
         plt.plot(sample_x, sample_y, ".b")
 
-    road_map = generate_roadmap(sample_x, sample_y, rr, obkdtree)
+    road_map = generate_road_map(sample_x, sample_y, rr, obstacle_kd_tree)
 
     rx, ry = dijkstra_planning(
-        sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y)
+        sx, sy, gx, gy, road_map, sample_x, sample_y)
 
     return rx, ry
 
 
-def is_collision(sx, sy, gx, gy, rr, okdtree):
+def is_collision(sx, sy, gx, gy, rr, obstacle_kd_tree):
     x = sx
     y = sy
     dx = gx - sx
@@ -103,41 +105,41 @@ def is_collision(sx, sy, gx, gy, rr, okdtree):
         return True
 
     D = rr
-    nstep = round(d / D)
+    n_step = round(d / D)
 
-    for i in range(nstep):
-        idxs, dist = okdtree.search(np.array([x, y]).reshape(2, 1))
+    for i in range(n_step):
+        _, dist = obstacle_kd_tree.search(np.array([x, y]).reshape(2, 1))
         if dist[0] <= rr:
             return True  # collision
         x += D * math.cos(yaw)
         y += D * math.sin(yaw)
 
     # goal point check
-    idxs, dist = okdtree.search(np.array([gx, gy]).reshape(2, 1))
+    _, dist = obstacle_kd_tree.search(np.array([gx, gy]).reshape(2, 1))
     if dist[0] <= rr:
         return True  # collision
 
     return False  # OK
 
 
-def generate_roadmap(sample_x, sample_y, rr, obkdtree):
+def generate_road_map(sample_x, sample_y, rr, obstacle_kd_tree):
     """
     Road map generation
 
     sample_x: [m] x positions of sampled points
     sample_y: [m] y positions of sampled points
     rr: Robot Radius[m]
-    obkdtree: KDTree object of obstacles
+    obstacle_kd_tree: KDTree object of obstacles
     """
 
     road_map = []
-    nsample = len(sample_x)
-    skdtree = KDTree(np.vstack((sample_x, sample_y)).T)
+    n_sample = len(sample_x)
+    sample_kd_tree = KDTree(np.vstack((sample_x, sample_y)).T)
 
-    for (i, ix, iy) in zip(range(nsample), sample_x, sample_y):
+    for (i, ix, iy) in zip(range(n_sample), sample_x, sample_y):
 
-        index, dists = skdtree.search(
-            np.array([ix, iy]).reshape(2, 1), k=nsample)
+        index, dists = sample_kd_tree.search(
+            np.array([ix, iy]).reshape(2, 1), k=n_sample)
         inds = index[0]
         edge_id = []
         #  print(index)
@@ -146,7 +148,7 @@ def generate_roadmap(sample_x, sample_y, rr, obkdtree):
             nx = sample_x[inds[ii]]
             ny = sample_y[inds[ii]]
 
-            if not is_collision(ix, iy, nx, ny, rr, obkdtree):
+            if not is_collision(ix, iy, nx, ny, rr, obstacle_kd_tree):
                 edge_id.append(inds[ii])
 
             if len(edge_id) >= N_KNN:
@@ -159,10 +161,10 @@ def generate_roadmap(sample_x, sample_y, rr, obkdtree):
     return road_map
 
 
-def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y):
+def dijkstra_planning(sx, sy, gx, gy, road_map, sample_x, sample_y):
     """
-    sx: start x position [m]
-    sy: start y position [m]
+    s_x: start x position [m]
+    s_y: start y position [m]
     gx: goal x position [m]
     gy: goal y position [m]
     ox: x position list of Obstacles [m]
@@ -175,41 +177,42 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y):
     @return: Two lists of path coordinates ([x1, x2, ...], [y1, y2, ...]), empty list when no path was found
     """
 
-    nstart = Node(sx, sy, 0.0, -1)
-    ngoal = Node(gx, gy, 0.0, -1)
+    start_node = Node(sx, sy, 0.0, -1)
+    goal_node = Node(gx, gy, 0.0, -1)
 
-    openset, closedset = dict(), dict()
-    openset[len(road_map) - 2] = nstart
+    open_set, closed_set = dict(), dict()
+    open_set[len(road_map) - 2] = start_node
 
     path_found = True
 
     while True:
-        if not openset:
+        if not open_set:
             print("Cannot find path")
             path_found = False
             break
 
-        c_id = min(openset, key=lambda o: openset[o].cost)
-        current = openset[c_id]
+        c_id = min(open_set, key=lambda o: open_set[o].cost)
+        current = open_set[c_id]
 
         # show graph
-        if show_animation and len(closedset.keys()) % 2 == 0:
+        if show_animation and len(closed_set.keys()) % 2 == 0:
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(current.x, current.y, "xg")
             plt.pause(0.001)
 
         if c_id == (len(road_map) - 1):
             print("goal is found!")
-            ngoal.pind = current.pind
-            ngoal.cost = current.cost
+            goal_node.parent_index = current.parent_index
+            goal_node.cost = current.cost
             break
 
         # Remove the item from the open set
-        del openset[c_id]
+        del open_set[c_id]
         # Add it to the closed set
-        closedset[c_id] = current
+        closed_set[c_id] = current
 
         # expand search grid based on motion model
         for i in range(len(road_map[c_id])):
@@ -220,27 +223,27 @@ def dijkstra_planning(sx, sy, gx, gy, ox, oy, rr, road_map, sample_x, sample_y):
             node = Node(sample_x[n_id], sample_y[n_id],
                         current.cost + d, c_id)
 
-            if n_id in closedset:
+            if n_id in closed_set:
                 continue
             # Otherwise if it is already in the open set
-            if n_id in openset:
-                if openset[n_id].cost > node.cost:
-                    openset[n_id].cost = node.cost
-                    openset[n_id].pind = c_id
+            if n_id in open_set:
+                if open_set[n_id].cost > node.cost:
+                    open_set[n_id].cost = node.cost
+                    open_set[n_id].parent_index = c_id
             else:
-                openset[n_id] = node
+                open_set[n_id] = node
 
     if path_found is False:
         return [], []
 
     # generate final course
-    rx, ry = [ngoal.x], [ngoal.y]
-    pind = ngoal.pind
-    while pind != -1:
-        n = closedset[pind]
+    rx, ry = [goal_node.x], [goal_node.y]
+    parent_index = goal_node.parent_index
+    while parent_index != -1:
+        n = closed_set[parent_index]
         rx.append(n.x)
         ry.append(n.y)
-        pind = n.pind
+        parent_index = n.parent_index
 
     return rx, ry
 
@@ -255,19 +258,19 @@ def plot_road_map(road_map, sample_x, sample_y):  # pragma: no cover
                      [sample_y[i], sample_y[ind]], "-k")
 
 
-def sample_points(sx, sy, gx, gy, rr, ox, oy, obkdtree):
-    maxx = max(ox)
-    maxy = max(oy)
-    minx = min(ox)
-    miny = min(oy)
+def sample_points(sx, sy, gx, gy, rr, ox, oy, obstacle_kd_tree):
+    max_x = max(ox)
+    max_y = max(oy)
+    min_x = min(ox)
+    min_y = min(oy)
 
     sample_x, sample_y = [], []
 
     while len(sample_x) <= N_SAMPLE:
-        tx = (random.random() * (maxx - minx)) + minx
-        ty = (random.random() * (maxy - miny)) + miny
+        tx = (random.random() * (max_x - min_x)) + min_x
+        ty = (random.random() * (max_y - min_y)) + min_y
 
-        index, dist = obkdtree.search(np.array([tx, ty]).reshape(2, 1))
+        index, dist = obstacle_kd_tree.search(np.array([tx, ty]).reshape(2, 1))
 
         if dist[0] >= rr:
             sample_x.append(tx)
@@ -320,12 +323,13 @@ def main():
         plt.grid(True)
         plt.axis("equal")
 
-    rx, ry = PRM_planning(sx, sy, gx, gy, ox, oy, robot_size)
+    rx, ry = prm_planning(sx, sy, gx, gy, ox, oy, robot_size)
 
     assert rx, 'Cannot found path'
 
     if show_animation:
         plt.plot(rx, ry, "-r")
+        plt.pause(0.001)
         plt.show()
 
 
diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
index 5538731dcd..8ba11a23d2 100644
--- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
+++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
@@ -71,9 +71,9 @@ def quintic_polynomials_planner(sx, sy, syaw, sv, sa, gx, gy, gyaw, gv, ga, max_
     quintic polynomial planner
 
     input
-        sx: start x position [m]
-        sy: start y position [m]
-        syaw: start yaw angle [rad]
+        s_x: start x position [m]
+        s_y: start y position [m]
+        s_yaw: start yaw angle [rad]
         sa: start accel [m/ss]
         gx: goal x position [m]
         gy: goal y position [m]
diff --git a/PathPlanning/VoronoiRoadMap/dijkstra_search.py b/PathPlanning/VoronoiRoadMap/dijkstra_search.py
index 7bf5425ebf..54d15ea9fb 100644
--- a/PathPlanning/VoronoiRoadMap/dijkstra_search.py
+++ b/PathPlanning/VoronoiRoadMap/dijkstra_search.py
@@ -35,8 +35,8 @@ def search(self, sx, sy, gx, gy, node_x, node_y, edge_ids_list):
         """
         Search shortest path
 
-        sx: start x positions [m]
-        sy: start y positions [m]
+        s_x: start x positions [m]
+        s_y: start y positions [m]
         gx: goal x position [m]
         gx: goal x position [m]
         node_x: node x position
diff --git a/SLAM/GraphBasedSLAM/graph_based_slam.py b/SLAM/GraphBasedSLAM/graph_based_slam.py
index 9bb68c2821..9335dbe6a1 100644
--- a/SLAM/GraphBasedSLAM/graph_based_slam.py
+++ b/SLAM/GraphBasedSLAM/graph_based_slam.py
@@ -6,20 +6,22 @@
 
 Ref
 
-[A Tutorial on Graph-Based SLAM](http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf)
+[A Tutorial on Graph-Based SLAM]
+(http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf)
 
 """
 
-import numpy as np
-import math
 import copy
 import itertools
-import matplotlib.pyplot as plt
+import math
 
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.spatial.transform import Rotation as Rot
 
 #  Simulation parameter
-Qsim = np.diag([0.2, np.deg2rad(1.0)])**2
-Rsim = np.diag([0.1, np.deg2rad(10.0)])**2
+Q_sim = np.diag([0.2, np.deg2rad(1.0)]) ** 2
+R_sim = np.diag([0.1, np.deg2rad(10.0)]) ** 2
 
 DT = 2.0  # time tick [s]
 SIM_TIME = 100.0  # simulation time [s]
@@ -33,11 +35,11 @@
 
 MAX_ITR = 20  # Maximum iteration
 
-show_graph_dtime = 20.0  # [s]
+show_graph_d_time = 20.0  # [s]
 show_animation = True
 
 
-class Edge():
+class Edge:
 
     def __init__(self):
         self.e = np.zeros((3, 1))
@@ -52,26 +54,21 @@ def __init__(self):
         self.id2 = 0
 
 
-def cal_observation_sigma(d):
-
+def cal_observation_sigma():
     sigma = np.zeros((3, 3))
-    sigma[0, 0] = C_SIGMA1**2
-    sigma[1, 1] = C_SIGMA2**2
-    sigma[2, 2] = C_SIGMA3**2
+    sigma[0, 0] = C_SIGMA1 ** 2
+    sigma[1, 1] = C_SIGMA2 ** 2
+    sigma[2, 2] = C_SIGMA3 ** 2
 
     return sigma
 
 
 def calc_rotational_matrix(angle):
-
-    Rt = np.array([[math.cos(angle), -math.sin(angle), 0],
-                   [math.sin(angle), math.cos(angle), 0],
-                   [0, 0, 1.0]])
-    return Rt
+    return Rot.from_euler('z', angle).as_matrix()
 
 
 def calc_edge(x1, y1, yaw1, x2, y2, yaw2, d1,
-              angle1, phi1, d2, angle2, phi2, t1, t2):
+              angle1, d2, angle2, t1, t2):
     edge = Edge()
 
     tangle1 = pi_2_pi(yaw1 + angle1)
@@ -88,8 +85,8 @@ def calc_edge(x1, y1, yaw1, x2, y2, yaw2, d1,
     Rt1 = calc_rotational_matrix(tangle1)
     Rt2 = calc_rotational_matrix(tangle2)
 
-    sig1 = cal_observation_sigma(d1)
-    sig2 = cal_observation_sigma(d2)
+    sig1 = cal_observation_sigma()
+    sig2 = cal_observation_sigma()
 
     edge.omega = np.linalg.inv(Rt1 @ sig1 @ Rt1.T + Rt2 @ sig2 @ Rt2.T)
 
@@ -101,34 +98,33 @@ def calc_edge(x1, y1, yaw1, x2, y2, yaw2, d1,
     return edge
 
 
-def calc_edges(xlist, zlist):
-
+def calc_edges(x_list, z_list):
     edges = []
     cost = 0.0
-    zids = list(itertools.combinations(range(len(zlist)), 2))
+    z_ids = list(itertools.combinations(range(len(z_list)), 2))
 
-    for (t1, t2) in zids:
-        x1, y1, yaw1 = xlist[0, t1], xlist[1, t1], xlist[2, t1]
-        x2, y2, yaw2 = xlist[0, t2], xlist[1, t2], xlist[2, t2]
+    for (t1, t2) in z_ids:
+        x1, y1, yaw1 = x_list[0, t1], x_list[1, t1], x_list[2, t1]
+        x2, y2, yaw2 = x_list[0, t2], x_list[1, t2], x_list[2, t2]
 
-        if zlist[t1] is None or zlist[t2] is None:
+        if z_list[t1] is None or z_list[t2] is None:
             continue  # No observation
 
-        for iz1 in range(len(zlist[t1][:, 0])):
-            for iz2 in range(len(zlist[t2][:, 0])):
-                if zlist[t1][iz1, 3] == zlist[t2][iz2, 3]:
-                    d1 = zlist[t1][iz1, 0]
-                    angle1, phi1 = zlist[t1][iz1, 1], zlist[t1][iz1, 2]
-                    d2 = zlist[t2][iz2, 0]
-                    angle2, phi2 = zlist[t2][iz2, 1], zlist[t2][iz2, 2]
+        for iz1 in range(len(z_list[t1][:, 0])):
+            for iz2 in range(len(z_list[t2][:, 0])):
+                if z_list[t1][iz1, 3] == z_list[t2][iz2, 3]:
+                    d1 = z_list[t1][iz1, 0]
+                    angle1, phi1 = z_list[t1][iz1, 1], z_list[t1][iz1, 2]
+                    d2 = z_list[t2][iz2, 0]
+                    angle2, phi2 = z_list[t2][iz2, 1], z_list[t2][iz2, 2]
 
                     edge = calc_edge(x1, y1, yaw1, x2, y2, yaw2, d1,
-                                     angle1, phi1, d2, angle2, phi2, t1, t2)
+                                     angle1, d2, angle2, t1, t2)
 
                     edges.append(edge)
-                    cost += (edge.e.T @ (edge.omega) @ edge.e)[0, 0]
+                    cost += (edge.e.T @ edge.omega @ edge.e)[0, 0]
 
-    print("cost:", cost, ",nedge:", len(edges))
+    print("cost:", cost, ",n_edge:", len(edges))
     return edges
 
 
@@ -147,7 +143,6 @@ def calc_jacobian(edge):
 
 
 def fill_H_and_b(H, b, edge):
-
     A, B = calc_jacobian(edge)
 
     id1 = edge.id1 * STATE_SIZE
@@ -167,14 +162,14 @@ def fill_H_and_b(H, b, edge):
 def graph_based_slam(x_init, hz):
     print("start graph based slam")
 
-    zlist = copy.deepcopy(hz)
+    z_list = copy.deepcopy(hz)
 
     x_opt = copy.deepcopy(x_init)
     nt = x_opt.shape[1]
     n = nt * STATE_SIZE
 
     for itr in range(MAX_ITR):
-        edges = calc_edges(x_opt, zlist)
+        edges = calc_edges(x_opt, z_list)
 
         H = np.zeros((n, n))
         b = np.zeros((n, 1))
@@ -200,13 +195,12 @@ def graph_based_slam(x_init, hz):
 
 def calc_input():
     v = 1.0  # [m/s]
-    yawrate = 0.1  # [rad/s]
-    u = np.array([[v, yawrate]]).T
+    yaw_rate = 0.1  # [rad/s]
+    u = np.array([[v, yaw_rate]]).T
     return u
 
 
 def observation(xTrue, xd, u, RFID):
-
     xTrue = motion_model(xTrue, u)
 
     # add noise to gps x-y
@@ -220,16 +214,16 @@ def observation(xTrue, xd, u, RFID):
         angle = pi_2_pi(math.atan2(dy, dx)) - xTrue[2, 0]
         phi = pi_2_pi(math.atan2(dy, dx))
         if d <= MAX_RANGE:
-            dn = d + np.random.randn() * Qsim[0, 0]  # add noise
-            angle_noise = np.random.randn() * Qsim[1, 1]
+            dn = d + np.random.randn() * Q_sim[0, 0]  # add noise
+            angle_noise = np.random.randn() * Q_sim[1, 1]
             angle += angle_noise
             phi += angle_noise
             zi = np.array([dn, angle, phi, i])
             z = np.vstack((z, zi))
 
     # add noise to input
-    ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0]
-    ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1]
+    ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0]
+    ud2 = u[1, 0] + np.random.randn() * R_sim[1, 1]
     ud = np.array([[ud1, ud2]]).T
 
     xd = motion_model(xd, ud)
@@ -238,7 +232,6 @@ def observation(xTrue, xd, u, RFID):
 
 
 def motion_model(x, u):
-
     F = np.array([[1.0, 0, 0],
                   [0, 1.0, 0],
                   [0, 0, 1.0]])
@@ -277,7 +270,7 @@ def main():
     hxTrue = []
     hxDR = []
     hz = []
-    dtime = 0.0
+    d_time = 0.0
     init = False
     while SIM_TIME >= time:
 
@@ -290,22 +283,23 @@ def main():
             hxTrue = np.hstack((hxTrue, xTrue))
 
         time += DT
-        dtime += DT
+        d_time += DT
         u = calc_input()
 
         xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID)
 
         hz.append(z)
 
-        if dtime >= show_graph_dtime:
+        if d_time >= show_graph_d_time:
             x_opt = graph_based_slam(hxDR, hz)
-            dtime = 0.0
+            d_time = 0.0
 
             if show_animation:  # pragma: no cover
                 plt.cla()
                 # for stopping simulation with the esc key.
-                plt.gcf().canvas.mpl_connect('key_release_event',
-                        lambda event: [exit(0) if event.key == 'escape' else None])
+                plt.gcf().canvas.mpl_connect(
+                    'key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
                 plt.plot(RFID[:, 0], RFID[:, 1], "*k")
 
                 plt.plot(hxTrue[0, :].flatten(),

From a36ddb4cfff4f1f7677175c93e8d7935bf5fbbd4 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 8 Jun 2020 17:16:56 +0900
Subject: [PATCH 0397/1080] use scipy kd-tree directly (#337)

---
 PathPlanning/HybridAStar/car.py               |  4 +-
 PathPlanning/HybridAStar/hybrid_a_star.py     | 42 +----------
 .../probabilistic_road_map.py                 | 71 ++++---------------
 PathPlanning/VoronoiRoadMap/kdtree.py         | 49 -------------
 .../VoronoiRoadMap/voronoi_road_map.py        | 29 ++++----
 5 files changed, 31 insertions(+), 164 deletions(-)
 delete mode 100644 PathPlanning/VoronoiRoadMap/kdtree.py

diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py
index 9b956a0877..cc7529b9c7 100644
--- a/PathPlanning/HybridAStar/car.py
+++ b/PathPlanning/HybridAStar/car.py
@@ -31,7 +31,7 @@ def check_car_collision(x_list, y_list, yaw_list, ox, oy, kd_tree):
         cx = i_x + W_BUBBLE_DIST * cos(i_yaw)
         cy = i_y + W_BUBBLE_DIST * sin(i_yaw)
 
-        ids = kd_tree.search_in_distance([cx, cy], W_BUBBLE_R)
+        ids = kd_tree.query_ball_point([cx, cy], W_BUBBLE_R)
 
         if not ids:
             continue
@@ -71,7 +71,7 @@ def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
 def plot_car(x, y, yaw):
     car_color = '-k'
     c, s = cos(yaw), sin(yaw)
-    rot = Rot.from_euler('z', yaw).as_matrix()[0:2, 0:2]
+    rot = Rot.from_euler('z', -yaw).as_matrix()[0:2, 0:2]
     car_outline_x, car_outline_y = [], []
     for rx, ry in zip(VRX, VRY):
         converted_xy = np.stack([rx, ry]).T @ rot
diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
index ff9b3fe43b..45d6e11b18 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -13,7 +13,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
-import scipy.spatial
+from scipy.spatial import cKDTree
 
 sys.path.append(os.path.dirname(os.path.abspath(__file__))
                 + "/../ReedsSheppPath")
@@ -67,44 +67,6 @@ def __init__(self, x_list, y_list, yaw_list, direction_list, cost):
         self.cost = cost
 
 
-class KDTree:
-    """
-    Nearest neighbor search class with KDTree
-    """
-
-    def __init__(self, data):
-        # store kd-tree
-        self.tree = scipy.spatial.cKDTree(data)
-
-    def search(self, inp, k=1):
-        """
-        Search NN
-        inp: input data, single frame or multi frame
-        """
-
-        if len(inp.shape) >= 2:  # multi input
-            index = []
-            dist = []
-
-            for i in inp.T:
-                i_dist, i_index = self.tree.query(i, k=k)
-                index.append(i_index)
-                dist.append(i_dist)
-
-            return index, dist
-
-        dist, index = self.tree.query(inp, k=k)
-        return index, dist
-
-    def search_in_distance(self, inp, r):
-        """
-        find points with in a distance r
-        """
-
-        index = self.tree.query_ball_point(inp, r)
-        return index
-
-
 class Config:
 
     def __init__(self, ox, oy, xy_resolution, yaw_resolution):
@@ -297,7 +259,7 @@ def hybrid_a_star_planning(start, goal, ox, oy, xy_resolution, yaw_resolution):
     start[2], goal[2] = rs.pi_2_pi(start[2]), rs.pi_2_pi(goal[2])
     tox, toy = ox[:], oy[:]
 
-    obstacle_kd_tree = KDTree(np.vstack((tox, toy)).T)
+    obstacle_kd_tree = cKDTree(np.vstack((tox, toy)).T)
 
     config = Config(tox, toy, xy_resolution, yaw_resolution)
 
diff --git a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
index 790db074c0..8681420e2d 100644
--- a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
+++ b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
@@ -9,8 +9,8 @@
 import random
 import math
 import numpy as np
-import scipy.spatial
 import matplotlib.pyplot as plt
+from scipy.spatial import cKDTree
 
 # parameter
 N_SAMPLE = 500  # number of sample_points
@@ -36,49 +36,9 @@ def __str__(self):
                str(self.cost) + "," + str(self.parent_index)
 
 
-class KDTree:
-    """
-    Nearest neighbor search class with KDTree
-    """
-
-    def __init__(self, data):
-        # store kd-tree
-        self.tree = scipy.spatial.cKDTree(data)
-
-    def search(self, inp, k=1):
-        """
-        Search NN
-
-        inp: input data, single frame or multi frame
-
-        """
-
-        if len(inp.shape) >= 2:  # multi input
-            index = []
-            dist = []
-
-            for i in inp.T:
-                i_dist, i_index = self.tree.query(i, k=k)
-                index.append(i_index)
-                dist.append(i_dist)
-
-            return index, dist
-
-        dist, index = self.tree.query(inp, k=k)
-        return index, dist
-
-    def search_in_distance(self, inp, r):
-        """
-        find points with in a distance r
-        """
-
-        index = self.tree.query_ball_point(inp, r)
-        return index
-
-
 def prm_planning(sx, sy, gx, gy, ox, oy, rr):
 
-    obstacle_kd_tree = KDTree(np.vstack((ox, oy)).T)
+    obstacle_kd_tree = cKDTree(np.vstack((ox, oy)).T)
 
     sample_x, sample_y = sample_points(sx, sy, gx, gy,
                                        rr, ox, oy, obstacle_kd_tree)
@@ -108,15 +68,15 @@ def is_collision(sx, sy, gx, gy, rr, obstacle_kd_tree):
     n_step = round(d / D)
 
     for i in range(n_step):
-        _, dist = obstacle_kd_tree.search(np.array([x, y]).reshape(2, 1))
-        if dist[0] <= rr:
+        dist, _ = obstacle_kd_tree.query([x, y])
+        if dist <= rr:
             return True  # collision
         x += D * math.cos(yaw)
         y += D * math.sin(yaw)
 
     # goal point check
-    _, dist = obstacle_kd_tree.search(np.array([gx, gy]).reshape(2, 1))
-    if dist[0] <= rr:
+    dist, _ = obstacle_kd_tree.query([gx, gy])
+    if dist <= rr:
         return True  # collision
 
     return False  # OK
@@ -134,22 +94,19 @@ def generate_road_map(sample_x, sample_y, rr, obstacle_kd_tree):
 
     road_map = []
     n_sample = len(sample_x)
-    sample_kd_tree = KDTree(np.vstack((sample_x, sample_y)).T)
+    sample_kd_tree = cKDTree(np.vstack((sample_x, sample_y)).T)
 
     for (i, ix, iy) in zip(range(n_sample), sample_x, sample_y):
 
-        index, dists = sample_kd_tree.search(
-            np.array([ix, iy]).reshape(2, 1), k=n_sample)
-        inds = index[0]
+        dists, indexes = sample_kd_tree.query([ix, iy], k=n_sample)
         edge_id = []
-        #  print(index)
 
-        for ii in range(1, len(inds)):
-            nx = sample_x[inds[ii]]
-            ny = sample_y[inds[ii]]
+        for ii in range(1, len(indexes)):
+            nx = sample_x[indexes[ii]]
+            ny = sample_y[indexes[ii]]
 
             if not is_collision(ix, iy, nx, ny, rr, obstacle_kd_tree):
-                edge_id.append(inds[ii])
+                edge_id.append(indexes[ii])
 
             if len(edge_id) >= N_KNN:
                 break
@@ -270,9 +227,9 @@ def sample_points(sx, sy, gx, gy, rr, ox, oy, obstacle_kd_tree):
         tx = (random.random() * (max_x - min_x)) + min_x
         ty = (random.random() * (max_y - min_y)) + min_y
 
-        index, dist = obstacle_kd_tree.search(np.array([tx, ty]).reshape(2, 1))
+        dist, index = obstacle_kd_tree.query([tx, ty])
 
-        if dist[0] >= rr:
+        if dist >= rr:
             sample_x.append(tx)
             sample_y.append(ty)
 
diff --git a/PathPlanning/VoronoiRoadMap/kdtree.py b/PathPlanning/VoronoiRoadMap/kdtree.py
deleted file mode 100644
index d3dc01b1f6..0000000000
--- a/PathPlanning/VoronoiRoadMap/kdtree.py
+++ /dev/null
@@ -1,49 +0,0 @@
-"""
-
-Kd tree Search library
-
-author: Atsushi Sakai (@Atsushi_twi)
-
-"""
-
-import scipy.spatial
-
-
-class KDTree:
-    """
-    Nearest neighbor search class with KDTree
-    """
-
-    def __init__(self, data):
-        # store kd-tree
-        self.tree = scipy.spatial.cKDTree(data)
-
-    def search(self, inp, k=1):
-        """
-        Search NN
-
-        inp: input data, single frame or multi frame
-
-        """
-
-        if len(inp.shape) >= 2:  # multi input
-            index = []
-            dist = []
-
-            for i in inp.T:
-                idist, iindex = self.tree.query(i, k=k)
-                index.append(iindex)
-                dist.append(idist)
-
-            return index, dist
-
-        dist, index = self.tree.query(inp, k=k)
-        return index, dist
-
-    def search_in_distance(self, inp, r):
-        """
-        find points with in a distance r
-        """
-
-        index = self.tree.query_ball_point(inp, r)
-        return index
diff --git a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
index d060c8e46c..d184230fd5 100644
--- a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
+++ b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
@@ -8,10 +8,9 @@
 
 import math
 import numpy as np
-import scipy.spatial
 import matplotlib.pyplot as plt
 from dijkstra_search import DijkstraSearch
-from kdtree import KDTree
+from scipy.spatial import cKDTree, Voronoi
 
 show_animation = True
 
@@ -24,7 +23,7 @@ def __init__(self):
         self.MAX_EDGE_LEN = 30.0  # [m] Maximum edge length
 
     def planning(self, sx, sy, gx, gy, ox, oy, robot_radius):
-        obstacle_tree = KDTree(np.vstack((ox, oy)).T)
+        obstacle_tree = cKDTree(np.vstack((ox, oy)).T)
 
         sample_x, sample_y = self.voronoi_sampling(sx, sy, gx, gy, ox, oy)
         if show_animation:  # pragma: no cover
@@ -53,15 +52,15 @@ def is_collision(self, sx, sy, gx, gy, rr, obstacle_kd_tree):
         n_step = round(d / D)
 
         for i in range(n_step):
-            ids, dist = obstacle_kd_tree.search(np.array([x, y]).reshape(2, 1))
-            if dist[0] <= rr:
+            dist, _ = obstacle_kd_tree.query([x, y])
+            if dist <= rr:
                 return True  # collision
             x += D * math.cos(yaw)
             y += D * math.sin(yaw)
 
         # goal point check
-        ids, dist = obstacle_kd_tree.search(np.array([gx, gy]).reshape(2, 1))
-        if dist[0] <= rr:
+        dist, _ = obstacle_kd_tree.query([gx, gy])
+        if dist <= rr:
             return True  # collision
 
         return False  # OK
@@ -78,22 +77,20 @@ def generate_road_map_info(self, node_x, node_y, rr, obstacle_tree):
 
         road_map = []
         n_sample = len(node_x)
-        node_tree = KDTree(np.vstack((node_x, node_y)).T)
+        node_tree = cKDTree(np.vstack((node_x, node_y)).T)
 
         for (i, ix, iy) in zip(range(n_sample), node_x, node_y):
 
-            index, dists = node_tree.search(
-                np.array([ix, iy]).reshape(2, 1), k=n_sample)
+            dists, indexes = node_tree.query([ix, iy], k=n_sample)
 
-            inds = index[0]
             edge_id = []
 
-            for ii in range(1, len(inds)):
-                nx = node_x[inds[ii]]
-                ny = node_y[inds[ii]]
+            for ii in range(1, len(indexes)):
+                nx = node_x[indexes[ii]]
+                ny = node_y[indexes[ii]]
 
                 if not self.is_collision(ix, iy, nx, ny, rr, obstacle_tree):
-                    edge_id.append(inds[ii])
+                    edge_id.append(indexes[ii])
 
                 if len(edge_id) >= self.N_KNN:
                     break
@@ -119,7 +116,7 @@ def voronoi_sampling(sx, sy, gx, gy, ox, oy):
         oxy = np.vstack((ox, oy)).T
 
         # generate voronoi point
-        vor = scipy.spatial.Voronoi(oxy)
+        vor = Voronoi(oxy)
         sample_x = [ix for [ix, _] in vor.vertices]
         sample_y = [iy for [_, iy] in vor.vertices]
 

From fa1585d880ea771abd4215077eb699d194486eb4 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 8 Jun 2020 21:43:37 +0900
Subject: [PATCH 0398/1080] fix scanning error (#339)

---
 .../random_forward_kinematics.py              |  16 +-
 .../random_inverse_kinematics.py              |  12 +-
 Bipedal/bipedal_planner/bipedal_planner.py    |   3 +-
 .../inverted_pendulum_mpc_control.py          |  28 ++-
 .../lidar_to_grid_map/lidar_to_grid_map.py    |   3 +-
 .../batch_informed_rrtstar.py                 | 158 ++++++++------
 .../bidirectional_a_star.py                   | 135 ++++++------
 .../bidirectional_breadth_first_search.py     | 142 ++++++------
 .../VoronoiRoadMap/dijkstra_search.py         |   1 +
 PathTracking/cgmres_nmpc/cgmres_nmpc.py       | 206 ++++++++++--------
 ..._grid_based_sweep_coverage_path_planner.py | 175 ++++++++-------
 11 files changed, 487 insertions(+), 392 deletions(-)

diff --git a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py
index b3426e53e8..f9caace300 100644
--- a/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py
+++ b/ArmNavigation/n_joint_arm_3d/random_forward_kinematics.py
@@ -11,10 +11,9 @@ def random_val(min_val, max_val):
     return min_val + random.random() * (max_val - min_val)
 
 
-if __name__ == "__main__":
+def main():
     print("Start solving Forward Kinematics 10 times")
-
-    # init NLinkArm with Denavit-Hartenberg parameters of PR2    
+    # init NLinkArm with Denavit-Hartenberg parameters of PR2
     n_link_arm = NLinkArm([[0., -math.pi / 2, .1, 0.],
                            [math.pi / 2, math.pi / 2, 0., 0.],
                            [0., -math.pi / 2, 0., .4],
@@ -22,10 +21,13 @@ def random_val(min_val, max_val):
                            [0., -math.pi / 2, 0., .321],
                            [0., math.pi / 2, 0., 0.],
                            [0., 0., 0., 0.]])
-
     # execute FK 10 times
-    for i in range(10):
+    for _ in range(10):
         n_link_arm.set_joint_angles(
-            [random_val(-1, 1) for j in range(len(n_link_arm.link_list))])
+            [random_val(-1, 1) for _ in range(len(n_link_arm.link_list))])
 
-        ee_pose = n_link_arm.forward_kinematics(plot=True)
+        n_link_arm.forward_kinematics(plot=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py
index a3838a6a38..91f6f1bba0 100644
--- a/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py
+++ b/ArmNavigation/n_joint_arm_3d/random_inverse_kinematics.py
@@ -11,10 +11,9 @@ def random_val(min_val, max_val):
     return min_val + random.random() * (max_val - min_val)
 
 
-if __name__ == "__main__":
+def main():
     print("Start solving Inverse Kinematics 10 times")
-
-    # init NLinkArm with Denavit-Hartenberg parameters of PR2        
+    # init NLinkArm with Denavit-Hartenberg parameters of PR2
     n_link_arm = NLinkArm([[0., -math.pi / 2, .1, 0.],
                            [math.pi / 2, math.pi / 2, 0., 0.],
                            [0., -math.pi / 2, 0., .4],
@@ -22,12 +21,15 @@ def random_val(min_val, max_val):
                            [0., -math.pi / 2, 0., .321],
                            [0., math.pi / 2, 0., 0.],
                            [0., 0., 0., 0.]])
-
     # execute IK 10 times
-    for i in range(10):
+    for _ in range(10):
         n_link_arm.inverse_kinematics([random_val(-0.5, 0.5),
                                        random_val(-0.5, 0.5),
                                        random_val(-0.5, 0.5),
                                        random_val(-0.5, 0.5),
                                        random_val(-0.5, 0.5),
                                        random_val(-0.5, 0.5)], plot=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/Bipedal/bipedal_planner/bipedal_planner.py b/Bipedal/bipedal_planner/bipedal_planner.py
index 60c03024bf..01df63034f 100644
--- a/Bipedal/bipedal_planner/bipedal_planner.py
+++ b/Bipedal/bipedal_planner/bipedal_planner.py
@@ -46,9 +46,8 @@ def walk(self, t_sup=0.8, z_c=0.8, a=10, b=1, plot=False):
             print("No footsteps")
             return
 
-        com_trajectory_for_plot, ax = None, None
-
         # set up plotter
+        com_trajectory_for_plot, ax = None, None
         if plot:
             fig = plt.figure()
             ax = Axes3D(fig)
diff --git a/InvertedPendulumCart/inverted_pendulum_mpc_control.py b/InvertedPendulumCart/inverted_pendulum_mpc_control.py
index caf0c23e77..e7b3077740 100644
--- a/InvertedPendulumCart/inverted_pendulum_mpc_control.py
+++ b/InvertedPendulumCart/inverted_pendulum_mpc_control.py
@@ -3,11 +3,12 @@
 author: Atsushi Sakai
 """
 
-import matplotlib.pyplot as plt
-import numpy as np
 import math
 import time
+
 import cvxpy
+import matplotlib.pyplot as plt
+import numpy as np
 
 # Model parameters
 
@@ -39,10 +40,11 @@ def main():
     for i in range(50):
 
         # calc control input
-        optimized_x, optimized_delta_x, optimized_theta, optimized_delta_theta, optimized_input = mpc_control(x)
+        opt_x, opt_delta_x, opt_theta, opt_delta_theta, opt_input = \
+            mpc_control(x)
 
         # get input
-        u = optimized_input[0]
+        u = opt_input[0]
 
         # simulate inverted pendulum cart
         x = simulation(x, u)
@@ -86,17 +88,19 @@ def mpc_control(x0):
     print("calc time:{0} [sec]".format(elapsed_time))
 
     if prob.status == cvxpy.OPTIMAL:
-        ox = get_nparray_from_matrix(x.value[0, :])
-        dx = get_nparray_from_matrix(x.value[1, :])
-        theta = get_nparray_from_matrix(x.value[2, :])
-        dtheta = get_nparray_from_matrix(x.value[3, :])
+        ox = get_numpy_array_from_matrix(x.value[0, :])
+        dx = get_numpy_array_from_matrix(x.value[1, :])
+        theta = get_numpy_array_from_matrix(x.value[2, :])
+        d_theta = get_numpy_array_from_matrix(x.value[3, :])
 
-        ou = get_nparray_from_matrix(u.value[0, :])
+        ou = get_numpy_array_from_matrix(u.value[0, :])
+    else:
+        ox, dx, theta, d_theta, ou = None, None, None, None, None
 
-    return ox, dx, theta, dtheta, ou
+    return ox, dx, theta, d_theta, ou
 
 
-def get_nparray_from_matrix(x):
+def get_numpy_array_from_matrix(x):
     """
     get build-in list from matrix
     """
@@ -133,7 +137,7 @@ def plot_cart(xt, theta):
     radius = 0.1
 
     cx = np.array([-cart_w / 2.0, cart_w / 2.0, cart_w /
-                    2.0, -cart_w / 2.0, -cart_w / 2.0])
+                   2.0, -cart_w / 2.0, -cart_w / 2.0])
     cy = np.array([0.0, 0.0, cart_h, cart_h, 0.0])
     cy += radius * 2.0
 
diff --git a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
index 911a0bf0ea..57278d4a47 100644
--- a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
+++ b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
@@ -19,7 +19,8 @@ def file_read(f):
     """
     Reading LIDAR laser beams (angles and corresponding distance data)
     """
-    measures = [line.split(",") for line in open(f)]
+    with open(f) as data:
+        measures = [line.split(",") for line in data]
     angles = []
     distances = []
     for measure in measures:
diff --git a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
index 227b3f9d25..b5eabbb39b 100644
--- a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
+++ b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
@@ -27,7 +27,8 @@ class RTree(object):
     # Class to represent the explicit tree created
     # while sampling through the state space
 
-    def __init__(self, start=None, lowerLimit=None, upperLimit=None, resolution=1.0):
+    def __init__(self, start=None, lowerLimit=None, upperLimit=None,
+                 resolution=1.0):
 
         if upperLimit is None:
             upperLimit = [10, 10]
@@ -96,10 +97,11 @@ def grid_coordinate_to_node_id(self, coord):
     def real_world_to_node_id(self, real_coord):
         # first convert the given coordinates to grid space and then
         # convert the grid space coordinates to a unique node id
-        return self.grid_coordinate_to_node_id(self.real_coords_to_grid_coord(real_coord))
+        return self.grid_coordinate_to_node_id(
+            self.real_coords_to_grid_coord(real_coord))
 
     def grid_coord_to_real_world_coord(self, coord):
-        # This function smaps a grid coordinate in discrete space
+        # This function maps a grid coordinate in discrete space
         # to a configuration in the full configuration space
         config = [0] * self.dimension
         for i in range(0, len(coord)):
@@ -126,7 +128,8 @@ def node_id_to_grid_coord(self, node_id):
     def node_id_to_real_world_coord(self, nid):
         # This function maps a node in discrete space to a configuration
         # in the full configuration space
-        return self.grid_coord_to_real_world_coord(self.node_id_to_grid_coord(nid))
+        return self.grid_coord_to_real_world_coord(
+            self.node_id_to_grid_coord(nid))
 
 
 class BITStar(object):
@@ -137,9 +140,9 @@ def __init__(self, start, goal,
         self.start = start
         self.goal = goal
 
-        self.minrand = randArea[0]
-        self.maxrand = randArea[1]
-        self.maxIter = maxIter
+        self.min_rand = randArea[0]
+        self.max_rand = randArea[1]
+        self.max_iIter = maxIter
         self.obstacleList = obstacleList
         self.startId = None
         self.goalId = None
@@ -186,18 +189,19 @@ def setup_planning(self):
                             [(self.start[1] + self.goal[1]) / 2.0], [0]])
         a1 = np.array([[(self.goal[0] - self.start[0]) / cMin],
                        [(self.goal[1] - self.start[1]) / cMin], [0]])
-        etheta = math.atan2(a1[1], a1[0])
-        # first column of idenity matrix transposed
+        eTheta = math.atan2(a1[1], a1[0])
+        # first column of identity matrix transposed
         id1_t = np.array([1.0, 0.0, 0.0]).reshape(1, 3)
         M = np.dot(a1, id1_t)
         U, S, Vh = np.linalg.svd(M, True, True)
         C = np.dot(np.dot(U, np.diag(
-            [1.0, 1.0, np.linalg.det(U) * np.linalg.det(np.transpose(Vh))])), Vh)
+            [1.0, 1.0, np.linalg.det(U) * np.linalg.det(np.transpose(Vh))])),
+                   Vh)
 
         self.samples.update(self.informed_sample(
             200, cBest, cMin, xCenter, C))
 
-        return etheta, cMin, xCenter, C, cBest
+        return eTheta, cMin, xCenter, C, cBest
 
     def setup_sample(self, iterations, foundGoal, cMin, xCenter, C, cBest):
 
@@ -228,18 +232,19 @@ def setup_sample(self, iterations, foundGoal, cMin, xCenter, C, cBest):
 
     def plan(self, animation=True):
 
-        etheta, cMin, xCenter, C, cBest = self.setup_planning()
+        eTheta, cMin, xCenter, C, cBest = self.setup_planning()
         iterations = 0
 
         foundGoal = False
         # run until done
-        while iterations < self.maxIter:
+        while iterations < self.max_iIter:
             cBest = self.setup_sample(iterations,
                                       foundGoal, cMin, xCenter, C, cBest)
             # expand the best vertices until an edge is better than the vertex
             # this is done because the vertex cost represents the lower bound
             # on the edge cost
-            while self.best_vertex_queue_value() <= self.best_edge_queue_value():
+            while self.best_vertex_queue_value() <= \
+                    self.best_edge_queue_value():
                 self.expand_vertex(self.best_in_vertex_queue())
 
             # add the best edge to the tree
@@ -247,11 +252,17 @@ def plan(self, animation=True):
             self.edge_queue.remove(bestEdge)
 
             # Check if this can improve the current solution
-            estimatedCostOfVertex = self.g_scores[bestEdge[0]] + self.compute_distance_cost(
-                bestEdge[0], bestEdge[1]) + self.compute_heuristic_cost(bestEdge[1], self.goalId)
-            estimatedCostOfEdge = self.compute_distance_cost(self.startId, bestEdge[0]) + self.compute_heuristic_cost(
-                bestEdge[0], bestEdge[1]) + self.compute_heuristic_cost(bestEdge[1], self.goalId)
-            actualCostOfEdge = self.g_scores[bestEdge[0]] + self.compute_distance_cost(bestEdge[0], bestEdge[1])
+            estimatedCostOfVertex = self.g_scores[bestEdge[
+                0]] + self.compute_distance_cost(
+                bestEdge[0], bestEdge[1]) + self.compute_heuristic_cost(
+                bestEdge[1], self.goalId)
+            estimatedCostOfEdge = self.compute_distance_cost(
+                self.startId, bestEdge[0]) + self.compute_heuristic_cost(
+                bestEdge[0], bestEdge[1]) + self.compute_heuristic_cost(
+                bestEdge[1], self.goalId)
+            actualCostOfEdge = self.g_scores[
+                                   bestEdge[0]] + self.compute_distance_cost(
+                bestEdge[0], bestEdge[1])
 
             f1 = estimatedCostOfVertex < self.g_scores[self.goalId]
             f2 = estimatedCostOfEdge < self.g_scores[self.goalId]
@@ -277,10 +288,12 @@ def plan(self, animation=True):
                     try:
                         del self.samples[bestEdge[1]]
                     except KeyError:
+                        # invalid sample key
                         pass
                     eid = self.tree.add_vertex(nextCoord)
                     self.vertex_queue.append(eid)
-                if eid == self.goalId or bestEdge[0] == self.goalId or bestEdge[1] == self.goalId:
+                if eid == self.goalId or bestEdge[0] == self.goalId or \
+                        bestEdge[1] == self.goalId:
                     print("Goal found")
                     foundGoal = True
 
@@ -288,14 +301,18 @@ def plan(self, animation=True):
 
                 g_score = self.compute_distance_cost(
                     bestEdge[0], bestEdge[1])
-                self.g_scores[bestEdge[1]] = g_score + self.g_scores[bestEdge[0]]
-                self.f_scores[bestEdge[1]] = g_score + self.compute_heuristic_cost(bestEdge[1], self.goalId)
+                self.g_scores[bestEdge[1]] = g_score + self.g_scores[
+                    bestEdge[0]]
+                self.f_scores[
+                    bestEdge[1]] = g_score + self.compute_heuristic_cost(
+                    bestEdge[1], self.goalId)
                 self.update_graph()
 
                 # visualize new edge
                 if animation:
                     self.draw_graph(xCenter=xCenter, cBest=cBest,
-                                    cMin=cMin, etheta=etheta, samples=self.samples.values(),
+                                    cMin=cMin, eTheta=eTheta,
+                                    samples=self.samples.values(),
                                     start=firstCoord, end=secondCoord)
 
                 self.remove_queue(lastEdge, bestEdge)
@@ -330,7 +347,8 @@ def remove_queue(self, lastEdge, bestEdge):
         for edge in self.edge_queue:
             if edge[1] == bestEdge[1]:
                 dist_cost = self.compute_distance_cost(edge[1], bestEdge[1])
-                if self.g_scores[edge[1]] + dist_cost >= self.g_scores[self.goalId]:
+                if self.g_scores[edge[1]] + dist_cost >= \
+                        self.g_scores[self.goalId]:
                     if (lastEdge, bestEdge[1]) in self.edge_queue:
                         self.edge_queue.remove(
                             (lastEdge, bestEdge[1]))
@@ -338,8 +356,9 @@ def remove_queue(self, lastEdge, bestEdge):
     def connect(self, start, end):
         # A function which attempts to extend from a start coordinates
         # to goal coordinates
-        steps = int(self.compute_distance_cost(self.tree.real_world_to_node_id(
-            start), self.tree.real_world_to_node_id(end)) * 10)
+        steps = int(self.compute_distance_cost(
+            self.tree.real_world_to_node_id(start),
+            self.tree.real_world_to_node_id(end)) * 10)
         x = np.linspace(start[0], end[0], num=steps)
         y = np.linspace(start[1], end[1], num=steps)
         for i in range(len(x)):
@@ -409,8 +428,8 @@ def sample_unit_ball():
         return np.array([[sample[0]], [sample[1]], [0]])
 
     def sample_free_space(self):
-        rnd = [random.uniform(self.minrand, self.maxrand),
-               random.uniform(self.minrand, self.maxrand)]
+        rnd = [random.uniform(self.min_rand, self.max_rand),
+               random.uniform(self.min_rand, self.max_rand)]
 
         return rnd
 
@@ -418,7 +437,8 @@ def best_vertex_queue_value(self):
         if len(self.vertex_queue) == 0:
             return float('inf')
         values = [self.g_scores[v]
-                  + self.compute_heuristic_cost(v, self.goalId) for v in self.vertex_queue]
+                  + self.compute_heuristic_cost(v, self.goalId) for v in
+                  self.vertex_queue]
         values.sort()
         return values[0]
 
@@ -427,21 +447,25 @@ def best_edge_queue_value(self):
             return float('inf')
         # return the best value in the queue by score g_tau[v] + c(v,x) + h(x)
         values = [self.g_scores[e[0]] + self.compute_distance_cost(e[0], e[1])
-                  + self.compute_heuristic_cost(e[1], self.goalId) for e in self.edge_queue]
+                  + self.compute_heuristic_cost(e[1], self.goalId) for e in
+                  self.edge_queue]
         values.sort(reverse=True)
         return values[0]
 
     def best_in_vertex_queue(self):
         # return the best value in the vertex queue
-        v_plus_vals = [(v, self.g_scores[v] + self.compute_heuristic_cost(v, self.goalId))
-                       for v in self.vertex_queue]
-        v_plus_vals = sorted(v_plus_vals, key=lambda x: x[1])
-        # print(v_plus_vals)
-        return v_plus_vals[0][0]
+        v_plus_values = [(v, self.g_scores[v] +
+                          self.compute_heuristic_cost(v, self.goalId))
+                         for v in self.vertex_queue]
+        v_plus_values = sorted(v_plus_values, key=lambda x: x[1])
+        # print(v_plus_values)
+        return v_plus_values[0][0]
 
     def best_in_edge_queue(self):
-        e_and_values = [(e[0], e[1], self.g_scores[e[0]] + self.compute_distance_cost(
-            e[0], e[1]) + self.compute_heuristic_cost(e[1], self.goalId)) for e in self.edge_queue]
+        e_and_values = [
+            (e[0], e[1], self.g_scores[e[0]] + self.compute_distance_cost(
+                e[0], e[1]) + self.compute_heuristic_cost(e[1], self.goalId))
+            for e in self.edge_queue]
         e_and_values = sorted(e_and_values, key=lambda x: x[2])
 
         return e_and_values[0][0], e_and_values[0][1]
@@ -454,18 +478,19 @@ def expand_vertex(self, vid):
 
         # get the nearest value in vertex for every one in samples where difference is
         # less than the radius
-        neigbors = []
-        for sid, scoord in self.samples.items():
-            scoord = np.array(scoord)
-            if np.linalg.norm(scoord - currCoord, 2) <= self.r and sid != vid:
-                neigbors.append((sid, scoord))
+        neighbors = []
+        for sid, s_coord in self.samples.items():
+            s_coord = np.array(s_coord)
+            if np.linalg.norm(s_coord - currCoord, 2) <= self.r and sid != vid:
+                neighbors.append((sid, s_coord))
 
         # add an edge to the edge queue is the path might improve the solution
-        for neighbor in neigbors:
+        for neighbor in neighbors:
             sid = neighbor[0]
             h_cost = self.compute_heuristic_cost(sid, self.goalId)
             estimated_f_score = self.compute_distance_cost(
-                self.startId, vid) + h_cost + self.compute_distance_cost(vid, sid)
+                self.startId, vid) + h_cost + self.compute_distance_cost(vid,
+                                                                         sid)
             if estimated_f_score < self.g_scores[self.goalId]:
                 self.edge_queue.append((vid, sid))
 
@@ -476,17 +501,21 @@ def add_vertex_to_edge_queue(self, vid, currCoord):
         if vid not in self.old_vertices:
             neighbors = []
             for v, edges in self.tree.vertices.items():
-                if v != vid and (v, vid) not in self.edge_queue and (vid, v) not in self.edge_queue:
+                if v != vid and (v, vid) not in self.edge_queue and \
+                        (vid, v) not in self.edge_queue:
                     v_coord = self.tree.node_id_to_real_world_coord(v)
                     if np.linalg.norm(currCoord - v_coord, 2) <= self.r:
                         neighbors.append((vid, v_coord))
 
             for neighbor in neighbors:
                 sid = neighbor[0]
-                estimated_f_score = self.compute_distance_cost(self.startId, vid) + self.compute_distance_cost(
+                estimated_f_score = self.compute_distance_cost(
+                    self.startId, vid) + self.compute_distance_cost(
                     vid, sid) + self.compute_heuristic_cost(sid, self.goalId)
                 if estimated_f_score < self.g_scores[self.goalId] and (
-                        self.g_scores[vid] + self.compute_distance_cost(vid, sid)) < self.g_scores[sid]:
+                        self.g_scores[vid] +
+                        self.compute_distance_cost(vid, sid)) < \
+                        self.g_scores[sid]:
                     self.edge_queue.append((vid, sid))
 
     def update_graph(self):
@@ -511,37 +540,40 @@ def update_graph(self):
 
             # find a non visited successor to the current node
             successors = self.tree.vertices[currId]
-            for succesor in successors:
-                if succesor in closedSet:
+            for successor in successors:
+                if successor in closedSet:
                     continue
                 else:
-                    # claculate tentative g score
+                    # calculate tentative g score
                     g_score = self.g_scores[currId] + \
-                              self.compute_distance_cost(currId, succesor)
-                    if succesor not in openSet:
+                              self.compute_distance_cost(currId, successor)
+                    if successor not in openSet:
                         # add the successor to open set
-                        openSet.append(succesor)
-                    elif g_score >= self.g_scores[succesor]:
+                        openSet.append(successor)
+                    elif g_score >= self.g_scores[successor]:
                         continue
 
                     # update g and f scores
-                    self.g_scores[succesor] = g_score
-                    self.f_scores[succesor] = g_score + self.compute_heuristic_cost(succesor, self.goalId)
+                    self.g_scores[successor] = g_score
+                    self.f_scores[
+                        successor] = g_score + self.compute_heuristic_cost(
+                        successor, self.goalId)
 
                     # store the parent and child
-                    self.nodes[succesor] = currId
+                    self.nodes[successor] = currId
 
-    def draw_graph(self, xCenter=None, cBest=None, cMin=None, etheta=None,
+    def draw_graph(self, xCenter=None, cBest=None, cMin=None, eTheta=None,
                    samples=None, start=None, end=None):
         plt.clf()
         # for stopping simulation with the esc key.
-        plt.gcf().canvas.mpl_connect('key_release_event',
-                lambda event: [exit(0) if event.key == 'escape' else None])
+        plt.gcf().canvas.mpl_connect(
+            'key_release_event',
+            lambda event: [exit(0) if event.key == 'escape' else None])
         for rnd in samples:
             if rnd is not None:
                 plt.plot(rnd[0], rnd[1], "^k")
                 if cBest != float('inf'):
-                    self.plot_ellipse(xCenter, cBest, cMin, etheta)
+                    self.plot_ellipse(xCenter, cBest, cMin, eTheta)
 
         if start is not None and end is not None:
             plt.plot([start[0], start[1]], [end[0], end[1]], "-g")
@@ -556,11 +588,11 @@ def draw_graph(self, xCenter=None, cBest=None, cMin=None, etheta=None,
         plt.pause(0.01)
 
     @staticmethod
-    def plot_ellipse(xCenter, cBest, cMin, etheta):  # pragma: no cover
+    def plot_ellipse(xCenter, cBest, cMin, eTheta):  # pragma: no cover
 
         a = math.sqrt(cBest ** 2 - cMin ** 2) / 2.0
         b = cBest / 2.0
-        angle = math.pi / 2.0 - etheta
+        angle = math.pi / 2.0 - eTheta
         cx = xCenter[0]
         cy = xCenter[1]
 
diff --git a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
index cf76ab67cd..ecb875f410 100644
--- a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
+++ b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
@@ -17,7 +17,7 @@
 
 class BidirectionalAStarPlanner:
 
-    def __init__(self, ox, oy, reso, rr):
+    def __init__(self, ox, oy, resolution, rr):
         """
         Initialize grid map for a star planning
 
@@ -27,21 +27,24 @@ def __init__(self, ox, oy, reso, rr):
         rr: robot radius[m]
         """
 
-        self.reso = reso
+        self.min_x, self.min_y = None, None
+        self.max_x, self.max_y = None, None
+        self.x_width, self.y_width, self.obstacle_map = None, None, None
+        self.resolution = resolution
         self.rr = rr
         self.calc_obstacle_map(ox, oy)
         self.motion = self.get_motion_model()
 
     class Node:
-        def __init__(self, x, y, cost, pind):
+        def __init__(self, x, y, cost, parent_index):
             self.x = x  # index of grid
             self.y = y  # index of grid
             self.cost = cost
-            self.pind = pind
+            self.parent_index = parent_index
 
         def __str__(self):
             return str(self.x) + "," + str(self.y) + "," + str(
-                self.cost) + "," + str(self.pind)
+                self.cost) + "," + str(self.parent_index)
 
     def planning(self, sx, sy, gx, gy):
         """
@@ -58,18 +61,19 @@ def planning(self, sx, sy, gx, gy):
             ry: y position list of the final path
         """
 
-        nstart = self.Node(self.calc_xyindex(sx, self.minx),
-                           self.calc_xyindex(sy, self.miny), 0.0, -1)
-        ngoal = self.Node(self.calc_xyindex(gx, self.minx),
-                          self.calc_xyindex(gy, self.miny), 0.0, -1)
+        start_node = self.Node(self.calc_xy_index(sx, self.min_x),
+                               self.calc_xy_index(sy, self.min_y), 0.0, -1)
+        goal_node = self.Node(self.calc_xy_index(gx, self.min_x),
+                              self.calc_xy_index(gy, self.min_y), 0.0, -1)
 
         open_set_A, closed_set_A = dict(), dict()
         open_set_B, closed_set_B = dict(), dict()
-        open_set_A[self.calc_grid_index(nstart)] = nstart
-        open_set_B[self.calc_grid_index(ngoal)] = ngoal
+        open_set_A[self.calc_grid_index(start_node)] = start_node
+        open_set_B[self.calc_grid_index(goal_node)] = goal_node
 
-        current_A = nstart
-        current_B = ngoal
+        current_A = start_node
+        current_B = goal_node
+        meet_point_A, meet_point_B = None, None
 
         while 1:
             if len(open_set_A) == 0:
@@ -94,22 +98,23 @@ def planning(self, sx, sy, gx, gy):
 
             # show graph
             if show_animation:  # pragma: no cover
-                plt.plot(self.calc_grid_position(current_A.x, self.minx),
-                         self.calc_grid_position(current_A.y, self.miny), "xc")
-                plt.plot(self.calc_grid_position(current_B.x, self.minx),
-                         self.calc_grid_position(current_B.y, self.miny), "xc")
+                plt.plot(self.calc_grid_position(current_A.x, self.min_x),
+                         self.calc_grid_position(current_A.y, self.min_y),
+                         "xc")
+                plt.plot(self.calc_grid_position(current_B.x, self.min_x),
+                         self.calc_grid_position(current_B.y, self.min_y),
+                         "xc")
                 # for stopping simulation with the esc key.
-                plt.gcf().canvas.mpl_connect('key_release_event',
-                                             lambda event:
-                                             [exit(0) if event.key == 'escape'
-                                              else None])
+                plt.gcf().canvas.mpl_connect(
+                    'key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
                 if len(closed_set_A.keys()) % 10 == 0:
                     plt.pause(0.001)
 
             if current_A.x == current_B.x and current_A.y == current_B.y:
                 print("Found goal")
-                meetpointA = current_A
-                meetpointB = current_B
+                meet_point_A = current_A
+                meet_point_B = current_B
                 break
 
             # Remove the item from the open set
@@ -158,7 +163,7 @@ def planning(self, sx, sy, gx, gy):
                             open_set_B[n_ids[1]] = c_nodes[1]
 
         rx, ry = self.calc_final_bidirectional_path(
-            meetpointA, meetpointB, closed_set_A, closed_set_B)
+            meet_point_A, meet_point_B, closed_set_A, closed_set_B)
 
         return rx, ry
 
@@ -175,16 +180,16 @@ def calc_final_bidirectional_path(self, n1, n2, setA, setB):
 
         return rx, ry
 
-    def calc_final_path(self, ngoal, closedset):
+    def calc_final_path(self, goal_node, closed_set):
         # generate final course
-        rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
-            self.calc_grid_position(ngoal.y, self.miny)]
-        pind = ngoal.parent_index
-        while pind != -1:
-            n = closedset[pind]
-            rx.append(self.calc_grid_position(n.x, self.minx))
-            ry.append(self.calc_grid_position(n.y, self.miny))
-            pind = n.parent_index
+        rx, ry = [self.calc_grid_position(goal_node.x, self.min_x)], \
+                 [self.calc_grid_position(goal_node.y, self.min_y)]
+        parent_index = goal_node.parent_index
+        while parent_index != -1:
+            n = closed_set[parent_index]
+            rx.append(self.calc_grid_position(n.x, self.min_x))
+            ry.append(self.calc_grid_position(n.y, self.min_y))
+            parent_index = n.parent_index
 
         return rx, ry
 
@@ -210,69 +215,69 @@ def find_total_cost(self, open_set, lambda_, n1):
         f_cost = g_cost + h_cost
         return f_cost
 
-    def calc_grid_position(self, index, minp):
+    def calc_grid_position(self, index, min_position):
         """
         calc grid position
 
         :param index:
-        :param minp:
+        :param min_position:
         :return:
         """
-        pos = index * self.reso + minp
+        pos = index * self.resolution + min_position
         return pos
 
-    def calc_xyindex(self, position, min_pos):
-        return round((position - min_pos) / self.reso)
+    def calc_xy_index(self, position, min_pos):
+        return round((position - min_pos) / self.resolution)
 
     def calc_grid_index(self, node):
-        return (node.y - self.miny) * self.xwidth + (node.x - self.minx)
+        return (node.y - self.min_y) * self.x_width + (node.x - self.min_x)
 
     def verify_node(self, node):
-        px = self.calc_grid_position(node.x, self.minx)
-        py = self.calc_grid_position(node.y, self.miny)
+        px = self.calc_grid_position(node.x, self.min_x)
+        py = self.calc_grid_position(node.y, self.min_y)
 
-        if px < self.minx:
+        if px < self.min_x:
             return False
-        elif py < self.miny:
+        elif py < self.min_y:
             return False
-        elif px >= self.maxx:
+        elif px >= self.max_x:
             return False
-        elif py >= self.maxy:
+        elif py >= self.max_y:
             return False
 
         # collision check
-        if self.obmap[node.x][node.y]:
+        if self.obstacle_map[node.x][node.y]:
             return False
 
         return True
 
     def calc_obstacle_map(self, ox, oy):
 
-        self.minx = round(min(ox))
-        self.miny = round(min(oy))
-        self.maxx = round(max(ox))
-        self.maxy = round(max(oy))
-        print("min_x:", self.minx)
-        print("min_y:", self.miny)
-        print("max_x:", self.maxx)
-        print("max_y:", self.maxy)
+        self.min_x = round(min(ox))
+        self.min_y = round(min(oy))
+        self.max_x = round(max(ox))
+        self.max_y = round(max(oy))
+        print("min_x:", self.min_x)
+        print("min_y:", self.min_y)
+        print("max_x:", self.max_x)
+        print("max_y:", self.max_y)
 
-        self.xwidth = round((self.maxx - self.minx) / self.reso)
-        self.ywidth = round((self.maxy - self.miny) / self.reso)
-        print("x_width:", self.xwidth)
-        print("y_width:", self.ywidth)
+        self.x_width = round((self.max_x - self.min_x) / self.resolution)
+        self.y_width = round((self.max_y - self.min_y) / self.resolution)
+        print("x_width:", self.x_width)
+        print("y_width:", self.y_width)
 
         # obstacle map generation
-        self.obmap = [[False for _ in range(self.ywidth)]
-                      for _ in range(self.xwidth)]
-        for ix in range(self.xwidth):
-            x = self.calc_grid_position(ix, self.minx)
-            for iy in range(self.ywidth):
-                y = self.calc_grid_position(iy, self.miny)
+        self.obstacle_map = [[False for _ in range(self.y_width)]
+                             for _ in range(self.x_width)]
+        for ix in range(self.x_width):
+            x = self.calc_grid_position(ix, self.min_x)
+            for iy in range(self.y_width):
+                y = self.calc_grid_position(iy, self.min_y)
                 for iox, ioy in zip(ox, oy):
                     d = math.hypot(iox - x, ioy - y)
                     if d <= self.rr:
-                        self.obmap[ix][iy] = True
+                        self.obstacle_map[ix][iy] = True
                         break
 
     @staticmethod
diff --git a/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py b/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py
index 328898db85..dd4282573e 100644
--- a/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py
+++ b/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py
@@ -17,7 +17,7 @@
 
 class BidirectionalBreadthFirstSearchPlanner:
 
-    def __init__(self, ox, oy, reso, rr):
+    def __init__(self, ox, oy, resolution, rr):
         """
         Initialize grid map for bfs planning
 
@@ -27,22 +27,25 @@ def __init__(self, ox, oy, reso, rr):
         rr: robot radius[m]
         """
 
-        self.reso = reso
+        self.min_x, self.min_y = None, None
+        self.max_x, self.max_y = None, None
+        self.x_width, self.y_width, self.obstacle_map = None, None, None
+        self.resolution = resolution
         self.rr = rr
         self.calc_obstacle_map(ox, oy)
         self.motion = self.get_motion_model()
 
     class Node:
-        def __init__(self, x, y, cost, pind, parent):
+        def __init__(self, x, y, cost, parent_index, parent):
             self.x = x  # index of grid
             self.y = y  # index of grid
             self.cost = cost
-            self.pind = pind
+            self.parent_index = parent_index
             self.parent = parent
 
         def __str__(self):
             return str(self.x) + "," + str(self.y) + "," + str(
-                self.cost) + "," + str(self.pind)
+                self.cost) + "," + str(self.parent_index)
 
     def planning(self, sx, sy, gx, gy):
         """
@@ -59,15 +62,19 @@ def planning(self, sx, sy, gx, gy):
             ry: y position list of the final path
         """
 
-        nstart = self.Node(self.calc_xyindex(sx, self.minx),
-                           self.calc_xyindex(sy, self.miny), 0.0, -1, None)
-        ngoal = self.Node(self.calc_xyindex(gx, self.minx),
-                          self.calc_xyindex(gy, self.miny), 0.0, -1, None)
+        start_node = self.Node(self.calc_xy_index(sx, self.min_x),
+                               self.calc_xy_index(sy, self.min_y), 0.0, -1,
+                               None)
+        goal_node = self.Node(self.calc_xy_index(gx, self.min_x),
+                              self.calc_xy_index(gy, self.min_y), 0.0, -1,
+                              None)
 
         open_set_A, closed_set_A = dict(), dict()
         open_set_B, closed_set_B = dict(), dict()
-        open_set_B[self.calc_grid_index(ngoal)] = ngoal
-        open_set_A[self.calc_grid_index(nstart)] = nstart
+        open_set_B[self.calc_grid_index(goal_node)] = goal_node
+        open_set_A[self.calc_grid_index(start_node)] = start_node
+
+        meet_point_A, meet_point_B = None, None
 
         while 1:
             if len(open_set_A) == 0:
@@ -89,28 +96,29 @@ def planning(self, sx, sy, gx, gy):
 
             # show graph
             if show_animation:  # pragma: no cover
-                plt.plot(self.calc_grid_position(current_A.x, self.minx),
-                         self.calc_grid_position(current_A.y, self.miny), "xc")
-                plt.plot(self.calc_grid_position(current_B.x, self.minx),
-                         self.calc_grid_position(current_B.y, self.miny), "xc")
+                plt.plot(self.calc_grid_position(current_A.x, self.min_x),
+                         self.calc_grid_position(current_A.y, self.min_y),
+                         "xc")
+                plt.plot(self.calc_grid_position(current_B.x, self.min_x),
+                         self.calc_grid_position(current_B.y, self.min_y),
+                         "xc")
                 # for stopping simulation with the esc key.
-                plt.gcf().canvas.mpl_connect('key_release_event',
-                                             lambda event:
-                                             [exit(0) if
-                                              event.key == 'escape' else None])
+                plt.gcf().canvas.mpl_connect(
+                    'key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
                 if len(closed_set_A.keys()) % 10 == 0:
                     plt.pause(0.001)
 
             if c_id_A in closed_set_B:
                 print("Find goal")
-                meetpointA = closed_set_A[c_id_A]
-                meetpointB = closed_set_B[c_id_A]
+                meet_point_A = closed_set_A[c_id_A]
+                meet_point_B = closed_set_B[c_id_A]
                 break
 
             elif c_id_B in closed_set_A:
                 print("Find goal")
-                meetpointA = closed_set_A[c_id_B]
-                meetpointB = closed_set_B[c_id_B]
+                meet_point_A = closed_set_A[c_id_B]
+                meet_point_B = closed_set_B[c_id_B]
                 break
 
             # expand_grid search grid based on motion model
@@ -137,20 +145,18 @@ def planning(self, sx, sy, gx, gy):
                 if not self.verify_node(node_B):
                     breakB = True
 
-                if (n_id_A not in closed_set_A) and (n_id_A not in
-                                                     open_set_A) and (not
-                                                                      breakA):
+                if (n_id_A not in closed_set_A) and \
+                        (n_id_A not in open_set_A) and (not breakA):
                     node_A.parent = current_A
                     open_set_A[n_id_A] = node_A
 
-                if (n_id_B not in closed_set_B) and (n_id_B not in
-                                                     open_set_B) and (not
-                                                                      breakB):
+                if (n_id_B not in closed_set_B) and \
+                        (n_id_B not in open_set_B) and (not breakB):
                     node_B.parent = current_B
                     open_set_B[n_id_B] = node_B
 
         rx, ry = self.calc_final_path_bidir(
-            meetpointA, meetpointB, closed_set_A, closed_set_B)
+            meet_point_A, meet_point_B, closed_set_A, closed_set_B)
         return rx, ry
 
     # takes both set and meeting nodes and calculate optimal path
@@ -166,81 +172,81 @@ def calc_final_path_bidir(self, n1, n2, setA, setB):
 
         return rx, ry
 
-    def calc_final_path(self, ngoal, closedset):
+    def calc_final_path(self, goal_node, closed_set):
         # generate final course
-        rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
-            self.calc_grid_position(ngoal.y, self.miny)]
-        n = closedset[ngoal.parent_index]
+        rx, ry = [self.calc_grid_position(goal_node.x, self.min_x)], [
+            self.calc_grid_position(goal_node.y, self.min_y)]
+        n = closed_set[goal_node.parent_index]
         while n is not None:
-            rx.append(self.calc_grid_position(n.x, self.minx))
-            ry.append(self.calc_grid_position(n.y, self.miny))
+            rx.append(self.calc_grid_position(n.x, self.min_x))
+            ry.append(self.calc_grid_position(n.y, self.min_y))
             n = n.parent
 
         return rx, ry
 
-    def calc_grid_position(self, index, minp):
+    def calc_grid_position(self, index, min_position):
         """
         calc grid position
 
         :param index:
-        :param minp:
+        :param min_position:
         :return:
         """
-        pos = index * self.reso + minp
+        pos = index * self.resolution + min_position
         return pos
 
-    def calc_xyindex(self, position, min_pos):
-        return round((position - min_pos) / self.reso)
+    def calc_xy_index(self, position, min_pos):
+        return round((position - min_pos) / self.resolution)
 
     def calc_grid_index(self, node):
-        return (node.y - self.miny) * self.xwidth + (node.x - self.minx)
+        return (node.y - self.min_y) * self.x_width + (node.x - self.min_x)
 
     def verify_node(self, node):
-        px = self.calc_grid_position(node.x, self.minx)
-        py = self.calc_grid_position(node.y, self.miny)
+        px = self.calc_grid_position(node.x, self.min_x)
+        py = self.calc_grid_position(node.y, self.min_y)
 
-        if px < self.minx:
+        if px < self.min_x:
             return False
-        elif py < self.miny:
+        elif py < self.min_y:
             return False
-        elif px >= self.maxx:
+        elif px >= self.max_x:
             return False
-        elif py >= self.maxy:
+        elif py >= self.max_y:
             return False
 
         # collision check
-        if self.obmap[node.x][node.y]:
+        if self.obstacle_map[node.x][node.y]:
             return False
 
         return True
 
     def calc_obstacle_map(self, ox, oy):
 
-        self.minx = round(min(ox))
-        self.miny = round(min(oy))
-        self.maxx = round(max(ox))
-        self.maxy = round(max(oy))
-        print("min_x:", self.minx)
-        print("min_y:", self.miny)
-        print("max_x:", self.maxx)
-        print("max_y:", self.maxy)
+        self.min_x = round(min(ox))
+        self.min_y = round(min(oy))
+        self.max_x = round(max(ox))
+        self.max_y = round(max(oy))
+        print("min_x:", self.min_x)
+        print("min_y:", self.min_y)
+        print("max_x:", self.max_x)
+        print("max_y:", self.max_y)
 
-        self.xwidth = round((self.maxx - self.minx) / self.reso)
-        self.ywidth = round((self.maxy - self.miny) / self.reso)
-        print("x_width:", self.xwidth)
-        print("y_width:", self.ywidth)
+        self.x_width = round((self.max_x - self.min_x) / self.resolution)
+        self.y_width = round((self.max_y - self.min_y) / self.resolution)
+        print("x_width:", self.x_width)
+        print("y_width:", self.y_width)
 
         # obstacle map generation
-        self.obmap = [[False for _ in range(self.ywidth)]
-                      for _ in range(self.xwidth)]
-        for ix in range(self.xwidth):
-            x = self.calc_grid_position(ix, self.minx)
-            for iy in range(self.ywidth):
-                y = self.calc_grid_position(iy, self.miny)
+        self.obstacle_map = [[False for _ in range(self.y_width)]
+                             for _ in range(self.x_width)]
+        for ix in range(self.x_width):
+            x = self.calc_grid_position(ix, self.min_x)
+            for iy in range(self.y_width):
+                y = self.calc_grid_position(iy, self.min_y)
                 for iox, ioy in zip(ox, oy):
                     d = math.hypot(iox - x, ioy - y)
                     if d <= self.rr:
-                        self.obmap[ix][iy] = True
+                        self.obstacle_map[ix][iy] = True
                         break
 
     @staticmethod
diff --git a/PathPlanning/VoronoiRoadMap/dijkstra_search.py b/PathPlanning/VoronoiRoadMap/dijkstra_search.py
index 54d15ea9fb..aef07ebbaf 100644
--- a/PathPlanning/VoronoiRoadMap/dijkstra_search.py
+++ b/PathPlanning/VoronoiRoadMap/dijkstra_search.py
@@ -125,6 +125,7 @@ def find_id(self, node_x_list, node_y_list, target_node):
             if self.is_same_node_with_xy(node_x_list[i], node_y_list[i],
                                          target_node):
                 return i
+        return None
 
     @staticmethod
     def is_same_node_with_xy(node_x, node_y, node_b):
diff --git a/PathTracking/cgmres_nmpc/cgmres_nmpc.py b/PathTracking/cgmres_nmpc/cgmres_nmpc.py
index 98a12ab10c..bb5699b888 100644
--- a/PathTracking/cgmres_nmpc/cgmres_nmpc.py
+++ b/PathTracking/cgmres_nmpc/cgmres_nmpc.py
@@ -5,17 +5,18 @@
 author Atsushi Sakai (@Atsushi_twi)
 
 Ref:
-
-- Shunichi09/nonlinear_control: Implementing the nonlinear model predictive control, sliding mode control https://github.com/Shunichi09/nonlinear_control
+Shunichi09/nonlinear_control: Implementing the nonlinear model predictive
+control, sliding mode control https://github.com/Shunichi09/nonlinear_control
 
 """
 
-import numpy as np
+from math import cos, sin, radians, atan2
+
 import matplotlib.pyplot as plt
-import math
+import numpy as np
 
 U_A_MAX = 1.0
-U_OMEGA_MAX = math.radians(45.0)
+U_OMEGA_MAX = radians(45.0)
 PHI_V = 0.01
 PHI_OMEGA = 0.01
 WB = 0.25  # [m] wheel base
@@ -24,19 +25,18 @@
 
 
 def differential_model(v, yaw, u_1, u_2):
-
-    dx = math.cos(yaw) * v
-    dy = math.sin(yaw) * v
+    dx = cos(yaw) * v
+    dy = sin(yaw) * v
     dv = u_1
-    dyaw = v / WB * math.sin(u_2)  # tan is not good for nonlinear optimization
+    # tangent is not good for nonlinear optimization
+    d_yaw = v / WB * sin(u_2)
 
-    return dx, dy, dyaw, dv
+    return dx, dy, d_yaw, dv
 
 
-class TwoWheeledSystem():
+class TwoWheeledSystem:
 
     def __init__(self, init_x, init_y, init_yaw, init_v):
-
         self.x = init_x
         self.y = init_y
         self.yaw = init_yaw
@@ -47,12 +47,11 @@ def __init__(self, init_x, init_y, init_yaw, init_v):
         self.history_v = [init_v]
 
     def update_state(self, u_1, u_2, dt=0.01):
-
-        dx, dy, dyaw, dv = differential_model(self.v, self.yaw, u_1, u_2)
+        dx, dy, d_yaw, dv = differential_model(self.v, self.yaw, u_1, u_2)
 
         self.x += dt * dx
         self.y += dt * dy
-        self.yaw += dt * dyaw
+        self.yaw += dt * d_yaw
         self.v += dt * dv
 
         # save
@@ -62,13 +61,15 @@ def update_state(self, u_1, u_2, dt=0.01):
         self.history_v.append(self.v)
 
 
-class NMPCSimulatorSystem():
+class NMPCSimulatorSystem:
 
     def calc_predict_and_adjoint_state(self, x, y, yaw, v, u_1s, u_2s, N, dt):
+        # by using state equation
         x_s, y_s, yaw_s, v_s = self._calc_predict_states(
-            x, y, yaw, v, u_1s, u_2s, N, dt)  # by using state equation
+            x, y, yaw, v, u_1s, u_2s, N, dt)
+        # by using adjoint equation
         lam_1s, lam_2s, lam_3s, lam_4s = self._calc_adjoint_states(
-            x_s, y_s, yaw_s, v_s, u_1s, u_2s, N, dt)  # by using adjoint equation
+            x_s, y_s, yaw_s, v_s, u_2s, N, dt)
 
         return x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s
 
@@ -88,7 +89,7 @@ def _calc_predict_states(self, x, y, yaw, v, u_1s, u_2s, N, dt):
 
         return x_s, y_s, yaw_s, v_s
 
-    def _calc_adjoint_states(self, x_s, y_s, yaw_s, v_s, u_1s, u_2s, N, dt):
+    def _calc_adjoint_states(self, x_s, y_s, yaw_s, v_s, u_2s, N, dt):
         lam_1s = [x_s[-1]]
         lam_2s = [y_s[-1]]
         lam_3s = [yaw_s[-1]]
@@ -97,8 +98,8 @@ def _calc_adjoint_states(self, x_s, y_s, yaw_s, v_s, u_1s, u_2s, N, dt):
         # backward adjoint state calc
         for i in range(N - 1, 0, -1):
             temp_lam_1, temp_lam_2, temp_lam_3, temp_lam_4 = self._adjoint_state_with_oylar(
-                x_s[i], y_s[i], yaw_s[i], v_s[i], lam_1s[0], lam_2s[0], lam_3s[0], lam_4s[0],
-                u_1s[i], u_2s[i], dt)
+                yaw_s[i], v_s[i], lam_1s[0], lam_2s[0], lam_3s[0], lam_4s[0],
+                u_2s[i], dt)
             lam_1s.insert(0, temp_lam_1)
             lam_2s.insert(0, temp_lam_2)
             lam_3s.insert(0, temp_lam_3)
@@ -106,7 +107,8 @@ def _calc_adjoint_states(self, x_s, y_s, yaw_s, v_s, u_1s, u_2s, N, dt):
 
         return lam_1s, lam_2s, lam_3s, lam_4s
 
-    def _predict_state_with_oylar(self, x, y, yaw, v, u_1, u_2, dt):
+    @staticmethod
+    def _predict_state_with_oylar(x, y, yaw, v, u_1, u_2, dt):
 
         dx, dy, dyaw, dv = differential_model(
             v, yaw, u_1, u_2)
@@ -118,21 +120,21 @@ def _predict_state_with_oylar(self, x, y, yaw, v, u_1, u_2, dt):
 
         return next_x_1, next_x_2, next_x_3, next_x_4
 
-    def _adjoint_state_with_oylar(self, x, y, yaw, v, lam_1, lam_2, lam_3, lam_4, u_1, u_2, dt):
+    @staticmethod
+    def _adjoint_state_with_oylar(yaw, v, lam_1, lam_2, lam_3, lam_4, u_2, dt):
 
         # ∂H/∂x
         pre_lam_1 = lam_1 + dt * 0.0
         pre_lam_2 = lam_2 + dt * 0.0
-        pre_lam_3 = lam_3 + dt * \
-            (- lam_1 * math.sin(yaw) * v + lam_2 * math.cos(yaw) * v)
-        pre_lam_4 = lam_4 + dt * \
-            (lam_1 * math.cos(yaw) + lam_2 *
-             math.sin(yaw) + lam_3 * math.sin(u_2) / WB)
+        tmp1 = - lam_1 * sin(yaw) * v + lam_2 * cos(yaw) * v
+        pre_lam_3 = lam_3 + dt * tmp1
+        tmp2 = lam_1 * cos(yaw) + lam_2 * sin(yaw) + lam_3 * sin(u_2) / WB
+        pre_lam_4 = lam_4 + dt * tmp2
 
         return pre_lam_1, pre_lam_2, pre_lam_3, pre_lam_4
 
 
-class NMPCController_with_CGMRES():
+class NMPCControllerCGMRES:
     """
     Attributes
     ------------
@@ -145,7 +147,7 @@ class NMPCController_with_CGMRES():
     alpha : float
         gain of predict time
     N : int
-        predicte step, discritize value
+        predict step, discrete value
     threshold : float
         cgmres's threshold value
     input_num : int
@@ -226,20 +228,22 @@ def calc_input(self, x, y, yaw, v, time):
         dx_4 = x_4_dot * self.ht
 
         x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s = self.simulator.calc_predict_and_adjoint_state(
-            x + dx_1, y + dx_2, yaw + dx_3, v + dx_4, self.u_1s, self.u_2s, self.N, dt)
+            x + dx_1, y + dx_2, yaw + dx_3, v + dx_4, self.u_1s, self.u_2s,
+            self.N, dt)
 
         # Fxt:F(U,x+hx˙,t+h)
-        Fxt = self._calc_f(x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s,
-                           self.u_1s, self.u_2s, self.dummy_u_1s, self.dummy_u_2s,
-                           self.raw_1s, self.raw_2s, self.N, dt)
+        Fxt = self._calc_f(v_s, lam_3s, lam_4s,
+                           self.u_1s, self.u_2s, self.dummy_u_1s,
+                           self.dummy_u_2s,
+                           self.raw_1s, self.raw_2s, self.N)
 
         # F:F(U,x,t)
         x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s = self.simulator.calc_predict_and_adjoint_state(
             x, y, yaw, v, self.u_1s, self.u_2s, self.N, dt)
 
-        F = self._calc_f(x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s,
+        F = self._calc_f(v_s, lam_3s, lam_4s,
                          self.u_1s, self.u_2s, self.dummy_u_1s, self.dummy_u_2s,
-                         self.raw_1s, self.raw_2s, self.N, dt)
+                         self.raw_1s, self.raw_2s, self.N)
 
         right = -self.zeta * F - ((Fxt - F) / self.ht)
 
@@ -251,17 +255,19 @@ def calc_input(self, x, y, yaw, v, time):
         draw_2 = self.raw_2s * self.ht
 
         x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s = self.simulator.calc_predict_and_adjoint_state(
-            x + dx_1, y + dx_2, yaw + dx_3, v + dx_4, self.u_1s + du_1, self.u_2s + du_2, self.N, dt)
+            x + dx_1, y + dx_2, yaw + dx_3, v + dx_4, self.u_1s + du_1,
+            self.u_2s + du_2, self.N, dt)
 
         # Fuxt:F(U+hdU(0),x+hx˙,t+h)
-        Fuxt = self._calc_f(x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s,
+        Fuxt = self._calc_f(v_s, lam_3s, lam_4s,
                             self.u_1s + du_1, self.u_2s + du_2,
-                            self.dummy_u_1s + ddummy_u_1, self.dummy_u_2s + ddummy_u_2,
-                            self.raw_1s + draw_1, self.raw_2s + draw_2, self.N, dt)
+                            self.dummy_u_1s + ddummy_u_1,
+                            self.dummy_u_2s + ddummy_u_2,
+                            self.raw_1s + draw_1, self.raw_2s + draw_2, self.N)
 
         left = ((Fuxt - Fxt) / self.ht)
 
-        # calculationg cgmres
+        # calculating cgmres
         r0 = right - left
         r0_norm = np.linalg.norm(r0)
 
@@ -276,6 +282,9 @@ def calc_input(self, x, y, yaw, v, time):
 
         ys_pre = None
 
+        du_1_new, du_2_new, draw_1_new, draw_2_new = None, None, None, None
+        ddummy_u_1_new, ddummy_u_2_new = None, None
+
         for i in range(self.max_iteration):
             du_1 = vs[::self.input_num, i] * self.ht
             du_2 = vs[1::self.input_num, i] * self.ht
@@ -285,12 +294,15 @@ def calc_input(self, x, y, yaw, v, time):
             draw_2 = vs[5::self.input_num, i] * self.ht
 
             x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s = self.simulator.calc_predict_and_adjoint_state(
-                x + dx_1, y + dx_2, yaw + dx_3, v + dx_4, self.u_1s + du_1, self.u_2s + du_2, self.N, dt)
+                x + dx_1, y + dx_2, yaw + dx_3, v + dx_4, self.u_1s + du_1,
+                self.u_2s + du_2, self.N, dt)
 
-            Fuxt = self._calc_f(x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s,
+            Fuxt = self._calc_f(v_s, lam_3s, lam_4s,
                                 self.u_1s + du_1, self.u_2s + du_2,
-                                self.dummy_u_1s + ddummy_u_1, self.dummy_u_2s + ddummy_u_2,
-                                self.raw_1s + draw_1, self.raw_2s + draw_2, self.N, dt)
+                                self.dummy_u_1s + ddummy_u_1,
+                                self.dummy_u_2s + ddummy_u_2,
+                                self.raw_1s + draw_1, self.raw_2s + draw_2,
+                                self.N)
 
             Av = ((Fuxt - Fxt) / self.ht)
 
@@ -312,14 +324,17 @@ def calc_input(self, x, y, yaw, v, time):
 
             judge_value = r0_norm * e[:i + 1] - np.dot(hs[:i + 1, :i], ys[:i])
 
-            if np.linalg.norm(judge_value) < self.threshold or i == self.max_iteration - 1:
-                update_value = np.dot(vs[:, :i - 1], ys_pre[:i - 1]).flatten()
-                du_1_new = du_1 + update_value[::self.input_num]
-                du_2_new = du_2 + update_value[1::self.input_num]
-                ddummy_u_1_new = ddummy_u_1 + update_value[2::self.input_num]
-                ddummy_u_2_new = ddummy_u_2 + update_value[3::self.input_num]
-                draw_1_new = draw_1 + update_value[4::self.input_num]
-                draw_2_new = draw_2 + update_value[5::self.input_num]
+            flag1 = np.linalg.norm(judge_value) < self.threshold
+
+            flag2 = i == self.max_iteration - 1
+            if flag1 or flag2:
+                update_val = np.dot(vs[:, :i - 1], ys_pre[:i - 1]).flatten()
+                du_1_new = du_1 + update_val[::self.input_num]
+                du_2_new = du_2 + update_val[1::self.input_num]
+                ddummy_u_1_new = ddummy_u_1 + update_val[2::self.input_num]
+                ddummy_u_2_new = ddummy_u_2 + update_val[3::self.input_num]
+                draw_1_new = draw_1 + update_val[4::self.input_num]
+                draw_2_new = draw_2 + update_val[5::self.input_num]
                 break
 
             ys_pre = ys
@@ -335,9 +350,9 @@ def calc_input(self, x, y, yaw, v, time):
         x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s = self.simulator.calc_predict_and_adjoint_state(
             x, y, yaw, v, self.u_1s, self.u_2s, self.N, dt)
 
-        F = self._calc_f(x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s,
+        F = self._calc_f(v_s, lam_3s, lam_4s,
                          self.u_1s, self.u_2s, self.dummy_u_1s, self.dummy_u_2s,
-                         self.raw_1s, self.raw_2s, self.N, dt)
+                         self.raw_1s, self.raw_2s, self.N)
 
         print("norm(F) = {0}".format(np.linalg.norm(F)))
 
@@ -352,36 +367,38 @@ def calc_input(self, x, y, yaw, v, time):
 
         return self.u_1s, self.u_2s
 
-    def _calc_f(self, x_s, y_s, yaw_s, v_s, lam_1s, lam_2s, lam_3s, lam_4s,
-                u_1s, u_2s, dummy_u_1s, dummy_u_2s, raw_1s, raw_2s, N, dt):
+    @staticmethod
+    def _calc_f(v_s, lam_3s, lam_4s, u_1s, u_2s, dummy_u_1s, dummy_u_2s,
+                raw_1s, raw_2s, N):
 
         F = []
         for i in range(N):
             # ∂H/∂u(xi, ui, λi)
             F.append(u_1s[i] + lam_4s[i] + 2.0 * raw_1s[i] * u_1s[i])
             F.append(u_2s[i] + lam_3s[i] * v_s[i] /
-                     WB * math.cos(u_2s[i])**2 + 2.0 * raw_2s[i] * u_2s[i])
+                     WB * cos(u_2s[i]) ** 2 + 2.0 * raw_2s[i] * u_2s[i])
             F.append(-PHI_V + 2.0 * raw_1s[i] * dummy_u_1s[i])
             F.append(-PHI_OMEGA + 2.0 * raw_2s[i] * dummy_u_2s[i])
 
             # C(xi, ui, λi)
-            F.append(u_1s[i]**2 + dummy_u_1s[i]**2 - U_A_MAX**2)
-            F.append(u_2s[i]**2 + dummy_u_2s[i]**2 - U_OMEGA_MAX**2)
+            F.append(u_1s[i] ** 2 + dummy_u_1s[i] ** 2 - U_A_MAX ** 2)
+            F.append(u_2s[i] ** 2 + dummy_u_2s[i] ** 2 - U_OMEGA_MAX ** 2)
 
         return np.array(F)
 
 
-def plot_figures(plant_system, controller, iteration_num, dt):  # pragma: no cover
+def plot_figures(plant_system, controller, iteration_num,
+                 dt):  # pragma: no cover
     # figure
     # time history
     fig_p = plt.figure()
     fig_u = plt.figure()
     fig_f = plt.figure()
 
-    # traj
+    # trajectory
     fig_t = plt.figure()
-    fig_traj = fig_t.add_subplot(111)
-    fig_traj.set_aspect('equal')
+    fig_trajectory = fig_t.add_subplot(111)
+    fig_trajectory.set_aspect('equal')
 
     x_1_fig = fig_p.add_subplot(411)
     x_2_fig = fig_p.add_subplot(412)
@@ -443,11 +460,11 @@ def plot_figures(plant_system, controller, iteration_num, dt):  # pragma: no cov
     f_fig.set_xlabel("time [s]")
     f_fig.set_ylabel("optimal error")
 
-    fig_traj.plot(plant_system.history_x,
-                  plant_system.history_y, "-r")
-    fig_traj.set_xlabel("x [m]")
-    fig_traj.set_ylabel("y [m]")
-    fig_traj.axis("equal")
+    fig_trajectory.plot(plant_system.history_x,
+                        plant_system.history_y, "-r")
+    fig_trajectory.set_xlabel("x [m]")
+    fig_trajectory.set_ylabel("y [m]")
+    fig_trajectory.axis("equal")
 
     # start state
     plot_car(plant_system.history_x[0],
@@ -462,23 +479,25 @@ def plot_figures(plant_system, controller, iteration_num, dt):  # pragma: no cov
     plt.show()
 
 
-def plot_car(x, y, yaw, steer=0.0, cabcolor="-r", truckcolor="-k"):  # pragma: no cover
+def plot_car(x, y, yaw, steer=0.0, truck_color="-k"):  # pragma: no cover
 
     # Vehicle parameters
     LENGTH = 0.4  # [m]
     WIDTH = 0.2  # [m]
-    BACKTOWHEEL = 0.1  # [m]
+    BACK_TO_WHEEL = 0.1  # [m]
     WHEEL_LEN = 0.03  # [m]
     WHEEL_WIDTH = 0.02  # [m]
     TREAD = 0.07  # [m]
 
-    outline = np.array([[-BACKTOWHEEL, (LENGTH - BACKTOWHEEL), (LENGTH - BACKTOWHEEL),
-                         -BACKTOWHEEL, -BACKTOWHEEL],
-                        [WIDTH / 2, WIDTH / 2, - WIDTH / 2, -WIDTH / 2, WIDTH / 2]])
+    outline = np.array(
+        [[-BACK_TO_WHEEL, (LENGTH - BACK_TO_WHEEL), (LENGTH - BACK_TO_WHEEL),
+          -BACK_TO_WHEEL, -BACK_TO_WHEEL],
+         [WIDTH / 2, WIDTH / 2, - WIDTH / 2, -WIDTH / 2, WIDTH / 2]])
 
-    fr_wheel = np.array([[WHEEL_LEN, -WHEEL_LEN, -WHEEL_LEN, WHEEL_LEN, WHEEL_LEN],
-                         [-WHEEL_WIDTH - TREAD, -WHEEL_WIDTH - TREAD, WHEEL_WIDTH -
-                          TREAD, WHEEL_WIDTH - TREAD, -WHEEL_WIDTH - TREAD]])
+    fr_wheel = np.array(
+        [[WHEEL_LEN, -WHEEL_LEN, -WHEEL_LEN, WHEEL_LEN, WHEEL_LEN],
+         [-WHEEL_WIDTH - TREAD, -WHEEL_WIDTH - TREAD, WHEEL_WIDTH -
+          TREAD, WHEEL_WIDTH - TREAD, -WHEEL_WIDTH - TREAD]])
 
     rr_wheel = np.copy(fr_wheel)
 
@@ -487,10 +506,10 @@ def plot_car(x, y, yaw, steer=0.0, cabcolor="-r", truckcolor="-k"):  # pragma: n
     rl_wheel = np.copy(rr_wheel)
     rl_wheel[1, :] *= -1
 
-    Rot1 = np.array([[math.cos(yaw), math.sin(yaw)],
-                     [-math.sin(yaw), math.cos(yaw)]])
-    Rot2 = np.array([[math.cos(steer), math.sin(steer)],
-                     [-math.sin(steer), math.cos(steer)]])
+    Rot1 = np.array([[cos(yaw), sin(yaw)],
+                     [-sin(yaw), cos(yaw)]])
+    Rot2 = np.array([[cos(steer), sin(steer)],
+                     [-sin(steer), cos(steer)]])
 
     fr_wheel = (fr_wheel.T.dot(Rot2)).T
     fl_wheel = (fl_wheel.T.dot(Rot2)).T
@@ -516,20 +535,19 @@ def plot_car(x, y, yaw, steer=0.0, cabcolor="-r", truckcolor="-k"):  # pragma: n
     rl_wheel[1, :] += y
 
     plt.plot(np.array(outline[0, :]).flatten(),
-             np.array(outline[1, :]).flatten(), truckcolor)
+             np.array(outline[1, :]).flatten(), truck_color)
     plt.plot(np.array(fr_wheel[0, :]).flatten(),
-             np.array(fr_wheel[1, :]).flatten(), truckcolor)
+             np.array(fr_wheel[1, :]).flatten(), truck_color)
     plt.plot(np.array(rr_wheel[0, :]).flatten(),
-             np.array(rr_wheel[1, :]).flatten(), truckcolor)
+             np.array(rr_wheel[1, :]).flatten(), truck_color)
     plt.plot(np.array(fl_wheel[0, :]).flatten(),
-             np.array(fl_wheel[1, :]).flatten(), truckcolor)
+             np.array(fl_wheel[1, :]).flatten(), truck_color)
     plt.plot(np.array(rl_wheel[0, :]).flatten(),
-             np.array(rl_wheel[1, :]).flatten(), truckcolor)
+             np.array(rl_wheel[1, :]).flatten(), truck_color)
     plt.plot(x, y, "*")
 
 
 def animation(plant, controller, dt):
-
     skip = 2  # skip index for animation
 
     for t in range(1, len(controller.history_u_1), skip):
@@ -543,12 +561,13 @@ def animation(plant, controller, dt):
         if abs(v) <= 0.01:
             steer = 0.0
         else:
-            steer = math.atan2(controller.history_u_2[t] * WB / v, 1.0)
+            steer = atan2(controller.history_u_2[t] * WB / v, 1.0)
 
         plt.cla()
         # for stopping simulation with the esc key.
-        plt.gcf().canvas.mpl_connect('key_release_event',
-                lambda event: [exit(0) if event.key == 'escape' else None])
+        plt.gcf().canvas.mpl_connect(
+            'key_release_event',
+            lambda event: [exit(0) if event.key == 'escape' else None])
         plt.plot(plant.history_x, plant.history_y, "-r", label="trajectory")
         plot_car(x, y, yaw, steer=steer)
         plt.axis("equal")
@@ -568,7 +587,7 @@ def main():
 
     init_x = -4.5
     init_y = -2.5
-    init_yaw = math.radians(45.0)
+    init_yaw = radians(45.0)
     init_v = -1.0
 
     # plant
@@ -576,14 +595,15 @@ def main():
         init_x, init_y, init_yaw, init_v)
 
     # controller
-    controller = NMPCController_with_CGMRES()
+    controller = NMPCControllerCGMRES()
 
     iteration_num = int(iteration_time / dt)
     for i in range(1, iteration_num):
         time = float(i) * dt
         # make input
         u_1s, u_2s = controller.calc_input(
-            plant_system.x, plant_system.y, plant_system.yaw, plant_system.v, time)
+            plant_system.x, plant_system.y, plant_system.yaw, plant_system.v,
+            time)
         # update state
         plant_system.update_state(u_1s[0], u_2s[0])
 
diff --git a/tests/test_grid_based_sweep_coverage_path_planner.py b/tests/test_grid_based_sweep_coverage_path_planner.py
index fa2d0a7a64..12ef98df39 100644
--- a/tests/test_grid_based_sweep_coverage_path_planner.py
+++ b/tests/test_grid_based_sweep_coverage_path_planner.py
@@ -2,8 +2,10 @@
 import sys
 from unittest import TestCase
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../PathPlanning/GridBasedSweepCPP")
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../Mapping/grid_map_lib")
+sys.path.append(os.path.dirname(
+    os.path.abspath(__file__)) + "/../PathPlanning/GridBasedSweepCPP")
+sys.path.append(
+    os.path.dirname(os.path.abspath(__file__)) + "/../Mapping/grid_map_lib")
 try:
     import grid_based_sweep_coverage_path_planner
 except ImportError:
@@ -14,88 +16,109 @@
 
 class TestPlanning(TestCase):
 
+    RIGHT = grid_based_sweep_coverage_path_planner.\
+        SweepSearcher.MovingDirection.RIGHT
+    LEFT = grid_based_sweep_coverage_path_planner. \
+        SweepSearcher.MovingDirection.LEFT
+    UP = grid_based_sweep_coverage_path_planner. \
+        SweepSearcher.SweepDirection.UP
+    DOWN = grid_based_sweep_coverage_path_planner. \
+        SweepSearcher.SweepDirection.DOWN
+
     def test_planning1(self):
         ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0, 0.0]
         oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0, 0.0]
-        reso = 5.0
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
-                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT,
-                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN,
-                                                                 )
-        self.assertTrue(len(px) >= 5)
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
-                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.LEFT,
-                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN,
-                                                                 )
-        self.assertTrue(len(px) >= 5)
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
-                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT,
-                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.UP,
-                                                                 )
-        self.assertTrue(len(px) >= 5)
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
-                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT,
-                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN,
-                                                                 )
-        self.assertTrue(len(px) >= 5)
+        resolution = 5.0
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(
+            ox, oy, resolution,
+            moving_direction=self.RIGHT,
+            sweeping_direction=self.DOWN,
+        )
+        self.assertGreater(len(px), 5)
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(
+            ox, oy, resolution,
+            moving_direction=self.LEFT,
+            sweeping_direction=self.DOWN,
+        )
+        self.assertGreater(len(px), 5)
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(
+            ox, oy, resolution,
+            moving_direction=self.RIGHT,
+            sweeping_direction=self.UP,
+        )
+        self.assertGreater(len(px), 5)
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(
+            ox, oy, resolution,
+            moving_direction=self.RIGHT,
+            sweeping_direction=self.UP,
+        )
+        self.assertGreater(len(px), 5)
 
     def test_planning2(self):
         ox = [0.0, 50.0, 50.0, 0.0, 0.0]
         oy = [0.0, 0.0, 30.0, 30.0, 0.0]
-        reso = 1.3
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
-                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT,
-                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN,
-                                                                 )
-        self.assertTrue(len(px) >= 5)
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
-                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.LEFT,
-                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN,
-                                                                 )
-        self.assertTrue(len(px) >= 5)
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
-                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT,
-                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.UP,
-                                                                 )
-        self.assertTrue(len(px) >= 5)
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
-                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT,
-                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN,
-                                                                 )
-        self.assertTrue(len(px) >= 5)
+        resolution = 1.3
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(
+            ox, oy, resolution,
+            moving_direction=self.RIGHT,
+            sweeping_direction=self.DOWN,
+        )
+        self.assertGreater(len(px), 5)
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(
+            ox, oy, resolution,
+            moving_direction=self.LEFT,
+            sweeping_direction=self.DOWN,
+        )
+        self.assertGreater(len(px), 5)
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(
+            ox, oy, resolution,
+            moving_direction=self.RIGHT,
+            sweeping_direction=self.UP,
+        )
+        self.assertGreater(len(px), 5)
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(
+            ox, oy, resolution,
+            moving_direction=self.RIGHT,
+            sweeping_direction=self.DOWN,
+        )
+        self.assertGreater(len(px), 5)
 
     def test_planning3(self):
         ox = [0.0, 20.0, 50.0, 200.0, 130.0, 40.0, 0.0]
         oy = [0.0, -80.0, 0.0, 30.0, 60.0, 80.0, 0.0]
-        reso = 5.1
-        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
-                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT,
-                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN,
-                                                                 )
-        self.assertTrue(len(px) >= 5)
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
-                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.LEFT,
-                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN,
-                                                                 )
-        self.assertTrue(len(px) >= 5)
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
-                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT,
-                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.UP,
-                                                                 )
-        self.assertTrue(len(px) >= 5)
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(ox, oy, reso,
-                                                                 moving_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.MovingDirection.RIGHT,
-                                                                 sweeping_direction=grid_based_sweep_coverage_path_planner.SweepSearcher.SweepDirection.DOWN,
-                                                                 )
-        self.assertTrue(len(px) >= 5)
+        resolution = 5.1
+        px, py = grid_based_sweep_coverage_path_planner.planning(
+            ox, oy, resolution,
+            moving_direction=self.RIGHT,
+            sweeping_direction=self.DOWN,
+        )
+        self.assertGreater(len(px), 5)
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(
+            ox, oy, resolution,
+            moving_direction=self.LEFT,
+            sweeping_direction=self.DOWN,
+        )
+        self.assertGreater(len(px), 5)
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(
+            ox, oy, resolution,
+            moving_direction=self.RIGHT,
+            sweeping_direction=self.UP,
+        )
+        self.assertGreater(len(px), 5)
+
+        px, py = grid_based_sweep_coverage_path_planner.planning(
+            ox, oy, resolution,
+            moving_direction=self.RIGHT,
+            sweeping_direction=self.DOWN,
+        )
+        self.assertGreater(len(px), 5)

From effddd2f6bdba33f75ca9df3367b4c6fe0fb00df Mon Sep 17 00:00:00 2001
From: adi-vardi-enway <57910756+adi-vardi-enway@users.noreply.github.com>
Date: Sat, 4 Jul 2020 11:44:35 +0800
Subject: [PATCH 0399/1080] Potential field - potential range and ocillations
 (#345)

* Potential cover start and goal as well

* Oscillation detection and fix potential look if outside range

* Formatting

* cleaner and flexible oscillation detection

Co-authored-by: Adi Vardi <adidasv111@gmail.com>
---
 .../potential_field_planning.py               | 40 +++++++++++++++----
 1 file changed, 33 insertions(+), 7 deletions(-)

diff --git a/PathPlanning/PotentialFieldPlanning/potential_field_planning.py b/PathPlanning/PotentialFieldPlanning/potential_field_planning.py
index 31904fa45d..8f136b5ee3 100644
--- a/PathPlanning/PotentialFieldPlanning/potential_field_planning.py
+++ b/PathPlanning/PotentialFieldPlanning/potential_field_planning.py
@@ -9,6 +9,7 @@
 
 """
 
+from collections import deque
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -16,15 +17,17 @@
 KP = 5.0  # attractive potential gain
 ETA = 100.0  # repulsive potential gain
 AREA_WIDTH = 30.0  # potential area width [m]
+# the number of previous positions used to check oscillations
+OSCILLATIONS_DETECTION_LENGTH = 3
 
 show_animation = True
 
 
-def calc_potential_field(gx, gy, ox, oy, reso, rr):
-    minx = min(ox) - AREA_WIDTH / 2.0
-    miny = min(oy) - AREA_WIDTH / 2.0
-    maxx = max(ox) + AREA_WIDTH / 2.0
-    maxy = max(oy) + AREA_WIDTH / 2.0
+def calc_potential_field(gx, gy, ox, oy, reso, rr, sx, sy):
+    minx = min(min(ox), sx, gx) - AREA_WIDTH / 2.0
+    miny = min(min(oy), sy, gy) - AREA_WIDTH / 2.0
+    maxx = max(max(ox), sx, gx) + AREA_WIDTH / 2.0
+    maxy = max(max(oy), sy, gy) + AREA_WIDTH / 2.0
     xw = int(round((maxx - minx) / reso))
     yw = int(round((maxy - miny) / reso))
 
@@ -84,10 +87,26 @@ def get_motion_model():
     return motion
 
 
+def oscillations_detection(previous_ids, ix, iy):
+    previous_ids.append((ix, iy))
+
+    if (len(previous_ids) > OSCILLATIONS_DETECTION_LENGTH):
+        previous_ids.popleft()
+
+    # check if contains any duplicates by copying into a set
+    previous_ids_set = set()
+    for index in previous_ids:
+        if index in previous_ids_set:
+            return True
+        else:
+            previous_ids_set.add(index)
+    return False
+
+
 def potential_field_planning(sx, sy, gx, gy, ox, oy, reso, rr):
 
     # calc potential field
-    pmap, minx, miny = calc_potential_field(gx, gy, ox, oy, reso, rr)
+    pmap, minx, miny = calc_potential_field(gx, gy, ox, oy, reso, rr, sx, sy)
 
     # search path
     d = np.hypot(sx - gx, sy - gy)
@@ -106,14 +125,17 @@ def potential_field_planning(sx, sy, gx, gy, ox, oy, reso, rr):
 
     rx, ry = [sx], [sy]
     motion = get_motion_model()
+    previous_ids = deque()
+
     while d >= reso:
         minp = float("inf")
         minix, miniy = -1, -1
         for i, _ in enumerate(motion):
             inx = int(ix + motion[i][0])
             iny = int(iy + motion[i][1])
-            if inx >= len(pmap) or iny >= len(pmap[0]):
+            if inx >= len(pmap) or iny >= len(pmap[0]) or inx < 0 or iny < 0:
                 p = float("inf")  # outside area
+                print("outside potential!")
             else:
                 p = pmap[inx][iny]
             if minp > p:
@@ -128,6 +150,10 @@ def potential_field_planning(sx, sy, gx, gy, ox, oy, reso, rr):
         rx.append(xp)
         ry.append(yp)
 
+        if (oscillations_detection(previous_ids, ix, iy)):
+            print("Oscillation detected at ({},{})!".format(ix, iy))
+            break
+
         if show_animation:
             plt.plot(ix, iy, ".r")
             plt.pause(0.01)

From eb42d3937e7de41f8157457d384b52757a30a07a Mon Sep 17 00:00:00 2001
From: "dependabot-preview[bot]"
 <27856297+dependabot-preview[bot]@users.noreply.github.com>
Date: Sun, 5 Jul 2020 20:01:03 +0900
Subject: [PATCH 0400/1080] Create Dependabot config file (#347)

Co-authored-by: dependabot-preview[bot] <27856297+dependabot-preview[bot]@users.noreply.github.com>
---
 .github/dependabot.yml | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 .github/dependabot.yml

diff --git a/.github/dependabot.yml b/.github/dependabot.yml
new file mode 100644
index 0000000000..9117f1216c
--- /dev/null
+++ b/.github/dependabot.yml
@@ -0,0 +1,8 @@
+version: 2
+updates:
+- package-ecosystem: pip
+  directory: "/"
+  schedule:
+    interval: weekly
+    time: "20:00"
+  open-pull-requests-limit: 10

From 5f6e4f855551aac9ece93533ceeacca8e97e1ad1 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 5 Jul 2020 21:15:18 +0900
Subject: [PATCH 0401/1080] Dependency setting clean up (#348)

* requiirements.txt use specific version

* use appveyor

* fix appveyor

* fix appveyor

* fix appveyor
---
 .travis.yml      | 35 -----------------------------------
 CONTRIBUTING.md  |  2 +-
 appveyor.yml     |  1 +
 environment.yml  |  5 ++---
 requirements.txt | 10 +++++-----
 5 files changed, 9 insertions(+), 44 deletions(-)
 delete mode 100644 .travis.yml

diff --git a/.travis.yml b/.travis.yml
deleted file mode 100644
index 99d239217b..0000000000
--- a/.travis.yml
+++ /dev/null
@@ -1,35 +0,0 @@
-language: python
-
-matrix:
-  include:
-    - os: linux
-
-python:
-  - 3.8
-
-before_install:
-    - sudo apt-get update
-    - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
-    - chmod +x miniconda.sh
-    - bash miniconda.sh -b -p $HOME/miniconda
-    - export PATH="$HOME/miniconda/bin:$PATH"
-    - hash -r
-    - conda config --set always_yes yes --set changeps1 no
-    - conda update -q conda
-    # Useful for debugging any issues with conda
-    - conda info -a
-
-install:
-  - conda install numpy
-  - conda install scipy
-  - conda install matplotlib
-  - conda install pandas
-  - pip install cvxpy
-  - conda install coveralls
-
-script:
-  - python --version
-  - ./runtests.sh
-
-after_success:
-  - coveralls
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index fd41946b65..91f6dfa822 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -18,6 +18,6 @@ Please check this paper to understand the philosophy of this project.
 
 ## Check your Python version.
 
-We only accept a PR for Python 3.6.x or higher.
+We only accept a PR for Python 3.8.x or higher.
 
 We will not accept a PR for Python 2.x.
diff --git a/appveyor.yml b/appveyor.yml
index f34bcb2b28..b883397d71 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -34,6 +34,7 @@ install:
   - SET PATH=%PYTHON%;%PYTHON%\Scripts;%PYTHON%\Library\bin;%PATH%
   - SET PATH=%PATH%;C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin
   - conda config --set always_yes yes --set changeps1 no
+  - conda config --append channels conda-forge
   - conda update -q conda
   - conda info -a
   - conda env create -f C:\\projects\pythonrobotics\environment.yml
diff --git a/environment.yml b/environment.yml
index da1c3c0a6f..fdfd25ca0a 100644
--- a/environment.yml
+++ b/environment.yml
@@ -2,10 +2,9 @@ name: python_robotics
 dependencies:
 - python
 - pip
-- matplotlib
 - scipy
 - numpy
 - pandas
+- cvxpy
+- matplotlib
 - coverage
-- pip:
-    - cvxpy
diff --git a/requirements.txt b/requirements.txt
index 1398664be8..775486efa3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
-numpy
-pandas
-scipy
-matplotlib
-cvxpy
+numpy == 1.19.0
+scipy == 1.5.1
+pandas == 1.0.5
+matplotlib == 3.2.2
+cvxpy == 1.1.1

From 77f6277e9c86e3b20a74eabfe9be6da3ba339308 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 7 Jul 2020 22:22:53 +0900
Subject: [PATCH 0402/1080] improve test coverage (#352)

* improve test coverage

* improve test coverage

* add coveralls CI

* add coveralls CI

* remove cover alls
---
 Bipedal/bipedal_planner/bipedal_planner.py    | 153 +++++++++---------
 .../CubicSpline/cubic_spline_planner.py       |   2 +-
 2 files changed, 80 insertions(+), 75 deletions(-)

diff --git a/Bipedal/bipedal_planner/bipedal_planner.py b/Bipedal/bipedal_planner/bipedal_planner.py
index 01df63034f..9757fc42d6 100644
--- a/Bipedal/bipedal_planner/bipedal_planner.py
+++ b/Bipedal/bipedal_planner/bipedal_planner.py
@@ -110,83 +110,88 @@ def walk(self, t_sup=0.8, z_c=0.8, a=10, b=1, plot=False):
 
             # plot
             if plot:
-                # for plot trajectory, plot in for loop
-                for c in range(len(self.com_trajectory)):
-                    if c > len(com_trajectory_for_plot):
-                        # set up plotter
-                        plt.cla()
-                        # for stopping simulation with the esc key.
-                        plt.gcf().canvas.mpl_connect(
-                            'key_release_event',
-                            lambda event:
-                            [exit(0) if event.key == 'escape' else None])
-                        ax.set_zlim(0, z_c * 2)
-                        ax.set_xlim(0, 1)
-                        ax.set_ylim(-0.5, 0.5)
-
-                        # update com_trajectory_for_plot
-                        com_trajectory_for_plot.append(self.com_trajectory[c])
-
-                        # plot com
-                        ax.plot([p[0] for p in com_trajectory_for_plot],
-                                [p[1] for p in com_trajectory_for_plot], [
-                                    0 for _ in com_trajectory_for_plot],
-                                color="red")
-
-                        # plot inverted pendulum
-                        ax.plot([px_star, com_trajectory_for_plot[-1][0]],
-                                [py_star, com_trajectory_for_plot[-1][1]],
-                                [0, z_c], color="green", linewidth=3)
-                        ax.scatter([com_trajectory_for_plot[-1][0]],
-                                   [com_trajectory_for_plot[-1][1]],
-                                   [z_c], color="green", s=300)
-
-                        # foot rectangle for self.ref_p
-                        foot_width = 0.06
-                        foot_height = 0.04
-                        for j in range(len(self.ref_p)):
-                            angle = self.ref_p[j][2] + \
-                                    math.atan2(foot_height,
-                                               foot_width) - math.pi
-                            r = math.sqrt(
-                                math.pow(foot_width / 3., 2) + math.pow(
-                                    foot_height / 2., 2))
-                            rec = pat.Rectangle(xy=(
-                                self.ref_p[j][0] + r * math.cos(angle),
-                                self.ref_p[j][1] + r * math.sin(angle)),
-                                width=foot_width,
-                                height=foot_height,
-                                angle=self.ref_p[j][
-                                          2] * 180 / math.pi,
-                                color="blue", fill=False,
-                                ls=":")
-                            ax.add_patch(rec)
-                            art3d.pathpatch_2d_to_3d(rec, z=0, zdir="z")
-
-                        # foot rectangle for self.act_p
-                        for j in range(len(self.act_p)):
-                            angle = self.act_p[j][2] + \
-                                    math.atan2(foot_height,
-                                               foot_width) - math.pi
-                            r = math.sqrt(
-                                math.pow(foot_width / 3., 2) + math.pow(
-                                    foot_height / 2., 2))
-                            rec = pat.Rectangle(xy=(
-                                self.act_p[j][0] + r * math.cos(angle),
-                                self.act_p[j][1] + r * math.sin(angle)),
-                                width=foot_width,
-                                height=foot_height,
-                                angle=self.act_p[j][
-                                          2] * 180 / math.pi,
-                                color="blue", fill=False)
-                            ax.add_patch(rec)
-                            art3d.pathpatch_2d_to_3d(rec, z=0, zdir="z")
-
-                        plt.draw()
-                        plt.pause(0.001)
+                self.plot_animation(ax, com_trajectory_for_plot, px_star,
+                                    py_star, z_c)
         if plot:
             plt.show()
 
+    def plot_animation(self, ax, com_trajectory_for_plot, px_star, py_star,
+                       z_c):  # pragma: no cover
+        # for plot trajectory, plot in for loop
+        for c in range(len(self.com_trajectory)):
+            if c > len(com_trajectory_for_plot):
+                # set up plotter
+                plt.cla()
+                # for stopping simulation with the esc key.
+                plt.gcf().canvas.mpl_connect(
+                    'key_release_event',
+                    lambda event:
+                    [exit(0) if event.key == 'escape' else None])
+                ax.set_zlim(0, z_c * 2)
+                ax.set_xlim(0, 1)
+                ax.set_ylim(-0.5, 0.5)
+
+                # update com_trajectory_for_plot
+                com_trajectory_for_plot.append(self.com_trajectory[c])
+
+                # plot com
+                ax.plot([p[0] for p in com_trajectory_for_plot],
+                        [p[1] for p in com_trajectory_for_plot], [
+                            0 for _ in com_trajectory_for_plot],
+                        color="red")
+
+                # plot inverted pendulum
+                ax.plot([px_star, com_trajectory_for_plot[-1][0]],
+                        [py_star, com_trajectory_for_plot[-1][1]],
+                        [0, z_c], color="green", linewidth=3)
+                ax.scatter([com_trajectory_for_plot[-1][0]],
+                           [com_trajectory_for_plot[-1][1]],
+                           [z_c], color="green", s=300)
+
+                # foot rectangle for self.ref_p
+                foot_width = 0.06
+                foot_height = 0.04
+                for j in range(len(self.ref_p)):
+                    angle = self.ref_p[j][2] + \
+                            math.atan2(foot_height,
+                                       foot_width) - math.pi
+                    r = math.sqrt(
+                        math.pow(foot_width / 3., 2) + math.pow(
+                            foot_height / 2., 2))
+                    rec = pat.Rectangle(xy=(
+                        self.ref_p[j][0] + r * math.cos(angle),
+                        self.ref_p[j][1] + r * math.sin(angle)),
+                        width=foot_width,
+                        height=foot_height,
+                        angle=self.ref_p[j][
+                                  2] * 180 / math.pi,
+                        color="blue", fill=False,
+                        ls=":")
+                    ax.add_patch(rec)
+                    art3d.pathpatch_2d_to_3d(rec, z=0, zdir="z")
+
+                # foot rectangle for self.act_p
+                for j in range(len(self.act_p)):
+                    angle = self.act_p[j][2] + \
+                            math.atan2(foot_height,
+                                       foot_width) - math.pi
+                    r = math.sqrt(
+                        math.pow(foot_width / 3., 2) + math.pow(
+                            foot_height / 2., 2))
+                    rec = pat.Rectangle(xy=(
+                        self.act_p[j][0] + r * math.cos(angle),
+                        self.act_p[j][1] + r * math.sin(angle)),
+                        width=foot_width,
+                        height=foot_height,
+                        angle=self.act_p[j][
+                                  2] * 180 / math.pi,
+                        color="blue", fill=False)
+                    ax.add_patch(rec)
+                    art3d.pathpatch_2d_to_3d(rec, z=0, zdir="z")
+
+                plt.draw()
+                plt.pause(0.001)
+
 
 if __name__ == "__main__":
     bipedal_planner = BipedalPlanner()
diff --git a/PathPlanning/CubicSpline/cubic_spline_planner.py b/PathPlanning/CubicSpline/cubic_spline_planner.py
index 239ce16738..7ec01d1f4e 100644
--- a/PathPlanning/CubicSpline/cubic_spline_planner.py
+++ b/PathPlanning/CubicSpline/cubic_spline_planner.py
@@ -190,7 +190,7 @@ def calc_spline_course(x, y, ds=0.1):
     return rx, ry, ryaw, rk, s
 
 
-def main():
+def main():  # pragma: no cover
     print("Spline 2D test")
     import matplotlib.pyplot as plt
     x = [-2.5, 0.0, 2.5, 5.0, 7.5, 3.0, -1.0]

From b19476f175f7b7730068e5bb7883051e6521ae9d Mon Sep 17 00:00:00 2001
From: Todd Tang <reso1@users.noreply.github.com>
Date: Wed, 8 Jul 2020 21:02:27 +0800
Subject: [PATCH 0403/1080] Wavefront Coverage Path Planning  (#351)

* First Commit of Wavefront Coverage Planner

* Update wavefront_coverage_path_planner.py

* fix CI / CodeFactor problem

* Fix mypy error

* update

* Fix PyCodeStyle

* Fix Code Scanning Warning and  code styling problem

* add simple unittest

* followed second code review suggestions
---
 PathPlanning/WavefrontCPP/map/test.png        | Bin 0 -> 156 bytes
 PathPlanning/WavefrontCPP/map/test_2.png      | Bin 0 -> 150 bytes
 PathPlanning/WavefrontCPP/map/test_3.png      | Bin 0 -> 132 bytes
 .../wavefront_coverage_path_planner.py        | 219 ++++++++++++++++++
 tests/test_wavefront_coverage_path_planner.py |  64 +++++
 5 files changed, 283 insertions(+)
 create mode 100644 PathPlanning/WavefrontCPP/map/test.png
 create mode 100644 PathPlanning/WavefrontCPP/map/test_2.png
 create mode 100644 PathPlanning/WavefrontCPP/map/test_3.png
 create mode 100644 PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py
 create mode 100644 tests/test_wavefront_coverage_path_planner.py

diff --git a/PathPlanning/WavefrontCPP/map/test.png b/PathPlanning/WavefrontCPP/map/test.png
new file mode 100644
index 0000000000000000000000000000000000000000..4abca0bf3090e5b22cb5d6a48ed9c55a4024e787
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diff --git a/PathPlanning/WavefrontCPP/map/test_2.png b/PathPlanning/WavefrontCPP/map/test_2.png
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diff --git a/PathPlanning/WavefrontCPP/map/test_3.png b/PathPlanning/WavefrontCPP/map/test_3.png
new file mode 100644
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diff --git a/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py b/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py
new file mode 100644
index 0000000000..20d640f24a
--- /dev/null
+++ b/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py
@@ -0,0 +1,219 @@
+"""
+Distance/Path Transform Wavefront Coverage Path Planner
+
+author: Todd Tang
+paper: Planning paths of complete coverage of an unstructured environment
+         by a mobile robot - Zelinsky et.al.
+link: http://pinkwink.kr/attachment/cfile3.uf@1354654A4E8945BD13FE77.pdf
+"""
+
+import os
+import sys
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from scipy import ndimage
+
+
+do_animation = True
+
+
+def transform(
+    gridmap, src, distance_type='chessboard',
+    transform_type='path', alpha=0.01
+):
+    """transform
+
+    calculating transform of transform_type from src
+    in given distance_type
+
+    :param gridmap: 2d binary map
+    :param src: distance transform source
+    :param distance_type: type of distance used
+    :param transform_type: type of transform used
+    :param alpha: weight of Obstacle Transform usedwhen using path_transform
+    """
+
+    nrows, ncols = gridmap.shape
+
+    if nrows == 0 or ncols == 0:
+        sys.exit('Empty gridmap.')
+
+    inc_order = [[0, 1], [1, 1], [1, 0], [1, -1],
+                 [0, -1], [-1, -1], [-1, 0], [-1, 1]]
+    if distance_type == 'chessboard':
+        cost = [1, 1, 1, 1, 1, 1, 1, 1]
+    elif distance_type == 'eculidean':
+        cost = [1, np.sqrt(2), 1, np.sqrt(2), 1, np.sqrt(2), 1, np.sqrt(2)]
+    else:
+        sys.exit('Unsupported distance type.')
+
+    transform_matrix = float('inf') * np.ones_like(gridmap, dtype=np.float)
+    transform_matrix[src[0], src[1]] = 0
+    if transform_type == 'distance':
+        eT = np.zeros_like(gridmap)
+    elif transform_type == 'path':
+        eT = ndimage.distance_transform_cdt(1-gridmap, distance_type)
+    else:
+        sys.exit('Unsupported transform type.')
+
+    # set obstacle transform_matrix value to infinity
+    for i in range(nrows):
+        for j in range(ncols):
+            if gridmap[i][j] == 1.0:
+                transform_matrix[i][j] = float('inf')
+    is_visited = np.zeros_like(transform_matrix, dtype=bool)
+    is_visited[src[0], src[1]] = True
+    traversal_queue = [src]
+    calculated = [(src[0]-1)*ncols + src[1]]
+
+    def is_valid_neighbor(i, j):
+        return ni >= 0 and ni < nrows and nj >= 0 and nj < ncols \
+            and not gridmap[ni][nj]
+
+    while traversal_queue != []:
+        i, j = traversal_queue.pop(0)
+        for k, inc in enumerate(inc_order):
+            ni = i + inc[0]
+            nj = j + inc[1]
+            if is_valid_neighbor(ni, nj):
+                is_visited[i][j] = True
+
+                # update transform_matrix
+                transform_matrix[i][j] = min(
+                    transform_matrix[i][j],
+                    transform_matrix[ni][nj] + cost[k] + alpha*eT[ni][nj])
+
+                if not is_visited[ni][nj] \
+                        and ((ni-1)*ncols + nj) not in calculated:
+                    traversal_queue.append((ni, nj))
+                    calculated.append((ni-1)*ncols + nj)
+
+    return transform_matrix
+
+
+def wavefront(transform_matrix, start, goal):
+    """wavefront
+
+    performing wavefront coverage path planning
+
+    :param transform_matrix: the transform matrix
+    :param start: start point of planning
+    :param goal: goal point of planning
+    """
+
+    path = []
+    nrows, ncols = transform_matrix.shape
+
+    def get_search_order_increment(start, goal):
+        if start[0] >= goal[0] and start[1] >= goal[1]:
+            order = [[1, 0], [0, 1], [-1, 0], [0, -1],
+                     [1, 1], [1, -1], [-1, 1], [-1, -1]]
+        elif start[0] <= goal[0] and start[1] >= goal[1]:
+            order = [[-1, 0], [0, 1], [1, 0], [0, -1],
+                     [-1, 1], [-1, -1], [1, 1], [1, -1]]
+        elif start[0] >= goal[0] and start[1] <= goal[1]:
+            order = [[1, 0], [0, -1], [-1, 0], [0, 1],
+                     [1, -1], [-1, -1], [1, 1], [-1, 1]]
+        elif start[0] <= goal[0] and start[1] <= goal[1]:
+            order = [[-1, 0], [0, -1], [0, 1], [1, 0],
+                     [-1, -1], [-1, 1], [1, -1], [1, 1]]
+        else:
+            sys.exit('get_search_order_increment: cannot determine \
+                start=>goal increment order')
+        return order
+
+    def is_valid_neighbor(i, j):
+        is_i_valid_bounded = i >= 0 and i < nrows
+        is_j_valid_bounded = j >= 0 and j < ncols
+        if is_i_valid_bounded and is_j_valid_bounded:
+            return not is_visited[i][j] and \
+                transform_matrix[i][j] != float('inf')
+        return False
+
+    inc_order = get_search_order_increment(start, goal)
+
+    current_node = start
+    is_visited = np.zeros_like(transform_matrix, dtype=bool)
+
+    while current_node != goal:
+        i, j = current_node
+        path.append((i, j))
+        is_visited[i][j] = True
+
+        max_T = float('-inf')
+        i_max = (-1, -1)
+        i_last = 0
+        for i_last in range(len(path)):
+            current_node = path[-1 - i_last]  # get lastest node in path
+            for ci, cj in inc_order:
+                ni, nj = current_node[0] + ci, current_node[1] + cj
+                if is_valid_neighbor(ni, nj) and \
+                        transform_matrix[ni][nj] > max_T:
+                    i_max = (ni, nj)
+                    max_T = transform_matrix[ni][nj]
+
+            if i_max != (-1, -1):
+                break
+
+        if i_max == (-1, -1):
+            break
+        else:
+            current_node = i_max
+            if i_last != 0:
+                print('backtracing to', current_node)
+    path.append(goal)
+
+    return path
+
+
+def visualize_path(grid_map, start, goal, path, resolution):
+    oy, ox = start
+    gy, gx = goal
+    px, py = np.transpose(np.flipud(np.fliplr((path))))
+
+    if not do_animation:
+        plt.imshow(grid_map, cmap='Greys')
+        plt.plot(ox, oy, "-xy")
+        plt.plot(px, py, "-r")
+        plt.plot(gx, gy, "-pg")
+        plt.show()
+    else:
+        for ipx, ipy in zip(px, py):
+            plt.cla()
+            # for stopping simulation with the esc key.
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.imshow(grid_map, cmap='Greys')
+            plt.plot(ox, oy, "-xb")
+            plt.plot(px, py, "-r")
+            plt.plot(gx, gy, "-pg")
+            plt.plot(ipx, ipy, "or")
+            plt.axis("equal")
+            plt.grid(True)
+            plt.pause(0.1)
+
+
+def main():
+    dir_path = os.path.dirname(os.path.realpath(__file__))
+    img = plt.imread(os.path.join(dir_path, 'map', 'test.png'))
+    img = 1 - img  # revert pixel values
+
+    start = (43, 0)
+    goal = (0, 0)
+
+    # distance transform wavefront
+    DT = transform(img, goal, transform_type='distance')
+    DT_path = wavefront(DT, start, goal)
+    visualize_path(img, start, goal, DT_path, 1)
+
+    # path transform wavefront
+    PT = transform(img, goal, transform_type='path', alpha=0.01)
+    PT_path = wavefront(PT, start, goal)
+    visualize_path(img, start, goal, PT_path, 1)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/test_wavefront_coverage_path_planner.py b/tests/test_wavefront_coverage_path_planner.py
new file mode 100644
index 0000000000..a72818c259
--- /dev/null
+++ b/tests/test_wavefront_coverage_path_planner.py
@@ -0,0 +1,64 @@
+import os
+import sys
+import matplotlib.pyplot as plt
+from unittest import TestCase
+
+sys.path.append(os.path.dirname(
+    os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP")
+try:
+    import wavefront_coverage_path_planner
+except ImportError:
+    raise
+
+wavefront_coverage_path_planner.do_animation = False
+
+
+class TestPlanning(TestCase):
+    def wavefront_cpp(self, img, start, goal):
+        num_free = 0
+        for i in range(img.shape[0]):
+            for j in range(img.shape[1]):
+                num_free += 1 - img[i][j]
+
+        DT = wavefront_coverage_path_planner.transform(
+            img, goal, transform_type='distance')
+        DT_path = wavefront_coverage_path_planner.wavefront(DT, start, goal)
+        self.assertEqual(len(DT_path), num_free)  # assert complete coverage
+
+        PT = wavefront_coverage_path_planner.transform(
+            img, goal, transform_type='path', alpha=0.01)
+        PT_path = wavefront_coverage_path_planner.wavefront(PT, start, goal)
+        self.assertEqual(len(PT_path), num_free)  # assert complete coverage
+
+    def test_wavefront_CPP_1(self):
+        img_dir = os.path.dirname(
+            os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP"
+        img = plt.imread(os.path.join(img_dir, 'map', 'test.png'))
+        img = 1 - img
+
+        start = (43, 0)
+        goal = (0, 0)
+
+        self.wavefront_cpp(img, start, goal)
+
+    def test_wavefront_CPP_2(self):
+        img_dir = os.path.dirname(
+            os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP"
+        img = plt.imread(os.path.join(img_dir, 'map', 'test_2.png'))
+        img = 1 - img
+
+        start = (10, 0)
+        goal = (10, 40)
+
+        self.wavefront_cpp(img, start, goal)
+
+    def test_wavefront_CPP_3(self):
+        img_dir = os.path.dirname(
+            os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP"
+        img = plt.imread(os.path.join(img_dir, 'map', 'test_3.png'))
+        img = 1 - img
+
+        start = (0, 0)
+        goal = (30, 30)
+
+        self.wavefront_cpp(img, start, goal)

From 7dd6b6793a5e1e55303103709d151bfa378cba38 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 8 Jul 2020 22:53:20 +0900
Subject: [PATCH 0404/1080] remove cover alls (#353)

---
 .travis.yml | 35 +++++++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)
 create mode 100644 .travis.yml

diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 0000000000..9fcc2dbdc2
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,35 @@
+language: python
+
+matrix:
+  include:
+    - os: linux
+
+python:
+  - 3.8
+
+before_install:
+    - sudo apt-get update
+    - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
+    - chmod +x miniconda.sh
+    - bash miniconda.sh -b -p $HOME/miniconda
+    - export PATH="$HOME/miniconda/bin:$PATH"
+    - hash -r
+    - conda config --set always_yes yes --set changeps1 no
+    - conda config --append channels conda-forge
+    - conda update -q conda
+    # Useful for debugging any issues with conda
+    - conda info -a
+
+install:
+  - conda install numpy
+  - conda install scipy
+  - conda install matplotlib
+  - conda install pandas
+  - conda install cvxpy
+  - conda install coveralls
+
+script:
+  - python --version
+
+after_success:
+  - coveralls
\ No newline at end of file

From 72c4a891b33d2075df77f97d0bd88f0540fd455b Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 10 Jul 2020 21:34:47 +0900
Subject: [PATCH 0405/1080] remove cover alls (#357)

* remove cover alls

* add codecov

* try coverage

* try coverage

* try coverage
---
 .github/workflows/Linux_CI.yml                |   2 +
 .travis.yml                                   |  35 ------
 .../wavefront_coverage_path_planner.py        | 103 +++++++++---------
 README.md                                     |   2 +-
 4 files changed, 54 insertions(+), 88 deletions(-)
 delete mode 100644 .travis.yml

diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index 293c5621e2..de99bd446f 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -45,6 +45,8 @@ jobs:
       run: bash rundiffstylecheck.sh
     - name: do all unit tests
       run: bash runtests.sh
+    - name: Codecov
+      uses: codecov/codecov-action@v1.0.10
 
 
 
diff --git a/.travis.yml b/.travis.yml
deleted file mode 100644
index 9fcc2dbdc2..0000000000
--- a/.travis.yml
+++ /dev/null
@@ -1,35 +0,0 @@
-language: python
-
-matrix:
-  include:
-    - os: linux
-
-python:
-  - 3.8
-
-before_install:
-    - sudo apt-get update
-    - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
-    - chmod +x miniconda.sh
-    - bash miniconda.sh -b -p $HOME/miniconda
-    - export PATH="$HOME/miniconda/bin:$PATH"
-    - hash -r
-    - conda config --set always_yes yes --set changeps1 no
-    - conda config --append channels conda-forge
-    - conda update -q conda
-    # Useful for debugging any issues with conda
-    - conda info -a
-
-install:
-  - conda install numpy
-  - conda install scipy
-  - conda install matplotlib
-  - conda install pandas
-  - conda install cvxpy
-  - conda install coveralls
-
-script:
-  - python --version
-
-after_success:
-  - coveralls
\ No newline at end of file
diff --git a/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py b/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py
index 20d640f24a..aaf77e02fc 100644
--- a/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py
+++ b/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py
@@ -12,33 +12,31 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
-
 from scipy import ndimage
 
-
 do_animation = True
 
 
 def transform(
-    gridmap, src, distance_type='chessboard',
-    transform_type='path', alpha=0.01
+        grid_map, src, distance_type='chessboard',
+        transform_type='path', alpha=0.01
 ):
     """transform
 
     calculating transform of transform_type from src
     in given distance_type
 
-    :param gridmap: 2d binary map
+    :param grid_map: 2d binary map
     :param src: distance transform source
     :param distance_type: type of distance used
     :param transform_type: type of transform used
-    :param alpha: weight of Obstacle Transform usedwhen using path_transform
+    :param alpha: weight of Obstacle Transform used when using path_transform
     """
 
-    nrows, ncols = gridmap.shape
+    n_rows, n_cols = grid_map.shape
 
-    if nrows == 0 or ncols == 0:
-        sys.exit('Empty gridmap.')
+    if n_rows == 0 or n_cols == 0:
+        sys.exit('Empty grid_map.')
 
     inc_order = [[0, 1], [1, 1], [1, 0], [1, -1],
                  [0, -1], [-1, -1], [-1, 0], [-1, 1]]
@@ -49,30 +47,30 @@ def transform(
     else:
         sys.exit('Unsupported distance type.')
 
-    transform_matrix = float('inf') * np.ones_like(gridmap, dtype=np.float)
+    transform_matrix = float('inf') * np.ones_like(grid_map, dtype=np.float)
     transform_matrix[src[0], src[1]] = 0
     if transform_type == 'distance':
-        eT = np.zeros_like(gridmap)
+        eT = np.zeros_like(grid_map)
     elif transform_type == 'path':
-        eT = ndimage.distance_transform_cdt(1-gridmap, distance_type)
+        eT = ndimage.distance_transform_cdt(1 - grid_map, distance_type)
     else:
         sys.exit('Unsupported transform type.')
 
     # set obstacle transform_matrix value to infinity
-    for i in range(nrows):
-        for j in range(ncols):
-            if gridmap[i][j] == 1.0:
+    for i in range(n_rows):
+        for j in range(n_cols):
+            if grid_map[i][j] == 1.0:
                 transform_matrix[i][j] = float('inf')
     is_visited = np.zeros_like(transform_matrix, dtype=bool)
     is_visited[src[0], src[1]] = True
     traversal_queue = [src]
-    calculated = [(src[0]-1)*ncols + src[1]]
+    calculated = [(src[0] - 1) * n_cols + src[1]]
 
-    def is_valid_neighbor(i, j):
-        return ni >= 0 and ni < nrows and nj >= 0 and nj < ncols \
-            and not gridmap[ni][nj]
+    def is_valid_neighbor(g_i, g_j):
+        return 0 <= g_i < n_rows and 0 <= g_j < n_cols \
+               and not grid_map[g_i][g_j]
 
-    while traversal_queue != []:
+    while traversal_queue:
         i, j = traversal_queue.pop(0)
         for k, inc in enumerate(inc_order):
             ni = i + inc[0]
@@ -83,16 +81,35 @@ def is_valid_neighbor(i, j):
                 # update transform_matrix
                 transform_matrix[i][j] = min(
                     transform_matrix[i][j],
-                    transform_matrix[ni][nj] + cost[k] + alpha*eT[ni][nj])
+                    transform_matrix[ni][nj] + cost[k] + alpha * eT[ni][nj])
 
                 if not is_visited[ni][nj] \
-                        and ((ni-1)*ncols + nj) not in calculated:
+                        and ((ni - 1) * n_cols + nj) not in calculated:
                     traversal_queue.append((ni, nj))
-                    calculated.append((ni-1)*ncols + nj)
+                    calculated.append((ni - 1) * n_cols + nj)
 
     return transform_matrix
 
 
+def get_search_order_increment(start, goal):
+    if start[0] >= goal[0] and start[1] >= goal[1]:
+        order = [[1, 0], [0, 1], [-1, 0], [0, -1],
+                 [1, 1], [1, -1], [-1, 1], [-1, -1]]
+    elif start[0] <= goal[0] and start[1] >= goal[1]:
+        order = [[-1, 0], [0, 1], [1, 0], [0, -1],
+                 [-1, 1], [-1, -1], [1, 1], [1, -1]]
+    elif start[0] >= goal[0] and start[1] <= goal[1]:
+        order = [[1, 0], [0, -1], [-1, 0], [0, 1],
+                 [1, -1], [-1, -1], [1, 1], [-1, 1]]
+    elif start[0] <= goal[0] and start[1] <= goal[1]:
+        order = [[-1, 0], [0, -1], [0, 1], [1, 0],
+                 [-1, -1], [-1, 1], [1, -1], [1, 1]]
+    else:
+        sys.exit('get_search_order_increment: cannot determine \
+            start=>goal increment order')
+    return order
+
+
 def wavefront(transform_matrix, start, goal):
     """wavefront
 
@@ -104,32 +121,14 @@ def wavefront(transform_matrix, start, goal):
     """
 
     path = []
-    nrows, ncols = transform_matrix.shape
-
-    def get_search_order_increment(start, goal):
-        if start[0] >= goal[0] and start[1] >= goal[1]:
-            order = [[1, 0], [0, 1], [-1, 0], [0, -1],
-                     [1, 1], [1, -1], [-1, 1], [-1, -1]]
-        elif start[0] <= goal[0] and start[1] >= goal[1]:
-            order = [[-1, 0], [0, 1], [1, 0], [0, -1],
-                     [-1, 1], [-1, -1], [1, 1], [1, -1]]
-        elif start[0] >= goal[0] and start[1] <= goal[1]:
-            order = [[1, 0], [0, -1], [-1, 0], [0, 1],
-                     [1, -1], [-1, -1], [1, 1], [-1, 1]]
-        elif start[0] <= goal[0] and start[1] <= goal[1]:
-            order = [[-1, 0], [0, -1], [0, 1], [1, 0],
-                     [-1, -1], [-1, 1], [1, -1], [1, 1]]
-        else:
-            sys.exit('get_search_order_increment: cannot determine \
-                start=>goal increment order')
-        return order
+    n_rows, n_cols = transform_matrix.shape
 
-    def is_valid_neighbor(i, j):
-        is_i_valid_bounded = i >= 0 and i < nrows
-        is_j_valid_bounded = j >= 0 and j < ncols
+    def is_valid_neighbor(g_i, g_j):
+        is_i_valid_bounded = 0 <= g_i < n_rows
+        is_j_valid_bounded = 0 <= g_j < n_cols
         if is_i_valid_bounded and is_j_valid_bounded:
-            return not is_visited[i][j] and \
-                transform_matrix[i][j] != float('inf')
+            return not is_visited[g_i][g_j] and \
+                   transform_matrix[g_i][g_j] != float('inf')
         return False
 
     inc_order = get_search_order_increment(start, goal)
@@ -146,7 +145,7 @@ def is_valid_neighbor(i, j):
         i_max = (-1, -1)
         i_last = 0
         for i_last in range(len(path)):
-            current_node = path[-1 - i_last]  # get lastest node in path
+            current_node = path[-1 - i_last]  # get latest node in path
             for ci, cj in inc_order:
                 ni, nj = current_node[0] + ci, current_node[1] + cj
                 if is_valid_neighbor(ni, nj) and \
@@ -168,10 +167,10 @@ def is_valid_neighbor(i, j):
     return path
 
 
-def visualize_path(grid_map, start, goal, path, resolution):
+def visualize_path(grid_map, start, goal, path):  # pragma: no cover
     oy, ox = start
     gy, gx = goal
-    px, py = np.transpose(np.flipud(np.fliplr((path))))
+    px, py = np.transpose(np.flipud(np.fliplr(path)))
 
     if not do_animation:
         plt.imshow(grid_map, cmap='Greys')
@@ -207,12 +206,12 @@ def main():
     # distance transform wavefront
     DT = transform(img, goal, transform_type='distance')
     DT_path = wavefront(DT, start, goal)
-    visualize_path(img, start, goal, DT_path, 1)
+    visualize_path(img, start, goal, DT_path)
 
     # path transform wavefront
     PT = transform(img, goal, transform_type='path', alpha=0.01)
     PT_path = wavefront(PT, start, goal)
-    visualize_path(img, start, goal, PT_path, 1)
+    visualize_path(img, start, goal, PT_path)
 
 
 if __name__ == "__main__":
diff --git a/README.md b/README.md
index 4a64f53642..49a99959f1 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@
 [![Build Status](https://travis-ci.org/AtsushiSakai/PythonRobotics.svg?branch=master)](https://travis-ci.org/AtsushiSakai/PythonRobotics)
 [![Documentation Status](https://readthedocs.org/projects/pythonrobotics/badge/?version=latest)](https://pythonrobotics.readthedocs.io/en/latest/?badge=latest)
 [![Build status](https://ci.appveyor.com/api/projects/status/sb279kxuv1be391g?svg=true)](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics)
-[![Coverage Status](https://coveralls.io/repos/github/AtsushiSakai/PythonRobotics/badge.svg?branch=master)](https://coveralls.io/github/AtsushiSakai/PythonRobotics?branch=master)
+[![codecov](https://codecov.io/gh/AtsushiSakai/PythonRobotics/branch/master/graph/badge.svg)](https://codecov.io/gh/AtsushiSakai/PythonRobotics)
 [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/AtsushiSakai/PythonRobotics.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/AtsushiSakai/PythonRobotics/context:python)
 [![CodeFactor](https://www.codefactor.io/repository/github/atsushisakai/pythonrobotics/badge/master)](https://www.codefactor.io/repository/github/atsushisakai/pythonrobotics/overview/master)
 [![tokei](https://tokei.rs/b1/github/AtsushiSakai/PythonRobotics)](https://github.com/AtsushiSakai/PythonRobotics)

From 4c5e3ccc9eba866f743feb3ade462fb104f0f4b0 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 10 Jul 2020 23:46:19 +0900
Subject: [PATCH 0406/1080] fix VisibleDeprecation Warning (#358)

* try coverage

* add python warning setting

* add random seed for test coverage
---
 SLAM/EKFSLAM/ekf_slam.py                   | 7 ++++---
 runtests.sh                                | 1 +
 tests/test_batch_informed_rrt_star.py      | 5 ++++-
 tests/test_closed_loop_rrt_star_car.py     | 4 +++-
 tests/test_dubins_path_planning.py         | 1 +
 tests/test_lqr_rrt_star.py                 | 5 ++++-
 tests/test_n_joint_arm_to_point_control.py | 3 +++
 tests/test_rrt.py                          | 3 +++
 8 files changed, 23 insertions(+), 6 deletions(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py
index 6b349d858b..8fa1a771c2 100644
--- a/SLAM/EKFSLAM/ekf_slam.py
+++ b/SLAM/EKFSLAM/ekf_slam.py
@@ -117,7 +117,7 @@ def jacob_motion(x, u):
 
     jF = np.array([[0.0, 0.0, -DT * u[0] * math.sin(x[2, 0])],
                    [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])],
-                   [0.0, 0.0, 0.0]])
+                   [0.0, 0.0, 0.0]], dtype=np.float64)
 
     G = np.eye(STATE_SIZE) + Fx.T @ jF @ Fx
 
@@ -236,8 +236,9 @@ def main():
         if show_animation:  # pragma: no cover
             plt.cla()
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
 
             plt.plot(RFID[:, 0], RFID[:, 1], "*k")
             plt.plot(xEst[0], xEst[1], ".r")
diff --git a/runtests.sh b/runtests.sh
index 52ef50f001..d7df3cff04 100755
--- a/runtests.sh
+++ b/runtests.sh
@@ -1,5 +1,6 @@
 #!/usr/bin/env bash
 echo "Run test suites! "
+export PYTHONWARNINGS=default  # show warning
 #python -m unittest discover tests 
 #python -Wignore -m unittest discover tests #ignore warning
 coverage run -m unittest discover tests # generate coverage file
diff --git a/tests/test_batch_informed_rrt_star.py b/tests/test_batch_informed_rrt_star.py
index e83684d92c..bac8941460 100644
--- a/tests/test_batch_informed_rrt_star.py
+++ b/tests/test_batch_informed_rrt_star.py
@@ -1,14 +1,17 @@
 from unittest import TestCase
 import sys
 import os
+import random
 sys.path.append(os.path.dirname(__file__) + "/../")
 try:
     from PathPlanning.BatchInformedRRTStar import batch_informed_rrtstar as m
-except:
+except ImportError:
     raise
 
 print(__file__)
 
+random.seed(12345)
+
 
 class Test(TestCase):
 
diff --git a/tests/test_closed_loop_rrt_star_car.py b/tests/test_closed_loop_rrt_star_car.py
index 18513ce665..060389d6a2 100644
--- a/tests/test_closed_loop_rrt_star_car.py
+++ b/tests/test_closed_loop_rrt_star_car.py
@@ -1,5 +1,6 @@
 import os
 import sys
+import random
 from unittest import TestCase
 
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
@@ -7,9 +8,10 @@
                 "/../PathPlanning/ClosedLoopRRTStar/")
 try:
     from PathPlanning.ClosedLoopRRTStar import closed_loop_rrt_star_car as m
-except:
+except ImportError:
     raise
 
+random.seed(12345)
 
 print(__file__)
 
diff --git a/tests/test_dubins_path_planning.py b/tests/test_dubins_path_planning.py
index f9349217a1..1be70e390e 100644
--- a/tests/test_dubins_path_planning.py
+++ b/tests/test_dubins_path_planning.py
@@ -1,6 +1,7 @@
 from unittest import TestCase
 
 import numpy as np
+np.random.seed(12345)
 
 from PathPlanning.DubinsPath import dubins_path_planning
 
diff --git a/tests/test_lqr_rrt_star.py b/tests/test_lqr_rrt_star.py
index a9c2f5eece..0b59475b3d 100644
--- a/tests/test_lqr_rrt_star.py
+++ b/tests/test_lqr_rrt_star.py
@@ -1,16 +1,19 @@
 from unittest import TestCase
 import sys
 import os
+import random
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
 sys.path.append(os.path.dirname(os.path.abspath(__file__))
                 + "/../PathPlanning/LQRRRTStar/")
 try:
     from PathPlanning.LQRRRTStar import lqr_rrt_star as m
-except:
+except ImportError:
     raise
 
 print(__file__)
 
+random.seed(12345)
+
 
 class Test(TestCase):
 
diff --git a/tests/test_n_joint_arm_to_point_control.py b/tests/test_n_joint_arm_to_point_control.py
index a935216475..deb30f83be 100644
--- a/tests/test_n_joint_arm_to_point_control.py
+++ b/tests/test_n_joint_arm_to_point_control.py
@@ -1,5 +1,6 @@
 import os
 import sys
+import random
 from unittest import TestCase
 
 sys.path.append(os.path.dirname(__file__) + "/../ArmNavigation/n_joint_arm_to_point_control/")
@@ -8,6 +9,8 @@
 
 print(__file__)
 
+random.seed(12345)
+
 
 class Test(TestCase):
 
diff --git a/tests/test_rrt.py b/tests/test_rrt.py
index 6e8f3f300a..b7c03d25cf 100644
--- a/tests/test_rrt.py
+++ b/tests/test_rrt.py
@@ -1,5 +1,6 @@
 import os
 import sys
+import random
 from unittest import TestCase
 
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
@@ -12,6 +13,8 @@
 
 print(__file__)
 
+random.seed(12345)
+
 
 class Test(TestCase):
 

From cb796ddeffc83750672846db4353332a1a2243dd Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 11 Jul 2020 14:12:37 +0900
Subject: [PATCH 0407/1080] Add .circleci/config.yml (#359)

* Add .circleci/config.yml

* try doc build

* try doc build

* try doc build

* try doc build

* try doc build

* try doc build

* try doc build
---
 .circleci/artifact_path        |  1 +
 .circleci/config.yml           | 25 +++++++++++++++++++++++++
 .github/workflows/Linux_CI.yml |  7 +++++++
 3 files changed, 33 insertions(+)
 create mode 100644 .circleci/artifact_path
 create mode 100644 .circleci/config.yml

diff --git a/.circleci/artifact_path b/.circleci/artifact_path
new file mode 100644
index 0000000000..59e6bd06ee
--- /dev/null
+++ b/.circleci/artifact_path
@@ -0,0 +1 @@
+0/html/index.html
\ No newline at end of file
diff --git a/.circleci/config.yml b/.circleci/config.yml
new file mode 100644
index 0000000000..f63f094a3f
--- /dev/null
+++ b/.circleci/config.yml
@@ -0,0 +1,25 @@
+version: 2.1
+
+orbs:
+  python: circleci/python@0.2.1
+
+jobs:
+  build_doc:
+    executor: python/default
+    steps:
+      - checkout
+      - python/load-cache
+      - python/install-deps
+      - python/save-cache
+      - run: pip install sphinx sphinx-autobuild sphinx-rtd-theme
+      - run:
+          command: cd docs;make html
+          name: doc_build
+      - store_artifacts:
+          path: docs/_build/html/
+          destination: html
+
+workflows:
+  main:
+    jobs:
+      - build_doc
diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index de99bd446f..8338a5f4ce 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -43,6 +43,12 @@ jobs:
         find SLAM -name "*.py" | xargs mypy
     - name: do diff style check
       run: bash rundiffstylecheck.sh
+    - name: run-circleci-artifacts-redirector
+      uses: larsoner/circleci-artifacts-redirector-action@0.2.0
+      with:
+        repo-token: ${{ secrets.GITHUB_TOKEN }}
+        artifact-path: 0/html/index.html
+        circleci-jobs: build_doc
     - name: do all unit tests
       run: bash runtests.sh
     - name: Codecov
@@ -50,3 +56,4 @@ jobs:
 
 
 
+

From e8a805ef67b8c7ef8f623d1f594c539ffa288d1f Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 11 Jul 2020 16:18:13 +0900
Subject: [PATCH 0408/1080] doc build test (#360)

* try doc build

* try doc build

* try doc build

* try doc build

* try doc build

* try doc build
---
 .circleci/artifact_path        |  2 +-
 .github/workflows/Linux_CI.yml |  6 ------
 .github/workflows/main.yml     | 16 ++++++++++++++++
 3 files changed, 17 insertions(+), 7 deletions(-)
 create mode 100644 .github/workflows/main.yml

diff --git a/.circleci/artifact_path b/.circleci/artifact_path
index 59e6bd06ee..d2c64880b1 100644
--- a/.circleci/artifact_path
+++ b/.circleci/artifact_path
@@ -1 +1 @@
-0/html/index.html
\ No newline at end of file
+0/html/index.html
diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index 8338a5f4ce..a751c75d4d 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -43,12 +43,6 @@ jobs:
         find SLAM -name "*.py" | xargs mypy
     - name: do diff style check
       run: bash rundiffstylecheck.sh
-    - name: run-circleci-artifacts-redirector
-      uses: larsoner/circleci-artifacts-redirector-action@0.2.0
-      with:
-        repo-token: ${{ secrets.GITHUB_TOKEN }}
-        artifact-path: 0/html/index.html
-        circleci-jobs: build_doc
     - name: do all unit tests
       run: bash runtests.sh
     - name: Codecov
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
new file mode 100644
index 0000000000..55d33eb324
--- /dev/null
+++ b/.github/workflows/main.yml
@@ -0,0 +1,16 @@
+name: circleci-artifacts-redirector-action
+on: [status]
+jobs:
+  circleci_artifacts_redirector_job:
+    runs-on: ubuntu-latest
+    name: Run CircleCI artifacts redirector
+    steps:
+      - name: run-circleci-artifacts-redirector
+        uses: larsoner/circleci-artifacts-redirector-action@master
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          artifact-path: 0/html/index.html
+          circleci-jobs: build_doc
+      - name: Check the URL
+        run: |
+          curl --fail ${{ steps.step1.outputs.url }} | grep $GITHUB_SHA

From 9fe14e657ba6a6c2aa7ee8bc586312e1c2154b72 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 11 Jul 2020 17:12:47 +0900
Subject: [PATCH 0409/1080] doc build test again (#361)

* try doc build

* try doc build
---
 .../workflows/{main.yml => circleci-artifacts-redirector.yml}   | 2 +-
 docs/index.rst                                                  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)
 rename .github/workflows/{main.yml => circleci-artifacts-redirector.yml} (87%)

diff --git a/.github/workflows/main.yml b/.github/workflows/circleci-artifacts-redirector.yml
similarity index 87%
rename from .github/workflows/main.yml
rename to .github/workflows/circleci-artifacts-redirector.yml
index 55d33eb324..849a97f1a7 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/circleci-artifacts-redirector.yml
@@ -6,7 +6,7 @@ jobs:
     name: Run CircleCI artifacts redirector
     steps:
       - name: run-circleci-artifacts-redirector
-        uses: larsoner/circleci-artifacts-redirector-action@master
+        uses: larsoner/circleci-artifacts-redirector-action@0.2.0
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           artifact-path: 0/html/index.html
diff --git a/docs/index.rst b/docs/index.rst
index e8e6052879..3798a1a1a6 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -8,7 +8,7 @@ Welcome to PythonRobotics's documentation!
 
 Python codes for robotics algorithm. The project is on `GitHub`_.
 
-This is a Python code collection of robotics algorithms, especially for autonomous navigation.
+This is a Python code collection of robotics algorithms.
 
 Features:
 

From 0c23ebe62be248000151f6fe543bcd551d964177 Mon Sep 17 00:00:00 2001
From: Todd Tang <reso1@users.noreply.github.com>
Date: Sun, 12 Jul 2020 12:09:43 +0800
Subject: [PATCH 0410/1080] Spiral Spanning Tree Coverage Path Planning (#355)

* First commit of Spiral Spanning Tree Coverage

* Modify followed by first code review

* fix pycodestyle error

* modifies following 2nd code review
---
 .../SpiralSpanningTreeCPP/map/test.png        | Bin 0 -> 156 bytes
 .../SpiralSpanningTreeCPP/map/test_2.png      | Bin 0 -> 150 bytes
 .../SpiralSpanningTreeCPP/map/test_3.png      | Bin 0 -> 132 bytes
 ...ral_spanning_tree_coverage_path_planner.py | 313 ++++++++++++++++++
 ...ral_spanning_tree_coverage_path_planner.py |  58 ++++
 5 files changed, 371 insertions(+)
 create mode 100644 PathPlanning/SpiralSpanningTreeCPP/map/test.png
 create mode 100644 PathPlanning/SpiralSpanningTreeCPP/map/test_2.png
 create mode 100644 PathPlanning/SpiralSpanningTreeCPP/map/test_3.png
 create mode 100644 PathPlanning/SpiralSpanningTreeCPP/spiral_spanning_tree_coverage_path_planner.py
 create mode 100644 tests/test_spiral_spanning_tree_coverage_path_planner.py

diff --git a/PathPlanning/SpiralSpanningTreeCPP/map/test.png b/PathPlanning/SpiralSpanningTreeCPP/map/test.png
new file mode 100644
index 0000000000000000000000000000000000000000..4abca0bf3090e5b22cb5d6a48ed9c55a4024e787
GIT binary patch
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zVMk@i#}!F2466K(PS<ZKWvk3ndlGlcs#)bI!=an3siIFO%7U!*boFyt=akR{03ZZ1
At^fc4

literal 0
HcmV?d00001

diff --git a/PathPlanning/SpiralSpanningTreeCPP/map/test_2.png b/PathPlanning/SpiralSpanningTreeCPP/map/test_2.png
new file mode 100644
index 0000000000000000000000000000000000000000..0d27fa9f958d59ae264570a47838c83124d5a2c9
GIT binary patch
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wX*W{=W{Hce7ap_59#r^pctu+v@0Cjo3@cdKe2pcX#6T8$y85}Sb4q9e0F{U>A^-pY

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diff --git a/PathPlanning/SpiralSpanningTreeCPP/map/test_3.png b/PathPlanning/SpiralSpanningTreeCPP/map/test_3.png
new file mode 100644
index 0000000000000000000000000000000000000000..1a50b87ccff5a966f5e078282cefec2f14ba9112
GIT binary patch
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zJY5_^EP9jwoNtgfSoCow$BN?&i>2I5d1QGtPR<Rur*N9h>UU#f;|BY-CV2^o55JQI
eekEFDFfjD$u~_NcJpT`927{-opUXO@geCxLEGFFm

literal 0
HcmV?d00001

diff --git a/PathPlanning/SpiralSpanningTreeCPP/spiral_spanning_tree_coverage_path_planner.py b/PathPlanning/SpiralSpanningTreeCPP/spiral_spanning_tree_coverage_path_planner.py
new file mode 100644
index 0000000000..4a6b42db0b
--- /dev/null
+++ b/PathPlanning/SpiralSpanningTreeCPP/spiral_spanning_tree_coverage_path_planner.py
@@ -0,0 +1,313 @@
+"""
+Spiral Spanning Tree Coverage Path Planner
+
+author: Todd Tang
+paper: Spiral-STC: An On-Line Coverage Algorithm of Grid Environments
+         by a Mobile Robot - Gabriely et.al.
+link: https://ieeexplore.ieee.org/abstract/document/1013479
+"""
+
+import os
+import sys
+import math
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+do_animation = True
+
+
+class SpiralSpanningTreeCoveragePlanner:
+    def __init__(self, occ_map):
+        self.origin_map_height = occ_map.shape[0]
+        self.origin_map_width = occ_map.shape[1]
+
+        # original map resolution must be even
+        if self.origin_map_height % 2 == 1 or self.origin_map_width % 2 == 1:
+            sys.exit('original map width/height must be even \
+                in grayscale .png format')
+
+        self.occ_map = occ_map
+        self.merged_map_height = self.origin_map_height // 2
+        self.merged_map_width = self.origin_map_width // 2
+
+        self.edge = []
+
+    def plan(self, start):
+        """plan
+
+        performing Spiral Spanning Tree Coverage path planning
+
+        :param start: the start node of Spiral Spanning Tree Coverage
+        """
+
+        visit_times = np.zeros(
+            (self.merged_map_height, self.merged_map_width), dtype=np.int)
+        visit_times[start[0]][start[1]] = 1
+
+        # generate route by
+        # recusively call perform_spanning_tree_coverage() from start node
+        route = []
+        self.perform_spanning_tree_coverage(start, visit_times, route)
+
+        path = []
+        # generate path from route
+        for idx in range(len(route)-1):
+            dp = abs(route[idx][0] - route[idx+1][0]) + \
+                abs(route[idx][1] - route[idx+1][1])
+            if dp == 0:
+                # special handle for round-trip path
+                path.append(self.get_round_trip_path(route[idx-1], route[idx]))
+            elif dp == 1:
+                path.append(self.move(route[idx], route[idx+1]))
+            elif dp == 2:
+                # special handle for non-adjacent route nodes
+                mid_node = self.get_intermediate_node(route[idx], route[idx+1])
+                path.append(self.move(route[idx], mid_node))
+                path.append(self.move(mid_node, route[idx+1]))
+            else:
+                sys.exit('adjacent path node distance larger than 2')
+
+        return self.edge, route, path
+
+    def perform_spanning_tree_coverage(self, current_node, visit_times, route):
+        """perform_spanning_tree_coverage
+
+        recursive function for function <plan>
+
+        :param current_node: current node
+        """
+
+        def is_valid_node(i, j):
+            is_i_valid_bounded = 0 <= i < self.merged_map_height
+            is_j_valid_bounded = 0 <= j < self.merged_map_width
+            if is_i_valid_bounded and is_j_valid_bounded:
+                # free only when the 4 sub-cells are all free
+                return bool(
+                    self.occ_map[2*i][2*j]
+                    and self.occ_map[2*i+1][2*j]
+                    and self.occ_map[2*i][2*j+1]
+                    and self.occ_map[2*i+1][2*j+1])
+
+            return False
+
+        # counter-clockwise neighbor finding order
+        order = [[1, 0], [0, 1], [-1, 0], [0, -1]]
+
+        found = False
+        route.append(current_node)
+        for inc in order:
+            ni, nj = current_node[0] + inc[0], current_node[1] + inc[1]
+            if is_valid_node(ni, nj) and visit_times[ni][nj] == 0:
+                neighbor_node = (ni, nj)
+                self.edge.append((current_node, neighbor_node))
+                found = True
+                visit_times[ni][nj] += 1
+                self.perform_spanning_tree_coverage(
+                    neighbor_node, visit_times, route)
+
+        # backtrace route from node with neighbors all visited
+        # to first node with unvisited neighbor
+        if not found:
+            has_node_with_unvisited_ngb = False
+            for node in reversed(route):
+                # drop nodes that have been visited twice
+                if visit_times[node[0]][node[1]] == 2:
+                    continue
+
+                visit_times[node[0]][node[1]] += 1
+                route.append(node)
+
+                for inc in order:
+                    ni, nj = node[0] + inc[0], node[1] + inc[1]
+                    if is_valid_node(ni, nj) and visit_times[ni][nj] == 0:
+                        has_node_with_unvisited_ngb = True
+                        break
+
+                if has_node_with_unvisited_ngb:
+                    break
+
+        return route
+
+    def move(self, p, q):
+        direction = self.get_vector_direction(p, q)
+        # move east
+        if direction == 'E':
+            p = self.get_sub_node(p, 'SE')
+            q = self.get_sub_node(q, 'SW')
+        # move west
+        elif direction == 'W':
+            p = self.get_sub_node(p, 'NW')
+            q = self.get_sub_node(q, 'NE')
+        # move south
+        elif direction == 'S':
+            p = self.get_sub_node(p, 'SW')
+            q = self.get_sub_node(q, 'NW')
+        # move north
+        elif direction == 'N':
+            p = self.get_sub_node(p, 'NE')
+            q = self.get_sub_node(q, 'SE')
+        else:
+            sys.exit('move direction error...')
+        return [p, q]
+
+    def get_round_trip_path(self, last, pivot):
+        direction = self.get_vector_direction(last, pivot)
+        if direction == 'E':
+            return [self.get_sub_node(pivot, 'SE'),
+                    self.get_sub_node(pivot, 'NE')]
+        elif direction == 'S':
+            return [self.get_sub_node(pivot, 'SW'),
+                    self.get_sub_node(pivot, 'SE')]
+        elif direction == 'W':
+            return [self.get_sub_node(pivot, 'NW'),
+                    self.get_sub_node(pivot, 'SW')]
+        elif direction == 'N':
+            return [self.get_sub_node(pivot, 'NE'),
+                    self.get_sub_node(pivot, 'NW')]
+        else:
+            sys.exit('get_round_trip_path: last->pivot direction error.')
+
+    def get_vector_direction(self, p, q):
+        # east
+        if p[0] == q[0] and p[1] < q[1]:
+            return 'E'
+        # west
+        elif p[0] == q[0] and p[1] > q[1]:
+            return 'W'
+        # south
+        elif p[0] < q[0] and p[1] == q[1]:
+            return 'S'
+        # north
+        elif p[0] > q[0] and p[1] == q[1]:
+            return 'N'
+        else:
+            sys.exit('get_vector_direction: Only E/W/S/N direction supported.')
+
+    def get_sub_node(self, node, direction):
+        if direction == 'SE':
+            return [2*node[0]+1, 2*node[1]+1]
+        elif direction == 'SW':
+            return [2*node[0]+1, 2*node[1]]
+        elif direction == 'NE':
+            return [2*node[0], 2*node[1]+1]
+        elif direction == 'NW':
+            return [2*node[0], 2*node[1]]
+        else:
+            sys.exit('get_sub_node: sub-node direction error.')
+
+    def get_interpolated_path(self, p, q):
+        # direction p->q: southwest / northeast
+        if (p[0] < q[0]) ^ (p[1] < q[1]):
+            ipx = [p[0], p[0], q[0]]
+            ipy = [p[1], q[1], q[1]]
+        # direction p->q: southeast / northwest
+        else:
+            ipx = [p[0], q[0], q[0]]
+            ipy = [p[1], p[1], q[1]]
+        return ipx, ipy
+
+    def get_intermediate_node(self, p, q):
+        p_ngb, q_ngb = set(), set()
+
+        for m, n in self.edge:
+            if m == p:
+                p_ngb.add(n)
+            if n == p:
+                p_ngb.add(m)
+            if m == q:
+                q_ngb.add(n)
+            if n == q:
+                q_ngb.add(m)
+
+        itsc = p_ngb.intersection(q_ngb)
+        if len(itsc) == 0:
+            sys.exit('get_intermediate_node: \
+                 no intermediate node between', p, q)
+        elif len(itsc) == 1:
+            return list(itsc)[0]
+        else:
+            sys.exit('get_intermediate_node: \
+                more than 1 intermediate node between', p, q)
+
+    def visualize_path(self, edge, path, start):
+        def coord_transform(p):
+            return [2*p[1] + 0.5, 2*p[0] + 0.5]
+
+        if do_animation:
+            last = path[0][0]
+            trajectory = [[last[1]], [last[0]]]
+            for p, q in path:
+                distance = math.hypot(p[0]-last[0], p[1]-last[1])
+                if distance <= 1.0:
+                    trajectory[0].append(p[1])
+                    trajectory[1].append(p[0])
+                else:
+                    ipx, ipy = self.get_interpolated_path(last, p)
+                    trajectory[0].extend(ipy)
+                    trajectory[1].extend(ipx)
+
+                last = q
+
+            trajectory[0].append(last[1])
+            trajectory[1].append(last[0])
+
+            for idx, state in enumerate(np.transpose(trajectory)):
+                plt.cla()
+                # for stopping simulation with the esc key.
+                plt.gcf().canvas.mpl_connect(
+                    'key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
+
+                # draw spanning tree
+                plt.imshow(self.occ_map, 'gray')
+                for p, q in edge:
+                    p = coord_transform(p)
+                    q = coord_transform(q)
+                    plt.plot([p[0], q[0]], [p[1], q[1]], '-oc')
+                sx, sy = coord_transform(start)
+                plt.plot([sx], [sy], 'pr', markersize=10)
+
+                # draw move path
+                plt.plot(trajectory[0][:idx+1], trajectory[1][:idx+1], '-k')
+                plt.plot(state[0], state[1], 'or')
+                plt.axis('equal')
+                plt.grid(True)
+                plt.pause(0.01)
+
+        else:
+            # draw spanning tree
+            plt.imshow(self.occ_map, 'gray')
+            for p, q in edge:
+                p = coord_transform(p)
+                q = coord_transform(q)
+                plt.plot([p[0], q[0]], [p[1], q[1]], '-oc')
+            sx, sy = coord_transform(start)
+            plt.plot([sx], [sy], 'pr', markersize=10)
+
+            # draw move path
+            last = path[0][0]
+            for p, q in path:
+                distance = math.hypot(p[0]-last[0], p[1]-last[1])
+                if distance == 1.0:
+                    plt.plot([last[1], p[1]], [last[0], p[0]], '-k')
+                else:
+                    ipx, ipy = self.get_interpolated_path(last, p)
+                    plt.plot(ipy, ipx, '-k')
+                plt.arrow(p[1], p[0], q[1]-p[1], q[0]-p[0], head_width=0.2)
+                last = q
+
+            plt.show()
+
+
+def main():
+    dir_path = os.path.dirname(os.path.realpath(__file__))
+    img = plt.imread(os.path.join(dir_path, 'map', 'test_2.png'))
+    STC_planner = SpiralSpanningTreeCoveragePlanner(img)
+    start = (10, 0)
+    edge, route, path = STC_planner.plan(start)
+    STC_planner.visualize_path(edge, path, start)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/test_spiral_spanning_tree_coverage_path_planner.py b/tests/test_spiral_spanning_tree_coverage_path_planner.py
new file mode 100644
index 0000000000..622b6ba18c
--- /dev/null
+++ b/tests/test_spiral_spanning_tree_coverage_path_planner.py
@@ -0,0 +1,58 @@
+import os
+import sys
+import matplotlib.pyplot as plt
+from unittest import TestCase
+
+sys.path.append(os.path.dirname(
+    os.path.abspath(__file__)) + "/../PathPlanning/SpiralSpanningTreeCPP")
+try:
+    import spiral_spanning_tree_coverage_path_planner
+except ImportError:
+    raise
+
+spiral_spanning_tree_coverage_path_planner.do_animation = True
+
+
+class TestPlanning(TestCase):
+    def spiral_stc_cpp(self, img, start):
+        num_free = 0
+        for i in range(img.shape[0]):
+            for j in range(img.shape[1]):
+                num_free += img[i][j]
+
+        STC_planner = spiral_spanning_tree_coverage_path_planner.\
+            SpiralSpanningTreeCoveragePlanner(img)
+
+        edge, route, path = STC_planner.plan(start)
+
+        covered_nodes = set()
+        for p, q in edge:
+            covered_nodes.add(p)
+            covered_nodes.add(q)
+
+        # assert complete coverage
+        self.assertEqual(len(covered_nodes), num_free / 4)
+
+    def test_spiral_stc_cpp_1(self):
+        img_dir = os.path.dirname(
+            os.path.abspath(__file__)) + \
+            "/../PathPlanning/SpiralSpanningTreeCPP"
+        img = plt.imread(os.path.join(img_dir, 'map', 'test.png'))
+        start = (0, 0)
+        self.spiral_stc_cpp(img, start)
+
+    def test_spiral_stc_cpp_2(self):
+        img_dir = os.path.dirname(
+            os.path.abspath(__file__)) + \
+            "/../PathPlanning/SpiralSpanningTreeCPP"
+        img = plt.imread(os.path.join(img_dir, 'map', 'test_2.png'))
+        start = (10, 0)
+        self.spiral_stc_cpp(img, start)
+
+    def test_spiral_stc_cpp_3(self):
+        img_dir = os.path.dirname(
+            os.path.abspath(__file__)) + \
+            "/../PathPlanning/SpiralSpanningTreeCPP"
+        img = plt.imread(os.path.join(img_dir, 'map', 'test_3.png'))
+        start = (0, 0)
+        self.spiral_stc_cpp(img, start)

From 808aa0fc4032500982ba2e4111d533ccfc1bc65f Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 28 Jul 2020 07:16:33 +0900
Subject: [PATCH 0411/1080] Bump matplotlib from 3.2.2 to 3.3.0 (#366)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.2.2 to 3.3.0.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.2.2...v3.3.0)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 775486efa3..217a23e50f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.19.0
 scipy == 1.5.1
 pandas == 1.0.5
-matplotlib == 3.2.2
+matplotlib == 3.3.0
 cvxpy == 1.1.1

From a87c5ab88026b909564b0787f8ef19961ba90d60 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 28 Jul 2020 07:48:21 +0900
Subject: [PATCH 0412/1080] Bump cvxpy from 1.1.1 to 1.1.3 (#367)

Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.1 to 1.1.3.
- [Release notes](https://github.com/cvxgrp/cvxpy/releases)
- [Changelog](https://github.com/cvxgrp/cvxpy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.1...v1.1.3)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 217a23e50f..2b884c032f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy == 1.19.0
 scipy == 1.5.1
 pandas == 1.0.5
 matplotlib == 3.3.0
-cvxpy == 1.1.1
+cvxpy == 1.1.3

From d861cda9d4448f792be655bdfaedc6e8e8a0ed7d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 28 Jul 2020 07:49:42 +0900
Subject: [PATCH 0413/1080] Bump scipy from 1.5.1 to 1.5.2 (#368)

Bumps [scipy](https://github.com/scipy/scipy) from 1.5.1 to 1.5.2.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.5.1...v1.5.2)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 2b884c032f..ea6578e457 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.19.0
-scipy == 1.5.1
+scipy == 1.5.2
 pandas == 1.0.5
 matplotlib == 3.3.0
 cvxpy == 1.1.3

From a3808bca79e22a7fc7a131d6f357fca2f30c6d75 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 28 Jul 2020 08:54:56 +0900
Subject: [PATCH 0414/1080] Bump numpy from 1.19.0 to 1.19.1 (#369)

Bumps [numpy](https://github.com/numpy/numpy) from 1.19.0 to 1.19.1.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/master/doc/HOWTO_RELEASE.rst.txt)
- [Commits](https://github.com/numpy/numpy/compare/v1.19.0...v1.19.1)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index ea6578e457..a256740bbd 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.19.0
+numpy == 1.19.1
 scipy == 1.5.2
 pandas == 1.0.5
 matplotlib == 3.3.0

From c90ef2ccb3961620581b55fbac4555f1d5aec14b Mon Sep 17 00:00:00 2001
From: "Gaushik M.R" <Jivthesh16@gmail.com>
Date: Sun, 2 Aug 2020 16:40:28 +0530
Subject: [PATCH 0415/1080] Create CODE_OF_CONDUCT.md (#370)

---
 CODE_OF_CONDUCT.md | 76 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 76 insertions(+)
 create mode 100644 CODE_OF_CONDUCT.md

diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
new file mode 100644
index 0000000000..e8c4702596
--- /dev/null
+++ b/CODE_OF_CONDUCT.md
@@ -0,0 +1,76 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to making participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+ advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+ address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+ professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies both within project spaces and in public spaces
+when an individual is representing the project or its community. Examples of
+representing a project or community include using an official project e-mail
+address, posting via an official social media account, or acting as an appointed
+representative at an online or offline event. Representation of a project may be
+further defined and clarified by project maintainers.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at asakaig@gmail.com. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq

From 88cc03dd217e10f2672bde06b11b5d892c975f15 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 2 Aug 2020 21:50:27 +0900
Subject: [PATCH 0416/1080] refactor hybrid_a_star codes and clean up heuristic
 function names  (#371)

* refactor hybrid_a_star code heuristic function names and codes

* removed empty line
---
 .../circleci-artifacts-redirector.yml         |  3 -
 ...ic.py => dynamic_programming_heuristic.py} | 65 +------------------
 PathPlanning/HybridAStar/hybrid_a_star.py     |  8 +--
 3 files changed, 6 insertions(+), 70 deletions(-)
 rename PathPlanning/HybridAStar/{a_star_heuristic.py => dynamic_programming_heuristic.py} (76%)

diff --git a/.github/workflows/circleci-artifacts-redirector.yml b/.github/workflows/circleci-artifacts-redirector.yml
index 849a97f1a7..951e3a5947 100644
--- a/.github/workflows/circleci-artifacts-redirector.yml
+++ b/.github/workflows/circleci-artifacts-redirector.yml
@@ -11,6 +11,3 @@ jobs:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           artifact-path: 0/html/index.html
           circleci-jobs: build_doc
-      - name: Check the URL
-        run: |
-          curl --fail ${{ steps.step1.outputs.url }} | grep $GITHUB_SHA
diff --git a/PathPlanning/HybridAStar/a_star_heuristic.py b/PathPlanning/HybridAStar/dynamic_programming_heuristic.py
similarity index 76%
rename from PathPlanning/HybridAStar/a_star_heuristic.py
rename to PathPlanning/HybridAStar/dynamic_programming_heuristic.py
index 36c3342462..cc635260d6 100644
--- a/PathPlanning/HybridAStar/a_star_heuristic.py
+++ b/PathPlanning/HybridAStar/dynamic_programming_heuristic.py
@@ -42,7 +42,7 @@ def calc_final_path(goal_node, closed_node_set, resolution):
     return rx, ry
 
 
-def dp_planning(sx, sy, gx, gy, ox, oy, resolution, rr):
+def calc_distance_heuristic(gx, gy, ox, oy, resolution, rr):
     """
     gx: goal x position [m]
     gx: goal x position [m]
@@ -52,7 +52,6 @@ def dp_planning(sx, sy, gx, gy, ox, oy, resolution, rr):
     rr: robot radius[m]
     """
 
-    start_node = Node(round(sx / resolution), round(sy / resolution), 0.0, -1)
     goal_node = Node(round(gx / resolution), round(gy / resolution), 0.0, -1)
     ox = [iox / resolution for iox in ox]
     oy = [ioy / resolution for ioy in oy]
@@ -115,16 +114,7 @@ def dp_planning(sx, sy, gx, gy, ox, oy, resolution, rr):
                         priority_queue,
                         (node.cost, calc_index(node, x_w, min_x, min_y)))
 
-    rx, ry = calc_final_path(closed_set[calc_index(
-        start_node, x_w, min_x, min_y)], closed_set, resolution)
-
-    return rx, ry, closed_set
-
-
-def calc_heuristic(n1, n2):
-    w = 1.0  # weight of heuristic
-    d = w * math.sqrt((n1.x - n2.x) ** 2 + (n1.y - n2.y) ** 2)
-    return d
+    return closed_set
 
 
 def verify_node(node, obstacle_map, min_x, min_y, max_x, max_y):
@@ -184,54 +174,3 @@ def get_motion_model():
               [1, 1, math.sqrt(2)]]
 
     return motion
-
-
-def main():
-    print(__file__ + " start!!")
-
-    # start and goal position
-    sx = 10.0  # [m]
-    sy = 10.0  # [m]
-    gx = 50.0  # [m]
-    gy = 50.0  # [m]
-    grid_size = 2.0  # [m]
-    robot_size = 1.0  # [m]
-
-    ox, oy = [], []
-
-    for i in range(60):
-        ox.append(i)
-        oy.append(0.0)
-    for i in range(60):
-        ox.append(60.0)
-        oy.append(i)
-    for i in range(61):
-        ox.append(i)
-        oy.append(60.0)
-    for i in range(61):
-        ox.append(0.0)
-        oy.append(i)
-    for i in range(40):
-        ox.append(20.0)
-        oy.append(i)
-    for i in range(40):
-        ox.append(40.0)
-        oy.append(60.0 - i)
-
-    if show_animation:  # pragma: no cover
-        plt.plot(ox, oy, ".k")
-        plt.plot(sx, sy, "xr")
-        plt.plot(gx, gy, "xb")
-        plt.grid(True)
-        plt.axis("equal")
-
-    rx, ry, _ = dp_planning(sx, sy, gx, gy, ox, oy, grid_size, robot_size)
-
-    if show_animation:  # pragma: no cover
-        plt.plot(rx, ry, "-r")
-        plt.show()
-
-
-if __name__ == '__main__':
-    show_animation = True
-    main()
diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
index 45d6e11b18..9679e3979b 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -18,7 +18,7 @@
 sys.path.append(os.path.dirname(os.path.abspath(__file__))
                 + "/../ReedsSheppPath")
 try:
-    from a_star_heuristic import dp_planning
+    from dynamic_programming_heuristic import calc_distance_heuristic
     import reeds_shepp_path_planning as rs
     from car import move, check_car_collision, MAX_STEER, WB, plot_car
 except Exception:
@@ -274,9 +274,9 @@ def hybrid_a_star_planning(start, goal, ox, oy, xy_resolution, yaw_resolution):
 
     openList, closedList = {}, {}
 
-    _, _, h_dp = dp_planning(start_node.x_list[-1], start_node.y_list[-1],
-                             goal_node.x_list[-1], goal_node.y_list[-1],
-                             ox, oy, xy_resolution, VR)
+    h_dp = calc_distance_heuristic(
+        goal_node.x_list[-1], goal_node.y_list[-1],
+        ox, oy, xy_resolution, VR)
 
     pq = []
     openList[calc_index(start_node, config)] = start_node

From 765f752165d157b3342e9175380befc3b92e3d89 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 4 Aug 2020 07:53:43 +0900
Subject: [PATCH 0417/1080] Bump pandas from 1.0.5 to 1.1.0 (#372)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.0.5 to 1.1.0.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.0.5...v1.1.0)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index a256740bbd..1dd8dfe984 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.19.1
 scipy == 1.5.2
-pandas == 1.0.5
+pandas == 1.1.0
 matplotlib == 3.3.0
 cvxpy == 1.1.3

From 6d29bcd97d531b762b7c7cae39e5676930157d5f Mon Sep 17 00:00:00 2001
From: Mahyar Abdeetedal <mahyar.etedal@icloud.com>
Date: Tue, 11 Aug 2020 03:31:45 -0400
Subject: [PATCH 0418/1080] support no obstacle in RRT* (#375)

---
 PathPlanning/RRTStar/rrt_star.py |  4 ++--
 tests/test_rrt_star.py           | 11 +++++++++++
 2 files changed, 13 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py
index 7b46029791..10ad93a6ba 100644
--- a/PathPlanning/RRTStar/rrt_star.py
+++ b/PathPlanning/RRTStar/rrt_star.py
@@ -81,13 +81,13 @@ def planning(self, animation=True, search_until_max_iter=True):
 
             if (not search_until_max_iter) and new_node:  # check reaching the goal
                 last_index = self.search_best_goal_node()
-                if last_index:
+                if last_index is not None:
                     return self.generate_final_course(last_index)
 
         print("reached max iteration")
 
         last_index = self.search_best_goal_node()
-        if last_index:
+        if last_index is not None:
             return self.generate_final_course(last_index)
 
         return None
diff --git a/tests/test_rrt_star.py b/tests/test_rrt_star.py
index 514ed34829..d3661610ff 100644
--- a/tests/test_rrt_star.py
+++ b/tests/test_rrt_star.py
@@ -20,7 +20,18 @@ def test1(self):
         m.show_animation = False
         m.main()
 
+    def test_no_obstacle(self):
+        obstacle_list = []
+
+        # Set Initial parameters
+        rrt_star = m.RRTStar(start=[0, 0],
+                             goal=[6, 10],
+                             rand_area=[-2, 15],
+                             obstacle_list=obstacle_list)
+        path = rrt_star.planning(animation=False)
+        assert path is not None
 
 if __name__ == '__main__':  # pragma: no cover
     test = Test()
     test.test1()
+    test.test_no_obstacle()

From 750e8a185e20b7f28424f258f503e9ee4870515e Mon Sep 17 00:00:00 2001
From: yashvarshney003 <59112785+yashvarshney003@users.noreply.github.com>
Date: Sat, 15 Aug 2020 08:24:36 +0530
Subject: [PATCH 0419/1080] Update breadth_first_search.py (#374)

---
 .../breadth_first_search.py                   |  8 +++---
 .../DepthFirstSearch/depth_first_search.py    |  8 +++---
 PathPlanning/Dijkstra/dijkstra.py             | 16 ++++++------
 README.md                                     |  2 ++
 tests/test_breadth_first_search.py            | 26 +++++++++++++++++++
 tests/test_depth_first_search.py              | 26 +++++++++++++++++++
 tests/test_dijkstra.py                        | 16 +++++++++++-
 7 files changed, 85 insertions(+), 17 deletions(-)
 create mode 100644 tests/test_breadth_first_search.py
 create mode 100644 tests/test_depth_first_search.py

diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
index 01f1a31d64..406e5eb51f 100644
--- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py
+++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
@@ -33,16 +33,16 @@ def __init__(self, ox, oy, reso, rr):
         self.motion = self.get_motion_model()
 
     class Node:
-        def __init__(self, x, y, cost, pind, parent):
+        def __init__(self, x, y, cost, parent_index, parent):
             self.x = x  # index of grid
             self.y = y  # index of grid
             self.cost = cost
-            self.pind = pind
+            self.parent_index = parent_index
             self.parent = parent
 
         def __str__(self):
             return str(self.x) + "," + str(self.y) + "," + str(
-                self.cost) + "," + str(self.pind)
+                self.cost) + "," + str(self.parent_index)
 
     def planning(self, sx, sy, gx, gy):
         """
@@ -92,7 +92,7 @@ def planning(self, sx, sy, gx, gy):
 
             if current.x == ngoal.x and current.y == ngoal.y:
                 print("Find goal")
-                ngoal.pind = current.pind
+                ngoal.parent_index = current.parent_index
                 ngoal.cost = current.cost
                 break
 
diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py
index c8d2d5eaba..523225ee42 100644
--- a/PathPlanning/DepthFirstSearch/depth_first_search.py
+++ b/PathPlanning/DepthFirstSearch/depth_first_search.py
@@ -33,16 +33,16 @@ def __init__(self, ox, oy, reso, rr):
         self.motion = self.get_motion_model()
 
     class Node:
-        def __init__(self, x, y, cost, pind, parent):
+        def __init__(self, x, y, cost, parent_index, parent):
             self.x = x  # index of grid
             self.y = y  # index of grid
             self.cost = cost
-            self.pind = pind
+            self.parent_index = parent_index
             self.parent = parent
 
         def __str__(self):
             return str(self.x) + "," + str(self.y) + "," + str(
-                self.cost) + "," + str(self.pind)
+                self.cost) + "," + str(self.parent_index)
 
     def planning(self, sx, sy, gx, gy):
         """
@@ -88,7 +88,7 @@ def planning(self, sx, sy, gx, gy):
 
             if current.x == ngoal.x and current.y == ngoal.y:
                 print("Find goal")
-                ngoal.pind = current.pind
+                ngoal.parent_index = current.parent_index
                 ngoal.cost = current.cost
                 break
 
diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py
index b744bc6fc7..9956dff326 100644
--- a/PathPlanning/Dijkstra/dijkstra.py
+++ b/PathPlanning/Dijkstra/dijkstra.py
@@ -38,15 +38,15 @@ def __init__(self, ox, oy, resolution, robot_radius):
         self.motion = self.get_motion_model()
 
     class Node:
-        def __init__(self, x, y, cost, parent):
+        def __init__(self, x, y, cost, parent_index):
             self.x = x  # index of grid
             self.y = y  # index of grid
             self.cost = cost
-            self.parent = parent  # index of previous Node
+            self.parent_index = parent_index  # index of previous Node
 
         def __str__(self):
             return str(self.x) + "," + str(self.y) + "," + str(
-                self.cost) + "," + str(self.parent)
+                self.cost) + "," + str(self.parent_index)
 
     def planning(self, sx, sy, gx, gy):
         """
@@ -88,7 +88,7 @@ def planning(self, sx, sy, gx, gy):
 
             if current.x == goal_node.x and current.y == goal_node.y:
                 print("Find goal")
-                goal_node.parent = current.parent
+                goal_node.parent_index = current.parent_index
                 goal_node.cost = current.cost
                 break
 
@@ -126,12 +126,12 @@ def calc_final_path(self, goal_node, closed_set):
         # generate final course
         rx, ry = [self.calc_position(goal_node.x, self.min_x)], [
             self.calc_position(goal_node.y, self.min_y)]
-        parent = goal_node.parent
-        while parent != -1:
-            n = closed_set[parent]
+        parent_index = goal_node.parent_index
+        while parent_index != -1:
+            n = closed_set[parent_index]
             rx.append(self.calc_position(n.x, self.min_x))
             ry.append(self.calc_position(n.y, self.min_y))
-            parent = n.parent
+            parent_index = n.parent_index
 
         return rx, ry
 
diff --git a/README.md b/README.md
index 49a99959f1..07dbf7ae5a 100644
--- a/README.md
+++ b/README.md
@@ -124,6 +124,8 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation
 2. Install the required libraries. You can use environment.yml with conda command.
 
 > conda env create -f environment.yml
+> using pip :-
+         pip install -r requirements.txt
 
 
 3. Execute python script in each directory.
diff --git a/tests/test_breadth_first_search.py b/tests/test_breadth_first_search.py
new file mode 100644
index 0000000000..7dc36b2860
--- /dev/null
+++ b/tests/test_breadth_first_search.py
@@ -0,0 +1,26 @@
+from unittest import TestCase
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../PathPlanning/BreadthFirstSearch/")
+
+
+try:
+    import breadth_first_search as m
+except ImportError:
+    raise
+
+
+print(__file__)
+
+
+class Test(TestCase):
+
+    def test1(self):
+        m.show_animation = False
+        m.main()
+
+
+if __name__ == '__main__':  # pragma: no cover
+    test = Test()
+    test.test1()
diff --git a/tests/test_depth_first_search.py b/tests/test_depth_first_search.py
new file mode 100644
index 0000000000..15aa7a0a53
--- /dev/null
+++ b/tests/test_depth_first_search.py
@@ -0,0 +1,26 @@
+from unittest import TestCase
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../PathPlanning/DepthFirstSearch/")
+
+
+try:
+    import depth_first_search as m
+except ImportError:
+    raise
+
+
+print(__file__)
+
+
+class Test(TestCase):
+
+    def test1(self):
+        m.show_animation = False
+        m.main()
+
+
+if __name__ == '__main__':  # pragma: no cover
+    test = Test()
+    test.test1()
diff --git a/tests/test_dijkstra.py b/tests/test_dijkstra.py
index b86f2e6ce6..7650eee802 100644
--- a/tests/test_dijkstra.py
+++ b/tests/test_dijkstra.py
@@ -1,6 +1,15 @@
 from unittest import TestCase
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../PathPlanning/Dijkstra/")
+
+
+try:
+    import dijkstra as m
+except ImportError:
+    raise
 
-from PathPlanning.Dijkstra import dijkstra as m
 
 print(__file__)
 
@@ -10,3 +19,8 @@ class Test(TestCase):
     def test1(self):
         m.show_animation = False
         m.main()
+
+
+if __name__ == '__main__':  # pragma: no cover
+    test = Test()
+    test.test1()

From 007db0c844589a4677eb28a646833c86c549b988 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 18 Aug 2020 07:23:27 +0900
Subject: [PATCH 0420/1080] Bump cvxpy from 1.1.3 to 1.1.4 (#377)

Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.3 to 1.1.4.
- [Release notes](https://github.com/cvxgrp/cvxpy/releases)
- [Changelog](https://github.com/cvxgrp/cvxpy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.3...v1.1.4)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 1dd8dfe984..78ba2686c2 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy == 1.19.1
 scipy == 1.5.2
 pandas == 1.1.0
 matplotlib == 3.3.0
-cvxpy == 1.1.3
+cvxpy == 1.1.4

From 7442bdceb18b6157b733311ffcc2d544eeab901a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 18 Aug 2020 07:52:23 +0900
Subject: [PATCH 0421/1080] Bump matplotlib from 3.3.0 to 3.3.1 (#376)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.3.0 to 3.3.1.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.3.0...v3.3.1)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 78ba2686c2..2a7d5b3a91 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.19.1
 scipy == 1.5.2
 pandas == 1.1.0
-matplotlib == 3.3.0
+matplotlib == 3.3.1
 cvxpy == 1.1.4

From fa227083372445f0f440b5d5ad878d9201ed7476 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 25 Aug 2020 20:32:47 +0900
Subject: [PATCH 0422/1080] Bump pandas from 1.1.0 to 1.1.1 (#384)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.1.0 to 1.1.1.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.1.0...v1.1.1)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 2a7d5b3a91..3bcb2de332 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.19.1
 scipy == 1.5.2
-pandas == 1.1.0
+pandas == 1.1.1
 matplotlib == 3.3.1
 cvxpy == 1.1.4

From 409be20b472b5ec88077495aeb980b02d8edeb68 Mon Sep 17 00:00:00 2001
From: Sarim Mehdi <sarim.mehdi.550@gmail.com>
Date: Sun, 30 Aug 2020 06:04:08 +0200
Subject: [PATCH 0423/1080] Implemented bug algorithms (#378)

* Create bug.py

* Update bug.py

* Update bug.py

* Update bug.py

* Update bug.py

* Update bug.py

* Add files via upload

* Update test_bug.py

* Update test_bug.py

* Update bug.py

* Update test_bug.py

* Delete bug.py

* Create BugPlanning

* Delete BugPlanning

* Create bug.py

* Update bug.py

* Update test_bug.py

* Update bug.py

* Update bug.py
---
 PathPlanning/BugPlanning/bug.py | 327 ++++++++++++++++++++++++++++++++
 tests/test_bug.py               |  17 ++
 2 files changed, 344 insertions(+)
 create mode 100644 PathPlanning/BugPlanning/bug.py
 create mode 100644 tests/test_bug.py

diff --git a/PathPlanning/BugPlanning/bug.py b/PathPlanning/BugPlanning/bug.py
new file mode 100644
index 0000000000..21bbbbb1cb
--- /dev/null
+++ b/PathPlanning/BugPlanning/bug.py
@@ -0,0 +1,327 @@
+"""
+Bug Planning
+author: Sarim Mehdi(muhammadsarim.mehdi@studio.unibo.it)
+Source: https://sites.google.com/site/ece452bugalgorithms/
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+show_animation = True
+
+
+class BugPlanner:
+    def __init__(self, start_x, start_y, goal_x, goal_y, obs_x, obs_y):
+        self.goal_x = goal_x
+        self.goal_y = goal_y
+        self.obs_x = obs_x
+        self.obs_y = obs_y
+        self.r_x = [start_x]
+        self.r_y = [start_y]
+        self.out_x = []
+        self.out_y = []
+        for o_x, o_y in zip(obs_x, obs_y):
+            for add_x, add_y in zip([1, 0, -1, -1, -1, 0, 1, 1],
+                                    [1, 1, 1, 0, -1, -1, -1, 0]):
+                cand_x, cand_y = o_x+add_x, o_y+add_y
+                valid_point = True
+                for _x, _y in zip(obs_x, obs_y):
+                    if cand_x == _x and cand_y == _y:
+                        valid_point = False
+                        break
+                if valid_point:
+                    self.out_x.append(cand_x), self.out_y.append(cand_y)
+
+    def mov_normal(self):
+        return self.r_x[-1] + np.sign(self.goal_x - self.r_x[-1]), \
+               self.r_y[-1] + np.sign(self.goal_y - self.r_y[-1])
+
+    def mov_to_next_obs(self, visited_x, visited_y):
+        for add_x, add_y in zip([1, 0, -1, 0], [0, 1, 0, -1]):
+            c_x, c_y = self.r_x[-1] + add_x, self.r_y[-1] + add_y
+            for _x, _y in zip(self.out_x, self.out_y):
+                use_pt = True
+                if c_x == _x and c_y == _y:
+                    for v_x, v_y in zip(visited_x, visited_y):
+                        if c_x == v_x and c_y == v_y:
+                            use_pt = False
+                            break
+                    if use_pt:
+                        return c_x, c_y, False
+                if not use_pt:
+                    break
+        return self.r_x[-1], self.r_y[-1], True
+
+    def bug0(self):
+        '''Greedy algorithm where you move towards goal
+        until you hit an obstacle. Then you go around it
+        (pick an arbitrary direction), until it is possible
+        for you to start moving towards goal in a greedy manner again'''
+        mov_dir = 'normal'
+        cand_x, cand_y = -np.inf, -np.inf
+        if show_animation:
+            plt.plot(self.obs_x, self.obs_y, ".k")
+            plt.plot(self.r_x[-1], self.r_y[-1], "og")
+            plt.plot(self.goal_x, self.goal_y, "xb")
+            plt.plot(self.out_x, self.out_y, ".")
+            plt.grid(True)
+            plt.title('BUG 0')
+
+        for x_ob, y_ob in zip(self.out_x, self.out_y):
+            if self.r_x[-1] == x_ob and self.r_y[-1] == y_ob:
+                mov_dir = 'obs'
+                break
+
+        visited_x, visited_y = [], []
+        while True:
+            if self.r_x[-1] == self.goal_x and \
+                    self.r_y[-1] == self.goal_y:
+                break
+            if mov_dir == 'normal':
+                cand_x, cand_y = self.mov_normal()
+            if mov_dir == 'obs':
+                cand_x, cand_y, _ = self.mov_to_next_obs(visited_x, visited_y)
+            if mov_dir == 'normal':
+                found_boundary = False
+                for x_ob, y_ob in zip(self.out_x, self.out_y):
+                    if cand_x == x_ob and cand_y == y_ob:
+                        self.r_x.append(cand_x), self.r_y.append(cand_y)
+                        visited_x[:], visited_y[:] = [], []
+                        visited_x.append(cand_x), visited_y.append(cand_y)
+                        mov_dir = 'obs'
+                        found_boundary = True
+                        break
+                if not found_boundary:
+                    self.r_x.append(cand_x), self.r_y.append(cand_y)
+            elif mov_dir == 'obs':
+                can_go_normal = True
+                for x_ob, y_ob in zip(self.obs_x, self.obs_y):
+                    if self.mov_normal()[0] == x_ob and \
+                            self.mov_normal()[1] == y_ob:
+                        can_go_normal = False
+                        break
+                if can_go_normal:
+                    mov_dir = 'normal'
+                else:
+                    self.r_x.append(cand_x), self.r_y.append(cand_y)
+                    visited_x.append(cand_x), visited_y.append(cand_y)
+            if show_animation:
+                plt.plot(self.r_x, self.r_y, "-r")
+                plt.pause(0.001)
+        if show_animation:
+            plt.show()
+
+    def bug1(self):
+        '''Move towards goal in a greedy manner.
+        When you hit an obstacle, you go around it and
+        back to where you hit the obstacle initially.
+        Then, you go to the point on the obstacle that is
+        closest to your goal and you start moving towards
+        goal in a greedy manner from that new point.'''
+        mov_dir = 'normal'
+        cand_x, cand_y = -np.inf, -np.inf
+        exit_x, exit_y = -np.inf, -np.inf
+        dist = np.inf
+        back_to_start = False
+        second_round = False
+        if show_animation:
+            plt.plot(self.obs_x, self.obs_y, ".k")
+            plt.plot(self.r_x[-1], self.r_y[-1], "og")
+            plt.plot(self.goal_x, self.goal_y, "xb")
+            plt.plot(self.out_x, self.out_y, ".")
+            plt.grid(True)
+            plt.title('BUG 1')
+
+        for xob, yob in zip(self.out_x, self.out_y):
+            if self.r_x[-1] == xob and self.r_y[-1] == yob:
+                mov_dir = 'obs'
+                break
+
+        visited_x, visited_y = [], []
+        while True:
+            if self.r_x[-1] == self.goal_x and \
+                    self.r_y[-1] == self.goal_y:
+                break
+            if mov_dir == 'normal':
+                cand_x, cand_y = self.mov_normal()
+            if mov_dir == 'obs':
+                cand_x, cand_y, back_to_start = \
+                    self.mov_to_next_obs(visited_x, visited_y)
+            if mov_dir == 'normal':
+                found_boundary = False
+                for x_ob, y_ob in zip(self.out_x, self.out_y):
+                    if cand_x == x_ob and cand_y == y_ob:
+                        self.r_x.append(cand_x), self.r_y.append(cand_y)
+                        visited_x[:], visited_y[:] = [], []
+                        visited_x.append(cand_x), visited_y.append(cand_y)
+                        mov_dir = 'obs'
+                        dist = np.inf
+                        back_to_start = False
+                        second_round = False
+                        found_boundary = True
+                        break
+                if not found_boundary:
+                    self.r_x.append(cand_x), self.r_y.append(cand_y)
+            elif mov_dir == 'obs':
+                d = np.linalg.norm(np.array([cand_x, cand_y] -
+                                            np.array([self.goal_x,
+                                                      self.goal_y])))
+                if d < dist and not second_round:
+                    exit_x, exit_y = cand_x, cand_y
+                    dist = d
+                if back_to_start and not second_round:
+                    second_round = True
+                    del self.r_x[-len(visited_x):]
+                    del self.r_y[-len(visited_y):]
+                    visited_x[:], visited_y[:] = [], []
+                self.r_x.append(cand_x), self.r_y.append(cand_y)
+                visited_x.append(cand_x), visited_y.append(cand_y)
+                if cand_x == exit_x and \
+                        cand_y == exit_y and \
+                        second_round:
+                    mov_dir = 'normal'
+            if show_animation:
+                plt.plot(self.r_x, self.r_y, "-r")
+                plt.pause(0.001)
+        if show_animation:
+            plt.show()
+
+    def bug2(self):
+        '''Move towards goal in a greedy manner.
+        When you hit an obstacle, you go around it and
+        keep track of your distance from the goal.
+        If the distance from your goal was decreasing before
+        and now it starts increasing, that means the current
+        point is probably the closest point to the
+        goal (this may or may not be true because the algorithm
+        doesn't explore the entire boundary around the obstacle).
+        So, you depart from this point and continue towards the
+        goal in a greedy manner'''
+        mov_dir = 'normal'
+        cand_x, cand_y = -np.inf, -np.inf
+        if show_animation:
+            plt.plot(self.obs_x, self.obs_y, ".k")
+            plt.plot(self.r_x[-1], self.r_y[-1], "og")
+            plt.plot(self.goal_x, self.goal_y, "xb")
+            plt.plot(self.out_x, self.out_y, ".")
+
+        straight_x, straight_y = [self.r_x[-1]], [self.r_y[-1]]
+        hit_x, hit_y = [], []
+        while True:
+            if straight_x[-1] == self.goal_x and \
+                    straight_y[-1] == self.goal_y:
+                break
+            c_x = straight_x[-1] + np.sign(self.goal_x - straight_x[-1])
+            c_y = straight_y[-1] + np.sign(self.goal_y - straight_y[-1])
+            for x_ob, y_ob in zip(self.out_x, self.out_y):
+                if c_x == x_ob and c_y == y_ob:
+                    hit_x.append(c_x), hit_y.append(c_y)
+                    break
+            straight_x.append(c_x), straight_y.append(c_y)
+        if show_animation:
+            plt.plot(straight_x, straight_y, ",")
+            plt.plot(hit_x, hit_y, "d")
+            plt.grid(True)
+            plt.title('BUG 2')
+
+        for x_ob, y_ob in zip(self.out_x, self.out_y):
+            if self.r_x[-1] == x_ob and self.r_y[-1] == y_ob:
+                mov_dir = 'obs'
+                break
+
+        visited_x, visited_y = [], []
+        while True:
+            if self.r_x[-1] == self.goal_x \
+                    and self.r_y[-1] == self.goal_y:
+                break
+            if mov_dir == 'normal':
+                cand_x, cand_y = self.mov_normal()
+            if mov_dir == 'obs':
+                cand_x, cand_y, _ = self.mov_to_next_obs(visited_x, visited_y)
+            if mov_dir == 'normal':
+                found_boundary = False
+                for x_ob, y_ob in zip(self.out_x, self.out_y):
+                    if cand_x == x_ob and cand_y == y_ob:
+                        self.r_x.append(cand_x), self.r_y.append(cand_y)
+                        visited_x[:], visited_y[:] = [], []
+                        visited_x.append(cand_x), visited_y.append(cand_y)
+                        del hit_x[0]
+                        del hit_y[0]
+                        mov_dir = 'obs'
+                        found_boundary = True
+                        break
+                if not found_boundary:
+                    self.r_x.append(cand_x), self.r_y.append(cand_y)
+            elif mov_dir == 'obs':
+                self.r_x.append(cand_x), self.r_y.append(cand_y)
+                visited_x.append(cand_x), visited_y.append(cand_y)
+                for i_x, i_y in zip(range(len(hit_x)), range(len(hit_y))):
+                    if cand_x == hit_x[i_x] and cand_y == hit_y[i_y]:
+                        del hit_x[i_x]
+                        del hit_y[i_y]
+                        mov_dir = 'normal'
+                        break
+            if show_animation:
+                plt.plot(self.r_x, self.r_y, "-r")
+                plt.pause(0.001)
+        if show_animation:
+            plt.show()
+
+
+def main(bug_0, bug_1, bug_2):
+    # set obstacle positions
+    o_x, o_y = [], []
+
+    s_x = 0.0
+    s_y = 0.0
+    g_x = 167.0
+    g_y = 50.0
+
+    for i in range(20, 40):
+        for j in range(20, 40):
+            o_x.append(i)
+            o_y.append(j)
+
+    for i in range(60, 100):
+        for j in range(40, 80):
+            o_x.append(i)
+            o_y.append(j)
+
+    for i in range(120, 140):
+        for j in range(80, 100):
+            o_x.append(i)
+            o_y.append(j)
+
+    for i in range(80, 140):
+        for j in range(0, 20):
+            o_x.append(i)
+            o_y.append(j)
+
+    for i in range(0, 20):
+        for j in range(60, 100):
+            o_x.append(i)
+            o_y.append(j)
+
+    for i in range(20, 40):
+        for j in range(80, 100):
+            o_x.append(i)
+            o_y.append(j)
+
+    for i in range(120, 160):
+        for j in range(40, 60):
+            o_x.append(i)
+            o_y.append(j)
+
+    if bug_0:
+        my_Bug = BugPlanner(s_x, s_y, g_x, g_y, o_x, o_y)
+        my_Bug.bug0()
+    if bug_1:
+        my_Bug = BugPlanner(s_x, s_y, g_x, g_y, o_x, o_y)
+        my_Bug.bug1()
+    if bug_2:
+        my_Bug = BugPlanner(s_x, s_y, g_x, g_y, o_x, o_y)
+        my_Bug.bug2()
+
+
+if __name__ == '__main__':
+    main(bug_0=True, bug_1=False, bug_2=False)
diff --git a/tests/test_bug.py b/tests/test_bug.py
new file mode 100644
index 0000000000..31d8b2c297
--- /dev/null
+++ b/tests/test_bug.py
@@ -0,0 +1,17 @@
+from PathPlanning.BugPlanning import bug as b
+from unittest import TestCase
+import sys
+import os
+sys.path.append(os.path.dirname(__file__) + "/../")
+
+
+class Test(TestCase):
+
+    def test(self):
+        b.show_animation = False
+        b.main(bug_0=True, bug_1=True, bug_2=True)
+
+
+if __name__ == '__main__':  # pragma: no cover
+    test = Test()
+    test.test()

From f969069df9a5182dd006b17fdccf52a7c4e64b36 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 30 Aug 2020 13:07:08 +0900
Subject: [PATCH 0424/1080] fix docstring error

---
 PathPlanning/BugPlanning/bug.py | 18 ++++++++++++------
 1 file changed, 12 insertions(+), 6 deletions(-)

diff --git a/PathPlanning/BugPlanning/bug.py b/PathPlanning/BugPlanning/bug.py
index 21bbbbb1cb..62fec7c109 100644
--- a/PathPlanning/BugPlanning/bug.py
+++ b/PathPlanning/BugPlanning/bug.py
@@ -53,10 +53,12 @@ def mov_to_next_obs(self, visited_x, visited_y):
         return self.r_x[-1], self.r_y[-1], True
 
     def bug0(self):
-        '''Greedy algorithm where you move towards goal
+        """
+        Greedy algorithm where you move towards goal
         until you hit an obstacle. Then you go around it
         (pick an arbitrary direction), until it is possible
-        for you to start moving towards goal in a greedy manner again'''
+        for you to start moving towards goal in a greedy manner again
+        """
         mov_dir = 'normal'
         cand_x, cand_y = -np.inf, -np.inf
         if show_animation:
@@ -112,12 +114,14 @@ def bug0(self):
             plt.show()
 
     def bug1(self):
-        '''Move towards goal in a greedy manner.
+        """
+        Move towards goal in a greedy manner.
         When you hit an obstacle, you go around it and
         back to where you hit the obstacle initially.
         Then, you go to the point on the obstacle that is
         closest to your goal and you start moving towards
-        goal in a greedy manner from that new point.'''
+        goal in a greedy manner from that new point.
+        """
         mov_dir = 'normal'
         cand_x, cand_y = -np.inf, -np.inf
         exit_x, exit_y = -np.inf, -np.inf
@@ -187,7 +191,8 @@ def bug1(self):
             plt.show()
 
     def bug2(self):
-        '''Move towards goal in a greedy manner.
+        """
+        Move towards goal in a greedy manner.
         When you hit an obstacle, you go around it and
         keep track of your distance from the goal.
         If the distance from your goal was decreasing before
@@ -196,7 +201,8 @@ def bug2(self):
         goal (this may or may not be true because the algorithm
         doesn't explore the entire boundary around the obstacle).
         So, you depart from this point and continue towards the
-        goal in a greedy manner'''
+        goal in a greedy manner
+        """
         mov_dir = 'normal'
         cand_x, cand_y = -np.inf, -np.inf
         if show_animation:

From 46bdd2afd950046d3f57c181efb0b867c63a2536 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 1 Sep 2020 08:26:44 +0900
Subject: [PATCH 0425/1080] Bump cvxpy from 1.1.4 to 1.1.5 (#389)

Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.4 to 1.1.5.
- [Release notes](https://github.com/cvxgrp/cvxpy/releases)
- [Changelog](https://github.com/cvxgrp/cvxpy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.4...v1.1.5)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 3bcb2de332..bc087081b4 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy == 1.19.1
 scipy == 1.5.2
 pandas == 1.1.1
 matplotlib == 3.3.1
-cvxpy == 1.1.4
+cvxpy == 1.1.5

From 2a2374ee90c5c101e4f23c7171fc2e7610e32562 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 9 Sep 2020 22:35:56 +0900
Subject: [PATCH 0426/1080] Create pull_request_template.md (#397)

---
 .github/pull_request_template.md | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)
 create mode 100644 .github/pull_request_template.md

diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
new file mode 100644
index 0000000000..9fbb893c1c
--- /dev/null
+++ b/.github/pull_request_template.md
@@ -0,0 +1,23 @@
+<!-- 
+Thanks for contributing a pull request! 
+Please ensure that your PR satisfies the checklist before submitting:
+- [] This project only accept codes for python 3.8 or higher.
+- [] If you add a new algorithm sample code, please add a unit test file under `test` dir.
+     This sample test code might help you : https://github.com/AtsushiSakai/PythonRobotics/blob/master/tests/test_a_star.py
+- [] If you fix a bug of existing code please add a test function in the test code to show the issue was solved.
+- [] Please fix all issues on CI (All CI should be green), before code review.
+
+Note that this is my hobby project; I appreciate your
+patience during the review process.
+
+Again, thanks for contributing!
+-->
+
+#### Reference issue
+<!--Example: fix #392-->
+
+#### What does this implement/fix?
+<!--Please explain your changes.-->
+
+#### Additional information
+<!--Any additional information you think is important.-->

From 3b92ae8c84f86b02d8c5eab40628319f86c7e40f Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 12 Sep 2020 21:36:47 +0900
Subject: [PATCH 0427/1080] remove code cov check (#400)

---
 .github/workflows/Linux_CI.yml | 2 --
 1 file changed, 2 deletions(-)

diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index a751c75d4d..1174438dd5 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -45,8 +45,6 @@ jobs:
       run: bash rundiffstylecheck.sh
     - name: do all unit tests
       run: bash runtests.sh
-    - name: Codecov
-      uses: codecov/codecov-action@v1.0.10
 
 
 

From ff3ad5bb9cf1810363191f41c5ed39e5fb923876 Mon Sep 17 00:00:00 2001
From: weicent <weicent_zhang@outlook.com>
Date: Sun, 13 Sep 2020 10:17:42 +0800
Subject: [PATCH 0428/1080] dwa pr (#390)

* dwa pr

* dwa_pr

* dwa_pr

* dwa_pr

* dwa_pr

* make changes rerun CI

* rerun CI...again..

* rerun CI.....

* rerun CI.....

* rerun CI final time!

* modified const to class variable

* put back missing comment

* add test for dwa stuck case

* add test dwa stuck case

* add test dwa stuck case

* add test dwa stuck case

* add stuck test in original test file
---
 .../dynamic_window_approach.py                | 54 +++++++++++--------
 tests/test_dynamic_window_approach.py         | 31 +++++++++++
 2 files changed, 62 insertions(+), 23 deletions(-)

diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index 0df40410ab..74ff064e86 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -19,7 +19,6 @@ def dwa_control(x, config, goal, ob):
     """
     Dynamic Window Approach control
     """
-
     dw = calc_dynamic_window(x, config)
 
     u, trajectory = calc_control_and_trajectory(x, dw, config, goal, ob)
@@ -51,6 +50,7 @@ def __init__(self):
         self.to_goal_cost_gain = 0.15
         self.speed_cost_gain = 1.0
         self.obstacle_cost_gain = 1.0
+        self.robot_stuck_flag_cons = 0.001  # constant to prevent robot stucked
         self.robot_type = RobotType.circle
 
         # if robot_type == RobotType.circle
@@ -60,6 +60,23 @@ def __init__(self):
         # if robot_type == RobotType.rectangle
         self.robot_width = 0.5  # [m] for collision check
         self.robot_length = 1.2  # [m] for collision check
+        # obstacles [x(m) y(m), ....]
+        self.ob = np.array([[-1, -1],
+                            [0, 2],
+                            [4.0, 2.0],
+                            [5.0, 4.0],
+                            [5.0, 5.0],
+                            [5.0, 6.0],
+                            [5.0, 9.0],
+                            [8.0, 9.0],
+                            [7.0, 9.0],
+                            [8.0, 10.0],
+                            [9.0, 11.0],
+                            [12.0, 13.0],
+                            [12.0, 12.0],
+                            [15.0, 15.0],
+                            [13.0, 13.0]
+                            ])
 
     @property
     def robot_type(self):
@@ -72,6 +89,9 @@ def robot_type(self, value):
         self._robot_type = value
 
 
+config = Config()
+
+
 def motion(x, u, dt):
     """
     motion model
@@ -139,7 +159,6 @@ def calc_control_and_trajectory(x, dw, config, goal, ob):
         for y in np.arange(dw[2], dw[3], config.yaw_rate_resolution):
 
             trajectory = predict_trajectory(x_init, v, y, config)
-
             # calc cost
             to_goal_cost = config.to_goal_cost_gain * calc_to_goal_cost(trajectory, goal)
             speed_cost = config.speed_cost_gain * (config.max_speed - trajectory[-1, 3])
@@ -152,13 +171,19 @@ def calc_control_and_trajectory(x, dw, config, goal, ob):
                 min_cost = final_cost
                 best_u = [v, y]
                 best_trajectory = trajectory
-
+                if abs(best_u[0]) < config.robot_stuck_flag_cons \
+                        and abs(x[3]) < config.robot_stuck_flag_cons:
+                    # to ensure the robot do not get stuck in
+                    # best v=0 m/s (in front of an obstacle) and
+                    # best omega=0 rad/s (heading to the goal with
+                    # angle difference of 0)
+                    best_u[1] = -config.max_delta_yaw_rate
     return best_u, best_trajectory
 
 
 def calc_obstacle_cost(trajectory, ob, config):
     """
-        calc obstacle cost inf: collision
+    calc obstacle cost inf: collision
     """
     ox = ob[:, 0]
     oy = ob[:, 1]
@@ -238,29 +263,12 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle):
     x = np.array([0.0, 0.0, math.pi / 8.0, 0.0, 0.0])
     # goal position [x(m), y(m)]
     goal = np.array([gx, gy])
-    # obstacles [x(m) y(m), ....]
-    ob = np.array([[-1, -1],
-                   [0, 2],
-                   [4.0, 2.0],
-                   [5.0, 4.0],
-                   [5.0, 5.0],
-                   [5.0, 6.0],
-                   [5.0, 9.0],
-                   [8.0, 9.0],
-                   [7.0, 9.0],
-                   [8.0, 10.0],
-                   [9.0, 11.0],
-                   [12.0, 13.0],
-                   [12.0, 12.0],
-                   [15.0, 15.0],
-                   [13.0, 13.0]
-                   ])
 
     # input [forward speed, yaw_rate]
-    config = Config()
+
     config.robot_type = robot_type
     trajectory = np.array(x)
-
+    ob = config.ob
     while True:
         u, predicted_trajectory = dwa_control(x, config, goal, ob)
         x = motion(x, u, config.dt)  # simulate robot
diff --git a/tests/test_dynamic_window_approach.py b/tests/test_dynamic_window_approach.py
index 197dda2158..93deb6846d 100644
--- a/tests/test_dynamic_window_approach.py
+++ b/tests/test_dynamic_window_approach.py
@@ -1,5 +1,6 @@
 import os
 import sys
+import numpy as np
 from unittest import TestCase
 
 sys.path.append(os.path.dirname(__file__) + "/../")
@@ -20,8 +21,38 @@ def test_main2(self):
         m.show_animation = False
         m.main(gx=1.0, gy=1.0, robot_type=m.RobotType.rectangle)
 
+    def test_stuck_main(self):
+        m.show_animation = False
+        # adjust cost
+        m.config.to_goal_cost_gain = 0.2
+        m.config.obstacle_cost_gain = 2.0
+        # obstacles and goals for stuck condition
+        m.config.ob = -1 * np.array([[-1.0, -1.0],
+                                     [0.0, 2.0],
+                                     [2.0, 6.0],
+                                     [2.0, 8.0],
+                                     [3.0, 9.27],
+                                     [3.79, 9.39],
+                                     [7.25, 8.97],
+                                     [7.0, 2.0],
+                                     [3.0, 4.0],
+                                     [6.0, 5.0],
+                                     [3.5, 5.8],
+                                     [6.0, 9.0],
+                                     [8.8, 9.0],
+                                     [5.0, 9.0],
+                                     [7.5, 3.0],
+                                     [9.0, 8.0],
+                                     [5.8, 4.4],
+                                     [12.0, 12.0],
+                                     [3.0, 2.0],
+                                     [13.0, 13.0]
+                                     ])
+        m.main(gx=-5.0, gy=-7.0)
+
 
 if __name__ == '__main__':  # pragma: no cover
     test = TestDynamicWindowApproach()
     test.test_main1()
     test.test_main2()
+    test.test_stuck_main()

From 298f1090749d2789433255c0ea997bc18da53818 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 15 Sep 2020 07:52:00 +0900
Subject: [PATCH 0429/1080] Bump numpy from 1.19.1 to 1.19.2 (#403)

Bumps [numpy](https://github.com/numpy/numpy) from 1.19.1 to 1.19.2.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/master/doc/HOWTO_RELEASE.rst.txt)
- [Commits](https://github.com/numpy/numpy/compare/v1.19.1...v1.19.2)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index bc087081b4..67ceca7dc1 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.19.1
+numpy == 1.19.2
 scipy == 1.5.2
 pandas == 1.1.1
 matplotlib == 3.3.1

From 6f1642076498d9483ab002ff45abe94b854e1459 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 15 Sep 2020 20:12:00 +0900
Subject: [PATCH 0430/1080] Bump pandas from 1.1.1 to 1.1.2 (#404)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.1.1 to 1.1.2.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.1.1...v1.1.2)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 67ceca7dc1..094def17af 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.19.2
 scipy == 1.5.2
-pandas == 1.1.1
+pandas == 1.1.2
 matplotlib == 3.3.1
 cvxpy == 1.1.5

From 43ab313762d6688b0f27f963bfe2400cfb985137 Mon Sep 17 00:00:00 2001
From: weicent <weicent_zhang@outlook.com>
Date: Sun, 20 Sep 2020 11:40:32 +0800
Subject: [PATCH 0431/1080] New A star algorithm pr (#391)

* a star pr

* a star pr

* a star pr

* fix line excede 79 error

* fix pycodestyle errors, missing a whitespace

* add test file

* add test file

* rerun CI

* rerun CI

* rerun CI

* rerun CI

* rerun CI

* modified test file and rerun CI

* rerun CI

* fix CI error

* modified code resubmit pr

* fixed some minor error

* modified pr as suggested
---
 .../AStar/A_Star_searching_from_two_side.py   | 369 ++++++++++++++++++
 tests/test_a_star_searching_two_side.py       |  27 ++
 2 files changed, 396 insertions(+)
 create mode 100644 PathPlanning/AStar/A_Star_searching_from_two_side.py
 create mode 100644 tests/test_a_star_searching_two_side.py

diff --git a/PathPlanning/AStar/A_Star_searching_from_two_side.py b/PathPlanning/AStar/A_Star_searching_from_two_side.py
new file mode 100644
index 0000000000..0318970cc8
--- /dev/null
+++ b/PathPlanning/AStar/A_Star_searching_from_two_side.py
@@ -0,0 +1,369 @@
+"""
+A* algorithm
+Author: Weicent
+randomly generate obstacles, start and goal point
+searching path from start and end simultaneously
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+
+show_animation = True
+
+
+class Node:
+    """node with properties of g, h, coordinate and parent node"""
+
+    def __init__(self, G=0, H=0, coordinate=None, parent=None):
+        self.G = G
+        self.H = H
+        self.F = G + H
+        self.parent = parent
+        self.coordinate = coordinate
+
+    def reset_f(self):
+        self.F = self.G + self.H
+
+
+def hcost(node_coordinate, goal):
+    dx = abs(node_coordinate[0] - goal[0])
+    dy = abs(node_coordinate[1] - goal[1])
+    hcost = dx + dy
+    return hcost
+
+
+def gcost(fixed_node, update_node_coordinate):
+    dx = abs(fixed_node.coordinate[0] - update_node_coordinate[0])
+    dy = abs(fixed_node.coordinate[1] - update_node_coordinate[1])
+    gc = math.hypot(dx, dy)  # gc = move from fixed_node to update_node
+    gcost = fixed_node.G + gc  # gcost = move from start point to update_node
+    return gcost
+
+
+def boundary_and_obstacles(start, goal, top_vertex, bottom_vertex, obs_number):
+    """
+    :param start: start coordinate
+    :param goal: goal coordinate
+    :param top_vertex: top right vertex coordinate of boundary
+    :param bottom_vertex: bottom left vertex coordinate of boundary
+    :param obs_number: number of obstacles generated in the map
+    :return: boundary_obstacle array, obstacle list
+    """
+    # below can be merged into a rectangle boundary
+    ay = list(range(bottom_vertex[1], top_vertex[1]))
+    ax = [bottom_vertex[0]] * len(ay)
+    cy = ay
+    cx = [top_vertex[0]] * len(cy)
+    bx = list(range(bottom_vertex[0] + 1, top_vertex[0]))
+    by = [bottom_vertex[1]] * len(bx)
+    dx = [bottom_vertex[0]] + bx + [top_vertex[0]]
+    dy = [top_vertex[1]] * len(dx)
+
+    # generate random obstacles
+    ob_x = np.random.randint(bottom_vertex[0] + 1,
+                             top_vertex[0], obs_number).tolist()
+    ob_y = np.random.randint(bottom_vertex[1] + 1,
+                             top_vertex[1], obs_number).tolist()
+    # x y coordinate in certain order for boundary
+    x = ax + bx + cx + dx
+    y = ay + by + cy + dy
+    obstacle = np.vstack((ob_x, ob_y)).T.tolist()
+    # remove start and goal coordinate in obstacle list
+    obstacle = [coor for coor in obstacle if coor != start and coor != goal]
+    obs_array = np.array(obstacle)
+    bound = np.vstack((x, y)).T
+    bound_obs = np.vstack((bound, obs_array))
+    return bound_obs, obstacle
+
+
+def find_neighbor(node, ob, closed):
+    # generate neighbors in certain condition
+    ob_list = ob.tolist()
+    neighbor: list = []
+    for x in range(node.coordinate[0] - 1, node.coordinate[0] + 2):
+        for y in range(node.coordinate[1] - 1, node.coordinate[1] + 2):
+            if [x, y] not in ob_list:
+                # find all possible neighbor nodes
+                neighbor.append([x, y])
+    # remove node violate the motion rule
+    # 1. remove node.coordinate itself
+    neighbor.remove(node.coordinate)
+    # 2. remove neighbor nodes who cross through two diagonal
+    # positioned obstacles since there is no enough space for
+    # robot to go through two diagonal positioned obstacles
+
+    # top bottom left right neighbors of node
+    top_nei = [node.coordinate[0], node.coordinate[1] + 1]
+    bottom_nei = [node.coordinate[0], node.coordinate[1] - 1]
+    left_nei = [node.coordinate[0] - 1, node.coordinate[1]]
+    right_nei = [node.coordinate[0] + 1, node.coordinate[1]]
+    # neighbors in four vertex
+    lt_nei = [node.coordinate[0] - 1, node.coordinate[1] + 1]
+    rt_nei = [node.coordinate[0] + 1, node.coordinate[1] + 1]
+    lb_nei = [node.coordinate[0] - 1, node.coordinate[1] - 1]
+    rb_nei = [node.coordinate[0] + 1, node.coordinate[1] - 1]
+
+    # remove the unnecessary neighbors
+    if top_nei and left_nei in ob_list and lt_nei in neighbor:
+        neighbor.remove(lt_nei)
+    if top_nei and right_nei in ob_list and rt_nei in neighbor:
+        neighbor.remove(rt_nei)
+    if bottom_nei and left_nei in ob_list and lb_nei in neighbor:
+        neighbor.remove(lb_nei)
+    if bottom_nei and right_nei in ob_list and rb_nei in neighbor:
+        neighbor.remove(rb_nei)
+    neighbor = [x for x in neighbor if x not in closed]
+    return neighbor
+
+
+def find_node_index(coordinate, node_list):
+    # find node index in the node list via its coordinate
+    ind = 0
+    for node in node_list:
+        if node.coordinate == coordinate:
+            target_node = node
+            ind = node_list.index(target_node)
+            break
+    return ind
+
+
+def find_path(open_list, closed_list, goal, obstacle):
+    # searching for the path, update open and closed list
+    # obstacle = obstacle and boundary
+    flag = len(open_list)
+    for i in range(flag):
+        node = open_list[0]
+        open_coordinate_list = [node.coordinate for node in open_list]
+        closed_coordinate_list = [node.coordinate for node in closed_list]
+        temp = find_neighbor(node, obstacle, closed_coordinate_list)
+        for element in temp:
+            if element in closed_list:
+                continue
+            elif element in open_coordinate_list:
+                # if node in open list, update g value
+                ind = open_coordinate_list.index(element)
+                new_g = gcost(node, element)
+                if new_g <= open_list[ind].G:
+                    open_list[ind].G = new_g
+                    open_list[ind].reset_f()
+                    open_list[ind].parent = node
+            else:  # new coordinate, create corresponding node
+                ele_node = Node(coordinate=element, parent=node,
+                                G=gcost(node, element), H=hcost(element, goal))
+                open_list.append(ele_node)
+        open_list.remove(node)
+        closed_list.append(node)
+        open_list.sort(key=lambda x: x.F)
+    return open_list, closed_list
+
+
+def node_to_coordinate(node_list):
+    # convert node list into coordinate list and array
+    coordinate_list = [node.coordinate for node in node_list]
+    return coordinate_list
+
+
+def check_node_coincide(close_ls1, closed_ls2):
+    """
+    :param close_ls1: node closed list for searching from start
+    :param closed_ls2: node closed list for searching from end
+    :return: intersect node list for above two
+    """
+    # check if node in close_ls1 intersect with node in closed_ls2
+    cl1 = node_to_coordinate(close_ls1)
+    cl2 = node_to_coordinate(closed_ls2)
+    intersect_ls = [node for node in cl1 if node in cl2]
+    return intersect_ls
+
+
+def find_surrounding(coordinate, obstacle):
+    # find obstacles around node, help to draw the borderline
+    boundary: list = []
+    for x in range(coordinate[0] - 1, coordinate[0] + 2):
+        for y in range(coordinate[1] - 1, coordinate[1] + 2):
+            if [x, y] in obstacle:
+                boundary.append([x, y])
+    return boundary
+
+
+def get_border_line(node_closed_ls, obstacle):
+    # if no path, find border line which confine goal or robot
+    border: list = []
+    coordinate_closed_ls = node_to_coordinate(node_closed_ls)
+    for coordinate in coordinate_closed_ls:
+        temp = find_surrounding(coordinate, obstacle)
+        border = border + temp
+    border_ary = np.array(border)
+    return border_ary
+
+
+def get_path(org_list, goal_list, coordinate):
+    # get path from start to end
+    path_org: list = []
+    path_goal: list = []
+    ind = find_node_index(coordinate, org_list)
+    node = org_list[ind]
+    while node != org_list[0]:
+        path_org.append(node.coordinate)
+        node = node.parent
+    path_org.append(org_list[0].coordinate)
+    ind = find_node_index(coordinate, goal_list)
+    node = goal_list[ind]
+    while node != goal_list[0]:
+        path_goal.append(node.coordinate)
+        node = node.parent
+    path_goal.append(goal_list[0].coordinate)
+    path_org.reverse()
+    path = path_org + path_goal
+    path = np.array(path)
+    return path
+
+
+def random_coordinate(bottom_vertex, top_vertex):
+    # generate random coordinates inside maze
+    coordinate = [np.random.randint(bottom_vertex[0] + 1, top_vertex[0]),
+                  np.random.randint(bottom_vertex[1] + 1, top_vertex[1])]
+    return coordinate
+
+
+def draw(close_origin, close_goal, start, end, bound):
+    # plot the map
+    if not close_goal.tolist():  # ensure the close_goal not empty
+        # in case of the obstacle number is really large (>4500), the
+        # origin is very likely blocked at the first search, and then
+        # the program is over and the searching from goal to origin
+        # will not start, which remain the closed_list for goal == []
+        # in order to plot the map, add the end coordinate to array
+        close_goal = np.array([end])
+    plt.cla()
+    plt.gcf().set_size_inches(11, 9, forward=True)
+    plt.axis('equal')
+    plt.plot(close_origin[:, 0], close_origin[:, 1], 'oy')
+    plt.plot(close_goal[:, 0], close_goal[:, 1], 'og')
+    plt.plot(bound[:, 0], bound[:, 1], 'sk')
+    plt.plot(end[0], end[1], '*b', label='Goal')
+    plt.plot(start[0], start[1], '^b', label='Origin')
+    plt.legend()
+    plt.pause(0.0001)
+
+
+def draw_control(org_closed, goal_closed, flag, start, end, bound, obstacle):
+    """
+    control the plot process, evaluate if the searching finished
+    flag == 0 : draw the searching process and plot path
+    flag == 1 or 2 : start or end is blocked, draw the border line
+    """
+    stop_loop = 0  # stop sign for the searching
+    org_closed_ls = node_to_coordinate(org_closed)
+    org_array = np.array(org_closed_ls)
+    goal_closed_ls = node_to_coordinate(goal_closed)
+    goal_array = np.array(goal_closed_ls)
+    path = None
+    if show_animation:  # draw the searching process
+        draw(org_array, goal_array, start, end, bound)
+    if flag == 0:
+        node_intersect = check_node_coincide(org_closed, goal_closed)
+        if node_intersect:  # a path is find
+            path = get_path(org_closed, goal_closed, node_intersect[0])
+            stop_loop = 1
+            print('Path is find!')
+            if show_animation:  # draw the path
+                plt.plot(path[:, 0], path[:, 1], '-r')
+                plt.title('Robot Arrived', size=20, loc='center')
+                plt.pause(0.01)
+                plt.show()
+    elif flag == 1:  # start point blocked first
+        stop_loop = 1
+        print('There is no path to the goal! Start point is blocked!')
+    elif flag == 2:  # end point blocked first
+        stop_loop = 1
+        print('There is no path to the goal! End point is blocked!')
+    if show_animation:  # blocked case, draw the border line
+        info = 'There is no path to the goal!' \
+               ' Robot&Goal are split by border' \
+               ' shown in red \'x\'!'
+        if flag == 1:
+            border = get_border_line(org_closed, obstacle)
+            plt.plot(border[:, 0], border[:, 1], 'xr')
+            plt.title(info, size=14, loc='center')
+            plt.pause(0.01)
+            plt.show()
+        elif flag == 2:
+            border = get_border_line(goal_closed, obstacle)
+            plt.plot(border[:, 0], border[:, 1], 'xr')
+            plt.title(info, size=14, loc='center')
+            plt.pause(0.01)
+            plt.show()
+    return stop_loop, path
+
+
+def searching_control(start, end, bound, obstacle):
+    """manage the searching process, start searching from two side"""
+    # initial origin node and end node
+    origin = Node(coordinate=start, H=hcost(start, end))
+    goal = Node(coordinate=end, H=hcost(end, start))
+    # list for searching from origin to goal
+    origin_open: list = [origin]
+    origin_close: list = []
+    # list for searching from goal to origin
+    goal_open = [goal]
+    goal_close: list = []
+    # initial target
+    target_goal = end
+    # flag = 0 (not blocked) 1 (start point blocked) 2 (end point blocked)
+    flag = 0  # init flag
+    path = None
+    while True:
+        # searching from start to end
+        origin_open, origin_close = \
+            find_path(origin_open, origin_close, target_goal, bound)
+        if not origin_open:  # no path condition
+            flag = 1  # origin node is blocked
+            draw_control(origin_close, goal_close, flag, start, end, bound,
+                         obstacle)
+            break
+        # update target for searching from end to start
+        target_origin = min(origin_open, key=lambda x: x.F).coordinate
+
+        # searching from end to start
+        goal_open, goal_close = \
+            find_path(goal_open, goal_close, target_origin, bound)
+        if not goal_open:  # no path condition
+            flag = 2  # goal is blocked
+            draw_control(origin_close, goal_close, flag, start, end, bound,
+                         obstacle)
+            break
+        # update target for searching from start to end
+        target_goal = min(goal_open, key=lambda x: x.F).coordinate
+
+        # continue searching, draw the process
+        stop_sign, path = draw_control(origin_close, goal_close, flag, start,
+                                       end, bound, obstacle)
+        if stop_sign:
+            break
+    return path
+
+
+def main(obstacle_number=1500):
+    print(__file__ + ' start!')
+
+    top_vertex = [60, 60]  # top right vertex of boundary
+    bottom_vertex = [0, 0]  # bottom left vertex of boundary
+
+    # generate start and goal point randomly
+    start = random_coordinate(bottom_vertex, top_vertex)
+    end = random_coordinate(bottom_vertex, top_vertex)
+
+    # generate boundary and obstacles
+    bound, obstacle = boundary_and_obstacles(start, end, top_vertex,
+                                             bottom_vertex,
+                                             obstacle_number)
+
+    path = searching_control(start, end, bound, obstacle)
+    if not show_animation:
+        print(path)
+
+
+if __name__ == '__main__':
+    main(obstacle_number=1500)
diff --git a/tests/test_a_star_searching_two_side.py b/tests/test_a_star_searching_two_side.py
new file mode 100644
index 0000000000..6116c9bbe7
--- /dev/null
+++ b/tests/test_a_star_searching_two_side.py
@@ -0,0 +1,27 @@
+from unittest import TestCase
+import os
+import sys
+
+sys.path.append(os.path.dirname(__file__) + '/../')
+
+try:
+    from PathPlanning.AStar import A_Star_searching_from_two_side as m
+except ImportError:
+    raise
+
+
+class Test(TestCase):
+
+    def test1(self):
+        m.show_animation = False
+        m.main(800)
+
+    def test2(self):
+        m.show_animation = False
+        m.main(5000)  # increase obstacle number, block path
+
+
+if __name__ == '__main__':
+    test = Test()
+    test.test1()
+    test.test2()

From 5462ed7e6644341446c1efe394b01401f5f4a739 Mon Sep 17 00:00:00 2001
From: Sarim Mehdi <sarim.mehdi.550@gmail.com>
Date: Mon, 21 Sep 2020 06:32:38 +0200
Subject: [PATCH 0432/1080] Added variants of A* (#395)

* Add files via upload

* Add files via upload

* Update test_a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update test_a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update test_a_star_variants.py

* Add files via upload

* Delete test_a_star_variants.py

* Update test_a_star_variants_iterative_deepening.py

* Update test_a_star_variants_beam_search.py

* Update test_a_star_variants_dynamic_weighting.py

* Update test_a_star_variants_jump_point.py

* Update test_a_star_variants_theta_star.py

* Update test_a_star_variants_beam_search.py

* Update test_a_star_variants_beam_search.py

* Update test_a_star_variants_dynamic_weighting.py

* Update test_a_star_variants_iterative_deepening.py

* Update test_a_star_variants_jump_point.py

* Update test_a_star_variants_theta_star.py

* Update test_a_star_variants_beam_search.py

* Update test_a_star_variants_dynamic_weighting.py

* Update test_a_star_variants_iterative_deepening.py

* Update test_a_star_variants_jump_point.py

* Update test_a_star_variants_theta_star.py

* Update a_star_variants.py

* Add files via upload

* Add files via upload

* Delete test_a_star_variants_beam_search.py

* Delete test_a_star_variants_dynamic_weighting.py

* Delete test_a_star_variants_iterative_deepening.py

* Delete test_a_star_variants_jump_point.py

* Delete test_a_star_variants_theta_star.py

* Added requested changes

* Added requested changes
---
 PathPlanning/AStar/a_star_variants.py | 462 ++++++++++++++++++++++++++
 tests/test_a_star_variants.py         |  47 +++
 2 files changed, 509 insertions(+)
 create mode 100644 PathPlanning/AStar/a_star_variants.py
 create mode 100644 tests/test_a_star_variants.py

diff --git a/PathPlanning/AStar/a_star_variants.py b/PathPlanning/AStar/a_star_variants.py
new file mode 100644
index 0000000000..cbf5395fd2
--- /dev/null
+++ b/PathPlanning/AStar/a_star_variants.py
@@ -0,0 +1,462 @@
+"""
+astar variants
+author: Sarim Mehdi(muhammadsarim.mehdi@studio.unibo.it)
+Source: http://theory.stanford.edu/~amitp/GameProgramming/Variations.html
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+show_animation = False
+use_beam_search = False
+use_iterative_deepening = False
+use_dynamic_weighting = False
+use_theta_star = False
+use_jump_point = False
+
+beam_capacity = 30
+max_theta = 5
+only_corners = False
+max_corner = 5
+w, epsilon, upper_bound_depth = 1, 4, 500
+
+
+def draw_horizontal_line(start_x, start_y, length, o_x, o_y, o_dict):
+    for i in range(start_x, start_x + length):
+        for j in range(start_y, start_y + 2):
+            o_x.append(i)
+            o_y.append(j)
+            o_dict[(i, j)] = True
+
+
+def draw_vertical_line(start_x, start_y, length, o_x, o_y, o_dict):
+    for i in range(start_x, start_x + 2):
+        for j in range(start_y, start_y + length):
+            o_x.append(i)
+            o_y.append(j)
+            o_dict[(i, j)] = True
+
+
+def in_line_of_sight(obs_grid, x1, y1, x2, y2):
+    t = 0
+    while t <= 0.5:
+        xt = (1 - t) * x1 + t * x2
+        yt = (1 - t) * y1 + t * y2
+        if obs_grid[(int(xt), int(yt))]:
+            return False, None
+        xt = (1 - t) * x2 + t * x1
+        yt = (1 - t) * y2 + t * y1
+        if obs_grid[(int(xt), int(yt))]:
+            return False, None
+        t += 0.001
+    dist = np.linalg.norm(np.array([x1, y1] - np.array([x2, y2])))
+    return True, dist
+
+
+def key_points(o_dict):
+    offsets1 = [(1, 0), (0, 1), (-1, 0), (1, 0)]
+    offsets2 = [(1, 1), (-1, 1), (-1, -1), (1, -1)]
+    offsets3 = [(0, 1), (-1, 0), (0, -1), (0, -1)]
+    c_list = []
+    for grid_point, obs_status in o_dict.items():
+        if obs_status:
+            continue
+        empty_space = True
+        x, y = grid_point
+        for i in [-1, 0, 1]:
+            for j in [-1, 0, 1]:
+                if (x + i, y + j) not in o_dict.keys():
+                    continue
+                if o_dict[(x + i, y + j)]:
+                    empty_space = False
+                    break
+        if empty_space:
+            continue
+        for offset1, offset2, offset3 in zip(offsets1, offsets2, offsets3):
+            i1, j1 = offset1
+            i2, j2 = offset2
+            i3, j3 = offset3
+            if ((x + i1, y + j1) not in o_dict.keys()) or \
+                    ((x + i2, y + j2) not in o_dict.keys()) or \
+                    ((x + i3, y + j3) not in o_dict.keys()):
+                continue
+            obs_count = 0
+            if o_dict[(x + i1, y + j1)]:
+                obs_count += 1
+            if o_dict[(x + i2, y + j2)]:
+                obs_count += 1
+            if o_dict[(x + i3, y + j3)]:
+                obs_count += 1
+            if obs_count == 3 or obs_count == 1:
+                c_list.append((x, y))
+                plt.plot(x, y, ".y")
+                break
+    if only_corners:
+        return c_list
+
+    e_list = []
+    for corner in c_list:
+        x1, y1 = corner
+        for other_corner in c_list:
+            x2, y2 = other_corner
+            if x1 == x2 and y1 == y2:
+                continue
+            reachable, _ = in_line_of_sight(o_dict, x1, y1, x2, y2)
+            if not reachable:
+                continue
+            x_m, y_m = int((x1 + x2) / 2), int((y1 + y2) / 2)
+            e_list.append((x_m, y_m))
+            if show_animation:
+                plt.plot(x_m, y_m, ".y")
+    return c_list + e_list
+
+
+class SearchAlgo:
+    def __init__(self, obs_grid, goal_x, goal_y, start_x, start_y,
+                 limit_x, limit_y, corner_list=None):
+        self.start_pt = [start_x, start_y]
+        self.goal_pt = [goal_x, goal_y]
+        self.obs_grid = obs_grid
+        g_cost, h_cost = 0, self.get_hval(start_x, start_y, goal_x, goal_y)
+        f_cost = g_cost + h_cost
+        self.all_nodes, self.open_set = {}, []
+
+        if use_jump_point:
+            for corner in corner_list:
+                i, j = corner
+                h_c = self.get_hval(i, j, goal_x, goal_y)
+                self.all_nodes[(i, j)] = {'pos': [i, j], 'pred': None,
+                                          'gcost': np.inf, 'hcost': h_c,
+                                          'fcost': np.inf,
+                                          'open': True, 'in_open_list': False}
+            self.all_nodes[tuple(self.goal_pt)] = \
+                {'pos': self.goal_pt, 'pred': None,
+                 'gcost': np.inf, 'hcost': 0, 'fcost': np.inf,
+                 'open': True, 'in_open_list': True}
+        else:
+            for i in range(limit_x):
+                for j in range(limit_y):
+                    h_c = self.get_hval(i, j, goal_x, goal_y)
+                    self.all_nodes[(i, j)] = {'pos': [i, j], 'pred': None,
+                                              'gcost': np.inf, 'hcost': h_c,
+                                              'fcost': np.inf,
+                                              'open': True,
+                                              'in_open_list': False}
+        self.all_nodes[tuple(self.start_pt)] = \
+            {'pos': self.start_pt, 'pred': None,
+             'gcost': g_cost, 'hcost': h_cost, 'fcost': f_cost,
+             'open': True, 'in_open_list': True}
+        self.open_set.append(self.all_nodes[tuple(self.start_pt)])
+
+    def get_hval(self, x1, y1, x2, y2):
+        x, y = x1, y1
+        val = 0
+        while x != x2 or y != y2:
+            if x != x2 and y != y2:
+                val += 14
+            else:
+                val += 10
+            x, y = x + np.sign(x2 - x), y + np.sign(y2 - y)
+        return val
+
+    def get_farthest_point(self, x, y, i, j):
+        i_temp, j_temp = i, j
+        counter = 1
+        got_goal = False
+        while not self.obs_grid[(x + i_temp, y + j_temp)] and \
+                counter < max_theta:
+            i_temp += i
+            j_temp += j
+            counter += 1
+            if [x + i_temp, y + j_temp] == self.goal_pt:
+                got_goal = True
+                break
+            if (x + i_temp, y + j_temp) not in self.obs_grid.keys():
+                break
+        return i_temp - 2*i, j_temp - 2*j, counter, got_goal
+
+    def jump_point(self):
+        """Jump point: Instead of exploring all empty spaces of the
+        map, just explore the corners."""
+        plt.title('Jump Point')
+
+        goal_found = False
+        while len(self.open_set) > 0:
+            self.open_set = sorted(self.open_set, key=lambda x: x['fcost'])
+            lowest_f = self.open_set[0]['fcost']
+            lowest_h = self.open_set[0]['hcost']
+            lowest_g = self.open_set[0]['gcost']
+            p = 0
+            for p_n in self.open_set[1:]:
+                if p_n['fcost'] == lowest_f and \
+                        p_n['gcost'] < lowest_g:
+                    lowest_g = p_n['gcost']
+                    p += 1
+                elif p_n['fcost'] == lowest_f and \
+                        p_n['gcost'] == lowest_g and \
+                        p_n['hcost'] < lowest_h:
+                    lowest_h = p_n['hcost']
+                    p += 1
+                else:
+                    break
+            current_node = self.all_nodes[tuple(self.open_set[p]['pos'])]
+            x1, y1 = current_node['pos']
+
+            for cand_pt, cand_node in self.all_nodes.items():
+                x2, y2 = cand_pt
+                if x1 == x2 and y1 == y2:
+                    continue
+                if np.linalg.norm(np.array([x1, y1] -
+                                           np.array([x2, y2]))) > max_corner:
+                    continue
+                reachable, offset = in_line_of_sight(self.obs_grid, x1,
+                                                     y1, x2, y2)
+                if not reachable:
+                    continue
+
+                if list(cand_pt) == self.goal_pt:
+                    current_node['open'] = False
+                    self.all_nodes[tuple(cand_pt)]['pred'] = \
+                        current_node['pos']
+                    goal_found = True
+                    break
+
+                g_cost = offset + current_node['gcost']
+                h_cost = self.all_nodes[cand_pt]['hcost']
+                f_cost = g_cost + h_cost
+                cand_pt = tuple(cand_pt)
+                if f_cost < self.all_nodes[cand_pt]['fcost']:
+                    self.all_nodes[cand_pt]['pred'] = current_node['pos']
+                    self.all_nodes[cand_pt]['gcost'] = g_cost
+                    self.all_nodes[cand_pt]['fcost'] = f_cost
+                    if not self.all_nodes[cand_pt]['in_open_list']:
+                        self.open_set.append(self.all_nodes[cand_pt])
+                        self.all_nodes[cand_pt]['in_open_list'] = True
+                    plt.plot(cand_pt[0], cand_pt[1], "r*")
+
+                if goal_found:
+                    break
+            plt.pause(0.001)
+            if goal_found:
+                current_node = self.all_nodes[tuple(self.goal_pt)]
+            while goal_found:
+                if current_node['pred'] is None:
+                    break
+                x = [current_node['pos'][0], current_node['pred'][0]]
+                y = [current_node['pos'][1], current_node['pred'][1]]
+                plt.plot(x, y, "b")
+                current_node = self.all_nodes[tuple(current_node['pred'])]
+                plt.pause(0.001)
+            if goal_found:
+                break
+
+            current_node['open'] = False
+            current_node['in_open_list'] = False
+            plt.plot(current_node['pos'][0], current_node['pos'][1], "g*")
+            del self.open_set[p]
+            current_node['fcost'], current_node['hcost'] = np.inf, np.inf
+        if show_animation:
+            plt.show()
+
+    def astar(self):
+        """Beam search: Maintain an open list of just 30 nodes.
+        If more than 30 nodes, then get rid of ndoes with high
+        f values.
+        Iterative deepening: At every iteration, get a cut-off
+        value for the f cost. This cut-off is minimum of the f
+        value of all nodes whose f value is higher than the
+        current cut-off value. Nodes with f value higher than
+        the current cut off value are not put in the open set.
+        Dynamic weighting: Multiply heuristic with the following:
+        (1 + epsilon - (epsilon*d)/N) where d is the current
+        iteration of loop and N is upper bound on number of
+        iterations.
+        Theta star: Same as A star but you don't need to move
+        one neighbor at a time. In fact, you can look for the
+        next node as far out as you can as long as there is a
+        clear line of sight from your current node to that node."""
+        if use_beam_search:
+            plt.title('A* with beam search')
+        elif use_iterative_deepening:
+            plt.title('A* with iterative deepening')
+        elif use_dynamic_weighting:
+            plt.title('A* with dynamic weighting')
+        elif use_theta_star:
+            plt.title('Theta*')
+        else:
+            plt.title('A*')
+
+        goal_found = False
+        curr_f_thresh = np.inf
+        depth = 0
+        no_valid_f = False
+        while len(self.open_set) > 0:
+            self.open_set = sorted(self.open_set, key=lambda x: x['fcost'])
+            lowest_f = self.open_set[0]['fcost']
+            lowest_h = self.open_set[0]['hcost']
+            lowest_g = self.open_set[0]['gcost']
+            p = 0
+            for p_n in self.open_set[1:]:
+                if p_n['fcost'] == lowest_f and \
+                        p_n['gcost'] < lowest_g:
+                    lowest_g = p_n['gcost']
+                    p += 1
+                elif p_n['fcost'] == lowest_f and \
+                        p_n['gcost'] == lowest_g and \
+                        p_n['hcost'] < lowest_h:
+                    lowest_h = p_n['hcost']
+                    p += 1
+                else:
+                    break
+            current_node = self.all_nodes[tuple(self.open_set[p]['pos'])]
+
+            while len(self.open_set) > beam_capacity and use_beam_search:
+                del self.open_set[-1]
+
+            f_cost_list = []
+            if use_dynamic_weighting:
+                w = (1 + epsilon - epsilon*depth/upper_bound_depth)
+            for i in range(-1, 2):
+                for j in range(-1, 2):
+                    x, y = current_node['pos']
+                    if (i == 0 and j == 0) or \
+                            ((x + i, y + j) not in self.obs_grid.keys()):
+                        continue
+                    if (i, j) in [(1, 0), (0, 1), (-1, 0), (0, -1)]:
+                        offset = 10
+                    else:
+                        offset = 14
+                    if use_theta_star:
+                        new_i, new_j, counter, goal_found = \
+                            self.get_farthest_point(x, y, i, j)
+                        offset = offset * counter
+                        cand_pt = [current_node['pos'][0] + new_i,
+                                   current_node['pos'][1] + new_j]
+                    else:
+                        cand_pt = [current_node['pos'][0] + i,
+                                   current_node['pos'][1] + j]
+
+                    if use_theta_star and goal_found:
+                        current_node['open'] = False
+                        cand_pt = self.goal_pt
+                        self.all_nodes[tuple(cand_pt)]['pred'] = \
+                            current_node['pos']
+                        break
+
+                    if cand_pt == self.goal_pt:
+                        current_node['open'] = False
+                        self.all_nodes[tuple(cand_pt)]['pred'] = \
+                            current_node['pos']
+                        goal_found = True
+                        break
+
+                    cand_pt = tuple(cand_pt)
+                    if not self.obs_grid[tuple(cand_pt)] and \
+                            self.all_nodes[cand_pt]['open']:
+                        g_cost = offset + current_node['gcost']
+                        h_cost = self.all_nodes[cand_pt]['hcost']
+                        if use_dynamic_weighting:
+                            h_cost = h_cost * w
+                        f_cost = g_cost + h_cost
+                        if f_cost < self.all_nodes[cand_pt]['fcost'] and \
+                                f_cost <= curr_f_thresh:
+                            f_cost_list.append(f_cost)
+                            self.all_nodes[cand_pt]['pred'] = \
+                                current_node['pos']
+                            self.all_nodes[cand_pt]['gcost'] = g_cost
+                            self.all_nodes[cand_pt]['fcost'] = f_cost
+                            if not self.all_nodes[cand_pt]['in_open_list']:
+                                self.open_set.append(self.all_nodes[cand_pt])
+                                self.all_nodes[cand_pt]['in_open_list'] = True
+                            plt.plot(cand_pt[0], cand_pt[1], "r*")
+                        if curr_f_thresh < f_cost < \
+                                self.all_nodes[cand_pt]['fcost']:
+                            no_valid_f = True
+                if goal_found:
+                    break
+            plt.pause(0.001)
+            if goal_found:
+                current_node = self.all_nodes[tuple(self.goal_pt)]
+            while goal_found:
+                if current_node['pred'] is None:
+                    break
+                if use_theta_star or use_jump_point:
+                    x, y = [current_node['pos'][0], current_node['pred'][0]], \
+                             [current_node['pos'][1], current_node['pred'][1]]
+                    plt.plot(x, y, "b")
+                else:
+                    plt.plot(current_node['pred'][0],
+                             current_node['pred'][1], "b*")
+                current_node = self.all_nodes[tuple(current_node['pred'])]
+                plt.pause(0.001)
+            if goal_found:
+                break
+
+            if use_iterative_deepening and f_cost_list:
+                curr_f_thresh = min(f_cost_list)
+            if use_iterative_deepening and not f_cost_list:
+                curr_f_thresh = np.inf
+            if use_iterative_deepening and not f_cost_list and no_valid_f:
+                current_node['fcost'], current_node['hcost'] = np.inf, np.inf
+                continue
+
+            current_node['open'] = False
+            current_node['in_open_list'] = False
+            plt.plot(current_node['pos'][0], current_node['pos'][1], "g*")
+            del self.open_set[p]
+            current_node['fcost'], current_node['hcost'] = np.inf, np.inf
+            depth += 1
+        if show_animation:
+            plt.show()
+
+
+def main():
+    # set obstacle positions
+    obs_dict = {}
+    for i in range(51):
+        for j in range(51):
+            obs_dict[(i, j)] = False
+    o_x, o_y = [], []
+
+    s_x = 5.0
+    s_y = 5.0
+    g_x = 35.0
+    g_y = 45.0
+
+    # draw outer border of maze
+    draw_vertical_line(0, 0, 50, o_x, o_y, obs_dict)
+    draw_vertical_line(48, 0, 50, o_x, o_y, obs_dict)
+    draw_horizontal_line(0, 0, 50, o_x, o_y, obs_dict)
+    draw_horizontal_line(0, 48, 50, o_x, o_y, obs_dict)
+
+    # draw inner walls
+    all_x = [10, 10, 10, 15, 20, 20, 30, 30, 35, 30, 40, 45]
+    all_y = [10, 30, 45, 20, 5, 40, 10, 40, 5, 40, 10, 25]
+    all_len = [10, 10, 5, 10, 10, 5, 20, 10, 25, 10, 35, 15]
+    for x, y, l in zip(all_x, all_y, all_len):
+        draw_vertical_line(x, y, l, o_x, o_y, obs_dict)
+
+    all_x[:], all_y[:], all_len[:] = [], [], []
+    all_x = [35, 40, 15, 10, 45, 20, 10, 15, 25, 45, 10, 30, 10, 40]
+    all_y = [5, 10, 15, 20, 20, 25, 30, 35, 35, 35, 40, 40, 45, 45]
+    all_len = [10, 5, 10, 10, 5, 5, 10, 5, 10, 5, 10, 5, 5, 5]
+    for x, y, l in zip(all_x, all_y, all_len):
+        draw_horizontal_line(x, y, l, o_x, o_y, obs_dict)
+
+    plt.plot(o_x, o_y, ".k")
+    plt.plot(s_x, s_y, "og")
+    plt.plot(g_x, g_y, "xb")
+    plt.grid(True)
+
+    if use_jump_point:
+        keypoint_list = key_points(obs_dict)
+        search_obj = SearchAlgo(obs_dict, g_x, g_y, s_x, s_y, 101, 101,
+                                keypoint_list)
+        search_obj.jump_point()
+    else:
+        search_obj = SearchAlgo(obs_dict, g_x, g_y, s_x, s_y, 101, 101)
+        search_obj.astar()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tests/test_a_star_variants.py b/tests/test_a_star_variants.py
new file mode 100644
index 0000000000..53efeddd20
--- /dev/null
+++ b/tests/test_a_star_variants.py
@@ -0,0 +1,47 @@
+import PathPlanning.AStar.a_star_variants as astar
+from unittest import TestCase
+import sys
+import os
+sys.path.append(os.path.dirname(__file__) + "/../")
+
+
+class Test(TestCase):
+
+    def test(self):
+        # A* with beam search
+        astar.show_animation = False
+        astar.use_beam_search = True
+        astar.main()
+        self.reset_all()
+
+        # A* with iterative deepening
+        astar.use_iterative_deepening = True
+        astar.main()
+        self.reset_all()
+
+        # A* with dynamic weighting
+        astar.use_dynamic_weighting = True
+        astar.main()
+        self.reset_all()
+
+        # theta*
+        astar.use_theta_star = True
+        astar.main()
+        self.reset_all()
+
+        # A* with jump point
+        astar.use_jump_point = True
+        astar.main()
+        self.reset_all()
+
+    def reset_all(self):
+        astar.use_beam_search = False
+        astar.use_iterative_deepening = False
+        astar.use_dynamic_weighting = False
+        astar.use_theta_star = False
+        astar.use_jump_point = False
+
+
+if __name__ == '__main__':  # pragma: no cover
+    test = Test()
+    test.test()

From d7cc364b2f8d83f614a316cb5916e9f0b17dc10e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 22 Sep 2020 07:13:16 +0900
Subject: [PATCH 0433/1080] Bump matplotlib from 3.3.1 to 3.3.2 (#406)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.3.1 to 3.3.2.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.3.1...v3.3.2)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 094def17af..58511e90f4 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.19.2
 scipy == 1.5.2
 pandas == 1.1.2
-matplotlib == 3.3.1
+matplotlib == 3.3.2
 cvxpy == 1.1.5

From 468be047aa5975265cbdc5b89740bc6b61079a9e Mon Sep 17 00:00:00 2001
From: ajwahab <1449672+ajwahab@users.noreply.github.com>
Date: Sun, 27 Sep 2020 00:10:46 -0400
Subject: [PATCH 0434/1080] robustified 2 joint arm example (#402)

two joint arm point control example will no longer fail if target point is out of reach
---
 .../two_joint_arm_to_point_control.py                      | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
index 75e7cf301f..85ed612360 100644
--- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
+++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
@@ -37,8 +37,11 @@ def two_joint_arm(GOAL_TH=0.0, theta1=0.0, theta2=0.0):
     """
     while True:
         try:
-            theta2_goal = np.arccos(
-                (x**2 + y**2 - l1**2 - l2**2) / (2 * l1 * l2))
+            if np.sqrt(x**2 + y**2) > (l1 + l2):
+                theta2_goal = 0
+            else:
+                theta2_goal = np.arccos(
+                    (x**2 + y**2 - l1**2 - l2**2) / (2 * l1 * l2))
             theta1_goal = np.math.atan2(y, x) - np.math.atan2(l2 *
                                                               np.sin(theta2_goal), (l1 + l2 * np.cos(theta2_goal)))
 

From 60e9e8f39b528d664a3d641f916549a2545555d4 Mon Sep 17 00:00:00 2001
From: Sarim Mehdi <sarim.mehdi.550@gmail.com>
Date: Tue, 29 Sep 2020 08:32:21 +0200
Subject: [PATCH 0435/1080] Implemented Flowfield Pathfinding (#408)

* Add files via upload

* Add files via upload

* Update test_a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update test_a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update a_star_variants.py

* Update test_a_star_variants.py

* Add files via upload

* Delete test_a_star_variants.py

* Update test_a_star_variants_iterative_deepening.py

* Update test_a_star_variants_beam_search.py

* Update test_a_star_variants_dynamic_weighting.py

* Update test_a_star_variants_jump_point.py

* Update test_a_star_variants_theta_star.py

* Update test_a_star_variants_beam_search.py

* Update test_a_star_variants_beam_search.py

* Update test_a_star_variants_dynamic_weighting.py

* Update test_a_star_variants_iterative_deepening.py

* Update test_a_star_variants_jump_point.py

* Update test_a_star_variants_theta_star.py

* Update test_a_star_variants_beam_search.py

* Update test_a_star_variants_dynamic_weighting.py

* Update test_a_star_variants_iterative_deepening.py

* Update test_a_star_variants_jump_point.py

* Update test_a_star_variants_theta_star.py

* Update a_star_variants.py

* Add files via upload

* Add files via upload

* Delete test_a_star_variants_beam_search.py

* Delete test_a_star_variants_dynamic_weighting.py

* Delete test_a_star_variants_iterative_deepening.py

* Delete test_a_star_variants_jump_point.py

* Delete test_a_star_variants_theta_star.py

* Added requested changes

* Added requested changes

* Added flowfield

* Added requested changes

* Update flowfield.py
---
 PathPlanning/FlowField/flowfield.py | 223 ++++++++++++++++++++++++++++
 tests/test_flow_field.py            |  17 +++
 2 files changed, 240 insertions(+)
 create mode 100644 PathPlanning/FlowField/flowfield.py
 create mode 100644 tests/test_flow_field.py

diff --git a/PathPlanning/FlowField/flowfield.py b/PathPlanning/FlowField/flowfield.py
new file mode 100644
index 0000000000..f222e8ecf2
--- /dev/null
+++ b/PathPlanning/FlowField/flowfield.py
@@ -0,0 +1,223 @@
+"""
+flowfield pathfinding
+author: Sarim Mehdi (muhammadsarim.mehdi@studio.unibo.it)
+Source: https://leifnode.com/2013/12/flow-field-pathfinding/
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+show_animation = True
+
+
+def draw_horizontal_line(start_x, start_y, length, o_x, o_y, o_dict, path):
+    for i in range(start_x, start_x + length):
+        for j in range(start_y, start_y + 2):
+            o_x.append(i)
+            o_y.append(j)
+            o_dict[(i, j)] = path
+
+
+def draw_vertical_line(start_x, start_y, length, o_x, o_y, o_dict, path):
+    for i in range(start_x, start_x + 2):
+        for j in range(start_y, start_y + length):
+            o_x.append(i)
+            o_y.append(j)
+            o_dict[(i, j)] = path
+
+
+class FlowField:
+    def __init__(self, obs_grid, goal_x, goal_y, start_x, start_y,
+                 limit_x, limit_y):
+        self.start_pt = [start_x, start_y]
+        self.goal_pt = [goal_x, goal_y]
+        self.obs_grid = obs_grid
+        self.limit_x, self.limit_y = limit_x, limit_y
+        self.cost_field = {}
+        self.integration_field = {}
+        self.vector_field = {}
+
+    def find_path(self):
+        self.create_cost_field()
+        self.create_integration_field()
+        self.assign_vectors()
+        self.follow_vectors()
+
+    def create_cost_field(self):
+        """Assign cost to each grid which defines the energy
+        it would take to get there."""
+        for i in range(self.limit_x):
+            for j in range(self.limit_y):
+                if self.obs_grid[(i, j)] == 'free':
+                    self.cost_field[(i, j)] = 1
+                elif self.obs_grid[(i, j)] == 'medium':
+                    self.cost_field[(i, j)] = 7
+                elif self.obs_grid[(i, j)] == 'hard':
+                    self.cost_field[(i, j)] = 20
+                elif self.obs_grid[(i, j)] == 'obs':
+                    continue
+
+                if [i, j] == self.goal_pt:
+                    self.cost_field[(i, j)] = 0
+
+    def create_integration_field(self):
+        """Start from the goal node and calculate the value
+        of the integration field at each node. Start by
+        assigning a value of infinity to every node except
+        the goal node which is assigned a value of 0. Put the
+        goal node in the open list and then get its neighbors
+        (must not be obstacles). For each neighbor, the new
+        cost is equal to the cost of the current node in the
+        integration field (in the beginning, this will simply
+        be the goal node) + the cost of the neighbor in the
+        cost field + the extra cost (optional). The new cost
+        is only assigned if it is less than the previously
+        assigned cost of the node in the integration field and,
+        when that happens, the neighbor is put on the open list.
+        This process continues until the open list is empty."""
+        for i in range(self.limit_x):
+            for j in range(self.limit_y):
+                if self.obs_grid[(i, j)] == 'obs':
+                    continue
+                self.integration_field[(i, j)] = np.inf
+                if [i, j] == self.goal_pt:
+                    self.integration_field[(i, j)] = 0
+
+        open_list = [(self.goal_pt, 0)]
+        while open_list:
+            curr_pos, curr_cost = open_list[0]
+            curr_x, curr_y = curr_pos
+            for i in range(-1, 2):
+                for j in range(-1, 2):
+                    x, y = curr_x + i, curr_y + j
+                    if self.obs_grid[(x, y)] == 'obs':
+                        continue
+                    if (i, j) in [(1, 0), (0, 1), (-1, 0), (0, -1)]:
+                        e_cost = 10
+                    else:
+                        e_cost = 14
+                    neighbor_energy = self.cost_field[(x, y)]
+                    neighbor_old_cost = self.integration_field[(x, y)]
+                    neighbor_new_cost = curr_cost + neighbor_energy + e_cost
+                    if neighbor_new_cost < neighbor_old_cost:
+                        self.integration_field[(x, y)] = neighbor_new_cost
+                        open_list.append(([x, y], neighbor_new_cost))
+            del open_list[0]
+
+    def assign_vectors(self):
+        """For each node, assign a vector from itself to the node with
+        the lowest cost in the integration field. An agent will simply
+        follow this vector field to the goal"""
+        for i in range(self.limit_x):
+            for j in range(self.limit_y):
+                if self.obs_grid[(i, j)] == 'obs':
+                    continue
+                if [i, j] == self.goal_pt:
+                    self.vector_field[(i, j)] = (None, None)
+                    continue
+                offset_list = [(i + a, j + b)
+                               for a in range(-1, 2)
+                               for b in range(-1, 2)]
+                neighbor_list = [{'loc': pt,
+                                  'cost': self.integration_field[pt]}
+                                 for pt in offset_list
+                                 if self.obs_grid[pt] != 'obs']
+                neighbor_list = sorted(neighbor_list, key=lambda x: x['cost'])
+                best_neighbor = neighbor_list[0]['loc']
+                self.vector_field[(i, j)] = best_neighbor
+
+    def follow_vectors(self):
+        curr_x, curr_y = self.start_pt
+        while curr_x is not None and curr_y is not None:
+            plt.plot(curr_x, curr_y, "b*")
+            curr_x, curr_y = self.vector_field[(curr_x, curr_y)]
+            plt.pause(0.001)
+        if show_animation:
+            plt.show()
+
+
+def main():
+    # set obstacle positions
+    obs_dict = {}
+    for i in range(51):
+        for j in range(51):
+            obs_dict[(i, j)] = 'free'
+    o_x, o_y, m_x, m_y, h_x, h_y = [], [], [], [], [], []
+
+    s_x = 5.0
+    s_y = 5.0
+    g_x = 35.0
+    g_y = 45.0
+
+    # draw outer border of maze
+    draw_vertical_line(0, 0, 50, o_x, o_y, obs_dict, 'obs')
+    draw_vertical_line(48, 0, 50, o_x, o_y, obs_dict, 'obs')
+    draw_horizontal_line(0, 0, 50, o_x, o_y, obs_dict, 'obs')
+    draw_horizontal_line(0, 48, 50, o_x, o_y, obs_dict, 'obs')
+
+    # draw inner walls
+    all_x = [10, 10, 10, 15, 20, 20, 30, 30, 35, 30, 40, 45]
+    all_y = [10, 30, 45, 20, 5, 40, 10, 40, 5, 40, 10, 25]
+    all_len = [10, 10, 5, 10, 10, 5, 20, 10, 25, 10, 35, 15]
+    for x, y, l in zip(all_x, all_y, all_len):
+        draw_vertical_line(x, y, l, o_x, o_y, obs_dict, 'obs')
+
+    all_x[:], all_y[:], all_len[:] = [], [], []
+    all_x = [35, 40, 15, 10, 45, 20, 10, 15, 25, 45, 10, 30, 10, 40]
+    all_y = [5, 10, 15, 20, 20, 25, 30, 35, 35, 35, 40, 40, 45, 45]
+    all_len = [10, 5, 10, 10, 5, 5, 10, 5, 10, 5, 10, 5, 5, 5]
+    for x, y, l in zip(all_x, all_y, all_len):
+        draw_horizontal_line(x, y, l, o_x, o_y, obs_dict, 'obs')
+
+    # Some points are assigned a slightly higher energy value in the cost
+    # field. For example, if an agent wishes to go to a point, it might
+    # encounter different kind of terrain like grass and dirt. Grass is
+    # assigned medium difficulty of passage (color coded as green on the
+    # map here). Dirt is assigned hard difficulty of passage (color coded
+    # as brown here). Hence, this algorithm will take into account how
+    # difficult it is to go through certain areas of a map when deciding
+    # the shortest path.
+
+    # draw paths that have medium difficulty (in terms of going through them)
+    all_x[:], all_y[:], all_len[:] = [], [], []
+    all_x = [10, 45]
+    all_y = [22, 20]
+    all_len = [8, 5]
+    for x, y, l in zip(all_x, all_y, all_len):
+        draw_vertical_line(x, y, l, m_x, m_y, obs_dict, 'medium')
+
+    all_x[:], all_y[:], all_len[:] = [], [], []
+    all_x = [20, 30, 42] + [47] * 5
+    all_y = [35, 30, 38] + [37 + i for i in range(2)]
+    all_len = [5, 7, 3] + [1] * 3
+    for x, y, l in zip(all_x, all_y, all_len):
+        draw_horizontal_line(x, y, l, m_x, m_y, obs_dict, 'medium')
+
+    # draw paths that have hard difficulty (in terms of going through them)
+    all_x[:], all_y[:], all_len[:] = [], [], []
+    all_x = [15, 20, 35]
+    all_y = [45, 20, 35]
+    all_len = [3, 5, 7]
+    for x, y, l in zip(all_x, all_y, all_len):
+        draw_vertical_line(x, y, l, h_x, h_y, obs_dict, 'hard')
+
+    all_x[:], all_y[:], all_len[:] = [], [], []
+    all_x = [30] + [47] * 5
+    all_y = [10] + [37 + i for i in range(2)]
+    all_len = [5] + [1] * 3
+    for x, y, l in zip(all_x, all_y, all_len):
+        draw_horizontal_line(x, y, l, h_x, h_y, obs_dict, 'hard')
+
+    plt.plot(o_x, o_y, "sr")
+    plt.plot(m_x, m_y, "sg")
+    plt.plot(h_x, h_y, "sy")
+    plt.plot(s_x, s_y, "og")
+    plt.plot(g_x, g_y, "o")
+    plt.grid(True)
+
+    flow_obj = FlowField(obs_dict, g_x, g_y, s_x, s_y, 50, 50)
+    flow_obj.find_path()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tests/test_flow_field.py b/tests/test_flow_field.py
new file mode 100644
index 0000000000..bf8fe6b802
--- /dev/null
+++ b/tests/test_flow_field.py
@@ -0,0 +1,17 @@
+import PathPlanning.FlowField.flowfield as flowfield
+from unittest import TestCase
+import sys
+import os
+sys.path.append(os.path.dirname(__file__) + "/../")
+
+
+class Test(TestCase):
+
+    def test(self):
+        flowfield.show_animation = False
+        flowfield.main()
+
+
+if __name__ == '__main__':  # pragma: no cover
+    test = Test()
+    test.test()

From 2b316502e336de102888cb62cd0ae39769b191b3 Mon Sep 17 00:00:00 2001
From: Rafael Rojas <rafaelrojasmiliani@gmail.com>
Date: Thu, 1 Oct 2020 13:15:52 +0200
Subject: [PATCH 0436/1080] Bug RRT* fix, issues #382 and #383 (#401)

---
 PathPlanning/RRT/rrt.py          |  55 +++++++------
 PathPlanning/RRTStar/rrt_star.py | 132 +++++++++++++++++++++++--------
 2 files changed, 131 insertions(+), 56 deletions(-)

diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py
index 4acea0c10e..834fb24e39 100644
--- a/PathPlanning/RRT/rrt.py
+++ b/PathPlanning/RRT/rrt.py
@@ -32,8 +32,15 @@ def __init__(self, x, y):
             self.path_y = []
             self.parent = None
 
-    def __init__(self, start, goal, obstacle_list, rand_area,
-                 expand_dis=3.0, path_resolution=0.5, goal_sample_rate=5, max_iter=500):
+    def __init__(self,
+                 start,
+                 goal,
+                 obstacle_list,
+                 rand_area,
+                 expand_dis=3.0,
+                 path_resolution=0.5,
+                 goal_sample_rate=5,
+                 max_iter=500):
         """
         Setting Parameter
 
@@ -75,8 +82,10 @@ def planning(self, animation=True):
             if animation and i % 5 == 0:
                 self.draw_graph(rnd_node)
 
-            if self.calc_dist_to_goal(self.node_list[-1].x, self.node_list[-1].y) <= self.expand_dis:
-                final_node = self.steer(self.node_list[-1], self.end, self.expand_dis)
+            if self.calc_dist_to_goal(self.node_list[-1].x,
+                                      self.node_list[-1].y) <= self.expand_dis:
+                final_node = self.steer(self.node_list[-1], self.end,
+                                        self.expand_dis)
                 if self.check_collision(final_node, self.obstacle_list):
                     return self.generate_final_course(len(self.node_list) - 1)
 
@@ -108,6 +117,8 @@ def steer(self, from_node, to_node, extend_length=float("inf")):
         if d <= self.path_resolution:
             new_node.path_x.append(to_node.x)
             new_node.path_y.append(to_node.y)
+            new_node.x = to_node.x
+            new_node.y = to_node.y
 
         new_node.parent = from_node
 
@@ -130,8 +141,9 @@ def calc_dist_to_goal(self, x, y):
 
     def get_random_node(self):
         if random.randint(0, 100) > self.goal_sample_rate:
-            rnd = self.Node(random.uniform(self.min_rand, self.max_rand),
-                            random.uniform(self.min_rand, self.max_rand))
+            rnd = self.Node(
+                random.uniform(self.min_rand, self.max_rand),
+                random.uniform(self.min_rand, self.max_rand))
         else:  # goal point sampling
             rnd = self.Node(self.end.x, self.end.y)
         return rnd
@@ -139,8 +151,9 @@ def get_random_node(self):
     def draw_graph(self, rnd=None):
         plt.clf()
         # for stopping simulation with the esc key.
-        plt.gcf().canvas.mpl_connect('key_release_event',
-                                     lambda event: [exit(0) if event.key == 'escape' else None])
+        plt.gcf().canvas.mpl_connect(
+            'key_release_event',
+            lambda event: [exit(0) if event.key == 'escape' else None])
         if rnd is not None:
             plt.plot(rnd.x, rnd.y, "^k")
         for node in self.node_list:
@@ -167,8 +180,8 @@ def plot_circle(x, y, size, color="-b"):  # pragma: no cover
 
     @staticmethod
     def get_nearest_node_index(node_list, rnd_node):
-        dlist = [(node.x - rnd_node.x) ** 2 + (node.y - rnd_node.y)
-                 ** 2 for node in node_list]
+        dlist = [(node.x - rnd_node.x)**2 + (node.y - rnd_node.y)**2
+                 for node in node_list]
         minind = dlist.index(min(dlist))
 
         return minind
@@ -184,7 +197,7 @@ def check_collision(node, obstacleList):
             dy_list = [oy - y for y in node.path_y]
             d_list = [dx * dx + dy * dy for (dx, dy) in zip(dx_list, dy_list)]
 
-            if min(d_list) <= size ** 2:
+            if min(d_list) <= size**2:
                 return False  # collision
 
         return True  # safe
@@ -202,20 +215,14 @@ def main(gx=6.0, gy=10.0):
     print("start " + __file__)
 
     # ====Search Path with RRT====
-    obstacleList = [
-        (5, 5, 1),
-        (3, 6, 2),
-        (3, 8, 2),
-        (3, 10, 2),
-        (7, 5, 2),
-        (9, 5, 2),
-        (8, 10, 1)
-    ]  # [x, y, radius]
+    obstacleList = [(5, 5, 1), (3, 6, 2), (3, 8, 2), (3, 10, 2), (7, 5, 2),
+                    (9, 5, 2), (8, 10, 1)]  # [x, y, radius]
     # Set Initial parameters
-    rrt = RRT(start=[0, 0],
-              goal=[gx, gy],
-              rand_area=[-2, 15],
-              obstacle_list=obstacleList)
+    rrt = RRT(
+        start=[0, 0],
+        goal=[gx, gy],
+        rand_area=[-2, 15],
+        obstacle_list=obstacleList)
     path = rrt.planning(animation=show_animation)
 
     if path is None:
diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py
index 10ad93a6ba..bef0086095 100644
--- a/PathPlanning/RRTStar/rrt_star.py
+++ b/PathPlanning/RRTStar/rrt_star.py
@@ -12,8 +12,7 @@
 
 import matplotlib.pyplot as plt
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../RRT/")
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../RRT/")
 
 try:
     from rrt import RRT
@@ -33,15 +32,17 @@ def __init__(self, x, y):
             super().__init__(x, y)
             self.cost = 0.0
 
-    def __init__(self, start, goal, obstacle_list, rand_area,
+    def __init__(self,
+                 start,
+                 goal,
+                 obstacle_list,
+                 rand_area,
                  expand_dis=30.0,
                  path_resolution=1.0,
                  goal_sample_rate=20,
                  max_iter=300,
-                 connect_circle_dist=50.0
-                 ):
-        super().__init__(start, goal, obstacle_list,
-                         rand_area, expand_dis, path_resolution, goal_sample_rate, max_iter)
+                 connect_circle_dist=50.0,
+                 search_until_max_iter=False):
         """
         Setting Parameter
 
@@ -51,15 +52,17 @@ def __init__(self, start, goal, obstacle_list, rand_area,
         randArea:Random Sampling Area [min,max]
 
         """
+        super().__init__(start, goal, obstacle_list, rand_area, expand_dis,
+                         path_resolution, goal_sample_rate, max_iter)
         self.connect_circle_dist = connect_circle_dist
         self.goal_node = self.Node(goal[0], goal[1])
+        self.search_until_max_iter = search_until_max_iter
 
-    def planning(self, animation=True, search_until_max_iter=True):
+    def planning(self, animation=True):
         """
         rrt star path planning
 
-        animation: flag for animation on or off
-        search_until_max_iter: search until max iteration for path improving or not
+        animation: flag for animation on or off .
         """
 
         self.node_list = [self.start]
@@ -67,19 +70,28 @@ def planning(self, animation=True, search_until_max_iter=True):
             print("Iter:", i, ", number of nodes:", len(self.node_list))
             rnd = self.get_random_node()
             nearest_ind = self.get_nearest_node_index(self.node_list, rnd)
-            new_node = self.steer(self.node_list[nearest_ind], rnd, self.expand_dis)
+            new_node = self.steer(self.node_list[nearest_ind], rnd,
+                                  self.expand_dis)
+            near_node = self.node_list[nearest_ind]
+            new_node.cost = near_node.cost + \
+                math.hypot(new_node.x-near_node.x,
+                           new_node.y-near_node.y)
 
             if self.check_collision(new_node, self.obstacle_list):
                 near_inds = self.find_near_nodes(new_node)
-                new_node = self.choose_parent(new_node, near_inds)
-                if new_node:
+                node_with_updated_parent = self.choose_parent(
+                    new_node, near_inds)
+                if node_with_updated_parent:
+                    self.rewire(node_with_updated_parent, near_inds)
+                    self.node_list.append(node_with_updated_parent)
+                else:
                     self.node_list.append(new_node)
-                    self.rewire(new_node, near_inds)
 
-            if animation and i % 5 == 0:
+            if animation:
                 self.draw_graph(rnd)
 
-            if (not search_until_max_iter) and new_node:  # check reaching the goal
+            if ((not self.search_until_max_iter)
+                    and new_node):  # if reaches goal
                 last_index = self.search_best_goal_node()
                 if last_index is not None:
                     return self.generate_final_course(last_index)
@@ -93,6 +105,21 @@ def planning(self, animation=True, search_until_max_iter=True):
         return None
 
     def choose_parent(self, new_node, near_inds):
+        """
+        Computes the cheapest point to new_node contained in the list
+        near_inds and set such a node as the parent of new_node.
+            Arguments:
+            --------
+                new_node, Node
+                    randomly generated node with a path from its neared point
+                    There are not coalitions between this node and th tree.
+                near_inds: list
+                    Indices of indices of the nodes what are near to new_node
+
+            Returns.
+            ------
+                Node, a copy of new_node
+        """
         if not near_inds:
             return None
 
@@ -113,14 +140,18 @@ def choose_parent(self, new_node, near_inds):
 
         min_ind = near_inds[costs.index(min_cost)]
         new_node = self.steer(self.node_list[min_ind], new_node)
-        new_node.parent = self.node_list[min_ind]
         new_node.cost = min_cost
 
         return new_node
 
     def search_best_goal_node(self):
-        dist_to_goal_list = [self.calc_dist_to_goal(n.x, n.y) for n in self.node_list]
-        goal_inds = [dist_to_goal_list.index(i) for i in dist_to_goal_list if i <= self.expand_dis]
+        dist_to_goal_list = [
+            self.calc_dist_to_goal(n.x, n.y) for n in self.node_list
+        ]
+        goal_inds = [
+            dist_to_goal_list.index(i) for i in dist_to_goal_list
+            if i <= self.expand_dis
+        ]
 
         safe_goal_inds = []
         for goal_ind in goal_inds:
@@ -139,17 +170,48 @@ def search_best_goal_node(self):
         return None
 
     def find_near_nodes(self, new_node):
+        """
+        1) defines a ball centered on new_node
+        2) Returns all nodes of the three that are inside this ball
+            Arguments:
+            ---------
+                new_node: Node
+                    new randomly generated node, without collisions between
+                    its nearest node
+            Returns:
+            -------
+                list
+                    List with the indices of the nodes inside the ball of
+                    radius r
+        """
         nnode = len(self.node_list) + 1
         r = self.connect_circle_dist * math.sqrt((math.log(nnode) / nnode))
-        # if expand_dist exists, search vertices in a range no more than expand_dist
-        if hasattr(self, 'expand_dis'): 
+        # if expand_dist exists, search vertices in a range no more than
+        # expand_dist
+        if hasattr(self, 'expand_dis'):
             r = min(r, self.expand_dis)
-        dist_list = [(node.x - new_node.x) ** 2 +
-                     (node.y - new_node.y) ** 2 for node in self.node_list]
-        near_inds = [dist_list.index(i) for i in dist_list if i <= r ** 2]
+        dist_list = [(node.x - new_node.x)**2 + (node.y - new_node.y)**2
+                     for node in self.node_list]
+        near_inds = [dist_list.index(i) for i in dist_list if i <= r**2]
         return near_inds
 
     def rewire(self, new_node, near_inds):
+        """
+            For each node in near_inds, this will check if it is cheaper to
+            arrive to them from new_node.
+            In such a case, this will re-assign the parent of the nodes in
+            near_inds to new_node.
+            Parameters:
+            ----------
+                new_node, Node
+                    Node randomly added which can be joined to the tree
+
+                near_inds, list of uints
+                    A list of indices of the self.new_node which contains
+                    nodes within a circle of a given radius.
+            Remark: parent is designated in choose_parent.
+
+        """
         for i in near_inds:
             near_node = self.node_list[i]
             edge_node = self.steer(new_node, near_node)
@@ -161,7 +223,12 @@ def rewire(self, new_node, near_inds):
             improved_cost = near_node.cost > edge_node.cost
 
             if no_collision and improved_cost:
-                self.node_list[i] = edge_node
+                near_node.x = edge_node.x
+                near_node.y = edge_node.y
+                near_node.cost = edge_node.cost
+                near_node.path_x = edge_node.path_x
+                near_node.path_y = edge_node.path_y
+                near_node.parent = edge_node.parent
                 self.propagate_cost_to_leaves(new_node)
 
     def calc_new_cost(self, from_node, to_node):
@@ -192,10 +259,12 @@ def main():
     ]  # [x,y,size(radius)]
 
     # Set Initial parameters
-    rrt_star = RRTStar(start=[0, 0],
-                       goal=[6, 10],
-                       rand_area=[-2, 15],
-                       obstacle_list=obstacle_list)
+    rrt_star = RRTStar(
+        start=[0, 0],
+        goal=[6, 10],
+        rand_area=[-2, 15],
+        obstacle_list=obstacle_list,
+        expand_dis=1)
     path = rrt_star.planning(animation=show_animation)
 
     if path is None:
@@ -206,10 +275,9 @@ def main():
         # Draw final path
         if show_animation:
             rrt_star.draw_graph()
-            plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
+            plt.plot([x for (x, y) in path], [y for (x, y) in path], 'r--')
             plt.grid(True)
-            plt.pause(0.01)  # Need for Mac
-            plt.show()
+    plt.show()
 
 
 if __name__ == '__main__':

From 2a5bbdc0be30a9adfa1b074727548f5d0160a873 Mon Sep 17 00:00:00 2001
From: hotsuyuki <cube.0529@gmail.com>
Date: Thu, 1 Oct 2020 20:21:51 +0900
Subject: [PATCH 0437/1080] More intuitive way of plot_covariance_ellipse()
 (#407)

* More intuitive way of plot_covariance_ellipse() in EKF and UKF

* Add the same changes in UKF as well

* Modified rotation matrix to scipy.spatial.transform.Rotation

* Modified angle of covariance matrix

* Fixed typos
---
 .../ensemble_kalman_filter/ensemble_kalman_filter.py        | 2 +-
 .../extended_kalman_filter/extended_kalman_filter.py        | 6 +++---
 Localization/particle_filter/particle_filter.py             | 2 +-
 .../unscented_kalman_filter/unscented_kalman_filter.py      | 6 +++---
 4 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
index 1307b1ebd1..8ef2089b01 100644
--- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
+++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
@@ -167,7 +167,7 @@ def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
 
     x = [a * math.cos(it) for it in t]
     y = [b * math.sin(it) for it in t]
-    angle = math.atan2(eig_vec[big_ind, 1], eig_vec[big_ind, 0])
+    angle = math.atan2(eig_vec[1, big_ind], eig_vec[0, big_ind])
     rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2]
     fx = np.stack([x, y]).T @ rot
 
diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py
index 2d0d86bf77..51e8f22f4d 100644
--- a/Localization/extended_kalman_filter/extended_kalman_filter.py
+++ b/Localization/extended_kalman_filter/extended_kalman_filter.py
@@ -10,6 +10,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+from scipy.spatial.transform import Rotation as Rot
 
 # Covariance for EKF simulation
 Q = np.diag([
@@ -147,9 +148,8 @@ def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
     b = math.sqrt(eigval[smallind])
     x = [a * math.cos(it) for it in t]
     y = [b * math.sin(it) for it in t]
-    angle = math.atan2(eigvec[bigind, 1], eigvec[bigind, 0])
-    rot = np.array([[math.cos(angle), math.sin(angle)],
-                    [-math.sin(angle), math.cos(angle)]])
+    angle = math.atan2(eigvec[1, bigind], eigvec[0, bigind])
+    rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2]
     fx = rot @ (np.array([x, y]))
     px = np.array(fx[0, :] + xEst[0, 0]).flatten()
     py = np.array(fx[1, :] + xEst[1, 0]).flatten()
diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py
index 89a43ea600..002718f028 100644
--- a/Localization/particle_filter/particle_filter.py
+++ b/Localization/particle_filter/particle_filter.py
@@ -188,7 +188,7 @@ def plot_covariance_ellipse(x_est, p_est):  # pragma: no cover
 
     x = [a * math.cos(it) for it in t]
     y = [b * math.sin(it) for it in t]
-    angle = math.atan2(eig_vec[big_ind, 1], eig_vec[big_ind, 0])
+    angle = math.atan2(eig_vec[1, big_ind], eig_vec[0, big_ind])
     rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2]
     fx = rot.dot(np.array([[x, y]]))
     px = np.array(fx[0, :] + x_est[0, 0]).flatten()
diff --git a/Localization/unscented_kalman_filter/unscented_kalman_filter.py b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
index 7bf279ced0..7f80bccf51 100644
--- a/Localization/unscented_kalman_filter/unscented_kalman_filter.py
+++ b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
@@ -10,6 +10,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+from scipy.spatial.transform import Rotation as Rot
 import scipy.linalg
 
 # Covariance for UKF simulation
@@ -180,9 +181,8 @@ def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
     b = math.sqrt(eigval[smallind])
     x = [a * math.cos(it) for it in t]
     y = [b * math.sin(it) for it in t]
-    angle = math.atan2(eigvec[bigind, 1], eigvec[bigind, 0])
-    rot = np.array([[math.cos(angle), math.sin(angle)],
-                    [-math.sin(angle), math.cos(angle)]])
+    angle = math.atan2(eigvec[1, bigind], eigvec[0, bigind])
+    rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2]
     fx = rot @ np.array([x, y])
     px = np.array(fx[0, :] + xEst[0, 0]).flatten()
     py = np.array(fx[1, :] + xEst[1, 0]).flatten()

From b10bfbbc40988c8d475427d88188b7d261cf9342 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 7 Oct 2020 19:57:58 +0900
Subject: [PATCH 0438/1080] Bump pandas from 1.1.2 to 1.1.3 (#416)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.1.2 to 1.1.3.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.1.2...v1.1.3)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 58511e90f4..da40bbae8f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.19.2
 scipy == 1.5.2
-pandas == 1.1.2
+pandas == 1.1.3
 matplotlib == 3.3.2
 cvxpy == 1.1.5

From b3ab809890d0d27c6242266ddc05e4fcae7226e8 Mon Sep 17 00:00:00 2001
From: Raghuram Shankar <raghuram.shankar98@gmail.com>
Date: Fri, 9 Oct 2020 16:43:21 +0200
Subject: [PATCH 0439/1080] Add Cubature Kalman Filter (#410)

* cubature kalman filter

* Revert "cubature kalman filter"

This reverts commit 172772897c3f8407390fb9d57476c15b4f21fa75.

* add ckf test

* update flags for CI

* update flags for CI

* update flags for CI

* remove comments

* remove comments

* change postpross

* changes requested

* remove comments

* Changes to comments, remove linear_update

* changes to comments

* removed comments

* change comments

* update comments

* update comments

* update comments

* update comments

* fix comment
---
 .../cubature_kalman_filter.py                 | 246 ++++++++++++++++++
 tests/test_cubature_kalman_filter.py          |  14 +
 2 files changed, 260 insertions(+)
 create mode 100644 Localization/cubature_kalman_filter/cubature_kalman_filter.py
 create mode 100644 tests/test_cubature_kalman_filter.py

diff --git a/Localization/cubature_kalman_filter/cubature_kalman_filter.py b/Localization/cubature_kalman_filter/cubature_kalman_filter.py
new file mode 100644
index 0000000000..0fc4c93760
--- /dev/null
+++ b/Localization/cubature_kalman_filter/cubature_kalman_filter.py
@@ -0,0 +1,246 @@
+"""
+Cubature Kalman filter using Constant Turn Rate and Velocity (CTRV) model
+Fuse sensor data from IMU and GPS to obtain accurate position
+
+https://ieeexplore.ieee.org/document/4982682
+
+Author: Raghuram Shankar
+
+state matrix:                       2D x-y position, yaw, velocity and yaw rate
+measurement matrix:                 2D x-y position, velocity and yaw rate
+
+dt:                                 Duration of time step
+N:                                  Number of time steps
+show_final:                         Flag for showing final result
+show_animation:                     Flag for showing each animation frame
+show_ellipse:                       Flag for showing covariance ellipse
+z_noise:                            Measurement noise
+x_0:                                Prior state estimate matrix
+P_0:                                Prior state estimate covariance matrix
+q:                                  Process noise covariance
+hx:                                 Measurement model matrix
+r:                                  Sensor noise covariance
+SP:                                 Sigma Points
+W:                                  Weights
+
+x_est:                              State estimate
+P_est:                              State estimate covariance
+x_true:                             Ground truth value of state
+x_true_cat:                         Concatenate all ground truth states
+x_est_cat:                          Concatenate all state estimates
+z_cat:                              Concatenate all measurements
+
+"""
+
+import math
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.linalg import sqrtm
+
+
+dt = 0.1
+N = 100
+
+show_final = 1
+show_animation = 0
+show_ellipse = 0
+
+
+z_noise = np.array([[0.1, 0.0, 0.0, 0.0],               # x position    [m]
+                    [0.0, 0.1, 0.0, 0.0],               # y position    [m]
+                    [0.0, 0.0, 0.1, 0.0],               # velocity      [m/s]
+                    [0.0, 0.0, 0.0, 0.1]])              # yaw rate      [rad/s]
+
+
+x_0 = np.array([[0.0],                                  # x position    [m]
+                [0.0],                                  # y position    [m]
+                [0.0],                                  # yaw           [rad]
+                [1.0],                                  # velocity      [m/s]
+                [0.1]])                                 # yaw rate      [rad/s]
+
+
+p_0 = np.array([[1e-3, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 1e-3, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 1.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 1.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 1.0]])
+
+
+q = np.array([[1e-11, 0.0,    0.0,               0.0, 0.0],
+              [0.0, 1e-11,    0.0,               0.0, 0.0],
+              [0.0, 0.0,    np.deg2rad(1e-4),   0.0, 0.0],
+              [0.0, 0.0,    0.0,               1e-4, 0.0],
+              [0.0, 0.0,    0.0,                0.0, np.deg2rad(1e-4)]])
+
+
+hx = np.array([[1.0, 0.0, 0.0, 0.0, 0.0],
+               [0.0, 1.0, 0.0, 0.0, 0.0],
+               [0.0, 0.0, 0.0, 1.0, 0.0],
+               [0.0, 0.0, 0.0, 0.0, 1.0]])
+
+
+r = np.array([[0.015, 0.0, 0.0, 0.0],
+              [0.0, 0.010, 0.0, 0.0],
+              [0.0, 0.0, 0.1, 0.0],
+              [0.0, 0.0, 0.0, 0.01]])**2
+
+
+def cubature_kalman_filter(x_est, p_est, z):
+    x_pred, p_pred = cubature_prediction(x_est, p_est)
+    x_upd, p_upd = cubature_update(x_pred, p_pred, z)
+    return x_upd, p_upd
+
+
+def f(x):
+    """
+    Motion Model
+    References:
+    http://fusion.isif.org/proceedings/fusion08CD/papers/1569107835.pdf
+    https://github.com/balzer82/Kalman
+    """
+    x[0] = x[0] + (x[3]/x[4]) * (np.sin(x[4] * dt + x[2]) - np.sin(x[2]))
+    x[1] = x[1] + (x[3]/x[4]) * (- np.cos(x[4] * dt + x[2]) + np.cos(x[2]))
+    x[2] = x[2] + x[4] * dt
+    x[3] = x[3]
+    x[4] = x[4]
+    return x
+
+
+def h(x):
+    """Measurement Model"""
+    x = hx @ x
+    return x
+
+
+def sigma(x, p):
+    """
+    Unscented Transform with Cubature Rule
+    Generate 2n Sigma Points to represent the nonlinear motion
+    Assign Weights to each Sigma Point, Wi = 1/2n
+    Cubature Rule - Special Case of Unscented Transform
+    W0 = 0, no extra tuning parameters, no negative weights
+    """
+    n = np.shape(x)[0]
+    SP = np.zeros((n, 2*n))
+    W = np.zeros((1, 2*n))
+    for i in range(n):
+        SD = sqrtm(p)
+        SP[:, i] = (x + (math.sqrt(n) * SD[:, i]).reshape((n, 1))).flatten()
+        SP[:, i+n] = (x - (math.sqrt(n) * SD[:, i]).reshape((n, 1))).flatten()
+        W[:, i] = 1/(2*n)
+        W[:, i+n] = W[:, i]
+    return SP, W
+
+
+def cubature_prediction(x_pred, p_pred):
+    n = np.shape(x_pred)[0]
+    [SP, W] = sigma(x_pred, p_pred)
+    x_pred = np.zeros((n, 1))
+    p_pred = q
+    for i in range(2*n):
+        x_pred = x_pred + (f(SP[:, i]).reshape((n, 1)) * W[0, i])
+    for i in range(2*n):
+        p_step = (f(SP[:, i]).reshape((n, 1)) - x_pred)
+        p_pred = p_pred + (p_step @ p_step.T * W[0, i])
+    return x_pred, p_pred
+
+
+def cubature_update(x_pred, p_pred, z):
+    n = np.shape(x_pred)[0]
+    m = np.shape(z)[0]
+    [SP, W] = sigma(x_pred, p_pred)
+    y_k = np.zeros((m, 1))
+    P_xy = np.zeros((n, m))
+    s = r
+    for i in range(2*n):
+        y_k = y_k + (h(SP[:, i]).reshape((m, 1)) * W[0, i])
+    for i in range(2*n):
+        p_step = (h(SP[:, i]).reshape((m, 1)) - y_k)
+        P_xy = P_xy + ((SP[:, i]).reshape((n, 1)) -
+                       x_pred) @ p_step.T * W[0, i]
+        s = s + p_step @ p_step.T * W[0, i]
+    x_pred = x_pred + P_xy @ np.linalg.pinv(s) @ (z - y_k)
+    p_pred = p_pred - P_xy @ np.linalg.pinv(s) @ P_xy.T
+    return x_pred, p_pred
+
+
+def generate_measurement(x_true):
+    gz = hx @ x_true
+    z = gz + z_noise @ np.random.randn(4, 1)
+    return z
+
+
+def plot_animation(i, x_true_cat, x_est_cat, z):
+    if i == 0:
+        plt.plot(x_true_cat[0], x_true_cat[1], '.r')
+        plt.plot(x_est_cat[0], x_est_cat[1], '.b')
+    else:
+        plt.plot(x_true_cat[0:, 0], x_true_cat[0:, 1], 'r')
+        plt.plot(x_est_cat[0:, 0], x_est_cat[0:, 1], 'b')
+    plt.plot(z[0], z[1], '+g')
+    plt.grid(True)
+    plt.pause(0.001)
+
+
+def plot_ellipse(x_est, p_est):
+    phi = np.linspace(0, 2 * math.pi, 100)
+    p_ellipse = np.array(
+        [[p_est[0, 0], p_est[0, 1]], [p_est[1, 0], p_est[1, 1]]])
+    x0 = 3 * sqrtm(p_ellipse)
+    xy_1 = np.array([])
+    xy_2 = np.array([])
+    for i in range(100):
+        arr = np.array([[math.sin(phi[i])], [math.cos(phi[i])]])
+        arr = x0 @ arr
+        xy_1 = np.hstack([xy_1, arr[0]])
+        xy_2 = np.hstack([xy_2, arr[1]])
+    plt.plot(xy_1 + x_est[0], xy_2 + x_est[1], 'r')
+    plt.pause(0.00001)
+
+
+def plot_final(x_true_cat, x_est_cat, z_cat):
+    fig = plt.figure()
+    subplot = fig.add_subplot(111)
+    subplot.plot(x_true_cat[0:, 0], x_true_cat[0:, 1],
+                 'r', label='True Position')
+    subplot.plot(x_est_cat[0:, 0], x_est_cat[0:, 1],
+                 'b', label='Estimated Position')
+    subplot.plot(z_cat[0:, 0], z_cat[0:, 1], '+g', label='Noisy Measurements')
+    subplot.set_xlabel('x [m]')
+    subplot.set_ylabel('y [m]')
+    subplot.set_title('Cubature Kalman Filter - CTRV Model')
+    subplot.legend(loc='upper left', shadow=True, fontsize='large')
+    plt.grid(True)
+    plt.show()
+
+
+def main():
+    print(__file__ + " start!!")
+    x_est = x_0
+    p_est = p_0
+    x_true = x_0
+    x_true_cat = np.array([x_0[0, 0], x_0[1, 0]])
+    x_est_cat = np.array([x_0[0, 0], x_0[1, 0]])
+    z_cat = np.array([x_0[0, 0], x_0[1, 0]])
+    for i in range(N):
+        x_true = f(x_true)
+        z = generate_measurement(x_true)
+        if i == (N - 1) and show_final == 1:
+            show_final_flag = 1
+        else:
+            show_final_flag = 0
+        if show_animation == 1:
+            plot_animation(i, x_true_cat, x_est_cat, z)
+        if show_ellipse == 1:
+            plot_ellipse(x_est[0:2], p_est)
+        if show_final_flag == 1:
+            plot_final(x_true_cat, x_est_cat, z_cat)
+        x_est, p_est = cubature_kalman_filter(x_est, p_est, z)
+        x_true_cat = np.vstack((x_true_cat, x_true[0:2].T))
+        x_est_cat = np.vstack((x_est_cat, x_est[0:2].T))
+        z_cat = np.vstack((z_cat, z[0:2].T))
+    print('CKF Over')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tests/test_cubature_kalman_filter.py b/tests/test_cubature_kalman_filter.py
new file mode 100644
index 0000000000..e6ac71a8ee
--- /dev/null
+++ b/tests/test_cubature_kalman_filter.py
@@ -0,0 +1,14 @@
+from unittest import TestCase
+
+from Localization.cubature_kalman_filter import cubature_kalman_filter as m
+
+print(__file__)
+
+
+class Test(TestCase):
+
+    def test1(self):
+        m.show_final = False
+        m.show_animation = False
+        m.show_ellipse = False
+        m.main()

From 1b1e649b7d74dc3eda20ac3d63d43f60a3681dcd Mon Sep 17 00:00:00 2001
From: Raghuram Shankar <raghuram.shankar98@gmail.com>
Date: Sat, 10 Oct 2020 03:51:16 +0200
Subject: [PATCH 0440/1080] fixed TypeError (#414)

* fixed TypeError

* updated global variables

* updated global variables

* fix comment

* Change try-except to if
---
 .../two_joint_arm_to_point_control.py             | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
index 85ed612360..90fcea1df9 100644
--- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
+++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
@@ -34,9 +34,14 @@
 def two_joint_arm(GOAL_TH=0.0, theta1=0.0, theta2=0.0):
     """
     Computes the inverse kinematics for a planar 2DOF arm
+    When out of bounds, rewrite x and y with last correct values
     """
+    global x, y
     while True:
         try:
+            if x is not None and y is not None:
+                x_prev = x
+                y_prev = y
             if np.sqrt(x**2 + y**2) > (l1 + l2):
                 theta2_goal = 0
             else:
@@ -54,11 +59,15 @@ def two_joint_arm(GOAL_TH=0.0, theta1=0.0, theta2=0.0):
             theta2 = theta2 + Kp * ang_diff(theta2_goal, theta2) * dt
         except ValueError as e:
             print("Unreachable goal")
+        except TypeError:
+            x = x_prev
+            y = y_prev
 
         wrist = plot_arm(theta1, theta2, x, y)
 
         # check goal
-        d2goal = np.hypot(wrist[0] - x, wrist[1] - y)
+        if x is not None and y is not None:
+            d2goal = np.hypot(wrist[0] - x, wrist[1] - y)
 
         if abs(d2goal) < GOAL_TH and x is not None:
             return theta1, theta2
@@ -118,8 +127,8 @@ def main():  # pragma: no cover
     fig = plt.figure()
     fig.canvas.mpl_connect("button_press_event", click)
     # for stopping simulation with the esc key.
-    fig.canvas.mpl_connect('key_release_event',
-            lambda event: [exit(0) if event.key == 'escape' else None])
+    fig.canvas.mpl_connect('key_release_event', lambda event: [
+                           exit(0) if event.key == 'escape' else None])
     two_joint_arm()
 
 

From d90fba3717c556229a2cac8778324b915438b65e Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 10 Oct 2020 16:29:57 +0900
Subject: [PATCH 0441/1080] fix circle ci (#419)

* fix circle ci

* fix circle ci

* fix circle ci
---
 .circleci/config.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index f63f094a3f..d65f0fdf3c 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -5,7 +5,8 @@ orbs:
 
 jobs:
   build_doc:
-    executor: python/default
+    docker:
+      - image: circleci/python:3.8
     steps:
       - checkout
       - python/load-cache

From fa636b26406baad31e8c2840657ff0dcbb6fc4b2 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 13 Oct 2020 06:46:11 +0900
Subject: [PATCH 0442/1080] Bump cvxpy from 1.1.5 to 1.1.6 (#421)

Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.5 to 1.1.6.
- [Release notes](https://github.com/cvxgrp/cvxpy/releases)
- [Changelog](https://github.com/cvxgrp/cvxpy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.5...v1.1.6)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index da40bbae8f..932089a068 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy == 1.19.2
 scipy == 1.5.2
 pandas == 1.1.3
 matplotlib == 3.3.2
-cvxpy == 1.1.5
+cvxpy == 1.1.6

From 840d6afb6d8bb4835fdb1682a535cea034ea4ca9 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 20 Oct 2020 07:22:35 +0900
Subject: [PATCH 0443/1080] Bump scipy from 1.5.2 to 1.5.3 (#424)

Bumps [scipy](https://github.com/scipy/scipy) from 1.5.2 to 1.5.3.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.5.2...v1.5.3)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 932089a068..e175ed2d00 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.19.2
-scipy == 1.5.2
+scipy == 1.5.3
 pandas == 1.1.3
 matplotlib == 3.3.2
 cvxpy == 1.1.6

From 48b6ee3cf9ee65fec86124948c277f43b8cf06ff Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 Oct 2020 07:23:42 +0900
Subject: [PATCH 0444/1080] Bump cvxpy from 1.1.6 to 1.1.7 (#426)

Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.6 to 1.1.7.
- [Release notes](https://github.com/cvxgrp/cvxpy/releases)
- [Changelog](https://github.com/cvxgrp/cvxpy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.6...v1.1.7)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index e175ed2d00..92f1b04fb6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy == 1.19.2
 scipy == 1.5.3
 pandas == 1.1.3
 matplotlib == 3.3.2
-cvxpy == 1.1.6
+cvxpy == 1.1.7

From 2bf0dcedc906332e1b4d1b5b4a307fb32de4afc5 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 1 Nov 2020 14:05:46 +0900
Subject: [PATCH 0445/1080] fix unittest animation bugs (#429)

* fix unittest animation bugs

* exstract a function
---
 ...e.py => a_star_searching_from_two_side.py} |   0
 PathPlanning/AStar/a_star_variants.py         | 129 ++++++++++--------
 PathPlanning/FlowField/flowfield.py           |  20 +--
 tests/test_a_star_searching_two_side.py       |   2 +-
 tests/test_a_star_variants.py                 |   5 +-
 5 files changed, 92 insertions(+), 64 deletions(-)
 rename PathPlanning/AStar/{A_Star_searching_from_two_side.py => a_star_searching_from_two_side.py} (100%)

diff --git a/PathPlanning/AStar/A_Star_searching_from_two_side.py b/PathPlanning/AStar/a_star_searching_from_two_side.py
similarity index 100%
rename from PathPlanning/AStar/A_Star_searching_from_two_side.py
rename to PathPlanning/AStar/a_star_searching_from_two_side.py
diff --git a/PathPlanning/AStar/a_star_variants.py b/PathPlanning/AStar/a_star_variants.py
index cbf5395fd2..e0053ee224 100644
--- a/PathPlanning/AStar/a_star_variants.py
+++ b/PathPlanning/AStar/a_star_variants.py
@@ -1,5 +1,5 @@
 """
-astar variants
+a star variants
 author: Sarim Mehdi(muhammadsarim.mehdi@studio.unibo.it)
 Source: http://theory.stanford.edu/~amitp/GameProgramming/Variations.html
 """
@@ -7,7 +7,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-show_animation = False
+show_animation = True
 use_beam_search = False
 use_iterative_deepening = False
 use_dynamic_weighting = False
@@ -89,7 +89,8 @@ def key_points(o_dict):
                 obs_count += 1
             if obs_count == 3 or obs_count == 1:
                 c_list.append((x, y))
-                plt.plot(x, y, ".y")
+                if show_animation:
+                    plt.plot(x, y, ".y")
                 break
     if only_corners:
         return c_list
@@ -148,7 +149,8 @@ def __init__(self, obs_grid, goal_x, goal_y, start_x, start_y,
              'open': True, 'in_open_list': True}
         self.open_set.append(self.all_nodes[tuple(self.start_pt)])
 
-    def get_hval(self, x1, y1, x2, y2):
+    @staticmethod
+    def get_hval(x1, y1, x2, y2):
         x, y = x1, y1
         val = 0
         while x != x2 or y != y2:
@@ -178,7 +180,6 @@ def get_farthest_point(self, x, y, i, j):
     def jump_point(self):
         """Jump point: Instead of exploring all empty spaces of the
         map, just explore the corners."""
-        plt.title('Jump Point')
 
         goal_found = False
         while len(self.open_set) > 0:
@@ -232,11 +233,13 @@ def jump_point(self):
                     if not self.all_nodes[cand_pt]['in_open_list']:
                         self.open_set.append(self.all_nodes[cand_pt])
                         self.all_nodes[cand_pt]['in_open_list'] = True
-                    plt.plot(cand_pt[0], cand_pt[1], "r*")
+                    if show_animation:
+                        plt.plot(cand_pt[0], cand_pt[1], "r*")
 
                 if goal_found:
                     break
-            plt.pause(0.001)
+            if show_animation:
+                plt.pause(0.001)
             if goal_found:
                 current_node = self.all_nodes[tuple(self.goal_pt)]
             while goal_found:
@@ -244,23 +247,26 @@ def jump_point(self):
                     break
                 x = [current_node['pos'][0], current_node['pred'][0]]
                 y = [current_node['pos'][1], current_node['pred'][1]]
-                plt.plot(x, y, "b")
                 current_node = self.all_nodes[tuple(current_node['pred'])]
-                plt.pause(0.001)
+                if show_animation:
+                    plt.plot(x, y, "b")
+                    plt.pause(0.001)
             if goal_found:
                 break
 
             current_node['open'] = False
             current_node['in_open_list'] = False
-            plt.plot(current_node['pos'][0], current_node['pos'][1], "g*")
+            if show_animation:
+                plt.plot(current_node['pos'][0], current_node['pos'][1], "g*")
             del self.open_set[p]
             current_node['fcost'], current_node['hcost'] = np.inf, np.inf
         if show_animation:
+            plt.title('Jump Point')
             plt.show()
 
-    def astar(self):
+    def a_star(self):
         """Beam search: Maintain an open list of just 30 nodes.
-        If more than 30 nodes, then get rid of ndoes with high
+        If more than 30 nodes, then get rid of nodes with high
         f values.
         Iterative deepening: At every iteration, get a cut-off
         value for the f cost. This cut-off is minimum of the f
@@ -275,21 +281,23 @@ def astar(self):
         one neighbor at a time. In fact, you can look for the
         next node as far out as you can as long as there is a
         clear line of sight from your current node to that node."""
-        if use_beam_search:
-            plt.title('A* with beam search')
-        elif use_iterative_deepening:
-            plt.title('A* with iterative deepening')
-        elif use_dynamic_weighting:
-            plt.title('A* with dynamic weighting')
-        elif use_theta_star:
-            plt.title('Theta*')
-        else:
-            plt.title('A*')
+        if show_animation:
+            if use_beam_search:
+                plt.title('A* with beam search')
+            elif use_iterative_deepening:
+                plt.title('A* with iterative deepening')
+            elif use_dynamic_weighting:
+                plt.title('A* with dynamic weighting')
+            elif use_theta_star:
+                plt.title('Theta*')
+            else:
+                plt.title('A*')
 
         goal_found = False
         curr_f_thresh = np.inf
         depth = 0
         no_valid_f = False
+        w = None
         while len(self.open_set) > 0:
             self.open_set = sorted(self.open_set, key=lambda x: x['fcost'])
             lowest_f = self.open_set[0]['fcost']
@@ -351,30 +359,14 @@ def astar(self):
                         break
 
                     cand_pt = tuple(cand_pt)
-                    if not self.obs_grid[tuple(cand_pt)] and \
-                            self.all_nodes[cand_pt]['open']:
-                        g_cost = offset + current_node['gcost']
-                        h_cost = self.all_nodes[cand_pt]['hcost']
-                        if use_dynamic_weighting:
-                            h_cost = h_cost * w
-                        f_cost = g_cost + h_cost
-                        if f_cost < self.all_nodes[cand_pt]['fcost'] and \
-                                f_cost <= curr_f_thresh:
-                            f_cost_list.append(f_cost)
-                            self.all_nodes[cand_pt]['pred'] = \
-                                current_node['pos']
-                            self.all_nodes[cand_pt]['gcost'] = g_cost
-                            self.all_nodes[cand_pt]['fcost'] = f_cost
-                            if not self.all_nodes[cand_pt]['in_open_list']:
-                                self.open_set.append(self.all_nodes[cand_pt])
-                                self.all_nodes[cand_pt]['in_open_list'] = True
-                            plt.plot(cand_pt[0], cand_pt[1], "r*")
-                        if curr_f_thresh < f_cost < \
-                                self.all_nodes[cand_pt]['fcost']:
-                            no_valid_f = True
+                    no_valid_f = self.update_node_cost(cand_pt, curr_f_thresh,
+                                                       current_node,
+                                                       f_cost_list, no_valid_f,
+                                                       offset, w)
                 if goal_found:
                     break
-            plt.pause(0.001)
+            if show_animation:
+                plt.pause(0.001)
             if goal_found:
                 current_node = self.all_nodes[tuple(self.goal_pt)]
             while goal_found:
@@ -383,12 +375,13 @@ def astar(self):
                 if use_theta_star or use_jump_point:
                     x, y = [current_node['pos'][0], current_node['pred'][0]], \
                              [current_node['pos'][1], current_node['pred'][1]]
-                    plt.plot(x, y, "b")
+                    if show_animation:
+                        plt.plot(x, y, "b")
                 else:
-                    plt.plot(current_node['pred'][0],
-                             current_node['pred'][1], "b*")
+                    if show_animation:
+                        plt.plot(current_node['pred'][0],
+                                 current_node['pred'][1], "b*")
                 current_node = self.all_nodes[tuple(current_node['pred'])]
-                plt.pause(0.001)
             if goal_found:
                 break
 
@@ -402,13 +395,40 @@ def astar(self):
 
             current_node['open'] = False
             current_node['in_open_list'] = False
-            plt.plot(current_node['pos'][0], current_node['pos'][1], "g*")
+            if show_animation:
+                plt.plot(current_node['pos'][0], current_node['pos'][1], "g*")
             del self.open_set[p]
             current_node['fcost'], current_node['hcost'] = np.inf, np.inf
             depth += 1
         if show_animation:
             plt.show()
 
+    def update_node_cost(self, cand_pt, curr_f_thresh, current_node,
+                         f_cost_list, no_valid_f, offset, w):
+        if not self.obs_grid[tuple(cand_pt)] and \
+                self.all_nodes[cand_pt]['open']:
+            g_cost = offset + current_node['gcost']
+            h_cost = self.all_nodes[cand_pt]['hcost']
+            if use_dynamic_weighting:
+                h_cost = h_cost * w
+            f_cost = g_cost + h_cost
+            if f_cost < self.all_nodes[cand_pt]['fcost'] and \
+                    f_cost <= curr_f_thresh:
+                f_cost_list.append(f_cost)
+                self.all_nodes[cand_pt]['pred'] = \
+                    current_node['pos']
+                self.all_nodes[cand_pt]['gcost'] = g_cost
+                self.all_nodes[cand_pt]['fcost'] = f_cost
+                if not self.all_nodes[cand_pt]['in_open_list']:
+                    self.open_set.append(self.all_nodes[cand_pt])
+                    self.all_nodes[cand_pt]['in_open_list'] = True
+                if show_animation:
+                    plt.plot(cand_pt[0], cand_pt[1], "r*")
+            if curr_f_thresh < f_cost < \
+                    self.all_nodes[cand_pt]['fcost']:
+                no_valid_f = True
+        return no_valid_f
+
 
 def main():
     # set obstacle positions
@@ -443,10 +463,11 @@ def main():
     for x, y, l in zip(all_x, all_y, all_len):
         draw_horizontal_line(x, y, l, o_x, o_y, obs_dict)
 
-    plt.plot(o_x, o_y, ".k")
-    plt.plot(s_x, s_y, "og")
-    plt.plot(g_x, g_y, "xb")
-    plt.grid(True)
+    if show_animation:
+        plt.plot(o_x, o_y, ".k")
+        plt.plot(s_x, s_y, "og")
+        plt.plot(g_x, g_y, "xb")
+        plt.grid(True)
 
     if use_jump_point:
         keypoint_list = key_points(obs_dict)
@@ -455,7 +476,7 @@ def main():
         search_obj.jump_point()
     else:
         search_obj = SearchAlgo(obs_dict, g_x, g_y, s_x, s_y, 101, 101)
-        search_obj.astar()
+        search_obj.a_star()
 
 
 if __name__ == '__main__':
diff --git a/PathPlanning/FlowField/flowfield.py b/PathPlanning/FlowField/flowfield.py
index f222e8ecf2..e50430de3c 100644
--- a/PathPlanning/FlowField/flowfield.py
+++ b/PathPlanning/FlowField/flowfield.py
@@ -129,9 +129,12 @@ def assign_vectors(self):
     def follow_vectors(self):
         curr_x, curr_y = self.start_pt
         while curr_x is not None and curr_y is not None:
-            plt.plot(curr_x, curr_y, "b*")
             curr_x, curr_y = self.vector_field[(curr_x, curr_y)]
-            plt.pause(0.001)
+
+            if show_animation:
+                plt.plot(curr_x, curr_y, "b*")
+                plt.pause(0.001)
+
         if show_animation:
             plt.show()
 
@@ -208,12 +211,13 @@ def main():
     for x, y, l in zip(all_x, all_y, all_len):
         draw_horizontal_line(x, y, l, h_x, h_y, obs_dict, 'hard')
 
-    plt.plot(o_x, o_y, "sr")
-    plt.plot(m_x, m_y, "sg")
-    plt.plot(h_x, h_y, "sy")
-    plt.plot(s_x, s_y, "og")
-    plt.plot(g_x, g_y, "o")
-    plt.grid(True)
+    if show_animation:
+        plt.plot(o_x, o_y, "sr")
+        plt.plot(m_x, m_y, "sg")
+        plt.plot(h_x, h_y, "sy")
+        plt.plot(s_x, s_y, "og")
+        plt.plot(g_x, g_y, "o")
+        plt.grid(True)
 
     flow_obj = FlowField(obs_dict, g_x, g_y, s_x, s_y, 50, 50)
     flow_obj.find_path()
diff --git a/tests/test_a_star_searching_two_side.py b/tests/test_a_star_searching_two_side.py
index 6116c9bbe7..013e9253b8 100644
--- a/tests/test_a_star_searching_two_side.py
+++ b/tests/test_a_star_searching_two_side.py
@@ -5,7 +5,7 @@
 sys.path.append(os.path.dirname(__file__) + '/../')
 
 try:
-    from PathPlanning.AStar import A_Star_searching_from_two_side as m
+    from PathPlanning.AStar import a_star_searching_from_two_side as m
 except ImportError:
     raise
 
diff --git a/tests/test_a_star_variants.py b/tests/test_a_star_variants.py
index 53efeddd20..13009c1365 100644
--- a/tests/test_a_star_variants.py
+++ b/tests/test_a_star_variants.py
@@ -10,6 +10,7 @@ class Test(TestCase):
     def test(self):
         # A* with beam search
         astar.show_animation = False
+
         astar.use_beam_search = True
         astar.main()
         self.reset_all()
@@ -34,7 +35,9 @@ def test(self):
         astar.main()
         self.reset_all()
 
-    def reset_all(self):
+    @staticmethod
+    def reset_all():
+        astar.show_animation = False
         astar.use_beam_search = False
         astar.use_iterative_deepening = False
         astar.use_dynamic_weighting = False

From 0c76141b8057630865880fd57368be3312f58c6f Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 3 Nov 2020 07:30:43 +0900
Subject: [PATCH 0446/1080] Bump numpy from 1.19.2 to 1.19.4 (#430)

Bumps [numpy](https://github.com/numpy/numpy) from 1.19.2 to 1.19.4.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/master/doc/HOWTO_RELEASE.rst.txt)
- [Commits](https://github.com/numpy/numpy/compare/v1.19.2...v1.19.4)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 92f1b04fb6..526d314410 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.19.2
+numpy == 1.19.4
 scipy == 1.5.3
 pandas == 1.1.3
 matplotlib == 3.3.2

From 35e23c89cdd789e09a4849eb87b345790ac2efcc Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 3 Nov 2020 07:30:59 +0900
Subject: [PATCH 0447/1080] Bump pandas from 1.1.3 to 1.1.4 (#431)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.1.3 to 1.1.4.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.1.3...v1.1.4)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 526d314410..600dbc480e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.19.4
 scipy == 1.5.3
-pandas == 1.1.3
+pandas == 1.1.4
 matplotlib == 3.3.2
 cvxpy == 1.1.7

From 31b75f135865bcd9e92376088b1064b84d155068 Mon Sep 17 00:00:00 2001
From: Nguyen Mau Dung <dzung@seoulrobotics.org>
Date: Sun, 8 Nov 2020 14:09:59 +0900
Subject: [PATCH 0448/1080] Using vectorization to speed up computation (#427)

---
 .../rectangle_fitting/rectangle_fitting.py    | 41 ++++++-------------
 1 file changed, 12 insertions(+), 29 deletions(-)

diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py
index ae41b3c53e..a60a6fbfd1 100644
--- a/Mapping/rectangle_fitting/rectangle_fitting.py
+++ b/Mapping/rectangle_fitting/rectangle_fitting.py
@@ -69,15 +69,11 @@ def _calc_closeness_criterion(self, c1, c2):
         c1_min = min(c1)
         c2_min = min(c2)
 
-        D1 = [min([np.linalg.norm(c1_max - ic1),
-                   np.linalg.norm(ic1 - c1_min)]) for ic1 in c1]
-        D2 = [min([np.linalg.norm(c2_max - ic2),
-                   np.linalg.norm(ic2 - c2_min)]) for ic2 in c2]
-
-        beta = 0
-        for i, _ in enumerate(D1):
-            d = max(min([D1[i], D2[i]]), self.min_dist_of_closeness_criteria)
-            beta += (1.0 / d)
+        # Vectorization
+        D1 = np.minimum(c1_max - c1, c1 - c1_min)
+        D2 = np.minimum(c2_max - c2, c2 - c2_min)
+        d = np.maximum(np.minimum(D1, D2), self.min_dist_of_closeness_criteria)
+        beta = (1.0 / d).sum()
 
         return beta
 
@@ -88,26 +84,13 @@ def _calc_variance_criterion(c1, c2):
         c1_min = min(c1)
         c2_min = min(c2)
 
-        D1 = [min([np.linalg.norm(c1_max - ic1),
-                   np.linalg.norm(ic1 - c1_min)]) for ic1 in c1]
-        D2 = [min([np.linalg.norm(c2_max - ic2),
-                   np.linalg.norm(ic2 - c2_min)]) for ic2 in c2]
-
-        E1, E2 = [], []
-        for (d1, d2) in zip(D1, D2):
-            if d1 < d2:
-                E1.append(d1)
-            else:
-                E2.append(d2)
-
-        V1 = 0.0
-        if E1:
-            V1 = - np.var(E1)
-
-        V2 = 0.0
-        if E2:
-            V2 = - np.var(E2)
-
+        # Vectorization
+        D1 = np.minimum(c1_max - c1, c1 - c1_min)
+        D2 = np.minimum(c2_max - c2, c2 - c2_min)
+        E1 = D1[D1 < D2]
+        E2 = D2[D1 >= D2]
+        V1 = - np.var(E1) if len(E1) > 0 else 0.
+        V2 = - np.var(E2) if len(E2) > 0 else 0.
         gamma = V1 + V2
 
         return gamma

From 7e312daa705e4b9939719fca3719e5956614116f Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 10 Nov 2020 06:55:51 +0900
Subject: [PATCH 0449/1080] Bump scipy from 1.5.3 to 1.5.4 (#438)

Bumps [scipy](https://github.com/scipy/scipy) from 1.5.3 to 1.5.4.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.5.3...v1.5.4)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 600dbc480e..5035bbbc69 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.19.4
-scipy == 1.5.3
+scipy == 1.5.4
 pandas == 1.1.4
 matplotlib == 3.3.2
 cvxpy == 1.1.7

From 40df009180e7541e6bc85e7f455d7d973a064aed Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 17 Nov 2020 20:18:04 +0900
Subject: [PATCH 0450/1080] Bump matplotlib from 3.3.2 to 3.3.3 (#441)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.3.2 to 3.3.3.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.3.2...v3.3.3)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 5035bbbc69..5cc619a841 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.19.4
 scipy == 1.5.4
 pandas == 1.1.4
-matplotlib == 3.3.2
+matplotlib == 3.3.3
 cvxpy == 1.1.7

From f9e2293a8d0478c51752809efb878c9f3cea4a4d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 8 Dec 2020 07:49:13 +0900
Subject: [PATCH 0451/1080] Bump pandas from 1.1.4 to 1.1.5 (#443)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.1.4 to 1.1.5.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.1.4...v1.1.5)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 5cc619a841..b66011e4a4 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.19.4
 scipy == 1.5.4
-pandas == 1.1.4
+pandas == 1.1.5
 matplotlib == 3.3.3
 cvxpy == 1.1.7

From 2d5024dc386b1d84ade7cdbdda7d5aaab58cf57b Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 20 Dec 2020 16:16:35 +0900
Subject: [PATCH 0452/1080] add test_rectangle_fitting.py (#444)

* add test_rectangle_fitting.py

* add test_rectangle_fitting.py
---
 tests/test_rectangle_fitting.py | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100644 tests/test_rectangle_fitting.py

diff --git a/tests/test_rectangle_fitting.py b/tests/test_rectangle_fitting.py
new file mode 100644
index 0000000000..2a28320626
--- /dev/null
+++ b/tests/test_rectangle_fitting.py
@@ -0,0 +1,22 @@
+from unittest import TestCase
+
+import sys
+import os
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../Mapping/rectangle_fitting/")
+
+try:
+    from Mapping.rectangle_fitting import rectangle_fitting as m
+except ImportError:
+    raise
+
+
+print(__file__)
+
+
+class Test(TestCase):
+
+    def test1(self):
+        m.show_animation = False
+        m.main()

From 4264cb8b1010e5eaa448b3ef6d0a1c096bba65e8 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 20 Dec 2020 20:29:36 +0900
Subject: [PATCH 0453/1080] fix pure_pursuit sample (#445)

---
 .../ClosedLoopRRTStar/pure_pursuit.py         | 41 +------------------
 1 file changed, 2 insertions(+), 39 deletions(-)

diff --git a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
index f3984f87b0..ca763f0d4f 100644
--- a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
+++ b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
@@ -250,7 +250,7 @@ def main():  # pragma: no cover
     yaw = [state.yaw]
     v = [state.v]
     t = [0.0]
-    target_ind = calc_target_index(state, cx, cy)
+    target_ind, dis = calc_target_index(state, cx, cy)
 
     while T >= time and lastIndex > target_ind:
         ai = PIDControl(target_speed, state.v)
@@ -291,43 +291,6 @@ def main():  # pragma: no cover
     plt.show()
 
 
-def main2():  # pragma: no cover
-    import pandas as pd
-    data = pd.read_csv("rrt_course.csv")
-    cx = np.array(data["x"])
-    cy = np.array(data["y"])
-    cyaw = np.array(data["yaw"])
-
-    target_speed = 10.0 / 3.6
-
-    goal = [cx[-1], cy[-1]]
-
-    cx, cy, cyaw = extend_path(cx, cy, cyaw)
-
-    speed_profile = calc_speed_profile(cx, cy, cyaw, target_speed)
-
-    t, x, y, yaw, v, a, d, flag = closed_loop_prediction(
-        cx, cy, cyaw, speed_profile, goal)
-
-    plt.subplots(1)
-    plt.plot(cx, cy, ".r", label="course")
-    plt.plot(x, y, "-b", label="trajectory")
-    plt.plot(goal[0], goal[1], "xg", label="goal")
-    plt.legend()
-    plt.xlabel("x[m]")
-    plt.ylabel("y[m]")
-    plt.axis("equal")
-    plt.grid(True)
-
-    plt.subplots(1)
-    plt.plot(t, [iv * 3.6 for iv in v], "-r")
-    plt.xlabel("Time[s]")
-    plt.ylabel("Speed[km/h]")
-    plt.grid(True)
-    plt.show()
-
-
 if __name__ == '__main__':  # pragma: no cover
     print("Pure pursuit path tracking simulation start")
-    #  main()
-    main2()
+    main()

From e0a8ab2368d83797d2246af1f9b4fa80788d74dd Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 29 Dec 2020 07:28:19 +0900
Subject: [PATCH 0454/1080] Bump pandas from 1.1.5 to 1.2.0 (#449)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.1.5 to 1.2.0.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.1.5...v1.2.0)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index b66011e4a4..c8aa78aeb7 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.19.4
 scipy == 1.5.4
-pandas == 1.1.5
+pandas == 1.2.0
 matplotlib == 3.3.3
 cvxpy == 1.1.7

From 65debdc332b23083a434c24fa5b73e917ee71aa2 Mon Sep 17 00:00:00 2001
From: Mahyar Abdeetedal <mahyar.etedal@icloud.com>
Date: Tue, 29 Dec 2020 06:32:13 -0500
Subject: [PATCH 0455/1080] RRT* for seven joint arm control (#439)

---
 .../rrt_star_seven_joint_arm.py               | 403 ++++++++++++++++++
 tests/test_rrt_star_seven_joint_arm.py        |  26 ++
 2 files changed, 429 insertions(+)
 create mode 100755 ArmNavigation/seven_joint_arm_to_point_control/rrt_star_seven_joint_arm.py
 create mode 100644 tests/test_rrt_star_seven_joint_arm.py

diff --git a/ArmNavigation/seven_joint_arm_to_point_control/rrt_star_seven_joint_arm.py b/ArmNavigation/seven_joint_arm_to_point_control/rrt_star_seven_joint_arm.py
new file mode 100755
index 0000000000..d10733b41e
--- /dev/null
+++ b/ArmNavigation/seven_joint_arm_to_point_control/rrt_star_seven_joint_arm.py
@@ -0,0 +1,403 @@
+"""
+RRT* path planner for a seven joint arm
+Author: Mahyar Abdeetedal (mahyaret)
+"""
+import math
+import os
+import sys
+import random
+import numpy as np
+from mpl_toolkits import mplot3d
+import matplotlib.pyplot as plt
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../n_joint_arm_3d/")
+try:
+    from NLinkArm3d import NLinkArm
+except ImportError:
+    raise
+
+show_animation = True
+verbose = False
+
+
+class RobotArm(NLinkArm):
+    def get_points(self, joint_angle_list):
+        self.set_joint_angles(joint_angle_list)
+
+        x_list = []
+        y_list = []
+        z_list = []
+
+        trans = np.identity(4)
+
+        x_list.append(trans[0, 3])
+        y_list.append(trans[1, 3])
+        z_list.append(trans[2, 3])
+        for i in range(len(self.link_list)):
+            trans = np.dot(trans, self.link_list[i].transformation_matrix())
+            x_list.append(trans[0, 3])
+            y_list.append(trans[1, 3])
+            z_list.append(trans[2, 3])
+
+        return x_list, y_list, z_list
+
+
+class RRTStar:
+    """
+    Class for RRT Star planning
+    """
+
+    class Node:
+        def __init__(self, x):
+            self.x = x
+            self.parent = None
+            self.cost = 0.0
+
+    def __init__(self, start, goal, robot, obstacle_list, rand_area,
+                 expand_dis=.30,
+                 path_resolution=.1,
+                 goal_sample_rate=20,
+                 max_iter=300,
+                 connect_circle_dist=50.0
+                 ):
+        """
+        Setting Parameter
+
+        start:Start Position [q1,...,qn]
+        goal:Goal Position [q1,...,qn]
+        obstacleList:obstacle Positions [[x,y,z,size],...]
+        randArea:Random Sampling Area [min,max]
+
+        """
+        self.start = self.Node(start)
+        self.end = self.Node(goal)
+        self.dimension = len(start)
+        self.min_rand = rand_area[0]
+        self.max_rand = rand_area[1]
+        self.expand_dis = expand_dis
+        self.path_resolution = path_resolution
+        self.goal_sample_rate = goal_sample_rate
+        self.max_iter = max_iter
+        self.robot = robot
+        self.obstacle_list = obstacle_list
+        self.connect_circle_dist = connect_circle_dist
+        self.goal_node = self.Node(goal)
+        self.ax = plt.axes(projection='3d')
+        self.node_list = []
+
+    def planning(self, animation=False, search_until_max_iter=False):
+        """
+        rrt star path planning
+
+        animation: flag for animation on or off
+        search_until_max_iter: search until max iteration for path
+        improving or not
+        """
+
+        self.node_list = [self.start]
+        for i in range(self.max_iter):
+            if verbose:
+                print("Iter:", i, ", number of nodes:", len(self.node_list))
+            rnd = self.get_random_node()
+            nearest_ind = self.get_nearest_node_index(self.node_list, rnd)
+            new_node = self.steer(self.node_list[nearest_ind],
+                                  rnd,
+                                  self.expand_dis)
+
+            if self.check_collision(new_node, self.robot, self.obstacle_list):
+                near_inds = self.find_near_nodes(new_node)
+                new_node = self.choose_parent(new_node, near_inds)
+                if new_node:
+                    self.node_list.append(new_node)
+                    self.rewire(new_node, near_inds)
+
+            if animation and i % 5 == 0 and self.dimension <= 3:
+                self.draw_graph(rnd)
+
+            if (not search_until_max_iter) and new_node:
+                last_index = self.search_best_goal_node()
+                if last_index is not None:
+                    return self.generate_final_course(last_index)
+        if verbose:
+            print("reached max iteration")
+
+        last_index = self.search_best_goal_node()
+        if last_index is not None:
+            return self.generate_final_course(last_index)
+
+        return None
+
+    def choose_parent(self, new_node, near_inds):
+        if not near_inds:
+            return None
+
+        # search nearest cost in near_inds
+        costs = []
+        for i in near_inds:
+            near_node = self.node_list[i]
+            t_node = self.steer(near_node, new_node)
+            if t_node and self.check_collision(t_node,
+                                               self.robot,
+                                               self.obstacle_list):
+                costs.append(self.calc_new_cost(near_node, new_node))
+            else:
+                costs.append(float("inf"))  # the cost of collision node
+        min_cost = min(costs)
+
+        if min_cost == float("inf"):
+            print("There is no good path.(min_cost is inf)")
+            return None
+
+        min_ind = near_inds[costs.index(min_cost)]
+        new_node = self.steer(self.node_list[min_ind], new_node)
+        new_node.parent = self.node_list[min_ind]
+        new_node.cost = min_cost
+
+        return new_node
+
+    def search_best_goal_node(self):
+        dist_to_goal_list = [self.calc_dist_to_goal(n.x)
+                             for n in self.node_list]
+        goal_inds = [dist_to_goal_list.index(i)
+                     for i in dist_to_goal_list if i <= self.expand_dis]
+
+        safe_goal_inds = []
+        for goal_ind in goal_inds:
+            t_node = self.steer(self.node_list[goal_ind], self.goal_node)
+            if self.check_collision(t_node, self.robot, self.obstacle_list):
+                safe_goal_inds.append(goal_ind)
+
+        if not safe_goal_inds:
+            return None
+
+        min_cost = min([self.node_list[i].cost for i in safe_goal_inds])
+        for i in safe_goal_inds:
+            if self.node_list[i].cost == min_cost:
+                return i
+
+        return None
+
+    def find_near_nodes(self, new_node):
+        nnode = len(self.node_list) + 1
+        r = self.connect_circle_dist * math.sqrt((math.log(nnode) / nnode))
+        # if expand_dist exists, search vertices in
+        # a range no more than expand_dist
+        if hasattr(self, 'expand_dis'):
+            r = min(r, self.expand_dis)
+        dist_list = [np.sum((np.array(node.x) - np.array(new_node.x)) ** 2)
+                     for node in self.node_list]
+        near_inds = [dist_list.index(i) for i in dist_list if i <= r ** 2]
+        return near_inds
+
+    def rewire(self, new_node, near_inds):
+        for i in near_inds:
+            near_node = self.node_list[i]
+            edge_node = self.steer(new_node, near_node)
+            if not edge_node:
+                continue
+            edge_node.cost = self.calc_new_cost(new_node, near_node)
+
+            no_collision = self.check_collision(edge_node,
+                                                self.robot,
+                                                self.obstacle_list)
+            improved_cost = near_node.cost > edge_node.cost
+
+            if no_collision and improved_cost:
+                self.node_list[i] = edge_node
+                self.propagate_cost_to_leaves(new_node)
+
+    def calc_new_cost(self, from_node, to_node):
+        d, _, _ = self.calc_distance_and_angle(from_node, to_node)
+        return from_node.cost + d
+
+    def propagate_cost_to_leaves(self, parent_node):
+
+        for node in self.node_list:
+            if node.parent == parent_node:
+                node.cost = self.calc_new_cost(parent_node, node)
+                self.propagate_cost_to_leaves(node)
+
+    def generate_final_course(self, goal_ind):
+        path = [self.end.x]
+        node = self.node_list[goal_ind]
+        while node.parent is not None:
+            path.append(node.x)
+            node = node.parent
+        path.append(node.x)
+
+        return path
+
+    def calc_dist_to_goal(self, x):
+        distance = np.linalg.norm(np.array(x) - np.array(self.end.x))
+        return distance
+
+    def get_random_node(self):
+        if random.randint(0, 100) > self.goal_sample_rate:
+            rnd = self.Node(np.random.uniform(self.min_rand,
+                                              self.max_rand,
+                                              self.dimension))
+        else:  # goal point sampling
+            rnd = self.Node(self.end.x)
+        return rnd
+
+    def steer(self, from_node, to_node, extend_length=float("inf")):
+        new_node = self.Node(list(from_node.x))
+        d, phi, theta = self.calc_distance_and_angle(new_node, to_node)
+
+        new_node.path_x = [list(new_node.x)]
+
+        if extend_length > d:
+            extend_length = d
+
+        n_expand = math.floor(extend_length / self.path_resolution)
+
+        start, end = np.array(from_node.x), np.array(to_node.x)
+        v = end - start
+        u = v / (np.sqrt(np.sum(v ** 2)))
+        for _ in range(n_expand):
+            new_node.x += u * self.path_resolution
+            new_node.path_x.append(list(new_node.x))
+
+        d, _, _ = self.calc_distance_and_angle(new_node, to_node)
+        if d <= self.path_resolution:
+            new_node.path_x.append(list(to_node.x))
+
+        new_node.parent = from_node
+
+        return new_node
+
+    def draw_graph(self, rnd=None):
+        plt.cla()
+        self.ax.axis([-1, 1, -1, 1])
+        self.ax.set_zlim(0, 1)
+        self.ax.grid(True)
+        for (ox, oy, oz, size) in self.obstacle_list:
+            self.plot_sphere(self.ax, ox, oy, oz, size=size)
+        if self.dimension > 3:
+            return self.ax
+        if rnd is not None:
+            self.ax.plot([rnd.x[0]], [rnd.x[1]], [rnd.x[2]], "^k")
+        for node in self.node_list:
+            if node.parent:
+                path = np.array(node.path_x)
+                plt.plot(path[:, 0], path[:, 1], path[:, 2], "-g")
+        self.ax.plot([self.start.x[0]], [self.start.x[1]],
+                     [self.start.x[2]], "xr")
+        self.ax.plot([self.end.x[0]], [self.end.x[1]], [self.end.x[2]], "xr")
+        plt.pause(0.01)
+        return self.ax
+
+    @staticmethod
+    def get_nearest_node_index(node_list, rnd_node):
+        dlist = [np.sum((np.array(node.x) - np.array(rnd_node.x))**2)
+                 for node in node_list]
+        minind = dlist.index(min(dlist))
+
+        return minind
+
+    @staticmethod
+    def plot_sphere(ax, x, y, z, size=1, color="k"):
+        u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]
+        xl = x+size*np.cos(u)*np.sin(v)
+        yl = y+size*np.sin(u)*np.sin(v)
+        zl = z+size*np.cos(v)
+        ax.plot_wireframe(xl, yl, zl, color=color)
+
+    @staticmethod
+    def calc_distance_and_angle(from_node, to_node):
+        dx = to_node.x[0] - from_node.x[0]
+        dy = to_node.x[1] - from_node.x[1]
+        dz = to_node.x[2] - from_node.x[2]
+        d = np.sqrt(np.sum((np.array(to_node.x) - np.array(from_node.x))**2))
+        phi = math.atan2(dy, dx)
+        theta = math.atan2(math.hypot(dx, dy), dz)
+        return d, phi, theta
+
+    @staticmethod
+    def calc_distance_and_angle2(from_node, to_node):
+        dx = to_node.x[0] - from_node.x[0]
+        dy = to_node.x[1] - from_node.x[1]
+        dz = to_node.x[2] - from_node.x[2]
+        d = math.sqrt(dx**2 + dy**2 + dz**2)
+        phi = math.atan2(dy, dx)
+        theta = math.atan2(math.hypot(dx, dy), dz)
+        return d, phi, theta
+
+    @staticmethod
+    def check_collision(node, robot, obstacleList):
+
+        if node is None:
+            return False
+
+        for (ox, oy, oz, size) in obstacleList:
+            for x in node.path_x:
+                x_list, y_list, z_list = robot.get_points(x)
+                dx_list = [ox - x_point for x_point in x_list]
+                dy_list = [oy - y_point for y_point in y_list]
+                dz_list = [oz - z_point for z_point in z_list]
+                d_list = [dx * dx + dy * dy + dz * dz
+                          for (dx, dy, dz) in zip(dx_list,
+                                                  dy_list,
+                                                  dz_list)]
+
+                if min(d_list) <= size ** 2:
+                    return False  # collision
+
+        return True  # safe
+
+
+def main():
+    print("Start " + __file__)
+
+    # init NLinkArm with Denavit-Hartenberg parameters of panda
+    # https://frankaemika.github.io/docs/control_parameters.html
+    # [theta, alpha, a, d]
+    seven_joint_arm = RobotArm([[0., math.pi/2., 0., .333],
+                                [0., -math.pi/2., 0., 0.],
+                                [0., math.pi/2., 0.0825, 0.3160],
+                                [0., -math.pi/2., -0.0825, 0.],
+                                [0., math.pi/2., 0., 0.3840],
+                                [0., math.pi/2., 0.088, 0.],
+                                [0., 0., 0., 0.107]])
+    # ====Search Path with RRT====
+    obstacle_list = [
+        (-.3, -.3, .7, .1),
+        (.0, -.3, .7, .1),
+        (.2, -.1, .3, .15),
+    ]  # [x,y,size(radius)]
+    start = [0 for _ in range(len(seven_joint_arm.link_list))]
+    end = [1.5 for _ in range(len(seven_joint_arm.link_list))]
+    # Set Initial parameters
+    rrt_star = RRTStar(start=start,
+                       goal=end,
+                       rand_area=[0, 2],
+                       max_iter=200,
+                       robot=seven_joint_arm,
+                       obstacle_list=obstacle_list)
+    path = rrt_star.planning(animation=show_animation,
+                             search_until_max_iter=False)
+
+    if path is None:
+        print("Cannot find path")
+    else:
+        print("found path!!")
+
+        # Draw final path
+        if show_animation:
+            ax = rrt_star.draw_graph()
+            for i, q in enumerate(reversed(path)):
+                x_points, y_points, z_points = seven_joint_arm.get_points(q)
+                if i == 0 or i == len(path) - 1:
+                    color = None
+                else:
+                    color = "grey"
+                ax.plot([x for x in x_points],
+                        [y for y in y_points],
+                        [z for z in z_points],
+                        "o-", color=color,  ms=4, mew=0.5)
+                plt.pause(0.01)
+            plt.show()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tests/test_rrt_star_seven_joint_arm.py b/tests/test_rrt_star_seven_joint_arm.py
new file mode 100644
index 0000000000..fe90801b3b
--- /dev/null
+++ b/tests/test_rrt_star_seven_joint_arm.py
@@ -0,0 +1,26 @@
+import os
+import sys
+from unittest import TestCase
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../ArmNavigation/seven_joint_arm_to_point_control/")
+
+try:
+    import rrt_star_seven_joint_arm as m
+except ImportError:
+    raise
+
+
+print(__file__)
+
+
+class Test(TestCase):
+
+    def test1(self):
+        m.show_animation = False
+        m.main()
+
+
+if __name__ == '__main__':  # pragma: no cover
+    test = Test()
+    test.test1()

From d29dacbf9e89db2e06be3fcb799a5cf78f22e4d9 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 29 Dec 2020 21:41:14 +0900
Subject: [PATCH 0456/1080] clean up seven joint arm script

---
 .../rrt_star_seven_joint_arm_control.py}      | 19 ++++++++++++-------
 tests/test_rrt_star_seven_joint_arm.py        |  4 ++--
 2 files changed, 14 insertions(+), 9 deletions(-)
 rename ArmNavigation/{seven_joint_arm_to_point_control/rrt_star_seven_joint_arm.py => rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py} (96%)

diff --git a/ArmNavigation/seven_joint_arm_to_point_control/rrt_star_seven_joint_arm.py b/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py
similarity index 96%
rename from ArmNavigation/seven_joint_arm_to_point_control/rrt_star_seven_joint_arm.py
rename to ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py
index d10733b41e..0de4c7edb8 100755
--- a/ArmNavigation/seven_joint_arm_to_point_control/rrt_star_seven_joint_arm.py
+++ b/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py
@@ -224,7 +224,7 @@ def generate_final_course(self, goal_ind):
             path.append(node.x)
             node = node.parent
         path.append(node.x)
-
+        reversed(path)
         return path
 
     def calc_dist_to_goal(self, x):
@@ -385,17 +385,22 @@ def main():
         # Draw final path
         if show_animation:
             ax = rrt_star.draw_graph()
-            for i, q in enumerate(reversed(path)):
+
+            # Plot final configuration
+            x_points, y_points, z_points = seven_joint_arm.get_points(path[-1])
+            ax.plot([x for x in x_points],
+                    [y for y in y_points],
+                    [z for z in z_points],
+                    "o-", color="red", ms=5, mew=0.5)
+
+            for i, q in enumerate(path):
                 x_points, y_points, z_points = seven_joint_arm.get_points(q)
-                if i == 0 or i == len(path) - 1:
-                    color = None
-                else:
-                    color = "grey"
                 ax.plot([x for x in x_points],
                         [y for y in y_points],
                         [z for z in z_points],
-                        "o-", color=color,  ms=4, mew=0.5)
+                        "o-", color="grey",  ms=4, mew=0.5)
                 plt.pause(0.01)
+
             plt.show()
 
 
diff --git a/tests/test_rrt_star_seven_joint_arm.py b/tests/test_rrt_star_seven_joint_arm.py
index fe90801b3b..0788b3675a 100644
--- a/tests/test_rrt_star_seven_joint_arm.py
+++ b/tests/test_rrt_star_seven_joint_arm.py
@@ -3,10 +3,10 @@
 from unittest import TestCase
 
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../ArmNavigation/seven_joint_arm_to_point_control/")
+                "/../ArmNavigation/rrt_star_seven_joint_arm_control/")
 
 try:
-    import rrt_star_seven_joint_arm as m
+    import rrt_star_seven_joint_arm_control as m
 except ImportError:
     raise
 

From 581b3cf27ab188a319162dc13dc926503b52cda9 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 30 Dec 2020 13:52:00 +0900
Subject: [PATCH 0457/1080] bump python version to 3.9. (#433)

---
 .circleci/config.yml           |  4 +++-
 .github/workflows/Linux_CI.yml |  6 ++++--
 .github/workflows/MacOS_CI.yml |  4 +++-
 README.md                      |  2 +-
 appveyor.yml                   | 16 ++++++----------
 docs/getting_started.rst       |  2 +-
 6 files changed, 18 insertions(+), 16 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index d65f0fdf3c..0ec05a3a20 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -6,10 +6,12 @@ orbs:
 jobs:
   build_doc:
     docker:
-      - image: circleci/python:3.8
+      - image: circleci/python:3.9
     steps:
+      - run: sudo apt-get update && sudo apt-get install -y cmake # TODO: remove after osqp wheels released
       - checkout
       - python/load-cache
+      - run: pip install numpy # TODO: remove after osqp wheels released
       - python/install-deps
       - python/save-cache
       - run: pip install sphinx sphinx-autobuild sphinx-rtd-theme
diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index 1174438dd5..61be099152 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -8,7 +8,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.8']
+        python-version: ['3.9']
 
     name: Python ${{ matrix.python-version }} CI
 
@@ -21,9 +21,11 @@ jobs:
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
+      # TODO: remove numpy installation after fix cvxpy install issue
       run: |
         python -m pip install --upgrade pip
-        pip install -r requirements.txt
+        pip install numpy
+        python -m pip install -r requirements.txt
     - name: install coverage
       run: pip install coverage
     - name: install mypy
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index 3a28347b18..84ef75157f 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ '3.8' ]
+        python-version: [ '3.9' ]
     name: Python ${{ matrix.python-version }} CI
     steps:
       - uses: actions/checkout@v2
@@ -23,8 +23,10 @@ jobs:
           python-version: ${{ matrix.python-version }}
 
       - name: Install dependencies
+        # TODO: remove numpy installation after fix cvxpy install issue
         run: |
           python -m pip install --upgrade pip
+          pip install numpy
           pip install -r requirements.txt
       - name: install coverage
         run: pip install coverage
diff --git a/README.md b/README.md
index 07dbf7ae5a..2dcb4bab7e 100644
--- a/README.md
+++ b/README.md
@@ -92,7 +92,7 @@ See this paper for more details:
 
 # Requirements
 
-- Python 3.8.x
+- Python 3.9.x
 
 - numpy
 
diff --git a/appveyor.yml b/appveyor.yml
index b883397d71..9fd7430223 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -8,11 +8,10 @@ environment:
     CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd"
 
   matrix:
-    - MINICONDA: C:\Miniconda38-x64
-      PYTHON_VERSION: "3.8"
+     - PYTHON_DIR: C:\Python39-x64
 
 init:
-  - "ECHO %MINICONDA% %PYTHON_VERSION% %PYTHON_ARCH%"
+  - "ECHO %PYTHON_DIR%"
 
 install:
   # If there is a newer build queued for the same PR, cancel this one.
@@ -30,15 +29,12 @@ install:
   # Prepend newly installed Python to the PATH of this build (this cannot be
   # done from inside the powershell script as it would require to restart
   # the parent CMD process).
-  - SET PATH=%MINICONDA%;%MINICONDA%\\Scripts;%PATH%
+  - SET PATH=%PYTHON_DIR%;%PYTHON_DIR%\\Scripts;%PATH%
   - SET PATH=%PYTHON%;%PYTHON%\Scripts;%PYTHON%\Library\bin;%PATH%
   - SET PATH=%PATH%;C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin
-  - conda config --set always_yes yes --set changeps1 no
-  - conda config --append channels conda-forge
-  - conda update -q conda
-  - conda info -a
-  - conda env create -f C:\\projects\pythonrobotics\environment.yml
-  - activate python_robotics
+  - "python -m pip install --upgrade pip"
+  - "pip install numpy" # TODO: remove this line after osqp wheel released
+  - "python -m pip install -r requirements.txt"
 
   # Check that we have the expected version and architecture for Python
   - "python --version"
diff --git a/docs/getting_started.rst b/docs/getting_started.rst
index 93ab70503a..83565eb872 100644
--- a/docs/getting_started.rst
+++ b/docs/getting_started.rst
@@ -25,7 +25,7 @@ See this paper for more details:
 Requirements
 -------------
 
--  Python 3.6.x
+-  Python 3.9.x
 -  numpy
 -  scipy
 -  matplotlib

From c2debe3b05b095046f63f72bb9b9c3b0c52c2635 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 31 Dec 2020 22:54:40 +0900
Subject: [PATCH 0458/1080] use pytest for test runner (#452)

---
 .github/pull_request_template.md              |   2 +-
 .../two_joint_arm_to_point_control.py         |  26 ++-
 .../Eta3SplinePath/eta3_spline_path.py        | 178 ++++++++++--------
 .../eta3_spline_trajectory.py                 |  48 ++---
 README.md                                     |   2 +
 requirements.txt                              |   1 +
 runtests.sh                                   |   8 +-
 tests/__init__.py                             |   0
 8 files changed, 152 insertions(+), 113 deletions(-)
 create mode 100644 tests/__init__.py

diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index 9fbb893c1c..fb412d571c 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -1,7 +1,7 @@
 <!-- 
 Thanks for contributing a pull request! 
 Please ensure that your PR satisfies the checklist before submitting:
-- [] This project only accept codes for python 3.8 or higher.
+- [] This project only accept codes for python 3.9 or higher.
 - [] If you add a new algorithm sample code, please add a unit test file under `test` dir.
      This sample test code might help you : https://github.com/AtsushiSakai/PythonRobotics/blob/master/tests/test_a_star.py
 - [] If you fix a bug of existing code please add a test function in the test code to show the issue was solved.
diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
index 90fcea1df9..9f835c8b32 100644
--- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
+++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
@@ -5,8 +5,10 @@
 Author: Daniel Ingram (daniel-s-ingram)
         Atsushi Sakai (@Atsushi_twi)
 
-Ref: P. I. Corke, "Robotics, Vision & Control", Springer 2017, ISBN 978-3-319-54413-7 p102
-- [Robotics, Vision and Control \| SpringerLink](https://link.springer.com/book/10.1007/978-3-642-20144-8)
+Ref: P. I. Corke, "Robotics, Vision & Control", Springer 2017,
+ ISBN 978-3-319-54413-7 p102
+- [Robotics, Vision and Control]
+(https://link.springer.com/book/10.1007/978-3-642-20144-8)
 
 """
 
@@ -37,6 +39,7 @@ def two_joint_arm(GOAL_TH=0.0, theta1=0.0, theta2=0.0):
     When out of bounds, rewrite x and y with last correct values
     """
     global x, y
+    x_prev, y_prev = None, None
     while True:
         try:
             if x is not None and y is not None:
@@ -47,18 +50,20 @@ def two_joint_arm(GOAL_TH=0.0, theta1=0.0, theta2=0.0):
             else:
                 theta2_goal = np.arccos(
                     (x**2 + y**2 - l1**2 - l2**2) / (2 * l1 * l2))
-            theta1_goal = np.math.atan2(y, x) - np.math.atan2(l2 *
-                                                              np.sin(theta2_goal), (l1 + l2 * np.cos(theta2_goal)))
+            tmp = np.math.atan2(l2 * np.sin(theta2_goal),
+                                (l1 + l2 * np.cos(theta2_goal)))
+            theta1_goal = np.math.atan2(y, x) - tmp
 
             if theta1_goal < 0:
                 theta2_goal = -theta2_goal
-                theta1_goal = np.math.atan2(
-                    y, x) - np.math.atan2(l2 * np.sin(theta2_goal), (l1 + l2 * np.cos(theta2_goal)))
+                tmp = np.math.atan2(l2 * np.sin(theta2_goal),
+                                    (l1 + l2 * np.cos(theta2_goal)))
+                theta1_goal = np.math.atan2(y, x) - tmp
 
             theta1 = theta1 + Kp * ang_diff(theta1_goal, theta1) * dt
             theta2 = theta2 + Kp * ang_diff(theta2_goal, theta2) * dt
         except ValueError as e:
-            print("Unreachable goal")
+            print("Unreachable goal"+e)
         except TypeError:
             x = x_prev
             y = y_prev
@@ -66,6 +71,7 @@ def two_joint_arm(GOAL_TH=0.0, theta1=0.0, theta2=0.0):
         wrist = plot_arm(theta1, theta2, x, y)
 
         # check goal
+        d2goal = None
         if x is not None and y is not None:
             d2goal = np.hypot(wrist[0] - x, wrist[1] - y)
 
@@ -73,7 +79,7 @@ def two_joint_arm(GOAL_TH=0.0, theta1=0.0, theta2=0.0):
             return theta1, theta2
 
 
-def plot_arm(theta1, theta2, x, y):  # pragma: no cover
+def plot_arm(theta1, theta2, target_x, target_y):  # pragma: no cover
     shoulder = np.array([0, 0])
     elbow = shoulder + np.array([l1 * np.cos(theta1), l1 * np.sin(theta1)])
     wrist = elbow + \
@@ -89,8 +95,8 @@ def plot_arm(theta1, theta2, x, y):  # pragma: no cover
         plt.plot(elbow[0], elbow[1], 'ro')
         plt.plot(wrist[0], wrist[1], 'ro')
 
-        plt.plot([wrist[0], x], [wrist[1], y], 'g--')
-        plt.plot(x, y, 'g*')
+        plt.plot([wrist[0], target_x], [wrist[1], target_y], 'g--')
+        plt.plot(target_x, target_y, 'g*')
 
         plt.xlim(-2, 2)
         plt.ylim(-2, 2)
diff --git a/PathPlanning/Eta3SplinePath/eta3_spline_path.py b/PathPlanning/Eta3SplinePath/eta3_spline_path.py
index 414f6b4534..dc07d3c84b 100644
--- a/PathPlanning/Eta3SplinePath/eta3_spline_path.py
+++ b/PathPlanning/Eta3SplinePath/eta3_spline_path.py
@@ -1,13 +1,13 @@
 """
 
-\eta^3 polynomials planner
+eta^3 polynomials planner
 
 author: Joe Dinius, Ph.D (https://jwdinius.github.io)
         Atsushi Sakai (@Atsushi_twi)
 
 Ref:
-
-- [\eta^3-Splines for the Smooth Path Generation of Wheeled Mobile Robots](https://ieeexplore.ieee.org/document/4339545/)
+- [eta^3-Splines for the Smooth Path Generation of Wheeled Mobile Robots]
+(https://ieeexplore.ieee.org/document/4339545/)
 
 """
 
@@ -15,23 +15,25 @@
 import matplotlib.pyplot as plt
 from scipy.integrate import quad
 
-# NOTE: *_pose is a 3-array: 0 - x coord, 1 - y coord, 2 - orientation angle \theta
+# NOTE: *_pose is a 3-array:
+# 0 - x coord, 1 - y coord, 2 - orientation angle \theta
 
 show_animation = True
 
 
-class eta3_path(object):
+class Eta3Path(object):
     """
-    eta3_path
+    Eta3Path
 
     input
-        segments: list of `eta3_path_segment` instances definining a continuous path
+        segments: a list of `Eta3PathSegment` instances
+        defining a continuous path
     """
 
     def __init__(self, segments):
         # ensure input has the correct form
         assert(isinstance(segments, list) and isinstance(
-            segments[0], eta3_path_segment))
+            segments[0], Eta3PathSegment))
         # ensure that each segment begins from the previous segment's end (continuity)
         for r, s in zip(segments[:-1], segments[1:]):
             assert(np.array_equal(r.end_pose, s.start_pose))
@@ -39,7 +41,7 @@ def __init__(self, segments):
 
     def calc_path_point(self, u):
         """
-        eta3_path::calc_path_point
+        Eta3Path::calc_path_point
 
         input
             normalized interpolation point along path object, 0 <= u <= len(self.segments)
@@ -47,7 +49,7 @@ def calc_path_point(self, u):
             2d (x,y) position vector
         """
 
-        assert(u >= 0 and u <= len(self.segments))
+        assert(0 <= u <= len(self.segments))
         if np.isclose(u, len(self.segments)):
             segment_idx = len(self.segments) - 1
             u = 1.
@@ -57,11 +59,12 @@ def calc_path_point(self, u):
         return self.segments[segment_idx].calc_point(u)
 
 
-class eta3_path_segment(object):
+class Eta3PathSegment(object):
     """
-    eta3_path_segment - constructs an eta^3 path segment based on desired shaping, eta, and curvature vector, kappa.
-                        If either, or both, of eta and kappa are not set during initialization, they will
-                        default to zeros.
+    Eta3PathSegment - constructs an eta^3 path segment based on desired
+    shaping, eta, and curvature vector, kappa. If either, or both,
+    of eta and kappa are not set during initialization,
+    they will default to zeros.
 
     input
         start_pose - starting pose array  (x, y, \theta)
@@ -110,70 +113,96 @@ def __init__(self, start_pose, end_pose, eta=None, kappa=None):
         self.coeffs[1, 3] = 1. / 6 * eta[4] * sa + 1. / 6 * \
             (eta[0]**3 * kappa[1] + 3. * eta[0] * eta[2] * kappa[0]) * ca
         # quartic (u^4)
-        self.coeffs[0, 4] = 35. * (end_pose[0] - start_pose[0]) - (20. * eta[0] + 5 * eta[2] + 2. / 3 * eta[4]) * ca \
-            + (5. * eta[0]**2 * kappa[0] + 2. / 3 * eta[0]**3 * kappa[1] + 2. * eta[0] * eta[2] * kappa[0]) * sa \
-            - (15. * eta[1] - 5. / 2 * eta[3] + 1. / 6 * eta[5]) * cb \
-            - (5. / 2 * eta[1]**2 * kappa[2] - 1. / 6 * eta[1] **
-               3 * kappa[3] - 1. / 2 * eta[1] * eta[3] * kappa[2]) * sb
-        self.coeffs[1, 4] = 35. * (end_pose[1] - start_pose[1]) - (20. * eta[0] + 5. * eta[2] + 2. / 3 * eta[4]) * sa \
-            - (5. * eta[0]**2 * kappa[0] + 2. / 3 * eta[0]**3 * kappa[1] + 2. * eta[0] * eta[2] * kappa[0]) * ca \
-            - (15. * eta[1] - 5. / 2 * eta[3] + 1. / 6 * eta[5]) * sb \
-            + (5. / 2 * eta[1]**2 * kappa[2] - 1. / 6 * eta[1] **
-               3 * kappa[3] - 1. / 2 * eta[1] * eta[3] * kappa[2]) * cb
+        tmp1 = 35. * (end_pose[0] - start_pose[0])
+        tmp2 = (20. * eta[0] + 5 * eta[2] + 2. / 3 * eta[4]) * ca
+        tmp3 = (5. * eta[0] ** 2 * kappa[0] + 2. / 3 * eta[0] ** 3 * kappa[1]
+                + 2. * eta[0] * eta[2] * kappa[0]) * sa
+        tmp4 = (15. * eta[1] - 5. / 2 * eta[3] + 1. / 6 * eta[5]) * cb
+        tmp5 = (5. / 2 * eta[1] ** 2 * kappa[2] - 1. / 6 * eta[1] ** 3 *
+                kappa[3] - 1. / 2 * eta[1] * eta[3] * kappa[2]) * sb
+        self.coeffs[0, 4] = tmp1 - tmp2 + tmp3 - tmp4 - tmp5
+        tmp1 = 35. * (end_pose[1] - start_pose[1])
+        tmp2 = (20. * eta[0] + 5. * eta[2] + 2. / 3 * eta[4]) * sa
+        tmp3 = (5. * eta[0] ** 2 * kappa[0] + 2. / 3 * eta[0] ** 3 * kappa[1]
+                + 2. * eta[0] * eta[2] * kappa[0]) * ca
+        tmp4 = (15. * eta[1] - 5. / 2 * eta[3] + 1. / 6 * eta[5]) * sb
+        tmp5 = (5. / 2 * eta[1] ** 2 * kappa[2] - 1. / 6 * eta[1] ** 3 *
+                kappa[3] - 1. / 2 * eta[1] * eta[3] * kappa[2]) * cb
+        self.coeffs[1, 4] = tmp1 - tmp2 - tmp3 - tmp4 + tmp5
         # quintic (u^5)
-        self.coeffs[0, 5] = -84. * (end_pose[0] - start_pose[0]) + (45. * eta[0] + 10. * eta[2] + eta[4]) * ca \
-            - (10. * eta[0]**2 * kappa[0] + eta[0]**3 * kappa[1] + 3. * eta[0] * eta[2] * kappa[0]) * sa \
-            + (39. * eta[1] - 7. * eta[3] + 1. / 2 * eta[5]) * cb \
-            + (7. * eta[1]**2 * kappa[2] - 1. / 2 * eta[1]**3 *
-               kappa[3] - 3. / 2 * eta[1] * eta[3] * kappa[2]) * sb
-        self.coeffs[1, 5] = -84. * (end_pose[1] - start_pose[1]) + (45. * eta[0] + 10. * eta[2] + eta[4]) * sa \
-            + (10. * eta[0]**2 * kappa[0] + eta[0]**3 * kappa[1] + 3. * eta[0] * eta[2] * kappa[0]) * ca \
-            + (39. * eta[1] - 7. * eta[3] + 1. / 2 * eta[5]) * sb \
-            - (7. * eta[1]**2 * kappa[2] - 1. / 2 * eta[1]**3 *
-               kappa[3] - 3. / 2 * eta[1] * eta[3] * kappa[2]) * cb
+        tmp1 = -84. * (end_pose[0] - start_pose[0])
+        tmp2 = (45. * eta[0] + 10. * eta[2] + eta[4]) * ca
+        tmp3 = (10. * eta[0] ** 2 * kappa[0] + eta[0] ** 3 * kappa[1] + 3. *
+                eta[0] * eta[2] * kappa[0]) * sa
+        tmp4 = (39. * eta[1] - 7. * eta[3] + 1. / 2 * eta[5]) * cb
+        tmp5 = + (7. * eta[1] ** 2 * kappa[2] - 1. / 2 * eta[1] ** 3 * kappa[3]
+                  - 3. / 2 * eta[1] * eta[3] * kappa[2]) * sb
+        self.coeffs[0, 5] = tmp1 + tmp2 - tmp3 + tmp4 + tmp5
+        tmp1 = -84. * (end_pose[1] - start_pose[1])
+        tmp2 = (45. * eta[0] + 10. * eta[2] + eta[4]) * sa
+        tmp3 = (10. * eta[0] ** 2 * kappa[0] + eta[0] ** 3 * kappa[1] + 3. *
+                eta[0] * eta[2] * kappa[0]) * ca
+        tmp4 = (39. * eta[1] - 7. * eta[3] + 1. / 2 * eta[5]) * sb
+        tmp5 = - (7. * eta[1] ** 2 * kappa[2] - 1. / 2 * eta[1] ** 3 * kappa[3]
+                  - 3. / 2 * eta[1] * eta[3] * kappa[2]) * cb
+        self.coeffs[1, 5] = tmp1 + tmp2 + tmp3 + tmp4 + tmp5
         # sextic (u^6)
-        self.coeffs[0, 6] = 70. * (end_pose[0] - start_pose[0]) - (36. * eta[0] + 15. / 2 * eta[2] + 2. / 3 * eta[4]) * ca \
-            + (15. / 2 * eta[0]**2 * kappa[0] + 2. / 3 * eta[0]**3 * kappa[1] + 2. * eta[0] * eta[2] * kappa[0]) * sa \
-            - (34. * eta[1] - 13. / 2 * eta[3] + 1. / 2 * eta[5]) * cb \
-            - (13. / 2 * eta[1]**2 * kappa[2] - 1. / 2 * eta[1] **
-               3 * kappa[3] - 3. / 2 * eta[1] * eta[3] * kappa[2]) * sb
-        self.coeffs[1, 6] = 70. * (end_pose[1] - start_pose[1]) - (36. * eta[0] + 15. / 2 * eta[2] + 2. / 3 * eta[4]) * sa \
-            - (15. / 2 * eta[0]**2 * kappa[0] + 2. / 3 * eta[0]**3 * kappa[1] + 2. * eta[0] * eta[2] * kappa[0]) * ca \
-            - (34. * eta[1] - 13. / 2 * eta[3] + 1. / 2 * eta[5]) * sb \
-            + (13. / 2 * eta[1]**2 * kappa[2] - 1. / 2 * eta[1] **
-               3 * kappa[3] - 3. / 2 * eta[1] * eta[3] * kappa[2]) * cb
+        tmp1 = 70. * (end_pose[0] - start_pose[0])
+        tmp2 = (36. * eta[0] + 15. / 2 * eta[2] + 2. / 3 * eta[4]) * ca
+        tmp3 = + (15. / 2 * eta[0] ** 2 * kappa[0] + 2. / 3 * eta[0] ** 3 *
+                  kappa[1] + 2. * eta[0] * eta[2] * kappa[0]) * sa
+        tmp4 = (34. * eta[1] - 13. / 2 * eta[3] + 1. / 2 * eta[5]) * cb
+        tmp5 = - (13. / 2 * eta[1] ** 2 * kappa[2] - 1. / 2 * eta[1] ** 3 *
+                  kappa[3] - 3. / 2 * eta[1] * eta[3] * kappa[2]) * sb
+        self.coeffs[0, 6] = tmp1 - tmp2 + tmp3 - tmp4 + tmp5
+        tmp1 = 70. * (end_pose[1] - start_pose[1])
+        tmp2 = - (36. * eta[0] + 15. / 2 * eta[2] + 2. / 3 * eta[4]) * sa
+        tmp3 = - (15. / 2 * eta[0] ** 2 * kappa[0] + 2. / 3 * eta[0] ** 3 *
+                  kappa[1] + 2. * eta[0] * eta[2] * kappa[0]) * ca
+        tmp4 = - (34. * eta[1] - 13. / 2 * eta[3] + 1. / 2 * eta[5]) * sb
+        tmp5 = + (13. / 2 * eta[1] ** 2 * kappa[2] - 1. / 2 * eta[1] ** 3 *
+                  kappa[3] - 3. / 2 * eta[1] * eta[3] * kappa[2]) * cb
+        self.coeffs[1, 6] = tmp1 + tmp2 + tmp3 + tmp4 + tmp5
         # septic (u^7)
-        self.coeffs[0, 7] = -20. * (end_pose[0] - start_pose[0]) + (10. * eta[0] + 2. * eta[2] + 1. / 6 * eta[4]) * ca \
-            - (2. * eta[0]**2 * kappa[0] + 1. / 6 * eta[0]**3 * kappa[1] + 1. / 2 * eta[0] * eta[2] * kappa[0]) * sa \
-            + (10. * eta[1] - 2. * eta[3] + 1. / 6 * eta[5]) * cb \
-            + (2. * eta[1]**2 * kappa[2] - 1. / 6 * eta[1]**3 *
-               kappa[3] - 1. / 2 * eta[1] * eta[3] * kappa[2]) * sb
-        self.coeffs[1, 7] = -20. * (end_pose[1] - start_pose[1]) + (10. * eta[0] + 2. * eta[2] + 1. / 6 * eta[4]) * sa \
-            + (2. * eta[0]**2 * kappa[0] + 1. / 6 * eta[0]**3 * kappa[1] + 1. / 2 * eta[0] * eta[2] * kappa[0]) * ca \
-            + (10. * eta[1] - 2. * eta[3] + 1. / 6 * eta[5]) * sb \
-            - (2. * eta[1]**2 * kappa[2] - 1. / 6 * eta[1]**3 *
-               kappa[3] - 1. / 2 * eta[1] * eta[3] * kappa[2]) * cb
-
-        self.s_dot = lambda u: max(np.linalg.norm(self.coeffs[:, 1:].dot(np.array(
-            [1, 2. * u, 3. * u**2, 4. * u**3, 5. * u**4, 6. * u**5, 7. * u**6]))), 1e-6)
+        tmp1 = -20. * (end_pose[0] - start_pose[0])
+        tmp2 = (10. * eta[0] + 2. * eta[2] + 1. / 6 * eta[4]) * ca
+        tmp3 = - (2. * eta[0] ** 2 * kappa[0] + 1. / 6 * eta[0] ** 3 * kappa[1]
+                  + 1. / 2 * eta[0] * eta[2] * kappa[0]) * sa
+        tmp4 = (10. * eta[1] - 2. * eta[3] + 1. / 6 * eta[5]) * cb
+        tmp5 = (2. * eta[1] ** 2 * kappa[2] - 1. / 6 * eta[1] ** 3 * kappa[3]
+                - 1. / 2 * eta[1] * eta[3] * kappa[2]) * sb
+        self.coeffs[0, 7] = tmp1 + tmp2 + tmp3 + tmp4 + tmp5
+
+        tmp1 = -20. * (end_pose[1] - start_pose[1])
+        tmp2 = (10. * eta[0] + 2. * eta[2] + 1. / 6 * eta[4]) * sa
+        tmp3 = (2. * eta[0] ** 2 * kappa[0] + 1. / 6 * eta[0] ** 3 * kappa[1]
+                + 1. / 2 * eta[0] * eta[2] * kappa[0]) * ca
+        tmp4 = (10. * eta[1] - 2. * eta[3] + 1. / 6 * eta[5]) * sb
+        tmp5 = - (2. * eta[1] ** 2 * kappa[2] - 1. / 6 * eta[1] ** 3 * kappa[3]
+                  - 1. / 2 * eta[1] * eta[3] * kappa[2]) * cb
+        self.coeffs[1, 7] = tmp1 + tmp2 + tmp3 + tmp4 + tmp5
+        self.s_dot = lambda u: max(np.linalg.norm(
+            self.coeffs[:, 1:].dot(np.array(
+                [1, 2. * u, 3. * u**2, 4. * u**3,
+                 5. * u**4, 6. * u**5, 7. * u**6]))), 1e-6)
         self.f_length = lambda ue: quad(lambda u: self.s_dot(u), 0, ue)
         self.segment_length = self.f_length(1)[0]
 
     def calc_point(self, u):
         """
-        eta3_path_segment::calc_point
+        Eta3PathSegment::calc_point
 
         input
             u - parametric representation of a point along the segment, 0 <= u <= 1
         returns
             (x,y) of point along the segment
         """
-        assert(u >= 0 and u <= 1)
+        assert(0 <= u <= 1)
         return self.coeffs.dot(np.array([1, u, u**2, u**3, u**4, u**5, u**6, u**7]))
 
     def calc_deriv(self, u, order=1):
         """
-        eta3_path_segment::calc_deriv
+        Eta3PathSegment::calc_deriv
 
         input
             u - parametric representation of a point along the segment, 0 <= u <= 1
@@ -181,8 +210,8 @@ def calc_deriv(self, u, order=1):
             (d^nx/du^n,d^ny/du^n) of point along the segment, for 0 < n <= 2
         """
 
-        assert(u >= 0 and u <= 1)
-        assert(order > 0 and order <= 2)
+        assert(0 <= u <= 1)
+        assert(0 < order <= 2)
         if order == 1:
             return self.coeffs[:, 1:].dot(np.array([1, 2. * u, 3. * u**2, 4. * u**3, 5. * u**4, 6. * u**5, 7. * u**6]))
 
@@ -199,10 +228,10 @@ def test1():
         # NOTE: The ordering on kappa is [kappa_A, kappad_A, kappa_B, kappad_B], with kappad_* being the curvature derivative
         kappa = [0, 0, 0, 0]
         eta = [i, i, 0, 0, 0, 0]
-        path_segments.append(eta3_path_segment(
+        path_segments.append(Eta3PathSegment(
             start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
-        path = eta3_path(path_segments)
+        path = Eta3Path(path_segments)
 
         # interpolate at several points along the path
         ui = np.linspace(0, len(path_segments), 1001)
@@ -214,8 +243,9 @@ def test1():
             # plot the path
             plt.plot(pos[0, :], pos[1, :])
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
             plt.pause(1.0)
 
     if show_animation:
@@ -232,10 +262,10 @@ def test2():
         # NOTE: The ordering on kappa is [kappa_A, kappad_A, kappa_B, kappad_B], with kappad_* being the curvature derivative
         kappa = [0, 0, 0, 0]
         eta = [0, 0, (i - 5) * 20, (5 - i) * 20, 0, 0]
-        path_segments.append(eta3_path_segment(
+        path_segments.append(Eta3PathSegment(
             start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
-        path = eta3_path(path_segments)
+        path = Eta3Path(path_segments)
 
         # interpolate at several points along the path
         ui = np.linspace(0, len(path_segments), 1001)
@@ -261,7 +291,7 @@ def test3():
     # NOTE: The ordering on kappa is [kappa_A, kappad_A, kappa_B, kappad_B], with kappad_* being the curvature derivative
     kappa = [0, 0, 0, 0]
     eta = [4.27, 4.27, 0, 0, 0, 0]
-    path_segments.append(eta3_path_segment(
+    path_segments.append(Eta3PathSegment(
         start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
     # segment 2: line segment
@@ -269,7 +299,7 @@ def test3():
     end_pose = [5.5, 1.5, 0]
     kappa = [0, 0, 0, 0]
     eta = [0, 0, 0, 0, 0, 0]
-    path_segments.append(eta3_path_segment(
+    path_segments.append(Eta3PathSegment(
         start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
     # segment 3: cubic spiral
@@ -277,7 +307,7 @@ def test3():
     end_pose = [7.4377, 1.8235, 0.6667]
     kappa = [0, 0, 1, 1]
     eta = [1.88, 1.88, 0, 0, 0, 0]
-    path_segments.append(eta3_path_segment(
+    path_segments.append(Eta3PathSegment(
         start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
     # segment 4: generic twirl arc
@@ -285,7 +315,7 @@ def test3():
     end_pose = [7.8, 4.3, 1.8]
     kappa = [1, 1, 0.5, 0]
     eta = [7, 10, 10, -10, 4, 4]
-    path_segments.append(eta3_path_segment(
+    path_segments.append(Eta3PathSegment(
         start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
     # segment 5: circular arc
@@ -293,11 +323,11 @@ def test3():
     end_pose = [5.4581, 5.8064, 3.3416]
     kappa = [0.5, 0, 0.5, 0]
     eta = [2.98, 2.98, 0, 0, 0, 0]
-    path_segments.append(eta3_path_segment(
+    path_segments.append(Eta3PathSegment(
         start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
     # construct the whole path
-    path = eta3_path(path_segments)
+    path = Eta3Path(path_segments)
 
     # interpolate at several points along the path
     ui = np.linspace(0, len(path_segments), 1001)
diff --git a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
index 22928354a5..01a687679f 100644
--- a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
+++ b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
@@ -1,13 +1,14 @@
 """
 
-\eta^3 polynomials trajectory planner
+eta^3 polynomials trajectory planner
 
 author: Joe Dinius, Ph.D (https://jwdinius.github.io)
         Atsushi Sakai (@Atsushi_twi)
 
 Refs:
 - https://jwdinius.github.io/blog/2018/eta3traj
-- [\eta^3-Splines for the Smooth Path Generation of Wheeled Mobile Robots](https://ieeexplore.ieee.org/document/4339545/)
+- [eta^3-Splines for the Smooth Path Generation of Wheeled Mobile Robots]
+(https://ieeexplore.ieee.org/document/4339545/)
 
 """
 
@@ -19,8 +20,8 @@
 sys.path.append(os.path.relpath("../Eta3SplinePath"))
 
 try:
-    from eta3_spline_path import eta3_path, eta3_path_segment
-except:
+    from eta3_spline_path import Eta3Path, Eta3PathSegment
+except ImportError:
     raise
 
 show_animation = True
@@ -32,7 +33,7 @@ def __init__(self, actual_vel, max_vel):
             actual_vel, max_vel)
 
 
-class eta3_trajectory(eta3_path):
+class eta3_trajectory(Eta3Path):
     """
     eta3_trajectory
 
@@ -41,7 +42,7 @@ class eta3_trajectory(eta3_path):
     """
 
     def __init__(self, segments, max_vel, v0=0.0, a0=0.0, max_accel=2.0, max_jerk=5.0):
-       # ensure that all inputs obey the assumptions of the model
+        # ensure that all inputs obey the assumptions of the model
         assert max_vel > 0 and v0 >= 0 and a0 >= 0 and max_accel > 0 and max_jerk > 0 \
             and a0 <= max_accel and v0 <= max_vel
         super(eta3_trajectory, self).__init__(segments=segments)
@@ -61,19 +62,19 @@ def __init__(self, segments, max_vel, v0=0.0, a0=0.0, max_accel=2.0, max_jerk=5.
         self.prev_seg_id = 0
 
     def velocity_profile(self):
-        '''                   /~~~~~----------------~~~~~\
-                             /                            \
-                            /                              \
-                           /                                \
-                          /                                  \
-        (v=v0, a=a0) ~~~~~                                    \
-                                                               \
-                                                                \~~~~~ (vf=0, af=0)
+        r"""                  /~~~~~----------------\
+                             /                       \
+                            /                         \
+                           /                           \
+                          /                             \
+        (v=v0, a=a0) ~~~~~                               \
+                                                          \
+                                                           \ ~~~~~ (vf=0, af=0)
                      pos.|pos.|neg.|   cruise at    |neg.| neg. |neg.
                      max |max.|max.|     max.       |max.| max. |max.
                      jerk|acc.|jerk|    velocity    |jerk| acc. |jerk
             index     0    1    2      3 (optional)   4     5     6
-        '''
+        """
         # delta_a: accel change from initial position to end of maximal jerk section
         delta_a = self.max_accel - self.a0
         # t_s1: time of traversal of maximal jerk section
@@ -112,7 +113,6 @@ def velocity_profile(self):
         self.seg_lengths = np.zeros((7,))
 
         # Section 0: max jerk up to max acceleration
-        index = 0
         self.times[0] = t_s1
         self.vels[0] = v_s1
         self.seg_lengths[0] = s_s1
@@ -195,7 +195,7 @@ def f(u):
 
         def fprime(u):
             return self.segments[seg_id].s_dot(u)
-        while (ui >= 0 and ui <= 1) and abs(f(ui)) > tol:
+        while (0 <= ui <= 1) and abs(f(ui)) > tol:
             ui -= f(ui) / fprime(ui)
         ui = max(0, min(ui, 1))
         return ui
@@ -301,7 +301,7 @@ def test1(max_vel=0.5):
         # NOTE: The ordering on kappa is [kappa_A, kappad_A, kappa_B, kappad_B], with kappad_* being the curvature derivative
         kappa = [0, 0, 0, 0]
         eta = [i, i, 0, 0, 0, 0]
-        trajectory_segments.append(eta3_path_segment(
+        trajectory_segments.append(Eta3PathSegment(
             start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
         traj = eta3_trajectory(trajectory_segments,
@@ -335,7 +335,7 @@ def test2(max_vel=0.5):
         # NOTE: INTEGRATOR ERROR EXPLODES WHEN eta[:1] IS ZERO!
         # was: eta = [0, 0, (i - 5) * 20, (5 - i) * 20, 0, 0], now is:
         eta = [0.1, 0.1, (i - 5) * 20, (5 - i) * 20, 0, 0]
-        trajectory_segments.append(eta3_path_segment(
+        trajectory_segments.append(Eta3PathSegment(
             start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
         traj = eta3_trajectory(trajectory_segments,
@@ -366,7 +366,7 @@ def test3(max_vel=2.0):
     # NOTE: The ordering on kappa is [kappa_A, kappad_A, kappa_B, kappad_B], with kappad_* being the curvature derivative
     kappa = [0, 0, 0, 0]
     eta = [4.27, 4.27, 0, 0, 0, 0]
-    trajectory_segments.append(eta3_path_segment(
+    trajectory_segments.append(Eta3PathSegment(
         start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
     # segment 2: line segment
@@ -376,7 +376,7 @@ def test3(max_vel=2.0):
     # NOTE: INTEGRATOR ERROR EXPLODES WHEN eta[:1] IS ZERO!
     # was: eta = [0, 0, 0, 0, 0, 0], now is:
     eta = [0.5, 0.5, 0, 0, 0, 0]
-    trajectory_segments.append(eta3_path_segment(
+    trajectory_segments.append(Eta3PathSegment(
         start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
     # segment 3: cubic spiral
@@ -384,7 +384,7 @@ def test3(max_vel=2.0):
     end_pose = [7.4377, 1.8235, 0.6667]
     kappa = [0, 0, 1, 1]
     eta = [1.88, 1.88, 0, 0, 0, 0]
-    trajectory_segments.append(eta3_path_segment(
+    trajectory_segments.append(Eta3PathSegment(
         start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
     # segment 4: generic twirl arc
@@ -392,7 +392,7 @@ def test3(max_vel=2.0):
     end_pose = [7.8, 4.3, 1.8]
     kappa = [1, 1, 0.5, 0]
     eta = [7, 10, 10, -10, 4, 4]
-    trajectory_segments.append(eta3_path_segment(
+    trajectory_segments.append(Eta3PathSegment(
         start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
     # segment 5: circular arc
@@ -400,7 +400,7 @@ def test3(max_vel=2.0):
     end_pose = [5.4581, 5.8064, 3.3416]
     kappa = [0.5, 0, 0.5, 0]
     eta = [2.98, 2.98, 0, 0, 0, 0]
-    trajectory_segments.append(eta3_path_segment(
+    trajectory_segments.append(Eta3PathSegment(
         start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
     # construct the whole path
diff --git a/README.md b/README.md
index 2dcb4bab7e..ec0d1f74ec 100644
--- a/README.md
+++ b/README.md
@@ -103,6 +103,8 @@ See this paper for more details:
 - pandas
 
 - [cvxpy](https://www.cvxpy.org/index.html) 
+  
+- pytest (only for unit tests)
 
 # Documentation
 
diff --git a/requirements.txt b/requirements.txt
index c8aa78aeb7..d1d7a2b037 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,3 +3,4 @@ scipy == 1.5.4
 pandas == 1.2.0
 matplotlib == 3.3.3
 cvxpy == 1.1.7
+pytest == 6.2.1
diff --git a/runtests.sh b/runtests.sh
index d7df3cff04..d9abc370a9 100755
--- a/runtests.sh
+++ b/runtests.sh
@@ -1,6 +1,6 @@
 #!/usr/bin/env bash
 echo "Run test suites! "
-export PYTHONWARNINGS=default  # show warning
-#python -m unittest discover tests 
-#python -Wignore -m unittest discover tests #ignore warning
-coverage run -m unittest discover tests # generate coverage file
+# tests: include unittest based tests
+# -Werror: warning as error
+# --durations=0: show ranking of test durations
+pytest tests -Werror --durations=0
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000000..e69de29bb2

From 2afa10a457769ed0eed92b5bfd47087492c860e1 Mon Sep 17 00:00:00 2001
From: karaage <coldchicken@gmail.com>
Date: Sun, 3 Jan 2021 13:50:54 +0900
Subject: [PATCH 0459/1080] Add plot initialize for google colab visualization
 (#453)

---
 ArmNavigation/n_joint_arm_3d/NLinkArm3d.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py b/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py
index b411cd7114..3f6c5ddd2a 100644
--- a/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py
+++ b/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py
@@ -43,8 +43,6 @@ def basic_jacobian(trans_prev, ee_pos):
 
 class NLinkArm:
     def __init__(self, dh_params_list):
-        self.fig = plt.figure()
-        self.ax = Axes3D(self.fig)
         self.link_list = []
         for i in range(len(dh_params_list)):
             self.link_list.append(Link(dh_params_list[i]))
@@ -64,6 +62,9 @@ def forward_kinematics(self, plot=False):
         alpha, beta, gamma = self.euler_angle()
 
         if plot:
+            self.fig = plt.figure()
+            self.ax = Axes3D(self.fig)
+
             x_list = []
             y_list = []
             z_list = []

From e30696a25f99c59f8facda851ce1e72f4ec9a3ff Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 5 Jan 2021 06:44:33 +0900
Subject: [PATCH 0460/1080] Bump scipy from 1.5.4 to 1.6.0 (#454)

Bumps [scipy](https://github.com/scipy/scipy) from 1.5.4 to 1.6.0.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.5.4...v1.6.0)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index d1d7a2b037..e7af23e6e2 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.19.4
-scipy == 1.5.4
+scipy == 1.6.0
 pandas == 1.2.0
 matplotlib == 3.3.3
 cvxpy == 1.1.7

From 5efb8fd0afd49dd2a53bb0a6eb448ff79f683f76 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 6 Jan 2021 21:34:45 +0900
Subject: [PATCH 0461/1080] Remove workaround codes for CI (#455)

* remove workaround codes

* remove workaround codes

* remove workaround codes
---
 .circleci/config.yml           | 4 +---
 .github/workflows/Linux_CI.yml | 2 --
 .github/workflows/MacOS_CI.yml | 2 --
 appveyor.yml                   | 3 +--
 4 files changed, 2 insertions(+), 9 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 0ec05a3a20..4cb628ecd8 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -8,13 +8,11 @@ jobs:
     docker:
       - image: circleci/python:3.9
     steps:
-      - run: sudo apt-get update && sudo apt-get install -y cmake # TODO: remove after osqp wheels released
       - checkout
       - python/load-cache
-      - run: pip install numpy # TODO: remove after osqp wheels released
       - python/install-deps
       - python/save-cache
-      - run: pip install sphinx sphinx-autobuild sphinx-rtd-theme
+      - run: pip install sphinx sphinx-autobuild sphinx-rtd-theme pytest
       - run:
           command: cd docs;make html
           name: doc_build
diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index 61be099152..d4ec886207 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -21,10 +21,8 @@ jobs:
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
-      # TODO: remove numpy installation after fix cvxpy install issue
       run: |
         python -m pip install --upgrade pip
-        pip install numpy
         python -m pip install -r requirements.txt
     - name: install coverage
       run: pip install coverage
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index 84ef75157f..8c2fe84940 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -23,10 +23,8 @@ jobs:
           python-version: ${{ matrix.python-version }}
 
       - name: Install dependencies
-        # TODO: remove numpy installation after fix cvxpy install issue
         run: |
           python -m pip install --upgrade pip
-          pip install numpy
           pip install -r requirements.txt
       - name: install coverage
         run: pip install coverage
diff --git a/appveyor.yml b/appveyor.yml
index 9fd7430223..2a925433f4 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -33,7 +33,6 @@ install:
   - SET PATH=%PYTHON%;%PYTHON%\Scripts;%PYTHON%\Library\bin;%PATH%
   - SET PATH=%PATH%;C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin
   - "python -m pip install --upgrade pip"
-  - "pip install numpy" # TODO: remove this line after osqp wheel released
   - "python -m pip install -r requirements.txt"
 
   # Check that we have the expected version and architecture for Python
@@ -43,4 +42,4 @@ install:
 build: off
 
 test_script:
-  - "python -Wignore -m unittest discover tests"
+  - "pytest tests -Werror --durations=0"

From d5ce0351b62f6574d02730e1156d49ad6a34d9ba Mon Sep 17 00:00:00 2001
From: Shamil <shamilgemuev@mail.ru>
Date: Sun, 10 Jan 2021 05:16:22 +0100
Subject: [PATCH 0462/1080] fix ICP negative dError. Issue #446 (#447)

---
 .../iterative_closest_point.py                | 20 +++++++++++--------
 1 file changed, 12 insertions(+), 8 deletions(-)

diff --git a/SLAM/iterative_closest_point/iterative_closest_point.py b/SLAM/iterative_closest_point/iterative_closest_point.py
index 0b2802603c..db252247a8 100644
--- a/SLAM/iterative_closest_point/iterative_closest_point.py
+++ b/SLAM/iterative_closest_point/iterative_closest_point.py
@@ -27,8 +27,8 @@ def icp_matching(previous_points, current_points):
     """
     H = None  # homogeneous transformation matrix
 
-    dError = 1000.0
-    preError = 1000.0
+    dError = np.inf
+    preError = np.inf
     count = 0
 
     while dError >= EPS:
@@ -37,8 +37,9 @@ def icp_matching(previous_points, current_points):
         if show_animation:  # pragma: no cover
             plt.cla()
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(previous_points[0, :], previous_points[1, :], ".r")
             plt.plot(current_points[0, :], current_points[1, :], ".b")
             plt.plot(0.0, 0.0, "xr")
@@ -47,15 +48,18 @@ def icp_matching(previous_points, current_points):
 
         indexes, error = nearest_neighbor_association(previous_points, current_points)
         Rt, Tt = svd_motion_estimation(previous_points[:, indexes], current_points)
-
         # update current points
         current_points = (Rt @ current_points) + Tt[:, np.newaxis]
 
-        H = update_homogeneous_matrix(H, Rt, Tt)
+        dError = preError - error
+        print("Residual:", error)
+
+        if dError < 0:  # prevent matrix H changing, exit loop
+            print("Not Converge...", preError, dError, count)
+            break
 
-        dError = abs(preError - error)
         preError = error
-        print("Residual:", error)
+        H = update_homogeneous_matrix(H, Rt, Tt)
 
         if dError <= EPS:
             print("Converge", error, dError, count)

From 44142564cc8c6c27ebe7e69bb4a750c74b95180f Mon Sep 17 00:00:00 2001
From: Rafael Rojas <rafaelrojasmiliani@gmail.com>
Date: Sun, 10 Jan 2021 05:26:40 +0100
Subject: [PATCH 0463/1080] Sobol sampler implemented (#413)

---
 PathPlanning/RRT/rrt_with_sobol_sampler.py | 270 ++++++
 PathPlanning/RRT/sobol/__init__.py         |   1 +
 PathPlanning/RRT/sobol/sobol.py            | 911 +++++++++++++++++++++
 tests/test_rrt_with_sobol_sampler.py       |  30 +
 4 files changed, 1212 insertions(+)
 create mode 100644 PathPlanning/RRT/rrt_with_sobol_sampler.py
 create mode 100644 PathPlanning/RRT/sobol/__init__.py
 create mode 100644 PathPlanning/RRT/sobol/sobol.py
 create mode 100644 tests/test_rrt_with_sobol_sampler.py

diff --git a/PathPlanning/RRT/rrt_with_sobol_sampler.py b/PathPlanning/RRT/rrt_with_sobol_sampler.py
new file mode 100644
index 0000000000..b88e51c680
--- /dev/null
+++ b/PathPlanning/RRT/rrt_with_sobol_sampler.py
@@ -0,0 +1,270 @@
+"""
+
+Path planning Sample Code with Randomized Rapidly-Exploring Random
+Trees with sobol low discrepancy sampler(RRTSobol).
+Sobol wiki https://en.wikipedia.org/wiki/Sobol_sequence
+
+The goal of low discrepancy samplers is to generate a sequence of points that
+optimizes a criterion called dispersion.  Intuitively, the idea is to place
+samples to cover the exploration space in a way that makes the largest
+uncovered area be as small as possible.  This generalizes of the idea of grid
+resolution.  For a grid, the resolution may be selected by defining the step
+size for each axis.  As the step size is decreased, the resolution increases.
+If a grid-based motion planning algorithm can increase the resolution
+arbitrarily, it becomes resolution complete.  Dispersion can be considered as a
+powerful generalization of the notion of resolution.
+
+Taken from
+LaValle, Steven M. Planning algorithms. Cambridge university press, 2006.
+
+
+authors:
+    First implementation AtsushiSakai(@Atsushi_twi)
+    Sobol sampler Rafael A.
+Rojas (rafaelrojasmiliani@gmail.com)
+
+
+"""
+
+import math
+import random
+from sobol import sobol_quasirand
+import sys
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+show_animation = True
+
+
+class RRTSobol:
+    """
+    Class for RRTSobol planning
+    """
+
+    class Node:
+        """
+        RRTSobol Node
+        """
+
+        def __init__(self, x, y):
+            self.x = x
+            self.y = y
+            self.path_x = []
+            self.path_y = []
+            self.parent = None
+
+    def __init__(self,
+                 start,
+                 goal,
+                 obstacle_list,
+                 rand_area,
+                 expand_dis=3.0,
+                 path_resolution=0.5,
+                 goal_sample_rate=5,
+                 max_iter=500):
+        """
+        Setting Parameter
+
+        start:Start Position [x,y]
+        goal:Goal Position [x,y]
+        obstacle_list:obstacle Positions [[x,y,size],...]
+        randArea:Random Sampling Area [min,max]
+
+        """
+        self.start = self.Node(start[0], start[1])
+        self.end = self.Node(goal[0], goal[1])
+        self.min_rand = rand_area[0]
+        self.max_rand = rand_area[1]
+        self.expand_dis = expand_dis
+        self.path_resolution = path_resolution
+        self.goal_sample_rate = goal_sample_rate
+        self.max_iter = max_iter
+        self.obstacle_list = obstacle_list
+        self.node_list = []
+        self.sobol_inter_ = 0
+
+    def planning(self, animation=True):
+        """
+        rrt path planning
+
+        animation: flag for animation on or off
+        """
+
+        self.node_list = [self.start]
+        for i in range(self.max_iter):
+            rnd_node = self.get_random_node()
+            nearest_ind = self.get_nearest_node_index(self.node_list, rnd_node)
+            nearest_node = self.node_list[nearest_ind]
+
+            new_node = self.steer(nearest_node, rnd_node, self.expand_dis)
+
+            if self.check_collision(new_node, self.obstacle_list):
+                self.node_list.append(new_node)
+
+            if animation and i % 5 == 0:
+                self.draw_graph(rnd_node)
+
+            if self.calc_dist_to_goal(self.node_list[-1].x,
+                                      self.node_list[-1].y) <= self.expand_dis:
+                final_node = self.steer(self.node_list[-1], self.end,
+                                        self.expand_dis)
+                if self.check_collision(final_node, self.obstacle_list):
+                    return self.generate_final_course(len(self.node_list) - 1)
+
+            if animation and i % 5:
+                self.draw_graph(rnd_node)
+
+        return None  # cannot find path
+
+    def steer(self, from_node, to_node, extend_length=float("inf")):
+
+        new_node = self.Node(from_node.x, from_node.y)
+        d, theta = self.calc_distance_and_angle(new_node, to_node)
+
+        new_node.path_x = [new_node.x]
+        new_node.path_y = [new_node.y]
+
+        if extend_length > d:
+            extend_length = d
+
+        n_expand = math.floor(extend_length / self.path_resolution)
+
+        for _ in range(n_expand):
+            new_node.x += self.path_resolution * math.cos(theta)
+            new_node.y += self.path_resolution * math.sin(theta)
+            new_node.path_x.append(new_node.x)
+            new_node.path_y.append(new_node.y)
+
+        d, _ = self.calc_distance_and_angle(new_node, to_node)
+        if d <= self.path_resolution:
+            new_node.path_x.append(to_node.x)
+            new_node.path_y.append(to_node.y)
+            new_node.x = to_node.x
+            new_node.y = to_node.y
+
+        new_node.parent = from_node
+
+        return new_node
+
+    def generate_final_course(self, goal_ind):
+        path = [[self.end.x, self.end.y]]
+        node = self.node_list[goal_ind]
+        while node.parent is not None:
+            path.append([node.x, node.y])
+            node = node.parent
+        path.append([node.x, node.y])
+
+        return path
+
+    def calc_dist_to_goal(self, x, y):
+        dx = x - self.end.x
+        dy = y - self.end.y
+        return math.hypot(dx, dy)
+
+    def get_random_node(self):
+        if random.randint(0, 100) > self.goal_sample_rate:
+            rand_coordinates, n = sobol_quasirand(2, self.sobol_inter_)
+
+            rand_coordinates = self.min_rand + \
+                rand_coordinates*(self.max_rand - self.min_rand)
+            self.sobol_inter_ = n
+            rnd = self.Node(*rand_coordinates)
+
+        else:  # goal point sampling
+            rnd = self.Node(self.end.x, self.end.y)
+        return rnd
+
+    def draw_graph(self, rnd=None):
+        plt.clf()
+        # for stopping simulation with the esc key.
+        plt.gcf().canvas.mpl_connect(
+            'key_release_event',
+            lambda event: [sys.exit(0) if event.key == 'escape' else None])
+        if rnd is not None:
+            plt.plot(rnd.x, rnd.y, "^k")
+        for node in self.node_list:
+            if node.parent:
+                plt.plot(node.path_x, node.path_y, "-g")
+
+        for (ox, oy, size) in self.obstacle_list:
+            self.plot_circle(ox, oy, size)
+
+        plt.plot(self.start.x, self.start.y, "xr")
+        plt.plot(self.end.x, self.end.y, "xr")
+        plt.axis("equal")
+        plt.axis([-2, 15, -2, 15])
+        plt.grid(True)
+        plt.pause(0.01)
+
+    @staticmethod
+    def plot_circle(x, y, size, color="-b"):  # pragma: no cover
+        deg = list(range(0, 360, 5))
+        deg.append(0)
+        xl = [x + size * math.cos(np.deg2rad(d)) for d in deg]
+        yl = [y + size * math.sin(np.deg2rad(d)) for d in deg]
+        plt.plot(xl, yl, color)
+
+    @staticmethod
+    def get_nearest_node_index(node_list, rnd_node):
+        dlist = [(node.x - rnd_node.x)**2 + (node.y - rnd_node.y)**2
+                 for node in node_list]
+        minind = dlist.index(min(dlist))
+
+        return minind
+
+    @staticmethod
+    def check_collision(node, obstacle_list):
+
+        if node is None:
+            return False
+
+        for (ox, oy, size) in obstacle_list:
+            dx_list = [ox - x for x in node.path_x]
+            dy_list = [oy - y for y in node.path_y]
+            d_list = [dx * dx + dy * dy for (dx, dy) in zip(dx_list, dy_list)]
+
+            if min(d_list) <= size**2:
+                return False  # collision
+
+        return True  # safe
+
+    @staticmethod
+    def calc_distance_and_angle(from_node, to_node):
+        dx = to_node.x - from_node.x
+        dy = to_node.y - from_node.y
+        d = math.hypot(dx, dy)
+        theta = math.atan2(dy, dx)
+        return d, theta
+
+
+def main(gx=6.0, gy=10.0):
+    print("start " + __file__)
+
+    # ====Search Path with RRTSobol====
+    obstacle_list = [(5, 5, 1), (3, 6, 2), (3, 8, 2), (3, 10, 2), (7, 5, 2),
+                     (9, 5, 2), (8, 10, 1)]  # [x, y, radius]
+    # Set Initial parameters
+    rrt = RRTSobol(
+        start=[0, 0],
+        goal=[gx, gy],
+        rand_area=[-2, 15],
+        obstacle_list=obstacle_list)
+    path = rrt.planning(animation=show_animation)
+
+    if path is None:
+        print("Cannot find path")
+    else:
+        print("found path!!")
+
+        # Draw final path
+        if show_animation:
+            rrt.draw_graph()
+            plt.plot([x for (x, y) in path], [y for (x, y) in path], '-r')
+            plt.grid(True)
+            plt.pause(0.01)  # Need for Mac
+            plt.show()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/PathPlanning/RRT/sobol/__init__.py b/PathPlanning/RRT/sobol/__init__.py
new file mode 100644
index 0000000000..c95ac8983b
--- /dev/null
+++ b/PathPlanning/RRT/sobol/__init__.py
@@ -0,0 +1 @@
+from .sobol import i4_sobol as sobol_quasirand
diff --git a/PathPlanning/RRT/sobol/sobol.py b/PathPlanning/RRT/sobol/sobol.py
new file mode 100644
index 0000000000..428ba58a98
--- /dev/null
+++ b/PathPlanning/RRT/sobol/sobol.py
@@ -0,0 +1,911 @@
+"""
+  Licensing:
+    This code is distributed under the MIT license.
+
+  Authors:
+    Original FORTRAN77 version of i4_sobol by Bennett Fox.
+    MATLAB version by John Burkardt.
+    PYTHON version by Corrado Chisari
+
+    Original Python version of is_prime by Corrado Chisari
+
+    Original MATLAB versions of other functions by John Burkardt.
+    PYTHON versions by Corrado Chisari
+
+    Original code is available at
+    http://people.sc.fsu.edu/~jburkardt/py_src/sobol/sobol.html
+
+    Note: the i4 prefix means that the function takes a numeric argument or
+          returns a number which is interpreted inside the function as a 4
+          byte integer
+    Note: the r4 prefix means that the function takes a numeric argument or
+          returns a number which is interpreted inside the function as a 4
+          byte float
+"""
+import math
+import sys
+import numpy as np
+
+atmost = None
+dim_max = None
+dim_num_save = None
+initialized = None
+lastq = None
+log_max = None
+maxcol = None
+poly = None
+recipd = None
+seed_save = None
+v = None
+
+
+def i4_bit_hi1(n):
+    """
+     I4_BIT_HI1 returns the position of the high 1 bit base 2 in an I4.
+
+      Discussion:
+
+        An I4 is an integer ( kind = 4 ) value.
+
+      Example:
+
+           N    Binary    Hi 1
+        ----    --------  ----
+           0           0     0
+           1           1     1
+           2          10     2
+           3          11     2
+           4         100     3
+           5         101     3
+           6         110     3
+           7         111     3
+           8        1000     4
+           9        1001     4
+          10        1010     4
+          11        1011     4
+          12        1100     4
+          13        1101     4
+          14        1110     4
+          15        1111     4
+          16       10000     5
+          17       10001     5
+        1023  1111111111    10
+        1024 10000000000    11
+        1025 10000000001    11
+
+      Licensing:
+
+        This code is distributed under the GNU LGPL license.
+
+      Modified:
+
+        26 October 2014
+
+      Author:
+
+        John Burkardt
+
+      Parameters:
+
+        Input, integer N, the integer to be measured.
+        N should be nonnegative.  If N is nonpositive, the function
+        will always be 0.
+
+        Output, integer BIT, the position of the highest bit.
+
+    """
+    i = n
+    bit = 0
+
+    while True:
+
+        if i <= 0:
+            break
+
+        bit = bit + 1
+        i = i // 2
+
+    return bit
+
+
+def i4_bit_lo0(n):
+    """
+     I4_BIT_LO0 returns the position of the low 0 bit base 2 in an I4.
+
+      Discussion:
+
+        An I4 is an integer ( kind = 4 ) value.
+
+      Example:
+
+           N    Binary    Lo 0
+        ----    --------  ----
+           0           0     1
+           1           1     2
+           2          10     1
+           3          11     3
+           4         100     1
+           5         101     2
+           6         110     1
+           7         111     4
+           8        1000     1
+           9        1001     2
+          10        1010     1
+          11        1011     3
+          12        1100     1
+          13        1101     2
+          14        1110     1
+          15        1111     5
+          16       10000     1
+          17       10001     2
+        1023  1111111111    11
+        1024 10000000000     1
+        1025 10000000001     2
+
+      Licensing:
+
+        This code is distributed under the GNU LGPL license.
+
+      Modified:
+
+        08 February 2018
+
+      Author:
+
+        John Burkardt
+
+      Parameters:
+
+        Input, integer N, the integer to be measured.
+        N should be nonnegative.
+
+        Output, integer BIT, the position of the low 1 bit.
+
+    """
+    bit = 0
+    i = n
+
+    while True:
+
+        bit = bit + 1
+        i2 = i // 2
+
+        if i == 2 * i2:
+            break
+
+        i = i2
+
+    return bit
+
+
+def i4_sobol_generate(m, n, skip):
+    """
+
+
+     I4_SOBOL_GENERATE generates a Sobol dataset.
+
+      Licensing:
+
+        This code is distributed under the MIT license.
+
+      Modified:
+
+        22 February 2011
+
+      Author:
+
+        Original MATLAB version by John Burkardt.
+        PYTHON version by Corrado Chisari
+
+      Parameters:
+
+        Input, integer M, the spatial dimension.
+
+        Input, integer N, the number of points to generate.
+
+        Input, integer SKIP, the number of initial points to skip.
+
+        Output, real R(M,N), the points.
+
+    """
+    r = np.zeros((m, n))
+    for j in range(1, n + 1):
+        seed = skip + j - 2
+        [r[0:m, j - 1], seed] = i4_sobol(m, seed)
+    return r
+
+
+def i4_sobol(dim_num, seed):
+    """
+
+
+     I4_SOBOL generates a new quasirandom Sobol vector with each call.
+
+      Discussion:
+
+        The routine adapts the ideas of Antonov and Saleev.
+
+      Licensing:
+
+        This code is distributed under the MIT license.
+
+      Modified:
+
+        22 February 2011
+
+      Author:
+
+        Original FORTRAN77 version by Bennett Fox.
+        MATLAB version by John Burkardt.
+        PYTHON version by Corrado Chisari
+
+      Reference:
+
+        Antonov, Saleev,
+        USSR Computational Mathematics and Mathematical Physics,
+        olume 19, 19, pages 252 - 256.
+
+        Paul Bratley, Bennett Fox,
+        Algorithm 659:
+        Implementing Sobol's Quasirandom Sequence Generator,
+        ACM Transactions on Mathematical Software,
+        Volume 14, Number 1, pages 88-100, 1988.
+
+        Bennett Fox,
+        Algorithm 647:
+        Implementation and Relative Efficiency of Quasirandom
+        Sequence Generators,
+        ACM Transactions on Mathematical Software,
+        Volume 12, Number 4, pages 362-376, 1986.
+
+        Ilya Sobol,
+        USSR Computational Mathematics and Mathematical Physics,
+        Volume 16, pages 236-242, 1977.
+
+        Ilya Sobol, Levitan,
+        The Production of Points Uniformly Distributed in a Multidimensional
+        Cube (in Russian),
+        Preprint IPM Akad. Nauk SSSR,
+        Number 40, Moscow 1976.
+
+      Parameters:
+
+        Input, integer DIM_NUM, the number of spatial dimensions.
+        DIM_NUM must satisfy 1 <= DIM_NUM <= 40.
+
+        Input/output, integer SEED, the "seed" for the sequence.
+        This is essentially the index in the sequence of the quasirandom
+        value to be generated.    On output, SEED has been set to the
+        appropriate next value, usually simply SEED+1.
+        If SEED is less than 0 on input, it is treated as though it were 0.
+        An input value of 0 requests the first (0-th) element of the sequence.
+
+        Output, real QUASI(DIM_NUM), the next quasirandom vector.
+
+    """
+
+    global atmost
+    global dim_max
+    global dim_num_save
+    global initialized
+    global lastq
+    global log_max
+    global maxcol
+    global poly
+    global recipd
+    global seed_save
+    global v
+
+    if not initialized or dim_num != dim_num_save:
+        initialized = 1
+        dim_max = 40
+        dim_num_save = -1
+        log_max = 30
+        seed_save = -1
+        #
+        #    Initialize (part of) V.
+        #
+        v = np.zeros((dim_max, log_max))
+        v[0:40, 0] = np.transpose([
+            1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+            1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
+        ])
+
+        v[2:40, 1] = np.transpose([
+            1, 3, 1, 3, 1, 3, 3, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 3, 1, 3, 1, 3,
+            3, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 3, 1, 3, 1, 3
+        ])
+
+        v[3:40, 2] = np.transpose([
+            7, 5, 1, 3, 3, 7, 5, 5, 7, 7, 1, 3, 3, 7, 5, 1, 1, 5, 3, 3, 1, 7,
+            5, 1, 3, 3, 7, 5, 1, 1, 5, 7, 7, 5, 1, 3, 3
+        ])
+
+        v[5:40, 3] = np.transpose([
+            1, 7, 9, 13, 11, 1, 3, 7, 9, 5, 13, 13, 11, 3, 15, 5, 3, 15, 7, 9,
+            13, 9, 1, 11, 7, 5, 15, 1, 15, 11, 5, 3, 1, 7, 9
+        ])
+
+        v[7:40, 4] = np.transpose([
+            9, 3, 27, 15, 29, 21, 23, 19, 11, 25, 7, 13, 17, 1, 25, 29, 3, 31,
+            11, 5, 23, 27, 19, 21, 5, 1, 17, 13, 7, 15, 9, 31, 9
+        ])
+
+        v[13:40, 5] = np.transpose([
+            37, 33, 7, 5, 11, 39, 63, 27, 17, 15, 23, 29, 3, 21, 13, 31, 25, 9,
+            49, 33, 19, 29, 11, 19, 27, 15, 25
+        ])
+
+        v[19:40, 6] = np.transpose([
+            13, 33, 115, 41, 79, 17, 29, 119, 75, 73, 105, 7, 59, 65, 21, 3,
+            113, 61, 89, 45, 107
+        ])
+
+        v[37:40, 7] = np.transpose([7, 23, 39])
+        #
+        #    Set POLY.
+        #
+        poly = [
+            1, 3, 7, 11, 13, 19, 25, 37, 59, 47, 61, 55, 41, 67, 97, 91, 109,
+            103, 115, 131, 193, 137, 145, 143, 241, 157, 185, 167, 229, 171,
+            213, 191, 253, 203, 211, 239, 247, 285, 369, 299
+        ]
+
+        atmost = 2**log_max - 1
+        #
+        #    Find the number of bits in ATMOST.
+        #
+        maxcol = i4_bit_hi1(atmost)
+        #
+        #    Initialize row 1 of V.
+        #
+        v[0, 0:maxcol] = 1
+
+        # Things to do only if the dimension changed.
+
+    if dim_num != dim_num_save:
+        #
+        #    Check parameters.
+        #
+        if (dim_num < 1 or dim_max < dim_num):
+            print('I4_SOBOL - Fatal error!')
+            print('    The spatial dimension DIM_NUM should satisfy:')
+            print('        1 <= DIM_NUM <= %d' % dim_max)
+            print('    But this input value is DIM_NUM = %d' % dim_num)
+            return None
+
+        dim_num_save = dim_num
+        #
+        #    Initialize the remaining rows of V.
+        #
+        for i in range(2, dim_num + 1):
+            #
+            #    The bits of the integer POLY(I) gives the form of polynomial
+            #    I.
+            #
+            #    Find the degree of polynomial I from binary encoding.
+            #
+            j = poly[i - 1]
+            m = 0
+            while True:
+                j = math.floor(j / 2.)
+                if (j <= 0):
+                    break
+                m = m + 1
+            #
+            #    Expand this bit pattern to separate components of the logical
+            #    array INCLUD.
+            #
+            j = poly[i - 1]
+            includ = np.zeros(m)
+            for k in range(m, 0, -1):
+                j2 = math.floor(j / 2.)
+                includ[k - 1] = (j != 2 * j2)
+                j = j2
+            #
+            #    Calculate the remaining elements of row I as explained
+            #    in Bratley and Fox, section 2.
+            #
+            for j in range(m + 1, maxcol + 1):
+                newv = v[i - 1, j - m - 1]
+                l_var = 1
+                for k in range(1, m + 1):
+                    l_var = 2 * l_var
+                    if (includ[k - 1]):
+                        newv = np.bitwise_xor(
+                            int(newv), int(l_var * v[i - 1, j - k - 1]))
+                v[i - 1, j - 1] = newv
+#
+#    Multiply columns of V by appropriate power of 2.
+#
+        l_var = 1
+        for j in range(maxcol - 1, 0, -1):
+            l_var = 2 * l_var
+            v[0:dim_num, j - 1] = v[0:dim_num, j - 1] * l_var
+#
+#    RECIPD is 1/(common denominator of the elements in V).
+#
+        recipd = 1.0 / (2 * l_var)
+        lastq = np.zeros(dim_num)
+
+    seed = int(math.floor(seed))
+
+    if (seed < 0):
+        seed = 0
+
+    if (seed == 0):
+        l_var = 1
+        lastq = np.zeros(dim_num)
+
+    elif (seed == seed_save + 1):
+        #
+        #    Find the position of the right-hand zero in SEED.
+        #
+        l_var = i4_bit_lo0(seed)
+
+    elif (seed <= seed_save):
+
+        seed_save = 0
+        lastq = np.zeros(dim_num)
+
+        for seed_temp in range(int(seed_save), int(seed)):
+            l_var = i4_bit_lo0(seed_temp)
+            for i in range(1, dim_num + 1):
+                lastq[i - 1] = np.bitwise_xor(
+                    int(lastq[i - 1]), int(v[i - 1, l_var - 1]))
+
+        l_var = i4_bit_lo0(seed)
+
+    elif (seed_save + 1 < seed):
+
+        for seed_temp in range(int(seed_save + 1), int(seed)):
+            l_var = i4_bit_lo0(seed_temp)
+            for i in range(1, dim_num + 1):
+                lastq[i - 1] = np.bitwise_xor(
+                    int(lastq[i - 1]), int(v[i - 1, l_var - 1]))
+
+        l_var = i4_bit_lo0(seed)
+#
+#    Check that the user is not calling too many times!
+#
+    if maxcol < l_var:
+        print('I4_SOBOL - Fatal error!')
+        print('    Too many calls!')
+        print('    MAXCOL = %d\n' % maxcol)
+        print('    L =            %d\n' % l_var)
+        return None
+
+
+#
+#    Calculate the new components of QUASI.
+#
+    quasi = np.zeros(dim_num)
+    for i in range(1, dim_num + 1):
+        quasi[i - 1] = lastq[i - 1] * recipd
+        lastq[i - 1] = np.bitwise_xor(
+            int(lastq[i - 1]), int(v[i - 1, l_var - 1]))
+
+    seed_save = seed
+    seed = seed + 1
+
+    return [quasi, seed]
+
+
+def i4_uniform_ab(a, b, seed):
+    """
+
+
+     I4_UNIFORM_AB returns a scaled pseudorandom I4.
+
+      Discussion:
+
+        The pseudorandom number will be scaled to be uniformly distributed
+        between A and B.
+
+      Licensing:
+
+        This code is distributed under the GNU LGPL license.
+
+      Modified:
+
+        05 April 2013
+
+      Author:
+
+        John Burkardt
+
+      Reference:
+
+        Paul Bratley, Bennett Fox, Linus Schrage,
+        A Guide to Simulation,
+        Second Edition,
+        Springer, 1987,
+        ISBN: 0387964673,
+        LC: QA76.9.C65.B73.
+
+        Bennett Fox,
+        Algorithm 647:
+        Implementation and Relative Efficiency of Quasirandom
+        Sequence Generators,
+        ACM Transactions on Mathematical Software,
+        Volume 12, Number 4, December 1986, pages 362-376.
+
+        Pierre L'Ecuyer,
+        Random Number Generation,
+        in Handbook of Simulation,
+        edited by Jerry Banks,
+        Wiley, 1998,
+        ISBN: 0471134031,
+        LC: T57.62.H37.
+
+        Peter Lewis, Allen Goodman, James Miller,
+        A Pseudo-Random Number Generator for the System/360,
+        IBM Systems Journal,
+        Volume 8, Number 2, 1969, pages 136-143.
+
+      Parameters:
+
+        Input, integer A, B, the minimum and maximum acceptable values.
+
+        Input, integer SEED, a seed for the random number generator.
+
+        Output, integer C, the randomly chosen integer.
+
+        Output, integer SEED, the updated seed.
+
+    """
+
+    i4_huge = 2147483647
+
+    seed = int(seed)
+
+    seed = (seed % i4_huge)
+
+    if seed < 0:
+        seed = seed + i4_huge
+
+    if seed == 0:
+        print('')
+        print('I4_UNIFORM_AB - Fatal error!')
+        print('  Input SEED = 0!')
+        sys.exit('I4_UNIFORM_AB - Fatal error!')
+
+    k = (seed // 127773)
+
+    seed = 167 * (seed - k * 127773) - k * 2836
+
+    if seed < 0:
+        seed = seed + i4_huge
+
+    r = seed * 4.656612875E-10
+    #
+    #  Scale R to lie between A-0.5 and B+0.5.
+    #
+    a = round(a)
+    b = round(b)
+
+    r = (1.0 - r) * (min(a, b) - 0.5) \
+        + r * (max(a, b) + 0.5)
+    #
+    #  Use rounding to convert R to an integer between A and B.
+    #
+    value = round(r)
+
+    value = max(value, min(a, b))
+    value = min(value, max(a, b))
+    value = int(value)
+
+    return value, seed
+
+
+def prime_ge(n):
+    """
+
+
+     PRIME_GE returns the smallest prime greater than or equal to N.
+
+      Example:
+
+          N    PRIME_GE
+
+        -10     2
+          1     2
+          2     2
+          3     3
+          4     5
+          5     5
+          6     7
+          7     7
+          8    11
+          9    11
+         10    11
+
+      Licensing:
+
+        This code is distributed under the MIT license.
+
+      Modified:
+
+        22 February 2011
+
+      Author:
+
+        Original MATLAB version by John Burkardt.
+        PYTHON version by Corrado Chisari
+
+      Parameters:
+
+        Input, integer N, the number to be bounded.
+
+        Output, integer P, the smallest prime number that is greater
+        than or equal to N.
+
+    """
+    p = max(math.ceil(n), 2)
+    while not isprime(p):
+        p = p + 1
+
+    return p
+
+
+def isprime(n):
+    """
+
+
+     IS_PRIME returns True if N is a prime number, False otherwise
+
+      Licensing:
+
+        This code is distributed under the MIT license.
+
+      Modified:
+
+        22 February 2011
+
+      Author:
+
+        Corrado Chisari
+
+      Parameters:
+
+        Input, integer N, the number to be checked.
+
+        Output, boolean value, True or False
+
+    """
+    if n != int(n) or n < 1:
+        return False
+    p = 2
+    while p < n:
+        if n % p == 0:
+            return False
+        p += 1
+
+    return True
+
+
+def r4_uniform_01(seed):
+    """
+
+
+     R4_UNIFORM_01 returns a unit pseudorandom R4.
+
+      Discussion:
+
+        This routine implements the recursion
+
+          seed = 167 * seed mod ( 2^31 - 1 )
+          r = seed / ( 2^31 - 1 )
+
+        The integer arithmetic never requires more than 32 bits,
+        including a sign bit.
+
+        If the initial seed is 12345, then the first three computations are
+
+          Input     Output      R4_UNIFORM_01
+          SEED      SEED
+
+             12345   207482415  0.096616
+         207482415  1790989824  0.833995
+        1790989824  2035175616  0.947702
+
+      Licensing:
+
+        This code is distributed under the GNU LGPL license.
+
+      Modified:
+
+        04 April 2013
+
+      Author:
+
+        John Burkardt
+
+      Reference:
+
+        Paul Bratley, Bennett Fox, Linus Schrage,
+        A Guide to Simulation,
+        Second Edition,
+        Springer, 1987,
+        ISBN: 0387964673,
+        LC: QA76.9.C65.B73.
+
+        Bennett Fox,
+        Algorithm 647:
+        Implementation and Relative Efficiency of Quasirandom
+        Sequence Generators,
+        ACM Transactions on Mathematical Software,
+        Volume 12, Number 4, December 1986, pages 362-376.
+
+        Pierre L'Ecuyer,
+        Random Number Generation,
+        in Handbook of Simulation,
+        edited by Jerry Banks,
+        Wiley, 1998,
+        ISBN: 0471134031,
+        LC: T57.62.H37.
+
+        Peter Lewis, Allen Goodman, James Miller,
+        A Pseudo-Random Number Generator for the System/360,
+        IBM Systems Journal,
+        Volume 8, Number 2, 1969, pages 136-143.
+
+      Parameters:
+
+        Input, integer SEED, the integer "seed" used to generate
+        the output random number.  SEED should not be 0.
+
+        Output, real R, a random value between 0 and 1.
+
+        Output, integer SEED, the updated seed.  This would
+        normally be used as the input seed on the next call.
+
+    """
+
+    i4_huge = 2147483647
+
+    if (seed == 0):
+        print('')
+        print('R4_UNIFORM_01 - Fatal error!')
+        print('  Input SEED = 0!')
+        sys.exit('R4_UNIFORM_01 - Fatal error!')
+
+    seed = (seed % i4_huge)
+
+    if seed < 0:
+        seed = seed + i4_huge
+
+    k = (seed // 127773)
+
+    seed = 167 * (seed - k * 127773) - k * 2836
+
+    if seed < 0:
+        seed = seed + i4_huge
+
+    r = seed * 4.656612875E-10
+
+    return r, seed
+
+
+def r8mat_write(filename, m, n, a):
+    """
+
+
+     R8MAT_WRITE writes an R8MAT to a file.
+
+      Licensing:
+
+        This code is distributed under the GNU LGPL license.
+
+      Modified:
+
+        12 October 2014
+
+      Author:
+
+        John Burkardt
+
+      Parameters:
+
+        Input, string FILENAME, the name of the output file.
+
+        Input, integer M, the number of rows in A.
+
+        Input, integer N, the number of columns in A.
+
+        Input, real A(M,N), the matrix.
+    """
+
+    with open(filename, 'w') as output:
+        for i in range(0, m):
+            for j in range(0, n):
+                s = '  %g' % (a[i, j])
+                output.write(s)
+            output.write('\n')
+
+
+def tau_sobol(dim_num):
+    """
+
+
+     TAU_SOBOL defines favorable starting seeds for Sobol sequences.
+
+      Discussion:
+
+        For spatial dimensions 1 through 13, this routine returns
+        a "favorable" value TAU by which an appropriate starting point
+        in the Sobol sequence can be determined.
+
+        These starting points have the form N = 2**K, where
+        for integration problems, it is desirable that
+                TAU + DIM_NUM - 1 <= K
+        while for optimization problems, it is desirable that
+                TAU < K.
+
+      Licensing:
+
+        This code is distributed under the MIT license.
+
+      Modified:
+
+        22 February 2011
+
+      Author:
+
+        Original FORTRAN77 version by Bennett Fox.
+        MATLAB version by John Burkardt.
+        PYTHON version by Corrado Chisari
+
+      Reference:
+
+        IA Antonov, VM Saleev,
+        USSR Computational Mathematics and Mathematical Physics,
+        Volume 19, 19, pages 252 - 256.
+
+        Paul Bratley, Bennett Fox,
+        Algorithm 659:
+        Implementing Sobol's Quasirandom Sequence Generator,
+        ACM Transactions on Mathematical Software,
+        Volume 14, Number 1, pages 88-100, 1988.
+
+        Bennett Fox,
+        Algorithm 647:
+        Implementation and Relative Efficiency of Quasirandom
+        Sequence Generators,
+        ACM Transactions on Mathematical Software,
+        Volume 12, Number 4, pages 362-376, 1986.
+
+        Stephen Joe, Frances Kuo
+        Remark on Algorithm 659:
+        Implementing Sobol's Quasirandom Sequence Generator,
+        ACM Transactions on Mathematical Software,
+        Volume 29, Number 1, pages 49-57, March 2003.
+
+        Ilya Sobol,
+        USSR Computational Mathematics and Mathematical Physics,
+        Volume 16, pages 236-242, 1977.
+
+        Ilya Sobol, YL Levitan,
+        The Production of Points Uniformly Distributed in a Multidimensional
+        Cube (in Russian),
+        Preprint IPM Akad. Nauk SSSR,
+        Number 40, Moscow 1976.
+
+      Parameters:
+
+                Input, integer DIM_NUM, the spatial dimension.    Only values
+                of 1 through 13 will result in useful responses.
+
+                Output, integer TAU, the value TAU.
+
+    """
+    dim_max = 13
+
+    tau_table = [0, 0, 1, 3, 5, 8, 11, 15, 19, 23, 27, 31, 35]
+
+    if 1 <= dim_num <= dim_max:
+        tau = tau_table[dim_num]
+    else:
+        tau = -1
+
+    return tau
diff --git a/tests/test_rrt_with_sobol_sampler.py b/tests/test_rrt_with_sobol_sampler.py
new file mode 100644
index 0000000000..43f25e85bc
--- /dev/null
+++ b/tests/test_rrt_with_sobol_sampler.py
@@ -0,0 +1,30 @@
+import os
+import sys
+import random
+from unittest import TestCase
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
+sys.path.append(os.path.dirname(
+    os.path.abspath(__file__)) + "/../PathPlanning/RRT")
+
+try:
+    from PathPlanning.RRT import rrt_with_sobol_sampler as m
+except ImportError:
+    raise
+
+
+print(__file__)
+
+random.seed(12345)
+
+
+class Test(TestCase):
+
+    def test1(self):
+        m.show_animation = False
+        m.main(gx=1.0, gy=1.0)
+
+
+if __name__ == '__main__':  # pragma: no cover
+    test = Test()
+    test.test1()

From 353314742c494570a8cd54e3fb56f33bae83b32b Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 10 Jan 2021 13:30:09 +0900
Subject: [PATCH 0464/1080] Create SECURITY.md

---
 SECURITY.md | 13 +++++++++++++
 1 file changed, 13 insertions(+)
 create mode 100644 SECURITY.md

diff --git a/SECURITY.md b/SECURITY.md
new file mode 100644
index 0000000000..5047a6fc3d
--- /dev/null
+++ b/SECURITY.md
@@ -0,0 +1,13 @@
+# Security Policy
+
+## Supported Versions
+
+In this project, we are only support latest code.
+
+| Version | Supported          |
+| ------- | ------------------ |
+| latest   | :white_check_mark: |
+
+## Reporting a Vulnerability
+
+If you find any sequrity related problem, let us know by creating an issue about it.

From 8861977e8cf4d8de58d7be78a88d6089d1b54825 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 12 Jan 2021 07:23:04 +0900
Subject: [PATCH 0465/1080] Bump numpy from 1.19.4 to 1.19.5 (#457)

Bumps [numpy](https://github.com/numpy/numpy) from 1.19.4 to 1.19.5.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/master/doc/HOWTO_RELEASE.rst.txt)
- [Commits](https://github.com/numpy/numpy/compare/v1.19.4...v1.19.5)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index e7af23e6e2..7a4a17fcca 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.19.4
+numpy == 1.19.5
 scipy == 1.6.0
 pandas == 1.2.0
 matplotlib == 3.3.3

From 67d7d5c6105f6fd436435eef71651059f4ca9d54 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 12 Jan 2021 22:13:46 +0900
Subject: [PATCH 0466/1080] Test code clean up (#456)

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up

* Test code clean up
---
 .../__init__.py                               |   0
 .../drone_3d_trajectory_following/__init__.py |   4 +
 ArmNavigation/n_joint_arm_3d/__init__.py      |   3 +
 .../n_joint_arm_to_point_control.py           |   2 +-
 .../inverted_pendulum_mpc_control.py          |   0
 .../__init__.py                               |   0
 LICENSE                                       |   2 +-
 Localization/__init__.py                      |   0
 Mapping/__init__.py                           |   0
 Mapping/grid_map_lib/__init__.py              |   0
 Mapping/rectangle_fitting/__init_.py          |   4 +
 .../rectangle_fitting/rectangle_fitting.py    |   3 +-
 PathPlanning/ClosedLoopRRTStar/__init__.py    |   8 +
 .../closed_loop_rrt_star_car.py               |  24 +-
 PathPlanning/GridBasedSweepCPP/__init__.py    |   7 +
 .../grid_based_sweep_coverage_path_planner.py |   9 +-
 PathPlanning/HybridAStar/__init__.py          |   4 +
 PathPlanning/VoronoiRoadMap/__init__.py       |   4 +
 PathTracking/__init__.py                      |   0
 __init__.py                                   |   0
 runtests.sh                                   |   6 +-
 tests/conftest.py                             |  13 +
 tests/test_LQR_planner.py                     |  16 +-
 tests/test_a_star.py                          |  23 +-
 tests/test_a_star_searching_two_side.py       |  29 +--
 tests/test_a_star_variants.py                 |  94 ++++---
 tests/test_batch_informed_rrt_star.py         |  27 +-
 tests/test_bezier_path.py                     |  20 +-
 tests/test_bipedal_planner.py                 |  32 +--
 tests/test_breadth_first_search.py            |  29 +--
 tests/test_bug.py                             |  20 +-
 tests/test_cgmres_nmpc.py                     |  18 +-
 tests/test_circle_fitting.py                  |  13 +-
 tests/test_closed_loop_rrt_star_car.py        |  31 +--
 tests/test_cubature_kalman_filter.py          |  17 +-
 tests/test_depth_first_search.py              |  29 +--
 tests/test_dijkstra.py                        |  29 +--
 tests/test_drone_3d_trajectory_following.py   |  20 +-
 tests/test_dubins_path_planning.py            |  89 +++----
 tests/test_dynamic_window_approach.py         | 104 ++++----
 tests/test_ekf_slam.py                        |  13 +-
 tests/test_eta3_spline_path.py                |  13 +-
 tests/test_extended_kalman_filter.py          |  13 +-
 tests/test_fast_slam1.py                      |  28 +--
 tests/test_fast_slam2.py                      |  28 +--
 tests/test_flow_field.py                      |  20 +-
 tests/test_frenet_optimal_trajectory.py       |  30 +--
 tests/test_gaussian_grid_map.py               |  13 +-
 tests/test_graph_based_slam.py                |  28 +--
 ..._grid_based_sweep_coverage_path_planner.py | 236 +++++++++---------
 tests/test_grid_map_lib.py                    |  43 ++--
 tests/test_histogram_filter.py                |  27 +-
 tests/test_hybrid_a_star.py                   |  25 +-
 tests/test_informed_rrt_star.py               |  27 +-
 tests/test_inverted_pendulum_mpc_control.py   |  17 +-
 tests/test_iterative_closest_point.py         |  13 +-
 tests/test_kmeans_clustering.py               |  13 +-
 tests/test_lqr_rrt_star.py                    |  27 +-
 tests/test_lqr_speed_steer_control.py         |  16 +-
 tests/test_lqr_steer_control.py               |  16 +-
 ...odel_predictive_speed_and_steer_control.py |  20 +-
 tests/test_move_to_pose.py                    |  13 +-
 tests/test_n_joint_arm_to_point_control.py    |  20 +-
 tests/test_particle_filter.py                 |  13 +-
 tests/test_potential_field_planning.py        |  13 +-
 tests/test_probabilistic_road_map.py          |  12 +-
 tests/test_pure_pursuit.py                    |  12 +-
 tests/test_quintic_polynomials_planner.py     |  13 +-
 tests/test_raycasting_grid_map.py             |  13 +-
 tests/test_rear_wheel_feedback.py             |  16 +-
 tests/test_rectangle_fitting.py               |  25 +-
 tests/test_reeds_shepp_path_planning.py       |  60 ++---
 tests/test_rocket_powered_landing.py          |  13 +-
 tests/test_rrt.py                             |  34 +--
 tests/test_rrt_dubins.py                      |  30 +--
 tests/test_rrt_star.py                        |  46 ++--
 tests/test_rrt_star_dubins.py                 |  29 +--
 tests/test_rrt_star_reeds_shepp.py            |  31 +--
 tests/test_rrt_star_seven_joint_arm.py        |  28 +--
 tests/test_rrt_with_sobol_sampler.py          |  30 +--
 ...ral_spanning_tree_coverage_path_planner.py | 102 ++++----
 tests/test_stanley_controller.py              |  16 +-
 tests/test_state_lattice_planner.py           |  34 +--
 tests/test_two_joint_arm_to_point_control.py  |  16 +-
 tests/test_unscented_kalman_filter.py         |  13 +-
 tests/test_visibility_road_map_planner.py     |  18 +-
 tests/test_voronoi_road_map_planner.py        |  18 +-
 tests/test_wavefront_coverage_path_planner.py |  93 ++++---
 88 files changed, 850 insertions(+), 1310 deletions(-)
 rename ArmNavigation/n_joint_arm_3d/__init__py.py => AerialNavigation/__init__.py (100%)
 create mode 100644 AerialNavigation/drone_3d_trajectory_following/__init__.py
 create mode 100644 ArmNavigation/n_joint_arm_3d/__init__.py
 rename InvertedPendulumCart/{ => InvertedPendulumMPCControl}/inverted_pendulum_mpc_control.py (100%)
 rename tests/.gitkeep => InvertedPendulumCart/__init__.py (100%)
 create mode 100644 Localization/__init__.py
 create mode 100644 Mapping/__init__.py
 create mode 100644 Mapping/grid_map_lib/__init__.py
 create mode 100644 Mapping/rectangle_fitting/__init_.py
 create mode 100644 PathPlanning/GridBasedSweepCPP/__init__.py
 create mode 100644 PathPlanning/VoronoiRoadMap/__init__.py
 create mode 100644 PathTracking/__init__.py
 create mode 100644 __init__.py
 create mode 100644 tests/conftest.py

diff --git a/ArmNavigation/n_joint_arm_3d/__init__py.py b/AerialNavigation/__init__.py
similarity index 100%
rename from ArmNavigation/n_joint_arm_3d/__init__py.py
rename to AerialNavigation/__init__.py
diff --git a/AerialNavigation/drone_3d_trajectory_following/__init__.py b/AerialNavigation/drone_3d_trajectory_following/__init__.py
new file mode 100644
index 0000000000..087dab646e
--- /dev/null
+++ b/AerialNavigation/drone_3d_trajectory_following/__init__.py
@@ -0,0 +1,4 @@
+import os
+import sys
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
diff --git a/ArmNavigation/n_joint_arm_3d/__init__.py b/ArmNavigation/n_joint_arm_3d/__init__.py
new file mode 100644
index 0000000000..ba5dac65f6
--- /dev/null
+++ b/ArmNavigation/n_joint_arm_3d/__init__.py
@@ -0,0 +1,3 @@
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
diff --git a/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py b/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py
index 3972474b8d..b7834ee98b 100644
--- a/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py
+++ b/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py
@@ -6,7 +6,7 @@
 """
 import numpy as np
 
-from NLinkArm import NLinkArm
+from ArmNavigation.n_joint_arm_to_point_control.NLinkArm import NLinkArm
 
 # Simulation parameters
 Kp = 2
diff --git a/InvertedPendulumCart/inverted_pendulum_mpc_control.py b/InvertedPendulumCart/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py
similarity index 100%
rename from InvertedPendulumCart/inverted_pendulum_mpc_control.py
rename to InvertedPendulumCart/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py
diff --git a/tests/.gitkeep b/InvertedPendulumCart/__init__.py
similarity index 100%
rename from tests/.gitkeep
rename to InvertedPendulumCart/__init__.py
diff --git a/LICENSE b/LICENSE
index 9d60d1828f..2867142fc7 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 The MIT License (MIT)
 
-Copyright (c) 2016 Atsushi Sakai
+Copyright (c) 2016 - 2021 Atsushi Sakai
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/Localization/__init__.py b/Localization/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/Mapping/__init__.py b/Mapping/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/Mapping/grid_map_lib/__init__.py b/Mapping/grid_map_lib/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/Mapping/rectangle_fitting/__init_.py b/Mapping/rectangle_fitting/__init_.py
new file mode 100644
index 0000000000..087dab646e
--- /dev/null
+++ b/Mapping/rectangle_fitting/__init_.py
@@ -0,0 +1,4 @@
+import os
+import sys
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py
index a60a6fbfd1..078916f3b8 100644
--- a/Mapping/rectangle_fitting/rectangle_fitting.py
+++ b/Mapping/rectangle_fitting/rectangle_fitting.py
@@ -18,7 +18,8 @@
 from enum import Enum
 from scipy.spatial.transform import Rotation as Rot
 
-from simulator import VehicleSimulator, LidarSimulator
+from Mapping.rectangle_fitting.simulator \
+    import VehicleSimulator, LidarSimulator
 
 show_animation = True
 
diff --git a/PathPlanning/ClosedLoopRRTStar/__init__.py b/PathPlanning/ClosedLoopRRTStar/__init__.py
index e69de29bb2..0e49cd14d3 100644
--- a/PathPlanning/ClosedLoopRRTStar/__init__.py
+++ b/PathPlanning/ClosedLoopRRTStar/__init__.py
@@ -0,0 +1,8 @@
+import os
+import sys
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+sys.path.append(os.path.dirname(
+    os.path.abspath(__file__)) + "/../ReedsSheppPath/")
+sys.path.append(os.path.dirname(
+    os.path.abspath(__file__)) + "/../RRTStarReedsShepp/")
diff --git a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
index aea0080112..740fc5590a 100644
--- a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
+++ b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
@@ -6,25 +6,13 @@
 
 """
 
-import os
-import sys
-
 import matplotlib.pyplot as plt
 import numpy as np
+import reeds_shepp_path_planning
+from rrt_star_reeds_shepp import RRTStarReedsShepp
 
 import pure_pursuit
-
-sys.path.append(os.path.dirname(
-    os.path.abspath(__file__)) + "/../ReedsSheppPath/")
-sys.path.append(os.path.dirname(
-    os.path.abspath(__file__)) + "/../RRTStarReedsShepp/")
-
-try:
-    import reeds_shepp_path_planning
-    import unicycle_model
-    from rrt_star_reeds_shepp import RRTStarReedsShepp
-except ImportError:
-    raise
+import unicycle_model
 
 show_animation = True
 
@@ -77,7 +65,8 @@ def search_best_feasible_path(self, path_indexs):
         for ind in path_indexs:
             path = self.generate_final_course(ind)
 
-            flag, x, y, yaw, v, t, a, d = self.check_tracking_path_is_feasible(path)
+            flag, x, y, yaw, v, t, a, d = self.check_tracking_path_is_feasible(
+                path)
 
             if flag and best_time >= t[-1]:
                 print("feasible path is found")
@@ -186,7 +175,8 @@ def main(gx=6.0, gy=7.0, gyaw=np.deg2rad(90.0), max_iter=100):
                                              obstacle_list,
                                              [-2.0, 20.0],
                                              max_iter=max_iter)
-    flag, x, y, yaw, v, t, a, d = closed_loop_rrt_star.planning(animation=show_animation)
+    flag, x, y, yaw, v, t, a, d = closed_loop_rrt_star.planning(
+        animation=show_animation)
 
     if not flag:
         print("cannot find feasible path")
diff --git a/PathPlanning/GridBasedSweepCPP/__init__.py b/PathPlanning/GridBasedSweepCPP/__init__.py
new file mode 100644
index 0000000000..f1c41ce433
--- /dev/null
+++ b/PathPlanning/GridBasedSweepCPP/__init__.py
@@ -0,0 +1,7 @@
+import os
+import sys
+
+GRID_MAP_LIB = os.path.dirname(os.path.abspath(__file__)) + \
+               "/../../Mapping/"
+
+sys.path.append(GRID_MAP_LIB)
diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
index 09df21c4c8..ca318ad596 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -5,19 +5,12 @@
 """
 
 import math
-import os
-import sys
 from enum import IntEnum
 
 import matplotlib.pyplot as plt
 import numpy as np
 from scipy.spatial.transform import Rotation as Rot
-
-sys.path.append(os.path.relpath("../../Mapping/grid_map_lib/"))
-try:
-    from grid_map_lib import GridMap
-except ImportError:
-    raise
+from Mapping.grid_map_lib.grid_map_lib import GridMap
 
 do_animation = True
 
diff --git a/PathPlanning/HybridAStar/__init__.py b/PathPlanning/HybridAStar/__init__.py
index e69de29bb2..087dab646e 100644
--- a/PathPlanning/HybridAStar/__init__.py
+++ b/PathPlanning/HybridAStar/__init__.py
@@ -0,0 +1,4 @@
+import os
+import sys
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
diff --git a/PathPlanning/VoronoiRoadMap/__init__.py b/PathPlanning/VoronoiRoadMap/__init__.py
new file mode 100644
index 0000000000..087dab646e
--- /dev/null
+++ b/PathPlanning/VoronoiRoadMap/__init__.py
@@ -0,0 +1,4 @@
+import os
+import sys
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
diff --git a/PathTracking/__init__.py b/PathTracking/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/__init__.py b/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/runtests.sh b/runtests.sh
index d9abc370a9..12d1b80453 100755
--- a/runtests.sh
+++ b/runtests.sh
@@ -1,6 +1,8 @@
 #!/usr/bin/env bash
 echo "Run test suites! "
-# tests: include unittest based tests
+
+# === pytest based test runner ===
 # -Werror: warning as error
 # --durations=0: show ranking of test durations
-pytest tests -Werror --durations=0
+# -l (--showlocals); show local variables when test failed
+pytest tests -l -Werror --durations=0
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000000..3485fe8150
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,13 @@
+"""Path hack to make tests work."""
+import sys
+import os
+import pytest
+
+TEST_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(TEST_DIR)  # to import this file from test code.
+ROOT_DIR = os.path.dirname(TEST_DIR)
+sys.path.append(ROOT_DIR)
+
+
+def run_this_test(file):
+    pytest.main([os.path.abspath(file)])
diff --git a/tests/test_LQR_planner.py b/tests/test_LQR_planner.py
index 2bcf828c38..b535b99baf 100644
--- a/tests/test_LQR_planner.py
+++ b/tests/test_LQR_planner.py
@@ -1,15 +1,11 @@
-import sys
-from unittest import TestCase
-
-sys.path.append("./PathPlanning/LQRPlanner")
-
+import conftest  # Add root path to sys.path
 from PathPlanning.LQRPlanner import LQRplanner as m
 
-print(__file__)
 
+def test_1():
+    m.SHOW_ANIMATION = False
+    m.main()
 
-class Test(TestCase):
 
-    def test1(self):
-        m.SHOW_ANIMATION = False
-        m.main()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_a_star.py b/tests/test_a_star.py
index 61276e6a31..82f76401ad 100644
--- a/tests/test_a_star.py
+++ b/tests/test_a_star.py
@@ -1,20 +1,11 @@
-from unittest import TestCase
-import sys
-import os
-sys.path.append(os.path.dirname(__file__) + "/../")
-try:
-    from PathPlanning.AStar import a_star as m
-except:
-    raise
+import conftest
+from PathPlanning.AStar import a_star as m
 
 
-class Test(TestCase):
+def test_1():
+    m.show_animation = False
+    m.main()
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
 
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_a_star_searching_two_side.py b/tests/test_a_star_searching_two_side.py
index 013e9253b8..ad90ebc509 100644
--- a/tests/test_a_star_searching_two_side.py
+++ b/tests/test_a_star_searching_two_side.py
@@ -1,27 +1,16 @@
-from unittest import TestCase
-import os
-import sys
+import conftest
+from PathPlanning.AStar import a_star_searching_from_two_side as m
 
-sys.path.append(os.path.dirname(__file__) + '/../')
 
-try:
-    from PathPlanning.AStar import a_star_searching_from_two_side as m
-except ImportError:
-    raise
+def test1():
+    m.show_animation = False
+    m.main(800)
 
 
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.main(800)
-
-    def test2(self):
-        m.show_animation = False
-        m.main(5000)  # increase obstacle number, block path
+def test2():
+    m.show_animation = False
+    m.main(5000)  # increase obstacle number, block path
 
 
 if __name__ == '__main__':
-    test = Test()
-    test.test1()
-    test.test2()
+    conftest.run_this_test(__file__)
diff --git a/tests/test_a_star_variants.py b/tests/test_a_star_variants.py
index 13009c1365..b9ef791402 100644
--- a/tests/test_a_star_variants.py
+++ b/tests/test_a_star_variants.py
@@ -1,50 +1,44 @@
-import PathPlanning.AStar.a_star_variants as astar
-from unittest import TestCase
-import sys
-import os
-sys.path.append(os.path.dirname(__file__) + "/../")
-
-
-class Test(TestCase):
-
-    def test(self):
-        # A* with beam search
-        astar.show_animation = False
-
-        astar.use_beam_search = True
-        astar.main()
-        self.reset_all()
-
-        # A* with iterative deepening
-        astar.use_iterative_deepening = True
-        astar.main()
-        self.reset_all()
-
-        # A* with dynamic weighting
-        astar.use_dynamic_weighting = True
-        astar.main()
-        self.reset_all()
-
-        # theta*
-        astar.use_theta_star = True
-        astar.main()
-        self.reset_all()
-
-        # A* with jump point
-        astar.use_jump_point = True
-        astar.main()
-        self.reset_all()
-
-    @staticmethod
-    def reset_all():
-        astar.show_animation = False
-        astar.use_beam_search = False
-        astar.use_iterative_deepening = False
-        astar.use_dynamic_weighting = False
-        astar.use_theta_star = False
-        astar.use_jump_point = False
-
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test()
+import PathPlanning.AStar.a_star_variants as a_star
+import conftest
+
+
+def test_1():
+    # A* with beam search
+    a_star.show_animation = False
+
+    a_star.use_beam_search = True
+    a_star.main()
+    reset_all()
+
+    # A* with iterative deepening
+    a_star.use_iterative_deepening = True
+    a_star.main()
+    reset_all()
+
+    # A* with dynamic weighting
+    a_star.use_dynamic_weighting = True
+    a_star.main()
+    reset_all()
+
+    # theta*
+    a_star.use_theta_star = True
+    a_star.main()
+    reset_all()
+
+    # A* with jump point
+    a_star.use_jump_point = True
+    a_star.main()
+    reset_all()
+
+
+def reset_all():
+    a_star.show_animation = False
+    a_star.use_beam_search = False
+    a_star.use_iterative_deepening = False
+    a_star.use_dynamic_weighting = False
+    a_star.use_theta_star = False
+    a_star.use_jump_point = False
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_batch_informed_rrt_star.py b/tests/test_batch_informed_rrt_star.py
index bac8941460..3ad78bdb23 100644
--- a/tests/test_batch_informed_rrt_star.py
+++ b/tests/test_batch_informed_rrt_star.py
@@ -1,25 +1,14 @@
-from unittest import TestCase
-import sys
-import os
 import random
-sys.path.append(os.path.dirname(__file__) + "/../")
-try:
-    from PathPlanning.BatchInformedRRTStar import batch_informed_rrtstar as m
-except ImportError:
-    raise
 
-print(__file__)
+import conftest
+from PathPlanning.BatchInformedRRTStar import batch_informed_rrtstar as m
 
-random.seed(12345)
 
+def test_1():
+    m.show_animation = False
+    random.seed(12345)
+    m.main(maxIter=10)
 
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main(maxIter=10)
-
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_bezier_path.py b/tests/test_bezier_path.py
index 94ec8f5c31..67a5d520de 100644
--- a/tests/test_bezier_path.py
+++ b/tests/test_bezier_path.py
@@ -1,16 +1,16 @@
-from unittest import TestCase
+import conftest
+from PathPlanning.BezierPath import bezier_path as m
 
-import sys
-sys.path.append("./PathPlanning/BezierPath/")
 
-from PathPlanning.BezierPath import bezier_path as m
+def test_1():
+    m.show_animation = False
+    m.main()
 
-print(__file__)
 
+def test_2():
+    m.show_animation = False
+    m.main2()
 
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
-        m.main2()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_bipedal_planner.py b/tests/test_bipedal_planner.py
index 66049c68ec..818957bcdc 100644
--- a/tests/test_bipedal_planner.py
+++ b/tests/test_bipedal_planner.py
@@ -1,24 +1,18 @@
-from unittest import TestCase
+import conftest
+from Bipedal.bipedal_planner import bipedal_planner as m
 
-import sys
-sys.path.append("./Bipedal/bipedal_planner/")
-try:
-    from Bipedal.bipedal_planner import bipedal_planner as m
-except Exception:
-    raise
 
-print(__file__)
+def test_1():
+    bipedal_planner = m.BipedalPlanner()
 
+    footsteps = [[0.0, 0.2, 0.0],
+                 [0.3, 0.2, 0.0],
+                 [0.3, 0.2, 0.2],
+                 [0.3, 0.2, 0.2],
+                 [0.0, 0.2, 0.2]]
+    bipedal_planner.set_ref_footsteps(footsteps)
+    bipedal_planner.walk(plot=False)
 
-class Test(TestCase):
 
-    def test(self):
-        bipedal_planner = m.BipedalPlanner()
-
-        footsteps = [[0.0, 0.2, 0.0],
-                     [0.3, 0.2, 0.0],
-                     [0.3, 0.2, 0.2],
-                     [0.3, 0.2, 0.2],
-                     [0.0, 0.2, 0.2]]
-        bipedal_planner.set_ref_footsteps(footsteps)
-        bipedal_planner.walk(plot=False)
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_breadth_first_search.py b/tests/test_breadth_first_search.py
index 7dc36b2860..14c1ac6988 100644
--- a/tests/test_breadth_first_search.py
+++ b/tests/test_breadth_first_search.py
@@ -1,26 +1,11 @@
-from unittest import TestCase
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../PathPlanning/BreadthFirstSearch/")
+import conftest
+from PathPlanning.BreadthFirstSearch import breadth_first_search as m
 
 
-try:
-    import breadth_first_search as m
-except ImportError:
-    raise
+def test_1():
+    m.show_animation = False
+    m.main()
 
 
-print(__file__)
-
-
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.main()
-
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_bug.py b/tests/test_bug.py
index 31d8b2c297..f94794a45e 100644
--- a/tests/test_bug.py
+++ b/tests/test_bug.py
@@ -1,17 +1,11 @@
-from PathPlanning.BugPlanning import bug as b
-from unittest import TestCase
-import sys
-import os
-sys.path.append(os.path.dirname(__file__) + "/../")
+import conftest
+from PathPlanning.BugPlanning import bug as m
 
 
-class Test(TestCase):
+def test_1():
+    m.show_animation = False
+    m.main(bug_0=True, bug_1=True, bug_2=True)
 
-    def test(self):
-        b.show_animation = False
-        b.main(bug_0=True, bug_1=True, bug_2=True)
 
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_cgmres_nmpc.py b/tests/test_cgmres_nmpc.py
index 8420ca1a5a..db3ada5065 100644
--- a/tests/test_cgmres_nmpc.py
+++ b/tests/test_cgmres_nmpc.py
@@ -1,15 +1,11 @@
-from unittest import TestCase
+import conftest
+from PathTracking.cgmres_nmpc import cgmres_nmpc as m
 
-import sys
-if 'cvxpy' in sys.modules:  # pragma: no cover
-    sys.path.append("./PathTracking/cgmres_nmpc/")
 
-    from PathTracking.cgmres_nmpc import cgmres_nmpc as m
+def test1():
+    m.show_animation = False
+    m.main()
 
-    print(__file__)
 
-    class Test(TestCase):
-
-        def test1(self):
-            m.show_animation = False
-            m.main()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_circle_fitting.py b/tests/test_circle_fitting.py
index d184548227..b3888d7cc3 100644
--- a/tests/test_circle_fitting.py
+++ b/tests/test_circle_fitting.py
@@ -1,12 +1,11 @@
-from unittest import TestCase
-
+import conftest
 from Mapping.circle_fitting import circle_fitting as m
 
-print(__file__)
 
+def test_1():
+    m.show_animation = False
+    m.main()
 
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_closed_loop_rrt_star_car.py b/tests/test_closed_loop_rrt_star_car.py
index 060389d6a2..c33e413e92 100644
--- a/tests/test_closed_loop_rrt_star_car.py
+++ b/tests/test_closed_loop_rrt_star_car.py
@@ -1,28 +1,13 @@
-import os
-import sys
+import conftest
+from PathPlanning.ClosedLoopRRTStar import closed_loop_rrt_star_car as m
 import random
-from unittest import TestCase
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../PathPlanning/ClosedLoopRRTStar/")
-try:
-    from PathPlanning.ClosedLoopRRTStar import closed_loop_rrt_star_car as m
-except ImportError:
-    raise
 
-random.seed(12345)
+def test_1():
+    random.seed(12345)
+    m.show_animation = False
+    m.main(gx=1.0, gy=0.0, gyaw=0.0, max_iter=5)
 
-print(__file__)
 
-
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.main(gx=1.0, gy=0.0, gyaw=0.0, max_iter=5)
-
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_cubature_kalman_filter.py b/tests/test_cubature_kalman_filter.py
index e6ac71a8ee..00f9d8bc5f 100644
--- a/tests/test_cubature_kalman_filter.py
+++ b/tests/test_cubature_kalman_filter.py
@@ -1,14 +1,13 @@
-from unittest import TestCase
-
+import conftest
 from Localization.cubature_kalman_filter import cubature_kalman_filter as m
 
-print(__file__)
 
+def test1():
+    m.show_final = False
+    m.show_animation = False
+    m.show_ellipse = False
+    m.main()
 
-class Test(TestCase):
 
-    def test1(self):
-        m.show_final = False
-        m.show_animation = False
-        m.show_ellipse = False
-        m.main()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_depth_first_search.py b/tests/test_depth_first_search.py
index 15aa7a0a53..8dd009278f 100644
--- a/tests/test_depth_first_search.py
+++ b/tests/test_depth_first_search.py
@@ -1,26 +1,11 @@
-from unittest import TestCase
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../PathPlanning/DepthFirstSearch/")
+import conftest
+from PathPlanning.DepthFirstSearch import depth_first_search as m
 
 
-try:
-    import depth_first_search as m
-except ImportError:
-    raise
+def test_1():
+    m.show_animation = False
+    m.main()
 
 
-print(__file__)
-
-
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.main()
-
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_dijkstra.py b/tests/test_dijkstra.py
index 7650eee802..40404153d8 100644
--- a/tests/test_dijkstra.py
+++ b/tests/test_dijkstra.py
@@ -1,26 +1,11 @@
-from unittest import TestCase
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../PathPlanning/Dijkstra/")
+import conftest
+from PathPlanning.Dijkstra import dijkstra as m
 
 
-try:
-    import dijkstra as m
-except ImportError:
-    raise
+def test_1():
+    m.show_animation = False
+    m.main()
 
 
-print(__file__)
-
-
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.main()
-
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_drone_3d_trajectory_following.py b/tests/test_drone_3d_trajectory_following.py
index dfadcdaee4..e3fd417024 100644
--- a/tests/test_drone_3d_trajectory_following.py
+++ b/tests/test_drone_3d_trajectory_following.py
@@ -1,16 +1,12 @@
-import os
-import sys
-from unittest import TestCase
+import conftest
+from AerialNavigation.drone_3d_trajectory_following \
+    import drone_3d_trajectory_following as m
 
-sys.path.append(os.path.dirname(__file__) + "/../AerialNavigation/drone_3d_trajectory_following/")
 
-import drone_3d_trajectory_following as m
+def test1():
+    m.show_animation = False
+    m.main()
 
-print(__file__)
 
-
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.main()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_dubins_path_planning.py b/tests/test_dubins_path_planning.py
index 1be70e390e..99fbd29100 100644
--- a/tests/test_dubins_path_planning.py
+++ b/tests/test_dubins_path_planning.py
@@ -1,60 +1,65 @@
-from unittest import TestCase
-
+import conftest
 import numpy as np
+from PathPlanning.DubinsPath import dubins_path_planning
+
 np.random.seed(12345)
 
-from PathPlanning.DubinsPath import dubins_path_planning
 
+def check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw,
+                         end_x, end_y, end_yaw):
+    assert (abs(px[0] - start_x) <= 0.01)
+    assert (abs(py[0] - start_y) <= 0.01)
+    assert (abs(pyaw[0] - start_yaw) <= 0.01)
+    assert (abs(px[-1] - end_x) <= 0.01)
+    assert (abs(py[-1] - end_y) <= 0.01)
+    assert (abs(pyaw[-1] - end_yaw) <= 0.01)
 
-class Test(TestCase):
 
-    @staticmethod
-    def check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, end_y, end_yaw):
-        assert (abs(px[0] - start_x) <= 0.01)
-        assert (abs(py[0] - start_y) <= 0.01)
-        assert (abs(pyaw[0] - start_yaw) <= 0.01)
-        print("x", px[-1], end_x)
-        assert (abs(px[-1] - end_x) <= 0.01)
-        print("y", py[-1], end_y)
-        assert (abs(py[-1] - end_y) <= 0.01)
-        print("yaw", pyaw[-1], end_yaw)
-        assert (abs(pyaw[-1] - end_yaw) <= 0.01)
+def test_1():
+    start_x = 1.0  # [m]
+    start_y = 1.0  # [m]
+    start_yaw = np.deg2rad(45.0)  # [rad]
 
-    def test1(self):
-        start_x = 1.0  # [m]
-        start_y = 1.0  # [m]
-        start_yaw = np.deg2rad(45.0)  # [rad]
+    end_x = -3.0  # [m]
+    end_y = -3.0  # [m]
+    end_yaw = np.deg2rad(-45.0)  # [rad]
 
-        end_x = -3.0  # [m]
-        end_y = -3.0  # [m]
-        end_yaw = np.deg2rad(-45.0)  # [rad]
+    curvature = 1.0
 
-        curvature = 1.0
+    px, py, pyaw, mode, clen = dubins_path_planning.dubins_path_planning(
+        start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
 
-        px, py, pyaw, mode, clen = dubins_path_planning.dubins_path_planning(
-            start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
+    check_edge_condition(px, py, pyaw,
+                         start_x, start_y, start_yaw,
+                         end_x, end_y, end_yaw)
 
-        self.check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, end_y, end_yaw)
 
-    def test2(self):
-        dubins_path_planning.show_animation = False
-        dubins_path_planning.main()
+def test_2():
+    dubins_path_planning.show_animation = False
+    dubins_path_planning.main()
 
-    def test3(self):
-        N_TEST = 10
 
-        for i in range(N_TEST):
-            start_x = (np.random.rand() - 0.5) * 10.0  # [m]
-            start_y = (np.random.rand() - 0.5) * 10.0  # [m]
-            start_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0)  # [rad]
+def test_3():
+    N_TEST = 10
 
-            end_x = (np.random.rand() - 0.5) * 10.0  # [m]
-            end_y = (np.random.rand() - 0.5) * 10.0  # [m]
-            end_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0)  # [rad]
+    for i in range(N_TEST):
+        start_x = (np.random.rand() - 0.5) * 10.0  # [m]
+        start_y = (np.random.rand() - 0.5) * 10.0  # [m]
+        start_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0)  # [rad]
+
+        end_x = (np.random.rand() - 0.5) * 10.0  # [m]
+        end_y = (np.random.rand() - 0.5) * 10.0  # [m]
+        end_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0)  # [rad]
+
+        curvature = 1.0 / (np.random.rand() * 5.0)
+
+        px, py, pyaw, mode, clen = dubins_path_planning.dubins_path_planning(
+            start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
 
-            curvature = 1.0 / (np.random.rand() * 5.0)
+        check_edge_condition(px, py, pyaw,
+                             start_x, start_y, start_yaw,
+                             end_x, end_y, end_yaw)
 
-            px, py, pyaw, mode, clen = dubins_path_planning.dubins_path_planning(
-                start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
 
-            self.check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, end_y, end_yaw)
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_dynamic_window_approach.py b/tests/test_dynamic_window_approach.py
index 93deb6846d..fdb452b4a7 100644
--- a/tests/test_dynamic_window_approach.py
+++ b/tests/test_dynamic_window_approach.py
@@ -1,58 +1,48 @@
-import os
-import sys
+import conftest
 import numpy as np
-from unittest import TestCase
-
-sys.path.append(os.path.dirname(__file__) + "/../")
-try:
-    from PathPlanning.DynamicWindowApproach import dynamic_window_approach as m
-except ImportError:
-    raise
-
-print(__file__)
-
-
-class TestDynamicWindowApproach(TestCase):
-    def test_main1(self):
-        m.show_animation = False
-        m.main(gx=1.0, gy=1.0)
-
-    def test_main2(self):
-        m.show_animation = False
-        m.main(gx=1.0, gy=1.0, robot_type=m.RobotType.rectangle)
-
-    def test_stuck_main(self):
-        m.show_animation = False
-        # adjust cost
-        m.config.to_goal_cost_gain = 0.2
-        m.config.obstacle_cost_gain = 2.0
-        # obstacles and goals for stuck condition
-        m.config.ob = -1 * np.array([[-1.0, -1.0],
-                                     [0.0, 2.0],
-                                     [2.0, 6.0],
-                                     [2.0, 8.0],
-                                     [3.0, 9.27],
-                                     [3.79, 9.39],
-                                     [7.25, 8.97],
-                                     [7.0, 2.0],
-                                     [3.0, 4.0],
-                                     [6.0, 5.0],
-                                     [3.5, 5.8],
-                                     [6.0, 9.0],
-                                     [8.8, 9.0],
-                                     [5.0, 9.0],
-                                     [7.5, 3.0],
-                                     [9.0, 8.0],
-                                     [5.8, 4.4],
-                                     [12.0, 12.0],
-                                     [3.0, 2.0],
-                                     [13.0, 13.0]
-                                     ])
-        m.main(gx=-5.0, gy=-7.0)
-
-
-if __name__ == '__main__':  # pragma: no cover
-    test = TestDynamicWindowApproach()
-    test.test_main1()
-    test.test_main2()
-    test.test_stuck_main()
+
+from PathPlanning.DynamicWindowApproach import dynamic_window_approach as m
+
+
+def test_main1():
+    m.show_animation = False
+    m.main(gx=1.0, gy=1.0)
+
+
+def test_main2():
+    m.show_animation = False
+    m.main(gx=1.0, gy=1.0, robot_type=m.RobotType.rectangle)
+
+
+def test_stuck_main():
+    m.show_animation = False
+    # adjust cost
+    m.config.to_goal_cost_gain = 0.2
+    m.config.obstacle_cost_gain = 2.0
+    # obstacles and goals for stuck condition
+    m.config.ob = -1 * np.array([[-1.0, -1.0],
+                                 [0.0, 2.0],
+                                 [2.0, 6.0],
+                                 [2.0, 8.0],
+                                 [3.0, 9.27],
+                                 [3.79, 9.39],
+                                 [7.25, 8.97],
+                                 [7.0, 2.0],
+                                 [3.0, 4.0],
+                                 [6.0, 5.0],
+                                 [3.5, 5.8],
+                                 [6.0, 9.0],
+                                 [8.8, 9.0],
+                                 [5.0, 9.0],
+                                 [7.5, 3.0],
+                                 [9.0, 8.0],
+                                 [5.8, 4.4],
+                                 [12.0, 12.0],
+                                 [3.0, 2.0],
+                                 [13.0, 13.0]
+                                 ])
+    m.main(gx=-5.0, gy=-7.0)
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_ekf_slam.py b/tests/test_ekf_slam.py
index e651e0079c..4181bb64ba 100644
--- a/tests/test_ekf_slam.py
+++ b/tests/test_ekf_slam.py
@@ -1,12 +1,11 @@
-from unittest import TestCase
-
+import conftest
 from SLAM.EKFSLAM import ekf_slam as m
 
-print(__file__)
 
+def test_1():
+    m.show_animation = False
+    m.main()
 
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_eta3_spline_path.py b/tests/test_eta3_spline_path.py
index 33a4223d31..7fa3883ea5 100644
--- a/tests/test_eta3_spline_path.py
+++ b/tests/test_eta3_spline_path.py
@@ -1,10 +1,11 @@
-
-from unittest import TestCase
+import conftest
 from PathPlanning.Eta3SplinePath import eta3_spline_path as m
 
 
-class Test(TestCase):
+def test_1():
+    m.show_animation = False
+    m.main()
+
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_extended_kalman_filter.py b/tests/test_extended_kalman_filter.py
index 299ee71b0a..d9ad6437a8 100644
--- a/tests/test_extended_kalman_filter.py
+++ b/tests/test_extended_kalman_filter.py
@@ -1,12 +1,11 @@
-from unittest import TestCase
-
+import conftest
 from Localization.extended_kalman_filter import extended_kalman_filter as m
 
-print(__file__)
 
+def test_1():
+    m.show_animation = False
+    m.main()
 
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_fast_slam1.py b/tests/test_fast_slam1.py
index 4b15e65c2d..b72ab6b9ef 100644
--- a/tests/test_fast_slam1.py
+++ b/tests/test_fast_slam1.py
@@ -1,24 +1,12 @@
-from unittest import TestCase
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
-try:
-    from SLAM.FastSLAM1 import fast_slam1 as m
-except:
-    raise
+import conftest
+from SLAM.FastSLAM1 import fast_slam1 as m
 
 
-print(__file__)
+def test1():
+    m.show_animation = False
+    m.SIM_TIME = 3.0
+    m.main()
 
 
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.SIM_TIME = 3.0
-        m.main()
-
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_fast_slam2.py b/tests/test_fast_slam2.py
index 4c1c967a41..95cdc69d42 100644
--- a/tests/test_fast_slam2.py
+++ b/tests/test_fast_slam2.py
@@ -1,24 +1,12 @@
-from unittest import TestCase
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
-try:
-    from SLAM.FastSLAM2 import fast_slam2 as m
-except:
-    raise
+import conftest
+from SLAM.FastSLAM2 import fast_slam2 as m
 
 
-print(__file__)
+def test1():
+    m.show_animation = False
+    m.SIM_TIME = 3.0
+    m.main()
 
 
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.SIM_TIME = 3.0
-        m.main()
-
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_flow_field.py b/tests/test_flow_field.py
index bf8fe6b802..d049731fe5 100644
--- a/tests/test_flow_field.py
+++ b/tests/test_flow_field.py
@@ -1,17 +1,11 @@
-import PathPlanning.FlowField.flowfield as flowfield
-from unittest import TestCase
-import sys
-import os
-sys.path.append(os.path.dirname(__file__) + "/../")
+import conftest
+import PathPlanning.FlowField.flowfield as flow_field
 
 
-class Test(TestCase):
+def test():
+    flow_field.show_animation = False
+    flow_field.main()
 
-    def test(self):
-        flowfield.show_animation = False
-        flowfield.main()
 
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_frenet_optimal_trajectory.py b/tests/test_frenet_optimal_trajectory.py
index 7a0ba5179e..72bb7a8341 100644
--- a/tests/test_frenet_optimal_trajectory.py
+++ b/tests/test_frenet_optimal_trajectory.py
@@ -1,26 +1,12 @@
-from unittest import TestCase
+import conftest
+from PathPlanning.FrenetOptimalTrajectory import frenet_optimal_trajectory as m
 
-import sys
-import os
-sys.path.append("./PathPlanning/FrenetOptimalTrajectory/")
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
-try:
-    from PathPlanning.FrenetOptimalTrajectory import frenet_optimal_trajectory as m
-except:
-    raise
 
+def test1():
+    m.show_animation = False
+    m.SIM_LOOP = 5
+    m.main()
 
-print(__file__)
 
-
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.SIM_LOOP = 5
-        m.main()
-
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_gaussian_grid_map.py b/tests/test_gaussian_grid_map.py
index d6a6cf53b5..af1e138721 100644
--- a/tests/test_gaussian_grid_map.py
+++ b/tests/test_gaussian_grid_map.py
@@ -1,12 +1,11 @@
-from unittest import TestCase
-
+import conftest
 from Mapping.gaussian_grid_map import gaussian_grid_map as m
 
-print(__file__)
 
+def test1():
+    m.show_animation = False
+    m.main()
 
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_graph_based_slam.py b/tests/test_graph_based_slam.py
index 89a6e14dfd..67d75f0f85 100644
--- a/tests/test_graph_based_slam.py
+++ b/tests/test_graph_based_slam.py
@@ -1,24 +1,12 @@
-from unittest import TestCase
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
-try:
-    from SLAM.GraphBasedSLAM import graph_based_slam as m
-except:
-    raise
+import conftest
+from SLAM.GraphBasedSLAM import graph_based_slam as m
 
 
-print(__file__)
+def test_1():
+    m.show_animation = False
+    m.SIM_TIME = 20.0
+    m.main()
 
 
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.SIM_TIME = 20.0
-        m.main()
-
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_grid_based_sweep_coverage_path_planner.py b/tests/test_grid_based_sweep_coverage_path_planner.py
index 12ef98df39..8cbe36eb49 100644
--- a/tests/test_grid_based_sweep_coverage_path_planner.py
+++ b/tests/test_grid_based_sweep_coverage_path_planner.py
@@ -1,124 +1,118 @@
-import os
-import sys
-from unittest import TestCase
-
-sys.path.append(os.path.dirname(
-    os.path.abspath(__file__)) + "/../PathPlanning/GridBasedSweepCPP")
-sys.path.append(
-    os.path.dirname(os.path.abspath(__file__)) + "/../Mapping/grid_map_lib")
-try:
+import conftest
+from PathPlanning.GridBasedSweepCPP \
     import grid_based_sweep_coverage_path_planner
-except ImportError:
-    raise
 
 grid_based_sweep_coverage_path_planner.do_animation = False
-
-
-class TestPlanning(TestCase):
-
-    RIGHT = grid_based_sweep_coverage_path_planner.\
-        SweepSearcher.MovingDirection.RIGHT
-    LEFT = grid_based_sweep_coverage_path_planner. \
-        SweepSearcher.MovingDirection.LEFT
-    UP = grid_based_sweep_coverage_path_planner. \
-        SweepSearcher.SweepDirection.UP
-    DOWN = grid_based_sweep_coverage_path_planner. \
-        SweepSearcher.SweepDirection.DOWN
-
-    def test_planning1(self):
-        ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0, 0.0]
-        oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0, 0.0]
-        resolution = 5.0
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(
-            ox, oy, resolution,
-            moving_direction=self.RIGHT,
-            sweeping_direction=self.DOWN,
-        )
-        self.assertGreater(len(px), 5)
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(
-            ox, oy, resolution,
-            moving_direction=self.LEFT,
-            sweeping_direction=self.DOWN,
-        )
-        self.assertGreater(len(px), 5)
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(
-            ox, oy, resolution,
-            moving_direction=self.RIGHT,
-            sweeping_direction=self.UP,
-        )
-        self.assertGreater(len(px), 5)
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(
-            ox, oy, resolution,
-            moving_direction=self.RIGHT,
-            sweeping_direction=self.UP,
-        )
-        self.assertGreater(len(px), 5)
-
-    def test_planning2(self):
-        ox = [0.0, 50.0, 50.0, 0.0, 0.0]
-        oy = [0.0, 0.0, 30.0, 30.0, 0.0]
-        resolution = 1.3
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(
-            ox, oy, resolution,
-            moving_direction=self.RIGHT,
-            sweeping_direction=self.DOWN,
-        )
-        self.assertGreater(len(px), 5)
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(
-            ox, oy, resolution,
-            moving_direction=self.LEFT,
-            sweeping_direction=self.DOWN,
-        )
-        self.assertGreater(len(px), 5)
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(
-            ox, oy, resolution,
-            moving_direction=self.RIGHT,
-            sweeping_direction=self.UP,
-        )
-        self.assertGreater(len(px), 5)
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(
-            ox, oy, resolution,
-            moving_direction=self.RIGHT,
-            sweeping_direction=self.DOWN,
-        )
-        self.assertGreater(len(px), 5)
-
-    def test_planning3(self):
-        ox = [0.0, 20.0, 50.0, 200.0, 130.0, 40.0, 0.0]
-        oy = [0.0, -80.0, 0.0, 30.0, 60.0, 80.0, 0.0]
-        resolution = 5.1
-        px, py = grid_based_sweep_coverage_path_planner.planning(
-            ox, oy, resolution,
-            moving_direction=self.RIGHT,
-            sweeping_direction=self.DOWN,
-        )
-        self.assertGreater(len(px), 5)
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(
-            ox, oy, resolution,
-            moving_direction=self.LEFT,
-            sweeping_direction=self.DOWN,
-        )
-        self.assertGreater(len(px), 5)
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(
-            ox, oy, resolution,
-            moving_direction=self.RIGHT,
-            sweeping_direction=self.UP,
-        )
-        self.assertGreater(len(px), 5)
-
-        px, py = grid_based_sweep_coverage_path_planner.planning(
-            ox, oy, resolution,
-            moving_direction=self.RIGHT,
-            sweeping_direction=self.DOWN,
-        )
-        self.assertGreater(len(px), 5)
+RIGHT = grid_based_sweep_coverage_path_planner. \
+    SweepSearcher.MovingDirection.RIGHT
+LEFT = grid_based_sweep_coverage_path_planner. \
+    SweepSearcher.MovingDirection.LEFT
+UP = grid_based_sweep_coverage_path_planner. \
+    SweepSearcher.SweepDirection.UP
+DOWN = grid_based_sweep_coverage_path_planner. \
+    SweepSearcher.SweepDirection.DOWN
+
+
+def test_planning1():
+    ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0, 0.0]
+    oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0, 0.0]
+    resolution = 5.0
+
+    px, py = grid_based_sweep_coverage_path_planner.planning(
+        ox, oy, resolution,
+        moving_direction=RIGHT,
+        sweeping_direction=DOWN,
+    )
+    assert len(px) >= 5
+
+    px, py = grid_based_sweep_coverage_path_planner.planning(
+        ox, oy, resolution,
+        moving_direction=LEFT,
+        sweeping_direction=DOWN,
+    )
+    assert len(px) >= 5
+
+    px, py = grid_based_sweep_coverage_path_planner.planning(
+        ox, oy, resolution,
+        moving_direction=RIGHT,
+        sweeping_direction=UP,
+    )
+    assert len(px) >= 5
+
+    px, py = grid_based_sweep_coverage_path_planner.planning(
+        ox, oy, resolution,
+        moving_direction=RIGHT,
+        sweeping_direction=UP,
+    )
+    assert len(px) >= 5
+
+
+def test_planning2():
+    ox = [0.0, 50.0, 50.0, 0.0, 0.0]
+    oy = [0.0, 0.0, 30.0, 30.0, 0.0]
+    resolution = 1.3
+
+    px, py = grid_based_sweep_coverage_path_planner.planning(
+        ox, oy, resolution,
+        moving_direction=RIGHT,
+        sweeping_direction=DOWN,
+    )
+    assert len(px) >= 5
+
+    px, py = grid_based_sweep_coverage_path_planner.planning(
+        ox, oy, resolution,
+        moving_direction=LEFT,
+        sweeping_direction=DOWN,
+    )
+    assert len(px) >= 5
+
+    px, py = grid_based_sweep_coverage_path_planner.planning(
+        ox, oy, resolution,
+        moving_direction=RIGHT,
+        sweeping_direction=UP,
+    )
+    assert len(px) >= 5
+
+    px, py = grid_based_sweep_coverage_path_planner.planning(
+        ox, oy, resolution,
+        moving_direction=RIGHT,
+        sweeping_direction=DOWN,
+    )
+    assert len(px) >= 5
+
+
+def test_planning3():
+    ox = [0.0, 20.0, 50.0, 200.0, 130.0, 40.0, 0.0]
+    oy = [0.0, -80.0, 0.0, 30.0, 60.0, 80.0, 0.0]
+    resolution = 5.1
+    px, py = grid_based_sweep_coverage_path_planner.planning(
+        ox, oy, resolution,
+        moving_direction=RIGHT,
+        sweeping_direction=DOWN,
+    )
+    assert len(px) >= 5
+
+    px, py = grid_based_sweep_coverage_path_planner.planning(
+        ox, oy, resolution,
+        moving_direction=LEFT,
+        sweeping_direction=DOWN,
+    )
+    assert len(px) >= 5
+
+    px, py = grid_based_sweep_coverage_path_planner.planning(
+        ox, oy, resolution,
+        moving_direction=RIGHT,
+        sweeping_direction=UP,
+    )
+    assert len(px) >= 5
+
+    px, py = grid_based_sweep_coverage_path_planner.planning(
+        ox, oy, resolution,
+        moving_direction=RIGHT,
+        sweeping_direction=DOWN,
+    )
+    assert len(px) >= 5
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_grid_map_lib.py b/tests/test_grid_map_lib.py
index 63b3cc86b7..ffdb72b1ae 100644
--- a/tests/test_grid_map_lib.py
+++ b/tests/test_grid_map_lib.py
@@ -1,38 +1,25 @@
-import os
-import sys
-import unittest
+from Mapping.grid_map_lib.grid_map_lib import GridMap
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../Mapping/grid_map_lib")
-try:
-    from grid_map_lib import GridMap
-except ImportError:
-    raise
 
+def test_position_set():
+    grid_map = GridMap(100, 120, 0.5, 10.0, -0.5)
 
-class MyTestCase(unittest.TestCase):
+    grid_map.set_value_from_xy_pos(10.1, -1.1, 1.0)
+    grid_map.set_value_from_xy_pos(10.1, -0.1, 1.0)
+    grid_map.set_value_from_xy_pos(10.1, 1.1, 1.0)
+    grid_map.set_value_from_xy_pos(11.1, 0.1, 1.0)
+    grid_map.set_value_from_xy_pos(10.1, 0.1, 1.0)
+    grid_map.set_value_from_xy_pos(9.1, 0.1, 1.0)
 
-    def test_position_set(self):
-        grid_map = GridMap(100, 120, 0.5, 10.0, -0.5)
 
-        grid_map.set_value_from_xy_pos(10.1, -1.1, 1.0)
-        grid_map.set_value_from_xy_pos(10.1, -0.1, 1.0)
-        grid_map.set_value_from_xy_pos(10.1, 1.1, 1.0)
-        grid_map.set_value_from_xy_pos(11.1, 0.1, 1.0)
-        grid_map.set_value_from_xy_pos(10.1, 0.1, 1.0)
-        grid_map.set_value_from_xy_pos(9.1, 0.1, 1.0)
+def test_polygon_set():
+    ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0]
+    oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0]
 
-        self.assertEqual(True, True)
+    grid_map = GridMap(600, 290, 0.7, 60.0, 30.5)
 
-    def test_polygon_set(self):
-        ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0]
-        oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0]
-
-        grid_map = GridMap(600, 290, 0.7, 60.0, 30.5)
-
-        grid_map.set_value_from_polygon(ox, oy, 1.0, inside=False)
-
-        self.assertEqual(True, True)
+    grid_map.set_value_from_polygon(ox, oy, 1.0, inside=False)
 
 
 if __name__ == '__main__':
-    unittest.main()
+    conftest.run_this_test(__file__)
diff --git a/tests/test_histogram_filter.py b/tests/test_histogram_filter.py
index 6d7df824e0..4474ead097 100644
--- a/tests/test_histogram_filter.py
+++ b/tests/test_histogram_filter.py
@@ -1,25 +1,12 @@
-from unittest import TestCase
+import conftest
+from Localization.histogram_filter import histogram_filter as m
 
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
-try:
-    from Localization.histogram_filter import histogram_filter as m
-except:
-    raise
 
-
-print(__file__)
-
-
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.SIM_TIME = 1.0
-        m.main()
+def test1():
+    m.show_animation = False
+    m.SIM_TIME = 1.0
+    m.main()
 
 
 if __name__ == '__main__':
-    test = Test()
-    test.test1()
+    conftest.run_this_test(__file__)
diff --git a/tests/test_hybrid_a_star.py b/tests/test_hybrid_a_star.py
index 8cf5c30ad6..dbcf3ba9f4 100644
--- a/tests/test_hybrid_a_star.py
+++ b/tests/test_hybrid_a_star.py
@@ -1,22 +1,11 @@
-from unittest import TestCase
-import sys
-import os
-sys.path.append(os.path.dirname(__file__) + "/../")
-sys.path.append(os.path.dirname(os.path.abspath(__file__))
-                + "/../PathPlanning/HybridAStar")
-try:
-    from PathPlanning.HybridAStar import hybrid_a_star as m
-except Exception:
-    raise
+import conftest
+from PathPlanning.HybridAStar import hybrid_a_star as m
 
 
-class Test(TestCase):
+def test1():
+    m.show_animation = False
+    m.main()
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
 
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_informed_rrt_star.py b/tests/test_informed_rrt_star.py
index a19f2465f2..10974ecfcb 100644
--- a/tests/test_informed_rrt_star.py
+++ b/tests/test_informed_rrt_star.py
@@ -1,24 +1,11 @@
-from unittest import TestCase
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
-sys.path.append(os.path.dirname(os.path.abspath(__file__))
-                + "/../PathPlanning/InformedRRTStar/")
-try:
-    from PathPlanning.InformedRRTStar import informed_rrt_star as m
-except:
-    raise
+import conftest
+from PathPlanning.InformedRRTStar import informed_rrt_star as m
 
-print(__file__)
 
+def test1():
+    m.show_animation = False
+    m.main()
 
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
-
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_inverted_pendulum_mpc_control.py b/tests/test_inverted_pendulum_mpc_control.py
index bf6c8576e4..d88d4d7915 100644
--- a/tests/test_inverted_pendulum_mpc_control.py
+++ b/tests/test_inverted_pendulum_mpc_control.py
@@ -1,15 +1,14 @@
-from unittest import TestCase
-
+import conftest
 import sys
 if 'cvxpy' in sys.modules:  # pragma: no cover
-    sys.path.append("./InvertedPendulumCart/inverted_pendulum_mpc_control/")
 
-    import inverted_pendulum_mpc_control as m
+    from InvertedPendulumCart.InvertedPendulumMPCControl \
+        import inverted_pendulum_mpc_control as m
 
-    print(__file__)
+    def test1():
+        m.show_animation = False
+        m.main()
 
-    class Test(TestCase):
 
-        def test1(self):
-            m.show_animation = False
-            m.main()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_iterative_closest_point.py b/tests/test_iterative_closest_point.py
index 9c71688ec7..3f212f7298 100644
--- a/tests/test_iterative_closest_point.py
+++ b/tests/test_iterative_closest_point.py
@@ -1,12 +1,11 @@
-from unittest import TestCase
-
+import conftest
 from SLAM.iterative_closest_point import iterative_closest_point as m
 
-print(__file__)
 
+def test_1():
+    m.show_animation = False
+    m.main()
 
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_kmeans_clustering.py b/tests/test_kmeans_clustering.py
index b5b2cb7a60..5e39d64ae6 100644
--- a/tests/test_kmeans_clustering.py
+++ b/tests/test_kmeans_clustering.py
@@ -1,12 +1,11 @@
-from unittest import TestCase
-
+import conftest
 from Mapping.kmeans_clustering import kmeans_clustering as m
 
-print(__file__)
 
+def test_1():
+    m.show_animation = False
+    m.main()
 
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_lqr_rrt_star.py b/tests/test_lqr_rrt_star.py
index 0b59475b3d..444b4616b8 100644
--- a/tests/test_lqr_rrt_star.py
+++ b/tests/test_lqr_rrt_star.py
@@ -1,27 +1,14 @@
-from unittest import TestCase
-import sys
-import os
+import conftest  # Add root path to sys.path
+from PathPlanning.LQRRRTStar import lqr_rrt_star as m
 import random
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
-sys.path.append(os.path.dirname(os.path.abspath(__file__))
-                + "/../PathPlanning/LQRRRTStar/")
-try:
-    from PathPlanning.LQRRRTStar import lqr_rrt_star as m
-except ImportError:
-    raise
-
-print(__file__)
 
 random.seed(12345)
 
 
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.main(maxIter=5)
+def test1():
+    m.show_animation = False
+    m.main(maxIter=5)
 
 
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_lqr_speed_steer_control.py b/tests/test_lqr_speed_steer_control.py
index fb1a14af54..9e921895ba 100644
--- a/tests/test_lqr_speed_steer_control.py
+++ b/tests/test_lqr_speed_steer_control.py
@@ -1,15 +1,7 @@
-from unittest import TestCase
-
-import sys
-sys.path.append("./PathTracking/lqr_speed_steer_control/")
-
+import conftest  # Add root path to sys.path
 from PathTracking.lqr_speed_steer_control import lqr_speed_steer_control as m
 
-print(__file__)
-
-
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+def test_1():
+    m.show_animation = False
+    m.main()
diff --git a/tests/test_lqr_steer_control.py b/tests/test_lqr_steer_control.py
index 4807afef6c..24427a8ffd 100644
--- a/tests/test_lqr_steer_control.py
+++ b/tests/test_lqr_steer_control.py
@@ -1,15 +1,11 @@
-from unittest import TestCase
-
-import sys
-sys.path.append("./PathTracking/lqr_steer_control/")
-
+import conftest  # Add root path to sys.path
 from PathTracking.lqr_steer_control import lqr_steer_control as m
 
-print(__file__)
 
+def test1():
+    m.show_animation = False
+    m.main()
 
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_model_predictive_speed_and_steer_control.py b/tests/test_model_predictive_speed_and_steer_control.py
index 4356fd40e2..27b6b79cf5 100644
--- a/tests/test_model_predictive_speed_and_steer_control.py
+++ b/tests/test_model_predictive_speed_and_steer_control.py
@@ -1,16 +1,14 @@
-from unittest import TestCase
-
 import sys
-if 'cvxpy' in sys.modules:  # pragma: no cover
-    sys.path.append("./PathTracking/model_predictive_speed_and_steer_control/")
 
-    from PathTracking.model_predictive_speed_and_steer_control import model_predictive_speed_and_steer_control as m
+if 'cvxpy' in sys.modules:  # pragma: no cover
 
-    print(__file__)
+    from PathTracking.model_predictive_speed_and_steer_control \
+        import model_predictive_speed_and_steer_control as m
 
-    class Test(TestCase):
+    def test_1():
+        m.show_animation = False
+        m.main()
 
-        def test1(self):
-            m.show_animation = False
-            m.main()
-            m.main2()
+    def test_2():
+        m.show_animation = False
+        m.main2()
diff --git a/tests/test_move_to_pose.py b/tests/test_move_to_pose.py
index 0125792039..6a5a4cf4fb 100644
--- a/tests/test_move_to_pose.py
+++ b/tests/test_move_to_pose.py
@@ -1,12 +1,7 @@
-from unittest import TestCase
-
+import conftest  # Add root path to sys.path
 from PathTracking.move_to_pose import move_to_pose as m
 
-print(__file__)
-
-
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+def test_1():
+    m.show_animation = False
+    m.main()
diff --git a/tests/test_n_joint_arm_to_point_control.py b/tests/test_n_joint_arm_to_point_control.py
index deb30f83be..a87271b471 100644
--- a/tests/test_n_joint_arm_to_point_control.py
+++ b/tests/test_n_joint_arm_to_point_control.py
@@ -1,19 +1,11 @@
-import os
-import sys
+import conftest  # Add root path to sys.path
+from ArmNavigation.n_joint_arm_to_point_control\
+    import n_joint_arm_to_point_control as m
 import random
-from unittest import TestCase
-
-sys.path.append(os.path.dirname(__file__) + "/../ArmNavigation/n_joint_arm_to_point_control/")
-
-import n_joint_arm_to_point_control as m
-
-print(__file__)
 
 random.seed(12345)
 
 
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.animation()
+def test1():
+    m.show_animation = False
+    m.animation()
diff --git a/tests/test_particle_filter.py b/tests/test_particle_filter.py
index 3e65503976..dad823ee9e 100644
--- a/tests/test_particle_filter.py
+++ b/tests/test_particle_filter.py
@@ -1,12 +1,7 @@
-from unittest import TestCase
-
+import conftest  # Add root path to sys.path
 from Localization.particle_filter import particle_filter as m
 
-print(__file__)
-
-
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+def test_1():
+    m.show_animation = False
+    m.main()
diff --git a/tests/test_potential_field_planning.py b/tests/test_potential_field_planning.py
index 58b960363e..c473a23968 100644
--- a/tests/test_potential_field_planning.py
+++ b/tests/test_potential_field_planning.py
@@ -1,12 +1,7 @@
-from unittest import TestCase
-
+import conftest  # Add root path to sys.path
 from PathPlanning.PotentialFieldPlanning import potential_field_planning as m
 
-print(__file__)
-
-
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+def test1():
+    m.show_animation = False
+    m.main()
diff --git a/tests/test_probabilistic_road_map.py b/tests/test_probabilistic_road_map.py
index 7b2f1fc557..aa58e28bce 100644
--- a/tests/test_probabilistic_road_map.py
+++ b/tests/test_probabilistic_road_map.py
@@ -1,9 +1,11 @@
-from unittest import TestCase
+import conftest  # Add root path to sys.path
 from PathPlanning.ProbabilisticRoadMap import probabilistic_road_map
 
 
-class Test(TestCase):
+def test1():
+    probabilistic_road_map.show_animation = False
+    probabilistic_road_map.main()
 
-    def test1(self):
-        probabilistic_road_map.show_animation = False
-        probabilistic_road_map.main()
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_pure_pursuit.py b/tests/test_pure_pursuit.py
index 8803bba458..2f0b83ae36 100644
--- a/tests/test_pure_pursuit.py
+++ b/tests/test_pure_pursuit.py
@@ -1,11 +1,7 @@
-from unittest import TestCase
+import conftest  # Add root path to sys.path
 from PathTracking.pure_pursuit import pure_pursuit as m
 
-print("pure_pursuit test")
 
-
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.main()
+def test1():
+    m.show_animation = False
+    m.main()
diff --git a/tests/test_quintic_polynomials_planner.py b/tests/test_quintic_polynomials_planner.py
index 0f8d781071..bee383a7ea 100644
--- a/tests/test_quintic_polynomials_planner.py
+++ b/tests/test_quintic_polynomials_planner.py
@@ -1,12 +1,7 @@
-from unittest import TestCase
-
+import conftest  # Add root path to sys.path
 from PathPlanning.QuinticPolynomialsPlanner import quintic_polynomials_planner as m
 
-print(__file__)
-
-
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+def test1():
+    m.show_animation = False
+    m.main()
diff --git a/tests/test_raycasting_grid_map.py b/tests/test_raycasting_grid_map.py
index 3c77096bd0..60ecf76902 100644
--- a/tests/test_raycasting_grid_map.py
+++ b/tests/test_raycasting_grid_map.py
@@ -1,12 +1,7 @@
-from unittest import TestCase
-
+import conftest  # Add root path to sys.path
 from Mapping.raycasting_grid_map import raycasting_grid_map as m
 
-print(__file__)
-
-
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+def test1():
+    m.show_animation = False
+    m.main()
diff --git a/tests/test_rear_wheel_feedback.py b/tests/test_rear_wheel_feedback.py
index 7fba28bd6d..895eb188b3 100644
--- a/tests/test_rear_wheel_feedback.py
+++ b/tests/test_rear_wheel_feedback.py
@@ -1,15 +1,11 @@
-from unittest import TestCase
-
-import sys
-sys.path.append("./PathTracking/rear_wheel_feedback/")
-
+import conftest  # Add root path to sys.path
 from PathTracking.rear_wheel_feedback import rear_wheel_feedback as m
 
-print(__file__)
 
+def test1():
+    m.show_animation = False
+    m.main()
 
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_rectangle_fitting.py b/tests/test_rectangle_fitting.py
index 2a28320626..20dc9cafbf 100644
--- a/tests/test_rectangle_fitting.py
+++ b/tests/test_rectangle_fitting.py
@@ -1,22 +1,7 @@
-from unittest import TestCase
+import conftest  # Add root path to sys.path
+from Mapping.rectangle_fitting import rectangle_fitting as m
 
-import sys
-import os
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../Mapping/rectangle_fitting/")
-
-try:
-    from Mapping.rectangle_fitting import rectangle_fitting as m
-except ImportError:
-    raise
-
-
-print(__file__)
-
-
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.main()
+def test1():
+    m.show_animation = False
+    m.main()
diff --git a/tests/test_reeds_shepp_path_planning.py b/tests/test_reeds_shepp_path_planning.py
index 2c3b38dc31..150f84fb35 100644
--- a/tests/test_reeds_shepp_path_planning.py
+++ b/tests/test_reeds_shepp_path_planning.py
@@ -1,42 +1,42 @@
-from unittest import TestCase
+import conftest  # Add root path to sys.path
 from PathPlanning.ReedsSheppPath import reeds_shepp_path_planning as m
-
 import numpy as np
 
 
-class Test(TestCase):
+def check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x,
+                         end_y, end_yaw):
+    assert (abs(px[0] - start_x) <= 0.01)
+    assert (abs(py[0] - start_y) <= 0.01)
+    assert (abs(pyaw[0] - start_yaw) <= 0.01)
+    print("x", px[-1], end_x)
+    assert (abs(px[-1] - end_x) <= 0.01)
+    print("y", py[-1], end_y)
+    assert (abs(py[-1] - end_y) <= 0.01)
+    print("yaw", pyaw[-1], end_yaw)
+    assert (abs(pyaw[-1] - end_yaw) <= 0.01)
+
 
-    @staticmethod
-    def check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, end_y, end_yaw):
-        assert (abs(px[0] - start_x) <= 0.01)
-        assert (abs(py[0] - start_y) <= 0.01)
-        assert (abs(pyaw[0] - start_yaw) <= 0.01)
-        print("x", px[-1], end_x)
-        assert (abs(px[-1] - end_x) <= 0.01)
-        print("y", py[-1], end_y)
-        assert (abs(py[-1] - end_y) <= 0.01)
-        print("yaw", pyaw[-1], end_yaw)
-        assert (abs(pyaw[-1] - end_yaw) <= 0.01)
+def test1():
+    m.show_animation = False
+    m.main()
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
 
-    def test2(self):
-        N_TEST = 10
+def test2():
+    N_TEST = 10
 
-        for i in range(N_TEST):
-            start_x = (np.random.rand() - 0.5) * 10.0  # [m]
-            start_y = (np.random.rand() - 0.5) * 10.0  # [m]
-            start_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0)  # [rad]
+    for i in range(N_TEST):
+        start_x = (np.random.rand() - 0.5) * 10.0  # [m]
+        start_y = (np.random.rand() - 0.5) * 10.0  # [m]
+        start_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0)  # [rad]
 
-            end_x = (np.random.rand() - 0.5) * 10.0  # [m]
-            end_y = (np.random.rand() - 0.5) * 10.0  # [m]
-            end_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0)  # [rad]
+        end_x = (np.random.rand() - 0.5) * 10.0  # [m]
+        end_y = (np.random.rand() - 0.5) * 10.0  # [m]
+        end_yaw = np.deg2rad((np.random.rand() - 0.5) * 180.0)  # [rad]
 
-            curvature = 1.0 / (np.random.rand() * 5.0)
+        curvature = 1.0 / (np.random.rand() * 5.0)
 
-            px, py, pyaw, mode, clen = m.reeds_shepp_path_planning(
-                start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
+        px, py, pyaw, mode, clen = m.reeds_shepp_path_planning(
+            start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
 
-            self.check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x, end_y, end_yaw)
+        check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw,
+                             end_x, end_y, end_yaw)
diff --git a/tests/test_rocket_powered_landing.py b/tests/test_rocket_powered_landing.py
index 96cebbfe96..01d2f4f832 100644
--- a/tests/test_rocket_powered_landing.py
+++ b/tests/test_rocket_powered_landing.py
@@ -1,15 +1,10 @@
-from unittest import TestCase
-
+import conftest  # Add root path to sys.path
 import sys
 
 if 'cvxpy' in sys.modules:  # pragma: no cover
-    sys.path.append("./AerialNavigation/rocket_powered_landing/")
 
     from AerialNavigation.rocket_powered_landing import rocket_powered_landing as m
-    print(__file__)
-
-    class Test(TestCase):
 
-        def test1(self):
-            m.show_animation = False
-            m.main()
+    def test1():
+        m.show_animation = False
+        m.main()
diff --git a/tests/test_rrt.py b/tests/test_rrt.py
index b7c03d25cf..14a8175931 100644
--- a/tests/test_rrt.py
+++ b/tests/test_rrt.py
@@ -1,33 +1,21 @@
-import os
-import sys
+import conftest
 import random
-from unittest import TestCase
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
-try:
-    from PathPlanning.RRT import rrt as m
-    from PathPlanning.RRT import rrt_with_pathsmoothing as m1
-except ImportError:
-    raise
-
-
-print(__file__)
+from PathPlanning.RRT import rrt as m
+from PathPlanning.RRT import rrt_with_pathsmoothing as m1
 
 random.seed(12345)
 
 
-class Test(TestCase):
+def test1():
+    m.show_animation = False
+    m.main(gx=1.0, gy=1.0)
 
-    def test1(self):
-        m.show_animation = False
-        m.main(gx=1.0, gy=1.0)
 
-    def test2(self):
-        m1.show_animation = False
-        m1.main()
+def test2():
+    m1.show_animation = False
+    m1.main()
 
 
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
-    test.test2()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_rrt_dubins.py b/tests/test_rrt_dubins.py
index 19983252ef..66130484bc 100644
--- a/tests/test_rrt_dubins.py
+++ b/tests/test_rrt_dubins.py
@@ -1,27 +1,11 @@
-from unittest import TestCase
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../PathPlanning/RRTDubins/")
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../PathPlanning/DubinsPath/")
+import conftest  # Add root path to sys.path
+from PathPlanning.RRTDubins import rrt_dubins as m
 
-try:
-    import rrt_dubins as m
-except:
-    raise
 
+def test1():
+    m.show_animation = False
+    m.main()
 
-print(__file__)
 
-
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.main()
-
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_rrt_star.py b/tests/test_rrt_star.py
index d3661610ff..5327f4ca20 100644
--- a/tests/test_rrt_star.py
+++ b/tests/test_rrt_star.py
@@ -1,37 +1,23 @@
-import os
-import sys
-from unittest import TestCase
+import conftest  # Add root path to sys.path
+from PathPlanning.RRTStar import rrt_star as m
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../PathPlanning/RRTStar/")
 
-try:
-    import rrt_star as m
-except ImportError:
-    raise
+def test1():
+    m.show_animation = False
+    m.main()
 
 
-print(__file__)
+def test_no_obstacle():
+    obstacle_list = []
 
+    # Set Initial parameters
+    rrt_star = m.RRTStar(start=[0, 0],
+                         goal=[6, 10],
+                         rand_area=[-2, 15],
+                         obstacle_list=obstacle_list)
+    path = rrt_star.planning(animation=False)
+    assert path is not None
 
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
-
-    def test_no_obstacle(self):
-        obstacle_list = []
-
-        # Set Initial parameters
-        rrt_star = m.RRTStar(start=[0, 0],
-                             goal=[6, 10],
-                             rand_area=[-2, 15],
-                             obstacle_list=obstacle_list)
-        path = rrt_star.planning(animation=False)
-        assert path is not None
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
-    test.test_no_obstacle()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_rrt_star_dubins.py b/tests/test_rrt_star_dubins.py
index db1ca2bfa4..c55ca23e43 100644
--- a/tests/test_rrt_star_dubins.py
+++ b/tests/test_rrt_star_dubins.py
@@ -1,26 +1,11 @@
-from unittest import TestCase
+import conftest  # Add root path to sys.path
+from PathPlanning.RRTStarDubins import rrt_star_dubins as m
 
-import sys
-import os
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__))
-                + "/../PathPlanning/RRTStarDubins/")
+def test1():
+    m.show_animation = False
+    m.main()
 
-try:
-    import rrt_star_dubins as m
-except:
-    raise
 
-print(__file__)
-
-
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.main()
-
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_rrt_star_reeds_shepp.py b/tests/test_rrt_star_reeds_shepp.py
index f7eeae3e6b..0a4663056a 100644
--- a/tests/test_rrt_star_reeds_shepp.py
+++ b/tests/test_rrt_star_reeds_shepp.py
@@ -1,28 +1,11 @@
-import os
-import sys
-from unittest import TestCase
+import conftest  # Add root path to sys.path
+import rrt_star_reeds_shepp as m
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__))
-                + "/../PathPlanning/RRTStarReedsShepp/")
-sys.path.append(os.path.dirname(os.path.abspath(__file__))
-                + "/../PathPlanning/ReedsSheppPath/")
 
-try:
-    import rrt_star_reeds_shepp as m
-except:
-    raise
+def test1():
+    m.show_animation = False
+    m.main(max_iter=5)
 
 
-print(__file__)
-
-
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.main(max_iter=5)
-
-
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_rrt_star_seven_joint_arm.py b/tests/test_rrt_star_seven_joint_arm.py
index 0788b3675a..2f6622cf6c 100644
--- a/tests/test_rrt_star_seven_joint_arm.py
+++ b/tests/test_rrt_star_seven_joint_arm.py
@@ -1,26 +1,12 @@
-import os
-import sys
-from unittest import TestCase
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../ArmNavigation/rrt_star_seven_joint_arm_control/")
-
-try:
+import conftest  # Add root path to sys.path
+from ArmNavigation.rrt_star_seven_joint_arm_control \
     import rrt_star_seven_joint_arm_control as m
-except ImportError:
-    raise
-
-
-print(__file__)
-
 
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+def test1():
+    m.show_animation = False
+    m.main()
 
 
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_rrt_with_sobol_sampler.py b/tests/test_rrt_with_sobol_sampler.py
index 43f25e85bc..affe2b165a 100644
--- a/tests/test_rrt_with_sobol_sampler.py
+++ b/tests/test_rrt_with_sobol_sampler.py
@@ -1,30 +1,14 @@
-import os
-import sys
+import conftest  # Add root path to sys.path
+from PathPlanning.RRT import rrt_with_sobol_sampler as m
 import random
-from unittest import TestCase
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../")
-sys.path.append(os.path.dirname(
-    os.path.abspath(__file__)) + "/../PathPlanning/RRT")
-
-try:
-    from PathPlanning.RRT import rrt_with_sobol_sampler as m
-except ImportError:
-    raise
-
-
-print(__file__)
 
 random.seed(12345)
 
 
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.main(gx=1.0, gy=1.0)
+def test1():
+    m.show_animation = False
+    m.main(gx=1.0, gy=1.0)
 
 
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_spiral_spanning_tree_coverage_path_planner.py b/tests/test_spiral_spanning_tree_coverage_path_planner.py
index 622b6ba18c..44170fa4cc 100644
--- a/tests/test_spiral_spanning_tree_coverage_path_planner.py
+++ b/tests/test_spiral_spanning_tree_coverage_path_planner.py
@@ -1,58 +1,58 @@
+import conftest  # Add root path to sys.path
 import os
-import sys
 import matplotlib.pyplot as plt
-from unittest import TestCase
-
-sys.path.append(os.path.dirname(
-    os.path.abspath(__file__)) + "/../PathPlanning/SpiralSpanningTreeCPP")
-try:
+from PathPlanning.SpiralSpanningTreeCPP \
     import spiral_spanning_tree_coverage_path_planner
-except ImportError:
-    raise
 
 spiral_spanning_tree_coverage_path_planner.do_animation = True
 
 
-class TestPlanning(TestCase):
-    def spiral_stc_cpp(self, img, start):
-        num_free = 0
-        for i in range(img.shape[0]):
-            for j in range(img.shape[1]):
-                num_free += img[i][j]
-
-        STC_planner = spiral_spanning_tree_coverage_path_planner.\
-            SpiralSpanningTreeCoveragePlanner(img)
-
-        edge, route, path = STC_planner.plan(start)
-
-        covered_nodes = set()
-        for p, q in edge:
-            covered_nodes.add(p)
-            covered_nodes.add(q)
-
-        # assert complete coverage
-        self.assertEqual(len(covered_nodes), num_free / 4)
-
-    def test_spiral_stc_cpp_1(self):
-        img_dir = os.path.dirname(
-            os.path.abspath(__file__)) + \
-            "/../PathPlanning/SpiralSpanningTreeCPP"
-        img = plt.imread(os.path.join(img_dir, 'map', 'test.png'))
-        start = (0, 0)
-        self.spiral_stc_cpp(img, start)
-
-    def test_spiral_stc_cpp_2(self):
-        img_dir = os.path.dirname(
-            os.path.abspath(__file__)) + \
-            "/../PathPlanning/SpiralSpanningTreeCPP"
-        img = plt.imread(os.path.join(img_dir, 'map', 'test_2.png'))
-        start = (10, 0)
-        self.spiral_stc_cpp(img, start)
-
-    def test_spiral_stc_cpp_3(self):
-        img_dir = os.path.dirname(
-            os.path.abspath(__file__)) + \
-            "/../PathPlanning/SpiralSpanningTreeCPP"
-        img = plt.imread(os.path.join(img_dir, 'map', 'test_3.png'))
-        start = (0, 0)
-        self.spiral_stc_cpp(img, start)
+def spiral_stc_cpp(img, start):
+    num_free = 0
+    for i in range(img.shape[0]):
+        for j in range(img.shape[1]):
+            num_free += img[i][j]
+
+    STC_planner = spiral_spanning_tree_coverage_path_planner.\
+        SpiralSpanningTreeCoveragePlanner(img)
+
+    edge, route, path = STC_planner.plan(start)
+
+    covered_nodes = set()
+    for p, q in edge:
+        covered_nodes.add(p)
+        covered_nodes.add(q)
+
+    # assert complete coverage
+    assert len(covered_nodes) == num_free / 4
+
+
+def test_spiral_stc_cpp_1():
+    img_dir = os.path.dirname(
+        os.path.abspath(__file__)) + \
+        "/../PathPlanning/SpiralSpanningTreeCPP"
+    img = plt.imread(os.path.join(img_dir, 'map', 'test.png'))
+    start = (0, 0)
+    spiral_stc_cpp(img, start)
+
+
+def test_spiral_stc_cpp_2():
+    img_dir = os.path.dirname(
+        os.path.abspath(__file__)) + \
+        "/../PathPlanning/SpiralSpanningTreeCPP"
+    img = plt.imread(os.path.join(img_dir, 'map', 'test_2.png'))
+    start = (10, 0)
+    spiral_stc_cpp(img, start)
+
+
+def test_spiral_stc_cpp_3():
+    img_dir = os.path.dirname(
+        os.path.abspath(__file__)) + \
+        "/../PathPlanning/SpiralSpanningTreeCPP"
+    img = plt.imread(os.path.join(img_dir, 'map', 'test_3.png'))
+    start = (0, 0)
+    spiral_stc_cpp(img, start)
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_stanley_controller.py b/tests/test_stanley_controller.py
index 5aa24acb97..7920273db4 100644
--- a/tests/test_stanley_controller.py
+++ b/tests/test_stanley_controller.py
@@ -1,15 +1,7 @@
-from unittest import TestCase
-
-import sys
-sys.path.append("./PathTracking/stanley_controller/")
-
+import conftest  # Add root path to sys.path
 from PathTracking.stanley_controller import stanley_controller as m
 
-print(__file__)
-
-
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+def test1():
+    m.show_animation = False
+    m.main()
diff --git a/tests/test_state_lattice_planner.py b/tests/test_state_lattice_planner.py
index 9bf9095282..9e9e40c892 100644
--- a/tests/test_state_lattice_planner.py
+++ b/tests/test_state_lattice_planner.py
@@ -1,30 +1,10 @@
-from unittest import TestCase
-
-import sys
-
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../PathPlanning/ModelPredictiveTrajectoryGenerator/")
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../PathPlanning/StateLatticePlanner/")
-
-try:
-    import state_lattice_planner as m
+import conftest  # Add root path to sys.path
+from PathPlanning.StateLatticePlanner import state_lattice_planner as m
+from PathPlanning.ModelPredictiveTrajectoryGenerator \
     import model_predictive_trajectory_generator as m2
-except:
-    raise
-
-print(__file__)
-
-
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m2.show_animation = False
-        m.main()
 
 
-if __name__ == '__main__':  # pragma: no cover
-    test = Test()
-    test.test1()
+def test1():
+    m.show_animation = False
+    m2.show_animation = False
+    m.main()
diff --git a/tests/test_two_joint_arm_to_point_control.py b/tests/test_two_joint_arm_to_point_control.py
index e2b9c21ac3..24cc26f118 100644
--- a/tests/test_two_joint_arm_to_point_control.py
+++ b/tests/test_two_joint_arm_to_point_control.py
@@ -1,12 +1,8 @@
-from unittest import TestCase
+import conftest  # Add root path to sys.path
+from ArmNavigation.two_joint_arm_to_point_control \
+    import two_joint_arm_to_point_control as m
 
-from ArmNavigation.two_joint_arm_to_point_control import two_joint_arm_to_point_control as m
 
-print(__file__)
-
-
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.animation()
+def test1():
+    m.show_animation = False
+    m.animation()
diff --git a/tests/test_unscented_kalman_filter.py b/tests/test_unscented_kalman_filter.py
index 8788043e8a..c4f79e8906 100644
--- a/tests/test_unscented_kalman_filter.py
+++ b/tests/test_unscented_kalman_filter.py
@@ -1,12 +1,7 @@
-from unittest import TestCase
-
+import conftest  # Add root path to sys.path
 from Localization.unscented_kalman_filter import unscented_kalman_filter as m
 
-print(__file__)
-
-
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+def test1():
+    m.show_animation = False
+    m.main()
diff --git a/tests/test_visibility_road_map_planner.py b/tests/test_visibility_road_map_planner.py
index 5295769b40..bd758da5f4 100644
--- a/tests/test_visibility_road_map_planner.py
+++ b/tests/test_visibility_road_map_planner.py
@@ -1,17 +1,7 @@
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__))
-                + "/../PathPlanning/VoronoiRoadMap/")
-
-from unittest import TestCase
+import conftest  # Add root path to sys.path
 from PathPlanning.VoronoiRoadMap import voronoi_road_map as m
 
 
-print(__file__)
-
-
-class Test(TestCase):
-
-    def test1(self):
-        m.show_animation = False
-        m.main()
+def test1():
+    m.show_animation = False
+    m.main()
diff --git a/tests/test_voronoi_road_map_planner.py b/tests/test_voronoi_road_map_planner.py
index a1f860a108..51fe38ea33 100644
--- a/tests/test_voronoi_road_map_planner.py
+++ b/tests/test_voronoi_road_map_planner.py
@@ -1,17 +1,7 @@
-import os
-import sys
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__))
-                + "/../PathPlanning/VisibilityRoadMap/")
-
-from unittest import TestCase
+import conftest  # Add root path to sys.path
 from PathPlanning.VisibilityRoadMap import visibility_road_map as m
 
-print(__file__)
-
-
-class Test(TestCase):
 
-    def test1(self):
-        m.show_animation = False
-        m.main()
+def test1():
+    m.show_animation = False
+    m.main()
diff --git a/tests/test_wavefront_coverage_path_planner.py b/tests/test_wavefront_coverage_path_planner.py
index a72818c259..28bb5987e7 100644
--- a/tests/test_wavefront_coverage_path_planner.py
+++ b/tests/test_wavefront_coverage_path_planner.py
@@ -1,64 +1,63 @@
+import conftest  # Add root path to sys.path
 import os
-import sys
 import matplotlib.pyplot as plt
-from unittest import TestCase
-
-sys.path.append(os.path.dirname(
-    os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP")
-try:
-    import wavefront_coverage_path_planner
-except ImportError:
-    raise
+from PathPlanning.WavefrontCPP import wavefront_coverage_path_planner
 
 wavefront_coverage_path_planner.do_animation = False
 
 
-class TestPlanning(TestCase):
-    def wavefront_cpp(self, img, start, goal):
-        num_free = 0
-        for i in range(img.shape[0]):
-            for j in range(img.shape[1]):
-                num_free += 1 - img[i][j]
+def wavefront_cpp(img, start, goal):
+    num_free = 0
+    for i in range(img.shape[0]):
+        for j in range(img.shape[1]):
+            num_free += 1 - img[i][j]
+
+    DT = wavefront_coverage_path_planner.transform(
+        img, goal, transform_type='distance')
+    DT_path = wavefront_coverage_path_planner.wavefront(DT, start, goal)
+    assert len(DT_path) == num_free  # assert complete coverage
+
+    PT = wavefront_coverage_path_planner.transform(
+        img, goal, transform_type='path', alpha=0.01)
+    PT_path = wavefront_coverage_path_planner.wavefront(PT, start, goal)
+    assert len(PT_path) == num_free  # assert complete coverage
+
+
+def test_wavefront_CPP_1():
+    img_dir = os.path.dirname(
+        os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP"
+    img = plt.imread(os.path.join(img_dir, 'map', 'test.png'))
+    img = 1 - img
+
+    start = (43, 0)
+    goal = (0, 0)
 
-        DT = wavefront_coverage_path_planner.transform(
-            img, goal, transform_type='distance')
-        DT_path = wavefront_coverage_path_planner.wavefront(DT, start, goal)
-        self.assertEqual(len(DT_path), num_free)  # assert complete coverage
+    wavefront_cpp(img, start, goal)
 
-        PT = wavefront_coverage_path_planner.transform(
-            img, goal, transform_type='path', alpha=0.01)
-        PT_path = wavefront_coverage_path_planner.wavefront(PT, start, goal)
-        self.assertEqual(len(PT_path), num_free)  # assert complete coverage
 
-    def test_wavefront_CPP_1(self):
-        img_dir = os.path.dirname(
-            os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP"
-        img = plt.imread(os.path.join(img_dir, 'map', 'test.png'))
-        img = 1 - img
+def test_wavefront_CPP_2():
+    img_dir = os.path.dirname(
+        os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP"
+    img = plt.imread(os.path.join(img_dir, 'map', 'test_2.png'))
+    img = 1 - img
 
-        start = (43, 0)
-        goal = (0, 0)
+    start = (10, 0)
+    goal = (10, 40)
 
-        self.wavefront_cpp(img, start, goal)
+    wavefront_cpp(img, start, goal)
 
-    def test_wavefront_CPP_2(self):
-        img_dir = os.path.dirname(
-            os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP"
-        img = plt.imread(os.path.join(img_dir, 'map', 'test_2.png'))
-        img = 1 - img
 
-        start = (10, 0)
-        goal = (10, 40)
+def test_wavefront_CPP_3():
+    img_dir = os.path.dirname(
+        os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP"
+    img = plt.imread(os.path.join(img_dir, 'map', 'test_3.png'))
+    img = 1 - img
 
-        self.wavefront_cpp(img, start, goal)
+    start = (0, 0)
+    goal = (30, 30)
 
-    def test_wavefront_CPP_3(self):
-        img_dir = os.path.dirname(
-            os.path.abspath(__file__)) + "/../PathPlanning/WavefrontCPP"
-        img = plt.imread(os.path.join(img_dir, 'map', 'test_3.png'))
-        img = 1 - img
+    wavefront_cpp(img, start, goal)
 
-        start = (0, 0)
-        goal = (30, 30)
 
-        self.wavefront_cpp(img, start, goal)
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)

From 2731cdba001c7f6b1a4d77c5569d0614a911601e Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 16 Jan 2021 14:28:24 +0900
Subject: [PATCH 0467/1080] Use pytest_xdist for unit-test speed up in CI
 (#459)

* Remove unnecessary plot

* Remove unnecessary plot

* update appveyor.yml
---
 .github/workflows/Linux_CI.yml                |  2 ++
 .github/workflows/MacOS_CI.yml                |  5 ++---
 .../grid_based_sweep_coverage_path_planner.py | 21 +++++++++----------
 .../state_lattice_planner.py                  |  3 ++-
 appveyor.yml                                  |  3 ++-
 runtests.sh                                   |  2 +-
 tests/test_state_lattice_planner.py           |  3 ---
 7 files changed, 19 insertions(+), 20 deletions(-)

diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index d4ec886207..aa175eeef1 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -30,6 +30,8 @@ jobs:
       run: pip install mypy
     - name: install pycodestyle
       run: pip install pycodestyle
+    - name: install pytest-xdist
+      run: pip install pytest-xdist
     - name: mypy check
       run: |
         find AerialNavigation -name "*.py" | xargs mypy
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index 8c2fe84940..f4a5d48328 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -28,13 +28,12 @@ jobs:
           pip install -r requirements.txt
       - name: install coverage
         run: pip install coverage
-
       - name: install mypy
         run: pip install mypy
-
       - name: install pycodestyle
         run: pip install pycodestyle
-
+      - name: install pytest-xdist
+        run: pip install pytest-xdist
       - name: mypy check
         run: |
           find AerialNavigation -name "*.py" | xargs mypy
diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
index ca318ad596..984b0affdf 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -7,10 +7,10 @@
 import math
 from enum import IntEnum
 
-import matplotlib.pyplot as plt
 import numpy as np
 from scipy.spatial.transform import Rotation as Rot
 from Mapping.grid_map_lib.grid_map_lib import GridMap
+import matplotlib.pyplot as plt
 
 do_animation = True
 
@@ -240,8 +240,6 @@ def sweep_path_search(sweep_searcher, grid_map, grid_search_animation=False):
             grid_map.plot_grid_map(ax=ax)
             plt.pause(1.0)
 
-    grid_map.plot_grid_map()
-
     return px, py
 
 
@@ -289,13 +287,13 @@ def planning_animation(ox, oy, resolution):  # pragma: no cover
             plt.grid(True)
             plt.pause(0.1)
 
-    plt.cla()
-    plt.plot(ox, oy, "-xb")
-    plt.plot(px, py, "-r")
-    plt.axis("equal")
-    plt.grid(True)
-    plt.pause(0.1)
-    plt.close()
+        plt.cla()
+        plt.plot(ox, oy, "-xb")
+        plt.plot(px, py, "-r")
+        plt.axis("equal")
+        plt.grid(True)
+        plt.pause(0.1)
+        plt.close()
 
 
 def main():  # pragma: no cover
@@ -316,7 +314,8 @@ def main():  # pragma: no cover
     resolution = 5.0
     planning_animation(ox, oy, resolution)
 
-    plt.show()
+    if do_animation:
+        plt.show()
     print("done!!")
 
 
diff --git a/PathPlanning/StateLatticePlanner/state_lattice_planner.py b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
index c2819eb250..51eeb000d2 100644
--- a/PathPlanning/StateLatticePlanner/state_lattice_planner.py
+++ b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
@@ -25,7 +25,7 @@
 try:
     import model_predictive_trajectory_generator as planner
     import motion_model
-except:
+except ImportError:
     raise
 
 
@@ -323,6 +323,7 @@ def lane_state_sampling_test1():
 
 
 def main():
+    planner.show_animation = show_animation
     uniform_terminal_state_sampling_test1()
     uniform_terminal_state_sampling_test2()
     biased_terminal_state_sampling_test1()
diff --git a/appveyor.yml b/appveyor.yml
index 2a925433f4..efbb96131c 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -34,6 +34,7 @@ install:
   - SET PATH=%PATH%;C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin
   - "python -m pip install --upgrade pip"
   - "python -m pip install -r requirements.txt"
+  - "python -m pip install pytest-xdist"
 
   # Check that we have the expected version and architecture for Python
   - "python --version"
@@ -42,4 +43,4 @@ install:
 build: off
 
 test_script:
-  - "pytest tests -Werror --durations=0"
+  - "pytest tests -n auto -Werror --durations=0"
diff --git a/runtests.sh b/runtests.sh
index 12d1b80453..a2a683e571 100755
--- a/runtests.sh
+++ b/runtests.sh
@@ -5,4 +5,4 @@ echo "Run test suites! "
 # -Werror: warning as error
 # --durations=0: show ranking of test durations
 # -l (--showlocals); show local variables when test failed
-pytest tests -l -Werror --durations=0
+pytest -n auto tests -l -Werror --durations=0
diff --git a/tests/test_state_lattice_planner.py b/tests/test_state_lattice_planner.py
index 9e9e40c892..89ee042746 100644
--- a/tests/test_state_lattice_planner.py
+++ b/tests/test_state_lattice_planner.py
@@ -1,10 +1,7 @@
 import conftest  # Add root path to sys.path
 from PathPlanning.StateLatticePlanner import state_lattice_planner as m
-from PathPlanning.ModelPredictiveTrajectoryGenerator \
-    import model_predictive_trajectory_generator as m2
 
 
 def test1():
     m.show_animation = False
-    m2.show_animation = False
     m.main()

From 98f9b224657dc2d097666bde3e1979339d66a703 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 16 Jan 2021 16:07:28 +0900
Subject: [PATCH 0468/1080] add main script for missing tests (#461)

* add main script for missing tests

* Fix CodeQL warning
---
 .github/workflows/codeql.yml                           | 5 -----
 tests/test_lqr_speed_steer_control.py                  | 4 ++++
 tests/test_model_predictive_speed_and_steer_control.py | 3 +++
 tests/test_move_to_pose.py                             | 4 ++++
 tests/test_n_joint_arm_to_point_control.py             | 4 ++++
 tests/test_particle_filter.py                          | 4 ++++
 tests/test_potential_field_planning.py                 | 4 ++++
 tests/test_pure_pursuit.py                             | 4 ++++
 tests/test_quintic_polynomials_planner.py              | 4 ++++
 tests/test_raycasting_grid_map.py                      | 4 ++++
 tests/test_rectangle_fitting.py                        | 4 ++++
 tests/test_reeds_shepp_path_planning.py                | 4 ++++
 tests/test_rocket_powered_landing.py                   | 4 ++++
 tests/test_stanley_controller.py                       | 4 ++++
 tests/test_state_lattice_planner.py                    | 4 ++++
 tests/test_two_joint_arm_to_point_control.py           | 4 ++++
 tests/test_unscented_kalman_filter.py                  | 4 ++++
 tests/test_visibility_road_map_planner.py              | 4 ++++
 tests/test_voronoi_road_map_planner.py                 | 4 ++++
 19 files changed, 71 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index 526b465eec..078590997d 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -20,11 +20,6 @@ jobs:
         # a pull request then we can checkout the head.
         fetch-depth: 2
 
-    # If this run was triggered by a pull request event, then checkout
-    # the head of the pull request instead of the merge commit.
-    - run: git checkout HEAD^2
-      if: ${{ github.event_name == 'pull_request' }}
-      
     # Initializes the CodeQL tools for scanning.
     - name: Initialize CodeQL
       uses: github/codeql-action/init@v1
diff --git a/tests/test_lqr_speed_steer_control.py b/tests/test_lqr_speed_steer_control.py
index 9e921895ba..cee9759a26 100644
--- a/tests/test_lqr_speed_steer_control.py
+++ b/tests/test_lqr_speed_steer_control.py
@@ -5,3 +5,7 @@
 def test_1():
     m.show_animation = False
     m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_model_predictive_speed_and_steer_control.py b/tests/test_model_predictive_speed_and_steer_control.py
index 27b6b79cf5..9a50e48044 100644
--- a/tests/test_model_predictive_speed_and_steer_control.py
+++ b/tests/test_model_predictive_speed_and_steer_control.py
@@ -12,3 +12,6 @@ def test_1():
     def test_2():
         m.show_animation = False
         m.main2()
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_move_to_pose.py b/tests/test_move_to_pose.py
index 6a5a4cf4fb..c6a6b8bc76 100644
--- a/tests/test_move_to_pose.py
+++ b/tests/test_move_to_pose.py
@@ -5,3 +5,7 @@
 def test_1():
     m.show_animation = False
     m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_n_joint_arm_to_point_control.py b/tests/test_n_joint_arm_to_point_control.py
index a87271b471..1d886d3670 100644
--- a/tests/test_n_joint_arm_to_point_control.py
+++ b/tests/test_n_joint_arm_to_point_control.py
@@ -9,3 +9,7 @@
 def test1():
     m.show_animation = False
     m.animation()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_particle_filter.py b/tests/test_particle_filter.py
index dad823ee9e..13a20f602a 100644
--- a/tests/test_particle_filter.py
+++ b/tests/test_particle_filter.py
@@ -5,3 +5,7 @@
 def test_1():
     m.show_animation = False
     m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_potential_field_planning.py b/tests/test_potential_field_planning.py
index c473a23968..ce178d793d 100644
--- a/tests/test_potential_field_planning.py
+++ b/tests/test_potential_field_planning.py
@@ -5,3 +5,7 @@
 def test1():
     m.show_animation = False
     m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_pure_pursuit.py b/tests/test_pure_pursuit.py
index 2f0b83ae36..0e0b83bf6c 100644
--- a/tests/test_pure_pursuit.py
+++ b/tests/test_pure_pursuit.py
@@ -5,3 +5,7 @@
 def test1():
     m.show_animation = False
     m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_quintic_polynomials_planner.py b/tests/test_quintic_polynomials_planner.py
index bee383a7ea..43f3c6bada 100644
--- a/tests/test_quintic_polynomials_planner.py
+++ b/tests/test_quintic_polynomials_planner.py
@@ -5,3 +5,7 @@
 def test1():
     m.show_animation = False
     m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_raycasting_grid_map.py b/tests/test_raycasting_grid_map.py
index 60ecf76902..f08ae9277e 100644
--- a/tests/test_raycasting_grid_map.py
+++ b/tests/test_raycasting_grid_map.py
@@ -5,3 +5,7 @@
 def test1():
     m.show_animation = False
     m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_rectangle_fitting.py b/tests/test_rectangle_fitting.py
index 20dc9cafbf..cb28b6035e 100644
--- a/tests/test_rectangle_fitting.py
+++ b/tests/test_rectangle_fitting.py
@@ -5,3 +5,7 @@
 def test1():
     m.show_animation = False
     m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_reeds_shepp_path_planning.py b/tests/test_reeds_shepp_path_planning.py
index 150f84fb35..f1ce82d4c2 100644
--- a/tests/test_reeds_shepp_path_planning.py
+++ b/tests/test_reeds_shepp_path_planning.py
@@ -40,3 +40,7 @@ def test2():
 
         check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw,
                              end_x, end_y, end_yaw)
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_rocket_powered_landing.py b/tests/test_rocket_powered_landing.py
index 01d2f4f832..7cf69567e1 100644
--- a/tests/test_rocket_powered_landing.py
+++ b/tests/test_rocket_powered_landing.py
@@ -8,3 +8,7 @@
     def test1():
         m.show_animation = False
         m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_stanley_controller.py b/tests/test_stanley_controller.py
index 7920273db4..a1d8073764 100644
--- a/tests/test_stanley_controller.py
+++ b/tests/test_stanley_controller.py
@@ -5,3 +5,7 @@
 def test1():
     m.show_animation = False
     m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_state_lattice_planner.py b/tests/test_state_lattice_planner.py
index 89ee042746..0c14222e81 100644
--- a/tests/test_state_lattice_planner.py
+++ b/tests/test_state_lattice_planner.py
@@ -5,3 +5,7 @@
 def test1():
     m.show_animation = False
     m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_two_joint_arm_to_point_control.py b/tests/test_two_joint_arm_to_point_control.py
index 24cc26f118..1de4fcd805 100644
--- a/tests/test_two_joint_arm_to_point_control.py
+++ b/tests/test_two_joint_arm_to_point_control.py
@@ -6,3 +6,7 @@
 def test1():
     m.show_animation = False
     m.animation()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_unscented_kalman_filter.py b/tests/test_unscented_kalman_filter.py
index c4f79e8906..b7dda6e276 100644
--- a/tests/test_unscented_kalman_filter.py
+++ b/tests/test_unscented_kalman_filter.py
@@ -5,3 +5,7 @@
 def test1():
     m.show_animation = False
     m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_visibility_road_map_planner.py b/tests/test_visibility_road_map_planner.py
index bd758da5f4..5a663d289d 100644
--- a/tests/test_visibility_road_map_planner.py
+++ b/tests/test_visibility_road_map_planner.py
@@ -5,3 +5,7 @@
 def test1():
     m.show_animation = False
     m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_voronoi_road_map_planner.py b/tests/test_voronoi_road_map_planner.py
index 51fe38ea33..b0b7550fb2 100644
--- a/tests/test_voronoi_road_map_planner.py
+++ b/tests/test_voronoi_road_map_planner.py
@@ -5,3 +5,7 @@
 def test1():
     m.show_animation = False
     m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)

From 6d0b8ccfdbfeeb23961687926d103867b4bf1d72 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 26 Jan 2021 21:16:22 +0900
Subject: [PATCH 0469/1080] Try to fix CI (#469)

---
 .github/workflows/Linux_CI.yml | 18 +++++++++---------
 .github/workflows/MacOS_CI.yml | 19 +++++++++----------
 2 files changed, 18 insertions(+), 19 deletions(-)

diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index aa175eeef1..2962028d6f 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -34,15 +34,15 @@ jobs:
       run: pip install pytest-xdist
     - name: mypy check
       run: |
-        find AerialNavigation -name "*.py" | xargs mypy
-        find ArmNavigation -name "*.py" | xargs mypy
-        find Bipedal -name "*.py" | xargs mypy
-        find InvertedPendulumCart -name "*.py" | xargs mypy
-        find Localization -name "*.py" | xargs mypy
-        find Mapping -name "*.py" | xargs mypy
-        find PathPlanning -name "*.py" | xargs mypy
-        find PathTracking -name "*.py" | xargs mypy
-        find SLAM -name "*.py" | xargs mypy
+        mypy -p AerialNavigation
+        mypy -p ArmNavigation
+        mypy -p Bipedal
+        mypy -p InvertedPendulumCart
+        mypy -p Localization
+        mypy -p Mapping
+        mypy -p PathPlanning
+        mypy -p PathTracking
+        mypy -p SLAM
     - name: do diff style check
       run: bash rundiffstylecheck.sh
     - name: do all unit tests
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index f4a5d48328..5bd9a60828 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -36,16 +36,15 @@ jobs:
         run: pip install pytest-xdist
       - name: mypy check
         run: |
-          find AerialNavigation -name "*.py" | xargs mypy
-          find ArmNavigation -name "*.py" | xargs mypy
-          find Bipedal -name "*.py" | xargs mypy
-          find InvertedPendulumCart -name "*.py" | xargs mypy
-          find Localization -name "*.py" | xargs mypy
-          find Mapping -name "*.py" | xargs mypy
-          find PathPlanning -name "*.py" | xargs mypy
-          find PathTracking -name "*.py" | xargs mypy
-          find SLAM -name "*.py" | xargs mypy
-
+          mypy -p AerialNavigation
+          mypy -p ArmNavigation
+          mypy -p Bipedal
+          mypy -p InvertedPendulumCart
+          mypy -p Localization
+          mypy -p Mapping
+          mypy -p PathPlanning
+          mypy -p PathTracking
+          mypy -p SLAM
       - name: do diff style check
         run: bash rundiffstylecheck.sh
 

From 51f675b9c3bb2c7a28efbb891f9bb6edaa1276b5 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 26 Jan 2021 21:36:51 +0900
Subject: [PATCH 0470/1080] Bump pytest from 6.2.1 to 6.2.2 (#466)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 6.2.1 to 6.2.2.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/master/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/6.2.1...6.2.2)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 7a4a17fcca..08965aeefa 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,4 +3,4 @@ scipy == 1.6.0
 pandas == 1.2.0
 matplotlib == 3.3.3
 cvxpy == 1.1.7
-pytest == 6.2.1
+pytest == 6.2.2

From 6e2a741c7339fdaa74ecd6a671fbc25a69f7a649 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 26 Jan 2021 21:37:15 +0900
Subject: [PATCH 0471/1080] Bump pandas from 1.2.0 to 1.2.1 (#467)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.2.0 to 1.2.1.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.2.0...v1.2.1)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 08965aeefa..78aacf133c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.19.5
 scipy == 1.6.0
-pandas == 1.2.0
+pandas == 1.2.1
 matplotlib == 3.3.3
 cvxpy == 1.1.7
 pytest == 6.2.2

From 9425e44208065a0e08b1d6425ed101d707091a2f Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 29 Jan 2021 22:34:29 +0900
Subject: [PATCH 0472/1080] Update README.md (#472)

---
 README.md        | 77 ++++++++++++++++++++++++++----------------------
 environment.yml  |  1 -
 requirements.txt |  1 -
 3 files changed, 41 insertions(+), 38 deletions(-)

diff --git a/README.md b/README.md
index ec0d1f74ec..804966d121 100644
--- a/README.md
+++ b/README.md
@@ -71,11 +71,13 @@ Python codes for robotics algorithm.
    * [Contribution](#contribution)
    * [Citing](#citing)
    * [Support](#support)
+   * [Sponsors](#Sponsors)
+      * [JetBrains](#JetBrains)
    * [Authors](#authors)
 
 # What is this?
 
-This is a Python code collection of robotics algorithms, especially for autonomous navigation.
+This is a Python code collection of robotics algorithms.
 
 Features:
 
@@ -92,6 +94,8 @@ See this paper for more details:
 
 # Requirements
 
+For running each sample code:
+
 - Python 3.9.x
 
 - numpy
@@ -103,8 +107,18 @@ See this paper for more details:
 - pandas
 
 - [cvxpy](https://www.cvxpy.org/index.html) 
+
+For development:
+  
+- pytest (for unit tests)
+  
+- pytest-xdist (for parallel unit tests)
+  
+- mypy (for type check)
   
-- pytest (only for unit tests)
+- Sphinx (for document generation)
+  
+- pycodestyle (for code style check)
 
 # Documentation
 
@@ -123,11 +137,15 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation
 > git clone https://github.com/AtsushiSakai/PythonRobotics.git
 
 
-2. Install the required libraries. You can use environment.yml with conda command.
+2. Install the required libraries.
+
+using conda :
 
 > conda env create -f environment.yml
-> using pip :-
-         pip install -r requirements.txt
+ 
+using pip :
+
+> pip install -r requirements.txt
 
 
 3. Execute python script in each directory.
@@ -150,11 +168,11 @@ This is a sensor fusion localization with Particle Filter(PF).
 
 The blue line is true trajectory, the black line is dead reckoning trajectory,
 
-and the red line is estimated trajectory with PF.
+and the red line is an estimated trajectory with PF.
 
 It is assumed that the robot can measure a distance from landmarks (RFID).
 
-This measurements are used for PF localization.
+These measurements are used for PF localization.
 
 Ref:
 
@@ -222,7 +240,7 @@ Simultaneous Localization and Mapping(SLAM) examples
 
 This is a 2D ICP matching example with singular value decomposition.
 
-It can calculate a rotation matrix and a translation vector between points to points.
+It can calculate a rotation matrix, and a translation vector between points and points.
 
 ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/iterative_closest_point/animation.gif)
 
@@ -267,7 +285,7 @@ This is a 2D navigation sample code with Dynamic Window Approach.
 
 ### Dijkstra algorithm
 
-This is a 2D grid based shortest path planning with Dijkstra's algorithm.
+This is a 2D grid based the shortest path planning with Dijkstra's algorithm.
 
 ![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Dijkstra/animation.gif)
 
@@ -275,7 +293,7 @@ In the animation, cyan points are searched nodes.
 
 ### A\* algorithm
 
-This is a 2D grid based shortest path planning with A star algorithm.
+This is a 2D grid based the shortest path planning with A star algorithm.
 
 ![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/AStar/animation.gif)
 
@@ -369,7 +387,7 @@ This is a path planning simulation with LQR-RRT\*.
 
 A double integrator motion model is used for LQR local planner.
 
-![LQRRRT](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif)
+![LQR_RRT](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif)
 
 Ref:
 
@@ -384,7 +402,7 @@ Motion planning with quintic polynomials.
 
 ![2](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/QuinticPolynomialsPlanner/animation.gif)
 
-It can calculate 2D path, velocity, and acceleration profile based on quintic polynomials.
+It can calculate a 2D path, velocity, and acceleration profile based on quintic polynomials.
 
 Ref:
 
@@ -420,7 +438,7 @@ This is optimal trajectory generation in a Frenet Frame.
 
 The cyan line is the target course and black crosses are obstacles.
 
-The red line is predicted path.
+The red line is the predicted path.
 
 Ref:
 
@@ -507,9 +525,9 @@ Ref:
 
 N joint arm to a point control simulation.
 
-This is a interactive simulation.
+This is an interactive simulation.
 
-You can set the goal position of the end effector with left-click on the ploting area. 
+You can set the goal position of the end effector with left-click on the plotting area. 
 
 ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/n_joint_arm_to_point_control/animation.gif)
 
@@ -544,9 +562,9 @@ Ref:
 
 ## bipedal planner with inverted pendulum
 
-This is a bipedal planner for modifying footsteps with inverted pendulum.
+This is a bipedal planner for modifying footsteps for an inverted pendulum.
 
-You can set the footsteps and the planner will modify those automatically.
+You can set the footsteps, and the planner will modify those automatically.
 
 ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Bipedal/bipedal_planner/animation.gif)
 
@@ -566,8 +584,6 @@ This is a list of other user's comment and references:[users\_comments](https://
 
 Any contribution is welcome!!
 
-If your PR is merged multiple times, I will add your account to the author list.
-
 # Citing
 
 If you use this project's code for your academic work, we encourage you to cite [our papers](https://arxiv.org/abs/1808.10703) 
@@ -584,26 +600,15 @@ If you or your company would like to support this project, please consider:
 
 - [One-time donation via PayPal](https://www.paypal.me/myenigmapay/)
 
-# Authors
-
-- [Atsushi Sakai](https://github.com/AtsushiSakai/)
-
-- [Daniel Ingram](https://github.com/daniel-s-ingram)
-
-- [Joe Dinius](https://github.com/jwdinius)
+If you would like to support us in some other way, please contact with creating an issue.
 
-- [Karan Chawla](https://github.com/karanchawla)
+# Sponsors
 
-- [Antonin RAFFIN](https://github.com/araffin)
+## [JetBrains](https://www.jetbrains.com/)
 
-- [Alexis Paques](https://github.com/AlexisTM)
+They are providing a free license of their IDEs for this OSS development.   
 
-- [Ryohei Sasaki](https://github.com/rsasaki0109)
-
-- [Göktuğ Karakaşlı](https://github.com/goktug97)
-
-- [Guillaume Jacquenot](https://github.com/Gjacquenot)
-
-- [Erwin Lejeune](https://github.com/guilyx)
+# Authors
 
+- [Contributors to AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/graphs/contributors)
 
diff --git a/environment.yml b/environment.yml
index fdfd25ca0a..a5361d4a29 100644
--- a/environment.yml
+++ b/environment.yml
@@ -7,4 +7,3 @@ dependencies:
 - pandas
 - cvxpy
 - matplotlib
-- coverage
diff --git a/requirements.txt b/requirements.txt
index 78aacf133c..3a582f4cff 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,4 +3,3 @@ scipy == 1.6.0
 pandas == 1.2.1
 matplotlib == 3.3.3
 cvxpy == 1.1.7
-pytest == 6.2.2

From b3d39f5039aabbc45a4f85fa60096ae8a8bbbe01 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 2 Feb 2021 07:22:13 +0900
Subject: [PATCH 0473/1080] Bump matplotlib from 3.3.3 to 3.3.4 (#475)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.3.3 to 3.3.4.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.3.3...v3.3.4)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 3a582f4cff..517aef344f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.19.5
 scipy == 1.6.0
 pandas == 1.2.1
-matplotlib == 3.3.3
+matplotlib == 3.3.4
 cvxpy == 1.1.7

From f495ae8983b247cbfca5c6fa44f4a38a9772a3a0 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Feb 2021 07:22:11 +0900
Subject: [PATCH 0474/1080] Bump cvxpy from 1.1.7 to 1.1.10 (#478)

Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.7 to 1.1.10.
- [Release notes](https://github.com/cvxgrp/cvxpy/releases)
- [Changelog](https://github.com/cvxgrp/cvxpy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.7...v1.1.10)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 517aef344f..ef4a59c522 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy == 1.19.5
 scipy == 1.6.0
 pandas == 1.2.1
 matplotlib == 3.3.4
-cvxpy == 1.1.7
+cvxpy == 1.1.10

From b8f28cc6a3c42b9d7a151516fe2d9348d6fd6393 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 10 Feb 2021 22:02:51 +0900
Subject: [PATCH 0475/1080] fix deprecation warning for latest numpy (#480)

* fix deprication version for latest numpy

* fix deprication version for latest numpy

* fix deprication version for latest numpy

* fix deprication version for latest numpy
---
 .../lidar_to_grid_map/lidar_to_grid_map.py    |  2 +-
 ...ral_spanning_tree_coverage_path_planner.py |  2 +-
 .../wavefront_coverage_path_planner.py        |  2 +-
 SLAM/EKFSLAM/ekf_slam.py                      |  6 +-
 SLAM/GraphBasedSLAM/graphslam/graph.py        | 57 ++++++++++++-------
 SLAM/GraphBasedSLAM/graphslam/load.py         |  8 +--
 SLAM/GraphBasedSLAM/graphslam/pose/se2.py     | 27 ++++++---
 SLAM/GraphBasedSLAM/graphslam/util.py         |  2 +-
 requirements.txt                              |  2 +-
 9 files changed, 69 insertions(+), 39 deletions(-)

diff --git a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
index 57278d4a47..ad987392f5 100644
--- a/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
+++ b/Mapping/lidar_to_grid_map/lidar_to_grid_map.py
@@ -193,7 +193,7 @@ def generate_ray_casting_grid_map(ox, oy, xy_resolution, breshen=True):
                                         (x_w, y_w),
                                         (min_x, min_y), xy_resolution)
         flood_fill((center_x, center_y), occupancy_map)
-        occupancy_map = np.array(occupancy_map, dtype=np.float)
+        occupancy_map = np.array(occupancy_map, dtype=float)
         for (x, y) in zip(ox, oy):
             ix = int(round((x - min_x) / xy_resolution))
             iy = int(round((y - min_y) / xy_resolution))
diff --git a/PathPlanning/SpiralSpanningTreeCPP/spiral_spanning_tree_coverage_path_planner.py b/PathPlanning/SpiralSpanningTreeCPP/spiral_spanning_tree_coverage_path_planner.py
index 4a6b42db0b..a8c513e6b1 100644
--- a/PathPlanning/SpiralSpanningTreeCPP/spiral_spanning_tree_coverage_path_planner.py
+++ b/PathPlanning/SpiralSpanningTreeCPP/spiral_spanning_tree_coverage_path_planner.py
@@ -42,7 +42,7 @@ def plan(self, start):
         """
 
         visit_times = np.zeros(
-            (self.merged_map_height, self.merged_map_width), dtype=np.int)
+            (self.merged_map_height, self.merged_map_width), dtype=int)
         visit_times[start[0]][start[1]] = 1
 
         # generate route by
diff --git a/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py b/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py
index aaf77e02fc..8968c54dcc 100644
--- a/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py
+++ b/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py
@@ -47,7 +47,7 @@ def transform(
     else:
         sys.exit('Unsupported distance type.')
 
-    transform_matrix = float('inf') * np.ones_like(grid_map, dtype=np.float)
+    transform_matrix = float('inf') * np.ones_like(grid_map, dtype=float)
     transform_matrix[src[0], src[1]] = 0
     if transform_type == 'distance':
         eT = np.zeros_like(grid_map)
diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py
index 8fa1a771c2..bb648ce9d9 100644
--- a/SLAM/EKFSLAM/ekf_slam.py
+++ b/SLAM/EKFSLAM/ekf_slam.py
@@ -115,9 +115,9 @@ def jacob_motion(x, u):
     Fx = np.hstack((np.eye(STATE_SIZE), np.zeros(
         (STATE_SIZE, LM_SIZE * calc_n_lm(x)))))
 
-    jF = np.array([[0.0, 0.0, -DT * u[0] * math.sin(x[2, 0])],
-                   [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])],
-                   [0.0, 0.0, 0.0]], dtype=np.float64)
+    jF = np.array([[0.0, 0.0, -DT * u[0, 0] * math.sin(x[2, 0])],
+                   [0.0, 0.0, DT * u[0, 0] * math.cos(x[2, 0])],
+                   [0.0, 0.0, 0.0]], dtype=float)
 
     G = np.eye(STATE_SIZE) + Fx.T @ jF @ Fx
 
diff --git a/SLAM/GraphBasedSLAM/graphslam/graph.py b/SLAM/GraphBasedSLAM/graphslam/graph.py
index 83c181dc79..672f63d60e 100644
--- a/SLAM/GraphBasedSLAM/graphslam/graph.py
+++ b/SLAM/GraphBasedSLAM/graphslam/graph.py
@@ -8,16 +8,14 @@
 
 """
 
-
+import warnings
 from collections import defaultdict
 from functools import reduce
-import warnings
 
+import matplotlib.pyplot as plt
 import numpy as np
 from scipy.sparse import SparseEfficiencyWarning, lil_matrix
 from scipy.sparse.linalg import spsolve
-import matplotlib.pyplot as plt
-
 
 warnings.simplefilter("ignore", SparseEfficiencyWarning)
 warnings.filterwarnings("ignore", category=SparseEfficiencyWarning)
@@ -44,6 +42,7 @@ class _Chi2GradientHessian:
         The contributions to the Hessian matrix
 
     """
+
     def __init__(self, dim):
         self.chi2 = 0.
         self.dim = dim
@@ -59,7 +58,6 @@ def update(chi2_grad_hess, incoming):
         chi2_grad_hess : _Chi2GradientHessian
             The ``_Chi2GradientHessian`` that will be updated
         incoming : tuple
-            TODO
 
         """
         chi2_grad_hess.chi2 += incoming[0]
@@ -100,6 +98,7 @@ class Graph(object):
         A list of the vertices in the graph
 
     """
+
     def __init__(self, edges, vertices):
         # The vertices and edges lists
         self._edges = edges
@@ -117,14 +116,16 @@ def _link_edges(self):
 
         """
         index_id_dict = {i: v.id for i, v in enumerate(self._vertices)}
-        id_index_dict = {v_id: v_index for v_index, v_id in index_id_dict.items()}
+        id_index_dict = {v_id: v_index for v_index, v_id in
+                         index_id_dict.items()}
 
         # Fill in the vertices' `index` attribute
         for v in self._vertices:
             v.index = id_index_dict[v.id]
 
         for e in self._edges:
-            e.vertices = [self._vertices[id_index_dict[v_id]] for v_id in e.vertex_ids]
+            e.vertices = [self._vertices[id_index_dict[v_id]] for v_id in
+                          e.vertex_ids]
 
     def calc_chi2(self):
         r"""Calculate the :math:`\chi^2` error for the ``Graph``.
@@ -144,22 +145,34 @@ def _calc_chi2_gradient_hessian(self):
         """
         n = len(self._vertices)
         dim = len(self._vertices[0].pose.to_compact())
-        chi2_gradient_hessian = reduce(_Chi2GradientHessian.update, (e.calc_chi2_gradient_hessian() for e in self._edges), _Chi2GradientHessian(dim))
+        chi2_gradient_hessian = reduce(_Chi2GradientHessian.update,
+                                       (e.calc_chi2_gradient_hessian()
+                                        for e in self._edges),
+                                       _Chi2GradientHessian(dim))
 
         self._chi2 = chi2_gradient_hessian.chi2
 
         # Fill in the gradient vector
-        self._gradient = np.zeros(n * dim, dtype=np.float64)
-        for idx, contrib in chi2_gradient_hessian.gradient.items():
-            self._gradient[idx * dim: (idx + 1) * dim] += contrib
+        self._gradient = np.zeros(n * dim, dtype=float)
+        for idx, cont in chi2_gradient_hessian.gradient.items():
+            self._gradient[idx * dim: (idx + 1) * dim] += cont
 
         # Fill in the Hessian matrix
-        self._hessian = lil_matrix((n * dim, n * dim), dtype=np.float64)
-        for (row_idx, col_idx), contrib in chi2_gradient_hessian.hessian.items():
-            self._hessian[row_idx * dim: (row_idx + 1) * dim, col_idx * dim: (col_idx + 1) * dim] = contrib
+        self._hessian = lil_matrix((n * dim, n * dim), dtype=float)
+        for (row_idx, col_idx), cont in chi2_gradient_hessian.hessian.items():
+            x_start = row_idx * dim
+            x_end = (row_idx + 1) * dim
+            y_start = col_idx * dim
+            y_end = (col_idx + 1) * dim
+            self._hessian[x_start:x_end, y_start:y_end] = cont
 
             if row_idx != col_idx:
-                self._hessian[col_idx * dim: (col_idx + 1) * dim, row_idx * dim: (row_idx + 1) * dim] = np.transpose(contrib)
+                x_start = col_idx * dim
+                x_end = (col_idx + 1) * dim
+                y_start = row_idx * dim
+                y_end = (row_idx + 1) * dim
+                self._hessian[x_start:x_end, y_start:y_end] = \
+                    np.transpose(cont)
 
     def optimize(self, tol=1e-4, max_iter=20, fix_first_pose=True):
         r"""Optimize the :math:`\chi^2` error for the ``Graph``.
@@ -189,8 +202,10 @@ def optimize(self, tol=1e-4, max_iter=20, fix_first_pose=True):
 
             # Check for convergence (from the previous iteration); this avoids having to calculate chi^2 twice
             if i > 0:
-                rel_diff = (chi2_prev - self._chi2) / (chi2_prev + np.finfo(float).eps)
-                print("{:9d} {:20.4f} {:18.6f}".format(i, self._chi2, -rel_diff))
+                rel_diff = (chi2_prev - self._chi2) / (
+                            chi2_prev + np.finfo(float).eps)
+                print(
+                    "{:9d} {:20.4f} {:18.6f}".format(i, self._chi2, -rel_diff))
                 if self._chi2 < chi2_prev and rel_diff < tol:
                     return
             else:
@@ -207,7 +222,7 @@ def optimize(self, tol=1e-4, max_iter=20, fix_first_pose=True):
                 self._gradient[:dim] = 0.
 
             # Solve for the updates
-            dx = spsolve(self._hessian, -self._gradient)  # pylint: disable=invalid-unary-operand-type
+            dx = spsolve(self._hessian, -self._gradient)
 
             # Apply the updates
             for v, dx_i in zip(self._vertices, np.split(dx, n)):
@@ -216,7 +231,8 @@ def optimize(self, tol=1e-4, max_iter=20, fix_first_pose=True):
         # If we reached the maximum number of iterations, print out the final iteration's results
         self.calc_chi2()
         rel_diff = (chi2_prev - self._chi2) / (chi2_prev + np.finfo(float).eps)
-        print("{:9d} {:20.4f} {:18.6f}".format(max_iter, self._chi2, -rel_diff))
+        print("{:9d} {:20.4f} {:18.6f}".format(
+            max_iter, self._chi2, -rel_diff))
 
     def to_g2o(self, outfile):
         """Save the graph in .g2o format.
@@ -234,7 +250,8 @@ def to_g2o(self, outfile):
             for e in self._edges:
                 f.write(e.to_g2o())
 
-    def plot(self, vertex_color='r', vertex_marker='o', vertex_markersize=3, edge_color='b', title=None):
+    def plot(self, vertex_color='r', vertex_marker='o', vertex_markersize=3,
+             edge_color='b', title=None):
         """Plot the graph.
 
         Parameters
diff --git a/SLAM/GraphBasedSLAM/graphslam/load.py b/SLAM/GraphBasedSLAM/graphslam/load.py
index b0dd023d93..ebf4291456 100644
--- a/SLAM/GraphBasedSLAM/graphslam/load.py
+++ b/SLAM/GraphBasedSLAM/graphslam/load.py
@@ -8,7 +8,6 @@
 
 """
 
-
 import logging
 
 import numpy as np
@@ -19,7 +18,6 @@
 from .util import upper_triangular_matrix_to_full_matrix
 from .vertex import Vertex
 
-
 _LOGGER = logging.getLogger(__name__)
 
 
@@ -44,7 +42,8 @@ def load_g2o_se2(infile):
         for line in f.readlines():
             if line.startswith("VERTEX_SE2"):
                 numbers = line[10:].split()
-                arr = np.array([float(number) for number in numbers[1:]], dtype=np.float64)
+                arr = np.array([float(number) for number in numbers[1:]],
+                               dtype=float)
                 p = PoseSE2(arr[:2], arr[2])
                 v = Vertex(int(numbers[0]), p)
                 vertices.append(v)
@@ -52,7 +51,8 @@ def load_g2o_se2(infile):
 
             if line.startswith("EDGE_SE2"):
                 numbers = line[9:].split()
-                arr = np.array([float(number) for number in numbers[2:]], dtype=np.float64)
+                arr = np.array([float(number) for number in numbers[2:]],
+                               dtype=float)
                 vertex_ids = [int(numbers[0]), int(numbers[1])]
                 estimate = PoseSE2(arr[:2], arr[2])
                 information = upper_triangular_matrix_to_full_matrix(arr[3:], 3)
diff --git a/SLAM/GraphBasedSLAM/graphslam/pose/se2.py b/SLAM/GraphBasedSLAM/graphslam/pose/se2.py
index 88518510e7..e89317a06f 100644
--- a/SLAM/GraphBasedSLAM/graphslam/pose/se2.py
+++ b/SLAM/GraphBasedSLAM/graphslam/pose/se2.py
@@ -9,7 +9,6 @@
 """
 
 import math
-
 import numpy as np
 
 from ..util import neg_pi_to_pi
@@ -26,8 +25,10 @@ class PoseSE2(np.ndarray):
         The angle of the pose (in radians)
 
     """
+
     def __new__(cls, position, orientation):
-        obj = np.array([position[0], position[1], neg_pi_to_pi(orientation)], dtype=np.float64).view(cls)
+        obj = np.array([position[0], position[1], neg_pi_to_pi(orientation)],
+                       dtype=float).view(cls)
         return obj
 
     # pylint: disable=arguments-differ
@@ -73,7 +74,9 @@ def to_matrix(self):
             The pose as an :math:`SE(2)` matrix
 
         """
-        return np.array([[np.cos(self[2]), -np.sin(self[2]), self[0]], [np.sin(self[2]), np.cos(self[2]), self[1]], [0., 0., 1.]], dtype=np.float64)
+        return np.array([[np.cos(self[2]), -np.sin(self[2]), self[0]],
+                         [np.sin(self[2]), np.cos(self[2]), self[1]],
+                         [0., 0., 1.]], dtype=float)
 
     @classmethod
     def from_matrix(cls, matrix):
@@ -90,7 +93,8 @@ def from_matrix(cls, matrix):
             The matrix as a `PoseSE2` object
 
         """
-        return cls([matrix[0, 2], matrix[1, 2]], math.atan2(matrix[1, 0], matrix[0, 0]))
+        return cls([matrix[0, 2], matrix[1, 2]],
+                   math.atan2(matrix[1, 0], matrix[0, 0]))
 
     # ======================================================================= #
     #                                                                         #
@@ -131,7 +135,9 @@ def inverse(self):
             The pose's inverse
 
         """
-        return PoseSE2([-self[0] * np.cos(self[2]) - self[1] * np.sin(self[2]), self[0] * np.sin(self[2]) - self[1] * np.cos([self[2]])], -self[2])
+        return PoseSE2([-self[0] * np.cos(self[2]) - self[1] * np.sin(self[2]),
+                        self[0] * np.sin(self[2]) - self[1] * np.cos(
+                            [self[2]])], -self[2])
 
     # ======================================================================= #
     #                                                                         #
@@ -152,7 +158,10 @@ def __add__(self, other):
             The result of pose composition
 
         """
-        return PoseSE2([self[0] + other[0] * np.cos(self[2]) - other[1] * np.sin(self[2]), self[1] + other[0] * np.sin(self[2]) + other[1] * np.cos(self[2])], neg_pi_to_pi(self[2] + other[2]))
+        return PoseSE2(
+            [self[0] + other[0] * np.cos(self[2]) - other[1] * np.sin(self[2]),
+             self[1] + other[0] * np.sin(self[2]) + other[1] * np.cos(self[2])
+             ], neg_pi_to_pi(self[2] + other[2]))
 
     def __sub__(self, other):
         r"""Subtract poses (i.e., inverse pose composition): :math:`p_1 \ominus p_2`.
@@ -168,4 +177,8 @@ def __sub__(self, other):
             The result of inverse pose composition
 
         """
-        return PoseSE2([(self[0] - other[0]) * np.cos(other[2]) + (self[1] - other[1]) * np.sin(other[2]), (other[0] - self[0]) * np.sin(other[2]) + (self[1] - other[1]) * np.cos(other[2])], neg_pi_to_pi(self[2] - other[2]))
+        return PoseSE2([(self[0] - other[0]) * np.cos(other[2]) + (
+                    self[1] - other[1]) * np.sin(other[2]),
+                        (other[0] - self[0]) * np.sin(other[2]) + (
+                                    self[1] - other[1]) * np.cos(other[2])],
+                       neg_pi_to_pi(self[2] - other[2]))
diff --git a/SLAM/GraphBasedSLAM/graphslam/util.py b/SLAM/GraphBasedSLAM/graphslam/util.py
index 070dc1aa06..8f7c8e174b 100644
--- a/SLAM/GraphBasedSLAM/graphslam/util.py
+++ b/SLAM/GraphBasedSLAM/graphslam/util.py
@@ -71,7 +71,7 @@ def upper_triangular_matrix_to_full_matrix(arr, n):
     triu1 = np.triu_indices(n, 1)
     tril1 = np.tril_indices(n, -1)
 
-    mat = np.zeros((n, n), dtype=np.float64)
+    mat = np.zeros((n, n), dtype=float)
     mat[triu0] = arr
     mat[tril1] = mat[triu1]
 
diff --git a/requirements.txt b/requirements.txt
index ef4a59c522..79cbc1b11e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.19.5
+numpy == 1.20.1
 scipy == 1.6.0
 pandas == 1.2.1
 matplotlib == 3.3.4

From 58390e74f33e26071c25a82fcd30402614d19152 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 16 Feb 2021 07:29:05 +0900
Subject: [PATCH 0476/1080] Bump pandas from 1.2.1 to 1.2.2 (#482)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.2.1 to 1.2.2.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.2.1...v1.2.2)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 79cbc1b11e..8402a5206e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.1
 scipy == 1.6.0
-pandas == 1.2.1
+pandas == 1.2.2
 matplotlib == 3.3.4
 cvxpy == 1.1.10

From ef3fcec685a41be16bb9aa39915a9534596cb460 Mon Sep 17 00:00:00 2001
From: Yang Wang <purduewang@gmail.com>
Date: Sat, 20 Feb 2021 07:06:16 -0500
Subject: [PATCH 0477/1080] Fix: cyxpy cannot be installed (#483)

Add a few lines in yml file to clarify dependencies
---
 environment.yml | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/environment.yml b/environment.yml
index a5361d4a29..1441b79080 100644
--- a/environment.yml
+++ b/environment.yml
@@ -1,9 +1,11 @@
 name: python_robotics
+channels:
+  - conda-forge
 dependencies:
-- python
-- pip
-- scipy
-- numpy
-- pandas
-- cvxpy
-- matplotlib
+  - python=3.9
+  - pip
+  - scipy
+  - numpy
+  - pandas
+  - cvxpy
+  - matplotlib

From 078c62bc3eb0ed59e6c41b10dbb52fcafa39037a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 20 Feb 2021 21:30:55 +0900
Subject: [PATCH 0478/1080] enable rundiffstylecheck.sh to run (#484)

---
 rundiffstylecheck.sh | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 mode change 100644 => 100755 rundiffstylecheck.sh

diff --git a/rundiffstylecheck.sh b/rundiffstylecheck.sh
old mode 100644
new mode 100755

From c025299dcfe8db7383704601c28dc48f6dc262db Mon Sep 17 00:00:00 2001
From: FXCarl <fxcarlcn@gmail.com>
Date: Sun, 21 Feb 2021 14:23:13 +0800
Subject: [PATCH 0479/1080] Update greedy_best_first_search - calc_final_path
 method (#477)

* Update greedy_best_first_search.py

parent_index should be pind. other wise it cann't work

* add breadth first search test

* Add greedy best first search test
---
 .../greedy_best_first_search.py               |  2 +-
 tests/test_breadth_first_search.py            | 22 +++++++++----------
 tests/test_greedy_best_first_search.py        | 11 ++++++++++
 3 files changed, 23 insertions(+), 12 deletions(-)
 create mode 100644 tests/test_greedy_best_first_search.py

diff --git a/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py b/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py
index 7100b0e604..f2416fba98 100644
--- a/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py
+++ b/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py
@@ -132,7 +132,7 @@ def calc_final_path(self, ngoal, closedset):
         # generate final course
         rx, ry = [self.calc_grid_position(ngoal.x, self.minx)], [
             self.calc_grid_position(ngoal.y, self.miny)]
-        n = closedset[ngoal.parent_index]
+        n = closedset[ngoal.pind]
         while n is not None:
             rx.append(self.calc_grid_position(n.x, self.minx))
             ry.append(self.calc_grid_position(n.y, self.miny))
diff --git a/tests/test_breadth_first_search.py b/tests/test_breadth_first_search.py
index 14c1ac6988..bfc63e39d6 100644
--- a/tests/test_breadth_first_search.py
+++ b/tests/test_breadth_first_search.py
@@ -1,11 +1,11 @@
-import conftest
-from PathPlanning.BreadthFirstSearch import breadth_first_search as m
-
-
-def test_1():
-    m.show_animation = False
-    m.main()
-
-
-if __name__ == '__main__':
-    conftest.run_this_test(__file__)
+import conftest
+from PathPlanning.BreadthFirstSearch import breadth_first_search as m
+
+
+def test_1():
+    m.show_animation = False
+    m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_greedy_best_first_search.py b/tests/test_greedy_best_first_search.py
new file mode 100644
index 0000000000..e573ecf625
--- /dev/null
+++ b/tests/test_greedy_best_first_search.py
@@ -0,0 +1,11 @@
+import conftest
+from PathPlanning.GreedyBestFirstSearch import greedy_best_first_search as m
+
+
+def test_1():
+    m.show_animation = False
+    m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)

From d391cdbb8c82f9c3e643f4c2fd95e85d03b47c9e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 23 Feb 2021 07:27:33 +0900
Subject: [PATCH 0480/1080] Bump scipy from 1.6.0 to 1.6.1 (#485)

Bumps [scipy](https://github.com/scipy/scipy) from 1.6.0 to 1.6.1.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.6.0...v1.6.1)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 8402a5206e..cb46f475da 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.1
-scipy == 1.6.0
+scipy == 1.6.1
 pandas == 1.2.2
 matplotlib == 3.3.4
 cvxpy == 1.1.10

From d53ec936bce2f01ffc99297372cba9081b92c24e Mon Sep 17 00:00:00 2001
From: zarmars <sxysgx410@126.com>
Date: Tue, 16 Mar 2021 22:13:11 +0800
Subject: [PATCH 0481/1080] Fix the problem that the length of candidate path
 is not correctly compared. Optimal path may be discarded. (#487)

---
 PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
index 3bed9e2997..7808f2c9f9 100644
--- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
+++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
@@ -40,7 +40,8 @@ def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
 
 
 def mod2pi(x):
-    v = np.mod(x, 2.0 * math.pi)
+    # Be consistent with fmod in cplusplus here.
+    v = np.mod(x, np.copysign(2.0 * math.pi, x))
     if v < -math.pi:
         v += 2.0 * math.pi
     else:
@@ -76,7 +77,7 @@ def set_path(paths, lengths, ctypes):
     for tpath in paths:
         typeissame = (tpath.ctypes == path.ctypes)
         if typeissame:
-            if sum(tpath.lengths) - sum(path.lengths) <= 0.01:
+            if sum(np.abs(tpath.lengths)) - sum(np.abs(path.lengths)) <= 0.01:
                 return paths  # not insert path
 
     path.L = sum([abs(i) for i in lengths])

From b30ba3e9d022e519431993ab173e610e841f431d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sat, 20 Mar 2021 14:32:52 +0900
Subject: [PATCH 0482/1080] Bump cvxpy from 1.1.10 to 1.1.11 (#488)

Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.10 to 1.1.11.
- [Release notes](https://github.com/cvxgrp/cvxpy/releases)
- [Changelog](https://github.com/cvxgrp/cvxpy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.10...v1.1.11)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index cb46f475da..d2fc6db92a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy == 1.20.1
 scipy == 1.6.1
 pandas == 1.2.2
 matplotlib == 3.3.4
-cvxpy == 1.1.10
+cvxpy == 1.1.11

From dbcb8d7ea5c78cb55aae13610cf923ecf7602c33 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 Mar 2021 07:24:34 +0900
Subject: [PATCH 0483/1080] Bump scipy from 1.6.1 to 1.6.2 (#491)

Bumps [scipy](https://github.com/scipy/scipy) from 1.6.1 to 1.6.2.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.6.1...v1.6.2)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index d2fc6db92a..5b6b9eff2a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.1
-scipy == 1.6.1
+scipy == 1.6.2
 pandas == 1.2.2
 matplotlib == 3.3.4
 cvxpy == 1.1.11

From 5c408b5d80b53b6835d1d432f1669f06cfe7fce6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 Mar 2021 07:25:17 +0900
Subject: [PATCH 0484/1080] Bump matplotlib from 3.3.4 to 3.4.0 (#492)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.3.4 to 3.4.0.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.3.4...v3.4.0)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 5b6b9eff2a..5e8cef887e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.1
 scipy == 1.6.2
 pandas == 1.2.2
-matplotlib == 3.3.4
+matplotlib == 3.4.0
 cvxpy == 1.1.11

From f17c991c68d72cf84bb3a89393d0a12454f66583 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 Mar 2021 20:23:54 +0900
Subject: [PATCH 0485/1080] Bump numpy from 1.20.1 to 1.20.2 (#493)

Bumps [numpy](https://github.com/numpy/numpy) from 1.20.1 to 1.20.2.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt)
- [Commits](https://github.com/numpy/numpy/compare/v1.20.1...v1.20.2)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 5e8cef887e..2d7c47554b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.20.1
+numpy == 1.20.2
 scipy == 1.6.2
 pandas == 1.2.2
 matplotlib == 3.4.0

From bf2d9df83dc6b4927763f6c47786813900f14b47 Mon Sep 17 00:00:00 2001
From: Shamil <shamilgemuev@mail.ru>
Date: Fri, 2 Apr 2021 13:49:16 +0200
Subject: [PATCH 0486/1080] Add ICP support for 3d point clouds (#465)

* Add 3d support ICP

* icp_matching function returns R,T corresponding to 2D or 3D set of points
* update_homogeneuous_matrix - general operations for translation and rotation matrixes

* Add test for 3d point cloud (with 2d visualization)

* Separate test for 3d points to main_3d_points

* Add test for ICP 3d

* Correct style

* Add space

* Style correction

* Add more spaces

* Add 3d visualizing for ICP

* Style corrections

* Delete spaces

* Style correction

* remove space

* Separate plot drawing

* plot drawing in a separate function for both 2D and 3D versions
* figure creating before while loop

* Style correction

* Comment 3d plot drawing

Co-authored-by: Shamil GEMUEV <https://github.maf-roda.com/>
---
 .../iterative_closest_point.py                | 94 ++++++++++++++-----
 tests/test_iterative_closest_point.py         |  5 +
 2 files changed, 76 insertions(+), 23 deletions(-)

diff --git a/SLAM/iterative_closest_point/iterative_closest_point.py b/SLAM/iterative_closest_point/iterative_closest_point.py
index db252247a8..5ed01fa383 100644
--- a/SLAM/iterative_closest_point/iterative_closest_point.py
+++ b/SLAM/iterative_closest_point/iterative_closest_point.py
@@ -1,10 +1,11 @@
 """
 Iterative Closest Point (ICP) SLAM example
-author: Atsushi Sakai (@Atsushi_twi), Göktuğ Karakaşlı
+author: Atsushi Sakai (@Atsushi_twi), Göktuğ Karakaşlı, Shamil Gemuev
 """
 
 import math
 
+# from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -19,8 +20,8 @@ def icp_matching(previous_points, current_points):
     """
     Iterative Closest Point matching
     - input
-    previous_points: 2D points in the previous frame
-    current_points: 2D points in the current frame
+    previous_points: 2D or 3D points in the previous frame
+    current_points: 2D or 3D points in the current frame
     - output
     R: Rotation matrix
     T: Translation vector
@@ -31,19 +32,16 @@ def icp_matching(previous_points, current_points):
     preError = np.inf
     count = 0
 
+    if show_animation:
+        fig = plt.figure()
+        # if previous_points.shape[0] == 3:
+        #    fig.add_subplot(111, projection='3d')
+
     while dError >= EPS:
         count += 1
 
         if show_animation:  # pragma: no cover
-            plt.cla()
-            # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect(
-                'key_release_event',
-                lambda event: [exit(0) if event.key == 'escape' else None])
-            plt.plot(previous_points[0, :], previous_points[1, :], ".r")
-            plt.plot(current_points[0, :], current_points[1, :], ".b")
-            plt.plot(0.0, 0.0, "xr")
-            plt.axis("equal")
+            plot_points(previous_points, current_points, fig)
             plt.pause(0.1)
 
         indexes, error = nearest_neighbor_association(previous_points, current_points)
@@ -68,24 +66,20 @@ def icp_matching(previous_points, current_points):
             print("Not Converge...", error, dError, count)
             break
 
-    R = np.array(H[0:2, 0:2])
-    T = np.array(H[0:2, 2])
+    R = np.array(H[0:-1, 0:-1])
+    T = np.array(H[0:-1, -1])
 
     return R, T
 
 
 def update_homogeneous_matrix(Hin, R, T):
 
-    H = np.zeros((3, 3))
-
-    H[0, 0] = R[0, 0]
-    H[1, 0] = R[1, 0]
-    H[0, 1] = R[0, 1]
-    H[1, 1] = R[1, 1]
-    H[2, 2] = 1.0
+    r_size = R.shape[0]
+    H = np.zeros((r_size + 1, r_size + 1))
 
-    H[0, 2] = T[0]
-    H[1, 2] = T[1]
+    H[0:r_size, 0:r_size] = R
+    H[0:r_size, r_size] = T
+    H[r_size, r_size] = 1.0
 
     if Hin is None:
         return H
@@ -124,6 +118,28 @@ def svd_motion_estimation(previous_points, current_points):
     return R, t
 
 
+def plot_points(previous_points, current_points, figure):
+    # for stopping simulation with the esc key.
+    plt.gcf().canvas.mpl_connect(
+        'key_release_event',
+        lambda event: [exit(0) if event.key == 'escape' else None])
+    # if previous_points.shape[0] == 3:
+    # plt.clf()
+    # axes = figure.add_subplot(111, projection='3d')
+    # axes.scatter(previous_points[0, :], previous_points[1, :],
+    #             previous_points[2, :], c="r", marker=".")
+    # axes.scatter(current_points[0, :], current_points[1, :],
+    #             current_points[2, :], c="b", marker=".")
+    # axes.scatter(0.0, 0.0, 0.0, c="r", marker="x")
+    # figure.canvas.draw()
+    # else:
+    plt.cla()
+    plt.plot(previous_points[0, :], previous_points[1, :], ".r")
+    plt.plot(current_points[0, :], current_points[1, :], ".b")
+    plt.plot(0.0, 0.0, "xr")
+    plt.axis("equal")
+
+
 def main():
     print(__file__ + " start!!")
 
@@ -153,5 +169,37 @@ def main():
         print("T:", T)
 
 
+def main_3d_points():
+    print(__file__ + " start!!")
+
+    # simulation parameters for 3d point set
+    nPoint = 1000
+    fieldLength = 50.0
+    motion = [0.5, 2.0, -5, np.deg2rad(-10.0)]  # [x[m],y[m],z[m],roll[deg]]
+
+    nsim = 3  # number of simulation
+
+    for _ in range(nsim):
+
+        # previous points
+        px = (np.random.rand(nPoint) - 0.5) * fieldLength
+        py = (np.random.rand(nPoint) - 0.5) * fieldLength
+        pz = (np.random.rand(nPoint) - 0.5) * fieldLength
+        previous_points = np.vstack((px, py, pz))
+
+        # current points
+        cx = [math.cos(motion[3]) * x - math.sin(motion[3]) * z + motion[0]
+              for (x, z) in zip(px, pz)]
+        cy = [y + motion[1] for y in py]
+        cz = [math.sin(motion[3]) * x + math.cos(motion[3]) * z + motion[2]
+              for (x, z) in zip(px, pz)]
+        current_points = np.vstack((cx, cy, cz))
+
+        R, T = icp_matching(previous_points, current_points)
+        print("R:", R)
+        print("T:", T)
+
+
 if __name__ == '__main__':
     main()
+    main_3d_points()
diff --git a/tests/test_iterative_closest_point.py b/tests/test_iterative_closest_point.py
index 3f212f7298..3e726b5649 100644
--- a/tests/test_iterative_closest_point.py
+++ b/tests/test_iterative_closest_point.py
@@ -7,5 +7,10 @@ def test_1():
     m.main()
 
 
+def test_2():
+    m.show_animation = False
+    m.main_3d_points()
+
+
 if __name__ == '__main__':
     conftest.run_this_test(__file__)

From 3abc9c69e3074d2ead16a5ae87c529536cafb406 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 2 Apr 2021 20:53:26 +0900
Subject: [PATCH 0487/1080] enable 3d plot

---
 .../iterative_closest_point.py                | 36 +++++++++----------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/SLAM/iterative_closest_point/iterative_closest_point.py b/SLAM/iterative_closest_point/iterative_closest_point.py
index 5ed01fa383..2b44de2c07 100644
--- a/SLAM/iterative_closest_point/iterative_closest_point.py
+++ b/SLAM/iterative_closest_point/iterative_closest_point.py
@@ -5,7 +5,7 @@
 
 import math
 
-# from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import
+from mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -34,8 +34,8 @@ def icp_matching(previous_points, current_points):
 
     if show_animation:
         fig = plt.figure()
-        # if previous_points.shape[0] == 3:
-        #    fig.add_subplot(111, projection='3d')
+        if previous_points.shape[0] == 3:
+           fig.add_subplot(111, projection='3d')
 
     while dError >= EPS:
         count += 1
@@ -123,21 +123,21 @@ def plot_points(previous_points, current_points, figure):
     plt.gcf().canvas.mpl_connect(
         'key_release_event',
         lambda event: [exit(0) if event.key == 'escape' else None])
-    # if previous_points.shape[0] == 3:
-    # plt.clf()
-    # axes = figure.add_subplot(111, projection='3d')
-    # axes.scatter(previous_points[0, :], previous_points[1, :],
-    #             previous_points[2, :], c="r", marker=".")
-    # axes.scatter(current_points[0, :], current_points[1, :],
-    #             current_points[2, :], c="b", marker=".")
-    # axes.scatter(0.0, 0.0, 0.0, c="r", marker="x")
-    # figure.canvas.draw()
-    # else:
-    plt.cla()
-    plt.plot(previous_points[0, :], previous_points[1, :], ".r")
-    plt.plot(current_points[0, :], current_points[1, :], ".b")
-    plt.plot(0.0, 0.0, "xr")
-    plt.axis("equal")
+    if previous_points.shape[0] == 3:
+        plt.clf()
+        axes = figure.add_subplot(111, projection='3d')
+        axes.scatter(previous_points[0, :], previous_points[1, :],
+                    previous_points[2, :], c="r", marker=".")
+        axes.scatter(current_points[0, :], current_points[1, :],
+                    current_points[2, :], c="b", marker=".")
+        axes.scatter(0.0, 0.0, 0.0, c="r", marker="x")
+        figure.canvas.draw()
+    else:
+        plt.cla()
+        plt.plot(previous_points[0, :], previous_points[1, :], ".r")
+        plt.plot(current_points[0, :], current_points[1, :], ".b")
+        plt.plot(0.0, 0.0, "xr")
+        plt.axis("equal")
 
 
 def main():

From 6bd25b922159dfe6df77f03bd24d61a9ba78ae67 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 2 Apr 2021 21:43:29 +0900
Subject: [PATCH 0488/1080] fix state_lattice_planner.py coordinate conversion
 (#495)

---
 .../StateLatticePlanner/state_lattice_planner.py         | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/PathPlanning/StateLatticePlanner/state_lattice_planner.py b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
index 51eeb000d2..b5fec697a4 100644
--- a/PathPlanning/StateLatticePlanner/state_lattice_planner.py
+++ b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
@@ -156,8 +156,8 @@ def calc_lane_states(l_center, l_heading, l_width, v_width, d, nxy):
     :param nxy: sampling number
     :return: state list
     """
-    xc = math.cos(l_heading) * d + math.sin(l_heading) * l_center
-    yc = math.sin(l_heading) * d + math.cos(l_heading) * l_center
+    xc = d
+    yc = l_center
 
     states = []
     for i in range(nxy):
@@ -301,7 +301,7 @@ def lane_state_sampling_test1():
     k0 = 0.0
 
     l_center = 10.0
-    l_heading = np.deg2rad(90.0)
+    l_heading = np.deg2rad(0.0)
     l_width = 3.0
     v_width = 1.0
     d = 10
@@ -309,6 +309,9 @@ def lane_state_sampling_test1():
     states = calc_lane_states(l_center, l_heading, l_width, v_width, d, nxy)
     result = generate_path(states, k0)
 
+    if show_animation:
+        plt.close("all")
+
     for table in result:
         xc, yc, yawc = motion_model.generate_trajectory(
             table[3], table[4], table[5], k0)

From d4153bcaa1ac81aeedefa60fbde56fad2773b51f Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 2 Apr 2021 21:43:52 +0900
Subject: [PATCH 0489/1080] add workaround for dependabot related issue (#496)

---
 .github/workflows/codeql.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index 078590997d..5e6c03aa16 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -2,6 +2,8 @@ name: "Code scanning - action"
 
 on:
   push:
+    branches-ignore:
+      - 'dependabot/**'
   pull_request:
   schedule:
     - cron: '0 19 * * 0'

From 079de036f3ce80eb6245396e7afabe728bcfb280 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Fri, 2 Apr 2021 21:45:09 +0900
Subject: [PATCH 0490/1080] Bump pandas from 1.2.2 to 1.2.3 (#489)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.2.2 to 1.2.3.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.2.2...v1.2.3)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 2d7c47554b..07ec77dde0 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.2
 scipy == 1.6.2
-pandas == 1.2.2
+pandas == 1.2.3
 matplotlib == 3.4.0
 cvxpy == 1.1.11

From add704866aa2df4fd7699875b6ec199dfa817f56 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 6 Apr 2021 07:07:34 +0900
Subject: [PATCH 0491/1080] Bump matplotlib from 3.4.0 to 3.4.1 (#497)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.4.0 to 3.4.1.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.4.0...v3.4.1)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 07ec77dde0..b3610549b6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.2
 scipy == 1.6.2
 pandas == 1.2.3
-matplotlib == 3.4.0
+matplotlib == 3.4.1
 cvxpy == 1.1.11

From 52e6225e73780e13c4f79015f6efb3ebfcf38ccc Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 13 Apr 2021 07:22:10 +0900
Subject: [PATCH 0492/1080] Bump cvxpy from 1.1.11 to 1.1.12 (#501)

Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.11 to 1.1.12.
- [Release notes](https://github.com/cvxgrp/cvxpy/releases)
- [Changelog](https://github.com/cvxgrp/cvxpy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.11...v1.1.12)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index b3610549b6..8ab982f887 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy == 1.20.2
 scipy == 1.6.2
 pandas == 1.2.3
 matplotlib == 3.4.1
-cvxpy == 1.1.11
+cvxpy == 1.1.12

From 6e98da47fd5535f8535f84ecb57964945ab0b041 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 13 Apr 2021 07:22:32 +0900
Subject: [PATCH 0493/1080] Bump pandas from 1.2.3 to 1.2.4 (#502)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.2.3 to 1.2.4.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.2.3...v1.2.4)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 8ab982f887..d2fd8f7f2b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.2
 scipy == 1.6.2
-pandas == 1.2.3
+pandas == 1.2.4
 matplotlib == 3.4.1
 cvxpy == 1.1.12

From 0705b1cb5b7c6626c54ff7868d4416240fcb8ed6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 Apr 2021 07:07:54 +0900
Subject: [PATCH 0494/1080] Bump scipy from 1.6.2 to 1.6.3 (#504)

Bumps [scipy](https://github.com/scipy/scipy) from 1.6.2 to 1.6.3.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.6.2...v1.6.3)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index d2fd8f7f2b..0e1d0dfe94 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.2
-scipy == 1.6.2
+scipy == 1.6.3
 pandas == 1.2.4
 matplotlib == 3.4.1
 cvxpy == 1.1.12

From 3ff6b7d1ec9ecc1865b74225d9c31d888f0fa884 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 8 May 2021 13:27:33 +0900
Subject: [PATCH 0495/1080] Update MacOS_CI.yml (#507)

* Update MacOS_CI.yml

* Update MacOS_CI.yml

* Update MacOS_CI.yml

* Update MacOS_CI.yml

* Update MacOS_CI.yml
---
 .github/workflows/MacOS_CI.yml | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index 5bd9a60828..6b3ffa2588 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -8,7 +8,7 @@ on: [push, pull_request]
 
 jobs:
   build:
-    runs-on: ubuntu-latest
+    runs-on: macos-latest
     strategy:
       matrix:
         python-version: [ '3.9' ]
@@ -17,6 +17,9 @@ jobs:
       - uses: actions/checkout@v2
       - run: git fetch --prune --unshallow
 
+      - name: Update bash
+        run: brew install bash
+
       - name: Setup python
         uses: actions/setup-python@v2
         with:
@@ -25,6 +28,7 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
+          pip install numpy # cvxpy install workaround
           pip install -r requirements.txt
       - name: install coverage
         run: pip install coverage

From 02cb3ad556c999fba9f793c9b995486aa0fed830 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 11 May 2021 07:14:13 +0900
Subject: [PATCH 0496/1080] Bump matplotlib from 3.4.1 to 3.4.2 (#509)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.4.1 to 3.4.2.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.4.1...v3.4.2)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 0e1d0dfe94..2c05e5ec72 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.2
 scipy == 1.6.3
 pandas == 1.2.4
-matplotlib == 3.4.1
+matplotlib == 3.4.2
 cvxpy == 1.1.12

From 0e232037c751cbeb8bb8cdac0b33bf48e4937f2a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 11 May 2021 07:14:31 +0900
Subject: [PATCH 0497/1080] Bump numpy from 1.20.2 to 1.20.3 (#510)

Bumps [numpy](https://github.com/numpy/numpy) from 1.20.2 to 1.20.3.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt)
- [Commits](https://github.com/numpy/numpy/compare/v1.20.2...v1.20.3)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 2c05e5ec72..6241a11e5b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.20.2
+numpy == 1.20.3
 scipy == 1.6.3
 pandas == 1.2.4
 matplotlib == 3.4.2

From 2cf4f6f001ec7fa74dc2d3bce3818b5eff11d9eb Mon Sep 17 00:00:00 2001
From: nirnayroy <32942494+nirnayroy@users.noreply.github.com>
Date: Sat, 15 May 2021 11:31:29 +0530
Subject: [PATCH 0498/1080] Added D* Search to path planning folder (#490)

* changes

* updated docs and readme

* Update a_star.py

* Update a_star.py

* Create test_dstar.py

* trailing loc

* Update dstar.py

* Update dstar.py

* Update dstar.py

* Update dstar.py

* Update dstar.py

* newline

* corrected changes requested

* 13, five, 21

* corrected changes

* latest

* linted

* lint

* removed diff
---
 PathPlanning/DStar/dstar.py    | 241 +++++++++++++++++++++++++++++++++
 README.md                      |  14 ++
 docs/modules/path_planning.rst |  16 +++
 tests/test_dstar.py            |  11 ++
 4 files changed, 282 insertions(+)
 create mode 100644 PathPlanning/DStar/dstar.py
 create mode 100644 tests/test_dstar.py

diff --git a/PathPlanning/DStar/dstar.py b/PathPlanning/DStar/dstar.py
new file mode 100644
index 0000000000..bc7e7c966e
--- /dev/null
+++ b/PathPlanning/DStar/dstar.py
@@ -0,0 +1,241 @@
+"""
+
+D* grid planning
+
+author: Nirnay Roy
+
+See Wikipedia article (https://en.wikipedia.org/wiki/D*)
+
+"""
+import math
+
+from sys import maxsize
+
+import matplotlib.pyplot as plt
+
+show_animation = True
+
+
+class State:
+
+    def __init__(self, x, y):
+        self.x = x
+        self.y = y
+        self.parent = None
+        self.state = "."
+        self.t = "new"  # tag for state
+        self.h = 0
+        self.k = 0
+
+    def cost(self, state):
+        if self.state == "#" or state.state == "#":
+            return maxsize
+
+        return math.sqrt(math.pow((self.x - state.x), 2) +
+                         math.pow((self.y - state.y), 2))
+
+    def set_state(self, state):
+        """
+        .: new
+        #: obstacle
+        e: oparent of current state
+        *: closed state
+        s: current state
+        """
+        if state not in ["s", ".", "#", "e", "*"]:
+            return
+        self.state = state
+
+
+class Map:
+
+    def __init__(self, row, col):
+        self.row = row
+        self.col = col
+        self.map = self.init_map()
+
+    def init_map(self):
+        map_list = []
+        for i in range(self.row):
+            tmp = []
+            for j in range(self.col):
+                tmp.append(State(i, j))
+            map_list.append(tmp)
+        return map_list
+
+    def get_neighbers(self, state):
+        state_list = []
+        for i in [-1, 0, 1]:
+            for j in [-1, 0, 1]:
+                if i == 0 and j == 0:
+                    continue
+                if state.x + i < 0 or state.x + i >= self.row:
+                    continue
+                if state.y + j < 0 or state.y + j >= self.col:
+                    continue
+                state_list.append(self.map[state.x + i][state.y + j])
+        return state_list
+
+    def set_obstacle(self, point_list):
+        for x, y in point_list:
+            if x < 0 or x >= self.row or y < 0 or y >= self.col:
+                continue
+
+            self.map[x][y].set_state("#")
+
+
+class Dstar:
+    def __init__(self, maps):
+        self.map = maps
+        self.open_list = set()
+
+    def process_state(self):
+        x = self.min_state()
+
+        if x is None:
+            return -1
+
+        k_old = self.get_kmin()
+        self.remove(x)
+
+        if k_old < x.h:
+            for y in self.map.get_neighbers(x):
+                if y.h <= k_old and x.h > y.h + x.cost(y):
+                    x.parent = y
+                    x.h = y.h + x.cost(y)
+        elif k_old == x.h:
+            for y in self.map.get_neighbers(x):
+                if y.t == "new" or y.parent == x and y.h != x.h + x.cost(y) \
+                        or y.parent != x and y.h > x.h + x.cost(y):
+                    y.parent = x
+                    self.insert(y, x.h + x.cost(y))
+        else:
+            for y in self.map.get_neighbers(x):
+                if y.t == "new" or y.parent == x and y.h != x.h + x.cost(y):
+                    y.parent = x
+                    self.insert(y, x.h + x.cost(y))
+                else:
+                    if y.parent != x and y.h > x.h + x.cost(y):
+                        self.insert(y, x.h)
+                    else:
+                        if y.parent != x and x.h > y.h + x.cost(y) \
+                                and y.t == "close" and y.h > k_old:
+                            self.insert(y, y.h)
+        return self.get_kmin()
+
+    def min_state(self):
+        if not self.open_list:
+            return None
+        min_state = min(self.open_list, key=lambda x: x.k)
+        return min_state
+
+    def get_kmin(self):
+        if not self.open_list:
+            return -1
+        k_min = min([x.k for x in self.open_list])
+        return k_min
+
+    def insert(self, state, h_new):
+        if state.t == "new":
+            state.k = h_new
+        elif state.t == "open":
+            state.k = min(state.k, h_new)
+        elif state.t == "close":
+            state.k = min(state.h, h_new)
+        state.h = h_new
+        state.t = "open"
+        self.open_list.add(state)
+
+    def remove(self, state):
+        if state.t == "open":
+            state.t = "close"
+        self.open_list.remove(state)
+
+    def modify_cost(self, x):
+        if x.t == "close":
+            self.insert(x, x.parent.h + x.cost(x.parent))
+
+    def run(self, start, end):
+
+        rx = []
+        ry = []
+
+        self.open_list.add(end)
+
+        while True:
+            self.process_state()
+            if start.t == "close":
+                break
+
+        start.set_state("s")
+        s = start
+        s = s.parent
+        s.set_state("e")
+        tmp = start
+
+        while tmp != end:
+            tmp.set_state("*")
+            rx.append(tmp.x)
+            ry.append(tmp.y)
+            if show_animation:
+                plt.plot(rx, ry)
+                plt.pause(0.01)
+            if tmp.parent.state == "#":
+                self.modify(tmp)
+                continue
+            tmp = tmp.parent
+        tmp.set_state("e")
+
+        return rx, ry
+
+    def modify(self, state):
+        self.modify_cost(state)
+        while True:
+            k_min = self.process_state()
+            if k_min >= state.h:
+                break
+
+
+def main():
+    m = Map(100, 100)
+    ox, oy = [], []
+    for i in range(-10, 60):
+        ox.append(i)
+        oy.append(-10)
+    for i in range(-10, 60):
+        ox.append(60)
+        oy.append(i)
+    for i in range(-10, 61):
+        ox.append(i)
+        oy.append(60)
+    for i in range(-10, 61):
+        ox.append(-10)
+        oy.append(i)
+    for i in range(-10, 40):
+        ox.append(20)
+        oy.append(i)
+    for i in range(0, 40):
+        ox.append(40)
+        oy.append(60 - i)
+    print([(i, j) for i, j in zip(ox, oy)])
+    m.set_obstacle([(i, j) for i, j in zip(ox, oy)])
+
+    start = [10, 10]
+    goal = [50, 50]
+    if show_animation:
+        plt.plot(ox, oy, ".k")
+        plt.plot(start[0], start[1], "og")
+        plt.plot(goal[0], goal[1], "xb")
+
+    start = m.map[start[0]][start[1]]
+    end = m.map[goal[0]][goal[1]]
+    dstar = Dstar(m)
+    rx, ry = dstar.run(start, end)
+
+    if show_animation:
+        plt.plot(rx, ry)
+        plt.show()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/README.md b/README.md
index 804966d121..ca9e6192b9 100644
--- a/README.md
+++ b/README.md
@@ -37,6 +37,7 @@ Python codes for robotics algorithm.
       * [Grid based search](#grid-based-search)
          * [Dijkstra algorithm](#dijkstra-algorithm)
          * [A* algorithm](#a-algorithm)
+         * [D* algorithm](#d-algorithm)
          * [Potential Field algorithm](#potential-field-algorithm)
          * [Grid based coverage path planning](#grid-based-coverage-path-planning)
       * [State Lattice Planning](#state-lattice-planning)
@@ -301,6 +302,19 @@ In the animation, cyan points are searched nodes.
 
 Its heuristic is 2D Euclid distance.
 
+### D\* algorithm
+
+This is a 2D grid based the shortest path planning with D star algorithm.
+
+![figure at master · nirnayroy/intelligentrobotics](https://github.com/nirnayroy/intelligent-robotics/blob/main/dstar.gif)
+
+The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm.
+
+Ref:
+
+- [D* Algorithm Wikipedia](https://en.wikipedia.org/wiki/D*)
+
+
 ### Potential Field algorithm
 
 This is a 2D grid based path planning with Potential Field algorithm.
diff --git a/docs/modules/path_planning.rst b/docs/modules/path_planning.rst
index f74867dcdf..a0bdd45511 100644
--- a/docs/modules/path_planning.rst
+++ b/docs/modules/path_planning.rst
@@ -39,6 +39,21 @@ In the animation, cyan points are searched nodes.
 
 Its heuristic is 2D Euclid distance.
 
+.. _a*-algorithm:
+
+D\* algorithm
+~~~~~~~~~~~~~
+
+This is a 2D grid based shortest path planning with D star algorithm.
+
+|dstar|
+
+The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm.
+
+Ref:
+
+-  `D* search Wikipedia <https://en.wikipedia.org/wiki/D*>`__
+
 Potential Field algorithm
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -427,6 +442,7 @@ Ref:
 .. |DWA| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif
 .. |Dijkstra| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Dijkstra/animation.gif
 .. |astar| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/AStar/animation.gif
+.. |dstar| image:: https://github.com/nirnayroy/intelligent-robotics/blob/main/dstar.gif
 .. |PotentialField| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/PotentialFieldPlanning/animation.gif
 .. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/kn05animation.gif
 .. |5| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable.png?raw=True
diff --git a/tests/test_dstar.py b/tests/test_dstar.py
new file mode 100644
index 0000000000..f8f40fecef
--- /dev/null
+++ b/tests/test_dstar.py
@@ -0,0 +1,11 @@
+import conftest
+from PathPlanning.DStar import dstar as m
+
+
+def test_1():
+    m.show_animation = False
+    m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)

From 1de0ffddb1ea11396a019403bbb986c8df8e0bdd Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 15 May 2021 15:21:15 +0900
Subject: [PATCH 0499/1080] change DStar animation

---
 PathPlanning/DStar/dstar.py    | 13 +++++++------
 README.md                      |  2 +-
 docs/modules/path_planning.rst |  2 +-
 3 files changed, 9 insertions(+), 8 deletions(-)

diff --git a/PathPlanning/DStar/dstar.py b/PathPlanning/DStar/dstar.py
index bc7e7c966e..e01508b3df 100644
--- a/PathPlanning/DStar/dstar.py
+++ b/PathPlanning/DStar/dstar.py
@@ -63,7 +63,7 @@ def init_map(self):
             map_list.append(tmp)
         return map_list
 
-    def get_neighbers(self, state):
+    def get_neighbors(self, state):
         state_list = []
         for i in [-1, 0, 1]:
             for j in [-1, 0, 1]:
@@ -99,18 +99,18 @@ def process_state(self):
         self.remove(x)
 
         if k_old < x.h:
-            for y in self.map.get_neighbers(x):
+            for y in self.map.get_neighbors(x):
                 if y.h <= k_old and x.h > y.h + x.cost(y):
                     x.parent = y
                     x.h = y.h + x.cost(y)
         elif k_old == x.h:
-            for y in self.map.get_neighbers(x):
+            for y in self.map.get_neighbors(x):
                 if y.t == "new" or y.parent == x and y.h != x.h + x.cost(y) \
                         or y.parent != x and y.h > x.h + x.cost(y):
                     y.parent = x
                     self.insert(y, x.h + x.cost(y))
         else:
-            for y in self.map.get_neighbers(x):
+            for y in self.map.get_neighbors(x):
                 if y.t == "new" or y.parent == x and y.h != x.h + x.cost(y):
                     y.parent = x
                     self.insert(y, x.h + x.cost(y))
@@ -178,7 +178,7 @@ def run(self, start, end):
             rx.append(tmp.x)
             ry.append(tmp.y)
             if show_animation:
-                plt.plot(rx, ry)
+                plt.plot(rx, ry, "-r")
                 plt.pause(0.01)
             if tmp.parent.state == "#":
                 self.modify(tmp)
@@ -226,6 +226,7 @@ def main():
         plt.plot(ox, oy, ".k")
         plt.plot(start[0], start[1], "og")
         plt.plot(goal[0], goal[1], "xb")
+        plt.axis("equal")
 
     start = m.map[start[0]][start[1]]
     end = m.map[goal[0]][goal[1]]
@@ -233,7 +234,7 @@ def main():
     rx, ry = dstar.run(start, end)
 
     if show_animation:
-        plt.plot(rx, ry)
+        plt.plot(rx, ry, "-r")
         plt.show()
 
 
diff --git a/README.md b/README.md
index ca9e6192b9..fac8b53dce 100644
--- a/README.md
+++ b/README.md
@@ -306,7 +306,7 @@ Its heuristic is 2D Euclid distance.
 
 This is a 2D grid based the shortest path planning with D star algorithm.
 
-![figure at master · nirnayroy/intelligentrobotics](https://github.com/nirnayroy/intelligent-robotics/blob/main/dstar.gif)
+![figure at master · nirnayroy/intelligentrobotics](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStar/animation.gif)
 
 The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm.
 
diff --git a/docs/modules/path_planning.rst b/docs/modules/path_planning.rst
index a0bdd45511..cfe7b6ad40 100644
--- a/docs/modules/path_planning.rst
+++ b/docs/modules/path_planning.rst
@@ -442,7 +442,7 @@ Ref:
 .. |DWA| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif
 .. |Dijkstra| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Dijkstra/animation.gif
 .. |astar| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/AStar/animation.gif
-.. |dstar| image:: https://github.com/nirnayroy/intelligent-robotics/blob/main/dstar.gif
+.. |dstar| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStar/animation.gif
 .. |PotentialField| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/PotentialFieldPlanning/animation.gif
 .. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/kn05animation.gif
 .. |5| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable.png?raw=True

From 1050aea5277eb27e1dab13e71f19588b19d1763d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 1 Jun 2021 07:54:57 +0900
Subject: [PATCH 0500/1080] Bump cvxpy from 1.1.12 to 1.1.13 (#512)

Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.12 to 1.1.13.
- [Release notes](https://github.com/cvxgrp/cvxpy/releases)
- [Changelog](https://github.com/cvxpy/cvxpy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.12...v1.1.13)

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 6241a11e5b..fea1a9562b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy == 1.20.3
 scipy == 1.6.3
 pandas == 1.2.4
 matplotlib == 3.4.2
-cvxpy == 1.1.12
+cvxpy == 1.1.13

From 2ac1d9c8156eda2e6b87f501197a8ea42b649653 Mon Sep 17 00:00:00 2001
From: vss2sn <28676655+vss2sn@users.noreply.github.com>
Date: Mon, 7 Jun 2021 07:28:23 -0400
Subject: [PATCH 0501/1080] Add D* Lite. (#511)

* Add D* Lite.

* Add test. Minor changes

* Modified based on LGTM report

* Fix linter errors

* Fixes and lint.

* Update README.md
Made requested changes
Add transform between world and grid coordinates to allow negative wold coordinates
Modify to allow diagonal motion

* Added display of previous and new computed path
---
 PathPlanning/DStarLite/d_star_lite.py | 398 ++++++++++++++++++++++++++
 README.md                             |  13 +
 tests/test_d_star_lite.py             |  11 +
 3 files changed, 422 insertions(+)
 create mode 100644 PathPlanning/DStarLite/d_star_lite.py
 create mode 100644 tests/test_d_star_lite.py

diff --git a/PathPlanning/DStarLite/d_star_lite.py b/PathPlanning/DStarLite/d_star_lite.py
new file mode 100644
index 0000000000..603c053dab
--- /dev/null
+++ b/PathPlanning/DStarLite/d_star_lite.py
@@ -0,0 +1,398 @@
+"""
+D* Lite grid planning
+author: vss2sn (28676655+vss2sn@users.noreply.github.com)
+Link to papers:
+D* Lite (Link: http://idm-lab.org/bib/abstracts/papers/aaai02b.pd)
+Improved Fast Replanning for Robot Navigation in Unknown Terrain
+(Link: http://www.cs.cmu.edu/~maxim/files/dlite_icra02.pdf)
+Implemented maintaining similarity with the pseudocode for understanding.
+Code can be significantly optimized by using a priority queue for U, etc.
+Avoiding additional imports based on repository philosophy.
+"""
+import math
+import matplotlib.pyplot as plt
+import random
+
+show_animation = True
+pause_time = 0.001
+p_create_random_obstacle = 0
+
+
+class Node:
+    def __init__(self, x: int = 0, y: int = 0, cost: float = 0.0):
+        self.x = x
+        self.y = y
+        self.cost = cost
+
+
+def add_coordinates(node1: Node, node2: Node):
+    new_node = Node()
+    new_node.x = node1.x + node2.x
+    new_node.y = node1.y + node2.y
+    new_node.cost = node1.cost + node2.cost
+    return new_node
+
+
+def compare_coordinates(node1: Node, node2: Node):
+    return node1.x == node2.x and node1.y == node2.y
+
+
+class DStarLite:
+
+    # Please adjust the heuristic function (h) if you change the list of
+    # possible motions
+    motions = [
+        Node(1, 0, 1),
+        Node(0, 1, 1),
+        Node(-1, 0, 1),
+        Node(0, -1, 1),
+        Node(1, 1, math.sqrt(2)),
+        Node(1, -1, math.sqrt(2)),
+        Node(-1, 1, math.sqrt(2)),
+        Node(-1, -1, math.sqrt(2))
+    ]
+
+    def __init__(self, ox: list, oy: list):
+        # Ensure that within the algorithm implementation all node coordinates
+        # are indices in the grid and extend
+        # from 0 to abs(<axis>_max - <axis>_min)
+        self.x_min_world = int(min(ox))
+        self.y_min_world = int(min(oy))
+        self.x_max = int(abs(max(ox) - self.x_min_world))
+        self.y_max = int(abs(max(oy) - self.y_min_world))
+        self.obstacles = [Node(x - self.x_min_world, y - self.y_min_world)
+                          for x, y in zip(ox, oy)]
+        self.start = Node(0, 0)
+        self.goal = Node(0, 0)
+        self.U = list()
+        self.km = 0.0
+        self.kold = 0.0
+        self.rhs = list()
+        self.g = list()
+        self.detected_obstacles = list()
+        if show_animation:
+            self.detected_obstacles_for_plotting_x = list()
+            self.detected_obstacles_for_plotting_y = list()
+
+    def create_grid(self, val: float):
+        grid = list()
+        for _ in range(0, self.x_max):
+            grid_row = list()
+            for _ in range(0, self.y_max):
+                grid_row.append(val)
+            grid.append(grid_row)
+        return grid
+
+    def is_obstacle(self, node: Node):
+        return any([compare_coordinates(node, obstacle)
+                    for obstacle in self.obstacles]) or \
+               any([compare_coordinates(node, obstacle)
+                    for obstacle in self.detected_obstacles])
+
+    def c(self, node1: Node, node2: Node):
+        if self.is_obstacle(node2):
+            # Attempting to move from or to an obstacle
+            return math.inf
+        new_node = Node(node1.x-node2.x, node1.y-node2.y)
+        detected_motion = list(filter(lambda motion:
+                                      compare_coordinates(motion, new_node),
+                                      self.motions))
+        return detected_motion[0].cost
+
+    def h(self, s: Node):
+        # Cannot use the 2nd euclidean norm as this might sometimes generate
+        # heuristics that overestimate the cost, making them inadmissible,
+        # due to rounding errors etc (when combined with calculate_key)
+        # To be admissible heuristic should
+        # never overestimate the cost of a move
+        # hence not using the line below
+        # return math.hypot(self.start.x - s.x, self.start.y - s.y)
+
+        # Below is the same as 1; modify if you modify the cost of each move in
+        # motion
+        # return max(abs(self.start.x - s.x), abs(self.start.y - s.y))
+        return 1
+
+    def calculate_key(self, s: Node):
+        return (min(self.g[s.x][s.y], self.rhs[s.x][s.y]) + self.h(s)
+                + self.km, min(self.g[s.x][s.y], self.rhs[s.x][s.y]))
+
+    def is_valid(self, node: Node):
+        if 0 <= node.x < self.x_max and 0 <= node.y < self.y_max:
+            return True
+        return False
+
+    def get_neighbours(self, u: Node):
+        return [add_coordinates(u, motion) for motion in self.motions
+                if self.is_valid(add_coordinates(u, motion))]
+
+    def pred(self, u: Node):
+        # Grid, so each vertex is connected to the ones around it
+        return self.get_neighbours(u)
+
+    def succ(self, u: Node):
+        # Grid, so each vertex is connected to the ones around it
+        return self.get_neighbours(u)
+
+    def initialize(self, start: Node, goal: Node):
+        self.start.x = start.x - self.x_min_world
+        self.start.y = start.y - self.y_min_world
+        self.goal.x = goal.x - self.x_min_world
+        self.goal.y = goal.y - self.y_min_world
+        self.U = list()  # Would normally be a priority queue
+        self.km = 0.0
+        self.rhs = self.create_grid(math.inf)
+        self.g = self.create_grid(math.inf)
+        self.rhs[self.goal.x][self.goal.y] = 0
+        self.U.append((self.goal, self.calculate_key(self.goal)))
+        self.detected_obstacles = list()
+
+    def update_vertex(self, u: Node):
+        if not compare_coordinates(u, self.goal):
+            self.rhs[u.x][u.y] = min([self.c(u, sprime) +
+                                      self.g[sprime.x][sprime.y]
+                                      for sprime in self.succ(u)])
+        if any([compare_coordinates(u, node) for node, key in self.U]):
+            self.U = [(node, key) for node, key in self.U
+                      if not compare_coordinates(node, u)]
+            self.U.sort(key=lambda x: x[1])
+        if self.g[u.x][u.y] != self.rhs[u.x][u.y]:
+            self.U.append((u, self.calculate_key(u)))
+            self.U.sort(key=lambda x: x[1])
+
+    def compare_keys(self, key_pair1: tuple[float, float],
+                     key_pair2: tuple[float, float]):
+        return key_pair1[0] < key_pair2[0] or \
+               (key_pair1[0] == key_pair2[0] and key_pair1[1] < key_pair2[1])
+
+    def compute_shortest_path(self):
+        self.U.sort(key=lambda x: x[1])
+        while (len(self.U) > 0 and
+               self.compare_keys(self.U[0][1],
+                                 self.calculate_key(self.start))) or \
+                self.rhs[self.start.x][self.start.y] != \
+                self.g[self.start.x][self.start.y]:
+            self.kold = self.U[0][1]
+            u = self.U[0][0]
+            self.U.pop(0)
+            if self.compare_keys(self.kold, self.calculate_key(u)):
+                self.U.append((u, self.calculate_key(u)))
+                self.U.sort(key=lambda x: x[1])
+            elif self.g[u.x][u.y] > self.rhs[u.x][u.y]:
+                self.g[u.x][u.y] = self.rhs[u.x][u.y]
+                for s in self.pred(u):
+                    self.update_vertex(s)
+            else:
+                self.g[u.x][u.y] = math.inf
+                for s in self.pred(u) + [u]:
+                    self.update_vertex(s)
+            self.U.sort(key=lambda x: x[1])
+
+    def detect_changes(self):
+        changed_vertices = list()
+        if len(self.spoofed_obstacles) > 0:
+            for spoofed_obstacle in self.spoofed_obstacles[0]:
+                if compare_coordinates(spoofed_obstacle, self.start) or \
+                   compare_coordinates(spoofed_obstacle, self.goal):
+                    continue
+                changed_vertices.append(spoofed_obstacle)
+                self.detected_obstacles.append(spoofed_obstacle)
+                if show_animation:
+                    self.detected_obstacles_for_plotting_x.append(
+                        spoofed_obstacle.x + self.x_min_world)
+                    self.detected_obstacles_for_plotting_y.append(
+                        spoofed_obstacle.y + self.y_min_world)
+                    plt.plot(self.detected_obstacles_for_plotting_x,
+                             self.detected_obstacles_for_plotting_y, ".k")
+                    plt.pause(pause_time)
+            self.spoofed_obstacles.pop(0)
+
+        # Allows random generation of obstacles
+        random.seed()
+        if random.random() > 1 - p_create_random_obstacle:
+            x = random.randint(0, self.x_max)
+            y = random.randint(0, self.y_max)
+            new_obs = Node(x, y)
+            if compare_coordinates(new_obs, self.start) or \
+               compare_coordinates(new_obs, self.goal):
+                return changed_vertices
+            changed_vertices.append(Node(x, y))
+            self.detected_obstacles.append(Node(x, y))
+            if show_animation:
+                self.detected_obstacles_for_plotting_x.append(x +
+                                                              self.x_min_world)
+                self.detected_obstacles_for_plotting_y.append(y +
+                                                              self.y_min_world)
+                plt.plot(self.detected_obstacles_for_plotting_x,
+                         self.detected_obstacles_for_plotting_y, ".k")
+                plt.pause(pause_time)
+        return changed_vertices
+
+    def compute_current_path(self):
+        path = list()
+        current_point = Node(self.start.x, self.start.y)
+        while not compare_coordinates(current_point, self.goal):
+            path.append(current_point)
+            current_point = min(self.succ(current_point),
+                                key=lambda sprime:
+                                self.c(current_point, sprime) +
+                                self.g[sprime.x][sprime.y])
+        path.append(self.goal)
+        return path
+
+    def compare_paths(self, path1: list, path2: list):
+        if len(path1) != len(path2):
+            return False
+        for node1, node2 in zip(path1, path2):
+            if not compare_coordinates(node1, node2):
+                return False
+        return True
+
+    def display_path(self, path: list, colour: str, alpha: int = 1):
+        px = [(node.x + self.x_min_world) for node in path]
+        py = [(node.y + self.y_min_world) for node in path]
+        drawing = plt.plot(px, py, colour, alpha=alpha)
+        plt.pause(pause_time)
+        return drawing
+
+    def main(self, start: Node, goal: Node,
+             spoofed_ox: list, spoofed_oy: list):
+        self.spoofed_obstacles = [[Node(x - self.x_min_world,
+                                        y - self.y_min_world)
+                                   for x, y in zip(rowx, rowy)]
+                                  for rowx, rowy in zip(spoofed_ox, spoofed_oy)
+                                  ]
+        pathx = []
+        pathy = []
+        self.initialize(start, goal)
+        last = self.start
+        self.compute_shortest_path()
+        pathx.append(self.start.x + self.x_min_world)
+        pathy.append(self.start.y + self.y_min_world)
+
+        if show_animation:
+            current_path = self.compute_current_path()
+            previous_path = current_path.copy()
+            previous_path_image = self.display_path(previous_path, ".c",
+                                                    alpha=0.3)
+            current_path_image = self.display_path(current_path, ".c")
+
+        while not compare_coordinates(self.goal, self.start):
+            if self.g[self.start.x][self.start.y] == math.inf:
+                print("No path possible")
+                return False, pathx, pathy
+            self.start = min(self.succ(self.start),
+                             key=lambda sprime:
+                             self.c(self.start, sprime) +
+                             self.g[sprime.x][sprime.y])
+            pathx.append(self.start.x + self.x_min_world)
+            pathy.append(self.start.y + self.y_min_world)
+            if show_animation:
+                current_path.pop(0)
+                plt.plot(pathx, pathy, "-r")
+                plt.pause(pause_time)
+            changed_vertices = self.detect_changes()
+            if len(changed_vertices) != 0:
+                print("New obstacle detected")
+                self.km += self.h(last)
+                last = self.start
+                for u in changed_vertices:
+                    if compare_coordinates(u, self.start):
+                        continue
+                    self.rhs[u.x][u.y] = math.inf
+                    self.g[u.x][u.y] = math.inf
+                    self.update_vertex(u)
+                self.compute_shortest_path()
+
+                if show_animation:
+                    new_path = self.compute_current_path()
+                    if not self.compare_paths(current_path, new_path):
+                        current_path_image[0].remove()
+                        previous_path_image[0].remove()
+                        previous_path = current_path.copy()
+                        current_path = new_path.copy()
+                        previous_path_image = self.display_path(previous_path,
+                                                                ".c",
+                                                                alpha=0.3)
+                        current_path_image = self.display_path(current_path,
+                                                               ".c")
+                        plt.pause(pause_time)
+        print("Path found")
+        return True, pathx, pathy
+
+
+def main():
+
+    # start and goal position
+    sx = 10  # [m]
+    sy = 10  # [m]
+    gx = 50  # [m]
+    gy = 50  # [m]
+
+    # set obstacle positions
+    ox, oy = [], []
+    for i in range(-10, 60):
+        ox.append(i)
+        oy.append(-10.0)
+    for i in range(-10, 60):
+        ox.append(60.0)
+        oy.append(i)
+    for i in range(-10, 61):
+        ox.append(i)
+        oy.append(60.0)
+    for i in range(-10, 61):
+        ox.append(-10.0)
+        oy.append(i)
+    for i in range(-10, 40):
+        ox.append(20.0)
+        oy.append(i)
+    for i in range(0, 40):
+        ox.append(40.0)
+        oy.append(60.0 - i)
+
+    if show_animation:
+        plt.plot(ox, oy, ".k")
+        plt.plot(sx, sy, "og")
+        plt.plot(gx, gy, "xb")
+        plt.grid(True)
+        plt.axis("equal")
+        label_column = ['Start', 'Goal', 'Path taken',
+                        'Current computed path', 'Previous computed path',
+                        'Obstacles']
+        columns = [plt.plot([], [], symbol, color=colour, alpha=alpha)[0]
+                   for symbol, colour, alpha in [['o', 'g', 1],
+                                                 ['x', 'b', 1],
+                                                 ['-', 'r', 1],
+                                                 ['.', 'c', 1],
+                                                 ['.', 'c', 0.3],
+                                                 ['.', 'k', 1]]]
+        plt.legend(columns, label_column, bbox_to_anchor=(1, 1), title="Key:",
+                   fontsize="xx-small")
+        plt.plot()
+        plt.pause(pause_time)
+
+    # Obstacles discovered at time = row
+    # time = 1, obstacles discovered at (0, 2), (9, 2), (4, 0)
+    # time = 2, obstacles discovered at (0, 1), (7, 7)
+    # ...
+    # when the spoofed obstacles are:
+    # spoofed_ox = [[0, 9, 4], [0, 7], [], [], [], [], [], [5]]
+    # spoofed_oy = [[2, 2, 0], [1, 7], [], [], [], [], [], [4]]
+
+    # Reroute
+    # spoofed_ox = [[], [], [], [], [], [], [], [40 for _ in range(10, 21)]]
+    # spoofed_oy = [[], [], [], [], [], [], [], [i for i in range(10, 21)]]
+
+    # Obstacles that demostrate large rerouting
+    spoofed_ox = [[], [], [],
+                  [i for i in range(0, 21)] + [0 for _ in range(0, 20)]]
+    spoofed_oy = [[], [], [],
+                  [20 for _ in range(0, 21)] + [i for i in range(0, 20)]]
+
+    dstarlite = DStarLite(ox, oy)
+    dstarlite.main(Node(x=sx, y=sy), Node(x=gx, y=gy),
+                   spoofed_ox=spoofed_ox, spoofed_oy=spoofed_oy)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/README.md b/README.md
index fac8b53dce..e2eafe74f0 100644
--- a/README.md
+++ b/README.md
@@ -38,6 +38,7 @@ Python codes for robotics algorithm.
          * [Dijkstra algorithm](#dijkstra-algorithm)
          * [A* algorithm](#a-algorithm)
          * [D* algorithm](#d-algorithm)
+         * [D* Lite algorithm](#dstarlite-algorithm)
          * [Potential Field algorithm](#potential-field-algorithm)
          * [Grid based coverage path planning](#grid-based-coverage-path-planning)
       * [State Lattice Planning](#state-lattice-planning)
@@ -314,6 +315,18 @@ Ref:
 
 - [D* Algorithm Wikipedia](https://en.wikipedia.org/wiki/D*)
 
+### D\* Lite algorithm
+
+This algorithm finds the shortest path between two points while rerouting when obstacles are discovered. It has been implemented here for a 2D grid.
+
+![D* Lite](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStarLite/animation.gif)
+
+The animation shows a robot finding its path and rerouting to avoid obstacles as they are discovered using the D* Lite search algorithm.
+
+Refs:
+
+- [D* Lite](http://idm-lab.org/bib/abstracts/papers/aaai02b.pd)
+- [Improved Fast Replanning for Robot Navigation in Unknown Terrain](http://www.cs.cmu.edu/~maxim/files/dlite_icra02.pdf)
 
 ### Potential Field algorithm
 
diff --git a/tests/test_d_star_lite.py b/tests/test_d_star_lite.py
new file mode 100644
index 0000000000..b60a140a89
--- /dev/null
+++ b/tests/test_d_star_lite.py
@@ -0,0 +1,11 @@
+import conftest
+from PathPlanning.DStarLite import d_star_lite as m
+
+
+def test_1():
+    m.show_animation = False
+    m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)

From a0db9d266c65e54f61a525fb5305772e929cee08 Mon Sep 17 00:00:00 2001
From: vss2sn <28676655+vss2sn@users.noreply.github.com>
Date: Wed, 9 Jun 2021 10:02:32 -0400
Subject: [PATCH 0502/1080] Fix D* Lite README.md link (#513)

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index e2eafe74f0..c9e45d9363 100644
--- a/README.md
+++ b/README.md
@@ -38,7 +38,7 @@ Python codes for robotics algorithm.
          * [Dijkstra algorithm](#dijkstra-algorithm)
          * [A* algorithm](#a-algorithm)
          * [D* algorithm](#d-algorithm)
-         * [D* Lite algorithm](#dstarlite-algorithm)
+         * [D* Lite algorithm](#d-lite-algorithm)
          * [Potential Field algorithm](#potential-field-algorithm)
          * [Grid based coverage path planning](#grid-based-coverage-path-planning)
       * [State Lattice Planning](#state-lattice-planning)

From fe4ce92c4bc5bdbc6549d2512514f5bfb7cb72e6 Mon Sep 17 00:00:00 2001
From: Haegu Lee <34788458+benthebear93@users.noreply.github.com>
Date: Mon, 21 Jun 2021 22:44:06 +0900
Subject: [PATCH 0503/1080] Fix No module error in GridBasedSweepCPP and
 ClosedLoopRRTStart (#516)

* Fix No module error in GridBasedSweepCPP and ClosedLoopRRTStart

* Fix No module error in GridBasedSweepCPP and ClosedLoopRRTStart

* Fix No module error in GridBasedSweepCPP and ClosedLoopRRTStart

* Fix No module error in GridBasedSweepCPP and ClosedLoopRRTStart

* Fix No module error in GridBasedSweepCPP and ClosedLoopRRTStart
---
 PathPlanning/ClosedLoopRRTStar/__init__.py      |  8 --------
 .../closed_loop_rrt_star_car.py                 | 17 +++++++++++++++--
 PathPlanning/GridBasedSweepCPP/__init__.py      |  7 -------
 .../grid_based_sweep_coverage_path_planner.py   | 11 ++++++++++-
 4 files changed, 25 insertions(+), 18 deletions(-)
 delete mode 100644 PathPlanning/ClosedLoopRRTStar/__init__.py
 delete mode 100644 PathPlanning/GridBasedSweepCPP/__init__.py

diff --git a/PathPlanning/ClosedLoopRRTStar/__init__.py b/PathPlanning/ClosedLoopRRTStar/__init__.py
deleted file mode 100644
index 0e49cd14d3..0000000000
--- a/PathPlanning/ClosedLoopRRTStar/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import os
-import sys
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-sys.path.append(os.path.dirname(
-    os.path.abspath(__file__)) + "/../ReedsSheppPath/")
-sys.path.append(os.path.dirname(
-    os.path.abspath(__file__)) + "/../RRTStarReedsShepp/")
diff --git a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
index 740fc5590a..c0c8c356e8 100644
--- a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
+++ b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
@@ -5,15 +5,28 @@
 author: AtsushiSakai(@Atsushi_twi)
 
 """
+import os
+import sys
 
 import matplotlib.pyplot as plt
 import numpy as np
-import reeds_shepp_path_planning
-from rrt_star_reeds_shepp import RRTStarReedsShepp
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
 
 import pure_pursuit
 import unicycle_model
 
+sys.path.append(os.path.dirname(
+    os.path.abspath(__file__)) + "/../ReedsSheppPath/")
+sys.path.append(os.path.dirname(
+    os.path.abspath(__file__)) + "/../RRTStarReedsShepp/")
+
+try:
+    import reeds_shepp_path_planning
+    from rrt_star_reeds_shepp import RRTStarReedsShepp
+except ImportError:
+    raise
+
 show_animation = True
 
 
diff --git a/PathPlanning/GridBasedSweepCPP/__init__.py b/PathPlanning/GridBasedSweepCPP/__init__.py
deleted file mode 100644
index f1c41ce433..0000000000
--- a/PathPlanning/GridBasedSweepCPP/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import os
-import sys
-
-GRID_MAP_LIB = os.path.dirname(os.path.abspath(__file__)) + \
-               "/../../Mapping/"
-
-sys.path.append(GRID_MAP_LIB)
diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
index 984b0affdf..c0c8fc4319 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -5,13 +5,22 @@
 """
 
 import math
+import os
+import sys
 from enum import IntEnum
 
 import numpy as np
 from scipy.spatial.transform import Rotation as Rot
-from Mapping.grid_map_lib.grid_map_lib import GridMap
 import matplotlib.pyplot as plt
 
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../../Mapping/")
+
+try:
+    from grid_map_lib.grid_map_lib import GridMap
+except ImportError:
+    raise
+
 do_animation = True
 
 

From 9036e1527028347469a474ccc26f748a3a7bef82 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 22 Jun 2021 07:14:54 +0900
Subject: [PATCH 0504/1080] Bump scipy from 1.6.3 to 1.7.0 (#517)

Bumps [scipy](https://github.com/scipy/scipy) from 1.6.3 to 1.7.0.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.6.3...v1.7.0)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index fea1a9562b..e0b9e76a34 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.20.3
-scipy == 1.6.3
+scipy == 1.7.0
 pandas == 1.2.4
 matplotlib == 3.4.2
 cvxpy == 1.1.13

From ad366d2ec6c555bba722e1a16ed32203b16c58bd Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 29 Jun 2021 07:26:28 +0900
Subject: [PATCH 0505/1080] Bump numpy from 1.20.3 to 1.21.0 (#519)

Bumps [numpy](https://github.com/numpy/numpy) from 1.20.3 to 1.21.0.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt)
- [Commits](https://github.com/numpy/numpy/compare/v1.20.3...v1.21.0)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index e0b9e76a34..d98897d70d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.20.3
+numpy == 1.21.0
 scipy == 1.7.0
 pandas == 1.2.4
 matplotlib == 3.4.2

From 01874cee24e6944015a72e701140b23e5d291d47 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 29 Jun 2021 07:26:50 +0900
Subject: [PATCH 0506/1080] Bump pandas from 1.2.4 to 1.2.5 (#520)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.2.4 to 1.2.5.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.2.4...v1.2.5)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index d98897d70d..70af779df9 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.0
 scipy == 1.7.0
-pandas == 1.2.4
+pandas == 1.2.5
 matplotlib == 3.4.2
 cvxpy == 1.1.13

From b0df3c7db3c100b0b3c4680305921482bf9ae2b0 Mon Sep 17 00:00:00 2001
From: mbosetti <michael.bosetti@gmail.com>
Date: Tue, 29 Jun 2021 18:37:49 -0500
Subject: [PATCH 0507/1080] fix dijkstra hypot check bug (#522)

---
 PathPlanning/VoronoiRoadMap/dijkstra_search.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/VoronoiRoadMap/dijkstra_search.py b/PathPlanning/VoronoiRoadMap/dijkstra_search.py
index aef07ebbaf..503ccc342e 100644
--- a/PathPlanning/VoronoiRoadMap/dijkstra_search.py
+++ b/PathPlanning/VoronoiRoadMap/dijkstra_search.py
@@ -136,5 +136,5 @@ def is_same_node_with_xy(node_x, node_y, node_b):
     @staticmethod
     def is_same_node(node_a, node_b):
         dist = np.hypot(node_a.x - node_b.x,
-                        node_b.y - node_b.y)
+                        node_a.y - node_b.y)
         return dist <= 0.1

From 8f3337e78d0258386d220192caa97763e17a33d6 Mon Sep 17 00:00:00 2001
From: Jonathan Schwartz <jschwar4@andrew.cmu.edu>
Date: Thu, 1 Jul 2021 09:46:32 -0500
Subject: [PATCH 0508/1080] Dubins path bug fix (#521)

* Without equals sign, sometimes get points that are in the wrong direction - relative to the points before and after it- when change in x or change in y along path is 0

* Created test script for dubins path generator

* Broke == 0 into its own case in dubins planner, also Renaming files to appease CI

* Reverting some naming changes

* Turns out theres already a test for dubins.. not sure how I missed that

* Note to self: run the test cases on your own before throwing them at CI
---
 .../DubinsPath/dubins_path_planning.py        | 22 ++++++++++---------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py
index 9c13198aff..64d29e5061 100644
--- a/PathPlanning/DubinsPath/dubins_path_planning.py
+++ b/PathPlanning/DubinsPath/dubins_path_planning.py
@@ -254,11 +254,13 @@ def generate_local_course(total_length, lengths, mode, max_curvature,
 
     ll = 0.0
 
-    for (m, l, i) in zip(mode, lengths, range(len(mode))):
-        if l > 0.0:
-            d = step_size
+    for (m, length, i) in zip(mode, lengths, range(len(mode))):
+        if length == 0:
+            continue
+        elif length > 0.0:
+            dist = step_size
         else:
-            d = -step_size
+            dist = -step_size
 
         # set origin state
         origin_x, origin_y, origin_yaw = \
@@ -266,22 +268,22 @@ def generate_local_course(total_length, lengths, mode, max_curvature,
 
         index -= 1
         if i >= 1 and (lengths[i - 1] * lengths[i]) > 0:
-            pd = - d - ll
+            pd = - dist - ll
         else:
-            pd = d - ll
+            pd = dist - ll
 
-        while abs(pd) <= abs(l):
+        while abs(pd) <= abs(length):
             index += 1
             path_x, path_y, path_yaw, directions = interpolate(
                 index, pd, m, max_curvature, origin_x, origin_y, origin_yaw,
                 path_x, path_y, path_yaw, directions)
-            pd += d
+            pd += dist
 
-        ll = l - pd - d  # calc remain length
+        ll = length - pd - dist  # calc remain length
 
         index += 1
         path_x, path_y, path_yaw, directions = interpolate(
-            index, l, m, max_curvature, origin_x, origin_y, origin_yaw,
+            index, length, m, max_curvature, origin_x, origin_y, origin_yaw,
             path_x, path_y, path_yaw, directions)
 
     if len(path_x) <= 1:

From 51689d62b9c0c2d245f59fd711ff36774d89a833 Mon Sep 17 00:00:00 2001
From: Jonathan Schwartz <jonbs98@gmail.com>
Date: Fri, 2 Jul 2021 07:53:02 -0500
Subject: [PATCH 0509/1080] Issue #523 fix (Reeds-Shepp planner handling
 length=0 case) (#524)

* Without equals sign, sometimes get points that are in the wrong direction - relative to the points before and after it- when change in x or change in y along path is 0

* Created test script for dubins path generator

* Made len == 0 it's own case, also changed 'l' to 'len' to appease travisCI

* More variable renaming to appease CI

* Broke == 0 into its own case in dubins planner, also Renaming files to appease CI

* Reverting some naming changes

* Turns out theres already a test for dubins.. not sure how I missed that

* Note to self: run the test cases on your own before throwing them at CI

* Added handling of length=0 case in generate_local_course()

* Missed reverting 'mode' back to 'm' in one spot

* Addressing style issues (line length)
---
 .../DubinsPath/dubins_path_planning.py        |  2 +-
 .../reeds_shepp_path_planning.py              | 19 ++++++++++++-------
 2 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py
index 64d29e5061..a813d79b0d 100644
--- a/PathPlanning/DubinsPath/dubins_path_planning.py
+++ b/PathPlanning/DubinsPath/dubins_path_planning.py
@@ -255,7 +255,7 @@ def generate_local_course(total_length, lengths, mode, max_curvature,
     ll = 0.0
 
     for (m, length, i) in zip(mode, lengths, range(len(mode))):
-        if length == 0:
+        if length == 0.0:
             continue
         elif length > 0.0:
             dist = step_size
diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
index 7808f2c9f9..0b31e73793 100644
--- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
+++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
@@ -285,8 +285,10 @@ def generate_local_course(total_length, lengths, mode, max_curvature, step_size)
 
     ll = 0.0
 
-    for (m, l, i) in zip(mode, lengths, range(len(mode))):
-        if l > 0.0:
+    for (m, length, i) in zip(mode, lengths, range(len(mode))):
+        if length == 0.0:
+            continue
+        elif length > 0.0:
             d = step_size
         else:
             d = -step_size
@@ -300,17 +302,19 @@ def generate_local_course(total_length, lengths, mode, max_curvature, step_size)
         else:
             pd = d - ll
 
-        while abs(pd) <= abs(l):
+        while abs(pd) <= abs(length):
             ind += 1
             px, py, pyaw, directions = interpolate(
-                ind, pd, m, max_curvature, ox, oy, oyaw, px, py, pyaw, directions)
+                ind, pd, m, max_curvature, ox, oy, oyaw,
+                px, py, pyaw, directions)
             pd += d
 
-        ll = l - pd - d  # calc remain length
+        ll = length - pd - d  # calc remain length
 
         ind += 1
         px, py, pyaw, directions = interpolate(
-            ind, l, m, max_curvature, ox, oy, oyaw, px, py, pyaw, directions)
+            ind, length, m, max_curvature, ox, oy, oyaw,
+            px, py, pyaw, directions)
 
     # remove unused data
     while px[-1] == 0.0:
@@ -390,7 +394,8 @@ def main():
     step_size = 0.1
 
     px, py, pyaw, mode, clen = reeds_shepp_path_planning(
-        start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature, step_size)
+        start_x, start_y, start_yaw, end_x, end_y, end_yaw,
+        curvature, step_size)
 
     if show_animation:  # pragma: no cover
         plt.cla()

From 21e748731973fef52a8e4af22d2fb0c53b53e8c1 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 6 Jul 2021 07:06:53 +0900
Subject: [PATCH 0510/1080] Bump pandas from 1.2.5 to 1.3.0 (#525)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.2.5 to 1.3.0.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.2.5...v1.3.0)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 70af779df9..d393dcec01 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.0
 scipy == 1.7.0
-pandas == 1.2.5
+pandas == 1.3.0
 matplotlib == 3.4.2
 cvxpy == 1.1.13

From 6f06b535b9d614ed0c6f00dab7811d6917b8ffa1 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 10 Jul 2021 07:15:10 +0900
Subject: [PATCH 0511/1080] fix dubins path length bug and clean up codes.
 (#527)

* fix dubins path length bug and clean up codes.

* fix line length CI error

* fix line length CI error

* fix line length CI error

* fix line length CI error

* fix line length CI error

* fix line length CI error

* fix line length CI error

* fix line length CI error
---
 .../DubinsPath/dubins_path_planning.py        | 164 ++++++++++--------
 PathPlanning/RRTDubins/rrt_dubins.py          |   9 +-
 PathPlanning/RRTStarDubins/rrt_star_dubins.py |  15 +-
 tests/test_dubins_path_planning.py            |  30 ++--
 4 files changed, 124 insertions(+), 94 deletions(-)

diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planning.py
index a813d79b0d..f8e6d7b1c1 100644
--- a/PathPlanning/DubinsPath/dubins_path_planning.py
+++ b/PathPlanning/DubinsPath/dubins_path_planning.py
@@ -14,6 +14,46 @@
 show_animation = True
 
 
+def dubins_path_planning(s_x, s_y, s_yaw, g_x, g_y, g_yaw, curvature,
+                         step_size=0.1):
+    """
+    Dubins path planner
+
+    :param s_x: x position of start point [m]
+    :param s_y: y position of start point [m]
+    :param s_yaw: yaw angle of start point [rad]
+    :param g_x: x position of end point [m]
+    :param g_y: y position of end point [m]
+    :param g_yaw: yaw angle of end point [rad]
+    :param curvature: curvature for curve [1/m]
+    :param step_size: (optional) step size between two path points [m]
+    :return:
+        x_list: x positions of a path
+        y_list: y positions of a path
+        yaw_list: yaw angles of a path
+        modes: mode list of a path
+        lengths: length of path segments.
+    """
+
+    g_x -= s_x
+    g_y -= s_y
+
+    l_rot = Rot.from_euler('z', s_yaw).as_matrix()[0:2, 0:2]
+    le_xy = np.stack([g_x, g_y]).T @ l_rot
+    le_yaw = g_yaw - s_yaw
+
+    lp_x, lp_y, lp_yaw, modes, lengths = dubins_path_planning_from_origin(
+        le_xy[0], le_xy[1], le_yaw, curvature, step_size)
+
+    rot = Rot.from_euler('z', -s_yaw).as_matrix()[0:2, 0:2]
+    converted_xy = np.stack([lp_x, lp_y]).T @ rot
+    x_list = converted_xy[:, 0] + s_x
+    y_list = converted_xy[:, 1] + s_y
+    yaw_list = [pi_2_pi(i_yaw + s_yaw) for i_yaw in lp_yaw]
+
+    return x_list, y_list, yaw_list, modes, lengths
+
+
 def mod2pi(theta):
     return theta - 2.0 * math.pi * math.floor(theta / 2.0 / math.pi)
 
@@ -148,14 +188,14 @@ def dubins_path_planning_from_origin(end_x, end_y, end_yaw, curvature,
     alpha = mod2pi(- theta)
     beta = mod2pi(end_yaw - theta)
 
-    planners = [left_straight_left, right_straight_right, left_straight_right,
-                right_straight_left, right_left_right,
-                left_right_left]
+    planning_funcs = [left_straight_left, right_straight_right,
+                      left_straight_right, right_straight_left,
+                      right_left_right, left_right_left]
 
     best_cost = float("inf")
     bt, bp, bq, best_mode = None, None, None, None
 
-    for planner in planners:
+    for planner in planning_funcs:
         t, p, q, mode = planner(alpha, beta, d)
         if t is None:
             continue
@@ -166,10 +206,15 @@ def dubins_path_planning_from_origin(end_x, end_y, end_yaw, curvature,
             best_cost = cost
     lengths = [bt, bp, bq]
 
-    x_list, y_list, yaw_list, directions = generate_local_course(
-        sum(lengths), lengths, best_mode, curvature, step_size)
+    x_list, y_list, yaw_list, directions = generate_local_course(sum(lengths),
+                                                                 lengths,
+                                                                 best_mode,
+                                                                 curvature,
+                                                                 step_size)
+
+    lengths = [length / curvature for length in lengths]
 
-    return x_list, y_list, yaw_list, best_mode, best_cost
+    return x_list, y_list, yaw_list, best_mode, lengths
 
 
 def interpolate(ind, length, mode, max_curvature, origin_x, origin_y,
@@ -203,49 +248,15 @@ def interpolate(ind, length, mode, max_curvature, origin_x, origin_y,
     return path_x, path_y, path_yaw, directions
 
 
-def dubins_path_planning(s_x, s_y, s_yaw, g_x, g_y, g_yaw, c, step_size=0.1):
-    """
-    Dubins path planner
-
-    input:
-        s_x x position of start point [m]
-        s_y y position of start point [m]
-        s_yaw yaw angle of start point [rad]
-        g_x x position of end point [m]
-        g_y y position of end point [m]
-        g_yaw yaw angle of end point [rad]
-        c curvature [1/m]
-
-    """
-
-    g_x = g_x - s_x
-    g_y = g_y - s_y
-
-    l_rot = Rot.from_euler('z', s_yaw).as_matrix()[0:2, 0:2]
-    le_xy = np.stack([g_x, g_y]).T @ l_rot
-    le_yaw = g_yaw - s_yaw
-
-    lp_x, lp_y, lp_yaw, mode, lengths = dubins_path_planning_from_origin(
-        le_xy[0], le_xy[1], le_yaw, c, step_size)
-
-    rot = Rot.from_euler('z', -s_yaw).as_matrix()[0:2, 0:2]
-    converted_xy = np.stack([lp_x, lp_y]).T @ rot
-    x_list = converted_xy[:, 0] + s_x
-    y_list = converted_xy[:, 1] + s_y
-    yaw_list = [pi_2_pi(i_yaw + s_yaw) for i_yaw in lp_yaw]
-
-    return x_list, y_list, yaw_list, mode, lengths
-
-
-def generate_local_course(total_length, lengths, mode, max_curvature,
+def generate_local_course(total_length, lengths, modes, max_curvature,
                           step_size):
     n_point = math.trunc(total_length / step_size) + len(lengths) + 4
 
-    path_x = [0.0 for _ in range(n_point)]
-    path_y = [0.0 for _ in range(n_point)]
-    path_yaw = [0.0 for _ in range(n_point)]
+    p_x = [0.0 for _ in range(n_point)]
+    p_y = [0.0 for _ in range(n_point)]
+    p_yaw = [0.0 for _ in range(n_point)]
     directions = [0.0 for _ in range(n_point)]
-    index = 1
+    ind = 1
 
     if lengths[0] > 0.0:
         directions[0] = 1
@@ -254,7 +265,7 @@ def generate_local_course(total_length, lengths, mode, max_curvature,
 
     ll = 0.0
 
-    for (m, length, i) in zip(mode, lengths, range(len(mode))):
+    for (m, length, i) in zip(modes, lengths, range(len(modes))):
         if length == 0.0:
             continue
         elif length > 0.0:
@@ -263,54 +274,57 @@ def generate_local_course(total_length, lengths, mode, max_curvature,
             dist = -step_size
 
         # set origin state
-        origin_x, origin_y, origin_yaw = \
-            path_x[index], path_y[index], path_yaw[index]
+        origin_x, origin_y, origin_yaw = p_x[ind], p_y[ind], p_yaw[ind]
 
-        index -= 1
+        ind -= 1
         if i >= 1 and (lengths[i - 1] * lengths[i]) > 0:
             pd = - dist - ll
         else:
             pd = dist - ll
 
         while abs(pd) <= abs(length):
-            index += 1
-            path_x, path_y, path_yaw, directions = interpolate(
-                index, pd, m, max_curvature, origin_x, origin_y, origin_yaw,
-                path_x, path_y, path_yaw, directions)
+            ind += 1
+            p_x, p_y, p_yaw, directions = interpolate(ind, pd, m,
+                                                      max_curvature,
+                                                      origin_x,
+                                                      origin_y,
+                                                      origin_yaw,
+                                                      p_x, p_y,
+                                                      p_yaw,
+                                                      directions)
             pd += dist
 
         ll = length - pd - dist  # calc remain length
 
-        index += 1
-        path_x, path_y, path_yaw, directions = interpolate(
-            index, length, m, max_curvature, origin_x, origin_y, origin_yaw,
-            path_x, path_y, path_yaw, directions)
+        ind += 1
+        p_x, p_y, p_yaw, directions = interpolate(ind, length, m,
+                                                  max_curvature,
+                                                  origin_x, origin_y,
+                                                  origin_yaw,
+                                                  p_x, p_y, p_yaw,
+                                                  directions)
 
-    if len(path_x) <= 1:
+    if len(p_x) <= 1:
         return [], [], [], []
 
     # remove unused data
-    while len(path_x) >= 1 and path_x[-1] == 0.0:
-        path_x.pop()
-        path_y.pop()
-        path_yaw.pop()
+    while len(p_x) >= 1 and p_x[-1] == 0.0:
+        p_x.pop()
+        p_y.pop()
+        p_yaw.pop()
         directions.pop()
 
-    return path_x, path_y, path_yaw, directions
+    return p_x, p_y, p_yaw, directions
 
 
 def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r",
                ec="k"):  # pragma: no cover
-    """
-    Plot arrow
-    """
-
     if not isinstance(x, float):
         for (i_x, i_y, i_yaw) in zip(x, y, yaw):
             plot_arrow(i_x, i_y, i_yaw)
     else:
-        plt.arrow(x, y, length * math.cos(yaw), length * math.sin(yaw),
-                  fc=fc, ec=ec, head_width=width, head_length=width)
+        plt.arrow(x, y, length * math.cos(yaw), length * math.sin(yaw), fc=fc,
+                  ec=ec, head_width=width, head_length=width)
         plt.plot(x, y)
 
 
@@ -327,9 +341,13 @@ def main():
 
     curvature = 1.0
 
-    path_x, path_y, path_yaw, mode, path_length = dubins_path_planning(
-        start_x, start_y, start_yaw,
-        end_x, end_y, end_yaw, curvature)
+    path_x, path_y, path_yaw, mode, lengths = dubins_path_planning(start_x,
+                                                                   start_y,
+                                                                   start_yaw,
+                                                                   end_x,
+                                                                   end_y,
+                                                                   end_yaw,
+                                                                   curvature)
 
     if show_animation:
         plt.plot(path_x, path_y, label="final course " + "".join(mode))
diff --git a/PathPlanning/RRTDubins/rrt_dubins.py b/PathPlanning/RRTDubins/rrt_dubins.py
index 55cea0bde3..8ab06c7323 100644
--- a/PathPlanning/RRTDubins/rrt_dubins.py
+++ b/PathPlanning/RRTDubins/rrt_dubins.py
@@ -133,9 +133,10 @@ def plot_start_goal_arrow(self):  # pragma: no cover
 
     def steer(self, from_node, to_node):
 
-        px, py, pyaw, mode, course_length = dubins_path_planning.dubins_path_planning(
-            from_node.x, from_node.y, from_node.yaw,
-            to_node.x, to_node.y, to_node.yaw, self.curvature)
+        px, py, pyaw, mode, course_lengths = \
+            dubins_path_planning.dubins_path_planning(
+                from_node.x, from_node.y, from_node.yaw,
+                to_node.x, to_node.y, to_node.yaw, self.curvature)
 
         if len(px) <= 1:  # cannot find a dubins path
             return None
@@ -148,7 +149,7 @@ def steer(self, from_node, to_node):
         new_node.path_x = px
         new_node.path_y = py
         new_node.path_yaw = pyaw
-        new_node.cost += course_length
+        new_node.cost += sum([abs(c) for c in course_lengths])
         new_node.parent = from_node
 
         return new_node
diff --git a/PathPlanning/RRTStarDubins/rrt_star_dubins.py b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
index 9cfd4e692a..26367eecea 100644
--- a/PathPlanning/RRTStarDubins/rrt_star_dubins.py
+++ b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
@@ -139,9 +139,10 @@ def plot_start_goal_arrow(self):
 
     def steer(self, from_node, to_node):
 
-        px, py, pyaw, mode, course_length = dubins_path_planning.dubins_path_planning(
-            from_node.x, from_node.y, from_node.yaw,
-            to_node.x, to_node.y, to_node.yaw, self.curvature)
+        px, py, pyaw, mode, course_lengths = \
+            dubins_path_planning.dubins_path_planning(
+                from_node.x, from_node.y, from_node.yaw,
+                to_node.x, to_node.y, to_node.yaw, self.curvature)
 
         if len(px) <= 1:  # cannot find a dubins path
             return None
@@ -154,18 +155,20 @@ def steer(self, from_node, to_node):
         new_node.path_x = px
         new_node.path_y = py
         new_node.path_yaw = pyaw
-        new_node.cost += course_length
+        new_node.cost += sum([abs(c) for c in course_lengths])
         new_node.parent = from_node
 
         return new_node
 
     def calc_new_cost(self, from_node, to_node):
 
-        _, _, _, _, course_length = dubins_path_planning.dubins_path_planning(
+        _, _, _, _, course_lengths = dubins_path_planning.dubins_path_planning(
             from_node.x, from_node.y, from_node.yaw,
             to_node.x, to_node.y, to_node.yaw, self.curvature)
 
-        return from_node.cost + course_length
+        cost = sum([abs(c) for c in course_lengths])
+
+        return from_node.cost + cost
 
     def get_random_node(self):
 
diff --git a/tests/test_dubins_path_planning.py b/tests/test_dubins_path_planning.py
index 99fbd29100..18492ccc59 100644
--- a/tests/test_dubins_path_planning.py
+++ b/tests/test_dubins_path_planning.py
@@ -1,12 +1,13 @@
-import conftest
 import numpy as np
+
+import conftest
 from PathPlanning.DubinsPath import dubins_path_planning
 
 np.random.seed(12345)
 
 
-def check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw,
-                         end_x, end_y, end_yaw):
+def check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x,
+                         end_y, end_yaw):
     assert (abs(px[0] - start_x) <= 0.01)
     assert (abs(py[0] - start_y) <= 0.01)
     assert (abs(pyaw[0] - start_yaw) <= 0.01)
@@ -15,6 +16,12 @@ def check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw,
     assert (abs(pyaw[-1] - end_yaw) <= 0.01)
 
 
+def check_path_length(px, py, lengths):
+    path_len = sum(
+        [np.hypot(dx, dy) for (dx, dy) in zip(np.diff(px), np.diff(py))])
+    assert (abs(path_len - sum(lengths)) <= 0.1)
+
+
 def test_1():
     start_x = 1.0  # [m]
     start_y = 1.0  # [m]
@@ -26,12 +33,12 @@ def test_1():
 
     curvature = 1.0
 
-    px, py, pyaw, mode, clen = dubins_path_planning.dubins_path_planning(
+    px, py, pyaw, mode, lengths = dubins_path_planning.dubins_path_planning(
         start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
 
-    check_edge_condition(px, py, pyaw,
-                         start_x, start_y, start_yaw,
-                         end_x, end_y, end_yaw)
+    check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x,
+                         end_y, end_yaw)
+    check_path_length(px, py, lengths)
 
 
 def test_2():
@@ -53,12 +60,13 @@ def test_3():
 
         curvature = 1.0 / (np.random.rand() * 5.0)
 
-        px, py, pyaw, mode, clen = dubins_path_planning.dubins_path_planning(
+        px, py, pyaw, mode, lengths = \
+            dubins_path_planning.dubins_path_planning(
             start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
 
-        check_edge_condition(px, py, pyaw,
-                             start_x, start_y, start_yaw,
-                             end_x, end_y, end_yaw)
+        check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x,
+                             end_y, end_yaw)
+        check_path_length(px, py, lengths)
 
 
 if __name__ == '__main__':

From 177f04618c4bbba40bea38cd57d642f7ab9e3725 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 12 Jul 2021 21:04:27 +0900
Subject: [PATCH 0512/1080] fix example run issue and clean up for arm
 navigation codes. (#528)

* clean up arm navigation codes.

* add project toml

* add pylintrc

* add pylintrc

* add pylintrc
---
 ArmNavigation/n_joint_arm_3d/NLinkArm3d.py                    | 3 ++-
 .../n_joint_arm_to_point_control.py                           | 4 ++++
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py b/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py
index 3f6c5ddd2a..0cea963168 100644
--- a/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py
+++ b/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py
@@ -63,7 +63,8 @@ def forward_kinematics(self, plot=False):
 
         if plot:
             self.fig = plt.figure()
-            self.ax = Axes3D(self.fig)
+            self.ax = Axes3D(self.fig, auto_add_to_figure=False)
+            self.fig.add_axes(self.ax)
 
             x_list = []
             y_list = []
diff --git a/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py b/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py
index b7834ee98b..36093df08d 100644
--- a/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py
+++ b/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py
@@ -4,7 +4,11 @@
 Author: Daniel Ingram (daniel-s-ingram)
         Atsushi Sakai (@Atsushi_twi)
 """
+
 import numpy as np
+import sys
+from pathlib import Path
+sys.path.append(str(Path(__file__).parent.parent.parent))
 
 from ArmNavigation.n_joint_arm_to_point_control.NLinkArm import NLinkArm
 

From 2c3896879b91117e8af1016acf25effd310e2def Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 17 Jul 2021 18:28:26 +0900
Subject: [PATCH 0513/1080] Fix reeds shepp path issue (#529)

* code clean up

* code clean up

* code clean up

* code clean up

* fix length handling issues
---
 .../reeds_shepp_path_planning.py              | 292 ++++++++----------
 tests/test_reeds_shepp_path_planning.py       |  27 +-
 2 files changed, 146 insertions(+), 173 deletions(-)

diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
index 0b31e73793..5bc04c8ee3 100644
--- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
+++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
@@ -14,28 +14,29 @@
 
 
 class Path:
+    """
+    Path data container
+    """
 
     def __init__(self):
+        # course segment length  (negative value is backward segment)
         self.lengths = []
+        # course segment type char ("S": straight, "L": left, "R": right)
         self.ctypes = []
-        self.L = 0.0
-        self.x = []
-        self.y = []
-        self.yaw = []
-        self.directions = []
+        self.L = 0.0  # Total lengths of the path
+        self.x = []  # x positions
+        self.y = []  # y positions
+        self.yaw = []  # orientations [rad]
+        self.directions = []  # directions (1:forward, -1:backward)
 
 
 def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
-    """
-    Plot arrow
-    """
-
-    if not isinstance(x, float):
+    if isinstance(x, list):
         for (ix, iy, iyaw) in zip(x, y, yaw):
             plot_arrow(ix, iy, iyaw)
     else:
-        plt.arrow(x, y, length * math.cos(yaw), length * math.sin(yaw),
-                  fc=fc, ec=ec, head_width=width, head_length=width)
+        plt.arrow(x, y, length * math.cos(yaw), length * math.sin(yaw), fc=fc,
+                  ec=ec, head_width=width, head_length=width)
         plt.plot(x, y)
 
 
@@ -68,35 +69,35 @@ def straight_left_straight(x, y, phi):
     return False, 0.0, 0.0, 0.0
 
 
-def set_path(paths, lengths, ctypes):
+def set_path(paths, lengths, ctypes, step_size):
     path = Path()
     path.ctypes = ctypes
     path.lengths = lengths
+    path.L = sum(np.abs(lengths))
 
     # check same path exist
-    for tpath in paths:
-        typeissame = (tpath.ctypes == path.ctypes)
-        if typeissame:
-            if sum(np.abs(tpath.lengths)) - sum(np.abs(path.lengths)) <= 0.01:
-                return paths  # not insert path
-
-    path.L = sum([abs(i) for i in lengths])
+    for i_path in paths:
+        type_is_same = (i_path.ctypes == path.ctypes)
+        length_is_close = (sum(np.abs(i_path.lengths)) - path.L) <= step_size
+        if type_is_same and length_is_close:
+            return paths  # same path found, so do not insert path
 
-    # Base.Test.@test path.L >= 0.01
-    if path.L >= 0.01:
-        paths.append(path)
+    # check path is long enough
+    if path.L <= step_size:
+        return paths  # too short, so do not insert path
 
+    paths.append(path)
     return paths
 
 
-def straight_curve_straight(x, y, phi, paths):
+def straight_curve_straight(x, y, phi, paths, step_size):
     flag, t, u, v = straight_left_straight(x, y, phi)
     if flag:
-        paths = set_path(paths, [t, u, v], ["S", "L", "S"])
+        paths = set_path(paths, [t, u, v], ["S", "L", "S"], step_size)
 
     flag, t, u, v = straight_left_straight(x, -y, -phi)
     if flag:
-        paths = set_path(paths, [t, u, v], ["S", "R", "S"])
+        paths = set_path(paths, [t, u, v], ["S", "R", "S"], step_size)
 
     return paths
 
@@ -131,22 +132,22 @@ def left_right_left(x, y, phi):
     return False, 0.0, 0.0, 0.0
 
 
-def curve_curve_curve(x, y, phi, paths):
+def curve_curve_curve(x, y, phi, paths, step_size):
     flag, t, u, v = left_right_left(x, y, phi)
     if flag:
-        paths = set_path(paths, [t, u, v], ["L", "R", "L"])
+        paths = set_path(paths, [t, u, v], ["L", "R", "L"], step_size)
 
     flag, t, u, v = left_right_left(-x, y, -phi)
     if flag:
-        paths = set_path(paths, [-t, -u, -v], ["L", "R", "L"])
+        paths = set_path(paths, [-t, -u, -v], ["L", "R", "L"], step_size)
 
     flag, t, u, v = left_right_left(x, -y, -phi)
     if flag:
-        paths = set_path(paths, [t, u, v], ["R", "L", "R"])
+        paths = set_path(paths, [t, u, v], ["R", "L", "R"], step_size)
 
     flag, t, u, v = left_right_left(-x, -y, phi)
     if flag:
-        paths = set_path(paths, [-t, -u, -v], ["R", "L", "R"])
+        paths = set_path(paths, [-t, -u, -v], ["R", "L", "R"], step_size)
 
     # backwards
     xb = x * math.cos(phi) + y * math.sin(phi)
@@ -154,55 +155,55 @@ def curve_curve_curve(x, y, phi, paths):
 
     flag, t, u, v = left_right_left(xb, yb, phi)
     if flag:
-        paths = set_path(paths, [v, u, t], ["L", "R", "L"])
+        paths = set_path(paths, [v, u, t], ["L", "R", "L"], step_size)
 
     flag, t, u, v = left_right_left(-xb, yb, -phi)
     if flag:
-        paths = set_path(paths, [-v, -u, -t], ["L", "R", "L"])
+        paths = set_path(paths, [-v, -u, -t], ["L", "R", "L"], step_size)
 
     flag, t, u, v = left_right_left(xb, -yb, -phi)
     if flag:
-        paths = set_path(paths, [v, u, t], ["R", "L", "R"])
+        paths = set_path(paths, [v, u, t], ["R", "L", "R"], step_size)
 
     flag, t, u, v = left_right_left(-xb, -yb, phi)
     if flag:
-        paths = set_path(paths, [-v, -u, -t], ["R", "L", "R"])
+        paths = set_path(paths, [-v, -u, -t], ["R", "L", "R"], step_size)
 
     return paths
 
 
-def curve_straight_curve(x, y, phi, paths):
+def curve_straight_curve(x, y, phi, paths, step_size):
     flag, t, u, v = left_straight_left(x, y, phi)
     if flag:
-        paths = set_path(paths, [t, u, v], ["L", "S", "L"])
+        paths = set_path(paths, [t, u, v], ["L", "S", "L"], step_size)
 
     flag, t, u, v = left_straight_left(-x, y, -phi)
     if flag:
-        paths = set_path(paths, [-t, -u, -v], ["L", "S", "L"])
+        paths = set_path(paths, [-t, -u, -v], ["L", "S", "L"], step_size)
 
     flag, t, u, v = left_straight_left(x, -y, -phi)
     if flag:
-        paths = set_path(paths, [t, u, v], ["R", "S", "R"])
+        paths = set_path(paths, [t, u, v], ["R", "S", "R"], step_size)
 
     flag, t, u, v = left_straight_left(-x, -y, phi)
     if flag:
-        paths = set_path(paths, [-t, -u, -v], ["R", "S", "R"])
+        paths = set_path(paths, [-t, -u, -v], ["R", "S", "R"], step_size)
 
     flag, t, u, v = left_straight_right(x, y, phi)
     if flag:
-        paths = set_path(paths, [t, u, v], ["L", "S", "R"])
+        paths = set_path(paths, [t, u, v], ["L", "S", "R"], step_size)
 
     flag, t, u, v = left_straight_right(-x, y, -phi)
     if flag:
-        paths = set_path(paths, [-t, -u, -v], ["L", "S", "R"])
+        paths = set_path(paths, [-t, -u, -v], ["L", "S", "R"], step_size)
 
     flag, t, u, v = left_straight_right(x, -y, -phi)
     if flag:
-        paths = set_path(paths, [t, u, v], ["R", "S", "L"])
+        paths = set_path(paths, [t, u, v], ["R", "S", "L"], step_size)
 
     flag, t, u, v = left_straight_right(-x, -y, phi)
     if flag:
-        paths = set_path(paths, [-t, -u, -v], ["R", "S", "L"])
+        paths = set_path(paths, [-t, -u, -v], ["R", "S", "L"], step_size)
 
     return paths
 
@@ -222,7 +223,7 @@ def left_straight_right(x, y, phi):
     return False, 0.0, 0.0, 0.0
 
 
-def generate_path(q0, q1, max_curvature):
+def generate_path(q0, q1, max_curvature, step_size):
     dx = q1[0] - q0[0]
     dy = q1[1] - q0[1]
     dth = q1[2] - q0[2]
@@ -232,98 +233,70 @@ def generate_path(q0, q1, max_curvature):
     y = (-s * dx + c * dy) * max_curvature
 
     paths = []
-    paths = straight_curve_straight(x, y, dth, paths)
-    paths = curve_straight_curve(x, y, dth, paths)
-    paths = curve_curve_curve(x, y, dth, paths)
+    paths = straight_curve_straight(x, y, dth, paths, step_size)
+    paths = curve_straight_curve(x, y, dth, paths, step_size)
+    paths = curve_curve_curve(x, y, dth, paths, step_size)
 
     return paths
 
 
-def interpolate(ind, length, mode, max_curvature, origin_x, origin_y, origin_yaw, path_x, path_y, path_yaw, directions):
-    if mode == "S":
-        path_x[ind] = origin_x + length / max_curvature * math.cos(origin_yaw)
-        path_y[ind] = origin_y + length / max_curvature * math.sin(origin_yaw)
-        path_yaw[ind] = origin_yaw
-    else:  # curve
-        ldx = math.sin(length) / max_curvature
-        ldy = 0.0
-        if mode == "L":  # left turn
-            ldy = (1.0 - math.cos(length)) / max_curvature
-        elif mode == "R":  # right turn
-            ldy = (1.0 - math.cos(length)) / -max_curvature
-        gdx = math.cos(-origin_yaw) * ldx + math.sin(-origin_yaw) * ldy
-        gdy = -math.sin(-origin_yaw) * ldx + math.cos(-origin_yaw) * ldy
-        path_x[ind] = origin_x + gdx
-        path_y[ind] = origin_y + gdy
-
-    if mode == "L":  # left turn
-        path_yaw[ind] = origin_yaw + length
-    elif mode == "R":  # right turn
-        path_yaw[ind] = origin_yaw - length
+def calc_interpolate_dists_list(lengths, step_size):
+    interpolate_dists_list = []
+    for length in lengths:
+        d_dist = step_size if length >= 0.0 else -step_size
+        interp_dists = np.arange(0.0, length, d_dist)
+        interp_dists = np.append(interp_dists, length)
+        interpolate_dists_list.append(interp_dists)
 
-    if length > 0.0:
-        directions[ind] = 1
-    else:
-        directions[ind] = -1
+    return interpolate_dists_list
 
-    return path_x, path_y, path_yaw, directions
 
+def generate_local_course(lengths, modes, max_curvature, step_size):
+    interpolate_dists_list = calc_interpolate_dists_list(lengths, step_size)
 
-def generate_local_course(total_length, lengths, mode, max_curvature, step_size):
-    n_point = math.trunc(total_length / step_size) + len(lengths) + 4
+    origin_x, origin_y, origin_yaw = 0.0, 0.0, 0.0
 
-    px = [0.0 for _ in range(n_point)]
-    py = [0.0 for _ in range(n_point)]
-    pyaw = [0.0 for _ in range(n_point)]
-    directions = [0.0 for _ in range(n_point)]
-    ind = 1
+    xs, ys, yaws, directions = [], [], [], []
+    for (interp_dists, mode, length) in zip(interpolate_dists_list, modes,
+                                            lengths):
 
-    if lengths[0] > 0.0:
-        directions[0] = 1
-    else:
-        directions[0] = -1
+        for dist in interp_dists:
+            x, y, yaw, direction = interpolate(dist, length, mode,
+                                               max_curvature, origin_x,
+                                               origin_y, origin_yaw)
+            xs.append(x)
+            ys.append(y)
+            yaws.append(yaw)
+            directions.append(direction)
+        origin_x = xs[-1]
+        origin_y = ys[-1]
+        origin_yaw = yaws[-1]
 
-    ll = 0.0
+    return xs, ys, yaws, directions
 
-    for (m, length, i) in zip(mode, lengths, range(len(mode))):
-        if length == 0.0:
-            continue
-        elif length > 0.0:
-            d = step_size
-        else:
-            d = -step_size
 
-        # set origin state
-        ox, oy, oyaw = px[ind], py[ind], pyaw[ind]
-
-        ind -= 1
-        if i >= 1 and (lengths[i - 1] * lengths[i]) > 0:
-            pd = - d - ll
-        else:
-            pd = d - ll
-
-        while abs(pd) <= abs(length):
-            ind += 1
-            px, py, pyaw, directions = interpolate(
-                ind, pd, m, max_curvature, ox, oy, oyaw,
-                px, py, pyaw, directions)
-            pd += d
-
-        ll = length - pd - d  # calc remain length
-
-        ind += 1
-        px, py, pyaw, directions = interpolate(
-            ind, length, m, max_curvature, ox, oy, oyaw,
-            px, py, pyaw, directions)
-
-    # remove unused data
-    while px[-1] == 0.0:
-        px.pop()
-        py.pop()
-        pyaw.pop()
-        directions.pop()
+def interpolate(dist, length, mode, max_curvature, origin_x, origin_y,
+                origin_yaw):
+    if mode == "S":
+        x = origin_x + dist / max_curvature * math.cos(origin_yaw)
+        y = origin_y + dist / max_curvature * math.sin(origin_yaw)
+        yaw = origin_yaw
+    else:  # curve
+        ldx = math.sin(dist) / max_curvature
+        ldy = 0.0
+        yaw = None
+        if mode == "L":  # left turn
+            ldy = (1.0 - math.cos(dist)) / max_curvature
+            yaw = origin_yaw + dist
+        elif mode == "R":  # right turn
+            ldy = (1.0 - math.cos(dist)) / -max_curvature
+            yaw = origin_yaw - dist
+        gdx = math.cos(-origin_yaw) * ldx + math.sin(-origin_yaw) * ldy
+        gdy = -math.sin(-origin_yaw) * ldx + math.cos(-origin_yaw) * ldy
+        x = origin_x + gdx
+        y = origin_y + gdy
 
-    return px, py, pyaw, directions
+    return x, y, yaw, 1 if length > 0.0 else -1
 
 
 def pi_2_pi(angle):
@@ -334,17 +307,18 @@ def calc_paths(sx, sy, syaw, gx, gy, gyaw, maxc, step_size):
     q0 = [sx, sy, syaw]
     q1 = [gx, gy, gyaw]
 
-    paths = generate_path(q0, q1, maxc)
+    paths = generate_path(q0, q1, maxc, step_size)
     for path in paths:
-        x, y, yaw, directions = generate_local_course(
-            path.L, path.lengths, path.ctypes, maxc, step_size * maxc)
+        xs, ys, yaws, directions = generate_local_course(path.lengths,
+                                                         path.ctypes, maxc,
+                                                         step_size * maxc)
 
         # convert global coordinate
-        path.x = [math.cos(-q0[2]) * ix + math.sin(-q0[2])
-                  * iy + q0[0] for (ix, iy) in zip(x, y)]
-        path.y = [-math.sin(-q0[2]) * ix + math.cos(-q0[2])
-                  * iy + q0[1] for (ix, iy) in zip(x, y)]
-        path.yaw = [pi_2_pi(iyaw + q0[2]) for iyaw in yaw]
+        path.x = [math.cos(-q0[2]) * ix + math.sin(-q0[2]) * iy + q0[0] for
+                  (ix, iy) in zip(xs, ys)]
+        path.y = [-math.sin(-q0[2]) * ix + math.cos(-q0[2]) * iy + q0[1] for
+                  (ix, iy) in zip(xs, ys)]
+        path.yaw = [pi_2_pi(yaw + q0[2]) for yaw in yaws]
         path.directions = directions
         path.lengths = [length / maxc for length in path.lengths]
         path.L = path.L / maxc
@@ -352,54 +326,42 @@ def calc_paths(sx, sy, syaw, gx, gy, gyaw, maxc, step_size):
     return paths
 
 
-def reeds_shepp_path_planning(sx, sy, syaw,
-                              gx, gy, gyaw, maxc, step_size=0.2):
+def reeds_shepp_path_planning(sx, sy, syaw, gx, gy, gyaw, maxc, step_size=0.2):
     paths = calc_paths(sx, sy, syaw, gx, gy, gyaw, maxc, step_size)
-
     if not paths:
-        return None, None, None, None, None
-
-    minL = float("Inf")
-    best_path_index = -1
-    for i, _ in enumerate(paths):
-        if paths[i].L <= minL:
-            minL = paths[i].L
-            best_path_index = i
+        return None, None, None, None, None  # could not generate any path
 
-    bpath = paths[best_path_index]
+    # search minimum cost path
+    best_path_index = paths.index(min(paths, key=lambda p: abs(p.L)))
+    b_path = paths[best_path_index]
 
-    return bpath.x, bpath.y, bpath.yaw, bpath.ctypes, bpath.lengths
+    return b_path.x, b_path.y, b_path.yaw, b_path.ctypes, b_path.lengths
 
 
 def main():
     print("Reeds Shepp path planner sample start!!")
 
-    # start_x = -1.0  # [m]
-    # start_y = -4.0  # [m]
-    # start_yaw = np.deg2rad(-20.0)  # [rad]
-    #
-    # end_x = 5.0  # [m]
-    # end_y = 5.0  # [m]
-    # end_yaw = np.deg2rad(25.0)  # [rad]
-
-    start_x = 0.0  # [m]
-    start_y = 0.0  # [m]
-    start_yaw = np.deg2rad(0.0)  # [rad]
+    start_x = -1.0  # [m]
+    start_y = -4.0  # [m]
+    start_yaw = np.deg2rad(-20.0)  # [rad]
 
-    end_x = 0.0  # [m]
-    end_y = 0.0  # [m]
-    end_yaw = np.deg2rad(0.0)  # [rad]
+    end_x = 5.0  # [m]
+    end_y = 5.0  # [m]
+    end_yaw = np.deg2rad(25.0)  # [rad]
 
-    curvature = 1.0
-    step_size = 0.1
+    curvature = 0.1
+    step_size = 0.05
 
-    px, py, pyaw, mode, clen = reeds_shepp_path_planning(
-        start_x, start_y, start_yaw, end_x, end_y, end_yaw,
-        curvature, step_size)
+    xs, ys, yaws, modes, lengths = reeds_shepp_path_planning(start_x, start_y,
+                                                             start_yaw, end_x,
+                                                             end_y, end_yaw,
+                                                             curvature,
+                                                             step_size)
 
     if show_animation:  # pragma: no cover
         plt.cla()
-        plt.plot(px, py, label="final course " + str(mode))
+        plt.plot(xs, ys, label="final course " + str(modes))
+        print(f"{lengths=}")
 
         # plotting
         plot_arrow(start_x, start_y, start_yaw)
@@ -410,7 +372,7 @@ def main():
         plt.axis("equal")
         plt.show()
 
-    if not px:
+    if not xs:
         assert False, "No path"
 
 
diff --git a/tests/test_reeds_shepp_path_planning.py b/tests/test_reeds_shepp_path_planning.py
index f1ce82d4c2..34ccfe7730 100644
--- a/tests/test_reeds_shepp_path_planning.py
+++ b/tests/test_reeds_shepp_path_planning.py
@@ -1,6 +1,7 @@
+import numpy as np
+
 import conftest  # Add root path to sys.path
 from PathPlanning.ReedsSheppPath import reeds_shepp_path_planning as m
-import numpy as np
 
 
 def check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x,
@@ -8,14 +9,21 @@ def check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x,
     assert (abs(px[0] - start_x) <= 0.01)
     assert (abs(py[0] - start_y) <= 0.01)
     assert (abs(pyaw[0] - start_yaw) <= 0.01)
-    print("x", px[-1], end_x)
     assert (abs(px[-1] - end_x) <= 0.01)
-    print("y", py[-1], end_y)
     assert (abs(py[-1] - end_y) <= 0.01)
-    print("yaw", pyaw[-1], end_yaw)
     assert (abs(pyaw[-1] - end_yaw) <= 0.01)
 
 
+def check_path_length(px, py, lengths):
+    sum_len = sum(abs(length) for length in lengths)
+    dpx = np.diff(px)
+    dpy = np.diff(py)
+    actual_len = sum(
+        np.hypot(dx, dy) for (dx, dy) in zip(dpx, dpy))
+    diff_len = sum_len - actual_len
+    assert (diff_len <= 0.01)
+
+
 def test1():
     m.show_animation = False
     m.main()
@@ -23,6 +31,7 @@ def test1():
 
 def test2():
     N_TEST = 10
+    np.random.seed(1234)
 
     for i in range(N_TEST):
         start_x = (np.random.rand() - 0.5) * 10.0  # [m]
@@ -35,11 +44,13 @@ def test2():
 
         curvature = 1.0 / (np.random.rand() * 5.0)
 
-        px, py, pyaw, mode, clen = m.reeds_shepp_path_planning(
-            start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
+        px, py, pyaw, mode, lengths = m.reeds_shepp_path_planning(
+            start_x, start_y, start_yaw,
+            end_x, end_y, end_yaw, curvature)
 
-        check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw,
-                             end_x, end_y, end_yaw)
+        check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x,
+                             end_y, end_yaw)
+        check_path_length(px, py, lengths)
 
 
 if __name__ == '__main__':

From 4df7b7a6a176527e44aaee4ccc16184e02127437 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 20 Jul 2021 07:22:44 +0900
Subject: [PATCH 0514/1080] Bump numpy from 1.21.0 to 1.21.1 (#531)

Bumps [numpy](https://github.com/numpy/numpy) from 1.21.0 to 1.21.1.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt)
- [Commits](https://github.com/numpy/numpy/compare/v1.21.0...v1.21.1)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index d393dcec01..e98fafb68d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.21.0
+numpy == 1.21.1
 scipy == 1.7.0
 pandas == 1.3.0
 matplotlib == 3.4.2

From d777bea5005eef54e461152f9207cf0fa0e4458b Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 Jul 2021 07:21:13 +0900
Subject: [PATCH 0515/1080] Bump pandas from 1.3.0 to 1.3.1 (#532)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.3.0 to 1.3.1.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.3.0...v1.3.1)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index e98fafb68d..b5f4ad9244 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.1
 scipy == 1.7.0
-pandas == 1.3.0
+pandas == 1.3.1
 matplotlib == 3.4.2
 cvxpy == 1.1.13

From 74ca49b813dd5665c01a134dec3272a149d2e087 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 Jul 2021 07:21:43 +0900
Subject: [PATCH 0516/1080] Bump cvxpy from 1.1.13 to 1.1.14 (#533)

Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.13 to 1.1.14.
- [Release notes](https://github.com/cvxgrp/cvxpy/releases)
- [Changelog](https://github.com/cvxpy/cvxpy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.13...v1.1.14)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index b5f4ad9244..4cc16fa789 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy == 1.21.1
 scipy == 1.7.0
 pandas == 1.3.1
 matplotlib == 3.4.2
-cvxpy == 1.1.13
+cvxpy == 1.1.14

From a942f6e4ed6f7de8be7a0f0a4d1b2236ae89bae6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 3 Aug 2021 07:20:27 +0900
Subject: [PATCH 0517/1080] Bump scipy from 1.7.0 to 1.7.1 (#534)

Bumps [scipy](https://github.com/scipy/scipy) from 1.7.0 to 1.7.1.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.7.0...v1.7.1)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 4cc16fa789..fc636e01ca 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.1
-scipy == 1.7.0
+scipy == 1.7.1
 pandas == 1.3.1
 matplotlib == 3.4.2
 cvxpy == 1.1.14

From 6ee8985004879604d9417eef8604891bd6a4d423 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 17 Aug 2021 07:21:08 +0900
Subject: [PATCH 0518/1080] Bump cvxpy from 1.1.14 to 1.1.15 (#536)

Bumps [cvxpy](https://github.com/cvxgrp/cvxpy) from 1.1.14 to 1.1.15.
- [Release notes](https://github.com/cvxgrp/cvxpy/releases)
- [Changelog](https://github.com/cvxpy/cvxpy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/cvxgrp/cvxpy/compare/v1.1.14...v1.1.15)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index fc636e01ca..c195634a32 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy == 1.21.1
 scipy == 1.7.1
 pandas == 1.3.1
 matplotlib == 3.4.2
-cvxpy == 1.1.14
+cvxpy == 1.1.15

From d3db011ab5f9a747ffbf8763206d06456a278d39 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 17 Aug 2021 07:21:27 +0900
Subject: [PATCH 0519/1080] Bump numpy from 1.21.1 to 1.21.2 (#538)

Bumps [numpy](https://github.com/numpy/numpy) from 1.21.1 to 1.21.2.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt)
- [Commits](https://github.com/numpy/numpy/compare/v1.21.1...v1.21.2)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index c195634a32..2e35c69679 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.21.1
+numpy == 1.21.2
 scipy == 1.7.1
 pandas == 1.3.1
 matplotlib == 3.4.2

From 3b7f88ea95e4f025e05dbe89bb3dc58066f7bdbc Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 17 Aug 2021 08:07:06 +0900
Subject: [PATCH 0520/1080] Bump matplotlib from 3.4.2 to 3.4.3 (#539)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.4.2 to 3.4.3.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.4.2...v3.4.3)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 2e35c69679..469e3e96d1 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.2
 scipy == 1.7.1
 pandas == 1.3.1
-matplotlib == 3.4.2
+matplotlib == 3.4.3
 cvxpy == 1.1.15

From 4aae5f35c5b63498ba35fe54a3afcab21a8687ff Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 23 Aug 2021 20:42:32 +0900
Subject: [PATCH 0521/1080] Bump pandas from 1.3.1 to 1.3.2 (#537)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 469e3e96d1..55d3e523fd 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.2
 scipy == 1.7.1
-pandas == 1.3.1
+pandas == 1.3.2
 matplotlib == 3.4.3
 cvxpy == 1.1.15

From 06b2450591d63901c07d43fea53d1ec5fafb0cd2 Mon Sep 17 00:00:00 2001
From: Trung Kien <kienltb.hut@gmail.com>
Date: Sun, 29 Aug 2021 11:49:15 +0700
Subject: [PATCH 0522/1080] Fix markdown equations syntax (#535)

---
 ...redictive_speed_and_steering_control.ipynb |  78 +-
 SLAM/EKFSLAM/ekf_slam.ipynb                   | 948 ++----------------
 2 files changed, 128 insertions(+), 898 deletions(-)

diff --git a/PathTracking/model_predictive_speed_and_steer_control/Model_predictive_speed_and_steering_control.ipynb b/PathTracking/model_predictive_speed_and_steer_control/Model_predictive_speed_and_steering_control.ipynb
index 6e3fe45be3..244f63c8c1 100644
--- a/PathTracking/model_predictive_speed_and_steer_control/Model_predictive_speed_and_steering_control.ipynb
+++ b/PathTracking/model_predictive_speed_and_steer_control/Model_predictive_speed_and_steering_control.ipynb
@@ -2,16 +2,15 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## Model predictive speed and steering control\n",
     "\n",
     "![Model predictive speed and steering control](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/model_predictive_speed_and_steer_control/animation.gif?raw=true)\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "\n",
     "\n",
@@ -26,11 +25,11 @@
     "This code uses cvxpy as an optimization modeling tool.\n",
     "\n",
     "- [Welcome to CVXPY 1\\.0 — CVXPY 1\\.0\\.6 documentation](http://www.cvxpy.org/)"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "### MPC modeling\n",
     "\n",
@@ -46,22 +45,22 @@
     "\n",
     "a: accellation, δ: steering angle\n",
     "\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "The MPC cotroller minimize this cost function for path tracking:\n",
     "\n",
     "$$min\\ Q_f(z_{T,ref}-z_{T})^2+Q\\Sigma({z_{t,ref}-z_{t}})^2+R\\Sigma{u_t}^2+R_d\\Sigma({u_{t+1}-u_{t}})^2$$\n",
     "\n",
     "z_ref come from target path and speed."
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "subject to:\n",
     "\n",
@@ -89,20 +88,20 @@
     "\n",
     "$$u_{min} < u_t < u_{max}$$\n",
     "\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "This is implemented at \n",
     "\n",
     "[PythonRobotics/model\\_predictive\\_speed\\_and\\_steer\\_control\\.py at f51a73f47cb922a12659f8ce2d544c347a2a8156 · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/blob/f51a73f47cb922a12659f8ce2d544c347a2a8156/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py#L247-L301)"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "### Vehicle model linearization\n",
     "\n",
@@ -120,21 +119,21 @@
     "\n",
     "\n",
     "\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "ODE is\n",
     "\n",
     "$$ \\dot{z} =\\frac{\\partial }{\\partial z} z = f(z, u) = A'z+B'u$$\n",
     "\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "where\n",
     "\n",
@@ -168,11 +167,11 @@
     "\\end{bmatrix}\n",
     "\\end{equation*}$\n",
     "\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "$\\begin{equation*}\n",
     "B' =\n",
@@ -196,11 +195,11 @@
     "\\end{bmatrix}\n",
     "\\end{equation*}$\n",
     "\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "You can get a discrete-time mode with Forward Euler Discretization with sampling time dt.\n",
     "\n",
@@ -210,11 +209,11 @@
     "$$z_{k+1}=z_k+(f(\\bar{z},\\bar{u})+A'z_k+B'u_k-A'\\bar{z}-B'\\bar{u})dt$$\n",
     "\n",
     "$$z_{k+1}=(I + dtA')z_k+(dtB')u_k + (f(\\bar{z},\\bar{u})-A'\\bar{z}-B'\\bar{u})dt$$\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "So, \n",
     "\n",
@@ -222,7 +221,7 @@
     "\n",
     "where,\n",
     "\n",
-    "$\\begin{equation*}\n",
+    "$$\\begin{equation*}\n",
     "A = (I + dtA')\\\\\n",
     "=\n",
     "\\begin{bmatrix} \n",
@@ -231,14 +230,14 @@
     "0 & 0 & 1 & 0 \\\\\n",
     "0 & 0 &\\frac{tan(\\bar{\\delta})}{L}dt & 1 \\\\\n",
     "\\end{bmatrix}\n",
-    "\\end{equation*}$"
-   ]
+    "\\end{equation*}$$"
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
-    "$\\begin{equation*}\n",
+    "$$\\begin{equation*}\n",
     "B = dtB'\\\\\n",
     "=\n",
     "\\begin{bmatrix} \n",
@@ -247,14 +246,14 @@
     "dt & 0 \\\\\n",
     "0 & \\frac{\\bar{v}}{Lcos^2(\\bar{\\delta})}dt \\\\\n",
     "\\end{bmatrix}\n",
-    "\\end{equation*}$"
-   ]
+    "\\end{equation*}$$"
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
-    "$\\begin{equation*}\n",
+    "$$\\begin{equation*}\n",
     "C = (f(\\bar{z},\\bar{u})-A'\\bar{z}-B'\\bar{u})dt\\\\\n",
     "= dt(\n",
     "\\begin{bmatrix} \n",
@@ -285,28 +284,29 @@
     "0\\\\\n",
     "-\\frac{\\bar{v}\\bar{\\delta}}{Lcos^2(\\bar{\\delta})}dt\\\\\n",
     "\\end{bmatrix}\n",
-    "\\end{equation*}$"
-   ]
+    "\\end{equation*}$$"
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "This equation is implemented at \n",
     "\n",
     "[PythonRobotics/model\\_predictive\\_speed\\_and\\_steer\\_control\\.py at eb6d1cbe6fc90c7be9210bf153b3a04f177cc138 · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/blob/eb6d1cbe6fc90c7be9210bf153b3a04f177cc138/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py#L80-L102)\n"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "### Reference\n",
     "\n",
     "- [Vehicle Dynamics and Control \\| Rajesh Rajamani \\| Springer](http://www.springer.com/us/book/9781461414322)\n",
     "\n",
     "- [MPC Course Material \\- MPC Lab @ UC\\-Berkeley](http://www.mpc.berkeley.edu/mpc-course-material)\n"
-   ]
+   ],
+   "metadata": {}
   }
  ],
  "metadata": {
@@ -330,4 +330,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file
diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index a64c145ad4..f4c9d61436 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -2,40 +2,39 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "# EKF SLAM"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename=\"animation.png\",width=600)"
+   ],
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
-      "image/png": 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yzSzesZt22c0kt0ne/n7HZWp2/vPI3rxMcbjEBV4kQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIk8B8BBw0jaoEESIAESIAESIAESIAESIAESIAESIAESEBPgIaRngZfkwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJgIYRRUACJEACJEACJEACJEACJEACJEACJEACJGAgQMPIgIMBEiABEiABEiABEiABEiABEiABEiABEiABGkbUAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgIEADSMDDgZIgARIgARIgARIgARIgARIgARIgARIgARoGFEDJEACJEACJEACJEACJEACJEACJEACJEACBgI0jAw4GCABEiABEiABEiABEiABEiABEiABEiABEqBhRA2QAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAkYCNAwMuBggARIgARIgARIgARIgARIgARIgARIgARIgIYRNUACJEACJEACJEACJEACJEACJEACJEACJGAgQMPIgIMBEiABEiABEiABEiABEiABEiABEiABEiABGkbUAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgIEADSMDDgZIgARIgARIgARIgARIgARIgARIgARIgARoGFEDJEACJEACJEACJEACJEACJEACJEACJEACBgI0jAw4GCABEiABEiABEiABEiABEiABEiABEiABEqBhRA2QAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAkYCNAwMuBggARIgARIgARIgARIgARIgARIgARIgARIgIYRNUACJEACJEACJEACJEACJEACJEACJEACJGAgQMPIgIMBEiABEiABEiABEiABEiABEiABEiABEiABGkbUAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgIEADSMDDgZIgARIgARIgARIgARIgARIgARIgARIgARoGFEDJEACJEACJEACJEACJEACJEACJEACJEACBgI0jAw4GCABEiABEiABEiABEiABEiABEiABEiABEqBhRA2QAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAkYCNAwMuBggARIgARIgARIgARIgARIgARIgARIgARIgIYRNUACJEACJEACJEACJEACJEACJEACJEACJGAgQMPIgIMBEiABEiABEiABEiABEiABEiABEiABEiABGkbUAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgIEADSMDDgZIgARIgARIgARIgARIgARIgARIgARIgARoGFEDJEACJEACJEACJEACJEACJEACJEACJEACBgI0jAw4GCABEiABEiABEiABEiABEiABEiABEiABEqBhRA2QAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAkYCNAwMuBggARIgARIgARIgARIgARIgARIgARIgARIgIYRNUACJEACJEACJEACJEACJEACJEACJEACJGAgQMPIgIMBEiABEiABEiABEiABEiABEiABEiABEiABGkbUAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgIEADSMDDgZIgARIgARIgARIgARIgARIgARIgARIgARoGFEDJEACJEACJEACJEACJEACJEACJEACJEACBgI0jAw4GCABEiABEiABEiABEiABEiABEiABEiABEqBhRA2QAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAkYCNAwMuBggARIgARIgARIgARIgARIgARIgARIgARIgIYRNUACJEACJEACJEACJEACJEACJEACJEACJGAgQMPIgIMBEiABEiABEiABEiABEiABEiABEiABEiABGkbUAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQgIEADSMDDgZIgARIgARIgARIgARIgARIgARIgARIgARoGFEDJEACJEACJEACJEACJEACJEACJEACJEACBgI0jAw4GCABEiABEiABEiABEiABEiABEiABEiABEqBhRA2QAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAkYCNAwMuBggARIgARIgARIgARIgARIgARIgARIgARIgIYRNUACJEACJEACJEACJEACJEACJEACJEACJGAgQMPIgIMBEiABEiABEiABEiABEiABEiABEiABEiABGkYh0kBmZib++usvrfaSJUsiPj4+RO/EakmABEiABEiABEiABEiABEiABEggdglkZ2fj0KFDGoDatWsjMTExdmEobDkNI4Uw9VWtW7cOdevW1UfxNQmQAAmQAAmQAAmQAAmQAAmQAAmQQAgJrF27FnXq1AnhO8RO1TSMQtTXNIxCBJbVkgAJkAAJkAAJkAAJkAAJkAAJkIAPAjSMfICxEE3DyAK0QIrs2rULlStX1rKuXLkSVapUCaQY85CAVwIZGRlYvny5ltakSRMkJyd7zcdIEgiEAPUUCCXmCYQAtRQIJeYJlAD1FCgp5guEAPUUCCXmCZQA9RQoqcLLt2/fvvwVPjt37kSlSpUK72ai6J1pGIWoM/fu3Yvy5ctrtf/999+oVq1aiN6J1cYCgVOnTmHJkiVaU5s3b46kpKRYaDbbGCIC1FOIwMZgtdRSDHZ6CJtMPYUQbgxWTT3FYKeHsMnUUwjhKqpaP/7es2cPypUrp6jm2K6GhlGI+l8vWBpGIYIcQ9XyIRVDnR2GplJPYYAcI29BLcVIR4epmdRTmEDHyNtQTzHS0WFqJvUUJtBBvI1+/E3DKAiQbkVpGLkBURXUC5aGkSqqsVsPH1Kx2/ehaDn1FAqqsVkntRSb/R6qVlNPoSIbm/VST7HZ76FqNfUUKrLq6tWPv2kYqeNKw0gdS0NNesHSMDKgYcACAT6kLEBjEZ8EqCefaJhgkgC1ZBIYs/slQD35xcNEkwSoJ5PAmN0vAerJLx5bJOrH3zSM1HUJDSN1LA016QVLw8iAhgELBPiQsgCNRXwSoJ58omGCSQLUkklgzO6XAPXkFw8TTRKgnkwCY3a/BKgnv3hskagff9MwUtclNIzUsTTUpBcsDSMDGgYsEOBDygI0FvFJgHryiYYJJglQSyaBMbtfAtSTXzxMNEmAejIJjNn9EqCe/OKxRaJ+/E3DSF2X0DBSx9JQk16wNIwMaBiwQIAPKQvQWMQnAerJJxommCRALZkExux+CVBPfvEw0SQB6skkMGb3S4B68ovHFon68TcNI3VdQsNIHUtDTXrB0jAyoGHAAgE+pCxAYxGfBKgnn2iYYJIAtWQSGLP7JUA9+cXDRJMEqCeTwJjdLwHqyS8eWyTqx980jNR1CQ0jdSwNNekFS8PIgIYBCwT4kLIAjUV8EqCefKJhgkkC1JJJYMzulwD15BcPE00SoJ5MAmN2vwSoJ794bJGoH3/TMFLXJTSM1LE01KQXLA0jAxoGLBDgQ8oCNBbxSYB68omGCSYJUEsmgTG7XwLUk188TDRJgHoyCYzZ/RKgnvzisUWifvxNw0hdl9AwUsfSUJNesDSMDGgYsECADykL0FjEJwHqyScaJpgkQC2ZBMbsfglQT37xMNEkAerJJDBm90uAevKLxxaJ+vE3DSN1XULDSB1LQ016wdIwMqBhwAIBPqQsQGMRnwSoJ59omGCSALVkEhiz+yVAPfnFw0STBKgnk8CY3S8B6skvHlsk6sffNIzUdQkNI3UsDTXpBUvDyICGAQsE+JCyAI1FfBKgnnyiYYJJAtSSSWDM7pcA9eQXDxNNEqCeTAJjdr8EqCe/eGyRqB9/0zBS1yU0jNSxNNSkFywNIwMaBiwQ4EPKAjQW8UmAevKJhgkmCVBLJoExu18C1JNfPEw0SYB6MgmM2f0SoJ784rFFon78TcNIXZfQMFLH0lCTXrA0jAxoGLBAgA8pC9BYxCcB6sknGiaYJEAtmQTG7H4JUE9+8TDRJAHqySQwZvdLgHryi8cWifrxNw0jdV1Cw0gdS0NNesHSMDKgYcACAT6kLEBjEZ8EqCefaJhgkgC1ZBIYs/slQD35xcNEkwSoJ5PAmN0vAerJLx5bJOrH3zSM1HUJDSN1LA016QVLw8iAhgELBPiQsgCNRXwSoJ58omGCSQLUkklgzO6XAPXkFw8TTRKgnkwCY3a/BKgnv3hskagff9MwUtclNIzUsTTUpBcsDSMDGgYsEOBDygI0FvFJgHryiYYJJglQSyaBMbtfAtSTXzxMNEmAejIJjNn9EqCe/OKxRaJ+/E3DSF2X0DBSx9JQk16wNIwMaBiwQIAPKQvQWMQnAerJJxommCRALZkExux+CVBPfvEw0SQB6skkMGb3S4B68ovHFon68TcNI3VdQsNIHUtDTXrB0jAyoGHAAgE+pCxAYxGfBKgnn2iYYJIAtWQSGLP7JUA9+cXDRJMEqCeTwJjdLwHqyS8eWyTqx980jNR1CQ0jdSwNNekFS8PIgIYBCwT4kLIAjUV8EqCefKJhgkkC1JJJYMzulwD15BcPE00SoJ5MAmN2vwSoJ794bJGoH3/TMFLXJTSM1LE01KQXLA0jAxoGLBDgQ8oCNBbxSYB68omGCSYJUEsmgTG7XwLUk188TDRJgHoyCYzZ/RKgnvzisUWifvxNw0hdl9AwUsfSUJNesDSMDGgYsECADykL0FjEJwHqyScaJpgkQC2ZBMbsfglQT37xMNEkAerJJDBm90uAevKLxxaJ+vE3DSN1XULDSB1LQ016wdIwMqBhwAIBPqQsQGMRnwSoJ59omGCSALVkEhiz+yVAPfnFw0STBKgnk8CY3S8B6skvHlsk6sffNIzUdQkNI3UsDTXpBUvDyICGAQsE+JCyAI1FfBKgnnyiYYJJAtSSSWDM7pcA9eQXDxNNEqCeTAJjdr8EqCe/eGyRqB9/0zBS1yU0jNSxNNSkFywNIwMaBiwQ4EPKAjQW8UmAevKJhgkmCVBLJoExu18C1JNfPEw0SYB6MgmM2f0SoJ784rFFon78TcNIXZfQMFLH0lCTXrA0jAxoGLBAgA8pC9BYxCcB6sknGiaYJEAtmQTG7H4JUE9+8TDRJAHqySQwZvdLgHryi8cWifrxNw0jdV1Cw0gdS0NNesHSMDKgYcACAT6kLEBjEZ8EqCefaJhgkgC1ZBIYs/slQD35xcNEkwSoJ5PAmN0vAerJLx5bJOrH3zSM1HUJDSN1LA016QVLw8iAhgELBPiQsgCNRXwSoJ58omGCSQLUkklgzO6XAPXkFw8TTRKgnkwCY3a/BKgnv3hskagff9MwUtclNIzUsTTUpBcsDSMDGgYsEOBDygI0FvFJgHryicYWCS6XC7KPMjIycPLkSe33mjW/49Chg7jwwgsh0/U/8qZlODc31yM+Pj4e3n4SExORnJyMpKQk7bfM43A4TLefWjKNjAX8EKCe/MBhkmkC1JNpZCzghwD15AeOTZL0428aRuo6hYaROpaGmvSCpWFkQMOABQJ8SFmAxiI+CVBPPtEoT5BGztGjR7F//37s27dP+5k/fz5ee+21/Pdq1aqVZgzlpaenp2vGT36GMLxwOp0oWrQoSpQogdTUVBQrVkz7LePyfmSa/ElLS8v/kabTli1bkJKSgjat26BCxQqaCRWGW+ZbRCEBfjZFYacWYpOop0KEH4VvTT3Zv1P1428aRur6i4aROpaGmvSCpWFkQMOABQJ8SFmAxiI+CVBPPtEEnJCZmYmDBw9qBpA0g+RnvvzjZPfu3drPjh07sH37dmzevBnZ2dkB1xsNGaXZVKNGDVSuXBmVKlVCuXLltJ+yZcuiTJkyKF26NEqWLImE+AQUK15MM8tkXl4kwM8makAlAepJJU3WRT3ZXwP68TcNI3X9RcNIHUtDTXrB0jAyoGHAAgE+pCxAYxGfBKgnn2gMCXJm0Lat27Bl6xZs3bpV+1mxYgWWLl1qyMeAWgLt27fHkCFD0KRJE8iZT7xihwA/m2Knr8PRUuopHJRj5z2oJ/v3tX78TcNIXX/RMFLH0lCTXrA0jAxoGLBAgA8pC9BYxCcB6ukMGvkHxbJlv4j9e4ADBw5g48aN2LBhA1auXImdO3eeyWijV6VKldKMFLnnkDRU5O+4uLj813lx8pblTCj5I/dDkv0uZzvJvY4i7XrwwQfx+uuva/sxRdq9834DJ8DPpsBZMWfBBKinghkxR+AEqKfAWRVWTv34m4aRul6gYaSOpaEmvWBpGBnQMGCBAB9SFqCxiE8CsaonuYH0M888g9GjR/tkE8qEChUqoGbNmli/fj2OHDmivZWcTdOtWzdtiVbx4sW1fYPkfkByb6AiRYpov+VraQipuqRhlJWVlb+xtjSTtJ+T4vepMxtt6zfdPn78uLYX0+HDhyF/Dh06lL8vk1x6d+LEibAYUZKVnPkl91l6ov8TqN+gviosrMcGBGL1s8kG6KPyFqinqOzWQmsU9VRo6AN+Y/34m4ZRwNgKzEjDqEBE1jLoBUvDyBpDljpDgA+pMyz4KngCsaInaWL8/vvvWL58OUaNGoV//vkneHheajjvvPNQrVo1VKlSBdIUkj9y357y5ctre/bIvXvkrKCEhAQvpSM7Kk9L0oRq0KCBZhzJvZ3kbC39Rt/yDze5v9OuXbsgl/WpvFq3bo2XXnoJjRo1gjz9jVfkEsjTk2xB8+bNuYF65HalLe6cerJFN0TNTVBP9u9K/fibhpG6/qJhpI6loSa9YGkYGdAwYIEAH1IWoLGITwLRqic58+SXX37BokWLsHDhQixevDjoWS9yZk/Dhg1Rt25d1KpVC9WrV9fMIXn6mYyXJ4ZZOY7eZ+dEWIJVLcly8jm5bds2jBw5EnPmzAm65XImVpcuXXD11VdDmkjSwOMVWQSs6imyWsm7DRcB6ilcpGPjfagn+/ezfvxNw0hdf9EwUsfSUJNesDSMDGgYsECADykL0FjEJ4Fo0ZM8fn7VqlWaMTRv3jzNJJJGjtWrTp06uOaaa3DOOefg7LPPRs0aNVG5SmXum+MHaCi0JGeGTZ8+Hffee6+fdy446cILL0SnTp1wxRVXaBtoJyUlFVyIOQqVQCj0VKgN4psXKgHqqVDxR92bU0/271L9+JuGkbr+omGkjqWhJr1gaRgZ0DBggQAfUhagsYhPApGqJ7nvzpo1azSDaP78+fj666+Rk5Pjs52BJNx66614/vnntdlDsTxTKBBW3vKEQ0vSBJRL2X777Td8+umnmDBhgrdb8Rsn94W67bbb0KHDdWjTprU2M8xvASYWCoFw6KlQGsY3LRQC1FOhYI/aN6We7N+1+vE3DSN1/UXDSB1LQ016wdIwMqBhwAIBPqQsQGMRnwQiRU9yXxx5atnPP/8MaRDNnj0bclaRmSs+Ph5t27ZFy5YttVkmchlZmTJlzFTBvH4IFJaW5Kbb0ujbtGkT5J5JS5cuDXj5oTxFTs48kj9SGxUrVvTTQiaFk0Bh6SmcbeR7hY8A9RQ+1rHwTtST/XtZP/6mYaSuv2gYqWNpqEkvWBpGBjQMWCDAh5QFaCzik4Bd9SRnksjPyyVLluCHH37ArFmzsG/fPp/t8JYg97GRRsDll1+uGUQXXHABN0L2BkpRnF20JE+dk7r57rvv8Mknn5ja4LxNmza48cYb0a5dO20pImeaKRKHhWrsoicLt84iNiRAPdmwUyL4lqgn+3eefvxNw0hdf9EwUsfSUJNesDSMDGgYsECADykL0FjEJwE76Uke175s2TIsWLAAn3/+Of744w+f9+0tQW5KLfcdkvvUtGjRAvXq1aNB5A1UiOLspKW8JubNTPvxxx+1ZYty6aKMC+SS+1idXrrWQdMSzaNAqKnLY0c9qWsdawo3Aeop3MSj+/2oJ/v3r378TcNIXX/RMFLH0lCTXrA0jAxoGLBAgA8pC9BYxCeBwtSTHLhLU0ieYvbNN9/gq6++CngwLxskB/DyBCw5G0QaRPIodbk/Da/CIVCYWgq0xYcPH9b09uWXX+Ljjz9GRkZGQEXPPfdc3HHHHbjuuus080guZeMVWgKRoKfQEmDtKglQTyppsi7qyf4a0I+/aRip6y/bGUbr16/XvmmWxyGvXLlSW56QmJionVojlxjcfvvt2lHGgSKQ9UycOFH7Y1EaN/JIZDnYuOuuu7TBRqD1mM2nFywNI7P0mN+dAB9S7kQYDoZAuPUk9x168cUXMWzYMEu3LU0hOYtI7kN0ySWXcMNiSxRDUyjcWgq2FdIskvsdSaNSmke7d+8OqMratWujW7duuP7661G/fn3NuAyoIDOZIhBpejLVOGYOOwHqKezIo/oNqSf7d69+/E3DSF1/2cow6tixo7apaUHN69evH0aMGIGEhASfWeW32AMGDMDIkSN95nnyySfxwgsvIBTfGuoFS8PIZxcwIUACfEgFCIrZAiIg9bRo0SItb/PmzUMyQ0duRJy3D9GHH34Y0H3lZapVqxbk86B16zZo1qwpypUrl5fE3zYjEMmfTfKEvdWrV2Pu3LmYNm0a1q1bFxDdunXr4t5779X2yqpRo0ZAZZgpMAKRrKfAWshc4SRAPYWTdvS/F/Vk/z7Wj79pGKnrL1sZRhdeeCH+97//accbyxlATZs2RaVKlbRjk1etWoXhw4drJ6LI5j/88MN44403fJIYPXo0+vfvr6XLDS2feOIJVKtWDdu3b9dMJLm3gbxkvscee0x7rfIfvWBpGKkkG5t18SEVm/0eqlZLM0eeDJWZmYlixYppsyxULOvasWOHtumw3Itozpw5kJtYB3JJ8/+GG27A1Vdfrc0iOuussziDIxBwNsgTLZ9NUqt//fWXtkxSzjxavnx5QHTlzOfu3bujQ4cOKF26dEBlmMk3gWjRk+8WMiWcBKincNKO/veinuzfx/rxNw0jdf1lK8NI/tEll5xdeeWVXmf9yH0I5Mam0jyS14oVK3DRRRd50JAGjRxwyG8P5ZG5cuCSlJSUn09OSZd7Enz//feQG6Zu3boVVatWzU9X8UIvWBpGKojGdh18SMV2/6tuvRwQy71Z8q4ZM2aga9euecGAf8tBttyP6Ntvv4WsQ55SFegljSpp1ssBd+PGjVGkSJFAizKfjQhE42eT1PXmzZu1ZWtTpkzBr7/+WiBxOVP5pptuwq233qr93aHCgC3wTaMwQzTqKQq7KWKaRD1FTFdFxI1ST/bvJv34m4aRuv6ylWEUSLPkRqlyxpC8hg4diiFDhmiv9f8MHjw4f68MOWNJ7jfgfq1ZswYNGjTQomX+5557zj1LUGG9YGkYBYWShQUBPqQog0AJrN71GxpWutBvdvkZKj9L8y5p2oz+8OUCy8n8eSaRNOInT56MtWvX5lUT8G9pMskNhXlFPoFY+GzKM48++uijgGYeSbPonnvu0QykZs2aIT4+PvI7OkwtiAU9hQkl30YQoJ4oA5UEqCeVNENTl378TcNIHeOIM4xOnjyZv9+G/IbcfW8MOZg5++yzsWXLFjRp0sTvN4MyXc5Skvn//PNPpUsg9IKlYaROsLFaEx9Ssdrz5tr9+rI38ciSZ/F+qxfRo/F9Xgtv2LABF1xwgTGtfSLQtjheaz4EfZo+bEwTIfm5unHjRm22pjSJfv/9d4883iKKFi2af8pUq1attOVv3vIxLnIJxNJnU55Z+tlnn2HcuHHYtm1bgR1XuXJl9OjRAzfeeCPq1Kmj9O+MAt88AjPEkp4isHsi7papp4jrMlvfMPVk6+7Rbk4//qZhpK6/Is4wOnr0aP4JOXKfowkTJhho7Ny5E1WqVNHinn76aW3fI0MGXUCmy5N75CX33pB/2Km69IKlYaSKauzWw4dU7PZ9oC2XM4saTbsyP7s0jbbP+lvbzDc/UryQ+xfJn/zrP7MoL1xxdhKK7iyiHXV/1VVXQZ4OJY353377LS+L39/yc1Sa+fJUM7kPnTzlklf0EojVzyZ5sMYvv/yCmTNnaubRsWPHCuxkuU+jnHnUuXNnpX9vFPjGEZQhVvUUQV0UUbdKPUVUd9n+Zqkn23cR9ONvGkbq+iviDKP58+dr+wNIBPKkNLmZtf7SL1mbOHEi7rzzTn2y4bVMv/vuu7W4n376Sdts1ZAhiIBesDSMggDJohoBPqQohEAIjF0xDvf/NCA/q/OndOTOSs8Pe7xwM4vw/VHgm0yPbAVF1KtXT9u7RW5aLZf6huLkyYLugemFQ4CfTYDcF1GeCDh16lTI/cGkmVTQJTd5l3+fyD0bud/RGVrU0xkWfBU8AeopeIas4QwB6ukMC7u+0o+/aRip66WIMozkH2Ht27fXNliVCNavX4/zzz/fQGP69Om45ZZbtLh58+ZBfkPu65IbtbZr105Llkfq3nzzzb6yesRLQfq79u3bB3n0rrzkySuqN9X2995Miz4C8iG1cuVKrWFyo3f9Ju7R11q2KBgC41dPQK8lg89U4csE0ptF8jCzmYeAX3LOlCvglfx8k4cUyM9kuR+Rw+EooAST7UZA+hrHjrlw7IQD6SdcSM8Qv4W/mH7ShRPpDvGD/J+MUw5kC3lkZwFZ2UCOKJslXp88lYtde48IkwQoUyoNCfFOcZgEIH6J3y7EOR1aWPwSe/lAfHYBRYu4kJwsfwNFxG/tR8QVSXYgRcSlpgJpxV0oXtyBpAiboHbo0CHMnTtXM4/k3xgFXXLZ5n333aftd9SwYcOYN1v5rCtIMUw3Q4B6MkOLeQsiQD0VRKjw06VJVL16de1GaBip64+IMozk8jM5nVteck+A999/34PE+PHjtT++ZMLixYtx6aWXeuTJi5DpLVu21IIffPBB/myjvHR/v80Mjj755BOUKlXKX3VMIwESIAFlBL7c/RVe+XfSmfrcTaM8syhdjPJ/OwF8eQoIYGKRNL6lyd68eXPtgWzmc/DMzfBVKAlkZTtw+HAiDh5OwiH5czQRR4/Haz8l9+1B/Z3L0ejoT6jlWINjuWlYgVZYntgYiUIAlbL/RcXcfxHnysZnRTviixKt4BSvq5/ajXNO7UCtrB3YE18Gs4s3Q7ZTOD9BXPXSN6HHkWlIcp0S73Ut5hW/BMJ19KgxDjkoFpeNtKQsFE8Wv1OyUSwlC6XSMrWfkmmnUFK8LiF+UotmaeaURyWFFCG/WFq0aBG+/vpr7TTWgm6jWrVq2gmucr+vsmXLFpSd6SRAAiRAAiRAAjoCcsuFLl26aDE0jHRggnwZMYaR3Jxa7oeRk5OjzdaR+2l4M2HeeustPPzw6U1b5f4CF198sU9E8qjcSy4Rf6SKS5Z76KGHfOZ1TzAzUKJh5E6PYRIggVAT8GkaSbOojpjGsVQYRUvFVBHhGfm75J5EcqmZNIlq1KjBmUT+YIUh7fiJOOzdXwR79xXBngNJOCBMoX1HkrD3qPg5mYQTuQn5d5GYcxIdjv2CK9N/QEvHN6gcvyc/LdAXOS4xQ8ghp6CduQ7mFMM4Zz98ndoK2xPK4kh8Kmqd2oVTjgTsTCyLXIfvU8EuOLkFzxx8Be0SZ5+pULzanlUZH8f3xFepLYSJlIWU3FMomnsSieL1huTq2JRcVeRyoGLmXgza/w4uxk9IcqQjHlmnfxzCLBLm0l5XVfye1AIbyzXCrrPPRbGKcShbOgPlymSgdKkMJCYY22K4iRAF5GbZW7du1ZatzZ49W8zqKni/I/n/Te4D1rixMPO4D1iIeobVkgAJkAAJRBMBGkah6c2IMIzkkbbyaFq5zEv+4bRkyRLIZTnernDNMOKSNG/0GRcqApwGGyqy0V2vYXmaXG0mZxMtE7OJtvh3ieTnrJzFedttt6FRo0Y0icIpk/37cWr+YmR/PR/Y+Q/+LtUUE2r3x4ZdCdgoVkIfEzOICrqkKfPQoY/QKW4yijrFurEwXlJZ+7JLY1duTexCNfwdVxMzircXZlK8MIpeFUbRFxCr1UxfB3NSsS+3Ms5N2Giq7N7skvg79zyI81XFHKokbCx6CZbU7YzyNVJRvYoLtWu5UKuGA1Urn14yZ6pyC5kzMzMh90ycMmWKtiF9QfsdlS9fHg888IC2ZL5WrVoW3jGyivBZF1n9Zfe7pZ7s3kORdX/Uk/37i0vSQtNHtjeM5Klncnr2li1btLX933//PS677DKfNMK1h5HPG/gvQb/pFje9LogW0wsiIB9S0iiVl/zmmXsYFUSM6XkERs99Gf3ffBpYINacncyL9f37qaeewoABA8T+McV9Z2KKeQJilgl+Ww189gmw+S+gbDlklq+O3aXr4Z9DqXD8uhKV//wa1V3rPOpeldkE11aYjIy4FI+0vIi43Czccng+7j45Hhcl/poXzd9eCBzPTcTXObfgmKO4mGDnFDOipAnnRErRZGTUPh+Oixugcr1SOKumAzWrO5BwZtKWl9qsR+0X5qCccSSX18sZ0QVdHTp0EEvue4hloVeJPaDE5k9RePFZF4WdWohNop4KEX4UvjX1ZP9O1Y+/uSRNXX/Z2jCSM4quuOIK/P7779o33J9//gU6drzeb+t5SppfPEyMUAJ8SEVoxxXSbcslMMuXL8fYsWMh92craBaD/jb79euHMWPG6KP4OhgChw/BNWsWciaMQ/y+rcHUhH+yK+GP3IZYldAYvxRpgJOOJDTK2IBLT/2MZnHfomScmEFWwJUeXwqH6l2DjItboVjWAaRtWIqEbWvhSCsBR6Uqwlg0LhXLry5BGBTZwnR0+Z+dlp8/kBeJos6yYmrPzs2B5A5rHsl6dW4L/JrcFGvOuRrn1UtFgwscqHO+A+ee7UARsapT1SX/v27YsEGbcfTuu+9qs6n91S2X4/fu3VvbdL527dr+skZcGp91Eddltr5h6snW3RNxN0c92b/LaBiFpo9saxgdPnxY2zcj71s3eVStXB5R0CVnJFWpIv7oFdfTTz+N4cOH+ywi01988UUtfceOHZB7dai69ILlDCNVVGO3Hj6kYrfvzbRcfu68/PLLmDlzZsGb7MpZE1metZcoUQLyWxnum+LJJtCY/ftd2DlrBYrOmIga/8xCvNseQIHWoyxfo5bAFeLE0JatgFpned1cOv+95EyoBWI53I5/gIqVIB6o4qcqUKzY6XLrxQyose8CPy8QR6gdzi9m6oU0n3r0Be68SxyVJo5EE3sS4uPJwLefi+PXhCgdYtFavNhrK0m4MjnZYmac+54/YkZQB3Ea6hVXivJFtT2TjmfE4/CBbLiWr0KRFT+g9J5lYk8jNebWpqyzcFm5WTgeLxiIS85HOlecY9HkXAcurANcWNeJC85zaCe/aRmC+EcuWVuwYIFm9Mr/xwVdchP6+++/XzutsIhKF6ugNw5ROp91IQIbo9VSTzHa8SFqNvUUIrAKq9WPvznDSB1YWxpGx48fx/XXX69tECmbKqdry/00ArnkN3Vnn322toStSZMmkBtb+7rkhtjyW3iZ/88//1S6T4desDSMfPUA4wMlwIdUoKRiM58cYMrZmAFd54vBeAsxED+nCFodqY+1b/8GuUmg/vrxxx/RunVrfRRf6wnIM+S3bQNWLEfusiXIWfQjXBknsKTCffj+aAPcKvbqqZO4Vl/C1Gu5ZCrVKWbzBHOlpAHd7gduuhmoUDGYmnyXzc7GqX934Xehv8ziabhInEqaJI6Vh4jDrp3An2K/oVlTxAl8GafriBcnq2lG0d2nDSj3miVXaVjFxZ1JkeFdor61vwvjSKyplEuxLryw4DZlCeNJml5is2ns3IHMoxnIWrwMKWu+1QyfM28Q2Cu5F9J78Y8jQ8zqSs1NFxtyn0BR1+kZXZlis+8cRyKc5Soio+3lOL9pSTSs70D5cg7xd0Vg9XvLJb/ImjZtGl5//XX8849oi59Lnqr25JNPolu3bihXrpyfnPZO4rPO3v0TaXdHPUVaj9n7fqkne/ePvDv9+JuGkbr+sp1hlJGRga5du2LOnDlaK+XSCLlEwsw1ePBgDBs2TCvyv//9D/Xr1/covmbNGjRo0ECLl/mfe+45jzzBROgFS8MoGJIsKwnwIUUdeCOwbNky7UAAb2mGOHloVSUxct0uBt/ylLS2xfOTnz+/H+Y8O9uwhwqXpeXjMb44cADZL46A67svkJBd8PIvY+HToW1Z1THbeQuSXSdxQe7vaBz/E5KdOdjjrIl/z+uAnCsuR6krG4rTQOMQ/4GYyfPFDGF8bDptpHir0D3uwkvFzJ17gcsuR8g239G9Z4GfTUePAvPmii2ChFF55VUQm2PpShfCS2n0ffkFsHu3OCFQGFTi5NWsU7lispT4veNfpO3+FYkQs5osXlniVLmFWe3xcdFbsbJCc7Son4BLGgJNGztR+yyHhsFs1VnC/JIm7oQJEzQDSX4x5uuKE2abPCm2Z8+eOP/8831ls218gXqy7Z3zxuxIgHqyY69E7j1RT/bvO/34m4aRuv6ylWEk/yjq3r279geRbKI0caSZY/aSBo08TUTu29G2bVvNfNJvEiz/w8vNI+UG2vKPK7mhdrVq1cy+jd/8esHSMPKLiokBEOBDKgBIMZTlqBiEDx06FK+88or/VpcXJtFlyehzdU98PvhTbZaCnK15/h118OiK06a6rOCNps/ifxNWY9y4ccKoqKqd4NSiRQv/dcdIqpyosmZtLlYuOITO49qijHOv6ZZnChNhblYXTC95OzLrN0Tjeg7UE3vhSAOhRuVcJAjDSKwB9F2vWKYkHlTAxj+A1SuBJQvF12hiBk+WmLmTVga49AqgWXPg4qanl5D5rkl5StR9NgkDCZuEQbfge+DNF4LidTQnGetymmCtsyFWJ9fD4tIt0LxRUbRu6kDTJqLvq5mfgbRLzLaaMWOGNutoq5w95ee64YYb0KdPH+3gEKc07CLgijo9RQDzaL5F6imaezf8baOews/c7Dvqx980jMzS853fVoaRXIcvN2mVV5cuXfL3F/J1+wni6JLq1at7TR49ejT69++vpbVp00abqi1Noe3bt2PEiBHat3UyUeZ77LHHvNYRTKResDSMgiHJspIAH1LUgSSQnp6OSZMmYdCgQR7LyDwIPSj2Wzk7Ce+3ehE9Gt8HuS/cDz/8gE6dOkHukzLgs4F45d9J+cXea/kiyu4orZ1CKfcxitVLTjr5a7MLP/+SiwVLXZi/LgeNj63Fs0efR6PE5aaw7HVVwtqLHkZO5444p3GaOLrd2gwTn28qzQ398i2fGUOXENWfTZs3A0/0Bf5YpQTgwZxiuD9lIuYXb6zVV06srmt/kQMtL3Gg2cUOVKogDN4ArxzR9z/99BPGjx8Pucejv6thw4a49957cffddyMlJcVf1kJPi2o9FTrd2LsB6in2+jyULaaeQklXTd368TcNIzVMZS22MowcJhf7y+nW69ev90pDzi6Sx0OPGjXKa7qMfOKJJzRTKhTfvOkFS8PIZxcwIUACfEgFCCpKs8nZl3JWgTzuvqC9THCL2J+oSVGNRJ5Z5I4lT09f7v7KYBr5yu9ePtrCh48Ai5fmYvF3R7D75804kZmF6lm70DZjPprHzUPxODGTx8R1qkp9JDzaF04xw7WwDR0Tt20pa56WZOHmzZtDP5vXUoV2KySXfy35+fSm3H+LWV5yk+5UYcbK3yni/5mYuZOdfgondhxB0T/mIz6AjbY3ZJ6Pu0q9hz+LGL/wqiMmi13X0oHLxc+F9ZwBryjcJpbZydmBcsN7uay/oEuePFu3bt2CshVKetTrqVCoxu6bUk+x2/ehaDn1FAqqauvUj79pGKljG7WGUR6iRYsWYeLEidqMIjnQksst5Iwj+U1bKJdc6AVLwyivN/jbKgE+pKySi+xycq8SuaG1nC25evVqv415d8Z7eOCfgfl5/Jk/ej1tKLIRvZYMzi+36pbv0LCS2FQ4ii/pAWzeKtguzMX+mT+j4V8zUM+5DFXi/w2o1XuyS2HIBbNQrVV1tKy2BxdmrUKxMmJD5woVTm/GXEaM/GPk0mspKg0jM/14XJzmNncuXFMnw1HArKQTuQm4P3kyvk4TSwm9XEXigOsbOnBVKzEDqbkDZcsUPPtIziKcOnUqRo4cKfZk3+alVmOUPPSjcePTs52MKYUXop4Kj300vjP1FI29Wnhtop4Kj32g76wff9MwCpRawflsZRgVfLuRk0MvWBpGkdNvdr1TPqTs2jOhu69169bh6aefxuzZs32+Sa9evbSZknl7sL2+7E08suTZ/GVovgq662ny7x/i/p8G4LXmQ9Cn6cO+ikV0vNwG6NeVufjuJxe++CkXTXb8iEfTR+GCRHFMvIkr25mEnEmzkdTInjM0TDRFSVZ3LUXdDCOrlOSm2uuFtuTpbj98J06M+82jJrH6Ea/kDsSq5Lo45kzBsbgUbEiqilNxYpag29VIeJGdLnegfVsnzqrpf+8jueT0m2/mYsyYl7Vla25VGYLnnXceVq1ahSJFPN/TkDFMAeopTKBj5G2opxjp6DA1k3oKE+gg3kY//qZhFARIt6I0jNyAqArqBUvDSBXV2K2HD6nY6ft///1XWyr7xhtv+Gy0PDp74MCBOPfccz3yrN71W4EzhLzpKZByHm9m8whxvgEWL8vFl9+6MGOZCyezXbj90Dz0zRiFsxPExsZmrurnidPl2gN3dIc4t9xMyajO601LUd1gK42TO6f36gn8PK/A0jlig/S1WQ0xM+lWfFDyWpyMO728VF/wbLHF2M1tHbj6cifqiM3Tfa3mlzMUf/31VzRt2lRf3OvrKVOmaCfUxsfLIxUL76KeCo99NL4z9RSNvVp4baKeCo99oO+sH3/TMAqUWsH5aBgVzMhSDr1gaRhZQshCOgJ8SOlgROnL48eP4+2339ZOhpQzBLxd7dq108wkuYltMFc06+mk2MJF7kc0W5hEM39xQZyYrl0lsw5j2t4HcXHiksDQxYslZq3aAZeLfYgubUmTyAe1aNaSjyZbi5b/p8eIPRU/fDvg8idz4/B99g3IcBRBcdcRpOIoEpAJB1zaj/z376QG2HJbX7S9JhUX1ve9qfqrr76Kfv36+X3vBg0a4IUXXsDVV18ttmYqnFPVqCe/XcREkwSoJ5PAmN0vAerJLx5bJOrH3zSM1HUJDSN1LA016QVLw8iAhgELBPiQsgAtQopkZ2dj+vTp2tIyeWS2t6tOnTraiY7SMDJ7OIC3+qJNT3Im0Y+LczFrngufrThjEuW1/aITG/DR0TtQIX5/XpTh98nUKkh4uA/iLxJ7Nx04ABQvDtQRS84KebaF4SZtGog2LYUcs9xA6y0xe/CdEcreaklma1xbeRLKJTvQ7UoHbrjGiQvO8z7zSJ6odscdd/h9b7nP4/Dhw9GsWTMlnzd+38wtkXpyA8JgUASop6DwsbAbAerJDYgNg/rxNw0jdR1Ew0gdS0NNesHSMDKgYcACAT6kLECLgCJyucjDDz8Mufmst6tUqVKaUXT77bcjMTHRWxZLcdGgJ3EQJlb+lotPvnThQ7Ev0Ulxwry3664Dc/BSTm8kOf6baqTL5KpyNhx9nwSuEjOJCvl4et1tRdTLaNBSoQBf9BMwYRywTSyNPHkcyDgBZArnU8wesnI95XgF75XpnF/03JJi9eQ1DnRs70TVKp4bZq9Zs0b77JEHg/i6unbtimHDhuGcc87xlUV5PPWkHGlMV0g9xXT3K2889aQcqfIK9eNvGkbq8NIwUsfSUJNesDSMDGgYsECADykL0Gxc5ICYxfLss8/C1z5FccK8GDJEbEDdpw/S0tKUtySS9bRlmwuff5WLiV+7sEOMsd2vKqd249Yj32onlzVwLkHl+D3uWYA0cYrZ8y8Dl12uHYvumYExgRKIZC0F2saw5csQ6ylXCPN42sdiytwc4R2ZM49WZTbB50ldMS2tLfYnlM6/7WZVgds6OHHNVQ6UKmk0j1asWKEtg50rTnfzdskZjY8//rh2UmPZsmW9ZVEaRz0pxRnzlVFPMS8BpQCoJ6U4Q1KZfvxNw0gdYhpG6lgaatILloaRAQ0DFgjwIWUBmg2L5IppMfLYazmrSB6B7e3q0aMHBg0ahLyTz7zlCTYu0vR0SKD6cm4OPpztwgrvq/bQ8MRGjD48EI0Svc/WymdWv5lYEvQ+IGZv8QqeQKRpKfgWh6mG/WL55NfCNNr0F8QRZkCxYuJHLJWUMw2FieMS6Q4/y9oWZF6N+8u9iAMJZ3QudyXq1NCB7jc60KKZM39Sndwc+4cffsCAAQO0TbK9tTAlJQUjRozAvffeG9IT1agnb/QZZ5UA9WSVHMt5I0A9eaNirzj9+JuGkbq+oWGkjqWhJr1gaRgZ0DBggQAfUhag2azIunXrtBlDCxYs8Hpnbdu21QZkjRo18pquMjIS9CSXnK1YnYsPP3FhqjjhLCdvsoUY3HY4+jPuOT4ZtRzrsRhX4X9J9fFc9qNIdvpYl5YH7zZxUtWTT3NvojweCn5HgpYUNNOeVTwtllPO+sjnvW3KOhtXlPsMR+M9ZymWFx5Uz+sc6NrRiSqVT886kob2rFmztP3UNm3yfopgrVq1MGbMGFx33XUh2RibevLZnUywQIB6sgCNRXwSoJ58orFNgn78TcNIXbfQMFLH0lCTXrA0jAxoGLBAgA8pC9BsUuTo0aMYNWqUtoms/Cbf/SpTpgzefPNN7UjrcJ1MZGc9HTokNq6ek4t3P3Vhk24SlsOVi/sOzkafzJdQJf5fd4z+w844YLjYaPj6jv7zMdU0ATtryXRjIq1AjjBI3xa6nvAWcCrd692vy6yLKyvMwMm4ol7TZeSVYouiu7o40baNU5vAlCGWxn3wwQeacXTihJd1n6KM3Bh75MiRaNKkic96rSRQT1aosYwvAtSTLzKMt0KAerJCLbxl9ONvGkbq2NMwUsfSUJNesDSMDGgYsECADykL0Aq5iDSHvvzyS2352T///OP1bvr27avtHyI3tw7nZTc9ydlEv67MxeSZLsz4VcwmcoPR7sgyPHdiMM5J+NMtxUcwWQyO214P1KgJlCghXl8FhGH/FR93E9XRdtNSVMP21bisLGDpEjHb6DNg7iceuQ47yuJQZioSHSexPbc2Fse3wYKiTXEwrhjixf+2RFe29lM6LhtNr6mEG+4pr22UvV8sexs9erRmeMvZR96uu+++G0OeGYLqNap7SzYdRz2ZRsYCfghQT37gMMk0AerJNLKwF9CPv2kYqcNPw0gdS0NNesHSMDKgYcACAT6kLEArxCL//vsvpBk0Y8YMr3dx8cUX46233kLjxo29poc60i56kpMXPheziV6bmmuYTZTX/gtObsGIg8+hReIPeVEF/77pXuDpwUBCQsF5mSNoAnbRUtANiZYKduwAbuoAHNlnqUXSFhrjGozf2/bA/bc50OxiJzZv/ksztqdPn+61TrlJ//PPP49HHnkEcq+jYC7qKRh6LOtOgHpyJ8JwMASop2DohaesfvxNw0gdcxpG6lgaatILloaRAQ0DFgjwIWUBWiEUkbOKpEkkN64+duyYxx0kJyfj9ddfx1133SX8jMIzNApbT//scOGLsbuAT2ajeuZGfJ1yNaaXbJvPKzHnJF7dMwI3J0yA3Kg3oEtsBIxnxMlnN90cUHZmUkOgsLWkphVRVsv27UCX9sAJ3ZpOk018M6c/BlfohdolHOh9qxOdrnXgf7/9jMcee8znxthyfyN58mP79u3FvtzG09gCfXvqKVBSzBcIAeopEErMEygB6ilQUoWXTz/+pmGkrh9oGKljaahJL1gaRgY0DFggwIeUBWhhLrJr1y7tG/ZPPvFcEiJv5Z577sGwYcNQsWLFMN+Z59sVhp7k9k2/rMjF8lGL0WT1W7g4cbHBDLo74UN8UaIV6qRvxoeH70XNhK2eNy5jqp8njKFhwCVNgYULgVHidao4QeopMauoYeg3DPd+U7EbWxhail3aJlq+eTNwy3VA+hEThYxZx2c/jCfL90WOMwFFhHPbo504Ya2rOKlw+aeacbRz505jgf9CXbt21YzxChUqeE33F0k9+aPDNLMEqCezxJjfHwHqyR8de6Tpx980jNT1CQ0jdSwNNekFS8PIgIYBCwT4kLIALUxF5KwiaRLJ46a9zSqqXbs23n//fW2T2DDdUoFvE049ZWYCs7/JxUfjD6DnhqFon/iFz/ublfQQrj31nravikem1JLAgOfExtWdII5n8khmROEQCKeWCqeFEfyuhw8BixYBOdlAitjXS7q2y38BfvwO+FdnyIrZQLnCvnW63HcPA7Zk1UT/1JexoPhF+SBubOTAfbekY9HCdzFw4EBkyT2U3K4SYu+wyZMno0OHDqZmG1FPbiAZDIoA9RQUPhZ2I0A9uQGxYVA//qZhpK6DaBipY2moSS9YGkYGNAxYIMCHlAVoYShy8OBBPProo5g0aZLXd3vyyScxaNAgpKamek0vrMhw6EnuTzTtsxyMnXQUN+2cgntdryAt7qT5JjvjgQf7A3eLvYmKiLPAedmKQDi0ZKsGR8vNSJNHLhsT+w9pv2W7PhWzI595xGsLx2b3wRMVHxVpZ5aataoJ3HbtDrHX9kBMnTrFa7mePXtixIgRSEtL85ruHkk9uRNhOBgC1FMw9FjWnQD15E7EfmH9+JuGkbr+oWGkjqWhJr1gaRgZ0DBggQAfUhaghbjIggUL0L17d3hblnHuuedqx1I3b948xHdhrfpQ6ungIRcmThEziqYfwsN73sctzveR4vScgRDQnZ9VVxwbPg6oUjWg7MwUfgKh1FL4W8N3xBefAwN7CxBiNpLbtSKzKXahGlYkNsbYUtchI+70BtfnlXShXaOFmPLhw/jrr41upYCaNWviww8/xKWXXuqR5h5BPbkTYTgYAtRTMPRY1p0A9eROxH5h/fibhpG6/qFhpI6loSa9YGkYGdAwYIEAH1IWoIWoSHp6OoYOHaodNe3tLeSsosGDB6NoUbEExKZXKPS0c5cL70/OxdvzXLj+0I8YldELpeKOeyWQ6RQzhQaOQGI5ceR97+5e8+DW+4EnBgCJid7TGWsLAqHQki0aFss3sX6dWP75OLBpjU8Kx3MT8WnuPXitZDdsTa6i5SsTfxzV457F/G9f9VpOfi4OGDBATBT0PVOQevKKjpEWCVBPFsGxmFcC1JNXLLaK1I+/aRip6xoaRupYGmrSC5aGkQENAxYI8CFlAVoIiqxevVqbVbR27VqP2qtWrap9i966dWuPNLtFqNTTtr9deG1sLiYvdiExJx1v7BmOLgkf+Wyyq+0NcAwRexGVKnU6z2Kxx8rE8cDvK06Hz75A7K77INDmMp91MME+BFRqyT6t4p0gNxf4QMzue+VZvzBELizOvAI/J7TCiuR6WJhaH46jPyL9r7uRnr7Ho2zDhg21Jbz16tXzSJMR1JNXLIy0SIB6sgiOxbwSoJ68YrFVpH78TcNIXdfQMFLH0lCTXrA0jAxoGLBAgA8pC9AUFpGbur722mt44oknxL6xnks15IbXo0aNQsmSYmPmCLhU6Gn7DhdefS8Xkxa5xIa5wJVHf8UrJ3qjcvxuDwIuse+J4+obgfsfAsRyPV7RQ0CFlqKHRpS1RH7W3ShOWtu4OuCG7c4ug15F38f3ydWR8edjyDzgubeRU2xa/8orr+Chhx5CfLzYo0x3UU86GHwZNAHqKWiErEBHgHrSwbDpS/34m4aRuk6iYaSOpaEmvWBpGBnQMGCBAB9SFqApKrJp0ybtBLSffvrJo0a5kevEiRPRsWNHUycBeVQU5ohg9LRjp5hR9H4uPlh42igqmn0cr+590fesojYdxPKWwWIfotPLVsLcVL5diAkEo6UQ3xqrV0Hgjz+Am9sD2eK4wwCvHJcDozAEI8rehfTdM5H5Vw+xJVK6R+m2bdti3NhxqF6jen4a9ZSPgi8UEKCeFEBkFfkEqKd8FLZ9oR9/0zBS1000jNSxNNSkFywNIwMaBiwQ4HykPnUAAEAASURBVEPKArQgi8iZRFOmTME999yDTHk2vNt13XXX4Z133kHlypXdUuwfNK0n0f7DEz/Hvg++EjMG9uAEiuMY0nBc/G7unIey8Yc9Gx0v9h567hXg+o5nTmHyzMWYCCdgWksR3t6YvP09YmnZmv8B54vlotL43bkTYodr8TMWyMzwieT7zGvQvfwoHM86iONrHxCe0XceeVNSUrTPWWm6y4t68kDEiCAIUE9BwGNRDwLUkwcS20Xox980jNR1Dw0jdSwNNekFS8PIgIYBCwT4kLIALYgiR48eRb9+/bSTztyrkUsopFEkjSS5tCISLzN6OrRsI7J79kDZ7M2BN7VeU+Dl1yHctMDLMGdEEjCjpYhsIG/aN4GTJ4HvhQm0cjmwVMzA3LHJI++hnKKYl9sVqxLrY+6Rjfh9h9wTSS5iNV59+/bF8OHDERcXhyVLlmiJ8pTJpKQkY0aGSMAEAX4+mYDFrAUSoJ4KRFToGfTjbxpG6rqDhpE6loaa9IKlYWRAw4AFAnxIWYBmsYjc2PrWW2/Fxo2ex0PLAcyECRNwzjnnWKzdHsUC0dOJ47lY3WsiLl7+DBIdnvs2eW2JnFU08EWgy00QbprXLIyMLgKBaCm6WszWeCUgN8ke+x7w+jCvyXmRM062R4+dK3E087e8qPzfjRs31kz6/fv3a3E0jPLR8IVFAvx8sgiOxbwSoJ68YrFVpH78TcNIXdfQMFLH0lCTXrA0jAxoGLBAgA8pC9BMFskVA553330XvXv3FgcEeX4DPmzYMPTv31+c8i5MkQi//OlJ7O+NmZ9modqQnmgRPy/wljZrCwwbAVSoEHgZ5ox4Av60FPGNYwPME1gqZgc9fBeQccJn2Z3Z5fHgofr4ct8kjzzy81V+zl5++eW49NJLOcPIgxAjzBDg55MZWsxbEAHqqSBChZ+uH3/TMFLXHzSM1LE01KQXLA0jAxoGLBDgQ8oCNBNF5DfaDz74ID755BOPUpUqVcL06dPRokULj7RIjfCmJ3kg0qJPdqLM4IdwQdx/R9y7NfDPrHOx45I7cUlLJ4q6jgNi6Z52XSrYNGvOvYrceMVC0JuWYqHdbKMfArv/Bfr1FvseLfWZSVryo090xlPbJ4uTJ//7HNHlvvbaazFp0iSULl1aF8uXJGCOAD+fzPFibv8EqCf/fOyQqh9/0zBS1yM0jNSxNNSkFywNIwMaBiwQ4EPKArQAi/z888+4+eabxT6uYiNXt6tz587arKOyZcu6pUR20F1P6/5IxLMjMzBq+TU4J+FPj8bJwd2SGo/g3HH9ULZigkc6I2KXgLuWuOdM7GrB0HLpQG8WexotXCiMo9XAD18BOWL6otu18lQ9DN29H3NOrHRLAerWrYsZM2bg/PPP90hjBAkEQoCfT4FQYp5ACVBPgZIqvHz68TcNI3X9QMNIHUtDTXrB0jAyoGHAAgE+pCxAK6BIdnY2Xn75ZTz11FMeOeVm1m+88QYeeOCBiN3Y2qNRuog8PR04lIjf5ldF6g/z8EDuSJSI8zz6Whbb+8RYlLvzGl0NfEkCpwnkaUmGuOcMVeGTgDTkH+sD/L7Ma5afT5TB0J1r8X22cTmwXKImZ3h26tTJazlGkoA/Avx88keHaWYJUE9miYU/v378TcNIHX8aRupYGmrSC5aGkQENAxYI8CFlAZqfIvL/pzzl7KuvxLfeblft2rUxbdo0NGrUyC0leoJHj2Tg857TUWflLDRK/AW+tqfOhQPOPgOBng9GT+PZEqUE+NmkFGd0VxbAxthzDmegx78Hsdttn/0BA57G0KFDomIPuejuZHu1jp9P9uqPSL8b6sn+Pagff9MwUtdfNIzUsTTUpBcsDSMDGgYsEOBDygI0H0V++eUX3HjjjV6XoEkTacyYMUhLS/NROrKj5SqRBT/mYO8jL+PWnNf8Nsb1+mQ4mjUDUlL85mNibBPgZ1Ns97+l1i//FejbEzi812vxg9kuDNm5H2+eMC5ha37pVfjs08koX76813KMJAF3Avx8cifCcDAEqKdg6IWnrH78TcNIHXMaRupYGmrSC5aGkQENAxYI8CFlAZpbEXny2XvvvYeHH37Y4xS0hIQE7Tjn22+/HQ6Hw61kdAQ3b3Hh6RE5uHTh2+gTJ04z83G50srC8dr7QJOLfeRgNAmcIcDPpjMs+MoEgcxM4NOZwKsvAscPeS247HgGeu04hFW5Z6YbJSZXxLxvPkebNpd4LcNIEtAT4OeTngZfB0uAegqWYOjL68ffNIzU8aZhpI6loSa9YGkYGdAwYIEAH1IWoOmKHBWnefXq1QsfffSRLvb0yzp16mDmzJlRu7HqyZNiTPZeDkZ/4cJTe8ahv/N5DwYZSILzmpuReFNn4KKLIDZu8sjDCBLwRoCfTd6oMC5gAhkZwPSp4kNqOJApPqzcrkwxLXLsviN4dH86hMX03+XEY/3HY+RLd4qPqug0+PNayt/BEeDnU3D8WNpIgHoy8rBjSD/+pmGkrodoGKljaahJL1gaRgY0DFggwIeUBWj/FVm/fj26dOmCDRs2eFRy55134vXXX0fx4sU90qIhYuHiXDz8Yi72HT2Jgfs+QO+4kR7N2nXnMFTqezvE5iAeaYwggYII8LOpIEJMD4jA/v3A4AHAT197zb4zMxtP7jiAjzNy8tPPqf8k5s56DjVr8LMrHwpfGAjw88mAg4EgCVBPQQIMQ3H9+JuGkTrgNIzUsTTUpBcsDSMDGgYsEOBDyjw0l/hmWh7J3K1bN2RlGffCkKegvf322+jRo0dUnoK2d58Lg0fkYvGS3XjqwFjc4JyEVOeZ7+fzaG7seh9qiM1keRR6HhH+NkuAn01miTG/TwLiMzvz23lwDeiDpFMnvGb7/mg6xuw5igyxSq1yvBMbEi7DbS9MR++eJSFWFvMiAQMBfj4ZcDAQJAHqKUiAYSiuH3/TMFIHnIaROpaGmvSCpWFkQMOABQJ8SJmDdlKswxowYABee81zY+eqVavi008/RZMmTcxVGgG55SFEU2bm4gmxBO2OfbMwJPtxFHUazbK8Zmy64U78c8WVPAo9Dwh/WyLAzyZL2FjIBwGpp2ULFqDm7M9RddHXEH+k+sh5OjpHpD+xMxELms7Hey/UxMUXcTmtX2AxlsjPpxjr8BA3l3oKMWAF1evH3zSMFAD9rwoaRupYGmrSC5aGkQENAxYI8CEVOLS/t/2NW2+7FUuXLvUodN1112H8+PEoW7asR1qkR2z804VHnsvB5q0HMHbvk2iT+J3PJmX3G4JFZ9XW0ps3b84ZRj5JMaEgAvxsKogQ080Q0Ovp0grlkfjcM8Aaz89yfZ2nhGnU4s8TWF/3K/Tt0gwD+sUhtag+B1/HKgG9nvisi1UVqGs39aSOZahq0o+/aRipo0zDSB1LQ016wdIwMqBhwAIBPqQCgzZv3jx07doVx44d8yjwwgsvoH///oiPj/dIi+QIudrurXE5eH56Lrod+BpDsx5Hibh0jybJ7+kdLdsD9z+IU3XqYsmSJVoe/hHtgYoRJgjws8kELGYtkICHnuTeanO+BJ59Ajjp+bmeV+G6k5losPUg4mpPQu2zb8E7g51o2YyzjfL4xOpvDz0lJcUqCrZbAQHqSQHEEFehH3/TMFIHm4aROpaGmvSCpWFkQMOABQJ8SPmHlpOTgxEjRmDgwIEeGdPS0vDJJ5+gbdu2HmmRHrFhows9B2Wj3OaVeP7YYJyfuN57kzreAfTuC1SsqKVTT94xMdY8AWrJPDOW8E3Ap57ESZeYMB74aCyQfsRrBa/uOYx+B9IRX2kQUs8ehHvbxOOZx+LEoQZeszMyBgj41FMMtJ1NVE+AelLPVHWN+vE3DSN1dGkYqWNpqEkvWBpGBjQMWCDAh5RvaHI2Uc+ePTF1qjia2e269NJLMWXKFFSrVs0tJbKDclbRtJGbUXTSe7jUMRfl4w94b1BKGjD6baB1G0M69WTAwUAQBKilIOCxqAeBAvUk9zQ6chhISERux3Zw/rs1v45cuDBp/1H03nsCJ9O6oljd91GuaCreedqJK1pztlE+qBh6UaCeYogFmxo8AeopeIahrkE//qZhpI42DSN1LA016QVLw8iAhgELBPiQ8g5N/t/q3LkzVq1a5ZHh0UcfxfDhw5GcnOyRFskRf4i9ioY+/i/e/bO116Vn+W27/Hrg+ReAEiXzo/JeUE95JPg7WALUUrAEWV5PwJSe1q+D66Z2Hhtj78rMxiPbD+BTNERqw88Ql1wRtzd1YNiAOJQQHjqv2CFgSk+xg4UttUiAerIILozF9ONvGkbqwNMwUsfSUJNesDSMDGgYsECADylPaL/++iuuvfZa7N+/35CYIM5W/vjjj9GlSxc4HA5DWiQH5Kyit8fn4Llpufhm5224OPH0HkQebapUCxj0PNCqtdi0yHv7qScPaoywSIBasgiOxbwSMK2nd94C3hTGuNslZxu9vPswnjhcVphGXyM+9VyUE98djB/iRAvubeRGK3qDpvUUvSjYMgUEqCcFEENchX78TcNIHWwaRupYGmrSC5aGkQENAxYI8CFlhPbNN9/g+uuvR3Z2tiGhatWq+PLLL9GgQQNDfEQHxBKMXQv/xNeDfkHCvr9RO3c9mif+5NEkV8nycDzyFND5RiAuziNdH0E96WnwdTAEqKVg6LGsOwHTepJL1CZ+AIx5FsjNca8O68Vm2KP3pGP62d8gsWRzLf2Raxx4qk+cmH3qkZ0RUUbAtJ6irP1sjloC1JNanqGoTT/+pmGkjjANI3UsDTXpBUvDyICGAQsE+JA6A23SpEm46667zkT896p169aYNm0aKlSo4JEWqRGuLVtw5I4HUOLIOp9NyHXEwTl1DlC3ns8ZRe6FqSd3IgxbJUAtWSXHct4IWNbT9u3AgMeB3372Vi0+O3QC3Yu/j4QKnbX080sBH7wUh/PO8T4L02sljIw4Apb1FHEt5Q2HgwD1FA7Kwb2HfvxNwyg4lvrSNIz0NBS+1guWhpFCsDFaFR9S4svj3FyMGjUKTz0lZtG4Xffffz9effVVFClSxC0lcoPpn4hlFEMeQCI8vzXPa5X4bh2OD2cDjS7KiwroN/UUECZmCoAAtRQAJGYJmEBQehLPCHwwHq5XhsKbDbTyRAY6uZ7C8ZrixEiRQ87DfOEuB+7rLkx37okdcB9FUsag9BRJDeW9hoUA9RQWzEG9iX78TcMoKJSGwjSMDDjUBfSCpWGkjmus1hTrDym59Kx///6aKeSugSFDhuCZZ54Rf/BHyV/8YrOiPb2Hofyice5N9Qw/Mhi4/wHP+AJiYl1PBeBhsgkC1JIJWMxaIAElevplGXKf7Avnvn883u/vzCxcdfRm7D1vtPCMTi/dbV1L7A/3QhwqVfBmM3lUwYgIIqBETxHUXt5qaAlQT6Hlq6J2/fibhpEKoqfroGGkjqWhJr1gaRgZ0DBggUAsP6TS09Nx3333YerUqR7k3nvvPfTo0SNqNrfO/Gc3DnS+FxXTf/No66GcothVsT3OvaYa4quIZXf16p9ehuaRs+CIWNZTwXSYwwwBaskMLeYtiIAyPYnZRrmzZyFnYG8kiA2w9ddOcYra1fta4J/6k+Bwnt7IKFV4RxOfceKK1lHyxYO+wTH8WpmeYpghm36GAPV0hoVdX+nH3zSM1PUSDSN1LA016QVLw8iAhgELBGL1IXXw4EHcfPPN+P777w3U5GyiTz/9FJ06dTLER3Jg3+yfkfTkXSjuTPdoxuzc7ij5zhC0bKNml9ZY1ZMHWEYETYBaChohK9ARUK6njRuRftM1SMnO0L0LsCcrB9fuPA+bGn4OR3yx/LR+1zowoG8cxGGbvKKAgHI9RQETNsE6AerJOrtwldSPv2kYqaNOw0gdS0NNesHSMDKgYcACgVh8SMn/Q+3bt8eqVasMxFJSUjB37ly0bNnSEB+xAfFN+NZH30T170bA/bvtUy4nptR6HddP7oTSpdQtl4hFPUWsPmx+49SSzTsowm4vJHravw/Hr2uL1KP7DTSO5uTi8V2lML3uAjgTS+enNakstkIaGYcqldV95uZXzhdhJRASPYW1BXwzOxGgnuzUG97vRT/+pmHknZGVWBpGVqgFUEYvWBpGAQBjFr8EYu0htXPnTrRr1w7r1hlPB5MnoH377beoV0+cCBYFV9bOvdjZtS9qHFno0Zp/sqtgzUOTcU2fc8WSO4/koCJiTU9BwWJhvwSoJb94mGiSQMj0dPgQ0ju1R4qXfY2Wn8jGt4n98UWpztiUXE2746JiidoHA5246nJ3G99kg5i9UAmETE+F2iq+eWERoJ4Ki3zg76sff9MwCpxbQTlpGBVEyGK6XrA0jCxCZLF8ArH0kNq2bRuuvPJKbNq0Kb/98kWdOnXw1VdfoXr16ob4iAuITa3x00JkfDQTyb/O8Xr7Sx3XoNzUMTir3pmlEl4zWoyMJT1ZRMRiARKglgIExWwBEQipno4dQ8ZNHZG8faPPe/lfZkNMTL4PH5W8CtnORPRp78DAfnFITPRZhAk2JhBSPdm43by10BCgnkLDVWWt+vE3DSN1ZGkYqWNpqEkvWBpGBjQMWCAQKw+pv/76C5dddhnkDCP91aRJE8yZMwflypXTR0fea7E0AvfcAWxe6/Xec1wOfFP7WVwx9R4USVE8rUj3jrGiJ12T+TJEBKilEIGN0WpDridxiEJ6v95IWTzXL+Hd2WVwd7FJWJZaF40rAZPHxKEiT1Hzy8yOiSHXkx0bzXsKGQHqKWRolVWsH3/TMFKGFTSM1LE01KQXLA0jAxoGLBCIhYeUXH4mzaJ9+4SportatWqFzz//HKVKldLFRuBLsVeRq9utcPy22OvN78sugfUPTEarvhcpX4Lm/oaxoCf3NjMcGgLUUmi4xmqtYdGTy4VjU6fA8Xx/pDp9G/Mnc+Nwb/LH+CatGUqITbCnv+TExRdxiVokaTMseookILzXoAhQT0HhC0th/fibhpE65DSM1LE01KQXLA0jAxoGLBCI9oeU3Nj68ssvx5EjRwx0rrrqKsycORPFixc3xEdi4NRbY5H09lCPW88VMUtc16HipGE4q0kZj/RQRES7nkLBjHV6J0AteefCWGsEwqmno+Jghfc6dkCNbX/gsuIpKBMvNi5yu7LFrM8xGIzRZW5HjjMZo3s4cNdtcSE39d1ug0GLBMKpJ4u3yGIRRIB6sn9n6cffNIzU9RcNI3UsDTXpBUvDyICGAQsEovkhtXTpUs0sysgwHnvcsWNHfPzxxyhatKgFYvYqsvO7tSj/yNWId7jyb2xPdimMSXgGOW2vxNDhJZAaxmZGs57yAfNFWAhQS2HBHDNvEm49nTx5Evfccw9mTJuGu1Pi0btcGhqkJHnwljNAByW/jBkl26JbMwdGDIlDkWSPbIywGYFw68lmzeftKCZAPSkGGoLq9ONvGkbqANMwUsfSUJNesDSMDGgYsEAgWh9SP/74I+Qsoiy5EbTuuuWWWzBhwgQkJ0f+X+SLvjuOGg9fgarxO/JbKGcVdS3yBW7ofxFuv8kZ9m+ro1VP+YD5ImwEqKWwoY6JNyoMPcnnT58+ffDuu+8iXlD+vFIaOpTw7uC/4Hoeo8p1RwOxnd5Hr8ShSmXfS9piosNs3sjC0JPNkfD2giBAPQUBL0xF9eNvGkbqoNMwUsfSUJNesDSMDGgYsEAgGh9Sc+fORYcOHZCTk2MgIr/tfeedd8SpNJF9LI3YsgiTXtuLhm/fhwsTVxraOMHxOBp83BeNGhTOYCMa9WQAzEDYCFBLYUMdE29UWHrKFR/YTz75JEaPHg35qTyuXFHcUybNK/MPsnvhsQr9USzBgWnDnWh+Cfc18grKBpGFpScbNJ23EAIC1FMIoCquUj/+pmGkDi4NI3UsDTXpBUvDyICGAQsEou0h9cUXX+DGG2+E/CNdf/Xq1Quvvvoq4uPl97yRe504AXx857fosrYPSsUdMzTkL1dDFJ//BcpXLLw2RpueDIAZCCsBaimsuKP+zQpTT/J5NGjQILz44osa57bxTjxbqQSap3rOdP0yswvurvgSXM4EvNHLidu60jSyozgLU0925MF7Co4A9RQcv3CU1o+/aRipI07DSB1LQ016wdIwMqBhwAKBaHpIffjhh+jevbsHhf79++Oll16C0xnZf3jv2XgIf3Z5Bi1zP/NoYwYS4ZyzGIk1K3ukhTMimvQUTm58L08C1JInE8ZYJ1DYepKm0dChQ/H888/nN+Klksl4sqLnKZ1LMluhc4X3cSquCB65xoHBj8UhznPf7Px6+CL8BApbT+FvMd8xlASop1DSVVO3fvxNw0gNU1kLDSN1LA016QVLw8iAhgELBKLlIeVweF+CJf9AHzx4cGSbRbv/xcGR45AydyySHcZldrLLTySURYrYxNtRp44FBagtEi16UkuFtVkhQC1ZocYyvgjYQU8ulwvDhg3DM888k3+bfVMTMapqaXFwgfEZtijzcnSsNB4uhxPXio/2d0fGh/UAg/wb5AuvBOygJ683xsiIJEA92b/b9ONvGkbq+ouGkTqWhpr0gqVhZEDDgAUC0fCQ8mUWjRw5Eo8//rjY+Nn4h7gFTIVTZMsW4L23kTtnGpw4cwqa/mYO1bkGJceOBtK874mhzxuO19Ggp3Bw4nsUTIBaKpgRcwROwC56kqbRiBEjMGDAgPybvzUpDuNrlkMRp/FZ9U5OPzxdoa+Wr04ZYMYbcahU0ZgnvxK+CCsBu+gprI3mm4WMAPUUMrTKKtaPv2kYKcPKGUbqUBpr0guWhpGRDUPmCUT6Q0ruCfH00097NPzNN9/EQw89FLlm0RefwzWot/wg9WibjDieWwSZA19FqduvFfM57TOAiHQ9eYXNyEIhQC0VCvaofVM76UmaRvKkzszMzHzeVyY4MaNWWZRwW3v2fnYfPFPuIW15WilxXsOnL8fhwnr2+czPb0CMvbCTnmIMfVQ2l3qyf7fqx980jNT1F2cYqWNpqEkvWBpGBjQMWCAQyQ+pKVOm4Pbbb/dodZz4gzs7O9sjPmIiNm6E68YrvJpFOS4Hfi16E+p98jRSq4uvnG12RbKebIYy5m+HWop5CSgFYDc9SdPIfV+96xOd+PSs8h7L0zLF5/6KrFb4PrEd5qRdjqHPVsY1V0b2nnxKO7cQKrObngoBAd9SIQHqSSHMEFWlH3/TMFIHmYaROpaGmvSCpWFkQMOABQKR+pCaOXMmbrrpJq8tln+IR+y1fz9yO7WD89BuQxMycuMwI/de7L7+XvR7oQoSEgzJtglEqp5sA5A3kk+AWspHwRcKCNhRT95Mo2fSkvBs5dI+WyzP/xyTOwilB92Pu2/nTtg+QYU4wY56CnGTWX0ICVBPIYSrqGr9+JuGkSKoohoaRupYGmrSC5aGkQENAxYIROJDSi436927t0drX3nlFfTte3q/B4/ESIg4eRLZnTsifvs6w93+mHkl7io3GgPvL4n7usfZaQWa4T5lIBL15NEIRtiCALVki26Impuwq57k6WndunWDnDGbd71RJgUPlUsTe9d5X3omTaPOyV/gkm6N8GSfODFTKa8kf4eLgF31FK72833UEqCe1PIMRW368TcNI3WEaRipY2moSS9YGkYGNAxYIBBpDylfG1i//fbbePDBBy0QsE+RI2PGI238mdNz5J39nlkfV1X6DJOGJOGqy+0/Kog0Pdmn93kn7gSoJXciDAdDwM56kqbRI488AvllSN51aZwTI6uUQrPUJK/Lk//JroTG5RbglpYpGP1snG1nnea1J9p+21lP0cY6FtpDPdm/l/XjbxpG6vqLhpE6loaa9IKlYWRAw4AFApH0kEpNTcWJEyc8Wvnqq69qf2x7JERQxJYFW1Crd0vDHe/PThPHKi/EO2+UQf263r9pNhSwQSCS9GQDXLwFPwSoJT9wmGSagN31JPfd69mzJz744AND2xpUroTvxYmfpZcsguOHLw1pE7MfRL+KT+Hq84Fxo+NRtKghmYEQErC7nkLYdFYdAgLUUwigKq5SP/6mYaQOLg0jdSwNNekFS8PIgIYBCwQi5SHVrl07fPvttx4tvO+++/D+++9H7mlookWbxi5AlTF3IdmZY2jf60VG4IYvbkfVKpFhFsmbjxQ9GUAzYEsC1JItuyVibyoS9CRPTbvzzjsxbdo0A+c6dergh/nzUbZ3T2Dd8vy0k2Jvu5pl1mknqF1UEZj+VhxKl4qc50V+QyLwRSToKQKxxuwtU0/273r9+JuGkbr+omGkjqWhJr1gaRgZ0DBggUAkPKTkcrNevXp5bZ2cyu9rmZrXAjaLXPPKfFww9k5xKo5xo+6juSnI/no5StUsYbM79n87kaAn/y1gql0IUEt26YnouI9I0VNGRgZuvvlmzJ492wC+efPmmDd2LFJvugrQHezQJ+4dfFjqGi1vzeLA7HfiUKUyTSMDvBAEIkVPIWg6qwwBAeopBFAVV6kff9MwUgeXhpE6loaa9IKlYWRAw4AFAnZ/SB0+fBglS5b02rKcnByPY4m9ZrRp5M/Pz8clUz3Nop04GyVnTEBKnVo2vXPft2V3Pfm+c6bYjQC1ZLceiez7iSQ9yaXX119/PRYsWGCA3qFDB8wqLTbDXv5Dfrw8FOGGyuPywxWKAF+/K2YeVadplA8lBC8iSU8haD6rVEyAelIMNATV6cffNIzUAaZhpI6loSa9YGkYGdAwYIGAnR9Scnr+jTfeiDlz5ni0TN53YmKiR3ykRMwftACtPuuOBLeZRb+WuBUXzh2OxGJJkdIUw33aWU+GG2XA9gSoJdt3UUTdYKTp6eDBg2jbti1Wr15t4PzBFZfh7n83GuK+yuyMIaUfxebkqlp8KfFo/FosTzu3Nk0jAyiFgUjTk8Kms6oQEKCeQgBVcZX68TcNI3VwaRipY2moSS9YGkYGNAxYIGDXh5RLTLnv06eP4dSYvOYdOnQIJUpE1lItbQnBpr+AjRuxcsbfqL/iJQ+zaFXF7ppZ5Iy3/2loeX3h/tuuenK/T4btT4Basn8fRdIdRqKedu3ahRYtWmDr1q35qNPEq30XVEJCfsyZFyszL0a/ki/i95SzUSwe+Oq1ONSrQ9PoDCF1ryJRT+paz5pUE6CeVBNVX59+/E3DSB1fGkbqWBpq0guWhpEBDQMWCNj1IfXaa6+hb9++hhbJGUU7duxA2bJlDfEREXjvHeD1YT5vdUOt7jhv1nA4nJFrFsnG2VVPPsEzwbYEqCXbdk1E3lik6mnTpk1o1qwZ9u/fn899cvlUdCstNizych3NSUa7tLn4o0hNJInHyZyXnWjcMLKfK16aWehRkaqnQgfHG/BKgHryisVWkfrxNw0jdV1Dw0gdS0NNesHSMDKgYcACATs+pGbNmoVOnToZWlOsWDGsWLEC55xzjiE+IgLr1sElNir19T3vX+d1R+2ZwyE2ZIqI5vi7STvqyd/9Ms2+BKgl+/ZNJN5ZJOtJLktr2rQp5DJtecknxfCSyXi8cjnE52Zrcfp/dmaXR+syc3EgoZSYyQp8+oITLZtF/vNF38bCfh3Jeipsdnx/TwLUkycTu8Xox980jNT1Dg0jdSwNNekFS8PIgIYBCwTs9pCSppD8NjU7+8wfwU5hpCxcuFCbmm+hiYVbRLTD1fFqOLZt8HofWy7ohlrTX4gKs0g20G568gqdkRFBgFqKiG6KmJuMdD3JDbDlnkZyuXbedW5KESzp9RBKfTMTcDOO1mbWwxUVZiIzrgjihGk0fYgTV7ShaZTHLtjfka6nYNvP8moJUE9qeYaiNv34m4aROsI0jNSxNNSkFywNIwMaBiwQsNNDatu2bWjSpIlh6r1s0tSpU3HLLbdYaF3hF8kdPx7OMc8YbmRdZl1sFyehVejSBg2H3xg1ZpFspJ30ZIDOQMQRoJYirstsfcPRoKdx48ahR48eBs61a9fG0u++Q+nePYHNvxvSlmS2xv/ZOw84KWr2j//u9mhHP/od0hGk995EOkjvKB2R3jsqSEdQuoDSFBCU3os0eZFeleorTQR9QURA4H/1P1nY3OTq7G5md2bnmc9HNk8mySTf/LzMPJNJGmdbggj/JLApZ9aOV5xG1clpJEBy0fAFPbnYdMqmAwHSkw5QJRepfv4mh5E8uOQwksdSKEktWHIYCWjIcIGAUQYptpB19erV8dNP4g3vpEmTMHr0aBda5v0skbduI7xBFSRFBK/ML2H5UTHbLiwflRSN6vnejbtR9MSBU8C0BEhLpu06Q1bcF/TEZhcNHz4cM2bMEBjXrl0bW1csR7K2yqfcD34Xzu0ObYx2wbMR5edvdxqtm+CPGlV9b+wRGu0Bwxf05AFMdAmNBEhPGkF5MZn6+ZscRvI6ghxG8lgKJakFSw4jAQ0ZLhAwwiDF1mVgaxbt3LlTaEG3bt2wePFiZWkf893cRr34P/yvQn1kCYve/jhSaV2zwG3oM6046tQ0X5uEzonHMIKe4qkaRZuMAGnJZB1m8Or6ip7YeNmqVSts2bJFIN6rVy/MHz4Mfi3qAS+eCue+DeuInsEfK3F+9s/TNkz0R7XKvjkGCQ3X0fAVPemIiIp2ggDpyQlYXkqqfv4mh5G8TiCHkTyWQklqwZLDSEBDhgsEvD1IRUZGol+/fliwYIFQe7ZWA7shTpEihRBvBoMtMXGu3miUvLNCqO6a8G7I+uU4n3676209CcDJMDUB0pKpu89wlfclPT169AhVq1bFzz//LHBmu4v2r/UW0K4REP5ygWxHglVh3TAw6wiE+ydDgLKm0QZaCNuBxqVfX9KTSwAok1QCpCepOHUpTP38TQ4jeYjJYSSPpVCSWrDkMBLQkOECAW8PUp9++imGDBki1LxgwYL4z3/+gwwZMgjxZjCYs2j7+9vQ6D/KehKq49ewvLi7cBeq1gxUxfpe0Nt68j2i1m0Racm6fa9Hy31NT9evX0fp0qXBnEfqY9q0aRj+5ptAl+ZAFJvXGn1cDSuAN7NsxHNbSrvTaPNUf1QqTzONoglpD/manrS3nFLqQYD0pAdVuWWqn7/JYSSPLTmM5LEUSlILlhxGAhoyXCDgzUFq48aNaN5cualVHWnTpgXbKS1fvnyqWHMEmbNo+cQbaLP6TQT6h/FKP4+04fy4A6jQJi+P89WAN/Xkq0yt2i7SklV7Xp92+6Kejhw5gmrVqoHN1FUf3bt3xxcd2gN93lWio3dVY2nG+n2C+Rlb25OzmUZbp/ujQllyGtmBOPGPL+rJieZTUskESE+SgepQnPr5mxxG8gCTw0geS6EktWDJYSSgIcMFAt4apC5cuIAyZcogLCzascLWKjp8+DAqVarkQku8nOX//g97Ru5H5V29kFLlLGK1+qndAhQd28TLFfTM5b2lJ8+0jq7iSQKkJU/S9v1r+aqeVq5ciXffZY4h8fjnn3+Q5vAhYPj74gnF6hXwJdakq6UsaeSHpIrTaPccG0oUVQJ0aCbgq3rSDIASSiVAepKKU5fC1M/f5DCSh5gcRvJYCiWpBUsOIwENGS4Q8MYgxXZEY84iNqVefaxduxatW79886mON3z45k2gYeU4q/lb8Q54bfX0OM/5YqQ39OSLHKlNAGmJVCCTgK/qie2cFtfGEGzm0cGDB+E3dRKw8vNYKPeENkLb4Ln23dNS2YB9i2x4PR85jWKBiifCV/UUT3MpWmcCpCedAUsoXv38TQ4jCUBfFUEOI3kshZLUgiWHkYCGDBcIeHqQYlPnO3bsiFWrVgm1nThxIsaMGSPEGd5gjqIN64Aln8VZ1cep8yHNwd1A8uRxnvfFSE/ryRcZUpteEiAtkRJkEvBlPb148SLODSLYZ99Ny5YF6pRXUIqfpjG2n0R+gMlZutsxZ0oGfL/EhhzZyWlkB5LIP76sp0SaTqd1IEB60gGq5CLVz9/kMJIHlxxG8lgKJakFSw4jAQ0ZLhDw9CC1ZMkSsPUV1EfLli2xZs0a2GzKa06zHGu/ASYMU+7BY9+EsyY8D0iHFJu3Ablym6VFUurpaT1JqTQVYkgCpCVDdotpK+Xrevr999+RPXt2oX9SpkyJixcvIueVy8BHynj15KFwPizKD3VT7sbZlAXs8TlSKU6jZTZkykROIwFUHIav6ymOJlOUjgRITzrClVS0+vmbHEaSoCrFkMNIHkuhJLVgyWEkoCHDBQKeHKTYukWlSpVCREQEr2muXLlw+vRpBAUF8ThDB5iD6ItFwOwJCVfz+FUglXL3bbHDk3qyGFrLNZe0ZLku17XBVtDT3r17UadOHYFjzZo1sWvXLiRhsTu2A6P7KoHoFx3Xw3KjeuateBqQ2p6vkLI56falAUiX1m7SP/EQsIKe4mk6RetAgPSkA1TJRaqfv8lhJA8uOYzksRRKUguWHEYCGjJcIOCpQerx48eoUKECLl9W3nS+OvyUBTePHj2K8uXZdHmTHJ/NBL78NM7K3gzLiXtl3kHFpV0t9RmaGoan9KS+JoV9kwBpyTf71VutsoqeRo0ahalTpwqYp0yZgpEjR76Mm618Qr14hnD+v2H50CjjGvyZNJM9voIyUWn94gAEBgrJyFARsIqeVE2moI4ESE86wpVUtPr5mxxGkqAqxZDDSB5LoSS1YMlhJKAhwwUCnhik2KKc7733Hr788kuhhrNmzcKAAQOEOEMbJ08AnZvFquI3YV2wOF17VGr8OiaOCWAbz1j28ISeLAvXYg0nLVmsw3VurlX09Pz5c1SvXh0nT54UiB45cuTlDqRsZ9IWjYBffxbO3wvPhGbp1uFqilz2+DrKV2or5wUgiX1qkpCUDIWAVfREne0ZAqQnz3B25yrq529yGLlDUsxLDiORhzRLLVhyGEnDatmCPDFIsQWu33nnHYFxkyZNsH79enOtW/RuW+DMYaEd4zAVszO1Q5syflgw3absViOctpzhCT1ZDqpFG0xasmjH69RsK+np2rVrKFKkCMKYc+jVkSNHDpw9e/bl59+/3QZaNYy1ptGjiEB0D/wa+9KUsefqVNkPn020WfoliINfzF8r6Slm28mWT4D0JJ+p7BLVz9/kMJJHlxxG8lgKJakFSw4jAQ0ZLhDQe5Bin6AVL15cuHHNmjUrzp8/j8yZM7tQYy9l+Vl5G9umrnDxKVEfY3rmTqj9+ss3sUmTCqctaeitJ0tCtWijSUsW7Xidmm01PX311Vfo1KmTQJO9uGHx7HNw3LsHvNMS+OOmkIYZW0JbYUjmUXiQJAM+bO2HQb1NtCFFrNboE2E1PelDkUp1ECA9OUgY91f9/E0OI3n9RA4jeSyFktSCJYeRgIYMFwjoOUg9e/YMlStXxrlz54SaHTp0CNWqVRPijG5EFc8Nv/BQoZoZM/wXJbMnwaYvAqBsRkOHQkBPPRFgaxEgLVmrv/VurdX0xD4F79ixI1auXCmgZXaHDh1exj36G+iizP69Jo7R7OTTyKQYmHQx1qd/E4sH+qNVU4tPnxUo0lgXAweZbhKw2t8nN3F5Jbv6+ZscRvK6gBxG8lgKJakFSw4jAQ0ZLhDQc5Bi6xPNmTNHqJWw+KZwxrhG1Nat8Bv5vlDBI6E1MKjwCuxWtiBOn97CixYJVOgmOgYOMt0goOffJjeqRVlNSsCKenr48CFKly6Nmzdv8l5Lnjw5rly+gpy5cr6MU9Y8Qp+ewPF9PI0jwJxGeTNeQLgtBTZN8UfViuQ0crCxop4cbadf+QRIT/KZyi5R/fxNDiN5dMlhJI+lUJJasOQwEtCQ4QIBvQap7du3o1EjZWFN1VG3bl1sVZwvScy0iubePYgc2BX+qm2II5U2tU+7EzO+K4rsIeQsUnUxzTBSw6CwWwT0+tvkVqUos2kJWFVPbCfSSpUqCf1Wu3Zt7NixAwEBAS/jI5VR7ds1wLQPgNAXQtp3k67CtrRVkEzxFR1caEPB12nMY4CsqidBHGRII0B6koZSt4LUz9/kMJKHmRxG8lgKJakFSw4jAQ0ZLhDQY5C6e/cuChcujEePHvEaBQUF4cKFCwgJCeFxhg/s+x6R/TsLziJW5yl+k9Dgm04oXpRunGP2oR56inkNsq1BgLRkjX72VCutrKfx48dj3LhxAurZs2ejf//+QhyUGUl4pxVw6wqPXxH+PgZmG2W3MydXnEbLbciWlcY+K+uJi4MC0giQnqSh1K0g9fM3OYzkYSaHkTyWQklqwZLDSEBDhgsEZA9SkcqbymbNmmHLli1CbXbu3Il69eoJcYY2Dh5EZJ93YjmLloX3Rsjno1CnJk3Nj6v/ZOsprmtQnDUIkJas0c+eaqWV9fTixQv7uoEnT57kuP2VLT3ZSxz2ckc4Fn0OzJnIo/6OSIliGY/jaUBqe1zhjMDuFbRun5X1xMVBAWkESE/SUOpWkPr5mxxG8jCTw0geS6EktWDJYSSgIcMFArIHqYULF6JXr15CTQYOHIjPPvtMiDO08ccfiKxTEf4R4iLXq8O64tmYcejeiXaMia//ZOspvutQvO8TIC35fh97soVW19OlS5dQrFgxREREcOwVK1YE24RC+Ez86lWgeU2ehgVWhPdUZhmN5nFNi/thyWc2KD4nyx5W15NlO16nhpOedAIrsVj18zc5jOSBJYeRPJZCSWrBksNIQEOGCwRkDlJx3ZCyt5fHjx9XdhEzyTZiys4yEZ07wXZKXAD0m7Au+LnjOEwcE6BsSewCaItkkakniyCjZsZDgLQUDxiKdokA6QmYN28e+vXrJ/BjG1PEjENfZZOHA1t5uogoPzQM3I7jqaJnI41p6Yehfa378oT0xOVBAQkESE8SIOpchPr5mxxG8mCTw0geS6EktWDJYSSgIcMFArIGqefKTitVqlTBmTNneC3YlPfTp0+jRIkSPM7ogcgVy+E/fYxQza2hLbG21gwsn5tEWSRUOEVGDAKy9BSjWDItSIC0ZMFO17HJpCfYZxfVqlULB5VPrh0HW/j68uXLyJcvnyMKUNYhRN0KQGT0bKT/RWRAuQz78U+SdDzd1yP90aieNacZkZ64DCgggQDpSQJEnYtQP3+Tw0gebHIYyWMplKQWLDmMBDRkuEBA1iA1YcIEfPjhh0IN2Gdo7HM00xzKTXRUnw5QTyD6X3h6dH3jP1i7Kp0yS8o0LfFaRWXpyWsNoAsbhgBpyTBd4RMVIT297MYrV67Y1y1i6w06jjp16oCtM8he8vBj/lxgwVRussCOsBboEPwpjwtQBsv9820oWkg9avLTPh0gPfl093q8caQnjyN3+oLq529yGDmNL94M5DCKF417J9SCJYeReywpt5ytYdmsojJlyiBK+ZzLcbAb0O3bt0dv2+s4YdRfZd2GiBZ1YIsKF2rYJ8VKjNpcA9lDrHdDLIDQaNBNj0ZQlCxRAqSlRBFRAicIkJ6iYc2cORNDhw6NjlBCy5YtQ+fOnaPj2FpHndoDZ/8THaeEOiZZia3pqvK4LCmAw1/ZkCmTtcZI0hOXAAUkECA9SYCocxHq529yGMmDTQ4jeSyFktSCJYeRgIYMFwi4O0ix3VfYwpnnzp3jV0+aNCmuXb2GnLly8jhDBx7cR3j9Wgh49kCo5syosaiyoifKl1W9dRVSkBGTgLt6ilke2dYlQFqybt/r0XLSUzTV0NBQ+7it/oQ8MDAQ165dQ0hISHTCv/4CapUHQp/zuGdIhVqptuFyitw8rnx2YPOSACRLxqN8PkB68vku9mgDSU8exe3SxdTP3+QwcglhnJnIYRQnFvcj1YIlh5H7PK1egruD1OTJkzFmjLjmT6w3lUaG/PgxIlo2ge33a0ItN4W1xePx09GxnXUX9RSAaDTc1ZPGy1AyCxAgLVmgkz3YRNKTCJs5i0qXLi1EtmrVCmvXrlU2dlDNFtqyGRjVW0j3OCotaqfeimspol8Kdanqh5kf2yyzKQTpSZAEGW4SID25CdAD2dXP3+QwkgecHEbyWAolqQVLDiMBDRkuEHBnkLp48aJ9m171Wgj169fHtm3bxLUQXKiXR7IoC3VHdWgNv6vRC3Wz654KLY91bVZjyoTklrn5lcXbHT3JqgOV4xsESEu+0Y9GaQXpKXZPsHUH2fqD6mP9+vVo3rx5dBT71Py9rsCPe6LjlNBfEelRJ902XE+uTC96dSzs7482za0xI5f05Oh1+pVBgPQkg6K+Zaifv8lhJI81OYzksRRKUguWHEYCGjJcIODqIBUeHo6aNWvi8OHD/KrsU7SrylpAuXLl4nGGDvTpCRzcJlTxVthrGFlyO1Z8nQFKc+hwkoCrenLyMpTcAgRISxboZA82kfQUG/a///5rn2XExm3HERQUBLYwdqZMmRxRwLNnQGdlPaOLJ6PjlNCf4RlRN2grbiULtsezRbAPKItgF7HAItikJ0EKZLhJgPTkJkAPZFc/f5PDSB5wchjJYymUpBYsOYwENGS4QMDVQWrRokV4//33hSt+/vnnseKEBEYyflE+QWv6plCjP8OD0DF4J1Z+F4JMGVVT8oVUZCREwFU9JVQmnbMmAdKSNftdr1aTnuImy176VKtWTTjZrVs3fPHFF+KnaU+fKitetwWunhXS3gnPhnKZ9+G57eU2otmVn8OrA5AurZDM5wzSk891qVcbRHryKn5NF1c/f5PDSBMyTYnIYaQJk/OJ1IIlh5Hz/CiHSMCVQerWzVt4vcDrYAtnOo6qVati//79ptkV7enEz5DqmxmO6uNxRHI0SrcL877Mh2JFyFnEwTgZcEVPTl6CkluEAGnJIh3toWaSnuIHPWDAAMyZM0dIsHfvXtSqVUuIw9MnQPtWwK8/CfGzIkdhfJboF0gNCgFfzwtQPk0XkvmUQXryqe70emNIT17vgkQroH7+JodRorg0JyCHkWZUziVUC5YcRs6xo9SxCTg7SLH1itj6Bps3Kwthvjr8lbvCCxcuoHDhwo4oQ/+GhUbhr+IVkNX/Dq/nkvC+SPrxCLzbxofvcHlr9Qs4qyf9akIlm50AacnsPWis+pOe4u+PR48e2cfvu3fv8kQFChQAWxib7Z4mHMpGEWjfArhxiUf/G5UC+TOc5rOM2Ilx7fwwoKfvbhpBeuLdTwEJBEhPEiDqXIT6+ZscRvJgk8NIHkuhJLVgyWEkoCHDBQLODlLffvst2rRpI1xp0qRJGD16tBBnZOOr/ifQcV8zoYozyu/EkCXFaJFrgYrzhrN6cv4KlMMqBEhLVulpz7ST9JQw5+3bt6NRo0ZComnTpmH48OFCnN349b9A4+pC/MKAsRiVvocQt2WyP6pW8s2XMKQnoavJcJMA6clNgB7Irn7+JoeRPODkMJLHUihJLVhyGAloyHCBgDOD1P379/H666+DvY10HGxW0cmTJ5EiRQpHlKF/9238C0VG1kCWgIe8nn9GZEGq06eRMiV9isahuBhwRk8uXoKyWYQAackiHe2hZpKeEgYdpeyG1rp1a6xbt44ntNls9o0s8ubNy+N4oF8vYP8WbkbYUuCtFNtxPkV02jQBwInVNmTJ7HtjK+mJdz0FJBAgPUmAqHMR6udvchjJg00OI3kshZLUgiWHkYCGDBcIODNI9ejRA19++aVwlSNHjqBSpUpCnFGN3878ib/bdkexJGeEKj7oOB4ZR3QX4shwjYAzenLtCpTLKgRIS1bpac+0k/SUOOebN28if/78YLugOo4mTZpg48aN4gLY7OS1a0CzNx3J7L8v/FOhl+1zbEpXVbFfOolqKP6jdYsDoPiefOogPflUd3q9MaQnr3dBohVQP3+TwyhRXJoTkMNIMyrnEqoFSw4j59hR6tgEtA5SbAHMOnXqCAUMHjwYM2fOFOKMaoTv3IMXQ3ojld9zoYoPslVGxt3fwOfuZoVWes7QqifP1YiuZFYCpCWz9pwx60160tYvn376KYYMGSIk3rlzJ+rVqyfE2Y3B/YHd62PFXwotjE9SjuSOowkd/NC3h295jEhPsbqdItwgQHpyA56Hsqqfv8lhJA86OYzksRRKUguWHEYCGjJcIKBlkHqsLHJZvHhxsLePjiMkJAQXL15E2rQG3ztXWaQbE8YD34ozo1g7HvsFIc0Ph4CgIEez6NdNAlr05OYlKLtFCJCWLNLRHmom6Ukb6BcvXqBMmTL28d2Rgy2Afe7cOSRPntwR9fL32TOg67vAT8fE+FfWN2Fd0Dt4HNgqRntn21Cq+MtZR3EmNlkk6clkHWbw6pKeDN5BSvXUz9/kMJLXX+QwksdSKEktWHIYCWjIcIGAlkFq5MiRYItfqo8dO3agfv366ijjhZmzaMxIYMuqWHX7KzIjUq1di2TFCsY6RxGuE9CiJ9dLp5xWIkBaslJv699W0pN2xt9//z1q164tZJgzZw769esnxNmN58qs3fe7AaeUly9xHKVTH8H15NmRPSVw5JsApEkTRyITRpGeTNhpBq4y6cnAnfOqaurnb3IYyesvchjJYymUpBYsOYwENGS4QCCxQer8+fMoUaKEUPK7776LFStWxF7TQEjlZSMBZ9HusGbIt24S8hYz+OwoLyN05fKJ6cmVMimPNQmQlqzZ73q1mvSknSxbALtly5bYsGEDz8RmF924cQNZs2blcTzAxttdO4AZk4E/b/FoFtgQ1h7dgqfY41qX8cPCT2w+sRsp6UnoZjLcJEB6chOgB7Krn7/JYSQPODmM5LEUSlILlhxGAhoyXCCQ0CAVERGBmjVr4ocffuAlp06dWlnr8lrcN408lZcDoaHA8MHKHPiNQkVCo/wwwjYPZca+jQ5tfGs9BaGhXjQS0pMXq0WXNiEB0pIJO83AVSY9Odc5v/zyCwoWLIhI5gx6dfTp0wfz5s1zmLF/lXsGNKoF3FYWxFYdPQOW4Nv0SrxyLOzvjzbN2Udq5j5IT+buP6PVnvRktB6JXR/18zc5jGLzcTWGHEaukkskn1qw5DBKBBadTpRAQoPU119/jY4dOwplLFu2DJ07dxbiDGU8fQL0VKbHnzsiVIs5i7olWwn/qlWxXFlLwc93llIQ2ultIyE9ebtudH1zESAtmau/jF5b0pPzPfTBBx9g4sSJQsbTp0+jVKlSQpxg3LkDNKgCRITx6McRyVE5/SHcSZYVSZSx99gyG/LkMvcgTHri3UsBCQRITxIg6lyE+vmbHEbyYJPDSB5LoSS1YMlhJKAhwwUC8Q1SDx48QL58+fDPP//wUitXroyDBw8iICCAxxkqcP8+0KktcOuKUC2Hs+h45io4vsaG9OnNfaMqNM5gRnx6Mlg1qTomIEBaMkEnmaiKpCfnO+vRo0fInz8/2P2A46hRowb27dsHf/8EZglt2QyM6u3IYv89G1oGtYK/RaSfDWVDgB0rApR7CSGJqQzSk6m6y/CVJT0Zvoto0WuduogcRjqBJYeRTmAtWmx8g1T//v0xd+5cgcqZM2dQsmRJIc4wBnur2bYx8PefQpX+jUyCLilWY2+actg+zR+VyidwkyvkJMMVAvHpyZWyKI+1CZCWrN3/sltPenKNaFwzjdetW4cWLVrEX6CyBhKGDQJ2fiekmRb1EaZm7mqPm9DBD317mPfTcNKT0LVkuEmA9OQmQA9kVz9/0wwjecDJYSSPpVCSWrA0w0hAQ4YLBOIapE6ePIly5coJpQ0ePBgzZ84U4gxldHkHOHFAqNJfEWnQKvU6nE1ZAMOa+GH0IPPenAoNM7ARl54MXF2qmoEJkJYM3DkmrBrpybVOCw8PR5Uqyuzc48d5Aa+99houXbqEVKlS8bhYgX//VT5NexN48Ds/9Xt4VhTJdkyx/cBG48OLbHijgDln/JKeeLdSQAIB0pMEiDoXoX7+JoeRPNjkMJLHUihJLVhyGAloyHCBQMxBik0zZ5+eMaeR48iYMSOuXr2KoKAgR5TxfovnAcL/T6hXiTRHcStZMIpnVta/XhWAJEmE02ToQCCmnpIlS6bDVahIKxAgLVmhlz3XRtKT66yULuyTAABAAElEQVSPHj2KSpUqCQV88sknGDp0qBAXyzh7FninkRBdPeX3uBCY3x5XOCOwf3UAkiYVkpjCID2ZoptMU0nSk/G7Sv38TQ4jef1FDiN5LIWS1IIlh5GAhgwXCMQcpL766iu89957QkmrV69Gu3bthDhDGYozC81rClXqlmQFNqSrYX+LeWSxDQVeN+dbTKFRJjBi6okcRiboNINWkbRk0I4xabVIT+51XJcuXbB8+XJeSFLFy3P79m1kyZKFx8UKsE/Tqiuzlf+6y08tC++NwdlGcHtEUz+MHGi+2b+kJ96FFJBAgPQkAaLORaifv8lhJA82OYzksRRKUguWHEYCGjJcIKAepPLkyYNChQrh2bNnvKSaNWti7969CS9wyVN7KTBkALBrHb/4w4hUKJD5LML9k8Ls6yTwRpkkoNYTeyNNDiOTdJwBq0laMmCnmLhKpCf3Ou/WzVvIkzcPIiMjeUFDhgzBjBkzuB1nYNpk4Kv5winmNBqSdRii/PyVj9OA72fZUKqEuV7qkJ6ELiXDTQKkJzcBeiC7+vmbHEbygJPDSB5LoSS1YMlhJKAhwwUC6kGKLW65bNkyXoqfsvf8hQsXUKRIER5nuMDlS0DL2kK1Po0cjQlZeqJ0NsWP9LW5d2IRGmYCQ60nchiZoMMMXEXSkoE7x4RVIz2532kffPABJk6cyAti9whXrlzB66+/zuNiBS5dBFrViRX9Q+hbaJt1Lp7bUiJ3GuDI2gCkSBErmWEjSE+G7RpTVoz0ZPxuUz9/k8NIXn+Rw0geS6EktWDJYSSgIcMFAo5B6qeffsKAAcpMHdUxevRoTJo0SRVjsCB709m6KXD5NK/YM2VXtAIZzuJ5ktQ4tsSGfHnM9daSN8SkAYeeWPXJYWTSTjRItUlLBukIH6kG6cn9jnz48CFy5cqFJ0+e8MLY5+rss/UEj8+VGUbzlJlGMY7/huXFW5k34nFAWgx52w9jh5jn0zTSU4zOJNMtAqQnt/B5JLP6+ZscRvKQk8NIHkuhJLVgyWEkoCHDBQJskDp06BB69eqF69ev8xKCg4Nx+fJlpEmjvPoz6rFtKzDifaF2MyLHYlKWHpjayQ89u5jn5lNohIkNuukxcecZrOqkJYN1iMmrQ3qS04Hz589H3759hcJ+/PFHVKxYUYiLZWzeBIzpB0RFf9LG0qwP64DuwZPtn6YdWmBD0ULmeMlDeorVwxThBgHSkxvwPJRV/fxNDiN50MlhJI+lUJJasOQwEtCQ4QIBNkixmUSffvqpkHvdunVo0aKFEGcog23Z+6aymOa/j3i1boeFoFTWQyiTIwm2rwiAjfxFnI2nAnTT4ynSvn8d0pLv97EnW0h6kkP7+fPnKFy4MG7cuMELrFatGg4cOJD4Wods17TubYEXT3leNis4R6bLiPBPgqKZgH3Krmlm2NGU9MS7kAISCJCeJEDUuQj18zc5jOTBJoeRPJZCSWrBksNIQEOGCwTu3LmDvHnzIjQ0lOeuXbs2du3alfjNH8/hhcC4D4DvlgoX7phkJXamr4rjy2zIk8scbymFBviAQTc9PtCJBmkCackgHeEj1SA9yetI9kKpVatWQoE7duxA/fr1hbg4DWVnNdQXZyM5Zgaz9FOU2cHvm2B2MOkpzt6lSBcJkJ5cBOfBbOrnb3IYyQNPDiN5LIWS1IIlh5GAhgwXCPTr1w/z5s0TcrKFrosWLSrEGcrYsxsY1FWo0qHQWmgasgQT3/FDn+40tUiA40GDbno8CNvHL0Va8vEO9nDzSE/ygEdERKBy5co4fvw4L7RUqVI4ceKEMrNXw/jbqglw6RTPywL9bAuxMqg+ApR3PSeX25Arp7Ff+pCehO4jw00CpCc3AXogu/r5mxxG8oCTw0geS6EktWDJYSSgIcNJAufOnUPJkiWFXEOHDsUnn3wixBnKuPMb0LA6EP5/vFrPI22okvYHJA/JjoNrzDGdnVfexwJ00+NjHerF5pCWvAjfBy9NepLbqfv27UOtWrWEQjds2IBmzZoJcXEamzYq6xmJ6yD9E5ECb2Q6bd81rUZeZW2jLwKUWc5x5jZEJOnJEN3gM5UgPRm/K9XP3+QwktdfhnMYsd0d2E5Q7CGZvQXZv38//vjjD3uLBw8ejJkzZyba+k2bNmkbDJWSNm7ciKZNlR2cJB9qwZLDSDJcCxUXqewwVrNmTfuC145mp0uXDr/++iuCgoIcUcb6DQsDWjQCfv1ZqNcQ/7lYmqEx9s+xoWQxY7+VFCrugwbd9Phgp3qpSaQlL4H30cuSnuR2bFRUlN1hxO6lHccbb7yB8+fPK2sQJXFExf2r5MXYUcCmr4XzcyOG48Osfexxiwb4o3Uz43qMSE9C15HhJgHSk5sAPZBd/fxNDiN5wA3nMPLzi/9BkhxG8jqeSjIHgbVr16JtW2XxSdWxZMkSdO0qfuqlOu394HZlV7Th4q5om8LaokvwNPSu5YdJYzVMhfd+K3y6BnTT49Pd69HGkZY8g5t9XuQ4NH1O5Ehssl/Sk/wOO3LkCKpUqSIUvHr1arRr106Ii9NgTqNunYDj+/hptgB2yfTH8L+kGZFSGc7Pf2dDhqD47915Ri8ESE9egO7DlyQ9Gb9zyWGkTx8Z2mFUsGBBlChRAmvWrLG33hWHERsoM2fOHC+9LFmyIHXq1PGed/WEWrA0w8hVitbO9/jxY7z++utgHnLHwf6fOHXqFFKmTOmIMt7vsMHAjrW8XnfCs6F05gNIG5gCp9cFKP+/8VMU8BIBuunxEngfvCxpyf1OPXtX+ew4uES8BT179gxZs2bFkydP7PcrbNZ1YGAgEssXb4EGPkF6kt85bJZR48aNsW3bNl547ty5cfnyZSRLlozHxRtQZjSjcTXh9NbQlugY8nLGf9dqfpj5sTFfBJGehG4jw00CpCc3AXogu/r5m2YYyQNuOIfR7Nmz7U4itpgv++Tm7t27CAkJsbfYFYfR77//juDgYHnENJakFiw5jDRCo2QCgQkTJuDDDz8U4ubPn49u3bppu8kTcnrIYG/BKxUHnv7NLzgOUzE7Uzt8M9Yf9WoZd+o6r7AFAnTTY4FO9lATSUvugZ5zbB4G/Dgei6tNQY8y3eMs7Ntvv0WbNm34OWY/yv0P3vthFGZX+gj9K4jrzPCEJgyQnvTpNPaiqWzZskLhy5cvR6dOyuwhLQf7NG3jV0LKZsk34mDqUvY4o35qTnoSuowMNwmQntwE6IHs6udvchjJA244h1HMppHDKCYRsq1A4NbNW8ibLy/UnyE0bNgQQ4YMQaVKlYzrMDp4EOjTQeii8ql+QMHyubB8tg0JfHEq5CFDXwJ006MvXyuVTlpyvbfZDKFSa2rzAuJzGrF17A4cOMDT5X2vAH4t8Be3z7Tdm+AMJZ7QBAHSk36dxJyOzNnoOPLkyWOfZZQ0aVJHVPy/fysvgWqWAUJf8DS/hOVHhWy7EelnQ4kswPerA5Td1/hpQwRIT4boBp+pBOnJ+F1JDiN9+ogcRvpwhVqwNMNIJ8g+XGyXLl3A3v45DrY45cqVK5EpUyZDO4yi3usKvyO7HdXG+dCSqJ19E86ttiE4mzHXOOCVtVCAbnos1Nk6N5W05B7gL059aZ8p5CglptOIfTZUqFAhx2mgvvJwXysNt2Om5ydMGiA96ddxce24yhxIrVq10nbRb1YBE4cLaWdGjsHELO/Z4+b19keH1saaRUx6ErqLDDcJkJ7cBOiB7Ornb5phJA84OYzksRRKUguWHEYCGjISIXDs2DFUrFhRSDV58mRUqFDBHmfYGUbKuhqRb5WG+nZxuP9sZO/XDP3fM9hrR4Gu9Qy66bFen+vVYtKS+2RjOo3SnvJHppMvnUJs51j2n/2I4SxSp2Pn2QYJ7FNmMx+kJ317r1mzZmA7CTsOtvzD2bNnlZlBGsZo9sl547rAzcuO7AiP8kP11PtxKUUepFKKOL/OhqD0xnk5RHriXUUBCQRITxIg6lyE+vmbHEbyYPu8w4gNjuytym+//YaMGTOiePHiqFu3Ljp06JDgYtjuIlYLlhxG7tK0Tv7IyEhUq1YNbLF2x/Haa6/ZNcy2wWWHUR1G4ZOnImDVXEe18a+yk8pbuX7GwY2pkDw5j6aAAQjQTY8BOsFHqkBaktOR849+jr5HVWvWff8Y2BkaXXgMZxFinldSss0QHjx4oPy9Ne8fXNJTdJfrETp69Kj9HkJd9tatW9GoUSN1VPzhn38C2tRXziu7p706vgnrgt7B4+xWjxp+mD5Og/PJkVnnX9KTzoAtVjzpyfgdrn7+JoeRvP7yeYdRfKjYN9tfffWVsJBkfGnjimeCTOi4f/8+ihQpYk/yyy+/gD3000EEEiPA3vyxt8Tqg32K9vbbb+P06dP26NKlSxtuDSO/iz8j4N3GwuwidhOZdt5YvFXdOG8b1VytHGY3PUbWk5X7xmxtJy3J6bGff/4ZZT6sBLwV/bkZdwppcBaxWvgpi8SdPHmS33vIqZlnSyE96cub7ZjG1kPcv38/v1C5cuVwUFl/0N9fPT+Yn44VCPhkKmzfLObxf4RnxBvZTin2y7F+z8wIFHt5+8vTeCtAevIWed+8LunJ+P3KnEQ5c+a0V5QcRvL6y2cdRmx3qfbt26NMmTLIli2bnRibZbR582YsWrQIbNBkx9q1a9G6dWt72Jl/2I2Z1mPdunX2Hd+0pqd01iQQGhqKzp07g22Z7DjeeOMNzJkzR9t0cUcmD//6KfUuNXYE0jy7z68cpkxT75lzK94Z9ogWuuZUKEAEiAARiJ8Am0U65OAYRFYLjE70PBJIoXqQj2NmEUscGBiISZMm2WdRR2emEBGITeDMmTMYOnSocOKTTz4Bexml5Uip7F5cbrK4llGDFNtwNFVRe/b8aZ5gyoBzigNKS2mUhggQASIgjwD7hLtly5b2AslhJI+rTzqMnj59ap+aHZ9T59ChQ6hVqxbCw8PtN1m3b99GhgwZnKIaX9lxFUIOo7ioUFxMAlu2bMGsWbOEaGYXK1ZMiDOakf+blch+ZJdQLbYQZtYPSyM46zMhngwiQASIABGInwC7we29dSD+Lq2sFxPziMdZVKBAAYwbNw5ZsihbVdFBBBIhwD5979u3L65cucJTsuUaPv30U/ssNR4ZX0B54VphRF+kePYPT/F/Uf5YGjkAC9K3xZ1kWTGs4RVULBP9EoknpAARIAJEQEcC5DDSB65POoy0oFqwYAH69OljT8rCvXr10pKNp6FP0jgKCkgg8M8//4DNJuKLmyplsvW3Vq9ebb+BM+o0WP+1a5Bk2miBwM+hRbGx20YMH0SvFwUwBjKMqicDIaKqaCRAWtIIyolk//77LzIseQOwKbOLHAebaTT21eLXjjjlt2vXrmCzQ9j6Rb5wkJ4804u7du1C06ZNhYsdOHAg1oYbQgKVETBjGmyrF6liooM/hlbHxNemYs2mLMrn89Hx3giRnrxB3XevSXoyft/SJ2n69JFlHUbswZwtgs0+TWvTpg3WrFkjlbB60S1a9FoqWp8srHz58jhx4oTQtkuXLtmdSCySDVI//vij/bxhFr2+fAlRrepA+SPC6x2qfIrWKugAVu3Oj1S+8fzC2+ZLAUPqyZcAW6gtpCX5nR1z1zR+hThmGLG1Z6pXr86TmD1AevJMD7JZRuwTNLYpjONo3rw51q9f7zAT/v3jHtC0DvAkthOTZTwcWhOXxyzHe529uwA26SnhbqSzzhEgPTnHyxup1c/f9EmavB6wrMOIIQwJCcFd5Vts9rDOtjKXeagFSw4jmWR9ryy2jlbMha7ZdPG5c6N3HDPcIKU4sPB2beD3X4UOmRI1HnkmdkWb5jS7SABjMMNwejIYH6qOdgKkJe2stKSM5SxKZA2jQYMG2T8l0lK2GdKQnjzXS8w55Fjrw3FV9pka+8RR06HMjMZc5TP6NV8om6ZFvzhiedknarmzXsfF9QFIn177mp+arutEItKTE7AoaaIESE+JIvJ6AvXzNzmM5HWHpR1G2bNnx++//04OI3l6opJcIBDXelh37tyxOzQdxRlukPpCmYo+62NH9ey/bFe0OYU/wuHvkiAgQDhFhsEIGE5PBuND1dFOgLSknVViKWM5ixwzihLYJS1dunRgN8Vs51dfOEhPnutFttFG/vz5wdbxdByDBw/GzJkzHaa23+vXgakTgSO7hfSvpz2Ntm9nwsejvDfLiPQkdAkZbhIgPbkJ0APZyWGkD2TLOoz+/vtv+0LX7JM0tksam+Uh81ALlmYYySTrW2WxrW3feuutWI1y7OLnOGG4QaqRMrvoxiVH9XAptDCqBm/BuolJ8WZVml3EwRg0YDg9GZQTVStxAqSlxBlpSRGvs+hV5iTNUyOssmpBGIczSTnvS5+lkZ60qEVemvnz59sXwHaUGKC87bl37559yQZHnKZf5RM3FH1NSNowxVYcS1UMJ5fbkCeXd2YZkZ6ELiHDTQKkJzcBeiC7+vmbZhjJA25Zh9Hnn3+O3r1720myAdMRloVWLVhyGMmi6nvlpEiRAi9evBAaxrZWjrkzmqEGqWfPEFX2dfghegp6y2TrEV6iDDYuCVAW6RaaQ4YBCRhKTwbkQ1XSToC0pJ1VfCljOotqPCqOgxP28eQVK1a0v9Ta9eduvPfDKB6PV04jX/osjfQU3b2eCLGXp1mzZgWbbeQ42G5pTFNOHzUrAX/e4tk+xHTMzdQGjYv5YcUc78wyIj3x7qCABAKkJwkQdS5C/fxNDiN5sH3OYcTWJHr8+DEKFiwYL6VDhw6hdu3aCAsLQ/LkycEcOpkzZ443vSsn1IIlh5ErBH0/z7Vr1+JcKyDm7CJGwlCD1KmTQKfo3VXClYWus2W8iv0LA1G0EHmLzKBcQ+nJDMCojvESIC3Fi0bTiZjOosXVpuCNFwXRvn17/Pbbb+jRowdmzZqFwMBAe3kx0wcej8TufptRpUoVTdczeiLSk+d7aNSoUZg6dSq/MHMg3bhxw35/zCO1BLq+Cxzfz1Mqc44wO3IUPs7SE/vnBKCk4jjy9EF68jRx374e6cn4/at+/iaHkbz+MpzD6OzZs3YHjqOJbDezbt262c1GjRrxsON8zG1BT506hbJly6J+/fpo0qQJChUqZP/0jKVnN1+bNm3CokWL7LujsTi2bXm7du1YUOqhFiw5jKSi9ZnCmK6XLl0qtIc5O1OnTi3EMcNIg9TjmYuQZmn0+kUXQ4vg8xY7MG+qd94gxoJFEYkSMJKeEq0sJTA0AdKS691z9u45lFqjfN776mDOoh5lututR48egW1zzu5xYq5zF9NpdKbtXpQMLuEoxtS/pCfPd991ZQ2ivHnzChdmOwezHYSdOuYrG3UsiHY8OfKO8ZuByxVaYf0Xnp+BTHpy9AL9yiBAepJBUd8y1M/f5DCSx9pwDqMhQ4Y4teNHzNkYDodRYojY27ply5bZ1y9KLK0r59WCJYeRKwR9O8/FixdRpEgRoZELFixAr169hDiHYZhBSlmn4M+SNZEl/BdH1bAqvAeq7/8I2UM8//aQV4ICThEwjJ6cqjUlNiIB0pJ7vTLn2DwM+HE81M4iLSU6nEazK32E/hX6asliijSkJ+90U4cOHewvUB1XZy9e2e7B/v5OrEn4/DnQuwdw4oCjGPvv0dCqaBCyEjum+6NiOSfKE0pxzSA9ucaNcsVNgPQUNxcjxaqfv8lhJK9nfM5h9ExZX4UtJHzy5EkcP37cPluJ7QDBPj/Lli0bypUrZ19kmC10HRQUJI9kjJLUgiWHUQw4ZMZ6Y8ymgP/666/8s4OYiIwySN3f+iMyjWwlVG9t9VVos6CGEEeGsQkYRU/GpkS100KAtKSFUsJp2EwjV2YIuZov4dp49yzpyTv8jx49ikqVlDWIVMcPP/yAqlWrqmI0BNni15/OAJbN5okfRqRG3qw/o3x2YOfXnp1lRHri3UABCQRITxIg6lyE+vmbHEbyYBvOYSSvad4tSS1Ychh5ty+MdvXOnTtjxYoVQrWWLFmCrl27CnFqwyiD1KVK76HQP9t51a6H5UHaHw8hQ0bPvjXkFaCASwSMoieXKk+ZDEWAtGSo7jB9ZUhP3ulCNlufrYP1448/8gq4vIPwnd+AuhV4OSyQP90ZPEiSAes/9kfNap67XyA9Cd1AhpsESE9uAvRAdvXzNzmM5AEnh5E8lkJJasGSw0hAY3kj5noUDMhzZSo3W4A9vsMIg9T/Vu9B5kldhCruKzMNb614R4gjw/gEjKAn41OiGmohQFrSQonSaCVAetJKSn66DRs2oEWLFkLBbHOO/PnzC3GJGmyWUYk8QEQYT9otyQpsSFcDRTMBB9cGKJ+68VO6BkhPuuK1XOGkJ+N3ufr5mxxG8vqLHEbyWAolqQVLDiMBjaUNtvB6jhw5YjGIuRZXzAReH6T+/BPPlC1zA/GCV+1pZFKE7zmPdK+l4XEUMAcBr+vJHJiolhoIkJY0QKIkmgmQnjSjkp4wNDQU+fLls28Q4yic7aA2efJkh6n9t11L4MJRnn5vaEO0Dllgt1eN9keDOp7xGJGeeBdQQAIB0pMEiDoXoX7+JoeRPNjkMJLHUihJLVhyGAloLG2MGzcO48ePFxg8ffoUKVOmFOJiGl4dpI4qU9S7i+sWsfrtqT4fdRY0jVlVsk1AwKt6MgEfqqJ2AqQl7awoZeIESE+JM9IzxezZszFw4EB+CbZBzB9//BHn7q08UVyBb9cC4wfzM+FRfiiQ/iweJkmPfOmAY+sDYPPAxqqkJ94FFJBAgPQkAaLORaifv8lhJA82OYzksRRKUguWHEYCGssaT548sS+8/u+//3IGU6dOxYgRI7gdX8Brg9SZ08C7jWNVa094M1Q4ORdp0tDOaLHgmCDCa3oyARuqonMESEvO8aLUCRMgPSXMR++z7N6VbRATyT4re3UsX74cnTp1cpjafp8+ASoWBiIjePopUR9jeuaX5awc5Y+GdfWfZUR64vgpIIEA6UkCRJ2LUD9/k8NIHmxyGMljKZSkFiw5jAQ0ljXYwtbdu3fn7Wfb1d69exdZsmThcfEFvDZItVdmFp2PXgST1e9eeCZs63sIPfqlja+6FG9wAl7Tk8G5UPWcJ0Bacp4Z5YifAOkpfjaeOtOrVy8sXLiQX65EiRI4ffq0su6Qkw6e4UOA7Wt4OdfDcqN08AHF9kPhjMAP3+q/lhHpieOngAQCpCcJEHUuQv38TQ4jebDJYSSPpVCSWrDkMBLQWNKIiIhA4cKFcfXqVd7+3r17Y/78+dxOKOCVQeqnC0Db+rGq1T71VizeVwqpEv6KLlY+ijAOAa/oyTjNp5pIJEBakgiTigLpyfsiOHXqFMqWLStU5MiRI6hUqZIQl6hx+hTQsYmQrGGKrfgxVTF73Lpx/nirhpNOKKG0xA3SU+KMKIV2AqQn7ay8lVL9/E0OI3m9QA4jeSyFktSCJYeRgMaSxq5du1C/vuh8uXDhAooWLaqJh8cHqd9uA/Uqxqpb4bQn0KdbVvTu5oHFB2JdnSJkEfC4nmRVnMoxHAHSkuG6xNQVIj15v/vYJhyVK1fG0aPRi1Z37twZy5Ytc65ySjmoqdxH/O83nm9zaBt0Dplut0tnA/auDoCfjl+2k544egpIIEB6kgBR5yLUz9/kMJIHmxxG8lgKJakFSw4jAY0ljSZNmmDLli287cx5tGPHDm4nFvDoIPXLNaDpm7GqNDlqAuZl64irmwKUBTBjnaYIExHwqJ5MxIWq6jwB0pLzzChH/ARIT/Gz8eSZtWvXom3btvySfopXh+3yGhISwuM0Bb78AvhsnJB0kP98LM/QyB63dYo/qlTUb5YR6UlAT4abBEhPbgL0QHb18zc5jOQBJ4eRPJZCSWrBksNIQGM549dff7VvVatuOJtxVLduXXVUgmGPDlLjPgC+WyrU53FEchTIdBaDW6fEsH40u0iAY0LDo3oyIR+qsnYCpCXtrChl4gRIT4kz8kSKZ8+e2Z1Djx494pebNm0ahg8fzm1NgQf3geollaTKbKNXB1tOe7D/AqzI0BBVcwGbl+k3y4j05KBOvzIIkJ5kUNS3DPXzNzmM5LEmh5E8lkJJasGSw0hAYznj448/xkcffcTbnSNHDvz3v/9FkiRJeFxiAY8OUo1qAzcuCVWaHzEU47L1w5X1NmQI0nH+uHBVMvQi4FE96dUIKtcQBEhLhugGn6kE6ck4XTl+/HiMGzeOVyhr1qxg97NJkyblcZoCH4wGNqwQkjKnUX/bIqwKqoe9n/mjTEl9ZhmRngTsZLhJgPTkJkAPZFc/f5PDSB5wchjJYymUpBYsOYwENJYyXrx4gddeew0PHjzg7Z4xYwaGDFF2D3Hi8NggpdQXZfIpLwOj3wayauZMfwGdGqbDx6NodpET3WbYpB7Tk2EJUMVkESAtySJJ5TACpCfj6ODWzVvIlTuXUKHdu3ejTp06QlyiRlgYMGaksGMayxMa5YfyaY6gQIkQrFkYkGgxriQgPblCjfLER4D0FB8Z48Srn7/JYSSvX8hhJI+lUJJasOQwEtBYyti0aROaNWvG2+zqOgAeG6TOngXeebm2gKPSpVMfwc3k2XFhtQ0hwTS7yMHFzL8e05OZIVHdNREgLWnCRIk0EiA9aQTloWRNmzbF5s2b+dU6dOiAlStXcltzgL2EmqEsdr18jpBl4KtP0459YUOB/PLvL0hPAm4y3CRAenIToAeyq5+/yWEkDzg5jOSxFEpSC5YcRgIaSxlscWu2XpHjaN++PVatWuUwNf96bJD6fD4wbzKv19WwAqgQvAedq/jhs4k0u4iDMXnAY3oyOSeqfuIESEuJM6IU2gmQnrSz8kRK5ixiTiPH4e/vj3v37iFz5syOKO2/zGlUuypw7wbP43AYdavuhxnj5d9jkJ44agpIIEB6kgBR5yLUz9/kMJIHmxxG8lgKJakFSw4jAY1ljGvXrqFAgQJCe/ft24eaNWsKcVoMjw1S7VoCF6K30l0e3guDso3EqaU25M0j/+2flrZTGvkEPKYn+VWnEg1GgLRksA4xeXVIT8bqwOfPnyNLlix48uQJr9jChQvRs2dPbjsVqFsduPNfnqWfbSFWBtWHTbm9uPydDZkyyr3PID1x1BSQQID0JAGizkWon7/JYSQPNjmM5LEUSlILlhxGAhrLGB988AEmTpzI25snTx4wJ5LN5vxbNI8MUsqNYVTZ1+EXxZajfHl0TLIS/tWrYfls5+vsKIN+jUfAI3oyXrOpRjoQIC3pANXCRZKejNf5I0eOBNshzXGUKFECZ86cAfvE3umjZiXgz1s824PwtBgUuADb0lbBB638MLiP3HsN0hNHTQEJBEhPEiDqXIT6+ZscRvJgk8NIHkuhJLVgyWEkoLGEwQaV4OBgPHz4kLd31qxZGDBgALedCXhkkPrxCNCjNa9WuLIg5WsZLmHTZ6lQvow+O5jwi1HAowQ8oiePtogu5i0CpCVvkffN65KejNevF85fQPESxYWKnT59GqVKlRLiNBmdOwAnD8ZKOsh/PjZkaYQr2wKQInms0y5HkJ5cRkcZ4yBAeooDisGi1M/f5DCS1znkMJLHUihJLVhyGAloLGHs2LEDDRs25G1lb+Lu3r0Lti2tK4fug1SkMquocV3gxiVevXOhpdG/xAYc+jZAeZPIoyngAwR015MPMKImaCNAWtLGiVJpI0B60sbJk6milLWHypUrh1OnTvHLsp1e2Y6vTh9XrgDt31a2w3smZL0Rlgulgg9hQT9/tGsh7wUV6UnATIabBEhPbgL0QHb18zc5jOQBJ4eRPJZCSWrBksNIQGMJo23btli7di1va+vWrQWbn9AY0H2Q2rAO+ECc/TQ/YiiyTR+A5o3l3bxpbC4l05mA7nrSuf5UvHEIkJaM0xe+UBPSkzF78csvv0SPHj145QIDA3H//n2wX6ePB/eBkcOAo3uFrMXSHEOyLNlwfEMAlLW1pRykJykYqZBXBEhPxpeC+vmbHEby+oscRvJYCiWpBUsOIwGNzxt//PGH/XM09lbOcWzbtk2YceSI1/qr6yDFZhexnUv+uMmr8zQyKepnOYrvd2dFsmQ8mgI+QkBXPfkII2qGNgKkJW2cKJU2AqQnbZw8nerBgwf2xa8j2f3Cq2P9+vVo3ry5w3Tul5VTrhDwPHox7Q/8pmNexjbYMMEfb1aV4zEiPTnXLZQ6YQKkp4T5GOGs+vmbHEbyeoQcRvJYCiWpBUsOIwGNzxsLFixAnz59eDuDgoLsn6Mlc8PzousgFcfsol4BX6JgrzoY8L7cBSg5FAp4lYCuevJqy+jiniZAWvI0cd++HunJuP3bpUsXLF++nFewWbNm2LBhA7edDgwbDOyInol9PLQy6oWsRn3Fj7R6QYDTxcWVgfQUFxWKc5UA6clVcp7Lp37+JoeRPO7kMJLHUihJLVhyGAlofNpgs4pKliyJ8+fP83aOHj0akyZN4rYrAd0Gqf8cBt5vB6hmQ10Lex2Vgvfg8roA6VvcutJ2yiOfgG56kl9VKtHgBEhLBu8gk1WP9GTcDtuzZw/q1lXWOlQd9+7dc3ltRuzdAwzswktjc5dKKJ+l/ZYsG86vtCFHdvcXTyQ9cbwUkECA9CQBos5FqJ+/yWEkDzY5jOSxFEpSC5YcRgIanzbOnTtndxipG3nhwgUULVpUHeV0WJdBSrnRQ4MqQOgLoT79bAuRpEkDfDqBZhcJYHzI0EVPPsSHmqKdAGlJOytKmTgB0lPijLyVIjQ0FNmyZRN2f124cCF69uzpWpWU3WRRXplOFBZ9D/JXRBoMTPE5CneqipED3b8HIT251jWUK24CpKe4uRgpVv38TQ4jeT1DDiN5LIWS1IIlh5GAxqeNcePGYfz48byNZcuWxfHjx5Vdxtx7UyZ9kAoPB9oqaw9cPs3rygJrwjqjV/A4HFamgxcp5F6dhYLJMBQB6XoyVOuoMp4kQFryJG3fvxbpydh9PGrUKEydOpVXku2eduzYMdfvcT4YDWxYwctzBCYHzMCQE+3cXkOR9OQgSr8yCJCeZFDUtwz18zc5jOSxJoeRPJZCSWrBksNIQOOzBlsMMnfu3Lh9+zZv49y5c9G3b19uuxqQPkjNmQUs+kSozs7QpmgfPAulg/2wd3WAcgMonCbDhwhI15MPsaGmOEeAtOQcL0qdMAHSU8J8vH32zJkzKF26tFCNS5cu4Y033hDiNBt//QU0ehN4rPyqjkcRgfjh06to3MC9xa9JTyqoFHSbAOnJbYS6F6B+/iaHkTzc5DCSx1IoSS1YchgJaHzWOHHiBMqXLy+07+aNm8iZK6cQ54ohdZBStsLFmyWFdYt+Cw9Gucz78MIWiEUD/NG6mXs3aa60kfJ4joBUPXmu2nQlAxIgLRmwU0xcJdKTsTuPvRgrVqwYLl68yCs6YcIEjB07lttOB5jTaKCyUcgZZU3FVwfbqbVtuV+wbYV7i1+TnhxE6VcGAdKTDIr6lqF+/iaHkTzW5DCSx1IoSS1YchgJaHzWYItbT5kyhbevevXqOHjwILfdCUgdpOYqs4sWRs8uCovyQ4OUO3AqZSGkUPxE/90WgMBAd2pLeY1OQKqejN5Yqp+uBEhLuuK1XOGkJ+N3+axZszBo0CBe0Rw5cuD69euw2dxYcyjGfcnJ0IqoE7IGx76woUB+16c7k554N1FAAgHSkwSIOhehfv4mh5E82OQwksdSKEktWHIYCWh80ghX1gTKnj072B8nx7F48WL06NHDYbr1K22QUhatRKViwPMnvD7fhnVEz+AJdrtPbT9MHOPGTR8vlQJGJiBNT0ZuJNXNIwRISx7BbJmLkJ6M39W3bt5Crty5hIoePnwYVaoom2i4enTtCBzfx3N/Gd4Pw7INhbv3JKQnjpQCEgiQniRA1LkI9fM3OYzkwSaHkTyWQklqwZLDSEDjk8aRI0di3SzduXMHISEhUtorbZDavAkYrUz9Vh2VUx3ApRR57DFHF9tQ8HXX3+apiqWggQlI05OB20hV8wwB0pJnOFvlKqQnc/R0nTp1sHfvXl7ZwYMHY+bMmdx2OtCuJXDhKM+2JLwvhmYbhmTKrOcb2wOQIgU/5VSA9OQULkqcCAHSUyKADHBa/fxNDiN5HUIOI3kshZLUgiWHkYDGJ42hQ4cKN0t169bFrl27pLVV2iDV9V3lLd5+Xq+joVXRIGSl3S6r+Lb2rHJvvQBeMAUMTUCangzdSqqcJwiQljxB2TrXID2Zo69XrFiBzp0788oGBQXh3r17SJo0KY9zKvDJNGD5HJ7lQXhaFMh6FpF+NqwY7u/y4tekJ46UAhIIkJ4kQNS5CPXzNzmM5MEmh5E8lkJJasGSw0hA43NGqPKZV7Zs2fDw4UPetmXLlgk3U/yEiwFpg1T5IsDTv3kt+toWYVVQPbv9xSB/tGxCi11zOD4ckKYnH2ZETdNGgLSkjROl0kaA9KSNk7dTqe9xHXVhazaytRtdOpQ1kPB2VSFrf9vn+DqoAWrlB777wrWXWaQnASkZbhIgPbkJ0APZ1X+byGEkDzg5jOSxFEpSC5YcRgIanzN++OGHWDdJsv9ISRmknijrFlUoKPCvkOoQrqbIZV/s+lc3pn0LhZJheAJS9GT4VlIFPUGAtOQJyta5BunJPH3duHFjbN26lVd44MCB+Oyzz7jtdKDF28CVMzxbqLIhR5dkq7EjbSVc/MaG4GzOfy5PeuI4KSCBAOlJAkSdi1A/f8t+FtO56oYunhxGOnWPWrDkMNIJskGKHTVqFKZOncpr07BhQ2zbto3bMgJSBqmfLgBt6/PqhCs3Y1ky/VeZ8h2AHjX8MH0cLXbN4fh4QIqefJwRNU8bAdKSNk6UShsB0pM2TkZItXLlSrz7rvKZ+6sjXbp09o0/XP4sbbvifBr+vqM4+++zyACUSn8MfbtkRt8ezt+jkJ4EnGS4SYD05CZAD2RXP3+Tw0gecHIYyWMplKQWLDmMBDQ+ZURGRiJv3ry4efMmb9eXX36Jbt26cVtGQMog9b2yQOWAzrw6v4UHo1i2l4tM7vnUH2VL0edoHI6PB6ToyccZUfO0ESAtaeNEqbQRID1p42SEVPfv30eWLFkQFRXFq7N//368+eab3HYqwMoZMRTYvkbI9knkB/gmf3ec2hQAfydvU0hPAkoy3CRAenIToAeyq5+/yWEkDzg5jOSxFEpSC5YcRgIanzIuXLiA4sWLC21izqOcOXMKce4aUgaptd8AHys3Y6+Os6FlUDNkPbKnBM5vdf5GzFEO/ZqPgBQ9ma/ZVGMdCJCWdIBq4SJJT+bq/KZNm2Lz5s280v369cOcOdGLV/MTWgPKSzj0fg84vJPnOBZaBfVDVuH7z/xRuqRzHiPSE8dIAQkESE8SIOpchPr5mxxG8mCTw0geS6EktWDJYSSg8SljxowZGDZsGG9T2bJlceLECW7LCkgZpObPBRZEfzr3fWgDtAr5HB+29sOg3s5P9ZbVNirH8wSk6Mnz1aYrGpAAacmAnWLiKpGezNV5q1evRocOHXilU6dODTbzKFmyZDzO6cDBA0Cfd3g2tpZR/qDzaFMrndOfzpOeOEYKSCBAepIAUeci1M/f5DCSB5scRvJYCiWpBUsOIwGNTxnly5cXHETTp08XHEiyGitlkBr/IfDtEl6lNWGd0St4PM59ZUPOHM4vJskLooDpCEjRk+laTRXWgwBpSQ+q1i2T9GSuvv/rr7+QKVMm4bO077//Hm+99ZbrDXn69OUGHapP3eZFDMPE4L64sSMAKZJrL5r0pJ0VpUycAOkpcUbeTqF+/iaHkbzeIIeRPJZCSWrBksNIQOMzxu3bt2N9enb+3HkUK15MehulDFLtWgIXXq5ZxCo4N2I4dpXrg+1fubZdrfRGUoEeIyBFTx6rLV3IyARIS0buHfPVjfRkvj5r3rw5Nm7cyCvep08fzJs3j9suBYYPEdYyehyRHAUyncWyj1KhXi3tn6WRnlyiT5niIUB6igeMgaLVz9/kMJLXMeQwksdSKEktWHIYCWh8xli2bBm6du3K25MjRw7cuHFDWZRR+80Mz5xIwO1B6pjiKOqmOIxUR7ckK1Drg5po10J+fVWXoaABCbitJwO2iarkHQKkJe9w99Wrkp7M17Nr1qxBu3bteMXZbmnsHjhJkiQ8zunA9evA21WFbFOixuNmgy5YNEP7J/SkJwEhGW4SID25CdAD2dXP3+QwkgecHEbyWAolqQVLDiMBjc8YTZo0wZYtW3h7Ro4ciSlTpnBbZsCtQeqff4C6VYAnD3mV7G/rsvyEK1sDkTYNj6aARQi4pSeLMKJmaiNAWtLGiVJpI0B60sbJSKkePHhg/yxNXacjR46gUqVK6ijnwz2V3Wb/s4vn+zG0Oppk/wo3twUgpbJZh5aD9KSFEqXRSoD0pJWU99Kpn7/JYSSvH8hhJI+lUJJasOQwEtD4hPHo0SNkyJABkWxHj1fHoUOHUK1aNYcp9dflQYqtATCoH7A3ero4q9hHmIZbtdph+Wztb+qkNogK8yoBl/Xk1VrTxY1IgLRkxF4xb51IT+bsu3r16mH37t288mPHjsWECRO47VKgbQvgp2M867dhHdEzeAK+GuGPt+trmxlNeuL4KCCBAOlJAkSdi1A/f5PDSB5schjJYymUpBYsOYwEND5hbN26FY0bN+ZtCQwMxN9//42kSZPyOJkBlwepvXuAgV2EqhwJrYFGIcuxfLgNTRpou+kSCiDD9ARc1pPpW04NkE2AtCSbqLXLIz2Zs/8XL16Mnj178srnyZMHv/zyi+uf6D9/DpTJr5SnvPR6dXRO8jU2p6uGxsX8sGKOtpddpCcHPfqVQYD0JIOivmWon7/JYSSPNTmM5LEUSlILlhxGAhqfMPr374+5c5Vt6l8dXbp0wdKlSx2m9F+XBil2w/VWBeCfB7w+7FO0skFH8FeyjLixNQCpU/FTFLAQAZf0ZCE+1FTtBEhL2llRysQJkJ4SZ2TEFDdv3kTu3LmFqv38888oXLiwEKfZUD5zQ/XiQvICaU/jf0kzgrmKft0SoOlzetKTgJAMNwmQntwE6IHs6udvchjJA04OI3kshZLUgiWHkYDG9Ab7DC0kJAR//PEHbwtb9LFNmzbclh1waZCaMwtY9IlQlb62RVgVVA9NivvR52gCGWsZLunJWoiotRoJkJY0gqJkmgiQnjRhMmSi0qVL48yZM7xuM2fOxODBg7ntVCAiAiiWU8kSPcOoZuAenE1ZwF7Ml4P90aJx4jOkSU9OUafEiRAgPSUCyACn1c/f5DCS1yHkMJLHUihJLVhyGAloTG/89NNPKFasmNCOu3fvIlu2bEKcTMPpQUqpD+qUV+61otdYOhpaFQ1CvlaqpUznHu6PxvQ5mswuMlVZTuvJVK2jynqSAGnJk7R9/1qkJ/P28fTp0zFixAjegPLly+PYseg1iPgJrYGKyn3W4794ascnaSyibkFgzcIAfi6+AOkpPjIU7woB0pMr1DybR/38TQ4jeezJYSSPpVCSWrDkMBLQmN6YPXs2Bg4cyNtRrlw5HD9+nNt6BJwepEYrN22bV/KqhEf5oWrqA7iSIjdsfqDP0TgZawac1pM1MVGrNRAgLWmAREk0EyA9aUZluIQXzl9A8RLiZ2Ru3f82awhcO8fbOTVqHKZlfrkmI5tbdEP5LC1NGn46zgDpKU4sFOkiAdKTi+A8mE39/E0OI3ngyWEkj6VQklqwbg2YQqlkGIFAnTp1sHfvXl6VyZMnY9SoUdzWI+DUIHXrFtCgslKN6KncK8Lfx8BsL+vYVPkcbRntjqZHN5mmTKf0ZJpWUUW9QYC05A3qvntN0pN5+5Z9rs/WMbp9+zZvxJIlS9C1a1duOxXo/R5waLuQZXVYVwzMOgJh/sk17ZZGehLwkeEmAdKTmwA9kF39/E0OI3nAyWEkj6VQklqw5DAS0JjaYDuhZciQAVFsu/pXB5tyzaZe63k4NUgNGQDsWser8ywyAEWCTuPvJOnscfQ5Gkdj2YBTerIsJWq4FgKkJS2UKI1WAqQnraSMmY69PJs6dSqvXKNGjcB2lXXpmDwBWLUwVtYpUR9jeuZOaFvWD59/kvBuaaSnWPgowg0CpCc34Hkoq/r5mxxG8qCTw0geS6EktWDJYSSgMbXBbnwaN27M25AyZUo8fPgQSZMm5XF6BDQPUkpdULWoUIUFEYMxJqviRFIONo37JtsdLbXdpH8sSkCznizKh5qtnQBpSTsrSpk4AdJT4oyMnOLw4cOoVq0ar6Kfnx8eKDueBQUF8TjNgfPK52jtlc/SYhyHQ2uiccgyJFNuaG7vClDuv2IkUJmkJxUMCrpNgPTkNkLdC1A/f5PDSB5uchjJYymUpBYsOYwENKY2+vXrh3nz5vE2dOnSBUuXLuW2XgHNg9TpU0DHJrwaYYqLKE/6C3ga8NJDVCs/8N0XiS8UyQuggE8S0Kwnn2w9NUomAdKSTJpUFunJ3BoIDQ1FxowZ8eTJE96QTZs2o0mT6Bdt/ISWwJnTwLti3uthuVE6+KA995Yp/qhakb0Ki/sgPcXNhWJdI0B6co2bJ3Opn7/JYSSPPDmM5LEUSlILlhxGAhrTGuz7/ODgYLA/QI5jzZo1aNOmjcPU7VfzILXve6B/J16P3yOyo0jWI9z+7D0/dG6f8BRunpgCPktAs558lgA1TBYB0pIsklQOI0B6Mr8OunfvDrZ2kePo3bs35s+f7zCd/z17FninEc8XqmzikT/oPB4HpEWf2n6YOCb+exrSE8dGAQkESE8SIOpchPr5mxxG8mCTw0geS6EktWDJYSSgMa3x888/o2hR8XOvu8r29dmyZdO9TZoHqfXK2kUfvvz8jFXqp9BiqBYSvX7A+ZU25MiubJNGh6UJaNaTpSlR47UQIC1poURptBIgPWklZdx03333HVq3bs0rmClTJty7dw82W/yOHZ44rsCjv4HKRYQz34Z1RM/gCciSAri0PQD+8UwyIj0J2MhwkwDpyU2AHsiufv4mh5E84OQwksdSKEktWHIYCWhMayxcuBC9evXi9S9btixOnDjBbT0DmgepLxcDn43nVXF8688iCipLCBzdQJ+jcTgWDmjWk4UZUdO1ESAtaeNEqbQRID1p42TkVOr7X0c9zyqzhEqUKOEwnf8d2BfYu5Hni1RCNVLuw0+B+XB4gQ1FCsX9Ioz0xJFRQAIB0pMEiDoXof77Qw4jebDJYSSPpVCSWrDkMBLQmNZo0aIFNmzYwOs/btw4fPTRR9zWM6B5kJqgOIvWKE6jV8f6sA7oHjzZbo1s5ocRA1x8w+cokH59goBmPflEa6kRehIgLelJ13plk558o8+rVKmCI0eiP4f/9NNPMWjQINcbx5YCqK3sRhsRxstYF/YOegRPwkdt/DCwV9z3NqQnjosCEgiQniRA1LkI9fM3OYzkwSaHkTyWQklqwZLDSEBjSuPFixdIkyYNwsKib1b279+PN9980yPt0TxI9eoB/LCD12luxHB8mLWP3d43y4ZSJeJ+C8czUMASBDTryRI0qJHuECAtuUOP8sYkQHqKScSc9ieffILhw4fzyteoUQMHDhzgtkuB+XOBBVN51v+L8sfrQefwRq602LUy7tnTpCeOiwISCJCeJEDUuQj18zc5jOTBJoeRPJZCSWrBksNIQGNK4+jRo6hUqRKvu7/ywfw///yDVKlS8Tg9A5oHqcb1gF9/4lXZHtoc74R8hjTKvdT13QHKGgL8FAUsTECznizMiJqujQBpSRsnSqWNAOlJGyejpzp16hTYZ/vq48GDB8iQIYM6yrmwkh81lM/aoqJ4vmlRH2Fa5q64vjkA6dLyaB4gPXEUFJBAgPQkAaLORaifv8lhJA82OYzksRRKUguWHEYCGlMaM2bMwLBhw3jd69Wrh507d3Jb74DmQapYLmHKNqtX+ky30KmyH2ZNIm+R3v1klvI168ksDaJ6eo0Aaclr6H3ywqQn3+jW8PBwZMyY0f5izdGiXbt2oW7dug7Ttd+hA4Gd3/G8V0ILomLIbqwa448GtWOvfE164qgoIIEA6UkCRJ2LUD9/k8NIHmxyGMljKZSkFiw5jAQ0pjTefvttbNu2jdedOZCGDBnCbb0DmgepwiGxqsIcRsuH+6NJg9g3U7ESU4QlCGjWkyVoUCPdIUBacoce5Y1JgPQUk4h57XfeeQerVq3iDWBrPrK1H906Tp8COjYRiiiR5ijq1A3B9HGxX4qRngRUZLhJgPTkJkAPZFc/f5PDSB5wchjJYymUpBYsOYwENKYz2ADBPj1jb8wcB1vMUf2JmiNer1/Ng1QMh9HJ0IqoE7IG176zIVMmWr9Ir/4xW7ma9WS2hlF9PU6AtORx5D59QdKT73Tv4sWL0bNnT96gqlWr4ocffuC2S4GICKBcIeDFU559IiZiVY53cWlHAPxi3OaQnjgmCkggQHqSAFHnItTP3+QwkgebHEbyWAolqQVLDiMBjemMmN/is/WLHj9+jJQpU3qsLZoGKfZdf+WSwD/3eb2mRo3DtkJd8J91cS8IyRNSwFIENOnJUkSosa4SIC25So7yxUWA9BQXFXPGnT9/HiVKKGsOvTr8FG/Oo0eP7BuIOOJc+h0zEtj0Nc96JLQGGoWswMmlNuTLI3qMSE8cEwUkECA9SYCocxHq529yGMmDTQ4jeSyFktSCJYeRgMZ0xvz589G3b19eb7YzGtshzZOHpkHqwnmgXQOhWpVSHUSD1nkwdkjsqdpCQjIsRUCTnixFhBrrKgHSkqvkKF9cBEhPcVExZxzbVZbNzg4NDeUNYDulsR3T3Dq+3wsM6MyLeBFpw2uZrmB6z2To0kG81yE9cUwUkECA9CQBos5FqJ+/yWEkDzY5jOSxFEpSC5YcRgIa0xlt27bF2rVreb0nTJiAsWPHctsTAU2D1OgRwOaVvDq3wl5DieDD2DLZhqqVaP0iDoYC0KQn4kQENBAgLWmAREk0EyA9aUZlioTNmzfHxo0beV2nTp2KESOUexV3DmWGNyq+IZTQMtl6JK1QBqsXiLOpSU8CJjLcJEB6chOgB7Krn7/JYSQPODmM5LEUSlILlhxGAhpTGRHK9/Jp06bFv//+y+u9d+9e1KpVi9ueCCQ6SD1VvuevWBiIjF5naXrUh5ieuRtuK9/1BwZ6opZ0DbMQSFRPZmkI1dPrBEhLXu8Cn6oA6cmnuhOzZ8/GwIHKzmavDrZLGtstze3jrcrAHzd5MQP9F2B1xob4fU8AkiTh0fRyJBoFhSQQoL9PEiDqXIT6+ZscRvJgk8NIHkuhJLVgyWEkoDGVcfHiRRQpUkSo88OHD5E+fXohTm8j0UFqy2ZgVG9ejfAoP7yR/jSKFMyAjUvEN248EQUsSyBRPVmWDDXcWQKkJWeJUfqECJCeEqJjvnPHjx9HhQoVeMVtNpt9DchAd99ilS0EPPuHl9s+6RrsTFsRB+baUKJo9DpGpCeOiAISCJCeJEDUuQj18zc5jOTBJoeRPJZCSWrBksNIQGMqY/ny5ejSpQuvc9myZXHixAlueyqQ6CC1cAEwdxKvzqnQ8qgd8i0mveuH3t3Eb/p5IgpYlkCierIsGWq4swRIS84So/QJESA9JUTHfOeeP3+O1KlTg83WdhxHjx4VnEiOeM2/bMfa4jmF5NVS7sNPgfkwtZMfenaJvuchPQmYyHCTAOnJTYAeyK5+/iaHkTzg5DCSx1IoSS1YchgJaExldO/eHUuWLOF1HjlyJKZMmcJtTwUSHaQ2bwJG9+HVuRmWEyWDf8D+OTaULBb9to0noIClCSSqJ0vTocY7Q4C05AwtSpsYAdJTYoTMd75evXrYvXs3r/isWbMwYMAAbjsd+PNPoGYpIVvO9BfwOCAtGipf5q+cHz2rmvQkYCLDTQKkJzcBeiC7+vmbw0SPIAAAPatJREFUHEbygJPDSB5LoSS1YMlhJKAxjRGlbFOfPXt23L17l9d5y5YtePvtt7ntqUCig9TPPwFt6vHqRCifpGXP9Atuf59C+J6fJ6CApQkkqidL06HGO0OAtOQMLUqbGAHSU2KEzHd+2rRpYC/bHEezZs2wYcMGh+n876+/Ao2rCfkyZ7iKMP/kSKHs7/Hb3gAoX77ZD9KTgIkMNwmQntwE6IHs6udvchjJA04OI3kshZLUgiWHkYDGNMZvv/2GHDlyCPW9c+cOQkJChDhPGIkOUmzR6/IFhKoMKnQQn32XX4gjgwgwAonqiTARAY0ESEsaQVEyTQRIT5owmSrRoUOHUKNGDV7npEmT4smTJ2C/Lh3KZ24ow+5tonj2MX4zsCBjK7v9n89tKPzGy5nVpCeOiAISCJCeJEDUuQj18zc5jOTBJoeRPJZCSWrBksNIQGMaY9u2bcJsotdeew2sL/38PP+JV6KDlLI4N1rXEdgubf8juo4Rv/MXEpBhWQKJ6smyZKjhzhIgLTlLjNInRID0lBAdc557/Pgx0qVLBzZr23GcPXsWJUqUcJjO/9Z/E7h9Tcg3N2I4PszaBzO7+6HrOy+nGJGeBERkuEmA9OQmQA9kVz9/k8NIHnByGMljKZSkFiw5jAQ0pjHGjRuH8ePH8/qyxa+XLl3KbU8GEhukIiZOgu0bZeHrV8dv4cG4uewYqlaKXvzRcY5+iUBieiJCREArAdKSVlKUTgsB0pMWSuZLU61aNRw+fJhXnK0N2bVrV247HfhgNLBhhZAtUrGKpTmOcuWzYekschgJcMiQQoD+PknBqGsh6udvchjJQ00OI3kshZLUgiWHkYDGNEadOnWwd+9eXt/PP/8c77//Prc9GUhskHpWuwEC757nVVoQMRTvnhqE1Kl4FAWIACeQmJ54QgoQgUQIkJYSAUSnnSJAenIKl2kSjxkzBpMnT+b17dGjBxYvXsxtpwNXrwItawOR0buvsTJmR47EzJBeuLknAP7KekakJ6fJUoYECJCeEoBjkFPq529yGMnrFHIYyWMplKQWLDmMBDSmMEJDQ5EyZUqEs+1bXx3Hjh1D+fLlHaZHfxMcpCIjEV40FwIQfeM0NvhbTNxb2aN1pIuZh0CCejJPM6imBiBAWjJAJ/hQFUhPPtSZqqZs2rQJbLFrx5E7d278qixe7dYn/rdvA/UrOoq0/94Lz4TCWU/g9FdJkDunHzmMBDpkuEuA/j65S1D//Ornb3IYyeNNDiN5LIWS1IIlh5GAxhTGhfMXULxEcV5XdlPDFmlkTiRvHAkOUr8pN031xJumma3OYci4TN6oKl3TBAQS1JMJ6k9VNA4B0pJx+sIXakJ68oVejN2GWzdvIVfuXMKJe/fuIWvWrEKc08aVy0CLWkK2moF7MGD8G2jSwJ8cRgIZMtwlQH+f3CWof3718zc5jOTxJoeRPJZCSWrBksNIQGMKY8WKFejcuTOva8WKFfHjjz9y29OBhAapqH374Ne/I6/Sw4hUOD73MurXVuZj00EE4iCQkJ7iSE5RRCBeAqSleNHQCRcIkJ5cgGaCLGzB6yxZsuD+/fu8trt27ULdunW57VKALaRduSTwT3S53QOWI6R9TYwfYSOHkUtQKVN8BOjvU3xkjBOvfv4mh5G8fiGHkTyWQklqwZLDSEBjCqNv376YP38+r+uwYcMwffp0bns6EO8gpXyOFlG+OGzPHvIqHQ+tjOyH1iIk2O//2TsPOCmKLIx/m8hJksBKkAwiLpIXkYyIoIAEEyISlCBZgqQjI0FyzklBgoKBHCW7SkbJiMrpoQgqaeNNjU5t1+7OTPdMs9MzfH2/c+pVvaqt/tejp+ub7iqZxwQJaAk4jSetE9MkoIMAY0kHJLroJsB40o3K7xxfe+01rFixQvZ7xIgRGDRokLQ9TrR4ATgdJasPwTgcLtcKm5aHUjCSVJgwgwCvT2ZQvL9taOffFIzMY03ByDyWSkvagKVgpKDxC6NcuXI4evSo7OuqVavQsmVLaad2wumX1LgxwJLpSndWJryNVqcG29YGULJpkIAk4DSepAcTJKCPAGNJHyd66SPAeNLHyR+9xI9w4sc4x9GgQQNs3LjRYXr+2a0zsH29rD8zrheG5O2Oq5tDkZBwTz4dHhkZibRp00o/JkjAKAFen4wSS31/7fybgpF5/CkYmcdSaUkbsBSMFDSWN/7++29kzpxZ6efp06dRqlQpJS81DadfUhVtfbr9p9KVWUXmo9OGZ5U8GiSgJeA0nrROTJOADgKMJR2Q6KKbAONJNyq/czxw4ACEaOM4wsLCIO630qRJ48jy7HP0CGDFbFl3bcyraJ9vNPbMCEHxotEUjCQZJrwlwOuTtwTvf33t/JuCkXm8KRiZx1JpSRuwFIwUNJY3vvnmG1SoUEH2MzQ0FLdu3fL+pka2aDyR4peU7XU0PJ5faexyTEGs77YT3bvyVzQFDA2FQIrxpHjQIAF9BBhL+jjRSx8BxpM+Tv7oJcShLFmy2J76sa079O9x7NgxlC1b1mF69jl9KjDrfVl3Z/QzaBY+F9M6BaNFkxgKRpIME94S4PXJW4L3v752/k3ByDzeFIzMY6m0pA1YCkYKGssbSRe8rlGjBnbt2uXTfqf4JWXbtQ1VSir9Kp31a0wfmwe1n+aC1woYGgqBFONJ8aBBAvoIMJb0caKXPgKMJ32c/NWrfPny+Pbbb2X3TVkf8p1OwI4Nss0VMe3QNd8QvPFUEMYOjqVgJMkw4S0BXp+8JXj/62vn3xSMzONNwcg8lkpL2oClYKSgsbzRq1cvTJo0SfbTlBsa2ZpniRS/pK5eBepVVBrMk/17nFiXCblycQEjBQwNhUCK8aR40CABfQQYS/o40UsfAcaTPk7+6lWmTBmcOnVK6b72iSOlQK8hdkm78T/p/V7QRMzK2RzFsgFfrYqjYCTJMOEtAV6fvCV4/+tr598UjMzjTcHIPJZKS9qApWCkoLG8Ua1aNXmDITq7fPlyvPrqqz7td4pfUj/8ADRMXA9AdLBB7oPYtFN9Tc2nHecftySBFOPJkj1lp6xOgLFk9RHyr/4xnvxrvIz29qGHHsKNGzeUavG21+uDPN2lIzYWeKKg0t7ZmOKonG8Lgmz/u/BpHI4e2W8v56LXCiYaHhDg9ckDaKlcRTv/pmBkHnwKRuaxVFrSBiwFIwWNpQ3xZZAhQwaIGxjHIXZLe+KJJxymTz5T/JKKicHdiJJIh7uyT5/lHYjG22w7hvAgARcEUownF/4sIgFnBBhLzsgw3xMCjCdPqPlPnWnTpqFbt25Kh8UramJnWo8OsR5Sg5rAT+eV6s3TrsX2LBWwbVIs/rxxwF5GwUhBRMMDArw+eQAtlato598UjMyDT8HIPJZKS9qApWCkoLG0IR6VFo9MOw7xq5dYqFGISL48nH1JHS3XHhHRidvSnitgewx74xRfdpV/2w8IOIsnP+g6u2gxAowliw2In3eH8eTnA+im+7dv30bGjBkVr9q1a2P79u1KniHjzBngxbpAQuIPffNiu6Fv3t6Y0SUOeXPyCSNDPOnslACvT07RWKZAO/+mYGTesFAwMo+l0pI2YCkYKWgsbaxfvx5NmjSRfRS/emkXaJQFqZxw9iW1s2Rn1ApZL3vzY+TbyD9vsLSZIIGUCDiLp5R8mUcCrggwllzRYZlRAowno8T8zz+l18+8Xsdo7Chg2UwJIyq6MuqFf4wu9eNRp+o+ez6fMJJ4mPCQAK9PHoJLxWra+TcFI/PAUzAyj6XSkjZgKRgpaCxtTJw4EX369JF9bNu2LRYuXChtXyVS+pL6w7YMwP8q1UaJMNuva/8ev3f9ADk6tXKY/CSBFAmkFE8pOjKTBNwQYCy5AcRiQwQYT4Zw+aXzfRGM9u0FOibe+9yND0F47nOo9mgQerahYOSXgWLBTvP6ZMFBSdIl7fybglESOF6YFIy8gOeqqjZgKRi5ImWtsg4dOmD+/PmyU2PHjkW/fv2k7atESl9SX0fFoezrhZA2KPEx7PjF6xFcsYKvusm/6ycEUoonP+k6u2kxAowliw2In3eH8eTnA6ij+ykJRl4tfC3+5s2bQGRp5a9XzrQHP2UqgGUD99oW1bYVR0Yibdq0ig8NEjBCgNcnI7R846udf1MwMm8MKBiZx1JpSRuwFIwUNJY2KleujMOHD8s+rl27Fs2aNZO2rxIpfUl9OvtHNJlWRe3SnuNAjhxqHi0SSEIgpXhK4kKTBHQRYCzpwkQnnQQYTzpB+bGbWPRaLH6tPU6ePInHHntMm2Us/ddfQJWSSp3HsxzCT2nzYG7Xw8iZ4x4FI4UODU8I8PrkCbXUraOdf1MwMo89BSPzWCotaQOWgpGCxrJGXFwc0qVLh1ixTeu/x5EjRxAREeEwffaZ0pfUxpZL8eypAbJPd2z7paU/adspxNPtaWVLTAQ6gZTiKdDPmed3fwgwlu4P1we1VcZT4I/8999/j1KlSikn2r17d0yePFnJM2RcvAg0rq5UyZnjPOKCwzC42SmUe/w6BSOFDg1PCPD65Am11K2jnX9TMDKPPQUj81gqLWkDloKRgsayxpUrV1CwYEGlfzdu3EDWrFmVPF8Yyb6k0qTBfx+rhrxBP8juXC74Agp9mbjooyxgggSSEEgWT3xMPwkhmnoJMJb0kqKfHgKMJz2U/NsnOjo62athw4YNw5AhQzw/sYMHgHbNZf3rcZlQJM8pu92mymW88MyPFIwkHSY8JcDrk6fkUq+edv5Nwcg87hSMzGOptKQNWApGChrLGrt370bNmjVl//LkyYP//ve/0vZlIumXVNjxEwh+4wWlSyf7fIIybSspeTRIICUCSeOJ6zqkRIl5eggwlvRQoo9eAownvaT82y/pOkaFCxfGhQsXPD+pDbbdYgd0lvXPxJRAlXxb7Hbdwr+ic+uzFIwkHSY8JcDrk6fkUq+edv5Nwcg87hSMzGOptKQNWApGChrLGkuXLkWbNm1k/5599ll8+eWX0vZlIumX1L1Zy5Bl3lDZpUsxhZBx717kzm1b2ZEHCbghkDSeKBi5AcZipwQYS07RsMADAownD6D5YZWkglGDBg2wceNGz89k/lxg0jBZ/6vo2ng+fJHdLpH1T4zpcYyCkaTDhKcEeH3ylFzq1dPOvykYmcedgpF5LJWWtAFLwUhBY1lj5MiRGDx4sOzfO++8g6lTp0rbl4mkX1K/jv0QBT4eJLt0KOZpVDrzEZcvkkSYcEUgaTxRMHJFi2WuCDCWXNFhmVECjCejxPzT/6233sLcuTaR59+jS5cumD59usM0/jnSJhZ9lNjempjX0CHfKHs76YJisGLwQVSrxl3SjINlDS0BXp+0NKyZ1s6/KRiZN0YUjMxjqbSkDVgKRgoayxrt27fHggULZP8mTpyIXr16SduXiaRfUieGfo4Kn3WTXbqQ8DiKnN4kbSZIwBWBpPFEwcgVLZa5IsBYckWHZUYJMJ6MEvNP/+XLl6N169ay814/0d31bWDnZ7K9GXF9MCjPO9Je2vsA6tevlGztJOnABAnoIMDrkw5IPnbRzr8pGJk3GBSMzGOptKQNWApGChrLGrVq1cKuXbtk/z7++GO0aNFC2r5MJP2SOtN0OMr+sFh26begR5Dz5CFpM0ECrggkjScKRq5oscwVAcaSKzosM0qA8WSUmH/6J10zskCBAhD3yh4fnTsCu7+Q1ZfEdUKPPP2l/X7rY2jzWhkKRpIIE54Q4PXJE2qpW0c7/6ZgZB57CkbmsVRa0gYsBSMFjWWN3Llz49q1a7J/+/fvR9WqVaXty0SyL6knCyvduR2UGRlOfq/k0SABZwSSxRN3SXOGivluCDCW3ABisSECjCdDuPzWWSxwXbRoUaX/d+7cQbp06ZQ83UaSV9LibRVbp/kIX2YV93BB6PnMWfTrVYSCkW6gdEyJAK9PKVGxVp52/k3ByLyxoWBkHkulJW3AUjBS0FjS+Pvvv5E5c2alb1Yat6RfUnHlSiJDULTsb2xQKEJPevHrnGyJiQeBQNJ44hNGD8Ko359zZCzdH64PaquMpwdj5O/evYv06dMrJ3v+/HkUKVJEydNtbLa9kt+rXTL3qOgqeDbvUrxY/ldMGRtOwSgZIWYYIcDrkxFavvHVzr8pGJk3BhSMzGOptKQNWCsJD0onaUgC586dQ/HixaUtEuKLIU2aNEqerwzlSypdeqRt/6LSlUs9luDRDnWVPBok4IyAEk+RXAjUGSfmuyfAWHLPiB76CTCe9LPyd8+CBQviypUr8jTEkgA1atSQtqFEbCzwsu2+6HRUsmrDMRqHI57B2oUPUTBKRocZRgjw+mSElm98tfNvCkbmjQEFI/NYKi1pA5aCkYLGkoa4URFrGDmOwoULQzwybZXD8SUVbPtVrkaf9sm69cem7/FQfvUJqWROzCCBfwk44kmYkRSMGBdeEGAseQGPVZMRYDwlQxKwGQ0bNsTGjRvl+S1dulRZCFsW6E3cvAk0bwRcvajUOBj9FN4uNgeHPrH92MbXrxU2NIwR4PXJGC9feGvn3xSMzBsBCkbmsVRa0gYsBSMFjSUN03fsMPksHV9Sub79BmUWTlJa/zMuHTKfPo+g4CAlnwYJOCPgiCdRTsHIGSXm6yHAWNJDiT56CTCe9JLyf79u3bph2rRp8kRGjBiBQYMGSdujxK1b/4hGV87K6tEJQSiV8wROb81AwUhSYcITArw+eUItdeto598UjMxjT8HIPJZKS9qApWCkoLGkMXr0aAwcOFD2rWvXrsqNjCzwUcLxJVVy4Vzk/XaP0ovlmQfhtYOdlDwaJOCKgCOehA8FI1ekWOaOAGPJHSGWGyHAeDJCy799J0+ejJ49e8qTaNu2LRYuXChtjxO2NSlRuYRSvVbGrVizsTBy5PBwUW2lNRoPKgFen6w/8tr5NwUj88aLgpF5LJWWtAFLwUhBY0mjY8eOmDdvnuzbhAkT0Lt3b2n7OmH/ktq7F9V6dUSa2LuyO5/EvIwvGr2P+R+EyDwmSMAdAd70uCPEcr0EGEt6SdFPDwHGkx5KgeGzbt06vPhi4nqM1apVw17bfY7Xx9WrQL2KSjMls0Zh7YLseKxUWiWfBgkYIcDrkxFavvHVzr8pGJk3BhSMzGOptKQNWApGChpLGvXq1cO2bdtk31auXIlWrVpJ29cJ8SV1bM0aVBrdV+nKk5n34YVm+TG0LwUjBQwNlwR40+MSDwsNEGAsGYBFV7cEGE9uEQWMw6FDh1ClShV5Prly5YK4d/b62L0L6PyqbEa8tl8wz/dYPTQedWtRMJJgmDBMgNcnw8hSvYJ2/k3ByDz8FIzMY6m0pA1YCkYKGksaxYoVg9jS1XF4tVuHoxETP8WX1Mlly1B+0lDZ6u34UIQ/fAET2gWhXWsKRhIME24J8KbHLSI66CTAWNIJim66CDCedGEKCKfLly/j0UcfVc7lzp07SJfOy9fG5s0BJg+X7UZFV0G98FWY8lYcXn+ZgpEEw4RhArw+GUaW6hW0828KRubhp2BkHkulJW3AUjBS0FjOiI+PR1hYGMSn4zh9+jRKlSrlMH3+Kb6kTnz0ISqMVxeEfCjXD1jxXjAa1g/2eR/ZAf8hwJse/xkrq/eUsWT1EfKv/jGe/Gu8vOnt7du3kTFjRqUJU+6X/zMYWJ24FtLqmNfRMd8I9H0hDgN6UjBSgNMwRIDXJ0O4fOKsnX9TMDJvCCgYmcdSaUkbsKZ8ASqt0zCTwPXr120LIeZQmhTjJx6PtsohvqQujxmNEqvnK10SgtH2KSF48gnukKaAoeGSAG96XOJhoQECjCUDsOjqlgDjyS2igHIQ917iHsxxHD58GBUrqusPOcp0f06dDMwZL923RTdEi/BZeLlSHGaOo2AkwTBhmACvT4aRpXoF7fybgpF5+CkYmcdSaUkbsBSMFDSWM86cOYOSJUvKfgUHByM6OhohIdZ5zUt8Sd1o2xoPH9sn+ykSD+W8jFOrQpEvDwUjBQwNlwR40+MSDwsNEGAsGYBFV7cEGE9uEQWUg9il88CBA/Kc1q9fj+eff17aHiXWrQEGd5dVv4suhcjwTaiaPwFfLguT+UyQgFECvD4ZJZb6/tr5NwUj8/hTMDKPpdKSNmApGCloLGeIXTmqV68u+1WoUCFcunRJ2lZIiC+pv1s2QY7zx5XuZLc9YfS/baEIDVWyaZCASwK86XGJh4UGCDCWDMCiq1sCjCe3iALK4ZVXXsFHH30kz2n27Nl46623pO1R4tBB4M3E3df+WfT6DB7JmIATX1Aw8ogpK9kJ8Ppk/UDQzr8pGJk3XhSMzGOptKQNWApGChrLGWvXrkXz5s1lv2rWrImdO3dK2woJ8SUVW6sqMt78VXZnRlwfTHv0HXy/kWqRhMKELgK86dGFiU46CDCWdECii24CjCfdqALCsV+/fhg3bpw8l8GDB2P48MQFq2WBkcSPV4AGVZUa+R46hZiwjPjfjjAE8YFshQ0N/QR4fdLPylee2vk3BSPzRoGCkXkslZa0AUvBSEFjOWPmzJno0qWL7Ffr1q2xdOlSaVshce/2HYRVLIZgJMjuNEn3Ke6WKodNyykYSShM6CLAmx5dmOikgwBjSQckuugmwHjSjSogHKdMmYIePXrIc2nbti0WLkxcsFoWGEnExAARhZQaNTNuw7EMxfDjF6HIpK6zrfjRIAFXBHh9ckXHGmXa+TcFI/PGhIKReSyVlrQBS8FIQWM5Y8iQIRgxYoTsl/jFa+zYsdK2QiL64kWkaZz42pzoU+Fsx1C9/ENYMtU6ay1ZgRX74J4Ab3rcM6KHPgKMJX2c6KWPAONJH6dA8Vq9ejVatmwpT6dWrVrYsWOHtD1OVC0L/Pm7rN4jeCaW5HgOR5eFoGB+PmIkwTBhiACvT4Zw+cRZO/+mYGTeEFAwMo+l0pI2YCkYKWgsZ3To0AHz5yfuPjZ58mR07564YKIVOhy9dQvS9Ggru3IzLj0K5fkO7WsEY/wwCkYSDBO6CPCmRxcmOukgwFjSAYkuugkwnnSjCgjHpGtI5s+fH1eu2F4p8/awrfmIU1/LVm7Hh+L19Kvw3qLK3FVWUmHCKAFen4wSS31/7fybgpF5/CkYmcdSaUkbsBSMFDSWM5599lls2rRJ9mvlypVo1aqVtK2QiB07CqHLZsquREVXQb3wVejfNAj9ulMwkmCY0EWANz26MNFJBwHGkg5IdNFNgPGkG1VAOJ4/fx7FihVTzkXsUhsW5uXi1NOnArPeV9q9lxCMqKF7Ua1VQSWfBgnoJcDrk15SvvPTzr8pGJk3DhSMzGOptKQNWApGChrLGRERETh27Jjs19atW1G3bl1pWyER/1xdBF/+TnZlalw/DM3TGRPaBaFdawpGEgwTugjwpkcXJjrpIMBY0gGJLroJMJ50owoIxz///BNZs2ZVzkWISEWKFFHyDBs20QmdOwAHtilVj9UciidmdFTyaJCAXgK8Pukl5Ts/7fybgpF540DByDyWSkvagKVgpKCxnJEjRw5cv35d9isqKgrly5eXts8TN28CkaWVbogFr3dnLoeFfYLRtFGwUkaDBNwR4E2PO0Is10uAsaSXFP30EGA86aEUOD4JCQkIDlbvYV5++WV8+OGH3p+kbfHrv54oi8xBf8q2dlebgBpzX5Y2EyRghACvT0Zo+cZXO/+mYGTeGFAwMo+l0pI2YCkYKWgsZcTGxiZ79PnChQsoXLiwdfq507YAZNfWsj934kOQP9cZxAWHYcPoYFSPVG+2pCMTJOCEAG96nIBhtmECjCXDyFjBBQHGkws4AVoUlMI+90JI8vq4dQuoVFxpZm69jeg42bYgNg8S8IAAr08eQEvlKtr5NwUj8+BTMDKPpdKSNmApGCloLGX8/vvvyJkzp9InkZc9e3Ylz6fGujXA4MRFuM/FFEOlfP88Zv3VzBCUKc0dP3w6Pn74x3nT44eDZtEuM5YsOjB+2i3Gk58OnBfdvm+C0bffAK2flz2LSQhCn4bnMGVCepnHBAkYIcDrkxFavvHVzr8pGJk3BhSMzGOptKQNWApGChpLGeJpoqJFiyp9Ek8dhYRYaF2grw8DbzSVfRQLN+bNdQEJQcE49VEI8uWlYCThMKGLAG96dGGikw4CjCUdkOiimwDjSTeqgHFMKhjVq1cPW7Zs8f78li4G3h8o2/kuuhRG1tuEFTNDZR4TJGCEAK9PRmj5xlc7/6ZgZN4YUDAyj6XSkjZgKRgpaCxliPWKKlasKPuUK1cuiLGz1HHjD6BaGaVLlTPtwdn0BfHjF6HIlFEpokECbgnwpsctIjroJMBY0gmKbroIMJ50YQoop44dO2LevHnynAYNGoQRI0ZI2+NEnx7AxtWy+uqY17G4ygh8sZSCkYTChCECvD4ZwuUTZ+38m4KReUNAwcg8lkpL2oClYKSgsZQhdkSrX7++7JPYMe3IkSPStkoioWJpBN22LX797/F26AKseqguft8Ralsw0pHLTxLQR4A3Pfo40cs9AcaSe0b00E+A8aSfVaB4TpgwAe+++648nU6dOmHmzJnS9jhROxL49QdZfWDQBOwo3gIH1lEwklCYMESA1ydDuHzirJ1/UzAybwgoGJnHUmlJG7AUjBQ0ljJWrVqFl156SfapQYMG2Lhxo7Stkoh//WUEf7NHdmdpbEf0DR+IX2yCEQ8SMEqANz1GidHfGQHGkjMyzPeEAOPJE2r+XWfx4sVo27atPIlmzZph7dq10vYo8fffQOUSStW6GTbhxxylcGYT75sUMDR0E+D1STcqnzlq598UjMwbBgpG5rFUWtIGLAUjBY2ljFmzZqFz586yT23atIG4ebHaETttCkJnj5Pd+m9sLtQs9DXObA6TeUyQgF4CvOnRS4p+7ggwltwRYrkRAownI7QCw3fDhg144YUX5MlERkZi37590vYoEfU10KaJrCoWvM6b85xtfcow/LqTgpEEw4QhArw+GcLlE2ft/JuCkXlDQMHIPJZKS9qApWCkoLGUMWrUKIj35R1H7969IR6PttoRfeok0rR8RulWh7ybMW+buraR4kCDBJwQ4E2PEzDMNkyAsWQYGSu4IMB4cgEnQIuEOPTUU0/JsytQoADEfbNXx+KFwPjBsolb8WF45OHzdvvatlCEUjOSbJjQT4DXJ/2sfOWpnX9TMDJvFCgYmcdSaUkbsBSMFDSWMnr27InJkyfLPo0ZMwb9+/eXtlUS9+7exb2K5ZEl/obs0pqMvdH8cC9pM0ECegnwpkcvKfq5I8BYckeI5UYIMJ6M0AoM3++++w6lS5eWJxNsW5gxJibGtj6jFws0Tv4AmDdRtikSOXOcR1xwGC6tD0W2rEoRDRLQRYDXJ12YfOqknX9TMDJvKCgYmcdSaUkbsBSMFDSWMl577TWsWLFC9mnOnDkQO3ZY7RBfUjcrVkfuuJ9l184FV0KxE59ImwkS0EuANz16SdHPHQHGkjtCLDdCgPFkhFZg+P7yyy/ImzevcjI3b95ElixZlDxDxulTQIvEDU1E3RoZt+F4hmI4sSIEj4QHGWqOziQgCPD6ZP040M6/KRiZN14UjMxjqbSkDVgKRgoaSxnPPPMMtmzZIvv08ccfo0WLFtK2SkJ8SX1X6y1E3Nwqu3Q2zVMofmSVtJkgAb0EeNOjlxT93BFgLLkjxHIjBBhPRmgFhq8Y83Tp0iknc/nSZRQsVFDJM2w8XgCIj5PVGqX/DPsylUXUwhAUKUzBSIJhQjcBXp90o/KZo3b+TcHIvGGgYGQeS6UlbcBSMFLQWMooX748vv32W9mnzZs3o3599VcpWejDhPiS+qFKcxSPTuzr9gLvos7GHj7sFf+0vxLgTY+/jpz1+s1Yst6Y+HOPGE/+PHqe9z1Tpky4deuWbEDcl5UrV07aHiWqlgX+/F1W7RoyByuyN8C+2SEoXZKCkQTDhG4CvD7pRuUzR+38m4KRecNAwcg8lkpL2oClYKSgsZRRsGBBXLlyRfZp//79qFq1qrStkhBfUndtN09Zg27KLq2qtACtFjWQNhMkoJcAb3r0kqKfOwKMJXeEWG6EAOPJCK3A8S1TpgxOnbK9RvbvsW3bNtSpU8dhevb55uvAoe2y7mfRzfF6+ETsnBaCiMcpGEkwTOgmwOuTblQ+c9TOvykYmTcMlhOMrl+/jhMnTuDo0aM4fPgwduzYAfF+szh69eqFiRPVRezcodi7d699m/Tdu3fbd10QAkGNGjXwxhtvKLsyuGvHaLk2YCkYGaWXev5p06ZFdHS0/IMi9sSNi9WOe7Z/F2mrP650a/ZzO/H2uOJKHg0S0EOANz16KNFHDwHGkh5K9NFLgPGkl1Rg+Yknu7duTXzlftWqVWjZsqV3J/mx7ZX9YYkbg9yMS49Ceb7DpgkhqFzBiwW1vesVa/sxAV6frD942vk3BSPzxstyglFQkHPV34hgFB8fjwEDBmDcuHFOafXr1w+jR4/2bicGJ61rA5aCkRNIPs4WQpEQjLTH5cu29+ZtoqLVjugLF5Dm+aeVbk1o/T369M+s5NEgAT0EeNOjhxJ99BBgLOmhRB+9BBhPekkFll/r1q2xfPlyeVIzZ85Ep06dpO1R4vw54IWaStUcOS/i07FhqF6VgpEChoYkcOTqUZTLFyFtbUJcn7766it7VmRkJDJkyCCLXdWTTkzcdwLa+TcFI/NwW1owKlmyJCIiIrBy5Ur7GRsRjCZMmIB3333XXq9mzZro27cvChQoYH/9SIhIu3btspcJv969e9vTZv5HG7AUjMwka15b4mm2HDlyKA3+/vvvyJ49u5JnBePetWtIW1P9Apv7ymF0HBhuhe6xD35GgJMyPxswC3eXsWThwfHDrjGe/HDQTOiyuL+fNGmSbGnUqFF47733pO1R4uQJoFXia/t34kOQ7+GLWPOfYNSpScHII6YBXmnqwenovn8Y5j49Bh0qtE92tmLeIHb0Ez84Z86c2f4GjBCN5kXNR8c9AzAlcii6VemarB4zUo+Adv5Nwcg87pYTjKZMmWIXiR5//HH7xP3q1asID/9nUqxXMBICTZEiRRAXF4e6devi888/V54kuXv3Lho3bgzxjnRISAguXbqE/Pnzm0fV1pI2YCkYmYrWtMbEuBQqVEhpT9yspkmTRsmzgnHPFrPBTxZFWFCC7M6qFzei1XDboo48SMAgAU7KDAKju1MCjCWnaFjgAQHGkwfQAqDK0KFDMXz4cHkm4g0B8QaAV8eB/UD7xF1vr8VmQ/G8x7BiYDAa1qNg5BXbAKwsnhB6cmU9eWYpiUYrVqzAa6+9Jn3Ezso3Hr1pF4scmd++tNXpE0oOH37ePwLa+TcFI/M4W04wSnpqnghGgwcPxsiRI+1NHTt2DGXLJp9UHz9+HE888YTdR/hrv6iS9sETWxuwFIw8IXj/64gFFrXrFQmhSNysWvEQ/forogJyBl+X3dv03DI0GFdb2kyQgF4CnJTpJUU/dwQYS+4IsdwIAcaTEVqB46t9K0CcVdeuXTFt2jTvTnDrFqBHW9nGpZhCeDLfbizsE4ymjSgYSTBMSAKOJ4UcGUlFI/HGilgT13EU6VgCF0r87jCdPpkkHZi47wS0828KRubhDjjBKCEhAUWLFsXFixdRsWJF+8LZznCJ8qioKLv/2bNn4Wr9JGdtOMvXBiwFI2eUfJt/8OBBZUc08ZSZdsc03/ZO/espCUb/y1YeufdtUB1pkYAOApyU6YBEF10EGEu6MNFJJwHGk05QAeY2e/ZsZc2iNm3a2Des8eo0160BBneXTRyPjkCN8PWY9U4wXnqRgpEEw4RCwJlo9N1336F06dKJvs/a3kaom0XaScUlWcBEqhLQzr8pGJmHPuAEo59//hmPPPKInZB4/1m8B+3sEOVjxoyxF//000/y1Tdn/kbytQFLwcgIudTz3bx5Mxo0SHy/PVu2bPjjjz9SrwMG/pK4ib4TURbZgv9Wax29DISFqXm0SMANAU7K3ABisW4CjCXdqOiogwDjSQekAHRZtmwZXn/9dXlmTZo0wSeffCJtjxKTJgLzP5BV90bXQuPwxRSMJBEmnBF4dmQjbMpwSBZnjQpGyOZYiDWM7EcSsUiU5/o6UTx66aWXMGLECFmfidQjoJ1/UzAyj3vACUbiUUHxyKA4Fi9eDPErhbNDlLdt+8/jqnv27EH16tWduRrO1wYsBSPD+FKlQo8ePSDWzNIe4gk1Kx7iJnp3xf6oH/ex2r1R04EmTdU8WiTghgAnZW4AsVg3AcaSblR01EGA8aQDUgC6CHGoWbNm8sxq166N7du3S9ujxIuNge+/lVVnxvXGwDzdKBhJIkykRECscys2xLldI1Z5ggjb/gQ2RgNJxCKZr2ksY8aM+O2335AuXTpNLpOpQUA7/6ZgZB7xgBOMVq1aBaHsikM8QVK/fn2ntLZs2YJnnnnGXi52YmvVqpVT36QFIiBdHddsu1o51sc5d+6c6Ytqu/rbLNNHIKULufiisOIhbqK7tLyEZT82VLoXH14EMRts7+kHBSn5NEjAFQERT998843dpXz58sqmAK7qsYwEkhJgLCUlQtsbAownb+j5b12xCU2jRo3kCZQrVw4HDhyQtuGEbRerNFVLwTbJkVVbpf0YW7JUxtS349CSv7NJLkyoBE6ePGlf0sT+A3JScehOPJBe8zqjQ0RSm7AvcfL111/LeWCSYpr3kYAQiQoWLGj/CxSMzAMdcILRggUL0L79P1sh7t27F9WqVXNKS5Q7nipauHChfNrIaQVNgZH1jtasWWPJrdo1p/NAJlu3bg3xCqP22LFjh9a0VPo/0x/HY+cPY2ZsB6Vfx97ui+tlyip5NEiABEiABEiABEjAHwgcOXIEvXv3ll0tUKCA/S0BmWEwEWTbJblmd/UNgw6hi7DmodroUvsc6lT/xWCLdH+QCIgNkwYOHIjbt28nf6LIAcKJWJQhQwb7ciiOjZUc7vxMHQLitcHmzZvb/xgFI/OYB5xgNGPGDPvuCgLRoUOHUKlSJae0Dh8+jMqVK9vLRb3OnTs79U1aQMEoKRH/sxctWgTx3rzjEIulz50712Fa7nP4jDI4ei0bov5bG0XCLsr+/ZG/BI72GyxtJkiABEiABEiABEjAXwiICXrPnj1ld8PDw5X7M1lgIFFuzDBk+/mcrLEluhFahc+gYCSJMOGKgBAb/vOf/+DMmTPAyOzqk0XiSaNB/65npGmkRIkS9joPP/ywJpfJ1CRAwej+0A44wSi1njDiK2n3JyBTs9VJkyZhwIAB8k926dIFEyfaFkm04CEe02/a7h6+vfYQul1biWHop/Ty3mbb4ny5cil5NEjAGQG+9uGMDPONEmAsGSVGf1cEGE+u6ARu2f79+yHWLXIchQsXxunTpx2mR5/Btl3Swkb2lXWvx2VGkTwnMa1THFo0kdlMkIBTArdu3ULDsY1xKM/Z5D5JnjB68803MX78eIj1i3j4jgBfSbs/7ANOMEqtNYzcDYd20S0ueu2Olm/KxQ56gwYNkn/83Xffxbhx46RtpYS4iW70+k1E/foQZl8dilZhS9Tu7TkO2yp9ah4tEnBCQMSTuEEXR2RkJNcwcsKJ2e4JMJbcM6KHfgKMJ/2sAsnz4MGDqFq1qjylQoUK4dKlS9L2KLHc9gT5mP6y6q+x2VEy7xHM7haMVs0069BIDyZIQCUwL2o+Ou5J/GEZLtYw2rVrF2rUqKE2QCvVCWjn33wlzTz8AScYcZc084Ij0FsaMmSIsu2leF955MiRljxtcRP98itX0eH4BNsGDZ+qfaxUC1i0XM2jRQIuCHBS5gIOiwwRYCwZwkVnNwQYT24ABWixdokIcYr58+fHlStXvDvbV1sCR/fJNtbFvIJ2+cZQMJJEmHBFIJlY5HiiKOlC2P/mi1cqP/jgA1dNsiwVCFAwuj+QA04wEosYP/LII3Za7733nn3hMWfoRPmYMWPsxT/99BPEO9NmHdqA5RNGZlE1t53+/fvj/fffl40OHz4cgwdbcy2ge7Z3qX+v2Rj5gtVFuu2dX7AGqJL4y5w8ISZIwAkBTsqcgGG2YQKMJcPIWMEFAcaTCzgBXLRv3z489dRT8gyLFSuGs2dTeA1IerhJxMQAEY/anBKk45thS/FJthoUjCQRJpwRcCoWOSqkIBplO5AB4omWNGnSOLz46QMC2vk3nzAybwACTjAS2yCKxYsvXrxo3xZR/Grh7BALYottD4W/+GIyspC1szYd+dqApWDkoGKtT/FrwOTJk2WnhHjUt2/i++6ywAKJ2HFjEbpkWvKedLE9Ktu5a/J85pCACwKclLmAwyJDBBhLhnDR2Q0BxpMbQAFaLHaorVOnjjy7iIgIiJ3TPD7u3AEqFFWq18y4DccyFMOid4PR5Dm+kqbAoSEJuBOLxC5oYjfuWT8tQky1tLIebE8a7eq3ha+lJRLxSUo7/6ZgZN4QBJxgJNCIp0QcrxaJnRfKlk2+5fjx48fh2PJQ+IunS8w8tAFLwchMsua1JXbFmzVrlmxQiEfdu3eXtpUScX17I+SLlbJLf8alw/7Gc9FgfOINlixkggTcEOCkzA0gFusmwFjSjYqOOggwnnRACkCXjRs3omHDhvLMxNp64qkjr46nngT++FU20SN4JpbkeA4rBgajYT0KRhIME5JAUrGo5o0nsGvEdlkudtYWPzbnzp3b/tpkrTH18dNjf8nyWjcisGP4VmkzkfoEtPNvCkbm8Q9IwUgINGKHhfj4eNStWxeff/65sqiruCFp1KgRtm3bhpCQEPvTSAUKFDCPqq0lbcBSMDIVrWmNtWvXDgsXLpTtzZw5E506dZK2lRKxE8cjdGHi01Bbo5/DyZ6z0LNziJW6yb74CQFOyvxkoPygm4wlPxgkP+oi48mPBsvErn766ado2rSpbFHcu2/d6uXE++32wFcbZZsLYruiT953sWZYMOrUoGAkwTBhJ3Dk6lE8ubKepDH36TEodbckXnnlFfz444/o0KEDxo4dC/EggjiEqBkXF4c6w+rj4MNnZL1vX9qKcvkipM1E6hLQzr8pGJnH3nKCkXgEVQgsjuP69esQE3txCJHHkXaUN2nSxJFUPidMmACx65U4atasiX79+kGIQmIRPfHqkVjNXhzCr3fv3va0mf/RBiwFIzPJmtfWG2+8gSVLlsgG58yZg44dO0rbSomYD5cjbFQ/2aUT0WWxodPnGNiLgpGEwoRuApyU6UZFRzcEGEtuALHYEAHGkyFcAeOs3eFYnFTjxo2xYcMG785v+hRgVuLOt/ujn8Zz4cvw2ZhgPFWVgpF3cAOz9tSD09F9/zAIsahDBZvgaDtu3LiBnTt3Qsw3o6Ojk+0wKx5O6LykK+b8sRpTIoeiWxUuE+HL6NDOvykYmTcSlhOMhHhjZJV5sWZRSof4BywWNR4/fnxKxfY8sV6NWPQ6ONj8Lw5twFIwcjoEPi3wJ8Eoevs2pOnWRvK6FpsNU9scx4j3KBhJKEzoJsBJmW5UdHRDgLHkBhCLDRFgPBnCFTDO4sc7cU/mOFq2bAkhInl1LJgPfDBUNnE6+jFUC/8SWz4IRsUnzb/vl3+ICb8mIJ40cvaEkKvrk6t6fg3EzzqvnX9TMDJv8AJWMHIg+uqrr7B48WL7E0XikUKxVad44qht27bKjgwOf7M+tQFLwcgsqua2I2JAxIbjmD17Nt566y2HaanP6JMnkabVM7JP8bZU/yYXMG5UOpnHBAnoJeDqpkdvG/QjAUGAscQ4MJMA48lMmv7T1ty5c5X7rzZt2ij3Zx6dSX/bWwaffSirbohuiTbh47F7egjKlgmS+UyQgF4CvD7pJeU7P+38m4KReeNgOcHIvFPzbUvagKVg5NuxcPbX33zzTSxatEgWiwWw3377bWlbKXHv2jWkram+Ez3o6UMYOesRK3WTffETArzp8ZOB8oNuMpb8YJD8qIuMJz8aLBO7OnXqVGXTEfHjnfgRz6ujzStA1G7ZxJiEYRiX+w0cnBuCEsUpGEkwTOgmwOuTblQ+c9TOvykYmTcMFIzMY6m0pA1YCkYKGssY/rTo9b27dxFUvhjSQDxb9M8x5onPMeDDcg6TnySgmwBvenSjoqMbAowlN4BYbIgA48kQroBxFstHiGUiHEe3bt0wZYptDSJvjsb1gYunZAt9gqdiQY4X8M3iEBQuRMFIgmFCNwFen3Sj8pmjdv5Nwci8YaBgZB5LpSVtwFIwUtBYxmjfvj0WLFgg+zNjxgx07txZ2lZKiC+p6PJPIHNC4vad44quRN/11a3UTfbFTwjwpsdPBsoPuslY8oNB8qMuMp78aLBM7OqIESMwZMgQ2aIQj8QGNV4dTz0J/PGrbKJt2DJ8mu1pnPwwBOH5KBhJMEzoJsDrk25UPnPUzr8pGJk3DBSMzGOptKQNWApGChrLGGKLzPnzbYsi/ntMnz4dXbp0cZiW+hRfUrcqVUX22MSbn0l556DntkaW6ic74x8EeNPjH+PkD71kLPnDKPlPHxlP/jNWZvZ04MCBGD16tGxSiEfDhg2TtkeJsoWAuBhZtUfwTCzJ8Rx+2BCKLFlkNhMkoJsAr0+6UfnMUTv/pmBk3jBQMDKPpdKSNmApGCloLGN07NgR8+bNk/2ZNm0auna15naY4kvqt8hnEH73nOxvVHRVVHjxUaBdB6BYcZnPBAm4I8CbHneEWK6XAGNJLyn66SHAeNJDKfB8ku6QLMSjAQMGeHei1Wyv7N/4n2wjKroK6oWvwm/bQxHCDWYlFyb0E+D1ST8rX3lq598UjMwbBQpG5rFUWtIGLAUjBY1lDLGootiZw3GIRRffeecdh2mpz5jPNuBS7w9QPCxRMJIdDLI9Wj3BJnw1eFZmMUECrgjwpscVHZYZIcBYMkKLvu4IMJ7cEQrMcrHhyJw5c+TJTZo0CT169JC2R4khA4G1i5WqZXMcx/E9OZQ8GiSglwCvT3pJ+c5PO/+mYGTeOFAwMo+l0pI2YCkYKWgsYyS9QRELLIqFFi13nDuLhKa1cTm6AMJDryBNUELyLgrR6JMdfNIoORnmpECANz0pQGGWRwQYSx5hYyUnBBhPTsAEeHbLli2xevVqeZZiB9s33nhD2h4lNm0EereXVaMTglC5wDkc2ZRe5jFBAkYI8PpkhJZvfLXzbwpG5o0BBSPzWCotaQOWgpGCxjJGp06dlG1bJ0+erGzrapmO9n8X+OxD9915/lVgzDj3fvR44AnwpueBDwHTADCWTEPJhmwEGE8PZhjUqVMHO3bYfvT691i3bh2aNm3qMD37fN+2JtLSGbLu17bX+HtFrMRXq0NlHhMkYIQAr09GaPnGVzv/pmBk3hhQMDKPpdKSNmApGCloLGOIHdFmzZol+2NJwSg+HoiwrVMUFyv76TQRYrsJOnoJCA526sICEhAEeNPDODCLAGPJLJJsRxBgPD2YcVC2bFmcOHFCnvy2bdsgRCSvjkb1gEunZRNT4vtjR5VOWL+IgpGEwoQhArw+GcLlE2ft/JuCkXlDQMHIPJZKS9qApWCkoLGM4ReC0Z07QIWi+plFnQfS83Fr/cAeTE/e9DyY434/zpqxdD+oPrhtMp4ezLHPkycPxOTOcRw+fBgVK1Z0mMY/Y2JsP7YVUuq9kmYlwqpHYslUrnitgKGhmwCvT7pR+cxRO/+mYGTeMFAwMo+l0pI2YCkYKWgsY3Tp0gUzZ86U/TFlkUXZmkkJPmFkEkg2oyXAmx4tDaa9IcBY8oYe6yYlwHhKSiTw7XjbfU5YWBjEp+P4/vvvUaJECYdp/DMuDihbQKnXLN06FGhQAZNHUTBSwNDQTYDXJ92ofOaonX9TMDJvGCgYmcdSaUkbsBSMFDSWMa5evYq//voLQbYFo8X/s2fPjhw5LLh7BtcwskzMBEpHeNMTKCPp+/NgLPl+DAKpB4ynQBpNfedy69YtZMqUSXH++eefkS9fPiXPkJHCE0Z1M2xCtealMawfBSNDLOksCfD6JFFYNqGdf1MwMm+YKBiZx1JpSRuwFIwUNDSMEvh3lzTbP1bnNblLmnM2LElGgDc9yZAww0MCjCUPwbFaigQYTyliCejMX375BXnz5lXOUfyYl1REUhzcGZcvA89VU7zyZT+NgW0yoUt7CkYKGBq6CfD6pBuVzxy1828KRuYNAwUj81gqLWkDloKRgoaGBwRiPtuAkP6dEYwURCMhFk2YBzR41oOWWeVBJMCbngdx1O/POTOW7g/XB7VVxtODN/Jnz55VXj8TT3zHxsba9u/wYgOPPbuBTq9ImNfjMqNInpOY2yMYLZp40a5skYkHkQCvT9Yfde38m4KReeNFwcg8lkpL2oClYKSgoeEBAfEldXzVKuTf/AUePnEQQWLXNLEr2nOtgDfbA8WKe9AqqzyoBHjT86COvPnnzVgyn+mD3CLj6cEb/aioKGWB61y5ckHcQ3t1LFsCjH1PNnE0ujxqha/DuhHBqFWdgpEEw4QhArw+GcLlE2ft/JuCkXlDQMHIPJZKS9qApWCkoKHhAQHlS6pKFaQVbaS1/debX+A86AerBAYBJZ4iI22hZI+owDg5nkWqEmAspSrugP9jjKeAH+JkJ7hjxw7UqVNH5pcpUwYnTpyQtkeJgf2BT5fJqutiXkG7fGOwZ2YIHi9teyqbBwl4QIDXJw+gpXIV7fybgpF58CkYmcdSaUkbsBSMFDQ0PCDALykPoLGKUwKMJ6doWGCQAGPJIDC6uyTAeHKJJyALP/30UzRt2lSeW61atSBEJK+OF2yv6J8/LpsYlTACE3K/jlMrQ5AvDwUjCYYJQwR4fTKEyyfO2vk3BSPzhoCCkXkslZa0AUvBSEFDwwMC/JLyABqrOCXAeHKKhgUGCTCWDAKju0sCjCeXeAKycPHixWjbtq08t+bNm2P16tXSNpyIjgaeLAxoNgppkXYNtmWpiF82h9ofzjbcJiuQgI0Ar0/WDwPt/JuCkXnjRcHIPJZKS9qApWCkoKHhAQF+SXkAjVWcEmA8OUXDAoMEGEsGgdHdJQHGk0s8AVk4ZcoU9OjRQ55bhw4dMHfuXGkbTvx2DagRoVQrlO0YYtJlxc/bw5R8GiRghACvT0Zo+cZXO/+mYGTeGPwfAAD//3Kx8Q0AAEAASURBVOydebxVU//HP3do1kiDRAOZCikZmmhQkSJpJKHkUU9KoQnhVyqSMoSopEEpkUIpScgjQ1EeKUOiHhRSSu74O/tyd2vdc/a+Z62z9r7nnP05fzhrre9a373Xe31ed7U+ztknJTf0Al/GCfz888+oWrVqXt7vvvsOJ5xwgvFrMGFwCPz1119Yv3593oSbNGmCEiVKBGfynKlxAtSTcaSBTUgtBXbpPZk49eQJ1rhOetddd2Hs2LH2PY4YMQLjx4+368qFH74H2p0vDat89DbUqlgcHy4tJrWzQgIqBPj3SYVW0fQVz98//fQTqlSpUjQ3kmRXTaFh5M2KioKlYeQN4yBl5SYVpNX2fq7Uk/eMg3IFaikoK+3PPKknfzjH01X69++Pp59+2r6lhx9+GEOGDLHryoWvvwI6XSgNq1h5By6sk4uXZxaX2lkhARUC/PukQqto+ornbxpG5taAhpE5llImUbA0jCQ0rGgQ4CalAY1DHAlQT45oGFAkQC0pAmN3VwLUkyuepAxedtllePXVV+25zZ07F1dffbVdVy58/jnQra097HBOGo6t+g36NM3BlHE0jGwwLCgT4N8nZWS+DxDP3zSMzOGnYWSOpZRJFCwNIwkNKxoEuElpQOMQRwLUkyMaBhQJUEuKwNjdlQD15IonKYMNGjTAp59+as9t5cqVaNv2iOFjB6ItfPIx0LuT1LviMTsw+qoc3DaIX+eXwLCiRIB/n5RwFUln8fxNw8jcEtAwMsdSyiQKloaRhIYVDQLcpDSgcYgjAerJEQ0DigSoJUVg7O5KgHpyxZOUwXLlyuHAgQP23D7++GM0bNjQrisX1r8H3NhNGjYwbTqa3d0GPa+iYSSBYUWJAP8+KeEqks7i+ZuGkbkloGFkjqWUSRQsDSMJDSsaBLhJaUDjEEcC1JMjGgYUCVBLisDY3ZUA9eSKJ+mCf/75J0qXLi3Na8eOHahZs6bUplQJjUeHptKQadlDUX/Gv9GiKQ0jCQwrSgT490kJV5F0Fs/fNIzMLQENI3MspUyiYGkYSWhY0SDATUoDGoc4EqCeHNEwoEiAWlIExu6uBKgnVzxJF9y1axdq1KghzeuPP/5AmTJlpDalivXjz/XlnMNSH8W/XuqAuifRMFJiyc4SAf59knDEZUU8f9MwMrdENIzMsZQyiYKlYSShYUWDADcpDWgc4kiAenJEw4AiAWpJERi7uxKgnlzxJF3QenaR9Qyj/Ffx4sVx+PBhpKSk5Depv+/dC1x4ljTugqPWYuXqE1CuLA0jCQwrSgT490kJV5F0Fs/fNIzMLQENI3MspUyiYGkYSWhY0SDATUoDGoc4EqCeHNEwoEiAWlIExu6uBKgnVzxJF3zzzTfRpk0be1716tXDli1b7LpWYe1aYOCRX1k7mFMMp1Xbgu0riqFECRpGWkw5KI8A/z7FvxDE8zcNI3PrRcPIHEspkyhYGkYSGlY0CHCT0oDGIY4EqCdHNAwoEqCWFIGxuysB6skVT9IFFy5ciB49etjzateuHVasWGHXtQq3DgLeWGIP/SCjKQad9hTeWVSahpFNhQUdAvz7pEPN3zHi+ZuGkTn2NIzMsZQyiYKlYSShYUWDADcpDWgc4kiAenJEw4AiAWpJERi7uxKgnlzxJF1wwIABeOKJJ+x51a9fH5s3b7bryoUPNwDXdZaGTc0Zgbcad8HCp4+mYSSRYUWVAP8+qRLzv794/qZhZI4/DSNzLKVMomBpGEloWNEgwE1KAxqHOBKgnhzRMKBIgFpSBMburgSoJ1c8SReM9KyiXOuh1TqvzMzQr6O1BnZ9bY/en10SZx79AS5r8jseGluDhpFNhgUdAvz7pEPN3zHi+ZuGkTn2NIzMsZQyiYKlYSShYUWDADcpDWgc4kiAenJEw4AiAWpJERi7uxKgnlzxJF2woGHUvHlzrFu3Tm+eLywE7h0qjR2dMgnTjumK2ztsxbBb6tIwkuiwokqAf59UifnfXzx/0zAyx5+GkTmWUiZRsDSMJDSsaBDgJqUBjUMcCVBPjmgYUCRALSkCY3dXAtSTK56kC9atWxdfffWVPa/ly5ejQ4cOdl2pMGwwsGKxPWRXVjWcUe195KakYvL1n6BXj7NoGNl0WNAhwL9POtT8HSOev2kYmWNPw8gcSymTKFgaRhIaVjQIcJPSgMYhjgSoJ0c0DCgSoJYUgbG7KwHqyRVPUgUzMjJQsmRJiF9B++KLL3DqqafqzXPObGDCKHtsTqjUsNz7+K5Edcwfvh6tWp1Hw8imw4IOAf590qHm7xjx/E3DyBx7GkbmWEqZRMHSMJLQsKJBgJuUBjQOcSRAPTmiYUCRALWkCIzdXQlQT654kir49ddf46STTpLmdPDgQZQuXVpqi7ryxwGg6ZlAVoY9ZFLOnXis+rWYdecGNGnShIaRTYYFHQL8+6RDzd8x4vmbhpE59jSMzLGUMomCpWEkoWFFgwA3KQ1oHOJIgHpyRMOAIgFqSREYu7sSoJ5c8SRVcM2aNWjdOvSQ6n9etWrVwrfffptf1Xu/4Vrggzftsc9m3YxZ9f+Fe/69mYaRTYUFXQL8+6RLzr9x4vmbhpE57jSMzLGUMomCpWEkoWFFgwA3KQ1oHOJIgHpyRMOAIgFqSREYu7sSoJ5c8SRVcMaMGejXr589p06dOmHp0qV2XbmQE/oSWuPQ19kOH7SHDk19DD83aYS+Pb6mYWRTYUGXAP8+6ZLzb5x4/qZhZI47DSNzLKVMomBpGEloWNEgwE1KAxqHOBKgnhzRMKBIgFpSBMburgSoJ1c8SRUcOXIkJkyYYM/p9ttvxwMPPGDXlQvbtgGdW0rD6pXfgMva/IH2LXfTMJLIsKJDgH+fdKj5O0Y8f9MwMseehpE5llImUbA0jCQ0rGgQ4CalAY1DHAlQT45oGFAkQC0pAmN3VwLUkyuepAp26dIFS5Yssef01FNPoX///nZduTDnudBDr0faw77Pqo4zj30f93XbjPqn7aNhZJNhQZcA/z7pkvNvnHj+pmFkjjsNI3MspUyiYGkYSWhY0SDATUoDGoc4EqCeHNEwoEiAWlIExu6uBKgnVzxJFTz55JOxfft2e05vvPEGLr74YruuXGjdFPhxhz1sUWZv9K8+Fs8O/Q/Klc2kYWSTYUGXAP8+6ZLzb5x4/qZhZI47DSNzLKVMomBpGEloWNEgwE1KAxqHOBKgnhzRMKBIgFpSBMburgSoJ1c8SRM8fPgwSpUqJc1nW+grZXXr1pXaoq789RfQsI7UfWlGd9xWZyKmj3g3r52/kibhYUWDAP8+aUDzeYh4/qZhZA4+DSNzLKVMomBpGEloWNEgwE1KAxqHOBKgnhzRMKBIgFpSBMburgSoJ1c8SRP873//i3r16tnzSUlJwaFDh1CyZEm7TamQnf33A6//OmQP+zHrGNza8kP07/5eXhsNIxsNC5oE+PdJE5yPw8TzNw0jc+BpGJljKWUSBUvDSELDigYBblIa0DjEkQD15IiGAUUC1JIiMHZ3JUA9ueJJmuBLL72EK6+80p7P2WefjU8++cSuaxX69QHeX20P/TW7LJ648VM0abg+r42GkY2GBU0C/PukCc7HYeL5m4aROfA0jMyxlDKJgqVhJKFhRYMANykNaBziSIB6ckTDgCIBakkRGLu7EqCeXPEkTXDs2LG466677PnceOONmD59ul3XKtz/f8C8J+2h72VchJ8nz0SFo2gY2VBYiIkA/z7FhM+XweL5m4aROeQ0jMyxlDKJgqVhJKFhRYMANykNaBziSIB6ckTDgCIBakkRGLu7EqCeXPEkTbBr165YvHixPZ9HHnkEgwYNsus6hewO7ZG2Y7M9dHLOKLRb0g8//0jDyIbCQkwE+PcpJny+DBbP3zSMzCGnYWSOpZRJFCwNIwkNKxoEuElpQOMQRwLUkyMaBhQJUEuKwNjdlQD15IonKYK5ubmoUaMGdu/ebc9n9erVaN26tV1XLhw8iNxzT0EKcu2h3Ustwcy3G2DDBhpGNhQWYiLAv08x4fNlsHj+pmFkDjkNI3MspUyiYGkYSWhY0SDATUoDGoc4EqCeHNEwoEiAWlIExu6uBKgnVzxJEfzll19wzDHHSHPZuXMnjj/+eKlNqfKf94G+V9lDMnNTcEX9L7FkTjrWr6dhZINhISYC/PsUEz5fBovnbxpG5pDTMDLHUsokCpaGkYSGFQ0C3KQ0oHGIIwHqyRENA4oEqCVFYOzuSoB6csWTFEHLwGnatKk9l9KlS+PAgQNITU2125QKoU8s4cbrQw+8XmUP+zSjIRb2eRljbs+iYWRTYSFWAvz7FCtB78eL528aRuZ40zAyx1LKJAqWhpGEhhUNAtykNKBxiCMB6skRDQOKBKglRWDs7kqAenLFkxTBmTNnom/fvvZc2rVrhxUrVth15cKboV9GuyX0C2nCa2rOCNSYOBCXtcukYSRwYTE2Avz7FBs/P0aL528aRuaI0zAyx1LKJAqWhpGEhhUNAtykNKBxiCMB6skRDQOKBKglRWDs7kqAenLFkxTBIUOGYOrUqfZchg8fjgkTJth1pUJODnDRecAvR56HtDerPM6svB7rniuP44/LoGGkBJSd3Qjw75MbnfiIiedvGkbm1oSGkTmWUiZRsDSMJDSsaBDgJqUBjUMcCVBPjmgYUCRALSkCY3dXAtSTK56kCDZu3BgfffSRPZd58+ahV69edl2p8O03wGXNpSED06bjxWPaYffqdGRm/kXDSKLDSiwE+PcpFnr+jBXP3zSMzDGnYWSOpZRJFCwNIwkNKxoEuElpQOMQRwLUkyMaBhQJUEuKwNjdlQD15Ion4YN//PEHypYtK81j8+bNqF+/vtQWdWX5MmD4v+zuP2UdjVOP/Rit66Zg8dPpoJ5sNCwYIEA9GYDocQrx/E3DyBxsGkbmWEqZRMHSMJLQsKJBgJuUBjQOcSRAPTmiYUCRALWkCIzdXQlQT654Ej64YcMGnHde6Ctk/7zS09Nx8OBBFC9ePL9J7X3KZODph+wxazLao8txT2FUlxTcPiiNhpFNhgUTBPj3yQRFb3OI528aRuZY0zAyx1LKJAqWhpGEhhUNAtykNKBxiCMB6skRDQOKBKglRWDs7kqAenLFk/DBp59+Gv3797fn0apVK7z55pt2Xbnwf/cCC6bbwxZkXoebq9+LxfekovVFqTSMbDIsmCDAv08mKHqbQzx/0zAyx5qGkTmWUiZRsDSMJDSsaBDgJqUBjUMcCVBPjmgYUCRALSkCY3dXAtSTK56ED950002YPv2IwTN69GiMHTtWf14jbgeWzbfHz8waiGHH3oEvF6WhSuUUGkY2GRZMEODfJxMUvc0hnr9pGJljTcPIHEspkyhYGkYSGlY0CHCT0oDGIY4EqCdHNAwoEqCWFIGxuysB6skVT8IHTzvtNGzdutWex4svvogrr7zSrisXbrgW+ODIJ5Qey74dT9T5Nz5/LT0vFfWkTJQDXAhQTy5w4iQknr9pGJlbFBpG5lhKmUTB0jCS0LCiQYCblAY0DnEkQD05omFAkQC1pAiM3V0JUE+ueBI6+Ouvv+Loo4+W5rB9+3acdNJJUlvUlV9+AS48C8jNtYcMTX0M2R07Yer9aXlt1JONhgUDBKgnAxA9TiGev2kYmYNNw8gcSymTKFgaRhIaVjQIcJPSgMYhjgSoJ0c0DCgSoJYUgbG7KwHqyRVPQgffeecdtGjRwp5D6dKlsX//fqSl/W3u2IFoC7NmAJPutnv/mZOGE4/ZjClDy6Jb59S8durJxsOCAQLUkwGIHqcQz980jMzBpmFkjqWUSRQsDSMJDSsaBLhJaUDjEEcC1JMjGgYUCVBLisDY3ZUA9eSKJ6GDDz/8MIYOHWrPoWPHjnjllVfsulLB+lRR66bAT9/Zw17K7Ikbqk/AxtlpqFUzJa+derLxsGCAAPVkAKLHKcTzNw0jc7BpGJljKWUSBUvDSELDigYBblIa0DjEkQD15IiGAUUC1JIiMHZ3JUA9ueJJ6GCnTp2wbNkyew4TJ07EHXfcYdeVCtu2AZ1bSkPuwQTMr9ETX65IR8rffhEfei0RYiVWAvz7FCtB78eL528aRuZ40zAyx1LKJAqWhpGEhhUNAtykNKBxiCMB6skRDQOKBKglRWDs7kqAenLFk7BBa13Lli2LzMxMew7r1q1D8+bN7bpS4ZOPgd6dpCFLM7rjjSsfxOMTj3zFjXqSELESIwHqKUaAPgwXz980jMwBp2FkjqWUSRQsDSMJDSsaBLhJaUDjEEcC1JMjGgYUCVBLisDY3ZUA9eSKJ2GDH330ERo3bmzff2pqat7zi8qUKWO3KRV+/DH0lbRG0pBZWQNQctxI9Lzq7+cXWUHqSULESowEqKcYAfowXDx/0zAyB5yGkTmWUiZRsDSMJDSsaBDgJqUBjUMcCVBPjmgYUCRALSkCY3dXAtSTK56EDT766KO45ZZb7Ptv1aoV3nzzTbuuXLh3DPDCM/awrNwUnFvuXSyaXxMn1vnn+2ihKPVkI2LBAAHqyQBEj1OI528aRuZg0zAyx1LKJAqWhpGEhhUNAtykNKBxiCMB6skRDQOKBKglRWDs7kqAenLFk7DBLl26YMmSJfb9jx07FqNHj7brSoUffgDanw9YD77+57Ug8zqMrHkvvl6ZjtCHl+wX9WSjYMEAAerJAESPU4jnbxpG5mDTMDLHUsokCpaGkYSGFQ0C3KQ0oHGIIwHqyRENA4oEqCVFYOzuSoB6csWTkMGMjIy85xdZ7/mvNWvWoGVL+aHV+bFC3yc9AMyaanf7KzcVZ1XYgJZNq+CJB488v8jqQD3ZmFgwQIB6MgDR4xTi+ZuGkTnYNIzMsZQyiYKlYSShYUWDADcpDWgc4kiAenJEw4AiAWpJERi7uxKgnlzxJGRw06ZNOPvss+17Twn9hNm+fftQrlw5u02pcFNf4N0V9pB5mX3x7+p34+lbU3HV5cLHi0I9qCcbEwsGCFBPBiB6nEI8f9MwMgebhpE5llImUbA0jCQ0rGgQ4CalAY1DHAlQT45oGFAkQC0pAmN3VwLUkyuehAw++eSTuPnmm+17t34ZzfqFNO1Xvz7A+6vt4eNz78UDVa7DFwvTUK3qkecXWR2oJxsTCwYIUE8GIHqcQjx/0zAyB5uGkTmWUiZRsDSMJDSsaBDgJqUBjUMcCVBPjmgYUCRALSkCY3dXAtSTK56EDPbs2RMLFiyw7/2ee+7BmDGhh1brvHJygCZnAQd+tUePTpmEt07uivVL0u22/AL1lE+C7yYIUE8mKHqbQzx/0zAyx5qGkTmWUiZRsDSMJDSsaBDgJqUBjUMcCVBPjmgYUCRALSkCY3dXAtSTK56EC2ZlZaFChQo4ePCgfe8rV65E27Zt7bpSYfNnQI9LpCHnHbUOHbvVwp3D5OcXWZ2oJwkVKzESoJ5iBOjDcPH8TcPIHHAaRuZYSplEwdIwktCwokGAm5QGNA5xJEA9OaJhQJEAtaQIjN1dCVBPrngSLrhlyxacccYZ0n3/8ssvqFSpktQWdeWhB4GZU+zuX2fWwTnV38Ky8alodoH8/CKrE/Vko2LBAAHqyQBEj1OI528aRuZg0zAyx1LKJAqWhpGEhhUNAtykNKBxiCMB6skRDQOKBKglRWDs7kqAenLFk3DBZ555BjfeeKN93+eeey4++OADu65UyM0FWl0A/Py9PWxa9lCMOXYwvn89HSVL2s12gXqyUbBggAD1ZACixynE8zcNI3OwaRiZYyllEgVLw0hCw4oGAW5SGtA4xJEA9eSIhgFFAtSSIjB2dyVAPbniSbhgjx49sHDhQvu+R48ejbFjx9p1pcK2bUDnltKQ1qVXoso5p+L5J8KfX2R1pJ4kXKzESIB6ihGgD8PF8zcNI3PAaRiZYyllEgVLw0hCw4oGAW5SGtA4xJEA9eSIhgFFAtSSIjB2dyVAPbniSaigtZblypVDRkaGfd+rVq1CmzZt7LpS4b57gIVP20P+l1UZpx/7IR7ql4obrgl/fpHVkXqycbFggAD1ZACixynE8zcNI3OwaRiZYyllEgVLw0hCw4oGAW5SGtA4xJEA9eSIhgFFAtSSIjB2dyVAPbniSajg+vXr0bRpU/ueU1NTsW/fPpQtW9Zui7pw6BBwQT0g64j59Hj2bbiz2iBsei4NNU9IiZiKeoqIhY2aBKgnTXA+DhPP3zSMzIGnYWSOpZRJFCwNIwkNKxoEuElpQOMQRwLUkyMaBhQJUEuKwNjdlQD15IonoYITJkzAyJEj7Xtu3749Xn/9dbuuVFi7Fhh4tT0kOzcFZ1TYgArVqmD9kshfR7M6U082MhYMEKCeDED0OIV4/qZhZA42DSNzLKVMomBpGEloWNEgwE1KAxqHOBKgnhzRMKBIgFpSBMburgSoJ1c8CRVs0aIF3nnnHfuep0yZgsGDB9t1pcKc54AJR8ynjRnnoNVxL2L0VSm47d+Rv45m5aeelCizcyEEqKdCAMVBWDx/0zAytyA0jMyxlDKJgqVhJKFhRYMANykNaBziSIB6ckTDgCIBakkRGLu7EqCeXPEkTHDv3r2oXLmydL8bN25EgwYNpLaoKw+MB2Y/ZndflnEVrj3uIax7PA1n1Iv8dTSrM/VkI2PBAAHqyQBEj1OI528aRuZg0zAyx1LKJAqWhpGEhhUNAtykNKBxiCMB6skRDQOKBKglRWDs7kqAenLFkzDBV155BZdffrl9vxUqVMCePXuQnu789TG7c6TC4IHA6pftyFPZgzG51lB88Vo6Qo9GcnxRT45oGNAgQD1pQPN5iHj+pmFkDj4NI3MspUyiYGkYSWhY0SDATUoDGoc4EqCeHNEwoEiAWlIExu6uBKgnVzwJE7z11lthfQUt/9W3b18888wz+VW199xcoFM74JvP7XF34wHk9OqBsaOdv45mdaaebGQsGCBAPRmA6HEK8fxNw8gcbBpG5lhKmUTB0jCS0LCiQYCblAY0DnEkQD05omFAkQC1pAiM3V0JUE+ueBIimBsyeGrVqoWdO3fa9zt//nz07NnTrisVnp8HjL1DGnJdsTno+8RFaH6By8eLQiOoJwkbKzESoJ5iBOjDcPH8TcPIHHAaRuZYSplEwdIwktCwokGAm5QGNA5xJEA9OaJhQJEAtaQIjN1dCVBPrngSIrh9+3acfPLJ0r1a5tHxxx8vtUVVCT33CL07AtanjP557c8uiQbVPsXWN45C8eL5rZHfqafIXNiqR4B60uPm5yjx/E3DyBx5GkbmWEqZRMHSMJLQsKJBgJuUBjQOcSRAPTmiYUCRALWkCIzdXQlQT654EiI4c+ZMWF9By3/Vr18fn332GVJSnB9Ond9Xes/MBC46F9j3s9TcL/1ZlGjfCo9PdP86mjWIepLQsRIjAeopRoA+DBfP3zSMzAGnYWSOpZRJFCwNIwkNKxoEuElpQOMQRwLUkyMaBhQJUEuKwNjdlQD15IonIYJdu3bF4sWL7XsdNWoUxo0bZ9ejLqx/D7ixm9R9WvZQjK42GIvvSUXri9y/jmYNpJ4kfKzESIB6ihGgD8PF8zcNI3PAaRiZYyllEgVLw0hCw4oGAW5SGtA4xJEA9eSIhgFFAtSSIjB2dyVAPbniifvg/v37UbFiReTk5Nj3unr1arRu3dquR1WwvoJ2/TXAh2vt7t9m1kKjY99CyfRUfBv6dbQSJeyQY4F6ckTDgAYB6kkDms9DxPM3DSNz8GkYmWMpZRIFS8NIQsOKBgFuUhrQOMSRAPXkiIYBRQLUkiIwdnclQD254on74LJly9CpUyf7PouHHjK0b98+lCpVym6LqrAk9AmluwZLXSfnjML/Vb0JN7RIwUP3Ff51NGsw9SQhZCVGAtRTjAB9GC6ev2kYmQNOw8gcSymTKFgaRhIaVjQIcJPSgMYhjgSoJ0c0DCgSoJYUgbG7KwHqyRVP3Af/9a9/4amnnrLv87rrrsOsWbPsetSFlhcAPx/5lbW9WeXRqPI72J9eHq+MTy3019Hyr0M95ZPguwkC1JMJit7mEM/fNIzMsaZhZI6llEkULA0jCQ0rGgS4SWlA4xBHAtSTIxoGFAlQS4rA2N2VAPXkiieugxkZGahUqRIOHjxo36f1LKMuXbrY9agLF50H7PnB7j476yYMOXYUyhcDtoe+jlYs9B7Ni3qKhhL7REuAeoqWVNH1E8/fNIzMrQMNI3MspUyiYGkYSWhY0SDATUoDGoc4EqCeHNEwoEiAWlIExu6uBKgnVzxxHVy7di1atmxp36P1q2h79+7NM5HsxmgLD4wHZj9m9/5fVmWcXu1DDLg4FePujO7raNZg6slGyIIBAtSTAYgepxDP3zSMzMGmYWSOpZRJFCwNIwkNKxoEuElpQOMQRwLUkyMaBhQJUEuKwNjdlQD15IonroNDhw7Fww8/bN9jx44d8corr9h1pcLWrUAX+UHZp5b/CM89WhXnNir819Hyr0U95ZPguwkC1JMJit7mEM/fNIzMsaZhZI6llEkULA0jCQ0rGgS4SWlA4xBHAtSTIxoGFAlQS4rA2N2VAPXkiidug9bX0Y499lj8+uuv9j0+99xz6N27t11XKny6CejVQRrStNqneGfVMUiN3i/iJ4wkgqzESoB/n2Il6P148fxNw8gcbxpG5lhKmUTB0jCS0LCiQYCblAY0DnEkQD05omFAkQC1pAiM3V0JUE+ueOI2uG7dOlx44YXS/cVyWMt5/HGkTrvfzrct82QsGbAaI4ZE/3U0azD1ZCNkwQAB6skARI9TiOfvWP4GeXybCZeehpFHSyYKloaRR5ADlJabVIAW24epUk8+QA7IJailgCy0T9OknnwCbfgyw4YNw+TJk+2sHTp0wPLly+26amF/284ot2uDPWxm1kBc+PpInFgnxW6LpkA9RUOJfaIlQD1FS6ro+onnbxpG5taBhpE5llImUbA0jCQ0rGgQ4CalAY1DHAlQT45oGFAkQC0pAmN3VwLUkyueuAxmZmaievXqeQ+4zr/BZ599Fn369Mmvqr1v3IicazoiFbn2uPtqPI+7V7aw69EWqKdoSbFfNASop2goFW0f8fxNw8jcWtAwMsdSyiQKloaRhIYVDQLcpDSgcYgjAerJEQ0DigSoJUVg7O5KgHpyxROXwXfffRfNmzeX7k37oBZ62HVO90uRmvWXnS8jNwUv3/kluvUqY7dFW6CeoiXFftEQoJ6ioVS0fcTzt/bfoaKdQlxenYaRR8siCpaGkUeQA5SWm1SAFtuHqVJPPkAOyCWopYAstE/TpJ58Am3wMrfffjsmTZpkZ7zkkkvw2muv2fWoC9nZwMUh4+mn76Qhs7MHovOGUShXTmqOqkI9RYWJnaIkQD1FCaoIu4nnbxpG5haChpE5llImUbA0jCQ0rGgQ4CalAY1DHAlQT45oGFAkQC0pAmN3VwLUkyueuAtmZWXlfR1tz5499r3NmjUL1113nV2PurD+PeDGblL3NRntsbTzNEx9oITUHm2FeoqWFPtFQ4B6ioZS0fYRz980jMytBQ0jcyylTKJgaRhJaFjRIMBNSgMahzgSoJ4c0TCgSIBaUgTG7q4EqCdXPHEXfO+999CsWTPpvv73v/+hWrVqUltUlaG3ACtftLt+lXkSLqj2Ol6aWBLNLki121UK1JMKLfYtjAD1VBihoo+L528aRubWg4aROZZSJlGwNIwkNKxoEOAmpQGNQxwJUE+OaBhQJEAtKQJjd1cC1JMrnrgLDh8+HA888IB9X+3atcOKFSvsetSFAweAJvWAnNDX0v55jcFEvHBCD3z+ajpS9fwiUE/5NPluggD1ZIKitznE8zcNI3OsaRiZYyllEgVLw0hCw4oGAW5SGtA4xJEA9eSIhgFFAtSSIjB2dyVAPbniiaug9XW0GjVqwDqU5b9mzpyJ66+/Pr8a/fvroWce3Xaj3d960HXdSp/i1qsrYMjNaXa7aoF6UiXG/m4EqCc3OvERE8/fNIzMrQkNI3MspUyiYGkYSWhY0SDATUoDGoc4EqCeHNEwoEiAWlIExu6uBKgnVzxxFXz77bdx0UUXSfe0e/duHHvssVJbVJXHHwWmTbC7rs9ogY7HzcHnC9NQrWqK3a5aoJ5UibG/GwHqyY1OfMTE8zcNI3NrQsPIHEspkyhYGkYSGlY0CHCT0oDGIY4EqCdHNAwoEqCWFIGxuysB6skVT1wFb775Zjz55JP2PbVv3x6vv/66XY+6kJOD3MsuRsp3W+0hizJ7Y0WHcZjxsP6ni6xk1JONlAUDBKgnAxA9TiGev2kYmYNNw8gcSymTKFgaRhIaVjQIcJPSgMYhjgSoJ0c0DCgSoJYUgbG7KwHqyRVP3AT379+PypUrIyMjw76nBQsWoHv37nY96sKaN4FB10rdb05/Br2nt0OT8zQfXvRPNupJwspKjASopxgB+jBcPH/TMDIHnIaROZZSJlGwNIwkNKxoEOAmpQGNQxwJUE+OaBhQJEAtKQJjd1cC1JMrnrgJvvDCC5I5VKxYMezduxflypVTu8fQp4vQoTWwc5s97tvMWuh+6lv4z8vFtR92nZ+MesonwXcTBKgnExS9zSGev2kYmWNNw8gcSymTKFgaRhIaVjQIcJPSgMYhjgSoJ0c0DCgSoJYUgbG7KwHqyRVP3AQ7duyI5cuX2/fTv39/PPXUU3Y96sJ/3gf6XiV1H5g2HY3uaI8bront62hWUupJQstKjASopxgB+jBcPH/TMDIHnIaROZZSJlGwNIwkNKxoEOAmpQGNQxwJUE+OaBhQJEAtKQJjd1cC1JMrnrgI7ty5EzVr1pTu5a233gp7ALbUwakyfiww9wk7+nVmHZxffQ2+eqUYyit+WMlOIhSoJwEGizEToJ5iRuh5AvH8TcPIHG4aRuZYSplEwdIwktCwokGAm5QGNA5xJEA9OaJhQJEAtaQIjN1dCVBPrnjiIjh+/HiMGjXKvpdq1arBMpGsr6WpvnJbNUXKTzvsYQ/njMSubjdj0r2xf7rISko92WhZMECAejIA0eMU4vmbhpE52DSMzLGUMomCpWEkoWFFgwA3KQ1oHOJIgHpyRMOAIgFqSREYu7sSoJ5c8RR50Fof69NF1kEs/2UZSCNGjMivRv/+1Xbg8ouk/m1Kr8CU2fVR//QUqV23Qj3pkuO4SASop0hU4qtNPH/TMDK3NjSMzLGUMomCpWEkoWFFgwA3KQ1oHOJIgHpyRMOAIgFqSREYu7sSoJ5c8RR5cOnSV3DFFZdL96H7b9zc24ci5bWFdq69WeXR74JP8fKz6p9UspMUKFBPBYCwGhMB6ikmfL4MFs/fNIzMIadhZI6llEkUrO5mKiVkJdAEuEkFevmNT556Mo40sAmppcAuvScTp548wWos6aWXXorXX3/dzte9e3csWLDArkdd+O475F4a+joacu0hU3NGoP4TA9H6olS7LdYC9RQrQY4XCVBPIo34LIvnbxpG5taIhpE5llImUbA0jCQ0rGgQ4CalAY1DHAlQT45oGFAkQC0pAmN3VwLUkyueIg1u27YNp5xyinQPq1atQps2baS2Qiu5IZOo77XAB2vsrody0nF5rU+w8tWjkWrOL+IzjGzCLJggwL9PJih6m0M8f9MwMseahpE5llImUbA0jCQ0rGgQ4CalAY1DHAlQT45oGFAkQC0pAmN3VwLUkyueIg1ee+21mDNnjn0PderUwZdffon09HS7LarC2reAgddIXZ/KHoyj7rsNV3cz6BaFrkA9SZhZiZEA9RQjQB+Gi+dvGkbmgNMwMsdSyiQKloaRhIYVDQLcpDSgcYgjAerJEQ0DigSoJUVg7O5KgHpyxVNkwT179qBKlSrS9adMmYLBgwdLbYVWQg/NxsXNgF92211/zqqIi6q/j49fK4tSpexmIwXqyQhGJvmHAPUU/1IQz980jMytFw0jcyylTKJgaRhJaFjRIMBNSgMahzgSoJ4c0TCgSIBaUgTG7q4EqCdXPEUWHDp0KB5++GHp+lqHsSmTgacfkvIMSnsSNftfitv+nSa1m6hQTyYoMkc+Aeopn0T8vovnb62/UfE7tSK9MxpGHuEXBUvDyCPIAUrLTSpAi+3DVKknHyAH5BLUUkAW2qdpUk8+gVa4jLUmlStXxoEDB6RRudaziFRe27cBnVsBwriPMs5D++MW4otF6aFrpKhki6ov9RQVJnaKkgD1FCWoIuwmnr9pGJlbCBpG5lhKmUTB0jCS0LCiQYCblAY0DnEkQD05omFAkQC1pAiM3V0JUE+ueIokaP0KWs+ePaVrb9q0CWeddZbUVmjl5huBda9J3Zoe9RbOb3ciHrrP/KeLrAtRTxJuVmIkQD3FCNCH4eL5m4aROeA0jMyxlDKJgqVhJKFhRYMANykNaBziSIB6ckTDgCIBakkRGLu7EqCeXPH4HszOzkaDBg2wZcsW+9rdunXDwoUL7XrUhfrHhz5dlCN1P+aYr7FhdgnUqWX+00XWhagnCTcrMRKgnmIE6MNw8fxNw8gccBpG5lhKmUTB0jCS0LCiQYCblAY0DnEkQD05omFAkQC1pAiM3V0JUE+ueHwPLl++HB07dpSu+8EHH+Dcc8+V2qKq1DsurNvjZy/CwLlNwtpNNVBPpkgyj0WAeop/HYjnbxpG5taLhpE5llImUbA0jCQ0rGgQ4CalAY1DHAlQT45oGFAkQC0pAmN3VwLUkyseX4M5OTlo1qwZ3n//ffu6bdu2xYoVK5CSoviJoF27gLbhJtM3T32AOs1q2PlNF6gn00SDnY96iv/1F8/fNIzMrRcNI3MspUyiYGkYSWhY0SDATUoDGoc4EqCeHNEwoEiAWlIExu6uBKgnVzy+BteuXYuWLVtK11yzZk1Ym9TBoZI9fgLS5j4qRWfWfRI3vCx/eknqYKBCPRmAyBQ2AerJRhG3BfH8TcPI3DLRMDLHUsokCpaGkYSGFQ0C3KQ0oHGIIwHqyRENA4oEqCVFYOzuSoB6csXjW9D6BbRLL70079NE+Rc977zzsH79eqSmpuY3Rff+55/IaFwfxXMP2/1nZ/0LDZfciTPqKX5Syc4QXYF6io4Te0VHgHqKjlNR9hLP3zSMzK0EDSNzLKVMomBpGEloWNEgwE1KAxqHOBKgnhzRMKBIgFpSBMburgSoJ1c8vgWt5xSdf/750vWWLl2KTp06SW3RVLLnzEXahOF2V+ux18Mavo+H55xgt3lVoJ68IhvMvNRT/K+7eP6mYWRuvWgYmWMpZRIFS8NIQsOKBgFuUhrQOMSRAPXkiIYBRQLUkiIwdnclQD254vEteMUVV8AyiPJfp512Gj777DOkp6fnN0X3fuAA/mrSCCVyDtr938poh3IvPINGZyt+UsnOEH2BeoqeFXsWToB6KpxRUfcQz980jMytBg0jcyylTKJgaRhJaFjRIMBNSgMahzgSoJ4c0TCgSIBaUgTG7q4EqCdXPL4EP/roIzRu3Fi61oIFC9C9e3epLZpK1v+NQ/qCaVLXe056GWNebhx6cLbU7EmFevIEa2CTUk/xv/Ti+ZuGkbn1omFkjqWUSRQsDSMJDSsaBLhJaUDjEEcC1JMjGgYUCVBLisDY3ZUA9eSKx5dg165dsXjxYvtadevWxZYtW1C8eHG7LarCd98h59KmSEWu3X1VRgccvegpNGzgg1sUuir1ZKNnwQAB6skARI9TiOdvGkbmYNMwMsdSyiQKloaRhIYVDQLcpDSgcYgjAerJEQ0DigSoJUVg7O5KgHpyxeN5cNOmTTj77LOl68ybNw+9evWS2qKpZIybiOLzH7G7ZuSmYETj9Zg82/tnF+VflHrKJ8F3EwSoJxMUvc0hnr9pGJljTcPIHEspkyhYGkYSGlY0CHCT0oDGIY4EqCdHNAwoEqCWFIGxuysB6skVj+dByxh6/vnn7evUrl0bW7duVf90UehX1g40aISyWT/ZuWZlDcB5S0fh9FP9+XSRdWHqycbPggEC1JMBiB6nEM/fNIzMwaZhZI6llEkULA0jCQ0rGgS4SWlA4xBHAtSTIxoGFAlQS4rA2N2VAPXkisfTYKRnF82ePRvXXnut8nUP/GcLyvZtJ427//zVGDXjNKnN6wr15DXhYOWnnuJ/vcXzNw0jc+uVtIZRSpRP07N+CeKll14yR/SfTKJgaRgZxxu4hNykArfknk6YevIUb6CSU0uBWm7PJ0s9eY7Y8QKR/t38559/omTJko5jIgYOHwYanSiFdmSegOwV7+HEOt7/Mpp4YepJpMFyrASop1gJej9ePH/TMDLHm4YRDSNzamImzwhwk/IMbSATU0+BXHZPJk0teYI1sEmpp6JZ+rFjx+Kuu+6SLm79Kpr162iqr4Pjp6DM3AelYW+dMAwtXx8qtflRoZ78oByca1BP8b/WNIy8WaOkN4zuvPNO9OnTx5FeqVKlcNxxxznGdQOiYPkJI12KHJdPgJtUPgm+myBAPZmgyBwWAWqJOjBJgHoySTP6XJE+XfTDDz+o//s49OmiQ43ORGkclC7+01NvoWqzk6U2PyrUkx+Ug3MN6in+11o8f/MTRubWK+kNo6lTp+KWW24xRyzKTKJgaRhFCY3dHAlwk3JEw4AGAepJAxqHRCRALUXEwkZNAtSTJrgYhn388cc455xzwjLkhh5crfr636OLcOyTQ6RhP5Q6BzU+Wiq1+VWhnvwiHYzrUE/xv87i+ZuGkbn1omFkjqWUSRQsDSMJDSsaBLhJaUDjEEcC1JMjGgYUCVBLisDY3ZUA9eSKx3jQMoU6dOiA119/Xcq9Y8cO1KxZU2orrJKbk4v/ndEC1fGN3fW37KOA97eiYkX/fhnNvnioQD2JNFiOlQD1FCtB78eL528aRuZ40zAyx1LKJAqWhpGEhhUNAtykNKBxiCMB6skRDQOKBKglRWDs7kqAenLFYzy4bt06XHjhhVLekSNH4v7775faoqlsu28BTl44TOr6eueFuGRsM6nNzwr15Cft5L8W9RT/ayyev2kYmVsvGkbmWEqZRMHSMJLQsKJBgJuUBjQOcSRAPTmiYUCRALWkCIzdXQlQT654jAZzcnLQsmVLWKZR/qtevXrYuHEjihUrlt8U1XvWp1uQ3fNSlEjJtvt/n10LVTa9ixIli+bTRdaNUE/2crBggAD1ZACixynE8zcNI3Owk94wOu+882B95Pazzz7L2wBPPPHEvA2yZ8+eaNy4sTmSBTKJgqVhVAAOq8oEuEkpI+MAFwLUkwschpQIUEtKuNi5EALUUyGADIaXLl2KK0K/FCy+rLZOnTqJTe7l73cCUycj9/VFKGgLfdhvARrf2tx9vMdR6sljwAFLTz3F/4KL528aRubWK+kNIzdUffv2xZQpU3DUUaHvWCu+LEG6vfbs2YP69evnddm+fTuOP/54t+6MkYArAWuTsh5Mab0aNWqEEiVKuPZnkATcCFBPbnQYUyFALanQYt/CCFBPhREyEz8c+jWzhg0b4ptvjjxvyPofrGvWrEFaWlpUF0n55GMU698DKTlHPlWUP3BFyX648N1RSE3Nbymad+qpaLgn61Wpp/hfWcskyn/+Gg0jc+uVtIZRmTJlcP311+Oiiy6C9ami0qVL49dff8V//vMfPPDAA/jxxx/zKFr/d2XRokVIT09XohrpJ0idEixevBiVKlVyCrOdBEiABEiABEiABEiABHwhsGTJEjz22GPStaz/gXrmmWdKbW6Vsybdj0o7/hvW5bOMBvjotjtR++TDYTE2kAAJkICXBKyz/lVXXZV3CRpG5kgnrWF04MABlC1bNiIpS0xdunTB2rVr8+LPPfccevfuHbGvUyMNIycybCcBEiABEiABEiABEohHAvv27UOPHj2QkZFh316LFi0wZswYRPtv25TMTDQf2hdpuTl2jj9yiuMZDMN7DS7DgP7f2u0skAAJkIBfBGgYeUM6aQ2jwnDt2rULtWrVQlZWFho0aJD3kL/CxohxfiVNpMGy1wT4MVivCQcrP/UUrPX2crbUkpd0g5ebevJ+zUeNGoXJkyfbF7JMIus5n3Xr1rXbCiukbP4MxfvIzz86tfxH+KXEMfhgVg6OO7awDP7EqSd/OAflKtRT/K80v5LmzRoF1jCycFqfKpo7d24eWcsAqly5sjHK4kO3+NBrY1gDm8japNavX583/yZNmvAZRoFVgpmJU09mODILf4WIGjBLgH+bzPIsmG3btm049dRT834MJj82fPhwTJgwIb8a3fuc2cCEUXbf7Zl1cW711bivVwoG9Y/uGUj2YA8L1JOHcAOYmnqK/0UXz9/8Spq59Qq0YWRtkCNHjsyjuWXLFlg/J2rqJQqWhpEpqsHNw00quGvvxcypJy+oBjMntRTMdfdq1tSTV2SRZxJZX0V74YUX7ItYz/u0/o169NFH223RFDLuHYviLzxhd12WcRXGnPYQ3l+UHvofWnZzkReopyJfgqS6Aeop/pdTPH/TMDK3XoE2jCZOnIgRI0bk0aRhZE5UzGSeADcp80yDnJF6CvLqm507tWSWZ9CzUU/eKeCdd96B9awi8WU9+HrgwIFiU+Hl0POLfm94Hsrn/GT3nZ31L9SeORotmhbxz6LZd/R3gXoqAITVmAhQTzHh82UwDSNvMAfaMLr22msxZ86cPLKmXUhRsPyEkTfiDVJWblJBWm3v50o9ec84KFegloKy0v7Mk3ryhrP1vM6mTZtiw4YN9gVOOukkbN68GSVLlrTboin8/NBzqDLz70/n5/efdvocDFjUKr8aN+/UU9wsRVLcCPUU/8sonr9Nn+3jf/be3WFgDSProde1a9dGZuj/lJx11lnYtGmTUcqiYGkYGUUbyGTcpAK57J5NmnryDG3gElNLgVtyTydMPXmD99lnn8X1118vJX/55aW4/PJOUlthlZx9v+Nwk4YonXLY7rolswEqrV2G6tXj69NF1g1ST/YysWCAAPVkAKLHKcTzNw0jc7CT0jBau3YtmjdvjrS0yA/es35yr0uXLrD6Wa9Zs2bhuuuuyyub+o8oWBpGpqgGNw83qeCuvRczp568oBrMnNRSMNfdq1lTT+bJ7t27F9aniX7//Xc7ecuWLbF69WqkpqqZPF/1GYuTPjry7CIr4ZKur+LKexrYueOpQD3F02ok/r1QT/G/huL5m4aRufVKSsPo9NNPz/u/CtZXzho3bowax9VAiZIl8Ntvv+H999/HpEmTsHv37jyKHTt2xOLFi1G8eHFzVEOZRMHSMDKKNpDJuEkFctk9mzT15BnawCWmlgK35J5OmHoyj/fWW2/FlClTpMSfbvoUZ551ptRWWOW3tZtQfmAHiBbT2tzOaLLpsdC/oQsbXTRx6qlouCfrVamn+F9Z8fxNw8jceiWtYfTFF18USql///555lHZsmUL7avaQRQsDSNVeuxfkAA3qYJEWI+FAPUUCz2OFQlQSyINlmMlQD3FSlAev3HjRjRs2FBqHDZsWN6/faXGQiq5X3+NPzu1RWkc+SqaNWTTuHfQ4Io6hYwuujD1VHTsk/HK1FP8r6p4/qZhZG69ktIw+uijj/Cf//wn79NEW7duhfW8IusjuRUrVsQpp5yCCy+8EN27dVf+vysq2EXB0jBSIce+kQhwk4pEhW26BKgnXXIcV5AAtVSQCOuxEKCeYqEnj83Ozob11TPr19HyX5UqVcL27dthvUf9Cj0w+1CzFih94DtpyIdV+6DxmvultnirUE/xtiKJfT/UU/yvn3j+pmFkbr2S0jAyh0c/kyhYGkb6HDnybwLcpKgEkwSoJ5M0g52LWgr2+puePfVkjui8efNwzTXXSAmfe+459O7dW2orrHJoyWsofdeNUrf1WW1Q7/0ZKF8pXWqPtwr1FG8rktj3Qz3F//qJ528aRubWi4aROZZSJlGwNIwkNKxoEOAmpQGNQxwJUE+OaBhQJEAtKQJjd1cC1JMrnqiD1o+7WJ+otz5dn/9q0qQJ1q1b5/iDMPn9pPfcXPx4dhtUy9xqN2/NOBXfT12Giy8tbbfFa4F6iteVScz7op7if93E8zcNI3PrRcPIHEspkyhYGkYSGlY0CHCT0oDGIY4EqCdHNAwoEqCWFIGxuysB6skVT9RB61NEc+fOlfpbj2to1KiR1FZY5ctZ7+GUSd2kbk+eOhs3LW6DlBSpOS4r1FNcLkvC3hT1FP9LJ56/aRiZWy8aRuZYSplEwdIwktCwokGAm5QGNA5xJEA9OaJhQJEAtaQIjN1dCVBPrniiCk6ePBnWg63F16BBg/DII4+ITe7l0CeL/nplJf4aMQjlUg/Zfb/LqomSb7+LqtXE30qzw3FXoJ7ibkkS+oaop/hfPvH8TcPI3HrRMDLHUsokCpaGkYSGFQ0C3KQ0oHGIIwHqyRENA4oEqCVFYOzuSoB6csVTaNB60HV6evhzhax/k1auXLnQ8XkdfvkFGDMaeGtZWP8PO0xF4weuCmuP1wbqKV5XJjHvi3qK/3UTz980jMytFw0jcyylTKJgaRhJaFjRIMBNSgMahzgSoJ4c0TCgSIBaUgTG7q4EqCdXPIUGZ8yYgX79+kn9ateujW+++UZqc6wcOAB0bAPs+SGsyw7UwwmfLEdqieJhsXhtoJ7idWUS876op/hfN/H8TcPI3HrRMDLHUsokCpaGkYSGFQ0C3KQ0oHGIIwHqyRENA4oEqCVFYOzuSoB6csXjGty9ezfq1q2LQ4eOfIXMGvD777+jXLlyrmPt4KNTgCcftKv5hdVZnXD60vtR/dSK+U0J8U49JcQyJcxNUk/xv1Ti+ZuGkbn1omFkjqWUSRQsDSMJDSsaBLhJaUDjEEcC1JMjGgYUCVBLisDY3ZUA9eSKxzGYG3rmkPWg63nz5kl93n33XTRt2lRqc6yEcuCi84C9u+wuWbkpGFTsabS6tx26XpEYzy2ybz5UoJ5EGizHSoB6ipWg9+PF8zcNI3O8aRiZYyllEgVLw0hCw4oGAW5SGtA4xJEA9eSIhgFFAtSSIjB2dyVAPbnicQyuWLECl1xyiRQfNWoUxo0bJ7W5VrZvA65oKXXpXuIFlGh6PmY/kpYQv4om3XyoQj0VJMJ6LASop1jo+TNWPH/TMDLHnIaROZZSJlGwNIwkNKxoEOAmpQGNQxwJUE+OaBhQJEAtKQJjd1cC1JMrnojB/fv344wzzsDOnTvteK1atfDZZ5+hbNmydlthhYOPzkCZJ++2u+3Kqoomx3+Ajxako/IxKXZ7IhWop0Rarfi/V+op/tdIPH/TMDK3XjSMzLGUMomCpWEkoWFFgwA3KQ1oHOJIgHpyRMOAIgFqSREYu7sSoJ5c8UQMjhw5EhMmTJBi1ieO2rVrJ7W5VXJygG0Ne+DUzHfsbgsyr0P5x/8Pl1yceF9Fy58E9ZRPgu8mCFBPJih6m0M8f9MwMseahpE5llImUbA0jCQ0rGgQ4CalAY1DHAlQT45oGFAkQC0pAmN3VwLUkyuesODHH3+Mc845R2rv06cPZs2aFfoKWfSfCpr/9G+46uEzUDwl9Byjf14zzpqNvvNDv5iWwC/qKYEXLw5vnXqKw0UpcEvi+ZuGUQE4MVRpGMUAz22oKFgaRm6kGIuGADepaCixT7QEqKdoSbFfYQSopcIIMa5CgHqKnpbF6oILLsDGjRvtQWXKlMFXX32FatWq2W2FFb7YkoGDnXvhnOLv212zQw+7PvjmVpQ79ii7LREL1FMirlr83jP1FL9rk39n4vmbhlE+ldjfaRjFzjBiBlGwNIwiImKjAgFuUgqw2LVQAtRToYjYIUoC1FKUoNgtKgLUU1SY8jpNnDgRI0aMkAY8++yzsD5hFO3r0MFcvHP+CLTLmSsN+bV+B1RaOF1qS8QK9ZSIqxa/90w9xe/a5N+ZeP6mYZRPJfZ3GkaxM4yYQRQsDaOIiNioQICblAIsdi2UAPV5a4vvAABAAElEQVRUKCJ2iJIAtRQlKHaLigD1FBUmfP755zjzzDORYz186J+X9cyi1157Damp0T9z6NXLZ6DDV0cedG2l+iO1Eo5aswaoXDk/dcK+U08Ju3RxeePUU1wui3RT4vmbhpGEJqYKDaOY8DkPFgVLw8iZEyPREeAmFR0n9oqOAPUUHSf2KpwAtVQ4I/aIngD1VDirzMxMtGzZEu+9957duVixYti6dSvq1KljtxVW2DBxDc55rjdEeykjNw2pL6xEev3TChueEHHqKSGWKWFuknqK/6USz980jMytFw0jcyylTKJgaRhJaFjRIMBNSgMahzgSoJ4c0TCgSIBaUgTG7q4EqCdXPHnBRx99FLfccovU8fHHH8eAAQOkNrfKro0/okKv81EmNVPq9us9s1Gpa2I/6FqcEPUk0mA5VgLUU6wEvR8vnr9pGJnjTcPIHEspkyhYGkYSGlY0CHCT0oDGIY4EqCdHNAwoEqCWFIGxuysB6skVT94DrU877TRkZWXZHVu0aIE333wT6enpdptbIeOvXGw+5wY0ynlD6ra9/d2o+9BNUluiV6inRF/B+Lp/6im+1iPS3YjnbxpGkQjptdEw0uNW6ChRsDSMCsXFDoUQ4CZVCCCGlQhQT0q42NmFALXkAochZQLUkzOy7OxsXHLJJVi1apXdyXpe0ZYtW2CZSNG+Fly/Aj029JW6bz6mC85YOxVISZHaE71CPSX6CsbX/VNP8bUeke5GPH/TMIpESK+NhpEet0JHiYKlYVQoLnYohAA3qUIAMaxEgHpSwsXOLgSoJRc4DCkToJ6ckc2YMQP9+vWTOkyaNAnDhg2T2twq6x75BGdPuwpl0/6yu+3LKYeS6z5Aycrl7LZkKVBPybKS8TEP6ik+1sHtLsTzNw0jN1JqMRpGaryi7i0KloZR1NjY0YEANykHMGzWIkA9aWHjoAgEqKUIUNikTYB6iozO+ndkrVq1pGDjxo3x7rvvonjx4lK7U+X7eW+jyrhrUCLlyC+rWX1/Gj4TVa9t5zQsodupp4Revri7eeop7pYk7IbE8zcNozA82g00jLTRuQ8UBUvDyJ0Vo4UT4CZVOCP2iJ4A9RQ9K/Z0J0AtufNhVI0A9RTOy/oqWsHnE6WEvjq2ceNGnHXWWeEDIrT8sWk7SvVqibSUXCn608mXoeqSJ5Puq2j5k6Se8knw3QQB6skERW9ziOdvGkbmWNMwMsdSyiQKloaRhIYVDQLcpDSgcYgjAerJEQ0DigSoJUVg7O5KgHoKx9OyZUusXbtWCtx77724++67pTanSk7oA0X/O7M1jsvdKnX5qlJ7nLR6GlCihNSeTBXqKZlWs+jnQj0V/RoUdgfi+ZuGUWG0oo/TMIqelVJPUbA0jJTQsXMEAtykIkBhkzYB6kkbHQcWIEAtFQDCakwEqCcZ37fffos6derIjaHawYMHUbp06bD2sIY//8TnvSag3rZnwkKZH+9EsZJpYe3J1EA9JdNqFv1cqKeiX4PC7kA8f9MwKoxW9HEaRtGzUuopCpaGkRI6do5AgJtUBChs0iZAPWmj48ACBKilAkBYjYkA9XQEX1ZWFtq1a4c1a9YcaQyVevTogeeff15qi1j54Xsc6todpfd/FxY+3LonSj4yKaw92Rqop2Rb0aKdD/VUtPyjubp4/qZhFA2x6PrQMIqOk3IvUbA0jJTxcUABAtykCgBhNSYC1FNM+DhYIEAtCTBYjJkA9XQE4dSpUzFkyJAjDaFSt27dsHDhQqktYuWXX5DZsT2K/b47LLy/bkuUmxt6btFRR4XFkq2Bekq2FS3a+VBPRcs/mquL528aRtEQi64PDaPoOCn3EgVLw0gZHwcUIMBNqgAQVmMiQD3FhI+DBQLUkgCDxZgJUE9/I9y8eTMaNGiAHOsBRP+8zj33XKxbty70yKFCnjl06BCyu16JtB2b84fmvR/OScPGVuNxwWO9kvYh19KEQxXqqSAR1mMhQD3FQs+fseL5m4aROeY0jMyxlDKJgqVhJKFhRYMANykNaBziSIB6ckTDgCIBakkRGLu7EqCegD9Dzx1q2rRp3q+g5cNKS0uDZSKddtpp+U2R3zMzkXPj9Uj98C0pvj2zLl5oPROjptVB6AfWAvOingKz1L5MlHryBXNMFxHP3zSMYkIpDaZhJOEwVxEFS8PIHNegZuImFdSV92be1JM3XIOYlVoK4qp7N2fqCRg9ejTuv/9+CfLjjz+OAQMGSG1SZf9+4LlZwIuhZxv9/L0U+iHrWNxxxmuYObcKSpaUQklfoZ6Sfol9nSD15CturYuJ528aRloIIw6iYRQRS+yNomBpGMXOM+gZuEkFXQFm5089meUZ5GzUUpBX3/zcg64n6wHXrVu3lsC2b98ey5cvh/Upo4iv0KeKcGkrYPc3YeHfssugT7WVmLGoFipXDtBHi/4hEXQ9hQmCDTERoJ5iwufLYPH8TcPIHHIaRuZYSplEwdIwktCwokGAm5QGNA5xJEA9OaJhQJEAtaQIjN1dCQRZT9a/G88880xYh5z8V+nSpfHFF1/ghBNOyG8Kf1+7Fhh4dVj7n6FnFvU4ahnGzT4T9U8PnllkAQmynsIEwYaYCVBPMSP0PIF4/qZhZA43DSNzLKVMomBpGEloWNEgwE1KAxqHOBKgnhzRMKBIgFpSBMburgSCqifr4dZdu3bFkiVLJD4vvvgirrzySqktrDJ6BPDyHKl5R2ZNDCr7KG4efzYubZsqxYJUCaqegrTGfs6VevKTtt61xPM3DSM9hpFG0TCKRMVAmyhYGkYGgAY8BTepgAvA8PSpJ8NAA5yOWgrw4nsw9aDq6Yknngh7RtG//vUvTJs2LfSQapdPB4WMppzG9ZB6OPQMo39eyzO6oPdxD+GurqkYOtDha2z5nZP8Pah6SvJlLbLpUU9Fhj7qC4vnbxpGUWMrtCMNo0IR6XUQBUvDSI8hRx0hwE3qCAuWYidAPcXOkBn+JkAtUQkmCQRRT+vXr0ezZs2Qm5trozzllFOwYcMGlCtXzm6LVMhYtRbFh8hfRzu77Ho0alID0yelITW4Hy7KwxVEPUXSCdvMEKCezHD0Mot4/qZhZI40DSNzLKVMomBpGEloWNEgwE1KAxqHOBKgnhzRMKBIgFpSBMburgSCpqddu3bh7LPPxp49e2wuqSGXxzKLGjVqZLdFKuS8uAQ5d92C9JQjRtNXmSdhQOM3sfSZdJQqFWlUsNqCpqdgra7/s6We/GeuekXx/E3DSJWec38aRs5sYoqIgqVhFBNKDg4R4CZFGZgkQD2ZpBnsXNRSsNff9OyDpKfDhw/D+gW0t99+W8I4ffp03HjjjVKbVMnORu4DE5Ayd5rUbFWmlxiDLqtvxNGVXL7GFjYqeRuCpKfkXcX4mRn1FD9r4XQn4vmbhpETJfV2GkbqzKIaIQqWhlFUyNjJhQA3KRc4DCkToJ6UkXGAAwFqyQEMm7UIBEVP1tfPhg0bhocfflji1L9/fzz55JPuzy2K8JBrK8n7Wa1w7GszUevEYlLOIFeCoqcgr7Gfc6ee/KStdy3x/E3DSI9hpFE0jCJRMdAmCpaGkQGgAU/BTSrgAjA8ferJMNAAp6OWArz4Hkw9KHp6/vnn0atXL4lg48aNsXbtWpQuXVpqlyrbtgGdW0pNVmV29kCc+fwdoa+3pYfFgtwQFD0FeY39nDv15CdtvWuJ528aRnoMI42iYRSJioE2UbA0jAwADXgKblIBF4Dh6VNPhoEGOB21FODF92DqQdDThx9+iAsuuADZoa+W5b/Kly+PTZs2oVatWvlNEd9/GPoQaqycbMcyclMwMu0RtJt6Bdq2CvgTrm0qRwpB0NOR2bLkNQHqyWvCsecXz980jGLnmZ+BhlE+CcPvomBpGBmGG8B03KQCuOgeTpl68hBuwFJTSwFbcI+nm+x62rFjB6xPEu3du1ciuWrVKrRp00ZqK1jZOWUJqk2/BcWFh1w/kzUIaXffjuuvTivYnfUQgWTXExfZXwLUk7+8da4mnr9pGOkQjDyGhlFkLjG3ioKlYRQzzsAn4CYVeAkYBUA9GcUZ6GTUUqCX3/jkk1lP+/btQ4sWLbB582aJ24MPPojbbrtNapMqWVn47Y5xqLhyutRsVWZeugLXP3BG6JlHYSE2hAgks564wP4ToJ78Z656RfH8TcNIlZ5zfxpGzmxiioiCpWEUE0oODhHgJkUZmCRAPZmkGexc1FKw19/07JNVTxkZGejSpQuWL18uIevbty+sX0VLTXX4Otlvv+HwDTei5Lb3pXFW5ZOje6DB2odCY8NCbPiHQLLqiQtcNASop6LhrnJV8fxNw0iFnHtfGkbufLSjomBpGGlj5MB/CHCTohRMEqCeTNIMdi5qKdjrb3r2yagn6xfRBg8ejEcffVTC1apVqzwDqVSpUlK7XfnuO2T3ugpp+3bbTfmFNccMQIs3RiC9BL+Kls8k0nsy6inSPNnmDwHqyR/OsVxFPH/TMIqFpDyWhpHMw1hNFCwNI2NYA5uIm1Rgl96TiVNPnmANZFJqKZDL7tmkk1FPU6dOxZAhQyRmp5xyCt59910cc8wxUrtd2bgROTd0Q2rGIbvJKvyVm4pZJzyGG165HMWLSyFWIhBIRj1FmCabfCJAPfkEOobLiOdvGkYxgCwwlIZRASCmqqJgaRiZohrcPNykgrv2XsycevKCajBzUkvBXHevZp1senr55ZfRuXNnCZf1i2jWL6XVrVtXas+r5OQAc2Yj98G7kZIbKguvH7OOwcST52HcwvooXVoIsOhIINn05DhRBnwhQD35gjmmi4jnbxpGMaGUBtMwknCYq4iCpWFkjmtQM3GTCurKezNv6skbrkHMSi0FcdW9m3My6entt9+G9bWzHMsE+udlPavIam/WrFl+k/x++1DgtYVyW6j234x6GH3qHMyaWxUVyoeF2eBAIJn05DBFNvtIgHryEbbmpcTzNw0jTYgRhtEwigDFRJMoWBpGJogGOwc3qWCvv+nZU0+miQY3H7UU3LX3YubJoqdPP/0U559/Pg4fPixhmj9/Pnr27Cm12ZUffgDanWdX8wtrMy7GqJMew8vPlUGVyvw5tHwu0bwni56imSv7eE+AevKecaxXEM/fNIxipXlkPA2jIyyMlkTB0jAyijaQybhJBXLZPZs09eQZ2sAlppYCt+SeTjgZ9PT111/jggsuwJ49eyRWEyZMwPDhw6U2u7JvH3I6XozUX+UHXE/PugWP1R6C12YUR43jaBbZvKIsJIOeopwqu/lAgHryAXKMlxDP3zSMYoQpDKdhJMAwWRQFS8PIJNlg5uImFcx192rW1JNXZIOXl1oK3pp7OeNE19OPP/6I5s2b46uvvpIwDR06FJMmTUJKSgHTJ/QLalj6MnLuG47Uvw5KY7ZmnIrOJ63EG0+n4fgaBcZJPVlxIpDoenKaF9uLhgD1VDTcVa4qnr9pGKmQc+9Lw8idj3ZUFCwNI22MHPgPAW5SlIJJAtSTSZrBzkUtBXv9Tc8+kfW0L/QpobZt2+Y90Frk0rt3b8ycORPp6eli89/lO0cCLz0X3h5qubfk47ju5ctR83iaRREBRdGYyHqKYnrs4jMB6sln4BqXE8/fNIw0ADoMoWHkACbWZlGwNIxipcnx3KSoAZMEqCeTNIOdi1oK9vqbnn2i6mn//v244oor8NZbb0lILrnkEixZsgQlS5aU2vMqu0NfP7u4cVj7wZxieLzk/ei+tCdqnkCzKAyQQkOi6klhiuzqIwHqyUfYmpcSz980jDQhRhhGwygCFBNNomBpGJkgGuwc3KSCvf6mZ089mSYa3HzUUnDX3ouZJ6KeDhw4gCuvvBKrV6+WkJx77rlYtWoVypUrJ7XnVUIPw86+/FKk/fClFFuZ0Qnja4zBrGeronZNmkUSHI1KIupJY5oc4hMB6skn0DFcRjx/0zCKAWSBoTSMCgAxVRUFS8PIFNXg5uEmFdy192Lm1JMXVIOZk1oK5rp7NetE09Mff/yBLl264I033pCQnHLKKXj77bdRtWpVqT2vEnrOUdb1fZC+c4sUO5STjrNO+Born0pDnVo0iyQ4mpVE05PmNDnMJwLUk0+gY7iMeP6mYRQDyAJDaRgVAGKqKgqWhpEpqsHNw00quGvvxcypJy+oBjMntRTMdfdq1omkp71796Jy5cphKOrUqYO1a9fi+OOPD4th4yfI6Xc1Ug/vl2KWWTSg4ku4a97ZOLE2zSIJTgyVRNJTDNPkUJ8IUE8+gY7hMuL5m4ZRDCALDKVhVACIqaooWBpGpqgGNw83qeCuvRczp568oBrMnNRSMNfdq1knip6+//57nHDCCWEYateunfcco5o1a4bF8NqryL3jJoT+4S3F9meXxK2VF+Pu+Q34gGuJTOyVRNFT7DNlBj8IUE9+UI7tGuL5m4ZRbCzF0TSMRBoGy6JgaRgZBBvQVNykArrwHk2bevIIbADTUksBXHQPp5wIerJ+Da1ixYoRKXz77beoVatWeOzXX5FzYUOk5mRKsR2ZJ2BUzTl4aM6JOLYaP1kkwTFQSQQ9GZgmU/hEgHryCXQMlxHP3zSMYgBZYCgNowJATFVFwdIwMkU1uHm4SQV37b2YOfXkBdVg5qSWgrnuXs063vX0a8j4OfrooyNOf8uWLahXr17E2LcvfIDa914pxaxPFvU69WM8O6M8jjmaZpEEx1Al3vVkaJpM4xMB6skn0DFcRjx/0zCKAWSBoTSMCgAxVRUFS8PIFNXg5uEmFdy192Lm1JMXVIOZk1oK5rp7Net41pP177rLLrsMH374Ydj0x48fjxEjRoS1IycH3901A8e/fA9SC0QXlxmGNquGokL5AgFWjRGIZz0ZmyQT+UaAevINtfaFxPM3DSNtjGEDaRiFITHTIAqWhpEZpkHOwk0qyKtvfu7Uk3mmQc1ILQV15b2Zd7zqadeuXWjfvj2sTxEVfE2ZMgWDBw8u2Azs34+feg5B1R0rw2OhlsN3P46S3a+IGGOjGQLxqiczs2MWvwlQT34TV7+eeP6mYaTOz2kEDSMnMjG2i4KlYRQjTA4HNymKwCQB6skkzWDnopaCvf6mZx+Pevpux3doc3EbfPXVV9J0K1WqhE8//RQ1atSQ2q1K7n//iz969UbZzB/DYlm5Kci9bgiK3TYUSC34uaOw7myIgUA86imG6XBoEROgnop4AaK4vHj+pmEUBbAou9AwihKUajdRsDSMVOmxf0EC3KQKEmE9FgLUUyz0OFYkQC2JNFiOlUC86Wn79u1o3bo1rF9FE1/nnHMOXn31VVSpUkVszivnLH0FOaMGIB3yr6HlhKIfVO2Hc+YMQLHjqoaNY4N5AvGmJ/MzZEY/CVBPftLWu5Z4/qZhpMcw0igaRpGoGGgTBUvDyADQgKfgJhVwARiePvVkGGiA01FLAV58D6YeT3ratGkT2rZtiz179kgzbd68OV5++WVYnzAq+Mqc+zyKjb+tYDN+zT4Kb146C10mNeGHisLoeNcQT3rybpbM7BcB6skv0vrXEc/fNIz0ORYcScOoIBFDdVGwNIwMQQ1wGm5SAV58D6ZOPXkANaApqaWALrxH044XPa1duxbt2rVDRkaGNFPLQFq0aBHKlSsntVuVP5+YhVKP3RnWvjHjHHxz+1Po0r9aWIwN3hKIFz15O0tm94sA9eQXaf3riOdvGkb6HAuOpGFUkIihuihYGkaGoAY4DTepAC++B1OnnjyAGtCU1FJAF96jaceDnhYvXozu3buHfuDM+hLZkdfll1+O+fPno3Tp0kca/yn9dv80VJw3Lqx9bvZNqPLIiNAnlYqHxdjgPYF40JP3s+QV/CJAPflFWv864vmbhpE+x4IjaRgVJGKoLgqWhpEhqAFOw00qwIvvwdSpJw+gBjQltRTQhfdo2kWpp9zcXEybNg3//ve/w2bXp08fPPnkkyhZsmRYbOeYGThh8d1h7dMwCufOvRnnnM0HW4fB8amhKPXk0xR5GR8JUE8+wta8lHj+pmGkCTHCMBpGEaCYaBIFS8PIBNFg5+AmFez1Nz176sk00eDmo5aCu/ZezLyo9GR9mmjMmDEYO3Zs2LRuv/12jB8/HmlpaX/HrE8ePfQAsOo1YNfXYf2thseK/R/avXg96p6YEjHORn8IFJWe/Jkdr+I3AerJb+Lq1xPP3zSM1Pk5jaBh5EQmxnZRsDSMYoTJ4eAmRRGYJEA9maQZ7FzUUrDX3/Tsi0JP1nOKrE8VPf3002HTmTx5MoYMGYKUFMH4mT8XGDc8rG9+wzNHT8TlS65G5WOEMflBvvtKoCj05OsEeTFfCVBPvuLWuph4/qZhpIUw4iAaRhGxxN4oCpaGUew8g56Bm1TQFWB2/tSTWZ5BzkYtBXn1zc/dbz0dOHAAvXv3xtKlS6XJWAbRnDlzcPXVV0vtoQcbIeei0C+d/fK93P5PbWnt0CeLFt8Q+upaxDAbfSbgt558nh4v5zMB6sln4BqXE8/fNIw0ADoMoWHkACbWZlGwNIxipcnx3KSoAZMEqCeTNIOdi1oK9vqbnr2ferL+nda5c2esX79emkaxYsWwbNmyvF9JkwLZ2Tg4YBjKvLtIas6vfHjmHWg0bzBS+ciifCRF/u6nnop8srwBzwlQT54jjvkC4vmbhlHMOO0ENIxsFGYLomBpGJllG8Rs3KSCuOrezZl68o5t0DJTS0FbcW/n65eevv32W1xyySX48ssvpQlVqFABb7zxBho3biy1448/8GuPm1Dp27Vye6i2J6sC9nS6Hac/0Aeh766FxdlQdAT80lPRzZBX9pMA9eQnbb1riedvGkZ6DCONomEUiYqBNlGwNIwMAA14Cm5SAReA4elTT4aBBjgdtRTgxfdg6n7o6bNPP0Pbdm1hHSbEV61atbBy5UqcfPLJYjNyfvwJv13WA0f/uU1qtyoTiz2IdnN6osEZNIrC4MRBgx96ioNp8hZ8IkA9+QQ6hsuI528aRjGALDCUhlEBIKaqomBpGJmiGtw83KSCu/ZezJx68oJqMHNSS8Fcd69m7bWe3n77bbRv3x6HDx+WptCwYcO8r6FVr15daj/0yRfIvKYryqf8JrVblT05VZC5/F1UP7FMWIwN8UHAaz3Fxyx5F34RoJ78Iq1/HfH8TcNIn2PBkTSMChIxVBcFS8PIENQAp+EmFeDF92Dq1JMHUAOakloK6MJ7NG0v9bRgwQL07Nkz7M7btm0LK1axYkUptvuFdah4T2+USsmS2q3K52U74MTZo1DylFphMTbEDwEv9RQ/s+Sd+EWAevKLtP51xPM3DSN9jgVH0jAqSMRQXRQsDSNDUAOchptUgBffg6lTTx5ADWhKaimgC+/RtL3QU25uLipVqoR9+/aF3bX1K2hPP/00SpUqJcU23/Mi6i26BQWfX/1tZi1sHLIAnW8+no8rkojFZ8ULPcXnTHlXfhCgnvygHNs1xPM3DaPYWIqjaRiJNAyWRcHSMDIINqCpuEkFdOE9mjb15BHYAKallgK46B5O2bSerK+e9evXD/PmzQu766FDh2LixIlIT0+3Y3/9BbxxwyJ03DTEbssvbMo6H7kzZuLsZuXzm/ge5wRM6ynOp8vb85gA9eQxYAPpxfM3DSMDQP9JQcPIHEspkyhYGkYSGlY0CHCT0oDGIY4EqCdHNAwoEqCWFIGxuysBk3qy/h1mfQVtzZo1YdccN24cRo4cGfqU0JGHVX+3MxcvXf0Shvw6KKz/u8W74uTlD6DKccXDYmyIXwIm9RS/s+Sd+UWAevKLtP51xPM3DSN9jgVH0jAqSMRQXRQsDSNDUAOchptUgBffg6lTTx5ADWhKaimgC+/RtE3p6bPPPsPll1+OHTt2hN1py5Ytw0yk5StyMGbsz1j/a2OUSMmRxrxTawjOW3Ibipc4Yi5JHViJWwKm9BS3E+SN+UqAevIVt9bFxPM3DSMthBEH0TCKiCX2RlGwNIxi5xn0DNykgq4As/OnnszyDHI2ainIq29+7ib09Morr6Br167IyMiQbjAtLQ0rVqxAmzZt7Hbrx9LGTMzG9Ldy8czuUehSTP7q2rYWd+DkabeADyyykSVUwYSeEmrCvFlPCVBPnuI1klw8f9MwMoI0LwkNI3MspUyiYGkYSWhY0SDATUoDGoc4EqCeHNEwoEiAWlIExu6uBGLRU05ODh588EGMGDEi7BoNGjTAkiVLULt2bTv29be56HN7Nnb+uB8Tf56MnsVm2TGr8EeTLjhq+lSaRRKVxKrEoqfEminv1g8C1JMflGO7hnj+pmEUG0txNA0jkYbBsihYGkYGwQY0FTepgC68R9OmnjwCG8C01FIAF93DKevq6Y8//sDNN9+MuXPnht1dt27d8n4JrVy5cnmxkK+E+YtyMPSJbPTduwTDs+5EhbRDYeOwcAVQ/4zwdrYkDAFdPSXMBHmjvhKgnnzFrXUx8fxNw0gLYcRBNIwiYom9URQsDaPYeQY9AzepoCvA7PypJ7M8g5yNWgry6pufu46evv/+e1x11VXYsGFD2A3dc889uPPOO2F9Hc167dmTi8FjsvHrhs8x6ffhqF98c9iYvIbzWgEznuOniyLTSZhWHT0lzOR4o74ToJ58R658QfH8TcNIGZ/jABpGjmhiC4iCpWEUG0uOBrhJUQUmCVBPJmkGOxe1FOz1Nz17VT198MEH6NixY8gI2iPdSnp6OubPn5/3LKP8wBtrcjBo7GHc88P9YV8/y++D1HRg8Gigz/VAsWJ2MwuJSUBVT4k5S961XwSoJ79I619HPH/TMNLnWHAkDaOCRAzVRcHSMDIENcBpuEkFePE9mDr15AHUgKaklgK68B5NO1o95ebm5hlC1157LaxnF4mv6tWrY9myZWjYsGFe8x8H/36w9Stv7sXin/vhrOKfiN2PlNtcAYy8C6hW7UgbSwlNIFo9JfQkefO+EaCefEOtfSHx/E3DSBtj2EAaRmFIzDSIgqVhZIZpkLNwkwry6pufO/VknmlQM1JLQV15b+YdjZ4OHTqEUaNGYerU0AOpC7yaN2+OBQsWwDKNrNcnm3Jx/Z3ZOPqnLzF/fy9US99bYESoWr0OMPZB4Lzzw2NsSWgC0egpoSfIm/eVAPXkK26ti4nnbxpGWggjDqJhFBFL7I2iYGkYxc4z6Bm4SQVdAWbnTz2Z5RnkbNRSkFff/NwL09M333yDnj17RnxeUb9+/fJMpNKlS+PPP4EHH8vGI8szMWDvixiZMxKlUrOlG85NS0fK0DHA1b359TOJTPJUCtNT8syUM/GDAPXkB+XYriGev2kYxcZSHE3DSKRhsCwKloaRQbABTcVNKqAL79G0qSePwAYwLbUUwEX3cMpuelq69BX06tUT1ieMCr6mTJmCQYMGITU1FR98mIOb781G852v4/a/7kON9P8V7I7cStWQMmsBcFLdsBgbkoeAm56SZ5aciV8EqCe/SOtfRzx/0zDS51hwJA2jgkQM1UXB0jAyBDXAabhJBXjxPZg69eQB1ICmpJYCuvAeTTuSniyDaPTo0bBMoYKvChUq4IUXXsDFF18M61lFkyf+jgovzEb33GdQNf2Xgt3/rp8R+urZk88AFSpGjrM1aQhE0lPSTI4T8Z0A9eQ7cuULiudvGkbK+BwH0DByRBNbQBQsDaPYWHI0fyWNGjBLgP/oMcszyNmopSCvvvm5F9TTjh07Qp8q6oVPPgl/UPWFF16IOXPm4Pjjj8e693Jw95hfMHtnR9Qs9r3zjXW5DrjrHn4FzZlQUkUK6qlEiRJJNT9Oxl8C1JO/vHWuJp6/aRjpEIw8hoZRZC4xt4qCpWEUM87AJ+AmFXgJGAVAPRnFGehk1FKgl9/45PP1ZP0K2s6dO9G/f39kZmaGXWfkyJEYM2YM/jxcAvdNysYLbx3AvJ8G48Liq8P65jWc1ggYEfoFtHMaR46zNSkJ5OvJmlyTJk1Awygpl9m3SVFPvqHWvpB4/qZhpI0xbCANozAkZhpEwdIwMsM0yFm4SQV59c3PnXoyzzSoGamloK68N/O29PTGG2/g8ccfx8qVK8MuYn0F7fnnn0fbtu3x0rJsvH/fKnTaPx8XFFuDYim5Yf1zTzgZKSNCD7ZucSGQkhIWZ0NyE+Dfp+ReX79nRz35TVz9euL5m4aROj+nETSMnMjE2C4KloZRjDA5HNykKAKTBKgnkzSDnYtaCvb6m579hg0b0LlzZ+zevTsstfWcolmzZuHgn9Ux4c6fcN3Hd6BF8TfD+tkNI8aHfgHtGoSehG03sRAsAvz7FKz19nq21JPXhGPPL56/aRjFzjM/Aw2jfBKG30XB0jAyDDeA6bhJBXDRPZwy9eQh3IClppYCtuAeTTcnJwfTp0/HwIEDYZULvsaPH48B19yIdfetR8raVbgwbRmOSs0o2C2vnhv6JFHKU6FfQGvaLGKcjcEhwL9PwVlrP2ZKPflBObZriOdvGkaxsRRH0zASaRgsi4KlYWQQbEBTcZMK6MJ7NG3qySOwAUxLLQVw0Q1P+ZdffsHNN9+MRYsWhWWuXr06ljw+DSe8sA5VNi1EWoSvneUPyk1JRUqzdsC/hwD16+c38z3ABPj3KcCL78HUqScPoBpOKZ6/aRiZg0vDyBxLKZMoWBpGEhpWNAhwk9KAxiGOBKgnRzQMKBKglhSBsbtEYP369ejRowe+/z78l816XXYZHjr9XFRaNh3FU8I/dZSfKKNERRS/ZxzQug1Qpkx+M99JgF/npwaMEuB+ZxSnJ8nE8zcNI3OIaRiZYyllEgVLw0hCw4oGAW5SGtA4xJEA9eSIhgFFAtSSIjB2zyOQlZWFyZMnY/jw4WFE0kNfKZvbpj067N6Bo3J/D4uLDTntuiF1zD1A+fJiM8skkEeAf58oBJMEqCeTNL3JJZ6/aRiZY0zDyBxLKZMoWBpGEhpWNAhwk9KAxiGOBKgnRzQMKBKglhSBsTt27dqFvn37RvwVtHp1amPxUTVwatbXEUn9lZuKL0u0QY2b2qNS54uAqlUj9mMjCVgE+PeJOjBJgHoySfP/27sbODmKOv/jv90km80jeSCQB5KIgEYIhAQBCY8qIurxEAVBAQUPRREJj3ryB+/vA4giCCpyFx59OA45hID4F44TEkIQggiJ4elAEJAQSUxIICGEZOdf1TBD1e5U7VRNzaZ3+jO+4m53VddOv+vL9PZve3oaM5Z5/k3BKJ0xBaN0ltZIZmApGFk0LEQIcJCKQGMTpwB5ctLQEChAlgLBCty9VCrJr3/9azn22GNlzZo1loR+I9kX9j5YPvaPcfJBudlq0wsbSy3ym5bPyWbnzZT9Dh4p6iIkHgh0K8DrU7dEdAgQIE8BWJuoq3n+TcEo3SRQMEpnaY1kBpaCkUXDQoQAB6kINDZxCpAnJw0NgQJkKRCsoN2XL18uo0aN6rL3JwzsJ18fO0wmtvXr0lZeMe+N/eWZY74hh5+xjQwYUF7LVwS6F+D1qXsjetQuQJ5qt9pUPc3zbwpG6WaBglE6S2skM7AUjCwaFiIEOEhFoLGJU4A8OWloCBQgS4FgBeuuryq66aab5BOf+ESXPd+9T6vMf9eW6pPPql8utK6jj1yzy3/IwT/YS8aOrt6ny6CsQMAQ4PXJwODbugXIU92EDR/APP+mYJSOm4JROktrJDOwFIwsGhYiBDhIRaCxiVOAPDlpaAgUIEuBYAXqrj/57LTTTpMbbrih6l7fO3GS7DFoddU2vfIvx/9Ytjn14852GhDoToDXp+6EaA8RIE8hWpumr3n+TcEo3RxQMEpnaY1kBpaCkUXDQoQAB6kINDZxCpAnJw0NgQJkKRCsAN03btwoV199tXz5y1+W9evXd9ljfbHQ+WMPkM9utthq61BLL23cUtYOHy9rZuwp75o5U/r372/1YQGBEAFen0K06NudAHnqTmjTt5vn3xSM0s0HBaN0ltZIZmApGFk0LEQIcJCKQGMTpwB5ctLQEChAlgLBmrz7Y489Jl/60pdk7ty5XfZ0S1Uo+vrIcXL85n1kUOsGq33ZxuFywwnz5YjP9peFC/+QtU2fPp2CkaXEQqgAr0+hYvT3CZAnn04+2szzbwpG6eaEglE6S2skM7AUjCwaFiIEOEhFoLGJU4A8OWloCBQgS4FgTdr9hRdekK222qrL3uk7D32irY8cN2qE7D+0TdpaSl366BWvnP1vMuRTB/Ex6FV1WBkrwOtTrBzbVRMgT9VU8rXOPP+mYJRubigYpbO0RjIDS8HIomEhQoCDVAQamzgFyJOThoZAAbIUCNZk3fXbz/r27Wvt1aTWFpnYp0UOGNJfjhgxRMa12e1m55K64XXLyWeLfP4EEfU9eTJ1+L5eAfJUryDbmwLkydTI5/fm+TcFo3RzRMEonaU1khlYCkYWDQsRAhykItDYxClAnpw0NAQKkKVAsCbprj/97Ne//rUcfvjhlT3aThWKZk8cKdsPaKusc32jrzNq+diRIiefKurSpEo38lSh4JsEAuQpASJDVATIU4Uit9+Y598UjNJNEwWjdJbWSGZgKRhZNCxECHCQikBjE6cAeXLS0BAoQJYCwXp5d10ouuuuu+Tb3/62zJkzx9qb60cPkcPVFUW+R0dLq7Qe+AmRL31FZJttunQlT11IWFGHAHmqA49NuwiQpy4kuVthnn9TMEo3PRSM0llaI5mBpWBk0bAQIcBBKgKNTZwC5MlJQ0OgAFkKBOul3Ts6OuT3v/+9fOc735G7777b2ouBaunEIW1ywfjNrfXmwtqR75IBJ/yztPzTQSKbbWY2Wd+TJ4uDhToFyFOdgGxuCZAniyOXC+b5NwWjdFNEwSidpTWSGVgKRhYNCxECHKQi0NjEKUCenDQ0BAqQpUCwXtZdF4ruuOMO+da3viX33nuv9ey3VvccOm+LwXLQsEEyqE+r1VZeWLbnsTJq5qdFtt8+u0dReb3rK3lyybA+RoA8xaixjUuAPLlk8rPePP+mYJRuXigYpbO0RjIDS8HIomEhQoCDVAQamzgFyJOThoZAAbIUCNZLuutC0W233Sbf/OY3ZcGCBV2e9W6qQPS7bUbJiL59urTpFW+0bSb9FiwU6devartrJXlyybA+RoA8xaixjUuAPLlk8rPePP+mYJRuXigYpbO0RjIDS8HIomEhQoCDVAQamzgFyJOThoZAAbIUCJbz7uvXr5dbbrlFzjvvPHnooYe6PFt9HdG3hrXLqaOHy0B1k+uqj83UW9MuniWy2+5Vm30ryZNPh7ZQAfIUKkZ/nwB58unko808/6ZglG5OKBils7RGMgNLwciiYSFCgINUBBqbOAXIk5OGhkABshQIltPuq1evluuuu06+973vydNPP93lWb5Dvf3s2KFt8umRw2S79q5XFWWferbnh0WO+ozIXnuL9Onap8ugVVaQpyoorIoWIE/RdGxYRYA8VUHJ2Srz/JuCUbrJoWCUztIayQwsBSOLhoUIAQ5SEWhs4hQgT04aGgIFyFIgWM66P/vXZ+WKK6+Qiy66SNauXVt5dvv1bZVTRg2WUf36yDBV/Jk0oJ+0iuOKovHbiVz+c5HxEyrbx35DnmLl2K6aAHmqpsK6WAHyFCvXc9uZ598UjNK5UzBKZ2mNZAaWgpFFw0KEAAepCDQ2cQqQJycNDYECZCkQLAfdS6WS3H///XLppZfKL3/5S+sZjVI1oUu2GCJHjBzsLhCZWxx2nMiZ/yIyeLC5Nvp78hRNx4ZVBMhTFRRWRQuQp2i6HtvQPP+mYJSOnYJROktrJDOwFIwsGhYiBDhIRaCxiVOAPDlpaAgUIEuBYJuwu74/0a233ppdTTR//nzrmQxRSyeqt5z9y5jh2RVFVmO1hYmTRM6/SGSnKdVao9eRp2g6NqwiQJ6qoLAqWoA8RdP12Ibm+TcFo3TsFIzSWVojmYGlYGTRsBAhwEEqAo1NnALkyUlDQ6AAWQoE2wTdV6xYIb/4xS/kggsukI1LXpD3t/WRif37yLi2vjJGveXsPe39/W85Kz/nvv1FDvy4yEGHiEzfU6RV3wI77YM8pfUs+mjkqegJSLv/5CmtZyNGM8+/KRilE6ZglM7SGskMLAUji4aFCAEOUhFobOIUIE9OGhoCBchSIFgPdn/88cfliiuukJt//CM5e1h/2W/oAJnYFvYx97LFeJEPfOTNG1nvuZdIW1tD94A8NZS3cIOTp8JNeUN3mDw1lDfJ4Ob5NwWjJKTZIBSM0llaI5mBpWBk0bAQIcBBKgKNTZwC5MlJQ0OgAFkKBGtw9zVr1mRvO5s1a5bceeed8pVB/eRbW42o7W1m5nP7wMEi/+cbIqPHmGsb/j15ajhxoX4AeSrUdDd8Z8lTw4nr/gHm+TcFo7o5KwNQMKpQpP3GDCwFo7S2RRyNg1QRZ71x+0yeGmdbtJHJ0qafcX0T68WLF2dvO/vxjy+VdevWyifVW85O3XIzed/g9tqfYIt6i9mUPUROOkVkj+m1b5ewJ3lKiMlQQp4IQUoB8pRSszFjmeffFIzSGVMwSmdpjWQGloKRRcNChAAHqQg0NnEKkCcnDQ2BAmQpECxh99WrV8stt9wiP7n03+T+++bL7n1a5aND+smnRwyVbdvdbz17raOPLO/3Hhm43QQZvtM4aR2/lSoU7Syyw2SR9oACU8J9KQ9FnsoSfE0hQJ5SKDJGWYA8lSXy+9U8/6ZglG6eKBils7RGMgNLwciiYSFCgINUBBqbOAXIk5OGhkABshQIVmd37T1nzhz5zZW/kNH/c5NMau8j7+zfpv717fZtZ/PlIFl+zFdkr8+9S0Zu4S4o1fkU69qcPNXFx8adBMhTJxAW6xIgT3Xx9cjG5vk3BaN05BSM0llaI5mBpWBk0bAQIcBBKgKNTZwC5MlJQ0OgAFkKBIvo/sYbb8iCBQvUW85+Lb+/epZ8bTORo0YOlQGtLTWN9mrrcFlx+iUy/rMflJbaNqlp3EZ0Ik+NUC3umOSpuHPfiD0nT41QTTumef5NwSidLQWjdJbWSGZgKRhZNCxECHCQikBjE6cAeXLS0BAoQJYCwWrs/tprr8k998yXa6+9Va79z19Ix+sr5NwR7XLSFsNlYI2ForWbbS3tp8yU1oMP2uRvNatxt7nnTK1Q9KtJgNenmpjoVKMAeaoRahN2M8+/KRilmwgKRuksrZHMwFIwsmhYiBDgIBWBxiZOAfLkpKEhUIAsBYJ5uut7Ev33HXPkyqtulgdv/6Xs22ejTB/cJtMG9ZddBrbLYHWPou4e6/pvIf32P0D6HKo+5ex96gbWrd1v092YPdlOnnpSu/l/Fnlq/jnuyT0kTz2pHfezzPNvCkZxhtW2omBUTSXBOjOwFIwSgBZ8CA5SBQ9A4t0nT4lBCzwcWapv8vXvB9f+5+1y7/X/T05ZNle2H9AmrdIio/q1Zl99o28siSwa/DEZsO/uMnH/bWXADtuIjBsnuX/fmWenyJMHh6ZgAfIUTMYGHgHy5MHJSZN5/k3BKN2kUDBKZ2mNZAaWgpFFw0KEAAepCDQ2cQqQJycNDYECZCkMrFQqyWX/dq18+cSjrQ3/e6th8qGhA611roUO1fDStE/JiP87U9q2Ge/q1ivXk6deOW25fdLkKbdT0yufGHnK/7SZ598UjNLNFwWjdJbWSGZgKRhZNCxECHCQikBjE6cAeXLS0BAoQJa6B3vqqeVy+pnfkYWLnpBnn74t22CYuvn0x/r3kd3V282mDuwvew0Z0P1AqkfHdjtL67nfF9lhh5r697ZO5Km3zVi+ny95yvf89LZnR57yP2Pm+TcFo3TzVYiC0YsvvihXX3213HTTTfLoo4/KoEGDZMqUKXLkkUfKUUcdJe3t7elE3xrJDCwFo+S8hRuQg1ThpryhO0yeGspbqMHJkj3d6gIi+fPiFer3jfvlhd/cIu945jey+4A1WVGoXd1PaOkbG6Sf+qiy8W197Q2rLHWot6atHz9F+u+zh7RM20VkpykiY8b06recVdlNaxV5sjhYqFOAPNUJyOaWAHmyOHK5YJ5/UzBKN0VNXzCaM2eOzJgxQ15++eWqatOmTZPZs2fL+PFpL+s2A0vBqCo9KwMEOEgFYNG1WwHy1C0RHWoUKHqW1IeZyfw/LJPZtyyQ126/SfZaebvs1L5W3t3eVtNNqp3MV/1aZOepIv37O7s0Y0PR89SMc7op94k8bUr95vvZ5Cn/c2qef1MwSjdfTV0wevzxx2Xq1Kmybt06GTJkiHz/+9+X6XtMl3Wvr8uuNjr//PMzSV00mjdvngwcWNv9A2rhNwNLwagWMfr4BDhI+XRoCxUgT6Fi9HcJFClLujj0+P9ulD/d9pAsv/13svSpP8vKFX9SVxC9KAcPGyzjarhqyOVYWT9yrMi31FvO9nt/ZVWRvilSnoo0r5tqX8nTppJvzp9LnvI/r+b5NwWjdPPV1AWjQw45RG655Rbp06eP/OnBP6mruXey5K688ko5/vjjs3WXXHKJnHzyyVZ7PQtmYCkY1SPJtlqAgxQ5SClAnlJqFnusZsySflvZypdL8r9PluTJec/IP26/TYY+eads1/GwbN++Rkb27ZNu0t+j3mq2+55vXk20444iW45u6recdQfXjHnqbp9pb5wAeWqcbRFHJk/5n3Xz/JuCUbr5atqC0eLFi2VH/cuXepx11lly7rnndlHr6OiQPfbYQxYsWCCbb7656Hsd9e3b/X0FugxUZYUZWApGVYBYFSTAQSqIi87dCJCnboBorlmgN2dJ/Qogf3+pJM8+8Zo8/chKuft/18rCPy6Q7R48Vg4dPlD2VTeirrc49FJpoKydvI+M/dTB0jZxK5EXl4g8/7yIvlzpIx8TmTSpZusidOzNeSrC/PS2fSRPvW3G8v18yVO+50c/O/P8m4JRuvlq2oLRhRdeKGeccUYmtWjRokrxqDPdrFmz5IQTTshWz58/X6ZPn965S9SyGVgKRlGEbGQIcJAyMPi2bgHyVDchA7wlkPcsbdggsvTvJXnq6dfkkUeXqreUvSRPP/U32eGxufLR9XfLLu3LZEgf9ZFliR7LWgbJmg/MkPEfP1D6TJ4ssvmoRCMXY5i856kYs9A8e0memmcu87An5CkPs+B/Dub5NwUjv1VIa9MWjMpvR9OfiLZq1arsbWnVYBYtXCRTdlafPKIeF198scycObNat+B1ZmApGAXzsUEnAQ5SnUBYrEuAPNXFx8aGwKbM0uuvb5Rn/rpKnn5mhTz77Ar5yzNL5cc/PE7Wr1+RPcOW1qEi/XaQAW88IZNkpWzTr1UOGNouHx8+WEbU+baytR0l2aA+xWxoq8j6gcOk72FHSeshM0Tera4YUp+CxiNOYFPmKe4Zs1WeBchTnmen9z038pT/OTPPvykYpZuvpi0YjRs3TpYsWSL77LOPzJ071ym2evVq2WyzzbL2L37xi3LZZZc5+4Y0mIGlYBQiR99qAhykqqmwLlaAPMXKsV1nAStL732v9FcfNiG77tq5m3O5QxVe1qx5XZb/41VZseJVWbbsFXnp76tlxdKVsuqll2WZWvfcilfU13+o9mWyYuVLsnrVi/LGa3+TUsdLalx1wyHjod9UfmhbHzlo+ADZbVC7jO3XV4b2UVWdOh6vbOyQv7zRR14eN0kGv39vmXDQx2SU+rCMllY17saNov4iVcfobGoKWHlSV3z3L9inxJkWfF+/AHmq35AR3hYgT29b5PU78/ybglG6WWrKgtH69esrv2Qcc8wx8vOf/9wrNnjwYPUL6xo58MAD5Xe/+523b7lRB9L3WLZsmUzWl6Orx5NPPinjx4/3dacNAa+APkg9+OCDWZ9ddtmlkm/vRjQi4BAgTw4YVgcLHP7pS+UB9aESHapwck3rIvnw4JXy76vGyLdatpYOVczZqNZ3dGxUX9fJG+vXysYNa2TDG69KaaP617FS/bz12c/sp/7/W8Pb5ZiRQ6t+2thadcOhVzeq4pL6qv+1qP9t3b+fPLluvTy4dp1s3dZPJqjl8eqTytrqvMJHP++HVBFr9fs+KFt85rMyfs+9pH97e7ANG4QL8NoUbsYWbgHy5LahJVyAPIWb9fQWukg0ceLE7MdSMEqn35QFo5UrV8qIESMypRNPPFEuvfRSr9iECRPUPSifz+5fpO9jVMujJeAX0htuuKHyfGoZmz4IIIAAAgj0BoEZR16srgS6Rb4zrF3+z9g3j7v6eb+qrspZ9Nrroq/OWb6hQ5Zt2Ki+6jdxtUi7On4OaG2R4X1bZYS6Oke/Pexd7W2yZb+evVLniQ1t8seJ28urO0+Wd6hi0zYrlsuGLcfIP6bsLOuHqrez8UAAAQQQQACBXiOwYsUKOeyww7LnS8Eo3bQ1ZcFIB2T0aPXRtOpxyimnyA9/+EOv2CT1KSVPPPGETJkyRR5++GFv33IjBaOyBF8RQAABBIoq8PFP/Vh2XH6zzHn3ltKqikF5fqxv7SuvD9tCVk6eKi/us5+sHT0mz0+X54YAAggggAACAQIUjAKwAro2ZcGoJ64w4i1pASmja90CXAZbNyEDGALkycDg27oEtnnP5+SFZ66Vc9UVRv8ydnhuikYd2+woHR/6iHTsPFXUX5CkNGoLkQED6tpXNm68AK9NjTcu0k8gT0Wa7cbvK3lqvHG9P4G3pNUrWH37piwY9cQ9jKpzvr3WvOkWN71+24Xv4gT0Qeree+/NNp7OjUDjENmqIkCeKhR8U6fAWefcJHff87C0qhtAH9y6Uk584QYZ2GLfiDrkR3RM3l1az/y6yJixIm1tb95UWt1jUNa8qt7npv7p7/U9BM/7mmpX9xUav63IO7dT/9TXie9489871Ndhw0J+LH1zIsBrU04mokmeBnlqkonMyW6Qp5xMhOdpmOffvCXNAxXY1JQFI20wZswYWbp0KZ+SFhgIuudTgINUPueltz4r8tRbZy5/z7tLltQNqWX+PaI+0kzktbWiPtJM1EefqSLP39W65SJ91e2t9Q2k+6t/w4aLjBj51j/1/XbvEtlBfVhEwD0C8yfCM6pHoEue+JS0ejgLvy15KnwEkgKQp6ScDRmMglFDWKVpC0aHHHKI3HLLLTJo0CBZtWqV+tTb6jfTXLRwkUzZeUqme/HFF8vMmTOTSJuB5QqjJKSFHoSDVKGnP/nOk6fkpIUdkCwVduobsuPkqSGshR2UPBV26huy4+SpIaxJBzXPv7nCKB1t0xaMLrzwQjnjjDMyqUWLFsmOO+5YVW3WrFlywgknZG36E9L0231SPMzAUjBKIVrsMThIFXv+U+89eUotWtzxyFJx574Re06eGqFa3DHJU3HnvhF7Tp4aoZp2TPP8m4JROtumLRgtXry4UiQ666yz5Nxzz+2i1qEund9jjz1kwYIFsvnmm8uLL74offv27dIvZoUZWApGMYJsYwpwkDI1+L5eAfJUryDblwXIUlmCrykEyFMKRcYoC5CnsgRfUwiQpxSKjR3DPP+mYJTOumkLRpqo/LY0/Xa0Pz34J9lpyk6W3JVXXinHH398tu6SSy6Rk08+2WqvZ8EMLAWjeiTZVgtwkCIHKQXIU0rNYo9Floo9/6n3njylFi32eOSp2POfeu/JU2rR9OOZ598UjNL5NnXB6PHHH5epU6fKunXrZMiQIXLBBRdkVxTp/+Bvuukm+e53v5tJTps2TebNmycDBw5MJmsGloJRMtbCDsRBqrBT35AdJ08NYS3koGSpkNPesJ0mTw2jLeTA5KmQ096wnSZPDaNNNrB5/k3BKBlr8970ukw0Z84cmTFjhrz88svlVdZXXSyaPXu2jB8/3lpf74IZWApG9WqyPQcpMpBSgDyl1Cz2WGSp2POfeu/JU2rRYo9Hnoo9/6n3njylFk0/nnn+TcEonW9TX2FUZtL3JrrqqqvkxhtvFH3Vkb6SaOedd5YjjjhCjj76aPUJYfddowAAMFRJREFUv+rjfRM/zMBSMEqMW8DhOEgVcNIbuMvkqYG4BRuaLBVswhu8u+SpwcAFG548FWzCG7y75KnBwAmGN8+/KRglAH1riEIUjNJx1T6SGVgKRrW70bO6AAep6i6sjRMgT3FubNVVgCx1NWFNvAB5irdjy64C5KmrCWviBchTvF1PbWmef1MwSqdOwSidpTWSGVgKRhYNCxECHKQi0NjEKUCenDQ0BAqQpUAwunsFyJOXh8ZAAfIUCEZ3rwB58vLkotE8/6ZglG5KKBils7RGMgNLwciiYSFCgINUBBqbOAXIk5OGhkABshQIRnevAHny8tAYKECeAsHo7hUgT16eXDSa598UjNJNCQWjdJbWSGZgKRhZNCxECHCQikBjE6cAeXLS0BAoQJYCwejuFSBPXh4aAwXIUyAY3b0C5MnLk4tG8/ybglG6KaFglM7SGskMLAUji4aFCAEOUhFobOIUIE9OGhoCBchSIBjdvQLkyctDY6AAeQoEo7tXgDx5eXLRaJ5/UzBKNyUUjNJZWiOZgaVgZNGwECHAQSoCjU2cAuTJSUNDoABZCgSju1eAPHl5aAwUIE+BYHT3CpAnL08uGs3zbwpG6aaEglE6S2skM7AUjCwaFiIEOEhFoLGJU4A8OWloCBQgS4FgdPcKkCcvD42BAuQpEIzuXgHy5OXJRaN5/k3BKN2UUDBKZ2mNZAaWgpFFw0KEAAepCDQ2cQqQJycNDYECZCkQjO5eAfLk5aExUIA8BYLR3StAnrw8uWg0z78pGKWbEgpG6SytkczAUjCyaFiIEOAgFYHGJk4B8uSkoSFQgCwFgtHdK0CevDw0BgqQp0AwunsFyJOXJxeN5vk3BaN0U0LBKJ2lNZIZWApGFg0LEQIcpCLQ2MQpQJ6cNDQECpClQDC6ewXIk5eHxkAB8hQIRnevAHny8uSi0Tz/pmCUbkooGKWztEYyA0vByKJhIUKAg1QEGps4BciTk4aGQAGyFAhGd68AefLy0BgoQJ4CwejuFSBPXp5cNJrn3xSM0k0JBaN0ltZIZmApGFk0LEQIcJCKQGMTpwB5ctLQEChAlgLB6O4VIE9eHhoDBchTIBjdvQLkycuTi0bz/JuCUbopoWCUztIayQwsBSOLhoUIAQ5SEWhs4hQgT04aGgIFyFIgGN29AuTJy0NjoAB5CgSju1eAPHl5ctFonn9TMEo3JRSM0llaI5mBpWBk0bAQIcBBKgKNTZwC5MlJQ0OgAFkKBKO7V4A8eXloDBQgT4FgdPcKkCcvTy4azfNvCkbppoSCUTpLayQzsBSMLBoWIgQ4SEWgsYlTgDw5aWgIFCBLgWB09wqQJy8PjYEC5CkQjO5eAfLk5clFo3n+TcEo3ZRQMEpnaY1kBpaCkUXDQoQAB6kINDZxCpAnJw0NgQJkKRCM7l4B8uTloTFQgDwFgtHdK0CevDy5aDTPvykYpZsSCkbpLK2RzMBSMLJoWIgQ4CAVgcYmTgHy5KShIVCALAWC0d0rQJ68PDQGCpCnQDC6ewXIk5cnF43m+TcFo3RTQsEonaU1khlYCkYWDQsRAhykItDYxClAnpw0NAQKkKVAMLp7BciTl4fGQAHyFAhGd68AefLy5KLRPP+mYJRuSigYpbO0RjIDS8HIomEhQoCDVAQamzgFyJOThoZAAbIUCEZ3rwB58vLQGChAngLB6O4VIE9enlw0muffFIzSTQkFo3SW1khmYCkYWTQsRAhwkIpAYxOnAHly0tAQKECWAsHo7hUgT14eGgMFyFMgGN29AuTJy5OLRvP8m4JRuimhYJTO0hrJDCwFI4uGhQgBDlIRaGziFCBPThoaAgXIUiAY3b0C5MnLQ2OgAHkKBKO7V4A8eXly0Wief1MwSjclFIzSWVojmYGlYGTRsBAhwEEqAo1NnALkyUlDQ6AAWQoEo7tXgDx5eWgMFCBPgWB09wqQJy9PLhrN828KRummhIJROktrJDOwFIwsGhYiBDhIRaCxiVOAPDlpaAgUIEuBYHT3CpAnLw+NgQLkKRCM7l4B8uTlyUWjef5NwSjdlFAwSmdpjWQGloKRRcNChAAHqQg0NnEKkCcnDQ2BAmQpEIzuXgHy5OWhMVCAPAWC0d0rQJ68PLloNM+/KRilmxIKRuksrZHMwFIwsmhYiBDgIBWBxiZOAfLkpKEhUIAsBYLR3StAnrw8NAYKkKdAMLp7BciTlycXjeb5NwWjdFNCwSidpTWSGVgKRhYNCxECHKQi0NjEKUCenDQ0BAqQpUAwunsFyJOXh8ZAAfIUCEZ3rwB58vLkotE8/6ZglG5KKBils7RGMgNLwciiYSFCgINUBBqbOAXIk5OGhkABshQIRnevAHny8tAYKECeAsHo7hUgT16eXDSa598UjNJNCQWjdJbWSGZgKRhZNCxECHCQikBjE6cAeXLS0BAoQJYCwejuFSBPXh4aAwXIUyAY3b0C5MnLk4tG8/ybglG6KaFglM7SGskMLAUji4aFCAEOUhFobOIUIE9OGhoCBchSIBjdvQLkyctDY6AAeQoEo7tXgDx5eXLRaJ5/UzBKNyUUjNJZWiOZgaVgZNGwECHAQSoCjU2cAuTJSUNDoABZCgSju1eAPHl5aAwUIE+BYHT3CpAnL08uGs3zbwpG6aaEglE6S2skM7AUjCwaFiIEOEhFoLGJU4A8OWloCBQgS4FgdPcKkCcvD42BAuQpEIzuXgHy5OXJRaN5/k3BKN2UUDBKZ2mNZAaWgpFFw0KEAAepCDQ2cQqQJycNDYECZCkQjO5eAfLk5aExUIA8BYLR3StAnrw8uWg0z78pGKWbEgpG6SytkczAUjCyaFiIEOAgFYHGJk4B8uSkoSFQgCwFgtHdK0CevDw0BgqQp0AwunsFyJOXJxeN5vk3BaN0U0LBKJ2lNZIZWApGFg0LEQIcpCLQ2MQpQJ6cNDQECpClQDC6ewXIk5eHxkAB8hQIRnevAHny8uSi0Tz/pmCUbkooGKWztEYyA0vByKJhIUKAg1QEGps4BciTk4aGQAGyFAhGd68AefLy0BgoQJ4CwejuFSBPXp5cNJrn3xSM0k0JBaN0ltZIZmApGFk0LEQIcJCKQGMTpwB5ctLQEChAlgLB6O4VIE9eHhoDBchTIBjdvQLkycuTi0bz/JuCUbopoWCUztIayQwsBSOLhoUIAQ5SEWhs4hQgT04aGgIFyFIgGN29AuTJy0NjoAB5CgSju1eAPHl5ctFonn9TMEo3JRSM0llaI5mBpWBk0bAQIcBBKgKNTZwC5MlJQ0OgAFkKBKO7V4A8eXloDBQgT4FgdPcKkCcvTy4azfNvCkbppoSCUTpLayQzsBSMLBoWIgQ4SEWgsYlTgDw5aWgIFCBLgWB09wqQJy8PjYEC5CkQjO5eAfLk5clFo3n+TcEo3ZRQMEpnaY1kBpaCkUXDQoQAB6kINDZxCpAnJw0NgQJkKRCM7l4B8uTloTFQgDwFgtHdK0CevDy5aDTPvykYpZsSCkbpLK2RzMBSMLJoWIgQ4CAVgcYmTgHy5KShIVCALAWC0d0rQJ68PDQ6BB5a8rBMHbtzl1adp3nz5mXrp0+fLgMHDrT6uLazOrGAwFsCvD7lPwrm+TcFo3TzRcEonaU1khlYCkYWDQsRAhykItDYxClAnpw0NAQKkKVAMLp7BciTl4fGKgI/uu8nMvPeb8qsfb4rn3/v8VaPFStWyJgxY2T9+vUyZMgQWbp0aaVodPkfr5Av3P11uWT6v8rJ7zvJ2o4FBKoJ8PpUTSVf68zzbwpG6eaGglE6S2skM7AUjCwaFiIEOEhFoLGJU4A8OWloCBQgS4FgdPcKkCcvD42dBPQVQtOu+1Blbeei0X/8x3/I0UcfXWm//vrr5fDDD5dysajc8Kcj76h6hVK5na8IaAFen/KfA/P8m4JRuvmiYJTO0hrJDCwFI4uGhQgBDlIRaGziFCBPThoaAgXIUiAY3b0C5MnLQ2MVgc7FH7NotN9++8ncuXMrW33gAx+QI7/3qezKovJKs395HV8RqCbA61M1lXytM8+/KRilmxsKRuksrZHMwFIwsmhYiBDgIBWBxiZOAfLkpKEhUIAsBYLR3StAnrw8NDoEqhWN9hq0p2y//fb2Fh9pE9l/aGUdxaIKBd/UIMDrUw1Im7iLef5NwSjdZFAwSmdpjWQGloKRRcNChAAHqQg0NnEKkCcnDQ2BAmQpEIzuXgHy5OWh0SFwzjnnyI//MktWvbej0qPf/NfljRtfqSxLp2LRZn9slVEPvFk8OvLII+Xb3/722335DoEqArw+VUHJ2Srz/JuCUbrJoWCUztIayQwsBSOLhoUIAQ5SEWhs4hQgT04aGgIFyFIgGN29AuTJy0NjFYF169bJyJEjZe3atdK5KCT/s1rkd+vd698ab9CgQbJ8+XJpb2+v8hNYhcCbArw+5T8J5vk3BaN080XBKJ2lNZIZWApGFg0LEQIcpCLQ2MQpQJ6cNDQECpClQDC6ewXIk5eHxioCixcvlp122klKpdKbrZ2uJJLX1FVHA1rf3rJcRHp7jbS0tMiiRYtk8uTJxlq+RcAW4PXJ9sjjknn+TcEo3QxRMEpnaY1kBpaCkUXDQoQAB6kINDZxCpAnJw0NgQJkKRCM7l4B8uTlodEhcPfdd8uMGTNkxYoVb/boXDQqb1elWDRixAiZPXu27L333uVefEWgqgCvT1VZcrXSPP+mYJRuaigYpbO0RjIDS8HIomEhQoCDVAQamzgFyJOThoZAAbIUCEZ3rwB58vLQ6BF47rnn5JOf/KTcf//9b/b6zgj7yiJ9pdHZbxWU3hpn9913l+uvv14mTJjgGZkmBN4U4PUp/0kwz78pGKWbLwpG6SytkczAUjCyaFiIEOAgFYHGJk4B8uSkoSFQgCwFgtHdK0CevDw0diOg72N0yimnyOV/+5n1aWiVzYwrjD7/+c/LxRdfLAMHDqw08w0CPgFen3w6+Wgzz78pGKWbEwpG6SytkczAUjCyaFiIEOAgFYHGJk4B8uSkoSFQgCwFgtHdK0CevDw01iBw+nVnykVLrnm7p+MeRnPmzJF999337X58h0A3Arw+dQOUg2bz/JuCUboJoWCUztIayQwsBSOLhoUIAQ5SEWhs4hQgT04aGgIFyFIgGN29AuTJy0NjNwKX//EK+cLdX3+7V/mKos73NFLrT530Zbnooove7st3CHQjwOtTN0A5aDbPvykYpZsQCkbpLK2RzMBSMLJoWIgQ4CAVgcYmTgHy5KShIVCALAWC0d0rQJ68PDR6BJzFovI2nYpGfee/Lmv+c7m0tbWVe/AVAa8Ar09enlw0muffFIzSTQkFo3SW1khmYCkYWTQsRAhwkIpAYxOnAHly0tAQKECWAsHo7hUgT14eGh0CvmKRvkfR8ccfL7/85S9lxe6vWvc2Om3csXLhERc4RmU1ArYAr0+2Rx6XzPNvCkbpZoiCUTpLayQzsBSMLBoWIgQ4SEWgsYlTgDw5aWgIFCBLgWB09wqQJy8PjVUEfMUi/Slop556qmyxxRYyfvx4Ofroo+X+EQ9ZRaNZ+3xXPv/e46uMzCoEbAFen2yPPC6Z598UjNLNEAWjdJbWSGZgKRhZNCxECHCQikBjE6cAeXLS0BAoQJYCwejuFSBPXh4aOwk8tORhmXbdhyprz9jqc/KrM/5Tnn/+edGfgnb++efLwoULs/bp06fLxo0bZebMmXLFCz+3ikZ/OvIOmTp258o4fINANQFen6qp5Gudef5NwSjd3FAwSmdpjWQGloKRRcNChAAHqQg0NnEKkCcnDQ2BAmQpEIzuXgHy5OWhsYrAj+77icy895tSvlLo5ZdflrvuuksOPfRQWb9+vdx7773ZVrpg1L9/f+no6JCbb75Znt38eTn1gW/LJdP/VU5+30lVRmYVArYAr0+2Rx6XzPNvCkbpZoiCUTpLayQzsBSMLBoWIgQ4SEWgsYlTgDw5aWgIFCBLgWB09wqQJy8PjQ4BfaVRtSuEusuTazvHj2F1wQW6y1PBeXKx++b5NwWjdFNCwSidpTWSGVgKRhYNCxECHKQi0NjEKUCenDQ0BAqQpUAwunsFyJOXh8ZAAfIUCEZ3rwB58vLkotE8/6ZglG5KKBils7RGMgNLwciiYSFCgINUBBqbOAXIk5OGhkABshQIRnevAHny8tAYKECeAsHo7hUgT16eXDSa598UjNJNCQWjdJbWSGZgKRhZNCxECHCQikBjE6cAeXLS0BAoQJYCwejuFSBPXh4aAwXIUyAY3b0C5MnLk4tG8/ybglG6KaFglM7SGskMLAUji4aFCAEOUhFobOIUIE9OGhoCBchSIBjdvQLkyctDY6AAeQoEo7tXgDx5eXLRaJ5/UzBKNyUUjNJZWiOZgaVgZNGwECHAQSoCjU2cAuTJSUNDoABZCgSju1eAPHl5aAwUIE+BYHT3CpAnL08uGs3zbwpG6aaEglE6S2skM7AUjCwaFiIEOEhFoLGJU4A8OWloCBQgS4FgdPcKkCcvD42BAuQpEIzuXgHy5OXJRaN5/k3BKN2UUDBKZ2mNZAaWgpFFw0KEAAepCDQ2cQqQJycNDYECZCkQjO5eAfLk5aExUIA8BYLR3StAnrw8uWg0z78pGKWbEgpG6SytkczAUjCyaFiIEOAgFYHGJk4B8uSkoSFQgCwFgtHdK0CevDw0BgqQp0AwunsFyJOXJxeN5vk3BaN0U0LBKJ2lNZIZWApGFg0LEQIcpCLQ2MQpQJ6cNDQECpClQDC6ewXIk5eHxkAB8hQIRnevAHny8uSi0Tz/pmCUbkooGKWztEYyA0vByKJhIUKAg1QEGps4BciTk4aGQAGyFAhGd68AefLy0BgoQJ4CwejuFSBPXp5cNJrn3xSM0k0JBaN0ltZIZmApGFk0LEQIcJCKQGMTpwB5ctLQEChAlgLB6O4VIE9eHhoDBchTIBjdvQLkycuTi0bz/JuCUbopoWCUztIayQwsBSOLhoUIAQ5SEWhs4hQgT04aGgIFyFIgGN29AuTJy0NjoAB5CgSju1eAPHl5ctFonn9TMEo3JRSM0llaI5mBpWBk0bAQIcBBKgKNTZwC5MlJQ0OgAFkKBKO7V4A8eXloDBQgT4FgdPcKkCcvTy4azfNvCkbppoSCUTpLayQzsBSMLBoWIgQ4SEWgsYlTgDw5aWgIFCBLgWB09wqQJy8PjYEC5CkQjO5eAfLk5clFo3n+TcEo3ZRQMEpnaY1kBpaCkUXDQoQAB6kINDZxCpAnJw0NgQJkKRCM7l4B8uTloTFQgDwFgtHdK0CevDy5aDTPvykYpZsSCkbpLK2RzMBSMLJoWIgQ4CAVgcYmTgHy5KShIVCALAWC0d0rQJ68PDQGCpCnQDC6ewXIk5cnF43m+TcFo3RTQsEonaU1khlYCkYWDQsRAhykItDYxClAnpw0NAQKkKVAMLp7BciTl4fGQAHyFAhGd68AefLy5KLRPP+mYJRuSigYpbO0RjIDS8HIomEhQoCDVAQamzgFyJOThoZAAbIUCEZ3rwB58vLQGChAngLB6O4VIE9enlw0muffFIzSTQkFo3SW1khmYCkYWTQsRAhwkIpAYxOnAHly0tAQKECWAsHo7hUgT14eGgMFyFMgGN29AuTJy5OLRvP8m4JRuimhYJTO0hrJDCwFI4uGhQgBDlIRaGziFCBPThoaAgXIUiAY3b0C5MnLQ2OgAHkKBKO7V4A8eXly0Wief1MwSjclFIzSWVojmYGlYGTRsBAhwEEqAo1NnALkyUlDQ6AAWQoEo7tXgDx5eWgMFCBPgWB09wqQJy9PLhrN828KRummhIJROktrpCVLlsi4ceOydQ8++KBstdVWVjsLCIQIrFu3Th544IFsk1133VXa29tDNqcvApYAebI4WKhDgCzVgcemXQTIUxcSVtQhQJ7qwGPTLgLkqQtJ7lYsW7ZMJk+enD2vF154QcaOHZu759gbnxAFowbN2iOPPFIJbIN+BMMigAACCCCAAAIIIIAAAggggIAhsHjxYtlhhx2MNXwbK0DBKFaum+3mzp0r++23Xze9aEYAAQQQQAABBBBAAAEEEEAAgVQCFIxSSYpQMEpnaY30/PPPy4QJE7J1Cx9eKKPHjLbaWUAgRMC8xFK/AI4aNSpkc/oiYAmQJ4uDhToEyFIdeGzaRYA8dSFhRR0C5KkOPDbtIkCeupDkbsWGDRtk5cqV2fPabrvtpK2tLXfPsTc+IQpGDZo1brrVINiCDkueCjrxDdpt8tQg2AIOS5YKOOkN3GXy1EDcAg5Nngo46Q3cZfLUQFyGzrUABaMGTQ8vKg2CLeiw5KmgE9+g3SZPDYIt4LBkqYCT3sBdJk8NxC3g0OSpgJPewF0mTw3EZehcC1AwatD08KLSINiCDkueCjrxDdpt8tQg2AIOS5YKOOkN3GXy1EDcAg5Nngo46Q3cZfLUQFyGzrUABaMGTQ8vKg2CLeiw5KmgE9+g3SZPDYIt4LBkqYCT3sBdJk8NxC3g0OSpgJPewF0mTw3EZehcC1AwatD08KLSINiCDkueCjrxDdpt8tQg2AIOS5YKOOkN3GXy1EDcAg5Nngo46Q3cZfLUQFyGzrUABaMGTQ8vKg2CLeiw5KmgE9+g3SZPDYIt4LBkqYCT3sBdJk8NxC3g0OSpgJPewF0mTw3EZehcC1AwatD08KLSINiCDkueCjrxDdpt8tQg2AIOS5YKOOkN3GXy1EDcAg5Nngo46Q3cZfLUQFyGzrUABaMGTQ8vKg2CLeiw5KmgE9+g3SZPDYIt4LBkqYCT3sBdJk8NxC3g0OSpgJPewF0mTw3EZehcC1AwyvX08OQQQAABBBBAAAEEEEAAAQQQQACBnhegYNTz5vxEBBBAAAEEEEAAAQQQQAABBBBAINcCFIxyPT08OQQQQAABBBBAAAEEEEAAAQQQQKDnBSgY9bw5PxEBBBBAAAEEEEAAAQQQQAABBBDItQAFo1xPD08OAQQQQAABBBBAAAEEEEAAAQQQ6HkBCkY9b85PRAABBBBAAAEEEEAAAQQQQAABBHItQMEo19PDk0MAAQQQQAABBBBAAAEEEEAAAQR6XoCCUc+b8xMRQAABBBBAAAEEEEAAAQQQQACBXAtQMMr19PDkEEAAAQQQQAABBBBAAAEEEEAAgZ4XoGDU8+b8RAQQQAABBBBAAAEEEEAAAQQQQCDXAhSMcj09PDkEEEAAAQQQQAABBBBAAAEEEECg5wUoGDXAfO3atfLYY4/JwoUL5YEHHpB58+bJI488kv2k97znPfLoo492+1OXLFki48aN67af7nDaaafJhRdeWFNfOvU+gRR5Mvf6qaeekquuukpuvfVWeeKJJ2TzzTeXXXbZRY466ig57LDDpE+fPmZ3vi+YwIwZM2T27Nk17XWpVKqpH52aU+DFF1+Uq6++Wm666absuDZo0CCZMmWKHHnkkdnrSXt7e3PuOHuVTKClpaWmsQ499NAsZzV1plNTCqxYsUL+/Oc/y8MPPywLFiyQO++8U5YuXZrta8zvwffcc49cc801MnfuXHn22Wdl4sSJsu+++8qxxx4re+21V1MaslNvC6TIk/5dSf/OVMtDHyf16xgPBHqlgPqFn0diAfXioc+iqv5TBaOaftoLL7xQdftq46oDZU1j0ql3CqTIU3nPr7/++lK/fv2c2TrggANKK1euLHfnawEF1C80znx0fv0pIA+7/JbAXXfdVRo2bJgzK9OmTSs999xzeCHgFej8muJa1q9LPIot4MqGXh/ye/DGjRtLX/3qV52vXXq8r33tayXdj0fzCqTIkyoCeXNk/gzdlwcCvVWAK4zUf82pH+Zf6Lfcckt5//vfL7/97W/llVdekZgrjH7605/Khz70IefTHDp0qGyxxRbOdhp6t0CKPGkBfaXbfvvtJx0dHTJhwgQ599xzZeedd5ZVq1bJz372M7n88sszqIMOOii7wqS1tbV3w/HsowTKedt///3lsssu846x7bbbettpbE6Bxx9/XKZOnSrr1q2TIUOGyPe//32Zvsd0Wff6uuwqkPPPPz/bcVU0yl53Bg4c2JwQ7FXdAuUrjM4++2z57Gc/6xxvwIABNV917RyEhl4tUM6K3olJkyZlv79cd9112T6FXGH0gx/8QM4888xsO/07kSoeZb8TqQJ39lo2Z86crE33O/3007Pv+b/mE0iRJ/MKo/nz53vPxfT5oD5e8kCgVwr01kpXnp/3r371q5IqEFl/XdVXFqmAlGKuMKIqnefZbvxzS5GnN954o7TjjjtmGdRXBTz//PNdnvg555xT+UsJmevCU5gV5SuM+It+YaY8eEcPPvjg7LVCvX21tPDhhV22v+KKKyqvJZdcckmXdlYgUBbQvxfpf+SkLMJXl8DFF19cUsWc0j/+8Y+si3klfq1XGP31r38t6dctnTn1R5GSKnpbP+61117L1ut23Y+rJC2eplpIkSfzCiOdRx4INKuANOuO5W2/KBjlbUZ69/MJzdNtt91WOYH793//96o7r39RGjVqVNZv9913r9qHlc0vQMGo+ee4nj1U9xCpvJacddZZVYfSb+XYbbfdsn7qHmklXbDmgUA1AQpG1VRYV4tATMFIXclWef1S9xmt+mP0+nIu9R/SeBRDICZPFIyKkQ32Ut2wFISeEQg9wTdfuLjao2fmqDf9lNA8nXTSSZVfgJYvX+7cVX0CWP5FSf8ljkfxBCgYFW/OQ/ZYvU2j8hqxaNEi56a6MF1+LVGX6jv70VBsgXJGuMKo2DmI2Xvz9+RarjBSb8cvvfOd78xel3bddVfvj3zve9+b9VNvuy7p7Xg0v0BonrQIBaPmzwV7+KYABaMeSkLoCb75wkXBqIcmqRf9mNA8vfvd785++dG/BPke+q2U5V/gb7zxRl9X2ppUgIJRk05sot0qvx1NfSJaacOGDc5R9VvVyq8l+tJ/HghUEyhnhIJRNR3W+QTM35NrKRj97W9/q7wmua6OLP+8r3/965W+ejsezS8QmictQsGo+XPBHr4pQMGoh5IQeoJvvnCpm2aX1EcVl9ra2rJPpdFvF9IHu0cffbSHnj0/Jm8CIXlas2ZN5Refz33uc95deeyxxyp91Y1rvX1pbE6BcsFI3Ri9pD81b8SIEdlrj/5L63HHHVeaPftm3mLUnFNf016NHTs2e43YZ599vP3VzfQrryVf/OIXvX1pLK5AuWCkf6/Rb2Nsb28vqRvDltQHMpROPfXUkvr49OLisOdeAfP35FoKRvr+R+W8XXPNNd6xr7766krfu+++29uXxuYQCM2T3muzYKQ/0Xjrrbcu9e3btzR69OjShz/84dJFF11U+vvf/94cQOxFoQUoGPXQ9Iec4OunZL5wlQ9w1b7qwtHrr7/eQ3vBj8mLQEiennnmmcovPt/4xje8u7By5cpK3y9/+cvevjQ2p0C5YFTt9aa8Tp/cPf30080JwF45BfSxppyBY445xtmv3KCvQtL9DzzwwPIqviJgCZTz5Pv6z//8zyX1KbPWdiwgYP6eXEvBSH2iWuX16/bbb/cC6vZyJvV2PJpfIDRPWsQsGJXz0vmr/mM/GWr+/DT7HrboHVTh5tFgge23317U1RuiTvRFXRnU7U9bsmSJqKuK5Nhjj5W99tpbtn7HO6Stf5ssW7ZM9Ed+XnDBBaJ+gcrG+cpXviI/+tGPuh2TDs0jEJKnRx55RCZPnpztvP746/LHyVbTUCeEov7CmzXpjzhWf4Wr1o11TSzwiU98QtSnw8hHP/rR7PVKfaqeqKvUZPHixXLppZeK+ot/tvfqCiS5//77Rf0lrYk12DVTQBWURV1xlq068cQTszyY7Z2/1xlRn8go06dPF/2RwzwQ6CygioqirlwU/fHm22yzjQwcOFBWrFgh9913X/YR50uXLs02UYVs+a//+i9Rf73vPATLBRXQvyePGzcu23tVMJILL7zQK3HllVfK8ccfn/W55557ZM8993T21+1777131n7VVVdlGXV2pqEpBELzpHd69uzZov4QK5/+9KdF3fJBxowZk1no497NN98s6l5++sKMbJ36xGP55Cc/mX3P/yHQ6wSavSKWl/0LuSJEP+f169eX9KdWuR7PPvtsadKkSZW/gNx1112urqxvQoGQPD344IOVnHR3LxH96UbqRSz7d/jhhzehHLvUncDq1audXfSnXem/5JYzon75dvalofkE1Ml7Ze5POeWUbnewfO80/ZZqHghUE/C93uiPT1eFpErmfv7zn1cbgnUFFQi9IuQnP/lJJUvqjx1eNd1ePs6pP5R4+9LYHAKhedJ7ra989N0UXb8NUr9FTWdJFcNLvg+daQ5F9qJZBQr7lrTygaCeryE3ow45wa81bGYhQFWta92Mfg0QqCdH5W0blSd1ZUjlFx91hZF379etW1fpq64w8valcdMJlDNTz9eQvJl7qm90XP4EGf3zeX++qdPc36srPyqvD+oKo253dvz48Vl/dYVRt33pgEA1AX3D4fIJl76vEQ8EygKhJ/hXXHFF5fVLXUFUHqbq13nz5lX6qiuMqvZhZXMJhOap1r3XBcfy72o//elPa92MfgjkSoCC0VtXU5T/Yw75GnLC1YiCkU6SvpeIfs76PbL66hAem0YgJDeuvo3KE/cw2jSZaORPdWUoZH1I3jrvi/mL96233tq5meUmFeAeRk06sTnfraOPPrpywvXSSy/l/Nny9HpKIPQEn3sY9dTM9M6fE5qnWvdSXynZ0tKSvYYdccQRtW5GPwRyJVDYexj99a9/VedW9T0233xzGTx4cE2DhNxzpqYB3+qkPn0me4+sXtTv+x8+fHjI5vRNJJDnPK1du1b0fSL0Q31Kmuj38bsejz/+eHbfGt2uPiVNvva1r7m6sn4TCvR03jrvqvrUGNl3332z1ap4JOqmtJ27sNykAvoeDfq+MupT0mTu3LnOvVRvNZLNNtssa9fHqcsuu8zZlwYEfAL6WKQ+5jzrou+ltsMOO/i601YQgdB7zujXK32vLP3Q92fU92l0PXS7vreWfujjXfl+Rq7+rO/9AqF5Ctljfa8tPb76I392f7aQbemLQC4EclW+auIn06grjPTHFasgZf/02wV4FEMgNE/le4notxL5Hr/97W8rebrxxht9XWkrsID+mOHy646+2ohHcQQOPvjgbO71J6Dptye6HgsfXljJSHf3TnONwXoEtIAqGFWypN9izQMBLRB6RYh+e2P5uKU/Ydj3UAXKSl+9HY/mFwjNU4iIKhhledLvCuGBQG8UKOxb0np6skJP8Gt9fnvssUf2IsRb0moVa45+oXk66aSTKr/8+G66p3+JKv9Cpa5iaQ4s9iK5gLpKrZKT3/zmN8nHZ8D8CvzgBz+ozP2iRYucT1R9Okyln/qENGc/GhDoTuCYY46pZIl7pnWnVZz20BN8fXPid77znVmWdt11Vy+Ubte/C2277bbemxp7B6GxVwmE5qnWndN/zC+/JY37zdaqRr+8CVAw6qEZCT3Br+VpPfTQQ5VfovhEq1rEmqdPaJ5uu+22Slb0iVy1h/5UvlGjRmX9+CtINSHWaQF9Vcluu+1WyZP+5CwexRH485//XJl711/p9f30yhlRb90u6U/X44FAjIC+uqNfv35Z5vi0vRjB5t0m5gT/7LPPrrx+LVy4sCqOXl/+w9k555xTtQ8rm08gJk+1KOgbXZfzxCfu1SJGnzwKUDDqoVkJPcH//e9/7/2rxnPPPVeaNGlS5UXojjvu6KE94cfkQSA0T/qEbccdd8zyMmzYsNLzzz/fZTf0L0blg1o9N0TuMjAreo3AggULSr6PudY5Ou200yo5OfbYY3vNvvFE0wmU35bWp0+fkn7rWeeHeVP0Sy65pHMzywhkAnfddZf3bY36ZrHqnjOV15urr74aOQQqAjEn+PrK6dbW1ixT+++/f0l/Mqz50Mt6vf5dSL++Pfvss2Yz3zexQGiedP/HHnvMKzJnzpxKwbu9vZ1PlfVq0ZhngcLe9FodDBr2UCfjoj7y3hr/S1/6Unaj0NGjR3e5+ae+md7IkSOt/uryRVEfISuf+tSnZOrUqaK3U287k2XLlon6JUsuuOACeeWVV7Jt9Niqgm1tz0LzCKTIk9ZQHxOb3fBRXZYtEyZMkPPOO0/UX2xF35z2Zz/7mcyaNStDO+igg2T27NmifqlqHkT2pCaB008/XS6//HJRhaDsJp/q8v3sxv76xunqypLsdeb+++/PxlIfmS5/+MMfRN/MkUexBPTN8fVxSZ1cyZAhQ7LjkXp7tKhPURNVbJbvfve7Gci0adOy152BAwcWC4i9rUlAfxiIzsxnPvMZUW8Bkq3GbSX92/vLypUrs9cW9fbH7EaxejB9XLrhhhuy34NqGpxOTSegrqoXVcCp7Jf+oJfyBy780z/9U+X7codDDz20/K31VefqzDPPzNbpm2DrD/fQvxOpP8TK9773PVEn+Vmb7qePiTyaU6DePP3xj3/MXrc+8pGPyCGHHCL69ax8Lqd/b9e/R6sr+vWFGRngtddem53TNacme9X0AnmuZvXW56avzlDBqfnfAw880GVXa91eX16rP+qYR/MKpMhTWef666+v/LWjWsYOOOCAkvplvdydrwUTMK8eqpaP8ro999yz9Je//KVgOuyuKaCvDtFXK5Yz0fmrKhaV9JWwPBBwCZSvlO2cnc7LX/jCF7xXPrrGZ31zCdR6fCrnx7X3+i2zqmDkfO3S23/1q18t6X48mleg3jzpc7dy1nxf1R9MSr/61a+aF5I9K4QAVxip/8pTP3RVecaMGTUPq150RH16ldVf/4VDX6V03333yZNPPim6Wr1q1SpR94MQ9dai7EqRI444QtQN+aztWGg+gRR5MlWeeuopUTctFvWJaPLEE0/IiBEjsr+SHHXUUXLYYYeJugzb7M73BRJ45plnso8Q1lcRqfs4iLp/SPYX/gEDBoi+2kgVirK/pKlL9rkCrUC5cO3qiy++KFdddZWoT1QUfdWRvpJIXxmrj01HH320qEvwXZuyHgHRf6HXv+PoKxV1ftRbPER9KIMMHz5c1Cd7yr777itHfPII2WnKTmghkF3tc9FFF9Usoc7ivH31VdfXXHNNdkWR/h1bXzWrrzg67rjjZK+99vJuS2PvF9BXj9WTJ33l9Z133in6HE7/zqSvftNXqam37suYMWNE3cdPPvjBD4q60XX2e3bvF2MPiixAwajIs8++I4AAAggggAACCCCAAAIIIIAAAlUEKBhVQWEVAggggAACCCCAAAIIIIAAAgggUGQBCkZFnn32HQEEEEAAAQQQQAABBBBAAAEEEKgiQMGoCgqrEEAAAQQQQAABBBBAAAEEEEAAgSILUDAq8uyz7wgggAACCCCAAAIIIIAAAggggEAVAQpGVVBYhQACCCCAAAIIIIAAAggggAACCBRZgIJRkWeffUcAAQQQQAABBBBAAAEEEEAAAQSqCFAwqoLCKgQQQAABBBBAAAEEEEAAAQQQQKDIAhSMijz77DsCCCCAAAIIIIAAAggggAACCCBQRYCCURUUViGAAAIIIIAAAggggAACCCCAAAJFFqBgVOTZZ98RQAABBBBAAAEEEEAAAQQQQACBKgIUjKqgsAoBBBBAAAEEEEAAAQQQQAABBBAosgAFoyLPPvuOAAIIIIAAAggggAACCCCAAAIIVBGgYFQFhVUIIIAAAggggAACCCCAAAIIIIBAkQUoGBV59tl3BBBAAAEEEEAAAQQQQAABBBBAoIoABaMqKKxCAAEEEEAAAQQQQAABBBBAAAEEiixAwajIs8++I4AAAggggAACCCCAAAIIIIAAAlUEKBhVQWEVAggggAACCCCAAAIIIIAAAgggUGQBCkZFnn32HQEEEEAAAQQQQAABBBBAAAEEEKgiQMGoCgqrEEAAAQQQQAABBBBAAAEEEEAAgSILUDAq8uyz7wgggAACCCCAAAIIIIAAAggggEAVAQpGVVBYhQACCCCAAAIIIIAAAggggAACCBRZgIJRkWeffUcAAQQQQAABBBBAAAEEEEAAAQSqCFAwqoLCKgQQQAABBBBAAAEEEEAAAQQQQKDIAhSMijz77DsCCCCAAAIIIIAAAggggAACCCBQRYCCURUUViGAAAIIIIAAAggggAACCCCAAAJFFqBgVOTZZ98RQAABBBBAAAEEEEAAAQQQQACBKgIUjKqgsAoBBBBAAAEEEEAAAQQQQAABBBAosgAFoyLPPvuOAAIIIIAAAggggAACCCCAAAIIVBGgYFQFhVUIIIAAAggggAACCCCAAAIIIIBAkQUoGBV59tl3BBBAAAEEEEAAAQQQQAABBBBAoIoABaMqKKxCAAEEEEAAAQQQQAABBBBAAAEEiixAwajIs8++I4AAAggggAACCCCAAAIIIIAAAlUEKBhVQWEVAggggAACCCCAAAIIIIAAAgggUGQBCkZFnn32HQEEEEAAAQQQQAABBBBAAAEEEKgiQMGoCgqrEEAAAQQQQAABBBBAAAEEEEAAgSIL/H8oVzrPrACkCwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
-      ]
+      ],
+      "image/png": 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"
      },
-     "execution_count": 11,
      "metadata": {
       "image/png": {
        "width": 600
       }
      },
-     "output_type": "execute_result"
+     "execution_count": 11
     }
    ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename=\"animation.png\",width=600)"
-   ]
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## Simulation\n",
     "\n",
@@ -46,39 +45,39 @@
     "- Black line: dead reckoning \n",
     "- Blue line: ground truth\n",
     "- Red line: EKF SLAM position estimation"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## Introduction\n",
     "\n",
     "EKF SLAM models the SLAM problem in a single EKF where the modeled state is both the pose $(x, y, \\theta)$ and \n",
     "an array of landmarks $[(x_1, y_1), (x_2, x_y), ... , (x_n, y_n)]$ for $n$ landmarks. The covariance between each of the positions and landmarks are also tracked. "
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
-    "\\begin{equation}\n",
+    "$\\begin{equation}\n",
     "X = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\\\ x_1 \\\\ y_1 \\\\ x_2 \\\\ y_2 \\\\ \\dots \\\\ x_n \\\\ y_n \\end{bmatrix}\n",
-    "\\end{equation}\n",
+    "\\end{equation}$\n",
     "\n",
-    "\\begin{equation}\n",
+    "$\\begin{equation}\n",
     "P = \\begin{bmatrix} \n",
     "\\sigma_{xx} & \\sigma_{xy} & \\sigma_{x\\theta} & \\sigma_{xx_1} & \\sigma_{xy_1} & \\sigma_{xx_2} & \\sigma_{xy_2} & \\dots & \\sigma_{xx_n} & \\sigma_{xy_n} \\\\\n",
     "\\sigma_{yx} & \\sigma_{yy} & \\sigma_{y\\theta} & \\sigma_{yx_1} & \\sigma_{yy_1} & \\sigma_{yx_2} & \\sigma_{yy_2} & \\dots & \\sigma_{yx_n} & \\sigma_{yy_n} \\\\\n",
     " &  &  &  & \\vdots &  &  &  &  &  \\\\\n",
     "\\sigma_{x_nx} & \\sigma_{x_ny} & \\sigma_{x_n\\theta} & \\sigma_{x_nx_1} & \\sigma_{x_ny_1} & \\sigma_{x_nx_2} & \\sigma_{x_ny_2} & \\dots & \\sigma_{x_nx_n} & \\sigma_{x_ny_n}\n",
     "\\end{bmatrix}\n",
-    "\\end{equation}"
-   ]
+    "\\end{equation}$"
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "A single estimate of the pose is tracked over time, while the confidence in the pose is tracked by the \n",
     "covariance matrix $P$. $P$ is a symmetric square matrix whith each element in the matrix corresponding to the \n",
@@ -86,26 +85,26 @@
     "belief of $x$ and $y$ and is equal to $\\sigma_{yx}$. \n",
     "\n",
     "The state can be represented more concisely as follows. "
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
-    "\\begin{equation}\n",
+    "$\\begin{equation}\n",
     "X = \\begin{bmatrix} x \\\\ m \\end{bmatrix}\n",
-    "\\end{equation}\n",
-    "\\begin{equation}\n",
+    "\\end{equation}$\n",
+    "$\\begin{equation}\n",
     "P = \\begin{bmatrix} \n",
     "\\Sigma_{xx} & \\Sigma_{xm}\\\\\n",
     "\\Sigma_{mx} & \\Sigma_{mm}\\\\\n",
     "\\end{bmatrix}\n",
-    "\\end{equation}"
-   ]
+    "\\end{equation}$"
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Here the state simplifies to a combination of pose ($x$) and map ($m$). The covariance matrix becomes easier to \n",
     "understand and simply reads as the uncertainty of the robots pose ($\\Sigma_{xx}$), the uncertainty of the \n",
@@ -113,13 +112,12 @@
     "($\\Sigma_{xm}$, $\\Sigma_{mx}$).\n",
     "\n",
     "Take care to note the difference between $X$ (state) and $x$ (pose). "
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "\"\"\"\n",
     "Extended Kalman Filter SLAM example\n",
@@ -148,24 +146,24 @@
     "LM_SIZE = 2  # LM state size [x,y]\n",
     "\n",
     "show_animation = True"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## Algorithm Walkthrough\n",
     "\n",
     "At each time step, the following is done. \n",
     "- predict the new state using the control functions\n",
     "- update the belief in landmark positions based on the estimated state and measurements"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def ekf_slam(xEst, PEst, u, z):\n",
     "    \"\"\"\n",
@@ -187,11 +185,12 @@
     "    xEst, PEst = update(xEst, PEst, u, z, initP)\n",
     "\n",
     "    return xEst, PEst\n"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## 1- Predict\n",
     "**Predict State update:** The following equations describe the predicted motion model of the robot in case we provide only the control $(v,w)$, which are the linear and angular velocity repsectively. \n",
@@ -287,13 +286,12 @@
     "$\n",
     "\n",
     "Notice this uncertainty is only growing with respect to the pose, not the landmarks. "
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def predict(xEst, PEst, u):\n",
     "    \"\"\"\n",
@@ -311,13 +309,13 @@
     "    # sigma = G*sigma*G.T + Noise\n",
     "    PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx\n",
     "    return xEst, PEst, G, Fx"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def motion_model(x, u):\n",
     "    \"\"\"\n",
@@ -337,11 +335,12 @@
     "\n",
     "    x = (F @ x) + (B @ u)\n",
     "    return x"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## 2 - Update\n",
     "In the update phase, the observations of nearby landmarks are used to correct the location estimate. \n",
@@ -392,13 +391,12 @@
     "$\n",
     "P_{t} = (I-K_tH_t)\\bar{P_t}\n",
     "$"
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def update(xEst, PEst, u, z, initP):\n",
     "    \"\"\"\n",
@@ -434,13 +432,13 @@
     "    \n",
     "    xEst[2] = pi_2_pi(xEst[2])\n",
     "    return xEst, PEst\n"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def calc_innovation(lm, xEst, PEst, z, LMid):\n",
     "    \"\"\"\n",
@@ -492,11 +490,12 @@
     "    H = G @ F\n",
     "\n",
     "    return H"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "## Observation Step\n",
     "The observation step described here is outside the main EKF SLAM process and is primarily used as a method of\n",
@@ -506,13 +505,12 @@
     "\n",
     "Observations are based on the TRUE position of the robot. Error in dead reckoning and control functions are \n",
     "passed along here as well. "
-   ]
+   ],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def observation(xTrue, xd, u, RFID):\n",
     "    \"\"\"\n",
@@ -548,13 +546,13 @@
     "\n",
     "    xd = motion_model(xd, ud)\n",
     "    return xTrue, z, xd, ud"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def calc_n_LM(x):\n",
     "    \"\"\"\n",
@@ -590,13 +588,13 @@
     "    return G, Fx,\n",
     "\n",
     "\n"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def calc_LM_Pos(x, z):\n",
     "    \"\"\"\n",
@@ -665,13 +663,13 @@
     "\n",
     "def pi_2_pi(angle):\n",
     "    return (angle + math.pi) % (2 * math.pi) - math.pi"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "def main():\n",
     "    print(\" start!!\")\n",
@@ -731,18 +729,22 @@
     "            plt.axis(\"equal\")\n",
     "            plt.grid(True)\n",
     "            plt.pause(0.001)"
-   ]
+   ],
+   "outputs": [],
+   "metadata": {}
   },
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "scrolled": false
-   },
+   "source": [
+    "%matplotlib notebook\n",
+    "%matplotlib notebook\n",
+    "main()"
+   ],
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       " start!!\n",
       "New LM\n",
@@ -751,810 +753,38 @@
      ]
     },
     {
+     "output_type": "display_data",
      "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        fig.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
       "text/plain": [
        "<IPython.core.display.Javascript object>"
-      ]
+      ],
+      "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.get_websocket_type = function() {\n    if (typeof(WebSocket) !== 'undefined') {\n        return WebSocket;\n    } else if (typeof(MozWebSocket) !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert('Your browser does not have WebSocket support.' +\n              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        fig.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        event.shiftKey = false;\n        // Send a \"J\" for go to next cell\n        event.which = 74;\n        event.keyCode = 74;\n        manager.command_mode();\n        manager.handle_keydown(event);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n"
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     },
     {
+     "output_type": "display_data",
      "data": {
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xxLZq40P/m/CjsjIyETrxl95ui691kXXBu62YuNG8ctCfMH0MW8B5bixm+w6+95/6eMMOYvUFmjRooX+88J/tKrwqn9fylruxbXki41FeEG+W9x4ZgPdmGc5LPHCeSz79+/3spV/9dVXsh9fktJI3lrpWYZba1V9++23p81Weq7ZXZjryhnhklWEu66dYa5FosvgoaNPPKVth3Ipg0vMEL55k8L271PmI3uVPWqvMivwc6ZSXHdYn2tUXBt95oI/PaEILVUFF5q7Vo/ow+CV3NJohbnQWqSyyAUX3ZzNT1Jj3aOh2hV2sYZkbCfXMFIXZ9irqjHzVS16rioe+1lFojYrT8xub7/R3ih84TrkbrSdYXl1IiZCdaP/HeM27sV3K7dGqKW+0L2a4zr6xrjtfEu4olROK1RUm1RQ21RA273HBvrRBRgTGLfPZwefqJ1K7F+vF/e/4gJseXzWxD6tmWmBXs7ymq7b992pdUeLlFB45YpuXMKKinLdcmNsrL8UKPZ+boG7adOmeZON6WdddhMrf//9tyZOnOhNGVwyFEv8YgFEmyzgZ2P6pbfC5156u6Ln/3y4p87/NUhvR8A9ld6uqP/5nG6oLP/azKVHAQJ8IXxVLSuuDai9e/dub9yb4cOHewGzhE7J94uv/TqfWPHNvJc9mS0CkhOws+BhcgOIiR0369KmgAX3uM5p89qk5aN6b9F7/sG9uDH3rOVeXHDv5KN1R0jox41zcY7WBbdHjx5ecC8pr2ddGO+66y5ZEPPyyy9PyiapV2evy2TiEpu4fsRyHyixk31GWHIIG8fQ/YAUM2yYwtauDTiGyEc6qETA0rNf8I9r+zZXNbzA2bywGtqQt4qy5svmWq1JS7J1U8stfXXJ0T+UPeaAYrJl195SVXTo6uo6UbGKIgvklH1sZc9yQjmyRes/uTLpbxfU848n5XYH2diNW1hHkyZN0m825z5PE3yf6ttXru/3qRP754MR2lHzZlU+kVllXMNDywNikyX33bVLruuxPUa4qYJ7XkEbTi6z5cePRet2jXSvPkkZ3JiBf6qI1+qvsPv/C3rt1GvYk256XQ9poBaqitaqtNcyMJtr6WhBwzrHZil+DDXzny6jr03ffauM3btrcZWHtOO5d1S+SiYVPOyW2xi49qOdZcO1ZB95AgOHfgdwcsZcmjZt6k22aI870emuZerUqVM1ZcoUr7VfsO3iltmPkJZQxqbu7rjiim1r4/elx2Afn3txV5nHlBLgnkopSfYTJ8A9FSeRfh4T/Dsm/ZwiZ3IaAQJ8pwFKq6v/cgNyWxY7e7QxmQYNGuQS8t2S6OH6Bt5OF933bYUXfyD3RF+ElQgggEAKCLw56009NzV26AFvd3HBPZuZffIFTgb3LNjXfVJ33XTTTSdXnJsHa51nAY633npLkyfbAZ6+2Pu2jZlqLZjOabEkFS644mWRda2pvGZs9rh8udwBxUam4h+Qa6nnZal1y8Pir0uF+aiwCK0rc5M239Be4U0a64pLI1Q7n5TbxeL8g3MuI63eTsIRpOCfOE8+KXXo4LVatINxh+VNSTgIvyo2/uCePeHus7vlyUmK+MtNW6TF7nHwvGi1+esNv23yaYfrzjzBm/xWJGHmmoUDdeD2L11bRGv66l7cp1hCDzfYrsIseOkyMien5HYX5dZbb/Um2866oNu/hbiAn/1tkpRi/x5srD5LDPbf//7Xa92fHsaZTMq5UwcBBBBAAAEEEEhpgRT86zelD439JSRgg2daF5c//vjDq9K/f3/dd999CVU/tfyqq6469dzG1kms+K6/8sorE6vKOgQQQCBFBRIN7sW90uyTT1xwz8riixfrtRmvqVu9brELUvH/lpTg22+/9TKJWibSxEou11Lqzjvv9N6jbWy9cx68cF2G3S9AUr9+kmv1fa7LQRdaWpP1Gm3PX16FMu9UmT+nKNOJQzpavZ4y3dxEGUoWk9xYhdbdN8IFe0q7VoOlz/VBJvX1XEvksy0WU7NGczaVLRtkbzGvSQMuVUynTl523SA1kr0oobENXZY16fvvdfyHyZpf5xmpY0dVuKGw1/oxuS9S2F03+zvEJgt8r1ixQhMmTPC659pQDZY9OqFiwcB3333Xm4oXL+4F+uzfjLVyPef/XhI6SJYjgAACCCCAAAIhIECALwQuku8h7nVdqa534+rYoNdWevXqpUceecS3SoLPS5Qo4f1Sbn9M29hQiZW4sdPsj3b7g5uCAAIInAuB0wX3bKB+G3d03Lhx2jt7b+whnQzyvTD9BWWIyKAutbukyqHa2KRffPGF12LPso0mVuz99s0331SzZs3Oz/iTGzdK7scfffyxtG9fYoearHXrVdyNINdAD8oFDX3K4ozVNOuy1sp+eUEVKJ1Lha7IqUvK5lH+asVVOZPPnxo2bpuLcmV1EyWIgLm4loJh9oOctRh0SS4SK4dy5Nekck9pSIa22r9qs4ruXOw68y7Uffo8yUlLMkYfU60Zryt6xhuarv9oZtFW2tP0LlWpEykXk1bx4t4lS+ww/NZZUM7GlbTp2WeflQ0JYt15LeD3wQcf+NWNP7Nhwwbv7xr728bGpbz//vt1zz33nNfu9/GPkXkEEEAAAQQQQCCtCrgfcO0nXEooCBw6dMgbI2j27NimK926ddOrr76arEPv6H6hj8uyO3fuXG/Q6/g7sIG0a9So4S22+qf7gzz+9qebt+7Bcd1+Sd99Oq3QXW/Zl21sKyuJjm0VuqfIkaewwKxNs1Tnszqn9lppZyUt6b/k1Ly1Qn7sscdUqFAhXXbZZV5LH+/Hjlquyskgn1Weef9M1S5a+9R2Z/vEEmdYAo8+ffp4wyIktD/rtmgBvRdffNH1eCyVULXUXT5vntyBSqNGSdYtNxlltG7RElXSbuVxKSqyqKxWqqoWKJMb6M2SSHylu7UmZ3VVqBimukU3qOXm3sqTL0I52t2lXI2rJ+OV0k7VNP0+ZX+eWavLhQtjJ9d6P3ZwQRtg0E0lS0o33xw7pt5JUhtC0WKCm2f8oWs+eECltszww7bEIHY9s3lddv1W+c38oRLqqje8BCVZL8mjG2vtUbPLV+vai39TvpI5peuuk8to47dNUmeGuPEehw4dqqVLl7rGm6715mmKJei48cYbvWDfDa5LcUbLkpJGS5q+n9KoGYeVuAD3VOI+rE2+APdU8s1CaQu+Z4fS1UqdY/X5WT11XoC9poyAZb21Vitxwb3HH3882cE9O5InnnjC+6JqXczsi7K11PMdjNPe9G25Ffuj2upTEEAAgXMhYEG57vW6q+eMnup1XS+V319ebb5uI8vGedttt3k/TtgPE1YsMYX9GNG6dWuvu2y27C71QI2D3vYpFdyz4MN7772n999/30sqkJBBSRdosZZK1j3R9/00ofqpstxluFXXrnJ9IhPd/Tpd5mWZjXAZXi3Bg5V5qu5GtHtWy1yWWt+S08VxKleOnapUkYa65xZTskZmrl2Xm1wLQUrqCRi0C2R7kxujLinFuv5aqzvVdNt1+VHuQ15RO3Zpc0QJLdhVUgt+zSmXAFe/LdqnR/e8oif0rjKevA9893+Z1utr/ddbtOevXMr9zcnWsicrHY2I1NaqtyhHh3t08V2NY8d09N1BIs/t36xNVixA//XXX8uShK0NksTF6liCju+++86bLJGOteizln3Wwo+CAAIIIIAAAggg8K8AAb5/LdL0M8u2GNcaqkED1z3qwQe9MW4SOuhMbjyj0qUDRzKyZfZF1Lq/LHStAmrVqqUuXbq4L20ltW7dOq9L2RL7ouiK1TtvrVASOjGWI4BAuhboUb+HGl7W8FQLvJUrV2rs2LFq06aNjhw54nfulhl85MiRLgHsEK/l3JpDa05t51cxmTPWTdASYXz66acuy+rhBLeuVKmSnnvuOS8TbkRERIL1Um2FtdD78Ued+HCgMowemeDLRCtM37nWeX30lGbJWjZ6ETq/+tYoqoYL4lnjbQvm2WTBPP/kFn6bMJPWBezi1a8vuzOLn5zuOHnMMTE5tXHj25r8TRtdNLiPyv06Utmjgnflzi3/4J7tInPUYRX/ebjkpl0P5NWKq+5UxvvvUcX21RWZNfD+OvmyAQ/WKrdnz57q0aOHS+L8y6lg30brYh6kWLDfWtLaVNVleLZA39133+2SAucKUptFCCCAAAIIIIDAhSVAF90Qud7JHWi6WLFisi+pwUq0GwOpXbt2XubdYOttmQUQrUtaeCp8u6PpcELq6Ws5XQDS1/VMC2eT2veUvWfasAeDBw92vVsT7t5a3wVNLLBnXc+T+958Vo4WbHTJC1zfRu2b+YvCxnynHHv+THCXB1xHzEF6QO+pk9bpcr96lpnWWnrVdvE+myygFxnpV+WCmEnteypkEF3w/OiocTrW83Xl+C32R74zOfZ1YSU17/JWynJTI9WselwFV7lWhAsWyPWrl/vHJblxfU9X7G8UG7Pvs88+cz3NRyUaZLd9ZcuWTffee6/LEdLRG/fvdPtPzfXcT6mpe2Hum3vqwrzuqXnW3FOpqXv+98337PN/Dc73EWQ43wfA6597AQvaWcuUFi1aeEG8Be6Pb8vMmzdvXu8X8fbt26tp06bn/sB4RQQQQOA8CFhroeLFi5/2lW+99VavxfO111572ropVmHnTumNNxTjsp26PowKsyQVrrjeswmWrSrotdb7WO1c2ysXyXOlQIHYYdPq1pVruS1ZDodU+P0mwWNiRRoXcBmCM7e6XZnvuDl2DMdhwxTjBvML88l+e9x16t7g2gIWd/8P1q3XzrBkzDqV/O1lqa+b4pUYF7QLswRfRYvGW+M/a3+jWE8Fm6x7/IgRI7xgX1z3fP/a0sGDBzVgwABvqlOnjh599FGvS78NM0JBAAEEEEAAAQQuJAH++gmRq50auVBssGqbKAgggMCFKGCBAeuK27179wRP34IErVq1UufOnV1QzEXFUqJY8gRLgvHll67vZHHphRekiy/+d8/WetAFQk58MUzRI0cp04HdCvt3bYLP9iiXeuk5NzLeY4rIkU316kkNG8YG9sqWjRs7L8HNWYGA5Ib3cM1TvckL7lm26C1b5DJjKaJESR1Zm1GDxuxQzNcjVGnVUFWPmptktTDXQnZ7uQba0PkjVexYU5kvynbaba3rrfU4sGnNmjVe69rPP/9cW7duDbrtzJkzZVNRF0Ts1KmT2rZtS/fdoFIsRAABBBBAAIH0KECALz1eVc4JAQQQQCBBARu037r/vfTSS9q2bVvQepYsw4IKTz/9tBcsCFrpTBZai7wOHaRvvvl3671ujDPXqlqLFun4kK904svhity91bWXSlpZ7DLffhHeRr9Wu1fXNs2jyS6o54Ync9lGk7Y9tRAIKmA3kAW1Twa2w12lq6+2Ka/UraNLftFRy8au0+73v1SJOUNV9IgLBp6mFNi/TgVebKzjL2bQuosqKuNVpVWw9uXKdGOj2KalYQmHssuUKeONH2zd6G1M4k8++URjxowJ2p1+06ZNeuaZZ7yx/SzIZ8G+EiVKnOboWI0AAggggAACCIS2QFK/P4T2WXL0CCCAAAIXvIC1hB4/frzXzdaSdyRULBvum2++qYIFCyZUJfnLXVDRZQuRHnlErvmR//Yu2Hhw7FRl27HJdX10gTn/tX5zljDjN5XSUpfxdm1kRR1ucKMq3FdB3RtLNq4eBYFzJWA9YMs3d5lYmr8kxbyovVMWaFufr5Rt7hQV2Rv77+sXd5+6/Nbujv3d77Csi2/JXQulWTa5Vb1e1t+laytH7+6KvPG6RJubWqvauB4INtaQjRds0/bt2/1ew2YOHDigd99918uG3bx5c68lbrVq1QLqsQABBBBAAAEEEEgPAvaDLAUBBBBAAIF0LbDItY677rrrdNNNNymx4N6sWbO8rLwpEtyzoN7kydJDD8UmGbjttsDg3kl1C+4lVkbpNtXVDNUou19Duv6qIrNHqOv+5/XauApq2ZLgXmJ2rDsHAq7lXa5G1XTFD++qyJ4Vitq9Twum7tO3L/2iR66eqTW64rQHkX/tLEU2a6S1+Wtr2eOf6PiCXySfMQCD7aBIkSJ6+eWXZS32hg8froTGx7TEHZawo3r16l5ynJ9++inY7liGAAIIIIAAAgiEtAABvpC+fBw8AggggEBiAps3b/bG0Kvi0sROmzYtaNUmTZq4xLRLZS38alkGipQo770n1wRQLpogffyxXCajZO91iSqqS9ibalX9d23tP0pDN9bVvBXZ9PrrsRlwIyKSvUs2QOCcCETkzqGqDXKoZ09p0rKCyrp8vuZc31MrclwrS9aRWCm9Y47Kv9dOGatV0vHIHNpftrpiHuvkZY9OaLtMbuzAO++8U5aIY86cObrjjjtcEpngf+JOdkH3em6ASkvIMWHCBO/ffUL7ZTkCCCCAAAIIIBBKAon/lRVKZ8KxIoAAAgggcFLgyJEjXgKN11007NChQ0FdKlasqLffftslomgYdH2SFlrCDJesQy4Tr9avd10V3fzixXKDfyVp8/iV1ukyDdPd+qP6XarW5io91Tw2A278eswjEEoCRcvlVNEJriuvXtL2dQc0/72ftXniKkWtXaebY0armIK3YM0YdVQZV82XbHq/vw5c30LZ3+khlSuX4OnXqFFDNq13/x779+/vjdW3f//+gPrWWrdp06aqXLmyunXrpltuuSXBoGDAxixAAAEEEEAAAQTSoAABvjR4UTgkBBBAAIEzE7BWeGPdWHdPPvmk/vjjj6A7udRlBLWB+u+5557kf6F3XQE1enTseHouq6fXMs8FE5NaFqqyPtAjmqKGGqGWLj3GEq1QOY0Lu1nbqt+sa9pUUPvbwpQvX1L3SD0EQkugQMnsatbPguoNtWuX9P23b2vXu0N0y4rXVFwuUJ5IyT5xlKIn/p82X3uHCr7yqDI3cC1uE2ipZ0k1+vTp42XJtjH6LGN2sHH6rPv+ba77fFmXavr55593Xd5bysb5oyCAAAIIIIAAAqEmELz/QqidBceLAAIIIHDBC/z666/e4PvWEidYcC9nzpxeFk6rZ4k0EurCFwDpxu9yzYDkmvpIxYpJjz8uTZkiuQH+lYTg3i7lUWe96drmrVNVLdRg3a8/dalqaq4a1DiiBR8u1KN/v6QBcyvqofYE9wL8WZBuBS66SLr3wUx6fHk7Zd28VlPuGaypF93h/qVcluA5hytGxX4eocyN6mpn7su0vV232NazCWyRK1cuPfvss16Lvvfff18W4A9WbGzOVq1aybL1WobeY8eOBavGMgQQQAABBBBAIM0KEOBLs5eGA0MAAQQQSIqAdb/r3Lmzrr76am9MrfjbhLkEAA8//LDWrVvnZdCNjIyMXyXh+agoqV272Mm63iazWHKBfPpHb7sQ3/qTQYuSLvFo9+7Sb7/JjRcmdegg5c2bzB1THYF0JpC/SCY1/KK1rts5QjG/rdNbXXaqTeHJelXdtFvBU0RfvH+jCnzyuqIvK6lN5ZrqaNce0pgxscH3eD727/4Rl8X6999/16effqrLL788Xo3YWXufaOf+zdt6q3fCkuVQEEAAAQQQQACBEBAgwBcCF4lDRAABBBAIFLDMmF988YVKly7tjaV3PEjGTUuaYV3wPvroIxdES2YUzVrw3H23NGhQ4IsHWbJNBVwHw6I6pNgA4nLX9baZxipaEbKWShbIs4CeBfZ69JALIATZCYsQQMD7t9G510X6bHNDNVnwqnq136BekT20VzmD6lirvqIrJyhzL5fVw7Xgdc30pMtcK8C2baUvv5Trm3tqO0vI8cADD2iN62I/bNgwN5xf8PH8LEFPW7e9rR85ciTJOE4J8gQBBBBAAAEE0qoAAb60emU4LgQQQACBBAWWLFmi2rVre11tt23bFlCvUKFCGjp0qGbOnKlKlSoFrD/tgsOH5QbmkkaMCKh6LMdFmliwte5wo+hV189e19tsOqBC2uaNIZbNhfgy6pjKa7kua1xK33wjbd0qffih3OD/kmtQSEEAgSQI2L8VlwBbbw7IpSd2ddeUjzfo05Kva5WuPP3WlvTGtcBzg21KRYrIDbAn1xzv1HYRLg31f//7Xy+D9mg3rmbVqlVPrfN9Yl36LStvtWrVZBl4bZxPCgIIIIAAAgggkBYFCPClxavCMSGAAAIIBBWwjLg2PladOnU0d+7cgDoZM2b0uuHal3IbT8u65ya7HDgg3XijG/3/e79NT0Rk0hP5v1TW/dvVZNtgjXQhvvkuxGddbw8p26m6BQtKXbpldOMAShMnSrffLrlGQxQEEDgLgSxZpBZt8+jB37sqy+8r9WHbxfo868M64VrInrZYYO+NN6Trrgvovmtjcdq4nfPmzXP/Xid67y3B9rdw4UI1btxYjRo10ooVK4JVYRkCCCCAAAIIIHBeBQjwnVd+XhwBBBBAICkC1mrm22+/1aOPPqpx48bJuufGL02bNvW+ePfq1Us5cuSIvzpp87t3y32Dl6ZN86t/UFnVJOp79fv7bkUpg986m7E4ont5d4ySJdp1SXrlknhSEEAgFQQuKxmmjh9X0t17P9L0wRvVp/KX6q2nNU31E+zG6x3GTz8pquzVsUlzjh71OzL7McACeDNmzPACfZUtqU6QMnXqVFWsWFGPPfaYywLs0gBTEEAAAQQQQACBNCJAgC+NXAgOAwEEEEAguMB619Xupptu8lrkBftCXdJlrRg7dqxrcPe9Nx5f8L0kYenff0sNGkg//+xXeY9yqZEma6oa+i23mXz5pG4nk3iOHy/deqvkGhFSEEDgHAhkcLH2hq0L66mFd+v2De9oYpdpKplnt+sev1RPqK++082us7z/P8iIfXu8pDnHCxWVXnwxoEVfXKBvwYIFrnv9N7riiisCziTKJd+xjLw2/qeN70kijgAiFiCAAAIIIIDAeRAgwHce0HlJBBBAAIHTCxxzSS6sNV7ZsmU13qJn8UoW12fvlVde8VrtWQDwjLrjxu3TZb6IqVtX+uWXuCXe4z/Kqwb6UXNV02+5a8Cjzz77t7VesWJ+q5lBAIFzLGD/Bt3bhTZvCdeTg8rrp0pP6FYX4qujmV7ym/iHk3G3C+i7prbRxYorps39ksvG7Vvs/eR217/euuNaNt0iNo5fvLJz50517NhR1tpv+vTp8dYyiwACCCCAAAIInFsBAnzn1ptXQwABBBBIgoAlx7jmmmvUtWtXHbaEF/FKw4YNvS/eL7zwgizQdzYlatRoHatQRWFu3D7fskWXqJ5maImu8Ra7obq8vBuuB58WL5batJF7bd8teI4AAudbIDJSut/F61zybM2a5brK31ldVSJ+0VC1Cnpo4dFRChsyWHvKVNfxUWPk+v/71cvgmgla1l0b17N79+5B32+WLVum//znP17Sn93WzZ+CAAIIIIAAAgicBwECfOcBnZdEAAEEEAgusGPHDj344IOq61rTrVy5MqBSnjx59Mwzz+i7776Tdc09m3J47zEtbtxFEbc3V6bD+/x2tV7FvZY/q3WVcueWnn1WWrdOGjVK7tjIhOuHxQwCaVDAxsWsVUsaPlxauimPVnQZqtsjv/fG6Qt2uLn/Wq2Mt9+iHUUr6diCpQFVsmbNqh49emjNmjVeVt2ACm7ByJEjvXFCJ02aFHSc0GDbsAwBBBBAAAEEEEgpAQJ8KSXJfhBAAAEEzljAkmgMHjxYZcqU0aBBgwL2Y93lHn74YW/cq9q1a59Vd9zDh2I07v5R2npxWV0z+a2A11qucl5w73iRy9S3r+vyt1l6y1UrXjygKgsQQCAEBC65JLb77qBtN2jR29PUIN9yfaSHdUSZA44+75ZliqlWTbNvfVtHDkYFrC/m+gKPGDHC5eGZpvLlywesP3jwoD788ENdf/31Wr16dcB6FiCAAAIIIIAAAqklQIAvtWTZLwIIIIBAkgQsiYZ1ub3f9auzMa3iF+uqO3/+fPXp00fZsmWLvzrJ80d2HtSke7/Qyty1dNPg23VZ1O8B21o3vtalf9arnxXxWuw98YSUPXtANRYggEAICuTMKdcCWJrwZzlFfvaRbiu5TKt0ZcCZZHapOWp911mLL7pOn/XcpCNHAqqofv36rhvwIi+YZy2L45fZs2erQoUKXqu/o/Ey9savyzwCCCCAAAIIIJASAgT4UkKRfSCAAAIIJFsg2o111b9/f5UrV04//vhjwPY5cuRQv379NG/ePFWpUiVgfVIXHF31u1bXeEAn8hVU46H3qcrxuQGbWqbNt4p9oOz/94UWrs7mja+XKVNANRYggEA6ELB/2zaG5ri1pbX+/37Ra6WHaKXrjh+/1Dw2Q3f2KKNZuW/S9Nv76/CG7X5VbHy+Dh06eN12W7UKHOPv+PHj6tmzpxfo++mnn/y2ZQYBBBBAAAEEEEhpAQJ8KS3K/hBAAAEETivwm8taW69ePXXq1EmHDh0KqG/ZK617m623L9FnUo5t+EtLmj4vl4ZXV/78mbLHHAi6m9W5qmv5gDl6dn1H3do8TJZMg4IAAulfwP6t39g8k7r9ep92Tl6iYUW7KFpu8D6fklWH1fDo96o/qpNiLrtMM+/6UMePxfjUkPLnz6+hQ4dqzJgxKlCggN86m7EEHfZ+165dO5GEI4CHBQgggAACCCCQQgJ8jUkhSHaDAAIIIHB6gaioKPXu3dsbu2qWpbiMVy699FKNGzdO33zzjQoXLhxvbRJm3Vh+0VOnaXPV5ooocakqTXjDjbJ1LOiGWyNLaO0rX+vK3XNVub3Louv/vT7oNixEAIH0KVC3YSbdtbGXlr83XduyFAt6klljDqnO8Ec0P3djje+/Tu7txq/YUAPvvfeemjd37z8REX7rbOaTTz7xxhm19zcKAggggAACCCCQ0gIE+FJalP0hgAACCAQVWLVqlctqWcvLgnskyKBWlkRjxYoVuvHGG4Nun+jCw4elTz/V/ssrKrxhA126cLQiXFuc+CVK4Zp/UROt6T5MhXavVukXWpISNz4S8whcwAIVHqurgtuWatsND8jeL4KVWoen6PpOpTUx791a1uP/pCVL5Joie1UzZ86s1q1by37AqFq1asDmf//9t1q2bKm77rpLu3btCljPAgQQQAABBBBA4EwFgv/lcqZ7YzsEEEAAAQTiCdg4VK+//roqVarkjacXb7VKlCihqVOn6qOPPlJOGwU/OeXPP6Xnn9eJSy6V2rZVjj+WBd36uDLo68JPad7IP1Vt5w8q0+O/kvsiTkEAAQQCBHLlUsHvP1XEP9v1a/evNL2A+yEgXrEfEJrsGqbyPVtILhFQ1MX5FOG66cYVS7Axd+5cbxzR7EEy9QwfPtyNHlBW48ePj9uERwQQQAABBBBA4KwECPCdFR8bI4AAAggkJrB27Vqv1V63bt107Jh/V9kw1yfWxthbvny5GjRokNhu/Ne5fnHhP/8s3XmnYooXl954Qxn2BGbftY32K7vG5ntACz9dppabe6tmi0L++2IOAQQQSEggb15d0eMu1d/2tX55c6K2Z0x42ICII4cU0b6DMsxYc2pv1k3X3uOs9fLNN998annck23btnktlm1svn379sUt5hEBBBBAAAEEEDgjAQJ8Z8TGRggggAACiQnEuCDcwIEDvVZ7CxYsCKhaqlQpWVZJy5KbLVu2gPUJLci8Z49quWBhZgsIjhihMDemX7CyVqX0av739NOwv3TT9k9V44ErGWMvGBTLEEAgSQIVOzdW/u0rtLbZ0zoYFvw9KyImSjX6vqltryzT0SmzJfd+ZcXGFh09erS++uor5cmTJ+D1bGy+8uXLa9q0aQHrWIAAAggggAACCCRVgABfUqWohwACCCCQJAEbY+qWW25R+/btAzLkhru0lc8884yWLl2q2rVrJ2l/vpWuGvSZ8rrWMAmViWqsVnm+1/SP1ui5LY/pxv/mILCXEBbLEUAgWQJheXKr9Jh3lHnbJi249TXNyVRPf8q/Vd9F2q32i15S7jp3FpUAAEAASURBVJsbKSpvfkX37uO9hrVYtnH3bJzRpk2bBrzuxo0bvZbMTzzxhA7bmKIUBBBAAAEEEEAgmQIE+JIJRnUEEEAAgYQFJk+e7LVEGTt2bEClK664QnPmzNHbb7+tyMjIgPWnW7BwYZj2z9sdUO2gsupDdVCVrKs0/+WJGrj5Bj30cLgyZAioygIEEEDgrAUy5L9IVb99XhV3T9eXr2/S2IzNg+4zIuq43C8aWjXgp1PrL7nkEn3//ff6+OOPFWxsPmvVnNB4pad2whMEEEAAAQQQQCCIAAG+ICgsQgABBBBInsCJEyf0wgsv6Prrr9f27dsDNu7QoYMWL16s6tWrB6xLdMGWLdoz5DuNqdRde+reobJH/ZNoDNedKhaxRasf/VDj11+pF1+U6/Kb6B5ZiQACCKSIQNasUpeu4ar52+f6M2+FoPsMV4wiO7RW+zv3yL2decVa87V1SYGWLVumevXqBWz366+/qmbNmnr55Zdl760UBBBAAAEEEEAgKQIE+JKiRB0EEEAAgQQF/nSZbC1JxmuvvSYbe8+35M+fX+PGjdOHH36orPZtOKll9mxFN2wkFSmi3G1u1c2/vKxmGhewdfhlJTR7ZW717y+5l6IggAAC51zg4mLZVWTtNG3p0ENTc92oNbrC7xhKaIOeHlFN95Sap7595YJ2sastg/iPP/7olvVVlixZ/LaJjo5W9+7dVb9+fW3YsMFvHTMIIIAAAggggEAwAQJ8wVRYhgACCCCQJIHx48erYsWKmjlzZkD9G264wcuQe+ONNwasS3CBjT3VqpXcAH0KnzolwWpxK1p2KyXX85eCAAIInF8Blzzjot6dtX9wO33Z9VMtyOI/xmhp/aZJh2vrt6c+VJXKMbJE4FZsXFIbd2/JkiWqVq1a7EKf/892P3ZUqFBBw4cP91nKUwQQQAABBBBAIFCAAF+gCUsQQAABBE4jsGnTJpe8IkwWvNu5c6df7Qxu8LvevXt7LfesBV+Si0vOcaLedXKpJk+7SYz1w330Ual169PWpQICCCBwrgTc26KqVN+hMnM+0pEsufxeNqNOuPFCH1GnZQ+qQY3DevhhaffJYUXLlCkjC+ZZt9yIiAi/7fbt2+cl6Gjt3u/279/vt44ZBBBAAAEEEEAgToAAX5wEjwgggAACSRKwliTFihULWteWz5o1S0899ZQXAAxaKf7C48cV824/HStxhTIsmBt/rfYph6apvr6/8knNeKizpr7/vo5s2yavX268L8IBG7MAAQQQOA8CGcqUVJaFs3W8QuWAV39An7n2fJcr+//eUeVS+zR0qNzwBnKJgTK4cURf9N5Drftu/PL55597CTjmz58ffxXzCCCAAAIIIICACPBxEyCAAAIIJFmgles+e9dddwWt37x5c6+bWbISaUyapGNXVVDYk08o06E9fvvdq5zqpH66vsJ2Rc6dpgaLXtOeG2rqgBuXzzVx8avLDAIIIJDmBMqWVcaFri/uc88FHFph/aV39KwW7Symdfd21431Dmjt2thq1157rX755Rfde++9AdutW7fOS8Dx1ltvBYx5GlCZBQgggAACCCBwQQkQ4LugLjcniwACCJy5wDfffON6zwbvPvvmm29q1KhRyuPGoUpSOXBA0W0ekEu7q0y/rw7YZJMuVcPIOSrRp5NmLoyU+75LQQABBEJPwLXK0xtvSO790xtaIN4Z5NEeddfL+mRmab1ddrDeeStaUVFSzpw5ZS32hrrmfTly5PDbKspV6NKli2699VbXxfdkH1+/GswggAACCCCAwIUoQIDvQrzqnDMCCCCQTIEJEyaoZcuWQbey4F7nzp2T3iXXtUw5Wq6ywod8FnR/X+kuvdBovkatKasnn7Rua0GrsRABBBAIHYHbb1fYvHlyaXGDHvMl2qqPT9yv+l2qqePVM7VyZWw1azW9dOlS1ahRI2C7MWPGqHLlylq0aFHAOhYggAACCCCAwIUnQIDvwrvmnDECCCCQLAEbc69Zs2ZBt7HMjxbcS7S4L6dq2tRrrRf99LM6Xq2mMm882RfNZ8NFukbN885U5P99pSETC6poUZ+VPEUAAQRCXcB12dW0adJcN9aoa30XrFTRIv1vdV2tLt9S7z+9Xm6IUtl4fD/99JM3Pp8lN/It69ev97rsDhgwgC67vjA8RwABBBBA4AIUIMB3AV50ThkBBBBIqsBHH32ku+++WydOnAjYxDI7VqxYMWC53wLbzuq4FoBy4+2F93lHGY8f9qtiSTQe0kB98dgCDVlXW24oP9ca0K8KMwgggED6EbAxB779Vlq+XCcaXh/0vG6P/kaP9rlMByLz6q9OvZTBvSlaht2JEycqb968ftscO3ZMHTp08MbsO+CGP6AggAACCCCAwIUpQIDvwrzunDUCCCBwWgEbxL1jx44BrULuuece2RfK+ONC+e3wyBHp00+l3Ln9Fsefma+quqPkEj34czu9+164G3cqfg3mEUAAgXQqUK6cMkx2P36MH69DxcoEPck8UTt1Sf+u+r30DTq+dYcaNWrkJTOqWbNmQP0vv/xS1apV0+rVgeOaBlRmAQIIIIAAAgikOwECfOnuknJCCCCAwNkLWHDPBnGPXx577DENGTJEGTNmjL8qdj4mRho0SCpeXGrbVjp4MHg9t/RTPajvnpml71aUVPXqCVZjBQIIIJC+BdwQBll/W6ZjvfvrYJaLgp7r5X9M0q5ilbRxxDwVcZnEp0+frqeeeiqgrgX3qlatmmBCpIANWIAAAggggAAC6UaAAF+6uZScCAIIIJAyAm+//XbQ4F6PHj3Ur18/hYcn8NHhWvXp4YelBx+Utm9P9GDeyf+mys39WK+9nUlZsiRalZUIIIBA+hdwP5pkeupRZdvym7b993GdUETAORc4/qfy31lfP7Qf7ZIPZVTv3r297OWWcde3HHQ/rFhyDmuBffToUd9VPEcAAQQQQACBdCyQwLe0dHzGnBoCCCCAQIIC77zzTtCkGX369FH37t0TzpRrAb0GDaSBA4Pue4XK6lvdqnf0jD5sNVuPbuys6tcy0F5QLBYigMCFK3DRRSo47F1Fbf1Hkxu8od3yH+YgUkfUeGALDbj6A+93lNtuu83LoluhQoUAMxtDtXbt2tqwYUPAOhYggAACCCCAQPoTIMCX/q4pZ4QAAgickcBLL72kZ599NmDbvn376sknnwxYfmrB4sVyfcKk2bNPLYp7MkXXqYl+0NVarm5Xfqs6P7+tjkNr0movDohHBBBAIIhA5oJ51Gjqc9o0YbXmZa3vVyNC0eqw8lFNKt5O40Ye0eWXX+4S8851oyK4YRHilYULF+qaa67RuHHj4q1hFgEEEEAAAQTSmwABvvR2RTkfBBBA4AwELGHGK6+8ErCltdx74oknApZ7Cw67bLiu265ssPfNm/3qHFKk7tRwNdIUTXQhvieeCJPFARlrz4+JGQQQQCBRgQrXF9TVf03SnCsfCKh375FPVOCOOupy1yZFR0fq448/1uDBgxUZGelXd/fu3WrWrJm6du2qqKgov3XMIIAAAggggED6ESDAl36uJWeCAAIInJGAjat34MCBgG1tfKcEW+5NcJkfy5aVevaUG+TJb9uNKqqamqMRLsRXsKBkVV0jQFrt+SkxgwACCCRNIGuujKq58hP91qpHwAZVtVDPDK+sp68cr5XLotS6dWvNmzdPpUuXDqjbq1cvNXUJPXbu3BmwjgUIIIAAAgggEPoCBPhC/xpyBggggMAZC3z//fdBW+jZeHvBMjR6L/Tuu3LfEqX16wNe9yfVUVUt0FJV1M03S8uWSddfH1CNBQgggAACyREIC1Opod21b+AwHYnI6rdlPu3QgM03qmiF3NpStrHKbdqsBQsWqGXLln71bGby5MmqXLmylixZErCOBQgggAACCCAQ2gIE+EL7+nH0CCCAwBkLzHZj5t1xxx1Bt7eMuQElOlp65hm5Zn0BqyzjY289pYauS+6ByPxyY7tr9GgpX76AqixAAAEEEDhDgZzt/qvMS+Zpb4FSAXvIoQMqvGqyYm66SZm+HKXhw4frvffeU0aXode3bNy40Y2sUFNffPGF72KeI4AAAggggECICxDgC/ELyOEjgAACZyKwfPly3eS+BB62cfR8yoMPPqiYmBifJSefWjfce+6RXLfd+MVa7V2jxS4/bm+VrZjJZXSUHn5YLuNu/JrMI4AAAgicrUDY1eWU69cFOnCdayYdpIQrRlk6PqC9N9ytx+6+W9OnT3fDJbjxEnzKkSNHdN9996lTp046fvy4zxqeIoAAAggggECoChDgC9Urx3EjgAACZyiwYcMG1232eu3Zs8dvDzZ208CBA/2WeTP79kk33CANGxaw7nm9pnqa4XLkltfjj0s//yxdeWVANRYggAACCKSkQK5cyj7pWx1/p5/+yVky6J5zTxiuQ5eVU42du1ySo8Veq734Ffv376/GjRszLl98GOYRQAABBBAIQQECfCF40ThkBBBA4EwFduzY4QX3tm7d6rcLa81nGRjDw30+FqxVx5gxUp060o8/+tU/rgxqrcF6Q88rZ84wjRol2dB8mTP7VWMGAQQQQCC1BNz7dcanOynf3t81buBfmhlRL+CVsu7bprCbmynvoC80bdo0dezYMaCOtfCrWrWqVq5cGbCOBQgggAACCCAQOgI+3+RC56A5UgQQQACB5AscOnRIzZo109q1a/02rl27tr7++uvYcZoWLpS6dJEaNpTy55duuSU2U4bPFgeUTc00Vp+7EF+lSnItQ6TbbvOpwFMEEEAAgXMqcFO7QrpkzTR1K/GVdilPwGtnfKGLDj/+kj54/3199tln7scY/19j1rukSTYu34/xfswJ2BELEEAAAQQQQCDNChDgS7OXhgNDAAEEUk7gxIkTuuuuu1wXWteH1qdcffXVrpHeGGXN6rIy/vCDVL269NZb0tSpcn14fWrGPt2u/Kqv6ZqoJt44e3PmSCWD9w4L2JYFCCCAAAKpJ1Dy8jC9tPou9bl/hcbpxoAXyjXgTW1p1l5t7r1XM2fO1CWXXOJXZ58bjqFJkyYk3/BTYQYBBBBAAIHQESDAFzrXiiNFAAEEzkjAkmY89thjXiDPdweXXnqpi+n9oDx5XGsPN+C667slWabcBMpalVJNzdGabFX05ZfyMuVmyZJAZRYjgAACCJxzAWuY9+qgS3T0m7F6PtM7Aa9f+PuP9es1d6lK+QpasGCBqlWr5lfHEm5Y8o1XXnkleMIlv9rMIIAAAggggEBaEiDAl5auBseCAAIIpILAG2+8oQEDBvjtOXfu3F5wr3DhwrHLbQC9DRv86sTNzFENtdcAVdBSZbmqpKwXr0vMSEEAAQQQSKMCLW4P072/PK3nCw5SlPz/3L9i2TeaU7qNcuUo6GXYbdGiRcBZvPTSS2rbti0ZdgNkWIAAAggggEDaFfD/xE+7x8mRIYAAAgicgcDnn3+ubt26+W2ZKVMmfffddypbtmzs8k2bpNde86uzSNfoNo3SpdqkWq7V3kAX4mtya6SXJbdMGb+qzCCAAAIIpEEBy2jeZc39eqvqSB1VJr8jrLVpmIaVfEFbt0ZqxIgRevLJJ/3W28ygQYNkCZis6y4FAQQQQAABBNK+AAG+tH+NOEIEEEDgjARmzJjhtcDw3TgsLExDhw5V3bp1Yxdv3y41aiQdOHCqWrTC9JAL6X3rQnx/uhCflZ495WXKzZHjVDWeIIAAAgikcYFcuVyQ7+fmGtF6vCxBkm9p+88bGnh1f82ZE64+ffqoX79+ss8I3zJp0iTv82LLli2+i3mOAAIIIIAAAmlQgABfGrwoHBICCCBwtgKWKbd58+YB3av69u2rO+64I3b3bmw+3X67XFpdv5cbrDZarMreMgvoucZ+cr21FM4nhp8TMwgggEAoCNh7972Dr9OqHt/ohCL8DrnXoU6aU6+rhg4+oU6dOrkfckYpS7zBVZcuXaprr71Wy5cv99uWGQQQQAABBBBIWwJ8XUtb14OjQQABBM5aYNeuXV63qt27d/vt66mnntLjjz/+77L586VZs/6dd89Wq4yeUezA7KVKSfPmSTff7FeFGQQQQACBEBSo1r2pdr7yUcCRd47uper3X6n/a/aZbmncRNOmTVPevHn96v3555+qXbu2S7DuMqxTEEAAAQQQQCBNChDgS5OXhYNCAAEEzkzgxIkTatmypX777Te/HdzsonRvvfWW37Lj4yf7zf+lQmqoKdqti3TDDZLF/2wMJwoCCCCAQPoQKPBCOx3p9nLAyZTS77pt3AM6lKewKn85QguHD1cp+5XHp9hYfE2aNNGQIUN8lvIUAQQQQAABBNKKAAG+tHIlOA4EEEAgBQSeffbZgBYWFStW1JdffqmICNc1y7rlujS4h9p00LHX/AN+3+gO/aXCevppacwYySXapSCAAAIIpDOBLK++qKiPBioqzL+7rp1m9uO7lfH9virqkmssePll1axZ0+/s7UekNm3a6GW3LsY+TygIIIAAAgggkGYECPClmUvBgSCAAAJnJzB48GC9++67fjspWLCgxo4dq+zZs0vui5nLuiFVraqsQwYoW9R+v7o/hjfSgAHSO66HrsUCKQgggAAC6VMg4uF2ipgxTduLVQ16gmFHjijbw49oypQpatGiRUCd7t2766GHHnIfK+5zhYIAAggggAACaUKAAF+auAwcBAIIIHB2AvPcYHnt27f320mmTJn07bffqkiRIrHLrYvuoEF+deJmtoUV1KP/18DtI24JjwgggAAC6VqgTh0VWD9Pi1/5XlMjGgecaoa9u/TL+N0aMWKEbAzX+OWTTz7xhoQ44oKBFAQQQAABBBA4/wIE+M7/NeAIEEAAgbMS+Ouvv7yMuceOHfPbzwDXHM8yH3plyRJFv9Tdb73NRCtMM7I01v5Rk9XolqwB61mAAAIIIJCOBcLCdM0LN6jQsonqm7tnwIkev+NujR4Zrd69e6tfv34Kc/V9i/2I1LRpU9n4fBQEEEAAAQQQOL8CBPjOrz+vjgACCJyVwN9//63ChQtr69atfvuxbLn333+/3Lcuac4c7bzxPoVH/duVKkrh6qHuuqX8Bl2xfqJKNS/ntz0zCCCAAAIXjsBVV0n3/PqiZuVp5nfSdWNmaOWdL+uDD6ROnTpp1KhRypw5s1+d6dOnq379+tq+fbvfcmYQQAABBBBA4NwKEOA7t968GgIIIJCiAgUKFAjYX4MGDfTOCy9IDz+smLx5pVq1dPHWFX713lBXLWveQ1/PLSo3TB8FAQQQQOACF8iXP0yVlw/W35FF/SS66VUNe3SWunWTbr21uSZMmKAcOXL41VniWonXcp81f/zxh99yZhBAAAEEEEDg3AkQ4Dt31rwSAgggkKICNv5RsPLtAw8oQ4UK0v/+p7DjxwOqLFFF7ejwkkaOlLLSKzfAhwUIIIDAhSoQWfgiXTRxuMuwm+EUQbhi9KkeVJ/XD8t9vLhAXn3NmDFD+fPnP1XHnqxbt84L8i1btsxvOTMIIIAAAgggcG4ECPCdG2deBQEEEEhRgYULF+rRRx8N2Oc81y035z33SG5cvmDlkCI175Ev1PeDTArnEyAYEcsQQACBC1ogQ50aCn/jNT+DK7RWffSUvh58yLXik664opJmz56tEiVK+NXbtm2bKrgfmLp27eq3nBkEEEAAAQQQSH0Bvt6lvjGvgAACCKSowI4dO9SiRQsdPXrUb79fPvigqn3+ud8ymzmuDFqhshqqezShy3Q9/H45N1B6QDUWIIAAAggg4AmEPfO0VL26n0YHDdB2FdAd49vohRpTVSBvCS/IV758eb96NtOrV6+AsfoCKrEAAQQQQAABBFJUgABfinKyMwQQQCB1BaKiotSqVStt2rTJ74Vecf2m7h49WnLrfcsEXa/L9bsqZ1qhrKO+0G29qvmu5jkCCCCAAAKBAhER0qBBUqZMfuty6IDaaIj6LGuogwVKKMekOV533Tp16vjVsxnL7B4/625AJRYggAACCCCAQIoJEOBLMUp2hAACCKS+QI8ePTRp0iS/F/qPG9i826JF0s6dfstfd4k0muoH7clZTBMnSrfd5reaGQQQQAABBBIWsNS6vXsnuL7gsc3K3uZ2RXw01H3GuA+ZBEqHDh0SWMNiBBBAAAEEEEhJAQJ8KanJvhBAAIFUFLDMha+++qrfKxQqVEjf1aunsKVL/ZaP1i16wWU+LFAgzLWukOrX91vNDAIIIIAAAqcXsLFe3WePmjRRTAIDt+Z4/jEd69lXRw4fDrq/AQMGaODAgUHXsRABBBBAAAEEUk7g3xRZKbdP9oQAAgggkMIC27dvV+vWrf32miFDBn337rvK1rqN3/JVulL36XMVLhKuH3+USpXyW80MAggggAACSRe4/nrJTWFbt2p7v2Ha0ftzlT3h/6NSrje7aWeGSDdKRJQirHtvvNK+fXuvy26w5FDxqjKLAAIIIIAAAmcoQAu+M4RjMwQQQOBcCURHR6tNmzb6+++//V6yf/fuKvv+pwo/8m+riWiFecG9vCVy6qefCO75gTGDAAIIIHDmAq7FeIFeTylyzRK9nds/y67tNOvr3bRh6nrZZ1alSpUCXuexxx7zMuwGrGABAggggAACCKSIAAG+FGFkJwgggEDqCfTr18/1kHJdpP6fvfsAk6LI+zj+28AuOeckSRBRFEWUU+AMgBjOgAEFA8ohgohiQoKKih6YMB8qKHIGQFEPXhUUBcGA5CTgITmnJccNb9csMzvVMyy7sDszO/Ot5+G2q7q6q/rTezL8p8LRdLvzc2758rrHWY+v6DR7Pb431UO7T23qCe7Vru29gp8IIIAAAgjkjUCdunG6ZWFfPVvxNeuGRTIOqPDlrbRu7G+a7awL+8QTT1jnTWbBggVsvBGgQgECCCCAAAJ5I0CAL28cuQsCCCCQLwJz587VY4895rv3J87RSOfP2du2Kc61Y+5GVdZ/GjzrWXOvenXfJRwggAACCCCQpwLm75iuC3tqbNl7rPtWTV+vKje30I5H/qWBzihz97qx3soVK1b0HvITAQQQQAABBPJIgABfHkFyGwQQQCCvBfbt26dbbrlFR44c8dy6p/O/HbJp5MVab2rCtFJyZlGREEAAAQQQyFcBE6O7bMYgbUyqabWTqDSVe6mv9rdoo37duuniiy+2zpvM1q1b9dpr9gjAgEoUIIAAAggggECuBAjw5YqLyggggEDoBB544AEtW7bM0+CZzv++EKTp/Sqiz9RePetPVP8516tChSCVKEIAAQQQQCAfBMrUK6eis6drTvEWAXcv+stkHWx3nX7wW2LCv1KvXr1kdtglIYAAAggggEDeCBDgyxtH7oIAAgjkqcDnn3+u9957z3NPMzZijPMn2dVCVw1TBW3VkPM+07O/t1GZMq4KZBFAAAEEEMhngVJn1FDtFT/o31Wecsbu2f+0KDxzmvbe0kUZzsYbZ55pvqqy07333qsRI0bYheQQQAABBBBA4IQE7L+FT+gWXIQAAgggkJcCZrfcbs60JpOucv7Mdf6cZjJ+6SX11rvqqgZNimniRKlUKb+THCKAAAIIIBBCgTIVEnXToifVtd6P2iB7nYji40ZJ8fFaMHasnn/++YBedenSRaNGOXVICCCAAAIIIHBSAgT4ToqPixFAAIG8FcjIyPAE93Y5m2iYKbnjnT9lXU3MURP11XPOaAhpkrOJLiP3XEBkEUAAAQRCLlDW+ctq8K8t9WC9CTLLR7jTkRs6qM+jj2rgwIHWKfP33p133qnRo0db5WQQQAABBBBAIHcCBPhy50VtBBBAIF8FPvnkE33xxReenXIfDtLSctV1Vtz7XHUbJuv776Xy5YNUoggBBBBAAIEwCJi/k9745Rw9UeP9gNYLLZqnfe9+rAEDBqhv377W+XRnCm/Hjh01btw4q5wMAggggAACCORcgABfzq2oiQACCOSrwMaNG3XffffpSqeVW4K0NFo36RzNUaFTa2vyZMnsYEhCAAEEEEAgkgTMZk+PzrpZ/y7fP6Bbe3r104GUQ3r22Wf10EMPWefT0tLUoUMHjR9vxq6TEEAAAQQQQCC3AgT4citGfQQQQCAfBMwUpa5du2p/SoqGuu5/0Nleo5veVgd9qvK1S+qHH6Qq9hJHrivIIoAAAgggED4B8wXUNQue0XuleludqHxojT668E0dORKnF154QT179rTOHzlyRDfccIO+PcbOu1ZlMggggAACCCBgCRDgszjIIIAAAuER+PDDDzVhwgQNcJqv5+pCV73j7JfbTdWrx3mCe9WruyqQRQABBBBAIMIEzBdRl8x+Ub8mtbR61n7pIN3bIUXp6XF69dVXdc8991jnDx8+rHbt2mnoUPfXXVY1MggggAACCCDgEiDA5wIhiwACCIRaYNOmTXrggQfUyWm4n6vxn/U3jdJtMouXmw01atVyVSCLAAIIIIBAhArUqRuniu8PsXpXVikq88Vw9ehhiuP01ltvqXPnzlYdk3nwwQfVoEGDgHIKEEAAAQQQQCC4AAG+4C6UIoAAAiET6NWrl+J37tRbrhZTlaCeel1Fi8bp66+lhg1dFcgigAACCCAQ4QJ1bz1f2y9ub/XyDmcrqWHDMtTP+VYrPj5e7777rjp1Ml9z2enPP//0TNm1S8khgAACCCCAQDABAnzBVChDAAEEQiRgpuWOGTNG9zvtlXC12UuvalGhc5xdBaXzz3edJIsAAggggEABESg3wDNcz9fbM7VITTVLzz8vZwSflJCQoPffD9x511zw+eefezbl8F3MAQIIIIAAAggEFSDAF5SFQgQQQCD/Bfbs2aN7773XE9gzAT7/9ImzpcbbcT3kLM2ntm39z3CMAAIIIIBAARNo1Uo65RSr0x/rVlXUZmejDTk750qJiYk6dOiQVcebGTBggH777Tdvlp8IIIAAAgggEESAAF8QFIoQQACBUAj07dtX69at07+cxsq4GnxW/fX661KHDq4TZBFAAAEEEChoAs40XN11l9XrU7Vc3+pyFU/f5fm7buZMKSkpSbt27bLqeTPNmzdXamqqN8tPBBBAAAEEEHAJEOBzgZBFAAEEQiEwe/Zsvfnmm7rUaay7q8Fxuk43PNHo6ALkrpNkEUAAAQQQKIgCvXtLjRtbPW+ieRqvq5W0P0VXXSWtXCmVLFlSK1assOp5Mw899JD3kJ8IIIAAAggg4BIgwOcCIYsAAgjkt0B6erp6d+um3hkZGuNqbJ+KauaNL+ipp1wnyCKAAAIIIFCQBYoXl779VqpTx3qKlpqmBWqs07f8qHbtpB07pNq1a2vq1KlWPZN57bXXPLvuBpygAAEEEEAAAQREgI9fAgQQQCDEAl87U3M/nTVLLzrtlnW1PeK0IXr6o7qKi3OdIIsAAggggEBBF6hSRfruO6lyZetJamidvlNrtVo2TNdcIx08KLVs6YT+pk2z6pnM/fffr4kTJwaUU4AAAggggECsCxDgi/XfAJ4fAQRCKrBz3jxdNGSInH/iBKQZxS7W7b/cq0KFAk5RgAACCCCAQHQImBF8kyZJ5cpZz5OoNA1TN105vY/uvCNDziB3XXTRRRo1apRVLy0tTTfddJMWL15slZNBAAEEEEAg1gUI8MX6bwDPjwACoRM4fFgpzpa4pc2/Wlzp6+TrVPXXcSpVhv8su2jIIoAAAghEm8CZZ0rOF1667LKAJ+ujwSo9ZpiefTbzVKdOndS/f3+r3u7du501+67S9u3brXIyCCCAAAIIxLIA/5KM5bfPsyOAQOgExjir7SUnq/aWLVab01ROLZNnqNLP41TjzNLWOTIIIIAAAghErUD16nLm2sqJ3gU84qMaoiefSNe4cZmnBg4cqBtvvNGqt2rVKnXs2FFmRB8JAQQQQAABBMQafPwSIIAAAvku8Mkn0s03BzTzp+J0hX7Sw2Oa6dxzA05TgAACCCCAQHQLxDtjDZ55Rho2zHrOOlqpfhqkuzsd0vz5zj9YnHojR45Us2bNrHpmLb5nzPUkBBBAAAEEECDAx+8AAgggkK8CZjquM/LAnZz1w3WTumvQq6frH/9wnyWPAAIIIIBADAn8859So0bWAz+jJ/Tngep6ts1PMoPfixQpoi+//FKVKlWy6j399NP65ptvrDIyCCCAAAIIxKIAU3Rj8a3zzAggEDqBH3+Uli0LaK+XKurcu15Sz54BpyhAAAEEEEAgtgTM1vF33hnwzBW0Ta9vuUl3XbNdzjK2quLswjvGWfIiISHBVzfD+SLNTNU1U3ZJCCCAAAIIxLIAAb5Yfvs8OwII5L/A228HtHGNUzL99JF6++1kmX/TkBBAAAEEEIh5gc6dpYoVAxgqa7M6/dZDPXrIs7Nuy5YtNXjwYKteSkqKbrjhBh08aMbHkxBAAAEEEIhNAQJ8sfneeWoEEAiFwOrVShv3hdWSGbA3qfB1+uGHy5WUZJ0igwACCCCAQOwKlCsn/fab9OCDAQYdNFop732mN9/MPNW7d2+1b9/eqjd79mzdf//9VhkZBBBAAAEEYkmAAF8svW2eFQEEQieQnq4tV96ihPSs3f32Oa1/qMLO9KJXnDWEQtcVWkIAAQQQQKBACNSuLb38srRhg1JLlrG6PESP6tEHDuvXX+WMfo/TiBEjVL9+favOu+++q/fff98qI4MAAggggECsCBDgi5U3zXMigEBIBfb0GaSKi51/hfilD5zjtjf219VXn+JXyiECCCCAAAIIWALOWnuJ/z46XO/oCbOz7h1pw3XjjfJsulGyZEmNGzdORYsWtS7t3r275s2bZ5WRQQABBBBAIBYECPDFwlvmGRFAIHQCqalKv+9+lXjhCavN1U6un/Nn1KiHrXIyCCCAAAIIIBBEoEMH6W9/s04M0DPavv6AzCnnr1tn491GMqP2/JNZh89M3zXr8pEQQAABBBCIJQECfLH0tnlWBBDIf4G77lL8m69b7aQ7uducP8+89oaSk5Otc2QQQAABBBBAIIiA2YXqueesE1W1UT30pswG9U8c/R7t1ltv1X333WfVW7FihW6//XalO8tlkBBAAAEEEIgVAQJ8sfKmeU4EEMh/gWnTzBC9gHYec0qcM+rZ09kCkIQAAggggAACORNo1Upq3dqq+6Ie0V0arjee362vvso89dJLL+mCCy6w6k2YMCFgt12rAhkEEEAAAQSiTIAAX5S9UB4HAQTCJLBunY7cervVuNleo5Pz50Xnz08//WSdI4MAAggggAACORAYNCig0nB10Z+qr49vnaDly+XsSp+ksWPHqkKFClbdAQMGaPr06VYZGQQQQAABBKJVgABftL5ZngsBBEInsGaNMlq2UqF1q6w2H3dyHx0tadGihXWODAIIIIAAAgjkQOC886TrrguoWFmbNXr/1VrY7G4d2Lxb1atX16effqr4+Kx/3qSlpTnr9XXQtm3bAq6nAAEEEEAAgWgTyPobMNqejOdBAAEEQiGwapXkTCGKW7nCam2Rk/OuxGfWAiIhgAACCCCAwAkKPPusVLhw0IuvSxmhrWdeLB04oEsuuURPPvmkVW/9+vW64447WI/PUiGDAAIIIBCNAgT4ovGt8kwIIBAagZUrPcE9mSCfX/rDOTYrBh08Wla7dm2/sxwigAACCCCAQK4ETj9d+vpr6cYblVGmTMClNbfO0bLbnCCgk/r16+cJ9PlX+tq51qzTR0IAAQQQQCCaBQjwRfPb5dkQQCD/BP76KzO450zP9U9m5J4zjkCbjhbu3r3b/zTHCCCAAAIIIHAiAhc7f7uOGaO47du14Znh2q0S1l3qfD5EmybOV0JCgj766CNVqlTJOv/444/rl19+scrIIIAAAgggEE0CBPii6W3yLAggEBqBjAypY0dp7VqrvQVOzgT3thwtNf/IKFHC/geIdQEZBBBAAAEEEMidQFycqva/S989MV2HVch3bSGlastNPeQsu6fKlSt7gnxxTl1v8q7Ht90JEJIQQAABBBCIRgECfNH4VnkmBBDIX4E/nEm4M2ZYbcxTSV3ilPgv483oPYuIDAIIIIAAAnkmcP1TjfXV6WY7q6zUePfPevNhZ/kMJ1166aXq379/1knnaK3zxVznzp2VYb6oIyGAAAIIIBBlAgT4ouyF8jgIIBACgQVmrF5W2qIyutSZLOQ/JmDw4MEqWrRoViWOEEAAAQQQQCDPBMzgvNY/9NX2+PLWPRe++oOmT88sMhtutHI2wvJP48eP1yuvvOJfxDECCCCAAAJRIUCALypeIw+BAAKhFDg8aYrV3EwV1g6/krJly+ree+/1K+EQAQQQQAABBPJaoHSlZGX83Yyfz0oXZ0zWrbdKKSnyrMf38ccfq0KFClkVnKPHHnvMGYhvj8S3KpBBAAEEEECgAAoQ4CuAL40uI4BAGAV27FDGqFFWB37TRiv/wAMPsPaeJUIGAQQQQACB/BEof5Md4LtVn6jX2ofU7+7M7a6qVq2qUa6/t1NTU3XzzTc7QUAnCkhCAAEEEEAgSgQI8EXJi+QxEEAgNALz7ntPyWkHfI0dUZxG+HJSyZIl1bNnT78SDhFAAAEEEEAg3wQusQN8pp2H9LJ6fnGxxn1yyNNs27Zt1bdvX6sLq1ev9oy2Zz0+i4UMAggggEABFiDAV4BfHl1HAIHQCmxcl6YKn75uNTpGGdrgV2KCe6VLl/Yr4RABBBBAAAEE8k2gXj2pUaOA2zfUUv3yz/e1eXPmqYEDB6pFixZWvdGjR8tM4SUhgAACCCAQDQIE+KLhLfIMCCCQ7wJmw71BnZaoWsY6q61X/XLFihWTmZ5LQgABBBBAAIEQCZjdNj78UGrcOKDBXvsGqfvdh5xdc6XExERPMK9MmTJWve7du8uM5iMhgAACCCBQ0AUI8BX0N0j/EUAgJALm3w7Lp9rBPTMoYKZf6+YfCeXL27v5+Z3mEAEEEEAAAQTyQ+Ccc6R585Tx6mvW3Wtoner931CNHJlZXL16dQ0bNsyqs3v3bt1xxx1KS0uzyskggAACCCBQ0AQI8BW0N0Z/EUAg5ALrnLher15SFddmGv5TcwsXLqzevXuHvG80iAACCCCAAAKOgDOSL67nfTrcvKXF8Zz6akL3r7VmTWbxjTfeqNtuu82qM3XqVL388stWGRkEEEAAAQQKmgABvoL2xugvAgiEVMBM6/nnP6Vdu6QztMhq23/v3K5du6py5crWeTIIIIAAAgggEEIBJ8iXNGSQ1WCC0jXiQAc9deNipadnnnr99dd1yimnWPX69eun+fPnW2VkEEAAAQQQKEgCBPgK0tuirwggEHKBEc4Wud9+K52tuequt6z2/+eXe+SRR/xyHCKAAAIIIIBAWAQuukhyNtTwTyW1R3f83l3//ndmaalSpZxl+z50Bv056/cdTUeOHFHHjh118OBBbxE/EUAAAQQQKFACBPgK1OuiswggEEoBM53nvfsXaLRu0mydqyLK+tBvVuoZ7tcZs64PCQEEEEAAAQQiQGDAAB2+4RarI630k8Y8MlNm2Q2TWrZsqUcffTQzc/R/Fy9erMcff9wqI4MAAggggEBBESDAV1DeFP1EAIGQCmTs3KWVF3TQr/vPcsJ7YxUvZ66uX3rDOV54ND9nzhy/MxwigAACCCCAQFgFzFTdUSN0sHw1qxuP7H9KPe5N9+yqa048/fTTOvvss606Q4cO1ffff2+VkUEAAQQQQKAgCBDgKwhviT4igEDIBZbf3FetNo4O2u48p/SJo2datWqlJk2aBK1HIQIIIIAAAgiEScDZ/Krwwz2txq/U12o2YYA++yyzOCkpSf/5z3+UnJxs1bvrrrtkdtclIYAAAgggUJAECPAVpLdFXxFAICQCKat3q+p3IwPaSi1WXGZVH7M/n/dj//333x9QjwIEEEAAAQQQiAABZwOs9NJlrI7003Oafvf72rEjs7hRo0YaMmSIVWft2rXq06ePVUYGAQQQQACBSBcgwBfpb4j+IYBAyAX+r9MnKpaxz9duqhK0+Oan1afDzXrKKd1z9IxZd+8f//iHrx4HCCCAAAIIIBBBAmXKKP7TT5Qen2B16rk99+m57kcX43PO3Hfffbr00kutOm+//bamTJlilZFBAAEEEEAgkgUI8EXy26FvCCAQcoFp06Ry07+02p1V6SrVHvGw3vPO6Tl61kzhSUxMtOqSQQABBBBAAIEIEmjbVnFvvml1qJj265zRj+qHHzKL4+Pj9e6776po0aJWvS5dumj//v1WGRkEEEAAAQQiVYAAX6S+mSD92rJliyZMmKAnnnhC7dq1U/ny5RXnLCJs/tx5551Brggs+uCDD3zXeK891k9Tl4RALAkcOpihJzuvUX39aT32qY9ep3HjPteuXbt85eb/N507d/blOUAAAQRiWWD6mukn9Pgnet0JNcZFMSsQ1+0e7b2li/X8t+oTDbttug4cyCyuXbu2nn/+eavOX3/9pQHOjrwkBBBAAAEECoIAAb6C8JaO9rFSpUq6+uqr9cwzz+jbb7/V9u3bC1Dv6SoCES7gBM/3VquvH/46RXW1wupsuTOqaPjw4VbZZZddplq1alllZBBAAIFYFHhqylNq8X4LDZ4+OOjjp6amyvvHv4Kpb64z15MQyG+B4q89r4NFSlvNPLbhfg1+Ls1XZqbqXnjhhb68OXjllVf022+/WWVkEEAAAQQQiEQBAnyR+FZy0KeaNWuqTZs2Oah57CoTJ07UwoULj/nn2muvPfbFnEEgmgRSUpR2S0eV27E86FOtSUsLWIfn7rvvDlqXQgQQQCCWBMwIvIFTzfZDUp/JfQKCfHv27FEZZx20QoUKeX6avEkmuGfqm2SuZySfh4L/yU8BZ+ZL0vOZv6veZs7RXCU+/4ycgXqeZKbqmi/0/HfVzcjIkFmS49ChQ97L+IkAAggggEBEChDgi8jXErxTZmru+PHjtWnTJq1evVrDhg0LXjGHpfXr19cZZ5xxzD+lS9vfcubwtlRDoOAIHDkivf++MpyReAl7vfviurrfrJmGmYX5/FLZsmVFANwPhEMEEIhZgYtqXqR/Xfov3/O7g3yffPKJ9u7d6zlvfn766adWcM+cMNeb+5AQyG+B+O736mDd061m+qY9rRGdji7G55xp0KCBBg60A4FLlizxzKCxLiSDAAIIIIBAhAkQ4IuwF5Jdd8yHjauuukpmqi4JAQROUmDyZOnss+V8La+43XZwb7dKaEntK6QXX1Tqd9/pOdeaPJ06dbK+3T/JnnA5AgggUKAFHrvosWMG+dxfRg78fqBv5J55aBPcM9eTEAiJgDOStPCo95QWl+BrLl4Zuu+3jpo0cqOv7KGHHtK5557ry5uDQYMGae7cuVYZGQQQQAABBCJJgABfJL0N+oIAAqERGDFCctbQ0x9/BG3v1RIDVH3+/0nOB/wrbrghoA7TcwNIKEAAgRgXCBbk6zm6p+bMmZMl4yxttv709b48wT0fBQehFGjeXIefHGS1WEWbdP5dp+vI8y/KWTBSiYmJzgD/9z1Ty/0rnnPOOb4Rqf7lHCOAAAIIIBAJAgT4IuEt0AcEEAidgBm5d8892bbX9NFLVKJEZpXvnBF87tS4cWN3EXkEEEAgpgW6d++u1zu8rpK/l/Q5vLH0Dcm7X4H52dp3ylPP1K9evbrnj7mehECoBIoMeETrG19uNVcqfacK9X1EuvdeT/mZZ56pvn37WnVMxqyDTUIAAQQQQCASBQjwReJbCVGfOnfurKpVqyopKUnlnYWHL7jgAvXv31/r12d9ux6irtAMAqERWLpUMiPynG/nj5UWlmiuyx9v4jk9f/78Y1WjHAEEEEDgqMD+/fs1cuRIz+eH3V87Sx74fy9ignqPOn/8gnvmvKlnPm94/3z44Ycy9yEhEBIBZzONqt99qE3JgcG69P98JDmba5kULMCX4mzMtWHDhpB0k0YQQAABBBDIjUBibipTN7oEpkyZ4nug7du3y/yZMWOGXnrpJQ0dOtQZ5JT9KCffxa6DdevWuUrs7MaNWWucHDhwQOYPKfoEDh486Hso/2NfYagP9u1TsrOGZfzOnVbLL6m3XtGDul0fKlmHdNVHd+jg4cyd8jp27GjVNZmffvqJ39kAldAU+P8e+R+HpnVaiUYB/98j/+NofNb8fKZZs2bZ/138+Whr3qBeUb/WTfDPe96v2AT3zH3OO+88v9KCdej/O+R/XLCeIoZ6W6K4Nn7+o3Y4nw1O1xLfg2ccPKQDZsfcuDhPWY8ePfTmm2/6zpuD3r17e6bwWoX5kPH/PfI/zoemuGWMCPj/Hvkfx8jjR/1j8u/qqH/Fx31AAnzHJYq+CnXq1NH111+v5s4aJDVq1PA84IoVK/T555/rs88+k/mPfbdu3ZzPNXHq2rVrrgG898zJhSZYYkYPkqJbwLzncKd648ap0V9/Wd34XNfrEb3gLK8dr+fVV5dfvlJnpy/QukkLPfUWL15s1TeZHTt2aNKkSQHlFIRWIBJ+p0L7xLSW3wL8Tp2ccL9+/fTyyy9njcIzQbwLnT/+wT0zQC9IcK9o0aKegIkZGRUt/33l9+nkfp9CefWvzQfoX7/e6mtyt0rqzZfn6swzt3nKWrdu7flyb+HCzM8GpnD06NEyy3U0atTId11+H/A7ld/CsXd/fqei751v25b5363oezKeKKcCTNHNqVSU1Lvuuuu0fPlyvfDCC54gn/mm3Py5+eabNWbMGP33v//1LSj84IMPatOmTVHy5DxGLAskOqNE633xhUWwIOls3aZRnuCeOVGixGHdemvWN/j7nBF/7nTrrVn/AHCfI48AAgjEskDTpk2djcdf9H1xGBDcMzgm2GeCfn7JfClorjPXkxAIh8Dlf3OW7/BLJsD3/vuNlJ6eVfjwww+rWLFiWQXO0TvvvOPM5M2cymudIIMAAggggECYBBjBFyb4cDVbqlSpbJu+ypmm8MQTT2jAgAGeb+GHDx8u8618btLatWuzrW6m6DZr1sxTp2XLlp7FtbO9gJMFUsCMBPV+M2jec+HChcP2HIlOQLvQnj1W+3cdflcH/IaWPPOMWZ7vYl+djz76yHdsDsyOeuYfoaVLl7bKyYROIJJ+p0L31LSUnwL8TuW9bvv27XVx/4v1R7U/sm5uRu55R/K1PlrsjOS75pprPEGSEt5djbKuKJBH/D4VyNemeNcXemWUoh0rMrRtWzt16pQVwDMjYx566CHfQ65evVqrVq1y9uTI3JTDdyIPD/idykNMbuUR4Hcqun8RjrdUVnQ/PU9nBAjw8XsQIGCm5ZogX0ZGhqZOnZrrAJ/ZES+nqUiRIjJ/SNEtYIJ7YXvPu50F31991QKelHy1Zh/KGi3ibJSn++5LUkJCVrVxzpRe/9SuXTtVqVLFv4jjMAqE9XcqjM9N0/knwO9U3ti+Nvs1O7jnXXPPjNzzBveO/uzSpYsqVqyYNw1H2F34fYqwF5Jdd45+6eytUlJ79LizcMfAgUNkluJ1ZpB70v333+/ZTGbBggXeqnr66aedOh1VqVIlX1l+HfA7lV+ysXtffqei792H7d9b0UdZYJ+IKboF9tXlX8fNh+1y5cp5GmBH3fxz5s4hEnB2ZnQWzrMa63PoKSvvDPCzgntmw5nvvjP/Ks1KHTp0yMpwhAACCCAQIDB4+mD1mdwnq9wb3DMlzog99+66T056MqsuRwiES6BePTOE32r9fr2mhPWrnY3nsorNSP433ngjq8A52rVrlx5//HGrjAwCCCCAAALhEiDAFy75CG/XbLBBQiAqBCZPth7j/xL/obk6x1fmrJ2ttm19Wc+BGb2XmprqKzTfcF599dW+PAcIIIAAArZAtsE9b1VXkG9OuTkaNHWQ9yw/EQifwHPPKcMJ4HlTYR3SIPXT4MGSs7KML7Vo0cKZttvJlzcH77//vmbMmGGVkUEAAQQQQCAcAgT4wqEe4W1u3brVWXckcweeqlWrRnhv6R4C2Qg408z1s/kXZVYandrelzFx7CFDfFnfgdkdzz9deeWVziYcJfyLOEYAAQQQOCpwvOCeWf930KBB8qwD7Ary9Z/SX+Z6EgJhFTj1VMV162Z1oZM+Uut9XzjL1ljFzueGISpevLhV2Lt3b8/SNlYhGQQQQAABBEIsQIAvxOAFoTmzK5hZf8+kVq1aFYQu00cEggssdXbGcwLW/ulnvy0cb7tNOvts/7PyBLd//PFHq5DpuRYHGQQQQMAnMH3NdGtabpPtTTKn4x6t0ahRI82aNUt9+/bVzJkzdfrppwdM1zXTes19SAiEVcBE8kqWtLowXHdr2vA/tXhxVrFZj/fJJ5/MKnCOfvnlF40ZM8YqI4MAAggggECoBQjwhVo8jO2Znb7mzp2bbQ8mTJjgWTDYVDKLdHbu3Dnb+pxEIKIFnF1v/dMmVdIK1fEUJSdLzz7rfzbz2Px/ID093XfC/P/giiuu8OU5QAABBBDIErio5kV6slVmsONfl/5Lg9oN8m2ccf311+vXX39VPbPGmZNOdUZJ/fbbb7ruuus8Qb5ivxbzlJvrzX1ICIRVoEIF6ZVXrC6UdXbU/SajrV5+xG+erlOjZ8+eqlu3rlX3sccek9mhlIQAAggggEC4BLIWmwhXD2g3xwLTp0/X8uXLffW902hNgSn/4IMPfOfMwZ133mnlTYDv4osvVvPmzT3riZ111lm+D+ErVqzQZ5995vnjHb33ohMcqVatmnUPMggUGIEtWySzwYZf+laXO7nM9SUfeECqUcPv5NHDL7/80iq8/PLLnR30ilplZBBAAAEEsgSe+vtTuqzOZb4g3WJnuNP48eM9n0Pca/qa5Q7M542RI0d6Poss3b/Ud13WHTlCIEwC5ovtb7+Vxo71daC2VunKb3po9uxxOvfczOJk51tCM1W3ffusZT9Wr16toUOHqk8fv41mfHfhAAEEEEAAgfwXIMCX/8Z51sJ7773n+UAc7IY/O+uMmT/+yR3g854z36abP8dKJpjxivMNZteuXY9VhXIEIl/gP/+Rs1OGr58HVFgDlTnKxGwSHWzTu3379mnixIm+a8yBZ6SJVUIGAQQQQMAt4D8Cr3z58tnOAIiPj/edv0iM3HNbkg+jgFmcd8QIpS1fqYS5s3wduV5fqMf983Tuz1nrepjPBy1bttRPP/3kq/ecs1mHmf1SqVIlXxkHCCCAAAIIhEqAKbqhko6Ads51vnb8jxP06NGjh84//3zVrFnTMzIpKSnJ80Hkkksu8SyCvXLlSoJ7EfC+6MJJCJg1JJ0P6P7pM93gfAdf21PkLAXlLPbufzbzeNKkSdb0moSEBJkNNkgIIIAAAgggECMCzgYaCd/+nw4ULWs98KW/PC1nMo0vmdGpL7/8svxHqe7Zs8fZlMO1K4fvCg4QQAABBBDIXwECfPnrm6d3N1NwzfTZnP5xN26mxXTs2FFvvPGGZw0cM5XAjFg6dOiQNm3apMmTJ3sWwa5YsaL7UvIIFCwBZ0F3a0Vsp/cjdJfnGZy1sXXvvcEfxz0912wyU7as/QE/+JWUIoAAAggggEDUCDifhRMe6W09jhnFN6LXfOdzeFax+fL89ttvzypwjsyMm4ULF1plZBBAAAEEEAiFAAG+UCjTBgIIhFZg2DCrvRXOyL2pauUpM6P3nH0zAlKqM53XrBnln6699lr/LMcIIIAAAgggECMCSb176mDRMtbTXjZnsL77ziryzH7xX6vXbNQ1YMAAuxI5BBBAAAEEQiBAgC8EyDSBAAIhFNi6Vc5cdKvBD3SnMhSv6tWlLl2sU77MtGnTlJKS4subAwJ8FgcZBBBAAAEEYkegZEklPvyg9bw3aqxeeWSDNYrPbEj36KOPWvW++uorzZw50yojgwACCCCAQH4LEODLb2HujwACoRUwo/ecaefedEhJekeZG8b07y8VLuw9Y//84osvrAIz7aZGsG12rVpkEEBfXWKBAABAAElEQVQAAQQQQCBaBRJ79VBqUtaw/0JKVfMF/3ZG/NtP3Lt3b5UzO3j5pf7mQwcJAQQQQACBEAoQ4AshNk0hgEA+CzjTYvTvf1uNfKJbtFmVVauWnJ3trFO+jFnX0r3+HqP3fDwcIIAAAgggEJsCzjq88bd1sp79No3Ss8+YNbGzis0613369MkqcI7Mxl3+O+xaJ8kggAACCCCQDwIE+PIBlVsigECYBCZMkNavtxp/Vb08ebMcjrNhdNA0d+5crV271jp33XXXWXkyCCCAAAIIIBB7AvHdu1kPXVurtH/W4oC1+Lp3767KlStbdc0oPvMlIgkBBBBAAIFQCBDgC4UybSCAQP4LOLtC65//tNpZotM0T01Ur56cXe6sU1bGPXqvnnPB6aefbtUhgwACCCCAAAIxKNCkiTJcS3aYjbuG919pYZiNNtzTcs36vt+5d+WwriKDAAIIIIBA3gkQ4Ms7S+6EAALhEtizR7rqKmnLFqsHI3SXJ292zk1MtE5ZGTONxj+Z6blxcXH+RRwjgAACCCCAQCwKOJ8H4q6+2nryctqhd2aerdkfLLDKuzg7edWsWdMq69evH6P4LBEyCCCAAAL5JUCAL79kuS8CCIRO4LnnpEWLrPYmqo1e0YPORhlSx47WKSuzb98+zZgxwypr27atlSeDAAIIIIAAAjEs0Mleh89IlNJubev/qoWSnJysJ5980iqbNWuW/vvf/1plZBBAAAEEEMgPAQJ8+aHKPRFAILQCc+ZY7f2hhrpJY5SmRD388LHX3jMXPWeCg67UvHlzVwlZBBBAAAEEEIhZAfO5YNSogMevt36KZs60i2931gQ59dRTrULzWYO1+CwSMggggAAC+SBAgC8fULklAgiEWODIEavBt3Wv8716KZUvLzmzZbJNwQJ8xYoVy/YaTiKAAAIIIIBAjAk4o/gOTZxiPXRdrdCwAeusskRnTRD3KL7ff/9dU6bY11oXkUEAAQQQQCAPBAjw5QEit0AAgTAKrHQWuV6yxOrAERXy5B94QHLWvCYhgAACCCCAAAInLZB8WQsdLFLaus/+iT9p8WKrSDfffLNq165tFQ4ePNjKk0EAAQQQQCCvBQjw5bUo90MAgdAJmHX3LrxQ2rTJanOHyqpECalHD6s4aMa9GPbjjz8etB6FCCCAAAIIIBDjAvHxSmzxNwuhoZbo5ZetImdjL7NEiLNGiF+aOHGi5s6d61fCIQIIIIAAAnkrQIAvbz25GwIIhErA7JxrNsPYuNFqcZnq6xu1U/fuUmn7S3arnsmY9XD2mPv4pQtNwJCEAAIIIIAAAggEEUhsaK+vV0ur9J//SJs325U7d+6sChUqWIWM4rM4yCCAAAII5LEAAb48BuV2CCAQIgHzLfiGDVZj89VYf9cUHUkuITM993hp27ZtSklJsaqddtppVp4MAggggAACCCDgE6hVy3doDi7Wjyp9eLPeessqVpEiRZzPIvaHkbFjx2r58uV2RXIIIIAAAgjkkQABvjyC5DYIIBBigUOHrAZ3qaRaaao2qYqcDexUubJ1Omhm2bJlVnlSUpJquT64WxXIIIAAAggggEBsC5x1lvX81bVeE3SVRr6xRwcOWKec2QTdnSVDnDVDjqb09HS9+OKL3iw/EUAAAQQQyFMBAnx5ysnNEEAgZAJxcVZTB1REu5Q5J9f1hblVzz+zdOlS/6zq16+vhIQEq4wMAggggAACCCDgE/j736ULLvBlzcF5mqXROy7TmGH2rIDSzloh99xzj1X3gw8+cJYOttcOtiqQQQABBBBA4AQFCPCdIByXIYBAmAVci93EKcPToTZtpNNPz1nf3AE+pufmzI1aCCCAAAIIxKyA+YLxyy/lbJNrEZyv31X2yZ5yBulZ6cEHH5SZIeBNh5wZCK+99po3y08EEEAAAQTyTIAAX55RciMEEAiZwN69ci+yl67M/5zldPSe6as7wNegQYOQPQINIYAAAggggEABFahUSfrmGx0uVd56gCt2f6Kpo9ZYZVWrVnWWDnHWDvFLw4YN0/79+/1KOEQAAQQQQODkBQjwnbwhd0AAgVALmG++nQ0y/NNvukAmPmc21s1pcgf4GMGXUznqIYAAAgggEOMCzoeOQtOdjb2U6INIULo2PjXMl/cePPTQQ95Dz88dO3Y4O+86W++SEEAAAQQQyEMBAnx5iMmtEEAgRAIff2w1tFK11F1vqVcvKT6H/1UzU2RWrlxp3YcAn8VBBgEEEEAAAQSyEYg7o5GW//2fVo3LVr2rPxfaG4GZzxeXX365Ve/VV19VRkbm8iLWCTIIIIAAAgicoEAO/yl8gnfnMgQQQCCvBZYskRYvtu56j4bpYOnM3XOtE9lkli9f7qyTYy+UwxTdbMA4hQACCCCAAAIBAnVe6mGVVdRW/f7IWKvMZB5wrSHyxx9/6Pvvvw+oRwECCCCAAAInKkCA70TluA4BBMIj8NlnVrsbVEXf6zJ17SoVK2adyjbjnp5brVo1lShRIttrOIkAAggggAACCPgLJJ/jjOKrebF/kRp8/4azxp5VpDbOLmANGza0CocOHWrlySCAAAIIIHAyAgT4TkaPaxFAIPQCY+1vxT9XeykuXt27564rn7kChRUrVszdDaiNAAIIIIAAAgg4AsX73Gc5nJc2Q5NeXGCVxTm77/Yya4n4pa+//lrLli3zK+EQAQQQQACBExcgwHfidlyJAAKhFjBTcxcutFodqxvVrp10yilW8XEzn376qVXHvR6fdZIMAggggAACCCBwDIHK//yHdiRXts7O+cAO8JmTt912m8qUKWPVe81sHEZCAAEEEEAgDwQI8OUBIrdAAIEQCTRtajW0UZX1sy7UPfdYxSeU2blz5wldx0UIIIAAAgggEOMCiYlKqGLPBPhrZZxmz7ZdihYt6iwp4qwp4pc++OADpaSk+JVwiAACCCCAwIkJEOA7MTeuQgCBUAv897/SwYNWq++rs6pUS9AVV1jFJ5Tp2LHjCV3HRQgggAACCCCAQMmiqRZCqhL19ttWkSfTo0cPJSQk+E7sdxbrGz58uC/PAQIIIIAAAicqQIDvROW4DgEEQivQpUtAe/9SH5li54vzXKW9e/cG1O/Xr19AGQUIIIAAAggggEBOBOJS7QDfERXSxx9L7gkCNWrU0A033GDd8vXXX1eq63qrAhkEEEAAAQRyIECALwdIVEEAgTALmA+9W7danZihZtoXX1J3320V5yizevXqgHq1atUKKKMAAQQQQAABBBDIkYArQJeueB04IH30UeDV7s021qxZoy+//DKwIiUIIIAAAgjkQoAAXy6wqIoAAmESmDgxoOF/6l3P1Fzni/Bcp1WrVlnXVKpUSUWKFLHKyCCAAAIIIIAAAjkWKFnSqtpEcz35kSOtYk/mggsuULNmzawTQ4cOtfJkEEAAAQQQyK0AAb7cilEfAQRCL+Bam2ammmqhGp/w5hruAB+j90L/SmkRAQQQQACBqBK45BLrca7WeE9+5kxp8WLrlOLi4vTAAw9YhT///LNmzZpllZFBAAEEEEAgNwIE+HKjRV0EEAi9wJYt0vjMD8next9TF1WrJrVr5y3J3U8CfLnzojYCCCCAAAIIHEfg6qutCuc4I/iqa62nLNgoPrMOX9WqVa1rXnvtNStPBgEEEEAAgdwIEODLjRZ1EUAg9ALm22y/dW32q4g+VQfddpucXehOrDsE+E7MjasQQAABBBBA4BgCF14olS5tnbxKEzz5UaOsjzKeskKFCsnsqOufxowZox07dvgXcYwAAggggECOBQjw5ZiKigggEAkCW1VBu1VKt99+4r1Zt26ddXHNmjWtPBkEEEAAAQQQQCBXAk7AzrM4sN9FbTTJk9u0SQqynLC6dOkiE+jzpkOHDmlksOF+3gr8RAABBBBAIBsBAnzZ4HAKAQQiQMC1+UVR7dd550kNG55439avX29dXM3M9yUhgAACCCCAAAInI9C2rXV1c/3q5DM8ZR98YJ3yZCpWrKj27dtbJ4YNG6aMjMxrrBNkEEAAAQQQOI4AAb7jAHEaAQTCK7B/x0GrA0V04KRG76Wnp2vjxo3WPd1r4FgnySCAAAIIIIAAAjkRaN7cqlVZm1VPyz1l//2vtH27ddqTueeee6zCZcuWaerUqVYZGQQQQAABBHIiQIAvJ0rUQQCBsAlsGfSu1fYq1VKHDlZRrjJbt251lvRLta5hBJ/FQQYBBBBAAAEETkSgXj2pfHnryv+pvgaprxocXqDRo61TnkyrVq3UoEED64QZxUdCAAEEEEAgtwIE+HIrRn0EEAidwP/+p5pzv7Ta+73RXe7Pztb542U2bNhgVYmPj5eZIkNCAAEEEEAAAQROSiAuTrr22oBb9NXzmqHzNeWDVQHn4pxr3KP4Pv/8c23ZsiWgLgUIIIAAAghkJ0CALzsdziGAQFgF9jz9iuKPrl1jOrJLJVXh8S4n1Sd3gK9y5cpKTEw8qXtyMQIIIIAAAggg4BEYNEgKsrZvER3UGTNHyLXPl+eSO+64Q8nJyT7AI0eO6INgi/b5anCAAAIIIIBAoAABvkATShBAIBIEnJ3kCo/+wOrJyOR71OaGklZZbjPuDTZYfy+3gtRHAAEEEEAAgWMKmFkBY8bI+fYwoIrZdGPs2IBilS1bVjfddJN14p133pFZN5iEAAIIIIBATgUI8OVUinoIIBBagTVrVOjIAavNDdd2d77htopynXGP4GP9vVwTcgECCCCAAAIIZCfwt79JQdbRO9+ZqDvmk7SgV3br1s0q/+uvv/Tjjz9aZWQQQAABBBDIToAAX3Y6nEMAgbAJ7Hx1ZEDbrTuUCyjLbcHmzZutS8wUXRICCCCAAAIIIJCnAnfdJc2aZd2ypPYodeYcrVxpFXsyzZ0deM844wzrxPDhw608GQQQQAABBLITIMCXnQ7nEEAgPAKvvabSbzpr2PilvSqmlpcX9Ss5scOUlBTrQjMthoQAAggggAACCOS5wLnnKuPU+tZtr9MXQXfTDbbZxvjx47Vjxw7rejIIIIAAAggcS4AA37FkKEcAgdALpKZKPXtKvXoFtP3V3warUOGEgPLcFrgDfGXKlMntLaiPAAIIIIAAAgjkSCDu+uusescK8JlKt912m4oWzfoyMy0tTZMnT7auJ4MAAggggMCxBAjwHUuGcgQQCL1A9+7SG28EtPuUnlSFJ3sElJ9Iwc6dO63LCPBZHGQQQAABBBBAIC8Frr/eultDLdXBeUu0dKlV7MmUKlVKHTp0sE589NFHMrvqkhBAAAEEEDieAAG+4wlxHgEEQiOwb580YkRAWwP0tN4o+6Quvjjg1AkVMILvhNi4CAEEEEAAAQRORKBpU2VUq2Zd+YCGatw4q8iXcW+2YU7ceOONvvMcIIAAAgggcCwBAnzHkqEcAQRCK2BWnHamovinW/SxntUAXXd9nAoV8j9z4sfuAF/p0qVP/GZciQACCCCAAAIIZCcQH6+4m26yatyt4Zrz6Z9WmTfT1AkIBksZGRnBiilDAAEEEEDAJ0CAz0fBAQIIhFXAtaXcRlXWp7rF06W8+uLafDhmim5Y3zKNI4AAAgggEHsCDz+stOQivudOVJrOXfi+NmzwFfkOzGYbNWrU8OW9B0OGDPEe8hMBBBBAAIGgAgT4grJQiAACIRdwBfhWqranC2aAXV5Nzz1w4IBSzUYefsmsd0NCAAEEEEAAAQTyTaBqVWcHjdut29fSKjmb5AZNCxYsCCgfOHBgQBkFCCCAAAII+AsQ4PPX4BgBBMInsHat1fYq1fLk27VTnk3P3bt3r9WGyZQoUSKgjAIEEEAAAQQQQCAvBRLKlLRud4szT2HSZ7utMm/mWMuH7DPrFZMQQAABBBA4hgABvmPAUIwAAiEW2LTJanCDnG+7nXT11VbxSWXWr18fcH3x4sUDyihAAAEEEEAAAQTyVCDITrivfd9Qe+csC9pM3759A8pbtmwZUEYBAggggAACXgECfF4JfiKAQHgFNm602t/krMGXkCBdfrlVfFKZDz/8MOD6IkWy1sQJOEkBAggggAACCCCQFwKnnRZwl2raoC2PvhhQbgqeffbZgPI5c+YElFGAAAIIIICAV4AAn1eCnwggEF4BV4Bvo6qoRQupTJm869aePXsCbmYWsyYhgAACCCCAAAL5KtCpk3TttQFNJM7+PaDMFJjPJ8WKFQs4l5aWFlBGAQIIIIAAAkaAAB+/BwggEH6BRYukP/6w+mGm6Obl9Fxz82uuucZqgwwCCCCAAAIIIBASAROs++IL/fKPf1nNVd65RKkHjlhl3sy0adO8h76fP/zwg++YAwQQQAABBPwFCPD5a3CMAALhEXjmGavdnSql39UszwN8hQsXttohgwACCCCAAAIIhFKg5rNdreaSdESLxv1plXkzpznTeosWLerNen62adPGypNBAAEEEEDAK0CAzyvBTwQQCI+AGb03dqzV9lA9oJqnFdOpp1rFJ505ePCgdY9atWpZeTIIIIAAAggggEB+ClQ/s4w2JVazmlj+xUIr75+pU6eOf9ZzzDTdABIKEEAAAQQcAQJ8/BoggED4BHbtkm6+WcrI8PVhl0rqVfXSVVf5ivLs4NChQ9a9kpOTrTwZBBBAAAEEEEAgvwV2V7U33Ng0c+0xm7zpppsCzv36668BZRQggAACCCBAgI/fAQQQCI+AWSS6Q4eAtfdMcG+nyqht27zvljvAx5TdvDfmjggggAACCCCQvUCZSklWhTVOfG/nTqvIl2ncuLHi4+1/sn311Ve+8xwggAACCCDgFbD/tvCW8hMBBBDIb4GPPpK+/dZqZaHO0BA9KrNU3kUXWafyJOMO8DGCL09YuQkCCCCAAAII5EKgbOmsmQvmMjORYfLkY9+gffv21skvv/zSuca+h1WBDAIIIIBATAoQ4IvJ185DIxABAsuWBXTiao3XPhX3BPfyYz8MAnwB5BQggAACCCCAQIgFEhLsBs/QIk2aZJf5584//3z/rJYvX64lS5ZYZWQQQAABBBAgwMfvAAIIhEfA2RnOnVarlqeodWv3mbzJE+DLG0fuggACCCCAAAInIVDN3mSjvT7XlG8O+C9JbN28bt26qlq1qlXGNF2LgwwCCCCAgCNAgI9fAwQQCI9A2bJWu3uckXvelF8BvvT0dG8Tnp8J7q/QrbNkEEAAAQQQQACBfBC4807rpsWd+QsV187S//5nFfsycXFxzuZj9u5jBPh8PBwggAACCBwVIMDHrwICCIRHYMUKq90/Vd+TL19eOuss6xQZBBBAAAEEEEAgegSchYYzihWznidZh/Tdd1aRlXEH+GbMmKENGzZYdcgggAACCMS2AAG+2H7/PD0C4REwC0OPGWO1vViNPPlLL3WGFvNfJsuGDAIIIIAAAghEl0BcyZLWA8UpQz/9ZBVZmZYtW6qk65rx48dbdcgggAACCMS2AP+Mju33z9MjEB6B0aOl6dOttqephSefX9Nzzc3Zcc4iJ4MAAggggAAC4RJwfZsZr3RNnZq5o26wLiUlJemKK66wTn3zzTdWngwCCCCAQGwLEOCL7ffP0yMQeoHUVOmxx6x2V6i2Ruk2T9kll1in8jVj1rQhIYAAAggggAACIRdwfQYxAb7Nm3XMdfhM/6688kqrm5MnT9bhw4etMjIIIIAAArErQIAvdt89T45AeASmTJHWrLHafkgvOSvPFFb16lKtWtYpMggggAACCCCAQPQJBBnBZx7SjOI7VmrTpo11au/evc6ECHtGhFWBDAIIIIBATAkQ4Iup183DIhABAp98YnVits7Rl7rWU+asOS3XF9pW3ZPNMEX3ZAW5HgEEEEAAAQTyRMC1yUZd/eW5bXbr8FWsWFFNmza1mv/222+tPBkEEEAAgdgVIMAXu++eJ0cg9AJ79khm/T2/NFo3O7nMqbItMpfh8zubv4dM0c1fX+6OAAIIIIAAAscQuOAC68Rl+t6Tz24dPlPh8ssvt65jHT6LgwwCCCAQ0wIE+GL69fPwCIRYYOxYad8+X6OpSvCtvWcKQx3g83WEAwQQQAABBBBAIJQCl11mtfZ3TVGijmjtWmn1auuUlWnXrp2VX7RokdatW2eVkUEAAQQQiE0BAnyx+d55agTCIzBihNXuN2qnTariKStdWmrUyDqd5xmm6OY5KTdEAAEEEEAAgRMRuPRS66qS2qMWmuYpy24dvmbNmqlMmTLWtUzTtTjIIIAAAjErQIAvZl89D45AiAXMLm+//mo1OkJ3+fIXXii51pv2ncuvA6bo5pcs90UAAQQQQACBbAUqVZKaNLGq3KqPPflffrGKrUxiYqJat25tlRHgszjIIIAAAjErQIAvZl89D45AiAV27pTS061Gf9HffHmm5/ooOEAAAQQQQACBWBDo0MF6yhv0mZJ0SL//bhUHZNzTdL/77jsdOXIkoB4FCCCAAAKxJUCAL7beN0+LQPgETIDPlXaplK/E7KBLQgABBBBAAAEEYkbgllusRy2tXbpUk7VwobR/v3XKyrRt29bK7969W7/99ptVRgYBBBBAIPYECPDF3jvniREIj0BKitXuARV2vqMu7ClzZpvonHOs0/mScU/JTXeNKMyXRrkpAggggAACCCAQTKBGDalK5lrE3tNNNFdpadKcOd6SwJ9VnGvOPvts6wS76VocZBBAAIGYFCDAF5OvnYdGIAwCrhF8O+XsqnE0nXGGVKSIN5d/PwsVKmTdnOksFgcZBBBAAAEEEAilQEZGwFC9Qeqv7npTM2Zk3xH3NF0CfNl7cRYBBBCIBQECfLHwlnlGBCJBwBXgS1HWDnDnnReaDroDfIfNxh8kBBBAAAEEEEAgXAKHDgW0/Ioe1LKpmwLK/QvcAb558+Zp8+bN/lU4RgABBBCIMQECfDH2wnlcBMIm4ArwHVKyrytNm/oO8/UgKSnJuj8j+CwOMggggAACCCAQSoG4OKlbt4AWk3RExX/9LqDcv+CCCy5QcnLWZylz7vnnn/evwjECCCCAQIwJEOCLsRfO4yIQNoGEBKvp07RUxbXHUxauEXwE+KxXQgYBBBBAAAEEQi3w8svS118HtNpk2yRt2RJQ7CswsxISzSLGfunVV1/1y3GIAAIIIBBrAgT4Yu2N87wIhEvgkksk80310VREB9VBnzrfPktmDb5QJPcUXQJ8oVCnDQQQQAABBBA4poD5bNSundLfeMuq0lrfaebvWZ+brJNHM4ULZ25W5n/u4MGD/lmOEUAAAQRiSIAAXwy9bB4VgbAK1KkjXXGF1YXGWuDsAie59r6w6uRlxh3gYw2+vNTlXggggAACCCBwogLxbVtbl1bWZm36/g+rzJ0ZOnSou0ivv/56QBkFCCCAAAKxIUCALzbeM0+JQGQIVK9u9SNOGQrV+numYfdaNXzLbb0OMggggAACCCAQLoG6dbWzWDWr9X3zV1p5d6ZDhw7uIj366KMBZRQggAACCMSGAAG+2HjPPCUCkSGQkWH1I13xIQ3wFS9e3Gp/1apVVp4MAggggAACCCAQFgFnqu7hinaAb+/Krdl2xb0GX7aVOYkAAgggEPUCBPii/hXzgAhEiMD8+Ur75TerMxmKU+PGVlHIM6mpqSFvkwYRQAABBBBAAAG3QNGidsnGLYk6eNDepMyuQQ4BBBBAAIEsAQJ8WRYcIYBAPgkkvPee1KSJEhYtsFo4oiQ1bGgV5WvGPUXXNLZhw4Z8bZObI4AAAggggAACOREomr7PqrZXxbVmTQmrzJ154okn3EVKT08PKKMAAQQQQCD6BQjwRf875gkRCK9AWpoKPfWU5Jqeazq1pNplKlIkdN3729/+FtAYAb4AEgoQQAABBBBAIAwC8SXtpURKaZdWry6ZbU+6desWcH758uUBZRQggAACCES/AAG+6H/HPCECYRUodOCA4nbssPqwWRV1u0Zq1wVtrfL8zsQ569tUqlTJamb9+vVWngwCCCCAAAIIIBAWgcqVrWaraKNWrixllbkzVapUUcWKFa3ihQsXWnkyCCCAAAKxIUCALzbeM0+JQNgEEpwAnzs11gKNckJ8Z5zhPpP/+fr161uNrFu3zsqTQQABBBBAAAEEwiLgBOv8U2VtOu4IPlP/rLPO8r9MCxbYS6JYJ8kggAACCEStAAG+qH21PBgCkSFQ6ODBgI5sVzlP2ZlnBpzK94Lq1atbbTCCz+IggwACCCCAAALhEggygm/VqpLBVjmxetjYtWPZfGdjMxICCCCAQOwJEOCLvXfOEyMQUgH3CL4DKqw0JXr6EI4AX7Vq1aznZwSfxUEGAQQQQAABBMIl4ArwmRF8+/Yladeu5Gx7RIAvWx5OIoAAAjEjQIAvZl41D4pAeAQSXVN09yhzN7jChaW6dUPfJ3eAjxF8oX8HtIgAAggggAACQQSCTNE1tdavtzffcF95pusb01WrVmn//v3uauQRQAABBKJcgABflL9gHg+BcAskuqbo7lXmh9TTTpMSEkLfO/cU3bVr14a+E7SIAAIIIIAAAgi4BVwj+KppveKUrg0birlrWvkGDRpYeZP53//+F1BGAQIIIIBAdAsQ4Ivu98vTIRB2AXeAzzuC79RTw9O1mjVrWg2bAF9qaqpVRgYBBBBAAAEEEAi5gGsZkURnUZOL9eNxR/AVLVpU7s83S5cuDXn3aRABBBBAILwCBPjC60/rCES9QEZcnPWMVbTR8210uAJ8derUsfpjgnusw2eRkEEAAQQQQACBcAhUrSqdfrrVckd95Izgy36KrrnAPYpv2bJl1n3IIIAAAghEvwABvuh/xzwhAmEVSKlf32q/orbqLM1XvXpWccgy5cqVU4kSmesAehv966+/vIf8RAABBBBAAAEEwiNgvhTt3Nlq+xzNOe4IPnPBaWbtE79EgM8Pg0MEEEAgRgQI8MXIi+YxEQiXwIEKFQKabquJCtcIvjjnw3Nd1+4eK1asCOgjBQgggAACCCCAQMgFmjWzmmyoJdq2MclZTsQqDsi4R/AxRTeAiAIEEEAg6gUI8EX9K+YBEQivQLWffgroQBtNCluAz3TGPU2XAF/AK6IAAQQQQAABBMIhcMYZVqvJOqw66X9p1Sp7yROrkpMJNoIvIyPDXY08AggggEAUCxDgi+KXy6MhEAkCFefPD+hGufgUVawYUByyAneAjym6IaOnIQQQQAABBBDITqBsWcmsxeeXztRCZ1fc7AN87hF8+/btc6b2rve7C4cIIIAAAtEuQIAv2t8wz4dAmAWKbdgQ0IMZFf8h194bAXXys8Ad4GMEX35qc28EEEAAAQQQyJVA48ZWdbMO3+rV2Qf4qjk78BYrVsy6jnX4LA4yCCCAQNQLEOCL+lfMAyIQPoG4tDQVd317/La6aXKLp8LXKadl1uALKz+NI4AAAggggEB2Ak2bWmeb6XetW5d9gM+sMewexUeAz2IkgwACCES9AAG+qH/FPCAC4ROo9c03SnKmiPinIXpUtetk/yHVv35+HLtH8KWkpMj8ISGAAAIIIIAAAmEXOPtsqwunaLXWrDn+Z6cyZcpY173yyitWngwCCCCAQHQLEOCL7vfL0yEQPoHNm9Xw44+t9ueoiVaplmrUsIpDnqlZs6bi4+3//DFNN+SvgQYRQAABBBBAIJhA5cpWaUVtcQJ89ucWq8LRjPuzzPLly4NVowwBBBBAIEoFjv83RZQ+OI+FAAL5K1BoyBAV2r/faqSXXnXycWEP8CUlJemUU06x+rZkyRIrTwYBBBBAAAEEEAiLgGsnsuLap22r7c9Uwfq1cuXKYMWUIYAAAgjEiAABvhh50TwmAqEWiJs712pykyppulp4ysI9gs90olGjRlb/Fi1aZOXJIIAAAggggAACYRFwBfhMH9I3b9Xhw9n35tVXzRepdkpz1kMmIYAAAgjEhgABvth4zzwlAiEXyKhVy2pzlrIWjI7EAN/ixYut/pJBAAEEEEAAAQTCIlCypDKc2Qb+qby2ae1a/5LA45tvvjmgcOPGjQFlFCCAAAIIRKcAAb7ofK88FQLhF3CtcbdRVTx9KlxYKlcu/N1zj+AjwBf+d0IPEEAAAQQQQMARcHbElWsUXzWtd9bhy16nonNNYfNByy+tWrXKL8chAggggEA0CxDgi+a3y7MhEEaB+KVLrdYX6QxP3ozeM59bw53cAT6zbs0+146/4e4j7SOAAAIIIIBAbArE1aljPfhZmn/cAF+c8wHLvcbw6tWrrfuQQQABBBCIXgECfNH7bnkyBMInkJ6uONemFf4BvvB1LKvlhg0bOoFGO9LIRhtZPhwhgAACCCCAQBgFmmYtbWJ60VSztGnT8fvjDvAxgu/4ZtRAAAEEokWAAF+0vEmeA4FIEnBGw8UdOGD1yBvgq17dKg5bpkiRIqpbt67VPtN0LQ4yCCCAAAIIIBAugSABvs2bMo7bm1q1all1CPBZHGQQQACBqBYgwBfVr5eHQyBMAq7pudtUTltU0dOZKplL8YWpY3az7mm6BPhsH3IIIIAAAgggECaBBQushqtokw6v2mCVBcu4R/CtOd7CfcFuQhkCCCCAQIEUIMBXIF8bnUYgwgUKFQrSwczpsBUqBDkVpiICfGGCp1kEEEAAAQQQOLbArl3SkCEB53dsTQsocxdUq1bNKmIXXYuDDAIIIBDVAgT4ovr18nAIhElgwgSr4fLarqLa5ymL5ADfokWLrH6TQQABBBBAAAEEQi5w+LDkrGfsTn/ssoN37vMmX8U1VWJTThbuC3YjyhBAAAEECpwAAb4C98roMAIRLvDjj9Lrrwd08hRl7uIWSQG+M87I3NnX21kzjWXnzp3eLD8RQAABBBBAAIHQC5gPS1dcYbV7QIW1b9MeqyxYpnLlylbx1q1bdeTIEauMDAIIIIBAdAoQ4IvO98pTIRA+gWXLgrbtDfBVzFyKL2idUBeedtppKuSaTrzAteZNqPtEewgggAACCCCAgJ591kIoooO6c9uLSjvOLF33CD5zky1btlj3IoMAAgggEJ0CBPii873yVAiET6B27aBt71VxT3kkjeBLSkqSex2+efPmBe0/hQgggAACCCCAQMgEmjTR3ivaW8310lBtX55ilbkz5cqVU2JiolXMOnwWBxkEEEAgagUI8EXtq+XBEAiTwFlnBTT8hxrqVzX3lEdSgM906CxXf+fPnx/QfwoQQAABBBBAAIGQCzwzwGqyuLOe8d5pc60ydyY+Pl6VKlWyilmHz+IggwACCEStAAG+qH21PBgCYRIwHypdUbwBekZpSlSxYlKRImHq1zGaPfvss60zjOCzOMgggAACCCCAQJgEEosHfmjaXqLWcXvjXoePEXzHJaMCAgggEBUCBPii4jXyEAhEkEBcnBkWZ3XoLGWOinPF/aw64cq4A3xmJ10Wow7X26BdBBBAAAEEEPAKxM2Z4z30/ExRaW0uGnwpFP+K7nX4GMHnr8MxAgggEL0CBPii993yZAiET6BxY6vtxlrgyZcubRVHRMY9Rffw4cNaunRpRPSNTiCAAAIIIIBA7ArEu9YFnqNztGu380XqcRIj+I4DxGkEEEAgSgUI8EXpi+WxEAirwDFG8JUoEdZeBW28TJkyqlmzpnWOaboWBxkEEEAAAQQQCINA/Fx7vb3ZOle7dh2/I4zgO74RNRBAAIFoFCDAF41vlWdCINwCrgBfba1SYR1QyZLh7ljw9t3TdGfPnh28IqUIIIAAAggggEAoBDIy5B7Bd6IBPtbgC8ULow0EEEAg/AIE+ML/DugBAtEnEGQnjXTFKxJH8Bn8pk2bWu9g1qxZVp4MAggggAACCCAQUoENGxS3ZYvV5GqdkqMRfEzRtdjIIIAAAjEjQIAvZl41D4pACAVSUwMaK6yDETuCzx3gm+tMiUkN8gwBD0UBAggggAACCCCQHwKu9fdME2N1oxLWrjpua8Gm6GY4IwJJCCCAAALRLUCAL7rfL0+HQHgE6tZVRqFCVtvnanaBGcG3f/9+Ntqw3h4ZBBBAAAEEEAipQHzgP9NqaJ0aLPnyuN1wj+A7dOiQdu7cedzrqIAAAgggULAFAv/mKNjPQ+8RQCASBJKTlXHGGVZPztPMiA3wVahQQaeccorVX6bpWhxkEEAAAQQQQCCUAq1bK+2yywJajNu7J6DMXeAO8JnzrMPnViKPAAIIRJ8AAb7oe6c8EQIRIZB+7rlWP87RnIidoms66p6mO3PmTKv/ZBBAAAEEEEAAgZAJJCbq8FdfBTS3Lb5iQJm7oHDhwipVqpRVvHXrVitPBgEEEEAg+gQI8EXfO+WJEIgIgYyqVa1+FNV+FS9uFUVUxh3gYwRfRL0eOoMAAggggEDsCcTFaVuJatZzb407foDPXFCmTBnrul27dll5MggggAAC0SdAgC/63ilPhEBECMRt22b1Y4sqyvlCOWKTO8A3f/58HT58OGL7S8cQQAABBBBAIMoF0tNV8oD9eWpzRoUcPbR7BB8BvhyxUQkBBBAo0AIE+ArQ69uyZYsmTJigJ554Qu3atVP58uUV53yzZ/7ceeeduX6Sb775Rtddd52qV6+uZGfNNPPT5E05CYGTFnBNBTEBvqSkk75rvt3gXNeUYrMg9eLFi/OtPW6MAAIIIIAAAghkJxC3dq2SUg9ZVVaqjpU/VoYA37FkKEcAAQSiVyAxeh8t+p6sUqVKefJQ6c63gV27dtXw4cOt+61fv17mz5dffqkuXbpo2LBhig+yg5d1ERkEjiEQl5JindmucjotggN8ZipLXWf337/++svXbzNNt0mTJr48BwgggAACCCCAQKgE4pYutZraqVJam1rFKjtWhgDfsWRio3z6mum6qOZFQR82LS3NU56amhpwPrvrAipTgAACESfACL6IeyU561DNmjXVpk2bnFV21erXr58vuGeCF5988ol+//13z09vMOO9995T//79XVeSRSAXAhkZVuU0JUT0CD7TWfc0XfP/CxICCCCAAAIIIBAOgXhXgG+JGurgobgcdaVkyZJWPaboWhxRnXlqylNq8X4LDZ4+OOA59+zZo44dO6p9+/aq6qyXbfLeZOqb68z1JAQQKJgCBPgK0HszU3PHjx+vTZs2afXq1Z4Rdrnt/p9//qkXX3zRc5kJZvz888/q0KGDzjvvPM/P6dOn+4IcL7zwgpYvX57bJqiPQKaAM3XcP8UrXYUK+ZdE3rH5/4F/Mv//ICGAAAIIIIAAAuEQcI/g+1P15awgkqPECL4cMUVdJTMCb+DUgZ7n6jO5T0CQb8yYMTp48KDn/N69e/Xpp596jk1wz9Q3yVxv7kNCAIGCJ0CArwC9s4EDB+qqq67SyUzVHTp0qLzDsV9//XUVKVLEEihatKhMuUmm3iuvvGKdJ4NAjgUSEqyqlbUp4kfwXXSRPZVhyZIl2upaS9B6KDIIIIAAAggggEA+CcS5vmhvre+c4EzOGiPAlzOnaKtlpuX+69J/+R7LHeRzL9FklmTyD+6ZC831x5re67sxBwggEJECBPgi8rXkT6cynCmTX331lefmp512mi644IKgDZnyBg0aeM6Z+uY6EgK5FUhv2NC65FZ9rKT4wLU+rEphzpxzzjkyQW7/ZEa1khBAAAEEEEAAgZALlChhNVlVG1Xr4FKr7FgZAnzHkon+8scueixokG/27NmaN2+eBTC78GzfyD1zwgT3zPUkBBAomAIE+ArmezuhXq9cuVIbNmzwXNuqVats7+E9bzbdWLVqVbZ1OYlAMIG0W26xiqtpgyrMmWiVRVqmkDOHuHnz5la3pk2bZuXJIIAAAggggAACoRA48vjjAc1cmvFdQFmwAgJ8wVRip2z1x6tV8vesdRjNSL6LHrNnquhCx6N1lomp/3qH11W9enV179496wRHCCBQYAQI8BWYV3XyHf3jjz98NzEj+LJL/ufNNEUSArkVyDjzzIBLKvz3vYCySCto2bKl1SUCfBYHGQQQQAABBBAIkUCGszbwn2e2sFo7K90egWWd9MsQ4PPDiLHD/fv3a+TIkdr99W45s7p96WALZ363CeqZ5ArumXqmvhncYf58+OGHMvchIYBAwRJILFjdpbcnI7Bu3Trf5eabmexSjRo1fKfXrl3rO87JgX87wepv3LjRV3zgwAGZP6ToE0gbNy7goYrPmhLx77tZs2ZWv+fMmaMtW7aohGuajFWJTEgEvItCm8b8j0PSOI1EpYD/75H/cVQ+LA+V7wL+v0P+x/neMA1ErYD5PTqcXMx6voYZi3P0Wapw4cLWdWYXXT5zWyRRm5k1a1bWu/buF+cdqWd+muCe/4o0TnBP3npHVUxwz9zHvQHd0dP8iFAB/j8eoS8mhN0iwBdC7HA35b8NevHixbPtTrFiWR8mzA5LuUn+wcHjXffTTz+pfPnyx6vG+QIocMFLL8n9W7a1TBX9PmlSRD/NIWd7usTERN9mNOnp6XrrrbfUpEmTiO53rHXO/LeDhEBeCvA7lZea3IvfJ34H8kKg+pQpOnfWt9at9jmRmUk5+Cy1bNky67odO3bk6DrrIjIFVqBfv356+eWXM0fheYN33iDfcYJ7Zj3q3r17KyUlhd+ZAvYbsG3btgLWY7qb1wJM0c1r0Qi+n/+3yUlJSdn2NDk52XeebwJ8FBzkQqDQvn0BtSd0eiKgLNIKzO9+vXr1rG4tWrTIypNBAAEEEEAAAQTyW6D+2LFWE0eUqEEJA6yyY2Xcm4aZz/PmS0tSbAg0bdpUL774onwDL0yQzz3j1uS9wb+jLKa+uc5cT0IAgYInwAi+gvfOTrjH/kP1Dx8+nO19zCgmbypSpIj3MEc/jzel10zR9U6DNOudHW+6cI4apVLECRR67jmrT931pjq0vdYZ6h/5uzL//PPPWro0a5e6FStWqE2bNtbzkAm9gPmSwjsqxvy3w/+/aaHvDS1GgwC/U9HwFiPnGfh9ipx3ES09SXItD/KN2umXxL9rQhtnLbXjpGCb5LVo0UL+s3SOcwtOR4FA+/bt1bVrV3217St7Wq55NjOSz0zXPRrku+aaa/TOO++wLI2xKaDpeEtlFdDHotu5ECDAlwusgl7Vfw2x40273ec3+up403ndLrkJ2JngYW4DiO72yEeggPn9+f13q2NltUNJSYWd920VR2Tmyiuv1AsvvODr27x582T+P1OhQgVfGQfhFTDBPf7bEd53EG2t8zsVbW80vM/D71N4/aOl9SM33aSEp5/2Pc7l+lZlM3Y4f/+V85Ud66Bs2bIBpzIyMvi7M0AlugvMZ6XSV5aWNvg9pxm5552m65226wT5unTpoooVK/pV5LCgCfDZuKC9sbzvL1N08940Yu/oH3g7XnTffxSeb2h3xD4ZHYsoAWfnLdWuHdClK/S1MzUkoDgiC5o3b259w20+EE+ePDki+0qnEEAAAQQQQCA6BVLvvlvpcVn/XEvSETXIyJphkN1Tu6fomrrsipqdWHSeGzx9sEZuGJn1cGZDjSHOH/PTm0yQzxnJN9Y1Jdx7mp8IIFBwBLL+xig4faanJyhw+umn+670n37oK/Q78D/fsGFDvzMcInAcAfNN89atAZXO14wCE+Aza1RefPHF1jPkZEFr6wIyCCCAAAIIIIDAyQgUKqT4DPvb0ZS4wJF5wZoINpKHAF8wqegtM8G9PpP7ZD2gCeo5I/U8yfx0BflGrx8t/2WajtbkBwIIFCABAnwF6GWdbFdrO6Oqqlat6rnN1KlTs72dd52ratWqqVatWtnW5SQCPoHUVGncOF/W/2CTKheYAJ/pd+vW5uvMrGQCfGYkHwkBBBBAAAEEEAiFQNyff1rNpCpBG4rUtcqOlUlMTHSWRkmyThPgsziiOpNdcM+M7uzYsaNKLSplBfkOtTike0bdE9UuPBwC0S5AgC/a37Df88XFxcksnmqSGaH322+/+Z3NOjTl3hF8pr65joRAjgSmTJGCbM8+TRfpJo2Rif8VlOTeVGO9M/V4yZIlBaX79BMBBBBAAAEECrhA/LJl1hMsVz0lFLGDdlYFV8Y9Tdd/jW1XVbJRJJBdcM/M6HrppZd04403atq0aTo9xZnh5TeSb+T6kTLXkxBAoGAKEOArmO/thHv9wAMPKCEhwXN9z549deDAAeteJm/KTTLf/Jn6JARyLDBxolV1XmITxSlDLTVNvziLeziboBaY1KBBA7nXn5zoer4C8zB0FAEEEEAAAQQKnIB7BN9SnebsIJ/zx3DvNn/48OGcX0zNAikwfc10a1pu5+qdVfF/mRtn/D979wEfVZX2cfw/qZSgIBBEBFEBKQsiqBSpgrgIqIgoCgoCguBiQbGshWKBXRVdFWw0sYNgQVBABF3QRQWkFxXpXToBUt97hzfjnJkkZJJJMnPnd/fDZp5z7z33nO/JR8KTc+654YYbNH/+fFWsWNHdt2rVqrknfHRO7Gwk+exlvXY9HAggEH4CJPjCaMwWLlyoSZMmef58/PHHntb/9ttvnvLMazwnvT7UqFFDQ4YMcZf8/PPPuuKKK/TRRx/J/mx/tWP7s33Y11WvXt39mf9DIFcCPhm83TGnloRn3ptk79oVJoc9c9V3Ft+sWbPCpPU0EwEEEEAAAQTCXSDKWnHjfVyvz1QyPvfLIexf1nsfqeG0lMK74XzOtUCzKs00tOVQ9/Wj2ozShD4TtHr1ak2YMEH2vx1LlSpl1GXHdrl93RNNnnCfs++36+FAAIHwEzD/qx9+7Y+oFo8bN05vv/12ln1etGiR7D/eR69evbxDz+dnnnlGe/bscf+HftmyZerWrZvnXOaHPtauXU8//XRmyFcEcidw2WXGdZcnf2/N4Eu35vCd+l1COCX47I5cc801Gj9+vKdP9rsrjxw54vfDkecCPiCAAAIIIIAAAsESOHTIr6arTsywyqwZV7k4fBN8aWlpubiLS8JdYFirYWp7QVtPkq5cuXK64447su1WVFSU53y7mu0892V7AycQQCBkBZjBF7JDU3ANs/8jbictZs6c6X4nn73xhv0SXvur/c49e5aSnUy0r+NAICCBVq2My8ukH1BdrfSUhVuCz95oIzY21tP+lJQUzZ3r9aISzxk+IIAAAggggAACwRVIb9LEr8JSMblfDuGb4GMGnx+nYwvyOgMvr/c5FpKOIRBmAmRwwmjA7KW39i6euf1zuq7Zs5M+/fRT2ZsH2Fui21/tuH379qe7lfMIZC1QpYp0wQXGuUZa7Il9XvnoKQ/VD/ayhZYtWxrN++KLL4yYAAEEEEAAAQQQKAiB9DZt/KpdU878ucTvAq+CzPduZxaR4MuU4CsCCCDgTAESfM4cV3qFQNEJnHuu8exYpXjicJvBZze8Y8eOnvbbH+wZrunp6UYZAQIIIIAAAgggEGyBKJ/NvZapvo6WNn/OyumZzODLSYdzCCCAgPMESPA5b0zpEQJFK3D0qPH8YyrpicMxwdehQwdP++0Pu3fv1pIlS4wyAgQQQAABBBBAINgC0fPmGVXO0jUB7aJLgs/gI0AAAQQcL0CCz/FDTAcRKGSB+HjjgYna44nDbYmu3fBq1arpoosu8vTB/vD5558bMQECCCCAAAIIIBB0AZ9dbxtoqUqXzv1TfBN89ruEORBAAAEEnCtAgs+5Y0vPECgagXr1jOc21F+z3awNaMPy8F2mO23atLDsB41GAAEEEEAAgfARSLvtNqOx7fWV6p340SjLKfB9pQgb6OWkxTkEEEAg/AVI8IX/GNIDBEJLoGFDoz2X6mdPfPCg52NYfejSpYvR3rVr12rNmjVGGQECCCCAAAIIIBBMgdS+fXUk+gyjyhbr3zLinALfTTViY2NzupxzCCCAAAJhLkCCL8wHkOYjEHICl15qNOlCbdQZOuQu27/fOBU2QaNGjXTOOecY7WUWn8FBgAACCCCAAALBFihZUttjKxu1nn1wrRHnFPguyfVdspvTvZxDAAEEEAg/ARJ84TdmtBiB0BaoUsWvfQk6tfHGgQN+p8KiwF7ScsMNNxhtJcFncBAggAACCCCAQLAFkpOVmLzTqPXPWs2NOKeAGXw56XAOAQQQcJ4ACT7njSk9QqBoBU6c8Hv+cRV3l4Vrgs9u/I033mj0a9OmTdq3b59RRoAAAggggAACCARLIGrWLJ2Vbi5/2HVN71xXzwy+XFNxIQIIIOAIgRhH9IJOIIBA6AhksVVuZoIvXJfo2rjNmjVT7dq11bhxY3eyr02bNoqLiwsdd1qCAAIIIIAAAo4SiJk0yejPd2qumFrVjbKcAmbw5aTDOQQQQMB5AiT4nDem9AiBohXIIsF3QsXcbTp8WEpLk6Kji7aJeXl6tNXoVatWyeVy5eV27kEAAQQQQAABBHIvsG2bor7+2rh+vPro3tJGUY6B7ww+NtnIkYuTCCCAQNgLsEQ37IeQDiAQYgI+S3SPu5N7fyXFwnUnXVuZ5F6Ifa/RHAQQQAABBJwqMHu2XOnpnt4dUYI+1o0qnY8EH5tseDj5gAACCDhSgASfI4eVTiFQhAI+M/gyl+dmtiic38OX2Qe+IoAAAggggAACBSrg8xvRJWqoJJVUhQq5fypLdHNvxZUIIICAEwRI8DlhFOkDAqEkcJoE3969odRY2oIAAggggAACCISggLWDrvdh/8I0IUEqWdK7NOfPvkt0mcGXsxdnEUAAgXAXIMEX7iNI+xEINQGfBJ+i7OW5GZ5W7tzp+cgHBBBAAAEEEEAAgawE1qwxSrerUkCz9+ybfRN8vIPPICVAAAEEHCdAgs9xQ0qHEChigXLljAZUSt+uK/WNp4wEn4eCDwgggAACCCCAQNYCS5ca5UvVIKAEX0ZGhrWxmbWzmddBgs8Lg48IIICAAwVI8DlwUOkSAkUq0KiRVLWq0YQnNcITk+DzUPABAQQQQAABBBDwF0hKktatM8rtBN/ZZxtFOQa+yT374nU+deZYAScRQAABBMJOgARf2A0ZDUYgxAViY6VHHzUa2VLfqYW+dZeR4DNoCBBAAAEEEEAAAVNg5UrJawfdNEVpheoFNIPv8OHDZp1WtHXrVr8yChBAAAEEnCNAgs85Y0lPEAgdgV69lH7uuUZ7hmq4OybBZ7AQIIAAAggggAACpoDPD0ubdZ6Oq0RACb6jR4+adVpR9erV/cooQAABBBBwjgAJPueMJT1BIHQE4uKU+uCDRnuu1Hw11SLt2GEUEyCAAAIIIIAAAgh4Cxw86B1pv85yx4Es0d20aZNRhx1U9XmFit8FFCCAAAIIhLUACb6wHj4aj0DoCqT17KnjZcsaDeyhd+XzS2njPAECCCCAAAIIIBDxAj7La4+olJskkATf8uXL/RjL+vxc5ncBBQgggAACYS1Agi+sh4/GIxDCAvHx+qN9e6OBrbRAe/dKKSlGMQECCCCAAAIIIIBApkBMTOYn99cYpbq/nneeUZxjULlyZb/ziYmJfmUUIIAAAgg4R4AEn3PGkp4gEHICey++2GhTLa1ThYydLNM1VAgQQAABBBBAAAEvgeLFvQKpmE6440ASfC6Xy6jDDqKi+KefHwoFCCCAgIME+K+8gwaTriAQagKHLrhAKSVKGM2yZ/H98YdRRIAAAggggAACCCCQKVCsWOYn99fi1hYbpUplqHRpozjH4M8//zTON2zY0IgJEEAAAQScJ0CCz3ljSo8QCBmBjOho/Vm7ttGe1tZmGyT4DBICBBBAAAEEEEDgLwGfJbqxSlGVKhnKYlLeX/f4fPJN8PH+PR8gQgQQQMCBAiT4HDiodAmBUBLYV7eu0RwSfAYHAQIIIIAAAgggYArs22fEf6qsKlfOMMpOF5DgO50Q5xFAAAHnCZDgc96Y0iMEQkrAN8FXXb/p4KptIdVGGoMAAggggAACCISMwJ49RlP2KFHnnUeCz0AhQAABBBDwEyDB50dCAQIIBFPgkPVG6BPFzZfGJK6eH8xHUBcCCCCAAAIIIOAcgSwSfPYS3UAO+TEi1AAAQABJREFUZvAFosW1CCCAgDMESPA5YxzpBQKhK2C9h29fneZG+6ptI8FngBAggAACCCCAAAKZAiT4MiX4igACCCAQgAAJvgCwuBQBBPImkNq8hXHjpUnf6eRJo4gAAQQQQAABBBBAwBJI320u0d2tCtYmG+kB2TCDLyAuLkYAAQQcIUCCzxHDSCcQCG2BMxpWMxpYWge1aZNRRIAAAggggAACCCBgCaRsNxN89jv4atQIbInu7t27Dcty5coZMQECCCCAgPMESPA5b0zpEQIhJ5Cwd5PRps06Txs2GEUECCCAAAIIIIAAAsePK37jOtOhZJzOPNMsyilKSUnRPp+deCtWrJjTLZxDAAEEEHCAAAk+BwwiXUAg1AVcO3caTUxWnNb5/OxqXECAAAIIIIAAAghEosA77/j1Oqni2X5lORX4zt6zryXBl5MY5xBAAAFnCJDgc8Y40gsEQlogo3p1o31N9D+lzf/WKCNAAAEEEEAAAQQiXmDxYoPgezXRyaqBJfh27dpl1BETE6OzzjrLKCNAAAEEEHCeAAk+540pPUIg5ATSOnfW0VLmD6cd//uwlBHY+2RCrmM0CAEEEEAAAQQQCKaAz6qHWbpGlc49FtATfBN8FSpUUFQU/+wLCJGLEUAAgTAU4L/0YThoNBmBsBMoUUKbejxhNPtvRxcrY+rHRhkBAggggAACCCAQ0QI+Cb4dOkeVKh0NiGSnTx0szw2Ij4sRQACBsBUgwRe2Q0fDEQgvgZL39tWvMnfTTXvoESk5Obw6QmsRQAABBBBAAIGCEEhNVfofm4yad6piwAk+3xl8Z59trqIwHkCAAAIIIOAYARJ8jhlKOoJAaAucVz1OQ2NHGo2M2bxRGjvWKCNAAAEEEEAAAQQiUuDbbxV16KDR9fVRNVWhQpJRdrqABN/phDiPAAIIOFOABJ8zx5VeIRByAvarX9bU6iL7ZdHG8dRT0kHzh1njPAECCCCAAAIIIBAJAj//bPRyiRootVJZRUcH9s5ilugajAQIIIBAxAiQ4IuYoaajCBS9QK3aLj2o582G7N8vPfecWUaEAAIIIIAAAghEmsDhw0aP1+siVa1qlhkXZBMwgy8bGIoRQAABhwuQ4HP4ANM9BEJJoE4d6Qc11RxdZTZrzhwzJkIAAQQQQAABBCJN4MgRo8dHlZCnBN+2bduMethkw+AgQAABBBwrQILPsUNLxxAIPYH69U+16Vu1NBtnvVSaAwEEEEAAAQQQiGSBjCPmbrmnEnyHAiJJtX6m2rFjh3FPlSpVjJgAAQQQQMCZAiT4nDmu9AqBkBS45JJTzSqmE2b7qlY1YyIEEEAAAQQQQCDCBI7tNJN5doLv/PMDW6Jrv38vLS3NkCPBZ3AQIIAAAo4VIMHn2KGlYwiEnsA550jly0sl5LMbXJkyoddYWoQAAggggAACCBSiQNq6X42nHS5ZUWXK+PxS1LjCP9i6datRWKxYMZUrV84oI0AAAQQQcKYACT5njiu9QiAkBVwuyV6mGyOfJbnR0SHZXhqFAAIIIIAAAggUioC1tLbktvXGo5Kr15L9s1Mgx5YtW4zLK1eubNURYCVGDQQIIIAAAuEiQIIvXEaKdiLgEAF7mW6GfH7QPH7cIb2jGwgggAACCCCAQB4ENm5UTFqycWOJS2sZcW4C3wQfy3Nzo8Y1CCCAgDMESPA5YxzpBQJhI2DP4PtdF5rtXbnSjIkQQAABBBBAAIFIEvjkE6O3u5WoCy8P/BUmvkt07Rl8HAgggAACkSFAgi8yxpleIhAyAvYMvl/0/9vp/n+rMtaskU6eDJk20hAEEEAAAQQQQKDQBDIylPrGOONxX6q99VqTdKMsN8Gvv5rv8TvvvPNycxvXIIAAAgg4QIAEnwMGkS4gEE4C1atLvxWvZzTZZb13RnaSjwMBBBBAAAEEEIg0gQULFPPHb0av3yt+p2rVyjDKchOsW7fOuKxmzZpGTIAAAggg4FwBEnzOHVt6hkBICtj7aVRreKY26nyzfT//bMZECCCAAAIIIICA0wXS0qSRI41erlEtpV7eVIHuQZaUlKTNmzcbdZHgMzgIEEAAAUcLkOBz9PDSOQRCU6BxY2mrfN4JM316aDaWViGAAAIIIIAAAgUhYC3N1T33SHPnGrW/pTt1eSOfDcmMK7IONmzY4HeiRo0afmUUIIAAAgg4U4AEnzPHlV4hENICTRpn6AotMtqYsXatERMggAACCCCAAAKOFpgyRRo71ujiXpXTJPVSo0ZGca4C3+W59vv3SpQokat7uQgBBBBAIPwFSPCF/xjSAwTCTqCFvlOMrCUpXsfBhm29Ij4igAACCCCAAAIOF/jyS6ODJxWnGzRdB1UmKAk+lucavAQIIICA4wVI8Dl+iOkgAqEnUG73ar9GzbziWb8yChBAAAEEEEAAAccKlC9vdG2O2mmhmqtSJbn/GCdzEfjO4CPBlws0LkEAAQQcJECCz0GDSVcQCBuB+vX9mrrk58B3ivOrhAIEEEAAAQQQQCBcBGrXNlpaTad20r38cqM418H69euNa0nwGRwECCCAgOMFSPA5fojpIAIhKHDZZToZX8poWKtZQyT7ZdMcCCCAAAIIIIBAJAjExRm9jFOyO87L+/fS09NFgs/gJEAAAQQiToAEX8QNOR1GIAQEYmOVdFkroyHXHXpHJx54zCgjQAABBBBAAAEEHCuwapXRtXWq6Y6bNTOKcxVs3bpVx48fN65lBp/BQYAAAgg4XoAEn+OHmA4iEJoCCQ/e5dewYi+OlH7/3a+cAgQQQAABBBBAwHECX31ldGml6qp4ccla6BDwsXbtWuOeM888UxUqVDDKCBBAAAEEnC1Ags/Z40vvEAhZgdjrrtFbVZ/xb9/Eif5llCCAAAIIIIAAAk4S+M16394vvxg9+kp/V5Mmks/KXeOa7IKVK1cap2rVqiWXy2WUESCAAAIIOFuABJ+zx5feIRDSAtt7/lOT1NNso53gS001y4gQQAABBBBAAAEnCUybZvRmtxL1X2sH3ZYtjeJcBytWrDCurVevnhETIIAAAgg4X4AEn/PHmB4iELICrVtLozXYbN+OHdLs2WYZEQIIIIAAAggg4CQBnwTfdN2gdEWrRYu8dZIEX97cuAsBBBBwkgAJPieNJn1BIMwE7F3iNsTX00+61Gz5+PFmTIQAAggggAACCDhFIC1NWrbM6I2d4LOX5uZlB92UlBT5voOvbt26Rv0ECCCAAALOFyDB5/wxpocIhKxAsWJS06bSh+pmtvH7782YCAEEEEAAAQQQcIrA3r1+ryNZpb+5k3v2JhuBHuvXr5ed5PM+SPB5a/AZAQQQiAwBEnyRMc70EoGQFbCX6W5RFbN98fFmTIQAAggggAACCDhFYPt2oyep1tLcPdY7+PL6/j3fDTbOPfdclSlTxngGAQIIIICA8wVI8Dl/jOkhAiEtYCf4ymmf0caUsmdL6elGGQECCCCAAAIIIOAIAft9w17HLp3tfv9eXhN8vH/PC5OPCCCAQAQLkOCL4MGn6wiEgsDll0sZseaMvaPLNkilSkk9rR12ly8PhWbSBgQQQAABBBBAIDgCO3ca9exURdlLc5s1M4pzHfgm+Fiem2s6LkQAAQQcJUCCz1HDSWcQCD8B+4XSiedEGw2Psn6PraQkafJk6VJrA44PPjDOEyCAAAIIIIAAAmEr4POivTN1yL081343cV6OZT4bdtSrVy8v1XAPAggggECYC5DgC/MBpPkIhL2ANUPvgi0LjG6cqcPKyCxJTZVuv52ZfJkefEUAAQQQQACB8BWwdtDNGDnSaH9VbdLV7Tw/+RjnThfstGYD2n+8j4YNG3qHfEYAAQQQiBABEnwRMtB0E4GQFRg9Wpdm/KgDKm00cYe1XMVz2Em+F1/0hHxAAAEEEEAAAQTCTiDDSuL16CHX2rVG0+138F39d5dRlttg6dKlxqUJCQmqXr26UUaAAAIIIBAZAiT4ImOc6SUCoSlgb6Tx8ceqozVaIvO3zftU3mzz1KlsvGGKECGAAAIIIIBAOAlMmyZ9+KHR4mTF6snyr6tmTaM418GSJUuMay+55BJFRfFPPAOFAAEEEIgQAf7rHyEDTTcRCEmB48fd79qzf2d90GcGXykdMZtsv5PPvp4DAQQQQAABBBAIR4FPPjFafULx6qxPFHf9NXLlbQKffGfwNWjQwHgGAQIIIIBA5AiQ4IucsaanCISegP2S6RIl3O06S3962ndECfpV1XTc+sHXc9jX+byU2nOODwgggAACCCCAQKgLrFhhtPCfelaz1EFXX20UBxSQ4AuIi4sRQAABRwuQ4HP08NI5BEJcwF5CcuON7kZeomW6T6PVwFqsW8Z6I9/fNUf/VYu/OtC1q6w1J3/FfEIAAQQQQAABBMJF4ORJad06o7WL1UjR0VKbNkZxroO9e/dq69atxvVssGFwECCAAAIRJcC/liNquOksAiEoMHiwFBNjJfUO6UfrB93lutj6/8UarietOXy/nWqwdV733x+CjadJCCCAAAIIIIBALgTsjTXsTcO8jpWqqyZNpNLmPmNeV+T88eeffzYuKG6tdLjooouMMgIEEEAAgcgRsP7VzIEAAggUocDFF0uTJ0u3367xqX2sfeR2Wcm+g381yE7u2eft6zgQQAABBBBAAIFwFNi3z2h1imKstw2foeuvN4oDCv73v/8Z19vv34uxf27iQAABBBCISAFm8EXksNNpBEJM4JZbJOu30LV6NlKZEsmnGme/c69nT3e57PMcCCCAAAIIIIBAuArExhotdynDijN03XVGcUDB4sWLjesbN25sxAQIIIAAApElwK94Imu86S0CoStgz9CbNEmaMOHUbrn2hhq8cy90x4uWIYAAAggggEDuBTZvNq79XReqdm2XqlUzinMdZGRk6McffzSub9SokRETIIAAAghElgAJvsgab3qLQOgL2Em9kiVDv520EAEEEEAAAQQQyK3AW28ZV65TzXwtz/3111914MABo04SfAYHAQIIIBBxAizRjbghp8MIIIAAAggggAACCCBQaAJffy0tXGg8zk7wBXN5bsWKFVW5cmXjGQQIIIAAApElQIIvssab3iKAAAIIIIAAAggggEBhCowd6/e0H89qr0sv9SvOdYHvBhv27D2Xy5Xr+7kQAQQQQMB5AiT4nDem9AgBBBBAAAEEEEAAAQRCQeD4cWn2bKMlYzVAiTe1yterhtlgwyAlQAABBBCwBEjw8W2AAAIIIIAAAggggAACCBSEwLx5UlKSp+Y0659fT2pEvpbnHreShsuXL/fUaX/g/XsGBwECCCAQkQIk+CJy2Ok0AggggAACCCCAAAIIFLjAZ58Zj1ikK5ReppyuvNIoDij46aeflJqa6rknytqg7NL8rPf11MQHBBBAAIFwFiDBF86jR9sRQAABBBBAAAEEEEAgNAXS0qTPPzfa9pmuU5cuUlycURxQsNBnw4769esrISEhoDq4GAEEEEDAeQIk+Jw3pvQIAQQQQAABBBBAAAEEilrg22+lPXuMVtgJvm7djKKAg0WLFhn3NGvWzIgJEEAAAQQiU4AEX2SOO71GAAEEEEAAAQQQQACBghLIyJCGDzdqX6U6Olqhmlq1MooDCtLT0+Wb4LviiisCqoOLEUAAAQScKUCCz5njSq8QQAABBBBAAAEEEECgqAS+/lr67jvj6a9Zu+d27SpFRxvFAQWrV6/WoUOHjHtI8BkcBAgggEDECsREbM/pOAIIIIAAAggggAACCCAQbIH9+6XBg41at6iyxqmvvgny8tyqVauqUqVKxrMIEEAAAQQiU4AZfJE57vQaAQQQQAABBBBAAAEEgi1gJ/dat5ZWrTJqHqEnVaFyvJo0MYoDDnw32OD9ewETcgMCCCDgWAESfI4dWjqGAAIIIIAAAggggAAChSowbJi0YoXxyLWqqbfVUzffLEXl819fvgk+luca1AQIIIBARAvk86+YiLaj8wgggAACCCCAAAIIIIDAKQF7Y42pUw2Nbapk7Zv7mVIVq1tuMU4FHGzbtk2bN2827mMGn8FBgAACCES0AAm+iB5+Oo8AAggggAACCCCAAAJBEdi0Sdq1y6iqo77Qr6qhunWlSy4xTgUcfPHFF8Y9UdZ0wNq1axtlBAgggAACkStAgi9yx56eI4AAAggggAACCCCAQLAEFi0yatqj8lqui91ld9whuVzG6YCDAQMGGPekp6dbS37555yBQoAAAghEsAB/I0Tw4NN1BBBAAAEEEEAAAQQQCIKAvTz3nXeMir5XUyt2KSZG6t7dOEWAAAIIIIBA0AVI8AWdlAoRQAABBBBAAAEEEEAgogQmTpTmzDG6vECt3HGHDlJionEq4GDjxo1+93Tp0sWvjAIEEEAAgcgVIMEXuWNPzxFAAAEEEEAAAQQQQCC/Anv2SPffb9SyXedoknq5y+zlufk9Zs6c6VfFOz4zBv0uoAABBBBAIKIESPBF1HDTWQQQQAABBBBAAAEEEAiqwOTJ0uHDRpX99KYOqbTKl5euucY4lafAd4ONK6+8UsWLF89TXdyEAAIIIOBMARJ8zhxXeoUAAggggAACCCCAAAKFIfDhh8ZTPtJNmiVrXa519OghxcYapwMODlvJw++//96475577jFiAgQQQAABBEjw8T2AAAIIIIAAAggggAACCORF4NdfpSVLjDvf0p2euFcvz8c8f1hi1Z+Wlua53565d9VVV3liPiCAAAIIIGALkODj+wABBBBAAAEEEEAAAQQQyIuAz7vx9qqcMjfXaNxYqlcvL5Wa9/z0009GgZ3cK1GihFFGgAACCCCAAAk+vgcQQAABBBBAAAEEEEAAgbwIxMQYd5XSEZXRAXfZwIHGqTwFKSkpWrp0qXHvtddea8QECCCAAAII2AIk+Pg+QAABBBBAAAEEEEAAAQTyItC1qxQX57mzmE7qDk1U2bKSfSq/x4oVK3TixAlPNS6XSx07dvTEfEAAAQQQQCBTgARfpgRfEUAAAQQQQAABBBBAAIFABM46S0pONu6oq5Xq3VsqVswozlPguzy3SZMmqlChQp7q4iYEEEAAAWcLkOBz9vjSOwQQQAABBBBAAAEEECgogSyyeF+rrfr3z/8D09PT9eOPPxoVsTzX4CBAAAEEEPASIMHnhcFHBBBAAAEEEEAAAQQQQCBXAqmpkpWE8z32Xd1DF17oWxp4bL97b//+/caN1113nRETIIAAAgggkClAgi9Tgq8IIIAAAggggAACCCCAQG4F3nvP78rVqq3+A6P9yvNS8Mknnxi3Va9eXRdddJFRRoAAAggggECmgLntU2YpXxFAAAEEEEAAAQQQQAABBLIWyMiQxo/3O9e58hKt7eBXHHBBhlX/tGnTjPtuvPFG2ZtscCCAAAIIIJCVADP4slKhDAEEEEAAAQQQQAABBBDITmDePOm//zXODtQY9R5YTNFBmMC3ePFibdmyxaj/pptuMmICBBBAAAEEvAWYweetwWcEEEAAAQQQQAABBBBAICcB+717jz9uXLFZVfRhiT76PQiba9gV27vl+h4XX3yxbxExAggggAACHgFm8Hko+IAAAggggAACCCCAAAIInEbgjTcka4ad9/G0HlePPvEqU8a7NG+fk5OT/W4sW7Ysy3P9VChAAAEEEPAWYAaftwafEUAAAQQQQAABBBBAAIHsBP78U3roIePsb7pQb6uX1t5rFOc5mD59ut+9ffv29SujAAEEEEAAAW8BZvB5a/AZAQQQQAABBBBAAAEEEMhOYOZM6ehR4+xdel2dbojVhRcaxXkOxowZ43fv0KFD/cooQAABBBBAwFuAGXzeGnxGAAEEEEAAAQQQQAABBLISSE2VnnnGOLNCdTVPbbVwsFGc52DFihVauHChcf+DDz5oxAQIIIAAAghkJcAMvqxUKEMAAQQQQAABBBBAAAEEMgXsjTV695Y2bMgscX/9SZepUSOpaVOjOM/Ba6+9ZtxbunRpq37rARwIIIAAAgicRoAE32mAOI0AAggggAACCCCAAAIRLJCRId19t/TOOwbCUZXUS7pPg63Zey6XcSpPweHDh61HmM9o166dYmNj81QfNyGAAAIIRJYACb7IGm96iwACCCCAAAIIIIAAAoEITJ0qvf66ccdxFVMnzdCx8+vqhhuMU3kO7OTesWPHPPdHR0fLTvBxIIAAAgggkBsB3sGXGyWuQQABBBBAAAEEEEAAgcgUGDfO6HeyYtVF07RArfXmo1JMEP5FlWHNEhw7dqzxnA4dOqhcuXJGGQECCCCAAALZCTCDLzsZyhFAAAEEEEAAAQQQQCCyBQ4elObPNwwG6RV9qWtUqZJ0++3GqTwH3333ndasWWPc369fPyMmQAABBBBAICcBEnw56XAOAQQQQAABBBBAAAEEIldg1izJ3j33/48Titf7utUdPfSQFB+feSZ/X31n79WoUUOtWrXKX6XcjQACCCAQUQIk+CJquOksAggggAACCCCAAAII5Frgs8+MS+epjY6qlBITpb59jVN5Dnbu3Knp06cb9w8cOFBRUfxTzUAhQAABBBDIUYC/NXLk4SQCCCCAAAIIIIAAAghEpIC9e+6cOUbXP9X17viBB6QSJYxTeQ7GjBljTRL8a5Zg8eLF1bNnzzzXx40IIIAAApEpQIIvMsedXiOAAAIIIIAAAggggEBOAlu2SPY7+LyOOWqnMmWkAQO8CvPxMSkpSa+99ppRQ48ePVS6dGmjjAABBBBAAIHTCZDgO50Q5xFAAAEEEEAAAQQQQCDyBFauNPp8UGdqi6ro3nulUqWMU3kOJk+erP379xv333fffUZMgAACCCCAQG4ESPDlRolrEEAAAQQQQAABBBBAILIEVq0y+rtSdXXGGS4NGmQU5zlIT0/Xiy++aNzfvn171a5d2ygjQAABBBBAIDcCJPhyo8Q1CCCAAAIIIIAAAgggEFEC++abM/hW6W968EHprLOCwzDL2qF3w4YNRmWDBw82YgIEEEAAAQRyK0CCL7dSXIcAAggggAACCCCAAALOF7Dei6cnntAZcz82+vpHyboK5upZ39l7devWVZs2bYxnEiCAAAIIIJBbgZjcXsh1CCCAAAIIIIAAAggggICjBebNk+64Q9q6VXE+HW3Y++KgvXtv2bJl+uabb4wn2LP3XC6XUUaAAAIIIIBAbgVI8OVWiusQQAABBBBAAAEEEEDAuQL2ctm2bbPs3y+xl+m6UU2yPJeXwn/961/GbRUqVNAtt9xilBEggAACCCAQiABLdAPRcsi19m8Gc/OnVatWDukx3UAAAQQQQAABBBBA4DQCS5dmecELGqxlz89TsRLB+afT77//rqlTpxrPGmTt3BEfH2+UESCAAAIIIBCIADP4AtHiWgQQQAABBBBAAAEEEHCmwIoVfv1aqkv02oUvaO0Av1N5LnjhhRdk76CbeSQkJGjgwIGZIV8RQAABBBDIkwAJvjyxOeOmAQMG5PjDRMmSJZ3RUXqBAAIIIIAAAggggEBOAj/+KD3/vHHFHpVXR32h54ZLsbHGqTwHu3fv1oQJE4z7+/fvrzJlyhhlBAgggAACCAQqQIIvUDEHXZ+YmKi//e1vDuoRXUEAAQQQQAABBBBAIECBw4elbt2klBTjxh56V+XqnuM+ZZzIR/Dyyy/r5MmTnhpirczh/fff74n5gAACCCCAQF4FSPDlVY77EEAAAQQQQAABBBBAIPwF7Jl7f/xh9ONZPaq5aqc5L0jR0capPAdHjhzR2LFjjft79OihSpUqGWUECCCAAAII5EUgOG+KzcuTuQcBBBBAAAEEEEAAAQQQKEqBAwek//zHaMFCXaEnNULt20tXXWWcylfw5ptv6uDBg5467E3vhgwZ4on5gAACCCCAQH4ESPDlR497EUAAAQQQQAABBBBAIHwFXnpJspfo/v+RLpf66U1r2l6M7yv5Mi/J01d7We7o0aONe6+77jrVqlXLKCNAAAEEEEAgrwIk+PIq54D7pk6dqtq1a6tEiRIqVaqUqlevrp49e2r+/PkO6B1dQAABBBBAAAEEEEAgBwF79p6d4PM6pugmrVVt3XmnrJ+TvU7k8+N7772nHTt2GLU8/PDDRkyAAAIIIIBAfgR4B19+9ML83jVr1hg9+O2332T/mTx5sq6//npNmjRJZ555pnFNboJt27bleNnOnTs9548fPy77D4fzBE6cOOHplPdnTyEfEAhQwPv7yPtzgNVwOQIeAe/vI+/Pngv4gEAAAt7fQ96fA6iCSwtZIGbMGMX6zN57Sk9Yv/jO0COPnLB+Rg1Og9LT0/Wvf/3LqKx58+a6+OKLc/w52Pv7yPuzUREBAgEIeH8feX8OoAouDWEB/l0dwoNTSE1zZVhHIT2Lx4SIQMmSJXXttdeqTZs2qlmzphISErR37159++23ev311/Xnn3+6W9qyZUvNnTtX9u5egRz2+0Rye4wbN07lypXL7eVchwACCCCAAAIIIIBAUARaDh6s0hs3eur6yJq9100f6bbb1qhLl1895fn98MMPP/gl+J544gk1bNgwv1VzPwIIIOAR2Ldvn/r27euOt27dqnPPPddzjg+RIcAMvsgYZ6OX27dvV+nSpY0yO7jKeovwoEGDrBcKt9eyZcvcCb/XXntN99xzj9+1FCCAAAIIIIAAAgggEK4CZVeuNJJ7dj9e110qXz5JnTr9HrRu2bP3PvroI6O+qlWrqkGDBkYZAQIIIIAAAvkVYAZffgUdeP9G6zeZ9sy+lJQUVatWTb/+GthvMHOzRPfyyy93y23YsIHfLDjwe8jukj3t/7vvvnP3rkWLFipWrJhDe0q3CkuA76nCko6c5/A9FTljXRg95fupMJSD84yoefMUd9NNcnmtwd2rcqqonZo4OV033pgWnAdZtXz66ae69dZbjfrs1+DcZD3/dAffU6cT4nygAnxPBSoWXtfb/w6vUaOGu9HM4AuvsQtWa5nBFyxJB9VzwQUXuGfzzZo1y/1OPvuFwOecc06uexjIVODixYvL/sPhbAE7ucc4O3uMC7t3fE8Vtrjzn8f3lPPHuDB7yPdTYWoH+KyPP5a6d5eSk40b31B/tbwyRj16SAG8bcaowzewZ+89++yzRrG9a24P6yHR0dFG+ekCvqdOJ8T5QAX4ngpULPSv599boT9GBd1CdtEtaOEwrd/eXTfzsJf0ciCAAAIIIIAAAgggENYC1rJcdevml9z7VNdpZPQTevXV4CX3bKdp06Zp1apVBtnQoUMDTu4ZFRAggAACCCCQjQAJvmxgIr04kI0yIt2K/iOAAAIIIIAAAgiEgcArr0hp5vLbD6xtNbpqqv7xQLysyXVBO+zZe8OHDzfqq1Onjrp27WqUESCAAAIIIBAsARJ8wZJ0WD1r1qzx9CiQ5bmem/iAAAIIIIAAAggggEAoCXz/vdGahbpCPfSuKlSKlbWpbVCPqVOnavXq1Uad9uy9qCj++WWgECCAAAIIBE2Av2GCRumciv744w/NnTvX3aELL7xQlSpVck7n6AkCCCCAAAIIIIBA5AlY75S2Mm5Gvx/U80pXtEaPlhISjFP5CtKsWYK+s/fq1q2rLl265KtebkYAAQQQQCAnARJ8Oek48NyMGTOUmpqabc92797t/uEj+f9fPDxw4MBsr+UEAggggAACCCCAAAJhITBsmNHMJBXXUjVQmzayls0ap/IdTJkyRWvXrjXqYfaewUGAAAIIIFAAAuyiWwCooVzloEGDlJKS4k7iNWnSRFWrVnXvbrpv3z4tWLBAb7zxhuzP9tGsWTPdfffdodwd2oYAAggggAACCCCAQM4C1sy9jPHj5fK66l1rcW5GTJzs1/IFa9dcu3r7F+m+s/fq1aunzp07ez2djwgggAACCARfgARf8E1DvsYd1hKFV6yfZuw/2R32EoJx48YpPj4+u0soRwABBBBAAAEEEEAg9AWefVYua9OLzOO4immY9b9HHlFQN9aw6588ebLWr1+f+Sj3Vzvhx7v3DBICBBBAAIECECDBVwCooVzl22+/rW+//VY//PCDNm7c6J6td/jwYeu9IwmqXLmymjZtqp49e8qe3ceBAAIIIIAAAggggEDYC7z/vtGFxWqkMy46R489ZhTnOzhx4oTspbjeR4MGDXTdddd5F/EZAQQQQACBAhEgwVcgrKFbacuWLWX/4UAAAQQQQAABBBBAwOkCGS+MNpbm2v39QU301ltSsWLB7f2YMWO0bds2o9KRI0daS4C9FwcbpwkQQAABBBAImgCbbASNkooQQAABBBBAAAEEEEAgZAQ++kiuBx8wmpOiGKXddKuaNzeK8x0cOnRIz1pLgb2P1q1b66qrrvIu4jMCCCCAAAIFJkCCr8BoqRgBBBBAAAEEEEAAAQSKRODHH5Vx++1+j36wzATd81Zdv/L8Fjz//PPav3+/UQ2z9wwOAgQQQACBAhYgwVfAwFSPAAIIIIAAAggggAAChShgzabL6NZNruRk46GPaKTaTLpNZ5xhFOc72LVrl0aPHm3Uc8MNN6hRo0ZGGQECCCCAAAIFKUCCryB1qRsBBBBAAAEEEEAAAQQKTyAjQ+rXT64//jCeOUYD9UfXh3XttUZxUIKnn35aSUlJnrrsHXPtMg4EEEAAAQQKU4BNNgpTm2chgAACCCCAAAIIIIBAwQmMGydNmWLUv1BXaHiZ/2jlK8Hf7OL333+XvbmG99GrVy/VqlXLu4jPCCCAAAIIFLgAM/gKnJgHIIAAAggggAACCCCAQIEK2DP3Xn5ZGXfdZTzmgErrVr2vV1+PUYUKxqmgBNWqVfOrZ9iwYX5lFCCAAAIIIFDQAiT4ClqY+hFAAAEEEEAAAQQQQKBgBUaMkO69V670dOM5vTVBzW6poptuMoqDEnz++ed+9bRs2VKVK1f2K6cAAQQQQACBghZgiW5BC1M/AggggAACCCCAAAIIFJzAhg2SneDzOV7SvVpcsbNWvepzIghhamqqunbt6lfTO++841dGAQIIIIAAAoUhwAy+wlDmGQgggAACCCCAAAIIIFAwAnZyz2fm3jAN1WCN1oQJ0llnBf+x9nv3kn126W3QoAGz94JPTY0IIIAAArkUYAZfLqG4DAEEEEAAAQQQQAABBEJM4KefpPffNxr1bw3RcA1T//7S3/9unApKsGnTJv3zn/806kpMTNTixYuNMgIEEEAAAQQKU4AZfIWpzbMQQAABBBBAAAEEEEAgOAInT0p33CHZG2z8/3FECfqXHtYFF0jPP59ZGryvGdaz+vXrp6SkJKPSjz/+WDExzJ0wUAgQQAABBApVgL+FCpWbhyGAAAIIIIAAAggggEBQBJ5+Wlq92qhqlB7RAVdZffa2lJBgnApK8Pbbb2vu3LlGXf2tqYLNmzc3yggQQAABBBAobAFm8BW2OM9DAAEEEEAAAQQQQACB/AksXaqMkSONOpapvv6th6zls1KzZsapoAS7du3S4MGDjboqVaqkf/3rX0YZAQIIIIAAAkUhQIKvKNR5JgIIIIAAAggggAACCORNYO9eqUcPudLSPPenKEa9NEmXN43VsGGe4qB+GDRokA4cOGDU+dprr+nMM880yggQQAABBBAoCgGW6BaFOs9EAAEEEEAAAQQQQACBwAX+/FNq00Zau9a491n9U1tKX6xfrP02CuJVeJ988ons9+x5H926dVOnTp28i/iMAAIIIIBAkQmQ4Csyeh6MAAIIIIAAAggggAACAQkMHSqtXGncslz19Iwe0wfjpPPOM04FJbBn7Q0cONCoq2zZsvrPf/5jlBEggAACCCBQlAIs0S1KfZ6NAAIIIIAAAggggAACuReYNs24dpPO03X6TL37x6lLF+NU0IIhQ4bIfv+e9/HSSy8pMTHRu4jPCCCAAAIIFKkACb4i5efhCCCAAAIIIIAAAgggkCuBTz+VlWkzLr1B05VQp6pefNEoDlowb948jR8/3qivffv26t69u1FGgAACCCCAQFELkOAr6hHg+QgggAACCCCAAAIIIJC9wOHDUu/eUufOxjXJitWG+Hr66COpeHHjVFCCY8eOqV+/fkZdCQkJev311+VyuYxyAgQQQAABBIpagHfwFfUI8HwEEEAAAQQQQAABBBDIWmDZslOJvc2b/c7PUCe99GqM6tTxOxWUgieffFIbN2406ho1apSqVKlilBEggAACCCAQCgIk+EJhFGgDAggggAACCCCAAAIImALffy9Zy2Flz+DzOT7R9Zrffbxe7etzIkjhjz/+KPs9e97HFVdcoQEDBngX8RkBBBBAAIGQESDBFzJDQUMQQAABBBBAAAEEEEDALWC9+07XXislJRkgh1VK9+hlrajfU4veKphlssnJyerTp4/S09M9z46Li9O4ceMUFcUbjjwofEAAAQQQCCkBEnwhNRw0BgEEEEAAAQQQQACBCBeYOVPuLXFPnjQgFqmpuus9HS5TVUumF8x79+wHjhw5UqtWrTKebS/XrVmzplFGgAACCCCAQCgJkOALpdGgLQgggAACCCCAAAIIRLLAgQNSt26ST3LvC3XQjfpYya5i+uJd6fzzCwZp6dKlevrpp43K69Wrp4ceesgoI0AAAQQQQCDUBJhjHmojQnsQQAABBBBAAAEEEIhUgV9+kY4eNXo/RV11g6brpIrpiSeka64xTgctOGklFW+//XalpqZ66oyOjtb48eMVGxvrKeMDAggggAACoSjADL5QHBXahAACCCCAAAIIIIBAJAosWODX61v0gdIVrauvlqyVsgV2DB06VKtXrzbq/+c//6lLL73UKCNAAAEEEEAgFAWYwReKo0KbEEAAAQQQQAABBBCINAF79t7o0Uav7dl7dnLPXpL73nuSNaGuQI7vrR17n3vuOaPu+vXr6/HHHzfKCBBAAAEEEAhVARJ8oToytAsBBBBAAAEEEEAAgUgR2LLl1Npbn+W5z2mIEhKkGTOksmULBuPgwYPq3r27sWuuvSR38uTJsnfP5UAAAQQQQCAcBFiiGw6jRBsRQAABBBBAAAEEEHCqgL2xRvv20s6dRg/Hq7eWuC7TZ+9LdeoYp4IWZGRkqG/fvtq0aZNR54gRI1S3bl2jjAABBBBAAIFQFmAGXyiPDm1DAAEEEEAAAQQQQMDpAj16SGvWGL1coJYaqLEaNUrq1Mk4FdSga9eumjZtmlFn8+bN9eCDDxplBAgggAACCIS6AAm+UB8h2ocAAggggAACCCCAgFMFNm6UZs3y6103faibb4vXkCF+p4JWMHz4cL/kXrFixax3/b2nmBgWOgUNmooQQAABBApFgARfoTDzEAQQQAABBBBAAAEEEDAEUlKUcd/9RlFmUOOy0nrzTcnlyiwJ7tc9e/Zo2LBhfpW++uqrqly5sl85BQgggAACCIS6AAm+UB8h2ocAAggggAACCCCAgNMEkpKkzp3lmvG5X8/uLz1RH31WTNZkugI5Tp48qVatWmVZd58+fbIspxABBBBAAIFQFyDBF+ojRPsQQAABBBBAAAEEEHCSgL2pRrt20syZRq9SFKPe8e/qtnm9VLGicSpogb2pRr9+/bR27Vq/Onfs2OFXRgECCCCAAALhIsDLJcJlpGgnAggggAACCCCAAALhLvDnn1Lr1tLKlUZPklRcN0VN092ftFeDBsapoAZPPfWUJk+e7Ffn77//biUVCyir6Pc0ChBAAAEEEAi+ADP4gm9KjQgggAACCCCAAAIIIJCVwJNP+iX3Dqi02uprXf9Ge7Vvn9VNwSmbOHGihg4dalRWsmRJLV++XBdccIFRToAAAggggEC4CZDgC7cRo70IIIAAAggggAACCISpwLFf/ZfBdtd7avN4U/XtW3Cdmj17tntprvcTXNYOHu+//77q1avnXcxnBBBAAAEEwlKABF9YDhuNRgABBBBAAAEEEEAgvASsjWt1/2r/LN6LiaM0Ylh6gXVm2bJluvHGG5Wammo845VXXtG1115rlBEggAACCCAQrgIk+MJ15Gg3AggggAACCCCAAAJhInDwzzQ91XimOu54w6/FF+35r1xLfvYrD0bB5s2bdc011+jo0aNGdQ899JDuvvtuo4wAAQQQQACBcBZgk41wHj3ajgACCCCAAAIIIIBAKAukpSn5uf/o5LCX9MrJrdm3NCUl+3N5PHPA2q23vfVSv127dhk1dOvWTSNHjjTKCBBAAAEEEAh3ARJ84T6CtB8BBBBAAAEEEEAAgVAUSEpSerdbFTfjM1XIqX29e0uNG+d0RcDnTpw4oeuvv15r16417m3ZsqUmTZqkqCgWMhkwBAgggAACYS9Agi/sh5AOIIAAAggggAACCCAQYgL79imjYydFLf5flg1LL5mgqNt6yNr5QrrkkiyvyWthenq6evbsqe+++86ook6dOvr0008VHx9vlBMggAACCCDgBAESfE4YRfqAAAIIIIAAAggggECoCFgz9zJat5Zr1Sq/Fi2Lbqjyj/XXuUNukRIS/M4Ho+Dhhx/WlClTjKrOOecczZo1S6VLlzbKCRBAAAEEEHCKAAk+p4wk/UAAAQQQQAABBBBAIAQEMoYO80vuHVBp3V5sih6bf5XODe5qXKPHL7/8sp5//nmjrFSpUpo5c6aqVKlilBMggAACCCDgJAESfE4aTfqCAAIIIIAAAggggEBRCVg71WZMnKT0F0Yr2qsNW1RZ18Z8qedn1An2q/a8niJNnz5d9913n1EWExOjadOmqX79+kY5AQIIIIAAAk4TIMHntBGlPwgggAACCCCAAAIIFKaAtVutnnlGGjdOrkOHjOTeScWpvWu2nvqoltq2LbhGLVy4UN27d1dGRobxkHFWm6666iqjjAABBBBAAAEnCpDgc+Ko0icEEEAAAQQQQAABBApDIDVVatZMWrMmy6c9q39qyIRauuGGLE8HpfCXX35Rx44dZe+c63089dRT7s02vMv4jAACCCCAgFMF2B/eqSNLvxBAAAEEEEAAAQQQKGiB9euzTe5N1Y06b+wj6tWr4BqxYcMGtWvXToesmYPeR9++ffXYY495F/EZAQQQQAABRwuQ4HP08NI5BBBAAAEEEEAAAQQKSODkSWnwYL/KN6uK2mquDrw+Rb0HxPudD1bB1q1brWW/bbV3716jSns232uvvSaXy2WUEyCAAAIIIOBkAZboOnl06RsCCCCAAAIIIIAAAgUhkJwsdekizZnjV/ul+lkjxpZXv/5+p4JWsGfPHve79ewkn/fRqlUrTZkyRfbmGhwIIIAAAghEkgAz+CJptOkrAggggAACCCCAAAL5FbDeu5dx663SzJlGTcmKtRblTtWwV8trwADjVFADeznu3//+d623lwd7HZdeeqk+//xzFS9e3KuUjwgggAACCESGAAm+yBhneokAAggggAACCCCAQP4F0tOV0bu3XNOmGXUdVim115dq+fKNuvtu41RQg8OHD6t9+/ZatmyZUW/t2rX15ZdfqlSpUkY5AQIIIIAAApEiQIIvUkaafiKAAAIIIIAAAgggkB+BgweV0eM2ud55x6jlqErqas1WpxfbaNAg41RQAzu5Z8/c++GHH4x6q1ataq0UnqNy5coZ5QQIIIAAAghEkgAvp4ik0aavCCCAAAIIIIAAAgjkReCDD5Rxzz1y7dtn3H1cxdRJM9T9lSb6xz+MU0ENjhw54p6555vcq1ChgubOnatKlSoF9XlUhgACCCCAQLgJMIMv3EaM9iKAAAIIIIAAAgggUFgCGRnS449L1jv3fJN77nfuuaar58TWBZ7cs2fuff/990avExMT9c0336hatWpGOQECCCCAAAKRKMAMvkgcdfqMAAIIIIAAAggggMDpBOzk3n33SS+/7HelPXPvtqj31evD9ura1e900AoyZ+5lldybP3++7HfvcSCAAAIIIICAxAw+vgsQQAABBBBAAAEEEEDAX2D48CyTe9PVWQ3jVumOzzsXaHLvwIEDuuqqq7Ro0SKjbeXLl3fP3CO5Z7AQIIAAAghEuAAz+CL8G4DuI4AAAggggAACCCDgJ/DSS5Kd4PM6UhWtQXpF75QcoBkzpNatvU4G+eOePXvUrl07LV++3KjZTu7ZM/fq1KljlBMggAACCCAQ6QIk+CL9O4D+I4AAAggggAACCCCQKZCeLg0bJj31VGaJ+2u6XLpN7+ir0rfo6y+lxo2N00ENtm3bprZt22r9+vVGvZkz90juGSwECCCAAAIIuAVI8PGNgAACCCCAAAIIIIAAAtLhw9Jtt0mff+6nMUCvacHZt2jBV9LFF/udDlrB77//7k7ubdq0yajT3iX366+/Vs2aNY1yAgQQQAABBBA4JcA7+PhOQAABBBBAAAEEEEAg0gVmzjyVucsiufewRunbi/rrhx8KNrn3yy+/qHnz5vJN7p1//vn673//S3Iv0r9H6T8CCCCAQI4CzODLkYeTCCCAAAIIIIAAAgg4XGDSJKl3b8neNdfrsN+5N1ij9VPje7ToC6lsWa+TQf44Z84cdenSRUePHjVqtmfs2TP37Bl8HAgggAACCCCQvQAz+LK34QwCCCCAAAIIIIAAAs4WmDBBGVkk9/aprNppjjZ1ukfz5hVscm+SlWDs0KGDX3Kvfv36+vbbb0nuOfs7kN4hgAACCARJgARfkCCpBgEEEEAAAQQQQACBsBHYvl164AGpTx+5fGbufasWukw/qdqdV2r6dKlEiYLpVYb13BEjRuiOO+5Qamqq8ZAWLVrom2++UWJiolFOgAACCCCAAAJZC5Dgy9qFUgQQQAABBBBAAAEEnCewZs2p5bjWe+00erRf/0bpYbXSAt0x/Hy98YYUU0Av9ElJSdGdd96poUOH+rXh5ptvlr1kt0yZMn7nKEAAAQQQQACBrAUK6K/srB9GKQIIIIAAAggggAACCBSyQFKSNGWK9N57sl5ol+3Dn9ZjGhHzlCa86bJm1WV7Wb5P2O/Z69q1q776ytqS1+cYMmSIRo0apago5iH40BAigAACCCCQowAJvhx5OIkAAggggAACCCCAQBgLLFok3XqrtGVLtp1IU5Se0FN6o8yjmvuJSy1bZntpvk/s2rXL/b69pUuXGnW5XC69/PLL+sc//mGUEyCAAAIIIIBA7gRI8OXOiasQQAABBBBAAAEEEAgfgbQ0aeRIadgwyf6cxXFcxTRRd1j75A5WzEXVtNjaKbdatSwuDFLRqlWr1LFjR23evNmosVixYnr//ffVuXNno5wAAQQQQAABBHIvQIIv91ZciQACCCCAAAIIIIBA6Avs2CF17y4tWJBlW/erjF7VP9x/9ipRbdueWsFbkK+8s5fj3nTTTTpy5IjRprJly2rGjBlq0qSJUU6AAAIIIIAAAoEJkOALzIurEUAAAQQQQAABBBAIXYFjx6TWraUNG/za+D810ou6XzPUScd1amvcu+6StTRWio31uzxoBa+++qruvfdepaenG3VecMEF+vLLL1WjRg2jnAABBBBAAAEEAhcgwRe4GXcggAACCCCAAAIIIBCaAvayXJ/knv2OvWHW/57VP5WuaHe77T0sXnxRGjRIsl5/VyBHamqq7r//ftkJPt+jcePG+uyzz5SYmOh7ihgBBBBAAAEE8iBAgi8PaNyCAAIIIIAAAggggEDICWzcKD3/vNGsLaqsW/SBvtcVnvKzzpI++EBq185TFPQP+/fv1y233KI5c+b41d2tWzdNmDBBxYsX9ztHAQIIIIAAAgjkTYD95/Pmxl0IIIAAAggggAACCISWwJAh0smTnjalWrP1rtZsI7lXv770888Fm9xbuXKlLrvssiyTe08++aR7Qw2Se55h4gMCCCCAAAJBESDBFxRGKkEAAQQQQAABBBBAoAgFvvlGmj7daIC9kcY61fKU9eghLVoknX++pyjoH6ZMmSJ7+e1Gezah1xEXF6d3331Xw4cPt5YEF9CaYK/n8REBBBBAAIFIEyDBF2kjTn8RQAABBBBAAAEEnCNgbVKhDh2kNm2MPu1VOfd79+zCGOulPPZGGpMnSyVO7a1hXBuMIC0tTY888ohuvvlmJSUlGVVWqFBB8+fPtzb2tXb25UAAAQQQQACBAhHgHXwFwkqlCCCAAAIIIIAAAggUoEBKivTQQ9JLL2X5kMf0jA6ptLWJhTR1qtSiRZaXBaXQft/erbfeqtmzZ/vV16hRI02bNk2VKlXyO0cBAggggAACCARPgARf8CypCQEEEEAAAQQQQACBghfYvl3WVLlT622zeNoy1dd49bGWyp5K7p17bhYXBalo2bJlatCgQZa19enTR2PGjFF8fHyW5ylEAAEEEEAAgeAJsEQ3eJbUhAACCCCAAAIIIIBAwQqsWCEro5Ztcu+QzlBvTdCDD0Xru++kgkruZWRkaNiwYdkm98aOHau33nqL5F7BfjdQOwIIIIAAAh4BZvB5KPiAAAIIIIAAAggggECIC/TvL+3ZYzTypOLcM/Zm6RqtK9NUL797lq65xrgkqMGhQ9bi39Kls63zyiuv1IABA7I9zwkEEEAAAQQQCL4ACb7gm1IjAggggAACCCCAAALBF3j/fel//zPqTVGMmup7LVVDNWsmLfig4Gbt2Q++/PLL9dNPPxlt8A3snXQ5EEAAAQQQQKBwBViiW7jePA0BBBBAAAEEEEAAgcAEjh5Vym13yNqG1u++5vqvlrka6rHHZO1UW3DJvd27d8vlcp02ubd3716VLVvWr50UIIAAAggggEDBCpDgK1hfakcAAQQQQAABBBBAIG8CqanSzJk6XqehYt+d5FfHArXUH+UbWbvXSk8/LcUUwNqctLQ0tW/fXmeffbbf870LPv30U9nv5StXrpx3MZ8RQAABBBBAoJAECuDHgEJqOY9BAAEEEEAAAQQQQMCpAuPGKePxx+WyZs4Vz6KPE9VLM69+RSvedqlChSwuCELR4sWL1bRpU6Wnp2db2/nnn6/ffvtNUVHMG8gWiRMIIIAAAggUggB/ExcCMo9AAAEEEEAAAQQQQCDXAs89J915pzu553vPYZXSHXHvKfn1iZr6ZUKBJPf2WJt49OnTR40bN84xuffzzz9r48aNJPd8B4kYAQQQQACBIhBgBl8RoPNIBBBAAAEEEEAAAQTcAtayVvfGGQsWSEuWKGPpUrn++CNLnJ90qUbW+1Cjpl6oGjWyvCRfhanWkuDXXntNTzzxhOydcrM77MTfDz/8kN1pyhFAAAEEEECgCARI8BUBOo9EAAEEEEAAAQQQQEDbt0v9+kmzZnkwXJ5Pf31YrMs1ydVb5zx2hz56Mk6xsX+dC9anefPmafDgwVqxYkWOVe7bt49NNHIU4iQCCCCAAAJFI8AS3aJx56kIIIAAAggggAACkSpgz9qbOFGqU8dI7mXF8YCeV49qi9Xrh/564qngJ/fWr1+va6+9Vm3bts0yuZeQkKDnrCXDJ0+edG+iwQ65WY0SZQgggAACCBS9ADP4in4MaAECCCCAAAIIIIBApAhs2+Z+v56++irHHv+ps/SYnlXcoP5a9qxk5dmCeuzfv1/Dhw/X2LFjZS/Nzero3r27/v3vf+ucc87J6jRlCCCAAAIIIBBCAiT4QmgwaAoCCCCAAAIIIICAgwU++UTq3Vs6eNCvk7tUQR/rRi1RQy1VA6VWr603JsSqWTO/S/NVkJyc7E7qjRgxQgcOHMiyrnr16umVV15RixYtsjxPIQIIIIAAAgiEngAJvtAbE1qEAAIIIIAAAggg4CSBY8ekhx+WxozJsleTdZvu00s6YM3ai7JeoPPgg9KwYVLx4llenqfCDGtZ8IwZM6y6H9Svv/6aZR2JiYl66qmn3DvoRkdHZ3kNhQgggAACCCAQmgIk+EJzXGgVAggggAACCCCAQLgL2O/ae+stWdvSSnv2+PVmhyqqv97QF+rkPve3v0kTJkiXXeZ3ab4KfvnlFz3wwAP65vmkehEAADxlSURBVJtvsqwnPj5e999/vx599FGdccYZWV5DIQIIIIAAAgiEtgAJvtAeH1qHAAIIIIAAAgggEGYCUQsXnpqC9/332bb8I92ku/S6DqqMYqyfyB9/XFaCTYqLy/aWgE/s3LnTqvdxaz+Pie4NMrKq4Oabb9aoUaNUtWrVrE5ThgACCCCAAAJhIkCCL0wGimYigAACCCCAAAIIhL7AebNnK86etZfNxhVJKq579LLGq4/VGZeaN5f1TjzJnr0XrGObtZFH5cqVc6zu8ssv14svvqimTZvmeB0nEUAAAQQQQCA8BEjwhcc40UoEEEAAAQQQQACBUBY4eVJ/GzdOF37xRbatPKgz1UQ/aJ1qqVw56bnnpJ49rTSfK9tbAjpx/PhxlShRIsd77MSfPWOvW7du1vv+rBf+cSCAAAIIIICAIwT4W90Rw0gnEEAAAQQQQAABBIpEYMMG964YxapVyzG5Z7etgna7k3t33imtWyf16hWc5N7Ro0f1/PPP55jcK1mypJ5++mnruet06623ktwrkm8WHooAAggggEDBCTCDr+BsqRkBBBBAAAEEEEDAqQLz5snKmEkLFrh7mNUkvA/UTZW1VR9aX8epr2rWi9frr0tNmgQH5dChQ3r11VfdS23//PPPbCu1Z/XZO+dWrFgx22s4gQACCCCAAALhLUCCL7zHj9YjgAACCCCAAAIIFKaAvTPuyJHSY49l+9QTirfSeeP0nnq4r0lIkJ4dLt1zj9wbamR7Yy5P7Nq1S6+88or17r6xOnjwYI533X333e4kYI4XcRIBBBBAAAEEwl6ABF/YDyEdQAABBBBAAAEEECgUAes9e7LX177zTraP+0at9ZD+rSW61P1uvd69T030O/vsbG/J9Ql7ee0LL7ygyZMnKzk5Ocf7nnjiCY0YMSLHaziJAAIIIIAAAs4RIMHnnLGkJwgggAACCCCAAALBFrBn7G3eLG3cKN13n7Rypd8T9qqcJqmX3tKd+lU13OdbtJBeekm65BK/ywMqyLCev3DhQmtDjuc0Y8aMHO/t1KmTHn/8cdk75HIggAACCCCAQGQJkOCLrPGmtwgggAACCCCAAAK5Edi3T3r3XcnaGVerV2d5R7pcekSj9B/dq2RrWa59JCYesxJ7MdYutfH52h03LS1Nn376qTuxt3jx4iyfn1nYpUsXd2Kvfv36mUV8RQABBBBAAIEIEyDBF2EDTncRQAABBBBAAAEEchBYskRWVk365BNZ62CzvfCIEnSr3tcX6uS+JiEhQ507r1WnTr+rY8e2eU7uHT582FoB/I6VJHxJv/32W7bPj4uL0+23364HHnhANWvWzPY6TiCAAAIIIIBAZAiQ4IuMcaaXCCCAAAIIIIAAAjkJ7NghaztaafRoKT09pyu1WVWstN4MrVQ9RUefei3fkCEnrNW7v+Z4X04nV1uzBO1NM+z36x09ejTbS0uXLq2BAwdq0KBBOjsYL/bL9kmcQAABBBBAAIFwEiDBF06jRVsRQAABBBBAAAEEgiswe7b08MPS8uWnrTdNUZqjdrpDE7VbZ+vmm6WnnpKqV5eOH8/y9Xw51pmSkuJehjtmzBh9++23OV573nnn6f7771efPn2UYG/Ly4EAAggggAACCHgJkODzwuAjAggggAACCCCAQIQI7N0rK2Mmvfdeth0+qThNUxeNU18tUCvrjXsZSle02rWTnn1Watgw21tzPLHZ2rTjrbfe0oQJE7Rz584cr73E2qVjyJAh6tq1q2Ji+NE9RyxOIoAAAgggEMEC/JQQwYNP1xFAAAEEEEAAgYgTWLXq1LS7L76QkpKy7H6yYvWMHtOr+of2q6znmksvk0aNkq680lOU6w+pqan6wnrmm2++qa+++kr27rjZHVFRUbr22mvdy3Bbt25tvc/Pld2llCOAAAIIIIAAAm4BEnx8IyCAAAIIIIAAAgg4X8BOqNk74lrvrtPJk1n295hK6Gu11ZMaoRW62HNNnTrS8OHSDTco4M0ztmzZYj12nMaPH68d9nv+cjgSExN15513qn///qpcuXIOV3IKAQQQQAABBBAwBUjwmR5ECCCAAAIIIIAAAk4TsDetuOuubJfjHlBpPaJRGq8+StNfPx7XrSs9+eSpxJ41qS7Xhz1bb8aMGXrjjTf05ZdfWnt25LxpR9OmTXX33XerS5cuio+Pz/VzuBABBBBAAAEEEMgU+OsnmMwSviKAAAIIIIAAAggg4ASBXbukDz6QrE0s9Pvvfj2yN834SDdrsEa7N83IvKB+/VOJveuukwJJ7O213uv39ddfu5N127dvz6wuy69nnHGGbrvtNvdsvbp2JpEDAQQQQAABBBDIhwAJvnzgcSsCCCCAAAIIIIBACArMnSu9+KI0Z46UlpZlA8dooLUQ90ntUQXP+QYNTiX2rNff5Xop7nFr+9zp06e7N8yYP39+ju/Wsx/UqFEjd1LvpptuUsmSJT3P5gMCCCCAAAIIIJAfARJ8+dHjXgQQQAABBBBAAIHQEnjnHen227Nt00Gdqd6aoE9kvVDv/w9rhawefVTq0CF3iT17g4zFixdr4sSJ+vDDD3X48OHMqrL8WqpUKfXo0cOd2Lv44r/e7ZflxRQigAACCCCAAAJ5ECDBlwc0bkEAAQQQQAABBBAIIQE7wTZvnqydLKSZM7Nt2I+6TLfoA23Uhe5r7CW4Q4ZIV1yR7S3GCXuTjHesBOKkSZO0bt0641xWwWWXXaZ+/fqpW7duSkhIyOoSyhBAAAEEEEAAgaAIkOALCiOVIIAAAggggAACCBSqQHKyNHasrCl00s8/Z7sU127T27pdk60/89VasXFR6mtN8HvgAalmzdO32F6CO9NKGtqz9b766qvTbphRokQJ3XLLLRo4cKAa2Gt+ORBAAAEEEEAAgUIQIMFXCMg8AgEEEEAAAQQQQCCIAvauuDdYS2ztd+3lcKxXDTXUEh1TgkqXlh4ZIA0aJFWsmMNN1qmkpCTNmjVLH3/8sb744gsdO3YsxxtcLpeuvPJK2ctv7XfsderUScWLF8/xHk4igAACCCCAAALBFCDBF0xN6kIAAQQQQAABBBAoGIF9+6Tly6UVK3RywnuKX7Uk2+cc0hnWW/Z661GNVNWLilm72kq9eknWq/CyPewknj1Tz07q2V/tJN/pjmrVqln19rJe+Xe7ypUrZ+3pYW3qwYEAAggggAACCBSBAAm+IkDnkQgggAACCCCAAALZCKSkSF9+KS1bJm3ZIm3eLK1dK1nvv8s84jM/eH09qTgt0hV6T931kbqpzbUlNeMfUtu22W+csXv3bvdMvRkzZriX39rLcU932O/Ss3fAtRN7zZo1s3bbdblvyc29p6ub8wgggAACCCCAQF4FSPDlVY77EEAAAQQQQAABBIIr8MMPsnalkFatynW9+1TWvSvuXF2lEmcVV9++0sq7pPPP968iPT3dyhsuc8/Qs5fe/vTTT/4XZVESFRWlVq1auWfqdenShQ0zsjCiCAEEEEAAAQSKVoAEX9H683QEEEAAAQQQQCByBTIyZG1He2rnW3v32wULArLYosq6WrNVokEtjbGW4Vp7W1jvvjOrOGq9r+/rr792v0vPfq/ezp07zQuyieykXuvWrdW1a1d17txZiYmJ2VxJMQIIIIAAAgggUPQCJPiKfgxoAQIIIIAAAgggEFkC9g64//63NH68tGlTrvt+QvFapb9puS7WquKXK+r2HvrgrpKqX9+sYuPGjZ5ZeguspGGy/bxcHNHR0e7NMuyk3vXXX6/y5cvn4i4uQQABBBBAAAEEil6ABF/RjwEtQAABBBBAAAEEIkcgNVVW9uzUe/Zy6PUq1dF3aqHNOk+bVFUrVE+/qrpaXhmjPn2kZzv/NVvvxIkTWrRokWbPnu2eqbfWfmdfLo9S1s4b7dq1U4cOHdy739qbZXAggAACCCCAAALhJkCCL9xGjPYigAACCCCAAALhKGAn9nbtUvKzzynO3kQjm+OwSmmEntRLuk9pOvWjaqVKp3bB7d1buuACKcNa2rt69Wr3rrX2zrXfffedAtnkwt79tmPHju4/zZs3V1xcXDatoRgBBBBAAAEEEAgPARJ84TFOtBIBBBBAAAEEEAhPgXnzlPrI44pa8qOiMtKtvW79j22qpJnqoFm6RvPURseUoDPOkKz9LNS9u6wNLqTt27fom2++0dCh8zTPqjO379KznxYTE6MWLVq4Z+nZib0aNWr4N4ISBBBAAAEEEEAgjAVI8IXx4NF0BBBAAAEEEEAgJATszTKsZbI69n/t3Qm0HFWdMPB/QkIWCGEJAQTZF9lEPgiyQ0Q4IKLixowwagYVBBH8OANuxwHnOIJjRpEzKrKjnomAwgyL38CHrApGFonI4scqm+wQIDvhq3+F7nTS7+W9l7ylu/p3z6l0ddWtqnt/t1Kv+9+3br0eUTzUInvqvXjno/Hsz66Jd9x6/lv98JpLOrcI930kflkG9yKGFT3pouhVF/GJT0Tssstzceut18fFF18XRx/9m3jwwQebd7CMJXmrbd52m1Pegjt+/Phl5LaKAAECBAgQINDeAgJ87d1+Sk+AAAECBAgQGDqBuXMj/uVfIs4+O+LZZ5cox5rFu5yWlY6N/4irh70/3jM5iifVzoy11roxpk//TXzzm7+JGTNmLGvTpnXDhg2LnXfeuQzmZS+9SZMmRT40QyJAgAABAgQIdIKAAF8ntLI6EiBAgAABAgT6UyCDedOnx/yTvhoj7/tTn/acvfYejk3j2i0/G6scuFEcF1+L3//+ujjhhNvjjTfe6NO+NtpoozKgt//++5dPv11rrbX6tL3MBAgQIECAAIGqCAjwVaUl1YMAAQIECBAg0N8C8+dHPPRQxP33R/FUi5h/2x2x4LbbY8zzj5dHGtnL470Wq8TXR3wjHthh/Rg+8Y/xzLM3x913nxwLflA8eKMPac0114zJkyeXwbwM6uXDMrLnnkSAAAECBAgQ6HQBAb5OPwPUnwABAgQIEOgsgRwv74knIu66a/H0wAMRa6wRseeeEWPHRtx7b8Sf/hRvFuPeDcun376VMqDXm6DevCLXY7FBPD58XDw8fkRcMHZ2/O6pL8ebdxTH7kNaddVVy4djvOc974n99tsv3vnOd8bw4cP7sAdZCRAgQIAAAQKdISDA1xntrJYECBAgQIBApws891zE8cdH/Od/di9x661LrOtt37i7451xdHw5HolnY9SaM2L2sFvjuRfui1hY7O6lt6Yl9tz1m5WLp2zsvvvuZTAvg3o5jt7Ikb0JKXa9P0sJECBAgAABAp0iIMDXKS3dTT0fe+yx+MEPfhBXXXVVPP744zFq1KjYbLPN4uMf/3gce+yxxY/4xa/4EgECBAgQINDeAtlrb9NNFz3htp9qUjwvN4o+gHF5jIkfj3g0Xl9QPPo204uLXnrz74gRI8og3j777FMG9fbYY48YM2ZMbzaVhwABAgQIECBAoEFAgK8Bo9Nmr7jiijjiiCNi5syZ9arPmjUrbr/99nI655xzysBfjm8jESBAgAABAm0okGPoFX/v3/z+92PYa68tdwX+FqvGAzE27o434vZ4Ie4o9lSMyld20IuYHbGgmHqR8ofEXXfdNTKgt/fee5fzq6yySi+2lIUAAQIECBAgQGBZAgJ8y9Kp8Lq7inF3DjvssJg9e3bk+DZf+cpXykGr8/20adPi7LPPjr/85S9x8MEHl8G+cePGVVhD1QgQIECAQBsIZC+8J59c9MCLuXMjNtlkUa+80aMXF764DXf+jPvib7+5N1677Z5Y/7ZLY7VZz0R3t9reFHsVwbqd4q+xVvFc2zvj3cW7uTEv/ljs8e7i3tp7infF6HzxcmRwsO8BwgzeZa+8DOZlUC9vuc0gn0SAAAECBAgQINC/AgJ8/evZNns7vhiDJ4N5eWvMNddcE7vttlu97DnmzRZbbBEnnXRSGeSbOnVqnHLKKfX1ZggQIECAAIF+FrjllogLLyw6wxU94b74xSgiYYsCeb/5TUROxRNsywdjvJ43xi5ObxZPkJ215gbx0qh1Y9zzj8T4ec+XD8F4++IsXc79v1g73hsHFoG9p4r104rpb13m6+vCDTbYIN797neXPfMyqLfjjjsaQ6+viPITIECAAAECBJZDQIBvOdDafZPp06fHzTffXFbjyCOPXCK4V6vbiSeeGOeff37cd999ccYZZ8TXvvY1H9BrOF4JECBAgEB/CGSPvF//OuLb347IAF8t/fzntbkeX4cV+1jlhcdjlXi8x7yZ4bFi+lIxXRbFAzfip8W04imDet8vbgHOW2/XX3/9Fd+hPRAgQIAAAQIECPRZQICvz2Ttv8Hll19er8SUKVPq840zw4cPj09+8pPlrbsvv/xyXH/99XHAAQc0ZjFPgAABAgQIdCWwsHh0bI5v+8orxb2tLy9+ffbZiL/+tT69+eCDMax4wNVgpAwfnl1MFxfTnD4ecPz48bH99tvHdtttV77m/LbbbhtrrrlmH/ckOwECBAgQIECAwEAJCPANlGwL7/eWt3oJ5Lg4O+20U7clzbFyaum3v/2tAF8NwysBAgQIEGgUyPHwzjsvFt54Uyy8/c5Y6ZEicJdBvh5Sd+Pi9bBZvFpkyBt11+0mYwbwcty8+4rpnmK6rJjuLaaeUo6Nt/XWWzcF87JX3rDiVmCJAAECBAgQIECgdQUE+Fq3bQasZHnbbaZ8Om6Owdddesc73lFfVdumvsAMAQIECBBoY4HTTrs2rrtuevzv2y+KmcNHxX+seXY8tOq68eCYtYvg2Yh4s7j1dWERpHvjjQWxYMG8mDt3XsybN7+Y5sWwebNj4zkvxFbzXor133g1Tpn/YCkxvPg3p/5MTxQ7u66Ybiymh4vpoWLKZZny2bObFdPmxbROMWVfwPwL/0gxLSu8mA/Xyr/xW221Vfma89k7r6fPBcVuJQIECBAgQIAAgRYV6D6606IFVqwVE5gzZ048//zz5U5yzJxlpTXWWCOyl9/rxYDej/fhFqInnqh99eh6708//XR9RT7oIyepegJ5rtVS43xtmVcCfRVoPI8a5/u6H/kJpMBPf3pZPHLvj+L/FPMr5YIX/5T/loGxDKJlD7gMkuW6nFYuptWLKW9KzVHmRhZTf6X7ix2dXkw58t7WxXRoMeVfyt8U06LQYTHTRcpefDPempZevd5668XGG28cG220UX3K91tuuWXkuq565M2fPz9ykpZPoPG61Di/fHuzFYHidnqfpZwG/SzgnOpn0Bbbne/VLdYgQ1AcAb4hQB/KQ776at7YsyjlL/g9pVqA77XXXuspa33929/e07P76lnjpptuigkTJixeYK6SAtnOEoH+FHBO9admZ+4rf7zatqh6GdxrIMgeeFu8NTUsXu7ZDBIWI/FFMRJfvFRMf11qygDeH4upeNxGmboL2L21unwZOXJkrL766uUYeBMnToyupsyzdMreh/fcc085Lb3O+/4VcI3qX097i/IzMwcC/SngOtWfmq2xr1pHntYojVIMhYAA31CoD+ExG3+1WXnl7I+w7JTj8WTya8CynawlQIAAgfYSyB5sOwxAkacW+/xFMf2tmDKolz+P1YJ3xWw9rbTSyFh55VExevToWHf0ysXrqMi/uTmNGzcuVltttSWmfNBF9qzPwF7++NZVD7z6zs0QIECAAAECBAh0nIAAX4c1eX6RqKX8Jb+nNDcHDi/SmDFjespaX9/T7bx5i+4uu+xS5t97772jp1uF6zs201YCGUyu/TKY7dx47rVVRRS2ZQScUy3TFJUoyO67PxUPvPJInDr76diuGEdv+4Wvx2bF69I9+pZV2WfHrh6vjV4l1p71csxdeXT8n/f9XcTO/yuOWn1kjB8/JsaOHVsG4/JvaAblaq+5fFlj4C7rmNa1roBrVOu2TbuWzDnVri3XuuV2TrVu2/RHyXoaKqs/jmEfrS0gwNfa7dPvpcteAbXUm9tu8xamTL25nbe2374E7PLLTl+Ch7VjeG0vgQzuaef2arNWL61zqtVbqPXL9/Off77onf7puOaaa8rCvv2AAxYF9+4tnjd7993FIHjFKHjDixt2VypCfjnlQ6mK3nPFfbERa68dsc02MbHoZTfxrarmX9cjWr/aSjhIAq5RgwTdQYdxTnVQYw9SVZ1TgwQ9iIfxfWsQsVv0UAJ8LdowA1WsvJCvtdZa8cILL0RPEf6XXnqpfMBGlqUv4+oNVNntlwABAgQIDKhA9lbfaadF04AeyM4JECBAgAABAgQI9K9AjiUtdZjANkWvg0wPPvhgLFiwoNva339/PtdvUdp663yun0SAAAECBAgQIECAAAECBAgQINBqAgJ8rdYig1CePffcszxK3n57xx13dHvEG2+8sb5ujz32qM+bIUCAAAECBAgQIECAAAECBAgQaB0BAb7WaYtBK8mHPvSh+rHOP//8+nzjzMKFC+Oiiy4qF+UT+yZPnty42jwBAgQIECBAgAABAgQIECBAgECLCAjwtUhDDGYx8gm2e+21V3nIc889N2699damw0+dOjXuu+++cvnxxx8fI0eObMpjAQECBAgQIECAAAECBAgQIECAwNALeMjG0LfBkJTgjDPOiLztdvbs2XFA8eTAr371q2UvvXw/bdq0+MlPflKWa8stt4wTTzxxSMrooAQIECBAgAABAgQIECBAgAABAj0LCPD1bFTJHDvuuGP84he/iCOOOCJmzpxZBviWrmgG96666qoYN27c0qu8J0CAAAECBAgQIECAAAECBAgQaBEBt+i2SEMMRTEOOeSQmDFjRnzpS1+KDOaNHTs2cry9nXfeOU4//fS46667YvPNNx+KojkmAQIECBAgQIAAAQIECBAgQIBALwX04OslVFWzbbTRRvHv//7v5VTVOqoXAQIECBAgQIAAAQIECBAgQKDKAnrwVbl11Y0AAQIECBAgQIAAAQIECBAgQKDyAgJ8lW9iFSRAgAABAgQIECBAgAABAgQIEKiygABflVtX3QgQIECAAAECBAgQIECAAAECBCovIMBX+SZWQQIECBAgQIAAAQIECBAgQIAAgSoLCPBVuXXVjQABAgQIECBAgAABAgQIECBAoPICAnyVb2IVJECAAAECBAgQIECAAAECBAgQqLKAAF+VW1fdCBAgQIAAAQIECBAgQIAAAQIEKi8gwFf5JlZBAgQIECBAgAABAgQIECBAgACBKgsI8FW5ddWNAAECBAgQIECAAAECBAgQIECg8gICfJVvYhUkQIAAAQIECBAgQIAAAQIECBCosoAAX5VbV90IECBAgAABAgQIECBAgAABAgQqLyDAV/kmVkECBAgQIECAAAECBAgQIECAAIEqCwjwVbl11Y0AAQIECBAgQIAAAQIECBAgQKDyAgJ8lW9iFSRAgAABAgQIECBAgAABAgQIEKiygABflVtX3QgQIECAAAECBAgQIECAAAECBCovIMBX+SZWQQIECBAgQIAAAQIECBAgQIAAgSoLCPBVuXXVjQABAgQIECBAgAABAgQIECBAoPICAnyVb2IVJECAAAECBAgQIECAAAECBAgQqLKAAF+VW1fdCBAgQIAAAQIECBAgQIAAAQIEKi8gwFf5JlZBAgQIECBAgAABAgQIECBAgACBKgsI8FW5ddWNAAECBAgQIECAAAECBAgQIECg8gICfJVvYhUkQIAAAQIECBAgQIAAAQIECBCosoAAX5VbV90IECBAgAABAgQIECBAgAABAgQqLyDAV/kmVkECBAgQIECAAAECBAgQIECAAIEqCwjwVbl11Y0AAQIECBAgQIAAAQIECBAgQKDyAgJ8lW9iFSRAgAABAgQIECBAgAABAgQIEKiygABflVtX3QgQIECAAAECBAgQIECAAAECBCovIMBX+SZWQQIECBAgQIAAAQIECBAgQIAAgSoLCPBVuXXVjQABAgQIECBAgAABAgQIECBAoPICAnyVb2IVJECAAAECBAgQIECAAAECBAgQqLKAAF+VW1fdCBAgQIAAAQIECBAgQIAAAQIEKi8gwFf5JlZBAgQIECBAgAABAgQIECBAgACBKgsI8FW5ddWNAAECBAgQIECAAAECBAgQIECg8gICfJVvYhUkQIAAAQIECBAgQIAAAQIECBCosoAAX5VbV90IECBAgAABAgQIECBAgAABAgQqLyDAV/kmVkECBAgQIECAAAECBAgQIECAAIEqCwjwVbl11Y0AAQIECBAgQIAAAQIECBAgQKDyAgJ8lW9iFSRAgAABAgQIECBAgAABAgQIEKiygABflVtX3QgQIECAAAECBAgQaFmBW/56yzLL9sYbb0ROCxYsWCJfT9stkdkbAgQIEOgIAQG+jmhmlSRAgAABAgQIECBAoJUETrnhlNjr/L3i9FtO77JYr776ahx++OHxkY98JN72trdFvs+U+XO73F4iQIAAAQI1gRG1Ga8ECBAgQIAAAQIECBAgMPAC2QPv1BtPLQ/05eu+XL6evOfJSxz44osvjjlz5pTLXnvttZg2bVq8uPWLUcuf27930/fGnhvuucR23hAgQIBAZwrowdeZ7a7WBAgQIECAAAECBAgMkUAG5U7b77T60TNot3RPvnPPPbe+PmdO/b+n1oN7+T63F9xLCYkAAQIEUkCAz3lAgAABAgQIECBAgACBQRbIHnvdBfnuuOOO+OMf/7i4RHtEPLnNk/X3ud3SPf7qK80QIECAQEcKCPB1ZLOrNAECBAgQIECAAAECQy2QQbpdX921XozsyTf+4PGx33771ZdFEdyL/Re/XW36anHm350ZG2ywQTkdc8wxi1eaI0CAAIGOFRDg69imV3ECBAgQIECAAAECBIZSYNasWTHjRzMirl1cipm7zIxXtntl0YKlgnuZb+bVM+PJJ5+sTxdddFHkfiQCBAgQ6GwBAb7Obn+1J0CAAAECBAgQIEBgiATuueeemD17dsRviwI0BPnKHnsnFcsaeu6V6zPfUimDe7kfiQABAgQ6W0CAr7PbX+0JECBAgAABAgQIEBgigV122SWuuOKKGD9+fHOQb2xDoTL410VwL7e78sorI/cjESBAgEBnCwjwdXb7qz0BAgQIECBAgAABAkMocPDBB8cf/vCH2GabbRYF8Za+2zbfdxHcy/y53fve974hLL1DEyBAgECrCAjwtUpLKAcBAgQIECBAgAABAh0psMUWW8Rtt90W2x61bURjz73UyPc5Fl9D+vCHP1zmz+0kAgQIECCQAgJ8zgMCBAgQIECAAAECBAgMscAP7/5h/Hm9Py8uRWNPvv2LxQ1BvilTpsS4ceMW5zVHgAABAh0vIMDX8acAAAIECBAgQIAAAQIEhlLg9FtOjy9f9+XFRcgx975TTPlaSw1BvksuuaS21CsBAgQIECgFRnAgQIAAAQIECBAgQIAAgaER6DK4Vxtzr/aawb1Mb71efvnlMXfu3Bg1atSi5f4lQIAAgY4X0IOv408BAAQIECBAgAABAgQIDIXAMoN7RYHGjh0bh298eIy+efTi4hVBvpnbz4xrr23s3rd4tTkCBAgQ6EwBAb7ObHe1JkCAAAECBAgQIEBgCAWWDu7t+MKOSzwtN5+SO3Xq1PjYxz4W0783PSbOmLi4tEWQ75+v+efF780RIECAQMcLCPB1/CkAgAABAgQIECBAgACBwRS45a+3LDHm3mn7nRbfOuhbMXHioiBePiX3+uuvj/XWW68s1uabbx4PXvBgbPtU8ZTdt9Kda90ZuR+JAAECBAikgACf84AAAQIECBAgQIAAAQKDKLDnhnvGP++zqAdeBvdO3vPkOOigg+LPf/5znHfeeXHppZc2PSU3n5o740cz4qOrf7QsaW6f+5EIECBAgEAKeMiG84AAAQIECBAgQIAAAQKDLHDKvqfEezd97xJBugkTJsSUKVO6Lcnw4cPjkuMvKXvuCe51y2QFAQIEOlJAD76ObHaVJkCAAAECBAgQIEBgqAWWN0i3vNsNdX0dnwABAgQGTkCAb+Bs7ZkAAQIECBAgQIAAAQIECBAgQIDAgAsI8A04sQMQIECAAAECBAgQIECAAAECBAgQGDgBAb6Bs7VnAgQIECBAgAABAgQIECBAgAABAgMuIMA34MQOQIAAAQIECBAgQIAAAQIECBAgQGDgBAT4Bs7WngkQIECAAAECBAgQIECAAAECBAgMuIAA34ATOwABAgQIECBAgAABAgQIECBAgACBgRMYMXC7tmcCXQssWLCgvuLpp5+uz5uplsDs2bPj+eefLyv1xBNPxJgxY6pVQbUZdAHn1KCTV/6AzqnKN/GgVtD5NKjcHXEw51RHNPOgVtI5Najcg36wxu/Wjd+5B70gDjhkAgJ8Q0bfuQd+7rnn6pXfZZdd6vNmCBAgQIAAAQIECBAgQIAAgRUTyO/cG2+88YrtxNZtJ+AW3bZrMgUmQIAAAQIECBAgQIAAAQIECBAgsFhg2JtFWvzWHIGBF5gzZ0786U9/Kg+09tprx4gROpIOvPrgHyG7iNd6aE6fPj3WW2+9wS+EI1ZKwDlVqeZsico4p1qiGSpTCOdTZZqyZSrinGqZpqhMQZxTlWnKLiuSt+XW7pbbfvvtY/To0V3ms7C6AiIr1W3blq1ZXmgmTZrUsuVTsP4XyODeBhts0P87tseOFXBOdWzTD1jFnVMDRtuRO3Y+dWSzD2ilnVMDytuRO3dOVbPZ3ZZbzXbtba3cottbKfkIECBAgAABAgQIECBAgAABAgQItKCAAF8LNooiESBAgAABAgQIECBAgAABAgQIEOitgABfb6XkI0CAAAECBAgQIECAAAECBAgQINCCAgJ8LdgoikSAAAECBAgQIECAAAECBAgQIECgtwICfL2Vko8AAQIECBAgQIAAAQIECBAgQIBACwoI8LVgoygSAQIECBAgQIAAAQIECBAgQIAAgd4KCPD1Vko+AgQIECBAgAABAgQIECBAgAABAi0oMOzNIrVguRSJAAECBAgQIECAAAECBAgQIECAAIFeCOjB1wskWQgQIECAAAECBAgQIECAAAECBAi0qoAAX6u2jHIRIECAAAECBAgQIECAAAECBAgQ6IWAAF8vkGQhQIAAAQIECBAgQIAAAQIECBAg0KoCAnyt2jLKRYAAAQIECBAgQIAAAQIECBAgQKAXAgJ8vUCShQABAgQIECBAgAABAgQIECBAgECrCgjwtWrLKBcBAgQIECBAgAABAgQIECBAgACBXggI8PUCSRYCBAgQIECAAAECBAgQIECAAAECrSogwNeqLaNcBAgQIECAAAECBAgQIECAAAECBHohIMDXCyRZCBAgQIAAAQIECBAgQIAAAQIECLSqgABfq7aMchEgQIAAAQIECBAgQIAAAQIECBDohYAAXy+QZCFAoPcCr732Wtx0003x3e9+Nz7+8Y/HJptsEsOGDSunjTfeuPc7eivnPffcE0cddVRsttlmMWbMmFh77bVjr732ih//+MexYMGCPu/PBtUU2HfffevnWe186+61mgJq1VeBxx57LE488cR4xzveEausskqsueaaMWnSpPi3f/u3mDVrVl93J3+HCnR3nVl6eV6jJALPPvtsXHnllfGNb3wjDjrooJgwYUL9b9enP/3pPgP9+te/jkMPPTQ22GCDGDVqVPma73O51BkC/XFOXXDBBfXzcOlr19LvM69EgEDrCoxo3aIpGQEC7ShwyCGHxA033NAvRT/77LPjC1/4QsybN6++vzlz5sQtt9xSTueff35cddVV5QfkegYzBAgQ6EHgiiuuiCOOOCJmzpxZz5lBvdtvv72czjnnnPLasvnmm9fXmyFAgMCKCqyzzjoruoty+4ULF8bnPve5OPfcc5fY35NPPhk5XX755fGZz3wmzjrrrBg+XH+OJZAq9qa/zqmKsagOgY4VEODr2KZXcQIDI/Dmm2/Wd5w9Ynbeeef43e9+F9mzry/p6quvjqOPPjryQ2x+ePna174W7373u+PFF1+MDPz96le/iunTp5e/XGdAcaWVVurL7uWtqECebxn4lQh0J3DXXXfFYYcdFrNnz45VV101vvKVr8TkyZPL99OmTSuvL3/5y1/i4IMPLoN948aN625XlhOoC3z+85+PY445pv5+6ZnsJSoRaBTYcMMNyx7E11xzTePiXs3nZ6JacG/HHXeMk046qbzT4aGHHorvfOc7kde5/KEi73r413/9117tU6b2F1iRc6pW+//5n/+Jt73tbbW3Ta/ZW1QiQKB1BQT4WrdtlIxAWwp84hOfKG+pzVvdar1f8tbcvgT45s+fH8cdd1wZ3FtttdXit7/9bfnBtQZy4IEHxrHHHhs//OEPy558P/3pT2N5bm2p7c9rdQTyS/R2221XnQqpSb8LHH/88WUwb8SIEZFfrHfbbbf6Md7znvfEFltsUX5ZziDf1KlT45RTTqmvN0OgO4GJEye69nSHY3ldIG/Nzc9HOeWPl48++mg5lEk9Qy9m8tqUw6Bkyh+1cliUHMIkU+73Ax/4QOyzzz7lDxQ55MA//uM/1j+PlZn8UymB/jinGkG23HLLWJ4hdRr3YZ4AgaET0Gd76OwdmUAlBfKWkb//+79foQ+Tl112WTz88MOlT/auyfH3lk75oXWNNdYoF+e8RIAAgZ4EstfvzTffXGY78sgjlwju1bbNcfm23nrr8u0ZZ5wR+YODRIAAgf4QOPXUU+P9739/Gdxb3v19//vfr49BfOaZZ9aDe7X9jR07NnJ5phyr+Hvf+15tldcKCvTHOVVBFlUi0LECAnwd2/QqTqB1BXLsmFrqrmdefoDNh3hkuvfeeyN/0ZYIECCwLIHGa8uUKVO6zJrjVX3yk58s17388stx/fXXd5nPQgIECAy2QA6D8l//9V/lYfMBQbvuumuXRcjlW221Vbku8zcOn9LlBhYSIECAQCUEBPgq0YwqQaBaAvkQjUz54XTdddfttnJ5C0ot5W28EgECBJYlULu25K3cO+20U7dZXVu6pbGCAIEhFHjkkUfiqaeeKkvQeJ3qqki19fnQjbwVWCJAgACB6gsI8FW/jdWQQFsJ5Fh9jz/+eFnm/HV6Walx/X333besrNZ1iMD9999fPoxl9dVXj9GjR0cOBv3BD34wLrroIrdadsg5sKxq1q4TOT5ojsHXXXJt6U7G8u4ELrnkkthmm20ie5fng1lyLMdPfepTeoB2B2b5cgnkHQu11Hidqi1rfG1cX7v2Na43T6Argezdng/ZWHnllWPChAllL9Gvf/3r5dOZu8pvGQECrSUgwNda7aE0BDpe4Iknnqgb9PSkrre//e31vLWgYH2BmY4UeOaZZ8qnK7/yyisxd+7c8gPpf//3f5dftN/1rneFLzkdeVqUlZ4zZ048//zz5XxP15Yc37P21FPXls49Z/pS8wy85PUln86cP1Q9+OCD5Q8L+eCWQw89NPKaJBFYUQGfkVZU0PY9Cdxwww3x9NNPlz+KvvDCC/H73/8+vvWtb5Vja5911lk9bW49AQJDLND9z9dDXDCHJ0CgMwVeffXVesVXXXXV+nxXM7Uv4LmuL0/p7WpflrW3QI6btt9++8X73ve+2GGHHWKttdaKPJfuvPPOyA+k+cU7v4BPnjy5DABuuOGG7V1hpe+zQF+uLbnzvL68/vrrri19lu6sDbLHXj61NK8/2WMq/24999xzceONN8aPf/zjyC/IOfZj9iS+9tprY+TIkZ0FpLb9KtCX65jPSP1KX/mdbbrppvHhD3+4fPhU7Qf0fODdL3/5y7j00ksjfyQ7+uijY9iwYZEP1JMIEGhNAQG+1mwXpSLQsQL5AaKW8vaAZaVRo0bVV2evCalzBX71q19F3pa7dNprr73imGOOic9+9rNx4YUXRvbwO+GEEyLzS50l0JdrS8rUri+uLZ11nvS1tjm+WVfXnv333z+OO+64OOigg+Kuu+4qA34/+tGP4otf/GJfDyE/gbpAX65jtWtYbuw6Vic004VA9jLOIQUyeNeYJk2aFIcddlhceeWVZfAvnyr/pS99qfxRY1ljZDfuwzwBAoMr4BbdwfV2NAItIZB/wFd0uuCCCwakLjluWi3NmzevNtvla96CWUtjxoypzXptUYEVPedy++7Ou66+YNcYssfMOeecU3+i4GWXXWYsmRpOB7325dqSLLXri2tLB50ky1HVZV171llnnbLnS63X3plnnrkcR7AJgcUCfbmO1a5hubXr2GJDc80C48ePbwruNeZ6//vfH9/4xjfKRbNmzYpzzz23cbV5AgRaSECAr4UaQ1EIEIhycPKaQ0+33ebtc7XU0+28tXxeO1MgH6hw5JFH1iuft89JnSWQDz6opZ6uLZmvdn1xbampeV0egbztLXvzZcpx+WpPQF2efdmGQF+uY7VrWKq5jjl3VlQgb8vNH1oz+Qy1opq2JzBwAm7RHThbeybQsgL98aCB9dZbb0Dqt/7669f32ziYdH1hw0zj4Pe18UIaVpttMYGhPu/yCZe1lLfVSZ0lkD1fcmzGHBOtp2vLSy+9VA/wubZ01nkyELXNa8/VV19d7jqvPfmESonA8gg0PiCop+uYz0jLI2yb7gQmTpxY/g3Nh1X5DNWdkuUEhl5AgG/o20AJCAy6QA4E3qopf53OL9T5wfT+++9fZjEb12+99dbLzGvl0AsM9XlX++V56CWUYKgEMtBy8803lz2pFixYENmzs6vk2tKVimXLK+Das7xytltaoPGHqsbr1NL58n3jep+RuhKyrK8CrmV9FZOfwOALuEV38M0dkQCBHgT23HPPMscDDzwQf/vb37rN3XiLwB577NFtPisIpEA+RbeW9KCpSXTWa+3akreu3XHHHd1W3rWlWxorlkPAtWc50GzSpcAmm2xS7wHaeJ3qKvNNN91ULs47IzbeeOOuslhGoNcC+XTw7L2XyWeoXrPJSGDQBQT4Bp3cAQkQ6EngQx/6UD1Ldw9VyEF+L7744jJf/qK95ZZb1rcxQ2Bpgeytdd5559UX77333vV5M50j0HhtOf/887us+MKFC+Oiiy4q1+UDFCZPntxlPgsJ9EbgkUceiWuvvbbMutlmm0XjMBS92V4eAo0C2YPqgx/8YLkoe+jddtttjavr87m81oMv8+t5Vacxs5wCP/nJT+LNN98st95nn32Wcy82I0BgoAUE+AZa2P4JEOizwKGHHho5MHmmb3/72/HQQw817eOf/umfIsfJypTzUucKXH/99fHyyy93CzB//vz4zGc+E7UxAA855JDyNvBuN7CisgK77LJL7LXXXmX98imAt956a1Ndp06dWj9Xjj/++Kg9AbUpowUdL3DFFVdE/njQXXrmmWfiIx/5SNSeCH/MMcd0l9VyAr0WOOGEE2KllVYq8x933HExe/bsJbbN97k8Uw5DkPklAt0JPProo3HXXXd1t7pcfuWVV8Y3v/nNcj6fyDxlypRl5reSAIGhE+h68JmhK48jEyDQ5gL5lMBbbrlliVrUnliZr0v3yDvwwANj3XXXXSJ/fqE+88wzIwMxM2fOjLz99utf/3rkl/MM6p199tnxy1/+stwmb7n7h3/4hyW296azBC688ML4wAc+UE777rtvbLXVVrHaaqtFnm95G2b+6ly7RS4HiT7jjDM6C0htlxDI9s9rSn4JPuCAA+KrX/1q2Usv30+bNq08X3KD7BV84oknLrGtNwQaBTKIkj8gZBBvt912K2+DzC+/eRvbDTfcEGeddVb9lrb8W3Xsscc2bm6+AwXy81F+Tqql2i2P+T6XL/0Z6dOf/nQta/01r035w+Zpp50Wt99+e3k9O/nkkyN7iOYPoqeffno9YJP5tthii/q2ZqonsKLnVAb4sqd6XsPyc/cOO+wQ+Vkp08MPPxyXXnppOdV67333u9/VE7l6p5EaVUhgWPGfdVFf2wpVSlUIEBg6gfxw2pdf9rL3VQZlukoZyPvCF75Q7/2wdJ4M+F111VUxYcKEpVd530EC+QUog3w9pe23374M4DQOUt7TNtZXUyB7Xh1xxBHlDwhd1TC/QOe1ZfPNN+9qtWUESoEc1+yxxx7rUSMDgOecc07kLd9SZwv09u9VTam7r2k5lMBnP/vZJYaeqG1Tez3yyCPLHyyGDx9eW+S1ggIrek7ljxG9GYpi7Nix8b3vfS8+97nPVVBRlQhUR0APvuq0pZoQqJxAfnjNXxR/8IMfxHXXXRdPPfVUrLLKKpFPgzv88MPL2y67ewpm5TBUqFuB7Lnwrne9q7zdMnvq5UDQL774YowaNSrWWWed2HnnneOjH/1o5K3ftduaut2ZFR0hkL0UZsyYUfbmzEDeE088ESuvvHIZ0PvYxz5W/rCQX2YkAssSyB8W8kEHeat39nTJ3ljZ63zVVVcthwHYfffd41Of+lT5d2xZ+7GOQF8FMmiXwwxk8Dh7qf/hD38oz7/8wXPSpElx1FFHxUEHHdTX3crfgQI77bRT/OxnPyuvY9kj9Omnny7PpRx+YI011ohtt9029ttvv/Izd61nXwcyqTKBthHQg69tmkpBCRAgQIAAAQIECBAgQIAAAQIECDQL6LPdbGIJAQIECBAgQIAAAQIECBAgQIAAgbYREOBrm6ZSUAIECBAgQIAAAQIECBAgQIAAAQLNAgJ8zSaWECBAgAABAgQIECBAgAABAgQIEGgbAQG+tmkqBSVAgAABAgQIECBAgAABAgQIECDQLCDA12xiCQECBAgQIECAAAECBAgQIECAAIG2ERDga5umUlACBAgQIECAAAECBAgQIECAAAECzQICfM0mlhAgQIAAAQIECBAgQIAAAQIECBBoGwEBvrZpKgUlQIAAAQIECBAgQIAAAQIECBAg0CwgwNdsYgkBAgQIECBAgAABAgQIECBAgACBthEQ4GubplJQAgQIECBAgAABAgQIECBAgAABAs0CAnzNJpYQIECAAAECBAgQIECAAAECBAgQaBsBAb62aSoFJUCAAAECBAgQIECAAAECBAgQINAsIMDXbGIJAQIECBAgQIAAAQIECBAgQIAAgbYREOBrm6ZSUAIECBAgQIAAAQIECBAgQIAAAQLNAgJ8zSaWECBAgAABAgQIECBAgAABAgQIEGgbAQG+tmkqBSVAgAABAgQIECBAgAABAgQIECDQLCDA12xiCQECBAgQIECAAAECBAgQIECAAIG2ERDga5umUlACBAgQIECAAAECBAgQIECAAAECzQICfM0mlhAgQIAAAQIECBAgQIAAAQIECBBoGwEBvrZpKgUlQIAAAQIECBAgQIAAAQIECBAg0CwgwNdsYgkBAgQIECBAgAABAgQIECBAgACBthEQ4GubplJQAgQIECBAgAABAgQIECBAgAABAs0CAnzNJpYQIECAAAECBAgQIECAAAECBAgQaBsBAb62aSoFJUCAAAECBAgQIECAAAECBAgQINAsIMDXbGIJAQIECBAgQIAAAQIECBAgQIAAgbYREOBrm6ZSUAIECBAgQIAAAQIECBAgQIAAAQLNAgJ8zSaWECBAgAABAgQIECBAgAABAgQIEGgbAQG+tmkqBSVAgAABAgQIECBAgAABAgQIECDQLCDA12xiCQECBAgQIECAAAECBAgQIECAAIG2ERDga5umUlACBAgQIECAAAECBAgQIECAAAECzQICfM0mlhAgQIAAAQIECBAgQIAAAQIECBBoGwEBvrZpKgUlQIAAAQIECBAgQIAAAQIECBAg0CwgwNdsYgkBAgQIECBAgAABAgQIECBAgACBthEQ4GubplJQAgQIECBAgAABAgQIECBAgAABAs0CAnzNJpYQIECAAAECBAgQIECAAAECBAgQaBsBAb62aSoFJUCAAAECBAgQIECAAAECBAgQINAsIMDXbGIJAQIECBAgQIAAAQIECBAgQIAAgbYREOBrm6ZSUAIECBAgQIAAAQIECBAgQIAAAQLNAgJ8zSaWECBAgAABAgQIECBAgAABAgQIEGgbAQG+tmkqBSVAgAABAgQIECBAgAABAgQIECDQLCDA12xiCQECBAgQIECAAAECBAgQIECAAIG2ERDga5umUlACBAgQIECAAAECBAgQIECAAAECzQICfM0mlhAgQIAAAQIECBAgQIAAAQIECBBoGwEBvrZpKgUlQIAAAQIECBAgQIAAAQIECBAg0CwgwNdsYgkBAgQIECBAgAABAgQIECBAgACBthEQ4GubplJQAgQIECBAgAABAgQIECBAgAABAs0CAnzNJpYQIECAAAECBAgQIECAAAECBAgQaBsBAb62aSoFJUCAAAECBAgQIECAAAECBAgQINAsIMDXbGIJAQIECBAgQIAAAQIECBAgQIAAgbYREOBrm6ZSUAIECBAgQIAAAQIECBAgQIAAAQLNAgJ8zSaWECBAgAABAgQIECBAgAABAgQIEGgbAQG+tmkqBSVAgAABAgQIECBAgAABAgQIECDQLCDA12xiCQECBAgQIECAAAECBAgQIECAAIG2ERDga5umUlACBAgQIECAAAECBAgQIECAAAECzQICfM0mlhAgQIAAAQIECBAgQIAAAQIECBBoGwEBvrZpKgUlQIAAAQIECBAgQIAAAQIECBAg0CwgwNdsYgkBAgQIECBAgAABAgQIECBAgACBthEQ4GubplJQAgQIECBAgAABAgQIECBAgAABAs0C/x8X78RZL+fPogAAAABJRU5ErkJggg==\" 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\" width=\"636\">"
       ]
      },
-     "metadata": {},
-     "output_type": "display_data"
+     "metadata": {}
     },
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       "New LM\n"
      ]
     }
    ],
-   "source": [
-    "%matplotlib notebook\n",
-    "%matplotlib notebook\n",
-    "main()"
-   ]
+   "metadata": {
+    "scrolled": false
+   }
   }
  ],
  "metadata": {
@@ -1578,4 +808,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file

From 4fe851cc1d1f2e5d21684e24267d78c5e0299e36 Mon Sep 17 00:00:00 2001
From: Geonhee <gunhee6392@gmail.com>
Date: Mon, 6 Sep 2021 20:46:28 +0900
Subject: [PATCH 0523/1080] Correct typos (#542)

* Update cubic_spline_planner.py

* Update rear_wheel_feedback.py
---
 PathPlanning/CubicSpline/cubic_spline_planner.py        | 2 +-
 PathTracking/rear_wheel_feedback/rear_wheel_feedback.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/CubicSpline/cubic_spline_planner.py b/PathPlanning/CubicSpline/cubic_spline_planner.py
index 7ec01d1f4e..d2ef8622ad 100644
--- a/PathPlanning/CubicSpline/cubic_spline_planner.py
+++ b/PathPlanning/CubicSpline/cubic_spline_planner.py
@@ -195,7 +195,7 @@ def main():  # pragma: no cover
     import matplotlib.pyplot as plt
     x = [-2.5, 0.0, 2.5, 5.0, 7.5, 3.0, -1.0]
     y = [0.7, -6, 5, 6.5, 0.0, 5.0, -2.0]
-    ds = 0.1  # [m] distance of each intepolated points
+    ds = 0.1  # [m] distance of each interpolated points
 
     sp = Spline2D(x, y)
     s = np.arange(0, sp.s[-1], ds)
diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
index 52b4a11a0b..ff72454f34 100644
--- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
+++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
@@ -12,7 +12,7 @@
 from scipy import interpolate
 from scipy import optimize
 
-Kp = 1.0  # speed propotional gain
+Kp = 1.0  # speed proportional gain
 # steering control parameter
 KTH = 1.0
 KE = 0.5

From a2fc9f914ade3ecb6cda7e687acb39b3f8fc2e46 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 16 Sep 2021 21:10:36 +0900
Subject: [PATCH 0524/1080] Bump pandas from 1.3.2 to 1.3.3 (#543)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.3.2 to 1.3.3.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.3.2...v1.3.3)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 55d3e523fd..7363658a5f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.2
 scipy == 1.7.1
-pandas == 1.3.2
+pandas == 1.3.3
 matplotlib == 3.4.3
 cvxpy == 1.1.15

From acc3604f738f2c47942f4e06f99c02b896bd3ae0 Mon Sep 17 00:00:00 2001
From: szaguldo-kamaz <86873213+szaguldo-kamaz@users.noreply.github.com>
Date: Sun, 3 Oct 2021 16:40:44 +0900
Subject: [PATCH 0525/1080] Limit play area in RRT (#540)

* add support for limiting the play area

* plot the play area

* Update rrt.py

* fix param list

* fix code style

* fix more code style

* fix code style even more
---
 PathPlanning/RRT/rrt.py | 46 ++++++++++++++++++++++++++++++++++++++---
 1 file changed, 43 insertions(+), 3 deletions(-)

diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py
index 834fb24e39..dfbd4b1b7f 100644
--- a/PathPlanning/RRT/rrt.py
+++ b/PathPlanning/RRT/rrt.py
@@ -32,6 +32,15 @@ def __init__(self, x, y):
             self.path_y = []
             self.parent = None
 
+    class AreaBounds:
+
+        def __init__(self, area):
+            self.xmin = float(area[0])
+            self.xmax = float(area[1])
+            self.ymin = float(area[2])
+            self.ymax = float(area[3])
+
+
     def __init__(self,
                  start,
                  goal,
@@ -40,7 +49,9 @@ def __init__(self,
                  expand_dis=3.0,
                  path_resolution=0.5,
                  goal_sample_rate=5,
-                 max_iter=500):
+                 max_iter=500,
+                 play_area=None
+                 ):
         """
         Setting Parameter
 
@@ -48,12 +59,17 @@ def __init__(self,
         goal:Goal Position [x,y]
         obstacleList:obstacle Positions [[x,y,size],...]
         randArea:Random Sampling Area [min,max]
+        play_area:stay inside this area [xmin,xmax,ymin,ymax]
 
         """
         self.start = self.Node(start[0], start[1])
         self.end = self.Node(goal[0], goal[1])
         self.min_rand = rand_area[0]
         self.max_rand = rand_area[1]
+        if play_area is not None:
+            self.play_area = self.AreaBounds(play_area)
+        else:
+            self.play_area = None
         self.expand_dis = expand_dis
         self.path_resolution = path_resolution
         self.goal_sample_rate = goal_sample_rate
@@ -76,7 +92,8 @@ def planning(self, animation=True):
 
             new_node = self.steer(nearest_node, rnd_node, self.expand_dis)
 
-            if self.check_collision(new_node, self.obstacle_list):
+            if self.check_if_outside_play_area(new_node, self.play_area) and \
+               self.check_collision(new_node, self.obstacle_list):
                 self.node_list.append(new_node)
 
             if animation and i % 5 == 0:
@@ -163,6 +180,15 @@ def draw_graph(self, rnd=None):
         for (ox, oy, size) in self.obstacle_list:
             self.plot_circle(ox, oy, size)
 
+        if self.play_area is not None:
+            plt.plot([self.play_area.xmin, self.play_area.xmax,
+                      self.play_area.xmax, self.play_area.xmin,
+                      self.play_area.xmin],
+                     [self.play_area.ymin, self.play_area.ymin,
+                      self.play_area.ymax, self.play_area.ymax,
+                      self.play_area.ymin],
+                     "-k")
+
         plt.plot(self.start.x, self.start.y, "xr")
         plt.plot(self.end.x, self.end.y, "xr")
         plt.axis("equal")
@@ -186,6 +212,18 @@ def get_nearest_node_index(node_list, rnd_node):
 
         return minind
 
+    @staticmethod
+    def check_if_outside_play_area(node, play_area):
+
+        if play_area is None:
+            return True  # no play_area was defined, every pos should be ok
+
+        if node.x < play_area.xmin or node.x > play_area.xmax or \
+           node.y < play_area.ymin or node.y > play_area.ymax:
+            return False  # outside - bad
+        else:
+            return True  # inside - ok
+
     @staticmethod
     def check_collision(node, obstacleList):
 
@@ -222,7 +260,9 @@ def main(gx=6.0, gy=10.0):
         start=[0, 0],
         goal=[gx, gy],
         rand_area=[-2, 15],
-        obstacle_list=obstacleList)
+        obstacle_list=obstacleList,
+        # play_area=[0, 10, 0, 14]
+        )
     path = rrt.planning(animation=show_animation)
 
     if path is None:

From ee729d6b6ef1809c4cd402531e8fdce91a54d155 Mon Sep 17 00:00:00 2001
From: The-Anh Vu-Le <26211510+vltanh@users.noreply.github.com>
Date: Sun, 3 Oct 2021 17:31:08 +0700
Subject: [PATCH 0526/1080] Modify notations (#548)

* Modify notations

I modified some of the notations so that it will be less confusing:
- It is better to specify of which the Jacobian matrix is, here it is the transition and measurement function `f` and `g` (writing it as `J_F` and `J_H` is misleading)
- It is better to use `Delta` so as not to confuse people with the `d` commonly used in derivative
- It is more correct to use `\partial` instead of `d` since it is more multivariate than it is not

* Update docs/modules/extended_kalman_filter_localization.rst

* Update docs/modules/extended_kalman_filter_localization.rst

Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>
---
 .../extended_kalman_filter_localization.rst   | 36 +++++++++++--------
 1 file changed, 22 insertions(+), 14 deletions(-)

diff --git a/docs/modules/extended_kalman_filter_localization.rst b/docs/modules/extended_kalman_filter_localization.rst
index 565fb4ed20..5021563b5b 100644
--- a/docs/modules/extended_kalman_filter_localization.rst
+++ b/docs/modules/extended_kalman_filter_localization.rst
@@ -77,32 +77,36 @@ Motion Model
 
 The robot model is
 
-.. math::  \dot{x} = vcos(\phi)
+.. math::  \dot{x} = v \cos(\phi)
 
-.. math::  \dot{y} = vsin((\phi)
+.. math::  \dot{y} = v \sin(\phi)
 
 .. math::  \dot{\phi} = \omega
 
 So, the motion model is
 
-.. math:: \textbf{x}_{t+1} = F\textbf{x}_t+B\textbf{u}_t
+.. math:: \textbf{x}_{t+1} = f(\textbf{x}_t, \textbf{u}_t) = F\textbf{x}_t+B\textbf{u}_t
 
 where
 
 :math:`\begin{equation*} F= \begin{bmatrix} 1 & 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 0 \\ \end{bmatrix} \end{equation*}`
 
-:math:`\begin{equation*} B= \begin{bmatrix} cos(\phi)dt & 0\\ sin(\phi)dt & 0\\ 0 & dt\\ 1 & 0\\ \end{bmatrix} \end{equation*}`
+:math:`\begin{equation*} B= \begin{bmatrix} cos(\phi) \Delta t & 0\\ sin(\phi) \Delta t & 0\\ 0 & \Delta t\\ 1 & 0\\ \end{bmatrix} \end{equation*}`
 
-:math:`dt` is a time interval.
+:math:`\Delta t` is a time interval.
 
 This is implemented at
 `code <https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_filter.py#L53-L67>`__
 
+The motion function is that
+
+:math:`\begin{equation*} \begin{bmatrix} x' \\ y' \\ w' \\ v' \end{bmatrix} = f(\textbf{x}, \textbf{u}) = \begin{bmatrix} x + v\cos(\phi)\Delta t \\ y + v\sin(\phi) \\ \phi + \omega \Delta t \\ v \end{bmatrix} \end{equation*}`
+
 Its Jacobian matrix is
 
-:math:`\begin{equation*} J_F= \begin{bmatrix} \frac{dx}{dx}& \frac{dx}{dy} & \frac{dx}{d\phi} & \frac{dx}{dv}\\ \frac{dy}{dx}& \frac{dy}{dy} & \frac{dy}{d\phi} & \frac{dy}{dv}\\ \frac{d\phi}{dx}& \frac{d\phi}{dy} & \frac{d\phi}{d\phi} & \frac{d\phi}{dv}\\ \frac{dv}{dx}& \frac{dv}{dy} & \frac{dv}{d\phi} & \frac{dv}{dv}\\ \end{bmatrix} \end{equation*}`
+:math:`\begin{equation*} J_f = \begin{bmatrix} \frac{\partial x'}{\partial x}& \frac{\partial x'}{\partial y} & \frac{\partial x'}{\partial \phi} & \frac{\partial x'}{\partial v}\\ \frac{\partial y'}{\partial x}& \frac{\partial y'}{\partial y} & \frac{\partial y'}{\partial \phi} & \frac{\partial y'}{\partial v}\\ \frac{\partial \phi'}{\partial x}& \frac{\partial \phi'}{\partial y} & \frac{\partial \phi'}{\partial \phi} & \frac{\partial \phi'}{\partial v}\\ \frac{\partial v'}{\partial x}& \frac{\partial v'}{\partial y} & \frac{\partial v'}{\partial \phi} & \frac{\partial v'}{\partial v} \end{bmatrix} \end{equation*}`
 
-:math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & -v sin(\phi)dt & cos(\phi)dt\\ 0 & 1 & v cos(\phi)dt & sin(\phi) dt\\ 0 & 0 & 1 & 0\\ 0 & 0 & 0 & 1\\ \end{bmatrix} \end{equation*}`
+:math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & -v \sin(\phi) \Delta t & \cos(\phi) \Delta t\\ 0 & 1 & v \cos(\phi) \Delta t & \sin(\phi) \Delta t\\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \end{bmatrix} \end{equation*}`
 
 Observation Model
 ~~~~~~~~~~~~~~~~~
@@ -111,15 +115,19 @@ The robot can get x-y position infomation from GPS.
 
 So GPS Observation model is
 
-.. math:: \textbf{z}_{t} = H\textbf{x}_t
+.. math:: \textbf{z}_{t} = g(\textbf{x}_t) = H \textbf{x}_t
 
 where
 
-:math:`\begin{equation*} B= \begin{bmatrix} 1 & 0 & 0& 0\\ 0 & 1 & 0& 0\\ \end{bmatrix} \end{equation*}`
+:math:`\begin{equation*} H = \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ \end{bmatrix} \end{equation*}`
+
+The observation function states that
+
+:math:`\begin{equation*} \begin{bmatrix} x' \\ y' \end{bmatrix} = g(\textbf{x}) = \begin{bmatrix} x \\ y \end{bmatrix} \end{equation*}`
 
 Its Jacobian matrix is
 
-:math:`\begin{equation*} J_H= \begin{bmatrix} \frac{dx}{dx}& \frac{dx}{dy} & \frac{dx}{d\phi} & \frac{dx}{dv}\\ \frac{dy}{dx}& \frac{dy}{dy} & \frac{dy}{d\phi} & \frac{dy}{dv}\\ \end{bmatrix} \end{equation*}`
+:math:`\begin{equation*} J_g = \begin{bmatrix} \frac{\partial x'}{\partial x} & \frac{\partial x'}{\partial y} & \frac{\partial x'}{\partial \phi} & \frac{\partial x'}{\partial v}\\ \frac{\partial y'}{\partial x}& \frac{\partial y'}{\partial y} & \frac{\partial y'}{\partial \phi} & \frac{\partial y'}{ \partialv}\\ \end{bmatrix} \end{equation*}`
 
 :math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ \end{bmatrix} \end{equation*}`
 
@@ -132,7 +140,7 @@ Localization process using Extendted Kalman Filter:EKF is
 
 :math:`x_{Pred} = Fx_t+Bu_t`
 
-:math:`P_{Pred} = J_FP_t J_F^T + Q`
+:math:`P_{Pred} = J_f P_t J_f^T + Q`
 
 === Update ===
 
@@ -140,13 +148,13 @@ Localization process using Extendted Kalman Filter:EKF is
 
 :math:`y = z - z_{Pred}`
 
-:math:`S = J_H P_{Pred}.J_H^T + R`
+:math:`S = J_g P_{Pred}.J_g^T + R`
 
-:math:`K = P_{Pred}.J_H^T S^{-1}`
+:math:`K = P_{Pred}.J_g^T S^{-1}`
 
 :math:`x_{t+1} = x_{Pred} + Ky`
 
-:math:`P_{t+1} = ( I - K J_H) P_{Pred}`
+:math:`P_{t+1} = ( I - K J_g) P_{Pred}`
 
 Ref:
 ~~~~

From 90c6cfa27584b5d72349459a93bc655b062e7444 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 19 Oct 2021 07:10:21 +0900
Subject: [PATCH 0527/1080] Bump pandas from 1.3.3 to 1.3.4 (#553)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.3.3 to 1.3.4.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.3.3...v1.3.4)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 7363658a5f..a4a84bdf76 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.2
 scipy == 1.7.1
-pandas == 1.3.3
+pandas == 1.3.4
 matplotlib == 3.4.3
 cvxpy == 1.1.15

From 187b6aa35f3cbdeca587c0abdb177adddefc5c2a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 26 Oct 2021 19:36:03 +0900
Subject: [PATCH 0528/1080] Bump numpy from 1.21.2 to 1.21.3 (#554)

Bumps [numpy](https://github.com/numpy/numpy) from 1.21.2 to 1.21.3.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt)
- [Commits](https://github.com/numpy/numpy/compare/v1.21.2...v1.21.3)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index a4a84bdf76..48660d08a6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.21.2
+numpy == 1.21.3
 scipy == 1.7.1
 pandas == 1.3.4
 matplotlib == 3.4.3

From ea052a9829210d378a47d35edddaf9c99492a113 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Nov 2021 07:53:34 +0900
Subject: [PATCH 0529/1080] Bump numpy from 1.21.3 to 1.21.4 (#558)

Bumps [numpy](https://github.com/numpy/numpy) from 1.21.3 to 1.21.4.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt)
- [Commits](https://github.com/numpy/numpy/compare/v1.21.3...v1.21.4)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 48660d08a6..0157212e47 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.21.3
+numpy == 1.21.4
 scipy == 1.7.1
 pandas == 1.3.4
 matplotlib == 3.4.3

From 26ed1ab631a4dc77bbdf861b951fc3a25bd57756 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Nov 2021 07:54:03 +0900
Subject: [PATCH 0530/1080] Bump cvxpy from 1.1.15 to 1.1.17 (#560)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.1.15 to 1.1.17.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Changelog](https://github.com/cvxpy/cvxpy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.1.15...v1.1.17)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 0157212e47..f9af4fd2ab 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,4 +2,4 @@ numpy == 1.21.4
 scipy == 1.7.1
 pandas == 1.3.4
 matplotlib == 3.4.3
-cvxpy == 1.1.15
+cvxpy == 1.1.17

From 4d3fb2825cf66f6a9f7680d5ef399d89df01c8d3 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Nov 2021 20:58:17 +0900
Subject: [PATCH 0531/1080] Bump scipy from 1.7.1 to 1.7.2 (#559)

Bumps [scipy](https://github.com/scipy/scipy) from 1.7.1 to 1.7.2.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.7.1...v1.7.2)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index f9af4fd2ab..77a0bea427 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.4
-scipy == 1.7.1
+scipy == 1.7.2
 pandas == 1.3.4
 matplotlib == 3.4.3
 cvxpy == 1.1.17

From 0df55e943e661162230aa96bdb352e5bb8459da9 Mon Sep 17 00:00:00 2001
From: Jonathan Schwartz <jonbs98@gmail.com>
Date: Fri, 12 Nov 2021 05:28:16 -0500
Subject: [PATCH 0532/1080] Dynamic Movement Primitives Implementation (#526)

* Without equals sign, sometimes get points that are in the wrong direction - relative to the points before and after it- when change in x or change in y along path is 0

* Created test script for dubins path generator

* Made len == 0 it's own case, also changed 'l' to 'len' to appease travisCI

* More variable renaming to appease CI

* Broke == 0 into its own case in dubins planner, also Renaming files to appease CI

* Reverting some naming changes

* Turns out theres already a test for dubins.. not sure how I missed that

* Note to self: run the test cases on your own before throwing them at CI

* Added handling of length=0 case in generate_local_course()

* Missed reverting 'mode' back to 'm' in one spot

* Addressing style issues (line length)

* Mostly works, now just need to setup linear regression to solve for weights

* Re-arranged class

* Wrote DMP program and added tests file

* Styling fixes

* More styling

* Missed one indent

* Multi-dimension path learning (e.g. in x and y instead of just x)

* Added potential field obstacle avoidance

* Potential field working much better but has issues with reaching goal state

* Path ending to short not a result of obstacles, should be fix-able

* Mostly working! end won't go to goal

* split DMP and path following

* pretty close

* Okay this is working pretty well

* looks.. okay. was using the wrong vector before

* a plan to fix this mess

* Okay seriously going to pivot to the dubins approach im done with potential field lol

* Finished obstacle circle handling (and merging circles that are closer than their radii)

* Finished circle event finder function

* Some progress in preparing for dubins curves

* Finished angle finding algo, need to test

* Okay getting back to this, going to ignore the navigation and just focus on path generation since that's what DMP is for

* Moved DMP files to path planning

* changed folder name

* Made demo path cooler

* All working and added visualization tools (will remove

* Fixed unit test and handled TODOs

* not gonna handle this one

* demo now scales with data

* CI errors

* CI errors

* Fixing code style issues

* more styling

* fixing CI errors

* formatting

* Removed dead code

* removed unused imports

* removed uneccesary initialization

* Applying PR feedback

* fixing CI errors

* added description to header and removed unused variable
---
 .../dynamic_movement_primitives.py            | 261 ++++++++++++++++++
 tests/test_dynamic_movement_primitives.py     |  49 ++++
 2 files changed, 310 insertions(+)
 create mode 100644 PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py
 create mode 100644 tests/test_dynamic_movement_primitives.py

diff --git a/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py b/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py
new file mode 100644
index 0000000000..468d3b9f97
--- /dev/null
+++ b/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py
@@ -0,0 +1,261 @@
+"""
+Author: Jonathan Schwartz (github.com/SchwartzCode)
+
+This code provides a simple implementation of Dynamic Movement
+Primitives, which is an approach to learning curves by modelling
+them as a weighted sum of gaussian distributions. This approach
+can be used to dampen noise in a curve, and can also be used to
+stretch a curve by adjusting its start and end points.
+
+More information on Dynamic Movement Primitives available at:
+https://arxiv.org/abs/2102.03861
+https://www.frontiersin.org/articles/10.3389/fncom.2013.00138/full
+
+"""
+
+
+from matplotlib import pyplot as plt
+import numpy as np
+
+
+class DMP(object):
+
+    def __init__(self, training_data, data_period, K=156.25, B=25):
+        """
+        Arguments:
+            training_data - input data of form [N, dim]
+            data_period   - amount of time training data covers
+            K and B       - spring and damper constants to define
+                            DMP behavior
+        """
+
+        self.K = K  # virtual spring constant
+        self.B = B  # virtual damper coefficient
+
+        self.timesteps = training_data.shape[0]
+        self.dt = data_period / self.timesteps
+
+        self.weights = None  # weights used to generate DMP trajectories
+
+        self.T_orig = data_period
+
+        self.training_data = training_data
+        self.find_basis_functions_weights(training_data, data_period)
+
+    def find_basis_functions_weights(self, training_data, data_period,
+                                     num_weights=10):
+        """
+        Arguments:
+            data [(steps x spacial dim) np array] - data to replicate with DMP
+            data_period [float] - time duration of data
+        """
+
+        if not isinstance(training_data, np.ndarray):
+            print("Warning: you should input training data as an np.ndarray")
+        elif training_data.shape[0] < training_data.shape[1]:
+            print("Warning: you probably need to transpose your training data")
+
+        dt = data_period / len(training_data)
+
+        init_state = training_data[0]
+        goal_state = training_data[-1]
+
+        # means (C) and std devs (H) of gaussian basis functions
+        C = np.linspace(0, 1, num_weights)
+        H = (0.65*(1./(num_weights-1))**2)
+
+        for dim, _ in enumerate(training_data[0]):
+
+            dimension_data = training_data[:, dim]
+
+            q0 = init_state[dim]
+            g = goal_state[dim]
+
+            q = q0
+            qd_last = 0
+
+            phi_vals = []
+            f_vals = []
+
+            for i, _ in enumerate(dimension_data):
+                if i + 1 == len(dimension_data):
+                    qd = 0
+                else:
+                    qd = (dimension_data[i+1] - dimension_data[i]) / dt
+
+                phi = [np.exp(-0.5 * ((i * dt / data_period) - c)**2 / H)
+                       for c in C]
+                phi = phi/np.sum(phi)
+
+                qdd = (qd - qd_last)/dt
+
+                f = (qdd * data_period**2 - self.K * (g - q) + self.B * qd
+                     * data_period) / (g - q0)
+
+                phi_vals.append(phi)
+                f_vals.append(f)
+
+                qd_last = qd
+                q += qd * dt
+
+            phi_vals = np.asarray(phi_vals)
+            f_vals = np.asarray(f_vals)
+
+            w = np.linalg.lstsq(phi_vals, f_vals, rcond=None)
+
+            if self.weights is None:
+                self.weights = np.asarray(w[0])
+            else:
+                self.weights = np.vstack([self.weights, w[0]])
+
+    def recreate_trajectory(self, init_state, goal_state, T):
+        """
+        init_state - initial state/position
+        goal_state - goal state/position
+        T  - amount of time to travel q0 -> g
+        """
+
+        nrBasis = len(self.weights[0])  # number of gaussian basis functions
+
+        # means (C) and std devs (H) of gaussian basis functions
+        C = np.linspace(0, 1, nrBasis)
+        H = (0.65*(1./(nrBasis-1))**2)
+
+        # initialize virtual system
+        time = 0
+
+        q = init_state
+        dimensions = self.weights.shape[0]
+        qd = np.zeros(dimensions)
+
+        positions = np.array([])
+        for k in range(self.timesteps):
+            time = time + self.dt
+
+            qdd = np.zeros(dimensions)
+
+            for dim in range(dimensions):
+
+                if time <= T:
+                    phi = [np.exp(-0.5 * ((time / T) - c)**2 / H) for c in C]
+                    phi = phi / np.sum(phi)
+                    f = np.dot(phi, self.weights[dim])
+                else:
+                    f = 0
+
+                # simulate dynamics
+                qdd[dim] = (self.K*(goal_state[dim] - q[dim])/T**2
+                            - self.B*qd[dim]/T
+                            + (goal_state[dim] - init_state[dim])*f/T**2)
+
+            qd = qd + qdd * self.dt
+            q = q + qd * self.dt
+
+            if positions.size == 0:
+                positions = q
+            else:
+                positions = np.vstack([positions, q])
+
+        t = np.arange(0, self.timesteps * self.dt, self.dt)
+        return t, positions
+
+    @staticmethod
+    def dist_between(p1, p2):
+        return np.linalg.norm(p1 - p2)
+
+    def view_trajectory(self, path, title=None, demo=False):
+
+        path = np.asarray(path)
+
+        plt.cla()
+        plt.plot(self.training_data[:, 0], self.training_data[:, 1],
+                 label="Training Data")
+        plt.plot(path[:, 0], path[:, 1],
+                 linewidth=2, label="DMP Approximation")
+
+        plt.xlabel("X Position")
+        plt.ylabel("Y Position")
+        plt.legend()
+
+        if title is not None:
+            plt.title(title)
+
+        if demo:
+            plt.xlim([-0.5, 5])
+            plt.ylim([-2, 2])
+            plt.draw()
+            plt.pause(0.02)
+        else:
+            plt.show()
+
+    def show_DMP_purpose(self):
+        """
+        This function conveys the purpose of DMPs:
+            to capture a trajectory and be able to stretch
+            and squeeze it in terms of start and stop position
+            or time
+        """
+
+        q0_orig = self.training_data[0]
+        g_orig = self.training_data[-1]
+        T_orig = self.T_orig
+
+        data_range = (np.amax(self.training_data[:, 0])
+                      - np.amin(self.training_data[:, 0])) / 4
+
+        q0_right = q0_orig + np.array([data_range, 0])
+        q0_up = q0_orig + np.array([0, data_range/2])
+        g_left = g_orig - np.array([data_range, 0])
+        g_down = g_orig - np.array([0, data_range/2])
+
+        q0_vals = np.vstack([np.linspace(q0_orig, q0_right, 20),
+                             np.linspace(q0_orig, q0_up, 20)])
+        g_vals = np.vstack([np.linspace(g_orig, g_left, 20),
+                            np.linspace(g_orig, g_down, 20)])
+        T_vals = np.linspace(T_orig, 2*T_orig, 20)
+
+        for new_q0_value in q0_vals:
+            plot_title = "Initial Position = [%s, %s]" % \
+                         (round(new_q0_value[0], 2), round(new_q0_value[1], 2))
+
+            _, path = self.recreate_trajectory(new_q0_value, g_orig, T_orig)
+            self.view_trajectory(path, title=plot_title, demo=True)
+
+        for new_g_value in g_vals:
+            plot_title = "Goal Position = [%s, %s]" % \
+                         (round(new_g_value[0], 2), round(new_g_value[1], 2))
+
+            _, path = self.recreate_trajectory(q0_orig, new_g_value, T_orig)
+            self.view_trajectory(path, title=plot_title, demo=True)
+
+        for new_T_value in T_vals:
+            plot_title = "Period = %s [sec]" % round(new_T_value, 2)
+
+            _, path = self.recreate_trajectory(q0_orig, g_orig, new_T_value)
+            self.view_trajectory(path, title=plot_title, demo=True)
+
+
+def example_DMP():
+    """
+    Creates a noisy trajectory, fits weights to it, and then adjusts the
+    trajectory by moving its start position, goal position, or period
+    """
+    t = np.arange(0, 3*np.pi/2, 0.01)
+    t1 = np.arange(3*np.pi/2, 2*np.pi, 0.01)[:-1]
+    t2 = np.arange(0, np.pi/2, 0.01)[:-1]
+    t3 = np.arange(np.pi, 3*np.pi/2, 0.01)
+    data_x = t + 0.02*np.random.rand(t.shape[0])
+    data_y = np.concatenate([np.cos(t1) + 0.1*np.random.rand(t1.shape[0]),
+                             np.cos(t2) + 0.1*np.random.rand(t2.shape[0]),
+                             np.sin(t3) + 0.1*np.random.rand(t3.shape[0])])
+    training_data = np.vstack([data_x, data_y]).T
+
+    period = 3*np.pi/2
+    DMP_controller = DMP(training_data, period)
+
+    DMP_controller.show_DMP_purpose()
+
+
+if __name__ == '__main__':
+
+    example_DMP()
diff --git a/tests/test_dynamic_movement_primitives.py b/tests/test_dynamic_movement_primitives.py
new file mode 100644
index 0000000000..3ab1c8a32c
--- /dev/null
+++ b/tests/test_dynamic_movement_primitives.py
@@ -0,0 +1,49 @@
+import conftest
+import numpy as np
+from PathPlanning.DynamicMovementPrimitives import \
+            dynamic_movement_primitives
+
+
+def test_1():
+    # test that trajectory can be learned from user-passed data
+    T = 5
+    t = np.arange(0, T, 0.01)
+    sin_t = np.sin(t)
+    train_data = np.array([t, sin_t]).T
+
+    DMP_controller = dynamic_movement_primitives.DMP(train_data, T)
+    DMP_controller.recreate_trajectory(train_data[0], train_data[-1], 4)
+
+
+def test_2():
+    # test that length of trajectory is equal to desired number of timesteps
+    T = 5
+    t = np.arange(0, T, 0.01)
+    sin_t = np.sin(t)
+    train_data = np.array([t, sin_t]).T
+
+    DMP_controller = dynamic_movement_primitives.DMP(train_data, T)
+    t, path = DMP_controller.recreate_trajectory(train_data[0],
+                                                 train_data[-1], 4)
+
+    assert(path.shape[0] == DMP_controller.timesteps)
+
+
+def test_3():
+    # check that learned trajectory is close to initial
+    T = 3*np.pi/2
+    A_noise = 0.02
+    t = np.arange(0, T, 0.01)
+    noisy_sin_t = np.sin(t) + A_noise*np.random.rand(len(t))
+    train_data = np.array([t, noisy_sin_t]).T
+
+    DMP_controller = dynamic_movement_primitives.DMP(train_data, T)
+    t, pos = DMP_controller.recreate_trajectory(train_data[0],
+                                                train_data[-1], T)
+
+    diff = abs(pos[:, 1] - noisy_sin_t)
+    assert(max(diff) < 5*A_noise)
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)

From 17920dcbf489dff0f804ac541973c1c7b8ed77f0 Mon Sep 17 00:00:00 2001
From: Kajal <kajalpuri95@gmail.com>
Date: Fri, 12 Nov 2021 11:29:36 +0100
Subject: [PATCH 0533/1080] Changes in Slam/Ekf_slam.ipynb file, added a
 dtype=object statement to jF=np.array so it can render the output without
 numerous depreciation warnings (#552)

---
 SLAM/EKFSLAM/ekf_slam.ipynb | 1163 ++++++++++++++++++++++++++++++++---
 1 file changed, 1068 insertions(+), 95 deletions(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
index f4c9d61436..7634bfde9e 100644
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ b/SLAM/EKFSLAM/ekf_slam.ipynb
@@ -2,39 +2,40 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# EKF SLAM"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename=\"animation.png\",width=600)"
-   ],
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
+      "image/png": "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        "<IPython.core.display.Image object>"
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"
+      ]
      },
+     "execution_count": 1,
      "metadata": {
       "image/png": {
        "width": 600
       }
      },
-     "execution_count": 11
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename=\"animation.png\",width=600)"
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Simulation\n",
     "\n",
@@ -45,21 +46,21 @@
     "- Black line: dead reckoning \n",
     "- Blue line: ground truth\n",
     "- Red line: EKF SLAM position estimation"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Introduction\n",
     "\n",
     "EKF SLAM models the SLAM problem in a single EKF where the modeled state is both the pose $(x, y, \\theta)$ and \n",
     "an array of landmarks $[(x_1, y_1), (x_2, x_y), ... , (x_n, y_n)]$ for $n$ landmarks. The covariance between each of the positions and landmarks are also tracked. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "$\\begin{equation}\n",
     "X = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\\\ x_1 \\\\ y_1 \\\\ x_2 \\\\ y_2 \\\\ \\dots \\\\ x_n \\\\ y_n \\end{bmatrix}\n",
@@ -73,11 +74,11 @@
     "\\sigma_{x_nx} & \\sigma_{x_ny} & \\sigma_{x_n\\theta} & \\sigma_{x_nx_1} & \\sigma_{x_ny_1} & \\sigma_{x_nx_2} & \\sigma_{x_ny_2} & \\dots & \\sigma_{x_nx_n} & \\sigma_{x_ny_n}\n",
     "\\end{bmatrix}\n",
     "\\end{equation}$"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "A single estimate of the pose is tracked over time, while the confidence in the pose is tracked by the \n",
     "covariance matrix $P$. $P$ is a symmetric square matrix whith each element in the matrix corresponding to the \n",
@@ -85,11 +86,11 @@
     "belief of $x$ and $y$ and is equal to $\\sigma_{yx}$. \n",
     "\n",
     "The state can be represented more concisely as follows. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "$\\begin{equation}\n",
     "X = \\begin{bmatrix} x \\\\ m \\end{bmatrix}\n",
@@ -100,11 +101,11 @@
     "\\Sigma_{mx} & \\Sigma_{mm}\\\\\n",
     "\\end{bmatrix}\n",
     "\\end{equation}$"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Here the state simplifies to a combination of pose ($x$) and map ($m$). The covariance matrix becomes easier to \n",
     "understand and simply reads as the uncertainty of the robots pose ($\\Sigma_{xx}$), the uncertainty of the \n",
@@ -112,12 +113,13 @@
     "($\\Sigma_{xm}$, $\\Sigma_{mx}$).\n",
     "\n",
     "Take care to note the difference between $X$ (state) and $x$ (pose). "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "\"\"\"\n",
     "Extended Kalman Filter SLAM example\n",
@@ -146,24 +148,24 @@
     "LM_SIZE = 2  # LM state size [x,y]\n",
     "\n",
     "show_animation = True"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Algorithm Walkthrough\n",
     "\n",
     "At each time step, the following is done. \n",
     "- predict the new state using the control functions\n",
     "- update the belief in landmark positions based on the estimated state and measurements"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def ekf_slam(xEst, PEst, u, z):\n",
     "    \"\"\"\n",
@@ -185,12 +187,11 @@
     "    xEst, PEst = update(xEst, PEst, u, z, initP)\n",
     "\n",
     "    return xEst, PEst\n"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## 1- Predict\n",
     "**Predict State update:** The following equations describe the predicted motion model of the robot in case we provide only the control $(v,w)$, which are the linear and angular velocity repsectively. \n",
@@ -286,12 +287,13 @@
     "$\n",
     "\n",
     "Notice this uncertainty is only growing with respect to the pose, not the landmarks. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def predict(xEst, PEst, u):\n",
     "    \"\"\"\n",
@@ -309,13 +311,13 @@
     "    # sigma = G*sigma*G.T + Noise\n",
     "    PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx\n",
     "    return xEst, PEst, G, Fx"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def motion_model(x, u):\n",
     "    \"\"\"\n",
@@ -335,12 +337,11 @@
     "\n",
     "    x = (F @ x) + (B @ u)\n",
     "    return x"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## 2 - Update\n",
     "In the update phase, the observations of nearby landmarks are used to correct the location estimate. \n",
@@ -391,12 +392,13 @@
     "$\n",
     "P_{t} = (I-K_tH_t)\\bar{P_t}\n",
     "$"
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def update(xEst, PEst, u, z, initP):\n",
     "    \"\"\"\n",
@@ -432,13 +434,13 @@
     "    \n",
     "    xEst[2] = pi_2_pi(xEst[2])\n",
     "    return xEst, PEst\n"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def calc_innovation(lm, xEst, PEst, z, LMid):\n",
     "    \"\"\"\n",
@@ -490,12 +492,11 @@
     "    H = G @ F\n",
     "\n",
     "    return H"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Observation Step\n",
     "The observation step described here is outside the main EKF SLAM process and is primarily used as a method of\n",
@@ -505,12 +506,13 @@
     "\n",
     "Observations are based on the TRUE position of the robot. Error in dead reckoning and control functions are \n",
     "passed along here as well. "
-   ],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def observation(xTrue, xd, u, RFID):\n",
     "    \"\"\"\n",
@@ -546,13 +548,13 @@
     "\n",
     "    xd = motion_model(xd, ud)\n",
     "    return xTrue, z, xd, ud"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def calc_n_LM(x):\n",
     "    \"\"\"\n",
@@ -580,7 +582,7 @@
     "\n",
     "    jF = np.array([[0.0, 0.0, -DT * u[0] * math.sin(x[2, 0])],\n",
     "                   [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])],\n",
-    "                   [0.0, 0.0, 0.0]])\n",
+    "                   [0.0, 0.0, 0.0]],dtype=object)\n",
     "\n",
     "    G = np.eye(STATE_SIZE) + Fx.T @ jF @ Fx\n",
     "    if calc_n_LM(x) > 0:\n",
@@ -588,13 +590,13 @@
     "    return G, Fx,\n",
     "\n",
     "\n"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def calc_LM_Pos(x, z):\n",
     "    \"\"\"\n",
@@ -663,13 +665,13 @@
     "\n",
     "def pi_2_pi(angle):\n",
     "    return (angle + math.pi) % (2 * math.pi) - math.pi"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def main():\n",
     "    print(\" start!!\")\n",
@@ -729,22 +731,18 @@
     "            plt.axis(\"equal\")\n",
     "            plt.grid(True)\n",
     "            plt.pause(0.001)"
-   ],
-   "outputs": [],
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "source": [
-    "%matplotlib notebook\n",
-    "%matplotlib notebook\n",
-    "main()"
-   ],
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       " start!!\n",
       "New LM\n",
@@ -753,38 +751,1013 @@
      ]
     },
     {
-     "output_type": "display_data",
      "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "/* global mpl */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function () {\n",
+       "    if (typeof WebSocket !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert(\n",
+       "            'Your browser does not have WebSocket support. ' +\n",
+       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "                'Firefox 4 and 5 are also supported but you ' +\n",
+       "                'have to enable WebSockets in about:config.'\n",
+       "        );\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById('mpl-warnings');\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent =\n",
+       "                'This browser does not support binary websocket messages. ' +\n",
+       "                'Performance may be slow.';\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = document.createElement('div');\n",
+       "    this.root.setAttribute('style', 'display: inline-block');\n",
+       "    this._root_extra_style(this.root);\n",
+       "\n",
+       "    parent_element.appendChild(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen = function () {\n",
+       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
+       "        fig.send_message('send_image_mode', {});\n",
+       "        if (fig.ratio !== 1) {\n",
+       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
+       "        }\n",
+       "        fig.send_message('refresh', {});\n",
+       "    };\n",
+       "\n",
+       "    this.imageObj.onload = function () {\n",
+       "        if (fig.image_mode === 'full') {\n",
+       "            // Full images could contain transparency (where diff images\n",
+       "            // almost always do), so we need to clear the canvas so that\n",
+       "            // there is no ghosting.\n",
+       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "        }\n",
+       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "    };\n",
+       "\n",
+       "    this.imageObj.onunload = function () {\n",
+       "        fig.ws.close();\n",
+       "    };\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function () {\n",
+       "    var titlebar = document.createElement('div');\n",
+       "    titlebar.classList =\n",
+       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
+       "    var titletext = document.createElement('div');\n",
+       "    titletext.classList = 'ui-dialog-title';\n",
+       "    titletext.setAttribute(\n",
+       "        'style',\n",
+       "        'width: 100%; text-align: center; padding: 3px;'\n",
+       "    );\n",
+       "    titlebar.appendChild(titletext);\n",
+       "    this.root.appendChild(titlebar);\n",
+       "    this.header = titletext;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function () {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
+       "    canvas_div.setAttribute(\n",
+       "        'style',\n",
+       "        'border: 1px solid #ddd;' +\n",
+       "            'box-sizing: content-box;' +\n",
+       "            'clear: both;' +\n",
+       "            'min-height: 1px;' +\n",
+       "            'min-width: 1px;' +\n",
+       "            'outline: 0;' +\n",
+       "            'overflow: hidden;' +\n",
+       "            'position: relative;' +\n",
+       "            'resize: both;'\n",
+       "    );\n",
+       "\n",
+       "    function on_keyboard_event_closure(name) {\n",
+       "        return function (event) {\n",
+       "            return fig.key_event(event, name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.addEventListener(\n",
+       "        'keydown',\n",
+       "        on_keyboard_event_closure('key_press')\n",
+       "    );\n",
+       "    canvas_div.addEventListener(\n",
+       "        'keyup',\n",
+       "        on_keyboard_event_closure('key_release')\n",
+       "    );\n",
+       "\n",
+       "    this._canvas_extra_style(canvas_div);\n",
+       "    this.root.appendChild(canvas_div);\n",
+       "\n",
+       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
+       "    canvas.classList.add('mpl-canvas');\n",
+       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
+       "\n",
+       "    this.context = canvas.getContext('2d');\n",
+       "\n",
+       "    var backingStore =\n",
+       "        this.context.backingStorePixelRatio ||\n",
+       "        this.context.webkitBackingStorePixelRatio ||\n",
+       "        this.context.mozBackingStorePixelRatio ||\n",
+       "        this.context.msBackingStorePixelRatio ||\n",
+       "        this.context.oBackingStorePixelRatio ||\n",
+       "        this.context.backingStorePixelRatio ||\n",
+       "        1;\n",
+       "\n",
+       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
+       "        'canvas'\n",
+       "    ));\n",
+       "    rubberband_canvas.setAttribute(\n",
+       "        'style',\n",
+       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
+       "    );\n",
+       "\n",
+       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
+       "    if (this.ResizeObserver === undefined) {\n",
+       "        if (window.ResizeObserver !== undefined) {\n",
+       "            this.ResizeObserver = window.ResizeObserver;\n",
+       "        } else {\n",
+       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
+       "            this.ResizeObserver = obs.ResizeObserver;\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
+       "        var nentries = entries.length;\n",
+       "        for (var i = 0; i < nentries; i++) {\n",
+       "            var entry = entries[i];\n",
+       "            var width, height;\n",
+       "            if (entry.contentBoxSize) {\n",
+       "                if (entry.contentBoxSize instanceof Array) {\n",
+       "                    // Chrome 84 implements new version of spec.\n",
+       "                    width = entry.contentBoxSize[0].inlineSize;\n",
+       "                    height = entry.contentBoxSize[0].blockSize;\n",
+       "                } else {\n",
+       "                    // Firefox implements old version of spec.\n",
+       "                    width = entry.contentBoxSize.inlineSize;\n",
+       "                    height = entry.contentBoxSize.blockSize;\n",
+       "                }\n",
+       "            } else {\n",
+       "                // Chrome <84 implements even older version of spec.\n",
+       "                width = entry.contentRect.width;\n",
+       "                height = entry.contentRect.height;\n",
+       "            }\n",
+       "\n",
+       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
+       "            // the canvas container.\n",
+       "            if (entry.devicePixelContentBoxSize) {\n",
+       "                // Chrome 84 implements new version of spec.\n",
+       "                canvas.setAttribute(\n",
+       "                    'width',\n",
+       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
+       "                );\n",
+       "                canvas.setAttribute(\n",
+       "                    'height',\n",
+       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
+       "                );\n",
+       "            } else {\n",
+       "                canvas.setAttribute('width', width * fig.ratio);\n",
+       "                canvas.setAttribute('height', height * fig.ratio);\n",
+       "            }\n",
+       "            canvas.setAttribute(\n",
+       "                'style',\n",
+       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
+       "            );\n",
+       "\n",
+       "            rubberband_canvas.setAttribute('width', width);\n",
+       "            rubberband_canvas.setAttribute('height', height);\n",
+       "\n",
+       "            // And update the size in Python. We ignore the initial 0/0 size\n",
+       "            // that occurs as the element is placed into the DOM, which should\n",
+       "            // otherwise not happen due to the minimum size styling.\n",
+       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
+       "                fig.request_resize(width, height);\n",
+       "            }\n",
+       "        }\n",
+       "    });\n",
+       "    this.resizeObserverInstance.observe(canvas_div);\n",
+       "\n",
+       "    function on_mouse_event_closure(name) {\n",
+       "        return function (event) {\n",
+       "            return fig.mouse_event(event, name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mousedown',\n",
+       "        on_mouse_event_closure('button_press')\n",
+       "    );\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mouseup',\n",
+       "        on_mouse_event_closure('button_release')\n",
+       "    );\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'dblclick',\n",
+       "        on_mouse_event_closure('dblclick')\n",
+       "    );\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mousemove',\n",
+       "        on_mouse_event_closure('motion_notify')\n",
+       "    );\n",
+       "\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mouseenter',\n",
+       "        on_mouse_event_closure('figure_enter')\n",
+       "    );\n",
+       "    rubberband_canvas.addEventListener(\n",
+       "        'mouseleave',\n",
+       "        on_mouse_event_closure('figure_leave')\n",
+       "    );\n",
+       "\n",
+       "    canvas_div.addEventListener('wheel', function (event) {\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        on_mouse_event_closure('scroll')(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.appendChild(canvas);\n",
+       "    canvas_div.appendChild(rubberband_canvas);\n",
+       "\n",
+       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
+       "    this.rubberband_context.strokeStyle = '#000000';\n",
+       "\n",
+       "    this._resize_canvas = function (width, height, forward) {\n",
+       "        if (forward) {\n",
+       "            canvas_div.style.width = width + 'px';\n",
+       "            canvas_div.style.height = height + 'px';\n",
+       "        }\n",
+       "    };\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
+       "        event.preventDefault();\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus() {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function () {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var toolbar = document.createElement('div');\n",
+       "    toolbar.classList = 'mpl-toolbar';\n",
+       "    this.root.appendChild(toolbar);\n",
+       "\n",
+       "    function on_click_closure(name) {\n",
+       "        return function (_event) {\n",
+       "            return fig.toolbar_button_onclick(name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    function on_mouseover_closure(tooltip) {\n",
+       "        return function (event) {\n",
+       "            if (!event.currentTarget.disabled) {\n",
+       "                return fig.toolbar_button_onmouseover(tooltip);\n",
+       "            }\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    fig.buttons = {};\n",
+       "    var buttonGroup = document.createElement('div');\n",
+       "    buttonGroup.classList = 'mpl-button-group';\n",
+       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            /* Instead of a spacer, we start a new button group. */\n",
+       "            if (buttonGroup.hasChildNodes()) {\n",
+       "                toolbar.appendChild(buttonGroup);\n",
+       "            }\n",
+       "            buttonGroup = document.createElement('div');\n",
+       "            buttonGroup.classList = 'mpl-button-group';\n",
+       "            continue;\n",
+       "        }\n",
+       "\n",
+       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
+       "        button.classList = 'mpl-widget';\n",
+       "        button.setAttribute('role', 'button');\n",
+       "        button.setAttribute('aria-disabled', 'false');\n",
+       "        button.addEventListener('click', on_click_closure(method_name));\n",
+       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+       "\n",
+       "        var icon_img = document.createElement('img');\n",
+       "        icon_img.src = '_images/' + image + '.png';\n",
+       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
+       "        icon_img.alt = tooltip;\n",
+       "        button.appendChild(icon_img);\n",
+       "\n",
+       "        buttonGroup.appendChild(button);\n",
+       "    }\n",
+       "\n",
+       "    if (buttonGroup.hasChildNodes()) {\n",
+       "        toolbar.appendChild(buttonGroup);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker = document.createElement('select');\n",
+       "    fmt_picker.classList = 'mpl-widget';\n",
+       "    toolbar.appendChild(fmt_picker);\n",
+       "    this.format_dropdown = fmt_picker;\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = document.createElement('option');\n",
+       "        option.selected = fmt === mpl.default_extension;\n",
+       "        option.innerHTML = fmt;\n",
+       "        fmt_picker.appendChild(option);\n",
+       "    }\n",
+       "\n",
+       "    var status_bar = document.createElement('span');\n",
+       "    status_bar.classList = 'mpl-message';\n",
+       "    toolbar.appendChild(status_bar);\n",
+       "    this.message = status_bar;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function (type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function () {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
+       "        fig.send_message('refresh', {});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
+       "    var x0 = msg['x0'] / fig.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
+       "    var x1 = msg['x1'] / fig.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0,\n",
+       "        0,\n",
+       "        fig.canvas.width / fig.ratio,\n",
+       "        fig.canvas.height / fig.ratio\n",
+       "    );\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch (cursor) {\n",
+       "        case 0:\n",
+       "            cursor = 'pointer';\n",
+       "            break;\n",
+       "        case 1:\n",
+       "            cursor = 'default';\n",
+       "            break;\n",
+       "        case 2:\n",
+       "            cursor = 'crosshair';\n",
+       "            break;\n",
+       "        case 3:\n",
+       "            cursor = 'move';\n",
+       "            break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
+       "    for (var key in msg) {\n",
+       "        if (!(key in fig.buttons)) {\n",
+       "            continue;\n",
+       "        }\n",
+       "        fig.buttons[key].disabled = !msg[key];\n",
+       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
+       "    if (msg['mode'] === 'PAN') {\n",
+       "        fig.buttons['Pan'].classList.add('active');\n",
+       "        fig.buttons['Zoom'].classList.remove('active');\n",
+       "    } else if (msg['mode'] === 'ZOOM') {\n",
+       "        fig.buttons['Pan'].classList.remove('active');\n",
+       "        fig.buttons['Zoom'].classList.add('active');\n",
+       "    } else {\n",
+       "        fig.buttons['Pan'].classList.remove('active');\n",
+       "        fig.buttons['Zoom'].classList.remove('active');\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function () {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message('ack', {});\n",
+       "};\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            var img = evt.data;\n",
+       "            if (img.type !== 'image/png') {\n",
+       "                /* FIXME: We get \"Resource interpreted as Image but\n",
+       "                 * transferred with MIME type text/plain:\" errors on\n",
+       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "                 * to be part of the websocket stream */\n",
+       "                img.type = 'image/png';\n",
+       "            }\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src\n",
+       "                );\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                img\n",
+       "            );\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        } else if (\n",
+       "            typeof evt.data === 'string' &&\n",
+       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
+       "        ) {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig['handle_' + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\n",
+       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
+       "                msg\n",
+       "            );\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\n",
+       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
+       "                    e,\n",
+       "                    e.stack,\n",
+       "                    msg\n",
+       "                );\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "};\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function (e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e) {\n",
+       "        e = window.event;\n",
+       "    }\n",
+       "    if (e.target) {\n",
+       "        targ = e.target;\n",
+       "    } else if (e.srcElement) {\n",
+       "        targ = e.srcElement;\n",
+       "    }\n",
+       "    if (targ.nodeType === 3) {\n",
+       "        // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "    }\n",
+       "\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    var boundingRect = targ.getBoundingClientRect();\n",
+       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
+       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
+       "\n",
+       "    return { x: x, y: y };\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys(original) {\n",
+       "    return Object.keys(original).reduce(function (obj, key) {\n",
+       "        if (typeof original[key] !== 'object') {\n",
+       "            obj[key] = original[key];\n",
+       "        }\n",
+       "        return obj;\n",
+       "    }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event);\n",
+       "\n",
+       "    if (name === 'button_press') {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * this.ratio;\n",
+       "    var y = canvas_pos.y * this.ratio;\n",
+       "\n",
+       "    this.send_message(name, {\n",
+       "        x: x,\n",
+       "        y: y,\n",
+       "        button: event.button,\n",
+       "        step: event.step,\n",
+       "        guiEvent: simpleKeys(event),\n",
+       "    });\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function (event, name) {\n",
+       "    // Prevent repeat events\n",
+       "    if (name === 'key_press') {\n",
+       "        if (event.key === this._key) {\n",
+       "            return;\n",
+       "        } else {\n",
+       "            this._key = event.key;\n",
+       "        }\n",
+       "    }\n",
+       "    if (name === 'key_release') {\n",
+       "        this._key = null;\n",
+       "    }\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.key !== 'Control') {\n",
+       "        value += 'ctrl+';\n",
+       "    }\n",
+       "    else if (event.altKey && event.key !== 'Alt') {\n",
+       "        value += 'alt+';\n",
+       "    }\n",
+       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
+       "        value += 'shift+';\n",
+       "    }\n",
+       "\n",
+       "    value += 'k' + event.key;\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
+       "    return false;\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
+       "    if (name === 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message('toolbar_button', { name: name });\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "\n",
+       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
+       "// prettier-ignore\n",
+       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";/* global mpl */\n",
+       "\n",
+       "var comm_websocket_adapter = function (comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
+       "    ws.readyState = comm.kernel.ws.readyState;\n",
+       "    function updateReadyState(_event) {\n",
+       "        if (comm.kernel.ws) {\n",
+       "            ws.readyState = comm.kernel.ws.readyState;\n",
+       "        } else {\n",
+       "            ws.readyState = 3; // Closed state.\n",
+       "        }\n",
+       "    }\n",
+       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
+       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
+       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
+       "\n",
+       "    ws.close = function () {\n",
+       "        comm.close();\n",
+       "    };\n",
+       "    ws.send = function (m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function (msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        var data = msg['content']['data'];\n",
+       "        if (data['blob'] !== undefined) {\n",
+       "            data = {\n",
+       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
+       "            };\n",
+       "        }\n",
+       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+       "        ws.onmessage(data);\n",
+       "    });\n",
+       "    return ws;\n",
+       "};\n",
+       "\n",
+       "mpl.mpl_figure_comm = function (comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = document.getElementById(id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm);\n",
+       "\n",
+       "    function ondownload(figure, _format) {\n",
+       "        window.open(figure.canvas.toDataURL());\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element;\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error('Failed to find cell for figure', id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "    fig.cell_info[0].output_area.element.on(\n",
+       "        'cleared',\n",
+       "        { fig: fig },\n",
+       "        fig._remove_fig_handler\n",
+       "    );\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
+       "    var width = fig.canvas.width / fig.ratio;\n",
+       "    fig.cell_info[0].output_area.element.off(\n",
+       "        'cleared',\n",
+       "        fig._remove_fig_handler\n",
+       "    );\n",
+       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable();\n",
+       "    fig.parent_element.innerHTML =\n",
+       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "    fig.close_ws(fig, msg);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width / this.ratio;\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] =\n",
+       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function () {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message('ack', {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () {\n",
+       "        fig.push_to_output();\n",
+       "    }, 1000);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function () {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var toolbar = document.createElement('div');\n",
+       "    toolbar.classList = 'btn-toolbar';\n",
+       "    this.root.appendChild(toolbar);\n",
+       "\n",
+       "    function on_click_closure(name) {\n",
+       "        return function (_event) {\n",
+       "            return fig.toolbar_button_onclick(name);\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    function on_mouseover_closure(tooltip) {\n",
+       "        return function (event) {\n",
+       "            if (!event.currentTarget.disabled) {\n",
+       "                return fig.toolbar_button_onmouseover(tooltip);\n",
+       "            }\n",
+       "        };\n",
+       "    }\n",
+       "\n",
+       "    fig.buttons = {};\n",
+       "    var buttonGroup = document.createElement('div');\n",
+       "    buttonGroup.classList = 'btn-group';\n",
+       "    var button;\n",
+       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            /* Instead of a spacer, we start a new button group. */\n",
+       "            if (buttonGroup.hasChildNodes()) {\n",
+       "                toolbar.appendChild(buttonGroup);\n",
+       "            }\n",
+       "            buttonGroup = document.createElement('div');\n",
+       "            buttonGroup.classList = 'btn-group';\n",
+       "            continue;\n",
+       "        }\n",
+       "\n",
+       "        button = fig.buttons[name] = document.createElement('button');\n",
+       "        button.classList = 'btn btn-default';\n",
+       "        button.href = '#';\n",
+       "        button.title = name;\n",
+       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
+       "        button.addEventListener('click', on_click_closure(method_name));\n",
+       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
+       "        buttonGroup.appendChild(button);\n",
+       "    }\n",
+       "\n",
+       "    if (buttonGroup.hasChildNodes()) {\n",
+       "        toolbar.appendChild(buttonGroup);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = document.createElement('span');\n",
+       "    status_bar.classList = 'mpl-message pull-right';\n",
+       "    toolbar.appendChild(status_bar);\n",
+       "    this.message = status_bar;\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = document.createElement('div');\n",
+       "    buttongrp.classList = 'btn-group inline pull-right';\n",
+       "    button = document.createElement('button');\n",
+       "    button.classList = 'btn btn-mini btn-primary';\n",
+       "    button.href = '#';\n",
+       "    button.title = 'Stop Interaction';\n",
+       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
+       "    button.addEventListener('click', function (_evt) {\n",
+       "        fig.handle_close(fig, {});\n",
+       "    });\n",
+       "    button.addEventListener(\n",
+       "        'mouseover',\n",
+       "        on_mouseover_closure('Stop Interaction')\n",
+       "    );\n",
+       "    buttongrp.appendChild(button);\n",
+       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
+       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
+       "    var fig = event.data.fig;\n",
+       "    if (event.target !== this) {\n",
+       "        // Ignore bubbled events from children.\n",
+       "        return;\n",
+       "    }\n",
+       "    fig.close_ws(fig, {});\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function (el) {\n",
+       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.setAttribute('tabindex', 0);\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    } else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager) {\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "    }\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which === 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "};\n",
+       "\n",
+       "mpl.find_output_cell = function (html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i = 0; i < ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code') {\n",
+       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] === html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel !== null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target(\n",
+       "        'matplotlib',\n",
+       "        mpl.mpl_figure_comm\n",
+       "    );\n",
+       "}\n"
+      ],
       "text/plain": [
        "<IPython.core.display.Javascript object>"
-      ],
-      "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.get_websocket_type = function() {\n    if (typeof(WebSocket) !== 'undefined') {\n        return WebSocket;\n    } else if (typeof(MozWebSocket) !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert('Your browser does not have WebSocket support.' +\n              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        fig.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overridden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        event.shiftKey = false;\n        // Send a \"J\" for go to next cell\n        event.which = 74;\n        event.keyCode = 74;\n        manager.command_mode();\n        manager.handle_keydown(event);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n"
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "display_data",
      "data": {
+      "text/html": [
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\" width=\"636\">"
       ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
       "New LM\n"
      ]
     }
    ],
-   "metadata": {
-    "scrolled": false
-   }
+   "source": [
+    "%matplotlib notebook\n",
+    "%matplotlib notebook\n",
+    "main()"
+   ]
   }
  ],
  "metadata": {
@@ -803,9 +1776,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}

From e7c990a72992a31366ef4bc1118fa664ce25ad7e Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 16 Nov 2021 22:27:18 +0900
Subject: [PATCH 0534/1080] clean up doc dir (#563)

---
 Introduction/introduction.ipynb               |  44 --------
 docs/README.md                                |  11 --
 docs/index.rst                                |  16 +--
 docs/jupyternotebook2rst.py                   |  93 -----------------
 .../aerial_navigation.rst                     |   0
 .../rocket_powered_landing.rst                |   0
 .../{ => appendix}/Kalmanfilter_basics.rst    |   0
 .../{ => appendix}/Kalmanfilter_basics_2.rst  |   0
 .../Kalmanfilter_basics_2_5_0.png             | Bin
 .../Kalmanfilter_basics_14_1.png              | Bin
 .../Kalmanfilter_basics_16_0.png              | Bin
 .../Kalmanfilter_basics_19_1.png              | Bin
 .../Kalmanfilter_basics_21_1.png              | Bin
 .../Kalmanfilter_basics_22_0.png              | Bin
 .../Kalmanfilter_basics_28_1.png              | Bin
 docs/modules/{ => appendix}/appendix.rst      |   0
 .../Planar_Two_Link_IK.rst                    |   0
 .../Planar_Two_Link_IK_12_0.png               | Bin
 .../Planar_Two_Link_IK_5_0.png                | Bin
 .../Planar_Two_Link_IK_7_0.png                | Bin
 .../Planar_Two_Link_IK_9_0.png                | Bin
 .../{ => arm_navigation}/arm_navigation.rst   |   0
 .../extended_kalman_filter_localization.rst   |   0
 ...xtended_kalman_filter_localization_1_0.png | Bin
 .../{ => localization}/localization.rst       |   0
 docs/modules/{ => mapping}/mapping.rst        |   0
 .../{ => path_planning}/path_planning.rst     |   0
 docs/modules/{ => path_planning}/rrt_star.rst |   0
 .../rrt_star_files/rrt_star_1_0.png           | Bin
 ..._predictive_speed_and_steering_control.rst |   0
 .../{ => path_tracking}/cgmres_nmpc.rst       |   0
 .../{ => path_tracking}/path_tracking.rst     |   0
 docs/modules/{ => slam}/FastSLAM1.rst         |   0
 .../FastSLAM1_files/FastSLAM1_12_0.png        | Bin
 .../FastSLAM1_files/FastSLAM1_12_1.png        | Bin
 .../FastSLAM1_files/FastSLAM1_1_0.png         | Bin
 docs/modules/{ => slam}/slam.rst              |   0
 docs/notebook_template.ipynb                  |  96 ------------------
 38 files changed, 8 insertions(+), 252 deletions(-)
 delete mode 100644 Introduction/introduction.ipynb
 delete mode 100644 docs/jupyternotebook2rst.py
 rename docs/modules/{ => aerial_navigation}/aerial_navigation.rst (100%)
 rename docs/modules/{ => aerial_navigation}/rocket_powered_landing.rst (100%)
 rename docs/modules/{ => appendix}/Kalmanfilter_basics.rst (100%)
 rename docs/modules/{ => appendix}/Kalmanfilter_basics_2.rst (100%)
 rename docs/modules/{ => appendix}/Kalmanfilter_basics_2_files/Kalmanfilter_basics_2_5_0.png (100%)
 rename docs/modules/{ => appendix}/Kalmanfilter_basics_files/Kalmanfilter_basics_14_1.png (100%)
 rename docs/modules/{ => appendix}/Kalmanfilter_basics_files/Kalmanfilter_basics_16_0.png (100%)
 rename docs/modules/{ => appendix}/Kalmanfilter_basics_files/Kalmanfilter_basics_19_1.png (100%)
 rename docs/modules/{ => appendix}/Kalmanfilter_basics_files/Kalmanfilter_basics_21_1.png (100%)
 rename docs/modules/{ => appendix}/Kalmanfilter_basics_files/Kalmanfilter_basics_22_0.png (100%)
 rename docs/modules/{ => appendix}/Kalmanfilter_basics_files/Kalmanfilter_basics_28_1.png (100%)
 rename docs/modules/{ => appendix}/appendix.rst (100%)
 rename docs/modules/{ => arm_navigation}/Planar_Two_Link_IK.rst (100%)
 rename docs/modules/{ => arm_navigation}/Planar_Two_Link_IK_files/Planar_Two_Link_IK_12_0.png (100%)
 rename docs/modules/{ => arm_navigation}/Planar_Two_Link_IK_files/Planar_Two_Link_IK_5_0.png (100%)
 rename docs/modules/{ => arm_navigation}/Planar_Two_Link_IK_files/Planar_Two_Link_IK_7_0.png (100%)
 rename docs/modules/{ => arm_navigation}/Planar_Two_Link_IK_files/Planar_Two_Link_IK_9_0.png (100%)
 rename docs/modules/{ => arm_navigation}/arm_navigation.rst (100%)
 rename docs/modules/{ => localization}/extended_kalman_filter_localization.rst (100%)
 rename docs/modules/{ => localization}/extended_kalman_filter_localization_files/extended_kalman_filter_localization_1_0.png (100%)
 rename docs/modules/{ => localization}/localization.rst (100%)
 rename docs/modules/{ => mapping}/mapping.rst (100%)
 rename docs/modules/{ => path_planning}/path_planning.rst (100%)
 rename docs/modules/{ => path_planning}/rrt_star.rst (100%)
 rename docs/modules/{ => path_planning}/rrt_star_files/rrt_star_1_0.png (100%)
 rename docs/modules/{ => path_tracking}/Model_predictive_speed_and_steering_control.rst (100%)
 rename docs/modules/{ => path_tracking}/cgmres_nmpc.rst (100%)
 rename docs/modules/{ => path_tracking}/path_tracking.rst (100%)
 rename docs/modules/{ => slam}/FastSLAM1.rst (100%)
 rename docs/modules/{ => slam}/FastSLAM1_files/FastSLAM1_12_0.png (100%)
 rename docs/modules/{ => slam}/FastSLAM1_files/FastSLAM1_12_1.png (100%)
 rename docs/modules/{ => slam}/FastSLAM1_files/FastSLAM1_1_0.png (100%)
 rename docs/modules/{ => slam}/slam.rst (100%)
 delete mode 100644 docs/notebook_template.ipynb

diff --git a/Introduction/introduction.ipynb b/Introduction/introduction.ipynb
deleted file mode 100644
index c2fcc709a5..0000000000
--- a/Introduction/introduction.ipynb
+++ /dev/null
@@ -1,44 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Introduction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Ref"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/README.md b/docs/README.md
index 1c6eea4a65..728a81290e 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -24,14 +24,3 @@ if you want to building each time a file is changed:
 sphinx-autobuild . _build/html
 ```
 
-### Jupyter notebook integration
-
-When you want to generate rst files from each jupyter notebooks,
-
-you can use
-
-```
-cd path/to/docs
-python jupyternotebook2rst.py
-```
-
diff --git a/docs/index.rst b/docs/index.rst
index 3798a1a1a6..978c216f11 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -35,14 +35,14 @@ See this paper for more details:
 
    getting_started
    modules/introduction
-   modules/localization
-   modules/mapping
-   modules/slam
-   modules/path_planning
-   modules/path_tracking
-   modules/arm_navigation
-   modules/aerial_navigation
-   modules/appendix
+   modules/localization/localization
+   modules/mapping/mapping
+   modules/slam/slam
+   modules/path_planning/path_planning
+   modules/path_tracking/path_tracking
+   modules/arm_navigation/arm_navigation
+   modules/aerial_navigation/aerial_navigation
+   modules/appendix/appendix
 
 
 Indices and tables
diff --git a/docs/jupyternotebook2rst.py b/docs/jupyternotebook2rst.py
deleted file mode 100644
index 3db67244b6..0000000000
--- a/docs/jupyternotebook2rst.py
+++ /dev/null
@@ -1,93 +0,0 @@
-"""
-
-Jupyter notebook converter to rst file
-
-author: Atsushi Sakai
-
-"""
-import subprocess
-import os.path
-import os
-import glob
-
-
-NOTEBOOK_DIR = "../"
-
-
-def get_notebook_path_list(ndir):
-    path = glob.glob(ndir + "**/*.ipynb", recursive=True)
-    return path
-
-
-def convert_rst(rstpath):
-
-    with open(rstpath, "r") as bfile:
-        filedata = bfile.read()
-
-        # convert from code directive to code-block
-        # because showing code in Sphinx
-        before = ".. code:: ipython3"
-        after = ".. code-block:: ipython3"
-        filedata = filedata.replace(before, after)
-
-    with open(rstpath, "w") as afile:
-        afile.write(filedata)
-
-
-def generate_rst(npath):
-    print("====Start generating rst======")
-
-    # generate dir
-    dirpath = os.path.dirname(npath)
-    # print(dirpath)
-
-    rstpath = os.path.abspath("./modules/" + npath[3:-5] + "rst")
-    # print(rstpath)
-
-    basename = os.path.basename(rstpath)
-
-    cmd = "jupyter nbconvert --to rst "
-    cmd += npath
-    print(cmd)
-    subprocess.call(cmd, shell=True)
-
-    rstpath = dirpath + "/" + basename
-    convert_rst(rstpath)
-
-    # clean up old files
-    cmd = "rm -rf "
-    cmd += "./modules/"
-    cmd += basename[:-4]
-    cmd += "*"
-    # print(cmd)
-    subprocess.call(cmd, shell=True)
-
-    # move files to module dir
-    cmd = "mv "
-    cmd += dirpath
-    cmd += "/*.rst ./modules/"
-    print(cmd)
-    subprocess.call(cmd, shell=True)
-
-    cmd = "mv "
-    cmd += dirpath
-    cmd += "/*_files ./modules/"
-    print(cmd)
-    subprocess.call(cmd, shell=True)
-
-
-def main():
-    print("start!!")
-
-    notebook_path_list = get_notebook_path_list(NOTEBOOK_DIR)
-    # print(notebook_path_list)
-
-    for npath in notebook_path_list:
-        if "template" not in npath:
-            generate_rst(npath)
-
-    print("done!!")
-
-
-if __name__ == '__main__':
-    main()
diff --git a/docs/modules/aerial_navigation.rst b/docs/modules/aerial_navigation/aerial_navigation.rst
similarity index 100%
rename from docs/modules/aerial_navigation.rst
rename to docs/modules/aerial_navigation/aerial_navigation.rst
diff --git a/docs/modules/rocket_powered_landing.rst b/docs/modules/aerial_navigation/rocket_powered_landing.rst
similarity index 100%
rename from docs/modules/rocket_powered_landing.rst
rename to docs/modules/aerial_navigation/rocket_powered_landing.rst
diff --git a/docs/modules/Kalmanfilter_basics.rst b/docs/modules/appendix/Kalmanfilter_basics.rst
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diff --git a/docs/modules/appendix.rst b/docs/modules/appendix/appendix.rst
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diff --git a/docs/modules/Planar_Two_Link_IK.rst b/docs/modules/arm_navigation/Planar_Two_Link_IK.rst
similarity index 100%
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rename to docs/modules/arm_navigation/Planar_Two_Link_IK.rst
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diff --git a/docs/modules/Planar_Two_Link_IK_files/Planar_Two_Link_IK_5_0.png b/docs/modules/arm_navigation/Planar_Two_Link_IK_files/Planar_Two_Link_IK_5_0.png
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diff --git a/docs/modules/Planar_Two_Link_IK_files/Planar_Two_Link_IK_7_0.png b/docs/modules/arm_navigation/Planar_Two_Link_IK_files/Planar_Two_Link_IK_7_0.png
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diff --git a/docs/modules/Planar_Two_Link_IK_files/Planar_Two_Link_IK_9_0.png b/docs/modules/arm_navigation/Planar_Two_Link_IK_files/Planar_Two_Link_IK_9_0.png
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diff --git a/docs/modules/arm_navigation.rst b/docs/modules/arm_navigation/arm_navigation.rst
similarity index 100%
rename from docs/modules/arm_navigation.rst
rename to docs/modules/arm_navigation/arm_navigation.rst
diff --git a/docs/modules/extended_kalman_filter_localization.rst b/docs/modules/localization/extended_kalman_filter_localization.rst
similarity index 100%
rename from docs/modules/extended_kalman_filter_localization.rst
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diff --git a/docs/modules/localization.rst b/docs/modules/localization/localization.rst
similarity index 100%
rename from docs/modules/localization.rst
rename to docs/modules/localization/localization.rst
diff --git a/docs/modules/mapping.rst b/docs/modules/mapping/mapping.rst
similarity index 100%
rename from docs/modules/mapping.rst
rename to docs/modules/mapping/mapping.rst
diff --git a/docs/modules/path_planning.rst b/docs/modules/path_planning/path_planning.rst
similarity index 100%
rename from docs/modules/path_planning.rst
rename to docs/modules/path_planning/path_planning.rst
diff --git a/docs/modules/rrt_star.rst b/docs/modules/path_planning/rrt_star.rst
similarity index 100%
rename from docs/modules/rrt_star.rst
rename to docs/modules/path_planning/rrt_star.rst
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diff --git a/docs/modules/Model_predictive_speed_and_steering_control.rst b/docs/modules/path_tracking/Model_predictive_speed_and_steering_control.rst
similarity index 100%
rename from docs/modules/Model_predictive_speed_and_steering_control.rst
rename to docs/modules/path_tracking/Model_predictive_speed_and_steering_control.rst
diff --git a/docs/modules/cgmres_nmpc.rst b/docs/modules/path_tracking/cgmres_nmpc.rst
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rename from docs/modules/cgmres_nmpc.rst
rename to docs/modules/path_tracking/cgmres_nmpc.rst
diff --git a/docs/modules/path_tracking.rst b/docs/modules/path_tracking/path_tracking.rst
similarity index 100%
rename from docs/modules/path_tracking.rst
rename to docs/modules/path_tracking/path_tracking.rst
diff --git a/docs/modules/FastSLAM1.rst b/docs/modules/slam/FastSLAM1.rst
similarity index 100%
rename from docs/modules/FastSLAM1.rst
rename to docs/modules/slam/FastSLAM1.rst
diff --git a/docs/modules/FastSLAM1_files/FastSLAM1_12_0.png b/docs/modules/slam/FastSLAM1_files/FastSLAM1_12_0.png
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rename to docs/modules/slam/FastSLAM1_files/FastSLAM1_12_0.png
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similarity index 100%
rename from docs/modules/FastSLAM1_files/FastSLAM1_12_1.png
rename to docs/modules/slam/FastSLAM1_files/FastSLAM1_12_1.png
diff --git a/docs/modules/FastSLAM1_files/FastSLAM1_1_0.png b/docs/modules/slam/FastSLAM1_files/FastSLAM1_1_0.png
similarity index 100%
rename from docs/modules/FastSLAM1_files/FastSLAM1_1_0.png
rename to docs/modules/slam/FastSLAM1_files/FastSLAM1_1_0.png
diff --git a/docs/modules/slam.rst b/docs/modules/slam/slam.rst
similarity index 100%
rename from docs/modules/slam.rst
rename to docs/modules/slam/slam.rst
diff --git a/docs/notebook_template.ipynb b/docs/notebook_template.ipynb
deleted file mode 100644
index 40943b09a2..0000000000
--- a/docs/notebook_template.ipynb
+++ /dev/null
@@ -1,96 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Title"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'hoge.png'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-d7c1437408c4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"hoge.png\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m600\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, url, filename, format, embed, width, height, retina, unconfined, metadata)\u001b[0m\n\u001b[1;32m   1149\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munconfined\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munconfined\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1150\u001b[0m         super(Image, self).__init__(data=data, url=url, filename=filename, \n\u001b[0;32m-> 1151\u001b[0;31m                 metadata=metadata)\n\u001b[0m\u001b[1;32m   1152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1153\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'width'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, url, filename, metadata)\u001b[0m\n\u001b[1;32m    607\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetadata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    608\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 609\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    610\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    611\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mreload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1180\u001b[0m         \u001b[0;34m\"\"\"Reload the raw data from file or URL.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1181\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1182\u001b[0;31m             \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mImage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1183\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretina\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1184\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_retina_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/anaconda3-4.4.0/lib/python3.6/site-packages/IPython/core/display.py\u001b[0m in \u001b[0;36mreload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    632\u001b[0m         \u001b[0;34m\"\"\"Reload the raw data from file or URL.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 634\u001b[0;31m             \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_flags\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    635\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    636\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'hoge.png'"
-     ]
-    }
-   ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename=\"hoge.png\",width=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![gif](https://github.com/AtsushiSakai/PythonRobotics/raw/master/Localization/extended_kalman_filter/animation.gif)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Section1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Section2\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "F=\n",
-    "\\begin{bmatrix}\n",
-    "1 & 0 & 0 & 0\\\\\n",
-    "0 & 1 & 0 & 0\\\\\n",
-    "0 & 0 & 1 & 0 \\\\\n",
-    "0 & 0 & 0 & 0 \\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Ref"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From 2fef7bc476929a62f304b8aeaebb2452659fb5c0 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 16 Nov 2021 23:06:25 +0900
Subject: [PATCH 0535/1080] clean up doc dir (#565)

* clean up doc dir

* clean up doc dir

* clean up doc dir
---
 .circleci/config.yml                                |   2 +-
 docs/conf.py                                        |   3 ++-
 docs/modules/path_planning/path_planning.rst        |   2 +-
 .../cgmres_nmpc_files/cgmres_nmpc_1_0.png           | Bin
 .../cgmres_nmpc_files/cgmres_nmpc_2_0.png           | Bin
 .../cgmres_nmpc_files/cgmres_nmpc_3_0.png           | Bin
 .../cgmres_nmpc_files/cgmres_nmpc_4_0.png           | Bin
 7 files changed, 4 insertions(+), 3 deletions(-)
 rename docs/modules/{ => path_tracking}/cgmres_nmpc_files/cgmres_nmpc_1_0.png (100%)
 rename docs/modules/{ => path_tracking}/cgmres_nmpc_files/cgmres_nmpc_2_0.png (100%)
 rename docs/modules/{ => path_tracking}/cgmres_nmpc_files/cgmres_nmpc_3_0.png (100%)
 rename docs/modules/{ => path_tracking}/cgmres_nmpc_files/cgmres_nmpc_4_0.png (100%)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 4cb628ecd8..4ced9fb90b 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -12,7 +12,7 @@ jobs:
       - python/load-cache
       - python/install-deps
       - python/save-cache
-      - run: pip install sphinx sphinx-autobuild sphinx-rtd-theme pytest
+      - run: pip install sphinx sphinx-autobuild sphinx-rtd-theme pytest IPython
       - run:
           command: cd docs;make html
           name: doc_build
diff --git a/docs/conf.py b/docs/conf.py
index 7422e2b106..64b32d5047 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -20,7 +20,7 @@
 # -- Project information -----------------------------------------------------
 
 project = 'PythonRobotics'
-copyright = '2018, Atsushi Sakai'
+copyright = '2018-2021, Atsushi Sakai'
 author = 'Atsushi Sakai'
 
 # The short X.Y version
@@ -43,6 +43,7 @@
     'sphinx.ext.mathjax',
     'sphinx.ext.viewcode',
     'sphinx.ext.viewcode',
+    'IPython.sphinxext.ipython_console_highlighting',
 ]
 
 # Add any paths that contain templates here, relative to this directory.
diff --git a/docs/modules/path_planning/path_planning.rst b/docs/modules/path_planning/path_planning.rst
index cfe7b6ad40..60651dd5ff 100644
--- a/docs/modules/path_planning/path_planning.rst
+++ b/docs/modules/path_planning/path_planning.rst
@@ -39,7 +39,7 @@ In the animation, cyan points are searched nodes.
 
 Its heuristic is 2D Euclid distance.
 
-.. _a*-algorithm:
+.. _D*-algorithm:
 
 D\* algorithm
 ~~~~~~~~~~~~~
diff --git a/docs/modules/cgmres_nmpc_files/cgmres_nmpc_1_0.png b/docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_1_0.png
similarity index 100%
rename from docs/modules/cgmres_nmpc_files/cgmres_nmpc_1_0.png
rename to docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_1_0.png
diff --git a/docs/modules/cgmres_nmpc_files/cgmres_nmpc_2_0.png b/docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_2_0.png
similarity index 100%
rename from docs/modules/cgmres_nmpc_files/cgmres_nmpc_2_0.png
rename to docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_2_0.png
diff --git a/docs/modules/cgmres_nmpc_files/cgmres_nmpc_3_0.png b/docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_3_0.png
similarity index 100%
rename from docs/modules/cgmres_nmpc_files/cgmres_nmpc_3_0.png
rename to docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_3_0.png
diff --git a/docs/modules/cgmres_nmpc_files/cgmres_nmpc_4_0.png b/docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_4_0.png
similarity index 100%
rename from docs/modules/cgmres_nmpc_files/cgmres_nmpc_4_0.png
rename to docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_4_0.png

From 7b0522b588301109cb0936e3412c36220ce70aff Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 16 Nov 2021 23:14:26 +0900
Subject: [PATCH 0536/1080] clean up doc system (#566)

* clean up doc dir

* clean up doc dir
---
 README.md        | 1 -
 requirements.txt | 1 +
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c9e45d9363..74c19d7a5e 100644
--- a/README.md
+++ b/README.md
@@ -8,7 +8,6 @@
 [![Build status](https://ci.appveyor.com/api/projects/status/sb279kxuv1be391g?svg=true)](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics)
 [![codecov](https://codecov.io/gh/AtsushiSakai/PythonRobotics/branch/master/graph/badge.svg)](https://codecov.io/gh/AtsushiSakai/PythonRobotics)
 [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/AtsushiSakai/PythonRobotics.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/AtsushiSakai/PythonRobotics/context:python)
-[![CodeFactor](https://www.codefactor.io/repository/github/atsushisakai/pythonrobotics/badge/master)](https://www.codefactor.io/repository/github/atsushisakai/pythonrobotics/overview/master)
 [![tokei](https://tokei.rs/b1/github/AtsushiSakai/PythonRobotics)](https://github.com/AtsushiSakai/PythonRobotics)
 
 Python codes for robotics algorithm.
diff --git a/requirements.txt b/requirements.txt
index 77a0bea427..7c60c4a24c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,3 +3,4 @@ scipy == 1.7.2
 pandas == 1.3.4
 matplotlib == 3.4.3
 cvxpy == 1.1.17
+IPython == 7.29.0 # For sphinx documentation

From b812cb1fc9baf2197e7ff8d834d1187d012c7021 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 17 Nov 2021 19:42:14 +0900
Subject: [PATCH 0537/1080] clean up doc dir (#567)

---
 README.md        | 2 +-
 requirements.txt | 3 +++
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 74c19d7a5e..5a06f09ca8 100644
--- a/README.md
+++ b/README.md
@@ -117,7 +117,7 @@ For development:
   
 - mypy (for type check)
   
-- Sphinx (for document generation)
+- sphinx (for document generation)
   
 - pycodestyle (for code style check)
 
diff --git a/requirements.txt b/requirements.txt
index 7c60c4a24c..dbe243878d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,4 +3,7 @@ scipy == 1.7.2
 pandas == 1.3.4
 matplotlib == 3.4.3
 cvxpy == 1.1.17
+pytest == 6.2.5 # For unit test
+pytest-xdist == 2.4.0 # For unit test
+sphinx == 4.3.0 # For sphinx documentation
 IPython == 7.29.0 # For sphinx documentation

From 6f309828ba0e204833574b9f72f1c6ee6e74369f Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 17 Nov 2021 20:04:09 +0900
Subject: [PATCH 0538/1080] clean up doc dir (#568)

* clean up doc dir

* clean up doc dir

* clean up doc dir

* clean up doc dir
---
 .circleci/config.yml | 2 +-
 requirements.txt     | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 4ced9fb90b..539c0eeac0 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -12,7 +12,7 @@ jobs:
       - python/load-cache
       - python/install-deps
       - python/save-cache
-      - run: pip install sphinx sphinx-autobuild sphinx-rtd-theme pytest IPython
+      - run: pip install -r requirements.txt
       - run:
           command: cd docs;make html
           name: doc_build
diff --git a/requirements.txt b/requirements.txt
index dbe243878d..e11ba13b8d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,5 +5,6 @@ matplotlib == 3.4.3
 cvxpy == 1.1.17
 pytest == 6.2.5 # For unit test
 pytest-xdist == 2.4.0 # For unit test
-sphinx == 4.3.0 # For sphinx documentation
+sphinx < 4.0 # For sphinx documentation
+sphinx_rtd_theme == 1.0.0
 IPython == 7.29.0 # For sphinx documentation

From 137e372db1fb6455cfe6fdce263d63e7dd8e259c Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 17 Nov 2021 22:58:23 +0900
Subject: [PATCH 0539/1080] fix CI (#569)

* fix CI

* fix CI

* fix CI

* fix CI

* fix CI

* fix CI

* fix CI

* fix CI
---
 .circleci/config.yml           | 12 ++++++------
 .github/workflows/Linux_CI.yml |  6 +++++-
 .github/workflows/MacOS_CI.yml |  7 ++++++-
 .github/workflows/codeql.yml   |  4 ++--
 appveyor.yml                   |  4 ++++
 5 files changed, 23 insertions(+), 10 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 539c0eeac0..4866240bd4 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -1,7 +1,7 @@
 version: 2.1
 
 orbs:
-  python: circleci/python@0.2.1
+  python: circleci/python@1.4.0
 
 jobs:
   build_doc:
@@ -9,13 +9,13 @@ jobs:
       - image: circleci/python:3.9
     steps:
       - checkout
-      - python/load-cache
-      - python/install-deps
-      - python/save-cache
-      - run: pip install -r requirements.txt
       - run:
-          command: cd docs;make html
           name: doc_build
+          command: |
+            python -m venv venv
+            . venv/bin/activate
+            pip install -r requirements.txt
+            cd docs;make html
       - store_artifacts:
           path: docs/_build/html/
           destination: html
diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index 2962028d6f..4e2e57f8e4 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -1,6 +1,10 @@
 name: Linux_CI
 
-on: [push, pull_request]
+on:
+  push:
+    branches:
+      - master
+  pull_request:
 
 jobs:
   build:
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index 6b3ffa2588..9bfb4b6fd3 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -4,7 +4,12 @@ name: MacOS_CI
 
 # Controls when the action will run. Triggers the workflow on push or pull request
 # events but only for the master branch
-on: [push, pull_request]
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+
 
 jobs:
   build:
diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index 5e6c03aa16..148260a835 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -2,8 +2,8 @@ name: "Code scanning - action"
 
 on:
   push:
-    branches-ignore:
-      - 'dependabot/**'
+    branches:
+      - master
   pull_request:
   schedule:
     - cron: '0 19 * * 0'
diff --git a/appveyor.yml b/appveyor.yml
index efbb96131c..16a5d7751d 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -10,6 +10,10 @@ environment:
   matrix:
      - PYTHON_DIR: C:\Python39-x64
 
+branches:
+  only:
+    - master
+
 init:
   - "ECHO %PYTHON_DIR%"
 

From 35984e8978cff3f71bcdff26aea235d3ea433ef0 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 18 Nov 2021 22:35:10 +0900
Subject: [PATCH 0540/1080] clean up localization docs (#570)

* clean up localization docs

* clean up localization docs

* clean up localization docs
---
 Localization/Kalmanfilter_basics.ipynb        | 769 ------------------
 Localization/Kalmanfilter_basics_2.ipynb      | 381 ---------
 Localization/bayes_filter.png                 | Bin 1088102 -> 0 bytes
 Localization/extended_kalman_filter/ekf.png   | Bin 113010 -> 0 bytes
 .../extended_kalman_filter_localization.ipynb | 264 ------
 .../particle_filter/particle_filter.ipynb     |  72 --
 docs/modules/appendix/Kalmanfilter_basics.rst |   8 +-
 .../extended_kalman_filter_localization.rst   |  12 +-
 docs/modules/localization/localization.rst    |  26 +-
 9 files changed, 28 insertions(+), 1504 deletions(-)
 delete mode 100644 Localization/Kalmanfilter_basics.ipynb
 delete mode 100644 Localization/Kalmanfilter_basics_2.ipynb
 delete mode 100644 Localization/bayes_filter.png
 delete mode 100644 Localization/extended_kalman_filter/ekf.png
 delete mode 100644 Localization/extended_kalman_filter/extended_kalman_filter_localization.ipynb
 delete mode 100644 Localization/particle_filter/particle_filter.ipynb

diff --git a/Localization/Kalmanfilter_basics.ipynb b/Localization/Kalmanfilter_basics.ipynb
deleted file mode 100644
index c501b117de..0000000000
--- a/Localization/Kalmanfilter_basics.ipynb
+++ /dev/null
@@ -1,769 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## KF Basics - Part I\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Introduction\n",
-    "#### What is the need to describe belief in terms of PDF's?\n",
-    "This is because robot environments are stochastic. A robot environment may have cows with Tesla by side. That is a robot and it's environment cannot be deterministically modelled(e.g as a function of something like time t). In the real world sensors are also error prone, and hence there'll be a set of values with a mean and variance that it can take. Hence, we always have to model around some mean and variances associated."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### What is Expectation of a Random Variables?\n",
-    " Expectation is nothing but an average of the probabilites\n",
-    " \n",
-    "$$\\mathbb E[X] = \\sum_{i=1}^n p_ix_i$$\n",
-    "\n",
-    "In the continous form,\n",
-    "\n",
-    "$$\\mathbb E[X] = \\int_{-\\infty}^\\infty x\\, f(x) \\,dx$$\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.4000000000000001\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import random\n",
-    "x=[3,1,2]\n",
-    "p=[0.1,0.3,0.4]\n",
-    "E_x=np.sum(np.multiply(x,p))\n",
-    "print(E_x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### What is the advantage of representing the belief as a unimodal as opposed to multimodal?\n",
-    "Obviously, it makes sense because we can't multiple probabilities to a car moving for two locations. This would be too confusing and the information will not be useful."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Variance, Covariance and Correlation\n",
-    "\n",
-    "#### Variance\n",
-    "Variance is the spread of the data. The mean does'nt tell much **about** the data. Therefore the variance tells us about the **story** about the data meaning the spread of the data.\n",
-    "\n",
-    "$$\\mathit{VAR}(X) = \\frac{1}{n}\\sum_{i=1}^n (x_i - \\mu)^2$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.0224618077401504"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x=np.random.randn(10)\n",
-    "np.var(x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Covariance\n",
-    "\n",
-    "This is for a multivariate distribution. For example, a robot in 2-D space can take values in both x and y. To describe them, a normal distribution with mean in both x and y is needed.\n",
-    "\n",
-    "For a multivariate distribution, mean $\\mu$ can be represented as a matrix, \n",
-    "\n",
-    "$$\n",
-    "\\mu = \\begin{bmatrix}\\mu_1\\\\\\mu_2\\\\ \\vdots \\\\\\mu_n\\end{bmatrix}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "Similarly, variance can also be represented.\n",
-    "\n",
-    "But an important concept is that in the same way as every variable or dimension has a variation in its values, it is also possible that there will be values on how they **together vary**. This is also a measure of how two datasets are related to each other or **correlation**.\n",
-    "\n",
-    "For example, as height increases weight also generally increases. These variables are correlated. They are positively correlated because as one variable gets larger so does the other.\n",
-    "\n",
-    "We use a **covariance matrix** to denote covariances of a multivariate normal distribution:\n",
-    "$$\n",
-    "\\Sigma = \\begin{bmatrix}\n",
-    "  \\sigma_1^2 & \\sigma_{12} & \\cdots & \\sigma_{1n} \\\\\n",
-    "  \\sigma_{21} &\\sigma_2^2 & \\cdots & \\sigma_{2n} \\\\\n",
-    "  \\vdots  & \\vdots  & \\ddots & \\vdots  \\\\\n",
-    "  \\sigma_{n1} & \\sigma_{n2} & \\cdots & \\sigma_n^2\n",
-    " \\end{bmatrix}\n",
-    "$$\n",
-    "\n",
-    "**Diagonal** - Variance of each variable associated. \n",
-    "\n",
-    "**Off-Diagonal** - covariance between ith and jth variables.\n",
-    "\n",
-    "$$\\begin{aligned}VAR(X) = \\sigma_x^2 &=  \\frac{1}{n}\\sum_{i=1}^n(X - \\mu)^2\\\\\n",
-    "COV(X, Y) = \\sigma_{xy} &= \\frac{1}{n}\\sum_{i=1}^n[(X-\\mu_x)(Y-\\mu_y)\\big]\\end{aligned}$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.08868895, 0.05064471, 0.08855629],\n",
-       "       [0.05064471, 0.06219243, 0.11555291],\n",
-       "       [0.08855629, 0.11555291, 0.21534324]])"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x=np.random.random((3,3))\n",
-    "np.cov(x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Covariance taking the data as **sample** with $\\frac{1}{N-1}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.1571437 , -0.00766623],\n",
-       "       [-0.00766623,  0.13957621]])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x_cor=np.random.rand(1,10)\n",
-    "y_cor=np.random.rand(1,10)\n",
-    "np.cov(x_cor,y_cor)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Covariance taking the data as **population** with $\\frac{1}{N}$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.14142933, -0.0068996 ],\n",
-       "       [-0.0068996 ,  0.12561859]])"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.cov(x_cor,y_cor,bias=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Gaussians \n",
-    "\n",
-    "#### Central Limit Theorem\n",
-    "\n",
-    "According to this theorem, the average of n samples of random and independent variables tends to follow a normal distribution as we increase the sample size.(Generally, for n>=30)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([ 1.,  4.,  9., 12., 12., 20., 16., 16.,  4.,  6.]),\n",
-       " array([5.30943011, 5.34638597, 5.38334183, 5.42029769, 5.45725355,\n",
-       "        5.49420941, 5.53116527, 5.56812114, 5.605077  , 5.64203286,\n",
-       "        5.67898872]),\n",
-       " <a list of 10 Patch objects>)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import random\n",
-    "a=np.zeros((100,))\n",
-    "for i in range(100):\n",
-    "    x=[random.uniform(1,10) for _ in range(1000)]\n",
-    "    a[i]=np.sum(x,axis=0)/1000\n",
-    "plt.hist(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Gaussian Distribution\n",
-    "A Gaussian is a *continuous probability distribution* that is completely described with two parameters, the mean ($\\mu$) and the variance ($\\sigma^2$). It is defined as:\n",
-    "\n",
-    "$$ \n",
-    "f(x, \\mu, \\sigma) = \\frac{1}{\\sigma\\sqrt{2\\pi}} \\exp\\big [{-\\frac{(x-\\mu)^2}{2\\sigma^2} }\\big ]\n",
-    "$$\n",
-    "Range is $$[-\\inf,\\inf] $$\n",
-    "\n",
-    "\n",
-    "This is just a function of mean($\\mu$) and standard deviation ($\\sigma$) and what gives the normal distribution the charecteristic **bell curve**. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.mlab as mlab\n",
-    "import math\n",
-    "import scipy.stats\n",
-    "\n",
-    "mu = 0\n",
-    "variance = 5\n",
-    "sigma = math.sqrt(variance)\n",
-    "x = np.linspace(mu - 5*sigma, mu + 5*sigma, 100)\n",
-    "plt.plot(x,scipy.stats.norm.pdf(x, mu, sigma))\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Why do we need Gaussian distributions?\n",
-    "Since it becomes really difficult in the real world to deal with multimodal distribution as we cannot put the belief in two seperate location of the robots. This becomes really confusing and in practice impossible to comprehend. \n",
-    "Gaussian probability distribution allows us to drive the robots using only one mode with peak at the mean with some variance."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Gaussian Properties\n",
-    "\n",
-    "**Multiplication**\n",
-    "\n",
-    "\n",
-    "For the measurement update in a Bayes Filter, the algorithm tells us to multiply the Prior P(X_t) and measurement P(Z_t|X_t) to calculate the posterior:\n",
-    "\n",
-    "$$P(X \\mid Z) = \\frac{P(Z \\mid X)P(X)}{P(Z)}$$\n",
-    "\n",
-    "Here for the numerator,  $P(Z \\mid X),P(X)$ both are gaussian.\n",
-    "\n",
-    "$N(\\mu_1, \\sigma_1^2)$ and $N(\\mu_2, \\sigma_2^2)$ are their mean and variances.\n",
-    "\n",
-    "New mean is \n",
-    "\n",
-    "$$\\mu_\\mathtt{new} = \\frac{\\mu_1 \\sigma_2^2 + \\mu_2 \\sigma_1^2}{\\sigma_1^2+\\sigma_2^2}$$\n",
-    "New variance is\n",
-    "$$\\sigma_\\mathtt{new} = \\frac{\\sigma_1^2\\sigma_2^2}{\\sigma_1^2+\\sigma_2^2}$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "New mean is at:  5.0\n",
-      "New variance is:  1.0\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.mlab as mlab\n",
-    "import math\n",
-    "mu1 = 0\n",
-    "variance1 = 2\n",
-    "sigma = math.sqrt(variance1)\n",
-    "x1 = np.linspace(mu1 - 3*sigma, mu1 + 3*sigma, 100)\n",
-    "plt.plot(x1,scipy.stats.norm.pdf(x1, mu1, sigma),label='prior')\n",
-    "\n",
-    "mu2 = 10\n",
-    "variance2 = 2\n",
-    "sigma = math.sqrt(variance2)\n",
-    "x2 = np.linspace(mu2 - 3*sigma, mu2 + 3*sigma, 100)\n",
-    "plt.plot(x2,scipy.stats.norm.pdf(x2, mu2, sigma),\"g-\",label='measurement')\n",
-    "\n",
-    "\n",
-    "mu_new=(mu1*variance2+mu2*variance1)/(variance1+variance2)\n",
-    "print(\"New mean is at: \",mu_new)\n",
-    "var_new=(variance1*variance2)/(variance1+variance2)\n",
-    "print(\"New variance is: \",var_new)\n",
-    "sigma = math.sqrt(var_new)\n",
-    "x3 = np.linspace(mu_new - 3*sigma, mu_new + 3*sigma, 100)\n",
-    "plt.plot(x3,scipy.stats.norm.pdf(x3, mu_new, var_new),label=\"posterior\")\n",
-    "plt.legend(loc='upper left')\n",
-    "plt.xlim(-10,20)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Addition**\n",
-    "\n",
-    "The motion step involves a case of adding up probability (Since it has to abide the Law of Total Probability). This means their beliefs are to be added and hence two gaussians. They are simply arithmetic additions of the two.\n",
-    "\n",
-    "$$\\begin{gathered}\\mu_x = \\mu_p + \\mu_z \\\\\n",
-    "\\sigma_x^2 = \\sigma_z^2+\\sigma_p^2\\, \\square\\end{gathered}$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "New mean is at:  15\n",
-      "New variance is:  2\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.mlab as mlab\n",
-    "import math\n",
-    "mu1 = 5\n",
-    "variance1 = 1\n",
-    "sigma = math.sqrt(variance1)\n",
-    "x1 = np.linspace(mu1 - 3*sigma, mu1 + 3*sigma, 100)\n",
-    "plt.plot(x1,scipy.stats.norm.pdf(x1, mu1, sigma),label='prior')\n",
-    "\n",
-    "mu2 = 10\n",
-    "variance2 = 1\n",
-    "sigma = math.sqrt(variance2)\n",
-    "x2 = np.linspace(mu2 - 3*sigma, mu2 + 3*sigma, 100)\n",
-    "plt.plot(x2,scipy.stats.norm.pdf(x2, mu2, sigma),\"g-\",label='measurement')\n",
-    "\n",
-    "\n",
-    "mu_new=mu1+mu2\n",
-    "print(\"New mean is at: \",mu_new)\n",
-    "var_new=(variance1+variance2)\n",
-    "print(\"New variance is: \",var_new)\n",
-    "sigma = math.sqrt(var_new)\n",
-    "x3 = np.linspace(mu_new - 3*sigma, mu_new + 3*sigma, 100)\n",
-    "plt.plot(x3,scipy.stats.norm.pdf(x3, mu_new, var_new),label=\"posterior\")\n",
-    "plt.legend(loc='upper left')\n",
-    "plt.xlim(-10,20)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#Example from:\n",
-    "#https://scipython.com/blog/visualizing-the-bivariate-gaussian-distribution/\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib import cm\n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "\n",
-    "# Our 2-dimensional distribution will be over variables X and Y\n",
-    "N = 60\n",
-    "X = np.linspace(-3, 3, N)\n",
-    "Y = np.linspace(-3, 4, N)\n",
-    "X, Y = np.meshgrid(X, Y)\n",
-    "\n",
-    "# Mean vector and covariance matrix\n",
-    "mu = np.array([0., 1.])\n",
-    "Sigma = np.array([[ 1. , -0.5], [-0.5,  1.5]])\n",
-    "\n",
-    "# Pack X and Y into a single 3-dimensional array\n",
-    "pos = np.empty(X.shape + (2,))\n",
-    "pos[:, :, 0] = X\n",
-    "pos[:, :, 1] = Y\n",
-    "\n",
-    "def multivariate_gaussian(pos, mu, Sigma):\n",
-    "    \"\"\"Return the multivariate Gaussian distribution on array pos.\n",
-    "\n",
-    "    pos is an array constructed by packing the meshed arrays of variables\n",
-    "    x_1, x_2, x_3, ..., x_k into its _last_ dimension.\n",
-    "\n",
-    "    \"\"\"\n",
-    "\n",
-    "    n = mu.shape[0]\n",
-    "    Sigma_det = np.linalg.det(Sigma)\n",
-    "    Sigma_inv = np.linalg.inv(Sigma)\n",
-    "    N = np.sqrt((2*np.pi)**n * Sigma_det)\n",
-    "    # This einsum call calculates (x-mu)T.Sigma-1.(x-mu) in a vectorized\n",
-    "    # way across all the input variables.\n",
-    "    fac = np.einsum('...k,kl,...l->...', pos-mu, Sigma_inv, pos-mu)\n",
-    "\n",
-    "    return np.exp(-fac / 2) / N\n",
-    "\n",
-    "# The distribution on the variables X, Y packed into pos.\n",
-    "Z = multivariate_gaussian(pos, mu, Sigma)\n",
-    "\n",
-    "# Create a surface plot and projected filled contour plot under it.\n",
-    "fig = plt.figure()\n",
-    "ax = fig.gca(projection='3d')\n",
-    "ax.plot_surface(X, Y, Z, rstride=3, cstride=3, linewidth=1, antialiased=True,\n",
-    "                cmap=cm.viridis)\n",
-    "\n",
-    "cset = ax.contourf(X, Y, Z, zdir='z', offset=-0.15, cmap=cm.viridis)\n",
-    "\n",
-    "# Adjust the limits, ticks and view angle\n",
-    "ax.set_zlim(-0.15,0.2)\n",
-    "ax.set_zticks(np.linspace(0,0.2,5))\n",
-    "ax.view_init(27, -21)\n",
-    "\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is a 3D projection of the gaussians involved with the lower surface showing the 2D projection of the 3D projection above. The innermost ellipse represents the highest peak, that is the maximum probability for a given (X,Y) value.\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "** numpy einsum examples **"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 0  1  2  3  4]\n",
-      " [ 5  6  7  8  9]\n",
-      " [10 11 12 13 14]\n",
-      " [15 16 17 18 19]\n",
-      " [20 21 22 23 24]]\n",
-      "[0 1 2 3 4]\n",
-      "[[0 1 2]\n",
-      " [3 4 5]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = np.arange(25).reshape(5,5)\n",
-    "b = np.arange(5)\n",
-    "c = np.arange(6).reshape(2,3)\n",
-    "print(a)\n",
-    "print(b)\n",
-    "print(c)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 204,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 30,  80, 130, 180, 230])"
-      ]
-     },
-     "execution_count": 204,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#this is the diagonal sum, i repeated means the diagonal\n",
-    "np.einsum('ij', a)\n",
-    "#this takes the output ii which is the diagonal and outputs to a\n",
-    "np.einsum('ii->i',a)\n",
-    "#this takes in the array A represented by their axes 'ij' and  B by its only axes'j' \n",
-    "#and multiples them element wise\n",
-    "np.einsum('ij,j',a, b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0, 22, 76])"
-      ]
-     },
-     "execution_count": 199,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A = np.arange(3).reshape(3,1)\n",
-    "B = np.array([[ 0,  1,  2,  3],\n",
-    "              [ 4,  5,  6,  7],\n",
-    "              [ 8,  9, 10, 11]])\n",
-    "C=np.multiply(A,B)\n",
-    "np.sum(C,axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0, 22, 76])"
-      ]
-     },
-     "execution_count": 197,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "D = np.array([0,1,2])\n",
-    "E = np.array([[ 0,  1,  2,  3],\n",
-    "              [ 4,  5,  6,  7],\n",
-    "              [ 8,  9, 10, 11]])\n",
-    "\n",
-    "np.einsum('i,ij->i',D,E)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 238,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.contour.QuadContourSet at 0x139196438>"
-      ]
-     },
-     "execution_count": 238,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from scipy.stats import multivariate_normal\n",
-    "x, y = np.mgrid[-5:5:.1, -5:5:.1]\n",
-    "pos = np.empty(x.shape + (2,))\n",
-    "pos[:, :, 0] = x; pos[:, :, 1] = y\n",
-    "rv = multivariate_normal([0.5, -0.2], [[2.0, 0.9], [0.9, 0.5]])\n",
-    "plt.contourf(x, y, rv.pdf(pos))\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### References:\n",
-    "\n",
-    "1. Roger Labbe's [repo](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python) on Kalman Filters. (Majority of the examples in the notes are from this)\n",
-    "\n",
-    "\n",
-    "\n",
-    "2. Probabilistic Robotics by Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press.\n",
-    "\n",
-    "\n",
-    "\n",
-    "3. Scipy [Documentation](https://scipython.com/blog/visualizing-the-bivariate-gaussian-distribution/)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
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-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  },
-  "pycharm": {
-   "stem_cell": {
-    "cell_type": "raw",
-    "source": [],
-    "metadata": {
-     "collapsed": false
-    }
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/Localization/Kalmanfilter_basics_2.ipynb b/Localization/Kalmanfilter_basics_2.ipynb
deleted file mode 100644
index 43f34ec6f2..0000000000
--- a/Localization/Kalmanfilter_basics_2.ipynb
+++ /dev/null
@@ -1,381 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## KF Basics - Part 2\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Probabilistic Generative Laws\n",
-    " \n",
-    "#### 1st Law:\n",
-    "The belief representing the state $x_{t}$, is conditioned on all past states, measurements and controls. This can be shown mathematically by the conditional probability shown below:\n",
-    "\n",
-    "$$p(x_{t} | x_{0:t-1},z_{1:t-1},u_{1:t})$$\n",
-    "\n",
-    "1) $z_{t}$ represents the **measurement**\n",
-    "\n",
-    "2) $u_{t}$ the **motion command**\n",
-    "\n",
-    "3) $x_{t}$ the **state** (can be the position, velocity, etc) of the robot or its environment at time t.\n",
-    "\n",
-    "\n",
-    "'If we know the state $x_{t-1}$ and $u_{t}$, then knowing the states $x_{0:t-2}$, $z_{1:t-1}$ becomes immaterial through the property of **conditional independence**'. The state $x_{t-1}$ introduces a conditional independence between $x_{t}$ and $z_{1:t-1}$, $u_{1:t-1}$\n",
-    "\n",
-    "Therefore the law now holds as:\n",
-    "\n",
-    "$$p(x_{t} | x_{0:t-1},z_{1:t-1},u_{1:t})=p(x_{t} | x_{t-1},u_{t})$$\n",
-    "\n",
-    "#### 2nd Law:\n",
-    "\n",
-    "If $x_{t}$ is complete, then:\n",
-    "\n",
-    "$$p(z_{t} | x_{0:t},z_{1:t-1},u_{1:t})=p(z_{t} | x_{t})$$\n",
-    "\n",
-    "$x_{t}$ is **complete** means that the past states, controls or measurements carry no additional information to predict future.\n",
-    "\n",
-    "$x_{0:t-1}$, $z_{1:t-1}$ and $u_{1:t}$ are **conditionally independent** of $z_{t}$ given $x_{t}$ of complete.\n",
-    "\n",
-    "The filter works in two parts:\n",
-    "\n",
-    "$p(x_{t} | x_{t-1},u_{t})$ -> **State Transition Probability**\n",
-    "\n",
-    "$p(z_{t} | x_{t})$ -> **Measurement Probability**\n",
-    "\n",
-    "\n",
-    "### Conditional dependence and independence example:\n",
-    "\n",
-    "\n",
-    "$\\bullet$**Independent but conditionally dependent**\n",
-    "\n",
-    "Let's say you flip two fair coins\n",
-    "\n",
-    "A - Your first coin flip is heads\n",
-    "\n",
-    "B - Your second coin flip is heads\n",
-    "\n",
-    "C - Your first two flips were the same\n",
-    "\n",
-    "\n",
-    "A and B here are independent. However, A and B are conditionally dependent given C, since if you know C then your first coin flip will inform the other one.\n",
-    "\n",
-    "$\\bullet$**Dependent but conditionally independent**\n",
-    "\n",
-    "A box contains two coins: a regular coin and one fake two-headed coin ((P(H)=1). I choose a coin at random and toss it twice. Define the following events.\n",
-    "\n",
-    "A= First coin toss results in an H.\n",
-    "\n",
-    "B= Second coin toss results in an H.\n",
-    "\n",
-    "C= Coin 1 (regular) has been selected.                                                             \n",
-    "\n",
-    "If we know A has occurred (i.e., the first coin toss has resulted in heads), we would guess that it is more likely that we have chosen Coin 2 than Coin 1. This in turn increases the conditional probability that B occurs. This suggests that A and B are not independent. On the other hand, given C (Coin 1 is selected), A and B are independent.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Bayes Rule:\n",
-    "\n",
-    "\n",
-    "Posterior = $$\\frac{Likelihood*Prior}{Marginal} $$\n",
-    "\n",
-    "Here,\n",
-    "\n",
-    "**Posterior** = Probability of an event occurring based on certain evidence.\n",
-    "\n",
-    "**Likelihood** = How probable is the evidence given the event.\n",
-    "\n",
-    "**Prior** = Probability of the just the event occurring without having any evidence.\n",
-    "\n",
-    "**Marginal** = Probability of the evidence given all the instances of events possible.\n",
-    "\n",
-    "\n",
-    "\n",
-    "Example:\n",
-    "\n",
-    "1% of women have breast cancer (and therefore 99% do not).\n",
-    "80% of mammograms detect breast cancer when it is there (and therefore 20% miss it).\n",
-    "9.6% of mammograms detect breast cancer when its not there (and therefore 90.4% correctly return a negative result).\n",
-    "\n",
-    "We can turn the process above into an equation, which is Bayes Theorem. Here is the equation:\n",
-    "\n",
-    "$\\displaystyle{\\Pr(\\mathrm{A}|\\mathrm{X}) = \\frac{\\Pr(\\mathrm{X}|\\mathrm{A})\\Pr(\\mathrm{A})}{\\Pr(\\mathrm{X|A})\\Pr(\\mathrm{A})+ \\Pr(\\mathrm{X | not \\ A})\\Pr(\\mathrm{not \\ A})}}$\n",
-    "\n",
-    "\n",
-    "$\\bullet$Pr(A|X) = Chance of having cancer (A) given a positive test (X). This is what we want to know: How likely is it to have cancer with a positive result? In our case it was 7.8%.\n",
-    "\n",
-    "$\\bullet$Pr(X|A) = Chance of a positive test (X) given that you had cancer (A). This is the chance of a true positive, 80% in our case.\n",
-    "\n",
-    "$\\bullet$Pr(A) = Chance of having cancer (1%).\n",
-    "\n",
-    "$\\bullet$Pr(not A) = Chance of not having cancer (99%).\n",
-    "\n",
-    "$\\bullet$Pr(X|not A) = Chance of a positive test (X) given that you didn't have cancer (~A). This is a false positive, 9.6% in our case.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Bayes Filter Algorithm\n",
-    "\n",
-    "The basic filter algorithm is:\n",
-    "\n",
-    "for all $x_{t}$:\n",
-    "\n",
-    "1. $\\overline{bel}(x_t) = \\int p(x_t | u_t, x_{t-1}) bel(x_{t-1})dx$\n",
-    "\n",
-    "2. $bel(x_t) = \\eta p(z_t | x_t) \\overline{bel}(x_t)$\n",
-    "\n",
-    "end.\n",
-    "\n",
-    "\n",
-    "$\\rightarrow$The first step in filter is to calculate the prior for the next step that uses the bayes theorem. This is the **Prediction** step. The belief, $\\overline{bel}(x_t)$, is **before** incorporating measurement($z_{t}$) at time t=t. This is the step where the motion occurs and information is lost because the means and covariances of the gaussians are added. The RHS of the equation incorporates the law of total probability for prior calculation.\n",
-    "\n",
-    "$\\rightarrow$ This is the **Correction** or update step that calculates the belief of the robot **after** taking into account the measurement($z_{t}$) at time t=t. This is where we incorporate the sensor information for the whereabouts of the robot. We gain information here as the gaussians get multiplied here. (Multiplication of gaussian values allows the resultant to lie in between these numbers and the resultant covariance is smaller.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Bayes filter localization example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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vWL169Uq9enW/nW5Qyr+yXL5yvVy5crV06tS5dO3atXTtXEpnjtKJ42ope3fuLGvXrC67d+0qR48eKdevXi1du3Quly9f5rnLpTP3deGB6zeulxvXb5T845zwOnmxUyfOlSKq3NH6J/xO1NO5dO7SjaNLAQAnC7D5Trki/MtXAhdIpVs37gPm9WvXAqsLOIjktY7fXQDJb/DoAowePXqk3nPnz6feW3r1yvmr3H/lypUcN67Xezulsan2L/75PkJbX6fOXUr3bl05upVr0ObChQvlGnBtG4iC/w3o0yW05QT4XqeOG9RYacNf7ue+Fi62Uxr4/CVocPHSpdC3e9duad8N2neVeq5evRb6dga2eFjSB9QXuvK8/dGc87r928C9cvVK6da9G3h3hy70AyCEaRsqTa8Dt3Pp0upHad+5qQdcxdd67Vfb66e/u0oLaG/fX7x0uVy9BhNZvA/cLaFNvlU41um5nj16po/tH2+1H6UTj+Z8F/C3TdLF4m/p3o065YsLFy+US9DLOm1T9x62DX6ibdLNI4W21HPQnv6wvd7XraFxixelqof8V2l+JXh27QIeIGXfdqad9q+c7W/p4iFMf3eiXV27Ahs8gz/wpNc1aO199rXyw83BD6ngjhvhT9vULf3Tpdhf4gCRQ1vhpg3ca/G39JeX/OzRnbbzeRVaSl/h5760HTmqD1F/18CTNtelxXXwEndwE5Pgrl6gDZYrXAsuwO0CcZQz++gKdShP1u1hfza84XNpI+dFI+3ni3LfFbqEH6CV/CecbtTXK3DhSeqz3bZBuI1+aOhvq3ze/pPXc7/4A09+l8cDn2fFz3Z1EpbyY//w/A3/5LOlq7zX31ztuJY7c1uFwTORVeoQL+FGX/HZI3zXGdorp9BFagPQaqRpp3yHTnw29dgf1ife0lhe6d27d9p1+fKlDh6lI6EF8gGPR+YaCOBQ5bOFO4CFxf/U2vBHpRE0RmZ6oROVU/FUDuQB5T20hA/UwfaJdJNPpKXXbK9yqJzZ7tQTPOx72gcMZU/6KKsXLl2MbPXojk4G3lX0us+3HhFBH6oHXxtZsn2i3/CTBJSe1inu8njlpSrXtj98xnPqzcvonzwb/aatEb+W3IQ29TnxVx5tv3SS/+Rry9lz50PDfn1vzXl1zGXwD5ahrV8rH9kG+/jm4m8upx1eFL58Lx09L7+Ls3pMOlea28YK0zZ1RS/ZX9Ij/OSDFPlaWKFX+oGT1KGsyj/S4Nz5C+jV7qX3LbeEhy5fvBjaS+7/1tL0g/Sx7kvoAPlD26oNPI+tPXfuXNomzsEv+hGag/J1+Mz+r30CjvCTSuQiPHJJOwFOncG90Qn6Jeoi6aPcqJ/o8MhNI0c3t0GqNPJ6HVh+r5QK6Mh77JjwONIXre/anHPnz4WX5auGruLqYd3qPXWKvBFfRBnhOfnBvlN2qk1FJ1K3fGp/hWfFhLoqJvStP/3FJ7fmk4+OIt7iHw7lszP0lTbyy2Vo2IXv3TjETUa6Bg7ycHfk1KMruHSCh+JH8UxXZRcY8npkFvh+xtaCW8+ePcG9e73OtQ649MtV2hmWa/Fdg6R4W2xDPutHiN5c0+eTlj169khb5Ed1UC/qs1+1kwL/VnIqbfwtfl63r7qDv+ek5WVoLt7C0fb2grf79Okd3X/q9JnoNHlSebH92sQORgDHBrcG5/q71QhubK5Hv/KjJ3Sx/vMXL5Wz+He90GO9e/UEP+CDT/SUtLEvKOrS69DMmsJjLXnxfnWmOtG2qd+lgzpMONoun+kFrbRz56nrIvJq+9UXlga3jvZYb1Cvn15P3+VuvqP77HeZTXn15l74Wd4XX4lzHfcHDrfSDunaoeuhs/0gjo1fo25XB3cG/hX4zjbYJv0/5cd+OnPubM5357e08XqDq31ZfUouWa/opy2V/l6XnF3g5cgi1/RNqk6p443GN5A/QyPo3DHmgNbaGD3IeJEAE/xl/Sju10fthl68wnfh2NY6/ql+8wV02aULFyNL3cRBfHg+9G3RVr0r6spodA0X1TtBnE/7vtEx8nrDj9LG4q2Ba0MBZBu6gYdtj84AN++yf9Ju7Rj3BD5XpMdldKS/tR+Ckb+qLQc3GmwdHhavf7eIv4TJJ19zCzfmOXjA/s14hHP239X4odU/9NHQHZz8zJiPBxsdE2g8Z1uksXDkBfWntL4Ab8vj3fmu762fo2zIWx55Tl3Hc8KPbwoOsQfgZntCf87Z1oZfQwN/a0s4umj3wVUf8rp6A1zUjeFz9KSfOWwD9wkzMgktua32I3S4wb1S1PaBEP/BgU9xvuuuu8vsWQ+UkSNHBteKs30HsB94wU+2xf+6coVAg46mTKIzSb9UbtMKopc2rl5V3nvzrbLik6Vlzzfry/i7p5Qx7W0Rumt0WhcdCagpKtV5rChFed1E5Q5W5pzKrxvKTgf0q2Wfl7fWry7jqO6OBx4so8aOLf37D6gdBCiZSzZRkGQ2mS8MI3OpTKhX5SLjirgGSOHoc+ut5dSpU+WN//h/lw1ceWLq7aV96uTSb+BAFGTPFr4oRwTnhozHPX9riXDB/Lt27ij/6d2FpQ8P/vzZp8vgobfFWMtkVBDBFH97SfgKqgZIWsf4InQOJC9hwM6dPRucfNbD+zauXlNe/+br8sT48WXGQw9lIK4A1GLb+RbgVZA0vNJE2DUQ4+VKo9AJ2uswXUKgjx87Xha/927ZCYiXH5tbJk+bmnoDG7jeL2yF0qCSzyn8DW7WYx9L+ypw1XDoiB89fLjsXL+xfLhjSxkMwFd+9vMybPhwDBJKXueRz87A7k4/dQLnyzjECnoPDJeCq+HSqMWI83wn22PdKKMzZ06XXTu2l68/XVb2cG322JHloccXlL639i060ddRDuLmINiA2HVgXQV/6Z/BsjSwXfTBdepSedAA+Kt7ru/fu68s+92rZTX3D+OYN/+JMnHy5LRbw6xDGdLYly1HR37TAEeZwoc6+RavW+ymKE54c+03q8obK5aXvpx7+M7by9gJE8vAQQO9rVxrOdZNX0lrnT+NvkG9GERgS6NrthNErKu57yw8tOz9hWXJ7n3lXuBNnftYuQ3Fdyu0EYng7nPgCmHi6EoTB9xxOIUHnTujByzVSFbDJwEP7t9ffv+nN8terv3qwQfK8FGjazAHHHSi8gw3Vtmv9UkXf1faSxPOS3fadqPVF7ZDPtu+ZUt5FT1zD4Ae/tnPcB77VMem5bj17Nkrzo33yzPRBfRDnCngKZcwErjYNzdwbnvG8TnP4OcIPLnstdfLOmA/f/u95e77Z5ZuncH7Cvhxr+rOT0t1nFsai3Zf50R1+sET0e7UDZpw6PxfgpcPHzlYtm1bXz76YnOe/4cfPVnaxrbTUO6H/wzOosjop2rImr7TKZOnIRD/qxGzbVe51/5SN+jw7Nu1s2z+cHFZCXT7dc4vfl4GDRqMEw0/AjuyyHNqAf8BMM/HIII4khm6yOfXEYxr/uyBMYe3DkuXN98pK08eDe7P3z+7TL/7rgxM48yH9pWP7Uf7ySM4S28L1Ul7D0ko3/dExzrY3LljR/nT739XDnHbPeAx45lnyugxo+MsOAgQd9ssfVOowxJd5nm+R89II+Crb2KrpD08sOqLFeXtr1eV4dw3854ZZezEidBmUPAL7Mhr7Vf54jJyJGwDIeJncMH6a6l1q4t0RE9jPxa/+p/LynPXyoLJE0rblKml74ABGZQJq9EFabQAeLzSvwWunkobUkfwt59xmqHrvj17yn9e+H6BA8or8+aWEaNGld59akBDudHORV9iC74t2Ft4vdq76hDJTzpXlVaVRuK3bfPm8tpny8qj7WPKPQ89UmVH+yOi3p0HvoX8F7+17pNePilPHjtypHzx7ntlDb9ffmBWmTx9evoltFVnoROkjzJ6GfumHDjA0WnXodQWqNeVXSxkdEI3Bli248iRw2XXlo3lg1WbSm/g/+NPXywjRowsV4DjgFocOoOTn2mDn5Q4+37hQgINrXPqHIMavW7pXU6jI9djV99Zttw7y+wxY8oD8+aV/v36xw4LUxxj96jPfjPIE90C4Oh52hSfA10p/pEF2wJv7tu3r3wEv28DTk+O5xdU++EA4gIDrxv0O7dV/FttaAYw/6wt3CfpbZe0sU1bNm0qv3n/I86W8tiUsWX6vbPKsGEjomMunj2HDcFpu4buAzdtGo9UGHzaNxkQ8ll9EAah9Kn2y+PsmbPl8w/fLx/t3Fvu5P475s8pw0ePxpfqW+0P9skizJtLfrfa4vmbr4u/9DHQcez48bLoN68WteTPH59bhg4b1qE3RLLRIQayHHjIw/pG8rcyGfkXFm3zyHV0AKciy7t37yr/6e1343O88KNnowekezxD+c12t/BUW6bfWvgFT7+3sI82FTCH+vsiA6KNX35V/rh9e/nxpEnlzlmzoiO+HdRhc5BZVYl6XfyEKb1zeAFc9M2adugT2IZjx46WPcBd+PmKco5n/s2CJ0v7hAmV1+xPijJ7M107dE4QB1s+Gz3Z0EoZPM9ged/ePWXT8i/KirMnyzRgzf/HfyxDhgyNDbV+9YlFfSN/K7c394dwpUPO8ymfGwAwqHb82LH4You3Vdv35F0zyr0Pzk4Q5YJBWvtOHKF91ZciWnlSuNH7fgYD/nbo9+rv76FP33rtjdiP8dxz3xNzS1t7e3jbIKd0sO3S0oGsfoEBAnnJc9oA25g2aJ+oS3PYDf2uf7kG//pPjEGkwIL7ZpRxk6eUwYOHBKay1BEcBWmt3Z/1Ab+1jOpz8Rf2jdzk+EZf9Uz58P/5p7KKa3Mnjy8T77ir9OvfP/YjtAC30ACdoH+nj23f6e+p36vOacHn3kb3ZEKA64cPHSr/8e23g9evnn4KH3sEckaQlHbGx7AfKVJX9mt4pmJrlyL31BfaiDv3S6vYYz63btxYfrf88/hjc//hl6UPwXbpyQMCa/3HX0Ev6CvEDw3f689VH8xqPZ+AkfQHkYsXzpeT6IIVb7wZ2vxszmPldgbqjnm0LWIsXtbj/Z5PH3Je2YnPCu7WKc/Gj4IfrcexyjEmxHdi+97fsBFYpfzi2adKe3u7CrD6vi2ecYxge+xn2y6/CF86689CCX7TF/B5Z+hNBeUMOnbXjh1lA7K6hcB01Yil/Id//7+Wn/3852U8YzWeQq9We8DXH3z5uwQawoCVHyv/NGRRYKDi5tWry/tvv1M2rlmLAe5c7sdBnzZpcqKu3uKgJY6nnSiwAJRHKgM3DmSYl/tzHjgOKs+hvJd/8UVZ8smSDFrGTxhfRuFsDR44KMIChDCZKNWIHUwAY+m8RCBgDiBG8SmYMrWMZh23Emg4duJ4ef+DRQxgNpf7Z88u7ePaGdQNKrcQARdPebkqj4r338oTDqxl6m07tpfX/vAH4PUujz32aBl222042L1iJBVmmbcxRFRYjTznxE9lYuQ3gQaUozNB0kgl4XWFaP2GDeWzJUvKveB+790zcJRqlN7rFu8VjorfUmnCYBH6ip8ldAeunz6n0dNBP3b8RFmxcmXZhEA+8dRTZeqUKS0a136TPvyP0Km8FT4dAOHGGHHR+oMD54IT8M8jfIcOEWhgcLR+7boy8LYhZe7ceUT6RkSxx5hh9LsAu1d3Bs0YIAME9qO003HrCDTIW9yXQIN1Y8BPniLQAOztGOZ9e/eWabffUWbffz+OYr868KJ9Im7wqyv46SSIv8pWB5Ivgam7fY17bkA/GpG225Y9OP9ffrGyHMRh1MGace89ZQr87oxGDTRUWsZQgLX0bwINBpV0xlV8Fgd6Df3lz0sYk7Vr15bln35aBg7sH6U0hkHXEAaNoAJvV4Wq8pOeGk6VnzSPUgW+zoMGQsNkXwo3s1vcd+r06bIMw/ANMjVpyuQyYfwEHPQRcaKFH6OL4c/A10AD+EVpg6vwIXXqq4GGKledOzUzpjfK3v37yvsMjOSzOXPnllEjRyZCLQ43BxqksfhLI4MjsmcNNNAfygEVXaXuaxwJ+tBe+WLL1q3low8+KO3gPRtHzlnkSj9xN1OgRwZL1q+cN3pA4+BsXyMLgQlfOViUdmfOnoEnD5Wvvv66rP1qVXnhhZfKfTPuyWzFtcuVDjorDgH9D7oUvvhXuvHvOoriBjdd4/e1fNJHBBsuXr1cDh46WLbv3IG8rufZTmXOY3PK+Lb26BY6CXrjEIGz/SlsB1k6/elbaCf/2afWDElCMz+lq8GjPbt2l23QZvvmLWXS1Cll3py5ZfhtQ3HCCaxBt/ALD0hz/7WQDu38HtjAv0FHiD+1E+hBB8Gn+w4cKCvhF2FL37vh9zuRKfWnfSKtq7xXuvjdw7rkez+94ndp3QxmhKVzsXXb1rL0k0/K6ZMnE+1Xj00cPz6yqVw2MgKQWgHwpLf97rV63jZwQCedLHlNWT2Pw/LNqlXlc+Rp8JAhZcKEiaW9vb0MHTo0uEjMOAzIjfDEWxkUx8zgyrdNHa3qrdEWOfNy/MSJ8sknS8vm9evK3ffcE/jDGBzdeqsBMGgIbRr59hlLpUf9/t2/4Xdwv4IM6uSqaz5Bvzuouu+++8pobF+/vv3oB2aWgS9/NxkO1mORxh4OWGxfBjG0SzkTcev3TmeeNsMzy6DNlGnTyj0zZkRHyoPNxEAA/jf8EbY01IE+RIBqNb7Bzu3by5z588v0qQSqoSe91AridM29yqmOvzLQG/1uZp561IBPE2jILCC86WDaWddDhw9hP3aVDevWZcAyF34fS0BAWfIIx9FIZbahu82w3VVeW59c1EPwXCd4sTuBx+MnTkYHr1+3vpykf6dBm/tn3hefw+wvODpyKu9fBG/7WDl0Eka62ycXmSCxrmT4qJ+5rg62nl306fLPl8c29WdS4x7kaRpB/J7Iw2X8HfVAlfQW/q02/MW2tNpgUNPZ2PU4/599ujT9Pw45mjZ1WvSwPthFBnZXL9WJkx7QOZkZ0SuQDZ4Pr8iz/LMtaRf4V/+hUzlx6mT5HF2wcvmyMhF/YDIB9lEEGgYQXJMO6oKbC6jVclM/5JzEaZXwjDJLnQfgmU/hxzOnT5X7Z80uw5GlStvqqzjD7f0GB5Q/9Zq8Ii/L/o2v4TUDhbbHDFLxUv9sJ/v2nbfeLoOGDikPMzEzdPDgBFx60DfdAKB+b4LJ4Rzqqp8d2HJHU6q+8Zd1nER/rV2/vnwNfWY+8FCZQTDWDMxKk5a+atFHP+nmQMMVeFb6a1fllehN9Jd9oN012Ku/tA5/yRneeY/PL5PQZdGD0CD+HH6GtKkI1vryPbQXayUPGnGP90krB51nsB9bkNENBKj27t2TwNFDDz4Y2puBp16JbQaEvN3ofGELJ0EFYPFDUrTOVdzF/xBBwW+++aZsWr8hNnnG/feV+2bORI/1TXBGXu/G89Hq0ME2pQjP5nhwImetInXWDBb97K3btpWPFy9m8upQGU6wUXltb2+PPKlT8ggPq0PFR5tiUMjvTbBKXS091DdxRnjIwKO65ht02OKPFpVB8MpkggwTkCn9eLOTDAo68y1ytiNyG0RtQctO8S2tsh22CT3mIY+eJFD9Efr9S+Rp9kMPl4kTJ+D3DUoQPwwdKHVi5ArZd01ATT9GuQjOwLSP5DN1fDJU+W77DuLTLIY23vfQww/F14vd5Vn1tLxg3/rZ2KlK6NqI2r9V5sA6trAJlmlnN27eXN7605+iCx55+OHYJm6jPu62rZIG3PRF5WP7K74v57Wx1baKA/LAIYcaSHasc4wA1Sps90Z0/LM//nGZcdddgo6sCdM2Nb6vbdEe2tmeb7JwYxu5FvjIlnJ1CToeJXC3a8/u2KeL1Dd3/rwyoX2cj6c/7Tn1rf2pXpOmjZ7XV7K+UIh2KrMJDkLvzujKk/jY+qnK01Ema7WD21etKf/jv/9fyssvvVKGtY1C2VpR6+Aj3/38gZa/S6AhFA1VoYKfHirM7nQDBNu6anVZ9O67ZceWraU/jv+jMPTtGLfuED7Rc+/lkDHhLDkv5AwTy4yUnOG8ghglyLM9GTSfJXq15LPPyocfflTGtI2NoLe1tZUhCH2TaqTyk/GajAYHY2EE66U09cgM3qdh0iF14Hnk+LHyp7fexFHcUB4hEDAR5T14yOAEGhRqcTWqpdz4vWIbsH/1TwQKAd60ZUv59W9+w2z6reXxefPLCGbtnUVVQMTFlmssUwCusqiGyTqrIDkwVWA0ZgqiwiIupr1+hSB+sHBheXTOnPIgAy9T7YQbyHwqdDEsfEoHlYSOtUrII/V7Pw/4nN0h7S4wS3AEYfx0+XLasLU896MfZXChE1rxbskJD4i/zqKwG+WnMfK+jnoETFEBmWp88OCBsg3j9g0Du0HDbivPPP10GYPDYoqR0U+AkdpJoAEadoIml8mCsCSjARxUqBpnU8h1Qo0memiknRXZtGVz2bxpc9nPwHfGPfeWR+DJAcxIGQ018OUAWv7RwPnbjAaVSm0fBtV+4Np1Z7SlG9e6dzedq0vZgbOynFnA/Xv2lFsxlvdD99uZrXN2yD6y2Nr0JTxfAw1moKhYm4F/TXOOgpT4FB0jefkrjPLHHy6CP/uWyZMmMnghGACN7L8EGmwzsCovVGOpo5FAA9caRyVOgYaQdsgzOrIatsUYNh3F6QwWp+Aojhw5qgwgii4TpG/B8cY1hpo4b5353s2Zeeo2TVlMM2uJwdB42lLh+ymv7dqzN/Kkg/LkE0+kTx2w+UzD1+JdZbLSSBrIHtI2PArYGmDgOpCVOpW89+lEv4Fhm4STO/fRR8sgHFzrFm95TrkyeOBv2x9+57s4youRHSqTf4SXWWscdAdGBw4eLCtwEr/+4qvy0ks/Kw/cN6v06EJ6Hm0x6CXP1FlSPxtdoKzxD/zNajDYcJVP4ujlKjd3ZWB0GVrug9+3bNtaVpH9JdmefvKpMoV+9fsN6apDhBzpgOq4XSY4kSVe/DZAUmurdPN7pSXOCjx3hlRgg2qboM1mAo+Tp00H/pNlJLrmEvwoHXiAinyy9a/lvdX+Fi6Ie1D3VdqCWU2QxNTRvQTUPkMHbwG2MnDf7FnlXmSqHzotKb7wmDMKFZbVVB0mzNCbHrI0fBg5AB+zT3Q6NoD3okWLymnk1kyGWQzqpk6chCziFBgo4d7AVqhkFApk4/g20OBv+U/+SX/QVnnh/PkL5fOVBKqxH8OQoSlTpuLMTYyj6P3qLnF3QCJ9/DTII4xku0D/OvNS5Q3yBRep2JO+PYYj8R6Brw3066zZD0Sehg0n0EDWQZVt4AqafxVzEAXHju/8TJEP+JKDiz7r4cDovfcWwhddGBg9XNoYTPdHVmvqMffQt1XOK/1tv7KqrUv70qaqL6ShfeAhbAfDDow+/PBDdOQ95SGC1QYx1NHCkB5/a/FO22TgyP4/efJUdO8XX35ZtuN0PfP88+WeO++KDOpcmgHQ4KETlwENfGQKusHexlk2qGx2k7Kgnu/es3u5yP0HCH5t274tjmgfaK39GN/WHn/DwUXkFKTEyaMpDX1v/hQuXRQnsXuvW5h8OJHg1Lo1a8rxo8fKHXfcWR4ik3IoExBmXhgKzAAFGhogUTep4+0jda/9Ycqvfd7MdCnDmUnkczsDxiUE1nbxaaDhfuTprjvuIJuDtuFga++iURv8/Wy+Nw3h0zaIu5+Kc1ftB7y8ikD1h8iTNmHiJGbWwd8gjHQx0OAMrH3bE51sO/yujEXnBmsrq/pcPsrMG7IsvBMMpvXHli/9hAD+7RnUjQa2gQblxEC6pYPmLbz/0rl6c5VZ+WEfAdmFyNNxfI/HHn2sjMUn6MFgXd8uLU1jq1+h7pOn9Y3kZy/FrsC/XtMOKNsOzG2f+n/ztm3lD6/9odw2bDj+2NwyjIBjDwaU3eV5AutdgCc29T+tUCcqCym5Yi2py09xtmj3T8A3XzEoXbZ0aXkUX+8BJjdcOiAOlugafEr530kJcfR7bBGfqCLgVRslXNuiPBlwMwi+fQdZml9/w+Tb+fLcc88lcCciPp92iy7/amWpMX3bQjZXvN6hI6jDjDUzeDYho2vQBTt27CgjyRieP29eGcWgXb9OeRYPW9341tLTmhqdWyut1Wm/5BvtuTo4dnXFF0V5MlPp/oceKg8+8ED8mwvhd/SBz0ADZck+bYIL0j7fqUwZ9XvaCi9qi7QvmzZvKe+TpXkU3hk1eky5m5nvydgPdaS8YeBI+VGv6N9pUwwQylMu1UtwDRrSGOhPffYpn/KPtP/8y5XlHbIChhK4v3367fhkk+PHC1P928Dn6egdadHqhZBePdDgLv62ybpcKnT85InYjxXI1CPwu5N5gxnb9Gn58QJQ7mSO+HfIsEX93PjaggzNaKdyID/42+t78H/ffued8P/jBHv1sWsmYQ00iKl+sCj5jPWJr//9ExsOLfz0HscOkbWWjV+LT/BHJlJvv/POMn/O3Iyp1AHyhbBjR3hOnaj/6Hn5Qr5XX1hN5fNqr/wufAMNR48eLdqP1YxxXnzlFfyCmaB0I/JsmyuP4R8B037wOZG0PbZdGapByOoX6lv5jEuNDMA48fM12cPXaN/T2I9JEyckcJRJNuqxBQaTaj8baEDvgLsy5CSAbRCeNsr+NVDtRKiBhnXQZTUBkiO0wUnVAzt3l5/99/9defHFF0v/MSNwsEJgK5BM9eDjh1r+LoGG60wvGvGTK6TJDRUnCqEbAwjLDgzbxx8sKvuZYdD5fBCHZSoG7ioD4XMQ3eGsM8cyiYrETzuvYV5hSHYZPUqEex2oOzt/FkdxKYOiTxZ/XNrHjStTmBUZx6fCKC4qPplJJlNxaEwaZSW2MrrKQ7gW2+H9Gv6BGPmjBBr+8PrrZRNtmPf443FCTTl2xihOLobBpR+dQVicK6b/Ml8k0AAumzBsr5JmP4i6DDQMQ1llTT31y7jipaA0hzSoSymojO85D7OLt8Jk+yKo/HZviRVffVne+OMfy5PPPlvmPPgQgQbW2QJX4+AzDmwtoQN1aSBidKGvsCzWmX/2Cb9VCucRRoXksxWflx2795Yfv/RSuZMBjPeq8OwwYQpDxzQDYGDrcElqgwXea9TeNmhsUy/CrlGOoO/YnsyAgcOGlueefa6MGTUqSsT15a4f1fh3B59O9MFlnGKLPKdw61Y4421A4AaC3gQa7NfD4L127bqygdnjo6Tv3gc/Pmq0Fd48jQNs8MuWdwNX137FwMFHnlNJ1cEG+GpcNVa01YBGZkn4vQMHMYGG3bsJNPSLo6gB0oi5BlkYGmYDVdKyBp1qBoN0lh4qQSO3WWNMH0nL3iwDoKryJcGXRe+9U/rc0jMzRs52jR45soPOjbHnVvqrRqSVJful0rj2rfTvMAxAdg32KVP1Pv64rMSwOQNroGE46XTOLtTBln0I1BsMYq5czKdrq8XZ67BW2qOboUHzN41JezXMe1g68RqBgDNklTyOPOko9sJ51+EQt6ZEzvmhrMYwwTPykrwjzon8o+rjeEkv/hk0WE2f/vbXvyaN7q6yAHkagvMvfYWtE2MJfQVEET2LDon3xZHgpEuDdCJ0Vpx1PI8DfpiZl89XrixfLF9Rnv/xK+WBe+8nWMq+KperA6oT2hnc8ilkftca+EQ5XGd0o+NvgOHSDfoehurZp3eyGw4cPsis0bbyJfKqPn3huR+VqROYkYLvrhFhh8HipMhj4nrxGjMAyEynruhN2mL7pJ50kN7STx5zdsJ05u3omQ3rNyT6P55ZlwWPP0FwalhHoMGZF/GVKsIIHLwedZAFLVRpA+zryjM3Kmu90Cf7Duwvn+I8b2KGV3mexWyXM1K34gw5kK8pmA09gAVuHQew4lRQj10i39sW+7w3WV46ugaPFrJE6wyO+lic3HuZWZ88fnxsxiXk3vurjqqwK8ZS3n82S37583rlAZ18+3nZihXlw/feS0BtGgHBBBqG3hbeUz7l69glYQHH3+JvEEqebAJ4nrNEb9Mu5cmg5pvAXkfK9GNk8BgYdEbKjLg4ZC3cfa4+7ZdQ31MdRVayNdEVyJv32K5tO1hW8uYb0YfO2rcRiHGZk05TI+/SR9jqgHwBVlBtwfC6RdrbHg/XsJsJ8/Wa1eXdt98qDxK0m4OO7EOfOMjvwDVP/st/xFX6ZE02cnYSB3rPnr1lOfZjK8H2l1kaN/Puu6PnlLum3xLECb2RUWrVxurgZeZauYf/vdflRw6iTWc248TBy3blCUe0Dzr4xwTCx49tSyDZYEBg8Zzwbm5L6m2dsK/9rczqvneGF3sRtDhOMHYN/sB6HMXjR46W2wnIPoJtHcxyTQdGnXnQgIiBQWcZ5R3xla46uPb7Rfgu/clvs4Tsge69ehIo71l2MJP2MRmaO3fuKLcSNLpv1n3lLuroQb9fwF8CQGxRg/vNn4DpKB34e4Y29ELX2MffrF0TedIOTUIX3EEQYywDMJcMXkJeO/Gg/pgZDd7jYDJ7igDD57Whyk8jczJTIw/Ofn/86dKyjGD1nWQeTcUfGz5ieOmL/VCWPFro5DPfJXKrUEUtN/WLl3XW5Z0D6OA/MvFjtuCCJ54s49vbszzOASEIRRdYh3D0o/zUjja+lMDl92Tw0UfKsgML71N3bdq2tfz2t78tI7BLT2GfhhNo6AqPdcbeXb/MferccIV/fYpDuWqdlRaer03iWuiE+oYnz6CHV5Jm/9H7H5QFBNYeYTDtPhDyuJwmXvo2flZfAtvCtRoogGOpR7gtlcy3ahMNfB7Bj3GCQ34/S6DhJy/qj00LLGU5egy+Ca+IdkUwNGu+t1qTvpRftTMGv5z42QbsDZs34zNtKCPb2soCAtVjRo5KMFL85W2LvK59ti/kCX0eSRRfk/rV9Z6X/rbRINFBMg2WM2G1lgGjvDb70UeSTdIPf0d/7Ab4G0ZyUkP4yk0oBtwsNeAzwUA/uRYepV71s76gWVkG1o4zc2ygYSa2aSp8b9bLRfi9CeJ3RS/J9+7Lc4UxgNkmCQQB0wwycW/qCz+iwz3/2RcryhuvvVZGjBxZ7iRYOqkVqNYfV9d0AllQi95Ss/sdkB0lLeKkpyKzuRn5Q5eZIbSQQO832KhH5swB78mMFcho0I+H7pAyGV7xv6Qv/kKjzxtfWzkNcOtI10gjg23dyi7k6HcE1sziePappwk4ErjLxE/VU8JqaN6Bc8BVmPal/n0dq2nL5eGKgz79Omz3G4yf7mWy7XHwv5U+NWNAvMMH9IG8I89Yl3ilLXxPkCbwnUiROPzlt2MXx14nyCxzkuCLzz8vL7Hk4AH6VTk3S7HRtT4m7Ib/xVW6GPAVB8d6BgUM6umDGrS5QJBJn2brtu1lDcGv7mQfPoe8TiJL1noz1gCmPrnBKf3hG6bYgqX6Uj4xMO7YQz6XV818MbOhL/r8LBnbBu3WMAYxaK2PcPzwkfLMKy+X5/H7+o4eZVp9DTKQsQ3QykA25gda/i6BhmxE2FKWKjIdfo2haYaWbRi2j99fVA7u3VsGQmhn1qdMmBgn9zRLE7qh1LIpEZ3XkoSWQqwGzHPpRj5lzkaR3MIAxaUTnzAoWvrR4tI+YUIi6ONwQs1o8CGdrYvMdjuwlZEyIw1O8FgYWngpVsBXmV3FrEPQh8H/KdJzXv3jH8pGsjLmo1wnT5qUFFujoc5+X2c2sqvCETDgJ5y/oTSDzU3bt5ZXf/sqezMMKQvmP15uI323GXzHyQeugucRXME3ioPGKXcqgXyhTlWYzypkCqmO4jIE8TUyJp4hUjbv4UcSCc3sD8+5Pq0qJYXGOloDFq7p0IpjmiP9raxVFFpnAQ4zm/MpKZ479+wrL7Ee/i4G0zpRKi0flLTiIuwENDiXgAZ4OpDwHo2Z7YpC47czmAYanJHaum1rlMjgkSNwFF9ItNVIptkFGoRuwO+G0nFjPteaW7rQL50Qbg2CzmHm2KmjboRVI5YHMTjfMMO4fs3acpxA0mzo8tgjjxQN2ylmZFzDq9k0mCFv+t2oNMPQ2ij41HZeh0cYCbUCDaTlw+8GoJxl/JQZqb07dkaxzH74oTiKKrhz586GHlFAOhHg3ihXqgn88Dj4J9DAdWlv/5sdIQorCTQsZHDhspEpU3UU2acBA6Gyt9+VP41QY2Aa3rEu+UXj2/CUVeY8ddxC+93n4/3FH5XPkacZzLhMw1F0cxodxWZgBCrQg1DOdQwGVHZmGYBR8rIJUsDsFyqAuhRpKkSemB3Aqdqz/wCG7Y/lJDO9TyJP4u2gS6ew8qIYpZk2FWA6LjhI0EMj4Lncp0W2b+RTFLh8ZX2rcP7/yz/9U7mTIMkzZEwMRZ6yjINr8qU09Xnbn1kYjYT18Tx/6ie/41zjHBocUW/IkwbWliNPy5cuK8+9+FMM2/0YGpxVAw00tKs8wqeBhsgkuIfJIwycUzkQZDCj4eINDBGM1atvn2Q5HMDZMtCwAodC0Xj5Jy+Wacy6XNdgXWSwcplAA7Cd6dLwXCZQ4fIFmdNaYtytFwqlb+ljZyPNFjl79lzZirO1gUDAenTxuElTylPQ3kCDMzc17Q8ZB770lcr+S+GcJb+htzQyg0e56k5/9oJnzD4yo2HT2vWR51kPPZhUcvv1PHXLN6FDBRUahyfpA8luf8ivVqluD/9yrjcDIwM96zduYGD0Xjl7nEBDextLwO7G6I+L3k2wl3s79JMAPVJjtRsa/tTP+fQzv6zH/Tu0DwZLF7G0zxTv6dOnl0noeNcfi4cDRfETUsMj/rIOHWVlV6fCYEqcLWDLh01g0BneN5jtWkuAat4T2A+c0P4D+lcHljZXXm4RhmctDY7117d/bYd0yyw4fK/N2Yqu+T2OIhJQniBdeix7nvTu3SeyksEhz0S/0Hd+Aj28Kb38l6Bd4MI38gy87qeycor09JVkT71JoHrOgifL/Ecey9IFZwH/W4v12S55QvincLB279lTPmOSYOvmLeUXv/pVlmYoN1dw5uSZ6Fho6XPaY3W+jrR8qh0XTvgFedLhl+4OjMzk24+jaHqqTmhfgo2vEAgf29ZWLjPYk+flcbg5RarYpSFNzvBTMrW+3xxo6EEwugYa1iXQcIzZKDfxeowgzOD+A8t54INIllTZFrMDFPvgC/4W+zyzitShrZKfrqIrEmiAPk2gYceO7eVWeOW+WfeXuwkG9IQG5wlydII2QkKVpAjfNqQdnvF8C3+/Nvzap9+t1Nc1qd6mM7v8ZTKD0bsIypoJY7Axa9M570ydOAvTpRQuubAfEmQI3dG34V9k2/O0rQeBxYv4RB8RJFlC1sHdOP4uhxnO0jvth7KmrWlKB76csA03l45rnJdPnSi4hcGVgYbf/fH1sm/njvLMM8+yPGBC6UfmqXpO/LV/6m6LPCPO8ZFaOIb23CPfeN1Bi3ISHoP3DTT8mkD1KLJjn3lyARkNZsaiN8hmuHaZIIy2qIVo9mbyF7RSczaH9LA5/m6CA7bdIMyKr74qH6APnmMG1omfWwgsxQfgbvH0PgM+HYEG+N2BUBcyJztzKEd1ppf7qcRJJXkpqd5MbHxBxp0ZCC8B3yxKOinyYB3aCHEHvfBI044A8nyuep3+bNGrG/r3DH26A9gbNm1J1sHw9raaYTqSQAOyKuz4dbZbpPzPp/5kMmKAnfrBJfqOirW9BgK69+zF3kcEGpDTdfjYBmRmPfJwAg23wE/nGEzeoD+7iw/0gUihrX+1Q6JslmBsoN+9St92Jjik7db/NBsjgQZmqUcTqL7//lllOpkB8vj5Myw3Bn+Dgvp7ntO/vIpNN8jQhcMG2SfxZ/iubrCOngxAaVD5FPvx+quvBrayZKD6NuyHetJAhrpGWav/gjIAvi3iLo+DeYVtu6BdDTScKu8RJPmaOh6bN69MA+/Bg2pGdfiGh6KzwUfaarPC78CKjaXW0Nx+EYfWod+tr7wXHfxfwd0A6QsMpp34kW7KRuABs5nYbPAXT3kmfd2qJ3UBW92sLfRZ9fdabPefCMLch35cMHdeJh/M5rKEz8DbPrJIgZsDFsHb+/iXOvnrWMJ65bvjjB2Xwe8rPv2svPIP/1AeZOmgdzo2qYEy4HKvYw/Hf7HL0FWf0fZJj5rRcCWZK9X2tQINZHps2Voz4nqgX1748U/wOcZnSY3LauJHg5s6UnjWE/oAXxrYN7bH/Wu0207Muuym/6CBycJfTaB6lRlx+Acep4+fLAt+8uPyLDrtVvoAhAEOYzMeyqc89wMuf5dAQ2WCFhX4oVCabu6gV0W3gUjlB++8Ww7t2RvH/6FWoOEqg0MzGhJooBMczDkykVliPBUKwAY+f8LYLUFRUblTsEz18WefliULWZc90UDD9KxbN9Dg/SpumdJPZ3kapq6KtGVIwdF7G2FRQBzw9iYocoLBxe9e/2NZ/9XXZd6TT2St/RCyDtzpV7huVHNzoEEqfNdotijzZx8OmmTSjVu3sXTi1zi2BBqYZTRVz2ib1xsnUNz8ZxHHOFjQwaLjpXH1vEpdw6MRdfBguvcyItyvE6F/+sWflHkPMSOF8pbxFTqFQepWga+DTwVIAU891MGtle6pTRzqANion5kBS5Z9Vrbu2FVe+cUvyt3T7+BeDTeGvvVch3zwW5QbYVQRWKIYudnAhnWqANy523TszZs2lc+XLSsatpcIlIzGiVYhGlHMwB88E2igDzJYsg8VTOhqZc3Mq791kHSy/EygAZ5cx2ydqa+zna0j0NCfmcCz8KO7ysYRBV4nnBcdOrHtCkwNka2QEteAew283acBdzhpURoHl3x8vHhx2b1la+mHYnmYjWruJtJtfxv8yYBOYgDfvlUR2tcp0gg62CdR9FzjltDGNe/etfLrr8q7b7yO49mFtdNTiKKb+mqgAeePPs1zwLMvfdbzlvQtJ/wlrVWQcdRxnuyPXsC/AH7vEUFfynIbAw0OvMaYjo3Dq2PpjEsGVQw1qR56iJH1yKEO9hVh20R90orDrAMVbndmCHaT0fBbUulOnzhFOtpTpY0ZBuVYJS/v2MAKSSoH6eANuuAr7eWZFr24VwcsS0GsG7ldTaT4v/y//1TuMNBA4M5AQxxKntPwy5vyvXrATIXQWXw7+N4BjNkkDnSuxtirx5pAwzJmLz5b8ll5/qe/ZOkEGQ1dSUfGKelKgz0MMhhsAGmO1mdoxHcZie9mNlzCcb3CeosefZiZoMX7Sevcsm1b+RyHginO8sqLL5fpDHiv4+RfvWCgAVmVF0MI2grtrwEneyZQl21Spm1PgoS2D7xdfiDPbQO2a8rXEWCbMO328vSCp/4s0GD7pX14g7/hGX7XNnieK8D2vJlCl6GPs8dm2RzAgVsB3lvWb0zfzSRD6H4GGb0JBJ8lsGZ/BZ6dZx0BBSza4nnrNgDkd3GPQwdP6mg66+JM2qL33y9nGZyOwVGcea8ZDRO4l0E+Ojh9J73FknOpizokv7woX9mUVMxHE+RUnuznZDS8+w4zUqPKtOnTmJGaVG5DD2sznN1SbvI4fJFgnvCAX5extDKFgBOHgxtti/LUl2WCJ5iRe4PU1HVfrixz0e8GGmpGgynTVdcqOxXBVFPxr1//7K9ykQLdoivp421kCf7+D7+PXnqSGdjRpDOb6ed1AyDSQl73t3SXRpFV6pbOfve87WpkQnnKjBEOkIGGt7B/c+CXx5lpNKPBAYvs0KATnP7FP+Ku80xwhgpdOrF3397yGRsqbt2yufyyCTSAk855+Bk6SpoMWJxxoh3NjF0GNrTJng1V7Hd+u2eIPsEeJjU2bdpYVjBTOpAlQj97+RWCVO3lKoEvB9PiYBMUzZClhX9gccLP5ru9Hw5AX3SHJ49Cl9U4iesIVB89eIg9FGaWeaQ1DxlAoMHAGjLr8gYHvh7i7bI0SzLUuCbRnRU10KD8m43RpQdLFbhvx97dZcnSpaXJaJj1wGzsB0sn0KEXsekGGhD/Dj+jaYfwv9sW+VQZgCFLb/gRZZj18K/DM+I4leUNd8+4p4xra4tNdQY2REeHNXDl0/CJPATOFmEmWMK1zHyra5CnK9B+0ZKPy6K33ip3QRcHu6NGs28IyxKVi/DOd/AUXvAOwf31z9uhXJvSvY/Azu//9HrZu307gYZnyuQJBu4GxB/Tl3BAoY5X5tX78rU96acyYFvMALDI77bLdkRO6ItNW7eR0fCbMnrcuBqoHmxGHOSTA1iuRodWXBUazoZKfMfzyHd/12w7rnsP9dosBzSn8S0SaEAf/IhAwGNkfvl2CH2XcJu4MdDWhtelE3XXfHkmdg6fV98hB09YbKP67DhB2D379mRpn8sff/LSy+UOMh2lh3U3ukOuD615NmYpUOqfnIce0iIDNRpu4O4MGQ070DPrWWq6jgHScIIw2g/3g9EHto7wjDBDF37KF7S99oE6tw48tdGWqmu6J0DkHg0GSDYyw2s200w2iX6QZWZ9kbUzBtagRw9wcXkigNMWgwwJBvKZJYkgrxxFTtUF9K1LD5zQ2biFpRP4M8fJchrT1p5AwzQyA/Tlzp0+k4wGTbOjAfWKwUH1TJZ04ufbtgQy6fv4NvYo+GRjQ1D6lMmBP5GVPGZsW4J2LmXV7zBYa1DTTAl1wPfRPk1q9U7aA7wEhMGjLp04Wd4haLeSfUnmMnFyO4E7N5o0o0H9Lj/XCRjwhraRddHj+arzq31tbJjnHD9If3Xl7j17y6/BXb/vRyy3GYOPrV/kxFB4hrYrI/KyMPliJ1QepC8sjWwJ23vtY7PanchcQ4bpHwhkGGh4et78VkaD/BiQ/hXEtx/W0dTFZ2yk/gN9qv+nf2AdTtg58aPt/hId/9Nf/ZvyEP6YOBrIiM6SB6U756o9wc8Fhm2vfMkS1JY/6MRxMsnRb45taqB6W1nJ+KkTwaafMP7I5DiwnbiObgcP0Gr1K73b+l4bxHnqMiNTKmmT5Bk38jzN+GbV6jXl61XfkNFwkqyVU2RrnizP/OyV8twzz5W+Y8eE52gwTAnQWkkD9gf5+a8ONMgwjfBFQck4KgOYTYeFHiZSuSp7NBxm8HgbQjKbyNNkhBEzwIyR6WgwkoaNjmsMv+YBSK2/Mru0D+tDdxgOBdIdp+cS0daPEcLFOKJt49nYiAj9OD6T0QAEBwtRhuDRbMYkMHHNjD3fE0Xm04GgjGktMmJPnGh3xn7tjT8RLFmdfQ5cI+VmXs7MOHC5zoCh8oGUAGfgNKXi3/z680+dUhneaOt/JdDgUo+ncERdOqGQh3Y84myuQlKzDypNZV4FxqJR8k0D4m4UvBF2jddZBuWfE0F/G/zns1mjhu3W3syg2kLwzKw338XTwE0NbuD8ItgKrMIdo3ZTmzyvYVaYzWhYQpBnEylGvhViBms9LToUgOXZalwysAYWbgq8UmGbqqQgagRVCgp6k8nhwMjZrjiKOKKjJowrL5MKOJxB6WWuXXI/BnDI/gAg3wWYCViIJzAdfNUUN+qDTK6p00HKHg04RIePHkHQVzO7i6NI/97H+lozGgYSubyAkys/GoF2LVbjNMorMUShFd+g11Wqu8S9vn1C2C4BML12JzPTHzFY34mB60+a26Pz5rJRDWnB3KNCtz+ljX0sD/g7wRlg67SpseTPHLaFevzujI60+oI+XfjWG+UWHNPJU8lowFnU6FuaTa8iKzwY2jewuC6sZkCfPgZ37xUPZ+uk73sfLiJwt5BN/ZiRun16ZgIHkN1j3cI3Xf0GfN+rJ/iDrufqIFd3TP6Qw+hvaCSdDDT46cB31559CTScxWF23XTbmLEZGDmgjHEIgkKBFjZIXqVeyF953r6+CnwH89zVpVOVI+/XiV2zYUP53e9+W6aSXfMEqepDBw2OrrD/5HcdOWewlPNkB0AbYel82g8xQuBiUQ8oU57TUJieaqDhU95W8vwrv2CN7SxktRVoAO8EGoDVFRxhdABoYsTTg+9RbnynLVfIp/TNDd16sc4TGd+PE2SgYQXwO93oQkbDS1lehodTrrUCDTd4u0+wkie60W9sUmIwLRF7ZE76ySfqgBhUdKTpn86kJdBAEEaenwBtnsFR9C0uDrqi/3hWNPM8dAVM+qAyDNVyURrTmfS1AyP5lywVHDGXOX1Nls22zVvCC/eg32fee28GpS4VsksdLAo/g35PBFTFU7x1TjJQBHftic5z9mhARtxP5ZOPl5QzpNubXeObIbQfAC3X4lS0HCz5nHq0DzbA/hOW/CNHtlqUurxmGqd61T79CH5Xr09l76DJOKEGGtShkcuKbodsOii0J+QL6STfKUNxXqCSzjKYZBb3JKmvb5GNYaD6EQKOUwhiuGmYe+UkYAaPVF5uVSKWAm2Vb8+GfKnHuprZwu0GGsxoQMaeSKBhRBxkHTJ5Wj4wAC3EOPl8kT7yTBxCKkjfiDHX1MEeBhp849KXBKbeYwb24Tlzy9xHHi59WTrQ8Fk6tkH0X/i0HeIgjcTfQZdZa58xk+nSiZ/98pflHjJV6HhsS10z730ePqd9MqMhhXPWnZRY+F5dn1k4MryUa/dt2s0M7MZNG8tKAmCDWG//CgOvMe3jynWuuXGw/SNcS/Pp94beOv3N7wzW/QFdumP3j+AUGmjwld1HDhws9957X5n32JzY8KvnGXhh0xywN9kMPbB1DlrE2aU60cnaI/kHGZU33f/AjWF1/reD+9LPDDTsZOlEPzaBe7Dcw0xpL+q/wMCoI9AQDMG/QZrf39eW8D40MhBgdHg1waM/CzSwxKG9rS18EZ8Gftb2aVPF2T5r/BH7Q96XB7TztsXBnE56dwIZ16HtBwTZF77xRrmTJU7JtgP2QIIw0bH4KpYOFv8LuOceu7lpkH2MLt6Hrvkj2XwH9uxmj58FZDSMzxvGpK++UHwl5FY8lU/5X6pUvWggvAZlrVZ/x2vhs1bPOyj9AzOwY9rbmfghUE2goRMETkAduUfbihoFxLSdfnI0r63zd87ZwA49VAMNp1guqO3+aNEH2dPqUfwx/Z4rV+pG0/qONdBwJbZJm+gg3iM+L7ZOLypF8NRh3+iPudHkHgJ3X5A5ZSr2C2yO56A0gQZ4rg4GW1zvs2LdQdwGpASvcEMv8O/MoPMcgQb1zAYCDRvWbyjDmNRolk7ov9mv6jKL9JYf9VuptPYBnzQtOic81Tov7voEbob3JZMnW9jh32ziGffNLLOY5OiHrjnPgLIzsJPRYAWxqZUv4uNp/0C6BhluDjS4xwGvP4UHEmhABx8nc8JAw0z8Gjfn1sczMNhkM4Qm8LP2AmQT5HbzPn9fwd+p45zaDhqat9JJsM8IkryBDnYvEvc7aQIN0t5gQ3xH6ZJ/NkLa108/6qFF8kKlv/DVaSeweW8ztlmJn22g+nbGN25UrP1QVuX3FPCVtyW0fdAc1hmby/VkhvI7fja8L23cePZV+F2d+Bz+mEtwlfXGFxenRt+nb6GZdcSmWh+lyha6i3ptRewa1xx7OfHzu9/8ls1PZ5enmPhxCa7+hrxhySSbvNNqd5XFaifkj0amM15Cz4iz+GQzYXSBLwJYBe+8zETngwYalAfoot1WlmI3eK7JvlIOxFPY4i0u2kH50MM2Ol7Up9m2fUcCYNe49ydkmE5h4sf786YucBCPRkeGNtKac8JtfHt9GkifQJXBL7OGXZ7s/gzfaD/I7D1C1vwpxlLP/JRAA8GeQW1toU2ca79Jqha96oUf3t+/S6DhMgxlap3Edc1yRIfoX9dOBBpQARvclfWDRRjl/Yn6z8QAudGZO0i7/t01yK6JUtFW1d+iLRSWxpzmgIH9QmkCDZ0w+tcYAHxM9P+jRYuSVtvs0TCU1CU7X2aQ6WSFd+eRAABAAElEQVQmmctzUSTAkgkyyIAhdVrMYpAx7VMZuiv4HYXh3nj7LTaDXF8eePChbMbiLvy9SZtqjLHRO4VZE2MR5++WnKuXc0n4VBIny80gfZ3aAiKW7larkGtUvW7GhMrcNqhUkoJOpNZ2WGR0BdtAg+2TWLZVB/rChfPly28I8ry/sDz02GPZzMt3RQdDGhmaQh9xTxAGgYwCieA3tFeptNpFI6xXhaYwHj5CRoMp01u3shnLT8sMZtYrfbmfe4XVCLKKQhrYBiE7O+G5OJXUZ5qaKcES/yzO1B4ciY0bN7FHw4oyqr2dzfdeKkNIKbrBoMm6zZpxVqendMwgpipZTkXZiXE2aRQPO5TDbAdnjEwzXIugb6BPfb2PA2o3KM1g2kGomEpflQqBh2TntPBPIIn+cckEhAZ/HEeUl5F/nVC0VdkLPT766KOyk08383Ijzrtw5vD4y1WCPxoI+8j1iXkdG78T4KAdypDoSsAofPuExqjAu3A/nkM2wPmAGdi+fW4pk3kzxNSpUxiAjUg/aahVdBb714ftkxw6PhR5xvqrzwRkbpN/upDRce3smbJwERkNHy4qd+D4u2Z9bHtbXukqWXSefSvI9etXCLbZhzpRONfAc+AJ4VCqOhnQBBp19hwOUuiPbO3cvYcZ2D9k/5BnWBPYxgy1M7DODmsIq2y2Ag1pBsjBR3ZxeAte1fjoJKkHDDRUTqU7OL9248byJmnBEyZO5M0NbgY5MHIufJ8XT3nSwZX0lc7yv/LloeyZleK9DlqVNyvP6y3hdyPoyxgcPfPST8tsNmmrgQZ0nzRETurSCfBVZkJ/jTHfPbJYk0+7AcL51oZOBIvEZz8DFjeDNIJudOzFH5GqhzPEiB4CY5gJONzwgBZJGe0OTU2tAzcdLfWc7bCP0v+hF6mr8KVG2Swb19du3MDrhc1oeGpBGcreG+5i38yyiq7BpxqACihg6jYD14sdRYcC3FtBHtfDr2XgtYM61FXu7XEvh4NSf9v3GvcMbh3EBFcgeF4dB2zPJZ2T3+piAw1N4NQU+GVkNp3Bmb4N/XjvjLvZlLcGGhzYCDd4Ak+Y6vUESUE5jkyrPmljSfYQ382kcSNPnZWPcf4N9k6ZMhUdP5XX5t2WmS77xscambI3r2HbMjCkLttmn3hdfKm9tpk29cap8k0Z7xLEWE+wfTYBTZfeDWWZXPZbQe81tIis3lRPgyunUhrqN4ET65OnHQC8hqOok/TE/Pl5e4ABGumqrnUwZaDBeuwLn9NOWJ86wN/SSpoof8qE9+ukyTff0K9mk9zPevJH2aTNVwCri+SRBucGx7/l0wCIuJxFz7gJ1ufodwMNzsDewy780lI8vU9+CL9zLoNz24wOUTbV//JtBu6cd5lNVwfztMVXNO7ea6B6U/kaeepPv774k59gR8a5K1+cf/upxQ4dn+Lf0DnfWzd4zn7vBI27E+w9mkDD6rJm9ZpyiGDJPTPuTaBhEPt6gBRZSMxkMjiTt7QlviWjLiMlQOdSHGgfHqV9CSzTHsd8WfIH/m4GuRS7umvXjtKH4PdDzAa6L4l74VxksKrdUYWIXlC8CekWylz5ti25zIVuOLkQj9cjsxyGrAD70LeJmBGnHgaZrIevg1vTx+uA3SWqblprvfKU8MI/8XuqrxQn3UAGPscHLLt7n30UpgNX2OPHjy+34t/cgC7K03fLzTh/tz25NyfhW/wDA7JvkhFwGLrPm8dO8OPGZelEBuzgZtAvg1we1L+IHqedFvlW3vNQbpURr/tdfWMAeuOWzeXNN99MGvzj8+cxYUWggX90FQC+9fF8hk4EorCRBY/W75xLncoW90Awl/MYXPuSQMMnZHwsYOLnEWbu7VMzNNOfjD6vIXfJpIPXnFm2PfaDDGKQIQMu8FZu0wbk1ftdr26gYSWBBmd6XVN+uxkNPJ+AI7ilrWLro/nHj6aAoy1pSq7Di/K8r5jcvnNXaOMG2sNGjUpQcxSfzWBXPG1o/FqANJNN4vlnfSDvc87glOeVc9+49DU86at0XSai32EwYCBBNgfqmfjhOccKIWZILr3TkNg/9THWMf8AGrnSjpvFuhm4ZjScYIJpNBMb95DB42aQ2iSXP2eJrH4/MOLr8mkgoOEN+cngcuNHRu9RtROR4rMcn8DJvJEjR5Xb77g9gYbb1AVcU14Q+HyPrm0ROIEGYHALOHv4l5I2QXdo3wX8T5PFZ6Dhy8+XlTlkBNw+bXpsoHs73YAvlKeMAeRr5NNelLbhfWClTupxjNDInuOcjKFo/849e8rr+Eu28SmWCo0i0GC7tWXCEb/0JZDjm4oj5zrkiB/yinbGOm2FeEgjPx1Qv0pG9YyZ9zGROj8bFTt+CG25Vxtkn1s8pxx6zvo9cp12Znwj3tQhXIPgB9EFK75cmcmTF7AfD9zP0gn1FLx4CT2UbBL63/bar9G71oMshP+gT/V9HBvWZfXKq/bnCLyykw0av1j5BUtUO5Ufu3SCCQgGA4z7pHO1n1eRV2mrHofMkVdpHt4DVvoHokg7X/tun/pmN9844T4NvkXHbNZDZPo+Qxuee/45MobagEShrSGogAPdzx9m+fsEGoj2GWwwy6AbwQXVLuzGBi410OC64A8R9EN79+J89smrLceTfnUrGQnu6u8MgO+3lqshLc8rINC5RdwwLddkFiE7SHA2oC6duJg1tks/WVJGMuM9cdJENgxrw2msr7f02SbFqAk0NIIgQ3g9xtO6NTwtJS7TyyyHmcVciBO6Y8uWMpMo66SJ7EjODElSlxBylTxSB151cNSwQROx7PjdfPFTxlMwqWvr9h3lddYcDhw0sMwlGFDxrhvZyPwKdLMxnXg6EHKWUtLI4DpiKjJhuWxCGmlY/PQ5NxxZwuZ+MxkU3ccso5kYFgXKIi0UQJ812NA4dl6Tv3VAaxClKi3p4v0O9o8QhVuOo7gNI+RbJ4y2SlNxDm1buIinu4jr2LgRjNddemJ/JGjA76Sp8dtiRkM289rGGilmSm/DoD3PZpYqwdM4vw5sxd6ItPyTNZR5EuEHtww8+K1zqHGo3Mi4DBzcbMtXzrmTvUszjqJQbicKbQR9YP8BjONxDoULPc0UcT2WEW+devlTesUZZxDtWy0uMDi1HgNU7ijrLIRphp+RZbMXh7EvKaOzGGDcQWaA9HVZiP0oDeQvD50QDZ5wVa6V37/NePC3hzRyoOzr+JZ8uIjslFsYUE9gQDoxr5rymrv8Kx9xcOw7/mlQq4MC1XBYbKM42E7/6Yz43TefuIHo4k+Wli+WfVqmQZdJkyeV4QQxTH3VGfa5pI6hcE37pCrwN1WNGQDaQsXggPEDbgINtNlzHrZv77795Q3Sap1JmDd/XvbdcEMs6atRuQFcgIX3ZNEEdnDwxM92JSWPfhHvakxrmxQqn9+8ZWt2mG4bN661S74DI+M/ZMzA49LdNliMMAtXZaPDJO3kyaR3U7k8rD6wuHTCNYFfMjNt+uv8535U7mEwrTxmxon2u0dDsxlkR8CBc5UD6QmUQv6Jsm1BB3Ql0HAZfA4ddjOvXThcXzPA7VSeffrZbHhoFsO1S+BrwCHyDgSfJcjg8+kA4Nou+TOEsx7rpWkGGly+4EZhW7ZuyaCubRJv5Jg3Nw6LaYIaep+XTqEzD9p2Z/ICE/pD+NoXVkBfOjNgoMF+UEc6qNtNHdLJHabvZgbWZVoGb4Slfqq0p49Tl/hVJ7Thb3WLjoT6RmfEwYv3uOfJVwwYTaM1GHAXGTzj29sj9zoF3p+mizO0kfd9zvZYZxOI8Lel0qZu1qgj4J4nS9GRA0nBdhf+yZMmJ+CrnDTLD4RnqZpebW9PVljC03kJ30APdZrnnL3RqVjEwGsjA/aZZHr46jPbUDMaHBTVvgJS4KWWVl2ASvkWb+ujZp6RVuoaZ6TeePMt9Ea32I8Rw4fxvQbKdBCTtqvDhowrtzKIdLVIG+klrDh06j76KTOpyJIBxXXMMi5ZzH4tLIVxl3wD1dZf6QtmDXItXAPYczf/zsn6R77RqTWw6syrr4vdhm19+rnn6dfpcWov4Qt0pO+Co86nzp19rAwr9/5Q31Xdcz0zRaYay4/qMJ3QHTu25/VkBnyeJqjZTkqqmQb2+V9A7yZM61ebot5Lk9ANboDqHg0b4Xff4nKIAOF0MoQeZnba/ZXE04Ghyz+ciJAvdJIN3ghFXRto4dUaYFCGI0+0rRPBx1179yYFfs+eXWmXby3yrRM96Lfz8JN6gEcyGKpY/vW/4m6J7efB1fgEHzD54EbHBmR1oLWt0lXdqHyGH5Atbbd+Vl6tCd1iN9A78QNyn9YPfqSN2o8LPLNk6acE7t4vU6dNT4ape2P41gk3482+ITzRsLh4pYDkX+0THpAnDqIn31+0KK8qfOThRxIgMTPJa3Hq1ZH8kw+0e1VP1f5TJvVdwvfAc3DQ+Hy2V39p89ZtZSG+6ih4JVmOA3lrEc/EpvL5bbEDbgo0AM9aPWxJMyi1fh8zy8lNeVet4a0Tny5lI+T5ZRb6QD/j0sXz6U83FDdYbhDNvtZnEi/ti04GGAdfcQ61qEq/Q/lw4LWf4ItvpHIixUCGG66nH4EZrCQ6R/75tWnM99C+3gMN4RHlaTc8uXX7dvb62VaGMtE2B191FJ/NBJg4Snd53WetN3o2vM0Z6GB96RtwCN1betqMBid+fM2t/DWFyY27sCEDCLKZLan/BVFifzpwbrVFoPa49iI9bxdRp32bLCfo495HHy/+mH2hjpaRo0blLTEGqJRN/S6Dl+o8O0p+tw7toP1WeaYVnGq1RR6yuMePdX6BT7DwnbfZNHs4PM+rM9t4PTK6wJL+BLcAA7KwPW4u0qRCbJ2lbm2Y9sS0+kXgvopNog04ujl37AcyKb9Lr4wDYgOrHaqDaPmuQk2QHxz0ve2BLBEI7QvBqf3lXbI9LPPn+crrYUFQvyYBae53MC7S+q/VDlYZqvVWW9D4mMJRv4du0HI9etLNIO9gom0eEz8uJ1Rvqz/kxfA3baiyyAbG9Lc8pY3SN0Zgc4/1Vh9Q+1GXTvjWCV8t6oThE/C7ExDJbKKt2mB9A/nMdlhCD0gCqJwXZgKT4KMcOSaTT9UDx8k02LN3b33rBPc9zTIts6fE10M6qkfsA21yxgcQKRuOU4d42IfVF6hvwzPQoF6wT/VpNsPvLj8/eORw2bN7Z3nxH39FJtILBMPahA40aCThOw6+/kDLvyrQIBt7RNBbBJAsEsnIT49OzPoSZ1xD+uXCt98qu7ZtyystRzArNZyMg54Q3iUoGk6PqGkAVmdX8noRUCoAmMuOtb5E+2AMZ8UvwpTfsFZyJQPeYexu3NbWHmUygDXlAQ3cZuDdRPIyuAaWjNYcTT3W5SGTyCxu1OHswj7SGR1YtBEgUdB1HHTgFXSVawZBYKdv3pSKff1183nPKJgK2x6Wkyzi1WpmNMy6j53aUV46/gqaitK22m6ZW4fZ4IOb6Phbh0ynQBWeiB8C5b1xBmibynfj1q3ls08+yeZ4d6LAdcZUlLbb0rRXYRRGaG1/hNL8BUbjWPqMgus9ppLrKPqqLKNy8594gvfMtqduFUV6jyqCH8LbZDCYSt0oEuurjnbtC/GxDhWE0X+dRdcE9iUrYM6jj2Y5jAZV4U6gAsWgIQKhluKQjapR4FQcRADWNnFCZ+QWglunzpzmlUeb81q108CbhHHQkeuD8naXfCmj4RH34Aqe8oK0NcIubOmQJQG0TcVpJoEBIO87QIRyNUttjvB5S+8+ZTqwx49rt3kEUc5FodVBVDUGCSRBM5/1aGZXGvPT8L28Ic/4buLlny5lZ/Nupa2tjQg6S4WYJdWJ0/H2Hh0/+cVu9Lf8pqGwGOnVAGamCufSDTalaf++zCDwqeHcuH5tXk/m6wRvxeCbhphNRAOzZVygrTLrYExF7Zp65eYcNLyscXJAAH3kBbMdDMQYnPqQgYubbs7GgXZgpAOsos7eLjFS8rR4+6QdqQGF5tSnEbM+zsYxbhyY9DM6wk2rDKy1jxsfw9ObbAn7R/5WZqWpzn6lqUa3psw5KJN+jWGKHHHOYl3SS4fLNyB8zUzmrLnsAE1qqo6Y8pbXW1J/3jgBvs2ngZK0IVCARDvUYmZ9OIBxJ3hp5Jr1fTiKpmRfvngtb6BpHzUmmxFevaizRXuhSgIf0EFTd02lwncx1NjBsPwW2/pb3aG8K6uuh9++Y0fZTfBrBLSZxbIPg5tm5DSBBh6M/Bm4i2Mtzg1ceF+4/pfmnQ0y0L8GBnxt206Ca/sY9BoknMIAw03geqmfMfhVb9QgprSSBj7nebGX1vJ84NLp0UGci77m9569+9j9eXU5h9y647ZLG8YwMNJOKI81kABY7g3v8xm9Ce2F7XX1TOjC3/AT97hHg+eddVlGoLo/wbTxEyZkRsq3laiHgj9YWhcIgjg4o3eyNwZ6Un60Dtvizve2x2CLbfCVsPLMJyy12USw3UCDrxH0FZG++jK48az38r+WVCMUivVR5Nl6vfXJD/nZwd9+9gh4h4GRM7qPkmI/GLzDI9xTbVN984h4Vqen0ZUVtveqG+QBr8v/TbDT9vu6v6VLPkmg4V4yDqzTe///FumkXhU3MyakvZtBPrFgActK2NWbOuVX22OQTCzDH8hLtdvqAjLi0Ec61AbKHMQpx8qiRZ19Gl4xY2I9TqgO3sNkY/g6V4NHynKYMHf/C39AoHIO9ykX1HEafbkPB30v/H4YHT+2bRx7KNyZja4NTgdP8JW7tXECaJzT6Ct5iDZoh9VNCR7x2/Zq23U812L39iGzbsY3/Q5emTdhIjaJvTPQrRAkz6cHG775q83wpqoLJKiD6c/ZhNMAr5My7pbvW68s4intxVs6qmvlNeUqr6qFj9Tz2jpl1gCt/G5qcn9moLW/K3nFojOwk1mGNK69PZtnG1izz0N72vrd8tfaIv/Lp3ldLDP3ZrAexTd4gCwbB7vZtE986Vt5RX6Obmnqqc1v0Rxdo/4Hpvc4oNLPUga1wzt27SYbcXEZid17YBbp+wyMbFvwa6lAv6vVAcA3tXv9ns/v/A6/c5c8oXy5se1nSz8pjz/xOK9zvTP+7wUCegYZYt+u1Rnq7iwDMhPGiazoTHV/S2dap3QWZ3le+qh3zWRwkzknUp7QH8Pn0NY5+GnuF5/osmAu/kJrlfASf/hv0+Rflz+6TNY3Q+zeQ7CBYPWQESN5nevsjn1s5IEaQKr2VF5xECpgvwun6mj1l3Q3YFJ1tcGtYwTwzTrYgw1RHiag312irL+jvCZjWHg8awnZBe5/fiR4pI7neoUv3lWPGZjcjf34gvGBr0ceyiShWc9ueui9ZirK3+pufyc4CM5pAzTOYLpVp3VZqXxv0X5oc9yA2gzTYWQImrFmpodBa2VJv0p6ttDlo4U3n/Zb/S/eAdnRJu2Xy4vPnDvLmxW+5G1Ra8sMJgrNPHJjVfEVtnpdXKWjujsHv6WF/aI82D51rrrVEt1Jv1IFvuphAhmLQ0c3tB2CHpBXAhPaVR1lEKnqMvFKf3KPshx6A1PetG4pZF2e97qTkIs/+rBMZ+nw7Jn3xn44sWaRdqEB35VBAyte0x+V1gZE9Atjn9BD8rF4WZft1lffsHlTWYs/dj/86Bs51J/pR3Cw7fV+vUnZp9JZHHOeOpQt+zj8CF9abwINx09kaZ8Tkp2h9dw5c5gUGxUZ9nrtVOQavrPdjkd81vGNPGuwP7AI7thOdSZfEog1UH2IyRnHTdqSk2dPl20bvyn/w//0P5cXX34J36aNHdCq/YCT+SfGHj/c8ncLNKiDmwKLRTn07Gyg4TqDxdWs83yLjcjYfO/IUYINnbPxXmajoZ+bssgK1SmHwWBwB0UZvCswGO84CDCOxY5TwRrxcjZwM4Pprz9bQcrJMHYLHsf7bHlPOUpAIY4xVFkBR8HVOXHg3DCKHajwRDBQDCoHBcU6hK8C95VBB7fsLDMefaC4bMJdjmWiU6dOxhjrmCnMwjFKKhNHgcjYCEQT+WsUvG3wHgVIhvvs7ffL0Ams70IYdZjc+d80QZnTAVwK9Qk/zgj1uUmZG6IozIq39WewBu46ghF6ntnpwOujpax7q6/d8T6Vufh72G5LM+BSiXhOwyjtgrv04F7bJlyVg86F68c2MSA9vPdIeewFMg5c842i8Bq355kKu3tmOxzoOaNvloAlMKGBKGSdP3VKo2YtvYOvTWu+Ym1p/3LvfbPqDCEDVJWMb2AQF5de6KhlPR6wGqc0CoR+Fg+VZRwo+rMPfOHeFdswmNvXriznTpYyeVbdKVjDbn+LUII84Ch9HSD7Lnbr0QmOAkc5STOXP2hkNVgqOfdpkC6+G/7k4f2lZ+++pY00veEYOHGwz8TV+/wdJ4A65MNbGHT2Iu1ZRXZz5kN1NErSznQ8d9GnX37wASl/pYy5nde54oT6KlYdAp9TNjykhe03OCXtlSnb4P4IpvCapeB5B/1G9h1oSStT7HetW1OmkYkxhPWArhvWKsUpBtM49fYbNKhjXWSle8+kL8tNp06fiRGAYQ05gkc1JMqk69OWLlmCU7GjPPzonMyqmx2kY6tT5VIh5Q8VQf8SHKEeqg5fKFRuRinvi2cGVDaQSpVXv7oE4ZP3F5cJd0wudzDT6EyvDnNowHceSz3ymHysk+ph2xy02g/KmTT3uegDaCavGhzYvp2NPr9cW+bMezBOtPX6rCazM+3sxL2meLpxqJvcijv/wbEaPulvGqb9DlQ2lOJVhOgl18GaGbDmS/YiOHKjPP7C82UEeuzSBQIBHMqeS9HsV9vtsw521SOByWDrGk5qiMFVzzlLEONNXYcYEEnz3Vt2lxHTJpY70DX2xwUCU+rQqgcICAFP50wdbMBBeL5Z5xrriKMX0yZprX6ugYYzZ8+Ug6T+7d+zs5zafwh5msXM/YQMWJ1FV6eod+RHg77ynPKlrFuvDpO05raqX5BbnZ1Gpx5mFnPz+jXlzKH9ZcDwUWXcRPbJIVjt88pidaKhCLirL5M9Fb1Z5V59GH0ATZqiLMRGAEP7sXTh4jKoXynjps8sbe3jCBL0S0AuuoyHpL+BIe2HafAOOIV75Sop5gbVaId2QD5RlmzXAOTJQLjv+v5m1YbyyKwZeb+6et1AZkfwHLyUm0YfZkAB7QERHfwtD2olKVxQBtUVR3GeF77xWrmF4MUDDz8aHRn9La2hq+3UsfScPCeRlTV53NIMfKW/elneykwS17S52o+3P/m8zL3/btZ8TwtfOOAU3s1FPqyBopZdoQ9rgKc6nakN2ljyLM/rVJoJs3bNprLg2SfYGHZ0HDXtnzirJ6WJxFFWtUXiJJ/4Zg1p4KvMknWW+it86WXbDTZs3bQBHrnExoSz8qrbyH2LD2LrgFsHPnWyQPzULTWoIb+0cBYNzssD6lkD4WbDHdi1qQwbPTmZMO5/pA5LrI/ntE/aZGVRG6E/ox6yTZEFcHSJivzrOfnKNruvhwO6g7u3sNyvRxk/ue7BI6yaRv8t7cWuw8kHhjhqH6XBd4v08kk3Wl63cmm5deCwMnzU6NgVg7016M+SH/jTQEKVy7qMxW64SFBS2H3hNXWmdTh4Pg2+fg5sBbnM9lj96Rdl6sw74y+5TEjZsM0eICnjRXdVfwngVhDcobH33SSrtkm9Z52+UcGNZw9u3lVmPf4oM8e8alynnv6PXfZe6F6DDfJk5ceAp0/sH9slze1a9ZBLjew0Z3yd+Hnt3Y/KHRPZCwafw8mHZKHICvqN/udIoJV6IqvohlwO3pXq3sP/HJ6R16x7K4G7hZ9/Wn7E5qHTWPaoz3vxO4EGfUYDeupuaVHtLn0rPP7In9W/wU5CF9shTxrwXb+apROHzpUFP36WfZtGYC9rpoY+tPWrMxt+kU48CswqW/Ztg3DoJt7Q1ckLgxcH2Il/3ZrNLEEawSu7H8iyRIN80j36G9w6fOHUU/1F+9zrBvOlg7DVFznAQVnaRQB83/b12dx3/B33l/Zx45iAwC6jY5vAnXLk8+oocfd5SyPvftoWz8eG6e9jA80WXLvqy3J676EyuH10GT9pavbjES8HtcqF9JYn9PcaH1t46psmSF1pclOgAb4WFzcQfeOdD8q4QWQITb83AQeze6R51bnKaOXFxof2t/0pzW1jgll0Rge/cl28DKSuwRfbsGpjuWf2jOCtblSH+5xA5AHprk2pmZvV1rp0Wl5XFtIH6FvhN3sfqY+OMnO/eOGbNRg758nYD5/xPmmsHNY3zlR8rMvxWg0yGhSrvkPTroaf5Fvp5+TJZ0uWlTtn3sXmpNMCUx9YPjYDPu0FP/W5S2jlEwPQPiuvaov0F9Tx5/Az1NX2T+NPmD28aunnZfoDNQjj5AQ3Vd4CrsJhHdJbeB2F+uUn+9u6OviSe2y/e54c4m1aW79aW3oM7l0eeMyNxQdjn85lfKM90oYKWx2iL2GfyE+XtKMGQ2ikPqU80sclh9zrROkxYOsHnzp3BtoSMLl0gQnhfeX//L/+9/Lzf/hlGXPbWJZr6MvRBwkzBLsO1H+IX/5VgQYbrLAYfXFzsDh6EAbVmMhgr66kvvBr4/p1WTqxnHW2b7/5p3Ls/LdGkJVMKZL1DIc7CLQNG1gGDB4aQXOG4AgO7LpTRM25ZujCF86weCCfvijlIMd3iwsEvEf8TrQuDuJz8ND+7Pw5EAb2qgqa1zTuwgHHzxVb2BTsEV4OVmCVwxzfLcb+e8DDF7AujFNTfG7cgG7Mvg/HWeiVeh10nzq6r2w9aesqPPH3l/j1ppLt35Ki4OfGYEkHC0lMsFqlC2GT0m9Yf3ZYHpJURmEfP3KAGZVT5bjXOAYB7zLwjvG95ZKWc3xvCi/8TPF+EzltL8Pq1DkWcgzEiXfGXwWjs3fi0G4G5eyYyj22z2c8pKntruECvrSKdLE51umn91ls8x0TWOfvQBahdvBxFifwJNHUcxDb+8W3OcTp+4r0EBf7R5jSxv4f4jF+dOmFslIkLyG4Jw4x23TyajnFtaaI++jB3crJo1dCs+b8933a3oYXxg3sXoaOHBcFp+E+ffIYyyIOhgb2i/iIlxwlTaXvd4v3CY/E1+Au/j6jgzJuwC1l0G0j4mTaqhNE+Lfv2pe+4XJHEb5Hw3PNBft12PB+rFs91cGvfTknXf0cO87Xiw2oPAntD+3YXnYx5vCa8JQPeVI6OhQRx6bIp16rHNyc/fZzFF+Ht7eF9rgr7K9xsmzcvf+f8YZPjIavDtD/0uvmoqyJh/jeXMRn+G2D2OdisLaD5Rany7njB+DNynvyl/j6nM+L6xGOm0tDB2W/D0Q/h5wf4rvnhw7sVQYNHRGedznO8aOHGeQTdKFT5C3xamjD1/Qbj3eUrigIbX2YsONs/TIKZhs8ijcAMKtvsMQ37Jw/dZw9X86Wfa175QH5Qln5bp96i/0qf9u+Rpb4mjK164AyYPzIGDn3t9m3aW3Z2br2t37YPuv+bhkCo45kNqw3TrazbGfPnCxrtu4ND6gTpfXNz0mj7xb1uP0hLzV9Yzv83d63Wxk+ZnwdfDB4PH6MGdy9h7lCf6MEXCWiDlM2/lIZyIVbuddMD2Eeb904lM/ho28r/QbUmX2dhqMHWa9/1Nq/LSREl9600xiB/PDdMoIT8rz8Je0bXWOfjJjURlCNjVOBrdN04uDecgRhljbqMGuSLaxD3JTzm4v39OcQpm1Uj6o3fL7/sAGl7wBeqYesOBA/cWx/2XZcLCqveJ/3+6z0rVTjS6sI1+ITjX4WD20mscDSb6hB+H5xyB2knzlxiKy9b/WhOMiT8t13+1W8lSP7Xjlo9Jd6bRjM1G/YEAKyBs4I7oH76VNHCbaeiUx6jzhIS5+Vtt/VA9qgA1TcyJht8X75Z8Bt/XDWBoRn9FK1IdrW7Wd0+WqxneLdtLt1uuPD+r3e0E57IB4DOTF03FgC+7Se7zqKR/dvL4hqdID3CLOph6/fW4Zwlr1dWepU5dm6lPHhOAuDRo8tPXCkdXZ9Beah7bvit0gD+1R9JF62/bt051SK991MM/tBGZ44CPsxbHRm57VPp44f4e0+Rztkr5EjZdI+PMrxfUVcPcTD+4QvP4xoG0lAlGw3BgHnz7MZs8fpswwwKw80+CobPXh433cIJY62TxpKE4v8oI7sz14ot8AzDmCUpws436cPHCrHaKg83PCCfSUuTV187Shes20W6aP8ycMDIFh/grV94HeL+4KcOrq/qArEQ/7qxcPd+8Cv56+UQ1TY1Of9f61Y59g+PYA/Grt9SwZC+qlnaYPN/z48vw+etG5kQzo1z03m+5AJ8Ax+qoPnU6dPlCNM6EhD+7PpU5+xzSc5vltG0InGa2zrKY7mGWkzZuQQXr09MIEwB9NHDzDQP3E5vCgP2D7bIS0bnPj6Z2VMn87l1Fk25uOs/CIu8syIWzrhL7XFrjrAc3C2a9O26Flpbnu9z8Oijvy+oo6XPt7X6FBlYHi/7mXgiDHVXwJJA13bduz9Mzjqo64oDmOrh28WGp632D4PaSN3qEsb/mwfPrAMHDo8g0j9bH3JncfJ9uIeadfQ/xZOOGDe1xCWa00ZyhdPCx81kqIMDKYBA8lUVNdIX18Hu2/rzrK/3hI6qqc8etMRp2HIm+kvzuoj6d3oDK97fgjEGozt64/9MMDspNOpE0fwOU5F5pURaW/xfuvY44+bijIsvupc71cejnBIn1FD+mBXhyRQ4cTgqRPHyskDx8N74uQ99pfkFj/LzX0rvtZ7zAsU5Vma278TGDsNHt5eAwgELwzwn8V+nD9xgQ3Xa/+Li3jJn9ZxM2x+Bl+vCU/aevjddgxm7NS3/yDsBx1Ap51Xz6ALLpxm8pDr8rD3q8PFvekPvnYU+7ThRfFuijwxceIY7GrfjG/crP7UiaMEuM52yOUwgHaF4IcRKNvxfeV/+z/+Q/m3/+7fltFDxtA+a5KeBEnyV8r+cMvfJdDg5li+B9oIj0SJdiPw4Dvm7ZqNa9eWD957lx2gV7Bh2M6sBxxJZoAzDa5ldAbKaJyRNwV7/WY2b1m+MlRt5++Dzy1IZNzoUWa7YcQa6awzAa5nczY6XQEThcFwYDWMRvKMDBsEUaEamV3JeqcNBxSfUh6eOjE71LuG0OtJiwKAyt1Il1EqI+niVmewWhFWNQw1JvWOwIIzdSdJ6/NtCR+s+CKw72CGfxqvpTKVyqi7wlOjZ1AF2EbTjNjp0Bu1PoMD4qy4s/U6yM7IiId1+Owxdid1Xej6/QeiZB55+CFeUTc8qVQKj+tP7QOd1IzMwKKutWIdILQw6u01HUHbaaQ+s4YQ7jSbTB1gVnL58s8jHLOJPk6ewIad4OY94vHtjKaUNqLnjKkpq0ZXmUWCBrZJmifoRF11RvlK2bVzZ1n58dIOxfb4zBm8SmdUZsHNNKmRRVVzjUiLo+lg1mRUVXo1mSGcwqoRMaStHseJiK9jFn7Vuk25NAhv79FHHsmrQnthqF2DR4PpXyLvRIhVwm4eZp+mH8DX9lm7UUiXCBjd9v3pRiEPkDb4h3cXBrZ/7r/nzrzPuC980Yl2Z5MiZzss3C9c6Sy/hhehtfVYR3OPnxph+9111q8v/qRe4+//R917QFtVXQ24U5AqioJgQYULUqSIDRUVpQhiR0VsYMcWe6/RJCYxJhqTqIkaCzZsgKKi9A5KEQlNOlJEBRXpVd73zc3xzxtvjPfeGH/eeCMbj/fec/ZZe63Z25qr81FHRFlZWfKE38jyMWDs+sVbET1GcplJYZ7udzazkJkjot1mzzbCW85BfrJB4iefT8vxm7G9yP3WdtVnsVmVkPMyA8lYzl9eFH8bGVO+NoJs+TFeZxE9J8smp2/n2I3vVn4foznHeMaiL6LZ7g3j4KNpLFZWD6FbLZ8tXbplxfXbl2FnMgy7URFkpsEsWGnri/fxaGiNjCrPKxyVVVmS33/Y4DSATmhFuSaZbBuxWr5uJt4+C47v3OUZ6Ubn3r83WvngmrjMHJiZkL9cj1Udy5cvj2mf04SKypm6lMgeBlwsPa9CRs+AmPJD/vQlHH25LrdjWCK3Zs2PuQ7HS8wKO+41P/0NNPPFnJkxefqifP5hB7MXmix/DRxgK2Tc9mR20kqQzN4iH7LiivWAhFyb68tsCuPmOlmbBsqmjZuzUeq7fQajqFfGnlVsdnc6ZcT7QHsulrlTgSD1WW4snQkbadp5Oqb9RWzUaWDRz0vrs2LkW+Y+ZdKk+Hzhgpz7MU0OzG1dVaEtq1+syNiZeRu1dzzlgxkVx/ESp6VeLNKRPOzlfVZZLEN+9f7of/ipc+sjs+zRDMs6aES+EY/KLukkeRQ+E07CNyuIoPliXzCGBzgtPV85Z3Paj9kn7uWzz+7UMbcsKIsyy8A9VsV4OWf5SzmfMkAYQYvSjU6bv8vPzsnMhNVQk6GZz+bOze8f1awJze6oKEJ/eJUyOf4u/9kjgWXlc/zbGUlb8r048XKdfk96s8LtvWHDUhY1Y1uRDfXsZu76zPyp97IhInDJ7CQyQLAzzUJm8YsVe8IyZdAO+JsZW736x+wDMobtOV4dKWW1bH53etOkDJdGoAszPWbGMpuEbjaDpe6z0ko5L0wcP3ELXJRLjm2/gs8/+SQ00ppTneD2GfWq9O4lHoVjVlMkwxSyzbGcn7izuknnynWa7ff+hAtB6Xmzv4iplEF7tTvyiKhfj9MMmLvGo86MJ6hIY7nuvIv/CRgvxhFQwkwaKPihaDpmFdsMtliM5/hXr32Yb3v20LudQBpwDvYzMOjGIjILzcD5nArQnn2BUr8SrPSnmbrKyAjxKvLdh/sJWex5SwoT+1gy/YfT8K4qdoN6fQu0aKd0aSXlDXyZOlc6QUYUGVX41rkzheLY4IKmXMuXS5ZGPyrcSlfn445lP/GBSdeuE4jnRykDwZW0Ls27LuEihKSZAr/YJMhPZZo84Ik17rMey3YIr1YND4yyBvQZqakzA21Dh8otHyHvirOiIoJjG50/kTx/luAlbpQb4tctA99SPjx+9NhYjsyoyV3KYI+wtWq0sOSYfU6/WIO/F1LGEf1MOkePMl5xws2OXkbgzcDCUuhl8MTCHutIj5G9CWyoJ52HgQ31qfRsJYmnOikPq2A/SNvygi/h/5OeDcCvQPLM9cqLS4H7zGmzYvKiqXF4g5ZxCFuL3C6lven4wkT54swL+wbehTZSVuWiqExgDsLM6kBXpO7zo6/RTXP+NT1GzJ6WzmrHw46Ihg3qJ26cm+v1ZYBGWVnKrjJIrkcZLdyzepcBtbHTnuJ3A4xfUtI+DhlZClCcS3PgfTl1RzkkztQHzsNKF+Vu9sHib2Hlc5N/GN/ne2XVAd91HvalmU1y8dN5X+ZnjffeE3vseComd0teTjtO7CFXhJPyNeHkA3dc0p7zN5vuqWHlgYv8K73bv6vfBx+Xbo12bCmwP5inVDiCJ+bksZXgTdgUL+bJ+H7fm7TrHN9jK4UVkCIQW+B9PrbqgCHDfx6/S6cTc6tF2ovAJnHJ/JQl2pLaS9otXumfiFNgVNAm9P7zZySpsDumzpgen30xJ+9vzfHkLVq2ZFtwraQF4V7wDATH/M3Qy0/SuNtulYVpjwE3ceUlDlyLWX/7XXw4fEQ64vvDKC2PPTb2p3edY2hTSQ/CXDw6lvO2l5I2pbKroJkdOhA9oM7zeVkxQ/XcgoUL41P69rjazoxdi14UVp5YdZB6AZi47oJ3pPei8lywW9GirBFH2iLZQ4jP3Z7zPWNbZTxm+gw+jWgEnzYDp9pju8CTSXfAVTopeKt4TtI1c9wED2XFFQ+SBr3Pl89XZ321/KuYhk0z+7sibNORnnX1aQyq7agcs9LMK7d2Ayf50Jf86nqy8oT5i1flUtoyjC38tAmsAh7+WaFXDaic0KZ1btUrJW619/yuNGjjeE+fkAbXozO+Bmfv9P8wHibQcPFlBBpqH/BzoEHpUfqXE/wv/d9/JNAg4H/+J3ECmmQ7ymshqZhJw6cBHEk0j/1p7gtXydYvq5eGwVoMlMoA3tIlkari/eSzKXHfY39NY6vrcUfF6Weczt6nhuBIg4cyWwjOPeAymkSrALVzrASRzA8BMKEs+dK4UVDmdzGUPPN9OEcmPtf3vWhFSO3Ert3pHn80mdO9CkXLuFB1ErbflQElRolW4SfBO7bObzrqzNuyGLNPyym1mYkAGciJA98vXRod6AJ+OEKwcaPGuR8yS9YVQPxTWMvoGr8KAcusVIwSpnunXeN333+Xz1MJGnRwX6jHxfR5p0+c0L4dx7kcEQ3qlVESShwSAaKx5Tw1cFRGXmnUAZd0JPJ57gOlTEym4RkKEeHmMY8LFSKU+Q4bMSquuuLyOIyma8V+MEv4ULbCg3GFpcaowE9GT2ZkCsDHNbk2BZTMqNOnkHKv7Gj2zg+mjKp+owZxJortoIYHZpVD7vtlLn5P2CrQVQ4+y/dSgLkeYF/an78zynw7z9jC28sx5CbRoO/jD/vFnOWro+tJHaN9m+OiDAHrue+VFILM3cyv3cA16GzkI60U+614Js/j0VmZk4qHZ7kHXlHucWMD6fz77vCRgjRuvfbKOAajpSblUjtxEoDHjTm+cEwhyD2p0PJuZQuA4r8MXkizrCWVq/OHnv81c1a82qtXTFz6dRx7wD5x5rnd8mgq4eKcCkVWBHs0jBTi0r7w8bJcS36oTl8Sy7KF93qiqgrPdRgV4yZOij/+459xIPd2Ovec3NtaD9gYxhGn21iDP4tLh0qFDv8gvD0eVKPZJmgALOGUx3iyR/+nzdtjGYbcuwM+iBfeei2uOfvi7LvgSRU6GNKjuJP/VM6WjQlzP1NAG/nV0BLKgCc/q8TeVNet0lzpXuVZM+Pt/u9xosO26NTO5pH7ZamvynUT33Vbh/whXFQ0OkyW/SqPVNjyaSop18lLw8nAof1FFtAkavLUqfH2+x/G2aefGkcfdlgen+geenlDg0ze9DvroRt7slgSWY1gQ0bECTRI/zoH2TCOtdk4zWfbs+AzGjuO+2Rcduzu1u2COKRF8+xMbed+jXK3RahELRusjOOucwexMF8UNfPeto01MGcdDNdlpdTuZKHcZvHJuE+j31vvxnujP4hzTjotLj7vwjzJpzzBII4GAX7rGWo7dG4gQeNyQ8oo8cqwid+KyGLLoDX87SVgyb/BuMWLv4xRdEZ//vXXMuNw5yUXxakntstzzT3i0y0kFZmLBgLEnIaOCl0kKgOEsy/hnrQvYfHMVOrcN2f+gnirT58YMOGzrNq69fZbcv+mPOG2I2WuvSUMzmpo2VMkcbEDVspK12MwQ54SnxkMhp98fxI4/f0fHs1MUOfDWsaZZ59T8BN04vwSFtBe6g3nzTyda86XqWo8KqN1AKShdKjkWe5dhb4aNm5cPNHr5QxSexJRc+SkTbRAU45flLfDN9CFPGppsesvNcB1Dso1DTtpxXVKv8LfLuAfDh0W03nGqWd3iSNwvOwFIg+tpL+Jpbgelyi8nY9yIB0Vxi8ZYCXZ4OcaqMJTZ1qnbgZBx34cPXgAgYDj2dfqFheP9VRWF4G/Qt5K09K983e+ygdp0XmnXtwBD/nZv90fvggD3SDM+8y/O3tNW7E9R8fOslKml9/1fuXfz/AWaHyoHPYZ3isuxbHveb/yTH6a9i9OrxqEg0Gw7vxzzo6WTZvlCR7yu45fbl9Djos/x3de8rFXBk126DppxrH93Pftw+RJMm+++FIs5t7z0U2nsIferYDOw0CLMrgCfASiCLDq5BVGb0X0G5PMwKDbkNaqg6FZkwUlx8VGeoOGDI0X+7yfc7n3hquzMVp1dJANAs1qmiQwWaLRb1CjHDLA4IPBE/WtzqlBdY9UdssUiEl8i5sZs7+IV197PcbNnhMNeMLVd90RRxxyaBrKhcPI/Vw6s645dTfjCCflSkE3ZBd5lvrbQG7igHW6f/6TyZ/FX/7+j2ix3z7Rus0J0RLHyOOU09jOuRVGuokSK038rvMtcKCjjnxnCt7v1iZlv8HCH+F1t+aMJLnxztChcQ4NNQ8/nBMBmjTJZnrSjF/M2fOzoEWcT/jesRMn4ptxxbM04LPU7f79A/us7THyHvvn5Z+T2rWjIShBDPSkvJH0Dt95KberEWxW3it7/NwLMQzMoKUtRbChckUdb04eWbGyOH570oR4742+ccaF53B8eBsagtJ7LNeP/cKc5T/XntsLsD+Uxc67MvaLPz1xwhJ39aNoLfZ6R57gM/HTT2IktuSsRfPimhtuiMObt8jtlQadlQOuUZ1hsEs5qyzRmRHHwko+S3yzDvFRkmM6uzNnzIjRI0bEu59OjBM4Lrj7uV2jYb2yvF8a0A53/ja0taRdHPxkQtHxfQE3MSPs87ng0/f8eyUJsQnYHB+/9mboNvY8p0uc1qkjR9tbeo6OYX7CRLrzfsfxu8oBfk257GcGAbSrf/I+8McHiV+PHbQB7kcjx/KFiAd/8Ytogw1vfwG3LZnQ+AH9bhNJ8e5YBXzydoIW0Iyw30YFDeNv4dlwNdtcq2YC43Ng8/if/0yFx5o46ZhWccapp0czaNLKRPk0E5LwkjJYHObFc4W5V0lGupa0g72H38WXsBlD4OuvvV6J5lV2is7dusfx9B8z6VaCuwEB5Y58Ko9q02nHS9/av9Ko9rHjpf5Aholnq5ANOg5ma+pAgg3ndOyQx0w3aXJQ4lE7Uf7QgYZ9klfVDeJXXegYymDv8aWcTPsKGaczbSLSpNV7+HKNG9FQmkCD8y71jJAnpRlfmXhEf/67/5S2AJ855/wcGZp0yVoMHs0gOKUdPwSavODMM6IVgbv9962TWxCdmzZh0gz3Cw/HK/k5BqQSZowtHoS7vOf4wmk+cmDipxNiBLpvZ3R1d47DbN6kcdqK4lEd4lXSIxlogK+0SQyy+VztVOllG1FHfShpSJtmFXjxtLAB77wW45atiVOPPgJbtW32rNNe1A6Xn7JJOnDfme9WI+im1buKyopF4GzIkEFxDDxyAVsn9t+nHl4zvhCf//u/nOB/6f/+14GGf1+3HePTyIEAoIDihbCYOeWz+AgEf/XlotgLo+Y4mvg0anhgHq1m2ZOddzWeKoE0G3yMYIvFvXffH18x+FWndopzz+uW+3skqiLIYObFagOUFu/tpOWsAcB7UMMOIcscuCS6QulZRrUxFrMH7wOaLz78/Atxyn57x6nnXRBt27bNpkIaNCoEGUZO5P8puBQSKvvCaCvG3crzNBSNtNosz+zpV0Sh/0VW5B266n+/ZAmd40+OYxCATZs2S4fXQEI6yjBwzg04ScC+HEtnRqbfnU7HCit7FGgIKGjct7kIQ+5TGPGF55+Pzhxf1AkB1RhD0Q63drteA8E7lhmJkjGnRNfpLQINGKkI3sKIKPZF6zSq+JcuXZoduofROHIkSv/m226PYzFyDWJ4j3jVuMoIOZNPBlSJ6tjokPO5ysL7BJ9CQCWi0heudtMfxLFUvd8fEIcQIOl54QVx8EFNcmwVoMJb4SMCUzjxXcdIR5TxFKwOrLARL5U0gq1TxShYhvMzAuH65msvx4T5S+IXF10UXU87NerjTFcoR8R/B41sAf/r1+zIquPsaUCmgcu4CiadRTHDKjJzBIGFR459gZB6q2+feLZPXz6NeOI3D0Xn9u2jBhH0LQh3iDK3CjmegrkwsDToEhA/Cz5pNvcpMh9hIx6kMgNrjz/2WAyZMy/OaNo4LrmyZ7TK48xQJtBBQSOF0SCMHFfYGEVOJwIHWPrbFeFlc0F5RAWtIFwNzQ0eNTpuffh30ZS5d+t5JceonhQN69ePbZsJdnFfHtfDVFU2PisNFOalQ7kJoarTuwk+XreZJpI8u7KwJ9CwjUBDniIx8KPo9U7vuLLLRdGKhncHgVf7PWSggXFsOiZ9mA2Xe6ozT4V74cjJywpVosQqBuacfQXAs8GvGQQn+w34MMpzz4l0uva4tLKysjTINMI1zh1f4yWNI2CroQgFpkFUCvgIE+nXXhsGCgw0zJ1LBJ39vs+/8EJ079EjOhx7XGa+s7oJuOvcml3VYXRsjSQDZx7L+xN787dh7KXRrn0BPkBqwkazZd6CBRzdNzGGElz7YuGiuOLSy+IoAhn7qZiBjc7ohvXImx10L6lI1yoWsJs8K6zFQb4PXtzDXX03GwziTE9EsfUfGO+O6B9dOp4cPQhkNAKnfBM5CG9vItDA79KYMlB6MKjl2IrLLfzPfh+WRPu5ctcAXA1wY0XD4EED4/UnHg9j9L/9xdXR7bRTYm8NRQyOLdDgViMW0LtGr4pUehYWOuYaKPyRMNGw9m9pXyPa/aEzZs+O5196KV4aMiyaM/4vH/tjdECWmTHWYdVQ+XcDUQPMl5lr5Zc41ajQWZEfrGjwfj+Xn0fhNP7ylpvDnFHPkzpH1/POyyBPwoHPdVSUK+oF5UrKLsaRDoW/+Fa3mBlTH7g2vytvrMQ5eh/5eN9fnoizyIqc2OHEOJKg43516iQuzU6LU+ncjLVrseLIjEg2X2UsZYCZcmGi3NGhV3auobfJ7Pnz4k0MuQkffxznXXJJHHfccenYSeNmqyzrl0fMqhqkVhYIG4nEoIxgT37iuZmp4V5/mmX8Ct03GWf95TfeyGZrJ6P3DGLYd0h+lMadu/MSNsLU8dVFXsoc4e7fPkve0CDzct//9OnTYiQBkg842vC6X1wfbTjXPCuQ0AHSht91DGHpy98dX+NW/SSdaJgVe3XVTzjf4hQ82IR1zJjR8f5rr0YVjP6rL76YQEbLqE0mUJqSLq2OdK2OK04d16CsRqnrEY/KMoOPWSXAfT7LPhcjRo6K3o//JcZsXBPXn3d+XEhwqoxKEuHBzUnPup6VmLd2hnNyXLdDsJh0WuzHYSUZgjTlgIEGey3NJ0jSp9+78dhLryWsHn/g7jjtpE7wWnUanLIFAedFuqmoUwHMrVjBMGMLBjDgM2FlFkztuAW+88g+9Yzy3bVM/te0+Puzz8QHkyZGO7ZoXHPrLRkIN4Cl46/OTp0hcXjt+NtfhZVOtc9IGQlt6nTrvIpnHSMDazf/9uE4kuedevElcQJy2NNTvISztCi95/5k8GjwRnh7+VzpyZ+FHisqKqUFEy8LCWp+SAXPc2/2ju4dOsYJ0KTBNQNU8rcy6+fvI598nj0DpHf5VP4VD2bJ5Wt/l8+8x6DotFmzove778Jvm6NL55M5/Yl+F+BVuaisUJbJe1kBwE/1v2PAuCl3AW+iH7AwhgErA9VVqeSjunTObByMSfE+SaszzzsbZ/fYtMXkG/lJoBSVAOoiZJW0xKWec+4ZaGAOBhrc666sMDDsd5csXRJTaaI+dMTwbHZ37733pj22O46/8lZ+ke/lw8xwY6eIXdctHoVL0jtwkffU6WlLwis6jZMIHn3U/714gUDG2cD82ksujWaNG+f8lHkGJoWppwR4ulTyE7aA9p2/CzNlZdplfCtlHjCrRtDcqqxByMiXHn8ipvDZ/T2viPPPPDP2pYrSRtzi0zU6P2nC8cQ1Ey1oxM94X52+XrkAvMozh3KJ63LAfW707v1m9Hqvf5b/P/HgQ9HphLaxJ9VNbrW2YWpWT7Jm+9hIm6mDnDewkHcyscQHW/BZNkHnBhuq7kalC599gtP4wK9+FTOXLIsLTzoxLsRHOBxZg8SKDQQxhI9ztlpW+ed6UiYyZ+W5f5doVriXjm0XRt+QPBmMHLvvz0/ECXX2jdO6nhvtCIDVLyuDRrTHCt1UVFIV/kvqKGCTQRruSd0IjFJfoYPVq85HnM1buDD6YGcPerdfdL+oe7TG/2hO4sdggI3PnZf84aUt7aVO1Z73M3GgTChVZpjQMwCn/khnnaRxb2zhZk2axinYwJ5ssheOu4EzfRsZ84hEEgAAQABJREFUU18pZS+8Vfrb52jrFgGAAkYGB1JvwwvLlpNEpYH6qPHj4g3svVuuvjra0SNsf3TTbmytEjYG4r2kY2Wfckv5p40s/OVhkyZ+7hqEfdqGfDYTOTACOfPhq6/EzvXK4vqeV0UrbEmDNsqv5NeEcXH8sbxpEsmeW8LEf0mnzFk/oRK+hzKuYuWKBGM5MGDs2Hjrxefiw/lfxeWnnpyB9ibYY85H2SevbjGhxzg7QzcGGuQgAw3zFlFB89GAqNeieVyAX7R/3Xq5FuGYvWCgvP/26z8TaACQwC8RgiTiFzAhd8N0QDi+gDg9/uVbjBGNmjYYTwc1bgRTbcyz0b3VgIEKSmNnOEh78JbbYxbv33T2GUWgAWaBen5Whj5ApWy2+icU8laygza4U9BIFApjDTiZSMWP1EI4b8yjo/rRpfvXf386zmzYME7t1i0Z3RJSjbhSeRGPTuWbRIKw83m53xriVsBK3Os1NjAKPOva7KpNy6bOnBm93+sXKxctiS6cidr6KAINGHM2iirKcsz4owiYn9sSvHQCjK6nMQ0MdIQkajNvGkK7YHzZnERG1zH6+z+eiVO7nBGntWtPFrNhnsag47KKbSEKpVIEVINcRlRoaADp2FVGyDimQk9lpQCSYeaTZRyPQTFo2NCYQMXHrXfdGScceVQ2MdwV58z5uNVFYeLvOoMyY26bYT2W7iv4UsEBE59tYGYXnCodMiN+H33wQTz7zjtxDNsabrvqqjgUuIhTx1Ow+lMjiLdS4RidVgFJH5aAS2NGbaWwqsCoEnMvX33X+BrnxIqDF559JkahhO7peXVcAl7L6uyXRtxOrL+8GSKMNg07Aw9mlZ2fMM8KAR5ipkjHaTvBk+LoQYxTnjtz3tzoRXb3cYxcr5dxMs48+ZRw68QGtg5sA0/boTvHM8OgElbZu5405BBY0qOCXOGqUWcktCp0g8SMyRMnxK8eeijeRxhegPC75sYbozUOu46/Roi05+ILx2rH93e2CqiI1mvgGcwxIl+JOaUjxhxU5D+gID7CmLjqgfujCaNcds110eWMLpTxN0ilaeBFQ1T+06hPw0FFwSN9rWXclau+j2XfLI+lKINvcbSKLB5ZFeD6LQbXRLYijfpkbBzf6lj2qh2YTpeGXFFh47GZ9s4oDFENLoV6ydCwgaEBNo0hacrnCx8NeZutLl6ylKqAT8hObIpDDz0izjj9zDgax26PXQjyMPfK5ckCkmGS9szKSzzSs3CXdxVLws5AXtI7MLcs18ZZ8+fPC4OaTz75F/bG9YyTMVbK6tWLPciMiDsVm3NROcovGhiK/ArguRr4M0C3DUNe2BuQKsd7FeGtzcB+3gKrgybFIGA/nUDDtT2vitaUqu+9N4qT5+9MAExZ9xVZgkWUsZohM5gIKtLIVUlmeSdjGXBQuUmbFcH5RnhiyZJlbJ+YF6Mmjo4jWxwSHdu2o3kk5ZcGg+A3gw02FXPtwsJ1pGEHvW0lyLB2vY6dRoaBCPoBID+bIJOPPPyINECGEDx654mnY9j6FfHba66K85Fl9akmYZCUpatXc+IOA3iEqeOWDENlbMH7RdDKsXUWhL3Z2l3gO4NH/3juuXhywIBozQx/jTw7sX07FsjpGwQ5nK9OUgYukLOl0nFu5TPxYbmlOCFjLT/xu/xl2b3G6QiMlfuuvS6W/LQ5rjj3fDo5d00HQ/yZzdOAllelUZ+jA5CGAJ/zhOx2rgEpfix5lFYLOigX3+J49R00MG7/8x/j4hPa5bGJxyHP9tu3DvvL1yUtZ7abNWjQaZza3Mpn+LeGtXMuyQZ1ng1BNeClycxOYySO6d83LrrsKpy646H7w5DpVenH801mvw2COD+dFS/nzmPS+HLcDGaDd5+lEyI/GORbTPb4U4L+z7/8Co3ujojT23fI8lR1svBT1jiWRpvrdd2pJ4QBfOlc1XvKIYN56hlLQLHs4iuMpU/JwA7GMRo8EEPxrvui7VGts/TVe3NefFeGzKwuc5JuDBT5DIMK3mNZu8ZnGtzAScdFmemRvgPJTL/11utRu2nzuP7yK+IoKib2wLkQDlluzDgQDbSvzsO5ZTz5KGkfGQzxZImt9FmCi2uwctCTcN785wvRd97MuLPHpXH5BRdGw7plP/O9tkF5xqvG3Mrxswiiopd2IdAAnD3eW2clAxngQptDZ91AhIbk62++FY+8+HLi668P3BtdTu6cGdgN6whCabMwXy0LgwxQW7EO3t+m/oPmNaq3MO9NPNvkRkVo16ya+n3i55/Hk08/GW+MHxPntTwiLr/u2jiGhqxJw8iwrOxi/KzgYa7aLwA71ya/JP8KH95Xzui86mgLw5XQpI7R1Q89kNUSl1xyRZx++hnRtHHjgjbga+lEntWGEZfaNiU57oIdp+SYJl1BSyW7avGypdGXbbV/e+HZuLD9SdGZ5Iz8tAcJKYP4GbyANrQPpGd1lTaaz/MkK3nBsTM5wbg+V92qTpFfPqdZ9avw08qly+LsM86IY485Jqy402bbxlzNmjvnFRx96LZXHR5lhJUpqT94jjYeHAF+qQyAh7WhzHzKT3Pmzo5xk8ZHxxPgJZqlunXCAKB0qBzwp2tW3qQzBw/IX8JMZ8nfC8e12IZQk1Jxt+cZDPDUrZFUxM2eMCUeuu8+9OuRUQt6F6/yR2l7pOO4btHq95S12r0GYIWX8CnxtE2j1YXj0an93nwznh42OHp0PAl+upIKoaYJT3lcp1R70Yq7ChVw+vnOTwQaXAsgSRnqLz5XXDh+BnyZ/1p0WT+CGH/+zW8y0PAIFQfdzzmHk+Zq5dZl+VznOL/LpP2uslZ4K6uEjc6i+sXjw7dCi3wQFcC3wXFP3XoFGfbkm29Efejr1w8/Ep3bdYg9sFM3aEvANyo3ZXsFeESlmttmWY/0ziLQ1cAf+Fh9tBE8bdiCfsL5M9usjX33fffEpAUL44rTTotLu18cRx56aI7pEcsGBcWrsHEd6gjtlRLtqa+EuetUh0uP/lTuGyz+mGrBex99JDo0bJwJwzZsK2lQVpZwzEQdcy/oRl5V3Rb4M0CdulUc8BJe8q56TF1pleTchQvj9f7vxrC33o5Lr7k2jkUOeOSmNrzHc+uwK1OVwVYKOkfHkH+UJQa7/FsdqV2jrVedxOcq1m0VzFhg05tqxIOaHBSnd+gQhyGD69UrS7yp//yeYzh/TZnkfZ7pGoSJ+DVAbeDHRsoGNa0AyiQtQfBhyJpn3+4dD91+V3SmYeq+tenhwuf6dfpPjmcgsETf6iL77CVeeaz2t/wgPLyskBNm09guOIjgfd+Xno8KZQ3ituuuj9boVSt2nJNXVnPsgL32jOsoAoRF8sBKBuWjwajS83cGPvZUM9H5+t+finfmL45bSIz/4tJLE6ebwIn0kT4GAMlAA3RZ1Xkx/o/ImTngrB8yqvaB9eOi7lQ0QAuuR3uvPPYtBk7O77/5f//7QIPUBNHkK61hMMF/UFoBIJTEXAhoOI7gtxjVZt+POeZojrJqmEhch1DPTDDEoyBWyA9nz/dv77wrG11d2q1rHvlhVM5xFYISqWRsBk6ljKiLclvJprF3WaEtM2l87kokrhxE7dy2KWRQKEuYQ9/Bg+JBAg3nNGgYp19wfrTH4KpVp05sR9hp8CicvVTMZqIkFC8NLYVJZukgSDMY27mnEgJHx/IHjLnps2fHy33fiS8nT4uu3c6hoqF1niW9Kw43E8ixVIJGut0m4aVAT6GUfxXwkzlkfA2vCiiedZYBzp0TozHmnn3+hejQ6cQ4tW17yn+aZJ8GDRWVgwaJClgloMBTOJWY3FVpnMhYBgacs0cIyZjz53EMJnA30PDZpMlxyx23o9gMNJRO8CCLCFObRVaB6vg7DrPMv7MyADgb3PF9I+sKhWoGGnjO51M/xwAdmA57MwJNd1x1NYGGpilcc9n+zzXznRL8/VsDQ4bX+JSk3NepCKmE4b0zRuJOwOYrosSDhgyJF599NkbNnxcPXHNdXEZWqi5O3QaMFawJggswK2NpuJk51akWRhBT4jhpigCD3eSzRJWHpWLDkPSI0F6vvxqPvPySs4wX//RYnMG5vTXA3xZ6W6C5lahJv35fp1cBKP58hvDVeCvhQ0GZ3fFZg5+b+f7tw7+J96Gdy9hqc/UNN+YZ2653PfOX3lQE6QwBS2nQKKxOnPdoKMkX9i1QcToHFZtbIOxw+xHGf89fPhCHMfeLrrk+upBdqA/tb11Lx3ZoUWdII06jzfkIC4VgJdZuoOELAi1Tpk3FYF7INpIvgQuKbWey5Ny3imqKL8nYLeL9OpRj19pzz+zY73g2DqvAfR6bmeeFQxMGF7eg1DVGVX7SyA+rVha0wt/CRbhpLGlk2lNlGVtK1hks53r84YdwqtvH3tVpbrluY1QtR08GYCHdGLBTmaUziixx/RpGrqlwZjCcyBhVhuZXraT0dc4cDLnx8fdnnopLLr0yFVtZPfZ9YyxpPAlv6U+llcY4c7MqZhvP2QU4awxhXXMfRgYw2YkXiElDeM6C+fEJewI/hi5nf/1t3HTddQQajoiawCedKeSSxtUs9jt+SmBvwQIa0hKQcP6ZcaH3icGLrSi33EdK5iXxAgzMam6ilHcNTsrsufNjv71qcMJGM/aoVs1mals3cmoNNGPVxYbVKxWbBPxsIotSrUSQkUDDj6s4GWSdBrs0xJaFJSviwusujdM6nwz/bYshgz6O/s+9FANXfBm/u/qq6HbKKexp3C92gvakuc1UY2xF5mrwlMqhC4ORbCM3SO9J99Ci2Uszq4Us2y3mEmh46h9/j79Q+XU8d9//5NPRsV3bDLqtJ7ss3yvH0uiCxpTl0lM6pdI5dO9xqUn/Sa/ABvxn9oL7hxtouO4amlj9FJece0GcRaDh4GbNU8ZqrEgrXiV57t/qDbfe8NAMQmpIbAa3vi/upSlp4uuVK6IPsLnl8UfjgqOOjdMwRM3w1qmzX8oas+o5f+CioSvs1R8+I3UH4yiTNRLVd2a73IaiIWyp94w5X8Tr778XE4DNeZdclkH5gykhdYuLQVa3fwkXZZj8yo+kC+cmjOQdZYEyVDmjLHadbltasnhJfDJlcjzHNi2zxmec2DF7Y+y99z55n/TuPNV5ft85qkuFrY65gU3p021a4sSx5SUAFMvmz4+xY8dmwHfkuOEY6g9FewINVgf5XWW4Os3x/FkEFYuy1uQznuHlmMJJWvL5bkHwO1PQHx8P+Cj69H4p9mx5WNx4ZZGRMmNpVVP2T4AuPZ5Oo9TvezmWfMpQGQSQlwpnGrgDF/XIKmhuOJVHb7/YK3rPnBJ3dL8kel7QPbKUXJ0BbsySlge2VXfYE5uQe1ohBnZhtgwy5Ckw0L19bdwzXB5HxmygR7y9hlP0q+deyDk9/cv7i0DDHjWyCgOABFyYpd5MMn/a9ycrNfmpfAbgWUxrsEEeNNigAyO+JxBo+JuBBvpAXHzEMdGDDHJrdLeJAGWM1REGF1LOJ90UGT8dDYMD0o1OovyqzBE/aehCPz9SETdozKjoee9dUcYcL7m0Z5x11tnRBLtjG3rPhnMZuOCzQm/7LEWucqF4jvSjfcOjk4eSTqUv3lvKdtN3Ph4Q9z/zZFzdmsDdGadT1XACTSL3js3oVsdPfgV/fi/pyLF8BvwjYsW1Msi94gbMvDwJx4o4Ez+vvtc3G0+fdeppuU32oKYHxS4EBLajN9OSZOwlBDwmTqYvDUkxj0X9hsBE8gBO9s7QyBbgohVYnt8NNLg1cQV2h+XkPqdxwwNz213R00pZpa2H08tc1UHSaQYI4LHEibwP3xROaVElIJ/UYutFDehCO0rcjieIP/eTz+JXd9xZ2GPopl2gQW03daRAVc8ljQMV5ayfyUe+AFjykTymjNiD72sHjyWp9Parr8UzI4fEee1OjKt7XByHNG2Wdpa4cxx1n1XGwlq9rV5IfuJ54jNtVuCfFzdZWVSeQMYG4NHnvXfjT9g0U/nwT9ffED2QwbWpaFiNPheuiStwJo87t6xU5ffkf2W7z/AzaGcrv2+X5qFR8TxzzmwCDa/G3956Iw5m/Pt+96c4uV372LXKLjQQXJ0BO2UC1JHjqy9AAluegJkyTjpkzByP9VmtuRFdaaIGocrWhglx/4MPxMSFC+NK9N4Vl15ONeLhCtpYS5VxsTXOSk37yWBLI9/lI+Wxes81CRVpS/zkezxTm3D5im/i45Ej4jYCDSc3aBwnn3lGtCGwdmBZ/aRvFp0Vpknzzp+/1ReOoxxXpvssaV79Jw0Z2FI3Gvy0ouF1Gu4Pf+ftuPjKq6MNgbXmLTi6nQD/ahxig47C3u9KB/Kml0k9Ea1O0u/xT59rJVUlZLzbnLSXPpk0Md7o1y8a1CuL0zqcGEfgrNevXx+5QXAPHeO8ddKlNYZL+lafJL0AF58rrWrLKGNMelqBbVDNYMDQMaPjObbh3n/rnXHy8VSaIwc8+UU4pD5m3eogA07KP+067T5pJfUdMJJGlfMGPfQBxce06fYJHBh9nn8mdqrXIO6+8eY4hgpTgy7CMPUS80lbBhg4d75Y+I7QTeoonlveAJU8Af2kXUJFg4GGkaPHEKh+Nt6YMz9uJcl5w2WXRT1wuo1Ag4EpZYCS0XUDgKxOVUOtg6/mfbko3iF4s1ejBtHj4h6xV1k9gI+ech7cq+z/b7/+I4GG7RC5Ct6AAVgrYCKSQAYYioUgeTSZveVLl2Z283D2hDVs0CCRqABTKFhOZtTUMsaRZBkf+83DOc7Z53eL04hEWxUg9tNQ4BMZpBS93IPSml3Y4/wTRCdSJQoZRaGv0a5AljlVUssRgu8OHRIPPP/P6Lbv/nF6j4uyl8I+dfbjGDkDDUW0spARRbZdhpRYZSCJUgLzKhyyYs0aX1YgzJqHU9rnnZg6+KM478JLM4LerGlzHDCdF5xF5mF2SafdsVSevlT0ClkFYWbdIEgVv9UNZl0sYfxi9hcxFiFoduSwww+LTgiow1q0zLN17VGhE65A0kgXVs5PozMVMXBwfBnEy/d9GZFVKCxctIgAw6QYSrR1ysTJ8Yubb4rjDj8iS1MtLxJ2Kh4VghkFhVsxniyJEMCw8rJCQkbXsNVR3I0osc+3dGnMmDHR55WXY68jWsX1lAUfAk4dQ4NIA8GZZWku8FZgCXNHN6CUz+IvDf8UKjx/G+vcxs/FZEFHjB4Vb/R6OcYvXRz3XHFV9DinK8fE7J1NgXZi3palVuKVjdQYU5r1cu1moRR6aSgiBLfw90bmYLSyCvCfvXB+vP7WW/GHV3vldx679744qW272IfsQgXWVkFSz7kWGSHhlFlLeYDxFazSoc9y7l7F//VTN8Z4soxP/u2vMRAcXEm2/tKeV9Nw8jBgQGaBcmrh6fozoMC8smQUeOnAmNlQwdlwyKoas+mW7LoP0cuM1BCE96O/+23sz99nXnVdZmEb129A0AWhLR2SXc+5AQPp2Ei2FR0196qdgnDMp+PZ190/FnG81ReL5scuBvCEP2vRoF69lv3RG9bl9ifLSnM7DjRtBoQlZ4ZCPBqA0Vjxd59XGJw8E0fdq0KF0pYfg1cGa2w+h8LgG9/SfdjrgTtvi840atu/Zu2ohLONacMxkoyIIlI2qMyFU4mnVPIK+SwFZCz5THh+/c3XROhnUmb/SbxGlvTC87tHB0r1PKe6Zs09CyMe/jFToUywYZ7r3rRmbawhA7ozsKroY1FCrmcXMmRorcS3JYxfzCdwRwBpwIAPYxkwvRVjyx4QZq+tItBI0eC0Me0H/fvnsWfuQxdgZhHKYeA6sMreYIOmiw6SSgdfhDmBJ/hQQ7wKyq9mjT3p8wBOtlJdsQ0nRyW1ne9uXpP6sjwVMOV2ho/LWylgmbTOOeMRaFizclV8vW193H3tjfSqODMdk2FDBsfHz78cY1Yvj19d1TPO7NQRpb9XymyVsSV96kwNHOErTMWv+7ydq3A3mCfdugbpPCvMMFqUBU898494GsXfhinc9LvfR/tjj2VLCgY8MJH3NUzSEYfHHUdZKR6kTTNdGixm082GGCDw9Ie8BxyPIuvywF23xVLGvu70s+KMs85i60SLNNRSJjKO8lcadO4l5yV7VPB9A2AZtICO1DMZgGMs11Xoj8Fx2xMYt/Uaxeldz44TyLy4VzVLW+FHg2hpbPEdxzZI6LMSEY6zQwak8cHvrtVqHPWCdGMgY8bo8XFG17PiqB0ZKQP00nSpbL/IMMoZhXxJp8sZQhvKGJ/hP/GigWpVhVsnJuKwP/fKK3Ew8LCiQT3sEbYGcjXmDGo6Vw2vhBHzFd+OI3yVW8rK1Cv8LKqqysX8BQsIVI8lazSIXgGfx4N3PxBtjzw6q4f8rk69+FQXq+N0uny/JNNKuEjY8HxJXT3mVgplwFS2ZQwdMiQ+7PVK7HZ4i/jFlVfFYQSPNJA1C1MmAp8tVjcyvnhzTNfl/NVPygcWKFKSt9TjWdGAoTgKGdn3ldfjrS+mxg0Ep67odn40qlsvcWOFhLaFWr8KclYcSCs6ghWQtSwMHoVeCQ5qNFvZ4DGMymC3QS5csjjeePudeOCZ57ID+p8euI89650o9d49xzFvqV7aDk+6jULdpC5Up2eAnM9BTRqe7lffJn7Ab8lgn0QQ+EkCd+9xhF/3w1vHeT26Rysco4rwoDrCoLTwdt7bCQ4m7IGt201sMKdTl0EX5q7NYabcl8G2DfwcMnY0/HR7nqJy7mVXxxnYYwc1bgI/wnca0sBbvDmuNKJM0LkoeKvIvPq5joD//F2alze+Qnf3oXrqgeeeju5ND4/OVGq2YRuVx9f+SD8U9ZsyRFpJeZC0CKwASG4T4HPpw+da9SPOlRNWHNice8bc2fEG2XUDeWd26px079YJ+1BIw8op+WMh1QkDBw+KIQSzfsTpXA6vuD3TgP12cC7Mt0o2zoNnqd83EEi3V8/mTWaJaepdrXrONfmU9ZsN5WvAQcerkAHCRTnp374vP4nbhAd/u44aBKObEDi2Gekk6H7+p5/FfTfcFG2wm/ZFN+2GnHTN6TQDy9I/+d5xHDNhz7wdL+3OHfaZTWvdZjmWYOw7BBr6jRtJ1rt1dD+ra7TEHnOrqxly5yZ+nL92iHqQX6CJYluZOJR33V7oM8WRvCZcvyZQ0/+jAfHXxx/LHg2/J7Pe/eyzo444JRCgJDXw4hzlU/GV4/NJgWfhxR/AKBt+s4bt4MnftwEs+8y8wfavZwlmHMFttz/8aLh1wgDMmh9+zCrnXRhfHjX5ZuXBrqzb+Wf2WxgpA6B7X4ab5NmdeIaVSQZ3Hnn0DzGF4FMPZGTPy6megp/s+WCTSWWjwbssuYce9Q+UlV7SX86dtSW989Pkj7gxGbfciobRI+OOPz8WJ+27f5xCpUduRapfP+1cnWODyQYvhK+wTTkGTmogSyA+TpX5PitXtMFLvohyVX62n5j6Y+QH78W5yLETqJY4DLvDShsbvpvUVD9Jo4JY+Psc8amd7ZwloKzA4T7hg5rPPjbL4Ikp0/4Vb/V/F1zuE6e0ax+HHtwy6mIzGZxYhRz9CePE4JQ6z8u5Jy0yjnSYSTie5zYHL/W3ATbtsekzZuD7jYtX3n07bux5XXQ8lq2sdYoeDc4tvwutGEhznq5dfe7Lxfgcq8ikeXlDHjWQ4Nps1jgCH/S9F1+LbfvXjrtvujmOphqjqJw0iWqQ/aekYf0R/Vn/tnpVPpBG0QIp37XN0n+CVnbG9voOG3scdmT/3q9H71lz4loqvq6m18JBBBoM7AjPithfEob29Vbmox+2mXlvAg4Lly7Jvnt1D20Rl156SVRny7e0ml8UjklQYuu/9/qPBBrANEqsYAiwX0ADJQzmSedsinkQ54ghQ+NrAGqG4yg6Rnuigd+xaZnZCJWITZA0uIbgkN5x9915jEtPylDMSDVtgmKDSTSuVQwyiozldomq/F0ZRKpIJY7koB04kcgUvP6UMb/BYX8LZXL3k3+L7i0PZgvCWdmMxfJRhZ0Ex83+l5foLaFY5eZVGGMoUOZt5lfDfwtEozCZg8H1zzd6xwSiij0ofT22detohtLYA0ckyzsh4J8jiTvGk2nSGMMgkIks25N5NCQ8MsVtAt/j3MgslnX1euXVaNPuhMzANmvUJJtWFRnjoqRQo7NgQODhWlhAlhTBFL6vkkrmZC0qB4XBkiVLskfDEDI7o8HVbffcnaV6lqYajEiFxToNMsh04ksDBdXGMwpml9Et+9NY0WjWaRDfPmv6jOkxasTIePOll6IO+8ZuvuIKAg1NUiCJS50JqzwU2ClgMVaEe5bRSVMihP+Yaq5JJjXQUAHn76vvVsbgYUPjhb/8NT5D2N2NEWqgoa44hZ5y6wSjZbBB4QmNWOKdNMuchZc4UPlkiRvr2YSQMkttidNsstOvgtM/vPwSM4r4K/v3TiGaWwvcbF/Pnmne25mJmb1QcDtGCmyeuR1LOHHA/H2OwtG/VSAaOTph49gv+ac/PhpDyWhfSqDhyuuui6OpbFChWbKWtIEwdmyDGKkEEEBG1m3kWDTkJKoNLjRAzUo5B/nPioZBBI9u+/Wvcz/8uVdfG6eecmo0rn9gBhm2YpireDQAxa3BCpWAJYQ19tk7MzcDhw+Np/7615g2d+b/5Ti9BMj/x/+rUY2TRdaCfK57bruZrPtJUY9j+ipvA6ewO1Kf2XOxZuEpD/l7BgahReEs/6sc0kkGz98ScJxLpcboCRPihV4vxmWXXl4oNoxQaVZ6l18NBkjvu6Ooza5uhjfXAlObU1biGUq7xCv3G9XH+sA435I0M37ipPjggw9iMbR27+23p7FiKafZ+N13d7vV5hhHUPXlF5/nRJaRscA1/P943UI5YXccLI2oD+mp8/Ljj8Zc5vPoLTdmz5O9CZauX7uGRnVFtl4RL2w0SpXJyoHitAmdPJQ+NGvGR/7XEFLWaFjY0PapZ56JZ9nCdiTj3/XHP8aJOBdmLkrwVg5I93lqhsqWS7llIFIaB+hF+SJywznodGQwjntGoPTvuuWm+JLv3Hp21+iCkdsSx1pjSNqQTzX4xa904bwMJuTWAfWLARKe7ZWOLHxU0iNWNLxLEOa2x+krsd8B0YWmUu3ZY1vvgLrJP/KnMgtgZFDWn+67dnydPnlauelclHvKAg0iAzEaR/O+tPSVioYB9GigA/WROBcNGzfKXjneb6bbMTKLBm2qi4Spl9+XEVJW55w1GHU2CCYRaFhODyEDDc9S0dCyxcFx+oknUlnYKGoTQKrMmoUvJF3IAsYqAsp0yMaxUfaIQ50s5bu8JNwTdvyt/viczPogqqf6Dx0Yv33gV9HhmGOzNNX5pEHM3IW/zykF7UuyMBfA/4SzVzFvnEfWZsbqXzhcw4YOi3d7/TP2pIT8JpyXw5q3AB7cDIy1HTSdhX/Rc6YImkgT8mc+F5yn/GWNGv1WftWstWfuPx4ybFi88ren4v3F8+Pmi3rENbwaYPDpSGt0urXMLXc2hLQZZPY+4NEZaOB9g4EgNeWm8nMduDHgu0fNGlkF9sprlAM/+0+XFlY0nOnWCeSM9K7zL8/9hCz3Wa4p4UMwr1SpIQ952oSBBoMavqrgWMtjE8Dp7+lz8iHVKl1bHBo9b7wht04Ilx8xgqVn95NbWajOqAKv+p6BAqsaRHoRDC8ydW5V0eFyfAPug3GMbrj1ljz6rftlV+7YOtEkdZiJB+lC+jDopI1V0lPymDpbXGpjqPO8/NvL975ie8NbAz6IB/7xZPyi0ynRoX2H7PNjN3uTFfKGNKDcNoAk37gFyzlr/4lHSSadDOhQp0Ca0gG254mBhpfZrmmgoSsVDSas9qbJpyXbjm2jY8urFxDk7dOvb7xAwH8FcyudxpAT/X/4n5JiR8Hd/8Od/+8/7n5x9zgAXfQZSbppwwn03HxL9jzZtyZbK4CHgXNpWphKG+o9152BAPmTz8z8ZgIHutTmFGbqMQOzBhqsaHhuzMg4h0DpNVQ0HIyNnUFSeUmcMV35qVQ+ztcTxyYFfWba49CPeDRgp6w202wZ/Idsh/n7o4/m1onfXHM1gYZzaHJH4setDfwzueiV40M7JYdauhehrg9k4pAhI5Ff23zfOcFrnoLyqtUY4KspYzzw+z9G57bt0w/4kaqwiuXZRgeMUn9j/ymPDZ6krAVOaRtRvWr/IwMM2RySzLT863Zok3m/+u3DMe2rr+IKKhp6XETgjq0T5aEztwdoN8pbRSU2Np0L4bOke8YUzsXcC9tS+1udVY7Kp++h6Q+HD4sb2FZyArLhXIIYJ9FH6MCyMnTr+qxgdWykTOpVYaGcFR/VwZ2y1yCa+iJ5lvcz8AV+5Yf5ZMffGvBhDOrbJy66qEccf9xxcYhbzJA19nBTrzqW9rt480q+5JleqRtZg2MaaBT+Jt5Mfq5YuTI+x47vRXXW/iTxDNxZSbnffvtlsNetf8JCWGtPqqeYfoJDOaU+l1/d3iMutMuscpRmlwHrKVQTuXWiV/9+cQ+BtY7Htsng1K4EijxZ0Ct1PWNYCcQjiupP5qreMMSWQT3opkT/+k/eZ1JpGIHqt557Jipg+/7yzjuj9SGHprzYitwVEr4yuMZvwoH/JY59X15yIRWgE+3rLduQpXxuYNlK9jFUCPV5tVe8Oe2LuOasLvGLiy8m0NAgdajyPWUehCDNKQXtN2IoAWskFhDQevvtt2MfTkC8hARsTWwJsMKLS7QknpzFf+/1nwk0CAxfpSv/5n8gHI7IUycGUgr6zdKlUWe/OtH2+DbZo8Fs6hqi1ipAI2CWXQvUgSNGROereuZo9xBkuJAIfXPK7D15YTUZxeq743xDfDK2gYHMYGA0OgkNuozIIjTsgmwErxCCZDZgnO9hllco/bn5949Ez/Y46yefHEfh+HpMpMxbis7LITKJDKkRLeM4loJRopZJZcJ0TCFKc0syp3uk/v5yr/is3wdx2U3XF80gmbvOuqWxjqdi86eMqOGswpOwFaJeKZgck5ell5UJUqzFgZ5FJHcUjslTTz4ZXTFwT+3QIRseGsQw+yFzZRCEeaVRzvgKI6N8aegxZ41RDQzhrAFotYJMYLRyOkrNEzPeI/vy69/+Ltq2JiMFoxq51YAoj+FjttHIojDIMlrGk+lyPASH63Q8hYn42RODQQdEITLwo4/jny+8FIe0PT7uYt9ey4PMihTVC0WgoViDpa8CP2GP4pL5DQIJj8zMAKM1BhB4Vg3oyQZt/aGvx+6+I2by1bvYlnFxt3MTNu5/3YnvMZk05gpjtDDUnZ+XJVjuG+OBhRHHfFUM7rc30ODWgRfYE/jHl17M+1964onocio9Goiar6EvRzngbKAnHQBwoEBVAJWCCgpXjeU0hhC4pcCMvQLK8fd4nN377rs/hs+dFxe0Oiyuv/U2mnm1Slj/SKme35O2hEmWWgp/lJ3ZriLjBfi5x60eRs89FtETI2x4ZI+GD4cOjWvuvT/2ZfbXX3ttnHVmF5qINsxy4E2UwGtcCGPp32yxBrIl0RVxLDeTKR48Ylg64/OJlk9duCBhIKUmm+df/+f/KRINvqgavMdaFyHtd3yxkhSjvg/U82xq71vDq6hb4BcucoXQder0n99/6J47CTR0jrrs3asEWsuZBdxh7Ki4pENp06yr+PU9s3XCXF7TydMJXgXNSPNmv//8xF/i+uuvD5vjHYBzYRWOQcV0ektGNLSmXFFm2QOhMvCuCG9B7LnIrdD7Wgy4ahgDZr3mETSyqdT70OVsnNMHqYI5+nDyLzwfhJJFrpX8Mw6D77VXexHk48jEBUty3W6oUs0IM1/+LkxL8Cu9z1v/t5fw350vAZJYxZeEd+lyPGaeYzu+17133R0XX3RR8tlbBNYeeuSRfP/Pv7wnenBaSU33ahKg+Ylqiooc8yYtWkEmbQobJymMlT3KBp0DK0jEhzJOp09ZMnXmjHj6mWfjhYGDM/j1B7KxJ3don1tPtmLQ6PzovMjrjqtcKxwiwU1AB/i5qFLZsc8Vp0WPhp9oXjc2bunZMxYx+zvILJxz7rkYRM1zTI2tNHqYs/iVRtynqTOt8+hzMrPP+Bq9yiBlfRpj/PwG/fEumc/bCAx2bNwwzmaLlg0h65eVpYOro5i6gvlreOn0ehSgMmEN+ssMrYZvdouHNh1boz2blPEseeyfZEY+7P1aXEvW5Ui2OBzYqBHNVaunkaUTWpF56TjYTE/ZoLHm/DwVRbgIM+lemDi+8Dc7/A3lqe7n//uLL3Ly06FxRqeOaSjWqVMndYENQQvjEOOW+asLPRHFqgIDFStZu8anzUx1Fgw2qIcNJnmixRwcgEEEzJ7p9WI8+vDvohPZNANqykFx6Nqdi0HY/MdP9bV/lxwO1yOP+r6/GyRRl0ydVlQ09HrtlTgQmNwBv9oMUgPUYFA66cp6fi8lLkqVJMqW7J3AnNXZBgJskqte3LtevQwefjBgQPzl1ltjFGu+i2a8N+AACJcfWNdm+pdURbZmMANaUweqj5x3Bnn4adCwaNyKXuQ5Gr3aHdU44WAhsuDZF16MR55/iZlEPEm/g3NoVlwb+erJM47lyRPSXQbD1besC+wVyRPeT1p3XIwDgwyOb+a7HLJq2mefxd0P/pJTXD6NTvXrxwN/+EP2OdgODr9iC4DZQAMN2kkmZgwqqae9pBtxouGfAQwlAutRTumE63gNHDkiukOLXvfTDPJ0KhqaNzko9by8rvEv3WhPZNAamnNc1yVerTbRJvA+n2W5tHzsljS3shpY+/VTf4sHLrsijj/22Ghx8MFkb2tkAEi6kZ61j1ajC/nSz4Fgm6zqYKcRz9zkZX8XJ25d8ESP6bO/iGdfey0DpBeSPfbI1dqUYzumARVxtCs48qSdPn37Re/nnopV28vH3O9WZfWJtKNlaSDB30uXusmX77ml4t8/B3opq5WvvpTbfu44u/KqwhubATOrAcOFPvRzL2X7gfUPiJM6n5a2sifojP3go/jNPffE8fTMqs1cdwGewlH+E28GdLTrnIs2hbaH1VXKNysddb5KVRTy48+Bhtdej39i712IM33PDTfkEZHbGCf5FHgLz9zWCw5dqfJQmSzdi5eSnFeeatOWY17qtm/QqwPYD//sr34Tn/DNX9Ljxx4NZqczMAQ9e0nvzl0ZJb+bQEl5yPOkFddWbGXFLoSx3WJmgGgWp6u8gAx76o030354/tE/cSpSx9gFml6FjS3fSOPOVTqzt5i04lOVi5a+CxdPiVmHvN9lt2qxK3CxokGY2ePntrvuillfLY/ru3WNrsz9MGjSwIJbqJhY2pLSZOlZynRh4kvcFIke+BcEp3PJ/G0a73GG/QcOisvZCnN09fJx6c33xlldukTtuvViNbyw4utvqFCkb490iVx324dHBzsv9ac8Jlxci8/xbyt5fMPqxrnzSYrh3/R79dW45jp6NLh1G5qXLuQH56dO097zMnCnbvJSp5Reymz5yK0TVreaALaB9hQCvk8De/ufdTu9kAOeWuR4JhfFoz6FsFGHO55BI30oxzMhoC4Sz/69G3AvD06XU/X1ySefxEBs1Wc+eD+eoCfJSSQf9mSLrPrOwwL0bbQvpCFh6mUw1GC3Mj3lEM8VPtof6o/dCbA4t6n/msbWuw/ZXv1c7Nm4cfzu/vvZOnF4ynIDUiW/xa2Pyi3liHrkJ4IErkVGze0S2nwEjNx+Iw3ZQNTAz3B81leefTr6TJ0d159/Xtzc88pogCy2j5g2tvJQGc7UsMtJwIgzvp+BhiVL4o233oxqdfaOHjQl379BfZHLA5UcBZ7+52cu+7/uf//7QINLVnCAC6jnfwAgoFRYCIzZO3o0LEXhepbu0UcdmRUNUCBROJqKcY/frI1BphE5aPToOPumG3OsG8m+XtTdQMNBaUh5rrPGTimDlmW2jCMxpGDime4VUxhqHBl11VgyaqYS1FB8C+P/gb/8LS445sjodNJJcSTlqR5PJhNoOEsUMq7LkXFU+oUA1Ig2+mqwgc+Zq/tozIRb1eCX5rLGFynr+hynujudU21cZ5mhhGyzRoWVne8duzC8LCE0QqxQx1CCqWV8HSMj1iqNKhhzHvu2cOHCGD95Ujz91FPR5dxu0Zm9wfWJetfA8JMkNQ41+nWc/a5rVwkILx+oUrYkUudeAaUTkJ/xXc9tno2hOBTYv9uvf/zyvnvjuFatUtgVgQaEHEKJxadhLAzSSGHNPyFES8JPZSSsfL6XEXSdDJt5DRlEw8Y334lWxxyNIXdZtOB4GZlPYMrYGp8ZxOA9hUM60uBMgWoZnDjWQPBajfGJ5I0a++4TK3+kDwHC+88PPhQL+ewmyo8uoFy6HsIwDX++lyX7jCudOY6GUYFnnp2w0BnFwPZ97lGImNU3Az0b4f3amzROYsuK19+Idp/asWNUZy7rUWweb+nsNW4U5MLUMnHHcEz3c6r4pRfx4SRUFdKlgSCPD3v44d/GuIVfxuktDoqr6Nh+FBUNCtaiUWISV36/NH/xUIHAj5UUGgTeYeMx9+5uAO8GGpy7cBpIJcnND/0mjZybgftpZHYaldVPA3ozilZl47WJHicqxowyk4WtwHgrV6+K0URr3+z7diwi8jpt3oK8l53ZUR2y2h2vuBooqWT0wAv5D2kA9+In5Aw8EK6MC2hRPNDpRtbP3xWwS6woK8d3iRegzPichVRgPA0xkok4BLzPexpzXveSXe/UoX3sX4tAA+/vpOJBOSS9QM/Stbwv/WlQCV8Vkc6cxpiGhvxlFNpmXmPZ3vDEc8/HTSiGE9scl2eJu35psVCShWxJvnJsnlkO3izHIt0uo/PhNoK14Hc9z7MXSzkUiRmyCZ9NgS4Hxsxvv6YS447MipQaUO2OwWiAbtKkidGv39sc/zojJs9elGvUGK0KSrDdcPIIxlSkTFA+A36giywMMEJP7STc+J1pwCuFUUsrB5wT0MC9gAFji/v4jNhAGrrCehOfU2EaK3jx9Xz54NtuuSW6EaCTP/r17Rt/eOxx347f3H5TnHfWGVEb4381csgtN4S++CJzwoiQdw3mpIyEFg3yKGukdz/zkteEu/iYRl+Kf774UrzJMbqN+OxXeepEm5SRbjuRh7JsEdj6HXnV9zQcxIMMlLwKjSszlXc+XzxrbHhc3s04Rl9z5/VnnRlnYyi2aNo0x9AZl0ak+Qw0MLZGmNvsxLdVBzrYGkeWniv/lT/OX+NrBQFfu+Tf95e/xvEN6sbpVEycgEFkRUPqINau8ake0fASOQYaLCd1G5TvO9+EC3hLkxE8iUMd7EVLl8ZLZDf6k5G67ir6ELQ6Iho2apTOvvOV7ysydwONbjt0TspEZcAa1uZclT/CzXU6vvgwwOLxmFOmT49/vsrRagS/T23fnmaQnjpBoIFxlFGlQIMOojLeJsY6EQbtDTSYUbM82vk6rjD3WX42HzlpI+cnCZQ8QkVie/YH16Aizu8UDqlcbCmtwQ9+Y90GmH22xqPVMM7Zl7jm1gzKaOROpwpm5MgRyOG3ol7LFnEjsDm8efM82SNlL981QG0gxuqDdFz4Kd4MGuc/Hmg2qhRoMChbZ786wG19fEx1zV/vuTf3lN+AvdGTgNsB++zL6QL0j4EuLL+WHsSxukR4y2Cu3bHVG27NUM6IAz8TL9WAnX1tXun9Rvz5ld6snwqhO29j68SJuS0xaQ69bTZSftI5qoRgLPbGQ+c8R1wIa4cFMPkMn1MdI9qeMxPRrQ/+/vcxnCDSMfvuFfegS9qwDcyxvyW45LaMDDQAI7dmaLSn8y+MubQ1DDBbxWCWTr21E+XBBmLWQnNDx4yJ6whUq3mvJ3DXGZupCTSZDizrlmbkF2WwdC1MQF3qLvk2M93g1GyqsJI+DYI5v8Vsx7NL/p96vRR3nH9BHENyoym8aqBBvAtnaUy4S8PKX080kkayjwCfO4+0l+Bb+VrHS3rT/ps5Z070gp9W0dC42xlnUl3aNANI2lVuwzKYIv8vhu8+pNz/PRrRfbeeyqIVVIKwBoO++gS7oMOqIHpSziZdpp/AGpCnoI4l4zjpuPEFLh4Prxv8kMd58Z188TkFYRIpdhPJsHVb49vv0XO8pbuHeI8myJXj23WMfaC/ydP+FaPf/yAexvHyFLAa0FNl1u56pQEvx08Hnp/VcdykO2HnS0Qol+UDYePnJj0+/XRC9MFOfW3s+DibrYi3X3NNNMcBSz6ERoSftCePitd8JV8Ba8ZSzspH2q467trj4kT8L8VhtkKo1x8fj8+Y022X9ojzCE4dUKdOMS7fd3xx6uX3tDF1gOUdP0sd4OfwkdtWsmcU69I5nT1vPlW9r8QL/d7L7z9HdcDJ7TtkRZzBqExUARWDDVbZOJbVaiU7T/vAoFom3UCgNFAVPvJzAwFjJ0yM+wjczf12ZVx22skZCHDrnfPTXrL6SF5VBotKn2HAM/lUuPHsfHGfssdj0l2Hz5CfBgwdFjc9+OtoxncvveMWGlyfzgk3dTMQvJLEVWVg4TbCrORhTiYGddoNLPloA7sZvGXu4laYSARW6S74km1aVCO+gpy88eLumehs1oz+TQTTdYitaDIokroauWIgN0/mYBz1njwqPwsjGz1b7aHMXAevfE+gwZ4nbr2rt+9+cfYpJxenTpD0kT4MfCf/MR9pRF1qMFFZXuijoqKhoEuqUVmHgT7ljKcWfTblsxiO//EivtOvb7wh9Uct+pVUQSZaAQMpphzUn9Bfkjhdu/LEQLL+jjjVJlCX+kx1lzTltoyhgwfHy71ejdoN6scv77gjjiRQLQ6124VB6kB0gYEGZWbqIZ+a6FaP8HISGLPCxO3Qyl+PvB6FjHyd/g8ff/FlXM72r6vQIZ46YWDX7XzqfWWkck8b3d4+bslSIi5E9lhNVbN+3biw+4VRv0GDpPe0sXhccfng/97rPxJoKClCHdGSYYkUKaQuEaFFs2bFeAhowdy5CIHNGWQoq3tARu+3o9wUaCLccieFwhiM71/syKZdclybOJfmGh69o2C1HF+GkqBSeCLYzGTJfH5XIZUBAIhLwhO5aQyAJ5/z9YoVMZC5/OPtvnHCfrWi0xln5REwRuUksIyKwYCiNQUna8Is4q9C2CocVfoqQI1dI66WTxph00i2oqEvjDJ7/KfsDe4SrXAYGzdqlAabwsJ1esnUGnYKKaPMMoOZG+cu45QCDRK3T1dIeDROnmqBIdeKSOXxBGyaUvq6L0ESJZDNwhT4KprMagOnItDgOe3FyRM6YM7dwItZfHg1YbmCzM3CBQQyaAQ5fNjIuKrnFXEEwnU3nC7HVDgpRApDy+gcAgTYZ/kmsM99ncBPeGtMKFxKAkbBMhPnYtzYMfHhkBHRpHnT6HYmfTcaHpiCxkCOuMltL8IUZiyeh8nO4hV8wk5AZCaGSZtpsRtxJQTJCgI4OsOv/eOZLJe+ous5KQTtWLsZJ5/J5jp1AFQMKoxS9Nyouv+8wf1TKmGzXmkM8VO4GTx6HyOk96Ahoi7uphSwDcGpWgRRyrNeu5Ab0HCe0oaw/fdAgwbNOhSdl0a3zpZ49vJ5n0+fEb2olpi07Jto2+CA6HZh9zyOz7UKZyOuJbquwHdTCPp96DT7kkBLzjOVAsJPWrTcTQW6jvFHjPskHvzz37Ki4bzzzo32bdsSoKqXe4GzsREwEPYG0Vy7fQTcb7wBAfzVyhXMzyY9Izl54uuYNf/LnHctrKK994jYi+qimruhFCtihSH8xWOW6Mp77HV1j1tR+ophyXhZrrsjoFGpsj1UsOBw1DchJ1TC23ZCCFflSFTW/M13P9CYi33AsIzVDl43XM6RfxijdfakuSF0Yv8NO8Kr6AysFApNx8VGZRgX4hKjUtmg7BAvKvwVK1ZyagONGNmP+cpHgznr+6w45vBDacRFF3vgXmrepUKWPHb8b0dWk2Ou4FUbjUqn8jCWBIakvEs2jfkuROF/hgPgsXlz2Ifc88or41D5iQCDneo1ztZtWAdfcJ75qGH0aJgbk2Yu9EFRwxctH2rXrBp77lGdTvcofJUcMF3Dc7OsWRwxhlt8thOBqETpB9wJHeKkb7X5IEYI49iTwYgEahkDl+qgigTHNm9jb/TKmLcYecn7vryuuOzS7N+hLPH89reeeSa+4f3re5xPxQE9SWrtybjcLa0whsGY8kQu5HPhlIY031V2Kc+Uj8KuxF/J0+DDPjavko0aMGFyBhp63sZ+SeSkR2SZVReHOlzOQ1mVzguGhs/xMohRBFILWWaGtNjzi7GDoTCeAM/Nv3sk7+1KMPlMMkZmMn2+4/nK0n94Q/63OZaOmJe6TD0gvXi/xoe8IR+KMyuEhtA09/fPvxBH1NmTprynZGPFOpRjF7LRklogz7gGkv2+R9K5Jqsl5OPUR4wnPfocDVxh5mdfYqS/S3Bq1KhRcX6XM/N0iMYEZDWYzLbIW8LfAKP6SUNROex3lSXCRl2VlTwY2sLeterIWlExY87ceL1P38wutj/umGh0YMM8glLHXMdNmGsMyU/OXV0ibJyn/KSB7vjKm5L+lT2saJg/fx76Y1K8OWp03EkJ6NGUG+9JqbfGoLjT6CyyOsyK5xhwSAeFZ2cGlucLG18ZgGbu4tXMs43I7JQ/mGMiqzVpFD3o3WSp9y7IEKsArFwrqtcK+GeVFs9MfCuXmCOLSn1tWNZsprrVoInnoI8ZNz56P/l0fMFtF1IxpRFtoCGD94zjUaXqDI1cKVoZ7lWM/z/0Lryle9enrNehN2v/MUH2l97pF/isyILL4vhjWhNoKCo4lZnSHF/dgdOC7sQBwyTspSX1b8KGN8VpBt1Y01Tk2LMvvBCfzpsfR+1bOy6iIsPj+LTHDNCbIXUeVmNtQfbmCUPyk3TKS6NdfAqXolEekoRxNajXkCUdh03wq8eeiAbMvXO3IrDWoKys4CPGzIs5ySfKAas/zCTbQ0t5X8rEur4C7wUcpX9hMwgjvRfJn25Ulh7NkbHNCCDtCWwKWBuMwrGCJnPrBD/Najt3bTXhIJDEgzzgerxfe8FTi+bMXxDvEkT6jlOTTmKrUEvgUoYDYPBjNYE5ZYv64CsCMqOZx6gRQ2PFKo7yIworhvcBYTUQxjWrkrVHlFYmkoVVkHpN/tCO24gcr4y+1Dl0W4H84PulbvimN7aj42DUDAxvAcjlK9HImij7SpyUefNXx9d8SdrwOqDufnHokZzYQmPIz2fOislUFD5w2215HN+u6Kas8pP/WLd0KG8pD5UhVQkMVkJfbAIvJs6EiXzqJT1aGaVunEQVzPs0oHvv8+nRHr13CQHmxvXLkt5SPnKP45qdlnbUJX4oX6rHHUOakR790OfwMbCvkCdRjaXy9p2XXskjhq/qSp+cTh2jDlsn0p5BxspXySPIMO2/rIThET4758tgBr+UZ1YHWGpudl15N3cBzfMIhL89bISzisfuvIMkQZsdJ7sVJ/8o79RPyhdxLO9q7+rcWrlpwlJ5IEyEY2bAuc/nfTZ9RjxFMm/O96vi5MMOjq5UxB2Ms56BBvkUGGiXGnDQptQes6LV58n/XgYYssoJnOhIG7gTXgaqR+IbPPrM81GX+8695MLo0L5d1KWS0sbc8kxqOb5vNZx85HHgyni3EonTrEIGbhl8UB8AH+Hms92K9MHQ4fH6RwOje7s2eTyyWyfUQSZXtPekfadZyDboH/2nbBGv8rB6TDynHANuBmTdCubpPDOQw+/0fz/qYFt3IsDe6MADs5+K39f/kE4MCohDdYa0Y9UbU0xeTd+Jz5xrQsq5AyO3si5ctDCs4OmLnX0hznpreksogz0GOPUw8PBe55VX4g/7A1gkDJBjhX9IMJU1mnCWHg1gu032E2T8R337R9X99o1rr7g8e5KIP9fr+MoY9as+jDrcqsOCp5FlwHbjzZ8AAEAASURBVF2fQ9trGzJAnBpoSJsAOTMBvff+m6/GuKU/xFnHH5f6o3FZGbIX2DJu6n50VJ6YxXydsz1f7NGwmIa4H7Gdv/HRh8d5VKqXNahf0HxKoGKp0tZ/8/W/DjSIco0PCV1mUInlteNvKC4WEGgYi+E0i2zKSkqDarM3aV8ce8/nNWtthsAGSEWfgc0xna7k/YlCL2Kgk+s3iLaUdtUHaYVAJVMEw+nUy/g6EUbFFELuw5O4k+kgRu9PR2EHkRugWEGPhs+psBg2cUocyPitTukYLemcqkGk4ioCDQQr+Mx9jBKfho60bfbS99OZYn0KJbuCmkk2m+GeQCPjn44fG99SBt3yiEOjcdNmUZdopWVQDqIhK4P7UnHawbjajkCDzovz1cmVcRxfYaUBqSFmadSXS5bExOFDM5PftFmLNBQNNDintTzfEyeEhwpY5pCgzaIKkzyeUsXDPLxPwWvWygZu33+3Msss586eFV/NWhCtOrXPfWPOW0eqxIiFEEJxAQ8FkP0jzPwrvI0aC38/k9EV8D7LzMKXXy6KmVOnxIyZ82MvPNRj27bPc8qt7lBZJd6Ym4yfjgXvIeJSgaUSY1w/U3EIG3sJ6Dzl8TKM7xnuwz4YEAt579SjW9Ew8ESctD0yymoAwP4PGif8x1pQDAggDWoFoi9pUOVpFlxacl7e7Mki4nQcwakhM2fHXozf8aROWWFTEwFodsGtGeJS3OYJBcyxIji0OkVl6rgakorWrJRgbSoQqzfE6bwFC2ne0zemEos4FJnZ9tyu0RABXk0BDQ1LK8LX+dqPQSdM5QaSKWNHYbO+NBaZ7zbWqVlhuVtpj+2kKVPjuT79s6LhtDatyZIehdLfNx0WezQIn214x85V47UyOAVIbAXYQLaFTCXZ+alfzOD373CEVjA6xhBB9Lq1ykdZ7do4/ZSeez9KUgPL4ILGkGpXZZyKAZqQd4yUKydcF2YpigP5wcsKhk3ajJTkV0BB/Mjxi3N57uzFBAv43kperqvbyZ2iJUq/FlkteyRYTWKDVXFrUM3tIkabrdrJ/i88S5qRj+QhHQv37dnzZO6c2TFz+r/iixnz4/BjWxGdb4xMwkgGBtV3q548m8pHGuE50omfmUHbSCOwjRiqKoAKZCDNzPrT44gMWH21/OuYO3tuTB/5WazY/l20ObVLHAhOq9dk3siL7SirdYyxePEieGIq2zi+jPkLV2blRl187L1rBY2/alMqu1tUx8itABxhhoyMq6zdq51ZRzJj+APMD4OMF5NMJVkJOuEbWRq6CcVplcVOlIqU59SJ1Ru3xryly2PqHI7T5J7SfuRToeuj2P8uHUzFEB1LptFAQ5fjj6bC5rAs9Va+65wbpZc/lZEGGLI81DkCD26BVossmnN1PPGt426mQRk2mDLGMYu+yj22bTnCuEmjhmlsSusGg8wgp7GOgSNedXjlx8Lpx+AQFuBU483PMqvDinWGs2Ki99uBKRkdG+4XrdsWWxuUM87FOZlNdQz/NnOjoauTwBvZ/0B+Vk4431KgwXm4ZcsjIl8eNDQOJt15aJsO0YiKNRu3Jf8n/MUJQHFsxjB7oSGmEaP8zIAHz1YuKqczY8vczSZqKI7HSJ87bwEdvVtE00MOIcNhQJatHQQa8tQJ6EtdZBWGvGR/oNR54N/xfOmASPdiRNmqUWogwQDYEIyt2vvXisOPOob9tfvnca5pRGOQMenUec7bQEbyFLBJPmZ8+UrZWwST8EWBj/SwAkNx0YL5MRs5vBTaOrp9O/o/QO/wqbhxrKQJDEVhAGgSn47vGtw6YMBQw188iSNvsr+F9y5Ef0wlKDjl00ls6aoa7TufHg3q1iPQQEaL+UEI2Ysnf3J/Pg+aU3eYeVX2sDAnTFM5bwdmzEV5bKBh1qyZ8Wn/j2I6t3VqfhAnYx0b+yDb1DnKWgM30p/GuOM4b+WaulnZIPwMnCgviqCR2sl5UAVDVYT8NG3MJ7l96agOJ3DcaoukmWwSzDPUBV5przhbniXP+HzxJ97VwfKbSNU2cA2bCOYqn0cMHBALVq6OFg3qxKGchlJWr25WLsjglZlr7olnnlYj5XMY12cJl9TprGUnBUkJPjxE18VAgz1VXnr3w9iHe9scd3S0ooS/Dv2PzH6ql3LtjCU/eRlsFf7KZflMelffcQuwEa1FRtzvrli5Iv5FttEG1PuytObtOkVTAg01sRGVo/KnMrfET/KRNAKAeF7Bw6m/JRL+8xlJT4xtI0CPrPx0zMhseNuSuTcB7nXLyhKfP/A5lJjyw4Zubh2dNm1KNndbuqZYy0F778IRe7Vjv90rRy32PFSgwe5PnCZRjrIxn6NuEoYSacoS+EGekH5t2MuUgCuyGr0gbDdSuree93euwvZZcLn8+x/ZFrQwlhDjg8KyomHvPXeP5oe0IsBRM2bNnRcTp0yK67qeG82QkVWhw510tuQh/ukMyesGGrTDrezS+fFz4aM9m/YTYxeBBk4Bgx9mgdMxH78XU5f9GE3r7RPHt6XBcp19maL2OHQN/LSfrfZQvvLI5COTEYXsLAJMBnXkA3Wr/KTsNik2heqaEUNHxhKeezZwb01ixsCa8JBOiqBmEUTKoKDPZRznqhOfW1GBYVZqgFT1unLBtS5d9lWeYjcIe8zrZoKOVoCWGizbhNzsunjR0fW70pB2tUENP3frpHLX5pfaZwYexJs8sWDRlzH8/X4xefXGaMkYJ1/eg1NFGiZ/G/CzKaSVU3o68mf+A1bqRnmhkHNFMNPm3Op7beUNmzipBP9jGsGj/gOHB2HGaNX2qDjkkMPTH3I8gJzJSBNX8o/4qA4diDtPN7AnikFn5ZG4Va5n0AdY6VSvwgeYMHlSTJ4wJZo12DcOQRYchA+iHLU5qrgTn8I4g5vMPvUTay9kJ3KAf+oTbSYbcJqB18k20GDD6smjhjKnvaJFy0OjrF4ZgeraRVCTuWWVBzCUhvQtHLsK/og6VP9LnIgLaVd6NRmsDLXSUL9u7nyO7B4/MQ5v0YRtfS1if+RMVmwzpnOXfnIQ5Kxy2EoXA1EZcGdwcetcM6GkfuQebfolSxbHjKlsT/3086xUOuWCbtGwrF7qZ3m3eDE3xpLufVC5cvg32CHO16039n1R3lrVULEqdqK8yPirCODPhJ/GvjsgA9Xt6bVwLBVlB1ApaJDeLedWkgmDPBUHnDrvzcpBXl9DE8OoWjrlykvjgosuiHpl9dMuYyYs1hUXcppf/muv/3WgQUNVlarYEyAFaCAjiQxEmW2bRSOOEUOGxBSiPl8gzMsjAD3qxuZfCgIJPLMRIFwhtJYI0RqAvw3C9ejI3emfsBvZ48Jw2p5bEIxYukexCg7zKoSEvRts1CShmPlT0VWnbNP95ipqI4EypkfArCGyt2Edx+BgoOwKE+9B5Fjm89lpmKDgFBhmYo2qfk3JkGVANXBSbDyZWwiYa5bwuGoITuKXsSx1X0XlwVaIssoudC8nwlyZNZQypEYpDSg4hpHJTTxzJftFVfbVUBoyTBoyPEch7jM0ntMoYk4bUHJrgI/GjfCosXuN7KOgEZsZVt6rDFxVJgqhNb4wCKoghHYh6i7hqhjM/qaSQCCYdVgNTFav/CbnUbEigQXmUplsUdGFmMyzjM33hI9w0giSwdeznjzbm/VnQ0TuURnIoApHnQGVkNm0tZQvbli7mozubrHXfuyFJ8rK15gr0U9wI2wyMrzjWUblVwEr6aMmsBL2AlpKc30KwCqMYZTRM7OXzZ8b87/6Jg48YJ9oeeTR6TT+gHDcoEPIfCGPVCbF98EfMNyCYNe+qoTDJg40pMXXrjiaChgVq0pp+aJFse67b6PyHrWiBh13Le3UwLWaIR194O9aDAZ4XNJ6/rYMlTcw+HDegYUOmZFu4ePYzkmj9EcUwLfLl8SGH75BgO0WtQ9okMcgplHJmCoXj7lUeDOcuggYULbPQqqgIGwOZVnvGmhuDc7EztC856zbs8E1LsZInz15ZgrYOg3rRr0G7vmmdN/9wRyRmLAHxpbUGqBYAy42oomMdEt3XxOE+nL50viObRTfrS0MjEY4WQ32Kh+NUQT196kRlfneCspNN8Ff1dhLURVaM+Cgwlq7tjjLvSJVD0UgDKeRtW/E4NpI7f9mog32dN+uvNgZwUwUeOWa9TGF4Ma4RZsyuAMVxA+8jmnSEAeDwB0C3N4YaJ+s6lA5yPPr4d0fUArlmH81aF7DQiWvDJDOK0NHblnR0FkFbaxlTZvhqSrVqBzYtXrysLy1R409M9ggrUu/vudafiQotxb41CTLkMdJ8pkNrqpiOFapxJFkrGUzTvhG9uWtX7OBLtU0rAXn1fbiWDKqP3YCBkZhctsDlQffEchZtmx+/LDyezJs0AdrrEeMa/9a5Qjg7EPFyK6xRxXwCb53htYMauWcdAohht12pSEccJZGtm7GOOT7lYB9Naom5POVP3Am/Eb6I2jsVEZpMscfNnAKzMKlMWGWB0CyRYWX8G1Rv0GU1SvLMVYsXRarFy/FINnKcUt1Yp99DQwTAIUeVJjZhBRDWeNuEzC3T4qNeqXpajxrVwwb1WNRVYbLAt0qb5VvGoHfLlsSG1f/AI3umvxksyp5R+PYU0s0As3G2txLOSOv6+BKq7sx9m443josOtWl4JXj+963VJAs+2Iya941qteqQyAX+mTMQraTGcbpKaoV4D94ajW4WgcvKCOr8hyPBlWWpcEBXMS99xm01fhatmxxrMAYrU7msUbtfdgHyvjQo3Nzj7N8qh6qTOBJHPzAHiADlrtAi1V4ZcADGK5D7gsvS+CVCRpc9iEy+7p+9fdUa1Vn/nsRnKqZ38ngNPf8xEsDxnJYj39b7vYt5lmTfTaGGa2MkzYyYM376ZRyn/LNrS8rF3wRVWrU4khnuu+jowys+n2N23RglDP+48210PKP0Nru6JSa1dAryEMNucK4w3hnHvLdanTSCsrgPbnJwPMuyBdPBdEZcyx/Ck/H1KEQnsrwdchb8VqFMXx5g7LN52gk2g9Co3oFOF26YC6lzWSaq+0e1ffcCxqjUgQ9WhG5oSyuAP2UA67qER09desG8cE/929bDbYR+rJk1SCs+mkrDqMVZ+6Dtg/SJnBSmWPyatbiWDXkRAYxmK9ZsVJgIo8/hs428N4acCu97IFtUA0aM+ConNA+kR5cr+XWHv32f1D3ntF1XVeC5mECEwCCBEiQBEgiMQeJpCiJSWIQKdGSbEl2ydluV1VPu6tW9cz0TE2vmanuWj3VPVUzy71Wr179o+vXrDXlKlm2bEuyEhVIiknMOSKTAAgSYM4J4Hzfvrgstkqa8oz8R1d6fA/v3XvCPjvvffa5Dr+5RR9DcfKWUIF/KPCxeF4Yh8xR+So+m6bsdg77E1fco1wA7GJ+vKs7SCt30K3MJPQoR/mYfH8YTlTnFTiug4R75Qn9IcDBtIPrLKKTbqkTr21T2aQu4fM6L8WpG/AmHQ1uwes6cyqdOtxMK2RZVZalmumzqWLP6QXQp/PUuaK+pI7g2sofb7MGsabMKxx2/K6sE7/kH66JTryAFbJNneZSRyv7lCelmmkcPwnsnZe8N+QefUdtEnGYrAxxZzj44nde6jLyjdzIV/fxFC7bVa/hh1RUiuwmwBVHPDJHo6iOyTpYRmx1SrS1NJJZlzm2bXd+VRlp4uWphuyycYXAD7jcAtYwW/BTx48ZbTiXoRPlgHCQ/woHdQvx3sr0Onr5J5wMV3H0DoDv6fRt676YdpFV0IpwUy/RjV/Ca8HipVEA1sy79sPHUw3OgLFj2IIFrrl1T7jJW1w3nWUam9Kl8s+tBiXQ6jACTBpn/qbscyyjSsuCXqWn7vZWYMvWFPSx4pHIJ4IrbmFwJEanpUGd8kZhhc9VjS3gKk6NhAcbKNSJoo6cbU1wa8bIkLUnWprSmfrDqQBHxYjR49NIMxDhIfIA8U4jWoNcHUf9z1oJ8j/pVn1mVJ9uGNvb+E4+GduIWeOr4EV7U2M619DCWJGVC+ZxtPbEaM/Th2KLDjBShujkdEujTiWNdmXYLfSkm8xvNAE6j7K2D/VEHR4GyNS1zqNPXqAw7J1rPanysYdTmVnDwEWa1skgVzOTys8XWfdL6DjDwcFiZJ/Z1fYV25TJLBwgnMBV/75K9svVax4tzRYPeNFAHFBDwIVC+HDRME78AB46dfsjmFnqgK30dF15z/0F0Ix2g5kZ3Bawtj1xQZ1VujrH2l5DfgwCv1zXkX1r7rzkjfIomuYzNgPtXoKepOGRGM/D+E0+aVTfQKW8zqLo4pB68DWcSD4XJ+OAB6PHjo9Ti8QJCxSLb66vgR/X+ib4qA0ColGbBF5DW66j6xmOUmBi5o/6cGQHYGudajyUCkvHsT7jgAnZ6u6vpU11OAcu3ih/1K/Vx2y/DN1qhM542jKrQRtIm04a1Tl8AXvyLPzl2plu5tM/jamiQD+2n/ph6Cngo04S7R1pQFvxBnC5FbIEBwGwFmZISE5AIbOMNfXkNZ2z2pZdyOxzHY2cHHQ7jR4/lZM4qsMJfvUS2/fRL4SP7UZwl7F7bPw1xmVWgycUvbdnZ/o//v2/S9/9/vfA5UmxjlmPmSym6y/19YUcDcAegICwfS8/uxheqBIYNiATgvzwQaLNpIMePXwohO6U2to0iUiKRpqeegnLlwga3kyEk0aPQtYGzV7QoyRTNZIgEXhZMEWE8PzjzT/9aZqxenWqnTw5EN9jIVWs4uXNLK6NqZTGJxBdZqCgiWrgGIcKRAlAYehvOkJkUOs3fJyadu1Ojz21Io6/G4mRaVqOwi48x4xbAvcZiUth45ULVecVWyboS8RXMYsoCUzD6N5bHJ1YWlkZxzrprZTZek8eRdE4dZwCO9+PbN/u+zGKqYfVaIfPaNgrZBUqKvdHjh1P7/3ff5uWf+flNH/u3GhT77eC0AYV4n4Srj7vOHOF0zmoMKtAyDi8vF/nh8zcExF24Tw6vm1r+so//XFE4UOJZPwZLFB7aTy+A6YCXmJTgZRpKCxlcF7CyBtMn7Zt98a24T3d+8m2VDqhMj311CqivKPDY6nBoGIRzIrnHL9GgMq6you/W/sjnF0wG9c+S8METtyjMu/+/NamhtTesCc9vPT5qHQtPqksmF4mHJxDnNZB23EWs3NhHirJ/BP3+LegUfFTSImzbSjcu3bsSB3Hj6dCHGpzSfWfAl7ankzQtXPQjtUIjfDxFWtOX8JFhu2YxaEw0GhX4eL+s6Nk/Gx75y0i3cNS3YyZaerUqamcfvSeh2LNeKQQM3F8l+mq0Gqgun8xE2TOwRdD6Zur0YsbCAvPlN/6mzfSs3/04xDEHr9zsOFYOoHxeay1gxZTmo6jYcrYfjgaKlJNOUYFjpqLRGfcqqICUgAzlg7CGKRPnXBenrig88+1vkV05w6GuXtVe4FDL4pYzwAiJP0pLnf5OhH342n7iVuJXQSpk5dz+dHXnkuPgsfuVYVQ4DDMQdgBW2mvqYVtLv/Xf0lTl6xgv+/CEDbyCIWYAtZxqCxw8314SyuhkMmLxHf+duzCSXj7vGvs5wOHDqUNv3ojrfnDf0Llfo7XY9wK4V6LJQDHiAQzB7cU9OPlPAWz3nsjqxoPav5DCt1udYdslsa0e9dW0gaPpUMnsw0iM8ihrSZ8OKG0AkdDYSpBcR3E876sCyHu6RCTniLtEWF3B8Glg5WvoHELOWXR7KvXUOCAUz/o7h4ORDxQ6TyOhsOtnBt/7Hw4NoSt7qMXlq1Ii8loGIcyHvTFd9YBGKjTCd6rgqFxLH9yHVUaVB5VhuUDreDlMHBo2pw54DtReH4L+IHLwtD1sV0VMenE70I5hw7k7aFAACzxVH4g7bk2bfDIfTt3pMsoIaVsUZg6YxpFOyvDWeBYxDFxWJ7i+lrTRqPDNZMXGOUwQif9FGAYZ7xURxrOB5630ODG119N42tncMLDtOTxd2YoGK3K+Fmm2NuWCk5WdBVcZR2kVcfrZaaR/EyHgHzBjCSrdW/ctCk1fLw+LXzhpSy7DdkUBin3eGXywkwLcSNL55WXyd/sU4eLfECYyb+839MnABLGUTvHav1NKquqTU+tWsXpRiVhuPu8sHZ8KtLCVoPWNlTqhG/Ai3fpRzhLQxoXGb4jJ4HfsfqGSIF+8tln0kIi2W5TiXEGpvmoilAmP5QhGbzgX4zbvp2D625/zkHaciyuh8Uy95KlcHTDxvTSv/iTNGPqFL5HGUfex1w1viUeaNzMQTPEhFgmsbK+Y8sEY1ZmqcyNgC/Yj0UQ3arZTKSpkFM1Fi5dGim+Ks86X5WtztnBe2SehphjU/4KX1OYla8aEsJBfqHDciCGihGs48ePpfqjR8JJPGPOw2muld1xPmYyOVtX4Rp8nk6Ege34VTjG5HfgrTxePPIaKJ3Do06zDWUf0bfWxvo46nbBwsWcusVR0LRxnYil8t4xus0BzA+cFKbOR9kqxISBBj0fsjXiy2HgvnK8obExbV+/Puhn0pSpUWy0HMeHY3GLn/pG1IpirvLNwHFgJm4rrSO9GJiLixqayhTX3aKh6hamzW/8+S/SCtLxJ9dmfEBclCa8HKvyUuD42bV2zG4DC+OZe10X24qaSTznfIcR+DiPo+Y9tqZ2N9Snp9lSW1lREbSkcUQHMWflq1gpbxbXfXn5vZkj4mbwAGBlwEWctnbXzt170s5tW3DOnk/tNxxRSo9MGpmqcTTUjhqaxuNoGNxDhgDBkv44eUJOgJOurTxAvYWPIVfUEaQTnQD2rxO9h8jmbb4ze+8eRXbuIufacM7vOVCf2qmXY49K1wrm/r0f/3GayZpHIM41JBruy0xBAyPBe/g+Mq6AvTyum4DA/n370mkM5JVf/Tqwr2E8mX4h8gl/o9jSovqrjsjY6kHH0rwyMEbRtz7SrjqJundbezv01JSaDuxPk8D3J6gPNhojXQed8k/nljSlA9e56zRwbYUtww26FicDZxwLffq7/Z87dz4dO3o0taIvyZNmzX+EjB+i8DibjIJrHMsHxGyfCbjTn9jkmgauu/bx8lapAhmLQatckUdu3byRrJauVFFVk6ZMmxGZXGYgKDeDh8VYdIpBl/Qnzudzso/bRrL5zraVC/KaYcBPGjtIRs7Hr72SJs54KNWhi42BloqRgyAt9xu55zlHRH/ig3D1Zb9e2jwCRB4mvUpnjk0njJH+rVs/Sfveejc99o0XUk1NLUGubHun9JHzWOGSzYNugg/IZ2mO8WkT2L006LswtQ95ndlWv6Gmww0cLU9Ru8tMc++zDfFXXqAdwReBx/aT8xTnxgRDFoVV5X387XqEo4F7G5qb0xt//dfpoTVr0pJFi5DLOHrg8bZvRoCDDBhBl9KQTog8KBCZqsCbmQR8lPPe7zw17rU/rL2w/6OP0jM/+hHFTKcHXMVHx8n/4HymWzOgoAXtLscW+jX35fAOWmCOZjLqoLyG48i6Qwew+8xCWv7ss2kSWWIulXxKh5Y6nTAsYKtSyLs+PJWzOWbXVXnqQ+r5Ibfp3+wZ7b5mdFUzqSyyebS1Pv2bn/wkvfztb6Ux6NS92M3OU/oN2NLml/lClxZb/v9d+YMSvJ/zv8U3X/qaQbt0kH03H773burEuLPg4xLqC0zH8HJ/+XUyEcLw8V4QW2EhI4kUcQUjBGeKqQqpBBDKHN9JKO41MnqzftPG9PEHH6Q5pJnOmjUrEMKUTb2sGt1698MIddFAMpmF4wvDWqSF2Ri5NvUolCIQyDG4baALZHubFOIGFJaFixaHwTiGVCG9isHguFfhJbHYdhAY4/MKwdoHmBBwzFdDJNKzGYuEcwxl6LXXXuPIpfHp6VVPpVKiY1FkByaVK2m+i8gqAipzKvi27VaRKHzDZ+EiU5WJCJNcUd2N4PkN5w2vfu65qOgsDCXSbOwSoYYyBj6Cx/nHsWnAQ2YqDHzn53A2eB8PhEDVYBY2G7dsiSM9v/md74bhlTkiVLyduMQtpDOFX0yLQjasXexfREEJxZd+ZIjCTi+0wvAUezcbgc1uivWNBjYvUExoAkrFJTyqMgnnavu2Y5/CJduXBQxQGs/gkZZY9VjrnBEPmG3g1TkUifr6eoTbkXSipTktenJFWrVyBfvYRpC+dTkEa+ANa2SkUpgpWBy/R+EIO/vkH76DGTBuIykySI2ZRtrciLFu28VEKxexl+0hjC/xTcERig5t5Mwn1oL5Cxv7Ehft33loCOmZ1aBTaCuId+zend57/depCGE7E0fDnDmkg06YEAIhipKFYMy2zqhyKfQyow/FzWiQAg0DH5aL4gKuMgedGYVEIzwO9HX2y27YgvIP0/N0hIMYkJt2bk9HSOPcsvtArOcMjOGpOPknI1QnjmI7QgGZHESF3Qcsmrj+4pZ4aISHpYqoz23hR39MCyVB+YVyzbyR6ihiGGlU/L5FtazTl2+koyc4crUdYcBlpMfrL//H/z49w1qNJ5vJ+gyZEMIIU4CwBoeOHUuvvvJKmjnnoWTRSJ1HKj3ihnBUOJt15CDFC6MYjlHngzB3jVwD10RYZwIE5wz3a9wL+483rE9Pf+1rbDNYEMpaGGcIT9OSVQ4GY9D3I2Whl3ncQ0orrENZBx4qHP2oKFY4wmymWxRA2p3eefcNlJWDacPe+pjjFJakbiLbU0rHpnLqX4woQEhBKwOZryqziqbwDH4DfamAebQvg+YHcbLvd/hGbN8Brnf47rZjIjpwDkfDUdLb9zZR74FbT/Dy+pc/+gOq4ZOSXlUd9GWKrlEQM8WKiZDpaHDfs1HXgcwzw8lhcTTV5s2bUzM0ZRR7Nnx4zuzZIYTlWZFuL09hPNKtL+leA1h6ltcbwVQQCycNdRXfLJtqKMZufRxNZTSlkto+M1FCTQ03GhGKFmN3vTTIVfbd02okTvxzTRXsvluYL99WZpRfHu5cPtm+PX303nvw4LFpOkro5Lo6igNb4Ir1o13lkjQj7gjhwTxnOq7ZDc5NPJHP6VgQf3Jl3u0f51EkLDRYf+gw6clPRttxZDD0LO9w3IEbfQqzCpj4F3yRtmzbyK73yRstOqYyrxNVJ9AxaPLnbDMczXhfgMeXe5oJ4w2HNGtkNCdq/zhu4B7Rc56Xh0a0kHZdF9u0T+vBGBXyO+e///Ch9Gvk06OLFqflGOvFwFXW56WxI30II3HBDEDnEpFJ2nHMrq0Ku/CTlwkfLzMLuyh8thmj9CjK4g9+9CO2isyNNTT12nG6vmEA8Zx81XZsQ/mtTJfvSnsqh+KMssotkPLmpqamdPjQoXQMuV0CTJ6m7kIVuKMj1XULEmHNwukCLK9eRWYy51LlBf0Y0b1FtEy6UtmL/sVbHHhdRMfc43744IFwSMzBybCI+gIWvvQ5ASQ/CsWSuToejTs7lWY13pVXwidqG8HnxcN8G6jF9Lbt2J4acTQ4vieXr4jTRwbT5mXq1phB5/qEPGYOOi9d8+Atti/Mudc+XR/7FVaRkcRY9h8y8PN+/D1t+vQ45aGysjKc4Crv3Bzjy+sviYPSlUaY3MbsKb8TH0Nn4DfnMpL6M679u+hi77/5ZnqaQt4ef2fmjRk44rvPq1fIwjLcyTL8dL5EQIXBhhHG72FE8oxrZd/DyTQ5jVx/7fXX09nOzvRVdILa6qoYh7CWHqJN+2HNdIb6kqbsW1rQoeTL/kImQMtDiIS2ovi/A0x+85s3qUt0Nh0/Q4VGrrnjBqZJZNRWlwxO44iE4h7gpCPqTvC8RnPQC3O2f2Hhmsh3whHAmjgXnTE61G+bigj/9VQiTPNkcmDbWbZsHO1KlCIKfbmVPqejM/xP//rP00KcvjoOC+inF5rsuUEhW9dXWIGzGjqhG/JZh2YnNZR2IZ+E0ZpnvxKnSIgHPHIfH9xOoqEnb42tV6HX6FCHhniJR8Im5z/SmfyzubklHT9Wz3bA46lqypT0LIajBdTP4txQf5b+c9oOHg5NKo/NbBX3Mp3z7wMCDsrvxWN1NY/GbTh2PNboofnz06PIVvUxeak0qHNNfLZt+wmdgecDDuI5MLBN4SE9ZHpTtj1Yx9qHHEl8CbqdWFVN3ZsFUcxUnnQBJ4d6mLQhjslD1AV85XqXAYEboYtlDjXHrT4o/GxjO8bo+xxjPQEdbPoMio1CS/IR9QrlhTJQfNCwDn4DRgct0I59Zw5qdFkcOsHH+tqPbG300fcxpPfAD55YvhyH47QIpMprzcSS7pQX4p36onq0fYTuzjr6tzw06B/9VGYQWUj0az0qa5P83auvkkV+KT2P/FCPVE7YhmPUCSueGcizHy9pX6IU5+0/4zFm8WV2Dw9Gfz5/kGz2V/72p2nhk8vSs6tXBa2KqyHn4On242XWtsZ7IfLFQtnKJ3mpLXqssPyij4EGLN1yfJb13L13bzqI8+s5ir9bD895x3Ybxmf/js/ngu/yW+h7tCdM3Aaj/aTszk7XG0S25VWyW85jc15NZ3D47tixkxNP+lNQ+hsU9Z8aPNbMoYuXLgTPNhNTh1aGMwQo7Q+YKwOUn25xNOiUXRl+nr94idoxTRzTeiyd6u6KV1fbqfR7f/SH6UUKV4/E2WPQSH7oeMXpL/v1hRwN/2DyLm58GaucAQhgHdq3N33w7jvp7KlO9oazHwxHg0UMdTSYwmT6o0QuU5HhqWQoBERukTYyGhBukXqC8hQMSmKE0G9jNKzb+DEnGryXZqDczkIJrZpUFantIpmMQITz5ZUpVZlDQePfiFvsu+RnlbNISYQxiLDDYHQXYKSvIzTrUVieWLYsHA2e8eyeWZleIBZI5bv9ZQInI3x/j+h3H1QyIZTdKwNX4WhAUXyFo+RKS8vSMxBiOUVWZFAafkwxnnec+dhDwZDAaVuCN4InESoYFHRR1wHBZ18yNw2jNzjuZg1CefnSJWEYBUPqG69gsR8VzUwogdR8KcNTiCpMRXbXJy5+k6jcj2/kZRNFGOubm9NL7CGc+9CcuFdYO/ds/hAdfzseYeoaeqloOn6Zl997r/eYiq0i0NbWFqdg7MLRMKaiIr2AYTcOJngbfMkYZx/xAXvnL1E7VgvkmZKmAeOKe1Sh8HTOClCdNO41O4iyZaZNO3u3Fj25HKN0ZRqsYGMLifvPA1+EDW24pqHY8hdTyZCcDyq8GSPjPtsGH+HS6WRLMxXS/97RsBhHw8PAxnnKyFw7vZ22mxn6NCnu8LvCWbhr1NiVhmJERVjfwUWkEXDfdmDyNlVqTcWehWCbi2E3nihsXIETKjo4K/gMBkJPpA0Dl3xdNJx8mZ6naNV73o81xjojj+tKHBW6afvW9CLH9JSRKXGo/ljavHsHhUiPpXWf7IpuZlDGIRwNGPyVI4elYqLoPcKNsWsEqfS6F7o/39u3gNSBcgdjXKX1HuG4gWQwuA8O4HEfOMZ9JLana2yC67hERkPrybThFJFhejS93+sn/8ufpueI3I6HBvvpLAFndFQqsMWfA32CTWN3DfeprCgIc8VJ/mJNEulGWsoMX5Rg/vM+1zPgTltemVMk+15h+AnVutd/9GF6kWMNF7Pv1DbkWxr/Zv/0I2rl6x5GPRolU0NgM3mFmoqcCy9Miqi94H6/nXt2pTff+nXad3B/+mj3oeizjramVKVUxdaJcSXDUgmpt/1wEPWLCCt4QnueyS3OMcKMvvgUDgdg4To7P+Gq0QFioSxAt/x9l2yLszfupMPtp9OOZhQTnjOK5vWnFKx86dnn07S6unCyqjjEtiNgMkT8gQ/FFg3+Fi/FK7fodHaeStu3bU9N9RhG9DX3kflpHkZjEam4KrUqWioOOe/N1kLnDgpcn1Gr4eX6OfYwJunDIr/uYzba9T7nspsGPXFSFTV1iBzV1oYhGu0ydo059/jbpg7iAhwXIAVIRxq7afHwm+Hgt1WiY8sUc7FYqtEPj5fdgCI6FoNi+rTpmaNBPsz8TRE1wynqK9BPRGKARaQMM0bHKt64Ehkvyww88UlDTGXobSKwh5GBTy6jbgHKioXuVPRlJuJXbhDK14RNITgLI5Vg0l3gF85U7hNX5XGuu3DSoFFheeVnr4Zz5WsoisrXkC3cxy0h/+TXwjZkhE49+rjHnIyChsxizaJt2lfB8XuVG9drP8b667/6ZVrw2MK0HD6mQmbfEZGOMWT45pjybD3xLY7J6+tXnMjGq1HJvICNwYNOC/DhqHa/uI6GefPmqcWGY8S+fWUXbcsHgbKwti3Ha0q3UUDxS1niHN2ywaQpsNcUdXvk8SXwimcxequqqxkX+AiP8l4B5PyVPSHrgLlp9NKN2xCFmzJR+SE83CJpvSMj3gdxHB09cjh1YyTNmjWH2gVLCKJkx1Vm/Ij7gX+uH8Q8grFIjrRHmwwiftc5bMRU55XZXka7tiBXW1ubA2eXLH0iPQJsNJpvoaSqyKoDCYcImvDZ+YhL2dpkBpztC2t1EL/XGBRR9+zbH0e9DQWG7t+ejaO6Atmqw+Y6DhcNuVwu2646VMhnHrZJmqJV/+Odv/2NxWJrGLwWXvg2jrW1b7yZnuJ0iocJ/IwieGJ26D34NRQV+OlA8m1GbtmQVv07giz0b7vWvZI/Bc2xDjr9T1H75jX0mfOkh38NnaauGkcD87AG013WNtf3YJD8b2RWHpxnHGC0RiYCuATDFqcGCxNo0SDYr99+J/3itV+xVfZcOna6i55xNIzG4Vs+OtWMHJrGDWeOLKpbf80sED/tTx7img7CoS5MxKmQIwIKgEkrIUdcB+RUD3Lv7oAh6SaO6BPdZ9HRqI/Fc2BeauU1nXT3f/1v/21a8tjjUWfKOlA98Mk78DCdvhpGZitGZg74qMS6AG9sJ5NyO7zMANCLL74UBQzFP51xGjxm52i8mXXjVl4NaQiJHmE1GHlufXMeedQdJAuc09HZ1NRMHZNjmaOhbnL6Co4Gg2M6f5WpOthzOhcfpB3hY4BDHAqjDpnkPcJEfPSzeKZjZDeZTfXwebcozXvs0SjQrrNYOnS7oCdsZXOBdmlXPuL6OUZhoYPLdkPPYT4aaRqAtq+j+j2ChToaqpEb8+ZRoB37Q5wR30VoeX3QOm2rG8lXog/oVF0qcBF+K9a7ngaWrEXi+m/ftTOtBXfG4aiegWOtpqY26haIDPIr4RHOUdbeOUtfgTu0q9wK3kmbARfmGroffel8v0T21Dtkg+/8ZGtaiT6jsetJbgFX5ulY1M3Fr3DKOG/kjvqw+obyMZwP3BM807XoW5sC8N6jSH/K6SPCWR17EnpkOFUZI0OLdcpsmIynOH5frl9mf6ALiEB9V/aZf/ndeVt/5ee0/wiOWPWxPOvJtcvbUYZmPEZdNQuYapPpwLI9s16cTyZjWB/a1nlufZdt6MJ7sXG+/q1vkXG3IO43COo9wlM4yysdq/gTW7rBCTNZ1GX8LrJTcIgpF0HAqDFyDlw50daethGEuMf9L3/zm6mWbEcaIljHNnyDteBJbJNhXQPO4ozjFBbRPToufEt8dTwWC/cIWGtRHATXla1dF85R9Pxc6oavffX730lfpXD1yIrxphhlvI91YBJ90P3yvn1xRwMAFajZiw8uMEQS7zBesfUYStb7REm78dRbYGnh3EeocFsTSqB7OhV4FiuKffww02FEz9xL7eLIRIy8GDnS85cVNULQspymrehp3AgRfrT+wzSJNqdMmYpnsTKOjlIwiWQazWE4gwgilc9JnHqtboWiq/fbiI5ePI+QI6IDcrtF4izerTcYe+PxY2nZ8hWptrYmzmbVERJp6MxPA0Phr5Ir8QQT4PmcycicjMZo5DoOq7/KbPTcWXjmVc5Qtar1iuXLKLLEnmLmLlMQpkbijNSpJOpBFbmNONmXDFCsVjA43ngxPwlMQ0O47cLj9y7e1qeeWZOWLloYUadckVcYekkYoSzQn7DRu2tUSe+3MHQ+oeByZ6wJwsDI/OkzePx274rju1Y8tSoEm0UUnZvCRyPD+UrMtuOcNAj9OyscmW2BkGnLXBVIHi0ogwxHAwLCVMBSjrBUsE1EGdJhoDCSqSo4C0mn1FCW0J0DX8WEMmGUraviwTYVQkY1zWjYj7J1GGdDNx7FR3B8PbmEqsX0bcEccUsh7PoqiHT0WNBFnHFuuVGr8AgmybuM3qI/KootHGe2FZxsR2HUaHgUJjt75oxgQgr6SH2F6ehkQOTwH0Nm4LnH3uiWY9YQKkBYuq/YdK1CnCbWUNit4+6dd6iLMCAMl4dnz0lVwEYBGVuRGL8oaRt694WZClyWCoji5X/grWnH/XlXSZCuTK+3fsnadXjQMXxXffV59iqXpeMovDv42wrYH27ZKYTTTGyhKVTGrC3F0TBCRwPMFGP4XjB01kHLGwO5l3f30rkuMn0FqycWsDIU0sToweFwixoC0t9wajv0oBhe6emfTl66lrbXN6dNpzP8jHWljX/3P/wJGQ3LcTRQqA30ZTUCdq6ZeGUh2V8S4Z05c3ZatWJ5Gj2qNPp1ANKjeOK7wlZ8USgb5VbAiTMKKZ1geeaQ3wk78dhjjDRKN6/fQHXg76ZFREaE9xW+H1FkPROdZDgycLh4egZWCXTrfkT2axKt0BCWqHWGFXGahLVFdu/fm9569y2cOIfThv1ZtkgtJDxlUv9Uw/7/SrJFSgYjbMgWuQc9Gc2USYWjQRjTnmvtJ1/+HUKW+QqzSHvkBu/pQfm+C8y7cTQcOHk6fdhEMVruOedjXH/83e9wHvlTFPGbTAGmkbE/3TFrAMjfNKBUcl3N7Bg8eCY8uQsaOnTgYGppbAzlYO78RyK6YMVmoyfhWIsxIPgdSIzUcfOZl+PVKSoN+VMoM/A7eax/u/3LiNRlsgMqUYTmzZ8XKdnSYyi0PCYvCGEPTusYtQBe1CjgnivwsUtXLoVRYZRWLuGpLDq2jE5IT9u3fRJOXrMZathfWV42GswCPWkvzsEGb6QT+bARHtvQABI/coej43dO0plz8N4ulCEjvAfgk4vhMToayolY6ITRURCLRD+OXd4gjywHtyGI2NevMuR65/zM9byNsSSvV5FuPdmWXvvlL8PhtmrlU0SkKpETuuaMXGVHAucKtODWga2cU54aFXLM8jSNUNvTaDLiJNyl10MoRCrpDxMtWspWpCKUX5gKPBBFEf6VG+HX4JO25/YrM6hiPqx9yJW+toSXfSl3VLbMWtsGPTU0NKSvkwbvSQnOS9kW2SPcF8YCfMn2NJB6cVQC9ZBJbj+xToF0pCST51h8zCwBi5YdI7vJIsHWwFjz9DOppqoqeLp80uCGeBnbUqApHeDisnzI8emYEi/zY+90Bug+Nfih8u/WO7Mmmhh7Xd2U9ASOhrHoNrYtL3fNolYE4/Zv8cErk7lhroQT0Misa6BTOKPVhPw4GfKjiwh1GZkqGkYzyDxw//Zt+Ii8G5DGXISLQQJhEZF0x8m8Yl3BP3FcfNVQGoqTDWGZ9h44QJXzd3hmUJo6bVqaNm16ZAuGDAL2HguoYeoJFRYxcy5e8k3p1W2pbjmwUKRbLXS6ue1D2XoN3eSjDRvSeoyjxymI5rGy4nPxcMxoDF7Hnhm98v2b0bYyH4SKgJJyzv3P/m19CHnNIHh0OPrhXzoazDC9jIL+zOrVFJQmAqsDiLW/x7MAmLnTrI5eTtwBUwMWOiFuM9YhZIeVjGB7DPffuk2hWdoeBL62tXHE3Dtr0yu/eCOdv3Ke4y0zR8O8MhwNY0ZQo6EojS+iCr1AIqNB45deAi4uhluqXBXHK95kK+w3/AfPVEeRnkzDvkH1435DR6Z76ETN6FDrtpyK0xkqaE2nbx01SP7sz/7XtIjjLMsY22CNXeSMTpwwumjHYxhvEWlXT9ZppU5zor2N6vc7g7a+TuBnNmtrlPwWfED57rG4kQELjiov1aeHSM+M7zLPu3biu3TgOsjj/WxQqbmlJdUfP56aGptSBcXNV67giGl0VfU85a6GYOgxwEHHhmvr3KVf+UToNMJMWAEf+b36mHzHAol70VOPI7s1LudyJLynj6jvWr9KOW/tANuXZqWhPEPAoKQ8S3rV+S3P990r0uHBY7OG3ydj7Qo1Bqqqa+L41Oqq6nB+OUb5ShReBD5mjEmrLrOyPA9Q+Z1r7PiFly9pQPvErOEN6Ewl8Blpafr0aWw/HBeOO7OicnpVhso3DabJbwyo6SDxJX8fgT1gm5dx7GjXFOP09JSp9z78MO1me/Jy+Pvk2tpYN9sIO4H10SYInYZ2lK9m0jnMy2RD2Ic8Vxkgb1V39V055ZxawRkdd67PcxTdd0uiNXvCeRF8BplNa+KNtoj6kjxevVceLp6bDS1fyAOiIRP5XfgdAWfkNZOnTkXHxhmL01deJRzkU663uKLc087Q4eB3ITdcRBeCeyOICJ47F6lOvcJtHzoadiNDXv7Od9LjjyxgXHeRXxyDy3/OL7c5tC09+UEnjXDTNpI21GeEjTzU+33XvuggeFKPPrMLB1h/7veI7KnoBjeQc3doR5tVOSTeWOvMQIQwz7f8hyNePBEuDPgOOmQ/2h7Gml5Avu7DAb6XjDhr5ZxHfzzT3pGeI4P4a1/7ahoFfQGIDM+gHduIxoXHl/T6Yo4GV9wr57kKpBBK/sALAQAFpCMwkbXvvJ3OoVh45ODjc/EoQvCmPt/GiA4DDmSSaUm47oU0eqDgzUyUvFkYtMivogP6K/D0wm7avi2t37g+Tayuiv3qZjSYQunCS4QSSSi7/K2jIbz19KeQzhFaIaCCKBPU2JCRmBbTjaPhV2Y0HDsSHsXJk+tSCQ4IvegqRI5nMG06JtvzeRmdxBYXSCIxhleeOalA5XtQLYbY1Nqafvbqz4J4V61ciSOmPMYXKTM04NhlFjIIiUZFxshDKG0wapVcla2AE31JLM5FJ4SK5s49e9Lbb76RVq35CkdqLYpImmMMZs3auFLCKcbNJ5VjmUDGAG71KTEoYHwnc/KldzBP1dtKhFcBpyfuIaIXCq+ILrIuYQTQpmPXmRBeyQCKHeYfWCPmoOCwbdNHo0psR1uqR0DsZfylFTgaYIJ6W8UPleTYvwXsh1JwDWwBJpnHMuYjQwUOroPCQUNRJqnSVQShm868H2XryCFOQUGhWLBocTBBU5/dlyVvkxkpIGV8ai4BI96Fm8zc9kV7i4nFr6HkWshwUKzppk2bKMLYEkVMFy5ewraS2ZFOayFQBhR4MxAEl1kFTrMmwkc4qyTGeqIUWoxHBUBnQ3EZReOA7y4Mo7U4v4aCT9OmTMWJMSsq3KrIeW6vbQhvC9VkSixKCozLZ1Wk4wKPoqAPk7CokfjmaQ9XcTAp2Lbv3ZVWcb5z6ejSdKy5KW0/uI8tFPVpw8598fgM9Mbc0VAxYmgqwdGAthweXxVedkCkXgzkOwBOhch1EbdUR8SPAURzUIMFYDgaFAxDhpCNgiF8EyWx/eqttOvEmbSOUydwV0YNATv+8//un+NoWJYqx4zFuELBxjFhoTPXSFo5eORI+gWOu9lEGVevWBFbtVwr+VLQOzAKxYjv/F68F/d0NKhU6WQ0VTRTWsAtFAJ5gQqHDkIdDVvWbcDD/e041zwiwAixYUN1JKLU074FhHo4SrIXRRdm5goHrGE2zCDjBcOhfY8P3XNgX3p77btp/9EjaT1OB69qhjt10gAyGkancThxzGjoD83H9gnGYqFLlcCM5v9eDoGWQVfOSXj7ct4QllOIkyru4vzowtGw78Sp9FYTBdW4BYyM609+8L301WeeTnUU2xzC9o9QTDHsNOItGgfgYn4sXRgsURcFGJ7GIDqIo6EJpUIF4DEMjMdRFjVK3esu73VMPqYA9nNucDk2X9J+nvIq79R5avaBUV6N3XeRH6Z3VtfVpfk4MibX1TIht3YQgaVNcVh81rjLCgtTKFVHHUqN1aKlIWkzoqe0rVFqH54isRNlYuuWzWQ0jOUo4qlUo66hHgyOLPEK+o9MHdpVCZG3xr5xYKG8kA+LP/LbqB3EJMUZDXDlzJmz3eltlNyDyMBl8PdpU6ZwbF1ZKIMq5OKiIHDcKmbKJ/mQvCecPMAu5725PLzL+g8AJ1SkNUpf/fnP0wDw/+lVq1MVPNKoEU3ej9aFLLET4J7zcPmCvM02hbd8wVekCaM8+bvK5P7Dh9NbpJMvXbY8rVi6hEJtw3E8IaPFatpUdksrKnHSjnNW8cyMXnljLG+Gi9zv7z53AaW/vZ2IEfTU0FCfvv57OBrIQlKRDIcgYxNHwvinD2WeW5MGIGcH9kMWanjQ73Xo8gaGn5KsPzDxBCedSCeAi7VsdhPtsqDkC/CyuqrqMJyjHcYlzxBXfJenu5bihNQTjg5uUWZ41KOwkN5Mf/aUihM4Mtzap3NtFlsnVi5bRvR5VGSsyYekt2gfPuv6yfv8KpwAzCvgwk2BP/zl9zodYk3b2tKmTRtTGw7rEvSYuTh5zFzzhI2oXi5O0odyStwXL10HMwZcU9fOyxoOrq9V0p3DsGKMSmTK7v3705voBDryjMC6/W5CRWU4FG9evU7RSHDLTADG7ktcdM3ET0fuSVMaRNLU9dsYUvQtbXmiz1Vo7UMcDR8inx7Dge+xgJXjKtJI+J1OIjPeHLNjV1bJC9RrBJgOIk9QEbdhzlnhNfq1fWt06LQ11ft19pRbTNbgQx1bqIazncVizr1khWTOUGBB/YN7/YfAJ1k7jPvIZEBWD4a3Wryxh1oL169dCX5QiD7Xcup0euOD9em1tz/A8O1MTV14uLkeH5dS1diyNKlkeBozjCASEECxAS7gA2NnKkE/wlruDirFnPgXmGU8T9zK3BAYr8Dzdi94NnxU6qVeTkv3ubRxy8l0iIfx28cpP9XDStJf/eVfpCU4Giyo2Ut/Fjm1LkvwIOChIXoTuRoZGdDBOQptt7adTFs4AvY8Out3vv2dNAeckYdoHGVOeeiVAas7CycDZPIGcUSHq7pVbEmgPQupijO5jt3U3BInsxw+eChVVlWlr+C4mwjOGPQyGyhrL8Pz3IGnnI9sE9YudAL6jOwa+vOStqI+Rnd32oHBqD7mFosFSxZHfSUDP5fJ4BFv1PEcu3geRrVrwGcdDa5D2A3QrnSmbFRH1cmhYVzf2JQ+/IAjTsnAtf7OXE6ZmwofdjuPeqO6pPqh252ybQwsKm3ahjqDfFG4Cyd1vh7W1c8GgZQFO/fuSR+8vzYCkzk9mVkmfutoiLEDc+WTCOIc5AnyfvmKeG+74dSEB+hEUqeXni5hlK5FH7NGkRkNUydz8gjOVAN30Y4IyFhtV1tH0DpWMc5go2sQv/N98H55Pf17o3+3wGt+8atfRX/PU4fAYJ7BMo1veVI/BqaOajvB1+Rl8HB19ixDk3UBh8IOAX8i24P+lYfKhMPw4LU4BmeydXgZW+/KoDV5nmMSb7QR5Fny8wju8dnt28pWcTEytKFr+W7oj/SvvL3vaEB+7ALnv/X977PVCEcDz5stJ3zD4Y889dKZ4XrKa4Lf0H/Yhtyf2YDQKvcaZHVNzA7SQaWjYQDr8I1vfCNNqa3BIUcdMujJwr9DkfPyR/V55Yh477xyGCgXzaxRHoWjgTmZ9XXx2tXIZvDY+EsEuy2kferEyfTCd7+bXuBI69JJVawn7UJ/cfnGWn2Zry/maHDm4HFQiQidXwEY/hBQEM3+iMC+m87hJRqPgbB4nhkNtTga2EcOoFXWXSBrLuhNU3G4IXOVcYAYMgs9WpGGCxIOgMplOnrurNC8afsnacOmj1M1yucMmKtRKffBXkEhUNGVIPXoykQUmgpMGWMWqc+iOzJCEZnlDSPb2RSDFKah/YI9gceOHEyrYa5Tp06NKK9jkiAyR0PEVQM5TBG7DvNVIKs0hDEDGCQOvYYqMXq4LXSm0nUcI+4V9pRXVFTAvJ9OpYxb5hYeYpBLxValTqYkQfjZ+bi/eJBOMUF3AABAAElEQVQRCgxIK9mKyF4yD5HS+/XauXXiTRjJapjIssWL46QHbggmJOHJQHxED6tjtcqznj4VPiMe/i5sfMa2ZWIykcuXr8Te7A0Y1K0d7en3/+AP0jyYicqzUSLXyucs6CPc9Rpn/aFI0I+pe/bhWpj6GuMGLv52lef1KLq3bicMtrRifHoeRVElWoEiM9HhAkfIlCHPR6SvMNRpU/gK71DnGLPRHAWYDE64ncdgPIwCbcruGfpZ+MSTaTn7p1XkuogwCGcL/eg1NtNEeAtPmVVECcEdcUUnhr1ocCk0jcQYXTCded36dRRPqg9F0T2281CijbJagVZFPZR1YKuzISsCBD7aF0IELhm4ZL2NK5yIEIKH+Y0sp2I09KBgewtFsZAxTiedy3S6SgSbTC+EMjiTRRpZWGAhHPyPiQRuOL97MEezGTQixSkN9qFkX9zCyH6L1NePUXSfefEFTnYpTYeZx1aq4h4lffKTg0cDz2YwzMkoYVlGw9A0YhB4qsIROM44mMJdvnM3tI4GFTFxJ6Kr0GB/vrjnVj+KKN5j/bIaAxhrOBp6qJbcdbM3HTh9LW07RVQUQXxJJs71Z//in6U1K5fF+cqDOaHCisQqTuKDWQemo1mj4aE5D+OQWBkFq3JaUrCEgcv6Reo6uHAPw9hjo1QmIuMImpB+FTYqE9KwglUcNT1VD/rmj9anl178OhGnBRjgZkxpTBP5QzVWuCDhwtFwh6yG22RvqFx5msEQaEw+B7hjn/81xquj4R2UoP04MtdRr8GrimUyo2EiKbSjh3P2+SA8+YyHWA04asQFpZxxqgyqZ0jA8cZHm4+1jndby+gbNGBNUNL6HA17TnSkd5rAY+5gGeL6l3/4++n3yGKZCC65fUjnqcrtUJwORfBa18hMJxXQIYVkEmkQ0HcHDuQ9u3an49CUitWSZcvSYrJ43M9viqIKhTxN5UcHmKjoOkTKO3OQloRzln0jP8gcpyUUvbLmwyHo9NdsFTK9c+qM6ekR4F5XUxM85eL5C5F5YbFKlSKVIavhy5tVGlQ2FfYqn45b+hyAAuPpH8oXIwqfwGPWkTExfvx4DDqKq9ZNSeOJJCtnYCS8M035IJ+lHR2XfMUyy2uyzDWjxnHRR0SM4BkacsqPN4noHMGx6b5pFVxPfDHlUoepSpZGl/xP2SSMHbsLGdERaYZ1c03D2AV8MXb6tv/Glpb0t3/3Sirg2edov5rq6/I4FS15rgqoMsT25GHC3ZcRYuEe/AHYmIYtX32QZ+o42Llvf/rZT/+Gei3fge6oeQKPkofZFg0xbrgH7Tt+jUYNDx3FZk0E7dCH/bvWtm+RTvm+joaTJ9vYirQtHA3fYCvSArbbeK/zpLm4go9Lt3ynsTQYJ4Np1NK0911B9nnSjjzGo45HczqKW5NaT3KcGXizdesWZF5J+ubvUZiwuiaiu560E6nRrK94Y1vydg2WcGzw/H0HNXjrekvX8nmjsMqPozi/jvPqbG9Pj5Op8vRTpgUXptMdnYF/GnDKDY1MP1tvwkvFOWQqn5Wj4rrtQxrhlLamTzvydD3G+vEjhyPi/iiZJMoPM4Q0ijzFQjozwyEc4lC/8NFZ5brI+11fC19eu0UGBH8buR/GCTZmxO1EH/vVL1+LkzfmzHmIbX0Pp4mV1GhApl/A8O0P3Q9hzMMxZoZD+85D2o1UbPrRqWAwSNeLGo28XaNPw8iTj95fty69A73qYJ87+6HY9z0K2dIDPvaQvabuJV2Jm9KURorw7oezAASIbCEdIvZzT9yhT4gKvC7i+Neu9Mprv0hNKOkvvvQi27xqcWJwshf03MNcaQHcw3EBn+vpZ8FdAyNmsmI04/juzyk6njJ047p1vzh5DLgU48w5eeZsemfj1vTW+s2cKtOamjhpw+uJKmQcfKGicEgaxRLCTVI/eZk8gHkEnvKPx9V52oP04/fBd/lMt4EDPRhLPXegGxwgiUKQaQgZbYzxRPfltPNAa2qwNAZPXeBVVTgy/ef/+B/SUgwnj6++Br4ZzIpjSpEh8i0zDiz+LC/rhYGcg54M+Hy8eTNb2TrTD3/ww9i2Ij+8hT7XI08B1mYRxmkJjpC/lZnSmHq28Ndw1sj1pCS3CY0s5ZhRvnPrhNtNd5JKPqm2Ln2DrRkTKyo4ieBiZOtJrhrq8lzXNbZlgY/yOek99LtYdngGQFHeZsbkoHAebaVWy36csQYbFy5fnpYsWcy2x+LI8jKj7A4vs5CGox85XusTyBOjHfDEd41R+9FpYsat+r1boXSSrPvoo9geU0527BNPLiMoNluSS5cYv3xSuomsD+DrZ3lVBJTo0/HL0+UPyhHpVefdUGSgjjF1gjfffD0y1WajA1u4dTwZDfIYcV5+xcgC/pEhx1r4m04S21buqb/n7Xryi9FvZdd5nOtrkU0HMHhXYR/MnDYttiIpG+KEDdvn3rwmjLBX95ZmzTwQLjonY3s4sImr7119uAXn1M+gJ+vWfE0d2yADDnr5hzJbnFEOGiy0TfV169kID+Er71e/DlkOromb2jWxRsjhg0ePpN+8/kZ6fOmS9PTyFdl2SnFO2PByvZRv4Yzjs7qoMsCt6erCZiKZrSJPt13XQOeEcz979lw4qneBkzoazDBVtmnDROYec3cdfSkPQw7xrPqY2zK1ebw3cBUe41zyOkMdODQbKNS9fdeu1L9oOIGll9OUmhq233ByHidJqMsVIQuLyR6MbcfgOkZG2Fz2pxxRZuhkkJZ8hzFRLLsIneN6ZN4KGwPZZ3C0tTU3p5d/9CP0ypdSKc5T4R7MpW/J4nO2el/Kf383jgaBkgMG4AYFyzX5cA8Ge+jggbQR4dOFl2gMR40tnr8gzaidDALjiQToFjVUaYkjp2BMpuNpYLnwGnAaABKkysBdFGCPs1IQDsSI7eHejzZuTO+993aqnTI1PUT6pWmGFgG8SuqQnq/cU6l3VYFpiqpIrtIjkUok8KxQIhyzHkavoTCpsyDBLymmWH/8GFW9V+NoMCI1OpwipkPLrAtUfvqIRaXa8csEw9FA+zQZSpGGtoQYR4LBFEX8o/X16W9++tPY7vHcmq9QUKokiFniUxEMwwzGrANDA16lVILTK6dTxn5uo2TJrBTYdiYfyZS+m30ZDW+mFXhDn+xzNKgcOv/wJgJ3cVlvosQRKbAQsR5ThUYo1twrQ3C89u2l17ADgbYBg9SsjB/88J+kh2Gy1t1wD7zM2nFIzDKRWDtgo+dSp5EMQYXLMdu2DNEHZCQKiTPdXam5pSXt3LUzinlFoRpSim7hhDJyGOjFe69R9Ls6GTIHjHPQPjCdzrZUumxXpVTm6JpY5fUIRH4Ew8iTLXQ0rFqxIhll9hiyWCfmKOxVEGV+GqlmlsjETbdSSLAUwVCzOgRExPmOgVCzoilqNLS2NEfarnts54KXpltpRCh89KRHRoNr0WdwhGFhG9IQYw9vrmvLfFQWCznCR4VmGzD5DcUgrXI+E8eaUVIdDRrcKpzCPgwKcQKEEedUBBl4KELiqMcTajyBheAu9OQ6FRanHpTNt9eujToQa/Su4miwGORmihEdaqhP66nM7TUHNJhWWZBq8FCPJaXUrRO5AubvvSxnL2mqd+jfbIwoKARs/E8cNaPB/3TRRY0I1xLccVPFXZTb09d70t6Oy2l9K8UC+TaruJHSv/9X/216bvXKVIEgH3gPY0ZHA/QnHWrkHDh0OLZOzEKReHrFyvCgB+4CN+/x0rBQiZLx90BT1vPQaMv2MKMwAkMFfyhg8J/gPfCoPKNh6/qP00svfSMt4QhV+ROaCutJYUVeCpd+jKmHbAbiQ/AS1HEU90LW1aMNrR9iu8OIOutM3bV/X3pr7TtpL7j44e5dMb5qmpxePSBNKi0DtoNx4rB+4PkA1qyAzcHiugqP5q647vwyqPo434GY4qbvfuJnYMR6wFPuMLbum+ydbD+X3jpO9JI7yN+K61/90T9L337phTRpfGUY7PfoT9w0uqmjwT5iXyPoaWaAeG/jpsCb+tqA4SWdL1y8NBwNQ3A08EUWNYIeHaW0L/y9pDMzRVQ4NcodroZXRDZ5H4WSOwiatBDvG/Bg9wPXTZmaHqM2Rt3kydARWSY6GnSSQdeaQ4EHOFg1xHSURvSCNdGQkUcKM40kiDvWTqfmVhQVHQ0WxJOeptRNjowG6UO5FkccSlv055ids+ssb3HMKl8Zf2Fu0q54Dq3pjNbR8Prbb6UjBw+mNWRlzcAp6JY8xyWsoj0akd8rn6RdszR8z1LY4T/0Jz+/iwGgUefYNQrsv6G5JWr8eJTm86a+ovzn0S55vAP0eS/5rbgdW8HgbY7Bedi2imbsNQc2fi+srEmyg4yyv/0bHA3sUbU2ivzzDlFv2/YljgVf5RlrCCijzNhznZ1/GO7AwrY1IpR9OpMvuqec/cE7drL1Dn5pRtw8eKS4rKImTjjueI5nVXRDxkLCA3h5H/9CYyjpvqBX4eKWJJX/1hOtOKgOp0+2boWHjU7fxpFRU1PD2DmBhPGbIaECbXq2Dh6d8vLMkNnAQLoSFzXWpbXAURR/z4T3tIw9wGUfTvxT7W0U+10WQQIdtZfYb66CrpMaNOAzspORykO8otge7+KHslteEMYp34VyjWOtEyfJxx9viEKWBcDwiRUrovCsusplUslzB1KkTqMD4QGIWleR5UBn8rF+yPN74OuV6+g6LJI4XwhsNN7D0fCrX8a6zSLTbv68+WwXqgqavwH/74XuzZDJjVJ1M+VfTIj2bU86UiYpq2IOoL1GnUaejob3wfkFFDPUoJuE86sUvcZMkl5knzQqbckPhI1wt+3QZ2jHbBJlXh5d96hMMxo8fti6UD9ja1wLdYOs0TCtjq2sOhrgRT1mV7BW1mUgP4SIP9+RWieuWFBuiCpGL7KI+3ooxHsPp8MA8LagsIiMhs5wMrzx4UYyERtTQ8dJRtbnaICmKnE0jOSMu6G03w88DkeDxMU4bf++owHYqHdIeK4w3cYs7ynHefWjRs49MjPuDiRIxhhPnL2Udh8kekoRIl1RnjtUR22e//STv0pPwOcG0N8t9CGdnRqs4oD0Jr5rvLgGugeNfBuc0dHgtp7vf+/7OHlmhz6goRnZi7RhQMEgHYMJ3DRz1cttmf2RidKFvECjV5wpwliVBpuacTSQzbALPWAS/PFlUsnHwC9vgo8G8+RXGqJmCUi38kR1EelWvVHnQvAw15LPodsqO6HzLow6HY6Hccaq8zy6ZElatGhR1Ne6Cb2aGaCBroNEnmK7Zh2HQ5K25Ds6AHNHg1nOGpBmrw5l/CeamtJasso05sagNyxfuTLNwnEnb78Mf7Y9o9kao+HsBa7ySucj3ebySp4oXHzJh4fTPlyHLMftIZ/G4ZCawzZWTxgbi6Pa9h23csmXjnC3Psr3DciFzg3+iUO2GSfiAEczJQZxkkHoqgRBfkNGwPbNG9NqMnikJ7dl+Ow11ly7wK1ImZ2AjKJ9dXhx0vpUKLAK2ujPwF/gJr/xP3AbGEVQzWhQDuuozhwN8ETmbts6L6VV9SzHI2+zYKwBgyJks3xMGaazQdniGig/pGkzGozav/bqq9RBezJ9ZfVqikpztDjfh34aOIOThHFk9ogBVmDVB3flWzgxaFs9y5drJc4a8DNzx63bOqheZKvQ42QAKZDk5YFftOvY5MGRscrfBlH9zkCTzte8VoM0IJ46duXPKbI0G1qa0Q22pR7W4lvUgJiOnm1G8i3kh1un0JjD2SuOu9bq9Y7Nubk+1h2L8csT5BOMfwj9XsZxcrShIZwNnThOO053po72tvTSt75NRsOLaTR0pa6hzhJ2ke1/ya8v7mgAABKNiJJ5KgEKCyarlbhU7Y4RrdsGA+wEmCOGDo+MhpmTp8A4EVIClOcRN5mhBqJ6RJ6OBgWwBpJGgIspkiukhuIJNsoGtlhVMH2It/L1X/4iTcHBYEEpT54oIfVVI0LFWOYhw1CQOTALUOrJ1WsXe44hJN/DC8Xiiogusnsau3E0vEGNg5amxrRsxXKYyJTIaDBKr1KlcA9jjXFGGhpIJdF62Z5eQy8NagWy4/BcWj7w7b3UeOxYOBrGYSiqKMapE8BSA1djFkwLRtQDgXj+vMxa412mYZr2AASs+yP5OpgVww4mE/NkjnvMJnlvbVoCoUeUUeZAzz6rMMj3VZkdYsqUhrAGmynHzjFTTGHEIj1zsZiJXOo2BGcxr42sawOweYnU14dzwQb8ZHw+I9FqVJuaJvOzArVzuoKy5l54CVsGJmPwd6MXOhp0Bpxsb+Pov12piMjmGms0sB/TaJmErBDgQ0RIJHKZhFE0xy2soyAMQLBtlURxM4xt4G5RICNShw9xfjVK7qNEpKIYJEXa9EoCmBC4jtv598LoIhpOPwLasQYzpX2VUZV1maDrzaQ5YrElfYKHXuZRyHwXoDDMASc1HFQSw7EA3qj86ASzH/f6ezmPWHDaEXdUDK10TodoouA8Y9uOYfT2m78mWj+KLQKzIp1OIcdEAybeo7EWCgOwEO/DIAa+oHrGfFH6QJhQXoaRQeBJCXj00h1SFdd+8EEUO1uDh3sUMLHuwWYYrun972zdHOOcS1ezJo1IVRQ1HD0MJdDqWMCp370wfxkL8CZP9S7TMfIe+6gV0vIK7hvAlgL7NErjOe5SA6ETlhLcIurTfuVW2t7cmdaeIjLNT7kx/JN/86fp+TWr07gy9kNzlMIg2hGGvcxFvPWoqbfY6jR9+sy0ctky6GlkjBdVJ/BG+PpSYImbEaVjTDrQjCCY9m79A+lMenVtvdxHqYGhUrFt0xYcDS+nxQsXAjPg1oOj9BYK/U20RcDgUWYeXcYKg8+YE6CNKbAeT2cRIYVPEdlStxjDrn17UCR0NBxMH+zMMhpqaHIGjoZasr8stGm2SO91nBkc8K6HX9o3s0WhLp+1z2yU/tHnaIgfsr9ZdtHyAUdDbzrSeSVtbUZJvHkvEUyL63/+4x+nb+FoqJ1UBe8GBsBFo9cIpwqkhp7NunUiDF63HwAf+YA1GpobG0OJegQDw6rhQ3Gm9CJ43WIm3kvf0mjGd7NMAHmlykrQP4O0fWHt9+55HUQbTfBIt05cAv6TqqvTQuCuUxlEJrX2Yqy/tGUtDOmzF/y3z1BYwK2YPPPX2ZUrifJ3j6XSMDJ9f+OGDeFomE7EqLa6JrbcKNPkLdIHjeHUzmoFhTxiHo5boyscBqypc8gdm/4mj3Af6ZuM/QjwWYWiNWM6VcPDUU0tAdrT2SuuKTusMg7VBA/OFfdQcvndewIngZDOQ+dkX2Y0eGqR2VBf4WSFCrZ/hNLJeG3D9uTDwt7n5WMq88Ev4S0q2P6WbUlDaUUGeKqGl2nXO4ikmSG05rnnURRXQdMUTjU4IIyVkyE7UbgwbvH28BDyA7noFTJXOQcs5HHSmt/Zn/toT4M3Vg0/0drKqRAo0bNnMcaMPwk75ycfV+HN6zjJO8MQZtwaFxrUpH7QGw9KaD6DXGnBgXwEB9UuslXGcNb7t8hoqKittbx5BDcCH3nEzDUWEphrcCFbc9kMjIzkykO5LfrSyUA6SuoFD+XB2+HxbYz90cVLol7ACIIbtwhuBGx1AMDvTLNVR4hMHtpR0fd3cTNTIjPKlaZDPkFPwmPzFk5xaWgIx8ZTZFI+jAxxfJepUSBd6rBXaQXwzIlaCcg018O1V+YQOlTIZzqQznnaL0aO4oWKU8B03KlPWaMhtiIBG2v0gBwsAHgPnENYgC/CxvVTliqPwuFuX+IPdKAMlOtbeFkjbx2FkDeQ7m3QZ9bMbFvGqCjSmRle4WgAqOKlsAAhgr4ggpijyr+GtO3Lf6183x/6KMARfr6LwA97yjvbTrCdck2aXFPFFi2UetYdixR6VZcEnxn+1dvQTD8DVURH6cLf++NoGEAaXzhJrX2DcY3imU6faGPrxIb02nsfAX8cDSdbeCClxZNSqkE3m1A0NJUVoBMi3/oz9zyjga5Anz5HA/eDgUFnfpKbObWgPxGbv+8pq5BZN8h2u4RwbOk+j+PnXKpn2KxYFD2eNn5i+su/+PO0lDUvgqb6g4tuOellUuqywXdoXwcYkwm+ZnDrNMGZzVu2RH2Sb778TfSxWUHv7h93u5EOfZ93Xf0u9A7exUUNcnFDnLkHziujnIsy8Ro02NzcEvRkdrIZDS/hGBxdUZnu4vzVeAv9n8kaLAndrA8O8iCnnunZOATFLeClfpvhD44GnHM70fMM5uk8mIvseIyCkFEMEtzWqIvxy0vUcRhjyFHeI1D1AD0pjzK5zlZT5wQtnG5oSO+8/TYOh8ZUjp5kUV5PpZI+jVCLGo5HXnafZzJG9QqzmELPZFzqA5mzgTkBRp0wPvwJGQ1vv/WbyIibOXNWqqmuohbBmMjckVeZwRMsm5v9rOHrGug0CfxjzPZhvTjlk3VmYosfv1+7cA77421qnrybVsEj5z38UJpQOYHC4MhW1kUa1jbSSeJgXFcDqfJ5ne/KJK8w7JmD/Cd0YfBAOXaivZ1i929A07fT08inSZzMI71Il+rs8i7pVflqLSvhoyNZuaFjVFxUnmSOg0w3NsvGRVen3bufjLhX/i49tmhxehb5JG/SIPcS7kG3wFmnqA6C+I62HY86gLqYDsc8+8O11ZjX9tJZvWfvvthys+a559ICbD+f93czGnR2e786fAQ4ma8yV0emjmADgzp+/D1wlnEoP5RhndCSR3NuIVv+FmOwGOQMsinlj2YHOTcLNZsZHDyHN4ugu7bCzkKe4rcyRB1BOtXxUICcvIR9YzDS+hXtBGtP4iA8g2Pj+Ze+nr5KQc7y8nHAWJejteJ0+jCuL/n1u3E0gHwimqucKeaZwmi6EGYmeyWPpC0ock0N9VTsvZfmzZiZplRVh6PB4nEROYIJyyR8XqPEglc6GoI9y6354EJKsEaw9LCZ6n4d79hWlP+PiUhNrK7GwJhOcabJUaNBZNWhEMwDworoNG0pkMNpAbHLqFTAjPIbAQsmDuJ5uX/MYpAKzo62tvQozE+PXyjAII2EEUoE8xwEs45z32lfxHXktiVQ/Ow8vEL5VUDzm/1qlL6FI0Pl88mlS9FjUGQgbBmExk/GQJD7zCOcJhCh35uFYf8SahZd1zACPvQfwpsOVQgOo6R/gvCZh7fPiJFVVwWmziHbsC+fCYMfwgsDDPir4Dp+1zWDX9auTg4v9z0brdu1Zy/nFJ8MRdEicjIdlXwVSseROaEyr6J/W2xSxniRVD9TlpyL43e+jkNBY9vWgFARPYgzwAIqT1LxfDxVfT2ezL1nKlTigh5jmbfCXMXNNRAOwkamGp5M2hbuwsq+Pbu2sRFlor4+dZJlM5s9ewsR6qUoYo5XZiGTUxmVUd2fv30AF+cRsKF9+1Jw6hSIKBz420akbj8MtpvK2CqnRo0m19VFap5Ks+nRMu9wgNFWSGJxsY+OwrnhnGCMOi+sWyGzVclTkdtPhtCmDetx2hRGuzXgvcfxCUNxSkPN+zQeEC0wPB0NpHLRFz/H/MRbOGBkFKgcFEBPGjuXSQvbQmG8xqZm9mUvC1w3Y0XP9FFgtnF/Zgw/jE4ybXxJqi4dkcpHsHViSOZWhPMGbfWgkDHBMIoi4iWd8Z+CIBQzxnEHJi2e6ZFn9WN7AItENgR7cXE0fNLYlt7r7Ilq3LJdrz/98Q/TsiWL0mj2Q7v1wmwM11R4Su8NTWSTrFufqmtr0uMYvKZfilcSoHhjf6GMMH/v97ccV4WPxk0oF/QVvIF3x2801uKx+4lM7yOrY+VTT6f5KCtDVc7gVT13Ucbvwg9cQ/q4i4Lbi7OBnBpmhmIFjkQGl0o0kS0zGoxIecTRpk+2cHRofdp04LBTTJPQDSZPSKm2vJQjLovSKLZL9Cej4R5FhzTwIgLDWovPTAuAgn++xwWU+dJX/rfTp/sHHA096WDHhfTh8atxX2YWpvQHL389rV6+LFVVVuL8IWOBNkzTRvtB0QPnbYhLQzc89ApPxmCF5ob6huCR8qnpGBfWDTFa7PYf6UmeK43mCkDwBuggjFXa9G9pyHcNUpUB9wcraJtaW6ihsCVdghePQel/iFTv2urqoMtwhES7OJLBA0YZ/FcYuZ6udTZm1xN6BebhbOBLFT154D6cADt3bItIkVFvi5yNJAKrIi5eacTL32PrHvfr4AkjkX5dbxUy+Zb0p/ImnJyHvEdes27DxxzddjSOR66rrYmjz+S3KkTyShdB/uR3KjzKQ5XewD/6l6a9bPcesHcOWbRjQPCatWvfj5RPi22NLrM2Uda/yOEz/B94n60ecGDMfQ0GjLxHniMs5MUaF8pD18FTXFSiH1+8OC3BwRN1AsBB5acv5+/zwtJMiswBrvJm9Awex5pI78FXkQ3O09kYcRRvdPh2wC8XMfapk+tCHmm4hqOBewNG9KPSLJ9yPaIOB/BxfeWNKnTCI7J86NMCap1EiVpaWuII45Glo9PqVRTKREEXP43wBv8DrvI84eJcnIfGdOgg/C3/NM3W8buuVpkXJ6NqOHzAY7t1Js8EH5csWhxOTRVg52xES5iG0g+M1D383ks+6JLarjhPt/G3AxFHPd5yH5lOp9ra4iSjx4nuPkSk1PvOky4sbotrbj/UAFAvsLaO66pcdP3MhFA2yeNyo9FtSOLu0frjadPGjaEfuGd92rTpsS1RGEs/YYACI+WrL40X21Y/8qQP5Yu4YVvSv8q0a22EU5iZSr7zk22xzXQyRml5+Zg0AieB446gEnOMtpmQ+o1jFg4yKWkzsljAFddB54u0ZEZDAY7pLub/Efz98oWz6YmlS8jgGQ+uqA9C68geZfVd5NqN2xhEt8BNsgeK6NuMrrs6guG93IrhoR4FHTOnXmSINRrWbduV1m7eTpZKa2o70xlr9djElKoZ/6TiYWkMMm4484yMBubiSgbyMA/xD46T6awuLr+p+blmTCs+e6+nTtylvxv9KLhNxkVL99m0dde51MITuaOhmiLA//y/+X3ky5w0CnwbzPwj24/inBnigCe0LRWbeSM/uMk6n0en2o0+1tHRnp4hMKM+pn6nE8B1lT8Kbzmi+C9+snB9vAp+z1rKx8Qb6dpZyMPUsU+Ci43I1kZwp3JSVVqxnILMODV1MuSZWfIs6dsIdTgUaUCe4veZ7qEchd8BFH+XptSZ5ANuczpxojXGNE35gc5kxo58Qn6o0S/9OH5xMXCad68H6cl+/N05uIVMB6hOR/H9THs7p2iVp/k4MqZOmSIjjK2Atik9yaMcX+hlwCJ0Rvig7QkXeVsmQ5gYcBffAR6F/Q6kDevXgeflYXtU4Mzw6EhuCfgL41xHdR1cQ3m07StHYszMQroya81gh9sS5cVmD6xn7BaDfAw+YE0u+zEiH/yXMYtj0pOX6x06AmOWbnL+a5/39Uq+t0/XwCNRP1q/Pua9mPbHI1+DB4K06qrijuun3q8stq9wjtNXLgNtO2QV96k/KeOVWfIeec27OHlmsy3uycXU+HHctOkVtNG3ppmOYOaaTljoA6eLxYXlC7H9BjoVXs7BdsU5s6qPYN80NTSmpQRSrWPjJY8P2HK/a+ZY8uCG87EN2/Uef3OdQnbztwtrZokFSt2K5NregWGsfvrpNLm25j4/VIZEdjJ0JB4FfjKh0Dt4N+BpHwZOg5KEOfA2g8oiui0noafWlnAOnsGO6uxsTz/8pz/meMuXyKbkNJdbVM5iTZWpZux+2a8v7Gjow+9gSkHwQMTvfOloQMxFdf+POJ5sz66dqetkBx7iCtJzKyIdDGrpU2L1ROFtQsBoWEaFZAUPC+We6XzfkVE1uEgovxoNfq9h19nZkUagHLqtwTNsRWgJTeahQSvRyABViGIPK0JMY1RFRsRTyZCYJJRAvkCWAirAXo0jWiwkM2Xq1DRmzOg4GUEktW9TlURUFUSVFhmnn2VOMo4w5iFAxyIyS5BWNdXokdl4JreRdSvOTqmri0JgdB1EoNfQsTg+CVBlRcapQqByqzHeA2OybRlgKHb0bf8+5x5gYeNe0joYaw17fyJNCfg5vnyePu8lDFSQVCjc/iCT99JAePBSCAm3i0RvGvDMXbpyLS1evChVInhkAMJVLpIz8BA6zDWDgd458AJDPhgZcPCSOWkUOCaZvW24F9a0WiNsD2PUjSSaZL8yKtu6LzgdH8+pHEvcwktGZfRcT7pMxnUN5sNzphm2YDyryMkYKnAeWWhSwWb/vvw+h1Gm1KHoYqzbloxOnBIOtuu7fbr23uv+41ZOE7mMx9UI4QS2fJhKJxTFGYswqVzZvuPWIPOlUitcnHscwQgVBUOkD99VTG/D2DrwgLY01kMHd0kLLsPDXYFjjTRzxifbFO/yAkMqaxbUC+EFjLNtM8wNHLDYnQJKn4B/g5pkk1xLx5saoafTaQqM2xobF40+ktnTjuOn4XRbrNcsAmk1ZVTjLh+ZKjCGRw5HOLKJuhdFzvEbcRCOcgLXwD2yVgYvQLkYhBA1HfbiFfbK3tDYh9Fz52DeCwrw4JL1dPoaxw42tKV3T/WkUfyWJXgmhNVc9spVpRLSaAdhzAcTppt+zEWhYmrtAQyAcs74DnpiTcUVFdDIPqEtYRCFkhifAtuXa6rg8h7HrSAW3uKaiq9r5zqfbG/nGMcGeMFMDJfKMEAt+jWEwTO1UMBvMrfe/kTY2Ys7eEi2F9DaFb2k7N5hfW3TrRt36KfrfHdqaT8ZsN3XdJJeUqqAxU0cDXzHDEhVo8uo0wDugRsD5XvgmcaWVIPKKyk5oVAefVZ4C3Zf2SVdZMpubJ3AgdB1/U7aeyKr0SDlC3vbe2wW0Xx4hAV7TbvUqIw0WxwNVlaWtuM8dPoYgHIvXhkZlBasbXKBY+FUQsZVVIKTwAZ+m6VJ0jrjcR2kQd/lNUFnrFme5SIPULF16NKGiqj3dmNctLQ0h6PBdMeJ0JOnF9m+SpAZMvJ511XeF7Qu7+Q/+aIyxPbCcAkaygxk191xNKOIuh++rJStKvCwUrY26FxQYdHockuDfTlOeay4wcCifXFCfmX70r4v+5d3m71hYVELKp5sbIyMOx2CRuq8T1iJC5kyxKS55O0jgL3LKj5qRNq2cNP4FifFURfcMZ0jdXnvvn3w7SFp5vTpRHcp1sg4XXSgHPhhW/IPYSMvti3HbLZA3rZwiMt3/g+ezeeTpJRvw/E4BpyYUlcbx1vKB127mLew7UM22/fkAfe5yqflJbmTNngzY491pyPnrXyRnnTIesxiBY5kv9fJpCx2DO4tli86d9dhMPjrSN0nrCIa0SLGKa5o7PmsvN+2rSfSDT/wGNY6xp5nN8krPNrOuevokdeLZ/JvHd+use1rrCgP/V5c8nIeOsItGmwk1uMtyysqI11a2AsTiU/Z7lyEg7Wn3C+t0hg8HPnv2gZfYcxBF+CMa+aY3KJ1Cge4pz7J08fjINGoi6014jPtijveaz/KWWEfuMe7fbrtxfaU5bkjX6PU0y08H76t7WQYKp744NHaOtbEPWWqz6tEh77BmEMnAAbKOPUZo9IGFGxfqOh0EAcsbq1SfRxcrwfnPSFmHHzYLEbHrjEhrDV+1Clsz7mrk5nxcYu1sC1xS5o1m0S89SQrIXqV2hoaAAYJCuA7M6ZP5XQcTmJCB9LRMCwyW8yIok4WjoZrLMXgYcVsVSrF3Yvz4cpFkhquQxfCTX7BqtK/jobuy1fTUXji4Zb2dOHs6ag14XrPr8TxO7o01VAMchzFfof7LOPsT5/ihLioJ8GtE7bq/DN6QO75OzfwMxeQYl49zF1Hw+2Bw2LrRHP3OWqMnUkt/FzEfVd4Lx1enB6ZOztVV1aksWWj0gjwysLpd+FHmYPAvrmZ/11T+xNnzc5qbG4BN7vTfAJLZjcJb2WbuqD9m5km/fIY40KnBD/Eo0vocvJx8VO6i5f0wLqJp2at6RC8RCp/GRlC06ZOCWPaaYmPygb7CT2I59VnbD/kBfJKnsOtwaulZy/7kN+Lq7btNmV5+Xi22pi9Ki3qyAj6jMkCceYgfw1ey+/yQPmhOoc4k1/OI3gNfYkzzc3NbEHhVBwcABPBS411cdBnbUN9zLFGMI+xyn+y59Xt1JfBF3AlMjMYgwsr7aizNqHr7du9K3QxnQwl1FmzBoC0Kf9jcWJY9ifuC3/x3mCW62Ib9uUaOge/0+HmHIV9CwbpOdZ0InpqKXqe0XlhI/xcR+HuGtiPz4R9Q3vWsZDm7MvLdfYBeUXolbxfuHgpgiduXzebTweJ/M7x2GY4WnlGeeYaRmAQ2Mjn5GG26Xderr1zEDzCSznUCX+0yOeEmpo0rbY2eIn3iAzirevpH9pO4oLOUzPVzFw2qm+bjtnxZC/ROJM918ENs55PtRMsZCuMARJ/Ex5ezjvjiTrNs+zk2FYI/iib3H4hDF0P5Yv4Kv/xN08YE290xAxABpj5lTnx6d95PvAySCePM+tOfUQHkOsm7LPZMRjGHzVcmIsFqE8BlzayGS6T3eDpOsfhO//xP/2H9DJbNMbhaAADYi5m8mSuwZjSl/afL+xocOYyXGnJdxlfdsGwwBBUQPZN70vvUil408aPOWZmQ/w8lX9Bt1BwjbFrfjbzMo33ycnj07iquvBm36DAT8fJhrSv6Vwax2/F3HwHZ0+WaM5nvuvglV+lfNAzjK8xlYzlTG3GcPI0hXL4ZD/TeFU9voC0HyokY0CdP30qnT3ZGXvjSISKNO1+BCkHkKY+aCiFOzq7016xpe+3Ot6Hl5KqJ2PqQlngb9mb6d3F48rSCNJeClEqgnmjeJ9vOp7O9VlJo6B3nOzpGPf6nPM5zauv+UTifjLRW8Vf1uD3+ODj+Llq3sdPrUujENwyOBniWYjg8ukT6eZ5DBh+99JYGzwSaILYty7fSfv5If9tAV/Lc5DDAXvvR70hKuw/PDeqJJUD9+EI9ovnzqb2PQfvwxk2Fu1Iwo7dv8/zyq9JfKgYRaoXY3ENbVL247qS2ZiKJ9EuxKihbmaKBYquIbQut59OujNIIotxEAQ26MD+ygw2Z/neaw4NjqwsT1c6z6CA/P24C6gEPXRUObWVSiKKI3PRmLtyrjtdOnk6xuL8xQmWFeOUY38A7HE+P3gJd+8R9o7Z8eevsknjUlkFRdaAu4zJfbIntm1PN2hneBG1Q65wpA73OwdswnSZ9XaN86uCD+P4nlpQgWfi4fBCIkFXUR74XPXwbIqYjY/b3e94nnU9fazh/nYB6cSX86CJ1MXrwcv2x/ByLcXFc7xcm7G8RvHDsNIJHGMF7FHSdSh4NNjVs+1kt3CEH8b69Y6udJH0AOeQw5uP9y9xe9TwsnSRiNC1HkdAxB0ci2rcOBnKORmhaDDQMpsBer0GXd25cRFHImsDE/ZoqmEYAhasG0gOq2vkHtzbCClPaFC4WsV7EEJiIFGr/jgazuhoqCejoaMnScXSgnpkAYAYxCSFNZms4az0N/X0AUP6pbZr9+7zgzl8X1SiEkslaoySmzcyA911dY2Hjh+TxtVQTMxq8RiEHU0N6dzJU0Fz4op9CMcTvKRVx3GUV34JdzMtYEdJX3oJD12GOHymiNfIyRwTyfYHhedthOZVKplfaWkJOLuWXkw33aP/yzeupq7LuGWZXyUAnwgzqC4blCaU4jxl64xjGUSkzoKfCrNMueTLXADbWFwKb9oUKH1/wwJZZ3BDJRccOIOjYc+J9vReU3bHcGBn+vGNyzeCXir5etrMqamM7SmO3RTBi+2nUjfwEfecr7wSX1MaXIzDFTzuYtyn+NtLvJvkjyA3UwqclK7ET1/F40enwlJwr0/JvYyh2QUOui7FhYhVtir04v3qRfG7S2fyQPu9yCu/avggzQ4ePhDF4G7grTDyu9K6qjQc40Klw3TUix0nyEyzbk5G027DARxpfCmGazGnqrSQusjfXrW8RgH/AuZ9+QK4zN+upW1Lg8KxqLYqFaD862S4CV5dYU/0VRbUucnzCKKy5aOQeyhmduls2nlBrpldk3nzHucq7edjcjzeNXZcaZowdUYopGdOtafOY40hI8RXOQRkxGkl2fNDESjnzvfe52VzaWRoCVsHLgA77rUPX7Y9mEkMGTWWk2vGwLOKUeQw2k42pyunLvxXssAxCJ9iXoXlOPzOXEsNfM4v11ZeY5vicI5m9ifNTHp4ZhpF2vBVFLVTx/bijGYN+N45y0eEkZfP+312oCvP8blyJNX0GbvywHuxP8E95kDDxTiXikeVkQ49Ev6Bg56ofysZQK6b9CbOimMdvHxeHuuayQu9/G00L9sN/gvfVmk5fw3HHd+NGTMyFY7mxCeUXde09VhTtOu4CkaSNXABxZfP4qAwUlaIGzlOTuez7cp//d175aXeQ5J2qn10PryoJHif231at+0Mfcd7hbV461r5t8+I7zmP4GPcM4H3krEj0+3LF9JZJtnK3459Dq8Jc2aGfNW46ag/nDZ3XeLblKp4jQF+w8dR7PFCZzp3icAL3+X9ZNw8Wx/bFz7SsTAt51UDmCrmL4hClRFFxHl+nfFfP91BlF3DKBuv8x4zpihod0db133HcB3fOz/58xVuEuelJ/UUklvBySKqrNdExt4V2r7afSb1UqSRXWLpCPd5/1xeLHnaw/3ON7+m8MHxOlZhP46OKI2TLndnY3Ju5bXTUkV1FbBlraGny82HcaJkuCcMfNmHOB9wQVaEAsLfXg9XgFvw4MkjC1PFcIpv4pzojwLl1olgsowrjET4sFle6sH/wNHAwuaR1R5koI6GOwWF6TrKTkv3hbRpV0dqYiDFKCe32VIRJ9DoPKH/R2fUYeCMjq2qVxj/9U6K0cXIsn+Ehy/x27kc5eU1idfEiaOgH/hT163ANelCmJWPLUqF1IEYrjMZPdhsvcaNW9IOiZiLKd/n74MALKWHUiPfZRjlHdQQ4jWyjC2XRRmfvdbZkUhCDNmG6EoTZs+Iozdv4pS7gT5wo5siqehJ4ol4LczlCYUsICpD4HJOS6P5vrbMAAFbrLquBj3nzwDiVME/Y8D3QnQ+DUmzeToO7Eln6d92FT3Sp8845hO8Hrycnzho/6B3GkawpGBYYbpGEOUSYyktK0ol6HvqKTp3Lp5sSmeRcXEv9wtvx1E8aTwBgyHpWH1zauNvr3G8nD9+X0UzWwIYRx9c8TemosqqVIKTQ+enx2JfBG7XT3UHL7R9x9/AyzUWDs4lH395399neBc3qngNAn7nuVk9RByoYGITZj1C+4PDMX/p1Il0A2aR07l9yBcHIScGDB6WLpy5cp8HV/G9Y2dnekwStAjZEPgF/RaUlaRR4ydSILQ0HDsn9u8gEMsWDW533I7Zl305Fvtx7D7vNZmX8/ESJK6PfNJnqniNZU1LcHqakWVm2AW2L1w+05auM37blz+qv1PeJPQCVL37+F7Ob1PLqdnHkbU3kSHimfcP65Pxd9k+ZlZU6Xi2nQD7061N6VhrZ4xTXHBcNB1wFL7neH36UuexTWWYL+foPMaBbBPnPIIzuYQ2CPQx9q621nS2uSPut10bZydXzLfVvz/j+qv/839P3/3ed6m3NpH5OgP7M2Bgr1/u63fiaNCo9hUIBUwi4gOAdDTwCx4zCp6xJ3ALKUBrP/g4IDaSfy98DuyMYBYXQhJ40G6yN/m0GPmpy4WWECWcTl7ZsvDhgSsnKpE5v+x3GGMswCJUQNxEuT6V//gF30XYEKqk2Mll7qARyujg0595OX4J6EE4iFIisFPu4098yi7holEwECeIqXS3sMZlzjkT6btNnL5P3Pl3v827zLmYAQxmz/1dlPfrLKhj+/Q4bMtxSpwyiU/D3vFLlF4qW/nlfAcPgWyAzd0bZB3w94Nzz+/7vHcFg8L0s64SvhyO5Snu6TsfhEGGPfWZaysDFH8cN3Lgc2HlPFxT+wUV8WziiUZRuH7lZiiCfP07uRx7MRkBRlwVbHduZcq5wuOzYG+nygKfc4zOQWXr0+swhu90bt2FELzHVbtNpEdG6iUcXPMH1+jzcYdfWDc0rBhU5ZhBqZoK7+Ujh6cSnD2D+F6j7vqVc9TfuJL6QXRFDK68sF8ajTE3Qi88kO4Pn9DT2w+vQQH7XgmZ4zgyIs13OBsIVwDwoakTR8O2hvb0QVvPfaVBenZ84p7zyebEh3/kytf6wdtsR6fkUCTXIATbbaJlKsLS6+ddwirnJTn80Wk/d43kBaYbRuQB4WO2VtdnNO685J2gFpEMlESIpwbJOQklfNyI4WydwEnjNhdw2mPO1GaNaAW/5T3+c0JeGNd+/2lHg4p6nDqho4HjLfee6EzvNhJR5F4fFcd9F9/8LG4MJvPESMM9nILyI/nY/xd65fbPvOQNo3FOudY38ejfwvsqL8hh+5kP/ZZfivPSRH/4jNWgPdHoIvT0eTzYeX6af9rVZ+GM3wsj+djQwmwrkmfbX4cpSX+/zeVay/MfvJRJ4pP06++DiZwqTa8jPx6kTX76wpd9FQ0Fd8Anx+16uuafdT0IA/HEeXv/gzxYXJEuxDnHPxwADcH5c+c6pyHRsPLpd3GpzKJfki2RReNuXL2d/t9oT3i6tsL689besQtv38UZbBZQEtl38266wITEC+f9j8FHOOUw0Jj79PryVTjfhhQ5ItpFfsiDM/qNr+7/Y1uORTyTv31e3/x0/5KeADlON1Ob+1FJ/9pnKsr3H+CDI3HeXp83Zn8T5rZfQFFas3ukVeHiM673py/H/hnq2qdvu9+/6+SRkx5Nehu4y7f8zrnncip/WFksfH5bnLIdK1IVcOqI2z8u4sz9LFrP2/+v3ll4eebMsRhv1CGaMqo4TaAgZOZosNYDs3ew3HPf0cAf0m0epZVD24ZiE+9w6Mg9Gq84M3Q0XMPR0Np9KX2yvy0dB0mLAcR1PPAGA/Tbg+oYRWSN0eqNK3d+ax6Tz0NYfdYaifPC0uKYBUMJ/CB/rwEYAw1e4qWvz3rW3/+xSx6jblhQTEAO5/YtskXafxtE/sca7vtdnFRPLRgGBjPQK+gKn+a/4qHyVxgoW9SlfptLnJGWUfXg8W65wZl77mLwmk8/LxzlDw/yw0/f8+DfjqmMl85i9R8L28MmP1emSnfyvdO8Pu+y/xy0olnwAiZdMEy8Qbe6cjucr67nF70cv2s7lJOwrJVDHPM+znxW2+P5UtjkMsznHa949Vk80vXS7CvAvlEX7EWBv8rAz/H9b3O5Hrb9eXPVfhJlBpL9dPs6WTz8/XmyQfoQz+RzOV3w8R9c4ov3uk6DCsm2YxHuXiPQyaLIp/K14WPMXXz8PP74F//bn6fv/eD7bM+vAT5myutiULsTal/u63fiaDA9yCgbEAmuaqEUF9ukjzv3bkc12Q/XrqXy9gEiezfTtOoa0lwm8LsKj5XU3X9lOj2V3fFqm2r61xyJ4rV4ak1atvIp0v6rIsojc3fvsEU4PFHC9B63WcSeRFOPEADyf5l97kU2Tcjok2mYZ0mH2cYe6/e3bAsEee5Z9lrPm0+NhLIsBQmBpNPEtB/bNn3QtBgj8UaB3PZgRN7JRjoqXMP986YpmU5pdd4P1n+YOs5fS88v5Rxp9lZWV1UTPR6KIYb4dPwwX1PkHb+XaYUayI5RoWWKUp5GlfUxiBTcKxQMOYP3tD69vm5jmjlxLMVPHkm1pCSNJa3V1MPY7xuQtw4F0VHGb+p77IvCyDG9LPaB8b3pipEWCuy97zRR9Aba3rGT0wU6zqaXVj6ZZk+fnkazVcRUX8cnPJ236UzZdpRb/w91bxmodZX1fy+6TRCVOnAowUJBRUQRUVRAwMbuMccavfU2phwnnXQcO7EQFVFBUloFlJTuVEK66/l81sXl3P/Xz7yZC48nr/3be+2V373W2nnllimXovi7oJkpVYX0ykLNq3VsGt9FpCeb0j5w3Pg4guD0ou49o5Q9Fb00nezHtDvWLpJsMOXnTDODJllmwXNlMXnL/bWHgaUkpvZ/Q1Oxj/r2S4NzKsjoqSe1yfRt5+4eupfuqWsopD0xK8ZwPNeW62Nk9yB/zjPdC/9g+cqV2ZxrBBkevq7s0TX7XZgqm+l6Bn68L9NXCW4YIJ0FPxfHT3QcvrGe1R/6PBaetYZz5s2Lt996IzNfGhxWmSZXF1EPVsoJPkGewSVv8CPr1/hsA5t97GcxNdPQ0vGs/8oGWtBzN+/zVpQ1q9dw5eO38c7QYensdjj1jDiJqwdtPiovF7qq8374oBI8ajq8AbFnojZGs8nUrLlz8qYL0+C+52TbF0k7UadW9TgI5VqV46ky+7ZkyvJmtPIGLMOBaOjaWI4mdSpGCVk+hwLnV2D/YBAcSniTk6G9OFb7SIEt6zExa8jGPKxqN1kQKzCQExesiOHLSPfktzoLvrp3bBfH0JRHnq9WpTqyShBJmrN7lenVzNnP8vkGTgzcS7/nD1KmlC1p5bVcy5cty7KozydOjrPJcvJ61sMovTL10L0TEFFO5EHTiZWfQtpiJdZqH4ydecVTVUFLDMM+Go2Z9rcL2i9d/l1MmUEtNJkv39Fw6qJOZ0XLZs0ooTksU4w9zTfVdeGSRQCxU7hmanFMJ4PDV33oVnoEQAP9L44kID+EpmWCDOXReXaDVzaYXn64rvwnT/Fe11uQjxxKqvon6fAWSicKt05MW74hhs7biEHH2O63hued2R65OSnHmTplanz12ZA8kbiA2vyTT2xNDwPSt6GH9DOt2RtgTBE09TNTEuEdT6HUJ9K/kPZo+nYhNdF5qB9MNf10wKcxbWFhvT/pdSnNUlvup7NXI9ootJCe7xgpt3x275Sxgm5Gv/CvqB+VJVPdNwFcfDtrdrz4bt8kwFENakfnzlwr2aRJloM4tn0dtpL2KR1TppAXwU9lOEFzicv3pugqwdmvBZlQ1ygvX078Jt7u/0mO371j+6wnPpxMMwEl52carmOZvu+4pqXrsP5AvwZPhe39Y5mIL1PQ5TPT8dWp1uePHD06Zn+3Orqedko2Ni5p2DDLupKm0M95W7Kn7tF2mLIunWyOlanA0M65SC9p4h6ZFmw5lmUir/X9KI6qXztOPblt1K1DPwrSZWtgJ0yjloucT9ay8gz30/W4b+po9YPlYPKAvKZNVTa0q8uWLY2p9C6YOHdxnNfuZPp0tMhr3g6k5E395745hmO5D/KF40vuYhmJabs/7jG0cR3OZwU9F+bMmxufDRoeG+CjO264hkaADdGRlLOJ0EETnJnUk37teO6BzzCN2rR87ZB+gjyTdIc28q510NPpRTHko08TCGx/wnHR/tS2Wa6gbTK12vkrX+obU2Mtq1HOkk/5bGquf1As9/F3WQoETS3nGj1mTHwyciw/jbisy7nY7laF1HO+N7CULtolB1VO5G0JI43lQ8fK/iLsib0RimWe2iuvU37r7d6xZO3mLDO74oaruWKvOaeqduiHp6GfNIcZCkqDzzkua5LW7iULLGSj8vDs/cJn6aU8TZo2Pd54j1uOmOtZnTsiq8fkvroWT99znswjbRXvU0cIUMp78qO6wrwRbw+Tf6xDl5fVp17t9tXXX1M+8E2c0KR+Xktc2rBRXktcATugfLoG7bdrcN7yYzmChYKvpL+UO8PfkXAMDW0euW0XmTEcIS6m98IH9MDatWlNdOvSIxo2qEsJzUEAVursgq7Upf9+zbqYNmd+TJo5l3KbFbERne3rGKIlMxqaHlyDmyf+b0YDdry4V9KKKRRLJ5yjvGhpBqyX84QZGa1QOrGLjIZt5ejRsJdrWNduiq9nrYhZ2E3OdzLwPIDU+xZNG+F31qBJ8NRYRRbDTb0uoet9k/SV5UT5Xr6UZ1IfQhupUA1f2D5VWT6EXLpCaV6VDCttkz00fiAjc9bs2fH58E9izvItZCYeEj26dE2f3F40wd/SwAAAQABJREFUqcP268TkH8aV1vkhr/Cysa7rVC7kdffa37v36rkxNEr9YMjw/NvzzziN/i6nRG1sq7ymjtUXdk/lnSzRgTfUlQU9gw+JPCi32ZScvVLnuLfqSHsujBgxIsbPmJPjX9vzgmwka7mfB0GO7/v1sfWJ1QH6ur58pvytPCn7zt1nph3jecqTpT//erV3Bq2lB1WIHpdeQ0+HJpQV4a9JZ98Ej6kD1JPVq1aDn4BW2GPLiuR3+d61KquWPxVl6Tt8qEnoyEGjv0gg5Nzzz47WxB9eh1nouUAmC/vHIrJ0yhJp91QfJnvz8OwCzfW3LW2wfFcfnqCcHlKW1X4xYWJMgDZtSutE65PaZg+Wg9DB6WPzHvetKFPqL+MDecdSAv1sfWRth/OWd8qwBkvIvV3Kngv9hozgxrHDs2dPo5KGcRgZN5buSRPLTIr6UfpYPqjcGx9KF58NYfwvP0sf99nm75aI2APry+mzo+MpJ+atKfZLsm+dY2pP1Qfqlpww/9OO2PTRchPX51jSJ+04tNGeaBstzZnx7XRu8xmXwMU13bsiT42zzMVrOrUJzs2/zdhJOrCX9gPM62CZcPIKfF7oP1b4Xvny1rKZyNNo7Md8ltXuqKbR6cyO9Ho6cr89Qj6S7gWbUOhtA7DKs7y6fgUZncM++Ch63HFz9LryiihB/7nXZSib0A6kLWDc/+bXfwRoYIdS6SYh4FjLJwqEwgBDyJnUv44jm2HZokVRnVzhtq1O5HrLxpyCwm1swL+Bhh3Zs2AUHZ3/+OsnMoXopq6d40Ku0jmGevE0OjrbPM5urt7CIIOpRA2SDM59rq9MLYbR/KwgpvKB0Q0aB40YGU+99GqcVLNiXHjtbXHWWWdlnXvWJsJgBee2YNgMLHQUdXgKgsj1JRps1ij4oPL2OhoDMgVxGg5Lv4GfxdplS+lE3Tna4bAcTWM0r3VRUBQ4x9LhzDvp+d5gO5UHc03lBNMXgQYFxXROlfci6Pc1wXq//h9TM9QsTkd5H0vjnPrUtFVA2BwzpYG5GUSqFA/AWB2AAlbB+nyNuz9XuAyo0vjw/WKU9+TJk2MEDtHoEWPjJ3feGqe2PjHq1LUxmumqCK/zwxjopFnTZ3qTQIb3e/vSEBQEvhCAaGVr0kPA99nsZ/ToUfFxn7fiiGNOjOt6XZ4Bo3XS0iProZmjNaJJCxUoX+tI60hmB1x+z9Ly5ZgG1dWo0VxNiYoNot559bkYu3hN3NCze5zbqSNXX1Fq4lVy7GHRkdX51HGVBvKE68kAhjkk37KGgjJEvJm/a7Du8RMa2jzT54NMJ33w0YfizNPbc70kyhs6s0DeU2gI413MyoO1lI7jfOU/n2EDIx3SYiO9MipIfvYNQd3Lr7zE1ZHT49xTT6DD7eV5HaZXTnrFoD0doAqlJSCc8H4aJJQqOwKtYEWnyVjeZuK94DYz1OBpeJYBDIz84st47ulnooS5dLn+lmzcqTPn1X6b4VsdJE+U7IgreLONsTFheX2TNWRf0VvlU65g9D7qb6fPTPrr23kqVoXnV1Lv870+J/YuTGlrxPe1ASOObkTa6ZG1Sf8n+GFOECB5ZAu8up56Wa/ArEZTywo4jgZPphluZ7U2g5y8ZHWMBGgQNfaEy9cDN10VHdq1jYb1SmgwVihRsoFl0cmxM7LNjHS0lBn3QPlygspU0cFYRffyeQQuX2KU+77VJy6/9qo4/ZSTuU++HsbnIOQU55+9td5cJ0VQUHo6tql3W+hZsBVn6QBq0mtQz1UGQHUvZSVe07gT/TB7/iJu6pgUQ0eP4VqthcnvJ59wAle91UmerMDfec3RVIzfsJHDY+rMmTFs3De5RksnmtQN+jMcFHWoDT5YoEE+gMDqvrKsSRX0I5+yf9Kf//Jn/AmfcyjUgTKbbJylE7vgqTXb98bMVVxduJS75MkYIys8X3cQoFzYtUs6fGNGjYkROOhT5y6MW66/gatCO0JzSnCghfKp02DQbQCrzpJG6hJ/pwOUPVKQJfdFmhUBCo24DQZffu216DdiTKZTPvDkr+N09tQyDXWKjqFyry7LAAN+1GFUT+hcG9AoA46dQIPP5eeCzOsAl8ZNnBi/53RgMevqeWa7uAx5OrHV8dl7QfncsnFTbKI2VblVt+hA6uSq13WYpZ2yr0Mg8QSRlVMdvo3wwYChw+Lh3/8pWnL01ePqG+lPcxpAeIPkLZtC5RzhH/nFPalJqqkOsP0C1L82xHWN7p+0yrpQHDntgbdIOP6ShfOjE3apLaCgzWQPRM8lsA4tnLf6RRpI94MInKSTzpp6TadcB0y943oM6uxn4HymzZwVr7/zTtTHCTrnzA4JaAqS2ENAYFMuEjxQX0pzaWzgoV4XcBFUr5yOWcEe+Dz3bDm2b95c5Gni1zFqyNC47Jprom2b1tGwpITU75q5T87XuTi26/cjdT10UP9LC38m/dTRrsl16EDaENReFJ99NjB2UFp16003RKujj85bI/I2FAWCjzy8UHaRTQMwHXQDigQC2FObR6beZS7qY/fYBoPjCIz693kzxsxfGTdfemFccP553L7CnrKfBkLaJR0+9YJ00JYKOijv0kg6yCsFGeDcio33a/l+0RJuNOAmnH+88Eqe9N77wH1xNrSvTZ8RXwaL+h46o2m/ZUD/y89k5zGO69DmGVC4r+6DvoE292vsxz+eeSaGA5ie2rR+3HLHT6MtQLt18vKJQIPZCEw4G+v5tbRJp50xtE154wW63D4X2gB+iZ3al/I0Ysy4ePTxX0crlNI553XDpzk1Shs2zIDL8eVH6aJcKk8JNDCuvGdg655r8zwQEmB1n/WXpKE9OgZzXfBrvd+JCzufSVPmU/ImpbpH0OQRXSXd0/mX/9DHGVgxX4MLwQbtRgXA6jJk1HllpWPvZj6bd3CjABlBM9DDb7/3PrffbI4e6LaWzZrQ8K42GYQoWexBNhHFki4EGB45/psYOm4ifuo8rr1elHtzHIBv/cMOiVKAhiPJOgTapkcDthe6u0ksK/ffceDA/HDfEmjg97Bcfgg6+McJ9rKmjdz/vBZRXvLDppi+cE3MRh05FtTMnhztCbQOO6wmvgG3+UybFI899EicDDhVBfnQF7AeXDkR6FGuCodMnK6iW+QV6etH+knwitdXanPkH8GdCYA7n/bvFx+OnBDntz81rrnsEsANg2n8CHkeGruXP5bpMS/5W153rQXbw9rQnawsfULtgL0JvLKv/6cD4hf/eMZ3xX03Xhc9oX2DenXz/Qaj3miTMgOtlPnCdbCFw0Z1gTLvgZy8VJm90seW1+158y228oMP+8XrHw+Moxn/lsf+F/tBg2j0rFRUnvSF059nHP0//d2ivi3KUwaXzF6QQf8yM0p57wRuMvjrX/6C/7EiOh13VNxy2+15o0HqQfSKW6kseYWy/K9v761tPCDXJL+rbxxPH9W5C7RZ979o6ZIYNe7L+BO6oF3TunF+156p5xuVlKTOUjarsn/S/QfAW2XEPdWH0X9xD+R9p+C8lQ3X49cbODzRfnyGPA3oPyDOO/v06NDxrLwB6jB0sPTQL/SgtBDjeBtDob+AfWvsiaDedB0Z26hjmEtZHLzsl8bYE9HBfft9RJ+s0uiMDjuaw8g6gAEeujlndZi8qJ2zvFugwQb00t49FXCRh+QV6SOY4WeBZGOEL8aPj48//AR/7Mpof8pJ0QQ/tdahNZPHvc5TftMmKCzqY9+f8cP+PfT3fiTQAL2UBXXC3LnYpi+/jGFDB8dGarZuwD614QYPm/AL1KsDC/oVoAifZhsZrt6OYemvH9oBfQL9gF2UdTpn9aOy4IUB3pry2UcfxoDpc+PGC7pye1fPaNawYfZBky4pn2yavdK8jlUdu515bURGF8ATgwcPjkbHHxuXXH5pNABA5y3QCYWcq1Qz/He//v8DDXI8TJQfECW1KsxUcHB1WshoAKUaTWfT75fT9ZU0tnacjrUobZKnczthRq+vYQAUDk4ghB82dlw8/MADWRf204t7xFVXX5UBtbcAWMPufcKafYVcgSkybX5m42UYP4ooa3ZRhTE0hja0+2DAwHjsb3+PbjQ/6841QB1xouvVqZOnSiKtCouG2L/XyTCY1ignCoujLBPLADowIn6Z0YDz4b2u01CCvd//IFbC2Bf17Mmp0ck0LWqRAYvX48kyGZhDMpVboocoVJlYJeIrSZoUJIgzsEHJeGKksIyjo/Mbb78Vp9Gl+xxuYvDkog6n0zpbKpI0AKxd4XIdVVD8KkGR2lS+PFMHTsWVad0wvAp/6dKl3JPMFXsjR8agAYPiocce4R7nk9JJNriQzgIMKdgIpbRQeDR2CuGPDXMUDpbh86SPN3R4iu81m4PJaunz6hvRsO3Jcdt118bxnGJqAPK0hr/RQOT8lTLWoGKvUolTEOhS3BcDAunk39mQrjpI7XdrVsdnQ4bEa08/FSNXrI97rr4yLunWNZWhjrgGU8UnPXT4eXcqRcfwQ75RWfnKjJjcAZQYilZFNWs+GQfvvBt/eOPN/JuXf//b6NL5bJyV6tTZbdzvYGEA2AM/0jjzrBybd6jE/LArul1oNTqCGpUwoHvZd53zP/zpj/H55G+jW7vW8ZNbb887gW3EZy+LstCDM7VszGegaeMeU0Ftrmg2gCdEjuk9vWUxqtmFnb/fvZNUfQJqgbUHn/hN1rpe8pO7olu3C6IUoM+mN5sJyA1MvJJIZ9A76LdZxMre1ax5aMrj2C/GxQcffcDNCHPjm2mzkgbF/5k2VpsPfLAoTy3HPk9lSD+gYoKU/6ChYY0owWE6mL0SaDBYhkBc7bg7NqEb9tIgpAqpcp5Kedc4lR0ADXRDJqNh6rIfYsQimvExPjPK12/vuTO6dDyTbuN1kE2ML8BjNrFiV12H+yWP2gwzmxVBlzRM7LEvf+cJodlHCxYsAAz4Kl741/NxHYHL2e3bEzCWcNJ4SMr/NnSRwa8f1pr6nIqUHngquhNExAweA42KZGbswJHdtXMz9e9En/DA3AVLkNUpMQAAzGsf77311mhHg67aZE5537y8JdAwccqkGDD4M7IfZsRw/t5XAxbcvEFECScFRxxIc0DAmLI74F3oVwHel4MFVAvGyHfwM+jtK3naz/l1/iTVir/fAw/uwmiuJkV5GllLA2YDjPDzrfrMvB6GtldddrGDxKcfD4i3fvvHmMzPn7z77rioSxeavR6ROlcZVAx1WtU5fiMYauCjkdchNBhTBytbOkJJd56v7pw8fXo8/+JL8cagIVlf/vjTf49OZ5yRV50abLs+5dEdcz0CPuooA2EzJ3T0lGX1mHQwWCznvgKWeUIwAmDt4XvuinnEVjd37xKX9+qV14LpQGszPKnwQwIK3CXQwNPS+VIHsbYC0IBDA+F0Gr0FST34A87iR4MGx71PPBntaaRx0RXXpf1o2KAk56QOS0AcZ9o7t3WGDgGoVf51BNXBqUPhwXQkfBb/5FeBhDnw5DsAyeNHDMLpuCpv2zm+Vat0NnU8vVO+Eo5KGdat/pXfvVZQfW8TWtegzci94BnpPDH/DWR9rYXnv+Z0+sXXX4/jAL+7kmXTvBlZNmSq2Pwy7Rr0NstQp1B767x11N0H9X/qLmiSOs09Yn99Zp52wcMjx33BVdP94raf3hEdAMLrA07ZHE0nuQAWMwJjaVeVS51Of+daHMefFeyJ9gBHUVnGZuXtLOPGxgevvxhVjjo+7rn91mh93LFRDftgiaZdwPMECtoYiNl5XftdkHfAKf6p3wqBJTwF77rPVQl+12CXh3NC+ubz/0TffB93Yz+uuuTiaMyeup/8WfKkfO/e7gb0UzfkOqC/dk575zNgHDfUr1I3CKTP44DgLcCdXz77Aj+N+Du2tVvnznQXr5U87DMEVpyvz2LgtCGO7ytPmOFx5b1on/axB+6zwdJ4nP8//vnPMfCridHp6CZx573352mjfKK/xOSSzytqn5if+2owpy3MgJ0x2Eh0P/NgL7SrOsJlkVuB5sEjRsUN9z0QLZnL5Xf8JM7pdHbeaKAceuViTozJFTMa3LMiyCVfCuiwugQZWAw8BBAETyq/NkT7iIOZPz33UtzWs1t0aH86stqKRoZHZLAu+Fyw/YWbptQ18gm76ZSZvw0N4QCAhl3YLXmpLM68fUzW879pcxbEK2+9GZvWrML5v5BTUpr41jMwovcMGVnWbZfl8GvB0hUxePSX8cnnYwH55nBqviBpf+LhwYl/rSgB8K1NyjgtmwtAA7Ryn9RR6go0Xe455iv3zu8MwfmVU/aHfMe/svhbPHMzhnItpFu6bkvMWMxVeiRQgDtAm4hGjZvGeZ3OIIA7HPswJb4A5P/147+M09rQKwNfrhLvd29Tz2CD0g+GnuoRs6XkecFRQWD5RGCtjDRCXiuwt+vRseMB2PsTGL06YHhcfM5ZcffNN8UxzZru14/oMHk9uZ4JuQB4RW6U/9Qp+XJd+TtZDJnGBgjSrwRQ7duvX/z0yd/lnz2OrF7as0eUADQU/ERAIfhdHa7O0WdSjzmcfncB5ET3MF91bhn22Bua5HVlzWsB3+j9VjzT94M4gffc87sn4+wOHfKGqbz+HP5wbP0wx3Qc7YmNJrUjBnrO1bkbI2ivlF/9bF9joc0vfvXLmLhoaVzV8fS4/qab46QTT0hqmA1n7KHd8IrDPfhY1ZijPoLPkqe1ffq80j1tE88yKPUmsBVkDg9Cnh7+41PR+dhm0bVbj+jU8ay89cVeEzu2bqfR9QFp63yW9tUrHl27pPZzMdBOAI+9UA+4Dv0dr2Ts++nA+KRP37jiqsu4maV9HE+jxJoE6wk0oBdTfpQh9wx9azyiLKvTBDrkowzYoVnqAua/DvthjCAI8wrxx7H0Eepxbudo1tgeIjXTRiSYyXykg7pA/e1+FX1rn1vwW5AL9xL6SHP3x6b1k6ZMjs/HjI43yUx+4J6fRqf23ipDRgNAi4CavQ/Sp4AQZsIJREkL/bniAac21exu/042NdNCHTQLnhkzejQl/B/GZvzGn915Z5wCcKftNGtG2XR+vi/1sXoN37IisWZFZFYdrK3agR8A16qUky8F79Zid0eOHhNvv/JCDJyxMG6/9OK4hZj1KHxsQV55ZBdlMtLbWMeY0YORnfD0VvhxPtms/T7qH7WaNIxeV/TKxplQMGWO/8mSvIqfC9/9t/3/PwM0QMDcVVefmpXPSRf/tzemw0BDP/ss1nDqcTjOc9vjTwTtaWTOUmzldEl0U5TtEDqq+v5hOBT33nNvLOTd917SM667/vpMw9zAKdT6NT+AtnL7Ag6/jqdCkaf0CJANUGTqguIoMI1MXAWjYjqZDGm3z7c/7h8P/umpuApH6PwLLoj2p52W17rY/VYUTiUFx6HIMcwwmApdxaJS1BmQYfLFXD0JKAOirjFV8GdySvoSQemSyVOjF6f2bUiLaoIwGpCvBunVuJshUVCs0AfG1oFzHYmSMb7z1wlyvqbrlsd4rMNJnE3a0mgCo+eeezbvXD3/zDMJpgt3vhtM68TmHBUy58kkHUPHza/9nadVjq+AFuhFAMx6V678LoVx8KiRIN0fxy+e4JSRNGqzHnyvp1Y6nzZE9P5c1+EzVCbZvAjB1DEysFfxsZyCIkFJeC/2hK8nck3aJ9H77XeiJSfHd1x/XRzXgnRplFieMiPE0tVGjhohOcfAwC7JOnjFEyqDgnROXRtGs+qBNUhNX1MAGv78mxixfmc8dMMNcRXXxDSqW68wpnyGE6/y0wmSHipShdn5q8Hzex6qImQZBSeUdVZm/vPmz4tXcM6ffP0N3hPxBqBAz/PPy678a1evScc8r1lkHOfNIPkzB/Inju3eSkddJI2mn6sdZPOsigkePfaLn8dYGr91aXN83P/Ag9EOgIqNis3rNuQpVHkYQoe6HNPN68bgGa9A0giZwr6Nfd3Fw/e538oB/O4VhRuQrwHDhsfNjzwSrXn+FbfcmUBD49LSTIfbBmhh4OstCHnKhLHRCfeUyBtWLBcSaPio3/vx7dy5MXbmAleYTY9MRkRig8OeqHoQc6GOd09ZbuSgtq4sHlN14rRadHuqSa1jDebl/eMGtgKKztvyiX0ADGX4cC9gJGjD6SOK/bvNu2PK0vXx+fyt/w/Q8Nef3R89zjk7jqRxm3u3B2Og0yxPGFxJZ/dTHjcQTqeI79Ur/k65U0cI+i2miZ/y9PTf/xHXomPOJdgtRS9564A86NV0nqp5um4gZKCkgZS2fAGP+CymDUCyeRNNurauz9tjKhD8LMRxnTBlZnwybFjMnzc/Hr333jj1RBrCIT+uX3oLNEwAgPt4IJkDM7kCmLRdXw05zG/RsCxAw2FRm7ruAwFjaE0cZZlDZrXwrd32lXCX5iRYXuEl7/IPNkk+Tg+Cr/39Xua+C55YRRaDZSlDFu5/z/5PP3/wvrj2ystTrvvgqDz82z/lb37/s59Frx494zCuf123dl0CX1WVcfi4kGqJrmR/UxcyIecl/Z2cjkb+nAmoc6Tf1G+/jRdfeSVeBPBtwxMe/+c/84RX53ndDwTL6l/Wp45R9j2xMxBzH3To1AHKcnYJZ09Q0DQCLZzaKVfDCXbvufnmzIi7F+DkEsDkY1u2oEnpZsp61kV1xqjBbSAJcKvnoZ//dDAyYIRWAiQ6QjwsZTUDMHU4DWw/AtS8//d/iPOalcaFl/fi6sRzMqDejKzpHLoV6kYDOkETT5bVZXny6FpcHy/T7A0K5En5VP6as3BBvPr++wTrfePmq6+OMzueGSfANzqzKwnKbMx5ADrZgNHx1GWVsQ86Saa5Z/DL3mj3Uq5yP6gDht9NH51AUPrCq69E+9PPiG5kTLTYDzRI663wezFYlN8z8wBe11lTZtRflvts3k4KK3M3CNAWOAeBhm/Z12E4c2/COw/ef2+c1fbUvG1HG+ZYZrn4HPnA8bSvyavspbQ3SNTe6kBKP1+eaOogTwYEH8RplGPXp7Th3tsAGo49NqoLRDOxbZu4JQLaVMVeVAZMEyxxH4pBho6dvLEN+7INgNOAyUD6IHka8GgoOvLVf/w5hi1bHfdC9xtx+BrXr5+nlgK2jicvZloz4xqwOPdihpS87h46rjzkWqRPDTJFFgA0vNL7jXji5VdcUjz96CPR87zzohZ6Rt9FuyefON8C7zAyY/i1z/Ql7bUmXm+6A71moG6GgI76BDIa/vDnp2LAhK+jy7Et4nZ8J+2HwYONi+XvysiWTXlNBU6ggdPYHF/bpDzxjd3Pt6PLzDooL2iL37SZ4F2g4ep77st5PAwI071rt2jZvHnapT040F4/mB/KLKOmvWPe2tKCnsKUCQSwpt1MKm0h+kCdYVD63iefxm//+WzcfTGnux3OjDYnCsgeln1ztgOUGGBImbwaG1m3BGgX2QtSRIBaoAHthn/CVYj8vnK1GjjxXKGdQMP8eAmgYdWihdEL8MhTzNJGJchTJfh9EzYU/iYzbuHylTHg8zHRf+ioWLJobsxftjDXe0oCDbWj/sFVoya1DVVYBw/6sUcDqpith3h85B7xP2XQjAYtvnrXD7vwJ48jK3vYi610ON1A6cQSSmy/XUTZ6ppC7ws1Q7OjWkZ3Uurr16uTJ8gD334zfvnIz6M9/tihZKe6j1jbpLlAjOPCcPxE4AUdy37qjwocqRttvCwAq709GB9b+25Gw8f9P4pn+38WvQgY/+fO2+OYps0SHN3Baa5Nh8vBFI6lnBo0p45i7Y5dBElds4wkgKcuqAF4tp6MCYGGx3796/iOX//q9tvi0h49MpW8cLhW4BN1vMCCtNLHll99+TN1m7Lrgdh25rt+80b2tWreKDdn/oK0H39/r2/6NA+gi8/BF9Ymrcd+JHiBHimCmY6pr6A/YNApv8t7ymnRTpXD3/HGHHX+mPET4mH8pQlLl8WdF3Ql+LsyTiRYl85mwgmMCNoZRPphIKp/r6y6BGUzacRYxgbFPRAwWcv7Px0+PH5Kxna3VkdTJlsA1zzo1Jfcjo2qefCheYiVNoSxtHO7lUvWpI/jQVIGveyFz/Mw0c8CBfMoS3zzo49iIDHITbf9hIPOk8h+PipjDk/qtdf6NvJoApjoY2OHwl7sj3v8Xp7lb1gY/Y70VbZyW833ANXT4nmyEVu1PCYuPv98shwLt7Up92Z4uI+VkanirUFMq8Cf+2WkCOykH8U6CuW6Ag3Lsvx5KPHHvz78KJ56+KHoTNZwbZo6q3ONv/R7iwCltDBbwswLaeG+Gtv4tWOrm+VRS9bd45mzuEZ81Kj44NWXYzu3pjyOT9MWAN9sGvVU2jvG0F+R391L+V9/Oz+zLnXkTrIZUoexrx7OVSP+WAdANJSD9Bf/9scYOv+7uO2yS+OuG64HaCilFwRyR6mwIIx9kXJs56w9YrzdKIeFy5bGu336xEGN6sfV11wddXmfuk1w798v/vi/+PWfARpkSP7798tt1qAgZHyezqndIFLPBRqOxIBYOtGsIcSE4bfj3KAZUhnLFBq7YePGxc/IaFjMu+++qEdcx6ZZIpCOooaTZxnYJaqLes0AG0Vi2pYOjICB6ah5cgQjqVjUwv5uJaff7xLwPvTXv8YVbVpHlwu6x2mgfkdytZalEYWxPJUiyEdxqfw8FVChy5QqGV0C15cOhftv0IRS14GctQDDhpDPHT0urrjumji5dWvqg5tmRoOOqI5cOlrQxmf4MqsBqch5OoZzd86eenp6qmIxLXbBwgWgrRPi2eefj3O6nB/nn9EhjmrchHr1w9Lx3FkEGhgzT5x4huCAtWqJ4jJ/DZ6Cp0D6DJ1c17JiOY4iJS6DEcbPBg6MRx5/PE4HQTelVrqZmqVzpeL3VFFlrvJO48D+axx85Wka83UdBhwCEyK59lAYNGhg9H63TxwNwHPHddfHcUcdlQpf41gFx0a6Cgh4OpUOKfTRWEjx4omPCq1wgsCWqqhqVIuVa1cj6MPjref+GYPp4PsICPT1l16SV6i6n4UTHBBy1px7igBryHxpSFW6ifKyl9LEeasQTRHW+MxmT1/v3TuefLOQ0fDKk0/GBZ3PKWQ0bNiIBoRfNPzKwX6Dn/PkW9mjIB5QGaXihw7vHvixogECLDCOjIYnnvxNjJ0zP7oCNNzNiZQ9JsoQRG/GMFUg+K7IvPweC5bPcdwMJBgHF4YGMzigbMZOf8HfeitK9aqW62yLTwl2b3zooQC6iMvIaDjvvPOjcWkj7url1ANec89Egbduw1lHWVdjLyo4T2i0CkdwCvI7avjQmLlwYQxesCwbDiG9UY94+4CqKnaCEJrv7eTWh+1lSNPmOg56TEVNToBqHXQId4FXi8o4hLvMzuBmikrsW3kNsHKD4pY/7DlRkTnA7ATDFQEadsXXi1bHCNpxKyUF7or4y733RY9OnRKwNJuBZSdPG5zoPGhwMp0f3jfDwb01i0Qj4V4X08rNaPCatNGk6r2EPF11zbVxDnqgUUnDzOLR+RcAlb/lRemjQdb4enqzD6RbOiu7e1mTZRP79u7IoI9ZxbzFy2P8pBnx8dChsWTJ0nj0vvvi1BNOyIDRALMSvLUZ+n8NCPsJcjEFoGHE1EIPkBLo2rxBucxoOPwAgQYkgGDHjIbyroMP52HQAUsnj/k5X8xXeXYOyXf+DV/ne6CJQMP3dAH8ZuFy9rLwluL/H7n/7rjq8ktSH3+Isf/5757KxkW/wyhfQXbWYWR6bMCRQ4Fk4KI8SV8flM/LJytDPBHa+FB+Wwg40BcGrAaOU2fMjH+98EK8BPh8Kr+/76mn4ixOMqtDkwR6WafgZRp7aQy91TX28VFn+lz3Rf2bH3yzl/2pjtHXeA/Hftx6yy2xhLHvvfSiLJ3QftizZBP2oxK2w3RIQRt1CxPMNUsH1+Na8vQ71wF/s197WY489B18I9DwAHM+p6ROXHzVdXHeuedG3bp1s8u3Ol4bkQEzY6sTCsApQSr6N4MA5Ep9ow7WmTYrxnUI6s1esCBe7/chjvqHcfMll8ZZZ3eKNuiCGtwEtAZZFGiognwINEj33Pfky6LTDNkZM4M51iEgYEbJOta9aPGi+Oqbb+KlN96Ik046Obp17Jh3j3slm7rX7CwmUrBL+/W7cy7aiJQt9IWBroGHp4IC0epvT7smMfZQ7Ec/ACQzEs+k7KMmwbTrk0t11OUH5yw4oY3STqjb/ZlylvyUfy0LsafYD23YN1On0p/hs+iLQ3bIccfEHTfdEG2OPS4OIfCqzH5uB+DZDYBmyreOv7KafCIfMrZ7mhllOpV8p3Op/ZBnfqBHw0jm3af369Fv6oy4iwyYm/ho2qAk7b77pw3RdutIG1Row12LNlF7oY2U5x3XgwMdYnXNAQINlCX2JqPhV4Brvp559NHoTnDn3DfJL8xZQN3AwkBAOsnf0l5ba4BkCrXypC9gfbzBok3TPB2bMHVK/Al/ZuDXk6L7CcfGzbfdkQGGmZ/yoxlxlqkaHPkzQQH3WVvlP/nQQFEnWhA8eR3dXKl6Vfoc7Eyf4Jr7fpbN0e679proTkZcS4JSe+p47e0+ZZUhLUsqzzjuZyGAY7HIpX6g9s4sNqlVLM3QqS8CDU88/Uzc1uW86Nzp7Dj1lLakS9fiVip6mhD0ZkDE3A0gfEnfvYDShbkXwAakDF4yQ4F5V62WGQ1raLo5FZv6Wp9344fly+LS7j3ilDYnRvOmjaE3J/uUXJZDlmqg1xav+J4AcHT0HwzQsHhuzFuxULaJtgIN3BJQF6DhUJBzPB82/d/NIFlW6imZDBIokqmv/A5uhgf3/x66ur/eOmFGww6uPt7MjU/2aJg4c2lMp3QCXD71bXOAhs5nnR51yWiwLGYk2YQ/f/ixaI8/pg6uRgaG2Ts2E4QQP9Jenpcf9V0Kp6bIF3NIG2v2IPxzcC16kcE7X2L3Pvygbzz36ZC4lMym+2/9SRwH6GjGlOCRvYC4rTpf6gb30W/zKmLGVxcayLmH8o58bxBsP5bvVq8CPB8Yz+InWQz4q9tvjysuujDqQEdLbiWSWVIJGDN/7bj+tfR2ryVkygDjqmd28rxt0FxgzSvsZ2G3n33xxXiazOHjec//AjSce2ZH9pTT5TVrc1wBae1H+qk8Q9orP865GOwWs4MEm/S1tU3K1ugJ4ykVejy+XrEy7ux6PpkBVyfQYAbDeoDqBO2QS21HGWyHNBNwKAKo8mcR5GFBhYASgTbj7gd07KcEpXf96tfRuXlpdO9xUXTs0DEaAmpa0reDWCMPUZEl6SQdnK9+RvI/axDElM9yPegcdbzP9KBzwZLF8RYHhQMBYW5AR5pRfVSLFtkPppjRoHzKK3lYwD5rF9VtHtyqx/ydz9Tn9+CqAnpgE4ciy1euiIno4ZffeitaNm4WF2L3mpQ0JGOPG9+Yr8G0fm8CIvBFETDWtqnL9c/UC+pG7V4FAAz5VP25nNKJmTNnxuccMj/T5734NQdKnfHH5HfpURm959gCs37Ia+pHyWBMk3KX3/ED+YYP6VUdnvH506ZPz4zq9wFJypaUxP9y8HMK4FFxDOeVPJfzZu6uAyb3gE85kI+0XnvYxz06YfxdGUpaq3JIJ9DwOQDJay88E4OmL4yb4fVbr6VPTmkpsgR4g88uyKAApa7lS/lsD3tZFhuxhLW/8+67Ub3+kQk01G9cyiJ4QTP1SuHDH/z3vv4zQIPrd8f9n/8hCAY/OiBSdyqndp8R3K/mRKXOYTShOqF1NG9USuAE08E0lWEa0SmVwQ8b1ifQ8DDGeBnvvoN0umuv4/SbekxPLgUbrFtXwHW2/JBBFA5LHGSuDYxh/bD3L6tATEu1plgh+p7T7744uI8/80xcdEzL6Nrzwjid0x3LD0yN9hlyrYicDbKc09rVq9PR8DSyMgoymQUBtV5H5wXN9iPQMHvhgngZZ2jmZ4Pi8ptuQtBPydKJQ7j6xOBWYfQZBUUKQysQMhSffelUOFeDNx0VgxIVuU0yl69YHl8RsD/PiVRbjLEnsMdwsmA9oydmCTToQDCUilwH05q2DThS3uVt0O8vTSFLwWINgjMqgMUEQ5MZewgCMxxFeB+OokCDNUw6kyo3neNEEBEQHSIVls/1o/hSAfh8A9wdGDcD9e0orKnTpjIuYABK5JhT2ybQcCx9Jvxj1yhty+kYkaK1HRRQI5QOO/sqjXSKdL4KGpbPzIEHZ7bHyrWrYsSo0fFe71djwPzF8ejNAg2XRX33FAfUgNn36iDquLmn/lw+FTxKpN595zk6ciomwR7pL83mLFwYb+Os/O7tt3OZz2KEzjurYxzKnmp8nFsGK9DHr5MvoYnKSodDuklv91Fe2YeS2qsHwtdbodGXOOh/+dvfYszsedHt5BPiVhzFU1qfmDy+DeNjYKQzvY9gcy9OgAGSBjmvAES+ykjziqTvs8htrEmHzvOwA2scmKeknw4dFjcDNJzC7C++jZQ0AvXS0kbov0LjogytWPM+nVfmWBPnGIg3lsydF7MJhJdhvObNmhHLCXRWLP2OkQERCIbrchpYHfp5urRtDzW3JH/aWJBLDaIKWQ6HHsQp0KHe0U6zVvbLrJi9rNd0V08Q3HeBjnVct7kXR6JG1RqcTFNXjCOVt07MWx6jFrnp/3794a6fRtczO8aRNWsVaK0DypyVewPcLfDdRk6qPOnTKdGIaYDTKYXu9lfwud9RQjVjBn1DyGjog+HsdfU1cdapp5LCyF3V3iXNuJ6sZ0o3fF7s1WBg5D3tBbsBJaC54lsFoKVSJXacb+w/MRs+HDdxanw8aEgGp/9L+UHbVicwDo3SoFlV5GIra5+EXAwcNiQmC/Lh/Pmqx3Y2qSfQUJPSiapcm4nxR1eW00FgJTqwhXC4EGi6H7JWviDXv39b+NpfKS7/zmjg1oklK+OzuerNAuv63gfuujUuu6hn8uxnlE+9/oe/5NWJv8EoX86J1BHMZ+smGtgyD7MBDF4ywOK98rfOibJkYCGNJYwOqLKk02KPA08lp3G68I9//SteHTwk2vHeWzn5Op3T70PZr5QYxja408wafKb887X7kUE7a1Tf+wyDah1pndHKOEQCbsPJwLn+ttuzMeydF3Yns6xXHE9TXiaYAalyVBbnttDoCYpCW4GMfA6E1HZ5EptpwhDOUxdlVcDKxk39AY8Eqk8/tFpcfvMdcT6n0/Xr1M1yHAM7bYROb5Yc8J6NgIUCJzYVNmhPfcD30suX69D5so+CQMPb2MpPBnwSVxMYdTrnnGjDqZRZcJvQ4wINBo46uepIHSDf657rvCXA4F4zrnuhDjOdej1zWLBwQeqa19BjLZo1pwSpY9rVevXqQ8/9QANjq8+lr3YuTwX53j3cChCis6XzLEPJ62Yr6FguXLgwvvrqyxhKqZA9Yf7nwQejg/NGRwpISIt0NNlDZSiBBt6fADa8IR+p0/ydWUqmxmqDTW2Vtybj4A4cMig+fqN3lD+mRdx8zdVxEo5i7UMoRSKrwVtjdmM3MqiGNrkGxocE+X6zvnbxTVlkyeZmAkmuoyIn25ZOfMHcP+Uk8C2aRN968cXYj0uzabU0Vti1rdpu7bY24wDSm6X5JvwRS9eUaXlRuhTBTp3oGpRSLVy6NN774IP4JUBDTR77a05Lu7KvB+KfWE6T6+Tv9uDXWIcsjwtoVMfuVuVvzAj8gfRcxzZLQifXfTDA8PP4KVPir08/HYNIa+7Z+njKwG7JHg32+NnJKSMTlAj5UZa1VCarwcAgQVL9BV4C4Nokg1FlSdDB2622QLeho0fHjQ8+HI35u2tuujEuoHHgUU1Is2ffdmxGpztfHHJ1pCWcgiKenurDGFxJh33Izw7G3UVGgECDttBg1VTyvp8OiN88/a+45oz20QUQ/PTT2sfhAA1rE1jbmTyk7TbA8NRSXS9/FBwCNTwfgL179lB8AN3sU7QJB/+7HzbH5Flz4x1O17cCKvQ4/zz8sTaZjVEZeylw59wOhIeWrFwVn9BkW6Bh6ZJ5MX/losDERWuAhgb4rHUOwZYBNFQyowGa2BVfELGgo5wL63FBMhwfhd+xR/wo7ROz5MsEEoTOd1WqEdu4R3DB6nUx6uslMQc3wudxPEY5U+M47eQTOUCqmYHdtDGj4vGHHonTWrfhlqdDMqNBgKdQdoy8QnOBKnnegxr9LbML8jYlbLxlX7udIrx/ILZtK/7wuC+/iD7vvB0vDx4RPaH7nfrY+JJ6LUhI7qeypF7UX9KfcHm5Tj6lnmcP9Pds/i7PF/WN4NGwEZ/H23/4Q4zib39x221xNdkkR+BLCgQ4nuW86hb9MvW6GasOn6fX6B7+CN2GDoYP5Ul9PW9VMWP22zmz45/Pv5AN4y3nefy3v6dfABkN6FYz4hwjA0z4Ul4sgLraZw7u+Bv9663wUoKNyJKHef5c0MSMrTEc5v0CmzSZ7OefnHN2XA1tzGjwxH4d8zejpAblBmZOaa+2I7PbkAOD1sxOY693wwPSxbl7Aq6GTqAB/3bA5yPi7id+E+2POCguuepGwLVzaGzbKG9y8DBmr34eczeLypf2Qd0p7dWFHqToj3tApCwIavgzAVl1TR+A3gEffhhXX3F59lM5mtipJpkBZpPYI0n9YkNFe0v49bq17Ak6Qh9cPlKfC1htxh7ZrLsKdmvDls3ECIvJnpoSb/R9L0rr1o8LOp0dRzdrlo0sbYgpWMHEUkeqJ/OAgHmy9Jyna9C3FhQxc0SARz28g2etWrMqM7/G4AM9/+abce8dt8fZ7drFEfiP1aF1DcZXB7if9hoytpHW0sO5FoEMgeoEq6GVNqU6c1fPTWLen3zUH6D63agKqGPpxMnHHZcZD8Y0vtTnTDX5UL4sC8MLbwg0JO2gcxn0hjRxT9VplfFpLCsZw+FGn7feiE8mfhvXXtA1ru91WfYhLMcCcww5RQFSfcDv9kHbwxwr0Z9sOT7FO8SM1eodwYHXVVFSWqpGSz8Kz3b/jJzhf+/r/z/Q4NrdHV9u8v5/MlB5AkdCrJhCACvQsBbkpsGRdaLDKadGi8ZNCZwsndiYyljHRkY0/XPo2LHxs589EMsY8q5LC6UTxx1zdAp6AaFnbDZAY6mzq7DI4DoAOkc6bDpf1l/qAKSji4FSgFYCGrw3YEA8+ve/x2Wg2xcANHQ4s0MKi/XBnoBrGDT+1q2i7dJRNODw1D2DatZbWCe/VglqRBEehX0mwdnLoFMzqDu84pZboj3Ccgz1sNUJ3vYZoMBkzldl60tFlcqb7xVClZ9jq0AyaEf5qVQ2oMxWrV6VQMPTnAaeQhqdtepHNzsq6gE0iEYmkMHYCrLoqvSwVm8r9HDthVIJzAjry5MkhNQTo7L87RLSOydMnBCfDR8ew4YNJ6PhsTgDNPTQQw8BpKBrLHurUPPW/fNlltDcfTOg1uFl2gnsqLB1IP1798C0vUk4QYM4kXoOoOF0UqKsCTSjQdZxbiLQOrsaSo2OJ5d54ihf7H+Gwi/dnItU0lEsT3rnCpTU8JEjos9rL3Nl3+J4HMN2w2WXQZcjfuyh4H6qqNI5x+lScWjIrFF1DkWnX+e5eGKkg+6+2dX79Td7x29ee52/jHjhiV9Hl7M7ZWC0E4PPm5P/PCnyZEfVUHAuIJZz3j9fd1zjI9BgyQAMnGmB476med0f/5ip8z3aU1Zy5100UiOjgXG3rt+U6Vsqq/IumQ+dOrcBYvg/xgJ0AGXdztiYqRxTJXYIxsXatU9oNHPd3fdkje3VBOrngkQ3blyKwrPBmw2NcG+Y48EYrvLQdxenDquWLaeJ64yYjeLfRWrxvo1kEjGfcvCWBtASiOrQVADBtL5dOHq7uMJg2z4aAe2l6WgZMmn0uwlEqsBjlTHy7i9TTYelHPOV9oJVyvS+XXTjZ7yyBA27+VhJ+cW4BStj5GLX+O/XH8ho6MYtNHU5HdEhsiNwAn/Qwp4JKeusR16X9wwWvSHC7A/Lp4pphwINs2fPjlE4XK++8hp9MW6Nc9q3T8DxAAFKeKVgNOE01lvIanDy8B57sFdBgC/1lMqy6RXYmHJuDjREuLj5YH4MHzM+3u//SazHOP7igQezdMK0QgOSqhhAy10mZkbDZ3QXpykkc/F1ZKWI0npVomGtQ3Fwa2QzSDZqf+mEuk/ZU+ftPwnm+/0c4W+cYtKYT4UXUy3LPPcgU7uhwSpSiict/h6goZDVU/yzh++9k/TiixiLHg2fDIhXn/xTzOKXv73//gQa6pA5tZ2Ajg3n2fACv8usLMbPkyMH8ms+OQ8DCnWYe2KWmY06bU5r48unARpe5Blm2dxPdkBHaH8gIIS19TkA4zNUfjCUA/7owKgPBILUMyqkBBrY/wQa+GxGw3U335LXat1+4QWcElwTJx7H2Rf8ayq2wVY5kSLWocyaHZdj8XUCqjw7T7/hHYM60yPVkduRlSWAvf0ASB566s/RseSI6HXdTdHl/C5xRN26sXHNmtiM7BhU2+BYvaiOFOh0/nZ0V9f9X2BZ3eDyrOvVfghq9iYwGvhRv7jmyivizI6F0gn7HGxHNxrcOX+DAHWMgcAu5M8xtBnON+XBfeAjA3h4TrB56bKlZNlMihd7v0mg2CR7NEiXkpKGGRj9QKd9KJpAiUF/0p+Bc47MU9tkxojj+iHt7bHjIcGCBQtptvVFDB46LAaNGhM//9+H48y2bbnFpWaCEa5ZRzTBKHmH/XOeOonarSLQYLMybaTfG4hURk4QbEonplAeNzjef/HlqETpxE9vvSVOxvk/mLIsA+ddgAx7kRFBWetq5c+CreBL5q1OT+dQvYveSToxb4GG1WQLjhk3Nvp/8H68NeqLuJOSxxsBp5qUlPx4Wq/tUbDcT5cuEOBLQCrpww+1HTrQyTusSSBAnp+PbX0LR/JXL76U73n2549Hd8ApgbXsL8PY8rNzNjPQefs81y8vFU8rfXCCGfI960iQDR61R8Nf/vGPGDB+YlzQ+ri45fY7oh3ZJAINmQYvr/AhwObhjgCb/kEyDc/y5X66t3vQZWYHedqmI71uyyayVEbHTQ88FHX5u5tvuTF6XtCDporN81TdHkL2adDuVUVvq+B3IevaV/lRGpg+rs0z2N0t2Rzfh7PIZZRsvvfxJ/HE356Oazt1AMQglZyDn0PIfN26ei0ZE9uz0Zynox7QeEginWGdfL/bYHacIMPWrdgR5l2NTJGtu8nSXLORWyTmRO/33ouNOPMXdb8g2rY+MY5q1gTfjnRwDm8qGDSSqr5o2cr48LNh0efjIfH9ykWxcOXi1G8nHSnQcDh6uBoZDQD+AA37BBrgUffLaWTmDDop51WYFkvjd9IWnmdD0+8wK84AdDtOwO79QMMi5vjVNG4e4hIcLAc0qkrfrVIyGlsl0GCp09Qvx8YTj/+S0ok28DvNHtVX+ADaJYNSgdK8LUIe4qVGKQI5mUEJjawHN1g/EH/Og7cxgLHvvPVmvDRgWFzYsQOlZjdFqxYtErQwXdyMCTMoM8uIMfXl+HFBx8gb8n3uAV/z80LQXi5truXJAwcPihd+/UQInf/iztsBGi6hx8/hCczJ3/K2ejzlSd5kfurLws8KciBxBU6LwbqlE5XxJezw//Szz8Yz771fyGj4wx+zdKIGukgfXlkqNhNOf9u588w8Ree5ypPBrXQSmFJm/RDEcx2jvxofP3vof2LKiu/iJ+efm2WVbU44IYHGDYCS+mL6KsqR9FHvmIUrOdRz2j3BdgEAbZMAnvJkPf96DkHMcrzt0cejDart6tsfiG7YD4PLfeiwHRyAeYiaWUfQOMeEBklj5q0eK8y7cLjiH3jDWAKp8OSipUspDf8kPn63T1x/w7XRDh3csmXLzNLMTCN8DmMZaWQGoQB4AQinNBFZSKABeyFvexPSPtZSmWB4w+bNNLVdhP2YHK8S35QeWTd6dO7MzXQt8uYigQaEkPHQs9BWu1RQMOyh/MF4fggQCOxoBwR8LCFwj79DPmfPmRMjsSEvvPFm/IweDWefRkk7B0o1aP7rXP3b3egD4zvprI1Qt+vHa1eNH+XL9EX47N/oZ/FD+s8BVBP39Xn5lSjfkIyG++7L0gmBq7RN+9/n/kts/T8BPLOJIXqCnB4sV+CGsYLfTnzDr2ocfGCCMB509n79pRjw5fS4nsONm666MlpiY8vzfv311AOShNce9tex9jK/ivioS4mLeyOLVerUhteui/oNG8GH+B7wljJRABsK7/1v/f9/BmjYv/rcZMVAzoI8uipebzmZjIYhoGxrvlsRJTDoWe1Oj6ObNs3TmU0gkAY6MoOBncw3bMzYuO+e+xJouPPyC5P4ZjQYhG6mw7zpf5ZNWINuMOpJg0LkY51D8YQnyw4QlOLplCj4CtBWawIf/evf4nKu4OrWo2ec2aFDXt3mqY+puzpZAgoymwbftMlC1gRP4Hu/1tLlCRLKWwXuyaxOw7cIy6sI+exhw+PyG28AaDgtr4My9dVaw2LQknNFQPLFN4nqIqDFcVV8WSLAc2R+MxPWgMrrKP79uefozgzQcBY1tpws2DhJNNd52Zk5TxMUEpSq9BDRVchVUCp4wRcNhz/3lFGHZTHO0ASQ3IEoweFDh8fPf/PrBBrsdu44Cq9pc46hgygKmM4RS9CB1GFMAyIYw/j+vXxQBUfL2n9rAgcANj2FcejW4fS455Zb6NFwVK7X8TKwYyxPjgRiHFtQIIMKBE7Hzr1QMboWFYzIYsXqVRNoGApA8uaLz8TnNFZ67LZb45pLL4l60MUGOb7PNDGdW99fcEBZCbTRwJlOp6JyvgamSRs+V8WoVSUVcP78+fHKa6/Fb15+NbfrRZoPdet8ThxssEjg5Z4WQAaCZ9bCoGwoH3zmuzRGLCT5UApqOAUayuHkmqpqOcwTlE58OXNedO9wStx5192F0gnG2LKOZpPwt8a/Kgi6aXsqPtNWM7j0ATxnJ4vcDisKNmxXiTH+IZzUmCb56SCAhjt/GvZkvuuuO6Irjf2aNGmMnJDpQ731HhwPzAQZGgcb8cR330yKKfDAnLlz4vuvJuIQV6Q+v3Ycyl5WgxfKQJty7IE4cN6AgFLcgwPmunbweRu/X7dzS3y/ZW2sNS5l7lVIQDiYE428HYa/sYmlDpoK2DuZy8A/5XnfPta4G9BpORdMj2MvP1+CQ/d/XqbxdwFoqM/eVmLPTJX29EjjmUg/f1s86daAuX5BL2U1Gw/x9/LP6tWr2NcFMYpu8/98/sW4/+6fUhN4Og3aDsuTOU9cHbfAd+gxaJQGmnEEMTQCOnN8gn9YO6UTaIpCYASfzaDfwmAai737/ofQY1/86n8eAmhonbLkvKpwImpGg45kf0onbJA4+qsvcqW1uMSlUZ3q3DpxCFer1SCjgSwbHHq7nVdFL1TSqye4LMM+64IU3JD9RJLt/FI2ZH4pr34Lf/xfoGEyQMPAuf8v0PDI/T+NKy69OPnpYwCSF578Y17X9CQZDZd2757ZaJnBoxMBDZPPeZjb6P9cV56wwJs6s2aTGWiZOSOALJiovrFh7rPPPR8v8AxTXx//5z/ibHSw9fXeJOQClHvp79fKq/rE7zW86mf1gydo7qXp2KY0erqgYzecZsK33vyTWMnYN/XsGtdd64nUcemQC5RUwn6wu5mmapqw/Q8qkEWDEs7TBnVAjstnT791JqzH1PFatIwbBAAa/vdPf45OzUvisiuvpc72/DiyXr3YTKAuaKYuc57aDdgjAWv5yNRVHTB7dKhT1aHuz070nYCfQfbcxYviDU6/h/JxDamvNv095thjyGgAaMA2GQAUrjh1Dwo6jEdAgQItUDaF4I7n+XWRB3TAvR7Z1NcXX38jm/l1Ayy18V6jkoZJY5tFOt9CH4yCXGWGAc/UHqXdQF/pQKvH3PM8OWUti6gNnjD+qxiMHu43eHj88uH/walHNPUAAEAASURBVM6fGkdSlmFWm/bN9atz1feCJP7TBrm30kenPZ/HzwQhBYcFKQWfpkynR8OwofHucy/GAW1axc9+eif19sdnGrn4nkBDGTIC7L9RjkBM51+9kswJHXhUBtLyh2n8AkdZekeG0aof1sSo0WPi/Xffjve5deAunER7NJRy8mVasX6MTrg+gayurbPMkiUkf6ee4WfugfwvzeHYBNksNZy7YCENnN+OJ1982a2Kp3/+WPSk9LE2TrSnkMqUWXzqQzPu3IO0n/CKgZggmPbJ8Q00fJ7BewFo2J3O/9/IaPj0i/HRuVWLuOPue+M0gAZ9JMs+LTWVIwVsEMZ0oIsAbU6I37MLmclg+FUeu+QtRAanq9avBWgYE7f9zyPcuEAp603XxYVcTe3V1xjKLJ3w9FX7J7ChjXU90ijnyT4mPdD3Ahd7+CyoIRgj7y8HaOhLYPTk3/4Z15/XKbqe3zUbQh4CkLzzh/XUOOOPGVjzHgMK7b56x38oBPiEk2pAA5uOr1+/RsURB9PbZgcPWbGWa6+/nR2vExitW7kiLr2wZ7RGDzRuVJLNIHeSoVMJfqlC1s3CJcvj/U8HxxsffkaPgWVkOCxJ0px0BLdO1D4sjiQ779BqZhZCf2yKulf5S/0L32aACQ8nH/tO9kn6WBKgrRYIqIguMatmO3PbVZGS0nKkTZN1MWn2kpj9g3rMfahGYNg42gE0HFG7VozHFn/z5Zj47a+eiNMAGjxNZ+dpgsopPPxoxoof+jXqTLNPeRD7Qoo5Otg+Vuqwok9wUE2Bhp30Xfoi3n33rXjl46Fx4dkd4z5KJxJoYAwNtgCPvKIMujabGxbABGQA/sslshdmeamH3SBP2PWX1hA09sPX++PPfxlz+MPH77gtepFO3qBu3bSh6X9Bu/zM++UZDw0dVb7Xn7eni7JkRpYAmH1VBBpq4I/OnD0nnnnu+fjnu+9lj4Z7f/+7tB9mCJn14r7wlpRXg1Gf4ymyuieFll9mjJJyCs3RZ3KjGZDarFFffhV33nMPDZNXxs1dz41rrrs+b52Qx23YmDeBMZSHnILUqRX4nXPWd7RUzD4nlpjZ8ySBTX1VdIEBe/8hQ+LWhx6J48j6vP6uBzJDqGHD0tjO2Ns2bs7SL/0abYgved05pn/DGjKTAbkq+qnKl+CJutOMhrc5ue//1jtx409upAzp5GgOGODNRxkH8DdFuySIqU3N/mfqBtZgfJAHrIypDS/42NWY96ZYvHTJ/maQ70RpHYCG884loxqgAR1vQ0l5Xd9doEFapw1Pvc5Y6AT3xmbZ1ciq2Q2obpa2NDFGMLPJrOfPx46N1956Nx64/544p317smAOA2hARuV76K1e0Z9TD6QFge+KsifQp26QH6WNlDOrXfp5ffFw7Eff55+NXfUbxuMPPpClrJbLyAva5aSh72NvMyMR3i+PrLrfZuNq9yrWAPBAN9qnaBd+mKVIlpUMp+H6Ky/+i6zUmXH9JT3jlmuvBmhomn57GUDPPFzmGQkayHd8CL6W4/mLOLx4vfcbWTpxw403UIZZwvE8+5F/7TsK8iYv/Le+4DN25T/0UhHpoKl0dCY0bhJs+nQYaCh3shOwHQDqnc0gGzVKp8nmJxoRjayI7yacudHjJ8TdpEP5uuzsM+Kyyy8n3a1ZItoGdtkYD6YzY6IYjCqSLkVhkslkrkIqacFAK6gqGW8o+HTY8PjLa2/EqUfWjAsu60Vn1tMBGmpnvaQlAekoyggwRmY36DjyPAP5REgRJon2I9CA0AuQ2MV/zoIFidDPHvtF9MRpP+Ukb504KlF5jX42v3FhzNX5msIk+ur8zD5IAZF+Cijj+lklKNCwirlPIiB5/vnn4xQc0E58eIXjkVxPppFX0AVZPE3I+UHPPF1D6XmyraJyPOedz2caph+pz5YuXRZTpk6h1mh0DKHW8zEQxVNaHZ/pvjqYSp8dg6VrCgD0UcB1vlSI0k0l4JzT2fKv+L2NalyX9+MOI0X85c+GxumMe9NVV3Aq0jRRyQJqzsiMr0DiP2QamoiiNPf7TGGFtUz9yqDDdWAUKh9QjRRJnKHPP49//OXvsYLn3sKp7CVdu2ZGg8GhYqoDoNMmICWvme0h/dMh4nvX6MpS2bAX8rGBqYFSdg3nROolAiNfv3vwfgzbGdkdWCPviaUNwyyZKJxk8kc8y3nLl8qCxsCASyRTw68hKluZfcHxnTB5Svz+T3+K2ctXxSktS+OWW2h0Bo00vaYE54kRoEM1gDWR9EytY10+z7WoAHcgezvJEtiBo0WuRiLoln3YhXkYe/rIk7916nH7lZdTU96ZHg0NmQ8GfQcBLDStBF0OxmjuAcibBWo9H+ev4tpVUW3HFk50KZGgjEhlXxneMTwC7iDgLSj2HYxjuq0OvKdWZlNsxjFYs20TYMN6yqEAPwCWq5I3XIWeDTYZE332itpMQZXW0IoWD2wUJ8esc+mWnTF68XcxbDEK15+7ObweuOmm6Ajf18UACTTgRiWP5JV88L9p2qZz65BbPqU8uJeerBUzeuTh1QRVC5DVLydOpHv1h3EPgcUZGOUjOLU3rdNAVgdF3vZfAaXf82Oz0x3KE4ZY8JJpMAcyW3AVbay5j/fOmrcoRhC09OvXn7KSiAfvuTtak76vvInMV8Qwb4Hu02fNIjAeEzPmzInxXxdKJ+j/yAkaDTcPqhK1DsC5wknZQ/+MSszn4CoHoENJy8fIVRSwYa8Fe4qvH9X5fp4rEk6gIa9WA8hZtX1PTONGj09m2UFb57bwuuNG6q+7nJe6YcjgofHCcy/Hen51HxkB3c6mASe0McVS58TmvRpn6yOVcx26IsDj6fwmdKGBkIGohl/9mKcZyJlrfYVgt/+YL/LGkscff5TSCa63ZF2WNCUbwNvKrbRPh5QxEnxMYsPfONauFZWQgQt91bKBnZllXwBq3vnoz9NU9zjj1Lgc+3EcpzrKpA60cmXw5fVVngYKWJsR5njqONN1U6bgHU8EvRrO5p3y0zJOHwaNHBV/eumVKOHZV992c5zdqVM60dklHGfK+Sv76jPn7LrUcWbL6QxmVgPryOARuVUH2adEHlu4fFl8SDbcWPTZJeix1pzANm3ePDuqq2MZIIM6s52ki4CFwax2Qxuhhi46oto7v5f/BVzXoienzJgZr7zxOvfCN4vO6LCjPZGqWzfnaVmiAZKOv/pSx07+VgYcI0FYaS4v5SIJLlwbf2ujyWmAGKPJJuk3bnw8dPMN0a5N6wTwdfa0ywLg6m5fOb7PgQ7qR/W8QHbW26o7/Rt+Z6mNa5s+E0dx5Ih4r2+/OKBxSdx24/VxfMsW2dTTE15LyvjDPO01AJD27kFmqjCW+6nuFdgV5LXZoTrYD69HHke5R9+3XosvATevpC778h7do2HdepnCLB+6jwmOMKZ8rx1y3doI6ayecZ7S2+/54wxadGQ9Zfzgo4/i6Xf7MlLEr+6+K87teCZN32jMh6x4q4K+gQCaJ3fS331I+WJMx/NZ6jT3wKwsAyLXYdCoT/Dciy/FF5zet6pzcPzkrvvokn9iXoVoir0BUn4gu2X386S21T1QvnTaf6QP8y8ADQRKZEytXk+2x1ekkj/1t5z7HZdeGOd3PjealZYmkGOzM8cWjJFfCh+snzlDiaRHgjv8SKBBMFpQ0OepF77j4GfA0OHxfO93ome7k+KsjmfR16pN9uCxwacnxY4pP8iDftZPlC8LBpa1aXewN9uwN+qcA6DrVta6dNV6et/Mjfeg/V70p1eWHtuiOTeMAX5RFrILfqxAwF6VEsPFnF7bDPIDSic2rFnBvFak/mlGFeHh6OJaBIUHYqBqaIPL8h7scCX0cnFuSRwiFrdeWqiTy8pr8CQbCeiDzscGefXx5l0Ed+W5MrlsxVhCj4ZvZi2PWYiea6Mwkiv9SqINjQJrkX3wtTIF/f/42MNk8ByX4JF8nY098R31lxJQY1z5U3lUbgzGtkMTNjkDGgEk6V8N+6Qem/D1N9Hvw77x8bhJcdZJ3HR1zVUc/jVJEAomSwBJUFPAXdAv+60xfmbwME/5UV5Xd+n3pw2AJ/U5vD7zM/z9x57+V5LlNtLIL+rKrUVHHM7fcwjCPno4I4O4s/J9Al/8tXpdH1t9IDF9rnzuwUnyPfZj9nz6bvR+K/oMGRb1eM/d99/L1dRt87rxwnv3+5G8X1o4vp/VQWZxOld9VOfv3JVbPzt3M/C+wv954Gf3x4pde6MLpUj2aDjhuGMTLDKDVZ7UdhQPPL0FRFnKvWAs+0oo/zv5eieZnpnBCu2V2Q3YxSGjRsfjT/01mnOgcMm1+DNndOBGjvroALJ5NwusYU/lPnmINVQELFKWLPfQP3UcfXjlx3+ZPSGogW2zseBH2O63Bg6K23p0jVM4kGzRotCjQV9YG6MN0hZKlzxQRRe4H77fZ1oC4uGbusaMBg/EBEgs3fZA5B1A8IZ16tMXowNXojYl/uCgEzBAsMsYxOdom9Tr+tmCFwKqlmLog6VfyvebyJaqxPsEkL4DBJ82Y3qMJZuk96Chce+1V2Xz07pevcy+qIMzNpAn+Ejbz57Jhxmb8bzUD/xO2iePsRYzFtxfD83GcoDdH3Bq98EHkDFxT5x4zNEFYFcZgcDqwTxM5XMF/FD31ew4SyeyvwW0L1+Vvl/s75adXM+KINY4mJJGQAiBuzdefSYmLVjPNb2d4upLL4qmJSUaHpQTH+pg9YNSDm31K/Yxd4idNyH27ds3GpxwDKDWtVGvbgk+mTFswfbufxcU+O99/WeBBhjTzTbbRGFGHCAXd7bP/jZGDR8eM0CstvywPprWaxAldepmkKDDVJ1MBhvEuZmr6aHwDc7Qv2AIXfezjm8RnXBySxs2SLviVSE6u6JMBngqemsZda4MJA3YnUMi3zCdyiRrvfi5e7yWmsgvqInvTQ0yh6zRpUtnhPFkuhzXygBdBSfQoKOyLdF0rmi0zhRhUUgVIpHLYrCrIkdiUSqFu2OX0PByFE7LStba+rT23Al9TJQ2apSnZo6XqCfKNoMY5leNAM57XNW6eesFRkCnRSVrDZPOuwiyJ1IrSPeeOXdujPh8eDTFQTwBQWlc0jCvyVJpOHYqataqQve03mZU1pMKPuhsahikSSpf1ilNXNP3GPyFCxcACEyPBfMXRpee3UHkGhfuV4e2ReH27xVKFbqvqpWrJm0cw/1TiamgHFjQwUBDNHw+Y9sDYuSYL6Nu44bRldO00pKSbMCjEVARqUgcXPqbiqVzJ5LoPlckQPJ3earHPlfkFKNSdU7YDzog1oKMfj56TPzj7T45pwvat6M852QMG6feBMcqKZ25TL1kLqL01vBBhkRZXVvRYRT5lodU4tLdgHgJjTIHDRseAyd8nePfdVWvOOXEVlljK3BhFoYp2VkryQmJAIDBWCoVH8JXKkYDU5saVYCeBtueGnnS8C0Ife+334753/8QTWofED0u9E5rGoiq8AEYMo2Rz1Vxciqj/KzT3o3B08H2pMRTBetfd3I7wS6DUoyyAb/P3YiB+GbqtHj5vfdy7hedfWacccbp0aB+XdaoYdgeFXk/UUrswNnayN4v5XSywrzZcXz9xlGH/SuHw1KW1FSYFokGyUdJVmJvBB2ShoncQze+MQDR8HF2CdiwJ1YDVHy/dU2sgqU5a4gKh3NTxWFcx8raRNKxAhhsgjneU4MPGD62sK7FNPIauWhFDF3M3HyoLMXrym7d4iRq62qDileUzszdbAN5Q+ehGoFJdVBzwa21635IWdIoy4t+FoiQ39Zy+rxo4cI8XR9NwHs6PHM8BtleLTZANWlAnnbNOnbyiO8TcNFYbuTk+gfSKO16XqmSdIA+uIk2EN3LvBYv/x4nd358NfEbSod2x4U4Wl53lLfIwH9EiBgrAstl9EaZNZMmTkti+rTJucZy/NrTQ68IrYSNqsLXZZjK4aSk1DuMzwfWjgPptlmtgg6tQAN/tP8FGQov3iu4xf/z+2LpROF6SwK3FRtj+Dxq/hl3+/73XESn8zM4hZaWE8ZPiOH9B8Zy3n3lOedE+9at0/kvBCcF+dfpUd8YzEkTgQRpLJ9vRNeoh/Nn6DD3RptgIGln7P5kuH05a24cyfi9rr6CoLFlBuQCotIbFkvdriOhkVZ+BGSVXWtlzW7zpXyqf3VG2YyUr2nI06svvRqr+P3pxzSLc87pHE1KG2UAkNkz2A/lyuZ46phMceaZ7q8BjNQUFNyCY2d9s2CjJ4P+bDUZeOMBBl/dDzpeStO2dvSXMHiRbsXUUdegw6Yu82ttk2svZmSp/9XxBefGsjv0EzrQjLtxACVL5s2K1oDUzUhRr9egfvYL0v6kgw7NM3DkeTptB+HsaDMsB1L35i0y8Nf/tX1m261HT85buAhdNoTT0iPipFatuOqvXvZRsXeI+ip7CCE7Om/S2hszbKBrVpo3V2BVAO2AanCc3U/XoC2wueoSeHjGrFkxG2f0jHPOpayvaV6RK/2kQdoG/lbbmU4u4+gICrZrO+yank4VY2qn3PODkEVfBhdfkFb71RcTogIHBGed2SHvKT8EELQGOtf99MTexmyWCVQmQ0X6GyQZHOmke8JrkCvQq77PXgHs91rWZQ3vgHfej0U869y2BLztuVpN+8HcnHvajrRPshuBkTTgM79KW6HNKGZPpX1l332GV0ivJHtq5OjR0WfQsNDS9+p1SZx8wonUSFdj3ew7a9c+OWbaPcYt8rw8lSeR6Dn31rWYFZY3jSD3m6HZzHnz48OPP44ZS1dGM+4U7n5xr2h5VHOa+pqlA98xbz+ki6evpvLmwQ68r3wpp0UQ3IC0cAqOXWH8dZs3xmROA19854MEBc8650yyKbm2lHJNsz1swmmQqB3SNzMwUkfaE0pgJGnCXipHBlyZRs4zCifh9L+C9l99M4nyvs/j9GObx/HHHk+2TVOy6Dz1RKfihzlXlp3843zzpqrUKfArtmnr1s3QyAMigjmeK4i7aRv16ivXxLfzFsVo/LHD8BPse1TaoF4cVvNQwEVLMchWgce9pWIlTf7GTZoeIydOI6NhBbp9TZYyHMR+VWM7asDqJDRQMw7oAEvWOuRgSr3wfeQB6JoApXqFedq0FswfWiMeTFz9Upn5VoAXd+C3btpBUFeuCuWFFWgGuZ4eG6viW9QBWEaAN0T9Qw7jGs7mAFEHxRz2ds70b+kTcFWmYjuGvJE0V38hpwVwx5AdGkFr91N7ZzCTN5TAN/K8J7EG6+q1b5HTUZ8PiXGzl0XbllyzqD+GLtCHUbcYSHugIYNrq81KcsxCsMvJLrpY2bcsTr5UF/ihv2rPk/EA+O9+9ElwxhAXkzHhVYXesmLGrfyMEkg50j9TB7vB6sQ8WGIlypgyZT8k+dGyOH0L5WU+zVUHDxsen0+amj785QCyrch4thRJHa5N0kYUdYg6zBJhQYYt+GmuwwBYcEF/z/nr73kY4Wev8X6j9+v4IDvieHRNt+4X0tOmad4y5jwFwLbhV6kbqqBninuR4Bfr1d+V79Mf0nxBp9Q7PDdvmqLB5wt9PogTS2oDJJ8M2NsyffgM1LV/6jL43r10Pfap0Y4aHwjiZ3DNWOof7V+hPJGbOdDvS5Ytx358g98xKdqdcHTeWHQUB532BzILyxghhYk9lm/N9PV37vMmyv7URem3wjuOj2BnYO28bQw+Z8HCLLs5/NDDos1xx0bDBg2Qp1rYbuIv5uwaBGH92pf63Swb97wIlPCo1A3aPjMaBPE9+Jk9dw43Uk2nj9C06Iyf2oqM53pH1MnMYffTbJIcj7305ZjFuMT4RtpIL8F3edOXvol2ddHiJTGVMtWJxB/lah5MFsn50axRo4w/pJ/vc6yMHxlXO+5hhD63zYXTdvHzfQi2JRMermkLbcgr0DAVmzfk0z4xa82+OPvk1nH+WWdmea9AL4vN+EU+h33Sv2AT80BuL/v3vQelHC60I/uyF+WS9es3zLi5ADPkMgp7Uvjyv/L//xGgIRUESiJfbBZ2LD9E/MCZaLo2PYZSnz8ZB2olTubhNQ7idKw2mQkIN4TPJosYCIVgA7XgCxYviqmjv8jYot5xzaJJi5bUetZKwdARwkqxeTi46avxMBjFOvXcRcZkD3NjNPj+U3nIeDKUaO68hQu4234mAnxIHH5knTgCh0IDn4pJBahziBDqXCh0Gi6doULNnScvhRokRk1lLoKpElRwdMbm0qxm0+qVcXidBlGvfgnjH5kn3Z54ZZoSikinwvpWDV2m0TDHvYxbcMZYI4LuTRGilyoZsyVMfV2yZGHMo5t93RaNorRRExrX0YXYcZinDp/gQgIrKjoEoxJG3y7M2SEaw2rQroK15jGdf+iiA2zH8bWAPN9zYrcFw92oaQv6VhyegZnopmtz3FRyEFgnENJiXHiyY/k9z0t0WEHllwbqBlbSfw1jL11CLeKXk6BFRIfu3fJKUcfwPe69aKj01jmvAoDhazM000mqjkJyjaK+jmdZTGVuM6gCQm8WzIyZM2LG5DEYpRpxZJ0S0rnq5P31przq/BTKa1g7z1CpVEQxOo7KT6NWDefNV55MQQ+mlWvXoBqwmu0xffKMjHnbntU+TwRV0umYowStnzb9L9POmKcGTH50PeWhgwGaytB+FZ4YVUb5lsWA4gLmidfoEZ/EgrURxzc9PI49tnU2+BTZzuwFHS543jvjzVzRwbP2tlhKkZks0DCBBprV7MVg6mSwMYlSL1i0MKaPGpmAS+kpraMFAFWtWvRvwDnbvWNrVHSuAA2bMODbv5yYiEAd9qhFowZRF6VeCUCCqjocAtgSuuTe81nXRtr6T5lPw82adyJDO/iMGYjNBGdrd22JldvXxgoap2+lOuOgwwDu2F/5rjzz9EaN6nzUkM8YdQODLdq4LUYt+i5GruBBVTAcdpjk1eHEE6J5aWkcDP2yFZNOgsY55QVnDsNWjj3TuGzdtDENh8GvvKgjpCzJ96bxrac2ewk8Ofvr6dGgecMoLW3KlZ6k6nEC62miRtc9dI3JEKynHA6Kp2by5G4Mm5kClk7splRkz57tidCXARRbs3ELSDVXaC5djtOzkz4tLRPE8M7mrEtkH7ehw5YThMyB7jYEWr54gYoLTxXasiV8Sh1YMKsRLYkbmjQi2+GgIzldo/6QPSqnLPNRfDHdwsspO+/CxKEq+8G6BRrs0TCFa/wGzMYw8xf7MZw48+QT6GVwbP7tbIKLeWMnBD3Cow0Oun1mPFkQWEsgTqdC2vNsH6n+1YGzAZx11Dvhc38v3aWXuszpqJM8QTabZO33y5C7Gly/dXTUr1s35VKa66zoBCULyxM8ywDLwFe9qZ6xnCi1O3uq/s0PgweevXiZV2WNiw0rN0U99OQxx7bKrC91TLFHgyCVmQwaf0//BDjUO5mRxJxdl8G1DqM3hHjybTBmCYhgwMrvVzDPnXH44XWiUaPS1DWuraBnqD2FKKl7HB+aGVxb5uc+6EwrQ+rV3CLop7Or/bHp35zZs2LD6iUAskdjQ+rGoTVr4hgTuKWORA60fYyvTqkIv6vj1VsbKQFw3KoAbcmjPDNtID+T3zfSa8U+PyuWLyJNuGbUqVMv7YdOuLSVW7R50sjgxdNR9aRlBtqNHdBDGpjKbPCRp+qM7by9GtMmzN+tXBFrvl8adUqaRR10cA1AblOJ9RG0be6rMiXPuP4KPEPesBGt9s/5+5EnmswoU8H5W9N2Z0z+giBmA02z6kej0iZ5OFCtYpV09Is9CKzh9SDCuTvH4hW0bAB7Co3ZVzOuysIr8ow8a+C1hPEXzZ8dP7CvdRo252aCZgRGh8IPyLt0IcCRBvKidHfeypdBhTJmoC2/m2Eg70h/Dz8EBLwx6uvxX8TwmQv524jubU+Mo1sek6eA8nsGh/CIvJ2pvBBHfpRn1LFo1/zQXmmZMphmbPVtZtlA8ymTx7MH66F782jYsFHUxr+Sl/mj9GX0Z8zkEWhwv3btBwW0fWwKc3ZsaIPQZY05Rlr6eLXgQprAff3lsNiFfWp0dEO62NOEk+BCIEDQWxnSNu9lXPm+PDzpIYn8JE2UoV0ADWYzZNkgn7UX6mNPOefMnRNLZk2JOgDb9fCZjqAszrrsBEjkGWguFfQ9pLl9DtT3ZhR4tfDWzTRb3UsPLU4b9QsqADxt5STx+3VbyGpYC+8sIygsGyV1j4zahwoQeEJKvxaerf9VhYyGddjTmdzEM33h0li3+jtKuDbEgWx8BVSr+w/2m8BDCeoHtqYuvVrUwveqCI3kCQ92dgAo5/rRH96gZP+i9IugvcCDAMROeHMrGQ32aNhZvnIsWrMegGNZzEKsBaEgcRxRpQZZHYDpgBmrSSlfueDbKD0aHYYf6X75DOVTuhvkZpkf7xPsEGCXJ/VntHXZ+0XZAohNgI2f6295Oj0PH3j5gkVxOABDq+O4UvRQUg4ZI+muTeVreV35LI8e0E5s9uCHPc1SVNaYB0TyO/MyUFfm9KmWLF1KluwiZGsL/WsacFDYMP1UwfvUf/C2/pL87t6m34WP4+/kW0Ey+d9yNRQzOo4rHKGhwIYB76wZ38YKrtqucXC5OKpVuwx4BbgESJUnb4xS1+T8kauyPEsA3GaCHmrmCTx8JG+mHYGvssyCZ3gDwoxvp6YNq1W7DifM9aIWOtjMNE+lBRnMxLbUT3/PW6j8cN7ps2kH4HdtVva0Yd8zi4rnbmduc+bPi8ljx0bdpg3howbZqLF61eoZXyinqdt5DlNJnnf98r0l5mlT5Sv2ReOhntFHdU82U97ggeE8QOoNa5ZFgybHwqvMHV0g6Jrp/zxff8ZnqIftW1UJWRMQs9xmL/pL/Zsv9rcMcy7Le82O8fDAKy4XLZhLc8YDoqRBScF+EF/YyN0byxIsgW/0eR3bQyd7ERQBt8wYVqZdA7+zNEbgej22afnypVwFPjPmckXkCScdG03R8TbR1+eV5l4taplFgl68X0BtJ7bQj73QhoenD6G/h4KAPMQSjC0frfr/qHsT4CzPPMHvAYTQhbgvSYBObmMbDL5wt8G323a37XZf0z3dm8xmpya7O7tJJdnUJJWd3WRrj2QnlWNSU1PJbE9f091ut+02PrnMZTBgDJhDQkhCEgghiUMICUkc+f3+r15379ZWT3U3qYpf++P79B3P+zz/539fD/Nub2tJF7DLCrE/GxYtDfkhDOV9fteHV8gg1uBeyNm0YxnRTwjgASd4h1la7qlO32HWc+ZMZ2ppRGdqPJNqa+aktQ98jqbqNF4FT5hg2DOWSsp7rzOuMLnO/eS3NpN8n+Osv/PP/zR9jf5A0a+DuwEh7uizV/ZvvPwM/nNbHA0yJr2IkdIWm5wZ3XG8CalDRzDSPN7y4wP7Ux9R0zls9FwI187fIoheWSMPPqCgQGoFnIwrT5eSOGRqHumjc0Ivkwa2BH6yrTUd/fijVE4d0myM45k4JVRQRBiFsQqgSG19sJtt92yZrZ5xhZ3KcyCAhAKCea88muPpCaYL9aK8LsNr6nFdEpCErkIdKj7IkjNZ75lFzjNl0g64Htsp0zNiE8dkgfimhE4nKtuH4mQHYJXnJUuWEE0lTRfikACco6+Nwmq42tRRhixhqEgZadJwtvuwTMOUS+elEBYtVexOE40/vO9DmPEqsh8Wxu/8fSgbjCUh6t0NWHE/P/NSSCnAhJ3MPWDPeDJjCVkY6Z1uxUg6hxd1xco7wzGhhzVgIEEBH08eCUOPOTkfm5LJIDTonP8AiruZHs7bMcOryH4oEC5zJFsHSk4JAmTZEryywExj3Tm5byqJKkMyLRVgx1C59/7RbZg5CBPTw/S0K3SEjJ7dnp4eOiD3oKRcTbMrKtL8+VUZM1bxc18Zwx4NZpvIxE1LVmjLtMQhYSaMFYw5czX92v2+RNZMe1tbuoKCaWpYFQx5DnWeQlbFUKERc2dOcOgwdFRQhZ37ap2uRo+GhQ6FaJCHwmjkzj0xctnReDJNJxIzczpdeYGLR2WOAocR5I/NvDT4xB0Fr3sn3ngcopHISRjuE3ge4bSIUTIOxoOX14F1Nw6+gpPnUx0Rm9pps9MccGA2Y5Tx3cnMzQwGbpFFSVi3ESsFTCmwiDIXlTf4wABZGyPge+EEUuZY6xW+d2a4PzX196YuULtkKnMoJ6pUguHIuOxgKgE4ZTxu4LG7jEbTdmUo7WrvSTu7AFo5TqCreIahzXUrEUA1NcACqxt8CoXOebG3Kvhd53vSR/v3p3nVC9Li+vqApTxAnHBPMxMH/GZdPqRVjTDxJIM9BgrjSR8aFyr//m3a+hUEQiue8U72ds0D61ItNdxAmyiLDsQBfkP6s4IZA3aUaNXILca5zh1hEq7T6JdIoPFTgPNyhNedKJKfNDenTvDxYvcZxuPiawX8Rpio4OoC05ypJ7y2aOFETqKYSwov78MIf52jQeHketmCeJX1aOB4SxwfH7WdSe+cYtBfuR5avTLdtWI5SgTlTgPgRBhXOPlwCk03ssycPBVGfiD9SVMam/0IUw21Hgwetj3Nr6tFOSNjDdgZqZePhfHF+uUfRhkyDsXs+L50ExEm6N7Itjwt+DDI5n8XyCI409kRRyGX4gSqqa2NXhqOKT9g88I4UukSQXXmxQkJTMY56siWn+tM7QfPVVxUEn1WbnmGd+uJxqD1mfCl6cgmM7Gctw95u+PKgzXcNTLlj8GDhAPrCqUYvmJ5RCi37jHvG4U63nQyXUIBXAx/n4WMCiOYNXtFBEZewiZ5L2GrsSCPk28GjJmn+2jtu7BRAVKpNcPDoxlNg/YUFxtzTaZxFxOL+TiWfMaTVhxvCLknnei0UFbLv3Sgu0/KQI/XlJf5fvBK5tTVfT4dIxK0aMUdpHTXhDy1LMZovXJN40Za8QrezX28V/53jMM9g0/CB7yXPElZplP+DPLJvhzL7rgDZxB1+coaaE188LvSrXJDZ5A83hN2LHswsiV8pVFTmeUNKs/Bh3h9FaX7EnjTRUlHEc7oOOKNiKd6hQZHGBisz3XId02ZFf4BXyYfxjPzFhYadsrvUJaZuyUgZhj20a3dkwtmEKioAzbijDiifNPhrwPe/XPuyj1xVdlsDwyzMMOw4bvCI3BG+c2czJRqa20N+aFiXQEtzQEv1WFC/oML4qP4J3FbluRDo03HvkQVOg331QmtQWfZm8/ijvLjFFk/ZsrZHFB9ySwuZWk4A+SX4IcpzREBB6/lfRpw3lp5Ks0qazWsdK6pA/q3MFQn6Dh1KlUswJEFLdlkT0edThIDMuKktOr+Sk+5AaTRLJ06toZ0yFXG9b7qkiUYNDqFjhw7hkPoTGpYvJjeJehLyKcC5Oh4S8kYexzrvIURP0qJwygGmz1QdBKNw7GOixg4ODYwQ+7pRJuEHO2HJzZzNHYzmWh9GCPyuRkw3wIYrwEtDY1JPDgMCIOfrIMFReFscc4RQQcG165xD74jberYUDZlARiNFgbhoZHhqRMIQfo0FONouJx27W9NTfi0dSh385hbWJKefvLRVFtVwdrp/UVWicEteaX6lHiv80XnnfJJLBWGl8AbDXydAStwGNtvCM4bzld7iBnQskZe2pLOxMvQ29lDA23KJ4MY16/BN5iH+yyuqg91I1fPwt97kVNl6K11dXXBV0MfE4f57cRJwBJe42t5szzR/ZWXyxM0kqUjaVo4qctoAJttdaazM/Wc7Qpan19Ty+lP82Oe6ks6TnTEhvMOeQPSB506vvgYDjpwzzHlFcLKdZUgY+VRvWQvHj9+LPUio+zHsYC5z4PXRLQb3PCHOsQjqg0spF91PvFT/uh9HFdako+ZuSMvsCeEn1sadfiDD5Abs4JWxXkbUcuv5DO5fArnG+PrwBb2Zv34+6AfxpYHyL/AmnBcqh+ru5nFfPpUEwZoPQEi6QnHN3gu7NVp8izNmDfzkwZdm3tnloU8Q7sj59O5TuO9L8HHzOay8afBkGnYH/Ijx5Wn6JzSmS18HN+x5LnioPMT96RTdVSdoyo78k55kdkqlqrv42Sv2fDIJfUNUebn2KEDsF4dETbqzmQfTkNkkGvW+W3AB4AHf3f84M3gsLzAdcmzDSo0Eci+Y/U9aWFlZeBW6Lpj98iaFeOoAOaRoQ5tCq9wgjCGMFTnCFwCf8QBL/dkCNvgHPLpJkGuRYsWhb1mWWWUCYPZ7quOEvFS2BjIiExLcFxbxf4dw2OlZeKiiDaEI1z52c/DxqBXuf8Vnnefbk3/+p/9afryV7+SasBP+TuAiblk/4jdn93rd3c0QOjhaAABw7OKQBmTzAALhghDP3b0k7R9y5bUjvAxkrScyGEtUQlTV29JBGy0tYrgUGyaqTRGewW2GygyBFGpTADrieNk5BhACGEV2T04MLZtpV6rpiYtQvjU1NamGTgEgjDYfBUoGYXIFQY0DESlSMKTIcq8VQBEZBUnGbjGng1ULmI0vvL66+kIja6efu6LqbaaRkIYnzJrx/QeKoFhyPBa5UElyDE0dmWEKpZhCPGeRKQyqtBXAW6D+XmGql3FN6xfjxCj3wLKguOFNxfk1NDxPjJ8HShGiXRyXMX4dTzv5Xok/lyRl1m4vgMwkR/+xZ+nZ77xrfTQ/feFg0OEV1nS85z/zvt5hRLM3OznIAEqNFR4Q2iM3cfXrkFFyxS51lMt6YvPvxDHRqlYCUPn7t6wEJStLHvjUzjwngaews6xjGzJ/GLuMB+FZvc5PKcYdOLOjFlz0nrqiU1rN3IQTB8GJHNSsdPTrVAQ7jIoFRMdCeKMBO5nKnDugcxcRe4kzPtUU1MoistXrUprqGmdQrfqUHT5fYaPzAtcDKWX/XZ+wkvjQcXRtclsA+8ZVwXJPeiA+R1E+T+HkqsT4E7GX7yogd9ncNVADxhCG65BZdJ9zZ00locE7sCwYt5gvemjpqcp6D45ejR9sGVrmlpUmuoa6lNtPdE9HGzWjoENCLhMsHtcmgqbzhIV6ijvobZsiMyW6yhdhRj6BWpR4NFlhPwB0vy7fvZWWjW3LNXNq0qTiRYR26esAaWT9anMSS8q69bYQqCMryMHHOFzcci1XXNPwDHPP0Ykp8u81z50MR3u7UltEHApNa/lzHfKTJRExoyz0lFuyljvBOoGrgPTM9GjgWOyWnCimdHAKRRef/zt71AWcz+GL8fegT8ejavBBXjYs1F6HhxPP/irv0r3PvxwemzD+kgLFMZBP8xLpcc18PXAB5Vgfy8d6YByX4R5KCt818iHQlMh1cv89310MO1+9930wre+Hc3oTGUVluM5cWMcD7mBmY9wQ6IZ4M4klAIUqhHoQT7naxWOQhS7QXD5ePPJaAZ6At740cF9sUbAGo4GXCm+TIoood3Ai4aFKLrTKO8oQvHWT+VaxtbDV0TJ7HKBsUre4LUuh+jRAA88T9OIw5296Y3GoXBkEAuI62vPPZ3WryM6hANlMsq3PFaBaxptPNxX9ojFhqIg7FTkrMn9iHK0RvBS+rn3oYfSPeC8io4OOmFrhEf8lwe4V+5DzIs/LJkKPsbkpS9xSb4jD/Z1Gyn5Oo8u9vSmqfD1Oymdqa2uiTV5HFkY/CpCwMEokr/1kfNl6d7LaJo8Xn4q/5VeTcU9/MmRtF9FkQh2DbJjYXU1Kf9E97jEh2wcWTkwjH+ZOjDI04flVzqr5EvOV54jz/C1JTpbt29PLSdOpM+Bk7U1NRk9AgeNQQ1SlRtpR+XT11FqwPjR0wHczZUfad9L/FRBFzdVcv/dX/91yKTnn3suTnnIcV3+K+/TAJbHOj95mL+VN2osM1TAWT4q33HejquDRxlx6JOj6W/+4v9MT3z19yireTD2T0Ndni4cHSccdczbzIXgt+gA0osKlOty/jryfNa578Z7rOMZygwPwCdPt5xKX3rpK+kOUmXFAx0t7r0ywTlopOhs1mkr7Vr77Fr8rkpvGKLCBNwuh9+Ko2cZu/nkyQhueDzbk09yBCnOZGGiAi1/cg/DaQ7uOE/xRHjxYSiN3lf+qXxVlvq5iquGUWtrazrF+O0Y1YvvWMFx0/dFdE9ntPJPXAzccf3CnP0O+cEYKqRXcbBLA8JQhV+5Kb44dx1fB8j+7O7oTOU4R1ZS4rIEvcb792MAS+KO5eYp+8R/f6uscl8cX/gLI8dT8TeIkxvFpijvJaKqkbAQfNcBVomSnhleKNjAXxg5liWK3ku+5R4CqJBNOr0M9EyCx2fOwVGU6/IwRHZAS9ve3JhWwadV0GdgHFnio4ERTl+R2Hkzf2VeZNOIn9xTmhDPde6I535HXcY1FnIvec1b8N8zrc1pw2NZSe00GhBavjZi3bQ4gQFyYzzOu0kcmQ3/1bFmT5kRTqWYgKEwiWyNCdzA93T6mo3Vfq4n7f7ocNq27+PUfbaDHg1daSpzNKjFUzh7KbZMNdg+FXN4zC1nv6dgTKhngYvBw6R/18N+g1OWGIIEkU2qo0EuFLIB/fVmEbDCGdLacwlHw+nUCBO2SEhHw4LSKemP/vA/jdLYKWRtqlPofBPfQxcBX8RH8d6/RQidDPZJOXTkMCn0l+NYXB3tGojKZA1nn73kY17ySHmBcNYQ9R46ZPw455HKxkGMo9bTbWSenKSsso2Mn5r0ILxA55d6ijxA+vEeQTOMa9+YMOYYO/gmeCg9iD/irfcUZ8Ut9cijyI7Wk82xrhXw97t4GIwTt+SL4qP4IyzCoRY8CjoF7sIhdHnu79xzulO/ll5Nm3///W2pt6srzamqSitpIltfVxf3DtyVn6BPuw75oL9RH/NyTe6ptCduKUPEB/nENPRF399HSfBr6PAV0NCKFXeQbVWH824m9EeGE0EX1yudqod4KTe0QwJm3C/gBy/IA0zKQ/V7ZZR7uYVs1AMf7EiPPPVMWtzQkKaR6SI/kU5D5gATeZkZxzYaVud0zu6NeqwYLEz8rvN1T8Ohx3c70ft+TH8ATzP6EvJjfkVl8CLnmR3jzZ7xG+We6wm5x7yEgfeDiALmwt0Xyo243G/WoJP95R/9IN0FL3h43bo4ppnNj6+LK1G2wtxyx6Owke+of2kjyHulm+ABDC3PzPhals310ccfp930THqBU4Js9Oo6LY/xGbDHWOJZVrKT2U86QLRPlFtOW5gom+Qx/sb9uEQp1VkCqIfQa8bhHX3q6adpIlsbcNCBFA/nKL3wIw8yCH2FcXQCmlGjQ8p9CEcY8sx9MKBouVgn8qkVvcajYHvImO4/dz699A/+MD3zRfagujruE04Y+BMTC5B+lv+5LY4GPeM6G0IhUgAKFxkKRgSYnk4cPxaCrbfrXJoOEdx31yrqlRuUluwqHBZHgEpVRAHYqInFGOYqJPzcWj4jMW4+nCaEprXpk0hpnABDuAExbdu1M23ZsjlV19ak5Qj9RYsWJyNT1rP6uzjuEgahsDW1S+asABPRVFyso5Sxivg+RNJQdGAWI7196ft/8zfpCIrus3Re12C0A7hpkSJ8eGlZqr9TEKgwmMIlE1GZEAyfKhmAJRQp5izhaBh7dOIPf/hDPJWz0pNPPB6RQD33zst5O45C3vuowFivVcj9hesACBqMJggExo1QyZQCiIb1ufbdNFf50fe+l77wpS+lx9Y/TGM/xRlMj89URmQOEprKqZPVeJAwVKZkVBK+jN0rM9AyB80ABkQXkZGdKCynYeRf/drXSb9bGTgg8w8PInvqmDIO1+rDtHbfu4qSK/wVdt7PPXHueisHgVtHRyclKE2khB5Ms2gI4xFyldQqI1FCGVEwKBSsu2O4UJIcvwj8Aig4EHr4auYZdfy87MN9tp5YY/04j3MQ/GqUxM9z4sA0jJjIIgC2wk4hI2z00mdzByeZ78jA1RCOMiWVrXBk8D0VGj/vaGlNu8HJM+0d0SX5Xhrd3YEAGscar4PLIfTBF5Ve12BPgTKUNLiSiITdPxBRPnFERSCPrrkPCiFP8HjvjY1pMvMyUncHUf4KauW8t4xbRSsiWhjv+senEoFgorppuecgHnWi7zdHKDthryFBQr2plz3cvm1n+uTP/iLdUz2b44sqUxFCspjfTGKdBSpm/KdH1zQ6DbqJOAXG42jwtXsgPHz2bxu1Zo4GlGPm1DZ4IX1EdLSJzydjw01bUEkkcCq/ucU5zpc4PpOUaPaypGRaKmK+Nizc0XaBIxgvBN2Hpc1U/9c/oWM7zcjm4AS0N4OCVQXbS+X3o8OH0/e++10yDh5Izz31VAhlhZP4nSsjIeCBs88qwIXSKwZNPzWK0jHLCMVDJV2FI3M04Pxi/rv37kk7t76fnv/yV9KD9HZRqbxF2m5hIcoT7e9VXoeJDgzfRMEbR/1vcVkoXNfY02HoWVy0R0dhSRk9GsgsOHIkvbV1K1lTR9J772+OdTBUmoa9U4ZeL+UVA7NbkNJsHDTz54xPs6fMJslD+HJP+ISOhk8vvhuXi4iV8AavUadRvimdwBjruXYzHaOsYGfrZWiQOu+x3/yD/+T3gdkT9AdZTL0rTTtV0BGWHmGnd9+IjvQTQhlclSbGgVeXUOB2f7A7HT18JGhu3fr1ad0DD8bRpiPsq4qiPFX6dq+EazTlYmLSu3QajaiYcWS2we/ko8XwQd5AAW0CN98n7bGHNNDZaTXRCxUulxjOUvkf85EXiv9ZJgCKnP+xxzlIRE5/o/BXYVUWuDadpbu2bcO5MCsc1Q2MPRdHmJfzjoe/5ccqu0bYVLyCL/Ke9Or8/VzlWkM9MwgKIhL45ttvhxNm/YYNYTDqwPa3yoLAN34ofioD5ZVTMS41+nR8BX9nrvJz1+I9dDrKl8dxn86WlvQXf/mXwUO+zhFyFThjnaO/yxwNZP6B3yqIOlnCCcM97OZv5pWX++g8NOp1ZkoTwkoHyl4cAd//7r9Lj0JLTz36aDj35VlhoAN3ebhyIo+gycvGs7ZR8EUlWZnuvssjVW59dh1mM3TgJNmNUapD4Gu/900a4N0V8zbqI137O/mwvDv6GKHMyR/l0RoImYGL8g7sgk+Dr/YkApiURJ3GeEHv2LMnTiN54fnnU11NLTDJeiAFHIBxwDX2WV6vIwTdA/iGExw6jp4djK+BId5rvFxGdh0/fjzkx+nTbekOnGqPrN/AaQfoCyiwRlPFOvdUuIoLOo8gKGgRHs/cdbTEZ8BDB5x4H3TFXFopL929exelVO2xnrX335fuXHknn3PcZOxZlhIsMrvXrt31CFsNd+eaGVBGXjNFWnzXcFEu2Jj5/c2boeUJZAUsCcfdgurqYOBZACILEjhu6EvMW17g+F42rgxcYZ/Fd/HWNZeBt6Zev7tpU3qb4Mxa5R762Fxw0n1xrsJEPSKjy0y3CdnKvdzr2G9gxpdiXTrxxQP3xhT+TiKMP+P417MdbfTXejqOqpxJOYR1/LfoBB8BEtavo/fWRDIyimhiDB8Zh9wavsrRhzwX4zwBHFzyRvaDezS1daQ3tmxPr7yzNXV1tqaWTjIp+YbaUjwjJ8vZvgoae1H5l2ZO5di9MnCFTZAeNMKu40VwHyfBuywJtFwiouDqxtxLg0cXt3+NTixNQzhDWnsu0rz2bGoksG4/iPM8aqfOSH/63/+36aF718Dzp0OTZLS6Jh7ip3CQBjR8/dtLneY0Ti9PDtOQeY5mppa7GS0W5vJA+Zh47VzFHeEaUW/wXVqXh2nYBX9hX0NWse9GaU+eOhU4L60urK+nofSTaWZlRRq9Au9j/93TcLggk6Wp0CPBe19nBhw35HJ8b24ZifjuWs5TEqyjuokggWu8e8090Qek2GACfMo5K7tdh+Ope0eEmCGFg3xUJ4nrk3YDt8C1KAlDP2ttbk7v4Zzq5T6VlIncg9xehnPN7CizgZVFuc6qrIpoOnqk9Ol9g2/Bj8Khwfycg1krhepVvD64a3f66Q9+EM66u3AKGuychfwI+4PxhLXwlx8LJ3UK900e7fiO6xd0cLgn6jGuR5r1pLmN77yTPtq3Nz3FaWErlnNUJXwmTujTycoYAV/G15Ehr7FBoXg9CP+VntxHL+8lTRsck64h3nQWHvPd738/HExf/fKXKYdYCF/g++yP+BVOKiYnzpidoex07u6mNA+wA+a+L34DjpDv8hkDvGaDv4wTRh37SeTHTJz5ji0vcD42JNeuMWvQcc3I0ka4Dk5ZCuJ95Fv52O6xY6v/W1atPrYLR8PXf//3073wSeczjDwWV2JuwND9Eg7up/y7gPEHyaKzHF14h6wGFvJQL2nhHLZqS/OpyMaQP7+EI7wenGFx0HSWrS3emZ0ivrmn7t8EHQrc+1o//fDYn8yhA40Cc/cEQEbw4VRbazhhOsgS6uo9H7J+/XNfSI9DV5Yyae/JpMxMZtNiXp/lf26Lo4FdBYA8gmnnQOFv0sc0iE+gRG/fsiV1d3bSSGdaevCeNWl5/aJg0rdAABu6qZC4YXa0ji6tIKMp8bJICUhjQSZimUV2fi1IAXKMgjDvI5TfZ/zqutq0nPKGSHNBIXVeYTiORUXCYwjD8Aqmy9xEyGB4YwgpwTsXBVtBWWmkvP4codna1JQewYPeQPqSzbdy5YwvMy8ECHMUmVXoiojkidzhkeNeoTxLvAghuFooZ65VJGxqaU4v//TlEMZPQIjzYFCmIzsPmZ6E5vzCsIbJycAnosxBpVG35Pc8Rzpgx7wVzBJMZDxASKbVvkvZygYQ+HN4oSUI1+58ZVJezln4OkYoK/xe4SEhuU4jSTERhL+cROFpRN+o0c7du1Mnnr+vfuMbaQXpr9w4FA+ZiPeQsTl/fydRa2T5WmYg45WZ+r6P+B7PwygynUR1moC5kcyZwORZTpGoWLAQfMqYq2tgugHniDhB/DJcFXjXIbMQdjIuo3muJfYBpfsqzPswBulRFK5uGMoa4LL+859PExUc/M5aOJmoMHcsf+9vhY3CyZ4Ajq8yGe9xXz251s7yxdTZ2pp27dhBiv3pNJloy32Mv1LY8FsFp3N3g2WwPmtc5EqczDCHvU4G1+PYLDWNV1lljw8Bk7fAyWI+X4rguXPV3WmOafzsTyhq7hNKz60R4M8t9GB775tElYcpZbrO4+Z4DDKi4iBSIrSZevGubt++Kx3+3//vdOfMial+YX2azl1LUcysg1VFKoJ29MxbGjHKGiYVqJSTwseccyWAHYl9kSZMHbvB59eAzxkcHAd7W9IJ+Ock5ODE6aQATiNCidrVf6k7jRsge4CpEMAh46EyXRiZkD48czG9f5rjDnk/AMDTn/+T/y59+YmnombSRmCgUoYD7JP4fITazZ+//LO0FAX3sQ0b4nvSg8atOOCVp90JayOs4qSOUgWj8xcP/Uxmr/ALZRpcvUApzB7KkHa/vyM9/eyX0v1r1uBswUkHfAox/EnRAu7gPcb5EMWZo5RO2ItCQXVdpya4K77b52FiSVka5O/9hw6nN957Nx1k3ps+3BXzm8NQNI9PsycXcLQl6a44MMYxZhlK3+xpc3AyWB7EWm65L3yWA8dfiyheIn/8I5GwZj745fGWnDpx5kLayKkTiLFouuu3/yHnbn/pC0+lRbU2r6X/CfxJQ8UjID1dxbTrSGtmP+WRgbPApw9Hw34U3CaML/fgXvDdBrvj4YPXcDREVFXex0RUtLzkj8GreF9+4t74PXlR/vkknYaMf5px38FY7yfyNR9F6J41a8nkaYi1juC8cSz5jDiorAickCbBQV+r1EjHOi+8Z+wt87D3jobRBx9+mN5/7z3Km+ZyFNjSkB+zSK0VkSNbj/k5b/FCHhP8k3H9Wx5gtEQepMIiH/Newty65osYABuZ+yco0g/BYzyBaC416J7GoyIij1GuyQs03pyfGXMsKJRg5yp/8Dte3i+MAeA2Hti0t7WlH/7oh2EIPoPhNb+yknlA71zyAuGic0cekkfUhJf7pCLpqI4J9NFdAABAAElEQVQfa2XerkH8ZJGkUQ+m/USMXiXide+6dekR5m9zRn8vHJyHPFxD07/9rfLJut6brE0HhLAQTgEbx+Vv72tjYg2M3aTVNrK/L5Ayuuauu2O+oYAyP2WYnFWnhr836sXyY+7ySI0DeWTOO8Ud+wMoG5VNTU0n04dkIhaCh18iSFANXnuUr2tXMXbx7qUw9p5eKuCxz8Kbe8VexotsvyfA469g7B87diwdRYnuwLi7e+3aOHmkGEN7EB7h+Cr3/tbXwlc5Kg/2kg94ukqOMzld+BuV4M6OjrQHB4wlWpbv3YOSfhfOZGulR5DpDAucMqp3DP6My732Ia4qzzM5w22BW8BIvGA+hw59nN4FJ605X7pseVpJ9HiW8oPPpCfh4bjOK8DADXx2H0Lh5xOdP0FHfM/7eF8zR26AD+9t3ZI2o3OshgfcgT5WUVEZzjPhEbQDX41oKOO4DvmjOOV47rm0kK9LegrDZgKwY8+62NdXXns19VFH/8yzz1DHXYPzC1eADXnhvQhuaBYZipwZusEeTyRIAA/WGTtKedsEZJ+9HVRNYWj8Dn0EXnyuvTP97O3NHA34Dg7l9tRMnxFEUaoFZHOwjWbwRzmv7Y0zedIUavM9qUaexi2dN/AQYuKODhzs++C5maMBtwfrU1bYKHiEbunDlHUM3JqQTtOj4cCRnnQKds7w0aNhYdmU9G/+1f+YPnfv2jQVmR9chjXdRJ6L72wEgSL1Mx3z4BX3vAhv1NHwAYbXeRyyL3z5JWC/POgtw0E5vTopdDNmLAddyuOZa9AT74tMeX198AVxDl2tGUeDWQeH4Qc18LAXCFxNrZofQQp1bPc/HMjqfeyz+lLOI7xBfM77wspLPMrlx0Uc+Pv370snGN8Isfj+IE6qAvj/KLqJeqT0Kv7kwZ/gs4zjeIErwN9LPAo8Y7+luXHgfGvzybQJ/t5HlmwVwSqb7C5ZuiR0tetjPMp1G0XXoDYzS0eukWn5gjxX/il/8b9sDchCyjBgzBw/uie99pOf0o9tQQR9qqurozTYdec6sGOYPedvnZdZjWaPeE/v7V4UoIcAqLCZtB2UV/Z3eYu5Hz74URihNnk1cOQYobPz47A/oMEsC5cjh3Gsee/oZxY6jDok9DDmDDewaomR12n0vldeeSX2Lpcf6kvCV/npZeBI2SEshG/IbdbtvB3XS73GPhgxf/eJ+cvjbZK48Revp3vYz8c3bKA3EIqNPEN6hN/ID9wv4Ri0g6xyHHHKDDudq/LFsFeg7bg3c1Du6KxWdu/FBnnxa19L98OHpYUbyAZ1tzy46JzDccwaDBqKdzrIhbH8Xv1Y3upvhZvB6w54cGNjE1m++3BUT04vfeUraf6iRXohcARlmd7ineOIM85RXbJAHsty7Hfh+DKFoDPua5mWMsASzqZTzTSBb4wTJ7rREZSVG559OvZ4Bo4GFpzBUnzw8Rm/fjdHAwAVqPGQy4owXmKfD126KAPHiUpr7Bp9mY2B8fm196Vl9Q0h2IYRuEYF3Sj2GKQ29ZpmLiAV3CWMZpFN5Ffp0NFg7TdkEP8ZGdgJoe8mPXVhrY2KlqX6emqZiE4FcoLMegwlRo1sG7EZzTeK5iRFFhHeSwT0XhlD0/c8Ls533bJtazpP9P4+iKW2piaEpsTqelUAo55QpgRx5Aqp8w2mB3AkJrMSVJYcWwXQZxHwZMup9DpGo17/R9avT7NnzAwGnUVSGB8kc0568UJx5H7BZBlT4jeCoUC4gRGp4PF3OklUWi/TPMUurvtR5u4nYr921eogKgWP48o4hQvLDEaj8RB7CSxygaDiGIYq7zlnhYefxSkYCH2PdTEF9iukLi1HaTGio6cx4AKss32FMUD83lcPukJI5dy/FThRr8U8nJPM1SiliqLlDfthJNPnzU3P4WiwQ7oMxt95Gel3fPcwOuqOMcIYh3sLF8fTANExoPAoJUIwCM58jMA8irPBGtt71uFo+NznUwGK0k2YQD5+KEWsNTyoYzDzfr+6t46tYBUX9LTLuGy+uFNHQ2tbeJfvf3AdjoYVwSSjhovvCnfhoAAVnpGiDnNRWLsPwlnYO2Z8rsDAww+Q0iEyGja+8Rr4NDEtXr4sHA3zhQ2C7Cq19TYsApNTIYrMBPiVeCmOe/wkxIMyDq1hFI/ScXB0BGNgoD9d6OgkirYnHfnL76elfK1h6cJUAeOcjMZ0jVILRGaagkLnXvWDWzoXTJtlQlEqoeMh6Il/ZfA3mUOgE7RqhLOH+x/BodA4eiHdpI33IHz9Gs6OMP2ZYyHytoSofal63xS6M18vT5/0XEn7ethrv+TwXP/HP/wv04uPPB5dmm3sFhkcKO4hsLhv45hSodB/gO7opncqlMVdGX+GY5ngyo0L6TSuMdqSvtzXXKlxL8QB65T3IfD3ckrF4099ga79q4GLhtMQNa7AtAhlDudHHH0ZjgZ4gLwKh8t49s+HeBJ7i/AaBF/3ofT/4q234uSJrY1HYhq1DFNbUZKqOIZpFlGzchVajP0i6HX6FDIs2MNBonfDo5Y+8RO8AToE4sqfY0n+wxs8wdXGSicmpfOUoRw8fTa9eQoDgW+MgTb9/e98Kz3/haei1tE3LZ2IzDGG0OBmFcGrrYWP6Di/lYdZ830Yh0kbyqi4etc996TVRHbkG2aJCE8VYukqot/gvrQov5UGAAn7p5GRwdzvSVNRN46j8CROmNd+/vM0xFgNKLn3EPGqr6vnR6RxY3h5ua8O5D7nJXDeV/4onxQ/VCBV8OSjRmdMMfUzlZUtGF7zKiriKLBFDYvSXPDGtZhSrNKjUhfzZc7ii7XmgXPek/+8lw1epU+zV8Qt5Y3NHVUUj+CIWcvpFEtRFCsrKUvCgHQeOjTDaGR+ocixWeFc5B7Srds3ER7m+ngZ97Sxnpf3tPRu45tvRi34Q5SsLKQk0ei/xmU4b5ifvDgMw4BxBgsVQ3lVrIV7i5PuZc6LNfhUog6h/L8HbO4g22Ad6a8zUHLDMGQyuWIunXg5x/z37kNGkxl2ZQ47IojARRxQPl1CPu1FkdPh+wyOgNV33pUp0MxHR7ZZdaXwHB332Z4i73BcOmdhJ2KX48i1Aewt5mv2o3Mwqt3TS5kWjt4DB/bjaCiKjLi6mprgs+oFjiGvddyIFutc5r7+XpkRtea8VkH1u+Kr8FZBz1O9j6FEd3V0plUPPoAS/UgqZk97kFvuYzSmg17dAxV2eXXsIfcU54Rd3EulmrkrH913cfIsjrsPcWh2ID+sAV6NAn0nR+MaxYzILb8XtuK/OJ/DOeQQUMlkalYn7z44d+UINxCBIkvwrTd+QZJbafSdWoEzYE5lJUfoUj8MjzOCqK4iTwm9I88o5W9lqlkr4o46gFmXvuf+Ondl95Yd29P2d9+DD9D0ctlyGt7ROR4DQ7j7EO7OW7j6LP7wFI4MdZIcV5VZ4pM16Tp7x8NLzwCbV5n75b6epGFUSy2/PRo0+kGNwAkJZYRmkP1ka41i1MdvfZOeROPJPrNsAnOCe9NgGVoZN3N26iFT4qcb300/+PFGGvmeS00Xz4aj4W6cDAvmTMPpWxxOBsvVJvJ7HRXy/pg/k+cVTnj0KeabOWPkyRhRsWZ4J59ZZhGOBvjo0K2JqR+B3N53OR0ha+80bExKucSjgpLI/+3P/jXH+63hdCdmyr7Yz2wCMsUgm+NHeQs4ZPaR+Go5qA1T7fd1DoP6JRx3K4G9uKhu4f7YTyN3KPhaPTHogHnK68QjcVU+EP3RfGafLdE1y+YofHjP9h2pZuWK9HUivLNwNNwAB7xHOIP4reuNsRwT3uZrYaTOJC5muhWw4f5mydhMz74PH8Ifj8MHHGsN+tgD6NoGiS5jaIuH6o2iSwSAwIsIAolHvBfliL/CI3PeIw67lqbm5rR1y+Z08XwPDUcXpFXIp8UYjfLAkEfMxSi+fNIr8JJn4eH81RvEReWZtGQAUL5gaQc/IuvrQHrnjTdwNCxEfiyN5vI2aVdeSONRfsXa5eeOZ4DReeV/Sz9he8BfXI9j5w6Pi5cvpXcJoh49dDCtJxDpiSzKFXWa2CfGd77SVejYyGnX4X9hi7DH9oKQNygrhU0WfMvki6V3mzZvDtg+jCN57uw5gcvKSmGeZVMrl9FzuUfwGsAuDjpv91bZqI0S6401wPfgZ/LwT3DIbt30XlqFLrYBGyR6ngHL0K8Yz7lnOJjxMdcvEOQJ3kv9QL4rv48sKtYtbzZb3Ob4Zvce5PE08uM+7Bud6oF/Y7pEjC8snD+PgA33DLzkTs5ZPPEeLEKhCr0NhQPm+IkT4ZQtolTl+RdfSDW1tekqtoNwNGtRGavskL4iOM19ckeI42cZPmPBbO5jwFZ+M8D4x5uaosy3C9zvAcev4pR57Pnn0lNPPZ2m4gxjsoFbgRDChDl/lq/fzdHgyuWwMEEoP3vteyBdPHQ0APAT1DC9DSGew3tWCSI/fP+DZDQ0pFEaZfQDZL+jshWKIBvi0SGe4WyNSjQOGiMmmYydcFV+bZriwzRGoyJ7du1MC2tqo6FiTU1NmjGDHg0QXTBaniUMCVTDTsbp+9aRyWxECAlQZFPQZYxKI19HQzeEvol0us70wIPrUh3OjKkwEetedXwo7PVa+yxxeyl2VHCNLMkQZWYSlkgtg/VSOGQZDafSjyjNcL5PPPIYhD4riDsQn3GMGGcKioxbbz9wZiwJKJwv3CfShrmPa8qdBxKmhH6IbJI9pHY9CBO5F8EfXXID3hmDEpHFYZlWELzrCcUxYwC+JzNRQEj8kVbFe0Z/z/f0pveBe1PTyfSNb30rjqiT2amgOp5GuUzBITyjXSVJZSrq07yPgli48Xmshb0RbiovZkk0Qow6SWbOr0zPcaxhDUq0tXrOxT1SAMnENXwC6mNjuibH8Tsqn8LF9Dg9veWUw1xF+d9LSclhoozWNq55aB31x+tCUboCzISxeCGuiDcaQNaPOl68x2efCk6VJO4u3HNltLm1leyA96MhVjmOjQdgsHeS0WA02HIVPeXCNCJNOKAiJQ/YeOWOH8czgsQNM8HG/K3XlR724bh7c+MviMhOTIuWL013UnNYVVkVY1+9QidtlKsiyhqmWqNKU8Ib3NPyo5toR7eI/IybBEMthPkiJ27cIAvnKkc1dnSmD3Z9kI79+f+TliE/a2vo+IuTwZyDUTIYbLRVBqNUkeoHtwaZj0oJmAheQP6sh76HcVznMN+Pvij9eOUphSgD5gOsp/EKHfWHzocy1Y+PYhx6L/2wwMmsPGASCQVMiajexHRxtDiduDicDlzCoTFqaieeCK5/+wd/lJ773MMYaxWkqeG4A3eilhD8d99OgDPvkWpYDS94CGVFR4M4x/R+6WgQrlwKnoA9+CS9qTxLa5KxuOm+i5zSswaDvGYPhtHeHbvTl7/+zfTA6jURlbjCuoooYygk+0ADzbPSb04gIkJqrNqve1rIOKj+0YBMQ2PKDGo4mYOOhtff3EhGw7H0buMnMa/F/Kxh/uRUPZPTeaaCs4UIv6tEjcHjkiKNcwQ5Dg3LX5yrhCAtxJU/+358yBu8/g8dDR+1daa3WjJ4Z5BN6R//wd+JYzjn4/TUwC4cTyQWxUo+65Fb8l6demY2SLOhbACvLhwNOu7aTjYHba4mYmSPBjuTG9WQF4SxCGyMUniFsQpOxH8+M6BwkVZDieQ7ljIVoEg3oeC++rNXiLYOhmPtLiLfGo3yjlCemJN8IGrduVcYwPB099N9jXRqcFY+Kf1oELApkc6uwyF3NMylq7tnji9evDjNU+ESP3Dw6GiQZuVnrsV0UvFDGhW4Osedv8q2gJF/yaNURC/CT95ByW3CKH2ArAAz4qaNRaTk567fOQpL+YuI6qi5EczLDCf5KAwyPjQdWjkiD/JEgNc42tBacrOyFqJI544GlaDgk8xJJVH5KS47V5UrH/zsU5gro/zMcaWlcDQA+3dxhK154P70eeS2Ta7ENWHjd/yu8/d3XiEneF84BaxYo+/lf7s46cro2wWUNmX3oY8OpOcp+1hLSaVOAyYbDqpQ6MgqlH7D4QNumCWlg1naBQg4d/g+slTF1uMrnY8N5OxM39rWFpk2k+GbZjTU19REFokKvvsZBq9r5r/MQSIvy2g+HNSsw/Ra91TjWsevka9LZMR9TO+jIwc/TudxLKz9/OfS4488mszAsYxIONvITJAoh72P6wdqsfYAFJ8JNx8aMcJa/cdmp51dZyj52EsDuFPwx6IoQbgbJ4xOB7P9lKPyI/WP6HTvOIwfOAONuademUGEEcgYNqcrQP9xDgetKX/lZ2HoWWpqXbnOAGlTOW054GRkZegqwMnMG7NWpE2QkagktensyRCpzZF+DF04N5Vo+f57729Lm3/xRjhIzOSrxgDTaWgvHvdJTFGOSk/CXvoUT3WG2HxSo0i6zBq6QndGIdGlismysqb5Z2Q0nOs4TZbjF8LRED0RkG0TkFHihhHRG8i94ZvodjwcW1VUsTcOR8P1EbIByLAb5dlS0qK589J5MhtffvO99KOX30p9V7rT8b4z0b/mwXkJp29FmldeTE8k8I17OMY45T7j8k/IIHmKzRazyx2AzviMd3nwLJ6p74C/N+Gr18bhqMHR0HlhIB1tOZ/aLxIs5VeXeVSRYaGjYR0O1QJ+Z6nmRDybuAqyNHLwXhz36Er3VZz3ZCx72eho8OQBI7A6GqR7aUd6jSxHaZZ5WiKlPioN+3Atwj37T9JiZxhXHmEDzs4zZ9IJIrAfbN+Rqhrq01ehV09su4r+pCGu0yBw3DG4V55RE0ET7udnQkXa8JIXBw9iz3t6zqe9OHuPE/jREak+ZkaDJV/98AhlNBIojDh1JucqzURgzLF5z4c8OdcNvVdeItB0qjlt2bQJ59SFyIizR0NDXR0jwquxBZT5oYuBN87T+0lf8mRhGzyN+6nfqbtaOuf9bS4PVHE0kGEKD64i2KP8qKqqounvzEzf53ObJeqAYbRMZjKmV8g6xnHu0lroHMBOeaKcUt+zWfob776T9mzblh6jfG1JQ8MvHQ38RjoKrAMm7mM4A7ifa9L+0EntOl2DPMb9DUcD99ehYZ+Ad3CEC8sND9Mjjuxh5yLPlR4dx15fXtn+ZnqT/FL80XYaGsuCCjiOyRXvp6P4yPFj6U14wd04zR5Bb5tuWQebE3vFb/2NurpXxr/AR2hLnSl4MnjonB1PvSAcZsBLvcIAx4f7D0Tg7bkvv8iR8zgaWHNuJ2V4Hew77uO9cpr1tXAJhwY8ziuX4doKOjTFm4/gleMnl9CD7vmQH9oLnspkf0AbSgfs4e/aZCFPxP+ggYyeFALqqOoM43EwlREs7EePsaHtIXQCm2We7+vlSN2+9IWvfzU9Q0B1Fk37Q3gwx+wGMbmY42f1n9vjaABhrOUFwrFZ5IgpRcFMIAyAs4yGN1Pv2TOci1qRNjywjohUQ7oFIl7Ay5idmkDqNIQbXnsQzc6e4xAeEnpsFuOEN597FeFo0NlQBFIbPd/54Z70wc6dydTIxUsWx7Er03EGaByqbIl4wVz1irJTeuJleCpkevuCQEN5DrQJhgueh5HcBRP8BRGjlpNN6eENG6KJzFQ8XEYDwlMJcSuIIwsPJiRm2ChE5qFiotKrgaLQl3BULGRWKt4SzQmQ+a+++904Ou4LlDdUIPRk2n4vh2fuoXR2rsUog4SiMiTDttxEglFRl1h8SJjWDn+EQrSF+rTPPfJI1JRLIHEFbWQE5lgyDRlJrpDqodSQNPNDZUnGK5zy76ksGq0zenGIaOY3v/3ttAqFxfvmTFrF33SinKE7jms2/S8UU+YQRgXPjitzlcnZRKwDx05jU2OUTsyaXxWlEzUoLCogenudp8wbYIdyoVCR6dp0zoiI4/tdPf8yDm4R3n+7bHus4QdkYny8f3+kMd1DRGrdmBItA3NsYZHVVzl/5onwUXj62jkKMwWzEXX3wP225MX3T7a24GjYHopiObjyII6Gu4lIua8qWjrAbrEGlaI4Fo01eDRQ7sRg8sFcYV0Y7za9QdEDpyab0QBNmM78zrtvomQXpkVLl1KysiKO8xzBKBoeIMoNWIoxcmeVWqOAIo6jIe7Lb2+SyWBGqU6GcZZOjANXYXyXUCR2b9+Zjv3Z/5VWzCxFOayOsokiUk1VqsaDYxMmimsY9sJVPATOPgBAHGd5lTleRoG7MnQ1DVBsSoJCwoeQ5tbMp8HjxNROM67WgZ50Dt/iNWpdp5GdPrmcEgrmQd5GmkQ2jHR0o4AoxzDnWfcNpQ9wNExmwlcuZwbqv/rOH6QnMdgW4HQqQvn3KDaFvnShAfHJ8RNpIxlClpRsIEtl9owZ0BKLZa8U7O6fPEUlQjirJNnAzaiXCrmIIk6aLpgZk5lw0lFj1GjXXnjN9l3p7/zdP0rryMwyhfUiZ64XAPQCjoqIyDx3sQnZeLqHm9mhYVYM7FGR4ixoBfwMUvMtEdsPff7i7bfSRxh0G3E6eC3BXl00vyjV4nRcQMPMKTgaRtjXGwg4HQZOnWQVlFZFsPAH33mKi3nzRvyf/ePnrJnv2QzyOuUu3XRRzjMaShhrMGMD6b/6e3+QXiQdWR59A74pfprV4PFOE1hHlDgAL2EpfajESLdnSWO2+WnziRPBm6zNXkt5XBkGi8qGMNfTrzIjfwhhz7vSWfRUYCz5XZadYuotdMYibXo1EUdDI8rK66++Fo6GBvj7ypV3pprq6hjXLukqciwjU57hLZY2udfSr12zNZJs/qSw90gq6dSGw0a65Q86j7Zt3kTpxJy0dMkyjjZcRFfyueHAjNIJDXZoX2VUviIvVxlUUZanqxzLe4MnAGfv7S7o2L5I6cgmIlJmezwEH3De1nL6W7+U8RlgDHz8vfLS34dy6l7ykH+GUwy8hBBJsSUiBq8Uj5vbWtNPKW2w0d+Tjz+eFlZVZbKJvZN3ixcZX2dc6FZZIj0oO+RZjq+8yhzElkBk8NHg1kF+EGXozddfSw8z9oZ1D8WJJOHAYL3yQ/m5ck64OEdpyc/FCx09yr+Q2+4p8sTvO3fv10u6944PdqeD8OGvfuP30r2rV2UprOynp1KYvRa9khwHmMg3xcVwfCmghRdjeWylUSIdDe6rDdhUFJubmyMjTlp76YUXU0N1TXY0sAo3c+eHyHCMdebqfWAK2X1Yh7CVTj12TfJQV1A/0RGi/LCm3HTmXtK+1z70UGQ0FGG0DrImx3bvha3KfR7xi0g9sBYOwkCcD7wHlr/Kg9o7cfoCF3HGLBlL7+7GOHKeA/1XotGYMFV2GGUt4djjSP1l3vnxdMomDUSNdUv67BFRWk4vGta7j+joq6RLu/alK5bTR2ElJTdVQSf2YpmCgVeOw0N5FIgM3E3zdj+kTU8uiD4c8D4bg7oPnipRwvpNK9ax9vbPX41a+LuDVrMGqIOXmTv8QL3JtSgPxRsdHM7Xo0CjNxfwFlc9+nKY+1oyU8gaJ9N8r7unN/3k56+kNur5n8HRUF+zED6DLqVsG81OfJlYiMNiEvAvLINO7f2QZTGUwEf5EvXf4BZ9ioaQU8WspWz2XHhYN/0ZNqefvP5e6r10Lh2ndMJrw/yU6oFNZTkn7+BoIDasx5o9ho+F7ONLGkPjkFvQqXxBOoDbBs+NXgR+zvc96QIhGidijOCIvoITpPPC1XT0VBclFPBhhtLRUI2B+T//y/8JR8PqOAL+Onzf7MS8N5mZCMLdgJwyyz21ll+n414yA6Sr5zGMVmL0yjs8htTSNyOvmqWetqMzScMHJhHyk7djHOWJsi9zakIb4I488iz0dPJkc9qHQ2Au+tjzX/wSR3DPi3LCYcaWNyov3E+GymDDs3/L1+RBXpmDUJTJIuLyYh0jeUaDOttqnJr3EwHXqal+r+7iGnSqqXfLw+S34ow3C31M2g06Mqs4CxKa7aGxfBI+8C7BhwFOHDLr2fLq2poafpo5GuSROuqUF0bQ5U3KFXmY/CrW4624h/eK3eXNOJmIvf4A/rURHmlW8nL0jtpaTkUiwCH9qOc5d/m59ooNazP4ZLIwdHbGZPgIosmL1bF0ME5njGHo9+cb38Bx9wua0T9HT5LF4cDOMzw0l+UzwsT7ObZ6kIa6csYaf5syKlPUy90PnQjirPK3hXIbM4TUn5584olwOOYZSrFnTEy+ayZZrreHI4A569BTfzILymxacUe5ljsy5GmHMahfQT7dQYbj4+vXhwNGnqczKs/AjmwS5uNmhkMBHAjeCPy9lzBRLokzylvpTj3djIa9wN4M3yidoJRVPUMndu7oEt8cWVkkjmbOmCzoayDZchJ5tAEiLzMVxJ/zvb2pBXr6ECf4CF5K+/M1sK9mHmjvlWK/6WgwSwlJHHI2nO/AMeifMcJRB/7YAkAd/gbfLeUEpEvYZWYKetpHdx8nsYH/59G/X/zOt9Nz7HFFTQ2gYNZjNBMT8+/P8HV7HA0htGHAADcDEAqATga3GGZoTeBWjN0LeKDC0YBRV9ewGBcum4zyzreDwEPx4Xcilkw0jzxJHF5h6HGLIph1RNYhmJswoi07t6fNm99LdSiIEnodUSPPvZWQwqgDMfUuy0x8L+8uL9OS0TlvkSOQc0xQ6FkvgOFfBeF+jPf/2JHDUT/T0NAQPRokYrnDBJYdHYU1KJinjEVvs4qKglMQ2BldxU0iCSOHz3yWcI5jTP+AGtuZeECfltDxnuuEUBirXGWRF37L39bOZYCApJlzCAX+jTReuCXTYcxsHjJRCV2PnzW2Gx5/Ij380LqosfW+4RCAAWpwCXcZlQP4vjByrjIu4RJRnhgdYud7vqcS23vhYtq2c0c61niC819/L626c2UQXCjafEe4BvNjXJ0MjivzViCpsITn271hznqLc8LS0WCPhpOnmqOXwkxKJ2w+tJD6bI3KYEbAhokRAdeAy5is9/Nzhb6MKnPCuCwFZ5axUsCeXGZPJXJTdvWm341R9DmMVyOoKlTijLDPBRCTDQYlFgqvDB8zpULjSAeJKdkquTLwUy0taceuncmjOY2m3U/KtBkNcfwXMI8O/gh9oB5KMwCNfXaP2UAmDC6yFm7GQnAMcGPLHgoJ/dtx3fT9dze9BUMvJPq6KBoELayaj+KJQe0Z3RiS4yhSLaQOFEtL17UbC7fF0CSTYZSFXQdxbzJdvRKlrPUKitaWTVvTnn/xvyS6SaT6lYsjo2EKcyiGJlXmL3EU5iAZEJ4tH+UAzN9kpkHmdBF8OD88kPr4jsc2XiNtdZg8UMXqjIWVNOYq4PNBHAgXUYAxROYWpOlzF6DIaGCiIDJuEXM0Ve8WzP4c3/mwoyftOI1yxhLCa8HTn/3jP07PPvJInCluvwyPGnSfdRxqUB468kl65ac/SatWr0lP0VNluim74iIPhYxo7P6Jxz4ryHSaheLPeAr+3DGlUhHHVTK+KcEqcDYf+mDXnvSVr3wzHA3i3NDgAEqKhihwRUiaRYM1yAO1lEwAv1PMGKCGWkDccwJKFEhGM8jD6S141wEMup/v3MsXUqpBH64hklY3d1aqmTM1TcOwvEH21034pcelhVrCnojn2YLkb77myp/jM//hM/7VURQ9Gtg7G20e6aTRZiNla3x4Zew3/80f/mfppS8+GwaZZTGqLSqobnJGY6wBmKDihePLzBNTX7vh69Gj4djxUDzue+BBFEUaZWKw3EJhU4mR9oV3pnAyd3BcOpZepVXpXzqVnpyO/LEQRRHAplMoK2++uZFO0H1pQXV11gwSXq/ykDciDscdv+GHhnIjiqezVEXrBrir8jyesXRMyS+ZUaQHexTcPpSJXbt2hcxY3LAoLapviKMWQ21j7Y4trwnHMuNogDrHIgxPGBqNRDNnlXxSmWXasXASp6yXfpfU1JaTJ9OD8JhFyI/pOL8sJYnoXuznGD66U/IaYKIyJb5EZMf3+d/99uERhDrhxc2Tp1rS977/PbKNSsa6hleEshW8UMOCeajcBg+Er/msoeulDAnez3xVTsVxx9TZ71rMzNhP1P4Vzvje8NjjoShqgGZ9K4wMY/jz2wINll/ZT38bEUJhwz3k9XEBG/da4OkEP4eiuGP3Lo6/Ppy+/s1vUtq3iq+BcyqK/M7/Yv38Lv4DJoSoSX9nDYwd8oKxPdPcU3BuQhMajuoO1h4fwxj19AbT9l/4Is0gq6txxOJwYr907CiHsmw4gMo95BFRUgnMJup0BM46GOUJOX/QALBXy8cfKz8Opwu9fenu1fdENslk5Md11uVcJwgT9BijX8JAXOUOAVf/9nWuC3ifMI7AF3mOx0Pupu64o70tlWFcr6Vm3R4NGudGYMUR524ZaeYwdT8ZkT3UmAw5pDGhHoUuorPaewS+QhMHmffbREmlj0WLFtMMcmWU3Bj5HiKjoRgeYX8WYX0LXAy5JHzcy9gVdobxpKVbzFfj0GBHKZk6Nuh9d9NmIrzUZSNXlXuW80ynN5cnjN3CqS6vcv0sNnBBY1cDPfQ/g1TSMV/ySEYz5XQ0FMCPPKnH06hexknSfuokacZPpqUN9TSYm8pvCYoMEEDSyCJrYAKOhgnFUxjXpqo4lpjjJNJ+LHswO08OYG+dCe7z5CnRYf6Vd7akH298j94hHamp47QzTA9WpVSL/rGQkrVZOMSLmec47sGP+ZSFsAT1lxvAJ8vqU9fIPgvnPJ+F3gaOGhi5pe5DtuHwmKOhneMtPz5xPjXjYRAm/TyWVc5L//RP/gnyZU2aAi3Kez1+0GqPiewNRMTWYjiyvzbBdH97cDRYRmUE1iasTz35VPIUF3mWvGs8jEO6kXbNSNNZJ84HLYP3oe+xn1jWwJ/1CHvpmL91+LS0tNLU/QTBnxNkBdSkp9BVLTUTX+Qd4qFGrzpd4L9/izNePPufV56FEPqv+8w9bdK4H52mCT1Sp+1K+MBajMapBFaiSTDzNWNLfPfIWOlUmtQ4lK+HEQrtSFVoNe4KtyRjTfmBLtF24gSO6lfTFWyNhiVLcFSv5ASBuuBF6gzZNrJPzNnfeZKN7yurpDl1Eu8R8op7ivdmUxaUEkhgHz7AufPGG6/HUa4rlq/AWb00TkjTMe9RocqRjFaZu3CQD/OZNO/9xusA415hVBMoM2NC3a+coJh9Fl4lG3z7lk3paaLddyzjCMoIdHIyHXDRPvA+0pQ2kVCOoBgyK9dxvIc8x/GFn+UFQWOs11NzfvSTH4dMiz426NhmVunMUg4BYMZkjmMPebL6lHxK+MjrhYv066XsU8cXD3RMHsag/jn0ugKYP75hQ5pNSbvfcd4Z7DMe71jiUMxT2mLO/B+2oPagDjUDA+oROibNdjPwo2PtIPL7K1//RrrPHg3A4Rqf5TzWtTvXgAXzCyc+99Yhoizyb/W9WCMw1/npes5hF+jE33tgH9lHKT2Ho2EZTp44ppbfx2k00KRBjdA9xE3+EzdjjxkLIIK02jroiMo0/i7G0eDRlkfBdZ0wXee7w9HQ29uTnnvxpfTsc8+m2VULWEcGzxjLTXXsz/D1uzsaXLyUnV/AQ2T0LUE/ggHxCYrENlKXerrOpnkY1A/dc29aWt8QgmcEQRyePpBfJqTQMiXMCK4qp4pVIAsIo8CTYK1TK0SY2Php6OoAUfUd1MG+FY4Gu/s31NdzzBchUwnRjXcyjH0LopF49MKJiHorw2HBx0Yiw4gFkSUkyyrKGeMyDPzHL/+MGuSD6QmYd3095weTOmgEXUJXCOhplplIPCpJKmwqnCpsMtQgQuefIx+vVb6cywkU0NdggjYIe/SRDTCoihhbpJUQNXx1gEgooRypIDGOxGBkzVMAaHCfCX4QOScqiUjDSkfDT3/8N1HftZ7Ii2m7ErUREdMcfS0MhFMwb8Z1/d5PZhMKM99xPL8roesZlYn0onDZo6HldFt6nojRnSvv+NRBEn0NmDuA/uU+AGc9jgwNM1TQK5DBE4jRvRBWEmU/qfymY5v6epxo5jT2YQOwsQ+BTCcYPnOgxB+nE/AAVs7LMd1DCVxmqlLoszAxSh2Mnde9KIdNJ5sQbI3RXMrjlNY98CANfMAZpiujtqeDsPfvzNmjcocCChxcgDBSEGvEKHgUyCrpKmAtzHsvTcjOksFTikGpwrWc6EIJ3xlF8Bgt1tmgk2oi+BwsivuwjVwINISNDNwjXm0OFoIIBcwma1dRHA9QOrFt23vhaFgEri9ZvChV42iYXEy6LtC4QfR7pB+jAX2oAC+FPMu0zutkJIzinR3BuTAC8EaZAO9GZ+3B8704GralXf/y36a7mMXi5Q1pNuuagRCczLM9QM5c6Uv9I/1pSvFUGhMSRQLvLZfoB05dRIxbSWM4e03lx4IL0uo4V3zCuElEQTE4wdl+UlWHiChJD6WcODFjDrjIfAauomXRb6AEaVmAkLtJOvTZq8NpW3Nber+VG8hjefL6N//1f5Gee/zROKVlBPrTQDW1Vvy1LOUwzqOXf/KTdC8RkacefzyiIuK6n0uvgY/ZUDGsuCF/AeFjXxU6gUO8p3FhXZ3RX73ePRi6+/Cg792zLz3xxDNp7eo1kcrqqRP4AsANo9koKeCh6bE3x0mDCGruPYnNtd7WCFfcjjH7UeBMLdy6c0f6CIH82tY9MbMK9JEqTuaom1fMY2aaUYLyR1RuHPDUMApHQzbdsZVwjzH4fLo+b5IDzuUBwDjeEkXVUyeOnu1PO5svOWwotw70j2gG+QJ1z/LmqRg46uP9RIFGh4gUM5Y0qvMUjpSu4DQaB36aUm5E6gBwaUIRlWfde+/9IfSnoOgZHRL+8lydjkHnwFxeb9ql8Ba+lli5BxmNAiN4QTgfee/Y8eM4kjdhZHA8GfxxDUqoXcNLMa5dt8qyBlaUdMA/Is0e/iDORPkduGfEtQjFUAXRjJxBcFFlTiX6IBlZ1vJPx1Csq61NddU1aR5n0Kuk5yUj8cyA4pAySryxNEIHY5RmsM488pKfcy6PPwsf27x1a5ygc+99NCGjy/88Il/5MbzyLHmX+Bl7OMYv3T3vHwq8z8xVPqlMzPoX6ZwtTJZp/fVffy+OFnwBZcjz4QPXkRkaAPJbZZU4rryRbyn7QtZBt8LYxpQqoUMo2B5naImZcxc2B3Q04Gj3aM5HH14PXpBVxdjyMPfVcZUL8tdwMDO27+VGtPIjf4hjmcJ+I8r6utjPD/bQDLLxBDXlX0urOJ5M3m4E1ay/LAIl3WaOCx2zN+CfABoDAL4OPNxL4oXxeiKO2FKME50OHsd3FNo6hEO5DP7zDMeTmRF3jcCE2ZdGo+xFYADCaKM83/m7txq8Rp7k5zoNfc8H5BAwV8G1GWTTicZ0ERloQ8UH2FvTpR1Hfm5Qw8wOFWJlufqEeyn85e2/KvvMMFH55mshg3U07NmL/Og6Q5nC1HT3KnodLF8Wa9ZxofHj/JHOyCYwgvm7t9Ke9xS3SoCD40VPDHrsDGE4Ch9h9gn0pCNc/lZbV5dWojPpDNBIH7pig1j2FNqxtFVDSUeVmYnChd2ITuvD3M8MCRvpDjK+OtsU0qKvcf+t71M6gSPjTso9li9dlirRbWaSYaq8cGzxx4d6jXvrJb4pT3UCCncAmOl/0L+R0+z4O449psThdaK7Z3HCPProIyj/HO1Mad5E5MjgpQvRwNQhx5HJN6l0GmNC4+z5KHMcj2PBErdSmukW0udmPMzUBm02DGzq6Ey/2Lw9/RRnw/mz7VGG4LzurkypGjlVBz7MIysPSZbGA6fxcROAxPyCb/GkLNToV76pAeeOhgkY7plsB9eg5VGiw8MTaAZJRkNbz4X04aHedBx2hFmccCOlOxbOT//o7/9heuCeVXG6Uil7rVNvFGatrjaBOZvxO4JMFk7iey+lEx1nOtMR5IjR3vXQ6lL0AnmIMC8hy0NdQ15uRDYMVHAneI80yzjqMFGKobxDz9CRYSCuH+fWicamdByct3dWdU0Np3R9PjIaIoCkUeu+Mo54KL6Lo9J9GMMBDz8GKt4r5wnwNA3fboy6wzi/DNAoD5YQLLzr7rtCBqlrFvIdHV82tpY/xIO557qg8A+HKWsIHRv+w1ZEVpHy2xNiNHav0O9gCfi48o6VBCNrs8AP9Bpfhq+zgtBV5QOWy7mO0I+Bh7qwMJHXq6PoYPch3tuj4e133gpatXTCviRxFDtwHoaGnHuROMykbFTqqRLBJ9gP6V7HYBjfzFun6SD3sP9UGfTVz16++fY76QBZ248RLJQPGJQ0M9m9i6AYNAcBBb90H3TAyAfkB/IbjWrXIezly/JW9XmfW9s7CHT+KLIpnqfBZw1p+5HZyTjqwI5rTzyfM/khZAEZwMp5v3NXtrsPuT6lPFD226PhzY0b0zL21NI+eaR8R547DHycq/NSTsnvtTvEmXBggN/yXempBD3DLBzxwSOzPT5S2Ow/QGYZGb4vUip0L6VGzvEKWV9eykzXrd3oPXTs60iW9wqTzCGSrSfgwf199h46AJSt+w4ewNFwKz3tiR9kD7uXBSxeXjmCA1N5Io80ozAcVcBNvBcftQV0PkqryqmbzMc+PleYu1nPx082pS6cbOcIRlzAznwWG8qMhpmziDKFtHEVgZW/8ux7n73r9jgaPl03jBSqzR+gM8LievoEJrL5nbfTSRRRjasVDYtSDYaRNb8eY2LtupEREUDmlv9eZI7XIF7GuDNHg0fn6dFXaTU18ACK4gf796QqGgPV1tZF522PCIsMBe5hiiCY9kvBBvHlRGJ0x7H9bqRMK0D4rgqYtYiX+vvTpm3b0qnmk9EMsqqqMtIQI5rm2CCCjDAiI4HMCHyQWbYlIUt4gcrii4TI/XwOQ5W1tnd0ph04SjyyZg01Rh59JgH4dZXnSKF1TlwqKipYpjM7VxmfQkY4idTh2WV8n1V0jNQeb2xMmzdtooHaGo6f5GxiEF1CUMHVKRJEwXclbufkfX2o8IdQAHZ+J/PsZmNKiD5MK9VjKbF8HkXU1CIvCVqCdz9V5ASAY2bOixBJoegKcz+Qict8AE7Ayj31GK9z3Rwx09KaSqeWY6zfE44AFWTHlckqeBSeKqDuWewxTElo6ejRgHEf3QOdKCoz3u8yTeU6OzvjYSOi2vr6aNY4DZxxGOcuE4x7uCDfBD4yUxmRfxt5lRmKn2HMMH8VAOunNS6OwmB78FKaRrYED3dtTXUotqbRGdWJmj3HZPPCoBDOPDQ2nKsOJG4WTgaVAA31SAOHYdnw8KOD+0jzmhi4XklkYQ6e4rJiFD+M2+tDRHQHMOxGwE80N+uaneMIYLoGosJqw9lwQ22S12WsZ/TCpXTkwMfp8k9fTWsxhIyml1E2UYK1WcSawFj6MqCkwPwVChFRA+4sgbKJcakbRe5oX0dqpixiPIFe9+AG5c2m6U3jqESF0VXmbu8VYVc2p5zTRHR6kb6IY2n8jWupjKiUzpRRBG87zpJtza2cOsFA2KB5RsPf++oL6UEEijW/9kLIGilh5bMPGpgqK1u2bKG7//J0HzivcNLgij10H7lcjfgovmX47zrc08wAcF/9gpkS0rkRDftOWHJ1HOPi6CfHoaf7UXKXBO+CeNhb9hLmpiATR2/haMC6ze7E2DaC9Az3rAkX62StA+C56a4ebdnU2pJ2HzrprKjR5RhLmpDVziklo2F6ml6MExYnA1o8YzBWTJ01MH+vjGp4Znnysuw9F+Df2SUXuqFjCP7RMwRP5kSPbScGYp8ysZzSi48/kh4ieqrTysi1PO0qQlucjYgMOKRhrcklLugA00jV8GoELu042IT1MhStFSh0pShiGqN5zwTx2/3KhD9CGDip1GXKj1F3yUzYszZoNgwOXlt7rCPgEsr0dBSVxTgZbAxrmqPcRKPXvRcZnbM8J2DBWLF+x4OGwmkHHqqgK/hVSo00NJ48Gc6MKRhnniNexWMGUfDgXUQtIoI+Nq4DhiwCFirhztMUULMcxBt5r5EXlRzvbddwnb2nMXzvMLq7kOguDg3nbglYKG/Qu/zX3XQ94Vxw22Ly2b4613DEsrBwNHgf7n+GlOa3UUR1upiVNZNoV8yRuajMyrNUqsT4rPdOxofFUXm4CpFGsLdyPyI7CxzREWR6duPJZs6gfz/6wKxG+S/HaBcuzlv5lNOQPNn35OPySMsE3Mvg7cydL8aClH86Ncxo6CWz7JNjx+HDHWkDWUo6TZVrGi/iieOE00Sc53fBK93jIE/Ww5Aq+teBiY4kDQCzvmxUq+xow2A/daqZXiElcQpKBeUwo+gb8l9lqDxMnGGywCyTf96fSYfRJSy8h/xKeMkjlClm3LVzLFxnR0c0tDTTbgUGhidyCIPYQ/BDGauDUryXDyqTlanB38fGC/7DOpX5fDFg455qMPYhPzwWsoFIWg33UBE32upzpGA7ddYipnlPnXnS3yQyIpU7BPPDUeI+eiSx8JEOTJc+dPhQrLOysjLKQXVQabSoPNu4UL1GR4MnzhgIcv5hIDD/KzigpBuVZlPrLVUxCltMho+ODbMFD0KvNmz1uLyZ02ZQGjoZRwO4wUyFd27oihXqNKKHY0ZDT2AcMo/3lHvuhwbpRLIaeqmz375zV+pjf9euWZ2q51cB98kEYSiRQI5oxIl7LBYnCNk33M/G0xoG/IMstMRK3JLHZLL7FrDv7O5Juw4eSduQgRfOnwX2NE3gWjmfZpAzp6V6jL5KHA2TgfUE8H48vDx4rQtg33TkiyvxLjDKPhNvWZpL8HtcOiNwU2THWyKr23r6ON6yNzUyvdzRUDtrenqRxnArKBObiU7i6VLS2rD8H1lklpYGqU4N4eTzZdLXu8GX5pYWsl6uhr5UjT6c8UNEKA5mebi80iis/DH4GzPNZZ86gvzcwEm+F/IYM3LNEGptbU19Pb2UCFREKc+smTNiXtKo/EVa0nCUVi3pEe9D71LP4xL3c/4u3oczkvkrPyxv0FEiT6whmLd4Mb0IcHiK0+r7GuqOL4+SVuWzYYzytzAPPsT8lS1hM/CdYviaDuw2ZOzO7Tsi7d1Ti8TLqsoK6Ag9Gnz15zFmNsl47b18T8R0rspa9XrxewJyT1kyEYeVzo5jJ+hdsXdPON1ra2rQ9WqjZFPZp1NnEvtlcFR4B95DZ+p/yg5h4B7CqONvrR/T7NXrS8ooPYR/7KGfWBNO0zVE7HUElBMEsHeS+rr2gXqq85XXR6kOc1UHz0oCLOkEZ/hMnHQt/i3f96Hu/g72wfUbo3HEe5x6B775XXVgxw1HA3OXtwmH4O/8LU90vLBXgscLrgzR3Rvli8FCy5Rr6+rSasq/ooFmgDsbL4J4jKXME7d9doTI7mB84csKozzPTAv331Idxzbj7viJpnDiP7xhQ1oOvYhvOndDXgDbPMNb3AtZIu9hW+M+3EsYerkm+aq6rXtsUKmdQGHjySaapo8jK/m+4MHKDdXmCG5AFyPwSB294qf83cGV5MIlZAe0aaN0M5HlZZPgy5421oHDzkac3qcPB1h3V3v6u//5H6cvUfK0oKqaFY/RFGNlEM3+jcl+Bv8B9wX7734JXhmWjMT/vCZRJ847GF1H0rtvvpU+pFHNkX10gcYrP4/0EAWanuGbbKzHTl0jBfkWHLAchaAMAauir5AfGhrEuwbyBNABOIQKJobSC4mlHqJu3Rd7iGZMDiK0EZNRfxv7KXyKSycjzGwSgkcY77VHrKh8ycRMLRRpZTiCQgTxbN9Q7JifTFYGaJTFTvZ6ElVAvExZktDBboxFHB+MqYKkIBAeemdFZAEs4UgkMhcdEBKkn13GK9d6ihT7KWVRNmHdpb0lgqBhTsEU+E0maIZY39TwnDrWFVIFVXBDwCF4HM95y3BEeNdkw62zZ/pSRdVsvInTQ+CpFMp4XKvMI2MgGO0IfMdXqbCmWQ+wMBIunrIh7Kz3yuHmd7q7iCKw3Q1La9NUInweiXaNvZJpOF4QcCiNmYEh2ajEqoQJk/D8MW9TVE27NHKhUiGcFHbtKOgKknqcU0bmNEpdZ0kp3ZYRaDaY0ptYNpka2pgjTaDYzx48kgMDl2FQNLUCJv5G/JQPykhkwldRGIdosjcVJ89MhKZGoPgQQoCxFVJRxiIDkZHzLGyFV3yPeSko3ANx1FpYGYnp2GfOdlC7T4MvjMaKqoU0wZkWGRJDKNhF4KLNmMQbo2sqjZZVyORkJyq7zlEmO4IAGDJihJI0iWiiRyBdvHSZRjiesV4CPmKwDF9gHM6NJqOhhLMjx1NnMeE6AoX3iibQLRtFT8/+APDs54SEa0TgR+jNcJOa03E8Y0Gnkgs9qaBnOK3AWbGW6FY1RmLBdUsaUJSBHXYx+J0JAoWgDF1l5ZZeLvC0D2Xz2IXTqZHeDNdBb8nCExiLisalqTNmhfJylTVcC0cD0bDZU9PM2dTgo/kNM6eJODXKcDqYXnYV2LcTWdvZihPuDEoaVQZMOZwNiypKadQ3L4SCXvtQKtlXlQbx7QpKlkfOlaIwzSETRuGhEeclbMXhMLxEBH/H3wpK8e8KjjOVrWgoFPQKX2Kd8jTxUfy5AOzPn+1Ns2ZXYNRND4UqUmMp/7gxSk0xR6iZzjmplPPPy+gBA/5JZwP0M7nSez4MFSNHRtKAfPSzuHD5Ak0DOWLUrGjeq+Hw9vlkNFTPmho9Gqah4N5C6I9D0cxKJ1gLY+aOBhmMawuHA/eOy/VxKfTiQ17njobeQU4A6ehObzdRrsL7dr+Qj9bOmMxpHnOiX4ylEyp4ADgEq7zaWtn+yxdxFjEP4OtRf5ZPWJ+pUXQZJRTSSLPnzQ6HaWRegcc5L5BnjQDnIhRIo67SpFEPYeylkyZSveGbztljCXVsyCPPtncC0yLK1mYA02wPpRnpUZoStjAQ+MJUsh4q0xSUMcd1fEvIjEQNUpOtw0hnltlwptnLa85SZ3/oxOmE74vjhSvZ15kRMR6Gjj1OUONIZ6CKovhShvIv3V8CFpdxfogzobgF7894r5/bu0AD7BTKVid8ckmdp03Aj9wwL57krZH6yp/yZLMJypBfKkLOXX5qDyMVeGEhrx+VGJBBOlHNQujo7BB4caSass+6ZPmsfEocF09UsoS3DfVsYiZsPKdcZ7MoI39wvt7zYt95eLPRzGIU3Wtx7ncxzi6zGUphaGEYQlMxb8Z23l6ueQp8zn4AGjsX+nrcxnDAGiEcxdg1+8gIvs6EAXi5GQHDozfj2LlpOHqco7zF+fpjadE9voohpYNg7sy54VQZgGf1Q69G/vDyUaaDdxP+cQUdop8MKZVg6/uN7hYx5zlkqEyGN9vjwXHsFTDK4yY4U1xanqbhwHL9Ckiz9Zz7EFk7xeCRRgVAzOgIYIWyy3oswVRGumdGGT3WUBqR3icRQQ5nDXvnejX8QyFnHH9vNqFy1H1QZ3DPwlHN50aQOztPkwJ8LZVTFjB1+oxUDq5m8hrYwFyFvaebjKADzJqL84oAhXJrELg7B2lJR4OnbFl6Z7lNAXSjUdOLvtTY2BpHQM6dVUBvkuwUlAhsYByVl0wO+TTK3t8AvzxBQ/mv/FMfuwQvuwjPsl+FWYNZ5A5lmnsOArMBylPsF2SfilK+o3JeKL/CaWdwSSeG2VKWsJQgt+fiKBe3dTz1XTgPDwQhmadwtzRD+pWPyUtH0Bs0eq+BDzrtiqHl0WtX4FXUV7MPlhbI7m3Ee2s8uENmg3SiM+YmtH8LBynAg1cLI+jKe3HvqyM3UveVwdQFHx5k/nwlrhXzcfzOmJYWlZemShz75Xy/ALwyS03dNi7uLQwMbigO1WH5N2g7hCTv5ReVjRwnjCyhUfMIjmgdDbsPnE8nyGhAzCWdvlORn/V1GKumyIMXoZfABwaZ2y3gLy7qXDBCWoic1smmw00HUMeZ09DrzXTXnXWUq9CAEzzzKPGb7OsEADORe5bIA9xT+Ku6qMZ99G3NEwAAQABJREFUL1kFFy90kwkDbwMfr0CbN9TFp86At9Ej6coAvI59xwFTjM5RwZ5FPbtOBGgi0zmzLKpCcH8K48h3gsfAe5UB0nbGbzIHpDxHupDedDZ2oqcK9rq6Spo9VwbvkL9GqRM8wAxTA5NTZs1N09HZQveCDiOjDDpT55YPK0MGmb+8WpzQcL9Ime8wJzWpD0xDHylHV1U/9nvaEo6lcaoeMZm5G6DQWDcrIOgJHJTPq/uH45F9EO7SlZm9La1d2Czj4QOz0fVwJANbnVvSkMqQzmrliOMrt9RVgp6BiXLpGhl2ge/s6VX+1hmuTutenzvfG6eszKFRYwn0ph0jHoQxDVzCQGfuvl+EU3Ua9/c9DW733zF0RsgTvMJGga6UPWateSyqQawF2GRFZLS5T2HLSauMHyW5/E554pzMspK/9hGkM4NTnu5v1N9DloPvOpR1BvWDNx2nz6WKhfMoR5wdMPfEGH9TSsmSPMXLuYhHWQNLcTLjkcyEwRBvrE3bQ50gyht4bQZrT18vuja9NxaQ+Yns0Znub9Xb88aQBjpiHYEPGR+OMjHWbi8gnZPyCPV9ZZtr6WWOZ1q6om9K8RT6neGILdXRIS8Cb9QHCqElnTDaHfJIM87FFXFG3Aonm3CDjqLERz4PT1Rm9cFDz/V00wiSvQ8IpPTP/8U/pYnrS+GUUX4bQs1Yh3/9ChMZ+/5n6ek2OBpkqtkVKd8gtAwmjDI8tjLcxuNHoyGhR/798Gev5l//tc9L+HRCOYpD/63U/mu/+Zt/SMyH8+jHEyG7mbp/85//rb+YzTeMsXb+rd/87b5QjyACj9Pp3+7nv/ZXNSgnJTMKUiPHCo7J2l/7/d/kQ3r/0UwpSw8k8H3bryUzMODLpqa+0923HWcAeZonOgOU242PAqKShxENtjW18LidVx2DzZw4jeMU+9MnmWr1Hx3+Md59gEyGlWQgLEDgYI6kSbdUqhRqpi9nHmKFkEIBUcITSg/0fhlh2kV/hi76NJi5MIgyf/O6ChEGD8Jq6BZGKou7hlwZB3F4EsXUGTqHbKiHYc54RQi24WEEGErqaYyII90D6cNeBKPWMHyWNggoDf/Rqf/Wb87ll/TUjHPMf+tBfs0Pa0B45E9qJrvjb7swc8Lor8fRsIAqnvk4MiqmldDtHKUbBbsAAarCrMjx31Bsea0B7Hu8E/vCS/7wnV/5Hn9fH09GA4p4L/0vjrafTVsbGZPvqH6U0BXyYt4V0h9yySeZSpwSQruN23rhR0keFefppVmbz9s6fFo1Z4rQSD3dl287HxYu86ZNTMcuSq23/6pgSPSWdA7HU55tcjvvsgB8h6RTZ+bfuZ1DpyVYS8Uo6h93Xgr439bBGQy7jwg5Dd4uXos089s5/mLgUkiH8c6+wZTFs2/n6ClB0tFu5uztHTZGq+ffSZMn4pQbve34rlyaDzvRSG7l9e1Gm2WMOWXBvNTV3pXaeH27r4WU+VlL35F7D36DG6yoQjbjxGnAqV/JEcblGA0FjGWGmoatclAuO8aAs+d4J3t/7NNwSRijvIGBcl2jmx4Soxgqp3suUjrBiQ6XM9yQF2acO9Mhp+vjAuDtt1sZ4z4NPMrnoRecv5iO3+5NZWxxBvU9Glzy52292JY0eU4ZRx4PpLbcUruNd2jAb4mwxfn0y9LC2zU8bCb4GOpMkhfcZpUmKT+mzsQR3nsjxufP23Y598Vzy8Ppder/A8Et2Kfz6LhtM/73B6pGycIHkVowQDK3y7//+W/71zx+OIug4ijVJu4p5PwbX//DP/sTmiJ/LTLMRD7zv+QG+X+/8YD/P/rBbXQ0ZGlTekeNZOlZRMWGB99IzceOpe3UqXpkkylMBRgUVXijw0sDw47InMoyD71u3USjG890hQK6gCEWLl5Mehxp7fwXac0CkO9aGiBTLpqUpTdnNVvho2Z/8Ajx0O/snDSOjMgaKW7vbE9N5y4k8ILUu7nRH8FzaY2yR+okzDHSfPH6mQ5jdMlLb5331OMVsmXMiy2PdnwVfdMqPaECXT4tmDcL7+KM8Owb2Teyytd4ZN917uGQ4R6uzVIC1xdj8aFRS1N/XItH+FmbZDp+K17lypIJaV7lAlKRiJrijdN7mKchKdy8hKu/1/PphCN6xhwcP/daGo3ye3r1hvDudp9pT6cHb6SlFbPpczE7vMJ6e52fk7cMhf+BaTZPy17sBux4AR+feYSHkvnrMTfq3N9/KfVQk3u6rz+iqEuWUQc+tqfCxEt4BFH5Yux1BvkMZgE3p8Fn8RPXhodYj/U5xm7qOBtK0PzyolRT2xCexlFg6l7aHdZf6aX2yuHu/uoFjfRKPI16md0d8cbXfu6etrY0p84rmURbOr8imhFOwgtsdEm8zS8jGoHPAolH7GvMOYN/LHLs61kk8FakVx870RzeU23qu5fQHwGPPeX9EZUzomFEWS+yXtLo9g8uG2k3KyRqQpmD+zSOiB1LDkPCkh4zbrpIxT3c2RHOrwXlGK/V86P+7zoG/nWio9fxupcRCZtGtL4BI38JnuGFREqmECEsJffSNFFUpHyDYqnigRklOhqusd+D7PEgS+5nfvZsuMpc/WyQKOaFoX6MpuupGyZ8CbZA0gXOrPFx1I+ROqM2ngdTQJPCQWB87uLl1E437nYkwVGEmdEvqkACdDXz5xA1mEVkldRYZqKzIze0hXfge+xhhjN6ksX3wF++n2cCGLkdwJnRc74rdVweTlXgzByi2ZYWmZkUeCCtjOGyKBlrBtf9TJzO+UqMzl74HdNy/Z4nzXj80tnzF2Jfl9TW0MSJqCN7cgu4RXSaeV0i46i7uytdudQLbyJdkTHq0ewXzhmfqtiHOVMoPcHRMIk9KJRX8hvp0FRdG5D5p3gdWMsfzuHTiz/8ROU2aoNxNNzA0dDHUaHHiAruagTf+bLGrFRRXTmXI7oWMibOXT7voB7aayHOp2qyuYrx6EeUHASLfgh8Jm0Jj09TFHkv5y8ZXjoCl99DLkgbNitrOnoiXWDu3n9l7YLo0i09iOPyAHmuaaDBw+UpY3QcsB+LENjg1aix0RO/J00b6bEB44HmNu8axt3iFUuJdtDolXGs+fTKuEGGUzlOACyuDL7xOgNuwMO//d41olSdZztT0/k+pVtqqJqX5ldRTkdUw+wCS0HkiUGvyBP3oxAeKA820iL9xqaNwc07fgov5m5JwelTzQkdMS2YNjnNJLvPMiFLeHK+GsfkxdqdFGsJWBlBYq9dI3MQJ+UHIb9wEJrldbl/gG7aHKWH7JsBo6kkgmVk02iOkbboH8KQIRuAt3zRFF333UjyVdJE3Wf5fQZLZb0RL8pHiCIOkD3Q3dWRWjB2F8+dSaR87lgUfww/WKu/k3Z0WkYjSX4vTpg5MYSMK8HBKfzMBJG+/I6w95jQc/QsaLs4EHJ7eT30BH2w6MyIY6/cLjmu+kekbgOP6GPEOM7T+4i/4rfZYwE3vmtGzln6R53sPBvRparSSamqujayF3XmiVuO636HLsDv7S/i74WN9x2Anwi3KIFgT5SFymQjqB4B2dbWnE6T4ea1pHJ2dHc3Mide5zAxi0O8VJ8xXd7PzKQUP9xbbhNyKaicOXmso+UfNqc88smxUG5hrTQcraOxKVlk/CbwjV+Kh44BEOI97+k+2mVfmnN/vfxNPGLdyHlwyfTqw6c74/NqjtsVb4wIB54wN/fI36ijXGfOZZSv+J5jmnXmvANIrsNxmbPzEb882aS9rSWdptRvPls9l9T2GTNmBk5e497OHzEE3xN34IHMebK9JhhHevMe0r/rGltlvHZu6nOXKf1qhZ5ssrgQfJxJVudkZE7oLPAO6cTLjLZLZKdcJKPi8mWc4sib/5e99w77qjga/ofeQcGukY4oNuygKKIGe8WGHXuJaGJiS6LRaEzyRBN9Eo2xxtjFhoKgiKISW0QEUUCKApIoNlAEpfw+n9nvuW9M8r7X815P/vG6fgfOfb7nnD27szOzM7Ozs7sem63XINYHl91WYxS/NWWjjCpHg4xZdL8lm0+BIBk278ovs1quw51Uy+HDFbS1lUTZLG3ATkwsBvnam3NixkekIZ3yv3kLVqdfnWg8IgP+vmBBzgPvyVSxtYm6bCHN4CsP861okJGs5O2UreRz8CYt5HVP25xpjOrxcKegN96aHvq/1yC7jTfbJKPAnLoinqu65Mi0H9hmoZv87mF7lT45qk6+8pHlitHPifSZN486fVjc013WbBcbduyEjCwL4ApLYqagPvWl7arYqb4qetd0/lPXGdlie7ItGsk0nXUXdFJ7bNFxA+yxdbNNuz4SKRMPjuIrQ/xG+JQB4kCeMp08I9+m/IUXfC9juujzxKnv5KBPB+67bdQ9OuD0F+vmV/FM4WXgJk/LsDpJD3iv2PrwA8/N1Kt8bGSXa9O8MY/oEd50WnuNXHvMCD/lnzhNXEhj8KA8SNlFHpV9Ldyl7FKmMEkzYVhM1MbsWe+wxtVyIhRbRweiP9oQiWYUp9/n9AEANV/rXjeVk/sS9Vb4JfmGZ0bQaa+Is2VEL2ivTGdrbGXhhug+9aqwK+fkEWXJUtq8uqS5ejBxiu3K9+ol66bcqmhglEju9IH+WIiM/+Dv82LO519F17Xaxzq0V6OYlXHWTXxUuE9C8Uie8F+2ZN9Tt0xV+536A1idiu26Lu8tIoqK+96b9EiaamN/LU2hGdkhF2hD1FdcyX9lwFxsWT2mHRNJYUSCstRIaaN9ss+KzTFzxntp663drCHTsXsQGdo24dZWUvd7iCf5pZpqZX/hs0WfMW12Slx62UUx+JjB0bVT12zbcG3WDS7Na2bwLf2DnJQ0/5uj+lwiY0RAoGwESTaUGopy2ttvx4svvBDT356aIes9e2wUXTp1KkxI8U4XkMiG+XxEI3+NxSPvveWW9GoN2qVf7L7HHizw2DWJLrE9ZAQ7CypCOwaG99jIZbJUZDCCv00vUxt6YyGGOT3/15filmEPxaZrNo/d9zs0+jAveR1CYzI/FSPfpLAiD0Nu0oimWOuWZSjAgUFmM9TfBcacv7WQcL4Zs2bH6KdZ/OulV2LXffbMhZY6d+6cq99nXjQYHQoyp3O4VAxOVVCg6+QoRkfpxAu/QsXSXPjERXPemjqNRftGR2fmmW3du3ds1J0V0hGyKk4Vq53PiqTiVFp4mo/ZmadCMJUJ94Y36gSZM3dOTHz9dRbnejFepxNw3DHsIsHijm751hqjwm8Me3OaicIkhTfGWhrVDtnW8CwOxYVlasAaZmW9nHP6zNin4/6xz8W2GP6HHXxQ9KDzpUNKgSdsHjZpBYdHwu/Vd/meP7V00tUQv5bMnVxASN+zRMv85sab/SwO3WtgrpBu5yjncYPbDIHDIaHhY3kaoq7xYd2tkx1DhYFhZRlGhUJVIEiPGbNmxaMsaPPw089k/hecdXrsBM+otJ26YRhl4ti3CXSBXyFSgV6uSVGelUpYvh+4p+5tt9wcE2fPi15rto0TTjkD3G+esIt35926+rJX25g0c9V6d4L4io68oYMa5o3oRBqS1ozwOVfeNjzsww8XxHhW5r3m5psS9mP33Df23HtPwrM6E7qJoWeoPyGZi+bOi3cmTIyvHng0ttxgfcL2WRmf6QwtGE7BLEL4AjOnvOh0DnnMBVmFfyn1ZzdK5rKxOBtPvrS9ca+SWQL8nxKuOu/LD2IGIQ3vYmuvpNpOh2iO+7oVRmNLRndaYXS1oIl+zWKSn4LTvzN1YhZW0Js4J8Rm5X0ecsyRbJ24TaxNp51dngmF5i3li2nNvFQ8PEvcA6N8Isxi3Hfym21Nx5SLDMrzk2ir/ffem5XdN8tFnHSA2fGSpz1tryoif6t4qraqISf/uH4JWVI6HRh4y1DfmfCMW+C9+PTIWNS4XRx46GG5IGhOY4F+rkq/mI7btOlTccCyoOqM6THhrSlASegoRXfERd6RqI91iWho34J2Bi0a0Y5cPT0NAtqcBoD1Ei5/lbvK2OWxEIGL5TifjDr52gUq2Qnjo8VMc5nNQpvToQGfV6O3xw4+kkUuB5oj8/LHxY233JK/h6D89v7uHrnomoaSYbguKkbjqBlFSEvpUOP3xLm6AAgShb4CL85vF3/ulDCMxbmefPHV2Lhd4xh07MnhNng6eeQr+SYNoJQG8j/TiCjXtphU5j0JUg7ZZg07tz1rxNhxcc2eq//rmqzXXtttHYccMohF6Xpmu895s8gnHQJklnADXu3qLw/hLrBbgfIbPYXD4FPk8Njxf43rWICx55ptYq/9Dmb+/w6x/nrrJpzqI3PRweccV2mVsAGzvytHtfjKUHp40nrl1A5gf3fe3HjmuedjAmX022NAro3QrTsr69PZkc/k9S8x5A0jNo/Eu8ZW4t+8ynoCPEh8G+or7sXLx4QhvzV1egx75JHowDSFfjvumOsirMuc6zbypPxE/uJG2JX3Lpgo/B7q0cRZtiM7NbYl+Ir084D7zclvJi9PmDojThlyfGy9xeY4A1ZLeaock1bi0iMNOHAizaSpUynsNKYuVyaTpzreAQB17pw5c+LNN6egu8fHAgzSww45JDbuUdvqjTTqVmmlLFdPp0FH3jqWhdF8vLrdqh1cp6fk1svU+YMFzM9nHvHtf7op5gPbIHh9IAsNqj/UBcJsGLI4V886713Y1CMa8CjDYkgmXnQA64igk0w51nYe83FfAO7HHhmRHd5TkWF92FEgV2BPXi/2j/jJtUb43jZgPXRopIwBDvOzI6KTUpvBOgqHizv++Y474qW3p8XGhPcPGfp95kP3LuHfvLcTrf7wG/Gg7aFzRXxkx4ty5UHn4xvSzYvU2Yag68R4hQVBb77tz7EVDvDt2bp2S9Z6MnQ+4RUecQIurLO0Uq8lbPwWvrRJ8jfyAp3qNAzLdrrhvPfnxzMvjI87Hhke/bp3jh136R9bMpd7nXXWVmgkDnRiGTatTNCplbghP0Pu3T5YGrjzSQP0kVOh/E576X3ydo2qJ58ekyHL/Xcsu76suy4h6OjP5EeklHzjmh6T3ng9Xp/wasyeayeZcCKOLViZd0Omk3VerVms2wrnOI6GRshgIxpsDDoalPtycb3TwTeFz+2+eOZCkV6VxdKWKR1fAe+cBZ/G62/9PWbTJ/cLxHOsDnzrd+mJI361eO2tt+ODmbPjh6efGlv22iRai1twpy6j6Gzv6UQAN+I2bSneSwfxTrOTnDzHsYf80mlpW/vryy/HX26+IV5lusIu224dRx86KLp37UK+hY6uRyQ9tTWUAU4D0OGvLLAM26fSWH2aOpFn3luuC+m99PIr8fAdd6UNf/ygg7I9rc1i25ZdDYilSUGddTDkAAH4kS+smPKi1IEyoK8LL8r/X8Az76A/Ro1+MkY+Pz5Qk3HMSSfGtlttldNTlWEJD/UWfsuzvYOFxI/vckAGePMKzpRrTvtJOUQ9J015K+69+66YPHd+7LJFrzj4oINzIUBlVcIPbP7L9mM+0oNTOWHbT31hGn5LJ9udOHNxxb+za8pL2AV/uPve2IJBhJ332p/Fk7fDWb0BOSreqS9ppZ1tVn1i/0j5lvmJr1p7o0rZbh1gFS7r4Datz9KeRo8ZG7vtvANrSW2H3cFOFThli2xEBlMGn6a8T9wLP/c+176UaYTFv9XOXjmdCxk/hb7ck2PG5OKufdlZapOeGyfsrhFUvwgli13a36L+ygKd8OalAyL7D8AprzjFvFWbVujvFjgz/84OcxNzF4nRL70aJx4+KLZFDqyF09HpV7Z9cVOg4gLACTVXn3pUbyk2ecc01ZoQb789NcYiB+4e/XR0W6tDHH/MMbHFJpswVbw4Yew7CZv4Tp1BfsoZpznrlJaXynoRlIPtoV6AKLTr5bnji4smP/bI/QyQfxL7Ddgl9hmIjY3+yN1dsJOkpdPdlJVpB6HLoQRTnhi4YEDqyZEjYruBu8XgowdjS3TC6ehbeCv/6daRQt/e4z/kaFiF1Elt2Ajk6slx4Y63UYQvYTh9gMLVE+6ih24j5siDIwypvMGjxqSL8z07fnxcc9FF8SZ4PefwQ+PwIw6nw75pCkw7uf5QCGkMpfLHySBT2FA8VJ4KzDqDF85TmMjs7+OdHz5qdFzyu+ti4Kbd44DDjmD14t3xzm2QThANUY2t4mgohlAKEvKoOhy+Jyuld3Z23dfcVZ0/wwCaDEPfM+zBmEwExwFHHslq1DuwMN0msSYdoxRMCiS+zzUXEA4qABc58Z1KWUOxwA204CQVCu8+YU7P3Hnz4lU6RsMefCg2xnDetd9OiRdXjNZAE3/ixfwFL1mTb807T54V8pRRFAWB3k6NgndmzMgt3tyK7ZVXX4mh534/dgZ2FxpUOQmThqc4t9Gl8YMCEu9poPJeQWNkgA1S/Gu4uGWko0ovvvJyPPzQQ/Hb+4bFPv37xZknDonNe/UqxjBpBSyVtrD6W/gF1qtn7VklBIVHB09rFuycjwNm+OMj4uqLfxLTSPsD8j6cRVU26toVjygLxpGnW+JIW/lNQV7NBdObqtJ0JFH8qzgdPRR2nQ4akW/Cv7fe8ee49s67BSdu+c2vY5/vfjdW0wvNHDSIVmAEUDt2Hqs6GXxpVTylTFaLZ2n88uyvr7wav7rql/HU65Nij002ih/86PzYicVnFN4am8It/C7W5aGXtzk8A8YR6MxrB796jBsgEBvjaMh1ARSOTENQsT0x9uk44ycX57eXnHhaHH7k4bFxr54sDEm9iTaAcePjWe/GC888F9N/e330oVPTpdMG0cStLJk6wVIOadxIJgXrl3i2VfpN6bja6XaxG7tXjtbboXU3CEcgNFBQN6yuvTzmMa1i2sJ5MZ3hc8f1vqKH6/oVgJmhoa2YZtW2YUt8uH7DziBfLoppHy2JCTgbdGfgb8jjZxedH3vstltsuNbadL4NLwWRGhL8FucAlLxe8F8Uv7zic2mpsSXMc+fOZYu61+MFOvnPg5/9Dzo0+oFzF11zvrWdumxH1EUaVMaRvKMxaztdxjvbQY7oJbPifOFeR8MUDMRxz42Lp0YMj48btoiTTz6FXSq2IaRxTab3sHgZ898X0WF8nR1ERrNi9eTJk2LsS+Ozjl3423kDHA3so70+oxLtmWPQZDnrIkBrDT7beANkHpZT8lJWGwMhjd3MoRgS/jWSIcN1eaujYSWKszgaWHzynRKu+2F+E3HReefF4COOyLvhwx+L3/7s50wtWxY/u+BHccShh8SGdKZdT0dHg4vGgYCU4eIz8SspgKgyuOp4HbynPKBdOQIwkQ7pn266OW579PHow+4jZ/3ox7ELCxm2Izqrap/iWRnsofIv63EgG32AfE8lD12kg8aMo8MakXYkx6Frfvr9oTGFRS9P2W/vOPHEk2IbDNGUAzhClZN2MpR/RS4CH9n6Ow95KRmKv/Iy6fyncfERIyMPPfFEnPPzK2LXjbvFoMMHxx44wuUb59zbVpVjGisu3Ke80tDMzpHwcko/DU/ljPLde0euXdxq2kycMGxn9vyIh2OfQwezJeaObA22GbtJrJO85jx8t+9S7yl3K7xXVzuMqSNrvK4xJ98blfUBu4O8hhPm5j//hV011kCG7cEiuJvRoe6YTh7rLp+Ln9zijfqoG5ojU8SdcGaUCumUjbYlDWCd+DPQHy+zaNkYjLmXn/9r/OiyS6M/nVLXvtERJK6zY8cVoFNHZ1uiDuJWvGmcihPblDBYF51P4mzqtGkxHmfAyFGjiJhaHqcNGRLbYIi2ZTcA5ajzY6VTmR9bDDrz91uNaReGE+e5tTVtxbnb8o3rIrgf+xOjR8f1F10YLwHdOcceG4PpeG3UrRvtlLn+gLyY3a1cAKwpPGZTVybILxqN4lcjuIxwlUgzaSBvShfX6HgMR/Wfbrgpp3tcdvEFbL27O3OW18k87EgIo3wu/PKMdoGda2W+uBaHzssvjgad4c4npgMIHK/Qcbnmt7+Nh+lg7LL+evGjn12etkEL6qhj0PWIHABIRwP5qO90IgibOJEu4lo7JA1onktTaeIc6KfHPRdXXXJpbNWbLepwyPZn9fiuXbqkYZ47hEAv4ZV+ylrhkoZSWp4RV5WDzc6wW4OLO51f7rDxyBOj4vLfXx9H9N0+Bu69T/TdsW9siE1jGm0KdZ9TH4yCcTRfXjS/7PTBS+KiOBrsRMKT6B2jpmbPfje3Z73/0YdZV6BV7I/xr5OkIwvquYaP3wNhdkDfY8HA5557Np4d+1RMm/1eTGQNG4+t12kSG0KnTqs1jbVbGuGHjQAs6Wigrrb1Inerq19R8/I/5ZVvjG1SR6rbPI0sWwrN5zKw9vqUT+M9FJzdCdg/1u7aOTbpvR3rEKwZL70+Md6fPiMuP/9HsdO220ZbeL2pOIBu4reSJ8pXn3lK1xz0U3aRRhkjLm1bLRj4WQ4+n372mbjpD7+P+3D2Hs42oUNPPTm2wB7Tdta5tRB5YRu0k6hOcxFJHQ3SNnkGuiiL6zruymTEp3DMwU4d/dRTcfPlV8brpPnp0LPiyEGHsHD1esmHua6ZgIk//hkBuGqHN4GG121T8qV2nmtRaMHLby4ge/c998SNDzyYW3EPveKK2A2etFOqBLfdKwu0sbNzTR7WP0/eqYOUi15tu7ZVO+n+Tlv11VfjuuuujWcmTYmjcPYOOWFIbE+fRd5NXoT3zF+8Cnfil5JTT/A87Q2u3osr5ZNXdZM2/Ohnno3LfvXr6NO1Y+x98KDYDXumW1dGsW0r1hd5YB6WJQ3srNY5GqiHcIvnpD94Eke5TgX8PItIxEdYD+8xbNX9jjgkdtmlf2y9zda5C58yQHmSjoYabHV4JzPhBYCUP0kecVnDvRFl78ycEa+wy8OwRx6N9bBN9txtADsF9Y6unbukU1P9J87V1SVyDBuP75U75q3ckR45EAnulGutjC7iveuuuFPbmGeeiYcffSzO/f65MWCnHdmxZp1oS9Rd9u+QI3K0sOUBLjzS7vOad6Ue4kR6u8Cz1ZqArffYo4/Gnbf+Odbu2SN+ePbZ7OqydW59rKNSGajOE77KBnHw2vVlXH8vHQ3Ic9e7ARjWY6OhcjXiw/7HOAY677r91hj7xttxGvb18YOPik16dMd2hU7YAmVwgL4HtFVvuk6XrgRt6Zlz3qXOj8a6PbrEUQzwrrF2R94YySZ/GdXw/zsakrxJeqgpYfNIhoW6NAZabu6T/cxTY+IjDB1HyHU0dO/SNQnqPrE6HBRmdp5Uik+NG8f2PmfFfDI757BBcfyQIbHFpr3SEHPkuRj3CDxIkB1QDN7cZ1hlzzc2/BzJqyn6yqBTEr6P5+x+lP4FV18dg/tsH3vvf0DsvPPOLD6DEKRslauKUe6UwTKc1FypTul40BCtq/9s7ApYYM/QIJj0rXemx60IwUmjx8YRJx4XfRnt2rjnxrnNnvlnvggOlaWwKkxTwCAwRFs+9B15a7ikMYcxY+iPozp//durGIq35yjanruyhVH37nj/10n8KZT03ppf5mV25lV38oDDd5UzQo+1zD8bY8gt6kbhIHlmzJg4/ycY/9ttnyukuyq5eZivYVpmoPBU6Xy1BMGhAEPxWCNxLcx8kHVogkJ3cUi3BRxOY/rdsGGxV/9dYujJKDY8igrgwj8FNrCZNK0qUDkYzDu5q1YxUWVEgwu6zcc59RQw303nZSRTHC466ZQ4etAgVs/fIJUmgCfshmA5Um0+aVxpAPFOxeTookrWfFPxYEy4OFhLHGPToektt98eV91xO28j7vj1f8V+jPy2QwAuhh8VJLAc3/Inr/7OWtWEoPzoM04q5NXauP2peNXR8Isrr4zRk9+MQ+kQnXXuubEjKwzbkdAYQ+IkvyhsVQwKdBfesZupkepzQ9DchcETglIAi8XRqfwMR8iIsWNi6I9/FL0od9AZ58a+B+xLOGC3WLSSBTFZSKs1dV00/x/x5FNj47VfXRv9mZbRbbNNCBGlo8HCf82pXLNGiDsryX/XYlER5mKB5Pk18KULBNhysSvxSkI9wYaIfcX5IQJ1xuefMJ/yM9ZroEpENLRkoSmwn3vDu0NGmwZ0GJtghBIy+hF1fPMf78fE2mIeLNWQx88vvij2xWD8DkaY6xY0oWw7X/JLhWN5TyyL57rOLs+UC+5koFG8YIGhbjPieaZz3XHbLYSsHR+779yPDmOnXMxJ40flYscr25N4pQzbis4ocayMceRBHlpBlAeJ6zzoU6dNxXB9PkY89ghOlpXxg3POxVjZhpG11ozQEMVBWJ1Tfl6b8FqMfPyxmPwmjoZXX8w69qARdOvYnJE0ws8Z9WiHp6cR+GsAzTW4Ew4M1bLobJIEWYgMBD7Iw1Gu/i0RDRq6OhroNDFk9jHzutzObdJ0Ftkl9Zz8JuLCs8+Jww85NO8eHzEi7vyv62PK8o/jkqFDY/+996I9rZ8drYbgYhkRDW75WHhCvig4ly/AFCAWelRyQFmWW+TBExOJbrrp5pvjj48OT0Px8t//PiOQDNNfRHuyk24dbSPKSeV8ykt+8wN+w8kD3nM0Ddmo4aTccyEnDYVn6ZBedt4P4lUcOeeyZdeRRx1FxMTmmW9uN0g+2cnNmha5YtZisuCvwC8MWQ+uHi4u5grRD+Jo+N4vrogD2L7vAEbWdxswIDtGjli5eGM6peRL+EIjUVmZHXJgswMnvMo95aSjanKqMkeZNH0221UOeyAevffuOO64E2KnfjvFZnSO2tNBcGR6JTzvom6aHsoCD3Ge/6iEnVs7H8oDnaXi3Tq4e9I/0H2v0HG5Cafplr02jX1xkLhl13pENCj/KiNamasOdARGfhev4ktaGjrq9BMjJXTWVrrhXTt1r76S0Xwj2WHqpz/7WQzo25dw7LZJp1zoFuSKSmEteK61Ux4WnVXwokwUFx5+7yDB60Q5jho1Ku6+9dZYf6ut47wzzoxtwYvGqrjMzgV4Kca0nVIMROouL1l/9alyUjg8sg48a8NI3yJ065Njnoo7/vv6ePjd6XHOUcfE8YcfHl07dU6HKuCkLM4V/KGp7cyKJK65lzvUHdLSUx61THfa0YjVeXTnvffG5URMePzh0kvioL32ysVkXdBMXKSspJ46BuTC7KjTzjR6xYe87oJtKed5ZodIA17avDrx9fjt734b949/IfbesEv8gPwdvRcvtifzl1eybdJuhE34pa1wG0kivnWqCK90Vq7pZHK65qhnn42jfnBubEg+p5xychx84EHYNDiqsWd00Iv7rDfvq8Oyxbd5pa61TYMpZW/aB9zpWHmPjtcDyJpL//CHOB1+3AMH/vY79MmICetuBzB3koDvjGaQ40vkrA41Ox7iHppSPyPMdDq0YZG5Lxngmjt3XvyNUdI7HxqWODxk731xlvSOTp074zxj20YiUN31yAW+35v7Xjz15GgWLX80ZhAJMZEpb83Ie8s1WCQP+6ojEQ1rtVrF0YCsF4dycHE2AIe/+cZ6Vn/tLJTQf9o5ddfZ8BXPjGb4kizeW/Aezj8WuOQTpo6jNwlJZzHEHfoTQca2oK9A29eefymuYOCtH46GdjhpnBJZ4Va7S/3jIc7lFXEujRM3PlNmgkufuX2lNsWrbsmIHXwTW6sPZKDtrBNPxB7bmE9LeqOVrIcDS8qCZtznVEnamPIleT/LKG3L9qWjqjmDUh8R0fAIztKrcU45WHjpWWfEYKLKNqwcDepS8vEQX8bzCHexifltvp6kkWft3C2nDm6lKs+/hdPx5ttujWtpU5vz/UW/+k0MHLBbLlarDK7sdL8XCbYlTyNr1eniJ+Uu+Yol5Ys7TmmPCdV45NjPrrg8/vrOjDhz3/1iMPpjW/hGm6DaySFH/mtwJ875ziPhBubqMFKjLPwIH1Bvp/U9/jQDP1f8PA7s2j32xaG5K/qjS6dO6URxMM/00jedSJUuNEvzBQZxLeIqp6+yIaMy+HYmDrO7cQQ8fuedcQxtdce+fWLTTTdNm0bZaJsTP+K/0qnqV7JLfBfZVqC3yMbY/s3hGacgzZg5g2iMv8Vf7ruf0fqOccB3B+Zg4QYbfAd4iSIiMtbvXdxcfaL8sH+jzlMGKOMT93bUea+TV6eJ01tza9ZJb7DV9/Nx1/33xbnYS3sw+KCjoQ0O0xLRUGS5aNDWywN8CLywepSr+oRfPHfnEnn1TWyOp58aE/f/8Y/RmD7TT35wXvTdeqt0oku/5DX5AB5R/+lcVybrYFuOY9t8076EniuQGS7+SoLcaedjbIJn6bPeiW56BEfM6QzWnHH8kOhFOS487K5d2gU6YCxL+Wffwjzche2dd2fHXfQZ1+zWMU444fhYs1NnhSPwQ2faurZ8VsYKfksP+EKy/e+OkkWSIhm2DikiiuynTJgQIxE8H3+4IEfIHV3q0rlzKpDPYWBIhsJsQmNgS0qY8Um8rYNOPTWBOuOQg+PU005l5L5XTqv4AO9RemYhVHMZWiIsofEwtzwNAcCQIRRQjqwrfF0F2w6GPGmYyj2PDY8LfntNnNBvZ8LI92brEhQb0w8Mb7ETZwMHkFTO2QgF0HxrDdRbGcYG7yr5KhINXzHpNnU33n1nTH58bBxz+pDok46GnrkKrQ3RUQpHX2xo1jk983bUyVuFYJ6eDQnPUYEqvF2N1vlL781hXjVC8Nrrr4/dCO3cD+HaDTw671DBpDcxhRN5pcADaEVI5iu8tTMRK7k4KkPREd5Jb7A7CLgfNWpEXPTjS1KxuZqqQjq9wNDTfM3PBqwgcZXrLwhTNPRIo8UICUcPFMQKEBuVYYyvMXI7CgP9JpT+juB9qIoNI1eiJCgiz7whXj7xofdc8hk/ym+hLjeuqK2n23mMTp14BAH4wOQJcfGQU+IYFNsGjES46njloTS6wfpYh8UYSIt1LNUMNkPLHJ1SIX+BUHAE0Ckj7Vkpd9asWTh3bosr7rjNwuP2K38Z++1BRAN5fQFNc5eKfIPaAjRB1/zMevncOgo8R3kvLWoRDfwYjxPmKhwNT02fFkdvs12c9r3vRR86peL0U6aFaBC6UnGOBPF7MauGL+S5o/nup94cfGNhxQr4aBE87LoURhS0btuOrXS+ipHPjonzLjk/ekeH2OeM42L3vfaITt06sYAjtFu6KNpDzy8XfMwoBOuo/Oq62BU4u/fehE4805I8ed+UpmynEhJnvayOikqyObKvgaQxa4joEnCo88G2o3HlYpAf0xGf9cVC1l1YxL7EfL1622i9BqvM8/vTzwhbWIKjoaHrfTDySEd8AR3ZN+bPjb/9oxji88nf47LzL4z99tyL7R/XYFcNtmyzrhpRlFc5ccR7/VloIu8jDJIfNTJsT/PmzYvnXn45rrnm6hgy5KTYi1ERR3fd5tSjMrKUBSpmOdMOVjH6iQ4AGTk6iIJ3/qJ8256Vwo1wmD59ekYIDR/+SMxcuCQuJkplezpIDZFZK/GUt11t9ZwnqqPh8eGPpqNh3Bt/y3J7ItK6dWKrxTVWizXbYjg3QRawSjpePUaVcGrBRGVnHBJS0aQFP9JIqF2zvuSGVMmIhoxqwNHg1IlPcDRMn/ePmDL9k4wUmZGlRpx/yplx6IEHp6IdQZTFsGtvYmG5T+OMU0+PgbvuGh1xNOS8S+UAu6U4wgVL5CFfgyJKL8Zhcj84T1kGnuwg5Qgy+HtjypS4GaV8Aw4WQ19vZDRz7912S3n3KYaYI40aZTKXzjblpBFbafxSjiuse7rft6fOYTs9GgcaXONeejGuIDpjNvg67oCD4mCcjptv0ivb+he0ezuh5ldAL21R2hausTrld/KUDF477IQZtv/Qk6Pi7F9fFYdsvnXse+ABOcK74fob5IiVIxca5hortluNLiOQbCvC6FaY0ka9lDKVNBZXnAUsSvre7Lj5vnvjrvvuiu8dOyT69euXjganNny84CO2pvuKxUtxyKEbNHw9rHN+n/kUx5c8WjnYNaI/wxhyzZBX6Hjd/Oc/Rx+cAPvvtjvTD3rkXFjrapuQiOqmNEKpu1fbtXXRaeFODovh9aotKSM1GN+fOycmvEZ0zjNj477HHo2fXfyTGNCnL/P1cRxSV2lvvmIznQrAVDkXlMdVJ8kObhp90p7vjKazfjqqH7z/gbjmicdi4Dbbx0VnD42tN908ZbmdLWFUP6f+xjhPnQXMPlMn1uknoonUX841ttx10fvuEvQ0cN97y21x19uvx9lEqQwh0rEzo+qGaosDR3bt6BrRI46ETaO0rKFApxzeyM42NLC+0tZV9RvjaJ89c2bi/Oe33pL0uvHSn8Uh++wbq4M7pyZYX3ncfIVVulYOEXVLDkYAqzs82NlyyztH7F0ZXz382uQ34trrrov7Xh4f+6/fLc695MexM7iXDz+CZ9Kw5Tvlmc4q89YJZXuShZRh6j1h1nGTBy9a08HQofHEuGdj0NAz8/F5Bxwcx59wQvTq1St3vfiM+fK2HfnD7+2o2pmRtnYKfW7nzo6GNK6jM+9t3/PgyftGjojL/vTHOGevfWM3Ol1uxZ1TWZED0tN68Ae8AAL84X3pSChxfWhH2lFdthJs3pJ96NfMAYb5rDEzYfLkuOOh+3NKyiF774ejYcvo0qVL8qLb5bp+Snt29rCjM2LE4zHi/nviPXTrROxSF8HcbPXiaNhwdR0N2JJENDQkuqwRcMjMdDuyHqhF9F02Zf7K5cIF2JzqRqMNYUoi/tAZPCQH1jFaGe8umMlUr7LgOVo8I166rN8+BuxzYO688AoOtmeHj46f//gn0W8btnWG3kbVyN/yWToaxE9dkZSbeCIz+Nx0ElmZqtPGdij/THlrSox/4YV4+J67osfOu8Rpxx4Xm/fsmWnlZefdSytlgiO9rYjAs7PnYJa0NE87uvKNsidtQr5pw0j3IqI0hj3ycFx+2aUxEzB+esqpcSQRphuut36OvFeO2EI5wZSDlDNFCis7U/ZSKbLO7XyN5GnF6LS7q0yd8U7c8Kc/xW+hFRZk/OyXV6M/ds/2sJB1ppx+ZLtXJqiLrbOyW17OiCbylVe1T7W77Yiqv7Rd5f/nX305zr/wwpjAQMc5+x4Yhx1+GNPAtsh2qWOwOHcEkjPRnX+SBqn3gClrwmNhUC5QNfCHHYQcHv70mDjtisvj4E41R4O6lTXrdNqZv7AqK/mk5Glb4TRPShQriXOjSlO20kfwuW1lBo6Gv+A8GoGj+pQzz8otvntstFHaNOloQP9Ukdnmn3jnmofVkZZ5w1/+N8Fmbs70BgesZtMhdtrHbffczbTnrnEQAz69NupZ0x8r2KmHnWioq9MRcm0FeE9++Zw+QnbgpQftzT5N7rxFWnlImfT+/Pk5oDfuxRfj9nvvjKFnnh177LgTUV9r1xwNJYrJfoVy2/oWQAucJZLYpwV6edLDwWvlz7Rp0+OvOGIfuue+WEIU3E9/+MPYgYg45bj4WFUupbyn3o11BqLPtS9S/9mWaFM6vrQ/SECEaofUH88+/1zczWDkfS+/GKcwWHM29uQmXbvl9qbLGIzNQWDseWFXN2u3aKO7Ta06/3Z08lobdYlT6Bet0bULQoM2DX5gHs56yZKV+hb++Y84GrI1UPlsgCp5EFSULghCeU55/fUYNXJkfAgzucDHNkQ0uLZAeu0QVCpTBYMLzai4n6Kze+SZRbGdTsjV6WecHpsxIrWYTqPzTFORo/R1LDQidqUpUqKJxnd2bSySETfy9JQxVHgaSSqoOfPfj/vw5F583e/i8K22if0PPjh2YYR9/fXXR6DqaCgRDQq6ysmQQkPm5pnCRUGYnXYYwO2inMck4y1GSLw5bWrccu898cYTI+Kok06LnWksm226WQqxpQop65uCozQEPZ6Odlkn87Th5QgpTcn7yuunQjfsyoiGa6+/IXbdlXlAA3ZLQ9E9sDVUZOZvOBpsbLZDBTinQs9rtsVSfI62qSTc4mYCQsSIhidGj4wLL/pJRjR0wIlhmGkqLGhso9VDaQdAuKsmkLghaxuRODedgtyFl1y0KR0NjEjdec+dsUnfHeN7Q4bkqFoxeDAIhVX8cojfxLM3/Cbb2kk9vKcOJnG/56YoN9euMPT15kt+Hs8xRnvBkJPjuEGHRZdc2E7DBIUIXcW1AtCPdTDID+ajEFGwK5yyfhiQ/jasqxlTM2bTabzxlpvjF3+5jdQRt17xyziAiIbV27TNbdJUnEJkXuWs/y3gPgPoOmeDv32ei4HBC+OZVnLZZZfFc7NmxqDNtohzz7+ASJjt80N5XrpahutNOIro9jrLrQ/GlZ1OgBfxzj1ipA6DDCOyAR0c26Ado8fGPhUnXHp+9ACME086g/UCDoruzLddtAKHypefRjMdFExren7s8zHlNzdE72YRXTdm/jNZt2LT+qZ8R+uBnuKFNlXAL7zEjWSDu3ASqBwd9QTfnJJUx4NrNyygczJ90YJ4l8WKjHBouNYa0XotFnNDWH/OmgGNVrA1XiOmTjRAOZHXPz5fHBPemxXPf/hlGnwlkBWj5Qc/JOR7IIsorcuiPrR98Z4CnPKAU3Tk1d/+4ya3G5P3kQEquiqiwXBvFdvvb7g+TkYxDCSyqbOOBhY4ytFQ86VyKje+TqXktwp+p4ssRdHnaBJMCWvRgcW5Q8fC9G+9PTXGYaAPf+RBFlddFhfgQe+zzbbwFFOV3F7MUTdo6DSlR5lS9AadhecmFUdDZ2DvvE6D+A5G2zrtW8dqzLtupHG7zHmy8JqVQtqlXOIXRQOfcqnUPI0FeYw3Grpl+gSOSBw5K3HkfJxrNExnZwBGuHj/PqeLpV1y9nlx7OFHgrCV8RCG4rW//nm8x/NfgvPDDzgwwyXd8lElKN+VElbBN99JhYRCWECKck0HpFfDsjXwXsf4//0NN8QtT4zM8u+67vdpKNqJ+hJ+VYaZScpJrhq46gqzVD5qYNlyHTFOecNzIxp0fCprnvnr+Ljg7LNiKrAPpXNxNKHwW2FUCK/GVhq6ZFbBnzXhxnvLqI6UST4o/9NgXYChOAy4z/zF5bFnx+5x8OAjY8CAATl1ws5s3YgR3ylvC+wl6kJHg/JfWSMehKCKiLGzpzHm6Mbtw+6Pxx9+IE48Zkj078+c9a16M1rXLkenV8IzTcm3CWc6GijHaDrzSRlDrrZHHdVuq2il5FFHbz/EiWPH5YbbbyOss0ccsMd3GZHaNOfY2uk0ulDDy1HyPPiudApK3inXefaVMgDa6nSrOpPqj9cYJR2N/hiG/rgM/TEAB74RCXZcTJeOBuDNEW5hJi8PO6fKZoG17fhcGa9ctr24bZuhryOZHnfbX26LLr23gb5DY9vNt+C7ooHkD3OQBvKCuFc/2Y49Kr3oc+kqvs2/dfv2OIs/jVFPjo6bLroqRq74IM4ZfEycdOTR0b0zgyHQK+HkGxQGUTwlAk5HlfkswckoxzsKqezR5sgWAPxOd2hKZ332rJlxx113xU9v/GPC8iemNhy87z5sP7hanXMnO468XUmUkAynvLE+XtVF0lBbw8WPdRwZkVFG3RrlqPd/Xf2beIiIqN2arRkX//43setO/VRs8WUtQkhY/SdepYPONkuyk5X0sG7gxnYqL1mm8/EX0wkYhRwbfN45pI647NgTcoS3K4MEK+AXp4tWtEpjnPzt1IpvaW691Kni0LYqfwlLthXq4H7yD+BouPymG+OM3QfmNNZtieZz4CdtCPJJbc83DeVxTuWCsGY0i3qP3y5Ore3YCCduK/C6lLb23ntzMtrjzocfTIP/4H32y3WtdDS0ZItGR7+bEQLdko7H+3Pei8cZgHrsvntiDk6oCTiAsBRi8zYNc8rYd3A0rFk5GpDDLpDskTJX2KQZBJNq0k3ceiTtrIF8R92Xc+LSSVm8BNv1vQXziWj4POaR1vJcL6fLWm3rHA0vw/cvP/NCXPmTS2Jn8NIuHV5VRAP5QbeUl3xXjuS+AgAAJV9ZpnYnCVrh3LJj9+JLL8ZDDMxc9+QTceiuA2LoSSfHlkSYKgeSL2yf4Do7/z4AvxSWNpN0bm40JteMOLGNkFZ6tKS9f0BEw8M4z39y5RXxAZ9ecvoZGWHacX0cDcjndByBs8qWS16Hb7x6VDglSdEn4o523FT7B56aMvXtuP5PN8Z/D7sv1iL91Vf8KvXH6shIIw6UWSlD+D7ta+okzPKTzgbtIu2A5HVqm/qE+toBll7PYY9dROSkjoYh/XfHZjoxdmDgR1mzBMej8KfeFy8c8n91yNt5yAvWiW9sax7K/n8ggx8bOybOuurK2HedDeOAo9EfOEm6dOmc0xq0S0WDdofwAhDO6hIdIM+bf7ZZ6J7ONsSbskfdYaTK9Nmz4+5HH4mH778Xm+bEXIfHvtMa7Tuk7ksnIPCbv1CXs/AsmSdNZICsE/zjWlKNwcun6A+nN7zCoPHttJFO621ARANr0NX0h/LQ8u0X6Fy3L2hfysPpydn2k7eAH17JklN0Fx6eR7/sbdZ/eGb8+Lj1/rvivLOGEtGwc7a9tjiH1aPmmXIMOMVyDdMCWypiYeLOd0kHHMTwqVFtb5H3OCKzHsZZvRC74aLvfz/69t4qB++0G9LpyneuX5NOaeqhrCr/zN82zBU+XAbj6nB32kQb+gcLv1jE1Inn45477oj7X/5rnMhgzRlEI27StXuJdgagkpf6DdgFkLqrt5cyAKdz6L5774v1WCD/hBOOj7adOqnISMQBDMq3rGN58q38S5vOav9HgJdINo4csYfxEjkQeQohMU8/OYb9VGfniHJvvIM9unfPkDUJYIimIUPQLUegn8XzdObFFydMJ+y/Xww5kfn8m22aDcWR5uLhp+PjqBpCpSXKpXkjvIDkZZMpToYSUlb6YXQiYT8ZaC4MPZz5Y1fdclMMxOO036GH4mjoz/yx9XMKgCNO6fgALSW3GmoK92bDF85siAoShIAN0dXIP8ETrqPhHoz0yc88HYcccUzs1Jc5toYuMUoqwztHyo5jxdAqfUeqsyNPfgoMhWExWCiIxq4jwMV5/o4Af3Uioa+33Bp9dtwh9sATvQneyvVRypWjQQHrWRhaoAuswrvqKdVtN4b1+jwdDQiRMePGMX/v6Rg69NzYiY7R2syFb4MiFj7zVFG4B7i4FA+uI5AjdtKRcjXMnIOVBj3vV8dRoSJ8YzKh4U8/HQ/iad2Akd2Tjj46NsdYse6VAapQLa0Q2IRP4Dn8nVf+VMrIR+5j7Hx350iNHD0qfv3LKxmBjTgLQ/HI/Q9MQ9HOvHA7kin+FSoqT3lI3jOKIYU6DVvj09OOkZ2ilnQaDXF6+x2mw+BxvO6BewQjfnfxT1OxtccI/hpcVHALkyKwBm4anqv+zgqJdE8O660B8CLhaFf94qr427y5MbBbjzjnhz+KvozqiHONIef2GW2jANfZ0Iznzv1qYEiXbSBzw8ADz66R0ALjthHwLyXv+Si2Uc89E+f86rJM9aMjj2Ze+aDo0bMbK04vjYWLP4mVGNqfYpRNGv9qzP/jndGDbUI7b8De7HRW2sJ7duaRmOQPXxLhwKMkTtaNBqZy1vvbktFyr+JWe9ldAZZ6AuH8pV/GW5/MjZlOQcaiarTuOtEKR0MDaPglOG/GtInVGreiI82iSexp/v6nC2PCzOkx9qOlud1RtR3quaeewXzM/tGZcO+24K+ZtKSeEDE71QkT8HmV7qo0KWIbcm2F4mhgMUjmZU9F+bxARMMdOAaPH3x07ErHaMMNvpMLJ8kn8qK84VVeqRyCPvoSeeW6LCofFWnzZsACLzlCpiybOnVqvPD884SnPhLvL1kZZ51xBuGXzAnssEbuHd+8ZRuM+CXxN2j/+PDhuUbDS1MnJY068nfDdZrGd4imWbdDm1idbdUaM4WlEWcDaCy/rViJ8lHTgVuNXI3xNMT9Dbwpn3iLSU4yRg48cTS4V+hHXzDPdcZb8YZhQPAAAEAASURBVCJhIuuSRmPwI86Lzzw3jjmsOBoeRoZdeM0vyC3iyu+fF0cfPIh1ZlaPTz5akJFTjs4oa6tD+Wpa/1J8uQM3GnM6eMS/IzUafBOJaLjhpj/FfXRKzeHWX/wSWbZzjkjZGS91okbKMuprOaZTjihfrDrEqJWnIUN7IF+NOqORnnvppTj7wvPTeTIEI+7oY45lcbwts+0r35W/xQgSb/wTTrKsDvkmD67lZ7k3LNsRqUefejLO+82vYvsWHeLgk45lHuwu0aVjp7pvKnkggObr6bzklIlkmJEdwKtx7lQ0I+/sJNlBM/T1/hGPxfin6AAcNpipE/1yQUijbHRUrwD+RuChEYwtT9oxNw/xn3KFwoRWvBdnv+1wBSvBf57RUROmvBk3E1a7HlOP9sZR7dpHHVm0Sph0xuZoN+3KIw1q5LwGsm1cXepoDk0UGhTDK0fiSWu03essiDX2uXEs0jYuLjzn+0TEbRerAXeGVOtowOBWRyjzPP0tzdIRBQ0EPHUXP3wuLMpgaeYI7Lhnx8XDt9wezTbuEUMZJd0KB74yVJ6SB9KJQJ6VgyqNfN+Tl/BrZGcnGCM96wk/tkJWusq8axNd//Ofhq6+0w49Io479PCMFhQP6mI7GEYMNpR+1KNa80F9IV40pC3Djpc6nJ85FUH9NhvcPMgCnFfccrPZxTUXXBj74OTRJnAqj/U29Di5kN/J99RDR5IyR36UhjrujQDLBagpT9yLr79hE/zuv6+NJya+FtsTgH/B734Tu/TZMTv6drw81dniv3L4iLO0k6CDHRfLoOg8ddQ4DUEnjTtmjMVx972fXxo9eX/kCSfFAUTx9OzeI6eyapfYATI/T3MwckIcuC+9cjRpTf6pe6GFJUlTR2Dnvj8vhoP7q//y5zi6b7/ov2v/2IqBqPXWWTfrbH7q62zt1JUKZxl2DJzKlM4GcCS8Ohltba5N4ZQP12hwYfEHaU+GLe/LtIzNN9s8OnXqhE3TOvnbKRR2Rt6FRqNHPxFPPPgAuufTmPTpZ7gDWJ2e/SU3xL5af7XmsUYL8jaiQYfvvzgaSvNTNFWH+FRXKIN1S/AlbQd6qiMbM80LQTaXUeA3Js2L+S4RQBr13HfaNYt+392PaJt1mPrxRkx6eUJcytSJvthNbdD/yt7iQCPf7HCKk3KA5sRv3smX4Mn2JX2VB0ZuKmscgb3tD9fHsDffiAN27h9nnnBC2mO2I2WsET/a8sodBxCWMLLrFBa5VJsoR//RdSVSorbwqTSmTbmjxcgnn4xfwJM6sH944olxBI7q76zH9GTy0BFgAwE8ziJ/k/t9APxp7/G+4snKxpbvxelk9PatTGO97fHh3EVcd8GPY/d+u7ADwuroCLHNAZ+ou83Dw2c66NQh8kwOcJGfL2wHLqatU0IHnvbYlb+4MiZ98Pc4YMtt4hQW4jQaUR2TzmLS2+6qo9K1pahSnu987sCENoGH375PBM+T4P5icLNrayJMjzky+x9du3RJ+ZVOBGDR4aDNpywoNrbruBU62raEOWU+FdDha3plvOvBPMyA3kNElR178CHRj+kHW6D71ujQIWmVjnBw9A1HA3BWOE/812BXh8BACqecevc+TsEJb06O+x5+iAGQNWLPXQfEZjgaNtyQXcxaEikMTIl7EPEFUQw6IZXPRqXZz9DWdpqWji7xnHaj8pkyXAzyrbfeQne/GDcPfyjOP/m0jIjbgLbXDudYynDwknpI3sh/iVYRnaeXCvsV3Z0aLq3efpuprDhMRzBd81Puzz799NgOvOjAT9ioo98ou1JH2L8hw1ZESDm4l31Oc6ct6Up2YNlri9Ytc4FoF9F/iCkfj8M7RwzcK44jIq5nl67Ys0SiqceIkFD+iXfpZxvThlyCLHEB6JGsq9F1q81i8ODBsVrHjvAvNq3YtFJ111p9v4WX/6ijwUasUrFBptIBSSsQLJMnTY4xrBI7kRGPjxYsiK5duxIq1DEjGNJYAvkaNYZm6xVz0af7MOY8bKIXMTrVgxEYF1nzvcyUjZgrA666hrgi5BDgGhc2VpWy/2wsChYP4bOz/uDvb4jXyqzyOIKGuO2OO+ZWjsUBUD+HSRpXyj9ZmDIsPEmfL3kL89jhVVCp3KZPmx43MBrl0Qfo++LI6Nqjey6CaUfX0XU7RjK1dch6cFUgqRAUIDoaUpBZPs9kNuv90ccfxQRWZB3x5oT0fp9w4CHRiYWN2iNgDVkS9zKyBqz5ZxkCUivHupQy6xukxr/H34k2eXviG3Hrc8/k/YFbbB077NQ3FZOKxe+SvjRAF32q4E+nEg1JeovvFJDQXKPazkUHwhjtmU6fNi2evfUOIg6Kp+4s5sBu3HOjNKTM2/z4UwBLHJff4tqjOB6sgdWxW0W24N6pEy7QNl6P4rinTcq875Zx2JknxwYotoq/hAekpuAzjYZ/5lHDjTCKcwWA+LI+OSKFE2k2o3V3YLxN8UOOw/rvFv2JgmmLQfMVClhlIFyJc68Aa94eeeVlqtUEnofkz59UQArONyZNimvvvtvkefzwqKNjC7zQrhGicrLjqmNHoe1oo8ZuQ3ryzpGX7/Uiw0DpWFjGu8YY9Q3hSZvGx0TC6G0d9uLYzLv/Bh1jj8MOYnvZDWLxyiV0dhdFI6IMvsbgmc8Wm02efCl6btg+OjMq1B7eY0CHaCFNJNuUo15FcEqDrxCG7kCxzNF2IhFaNHb+KJ1JcZpmk3PQWEgNmOZQxsSP5sRUBsQbt6UtEie6vAVKHl0vR7RAn63WZN1YuZhFVVkJ+x16v6MS4m/+6d1lo9iT0cB1UZyOADUmbxizhnvwLmopXQqI+4om0rfIgiIjPvn4k3h39qx45v774w3SbcH53cFHZSi1W8Yqw7ItaYTTZjV2k5fINJUd+HaEytEQt1TM7fpQVoZqLqOz8e4s1jwZOyYemTGbnFkbA2NuawzoVii2ZvBNcxSzXnFX6r/rluvjw0xV/4dWE9uC+/U3Y556a7C5lPqw7ugKotu9sm4mTk4SgV/bRk4bq/1OFFh5f5DHck4CRljtnHuQ9uGsiBcwbGdwu+qxY+decdQJx6VyfOyBh+LxyS/m67023RLn1KGxGnj5eMGH2eGSFzV8PBLXlFeH89oz+bw4eIqjITsF4PWdd2bEVTf+sSaBI07adz/mkvbN6QyGtioHBF38e2ZHg7zsAOVIkjKRU3lne812gRzzqux8CefRzYxOevTgHHzqadETh6x52Z7UNZWhWCRKQVV+UP0BBlFY94cb57B/RoTRE4+PjBFTJmbKg7beIbbrt2OOwCa/yIvIA0+/TRJUBiL0No28lUYcOWgsKq+th4a9o0bXMKLjsTXnLkccFT0YaTQybAkLcK6kw+QaDfJ91bF2ZERo1X3i3N/mL+6tg/y6mI6yYcVvvjEp7nz5BbOPU3bdI3rg7F2LiDhhMkQ6+Vz4eO/ourwtzPmPh8oXtEw6GtRNOhGUlf/AgH4L/XHb008hKSL23bx39AUv7u6gI87oitQRfC/us/PJNW2FNPIoj3c6TrhkWr+zs2pnfsZ0piLdNizG1nYoP+2AQ2IzBh/sPFtvT2kpj6SOBSfepwOCd5aZ/KORzT/r6TunVbru08sYijc/OUK0RBfOE844OyM9/E6958BALsbLb9eT0jhNfa0eAS/WzZE69V/Wjd/ye3McDU7Ruv+/r4vCMREDe2wcex58YKyOAZ56kvoKjzI18S6sNZkjfqRfLqYIXMqcbFPwlPzu4c4Kv/rzLfnbP+cMOjK22mqrdETkdFD0hx1787fOyig7nbYD7Q0P3yXvcJEOlqm8S8cdDp4HX3o+0+0SbWKfoSdFp86dcWDgBKdD4bfWP/kP3KahDl6qzlzSgzoVOlnPYqOpg3X4Pkg0gy7WNpyH7f7d2JQOQIc1OmR70GaSi6tR7tzaFdjMo8gTp3+Yn7injsBhBKZOB3lyMgss3zV+TMK+X6/tYtNttox1N1gvd1OQ11Juoyvfh0ZjHnokRs6YmmlX/dObm24d2CqXGXUtaWINMSXwNwAVKKtkLnjjVdKv+hYwk5dJkvJ3BbpuBebWSq7LkcXM+GUr9Ignqw9Wua7D741ZM2HslLfy6eBdBsR2DD60osOinVxwXWyWlGUyDoe0q7tCF+lsWnnY9tqOEVgXe5zAoNIf77wjHQGmv+D4E9iBpjtpnYayrG5xbBdvt43p5HSuuWximzTaU+eajgPbRoGBaBlk5IfY+M9i7z9GlJ7HDuusx8LoRzBotWY6eFM28ly9JdjyT561Z1zyXaW75XkYrC6CzV3Afnv9f9fBfmS/AblobhtkTc6DT34xF/O3BOUKOFd/gAfxYTRcOmKpr7DbDqyPaSa8PjFueOj+2vc4So45jgXdN872UE3lseNpviIkUS9iKMSn+Ydb65SRVcgxDzv57qr3JIsgj5kzM5/t361nbL/7ALaV/k6mV9YWONUhyBbyVBpnPuAg8wFWdQYPsy46GpQ59hHUH1cztcFDm2aXQYdGN+zs1ZA1yibbUzWQKuTCXl0TSVaJCtim1CHyrl1eFyp2IHUKfbkHmaLlcTR4L/pjrZxa5vfKMevvum3KBvmpOTyb0yWQV/KWukgeVBKnjQ3P6Ox9BwfSsBGPhutx9cfO223vgdGBKaYOrJmv8iplkwXkf4FNRCc84tuDp5nOWx2ryobZM2fFSyNGxaj572WaYwfsHr179y4DneRvfeXLKirfROJcp57X4migOAbfdLK7TpSDTM2IjFqEg+e1l1+NP+PQrI6fMCDWEZp+hbOoEQoio5FpR8lr4ECHtPafU+7d9eipW26MPc//YRxGO7G/6M5CLmxbDutV6lbl/227/kccDRLWI5UhBEjiKBw4liGIZs2YyWjHhJjC9lTTp09Lz6iILwaZCq0oKhWbbLIEIjgny0NPf26hSGOycakATWPH3Pk3MoFz7VUsKlE9vQoNU9mJ9pnfZMOEMWzES1jPQceADcP0KkQVego28+cspK0JEfJSKObBtZhv5S8ApBDUsWG5NvgMvaEuzWUW6wQDF6HDNwgSG0zO3QUHCp/PCEuywWi8CIPRERoTaUjwPGGvMZqhmosd3SbvVnSySwMsdalBWH8R5hp/1tdE7JXKWNVsWMDn6IjebxWH+2G3pIG2BL+mTENLDyTpbLhGCaj48znK3akU0kGhYp4OtKbiAHaPLM96UPfF5G+ebRip0tObW8Yg/OoOMzCBV446ZURFzK6uHubH++UkwB7Od47s6ZGUpi7wJD+l06ZmtIkrO+UprKiLeLUUR430NqZhzgPhTeNOfiNdBZIdAaM1jOBoi6K1c+diMdK3HHzHj5LenPldw38N+AKoL3juqJr8YZSISkJTOTtN0EJ+zEV0yF/aeNrhFeep5Mje9J7CLx9+TcHu8eu5Qv6BR1QWGoRfI/ibge+mLPS4Eu/cSiyl5Y11kBBNQJ0aMyd10bvzYu035rEv9RrRGaXdDhjZLR13GSd4Jstsj84R5cN0NLg9z9JljEIDTzOmPjRryCgW9XLtFHnCha8WQfd3geG1+e/GOzgZVluvc7To+J1out6asYK5al/qRMH6atMQvqJXvGTp8viE8P5/fM5cZHiRzzkpkNq2wMPcGkNSWJYxBx/NKZbztNWmEc5Lf0vHpDV4MYUwi2/P/AK6uUuK/J6dB0Z1zddDnMrPtmWNF3lJY9r2rZyzA6WXWqXpSILfNwO3NG7oU/hZHlrCt04lsb1muDV1cFQSjQVWwT48VY2C6sW2+NbNGkWH1s2IZGgabfjdvBHQ4sxpgFMneQ1cqBgLB/A7GQ5DPnOk5t57ZF1oJ9DC0N3llOn6EJ+D3/mfLIoP2a96ObRyzrD7ijdnHm77tqtnvT/HsNCxKxabMjm6pe2D345EmLtOPhIWPNbKsbzEK+9rGM/76rkg+VtjZxnOYY0OdYA7C7RhHqgdOTs22ckib7P1b3ZGwb33SVPwJzsoczMMk0Jt29mBop5fYBSbT47gEmniO79TPmh02O7sYORRQ5W/zb8AXPvtfe3I+pCPZToq81XqmOI0yBER22utk5myz9xIW+Up/6SMAT5lSpHporDwVcWTGh+uGSCc6jd1kzJBveV80Yzgw9nowmzygrpER4OdXvWdxnPWFZ5TfiSteCdLmIcL6KmnHKVUvqfOFY/mQ9ni3iOnxpFX4h4Yfa6sRrkrBFJ/eu8CnpXe0nisdLGy3XcioMjQxGDSQvykjqZcZamyregdd2EykqE+QuFr5IsdSKOjlHuOSvq90yzVof62w5iGK+/EsUdGBcJfVQfY+pnWeqsfzMt/5fuikx0VFy7bvREewqIjUST6LG0AytCRWmgpPyCTeeZpQuWtOPdeWaRONU8XfjW8P2FATmgbaPcon1LGgHvtpmorUaNIlJ8OIJhX0oZyMzpI2vub0zKNzlGHiDdlTFUn4ZcvBCgjX4DDvPI536a9Bvx+I49l+4Cm2kyelmF6aeMouLzmt25JrC4RXz6rcFwna/nG336fOhe4PLIOPM/8ySfz53vlr2WLDxeMdd0J8W0btnOkw4nUlKWdA/y1+ltE6mloKZzLMQSExRFI6+MaV8rrz5EH0rpEXmrnIQPI0/biVEB34CgdZm0c7VH4gzTK8XYtmkX7Nmwx3JyRWXifsU3aIHKa92RU2jd5SwlhrDuSNsIn+pVX8AVnSmnercBLrLT/aOHimP/xZ/EF8tB1dJzmprOkSVNGgO3go5td+LkpZbegc6w9LOrEb9qSIoFDnpb3PGxv4sOrPOihrBVX8q0yQv6S/nV0Il3qTmDLTiafyUfiWn5XDsv/tiPxl3mQv3yRuCWyNaNDKUN+t/1ZJi+TB40kFK+WVyAi/wJ63pN7wll0V3XHIx4bmbkYHDiNuPQbysiz/CQOnK6oQ8Q6uR4AhaRMEXZAz3pkjt4AkLa/8Am3/CzebCfi26u8oBwzdUY+NSly0Hwtz38eFX6yEO7FS+Iz31JvyvdQDksL7Wb5020e0y6VPhQiTVKeVLSiXrYb12AxQrSsJWb7tkNcZJsQ5NQkcOIz5aMj5Tpl1eW+bwq/qBNTTpAoYZMheSs+LMMzcWPdOPnj6/xGvCj/jDiV6Wyb6gFljfygjK/0R+ZFmiyYetnGHRSTX7Jt1XAg38gnpd2qi713GlfhG/tx9n9a07dxXSLhUa+CwMxaGOtwLIGAw//+EQ8e1tDvvGpfWJY3flcWkIVnwK0yTB5KBwxwyvfKX/ub4sT0KZuRTeaW+VkGdNLJ4FQ2+VpbRJyUNf60JZswVaUM/roun5FALbGtlOVV2xQv5uMgtY4i29H2O/bBOb9TdO7i1C4Goxo5lFaqlzTKu2/nH9q1VPjfHQoshYtINLuq4cgArsjvgoGfMeo8n9V8P8CDLXNlJxnFCsmSGZ2HqUEvOQ03dJFCv5cgjujagBQKPkvmQACntxtiuQ9rtS2TikLDXaNKZSP3aTBXSk4PWnvmZapkXKTQjoINMRtjDRMUUZ55zX/84J3PC8cWps1b6uJaAQp2Q4KcL6X30DpqAFRREn5v47OeuaAMwq4ofAWdjRYGx3gShwohC8twTO5TYHtPnnYebZjWx9E54dFAKbDVYKxaXAL4T3+gz6oET4YniQK8BXgXJo0HO76e0jMVECUohJ0XKhy+s3yNRA168zGtBppebWFUyX+It87Q/9WIumhHGJ5GqQ1/ASHYn4N/oyX8JtlQwHiXJz/r6lQDuKKFtPKRpl36Rbm3Y5hKle8zC/CmEEyDiXR2YopyRaDwLwUhuLRsBbTeV/nGfH2XtCAjaSGvyJMala5mnREd8FljynVV+aJOCk/4fYXh7BvnPX8qmnitVUyceSYs4EtFIsy2J/lE40taWCEVnOUKoHsDa4zZWdPDbjoNcD5O4WeUjb/lm6/pvKoE6Cui2KElhtVynuloaNqK9rESxQSNPpo5Kya/MjEW3T0qtlm7RXRhFKI1bUhHQyvpDo82NhMA+JrpE5XRot5awu4Utn8ndLjGgrTIHVvAjOGhqP6YC69MfPu9+Gj7brF+t07RpV+f6LjV5tF0NUbJNQ6WsfgYTgqnUDTUCKNzrKpfgnHuuhPL8V5RCxxJX8FPjHoDW9MmGAiApILPg3oWw6EoAnkqDUbxJ+LIQR4xTRoMfOfojIa2nZnFdK7LiEVxvPk8FQN1K84ehqM4pI8dG+VVGizk05YQbJWjdPNMQ1aaclJoGgsa1M6xpiEhK5AN5KM8SoMBHBcawbdwdROGypo2EKf8Zoy4Mb9dkFM4NGCWsfiFhrV8VeiK0aIRLI2pq5En1tejcjSsaIBSxHBZgv33GY6cr7lvwVoRK8H3Yhbf/ALeVtHLg4Yc6jiRd5YSjbKItXFWwDcZ3UT7t8Ndw3pRutKhVl6F7W9cASdpwFU5ku2VvPOQPvBv1R58p/yU4n5lfYWjKOnioLD8FhiYwqOz1pFhR1LtHNg2HNWsHEVuDexCW4bxG62iHHAUjqxTzhcYCig1iMrNP/2VlzSUHKW2M2BYuWHpfpOQkp+4U16k4cjz7KBTltesl8aJPMEX6WACFttS6dgYeswcc3DviFvRqyVMVnid2iBWWtQcDWlEUV/bvx1Qy1U2K3d8tqo8S9nItxp5GoDKZqdl5WgksClTxbkw2j7yN/fiHeDSWNRgbM4IjlOzvqRzpv60jVkf24o6TFlmXuLF9sRNvvd5tjvSFzrLPdAQ+vm9ctXw/sQRMFoP6a9Ota3ZRpTBptcAVl7r4KvasWmkgWnMzzI8LNc8xa/P1JvKU+0F85dn7AjIC6bVuWP+2gzaEqXzBD7Bv01Kh6L1MA/5UbknLnW6yBvqE/V2HsgacSE86m3brvUShpQ12jvkoczRQa7McNHJitfl+6IPtIGctoZ9RX7qJuWG9fE7dYTl2JGRB9zqTv2s0dyC/KyfZSiLcpBAoclhO0i5La6pk23IMzsDwCJt/e1IsfgXJ/KM9bVM27PlWidxIj6kh88khr+9qtfyPfllZ4zn2mfC5Le2S+0mR02tj9+Yl3X2OwcN7GC77oLfl/Zlp1Cnmm3MDoSdLKMtS3tyIMotL+VJnXUmVFbLTzrezdd2Jqhf4bAWDqfDiDM7Z+LWtDoV3E6yDfhoY9uxzjwDwMShGae85S9ZZX5ZgdpbpXLCSKP0N00tI34aMHWCRh6fowcWIXvlXrcsdr0vRzK1UkRfwS2wfyluWEOFQpRrKS/FNzi1Djq0TCtNKr7UJhUmE1Q08V1FJ/ndabF+5+Ki2sLyrx1u8y95azsUh5c11OauHOnmn3Y5uBGXynXbvc9tN9JA2GxH0tur9+aTcImYxF91z4N8VktDIm/leRoC+LNuOqpwEkFbYVQHA2i2GWkvz5ib8jdxBB2ltfhJGUQ+wmcbVE6Kl4Ib8V3qrGyQx0yjfKinbz18VsR2arvJ74WTfLNTm1CX91ZUuJTlpjdfF0uUL79AfqlDhN88XLfH+mkv6/zSrje9NrcyhKxqfIkNCg/6zDYgr9qObefKGbeU950ywDbrIS3LD3FrH7fYQVkHn/BevvAq+BVOQF7iyVF8n9mWzVs+WEr7yD4Yz83Tv9qo9nvkKRcwL3xQBtNs3/JXa3jO1Oon26Q0U+/Z/0hbCNqJl4X0GdVDOgiVvcm3iVtBrNUnK0XpApB/CvzV+6wTb8RjykfKt87iRjyXdR9sP+q8UjezckqbvKX8VNZanPa/ct5oBnloBUClrAQfFdzizyl2qVtr+lo66mRQlpuPeJRvACR5Wt5Wf6/WoX2szRbiRh0ZIew/UuVZMMzNt/T4jzga9BIp6FOxgAgFtQq5onthAoTlwuJwUOHasU3Ek15PtZ6jotwdLWRlXYz2/I4GpPKTKW2IEim9djBnGg1whUalhOZlMqPvZQTTe8jMNkIbow6MNZif2pQ1HT5bvDAZLhsb33pIUH8lM+QTftd4OlPkb/6U/8kgziFzVEnm1cvX3sXkYK6EG0Fl4zNX661y19GQsJO/OCgGAMYpjUGBoBdbSBTcNpQcSeKJTJ0jXKTTgaHH345FMWotQeC4lqrw+18P5UgCX3ulYPPQwLLTpYCwTA2h7NCRZ07rIFPL16up4K7WDVCY2Iitl7CnQUUahZH4djsfhYbGRLvV2zGKxpwp/umA0FBNpxKN3yOFoeCQT1WFpEUB0ZrVPVf5qHjS3LHCNUFIk818NIQ01BRs5iF/2SFR6JtG+HxnvTVSnWuaQpm8FHoKF5WGisY6OVfYe2kq7cxfR4Pzr8xPmBP0/FvDsI+tGEdxOvBAYISXFyoLlYT0E4dpkPEuFQdlpOKCPnlFUWns+l07wu9dFVpHg55U+V3Ps52fXDwU+HPrHNI7Aq0QzVEJ6ArggIpZo6OhtQYFaT74MP4+laktTz0Xk351Y/Rhue2uhPc2YwHH1qRtC3wtaUuuuu70ic+XYmzyvFWzlsnnluHolu0ELqk76R6yujZCnfN9eH7S9DmxbOAOsdEWG0ev7/aPzn22pfGuzujwFzgkMZRxMgRRDSwwQUZ2xhSzZQRiBRPDG/HM+rpTzUrgaE49E5UoAIrgN/wBb5ZOFVXl26oTB3lKGt7b3lNRgA+VmJ0XjV5HQ3IrNZ4nf2jM8KGdxDI6wGgh//zWTtcSnQXU3fzaMlfPkT5p5BZ1GkN6zBvTPlTWSxxlx6DM/d7hmcXpaHABKjpuwGD64miAjnTo6Y5wxYjyxAzFugUvVBLZSkPkERWi2lkpmQJ8QExOK8qZQktm46AdGEGRJ44GNKTeIfLD2G3djgQY1ihF4XP0xHbeFv4y+kX5shQDdNFClikTX0ZRUR93GqmVRL4iP0uqf8at+PcwpWDlPT+K4Vc6N34mfuX5EtJpx6Qofd+JfzlK2op326/y0KrbJoXH9mu7tKOq4dOaaCN3l/EbjYVPcTSks5MwTPfVNr309qiB/S+/80H1vlQj4beNaRB5NWRSw9pqmo+n8tORdo1aD+WEMHhNWc9zecvUlaMheRHaedXIFX6/T0dhti0MSWBWxqrRXCMgIxrATTEybc8apcXwlB7KFvWeQJlXGjZ8a9nKGmHS8FOPqLeliZFIwi8/q7+EVxxKPCN/lI/N4HF3FZL/rX86UKCXMrI4G4qzozLkHCWyTPnbMrI9UU/r6mHHTvhs1xrXabjV4BCx0lWaG36b9GZkSLmw6IuF6TwyD/OsDF7bUuoj+Ynnlm2dPMSV8tzOqrrOunmvoWta01XOdp/b8Si0KosH2xYcrRI/fJB1KPjX0F/F0WAb5ZBtSh4uhkkHtoGOSvUWI690HJUTHtLC8jRI06gm/8QrV/GRPAQO1KdiLZ2T2DGWLX5Ma13Ev/D6TL7UJlK/Vh0m7Q+/Na06XpxpaFuGtPE74ZBXxIU00gaxY+SI3FdElzi4YH6Vga6slVfs/JqneKxGxNO2kP9q+XsvPhJ3NVpYjusG6Ghw/S1tI+EzH3Fq3q1wbrkY3Ffwh7rQesgzwp34JMvS0dKBWDoBwqDDJx3J1FlayF8ZcVPnaHA5YWlbnDzpaJBOyEhySYfNSuU7sLQEDxRKanICdmVhobA5Vwe5mWH5881rJuMlOEhgWM8IZeBwN+UgH4C3gbyFA8KBgmU4T3QsNcnRdB0N2MBffg7n21YLrit8W5z4EJeAmji2zfjed+JC+vo7BzHs9FMH8VNtP+t8enFTHA2l81rJhZRb0pgM/E6+szryh78dkbVtSrcciCF/+VDe4WHSLL9J3lCDAog58N9fHnk1U9PkfXnjI2Wto8fiQ95TlqS9Dy61fYz0NHU6zqh3yi9kiM+zXQJDOhpol+JIvMgv5u2HlRNWG0r+NG/lcLYb2oQw5fRUkZtfqY/4LvFqyZ4mK7Zawlx773PrXtFKvGgLN2SK6RKmrS5iNwfzkj7CaB6Vw81cTW9fKR2EtbyUvdJXZ7Q4lw7WU31h/8CdORz8sz1V7dm8BL/qI4ijyoHvO19me+Wa97w3bwBLu0MMpyyDZ4RFXCvj5Qdhr6iqnkoHId8qh3UISS/hsL/iM6fdmLe0sy7qFeuZjgbq6m9hd8c1dVIrnglT8rOgFsoJdd2vhDWfWJVaCq+cIlgZpIyxjcsDDjxo7+k081tt78q5Lt9rL9hfaIUdIT+YY+KZ9Pb1klcsHR63/5b9MnBuW9HR4CDq19h4TvuwDupr8S08hT9pq7RjnRfpxAFHjaFdY9o/nJDlWWc4P/9BhW/18R9xNCg7Dd2RWT2y0YiZolMQFPkYiV7vxU6vPw0ESvNBsjajzkWB2cjh5vIRhF2OQrZh6R3zO72jpfHWyoPYKUiTHS22PK86HCkQa8JF4deKVd8l5pLlRWlmRzphkKjlqL/yrGRX3tXqaKp8DJMqCB05VsjKxBnaTWobkE4GG7CK0kbmR9ZFhekhk2eDr9XBPFMo+U6FQnmr3lt/R4sR8WmgVIwrvAVm4KmA59mqRwX6qu+zIa6SSHg8LdMz78m5atzWT1ynIsNQyXsakLRXEFlX89fwFH4FiQJQ2tkgNRzMSyNYGlb5VSAk6CChqsI3cG+iqhL+FE4S2on3t98Ir4IiDSieKHDLs6IUFZCmVDFXxorPMjSU+uqcSqXNVWUknPKMBosVK555+dYOUE0QJCPkn0zDY8mclag9rb8XfvLxEPdJP/CpgCoPVUyOGBUPuPgyN+dLq/ict6fR3UR4+F5jsTgahB3eUPmIY4owRNDoBfNoAi0aWj++JzNAgLb25+3IInAXzno3nhgxOsZddGXszEJZ7jrRcCke6K+/jFbgogU4cLFVqEwf1dE1HC3wQnZGgFCcUgL/rJv8UJ4sAVWLOOdSxsSZc6Lpgd+NrXbYJnr02z46bNkrHQ1ASr586eRVOr1itQgNecVOvY46RhubIPDBu4a5rTxRBoMkDxfiJ60FocJ7cTTIcQXt8kLSEdrKf3bOmuHY0PmlgszRGhLbJpMm0oszlam8Jc7lLx7bgquym4KjJjgCHB1bRrv3Wx1IzrWDgVKGGZlhREOG9JKBefi7tCHxZVmk8aR9wwnc4xCoO7lXTiX/iCN5xooLjOmBSHi9/xdHQw2nOhyIMpE+K1fKJ7Rl7gEt4fFr4bE+5mPY7kr4kZaG3CkKu2pjK2Uyi68O8cNvT//W/y530sLD7zPUt/ZxJWuEPbmI94nvzEB5UgqRXp7Z1pWXKTMLnMqdbDdcdXTb7j3MW7oqr4rhU+ZJ+rw6MvcCWvUor6tWzQeZRNjJX0h1eFimLzKtcFsDbtySN2tNObZljzpZRz1XLc47wKb6duzgI3mGQwde1jfvxBvkz3bFlWcaLNZbI1O+SFkHTsyviobwI8utdKF49Z/8njJc2MS76WxQwgHMydf89pntXNoJS36f98VYF7SkD+8tK2UCfF8Z/b6z45rOUtLk996bnqPCibCYv+mT9lw9dISov6WdBiiFpJ6paOr3dgKEzy+yrnyrrJTG+d401o3niU/SKuNNr4z3meULp511y890vM/OElcN6ux48dvv5FOPQjvpV3BopyvxwWv1nvrP/DRWxY21FjfCbzoNcfGfdSfPTAPcqad4n7jgeX4Hf4gL+U+j2e+kvXAJSdYVWZMdJMoVZ9kpQDaYf4UL8aLeyG9ruBAf+Zx0BX5wmLZLcbaIe/lNuplOfIjPggUxwZE3tWf8rnioOATAR40m1lFcm4dlpW7l83R8kUa4hdV6+Y8kye9mX0kY8+Z/PlHf2emr3tom/G0Zlm1eHuIuB5ykNZlZJ9ucjgYPp1Olw1f4KNc2YiFOkVTskmHeg4ByJY3/8/B9QQCXAml5wd8Ep8AHo5RviVpAYSugkCO0N/Om7fqpyZ3Ohs8E/uCsZbQSJ7atO2GTBnX4KXwir3iIG+uqPSZ+q9PM6+jH+8Q9fKkLYSlRiWmniSvS2R5MK610gKVeT9CBlXLNX9qY1jYi//jbPHyefMu9KCllSq/SRsSOR17Nwhsr7Z/qnjs5KcWo+UCAr+B1HZ7SyK3Kfb+q/E1+sc78q3jVMj3NV5jq2jMPyuMCk/lX+NQGbISzSfxlO0xYyCI/ICPvgak68wF/Slm+K09871HBkG2cZ8IOsbEtiQrGkSFc4q/IpOIktn1Sjbq6ZOc06VuzQYEl6U1+aXdzryyT15U14mAxC3CLH+3uqpNrpRNu3ptnDdTEj+/q8O8L3ov/tLN5Zzn2lRxcU94pw/xG2VryEoc12WANgSVxDm9rN8tTHpWMFJMpc5JPC20qPSWuxIF5Gw0gXP5JXPrTA/j+7ZGwWhfqmjir9bm4z4glbIPKMVNykJYF1+ZnGSnvuapTrUf2Obmv8MEvpUPypS1BPjETB15tLUYK6uhyMNL70n5q6fhMmlhXnWcurqycSsdDVhTa8K98x7ukpoV9e4//taMh6U/9vX6D7IXOcBLEzndJyZLIB36ggFXZyEgIK1pFCl4bfd5LfYgLFbNjo8dPz2Z9SbWMFHo1wWfTKWkUuhbiUdI5GpvCF+Yznb9NoaEiaU3lUV3L3f/kL42ff45QyWxNDYsj5+wYUkerkZ29FMbC/++OClbfFXj/NRUwky+sSYrCjKWO5dsC9/8NetP98/tSlg1PD54NSOFgA2vo6GcefucbaWn6f87DRCWf5Y7oQsc0TFSktaM0TBVU8SQX5aXhVfKu0pn/v8u9el9P0/JEiAqHlXu/FT+5cCFZW46YKgdvskPGE55XMGtspEKh3vJY9XwZ/CJecnQEHrXxl6PCwjdh//d4qX3CpcJi/ZP6upZ3UlchhwiDnxVwKrwKHp0MCkBhzzZTw5Sd/jRAgd3OYAVVqnzaj97UOkpQJxoLeXJ1JVXm/lPJiHnvx8hH2Ef8+xfGzs1wNPTuFQ1wNDTF0eC0iWacNktrURRbDceU14xyC56tj6aQyUqb0tGwkHPOZ4tiAquAtxm0f/Tp1ze6br9VtGXni2jXPvOkhXClAD1H9sCBeSX1d2RHR4qhZDoaakBYQg3dVW3rHuSr6o/4LDyWwFNCuYLJ5EXvUhkmdb0r7wu1qly8Uo5KAXyKPfdFr/ISgirXZctxLgKvuHJkXho20DEIzrUxS5uyrgVPq0Lvb8txektjHAW2drnZ6S25CjFOo6RdChQs0FV4WAdFSVvLsaqGWcq34LPguLq6jagLkjFljSIawGeNaPO5bSLPzcW4AUcBGwFLy6Z0lMwHvin1toBvFML9vz8KRFXtSpoKd+WupPBZybHKtzwv5fBM/MPrZZ66ztbCixpORaL7nWc9zUtO5W95XmTn/xT2At+//i218fk3S6jKKEYvqdRfHBp6wu3hdEKfZgcs6VLlVsFp26ofdUv+RDZV8gcpytfFgLGmyimPhvBZhUFxI497X/9dJqv7Y2lV+vLQ3ORTTuSMRqD864hMyUPZUmSNhqXllfpLB56rv/M5qWs6tnqvw7Ous7uKTCv4Kqmqv3UA8sN8NfzMz/eppyhH2MWfHSFNsnIUPAq/Rq3wyxtpCJsuce2Xpqtwbr7qJTtWtBDSFZyUdFWq8gz8qD9ImAZs5ldKroe9+oLOImuqGI7uYedZWLKDBlymykg2HAZI+UxT/TFaKw1XEiXt5Zsan8gXllUZ9qXcwu9C7H1FC78tjuryJl9nXeEv9Jr4sc6rdnJKfqaEr8BJdhxJJ12FV16oyqzSwi3gpda5TMqUkupxLN8Ue0jaaBsoz8tR8FXWSjDaxOmXdH7rbI/iHFDu+Z3HChwghb58K32BSSd70emljZW86/+WUsq9v8WIpy2xDAbJu8WSSJ5RdoPziu7lS7/g68xslWu+5B2wZK55LV+UtPXvcl0ZbWLbB4/NSopWh/eexrqolgloSEeD2KowVmCQdqYsR+lQWX45bMe+ty5KgH8+Sksv9fVdxfPmYK7SXuawPVTyw3R5QAt5xyNlWOZfD4vPvfvXJ6UU33vUQ+vdN1P75J9T5OLTdG51UjdrUqY+yXvWrtSxynHVvHhmm6m1uSRNTZZYZsoIlHWFowK32Ck48U3Jtf5N9eSf4auvQwWHdag/5HHLE6cedtSzM50OLvlXfqtoVeUBjLRV+zf5XR3svl+1nt4VuM1bvNh2PTIKCxmTPMHfUqf6WmSi/FPlV5Vd3lS8Upyp0LwGo205Ia6TgwVr9Xip8lvlOd+kc0i+TJuyUM8UllPJd2VSU6Z0VhDUfqxy/00Y//k93E+GyKTUUUQEEZ2r01oZopO26ILqqwo+P4FXbDeJZ2GrQQb+5fiih3TUF1yLRfXsYuxkdWZG1NFiha6cfu/BHXnnVux8W6bQlmfmax/DfNzu0i+a4IisWl6hl3l8e4//taPBqme/SKIm4ySdbNcQsyDb0RVXGNYT6f7eeRT65cdJXNCbxPUjPk4hqYJNgkNUnsmEPk8PXSqiwhSFiBgLacCV0Yo0wiWs+dYagt9XHd2iTOqND0qtO1b9Xffw3/4wpSXQhLMsE6H4UppZDeGTiUxmHWR8mmY6S3jme4+sI1d7IqatfV/wApvlfYFKz7NhkTaYliyMJxNmIfz9vx8i3KPkU67VM8GDRsAjrcSxo4J2ijTsi4Go8VgEpO/TscPV3LLRWgdudBylME2niiNLZURq1WiGxBXfelTCdVWo/hm2TLjqn/yUP3xUwyBvzaFgQ5FQebrTEF1VEFZVruFYungkv/mMtNY5nUY8L7xW49laGTWKl5GO2rO8ZE7/5z9V0bUS6xJmfuKS8rMG/C44Fr96jMG7p7BCpzxIK3d7p8e51N1rOSwj5/SSXo9wzvDzYRrKfOUoOaItHQ12EObNj8eGj4iHz7uQLdKCrXZ6RwvStkQ5NiPbjKHRGKfE9DDzTEPaI1cFBj5HBGTpVEh84Q4UuAjic8qds/Dz+NuMadHqgH2ib98+sRHTJtbcpEewvHjmQQW5kimg5I4gKAjXPhED5uvq3o10QphGXquEi49I5eP/0yGePAp31Kfyea7/ggJyZNG5kKaxjiW7KlOu4EjZoSyxw1gW9ir8Z+6qnnrHQOkIla95mwmsn6m49T4VYGlP3OXhY2FyoTEdDZoG6UBIo096cW+m2dZ8q7PCB4mlWvnm4rnqYRpPvxFKlZoK0t/lXtise8XpJZfCYUJZuppixmgL76sOBzcefmAR/4djVYjMVXkjj/udBke9UW8GvE9ekk/VK+Jc/vcVrZtnjiykXIT3lU8qeTsbLp5VjlVLLIDZgZOXMr9VZULti/+XS4IinPyrDK+qTpYmLuvR4S++EG4uBW4T8ZwHqSe4zQU2uTcSzjqmzFQHklOWx7O6MsCJkW0eGijCUUosJVfwFUiy9EyTH/Cngq6k9r1SU+6sf5fA5j2wga9SzwJD1hH4AYmTsmuVdW0I24kyS31vbuZuOo+UcZm4PFdOF/1SInuq9CV1+ZuyULojXJL2ZJYdINuBh3mX7Cin8NU3y6lPVAzFMhXEtM6VLnqtxo/AVnBT5EA1CldGGykvdTYF/jP/VAiwqNohXSqbJJ3DwCj8wlvDishL3Fqf0vqsDCd8WupFShPX5V/4xXysSzGYfSYNgJxTfIkr6+eR6aBHeY6AtYbmyeGz6vDbLMxL7XlCw+8qT9PqcCg4y4Spa1Pn65BST+VR5VtoX8rhN+8Sw6uUb1H5hqsglHrQ5hOeUhepkfYhiR3MsDw7uQWvtTaSMBf5XAOi7lLKKLdV3b0TrlJ2wVWBUNxKDWWjTqgi9+sda8KbX9eupU61muWb6k9JV6VHJ/NAZ1NGY9USlZKrL9QB5fRbayZGPctOM/zwyAoBLdfEU0EcLwpNqnaQ/FHDY36X7wvybe86xZN20DTbVL63fEqv5V1Sl7+Zh2XDb8JX5zy13XFveV6LnS0ei+Nula/9in+m+n87hFfcWd+M6oUuDublqLcRPsIuzwNf8odtNOUnr3jmCLO2nXyjPtFWtTNeF5GR2MYhhr5Vjim/nEZT4fR/Bm1ipZbUWntf6FTlU9pSqX8hm7wuiKYrn1rHIqvq65SJTJg0sh1AO/J2TQ3tEimiba089bftRdwUTq5gqeVfLt/8W1/4N56bVw2shFHcmluRP/kLtOOUJNLUZxnFaQ7qZfmEfHUO8afky7PsV5CPGQmn31ln65P5W3/qJPT/s+P/Y+894C6rqvP/zfShDDDD0MGhFwFRQQTBQFRssSAaRcUItlgSo4lJLDGJDRH/H7tJTGKLCIpKUaoo8FOKKE1QQFCkygwMQxmml//6rnPWeffd77n3LfO2GZ49c+7ee+3V9nPue+/Z6+4CX+NlhaVRqs9He7uaLWxiA6wqbKtZVnwHEXzwHxl5H5u/8JGqH7DNR+R45jSP8JFnX7D2DUSNZt9iXvcAPD6bKyy14++EPniQCLph4m6CBzawZ1rBgLvGLEmebeKHWCs6AuTrcxqhQAPRb9583EC+6AwS3lx8WBtCrJvjA41fyadMq99szgPO9R+G37RqxgIfANXDR/WB4EjbTWDaFMECfh3gi98/TO1G+kCMe2ofGujjgdM/YKyML/FhyK8E/PpbvZHsV3sfTNf++O3ObiX+RcrLQSPn7psB98PeNNWb14kZV/0WoU+2fok3rD9QwsGbjjdbfBhaX6p3FV8vVjb/vd3ehJ4blXXIj9v6IjDY1DYFrIIoxtvNR+z4DaFgCXt5cjl7cbrdO/w0jPzBhze/YcbDEnhXflt/za/qFy/7I6I/fEhkPro96nZP2dyJe8YGVqxPr1L8umEfks3DAi0ZdmV/yror4gPQ/plY9cHAFxh/29UDHmXvB30L+bz/4AIdRuh1Gx/g/JLLlEz29AiMQwX327/IzHDfPTcdkdBXpoLmXyrw4ILpwxV/uIkHNSOAPbMbwMgHWuG7tSHjw37cNqz5h3/gUav1L2UUI0+ggTN9/T3F+4pAA+v+bRmEfdumpffcm8499/x02j99MB1gCnbdcy/bZduWAdkfMMdbsm/ACpv9wMditTeCLQ1iaq0NiNml15zwpRrsrM6XC/spzJxqp6iw0aCZu8/2VfjVXb9Ps//iRenwww5LTz70GWn7J9uRUXOzQAM3kiUF9n4zMDzQwGwAT4aDGXC8sOV/M3y4cNV9dj6q/RKYWENHW1Xh74m1hkwxZkMr3gP8Dfg7CRYwR5y/C/PBH1IMF3cVsrXZV6Px02e+VPgqIiFkd8P+lqpBgd1D85sHJV9mRr94CDNauIUEt4i/G9/0y/R5YMiw50vLN3j0L6gqYICXRnU7VciAWvUQQEtfqvhoxUM4eO9QjrkubrYWoGxoOzfzNugPknZn7N7aZ4N1nk1Qvadhxvzz5J2xl+hU5H2N/rfjD6JVZ/3z27+IHWvTY/TqwYD3KZ/vhhGf6XU7mIKj/20bHqwL5YGTzyj/1TP0VB41r/7QYHI+GPL3TdPUWYg+dVKrGv3hcjf5u60+AyGCDb775yHvUfzw/tuL2eV9hSCzYaIv0Pmug4/9Vegvm9DhAlNgWVpD4u8dPh+oopa/j/qzgjvTfJ5YG3e1Nux6XB6epkarMdaJsv3Vuv/VTAU0VO/Lim6YWV+qYJAJ4Rz3u8aAsv+yyfvbPhOqh/880IC/7nQljCzYWOK5wL9f7LuYGTXuFu2R6nvJ9w39d0jNjr9fTGc1eKje84jwvoGJzzxjqrTw+Wp/u/Tf8TQe39vA6AQX2WPJO4PZsI2/xscmsbT5EgfwjvZKc11HkP7VRM+pByGYO3PeL/5wjr/mQzzc9ikyfoeixqNWVz0z2QCJ9wDvD/MJmssbzZPReIDm+4Op4PDF9zYDq74ZcSZrPOBLQkfdm+qext+J8TCzDH3sYZTfq+Z7yuz4e7tywF/dNzCz/9VzmHXCfOU7Gpq/J2k3u/6gjz2zUT2jWbnusyujjKy9x0h+3/yeWMV0EKBGwDHJ5WDGFnYsVQOGqu63KPoIU8XY2OUeMUPTwK78s7+Lmqvi9Vf+gkqD1pClkOFz1QMN6LNy0GFFQ9RDGznXJMPLj7KmhtN1Xzyn7pcxhhb7HHIM6Vvwo9yq/v1Jbjp5pubvimdhnxUSn1vWjDcEfPkach0uXNEb+7Vd3lskPpe4pwzUkffPaKNhrjKOsjplxSB5XtIr4Uq88cFwtPcjswIYxBIwdB/Nj+p9zuc83xuGMj6SzCf+Jjz4Yd8VLAnks4rPLv7+qmeJavAZ3yc8Z7vhwNsVdXlxv+0l7AUbPkE2f3CfvzVw9T3koJGMoXqmrnE0HdUPTNHuTM4a/fTp+Sbny5T5rLMy36tVcNS+o61P1fNpyIJDDW6dVQr7yE29o4DzRjCfwJa/B6r+GevvF+sTezPYsz73eypL3y3xfcfnMe9DXyrp3+H0x/RZm8+WsjZwQY4lFVXivUt37PMaSyWeNVfXzIXr1trvRofh7sr5u7Aiz/ng5XuTMD5xWe4B7dVzCIz+/cazl9H57OLHAP6O2ZthSj3zgve9f19a39DN8zafrb7klO827jX2vRHHSNQt+fugGkdj2t+71ntScHplPX1Z50BDDZvfHzbZ4s3D9Lqp7FhuoHDffEaDAc4bc/IUgw3kjL7GBiUMQok6+RsRsL2NN5ox2Bus+VIwEX/45APX+GLw5w/gvubYGar7ln+4Grl5kzVvopoWfPDQkbbUjd7wBkPlr9vCTpPqt4m/wasPEX9TweJ9tDz+kFyuxgf5aAeU4DG8+MMAA/+ihZ6bi3JttqMNnZHK9qZuCho/0G0+4zv3pv7jdIP+B5Mpw4/QEWRy+6PjIdkfbJr7a3T7I/Xkcm2C1hp9qThbXmsGxHMVkPGb3HHLGws1zlPTnNfK9mHNB4Q/uDVfVLUtWF1/1Evjta7IepiuWExPqIIQPoQNfw+YkqA3eu1DzZ4AbA9FhOorGqu8Qrj6sGa4wlpTx93vr7VOMYIFGtbaxjf32x4N5513Qfr/PvivaW9jm2fXVjbanWkbC61c8KAPPPl75uLrgDPB2U8QG7agwV9si5LEaldofD3P4mvCAgmLH1iQ5lv9GrsOefGL0nOP+vN0wKGHpJ3sfOdJs+2AciSav2GrRp8ZbceNtfvBA6grBwuu+PuFrS1BD2xLnrrONHYelP2BiC9CkuNDIROC5n8HRnO7xmZF+urumqFJdj+qx/TaKA35+5AvZdcNlwl3PNTVTZg1Me+il8EGK1yW6n5zV6uve3ys7DJc7CvDXPtRU+lPH2ftQ93HPs4+KTRzv8mrfuGLPTTQcVo8t2K3BFu3FLjQHvczeB03q0Qe9zpvb0AyIzzM2OUPcnzGtCU66Nhb7h2yl7zTbTLdaN4vE2701bq8jlKr43OZ/DPT2uuHs8q+1aHDju/o4EGFOg+P3h+j2X/ayDwVmHgLjW6203bIVNL9h0Rw0+YDE1PA516uoaKjuqa6L3hhhVCOPzTTF/v8LN/b1T2uRJBsEt8D9Nm/G2odjc6Gq9Ib+Lqt4DVmp2PcEmWK+d8WNOeBTqPV64fBDoytpa8/lI0vvqf8PrS8tzr0oqBOtTsBWZA7cseKv1lj5jOlJ3Mm6X879LOWs2LluNVRQ4IW/W74jAgt8Avm0OeCocAqDR9yVs/vleOBDS7uYc3v/ajpllWplnd9RnG/sjYv4nvtf7RT94RykhvrvCf+mVq1+nuPPoWemuxiUSYPtUHrqNc2gs/7Zp8v9XdN7kmIVzSUxBXCwdGX+99Z7VDz99Q42F97aPS9WWJGH3acNfgxDWeerI3mwAJMPRlfsELiWcfaqkEwbdFYs7uYK6oJGb2DtdYf8s3fDUwdjJ16qNWi/Rsyikc7rB766U/zWWP666Br33ur9jn6j6qQgcb99Lq9d71uf9vhpvnuz6y8t/x9Hg2ZP67PXqKp7EMH3RrdHSNCx28I8T0QuvCHi9T4HYoqcscrn7Ow42Pzd2cEsM/7VKvskI1Kr7bgcV+tgivwx70Hbhu8AABAAElEQVTN/ecXF//cN6bmXtSfCy4Pnc/PWklgb5TKV6Pjc54a3wp6zpM3NfwwRAW9mYCTeamJMT7BZwIJNOEbzQ0v7LUecmsnAMnfsget4rMQCrJ+mUjoQpn3DaVtCXu1/rAd/N1E2tRMYNqIBBrAAkyX2y+OREiJpM2wc4eh854kesfF4JhnJxJ4EvXkhvFL1OR8SUUGLnrLFDTYnNVsuAPRkAvAAD3TmTf3LLfpaxNodLcINKSGyTQEMWhRL5Vn7VFs3pCZmhDrpob2kKdc8tEWtJKvrCPfRoPelnLesp22sFu2daPnfKE78lCGLDg1Opxg9YaQa+lfDoxpcRF7aWxArCu0DUZlhyzyg0joDbnSBnS7GJvzcRdvfx5cQgQLIQaNj/jJEPxD0Ap8CtqX06q1NkvIdo+/1wIN59uMhs++/1/Ssw94Wpq77dZp4d13pRWLHkxzdt3Nj9S0bfQsar00zV/4QFr0+MNpqh3j6ucA28PYspXL0vxH7k9TNp6Tdt5+ngUaJqVHbN+HVY8vSxtvvY1tbrU2/e6a69Lhr3lNOuLQQ9OBzzgk7bLP3mmy6XBH3TfzL1LeERj4cuaCz5Mx+H0iD1qXvFd73tborvWU9VCPTFzQKDsvL3b5+6wu096kEMrzprEq0ERyfVWx77UmWr+bIEfd2KmxCieUSqCSqrwKHYSZyGkPF/I8whK0V284a82FvKF4CQUF2au0tckHLZcNWq4nb4feTR9tIZ/LBI32oaZcj8uirCC2ftYET248aCiq9eSk3PmgR+62s5egh/q6HtWMs6MYYg2xm0DQI28E6gL0fsoyppCDJy8HS9Cinuel3sCXz4CuKVcYfEGLOsJBC0W0Qct4SpZgLfNMpGwasD5YGyhq7IRQEMp6WIUePNCCj3JOp54lx7luz9m6iXejo7JXW5iEp5sdV2AM0d4R6AxiKBpk3k8sd7K2xXeljxqqb9o+jko4Plcrx6rP1T4n81L4FBrIq6v/p3HFU1lgAGQPzmvs4kt/MCn6FaaQgVbWB6MLnlyuTSbstbX1og2kN2Qb/blAQzT/utA7HM/4nW71nNTwZvRcLb6U9fCPPNeV85X0sh46cjq0XEfw5HnOD29ez/l6lQeygWyjt8VIg7sxBV9O67CdG8v4G56ivaFbIW/K6W3l8IO2Njnag96LN29zO7VQPj7I6V42odBNPcqBiddrB1x/XYa3qVOG0QhOizbL19M0IoGG6Hv1o6P9smT/Yr8G2jjnmFkNzFrwpRU2YGCKD9MxiaRCK58X4v4gn5epk+rb4AMor/AdQMqZ4yZVLSPzmutH43BslDq6edamO6cNVk83/SNFL/3IfexlI/hK+TaZNp6Qj3wwcm16kGvTAQ3+tjZkQlfk0Ejd+KvW4b3mNtDPs4flnYEGyJVx2EOkZq8DDWHepmLbZpCsNZtkf4sP3nd/OvecH6bLv/X99MpXvyptNXvLdMmll6Y7bro+vfBlL08777C9TfNdnhY+9GC65bbfpQWPLEjb7zIvTbejxxYvWZwWPfpw+tP8e9LcbXZOBz3tIPNiUvrFz69Ij8x/JB148MFpqk0Dv/Pue9PMzTZPu+yySzrgqQelvfbbL03bws5U7va3W3XFOmI94eIDpumUNXpEPZiiX5a3kLLW/kX4Q2//1v5t8MfVwV/7Gcriy6XhMSHkGmGvNK1DKXDv/aqF0BTaIu/URwerluhq5PDl5ZDvyPO+hOFcCCUhQHmgFLyljja5kidkgzfqJV+0DyUfSEfYGozOgXTlOgbS2629G32wugfrY/BFnusf63Luw2D6PxL+5Tbb9I20HwPZw4eRttnWr9yPsbDX5kNDy50xolfX0ak2caeFLcv9oZ+8CjT0fVrCmAcWQllF4zmYXz3J+2RCAv2ZjebngpxGGVm+7G22HdMIvWxZWwrzbW0jQQvXQtdo2RuunVyuE/LwuIIz+Hr5302+T9PYlMLX0lov30veodRLe8OxU+posz8cvW16BkvDp5GyGXoiL3Xn/Y9y5L38DX3BQz1okUfbepavc6CB/oIhV2DhM5zthbWAzEaxcYwFGtgtvwoqMPWfOkcecqQQqe0+tNFy3rgP8T3gigb7Es6W/N2MlnxlPdc3XB2lzm71sbTVzYd1oef+53qGg1s3XaF3ODpDdiDd8A1Hfze9Q9FlOphJaZP8/B+u8EDT93tHNZEeOgl6tXTCq8Zs+2RYmGK5HW9pK2vTwvnz07ln/zDddN7F6fjXvjZtZUsmzvzRD9Ndd9+R3nzCCWnP3XZNS+3oxvsXzE833Pjr9Efbb2Hb7bdN0zeenhYvW5Iet7Vui22T0p3n7ZYOPugg+5tfk84//8L0x5v/kJ75zENthtPMdOc996WVNkjdcqu5Fmh4ejrgwKeljbewfRHod95361eMZYHKH9uYGuXTo2r/6VEeaIBxMKmNL7c9GB3B06or74yVc93Oz0sIRh4KB587PoW4fw5m2gevrcXNXDjvQ04farnw18XXVXepc131DaVP2B4re2U/h+pnN/6h+j9U/m5214UePgwVk+AP+cH6kPOHjsHKwtcmk+scSBe86Chl2vT20tWmoxd/tIX9qE/kvMSol6/d8Ougo9AuDzJEoCFXGszkcdEe9Jw3nKt1+g0dqGx6mM3A5rt+/HPfN7xrRrzNVG52KOVSV7g8FB3BW+qCPlL61kVP+DeYnD6Mla3B+DNYnjbskR1uX3J9w9UxWN/hG2t7g/Et9ynnL+k5Pnk5l8nppXzwBT3yoK+H+YgEGnxGswEXS3Y4kq5a90wgoYruMkaIWQvs5UCwgSUW0zi/x1KOe1vdmeqXnDfuQeQ530B6St5e9W76Q6bNp2gr8166cj2lHPW29l762nSMFa3N1+HY7ta/XH9eLvnL+mB9yHUik9dDZ+Slzpy3bIs6PN3kg4e8jQdZQgzhFTzVZa+1cU6jCBs+CM2fk4xtjUUeltgeDSvsLOd77747/ejsc9Il//WN9Lq/fnOaY4GGs7//gzRj7hbpb//u3WmPffaxDX+Wp/vu/1O67ppr03W/+oXtJ8lZ8BZQtKVPm86enTabMzvtsffeaf+nPMU3Wzv7zLPTpRdcmPbb70Dbn2Vq+tP9822vjulp9py56dDDj0iHPPOwNGvLKtAQPYkAQ9SrPtm+ABZS4djH6JuHTvwDp9qcy7rfpLr7TX1dCtjvlpq2DoNRsTyKHQpCqs6jmvNCox5tyEd7TYsqTZFydqd1Y2qjh5JuOTK5Acp2talqo3VT24uem8v5hqu/m75c92DLpQ8D6e7VXurKfcjbch15Gf6cL5ePcq/2UlfItNF76Qm50cyH4tNgfW3TSR+60dv0ttHacICvm97g79bejd7Ndjd62CHvprONPhh9ue7xKDd+D+RszViylfUKI2YlxJencTSzu8IaeVyVRPe+1xbY+4G/Wr6YXR/08jKSPzQTXLDnZD8StP/3nTUOKWFlqCl62iY3HH1tenrZgD+3MxBvr/ZcT5sfbe299LXpGCtam6/Dsd2rf6WNXrzY7tVe6ip9Hai95I96L5vBU+bdbEEfjL5u8mEn15GXaS/rIbOh5iMTaLBIA7th+zIIQ5Cd533DQpvOwFFIEWAAXG4OezNwsQHbFAs0lKAPdAPjZuR8eZn2sh4yA7XRXvqTy0Yb+qM/eXtZDv6SHvVo7+Vv8A6GJ3jHMo8+hM1efvZqQ77UFTqjDXl4Sj1lvdRT1nO9ZbnUFe05vdQX9Zwn5CKPNnijTFteDz0hU+Z5O2GE6kKHaYxRugsZJ8z88WGMK56VaKfNnl2W2uaiS2wmwr333JMu++kl6bJzL0wHHLB/2nyLLdKNv7kp7bn/vuk1x70m7bzTzkilhx55KN3y29+mG669Lt33x7vSUjtNYisLMOy027y0wy47pSftvmvaaecn2QyHJemSSy5NP7/8cpvVNMVOH1mRlixemmZvPjttv90O6Wm2dOKAAw5Ms2ZNb1wL98Jdcu+CcUy22RcebHBuPOH0BE5fyHcBh94/hR7ybgk7wdeNBzp8pMqvvryi1q/Ng2gHNauElozUUuzlb7D30zQYoRAeKG/TVXec2TQ0xzUcVaVMv77UDEEv3Snr8EHL89LGYOthM+fPdef0vNwm19Ze+p7zUC7bQy95Xh5IrmwfbD1s5Pz4FBf03MecP+g5LeeHDk+0R7ktR44UvJFX1Oo17A1Ey9vbyqXuso5Mbisvt+kr+bvxtNkJ3rKtrHezMRjfutmAPpCdbvqRoy3P0Rf8OT1otIe9XBb6QCnkcr42Wt6el3MfoJd1aOjjW8YPJ3aG4LIWL8IRFxIDJJexbzuP/lOxqwk2IFvTyPn+9oA6337VaUPRikXKkSM5nISObgndeQpbkedtebmXTvhKvbls6M55uunLeXIdZTl0lvSy3s1OyTeW9dL3sl76MlAfemEWuoOnl67gKe3n9eDppQf+Xu3d2kJ32CvrQW/L23S20Qajs00ubLbJD4YWPORRDp3rYz6ygQb7QLTVEb4fA6dP8O5h/wXf7NFyEjeFz9Q4XxVKLKlwhponyr3yuMGucxBywd9LZ942kjd4uLqG6nPu/0QqD7UfQ8Wr1D9U+YGwWlf9pXwvewP5Hu08/OTBBl9L0RgyLhid2V6gM5LnItU/knBqBU2LHno4XX/NNem6X/zKj1WaYXsvrLUgIIGDpx/09LS9nQ6B6GKbxfCAzUy4+w9/TH+89fb0+EMPpbm2FGKn3Z6Udtj1SWn2NlvZkT+2nGLFkvTHO/+Ybv/9H9L9f7o/LVq4yG3vvP3Oac899krzdp6X5syem2baSUjhFjm+xGVFT/QxAg3xe0712MfDV1CCu3+OvqEkh6yLQLSRAyEpaFVt5F57+T3gMolewoNxsU0+OspnvOmI+9VLXZuaXvxhohdPtA2keyi6QudI5cO13a1PoY88v0p/u8mXfL3qYSvnCb3k+ZXzjGY5fIo8txW+Ba2sQ2+jlboGqud62vSF/Zwvp/Uql7ZL3miPPG8vfRmojmypZ6A6MrnevEzbWKWwO5C/ZXs3/0JftOd1yujhqr4ujcLDa8MUViIPLZY3PBmtXxGmuGiMcuRGcgfMuj1b2+Jj+8z1nxRgHpGEpV6ppWe92AfX7UzDUPVnov2Kw9U1EAb9DE1QwlD7MVy82ro/XF0D+dyrvbRZ1tv8DFquNy9He+S5zij34keuW3vIt+mGlrdTjqtso76+pZEJNNiHr5+3a1EGArBs/OhnOLOmwpLPXLC9GPy4QKsHoJwn75tC2tEik+tAhAvYS7ebRTvy0U6eX7SPdYr+jLXd3F7gEbTwKehRpx1a1HuV4YUv54EWOimvL6mtv/he9i1oE7lf0Ze+QAP3yXoSPzOH8/nfFLMdYkRPO0psRsMae4bhIWqFtd19xx/TPXb6xFI7p3IjCzJsssXmabadPjF3223SZraZIyIWPkzLl65Ijz74UFpgmzuuePRxm5WwWZqz7dy01Q5b+7nCy+yRaMmqpWmpnVDx0KKH0v0WmHjskUftPOHJaZs529jsCAtIbDHHzh8226aUe8DVNnDFZgQa8sesoQQaTMWIpsqnCsK+xz6oVaIvZYLWx9HX2kajtdSR1zvs1w15e5/2qgQ/7f1s9RIqlVCvFcTbrO1+tYltaLR+OI5RB7GbX5gtfRnqLW1zvdQJD3rLq012tGnhW+R5f7uV8Slvox7ylEm96rSFfJm78Bi8hH/kUQ5fMJ+XB6qHPHykXvVoC/2RV5Jj9xp2Iy/9Dj970Utvc11RjjznRTffkVwR4O0HeC5AuU1RydNaR7AWDmP2UM0ySI4zzlpbpTdUYn5/x6uP+S3Fn6h38432vC2vRzl0RJ+Cv6RH+3jk4dNY2w4MIi/tl37l9bwccrmevEx7WQ8Z9LTpivY8b9PRRsv1RblXHm25rfWtPCKBBgeTlxoRNoJjs0dPfEjaejQ/3pKlEnXAAVZEfMmFCRKgGGqKm0ge5aHqGCn+Ybg/IqbHu98j0olhKKHf44V56e54+cJjR3XVWOBI/oYwgHhAcZygV7tHZu7bhpD2MBNHyy5fvCytWGJhAvv7XWMRgI2mT02TpttBlbZp6xSLCrCbCg9bttoirVm2Kq2xgMNGtkyKv+kpdpzt5I2n+L5VKyxksMI2mVxt/zjVYsUKoxgfnkyfPC3NmD7T9maZ3ugLtwvX3U9/uLNOMXchsUdDdNB+4amoA89ocEWj8FI99vUp5m6Qoh+R93FUpXjfRl625/U2HcjFBS/lnC8vRzt5pLAbz7FBb81r5tBJnpdbZUaRiO3wf6TNRL+66c3t5uVu/KNBx65fAzk7GsZNZ6zOGifzrfe+9GWgeg5Nfh/zMjxlHRq6S/3QxyL5fW8xlPuTl2Et67l42b+8npdzmV76cr6RLIfNyHPduZ9t5ZyWy+W6Biqjo+PKBVBa1nND61quOxDBXUz1Mtetv+vqBvLYHU39A/k4XrZ74T2QzxtK+3hgn7/X83tQ+pLXoxx5jn/oiJy2vJzXQ548yrQPlHJ9eblNLvTmeZThz8tt8usTbUQCDXQYUAOYNbbzIxczGLjYr4Frsu3XwJ4NRBXW2iwI9nQImW6gdWvPb2KUI0dXNznacl+p56lXG3y5jZALW5EHvS1vky/5BqMHmcHoKnWPdL3Nh17+w0975PgTOnrJwUcK3qpWvQ5GLucfqXKbL+ju5k/wR/9LXtoHkkWmM1XBBgbkDk5mJAYFaPV2H6ebhZrH5hP5DIXqdJh2y4issIvNXKeaEoINJlYdlVlFAfrsmgoeiCwEYXllhOAC1nPWVe5A5VNuNXPdcaDN5bzBjPoGWvyObi2+bpU5Dn2BhlwXbpYJNb14urWHX536Ktz72iLs08AbMDd5bptyZ71PE3b6ahVX1EMu8vAp2jtlo7XKO+11t1/p6guj5Lo7NXbWcv2dLVV/2tpz3W3t6Ml5Sr3Uczl483obf0kbSH/On+vOyzlPWR6M/sHqcr5QGHlpcDTqmYN8rkTChawa5FHLO/pfG85hyMs4UdbbHCv9j3rkIYOu0Bc5bSVf8JN344OOXOTwlqmUhb+xVQvmPHkZXWW91E+90Vc35vW8HLKD0Rm8I5Hn9ijndfwrfczrXs4F2hwypmCJHLa8HGK5bqcZUz9aMGcK4KHalbdub2Mo/9ZC/WDyXvZy+fAtctrKcs4f5YH05zpCpo0WbZHDk6ewE3neVpZL2bI9rw9GH/xD0ZnrH6lym/1evsNPe+RD8aO0FXYiH0hXKd/GPxRd6Ct1lvJ5vVs51xHlyMPHso6uuODJdYcMeS4X5cjL9lyu1O36c8Fg7mY42teDfEQCDQQNPHBQBxaizt4LJOorV670vRhYRuGBB/tZdMqUqXZxNnD/1IFtG/iIGFM0RR6aOuSDOAJ5NztDsVfqKN0aSV2l7pGut/VlKP4PxZ+xtDUYv9r8QW40+t/NVvwFYNPH9sFohLVGYFiORz5gp1IRnMZ5l34yhfEiu9Eqe0F+shE8opDsCMxKBP2QOOVrLesnLHcWGpDBAAS7qHLspoUb7JUaHlQBB+qrULB2ki2dmOJiFYez1bzw22UN5J58NgPOB9EM+qyGvkADfA2/C43cS38foVRXFXJwj2tKtFT2c9nwr+Ku2mNIHznUXIZeYSNSWKoocAal4uiUDakqDy2deUhEHp5EXvoTFtt1d1LXvRZe5ZrC/5w23HKb/m66wm7k3fhy+kD6h6Kr440xkOLciXUttznZRltXO93kB+pr7UvJVtbb1OfdyMsdWNeCfcHbPk0dMn3kdS7lvmPD7eTEsFA7UDaV9WDP89L3pj6QcMOYaxudcrhCHmUshQuRN7ScCeJgkykK0chDtKznNhHqqIfQEPJGf62oqWc6hmpjqPyZqY5imy8wjJT+DmNWKe2FnchL/rJeypft1AerC97B6INvtFKb/aH4PxS/SlthJ/KBdJXybfxD0RX6Ig99uY6OcskYApEbc87SrQw7euPqEApdZV47EjrLvAt7fxshWOsb0pu1NDJB6iMSaIgZDGz8WAUXKoTYp4HTJZjVQICBi7Rs+XKfTj1t2rQ03TaOgzuwpT3w7SDS0C2ZQC4PW6OjTQbmbgy92kys1U5DbAptVvto3WwHxyDVdHSil070dWvP27qVw6+WvJur3cw5gDTmttBb1gtbA9vpxlEoGrFq1cM2qz37jv3of5TDpx4YtNkJGurcJgQL6nkyArMLmJFAqhYaWCHmYDrRggEuaC/IrbRZSLZvykaTLfhnwQb2bwh52CYby0ZGWBOTCxBDjyUmGBBXJPdA4yRj8nZbQFEveZjsx3PZWlNbmrGRBRom2zIK+2holKCLKsmDJlWxeo1zzJsGOOmVGSxS6CjIlZ2yMTealzPh6GOQquG3UTOs8Rxf4C2vkIscF+KChmT3IT36wunIkakiRlAq/0JLxVPR0N6ZQkNwlXnYqvLKq+hPaEImv3J6lPvlKKmMdTbljra05825YAtr1Rx2Ig+hXFEhnDcFe7fcRRuBptCNvaIX9voxD1JNO4D9tFUEdJZ2u9FCRckf9ImUN1g1hf79zPqRcbX2ImOt2kuBvO4fVsZmQjkZwX56Km2djDkTCqhHHvxZnttw0SDE5w68uc6WeohkaptiKdqvUx3CNXc/oUbdqBfKIE+44nmHr+ZKWR/Iu1AWfFbPVVDO67DlInk5GDtoobfQEzMWct15OcTQFVfQBso77KMUQuS5cNAipy0v19VchHKH/rKReuiIvI2nhQZ7ntyOE8uWnCsrD+iY8Q5SVUcvS73oKGmZG03/c1pZDh3hT4u+aMpFW9j6mnOdPRn7RKJU2nLxhtgUgr09H8jmINUAbtvfR66+KZc6yzqeNsy123U9Z83LNDePm/lnbi3eL8v1Wzl0lTlyweo2ILQxOaNxBDP19TSNSKCBgAJ7MvQFEypkHn54UVq06OG0+eaz0uzZsw2iir5sma0FX7kiTZs23dZr27bzbSmAb2tro3W7GUPV06YbWk/962Ak9A5bRSjo5vgo03uZH3afCp8HtNFiqJdMob5fFXWlfIsJZyr5cmWtMjnDIMo99Id6Z6ESBNQacbV9SvYFCtYm5g7lH5wr11gg0ISmEAS0jRonsXkr41ePGthQFh1WRS3j/ClW8PG+E6zNxvgEIzwZjUDEWgssrly5zDZ5XJOmzZxqTWvSsqVLrGGtBRVtQ8nJ0PC4nv5AGX3dEqwkZl+YN7xW5ll2RWOx/KqXLlc0xJewH2Ku317iHPSwB2YR9Ki7VD4YoyLUkXs59HmvqIRC8uC2opejXvM1N7PS1hcc6AtbIFmm4MZWpTFsBmdf0KK25A3BhQy9rfSETJYHY0YadrFysFN8JPWjuc1Gp8W+WmO7LpANRT54Gz19qruWOmyYAvs/GPEw1VVvS8NAel1nztThW4vCktSLP9oiz2WheWoKQajyts620Tql+mql2rLunKYwdEYeGlr5o3GIeakb8dDf7YG3lCnrvVwI3cFT1oMenQ/d8OVl+KjndGhlytvzcslX1k037Hly8yVxoHr4nCvKy3l7ZrP65kF5ZaD6/qkEc5FcVdOKiDP1cYabfRpDc38N8XlLS5+G/nwNJZQ3hHUsdDM6knZ62lhHQ+geiorgdZ+6ObaOmA5WvJv58HGwenrx9bSxDoZC71BUwBtyVsiDDUGOvOOeDtZGI5wBYrQQD/POxudtNMAejTkNeq4zyoVO2PIEm7OGrjJ3Bufo1J8rWU/KIxRoWG1ruO3wHWY02KClmqi91o62uz397tbfpV122TXtsccezTIJNodj+cSUqVNtUzgGHi0pQG9p6knivgxXtqfirLGfDTPY2GwK9uYIxvrNkqkYfBF9tXymukM+fmnpII5DJbo7pqa7gdLDiW63YxiqmnvTw9yINvXCGP+54LFrtV3VfghU2TqxGt5bZnxr0nIChBYIYPnSFGu1DwOf0eCBBhs4r7H3FUP71bZBwyQLNEy1WQjNw5U9+awyhX2BDNNtPGtX2CaQK5baQHRNmsrZlRZwWLXkcWuwgIadXJE6Ag2EPniE6pLoB8lYWAaCryzGqEh8yuBN9a8me9uovIQvjrG9EGjwYAPWaKQfdgVf5NFMnqeqGzUFXa64rnc01jQU2uV6a94INPjff91uDIQP2kIILupaKv1wmeFaf+Q5V+cMjZrRXYCruXMhGgyjkWNwrOx087+xb4W87PzmYEDnjU2lm7aB6W4jDPXp9+VOJh0tuaIRsNpTL/rjbdfKmDszKuW813m57nkbAG208C1XAa2jHpVMgRfrOlmwhL6RznMb/hmRG8iNG2PtVs7RSguGXNxpRnBabrRUWtZD2SjnYTZyzPXzv4XWxpO7muvrRjeeahFiLAWMT9d2yHM1feXcEOWqXn1Ww9X5aRztVUtV65OCaom+5WqDRr6+J/rV3DsrNGU6llVG5Bm7Bsv/vnLDGbgT5Rm7dnVUsxyCwLqBPArG1MADLYQa4iBdrGXbsA9Vhn1YRWmQO4g5wyAt9ynqU5qrcTv4FcR+n78thuJ9Ek5a3ogX7LBUNuqG0B+RlYbBCs5YKFiPqiMSaKj2ZLCHZZIBMsl+HSVdffUv0vkXXJAOf9bh6dBDD00bb7yxPU9XsJPHZpHO3PYSd6itraTFjchl2mil3FDroRM5t5UZzIpj8sYIe/HmHmpf1nv+AGCIHcnvYS46LHXdlOWKx6Bc+m5uEWio5gDwZ5kHGipmBu60s1UjvVhtQQKLKqSpU21JgwUfVtlAepUFGfhbnWwfftPqpQ/eGxNYYSPNVZYj60EMUzvZ5O2ICaNY7iNRyy2g4cmOsfVkSzQ8mDGFQET7Hi3Oh+L6qmZXVA96eI9qm4NRNxtT2X9XMBovZojPsOaqbRQzGpyK7wMl95uX8ioFAwyj+0jP+JsRn7X5Z0DwcLc7v5xzbZ2t7kBtH65wGq7ugQbwD86xwx7/JkIyzAI2L4D/KPnF+8xTbcOyuLfR0s3yUF0ajD50Nm+7gQS6ObZO9Daj0OreunNFz4tqh/lSXVnvYA4zvRSWAiNZN+d6+dfmVhstXOqnK/QjRGObcBstFI5SXpoM99rMlX0q67lMqbdbm/Px3RPz+yqlvcRzVX3lkOjL+VsmoTEv59jDUYfTK+5efULZBpfqDvfrtxFG+tm3sUEh7hPFrLzB4durQ4ZDg0nBN5KQ8D0Hxm6LFyuH/hr7mlo54XyZP2U9a+paDP3BEOat3jT5c17N4DY6vMgaTCL/7gkFdR7uRd7RHETP7WVDDjREAIDB/+CTTSYzXPpE7OGUwYnNVnj0sUfTT3/y0/SZz3wmvfa1r01HH/38tN1226VNN93U+OGzj9U+wU6TAXwntf8bvsNVhIwQNNdhL04OYq0Qp0ml/YZeNVeyzthDb20jmDHlNmsdkaGbC5ul3eBp8hYF4RtNJO8SupqKk59YLwHGEHvtmLXIDEtdN2Ut+keblPtvbhFoiN9g8DJ2NAiPYQ8RfrFZVQca2DtlI5uZRJCBi8SgfqoHEEO6CjRgo9JtSyuMz5dfuITJxXGUIYIx9K0k8GBBBwtoJNsQsnHC5ewl+KlTtqsKNMTCiSrQQJjBQw3RCfhHPZkxt0deX+GkeePF8CHvR9Ba89Djio0j8pw5U+ZF42lGfHWbf65QrkIJuTQa+zR0PtL2t9epA9nco6o105c35kY32HLWYYp9wI5Oj3MbZou7BynzosPuurgzkE6/9zkT5XUxmHseuiLP2yi7XX8pW6r6QH5Ee66/m7o2esiPWIfbu9Gdak61+YVA41sh3Y0OW5uuNlqhsvX5JeR62Qs98A6GL/jLHNmwV7ZFfaD24GvLS9/8c5bvHr4LuSrlJVubqopWcka9+jsGjHA3PplzObirq5YL5u4GN7CWcehwmIxbtU5v2PX5dgQQY9gHTDa4RzknGK3NrTZam9uFqoalje7PeA1He6HV35q11tnNNW/OG/MyKny82G52faL6jIbYzJGBf2zYWHaiLRCBXNBZNrGR/6qX0gMPLEi33npruuyyy9LXvvY1n83wnOc8Nz3rWc/yJRToXm4bQmIrTp0IPW63HlR7ICIblK+xX0uxyZvMfZ1igxW7U2uNtsbWl0+ezIN+351l3wiYvU9BN8oa6KbDN640v8Nm9GcS/UCv+YEd71fNxuCi0mtDJfMfxrXsjkepPmXDK/mLyaCbAMxGZm+qnbbhO+f5XwuKeXf1+eEDGOTxOfw2eewGTpM5GhT7tJdvTmTHMtWu9zM5Un6hP4PI7XTo7qj0udHNrz6O9lJPW7mIGehlo4tbuYYBy236c/+62TC5vkeiiglV/BJdpb4Vp0HxIICx+t+Fva9cPv4WraMdg3pTVgUyKmn0clVjX+zZ5UsLKtsVV0Wu3t/8jdVX09hSqPtPkJegCQnP+YXH7ZU3oDDXonHwpNq2m83LtR/N3yk2/e80mGoTRbXVsMvai/vtLzVblAslTTXasY1INPTlGUetM7LOx9naeDR26AodkcPUZyETKYu5QNk2lHoYy2VGSneus81O3k65w25HpeTsXg87wxTnfRaikZfGMEFbmCrbe9W76UQm9DXxrV6KRq2tl4eF0XC4ILdWB622VprrDlloUW41MghirjfYO3R2VIKjf96mpz9XRRmkyop5KIq7GRwmvTSN39CG5P8wbLtdjESgu/z8HKzOsgNVPXe/Kpd8dJN/LQmBtoZcaYvYkEltNlAS9tfVXjf9YWNdbzL6h+1jL+dwcJRT7nvuykhhj/u53rw7jtmwgevTuw4qmjFQ7hflddFZ6ura/8II1eDNm4KW681oOWuwNM1tjc5UczSMIbn+5QMGGmJgG13zwX9dYfDMxcCkGpBv5AGEW265OV1yyaUeaDjrrLOc+81vfnPiOuSQQ7zOhpB5oCH0e26DG7+f9lAVH69QPBDAhnVVa2PTuV3A3gPIuByzwG1oYheBgopeSa51HdWAioF/ZdMGMhYw8OBCBAxyP2qdtKOXVAUaTGddr07csLr5YuzVG9LKuEZwowo02C/Dti8F/sBYu406T+h3H7AHjyuqbER/YMTWJJuKXvFUsnp9giPAmykSb68oN3nnA1Leztu1esM2zO2FEMr0Q0K+0mEF/gA8RV5XI6vf080UsaB3yzNb9CrsYbGx2U12NOjerba+DeANzW1i7mPXhu496Gqua0OLrm52+3S0cfS1tqgUaWQR4AYUgLfdk5E12q6tcKOdaTSp3To+Go7ltkZD/3Bwyn0K+dHwrc3OaNoL3SOV9/K/zUZgiFyUO/hyhXm5g2mASqtil+nU2J+vP2UAU2pedwS6vhfWXbU09EBguLiHXOcfU5+hdfkj6qazT3tVWhcbaMjtrKuu0rdxrHuggYFtJB+41pUY9FINeuTQQi5oS5YssdkMD6Trr78+nXf+eeniH1+c7rzzzjRz5sx03HHHpTe96U1NoCGCFOhgwB6DdPSSQnfpA7a4CBYsX7Hcf+XniEx0rLBZEqtsQM/UbwbzJOyw+SSzAbABX2kv/Icfe1GnnPuJfNhniFMNeZCqv5uaAZThWftZtVZ6Q7frN5bVMRMik4s7AU8rJvW9gi/0lXxhU7kQ4H0y2p9XzXu2Fe5ozRvXzaOx6FPurcpCQAgIASEgBCoE4jstvsfavpGCVubCUAgIASHwxEKgdTNIBrBxAQeDan75jwF4G0TwP/LII+m+++5LV155ZfrSl76crrvuWl8awS/5xx57bHrHO96RDjvssDSD3ectMYinLQIApf7wIfJ8oJ8HEAgssASD5RgEFMpAA3TsEGDwHfaNNwbn6F7D4N0u7Lf5EEsWaAtf8R86KXThE/pILKOY1KbP2rw/FihhP4vcZsi6fIts7lsEQKDRLyUhIASEgBAQAkJACAgBISAEhIAQEAITAYHOQAPjbfsXA2YCDP6/HvTmA93cefgZyLMc4uGHH06XX355+vd/+/d06+9ubdgOPPDA9MEPfjAdccQRac6cOc3eDAzWkSeVMw0aYSswCM95Y4BO7gP3LFAQAQn0rvZ9HaqAAPq58n7AQyCCnCBEBA3gCd7QH4GAybZkwXFih32DCD7aVq5c6XrAbZLtF4G+0lbl02qn5/boaxWjqAIVVb0v4MO+EeiMhB6u8DPoyoWAEBACQkAICAEhIASEgBAQAkJACIwnAh2BhnxATZlBbH7haAy2KccgmgAAyxMee+yxtGDBgnTFFVekD3zgA2nhwoU+cGdATDrllFPS4YcfnubNm5e22morH4ijLwIIEQhwZnsJW2GHYEboghYDeWxDZyZDBApyvfBCRz+JNi5o+RKMGPjTlut3IXsJ2y5nPNThjUAD/lEnhT30BA06MvSX9vAfetikHAlaYFP6Q1voRZeSEBACQkAICAEhIASEgBAQAkJACAiBiYBAv0BDDKZxjsFtXNRjcBsDXNoi8Wv+XXfdlX7961+nq666Kn31q19NDz30UNp4440TezewZwL7NDzjGc9IBx98sJ8+sdlmm/lgm8E0Vwz0Q2cMshlIl4NpfGDmwBpbfhAzCWKvBuRjQA9fGcCIgEXYiz5Hf/L+QaMeNHSHL/gXbfBBDx3ojCtk8CPaoeW60UN/+B/64cEG/pKQpy1v9wa9CAEhIASEgBAQAkJACAgBISAEhIAQmCAIdA00MAiOK3xl4BwDa2jRzsCXAfENN9yQzjzzTDtx4hLbn+E6DzBEoIHZBsw8eNWrXpXe+MY3+qaQm2++uQcX0Il86EQfg242S8Rm2MlzeGMQjjwpBuKUw09kYoAPDX3IoX+yHY+JrZAnh58UecgELejUoy18LGcoREADXnzI/YCGrtBBTsIfrkjhL+3RhlzI5v6EjHIhIASEgBAQAkJACAgBISAEhIAQEALjhYAHGhjExkAXR2LwGnk4x6CXKxIDX3iC75e//GU66aSTPNhAG7wMrhnYR/3pT396+sQnPpGe/exn+6aQ8NCG/di0kdkPBCbQCz3sho9xPGS++SM6Qp5jH6dYECFmLKAHHcx8QAf0fNAP3YMPxoeekKOf8JczCtAFPfcPXurs3xBHZoa/wRt+oI8ydkihzzZ+cFl0WBykSciHroZohdCLz0pCQAgIASEgBISAEBACQkAICAEhIAQmAgL9Ag0MgLkixQC3HOwGX/AyWL/sssvS61//+jR//nwfyDMAhl4mZj0cddRRiaUToQceAgXwE0jgigF02GZAToogAbzQ8sADg/gIFgQdHgIJXKR8LwfakIlgSMhGv+CnDR/CbugJ3wMjePMU7UGDL+xhB/9I0T9yZPJ+0577kvNGW/BTVxICQkAICAEhIASEgBAQAkJACAgBITCeCDRLJ2KQmzuTD2qjPR88xwCY0yYeeOCB9POf/zz927/9W/rd737nQQSWShA8yNMmm2ySPv/5z/uMhu223S5tsukmTTM2GIiTQvdgB9ERKEAu/A5Z3yzSTohgqUQEC+DDVvQreBtnuhTgx1YsvSjZaCcAEnqxxxVy5NiC1pZCDt+48DPnpT3otHEpCQEhIASEgBAQAkJACAgBISAEhIAQmCgINIGGNodisMuUfqbyM6hlkJwPbhn43nvvvek3v/lNuvLKK9PXvvY13xRy00039ZkCMesA/fDOnDkzvfvd706HHXZY2n///dNOO+3kA2kG7zGgRoYNJB9//HEPVLDEIGYnoIN2+GOJQwQz2PNh1maz0pSp1ZIEbMIXSx/gDxvooY2cPpHDh66lS5f6fhLIYxefZ8yY4WXkwQV+EvyctkFihgb8tEeCP2xCj9kTyHHRhn42sowZDsjiG/3Et+gndOyiJ+4FNCUhIASEgBAQAkJACAgBISAEhIAQEAITBYEm0MAANg8g4CADWga8pBjYMvDNE7MFOGnirLPOSj/96U99Q0iCBCQGzgyS8wE29Kc+9anpz/7sz9IrXvEKLwcPA27sMHD/05/+lH7/+9+n22+/3X1guUMMuBmAY5cr0rbbbpv23nvvNG/evLTllls6bwzWkWNAn/cvBuwRbMDnRx55JN13333pjjvucPvwb7PNNq5zp512tiM55yRmZJCQJ1Bw5513pssvv9x1H3rooWnHHXf0oAT20B24hW1mf3AaBzYIzsyaNSvtt99+aYcddkgESgJfAhL0DzlwDP+xi174gjcwUC4EhIAQEAJCQAgIASEgBISAEBACQmC8Eei3R0M+eI1AQz5YppwP0pl1wED7Ax/4QLrxxhs7+sOv9PAzuC4Tx1x+5CMfSYcffniaNnVaWrlqpQcHfAC/bHl6cOGD6eabb05XXHGFD8g50YJZEltttZUfmYlugg+ku+++22dGHHHEEenAAw/0wAB8JOxHgALdXN5Hm5DA0ZgRaKAfBABuvfXWdNFFF6XPfe5zLs+eE/jIJpZ77LGHBwNoiKDEtddem/7nf/7Hgxwnnnhi2muvvTwYQWAA/Eg5pmCBLHZ+9rOfebDiyU9+ssvtvPPOrj9kI9AQ/tOX/J5AVxICQkAICAEhIASEgBAQAkJACAgBITCREPBAQwxeGRDnJx5A52KASxs5icE5g2CWGDz88MMeaHjDG97Qr1/8Wr948eJmwJ0zvPzlL0//8Pf/kA571mGuFzsM9tFJQIHB9j333JNuuvGmdOVVV6aTTz7ZxZ/ylKf4oPxJT3qSzx7A36t/eXX63ve+5/s+MFPi4IMPdh5mNsQMBJZ/rFq9ygMN6KY/eeCB/uArMyjOOeec9PGPf9ztvfjFL04ERQhiPO1pT/NAADMZ/vCHP6RLL73UT9j48Y9/nD784Q+nY489Nu2+++6+DAIb6CflAYKgLViwIN16y60ecLjvT/d5EINgBktJwC1kQj7HntkO+E+gIehuSC9CQAgIASEgBISAEBACQkAICAEhIATGGYEm0BABBQbIkWIgzmA2H9ASaGD5Ar/OMziPZQDUmebPDINrrrkmXXzxxa7qr//6r9O+++7rg2NkscGMAwICu+66q/NEoIEBP4EGZiwQeLjrrrvSJZdckt75znc6H/sgvPa1r/WZC8wemD5terr8isvTRz/6UV9yweyDF7/oxemZhz7TlyTEzAaE166xGQ32j0F6megrA3j68v3vf99naMDzrGc9y0/IOPLII91flncQZPjFL36R/uM//iNdddVV6WMf+5jvObHnnnumrbfeumOvhdwOfSRhf9nSZT5r4/rrr0+nnnqq057//Od7v8AEDMAK3jbsoYN13pbbUlkICAEhIASEgBAQAkJACAgBISAEhMB4INCxRwMOlANXBuBttAhCxOwGfuXnF3YG0yxz+MEPfpC+8IUveJ+++93v+mCdpQ7woQ/e2ASRAX78Ss8AGt3ogZ9Axk9+8pP0/ve/P91yyy0+U+E1r3lNOuSQQzyQgA4G/di4+uqrfUYEA/6XvvSlidkIu+yyy6BwjX7ef//9Hmh417ve5XIveMELEgGAZz7zmWn//fZPS5YuST/60Y98aQX9fN/73pdOOOGEtMfue6SNJnWeEDEYw+xvAU4sv3je856Xjj/+eN+/Yu7cuS6e788AIYI8BGtoUxICQkAICAEhIASEgBAQAkJACAgBITCREGgCDSPpFHsr8Ct9LD9gRsKRNiOgW2J2BIEFBs4EGhhMcxFoIADw44t+nN73j+/zTSfZz4B9E5hp8NSnPs2WRmzs+zj83//9n88uYPBPIjDxpje9Ke22224e3GBfBGZLENBAJ3YYrHOaBMsrCFiQFi1alM4444z0tre9zesELP7iL/7Cgxps9Mgxnv/1X/+VvvKVr3g7Szre8pa3+AaU9IGNLGMJCLZI0S/asYM9lkeQ/vjHP6ZTTjklffnLX/b6e97zHg+SsASDpR/wE5jhigAMeqjjP7mSEBACQkAICAEhIASEgBAQAkJACAiBiYLAqAQaGOyfdtppzb4KF154YTr66KO9zzFzIAcAWgz+gx58q1auShf9+CI/EpP9Ewg0vPrVr/YNGpnVsPHGG6f/9//+nw/U2TiSGQkMwD/0oQ/5TIPtt9/el0Ncd911fkIER3ESxGCgP2fOHN/EkWM2t9tuOzdNoOH0009P73jHO7zOXhIve9nL0pN2fpIfm8lpGOzhQCCF9IlPfCK98Y1vdPk4TYJlI2z2GIES+AhyEIBgZgQXMy0IcrBU47Of/Wz6/Oc/D5v3jSAKx3+y3CQCIOCB3wQWYtZHYERdSQgIASEgBISAEBACQkAICAEhIASEwERAYFQCDew7QKDhU5/6lPfxggsu8EADg+T4Nb7tl3ja8nYG0Mx2YLPF9773vT54Z4YCswzY34GNIQk0cOoFMwwIODBIP+aYY5pTItDxq1/9ypc7/Pd//7cHNOBhoP/ggw/6Ro/Pfe5zPeDAfg4LFy5M3zn9O+ldf1MtnXjpS16aXvLSl6TZs2c7P0duspSDzRzZeJJAxJ//+Z97UOGmm2zjyiuv9BkR2GVzSGYzsKcDwRaOs2TTTGZAEETYYost0vz58/30CWZ9sKcFwZRXvvKVvg8FG1DGHhMEFQIbdJf1ifBmkg9CQAgIASEgBISAEBACQkAICAEhIAQmVKCB/Rv45Z8gBHs48Is/gQYG4ByfSQCDWQgM8Ak4bLvtts7LAP5b3/qWL5Fgo8j3//P705577embUjK7gr0bmIHAKRbMrGCGAptKEjBghgN7MBC42G+//VwHJ1i8+93v9ncHbS984Qs9oIEd9oNgA8i//Mu/TC960Ytcjk0pmQmBjb/7u79zOY7HfNWrXuV9+O1vf5t+/vOfp3/+539Or3vd69KJJ5zo/jGrgoABJ3fQN/ZpYP8HEjMcsM1yDXCI4EIEaBRocJj0IgSEgBAQAkJACAgBISAEhIAQEAITDIEJFWggyEBggcE0yx84eSICDf/yL//iJ1mwlIAZCZzuAB97IvzsZz/zgT7YslHkP/7jP6Z58+ala6+91gfuDN6ZKcDsA46qJKBAOuuss9JXv/pVDxaw/OFlL31Zmjptqh+VGadcHHXUUR6cwB/s/PCHP3TZt7/97T7zgA0i524914MazOD44he/6O0nnXSSz7zAT5ZMcBIHMyvw/8ADD0w77rBjmrX5LJ8JwUwFNoUkOMFMEBJLMvCXYzwj0ACdAANXJDCI4EPQlAsBISAEhIAQEAJCQAgIASEgBISAEBgvBCZUoCEfRMcAOgINH/zgBxP7LGy++ebpSNtYcocddvBBOqdSsCyBgTzp2c9+ts8kYIDPPgns28DSDYITzDDYY489fLDPDISLLrrIN3ZEjtkGzGJgpsN3vvMd30gSOssXWFrBEggCBZymQeJUiuOOOy7ts88+vt8Dx3AyI4EAAYkABTMdmEHBngwEGNg/gn0WKNMPrsmTJqdly5d53zh9gv0hSAQdCDTQT2YzeDDB4gur11QbZULTZpAOlV6EgBAQAkJACAgBISAEhIAQEAJCYAIhMCECDWvX2K/09o/BcySCDgyuI9BAIIBf/XfeeWc/BYIBPsEEZj08+uijvgSCTRhZZvGc5zzH9z9go8Wf/vSnrpJAA8dd7rTTTr7XAfszsJ8CyyRI//AP/+D7QLCcIQ80oIslDAQa4OcYTRKbRTJ7Aj8IGBC4IBBx6aWXpssuu8yXa8D3N3/zN+kVr3hF4rhKZl8QyNhmm23S5rM299kT9A//CaJwmkX488UvfDG9/JiXN5tUxtKJ2MciAg3YUBICQkAICAEhIASEgBAQAkJACAgBITBREJgQgQaOgeSXfvZl4Fd6ygyoGdzTxsyD973vfYm9Djh1gpkELIFgnwb2bCCxqSIzF2JvBZYncKQl9CVLlnhQghkGbMBIEGPp0qV+CkUcQfnOd7wzHffa43y2AcEElkaQONqSPR2mTpmafvLTnySO0SSdeOKJvunkAQcc4HtF4DfBBoIh//u//5u+/e1vO9+uu+6aDjroIA9GYIuNLNk8Mo63JMiwYP6CdM2117juc8891+WY3YBd9qEg+eyFZEdZZqdZasmEQ6MXISAEhIAQEAJCQAgIASEgBISAEJhACEyIQEMEFhhM80t91PNAAzMObr755sRRlGz4yAwF9lrYcsstHU42kmTPBWYZ9Eq77767H3tJkIKBOjZJHG9J0AA9Z5xxRhNoYLB/zMuP8aMtWaIRgYa3vvWtfqoE/jBbAV3Mkrjzzjt9yQbLNtjkkc0o2QgyEksiOFWCYzcJpmCPmQ7MaODkjFia8ZnPfKYJNKAbLMAmDy7EXg05LewoFwJCQAgIASEgBISAEBACQkAICAEhMB4ITIhAQzlgZgAOjSAASws43jJmNOy9996+FIG9E572tKf5TAWAY7bAmWee6adB9ALyhBNOSO95z3s8YIEMF/awxTIMggPMaHjb297mapjRwF4JzLZgZkXMVGDmBDMr9t1n37TZrM2a5Q9s/MjRmWxsybGWBBCYafHLX/7S9X34wx/2WQ0EOjiac9q0aU5ntgabSX7jG9/w+sknn+x87NEQvkVQBAbw4SLIoECDQ6YXISAEhIAQEAJCQAgIASEgBISAEJgACEyIQEMbDgz+SQ899JAfQ8nAnmUQLJU45phj0mGHHeZLEhiIw3vffff5gP6f/umf/JQGAgT77ruvD+QZ5J999tmu7/Wvf73vxcDeCosfW5weX/K4BxuYMcCeDwsXLvRAw3vf+17nJ6DBHg3sw4D92267zfdwePOb3pxec9xr0gH7H+CnTjCbgRkVf/rTn9w39mKI5RmXX365z1ZAIcdfcjQmm1LCg12CKRxvySwG9ocgMbvhBS94ge/nQECB2Qwk8igr0OCQ6EUICAEhIASEgBAQAkJACAgBISAEJhACEzrQwIkS99xzT7rkkkv8lIfAjVMg2Pfg6U9/epo9e3Zi00c2goTvlltuSS960YvSkXYyBSdQcMLDOeeckzgekwTtjXaUJRsycuQlssxCIPDAxo/MDmDpxEc/+lHnZ3bCoYce6idIsBElPp166qm+ZIOAwSGHHOJtDzzwQPryl7+cPvKRj3iAAPvMuGDGBEEENqlkY0oCGMcff7zvL4Fv6IujL1mWQR/Yx+FNb3qT7+Ww6aabuh/MkGCZRcxuKJdROJNehIAQEAJCQAgIASEgBISAEBACQkAIjDMCEyrQkC+Z4Nd6Zhew/IAZAWywyEB73rx5vpEisw8Y+LP04MYbb/TlFcw6YAYCQQj2Tthzzz19YH711Ven888/32cjsP8DAQqWSVx11VW+5IHgA/wsZ2BAT1AAewQjCGQQEDj44IM94IAPX//6133mwVve8hbfp4HNHdlwkmUPsRnlX/3VX/kRl8xYIKBBXwhiELTguEv2aKCNmRicUnHeeeelb33rW74vwwtf+EKfFcEyEXhIBCwi0ECfY1bDOL9/ZF4ICAEhIASEgBAQAkJACAgBISAEhEAHAhMq0MASAgIBBAFILEcg0HDttdemn/3sZ74kgs0fY/ANP4EBLmgEBDhZgqDAbrvaHgibbOwD9Pvvv98H+7/5zW98Q0n2RmDQz8aNBBg49nLHHXf0TRnvvfdePzmCPRPQyQwC9MN3+OGH+2CfIASzF0hf/OIXfXNJBv8EKPCT0ycIDMSJGJx+QRCDEzPIOUmCZRMkZlSwTOKTn/yk1z/wgQ/4nhC777a77/3A3hAkAi8EYvBbQQaHRC9CQAgIASEgBISAEBACQkAICAEhMAERmFCBhtiYMQIJLCtgJsCCBQt8rwYG/bF54prVa9Ky5ct8QB/LCZAjELHTTjv5MZaBN3slsMkjeywQvCCYwcCdAMJWW22VmDnAsZcEBGhnGQQnQaCPgT0zCdDLiRXIMEOCDR7/8z//00+5eOc73+k2WYLBhd/YjBkaBAawwyyGmCEBjXaWVZxyyinp9NNPT5xkwd4SLMcg8wDfUwAAQABJREFUSNEWVFi7xgIOaxVwiHurXAgIASEgBISAEBACQkAICAEhIAQmFgITKtCQ1tov9/aPATaJgThXHHcJLdooEyyIFHQG8AQeuCK16Yk25Jg1ELMECHZEkICZFbQzc4J8k0028UADMxZYrvHNb37T92t49atf7UsoWLbBbIUVy1ekVatXud/YyX3CFroIWBDY4PjL0047zWdjHH300T7rgQ0uZ8yY4X2HlysSWOAjOkNXtCkXAkJACAgBISAEhIAQEAJCQAgIASEw3ghMrEDDOKERyxIiOBEBjHyAn7vGYJ8lFsxs4OhNlj6w2ST7MrA0YqBE4IMZFizpYOkEezgw2+HAAw/0nIAGiWAEKQ+EKNDgkOhFCAgBISAEhIAQEAJCQAgIASEgBCYoAk/4QANBBWYskLNUgpkCzBig3mvGAEskWNJx0003+dGZ7LnAsZssryBQwH4RkydNTpMmT2pmJBBgQDf2WBLCRpCPPvqoz75gJgRLPtBDgAM+lmxQZrkIQRDK+IUeyjELY4K+t+SWEBACQkAICAEhIASEgBAQAkJACDwBEVCgwQbuzBJoAgvJBvm27IE6A3kuBvUk+EgM+mknEMCshFtvvdXbdt111zR37lxf9hAnQ+TyyMSMBAIVLJ2gzjIJAgyzZs1qggcRaEA+dEWgAT0k2pSEgBAQAkJACAgBISAEhIAQEAJCQAhMJASe8IEGvxnZ3hARDMhnDRBYoF7OfECWgMCyZcs8GMGeDsyCiIQMKQ82UMdGfgVPHjggAIE9aLEpZS5rOzekjSb17d1Am5IQEAJCQAgIASEgBISAEBACQkAICIHxRkCBhvIOWNCBUx0IEkSwIYIHBBWCRgAg6KWKqOeBhqBFgIE6MxRitgR12kg+c8FOl1i9ZrWXY+8I2kr54CdXEgJCQAgIASEgBISAEBACQkAICAEhMN4IKNBgdyAf4McNIUhAYIFEQCFmG0CPmQ0EAKCzFwPBiTb+0Bd5yJNjNw9YRB19NmHBHLMrm7QQfqIrymWwgjYlISAEhIAQEAJCQAgIASEgBISAEBAC44WAAg2GfAz6Y9DuMwosCMDyBQb0EVCIm0RAATp8BAq40BGnRMSmkvDDF0EB9FBGb2kzeD1wYXzo9lTPsAgd4WPVWL02vDlRZSEgBISAEBACQkAICAEhIASEgBAQAuOAwBM+0MAAPgb94B/BA3La4gp6c4+K2QZtgQZkCSpwEUDI91oIPcjl9j3QEDMajIm2CHhM2siCGnaKBTxKQkAICAEhIASEgBAQAkJACAgBISAEJiICT/hAAzclBvtrVtvmjTaRIF8qEcECcgb4BBy4PNXBBuro6Fg6YUEBllOEPDzoJYcWV6WoCnBQpp0rUgQqqMfMimhHh3G7z8GvXAgIASEgBISAEBACQkAICAEhIASEwHgioEBDjT6BApY+MHjnOEkG9STqsScDA/wINkQbA31mGUSdPA8WIB8JOvUIHlBGX9tMB2RojyAIsvhEHm2057a8QS9CQAgIASEgBISAEBACQkAICAEhIATGEQEFGmrwGbQTaGBgz8yDmD1Asy9dsFMgfPKADfRjsE8bcjHgz4MQtLUleLGRy2GPBK1MOR/6I4UefMnp0a5cCAgBISAEhIAQEAJCQAgIASEgBITAeCCgQEONOgN3lj5EEIDBe76EotvNgZ9ABCmWRgRvBA7ywES0lXkEFHKZXgEE+LCtQEOJpOpCQAgIASEgBISAEBACQkAICAEhMJ4IPOEDDeUAPwb63BQG8XFRj7aSxoCfRGCAtghWQKMeAYNcnra2OjSuXA5ejrqMPR+8bi/hB7mSEBACQkAICAEhIASEgBAQAkJACAiBiYCAAg31TAYG9/kMBuq+ZMLyCBREQCH2Smgb4MMTchEsyAMQ3HTqoT94Gl0WULBQg783GprVgp+cCx3hhzPrRQgIASEgBISAEBACQkAICAEhIASEwARAQIEGG7RHACECAnFfYklEDPgZ4JM4ZnKjSe2zCOCJGQ3IcaGXFPLQgi9vd6YuL/DnFzpDbxcRkYWAEBACQkAICAEhIASEgBAQAkJACIw5Ak/4QMOYIy6DQkAICAEhIASEgBAQAkJACAgBISAENmAEFGjYgG+uuiYEhIAQEAJCQAgIASEgBISAEBACQmCsEVCgYawRlz0hIASEgBAQAkJACAgBISAEhIAQEAIbMAIKNGzAN1ddEwJCQAgIASEgBISAEBACQkAICAEhMNYIKNAw1ojLnhAQAkJACAgBISAEhIAQEAJCQAgIgQ0YAQUaNuCbq64JASEgBISAEBACQkAICAEhIASEgBAYawQUaBhrxGVPCAgBISAEhIAQEAJCQAgIASEgBITABoyAAg0b8M1V14SAEBACQkAICAEhIASEgBAQAkJACIw1Ago0jDXisicEhIAQEAJCQAgIASEgBISAEBACQmADRkCBhg345qprQkAICAEhIASEgBAQAkJACAgBISAExhoBBRrGGnHZEwJCQAgIASEgBISAEBACQkAICAEhsAEjoEDDBnxz1TUhIASEgBAQAkJACAgBISAEhIAQEAJjjYACDWONuOwJASEgBISAEBACQkAICAEhIASEgBDYgBFQoGEDvrnqmhAQAkJACAgBISAEhIAQEAJCQAgIgbFGQIGGsUZc9oSAEBACQkAICAEhIASEgBAQAkJACGzACCjQsAHfXHVNCAgBISAEhIAQEAJCQAgIASEgBNYvBNasWZNWrVrlTk+dOjVttNFGae2atSltZP+tvD4kBRrWh7skH4WAEBACQkAICAEhIASEgBAQAkLgCYHA6tWr07JlyzyoMGPGjDRp0qQEjUQ5gg1r11rwoU5Bi/p45wo0jPcdkH0hIASEgBAQAkJACAgBISAEhIAQeMIjQOCAixkNXAQPmNFAItAALRIBh+CHb8qUKdE0IXIFGibEbZATQkAICAEhIASEgBAQAkJACAgBIfBERoDAAUsmCBxMnjy5Y+YCQYY82EB7BCUIOsQSi4mCnwINE+VOyA8hIASEgBAQAkJACAgBISAEhIAQeMIiQDAh35sBIKJOYIEUMx1iRkMZfMgDFC4wTi8KNIwT8DIrBISAEBACQkAICAEhIASEgBAQAkIgEMgDDSyFYMbCypUrmyUUBBeaZNsz2EIL5yEYwUWQYdq0ac1MiIZ3HAoKNIwD6DIpBISAEBACQkAICAEhIASEgBAQAkIgRyCWQkCLoAIzFqCznAIaF7TYLHL69OkNLXjIxzsp0DDed0D2hYAQEAJCQAgIASEgBISAEBACQkAIdEEg9mcggMBMhxUrVqTFjy1Ok6dMTptuuqnPZOgiOm5kBRrGDXoZFgJCQAgIASEgBISAEBACQkAICAEhMHQEli9f7rMcWCrBjAeCDwQiJsqmkAo0DP2eSkIICAEhIASEgBAQAkJACAgBISAEhMCoIEDgIK42A7FEItpWrVyVlq9Y7ksoYilFtI1XrkDDeCEvu0JACAgBISAEhIAQEAJCQAgIASEgBAoE2IOBTSAff/zx9Nhjj6UlS5Y0p00wY2HzzTdPs2fP9tkL8BGUILF/g06dmAAbVBT3U1UhIASEgBAQAkJACAgBISAEhIAQEALjgsDaNWvT6jWrPcDwyCOPpAceeCDNnz8/LVq0qDnmkqUSO+64Y5o3b17aYostPLgAjb0bJsImkAGcZjQEEsqFgBAQAkJACAgBISAEhIAQEAJCQAiMEwIcUbl06dJ09913pxtvvDH94Q9/8OABsxZoW7x4sQcdtt5667Trrrumpz71qWnvvfdOm2222Th53N2sAg3dsVGLEBACQkAICAEhIASEgBAQAkJACAiBMUGAIAOzGK677rr01a9+NZ1zzjnpb//2b9P222+fZs6cmRYsWJAuueSSdMUVV7g/3/jGN9LLX/7yNGvWrDHxbyhGFGgYClriFQJCQAgIASEgBISAEBACQkAICAEhMAoIPPzww+nXv/51uvTSS9O//uu/phe84AXp+OOPT/vuu2+aM2dOYjnFNddck37xi1+k66+/Pr35zW9OxxxzTNpyyy19DwdcYvnERFhCMXKBBvaf2KhCm06ffvrp6eSTT3bCBRdckI4++mjvMGeATpTOV97qVQgIASEgBISAEBACQkAICAEhIASEwPgi8OCDD6bLL788nXfeeekrX/lKOuGEE9IrX/nKtP/++6dtt93WN4W8+eab02233eYzHwhAPPOZz/S9GlhawTibDSE3mEADwQMuNqAg3XDDDek73/lOOumkk7yeBxoAgM5zKQkBISAEhIAQEAJCQAgIASEgBISAENhQEIgTIOhPlAf7QzszFn73u9+ln//85+lzn/tcuvPOO9P73vc+34dh7ty5acaMGYkTKTh1gjqzHNifgXE4dA80bGTj7HoCwHhiOiIzGugUgQaO2iARaPjud7+bPvGJT3j9/PPP9xkNBBfYyIKcYzeUhIAQEAJCQAgIASEgBISAEBACQkAIbCgIRHAh8ujXYIINy5YtSw899FD67W9/my666KJ0yimnhLiPn0888cT0lKc8JT3jGc/w5RSbbLKJBzPC1mBsNApHuTAigQY6xhWzFK699tp02mmnpU9/+tPufj6jQUsnRvmOSr0QEAJCQAgIASEgBISAEBACQkAIjCsCMfgPJwgCkGLsTNl2U0g2kvacWQjRtnDhwnTHHXf4Moq///u/h9UTx1oeeuih6aijjkoHHHCAH3NJsGHTTTdNHHEZ4/HgH898RAINZQd+9atfpVNPPTV99rOf9aY80JADHmCX8qoLASEgBISAEBACQkAICAEhIASEgBBY3xCI8W7rWNf2NVyzdk33Llk7gQeOsSTYEMsoLrvsMl9BcPXVV/sxl0cccUQ68sgjPeiwzz77pG222cYDDRNp1cCYBxpYZpGDP6leQ9J6I7rfArUIASEgBISAEBACQkAICAEhIASEgBCYMAgwzmW8S2J2QbcZBs14OGY01LMdHnvsMZ/JwN4MixYtSrNnz0577bVXYknFjTfemK666qp09tlnp7vuusuDDO9617vS8573PN+vodmjwexOhDRigQbAimABR24wo+Ezn/mM9/HCCy5Mzzv6ed7OZpANsNmumCE7EUCRD0JACAgBISAEhIAQEAJCQAgIASEgBIaCAONcxrs2KSFNmlwFGgYa5+bj6AceeMCPtrz44ovT73//+3TcccelY4891jd7ZIzNiRTnnntuuuKKK/zoy7e+9a0eaGDpBHshktgYciCbQ+nTcHlHJNCQnzpB+brrrvNTJ2LzigsvvNA3g8RJgMwDDRMBhOGCJzkhIASEgBAQAkJACAgBISAEhIAQEAKBAONhEuPccqybj4VjTEwesx8eWPBAuuoXVyXGz1/60pfS85//fD/ekn0Y7r///nT99denb37zm75Hwxve8IZ08MEHp1122SVtPHPjtGz5MtfDAQ2l3fBtLPMRCTQwTYOLDSgAFgA4deLkk0/2vnDqxPOPfn7aaBI7XIB61cUAl00wgjaWnZctISAEhIAQEAJCQAgIASEgBISAEBACo4lAjHvJ4wp71AkMMBOB5RLsy8AP98xguOmmm3z/BY6y3GqrrXwJBe1HH310es5znpN22mmnNH36dD+RgpkU7NGwQQUa8hkNAAYwBBo++clPOn4/+tGPfEoHgQgAiOhOyAHIRNq4Im66ciEgBISAEBACQkAICAEhIASEgBAQAsNFgDFvBBriR/eYcdCcOlHPfmAvBjaBnD9/fmIZxeOPP94shUAHQYQZM2akbbfdNm233XYpjrdkRgQ6Ix+uryMpNyIzGiIqE5tdMKPh9NNPb2Y0nHPOOR51IdqyYsWKBgBmQQA80RsuJSEgBISAEBACQkAICAEhIASEgBAQAusrAhFUYOAf4+TI6VMEAyLYkPeTsTF7LRBwWLp0abPvAuNmfrAnyDBr1qw0c+ZM/6EeHcjkenN941kekUADHQC8AItAw7e//e0UezScd955Hmhg1kIDvK2V4GgP6gH2eAIh20JACAgBISAEhIAQEAJCQAgIASEgBIaLAGPbGPgzNo7xseurtxBo2zYAubjgpUxwgaDD8uXLXZzVAVOnTE2Tp0y2kXQ1lkY/P9jDHysHqHfYdemxfxmxQEPuOksnTjvttCbQcMEFF/hGFg1PfT7oRACg8UkFISAEhIAQEAJCQAgIASEgBISAEBAC64AAA34G/vyY3usHdXiaZEV+hGd8jEwkeJjZAI3ZDKQIKhCIgM5yCmisHECegMREGGePWKCBztEh8muvvdb3aPjUpz7lYPz4xz9Oz33uc72sFyEgBISAEBACQkAICAEhIASEgBAQAkKgDwHG0aQySEBAAVoEIDyQscbG3nbQQsmb8/VpHp/SiAcamCrCjIbvfe97vhkkyyV++MMf+q6YdPyRRx7x6SSUY2+GAG18IJBVISAEhIAQEAJCQAgIASEgBISAEBAC64YAwQICAyTGwWUggLEy7fEjPeNhZiDEcoeSP/cGGeS54IvDFAg8MJ6Oei4znuURCzTknWCPhu9///vpYx/7mJO/853vpCOOOMJ3zbzrrrt8cwuAAlSuAA3mXuDmNnzHThfooPasxA2N3T5bmQkkcfxmzGSpj+IseUNX5GW710NXa6MRB2rvJhf0NvkWv/v5mMnlbd3KYW6ouetzMGvJwDXPranhy7GufWx8Cp/LvMWpRqalrcS8lTdstMkPRGuTDRo5yfrZ1ufwJdpspVbf30PoQD4vd6s7m8ljLHAt5MJeP33orFPDk9dznQ1jXcBWZif6gkijK2sPcfKct6HDS8r0hp7IW+1lMoFjw+8K65dMf2s7bMaDDpLjWRW8PuALYpnvHfyZbacHL5W83CFUVwrZxvcuck07qu2zt+vnbIt8K3/JN9R69MnkGmztC5N++/1y0IwJ7Irk/lhDw5fz1H50+AzNUoeditCq35nbXmrdeVOHnR46+/F1KLFK3odcT+ABNmXK+4oC+9/YafEVbFvtGNmxDBttfGGbNlK404u34ux4bfzroFaVpq2HTucJ420+mGy/98UgfB6M7X4uh58t+gNP9JKaz+FaJrcXvD31l30N2/2EBiD0krO28KXxL1PnNLAvfYEHvSTaetmAJ2tvswNLpKY9k/G2Aew1cqFokHkpV9Yb24EBhNK3nFa3tepxZdWLtwewuW7UD/HzOlPb51v4GHnG1Oi3trj/rX0KmVxHXo72Og+9kTu55C/roaMbPdrJ23hKGnVSgWmrLHwh300Onjp19CuIeR66clpRRgeJz4cG+4LHq+FPxVyR6vdF40fYK/MWfY2MteXlSrG9Bl6mq9WvsNGiG1I/nV34gtzBX+t2WjhS+xN8TVv46UbtJeqlf2U958/bKJMKe/G+CPsVU/Xa0EJPnUPnIvG8xRW0yGljDLzxxhunWZvNSptutqlv8EidYAGBBFLIUyY4wV4NvGemTZ+WJk+a7P7CO9H2ZsBf0qgFGpjR8PWvfz0tXrzYAw4HHXSQH9PBbAfOBwUUdsvkAjg2ulASAkJACAgBISAEhIAQEAJCQAgIASGwviJQ/qDDuJfxLhdtzF7g5Aiu3Xff3S+OqiTQQJsHGixWkS+NIJjASRTMXGCvhlgRELrR2zaDAgwj8EE59w16Xu/GOxBfqZc6acQDDXT2mmuuScxiOPvss935V77ylWmvvfZK9957b7r88svTQw891MxmIJoTwFcu6VUICAEhIASEgBAQAkJACAgBISAEhMD6iUA+gGd8zGA9Ag0EBNjA8eGHH06vf/3r00te8pK08847Nx2NwECuo0O+ns3gAhaQiE0kgz8PDGCbi0Q7AYrgC3oELeCBFksxCHowq6PUDx889AdZ+pProJ00aoEGTp246KKL3Mhhhx3m4N1+++3pG9/4htP0IgSEgBAQAkJACAgBISAEhIAQEAJC4ImKwIc+9KH0zne+M2277bYOQR4kGC4muQ7KBA+CFkEGdActggTUCR6sWb0mTZo8yWdXwBcBCWS54MsDDbG/BLws7eC/l42xWkRS1UfklT0amNHAsZaPP/64H2256667pt///vfpS1/60ojYkBIhIASEgBAQAkJACAgBISAEhIAQEALrKwL//u//nt761rd6oIFhOYN6Bv4M2NknI08eJDBSSe/HkxOKMkGElatWesCAWRUReCDAwFYG+QyFaMOv/Ao6eZTxO3xnhgNpxGc0oJR9GM4444x07rnnpkcffTQde+yxaY899vBAwymnnAKLTxfBGTqlJASEgBAQAkJACAgBISAEhIAQEAJCYH1GgIE3A3gG7Pzqz3g3T2X94x//eDrxxBM90BD8+QyBXHagMrYJFrBpJLrW2hGY/IMehzBQZvxNMMCXRphSggjwc02ZPCVNnTbVTeEr/Fzw5ON2+scVibbQO2qBhqVLl6Zrr73WT50466yzvLOsPWGPhttuuz194hMfd39wLCIjEOiAkhAQAkJACAgBISAEhIAQEAJCQAgIgfUVgRjXMtbNUwzYc/pJJ52UTjjhhLTNNtukFStWeGAiAhW57EDlsLlw4UIbc9+W7rnnnvTYY4950AF9jMX33HPPNGfOHA8y5PqwywwKlkswRkcXQQeCFgQjkCflfufjeNrCPnlTNqZOBOAcZkJVBBp+8IMfpHPOOccjG6973evqQMNt6aMf/Wir9nCotVFEISAEhIAQEAJCQAgIASEgBISAEBAC6wEC+RA7Bt/kzBLI2wg0vPGNb2xmNDALYcrUKc1gfahdveuuu3z7AlYW8OM/AQcSY/AjjjjC903cbLPN/OSKGH/jEzMepk+b3uyvQKCBAASzEzzwsZHNXsjmBSDDRUJPBCicUL+M+NIJDLJHw5k/ODNd9OOLfI+GY445xiMoRFe6BRpyp1QWAkJACAgBISAEhIAQEAJCQAgIASGwPiHAoJvBOXksNYglBgQYghZ9+tSnPpXe8IY3+IyGoK1LTmDh1FNPTZ/73Od88M+pj/jywhe+0MfjbDo5e/bstOOOO3pwY6uttkoEHjhak2BDnmI5BH7Th+gX+giI+GkUzIWY1DeLIZcfkUADxsMBlN94443pe9/7Xrr44ovTokWL0nOe85zEZpB33HFH+sIXvpDbV1kICAEhIASEgBAQAkJACAgBISAEhMAGgUAMyPkBPh8jRz3v5Pvf/37fz3CXXXZxsg/i67F1zjdQmSUO2L355pvT17/+9fTlL3+5p8jb3/729JSnPCXNmzcvbb311h5smD59euJiBsPGMzdO02dM94DCipUrmn5gg4t+0R/8zQMp1CONSKAh1nDMnDnT9d5w/Q3p26d9O51//vk+XeOAAw7wqMn8+fM9+BDGlQsBISAEhIAQEAJCQAgIASEgBISAEHgiIvCSl7wkHXbYYWm77bbzATuDdmYSMJAfSkKOixkNl1xySbrwwgs9IFDOoEDnrFmzEuPz7bffPjGjgTp7N2y++ea+pGKTTTZJ++23nx/mQOCAgAL7NTDmJwjBzAdmNKxes7rxGX+54I9gw+gEGm64IX33u9/1Dl5zzTVDwUi8QkAICAEhIASEgBAQAkJACAgBISAEnhAIMDBn9v+WW27pwYHHH3/cB/YxYB8MCDHAZ79ENoTk5EcSwYfYzDECBt307bvvvh54wJfjjz8+PetZz/IZDvATaIjNIQk0EMAg8MDsBmZTjFqgAcU4jiESSyfOPPNMXz5BWUkICAEhIASEgBAQAkJACAgBISAEhIAQGH0E2HNhyZIlbohgA4kxe557peXlGc94RmJJx5FHHpm22GKLhiOWShDUWLliZWJJRcxwCL15cGREZjQ01uvCnXfelW789a/THX+8Iz344IPeKdZ7RKQDJynHFI9SXnUhIASEgBAQAkJACAgBISAEhIAQEALrKwIMuhnz8st/sjE+mybGHgf0CTpXjJGhUc4H69AGkxhXI/fAAw+k6667Ll155ZW+DGLZsmVdxVkysdtuu/lGlGwOOXfuXF8WQZCC5Rz777+/792AgvAp8lhKQX8INgQ9NzZigYYAhZwpG4sXL/YcJ6ZOsbUc06tdLGkHUKZbxKYVOAZdSQgIASEgBISAEBACQkAICAEhIASEwIaAQCwx4Id2ggGx1KAZ/9oQ2HY3WOeuht5bb701nX766emLX/yib/K4YMECt9m2VwNGX/3qV/umkMxi2GOPPXx8vnz5ct+3YYvNt/DgSARD4I/xO2V0Mobnir5BjzSigYaYqUBH26IaYVS5EBACQkAICAEhIASEgBAQAkJACAgBITByCNx9993pK1/5SvrYxz7WoZQgwp577umBAsbs7MPASRdsCMmpE3vttZcfdxlCMQkA3ggo0BYBBXJSBFIi0OHE+mXEAg3oiykURDpi0wnoHoCwnSknTa6mdOA4NBxUQAKElISAEBACQkAICAEhIASEgBAQAkJgQ0HAB+tMVrATH2PMCy1mFzBmHul02223eaDh05/+dIfqo446Kh199NFp0003dftPfvKT0z777JM4YYJxOadHxgmSCMYKhAgsQIsxPDmBBS74uOhL2Z8RDTSEIYyWgQYADUfDSQDPQacDSkJACAgBISAEhIAQEAJCQAgIASEgBNY3BGx068sN8JsBPCl+/afMOJgxM4kxc0eb/TA/3GUUoef2229PZ5xxRrr88svTDjvskNhvYbPNNkt77rFnmrfLPN+3gXH5Tjvt5LMZ3JGWl24zFaBzRaChrOeqRjTQAHBceQABY9B86UkRzaFNgQZQUBICQkAICAEhIASEgBAQAkJACAiB9RmBGNvSBx8DW57TGnox06GhUxhGCht333V3+slPf5Iee+wx3+hxu+22S3PmzPGZDMw4gI8ACEdUzpgxo59vYbrXuD4f73fjQ8+IBhrCMeVCQAgIASEgBISAEBACQkAICAEhIASEwNghsHDhwnTLLbd4AIEZDZwkwayGtlQGQjyA4Os82riHTlOgYeiYSUIICAEhIASEgBAQAkJACAgBISAEhMCEQoDTHxctWuRLMtiPgVkLsXcCyxyYzUBAIbY0iJkQvWYmDLeDCjQMFznJCQEhIASEgBAQAkJACAgBISAEhIAQmCAIEEhgDwgCCRFgwDXoBBpiFkO39ghAjER3FGgYCRSlQwgIASEgBISAEBACQkAICAEhIASEwDgi0G1mAnQCDcxgyC9cjTYCFOWhDuvSFQUa1gU9yQoBISAEhIAQEAJCQAgIASEgBISAEJhACMTMBVwisNAtAEF7BBoIRBBoyGdC0D7cpEDDcJGTnBAQAkJACAgBISAEhIAQEAJCQAgIgQmCQL5EggADSyG4IkErUwQhgp7zB204uQINw0FNMkJACAgBISAEhIAQEAJCQAgIASEgBCYQAgQa1qy2DR/tH4mgQQQccjfzGQ9BbwtCRNtwcgUahoOaZISAEBACQkAICAEhIASEgBAQAkJACEwwBDyIYHGGCDYQQOAf/0kxg4GgBG0slxiNpEDDaKAqnUJACAgBISAEhIAQEAJCQAgIASEgBMYRgQgq5MGGoJFDH6mlEmU3FWgoEVFdCAgBISAEhIAQEAJCQAgIASEgBITAeo4AwYSYuUBQgeR5PeMhaKPRTQUaRgNV6RQCQkAICAEhIASEgBAQAkJACAgBITCGCBBYyFPMXoAW+zXk7VEmGDHSMxwUaAh0lQsBISAEhIAQEAJCQAgIASEgBISAEFhPESBYwDGVpHy2AuW4yq6FjI63bJCJaE3/IzoaFhWEgBAQAkJACAgBISAEhIAQEAJCQAg8ARBgZsKqVauavRci2BBBhpjhEJtDkrNpJHKxxGLKlCkjgtSIzGiohvwx8A+/hhcA6C9V6g39QS8lynrwDzVHfy9d0R5+DFW/+CcEAnEbJ4Qz6+JE/l7N35M5vdQ/nM7nukt941kv+5n3LcqRswtvJNsEx4ulfLTXeYgyHW0A1kJS1QKB/5+9916Uq7jWfScKIEDkjAgK5IxJJjgnnLN3OHvvB7hPcO8L3D/PO5z9z9mOxzljvG2wyUlkkASIHE0WIInz/b5RY3Z1rdm9eklLAglK6q40Uo0aNSrMmr1S9x+qsVHMgrJoL40SxEy38SxEZ+2RhJuF5q6FyVbuWi6LR73W3Id2v7N6nUWDO2ohLV7dc/W8MWrDLNKMoKenxrlNh83aXc1/nP54LmUYjltdAtWWtfmW0o5opKXxwcgvVFML6ckPhgYX0sqd1954f43nQhIfASxEqDHY+qCBA4M8aACIQ4b6QCFfpTCMRKn/SsUY0R3M7PRBQ6in/m4lmd4hk2uDZtRHOijX6eQF1GRKCbWr4nGJMhfnQyFXlhUJmuyYXHOaQQEIcdo01M45KFOdedAa8azzk9Ij6PFUDU8NeUJKlPm2bNQmg8/Bi9JRXcLXdEYwNZfEGQ3RprbJjqiUlEWv5Y90DLyUI8oSKnmO08raZJj5cajs2xEN4BKH0qF8QL9fvkPalHmk+fG21dIC2+pjqCxwavo1ddcm25p8S1p1Q2A1CukBtMmIY8BJfaywIt/Wk58EW6H1ySH4lmYPPCFR06jTQ+CT6rOcWKFEkdH3YJOisAXtcUpihJqQlAzwGQGaYUJDJjA4OApfOcdWRhQL7EjkfmJ1DdTGA3xq+tTWvMmPiUZBHxK7L1CiLktK4xSidFQ3XlvTeq/SKRv8Z5UucVr4ujzTdbtG8FHLd5ZFn4+gK/wqOaqvUkmiL4qCuf4+eRSCNd05NCCWdHrCTQICYalNxShb86DUJPkK3BHgrKkkOCjwAJGWzzT8aXUt6Zpuna7hFkKvxtt16ZQoONS50OdIq3XdfPIkFhZHGtwsS9yhMurGy0dYC+c/jpG5EcVRKmV6r+KUDf47IxV0JuFPq6vbPSscOC3sUB64lIn6SQGYEf5cXzUJry4f4TNbBt86rmGTH2UpX12f6Rp/CC7rE76Op9VFa2voSIODRCnfOM+ozZbl6AqYwAie41gjzeaaIDkEz/q7wkxmdfVQWihzQaOkLU/qQ+uYIdJtWX2Q0P7ZyrzNkDcX8pZDfRjR0tuZ/CIcNGR3ZJzipJrI1+msHy4dQYbaYxCAk91AnGnKwag/lO3O0LZ7nPeoPVV5LX5V3CcHkKahDID3pPb2xIL1Mg0BZQ0ocxLKAOh7oO4pUiD4lOp+fpkm9UQaI62MUiNC09iOoOZPtbTH6LpyrGScYFXV0iFfVfd4c8paREOqcA5gT+KDkRjUi5o+oJdJoLWiBtDG3fwA8Cx0a7TdlR5sywKZD7VtnG6VA7jKLpDVAsGHJFsgiZ0An8Z9UAXTEJBjAGkSSg86L0A0cAiMsp5OgM3Ju3gIuUUs+B+sqCgh9bObdJKrvGSbOq/Z1+msH1uqUjgINO7qWh49rYFETa5O96BDxBrAGqROJw3AA6UgAtTQSNhFi8d4DEm1aJz2DEKpgkbvWUwj6vQsjapJ1elZcHcnzELbtStlCz2ltkq8QAHfnYOWBNLLRAs4YAB0Rw8aoMKBAmHoACHrJtUbcZG+Fu2gYVxFKd2oQ2hu5rI247Y88iPlx2HDKD8+pIDms6SQa6kll8WOU56UZpSvOc2RZhisRol0gzgNrQGdS2svLlmQXqYB1zpqFDoNrQGtqezidMUZAcufqxlj6vKxkvHMfPXj0JFrXh0YHveTx/oQyUllk/QeLdf3GECljyRYFSVoxglCXIGNpcfpCxDkfSoKRqzyEPughGnNrhUqfdSgdRpV1aB1egyp1WkBbGm1YO9VfqwdOyjEcNuGlhzilsCLwXiqvMkIoDo9FWlRK6dxndP8acC1VA3iJLQebF6AIJ5gGU9i2dNNgCGErJsDnBV7e1w3XOnUUV28S1QQjPgepcYZpQgZj9WmnHXhAGCCZZzgbX4AtfehQ3W9npIgcQNY86jTgTLyOYGmb4AaGgG7iN89j1qiOr2IvPYEUtn0Ru9ZTBPq9FC+QR3rwrZut/TxjHpv2zUj2qKDjUYCpNFY0doCBRw6aAgS44SCw+h70Ru0Gwku0kFDqikVVTqgdERcWolWZU3dxrYs8kkLbNL5ATPTQDaf7MWWKGiLEVKsasMRRbHlatm2eYs+ixwNYrIFNdMJkvEsZPc2mNQFca2HOt23OYH7AiVaROoa5BqtBW9Aa8q7OF1xtoAlT1QLjBQJ2gqf+YxribOsjl2vgqRX8oC0YQykrVxAfjJtcchK4qGDlkqIGnSIfYJmbJhEItOnlUigygcM0dyry3p9DLQy9VOqEjTjGqMGrdMjfdfQJV0BDtEcwNitRZV4O8x3qF1Bt6Ge8x2cmqodZl4jpiCmnZmMa8Ddk07OGddNrtOWJoHmE20O4mi4QyKrM55omz1AMEz2Gddi1KB12jBDCIk8Bzgr9va4brjSqSOKSdfVi6GKnn4k+M4Ucc0u0xn37AOhz44lGuAEzRjYOl3jNqi9LG35rARqPpkmHtGLLVbk9Z1AFIwD1mLueLqm3zODXFbsOOk9FrNu+qhj3JysypjCOl23uUadlB5DroFqQrsxPaktu1EEsxo8aJhPOOobHeaUPULNx3WjEhiCliOvIUH1HhUW5aABsw4V1d+hptDGsJra0jYP3cmHDKnn5KMbDdmDWUU8l2hdu7D0uB0U2tl2SI0DzGE9Xt2Cz5W1IdCip/ANWBZ/IOKZddICtvlWiU2+BUe5Dchu1nfhPmTzu1oSsx5ppB4ByXqxdDPiEpSDbrZdZXX7W6ZVPulknHISV2CjdAtY5/OAwYh1RU11L07P1+RaoVJDDV6n0VANWqfHkIZUWYBbekOgu6tsTP6dZDrUrjkLnRZoVwvgNomp+bTMd7LBC0Af4jyn6S1Qm5+DIAGqshYc8frqocoxADIjux8C72kJrk7Pavdm8IH5qjWk9HwK3Rm9TKDNHDeqGqVgldJl3LMfB+uLnWiAEzTjBG7zlDeofb4trwROcqO4AU4+GdeAgAZ4QWqBGloj3AWmWrqgm3ZdUacXSH9PBW+b3Og7qzOmmXW6bnaNmumMe7gWeQ5AD7lbE61Yu5L5cJOztIp3QKh6yUobRp5lLjHm/PF5f1e2etfR3umDhlBTKiji+KYzqg6Z0oaEakFGhwzUQDU/mU8eJW57ELAMgIA+iVnCZRyNyNxw3NMamUoC9lVZ0NJr88DNQZpb1qINoSTLD0o8r05agDafimqV2eRbtKY6qezGWBK0Qu0u7m78e8VcjTTrpv1th1T5WSXtUVqENj922LC7lP4+4dPqohWrV2JUzAcOVIMyv13PQWiF2JvzpfHzKXYhOoIW8PPRtFoFtBDai9wVQyKOidMCtPmUZwxJhU2+RXN1XUi6xqnTqqpBk+VQPIY2hDQPnyGae2eZNDWkn7qxY8qsK+ZJT6NbaI4fNgS9ZJdxz2UavQRqkBIl4wSbFieJjHvYWYg0SImScdICrN/stJUJRNzQc1/NVzaNXtI2jVkAE2Evi4eaXum1rq7T82khSWRs+EkExoDmo7w316ciFE/S1XzNH0RNYhlDJHj1Y28+uu/j+kU5aBhpvN5wVx0yxwMNayQxxmtRfH6oyY4gTgx+n0HpaQcNoC52MPuQp/6GTUpmlilyzX+obA5iS2jU+iQ1xicL329xtnWSsNTPV5cwGVdtTPJZNIdUDVCnEyHjFrHJt6hNdVABqK2oyzKdxGrYuo7yzKd8Y7EATKMmMAawazO50TaXbMyuZTminm1XST3mW1VU+Vkl7FFahLG8Mgk4poeRhHttakwP6F+f1EU2usm3KAlWxw1Kse0aoknPQWjq98ps1eja7ndrW0tvWpRZerYRbsheEiTrMs7yJm65VloZtpsWIemNIaqwyddofVVdSLqvKESrfA2aLIfiCmWy/DVQnR4iuNeVqcG9Mt+Dxpt9CFB/o+aUJuNe9b28fclwokKsUer0MOKIN/UVmUpXkzBL+RjSCG2cd2xzAlTf45XzMFisajHtZX1PBFishiyczqTm9voIkgmW8XyMEj3jHn4SgTmAPcYHKCEl9PrZCYVMRO2JNzqdiNDAvX+zi37QMGpqKod7CZke1dap6bWpfOI6nRTA5sOrE1m2m+IieH28Auc57RmSa6hsDmJpR1XeolVVu6nR7z82tU7m6KOuRHTyALXlcxABVqjKZ0UJxF38bWEq4XYxuznke9atVuZA7pqC3GjV7HuZCsuSr0HmE6Yn0SK1eQOWwh5pPup7QX2rh6EmNfqYBQUyY2jzIY0BDwmxt5aVhs+nn13afDF/D/XfNn1MlLYSPQyVjSFVymrKa1RX1QUVmpNTcFvQOj+GNo3+RKS6Yi9NWy9jmtp9DRXb6JbxlV4tTZ0etLdJ0laIddfX6RlQx4fjLMgQrXiTrdHqdD5RNXhdAdLuCh+0A/3U6yR9z9h3SaaNEz3jvn5Gfj38ByUxppc5WhvTwhjoWE0ZcqADNIfMJMw5gA3V9392EQ8aWiWlckqcG4NZdZLohk/aGVNIOoGI9amrAdnVYQrLlGyqTLW8PcKA0FVdjQJkVRWICZAVs+SHYMGryzMdXMa/a9i6JnlnGTQSNuuyDJhMt3HiZ0x9CUkm88R99VBlDZj1PUJdWdJVXYInVFUVRQnQVlBOWV2faTATvoXLuoStaVD3XoeUu5EjxW2Kdyo7xqpmUKfHgMRO+bp6VgFMpkVs8zWvOt0yAY/6NgYuaSZ+DUN94mWamJB4pFuYpEFdHRIn4ZPnJJi2PPHq8knpinaynQRal1do422sgTJdgBdCP1F3Jh6TsSG02LJM42XWi82wac/M2VZQ5GrLktikumwLeAlTlxX8LEpyxGOshgCGysaQamojgi2aUdrCGrWiOQ2sRsl0j7oQxB4pqUyJoQt8xgla55N3wgFT86jrqcs86QyTcLOeOHkmfs0j4bIu88Q17bp8V6aLbIjTilSLXafnAE6Tr0Ks6dfpSegV6lg3LTb/5JPxguhPEn4h5TCepJCsS+ESbihPXV0+KV3LljB1WdLJuK6r03V9nW5h6nzyA36+UGBr0Do9DT3ZZGzYWZATAVjSNU7WQSzrSWcYKmvrkl5Nq8BkVaLsyjjYi+Mg0wHhJIxBS1WN1kOXwj4/pwE1VlYKejJCAr3v41170FBrfqHamqPcthM4XeasdQ7gBONYxL4Qy1aamvqYRNMAa6T50oVoS26MFzQSoK6gbFK+rpuUnk+2SfUpS9YjQ/LImLqEG5Ix4dq40EzUknXUkxmqrAFnTReCQ+R6Xkkr5cw8cZYlAZDqdMK2cJQng6yjLNNJg7L3IqRshfeuFGeMVcso9dHqQEgtaAsyKW9+Q8hDvMaEaygmfMZ1ddJP/BqmTtc4mZ6EOwmvLq/TSS/jobq6jPS0kG0RzHygk8j0JKYRENC06km0F6O8l68ititlGeIHa/+lWSrfC+bZdnhPEjBh6ngafNZlXOOVNFVt6NkPVbbAs+R7guOqdfE0HgVvGsg09j3bWQn0CNOoNnXQnoSXfKkfgqvrIVvD1HV1OXBtqOvrdAtHflI95bs6SA81mzoN61qNddpitcCTZC2ILXibH0KvedbpMaGHELNsDGkBbZ1FuOSxM3Ej3xxSyNHCtGUp63xwSTzxJ+ElHHHC1mV1er76pFHjDMlZ12e6gktRh8gleBtX6OMqrInVSDVCMmrLavhZ2j4JvsGdJFKNvivSbl7PPIXKeJxjPkfvwcernUt1jdNtAJnUM+RfUkvELN8D48U7aLB+KiX15ptaynhGLVXgNdXApqQCGCDp2rmIA5ALKBLRWUmOSTcr0iRRKmJDpKrqSRT2yvIhXdDQXh+TABaijZ7YcN9X1Quhuniwi9HGHZGmafjuEsNsZ2CWjn9HmpY4M/Fq9JC4e3U8Sf9FF5OqF6KTMbW2BFXZFi2E9mLApny7S475+Ll+sYWZoueUZzF0uRAaQ00ck2UIYCEMgC0EW1I9nwkVbfFC2VasA3USwV6QHeGwZ+JMVcWkyh1tqvTbkmzzSbrtijafcPPFNf06PR9eza9Oz4dX17f82nzCtvSdnwScSAuNRXQSyZb/QknvjfCtrtr8pDa3umzzk/B2d/ms7dkVclkn0wQoSkuQNk6Zat1mevBNoCTQIiZSlu+B8eIcNPgUZhYtLVBjAm+ptvkhnU/kAjKViodgBmkPAA7CFdKtPEafhNACT8qLyM6SmER6by7/wOh+PuNIGwYu03R85jMeMoasI65DTacuV7oFbaoXlB1i47IJTFxcIU0AmypDhR5wIjKnjBoV7gj9qczf55W9HtqGD+iiBZmlaT39Atznk1jFJ4tmobtYML08EwgupkzTeNV8BuEAoKIGLEUpelM1wcjHSQzySoLvcWzZ5jRqgUKJyDQSc3gMwE/DnySN6VaVc/hk3QC/rPogxLVuW52Ntb8CHIJz9VBFRaQiUZUOJ+chNYw0UDrEM8sm8ZhUPkB+alHymQpUKgd5QiArCjGyA8XjcIlTMa5lGaiuID9MpgZqnVFW5yfpcFJ50ny/xHVbdodMqZfBAwEEKADIlbK1cQVGMlEcz6GbyIasvnpBqrI9MLk4Bw1WddFUr7AhDWXZDJoSaE9K4HUa7DafFIc4DJUl/Kxxyy/zLe02X9OfUweROYWBkfRr/EzXdRPQE/QDEc+ij4l6GuiDml6rwLpuIs0W6QOUr/XTNnta3Xy6nFoP4QZgGq9Wrkn5huQksA/LGw3sDt0vBo9G7JmzQ3YxnzyT6odo1YIM1Sct4ra+zde0Zk0n/Wnwi8FnGv1pdbV80+To6xIhCwYUlyAt3yxP1La+zidsXbbQ9Cx8Fkpzb4CfT7eLobchHkNlqc+WZ5tPuFniIT5DZUlriNdQWcJPi4f4DJUljZZPm0+4PobYvECT1/RJZwYSCfqBitu+avO1MibpcFJ5jftepKe1ZVfLg06sl1aIoiyKs2oornWaaeI+nUg0pE6Tz9ADZ8GeGS/iQQMKKNpyNKShLJuirAJS6z3TGU/BHqsa4jZUNoZUMpN4TSqfRnda3RDvtmwSzxbuw/xcDXyo+7k6WYySIb0O2elQ2Sz8h+gPldW0hngNldU4Q+lpfOq6HaE9xG9PK6t1kLIP6WKoLOEnxUO067KaZp2eRG+xy1OWjJN+K0ubT7j54pYu8HUZdCfRruGSz1BZ1mU8id6k8lloJu33It4Z+Sa1uW5HS7/FafM17qR0SxO4obIdoT2J555WPtT2IR1N0t1Qe4doDpUN4dZlk+SoYWZJ7w28Z9XFUFuHytDbrDRn0fHeCDNJb/O1dU/R6462b772T6pHL/kxTApQFJZZ4qF0TTd1XMc97Rq5Rsp0jZRle2C8yAcNjQZQYv6ghRWaWmvg6mwBafWf+QRt81lO3HJp80MwNX5Le1K+pluna1pteha4ll9L48P8jmngQ93vmN4mYbX6bO12vnxLt6U3X76lX9ObVlfDTUu3/KfBflg3roGd1f98ut9Z+uPSzp5LuTIGs5VlvnzLraZF3aR80m3jSfDJp63PcuKkNa2shklaGdd478f0LHLW7VuMNuwMvVnkXQwZ9zQaqdOMkb/V1Xz5oTbX9CalE6+ln+UZz1efcJPimv8kmEnlu5p3K1vNr07X8k0qB6alNy2fdDKueXyYnqvLhepkd+kVPm0/zyLrjuDMQncaTOqE2GmEKIUpTx2Tzjx0M13Tobynl+kEpLJOkye0BKJ0j/vetQcNVkdqiozSQ8pMtVWgCZYxIJPSiV7HFSkXz5evcafxqevASboZ13QmpafBtvQn0fiwfMc08KHud0xvQ1hDuqztd1J6iBZlLb06X6cTv6afZRlPq0uYWeIhvrPgfdBhdlb/8+l9Z+nvaP+kXBknnVqeOk19m0+cjFtadb5OJy3otTRbuDpfp5NnHde06nTyq2GTVsZ13fsxPZ+cbXsXqw07Snc+eRdLvj2NTuoz45S/1Vedr9MJ38Y1vUnpxJmP3nz1SWdSXPMfgoH+JJhdzXuIb82zTqfsQ2VZ19Kr83Ua+KSTcdL4MA4NtPraEb3sDt3CY0dkbXF2lM5C9JL6IM504qc8xENp4LI8cYfinnYCZ5yMEol8nc76PShepIMGFPtut237tm7btm3d0iVLuqVLl0k3oZ139WORfAguKwrdZ4nq88YDlSqHTiQFWZRbwEvNqBMNOPCV8KAXEoaalE4SiMhnnyVZEjH0kmbW9GVCWCJBE6Xm0eIk7qS4xgWmxk8d7iNe/hQiKUeLSz7rrHsyKqxxC4lFiwqLMXqUETJGLj7bXahS2kNagbiGc2H5qsszTVXSG4KtyzK9fTt/FFV4sr3km3UZ1/QpG9J9DUPaOq7swHiU65MybpetbN++3byXyMgm8QeXUPMYgp1Wn3Vp09DDrrHTrKOMAAxB4lumtj55R5+N4JIQeATzSuAoGvuG7iTaALaodb5OJ1FoqTulU/WphFiqxpkHX0KocbKcspQ36RBTr64JHVS4NY0aPtOhX/Up/dkAFzESdCymDqYtTgJByjAlrklnObBZPlRGfRtqum3dQvLJL+l5jBQCtInRNSRb8sg68tjVu+/yLRwhZ13GwBCSZ+SGv1ucGirxQ1ZsZknvt7Mu4ZMO5a5TR6dsWZewxIlPTH3i5R9hBqYNCUt50syYMtOEL/9KBVrahtGjJ5VR3NKpaUAnhUsaLitf4GLDhKx3mfLRIyPZ5uVjKvFVSFYlkcQH0tf4P+ZNgvlRrn/pFwNObddaIudWw5rwqC8CN2RPXQCXOkBVcMi+ixzfk0PiJoRJZKaJsy55swbCvmxbal/L1/OEaGR54kO25Yv+t4sesEtx3rs4IEsvX2VzLdvsmyXqm2w3ndjbj/ty1MZsV93WluZ8ecuGfPokT7FxYBlZa6cuDwjapU8xaIntPLbh9rqfKvkTqYqzDVnU5rN8oTFNQJ/08VgbVB52FJy2S/h3JTA6z3GTvExDkxd0WHu39Qm3IzG0M9TpsTJVICX975i0Puh361aU3nXLlsX8yBxLJbDQ274NZI0TNR4cytpAOSHjyC3sG1xYv1smeXSU/LAN9Os+QDDgVIY/IuRYdab5AgKMpJXVbT7LdyaGF5+0k5AuKIbUUd+P4dKWrAPSNGicQt2uSeUG3Mkv+G8TT/aG2C82Op9+LCFyln5KEbDx6Kuw85Q7x4/xCnDb7qSRcV2fZS0+MC2ceRbrCBsJKMq3vSv/r39L9lmqfkobi70xPJYuWdotLRTHaNNWM6dUCQYPSelqjgAQ2sPCIh007GPlbnl7S/f22291y5fv263Yb4UUvcTlTJbbt4XDwSA8iqUolL7P0qVFZSgaIyLSlwKLDMMrHSURZ9pAA18YNbTslLO+ILlz+VIokdOgsICD91LtWMoYNV87KPArPLJbNXDgs4zBI0+ZOCbYfAHft4s6A6s0/pt0trVGTTz0xyZ1iWbVZcs4xDGq5evpimbdJuhskwMFFxgPRsmKXtqQ9Civ0zVc0kamNtRlCQeM5dc3Dp50OJl9pOuRTMjlj+o9oSrOBaeSDkk/ZbOzUSFtqfklPA6JkHRJU7Z161aXLZPdUTctmIJw0Ps26TB1n7SIoQFdnCiHF7bpQpQWszigHCmxl3feeadbumxpt3zZ8kG5C6ojHBbNqNswXj/KDbWEscQmnPEHnWXiu1Sy0K6gG/jAYB9LZPdYRtYTQ1coLku4WMwEDQj5wFBw6Mk46JU+VVkGyq2PUpC1CZMx1bSbgM7q8jodEIzBd7u3pVNk33f5MuMhJ/yRO3Eo8kSncbp0aZQWMMMw9t2H4Mk2alwTnfAFjsel+C+TPyMgm9tAQqxoR0lSHfUsHFVI37LooT5Dykwe23LflDFLOmnbLoQbcPJShR7lhJqOC+b5qmWo8dty6rIMHvTrdvRAgxRiMZHTqYv8Ra39kOK0acrwUdu2xn7hcuAAAEAASURBVIbKNogvNcb4V+JTGk0ESqXxv6c5jjXKBX6MZeTAB6Qv7PUqzrkIBRMcfBWdBWxdl/WO6RcLHTabePRV+Ki5LQIGDlETeDUUdgXftBFi5ra35cNQQM4D2DEhbTZpEAvcPsA0ypqFMgcBZD30clqg2uNF/O3TQtleqFse1ZteUBn8Tha0T9AFJvwvvhTZ0T/B/hMfJab4KHz/1m1b7XPDZ4UtWS58jOBijpbvVZq+wzcjUx16eNV73lG95aLRwCp2XkjgjmPXlEJGSloetnkhYrHY8dtvv235sONletiyFBsrpNxOjRN4Uz6NHygcWrzzztbwbWW+SHkLSUctHWDaMgApp8W0dKjeawX6nH/oQx/sPYPxpbP0efQheqVd0QfR0qxnnhs6IKnlq9PJhzjLiQnEXtcpTn/JfIp0HseKCcBhH4SlyF7Elyp7/86mFwh0i+xup/oLm8oQFCI30kBPLsF2Osa30M/oMdcO8GZ9g21b/2rH1q3veDws1xy3XHZFAA7ZiN9+523BbOuW77u8r1fxooTURcYQTb45/rAZZMWHUMdnm+bmN7dscXrF/vurfTFfg88cTDe9/fY7Bl62fLnwoy3U1yHpZRn5lIV0hiwjn+WUZdq6ls14fqrGH3aEzTIm+RC8lpFtENLv1/Qpx+YJHitOTf+qZUnIlmaW13G212tjIcR8EjaCCJkHjr0KbcG35VyatKI+xi8KjHVOaAc5tsnnoq1ZfBM0U646phxahKAc6Xc0Z+Ebob3ffvvZJyZcQIy+KUe3jE1sKvVLOfMCdg4d5gb6if0NbWEM4d9yzY1N1oG6CG1NDYX8giug5j9e7Xpg0u9JQMkpP6iytzQO6ad999230wxnCbQy6t566y0apbbv2y3fZ5nJMxv1EqouCjVINDC20xfQxR4ZGHt42OmDhuw6FP/Otne6d+QQmWD31cRIyMUSadRFF/NEw8Zgx6QpWjFKJWBgowGsOjxXCfCyEUAjCxUzuMCxUybPYkQATBwjbLEoSNltwBDMWmkmWggzQAk2JBXwdAIjYpNKwClCI9tmB0tbqCztIFkH5O7bBXYRgjKcNWjwQXBvoqHHQYyIBAw6Y+HHhCQ41Qe91IgJuCyeDsLd2FEmHvyDZkyoURcykp4UipJcPYJLbCbJkfzIzJIr4BITebz4UNtycY/sfOxE1BbbheTLRYrLs62qRSeEGPhz867ML+swuLNQTnmgwYYG8bBReNDHEsS60Rf/g4fbEbpH7+n0aB/BeKJj2yjtS5kdCwaqSRMedoqK6T/4ZzBcyaSsyIHe4GNtStYgJoiqTaAJZMQLeOqBp1x5dEocC8MYO+CYpPHpCyhRZm6R8TcVUQYNgvvA4oiXq52JwyMxtQ2IDlXg0AZiNmLWjerKaAHC/0IAlQOnUiiiJyAdVO4KMshYill8MvEQGLcJ73YAAzEi+lAfZLP9q9y6RRbBsehhs0wm5C94sLVMQQj6wKevCXtloThapABpH6cEsB7XQc5i1/XeIKkU24QPExZIPpxlzKqctjCxUmdtWRR/FdpFB+ADV2yU5mdI+vAgeFErupFLKOIsqbHr+khHL5EO2dGDhO373lA0XmXI73YhvcnzFXqs8Q1Hn4Onets/Pk95zwNCzvHgPhJUkAtpom8my+1+FI3sG+yAvslFPDrmk/VhB9EX2TeWLRrRy4kUlqNnjV417iQ7cfRHlAEXVhp5ZdXS+PDdk3Bby3xEPXahcU37t4quCkwXeql7+jT8o6uDlgCwbWRM6rTFOhUzqmwT6LgwN03BQBc4L3YEyfiBBjYbXjApQn88FI2MFyoHTfSCnEFXhbQVXSnGR2ErMa9s7+0eQtTTNwidfeMDbJXnQwnbucdNSJDtdEPVvsiPGppyujw0BqvxYL6CNH6MQdqBnlIOEJCFhXDKR53rVRftM1To1GM05EAGaKEHdwKxAjhp77TPOqOf9C8gDLaAL/ER4si+RQmfQvvQvyilDSjlfiZOvaMj8nGoGH2DzSEjMNgF9N9hsa3F/3ItthlPbGBoC7dcYzOwAJELKLKxPoO+fQH54nMA4UDD41lpTIQgcRxYa/Yy2u5oczwowl7c9/gdPgUnMNUH0gptoZyD5KY6wcZiZJ0b5pbS68xd6BM95cMHIKOPlPJ/xgcJ/HscSABDZc4tcTAXdLC5qKe39Cn9ZpTma9b2oFJoJjyy54FOWGThZfqUhP32a2aP62K3qkxbYZNP36Bg+5Uiu+2esopn8jaLGb6AR2Z0ORp/MWbhhcSuU/9iB7E25qCh6NvYiMY4EbyIWQcWJKjH2LE2hB/ziR/sqL+wJ6/VixyKFhQKh5hHpCPLgBwE5WkTZV4nyf4Nr/L0TS73mLTU9h9ImrpAXkK/NnAu+jHakj1bKhYYpTz0JTZe+32vn9yUsJUwrEgjrdtHm0kX/ZNmXoiHfvKHGgvbtCdjDPngWjbmcaAyfA1llgEapS+d9Jcpj7J9ShoKAQqKM6pN2aKv3RcqJec6/Kj+sQdmrPqAXLcaCC7XvhjQ5RyoiRY615drocyc57FM/6ocG4Ku96LZ5xDbQ8OiHDSEsqNfSKO4MApNMDjRsgjPrn2XgS1FpqPykw0ZjYN1z5cCClYH9CEJiHqBcJybXTqFDnMnCoCntymLaSQSGaXfRQbFHnDQznqckCo4haOIgwUc0hs6nQVoxYr9fYshwNVeBgSZeQ2iMKjgPAnaqEIOBhHOGSfB6bTbj5B8MoCvPE7OpNBR0Q0LBjsa1+G8Wexw8seEJsektjGpGaHgJNmx2O0ZK2kyyLDdT+j9ZAGnyiQo2lwbQk8ZkJMn+Tg4b3Z1Egn/lj1wtB35cU5MBvvqdgxtCOfJJl2TfaW/ik2yC10J30G4PSOVIQMBnSAjTsGOVuXwjzodBMhJWbmjZrjOX4IL3asFkpF0OO8CLPm84FeMrHaOcoa0A/2MyS+C6egpjw1ZsIKm6dLI+O+msECPCSw0CAxOnP5l/PXjDTuGPuNNfUV5TLBBv/6m6dkGYHIB47bhFFUf46v0mvIuTCLWQW74pVu1BVzaTp+idezDG2GcaumUWMjFQQiUC3XrwW0UHQecstpoCPepyksVolgHsht06EMc1TG2kIEPAXD6IPXPWEFO56kUWDyV7klr4oqnHZBAFI8nJYIOPNQC8fHhDnKVEL6N8SkkPkMBsQpf+0MxibHJYp0nWKFP9ycLtfSRSavHj/FFO71YHYMTA5Xn+Kc/kIkJb58yBpKc46Q5VjicAZSQusdukB9dhQ+Sv9E/2332GQgCiD5RLTZKnnKCZEtt4X85BGCsejwKivHa0wuMHt94k3QNbOET9PUNrGx7y5a3zGc/3cLD59pepXt4Yq9pQ0gGru1GtLzhqfq8FxwYfTyelAKOFuLbCfaRFXDIE6ZgAL6KjvJw3LIirz7QTn3hJ9ERBaareuhZVuAzQK+MB2h7U+U+SQDFBRza+BM+kPD8IT3RbnTPDb6KckVg/iS6DH8bNoAt8sn2QN+LUJGiLPlkH0RdlgJUMN3W8MPYXh4o82QJ+elT5nD61P2WoiZ+zykrmhg42IoP/coTZAL08ZWMLwTO9gAbPaFiwcOfOvum4pdNQF+pk2xwthW6+Mx+fCnvtZLbmtgLiK2qsrmVvN7481Sw+Dj0ZP9DO4Etat7OZli6ox4fZN8mfXjspiwFnrby9I4+WLFihelt0bqJPE8yl2k+D+JF7opPKRmMAMMeiemDFJG5bSs32lS2n/taPthAhYzk9LoHNPxiJS/2kKDE9kKqxwYz4P85OOEgYz/5hpgXEyuhJsdAhqxK+X/GqpBsbIyQD7vEJye0KQKqfkqfF74SNCRVJdCSy+VKpx0BD20Yezy5PSOdlW41/sQvyBdAkql7bICAPcMDe/GGlkIB0hZsAPlYy6QusTGCDxfT/gt9/CIPKOkbDqcI6AWJw1ciClLsWIjNqG4GSXZuhKQNICdji8DY9NorZRtjFxn0m7BJA7HioJr5Yrlvq2KP6Ie5eGm5ZWnESV+QRRcZ93CsoeKWFH5+X9kfbaA9KTf6tf7TZgVX10FqLI/9RzPMEx0wzxI89hXnGiEfxrlyB7+wBw4a0S03nLBN8vStbUPijHxmMYjkJTlD5/I1yM2HUOmJdZJ1rbHDGHpbvocy7AjfPFKsMWf/KrxzXZYyWpealL0Psc5HyoxRqW8XhZAhcsjNN+3Gl9KHjCmA0YPXbWVsuX1BJNqc7Z5d+vcd5KIcNGi4SnlhDHIt0hNKjk0GhrxczmiJrouMh4Bxr8gIwyASIjopc7s0nsaqOCFZgkUIoxsNyF0il4xwOxMnPMutkAXzEY0+WLeZo7EZyqDN7A7HoomeekegvHU6mqyDtGCA82Caxht8TVRyUHYu1ZP/QniHJZ2OKL6aDJkQd0r30fi5rFiI2o6y7VbSCE7vdmnFoHzWj6qkODUdeIWJMAIpE9BSOdy02UDaTd+9jHUbNPFpEcHSg4lYgk0QZoY2sljBzsp4nEMI/ovtlEWTSQFbXFIOyObwXZSCxh6wF9pLW/mMjeMBhjnmW7i+vPRJP1Yn9cMA7XmLim2O2a7K3B8g7yAvjQkWESyA9lnSzh/zCjU7gHT9rnS9T7/Qly3K/2hlNDuNmSGbfp4ZbxEA1SQHTKEeoqV490bYhwSa4s92WB76U3aDL9jHG1sopT3uoC22wuCvCeWpVWSmfLut8yke2yAonjNX0nnz4Rt5hi/o61P70b4vJqEj24xGw40Gyb9EBwvGUZ6DhqXkW/80id0CylljWrK6PTX+AkSv0TLNus8HXLtAds/t9IW7FtucUccp3MSYRhPeA3qwznkmN+7peyYNv10xd7v9fEkg5lLNI3P1sRA9CdZyNo1g7NDenC/Iw4912GLoXxtzDhz2WRaHMGEz4oE9DtrkpDZRPmAPHvuq6udp0TbcACxgCw2mX+mMtQ3rtEHZJxGfoPsxXylc8cL374PuF0R/El/Ka72hG0LVHueB4ZPlmUeHrR6pkw4Y9wT7rTjA85zYjwV0BECLT9meFRbloMFPLdXBbNBGT2TV4WUCiBNPOqBWWFGyOyeVprIspsibhhon4VxZZ4bTGHh2Jrxt3JPoiYRhJQAdDxjOgmAHFcnp37Xw0yHn1ha5tmuBpHcHLWu9YYR0Ltytl7kUrLxWhB4W+qrEIdaTQK/jUj9EdqYy8EVbhyTerCN77WiRHf7mVw1GOVG3Cx33i7YiIyh+OlvB46QIHpyUCwi4oj6qxgJ8zVul8O71AVS2uSDj0LUo4tRRR99VvyfcGOUmMyREoYteUuc4P+xST4ncNg5eaBN5JmXrIdtb+NqOhWO7pC7pNiLoHTgv6JDdui/1iGYlKRprf6nPqNZTlhFPKs+61DEx9GlHOnl+HEcHDYiwxBu54lDBHQtFR5SNHUYJE7ps/LAV6rJtbCbQSY7PXscT9IMQE6rGRMkMfMWDze4ynkxMPPgrMta2aD1PYlYJgszwcd8Cz0d5fA/lfgqXfa48baacQ49+vChPGaHuX2hoPDpwum+9Frgo3YHvIp/bCl/JD0/LQh0BHkN8st5A1Rd09BnTmfJqqxf59O/S5mlfhb1jyVq+lItY5e4TtQt9IROypXy1fmHck0k8xZABzj7KmRoQLAXKCT2ByNbfVNX9at6FD+kcYzVOppnLPH8JDluBFnnwbFMFMHmYjzKmC2195g0g72DAbuDZ6ih9ncc0MgiGtnjDMk2mAgtNaOAvCPiK9LnQmqazwJjhW3T6/it8B7GqOuSy+P4SdE2DLHlgVJ/2h6y9L6R+EYLlSBmgR7rQtu6KDDVI39aKP7Dpu3ofBSmV20dJ//hL+pc8sPTFDvmgMWEqIaYkaRKyEGpbdjm2IZqepwwxz1eh0+sh8/OgTa3ONhVajE3WAegn57N58YULHu10HwjXYTHkS+bIGXx4gLiPN9MpO1VlnNXjCvasZwi0BfBch6Nz6pnLKYdeRU4l84QdbBs6sh0kM+jog1yUu05p20zmUxRwVIfMPoCmjyQ3ZW5/oZl2Zh+munZOT3IzxxVf5PK6R2XYifuc/i68TTPhi+7Nn7WDAjIxDgnpE90RUTT6LjRcIJ4OlNXlpXjBUaGBL+h1LiKpQ+uv5VPyPKzi92qQvX8gkPLVgqhsbL5o6dWw09LgDQXJPseWVIZt9H2EzoFTZDJJK2MqUnbK+ICj21O6kQEZbmTEjVMVj/Ux+T0vLM5BgwzFNxrkRLjRgBL5ZxOX1t584/XutVde7V5/7bVuy5tveiNaqzfVj4bzehKG6PIyeN01dd+olqeMXHWmY1ylNFh+Ii0ptvJjP15sIUvA+zqX0wFLl5mOiHBgAn9frRRt2sS/uCI4uqLmcrXZV6ZVz7W6aGsxCWSCcAl1OsskgoNl1xfXjpGbq0Zc/YFCXHUXX3QgGK4dkfR1ucQ3JF8jLoYHkGIN6rj2KBk1ibFwZzNqAQWT1+SBHlEw6gxfaCf0yLUowptv6gdBtVn3VUA5Bdpk/VIp4CKW8ZCN96ugAjzXQqMv8OcqVz3NyivZkGDTRx/RFj9lKO2OK7VAlKaVmCcr2S5o19cM09agxTtVXLflmiflXLHz9ShTTO1gQxToS/9tHSIeWqBt1Bf7Fw36yzwEi3xRzw87xeaVJ7W0m/6gDLvzVUGYJLy40QY+rhMOeslr2AgCDWhzZdXXxuSI4xqmZJMc+XFTyhfwOxPA9sf06aeiBdH1+IO+/nM9LK9F7qsnWvClrdgkaV+po03C980B6dNPsBFOZQKMSPbAWEa/vMrEGORH8bgOy00J2FmGovOgPdI5RKi3MVi0kM86KH2oqA+0Bh7Iiq75ER+u4tEPMX40FgWd/epruPBWGeXwz6uuPVFVIIK/EFjQ7kcV0ofoDdq+UqdaaMerJlwclTySI3QneOnM11AFByWHknAzpV/s2T+UKTr782NctjU27yF7IPbYSWXemBFF/5mP0siNPOTD36oPVVY6xTq37kXZeoHDmKxBL3SmG3EixAdd0Af2DRqPlKEDUIF1qDutL4i6BKFYqMFTsftKBeaH72bxK6rBN2xZzep9BfqiEbSpP0gHH1qmjfzRBmJC2KXGpezT41flUaM6i6cvYIOAcRBDVuMyXsczvGKDUyMA/xik9AIRDr+QB7kdDKvrxqrDrraWQ9N9vTDTWGT86B92gwygRT9K18qYDjLQTskNnNulsWc8BOzlLTyDc5RnOgQuMmVhxNH2UpVyK5v9ie2jL9pFe3MO8GtX2FSO7zGyCD0qQGbjqch968bCBBi1JGGbJrg2ywrJXkTl6REtHxHW+kEX9gVKWF7zQY/oNgjBKuxMCcr55yq+kCXGtsuKXOCGHQkGXmq35yLKPXZNwPj6mj0EY/cvfG2X0nVevfa8orwFLQqlSfDH7yIXMATSefXXP/rIBlkBufNqt/HoMxpe2pr9DGyEqMvcUOzWWpGSodKR118S0OsGIeIbUj7sBLbIj+5oDs3PNiKHX0XDphXgMfqE/mkj9mP64DLuFBcRellMoOBn2vycgWrYHGPIPCwIKe358OPSEfP222+9LTuK1wWQzzIyDsQw1hLMmTEukIGeiPkpXvOiH9xHEM5gYfUlnsExKlzcF+BjKKHANX4d2Gll6VN+FwZ+9gu2B1UIHJ1D23MVOtWHfvG4UD1to8xBEXywm7d5VUIBH8a8hM9hXkmfn+0H01qzzlI6o+prJG+WjJdRLxzpDBk8d8uXQSV8vpICSV74wpwbvQcAWcHySxD2E7xmEK8Ia8NrnWIj9GG0Gx6Wira675QwXNCa/B2yIlsd0l84hnIBQ6YYl0E/yz0usVMFrxfpK/1DLtZh1Mc6knEZNkb76WP2BhLa9fDzeFI5vPg3awDSOgDBuGFDyEAdclNOsL2q3HClLPUFBP2AfPyQ5Fv6sE7njw30/WcqhY6YWlb6wnpSK4q+AEu52nQhMR5ZvNAPsmGXyIosxWUIPuZmj4+yZ+MViHy9DPg6kHOJaIfNoQcVFjuhDj9w8CGHdMcdf3y334oDEwMgffbcsCgHDb0Cix5CJTLQ0rVPP/lE9/CDD3YbN2zoHn3kke6tN97oJwd+R4GJjsURDgpjJCjrzpDphI7FhA6FV9TFQoj8Fv2li/h15qV+j2l//YYCxsym8W29f4s8IUsYog8GirXgDOAPHTrZ/FXnQcwiUTwxcmIv7GTodopysO+oXEL7V36XIZSCh6SS0OMrHETEFLkxpW3wgOebOnzhtwgOXHmgZeWXePOApJdP/L3IFm4OVDjCx4MV0goMQDtW0UakN954s3v99df9vuSBB2K4sTiyQ5euoYXjTCWLTR9Kk/o8iagHCOfE+5j6lW39O/jgg/0OJrz4QJcNAk7NG8dCzLLSFvHOD4MOmISHD/KxcYYW9Yceeqg3evQFkyv0aT+L/Rj8tAEbYeJTWenfWEi7ymXgeaITHXQPnwMOOKA7RIObxr366mteKKXM8MhgJ4ZTKLRpixfoYphtAC9l38LGX3z2F/2V0j28aTN8+aSOKAMHu8IWHdTmnExSZxweQB+7pl/RA3lk33//A2RHb7icMnSJTCgj9Rx0aCbyojdsJFpIxIQJPAsev7cnODYbbLJ5n5eJKDdd6N36EEzKZ7lz7KgdWAk/4GU5NW4OkA545/Wll15y33JgsP/+K7oDpR9k8WmufIB1ILq5iHEryuIFnXkDhJxqP3+1htNfLwbVCA4y6UPaw1++4b1gNlrIiBz4GEJMSKHrkF/2SFtcixZCT7SZNGNnP71z/Ibs8ZWXX6bW79XtJ9roGhroOUNMhEx4QRE98+nh3Kjggaz77sshTNe9/PIr9gcHHLC/DweiH7EbDqPK712IDzZoXUhvxNmP8MfmsScOGlhcMPkxPgmvvPKK/Znfl5bc0VKEwR4c9V9FdOddhfzKQZ9+ZMbF1vyRDIzX16R7DukOWnmQy/Fj9utlXEIsdBGcIWj7VD32T1voBy968KbiYbuXrPTtG5o7yNO3yNTrfExYvLAZ8e2Q/UCMXmgDdNCvOsU+gXdr4Z3lOW6IkXm5YNm0sFnh1S5C9qf9DHl9WGqyeN5ffUh78AN8sD/ak++uY9+WXzx7P0abg3DYjuogGv2t3xqQHbwpn8KTHeirxu+lsrlDFn6EeX/ZKXS3yEdAjXfX6VsOH6BtO4SHCPPXVrbr1pH/4pIZya4YU/w6tvoRuV9//Q21Q3Q0lpinLZ/RaW3oIOPaDl1Z1SMEuOgk/RNzH/6MPHrPkBsrODBW3SeSi8U+bfX4VTnqgWjYlMaz04rEA30gP/D0B3aaY4U6PjS5fAk15PM3dKgxSPgFvul1FpL4HPqThyfY9wr5MewDLGyeOpBjbMQYgV/8MCKUYs5AHoLFQD/lQ9mbb23p5+6DDzrI8rzDr/SLn8B2IARS6p6xhP6Z//hgM54T8Xn4SYFj7+iedvDefLQL1moBIBIEemwA3HbR4J11bww0jxBWrlxp2wEXvwTdWcW3XiCCnxOPd8UP28CvbdFDDfz+IfJtxOS3oh+0GeKH/PSLGDLeeIDB/M98w3qOceV2KOZQkA01smM3/AU1rw0OPkTrR/2ivOgz9nvZS6KXMThXjcNfRi3vu6ND5Pb8Kl8D40NEm3Ufsr/66ivWOzbhMSLdo1dsOd771+snyuPP3R7h81AHnaY/ZzOG/PBlPt9X4xjeng/UdkJYeZG66MkVriupvlFqA+30Wpi/bsFmVb8p4fawBivU1CdYMjwB51AmxjByhI1gK4wB1hbMFdBlDcC8ykMIdPOy5ifsjHLmbcYU9PidECnHaWVnCvQrAfvlg70z/9EPHLxjt+kP4OGb2ZKbdMhKzAWAsP+wae0nVBjjlnGo3+7w+mip/SQ8eCCxYoX+sgK6sL2G3SELtGcNyI99xoOV7ZL9VY9P1pL0NwF6bqe+6BfGsNdNKve4VFuR1WtejUfmD3wnPh495MER/frKq6+6jPUkewKv7T1Woo9r2VO3lqGuyBaWMtuH+G2RL3tT8xFy4w8sk/rZ8ooHLRGY29L7ZuXt99VP2DXzBMG2p3EADfCx7Zy7mfuwJ8rxN+HjUWS0AXzMAvGKeRQFUqNSKkghDDjqQ3T3mtZ9+G7WNSvUBv8uh+ijS4TGb0KRuRa9wj/WmsyvgEhOwWFjXlNLRmLWUbIi98NyrQHpj/POv6C75NJLu4MPPcKyQLcXrJTsadGiHDTQaFQRC1xOyDU41EEZHnn4oe7mG2/s1t95Z/fw/fd1+0qZRxx+uDfo/KU5Flk4ES/g1EEEOtofdQLVBBaBZCj3Ykid+o46avPmzd3jjz3S7S8DPuqYY7tjjzvOGzsWphw0cLCAmfnHH9Xr2JLNTvSg5RNVxT6Jp0zyeyDKOb2u2xgbN27s/vHSi91Rxx3bHX7EEd1KTWwMgG04XRxTcbLQFBlT5xtn76aJZqSoAmY00F988cXugXvWdysOOLBbvXadB2FuNnGulk8DigGTTwvhkwOUyZcJU9Uugy8NZIGAvp54fHN3/003dCeeeXZ38tq1drAMUAYhixbLonwESYmsRcd1WwpAqWfyWOJJ7pFNm7rnnn6yO+vCj3THHntsnPpq8qMeJ+wBJ4LwIaQTYeAREo76WEyrrdKnJ52X/tE9sP5Ot+P8Sy+TzRzhgYojta1Al/4Urg+LJLcX3LRfgxh66MA8xcttVTkLiFc14bzw3HPdU7Kb41av7U49/Qw7ECYKNhFx+CIk0SeE9JGy05CdQt/Xm1RJ//rHtUT/9ddfE+3nu6ek+xcff6Q786NXdWvWrhN/LR7lbIGNJ2BBz5sU8QldxOmpf0BHfeibIoggXJw0bXjh+Re6J2Xzr7z4QrffASu7U848szvqqKM1MegAomwKoEUoqO5T9EACftiQF1rqI5wm/cSP4cH3maef7p598imnDzns8O64E0+wXbJAYgPIQocfhDPRMl7pDzZYoS0xlm5oMGU4anhx0IDDfuC++7pnHtvYHXLUMd1xq070eN1PTvZtTST0Lc6bccWGkHQsNkRLZdBE7yw4WTZBnz5mobJCNJ5Vn66/+eZumxYyq2TvRx19jA+p0DeTUWokx0/YRNp/1FpnEt/yl3bxA7CMtScef7x74M47tHGPU+dDDjnUi1FaDk0CukVGApMg5Z7U1Z/WU9G/miA4LSolG4sCJmN089jtN3enX/EJnWof53ZhV4lnW5atBK+wfcaZWJpvxuYvvcDfiyEtfND9fevXd1sUn37OOV7kYgvGtLghswXXV4wb8XCB2mSB1SboMmFKZhZVtJ0N4LPPPNPdf/st3VHHn9iddsaZ3QFazME/jI+2yu9JVi8MZHPwZWIl9phAF8qzQM0JGf0gI4u8p558stu84aFuzalndCeeeKJ9HLabE7rE6mWkD+g/ZLdV2vTpWfeu4fDh5BivL8vXPPv0U14wrD3t9O6oI4+0jrP/0IUXpqLowxP0auqFPjnaojZi78wf+x94gNPPPfds9/RTT3Uvvfh8d5DsZc26U3zwwyEkNk7fEpilsHdkZk50u2iUPuieQzUg39HBwFLpncXyG5rjnpAveEG6Zz467oQTpZuTOg7A8DVqhPXkvhOu7UhltDuDbwWWDG3gs0z0acML8jGPbtzQvaWxybx31FFHdYcfepjsOfoRGfGHiEnARPTt/8GE+lLppqgt0hGHr/ioxzWeNt53r3S1sjvymGO6w6V3FqL4gfRLUPT4EV3rS7pnfoMO/QN9+07RgxM2w2EOf2ps08aN8mWPd8esOsFzN3zxX4wfdGHZinxo3np2C9wQpZDf3+HfxBP9YDtsiu6/e323VX4RmznssMP8wAAd+EBAiNiMxyd0el2lTkJPyGvFKQuvXPw//czT3Z03XKf56ZSYn9Qn2zTOAELW2YO1EiwkHP2K3h7TQ5+Hb7ulO+Oyy7uTTj7ZY5N1AT6eMUg7kB3dk8HuQ9/hM9C59a469OYn6sLDj73yj390jzz8sDbnb3TnXHyp7OZIj+FYcyD57PJLWyhFNq81l/oM+i9p3bTxgQf09O+w7pzzztN6b2X3hg59GAO2DWuotLtSFP3tPlc/ogdocwDHj/btK5t5c8ubXYzXJ7vnHn+sW3vOed2pp56mNeuy7k3RRweMU/5DPUN/06IUuA4Y+koxB8lsGF+WXnjw9qLGKzZ42tnndqtWrfLc9LoOaaGam3VI0c/YuUg40P7w/XSJSvXBXry5Eq9nZDOPP/qo7O+t7iDZ4wknndwdooc0+Bp8qOkIx2vuQjNpj5epBWoUnoJ5bz89yNii8fSIbOZZyY8vOPLIo+0P2NxxwOPNnvwZ9omft33AC/mZoxTQB/6cNtjriTm+Zj8d1P1DB/j333O3bOclj9cjjz7acze/8cZDCCFZH0XseSPbjZqxn+Y+noI/9dST3V1/u647+IijupPWresO0hxOHdplvskORb7wySE7YxibYlPI+jLUHmtykFg3wYu18EO339addMZZ3fEnrJIPiluEMW7KawvRA/PKDgA08Q30LQdf98rXPP/EY915l10h3Wt+kt2iz9Cs4KWf8DHYnMaj9JbjkzpvaoEXTejSL8Gn617U/ubuW27u9petnHPBhd1BWqthL/AAypDCzeBUZlUZlOhngNFolKBX7OLpp57uNtx5W7f2vAu7U04/zYcFWzTW0A2+hmB7dkJf4gU7bJt5j0Bbw2fz46n8MOYSH/TiC5579hkdrL/RrTn9dO1DjjM+m39wMsR4QdZYb0i9QbPAUJ8ffMNWtf1VrZVelD2y3saGzjn//O6IIw6XX3uze4e9peRD/zGPx5rAKoCp6HHIxloVOVg3cdBDe/bTQwIOd57RehX6W1W3n+Zzwre+90/d1V/8Ynfk0ccrl/KHPg2wB34t2kEDbeekks7l7/z6uia6kZ4euvee7i/X/qm77+675Whf6k7UtZBV+iyXA9MIl/HI2HBQGgzWq/BsqtZtEMEWGHiucQfG32PlNsN9Dz7YrV9/l6+cnHjSSd1qTZqHaTGEgTMxhzEwUcq5ybDDWE3Qw8GDTjQd4F0GIpujlzQx3Hz77d1mbUhP1WLiBG26jjzyKG2aDrC8/gsaGLRo5yIxJx3oITFyE0dQy2R8yMQG4klt6v7+979r4C3tzj77bC9wecKLY2PThx6gCwEcYBDi6Ww8NWHBs6+cJYGT4HQ0GDgT6MNyfjffeEN3imQ/UwOc00o2dSwYcCSEHOAeyBSgbPFEI1lHMcEwqseJ8cTlrnvu6e7T5/KrrurWrV7tBZgnGOmQVrOwYPEyJ4g4kxF04BHOPRwkDpG+e0EO5LY77jD+FZdf3h2vgwxoE3xaqfYhKxO/B7Qc1jvSAYFJHb3Jutwc94Dy4HMy+sorL3dPaDP9wP33d6u0aTlfi5VD9NQIvSAP+gfRk7rUAf3IszDRkx3B4Yzgg6LY7HqBqDYxYTKpPfrIo92zcoCXXXFld9YZZxj/VZ0aM3FxaGDnU01wtjvJzhjiSQULjnxST5t8dV98WEw8sumR7nkdZuynyeycc8/tTlh1vG2CQxQEog25AUi5oEGIQx6eEsYTOHBoNzeBcH6btJh4RAt05DhaOj/ttFM9ntAb+lhRnpJ4vJYxC0104L/Frhh7pz21bmjzS9L73Xff0z35+ObuMB02Ml5P0uaIJ/hvv8WmMSY2T86yGw4ssVf60uMW+aEt3auT3N9Mwkz2B2pMPqkJ7frrruve0oHLunXrvIhj08jExCIBedANad+CYiyqP/LpA5LTL+kDsEsWdxxk0ecPa9N1+y232H+tXb1Gh1+H+bYDo8W2ItpiYR6KbKdMtnEIFYdo0AfI9i8fSEzgicIdOgh4SIcNF11ySXey7BKdYS8E5LZOlbZ+rHu0MR4YU/gFbB2ZcnPwnA6o/n7TTc5fqfF0lA5NaVstb1JyD4q0ZVUhvPVlueFIX/vpmhaJ+J03pe/HtGm89eZb5CNP7C7UhMxiBf5oBHz7YfUnY5snWpTlYRdCuH0qizFb+kC6YfH/suxmw8ZN3YaHHuwu1sblzNO0WJFeoIWMKWfID79syShOGC8cpSP0g41zw+YpHQQ8/LA2Rsp/9KOXd6vVBnSA7mgDuCaJTpSXApy3zKJDv7AgJ89iAh3lAvHxJx7vHtGYelo+4WjNexdccEF3pGw/faNpmz6akl0EJ5Qfxp68xUNCd+8yd6h/+QsZzE8PaMP16AbG69vd6VrknqXF1kodXMdCMeY7KKN/5ge3Cdpihg5inCrD/8IDn4FcT8l/PfjAgzrcfM43KE6T3k/WQQaHf3nIY7svaqb9DkQogQheJbYuleapHLAPbtjQ3XrTjT40YbO7Zs2a7sgj4pCHvsE+R/O1dB5i2z+Djy8Gxu1QHrtkfuVAgdf4brvrzu4h+fgzzjyzO+GEVT5co1+gaVmkjxQUkdFTaYFyyqs6IGQNqvCiEZ8hPT37wvPdDTfe5AXnherTY7Ux4paJnyCXOY95hPb7AFz0fCCuOH0zNkY70DtpZGJeoT2Pajz99a9/ldyan849R+NppWWzfRSdIuOsIfuG+C3WTerX22+9RfPTFd2Z2kwzH+Si331W+hCd2uYLo6zzRkbjk/EPXr6iwy0J5u577r23e/W1V7srNf+dILvH7gbXAvM0AP1j94wvbrG9rg3F01o33aG1weGHH9ldeflHuyO03nuThwTSHwdJsWaSPrVGgK8Ubh17Hlf76Wn0je2wbmUsYTOv6tCR8frYY5vF46nuTB3InnPWWd3+moPYXAih9y2WS5Ta9R6dlHVOKM/cjb6e05y9UXMIh6b0NU8v165ebXkZr9gHNkOdfTxrHBEBH7tiruOBAt2P/6Uc2dkE+WBNfuZ+HdwxVx+lgzvs/mgdDjKWmNtpOcFxEbIv64Uu9YKj7317SuP1NR0a3bX+btG/pztcDyDWrFnTrV2zzusmfu2fh3q0Ifog/rwgMqNu1qK2edHEtuOQJ9as9C2298zzz3e33Hpr9w/tEVaL9gknnGDZOTR9h9sx0knKShtmDZ4L5c8eefSx7m9/+5s2ikd0p596qmzncPc5yuTBFv3jsSs+caAQ60WPGzMO39D7GvpIH+hjQ3dr3r7lhhu6czX3nbJ2rdcFXrMVuFnlTTh3h5THX5tgw3uTdMOB9VVXXem1cH/QIBivjzAK/UdKdJ7jPf0vdEP3rDfCV7I2oQ1P6eDrb9qHHHToId3ll12mw+RDvZ60oRW6Jl7ZSMrpGH76B2NUlbyxhzdkN+j+Th3CXHDxJd2F551rX8GtXMafeRQcz6OiARvsBbthDRZ2Xl5dla5zTcT8h04e1eEa66cL9MBz7eqTxZ+D8HrfETpJ+dwMtQvdpH6QOT/Mr9wsf1J6wQ8/+cQTWq8e0V2qddlxxxzt24LY/DLZlWZ9t4Hx6vWI2mTfKLv2IZti+DJ3eG2j+cOHjjq4fHjjxu5BPYinf9lPPSuf/P/+z//Zfevb3+6OOf4ktKAPAa3uuWGnDxrqpr/D5lfOjM2Rr3dJv+/K+dx9113dNX/4vW8zcLXk0osv1kA/xSdB78gh4kBsbGXS98kqNoty0a90bVvnS4FBhaNawRMd4d98263ddX/5bz8NOU2LrDPOKM5VRo7D4OCDWw1eYMkYbFiiFUNi1IUxYQTPoL+ie+6FF7rfX3NN98C9d3eXXPbR7hQ5qGN0s4GFkidNJjENhniybvH8leZBnGm3pRiznZoWaxsffaT7xS9+6SfEl2vCPFGLikN9dWmZT6VZGCKn0MbinLSZfL3ZFR8chnmpbdBHvjt1CPCbn//MA/zSiy7ySShPe9g08ZQKwrGJRschKd+ona/g7RwlnniYSJjkcBTX3fD37obrr+u+9LWva0I+05vCHGTQw9Eghx0cA64O4g0s1N03ogsczodF7NPPPNv96dprPWl+5ctfjglZA5tGMsjBAdun56JF+5lQ6UccUy7eaBaLCtrKRP66HN8L6teNGzmEudFPoz4u583GCxqeSCSTFymSHeeTcnqhLhifgKs8FuYoSs6Dtoj+C1qAsrG4/777u81ygF/46le7y2TzOCL4IpuvqkowdEP7zVMOCDFZBHJ7wBs5LSDs/AQDHnxZBN2vSW2znDenoJdfeVV3qjbV1rc2Gw6iY3sQD2Rnk+EODU24X+EJjq8xypmv1LUweN99z73dei3i3pbjO3ntuu4i2c3RRx/l2xLol6uCjClsXgT8gQ7/OGigX1QaGy94kBZ/5H9Wi4m/azLeJOd6DIcYukly6imndAfpkIc+xbYY/5wQ8xQGnWKvHDhQx8LHi+FlLDyXe3HFrSNebThUT4s3Sd+/+PnPuzd1zfBcHR5x2HCcbjjxalK9KU3d9H0rOdWFtj3kZ1xxOEDnePOiGD3dqYXWn//4Bz3lOrU7VweDx+rGBO/Uqdr6hghp6KIPNpPQY2LkYz25DSxYtYjYT9fRi02z4f3zddd3d9xyU/eFL36pO0ObujxoAI9gOxQeNGkDzIIj3RAbFWwQXXtTozJsDPgntLj9ze9+Z7ivfuUr3SrpxfaHzLK9pAOflDO4mo3lhZ+4ejON36FvsAluCD3w0EPdH0T/zLPO6T71iY93h0kvYd9oJPw2fNy3+ATx5aAB6uZnjQVv+EAfP/yaJuRnn31Wul/f3anF1tVf/kp36Uc+4sWKF7ZqY8gLFyRWS4IlBXMC/gIdebOsxchzeqrAQuX2227rXtNm5atf/7oPMtBZ0jcRkYasJs1+A2o21p8WHLIZGHPYiRToBl/zsDbT92nxz+Hdyaec2n3yE5/ojtcc8pbmRvurMo48rxXZPQdCRLwIyWe79MeChae7bJg5PLpDB+EP6BCfDd5Fl1zaXaLxynVy6GPzzIG0mQUxdPCvvf2LHjpGbuzViitl2P2jmx8z/Se1+TrwwJU65Lm4O1OHpmwqrZsyJlNO8NNfIbelp39IyzdwQA7sIVrQcrh3h245/uG3v/H4PV2borPOOlu6kV1Kh8iPDMDTv+gE+W03khk+3KTxdWCP3zxoWOJbEWwar/nv/+5uvu6v3eUf+7gPTDnEwFfQ/tQB9EPQoG9di0/GSob8ireHALbNxzWefvXr3+hQ8/XuU5/6lA5MTzBtfFY+ufeCWOSxJQLjl5C8mbFZFOfBDn0DDP7nfj1A+fGPfqSHBKd1H7/yyrhJonb6NVNkHpPQZKd+ITp6xO+zYb351tu6P/3ut92XvvGN7uILL/BrNz4kkz0C574sOM5DXTq3vhTRNuwKv0j7sHf6hNcdn9YC/Xpt6pjvviH6zE/4AuzdAToOk9pBOSHgvDaTvfBgiVfjHpNdXvunP3WrTjip+5rWBsdoM/2aNhrYOq8PsVkHEz0jm9stnox72k8ltsUGkQ04rxgceNBKPyR4SHPT/ffr8O6RTd2lOiS5QhuvlTpA4jBVBMe0zljEOlilEepDB+dtOWU9Ilh88H2aX/EFbKCulF2eLbtnfYoePVdLPtun5Gaug3IeHDM/c6UdPXt9ID9AP3BY/qbWc/fpUA0/tkV+53gdCF544YU+YKt1X/t5ZMSfoRA8QOQd+cs1sscD0I3m1Ov//rfuVq2ZuDXMWD1bhzDccPLtYcnPeGR88nAg55+0ndw40gf4fWizwcf/cLNgs3TzR621n9fh5tll7uaB5P6aI/1aJevsou+RhKH/tBP6IoLK3S50H3+W9N4HHuh+qbXB8aJ5keYPbuCu1LqDdQW+DB3zYY1FO+CFbMGBcYpv8zGfWeQYxu6xIQ7xf/eLX3Sf/Oxnu/N0QMXNLOZh2k9f93KVVEQpbxbCLYPqxJPXtF56+R/d7/54TfeI5pJvfftb3dqTV4ddyz5Yv6PzbDppZPVDx+xTWmHSpU7l2D9tRb7HtJH+5S9/2R121JHdl6/+osaTbiAxVsvcFGQkD+JWImI1dYj+kY8uY4xXgl7Wg7d7tF699ve/6z6jufuTeijJ6wf4CR80VDKCh33ACNtnH8BhAzrm9TQeDvFgzHOZ2vi85r/N8gX33Xuf0s91n/rs57wPQQ76FGGj35CSNHEE2p2fgElYzZXS+Zuy5w1ax98rX7BJ4/UY2c3n1LertUd7XQeoHDyyLuV2b7z6r/2HcLAn+p12IAM+nrUMhxe+Maixukz+5kUdkqzXuuCuu9erf1/RRw88Nt7V/T//3/+vPv5Od+RxJ0jQtI9K8GzAHhQv6kHDtq0yDi0QcDA2NHXkO9oErL/zru5aLc43b9roK3Sf/fSnunPloLgezXU0HJ0Hh2I7B0WoNY0WfdogMHoFOpCJhFsFr6mzr9NJ3J+u+UN3hAbHWVrknqsnvMfJibBxYaHliUcOwhOOJ52g78GZtN2hcGXSYGO01O/OcbXlZ7/6lSaH9d0VV31Mhxin+4c6WKywGaQNLB28jQvxlAvzgFJfZDvRl5wC7eLdO56yPvjww933f/ADGysL0DVreEp6uNsXV4tiYgOHSR3dks4JlJHDIiV5ps5Y7DC53KRJ58eif8lHP9p9TE8ujuapizan1LEwBT4XP+jY7VcU4ubAU1za5AlQA4nJjavYLOSu1yHPN77zne4j558nB8APtUT/Q4+Bht5jMoxFFkoxLxGlHwnIgzPmRJkYR8+k/Kvf/Mbv1n3vu9/tTjtlnfseB8ohCRuWGOQjncTfN2cDF1d/4YND5Z/bitxl4/KAFnJ/0yHJ6ZowP/eZz3THaDOdBzvg+eYCE6daz4k7+HbehV7+PkP0AQc+sfBi48LT0fXaGD0im/+6nAYLRfTAhgkn6cUu7VS76ddeZ5KTBTaLDuDssMSXwKYTGhs3beru0gJ9k55i7qu+/LRkP0uHa/Qdp7ARkD8WKl68yB4yBLmgSX+GQ+582wW+t+rk+VZNmryTuu7007ortNhiY8QCD1i/Oyl6tjrpgskoLd3Xw+QDsCT0wc0D0j5oUJ889fQz3Z9lMw/pFarjVq3SLZ5zNGbP9JNGxiuHO0jGTSTy2PsBK3QFEfklK++SMzlCl79iwXVO7JCDCp5QsKn7/ve/323RxvfCj1zUna6DxxNPOMF6jFc/5PZFh+DNtZRBv7KAILDBqW+rMIkjPzBc2eWpwu9++Sv7gY/oiiFP6g7T6T82gL6BI+BbwENf9DF9ycaWMo8hwYKTBwngsSj/7R//2N0sf/aNb33LPpLDVMYOtLFJv7MrWTm0wq4tOwzhpzy8CD5oQPdqK+2BJzcOfvqznxnuOzoxP1l6oY6QfgX5CNDKD3nzoWniE32ssacFnCd/2Ry/W3H3vff6kOd86eVLn/98d4SeerGQZIMIraBD+1lExIEe/YrtWCeUa0wYsuiGK828V/uUbgOwMbpFi9xvfOvb3cd0I4NDa+y1lxOZzSW+7LWiO6rSkIU6dIQtPffscxpTG7sb9XSaJ7Df+6d/ku7P1qKG90K5IaQAHRHHSuhbfIJ9QXJkTioHDSzQkIRXJ/h9g/seuN837jY89HC3VvPH1Z//gmxyld/LjoMG2aNkT7rIZrHRmf73TUD34s/oQve8isT8dMtNN3f36jD/Dc21l3708u6qy6/oDtW1YBb/HNbhS+lfDrVsl6LLAs/2KvumDxgR9AEbJeZXNrrocqM2WzdK509oMbfyoIPlC67wIjp0HzecRvrHPCR7+QjdwfWihr6wT3TDE1Hm2Bv1mtMvf/pTjd+V3VnyBedpg8GTTPTCU9m0GwihhyX0geyfcUAMnMerZGY80wZ8w8EHH9S9rI3nL3/72+6v2rx8Uj7yHB0MHqOnvPiK8I1qLzqWXFY0kQJ8+j5wSRSiI45QWSwyvzy6+fHuRz/+cfeG/OIXr766W6MbGfh15l7sEhr4SvRhuRR7XSQaHgPYungit/vA/aL1hMY7eW4L/ud//qefSn9B8h95xBFe2NKX6HJHAu0Fn4OG63Tg+xttvL4le79Sm2l8FPqMsRibOtue5G4DJSyeaRv9xAafdQW2w+9WPK7r9df86VrPd//6P/5Hd7YOp6DNZ8FBzDwrSE889eOVlY260fd79e1Jq9d0//yd7+oJ7zF6vfUfvsKPztGfx6jai6+k3bZLyecHK+qbOGiI24O8NrFSNsNNjHu1KVqvxf8GzeEf+9Snu0/r0PRgHbLxaoYMx3SyDdkLecDgvOTN8oRDt9gDB3eshzfoMAP+n9HG6ELZPOOSV53oG9uC7Jh+IIDnw2jB2N41nplP3UbaSVo2w2tU63XgeOstN3vsr9JtwYu5GacYe8+5IWia9Jh/wU+P1SljvYk2Bw1sjP70Zx3c6bDheM3d5557Xnee1tr4eW6T4GsYS6zp2Eiib8qggd0TWwaVjw4a5Jvk41nLP7J5c/cLbXaf1Y3NC3QQcPppp3Un6WYZr2ZwW0IKkSYWHvDz6PeO9Xd3P/zB9+1fLtd6eJXawA1f5KrXW+gKPdsfS78ZaE8GcHIdwUMjDqyu1e2jn//4R93nv/Tl7hId8HDQwIYVJTPedyiIJX77OdnlT3WI8fBDD3b//m//3p1x6inSpeza+g5vhUyksG/+hf1g97G2QQ6kEBgS2f6Qi3ZteuzR7oc61DxCT+u/881v6an9MdIJv0kCsGAmiG+NVHrJPP0NXX7TitfDb9f89Fv17Ze1rvmiNuu8VvmqxpPHZlEM8IlHEfKjV/qDdQaHDRzgsg6zXxUPbvU+ooe1d4k+r25+8Stf7T5ywfk00HDWRNGHVGK9EFPvPqQf9a+XW7rCh6FzHmI/IB9wx933dA9pr3Ds8au6r+kBzSlr1vjBypbXXtcDN81F5bAB+7R/k1/H/uHGbQ7m2uXKc7jA7aAlsvflassLurmDXm5ff5fnqhd10PCCXrP8+j/9S/fVr32tO/TDVycwg1GQXm2QdBxGibOUlaFn9faW7h5tuP5yzR/17tgjmiwP7z6r0/9zdIrLO9kv6yneMhkC78DFCIhOB9WdzzfGUD5w5UkITnZfOSd+8OqveqrugwZdkT5Li4nzdXXpWA0UHDcnTNyuYEPqq0Z2VjJoyZdyYtA4DcYSfHgqi+M+gInn+ee7n+ug4X69/nHFx67SU5HTfJ2cH7F7602c3zbLzrmntxYQCZFtwGQJnHyjEr4YUGwecLYY8ve//wMZ7NLu05/8ZLdm9WpfKWfAcYLOIAPNTk9lOHHLDU1kldwszpMuemGg5EHDjdoY/fC//qu7VAvzj2uRyDVynoTh8FkgwAdZPNzkVNyHok1AzvxAX5Ue/OgLnDxouOFv13ff1EHAhZp0YrEXi2bazmYdmhxAmI9kxXnxj/9Jl8Ui7WQBJ3BPUJs5Zf31r3wF9p+1GDpt3To7R2i+vYX3x8pBg3RCO5CLzSk0vaiWE6CMnoUmE7LlloPj6iWn/3+//vrubF17vfpzn+uvGKZz5ZAqJxwWCixskRGdwIOnP35VQ2nshZNzbJPDBBYpOD8fNGghdJVOcaUMbWqe9QKbHwty/5VTeuTPWwec2LJBRWdsRFEU8vtH3qS/h3XAcLs2XRv0BHm/Aw7U0++rNZ7OcluZMK1TYbH4o/0s6KHvvhUh0jTEuhF/NlvYFU/mmVxuEe2bNaY4aDjlzDO6q668Sqe5x3VvakHd2wzyqt0s/KGDjNidsrF5FX10wqIc/XM7gPcwedL1Rz2J4t3m47WhOO+887tzdPp/qJ7AcvCVryBx0MBhEk96mGCwecYvvBifaoRWunGVlomHHzs8UAueR6X378veOWj4yEW6OeWDhhPdNxw0YPeMKcYJtkBAL8jNN/V8kJ3NXOgqxiuHWDfdcqs2Rv+nO01XjS/SYoJDDA4GCeA5YA/Ip0B/UO6DBtkQi0u3QTIQ2LAhB/xYKP/mD7/vbtCrH9/UQQBPRVjEUMeCLeVGJmggM7aYvoF2YK/UQZOw1PYwAABAAElEQVT+MJzytJeDu5/ooAF5vqMJnyew0MQYGBu2a4iiCeTUx4ZX+KTdo3/sHrk4CGGc8ErQPffe1/1MurlABzxf+cLVfq0EXownH4wgs2haZslm3Yo2fLBB+7KqXSwuWKjFQcNTvrnGIczXv6WDu/6gIRayvpZbZKcFEYrPzaxHL33MxiN+k4S28lSEGw036Qo/m9Pvfu97flLH3IFNWj9lseJ5QzZJ76Jb6xc1yd7ZnBP8JEjwK7U4f13492njcpeu8OMT1uoGz9VfqA8a4j1Y9Ix3cV8qFjlEs/6dpkQ0+bG6LdI9ixY2zNy443UVxhOLukv0vv1V8vMHqu4tLYSwG8YN9pgbMPwD/W57FVl+Z8aHu+oDWPo3CIove3jTRr/a99Tjm3UL4bDuo5d/tDtPBwL7qd43DtCBdImpIDCSMwegM7Hpyz3G6Gfpngp+3JcxfPMtt3S//tlPu4N1iHGm5m4+q+RreHKFLyJAE1ux95UtQR37RlccDPLEC1v33CMe+NEV8gdv6AnsL3Rb4r9///s4aDhHN5COPc6HEL6SKx0gF0IS8cAj+AVP5LdCiGiLYjS0j/oZn8TB3Q900PC6NmBXl4MGfisAXfPaBuh54Ec6FZI+oB8LkhcfTZ4x4DlcZdxG/M//9b90Q+js7urPfqY76vAjTBv6nqOgucAAD3TH/PJXjaVfyR98W3PrVdp4Uc6PrjFvhF+QyOLFWgL9g2ufIJ60x3JKSfgc4NEJgd85wtf8QQc8z8jf//t//IdfHdwq2vBdaGDt5I28ZGDxzxPSDZs2+fbUmnWndP+itccRuln2qtZqzP/7LuM1A/yZLSbYlb6NHlaRaGIvrDvw66wXDiwHDbzywXhlc/EpjdXPffKTPkB6Q08dY34atSDpxUGDV4BWTswoIzj8PxXcxLiLg4aHHrQuubn2Ea0/5Eh0WPuKxNJco/FofBG33ats3GZUXvqEcnwZawUOYe6Wzdx66y2eO1fpRsNFF19UDhrYtI1enUAy2yQJ8SGdm8m+nCrqZO/7rzxQG6OXuz/roRKvOq1adcKcGw38+UFsEzvC12CjPixU22LDHa+F2obMEN/PK2b6fSVtyjfLx/xUB1/P6FD5wos4aNBDAh00HKC+2ZmDBtZQ2Ort2of84H//b99ouFzzxwk6aDhUv2OBKP6LBoJB9vSN2Afropz3vEewvtF5fKjjVgQ4f/rLX7qf/+iHcdCggxIOHel3NVG6YI20wCDB8HbcIH1Ov6HwE+mGH9X/j3//D73qdGqZe2LegHLyYJ1K4LdW8J0+AFeMjMxXULXdKEY2bGzTY491P/zhD/UU/djue1obcFOTuY95u3QVaHMD44hSxY5KGt+Mbri1wAMUNtS/00HDV/RA8ks6aFiudQO/FeWDBoxMAXjr2jlsQ3JKXvss7ZV4MMvhPz6EG6qsWZi7H9PcfafGK77mq7o9dZHGE3j4IVFV+6IFiBj0yY/6EPoE6rzGF11+U+UN+ZIHtdbGD3M7+Wjd4vmGDgDWrV7TvSYfxENy9m3LpT88jecP8aXt9Dv0PD+JvucmwXnVpvHEA8Lny0HDHczd8mv/ePUVHZY81X3p29/tvqpb0Icff5Kkor9CfmTcU4N0U7S8Ey3YvpWnI1KwFIKjkYZDN6y7dMth0z13d3/VFfjHH9moX+1c6asz/F4AHfWiTqT4rYYVWlBGR6lThB5DRfjYgEREzJTUm22cnxbhLOSul+P785+v8Y89MSlz0HCMntzz4zocROD08qABIhgTcuZE6Q2MF0th2J7sRf8gbbye149h/UrXgR968P7uMj0tWrtubXeENutsFFlocX18RTnI6AW02CGvTRg7IdheZJQyfBwaH95R5SSR99A+8fGP+/clWIQxfHlCxcIMNIzXcsuwkZ2JxoasRQHv5kE8bknE0xDaxgKA35f4iRzIJXpiwXvZh8uxMil5s65660FyIDunm16w96IyWcfgRwb0z6LSG3zh8F7Rn7UpuvXmm7pvyoGcp0Wi6erU0aeq0jMDnv6MBVf+5YIcPKEjYHCKtI+NC4FN5mZd1/31b3/nSeCftfhft3q1F/FsbHIDw1/7sD2ovdCJjXZcW2aRxEKCchYC6IQFBTcantQTl/vuf6C7Udc7z9ep/xd10MCGMZxrODd+WKo/aFAf45DRA3S8+dMil6ucNJCnO3nQwI2GDRse7u7WCTrXL7+sE2IOeVAgThcHCg3y2Ac0kZUyeLA54CQXnTFJhn62e9Mp0O4hbVhu0wHShgce6PbXAv2LujrKQQM4PFmAFvbitjNRFLnZzNGHLBzpV+BT7zhGNus8FblVNsO7hm/p3fhTzjiju1K3MY4/7nhvZKwf+lSEcLJohFtMBHQCL8YPjfLiWTF5Nu0Hauw/rYMWrkfec+ftOmg40e+o8lTkEC3yXtfGiIUcT/5Z7HLtDvpMtMRxcCI7ZZKQ/Fwj5+k69PlVdn6gaoPG049++CMfNPBUhIPBE1dxo0EbIx80cJVcP2jp/tI7f8K3LZXxz+tf2DAnz7z7Sn/Qvyyi4cONhl9oc7F27RotEC/0Io53PllUxSJaChZ86hh7dF+KHzbCYiAnfPrefkB6hD6b3N/pRsMNejLChMl44ro6B5KMB9sifYlMJaTtwA96eSBhuZWHF30NfXTPUxGedDNh5kED9bZtxdCwXUov+Fz0Q8CebEeKOWjgXWn6l4NB9MWh6L16v5Crqedqov+SxhO/Q4CusV90zAdfi5/CtgnolHrkhK4PJSSFZZYvgD7jlQMqbtrc/Pcbuq9845vdx+TP4qk6Bw3QYCxJ1hDXtCWqgr/6PG1iEws/7Ip63gl+/PEntEC/tXtFNxq+8c1v6ncOzvAimb/kwoYF/YQPVt+y+BQmbXFfIrfyLHzwbGxc0NUhhx3qJ8c8IWXjwnXp1bJHrl+eoKcjLMLZfEGHNmDj5qE4/okY+oe4GKKTtzWG+asTSyU7B4P8iNcdt93RPSgeHDTw6sQVesXvYG0MXtVGGx8GMnTT1+Nn0Du2gs5yzNofaLdB3jdpZK8b5L+u12Hss9o4Hn7kEXp14hJf9Wa+wrbTN+UyAhn9EVcl9IUd0Qzi+KBLxivNulk6/71urfG64GnyNaeccoofEozki3Zj38wP3CCUUcbmV2XMRdiNN5XyB/DgwIfFP+/s/vr3f+iu023Kyz/2Mf8GEq9l4Odsd8L1k0b6T8LYUpSw7tF3lNASy4quOGjwO+u6Ss9V75/8n5/6R3k/85nP+kYD7aJ9+GGQ8DP4M/t70WGd5EM18UY9HEx70yjbISAXdgT3u+67t/svbYzOkG//3Kc+6SfHHLzSj6lvI836hf4Fizz4kut0U4Wr3l/T3H2FfuUcHePDsAPmWOtSvBir+C7r2nKjnKIdxd6Ama78m3TJHPGUDvKv0ZqPcftv//Zv3dk68PVfRJDNONg2IjnxG2HFigh/T7++oc30SzqQ3bhpU3et5pG1p5zWfVcbo6N1m/VF3UzCT/oGq9qChB5PaoPImI59kdpAH+FX8W+MV36Q8AA203r6ygMIfvPrwQfu7z6tgwAOGlbKF72qjTa/JQbNDHHAELQpa/NRw/X32Hhs3rxZ64L13cOau2XGeuX0ax2/78GPF/MjzwS/ligejCPPI5LVDz3UL9hRrh1pFIea+FU266S5on6HbrGiB37E+SM69D1JMWsBaEQI+bMVYwcMkinLgcViuCG0QpvmF7W5+qseKt0l+rx+cNppp+vAXb91oPUqDyVokO1dDxXQL76RuQY7Yh1DeywH9qU5g36lD1ZoHc9rKxzc/Uz2yI9lnv+RC32Yz2HAAZq7eCipwWEa0QYLNy5sX1ES4ktg7kNnPJn+8Q9/4Fu9l2nNx80pDhqQNV6VELBkTJ9m21Hb3d+Uo0N9PPZMWr5AuBz4Yke89vjLn/y4+4wOp9js9gcNqrOfQZhaueShk2V1mroS+AtAz8suf/brX3cbdRPmX//lX32jwbjAiD7zpg8aREOiOtAOfCZjl8CYx/cwLuhX2gFL/M0j0v1PfvIT/eD9cd23v/H17pgj9aqsfBh9yOhJEU2IryJrltO3hIg17uSLaZh/GF1rXl57vFYPUb7w9W92X/j0p3yjIV+dsD4Tt8jkNpngiCbSxiEoryYwnpbotgSvTmzW/Lo+Dho0d1+k/R9yw1vkLBNy+SO53H742O+HHmCFvPQn+thXv/nFQcPD+p21u3XIwMMCfpz0a7oxse7k1d2rGgtbRH9f2QerYBmHH7rhWz1mRQ06rIlRFX/O9S1/tAaSDNj8C/rdKX5D6C49xObHIF+Vvp964rHuS9/9p+4rOmg4Wj/kGnqO+QAJ99Sw8wcN0qL6y4Zud459oVkKl0lBcsz8qvTfdF2avz6xvwbNJ668SlfpTvci63V1GE9G2bR4mSuFhymIRk8rjIHBJGuJBZOcGVdTuZrChPnna6/pDtOC/2w9BeS9NE7keG+Yg4b9tLGm83EUDEYcK0YXE0YMEHdgSeZkz0HGsxokv9dtjIc06ZyvU9a1a/kl+6N9LQor9lVOYjW3p6REDuKMXYmRa3BwuoXjwygf2BAHDRgsr05wzY2DBjbPeRpsOdEzfCSoBwMDQh9ktfMr5ZxegksngH+L3rX/zS9+2V10mRagl17mq2JMBsiDIyKgC8sp+sT6otD6sbxK67+L2ZjCj1ccXtNBCKe4t+ig53v/8i/dBdI9kx7X95lAkC82FzxBZhMap6rwAD8WVCEHcMjBDxJymMH1y0f1WwR/kO5ZZHxXT3jXaODxjqIPX5AJeTX5oB/ahH3wdBthmVzgx+SLjgRpGK53chPjCW0s2ABwG+O8iy/uvshVbz2x431/6LCY86JEmxL6NXXKpsDvRrLwEn8WZRLAZSwCUN2zuv7EhvdePRl5XE7w8zoIuFJPjDjw4rpYbJiL7qHu/6Ihx8TEwJMH2sgmi0UebWCSYOJCpw+LNgcNG3Wj4cCDD/VBAz82ydMcNmXYlmVRO/jTdTBA59ClbSx6qGfD5b5QX6E/dI9ubtNBA+9hMrmv0wLRp//aGFkOFqL6SDmy39hweVEqLkxoSzTmpYISNDFIXzhZFnK8l80PPv3xmj9199xxe3fsquN10HChr19yAPmqnuiAzM0NximLFHSPDuhr9IZ+sRO0Zw1KHzyBpV/5QUheRfr5z3/W/0YDv6XAgoI/h+hNoWyXmP5FT+gZ3cII3dLnbFz8442q51iD1zWgjw1xQ4iDhnXr1um95gu71atX+0oz9ss7sugTu0Of0PNBg+izKaaMp+nwZCLGbmkPvNnYcXD3u2v/1N2g66mf1xNSDhr4xXAWaRxq0n8eN6ILojfY0i9yYZOMVdomsoajE5ALmRgP/ODTj3VNnQXEtzUhn3TCKo9l8LA9YtMVPf+XLvKgAZ1j58CwmKacMjal8GBhe/9DD3a//dWvu1Nli5//1Kd1e4o/zyRgwdJufAey+GBQ8lLO2ETnPR/xUDHsbf/0Ea8zPKPfa7lFBw0cgPmgQeOpPmjArqFt/dNwBcuXOjZFCl3DFwDeWHH4xw/A3XnHnX4q+BUtJvhBReyC20W0OXxwCAYJ5pDkZ1r0J3OM+NDn2CQ/dsp7nvdoIcEP123a8HC3WnQ/r0OYE8tBA2OJeUkKkGhhE/DyXCoe0oaVoW/LzmjeJua8NhS/0fC8fmSLg4Z7ddDwRnexfPyVOmhg8cuVfsaR5z7FLKaxSewQnfN0SAW2HefVj/wGgQ8atJHGTzy0cVP3Fz3FfEaLf/6CC1exuQbPQo+njB5TooX+LSKy205oDdLTJ7iLsFP7JvUpcx/X9znU/G/dcOKwjkPBNWvXeu4GDv0y5tmUBDVxUNk+MkDaBB/sxrwxM1XDhXmFa8svq/1//PO13Y06uDtPY/VMyc0TUn7L5f9S994BW1VXoveidxCpAipdQEDsYqEpKth7iYklGmOimUxMJmUyyWjamMRMMjNpmphEY9dgV5QqICoiRenwvvTee+f+fuu8h5CM935zv+T+4dGH53mf55y911579b322gXcVUHoKtqxAcAvDGs/+He2WrSddM/XEDDjd0/5snSMNhDA69+vf9aaaYGBLl0quwpedE4LXe2jjsnxFDJHOwBZDC+k7KEvn6HTxNmMObPjhedfwJnuFP3JiGsBPdWqgVwBtnLM4uUvL7Fw6Hf+XV7FfAiPevIttse9gfMylJokpzOvVsl3vpwrZZR87Wd1uXJHWhLfpRMhrMogZWWpywtZsIstPatZ/X6TFbrVZAhdFT2YW/WN407o/vwPwP0ljCW0fu8vOSJw7vFvrvytYg+/wfa3mNf2nbvEZTjrrTl1yerz0otpy44x4QNmkenfKe+RQW6HEl6PhHZODa4YxFAGG2iwONsMVjHnzZ0T/QgKns3cNsIWdBXTQFfKQuCS1hLXVeguoD1kNM4TfUtUynB17uIlS/L0tTnYBsrEiwiaGhDfDy+YPStc1jfIPnyWV7aRH+3P5qAp2vJdW0w43HpgkHw6WyfeJRtxB3ZMu6PbxykEZNtjU6Yso498Pv/lnwStioKy6aL9Q+kn5xvcmNFgoOFNFpamowOPQH6ZLXgM8+rWCTMQpQ/hzcCFsHMVrReOtvBK69KUQScJwZRyi5n7Wsiq+vOseq/ilJjjjiejgfaPAnYzGopABrZ7VYvZeOK/qhfngC5zDsph5E9ukcMpRc59OHNWvPTiCylrTkAeGHQ00ODquM9lIAEc2krCahCQ58RByi9oXaD9zpfz4n+5tQO6Hsui1XAC7f0GDcosFYOnZeHZpMV8OoHyn6orIc7PBRkV//qtMBT8Vp2tE2yrfOONtCcvQ3cf06lzobOBJ2HTDhAeXkULRfM5DmjF71OO0qYLjd4kTGkfI48qqff14ksvZqDhErYHWNhWPaacSX/JkSZeS+QyE7TF/8X3vOfc2Dlfih8vMx3XsvD2AfT+Lvjpj509iAXVBvCai1rCng34DC/7EFY/2FP2oZxnfLkoxlh03pX/2g4u6i0kU17d7Sk9F7God1LV1gmLSKecpK2inUKP+9k+7Fv8SrdlX/JosYBSldFQUQnss7LgcnP034VDh0bn9h1YWNkaewiu1QEWfcDd9EVjHNVeFANWptiHOlR8ZJAVPNtTdfRDA4LdBhreIftIH20vtLUZnl2+ZGFc/IlPxsXI5GZtjwYP4Ifn+Ud0fmyvv0ugIUfvTHn5Lu2AVFOukKZUUZ8ZE1E681Gc6VCfcUb0YuuEborFIKuBTB0JL1WhOJVoy8sJK19+n1F9hR/CX2Idz6kKo0cZaDg8K/AbxbXQyzYcF1cWPPbOKLGCpAw2SBT+VzBHlfCAyOxW4nNydfTSMcL4N7rdA0e6Y6eOuXXCfdlZ8AYgPd4KLv4zxLThaGym6MW/+LI0wGBClZ6vIqPhadJvavxFRoO/WWlXLCTTAu9BxVn1XcnMVWySXShQfcZ7dShMWRqFgDoeQ8KChO5J09C0fW+UEYuV7gLHCXiJl4RXB8Rb/8yctu2qpI7RCAJI70x8K65j1eKEXr3BnauXxYqx8KngfJXzp7CQ0TXUNDa98hkVFQhTMGssmB65kJSoMWPH4KTWictZ4VVhFsLPVEGUB8/u3Q2d8ZypeT7v7/6g0+m72R46u372HlPRDSYsY1uGgQaNlZ4o+jLQoKNtu263kRY1uJxIDZVCEVWtKtg/40gaEufgyllW8a/C8KyoqIjZ0MwK0gAHDD43+rJipLMqfI7Pl/2UeC0dAgWd+FMpqAA18rLSNA6Pq9sKwQUVnHzw/hSOnatgFb9JFg400GABKAW7sIufpD5g5//EsfMgbeu4q/DTCeUuDWODBNLEJnhmyrSpMXXSexlo6MgWgdNwXNwiYEM6KzpfZjEYHFQL5fwy+iLQwHf2DB3QWfbhmC1edxiG8up162Mkxv8sIrkWg+wJzbh1worqmzdtyUCacyTsOq9eBmgy0AD8Xq4Cyrn0kPioUTVejTS3Ir2C8bwd59TsJldIDTR4kksiQlygWMS9lwaSeJBWVV5e8pPz4EqO8+ArjXAeMtDwAoGGbsd0jZOgG0+4sTaEASdXv125yECDOOZ5gz/+5lxK7867q2LOkQ53rh7CEx53pKIZAb2/PfbNGDCgqGPjka7iQ8VV8J1KFiVOP87BwQwG5lPakFkdRwZnGEsWo7R9+l/GKuPTw/6UwaSrWMU0o0F6FV5hSQdCIpZgeCkbUhaKFO2GFARQrPcKg3MALg0sOldzMNBHkKbevkPHGIAx0YrML2ESt6nMbRvc+py8k4o+lXxhCBSru1WGHfSjE2Y/0rRGxGRo3rTdCzEmTPWWbsWvcBbt07b4p+2c3/K9qtfsme9U/sJl4MbA32pWQi0opXMhPs4/f0gGGmwzaQPetz3/zuwD3hNHVeMQfwYWdLiUB/Krsl5616H7gHanvD+ZQMOC6ECmxLnnGmhok4Fg50e5Ynv2orHlf4WZBqYE2rmoGoM6tbqGM+/KeleOpxpowJCzOJWBBquGH9bIGg3bs01pTR7V+fVSNigrpevqwCw+nGe3w0j7GWhAVpmFNA+YR8OvK5GXLdkH7/nex3brnsZ7bp2AljOAJN5pO4My4sP/+KIAXx1jy8VRcMo0594MHmXNRFYC3dLXBaelY4cOWV8pj4ajAem+3CanTCnaB9eMvaTXg7qF9pUJzoe2ge2PnTA+plAHwrZ1XNoTrPYYSnkw9Slt/pnOS9oR+qRSjEhHUDUO4NnPxBygfenHFdiXkTUeVWgdm+60775vHViPF1S2ZB/SC1eBk8I5dD7Ej/vTpXth9q5SF/vcrHlz4/Xhr1OHoH0WJLRYcS6acH/K12z1f/6PuHMk0rB67m3wMhrbYPDQIXEamSpmYyjD5R/7V85Ln/KvL3HilfDyR+GkFLo+HUhwpSyQJ1evXRPjyIRZQ6DhEnR3meqdtF4085H/FtjmJ9ovPzsFGQggELIdg37ZsuUxhyDAuwQdj+rYMS4ikH8EToBywkvHzzGk7K3iL2WxwVbH5UqqfxuMEic6AdK89L6erbwVlRWZ1WCaet/+/WIgtbkaQ0/WaCgCDQVsBuUOJINmtwfx41/CXr4cTJ5GAh9ayNmMg9kfzoAn98bFOI3Ho0f2A4PZSfKGQXFpI+UPdKINom6uWd2TXgp9pXyRBu2jzOCRf6dQu2kCtvY20rCP6tgptz12oAq/vFrq05QtPshFNwevqq+Kv6vwL+6VNWYjGmgYO35cTCVAZTHI7mTa9MCOb9msecK6b09RwNmjLp1nYXYvvr2oGx2UNpCXQXnH4Pa4LJLH5wU4jC+yTdmtE8cRCPDEDPWrGQ0uGCI0Et6DMH/EGLLxQ/7xltLWnTF7Tm63aUqmmfUlLBLt57KeRGHbFEezq0PTBgBmcZ4LSnz2O+WnNbvU9epzv7Po73iy7UayVet0CnyewKq6mc/qbuWjc+3lvP6PL25WTirfLKI9csyYWM0JC0NwdrsSaJBWSru0vE+qS+nFs4d2VcpIcVEswhULWmYf+XIr0mvUPGmOTXYhRyu2adUKXlEigUHGqQ76y4v2qzrIX8r+/JL/U17weTNZdZ6SZpbQdILKp2EXmN2rPWbATxoXtlLGJJx8V17FKMAzsJT6w+wdHkjdoF2woKKCosJTczv0hZdQ2JZsEuckg/i2z0vp9WfdVAxHv6PEm4uZygx1ivRuRoOLBLPRf9M+mEFtjHnRslXrLJTZuUOH2A3O9kHndaEFdcR2Ag/qFGWK9pm+l3STdjjtKivVsdXAv7ZevcOa5sl3E959OyZOoh4a/LJp2xYCDYvi8ptuyazKw1q1AQ0l3g+dzRI7H5937Pf/RkH/d9CLB1/SBq9sTloTLzX5B+TPwdAaj7HiucqmoA0848zohcEF9eQ+HddAJYSD5hREpBCyYScnic9J4r+MTKMcNDQUUlYJn2BGw9hRufpnRsOJBBqOgCi2oXg0rtyzp1Pk6rdEl0wDzGnUAajtJ5F5D/9pBNi/wtWMhuGkXs6ZM4t0LhyL9u3zdAureqeDBiz7vZ/tI0LM46BBhHDRtgaETJHONOPlq4zISYAyvYGGZ0lZMip2FqsWpmS7KiJsppL7uE5KRvtgeAW4isd2ZSZhdwVWpaORoCLNlSq6V7hZaOT1V1/Ls6Y97aN582ZphPmsTOblu8/5ShzTdsn4/i1O7FfB6nPiynFYlGnUm+PiHSLo7im3qFFG8HjGdGrbLQyUwnDJ/hw/hpJjdw7LfsWPfekgyaAaDYuIcHvqhKmqBhostpWBBsacK7sA4ZzmfDKOxAVwijTbdxZkcFctsj8EiHhSCKxdt5ZAwNxMv+yJUruQFeTmOHUKXWmER3hn5H6mIcebgirHDqR8J16yE7+jT/txLlYQaJi/YD7G0Fyqwq+JATgWp5HS7KrxVgSv4zaqXFPDj7HsZCVSOlUpG0wotp8Y8bdds1aYG+6ri4CyzxSsrpDOW0DKYeMYiqF1LEp/L4ahNFPACjA8Iw7826sw9kEPcPhl4oY7DO4kPODMQMM0jJVp1GnYTltdMJ77YWi1aUugAXry7GCLlO7bZ9aK+HTcULyw1iycRA08AwP1oEffNXIg+qjfpHGsgp/GjhufK7AtKL5pWrCFLHOVcY/8CcwJt22qgHhUeJnnTAPnXRlQA6OrOi8Gwk8EmggK+pqNjDEyb9rcsWzJ0MHwuEWNCY2CdOakNOeUfhI1Iol5zr4TYzbLLzmnxby64uL+eI3z5555itoSvTj5ADnDnkZTsb03gwbQvTStA6Hj6VyqJDM4Ctz2Vx1CAfqEJQsrKjjrYuQSHBmJkfg+8mxQvwHRC7wczjnx0oX1QAr+wyAGtgzRMR4NOB3GGvBNdeaDISXPGdRwjHtxph2XssqjdJ8eNiw2bd8al5NubPYUo04cesQwWMkgRcriQ/gKQNO5opmkIXm6hiurOkj8Z+aU2WNWyTfg2wXZPojsLLdpyfOlrBP+0sHy2ULuEHBLeaCzJV0Cs3QG3BpEbnUy6LhsGUdnYqh4wkKuwOJMl4EG5eEBXm5pkLeSN51PkVEMj+ks5lE6dW4krD3QsMHStRhxS3GkZ8z4EHzto7AfgQboxnbd2lDOHYhKmslm+UeZlX3QZspL5Ivw52od7ZvB4wqsheXex7lYvHBh1mgYzH57A3fyiWMVD6Uckz7srwg48IdDyLHYOZ/hhQwyMAZlmoGG96DJBRjRjvE4jsuzdkhTApDWJLG9nE/aUK56eV/St7/RnitEyi2DLL6rFXSMpHkDdyPYzrMC/LcmOHIq2++ySj5ydAe4Ez4zEgt+ShCzT3GUxCjMfJTOfDcgI+51TN1TPmXa9Hhr/LjMwLDuibLASvNuSZTfbRdEJ55SP/G8jpVjUA44NmY2eU64nVtxJD+abvzWO29TI2o6WQGdOemKjIkjCQyySKAs0OHOSyTRijAWegs9R/vOA1xU4I87dCz3I+/2iT7a99gzAw2bCZ5ag6crfZhJqaPrIkHqOxAh/5XBGHtSB2VKLbguDVDnQ5wIvzaPNORxeS+//FJ06tIliwk3Z+VYGleWqf/ER66cAXdpyySdS5v06eecazvlsn2xpQG+cdPGeIf6GDqlgwafG55IZUFOeVU9ZyBDXSu/SmcGq5wH57TsSxwrv4RB2tEecxxm1VmjYSxtu0p/1VXUPEGPiHNh9nmJQZtC/tGm8Dn1k7rO8QNm1Twzl1yOQxjcTpfFlhcsiPHQTRucaAu0Wfx7KzLfhl1kEPcuMtmn7TsuA9byt4EG8egpS8oc8WH/yut1rBx7jO5sHKMFC+bnlhur5B/Gir7F3/YjM3KxBFlbrTaLQchesyzdmlu3tNOAIosWg8PUMLRt8EmZWVnpSVfvxoKZs1PnXkCKtNk2AMrRzxtz3IWNVeBaHIl3gwxQR45HXIt7A+HyU27lIaPB1OypLCy9NX482zw2RvuOHTPTpkMHHCMcdbe3ic2ka3HOZWBImpcexYUQl7yqrLQf4TF72GKQ4wkeTX77nVyw0ln39IZWZJMoJw0w+BLnwqzezuwM+kmZyLvyXLmpnVUGMLRbtZ2s8P/Sq6/EqpXL84jIY9AjBhp0Sp0jgzxJM7TN7YCmHQDMfG+/ymDtmCI7rwjoyV8uGDq+D+QnMhoO40hqs56Ppm1PoZG2sgYEdCdtZpAB+vBK2cy70kv9rnw0EOS2O+WptC9/qAMtrjqKQLtF4zPQQGBQvKUe4d6CH2UhbRwaZRCOQ34T1dkXPxR/y7t+V8BkoMGMhiULK+NibOFunbtkoMG5sS2fLeV7wcOOXxknj9lHMY/iyHYLWFwI3A2f7MhjFl9C1rSAj8xoaIP/JK/6vAFT8Zx+iLKVS7p37Dbm+JVpfk44+C1lBjaH9ZWWIwty+yBZrKeBm35nnM6pfQ3z+WwfnixljLylToTscn513r3H36WT1N3AoOTetcfMKTMaFuXJDdZGO48gyfG9j8s5THuRG9POS4T7mQd9+UYfxQTIQ3qhVQuT3Gth2N30O3suxSCxhz1Cuk2btnEp2Y6d27cnu2kLGQ07kPUuYrMgBZ0JZ9owPJ88SovKaXHt38X8Flk89dBBFtB+a9I7MfG9SQQaWFzFllu1ekVcRHb4RZzi17x1W6Hk5SWwH9+LOaiagf+/YxAPzjqvfTjbEoqGQI3aEKS44fuZ7M8ZSwr84oqKOAwC639a3+iB8tyLItu6cVMVc6vcCmMEqYo0LJwWhZLM5n9p8EHwOmh+1tDUUDT9ZPyE8dEI4jgGQ6UP0USrprpn1ZQrsxlKp855E6wMWEAgMl1Gy+hbplWQGG3VkDCtah3C9bURr8dcIuiebW8ql3vBGyBANOZt120TufJB2xqFGRyh3dL4VIHJ9K50Sdw60woDhZ+RRB0jNFOcivHcuVMn6ku0Ahes+LAXUcFnSns6DkCuEFfoSrwyXxYk5DsFeO7T4zcd6UJZVctCJq+98nIekeUKrKvIFt5y4DohtuOlQeFLmGUOhXUKJfqUQexTPFmbQtj9zjNsPfHDKsdnY6z06nlsGoz2rcOrAjaia9qgQlDDznb93badb1dM/E04NHqdK5lSI0Hj30CDJHbJxRdnRkM6x/QtXlLRgH9xLuzSig4MP2R7hfHsqp3HJxVGkPeZBaMjOh9jRSO6Bw7pUIrXtSQI4z7+XOW2PQVHYgd0AYTzpuJUcheChYlPmpWoCsWvMbqStNF5ZB3MWzAvlXN/HAuPnHOcppHVr1Mvj5+zAKoOkgVRNzPXOtvWiVDAq7j2KJwcE/yUlWrp3+88EtV9nhXz5oPfBhnhzmKQPoewUrEJe7nqlfMIbsWxxoP0Kd2Y7eFnHTqddFeQ3df94Ycz4sOp0zNVVGXf/6z+cRRCVl7SWLGPvRhcNWujvGprHINz2tcAd7+rx4zVoN1m8LoZTOJ0P/3WxNhaiVHuMVCmkTdrdjhbEDpD851R+C0ICHqqBqttGKqoNxwdeaRQuCAvI8jckDKldg3ovbbZL24RouBrvToo9QYxt7KC9EsCDTu2ZXCtc1cDDe1SGYhTMJm8ZGDPl5lE0pxKKYNY0JISQmPLOa2BMemKi6tdGnIa58//6Vn2vvbJrRNmNtVvAH9C4xp9GlTiVYUjnXulXGNc9lcbY7MWRmN1ZKWyMQ1dxN0e5OWqLaSmspVn5tRpcfaZ/ePYLsdEE4xhHYuq0FfSo0GGumpi5nkHQQMNoJqMvxpOioa6oJcOt4paR0y6tUaDqakbeOaCiy6MI486MmGWBjS0NCG8T+cqnxMFjKOawSReBlrkPzAHDs3QYH7AnTRhyvFslPGE8eMI8PTOjAZXSMW5/O8Ks3QtX0uHpQzRYFGOyVviXfidB18Gej1GWDmzmKCjATC3I5099Pw8IrkINGjgQl8YJxr6rvhC1MBfyBtlA0Ih5bnz6Yq0f7v9wPHo7LqKuWLlimxbg/U8ToU4BqdRZ3G3+kMa4SUfKXOSj8CZsDsm0JTty6f24XyoP6wT4ErXjBkzs1jV8qVLowP06BY5TytRHsirqTNoR5mqnFG3FfKNOebv5GU/CD9fyWfSpI6de+AnwU8LlQXwVzf41VVGi6s6h+nk0nZpQNuMDp3wpY5gTNkucqYoblsEGzSQlZnKMWWk+6Zbt23H6UWnspLZjZnCoYTP1X91oAOwUhh0sg/tOxZftm1fKXfofA/zS+5aGuDymKneb40bl/SgHOiDkei2kh3oD4vP1oMebT/1E/hO56oKzzkmccWYxLmBydoESJRFnkZjwNejr63U3qFjp+jK62jG4IkcynjxLmzSuDpCvKiz1D/yT61qrGzRvmOUh6Wdvdwv7UhDi5ctjeGvDc9tWgYaDIQrw6XB1NGgQqfFOUgnm/btU7y6DU5aN6NGeeHlyQfiXX407dd04+fY6tStRw/46awMHtm1jpc2gThRj2kfCa9jcEVOPWvbOix+X14a6v4uT65fv6GoxUNQs9/AgVmpXV1S6GbmizbLAIJ8ppPhvNJwNifOtDm0ZzZQ48RaWWaUyusb4VcXCSwcuJF+rr32WgJ3BBrAuXLWhQb1ZmbzQHfpGNGeNpLzKP7NGlEnCY+4V1Y6Egtwb2G13vbfJJDRvN0R4VG9rcm2MUtDfWnmom25/dJnxZVBBnGhE6OTKwwNGK/87HcQatof69BPFRUVOBZzYyGO76k4RZ5wY6Bhj3qPudqzl+BjPeRfA1Y9d+/MWiDW6WrMnBpwEHd7wFXqb/mC7+qi880eWrCgghTyt2Ppgsq0w84ZfG4cR9BaKbJx62aewe5wsPzteKVxw0PVDzC/ML/6MfmKNjPgCC/kCix2mfWiPPlHebAZmXbU0e0zS9ajEHMxY2sRaJAmU06CVuuAqbPUX9oY2ky+lDN0nvzmHEizyuF3aHsKL3Vmb1aO+5Lt6LaVncj1g8E1xi6dKieT/pw+aYc2pVHlnpkpyjBt9sxG5bcK5vR1FvU8dcKisJ2QwQZkXSWWb5SVGXBInSZvoTe0gZHZ2rzayMpAF7vEubysrSfsFqI2cPcnMvrMaPKY3k6dOmW9BuHW6RMv9Qgi2KYyuMgsxSbmb2W7foE2vIXgtZnkDxdR5AMd9onvToo3R42K01ixt+6U2RLSnvQs/6cdJn9CH44nZaM4Ql6JHO+R91PPAH9e/O241rJA8zKyxi3cV7Cod0znLthG2F7Cxr3SeWbS8ZBZI8o1YTNgDyklbaeO5bMLRX7nViHhMOC9oJJsEoIwGWjgCMojWhE8gi+0c80oE4aDNSeYK2WIgT3luv0VgapC9wmHc2Cw2gVDM5CyIDL6+1Rwcyb85FbZsn37UAaYea4+NUCedM+4zBpwfvUjUjYC+C5lBDyyi+fWbmDbIxkw2h4uGPRHlnlilHxo+0WgGKSAc2WX4xVXUHYh98Fh+qvSPm2bBbcL/j6MRUfHNnf+fHT39JhDnQbrKl120cXRkay4DczHdu51C4j2mXQujbuwoOzJwAg483v7Svqlf2HSR2mEfl7P1ol3p7AdlADMTnXB7h0sbK+Ji669BvvsIhaxjgRw4Ob5j/v19wk0iAteyKdkFIkc6aicYlIjPmSiPCt7Fs7RPoi2VzcilVTE3QejakwkUcFsEoD/KfCcfJmucBYLIyxXwpk4p85nNHRVyJ6PO43JkhCPbt+BM+47574xGU0jVGXiMwLjcxoOSQC05SVxSyQKQd8VUAoqiWEzzDQRZbyosiJ6sGLfqjXHQ0L0ChrhKSvva+wUBJxNppClM5BAz/SpEWf02EuFn2lP/LaMVKgJGFp0nPUl2rZtRyp204TV1W+fTeMf2NLA5T4FUiHEEdq0m44GbarkdQQUJIXBwH5+lOb4MWOiXfv2GIjdcz95A5Sml3v6aB7MKCDKFD2MKxjEH5zWnFfwlkYBfdmHDqmMY+E0DcXZzOuJfU+LTh07pjDS+XB7ggJDIWT0V+NH/JQBDHsV1xkgAP/SjAyosNew1tFYjVPuap100H/AgAweOVafc/xJB86hih34GUoyuL/pRJQrJM6rysAbVA4KQtt31cVikB7h6HFHTTEShUf8+u4qo22KnxrAp7GlsLZ/x5arkAWWEl86mgonj62xkOXipUswQjbHibTd7RhqktCmR/2ozHTCdF6M0m/bvDX3U/tdE1b9VajC6FihzFR0CnKjuTo3K1asTGNoKZHcunXrx+lnnBEd27fPOVFpJD1znwoNcApaAAcGcpxTFZ/tWDvBvx1TRouZNxV2ZUUlhSbnZbFW+emEPgSoSAPUGTXzIvvYD77hcQMNGoRGhBXUGi2eMVwdHDVt0CgDDdIO7i8rQHUQrpszur0c/LiNR3o3UqyBXrtWEWhwxYnZo7K+WT/yKjNAewece3Bj8dma1aF3Ag37DTQwZqvwW2BnyYpluZK2fReFLLtSCBLjvyWZEypj5UHKDcaZhhD40YmR3tOIqcKdOJHmNKI1hsS7/LobnM1gpWvsG8OLCvnQjcrXFRPpS5kl/5WBBvvM6ufQj+3LAwaXaoK4avvohf6l+X3gcTeO/DoCAFNZfa1Enp16wsmsvro/VSOZbUHAIlxG57mdQIOGJ/yDo6wMMJ0+gwHMrXymjOORpAXp12K7OtQa55t2bI3T+w8gFbAlc1jI3VTu3G9fGlkGwLJ2DrjR6Nc51EmULxBqjIuACbhTBmp0lFudprHlpsuxPXJV3VU8Tw7QkFRe6vx4r/JYw0I4pdVyVZ2Gk2c1Ju1H2aExtBUdYYqk6YsV8+fHyWedmc6u/ctT6TQCr/NoITjnFuJP/Bps41PiTt4E9Ax67ecmV2OdK40h6zRUVFZmW8oCt5VogPoSlxouqUd851V0gQHJnKqjHItyRpmf8on3OsCucVpJu8Lu+fBt27ePPn2OJ6jZPPmwMHx43nZ4qZGUgeon+/N3DfPkZdqUDuQls1eUxc7pzBkzYsXiJeAL/Qe9W8vGAE3KXJ5PHDsG2k0dAryOIZ042hIP6VxAj/uo6eLfBq11Pgz2Tpr0bqwloNGM4H2PXj1zlZHHMsAknAbPhFu800PSnXCLB0Z2EPd+lobsS12tcFI/TcPYkg+OpDjsMfCshcjMltiBY1QGGpTJSefwmI4VDwtC9gfyE0fOSe36BJSh6Z3Q0DpS0WfBS0uXLmb71JFZZb51c+orISdM8xbGQu/AK+BGSAtcV+kXkA01MbairwzyMNAyo8HVtLcJtO8i0HY8e8p1dhvivOoAGcT3OflDTerc2p+U6LZBs9NydV09CQ1q+ipHDR45DuXogsqK3IpknRwXCYTbH00ld1VVHtGRsl1pz5U151S95zi8tGMK6P+sG217M0auzrRBzRP6npqF91xEkObECShN3nIOddKVC2kRQCPSopfwSo9mwRmws56MslB+MtNmMkFZ6+6cT6p3B7LKhEk5KC9JK+pi5WwGAcQ/dKFzrgMhrSg3lKk67slfoE/ZYKDACvMewXzYES3jzLPOygC9zqvtKatzYYeVWmnMDJNCViHHMPQ9EUQ94PYcxyt/SLXyhmOxUPSihYvJ4lkWvQgoH4fT2JB52Qsv72NeDQZUr4Mc57vdOCVmlVkctSHt2Y7taX/40oJwDtRtZm7pdM2c/kGsWbQ07cuTWFzq1KlTjnfrTpx19TY86EQzDYkr9QXaGx3InIC/vdBS/gjOlJ3yg1sTlWWVOIyekGaxOusoHI+sadP6CHT3TrIaHDdt0l65cKV8Flfi2nYs8iuPpqxEJ8mX9uHvBhZdmZ6DzSdNdenWnZORemXmmosQOoRJ48wBUNFXEWgQr6lPJSqunBvkqgsU9VmMqEkw31+WI+M9Yng92aYdu3SOtsjgFi1apB6QIJVnynvtQx9Qd1vzzTm1Po0yrbBP0e98TluY8ambzc6qIHA0kkBGYzLNeuCMuqXSoIP2SQZNfR7eFDfqwJRf4EIaBAnYGUWgwT4Mvsl/8q68oU31IXh//62J0YtFpS6dO2eARH4Q7gzugodC/jpa9HgVrxX4Qa7Qhn06DnFXXgZ1NsGv42l7EXrkHLJvLfDpGOX7lOPgR5tWfix8E7MVCpve4JtBx0Lnmk1SbEmVL7wc/3Kc9TEjKahP0GgA24Wsu5H8Cm7lOWWa+ljeEl5xK907Em0vYeCHfEY61EdQlmX2MAGAheB+7uzZWSg6T9NCVxXyoAhmeL84dfxFMFK7D/+GvqUrx6QtYC0DF3y0NeUVAwNroJdFHM9pgNOTxjp16JD40y5IGhQpyizalo7EbOIY/LkAxk1p4ylzXLg2SN0YWeY4Fy9dmvVgFlVU5tHIA87sl9tKNtOXCyz1pC3gLuZA3xGZBs2kPU374if/4YO8kYEG5sKjqbcg+/Vd3SK3h3HtQJYsnj0lvvD9H8flV16Bv9O26mEbEeqP7/X3CTQ4fjFahQumM42hWvytgWqgYTgphhPHj2cP1isIVZQlL91d3D+EEjTAS/JXxDbhVa9RgyQqnYhtpFSbFOeal6t5hQot2ij/LmLS/Fj1u2025OXvrhdoHnuP/R3GREvEm1EYJH9iTPwZBp+zH00ZL+ERriJEUNxbT+UHwW5X8PGbfXh5rzvQGkF8ErKMvx3iKhL6CnhtV1R5r8/62T5FnZ/tx5I9HP4VjWBqFfNWhQh/e4/wKx58przEnWP0HmHxHsdkxNNggjtzS/gdm7/5t+OyTdvyb59thQFo+r6pdythGtvy5e/2UeJG+H2V0247js2Xnx2bzwib34kX9Fbs5AHhOfR7+3d3pc9ZPk6lZmV14fM+4a2PINPJEp9+X14+Ixw+34JXKwUEinEDKxOb+LvEr3B7lXgXPi/HLD7rIGxMd7Qd73HMJY59lsV7ZdRBPAiD9xQ7DotnhNMVfBX1Nu51Puzf+2rDDHv2HEh6s0/btB8v59rVFTQNTjwGAX+Xz/q7bfgSF+XLvx17Od/CKl490CfHw7tX+ay/1+EBSD+20bHjlAf9Xvq0P/lFuMo++PgX7QuXv5W4cX5LPPq9bTjXDQAq76n60XEKh7/5lTD67j3CX34vTOKwMf+67rmWp+xTuJrxQ+OGrPah4LZsRcHznbCLA1+2YVvCJE/7u5djrk0wZPduIsYA4TPix/69x/kTDyUswuo4HI8w+9k2lVVmEeyBRsRz1dCyX/sWBu+3TZ+xHdttcbhZMPVi07pNsYKGyv6E1ZewVMPw21JtX6ylEUgkavNqAqB19GwgOtuyXV/2IY6E17YyZIhC3MJ90ntJl9KUcIlT3z2AU4NpK/LA72zLMZZ4Ey/ly7FJE618tTic+w6QoriB+SjoQxh8CYPw+9l+hbPEb134QBm5G34VH77sy/t99xJnwiAs4tTnxUkjXvUIMu3ctSeKOuzFc7ZtX97ny3bEhc/Yln0Iu/SS84Vxr5zfvmt7UGY0+yr7tB2f1/C2LeJVOc9+17gWjgHG5q4drFh5T9WLt2yj/E49ZQBwK8a5+CrnJbNN+HsbtCKtZz+8e9m/fztux+y782gf4kGcCr/BWdOAPWJzHTQvTUtP5bM5/+BIp28rBpX4t40cO06PsnIncIkPx2o/vny+pDuDMhpt6/nOMdmm8PmyLe8VN9K/z/q9n32VPONnYfN+vyNXLuqDd2/WcDYl1HF6XwmH99ahcY3WXbtZeaZx4RQu5008Co/PSC++fMaXe1wNqO2CNsSXMAqXn22jHB/xyQyQbWdibaceP5idYCaR9zse7/fl78Io7M2bNs7gooX/tu/y16J92/UZ+xG+BjhJGtbbgP/Qfv1NnEkPXs6tMtXnDTkYNN0FT0iPwuF4fRcGx+xzfteQL7WPd1ThxnvKy7bERXkJk322AJ6GOFE6lRtwJhyTl/MqDPZRwi+cjsfnfLe9Ek5pVjjEh7i3HZ9r5t/Q5BYMcflSmJIOefd5n5MOyzkUL7ZtX94rnfi3fONc+7f3+7vyykCWjq0r2qvBkXLA52xXOG3XsZcw+1s5Bvvye+F0nN7btJ7uLvvEdxA05e+GdgQR7uBmx+T9fuUYpU378W/DBbojVCLit2rYHgQ8oGNCZAdpRrh9OU8NGzOzyNV9DMg2y7n0d3Fq+yWctl+EbOyx6E9cCI/yu5yfJrRbD/j37jJQyGcQLL+6WCXepBXvFe++St7y3Vf5nffYtzAIS3k/Hw+OvS7ZabUNTCIz1rF9Yzm/CZMv7xfP5XN+5yW+vWzTq8S7n73HcUob9uss6BgKh235rLgWzhL3jt/vfbakb38vcWn73uu8+p38cxQ2dgtS/HXsNq5dF1uZI/Hiy8v75T/b8yrbEga/s03hUGZ6n/07Zz7vPc151UeWbkf+buCz7fnyHueg/JuPObZyjn3WS1pwDOJC/Nm+3zlGcdOA4IcO6hYcVsfjd95X4kKYfNY2ypd/N6wK+m2o0gslPwm3sJW825z2dXgN1PubbXsJh5/tz3Z9poTL9n15eZ+/i6sSN47NOXce6uEnGAjYgDzwd/FhO8LvZTt+Z9DaRTNtD5+th9x3W7djFi7b9+X95WdhK/62xz/bQM6heBYuf/GzL7/3aoTuNhBkDYWtAOMcO1bhFRYv+2jAAo5ZBxu3UUOCv8W1sNmOL58p27cvx+fvysRayNld8EqJU393zLbrXNQiCJmZmVX2vjTmb/ZvP4kTvhFuzoXK+RJvwurr/p/8KK6/4RNkCh3BX45VaSQ0H9/rbw80MHZsHaI1vMQFL99FvgwlAmfNnkHRIY5tI/o/8vVXiUBtjjZ837Sue7/Yx1rlFEpcIn8hr7++2iIN4JckzvI3FVFLXq06sJ2ByKRKyrTuyhmzY1HVTfajAQRvahfEJqhjWdVvh745pSt4cUt0rPphAe/2cdKx3ZKpNhI5275xXUbrdqxZH/MAVsLw8j6JcDkvx37o5bhU0hJvScAS5mpevQ+rG0d2601AYW8sqyC6tX5bEjU/Rc/GxfaRNXhI4lFCX+kPh1yO7zB+WAkg66u+T2Lns/d3BfbmCGNXMBZMnhJrAU4GUbiUl7B5qehkqEMvDYDDefm9DJi45F1nxbG079E1CxKuJ+Vn0byFB+EThoIpC/pQMK/hdeglzhtBJAw9Kqp+aMW7fXpvczrozHYVqwavokjKwoXLco6qbv3/fBPuEifioikvlQqyHQOYcbVtQZXddiksl1HwZc4hE+e9zZFOtcn+2LiK4pH8fehl24dBLLt5xrFt8G9e4rJJK/bVH96coi/1M+ixZgUV7ZcUo5dexb+4/j9dCln7kIdKmlFY4TNHg1YUGWpEyn59ilnx3WrSm6ewZ+zQqzP31TncvYN7Y8Ff/pS3daZxI7F7cabmHgLMMRBrux59yNppkFuPtrClY9vaNbGHSG9tVmHrUWSuDis3ZlKYartt244YNfXtQ7uOs09mD16dOrFmVkXsWreIY2KPinotqZhO8HA/yHdlQOfVeTXqvGPz9ti1ntWiTRzh2bphNG7dhG0ArEru3cX+u8WxYP3yg+07f+JYWvRb6bE1L/nQv7uDsDak7rtCaVbNpg2rY+0Kgmb8dujlM/LlokO/5LP0d0RjVuqw7rYRkZl/yO9H8vkoZE1j9l5uwSBbNbcyeb06xLUNAFRY8omXc+Z8CedfX+35wr6dY9+9dzOv6NAu2rI1q1E90nQ3bondqzfG/i3sZSVIUqMhRgPbIzzWEBuc1d5dsXbJqph+kMIjjq7dMpq0acpqNlkQRtNZtd25hSNyt+6I6gRrGx/ZPFqQarwPZ2cJWwXWkXnS+DDCOhiwCnFX7LZv2MRRhkv/G68Knlc7xrqfsYprL/HvnDiW9l05MaBNu8yoqnh3UiwHTr9fy6u8uvBBWVbye/m9cyoulIvSfQOYb/XabQfnTdy379kNeJvG+rWrY+LsBXxTzL14Szy/EAAAQABJREFULi/xrVz966sDX0gjygP78FJGNePhxm3IUoOuXfkys2DdsopYvI6tS/xu246hfJfP5d+/vo7GgmAxjEwQZCt6ah03lP2cdHSbaMpKuiskphivWTAnFtOIcqO8lJXNYWaDYX8tJ71H/GzkJe68vF/4m2K1NGrbPs/lVsHtkuYXLor13FjiXVnmJTwfIQpCOawBJO36Ek/ea1/teTXv2in1nyvd6zjn+/2lq7LvVkz+FqyokhYgjYP6kI8HL3lNHNoeJHFwHvgYvdoeHoe3pCYD/+1Cd6+ZuyDmc5O0oE4tL4aZOqscU/m975142bftOz/e47PNEcjN2nRCXnk83q7YsXF9bFnOyQT8pqyW7zTKxXfyH+8fdYln5bC4UVTatmNqBr82YY+32QrK+I0r1zOK4h7hOPQq4T/0Oz/3bgX/1aobm9nmsQTE76y6wfa7dO+CI3VEbhWYMXlq0rX6w7mxfdsUNuGS3j7qkk7kUccq7dt+C15tjjg82nXswqpr4zy7/f2pHyZOGjdne9ta5oF7OvJqdMRhMQ35+VGXsBCXAQdtM+tg0qKCEoSpW3tWo1lR1yZbMO2dmLFFrBfz5NzKT8IkPotf+FB1icN2vPxdepFP/E68S6d+36xrl2hNloSZjFPGjouFVb/ZlvTrdVznlmQUbY25SyHSQ67jO7XNjJANKxdzNCkZoPwmbryO5nXsGWdA725tYZGIFdMNK1ZGdYJOZhzmyiVSf78LBzUp0rdmZcxeVj5d2Bin9jZ7cXesXloZ9Zq1ikZNm2eGzta1q6JlO7YJkrFTuzoBM4JU29HbmymYuWXTuthRnVX5xg2jDltb3d5q5ses9z6MikPsEmHsCjPhYybupIPyZ6aC1dw21BBrlSvArv4vmP5ezK8avnLVsYp7aUDcLuR16KWsaMoNm0Ckc9OQl/Qln4j7E5EFTTxlAv1oAby1c+fGEu5VrsgjzXn5TD2M0o0bd8cSPh96dWPyXUTcgGwXa7YrHF69D68fHbA9XKTbgK29afXK2LZmY8IhLF6OUfgd80pef3014AvHKB1I920QDFsgiFV8tv22XXrwiX6pUbJg9vyDsuvYJmy7aApF0/fGNaxif4SgtL1u7VrE2qVrYjGfHWvLpjVj1YYi0Nv3RAtAcvoJ6fwLZ75PzaUCh9K7uFMGeqk/PopfW/O995Q4c6zykve24dXlxBMym1oZvBg5Wcl3Xv7W/HAcZ+yG7ZvXxWyVyP/mEnfqEmGSp8o56EbHJwwanKvuM98aG9O27U382X8jHiJWdxAuvvpvV8mbtmsf8qB8q4xohiyog62o/tuOY7++yo73d+mtxeFkYRGE2IgQ/ii8OGNOR4kX4ZfWlPlNmtSJhjjkaWszd9uwy9ZWLE0/QTrw8n7H4XgX8PrfXeJbeCwtuAoUSpddj2wVjQ9vkYsme9Dd25dUxjYmUPryEg7vUyav4PVRV3sYj+S6nFvx4TOO3eekixpm1ZDitXX7pljKSux9P/pBXHf9dWwhPBo6//gHGRiiCx+EZv6Gy4cNNLgarPOgseMykZMq0yik5sybFePHQrwWxJo3P9rihB1JxdfqPkNEzNQYK6ibaiPy51ZWxuOcf+3n4zsfnZWu25CWaOqzlbVNI6vl6gSvLERFfyq0ohAhUUpS5jz20jQcFYMpN/7m/ZvZtzedFJ7hb78bHeGIHlTR7twJ4ck2iUw1QoC6CuazVun1eY8KM53KVD8FbKbbEUX1b1N5cqsH95lBsA7HbNL7U2IKguAkDJHu3Y8lzYlidFihphGZPmO6qagy6mW6kClJGivCbOTPy5QoV3zEr/clXvm8lJTE0WNH53FLPbr3iHatMWJJOzedLKsi80CmejGtplYJu2k6bgcxpdF+nBzTFJ1506kco3vMthDJsSDP25Pej5OO44QN0nCbYtgLn/iwD+83BdfIuqlLRiuNmvpZ/JkOZaqYRqnpVkYXJTHPql+Bwp4xi2PYwOvpp/aNtke0zt/TADf1zvbAh2lepvT5vEd92qfpku7vLtMtMzUJ/Jjut4OxWXRqCamO702eTnXxzpyl2zv3KSvARLYpVLZnmpsDF1b7NW1TfJiSKI7MoDFVUGe6HJt7E+cvmB8zpnGk5Kbdcf5ZfaMnx7PWrwO9YixkWi/tqSATLvAj7rP4Fu/C7hF9rlC6b01c2r94l1MsGDl/7ryYRYBM52bQoAHsAzsyHUBxJ5yZAsb9PlOmRDsP0qJtCbcpa/KgfOSeROnHFOL58NPoV0ek8di745Fx6iknR1tWj+RRHf2sS8G8WWDR1MFMKU24ya7AkMsUVngi985CM26NMf23Yf1GecTp5GlTY9io4bQWMeSsAZxucjK1WBrE5jVrYy97rcV5beEh8MKmHwIA0rnjL1acJHILJIKxbLuo+UC6ZQ0Ka65ZlafKTJk9K/q0aM2WhR6Z1m6qqgEL58r0RAtaiYPc/0xRM2nb34XZ1MFMXeY99y2DN2nK/cqLSWV94/XhBCj3cDxrd2q8dMtUU3Eu//uS5jK1D1pWDthnmfbntgrn2W0V4krj1DnwVBbTUD0ecMTYt6NPx6PyWKQjWrQCNxiR5F+7fcKg7B5WaLcx1j11SIeHPtzvWx14qhMEqoGMzO1ZtGc/nrBh6uz27VZ253zqmTNjLkG4E08+kdTzthiB7gtlHU4+Z+w6zm43MR1R2vS8cs9x3kQNCxOUTWEXJ56cY10DKdJjm7aTQrxixYqYQnCybrNG0Qu8tySN1bPmpUVPC7JtV2hlIP91S5kp+8owt1KIN+WAuHcOTH9UligHLdxkKuu70Pyg00+L7l07F3s0kXXKcnlJWWtb8oB/uzc2Vyto1wJW9qmclJ+8x3HLH576Mo+UxA+nfpAGz+DzzyWG0zb5KOmh6n7b9P7clsS743JVT7hNF/VK3vM+YFKHyGMWAZwx44OYvpi9xEe3RZadktu6lOvixO0hbi8y5Vv5Ik5ymwdjUVbYRwmz2wgcp7xqJpnFjd2b/8qEt7P/fif1ieOP60U2T0PGTICO52vIL4xeupAXXZkRz16OL+Wwc89/wp9jZHx+9hSl90mXn8wecZ25M84ZFF06dcw928o/ZYuwJW4ZT7bKc5lOTD/SvTgo+Ig54l6/S7nEuOSnCePfjMVbd0ZvAjh9SGU9sm2bnFPHqSwucezfzllZZd++xbvppVnYEz4Th8LD5OS55nMWVsar4OZwLNs+PahM37FztGI/rXJQ3WqGhrwoXxU6tOBzTycyG6oawTe+yT6F2eyzXWQ2qsfX4XzIT5NmzYtTjjsGmjkq5FcLWOf2nCrcpJwHFwZqpQl1zBacLsfjd+LdFTvH6b2uuIqjVcjD95GVB4DDEzC0C0wNl260WQrboNhCYKaK9J6F8GjLujXykPOtfBEntp28SHvy1KKly2LCW2+n/uvasWOehqPud96FzfYhnXzO+XTvu8G1regmtwzkFgngNy1aJ1W7yT7cVmRNBAtie/LAGf36R/t2bTMLT32dK9a0b00sAzrKVnndfdXqQYtGG/Q1fV1ek2aEQ7ktrbq3eskSTmOYNDVaHdU2+rJ/3q16eXkj9ythnGPfHYu62RR4pHumTrt1Q53HYDOdXZ4Qd+Jt7tx5MWX8W7EE0+q8M06Nk6nh5R5x7cnd0IXbXHy3XbffuWVW2pGXnDft01oQVF1wL76l4QNE/A02WAdkOnM69v0PEtybLjyfQuddc+43MG6Pdc6Ub2jE7WluM3I7UK2suUBL0JRbgnYdQIezZWI/+yPk5UbIZfXjvIqF8dxTw9KpPqZFU7ZJnpkn1jinysay2LCde78y3nl0Dry0PZRH4s1npEXn1LmXRj9ETv5p5Ji896xePXKLTnNqbfhcbqfjXpVpmYYuv9qH8lYa9KV9Kq7sQzkm7qRpaeX96R/Em9M+yGBm3/5nccTsMTkvSSvgVTs0iQEIlJMWVrbNTdRuc2zaHl7Ojf3IF9q36sIVq9dkTaO5K9bEqV07RNduFJJF1mgXlVv0/KzMFf60td3eIh5oz21tjjPtPWw5bTO3A0n/m7GV5xOwHQluWpCi2b1XH7a4dM7TcdQP8pvtyf8WKNV2bUoAxq2Ujs3aK15lX37O7ZX0rZ1vjZRZ0OXcWXOo6XAqMrJtsZ2WNrU5pLuUL9BxKRdSr8MDblkQdnEif+XWEODQvnQrR3Fc8zLan5uo7dm9e8JtUWnbFKZ8MV/yuVtV9RPaHGHI4kCsAa/24bZR59Ii37Kh/RWLNxtjKduOJ0+bwRHc3aPnMV2jCYEx5an4lg6cV/W+NpYyS5rj66T1pFvm0ja11w5uF4M/1qxfm0fAzp07K5at3hIXDewfnY9uXwQv0KnaYeI8xQI0aPay82ffICPtSudHGrI/9ba0og0q/bh1yeLA71UsjrYN61LDZEC0ww+xwVK+CqvzSfOJW2VWbtmBhy3EzUjyb3kg6Z32hWELOJuPTTP5/bdi0cZ9cUrv7hTZPZ4sucOytgzKhky+YmvnHuUNMAq/9Vx2MG9r8ZFGvfJq3P3D78fV11xNTboOOc6iRzr9GF9/l0CDE8K05ksCcIK9jGy5m2bmjA/zrGyPt6zFjacdd3x06dCBeg1si0AYS5qubipkVPjj2Xt357/ek21cOqhfXHvdtdGta9d0NLfhDDtZ7nU2PVQmlAiEQYJVwUjI7pWxfoPKXmWrUpMY1qA0h48eEz976A9xLPlpF19/KxWjz8hK1wpeDR1fCieNHAWmhXMO7tmC2bLIFiSgwLPC9T4oL/dtQoSLliyJZ194KV4cPjIu7dc3zjtvSBYus2jIWhhY4Z3OiszOfyoumZ6PaUBoZMsYqXyEQ0EMs4pfhbl7en778CNExfbF4EFnxwkUz+p4VPs00hN2cGEQQKbS4EknD+Fi+xK2L5lQAWBf4stLYaPxP/G9yfHrh/8Yt153TZxKZd7WMGETimf5TBloSAPNfngBfDKLzJhOBXjTaVfQaBjUR3iL16VLl3Ce/KwYM24cDLkzrrr0kqxE7ZzrfMjcGppp5MN0CikVpwaLAYJ05hm/AljhqqFRHvNmEaVF1CuY9sGH8cTTj1Bs5py47MKhqZDlVPeDukKtEDTgI941cBSc0o/tiQeNU/HN7cV30KRFAN2nPGHiW/HKi0/FzOXb4h/vuC0G9jsTAdL04F77NER5NvuSLnk5z2WQZCuGlkpII0b8uJ9UgSn+PJ3i9deHx+OvjcwV9bu/+fU4+fg+qchsV+K23TT2wak48PuCfjC+mEsQmKnC2C/MB4Kedt37qAH8Dvvmf/WTH8Uiwq6DTz8lrrvac827ZJsQhBI2Fbi1D5xn6VEc7OV7BaF7zaW/DRji25k7zzOvU8cTMhoRqd0Yr48eGb/4/YPcEXHz9Z+MoecPibYWiMJ43UNaoE6u46yRcwYvYYxktfJ0xnRWoUOCHBZJ1GjXaLTgmvtgFy1fQiXqV+O1USPiuJZHxvU3fzKPetMQ28q8M+yUHdKNBUh1RF15cn4zsMZ4nNsMAEmj8hn4VBbIT9Mo1PeLBx6MmYuWxKcuOC8GDhyUxpD7IjVENczEj7RcGNTgHhilmVSsKGNxJC/sZI+dvFrys8WSxrPn9Ic/fyCux9ntSwDGY6mOYMUJLLD6jVMK3FvJ3HDrxA6CDbnfFjzW2o3TgmJi0SwVm5rQYnfVgGsHaePrWQlbuGR5jIMup1TOj/PPOSeO7dE92hGg0indjSFksMF6IBaq1TFXnqVSB94dOFfKLo1g+W6rRjFjbEbQshr3GWiYv2B+PPXcc1Gzbs24kAKJHpPmSQp1kaXSjSdVKBMcr7iQh/0sfRr0UXH7nU62tK4hpNyxgKF1Bd5Gzj8zZlzcfest0Z8aLwZ7nZvyknd8+Z2KP3nZH70HGOUxL/uTdw9vxvF/0Jj8ZAHDYY8/Hfvq14q7v/Rl5GSvNLLEAbcWz0MbtuHfCSdyUnmcQTvmxTFlIMl3aMd99VsxJt6fOjVeeuXFeH3C5BiAkXjDNVdxokHnXIFUN7hfWOfEYJiBOw1/+dfgqbi2LdsWV8obHXyNuqbU5vGkhNfI/vu3Xz4glHH7zTfEpRddEC2aNc+CZa6C1oFfDFLZh+04pzSW8sxgl7QoPpRdaSjyW4H/WtREqIynnnk2nn19ZLb/vX+6O4vdNWRswl3iR72nzhO36ldhTSOS7+3LOcn5dRyJH4LD3DcFx+JXD/w6Zi5dGYNO6IXBdC37uHsCJwYVui9pBIwbNJa/NJ59OafiJueSibbehEaxzkbue6VbHbc3334bfvp1wn7bJ6+OQeyZ7URNCmlXWhQGcZA8Dqwa4YCbx+Pto39YDPuB/cbwhTLBAo+7oWMDDotXLI+XXh8Rjz73QlxxUXFSkEV2mxHs3kWQJ+uWKHUlP/qSNg3UCbOyUXlisM35VQ5kx7y5hVKH1xM8nqHtA+i3cwYOzCNy27Lqb1ByO/UodLQsLuu8bqT2hk6oOCh5SxmcNTSgW8dXfq+ztAG60al74NcPxvkXnB9nEvw6GrzIE9K4zou495nSBkh9oq2BLpFm8nfuUTaWAVnl6Cpqi8yvqIg3J7wV03AAbr/11jieObUehXaYcIqbIhDwZyNc/GgjZQAWGNzLraxUvgt/I+oRObcG2SdPfi8ee/SpLBb3iSuviE4scqizlbnqB9v20i5wUUSaq89WOnGs47YXJ935FPZCKiCzGctqsgWmcFLN2FEjY8wHc+Ozn7gWWTY464BoJ+q4ppMsbjKICw3pxAGffdBB9luNLTjynEa/fZhaaG2dBQsJ4o8eFU+/+HLed+8X/yHOYPFK3tuMTSUdiHOBkrfEle3UqUZQH30nX6XdQQaf7W2nuKQ4aQpunJ9p1L965Pe/i3fnL4zTyB698cYb4adjsxhjOt+MQX2kHSMPpYykz3Ku5X9xpPx0nM699qULONLNiDFj41/uuz9h/9SlF8W1V12ZC0zS6w6yFWn44BxI9wZlShtBHSiOtD8S5/bL34wy71mM3fTS8NfhpxejCyi77q7Pc4LVGXm6l4561v1SrgO6eqEeNZd0hg3S6iw75+Vx7MpJaTT3/4Nbg0pzFlTEA3/4Q4x9d3JcPrh/nHMuBcmpp2FxviySCl6E2fpp0pF6TRz5Oe1u4Bc3FtEU5/KyL3csWn/sHRYMf/C9++K0E/tE/zPPRL9S14FAhmiWp8Wx86ftoQywX2netuQbbZH8m3vFmcEdn5FXK7FXx8BPoyk0/6lP3hjHMactWfxpSCDfhR/bUwarO1N/MEP2I24KXuXUFBYNtG30c5xXcWffLhDMRge+NnJU2hMXn3dedOnYMWuoKGuFRVr08rMyVh4TfidSfeQYs3YG47N+ADdkXxuQSwsrPE5yWvz2mefj5ssvjcHUdLAgaGnvlr5SBniUDTSm/6VuFnYLJCvL5JGsd4fukd/8vqKSU1nem8RJfy/FvMWb4p4v/QN204lsM2HtH77RT0x/AFo+aMNXtW8QQlwYhDFAZZ/yn7awY1M3fIjdMfzVV+LZ0eOjD4sbd33mM9Gre7fUcQZJbVtdlb4BOJB3pXP/Vj9lrSbxxt+pY11o4uVCmoHqsRMmxL0/+c8c3xUXnx9XXHpxHN2OQurUJqyGvVcbvq/Bg9K+bZs5ZS2XjQS2Fi5dzOmJY+LkQQPj6quvjiPaHkVPzlMhg/jwsb3+5kCDIxcVXr5LQF4lenbt2xUfQJRjMPyWL1kcRzRrEeee1S96du2W+2i2ENmziJEM0JDicBL98DGj44JbPp3t3HblpXHHHXewqtM7DSENYovAGGxAW6XT7z5WGUaHVYNKplMgSXA67AoPI8tGn1Zg5D71wovx9e/fFye1qhU33fn1GDJkSGhE6xBLbBa805GT4GSaFExAo2On8tMQUEAZ/QT42ItjtwdFJ8FoTPzukcfi4SeeiVsvHRpXXXlNDIQRa5HmvplsBKuYKxxKQ04mlIhLZ1eiLZQzKeUoZAMvMotjskjJZFajfvQf/4kDsicu58zYs049PY7tekwqfWH27Osy2knTgpnCP/tEwYgnFU6piBSEwuAK5NKlS2PEm+Pixz/+SXzxC3dixJ1RGCuchJBwIvxkZmFXSbsq7ZiFT6a2D5WeQrJ0QFzVULhULFjAEW9T4oXhr1EQcEt85sZPkXVAqhltNyIaqgBR6RvwUNHLW4kjjQz6y4vxGEBRmeXqSB0MeJ4zEjh79uw8yuxXv/15DBl0adyEQdG5fftsw9VycaOQ0PB3PjNwAZzCavu5al8VuZWGVWwWWHKOXXkd/sbr8fijv41J81fHffd+My4eOoTVLlajaFsFZvup7KtosjREVRKeimCEuAEOcGNoXLwZGa6F4HXep1DD5IknHo//evyZOAob9f5f/Zqzu08/GDxi0EnrOr/So31pHIIi5lcDCRyBezNrzPsSdunGYoobKUY5imyiH3zjH2IG6VtXDxkcd3729jihV0/gpgHmSwpXs1ggTXy4Yu5PGoVMavKOEdcVGJwWc/SM4SLQ0JjVqPUEAl6O+3/+U56I+Nxtn43LOH7p6CPaZjaDgYbkJ+jEubIqvPyofDDyL+3XysAhuGZ8u3GiVfLyU836daKS1fphzz8Xw156Ibof1ib+6dtfj/Nwqps1bYKzwukWIEEFpwK2yrjza6BBQ2ATmSjSrQo/lT5zab/KBJWENGxg7Z4f/FtMmjk7vnTj9XHxxRdTQKtP7ofdVbVqjqopgjLMrbzvc0bp5Z10KsCT8kaDWt5N5Qb+PQ7s1VGj4ov/ck98gWDpOQMGkDVBVf22RwIFc0VbOlzrKc64Yd+O2MSeBIsUahjVIchQG8XqSpqmswXCnFeINbahtNas3xTzFi6J0W+OZXV6blw8ZChFBo+jUjcGBeO3yG41cNuYVXKdMAO0zq2OF8Cz0l8ENTTYpKWNGD/urTdDyvoKOwhkzCKL5FcYubXr1Ywbrrw6HaOmzZpmpWXlIEye/K7RJg2m0a5M4Dfx7qURKu4zYMg8+b2ro++9916MGDU6HiQo+6NvfC0uOOfslAUKLI036VFYfdZnki6BX76VB+RljaVSnhlga96S7QnokVkfTI8/cWTwIz/7eewmf/773/lOnMlpCQ1xXktnSJ7KwqJVsPp9EYz2lBSMOecSPJTy2ECA2RxmxI17a0L88bFH4smXR8WlQ8+NL9xxexx37LEpa3S4LBKVp2Bo7BBcyaKv0L/taqyksYQczcAsY/N7ZU3j5s1YTUI/PfunuPNb9yT+vv21u+NTN1yfqy6u8kFo1ArBQYE+pHkNxHpslRFvwqwxJX3Ku8pI3+mCzxh68O2MWcwpgbVfPfZUtv/wz34SQ88dnMEpiw97c2YTgA9xYpDQALa4Tp3BPEvziXdlMTInAxp8p8E+nsLJ99x7b0yaVxmXDzg97vj8nXEajpeyXH4yWKH8zew0ThDQAPTlHKs7lO+OxaCs8tH5tl0DAmswcl8mCPOFb3wrYf/ml++My4deGN27dCU7snBwxYG0IvUpW3MFj8876dtC1GA9HTxXI4UbgDLIsHM/W8ww/h8lCHP/g7+NT3/qyjhv0DmZneXquoUpDfKYSZlBHvCivvNkFLP5PHnBlSlXRXWo01lnrI5FR9hCZVMJhD/w+4djP4Vqr7jkEk7H6s1JFe3TmZA/PSmkCWn1EESsX7M2jxLWGE35BV3rwJmtofzSUU+djhwyvX/lyhUxjqDmj+77t7iS4M75gwZG165ds6q+uNWpVGYZ1FDmaqR7yWv5Ai8GjryUa7btfKu7LBQ3a+6ceIUs0/Hvvxf//NWvRt+TT4omyBbrBhxAfmiL1QOnNag3YzHqNKTpS71UnmDgwoDz6Pn0yl+LR+oYTOeI07FjxsR//fwXce65F8Sdt93CiTtdaRMdDLVkgAu8M4yULTpcPi9966CKf/8W5/KsRy4nAfDP0qXLYiJbdl/jJKKn3nwnvomzez3Okae+7EO+goHEpTStjSh/mxFlALgocgce6Nuiw/t3FUFVx6Tdtw9BPXfBvHiNQucPP/Fk4u7fv/3t6M/WC+WqQUnnSRqwDele/WHAy0BDPeStOJZOPd54fy1WRbELtlMUshmLGC6KvE+R80d+91C8MvHdOOeUE+LLd3+Jo7JPTudTGaM8MctJ3knnB5x52aYyWRla2pPiSb7176bYXa66P/fiS3HzXV/KZ758681xx2dui47QzV5kgfrZYDjQ5TyLU20z7RfbN8hQ2B7qPO5iLpQ32qrysZmUjz79bPzsN7+LE0gf/+zXvhfnEwzwRDjpUVwIu7QjbqSldAihOZ1x592MWm168Wi/zksN7BodOxcJvv/vP43n3xgZn7nuMo5Yv5KT207Bl7CAMNm72kjAwgymDlQ2K8+qQSwWaTYr0+CUDu5mUvpr4z/Ux3m3BoxFAUePnxD/cOfncaQHsXhyHgWOT8hCu4CaMjv1FLgpdVPOJXPt317CIezKd3nORTXvWQN9GQh4cfjr8fxzw+ILd94ZpzOnnmgnT3i//CnNaNvI/y6qupCSi6eMSdgPY6VcuvUISe83kGEWZ2YHffBBPPGnYeiV2nHTNddkRmKzZmy5ZYzeKx3IJAZipE9tV2lJWSFPKSvUfS747GIuXQAxUODRsjORY+MJktz364fia7ffFpdfMDSOatsOei30nfSivihtbOnBMRhU0E5y8Ut+tdhwXWxBszhdRPHkmpnY8SPHjMoFwxnz1sZvfnxfDB7QP5pi02jHSyPi0LYNXJdj0b5Qf5stmyeNME4XBrRkDWKou9WN77K48cRjj8WDw16K/iedEN/552/EKSyoWmtCHZE2Nu2XuJCutbHFiwLIeRBnmYkjnsCRJ5PVIei5hqNxX3j1tbj17q+BgYjP3PyJ+PSNN5Ax2ClhRUGwwIQcAPXpj9SsnZkk+lDrCVLMWTA/XsE/OpoFo2s4eaJpqyLDJBuT6D7G198l0JBeiRPhpYxnAqo+Mrk7Y+aHH8YEnJ3lGJjNWB0feNrpBBqOSQW1EwWr0ShDSSiu9L4x7s348l135f7ou668LG4i6HA8K1IaPBpxOndp/EPMCqkDCFCFoApSZZNp/DC2gi+r5/rOS6fR1NGnEa7/9IP74vQ2DeP6z/wDWQfn5dGJEm1GDBF0Oo/1ERQatRZXUkhJhBJaBhoYaEboVRCsvFox1FXrmQiQPz75dDz37AtxC0GSC3AA+p5yKkIGxwjhvR/hKqFqnKcCg4ntV8Xu6qLw7ma1MdUFMOsw7sYIcnuAimsySvlnOKKHQdgXUX227wknRzdWSRuxuiAuXH1SUDkbCkJTqcxwUEjIjDJMmRbk72mI0c966mQsxKEeRUTud//187jhtltZrTuds8G7pLGiYBL/pVGSUWvaUojoHCmwVQy2n0YogkBhY58GGpYSZPoAOngBY2URke7bicyf1Ls3Rxw2y4wJ4RNo0ziNymoI2pe4sZ1aMLQZGOnM873pXDqutTCy12zakEffjR0/Ln75y9/E1VdeFTdcxRFAMLjbG3S0DCA5p4URXcW0Igl4fUs+VqhAP+Lc6tfVFSAotEpgH0Xwa9iwZ2LElNnxg3/5GoGG81mZLgINOoYqfd8Vsip8nRirO6dhx2fnxcwAHW0VZgrD+qw2M65p4OWZZ56OHz7yeBzF8//x0AMxEGPFwltWc1YZS38l3auUfSkAxZFKWPqubuAFvEgvvjTUFeqj3xwXP/ve1+Od5TvikzhGd95+e/TBMdqH05KrfLSW/CQS4C3UDW8IRPjF1b6NzMlStjDMX7woFhOo02HxVbcu9StY+Z7IWfV/enGYWITeL4izzjgzWqPU9sE3Zi3JU9KJyqqmygWcinPTGxXo0o2RZPFlsE9lgUlEBkQtjmRcFeMmjOfoqHey/Xu/+nWySfpxTF0bCvpgmNCmBqlp0fKlRfxMxXa+NTbEkzQo76fiBFc6Ixq/pllPnPx+3PvDH8ayOfPilltvjPPPH5JVxqUT99NrpOgoyltYCxmcAfOJe6yt5FfxpfFvIKBctcsznteuieGsGH3pO9+Nmy+4IM4ddDbZTacga9qDZ5Qlim0NDsoScLto/apYsmlt4t65qGX0nhc9J12hLot5ps+djHXjZuqGrFzNKiZHPi5fEv04ZqxL506c4NGGlVEUOnJSejeTyvbS0QJ+HWaV5154Np1R+MwMG4Myzan70eXo9tGUoGh15qGysiIefORhHPimce2ll8ex3bpFC07wqA1f2J7b3eSkDMgyf/KqK1VFcIHtG0mbxfF1GhppIADDkqVLkl/Nbnro2WHx9X/8YgwZyCkYOHRmPZSruE64tK0xq0NtENVVcJ09DQ55V1mTjgx8Wx9DSz7wiLfX3xgezz/zRKzfvT++9k9fJSB7SlbSdiUbYsxjs+Qp5YnyxUteLVbk6Ne+mVdlW+oQfqvfiErRGKDj354YTzz1eDxGoOEK+OkujK3e3bsDDw/RtlsCIYaqFVkXP9FL/kR/wpxGOrgBQbJb0o3zUrdJo6ym/6cXXohv3POdrMnwzS/dxSrjFRhybZOma2D412bA1u3w6K90RJEjwutKlCvzflauSPPSpPLUzwZ9Tad96He/j//44xO5T/fB+38YQwYTaEB/yC/SijykMS5fqf/comEwKleS/ZvfGR0UyTjBu3xt+zrBZiMaaJg4vzKuHXRWfPaOzxFoODlxsx1db/vK48Q9tKBOlf81xKVD5YR6WwfJQIDwq8917NZCoy+PGBGf/8a3GGHEPV+5K668kDPlkfMGMbQN1D/SXUkz0pw4T6dD3FXJutQj9KM8khfU3/MWL4zHhz0fP37od3HT1ReBl/M4vu/UXP221skB5GVNwXNugVO4dXiE2S0vrqpXZx49EcQ5ziAMf+sceDrIFIz/B/7wSOxBnl5xycWk1Z6UR/ua+eICiiu6efQ08j4Dg1W2grpPfEjnzq+Gqf/pbGjz6JR5lrxBngd/+Yu4BP03GBnZuXPnXCXV1tA5Fy/CpZGs3FUvq6vy+D/kgLaCAQJ1ue1mAJfnlhJomEnQ8dWRo2Ls5ElxN6v2p51wfAYadJaVkdaEcUuAgQa3t2qDyEvShjRigMGV0ZQ7fKcs1slwu+a0qVNjDIGG3/7ytzGYRZ/bWYTo2bVr1jFIWmG8kpp8mKyVslYKVIYVc+3YpBVlj3Pu3Ii35ctXxHjkzCsEqx+d+F58647PxtUcHdeOrbvKFuWNAQbbVucvYTV4GqcHrGJF3W0UOY+M6QBzbiBezeu4UArphC3lhIpJkyaRGTA6afKzn7gh9Ye2rDabfXjlCjpPS4faCnl0qnIBWIW5GjJ1P7p7G0EG+bUlK8SNCORUVFbG2zi8z7z+Wpx3yonxJWSlQR51kvyvflPmqPsy80gIaTMDXbwrv5A6UIuyjA/SAP24TWsjdbteenV43PblwjH6OoGGm264gRNIusR+9Ta2xz6Ca9K6/Wmv+tmAlO3ZrnPg/KZe4d2/lQvORwbuCDTc/5uHot9RzeJTn/tiDD77nAw0qJ8NVgNe2jfCn7KH+VBvFafFwZ/KedrKfnhPWuKNnNd4/8MZ8dNf/SqeH/NmfPHGa+O8oUM5dYA6Bsj4bcgy2xbPNQQREiq2EwI5+BIVmUkL/1o40AUZAwyFDcXpW2SWjJ34dnwFfA/sexbHn5+bQceO7TukzjFQ4hgzsCV8zLM8pg70e+15eUhZqVPs78pfQMkjzufNnx8vjRwZzz36aNzy2c9mILxr12Nyi4MLhfKPMJaLbcow25df7dO25VsvF+e0P8yIMhuvDDQ8PmwYNmBdZNnVBMJ7RHMWxrSNciESmBJO5K+2tgu8WwlkOL8u+qaMB4A9vpjt0hY2Q2gaGULj3hwfv3z4sfjCLbfEpQRhOh5JbSn6V56rK9QdXupWx27ARdtImaOPlzSiHOA75bzBIwMCc+aRjThqFJlfT8akGUvigX/7XpzLQm3zJgScoEH9r2KBwHks7Gxx43Zf+dggpwu0HhVsINIFMm1hswU9YcITBM0Q+s1Lw2MQ2xr+5ctfJtDQJ4PHqU+dU3RfCbejEN/F9r6ixlIG+IWdlzRTDXlfF39sNXL4ZRYkv/2Vb8QSnrvjpuvj5k/dwFGknZKPDDSY1YCgQQaYYU1dEPhQ/WGAc14lR7O/8Hy079MrbkCONMKWc2x5AdfH+frbAw3ioZD+Sh24mReCE42TAmI/wnY2ht/bE8bHUoRmA5TRmSeenBHrmkyojoi3i1AJUuE3BkPup9/+l5jC13eSYn815zD3xjHynj2sSmikOFkSncIpnfbsGCL0HphGoWpES2WvwNcI8m9XRf5EKvZXWbU/s2XduPKWz8fZZ5+d5/VmtJb2VQYyjCuDtu9RQTplOn4aXRKJ3alEq1GgrTp7fbbtYc/0urXxwaxZ8SxpdNNffSMuuOJSottnRR+K07Vg/1aRNoeRS/s6pZmiw1gUKq6oG3UTdp1Do//1+NstGcLvMZtGvSazWqex0q5NqzhvwMA4qWfv6HxkewrIkUbIPCiIjKgqKGQEz5bWgMy96xji0muuaHCPxobC1vuMVM6fNz/exKEbhvC7kLN6z8J5ObZHD85zRSlzb+EYIiLEO+2rzGSSQsGj3FSadsDvwqAToIOh4HHFJaO4CNc59HPbJ2+IE3v2ykCDkV4NKZ9TaAqfQtn2XKVWeMvoZqTQes7xbu4xfbEe1ajWgpd3VfajRsQDT78Yt1wGzVzEShcBmMPMlkBAaYgqSFzFdw5VBBoj9uEY0uiicaP9Bhp0Qmu6CogxtBhjYtz48Zxj/FK89O50DNwvkn55TjrTWVsAI6QwHnBKoRuj6NJIQZ/iRFYolJLEbtu+6jK/Ct/pszgvHUPoh48+kYV9/uNXv4iBp/fNtFTPvzY4ZfsqSnkm0+lQwAZeJEQdZw1QlaQGuW0rXOsShNmG0H3zrbfi1/d/L8Yt3Rg3nTc4Pkfqa2+cxt2kRu6mUKA0rmNtu0ayBfgAeKgFv2xB6c1byNnf06bEmwjpx18vajHQ8V9cKgsNmD3UDvh/eV2JPDjt5FPiRBRnZ/Y4N6Df3dCMzl2OQUeIlzUHdIYzi0XFKe0w1+JJ/CgfrIj/zrSp8f2f/Htsq1wYV336xpQFpkuLE7MCbNMUV4jSVIh0cGsRiYZE+MrsIF60qV/GzRmYEg8GHZeuXB4jCNzd85P741xk3nlsPzjrrH7RsUNH5pICRwQel61ZHTMr58e4Se/ELx59+P8edXqw9k17f+s19PR+bGHgqFTSuZtSWMogzx8ef4wCmO1wqC/IQINHYiq3PN5JWWPGjs6uRqirKabcKsvcBpH4RtEbEJPfNKiVI8sx5j1XewKy5qlnnolP3nxLnHvmmewRbZ0GsCsfOv3KRYNOrnLlagh/78AYspCaq6caA8oYFX06NOBdWWlWmVudRowcESvXbYjbCaydwb5vnQv3kirL90AbGrQaoxpXaVigwJQHOo/qDVdA3A6jPHCbSQMKtW3FkJmIrHl22NPwwri44rxz4o5bbkrHKOU6dFEfeA0KyrvKHA0k5YJGozrS7TjCrbrUcFbmKO81jJZScf9leOxX9/0oPuTnr3zmlrjoQjLuoHXp2iBDDQq8Vle+IHfEvXJKGWztG1egNPo1DH3PFSTnhHvMHJtXURGPPvZ4/Ozxp1LWfPf734nzBg5M/WFGg45dHehb58UAZx4viez1KGpXODPwQ586ZtK89T40tsQZ5nRMmPxefPd7342JZNtcP+CMuPUzt8cpHM2ozvaoQ2E2UHTo9sdSZ+WWOydEgQlemIwMHOmkKtvW4wy8yrbHu759b2hi//OXPh9XXnQJe3g7UMBtXWxGt+skSy/qHXnX7A7HkosZOB0GGmDaDIgp912J2g/829Hf85YsjqdefiX+Hf133ZABMeTcIXE6Z76b0bBzC0EMMjBqgPca8Jp4klaEU1ki/h2/7+psA17KRPEiX3is8dQZM+Khx56IHexD9jz209GvPZDDdXDWtTGES9pW9tBy4kwnRd5SR2soO78a5FKPOt2gnCukc8k4GP/upHgGfh1y2eU4Rx433SlrQKg31aml8Swferm33OBCQ4zd+ug59btbrNTr6gIDtGYn6Ux/gI4aMW4ctQjejc98+tPUsOgdTRs2icbYHfWgF+vKOKfCrhOpzlbXoWQLvQC8W8nksOhZ/caNUk5K92Z9TSfjdezo0fH4H5+OQeecEzdefVX06tI1GuE86Ci6cikcAJb4Vq64Yq5DIa3UM2DPd+rw3LIJ/NKAx4GawTqWRa5Xnn02Hp3+YXzzppvjchyjthz5CBpzNdXCxwZvzCaYjlz645NPxiOsLP5Pr9p1D8vTjXZT94aR/08f+z/ed/ZAjjBsdySExSLTlk3xzqgJHCfZJr5IRoZbWsWzOk78GGzYC84NNGjTqMOdQ2lEebgLGlJGKtNSToIr0+LXkU38Bvz0o3/9bjpG3/jUDaR6X5p2k3yqzZHZwrzbh/yUNljinwDgIbq0DLDrQCoX/NtaNo+SnXU/25TP7tA6rr7pthhAfY8jyDxz0SptYGhb+8YFFTNjlHGujHuss3I5Mx+AX1mpfHT1W729gW2Tk2d8GA/CqzOnvBtXUDyv34AB0bNnz+R99YRy1xM1ajAWBAEBMWwn2jd4YmaDxawt9r0HHHn6wE7tKIKFB7Sh6GM8WXf3fPOb0bfX8ZmJccZpfaNTh46JZ/Vc8gk+QW5tgA61tc3UkieVP4de0qwOvXS8niBWRWVlDIefhj32aFxBIKAfcqY326BbtW6dzrK86ZWBHeSXxFoGMLSVlfu5HQbclMENt4iXGQ0uXj354otRC/v+U5ddxvaAHikLPM1oG3pJWpFnhElbW3vdrCv78wQ05Y0ZWnswmPcgjGqCe7O13Pb4/rvvxZvw1OPPvRSfvu66uHAQdX7ad6AwbJOcP21t4dWR1jeTR5U56gxtpczKSjlZFaxC/tYjk0RZOa9iQS7qPf/SczH+g4r4z299M84543Tq8FBcHRkjzdu+tKMM1udRmsnv2h25DQaZJv0Z9N0CT+5gXlthW5g16ILV7x58MP4welycjQy7+3Ofi5NZ8Myt+OBZuj9oF9CH8FZnHG55BimxdcP61IPObw0WmKV39YcFTl3wfJ2A43/8+F9j+gYyeG64Jm5gC/oxnTqqIKBvAunYxwd2qZMKe9VgsTrVAJGBuWeffjq6sL35JhZi6x/VLseXAxSPH+Pr7xdoYMJV6O7bdaVMBZPGApM8G6IfxwQsWrAgGmO09D+FYnpdj0mnyUrnuksSZhloGM0K6b/d862wvM6dQ4dQgfMTuYdJG0Slw+ynUpYgUljBMPSYjKfxoKAzrUti0/jPdEoIQkfeVZGnX345vvzDH8eJPPOJr/xjnE9Gg3uvNIRM81aoamw1ggH9vB0DLA1GCE3hpcAClBS0WGJR+7CGsQ2DdBVOg4rqGQINk14nrRYD8ewBg9gi0CcDDbYh7BpwtuuKgatc7t3NqBlCQSZU4ckU9TFsfVfMbmIVZCXtv0P0/9cPP0wxpjZx/oCBcXKv4zLQ4L5ThbV4VIDIdAYDFNTbac/2kzn4zsvoqIrZFFVTeY1UzpkzJ8a4Ov3EE5mG1o/VnGOPPTaLxGioaIiU0VkdB9uUETPQUCVQ0oB2DkCQwtA9Z47RKOv0Dz+IF0aMYC8SGQ0EGk5iP53pXMJg2rVR1XLlReHnKmYqmiqhmEY14zSevh2c7UchNGp+eKxDEb+NEzz8jdfidy+8EbdcclFcdeEF0aNz5zi8MWd784xnSCf+oVGVtG1p+IsrYbZwn+NIo1FjDpwbZFCALFrGlpLRZDQ8+0SMmL0ovkc680XnDo6WpB8qPA4wr6XSz1Ud2pc25QcaT+ErrqRNlYMrO/bTgIr/0ugUFOZjGIg/e+7FrHr7m1/8J4Zi36Q1V6Y10g0y4D6kopTapR8F+AGI3eBUKkiNC4TeHkrfa+w2on2324wZPz5++s27YyKZ0TcOPie+yKpOHxTP3qpAg3AW6YRwYioD5g94XWHdhhKejfB/+/3JMXLC+Hie10ddtTg/ziKOOzfDn/8Pr0svvpAMoVPilF49oyt7kOsA71YcjFqgux7GiUElZYJGUrkqotGs0cuocBqdW+YF+UQoLCZOnRb3/vhHsbhyUdz6yWtjKA61Z4Nb12DrRmpAoBAMTOU77bo6ne2ZkcBvBhukm6yNQZu162NgMA8GC5esWBYj3poQX//pv8eQHj05T/6C6N9/YHTu2An6MHtne6ylSvK8pYvJ4hobP/v9b/7vMYfgq0bm/AG2A/+t17knnYrTidHA6kRzqtEbaPgtW3qaNmkQV15wYXTr0pmq2s1y/GZ7iBdlSpGZVQTrjPa7mueKmSQqP6eTAz5zlRTlvwpjZSbB53Hw7GPPPpP7js8786zMKrOYZRZuQt6Ycq5RZHvu2beS+26cPQ2Oehh4GkU6p8r4NKSRY9K751KPJStuOIHBlRu3xJ2k759JoOHINu0y8OhK3S4MUU30NFaAzSCwThdTTEChUfKlMmYbzpE0Y/uN2K6zjfG9hbHyJBkNT499J646f3DcddunM9CwB2NfXk3jFr7U+NdBVxa40uvKtfImg7DQTBpg0Eruc8YJMANpKQFZt5f9iODXUmC5+9ab4hL0iIGGzJByrzjGSk3ehT3lMM+qN3KlCNnlKn0G1qDHDFJjFBtkd1Vn3oKK+P0jf4z/eurZJJdH7vtBDMW5a4hTuYl6KwZJrZVSyi+3NZo94ipSOo9gyDHo7CojC9wDOwF3DVON82/de0+8t2xFXNX3pNw6ceqJJxaBBk40MchTg2c9WaU+fWr0Z8YT8lf9n8F7cKLjJ3/KSwZ5ahAo2YAOfAXH6Av3fJeZi7jnHz8X11x8WRq569BfmwgqaUi7uqXhiRRO3WcASMdFmrHmiYsU1qNQDmjgHgw0LF3MdphX4n6clysGnBoXXXBxnEUArDWZazuhl/2UG68OvDUhObM+vAxkGGhRjhsIsLCkDpi40bEzI06H2ECDqd6/f+rp2E41/UsINAwg6+A4Fgp0rDbh9CnrLS4sfbsn2TnQ3pHOU9bTTwZ2kGXaOAYCXPFdDZ+6ddBskice/WNcRAr5IPRHl85FRoMGv1l0qZvBsbrPOZQ3nVN52Jefc988tOnv6mXRtZBMNjOnDDSMm/Ze3HrzLXEazm67lq3Rr01SL+nMCa96Sl5yLjMDgYlSF6nztu9DFjAG09NrgRdp1EyPqdg0o0eMjN8+OYzibwPjJuoE9DbQAI0Y5NUpNaBL6ym3zZ7ROHc7orJXx87tEspiAw2+xI0LGNadGsnixvPs5X9y0eL41k23wLPnFxkNtFdsqWU1ldY9Dlb77YHfPcQ2i7E5v//Tf+rUIlixR5oSY3/71aMnRcrbt2cbG3OODHrjtdeoM9M1vvqlf8xsEnXcHmShAXY/Fzpc9BRzK35Kp056PICMVOdloAF+MmCwFpp7ne1rX7v3+1k1/6ts77vm8sujO3SjfnKrUAYaeAfz6TRKF9r3yixuSdtDPjWwJq/6cmuJQYGF2E2PkNHww988FH1ZB7vxK/8c5wwaxJbTlsX2WGxhF2OEXbnpy89uGXGlF0GZmQkuVKVTT5sZuEPWbdhmoGFG/PqPfyRI/17cfO3lcfa5g+M4nMdctU/5bmCBMfO8Y6mH7q5P29qBu9Qj0JFZpsJvQHk3dLObQIMr+JuhLdv97rf+NU7o1DkuICNxILr7mM5d0l53G5ur9NKZ/CS9a6saHJeXpEnlvC9x5j1+py60cPn8iooYPn58PPvkE9Qruyz6nX569MZPOAKHWL60ToO+lPcrv2zfNrTr7dMJcSuFOsvAkhnBbp0wW6GysiIL/j79ysvMd634BIszvbtREJIAj88K4/+i7jwD7Kyuc73Uu0YN9TLqnaIGqIAKkkAgiRZjYzA2xiEQ2/GN7cRx4sRx4sR2HJfYcQEMxgZsiugIkFBvIAkJ1KWRRr333qX7POvTIff+jv9w4OjMnDlnf/tbe6131b22eF6IE2vFfci2XlesMTiQsss1zrGkZ9ApNeuxtZ1Atf1aFtFwdsa06fHE5LfjS2zTGj9yRBFooL+TtrUBdnkyt1nLE4xngEUMclx1hrwj3XPLORfXFrZfwTpszakkCF5+bVLMW7U1fvrNv41R0KZVM06GYu2yUbUJIHSfOKlPKO585I9oC3Mt9bX8bqLNNW3eqkXi8bx334vHSOQ9M39RjMCG/MaXvkTTxivQecwTflQ3yIPaNhLorHzJz5mQhf4GksSjDLgxfuooqpuc/36S5G8y93//+3+KdXz7wbuprP7kJ7Ky2nmipOMcfoiv8qW4rC2p76P/sQGcfeG556LjwH4ZaKjd4VKgga9qv36cH3+aQINE9Imiyj1QLIAZFSQAi/987pmdMWVKbKpYF40oyx01ZFhc3r0n5UwINs4OSyzP8/HCQJ9GRcM3/+GbeRTJXwAen7nvs9l8SCEwWu4eFwE2KwR4z2yFDxWlzpyZBMsXZeZ0HJ0cY1v6updAw3Nsnfjr7/0guvH3h8hOj7eUjjIVAeAEwpvRfBjAJpAJ6AivjgvwmhkHDQNvVwPiPFn12mTVT5JB3scenWVrVsezOIxvvj0t7hs7ipKrcZmBdcvISTNCAJ6MnNlXDE/vScF3PBWvykFjOcEaZVyTrLRGzGEirtsoYXx3ydL4xRNPsJe6edw86oa45sp+0a28UzpCRwE/o54a5oKagQYNIQ05FU9GRLlmZgsRFhtT1iMQ4Gcz0EBWZAb7GZ8h0HAPADL8mmuyO3ZLoqxmndyv6auLXQWHzihlZlcAaCO2gqFg5WcERcvrG9kZmt83rN8Qi95fzNaJKbGHMtEHP30PkcS+CX4KsYafdPBhNFVl4RgJtNyDrz6MwHovx8kslgINB4huW9EwdfrUeGTS5Aw0fBKHtBcKogyHySBMVsKwhnz5Er9ZCeOIBo2ISjJmbpmA/vKMCtmKA4M9bp1w79QfHvvPmL8v4nvf/HpMpLnfZeyjdD3PUQVzhiirQah6zFtD3eCZ+xsNKmX5MeOd4fqW1Oc2B9ahPo6LgaRFGHGPPfZoPDp1eh7h9fjPf8r2omtwaomsKx8CKbM2a2TGyzE1CJlygqBAqIEjaKsks5EivFTWhP1+gPv02bPje1/8SixiTp+5YWR87S//Mvr2okIIZXvuuNuCkBTp4jiuoXKEvNQpa4hxeDbWb94Ui1csSyfi6cmTJRqZQWTw0tPfuXVo5k+wN/E0SxVLn5G3fUhun4qrq0klGbPms7xpeaN/83dWKfNCRtLPGyOoTpbseDHK3ZSRD2G/fT8qbTq3bcN1qILauzd7GRTNycCeHETaE41mLBW996Vs5aKLT9BHWi2gDPDbbJ1YVLkpvvoJ9nnefkf0u5IjthjDoz1d33S+GEnl4FpmmIfIgpzkGmAmFYEGbsTMseubFQ27d8bUeXPjqz/+UdyOnI4dS5O2oTSv69Q5DTlPEThCieYWPvfOvNnx7R//kNlCt0YoIZRRNXRSYh3vSSMJ5FJJO17SrqtN931x9gTnT7Gcxe3xlrRPmvrBSw95UliWOU/x/ikGcrwid0L/DiouRqDUS4GGHWTXH3n6GfjwPA7dhOjaqWPuIdUQlLbFnlqMHmigwaMj48OscklOxWS3PLglxoyIxtBunMLly9iXTdXB7156ib2Mn4txZLo6dexI8zo28/LQaDtJQMDvmvk0+JCOEtetjcGo057Orw4UfGslj6XYmGSxAgyeQpOtV595MvZfrBZf/erfxLCBAzhppVWWdyqrpwjyKEvFNgHoCb5kUBMeqQ/fW3p5jDLm42I/VoLbiBrRn+IE867ZVpoAAEAASURBVJkDRv7+qSfipfkf0PNkTHzlwS/Qab576ia3IhmItqJBAwgLK8uaNahrUxpfqmKz/FjMdBF1GqvAj2brDDS8hIH4lR//NOnw9QfvjzsJnHZo0yb1XTX4gigrTZXZEkQwXCx3DKsvnK8rYMBC/aegqUvccifO2xhw7YYN8ehvnohfvFBsdfrDj/4jJowZk07/QfqtmF3UWMwnvA7wJp3le50Yq1NccJ0XjUcrDQwCZNkqgj8HHP7WP3873t+xO+4YeGU8/CXK7AcQ0ocWx9BP6RzBdGXwQV2CRwZnDFqZbZceqbvRW24fNHiS9OH+vMYBAg1Z0fDP/5q0+ZevPPRRoOEQJ7Co/4pTc2zAWfDLUXSNZdn2KtHJqEqAxsypxqJ0rwVdDDScxAm2ouEPr9MzhEDMncMHUUkyMbeB6RhloAG6G2iQ9hq58qPBHeeorvC1mDc4DH10yNyHfBJH0S7znmjx5PMvxPFd22MCgYYbRoyk99SV3DPN69i6eJyEhrzpdr9GHLeqcerpLuo+19X5GnTxOqVtT+pIKxoq1lMVRaDht0/+NibiLI4ePCS6d+tGM+dWqffTxoDnxCu/49P5p15lXIOFPtThvuerssqXY+MmGrR9sCSmzJodc5Ytij+//wsxFHnq3L48mjei2ST0PA/4yPsG3tPZZXz1qvKoXrUnjNsm7EOQgTBtFNb0EJUcGWiYNp2KrhdiAvuxP3/XJzLQUJ/gkLJ+Hn7JID1zUi8bvOE2MvFh4D6dXzBXGDDIoHNnoKkB+933E9S0MeyLZDGf27U7/vn+B+KTOI3tsPfS6YHncisK37/Az6sqKuIJgv6Ps32Ju0/9UQfZrM48DFyJY6cMmPA3LpcPr+ta+bsY66vzc82AmNR3xP7h8eJ9/47fnfjjaW1YVTj6yAfvqfuUsesGX5dbDMSPU9g3L0+fEtf37kl/jK/HtQTu1HsmT7RLtSf/v6opxlCHn8UmlVd8ug4GfEq/62TvxxZ+k/4GX/x2IU9fh+73sJ+/V5eu6fSLBSZQDADIH2KY9JbQiTHIqbpPu0P8sfJIftemcW03bt0WTxJY+/6jj8dVXPovvv33cePoMTQOvCyzwgYdLX8Xa4qMMuMjlwbza4MNYk/azH6Ga2WlFrim43gMvfk+8vQzbOHJCxfFQ7ePi5tuGR/93TpBkM7vyYsGvnQerQCrB/4awCtVUGi/qquUKTGARU4ePc7YbmmcjTx995/+Na7q2CFuQf+NHnlDdOnG0aXoJnuyJNbAiy62spQ6kOtpT+v4agv7LFUi1AaXDYbpZ1SAw2/OmROTnns2Pv2Ju2IYNk0vbDJtbat1rDjIrWTwstjoRXKcDCq4BZqTOcA211n72aCgWCZ+bNiwPhbjJ7zw5mR0V9X49MQJ0TcDDZdltULiySVdrZwzWOKB89YfsZrCAJ70Poch5GkoVn41wt41cLeQLSXTp02P/351cnydioZbR42KLiR9ysAsHfSsOGDGOtLijPwiHQzcpy5XfqF94gJj+5pVTui/dcz97XemxktsnVhQsTsDDTcYaACDxW8TDWKx/K5/po7NLThcT8mTH7WF5XMDwFameJx3YxKSXnM+fWwef/TX8fS8hTG8b5/4x7+m58kVV6S9Jz/iMKU8mRiQ3hnwlkaOzu9p/zGOQl7wPnocH7AhtsEBEp6v49984+++FTv5/IN335mBBrf2ZeAOHrSiQR3k3OVz6SNPnOa6lQQaXqTyqt1VfeLeez4Tdcrb5VykXxp2OYuP5z9/mkBD6d4BJoVNZrIRHdqQv1ygeciHMZ0F2AITXcZemzHDhscVML6Lmk3LYBaPSauBoXiB709BAL9CE5a1fPuvJt4S93/+83F538sLgECQ0pBzpQURvluU54hLBQja3Z4VJBpL5gpDVKWsY2FWx713z9KA7Cvf/ffowvj/51vfiDtuvy1atGuHst+fR0QaBEjnUHBVyL3OpWuZ4bHrvotvydU57TmifTZMsamMPRp+99wLMenF1+KBCTfFrRNo2IjSr4ahcxZjwiyXAQtLaS3tSSYSTGDmFHoBT+aDbjK5kf/qBBuMpO3AIXFP+c8e+02U1amBszsOx2AoJwi4Pxjnm4yOgJRReoGZcTRoBZZStF9g9el7KmSjrxp4BwC/yspKOqnPi9888mg8xL6xkZTvdyynJAoDlS8UoMkVlDKNiJzzJSEwSOLDPdQ6FwJtRnbJDvJGrF+zNhbQMfzVt9+OwwQUvkCVyjXsjWqNAV2NUq1zZN2smLARTZ684ZoxL4Xb6+T+ZsYXvFx6M0eW99cuq5+RxA/ZUjJ77tz42S8ej7twRj/7CXo0dOyURpugpEJ2LXExk1ZF1qXQ/PIr6VZGR2ky13TUmX8NSjKrQP8tGFpv0PDp6Ud/EPN2R/zgH/4W2o+ONs1bZMBIxWWzMPnSqKv8o1KWZ3TiJRFELgw7gNX1lR+rEfhSuX2wZEk8Avj98o0p0ZmP/uev/zsrGgw0CKyOm4YEitLvErbO0m9UT6GA5XfGsyO0kXmBtmp1jjYlyGMp2TszZ8b3v/JwzCXrfd/okZSLEWigUgUrGaXJ+Mw3LSLHdrLwhXvz7H9xlO9XbtuSZYpmoH87qXBQNJTKIFk65IjdCX6XA5hhlpH6qrOrAcaf82/1mCa/xhEcYj/vw+/UZElqcelaXJOYTWziPZ3fFnzeQfw8Jz3m44FP3xXXw5f9qIbp1LYtkW2OakSuDMa4tUm58j00JQ4Sa84F5W8DSSrPpDu0kkZmq+chT//ywx/GgrXrUZp3xO3sbx7I/mOP+jwFT2aggXXUEMptJSoLgw9KscooaWfQwSoV6I4hrUNgyfrufXvjzVkz4+Hv/ls8eNO47NEwYMAgjrXrkPM7RRDpKBi1eQ8BiTmz4ps//F7eY1lTZBI5qsJ6QZp8Qp58SAZ/TiOYHzyeXlrVYy1YcpcuaVoXuqH/oAVGJ8Tew+csemjh9yG6cZsT2mC+8p6PO8eMooTz6ujRoTxaNmmaZcf/Tdlr42b14547boseXTqnsVGrOtuhTpHZ5f6LbST2AWCRGMvKHZ1pZTdpLi8hexqo7iGtRlbkAKXY71E59c6MmfHU62/EVwl83TxieHQqL2crVKPEGktffWTJODioIcetpPyafZD+F3NsDDLW1ZuvS8YeDzPWrlwZr7/xekz6/a/iUPWG8fff+GZcR5+Apo1ssEZWifW7gFEhluf2I+Ytvrl1TufZLUfuHXWvtDij0WLpaF3mdob1MnD32GO/jElzl+K03MgpNH+eW5HMPIkFGuaJBTiMztNeJQZFSpU1Xkt4UC8pbwbADDRUAyv3E9yZ9Nqr8fXvfi+OMK1/pkfD3Z+4E8eoVW4bQaFFDQwVQuDJ69wAY7ju9vHhOtIIGmTmEixNYw5M81jTujhe69eui//6xS/jZ5zI4eOFn/8ELLuRM9jRMchRKaBucFb95F5t18LAUXWN/5Qt5s96SivpVJVqBnFM2mic/+u/fTdm0Qzy3hFD4gsP/gWBhgFpgIqR0kdZFSer4qwr8AbCXVuD644nlrHgwCfX4fZgXQIalHqTMXrNxm/f/MfEmW/99cNxB8GA7p06Z4WKlSpWzajfrFZzm44nv0h7y+htOpsAgwznfXgdrweIea31W7fE72ig9m+/eTz+/M5xMXb02GxkaTZNA17aV7fCSQFX7qGBPGNgWudXejlfK2BcUw1b+ddghN3aP1ixMn7zhz/GYfD0NrKYVjRcTkWDzlme0ONYzM1tQTWltYsJD+ZDbIbP7Zmj0amcqWMl3GECotu2b0va//wnP4nbcBZvvP56mrd2j2Y4LtLSbWQunXIp76lT1a0+CseDtRQ7ma8ZNv/u1gwdmi1bOXpyxXKCPDNi6ntz4ssPfzGup9rRLSv1kSn10blLa6tB7RqkPpUG/sd8deTOcTkzpB/JK3hgufkKxp4ze0488dSvOW1pVDz8mXvj8m7dsumpxngG5Jiz936R+ZvVdQkyqcF7OozagIWtgM7lfq0Wrc74h3btyq0Tr1GS/JulH8a/YtfcSxNtjxaVZ30o5+ooHc6VFQQannk6fo0Np44D1TL4ryrirnJJQIRoyH3Ub8K2Pfj5IILqKvmZxgxZH5NHn+UgEAbXJF43RiECKwSTaGrKEjq2GO4KeG2DDPRqzs83QhFeP2wEgdGWWdHilqDp06bFQI66/Rv2lA8mcGdG1+xuqcJXXEv9xL1bdeo9SX2zxNWRT6t6S72/xB3x4BCO6qv0aPjc1/6OK7MV6b574v57741OXboiJ4Q/sA0MwCbawqMyzhl0oskr+4kZ/JOG0lvb16oVKxm0BeVPM7RPPkug4ZHHYxjx4y/87b/GTVQPN7uM5qoG1biGmKluzQoe1tgKMG2zAuFzWhAJYsv/KhjvD14yqGGg4ftg2cvwzl996tYYj0wNJABWD9kxYWgQMwO/MItHkxq08tQmgIsFA6cVBB7F9i8wnupksefIiWOxg8z9DOzV7/zDd2LE1QNIGI7l5Inrol3nLihNnGa2Iyt/8rqBgJI97ZgZaGCc7GsD3X1PuZNm0tCtE+s3VMZr06fHK/DaAw89FEMGDoyOHQm0Uz2VTAHdtUUd2/HEeWnjdQy6SIs8lhyaq1f8TFUw6CzytGnTpjwZ6Q+vvpa64D62QV9OYqZZs6J6OO+bW1c25RUTjuKUW4icXwZ3uIKVQwYHDeqY8GzUpEn6CUsXvx8z6T31X799Ov7q/s/FnTfdiC3WjmB1sd1ZjNf+zaC068bcIUYmErW/7BshhokDVgQYAHP7n1UyayrWZUXDpElP0ednfzz23e/E2OuGRYum9G9SX2hLoDMyuSwGu5bcgzyovZErytjypXyeATYubxWVVQPvUXH33B+fiZ+/Ss8TglLf+cbfxiASS45zDnnKrRPO3YHEFPhFGHYLp3xplRRRzPRpzmNrl/oH1SbQcIxAw6tvvRVf/+o3Ygff+SI9Gu6/926qYLpk0Etb8sIp9B86xEC1J61pfxgsOs61N2zeGK+x3aVNn16ctPipaFhenvwurZzLx/nxJwo0JNfCtBh9KF8dtwQbIBaVm2eOe+rE5vXrowlH4lnR0Ldb9zQ67G4ueNgFthEd+WW+qQQazLqugrJfvm18VjTY1duofO690sDl6ffy6TrwWdlMw1+l7+JYvmzVQc4F4TTKupvMx6TJb8U3/uNHAZzGl/7+63HbbXQhRvEcQWErZI6VjIaQKzA6U6XfZfCMpLHwZlwyg1yDoIaROZTeusqN8XvA9UWaQX524rjMXAy9djDZqroovT04jhhDCJQOkSClQSRPCyACuCU5locpFIK3TVLs1eD+2z3798ZClOUvn/gtpbMnYvy48XHDsOtpREbHc8BUg0PDQJAX2HwWDCoQFoab4JhRXK6qQWH2QeBxf9a2bVvJrM6nKdOv4yECPVY0tCHy7xFDGvqlfV0qdOdrliGDOIyRmSiuZ7BAA8DrCH6eZS0grlu7Nrc3vAkfHGcun6E8UgFv27Ztdu52/28RaNCBK4ILZhAEQH/3+hIqDQrWVuG3HLAaStRI4loCPO+STXv0N3SuHn0b+zzvxBDqkApZh0g+YSSE25/hSnhVQ9R7L0rgbMSpgQCYy0/ck3uwjOpuALjfJkM66alfxdztp+I7X/sruuSPzKyIUVYNWTOkUDt5w1evUTyLeftWrgVzl2aZmSHwpZG6mNLR39Kg5lcEGnqji/7pBz/O5nVmW1WWGhUq47qZEWWLDLQ6zvUcz6CS/KGxepJqCUulgdy8hsaERu7MOXPjv/7pqzFr77m4Z8R18dDnPx9XYISaXTyHRWSG2uh8Gq6sYbE3j/njsBzh+5t37ohla91Tvyh+dykTinkZzbCWGjDf2pCUW4L/vUcwnp91cj3Kx2qAs6eVbwytuvK6Rgl0Rj9kBQSfhWUpkcVRZK/y+Qtn4/BJtg9hfZ3GSz56hm1DQMQhx+bxhU/ekYEG8aB9mzYpS2YCDSqk48v6ytMMlO8lzzC2MmYE2vWUVw1AHiZTtIDI/49+/otYvm59PPCJW2MC3eCtaDBooSHkuI6lYnANzqDkLLuUWaqiKCwPFO+KY+f4bPKmK1B0En6LLOCXCGp+6Q5KO+nP0Kf35dGGEzkAOrI6JzMLvYVS6mnz58Y//ezHeY9NMWCrHD8bDbjnpmXuV6Ur/wUqYTiVQlpxd8wP/oTYp1FeDEUFGU/tD+nPs4a8Ad+4JUZHX5r7uVp1NfgpQ+ce9mMIW8lneNLH+BGDOUrKCqkOeTrQ7r374leP/y7alLekUzsOXVcCDTgWNavVJNCFMcEaqizFFhsF+tDYTfmF1sqWcitWaDCZVa+N4btj27aYP98eCtPixekz46+/+HDciNOl8V+/QYN0mj2eTIfCbI1OtNggVoqb3rdBQ9/XGLNU0uxFk+aXYSzWzm0Zb0x+PV5+5tE4Uq1R/N3ffoMMLEdkYYBmRoJ5+f1SQFls16AWt3Sei4o4ZA8811gvAudmSBtCx5MxA/30+G9+nRUNd4wdFX/5eXo0IE/Ku9ldcRJkjwZkhJNnwHwNagMwYlpiGeOqq+RJjRUNMAODO/fuiZcJvvwbpyJtg57f+eqXM9DQmt4Yh8R35RU8q8VTfmeZc46OocEinjhf8avgxYLv6xHEl7ar1q6Ln/+KoOazL7pc8fSPfhA3j2HrBLTJ8aEpmiMDMsqAe/atCqgDD3rWuvP3/qS5zrnVdj51Jq0ym7docfz0pz+Jd1ZXxGdHXx9347gMvOqqlMmsRsTIkt4GYqSG1QbKmLpKA9o1Vwcq7gbb81Wm5572s83ojWkz4mFOcRF/vvTFB+KW0WOjR6fOGdwRhw2uW2btQ750z7IGYqkp8FkEwYZcZrzFToGryLZfIKC6LZ56iV45TzwZX7oHnBk6jCbUV0RzjNxMEPA9t6/ZJyerv+RL8NZqKYM78o22hpnyDDSwFmK9jX891cmtE0/88bk4tG1zTCRDei09Gnr26JHN63SMxBn5U9yXbxAgaFPck7RxTLvuq6dcY2mmjFlCvW/fXoKm78fP/+uncTOO9FjkqVvXrlkxqPzZ+8itiOpT5cantPKZMiTPgo3q7JL+1bC33HkHzvqairXxFs7FlHkz4st/+SUqhAaxnad97st2y8fJo8yfuWZJM2OU5it/V2Hurp/HgJuNTppxD/UJrFnqrW3wHiXNT73wTPTo2Dceuu/e6NO5a+KcVUdFgI6qHXgcyMmxYaG8B/WJuOCcpRf/wFUua7H/fDdzf48A+Ttvvhm/mvdufPsLX4i7J4yPcrDGwJDjaVtZNu92wdXrK+IZsvCPPj8pg1kGccXbDvgWTRuSSABnlFqz0zVxrLQjTuCYcrhafk4dV7cOpx7xxmHk/gwXQDToN8GWkZr1sYWOIk/yP1hMhJ3VpUICXXHsfGBaZLChBXr1uqHDoy2nM7gd9iABwCXsKe8+qF/8JXbZ1QTCTTxYpWkFgPJjI1GxUdnUTs7ALzRoSHDUSi9prn1Q2GzVsOka0oDueB4/eT/N63z83X2fjvtIANlE2/J3bVUxzaCNPXKkr02DHV8b26ov5VWb/Sg0EBOsplWmXITKzVvimRdfjp8++TQ9GprEPQ/aDHIUR/U25ahesvHiInNXtxqElnfEA20eg4/ypg3Z5UvXVGzWPuNGM/D7werV8YNfEGiYRfCLQMM4KpMH9B+QJytox5tZN1FjkMHqmNSH3I/YLyYUNmyB8QaZbNRtoPMIp1lZWTYLnvz+v3w/xgwfFmNGjMjmrR3LyxO/s+kxPKcMiucf6TlopAxpS5Xe8zryp7rMh0fXerzl69NnxFv05noAnrTvRqtWrfMkiWJMk15UQ/Cf2K1eNdmYlUk5CiTWlpTvefgqnZTzHdu3xzKw5lkC1jWg32ewPfqCM540ktuhUk6KQZT93C4OP6mfEZ+8jgR3fbN6BFw3WOWRqAZMPXVi3vwF8ctHnojPf+6zGWhoT+WUVWTqVCsMHEecF4v1R6SB/k2hS4tAoXyj3ig91bdrkb9ZBPJfefnpWLjxSDz63e/E6GEciYrdkVv6mLvB8CIRjB+ivoVnMijN3xIBGFMc1sYu+toUQRN9waUffhiTSUL8+PlX4kZsnX/42lfpN3NFBnE8wljbQn8vfQNoKg3UQfpHYqYNq2sRkDnHWKdIVOmncdPRsElZ9hCaPHUqwYt/jI1856F776K56qeiW6dOhZ7DVrsIIBhoSDtG+wwdLqbbDLJy8yaaWJNs7H9V/BnVRU06lKfMZaDt0joXq/bx+/d/H2jgnt0u4SJIizRy9DRcMJ6nz5+KtQDCAphn9YoVcZxj2fp06Rad2renhAQDFSWsglVxN0TxuKgLyPB+65FHkprXtW8Vd33ms5x+0Dmjt57tKzMUDFEoFSPeZsJzAsqdwg/LpUJN5QQDyjSAyH4U/psI+PNTZ+T4908YG8NHsG/MI3cEJ0DO6FgqLRhXcJIpvJd0HHnPufKS1zD7bYOj7KbMiJsQ8ieImK3ZtDuua9Moxk74s7gSY8WIrQ3ULEXz+yp9HSJBPJUy9LMhiIaJzV6sZkhlAS0tNVRRHEFDLVuzJn76+2dz7veMHsYZv1dH5/KOZIVhWJSDAs3UUruVmkgJXL4vUAtIXsP1EQ2cR2GsHMuyKLPWz85bEPcQRfT8Wrsf22dB0My9cozlGAmu0geBds1dCasnpLn0K4wYHTEywwjlTuhiU9DXX38zt8T89Z23YZz3IILLkTsoMUtZVTqlh9HJBE+UkGur4GcTNQnP9QUv92HZrfnwJSF9n/LOF6fNj6s6tSXQcFe0A/x0tHK/FcrFuxagWK5ijfndtciMySWeURHzZt6TClMQ3LpjB8bKAvakTcvp3TZmeIwcMjj37hp0kn8tcZVHLCeXsi6C1/O95EPuTdDNII/04wOWH2o0rqmoiFdf+GNM27A9x/+b+z9DJ1z3jRXGhGPIhxqgOheOp8x5MoRGnAaujkVmLYiy6mx4TzbJ8iixRcjT9x57Msfuxb9//n++TLVHxwwyuF+xJiXY8mDBN8iSP8N3F3k1mr0D52fdpo2xEoNwxoJFOU5z/m1GgLlTeb3o2Ko5Thzd1U9jnGDMSPMaVSl/rEFGF3JaGl31Is2XyBhUBcXPXsDAMAhRg6NP0Ukn2b6hhDXUeCHgcRGa7qfEbO12GiVuPpjG4C7TSDxuHnZNdlAuBz+akXUvBWM0tjQeLKm367Q8mPuNec/1UAV5fzp03p8BUXuhfAg2ff+J3zt0jOndMYaPHkfJcdfMuGrk+yyVd1pOp+B7nVw47jOj9vInvK/J4sXySrwa6Jz57sJ4jK1aQ9q0ixEEp7p37Y6y4hhGlM0p7tEmdLsPHSA7syJ+9/orOY9Gxkmx8bq1qh89WacmDZQPsobsAVbSqnGtqsiyBnCNrKCwZwlBI65vSaxBW6M+zv0syi2Dr1bnsM7UGlEhcjZ2Yeht3b8n9u6P2G7ajsfgy7tEHwyS1uzlbEQF1h4CDf/2+DPRm7jIXXd+jqP42tMVvwwniJLKkywIETl5U/zQqUv+hrbSSKwRIxJLGVtZLjWv8/i45R9+ELPIGs5jUe66bmgGNZsTKHDPrEEJt0woKxovOjs60blVAyOOgbMqQbzQwFBmNRQbYDx7nJf7X2fNmh7PzFqQ9/W1+z4d/SmTzABxSS7zFedWLBHT+KRzdSwrX0RRVjmxRoNFw0WDXWd6yQcfxtO/eDRxzJ0rf/e1L0fXjuUFX7Cu9vfQ4MrSUcfWuOW9LE+XVvIV11D2vYZ08ogsAwE2K546Y0Y88tpk/hI0mxwZE8fdCK83TuMcIc9966KMciY2KvvynHPXGXX+GXAQ22EF+b44dahONpx66umn2P+6Icf/yj2fpEz72tw64UkHJV7/aP+oNAYjxHGdikIPFMKooVtgJFjBj+qoDzBw/+XRx3PsAU2rxW33PBi9CMJkPyLGEXtLhr76VPqXHEV5RGxXVzue9+TcxUqNuUPg/EyyjI+9UMjJxOsHEWgfER3JpnHzGSzK7/MdedJHDgmtxH0/cx7nRtmzs7r3ZJbdTPk5gGobZfaT3pgc0z5cHiP6daOagVOXOneNJgTaLcOuKo8zrEF9sb30kFfU19JCfej85Uv1iHpQutj4dfX69fH9xwusuZtqj35sp+rUsTwDvQZ0pIu0kBv9L+0Dvu+jtLbKlbQpbJ6Cb93yoyG8mO1Iv3rjzRjTrUuMGj48yss5QQbnwjUrnAntJALJrGlmpbErrFTRedH28G86K+pECeern9vPltDNW7fE21Peivd27o/7b785G6g1b4ptQIDqnNhj8BX6uJ4GX3NLDz8716QP92AW2gyjeso19ZhjjeytW7ZwCs0H8fTkGXmv//6lh6ObSQJpAOZKQ2mqI6vjIH7Le7nGrDM/wUpiEDTnZ/WmGCj99pFYWsbY0wiQL+Svd1x7TdzKFrEWYM1ZvqP8KTOOe4HveCLWOzS6e2vuAovpslKBlxjWqU6Uk4lvUhe9xpKoA07Jz9xfBrYZwyCj+kA6ag9mnQT3nHYYglgNoeTj+ayGfuTu2IpFvwHWfvvB3ei7iG1MCLGJvn17ZAXWcdZ1xwerY5mT4PGdLz9EYqlH8rMOlvMwmC/eeB1pkjgmLaCRNobB9WKrE06ZfMmkxRqDptPnzmXLSlHddDMl27fddnt0bNM2AxxWSzhfx1b2vdfsp8L1MtAlb3qvjJkVFOCOmO11XZ/tO3fFC2xFmrXUtrZkeOmjMHTwYI5xLcsG5dp84oHJO7FdO1taatukPPAdkyBpo7rGXFf+50Np+62qrIyv/2dxrPboq7rF8FEEHXt0T572++KWVW/aFkXzWfgCfhKLDSwX/gFr7+QMIKE7rSwzObP34H76oX0YT748ObSZbrxjYvTp1ScTb+o3bfNS5Y+4lTLjONDI+SqfXiPtcT7vZ6Sh63MCHDN4N2XGzHhj2fL43Mjh9N+gYTx6N4O5fNbP+X0fnoyiDHDBtFuS1vysreP78r60cYESC+gHs2FjZTzx/IvIG0eO33N3OrsenakOThsduqs75BV/l2ez9wPvG9RXPylnyq5VfQZSrY7xKM2N6zewVXlhvPLukhh75eUxYfTo3NpQE9ppI8mTJjz5ao6btjpzc7FL2J5bWf0McxA39UnEuE1btzL2u9lo2Xt/+BPwzMABufUtcZExxZkSRnID0EX65uW4lyLhq86WR9WDJg7zmFH4yoTk9ClvxtTVGx0+vo8t7LZHgxdWjBjgVVeYdHBbk/aINraJUOlko0x7WmSwHRzLigZ4xy3WR/iM/Zt+ckme+vfpEvd+4hPRoW2bDIgYZGABU/+I8q6dWKnt5BZNt6rOZov26PvupS/hp6JJ2w7cFPcnU3mDH+PHnyTQIPhoGKqk8phCCKP4aiacwvmoYHEXsR/3A8qUV1J2U5NIbIvLWrBnimYsRKyTmBA7969CWPfwb6O7p0rOI1PaoBQakZ3VcNZRdy+NZ8Tb9MnvyPwnOIvePa7VEUpBQEEvM/sGeJhR0NkVDM4wxn4iuUcxODySqS4VA/UAYzM2Gh4uqFE+l9UeA4JKlhIxF4VUgdRQFEgFjvw8Tov871yOE6jYwV7Xk7yW4Tg1YNy60EXDxvHy+8y9ZIALqgK6xrRgYZbF6/iq4a6BbVO0FHyE4Bifdf5WRTh+PWnO+5bOSf+Psovch+BmFPQ4e1uNzAkUCdZcq5rAzuf9jKCmYWQZ4AGCFQf5TgNo0QRDoCEKSTqXAEl6+HmZ3zlJ12K7TGGI5jEzGluXAMyyLKP+RmTNlO9kr6rObzv26elYWm7rmHXp3VEAcQF8CrrHip7CGXRNLSlzvp5CIXi7DUYFKpCYxT+MkXyI51HGdv9v22ZNaf6FgmUcHZc0ZnPW/MPvec9GQ42Qs6YFvTXYuTOtSOiSxotKlPX2SEF55jAKrIwysstwaqR74UCoaDDSoJlKRsNNZeLxfwmyjK8zYFfgbMbDnMzY+jcN05PQ6DBreoixNX6ce3Oym04jnRJ4zyBPOtPySF6H7Drva3ypBKSFRp0P5c4fawi0jLcfp3I/+5e9uQbQrQVzLzO7y7zEMdfHtc/xeEPZcVuKhhfUixNc5xDz3YZyrNy+N6/RjgtcxrNvn6bRq1MbaEI3YxS0jU0NClUnkFCnJqe+cE3XsQr7RRE1+AC6OkNod5ES/NMY/cc5+cLPNURWawPkNWgCtufYqVhcsTnmL9+FAUCmBztWf7h7s9qUlLZljelc7ufJVAj8XlOeURl772Z6xCUrPSwJVO7SuYMeyoDr7PMgdN8NbQxYlMHvdRmzFmuiwaNsFB8s+EU+yvJFnI4aZPUNijiuvKISxdXhGqpmMp/OifcdewcZqTp8zmMjG3GEZNHkzCoFghHw914ytVv27opFO7YyI7ak8NTI7depeVzVg4ZuZWT52f9QBBrASXjSBldya22JyuWPsj//LNUgtck8aGQXipMsN+taFQLa4LAq8z0NkxzAKdiy/3Cs37WFBrP0T6FixEf3VlVRil3z2LoMXIKzu8miX6B0oUkZZ4xTESaP16IiovpFrsu+CytiNK7lYwM7x6kScWuZhqz8LR7pdCi7xRqRbUOW7IR+FIxUXsXIxtAeritkG+VeC8yR+GKx8mSJuuvqukj37PQNTTXdvY4Gi1vZNJJOIE8+zShaxt6assam6A8frr3fUhcU68tqId8adYlrriVjKa8aAuK8BqX4aKBTGTnBPR+Dv05wv86nKVug6qNDxDGzrxojztXA8imMD7eQMVLeg2PWx9DWCHKuYpnYI7/41HnZgXO0B7prjDVm7Vsy/zrwpbT0GkXgwh+ZOxgjjfxZOhgwFAvkSecrc3gPXkeZOMY1jxDgEtPUV2JZIwK9yTPipGPwNFMkPcUagz0eWXiMrQvyuzLqcc1mOT1OzGy917A79yE+txenWtyqw/00BWsaEqh2a6QPcdKf1AuOn44772n0OlfnqcFZzAN85W3lVEPUgMBuxt59lGM4+XxTcL514ybokTrFfMFym3d5zCQD5jqKheoi9WxV3s4SagJupapEe1icwpBWao/zmd3gtnjXkMBcA9alNrTwOzpB8prBraR18l6xBtLfxoSuqU3sNNClvWWxyoUYrGwcRq9KG3mtDH5pAsZ7OouGrWPk2nINdZbj6YQpbxrtNkM0oGwSQhzS6HXsQuejn5Cl/cx9L8+G6Ppm8LvVdqVjoeV118i5m41VP7m2aWvAs/7NOTquOkpeFE9zawj0Vy9pl8n37Vq2SNq7NdXSdAPVzqMU0E8jnM+ZkXXtasInBuqUHelseb3yZbBXu/GU8gomHcEuVJ5awC9lfMeKGrklkxBcvwrXQF2kvSHvOL7l6I0JOHNnZP73J21NWLleBs2UsUNgvPrVdW1K0KgNGXV5wiqQKtCzjN/dbqtDshdeXr56TazbsQcH2CAEN8Bj/OXNonvrZtEcPVcXuPEej6Ov1fe1kU1lUswVX9QuVm9Y4u8JROmEMtBFmhIZpBW7qqIfz2O8HKWUeh9z27Rve3xYeTb4P/G/MSLcmIhzo8at8nSEU2IUY7uuZehw5+9a2iRX2ksj9azNG/2bFbQ6/WKndK6J7Sm/iCHqQelscGyvtsERjp2FBxugO8RhdaA85iP12yWay4cZcIDarpty6jpmQBg6+lntWOVYu1ss2w6WHYLna/G5xqxLU3i+JjSRX8UC/QWvJZ/J/66b8uG8vS+TUOoNtzFqQ4o50tfstHbqHmjn7w0ZR5wx++842tTayGIXS5EY7zGWZ7BvvXf1i1gpZmZQgLUvAnDMX/2JPBzks/spp7ThtLZ2GbhnA1txU5nWqZfuDeAp+0JoZ1v9ZUVD+gqMLQYoW85J2fLpfRv42Q3tD3BfjRlTDLb6t2TLSGvvV/ySNvKbmKgcW6HoOjSUlpfWuNiyjLyxVmKE+nU7dodBiRZgjLZN+mb8Xfl3m3ke7QpPGFR2PQ8ToHANywhOShftp9yqJD9AQ+/V8bQptIUPMa8G8FULnOx63IPjyOuul7zpPYgpyqrzThsbjNRe0EGXV0wMSCsrrbyGwa+D8MthaK+Oak1wpDl8w4Szgsdxsso5Jb7AdvmHt2G7AvO5CeYPcvAqjxp4FwcNchyh8uoAMn4cGnqvTcEP11Zek1cMvHoN+U8/RR18CQJYQy7B7+Jc8pWYyT2kPIGX4ocVwNrxzt9rtm7WJG0Dj8a131ZuBWO+8qdBkZwjUzcgdOT40XidIPfP/uN78el77o7GLdvxrlfPm+P14/v4kwQaZFrBwcUthAO+gECS5/Q5SuPWrIl358yJlUTc927bHu0vaxmW2hjTLe2t1tDRKRZALH+tDpjCtcmwGglZXunnZSYWyKyCginDrl67Jpa9Py8aNW8VrSjna86+xNxLA1NoHBg18nESo95FK6OpSQojgpLl0ZfmL3jIZAqZoKzCPQwYLF68KDZ9MC86Xzk4WjN+U5xky6A0igpHE+BREGBMneK6OEEaHc5bRzr3R3JlBT1LrWFwAUSFsGPnzlg8b24CRw/6UFyWFQQYK9BD51LDJrMYzo3vebazYKtweb2kzaX5+14aaACo4O811q1fH9Of/mP0uH5I9OrdO0FQGqjsBSmVl/chMDi+Tpkg5pGYxwFNBc8giUKZ4ME1is+7//V4LF++PNYvXxRDb7kjOnfsmNeXH/y+xpMGqcZAAg6/a2gIvgZ2NBbNtPg3wbswLi3lpNQbAN5DhG/ZwgU0JWwSQ0eMIDh1GQCggCKWzFcQScXGmgliKlMDKv4t+3MwtoDhvBNUWROvLfA5P0vMls+eEt2G3BBXs+e0LvM8jDFt9FJwlw71WUtppREtoBj4ycoLeBDipTGYPAN9EgB5Nbu0fdtWTjLYEPu3b4y+14+Jbt27Mz5lWICwc9IAUrkLcKKk86qXQZ3auf/PfZmO698FYEHbz+jw2EzIue9jH2EV7rl7n77Rmi0urpGOh5kOX1XaGqquq7S1XFjD7djhwgHyZAD5NfdXQtO6KCQV4PrKDVGxZlVWNNTFCGvepm0GdJatWh3T5rzLbCI6I4Zt0ek9OzWKbu1aEHEuAnyWNppFN5XqXn4d1upYzAYazp8zVFA4EVZLnDMzjrHlenNjONDKCE4mDsR+mjIs3bQzltAQ6ATxgy2Q28ewrh1yS0yn8o7Rkj2sKnUVoEaDMmumYAnNZA/v3BQtOveM8i5dow3zV/akvbyp0k7lBC00lFxbo/EqSmXS4JMliJ7WorzaxFBjwmusXVcRs196Ptp17RtdWVPLCbMkkozoORwWg27nuVcNBqPhNdOg0RDkbxhXZ7kvHZ6yeo3yentR7pu2b42Vmyrj9SWXqkWgbR1o0bN9o+jdiX4JjQiUoIqqunWCa1RlraqzTpohGk0XoW/RmwNc5D7SqVPZ81SZAUYEXDBOWevzrMcBHOVNezntYtc2MjcRFQQa5LF+XTCmu3Qn2NCOPZE0jmId5B3HP09FyhmqKjRmyVdSisrf0KxuI8mqD0bYTuXPPDKH3dieM4ieCJbDapRIcx1PH/Ko2KgzZkBCnNLIOYm8p1zzmRIO+DeNNLNTWW4KX1Yufz8uHzIyunVjDzdrmg8mr0xqGImZYkAGPFkDZegkSv8Ux1zpWBW4qDxZjSX+FceN7dy2LTZXrM0A2IAhw9jG0TZ5pHCaCpl3LOdmhkNnPvfcMgEDCqcxMOQD5dW5pG7gXpXzfVRw7Nq2NXZtXB/NO3aNK/sPiCZUKJzgvgzyKdcMnVicsso6yZeuirJssFVHvK7rAWYY3JUuXv8QgawKml9uWb08+bbnoKF5JLGGrwZl6mXmIf11HKW99DHoreyIcwYQnK/0UIaSLmIdn9+7Zy84VkkflL3Jy+07dSm2u6GDdLLEeMcoYVUNxvX7+T7rIRbrBHqfXqPQrQZVig7slZWVsWrhu2Bfw2gF3zVv2Sq358jX2g+ZHUMmJZBZKgQ1HU7vX+cOgWM7SVENp/5GIDIr6f5iA58rVq6MTetWRYfO3aI1eFFGsK8eAVCNRedscNZqGAic37PpZ2bPuXe3Szh2FWjnujt3M55pl3BP+3Bsl1Etdmz/vuhMtrMFVZHaHdLWuatb5R+vI46oozI4zvvqbtfX+/xIB/NOyhvvS6ut8OQH8+dE685d0d190t6RnlmppJHOd+U3x5c+Yk0GIviMAVHxyypH9VLqxkuftW/FfhzAzazr7i2bog+ngJWXl6eOUX60L5I3tF2ggdcwYJuVBOppbTDkJku5cTi8V+eS/Z6Yh3KYp2y8/34cZVvjqFsmsF2sFQmhwzhnp/L0L2UxHTXo49xt/mf1hcFvAwYG6Mxme28mjuQit13p6OwC47du3hQb3p9Ph/ahMZgseUNkYx96Ub1f0nPnwWL1vnJjYiUDhtBN2qtz7ClQjfuysgJFmdsntoBh0+fMjblLSjUE3ByPCX3qRq/WLXFMkHusGBYoeVx7NHWs/gDvyYM6vvaiMNBgRWSujxELAg0GTwxyGWQ4xe/HeR6Cxpv2b4mFa9BzDOO2IE5ej+69e8SAAYPZ5tEhvyNWGfA7raMID8krOvSuj7wpnTZt3BSVq1fl1oYm2JLNeDZEtuQ1A1fir/NRrsSCOtgArq3vyasn4A2Dh66nMpEBDe5BeTVhtwme2UXvBTGkJXq1LU0ArVDwIe65pmKOTlZNt3OA0dXQ8wyfdNcZ9zrqUufvw6CWvLPPk1mWvB/7d2yhZ1rHaFfeMSsI5Ldj2OHymbYot5FBgTqsa330sjh1BJvG67sWpXvM4AtrrUxqx22icmb6Sy+wnaRp9Lj8CmhzWQbEnYN4JU3TDpbHGcdKYuU25ZO/64NoV+UE+I6f92HFL6scK8Hhd1/leMJBw6JzF45WJwiqTjDQo/53HOcurcVI+dm1K3QMeMN9qCsdy4dz8V5qYzdK+yUkandUro+BbJd2S7N0c0DlXN3pmNkXDblx/ukkO8dLdo++ggvhWjq2eldZlW42aF447c1o2qFLXD14CEHQRh/Rw/UUj/yctpKBAbd3q7/922mCot6DD2ee/O6C87921EHWdQ8yewB7tU3HTtGhY8fEF+ec2MJ8/L52sRgjVnkNg/bnec/gUNKeOTusfOn96d+IMzu3b4sdWzfn93v1HxjtqbxUN0lvH96r4/u7cuK8DdCJi27Fd/6ZDGBME2XyQp5Gwvc8Hncr1Vz78EUaNGzMKXx9spmpnOucnLPrq7zYZ8KeetnbiL+nram/wrqqx62maYB/5dwMtNsIV/9vBz2rjrK+h09wksr6zfH9n/ww7uJYzKbN23oVnklVXj++jz9JoOEjyculd/mLdzApKdU9E2tQ9nNnzqQh4GoiD2fJ0vVmHzBOKYaCRwNmYwwWyii+gYZaZBPcL2UWQ+BWuQgaCqBlUHZTt5Tc6KWld++h7GfPnRHNUPRdunaNLp07Z1m1TKAguM9LBebJBj4UIh1olZ6fcbbptMJgzr3E/HVgmj0ovbfZarFs8XvRb9DVaeC2wQhNI9rvIsSW6tn0T6H1uwKbilQn2JJEr6OT6t9k7jQsAW8fGzZtjlcpLzNSOowS1rat28DQDS4xo+VZBmxQDAoZnxd07Nbt+xqx3p80UjgUphLYeC0DFEtWrIjn//CHGIQytvu3NLOUWkcog0LMSxBRCQtqgqYK/iiOuJUi3oeKWwM3Z8y1EpC5/mE+M2vBgli0eCGC8SnO6u3JmKw5StxzhgWSxtDaqLTGqApBEPL+nZ/Rz5w3dGLY/JuKwu/5/W0o/dkEqGyMNZHjKt2Tn4LN+DreRucTmJm3QFGAbFHeqzHu/k+v6UNw9V4EBA1wAapi/YaY9vabce2o0XHLmDF0za1PN+FDBDmKfhEGYixl0+jKrATjuA4CrvOG8EmTYiW5L9aWhUsD1D14q+H3TZs2xsibxrG/76oMXDl/lY5rlYEG5uTNu8ZlXEtaSff98J1GjIZEoisXSeMcuqswV65alUeG1uY7g4cMja5sLdJ4zrG5hiAoUNdifOdneWI9lJ4Oi86J9NVREbDlUd3AMpwfz5JeSkDwvcWL0ghrQjlfO/bsm0mav3AR52IXZcs9GLQ99ku3djWiI/vHrWiwhNKs1Rlk/AINAapTHlobGtbjHqqQZTxJtvvCeTJgGgsorFMEI3R8bbxoA73z7pHj8wYaDlBmtnLnwVi+bV/Y43Qr8/Nxx7Br48pePSnx75kOsQEClZlrZBBuAwbo65PfjINUCPQkANOzV6/oQBmuBorrpsOlMY0FBZKgdLl/6SQ++Mj1RR4Moh030MB8NLw9dlFaLVxKiS/9NPr2uiIGsK+yBfSx6kcr7hz35taRcwQaNLTkR+VVY9eI9mnocppAw0UqAcrq2aG5Gj1j9sVaggwLVyyLR954NefQGpZowBQ7t64VXdu0jpZlyiRVA1QWuDewyrkzmWWxFwMERRXxH9O3ugeWyfU2U2xpvb6YCJJP7vMixt8hjFsDDet27WZfYcQ6nq7/dVfiBPfunSeS2FFe2YWlcLxwjKiWOMN1VZjVOMmhDlUSVbjgOYJK0lNlW7GRZkavvBJXcvzomOHDycaXpZwYnJPvlSF5WBkUB3SKXDdlVFqLZb7PlJk8n4X2VkN4xKVOhGeDvzd/XozhqM1B/fploCGz/MhEGkLMw2CSQUzXW/l1HHn9GDKv/JsV1tzzP68vlu2CDpUEMZYsXcraXaRRH2fJo0d06MQtdY8/yyuOoXNqUMoAlOOb0ReLld00XsB7sde/+/7GTZuiYu3aqKxYFx3YLjYS2rRGV2k8lvBJDBCDdTwNoEgjMVGdot7SsfZ4Zq9X6t5tIEhndwn6byVz10kYMnxUDCFoaiWgGC5NxRH1RRqK8Ll01ZCTP5V9g6vST/6WPvK81zPDV8nc36PB40G2TxkE6EswvEvnTtC4kKdc+xKWOS7rqTwZWFBvK0+upzwgjzCZvI57hdWJS5ctj3dotNsEo7x79+5sJegULZEpjbJ0cqC7r/JFlsMyP7c/JZ/wvk09zap7b5pkVdVrVPloCB/CqZkxd24snD83Bl1zLdvFOkdzrtOgNsc2cr8+MmAhP4Lbjut3eTfXxXHJZGSgwff8jFv1DEyYCNiGkTj57SlxZO++GMZWQ/f9l1npAW2876w2QV7E9qQ59JY+8oa2jnpbmvuaFTNSyvtMLLoYa9ZviJdffjl6gGFub/GcescqgqFFJZH8JgxID50akyNeOyshwTB1ncZ1rgvXVgZ9T3kS5z+Ed0aPG5dy7xiuufre+TqGek6eVE59TxtBXlJH+zefXi91FZ9xMsrMnr17cdjnxF5o82nKgLt36pT8qDw4T20PdbT6Sg7xvaYEteV75yaml+wO5yFdxHcrRbcRZF9XUZFVsj3op3MLzeguI0hxgC0eBhHMNooi6lhL8rXVEgt4V9skAxzgJjeTQSuzqm4V1FVaB4a9SEO2l6ZM57f/eUzsGQQaWkfjGuhkcBCIZkqFTeSaiLcun/ciH3piko3CxWTfB1jgI+4T28q1OMPPx2moe5IPHIEfrGiYuzZiFx9twndao1sHXj0sbhp9c/TmhDarg9TpJmbSmYfmybPQSx5IWUbeVmAXLGb7q3q+LUkxG4K2bN4ivyuGuX6uVW6R4rtWWfpd1006i5OuizwjfZRRr2Mw5ggVowvfXxwrPlhKoFR57Zk9Rprxc9pErKV4JQa7jt6n6ypmwiSJB66r/J84KlnABBM52lnbcUbfeOttmsavi75XXhU9sSc7lpenf3Ccqgv53uSJBJUHxT/7h3lNj0lVtxso9drep7zlvaifrbBbSX+aP3KiSDP4bNAATiFiPd1OID201eVj1yrv+ZI8SZv0DaBt6hgxh/n7WZfV65jw9J4WLF4cr0+aFENoxHpFnz5JI+/f76Wc8Pl88F3lXgxUrrKiFKyR/tIxMYAPOr4yWSTAjnB04rRsqn77HbdHV+giTji39AN4da7yuY6sNMiKVcbQRpbvTYKUcMJ5qJPlJV83b92W/QFaEzi66YZR0YIKYuctzdPHuPSzcqsON3Hj9bxvK66de9KP+SaPiZHQ1e3eBjE2oF+tbO+LzdTviivynsSQ1MmMKX1cR393XAPl3ov9OkwUOId8MD6GU9qJ0mcnwQvHXr1mDf7IkRg5ekxu/9QPUTc5B7FAXnAMscx+ZAUGYVtgd+ifaWfL8/5uxWVD+EI51p5Upjas5yADgnY3XD8it/0rP1bXS3vpmwF6ZNQAt8ke19DAoLaw9oPXz0AD1brOwQSQgbW1zr1iHXbYUarJD8fOzR/Gg9/6IU3J7yQQ1oYZOAvuOZ+8fEwff6JAg3evYoX4EP0jAKRgFJUUq1cbaJgRGysqKCGuHkP7D4reGHKYM3Tk1lmHZQA0GQ+2LvZRo9BdeI26jD7LyACYWYwaMI97aRRUja7ZZDCnTHuLaoZ20atnrzzWqTmCkqDHggru7jMzggeKZHRYYcgIIoLkz2KMpXgq/YJpiIQDrrv374u3pr4Tq9lD3Q9w6tqdzAgOb12NSBhUQ8TiLIMfzk/DWANWQVcQ3I+YjhzXUSHIgL4v0NTgHisqN8ZzL7yQDqFdqNsZaAAUVZAKulFoZUtx0UgQPBSeEnj5R+ldKvsSuDQi/azXff/DD+N1jmoaSGPHa8kyNiC6nWVF/D2BgbkIPs5JdlYILbnPEikEyfcK+vAJaOPPKlb/N9Awc+48GsPMjYm33RZX9qFigntXAQueKgOVvgZ6AjPf/+iVv5WuySX4bOG0CzTem5HF7Rhy70xD4TOeJ4OUt22bBoh8kmCacxF3NPygrePAR5ZK+TCiKxBrTGo8O3fpdRQDdC+G0GqM/1kzpsdVBJBuZt+m+6AFHUFPI0XaS29p4FPaeOxmloRBG+/Pz3hN+UZD2Eyk+/vc07qK8TcCVNcD3FcPHJjbW/LoIO5PXjFAZXmfBkR2X4ZW7rvT+TCT5LpKDxYzn66rDXQ8Gun9pUtiI0ZRXYzPEaNuyH3QUqAUPc29idDF4IPjO8968Gw6dtybysWggOM559yDh+LOPegYoEuXfZjll2UY5pdRKnsaoJw1f0H85plnuQrZFp4tmVr3jhFdWjdhmw1bm7iI9HZ9LqIX4FYqGtzWg2Mg6J6gC9ZFnGSbaPG3I8iOvRqy0odPV+UaLBLOcK3YSw+AVbsOx7LtByl1i9ieV4144LYJMaT/gOjTrVu0oXLJEwh0JB3DzFXFJk4IofHXseNHMFR6RVc+1w5cMLBmuZp09yldNFoMcsqfKgINZNdUzPE+5FAj7Bm8I/hpIOVdsgqT/vhs9OnZh9Mp+nEeO8anQQiU33nOMJPvz+GUM3zKmdNWHqpxnzb+MQCj9VkbR/08Dv9ejGMDDXOWLI4fPvNU3mVr2LQxH+vWrl50a9uK0kTKYy2DYNxzzJEbYVsQ+AFfeNa2M72Apet+fBk1sYLvG2iALfmrgQYwg8zSRfj3MNfdQoShgtMudu2LWG2hF48xVxM8IdMzCPr27kbzqAwUYGARODlLRYNn1dekYadHhp21RwP3onx4/JxGzFqU8eQ33ohuGIijR47Ipl+IdcrSSbJk0kH+gwmSvmJk8jfvSHdlqIQPzkdDittJmd8HDtsb4b0F8+MWumgPvnpQGiLysbytoa8h7Q2bNTRYqrFt5YpYZqWNmWplyXXVAbChmUjhkV0e4WegQbm7dcJEjPvuGVQxg+heXyaX19NIyOAG39PxEluUDTFAA8zfS3jv9i6NsA0byNqvWkmPmpXRqUevGIdj5HHKJSMvjSjmLy3lSQ0sMV4jT8NTLHIdPbIS5KDUHwMKh9jjBz1SeekquFmhAABAAElEQVSSpXnShg0FB9FgcDDPhuhG+wKkc+F983A8vu5POVeNKw0e9ySX8CsrYuD5al6Lz69dv4HmXDPJSO1E77WLa669Ft7vmfM0o51VOsiV+rWWTj7zLlaNy3BZ17wkT8qPFVP+7tyl/fs0wp3+zlQCDY3R292jS6fOud/XNbUUHYMiaZ96Bzw0Q2xZsw/Z3cpG96h7H94DE6PqAblGXg8QQDFJMHvmtLiaQMPlvXpHZ7LDjekzYmWOQTPv0fkkrzE+i3BJx/EjE6wujrH28qXWiWfKX6TTrUHabTt3xMuvvBZHCfaMHDEyOhHQdCuEutuxNUb9Xsqja8u6pvHMfdm/RfqI6eK8y5L00X6S7+EzT7B6mWZx2hvDoHsbAj02GMWNSuM1ZYXvpTMA/xn8MsCZ+trxknfgFcbT9lBn+h31yyEywIvAsoUkCm4kiD8Ax055KbZaFTTws04s5ZT5pNHu99WP4BwTTYzLteEaLHZeU929m0qYmQQatm/diu6+nZOxuvFnLQd0MWP5KmakAwBPeA0DutomBuB0YKRNJi5YW60DbQv/tossY0XF+pg7d06079SRJMQt9NhqnlWK4ogOrnjgHF0DeUnaq7sluo6ElbBuHUBTJRZc4PpMLNZUVsYfXpgUz7z6BroL+00m5XErgYbeVDQ0yUADDhHvqd9cW7nR60krP+5sDTIYbMg96byXAStwV1xKBw2mO0XA+ThVYYfgh8p9W2MWeTg3Jtr3hbN3YtTYEXHXHZ/iaPfL2WIM34N1NlwXL7LiieCIPC+e6ayLOwaPFkAXeaIzlTD90VFWqEkHu94nkDBP/+68pbv8pgxoN2WTV17lSf6YeGkCxf40B0jCzJk3LxYR7G3KdpVeyFNfHGob5Gm/mCwoKgWljV+F75i3bCSOSaK8nteUV1h3cdngpoGAzdu3xSv0M9q+fUtc1a8/4/fKI49NFHrqi3ig7vb+c53B+Uwg8LvBM+/JJJY85LXTzuXexGTt42U4jK9hC3vk60CSbgYarEhMnXSJJs5TnaKTqoMvT4oRBiJSLhmrJFcp24xvgNn35tA/ZurkyXH9yJH0ZeuLvWV1cOH4Kxspp4yVcgUtUh54v3iIMR/9lH9TN0pP/ZKdBMOnTJuWdqvyZINBP863oEXxRfWHD+/BseX51M9cU5vaOToP7R1p4vWUQf2kShJjk9+YnD7UhJvHEWjgZBBoLl9b7u/IjpsYy3cLWhSBqBLPeG35Rlu8CPhUz2NAt4IBq6D9CnjzmqHDMhBu8E87W33hHL3PlFXmV/CNawhtmPsF7Auddnsm8KZZs6IBMT/uIgC+Ad29avXqTBKOuuGGuJxkiVKpg68+VTdpA8s3peulXoLP08/hbwYInLeJbuWpEcEooD9Wr1ubPc7Wrl0DxrSKWziivFtHaA+v25tGXnHcXGe+z0xTllIA/Icpy7PSripCVgv7KfGPOe07eCCWr1nDcxVbk4/Tlwy/ZP+uuOP+B2IiyY5mTVszAwbIB2N9jB//+0BDiQ4yPMrVrJfA5PGWsAOkOQ+TrYg5M6YTqdzAfph6ccPgYRwH1pN9vjA6zJz7nl0IGM7v6rBoCLq/jTcTVIySqpQ0PAwy1EaZ14RZLVmdMW9uvPXOm9GWzGsfSlt6kDWyzF4jqGh2p1EOaMqoPGUMuDaXTYbMAIBzEcRh7Jw/AOG5tEbXJ0+dEutguH4DCTSgMFu2aZ0gZFfbi4B3TSZrsMGssQKeJEHgBFXBWzDV6ZWRdQtUojKm5TsVlZXxx2efTYdpOGdIZ0UDTpsOp3MtKTIFJAWG72p4Ok+vk2AKzVJpojzNnmYWnL8Jvp5o8PbkN2IgJ18M5qxeMy7er+Ap8CjMzisfzNH5+56vPqS79BKoBMjM/vJ5jRLP8TVjNH/enBhLlvHKvn0uRbCLUjnBI7NYTFQwTyMdwNAoLIznYquHwFISKGnmvQlClhW99fbbjHGBIyv/LDNGZvudt06/++o/MmyZrkZ1AoXzRPF6X0klgZH1EBwFWgMNuzFWVq9ZG/PmzokeffpyvvMNGcVVwbgdRhR2vARXXlUS9XCQzbb7uKhTyrykk06G9Lc5JaiZALIpAw1rqD7YHENHjqTDuI6RXdypqsl1xfhxTXlm51yMda+Z96Mc8FT5+0xOZdzssYFsrK2oiPcWLcrzkus2bBRjxo5N499xba4qCMrfOuEGwNKB5j23buiQQwj+TNYeJ8hO3pbc1mB/plnAwwDeEnhm2YoVua+/IX1OmgC6Hr/jWv/iycIZNtDQlGePTjjEbcuiSX1OJiHzLY/kUwG2ARYc7AkIVXFWL5xhnyWmnWX8Z7ixQ2R0zhmI4Lruha7J/DUoTzO/3cfPxOq9R2P5rkNkKyJ2yOw8vkh2bLjOTteu0ZbMpzS0HFOnsS7GSsXGSgINk3GeTkQPFE6XrpR6t2mbytUO3oJ+Gp58zyCPQcgqYMl5ZNntNCpSebBoGlmsp7fi7yeR44WUA09+5VUCO73opt8v2rVpk05SNcoLPDHjAsEGlWKhYwpFpiNei0oCS0k1Tt3MV52qgDMcl6czVLltc0wjWPrtR36V99iKf6Vtdxpt9mzfJpo3IHCIo3+RbWgqtyqsWS14LQMN8J7/2ZT2IlZByi7fTQcMmsGW0BQM5X4v0FfCioYMNBw8Fhs47WL77guxmn68Pm66diAdmK/EqRmSxq2BBvn1LFtekNg0HgywWr2VXeYJEMvTyoYK18j85NffiI5dusQNI4utTsqc2GHkX6LoCEljMcCZafDoIGmYyDc68b7mQ95nbDP6+4n+LyGTthBjbjwn1gyFB5QRtzkpQ2KjPK9TmI3FwAZ/l88dzadBNY1byA+BwGwMSTnO7KsBwaVWBRw7kScC9EaHqDvEeA1cMaEWe9ETw8CfEv46f41b5d6fxXmNUmXWigb3nK7fsJ6syEq6dS+PcgIY48FKt2a4pS6NWq6j1ijN20oXeVTDC2LwN16UbeZOeBMHBh6lTNPAmhUNH1JlU7F6dZaB9uvXP08zaIAO8Qg2554YzyiJVdDT9ciqMnSQa6HTJU7qFBnMsKqoOoYXX4w169bFFAIBe3btiHbty2PI0KF5NK44dZRy5uwcz/wN2NknKPUF19RI1tB1DGnv+OpCT2rS5dCx8L3F0HzGtHc+CjTYfLEVGVgmmceYGdTS2HWtNQ7lc2lssFfdbf8EDdXUKUQ3rTYwEFBLxwjZsgv4zBnvxNXov359r8iqBnWgp09oOyS/MY/UDa4f9PFndYv9o6obvYP2zucMss03iA7gGNF7wkDD8zilR/fu56Sj0dG5vDzLyLU1XFu/J8Y4T2VEXKmGvFjS7/YFLkSWnioz9HFu1wAj7RlhwNpEhIGGl15+KUu85XcdxiZljdKWSRsA2mSQu8TnySuFPaNMlXQ45M+Hv8vvrpHBWU9pmjNrZtx0y/isdjRbaRAieZuxtBPSKGaeamhtEfEzeRYZ0IYptlQ4f2WVoB5BUPeL78YBmDVnbmyn+m4841uBJn/keJCz0BEa4IVtkckB5sclkhZ5bQMGrKf6Xx5yflY0eDygwfZZzL0Fx77eygkebVq1LHQrHzSo7Hy1Wwy8Ox9pr+Pnq/yjDXbGJ/eRx2ZKc3TjGsb97TPPcIzzy1EPCDmOb+bjtqxoINBA9KEWNm6NpLV0QWZy0nyIayv3Is5ZIMHtExkUc/JgpqedeGKPcnGRwLM60OOJD6AHKvdtjlmrIvbzUUOx1mF+moDk5++5H0zmlAlxiz3cVeV7aJRZU+bs6UlimidPGUARI+fQzNLm1F26dqP54pAoLy/PQMcFemAkprBODJHzFR+LapqC7mlzQ6NU32BkOo7Q08qH/Yc4ZnPmzHh39iyqT5oRZLg8riQ77aks4oBjezJA6iDok441a6dNpT3nfXsSlsF7g9fS3jlnoIFeNJu2bYuXqIjbsXNbJvVMGnYs5whVnX3sI3uzZQCvpDu4i9wCBX3FR28oZY1Xf8tAFcEqmCy7+ntE6xTsSe33Af37Fac8EGgwWKHtnvYqWGbzannGrLf3nQFA6JzVXuCc96KceBVxyey7v88mCD797Slxw9gxeVqNNq/8mw6/NAUDfPrw1e/I//KkyQwxU7tafPYG/L54ZD+vrdBm2owZVLsejdtJ6tkcP++ZhUxsLFY0v6u958OAlmMrB+J8Sa8W87aquOjZoTyv31gZr2LXtG7fLm7n1JrmzZrmdhWdY0+9UvrFwJQpeEZ7V+wRt+SRPDmCT1WRZ8C/DDRwPwY0tzH3Zeg+dffwUTfEyGHXZcWd11VfKz9iuE8xU7vb63h/rosBUnnUa6q38yho9JM86hHilZtpVE4gQ114A5XJV/btm/pT/y+TeepnxsprMV91avJk6mmowXtZJce1TiPb3ouBBvWUDdAXYfOZKGjVvGXcevP4POXIbShurbOCSj63X5j6SfzLZPul+1HPJk8iB5jHKRu5jQ6es3p6KTy5dOXy7O2wH/nec3BP3EQj+5snjEeu2kBR+YU5X1pffvhYPv50gQYlG+DI7CkBgnS6MJiw1ugw/mFMm/J2Hm/ZvKxxjLlueAYasMjiBMZKZi5YfB1kGfSMIAQ4lKJjSFIynYsqE9bB4Mu9bpcyRlY0TJ8zPQMNglOPbpwf3aRp7vPTuXCpZTj3xvmzyuciRvJHXVthFIU7DS7mAWcnkAveljZPZu5r1q6K/mS+rWhoBVC5BcGjc0DL4lxzDBLLxBVmt3OoGt2TqQIWqJKZUQwai+6lU0G7TcGjEzVWagPm1yOArcnS5pE+KFWVOoiUQuL8zYZkdAwmF1wEJ9nQozVV1P5sRYUKWWCxPHMRhtwbRHEHU841lKoGS80y0KACYL00jjOiyG0rMCpgjTgj71mqzxolILIeJfArZSEtuZ01f368v3hR3HjzzZwJTmd37rlk4EjT7P7LPRvBFPg1ctL4R/DT2FBxMu+MQHNP6ZgwL8HcrROvESTRGLzn7rs5Tqtd7mvSyEoaQZM0pCGytMl7JiKpbZj7p1lHsy/SXPpowHlPZhL3kiGtqKiIBWR0usAvOkbNyN4b3RX4pLevOhjSXCVqNLKaiosxT5E9lCAGdlxjjQvH97xwt9ts2Az4sXVCB2bEjTdmkMc55vGpsphjW8nD2GlI86PX8ppmXqrAD/KpFQbypQZ0WRMy57y/bj1df6G5JWN16jeMsQQaeuEYmaXzfGrHc/51uVdfi+wdfQxQOio+/pzzLY4Xgo+Qu6LkuCYNNY+mM21213OTm1Iu1pxs0QlAddrsOfGzJ3/Ht8nu8GyO/unaOaJzm1bsfeYMZ4DbtfVRxUXAmVZRQZKoxj1U5UhWcnHwH8oaChyj3v8cGCG/1oQPa0lr7v8MgL3nFJUbh0/HGjLve/adiO2Xsu733XZ7DKEypz9lm53JSBigOPFRoKE+pWiV8fJrr+LgHYs+lHl3owu1FQ0auGn8Q3Nmlkafiqc+xkQtAn4wSmGo8jeN/FSczEneUb6kjz8vxpCb9tYUSut74rhcnvJaxlYnAwxnzrpXnfG54eo4O/KyWSZ52SBD9eqWm8P/ZLYw0VO0DV5s37cnplIV9I2f/FjSRUsmWIYwd2tLsIHy1xYNKDVG7YEe0FIKwcssIio5ZVWsMcjDH7hmwVtmY/MzaOOLrIUnlFygiiIDDWfOx8a9R6KCruD2aFhT2CUxGif1ql59YsTQ62LgFf3SqbE/wFmUL7POe/Sa/MC6agQgT9BKo0mjQcfoVUq9O3XrGmMwKFpc1ixpwEeTBsq7hlQqX2iTmAvPaEgURs//lJiKbz59GKRwP6NHUy1879248daJZO2vTvwwOFQEnjHPkSWNIUsUDT6Y0fQayryGeToV8GEa/zjUZgHFhd0EGio3Vsb7lJFbpnkbhpaOkYGGsziMBla8X7MjylMJL800aQiaDWGyqbOUgQw6ggUe3+f6b2BsMy6rVq7gqNCOcdONY6N9Wzq78zeNITFHsmZDX/jUdfNe1HUa7Trx0u6Ee2pdCbKq1ZA3y5n3op9ssryO0x7Elv79B8Zg9FQj9iQbhMmgo+vFONLZ13zwUnCSwReCF/C2eooPJcZ47rj3tRqMfJOtDQcP7Ivyjp3jKkpfu3XpmjLksbIGSQ1meqpSXQKI6diBr/JFbhlybGgu1sinNslD4AkK1kuHzxNxptJpuwkBTUule1FJY6WSHfw9ftejwMRKcVHeQZBTlxbZagw8aGPQI41/7IbzfqYW1Y4EAg6CC29MmRJz5syM64aPiCuwDdqSlWqIw2kAyQonZUZ+1O5AeBPDmGaudU3GrSG4U3nlOhpuOwvtq9evk8cGbiXQ8AzVTQe37Yhx48aR6eqc2zLlEff+isPqUcez74s4pzMrj1oCz5InX2owS5+zzN91sJRf2itPk156kQBP+7h+yJB06EywFMe85dQZmsF9wB/yovpVuRHr1e3+l5UDYFAp+CMPeC78fLbETCEDOx7HZTDHVerYaCSb0PHV332mQ8PY3pfr8FGDZzBRA90ZFPaH1Q7aC6czeDebwPR+KhvupAKpJ06v8qSjaLhDfa88lWTc65ldlA7Kq8EXedFxrWg1myn9XGeDJJsJYEyfPj2aU2VwGxVIbai68335Im2J5HcIy/dMFjjJ1H/cAx/gV4OCjM3ThrH25jDwuHrDhvj1k7+Nxye9RC8GsEfa8pjYA53XtiVbJ8ANbNoajFcVXJU3rRzjSi5BweP8cgY+t6rBoKZ/M2CVvJR47HqDFzyP8TwEvmwkkzl3HTjHVLlNfeP41Pg/iwfv+3xcTaChJg7M2cOEIXgFGDKra6M+tzrKP03AWu/LCqFZ06fFUZwY5VWbsrxDOaetEFgjiCquJKZgt+gQXuD7ZqSVTemd68k4BjA82lA9qH3gMZmHSPIYaHhv7pxo1bIVWfsroz940Bib/siBgxlsMEBX4hPv22F1Gl039arBUd+0CbKnsXjdOmBNQxzbHdh7z7/4YmzZupFAw0D6WlGNSHBNbDHQ4Mkv4pk0z0AaBHdsr5dZeq6lrSi/y0v6E1ZiaDNpl3ywciXO9CvYNM3AyX7Z40l7WBkV0+VtlbKYLJ9a0eBefcez4bj2vPKQNiXjF3JWBBLcpjuTag+PUb2JCpv+bOnRFhZTSg/tYnlT+9uHek2HV91ng2MDd3KS1X9JL2X40vf3gPNTCMjuprLh9tsJNHTqnLqt0Kn/oyszWAKt/Z6BBjHTvmbqaPnfz1vdoR1oIMX3tYcrsCUnse2jXadOcdftd2Q14lG22IrDGXCFOkJ3YnjKDFjDWBkUxa6VT5Sn4slC8Lv08R7ztLwVK3OL3CgqAsaMGJnNI92iLCZoX6v7PIJYaqlTxDH9kQL3wQHWT3xzi1Mp0ODJLqWKhmU47HsJOtw07maCPJfn9zL4Bb9oZ2sPlPgkMYz1TDpAe22DPAaT+XsErBhkvzJfN26lGmPtWgIlyzjetlHcPp5jvjt2Sv3hMfPOX7vdLWtWX7lVJ20CaOL962O5Jpe4CwIWPkNNxjcRsPDDpZwW9AFNsenlRBXuzr0748a7Phk3jx9PxU17qAEt+XaBIrx8TB/QCCr9bx5+2ycEVEgTbWGy9C4gNFopPgT8ppJl3LJ+PR20L4ux1w+PvhgU5wgCHKEzqt/TEXCxjBqeYrE1wDPQgFCamZYBVXBYQ5mFzEADSvsoiz2XZlKz350TbWgC0rNHzww0uGfPUtJTgGtGWjE+3aOuUSqQwA3p0GXWW8cWZE9FrfAYNQMUa9ORexcC/sbbb2MorogBGLddcVxatW2N41w/mawqRns1nog0VRb2B+DMagRXJs2oM8ynQVEIJIqS61iqqiHRAGarpEfDpBcn5b7O64cNy4ZVRlhVwoKqTwXP7LQZr3QeANeMgCKcCmAqG0idlQkCE/fgkmjILqTE/qXnn49ho0bFdWRFBFY/l0vGd1NF8h2FOg1/QErwcd+ZQJQl5QqPAsvQVhKkI8ZnLKWbTRmdR1PdQqnPFb17pzEjSyXgoayOCSbcs9tBHC8zSHy3BKAKovPVCPLp+4KsUdkt27bhNL6WhuXnOI/dc7vdD+3fBSWj2R6rpUEnqDpBq0cEuewDwKu/p9JkDhovBiBUcPZAMCvyHkGqzgQaRg0fnsdJ6YwKRCWFaUQV9EQhk6XTKWYNbFBjs0qvZ4VA0lOeZ3wN6V04jus2rKdUb0VWNIzFEBpy7TUZDLHbt2uoySaIWxFj1kuD2eskaDFHbgjj4AQVBkeRByp5GL/RZU2p1KidzRqNsm5g/nVwKMYC3h4hp3F7jDURVOtgSNSDRsqVkdd0Zlh1/zOYpLNVBBgwJPnZQEZpX/MCDND33lsYzagYaMnWABtNGjybOmt2/PiJJ+CsiD48W5URZOjA8ZZtmuGwE0RRcdqsEMWva4Bny/3g4LC+NXDEq3OsZTUy8xrA56rSEbsqShtKqLCsbqrNq1HhM3x/HzWrG46ei7UHT0BPjvhi+4SPu8bfSvaNzDtOSfc2bcg60cCHiL/rUBvnxSPknkeejhw9HFdQNtqzd680tDREbIwm1sgvVl9ZSlofI6ZB3frJ/5baqRwy4yKdkOHTl4KeNvrRKF2Cszt9ytTo2aUHW4X6Zva1Idc9C2ad5mSIUpDBbVEqc4N0nnRTHWyoyj2f4ZrnKZmtwb1DmczQ7jq0P6ZgvH39h/+R99gcO6QBPl/31gYaWhJowFgi0GCPhlo4OhoP6Ri6jswzK0lkKliQP2U1g86fXY4Fn4sEfAw0XKxqoIGGp+DVBipF1m7bTWApYhMiAyLGqMsHxOVUatxw3Yi4pt/AaELmV2XovTmYzSBPnDyuPZrVMRpIGq3ihs7FKvbATpr0QnTr1Yu9xaMxVpolv0lTH+KKnxU/zPL4swahDw09nQ0NLz+v4ycfeU9igdVTBhqU1zETJ7ANbFBirdsDxF077Ivprqvrqxw5llhkgFO8NXhtBQ8RkgKPeU883pX7PCtjMTJ1ij4hd+B49e3RM4OYyo6Gik2L5epScMWJKVPir9VTTDZx3YyIRot6zOM2zVBv2LiR7YOr2X63Mlq2axdjxoym30zbxDF5zmC4+lOdlpk1jSz0XG5/ggZinTJi0PGMVUMcE1uNCiSvazXG0sXvRwWGnM6rgYZrcRpdOw0tgxUacmKaWKAxJH0dL403XuWhzC6C1+JAZhgbNUwj3aN3rRA6RJlnx06dow/B5M4dO+Z8T7Bn22CdR8c1UF8wT4MDBjzUX44jVksDZpE4ox60z4FBEjPKVghZcWcJs8fH9UWHtANz7L1wlvlXT/xINEndoCMkk2eTUTBZfW6gQT2iYyT+XiTQUJ+O7IfBhTeoaJg3f3YMHzEyj1Nu0RisYp5uYTNInZlzsNcAvWt6FvzSyHWtdQxr4QiK0+rs08jYeXCuRkNO3WhcFluouPv908/EvsrNMR7DsHtnekPRaM45ZRNGxnDt1G/ycDrX6XzAqty7NEnaw4PS32oNdYjVoFaVrMDAff4FmsuB7SOvvy4aE1RGSIoGu/BF4d4ya+ih3rSiQPuDy/E3yMR6K586Lt5vOl7Igg6Hv88haPc6jt1E9gMPIThlCbjykgEw1kx7TH0vbaWV96Vuybkzf9fStRCLdFbNhmtD6Vx4hK39lax6+dQnP8kxoV0yOH8SnpHXDXrZg8Hgu4Aij9qUWBnN5stghOimfOZa852i94fJixNsTdxKoGEajYpbx+0TJmTA18yveKvzKQ7qjIor0qKwR5B9qeZ7XFOZ1VlX/9kUUt26cn1F/PKJx+M3L76U9bh+w8cEAg292raKxgSQ7dFgoKEavGEgvbhCLrNwm0dbWtHg1gmDAF6zFnRDA0BHgzEEN0R0aHAMbD6IrG86cCjmrT+Aw8HHa/OlU1Xizol3x8P3PRDXsK2lJqe3nSUYcZFXGwInzaC3sqUuv4zKDogZy7G1Z0KX/dDfCqSRw4dHR17PYBucACvFSO0YnWj1XAb0oLHzvIjdq1Puz8ex+UpBDKsdGzdtSlf8Y5lVX4RD3aZ1m7iKeQ0kOO0pOofZh2/yxWSeDp2szO0nz8uQ8oz2l1jsmho4toGxfN4AWW2A87+TpnjP4uxWVq6jenhQ9kMz0GDPHXHFk+YcV7wxSSjPuE9e3S72SGftNPndLTba31aBmjCUNjaGff75Z2nS2Dj6DxhAsKRoGq+t58KJvfJ3gZnoWnhf51/7S59EvaXdrYOqTKj/zE4rY0ex62bOmxtvY6/ejC08ENpY0SbW+lkrrsTe/wn2kf1GZ/o35VHeVj6VaYOAypAyLT5pN0svg6abKyvjDns0gMXqUyAkhd3vOQ8fyrA/O1dpr9+UW/ukmfLNdQ0yZZ8cZNe+GW57fB4/oUO3bvEp8KCl620SmCqYajrqrKb6g8ukXQTbf4TnyUfwYAbuWKMzYigTywo0xvf6y9B9ryFTYwgE3MT2BqskjnGih0kw/Rtxsj4BYAP6qbdZ2ww0cB/eS1YhYX+Jj+oPgw0mgHZSObV+Y2V8wFZfMWf8xIkxgKpMfQMTwfZ40CawKbm8qZ3tmqi7xSyxRjtYLBZ7DChZ4aQt7Ot2Kp/Xb9oYyxi/rF7DuINAg/0F5XWx7AK8IZ/rszluKdBgn6W8DvRTn7ju4oQBrGxG2bQJY1BVtvT9eA8f7Si21b5jR2LHnu1xE0lVAw3t25VfWl5108f78acJNEBIAazgeDjQFCb/wyEI8PlYzd6cmZRf7mAfUPNGTWLUkGHRiwj3OZw6Aw0uVGZAEGqVo+e2uuiOo1KAY5PK7tURaOpeYhjCgAQTjsRMSpbemflOtCXQ0BfjvzeGbksysTJZydjS8C4ZcjYwy8oLp6iAeg3mCo9jkPMmgJBBDphn797d8frbb1Oas5yKhkEZaGjdtk02fPSeqzGnajhFNXQSASmVaBrM3HcaV5fGE8A1ds/yHcu6BQHLR9068QwNajwqcdTIkWS6aCjF+7XJgGosO89zMKvBEu9do0pAFFikeJaSwo6Or2GnojfTppGu0eGe8mefeiqGjBxOVmRoNsaRxukk8BmBR4NTYVAZZNk342YzFuhr9UAGeC4BR+5/ZD0EW0uV3pk1MxYtWhifuuee6E8ZnQIlAOa4gKvRYH83YuvJIpmd8F4Yw6dEl/5Giv2uoKuxoAPhvmkbZdr993Of+Uy0Yw/saYRb4zyNCOZpgyXBKenBq0ebJSe6hihiBsw1FQS5UNLJslU7V9ujYe7s2dmgZhw9GpoDrnmvzDeNIUaV3hr8HuPHaBhbgDV/l1YsRzqTKiDBDybOa23fvROna018QBR06/ZtMY4I9LAhgxPoPKsegqQzlIEF6J6RVpS/5ViCrsZXRuG5RpZGMneNoXqUF3o/a9ezdYJgwHqcgHqU0t5MKXZfeF6la/d+x/PoVh0uA3NWApnpUVHI1xlQg/SeGy2vq0htJuixRif47HyaSb1LmZvGRSsMCpW9n3lr2vT4wSO/ZlC2TPBsQSFA5051/59Ag6XIKGDpQWnohfNcD1JWY/5COWErFDlyzbXP8ffT0NKsjtmq6nyO+DrfI2/P9/ecAjcOnIrl+2iSc4Bj4YgR+Hjgns/GcPj4SgzXDmRB6qF47FtQg0AATBvr162LPzxHlvHQvsyK9IUnO3XqlIaJjpj00DnSYPfnjHTLI/I/78mDypHGVqpL6K4hZ5M4MendxYvjVbJdvahocOtEe4IdniCAJCITGAfQsQqEtzQ0HTnWGraE7hok8Af3WwXjsgZBB8QuTmDAbGULw9R5c+IbP/rPvMcWjNEYevQsrxa9yzlFh60TF6iWuHjWPQ7SiBfWzXlpQBQ4wxd4n7cwCpgv1/z/Ag1w70UrGmoYaCBSv+cwgYY9lMOejwrsVh83DhgW/ftcGWOHj4prBwyK6hhpp8hInsG4rU6AQ/NYPoJcyKvZF+SN333qUBr5f+G556Jv//4x/sYbsxze7I+yauAgs00ocrEhcb24bOJDYsYljJH/M7sDMZWzY8eO5hY2s9/zyZJOpCPzsMGDEzusaKhDpZFbYHSGDAxA+JTfREnkwGsn3zN+Si08A4imo2QAbefuXRm8ex+n9wxYe8dtt3NWPbXSYLrb+jLQIL47b+gqr1j1JQB4H9lAkFeDmv9vVYDZNA3etRXrMiOycsWKaNe5c9x8001RTpZafivwFZrIe8itePN/2XvveDurKgF7JaT3kF656T0EQksgkEIJhBKKCAQFdBQFxTIqjjOOY0Ud2ziKQ1NRei+hBUIaSYCEkEr6vSk3Cem9t+951psTM6jzOd/4D7/f98LJuaftd++1V29b/PQhz/d1dRwp3iwj0+ocOBrwhGb00Xr1dDSQ0aADvX//U3LvGpMhcBjYlxzV8i6NO68Sf2YV8KsCaZCIgI13wCuNeTMaqqIsLYKeXnjxhXh/DadFlXXgdJ4zomf37gkLjcjiyK7CUWo/Fnmzcte1CHeVOR2bZjimESPOcssaGC+mTb8FPb3M+I1wgFv2eCLp2CfAc+SDB3D6QDXQKD8QHvwH1FPpFC/ko/I675Xym7nraDhk1LleHY5J2xovvfZaTJw4Lns0nMj4Hdq2x2Cvn5krKn06GJTb4mRpfPm7uKSjoQ6p7klY3G8fi91LiVSVOhwXCy9evmpVPPjQw7Fu6bIYQZ+D7p26ZCRQXJQX60yuAi4nPgIX9zoNDuCiUeucNYpVzOUtZk2p4Iqbwm3mvLmpG3TF6XX+0KHZyFIYWy4mPNzHYi/dyWJ/lZ/y4MIQgo7QlcyyUa75mYaLirtRTGvKX3zm2TSMsoEoekdJ/krPymQdgjlvfp86CBuQ4zCWNKXcSCcJ8LN8zXls387x5MBm3PjxsYVI9ygU525k3IHAiRvurXsnPhYySbzUACroR8MlZTc05coSf1izmX5GZy1nNRtx7Nix0bJ927iSDKd2bdumgaj+kuWOMlfm5KWDJ+mW144lbMR7jVyzR7wfEwEvq8ScRQvj7t+T0UBmljQjvnld1hOZh8O9MaUTOhpqMLSOBh9gJ+PLDdlq58sb+2HA6Whgj713ZubAQ/gC35Bn0JSb9Wzj9Vr63ZSv3xjjFmwgwMYgoAzCJ0YMvya+8MmbcTT0x9FAE8XN73OsMQY0s8ru9sgkHQ06eupjuDiRd81oGPc6zVvXR1lHS9iGRbsOHXFcoANgHInXymT3VX0IYCTdS5PKFJ1HyhX1DuWfe1wNPlCfjAYzWF+j58mU8eOiOaXJJzGv0zHY1Rv2bKahLDqSkWn1DmlLg909EN/UOcWbhDPz3YXMVtfWWeqx9FXglZWUmT76xBMEUBYS1Dsj+vY9Mbp06Qy7q5V6DURR8MOkeWidtajfK8N1ChQyBfj7PnBxX9NpxfiudS762MM2gyRDcwBBw/bwYJtauj9mN6Vxztg6lUv0lbJKHiFcAFGWKoG7Xga31BVcowEwS0tfofzA5qr2b7J3k6cfpT0AnNVRS1lGYoHomf2+mO/R8bhP4VjTMcF9mbf6lL2cnqfJdTl4/xHKiHt065Z7KG6Lz+J9jg+s3UrnnXgu/BmjRLO+r0w2WOFJL8puj+TUofwUTscyHA0fJQOpOUEmnVMJc/BWfSIVD2Bj5pX6qPAVb9StfZ06ifjP69xvPpfPbMZJPRMbcMwLL8ZFZAvqaPCI5AMEfsyAMytAx7RlmOKje6E9IoBcu6c4eKXMVZbD381sMGiwWtm9rDzx3nLoy+hfoV6mjSQPFt/V85y/shSI5ljyNOWd+5dyibdZVdKSNpU4uZvv6GSwGeS8efMorW4eV1x8aXTp1CkO48Qo9Q2pwnp1FDHDdHK53/LddMzwrnukDsTHKaO1zaqDG3vQXd6agaNh5gyOhd6ZGQ3rNq2Li0eNonTiUnpCtOPX4ppzLubNHx/K6+/maFD5SsMRxpQIKfWC9FhleerEZAzSNaS7Na5bPwadenr07NyliAyTnuPmK/AlCJmPpqJpMf5egWD0RQIR8WQqtVDaM5IBApqWM+HNKTHm9TGUNLShK3YfoiK98XC3KhRPEPkQDMqGUanIcS+H1oBX+Cqo07ADob2fjECC8WEKnx6zl3GS2NDyxP4nk2Zf9Giw6ZPjVIMIq2nsgswqFGlAwyBUOEupgMmsMeoyVZf7GR3R0WCzyUVLy+OBhx5M7/kwhEIHjGnTSPOEAOYqs1Zx01hMIxSE1nuropWpZ0xXJmgpRnUEjw4A68tSkPCZpROPPfIwPRoGUDoxgMwCj84sPMt+x/XLkNJw5m/nbxRAwjEiK9P2c5mwioEOCi+dAZnONW4c0e+p8fGbbqKhVL8kLiMrqawx3rFXRtG8F+8nA+dD91yC9H7CxDk5thG0peUV8cqYV5Ipjbrm2jiBHhwe7eTnCgLxRk+lSnmuwfkzZ72f9tsQLio/erbFq5JSbXTUNL15RBmtZ/Q0kYtJZ26Bc0oGJMwVlOIManfhaPC1+81cfQgLGZ+Ki+PrDFFBl0mtgvm9R9nEbBTF1TgdhsM0zqRsRcXNTAijlRotCmSNQnFadcV7ZjqvnwEXIwCuAeCnEqRQFq8WLl0SGkXpaCCacBFlK3169kpFUFxREQWD00hyPNN2jehYRyaM07Ek7JmvgsI0RkVdsxYtktHa/+Htt6cRqWhA2UTLLD3QKLCx2k/uvptvRnT2+wRyO3eqF2VtmpGO7PGWKA/MT+HA6NyXWaSQIgsJB0NdGhrW4JEGKvDbU5WsBh0N0E015lf7iIA6CJNeu/tAzHqf1LLVW+idcDBTSr3vZ278B1L7z4k+HTn6CmZdF6FzEONVwxdNlOMnF2Zz1c2bN5DR0D/6lhwNwD57qkij4g6wF/4aYu63Cr84mY4G6QK4uJcKueM06hhfb/dUYPPUo49HdzIaTsOgLiPLphkOD1AClEQ7RIs7hCDes5cIKE6EQsAznoooa62GsV/NOt0qKvAcbYajtHzVyhg7dXJ8+85fucRozgO1MXp3rBZ9O5SR0YCjgfEOoGweRMm1h01V1sNAKdS5I7jNYnwfOhX6lk5oBAJS7guughOHjsOIIaNhC46G5fS/WLRmAzS8J5ZATl7DTx9MyUT/OC8zGk5Bma0T28HlvekYRUgzsAqKawU906Cx8Zv0KP2prDz79NNxIsrnCMp5GhHR19CWtlXKxDujpfIo+Y60Lk342giOc/e135X3emnwqMS9zzzM4pk6eXJccf11dOEfmN83/bKWjgZwRkedUQwdg4KjZCh5r8LRyB4c4e0+qxwZgTCjobyiImaipO9HWb70kkvzBB3xxKwX+YC8XZ6jsSVNiSul8ZUV3C5pR8NImlUJM8tmG/RuQ6lZRERsBlnWtVs2gyyju7c4p1GUSjl8FpRLPqBCJG56P+nY117S/kEULCP2h3SUsAk6SWbTo2HpgoXJu0xnNspoGZhzldeozAkP+Xry4CO4Ll9R5uX8eUpjg+/IQ2tQmqEh5p6OeW1MHifYEgfA2ZTg9cWZ74y2IbtVygu5mqif98yMAMZVoRLuwkT5ZKReZU5FVAVP+ajz6DV4vCc19ezeM8vv2utogBearVb0sQHhgLf7qFGV8oNx5JnJQ4GPjilp1SMFTVnXONqwdTMBiPFkNEzi9JlenKzQJwMcTUj1NvtLmaqT2Es8EzYpK3hOA4Ytr4+RUx19Qzgd4Ga7ySo6aDNIZPcKjOknKHvcsKIyzkV57tLRjAbOTYduxBflkzidTkxgblaDire4bTZjZjyx/zoZXJf83eir9K0hZv3uw8hudY7zhgyJ1s1aZJYa2JW4no4qfpuymzkbLTZLQHqUnqSv0ikUxXdcaaFLebTqmwQIxo4ZE8POPz+zxBpAr/5GWvYhL1QOqQPoQNLp5lqKSC/0qVxiPOlX+Wo9u+95vLOlDeMnToxN6zfE1VdfTYZQdz4Cd/m9Y4nz6gj8jPvgtATn0mnqPoMfhSOgcBCqT1g24Wlbwm9V5aosRZpEr4CWJ7SLS5F/Ou4KIw48BpY5Z/ZUBqisTnnK/d1neZLk5VGswtyUeEziNNptznb/I4/E/c8+y/6xjTy8LutVFUeDpROFo6Gm9ISXRfmd//GaaSYNpcHCj81qkKZcYxV00Krwe0/qqQo+1cTZVQUjVF5cuQMeDA8a+95Woqh8+ciNLzrv6rjlxk8WPRrIlju4dR3RZYMsZD8Ba3HcSLuyuRZ4Y6aAwY2pGLweuduhQ6cYDL127NCRyYAn0JM6pDJPvPSSj7mH4rf8MB98pONRh4NNJ4342lth49YtMR59aSoP+5zoCDgVx2ZLGq8rWwWAmQY6ZnXqpw7lcsAhcd79yYAW95PWqiJXzTwtspQ4ZrmiIp6mzHf58vLohww5CYOxBxnE6rs7cWqmM9asUuYq3ZfwMuU58/Q+7jNDMz56hu+Bwxk44J7vzpkTjxGEsBnkAPSxsrKyPOlEPLRkrsQjzfhgqunUkpbso6IemVlf6ABeynVpK0tnwTFxfhJluK+9/HIMHITDHl1YYz2b3jK+9OQlvP1bnFYfc75mFCgXlXvKFK/Uw4/wJl9bIjCasSuWLKF04oroBT3Z401d1DlmedCR37m3wlnZ5v3SISKNsSZfywuUHd7L36oPL2Lclyn7aIteNZJoemv65Bj1LznIhLc6q/qu/ErYas/It7RnxJuU2e4xsJamDKzaU20VfHLWrNkECejlRkbDeYOHcBxtffgAshq6cO7CXplyEHkrf3AT5RcZdAAOfJyyJI8X1tHAfcy4ES7ly5dlRoNNnS8me/iUk7BDoHnxT/kkfvgQP91rebOyRFKT75R07LQ1mYi6gSfcWKpo8MRy2aVLl5Bp15bT6YbnCTpmAmU5D7TkmDY9dUxL7HQ0JN6hB9hvqbAZWAP3N5Dh3npanI676egF0yid2IQeuH4np81t3RQjyd6+mKzN5se3Yoau3IvN+xBf/3dHg4tnx0RgMVlkVkFXWAhQU2oXwbzfnjIlVpSXZwrZGdSc9ejUOaOuprW48W6WhpsCC8mO9IFh8HsZkoIsiYOPNMrSqw+hKBQtnXgbr9Dkt6dkSlQXMiV6QIQ2lNKAUPlUuIEFqZiIgHlsEtNVaKk0SuwSXgoqF+Df3N8ux+s2bohxCMxyvGY9e/fC2O2AQSYD8YgsEIyhjXzY6EfnhUJTB4LE5hF5GrwpNI9heEYYFJwqovZo8AgriWzggIFZu2t0R+YqKERO15AGI7CypMFzkWVWRuZVukrE4jpU5o2aCBs3Q2N3zCuv5BFZ1tPVZd55b5iARqdwl3kkAQp6FPY0FIF7RmRlBIwpE5e5SjTutSUVdk2diOJv2tLlMD97NKQyKyGzl15+X9iWlCHH9rUCXwbjWEDv6HctbfD9zRxXpLJiDwWF0cV4iUt1zd470yP5nalQ/lqG6foVCjoaSrDxtyqPiZgwA5+zGSRMaTHMdfr0adGd2t2hgwdnRoMM2rHTmDiCM2BDCmafxWm9pSrR7oECQryRsWqQutdrN67PdOlFSxbT42NdDDz7nDjlZBo6Mdesm3bP+E99IiOWVRGGMDcZoWvXUeR6xD+ZuVJPOpC5m5q9YuXKbNK2YvmK/M4gxu/WpUviuEZFClzGVqmwS7U4ZNpeliYBf2GvgDDi6C5JQ75ufHyTIpKGQNYwskykIWmGrYjkWHs7YfKUuPuBB/kFDgZA2pSpdenQLDq1bU5NuGVD0DIjKiAOoWzs3sN6aHRVDaFfu8r+qF/Vej+EHNtgb4Y9rNuGWAd1RgD3WjBocbIqQn3tbo5mpV5iyopNOGc4LznvaunElXE6hlS/HhyF14ZeKWYaYAhraCjYFsFjXkRg7ty1I4+27AYvaE+6uh5lmzwJYwVxZpFwf/lCOhl4X6YuPgprKKKgW8YVfxRGOvY8xeXVl8fQYb4T0dc+2bn6eBwex6H8HdbRAN7bFHIvjgEji/KCmtSu79ljVBGY42SogbOhVjUiHSieRioWLefUiXfejp/+4Xe5yqb8SwwmTkLO9OvYhqPOiDQeAr8PquQUiuFB1rAfODrPYq7y22JtiVesoSpKrWQo5wPCGGFE/S2dIH13xaadnDqxGUG9LZYc6X8xuP9ZlD/1jUGncYQoRlljhKH9L/aTTaETxYaXNYE36M9aVZ7AI2AiPcrTLBGYiBLqqROeoGOHfMtppEuNFn6UvEaBDLSyjMl0bY0inQnScU2Vf+Atr/GSN6mseEqMSrRleMNRhDzFRZrJo6n4rnXr4ruOQvdUunJslSzHz1MvmIclcfh7CgOA10buLKNaWVmZDZ+kvfPOPQ9HUpfkiRq8jgdTyfkX+AJE+a3jS0NGoOTz4o3yK/mxc4IneLqAfMajblegEBldPIc0eJv+ih8po+DX4qC4mfyAOZay1kCmo3xSPmAPAo1pe8HoMLYudT7ZDJXLluf3unftTtft3nliiI6GdJomTBRr8HAeGrgSYWI8c3Y3igdLZP4+5GfKQEv73nz7rcxoaIgD3FMterG/0tBOIjKgXCp1JRilnIA3MspRWvI+Ok192A9CR4O8TcNoFrxGBdRSxM6dOkWPrpQm0nRLJ4DGfkGnmuwCttg3lUzX4r1Lyp7yLh29fKZDQCNmIzilY3DO3FkZvezRuRtKYudognzdTUmlCm1JThoBlXcrP5xvlrgh++pVp3wA3iG89nPD3ZSAHYAOzDxYTVbcGMqotm/YGKfjrFY+NUI2W1OucatiLk66r/wcEPFavst4pkl7P5XSzPqARwpzMz3kySqsc8GZ518YzdxPyAyeVjjCHdtxlU/KKulIR63wNhVaY9+yCI82leeL/8osfsL9wSVxjmd1n5mz58R0nA3uqQ3UNIpUkJMH8h3n7KVDQ/ws9jalYL4vnJyBst0vp1OD3xghXYUj/823387GjcNxOiqflH8O6fdLRqITk040evLejCe9lhz44nghv0ghr3XE0YDhsmjxYmTUzGjWqmUMwZhujfEoTyrpcM5XGZrjS0/A3D1RZutoEGI15DXAR0PUMh7PsrcvxtOjn48Hnnku6rHPOwgieV1KRkMPehF56kRNmuPWZAD7LvgQIkzb/5MXGyT7U+mE+g18DaKqkYzHfQB/ufdBMpU20BB5+dadsXTDmphSzr57M2zNJvXaxgU4fK8Z+ZHkxdUPYcDsRApybzhA6gU61kqloeoJ8qAFGEazkVGWl9hH4WSMdZuIaiiqnyZPEF8SJws6kpbkLTpF1CfTOeiSfEhr7IH4bvr+29OnU57xbjp9uqFr9+3TN3uHqKM6pvp56pTyTfY2Hff83nschT37VDJEffbe0oANtHVOrd+4jqbr3bJni9mIZgjpGHTuOgGE59EHeFfS2dwA99/LvfBL8g35jrzZnlkvY6y3IKByMrpwmzZt8ghnv6zD3HG8LFFWT01YQK/JW3h9VI6wRt8zuKEu7R0tnZhOCrwND3v16sXpdz3yOEQDe9KbOrnfcx55+h1/NyQbVbzfigPH/iLKPMcVZo7rD0qy0qDe6+MnxEqyPi4g+9ZmkGb4+F3pqZh7oUtL795LGPhc0H9Bp/men7uv0IN8T0eHuvZkyqCbYjcNoSm9QTdpSENdZ0Ma7IymXu9eqWtpjItzHivpuCVdVbhJU9oS2zCmDep5/KQZfcWpSKfjaKD0XJj4Q2Di3hZ8hj3gtQ4GHw7sPHyo16dTALkkXjqPjdgglZyIZA+kdTgd8tQJ7BBpMh0lPKf0YBz5ZtqZrElYCxdlkfOVjzqmfMx7KAd2wz90tM/C4WugoyUZDYPOGEhWXLsMPmTpM79lk1IeSi85FuO4LwwFny7oQnzS6aDNKNx1Cm+GT85ZuCBm0/9vHetYt4PG59u3xJUf+ziOhsuiZeOWOS8gxCrcyQ/v9X92NIgnXsc+6wXGxMl390HAi2F+HqO0aP6C2Atw+yDwu3ToEA3wyNl7IMsD2FSFi1tuPXSebQxsjeJkyigCW+RRETOVEl97GhcKzHkg8Rzq4WsRhWkDEnTq0DHPyi0hrwwwHyCVwk4FWIeDpzJkrSbvu4BkUiKaRChzhXnbh8BI2mpSRzt27pz16g1Jlc5jdSAUI6PVQSCRTQ+agt16U+GxDSVdhUEklpkeZR7cz/Gdi6n148aNZ2WH40Q8xC1wYtTH25dpRBKWUGT+GpAaiA1RZDzfXudLnsCA4iBGp5LLGD57FQpU0PG8PKYh8NvSTbZb166pHCvQ/TwFO/OWOASABqLKgvfWiNCIlvglfAnTR0YlmL/31ziw+dDiJYvibCIuXYGPBJHMke9IyI7l79xDwaAypDFpdMfx/a6MUgNbYVSHlCoVHRnUmtUwEFKWVEQ8Vq2VEXdgoMDy+97LUg6fVRpV+o0UCQO73erpLwkD4V/aX6M5m7ZsipUrK8lUeS/Pmz6FVLcmRgH5rXjjfWTewt998j7insqMMHCuKjLin78RlmmQotxtBi6r6dKu8SIj7NW3bzq/hIPf5wfFGMCnZFiYOqYiJ54qdGT+7oWGqvvrHsnkNXbXr98APq6m/GMta6qaUfsTiOj4W/fNuTlvmTPIk69dQzoamIM1epawOF+xRTrQW18Xw9LnxUuW4uFenEZBTRTbps2b5X7PmDk7nnnxZX4R0ZHgeFN0hM7tWlI60Yx0ZLwOmRdRCI/9GLO7dmECo6zXQJrUw8lQrwrRAZ7F9YPQ3x5KCTCfmSc0z1xrgCMK3mqseR1G+TurcDQsx9Gw83CszbtGnHPSGdGtcxfOYu5N3W8ZdFgDGubkAdYingjzyTg1LR/q0KFDtCdDSMVCw8iohZe0b72egqyAU7HPfua+q7yloww4CiMju+KPtGx0+p23p6PYtosunUiVbtoUx59ZQuANxvhhDIbDOBoKZeJQRn6MPOzaSX+DnURQq4JLZBbUooTBel27Ji9eTiNCSrPuf+FZpxDNeGiqnUxFxkmdmkZb6B2KhM9opEBfwG8vMNsLHqWiz+vDZFLoaGCZhQOLjVWIs/18BxjDo0qOhi3IvhWbcTSs3xJr6H+xlFNHvU7uaaO/btGvZ588PkuDzL06RBRtD9kUgA361Ihip8ETcU06LGiS5q2rVsccUiTbAvOTiSpoiJvqnITPSKkAAcfSJS/Q8Sk9auxLF9nzhBvlt1iM0UlpwXPSNS7M4hkwiFOLUCZK0ZyMcPGLowonN5CeNJx0KuvwNfNNAe9+JsykC2hBujKysB7Ho8dQqnCefhrRLsrwHE++K/+VvwOKnJf8RtwpOW09e1vjpfhQPgP9sU75nPW2y1EOK3Aob8BZYsdzlX9Tj4Wh9yiUn+JZY0heo2FURKaMhBd80rR3o4zWphbOa+q7kU/Lyis4FeJ9xjoYJ5Bho8Fev2795GO+p/MljTXmJY0kz+Rv+aTrT/4LPISL7/mss0RkWsO4Gi8bcbjLP3phkHZG+ZcHasykTHasI+uQfqUt6cf1JT91M0VM/vcEAQ0Z91nD2z2dPWtm1oy3bdOWyPQJNHI+Ptfs0aI6kFTShEXhUOdvAhHCh5tkw2czH1Tsiowy7gHiG5Wyv8176AbLcPA0bdKE45HbZ5+fRvXonQEvEHeFh3tlOrGIbcmIV55Ag5JYB2PQwEbOnXnvAfaeJqAibSPO6Rhe1uvayNKz5y05rJMyFCc+a1XOidfOzzkrA8QPr4SR8AfHhbsKbsnYVoHXcTfpjTeSfxXd/ZtQy4wjhN9YJsNPwGmdCKyX8eyDZNNlZYyy2fdSweWeygT3WVr1WaNJPr9k8aJ0tHfBcLEWXJqQTh1c2Vx6iPOpG7Bu9zXlI/BXHuaN+Ik6hXsqXggba+K3cFLBqdTbS0/ihuM7toZXGnd8X3x0bB9eGm3yz3Q6i1t8R9loGrrwFCdXSFMVFdGIbLKTceI3AWfUSYSHLI5SrgAAQABJREFUsBYmOkG9LD2qAd0Lf9eeMpv1FE6GwsFhdHQbfGIJY04E5k+9NIaa7JrgUDHGJTgaerZpGU3S0YAcY06eHGQPEfs0sHyXlUE2sL4onVAtBBc18mtD17U1NHBcHOBxiLnsY03rd+2PFdSqL920A3mHzsh3qyNKO7XuylHD/emXMyydntXg/wf20XwVR4MO/dwn8NU1+5CvicMajMsqylMfatzo+OjYsUO0ILKuzMtZsqfyAvfV10WfAYwi4SzMDqJfASP5SzoaeE8HhONvw3m1EF6wrLw8+V87eE2XTjju1JsYT3pMIPC3/ED+nNF01up+iHMapd7bYyNrqOezV5mBiszVcTcHY3QHQQJLNk8AZ+wPJT/R6ej4yQeEde5rkREnjzN67z0Td5mvz8nLeE+c8O/yiop0BDSF93an9KAp9GrA0NIpZZk4qbyxzFe5Ic0IA8dy/upJ8nlpWP6ftMxnfs+1LVi0KBaSZt8Gh2NZWZEtoYzzt8KUoQo4oEe4jqboDtKcQTWdOHyYuJswAy7CSUeka1X+TZv+TqzD4D2dbAydmqlbwzPyC3zH33u5r8Je3uZ9pNe0obBP5O3qgvI85yXOSGuW4M2dOy8bGPcjG0N6kkuJu0UzZHmN+i8yI/m3umndHFcZVHoPYCU/07kjP9DhaUnDcsrmV4KbOpB69exJPwb6wbCfzjN5E/poyTEondpDRH6g7LJvWspK18e8UzYxF++pI98jnsvLKyjl3oAjgz56XYoggbwi7UVxn3kl/IFpgfc4pqGfYv3qe+pQwI7xq3J/M1gMQpQjty1V1tY6nkw4m0S3hp6Ei5mOrkEHhoOnfGJMcVre5f6ZwaxdKMwtu84+b8zHjEftw6U41xYTxF6L/rN2++ZYt2Vt3PSZz8fIKy6P1o1xnh7Z3GL2ub0fyn/+Lo4GYF2Aw53kguXkQ0SFfJP4Jo4fF+9Omx7lCxfhqWmSx8LZwMvIjYadiKS3naHSe6rHzI2XQDVIRUIZQnrV2VUVHIWoRtMyEHjRkvnU1dRBKLfCCGidBrkb7dglZBAfTO0yepK/hTl5brrIrAAqKYgSi0gosqikLli4AGJZjQLdIZrAHOytoNB0fBFOL66pOY6jl7EBCrqIl01dmLuKhOtSUfQDFQyJS4Q0IjWNZpYaBUbWra01qu/8/FFhLBTKoAxVJ4RlG85XBTcVB+aRhMIPXKNX3ot16E2cO2M6tYwnZPdqiVcGmYoZCO9cEj7MV0dIbRRAiULhpSIg81WIKNiEfcnR4L7Y6HEhsFlMZOHM4RdyjGCbvLdzkyFJ3N5H5Tlhxe81VGQk7qf76r7LTP2NY3vUoevW2Nb4qMCJYfMUTxBoTHTd7/kQFl7VUAJT2QL+Rolk7K7JY4A0yo8qdjIo5uPeem8zHmwes2TRe5TctEfw9EgHlABUQXRerkE801nhvrp+YeX7PnRuuAZeJXxkruJQMj8Urfepvd+MQ6Nrj14ZUXN9fp9Z5Fph9fyNswRBWhgXRbaIWQ/C1ii9EV5/oEHgfWSMMr2NMFdrVR2hCw4kPdDuk/Mu9klBIlMFRuyBTD8VPdavUNQJU8JHhbT7rBdXL7EG47LlFfx9AIWgXnrmhV0FwmLW3PmCPdqCns3qVo32zZtE6yYNon4tFGsMbQW1e30QZWrfXqLW7HUjyioa0j0LFZjIDvDiOzoa9lWjFESzWDgjaKqRBaDwrIGybzPId9dsj+mUT2zfRRbHboQS9y1r3g4ax7nRoR1do5vxfVMHTYEGp1mjzfFmzqTWnvfalXXgO0ShwBvhIOwBWMInm32C5yVYJW7wmbiR6W/MUaEm3ruvIBk0gSKHYJhDTV3r1mXUBbdPWvTYUI38w0Q7UctgfuKoSm9k9pG4s2f3PhSV4v5wFjJx0Cb5qmmL79MHpmJ1ZUydP1fQRhv8GnWwe3q1iOhzQksyGnCMolySiwLtsBL2gmQIskEQ+nw/TcRjHQ18RQcobwlaBDJKJEruAe5pRsMWHBwVG1Ea125CwO2MZUcyGlpzbnNzosk2XW2Nc6Yh+1AdPniItey2CRl3ssmlsMw9dK/Bl6Rh8E58XDRvTrSCT3YFJ22IZLS/dAljH/I+4VpSJsw2UFlRUVTxTT7M97ykN/FTfqBxsXTezOh/9rmpQBcRSpRuZuR/zkUacE/Fd4+zUyGUh5vFlPKDvZSSVG7l8Rox8lGVvcrKlWmc9Omtw5eMOMbLVGOBKN0ztjQsbzbtVcNHxURHSDoanLDzPsJr5O/yUPmMjuqNG9ZF85Zt6KbejYyDRgUfY0w3ybFLyrn4Ihy8l8awhpE8z/GUiTYl9VneJlzW0JRwE3ivrGuF7LNRm7B1vBIvSxpjbiqxygDhL53Ke+Up6ch0h50L/7kHGhubcJTqKNm8eWNGYTrhXFO+qsAejRj5XcZyrxzf1Hr5ozSgnHDuzldGpnLoPeSpPKWjfeGCeZkG3ALcawVt60yXDs1q0EBKI4n7OWfLL6Rz1+76jHRqhCSfZ2+9j53+DR7blHnZyuXApxLZ0TRaEq0zKmVWgGMX+FYo3fI/6dwmle6pTslUEMGuAr/QRYCLqrrjHwff2UI6tw0+7T7eEYPL7vuJF6xfR5cwcR0uVJkn/03nJuMLb2GjnC3pB8LcOQCW3JdV7Otbb05APnVIw6ghDhKjrcJWXUPdwLl6SVPiu/JVnUYF33GT1vgs99N7cj/33XlVrqqMlUuXRIeunPTRpm3O/SjtifLSt5vEQ/jmSU/AXj7o71MOM5ZfYXv4TmFQivMaTuUq6Dga+pzUHz7cotgjcNrLsYveT2YxFTjps2uTnlJ2s/fSknjlOoSd/MSab42X1fDihjiQpKf6ZH25NlAM+i/kqDDydxqNJcPQwIn8Jk+xkP9LZ8zbPhk7MWg8ljOzJeajdyBwEEt5XcwxSz0pnWhCj4ZaGMU1mVN1mh2bzSp+eAkHMD2dDdmjQeczm+Q+WWpRE4tdemTbqTHHdcx95cVrcJSVU+o3HUfDGngxZB7H121Io2UyBsgwswy4Gsz84AHLEdSZwCn3HaAbiBOPdLT7bPr4KvbVcsl64EsrMj3kNRp08sicJBM9ClNwUl4pHomTaZACR6abtMQy+bvQb2zWuQKYr64kkxI53bpV0b/J6HTyGr7s/rG5eR/5pDzSe4tH6h2l4Fg26NUQPgbfPcJ4CTizk8BBc9asnNfglZaKdHr2ljXnOpif9FQLPiwPFx9LuO1eCBvxXDwTB5Q7OqjmoAs3atKMfjMcRYuerq7tZ5lZBvxKvEkeVujI8DaGE9d3sX55seMZrVfvTJnGa+e3cuVK7JsFNChtk3xYO0BHgnzA3zsnrwIvj0u9RJxXNiW98p3S2OozwlQa9h7y+ffmE/letYLSxNNxVDdPekq+57juk/oef6f8A7fVKb2ne2qZ2n4dDXznWEeD/Mf5yOeXMPc68N6u9FOxlM3ZirsibPIaxiqtN2kWvcffq8OIOzpfxRWdycpW5+ae62xcC+zXVK4ko68TTscTkiads9/RGeDeOY9CdtfIQK7zF646eNIOUe4Jc+At/rtXlm14/1XoT5vRifuQLa8dUvDeQjdQVvnwP/dM/iDO+7dw8eH8XWquj7nrpLFEZA18ZsWqlezPNrLVoMn2ZVnart3HTRLP84e8LHRQnAzAQdwx00xHg9ln8kbpwLUq112D8mkNJVMr0Q10lqzZugo96HD85Jc/iY9ed0205XhLb+PmOvcP8/V3czTIkIQF+4koFjR4ZnmoCM/HmHv91VepHZtMv4DHeefPL40IWt1l9/Mjfd/+25dMJSZm9GdXI95pwbEjtakvVHnYYydTlGK/S4/dHO/PfvQX3rAmupSeXfoYMg3TmBvgAa0GY/Fs4n1btqHkI5R4n5Ph/qZL94JjleZfQhlhhC0RLSAMleCdm0mhIgrjPIwDli5/73eF61+6jICqzm/hoRh3Lc5N2LcBoZsS7d4Dk1xPZ2DHENZ+94gM5a//b5f3aUm3Y4+FWo8Rugoh/r+9jGEYQ/rgfBy7EczOY8pUDlS68Pn+2eV6/xpcXOdfWqPvizcNMaLrcmSk9YVLIfTSd/2catPcr2P3gbf+7HJfP/gd98MaxtoIYBnptjXUgBOJ8JImSvvp6+Jd//rzy3l4leZVvOK0B/6oX7NeNjyT6HZsXHsUZxxPXPnfXOK4663k4Q52qMLRq61InQNRVfg2oXyVcF071yxyf9OMjWtWvw59V1AUmeyhgwgEakZdKvIxdZrmTVtEm2aNomktnEwHd0aNQxis9DCwdGIfxz1qAEsPxyFoquNoUDmpjrNo/V4cDWu3x4y1ZHeQXrqa8gnhJvxqo1yoSG1i4z+IcUlPlEqo/O3dtgN+sCn3UZ4i/Ykv7pm84f/tasAXpKsS3fr9E5hBo3ZtaAREHxEa2W4h2bX0+bG4KC8zDfZPZra/Li5hLf/wck3sJN9FUeLvw8CxDROsx497tj0uetPsrCU9GmA+9IGx8ZWCFiHLSoyRKGzz1AmpAOnHRzmmJ32oHCA/i+8C54M4Gg7iaNiMl6Ji445YsHY9ivXeWM4CnLtz8fG/wSHxwN9KA43Yt0bwyt048Rau38A7NLXk4XjC3+9JG37ywX3jrcD1kt8pwdP3vOQPDXFw1G/SNI0S6akCY8brg/T3wdf5pb/yj/vgHnvfGnUw4sBlFY2d6zaAW/uTJx37U3HvL+3nsd859m/HFw/sJ1QLPuYxqh4ntwnlZc2xXzzytzxJPvi3XNKMsBU2NVGAqunsgtfvxWniMXTkh/y3y32V15bw7r99yItjcdLP/L7veY+6KLUZoUFx2k7PmeW85+V3/tI+5ofH/POX9oQ8rWL+zL0mDi0dKnu3Y2ByLO0OPhMHxJu/5XJdfwlnhX3TJhj/yCcdRntpTLgZZVbal7eW5i/d+fqDfJa38jqWrkvv+dyCR1MMOY1VT+DYhrNq3bFf+Bv/Fs7KsGPx3vdaElRo0Kxp6jVbCBiIG8KmdPkd8VdKKPHn0md/6VmccS+8hFkzMqXqImNtILwZI0/5eqwsE9+9/iecd8wmPIRfSW9pzd8NanLKVJOGGKtVYjOKegln+OhvvhzTh/dwb5yH8/d+dcEbTxnSGbgVehLux87T9TVhgzeCoAWn4I0jlzitPrm69MaRZ+Hp+PKq5MU8uy9eOhpKpRO1MPRrMW51vFnVEJKFBCvknebKAZwCOhoOpKMBngYvNmOvOtl9JKXxDTCKtAVLJ7bhqFhH5kz55vfjzRXI4G18nwU7P36Wc8kJHPOP/OqDtOF74nvyAzIZsv8SzH8XOLkZYXws3hwz1F/8sznvOr74KEyFv3ARbg0JEtqIUqPQ7GSdwPIUYXYs/P3+sbjEy6OXup1jH4vvfqhs0MGQBir0tH3blljPe+6FeC6v+Wv8i4/+x0t4tkTHbgC9IjTR43fE1j306uJ94daQh/P5IN/krf92teSVOCGtHLsH8u7mOEVqE4S0j9hWHL/S5Adxj7f+x8t5eLnmY9cqbJpSJmSpyXZodaPw8Ytc0qn4Lsydl7/9a1eJpl2D8BRvUr4in+qTmaLDZgW85th7l8Yq8cvS62OfHUc8OZZW/VycaYrOUZNTjMzY2gXOvA+/+Uvj+/3/zVXatzq16kfNhkXvuT0byQ4h+1LY/zX8++A9/hrvF6a4c6IuAa1sfAnO78KRZ6GI83d81+vv5R1/TXb/tfGFjQ5FA2vyjc1kQcvHfvjjH8T1N3w82jRvwyv5xf/vaCgAoWWjUsuTDgejCnpyayBk/Gjh4oWc7TsuptPNfibH5tVH6dXr5NFyNTEsLJ3INEYMCCNNSyrK42XSKb061awaJ58zJFNNjaCY8lhkERjp0MtNNJgIjc0kS15NvUkqXXrJ9FTJuVMh5+8tpMnOeGd6LMJh4HVqWfvo3ffEbFym980xXITMLr2aPOsd0wuV4/IbvZ96CY185lEujJ+1bNx3I4z9+fGTcux+GGsdO3fNueup24nho/TQGyiCakD7t8fg6GgwUlCKABlNNtpkLau1UHqujbxVkL0x4d3ZOX7XBtVpUHlmtCHCJAw999bvm5aW82M9NvGph8IiLHbjbBAuphCbWm6E13npRfXvbUQhFr43P+bCyPrgBe/co3sehyl8hYFj+Oxc9aB7L9On7B2g19JaMyPwGTFjzPQOggPCdBPZCauWLYs3V1YWc+ffLtbJ4QSxPGAX+2qqk7/RU60n2GfeSNxwb/RuOlf+yTHs9m5TLpv0rQAus+jTUQlnl+jPO/usTOlzfelJZM3uq7WEGWFjHXo1sycESoCpst5LWOuRzKgW93Rd1n69X7k83iO90atT66Y0X+ydZ6Z7BnNGHY/gpThjWc3RDAjG1gvuUavShQp7zh+csaeDxnwljP01cKbEfE/u1jE6l3XMOjazHcR3G5rajMuacb2iekg1yt1LcTvr12hSZu327n0Yvxj8elgz/auCo/vmLcm5+8/lQ8/mVJam4OP2PEbueASM8925lTQvFPJ6eOLtbeH54tvpf+ARr8tXr4pN7G8l0XfsjdBQaMg21GeLaMKewo4yUo7rZK58nvjNdzp1bhhd2raOFnU52/7gDhwNOzPljEAOygleXzJSpGNcDjgiirUdAkbv79wX01evj8krjNhErDkiPbsRVWqFwLXRUkOOxZLB7MKwrcKkMl2eOt66NkkCRqWO++656edmPxlBMWVWxm4jrWWVlfEadeJeZTza9egSTTGsjqdfhdkxUEfSlmncOjLr1uFEmNqkDFLasZu0Vzf1OGBerbrZO0aExS9S8XCm7NpDNhR1oW+uWJc4eUbPXtGxS3eyljQeLAmgnII5bdm5LVa8Xxmzl8xzGtEcCVcXPO7bsWac1JnSDxwNB0knPbh7O7ggLzK6C47CR917Mw0snTCFQSVVhQAdNxVc94IYP3gGP0xHA7W2ZJus3LIrFm+wvpH64CMaS7tmZKfASxrXrUfKeDUUva3xKk2cvPojFTv27hUtWrdjnTXBZ9M/5b2FcS5vMGooPUnvW+GD0rLHi5l1pWdfvmA2mGVNS+bPjwlLlubYvSnNsWmnJWfSvN83iuFVil7Lj8V36dm08b3SG1hmuqIReU8q8l42LnN8I2+T5y3MMc48sRfRwbKEWzZCAyaJc9Bqicc7V/mCfFY+YOTHEylMYU/eDr9LvgpPLq+oiNemTsux/Wf4wNOjDUelKR+kTX6ca/fYWiMRRpKkhxLfV44Vsgqa5n7uoTxHXm2UYz6Rq8kLF+f43Vo3i5P7n5K8wAiw8/e4MZ0XRRaUWQ7chYf2i9E95YayxPIoeY6wE47KyLUowK+9yvF3OXrEoB7dqZ+npI59MwIofz8q7+At/taHcquIEJp5wZz5ntkJCXvupRJpFsFy6GnCzIKeVASHnTcksxcdU4euzb7M/rMHkcfWCZ+0qBxfXOahDNyHMWe0LCNYRpjIjtnLa/sWvD13Qc6+Z/sWNBnsHc0wfoS58/K38tTcC+aUBAEtGEn2vtlPib/NVhBnTW3POl1+swl8Hz/1TRRJnAhYJif2OzXatUZPgZ/oqMhTTcA15yvfFVctr8xME94TFnsyWk50D3iLQ0WJQRFpXIcMfGHy1Jz78fzb/6QTs/+CfZfkmFmuaHYGOJ4RQ2BkVpu8S1pUB1EWO2/ltfLLLL7dwHUTPHohZUtz0BHKGM2GrMfjmJOHOT8v9yD1IThGETktIm/upWPK+312D0Qn+015maG3nrTk96jNX0YvKo2+E/vQB4tsMRtrWs4hv7WExFIjMyucp7BXjzG7YSdO5BocF24DQ/fUPVIXMSK5Bvk3j0jz2iNW2YWnnoKS3QLdkDpv5IPwFk/UM5yYHE+0kfYTxsxZfScz26BTdRJ5jfxoCzrRXPoNTVxYyL/eyD37aVhiov62Fzzwt9XRReuRFbEVPcBa/tmLl9Cfozr9bNhvrhE2g2xDdkB19kBHAzykhs0g5f88mE7Czf5kZjX8ydGgWwH6BrbVwX04BLAHruDOQdYnL15DttsSjq58A3a4HvkJWKJD09ZkCdJLZO3OWLt3xVEjZviZZ9BzoW3iu7qAukE6lIGBNJB8knnLTyz5K2UMlHo5uCf8JB+WKFgW9e6iCpcYbXicNWxoNncU5tKMuq5rU9dL/Rf4y7OMAsvDMlLP53budz99rWz1u5aQTp4wLipNv+Ma0IUeLD17Zf8YdUjpVZ3P433V7cw8sfTViLP6qfcXZwt9B0c53899lr9RwlRJlHzMjHdz7LKmtaNzN3gBeoFy4iD6kidg2Cj4IPSzB8PcXg9mM5sVlIEI7q2cqYH+rR6pPrDF7ALgspXmfAvJkl6+0VM+6J9GaVGnzugGZNBIL9Kk+Ca9mN0gvpmNK804z5J+nVF6eLW6mEibuiL3NFv33Xemx0re7QksOqMLe8pXPWSA2U+ZYcL6LWXJTEzgnpnU6i6M731L2XZ+P/V4vmM27Cp6dy0gy1gJogPkrH4nk5XaNG0lnaJ5SUfssfghrzKCL69xXLMr3NvM5oTG/I76prhlZsXqypUxf9qMdB62ZrDTKOVoTGBNuLvvqaODZI7vvJL/gETinfQqn88H7yifUn5xHz/3JLvFi+bHnBXv5zTP6NktS4fMQE1cBMfFR/sd5LHwwMMMDe8l3OUN6sLqeuq+OtzETR/KhlXosTOmvBGroDOvYSf3i7Y6n4C1D3XI5DvsqfOWT6X8Zx1J5YyR8g84uZZklOh/wnUH9pUn2U1eIOQLZ+F5Q8/KBsHOHcqnbNYSCksjC5jK05Q/ZhhvIPv56Zdfih/85Mcx6vpRNGIXul5CxlV/eK//c0ZDwsD1Cwse7peM5jAcsIaCgLcXLVwQUyZOjLkorRtIy+7ctl10JD3XYyolP1M+TXFScIscM1G0Hvn9fVHOjy85a0CcM3hI1vip6O/eCeFzHxN4ZUCmVUoAboNEIhMQQRRCyfRAOv8Wob02kqI7hZ4Lfxg9OrrweujlI+M0GotZM2un3Ez1ZeNFqiRgCNJ1+fMi/YY/QHYFdjoDXGs11ll8IVbB/F6lacvCt6bF2UMH59ngHcvKkonaIV1lRwHoJdORqbpuwZeNmvyAe8rMVXr1YBYdb4nEQOQLly6NZ198EWVzf5xBE6c+3XvmmewqzjJsidz1CwsfMpFi/IIQS8qchOnDz1VSbeBkOuJsDIDJ48Yx92GcO94Ng846Nup9+a5MU5jyIhlKMicUHecrAzJ9ONMUXQ/f8ZIBqzC+T0bCkiVLYgbKkH0vzj7zzKwDb4ZRVxcmn/M9ougI8mwKxzq83NOSwpZKL5/LNDwJoA5p5Ttg3KY7zsQ5NW78G3HGGafH4IEDM/1bxpFpk+AKA+UaPCbVTRVWMsZMvUOwuY4ilRJ8En/AAw2NNWSC2MztnWlvxqw1G2LUZZdwhM6J0YQyF3FE/DU9SqHv/WS4PrsnooWprDp5ci+Ah/vquDZY0lPqETrjxr0eL739TrSkAdXVo65P2NdHYcu0YWCpgVF6OGqRDsZfrCuNFdajkeXRP55P7aMqjhidMHPpjfLis4/F0o174+x+fThhYzj01y7PqbdZo2cauw6FpymlKonZIwUY7MDRYLOdxRXl2WhxIseleunBlnb1oovNen+pnhCEMPkiuqGH96wOtTk9gRKGOuzVgW2kkO7K36FfxQ6Ixs4rwqkuv2uQhjLKKUJk1Y7dMa1yfYxF+RKGRsu8Bp/an4ZxXeikzZgoFSoNKis25oGZ5VGH2TCNdXgCh7iaKejskTijoqVCJL3KaxaUL42nnn8uNoP7A84amE7HMpxfKv9pNAp7fpdCPQWRjaLIqECv2y8esQ6VguoooKKr5RP7cTTobFBpnTGPs7uffp7jErtRm39K9CQVtgUCBPLF0cDxvuDFmk3rY/aCudQFP5tr1HlTAxie3rlGnNKtExkN1CbjjDiwcws4Vgis6lVxQLH3ScdMoujRoEEjb2RvmLcKqLIQSmG9ZjSg3KJsbQX4K7bujiU4Glatg3aOuOJP69ObRn89yUBpltkJlhOMGTcBPr4rT03pQM2silYNlLe9NOs8DBA1uMRB90jFRMVCOKvEy1dMkZfnHOVF8BuViUWLFtGJelaMw8lz7cUXc9rFiTm2Blb+Hl7i75PeGZulJJ7IL9NZqJLNvbyn/MUzr2thyEhPq20OBb1OR5F7f936uPaa66IXOCOuiA/y4KRVxpK+pCUvYZn8kdfKMPm7PCCNXRQJHQ1m+Hjc1euvjYk3UdAHntgnLjr3XMpNcMAIC8dRCDKWr32Wbyoz5EOJi/Ah5Z0yJBUu7qEiZE8JG0S9NX1avPLY46koXjviwjhr4IDsW6CjwbPkjwONlX8FfykZXkfwmnkLP3mMY8vH5HE+xPvllSs5RWIsvGZadGWuF19/ffTr3btozsXvnLu4LkxUhpxvQoex+J8H8PBvPndPPOrMcivnr9E+Hx7/JMekmS0zrP+JMWTI0LD+X1qSHvPEHdadTR7hOeKoD3l8jp3jFvug2ug+eC9LJnZA59NmzozfPPhwNAC0l1x0YQw4+ZRo17J1zls4y1ddt0pzRqGYrynIOuJtalYTOhB2qSyyJ9nzgrt4LPL74OVEgiHPv/FGXDDwVODSlz4wnVCi6bl0hP86Tu4vaxc3lYGpX3BP8UUFXbjI6wWc+CPu6/TRCTNm/LjYUFFOn5FzsmmcPSmyxEgn0hG8SUAzRpZ3MY4OAB3t4rv3y3IX7qOBY6nGdoyotdRuz124MF5+5eU466xBcTJ7atp8Q5zG/i5hICxYs/sHSJPnSz+Ji67PffU/nr1Sb+BPy/SWgTcz4WUz3pvFng7j9I4u0YoyFJtfCk8mRSYbeA4u6LzTCeLeCZPdyFbp0waddo2X90pTlseqMyyr4Ag5+MB4eE3P7r3ifJrRdWjTliw5HDngiLTjJf76l3STc2QMb+2cxXfhDMLm3quH6PQ0Hd8TmsY/+1S8g/N6FLJvCIZRE8rpbHBsmrf8Q+ezJ36ov70ydmyMnjSRfgw1YpMGL/cc0atGdG3T4ujxljoaquNoqM5ilIPJa50bq9bZYDZDkdEgrwEfuUcNjF9cAOANuAF89sMfNuFxX8Mxros3rItxi4pMO7YEvt+XjJNmGZ7e4+eULDZqXz9GXn4phlfnvKdOO7Qa+Bn3BfeVU9KreClOJj/mPv6ddCrcgJf7IiztufAu+thTDz+WmQMjzzozhp59Th4vq4EvzmsgCes0GPmNuqWvdWTqWEtnIMjkiWfeT1i5H8oF+w+No4H0U+iT+Gni3I9eEyf3o58GsHevSgFGdTuPnBffC6cb0eIjsjsN1yM8yHE1IjXMxKcllEU99/IrsQCec+7QQdGHgGFZ2QnJZ/19OmPZQ3WD/ZSn2Li9BnM0eKOOlnQFP5d3qTs5rri6i0DNVpxrc5FRzz35ZPTo2JmeUP2iN7LRpqPSkuU38iwv5Z+NV4W7tCZ8/CztD56Fhe9Lc67boNo6YDMdnH9rwnj6mw1Fp+ma/SgaEeATp6VTDVlx3d8IV3ma90jc5x6OKW0IP2WlsNMpUbmykn4RC2nGyslhwPoc8F3nlE4c+YUp/Y6d4zgpHtJryl7gqi7v3OVhOhn8vCgLwDGGTlC+tDz7r0xkbweDL2fgJGlmiQvyVdgKi6M8PSEkThR44VhJx9y2tJ/yeXmBuvcq7AT1gqlvTor5azbHrdddGyf16oWOioYJLJQjym4HZIh0Nqi7shjWxIMPkufy3eyPwj7Lc6pio+0GNkvL6Vk37e0YM+Xt6NqubVyO7tG1Q4dC1wMu8rK8R+4XdliOyb14nWU7eW/lSSGdhI9Odue/BXqahg3yJPJpJXO7cNDAGHbOIOyQlkmfnsZi81h7NaSzC7xwvjrQbTy5miDexLGvx/Drr41rWffxZPfkxRyE24f5+rs7GgRG4aUqYKNytAil7y2QcvnSpUQU8IDBEDx1wqOrDgFgsUYwKth2kYo1/s2p8bV/+Uamkdxw0XC8O9dzZGVPjLn9WTsp486IHb/KRjxuhAghcYJgiQsMKOIpeNKYBvlFCpnf86++Gt/59a+jL/e85gu30WX83DyHOaPmCnzGSUarAsUcJR7nl52eQbBk7iKiRIhh57ndNqDS4POIngefeDIefW503HzpJXREHhwnwaQ8zqXUpTsdDdwjvYZHiNy56q0VoZ2vq1BI21lYJNYjbSOdGbPnxJ2/vS+a4H0fPuzcOJH6/zIcN/XrGn090vgFmObahSzj+pBhZ4Sb9WkEyLR0AtiUy8vaPh0Bk1C0nnjkkRj1sY/HwFNOjQ5lZTStaZIw0YOrI0YiKzXfU/B4yXgVPI7pvYzaed9GMDqfK4DLuxy1+RJCfBOM7BPXXovxRVMYPLgeW+V8hPtOGLwRQBmfwqA0vsyUgfJ991ePug6euqRkSeCekjBp8htx16NPxscuvTSuuuRi6qlOOML8ECTAVSXXeckEHV/mnPsMrB3TjujCXKIWV0y5rUmD0RWVlfE6wvLZJx+NqctXxze+8Lm4YMiQZK4AOiMajiMDdGwNXvGyJmuqgbJkRoPrMuIqszeKs4W9rEPqp2UnczBcnnjy8fjt6BejHcj1L3fcEQNxftWFuSrsvQfIReffwrjX2HI9Tlp8dz8PoPCrWBkxMrq7l9fV69aKHdDTZPb0vrt+FdOXrCabYXB8guNz+nanczzjHsbRoMBXeDIc9FIoKDosTJGUHldU0peAOb41Y0Y8/MJLfitpQDFrqp/ssGlL0uSaWt/MOeebDsQmHNKbAWW/jnWiV8eyaFYbY2f3ZiJBNLkELmgldHDHOQbzVUmy13tj9rM69z0II15DacKM1RtiaqXRbfATMHh99OKLOFby5OiJE6xt69ZEGz39BaMNvNbRVhsjuAFwllZ3kLWkcDA7JvEIXqDgLkWprcFT2P/0179KZ8+NH7kqLrjwIjpp02gQujcLRWegsPe1ziMdkJ6ZjvYGOkpvGowoSSjROhtQDVC+rBflNAM81K/TBPfrv/jPuGLAgBgyeGicciqNnNqXMWcynFAgt4MLK9ethu6mxM/u/EWusSZshYSHGFaGs6EHx+k2QIGgpOrQnq14xJkH9/ZEiUJjL+i7yGhQ+QAfGaXkaEjhC5wP85sD4OIhjuuzdGIpp03MX0uJwPZDsWRr3jYuHjYUh8KA6IhScjz8RcfjfQ88hEOnflyNc61D+3bZ4LEmnfirUJ98gD4aNtNVkRLGgCYNKuld50tePMnPNIr8XGPQWtnFixbHFE4z+M1zz8Y3P/PZOG/QoGz2avNRDU6dXo6hEqEDVkOtugId3EgFNV+DU4ztPvqoVU/8q5rNmya9MSlGP/t0bCRa+M9f/3qc0b9/HhkM5NLRUDIcNe6k3cIpopJUZAElH3ANvAaJ8tn6/R1EjKeiqDzx2KPxwpvT49KhQ+KTH7s+enbtmvxXXFGBdXx+lfwg5QWvUlGRt4BLfph7w3fkPhrSRqzWkHEw+qWX4vaf/yzB9+VPfTIuR44YdZF3VcVJVB3C8zjlIqLFC8dUyQU+GR2R/zOm7zmuxqnrklfMR3l+6NHH4g84q5V/N3/zm3lUaGMMUjPsHNPzx537n2pKE+tzPiqgGrp5vyNGo6RpZF/VSx7xw3//Uby7fGVcMXhQXHUVXfP79sUJCe4xvnJbR08aRqwhjWn2WGVaHi8PSucIPEJHgQakRpKRqc3w+dcmTYqv3/HDnMtXPnVTXDZ8BCdJdMpsAx2OnnIijessTZkCLupUUKZA+BmBZuRUhl0DFhS9TuD7OBqWUef7JGfV/4YjJa+56Dyc4YPipD44Bmka575m53X4lXsrHotrKT+Bv010hYvzlYemUcD8XYvKv3XY8xYsjPsffTQOwNAuweC1AXFn9CDhvIWyDp079kFwD4y0ym885tpLg04cTaNRWMFfNAx8mIlYWVmZAZS77/6vuOrqj8bQAWdGWRkN6ZDdAjWjog4k3onzwEV9S1lt9E89zb+V66lD8bmZeeJo5apVMWc+zjVg/+Z7s+PTn/hknI5x0YaeGg1whptdtNcAEMPrXEvnHTAQB2WV0hDsDuOQeQNrWZdOB5sPu06bRk7BufPIYw/EaaecHR+76qroCVzk5RqLSaPMQ9zgyVEZVzpyIGDCw/ecq3JQWJnpoZNFg27iRHjBE4/Hi2QJffWmT8QVF14YbVT+wcP8DfiRjgbkxqLypfHbB/7I8ZbPpDMdV3WWMQzrXTe6tG4Sjchcqw5/t+eCp0joaEi5z/ecmTTgQyfDQQBiZq/6ak1xEtTA5GEfCQ7wIH8zezS8jxG8VCfXEppBMgpbEENPP5vjTFuC19R5k2k47Y3x0b53l7jlszdzjPiJyCPoGTzJUjpYgJkk4k3CHpyTBoS/OMqbieeHgJeOENcsT9hM5td49vSn3/9hNlv+zJVXxjU8pCezJZK/gCvy21I2afY347eZ1QANZL8L9lT4iTvivnuuEaxj7bHnnotfP/RQDGhUL6699bYYes7gdJoa2JLfpB4jP3DPMFTNrPJ9dWUvMy3Fez8v+Bl8hP1Wx5mFXXHn734fryJHbv7olTFk6FCcDX3S0bUT55tuX/GHRMM8cvMQ9oPrkueo00sDOmUTJoxphqxGqU6Mzehnk3H4fv/b347+3XB+nXdeDCSw16lDx+RVZhebvWAvHXUA5X3KPQks///Ts/P3Pfl+OhrAeWXgmDcmxuPg2vXX3xCnI586daaxJo58+WBmdDA/r9xDx+CSNpWH4pDNlpVdm3Cmee9WZNX5nNm96DXPvPJK0v1HOKWpT48eeaxn0aC5yMRw+JStwFZ+ZTNZbahsJMuHZlpLu/I0+a+8xp5ycxh7Kqdr/PapZ+KWUdfHCODucZjyMbNG5FtMJNfqeP7HhAtaPfLsytQflbOeeGY2kfLVoNvr48fH6Befi7cXVMSPv357OkoaYT9pi7mfaeewj+K8f3tfeZdw0k5SvzObJwNvyET8gXmvbejgNht99dUxyL9XYiAZml/4zGfiFHi8WQwQBoQENkBHKQuBbc4betLRlgEV3hPmws3P5DPK2JrAaj19LsYw9x997/tZhvwPH70qrrv6KrIpT0A3JYgH/nlctiuohR6WQRPG2sOcDXoth8++TEZDr7MGxHXXXRcNkfl5uVFH9r9448P379/H0SDW+ChdbKyE5XumHM9HkLxFf4b3YTxN8IAPRpntTcMRkfEQn6fAEJAg8x6Q4ZWJE+Km2z6XdTa3XDEyPnXzzWHX5UMQoKUTpqqVFBaRI7OGHYtNE7EVPk4no1EMq6csvU4wSM9dfRTm96U7fhBn8r2P/8s3Y8SIi0jhP4FzuzGWUQocU+GmgiKzSkcDA6bxnO9pjIF4IPFhDKNDCM10NMBw5y9ZHHf/4Y9x38OPxhevujIuGn5RnEHX5foY3IdIcXRcwaOwl3nKhCSSQuAVyCtDlVmnoOAe3sc0Xh0NKnL//p//GS05+eIyjKL+OG1MCzbrIIEOjPahbImYMvuSAqdhbTqdzg2VDgVNKmMYk17rMSoWogiNwZN/z733xOdvuTWGDjwzO5h76oAAVQGVAUqEKq7OO5U15poNVYCNyosODaP4Mr36eDrd52VLltA1d1o8SSbJOoTMlz71aebeh2wGHA0wzOooNV67EDB6o3O+jO99vFwHt8FbjYBAeO/mO5YJ1MHRYMrrDJwY4/AO//je38XN11wTN3zkI5kxYWmDTESGlAwCJqIjIL2gjOccxRUvFVqjHOkJ5b52ID8OBriG/hMvvvRiPPzH38fYReXx43/55xh54XAU0KaZsu/YKs+Fk4GBwEM5W54prYKNsW7kRsZ9HFHLQ+yPHWfrURdnbfVMPLi/+/3v4pd4zzvw818A/6FnnpVMW2aaOI6CK96kocS+iuO53zDp/RirCqfajFUFYWCjvn0ozjWoW9u2b3eMnTAxfvKj78bUhZUx6qIL4wu33JLM1XpRuBzMr4hMqAAe1qhCMVGw1KrHET97iTjB/GcSSTSt+O7Hn+ZHpPvxEOMa8KDVRdRpTi10k3rZkX0DvUY2b6EZJ5Um7ds14PjLVvRowBDfh5PhMJEG5q5yaJ8Go6GeLVyT+TaElhR4B8HVtbv3x2x6NLxLM8jNGMKr9UdyffKjH4mz4R8nIiTSwYaiqyIkbu6DmbvXNoU9DH5YGiIN2Ak+z1+GZt0bDQLXqnEwhZTdf/3hD6N88aK45aab4nKU3BMxAESK3azDchUYSQrPFEbyqcREn+UTCCbGPE5lzgdvH0IJ0mloqvRLCJ7PfPd78XEUlQvOu4CuyAOjrKwjX6Nekfma0bBsTWWMnTguvvXD77pE1gA9wQrOaYdTtnvHdDTsp3a9yr4dePZZH8r8YSJhPqQt8RfuB0yT7EGNQrkVz2WHYo4PMxp0NGzch0N07daYgyNnO+yu/Iij4SN494cOOju6lOEY4sjO1UQXfnHX3Zwf3SRuvObq6NyhQyqCtXE0VEV6G4VSgApjnVzCQaNFgX/UsQB83NOkb2AjPq8iq83ygAmTJ8fPH3wgvv/Vr8UlOE1boyjJk5QHGuteQrng68CYcRXOaXgwbtFwqoiQqkx4DyM1Zhy8SsbBo/fdE5tZ879jmJ6Do8cUeGlVHPA0lNI48nkvu42nsc695QWFkl7wYnlwLfiBDsOJUybHgyiID742Lq4ZMSK+hPJ/Ys9eOW6WOIFXGqOuo+QIdx0qmr6X/yUNFEq5UZOUUfCazcD88Weejpu/8+2c079+8bYYBS8rI2NCeWHZRDUM9CrMX6VZ2KTRBW5rmCvzVLbSmJNHA3uz4iybEE90GN77u9/Frx5/Ivrx+us//WlcMHRYNkY7gFzQuMiUbOaapR+JXGCPyAX8nYOGnFFAZUhReoDRikPWr05AAf2nf/6neLO8Im44/9y4/uM34Bjsn2USOhoSHiBlzp1xVBzl8Y6bey7MmKvzzqNIWU86fXhvA4bR6Ndei5u/8Y2Ezfe+/IX46Mgro3PXbnq4KC0qTjJSMXYvNXykDu8rf3DPLZ0wwFE4GZAtwgXeI79cQtPLB55+Kn54zz1x48gRwOU8DF/S+DFK5e9GRC3RMkJq6UWhaMJv2VznmEYL8xVONovUCSbsrWPfQhbfuyjovwb2VZjL1SNHxhmnnZZNU3XUbcNY0JnpqUvqOWY/AvE0msT5wpgGxViPRp6GpOnsKtRb+e2yZcuA/dT45c9/GqNu/EQMH1ycAtWErC8v9bC8gK9y0KskvzUilR8lma5M9KoBH5W/rFi+jKP73o3Rr46JyXNnxde+9KU4+3TKhSidMEiQvXCYb6YFg4PiupdzNHNBhR/CTeNwD3SlQ1/50ggeswtZOJOxXx87Nu696zcxaNiFccuNN0Zf9lR+q1O1CPy4k/Kz4qHeJD+QB8h/Ei7sobipoSFu1mX8DTRbe/311+MpgiePko333dugp5FXUBJDVhl7KS3KIzOiipxYsGhB/PKuu+I3jz2c8BdSFrYM61U7urRpGo1oBlmd/i01xAe6glo6kcY+8yp0CeYH/93P5nk6iXN2H3UXIZ3hm+wh98NfyAkmVTK7bD0GSDmyYtLiosYedTIuPGdYtCVTR8Nk++btMfG10dEDOrr9a1/NIIS4Ik6rD+jIEB/Fc408YVLKahI33QM2t5BP/O3uypPXgzcvE3T7p9v/Kfvm3A6t3khQr3uXriB0wQdK2XzqZMfx2zroL1icjOAiPCqUvkzyePZYnpAlAklfVTAal8fvH30kfoAMGda0cXzya/8UHnVqicAOHGs66+QxGpDijLygCvoSwhMnP/KOWxi4cr7Jc8CZauqJ0KylidPmzokfogs/M35cfPnjo+JiHLKnQK/qfNtp1CrNOqb0LlYeQM/II3O5p1k3XuKo/EIHrYEVT7zT6bgJHXUcfP4rt30hzujTLy4leHUOzvAOnbuwbsomKVFIHZU5qQ9LP8mL2Qsv6VW6Nbqf7/C+hqoOCUudjNw/i4y69ze/jls+c2ucM3BgnlxTn2zCw9Cq+jMonridRjQvhIF80jlLqwaupM+NGzak3GvWug2vD2WJ8nT0tYeeeSazoG+6+uroj/3kiXw1ydqRTzpf8TUdx4zpWkonZFjmKJ1lY3ZgLWyULe6xp1MYMPQY0l8+8GDc/umb03HXvlVxOoiZBzZXFckP51x144gtQIE1FPRayFRp1jWlo0FdmLnZQPZFsrIee/qxmDRrYdxL0O2Cs8/mqG36EPH7xG9plt+lQ19ewvipb7AX6hfKv5R74KlyO/VaYLmNcph3mPvz2H+/wkkyFD3vm1/5Sgw8uX/aLUmY8nfwz4CqsFYHqCL/de7uh5t5BG4irXRUBZzhGI3YSebX8zh3vv2Vr4bFfV/6xA3x6RtvwHHXMQMy4t9hdC/tV/FPvi8P0tGgfbcU+fMc2bXt+/aKUaNGRf02rfNeDJWwy+cP6T9/H0eDi3cDCoxiN/jbBxvhps2ZNTvGw9Dep3aoJUxmyABSlLt2zzRQG6fIbERAU/g2UBM/FgL/JsrKUoa47cqRcQ3enb69ehXKCJtVKwWQqZgoVDDiVFZACKMWSSAqqxBGybsloin8NcTW4nV6DGP3Kz/99xiBc+GKG26Ic889N3tGSGDZJ4C1yPpS0TqC1KloyLB4qHSp0CrQFCiHsQwOMf8qGGmLKiri3gcfivtQ5L6EMnEeCnT/PM4Mgc/vsPKJQHDED8xZo1khKfBkHEZdzQpQ+JrqY8mEXmiVGFPgjaa9M3t2/Oqee6MRn110/vlxBkTSrUPnTH9PYQPhCUcNacfUsWCmhgSTioX3Y8xUVliDyopKV3YXx6B+DeX/rrvvils/ewsphgOiXTuim9TwqyDrHGHwFPzJVJm3Xt0UbDIV1mTERaKU+cpcVJyMvqxcsTJmIxieQ1lZiTf3ZoTaKb37RGMcJqYMO44EbPRf2Og91VPpmD58T6VAhshTphoZJZFxbtq+Nd57b35MeQsv6933xoWXXxHXX3FFErhRGVOVMprGvPT4q2ilQ4mBsvs5O5BMnPcdWwYoKnumuZ7K5StXUpIxPp4nQvrc7Llxx1e/EpdgOLYm0qULQ5iYNirOqNBn6pV7Ld6BS0Zo01hifY5dYrh2+NW5NpPa13uA+Z0w2E785Ge/vhMaGZD1hSqBClyNRp/9uffzhYq0Am2vjgbwqmZVhAH7noITXKxJM67tpPC/NnFSfPdfvxizSQu4Fpz58q2fi/49e8dhsgYOchKCMDHdc9v+3bEb78Nx1EDUpMFj/Yb2ItgTyxYvihnTp8frU96M3z33WpZLnMU0sINpeslUxNvanAddi8gcnHg3+30QYY24iaYNGkaLxg2iRb1a0ZhQfXVwc6+CjKhQFXEHIFUBPlAQESIwhvXsZ9/XkZq/YNPOmEvknZ6L1HkyGNdNV1wZg6ix7d+nDxkr7VFuq+FQoD0PuGl0RHpKo8ix1OgSYCqTwl6HQJElI/1acjP13RlxB9HjnRXlcfUNN8WFOB374eEW33fQWLZISVWYsbfMy1NZzDI6hANnL488Hox90MF5gIe0JO7sQQm0t8UrkybFF3784/jkRSMyLbVvP7oit20PjuBUBWe27tqRJ06MnzIpvv+Ln7rEaKjPDV/hyciZfp3a4GjAiOdexx2i58oRR4NHYxaOBr7LMnW+MeXEB5/F4sRkPxNHeRw8ztIJMxoO0/x0Z7y3jmMdaQq5rAgexWXDLogBwLYrgrFV86aUHayN/8IwakTmzbXQUw+UrCaNaHgFDA+SjXEc65VHemVK6xFjS6M5MxOYh7RfwndhL2+wTnI2MmHC5DfSqL7tti+iWA/O8jUdlZl+yffk5zpFzQwysqVipMOxZAjJK1W67G0gH/A+KoCWTUycMCGevv8PsRGD7/vf/W6cjbPXcTOaCy+Rhkz1lF7kB0mfPIsx8jOVQnE4o55CELquD7LvhJdOmfYW5QFPxF0c9Xr1BefHFz/1aUrYuqfxrKEuHzPiaI2weKORJ/8xk831p0ENnsvj5MvKKYKjqTiuJgV+NGe93/WjO8Lq469/6h/iKpTcTqQEO9dqbOshYG9Gg7IoYYLS7byt91TWuQq5rzivoeE9zDiwp8N7ixbFb//wh/gPsu4wz+M74OaFw4aFZVrbcczBbJN/KVPlyRq9Zpdkt272z1Rzs0eMYNrHRRm4Gx6howmiySjgd3CSvLFiRXwa2XcthsspyD+PF8yMQGVI0ip8knlJt/J2aRXQpMxKGnJPoFH/dg18OdajG4we93p8lvE7ott9+tYvxCUXXJjZkY6T8k9e67r5ibBxDGGv0qiTphayoHQctRTiuDoZVHaXrloZj4x+Pn7yh/vjY8PPjfOGDItTka/tUKLTOcHvDwCLw+gchbnGxFhzPpi8cNeItgzDOWh4CRfx1P5Fs+a9F3c98EDsJwPhCqKMOjG6QFMeh2n/CvmWcBd/xCMDBsJevLHvgOMLe/dTg075Lt+xh1D50qUxiUybR373+7gUR/uwM89k7M7U+bZIOKfizBrF9aLe+ogxKlwYx70wa0j6Ev+9p82MvXQ0TyfA8eLrY2Mytd9fvPXWOJO5t86Mhno5TzPKNPCMSrufpQCHeKizRZ4gbxfWnl4gnjZp0hTc3xuzZ89Jer3nrrvjHIInn7/xpugFXJyrhnk65piHeyqdSj++l0dWys+Zv85jxxX26jkajbVR/u0aPwGj6PlnnoqHp70T/4Zecy301JqSzT04SYWFephzlH7mLlpIoOj3cdczT2Yju80CgGtEd04Fa9cmmlACWFsPPXPYjzPc49lrASvls0Y50ypkGL/xVCDXaSZTLb/Hc+KpTkK+eID93oGHdBOvyze8H+PfKxrK1eA35w+m95ZZF8iI7XjaJ4yfkEcG345T1uwsHZm7d9Bzi/vI3R1XIyiNX9Yv/qkHlAxp8VvdLPvYgJfShh3vXx47Nr707e9kY8NvjLo+riEDqSeOBseVbjTMlYHyGh0NaXCJL+CfY0v7BgscW6eXEV55pXui4XT/44/H9++9L4Y3rh/Xf/lrcT66djNsALNwdF6krgf868F/xEHvKR1ngIU5uJegeK7F/UmnKfPny/Hu/Hlxx69+FU9NGB9fvPLyGHHpZTga+ief37Zla64hnVXoN85f56blWzoAzGrw0jGr7BJPnbfj6mQ2S3YC+uR3vvHPMaD/aXGhujaysVOHjsCOdcvT+Z14mDoHsEl+I36KB3xWZAwJayPgUC/vZckJc1C2Pgc93XfPf8XNN30qzkbf607WQWOCV3vQj3YDd3VJZZR47ngeA62jQidaPnhfHuZc5HhmxJnd6lGSMwhePQYv84Scj6EP9+vVO1riyD+a0QAs1KvVc6R1YeI9nKOyUZin7HAvE97wGhDNI0hnz5kdk8jUfPSRR+NacOZiHNUdcWLUr0tzdZx1aSMJW9ZcwLdwFqdDinuV5J7wK9khZg+b5bRg6RJKz1+PJ557Iqa8VxH3fee7cf5ZZ3FyGbzISeXE4OPaB+CNeGKmdZE1SZAKWKeTjWe+nXxHW1DHr6cAzsAB8xL23z04YU5Bh/zq5z8fp5Nxrmx1Lwubz6AyWangfZa9iDviOPMVb9KGcHxe512w1Wo0qBdbcSa/zJ7+Ckf4G9z7tuuuiU+Mui5LMyx/YmJ5VLK6gbze+4lBOhl2oMMvWV4Rz+EE6XRaf7JcRkW9tm1zTxgKviJFfnivv7ujQeBr/CT3EzYI53kYUmNfeDFW4+FsiXAxo6EXzAyqIeUOzyCbl8YqkUAdDePwzN+BMqFX6LYrSIMnotOnZw8YnpGJvVm7p+C37kpHQ81qCAoIReNRwVsHpVOmZz1tRr7m58MAAEAASURBVHeYk0LOiPJ6aoweJzr91f/4eVyCo2EknqMhQ4ZkQ8UdO4jCQuAybscrZTJkNIBxfS2iSZQa8xr/exFsB0HiQyCbzNeoyG8hwD8+9XR8HsaXpRNkHbQislAVpnEA5WEzlpNC1JoqGbcCXoaqMmgk1F4MCmaFgoLKdcjQJZR3SP25+/77owFEaenEmaeeTs1eVxQUUtGJKsgsHNPxNIz0XKqsK6BliEVKKVEWxjTSosKoAWCTzJWVRFanTIm7iajfQhbJOTBWa9I8BiiNN2Cg0PTSkaCQUBF072Qa7n0+ksQhI2Cu507mV1m5MiONLxBdWEEN9SdQhvrjaGiGB9eULefsHDPFlXFkRsI5I13gic9eMl4mk84XU+iqEUnbRKbHgoXUpE2fFg/c99s4Z8Qlcd1ll+GA6ZiNAWUe4oYOAJuQCXvP+lYrMPXVOQsjGZPCxgihCqqRGPdk+arKmIjB+OLo5+LZue/FHV/6YhGBxdFgGlQVYGFUXkGscq4XW8aQChcwSkUOGJv1odNHL6ip5rUxkNgUmN+7cd/dd8ed48YGcdH4wc//IwaTpieu7YXhKQxkS+6tCrnMWeFVrYYCXwcb+6uihyTQdHfuBxRSnPawlejLa29Min/9yj/GIqLX1yDov/zZW+Pk7r3i0HaOtaQXgsqJaZ6b9++KnVVIycUpUA2nQRXShmxCVgnzX4CiOevdmTF37PTsDt2e6bRrRrpdLTrGM69t3KeS7IfVaIYHcdBUYY3VgUddnBYax81hxC3IsEDsUtKAAQ/uVWPuHp9YHacIUBepUBTxxbHv6/YciMWbyYbasJVSjD85Gj5+ycg465TT4lSERBcdDcBvG9ERWbZOM/GwFJ0Tzu6pTqRMa2Yv/E7uOXA0/XEqHu4f/fI/YjfK9JXXXR8XXDA8+vbunbhiVEW8U4nzIebXBd9q4cHeu5cTLfZuZyxwBJwyE2YvpQTViPaDRNArjob1G2IMjrtv/OJn8TEcDYPPPCt6Me82bdoCG3pLoERuwstesYoGr/C8HxPd8GqCzVaTAGSvdhxviYHZsgE4y9Fmxx2kB4pCDXzI1tLoM6lp5Mz4G3gn78XpUIhAFAYH5HMx6BB9HQpHA1kMW/bEfJw4q9dujpVHWpKP4NjIU+kj0bVTR+i+OWnH63Ca/hEHUDWM3ZGZhWaTNhtF7ofmjAnVRaEXF83YUeHkNvAu6Qajns8T9vBI90G4C/9VNH97l+jiBPByNLz40yj/5585KJqjZGkMm4Ei71IR3QOdSrNGWzRe5CXpCOBzeaI0ZYq02UHSh42s5AVTgPvoBx+JdWjf//atb8Wg/qeknHGMNGCAiDxGI0O+4z7Le/1bnqUTL+fO+8k7uV8DI7DQmnzm6eeejjtffiU+gpH+hX/4h+iDPNMpqIEonmgwOm//1iBQaVE5Sn7P95L3ADv3K8vAeLYHxJr1a+OlV1+N+3GET+VbtxMRGXnRRVkGpkJoGrCOhmwIyT64ZmWcNKiyLz/xWYjroJYXyM+MDKpk2kPh93/8Y/wc+QR6ke1xR4xgDfZpUS7xA/gOyiD3UvlyTZ5jruxQuRXl9gOjjF7ynXRksw/yM2XB5BnvxA++/72YCM/8LE2cr9OhfFI//aJkg9AQl9/Ky9Ig4vvye+/he/LJ1AW4l8ak6anpjJcnA5+1lCKNxuF7yw+/H32bV4lR134uhg8ZFl3LUP7BvXTy8Ft5gAq5yqF8WBi5LlVJHQ3CsBSUcB26afbBv5Yy58defCF++tCDMWrIoDh/2PlxOiVsbYncZ8aac0Ln0Mg7KgcdW7pL2OtsKIwinQtG7d1TjQvTkWfNfy/uQzfYvX59jLx4BE4MHQ2dU0EXP5wjyJa8R6epcN+B80cY6ExzXfbeMVqnHJeqxdn1lD1WVFTEZHoRPEWa+vkj0TvQsbp15dhjjAtlt5lGwkNZJ+yFtTLP/RM3NIqUTRot/p34As35/fIK6poZ+2Vg/9a8OXErusFAnEctlN0YAEUZjM33irRpack1q9eIf6kjAAt1GvHS42ZV0D0qXDhpuOgYvBe95pzhF8bnwJmenTpBk+wfMBE3pBv/kxazVwmgSn2MMcVN6dqmlWYTeExzdWTQccjvVWvXxKQ33ogXnnsmHqOJ9rdu/kxcc/ElqYd6hKnHY7tWjRH3bt5iHHGPPBT3jH425dx63vPS0dCjXZtoWhf+hIzL7FroUEdDbeCls9woq7JaGsSXC06BerzWgDaLrzofZSTWdSHjDkNfu/AwbuaLS9evol9N0YtIvjps8Gk4Gpqno2EHRvMb49+kBBfD6MtfJbh0csLc8gDpSviI40fpirUoAXQEZDCMeel00FAvGgqSFcp/a8CbF3E0fO4HP8g1fvO6UXEVRmn3Tp1zPOlJ2tRAUodSLy6VCGjYaYhmgCZ5DPPgvjoa1Cfd8/KVK9GDn4zv4/w6n9+OIhP0XIxSM0HlBfJ3x7esyiPbXYt6mfc9uufArqAp/uVv72GquveYOX9+/Oi/7oynyML9PHhzyRWXx6k4wJQhOhqSloCzel/SK7gkn5AexBl1qeSPrp798SEfTXpFvk0kuPKjb387BqBvnDd0KGOfGh3LyhIXdQCqZ6iLSU8+vBI/4WnaByX54j5kqj3rKzkadPKMHj8u7qcX3SevvzEGoe/16NkzS512sK/29JL/yg/TCAbG2ibSsDq78kkaEr/cfmGjviNtLVu+jOyp2fEENpcZs+rCfbr3SF1bm0NeI4zlsfIZ5a3wNlglHnlPnnKuMIosx2USaYusZt6z582NN3DCPPnYE3EFZVojBg9O+eTxu3WZG+SRU9IWS8OfmaobOPe0c6QX5i2s8mL+6try+AUVS2PspAmUsD0dby1cGb/91r/FeQPOxNFAIAE8TLsD2NbFCWrZsc5Yy6/kLfIt+b33LHQ+7ZPCLqtF5pW8UkfDCxjzD+GE6d6zV/zjLbekLqmtkbyRtYsbGShF5/NvwJzyQtxJxxXySJCnrgmfM0hgEEJd7tXx4+Pu734rXsdc+fzVV6Wd0wUdzr1jYmm7ZtkTY3sdh6zdw35Y1rF4GY6GZ5+NrmedEddT3lyfLEZx2DXlI3/x4fzn/+5ocN3CjM0QOb2gYxA//8z37YY8AeVpDY6G4+vVjzP7nxo9O8PMQBwdDenXAclU2neitIydOiW+evvtsYwhbiWF8aZPfIIoY+/0Ru7azvfZJNMYVRyy4y8cr/AUF0qhSCdTlcAzDV7E4LVM0IZPj1Gf+hVSDM9k/FH/+I+kcw2P9m3bZsdWhVjJsMslyIxAMAYTs1JoKOAU1BLGfrjjHhRpVD3WXTUWgSwqEw89/UzcRkbD0MFDsgGONbDWkBtR0zNWKFUoOQgBmauMX2blPBOLGUvlxYfOBhV5z5p/m14Ev/7tbznSs36MOO/8I46GzikwPaZKQSbBKJRT6B3ZBtcgYagsqqik1w9Fzoi9zNseDYtIW3oNR8PviXh95lOfwtFwGnBpR5d8GtSwdudabC3EKwFyyWw14lyPwiUvCEMClRnW0kBAmCxevDjenjaN/hhjYhPM4dMItVPYU6MaGhFeMiIdAhK5xrpZGTImESvf5/NSpoawhyNGdQw/U2pngWMTJ0+K3z3wMCl0lyZz7VpGqjdMVCEj7BVkwlgnkrRb4CzMkHFdg+hbgrnpUkZEjJRa8/YSKVGP3XNnvL5pe/yMurGRwL5FI8phcHRlGQ8/dshkdI4HrBRivlYouo5krqwlhRvPKv7CTKHwyIMPxi9eHB39UYT/5ac/S9gLYxU/maDwdf456byRTBzGi/e9Cniu8lwPQ5LN5CuIILeHusiNHIk4FkXrB9+7nVKEAzEKz/znP3Vz9O+BMb0HnNipsonDDEV7f03oqE61aEAmwx6yG+zwPnPWjNiybn3srKwMivaj+sqlpIByZCqR8Va16qTR6ekUHqG1EqZbyZx24ThgF6FJGpZh6NVknxrjTGqCs6E2DjJT6qowb4iBkZg7c+UnwA+BDPPYx1p1NCzYsDPmEXXX/lld+Jli1IjL4B84Gnr1wtHQLhqgJB4EZxTsCl7rJTWwpAEVB+GrA1CKkMak0cRN3pcGzGj41o9+GNOWLo2vXnZ5XH7lVYVhBMx3I/CTnqDZQvBI49Iu84UBVaUrnwa9AnUfEeaDpNhUr8Ge4vjcA16s3bARR8Mb8eWf/ySuhw+cO2Ro9MdwKTuhDLmNgEFZWQ9NL1mxjL40k+NHdxaOhsboLPWwO/p1aRwndWkfLRtSt7oP58yeLRIcTe1QNqqAU24y97ZXRF4itXTBHO3ZkP8JB3mBrhwzGqh73sTYSzZRG035xLr122OVPjeuy4YOh5+cEV06daRhZRMcJWszunfcgWqkel+ejoZWRAIbKNBRhFRX7ZtwGHjrdAXbE97Spnvha58lDOnK6K6OhtVrcDTguBqH8+4hBOvnUf4vpKlUm5at4AWUUYHDCnUVQQ1T1ygPUEFSYTaSbiNV/1ahcK/dUxU7eb6NJt+YSBTzoUdjNcrFd7/37XQ06EDwu9JiiX85fvIwxvFSCS14GbDN27IOvqPCUsf+Kqx70ltT46GHHog/vPlWXI3j7os4Gvp265YyyIicDxUEFVznnQYwzypwScf/D3XvGWdndd1tL/XeBRLqMxr1CgIJNRCiqACim44BY2zHdhzHzi/F/X3zJHacx86TOMUEV3rvICQhCfWGUO8z6r1X1PVe17rnYD9fX+cLNxzNzDnn3vfea6/632uvnQRxWHIkF/TReayjs4Ij9+pbb8U/ktFQxUff/9Lj1Me4hVV1ajSgN81oaAB9lHefoZ4xWJMFBEmlcwKl/O1X/Fv+L1bA6sRKQJhf/e738S/Yp/Z8/rMfl4AGMl0QNINGnU4d0NSTNkI/U4ehOy/QHlJQ0J458pL2ZqUJmsxEx3/ve99NoOFLw0fGo+yBHUxQqv62uKhBgIFd6lwd8up5LliXvkIz29N2qffMGKJhBA71w/1vTZ8aX/z7/ycqeO4XvvZncfMNY6Nbl7JcdLCY86eruLTrnAmAOO/aXGssWBASBZEBjrqhIXYUo8JWJWwUiwTPkLr6Y7ax3T9qRNwMYH3V8BHRBht13CxEwORSZsP51DHSvDoQkhY8z/Ridb46SFtukTzrRjj2JasEGp6Ps8j8rTfelFsnenTvkdtKDBrN+JDvpYEgs/Mg7WUQbUfyOzyv/VYGUq/BA27L2LmDgPqjRfHLf/23GAvQMGbkyOhNDSSPn1ZujhwhlZw2CjsEPbkKnwD9i7Pr+/KS86LDngEjfo5bxyqrKhNosL7S0s1V8Rc45wINF0EX7au2NXmT+RIgLPpcyJJDMHiT1wslbzYlz2QMZjOe4N7F6ILJkyYR4D8XV113fXyFVdI+3Spy/pXH9Jnor9kH6gQDVHlfQCwDYMZl+9ZnUPd4mlMWgAOI96i+6WzFffXZp+Ldjdvi//3a1ynmfEt0ADCV3iopu6ZdMM16JX7Kk888Hb9867U89k8M1m0PY/o3iR7tL4oWHNNc9wIKFFoZqOlbCJbXYL4FH9J3hAZatjMIZcofbUNY7uE+HwZRLLZsRt9xlpwPYIc37NkZ01aczdNgGkCza68Zij5sk885Adg0b+KHUdG3T3z7m99ihf2y5I30VbM1xo/OUZc5x9ye9DAIdnug/oaZAAai8qZjddHNUyHegu6P/fCH3BHxQ+py3U/Bxoqy8ty6IJ3NJkOrpA/n+IpL+8JTsi3HWCw2ST/btgPKsEDD7199Nf7ht7+Lodz42N/+TYy9nkxQsmwM1AsQX0pBQ/hPG1v4yIUMFNmCWhlAm2r5chVbPaMsfIw8/fN/PxGvY2P/nC0Zt7FF4Eq29Khf3DapzklAkXvT9+MZ8oxzJK3kdXlRPaEPnPLEZ9qDgyywfLhgAZmg38MG94px48fF8GFFlpC2o7RVWL8j+Uda8PJKAIK2E4BwXDzD72WGGL9bf2IL+/Hf/nB6bsd99MGHM6OhD1kHzQDvrOHi6RHyesYKtCufaTuyfea0AJGdB8hdrXey5gkr44KOH5HR8DKg6Xn6KtAwABDjYni+qYt6EFTZL/QNdgp6Oge+nz5m8ihP4pnpU/F3TRYJPJlj9/69sXj50pg+Z3b8K0VE/+pRap7gU3Zp1x7fDruMDnCLmu0nfZk94xd5Q/oqywl0yK/wZdpbaGQhVmtkrN1YFZNmTI2X3n4l5q3ZGr/5wQ9jDDq4FSCGfKj9lp7GB/KacVEu3qF39S201fk5wIjZCKV5cpuWfCRg+go6+GfEIWP794+/+4tvktEwIPlVYNSt4fZRWdFH83n+rV3Ujgsypy2BNvyfoIaAcpOWLOoBTr039YP4F/o8n2d96/574/GHHgQI75KgsRn4AtYwL7yOrNJ/MzkE7o+w6CXQ8AaZFhVDr0igoWnHTugLFJQC5cM+wxc0K3HV//9R2EK+Sk1U0yXNGYRayyr8rKnTwoyGZiBLQ0kfFjWtzU2mZrnfihayMOApFPYUFMfj3/zLrIT72E03xFf+7CtxGUwh4nYMg3+OCWN2KBYEc8EMhXOXLm01Ixp0MDdMpEwg05SUoYjcyxTb+vbPfhaA1PGlb/x53HzTTWGleZExhS/RT8cEAyh4Ol0CGWmY6HMi6ShuAQ23TOhinyQbw6No1lRWxm9eeDHeeOOt+BI1GkZfQ3BB4ST3B3tWtU6EBXU0yAqFQpeOLopISdBRUVE6GoUk+897Hnu2b/++mA8i929P/irat2qJk3UDaOsVrEBWIBSsoh5jBRZhsF3766WyNgsgHTtoq3Ply09NtzZrQgc1Cz6tXBFTZ8+OF194KR55BOU3ZDDV/cvToaDRVNr+lOUN+FWsOrkG6em8814G13yuMXDfVAMcuQt8Z80aTh6ZO5cqwRPjGDR8/P77SX/niEi2ZfzfQEORsiTtbVMEVoUhwmtKXaGYGAPjqoViqQcYYBGWhaDPUxDyJ19+LT4H0HDvhJszo8E9pNJa3kmHxflkvOl80k+DTWljv9Pxp2/yinNrZkxdFPM2HNC333k7nvrJT2MO9/wL23puu2FMXAR4dJZic1ACZZ3cDnmq0+MYs4TSOZIWGl75UASztJ3FrTw68EuRj1dJMXzuzTeiA46bxc6uAuTR+OYKLnR23NVhSY5H4+jqwlm3IEArODwaCTQgD3yZF0qQ7Q+7KZQwFaPwz//773ESD8f9GOQvP/KF3DpRz7w6+u/KzulzrBSzWlODTAaPatyzb3dMfu/9eOF//yLaMebOzWtQrK9jtII366CMmwIotMaRq8+zTBE9Qh/2MHZfJ/j7BDxxmnT/MwAW6egACGlMGlL3oX4DwR6NHfsQkX3NWS14sTZtXWDMZ6DT7k/OxsrdR2LJziM46QQZ1T7O/RNup0jpkLiMwK4rTnRLDRTD0PGXVjogTHH+TCeC9jT40ktlLQ3pXjryzrcZDd9nVXcuDuZfkc5sITWLtDlfAg3yoTecJ0Cx9sU5aH6ekyXq1XM+cSahfxZIvYAZrCGI51G1f8homALtv/Xzf4o7hwyNMcjrcNIAu5Z3ZYqo0cCKpeng6zazvxpw9Sf/8W/0XMCI2hc8dmC3i3h1IBukABrOUkzTTBCBhka12WKAXNCZfCWQwvj4H2ebvgo0+HKweCNwdAIN5wm69p++EOsAcZbt4jzuA6covJmPjVuvHUv9i2HRtawzAGCzTO988unfAyjUy4yGfj16ZQXlpuijCzg0tZg7XR+dqCKbgUAGWmdBQnjEAENeLAW7nwINOP/uy542a1Y8+cqr8W1WSMddfXWCvRbNld46ueo0Ay6dRYNITZU6Xdl0JVrnkCek3vZYwaYAAa7+rmMuZ374YbxFzZMt1Nn4IdlxVwPwqGcyYEa+IVMGVurenD/GoJ4UHNXB8yo57aXnuj/Z7Ws6Wb958r/i1ZVr4h4c568/+ghFeXulDvDeUuqvp9sUTgl9lg78J1+p12wzM80YgSCAq7C1AHQPs3XihddejS9RN8TrR1/9s7jvjtvzuD8LqLldpTFzqM7R+TE4VIfRMLxHuzBAOrbyOb/7ng6Yz5Q5VqCHn8Tx/8Xrb0YH2v/pj/8hxo++ltTlhnnyUDpT2A77LY0EG9SfCZDIb+hO27WCtxklBqWu2mcxUO6ZsWB+fPc734mZONKPkBH3ZfbEW6PBLYkWLWuAPrbgW66eY2/dkuGqMk2mrdV2aXPN3rBtn58fIuR7DwM0TJsWX/jRDzMI/OuvfDFuG4+eR54MUHTOi8wlnU2CFeZSAMRtNyknOom8BIdNoZZ/muCAwmTwwCmAhk3x9OtvJNDw4HVXxS033RKjqFnSCht1kFM0zvKMXLFDnjK1m05nlge2wwK/8rkFzbSvqYf43N+z+O+nQMMLcQaZt9q5xeV6wTf6E0cACzLIon/6H/Kxx38meMZ7As5uC7gg/9Nv+TTnlDmybsi+vftiJjbwp/DNjQAN40ddE33JzLqYWgRmUXqcnnNoe9LYl7xYesnrOv5MYepL2/Yz52njJlZIWeCYOHVqLK3aEN9k690wdKR0sY8GjcoraitlIINt5i0XTmhXH8GV3CIo0r6YYs/R2ADPZo0tXLgo3p/4XjzHdp4h11wTjxIweuKVfFLyXZQr/QB/6u8ljzAen6ldl1nVDWkM1PW1kTbmdQvbtKYBNLz89K9j8qY98UO2fRh4dYYuqMf0k5R/eawmNF/FYotZXL9kq0W2TftwSIzs1zIq2jaPJpjXWmfRTWmv1Kr8rTfF+GCKnDttPhYtQXNpaFBkkH4OHlOm/Fz9BSHiGK7v3hNs2wHMn776ZNZKaMK4Ro4clkCDpwydhK8/en9SdOQo1298/euctMJWJO7NObRFJs0gXbrwB6/iX/WAGQ3aADNgBHfUd36jefNmrKIejzdZQHnkO9/Ne37w0EPUaHgwupSVxVFARxfAPAbUE2JO4n+pb0pz6nwy6uTB1MfoBWmYuofnGVQKNDxN5tSPATYH893H/64AGjwKWJCkZFv1VTMwVZfl3OILQINcVIIP1TduSUq/jL+VceXBE53+nSyYN8gG/NoN1wM03B1D8Vkb4F+fpr/WQnAM+tjypAX43ErrpZ5LYA09oYzKk/rBXo7tELpWoOFH3/1uDKjoHuPJQBoxfER079aNYbKQgv4rhU7FtohCnvysBNilbOG36f/4viepKWN5cgMy9c706fHKc8/EIw8/WmQ0oAtaUg9NcE+5E4jJhQ5ooh5sgi6DebB3x7MIp/SXNvqXAo9NmVN1ssCgNVVeZkHV79/HgqfFID1ZrynbdTOeQY7MJtFW/QHc1IssaCMd1BfS3WM/62CfzOjbg6+9mO1TU2fPip/+/tn4Hrb7rvHjohOLBNprF36t+ZUgJvPKCHL7htvAtf8+04yNs/gH6hr7Ik8JklgfY83GKmr0CTS8TOFZMxp+QLbjCHzt5smHuaWROdPmyctmaul7lLIlzVoQMLC+hDpGOqp/crGUwc1D1zwHkPyfM2fFuIED4zt/+ZdxJT/Vqd5r35xbfYWMw/D7HIs86ny4Rcw5kT/1KbGyaWcatWgWh+GZdwAw/v4HP4rV0O9bD97HFvEHo1t5eZyGF60hlrWJBGKgv89sQKaGBXKVxXWbqrJGQ7lbJ+5/IJqzxRYhoSU6zjx/lq8/GWhQaf3xS2IYJJ9HQdbF4T7PSunqPwIaPKN9+KDLc+uEe31FiHTgdVZNtz2OwZ4EEzz4jb8I/d+Hxl8bX/7ylzhepn8qUpWuwR0aNJWtQINCnAEojO7vGfzSnsrOLRSlNC8Dyq2spr323sT4K1aNe9D+l771F3ELAYZAg6nUpsA7pRoyf8pQOgL+rbMks5VWXRLIAIWr1bA449vK0jpyvyFQn/rWO/HIA/dkqpg1GtyDdgBjr8JzL5PG0WNkDCZNZ5NhTa/RWVFAVHY6tTKjWQcKjEc1CTT84slfsZreNG5EuV5JWpdAQz2+p2JNxQQTK8U+SyHWadMAlFIZNfgZgKCgdJ41FJ47/zFtexzROzi6d8HoV3FMZK+evXJbiQpBQ5ZopGAGtPTSeInUSiwVqka7ZMykv8rRVccNlSg/0MR3Jk+JPThVX2DvkvvsWwKYWMjSvtrnDPwVaJUrtNfQqfCkjc5gggX0RUfLfZj1SCk9gOM2l1Su9ydNiqdefiNuv3UCpxPcFL26VUSzJs0+NS7SRgMHs2WbGnxBBzuvY5rpVnxH0VaJlJRjFUDDJDJyXnnmtzFl0674GQbzljFkNOBoWRjMAmcqBBWnlz9SAaUyKvaPSvvCwarLOAjumc+GzKsO3ZJly+N5Ul7/z9vvRD/u/QE1AzySqDFBtJdjTuMFTXyIq4K+EtxgLIJ17h12tTP3mGlMqRtymmB4x7HDzCmr5f/8w1i75XjcO35sPP7wIwXQwFaL2gB2NWmDycWy4LSeOBKbtmxEZpfFOlaa9k+i2jVnT17SoYxzxZkPHP46fLchXloTnDli7URkP4EGx1lhP8brFAQ4QV8/wUAdA2g4TlbFJ+TTnUOO6jZvSJo7K9veyMrQhfMEpDhutREwMx9q0vfzoOcCDUt2HIz5244G8UVwMmdeD9/zQIy4cnj061oenS5qGS1w7MFFoE9R2ySNLvLjHOiQJJ/zu1PkVXKs/V2DMm/xx/GPgI571q5nP/m9MZ4tDlbJdzVVAMZLOsuL/tS5UF/Bqanj1ClmFNSp41wCMhA0ncGQHD3GedYUfJoye2b84F9+EmMHXxljx47jqN5RWWD1PHtEjhwhQMA4VW7bGh/OZY5+8fN8HmIZjRhArzYUIOvQNlo3Jh3xAlt/LpCqy1pZA+apCXNRn7nwyCT5OYEGhZD/BRAvsI/4PK/CGRJoINAko8FikAdI1V279xhHte7HoToXO6prNNxKCv1I+tm5U3u2IzRKoOF3oP8tG7cioBsfl/btlxlODZD3T+j3BQxzyq20RX511NRXpi66NUsnxf5A9NRrBjmCbdtZfV2KIzQdoOE3LwE0sDI97ppRFGhrn/KubnFPozUOSlXIlRkDT+93FcP6GsqnAa6Vz9UPTZsBNPCddevWx4c4cW+TIrl+69b4Ps7KKFa6TNXO1XRk3OBIm6PBz+CW/mf/6L96IHWYAl0t1PKNlbF9rgDJr//7P+P1ZavjvrE3xJep8zOwT++8X9uQTizOjT8dfq6icX/hfMk7OHOMsaQnXT12LHWwC7vhGYtBfv2n/8y3ABq+zp5yKsF3bt8u+bUG4EVN9EGm8tOQavOPaS/NS7WJ1KXSSyAgARTGsnz16njiN7+LJ15/K8q59e9/+uMYO3p0OmLqdts1mMuMEp7j+H2EdNKGKEeuUHnut3TSeTQLRflwX/FMnPO/pybG7K3b4m5WXr/KNrOhgDw0hL3DnlXLUaHPKQLLfLmlRPoiUGljBfRLdku77mVgtv8oK7AEu4997/v53g++/IW457Y7omd3LDm01olVEsywUvals+3Io+lQI5dWzWAg2HmOxOVzgQG34JmRY0bD82ST/JwiZ49OGBM3jRkfI4cN5xz0FnGI7CSLeZkm7xw7l9oIt00JKmj7S+3J79pv/zaI0Rk2Vdyjo39J20eQ9ztuv51jS4eRdcDRbcy99aE8rtDgK+02tim32iCvGC0cYE6dge7aW/2OlGvnApoJsAjizAJo+Alp8BPgl/EE7P2wrxe3b09NidOZ9SDRtDfyu/f7u/SRh3J+GZPj0odK34bnKHc74Mk16zk6k+MnZyyYF98k2B3G3Ao0NAI4cqACU+kXea9/89O5hpOyDVfWGVjSIwF3/RLGYpam9R+mYLvffvGV6H8t221uu40TqXqRTo/thr/sqwFoAgKMQZ7R77M4nP6jfpnzre/ki45kRkNt6gZ5NLPbHt/keMvX5i+Lv3rs0fgcQaPHq7sCK38JZJoJ4dbW3DrxDBkNL7+cfJ9zyDOHc0xzl9aM14w27FpT2m7SyBVchsX812S+a/Byu6CycEEeoYWko7RAJ7ogwATn+35ujYbD4BN7T5ylRsPBmFV1MhfXWtZtHpcPvqzYbsu3T38C78yYE517lcVXvvR4bp1wm4qymbLqfFbLapEFahAk7ZVTgHBk1XkoglKBKvwyfE6D6TdY+Hn0b77DNyP+jqDosYcfjrKuXeMkWw8MaDN4hA/kCwGTBBTpt8Cu+svxaXP9qa0t2Vz18MZt2+IZgOSfoG/GtGse93/1m2xBuDaPpXbhSH+y1H99HJpJu1voCEGlIttMnaOP6TMck/reoO5jgIbfvvBCzKdI9e3jb4gbb7k1t07kCTr0Vx0v0KAGcWEp+YZ2sl34Wv2lHs45gial/qs3TWVXl/0vbMeVVwyJMdddF0NY/CkvK+ee4lSYjDm4L+WFOVaH+Xv6a7Th9xyfc6Gfb/ArKOFWJ7OH352GjaL/n//iY3nCWzl0v+giCozzn7609+qzukjmszyxDIHNjAfta/J6Nd21vQ3RZW6dWL9hQyxw5Z5aBB5vef9tt0YfgLtmANnpT+PbqOf1KUvxgn6jPMUICrskP0F3db76rS6+agMW1g4c3J8ZuFPnkCX7y9/E3z7+WNx143iOGL4ktwua8aV+VQc4odoji2u6cu/CnTYxdR1zr57Tp/IlUGK23BqAzPdnADS880rMX74lnmSR4AYWZ1rzbDMdvSe3RsB7uUAAR6Svzv3GMcZJ+tZpP7Ar8qaXYLPvz5k/P56i2O/TCz+KGy4dSI2Gr+XxnGb4qAPN/BLEks8LUBa5ga8Fenw2DF5dC6UAYhOc5e2a0P4IIIzA3V9/5/uxg2f++f13U6Ph3qwVl5k12B8IkrQRNDU7x8xMj7f0lBOzOd5+9+0oo1+eOtGybQdakZBexTiK3z97//5JQEOJBLAk6vAPJFGVmtZdl1U7sxVWLl+eWye2bKiMJkz44P4Ds4CTRtsgx8lKw43znCtGVK5+/Ps/SmrecvXgeAiktX+fXil4Z3BALZCTFaSZLNNbFPDCaJpWhXFEuaosFHCVqgFesW+H1G6M5kSyK/7hiV9l5fy7/+yLHF1zQ3TEkbMGgsi8jKXA2a4raqXV70zf1xngc/5hlKxGsRewAUrbVKaDREQr166LF15/M+ZNmxH33nlrXDVyJMfl9c9jqg6xuia7NBLFqjb+rmq3bn1RKqjcw0qb9tdshmMoO1ddFAINxiGU/8crV1DM62lS0RtSrGp0nu9b3qVLOrMa3ESJCx1B/wmAUKwG1NJYmmgIVB7SQ1plPQuesZUVKPdKzl2wMKa+NznG33YzIMagBBo8RlBnuVDQOJjQSAfESyc297zzu86Vq8oltDfb53MV2jaMzspVq2IKK43bKChzH3U3+vfqGS10VgQ7UDLSXCfIlwpJ+ru9QMPt8wQJDCiVPZWKQmrq64HDOCs4WVOnTY1n3/sgbrv+2pgw5nqK2nUt9m/ThspM8EIHV4faS2WkMvE5PlP03+eUaOM86BRt2b4tZhOsTySdcnrVzvje174cYzjr+2L6nqlQ0DsVNm1qaFR8KhJ5z7ady1MgzWYzGFRo+KWRykygY8Wq1ZnR8Dt4xuyB737/uxieQVkbIwM36Of3paPOeDrnOEnuoxbksKCqp7HodDnndVF6VsL9hNhy59FDMRuD+bNf/EOwKBc3j+b4sLvvjX7de5ICyncJeK1zYHYRrk8c3Lsn5uOYLfzP/8yjK9uXd4r2BEAtBQFYsakDOED2KEUYvY8gBMBBQ3QWgOE8K/pn2c9vyuhJ6OkRVEfg28NnAMmo/3CQn+ea14/GrJbXp6iW2w/gGsYFbQiKLfJaE2N3ljnZRaGtjzkVYd7206QasneV8iNe99xxN/umB0cPtk20bdYEh4/glf44pxZRNaBy1ZSOpaGX53UmnFMvnRh1he/pSH0EyPNfv3wizm7dEWPuuTNGjLwqemKU5ReNlsZF5y2dFOjtpa5K5Bx+URXUxADWr0f2C9kMgg7WXrBWwI6du3DMqb3wq3+P0X0GxOjrr+OM+xHRBXk9c+os+7aPxSHmbiur2HM+WsCWqF9m+1g1Vs3OR3vmr02TutG8kc8/E43rs5oNsNmS7JBWvAR+atAXDbu6OENCFIzZDISFCTQgtGqyaqABAAbaenb7+n3HyGjYD0h3PrZVnzoxdtiQPLbO+gyNAE89CvhlUvkubnkJBQNHR//efQEaOqVOPMoKrau6Bb/D69BVZ0edm7qW+dDJKBxcpkMeQ5ak/a5du2MlemwuRv+FdybFVx/9fIweMTyPt1QmlH0zGLI2A7KrwyXvZ2FX+m89GUFXHStBWmXWuXK1RnmtrKyKuWQdTKK46prKzfHtv6ayNOB2G+rkqE90VD3LXD3O9EKfIihSFzgOHejUA9A2dT20dVxNADXNLPN4y5eefSreXr4ubr16RDzwubtyBTZrS8hb9NUx5Mor98o78pFgo3yqrkldrC7j/dzWgF4QBNnB9iSLQf7wiSeTF/7y4QcpHHhjVsnXgU2wF/2b26V0fuhX6ht41J/KovrG+jvaDJ0ZV2eUC53XNevWxzNu7Zs0Ncp5wje/+7dxzcjhGTTKROlMQW+BBmUkVxuVb/qejnnOKFLLZ7an/hcMkk7WFloIgPSLn/88FhM4je1ZEY9+hRNuyOjT0Tbg9nuOQ1tamlPHrSPnPBsYGMQoc97j3MjbZvp5tv1kQJ5v/1MBwnzj/rs4xvhWAOVu2U/1e2nbjn2XFt6bGTcCzHxeryZ6h+eoT5Vj9wYLSbhqtxEb+OakyfFbFgnuH3cNR/FdQ82SQRRAbR5Hsb0W/M0ChPSVgcAr9JH7tf2OXXoIvEDET4M77YA2V/7M+hjPvxj7N6yMW++8NwYTuAiSZIYHdimDRfqSvgcOe6FzrAdzMhcl5BVrBjmmDOjgI2mpY2yxyflLlsbP//UXMeGmcXHdiJGZLdH2krZJZ/URXU77IP85D9o/bVzSmL47H86z4/AhyU+8J4ixie2DU1mEmDz5/fjaN74Rl7Pw0xwgwIxBfSRXvdWZBf9IHhpQF/Ez262eC1Oofa58K53cUuKRc7Nnzohpb02M7kOHxAROdPK4WFchlVcvwVsBRhs1WHJfP+Yn5ck6Lvoe2lpBBtutiZ70JJRde/fGPLJspkyeGG/NWxaP3kG2B7pMoKFxQ2oGMYfaY24mvbs2x1tujOdffSWefuvtfG7pn94tOMaZzLJ6AONuOWx/SfMsbN4A41MTOyf4Xgt+EniojRzaT5dipC09SjBcMN1JsGgw4S2ZgJw6cYZMHQofV+47EnM2H8tjJlvUaR29+7KnvlVreIF5OXUiVs/8ODr1axuff+h+jlztlyAUE5U+WfINv6uLlRnnVHn3bwEe5U5doN+XugYdqZ4VaJj4wbT49j/+Uw7zq7ffGg9w7Hi3srIMBl240m8s+cLe6+KIutzsX/VMDi7vLoJSdYTMYwaFIM/LHPP+nwBIY7q2iwn3PhgjhxM0tmyZwFv6YUkSHSjlSd4ATHYM/K4e9jle6ojSarZ++kEWl1asXRuvszizdunyuHr0yLj6Wo5779+PbI3mdIsgl/u1I86xW3xsN3099IBtZ8aAvI6sGZAqx45J+2VQOg9d9rMf/zNbJoagI0fGAIoGduzQAdsA8Ie8K/+CZf6U95XP0qWM6eOr57V5Ai+OQZ3jNmXBgCnossnomgc+/2BmbLcjBmkJbQRV7Ketpa6kL/4uOKTs2Dfp7BzL65lRwRdcoLROSVVVVXy8dEm8xVG9LQEAPOq9W3lZ+tnaUAFBL/vCYJMnpXcCmzwp6cJnBt7qAlgWoIAMJJ5vDb0Va1fHbGzgv/zuufjKPXdRGJ0To9iWYV0i9YA+sfRwPtMeIRvyg9nT6YM7pzTqYk5KCnRLPxf5cOu520jfnPRaLFu/P/7p239JTb/BeepEblujXQEh5yllPnkGPx09rE5RF0ofdZf8Kt2lv/RUBy6kNtTzv/11vLe2Kkb07MZRvY/GZf36olPY+kc//I4gmNkn3u88Oq/GUblNC1rk6Re06RidX7Rwjm8vIIyx5bfgGaXgEbb938mip0XLnUdpjWsG/QWe4Bt5h/k8ie46BFBduXkzp8BMjAHw2r0ADRdd3J5WeGBef+Ct6jc+Uz/+JKDBkUoGUz90KkXYZB3fMwxn6vn3HBXGl8REVpdWUEzuFIWxurbvwNE9bRMFNx3LVMEMikl1MyVzAQL+7AfTaaW4vv7QXVFRXp4Gy9XbGrkKi6FM46ajBSMx8TJUKlcUQYHicj+CKBNnn+jnPozmpA+mxEL263ndNWJwDBx0RQq4TKoDpBAUAQZBCW0nmooAGkzmi7EqpCokK9DXw+E1XdjApRJBefLVwkgNJC4ZfvOEqKioSETNvVnaGoVOYc4jclAOjTHYspHPVrBV5Bm0KzAY5vw+zzPjwnPQn5/yYfZ9zMCepEcODIEAlaiomQrbtux/ofyYD35PNqXtdMCq++77rqRJn3379lG5mhoNk6aHaFw5r6vHXRudu5RFawxe4QgWAEA60vRfKyMSaQDhJYIrGGAQpjJwLnTeVTYHWRHaWFUZz82cm9+9dcggnKzuGSjonGWgosJnpkqBhYZFYfezPCqHZ+aqlUqKPuskum9M9HnN6lXx/psTY2u2Tm2PO2+JTqyQuuKkshERFihJGjN+2xYIMPiX99AoSRsVuMGRysXPTdfbjWH4aNH8mLjEhKiIYX26xQ2jromWZEt4n4Y+jSafZUBF3+yfvzsbZ3BidWwdh06Ac+D8Zr/g4aqNVfH6i6/Glmw94u5rruI4ov5FwMx7Krs0CtI0n6FTR1sEuPQQAaTPrsrQrnTH8yN7AICD136ORVxInYXJU+dVtx7xtQfvjwoKoZoVdOEkFaX5pC6vxmyFOMvqyarlK+M4jt8A0vgq2CfaGoPWAMY9eRIDcf4Uq+kEvt5Ug4DqAgr/nGlwGJlabMMBbDiHA6WTZZh5DJ44CD/sZAvH9pMH4zALMQ1Ig6vfhBWGBgSfyTqslvBtpfQ0/HuM13YCwVX7TsXH+3EmYbW9ZDV4XTqAs+fLu0bH1q1Y6W8YjRirVHZLg4UzBaDq4fxqWApwrQjuNAg6vQke8R0Vv/vtXeF9oVqeRrZrEYNGjuaklU4pP/KKPOy8mRWkcXAGlNGUAcamMTaQr2tGA/UPJP9ZnGJl8SDA4uz5AHdrF2Xfb71ieAy8/NKs/izQcIKz509xwyFWIVZXruMozMn5vboAb/XRi43hG91syAQ/MT/8wW6Y6NAqogunQrTECa8hcXJs1SaJ/hb1GeRjZdT+wS/8x16juACQc4i3qw6fjDV7D8TOPadiUzVtB1V0ih5du0ULUpprASTt2LUjXp5e9H3CkMuiL0BDO1cuoIMpz543reyUQDVuAtyQQyECNJJvc3+2f1fLk7TcR2bXBvTYlA9mxB6+PYznjhxhinqLdDSlrSvOOp06P3lBZ+VdXayePY5htq6BulGnUfkzKFHudrA1Y+nC+fHWcssJU3tiyOVZd6OZ6ZeM33kVlLWr2Xd4pZSNoV5Qx8tP6UToIHDZb0FZ7cq6dWvjg1ffzPRIP/syuqZr5y7JH/JLBrzoPHW9erMEZtpeAhB8ls/wp6TBbsqXOvH7CbymTn4/JuMIefVt2yxuv/1zFFC7KMcp0GAaNtNTOCvoKAaReiqfDc3dTiXgm9uuoEeCtfC8/L9l27Z496lnY3UxrLhj5JVZ5KwJTqCrxl6uTtt3bYizyTt5bwaN8LwOuUGLlw5eHi3G79rP5awyPj15an7mP39GYNerZ8+UI+Wz4A1lGn1FGx5DKX2kk8JjYODc5GohNC9d1jrwxI+5AMqvz3EHLFuL2taPW255gGyPDmkXMvCBH0v2DjWU3K8+1/m1tpN1GpjYpL/j0W/wxB2r8gvy/IKgSKkxl+yOG6+LXt1ZWSfjzkxKaa9Dng4ytMlgmb+dV8ER2xNI9nkWh/WnNkQ5kG8qN20CxJho12M0W6IuHTwsOqGHXcgQTNCOSG+p7k95WfukPJxh7L5pf32OGXj8SJppW46yIubRbe+vWB3s7CcDaUx0wWdq2bJV2hudZy/52Pt95XZS6K6foMOvTUqAzbmlbUFw5+gIfssOMkGfZwHCmgU3Dx3E8cIGuzjj8K36QCfcObTfGaBwX8EhaB+ZiedJY+MadXSCYzxXgKaS1d050GUl93p9fszo6NqFwAjnX/4oVpqt30QGFffk+9BBHZ7bhxi/8uR8wzgJNrnfux5AwwEyJj5aTJHMDwv716NJjRh7852Amm2LFVzkPdP17T9+zA5s/XQAlbkrSr2hSdjwEl7N0cG1IaOmr3sF2wnbdmHhjLFj7TyeuY6LaxDSApGOPU+ske/Qhd5TAhr87AK2+xwNH6UK8z5SADdQK+fD9ftiJ581BOJvx7bAhtRAOrL1YGw6s5F3i+uLt94UfZAn+dDxpz8K/0li5V9fOPmGzw2e9ckSOGVuldkChCgWPMy6mzGbQplzCx3fh0fc9aXHMrOsJIcFk8GP0NagSH3rH/KcMizJS5f+sL5NMUcN2X65P37PCWlVhVqJezhJ4woWCawT4Lzry+WU8a+6pgBN8SOYW8enTs/FN3hHHaFu1qb77OOc1rSusjKenT4rH29Idv2466J7jx4ZNPqm/ZNnBMLk1dTttKUvpa+Ueo7vpZ9GR5TZzBSAbgJ5i5cti9dmFnxzz/Aro3efPglWq1cFGuyvYGACdtBdWyeNlSfH5X7/9AsZm2CEi4rK1SGC9S0ElS+//V7av+sqOseAgZdFc0CGzDCGL6Shl33MvtJibWjAB0k3syp9rrYj7RQNq4fdprWT7UIrVyyLiUsL+/cgdCkj2NXPNjvbzqVI0lf7Y2+Vx1ou2tJXQezcJqp88ZzUBeg1sxIOZ0BcFTNmvRsrd53NOj93P3hPFlfNeIw54iZa9CHqYvQBr5rVQXXGTcwJbxX6wq/ZB+WZX7agZ2Yvmh0zllXm+McOGpDbw5uhCzzNQh5PfYC8O0fyujGC9/s8QSDtu79r040J1Xm+tE+rVq2MX79b+Fk+4BH0ZO8e3ZOv6UZuX5MvBXEE6Z1j+cPnZj+hTdoSvuv37YPPakwcchSe9DSOV2Yv4BN81bZ1Y8xNd2dNEnWMR18X2ynVw/RLPcgzhMM94tKFnYnP/D4eY+v0/fjobS7qQCs+xUuKfXYv+BhO/hMub9aAuMIhsxr8FBeMD3EEHJZTPOS9N96MpTgJB1nNurhx07i4Vavcy3OayVeYdFjlUaQ0zjIBZ2UQGCULlaBcLuA86YCcw0F35bMBe5RVHiLwm0i3Wb9keRbtuahvz2iD8XaVXGRLw1liNJlOpj/hhPOoRqwO1IORcusGDHOWVcQ04BgHFZ2or2mJqxbMjn0UTuvap3u0ogK1yJxtiSTKaCoTFYNBt+l3GpA05ggDH+aqoyv2Mq6K2BVo0cfGrMDth7lmTv4AWlFhHoe4Ee/JuAqPL42Fjkq2x3cs7nIeGqtwVUAa8awpAaVzJlN54PDwLBXelk0bWYH7OLq3aREVBGkqC41xyXhbV8L7PMqqHo66aTyfsHJDVny+rK/gviINugokjQBj1NkSKV+3eEGs2ns4Lif4bt+lLAUoBZPx2q7HI9p/gz8N1Qnbgr4NMdB1+Y5OVAbRjE3l5FgyQOH9wzjd62bNS1Hrx0p8Ok3Sgpf9T9b1J+OEWTLIsbCKTk9jDRO/f0JQknyDoEsP58hnCvLs3ro5VmzcmnuV+143OnlGoEGlogLzp8/QTubLv2FSZpcVDJF+BqgAcPlDHvAen3lo/77YU7WW+T0e5Zf1j45l5SkfGknpk06qBOKqQd95I1N+0xmgNYNuFZ5K0z4IeLkHXeOyi5Xy9aSxbmf+G3P/oGGjo33HDrSDAwC/HTUQI0i1UKZVh/WtpWk6yrTVGL6qSx/SECIjtbjnHKsbR1YABB5m+w1tdsOwXNq5U5QTXDWFvHVxoj5hZcWtDo35DNEEYARkSKCBoA2goUEtEPcaxYqbs6k+OAmfHqL9HZzSUHVsT+yi8ZqwYN3mjaJRU7IA6gvIyMM6cCDVyODh0+diO4Z8NUDDMgpBSiUy/eFB+kWg6x7PdoAgrUiVd9vEaU67cG7lr/2sYK2ZNpOnR7Qb0Duasy+xMfpGmit7vp/AEj+li8Unz6fiMRjBEUM2Up74Ys49fKOzpj7w7y2AZQtXrI0yKnZ1GjiYdMbmOEHwFODnWXSTAZIAUqNGrKBgXE+QxeFLJzMdTdryObWsp8HWstN8/xAr1Fa8n8XeR6/6rJw3AASqjewY9DgfruW15eV+4V7lvDq1Qo+CwtMeiouxqGtp0n4Dm7Oewt9yrSuk6FFqSCTQgFwINGw6cirW7jsQ23exp7MaaOjTsi5FBztx4kQbtjS1gJ4W52UFnjnJjBfkv8hAo0/024KpJT5WD+4mwF+weGmUtWkefYcMzxUcnTIkln75/XQ/ku6mt8vzBjvSo6jVYf/hG77rQNSrWVsF+6L+2bRyaSzccySuv7RfVFAvQkfyGHyrnlSfqke8p6QPXKG0j5m2TP+UJfeG6ozrcGsXmNQMEo+ga7Ys+Ij6IhEDKATYBj4zONR+FDyjI1Y4LPbZgOk4DpP6oiHCYMp0OlHwi46fDpuZa/LOPrLo9qxbSdrpCVaYWkf3y65gm0ezlD+DY/vluE2t1Uact32emlkDtF+TNi6wKmQAB2FSz2h/1MXap52k/O9btxo9TQbMwH7RoUtZAgCfwFfaiXQe1Tn2kfttxmBcGhsEWfND2XDlOVdeoImOveDaIYD5LcsWxzme06xLebQgQGtGJpdZdupI9dkJgF71S9KVlUNr52SwSb8LeSq2RFj8VBqqw2xfedq5bWtsnLMwGrWqF606d4+WF7VJ8Flmtk/qer+Xupj+n1GO6XehJ9mGaJDL59LBrRsIVh6Tpp5P+4RO2755d3Tr1ZU5ZWsOwVsDglf1qW0maGL7ZBFJ99O0nxABz2byKQCJ7CJb+gLyusG4DCG/mUWw5P0Pcotnz8sH0PeLcVjNkEOXQ0v5RBoKTEon5/QCfou6RH6pyfsC6Mk38LjSYZ+KsZ+NHczrdMCyPjgHfUaNzgyenDvu0/fQP0qfhjcz2KBdGEhIkawNndpqu02b0l2e10Y5xwIPVatXxNJNO2No/15RVkFWCPfJ7yV6G+TbvqvhpwROuF+dUF+/jG/rx5gBKtBgMCefCT6eIIA1M27DylWpvwab/UcBQPePpz6g/+pj9aP+kuCk8nQa2XH4jZDnOrZF/w0UzUBSpi3GylRRp+tQ7N22LdauXhet6fOAa9n6A08mAEzbLnSY2VKHrD71p3Jq+44l94TzXk0bgg58PcdtpoWLRhaW/pgMixVkuKUaQhVpY2oy7dKVECbB36s7RVS075jHjNdlW2B9MvLq0VhmNChnOSfo4XyGNZRcGONmngu32Zk4x9iPATTsPQnQsO9oTF+zNwQa2LHOaR4tol371lHWpSOZDc3pLzqFbchNWAyoR/tmMHoqiSCV4JYgj3pkOwHsRhb0HHcrjpTsfNnl0QJf28wGeT0XT6BtCVCq4RzrK8IfgnAX8OnSZ8VqpMzZX8dPf03Lr0QHb9y6O7Md2/XpEe07AbaQVep3BVjzBADaaoZtptE4An+cwpapZ/T3GuDb64fkySPIdm4bw36qDwzAl8+cEifYT9IZnmyBPDVq1CRtu3ULDPSSj+EPV4PP0LcT8Jvk1OfTh9AK6herO5xY+5Wr7Hx3x9atFCT/KPte3rtntEbHN0EPCzDx1U/BWQEqTyk4SRvJO9gBbUhjdIrb+dT5yrNzzFSknVNm1lM8e8Hy1XEpwEHniu4ZJ6gzzGbSl8vFCu695E0EAABAAElEQVRPX4QHfgIdtNDN0KX19JX4nr6qOs9L3a2fbUaaGWoLJ74ds1FxE3pVRPvOXdKXlx7qpE/HKx3gY2lPsip6CD1wWv/DfhYLeuob9XURQxT69QALjUvmLEjaDOBo3yaAqwkE0RUzIOyTtvUsRD4Pv51HXvSd0meCHsZR6gHB3LPIm+07L4IvHsm7dRPbAtZWxtXN6kW/4ddktqL9KDKCoSPj16k8zT0nvQ95d+5q0rb+qUUm1ZslO+UY1WXWpzlEDLVzzdJgJ2r0GHo523w7JP0yawU96MJl+ofqKPqIEKQMpm2h7fP4ymZpK5mNGLf+nPGFNLfQ7r49u2LfxvVRv3Gz6NbvsmjBgoWy5mKIfKAdbEH9ChdCDvO3fGNtPLcC12NMjlHgzq1x0qgW8Zt9F+jbXLkhdm7YEPXbX4ztuRCLt+6Nn//8nzg29D6AhvZyQY4liVn922fxx58MNDhosxlkAJlFIyu44IsELj4lXXPVipg5ZWpUrgFhw3HtRdCVe+RgFhnVScs9X0YSKiNW1Dz6T8RH4+HKoyto7rOUkd2SIdhg2p6rJFZYnU016iacAlDB3r5eII8XkyarQrBfMpnCrZLXQXGFvVASqCXeM7Uymc7BMK++JyOYZuQewnns/99eVRU9+/SNDh074oS3wol0rbFwhlQQOgkqzYY4lyoG2xCNlcFKQISpUzogOkUqVo9l24Zz7l5ihWjI0KHRClRTR0z0UWfGKx3u6rHo8AiiSBefm+mhjoG/HYPKXgGWoTX8a9evj9lTp0Z3aNKvb980Cgqp7Ou9aXC43+NZTM1CugunhTZ07t0v68vfFdhMk0RQpalpxIs+5tjDRQtiOGlrPbp1S0fLVRgNmjTIPtJ3kXDHw1s+Iu/PsfGZAYPzYf/tmECAY7aA1dIlS3IOh1CzwNVUlY9XBs6ON/nOE0VIoa82mH7HtmxDZaGzotJRKZve7O+eeWtdCsGvDl275v47j1gyuHDunQP52baKIAmgCIWisyZqapVw+ymNE1WV3nzfMe8/sB/EegsAWCVAw764fMSIpL1z7FnfEsBnOBLb1jmVH+RR6eDqlnyjond8Pkf+kl8EkLZs3ZpZBzs2b8PYNI5B7D8vLytLebFgmKi22xas7ZFHfdFn3Ai2GzGHKDsITmvVqxs6UjzjKMDIogULY8Wzz+Wxd126d422ON+tMSwkRuNI8X1+1hIU0LryE/OGsRdkRC7ZOlG/JitcPEkgSbrU5f6zfPUEvLn71PHYcGxXbMWPOYPHVotkkMakONaBn60poFJuSD81kMAG1A04GUu3744Fm5k/urwbA+t166hRcWnvPlHepRPHIbbCedHBwpAyNwb4rtjOYevHKcCWsq4V0bFzZ05QaJMOWTr00pOXtHfe5BtfApbyildJXzCElFf5xeBXg75qDUVNp0+Prj16Rs+ePSiaeFEGlGfo5BmyQ+QxVw08ArMe2Ra10vvB4cRJPEstigL0chuAEFEt9p0fi41sJVqEU/4Ueyq9rGTfgOBex9YeIRXpOJbzsw0qoVsXXu3INGGPcGY0IPPKlTKdA0ugwXCpBDTAuwBB1mj4NKPhEEeH7tnHKu7ZqMRAe40ks6BXeVfSTznRo1t5ZhgUfIo84bWcZYxuCzmjg+QD0fWOV5BBOd+8ZUvMmzUrOqMHBrMvXx1pYCHAkAh+ypO6uJAp9aX8LN0twuQIzFjQSBfBmqmvyi/BLHKzevWa+JhaFmNuu4MtYwNSbxwmpV3dom51ngTPDHiVJ/Wwc2uRPR2Ski7KOdfhY37d56ku2MUcrGNu/c4IeKy8S5ece/WBvFLSO/5Mmuh88Z9yqTzkKrdywaUO9jIb4zgBwYYN62M12we1IV2wISNGjij2KfMs9a9bKnyI+iG3IgDuSQdtlXrA1Wq/Jw11XKV56iWeYYbe+nXrYju0FyTvympnr569sgii/Ko+0mnKMaBfvHxPeRGgcRVdmnnZLkPi88LB9R7laQkrwgdZ6W3O6nj3Xj2jSxeCC2yFY5ZebvHTrqnzDfgEUeyLn+V2EfpR/F7Ihw638+M9a+n7Ek6qkFYdWCCwbY88Vt4KuwyV6a+04M0MMHL1i7914OoQBbkVwraUa53VC8icsn0Iunmc2Vq2BHYqK4vyTmVkUlJAlgzCUvpw+giMN+sfmB5M0MTAsNEUk8PnOE3WEXFk8rL63dMpXLmVfrsBNWfPnpUB36UUvLyEk0EsPqntsL+5uMA8Og8GYAbuzSjY5jxqm+Rr9YGfS3cnyXHneBmLRRhnfzg9Opd3jcvYfpLHTDtHXAILzpf9SFvI366GyvPSwi0oti3faFf8nvwl3ygnpgivWLUqli1aGEOvHhW94Rv7bVvJW9zjPMlvrgRKpyZuZaB9+UW59NhvfTJT0w3gMQnYLFahoZsZkmvJFhOkuHL48Ew519+wHf1FHgL/aU8Jkn0O9ztuee6PAxsdeQEqHkCxuxbYgjOxfdv2qMI5X8PcyjMjaL+dGbK0mWnv6JMzAHPOY75ou0Qr+V1/ILcZGkz7RO5z/pXaTQC+k9ne+fr0GdGcyo9H0XccLgRvSfXiO1ipGNMZoKFDe4AGto7UpG4OOlcwPoEG24Mnz/Cs5EnG6/GW9bQFycvwNDRzQeoIBnLPJ+cBGg7HjPVHs0ZD/RqNAX66csxyrxhOnaxMe2ebXC0zBwmKzkJ3a3YI8rhdzK0kzq1zvg5/byV0OQUIcXHHDtGXzEj5MreFQnvlwiE7p/4iP8ozAm9mW3mctSCG7RW8SJf5vsG4+8g93Wsdq8LNkfMu+E1deemzqgtKfKWdlU/0VdW1XoWew2OgD4WeLvwsdXc97IC8twMdPOPDGbGfI0m79+odXbp0iUuYV31ZxwNRcx5zEY57BPLsm/PuHGZGLfyX/iR9KHQzcwxfOraNmziNYd78XLCp6NYtY4SmBI0+3/sLQA1AgD5nli1jlkbqYGmrPKinmMLUb+p59ZyAr+NbunxFLJo9K/pSrLNHd7b+CMgCHpfA5BIP+lMdYDaCdk5a47Dmwqh1JRIA4CElWXRrlVsiZrJ9t3L1qrgKX9uacvbTtuSx1AHVeqmgqan/Fv88m8UnpUlJN2WcAG86T86Fusg6LHNpvykyPmzYsNze4viwOAnU+F3HcQH9ak06j5YUzBW494QPQSprPKiTBRq8nBdX6l34Wr+RI3KhzQBqyl0BfRrph6ub+L66L20qPH4B238BvSBIqA08A+1PEpC7+KOeUbf5kh/1nffjt27FHzZb2iyTy+l794qKpIs6Uto4Rz5Dv6Jhw8aZOSj9tS/6HRZZVhfbX+2afKvNNJOrCuBOmXIBy8WHKwZdzmJMu7SbZj1KPxsXeFdXnmC8npBiNphjSnmnv/ogapvGDdj2BZDhvHnqy6q1a2PNhnWc+oYPzoLZpqpV8ehffTfuuOtOAMZLsm1pKR9+lq//EaBBxnViDC50IhUUGd/J1E3egEGbiwLZWlkV9WCOy3r3iZ7lXVkZJQOA+zLtmeBNh9NAqWGzJuwHInkRQdQ4qRRFb818sKqpR1S5X6cpaKepTrNxVKZ+8EGeNd2rX7+47LJB0Z7UMwVDw1c4hir/wlpodL10MhVAmoVVCqcxf3MsKACLXO0l2J005YPYgIB7NF0FilV03oKOGkbHqaDKyCoMV6VcyfIzU3w1koVTQCDGeHXKdWJUWjK1jPziyy+lkI666qoiKCLwTGaEWVWWggiOQ6ZOBxpHNPvP39JH45pAA89UkYsYG+w47iU4uO9S4GoAJ30MuRx0G+TNlGO6mwGijjRhCuAFJwIwJufQMSmgjsu2EiHmJw9JpWMgptAoiB/OmR3zKRB1E5VtBwBm6DTo2KSQSWPoa3vp6EB3hbl4pQymsiuUgYrP9TaDO8bFeHfv3hOz58zJcV937bWZ/qUw2/mSYynfaeQ0VCpWlb60d25t19/9qTPn91Qi8lMe57l+fcyj/73Ye3fDaFZFcJDd/+c9f2wU7K/vGchKf53mQygJ++mc57Ohdwlc2UUK7nraXg3f7+b3a8eNo6DQFSiimkkzZcR7SlcpuLJ/Dk6esY/SwmdrxByvjqZgSlXVRo4H/JjTMLazdaRpDEG5dqvoBtAgim39AMbOTw2RIEamtdJabq+ALq6iKaOu4tXB+WkK3Y6zojt52vSY8uOfRM9GtaKsa49oSRtNLwASIcP18SJdkXBV1LbhsgQRMpxlfmsCNNRjhV75lN/pdq58YSESpd7HCRSVx3bEJmotHMUO1bw4WN29mMChXhwDGBHAaILBr10P4KFO/dhznPPVt+2PjzYeIJCJ2CnDcn2Bgk/D4OM+Pbqz8t6W+Ua5Y4RNYXRuK6HNO+++EycwHr3RM90w+B7RaiCqI6LxLIwD6oW5E0zUMEtjg1l5N+emes7lG2nvypEytojVonfe5KxjQIZL4Zv27XE2cVbcLnHabSjyIn3Sp/RVlxUo/sTYoYsAGooMH+StgaAe9QoOHIzVgFIzF30UP3vqmRyj8y6MCRYTTbBV1B1LO6PYNyMbpGPLRtGpVVOOWkPGGRNKgPlMsUi6FzUaSkADtGZOcOlxbAqg4SA1Gio53nIllc537zkf6/U5ucYBMFwKUDuMzKp+/ahS3bpV8qDSztBzfMfRw7nyyDh19HRcpaeB1Fqcf4u69QTQvHbUqCwE675qdYqpiIVOKAJ1ZUFZ9f4iWD2e3ys5T/ZHfa0c+7mB0ccAygIZE26/I4axv10ePoqTI4+XgAbn0ecIYKi/De68X9DXIMd7lDf1kd/z+57m4xG2S3DQ7e9NN0/IdErHlen+TIhymErTjkFsdaD6QB6SP9LeSRO+lnqMX9yyoPOzfCUrx+wN3Yaur4A216PL2uP8u6rn87PtbLbQMdaeKOkHdcFhnP/kQfhUm5qikH2qyfztjqVLl0Xl+nX5nf44cYN4ub899QCyqP7zsq/qMekqXZQX29VOaWccj30p6Wvf21BVFbNmzcysjNbYvQEAPALKrng5Vm2sciOtvNeCYwKiCT7hXJWcPZ+TDh9tGoC7SGC/PiZw+XDKlHTgK5DV3r17sxWwPd8qdLzgtv1R9nxgbTLhMliS4bUX8L7875xkh2j7AnrNRYoD6OgZ6PeP2U/co2ev6N2tR5RRY6Q18po+i4Eg7Rr4utpbG6ChGUcumnKbe3XZ5nkep1mdZjCibjh9DntG5+TnbTjn71JPw+y4q7DdnVmE0JmXjo5NejuP8p00lifNSvP95C1ol8Aj35V2vuTJ0hy4TfLdd96JsvKuFI4ciuPZKsEhGUC9pFxpD6WR/wnsGeDpA5jNaZCe8yLN1Unwp4TVlzgCTy3EhsybPSuuGzsutxbZb2kt7e2Dfbc/bmd1ZdTFiLSftG3gZfBmoGCQmmnN3OeihECD2w0/ZivHkWNHY/R11+XWV5efBGB8QdLsu/0qyaI+RV7MZc4P3xMANgDky9EIv9C+bEaO1go64veVl5XnkYkd2+H8c588Jt/nNo1cAocXkHXny/HbbgE04J/iQ9oRs3sSaODxa+H3lzni9Nevv8nWyLpx4ATzj/i0hnTNyHyvBfLr3716snXikg7RFJ+uDtkMggx1lCuaNCjSLzmbwVzBm2YhCDQUfWT80EygwWKQeygGWbl3f8zYwOIKBBCwL6/oHkMHXx43jxtDbZxe0QjbXhOQ4Ry0Pw0vuVh1DpvTDB/S/eWl7CbBo48WLAAkOx7tunSOKziFoRNbAUtBtPRmctO/UI+4jUE9Jagg39iuAEb6eHzP/sIEGVgegGfmUD9tOXryIhbytK3deV3cmloSfMf7LfiqX+Q2Q3lFHSzdnZME1uB5dYFXCQATcNav2rx1a7zDiXC7AHv6cxKAwXpn+m4GklX7Yc58jmMQbJT39aXofI5JOVOfpr6j3+qvzIjGfrnFZA3yNGPatDzprC++altADOs5+Hy/rQ5IuZJf8In0C6RrFpXUH4MeCcBUy2jqNGRXOVEPWrtnGvbviiFXUlC6X9Ze069OnwOa8JBswz4rMyX7ocz5XGXV70pLL3Wo9G+AfTMonTh5ClsO18bN2KfuFeVpx5Lu9CF1JO2oW2zXl4CMc3JYX5XvqB/0z/O7/C2dfKbztWnL1iym7iLq2BtuiLaAvco3VCaDhqAcXlafXKiDPBFUW5vO9qW3QIN6IItJqq8dA7Ryfizuv5/adW5RnQloetU1o6nHNJItoPWT11KHSxfnkO/jZCbQkPUL+PusOswgnfbkI/srrTI7Az5wgUBf2y0R1hy5fuxYALq+SUO/K/3kc4ES25An9RXUOeqC4/CrNtrvSktBOzMavOcwPsfayg2xHJnasX0Hvl6HGD3yarZJdi5iIvviM2gngQ/sjlvoXYRRv/F/zrdzamFx9VtTZLUhPq7Hze7D91vMaT4eF3qcOPYQdnTvgT0x4YEH4uYJtwCEtacFe+Jla5/d638EaGA2/296QOwMZBAmOD3WrVjJ8ZZTE2hohLG9ov8AVs4qcgVTkfKsXgOYo1R9OwaxPeakNsKl4BuoyiwaBY9SMoXmLMeEWO3fYkSmhM376KP4cOaMRNh64ChfTlDXDkdO59JXThFd1Ahp7D3aR+NzhKMyDegUTpkMPsyh6NSoCAQC9uzbl+ear/hoEQVnRmehuNLKhYG4befxXPRRpvPvkiOkcKfhpmEZW8H0bwGVkmO3vqoyXnzpJSosN41rEUId0DzqjHF+6qxwr23bV2lhW74y4wNGFGxIxuY5GUxhgBrqlDIm610889vf5arC1SD/rVAkFpTSEJpimCAFfdbxzyCe3+Xt7De/1sNgiChXEyaNqMrBvh0wOGV1dy5Aw4MPP5wFbbzPviYh6I/tqqBN9Sxo4QM0YBhgfup0+tN+q8xScTKnFpqzOv0knFAV/W0c2dWtrDznShqo9JI+0Ela+p7Pkfa2Y7ql86rjp5Oho5RKGKNiZVkzDpYBwnw4ZXKMRDndjvJuxBwcAViybS9prAFUkYi+alBUVH5u1WPH43vOa84Jz/HaifJbjWJdRmC0ZfOmuIWzqa8aPiw/O0BlWgMd26HbaVg0wir77D99VjFJH/ei+j2Nvy/50ees38AJHosWx87tu7LewRVXDs06IPKAQIM0zRcykyYLeqCd81UzjYBTjJIl+K9FTt0lbYj6GZ9B4mt/970oo6fdTM8DWb4Y5dmQRuqwYiOfnQZ4OM1qkZkMhi+m0OukWOCGsCt5JyuM03+BCbdzmAZ34By1AI4fiI1HUaZ4U/V74aiVd8/7t+zYxjYZTmDhOQC+gIwd4ihpD+tIJ123+yAZItRsSOpFfPHW2/Poz/59++CEdEhH4jCGzMKBOvEGuy++8GICJ5cBSHh8XBcMg4CC6L4ORxH0VDuA6hReplkbRBu0SnOd4dxaAp+r6g2e5K25OHG///WvcLK6xRCcuIpyUrIphCQ7n2H1y5U9/VvBj1MnyVwy1ZWXGQ2CDRLI1LnGDQEaoNcu6jis2rg5ps6nwvWTv+U9bC0v8x3aAvi0JW22aQMBOmoW1ORcc2jdCiPftik7eeuht+if8gj7c8EAPpxneOoEbje/Apryfm2P3xRogE4F0MCWsN0HMGycClSd0TBh0GUxuG+/GM45zv379cYhI70fvmDqmWMdLgp7Hj6ahjRXNJAls5eU92MEFCtw/l+lWvvlBEUTCF50YjXi8rWrCfZPOVXXC+Sqj73X+VCWnAedAHWT76dO5rsJ7JF14JFdM7AjN024lZNHhqQza3aRQbMgo3KTadM4O16l4Eo9YXDt/KkDMnuKCdPp8tmCjhs3ssWM/ZXWe7n//gcoPtwn5TwBP/rvGPKibfumbk3gj/EfYaVP8Fhnye8Z+KkXXC30/qXLlsdSsgL2sG2wK/x4zahRWfhSHaIjKDjtfeo5++M0pq7CVtlvj0F1LE2xdwbBOrVMSeoCVwEXEnCtRNfo9A0l4B1OZlxL6l0k0E5fnUMekE6ztPB7OmoWt/Q7poeqQ12Vyf5Df4E1n7kOeRLEP7h3L9uzOsIX/XLFyFU258yXDrGAjHqsFDQ4jpxD6C0t1FvSxyDX7ytPPnPeokXx5isv43zViz74BS4SuFpnu+pZHUR/z8s5SN5B3yhnzgW+gMepudqlPcRw56suMmKR4EkU55o/e2Y6//17skravkM0ZyuVAb5gtu3pZ2SmGfc2a0UxNtoxYLJdneyspE4HzLwSvnN1XT2xiZX1F+n7cfyI8ePHJRBuRqPBiWPUMc7gHV4rVioL2500pj3nVrvosJxv7a88n6udvLcMB/eZp5+O8q5d47prrkmecaXVLsj3SRd+l9tt0+fadxnIz4pXsbLr5z5IW2bfjrKSZwX2KRRMvQP7NAyfqSRDyp96IwM6xuD37WdpFVFgyWA9M7ewfVaDN7j2P2kqCGEAMGvunATab6QYnfWYHJ82immDfwud5bP0t3g7aeBP5difBqvpD/C5YID7vF0dNaNhLdlH81mBrehaERM4KagDflPyC991nHBNpmy7hcJ+S9+ihwyNDqRO4x2HmvpAP4T5X7VhQ/z+xRfiCU5LAFfIjDJ+RK+L0MfNCLihXw3cgw7sv25HanMDAQDAp1rONy+3WeQJXbRr6nqhf7WfzH3+ych5aA3G5lHOZjRkMUhA32lrTifQ4PcIQ6k/Mj7uZWXzMoLWBuh9jxQ+j0wYfLlFxTn1NLMEy9EVgr3LVqyIhYABJwiSOnYtj2HDLT7cOYGnE/idzqn0UQeUeNQATF7RdOj3pmxV9z3fh6caATIdRF95VKhAhpm9ggwVFRVZtDJ5Gh5R19i2sm9QXlpFl8b6OOobaaLPZD/sg7rBTK6NW7bEa2+8Hju3buXUjcGcyNKLE5Ao2ikoCb9p57y3AKuYYWgtWJHMwu9ZEwN9YV9Snvjpf57I5k9BmHfefCvaApgKxgo06DP4fDgs5yVtD30yYNQW+fKZ+vKOTd7MBS10mrrUl/RX902bNTtee+H5GHXtdblqb2aW20rsi3avtFio3dNnyu1nfJaZz+gjaW/b6l795vw+NLPgpJlrr5HxaCbPQw8+SO2YbjlOhd85Kmwco+B3hprjzTnhCylP0Eld5Fz7fH1kQ3D9Yu+twk996ZVXog2r9Xfia7eDRp7AhwSyJRZe5fvO6zn6VZOVj/P4dUkr55q5YRagM34w45KPtPfaVzMxdu7dE4uWLon3OSL+znsfiJuuvz4D7U/wfZKP+b7slmBf+ov4UtDffuZWJ3SQ+sVLsEpAySLQ+ubbKeC7AiB/MTGg9Y3uuOceCvgOTBkXbC1sD0AX/fTlg2xJ/8MmpYP085LvXQBO/cvvR9A1lZs2U+B/LQWmN+DntY1x19+QxVLtt9s6kyfgF/tvhopxqzZFP0T65Okw8L5+EA50yquFSevyck7nEVfOW7QQX/wk2y4o6nvscIzjeN8x4znJo2Pn7Bc9q/752f3xpwMNUtgLov7hp7/zQojg5FhHQDeDgHF71Uac5oYxlOMee1F0zH09Vk3P1CUm6RQGTASsBsobzyHTbgUaVAROmMGz+61O50oDqUCk0sElFIhaGNNnfpiOandWRK7AaLbF8Jj6KGKlkNs/BUohds8ajWYwYgCnAOqEyoAKXxrVVK7N48C+vfHia6/FUioXi5aZltqGlVSPddKwmS7ovi2FSyHQwcrn0ZZKUGHXwZKxi0C+UJS+77gyMHrxRRyvpnkUpoUdRZi9dLA1tunUIRD+rYPhM3TKVVQKp4Ff8QzTTREclEcjHBLbn7twUTzx7/8OSHJtXHs1BdcYuxkNCrHKMUWMNhJwqBY4lbL0UimZWeH4kJpUYpprf1d5GTR7tvbc+XPi0S8+TgHDAYk2KuA6UY5fh9Z+GLRpfBS+Yjp0sAoapDPB3PiBDqNj0DHbtn1bvPXOu4z7VNx3z915ikTuM+ZzUzdT6aMY7K9zWwQxBUAjrXS6TOPV0fC7CXow9k9AeDdt2hRLcM6noLyv5ziwu2+7PWpgdE6jFDXYKiCVpVstbNsKzP7t3Kic/Y5gQAYvjE9jomJ1Hqyqv3btmlwhrdqwPm6jguyoESOyf66eWpBOpF9aqPzkowxeaMP5lnaOxVVD59hny6cGbtLSyvHzCEy3btsRjUhnHnb11Wn0iznFOOBMKCd0HI3NPPCzJkquFmMyxY0u4oiRqsXRkzVqXyCLBqCBPnwIYv7O33w3awGU92CfIfJ2MWCDhSB13T45x35ZAmZNVIaxyS9mRaCcCURV4WfJNjqJEjbrQeBA5YsGj8OMaTuKdPPxfRz7iPj1CFIvy/heTc7brow9W9DPxFiNKULQvHVTajs0jC3UEdh8iDTc/WdiK/Ll9YVbbucIxiExaGD/6FrWmedGHAQc0th7jvS69RviuWefjZM4/67m9CYFU2dFudcolAxLBjDMXymb5CSfCU4JMliDRdTcINHVTvlTuamJEZlH6uWv/uvfo6Jb1xhGQNetols6LgpSATQoIRgWVn9OAJyWgIYLADSGKcqUc1ibUyr0uvcfOhJrAL0+mLcgfvBfv84xUq8smjDeLpc0jXLrJXC8ZVwgte88ACX0b0Qut9kMjQh4nFsDLmVK+tM8P5ghX8ySHxQZDfCDQAN0EGjYsF+g4SBVpE99CjTcNvjKGEqWxvArr4i+ZDQ0JRA9cYwjPxhbPYpdnjnt3lqr6wOG8HyDSnleWrkvOwOjp34fI64ZTaXrCThxFP1ytYMuKSf2RVkqgikCZnjOoMVOq4NzBYmfyoRzYds6Cn5PXbNw8eKY8QFAA1ktVwGuaTd0xgTV1DUlhytXeHmWfytH6hllX0BC2uvw6jiqQ3XM9gImCzTMAKw+CN888uijjL9vkaLsSoT6WweKcaRuQvfo+LRA9miUo+AO5Qp4Sdc7FY7HNFQDWvXMUvq+h9NFKuDH0aNHZ9ruEfYkq0tcRZEm6iz1nrrSttQ9/pRvDaIFHs0iMR1VIZZegpry5HK2sKkTR9L2VSNHYE8AGqCttJRGNJO0lP91gtTrbsGTtgINDk6nOumIXPi7tF+LPE0BjD0MjToB2F1KZpypqcVKH/xIw96j/kpHjnb823k2ILAN6awulvcFJAwurK7uWOajx155/rm0V/1pexDgoM8REMxth/y0z9LHl1ztlSuL/HrmOLaG8Ulv9bzbJvgwT97xdJK3AU9nT5saI68exRGkfVmFbp90PI0+lS7ymLbP7JJz3NqIbZuulvpsdWVjV5+Ye8fm93JFrx5AMXZg07Zt8RS6xhNYbrvllk8Lrtk3M3HScYVH0z4xjhJN7L9j8dLfkLf0DaSb9kOaydNLVlC47Ne/jgqCinHXXZ+F6HSu6WbS2/bkdW2PAWTyf7Wdk8+dG/k/5YDv+bc3+57BzYw5c+O9N9+Iex/6fIwUuOO5+gS26z3OV/bJdnimvoK+kXyqPdXH0CdTMvR//OniizxvNuLUD6enDb/jzruygGGCDLZUao9+aLMy04jnZZ8dG7STOvKofOOznRuPvztKvwUGN6xfH3NnzoryzmVxK0BGW8BeC8Ta7zrQ3GfoF2mn0u+SaPaR9wWA8MZSL9KFtMPyTA34x+Mt//vpp+K/Xn6FG/5wjexah+MtWwO60x4AffPG9ckmIPPIjjK3Hm3pCy2bvCl0cpYBS3NX1d0j70iyyJ5zoZ/DvB0FaNjHUc6Ve9k2sOZCsXWCNtwyd/sNV8cXH/1CXMHqvvb79JEiWKlFp114k88MTv2pTyEAsWzF8ljIcY/WrulQXhbDR46kZksXAjp1KrJiH7hf30NaeWkLU0fwu9m4nmaV2azMtfxYC9mqj39qZtWU6dMBGuYD3ndJn6O8nC12yHLSlvvlZe9N/4j2XR02jVwZ8JklcEOgzr/V/8qbiyibtm6NVzna11o/V7AdtEePIhuxCXx2/nSRPUPnIXexAKk8Os88NOmsLDk2fe3kJeZW/e4CpX7lUkCYV/C127drH1fSvtsyBBrkDxdRtBEMOLOkzZRWHrJuDj6Z7KM/IO+bPakcSj9fZpQIVnqi2nO/+U2MnTCBE9suTx1nhpE+nZe+u/rPRTX5wtOxfKap+9kusqT/4fYM9YOgjLra5+1lO+5Lr78RGys3xBcfeyxPZSnJqTpAujgJ6srUM8yp7ym/qR/ULTxTmqfeqP5MkE+abdi4MZ557rm4BADp/s/dlUDDKU74M3vHreoIKjoHHwafQ6DhNPdJI9tSBJSn5El1Gc8xYK+B7Kpndu3bw4Ln4niDBdWHvvilGM8iBMSNs6w4ZWFjqGvvU7/wqLMok5PaE8ZuO86fbTqX0kT7bWFnC9nvhFdWks2wADtiJvG9n38ohgwahH4iu4ZnO4dp8+l76nDeT75nzPKqvCl95E/nWBr5qo08nUCeKrcANKxZG6upv9GqVWtOxBkf3cu6Um+BueFznyO/y9f6ug3IYNHXcfuyz0kbTrtutTYua8hpcdrW2tgPa9BZ3HjewgVxBDnwdLbjFFy/4Y7b44axY3JbWCKz0v8zfv3PAA1MWjK63JIsww9njQlkJmIDQMNMgpgdoEMtcG6GXXZ59K7oBsqLE4Vj5T4fmcd7nCyPJhJo0OjpxGYQp0JFWNw2YRqVKZNNmXhmM2bNnxcTJ03MtEG3Tgg0tCdg1+DpSGkE7V8CCPTJPWkyk3tz/TwnHob0O6kMYR4dkCakUVoM6xVQ1lU4i6Mx9t0x+p7HrpPrdg6Rvnr0TSOrwH+6SqFxgeF8tEyo0yXjqdRk/kz3RYDWVVXFCyi/+rx3zahrMqNBJ8ZxWdxKJZOr8tzn/aX9YiLCeVwM4nmSfaOpUARofCD9aEwbKvD5MPKT//EfcdVogAZWu1xJclVMBZErz84TdP2Dki4URclZL61UZEFI6OIRNqaHK/iHWclzP+O8eXPiPs+SJ2VJI6BB86d9ctVMh9jn6YxKC5VhyYGxHa+kDYJfHwPks93TtGXbVs6VfS/vu/fuuwskERpkOiXP8J4McqFNjlv+oQ3nIMdGuyLEKlIVlLTPDAFAgp2kva5AQU15990YydaGOzEODVgxPEbQKo3to860vGHfXaXRWGWaITzqs0vIuiRP5cQcOudW1TdwWYFhq6rcENfffHOMGHplzosopryjAVLxabzsl/Nccu41Oj5PYyPvO17H0JLApQbfW7d2bcycPSePSGpECuOYm26Ovn16J80FJNzqYsaPab81ARoaoTTNJNJhljcuIG9mJlzgnEprBFot+zDGYNHsubHkJz8l0G8Yl7S4OC7BULCIQzDrVgy2NvHSkW9QjxQ8eCArpdNe/dqg/gANnPoVJ5DPE8otc6DPb0YDRAl6FXtx/Laf3BvbDpLOhy/YmDgNnAEawIJYmYb0hfiV/XkcJ0dGw9bDp2IrgfARtlFsKbIt475xt8YQ9MeVgwZGz4qydPQO7tuLca6bvL2+sjJef/W1XPm5FEDTFRezhIrAiKUoJqvgOZ0S+geNdabkTdNLDYo0YNYW0EgakCovAg3KrMDd07/5FanYFTEYZ6Izx2y2ZC9msXVCkMftO8gX7Z0HmKmNYa6jl+laKDRPk5p9cF/n+TjIM6rI3PlgwaL40X8/xfdI0yUmb8J4e5S1pp5Nx2jdFEfrLODXmWNse+EZ0LEhPFgP2bE4Y1rovFMjyS8qvAQadCLQOTi8QGHoY0AstqUc4Lkeb7li1wFWfU/HuuqtE7cNGRHDBl7GFqtLyQTpjtNZn/2dB2jOwoas/HC/spAvxlKTuXW1RVmRF8xoePapp2L4qFEpTw0BytzCooOQQSLfS0cTnlYHqns9ScftQMqDQYX6V5nwmGP/1qFzLqyVs5iMhnmzZ8ett91BYHRlyqMp6UUdgCITwOfofPtThy7nj98T6OWnNsb+K0+uDjv3Hju8ddvWmEXbroo88OBDVBjvHaeYG1cilE+DxnR2GIPH6UnzzGiA7wQCHYvPtD2f4aqIp1S4Mu6xwR9/tCi2Yf96stoybszY3H53CD2hDlAn2WfnygBO50V98mlQSeCVepM+ZxFH5r4OesktFnv2UlsFHb8aXWO/Bg8fTpr9sAQpcx83/TKQSP3EM/zpJW2lu3yvsy89BDP8qV5L0Ic5Up6mASYf3r8vT2MZTNX4Xj26p93Tfio/Bvle2pBsn2c4j7ZV0IOgizH5LPWmOq01NlT7tJC+v8MRqk3wC3r36Rv9WL1164TfVQcW95ghIpgrfxSOscGVWxoEGVwpUr4t6OcJEuehTwNo455ygYaZH5C1hm3t36tPdGLFrlnDxkkr+yIPOL8CjWaD1ZQGjMXnWgSyMY6hdaTUBWZx1SJToh6FXi3Y6mra088/H0f27Y8J6PguOOkGVpJYwMifApyFI5skSprYvrPgz4JO+AbQKn/nXefF8S5bvTqeRp56YVdvGF1s7dM2lmymLUpvA5OSnLhtwecl7zgP9F0e05fSaVe/ycsGgrMAqIpVxntjOIGXNFXWnCt50P75LH0hnWf/Ni3Yz+2H2Y5uy0uexd4gXQkQGbQK3gk07AJcu/fe+ziCtHs634JkJXBe3nDMmc1Ie/YbAhH0shecv+1vyjFznqfZQBODG+uybCEAMKBuT3HS8coT+vkQsitwof8kLwsylLbFKD+2meAPK68C8TVx4A0v7IMggydVrADAeOLpp+KXr77KaIqrDpM1tl/z6N6WbUHo8loEBNZckOfyRA1ojAHIYF4ayKeGXxmCQWufWc8sGO5R96ivpKPHQB8B9N3H9oyNHAc7fyO1iLi/JY0coMn7xo6Irzz+5RiMzjh1jBVPvlO3JoFXHm2NPadd59YsEjP6BChXwTOLkSmPY23TsUNY26pLWZdkuFPwQGaj8H3lX+BVsNgAMYMm+panEtA3gWN5QDlyL75FpfdgC2bMnpVbJzoDBppNIuCgX1Lyl1wUk6bKbrYJbdT/Xs6lc5wAMjpDGdOH9O8W+NpuRXqLrUIHCaoHXnZplHcpS3CtIf3MLUy0Ky3VK86r/8mT6iBttJe6S5nQHy8FgUXGR71YunJFvIy8duzQke2B1PwiRnAhSj7UTkgTdZIBrlt0bFsAT5rYb+2S40ra8V7qN57lIqEZG1NmzIiX4J3xAOEGu1n7gjaTx+hXylW1Lk7gmzbsq/6en2kTMzuWZ9m2YLt8q//vtoBXqOXm1omHP/8wvk9XbI4ZFtoK+gk/63soryVeV8dIm09pxrN9jmOlU/l9bpBsuYX7+RdeiEvwZ+65k1NZ8Cs/cUGXrIaaLDBcQMcqJ7Xq4+Oi/wQazAKz/wIBjlG/E8ZOGbYfPvsT5NWjGxdjA19/6cW4674HYsINY/ALqU+C7XIhLGURne171oA4j4xZQNJF5pQn2rH91A3MjXMvQCQItJc2KjdsiEVkNLgwWWQ0XFrwFguKypsj1C/3JS0cvDbR9pJmvJdzAz/KP/bdovyn0SXrN26MpatWJdDQonnLuPGGsQANLJApw8ie+tW2vL8AGoy7qrMO+UwdAanIDiFewbbrN9fjdDJjN7fDmOnxEa9DLOAcJqvBLOMxd96eGQ0XtWlPX+VCr2Keit8/e//+zwINCJuTl9whA0NUZiE2LGMf5qTJsYu9de6PHHHFkAQaXIlzZeEUAqVxEYk25VHUzJV605d0LBLxh9DJyDB8DSbMlJ56KDg4KmbOA5l//z32V7bk/OG+uY/UzAANoAxgnxRcQQGnqzBoLuIWiFzhxKioeAbfE9xQ+TUi/fQwSu91Vr3XoqRGXj2KwKVrFoPUuKtIFUDNlS8Z1GelsoN5NXZp6BUOmNG2dWpUKDo4BjSVVVWhgGs0RrEyrZPVGAdVwdJZsa8+S6Pr7xoVARSVo0UdCSeLjAYEVUHy+RrUBBr4/vxFH8Xvnvzv3BvlWcA6dLkn0X7iOKmlVAjc5a30T4eav6sFyJhFurmVweNZDIhLFabdwzR11kxWpebFLXfeAdDQL/ugsTewT0cOI6Vzw3CyX9LW91VQggLORz6bPruKI8rrs3WYNrPSO3HS+9mve0gnKsOonZOWCLnzegGlJs2li466Y/HZ6Tjwezox/O2KiUZN2uuEqNj3kgq8BqX94bRpMQBg6iayVVqRIZPBpTxDfzSM6Ugxd/KKjpgIpT99lgo9UV6IZCAjqOEzLGK5adMm9o2tAnCoimvGjMGwDcn2TJcu8YC00FFIfoBOOoeORUcti6PSZ5WTl3Pb3AweeGrD2nUcozMnVq9dEw0wCBNuvz2LTcqPrmRYMFUZOQciLdDgqpx74pjo5A+W3pEtHEOOmLRG4DlS/PeDDC/GcVv5bz/PqrptedbF8FhTxnaBgPkMq+knLlARGSXapEGTqK/TT9/xpqARsoDTdACDdICVh+P029NLEI5UurVwjE4Dgh2lvT1njsbmA4djJ8FtDbKA61OcGp8997o2pp06nMZwHtDi0MnzfI8MiH3szwfB2Mo4vO656XOADFfE4AF9UPgdSV2lKOmRQyhvAjv2Xayv3BBvv/U29ShOU9RwQNZUaUdwIc/m3lyYXJ4rXUVwyIpsziU8wveUW+cmedjvw5eu/ipzC9hr//rLLxCId4tBl14KiEEVfgAqi0G6tUA+TFmFd7iNS14nQOE8whrQMp1q5sYMkDPoDo+2rNqxKz7gGMwfPvH77FYb5qQR5OvTtXX0o4J1m2akjp7B8TvtXkhoSl89BYIm+Zt2+Vkg38qyrMmcmRusI2b/mRuhUHKPARpIKRdoIKNhxa5DbEuhWGGxUyhuHXJVDB04iEKOZJwBNDQmm+YQ6efKmeCSBlI+PE8fTmMUnXxlVgHV4THF8Plnn4nBw4bH7TfehLFmTzU6oiRLnxpkaGM76kTl2JVoQTblVbob7BgY6dCph3W49gPQCTSYFnz7HXdlRoNOg3pC2U59UK2/pL/t62T4jHQ8+W7agGo9JxOkXuKZ1lsxaLTor8fh3X3vvYwfoAEQw1WR1C/aMu9VF/I86V4KIn1O6hz4Q12aug26KM9uq1i5ajV1FJbExvXro3v//nEjwGZbeNKsl9Tx3ONYfA4tpP6TR3UeUz9KF54pgGvwVCvtJJk3zI8BnfVa1hFgCJBaGHY4TrTZD0cZV/YVXVXieYOgdH6cPXTjpzTJ0fleAaAW2Sq1s+bJTDI9DrCK3LbtJWzLGJYpzRaW3YeTp/w4d8pIodN5Es/zgcmW8gZtquecW3uinsyz7vneIjI9Jr83MbcO9ujZEwCvZxb200ZIG1cBpW9uj4EffN+XlsNXLdi8sNzoe55pyq1gqPv5D8F775KNMeODKWwdHAHQQHZTO7ZONGoCkM0igXMIeJhBqf3k3uIIRhrlM2XGtGEGUIAeAhE42nUTaGiSRz4+h+0+DB1uHD8+t3y4dcIMGG207asPtBfSyZdXyd7lT//m2SVbJa3kA23ESvT7K2xF6mEWDNmIbq+U//JzBb36kv4C9Nq1BMZ4juCP9tVn6FvJP74nz2sz3Y4zh5XpKdSY0H4MxQZqh7SV0juDNPSU93ufAZB99OX8Jv2dZ8aoT2IGigGrY9ZXkC8/mD4tazXcd//9ud/e05hO0EeBBp1xx+glH/KYHJe6RDtm4KoMy1MJLjGmWo4R+38QXbAZ0G4RuqBDm3YADWPiIoArM4TstyCZe7xVwM6ndHFMzov/2Xe3u+r0u42NDxKkuoCNWrF+XQINTwB+SWG1qNedAxtHr3acdFQP2gK8e9qPusXvuBhzgb/NGkifhjaFXTJMgibOV1140pNGzGjI082cFxo/cuocJyqxrZBikIu2ROyivYvp0h70/4NjhsTXv/rnpOFfmkDDwT2HaKMaaBDAoj33fhvEuKqu36SucWuDIKbFIIcCPJaXlaV5kHYCTAbSZkDIM/KOuiczRPGrMruYfnkUuzyjv1QXP9WU8D3QN4EG9LDBejfAo4qu+MP44abPq6+sA2B76p2UeXij0M36d4Xvm/PP3Kjjc9UZfdaSLUsCDW+z8LOfRQ9rNNjvPIaUgPysWWTwSAKKtC/EkDJjfEC72gpZU5/MC84s+JgxmapulsYSwNgXn6XYdfsOZCNeGR06oAvoO7elrVcPZPYrdHShqhSwJwAAL8qX+oGOw0xIgWEvC3LKwx8ANLxBMG0NIQse6ivbdso3baq3vGzPNkrv55vV/5R0qJ9JN3WSxx67DeyNd96NTZUb4j6Au+7l5fTZFXV4EP5Tzzh3+u76j/KCz9A+lxbfimyAagARGirT9s+FOY/efZmtE20BGj6HPmjLiR95pC91pwQazChJGTKTsQkFZ/Ex3IqjTnOc+iDKXglocBxOgjzndidPcnmbbJUb2fZ4IzVbBCkPHziIL4Mdpp+pG5UjZPA88Z+ax/hEn0Gd5xj9jn1QHyQ4Dx0PoAusr6RvIPg4gezky8mM83vW2cktZtyvDjOOkva2I231rXMu+FsdaRbaefjHk6o8jURbYs0W+7523TrqHrWMcddyhGpZOX2iH9wPoVNXuVCOm5VFLOVH+yj4lEyZROYf/s54kc+1E9qnpSx2LiG23IcPu/+4pQOOx7h7Phfjb7opWrRuU80VNiBHf3av/zmgQZaVsAoTCrB4YUgg+DqKx3yAAtkNQ1wCA1995bDoSWEmZjtOQ2wnQyNQmzRtJCMr5ls1P7Mb+Dsv2hZFMp2xEQ5vDV5YTc46OpYZDdNnTue4mjZUgu/Fq2emM2v0ZNRk1moht48yoQYnBUSmQ+HJGAqrzKKCycCR1N8jGMw36ftaUtKsnizQ0Jox/H/snXW8ndWVsFdcIBAnxK/ElQgQkuDBpRRarFihRgUpbaelSmVqU6cCFCiUUmgZpDhJCBBCEkKI2725N3LjStzzPc967wG+v2f+mfnNCyfnnnPed8vay9faa+tllJgcj4rI+wo/zxdEVsBA5iLT8ruCsFWmi31pRqWWLl0aj5I6qkF7xmmnp7JiQS09bSnsGZtEoLKiYuuRbQwOFxmKJ6noEqRbTRTcKrnO1eeyMAt9TsMweoyoyMlnnEH6/uhMjZNJSwwaTjodRGMViiQKPjPyZFTCj7uSyCV2mYk1LrJ/iGwLHu5Jb06O6SgsZ6I8Dx86JFoTWUB6ue8DD/AHESkJXUaeDgzW23evHAfvqTzSt1stnIPRCbMCxk+YkELx45deRgpgj0zxNzNFxpNKjTgBc829UYxPoetcVDaciMzjAIYRP6Xi4vcWfDP9cjFKxTSUlZ7lFXE6R3Adw7p6CWvXSwPLQqWJQ8DCNbJ4oTieJ3IA58QbmJdef7gWT3PqBClcS2BM82FQK+tWZjHIMaNGoaSSHaFTzTXl9b6ApO1koLRrvyVDyXtKxrFjaonhJuyXVrF1gnSrJTVLoxkC+lyiaQP69ydKR00S9tAbAWihIMTwJUyfCrPKUNKn0lgGTgrowebMk3CNdHeAtZz4yoQYf+ddedxnWf8yTp1oFG1RkJrzKOoRxyLqbEBJgU6ao0y1xFjW4afavw1HwCr2S6/GC74d+O7eSd/IncYk5zRrjTcBBWQfyt+m/bs4ynFT7MRn0rmLJ7RQVM4+mFdLFqwxbR7GIN6880BUraNGw+ptMGTadQG5brzqxjj5pDExsFd5dO3Yhq1YCDnWV2GlgFAg/OvpZ9hetTMGDxlM9LXY59nY9QFXVIilJ/FPAaxBZZYIC50GbikdU1zVkScPKrom3RglbTqG0YQXn09DXHzv2qVzFoM8iNNgL0JZBiIvU4A15lkNck+baMYxnlmsE+GsctCMeUPMGINbYzFK88tkk9z5u3tzjh0g7xaQx4BuLWNwRVl0rM9owHPEPIGpI6I/+U4hyAoFmgkl23U/F5yPtqR0aJ0xNSJ1RLjqaNjCMGu2eurE9lizfkssrc9ouOjEU+OEIcNj9EnHUwyyP4KVLSwoeJ42cQDnhDwxt4nR/iFqbuhwUHHmn4TnAhxg/yBiNBgHjCmGGkaZmcO45GeOVTwv4bqOZXmg43ZdVIBVhowiqQi7Bl7biYhYXG46UYvpZB1cetnH4xSi9o7HI/ZSoYIvysdVIApFMKFEl0DAl/8BNslAuss/aFvFUWVFOrXWganZl+HU7As9HWStnL/jkcc4bmlI/FcqqdClrKA9lRdlh/Rr+342Om5WgVkBCxcuSLrtVl4RZ581LlNUxUeNMg0heYztOifHmHwSPLEd5ZSXirmczMi9ckAnjOmiFt2rIptERXgEjgajdS3x3u1h24qXPNv2VfyVcdbokbd4EoeOB40JZYFGbipIAEqFWzguhQdPoSDvOhwxbncYM3oMpzoNBPc4+g1F0bEagRe+PpuGFnifP9B3CfZ+Fja+/FuDVx6qo+HV8eMTV3r36ZPGixlIGUFnPMLGdfV++aJyInEI2ChzPX0KgKcu4VrksakQSXMipGakvICj4Y3XXo3jhg2PIf05raZbT7ZHUGaVdvMkJYmbuSs/lYWm22vApPLOgnqqBVNL3AU6gfscfoFSf2RLnFOrs5Dz1vUb4+yzz8p6LdbG8CSFdMwDo+QzPJ54AQ6ljGYO4qnySVwRzvKZNJx4xneV/3mLFsVzBDl6lJXFWHQPi+6JC66hVwZSmLPPa4AJm1JAI/sTB3mVLv+my4SfSvgUDNJJwP4snCQWK9ZIzHuAg21JG+JJRlmBvc5Yo4niU2aXMIdC2QZetJxbLIGVJwS4pWf8xAnpjLnyqqup+dPPAYPyRdRQXBEP8sWYHK9zENaFc8lsRenIdQFO0FKTI1qmoWwxyIXzF8Tb4GXvil5xCdmIrUmD58f6NaUR4KmzQX6u41L8NkggHWicyx7dUy7uNmBuAmYPaz9n8aL4M85SHQ1HgVbbIHmvixn+AAqMd0TetGzAvOFVOkB1VOQ+bPoxCKIMasB3++C3e2XBfFYeAFDwVQez/IGxuV6sx3s40ddt2x214ND02v/f0XDl6cPitltvJ0N3RMrS3VuZH0Yfwjh1lMxoYG46ZY50+wKTWExQb/KkSRzHvj66VpbHGePGRSd0VvF9D/qYuCXMXWdf4o44ao0ZeblGs/A4BN9TTjoF5WBTiojv2P5eTCAwY20MswEGElgazEu+4EkV0pPrqMNJZ4N/+588QLjTc37nezoK6Nf1MeDWFMNuXd3KeAK4r6itSRkyED5TWVGe+sxuto2YWSTuCz/noHHvEcDSrvI2HZqsgZf96/hIOjbIwueFFM19jK1OncmCcdujWypbCTfaMLPOewq5j4OLPtzWJu+xDdsSTs5FB5hwk9bSsQVvklZ0NLgV6aMfvzyOx9HgmJyrzzteacpLGnYOyhZtAZ09tqlzUh6fY+azDtnSPNeQGfT8yy/HWgJCHyPjoE9FRdKxOjCt51omX6APM6FdS3VuvxP37du2Hb+4p2PGd/tXRi9ZujSexBFwLDC57JJL8lSkfbTTBO9tA/QUHiYoDI7gBGjKseQQa2aAlXgxiJXjECet16XMFJ5m0GzCQWVGw0vYUWedc16chy1ijYKDtAe1cJ/8WzsN/oKRYR0IC5W7BsJXvFJ/MHvSy7mIW8LfLZV1davi3VnvZpHYCxn7SPQP10d5V9I1nLcyPIPK9Je/0bbwUbfO+7RxWDF5chMyDuR9C4GLDiqLjJuFd/ZpZ+QWbgAJ3rCe+a7uUugC1vFx3qljMFbHLw2kPQaXzBM5uKUJvNbTpOTxOhvWbNoQqzdvJNCxOS66hmKQF1/EccnwtLzEIlf5f+71X3c0OHeBne8AXmKSsfpSHSMaugDm9zoCbRVKi9GEMcPZO92rV0ZePb5ERqznTeIzKrEbZrEHR0MqbCoA9YuqUDMR6Siia1aidZ/bDhbLfS5vUifAqtiVtKuXtSOFWByBTDhfEJ44ILMoeQHNKpAhySRENIlPRUmGpfGj12n9po3x0iuv5JE+Ijb6mwAAQABJREFUQ0mHKi+viA7HdEAx8gQAlDF60dtHAykk7bQU1UomASNVqZBojMqokImUEr/Eo7H4T6IWCmkzGqyym5VwEYCpHDK2zGCgYRUt9y3KqCwouR0ni6mipvxJ2PYrY9S7nMyMzzNnzY6n8VSOGjOGvddEukBwBbdMxvtducIcETh8gIALuDEfmIfCX89/MhLWQeGssJQI3SJgNsk7FDo7+YzT4ziidR3Z798MuO1DCOutLkUTVFaEl427nsLbdxmsjDWZBz96j3MwnXk5HsrJb7yBkdYiLr7wokxNda1cQ8fvmHQkOF6ZtWiSp0YwkZLybLqgMFcxEr+cs159jQuzSSwi0wmhqYfbfZ72L5xlSo7JaE0aU/QBhia+OHbnr2Kr4DZyaX/OxWdUshahsCxcsCAdGqeefVacSHEj0wpVelw75yFM9LLK7P3svIVJRqhYZ5VNMyYYAHOsV5T43cI9c/CCLl2xLBrhXT4ND3HfPr1TwduzfSfnPZPBYMov9FIcZ8m7fzM225eJu2ViD/sVlCEdcajtxZh75eUJ8eI3vx+9mGuvgZXRCXi1gw5aINeNkG+HlncdchsFSiDwacWe/yYIFeM6m6CBmu07OM1gCwwUox02oFhQ3jSm6EDDVhRThHnbxvLVG6LBsZyewNFkR3JEIxoEdI0Cy/iAMGNrGpt2kQm1fmcsWvMe6eHb33c0XH/lDWxDGc22gu7RiXPCj+KopSYYvk3EZdZ/SVVVvPziS9Th2BZ9++F0xHjpaT/gpDeoPJkCKSxUYhXmGmyu4/tKMPiSRjBrIw8q0axpnh6X9yaGS7++vWPooIGZ2plF+hi3LOAgzilxQDxya0GRmcL2rBZsl2pWKBPSdZujMEgaNYn14OFC+OJEHF531ddowFUXuLNicOfGMaSinIwGTuLAQZOOBtaOZtPLryPM9ZSg8z9pF77SIB0NrLf/8bu/6WhA886tE1kMcsueWAhc166nQCc6ltfZI8fGiIFD48QTR6BE9ov2OLEKHuSRdWQwQG/iqyeEMCBgWEQApA957JLq6ngGRdHjhc85c1wc7dYJaE1BrpOUYaTgFd/liUYlzBSR3yZNsDbSlHSh8imNO35Pt/F0BYtBvj3lLYTwR2IsBrVREem/lJEmrUiXyhIVDTsUBq5HoWDzDXgrnQons0s8flI+Vle3kqN0Z2eq54Xst+8P3pTosmR8mUmFwExeLX+QL9tnRo3sj/nJX0p8xjE49mUYRlXgpUdwdUaRO+3UU9mG0C3h6XhV9HyXz6byzzo6b/tTPglzvy/ShZkW8zvE+OXVOhrmIl9rllRlG0OPo6AnW3qsRi/vVeY5DtfH1GoVK6P48huL4or7rXAIOW4Vd2nbvjUu7LOWseuAsRL8kayn2zKGpKOBbBycwSnH6ulHvpjyI3GwMDSEPUNI+STc/T3bZ0xmYHjqxFvweE9xKq+szK1OFnJWSebGXF+jRc7Bl8/nXMAVCc49r6beik869N3eoBJ8FIVMt6PkTnjttZjGkagDBg2OgX364mjowWkCRKuYv8qmzzm+3NfPfPfqWOYbjVwkRlHbhhuEhbJ2L8boQQhQB+VKlP5/kT1ljQZld0V5eRbhVEZLN8JDRdPxslrF2tKu+O56O5eSwl8opshFfnMd9rH2C8m4ewXdo0vXrik/dIQb3dQhlU5258t407gTNlzC1peTcszionDzb6+UXdCMaz8duT3l9dcz23EEUXP3yRdOhELnMmIqjFIP4/n99XTkOpZwyshe4hgzdN72Ij6vWbOWulmv874mLmHPsfSkI8e98Gmg1eOZsPfl+JS7wkkHnvBLQ5HxCycdDc2VHxiEtTU16cR/hwy83pW9sxhkN5wAOuUzY4z5H3JQtOkWBeHpmLO+EnC3CLiRRbcgqJOZxSa33A0szWh4mFPAHuCkrqPJXngvvQURFyIUB3ZpH8eQcXsEGWqHDuho0MkJX1fW2idjts/DH3I0HEI+SiMpf+E3GmKueQbV+Hsb7a/btidqqdEwtYpMBuZf2jpx2SlD43Of/RxbBYfB89k2gvPerLn9e+ELQEq9SfyC0rJOgjxpLkbRW5Mnx3rg3rm8LM7E0VBWUZ6ZX8V2NHXFgq8WjkHpqXC+iDc6GkyFV9cWR9VX5Det27eL99Dn3A4zjfY7oisNZJvy0CFDon2bdrnmBnnyJBLmoG6r7iieiJ+2nbgnvfG3MBFWLrhBGrfOLa9bGU+yPWBZTXUMIqNhUDoaKrJQbDoaeM42xRV1WGlFfCp4bmEsfthQd/99kaqOYU9H1mh4El37GGwDt4B1g65a40ARH7MYLOMUNjobTONPuUH7acyDToXzoqCn5Jc8J492PBrUr+P4mgS9WkPIIETyMHGX3xMGyE8+5vc+L504Xk/gcaxmGelkU3co6cvyIulQnfJVYL+RTNzzzz8/KsvKknBoLtsWF3TwCGO3dDt25YZ4D9iQf/AVfnMstl0EJouaOdZdqoamXiC7qQO89/zzzo3OZMq6ls3Bu5ZkmlpLIe0N1q3xkWyPAUaHWL50xsAjUp8Cxsp6+ZP9ynuk13XYUNZvehN+MHr0WE6dGIPedmQa3U2hFQv8J67Jb+DfB2hXJ6HObnVijXPnYgaOl+tltoDyTD1+FXx4Ltvz63CGW0RxGAEmHYjSvWtXrGPhFEheCKzsTzsw7RvGnPcBI3Uc18atDXvgBwurcTTgCFhZV4ejt2OcOnpMVPYsSzuUhnPeyhBlVOqPSA9tX+Wqepe8TNxT9zeLyrm4HjrO3Q6zEL1g7uKFsXojjoZNGzh8YF187KYbyYq5hCyt/3M05IJ/8A+Qq79U7Lx8Z4lzL+TSJZ6VPSOW4r0xyjKYbIY+ZeVpEDVmUQqhXKQJSVimzqhIyURkJu8jMYSkUmGUQRPfxTOddAZHhLw1/a04EoWpJ+32qqxMRwNN5bM+n8gGYnkls6ItkUPi8z4ZgQiWDFCiBfl0RGzCY/YajLV68aLoiwLdowfHY8GoNC6SkXKvjobc1mF/YJFzkKBlWEIjBatIz4eM1hlF5T+VaiNGL770Us5llPvGSMU27VWmViApzzAWCUA4KUzSA8ecMoJjbxCmiK0AM91UBc4iTSr38xYujJfxJKpoDYd5q0ykt5Fxp1LJ+Bxjad3SIKfvNHrpz71/Oi00uK3T4AkUydCAnWnGOnlmz50dp407MwZg2Jnp4XhdFxmkKWb2J1x8efku3H1PhiTc6793rdx7ZmqrBaXenvF2tnHm6adnZWkZc+IDeODAc/142nY0JlSMhL+fde7YY8KcOXivAkHjQsNGxvQO4++Cc+cknDBFUSOFAaPhwVQKYJp+SMHL/DVU/V1hIE7JZIS9zgaFhWMw+lpdXc0xlEvzaKFRp5zMcUdDct0U9jlm2hBHfNmO3yUOui606Xclp4dMVQXKtXdd1m5YHytWrYzlq+qCUgYxkuKIFSgVZtccIoOhSCdmjMBIyNqer4Q/dCUTN714O/vBmEEcSzThEPB+640p8e7PfhuVbVtEedcebJ04zBGLOHVg/g1wBohnKtu5pYm+tDdpDfjCNFHcFqPALcYru3MX9wLCo48q0jt3soXjEP6EFkd1iP3Mb/OOLdGoTYtod0xbcJ17AHFDmHwDYMtfav2xhT+XUZ+hmloCK1ZtiJVFoAKl8iIECTUEKsui2zHtsuq3kWf5iI6DFStWxlROD9hDBKQ7Rp1RCyOkKlDSqzRlHQthLY6YHSMdqWBkFJc1pKH3lRiFBjfnfDTc5syfH1Nefy2rPvfv2ye3Irmt4IgjjkKBsFCokQPWD5wR3gdwmB4Cfo2bmN5Ju9ANP7BPvBVNN8LRsCmWYMxNxSP/h8f+wfwjSPbIiufHdW0eQ5nnsWQ0HD5gFXxqNNAeBJA4rdFsH/zPd3AUxin/4mt6l9cVBjaQqXc0oDCgFG/edzhrNMxZ+x40vCPqqDHmNXYwp030R3nkmMuKXmWcpd0mafkAOb67cDQomOU9zALF2sgLzhrAc4gxqeTX1NbGpIkTyQYYQPbU2ORBGjTCXWVKHpNRMnDHS7rMyCjzKSmM8mBpSCXBNdHIMTPK2glzgb1H2p122hnsWz6ucB7Rjrgt3duqNGK70pU05dqKpcKGt+TxJZ4jX9YR4lan1SgrnhTjOIwC9q6oyGflRY474QxNlpROFTuVaZ3k8iTlVBpk8Bz7FmdS0cIZXLtsWVQvrY7VKCvdyzmqcMzoPEEgjz7mWRewJJ/sh2GmkSWs/Vt+lxdzy0gPcNEhL5zWo3guXrQ4VtKHSm8fDLqBwN/Ius+qUBmttA8VX3mK/Fm+lY4IxqrR7HhVRm3Tl7LE6Kz7XmfjDLBAm3uOT0RBH4DBbpRp86bNtIcBiJy0s5wDz+YcaFeYp4LL+F1X4cJXeTkGYbuQ7TauqTKme4+eWWBOGeglvtmG7SWMWUuzBWwn0+v5vTmGnnWDbFd55nnkRsWaE/22arinxMybNyczHfsA+26k2h+NfNUZLy91rgYVzKCRZ+0iA8nv3JetAtwIZ55b0cQlJFZG9Gzf/0z1fg1D/QAy9wQcyfIZdZI0uBmjMBc3dTTwIde0ZOS5LhKPuOjg7VMY5ny5W7xdsrQmJr46gehxlxgxfFh0aEudGOSMBri6gHDxEh6lNbVdP6f8Eyj8T9d5lWDpPWY0aHjNBPYnsB3GU3yU3T5nW7ZdkkuF/gJ+s9Z0Dk4hT+hfGGlUpK7APHVSaN+rf7g10X3TOvHOIIJZXlZW8ETGk5lizN12nac82LGJn4VuUcg/acwrQUO/1glQr1iPc622pibmznw3Oqn8jx2bx/HJ25XB4qP1Nt7Dee6a6dDMtoGbY7VAuFleFvQWlnkMpboCemcVTvx/4GT4G9t5YNe5vcExmNEwsPMx0c5sGZzlDcEzUCPHrdzR0WAqPVIR2gRGyEXu4m/kCfMAi/kELuVk5NHFfTuo0bBpF8cLbtwU0xazpZO+LAXuzMedOiIuI419UP9+wIX29gAXeLG6iC0yvOQN/m1hTvGsdlltEeCgIHXrDu05dWI0x1t2S0PaAnUWAneNhX3W72HsIoiwU4eVp7nNUANdXmfgRpnvtmQdd1Ohpznvzsy6RL169yFjsE+0yxpF0JJ4wdhSF6ZVdY8SbtqneKfjVb6oc1NasC6SepqZpnXwmPETJ4AzG2PAQAq3IrsNGLYArzx1QudfyWEtn5ZehWM6HBKXijnZl+stLkonmSnGWDzecgKOgI7tO+TpOZ4K4fGT4p7wUI9TZ3dMOljS4U07wls6TiMa+HiVaM9310o4WazYGkIG9QaqC8MvbePD94vS4lyO+0O47xpk0IwbpEP5qpm3BkVcaGsUTYNWN6BbjmZNe3TtWsg6YOK4tBekF3FA3khzaZwLf+EuH/bvhtCrvF2cdM46NswiXAGfnzz5zWhJ0GnM6JOo0XBM4oCZq01R/HTMKWMPgi6HCWzqDJDf2k5uHQJXhJMSK3UE+nKOOho2EKWvgl4XIr8tzj0cXdiTRKwj5to2J3gpP7H+ivx7v4Ensx2Bnc4E+as0LG+Vfl0beZR9b0Z/0gmwGDni9sqxp5yKHdI35+q8S+uUeMh4hEvyNvpz3ZSHjldcVyYaPJBEPeZYDSozGnCSKGc7tGuP3jGc7Xdd6uudMVZgkv2oG9C8Dpx80Zf9KJPFw3S8AUv5W9Iuv3v88mJshIXVVRTl3hobd27jKNBNcdVnPhMXX/IR6s78n6MhiScJiH8UurA/1zA/FSwQImepTNeqra6O2TPeiXmkLi2vqoqu7TpEGcyvHfUaNIg1+C0mZkQCXxVntBZ77CT43CetEsfyNAcp9YAZpW0IQbkvzSjPApDsnVkzMirRmb1jZWXlKMlFJdySkua7SGo7WdCPBd+BINIwl/AScRm9xOErmRVM2T3172AErFhanUrisXjOPbXCWgfeY3um/bq1QAwVuQqls/AcJkFDPCpKGt560DX2eSyZyIqVdRRTxElCex4d1g5lQu+d7cgwVMBU0mUgSQTMQ8aYTINxKlD3E2WRSUqY3mNqoJkLFsGpQQF9c9Ik0i/Ls4pvGlzcJ+MxYufaOUbXznbTYQHhJVPhd4WxaT96q02lUgjJKGUMCg2VlUWLF8To006PirKeKTxUUFRi7cN56b2TGFVQ6CIvido5CnfbU2H3Oefsmrhe72G4mhXguEaMGJmpo/lcPQz8nibySqOLWQj7dKDwgwJMQ8ZolQqauOR2D40L19h9pLOJknYh4u1++6NhsjKGVJiZn8LK/X0ycAVN6fQHo4LikON3Xq6FaV0aGn4nw3Pf2BqYt0WFhpAJ06tXL347lDBz3CmAuDcVTGbAV+BTEUXUEHaM9iGOOx4ZmnNXMOhZ3sL+7jXr1xKF2RumHHfr0iWV7kYyTISy3miFsm2qzKViIaSAiQa13uitu8iIITKjo6Ex989/d05s+PuT0a9LxygngtEBeLL7kHoApnxCgyjkCnf3vUp3TEfOyppDJ2hdS8CHKgzC7eAJD1DE0CPsKHi4dRtKF46G1swFw2A7kaAWHY4iM6gNmSCmF9IQONwAWDL8OMiYN+09HMu27aOWwHZOqdgSq+odDSOHj8yMpd6cONG1UwcqIrtPdTsCrhAaG9ZvSJzZyxYOU91UVKQp0/iLglkYXsBWg9RsBiO7CnOjvC0V8HzHgiatup84VUc+q4AIQ48WfYfsqcryiijv2TOdOY3IFGjdpgO4TqQUgbwH+O9H6KYyxLjoCtgV7+I1HxOvrJ2wCQGzfPUqjlVbEs/hwPAqORqGs3ViWK+eHGXplhsqqu/dxvKB16yB/wh+4QUa+UXSibiVGQ06hhy9P9IPq8SafOBoWEw2w7urN+NV3xtr6x0Ngyr6EimpiIrynnFs507RhqiwvIghp6PBLRRyigbU3vDVmE2zFr6U/0kbOu5m4bir7NsXp+ZxySs8w1tHg85T8V94K2jlneK+DoWSkaWxJ62K40Z3MiWW9ZB3uve8uqaGoryzOAJxJBHSvhxvzCks4JOOCIsu6mCQZm1bh0tmOCBbhIk0mlDiPfk2dCAPc+23kD21noyJ5bW1GAYNMLxGZbTL36U7eYo0yNRzDsoRFQb5uPxS56X8UsXZF9Mr1oM+rPmyYiVOQQyAzRs3RPeKSsY/LB0BWQ8GujOS6yLaRhoRjMHv5FkucPJS+nfcaaQxz4wOMz6PGF4Bj1+H0SuP6qr862mBtjb5vM9ugs+J7x61lsYg7Tuf5An0m3yUyYlGroWYU3LWrEPBqqpaQir2OiJKLdJhWtGzLLcGWChZPmY0MttgfXWKyvdTjtJP8mPGaevewz8CJ5U7eedy+ORiHDyujnUrKioryRLqAK8D9vzuesp/MzsOHUEjMhXQ5HFks/BkEUErYGLGgUW8GrK9YQd07pF2y5bVRDn02r1L1+hI9NWMr6wrkOOSNglegHfS0276cPzCXl2jMfM5vB/5Dd5m9iB7lJUvKsXrcPjOQadRBxhIEELnjgq/Ml657UzT0VCPO8o6FVtpUl4uLDS6hLk8Whkm3Jlwrs/ylXUx5U2ixzgwBtF+a3hYOrd4rjDOZVUYGjwjfgsv6Uv5JB8T3inL6N93P/uyb3lgFXrZIuT3ILJgelVUoMwXWUS5ToxemexYVezVFZqSTSF+6yxXRiubzCxVd9Nx4zqoVAsrTzJR+d8G/R9Hhs2xOI+Szpmva2g7RgBLDiSJRpgJo3Si1MPM/kv8oSGwS15AsGodEd7qRYujlThJEKUNJw4puy0QdxTF+Uypd33EvDbogu5f15iQnk1Zb4zhrm5D88mvG+AIVu+sA8/Hw4dfYCtbLqBEwfWRAWydYA5tcUY0pRhkYxcX2PtW8Fhvl3ZYGzDiAO3v53VQ5s9nQAnPoDHnwzcKBU2ddDTsPkgtovdi+hIMDW4BA6INLGHQ0IFsYxtFbaoePEtb0EQ+zDwASrZl//JT8U6+tBFaX4PBbuS7GfrDQLJMPVXKNVSfVhcT9urc4kRJ5zB7Uh0zjUVwRGeZbSeP4j6Pt1SXmb9wQRYkVI/sRoCmB7qTRXFzatzn2ikPcqzMVb4j3yrpXzp11bc7EDF3HDo/5BF+tw7++DZ2wh62BPfq3TudGS1ZE4NbDWlL/d92mDm2glnBhS4jPhs4kp95yTPUfTIjFLx0DK6CfHjm9LezsHMl+N6KOehkdRzSYQk2HsmobqsTwpO/dKrmUY/QmfRXoilpLmmFsekUWAS+e/qPJ9+V9yyrL1hIkMA1Z45eBW0V7+K6k5EOXLtcD3DcjGaNX3m7J304RuG0gO1xmzasi/79B2Qmpc8LmzSWwUUbE8flOfIS6clL+pQvp27PXOUxwtytijo35HepC+NQ9rf+8BqdMVlEEx7Y0GIi/C8PPwggD4D8mPCp3+QYWBc6gLZYE+aaxjSwEYF2ApfN1B/wGOY16DldOnUGNj0zE817WxJU0r5w7rl1EjzcDzLpaFCnTkMeXqOTwZf8Td4ljxVntgCj1egd1mdQRhw3fER4nKuX8lTYC/PCoYDtSD/2JW6nDkgb8tCSfagTUp2kDbqjGfPVtGvtKfFBh1q/yt44NztkEFKHWm5/AY724XiaIB/UFb0yQMhaZI0JerRwfRYWBTYexWpGQ+3KFVmI8z108W0E/lbUzImv/uS38VGOtT223tGQsjNHnM3+j/yHtWYl/guXD7swGrsirJ8FdPGfCLkvFYnJr06Kt6dNi2mvT8bC2RpdyivSGFbxUQGVODTEbeEIGJfVbhXoIovMzora3mvqvpFbC8M0hRkqoGvxCM2goJpXD15lROSORtGy3XQA+APTlJGKcB4TKMNQIKqI+S7jBVvypYEqA9bL6j7WqVOnB/VRg2zv6FrWlbTMdoWzAkKXwerJtX2Zg8+JcDIvMDjHZx+mUKcSQP+mC9uX/a5ZXRcza+scYZzQqwyDpW0qUiK+v6s0mEqvMPE7lXbhIGN06VSMtoOkMt006IGXTgGdExpPK2pr2fu3MiibGcf175MF2mQ4EpsMzvXyWccrbKyarTdVuNt/MmnHinDeT7upyEMwDCYJvhpFa4ltUyjpWNJSZTwSRjJOxidDUDH0e19pBHC/a21Ey31ZXo2Am9d+5rMPxsIAYwf716euWJ3fn87YVRiMPggHL+dfWlNTnVII2Le4xNidn/hUWkvx1COwTFnTiNiwenksoeq+qerHcYqBxpBwUUCJjzpAhInGlvNWuMtMzS7ISDgMJlOnFUD1c3RsHluzYt78WJyjjDixvEd071nG40YIzFNHMZDZMwbn4PpaBNO+ZIBGmyxsZfTOFHK/c57imOPaxl7JjWtXxkLS3736V3SCgXdNpQ9VL50MRQpjYdApcHQuyFCFhw4XBdcOikDScnSEVpojmFYvrY6OtWti5LHtozfOtA4YtW1B8JY4DTwH3LRk9zDvOyjeoBhiYDcEFw6SXmdGwzLguoztE5vxzGpotz6SPb+kme7cxVqDaA1agleI/G1b90WbPh2pWN0Z2HIiBeM4zFrpcqAZjtA8jPG7NxZv2Bpz1+B0wam/iZHKvlsy1iNat4vObVtHO/YKNgE4B4hCMsRUxjTm3lpYxayoAcGrC1GotnjnjzzSjIOi0vVuaFq8TsWihJfCBxqQn6RSBoxS+UdDNAogvYlXy2uXxoLVG4PzbnB4dGf8RMs4yaEl6dhN+Vt8KKLg6qEoAuCOSj8kxvoX+yGlTwsrqqgYobMq8zo888tQcr08daIZZDG8e8sY0btndOR4y10I69i3BeWL86MxonaSQrsL+pF+XYv3/1OjZa2K4y1ZbXBGhwbUB4DqHQ1E0ZZs2BGz127BKbYn1oIGRtGOatUWo7EVp+q0hQ8djaJ0ZDqCE2/JaNhDDQ4zMix8hrkCbwIvwGM/6+Csq14UC3BcdKGtkURF5IcWJ5Xu3f4kXclTxHej48JHYVwyOHRiKbDTKQsfSJ7EmhWG0c6onj0/VtF2Ga8+w49LerVteetuZETSOcqI7UuPNJc05vMlg8a5lBRLlTLHsx1lbkNtVSzaIzTJ7BjQF0dLZ8bFc/xe8ISC/kqGpIq4/diJThCVn8wmAa+yY+bh/BzbqmVLY/461o/LKjBDRo3MDBsdKir24oEKujCUP6rc2rZzk49qrKlwGY0UR3UyCCvX1t9q582JFYgvr3KWuXwwmWsYpbbl+HUIaKwfQbs6BVTW7VPFXOO0cKZokgEvZSbtOnblghGjefMWxrZsHRlVSW0UDAwQAZw0OglPp70CcgUvSyPFOdGO+9iT53GDW0wEjvdav0SDZBWK3MxVBd734PuKQQPSkS/M5Y/OQVmp/PezR4ZmxDqVf/BRGUI7rqnj1fHuTHQcSMfziALWQUvy+PJebJtozRGF4Ij1HeTVzl94en9jcEY6d07CxyieRej28/JkrMPQlsao7+oLG1H6p1WvoGXggl5wNMXCvByzMlb8c329kgbAGZV31075at/yGPHT+ZqunbqDsOcZFfMpS2rz+dH9KossStoVgCXZwdCTx2hYaVwol6UheYLtGhG0L+WGc9PxrhzZD+7VYlgsATZDOE64R2WfxC3XzM6Tn9BO0VbBy5oBN+WW0VuzZHLLEjJFfmZNCvUS6cM5S/dzFi+JzYz+hO44eKCnom1kErAWvtKkuCasCuOwMIxU3qUF5axr5HrwQAYOpElpffNajrVbtyXpaRjZGOpUOg11xB1pwABc2Yjz0MKp7eH/4ozYp16wnQCG8lHdxPbl197n0XRGYKuWVsfSdWh8he2W8PhIv6bRv1M7HA3IPZzz5qoeBt6uk1dmKvgd3+ho0MlgVoPZDeKXK6KxrPNGPHWt5aJunVi/Y19RDLJ6e542YZusQnRt05hCoEPS6BNeGu8HyFiUpl3/Dztk5LXikMXL11XXRg3PUxmJbU79oi1Gk3gm7Ep0XcIHdR71A9dGXmPavbiuLpW8mHX1N51MOhoWzpwedahnXn07tokuPcrS4SQvZQC5nkW9snq9C97g+hZ8Gqcnzh/HeTT6hXq2a2b70t9Wi03OnpdtD+rWCRlUHMVuJoNtlvRroaMTX/1cfiasXFd5jXxCWGkbCEdhJPTF+dUrlsXMurWpCw+oKMvAgnqnfJvJFnIa2rUt9VOPUDRQJj6Lj+lEYewJe97tx5fjE57VC+fF3M3bo4J+ewzsj+5GujxzTxqi/1wv3sVR8V29Uv1e+WefZgIJmyMz6xgZn3JlR7ahnvDu3IVh1Z1hXTuwTfxY+pZP8WLsvrzkAeKYtCiv8ZJWnY/jEPfkA7nejFke4aPqTVNrVub9J1TCJ9tRD4Y2zPzxxA8eKJ5lnTx5xzpBJRmkbiruiJfaW/IA19u+tE+2vsf2BnByFaywd6vG0bPXgBxf6u7cp4My2wBH3ZLRkD60XYSdgeod0Kvy3K0TZk4qU90aIS/yaNB1y6iH9l4hAEegU3bt0TPxUD3Xdc95M0llhLIpt0YzNn/LYAT47pq6hm6p82pJkEw+vwoePH3x0vxOeho0oH/hUJauWauUncKQttT1WrQisMo47VPHp2sof3SdhInOBn9T77H21Mol6B276yM92UvEz37x07iKOg2d2x+b34jfxerW3/A/8O2/0dFgZEozhoVVoLGQqNj5ecHieRRQezGmTXmLYl5vcQ58E6LI3dJTWAiUIo3FdEfUOVIpZ8RmoVt/Denfi1TTYxOZLTQEZWJdFN5ThfoRCBb3yKvQu4B60xV0Gm4il1cyCYjgPZSu2WsK49XvNRj04LkVIoU+CJdeQZBeBGlOux5nKWMQcSUmBaRKYiopPC8RiLQS2zraXrJR8frBNbB7F9roSFuFd1NDV4bi+EopQRquOlRkCPYvM1Bx1lmicavCIBEsrK4JEmHfv45DiLejEJLI69gKZlxsoXD8bvPQQ6qy4Zn2ztGxymjcv+h/EpnHxG1cszKW7ajXWOlBg6FDty5ZgdXohcLHLQ2+NML1tGoIyxCsheBLY97xl4jbedj3BuAyf62JgcVFsi0V9cujNdFmHVPbWDPH6/aXYs8ayhqEKXEWygDMn98zxY9x26dKt1FP4bZsztwoVL6i/Yr2bai9QJQfZp9EDT44b4+EU8HIaCkMTuVXZadkjLiGop79JrMErptIa5xZVV00XP/vqH594ygUdwWN0TGVH5U4K8ELkzR0+M2xO3/HLt6YCWC7KpTiruuwauXKmE9hsdLVA4XHI80UGuKDSpH4m0KNZ2y7CelrpuN7wkDuNwanuFlXP/OELQGXRoxjD+uxYNa8WFNqnPdu5cdE987dUtCZGbQLmtiPobVn+6EYzO8jwddKnH1tMIzbUsfhaNpiu2rhZGBfvlsnZH1WwIbYUZjIDuKrLQjkTYxhqxFeigUewRqRdxy7OJpxO86Jbd7DEFnqOLJ3kNLZG/xHeHAKhsaqyts+oofbUKbWUGtiyQaUVTatqiBq6LguFXjM3GPZ8ojWRC2pfnzAaAIGGoLHLB4NNGlVR4I0Jo16VOounAu78VYr/FVKWdYUWiuWLo0qGH7p6oty0bWyInEyhQ9wtx35SMIdWGgkHcbhkoVCFdaMeZfOlH3s+9XhgFG9l3XYgoCcVzO/1HS+D+gzKGsfQCjgjc5ZT2ghZXYLKYBrVuU97QFbM8hwZPdmcXyf8nQ0eLzg4T1r2RvbEQWiKU4inUUogSmCFEMoEUzKeRVfaW7BI8GvBuT0gQ0gNQplaesEp07MRVFfz6keq3E04PpMfD4S+nBLxEFOB6naVDiyHJT0OrB3L5x97ZIujWgCmOTzhYFd1LTxxBwdq+tJ3RV+RoUYQq6DioF0uwPjeCVR7GU2XH8NJmp7JDhXgrO0IS/J1EPm5Pc6JzSSFe4qT0ZRXRtp3DWXJ2/jWLdVVUv+P16g+WetjjQ2oD1p3bHJX3zOdU1ehhLipcKrca4SWkSGC+VM5c2oizizhFokpesYQFvWt1/y8zSKWABlocqj7VsszXHbr7DRMaFSmYoquKQSopKiASwve+3tdxLXS+2PGoR8gtfozLANlco0VngX360llM4s+hUGO3D2qYypvOlgLDkt5MtGO2eTRVC6mvHHUNZVhT0RJxFImaDRUMDWzCrXw0GZbeVpFiqh1gswMrYX/icOyEdrlyyJGuZXunogI3r265fKnb/LA+Vlyj2L8ykf5HPirr/rNHB9hV2pEK5wUg5bjPBVMtBKF1I3x94ampVva1yqe4CU2ZbFV5vjwBUGjl1csnjoAbZyibuulcVI5SvSYBYIXlM4PUp9HA9sPL5WXBFnDDz4Ls6Ij+K3l3BxvM7D310n56Me5GfntHxJVRQUXrQ+uFNHClgfmwZTwoY5277mq9kFFv9VrxHG4otbbXQQSOLSg/qBsHE96jDqP1hVeCT3dOxA5iUGnTLVAIJjUkfw+cR9cM519TsNNLk6DTIH/0iOkvqC7a9ctiLrBxS/EN3nxJ8MZtCuBo36gHqBQQJPQ7A/M2ikd7OTNAJtWA0xHR/AxjmKD0sXzYu11OQpXV1btYge5ZWJ187Pubu+4rw6k/Lb4r+mbKfDiN/kA24nctuauozbA49i/71OkSkzZsYHnAzHRx+i5TiseTrnrzx2HEYzPWVnHZmCa5gHwVzRJHHn4gHNcDS0j7Z4tj9wNNhCPbuFVgpng44GjAwdDbx0NHiHegCIkPguH7SYq9kOW3YfiNUc4+ypEzOX7Yqt3KYe4XGP6ibr4KMfvo5p2jB6DxgIHHCkMn/vdQzKOelKupSXiRteuVUUeeyVNCbMNETBX43XKTNn52+lf4ZUlLE33IyiQueoR4XUFX3mSAyp3P+ufgy+q8vadzomaERcF2huvXiP7NEZH+I15PHGKLJbpH2zVeWR0rXjsT/XQQNTfpBGXBrg8HjhxrilU+lEPHLSK5Yvy2NBS2Pv2bFNlFX0Ard1FhSOATrhZ+FERhdy3ywWx5j1EeBbyTPoX16QAdN6ulXfnj5/UalpbBnshK5dM9ItzNWbxTnH6bpLT+r36iGpc0BTyl7H69z44335o664Hj68gOyT0tWVP3rAJ3Uy6GgT35si511HHfTSu7qr/Sj3dNDIc+xDp7IBSPt1XjtxnC9buSqDM7ZfRkCgS69e4AXOF2jI+xyXtCXMha1jVy/y+0I+QTH8nYFOnvN7ebLbWmay1fPDVyeKeHXpXg4/NIALbJAdOgmdtyeWuMb2ozzRaWOGiO/CL9cnx1I4LjylyQz4jS4blwGQE4YNTToXbuJeyZBP/EePTp4AL7MPeYXGvesqr8lAaf08nfeKJYuiZme9p4y2exDoKu/bP2lGOeycXVNtvcM8dyR2obLZufhqQObuYd5tyzVyDuKWayN83mH7zIetv9Ec19yOTLdcU/DN7cZFscxCn9S5rUPDLGGzkd4iaPCjn/worrvh+ujc4f8cDeJAXuJD6eUXoBIKAYYxi9ESBIWlxgIEycSXXo5Z77yDJ3odZ8NXRhmOhsYwlExLYlFdxJatCu/knEWL4u5H/p7tD+9bFuedd0GUkQ5j5oDVbQ8QWdOgkggUlCoX7h+UgUlIGtQinIyxUJAKYhcxTGufPX9+jGcfd3d6GHnGmVSn12vcLpFTJiTx6j3WyBOJPWvXd5VMPWAyyPQuQ0wiu7+JcCqRq0hhfXP69JhCmtOYCvac9h+Qhb+K/WCq8xYA1nOJEiKRM15fzl9EtR0J33FrjArc/I22Va5rYdwTXn4pGgGDoRQw7D9oIBGmrsmAFODw33xeIaByY9HKoxm/hFFiTDJq51C8EFLMQ++aiG712XenzojBw4ZQaKmCAomdklDS0QC8VUjcmkETCeuMgAkDhJueWhmSc0iHA3+rcOixNb3JVMoZU94IRDj7oM+KXuUYURTwdJ22oLh7zKkFx1S4hY1iXCFje87LtXFeMgMVPR0UMmBTkJag4M58G7ivqIvRA/rHiSOGc1Ro52Tc4qcVXlU0hWkyK573Ei7C3zHYj4q6lwxez6froAI6k9M7Xp0wMdby2xXjxsVIcKYtcHWM4pUMXgFUcjI4/hRc/Gb/XvZlyp9jtj/bF14WXHtj0qvx6qIl4ag+c9WVMaB37zROxDPXNccFzqiYi29NiGrLF5FyQFMkgYEbcd5d78VlLM2JSO9jTlbSfuzvT+TYB/buEWefc1b07Nm9mDNt796wKbYBt/U1tdHi3XnRn3TI7jDOlvt3x1GcMtEGmLWgX1YVH4bMW/xBOXds6BVGbDyS8jCOh73MZztK+/6D+zj5AvrmqV0Y5Rv27Yw60v9XYNRuxg7ZjeXaHBdx6w4tUOQ7ZlRgPwbADnBrCxkRG6jzsAk5tA79VB9RyfF4/vF9YnBfIs7HdsPBSIYL97y3TQFEUTsEZUnhKWXi6KUWfvIDjWMNPh0S0q305vFFj/3tkagDlGdjjB4/dmxUgJcptMAF+ZjrKM6oNB8gmmV1cWwYXoXXeh/Fufa5dxbHassjW6ejYSdrsZ400XmLFsZTE56NY5q2jRNPPpHTHAbm8X1Qfm5xKYolrY9F1VXxwqvjGQXFwED2ZsD3xO6N4kSO0TyGOhZbN66PvdvWkKaqYs+WMfHOaAPwPqxjKTFRJdI/ebh+64Q0/oGjgUr/ZFJsZqzVm0grX4ejYfPeWF7vZxnas4ytE2w7a9caAweaxMB4+YXno00HiqVSP8Yz092nqDIhrUIwwEmzSJJEkYLWNUrlg0bCNWyTJwNrjSWfE/eTz8Dj57ElbSb88uzRo2MQa9oGwyAzqqANabJEP9KlnRQ088HeWw3pNIbp17VxjXUSWByquqoq5iD0dZuee9FF0Yc1VZEST2xXXmIf0qxKpunntu9lmqQGv7Tv/eLSh5VzTzZ5i4Kg01atjWE9OJP9pJNIy++SBqP380DyEvHLPlIRdHzAxe+Elf3LD8RDcVJjTcPNvbhTKAz66Isv51guPuVkTlkZlWnzKoA+J28qDHtwkDE6bsdqW8mLgEMqRQBNPE64y0/53QKGr06aFJOQgX3pYRTFs3qjhLbCgEiDn7EzvJQNKlyul04MDUkv6/QYvdaAVv66Lt4vHuhEXYyS+PjDD6fRO5KiyWPOHBcVZWX1cMe5Bj25nvJ4HSB+dsyOM1/0Y78JK+Alf5Xnqex5JvsMovB/oQBjf7ZdjRxxYqaydsSYth1p1S1hGpw6Q+VLOpTFDXltI+nF8UObOmdt29M5bH8Pc7L2zdSZ78Yr06ZHb3LXR48eFwNRQm1fw0k5tg05aQq6ckRcdR4CQMXftXEOsmPlFpSZfeTWUHjPCqJjL70yPvbjDBt1ylgKXxJAYStmHunMeHUkWJhN+AjzdJrSB9NIfUN8UaaLj/Zfymx8D5ypq6vL003mTp+BY6cPsps6VTglzQI00CBd+rxbnAw2lIIBDDC/l08qUzMzAhwoXfa3EZ1p5SpqdcydG9Nqlscl407L4/XULVrhbNFhpNGvXpAOCx1c4I30Jm4KN9fG9ZBVZbCDd7OcdEC4hWMmJ1dNrVoWx1Nkdwx6TY+u3XJrknim40zdSyNO+Cp/IGL0QeiH9wIXC/xRH1FvUx/0eN0tyH0LxU2Z8HzM23YgLhxzUoweOTKOxZhuSnZswpC19ai+LQgaU5nfpuDv69T9aopTYR/ZX14XDmgQA7p0xdFAAA3HeGN5LMBLdss6W2PIjAUX/xCT1NHgS2PF+8Txg64d4xcfjdweYB037ToQdZwAtGzj1pi1Eic7d7eg3k9lRc/cyqnOthnHxzJ4TddWzeO88y+MXmU9E+bKOl/Cw3XKwtWOhc/S7fvGcz2vETbiTdI0YzDVfCYZqY88/Sy9RpxzwgiKjp4Qx3J6G8Iscdh2lTE6buRtOXbXUH1DeuOdj/lbSRdyrsqG9QRoLDj6Es5Bec3Y8y/g6PnBmVHsVgN1dR0LIrj4IV6kg9M58Jv9Skd8ZE7wGNZJWpWPKU+WgfPPUd9s/oo6soF7xonUBZKXyU91nqnvGaQRHvJAcVs+IK8w+KAccOwlPdL+HIe4JmzcO/+3f70Qw4DHEOqblfXkBCiCHMnjea40PumqRPs6kQq5hI5KX65BwpDxSg/ShScRWcR3Fjz4lSlT4viKiuhPYKmc97Y4mBx3OkAcHFfyEQBgezl3x4gerONZOLXBkHVuOkfMht2KLr2ibmW8PmFCNEden3HyyejC8BnplP4dq2MHLFw6cAp9Wl6mcZ4ObcZgXxbbbdWyODpVmKxBD6lesSzmLZ4f86uWU7NnWNYQsZC6BX3dWk3jie+p68pTSg5paEzebpaEzhlJyKLi8lbnIX5uwDlVU8dxlfNmx8wly+Kmj34khvbvn7aigchSsEI+IP7kxTyUieKGW0Gk/1wD5uIcbFs+JFwtAD/1tUnxxvK66MOpWhdTuLNXWVnijPcmHcGvaDydgQ3hOToCdsObdYK3QH+y8Lh2q0FPcV046nDeCS9zK/lzzzwTqxnY2SccH2edflpxVCi6rfarVk2x1U8Zjo3AGC06uZMx17G95Kl77okbf/rDuOa6a6mTUTgaikn+z/73v5zR4PST4EAulR+hvhdGLLEeAYKpUC9cOD9efcWTGxZEIyyD44cMzSNCGoFsesD12InsIqRVn99in9a3fvjvGbn/yLhT47JLL6XoTG8YNQxiJ9F0vEoEWlPYFESIsa5CATMUWVTm0qjku5KCpPD2tw0o/xMmT45fPPxQnMCin3UDVexRdDVKZTISoVcKZZihCO1xkzKI4rjGggB5NNvLdEfukTGoHC1dviz+BfN7ZeKrcRFINvKEE6h+3/f9qsCKopKylR3lP8yCH1RAJcQ0DkB0+9aDKaFoyLofaS4OjH8+/VQcwfeO+7jjjsMwKktC0qBKwxzhqzLoXCxIo5PAPh2fzNUxyAB9ycHdz7glFXSKI86ak8f3nXL6mTGM/X1W7PdYLRkItksSmLDNsdKOjMkiKMLBcQoXLwWB8DbqI1xra2tjFgL8NRjrNpj8JeefR+G5fhiMndMYMQXYFEaNQPdmqQjJsGwPks+2hI1zFFgKPBWPJggqi1LOQgGdPPmNePz1N+Kys8+K8884I0+pyCgm4/I5BZkMSGalMLJx4cIwc339Tbx13Cog7veUgbv3/DWqlz/70IMxC6PyKzfeGGcCexVQMC+FmYqmKWvpAKF9lXbXUThIH4oL3/3LDoWVyp9rvABj9Gmq9f91wsQ8WvIr3/pmnMC+Was/i1cys2LLRoGbwkdHQy4IKyDZNaSfwxyveJjxpRJJuy1hpLuZ8xQcfH/47Y9j3nsRZ558Ylx99ZV5aoKjOQhONKSewY7V60iLnBUbHngYfKqIrpxDfyT1H3Q0HIXDoAVGtZJBsSR8nMselIz9eniZUiOcDM3YQmA66S7o/yDFApsysIMgzW7gsI59rTW7KQpEmuh6HA2bQZVGBANbUpDgKJR9MJ0jKXeAK4yJmg87sBY2evTXThRAXMTri6nH9eNOgH8Mjsre/amQ3I3+mmAYUtwReJYia4xUcCeelGjK9Vf5KBwNRiExDlC6ZyLsf//n+2IxRsC1OL/GnXtOFtNzXXZyprGebZWqQjkjAwX6sfhhE/ZuAZYUTPvgS4hqeBARjaYYHoTC9vDdBgT+1Hemx8/v+W2M6DcoTjn5lDj++JF5hC1qYG5H2ooivILtU2/Pmhn3PfKwIw8j8LQSJ/RoGCf26RWd2Dqxc9tmjgKuY+uYiirGBzCyujkom7xDXHberk26zckkAeOSn6gOwwm5H2daOhoOx9It7Plka8o6MhqW1jsaxo0YGSNRqLp16QTPOzILVf4DodmsUYs45/TTOZKY/YkcDZa1LoBLKprglzAuCXOdDcLMIpDSq/RQ7CF33Ci5rJMVtFX+LW7l8biXcRzYaCJdWQgXZcy2pMukHfC/RC/OLV/MU57gGjtdDV6VDMchHdbAa94B5yePn4DifihuuOGTMXzQoDRcHI/3yRsLXiv+FuPXKeK6O3YLxqoEiy8qqvarsZvKOQ6SF559Np59d1ach5Phsosvyj3u6XQB/72kffmeq2I7zkmZ4rzsV/T0O9v2UomUV6/H2Tse2fHzP92TdU0+f/VVccFZZ2UWjwqgDyqLnIMGrOPS2HDc8h/b16GmvJO/pzEi3PnbZ6qW1sST/3omHh0/PgbS71W33JJHG+qkM6rr5X0lWDtOvsg+VdZdcw0a5WqOgwGlYcz4fX+Hdb377t/FLNb4ImpRfOSyy7K+gHJMvEgll/t81u+UV65zyXHhu33L80pyxiEYpd3O/Ce+NSW+97vfxYiyznHWmefGSSNOiG7KbtpO2QB9uI9YZ4AUYEX7VNBpI1O6yT6CaWW/Guw6hjV+zcZbsWZ1vDhxYjz45FOs6/FUGB8L3gzNPblMjvXEUEdZVoF2IXQ+qpwLLx1TrmviSz2sXVn5ktlspvguqsYJA94cBkfHnXZaDCCT0oJ34l0hn4pMCOHolc4E8NVLuGmEyX/EmcyC4TfX1dM/3H/+7tx5nLiDYYQhbbHUHuztN8BR4DU0hdwurYFjTpyhrazBwPwde2a/MGYv8dP7V69dk6f5TGbr6zuLFsS1n/hEHhVqzYgjMF403qRHjbpU1MEPFjFlufRWcjQUggqo8JtwkZ7cD28R1mlTp8Urzz4fQ8eM5mjcc6MPQQ71FnFEvUW85qH3aVH9z+MOkwHyXsheHfcEclDaGzfD8Yzj0mK7b775Zjz/9H/GGzV1cfMVl8d58LLOGEYaPCr9wmEv7e9gXXQ8Pzf+lXhi4njq/xDPSgLA0dC/SfTv0hmnO+NGLiIJmIZ8Fd4hLKH3hmSnOQCl5MGSo6HED9Q9gHHSPLjfELgcol8dDSvgxTUbiP6v5GQn4N7yiKPjuKGDyCxolw6xHehmbwKfYf36APurE/bSibQjfDTU6DTxT/oUTxgC4Crg7BTcaiBWqfs4X3mSAZpJb7wZ3//Vb/gl4tOXXhJnE0QpQ+dLukTuJb+icR2yBvTEm9Ir8VtcpDNpVvw3MOK741oFD3gePnMf+uqpBKzOufzjWXS7Dfq0zlzHV5wOhiwENuqmXqknfagfsTEDB86HfsQf71kIPf31H/+IidPfjovHjIpzcJoORX6pB+igEd+dh+OTN6eBx7j8Xjy1rdSBaVLYiJPCTZ1LvJwxa3b8/te/Bt4D87Qd67B069I1dW37t215fNobvLsFRV7j89KrMBDWJXpWn2QGyQtWolO+gXPtWU7BOOu882ME4+7Vuxd1pToUPJb2lCEJazGKsdpWzp++k8+jz3ipM6sPC3f50GZsnKqa2nhuwvjExUvOOTuLG7fGISF9Jw+mDXFV3mtb8mN1deeVWVuso9+5Vd3aGPJ7HbirccbOWsDJJjOR3eMnxUWXfZSjpkexdZdCuPICnqGRxIN0KAJ36+XYrnCRj+pgMLLvuqgH5ZGYzE1+vRIePIstKK9RB+vV6bPiB7feEmNGkgWDw0ND3zGpi4kGtlmaSzrD+d61kF+4ruK6lzBX13aec+CRzz/zdPyD7fAnUcz7k1dLT2xzgRcJ2wzQwt/lr2Yd6SjMDCWeNVOjGXTQmPsyI4PvzNyQLqzX9B61wt5APj34q1+HuTDXoRd87OKLs3DnHh0Nyk14TkEv/Cu+0Z41hXYRwFoOTjzD2E44b1xcc8210QmY/m+5/lscDelFYoFdHIUj4jC9U2Y0gA/paHhjwsSsF2DxnrHHE4knYivj3Y1xkcijMsTLCN8kBMOXb78jU39uvOLSuBxFdCDIoCAzGuHRfVaEdhtFpgJKgCy2iCfHdRwydC/7d0FFIvdEeVzlE889F1/76U9iBL9d9eU7YhzMVYNagaix6aVSaLFIWzAioHDXW+j3MhMj+KbwOeZEUJ4VyRdVVcVDjz8ef3366fjixz9GxWUMdhQui9EZkZLpyDBkICJ+oUDUEzXj83uVjg+YU1EExX2uazky6m28w3968EGO9mvDEXJnEtUZQWSEQk4wEJUzCeXD21EylQ4GaLuOXcYoA3N/m2n+uS8dRDfKWIuT5DW2t/z5j/fENddfFycTWehdSUQV5pdt4yAovHGsscxUpgFsM4IEwclIVPiFv4DXAGhFESHnWQVcppHp8SJe1vUoRtdefnkMJyvA85jdB1fyolq0SUKUWdiHBr84paBJxggOFIYBuMa91mawIv3b78yICSiJv3/iibgJgXYV1ZrLUOIYYLFeMA1hmso6qyrDtc10vAAb4VNaC5mU45DxGmU04+Altv48/pcHY3LdqvjRHV+JC8CZYzluzMI3FoUR5jRRCGNHqJMkBWPhcMh1Zx7ijnCzr6PACRncLKInDz/0UNz9n/+ZdQX+4ze/jVNGjSrWFIavcpnHCLF+tuPeWF9gEGuJEcx8fDXESG+IIy9xmHVpiaIlA9Ox9qO7vhbvbtgbl5x3VnzhCzfHIPYQWkBpP4KpNQbr3k1bYsqrr8U7d/0wuh/Vmuh116JGAxIB0YTnWcVK09WMJUQCky2ceCiw/K3KpVCSFlTaDrPtQkPcY8b28r6B72r2UYBsF8f4sHVibwdSezt3oe4DdQ9wcmzFBdycOXTo0Tg6duqBc6JBVK3fEtXryLbAQbK2sIHic+eOjROGDibj5gT2FfeFDqkuj6PEDCFpXnr3yrUEV6Q1vdkqYNKV667i7filx6lEXH7yq1/F0qXV8akrroqLP3pJDENJt61t28iM0rAA7n5WwDVkPr4ao+kqgsw02U+GQNPmpq9bnFZFV6WuARGy7THxjdfiK9//VpzBkZznnn0OUdKTiPCWp4A1+reFVN0aIgSvI6B+9rvf5tjb8S/JHjG8Z5MYXtkzuhyF24HjLQ/voyAkTg7p4hA0Bidh/ZMs+KdQwgA/SAj+paOhUHzfzyEO8LwAAEAASURBVGjQ0QAstgDLZdRSWES67ur1rIlsjuvjZ46LU1EaKqkp0q59a35bH/c+/FecUQfj8o9cRDXnAUS7oFcMAJUalTWzrMRJadSogsqVfEBeKN7KH4S//Ebc93Lf4+zZs+N1+PyDT/5n3PKpT8dZp5ySGRMeNyb+Finq8pfCkFZGyJ+T3wNbP7s3VlqV1uRpKhPin0W5Xps0Kf715JOxGmX6W9+8E2Xl+KQ3FZJ0AjJm2zL6IV4oOVQWHGuuO0aH6ZDiioqK/2sYWRz4LQyufzz29/gH47/snHPii5/+NJHvvsnn5HXili8vZZJwSOcfc7EjlRMVd8crXmVaOd/LB9eyr/zFl16OP/3oR1nj5euf+1x87CMXZ7FXFWTv17AQn0tRdL8rveQtRraco/CWj2kQCxefMcPmkUcfjXtxbLoaf/zxT+IcnLI6Ocxqk88w+AK+PC9s5MXCJY3qXHer+7veRXTM9Va2MuuY8vaM+P7374rpK5bH1aecGjd86lNx/PDhiQemtBfGcsH/xGP7cqwaivITDSjf/SysxAV5tPhldPp5ePwtd30v+rZtTNTnC3EuOOtxY7mvn/X0qGcLenpcnVkNR+DETGNXfMT554k8msVpBNAXnaURZurqMoz1x5Dbv/3LX+L6j14c4045LY4fOizlnxkQwk8XkQq0slSYix/iv1FKJpJwtD/hnWtC+yr/GzdszPPYH/j73ykweSguxpg+DuOiZ1nPdGZ7j8aQOoXtCXfbEMZ+FgbioW36vZfrK38rZTS8iePuT3ffHRci+4xi9iG62xHHoIBMeUo74qXteWWWB+2LR2YDpU7AfEoGjX1Lx8uBy5x5c+MldLgZixfGl2/5Eo6MYUQ6jyicDKwTkEx5D+t5X/+Q17pu8s80MljTIsMEwwUjwN+2kkK/YOGCeIsM0388+tfoP2JUXH/lFZxs0idhq/yXdkoGkX+ri5kN66lKTAY6NvOloE//yIwTDOnW7drCw9bFSy+/FA/f98d4c9ma+LfPfjY+duEFmdGwD11SiZH1oWhPqCxcShQb4+/BZ57MNplOXhcNODr6dGoTrSGaptBXY+ShzyL94cKFo8HTmcRn/k9Hg86Goj4DfTCu9NU7UPpqxPhYwNjE1gkdDdXrNsfkpXsyo6Fxw2Zx8sljMrNA3WI3dDmBzLLhI4bHV26/LUYCe3mvOOcYdADq4BWnU/9TrsuH+E9cND07JwPcSs4H8VbZ8xy85kt3fivn+NUbPxmXXvLRzGRNXRt+U1xCAf4BDpYcVdKp+CdfcD0yECJ9sB7ij7zC7CkdAT+7/89xXq8+cdm11+QxsG1at6HQIdEDnpPneb8BHnsp4bs4I64WTrWCJ4jHynnn4r1zwJu77/tzPEcQ6KqzzoyPXXEFRzOfmAEaebj0Kj56d9IOf/k5dQP4XGn8+9CRLODsdlR5vPRlIdk3yCz7+lfuIOgzAtl9Nie/cLx9WXnO2XnK65UhbkHxb/m5OC/tlujXubkuyilpVz1aO2JlXV28QBbr3x64P2686dNxEqfWVEKvOgbli66fbSo7nG/KIMaffJK2vJQHromXY3Y+Og234GgwQ/IxggQe5foJnL1DcJIcw/ZEM9fkqcqlkgx3/K6nGRfC3PH5bkBB+B/EMSu+tKLg6tqNG9iKND1eef3VuPexJ+KOz3JCAsEZHQ2e/qN9UFJK0tkOnPLIYfm746ZdjMPEW7OgsNzS6aCjRDuienltTH57Gs6+l+LVaTPj7u99L84YM4Z6X2xTZp7is+3IT+TBym8zskuZkOr3ylPH68t5iALW5ROe05FPj/714XjglfFx6pDBcevNn8/sZNfNKx2l3GddLnUXdVuGm/BNZ5f8xzkwL9t2PvKyVgT1zAB9ASflD+68M5bR1mfhY9dddRXyqWcG1A8SBDQDy9o/jt2MBrfyHmY+HutZW7cy/on9UjZscFwLrRxDEPZ/y/Xf4mjYz55qGZyEpPCQgZhirSDzWjgfD9WECRRjWREdYDKnncTxL336poFW7G1H0QDY7ql8D+X/FRjHLTffHGt59gvXfyKuvOJKjl8aWDAz9iUf3aJIkd63gyKRCEiNffsujOiiXxdfZiAuyFzTeIQRbMbR8DcMui9+99sxKprF9d/+RpwLoXTD+38AIhUZJS4VLjMYJHCjPc5Jxu7WCY08GaxIr6Guk0PlQya8YMniuAej8QEY7L9df0OcgyJ6PEykBW1s4zgwmZyeT5E0vbj05ziN2GjU+n1GGHR68L3EIgMoORremvF2/OoPf4yepEJdSNsjhg0nza6c+3jWMRDN0uiFZlPwC/9Ucv2COYjgjlWlIveUqSDSp46GpbVLgf3r8ZNf/ipuv/lzccbYk6Nv795E0zpmRsUu4G2VaZXKUnTFojC269w1BNLRwFpK3a7BEaRX6omsqlqSjoZnMNjXwgg/fe21MQKD7hjSOxU2wt1xypSdt8qPzwlnlY2CSclcC7i4phYha8Y+UBWt6ShZr0DkP0NJ/Dxtf/LKK9Mz7zhVtoWt7eo8ktHabrYpfJh/YfwXjh8dJRokOhoagjOrly2L5zki9O8PPhgTYOC/+Na3MdjPS0eDUREaTgZFM4lrMn/7FRYaX8JbxqhTQbgrVFXgdGA1YH3n4zx64P774z8efSQdDX9AeJ5BGqBrrzDxcuzOXSNFYdwIK96/Dx4yuoiQUfEitcSsE8/sVtlulJW498bLGF3f/cYd8TYF6S6/+AKceLdSPX5Q7t88AGNu1xxvL5X33d40/o6v5RjKSGHtgie6PcWvmuo0wKiGxIAVygbGruy7qcUg+dIKuhq6nhMMJafRx42MC7gCnr28NrB9Yhk4UrV7a6ykPsDBLo2IuFXCyA+SKlcd26n2eDS6VwcqZLcn+rEDZjyvbmPMXbGWCEBEXSFP44sXnx2jhw+jkvkJFHfrx5Fn1ARAYRS+wki8U+ga7VX5UfgKR38T5q67RYVcc6PWU9hucxdOx1oMsM9cf2N8VKFM+052D0alWTbiZuKbPIo1zZMfcPJg+ee6O/8WLSk2yZaGPXvZx814FCLyMiOkN3/9jrj4zNPjgnPPj7Fjx0RFeTkGTwNOctiZyt7SFctwSLwed/38P3KtdTRQiiKGdG8cg3t2iy6tqfvBJoCGh0z5VokE/sD6MIpsgl4JnZyqmHseRVqf0eA6FMUgWTwcDYeh3y2kA9e+tycWktGwet2uqK4v+nINkZUzx44lmsjRlh3axKp1a+O3993PNA/FNR+/LAYPGJRH7TUjlZJBJM8300pYpnEFzNzK4Fj8P3EWuJuB44kF8mnvXYXnfu6cOTFp8hvxy4f+Et/80i1kIJ2ZR+CZPaaS61Yn75V+0ljmXR5T9KdCCo2ytvIL+b581Wf5gWr0i2I8GXRPwINr1q2Ln/743+PUUSelo0HacWy5JQl6UiFUuQBtkg+JHxrE4orKhxlN/gZKsd+0ZfKPyVPejEf/9kg8RB9Xsi3j3265Fdj0T3jYXuIhz0gbzsH2/c7sAvuWB6cTFuVHvMotEfyunFnDeJ9/4YW459vfipm08d1bb40rwcmeOE2dYxo3DEglWiU3+2JwKeM0lujP+xJfmWvCCDg5Fu+dy5ngf4aP3f3YY7Qe8RjZAReee146bHeiKInrTtjnlN/KEk/0EC5H4HiRRwtv4S4sSwqfNCaQ3pg2Ne78xtfjrdqa+ORZ58Rnv4AiRzq2124VWP9gjAl34KCCKywAS16+q9BpPOWY4aPORyNgM/T4zCsvxU1f+2qezPK1r3wlLrvwYk6q6IUfjrR9xmrV70O06ckIji0dDeBdpkrDDxrDp5pCN80SJtCP6yF/Bm9rVq6Ih8GZn9/zp/jCtVfH2adyjCrytWO79rllUwNAmYG5k2voYgonjYHdOOGFuRl0iWPMw3dPStChuHHDhngH59ofwXcQObNgRpJB1KuyMmGbx8Byrw4fDYCSY0AnjDgkXan4Fv0XtFXCf092kaZew1j/9x/8IK755A1E7c/gSM++0aFTpwzoZIYRYxLetiNsc3y8i+viksq+66z+k4YLfytzlqG3mTH4LAb71Llz4htf/QqZJBQ0RfkXjkb6NOAMati+OCMe6eBSB3AdlIloY6mHaczJC+xf5XwBma5vQlOP/PWv0Zts189efz1bqfqlnlGMBxiId4xFHE78gL8243kU2OzP75O/0IcZJxbtbNG+LUWTiaqT5fHgfX+KVxdUxXegp6txxJjRsI8oo22Jf2ZHqsvNR3+79+G/xO///kjio/9QFiHO7d8h+hxzdLSGfRQZDRrBwNM58cqTmRhDJqIzJo+1tAaDv4rTiA0cI66bhgv3w1saAN+tew/FKmo0VK3fHG9WsXefe3EfxbgzzsjjUhuw7ruIlL6IY3A42bHf/c632d5wfJENR8Zc4iSw0LAX/so9dXFhIC9IHGGNcrAOOHlFsafcbVpPkWFz0+1fdprxHYyuKwgA9e0FPUErpoeLB9KrXF19o8TfdJQWAQ/a5rJP1zjXhv4MANXVrQSWD8ddONDPK+8Vn7j5s2QhjctTH9ziJizMrCjktU5U1hSYqFuU1trfbLPES9WrEqB8/y52xS9+f3c8RdbEDRecTwG9a/NoRvm2OqE8XrwE7NmGdCUv8Z0usi/llvq2xbHNODDDQpXOk70mTn4jvvS5mzOz6UL0PfX4yvKKxBN1U3HccZbGar/q8fZb+k7YuAapa9KucJLfrwQ2z7z8cjzwpz/ELbd+OcYSWNLRYE01nUsftAvPAS4uQ/J13nVGqlvaT46BtfYG+3d9NpB1MI/M50f+84mshXEDBu+wDOp1SX5oCn/KDdY1+TdjSt6FY0E+Y00VAaTsU1fWiSsfbQbN7N60AR7/Vjw/4eX49QMPxbduuzU+TtS+K44Gaw+4rUCYC99cR3BiH3gj/mWghp+KNeEz6yr9qsS4DnriFi2tjlfemBT/fPbpmDxjdtz/05/Bh0+N9tThMfMh8YA2inmDj+jSzkG9WtiX6sHo9FEmCTPpwPpPTJrC/lPjofvvjz+A92cQAP63226nBsSwwlHOUHTS+0yRNQEvY43FU/mQfML6DHSYKMgwcl3kO00IqO7kGM9nkN1fve223Ir7RbY/3MSrT0Vl1tXw9CId3R5RK78F6OAb/J613AuMluKcf/yJf0anPhVkNFwTHbDx/rdc/3VHg0glF2IxgFz+XzACPkrhXAsQUhNefClWL19BGmL7OA0jSkGiwDuEYayglkm5YGY0jH/99bjt8zfHKp79wnXXxOUwv0H9B+RiHcTR0BpF16rte4kCuPWilDajAq7BqACzbRljyQMq4ljwaiOpdH8ziva978QZXcs4s/RT7Fk/O7p07YYyUSiYClovGZJELvOTcBT+KngyPm9x2g1xjmjwKkRlDguqluBo+Es8jsJy2ydvirNIe1Wh8BSNrexZkzmoOJcEZyrmMgmIPA0iiMIoahrutKdQNrKm4W0hlqmkBP/6D3+IChTPC/CyerRTT5wkeY4y41UxULFVqXXcMjafLSnmKokZoUVhEm5uV9EJswm41NTW4ql8LX7+i1/GbV/8Io4G9rxV4mVF0VLB37l9B0fRYKiRUeK4VYDS0QITMfU0HQ0ymoQ9xAiMLG4jU6ipqYkZ7Jl+FkfDeoTMp6+7PrdmWD9Cg74UxdKxo/BU+fE9jX7WruS0cr013G1cuB+BMW0BJ4uwTHj11fjpfffG5665Lm7Q0dCte97r8Tn24Xq639PxyPjFE+Ei0pYcDTItL2Gi4FFxXGZGA0Lhib/9LV4hovPLb383PkJE6hjgwoOZkptCgHZy7uCyY7R9lS0VWQVAKWKkx9UIlnP3CCqjuw+DMzoautP3b+65L06HRsSJHKswZb6urZ9lth41qSf+IAa8xQn1JjdtgGKLlFQIavw2YfuDRZF03P3wzm/EVCLUVyCQb2Ftj8PR4BanQ3iEW+uEwdEwHhp94Y6vZiGxssqe0Q3lvj0OjWY4A5riwfCUAbMaDuB4EN9TaYUWJP3DaOzWCtCoTcVAGLL+uiR289oAXFcy3mocDcvXkWbMRMtwNDSjD7coNARGRyCompOh0BTma0rp7JUbYlbNGgz2iOUuCtcXL70kTkXhGkRKc9ce7FXlXnFPx6C0KV7oDNAhmDDXycQ9rof0K+wU/MJTHHuLTJjv/+THsWLhgrjhuhvjItLdjht6XOKHCoHwFtfFQ3GlETAwo+HQQauiyw+YIW21bGkRSAshst8OBVZHwzbW+EVw8nNf/1pcfu6Zce5ZZ7Off1R49JV4s3M7xccwnmrrVqDUTI67/uNXOUdrNLQBpv26NCBd91iOH8U7j4Qipp+OhlSqoUGPXwKlE+5qRwyjYL16m3ilegiNKtgbu7+pEUYBz20mH7hq4w5O9NjI+dB7o6Y+W+Tqs89lS9AYtgEUGQ11pEz/7v4HqLXRLD7B9jV5TZcu3dB02NLD2AsnjDQK/qlggQjCvDEOKovwGdGQl0lLaTDw7tjXrFkTi3AGvI5xcfef743bb7ktHQ09unVLY18emRFeJqRSpWNIfmbbFjeT1pxsQb9FpoPPuFfVlOTqxYti4oSJ8SQO5aq6lfGTfy8cDfIix+qayqsBXMLbMYkPKmq2KX/095LBlXyO5yzOp/P5TY4jfvzxx+LeZ/8VV154UXz1S1+Kwf36Jz6JU7bLaiQOsSTZn9+Jd17e4zisR+OCGUGS/5hRYUbD8/DIP+MIn8G938WJcYWOBmAjjquEWifBhYcrZDsqibn43O+MVPCKmfGB3+wrs03A33mkqd9HFO13RNa9/v4bHA04rK1VsHtbUQNCY8n7NfZVMs0uES7u8VVpk0fKP30pT4RnZpPQz2QcDd8kg+St2qU4Gs6Nz9x8c24XEt9Nl3Ys8g2zIKUdYeLaKg/TcVqvBNu3ip1wcy5ucfB0Fh0NN3ylMIx+cOttccVHL4uKXr3jALTmWJtQXJZOMqNBBVce7jhz/3opowGeZYqvNG3GlRlaQDb3Bj/8TxwN994Tt6AgnklGg9HjDm3aQas4SbhPp5nP5bgYmzguTFJxdxnou/RZ+WoAwKib2x7fxUj/I47wnRs2xmWXXBKjMC769e2bjnQNI2Gh090Jux+54D3FSoqftu1ap7GKjHFNnJvbMlZDU6+Dlz/+Aft7b/xkOhr69O6dxwhKNzojbMM+UmnmWS/H7atwZgDr0lz4TX3Ez3U4Meaw9/h5HPmvv/M2ivTt6WiwoKLHk+toULZ61J7GQ+EQL/iCYxbHhYm4YgDK01Mch46NraSruXXCjIa/kWlTMWBAfAbdwJotGjreJx3yeOpOjsc+zGZqRn9eGsQa2O7xdouAr8bgwVHsd18DPb0yYXw8/tij8Ry1p74Dzlx16UejO9FCFf8mOGzFdYCCRQjvwOH8e3jSLx9+INVXOGlelww4Jvod2y7acFvhaCiMDR0N5ha6dcJXGk48IXWmo8F1Y8w6DEDwHKf41gjZ3pD5bUdkbUDWLd20LaaRWraMZ1vhaDj9tNOiGxHow8zFQsYvsN1p8MiR8YO7vp/bcQ+jB1mwVv1O2KvjqO/Jd6QY+UkJJ+UNyignJGfSSHVP+Zat78XTZPfedEe9o+Fzn4/LP/ZxHA2FYVQKcgh88VzY52VT4iLr4GU/8ku7ELfUWRuj1xhYvOfhh+J7v/l1nNyydXzqO3fGOeis7QkuqWunM4P5acwqY13rUoFYv0vnVP6GnklfJbqSp9nZrPnz4pd/+H08yfreBB++4uqrM2PQQNhW9BnxkAln5os8Rv1A3Zink27sT7tAR4NyysBbFl9lfdyOq6PhDraWjTv9zDgvMxpGRFnPnk45ebj6qRARP5VxwiRffPZ76U4aTl6Z8LLmh0VHcTRAU/Kyh/70x/jSbV+OMSfiaMDp6JHc6UTmPsefdCXPYf4ap66tdGlAt5Bj6DngvrStfeM8VpNJYq2AR5F/++j/ejI9hg4aVGx7ZI4GZ9OYho+lsQ9AbN/L/vzNtXZerpFOVOudHMHxrruwEya/PZUAyoT45Z/vj28hn9we0BWHJhDITCO3RahzoJIkTeLy4j+XDJkIHMzIy0KurkQ975AnGLRcWL0kXpg4Ph596vF4Z35VPPDTn6ejwQC1zzln5ae0LpDznbaVD+Ke83c9/VvnhXgpHrWhoK94PJXtKn998C9x9zNP42gYHl+DH+ho0L4Qv0q8S9xQh7MtvzdQKb+R1+ioYeSJ7/Jj68E0Rn5uJ0vnXzg1v3H77amvfoms8BuvvSb6VvbK4ElDdGSd3BaUlG5pIbf2mTWxE35ftawmniALszOHH3yC7WltjqVWipfIZIf/g6//HkdDCQACpASUXAk+E5mcgyH1yvMvZLXuNqSLn0Qaaz+UAysHl4SEQsjtCBZ8eo004s9gGHldfdGFcRmKVj8yIKy26lFTrYjAtiCaajqjCoRElx4+iESGJQY4jBSiLKKf/V0mshFkeIo0tO/95ldx4tEd47JP3xCnn86ePZh6FpHUGIHoNFaM2mgAS4wSXhI+hEFztK2CgpTgvjzyiu8kgiW1NfEw0aInQLgvXHZ5nEJK8NDBQzIaZEV0FeJMe6c9/xb5FcYithFvGYnMSCYlTGSEVp/V8aCyMHPu3PjjvfdyznuXOJtxu6WkO04S05xSCYVJyADNApDYLNpm5FalxPYlOttNIQSRyFztw8jCCiI6r015K/7j/j/H7Qj7saShlZeV5fmxRiks9mRqlApFcTF25q8Q05DT2yxTlSiEhQSqgW9/KxA6c02/HD8hNuIYuB6hMAjl3Cr1ErFMQVgrWGV0yezqhYzfq+CJUhbMSoNSmGGAqCBvRQmcjRI3GWPt9xgA1158SVyBItejS9dcJwWwRoRMVIdJkYIs02NtmbvCwfUtORsShYGNzE+h6f7XSa9NiqcfeTTe3rQuvg1zPY8IbHuZF8qKa5hRHeDgWgrbdDSwpiVmp7Ip/N2uIuO26JeGhoqwZ70/wXab+57/V1g94Dc//mmMIQqooiUchalM2nmLM4VhYdcII+CuAdwUI9LtRNyZ88soO8VVd0Ijr1MX4+df/2osoO3zSEP7zKc+FQOB/YFDCDKEVFvGtY/1n8zWiTd/+KOsE1GGV7UzIZgOGNYtkRrNeOlk0ICFyvhP5dy/FFKsG4asJ0BAOexjQ4lkLACclDCcO8BjHWPX0bBURwPp+gfI6O3WtWPWcZFm0d+iBXDUK34I2l7PcYpzl6+Md1ZwUgW/kWiZ13UYw6PlHyiinbK+B9kYwEAjTEdXSZnWGJKGMpoOfnmPjgbHWxL8wnYGvOmnP/9ZzFtdF5+/+NI4i+ymQQMGJu1bsEpa8kpHA7gu7SPe3nc0uHVFhau5WVZNW9InKe04Q71RR8Ork9+Mb/zsp3HBmJFxBvTqVqfuGI3uMd69Y3emgy9bVYeBNj1+c/9fbDwvzI0YDlAG9GmJowGHVEPwgF37hw9iyDKLBhxViYQD10Q/ldlibA7PjAakGX/5o8ovRrn3NIQGqCGxed+hWLJ+U8yuJoIGCzOK5nXhCSfFySg8ZWybadu2VawhMqKieFSLVkQt2DrRtz/KyrHApgnGMU4YBa68EhqSvwhz0zDl42ZhiR0lmMujpQ3pzOKqS6qqEPrT4w//fCzu+OSnqB1yMicLHVM4W6F7lSqVkuRj0JFGp+ubSgaTFgNTuWNN5BXyY9MjHcvSpdXxJnB/7qlnYsmqWlL5fxQnDR+ZvEjaES98iQfyeC8VdMcoT0jjkTEkH7IvXsoPiwK7R3/6jBnx1FNPxt8nTYzzTzk1Pn/Tp3IroG0qj4r2pAvWxA+SDW/2l3KkHk91PIqPuf2Ev3Q0WKPhZRTnP/76F7GS3277JFk2ZE10RT6pnCgvPdLRlo0m26a4plIHaIuO+CyESvJPWCkL5D9u7Xv4EbIxnnuWmyN++53vxrhTT0vYWbNEfHFQwt7xq5hnhhyflVF2IW8RHq6nay5fUrbKR6exFemuO78ZC7ZviUtPHB3X3kh9DBx3wlKHSkaPxRXnwXfy5lTygLdKof3ZXjoakDMJP/o0xXTbrh3xEnz4th/e5dDjVmTUx+H1fcor0xiTryoTGDgRyiLzx7pNKVfoKwsHMj1pIdeG+xh08jFNhuVs6XmcdON7Hn80rmW7ymmjx+KQHRLtUHJ3ge/uy80oLjB3rsJXWS18jfBqRCsDM0OC/nQ0KP/kM2YUzFu0KB589G+xfsGS+MhVl8dJ6WgoTuRQtsszddi4kAUcGBV9yNOEdcH3653iyMkPHA0Wl1sbU8HLX/z+d3HVxy7PdOPy8vKMHtu/jjuRw/GLBxpe0lM61GhbGKkbuCbyUpFAuSxurWbf9MLFi2IC2Y4WEv3S7bfl1okO7TjRgvlBMCn/EtsZe2E4Ki8cN7AWVrQnrNQJXJPMnoBPbMPoWkzb0+B/j91/f/Q96aS4jgzWfr16FY4GcEUnlHTji2YSJ3LrBDB3CYWR/F2DK2sR8F1jdKaWZDua6j0Zh+a/MCxefXdOfOmGG+KjF1wQ3Tp1xjGgESp+FI44gyYLoY/7//pQ/Pnpf4pi71/n9mwW/bp2oUYDMDxA4CbL8UInDAh3L+9GK+W1IBjflRwNws/11Iwz5Xo/BZY13iwG2YCaPtuRmRupgl+zfn9MJatvXX2P44aNJB29E3PjqFBg9MTk1+PEyt5x5ze/mVuRpJ88RQx6ES+Fsc4X11G+WN9tGmLpdBB+DEWA+bvZSe+xvcBU76/+8AfZ6y1XfiIuwWisLCtP/EvnXMIcmc+766muIS56lfhkydmmjFTeqvNI18uRaw+j1/yGIEpf7v/01/4tZaCFA0s8uJT94rpK8+Jm8jTud12lp9K6c0v+nf3/P/bOBO6vorr7k31PyEIS9oRFQBBZFBAUEGUvgjuouLxitaLW2k1sa7Vqbe2n+trWT1tRSl8VZVFBFhUQUFARAZV9DQkkQCALgex5lvf3PXPP/c//5v8kAR59niRnnuf+770zZ87M/O7MmTnnzsxVOe5UvfnPc7+ertIb9rce9er0ehlJDtEeSKS9TGNt6jZ1E1zoR5ArtCvavrUdlQNe1EvaAmNhZDJjbV5I3iil9C8++5l0quTYa199tDayfKn13eSLfo582XhD+abfoe1QV2n3lIEy8qypsKTJ7B5o2D+CDYuRZeec/w0ZNd9r++TspvY6RW2K9kpc8kvdQT6i9GdDQ172S3rkGfmLHGJJErKDfDGOxzD4vR9cZpt4nqYZPPtovMRsCbBheQF1BgwYyyMTGNqRLvnPhr3cf/gLm7Eap0+WEWSR3tr/WnsUXXvjz9K5F12cPvyu98hQfZy+kDAjL2dSOW2YKH6qcTZjjD1zMAjQR1ufrjQxNOBqI6VwR2G/b86D6cfqV795wf+k+fr09hc/8TeajXi4jL1T7DkyY5vZ3dRH6jzyhWdoxi2Vh2fJLAfwoY8CR/DhJQSNgheSF8mg+c3rrtUGoi9KH/3Qh9KBeoFC3wYdz4sX37wghL/pG/JnHwxmDGHk7xHu1FcV1dIGe14kL9IL1KvE928/+1nba+Xd2r/iHXpJvvvs2WZoYBYrLwMZnuW2hW2TF5EaI+sFBjMarpDhb+/DXq591N6epu6oGQ1UelyGK19vhr/9Z2gAED8ECpfUJQSJGRquuDLdIyVZLUMWnt3TLCnHCACfgoT6gtWMDoldf//l3K8ZnHtLKL39zP9jm21haFit9djD9cDU9MxSSaPAmo2wy1YoBIcauBogjR2hipRXHbeKx3r+639+Y/ruT642/u868eR02Ku0sZ/2IUCgk98siKQwqWLRMFDuaIBuraaRILwRZD2q6FjP6V5o+PPmz0+XXPoDvYl9MB25257pEPHe80V7pkkTJprSgpCiIwYhswirwVDBrSOVrzUc8Ud5Nz/DMr8NQIjc99CD6d81+Gd4/CYpuwg/9pfgDQ1vQYgDJnQ4lJ8KDR4M8BCACEam7tEA2XgTPzpYBoGPa7Byq6bxXyHF9KRXHKYBot4a76DOVVOSGUTYm3JhYTMNaGVyLuxs7aX4ILyy8JagVDlR8BkEPLbgMb3FvCf9SAPcBYr3gTe9WVO0d7cZD0z7s3hKw56l8CD/tC2UGcpEJ4SzzUN51mqg7NHAFEQ21Lvn7nvSLzQQ+sWcByRA9konSWFkg097y0Id0PPLHVf1LIWDGBv2CCR7qyEvnisYUW9skKp0F2ig9XMZMb5T1Zm3HH9COvLww9NUTedCYbSpbnQocggnawbizWAK5QX8eXYM9pkhQb1BAbM3qKJh1+3LvnNRuumJR4zHxyS8EX50jmAHQzCwekL+EK48RwaGwsKMYkMlWBUbIcbzob7zuVje9t+ijuFL3zjPeGsOhgaKf2GbA9nMBOVtnMrdped/3+13pMUXfVeCcZc0W2vOpov3NCmsE2RsGCUz9VoZNrqlWKuIOkuh7laHxbMYJiVDLVJVQ4MubSqkt+YYPTAgrFIel6kDe0zpPCJjywMrl6b5S/Utc9FO1mw2TWDQd5P1JkT5HcXLYE3jX6MxsWwR6XbltfUxO8t+OkyzIA486GVpt912SzNmTNfAhOm9GkCoLtNRUo8YLJAv6jjPkbpCvWLnYCoVNGBGu77rvnvTF/QGE/eqHWanY049Oe255542EKdjZ2AA7rQp5EK3GXdYPw0OtGMOBtNqRzKQDFFHQie4Trx5W3ezOrZvadnNdFGd+LrjbPfqndSmRg9XByOjxCLJI6Zs3/qb29JVN90qqpbbRZdHKKIZtlW9eFOgoli7kL6r6bPUi3xY5y763PlxUfFR9sjmCNHp0aRezXB/WvHufzSla+VF7t3N1sWRx54oA9D2WoYwTptZLkr/dt63zPD0xne8TRvX7aLlB0xhVJ1G2ZVRKA8MUbKQBRog6TmzMSwzHPAzRVTPAmftQdg/KQPG3XfdnX71sxvTrU/Mlxw7Nh3ysoNUH/Kmu0yRpM5Tn60gio/89ds80KSdqU0IANocchp5x8DoEXXYv73l1vSdG6+3KB85/Qxbo8pADFraHwf1gMNAzNytXcEH+OBFvTJ5Jz/2VEFm/k4z9L73ta9pw6futMeU6eltwma3WbMsDvF4FuSX+sg9gzuM1aRJO7Y39Qoww6baBwMw5DCGBurDNRqsfOvHV1qOTnn1a7Q++Fj78s9yKQb2xQfRI+95i0l5kAGuTOihGA70o9RdS0PhZmiQEj7vkUfTRRdenH6lN0e4M099g9YeH2hftSBvVnLln8EWg1GcyX61AZONSo/nSzo4jA60c2QZfrfruX7uv75iYbP1e8ZZH04v1iAXDFF2kbMMnG1Qrv6BfJmhQfxRnjhIl4EiRqCciuqO6hOb9TH4P+d7Fxn/E2QUO/mEk2y2B5+uZdoss2nYJZwBIoqnyXHxAX9TTlUpMTigcOAwYKhC29KJBcwm0cy1a277ddpbA/5jtJTopZLDyPm89CK/PUYW0B+BB2/QVGkkcyTElIYrvDxrymBvSIUlxqQHH56TvnnuuYklocdricBhMq6xvIH6wbJMUxhVYvB0wzX5hr85MNc1zxaZxHiHuooR4wktEeCrEBfqayivefFLbBbJDpIzDLDNQKRn6+MM6jz1hk8/skyIfobnR5/i/Qzp0ba5Z4d8vup0wzXXpduXLUqvP/o1af9999UeAtupzU4yAwl5Z0YJMpPc0n9bOpKzeNBOwYu8036ZvUpfiNJz3333pV+qf73mjt+lPafMSG897c1pt1mzbTkA7RoDUjY+SfjJ5TqitqV6yrJBa6/KO30Tz543o0NUD9ivgy//3KYxzaWq89Lj07GqM6959ZGmGGGc5y0sdZOxSpcOvjpxiZYp3CYDZen2183eMo5PVjUfKVk8XGkrSTNWIKeGS8QxLZo8qYi2ZNDMNRXdGPV12G16FFcfYDK5qxebaYkGQwsU7wFF+50Od9vrYqdtd7CXa08/81SaUwWc/YEP6iWBlgwKX/INPqTHSzczYJAnjfVoV67Y8lYb45eNFWhXOsCelx03aCbJuZrFgzt2/4PSCSeeoBc0mj1VtUVwJx0c/KgT1B0UOuQbPLk3f4VTX0xOiH6B6uTlesF43e9yL/6ePzrZ9lDgyw3UEWSAGY6rMR7jM+ITZgZs8aUe4ag/1E/qjo3pRfTA3LnpvHPPSw+oTr5k+Lh06vv/j74gt5/G7RjCtcxZfRR5p9/mBRl8xUj3efzEGMHySjpqRxjI4c+yqye11Ok2vYQ4/4dXpF0V7eiTTkn7vmTftIPGlOCNMcLyKqY2w0k0fE0OXOhnmJlBO2aMQb8ILZ8DpjzIQTZEZg+Iy355Qzpu3wPSy16uMY3Gw3wKmT4U+WR9nuJZs1ea4IOzl3s6kxaFYryDos2BEv6wcEHn+vZPrrHZqW95m/qnXfUpeY3jkQW8bKO9IsPckA8ujEmpv8hjHDKMSo7MtrG28OFF7R3aEPYXehl88333p2P0Naoj9fJqpvQnNo1k3GefgBQ/8KU9SpBb/SO/Jjv1LKi3/jzAkxkJbBrJPgXX//xn6dLrrrc80D8drBkH02TsHal41BfqraLYM6MuWPuXnMljYp4p9RUdKpeBfPAJZMbNd6p/+v5Xzkm/7dHm7aL6Gy0XYj8Y2gTyMRt48jJn6rV9pUf1k1kk6BLsK4geZJWAVCiH+g8M3OghLMU957vfU4jGbgcekF6vva14SbDGNoPU+JTeVA2f5aHKltpU3jyUr94skJz9ydcuSa//hz9Jp+uZ7bDrrJwMGd3MXf8ZGgSE5LweMh2JnrDAQRBTMR68/4F0iwYI98uiv3D+gjRGlXGyNhRDcTeLlhCn8bDZyFANuOZJsbtDCiO7cfLFAzZ74u0xD2u1puyq1mrgrB1rNdBC4NLAqVxY3VDsEDK8ebIdpxEeqoxUNoQtFnQGFE/KMkcjYynHjttr7ZIqEksMaOAIAwQTAwAG91jRUCZpHPhz4OC3Tny7RITBwRR2NQKMDUzR30bW/skyMEwaN0EbJ8kipvxag1Y6NDgX2lRwBkZ0+oCGZZW36Wa11NR4BIASt86ZgegD8+aaMGIAtLM6+xkaFIHjSpWN5kujkWSTkK0Ek3iSdxu8YVgQHrytXa43RJKSZg1lUIYyvFhvVpiiyuYrUzWIGK/ekdkSDGoZ9LDeFGMMZUERR5GwTswaX7bQWfrCQ1mQw4qvjTUVj0+DPS0hS5zJeuO5jbDhjYjhoDzS2cObiDRsBAgDPDo9Gj3Shem25J9v82IBXSfaFXrWy9TI+TwVb5HHKm/bTZkq3Flio8GJ6qQZkIQNAyqwpwMwJZK6Bz7iidBA+lFf6FRNYaTOiOeiZUvTMlkdMdJMkcIxXYJvwphxabTqAkKDvUboTGwpifjBhw4fPnRKPEMULQakwELapIlbohkevD1eprrD2+Jdd9zRBDcCjcEb9YM8gisHccGKwdpQKX10+FmpoSPWYYVQmURLJ7dQS26e0IGVdhJ1UrhMGItgV5llmRi+RvVeUwUXzp2XRsgYtKfeWu8qI8U01b1tpVBvI0HKjIZV3Xpjq5ESSzVUu9LqbrU74TdiqJ6FFGy+GKHhZf0JICQphoZnld/HhcE8yYI5a5enRVoqMXzfF+vbzrumUeNG2y7hw/SM9MRVx6Sg6m3902s0ZVcbFi5cISON2vtKYUwbHy/MaVPTpJRNHKd2Ilz45CR1jI4MBZcphGBMh80ZTEwJVt3hGdPZ4mgfbDC3UJ0nb4qpN9O0p8hkHdb2Vd/NOKW6Qp3k+dqgVLgMUZtn+QoYMyjiE5fr1momkdotHTPPY+Xqlfpc09K0+Jmn1YaGS5kbp2O88q1ZPHydQrMQlqrNLnxan/NUe1yqPFAnxiiPw2S4mqnns9u0SWnKGE3vXPG0Ornlmqqc3wau7dUbE33Lj0+pWSF1pj8aqnypdqvQDMQxUmmgJRDyjAa9kdYyiGcUb4Gm6z66fF1atlID1B694bJZYlICRTt2JLO/ZKRUOZcofxhWJ1PXVS5bGqO0+KONjkOOC1+mwiMHeKNIG+EPmQXerH8HD+o+Syqe0Zrj+Xqjw8a8bMI1SwrRjnrDiIEBRZo2a7JSzwd5jAxgIM2AijcHtDGKzaZvPHOeFQNrnjGDKGQ88gA5pmpl/GdMwcTGM2cQInhUJrBGLtKWbNq+4pNtMwDSdsSbg/amCLaEDYn62FML07wnHrNlfuy0jQzmrTf5tpk8aq/IBKaoIo/5DCBvAtmTAxxcqaNvBC9kMNiwtIMZfQ/NfzTNfXy+9QkYpV60224mf5exrlk4I3OYxmnr44U1OFB2Bs1m8FUbIL+0FzY5JE1kBXKWpToPyhDzrOQVfdJk9U3bSBbzmUKWxIEHfQlGazZbZtA8QX0BMpv+Uaqc8ivcJPtNvtAHKk+0KeQynyej/yZP4/S8Zgp32hMOHG0zM7UX2ODsLaziMaimrVmfpTL5s9Fjzu1O51Va6vX4M/p8qfru5ZIh08ZPTPvM2i1NlfFrhYwwPHv6TZYz2nIIpWeGKGFBn80gmHHDUKtXGjAqHZsBoXrKhmXL1F4fX7pYy7Y080K0yALyP17P2CqS5ECXysV1NjRILmnsQR1nTToYo0jzLPjyAS8LwIWxB7LoWeGJjF8hHFWx7K06z5e0mC0I5ihHlJnBvr1t1HNG/tMH0p6GaTBO+/L+A5xQ1p9Wv71Ehs3l4jFRz3Oq8g4PjDW0U549cawuyI8882KFPYuoj9kIJ3+lTXrIOtoUjk0DFy5alJarvrLnDuMB+u/Jwn2i2j/5ZHy0TNPVGYOwHwtp+osN2ihpM16wT8Wp/MiHCRpfgBdK12M6yPt41bcdtYElYxs2uCY/GGnorykD9YS+HHmgCm7tyvp0tS/a0hBbyqo+RH3WOvVZyID5qo9P6rky82uGxgU76OXVRMk79jQSmNZW12gWGtOkl2js+KTk9TrJ9nGaEYGhceWKZWnXbcamWdtukyZolt9wGZvpAckbB9f585Z5vIWvendhpXqtug7NiF7NrtOBjKa9M8bo1XNfLpGzeHVPeuxZ9Y/aq2GlZDN1a5ye82jtj9SlOs9syG7lnWVkUydonwhhz9gVeYNCyzNmOj5LW1VIq3MYuMDf+0XaLVjT7Hj2zKSkjcyb/6gZV5DnM6dOS7N32NHGZbygok+0Pk+4YnDAOO8zl8CKOk3dZn8LlmJQLtogY2/8GZcx3qO+07bptxmrImswtvIsWRNPe2LMTR13JZjxHksgqEtcYwQiL/o3GUdbo2+956EH00pZ3LfVc91J43g3Ytin3BUH9BlHoVjTHliOSpuBF/IZmYbMGKOxhICxPm+l+hu+OkHdeUZjctblj5dsZNbzVOkhtA8U0vwos+EDfjwDlsWO01JNa09q/8gYaMGDsS5jMMaCy9RWH5MxgzbLOHWaDILwps4z5qPe0a8hj9eCn/Jf13PlB/mKkZN6RDtCZ7BxvdLnhSHjPeZa0TbZgB+ZwLIsZA0bWCPjuebIL3wxFOb+CowYIyPHwGqk7mm39CXPSO4uenZZWqxjpXSWSerPp28zxc68PGGvMF5s8IUfq2+Kxx4E7MWFHLBxrMpkrULPkv4YPEeqPjILafHyZ9Lcp7RcVmNt8jZ97IQ0XXKefgq5A5Ysn6QeU16bhagzeQUz6jYHfSH9H/irKIYd47TF6kNtrK1nRX2gvjNbgnzl+gVnWrCcyoHcon5SXxmtU4+Y0UC+GYPQ32IgoT/ka2v0fQ8vfMyw2k6zPHbXsvaJqg/MiONDBixhZWYDS5zJD0Yq8rpK2PCJy4WLnkwnaCbESVrivP3OO+V+0jJDhjZf98INDSp7fjS8veLIRgM9e3sw8jJr+8MPPpjmPPCAlk/M0Wf41mjdrzpQVQ4OHrJZhdTgVevTs+rwlwt09g9gcOmDHzrj7lUawOjzbqOlkDHoMmGoNNgcD6sYlYw1paskCBFGWOI4Y8hQfbG3sljyMWIgyBDG9tZAFZJOGOHHgBc/hK9qms0KoILxvLGqEc/egKsRsYkHjU8t2qbWQoSRAXr2M6DrRkiQOJ0MApvBBIIVwcFABGWUN4R0/Agk3sAi/PhEE98mR6gwIMIKvULY8F1rhP54VeAxElzsvswAnYEhkhghYQqR0kVIo5waxrrGagseNHo6BJ4d2JiCrHNusHndP3mkM2cQARYIDzp4hBQKBp0LCj882IBm8aKnDBcGXeO1pIFyMAhCOJgCprLSOGm8NvtCZWO3WPJHRwT+DCJ4BsxyQMjxhsSmaJNPHbissOvVgvK7StggyMgHzx6lhDrFmwqenVkOhQM8eW4Ie8qCUEUAkS8EHcIfoWSKo/gJRBt40OGAPZ/KAjsGj+SdN2gjpeQzCGU6FZ0MQIAtjudIAggT3nIxUKec1DcEJA7ccFk45k4p51PPVO1juZQC1u6SXxQRMGewReeI8WvsWA04hB9rn5nKDt7QUQbSBnemwTJjgw6MgSGdylPqiJZq4DV+8nj5S3lUnVqrqYLzH5yTtFtO2mOHHdOuerbbqnOYrl5jstrkaEGyposBj6Y1qw2gMDDgtfwLbRRe+9SlhCj1xqbGCQN1sWm1yJ4SBvOU5/nq/pavWZJm6g3B3ofoE56TxkuZ1aeuVKcmakbAmBGqN6O0A77sTY8s0cB/2SpdE0dvUJQf6sAItRc9Cc2hUF0Xrr1SiCkrdRr8aSfkDMWX58A1z5YrsKFzskG3lEFkRh4USD6o3jBYW77sGVPsrK4IR+o77Zk6iiEDnOnUSA/8GTysUieyUp/ipHPKM2QwCoknHhrwjh7NveSFBgLUn6EIRuHGYHSd2sUaPZ8ePTvkwRhhO1plnqbzTjI0TBolZeLZJ7VZ7dPiI6VatL3CqmeI3gyLPftjgAtH/ioGFncpLBrYSvioY5NMY2mFbQYpRb97WJq/UntmPLtGRhAG/SO1Se9Ua9/PSLas05SSkbI4jB2bjTZqIFIgVEdklAAHgWmyzAwC5EXYolhRx5UdG/CDPe0TvK3N6znQ1hjg8jzADtlAXWZTOWZNgf8qpYO8hzf3tieLZCPyjEEusoXniGGUNDHi0a5cKRJra19m/FAaxDNjpgbpKPr0Dcgc9n3AUR6rO0qDN1D29kttFPkOnpSNukL9MTmqZ6pewja4MvkpLJhWyffGkbsYz9mlG5mAkYS+BEUd5YE2m99UqY+Uv8ke8QIDBnJmKFfaVA1mTvBmjXIwQESOm7FUD3ykZIq9n1NaDM7BATzA1Q0NyHNkAIZADEZcI3dM9orWBpDa54j8suaUfJoBXeWhT3XcyTt7ydB/gDVLEpC57FfBDETyvkyKKPkDV9K3/kT3Ym39BWWmXnMgy1HWqbHghbyjPuG459kz+EJ54h4mZoCmjHryw2WsGyKjHYZrlpBMksIoiWdtljyP1XIxm6UAb0U3pVn1lzY8VEolRgbWyTKYBpNVa/XyQvnhhQF9+IgJeu1s8iH30+vUpw7R8k/qI/0skpt6YfkSX2Q7z85m60g2mDKtYJ4Hii8lM4OP6vJI1TmWy9HP8TlnxgbjNFCnf2VsQ11EbiL7SS/vI6E+WfihfDCgpWy0LXv5oIwga5FxPFdUX7BH1mOoo+/k5QXGGx9Ic6at0A+ZDCRN2qnw5nkxk8XHQfQ1lIsXECi7I8SXIysJqjcqhwppCgHPEGM7/S0729NWyRuDaNoc4wqWMlDvF2uWAUoSX4VAViNLmT1kz4o2qHKAjY3AlF+rB7qnrlAWxlBklr6MdsXUb2QC46RuyVpeVvXKINCjcq1Sn4XhkXww3uLFyXjVGVsnLVmjgaDlMz9V1XHxXik+I8aLTn3+GtWPZ5Y+lXaePDbtvv30NEFG2KEyuAstqz+0H+VM7Uh9E3VIDunYjWyW8V0PxMK710g5WsOLBL29Vf5YYjBUz3mNjLzLtGfOfH154uHFq9NKzTPnLefYURrTKHqXXgLwHJFH+CNbMAIwhqTeYGhAYaKOmJFKePEsqUPUBWQbMoCxNONMxr72LNS/I5t5u47BExxNnqveMU5lHAV/MKPO8GyZFZjHFxpvKox6Z4qcnin9Ln6MYcF6lF5kUAfyuIxp/62lANQRytBDHyEYMZpgmKJdUQ4bs4kH/raJsMqTZUDeb4wvAqBY0255ecX4bKIMa9QD5DV5x9AALiz/ob9F0aPNM2sMOUS67LfDS0nqJGM76h/jbcbdZrAUfrQJ6NaKJwYE8mntkT5c8RhLgDWyFVkMttRX5La/zGI8AC0yD0c6YIRcYB8AZjqQV+hzm1VdUThyl+eyQuMRZDIz6ugzkCtmaFAeMOKTd9opzxspQJgSNPx5Ps/KmIEOwjIu8pobugpmsjfLRhvf6PmRR9N/lC+eO7hb3yJ+KNO9Ggt2ywhnLw9Ez/Ij+iNkq8kCNQH6JhsDKj/sP7BW/U+v4oAR/uCT23LWA6h/8B4hHLoYI+nNiN2D6QoZt7VJ11j1VSYrVV4wplz8wROs4YHxi3v6IJ6bGmQVLlkgPHGmVwnTLHNkvIVOYaZj6PEwxuZZ0C7WyfjI2JCXrDyPHrU9ZjTwLDDgwZ+6x4x2zl16abdU9XGFDF+8BDU5Tt1QGmvFa4jij5JMGKXZr8wKJZwxhaSz9ffdwkUjrHTgoQenwzVLZOrUqZbnLeHnBRsaeE44zsLROkEb7Ojh06zwxwK1TNbHRVqbu1RWPD1lq4wIMh4gFZqGwcZMGjNbx41ERnghBKhYtnZGjYUOgoGCHp8NHiw+aZGYDiogB0Nes1ApwAY+qvSkYTQixVFByR+V0CsibFodqfKnhmaCA3oRY4DA0oYwJz4DlKzsSjmQEEKg0AFDy2CJxozg9CloNDDC7M0OjU708EfwIKhpPJTZhJPi0zzoKJSwNRormwQNgzE6WloUFlc6YMOTFsa/ndXQxJs0rOBgK38wNsC4tyN3TpQJfIxG+bHBi/Jrg0H42TUDAZVNDcQaCsJd9zxjpqZbuRTGYIZBYxYKEt7qHOgIiIdjIzkauwtgT5PnYNiJznCp6ghpkG17FnoG9uxUNvBnUA32Nh2eAYvqifHT2QYlPEPFJj8tQZcFD1zBPNebXGeUKeuUsfgziEeIYDhBmMOXzpsBNgIEwxNCHD/qsQ3YKgyhtYMCK4w8U17Kgj/0HGCSB9ZV/aywN4zoNBQBvtnAJSGpTnqtyomCywwIlkdwkAZtjwEE6TBgBE/yyNv6Cep06QSWyqr77HKtCdaAfaQGZGOow1Iwf3vzr9MDX/lvTdkcn2ZNm562U36nq0pMUt1jRsM6zWagZTmOyj0FU1kwkrSOjKewVD3lDViX6upi5WOuBiFPaKDW1fts2uuMM9PLXntEmrDt1LREdadX7Xqs3tKP1rpV9jpY1aU3Fiu609JVemuowewa1b0eCXJ424BOsxjUK9u6WKbPIgf87bDPRjGLtmGf8Td8hAvPgenqYDNanTQDc+oFbQjl16Z46x5DoXUwqgd5k0ueoeKr/ikX1kFwjcQwfiqDtW+Vmbo4QofI7Rihtb3MgkBZtBlSmv2h7lQdktLQ4GGdjh7lhWcn9S+NVFuaoPxN04yPcRobrF29TPV6hZ45gzrVQz33XnVayrLw11MQ/pYTPachms2gFiY/YaROnrXB2dCgOMNHJw3B0lPrNOBXlVmhXc97hDsbPgpeQbpKna4MJlLrRulZjVPnTwIYUbq01EOZFl+G0vlc1u8sS5BXqiXKO/feTqkqJocFCHWZAYEbQ63dyN8HH9ZWVZcNez17zuSBeBzUAgY6yEXSwVEXzcCjcAbltCeb/q9w4vCcGIzQTrPskhxSWagDNpsBII2PvOFZtUvqOmVEPoichPLUbPmbMdG8ckuQh8VFHlAWLwePWtpZAABAAElEQVQ8MGCYAxvVJxz1FJ5uoCH/DLBQQjBo2gwR0dIrUH4lbQM72vAQ8q10KCN455YhAtVtcKaecg1/PeGcf9GZEq907Q2NaMxQoTpJ/RlJf6sIyE/rT5QG+yiN1Jsq8gavtcoPyhhpUFe933LZxyCNgbzhphjM1GMAyPMAd8pOvlU8w4d2g4HHnp88wclkvNKivBzKthl2UCKHS4FhSjyYIduHAaX4YdgBcxR90qmelhjAQTz0fDFm8rZNtSEbVRTEIJp9ZMCP6fYjmeklHHjeDCZXy9CQDTHqb5U3BuHUf+sbxddkie6RPcheXoyADQoLfV82AFF31a7VvlHeKZApXioDuQNvw0TlBDeWeGBwsLJbncrP12W7KRqqxxAgt8CefheZT3zSIl/5s+NadkB9Jq8c8AXjip570qY8NuZADgoPsESGUV+o/5ZWNTAGU+hR6HjBYeMh4lEe0aIg5vaXFTfkMHUHAy18mAVC3woe9GGkQ57IH+2RfiuPl/S8lBbPk3rhMpv2CS3jH8pLfnjevHFlDClP63ekEuhPTsTIAGv3xNH4sVuGa5bhYvBlpgA44kyuQ6/nhALRpdkLa1frE9BS/KdPkPIssqE8a9IRY/JhsajDOvBjtIGRAWODHoTR9HRJcZcCMkxCdoT6U9krrY12S+lY0zs8LVqpvWtWYphXWfX8pGLpSgLalPE8fhqifOc18ywxkvIG3voDo9xmeLbkKPdtYAk2yFoUYuolBl7ajtUl+UMNtuDItdcF2gt8eI7mr3zwzJE38LQxhviZbDRK2kB+dhau9Hi+GCpMZiuutQe1U2QOdYW4pGqyQWWmDLQN0ibv5MkUY/n3qG8l38hE6oq90DFy5RGZo7QIxwAHNvBBSS8VeNLCz8okHpSH9olDXtKuqKvUBzeogC/1EVmQ25nodM9h7Q18lD/7Iz/ilesvOAlb8eXAUSYrc+VHnQMb8kT9ZekXBjh4UHbyjr5BG6M8jP/wZ5Nbygqd9blqy8gvyiMQDQ97IcLYQvEYm1NviOsH+aW/Qjex2UGio92CEeM2qxPKm0jsuVlfLvmGEY9ZQ6QDb3U+9oJCD0ZyTJggN0RMvpExGBroq5WwtT8zTpn8z/KKemB4URrRDNGLr6F6mUL9Ug71aW293NLYA77IXuQRso2y8rysb5cffTgyxZ4R9VJxcXm8hn6V8QJDcCXv+TnmOpXrY8adegh/MDIDs3CgTiGnrZ0prslUyYD8glb0SlGTkaS76qAcOnC0px6NYfTwbWmVarl0V2SQnoXas7UN0bHU1uIJ2+k7bJ92mT3LDKLwoCxgsTm7fjE0AASHvtqhBqDKpgdjg3/5mZJMRUMIYdWkYxJsTHmm8VCTbTAqWgSVHou0WD0kKjkPTzRWoUVqlZfHrweHwYEOmUYMKyqwfZpEPBhsmOCgQereCFDURWuVWRHkmyuf+JswEC8cDzQ3tjxtl/7EFGri6hrlsn7oVEgRoEy58k5eaahUUjpg46o4DD7pPPFHsMDDBI/KTxwwADfyajwg0MGAk4O3UghqyoVgpRGbo3wMoHRjglq3PnDLAjCTGV9d2vovCVeUFe/4wcIypHSyUyNUw6KxeScgpjAWmf64VLrkn+fNMyJPdOQ4BeeyyZ/BC/mgUVMu6BB2xOVoOfFV8sZfnp534pjTidwRjp8pkXgIM2vUiow/A2Tzpm5wJT/yCz6E0cnhzxsdBlMoH2CPpZj4dMTcU4cQpvbWXrxJw4R5lWd4WF0QDxXEeGUhxzPMGBhG4knB7HlRQDnyDrYITQSN5Vs8qIdmLFE+qStWL+VHeGvgp3yo7iPoUPANkareWEcpWnhymKN+iZb1ozZ1WLjwGdQe1SeJamGjJUcq6zq9ZbpWu91fedafJm1xmXZ5EV+dGJmmqy2OEY2G9rRGhQhPJctzoDOiRjAAFECq73lgZYNspdGtNz9YaQVuWqLyPiBjxlOaojh8ZHc66P0fSoedeGwaoa8q9ApzllwNl/Kt1qZnqreNUrdX9+rTcBqErVM5u6nvasM8TPKs3jcNwQgkSpQhkrHc6Qz+5Mk6FwXw7Hg2BpeCjZhayj/PR4dhT3nAmvZEPJG6kRDFjPpNu8DIM1wWdo68CSXtRO1JA3Fz8COyEgBnMVQ7II2cFukyAmYDzV7x7dKxWnlcq4OlMMMVf5gs4KOFy1iTWxBrqrCGoVaZrYxqa8qv8TLhQ3pyVHYMDTabAUMDcUFGtLxh00wI9eRplWY3rBqi6dQaDEs0pS7NoCB7o5Gd1AxteKZWa/WR5GBBnbfBkc5C07BhGibYMdDj7YzQM1lIEU1+k0Gw1MG94a1r4ugE9FbXwRm5gJJKW+VhorQQnXtSNKWaek+GRJtlueLTXlS/vN7T1ojqMsbaEYnhCDCXn6/YWhr4Wx3R2Yzawo282Mw1yzdkKrPC6dMoC4YC2oAuSczSswG66h71hzfSpMIbEPq5LPtol9SLXD/JCtNfyQd8LXdUHjvU1pUe7RrHQGs42CmuKrcerZ6znMWnzLq2Q9GzaMh1Hj8b4FVldDwoOvWZN3Rgz6Aq9yG0scwMZQa5aQNUKQ9MBUcSrFYcm00ijJgdwuCf1ImGA0v+8OGZk1ZGXBROhF/j2h4TJMITOmuHKhtFltSyWQ321hpjiWQOMxSYaWdTUeFX4UB9ADvqBOXl+ZqRQXygN2VGCdCf8raNzwF3qwFQNmYo2AAVfiRsh2SCLnkG3DOwxTHQ5LlhTOGZYWhAiSNtlJj8lk3YGkb0g3kwbMqO+CHXre6RPyFk+MuL52uDVKVDGVDIrQ8SkMg1FA0w5rkY9gAsHvrP2OuePBh/XZfPw9uFY4PRg3EBLwc4MivFpYCZIQzsEl/6LRxvXzHC5CUeua5DZM9dmKCckRZ9KYoD+eGwBOAHEyVmZdZ9na4w4flbvRGR5dPqF9mh7aDU0/wrGQK9PHhuNtAXL75Lz2eYea7s22EKipIzjJVSj4ymfN6Sgb9NFVeZaJOMtSgrS2Hpc5SaKGQc6NXsNR1676x7ykBuodWJMuFnZ11KkTAjsLjTXsRG+UUJQx6r3ujoVd4o4xBmFcmwvqJ3ZFrRLXrJZ1tWoU0je3UwFMZgjZFBtQPmlpSBp/Rov7TRbETRMyDv4svztPGb6g1GhpzDqny6y+MHPUvVLcMRvio/cagzVj/x0j3lMqOo+KIII0uo58ZXjJFaFk/09VhMfqSJI0/eBqHDWbtWiYDPcFOa3Nk4CSjBRn/0NzxzZDzPxvjoDHfjr2ueBe2eMSbjN3jTF8PL/nguVdloU4RTp6xN2MPJdZE2RdsyTFRfAZo6YUmIE/8kCr5WN1X/SAP5iJ/x1D3GbPJC/0E+LK/iQxwcZSC/MLRfeIAtRhLFs5lEtBc6ZsVB5qD7kI/8bDVewl/tClkDX/oIZjKQAjOnaG+4nGIuB8+S9BjL0A64ZmaJzZTTHV+QIR3yQCh1QJm1Nkt/Av60C1OouVe6yEQMCyYnhYPLWNIm7wxNbHhiYxu9eNTzsf5GvJl1xTOADwYkDIW9LOeVIdlkhfLLUjcOZcrwAGPKxrMin9RL6j7lbKsTdm+ZqDDQNQCaq56FIpE+/px4jjwDxt7IBGQh5aM9MD6hTtKnkwfKRrZo3My0tRmdahfDwV7yxvNjFzZ2Ek4CghkgYIS/vRwyfKU7q/yGk9oqs/AxdqE/eo79nPO/+f32i6GBYoOd9B8TYHTS6AT4sSs7A4JqCA7pc3Y8tE0CGkI9bBpHy+GJ68xh47w3TpH5//5/Gd7S2XQuyaamrwaijm6IFJq+MHFOdLKk17fLvEzgoMBs1FEjNsRvowxeOIGEhUkVFzoIc4S+1xkJEAt/4SkNCAe6cRDepDpC1Vb7VM+aNOUo3Xzppekb2ohyG3nvvOceaQd1ZNMkyEeKZgR0Vh94hgwKSAMBXaVmA6psAV8rjLHwwrdLhLwxXCSFdK7WDS7XTIlx48ekw/74fenlJx2fZL4VpzxoN4GhQZpeKcqvVZ9yyvKqXE77OZTTIz7vc6PeqpNTb1NzQ+QYDLXPc78gBd6tUDY4G6p44qzAoFAi4U+ZwNLxUKEDUxRcmMiPXlFyOB8sG2jhCyUHnerIgh2cilvddXDN9tOBZFO8NMTDzr8ppH8Ymhf0UBvIcYtcQc70q2uk8zx5o15nOU+d6uBo+97O62AUaWaHYBjqI15N278XyJbWEOx58gY6XH89EvZDsT6lvQ5rWG3Y5hW+tMysQG1aL662q8GvtC1l9PeJMYoIhu2NjwkMs/LH6kZ7metgZA/1vpCVddgf4AKs6amoK50eM71UrrudQjtlUIYCk60eVsajQnl7xB8swcVpkMMul1VXeJMOPpothOEXeVxygFrqjXHJfOTRp/N0GwRS3Kxvb3i/oFsMJcjGTRrvkVIfeXtBmdhQ5I2kR33E+fgv3/X/74baRZ+pqb6qTuSxeZ9ECsi1o1kv9BrN0JbqvaHI64X1aKaqySNr/+sF978HRlrGnBt4BjwlStkuWfAp5WDz/oVnFQwxYIzUzM92pxxZ1fL23B7KWAvjzzBmMdVtvkmz6ff5CWfjGyn2leqmcxxYyn4zNFTN155FWSR7NhVQ6wFmkaqYVDqEQIVoVmWkyBbMTLHx+zKg9sMTHmJilVjXFc/MzwlzIiX3sitqhXosfLKvc8h82/1KftDlUKchL3XsKrTysDxWdJ7vmrQVn24Trq28ev5q4o1fkJZj1ChTW/ZEk1PLKXZmLArxMzrLt+cVaufmfs7P70uOnlLpt7Hrkn+2cJcxciqNtCi7lV+Ull+dm/ee7/XKQ4m8DCUmVT7aiuDpeh7Jmftx3eKT/Zt0OaX8nIlXhhM/O+pDK6Rl5c81pBXbUqacWXpVZ90wYGCww5RWLWm6SZ92+9oH3pe2E/sdtXP5FA1wt9ExRgy0glG0eQYE8UiNFLDOj9Cgg7vVEtBrpISvE0++Ec4eEtrRQ5+qXJae0OZ8i7TnxJCdZumLBtukV77jbenA445RQttr0KWOR/HtcGVYxgYzZMtXKdsQzcsKJR0QR6tLJdRKqjPOqf2uDMt+HuIxM0WTTqFWR+RPEMQWoXXv8duS9yQINKeLkrX5Vx46MQTlwOFL2czyjYcpvaDA4QxF5XXUvaC1cKet6L2O00mjMBrGrA20d3MtjmQRtsbHeeWb7NcKMV/4Om+L2AivOOfsEdaqr7l2Zt7ZN9enVjploSq+nhbR6qRafGsvCyzj53Tyb6Zv+bRi1X6WjuI7vq2A9a5yOYq06jwWfEs/4wkbxSGak5XZcr+qHM699rbo7us8PLTwh04u9xIZZadqJQx9jtOSGM5D1OSdo/F84YaB2d4Ktg3+PC4pt1LjLjvCO/kT6mEVD2dVYIZXVh/JLX9ORHznW/qV/romqH4ehMkRrYpqwfVtRiRHgrBwzsPzRhuFid/rDoyafTZ+ObkqwRyrYOyXykmdV9GuZ6QiENfik+839Zf4Hrf1LFt+m8KnyqOV2+nF07Hxc113PM/Qknbz3nkU/lxaNj2vHq0OcI+2rBPq/aM/RXnVJTZuyp+FFc8MGhzxOXDQ6j2ufiWh7TnjWzkCndAuMnWuByhFlpLOlUwGE+PBWQfyGKVLPRlmj9KRZu5n8a34GoHz1I1hTAbkV5ej4m3e7s9NX875VTQWr6I1L/0Yb531dtec1cdGvAb7irL2hRo/j5WvmlQ1eXUhaiOpYlpk5wRJixuEuX3hV/pXrAz7Kj3Hys9GsqG8OL8mDf6lX3Vvz5brZniVl/XyJx6KAyd7y+7xaj5VPO7dNfKeZQ0p5nSdkjtc5p7D3c98xdPu/W1JnYZ8W4TikG/guz6vKjVP1M8e3876gTcH95b/isDSFFf5kRvnX7agXNereJ5H5yvv5+Jy9qr2X0TkZa4Sl7i1DLZCLN+6dcyrbGeCXCaWbWGwz7xbUbnK3IpINZH7VR5WfsuCxfG47dw2v7sXbGigyDVmuga28p5wdw6p3XtF8UDO/hB1mStaWwyjbPNpJrReYJuHxeenL95lznPMMn6RmF8WwdmrGcsJSbUkdn/5Fd6thgQ9jsCC1vzcp5mi01VE5YmgOp36Qp7tccq7TOW0ZUjJ2MPdr6TzMPdr3nuc53tu8fMUKKT7Zq6tkHxfhFr90717QerXRtyMi2eLgDrUTM2itWHq9E1e7k+MZsKbcu9Pjlz4X06dPJUcuLfhC54cNt7RBQMGBjy8MWOgo/1TbrziinTOh96fxots2oxdtEZsRJqoNjmeTWyWsynP44bAiNHanG7KhNStKWK8bWP9P6y6xHOtNu9Zs3ihOGhfi913TWtEs2CFPr+mnbyfXbosjd9T33OeNi299l1npEOOOy7pe1HZ0EDbNyVYubXBl6bsyQvl27KsM9nHgR5DM1PGq/u689J9y7VwznGdQ4sCbk6Vn2kZxrWH6pI6U8ioJmWdwTKgiF6XoM5GFagTXn7g60fm6SF1xCqFKn6Znl07PTed4iieMM4deo7s6Tl1X5xr9vAtsXB5vl7E7AG2uVQ5vfZ8tZ5Buz+0xO8rV5383c/jcsaV/tyXfLkvXVmITvFatLnOlPStMEuT6I6TsSr41f6Vn98bi4KuroOlX5GOe/eRDQ8uYtSXOQq/G6OqwjlZpGZiHr/0d786uedwUfCxdkfU7Nfi6jK45dNKoIhflq0m7RBe4J/J8m+mdHpnwL1ft/LmfoS012rnlfnk9pBz65zznf82fcu0oPHwpr/H39iZ+B7XeRHH/fqK7/HKOFW8MmqBpXFqk51OWPIq/Tqk3Ra/CneZw20jO3AD/8y1hbaTlc+m4tZ28ty4Z+aEr45moBOVZyu/p0a0Km7hVZKrExZbz28uTibdWGIFQ8fDvAr/OsP4Ob9O4eTI/Zt0fd23l+L3d9fMV6eUnMbDPM9+3wyX/yZhRnznVfLoy69JU957Xjyu33Mu6Qj3e107uXkV/nV094PUietAu/A67+E5RiteKxHI5e/YVLfGRD8t7s7BfSpefusR6iQIqG8yJ6ct5EXOX4uuvVV4BJg7TemH/wtxzhMezrfh596Q1EF+4egS2O6cwnxLHjApAsugwrud2WZ21y+Ghs2szJHdQGAzRwBhhjgqzxSpFEv5ms6FK7sjClo7HQiGBmYzaO2ZnZ/QN3yvvjr974c/lhYlbQgoMn2wKk3WwZ4NTLbXHt3Gh71wmSCmb5yYH2cm4vMBQZT/pTqgxcDBcoBndTytY5GOaZO3lW1hZnrDh85KrznxRM1o2CHngY6GgyUsZnBQtnRbGhnIPo6ywNsP7rcE5+WjLOuXqQxtlhbqDYU36blfP4VOVOEXCAQCgUAgAAIvRMZuLG7I46hjgUAgsGUiEIaGLfO5Rqm2aAR80MIZVTy77Ov239bZwzkznEFBN2ODTUPQnAF5Ll8oQ8OPf5zO+fuz0xJZFZ7VZjh33rMgHfGS3dORh70izdplR/uknQRGWqMvyDz56Px07133pjt++dvENwr2OnTftO/u2tdh553TBH2nHAPDgiceT3PmzkvzHpqT5txyZ1o7UZtM7vKitK0+E/W2Mz+QTjjpj9KomZrRwJo9MmFWbQwNyh6HfHHlWd7mOJfXlXecOiDg+HmQ4+b3cQ4EAoFAIBAIBAKBQCAQCAT6G4EwNPQ3osEvEPhDIGDT2qRC+vQ21x6lrKNYMg0zr/DMinr2ayntZDEr69pBWqaHxauWpCt/cFn61CfenXbf62B9znNSuuSKq9Nbjj8uvfGNp6SX7rdPmjljunbK7U0LHnk03Xn7HemnP/15+u5530977zc+HXDYiWnPF7847bPvS9Iee+6pSQlD0733P5Bu+81v069/9et03VWXp5322DNNmTLddrl+y2lnpBM0o2HKZHaEsK0j6xz5FL/awkBm+9KWvdzQhAsEAoFAIBAIBAKBQCAQCAQCgUGBQBgaBsVjiEwEAs8BAVO69VOeTeHWj/7zhjqtZQe+/IAzUfzI6n1vGitDw9K0PH3voovTZ/7jc+l9b3i77aNw3fXXp7Hi95Y3vT4ddNABaaepmn0gN+/xh82A8JPrb0h3PTQ3veaII2SE2DatfGZZetEee6TDXvlKfSqPGRF3p5tvuSXdcstv0uOLFqf9ZIRg88g7774znfDaE9LrTjkl7TBxpmY/5FkZGBj8s0v1Z+88syRs5eVCjvKWh3nGTyAQCAQCgUAgEAgEAoFAIBAIDAYEwtAwGJ5C5CEQeC4IuMLNbAa/RulG89Y/yw7cqFAaGfwaSjZZ1Je2RdetzR9HpmVpRbrw/PPTd75/fvrzj/xZmrndzHT1j36c7r33jnTiCcen/Q94aZq1/U7ag2FomrPg4XTHHXemX970K325bEw66aST7NvNl19+aRo/Rssi3v4O+7LEnaL5zW9/m+7XzIYJYyekgw8+RF/1Wpcuu/LydPB+L0tvPe20tNNEllnkQrCkIxsbdMbLD122XVPW5gFNuEAgEAgEAoFAIBAIBAKBQCAQGBQIhKFhUDyGyEQg8BwRcAMD0bhG8cYVRga83eDgOjv3OM7sjJANDcPS092r0iUXfzdd8N3z02lvPz1Nmz4tXXvNNWnpE4+lU04+Je2//0vT9jNnmiFgwePz01133Z1++rMb0qLly9Mxxxybpk6emhbOfSxN2WZKOuCQg9LosWPSPO3PcNddd6Vf3/rrtHrt2vTyl79c3xkelm7XcooDX3KA9mg4MW0/cZrt58CCD4wMtf3AM8wZ5+d8F4YGxyHOgUAgEAgEAoFAIBAIBAKBwCBEIAwNg/ChRJYCgU1BoKl7+/3GziVvFHu+ILF89Zp0lTaD/J9zv5Ym6tOVw0cMSz+98ep04iuPTae/9TTt0fCSNHHyNlL4e9PSpxalu++5L/1Q9P/0lf8rA8LB6c2ve3M69qhj0uzZs9OwkSPS2q61adkzz2g2w/3pih9emf7fZRekww84JM3aaRczRrzi0EPN8DB54qQ2G4LZS/oqQDPjRixPP5fhcR0IBAKBQCAQCAQCgUAgEAgEAgOGQBgaBgz6SDgQeH4IlHp4ee36tvvB3f08Jb8vlybwNck1y9emh+c8lOY88EBa8cyzqUszEIaIaObM6WmvPfewpRRppD5iqS9V9K5cnZ6UseGhOY+kR+c/kdauXpd2mT0rHXTwQWncjElipqUZir9OX5N4ctFT6aGHHhLd/LROfuMmjk/TZ8xIO8/aOe24445p9Bg+nClXZjr7tPtZeEFEQezwEnmkOAcCgUAgEAgEAoFAIBAIBAKBwEAjEIaGgX4CkX4g8BwRcHWbsx9NFqX67de+waLda+0EX7fs0fqJ7q6u1Nvdo2UNWrogz651a3XfnYYPG2ozG9KI4fompmL1aGcH9oXAMtGj+65e80LjHzpMNKOGpaSTrcvwDIm8SwaH1WtWpxUrV6Ye8R89ZnQaM2ZMGjV6VBoCPYXo5Nr8deP3dYEUyT6J2Sly+AUCgUAgEAgEAoFAIBAIBAKBwEAhEIaGgUI+0g0EnicCrm9z9sNZuQ7evG8zMjgDDA2yHXSt67bJASPGyFCg//Uc9F2VVYLAYVpsMbyZkvzX6hCZ8WBnx7y7oy6yI60ehWOTqO0DnhcnKs9tYbrxe0+ac82ojBjXgUAgEAgEAoFAIBAIBAKBQCAwkAiEoWEg0Y+0A4HniQA6t+vdfoaV6+BNtuZfEjqt/DSZwWY3EIzePkzGBs498ujVDIZepj7ofqhmNQwxxd641elzB49Vq/XBTNGPHj3ceMAPHhgXLL4I3S5AnMyl/Wx+RHTn13b2GwXCqMnA48Q5EAgEAoFAIBAIBAKBQCAQCAQGFIEwNAwo/JF4IPD8EHCV289NLq6D1/4FIWHcYgCwlRB46MiGBV1rJgJ6PDRGh6FBV6yYKL4LIb+W6xbh2rV8NLM3jRolQ4OuuCONHjHGSDHC4uu+ilZOeKiyYLYDri3hTucqbotQHhbBA+IcCAQCgUAgEAgEAoFAIBAIBAIDjUAYGgb6CUT6gcDzQAADANp4PjuDbAbwO86+ZKL04xoDw7q1vZqJ0JNGjBxmyxnww9jQpekJ2vLR9mdAh8dg0N3LbIVsIhgydKhsEcNsZQS88M1GC4wRQ+pVFbbaggDxGqo4w9nnQa5LflwNNz7mVdsN3GbQlm8vpJ9rIsX168wmfgOBQCAQCAQCgUAgEAgEAoFAYBAg0C+Ghjy9WkqGXoPmqdW5ZEuXLk3Lli1LEyZMSFOmTKnD+qJ3PAjfFOdp9UhxIQ7KjPv1Fb/k7bSlH/Hcvy8eTf8y/obilnTGg2JKUdpQnGZacR8IgEA2MbihIbeXlpmhuuottPBmk9J9D5s5yrIwbLjaDZsywlf+3ZqegKEBf+qnGRJkZOghUBYA6iumBsUyPd/84V/VZVLlFt5s/kgAMxqGMk1CDuMGbhjt1a4sanXVum4zNhAKU3edInpYnAOBQCAQCAQCgUAgEAgEAoFAYEAR6BdDQ4/egHZrpzcU/WEs8JZD+b/99tvTzTffnPbff/90wAEHpBEjtIlcFdatXe1LeguoflDIXSlHqfFrP+PnB1HWrVtn6Q0fztrwTrvZZcYe38+kj3NDhfPkvKkOXn5sLH6vFC/7Q2GrHHE8H+4X50BgwwhQi3C5PrkGnmutmxt054aG+lxw9SrImYidqnzhX7JQi8zkxkM/zqtmr4jr8Wv5Ncn9fr0oNb+KnRPivyHiIl5cBgKBQCAQCAQCgUAgEAgEAoHAHx6BfjE0oEDz5tKVfBR/ZjPceOON6eKLL04nnnhiOvLII9O0qdPsk3Yo5hgaoPc4+LmCz7UpL6UyYV5Z03A64ILWDQXwcqUdf+MjmpK+E8QbovMw4nXi0ykcutLfr/1c5mGoFr4z3TxcILDpCNAOSiNDbhfEzzWJXx1GVly7dt4iJ0q7ywycUQ6rWLQIxcDaqCUgb+4JFSG0XLuxzs8Em6toqrxYNA8qzn35w95dee1+cQ4EAoFAIBAIBAKBQCAQCAQCgYFHoF8MDc1iLF68OD388MPpZz/7WbrgggvS8ccfn4466qi0xx57pBkzZphxwQ0NZhiQVoGhAgW9kzLf5O/38EB5h4cbGAjDj8PDSwNEzb9KE/pmuh7fz9Dg6rj5tvbbUHyI4IMr43uc0s+I4icQ2CgC1Cc3NOQ9EIiSFW9Xv3U2Mu6ra4jqa6czz9aPe/d1zhYF8Va6tixCiVT1OzOpItYGBt2bV4NhHU42PYzrTXMew8+bFiuoAoFAIBAIBAKBQCAQCAQCgUDgD4FAvxgaurq6EsfIkSNN4Z8zZ44tmbjuuuvSV7/61XTK605Jxxx7jBkb9tprr9rQQAFdmUfh7mQwKEFoKuWkSfzSkOD87FwtVXDenEsezITAlf5t8QsFymhQiDpoNqWRA37OA60JddCdp22T22FVKFtOE+dAYOMIUKc43Mjg90XlrNc64Ff5W1V0Gj+TWnXtXvU5116LJio/56UTbOhYGBsslIh1ZBjLVfdW18swuORwP9tt9eNplX6ZuuVTptbyjatAIBAIBAKBQCAQCAQCgUAgEBhoBPrF0LBq1aq0cuXKNGbMGDM23Hnnnenyyy9PGBpuuukm25vh9NNPT+95z3vSwQcfXJeZJRYYC3Ao6xgMOFwB9yURhJuiXxoKpIl0dfdtaCAODqXfHWk4b/w2ZGhohln6KE2uKznTDucyTYLdsNCBNLwCgeeBAHXaDzc2NNhYta8qqxsd6spbVuKiTjt5wd1TMXZ1EppFJCPDUH2PgnMrL86X8/rXeeZC9m9dt+ja06gTa7toUWdvv28jiptAIBAIBAKBQCAQCAQCgUAgEBhQBPrF0IDBYPXq1abUr1mzJt1yyy3pvPPOSxdeeGFig0aMCUcffXT6m7/5m/SqV73KDA8+G8GVcvYqGDpMh86uo9QzAwSRGxpKtNwQ4WHOC5oNGRQ8rDQmWBwlbBPSC+OE8/I43DddW7oVD6fZlHgbonE+cQ4EWgiU6r8r+q3QaqqAPFwNr86myVfX5Wya6rrkWl7D2aLWSWBoyEYGNzT4/IScZk6jZUwgYp4H0Z6nVv7K5RN1Mh0uWjFyoN93IA2vQCAQCAQCgUAgEAgEAoFAIBAYIAT6xdDgeX/66afTggUL0i9+8Yv0xS9+Md17771p7NixNtvh0EMPNUPDK1/5Spv5gJLPVygwRJhDk3keWoMbI/wMr3IJhvu7MQCl3o0Z/hUIS9+SVwbsP5/df0Nn5w+NGwz8vCnxmO0Qm0FuCKkIWx+B0gxQXovSLAJlQyqv4eT31bk6uaJfcjNWFUu/hgOJ+IwGlk+0jAyENfjbffZrGh7aadu5wKmT68S9E134BQKBQCAQCAQCgUAgEAgEAoHAwCHQr4aGJUuWpLlz56YbbrghffSjH7VSsW/D2rVr0y677JLOPvvsdPjhh6ftttsuTZw40YwMTaW8qbiX4T6DARr8fSkE9x5GouXyC+79qxhcE8+PMi0PK89cm5OW1ZzpUObLyfy8XlgV38M93WY5PDzOgcCGEXC1n7MfxOBaqrgH41U7V9Hdw+91ri49Wnl2AwSxsr+bC/IeDdnQ0AqtmTlTS85nM3BTpFuFcSrTMe/ih3SJ5WeCmlzwCxcIBAKBQCAQCAQCgUAgEAgEAoMDgX4zNKDoL1q0yAwNzGj4whe+kB5//PG6lKNGjUof/vCH02GHHZYOPODAtMusXSyMeKXiXxoMypkJELPcwr8kQRxmQ0BjTloIX67AuQHCbvSDQo+W4hszluk5Dee+9lIgfpkvoy0MFk3DgqVXMq6u8W8e5LVpGOkQNbwCgQIBVWZzzXNBwqUHt3m7io5neV0R1V6dZxjAMpNgGvBlG50SqhnVMTxmTqk9vMmheV/lrj55bD/XAXERCAQCgUAgEAgEAoFAIBAIBAIDjkC/GRooyQMPPGDLJjA0XHTRRWnp0qVtBWTZxBGvOiKd+vpT0wEHHGCGAgwHKOqlAk4k/JqGBmhR+PsKdx5G4JqKNBEzLEgtKg0NRqMf4rhrGgzwd56d6Iyv8tnmxM4NHu5f8nU+zpewYUOHtetgHjHOgUCfCLTqbR8Whc4xLVpZZ8vrzlH69vU8+LkvyjKN8lr0jVs4dOLW9PNofu4r5fAPBAKBQCAQCAQCgUAgEAgEAoE/PAL9ami4+eabbW+GCy64wN7SYxjAWODGAYr3kpe8JH3+859PRxxxRBo3blyt6EPjdG5g4Fwq6SU8KOqdwvCveUk7Yf8D5+fxLS4azka0FOhwfia9Tmk6X6f1cnDvcTYWr+QR14HAC0GAWtt5PkLJVZW/qb2XwVxvLHwj7afJ7jndV7wHMgvPKb9BHAgEAoFAIBAIBAKBQCAQCAQCNQL9ami46Zc3pXe+6502s8GVe5Ru//KEK+AXX3yxfYWCfRqgQ6FhFkCp0JdGhtK/znlx4Zs6Gi/5u6HB47E0oQzz5RdtSy8qfh6nYF9fNo0FTlv64+flJCJhnnbNqLroFL9JE/eBwIYQ2JgivqG4hJk+34nJxvyIuAFjQBW0seQ3PXy9jPZ7Cpuel6AMBAKBQCAQCAQCgUAgEAgEAoENItCvhgaWTLAPw2233ZbYk4E9FVzpRqnnM5jbb799+tKXvmQzGqZMmZLYLLIvtyGDAXEI9+UUKPQYFDhwKPFuUHBDA/TkiU9wEj52zNg0fET11YsqjsflDM/SiICfO88b9/CHzgwHUtB8iQZhnXhgGOnuyctAMEJ4fOjDBQKbisDGbAGbwqdU14c0GW7onog6bCGTzk5a8iP9Js9m+Mbz6Jyh1HXNwC/8vHFOQREIBAKBQCAQCAQCgUAgEAgEAn8YBPrF0MBeDI888kj6+c9/nv7ub/8uLVm6xHKPEo1C3nSf+MQn7OsT++67b9pxxx3tjT9Kuiv1bhBYvXp14nCDAZ/D5EDb6NUMCAwGhKGojx49Ok2YMKE2NJAmYTjCcRg64Ldq1SpLa/z48fapTQvUD3nwg40hWXbheYKGfPkXLODF1zQoI0tAPA0zNlQMa+NDFdc3s+yRoaGra53lB4MM+S7TqaLHKRDYIAKlCg5h874vP/yb6rnftxkGmgzLe0Xo9aNK23msx1/x+gyDuKMrEyuvK2JjyE/JuSOj8AwEAoFAIBAIBAKBQCAQCAQCgT8wAv1iaLjjjjvShRdemH784x+n3/zmNzZrYGPleM973pPOPPPMdPDBB9ebQrqyjgK/cuXK9PTTT9uBQQElH6V85AjNgJBuwT00GBPGjBmTJk+enGbMmJHGjh1bJw0Nzg0ezz77bFr+7PK0avUqMwxMnTp1PSXfjAkyOHg8NwBgQCDMjRUrVqxIHBg4SJe8dXJuuOhap5kUa9eYoYPyuBFlm222STNnzjQMOsUPv0CgLwQ6qN8djQ3EL2k7qebu12ZoaEZ0JhXxJhsaxMf5ejqw3rDzxKAqr6tYxoifTedYxYxTIBAIBAKBQCAQCAQCgUAgEAj8nhHoF0PD7373u3T++eenX/3qV6bAP/nkk/apyyeeeMKyz8wFlHpmEnDcf//96Y1vfGM664NnpUNfcagZAnxmAoo5NEuWLEnz5s1LDz30UFq2bJnNHmA2Awo9yzCgW758ufnjN23atLTddtvZDAlmSbjBAcMAhgaMDPfdd5/xQ8mHdv/997czMw3gh6EDWjcOcMbQgJ878jJnzpz08MMPWxl32WWXdMghhyQMBp0cPHCcMVSACfnwT38S/0UvepHhQ7lw69aus9kUXk43fnheOMMPzLh2A41Fjp+tCoEOKriVvy//JjhNNd3uO0Vu+okQL4wNOK6bvCyg9BdRXzRO2zo3EySkYmCJOSc/t2LGVSAQCAQCgUAgEAgEAoFAIBAIDCwC/WJowBjA/gwYBlD+H3300fTggw8mvkKBkn7qqaem2bNn20wHZiugQM+aNSsde+yxCSMErjQ0MFNg4cKF6e677za+nDFmoKTvvPPOxosZDCjZGA1IC0caxx9/fDrppJMSCjwOXhgSFixYkK688sr0mc98Ju21117p7W9/e3rd615n+fAlGBgsXGl3RZ4z+SUt3FNPPZWuvvrq9K1vfcv4ffrTn05nvvfMtP0O21s49E6LgQBXGirmz59v8VhmwkyMAw880D71Sd7Zs4K4zNQgDrMl4OeGEMLIHwd4YUTh05i2nCT0LcN6a/zppJI/Vxw6Vp+NMVakjZF4PuAPraVTX3hodXZmZWZU/82Vfm3R+gxoo4qbQCAQCAQCgUAgEAgEAoFAIBD4wyHQL4YGljhgGPDlBMxYwPDw3e9+12YnfOpTn7IlEj7LAOUZJZqZB9OnT7fSuqEBbWTturU2A2Hx4sU2cwCDxX/+53/WswBe85rXpFe84hU2IwGeN9xwg30yE0ZnnHFGOv3009N+++1nswxQxjGE3HLLLemSSy5JzLZ473vfmw499NDaYOGKvBsUyB/ODQZ2U/1gKLjqqqvSl7/85XT77benD33oQ7YBJrMSStcXDwwxzIi49957bWYDRgWWTvDZTwwgbkDBmDB0iGZSSI9yXvDn2u/JHwYJN46U6cd1INBEgFr9+1DLK1OAJff74N8sR9wHAoFAIBAIBAKBQCAQCAQCgcDgRqBfDA0U0RVgFGf2bPjRj36Uvv71ryeMBf/7v/+bXvva16ZJkyaZ8s6bfg5/O+9xUZxducePa5Yq8Pb/vPPOSxdddFGaNWtWOvHEE9ORRx6ZjjrqKFPMf/KTn6QvfOEL6brrrjPjw8knn5xe/vKXp5e+9KWmiF9++eXpT/7kT+xJfPKTn0zveMc70h577NHxyZAvZl2QPjMcfDYC94RhaCA/P/jBD9IFF1yQPvaxjyX2m8DQwMwC8ux7ShDX45vhQPfuHnvsMeNzzTXX2N4WGEdOOOGEtOeee9bLKHyzSeI6LhhO4I9RhPy5v/ONcyAQCAQCgUAgEAgEAoFAIBAIBAKBQCAwkAj0m6GhLASGhiuuuCJ95StfMcUcRf+4444z5bikc4NDqZATjj+KPQo2sw1++tOfpv/4j/+wGQksiWB5xNFHH52OOeYYMzT8+te/Tt/+9rcTBgdmGbD/w8te9jKb1QCPSy+91GZE7LbbbmYYeP3rX297M5R58WsMJRgBMHCgzLP3Aps9MgMDx/IN8oPRgxkbfEHj/e9/f9ppp53SM888Y/H4CgdGAj7dCQ8cX6aYOHGibT6JQYKZFb/4+S/SlT+8Mp1zzjmW37e+9a3p1a9+tc1uIK7vweDGCviAR2lowC9cIBAIBAKBQCAQCAQCgUAgEAgEAoFAIDBYEOgXQwMKMcYB2ytAJbvrzrvSZZdflv71X//VNkz84Q9/aIYGf/vuswOIx/XwYcPTsOH5E5QAg5KOQs2yCM7XXntt+qd/+iebsYBCz9IJZjRwxgiAYYN9E2666SYzKrzqVa9KGBUIgz8zEFjaAT0zKw4//PDa0EA4swQ4yB8zMDBWsLSBtHfffXczArDMA6MFm1SS1je/+c2EAYU9Gj74wQ/aLAQ2eWS/iMcWPJaWr1hef26TcrI8gv0lOGNwIB2+0MESE5Zi3HbbbTYD4+yzzzas+tpcEl7kC+ODL/UYLJUp8hEIBAKBQCAQCAQCgUAgEAgEAoFAIBAI9IuhwWcm+Nv7O++4M33v+99Lf//3f28I89lLNn7E8TYehd3f0hO3NFTAo7xHqWZ5AUsemLmAAYHZEeyxwNceWI6BP7MLbr31VjM6sIxh++23t6UFxGemA7Mg2PyRJRXwYC8EHAYGNq9kScSa1WvSwicX2t4JbEDJzIS9997bNmt86X4vtWUNK1etNEMDMygwoPzlX/5leve7320zHjAaYGhg9gJGC4wY7F8xd+5cM0SwLOKAAw6wvRgIe+SRR2yTSwwW5B/3+c9/3paGzJ41O40bP85wwvACP3DDEReHnx/mET+BQCAQCAQCgUAgEAgEAoFAIBAIBAKBwAAj0C+GBhRfDjce8IUI9i9AacZhaGCZA0oxn5lkWQD7C7hjuQIGiDGjx6TRY/ISBQ/DEMAMgo9//ONmRGBvBfZo4NOUbJ7ILApmPPzVX/2VRWG2ALMQ+KLDokWLbAYCX4pgHwWWJvgmkYTjyM+NN96YLrvssvqzk/vss48p9cxqoCy4P//zP7cvVRCPpRN8wYJ9GuB7yimn2CwD9qJg+QRLN/jcJl/EwJjAfhXMtjjooIPSRz7ykXrGAssz7rrrLjMysMwExzIMlk/wNYpZ2o+C8rHJJtiBG8YGrsEbIw3XjrsxiJ9AIBAIBAKBQCAQCAQCgUAgEAgEAoFAYAAR6DdDA2VA6cX99re/tT0T2KARh6KNoQGFmK8uoDBz4FCWMSb0dPek4SOGpxHDR+jDEy3DBWHMaPjbv/1bW17AcohXvvKVadddd81LI3p60/U/vd72hIAfMwZm61ORKOjMSmBZBe4v/uIv0mmnnWaf0yzTZjYDRoOzzjrL6P7oj/7IvlzBjAcMDcxS+M53vmOKPxtK4s9nPFkmgfGAJRUYBjAq/N3f/Z3tHfHOd77TZkLwuUoMGeSBA6MD/DGUTJ061Ywrd991d/r2d76d/uVf/sXSZ/8IynfEEUfYXg0YZDDCgC2zPUqjghsaHHdjED+BQCAQCAQCgUAgEAgEAoFAIBAIBAKBwAAi0C+Ghmb+MTScf/75tfKMIo+hAUV51apVdsYQgKLMgfLM4TMj8MNBj6GBpQ8sw+AzlziWIGBwmDBhgtGgzKNsb7fddqbAjx8/3vZ5IB/MhsAR/21ve5t9HYJ7llSwNAKDATMTXNH/zGc+k971rnfZjASMAxgaSJ/lDcxUYJ8FjA3MMmDvB9JlzweWN/z3f/+3GRIwJrAZJUs0WAbCcgo+scnyDPLOpzlZXoHDmMF+D5/73OfsniUmLO9geQgzINinIgwKBk38BAKBQCAQCAQCgUAgEAgEAoFAIBAIbAYI9IuhwQ0E/rad5QZ8lcGVZ5YlHPPaY9KIkSPs7TxGBowIzXjlRoe+j4MvnWAvBGYosCQBBZyZCyyDYEYBDoWfZQ3w4IsOKPe//OUv04UXXmjhzDbAgIDyj2NmxQMPPGDGCzZj/N73vmf+bGDJjATSueeee2zWAuHMamCpBp/MhAeGBb6swT17P3D/7//+77ZPBMssPG/MSCAtzmzwOH36dOPtsyrI5//8z/+kf/zHf7T0mcnAsomTTjopHXzwwbZxJDj5rAWuKSP3vmeDRYyfQCAQCAQCgUAgEAgEAoFAIBAIBAKBQGAQINAvhgbeuKMAu+LLVxvY3PAf/uEfTPlHSedrD3wiEoUcIwNH0xHGTAMMERw4DA1X/fiq9P4PvD8tWLDAlPyjjjrKvjqBUs7nLksHvRsQ2EvhvPPOs+C//uu/Tmeeeabt34DHksVL0o0/vzF9//vfrzeR3GGHHWyvB5ZYbLvttmasuOWWW2zTx2984xu2JAMjB3tAsJzhS1/6UvrTP/1TM0xQfval8JkRlqh+WAqx77772lII9pVgyQSGAi8/Mx2+/vWv1/tZ8MUMDA0sr2Bmg29a6fxKYww83ADh4XEOBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAYSgX4xNKBka1uFNGTokNS1rivd9pvb0sUXX1wr3bz5Z+kExgMMCeVSCV8WAAiEcQ+dGy18RgOzBFhmgJLPnghubMA4UDqWQzz04EPp1ttutU0eWZaA+9jHPpY+8IEPJDaTxEHHJpVswsjXKjCC8KUKaN785jfb0gyWOjA7gxkZLIvAsSyCr12QLwwp7P2AAQPDxGOPPWb7SHzxi1+sN5EkDp/VZMNIllOwfwQzG0gPN2fOHDOGsGQDR9mgO+GEE+zM8hCczWTo0udA9YdxgT/717XPJDHC+AkEAoFAIBAIBAKBQCAQCAQCgUAgEAgEBhCBfjE0lPlnD4bbbrvNlk58+ctftiA2gzzu2ONMMS5peTvvhgaUZzNYiMANEdCi0LMZJHss8BlLNoFEcWc2w2GHHWb30MGHJQosm2CDR77mwGaN3/rWtwi2mQcf/OAH6z0a2NcBvnwpgr0YiLfTTjulP/7jP05veMMbzNCwcOHCxKc6L/3BpemrX/2q8Tn99NNt6QazLz7xiU+kj370o2ZoYDkF+cZwQJqf/exnE5tBLlmyxOIxQ4FPch555JH2xQy+joGDntkSn/rUp+weQwblwuDAsgz/OgZYgQUGGAwxlBc/0nSjjDGIn0AgEAgEAoFAIBAIBAKBQCAQCAQCgUBgABHoN0MDRgKMBcxKYBbApZdemvwtvRkatLlh0xHHDQ2E+TIAeBDGPgacr7nmJ5o98GmbocCMAN72s8SAmQXc46AjHp+MZCYChobrr78+fe1rX7Nwlk5gRMBQgfOvQfzsZz+zr2KwzAL3yU9+Mr3pTW8yIwFLMFg6Qf7ZEBJ/4rP8gc9YsuyCpRSnnnqqGSb4TCdGAIwIGFwweLCM5Nvf/naaO3eu0b3jHe+w2RjwwOF/npZ3fPrTn7b7M844w8KZ1bDH7nukMWPzZzgpnxsWMC54ecHMl2EYg/gJBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAYQgX4xNKD0cqAA41DyWS7xb//2b7avwg9/+EP7ikI5a8GNCp3Kzv4HGA34MgPX1113nW0seeONN6ZZs2YZLzc0oPh7uvAiHrMT7r3n3nTV1Velf/7nf7YkPv7xj6d3v/vd9tUHPJj9sGjRIluOwWaP7LeAYxkEX5cgbWZEsLwCI8Pxxx9vB0YE+GMgYObGpEmT7CsS5JO02KPhve99rxkb+KIFX8pgY0y+vMFMCb58cZT2mHBDAzRsBun5ZCkG+1mw8STLMfjqBNg28cJA40aamNFgjy5+AoFAIBAIBAKBQCAQCAQCgUAgEAgEBgEC/WZooCyuDN95553pkksusdkBKMkYHfhcIwox9+6c3u8J83CWCfAJSTaAvPbaa22JgtNxZoYCX3tgo0WU8fKt/urVq202Aen+2Z/9mUXjixMse2A5AsYBZkuQxhNPPJH4DCbLKFD4mZXwkY98xJYsYGQgDMcyCQwFfD2C2Q/wZm+Hvffe25ZxMJMCwwpfijj77LNtE0dmNLAs4wtf+ILtUcGMCJZPsE8EMx9YVsHsDz4FyqwGHAYHZkiwjIM8YkTBeMIZ/DAusGwD7ODBuYmjMYqfQCAQCAQCgUAgEAgEAoFAIBAIBAKBQGAAEOgXQ0Mz3yjPbAbJPgW4yy+/PB177LGmGDutGxRKJZmlAfhjNEChRlHHaMHeDCj2LEfgKwzz5s0zwwWbNvKZS2YZYAAoHUsbWDpx7rnn2jIOllmgwLP/wYtf/GL7xCT0zER45JFHbIkDyyj+67/+y776wGaNGAJmzpxpx8knn2xLNsgvRhT2bMDgAE+WcuAwiGBY2GeffcyYgSGAPM+bOy+deNKJ6eijj7bNLMkv+ePzmXyCk09r3nDDDbb3BPtIsHGmbwKJkcH3Zhg5QsaRrnXGE4zYv6HEzzIRP4FAIBAIBAKBQCAQCAQCgUAgEAgEAoHAACLwezM0sFzgc5/7nBXtuRgaMDbwJh+HMv7UU0/ZUgW+EsHMhVEjR6WlTy81BZsvUPDVCRR6Xz6A4s2Bgo6hghkJ7BfBpo8sWXjnO9+ZTjrppDR9+nRLg9kPzBDAqPDwww/b1yj8qxDwQKEnP9DvvPPOpuRjwGB/hnPOOce+UsFsiRkzZlhc8km+KQeGAOKSN+Jvv932adI2k+yejSZ/9atf2aczMW6w3OKtbz1NMzT2MV7MYMAI4ssjyGxpVIAnR+lnBYqfQCAQCAQCgUAgEAgEAoFAIBAIBAKBQGAAEfi9GRqe64wGZjKgnKNYuxINLviznAGlf/z48RbG/grMFOAeZd7joXSjoHPAh4OlEVdffXXiCxjMtOAzme973/tsrwaMDND4lx2efvpp44WhoVyKUT4fNpG8//77zYDBTIv999/fZjUw8wFHPhcvXmxGAmZZMDNi9KjRacTIEZYv58XXJjBYsH8FZ/Z2YGkFZcKtW7surV6z2gwJ8CGfzGwgX/Dj05adZoU4/zgHAoFAIBAIBAKBQCAQCAQCgUAgEAgEAgOBwMAYGrRNg3ZjqN/Go5xzuKEAQwPGAnco2CjavjwCBduXE0DLPQdxODNDAeMDSj6zAlhqwcaNfP2B2RWf//zn7asV7JXAhos44hEHhzLfnClAOHnAOLFyxcr0zLPP2JcryBOzFTBOkBcchhDKw2wGjtJoQb7hgbGCfSHYWJJ8sHfDfvvtZ2Ug/44HPIlP2vhxzwwOzy/59HQt8fgJBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAYQgYExNKjAKMquzGMM8Lf1KNEYDAhz5R4lG4d/U6k2Prze1z8OWjaRxGjA7ACUdBR0PnnJZyrZsJHws846K5122mm2HAJaDtImXfICX+5xxHca0se44HknPZZK4HzGBYYCHLQcXhZ4UlbSf+ihh+yrFmxMyacsWQLC3g04ZmtQVl924fFJi2vCuCZPnTAxJvETCAQCgUAgEAgEAoFAIBAIBAKBQCAQCAwAAoPC0FAq8Z0MCeCCkg6dJkKkYcOLGQ+67+nNSjcGAmhcwec8dIiWUgwbaso9mz5icEDR5/OSfLGCzSXhjeLuaXONX3mPHweKvhsg/HmxzGHturU1j9JAQBybnSBLyPAReWYC+WTvCfLDvhOzZ8+25RBr1q6pDQwYSDAieLlJl/xwBgPKTJgbHjwvcQ4EAoFAIBAIBAKBQCAQCAQCgUAgEAgEBhKBATU0UHBTnBsIoEC7K8N9SQSKvi9HgNaVefZy4JovNqDso+Bz7zMVMEKg5LN0AYeSD11fzvPheeDe/YiDv4eRFvs3cE/6bqQgTWYx4M9MCIwHOMrCTAjKI+SbHAAAQABJREFUwbIL7tkjgrwyy8Hp4Eue4eflKPMAL88D1+ECgUAgEAgEAoFAIBAIBAKBQCAQCAQCgYFEYFAZGlDK3TgAKL4swN/kE47hgHvCuC6VbhRy7n1GAby4R5kvlXH4GK9uzVAYmmcouGLvD4N4zo8wPwgnLs7zxTX0GAs8PQ/j3vNNfuGDwYBZCXyqknBoe5QX7smr70UBX+JSjjqe/OBDecoyQRsuEAgEAoFAIBAIBAKBQCAQCAQCgUAgEBhoBAaVoQHFngPl2hVwFG8OU8YrwwJKN0q2K/CE4dd0zoewZjj82QsBpZ1NI0mjdPgzE4Ez/D0P0KD44zxdeLnSz7WHDx8mA4cMGTjywkwK6DCEeHrwx0BB/nx2BTwwRLBhJtccXgau4eX3xjx+AoFAIBAIBAKBQCAQCAQCgUAgEAgEAoFBgsCgMjSgdHP09piKbQo2OKGUo1ijwKNkMyMAJd+VcJR3V/RLXD0cnrhhQ7XHQaX4cw8vDlfk3XBAGM7DnD95wFkeKwOAp0EY+eQewwE0bpzweBhRcNxzeJ59xgJ+xCM+zvPTpIUPYb6UwojjJxAIBAKBQCAQCAQCgUAgEAgEAoFAIBAYBAgMDkND9fbeFW/HxQ0BrnCjgONXzgjwN/8ep8kDxR9FvlT88cNB64YBzqUBw8ONsMOP8V0nw0dPa/YF8Z0f6WHYIO8cpYGDMOffzC9GBN/TwfPTNDQQzmyJkaNG1saKDlkMr0AgEAgEAoFAIBAIBAKBQCAQCAQCgUDgD47AgBoaXMl2QwAKtSnlMgDgUNpRyl3R9hkGTkM8aLiHxmdCwNd5w8fpfLmCzzjwzRnhi4MPzsPh6YcFVD/44Yjn+eO+NCBwT97cEcf5e7k4k0/COLs/fNyfa/JPXDdkeH49XllWTy/OgUAgEAgEAoFAIBAIBAKBQCAQCAQCgcBAIDAoDA28xUeZRnH2ZRKueDsoTWUapZx4KOL+5p9rV96h9zj44+DPdbk3A3FLhxLvMydQ7jlcoXc67nFlWvClDDh4krYbIqDjM5t8lhN/aP3gnjQ4Q4dz/lzDkxkM+LlhBH9o4UG8kp6wcIFAIBAIBAKBQCAQCAQCgUAgEAgEAoHAQCEwKAwNrnSzDCJpMgOKMweKOrMUWHbAPUo1DiXbD+7LsJpX5U8YzhVzwrl2Bd3j4ueGC/wwDFh6OUm75h46NyhgFHE//HHOl2tPy/2dFv/Sz2704zw4Wx6UD2jdYAEdRgnShQZ/eOIXLhAYCAR8zo5aRpU8DaZqyPgUlxVB28ljtXl2uKlavkKqBtmBZqvz6oite24qsqBWYLqp0YooWx3uUeBAIBDYChHYmHAshOLzEcOOaMHGveIcCAQCgcDmisCAGhoADUXZHUo1hyvZKNIcfo/yz5gYJZuDuGV854MhAD4+OwJ/6PsyEMAfemYOQMfMgVJ5JwzndMx4wLFXBH7kEceeDG1jdvHCeR4xmvT0tsroaTh/Lxf38CXc45J3DCH4jRwxUvpbGBoM3PgZEARyzV5/4FW35vWDcj5rgmyDoMG0SFtXXqiCvGxaHqxzSVF4b+mXBtX6eLUXuxHeESp5NshqTNfzr7g7Hz+3Jxp3gUAgEAhsQQi4IPRzp6KVcrSDYCQq3p1YtJF3Iqjibq19XSe4wy8QCAQ2GwQGlaHBFW03IPg9aLof1/jjXAm3m+LHFfcyDsHujxLvDl7Qca4NBoWCD12ZHtclH48LXV/5IQznfDiXeXP/kobrZj5Lowvh5KPkg1+4QOD3jUBufZi63FXtUbf1mMm8dNciagVWRASVh3PzcyZTW6n4VtGqYL/zs8faCs6GqX7s7OUtbgyS4t5J6ghNzKr7Xvf3+zpi+wXBDdJ2grgLBAKBQGBLQQBZWh6NcrXJTQlGE70uIKuojSgdb4eUMru49pdxxrLg25FJeAYCgUAgMLgQGFSGhsEFTeQmEAgEOiHgJoY89MoDIuYlMASyYZB56SoTZBblYKkIchI/e3olv/LaUjD+JFan6NG2jrMMlXkwq+IaFhUgLfAzDn0NXGuULILueCDVtZ39uiZsvyC4PNpD4y4QCAQCgS0EAe9smNXKdZ7d2iqcBKGRVC+vXH66PHVCZ+P35bkStxUjhYi49qsI6/6zGVAyiutAIBAIBAYfAmFoGHzPJHIUCAxqBFqGhtYVwx8/8sBLdwyufIBVD5QyId4+dHOymlRhRISfviVT8+U+87MrJci5uiZsa3EGWIWWnarrpmGhBViFjOjwaxsEFxh2GiRXrNug9Sh+bguMm0AgEAgEthQEXADSW5U9FuVDAMq1yc1KKLbJ2Ey28V/ks6fnZ2KJpyXF2S42ziooAoFAIBAYJAiEoWGQPIjIRiCwOSGQTQz5lxkHuHoIxG3bQKsK4aTDgiHR4UM3XVZcMp/WLAZtjKow45CTybzNQz9b48DLAHQwQA7n9zq3g9W4z9SVp24gtggZV793dpCX1xWpkxEcLhAIBAKBLROBLPy0YFbF894qG79r2WkkEozrGRwaiLjsdO/mvQlaF7Y6+6XR8aNja+zvHK84BwKBwGaJQBgaNsvHFpkOBAYaAR8FVWem85vTYMgv/Z5zNU4iqNNhpNVPRaoopZFBsYwvoU6o661x4GU4CAM/c1HDUnnamzEn8HPGrXXnkcoz135f4dzHadOo+ogc3oFAIBAIbAYIYE7H0ODGdQQvsk/fJatyrzNGhjZDg4I8GCquIdHJD7y5drJsXHcjhkIIrJ2orK9z6jogLgKBQCAQGNQIhKFhUD+eyFwgMEgRqA0LzQGR57caEPm4SIMkxk3NA2r8nIz70sBgscpIEOAYdFkkP2fvreYXTEpXA5jBysEOHBh3juDD2ow6DGFUM+sYyyn8XGYjrgOBQCAQ2FIQcLmZTQ3MaMC1DA1ZUuq3NjQQnn39ZEHybc2HyH1eKZGzLIWvG9dzGhWnimemIoVwgUAgEAhsLgiEoWFzeVKRz0BgMCFQbkho+fJhU/sgywZd8nKV189elEYsG5vl4VSlAjtBnV7B3y71U3k5zy397JB4OZv3LbSh8FA/t3wcuAppY9cc3rZi5dSAmsOXs2xl0GcQ4jcQCAS2GgQwMpR/FDzLQH1e3VDQrwlK7rKPeeunaWQojQ1OwznHdOnbbmzI4c67nX/JI64DgUAgEBiMCIShYTA+lchTIDCYEXDtk3M9s0HX5RjIlzTIz8iq8njUZvE8Kme/zoM3xfBIfjYC/bQRNzlumfdAUB6Usg2WutiZKmPpFHVg4yLPNsmefp1j4lfGdsjD0NCAMG4DgUBgi0QAIwMun1vSEDNDlpKVrMSq4JdG35LVTQMDXJxTFUVRMTS4sQFW5TVUrRR1Ey4QCAQCgc0CgTA0bBaPKTIZCAwiBHyEVI6WyF5rxJQzq/smSVmKJrkRlwSeTunn1+tF9oAt+wwkDFr9KCFqQtIyGeSn4OEglK9LH3x5Xu6XzyX/TJHjEuqH+8c5EAgEAoEtE4EsQ1tlczmJj0vaVn/ncrOMVfq1+LSuMpdSAucYmBeyYTcMDS204ioQCAQ2FwTC0LC5PKnIZyAwWBDY0IipGn/xcgfnpPku/9YKqgc2z5CV47jmfTMss90qfoGquzr8LZkX3HF1eFr3DF5bA1jonaZ5XQ6ancofT5nO+vE8NM6BQCAQCGxBCLgALGfvIUFdiOrsshhSP0DAozoaHsXv289O3X5uGho2zKOdY9wFAoFAIDDQCAyMoUFy1KejDfEp1gONRKQfCAQCm4aAj4OgLq8ZAenAy7397IOjiqRF5AR+hieujFDd+3pXC69InMz9tvSzz2TA2FAObh3Xvs8Z4BxegF1ctkCvwK0eQhtJAfDWhn1R9LgMBAKBrQUBBKAfZZld2MqvR9dOUp6b5Ny73PSz0/iYGE7w4Jxp2mc0NON5/DgHAoFAIDAYERgYQ4OQ6K2sw2FoGIzVIvIUCGwCAnk0lAk1+mkOsDyYgVF5tBF6pGZyZQRdM5BzxRpSgrfGfQKAy40MnEvXgKzGHJxwhLftqeEPCP+mM2L7aYU0blsBcRUIBAKBwBaKgPdRdEA47pGFxYER3PsnJ4fUnYtOj4K/+zkNjFsiOV87jS+a4BwuEAgEAoHNCYEwNGxOTyvyGggMEgRaA6L1DQxk0cN9WFQbBXwUxrkcuJXlIpIfisggzpXrku8wkTnfMvqWfO2wAZ3DR3kdrk5nexgOHMT1dX2Bb7trzjSDcema92VYXAcCgUAgsCUg4CKyaUXoIGh9xh1RPBpnF5VlFKAxfyfEg1hOXHPIvFoGhprAYsRPIBAIBAKDHYEwNAz2JxT5CwQGGQI+NuJcHmTTwzzLPrjCIMBhBM1BWxnJI3CurAjMZujSbalcY2RwQ4Px1f3W4Eq8+4JtiAc0zwDkfuuBRYCABnd3fV0TXoY5fZwDgUAgENiSEHB56Z2P3yP//KC81bUbG/By0iI4i80yoLyGsC1WFWiyVj9+Nrr4CQQCgUBg80AgDA2bx3OKXAYCgwYBHxtxbh5kEj8bE1VnriubQTIlmGVTtbGhoiQSjls/iKSD2Qx+OBlBw3NwNmDoemtxYOA4VOhZ0WsDA3clkRMb1QZ+2phVdJ38NsAiggKBQCAQ2GIQcNlZ91dFybyfwsuvK3np0fzsYtTO7uln4pfX7TeEVvwV2xll3/gNBAKBQGDQIzAwhgYJVV+NFns0DPo6EhkMBNoQYEzU6XCicszk46JsM+jNRgEIevRTMqlHUNVgigi67NWZMV4YGhxdnR1gx68IqsM25ucPxun8vsM5J+OJ8lgykZM6izgHAoFAILDFIeBy1s9ewE4CEL/Kv5aYum8jrQNE69dt5+qGk0fkPLS6cT95hQsEAoEtFwH2MrTx1ia0ed/3EDQGm149MIYGAeGgDDZAttwqGyULBPoPAcZA2Ar8RY8PiJCHhHHgMCj2igq7wTARcdj3wmsGujBiYupgbwAONzTo0g0NdVqizPzy8gmRbD2uBWwGpkuoMENkmBaSAEp3hecwcKxgIY7Hc6QqfO3W6aoz039xHi2fWwxy1Gxu8Kg5RvwGAoFAILAFIuDiz8/NIkoMd3d1p6HDhqYhyF7Rdff02IB/aCWLy6i13HRPzn5wUV/LH2ITurrguo6s63CBQCCwRSKAjux68tAhEgBluzcRgZBouR7JG46hQ4dqOMjC4sHjwtAweJ5F5CQQ2GwQsHGQfmxiAkKPMdD/Z+9NwOyqrnvPVXVrLqk0CwkNIAkxGwzYgLEJsTHGQ5LXzuwMz50e8rXzOkn3193pbnfn5cWOYyf+knzpfO/153Tsfp04jglgm4ABG+OR2MYDEgIBRgxCAxKah1KN91b1/7f2WfeeuqqSBKoCUdpb2nfvs/faw1mn9trr/M/ae6MMFc7zFU9AQ81lZEXaUnpJLY7tAq2tWzZQAZJUXoIyhGocGxYgA/XiqCc8pc4aF4zlhjHzGNXuFQAN7e2JZ1J2E/CghSUwKJwzruBeCcjx7ILtUXU5JD9dp7IFacH7sG2IRnKYOZA5kDkwuziQJF+Sg9wZ4tbloH4IEZBjAnhrtWpS8tuk5Gteq9U078kKoRWlX4RRD0VwUdYvyAxPQjkOIbKcMLyi2WUOZA7MXg6UgQY+yMdH+QAfCMtpXAM0oJUBeAI4nCkuAw1nypPI/cgceD1xoNCaXB/ST2yC5YCD7qOhJyWogVtrLVQtt2jw60LZUrzFP6OHFkXIB3vKFtQpydOp+6zVu7h5HCHoiyYWj8ekImXXNeFWLBzgEoRiXrBWVx5vCqEqe7Ib18SSi2qoWd/u6lVFfg4zBzIHMgdmEwdCDnJPEY/pKOSh5wGa64WAFQ6AEYFI1F8GICpclPcKSYuKm+NcRyPx3lAvTGZ2mQOZA2cTBwJQ4J7LAITzQHJkbLxh2QDYEPLnteRRBhpeS+7ntjMHXo8cQCkKVyhI6FgIOPZU4CsOwi1lBXEADilMulPjRZVYumpoUeOqNH09Uo4jumqUbFWpy7PXuRab+JA0VFiRGDNe0zNQvqPZdSYpL9b3ineO6USRqOYEIW3wFIPlhOi88cQUzS5zIHMgc2BWcgDZhwfXDZfS+C3LxZCQE9MpnWRmkZ+yU1X1+CR5iaLcQCMeeTnMHMgcmLUcQJfDuT6teFwTJp04yQ3i4bCkGh0d9fyOjg4PI++1CjPQ8FpxPrebOfB65UAoR4RoX5iNsj5sXGb7AA1trFNNSCokCVqI33htTTefwIXQn9Kra11kCmhoUf2k+qILMuqZpXg5LVU7q3/Ta39wNm41MSFxOfGswazEZX9UmoRwrOE7dbalBx70jTBi0YccZg5kDmQOzD4O1ASiM8cB2La2VNzarqa1a8nqjvtNc1fIXIBYLL6YIMc1L7JssD3WFiKIcXXElwvRQl6IVLKS1CUvuVJ2JOUwcyBzYJZygGUQ1SoHu+uEtbY2BwwAEXB8SJpqaQRlhoeHPb+rs8s//Hmh1/AnAw2vIfNz05kDr0sOpHfcpAn526teb8dqUox0IW3IQQY2wOIruv6jjCWFLF6RnbIAINJLcFLT4jfpWxzX2EqV0rpaMJUoO9ULqFFWzsrZszWegITgZ9JYQyElTByMRSpwAUbhE8BTHSsmrtZ2Kb8N16AK6kZYV3mjoVSdCtcjjYpyLHMgcyBzYJZxAGs9V/IBGqTkM9tVNaslqAEJieRF7iYhCcigVwNdSUZrbmwHaGA5GwgC5n/HgQxKR5xqTkOq8zpBmGR6muqQ15Dgs8scyByY3RxoBhq4W9/3RdYL/qFIIZYNYeUQFg7QBNDQ2dmZLRpgXNnkg+vsMgcyB85wDqD94EMTortc8wM6ENqQhykNBSwBDtChSKGo8Qs5ahk+fiNFX95dMRMRIcR42gVkQPMKsEHRs8EloCEUXBiBMiuLEmcK7EhbbiZWJY4mviQ62Z34ZcXpIqfpkZFMBeEK8724VJPJlcz16nk5kjmQOZA5MOs4gEIv2VlMQ0jRBK0jexGWyNcWSVdkc00gg4AFyVhShcIrzolLmqzq8xkMKgQpchRfZAMyhIcKp619Vf6sm+649ewyB85KDpRBBBjAtW/2KFmBhQNLi0ero55GPuBDO5uCy0GHC/DBL17Dn2zR8BoyPzedOfC65QByLMmyur7k9+JvuGQo4noUYeFVYNzjLQIZEtCAQpUOvUyIQfoin1JQ09z8NJSzaFP7EBgWE21JOQt9zduf9T8ANvyDg2mH4QAaYD1AA1wrnoBzl1T4CqehxbEUBY7jEufT44pH5mSJlBkuEXqiKFJVCot4kZuDzIHMgcyB2ceBsvwjnqRomo6Qwj7buXQE8AVq8KUS/it6AQ1tzGFI2jhGCSFKVQEyFEBDTckBMhBCgnwGZAiwgZqyyxzIHDg7OADAgJUCIcBBLJsIiwcHFSQoOGkCsIH8M+0DfgYazo6/1XyXmQPTywH0J7QgeXQkd1xjFipTUT8NwZUopQEuQMgeDsR9WYUUKsXTwZeYnPq3H1esRCTlCtNTvrsXChkKGhod2hf1o321S/2Kt2Rdnh0OXvINzRkBd5xnaWkKHEjLJtLXNlEK2W7RmuK2Fox3G0sqEqADpxss9MdEFcXjSiHPjUScIhQolGIPuc4ucyBzIHNg1nIAGYhnApJDUBZyL+SuUtwBQfCP+Yu5y2c17WGUlkqoUFjmeQW69jmyCDWXBdDAAjckPPUyxQEyZKBBTMguc+As4wBAAps74trbpMcJUAhXtnqALpZWBBhBeCa4DDScCU8h9yFz4HXGAf+a40iDFCEpS61oRIAMVeWwgQ15dSVKeShp7A/gQIOEX1gjaM0rIEN6bU5YAsobL8XADL4QwJUzKWM0itKGWZhrX2cj0KB7d4VXHAO4IQ5PfUJpTCrwsFps0jmuTcgqmqAEN4iB4qO7ZONAlFKeqmpcsy2HJMQ1EQgDaCj24FBKdpkDmQOZA7OQAwg/OeYcl7fIwCLNZWHKTr+kKxH5yPynIj4nkskc5ulRqLiuz5GaByWImQfDUxxHiQAa6rLac/JP5kDmwGzmQCyXYINHrBRYGkEIoIDDgiFcWDhQpgw0nAnWDRloiKeUw8yBzIFT4gDqFEoQm2ERBwxA3LWMShjKOxjgOpfUIl6AXcFSCbdEUAaIrBdQHISCF1Y5rBsQn9SLYgbY0Kav8dTvm0HSqHv9UAbLCIqm4oqcBQ5gwb04BXBTIN3WobV5WrdXZwY0An6YdNIHNfFQz6KVDcnEz+QS4+os5JmFh8Dj+iF0V0QCYIgwsnOYOZA5kDkw6zgguedWepK3hMhWJp2Y24i7JR/zEnObZCxCt3gZ8DTniehc2BLii7nR4+zvoGpKPsQuRZDaPmUq5Dq7zIHMgdnNAdfdiiUTARwAGgA6DA0NOeDAZo/s1xCOMuGhzRYNIL5yZwLaEg8ph5kDmQMn5gCjFg8gMKIYihEmohXFKxKAbSO8AIuCpQ4CCWS7L8IR+WEhCDL/4mtQp16KOyUcWfrQjmImWsAHCUbqBWigCiUIT+A7PFtrATYoyb1nJrDhLNG6uGO/Vb6socBiNTI4aHbkiMAG8be7y0xnJjuDmHi6ugU8sDGQSiJrAR0EDLSQB9iAcqsaC04qXtQfCYT1eD2SqFLRVMdZwn/4k13mQObA2caBQhDKmmFsHBC9ppOQJIOxUACwJWQSRBb3DySZ2C45XC1kNHJ5Tq9kMfMbRfSDd5AccIH9dJJVX3AWsrpX88Td1SORkMPMgcyB2cqBsFDgHTk2eeReARpGpFOTDtAAmAC4UH6X5hpXTvOE1+gnWzS8RozPzWYOvB45gPhCr5LKJaAhAQ7oP9oD1zqU0z4qCv+aQ6peaAekgO0/YHbggFUHpYhJ32pD8Zo3x2xuj1kvL8iia5NXHkpYAht4N0bLqi+gkMJVmPuTLFJ+PCQ+yx18d6AFBGZEgM2w/IGDNv7CNju8f791z51jla5OG5KFQ9e8eda2aqXZ/PliIkqxfE1PDHABoMF5LWb7F7kSnlDwMHgaoQMVk/HXwYrJMnJa5kDmQObAbOAAkpddF9Imjw4LIE+ZAKuSkExWzHkv7bXath2y6BMQAcgrOTxcG7XOFeearZTvFvjAWc0sGZQfk+wc1eyFFV/a0QGwPk2BktDWQr0iV2ZyPjcqilCuC+YiLweZA5kDs44DfqSuAEvAgrLVwuvxRl81oOHWd71LH9jS0RsuuCVAXV5mZfX1+HeT+3wGcQBd5JR0j2bC5utTuCeKBBAA0IC+RdsADV0ADVgwkDikn2H5vfttdOs2e2n7Duvv19d3jfc58+bakpXLrVPeFullGAuHLnm3cEDdQglL7bB/Awsz+MXRFmofm20l78ln5A+8anan9JyaC+naH1VhmWADsg453G+2/UU7vOkx2/aTLdY1v89aurrsqL6sLVy1ws678oqk4PqSCvEUk19/UIoDNmBBUpj+UnfZl5tP/eUpUDzuKPZ3eKV3U24hxzMHMgcyB848DoS0A2RIXiCC4n6mDxMUE+CAfo4IQH9mq23b8JjVjg7YnLlzbXB4yAYF7q6+/GLrvuQis6Wa5+Z0Jks+id9R1aJvkppL00bIzGUADPh2GmYOxRNHzDIt4pkGs9gVE7LLHHj1OBDD8FRbDHrCE7njhnIUVCGsEsbQpyUEWrUci+UShw8f9o0hAR4AIKBho8jh4WEHI3p6eqy3t9e6u7sn7N9woj68GnnTAjSg+ONC/Xx000a74/Y77E/+5GOe/uV77rZ3OdBQmPaKvM7PJqAh1eTFTvnnuId1yiVPg7B+A6dRx6tVtLmv5etyvNyfqdLLNCeLl+uIeHNIHZE2WX2R1xxORnsqaVFP0DZfkz5ZWtC/krBcX8QJceU/3nIe6XENXTnOtVxUka7Sb7m6SQnKxJPFiwomqxty0hF96EDoWYAOuHbldCq1XV90fEPII8cEMsiS4bmttmXzZtvy2GY7NDQgK4Ux6Vuddvmb32QXXHO12QqBDR1Sr8K6AdBBTKm6AkZbdCg87aevSxXtnNWmNa4oaO6CP80hmZOlpVITf4NuYmq6Ig9XNJcuSr9N+VyGD6oo2ribyJkipILy7fElDeYfFW9f2mf29LO27QcP28Zvfcta+vqs1tlhB2XlcN4br7AbbniLdV0kBXfJIvFWFiT0xk12pa36MpUG0JBMd4/vLympeYCd5BLYgKRvuotSX52y+booP2UQ9IRlV9x/Oan+PCcknuCiXPdk9Z2g6LRlRR8mq/BkeZSZrN9RjhAHTTmtuUzkOXHppzm9+bpEOqPRaDfCqRojHxf310w/WX6Z3gs3/TTX0ZQ96WWUaW4P4siLgnEdYTmdeNxLpBMGbYRTpZXLnCxerutktM35Jyt7svzm+k7nmramciVeNpOVslLpSQgiiTB55hwOrqzqXb9mbWx4zMTHBLj/sNmO3Xbg8c324+88ZMcU71u0QIZ8gzasUhe/6Rq75A2XW+ul683OO9esR2CD9iWqylJvRFI1zXNJmgbQgJbsSHsADVwDMkDADYQwVrT+N0L8VBw31MyESIvwVOo5FZrTqe9Uyk5GM1naqfS1meZU6jkZzcnyaRMaXPMzSamn9tvcTvP1qdXSoCqXn6p/ZZpGyUbsZPkNyuNjUXaytst5L4dnUa7c2imkRRfKxU7UbJl+qni5PPH6dbkADfp1gjiheXHXLvvxj39sP/nJT3z5BOACjo0h8cuXL7fzzjvP1q9fb6tXr3bAwQnOgJ/TBhoSyJDU/7Ql3JhtfGyj3QnQ8NGP+y1++Z5/MSwaKu0SsijLOJgIyOBcTqwOPjeHTu8/kdNImaDyHp/dIMyxzIHZxAENGf7ck+eFMI0hfj0WY6E5bOZBKpZSi7gvPy3qJiOqiDjXjOLQgyiGRUNrdcjaqwg/pezRi/DjT9nOh39kWx/dYNvuusv03cfLoC9d/IEP2BVv+ymbe+klZsvPMVu8MJmXdmkBhr60cxI5NguprSRlVEzXpGg/COVx/KUDDeUOQvSaOnVGt48eCo94MnQPHpU9iSyFcBEIveK+UkTxCR+tSMSzpteBBsUFJthTW8zE2yd+9LA9ddvn6nrvUbWz5K1vs4vf+lN28fVvtsrlBX9dUQ2QgQu1ImWX/gE0lMEGZRZ9Tb0nl7tJ/Srugi3SPQXq7DIHMgcyB2aYA4genESguwiLSw+CBnFZpDNnRDJJ41pO1qJJzufM8oeugoj5D/mNpw6SUfd5++9gjwZ5F9gjmgG37rDhjY/Zlod/aD/52r02sGWLaUGgDckzP55/y/vsgje9yVa88yazKy4z6xPoK9E5ri+SI5rBgC6in0jUwBMq3gHlsFSODsTeDsTx2WUOZA7MLAc0zhj1uBij5eHXPAyDhrDZeyWln6inrkU1FygRjAwN26Ejh22zPth96Utfsg0bNuhD0g3Wp49LWDQ8//zz9ulPf9pr//3f/337mZ/5Gbv22ms9v9Tkaxo9TaAhAQzpd0xCkteNmm18fIMDDR/7yJ/7zd17z12yaLhFQIPWrnFEEA7GupAXR/U/poJmfjut/5Az0aVnkYyqff0y2ceTTSyUrzIHXu8cKIRQev1r/MGTzIs3oY+DqQdTgwNFXZ5QxFG0ykWjunLocREx7tjPsUVvyiNDh6WICU5gPeqLu2zPfV+zLX//eeXp287hvb7Jdk1K3rA2L2xdcr61zltoV/38z9u8t/90smzQwtSxdilgUsLGW9q15wPyRKZhbHyohlr0JT4AlXTSha6bURE6NtPOGdzUCAyDawUPsaxF0UwTVep1TCqQsJIBJbJdAAKbj7PtQlUKbEX3SBq4gj8ENhWTTB3XvgotLuTUxu7dNvLNb9kLn/snG9y724b27bDxipTW1jbn1dDchba/v2oXf/BX7Jqfe7fZBWuEBKlC8RZVFkmNoS5wDbYK+AQ2NNBzKFB6HYLQJmjj2siTLrX4RpLkhFc0u8yBzIHMgZniAIKncC5mFZ9S3wtahWkeYwapi2VtWSO7BJkat2qz4nbJTN8Yl7pduCmULE6QQjIqoGzI7Vad8tPJXjfse0PIBsebn7DH773f9siqrDJ41Fr277ZKZ7dVRwatVumwzjmLbMHFl9mlv/ILZte9WUBDnypUjdqod6y13WoKaSPN5YoU0pj9jlodZPYk/aiMgOHUUQXZZQ5kDswcBwp5gPbF+CzLkBAVhO6KzKBjLBOPMR3piTiN4LpMUaKP6uYCEBcq1pGDh2zT44/Zdx56yP7hs5+1nxWQ8IFf/YCtWLnC5RkAxCc/+Um7//777Y1vfKN9+MP/h73nPe+2OXO0D9oZ4mYAaKgKaNhoX8CiIYCGu++SRcMt1tohoME1bD0ihGh8zlMYD6M5THwidaKLh5wemJT1IIlwInm+yhyYPRwoJB2CTK+g9fuqjwVSQtqRHb5OWYoUddUVLYYmvqlYo5UkKCnGmzT7WzleSHvVQ9qXQX5MNqXPPWtP3flFe+HP/x9bukp7Pi5cLbq00eOIxv7Rbdq3QR/mr/i9D9kF75cStn6d1rBKPvR02ZDWo423SknTyzAdYZfdcQa4LnllZ8FEghwKoKHcOfr1mjl1xPkncEQRwAYmKhJjYqmrigk/SICCyoyKfzXxpSKEgQ3K/dRPFFo/u10hyAOndozqW5l49+KX77Gt/+u/t/kyBulevlarVca8zbG2Djt09IA9vmWvnfuB99l7f+1Xre0Nl2pjSCm42jDSBEbUBOEkoCEBDw2gQY9OVhM824qeVcAQLQANAodcxnJ0WwsPopgF/f7yT+ZA5kDmwAxyALlaqt5lEQnlRPIRXoXn4KP0EayeJLVzzGpCdSvKrAA0uByjoFyUk2hjVQSe6kPStQsIbsOaAbl8TLZjB/fZ4IYf20Of/0cb/sL9tvyq9dat+ttoQzTDmkiHdmzVkom1dsl/9SGb95a3mJ27QnJYsrhNcx1yVPuWjSkopoNiPtcSRE3gHd662tL/NPmJ0C3JuM4ucyBzYMY4UMgCxqUPv6Ih9Lgiq/HOWeSF3gx92TeLqSgfcoWwpbkAdZKhIb9v9x578JvfsH++83b7wh132u/+7u/ab/zGb9jatWv91IldWlLxkECIp59+2vdmwNrh+uuvt7naK+ZMcacJNCRBDlsR6BgzIzIbSyc+4fd5391p6URLh5ZOYAqGI+ANpeB6PAz4XVDUw0YK5Ck3FUtfCvnNQANMze6s4EAxZpJsSl9suO8TAg0QxMAijivqmRBXmo9F8go3aTElHiccKzph4vBLehF+zga0T8tzD33Xjn36Tlt6+TnWKZBxVF+EfPjrZRerhsPazHD+u3/R1r7xGmu//k1ml12szSEXudLF13ksGhjb4yoE0DDmW3HT7QQ2ENZRkejsaxXCJISQujSmMIAGnhGJZaDBJxhINYuBHaCUtsgsREYcCbRRiZrMclvErw5QBxxf0Pql3B7Yb/bsM7b929+2/X/5F7ZwzXzr7l1kI1KAh2RVUpV5xDHVt+eZF6zjlrfb+UK4z33zVdZ12UXFfhiSwS3y4q3sKeSBE/DpuXt//TYkz3UfPgnyRGjfwWH1pw4yFH2jf9llDmQOZA7MBAeKuQgZFQ5RWxdakUgILV6iKeZHknGR1aL5xOcu3gw8VQH1MTlpicKYhN4ovkhGBqLZdrC+jQ9lyMLdL5ptecp2CGh4Ui8B9v1v2LLz1lmfynfLYmFUdP2yRjtyYLsN962wZe98n62++mrruPIqszXrtCmkFliMqZNYmUnuq0bvL+H4+Ki1q4Nu6yDQgqUerT45sH+RytDX7DIHMgdmjgOFaEgypNEMGk9dakwyDhEplCErysZ11EJ56gmPbJlQIOpF8Mjv3fWSfe0bD9qdX/qirxTAauGWW26xK6+80tasWWOLFy/WyeaygZKcGNTeMGwEuXDhQmNjSI7FPBOOuDxtoAEeNbtNGx+1O4W8fORjH/Ws+/7ly/buW27VzvLiGhBRuOKJBRIEf8MHSYQADIk8PYWiqLKjkqDMYebA7OdAY8wkoCHGAy/froTBghhMachMzhQK4prDlJrqmCxeTguJWtE3oD3b7djD/2o7vnG/9T+z1doe/54tXrTKOmX6PyIFjVHcIhBhVF9mjunL0ljfMh13ucCWvv/nbOFNN5qtXiUlrFNLBfTVna83gJE4BXyNanSIO0VUv8quzMtyPJguUwREHAqjoAKfbKKnzBv0mInFFV0Rjg3KEkGAQqWnQxZf6e4oP6rlJqbj0bq0lKJCO2xAtl/HWW552vY/8iPbs3GDjX79Nlu68FwZgczRIR9VHaemTYHUwghgg+hHWMMnAGPxze+08298i7VfeKHZggWyGpFlwzi9oScCHAQcRPfBEtC34TS9aZXiDODQ4kve4oZjiow/GifOP5kDmQOZAzPGgZA+NODyioQJiWQkHwo/n8Aajo9h6O76JRlBG1MKgs+BVFXAfggSj1XIuKSM00uqAzRo6QVg74GHvm3bH/6u7X3maevYvsmWLFxl87Xsr1dWX6NS+vtHq3ao/6D1swzwvItt0eVX2tr3/IzZVdoEec489VWNaF6sKvCpju7QDf20yVSwwk2qPa5boNW8mW5QQXaZA5kDM84Bxn/ZhZ40QayUCYirDOKioaOn6yCLKgnxyJdUoBSSRob8kUOH7WnpfT/URpAPfec79rnPfY5c+8Vf/EV7y1tusEsuudjOPfdcfaNbZF06eQzQoVVgJyAD8dkBNDhHddeEkoUI7s0bn9SpE3faf/jEHyrB7P67vmK33vIu2fjqAg28idPJxA3KiQ8kpSR+B9Dgk4ET0mDxqEovI1Emh5kDZyQH4s/2VDpXpi3FiYYLe4bGkFJsIkGQTgyjgFJLVScaEqKO9MaZriOdshKAKEdkM+xdadOXGNv+vL34wP32zD/+rXXoeMveowdtns4V79QLc82BAhWW0sRu2yPS2fprFTu076gt/q3fsot++iZrv0gvw0sWS1bwxafoGQIXV7Q/jrKnMc+eDXVZkijSb1GsnHRK8ROUq2cRwRGGhx/MQAU/UG45Hz102DLQAK/qZ6QjC4cFDghoaNFu5C1diY3AMVV9T8Muok153ayraOkQtH3IBv71X+3pu75oQ9u3Wseun9g5c+ZbT3uHThJVCY5CEshQg7+a5Qa1n8LBA/us9+ZbbZ02B5p/5RVma9cIbNDGm3TOl0BIeUWJLTvdCl1DLrN/hHtdc5u4CCfE4UU5g8xTdadTtrmNqCvCqfKb01/L66n6Gn16JfknKxN1R/hy6aPcqx1GPyOcrP3mPK5xJ/r7bC6TSpza72T1n6y+yI9wspYmy4u0CCcrd6K0E5Ur55XjJ6ov8qDHnYjHiWLi7ym2E9V7YV1MqvDTduERb/goxzw5LrCUJXecVuR7+0CAlYLvg6OCLuiKG2DOKdWnTWpUYeHZUOeJJ+zZf7nbdj/wVRUftq6je62vp9f6BBz0aEnGmECLQdXbr30c+tXq0Oi4dVz8RnvTr/9ba73uBlnuSQbrJaCmOWxIXWDrmw61KZw6gdBqvt55Iui49AlXdNHjyppw7Ykv8yfqiPDlFD9RmciLcLJ6p8qbKn2yOl5J2lT1T5U+WRsvh3ay8s1pJ6uvOb/5urm+1+v1mXBf9CHcVPHIP5WwPGaDfrI08prS2fCRUyW2bNnim0F++MMfjhrsHe94hy1btsz3YyDOkgmuKwI7OQLzTHGnb9HAQwiJzn3pHeCJDVvsjjvusD/6s8SQ+7/0NQENN+tLmvLRYMNJcDrqI8bGs4wQEviN973nPUM/hI4+K8QhgF0Ip0UV5fKJIP9mDsxODqS/eO7N7QRKN1kaUKSWLmN8+DCaWCKNtSCIMe2hEkn3F3+FfPGRF0agl1opSPI2KGuGw9qf4SdP2RP332fP/9knbOn65dbX3aW9FnT0pejbHBjQ3gWqpyqUQqtZ7dDho7bzuR3W80u/Zhfc8FZboWMvWy/SUWALdO44L85CZsdVliZSHxR6ukLfzCCJH7JfDUc/XMl1ftCiIiS6LEt7k8OyYB8UKLd4XufDksEJkIWACAhNWXvVujhtA5ABywTsIWRCK985KMV2RA3s2Gt7H3jANv5P/7u1L22zeQv6bJEQ7F4ptqOaiHg8FWms/DUANBwbGrV9Tz5jlRt/2s657HJbfeNbrfImfU07d7k6ok7zhcy/kpUmJGd0YnV80aPjJHPLuOJ2PUwp+TdzIHMgc+DV4wAy2EVVCCWaJkGepJC/kY1kZv8ZXuTbJCMJHWRgLsFTkLf9VKvCiJKHR1jLD2qz48NHbFRWuxs/f4cd/fxt1nf5BdYlkB3rs27tpdOpOQtXlUAeUtEhSf8DT26xYVk1XPnf/LYt1FdIW3u+Hz08LtrDAonbtHFkD+KYgjSnKcFD7onEwsdXUqVklzmQOTBDHHBRgowITzvEyZjKkY+LsDleLusNiKAIGdcM+3JzZCVJgiVWi+3cudMefvhh++EPf2h79+61TZs2eZxmcL/3e79nP/dzP2eXXnqpLVmy5GwAGp4pgIY/1O2P2X13fVVLJ24+zqIB5sLoqYRnkS0SsT/AhXoIa+X8aehHLyNsY1Z+xokg/2YOzD4O8GePWIq/+HSdfiPHSZwqxUKIxRiJkFxK4kOn8YHEmyseQjxxHC+p0slGRYxVQwdfgg4BMjxjAz/6sW1++Ht28B/+Pzv3sjU2p0OnHFRHHGiY06lP9irbPzJiI5KqFZmU9msvgt17X7LaJTqOZ80Ftu7dt9riG643O2epKpdtgJS3MaGzCFp/wXclTD90VMsCQnYUPfPuzeRP8GgCX+iY+oMFAPohfYn+QA9MggrbGrMJ94DeCjHOtV/ZL7QLLFBdnNiu7cQEw1StC6IB7X2xVwru09tsx7e+aY9+7E9szjnttmCxgByt8e12cIEupJ3UZbVrgzLbHRjVvg06a7m26Fwb7+mz8973Hlv09rebrTlfD01arUzrvOP0jqUUqbMpSfH6/cBrXcd9QUYSPrvMgcyBzIFXmwMhqghdMHmkIaNCVkW/sDLDogFZjEWDn+ggcNZBBhfWqsABgqiokOBY4WHNgBkaOx/v32v23FZ76ZEN9uR9X7UWnay09MoLrUsIMksdONPHrRJUDfDyuGRzVRZjBzQ/Huros2XX32SrtXSi60aBDRfLek/z2wEdX9euE9l6O31RR5oXhC0LY/Z5lj0cQuCWrX/j3nKYOZA5ML0cQAq4foMYQF9rdoWYqCcX4qKuJJFRTgtCV2J1EQ0UIU2gDhKmYmlhMHrj4MAxbc213w7p9Ilh6XMHDhywPXv22E9+8hP7jpZSfFv7deHe+c532lVXXaUTJ95j1113ne/R4BlnwM/0WDRwI3CHJ6PwscKi4SMfTxYN9971gE6deKe16j3DuanAHUyWj5eFIrWeRWUJZFASAAOOIMAGrmlTIMO4XmAaD4mM7DIHZi8HGDq4ABriKoUph980ahKVD50irRhNRX6Se1ClIZm2B0yCVpQQB+DABUTSpkblGXVtDEJtWDP4nR/Y7nu/Zi9t3WJjTz0kE67zrFumpEMjQ24WyhIKgIbDQ0MONLRX2m1Q0vWghOdRrasaHW6z9f/tB23dzW/Xy/B5OqpCmxZ2d2qtbHpVr4BquDTWSFd0XEc2etf8V316FZzfutrx+aIOvChB/QE7QAbFuuDEywJkEKca5rrK8Y3FKKc4C4BbtfShTZtl6l6AGrBr6JBnorEj/TITe0Ebjm2yZ3/8Q9v22c/anFW9Nmf+EutS+Q4pyK3iK1/q2uUBGo6Jp8Nqo0U8HtYT2v/YZlv2737f1uv0H7vkIpnuymKkFxMzeglf5dFoaRBPshxbQyCfWWUBr2E/MY6/bPxVeWL+yRzIHMgcmBEOIHtwaYZK4gkRVYipeuhEpZ9GOV884TkAskjpMe0ZJAXYT9ipr2NGt3QLB5FIpqbNH3njl3TnNCWd+NP/40fshR/8yHbK9z7yuK14g06b4FQggHGBGWzKRhsVgAsJzpqAjcODQ3ZImz+29i6xubIuO+/nf9a636SNIXUS0CCWfh09+gKptdU0qy60qCm8y2IMziSTwYLT/BB3pfTsMgcyB6adA0gIPOPRFSDCsiPTXTEWY0gSNvuCsh5E5UUDgIfojQ39kbYT0MD5YPu1/PUpHaP70q7d1qH9y1gSwd4LAA6ADRs2bPAlFdR/66232m//9m876NDHUbpniDttoKHMU/RTrjc++pQ2g7zDPvYnf+i3+eW779fSiVt1vrAu4SgOwjLDi6SUQVbUXGQwAeA8uYhzzV+ClG2qxZdydJVd5sDs5QDDp+EQTeHS6IkrQh82pVGVrsvjJY0cSob3oeXXDLNQ8aKkziNXGl/dXTHbusNeuv/r9uKnbtM4PGZdIwdtQW8vRgdanzrsX3vmdnS5zD6ql2AtWdU55loYIPBgQC/Eh2XicGjvYVv1a79ul2NaeoVOoFi9TIrY3IQtSNNqd21Ld4ksUMDX/zTmU9/L9ztTcXic+KKIy7LUF17GUUdDBkEXnpfyhkWGUiHijZ1uo1VWlNAmGwZ9NQuQYVzwQIfXqL0Z9sta5Jvftx2fvduO7d5ho9rJvE2WDO0CcfwrmhTiVimzAA1tUm7HBIAMDI8IJGixLp3pzjFr2zc9bnN/6VftSvG2cpX2alh/vtlirRPGGkX54yN0Rqd8tEtIc4M4dYvtMNgcXY/Knx34CCeApL8wyuCzyxzIHMgcmBkOhJRpljRJvqZ5rzne3JOJdfhCColgVHtAU224qxh1MKvVdCIS120cf4kAlEWeCSy34WOyKtti277+Ldv+0EOSxXtt/sH9tmLJOQIaEJoCLwQy1FQGjAKZXJNsRbRyItCQ5rr+/mGrrl5rq/7N+2zVm6+2ytrzzZYu0RGXXZL4zLLtLn6FOVsbQAPV0hn5cQH7WLsBmWSXOZA5MHMcQBbgXG9LUf8NORJJXCddKA1VLxfDM4gjpFBUTMjYlqAIgIGwrhaKEL1RO3fZi7t22je//g178CsPKP6iXa9jcm+66SZ9J+q1vfv22ubNm+1v//Zv7fnnn7ff+Z3f8eUT7NUwb542nD1D3GkBDcE/QnRngFcYtVFfz+6843b7xEf+WFdmX77ny/aud90q5RhpqQR8CEsY7sz3RDLkmuJxWQ6JR1k9MNrHZyEsJmR3FnDAB43fZ4rFdYQTWcBwOZFvjJwG0EBNCNEAGdI1X2vwjHSNuKqUr/2HbezJZ+2Z+x+0fX/2f1nfecu1qXavdWu88yJc5cuR6mnXEgjGJ0cxYsRa0f4AbM/FOtaj+uJzeMcLNv/m/8LOu/wKW3DTddqd+1K9DC9SCXYrwNxVL9T6rMOOuvSM1/J0T/y+Og4e0DreBQ6hEtW1+oQRvWnQJqsGX4tApgsqCnEhPmKS256WS6BKaptMValpZ0RLJgakbb6wyw589Ru29Q+0ZGJJt3WvXFVUEkqnOCSlFquGigMHAj207pcn19HeJSV3zHa+8KS1XnGjrV5/sa14+9vMrrvGbKX2anAFWU9UyA+2KW2i97XK9FFdG2OjNIAM36ksuk6/4T3t49KvR/NP5kDmQObANHPARabqROogbYAXkK/JA+SmtPqLgYsocj3DA4DpKM8LO3vhUCNULs+VpjOArCpgAV22q00v/SydEPBg+qpoL+7Qsc2P2VMPft0OPvgV65h/ji3U68Dinm5ZlTHDSSIKaBhjiUYhi0cE+g5L/mphhVs2HNj+rB3s6rT5t/6870e08m1v0RIKWZi1ddqgylf1aqH94vVPRg4ADdw4Th0ab0sACbVllzmQOTCTHCh/uKOdJEtC00lSKLUfQEPIkSRLlOcyqAgTqX5LMkmEkKALM6KRRkkiKaI0DncHaHhJS4t/9PAP7Cv33m//8f/+T/bud7/bfvM3f9NPmTh69Kht3brVvvWtb9kFF1zgAMRll11mK1asmD1LJ4KPMAmPcIRRGx/f5Od9fuIjH9UVQMPdAhreJSVWX+cQ3LiwUIDvBe89faqf+hMWccShpayQXhfyU5XN6ZkDs5ADSUXixspCMQ2m8pCK4RLjlZBR2LgOsdlIIdeHloeNlgAZpDJJGeL7vUb7kf029vRztu9Hm2zXw4/Y0H++3RZeuNp6ujlWJ73HVlgeoHJVAQy0xJceXoz5mo5lAy/CQ1UtxdC+ApWlKrtkmS37+fdY703aq2GFrBpEN6IWx7TngCthkiO8usdrtip/1VziiZors6pIDGSaLFzQovz65BMLbIMAGTgmPrZoyQSi0Z/KqO4M3irvmJZMPL/dahs2y1T3ETvw95+1xeecY3P65ohSX84w1dU/uMou51TrbYlfTFL8AxIalPJ7SHWN9i3WMaM9dv6vvN/mv+sd2pBstRRYgb+yhGAZck3AT0UKbwtnttNXPZNxPRMeZAt7OogUB6iS+kqbcTMpL/9mDmQOZA5MFwdCuoSsL0ucJOGSbE0AA3EXTkl+0QkqQBAjqSQva9IV2YuB+tJeODETEiJJdeRyVZvwSuZ1Y8ZFBTpm2J57zo59/7s6zvL7tvup58y2bLKF87RHjurqlQxFvrPZZJKLzHsAv5ysVNPpEwLatYSCY50PDQzYfgnSqo4Znqv11Ff/6i9bx7UCfbWscMhb71J3O/0FQ1v2yIpC9bIETvNDi4zNEh/oa3aZA5kDM8mBJHtceCAF3EXYaLdBBSU+PszVCzhJqieVU7y4RJdiNLO/YAqTyIIOqaJhb8cGj9nuF3fZoxsetS/d9SXbuHGjL42YM2eO79kwoj3PRnXk7jve8Q678cYbbenSpZJ1khko4GeImwaLhqRqIgAx+kJUb3x8o4CG2+3jH/m43+a999wloOEWmd9qjTYarYMMcB+G4ydyo/FaQwbTCU6/8ZQj9PSU5ZmYIWeXOXBWcKCMo6Yb9nFygiGQXhDTMIIMj3CLOLE0mhmBKZUwjUKueb2vubeqNiccOKyTELbZvg2bbM9D37OBZ7dZ5bEnbYGO7uqUaT8vwhgfdMiSgbqH9aUIAdipNWat2iCLtjmBYggLB5mVVpQ2Kjv9wcNDtvhDH7SVN99odsEas3lzJL11hCPnYeoluF1f3RvGrqrEe0j4KjjYgCszDgahaco3kv1pFLwLoIGCZcfXL31XE9CAWSzSs4I1QxXtUg3t22+jP9poL3zlQRt45nlr0Xq8BXN6rUdfxFi+gGKLvK0SulcZ/Wf5BEoykhNFF7NdAJ1hbbhw9Mmttux/+JCte88taTMynVwh6Fudkg2FFGH4qwUYWtGhZ0YhgAZurENKN/eYbiuFLm9Fk13mQOZA5sAMcQAJw7yUXrLTDEVTATQQOtAQWjsFQhBDGE4ykmVgLVrPV5PsYkcc/qXaeavH8k4HL2vpA1B2qwBYTTo6g/mIzmzfbE/ffZftvuf/1eaOS6xP6YvnLfQlExVAXpe/1KH6pdMig7G848jhYbfok3Wf6jsqC7GD+Cd2WMt1F9n1v/s/2ry3yqph6UIb035EQy096hFAg5bG6T7GtWKjJoC9Iouylk4JX5e/9Dm7zIHMgZnkAJpviJGkBdPaRH1nohYeunIxTMvkXqysPBU9DzBAWZBMrD3VwwaQx/r7bffu3W69wN4M7NGALl0ViNmlk8cAHdasWaPTy9daO7raGeZOC2jgXhD7MQWgoCK+A2j42Ef+3G/3vru/qM0gbxEqK6CBL3h1VwjOYLYeLI8qHlc83Hg8FPO4nkaE9aqan1A9I0cyB2YxB2JwlP/+y/HyrQctoXwADyFMIU1F+Y2RmFIYlaSw2MG1uKMHTPb4NvLk47ZNQMN+7X7bfmzYuvuHrFcgQ5usGKoyIaUdgAbKxxKKDq19RRmj5lF9bR9y8/yKTFUFJui4x/17D1rPL9Cn7zsAAEAASURBVL/fVrzlTdZ3xWVm56/SppBzbVxKW40XYYEOOl+huAFpjqmLqu1Vcok9SZmlyYKfHsbLuNLK6iAf0ryf0Mrx+o6ymxIdLpCyPGodOibNhoolEzt3265vP2SbP/NpWyRlc4EsOTrE0zaVY8UDzw+Aocq6YFVe08QD+MCu6j2Y/apDnD5RVTrLVNgccp+WuPT80i/YuuvebL3XXGm2fq0fsybkR31ixw0pyAo7BDfYqBpQu360CCdUcG+44h7SRf7NHMgcyByYQQ4gb5CV8vxLLgkhh3BDjCJwYzKLkDxIgwbMW58JxxSyuAyrhjQTYcDM5ruSn5KXXohAO73bzh12VF8RN993r/V//h6be+lSW6DjKBfICkH7EbulHvsyxN4MWDOERQMyGfkMGIIAHVL8mPq2/9mtNrxujV3xyx+wcySL7SLJ4RXnaIPlHkHNHZrdOiXr1W1hzuMCfFvbVf7Me3/QPWWXOTALOcBwlS9jl3GXZOGmClOufpEfzS7SpgqDnspFUxurSifWKW3ygKLsG4PuzBItrBiwZmBzyB59LCK9Jl2a/bs6Ozut4h/4osLXNjxNoCEJ/iT8MdJlZVsBNPzz7fYnH/2k3919d3/BT51o0c66E4AGAAZ/Wvwg7if6xqNMTHLSFE3zDqV4YOHJiwdYJm5Oh2ayfNKiPGVwzXQpNdEFfdBE2UgP2nJ+xMkr96NcNsoFTVxHvVFHuUzUVU6bqjzpUddk8ShXbifi5J2Ki36UaaPNckh+0JbTy/dzovSoP+jL9RGPskE3W0Luq9kFH0kvx7kO+nJIXB5SBGrZxUj0XK+LH4gBDxTft0fHyzxi+x/6hj336GN25Etft8UXLrM+AQKy8OS9tfjSrlEt+orGOhtzo+clV0gNV8QY+drsUUJ0SF+K9u3ZaS033mzzLr/Elv/UDdZ9pTYvXLSIt2q13a6+StiidY2xcLV4+/U+Rt2vQkh75TbhX5Ona0ECy1xWiYZVCWwQNsqRaH7nfKuTua7sZPk6Zke178X2XWaPP2mPf/tf7bn/9ClbtazPlq5arX0jpQzrGbAcBQ6O6GsZFg1isvuaJiD2auAsd4AGvqjB3XYHGsZs767nbfyy62zR2rW28qdvtLZr36RNN881mZmoPD1Q4ECDrqu6AT92QiEbo9FG/YaK+41rXWaXOZA5kDkw7RyQ2JFQkkfYyBN3hzzC6yJ8AAwRkl6nV5zpAoxaoQ7+dUuytFmv5jV9BGtDllKAANm3Y6eNainw9h/8wLZ/+5tW+e4jtvTyFTZXinwXMlHyekRfFdOyCZYFYgkhyRvdUj7L2ngJ4J9WowlsGLdDe7baQPdiW37dTbZKp090/rSsGi7TBsgdcwQ0cI5Th7XVkMHqB32h33juhXvKLnMgc2DmOMA4w2vMlVe8RrKHpzIOIcSVaYmfyENfL1eTZshuMtKhBSC0C1QoOz/hRvLGnersl+UDZbu7u2cb0ADP4FoCGpJFwwbfo6Fh0QDQUFg08MWu7JDIBVdRiBt+wkqXOt+jqJdSs/UvhXQBF2G6yr+ZA7OXAyGMuMPmv/vydZku4j6ACtZE2gROqQJeesNHGyhiIAZ7dpsJZNh69232wtZtNvTU47Zy6Up95dExXaKlSqlusliQ6b7WvGLhMA+UVeN9SEsoanoBZuijgLGWFYWR0xIGhcj2C2wYXb3aWlYst/Pf915bcIP2atDeBC71BTK45PdFq6xgE3RRvlelvGou2i3zj3jhTwQ0oGyOikPt4ougExUasXbxyV/oD8lU9+Ef2bZ77reXnn7aBp9+1BYIsZ6vL2gdUrb5uBVAzqB4CdDQwXIUTTi+V0PxzJh+2rQcxffEUKewaDikM5mH5y22Ss8cW/Wz4u1NN5qtO89sjjaBlMUEu2BoStMzFF858pKHyI1gRkw3BWT4c8CkgvsMHiiaXeZA5kDmwLRzADmDmwxoQAghg8oeEVW+9rL6QSCCdCPWlD9U07IInfjDUr423ubZ9FHzkk9MsqwTgmD2zDO2Qxut7f7Xb9vIi9usb9/TWuWwyo8UTtYKsmQo5jK3YkAwq098fcSSAQACl4B2LdlQu8OS/QPDQzbMMWzaE2fu1VfbBb/+y9qgV6Bv71yoNe1q6dsoM2nxYhE8oLLsMgcyB2aWA2kYu47DcivECa6eHAkkluPlcap4OQtSEpyEjPCIiHIcOoiQV+El+7AQxmIBh7VCAjT1zix9zzehlbwhTjpWDujWZ4o7TYuGxB/Mz/DtEorw69HNaY+GP/3jP/X7vO/etEdDa0VLJ3x3G9GLKS1SVlGCARcavE5gAxwmvXgs6eHUr7za9CzUoIMNJNF4dpkDZxsHTvR3X5Y1RTysF7xYkeYjjQS8wAFpSImLReDpvGwOyqz/0H5tjrXF9n3327bzi5+xY8cGrVXpi7XZ4JwOds1OjjGdlkZog0MpYHO0rgyMAuCBF1bWsaKA8c+bVYFBKW1H9YXoiEz/j+kr+8p/8wt2CUCDvsDb/AWCanvVR1Wi5RN8geec8tfEOcNKLQefSFLecSi48l1OkSeSEd1/TfszdGgjMbeIxVJkUPteHDmqUya2217tefHCZz4nZh0xVpx1iak9kpedYmCbeAa4iyI7rI3KUHjbNPHAY1/HDNCg58dz4MhLTqKAx+yAfgwgRx3o37XDlvzKv7UL33ZD+pJ27hKzhfM1g4Vyq9K14kaoiWfP34T2fNCFKhbdGTSRqVPZZQ5kDsxaDiBgkZyEOMkmlz8KiePIiuwyKXlBhlCUx1BghE0epbmy/KEDQACQwcspoV+yeN9BG9f59U88+IAd0CkTXT0Vm18bsIXdPYICZC3mMhwwQZqq+oI1AyGy2Ze0qU7ipLCcDbmNCGVfIsAGjh0eemyrtd5wla37r/9L63vLtWbLZV02l2PpNL9VkbnqLPMdZtD6H7eqWHaZA5kDM8kBDTuGnvuiHV0mvJNwiraDviyOIA16r4NrZA3qFCEuZFfEkVWM+WLcA1pWtRQWMAEgoQw01FwvU52FDIqQqnCUQQeMTpSviQNUuAwrrCPK+amGVIenF23U69V12UETjjpxpw00wCOU5hG9nHTq5YBqNz2x2b5wxx328T/6D7oyu+8r9/qpE2zgxne8UX2F4/g11pK0adMyupVEMtSIZWpphM4gXcftpFxoE988XZWk/MZNJor8mzkw2ziQ/tL9A09xaymlIat8FCiRcCrBR1HK4ZFlyDVXtrT2K33ZUYoUpHoFGq8oX8d+9LA9840HbHDr02ZPfd/maHPGHn0556UWhYqRSruMaRQrQnqCzKEtBBFf8jtBZXXNCzMkgI6ckHBQX5L2au3Z/pEB67v+JltzySXaU+A6q1yq/RoW64WYl2HfEVyl1eYYfaTykoBTtTPiEi8TMIrs485w3Af3hselO26kATLgPV/hqJZAcAJPu8ADT6X/h7W55qbHbce3v2P7n3rKhh/dYHNbatpvQRxluYRmpk7RA874BpDiVeJxqli/3nL0w5+EaNMT0fo9EQzLsyHZPoFF7Ze9yVasXW/L3/pm67zqDWarVuiLmlANQBwq4wYppGfrPFe0Jrk9Ln63gphrUnoVWM5NZZc5kDlwlnLANUEEDWBAXeIqKtk2hgySVE2wasEgRJaiDuwqzgXzEHvbIqjRpckHBBjXJghj2mytGznHZrgQgBLv3mv29LN25NFN9twjP7Rjj/5AorFivWPag0h6LuBEyD6qxYcOS70eFwH/MHJgsR8gO87lsECNYaVVh/S7bJXNu+5aW6wlgt1XXW22Zq2sy7TMmJeQ4aoMKyT7uwTSaxNgv2dvzavKP5kDmQMzwgHJlGK8MrZxfllcpKz0ppokSSIIWQB9yATioYWleKoLmVTDakrj3HVnBEXRpo994oVizubfuMle4k+Uzos+ZfABPhCPJRekEWe/B4CLdvbikgvgIYCCSAPQgA6LiuZ6vKB+mtsj/bSAhmCqrzfWHNABU+Qe3fyk3XHb5+2TH/2IX99/75e1dOJdSVmFg3zRBE0GpeV4NX8kiZFeoM5tON3k/ekrbQI5F0xCuHJGSsm/mQOziwPx90/I+GhynqSfyJIwOX5cxDghlA8djpd+NgB0Ey2VIz7A5oTa/poKd79kT3zz6/b0X/+Nze0dtfmdI7awd571SEBJfrmnN9QaVaYWAB1SjO5wfBhAA8oja1wRaJ3aYKumkM2y9urr/s59u6y6aLXN11eeC955i51/tY4BW6EvPr2yaujTVx9CWUmgfKWbpf4ZdjTVYGxd2qDqTu5UIN12SUQpAV5ofwUbEl+P6evZiOThnn229wc/tGfvvNtGd++wzpGDtnhOj83pbNdjGFURgbkADRL0ySxXdRTV+5KJog90MeYs4gA//BNmLYCixQY0WRw6piUUnX2yqOi1hbfcZEuvvcr61q1JVg06es0tR7Q5p2YcatP/Qrjz1Q9+8+yc75PfdU7NHMgcyByYFg7ExALQ4HMItUoGuZBTiBwCbEZOke5eAXK37Lys6oDMnQjYnFzLGGTe4Aq/z3NHJY+37rChzU/Zjk2P2Z7nn7bxnc9Y39xu62lNJ1IANIRcpTmUa9ZRIy1DFvNywLxAj6D3vSCgVUpYNbAR8qj2NWo9Z7n1XniRrXrLW6zj4kvMFixOMthRERXqlCz2k3+orfnG/GbyT+ZA5sC0ccAHqmojZMyFi7Gn0JO5LqeVaAEHyPIkfuRdBkkAIa/Ik+7rQgP9ig86OE+XPKI878dCKAEsceUXf2RO2UVeOZ00rh04UPut0h9dVkkHDLoIORWn4u/jdFN1U33pdkgLgAKwgTjAA22EJz3qo2/Rp2kBGsQSB2DAQohv2PSE3fbZz9pfffLjujL76pfusltuuUW2v/papo45Mo2pcEwcMLR+QwXz/JoHQw1chFc0wAayIKC8h56QfzIHZjcHiiHiY6IuBH3ANO47LuNlsD586oVFqzgCxU1GSRdR0pQS0MCnl6PaXGanNibc8aJV9UK8d9cu+8nmTXbw+1+z5d3ttlgbBvRIEHYg0FQ+5BO14anO4wgpxbmiK3zdQfkaYw8HrY1FyPV2dGmzGx3bI4uFgzIR266NbQ6o0JiWYyy+5A36+r7Olpy/xuYsW2Yt69aarVyhvQUENvAyHPfpbczwDzcQb/LlpuLmSQsGeKgCE651gSWD9qIAuLFt22xw9x47/MI22/PsM3bwsU3WpqND54wLxJFy2d2m89gFztYkMznBA6CB6kDEcem7WUxFib900b34kuI8GwENSh9ViB+Q6ntUz7im/TC6Vp5r8y9cbwvOW2WLL7hAG0Sulgmvjr6Et/yNwV9CXKowRYokT88/mQOZA5kD08kBZCoOYRf6oidI8CCD8QiksGoDRSDJZVWR5/Qq74o/EwpSUNci9XrRSQe0DS7LJXZornv2eTvw1Bbbu/UF2//cszZ47KB1jRzVHjmd1q0y2D34PjlqiBZw9JIlbITJsiFJZZ8VRYQtr2/8q/7yj00hhyVb+zXP9esFY7RTexstO9cWrb/Izll/oc29UBtDavNfA/Tl+GHNAd5/5HA0SsPZZQ5kDkw/BxjIyJAIyy34+6YS4r2TvEgr002IqyKJHtenGMCyinLTKurn2oFSXaBzuz4uYul5MjEQbQFATKjv1C/KgABLLgIw4MQKLBlIY/PIAAWi5qDjOoCEiBMCMuCpH0c9+MnctAENNCXM1Xm5ccMTdvs//IP92V99wrRXvH3uP3/W3nXjTfRE65C1ozqCnnXgfNEjjuDEp6dKZKLzPP9RehG68luQ1R+yHlKQTazh1K9UhddBiDtZfUGfqCf+nihvIuUru5qq/ub05utya+W8qeJl+pcbjzqbw3I95OEm4/WJyqVSx/9GmXJOtFFOe13HxSy/p8mYprRILr+Ae1qJEf7yqGusFxBu9fpEyDXWDIdkzr9tu+2T8jWi+OGXXrLdO5+36osb7JyFy7X5Y5d1avyhSPGKm15+Ua8ao5l4w5SUroleChcnI9QkA46NyGJCaV3t2uywXSeIC2wYUEX79LX/gL68D/Xr3OB5K23eEp1qse4C6125ys659BJrXXu+XoYDaCgaVDCjDh66109Z6QuFOMCGZgZQqJ6mCAi2zGZt507bt+UZOyKQ4ajCgX27rXb4JevpaNPO5h3Wp+UqOkJdYMywrBiqfjIHoIxzUdVQJS5ghkiJLpLncbgumYmcZsNHzIQH9Hz364z4fj3mMV33XvlGW3jBOjv3ssvE57XaE0N7NngL9L3oPxWSlir1q/yTOZA5kDkw7RxAxiDgXKYi7OQDXXXZKwKAhogT1gVTxItecQlt6J186II+6tQ+Q3Yo7Y9z4IknbY/2Zji6Z5eNHNxlFQGune0Vt8DDEo+5jpDlEN48XZRHtqKcp3+6UIwTlzzXQZLYl0g1qDyHWgxKUe/X/Dsw2K8tGTT/LV5u89ZeYEuveKMtWLdORw9rqeDcOdKdJbNphfvAEVJ1dpkDmQMzwwF0nthsqz7wijEY49rHYwzEIi8u6ZXLGIUhw5BfpPnyX+lxxPFykkiuJ7frpd/69JGH/chimbCAiPQJqVy5F5vyJ4CDABpiyUMUAGjAk97B/mlYqRaubJUQaWWwgTTqDc815ctLKqCnbtxpAQ1UwG2DzvJe0lX087FHnrAvfvYf7dN/9RfWqwf0V5/8pL3jmmttXDueH9q5w8GGir5itvAlU8pzh/jcIYndKmGcXlH0WlKAB9Qfz9qfO40WVKH71sMseZ07+We2cyAJJu6y/k0l5I+ykkBKPIh4jBFyIWXjKk4q8PX+MtuvybdI6WlXZhdoK05f3Me0OeGePfraLl9BsIwM2bF+gQ/DR2xue6tbM6B4FWqQCqURTIijLfcStIANKW3cd+1muQUWDUeG+/XFXgKPfR60E3d7W5do034NQxIsbGLI7txtsngY7+y2sc4uW7BmjZ2zaqW1yUpqUErjmO5Hwszrn8kf56duzSWV81r3l25VzYoP6gL9qCjE1/dlKPjBNb5NflRrg/eLr/sENowePWJjx47q3PRBa6sO+74MvUKye+Q5231gdFC4z7AsGthQE1Q6LYUIPpc4Xdx+PHku01c0Nake8ny0x4LAiiGh54c10bBUpaYJrWXufOuQUj3n3BXWt/wc62SyU/stAiG4l3E9CxBwlOd2Kd6tktlpQqLm7DIHMgcyB6afA8wtFc0BSaYmWYPMZZldVR79EPmfZDLyLXzqCyWQVa5IS8cE3GauQ662KWwDaBfQUD181I7tP2BH9++zwYMHtKpC+qk27O30L4pp810kKHMmu5ElsEF1K44rz2/Q8U+k3htA4pqWabAxbxvyFLBYcQ63wLKBTZAHdVNVzXMtvX3WvmiJdS1abN2yaOjum2MdAp3b6Ifqm+wlwDuQfzIHMgemhQOM6VaNzTaNyYo8r6PIkSRrpMu6zEljPN5VadgljwiRCNQRm51zopqq05CvuNwalY44IuFQE4A4LOIh6cFj+qhU0Qboy9ZdYH3al8wWLVWjGvOt6Uh39iKj/lN1AQwABlS1RCOAgHJ50sMqgfyyxUMdmFCTbETp7xrcmFyAGCGLynlRZ9Tn9CI89Z57ExN/KOzWHop0JvDCnhDQcM/n/sm+pF3Tx47usz/+gz+wGy+/0o7s3m1bH/mx2YED1iVh3yVB3q2XBM6qaNOT1CPQA9UNadMzfy1RmscIiwfLw2KSod16yGMt0ib2Ll9lDsxWDoTI0R9+4Rqx41PKg5whj8Br57hJhWMj2hBL1gPjCivK6xGS2iEkclwnSQwfOWiHDuj4yqND1iWFp717no3xUgoKqjdmxi3D3r/cMCCbRKGPU6WmMPUC8cymkb5Hg+LsP1CVoKVfKIl8M0KoItoAHEgTIuHr2YaP7LWB7erL+iXWt2qttcicdVgyAzlx/P0HH6YzTK2k1tIz8OlGyT7h6B7AW+Fjm7rsvBFfnDVK5+4AGipSLMcFNPTLgmHgpZ1WEXjd1iP7LymaFTYbk2LcoeUV+FbJw5HqkBRVHYHZyjPTYWwSiGMFv9PdlTlNQ4nXKU+/9MEvCLmHdNRaVRMgnolwRBYWw4cPWq27zyqLFlnrvHk6BrNHoIKUXJUfExg1MtDvz7qjGwRcdYn36el65fkncyBzIHNgWjmADHWZidwsxBoBh+LgkWeADqQBOBBiMeCh4u40X7UIWHfJJ92zIt+qOYcPXq3y40cP2di+bVaTYcOYVipY53ztidsrWSyAWwp4VfLcXwZUqUSzAw0sF6RGlgFOdMwIuOiBrpmjsKKQY9NjP3JYITMCEjSdpqllhAKahwcP2shRgf8ib1s23zplOdipYy/bNdfRVJK5imSXOZA5MCMcaJW+A8DQJj0L3cfHnVqqSa+q6mLM30l5L9XYLnQtJA5RZJV/bNL49uPFVZojcH0TbVkOIKsG9cFmSMRVWa4OqkD/qPS7bul2Wo588dtvtdVv+yntR7ZaLWqdgD68ATbUVF9qKkmXk914WBMc1ibjL+58EVGjlVgLfMNHgIIe6XZdskjGAQ74e4HSw1EeurBaIA54ECBD0LneLvlIOj6AizpQIcLTsmiI20V8Ei++g9qTGzbb/bf9s933z1+w4V077MO/89/bDZdcZi9u22qPPHC/tezfYwv00rJQCM48hL+Q3nG9bIAwI8RDQCOuEcJsskMbxB14KOIKvN0Ga0iZeEVKw9FL8iNs5DRicVeRMlV9UUeEQV8OT5QH3Ynyy3nEcc19KdMkilRnM20zXfn6VOJR94nCcj1BR1rZlXlfpg+68v1F/snCqD/ouC7H4zroZkOYlKjmOwvuRcjfSyPe+Muoc0BCAcsGXzsqniEgEJCchOAmoryASiEblPIzJkHUrbHKHgHUCbCAipRMQxt/mdEiTwBH2IinWOqT4ryMA1ioTqleUsT0sivlb1j94OWXWnmpblc+FJRjB+4B7VcwqlMnWiQkayoPOvxqAw3pHhrKrE88xZ2iPmIZwmo1eg0td5PuiDTymMgk4XQvLYCuBc9ruv9Ref4xq7QLCKpoIgJkYI0yFg2tsmjQvCWAl5pTTxot0AouwkZOotVTU1ZVFVRkQYL1Cl/Y6CXWI4cEfhxRv4bE8zEABuVzBGeHaMf1XIallCOF+RvJFg3O6PyTOZA5MIMcQDYhMwFwQ+Ih3ZI+mOQb8ZTWkMlcu4hUyP4JnI7GF8Zuya5OKZoun5U2JrnWyhc/yeE20bmlgvJRmqnXy1K+iNMfaDo8TDKe/nnn6nIX7RXnvfB5Ft0WQLcK4KBkvy/NX1jyMQ9jpTaC7JUcHlCIhUNNcnicOUB9Zq5MLtVcXOQgcyBzYFo5EPImWS4luYMsSO+heiWvx0mLcY5sYoSGR1NOp7Bp3Gs8ozHzos4OMSyZGlKBqnTqQeUckV41gpnryB67+lf+O7v0Xe80O3+dhJ6+QHUI+XSrhqQDv9xb3bp1q331q1/VyfSDdvHFF9ucOfpgKGvilStX2rnnnluvDkAhTp/AssHln9IAIfCkUY70suO9Ac+94XHNoMVpAw0wOUAA9mjAbd74qH31ttvt3tvvtNFdO+2Pfuf3BDRcaluffcYe+tR/tHEBDYuXzLElfQu0xrtbyLJEeHVQE4m+jvo9pIfHLw8IoAFE2R+0h0wyaln//eHqxnmoxCeywLuTfzIHZhUHGHOMDcIYB9wg4z9ebBkNaSxECAUupSIIqvpKTk2dnXP05UZIq15wWcqALQGKTxvrw6TksDQKM08AgXZXjFSH/sfu2l6rrhGw5dbom+MFEBQu9TGVxYSVr0U9vNBK2YJ+VAJrEMEmAcdXKUxNedn2F3MaRR7ID+sFfUh7O2ANgfzxLkYjMxwGbwnVFe+3OOdyiqbhgyur3v/EEfqHxwGqsKGjm9CK7+16mce6AyV4RCa2w+ILIfxok4LJfhZ6y1drKMp6JuJJTeerY9EQdXrFDnczneFSTrSbNiojh7rUZ/G3XTzvBkgQ0MDfDefCHxPS3i/LlgF5juBkiQX875CnNF/lxmVOnL6qoYZnlzmQOZA5MLMccJkr+ZhkW2rLJZx+0kyYJJ5/XVN2mh9TDhKxVtPGZ5ovAEc7ZKngewFJpqX5kuUQ6ci2tKxBqWoLkAK9E7moxRZpmYbqpg9IQ/TdNtHFi0XSXZVYuOgDl2nJhPqhCdHnWdVLPVg2kMd824Jcpz15llOMCHBwWs0HfIgblzzGlXngCfkncyBzYFo5gKRBH2N8u+ajsPEuqnGpMcp7adKBaZrxTBn0u1SWcpRnvELHyK1Ip2YFwDHpr4PUoRf3AX1AOjx4xEbb9dFpq9l1f/q/2Pr3vddszXoJGIEM7ezVoOUTKv9Kxv5TOir9M5/5jD3++ON27bXX6rA2yT9ZVqxbt87Wrl1r82S5yjXpWDiEJYQ67C72cgBk6JQVNA79ERkZYERcN5cNUOK0gQZUTcQfyj4WDbB/88YN9hUBDXf90+es9aW99vH/+X+zt112uW0DaPi8llM8/pgtWH++LVDH58mqQSu19TDxxwMNvixD6bzsoHjTTgAbMB38RKq3K/bpRUcX2WUOzGIOILLwjD3GBDKMsaBxXyhOjdDTSwKKctABAPDlnLK+V4NefHm5968tTpOsHXiV5SsML5Z1oaF8lDDqQJmKNjhFAiGbFKz0JYg+epsKXSEraFjzNSoFCiWrG6BBSh909MEVO4VeVh2kfv+CJNouvZADeCAPBgRIjKoe6F4tF6I+KaipVZ6B1ECffLgHFE5kIX1OSuhEQID7gZdMRCiYDurwUOQAGYakVPJssJIAiAGUYEmZQ61qAI4U0lLPJeqmZeV4NYln6enwHJDRlMbRp9R2u2JMpvTD+6IQZRfaIT1cQhz0fjwbtPI8X0AIJtv0dJ0s/2QOZA5kDswIB5IkSjKSBiLWHIZEihCpiQOAcPmnkH/IXGRZu+Qdy9SI8495roqPeUjxBGRrCZ/kMC1To+ud1FHEecGgPBQhd5O8pbUENCTZqVzVTX/cFUGaW5G/ybsgV99izmVpIfMlLu7ZL/JP5kDmwLRzIMYyYxb9B8d4BngEYHBfyJQ0whv6bdKnGPPIB2pK8ocxDZCJPDkm3RWgYVQyZUD1HZEl6Zje4dsGd9qbf/v/tOXvfY/ZeWs12JXYLrBBlqx8eHslY/+JJ56wv//7v7e//Mu/tKuvvtoGBgbcOuES7QNxwQUXOOCwatUqu/DCC23FCp3kVvQ3ZA9WDsPqHxYNABHj0g3d+hm5Kf00XMg0QAfigA4BPEwL0ICSjQhkgxximzdtsAdkzXDPF75gg889b//+Q78vi4ZL7MUXXrBH7r3bWna9YPMWLrY56mivvAMN6lgDaEiimgfLRJEU5fSgHWSQcCYPpiPgXaknTtoreRIql13mwOuFA+gmGi4+NhgHXPNnj0coujqkgZDUItInDgovL1q+rrD3CSaZCJURWROg0Liyo3HpoegSvcSpBAgO4cKrrK/tpyNFmwnNhSKJ3vRym+Kpb4zRojcqBlARL9m0VVP9dZSYgUy/vDq9cKtOliNwUgVf1+nJECZbRVuQvRqO3qd7Sfzl7ukLgAdykGt2lQDJRi758/Cb0AUOhKbuUtzr437leHlH0eW5QKrH4A8WBRgBDkAzNq4nK2uEcXwZaPDlLPQgAQ30hit4hHyG31z7/hia8HgWYrqWrole7QL2dAo5p1HMjJ23PF/5+j3JwoJJhCUuDjTQP6+VMLvMgcyBzIHp5QDSzMWQqkV+4RA7zG/IRxdBnob+mSh8XlQU/ZEUn0704s4VSrIrwkpPlmXJokCX/iIByDsiOetWtCrIfkQuUb3q1Jq3LfroQ1xHXyClrSSJ/YK7EH1YL6SZhH74V1G159RqL8BnnyuLeQG5X5MXUeOGFc0ucyBzYPo5EHpe0lfTqEamoNPWrRkUdznjg7KQBaKhDLowYcgF5JdbL0l35h12QEuAjwEeanyzTOqYNuXWUWPWOXbQrvr1f2fnv/OdOmJ8jSoBaOi2EellVeln6Gmn6mIpA5YMn/70p+1v/uZvjisK0PC2t73NLrroIgN4WM1R54AJamfhwoXu0TsBGrBmII/r8hKL5kopi0enn3agYVQsbxdrUWufeHyjPXDnF+2B++6zXY9usg/90gfsmvXr7eDuXfbcDx629iOHba463Ckz3HYJz4rCisL6Q9GDS4I6KfE8TAcYFBKXalw8Wh5mAhp4EYnySqrnE88uc2A2cABxF2KGEZLGAneGOsO/JOwa8ZRGOi6UnhhbCILYNIsvOMMsQ1CYBBTgA6VUh0IEh6OYuujwdVqVBEyAAiotxh4qGQ61KTwpVIXnxZvQTUn1ooyjTZZMjOiLDUKcfrXJ0qlDHhDE1TzRtEg4txBSSDRlOZBaJWNmXdyDAwhqinZ5Dsg/bSnm/+gBVlZ47tDvWQW93/rVLXoZt9aS3AMAIM8RcE0mLJdwAa1E57vkIwAQzwj+cN8tWkLRAtBQ1NroiSoq+hG9wTIi5KeyHFDoZq8HxWs6x70mdJ3JgyUS3TpelLYBO2jb61KcY+Fc1jrIhEKuflOZU3iQfzIHMgcyB6aNA8jEJIEa0i3kXWO+iTmv0WwhtVzGIqMQYyyZaJfZMjKR9cbIOzJoA3lHGeQ44IJ/tVQGG0u2an8a9qmBdozTKURImeSjN/ShEVd2vT4o+cfcyTzHEkEsCH2JBsCH6q3V5XsBmysNWZtmR3qm+kUbSntK8eT8kzmQOTADHEjjG7026auMbmQJIxSPrOA6dCC6QJkYt5Qjjpyq16UxHEsnBqTrDkgmDGsws0fDkJZSVOb0ainruF32M++3q/Xy37LqPC2n6LL+jm4bxhJCcurlAA0sh8A/+eST9nd/93fuXeeXfMEBHLA55BIdoTtfR5nPnTvX929YtmyZYeHAMgvAB5ZMsHyCpRX4AB6QR1g54JBjXIeM8sTSz2lbNNBlvpbhaRL18/GNG+2+O+6w+//lLvvuY0/Yb115hV0mxOTYvn320te/4V/6ZAxSV8bjYUSoLLG+4dNDTRMB6VwT8gAp01DoU5qSssscmNUcKI8DbjQJs0YYY4n0sotxRVrk8RI6Ku+bXSnkS3xSchSRg45y0BFnlRYh9Ix7HNfRJtfRP8ZqOGjCU3/s6RJtDyuNNqiHPtAOYdQbfSfEkY6L63Q1s7/l/tN+3Cf3gOd+gxfIJWjCkx73AF3wnRAXPOGeoyx58Ji6CcuyL+pT8gQX/Ci3RTk8jvrZa5h+US9t4GlfJzgXcnwiX6kLevpFPOqLtpSUXeZA5kDmwLRzoCzHQt4g+5BXyKSQt9FwmT7kJbTIPVzI0XSVfkOekRf51FMuxzVthY++KMkd6bhoP+hJo05kLH1g3iOkbtoty/iokzx8uNBxo+5Iz2HmQObA9HMgxnJ5rDP2YryGXCEMBy3yiHFLWPa6rF9Tht3RdMCN61/ovXjKoZet/eWftQuvusZ6zllu/frwMyg/oo9AfoS78k/m0gciyZkCaGAzyLvuusu+8pWv1IuSB3gwlbv55psND9AALXWuXbPWVp+32gEKwIYAFsjDA2KUfbnu0wYaqAzG4WHUqKTmYwIa7vnCnfagLBoe2qDjLOWulh+Q3yuP0ESh5UUiFOp4oEqquxCq5ZBMrsNFuQgjPYeZA7OdA1ONA+6b8YCLsDyGyuUiPZQd6BF2jMsY12V68kMBmixf2e6iTDmMOATUgQ9FkbpCySOkHZSxULDiPpRUV+SI48r1ppSZ+6Uf5cmEtuk7IhtPHFemiThlocdHnDK87OOCDkU0lFHSqZOJKaYFyjZ7JbmL/vA8Kcd1uChDO/A1+gAtfYC2/EzIhzbKRagkV46hL9dPenaZA5kDmQPTyYGQM2V5FrKSEF920EMbnryQZSHPkHPIQBx0yMDwzD8B7Crq9SOPy8A36SFjo53oZzksy0ziMe8Rx0XbtEkcBw3tlWV0cxvQZZc5kDkwcxyIsUsYY7o8DomTHq4sW2Kso8MSj7ENLXHGO/IDRwjQEB99Fii+eMVia5u3UGBEq6wZtNxCPtlMlVsU4QkcQADWyceOHbPt27d7CDlpgAdYWQXYAEAQjlMp8Bx/SQjdmjVr7IMf/KC99a1vtUU6+jxOpaBMAA1uJaZr6qLtcp2nBTR4IzTkjUkgq6/qkz3/rI6x/OEP7Llnn7G9e1+yfQf329DosPV2d9nc3h7tcI8o1UYYMhcZ4bg0FWQdHI/EmenrmDE+UZrWI2O80kJYpHPGqS9gVjCmM+Zr8mOtMmiRiTD/ssscmN0cSCIH0dAqJBFHHJcudaX/Pi49NS01YpmELzxQngs7DVbGC4InLVHwEafhKDqZl/b3H/MjDft651h3h9QsBjfr+WUCxviTNX1a9w+9xl7yqtn3ChDiCcpZjEf2HOD8YFBZOsd45qwDpKybsiq5RXIBU1WO8hKl+qAFCVrOMcZu2xrjrTpEvUWL32ifhVJjOnkB+UB9yfvNzvgP8odlB626qRBFVSVU1b/gQ3BfrHA+cB4zZXgGnB8xqKUHTC6tMkvDnI6KKqLRggbrqmmDzFHtSSGvm/Q6Rzu1AWa7yrKkQc+gVesuJPK05Iw71w++kH+1VslENcaZzzg41KbH4n1WEks1MOMdZ4mG2q9JHA/KfHdMvG3TBKQGdAiQTgHS8+uSaRwbUda0IRBtcAoFp16kEy+C97SSXeZA5kDmwPRzwKWY5CYyPznmD10pozH/FZpfIQOZb1wfZGoonEsrJj7tS1OR/Kwgc1Wnm/tK/2RuqomevXZGCL2AZKKKdFfHrHukZh2UlTxEziPzXc4qTHHmOKQkjVI46aytmqPax7VcQq3V1G5VSzA4WYL5kMXcLW2iDeEsOdyi+a5dk2uHaNg3B5mr/ejdzHrM5z7qzy5zIHNgZjnAOJNPAsj1q9SeEnwIeoaSUuh6WGRBqPHr+jL6or94J5ngy6DQoSSLqlq6ymbrUmql30pCSP8aGB6xF/cfsGNDA9bXwVG8IpTMQScuGqb2k7oAAI4ePWq7d++2Xbt2Safvd30boAFgAN17KsdyCpZPsLyCYzDf+9732jXXXONLLChTBhNoC9ds2eCJ+jltoIGKYBjel2uovX37DtuObdtsn0CGI4cPyh+ykeFB6+7uFELSqzUfKKsCGmS6MSwAws0t9LKTmMiDTQ/ErxXnpSQBDgXYQJr+cWvjBciQgAYpzyjXfs/+l0D3JnEQTJUfeV7JKdBNUv1rnhT3EB1pvo50wnLeVPEy/cuJU1/ZwfNyG5F3orTIi/BEZV5OXtQXIWUjTlh28bcS6XEdNFGO63K8Ob9cPuLQRH1RtjkvrhP/ghpli3gIuEY9qU5egnH+uq94hNAhsqpaJ4ZgqGjgAjZ4XWoKoIEXy2NH+h1omN/XZz1azzXOGlVecrXOlBdjcAfpXYqqBxp3AH7jCEXVTp9S/+g7L+CiZxhDi+CV0sWeLuNSvEZGBCbQuORChX0ZBDbQw3GBkOM6lqwmsMGBBml8DjQASo7LlExAw7jLCu+5KsAFr4iTHtfNcfJxkZ+uUpmIR9igQe4gYgAZHPAUCXhHHWiQwgj4Sb3eK/EXHdJf9LlJ3T+ifUCKJFYKrRL4bTrikm60qaIO8aRbjOoaGbcOAQ3jYjLKbLVLyqaABvZpYIKoqBKUZep2XquCBLQWIAP9IJN+KKioSwmUELWeYQANTGxVPc5BjqyUwtuh56xGbXRAWLra7urQ2RS6h6qOuxSzEyjiQENalVzcpbejHznuGkfbES9fT5ZOPg568stuqjqaaaLe5hC65jqby8Z1lI3r5rCcH/GpwnJZaHCT3QvpUQfxcJF2orKT0ZTTmtuj7nIa11F/5DVfB02Ui/zydcShxUUf0tXE31PJOxlNuUbaLtMTDxf9irTydcSDNuooh+Q11x/0hJPRRjphlCWOi+sIU2r6Ja3ZleuPfNLKLtLLaZPFy3WRP1m5qJu8chz6uCYeZSOtmZ70SCvTNsfL5akXVy7LtcroP3JG0i5d8ysy5FICs6GjHDLQf10WBthALi271ijZNy7QoAWgAZnITCQFnw9dY/rghWx2oEFNATS0KI2jlbtlqts1XHWgoVVKP7K2DDTUJFyrovc2i9a8RSWgv7b7bKc5S8clATSwkTKgBCADfhzhLHnN0cEtmpcBGti/jH0lAmgY0c0xjyY+cFfcM467m8pBQ/5kIWWa80gLV643ypN3KvEy3VT0ZZoTxcnDleuZ7HqyNMrgyvfCdbmu5jj5ZddcNvLK5SKtHJ4oP/IIy665rRPRlfMoV66rfB11Bj3tRby5zMvJm6oN6sBFu8SjnXIa6eGiP1xHPMqQ1txW5EV9k11HmQipB1cuE3kRlvNd2/MSCBZ/t/SrJGdSP1O7jEovKZnkoX6QUWNstq2w4oAhkkTjXfoTns3YR4ZHpYeLRvkVjhqX/lWV3tXff1T5o9bRhW6uviFz6jz0Jqb8odf+X7KCd+sdO3bY9773PXvwwQddd+TUCZzr/Oijk4AN7NHw/ve/397whjc40MD+Deu1zyKbRQIwDA0NeRiWDWG9EGFz56YFaIC34oMaVvW6x8EBncV+9IgNDw3qJWLYhgYHPOzsbNeulZ3pXHjRpQ1y9LZCMQl0V1r9OSkzJhU9YGdc8UKBVUO6hpmwHoUawZ+Ev1eWfzIHZjUHfJC4AAsB17hdjQuyGUJyBWWKu5LiUU9PZwBLbUHwYVVUFCA2LkVnVAAACGKXrBnaAQJBE1WhCxONR8a8/rvTsJQShAAmJamFCNjoQcrXlfqgHx/D+pbkFfDi7PXQB98I0XugPFlc6OuOn3ahetXNQixQByADPSYRR/xVcNy/mkEO8R7PLfq96SIBKUkmOZEEY6JVL0WIp7R/NVPeqDJbXMllN2HlSynFyqNdYbsU0jZEo3hD/W7NoAlHdgfOs1ZNUmA6Xj/VIoTr8hCwg355g6IpaCHxEspXnGcBv2viO5tZjit0azM92DEBTSjxfiKJwjE9C6rj7wSeF09Yca6zyxzIHMgcmBkONKRYyJo0P7jMRO7VHQJKco1/HpZlYJJ9gN/+lRHZKZ9OeNAcwtwo7yJXFVdV3i0OFDLDdAiY6BBA0CbBSR3I1mS9puok2JG3ce1znHdE/dQ8RW+xaoj5zk978l6qTZqWTCWkQ36MtORvRXLfNzhHRvNSojrwmgW9r/VbzpHMgcyBmeMAgoQxF07jPkmcJrlTp0o6X0HksoairmO7Aos0YAyjP0tnFMiAHs41L/BYNSADWNKgT0a+MTjF3FLAZQa9KfVHdUePSCce+a2uS7fali1b7Pbbb7fPfOYzdOU4t27dOrvsssvsiiuusPPPP9/mzZvnp0tgyYDnpAk2hIwNI6mA/vEeEGAFaVOBDJ6nGyhzjLTTdpiHjQmVUT9ckFdlAo3nTHg6nPgUj6vcfDCwCCc8ZKXpOuUEHV1NQj+Uaq6ncuRQMsLJ6CIvwsloSIv8CCejO1HeZPQvN22y+knDTeTQxOtEcfxvub5y/HjKU0+Zqp5yOnFcuc8p5dT4HLQRluuOtKnCyWgnS5uq/MtJL9dLHDfVPafcRn65bOR5+aionFiKe7Y3kloqk5fjdMQpisRGPAmuuEaUJUqlOK1SPFOXaH5ySdilGFlFlU6X4mkcp6VQ0CWaojq/Tu2oJRdPynEBX9TklfIT3otM+IGSXFzEI0ypx/+eKJ88nNdZyCHikR4v9n6diJzeyygR1tT7I8EoHVeKKbxLqXDEaUTnJr0gARAVDkU0tZESAC3qdSruPVFVPul4qDgEhYu6630gnfYVRL2EOFJT3SmMu2yUjViEiaJxdfx1qvn430YPE3/imrqIl+tsLj1ZfqRF2FymfH0qNGX6yeKnWseJ6E6WR7sn4gP5UUdzSF64yItrQtJw5fono0tUp/97orojL8LJWiMvXPS5TB/5kRe0ZZpIK4eRH2E5b6p40EYYdFzj6EPE45oQF2Uiv7m/ZRovUPxEuXLay4mfrHzkR9hc94nSoY17nux+muvieqr6gpb8iX+djZqTeGN+Kjv0wXTtZUuZIQ6TbBONCBrZSRZSlnJJNqc4GEBF8hjg1RFar74oXFTq5ShbVOizJhV5CyHbyUwEKYs2o78pjPnOrTW8J96YYqJ1eV00kJJP+ks7lIhwsgKR5316GfST1VVOK9c7Va+DplyO+FTpzXQnu56qnlNJn4om2jzd/KgnwldaX5SLMOo7UVimLccnKzNZ/mRp5bInyy/TEp+MnjTcZH87J8pLpSb+TlV/c90NuuacVF+026h9Ykq5lMcnZKdcdL16ciEw/LrQBSe2pJwQXI1GTykGCIDj1IlPfepT9td//dcOBpRf+bFc4HjLSy+91K6//nrf/BFwAUuFKB+NlZdKRNqphpJrpw80UAPVgGjAq1Rjuo6O8FUy7cMQKTnMHMgcyBzIHMgcyBzIHMgcyBzIHMgcyBzIHMgcmE4OYNEA0PAXf/EXDiicc845tnLlSrv88stt7dq1tnjxYt/0cfny5bZ06dJkDDCdHVBd0wI0uOmzgAQ3/ZD5B6ADaQAPpE2VH/fCGceYh2WXOZA5kDmQOZA5kDmQOZA5kDmQOZA5kDmQOZA58Mo5gEXDbbfdZt/85jftvPPO840d2Wvhqquusosvvtg3d+QdPZZBlN/fm60aXmkvZgxoaFg4yExEwEP5ml02x7QOBUOSZAUB0JDMSl7pjeRymQOZA5kDmQOZA5kDmQOZA5kDM8UBzJyztjpT3M31Zg5kDkwnB9gMcsOGDX685ZIlS9x6obe315YtW+bWDM1tATqEw1BgOty0AA3NQAIdIw03GYDAjQTwMF034o3ln8yBzIHMgcyBzIHMgcyBzIHMgcyBzIHMgcyBs5gDHG+5Z88e40hLlkkQ4nj35v2cd/EyuEDadL+XTxvQQMfLoEIACaTjAniIONdxo06QfzIHMgcyBzIHMgcyBzIHMgcyBzIHMgcyBzIHMgdOiwOjOkFseHjY917o5PjywgEukMd7O5s/NjvSy+/0zfkv53pagIbjGmRzSP1TN93GDFAhfNDGTUzXjUS9OcwcyBzIHMgcyBzIHMgcyBzIHMgcyBzIHMgcOBs5UP7AH+/aAAzValVHVKbTIDkJsqenZ4J1AzQYApxRezTEA4ybijBujPwAGtifQUBJ3ZqhTBP15DBzIHMgcyBzIHMgcyBzIHMgcyBzIHMgcyBzIHPg1DkQ79xRgmsAhEiPONYMsZwCWtLxvJufcUBDdJ4QVwYQIl6+gVg2EXleKP9kDmQOZA5kDmQOZA5kDmQOZA5kDmQOZA5kDmQOvGwO+Du5PuzXxmo29v+zdx5wlhTV/q+dnV1gyUEWJKMiKmAgJwUUUDE9MXxUzL6nTxE/6lP/z/fMYkJRMYKiz4yKiqiAJBUUCSoiggSJEgVBJO7uzOz/fE/f352a3jsb2Ls7M7u/2u3p7gqnqr7dfbrq3Krq4TAexAcY8KPPrdEK+tJELVx9eWYkTIsvQvbD9WXqhApGgVQRGRB0rjCdE84xjmPFTw//MQETMAETMAETMAETMAETMAETMAETWHwCnSUMNHKB/nbd/9aP/fgRBye/fvfH+25ooLAyHLQrRljthofD0hIVlHWlDvOxCZiACZiACZiACZiACZiACZiACZhAfwjQP1cfnH44/Xb1xdn30/XN0FAXStYQKsHG8Ixecz0WFV7L9LEJmIAJmIAJmIAJmIAJmIAJmIAJmMA4BDojGjAo4DQAQLE1EGB+TK/gHw4DA0YHDQDo9TUKpV+SfV8MDeNlOHfu3FjZcu4Cn9VQfFa+pELjGSIUz3sTMAETMAETMAETMAETMAETMAETMIHxCWBIoH8tg4JGLCxqtII+h4mRgc9hauDA+DktOmSZGRqo4AMPPJDf79Sqllp4QqMbMDRoxEO/LCeLrrJjmIAJmIAJmIAJmIAJmIAJmIAJmMAKRqAzooE+Nv1xDAb0s9uGA8LYMECwEZ++Ocd8+rIfbpkYGhiKMW9oXrn77rvLPffck4VdffXV8xMaVFSVtaGhH5fQMkzABEzABEzABEzABEzABEzABEygIYARIQ0H0wbK9MHpCxgaMCwwikGGBQwR9XSLfnBcJoYGCkbBr7jiinLppZeWLbbYomyzzTZl1qxZ3bUa2hWhctrqihGvbYHphncsNoAk3vSBgNj5HAfndR61jAgK1wwrwR/AcqSRhUdh7Ov0ilvv6/wG4oLGl0HGdXW5xo20iIC6jBohsogkDjYBEzABEzABEzABEzABEzABE1jBCahPq/5su7rLoy+5TAwNVOzee+8tv/jFL8rRRx9dXvCCF5T999+/rL/++jmqQUMz6CAzumE8EN0OOb32Hh13pUMex8hSp1th7PGrDQUKAzCOcBkbCMNIQhhpCGunb18ozklHOXQx6/zq+MRjw9V5ct5Oo3htf+KKoaxQ+NmZgAmYgAmYgAmYgAmYgAmYgAmYwEQT6IuhgU45nWI65Li77rqrXHvtteWMM84ob3/728uhhx5aDjzwwLLDDjuUTTbZJONoDkh2tqPfPTK/6djTqVZnvFcHOxO3/shggCylkYw6qsrZjkenHSeDguQhiy3LGOEyaGTk+CM5xKnzw59z5EgWfopPmFx9rLITVsuTP3sdSzbn4i6Z3puACZiACZiACZiACZiACZiACZjARBHoi6FheJgO9XB34Ygrr7yynHXWWeXss88uJ510Ull11VXLwQcfXF784heXnXfeuVvX8TrZdKIJU8dc5ySkY610Cu8KHOdAnXIZCjTyIWXFehJz5s5JIwL+bL3kkifGEWTh1MFnzya/PIg/xFN80mIMYGENxcVPspS2DiOcTY6wXuVSuPcmYAImYAImYAImYAImYAImYAImMBkI9MXQQEXoNNORZ8rEH//4x3LiiSeW0047rfz5z3/Oeh5yyCHlNa95Tdltt93ykxl4Ep+NTnj9qzyy6GSrY00c/IhDh5tzwjknDotPDoehQ51z/LRl5vGH9Gw4yVX8Xv4ZMf6Q33gGgDqsHR/ZyFUedXnkx14ylIfksCe83kdJcg2KXnEzov+YgAmYgAmYgAmYgAmYgAmYgAmYwAQT6JuhgXrwhYlbb721nHfueeXYrxxbzjzzzKzeWmutVZ75zGcWjA277LJLWXvttdNIwC/+rIeAwYCRBOp0qyOuc40M0GiD9jmGB/mRVvJkUOjFmHikYS+5xONchg3OZSBQWRSn1x4/xWPfdshmk2FD8VnEstcaFEqvdOyRS5nsTMAETMAETMAETMAETMAETMAETGAyEui7oeGWW24pv/nNb8rhhx9emEIh96QnPam84Q1vKHvuuWfZYIMNclFIOtwYGuhA03nGQMCmzrgMBcSh88/UA8I1UkDhis9estTRlx/lIG3tL+OE/OswlRu/8fwVp94rP9K0DQIqt+SRrpZfy2kfS678JUN7+XtvAiZgAiZgAiZgAiZgAiZgAiZgAhNJoK+GBhaBvOGGG9LQ8P73v7/ceOON3bqtueaa5aMf/WjZe++9y6abblrWWWedDKOzrxEEGBIYXUCnmk65DAlz587tGhoIX1yHDGSzMe1gcMbYr1IoX8kjP+U5pgPfWaxyJKZoxJiHZlRBfMJSn9JUevaUnTqRXgYM+Wc5OgaIMfJrAQs5lrGBPUMgBjqf8nwwshaSjYNMwARMwARMwARMwARMwARMwARM4EET6Juhgc7v9ddfX/70pz+Vc845p3zxi18s//znP7PjTocfx4iGPfbYo+y0007lYQ972JjRCaSvO+YyNJBu7lxGNAzliAYZGupON3HpbMtQQBoccdLYMBRrPMRXLUjbzoM4csjQJj/tFY99HYdbDOhnAABAAElEQVRzDAi4welhJIl/Oq/zIt5ILJrJFAn8M12Ui3PVibLij1Me7GtHuOLU8eo4PjYBEzABEzABEzABEzABEzABEzCBiSLQN0MDnWSMDCwC+ctf/rKcf/75uTAknWp1vDfccMNy0EEHlVe84hW5VgNfo+jl1JFWJ1ujHmQoUGdbHXPJV7jSIZu4hBM3DRGdkQh1nF5lWFw/5DK1Azdz5szcL2pEA0xIx0gNysRIDhx+bDjK13N0RYSp/orXr7ogz84ETMAETMAETMAETMAETMAETMAEloZA3wwNFOJ3v/tdOfLII8t3vvOd7HTTkZahgU4453zekmkVT3ziE8usWbOy7LUxgI62RiaQlk40X5VgRII63nVHm2M5wnHIwL+bPo7xU4dcnXmFk4b4CuccN14+8mePU3nZI3to3lCOVKg/Z4mxA4MEecCCPX7s63KnwPgj2ZwTR1sdTpy2v8K9NwETMAETMAETMAETMAETMAETMIGJINBXQ8PZZ59dXv/61+cnLRldoA49HWs63XfffXd2qr/73e+WfffdN9dpoLNPvFzwsTPFAT+mIUwfbD5f2QajTjid7LYjTCMYkMNWO8LJiz1lknGAOO1OO3EwcuQ/jmPDsafMbKSnfrWxAIMKrvanTHPmzMk8VllllW78jNjjD3mQBsf6EqwHoTzwy7JFnHaZCbMzARMwARMwARMwARMwARMwARMwgYki0BdDAx3oO++8s/z6178u/+///b9y1VVXZQeYStEhprNPJ1lTDD7/+c/niAYWheRTlzgZBzgmbttAgP+SOKYvyNCB0UOddMrTNjSQNx12xRkvH9JKpuKoo89e7oEHHsh61wYF8iBf8sAAUTvJrPOXIUH7gZjyMTB99LOWKsvilLvOy8cmYAImYAImYAImYAImYAImYAImsCwJ9MXQ8Pe//71cfvnl5be//W059thjyxVXXNEtMx1hOsVyfG3ita99bdl9993L4x//+LL55psraMyezjcGDDrnjDzAWMAxHXadk4B4+LOnA8+mPBlZQHz8SI+jLI0RghENjQGCtN0yRlE55h9OBgSMABzrPAPjD3nfd999ecpUEOLhRzzylMGEPCgLDn+OqR/+iks5exkbFEeykEEZ5V+nIczOBEzABEzABEzABEzABEzABEzABCaKQF8MDX+59C/lxJ+cWH7wgx+UCy64oFsXFnukc3zvvfd2/XTw5je/uRxyyCHlCU94gry6e0YE8MWKv/3tb+WmG2/KKRQYFzRKgZECdNZliKBjv/rqq5fNNtusPPShDy1rrbVW5ktnnjSaIkEGMjSwR4Y66ci69dZbC0aTe+65p1AGHMYD5G2wwQZlvfXW664rQRgdfdL8+c9/zrJsvfXWhQUv11hjjTRukAdGBOXBOWVF9s0331yuvPLKstpqq+UXONZdd92uoUQGBeKzkY9HNEDczgRMwARMwARMwARMwARMwARMYLIT6Iuh4cILLyz/93//l5+1XHPNNcttt93W3TAAbLXVVtlJp+PPdt1115V99tmnHPqGQ8tuu+82hhHhGCbowP/xj38sv4wvWNx0003pxwgFjAwPechDssNPh511H6655pryqEc9quy///5puNhiiy0KHXc66NlJj1EGdWcf4wMd+NrQcP/99+dIjD/84Q/59Qz2uMc85jG5Pe5xj8s8MDbgMEyQ96WXXlqOO+64NGg897nPzbiUDyMLeeMwNrDhSIcRhXSnnHJKgdfee+1dNtl0k7LG6muUNddaM40PlLc2NJBeBgjkyABRGzLwtzMBEzABEzABEzABEzABEzABEzCBiSTQF0PDjTfemFMn+LWezjC/1J955pnl+OOPz7p94AMfKDvuuGN3FALGBH7Jf8QjHpGjEOjw4/DXtAOmI1x//fXZ6T/vvPPK5z73uYzDiAE6/RgvGMFAWtaG+OlPf1ro6GNs2GuvvXKUAJ31eqs7+xgBmKqgzjv5YiDBAHDqqaeWI444IvPbb7/90nix3777lV132zUNJnTyMX7wOc9f/epX5aMf/Wh54xvfWF71qldlnRhBwUZ+xKVeGpGAkQNDCiMarr322gI7jBwbbbRRlpl1K9Zff/2uYYT44kp5a4dsnOpVh/nYBEzABEzABEzABEzABEzABEzABCaCQF8MDfxKT2dZBoOLLroop1F88pOfzDp9//vfL3TY6XwzCoGOMR18dcjZYxBgxAId69VWXS2/svCvf/2rXH/d9eUXv/xFOeyww1IWhgYMCRgbmHbBNAUMDf/7v/+b4XzN4nWve13Zaaed0ihAOPlpo3NOx71taBgZjjUURoaz8/+jH/2ovOMd70h55LPLLruUpz3taeVJT3pSjpS4/fbby8UXX1y+/e1vly9/+cvlKU95SnnZy16W+4033jjrQH4alYChgXP4kC91hAPMmHbB50BxyNl+++0LIzIoN464cEEWozmQQx3Y25mACZiACZiACZiACZiACZiACZjAZCPQF0MDnWc66oOxuCLHTHlgvYYPfehDWV9GGzDSgF/k6WDj1BHXiAPO6ZDTicYIoY70P/7xjxwd8e53v7tcdtlluQYDnX4Wk9x99z2i479OrgvB6IlvfOMbKfvVr351dtr32GOPcRebzIidP+TLRvluueWWcsIJJ5T//M//zFDyeeITn1j2edI+ZZddd8lynX/++Tnq4cgjjywvetGLciTDtttum+tEMBVCBpc6j/YxnKgrBosvfOEL5Zhjjimvec1r0iDDIpmMbGDaCRwwNrQ/sylDTVuuz03ABEzABEzABEzABEzABEzABExgIgn0xdDQrgCGhh/+8IeFKRO4k08+uRxwwAH5qzwdbDk60TIoyI99Gi4inkYAnHHGGeWDH/xgOeuss3J6wTOf+cyy9957pwGANRN+//vfp3GAhShPO+20FPWGN7yhHHrooQUDAE4jCDAosH4Coyjo6DcbX4KYnoYG1k/48Y9/XN75znfm9AhGGWAkYYQE60AwyuIrX/lK+djHPpZyP/zhD5f/+I//yNETrNmAUQD5yGXEAvXjnKkT84ZGP29J/jg+BXr00Ud3p2rwRQ4MKYxswNig6RKUm5EY7HGwIawXv4zgPyZgAiZgAiZgAiZgAiZgAiZgAiYwAQT6bmigg42hgREGGtFQGxqoozr44xkamIpBp5rpAxgdWO8BWb/4xS9yDYRnP/vZOY2BqQyMIGANh29+85vld7/7XTn33HMTI1MtMDSwDgRTHViAkq9YsA4DUxD4mgSd9U022SSNEcjB/fPOf5YfnfCjHKXAOZ3+pz/96bnII3HvuOOO8vWvfz1HIRD+3ve+N6dqzJ49O79WwYgI8mIvw8Dg9FiDImY6YCRgWsQ222yTX6egHKxnwfQLGS4wbOwTC2WyYWzgixc4mJGePY6RIFpfIj38xwRMwARMwARMwARMwARMwARMwAQmAYG+GxowDPAVCtZlYJFE3EknnZQjGtQxJg4dZgwNdJjbjpEAdNLpZDNCAEMDIwd+GV+g4BOSjGjYc889cyPO2WefnVMPmPLACAamTDDigY0RD1dccUX57W9/WxgZwTSOtddeu+y888454uA5z3lOYXv4wx+exbjzzjsLazQw/QKHoeEZz3hG2XLLLdM4wdcwGPGgdRU+85nP5CKUfP4SIwZrLnzve9/LUQ+vfvVrYuTEYH4yk+kWN9xwQy4a+bznPS/LyZcxWFSSr0/AiMUlr40FIjE2vPKVr0xjAwYMGOXIBWwMXpohr4v/mIAJmIAJmIAJmIAJmIAJmIAJTE4Cy8XQ0GtEA8YGdaDbIxz45Z6NqQEshIih4fDDD89FH/l0JAs+8mv/ox/96Px6BZ+iZEQDazg861nPKm9605tyBALTJfi6A+F8SeInP/lJdvAZEYGB4je/+U2ObjjwwAO7UyMYTXHiiSemkeSSSy7JvJ761KemweKuu+7K0QpMC8GgobUitt9u+yznb8/9bU7dOOqoo8qb3/zmXL8BQwcjKpjewXQOjAsYYDCGYNzAoML0CaZ9sK4FRgcc6z8wZYMREKzV0DbIaGQDcT19Agp2JmACJmACJmACJmACJmACJmACk4FA3w0NdIAZ0XD8948vH/7Ih7OOvQwNGtFAJ1lTAuhMtzvUdPwZicB6D0yLYG0DRiOwfgFGBxyfpGRaBe7FL35xYeFIpkxgKMD/9NNPTyMDayywVsRuu+1WZgzOKCefcnI59thjs6P+8Y9/PNMypQKDBB19DBSMnKDDT77kTxiOEQ98ThODx0Mf+tAcmcAUiPe///0ZzucxKQthfM4SI8hxxx2XUyqY8kEdMFYwfYJPecLsi1/8YhpMEMBXNDBkUA+ME/UCkzLMEA9+NjRAws4ETMAETMAETMAETMAETMAETGAyEOi7oYFKaY0GRiHgmBbAqIExRoRqGgCGBo1w0PSKTBh/2oYGwnfcccfswK+//vo5teJrX/uaohdGH/D1BtZToHPPqAWmSyDn5S9/eY6C4BOUjHb4+c9/nusjkPgtb3lLfkKTkQ6MWOATmUzbwChA2TE0MP2CMNwrXvGKwsgIvhDBqAOmQHz605/urrWAkWGfWGfhMY95TJaFujO1AqMAUzcwHrAXE74+8YlPfKKoLhhLMDQ87GEPK+uss07mnxl3/sjYYENDTcXHJmACJmACJmACJmACJmACJmACE01gmRgaLrroolwMki9F4H72s59lZ71tRFDlZWggnI63OtEcM3WCEQ0YLTAa0Dln7QV+6WdUA190uPHGG8tf/vKX3JCJcYDO+TXXXJNTEtjvuuuu2XHfaqutcroFX48455xzci0F0rzxjW9MYwOGBqYw8CUJ3JOf/ORMxzQO1llgigbukEMOybUinvCEJ+S6EXytAoMK6zOwGCTrLeCQy2KSyhf5TIXAcKFRHcTDKPKRj3yka2hgZARGE9KxKCYjH9ojF0iPa/unp/+YgAmYgAmYgAmYgAmYgAmYgAmYwAQQ6IuhoTYMUAdGNNBZl6GB6QZMWaCzPn8kPikZ/3D6Nb6dntEGrF3AlyDYM/2BTjj7rbbcqjzr2c/qrqmw4YYbpqy///3vaZB4xzveUXbYYYf8qgMLN15++eVprGARSUYfMN2CTj7TFf76178WjCIYCd7znvfkKAbyZDFIpi5gMMDQwOKTlB3DhAwNL33pS9P/sY99bI5YIBzjBdM4MDZ89rOfzXJtvvnmOcViy1hMEoMBBhJGKbB2A4YVNupPOT71qU915TNVhBENpGc6R34iMwwvNiokVv8xARMwARMwARMwARMwARMwAROYpAT6Zmhg6oNGLCzM0EA8HJ3rgWnRcR5ofUYhbBD33ndvGgeYXsD0BQwMLKDIyAbWNXjBC16Qoxro5GudBuRiIOCLDm3HyAjlS6f94IMPztEQlBcDASMqME7wtQf8WAwSwwajEvCrDQ3f+MY3UjzTMPDfYfsdyuyNZnc/Q8noCgwrPzj+B2Vg+kCOsuCzmnLUY/+nxCKPW26RIytYewFjCoaGz33uc+Vb3/pWRsVIw0iIzTbbLOM1hobpYWho2EmeDQ8i4b0JmIAJmIAJmIAJmIAJmIAJmMBkINA3QwOVUaeXDjprGbzvfe/LOp70s/i85YEHdH+9V8UVX+fa0/Gm80/nGoPEmWecWT704Q/l1ycYEfBv//ZvaWjYaaedykYbbaRkaWhggcaFOdZVeO1rX1tYGJI8KAOd/VmrzSrrrb9e+cc//pFrOjCigCkXLAR50EEHZZxf/epX+dlO5LMYJJ/FxEBBGRip8bcb/pafsuTznIxuuOeee8qVV16Zn8PUVIp3vetd+blMvjjBCAcMHTjCGdHw1a9+Nc/f+9735hc0tooRHKuv0Uy1IADDC9NJVG4MI1rnIRP6jwmYgAmYgAmYgAmYgAmYgAmYgAlMIIG+GBra5V/A0NBrMch2os45hgU5OtN02vXVCdZIYCoBaxfsuceeZaedd8pzDBJ8QpJFH//93/89k2OE4IsPdMSvvfba/KoDAa+IRRz1+UsWiKTjjqFBoxtY1JERDYceemjK2ScWdGT6BOsqXH/99Tk1gk9lskYDBg8tBsmnL0877bT8qgSf3cSIgMGE6Rd8nlMGBD57ibEDQwfTJ8ibOjMKBEPD17/+9cyXr14wokHrUFA+HDKZWgIbpoCo7BnoPyZgAiZgAiZgAiZgAiZgAiZgAiYwwQT6YmhI40DYBzQNov3VCQwArNFAx1iuXqtBv8gzvYFRBlookV/ub7nllvw8JV+SkKMDzuch6eSzRgMjB+jQM8Xi29/+dk6vYH0DPk2JEeKUU07prpmA4YARDXwpAuPEHXfcUe6+++7Clyh23333zP+73/1uedvb3pbZMVqBr04wkoI4xCUPDAlM0+BTmRgW7rzzzlxYEkMBdd32kdvm9AgMJfDAOHHBBRfktI/nP//5hUUkmRaBoYC0v//978sXvvCFHJVBOTCGYOBg+ggGEeKJH8YRDA2aesKxnQmYgAmYgAmYgAmYgAmYgAmYgAlMBgJ9MzRgbJDB4MILL8wpBh/+8IezjowQoPPNlxNwxE3jRJ6NTrnAyICxgekE7OmAM32Br0186UtfKpdccklhXQY6/IxsYFFF1lxgwcdzzz03vz5BB57PX2KEeNzjHpd5nnXWWbnIIos/MsoBAwSGAr5UwdQGysJ6C0yFoIwsZImhgc9nPvKRj8wvWOyxxx6FjREFGBrYMFhg0OArGPfee2+ORmAxShwjHvbbb780dNxwww05qoIvS/D1i7322qtst912OVqBRSkpP5/OZNQDDBgpgXGDusACQwojGjCa2KjQuWm8MwETMAETMAETMAETMAETMAETmJQE+mJooGZ01tUJbo9oYHFEOs76RZ6PTqShofNDPEYFtvyVPhZuxGBBh54vSVx99dXZEb/44ovT8IA/v+6vttpquQAjcZnuQGecqQismYChgTUQ+MQl+dx8881pUPjzn/+cUx/4sgQOwwMjFVjwEaMF0yMY4cDICKZBMFqCjj7TIpiKwQgD8j/22GPT8IGMo48+urzoRS9KAwXGAowiGEQwIGyzzTZp0MB4gAEDowhTJtgwllAPysaIjy9/+cv5+cxXvvKVOVJi++237079IE/YEN/OBEzABEzABEzABEzABEzABEzABCYzgb4ZGupK8gWF448/vvt5y55TJyrDBFMB6IzTka7XImBqA1Mn6PzzCUqmENDpxngg4wTpSI9BgFEKTEfgyxQbbLBBFom4xEEGIwcwLhCf9BgDttxyy5wywdcrMCjcfNPN5eprrk4jB4YBOvgs7Dh79uwcTUH+jJDgE5ZMhzjssMPys5gYDm677bY0HPDlCRaVxGFcQQ5GEDbKx6gKjf7gc5if/vSnyzHHHJOjNN75znem4YM41Ek8Ulj8SQNN50SGHYV5bwImYAImYAImYAImYAImYAImYAITTWBSGBroPLOx5kDpjHLgnEUPWeNABgY65+pc1x1ujvHn38xVZuZIB03T0EgJZGBsYOQDHXiMGoRhCGCEA34YJFgXAgMEBgXiIJdjOvyMeMBIgeGB0RF8jhJjw7vf/e5cM2KrrbbqjqLgwsoYQvmQzygMjA5MgcBRP4wyjGZg5AZrTzA9Q4tJUhbypYyUAzmUEbmwGJweIxy8PEOy9B8TMAETMAETMAETMAETMAETMIHJQWBCDQ0gkOGA49p4wHnb1XHbYe1zZNEhx2lUwNC8oZi1MX90CkcnEZ334aEYUTFjsDvSoBM0ZofBAZkYMRi18POf/zzXZeCzl29961vLC1/4wpwWwciK2iGfsmsUA2EYUDB8sG4DIySY5sHCkltvvXVO2SA+cUiDkYJz8s6yhjzqJENInZePTcAETMAETMAETMAETMAETMAETGAiCUyooUGGAzrQMgpgIKgNDsShU1130mtgxJUcjumI5xoQ8Qd/pSeN5Cq+5ODPNl4eyCScdMRhz2gE1oZgRMJxxx2XhoIXvOAFucgj0za6xo0YAcEoCBkMlCdTQRjFwCczGSHB4paPecxjcnqF0rYNFDDKOnS+8DFeeZWH9yZgAiZgAiZgAiZgAiZgAiZgAiawvAlMGkNDGgii9ur0CwSden65b3eqiaeOtzr/tcGC9LVhgOPayXAgv+zAd04UV3kgV+UgCmXFj3xZzPHXv/51TsngSxIYDPhaBaMeiEPc2tAg2RgaWC+CqRyrr75GLA65UXfthraBgXKofKSXDPx0rHp4bwImYAImYAImYAImYAImYAImYAITSWBCDQ1UnI6yOtEC0T6vO9carVDHoUPPOR3/tkFCMpUX+zo+5zIc5CoPA80ICvIknpzKwLoJLFJJfoxcwB+jAf5MccDAwMa6CpSFeMghnkYqIJM1I/h8J+Gs3UBaDCqcM0WD9MjhHNmk11oNpMefDbewOmcE/zEBEzABEzABEzABEzABEzABEzCB5URgUhgasq588hIrQjg61b1c3bmu42EoIKyeYsE5cuiLj4yMhuPXjT8wPbMZGm6+QkEYC1JOH5zeSdt05vFnw2Eg4EsYOL5EoYUdOcegwOgF8qbzrzUUOO86DkMUcSkHZZYBAoOC1oEgrUZF4E8crdWAPG1Z5sjLzgRMwARMwARMwARMwARMwARMwAQmA4EJNTSo806nm00dcnXs6axzrE41cXDqXCt9nY5wOvvEpXNOHDrqxKGjjp/kEcbWnCOX1GMNHYqLv0YosD7D/JH5uXgk8trlJK6c8qA8KhdhpCEtm5wYkCeOPX4YJDA8UH7OMUaQXoYMpffeBEzABEzABEzABEzABEzABEzABCaawKQxNNCZrp066HSocep0c0yY/DmuHfHqUQHEaxsaiK/OfJ22fSzZMgAQjjz5Kx8ZG1Qm4pGGjbiEy0DAHoeRgKkQuIwXwxymxbQNOcWnnJJBGowV5IvMeiqF0nlvAiZgAiZgAiZgAiZgAiZgAiZgAhNJYGINDcwhiP91p59jbXSw662OJ2i1n+JitKCjTmccP8VROGmVB/F0LJnEw2hQGxWIN3fO3CyvpktohIIMDaTDkb/CkKEpD8qLOEzRwLBAXAwhdTzCKRMGBWSqHvgTn434HtEAETsTMAETMAETMAETMAETMAETMIHJRGBCDQ2AUOe8hkKHnE3h6vjXcWrjAMc4GQYURrpavuI10UcNGkyD4F/tkNXu4LM+AzIwHBDOcV02zmVMUB2Ix8gD9m1HfI1QIFwGCfy14a+0kokc1U37tmyfm4AJmIAJmIAJmIAJmIAJmIAJmMBEEJh0hgZ11OvO//RYtHFgeu+OOh1yOUYJMEKi7RgBgFwcHXPSyEDAvnbKHz9GDODq9BxjcCDdrFmzxsRh7QYcizhipJCxQGWsDRf4YWTAyZCAH/LZqPOMmaMGCsqFP3Fr2ZSjXYcU6j8mYAImYAImYAImYAImYAImYAImMAEEJoWhQR3x8fbqTGsvTo2NYdTQEF3uNDRIDvHVeaejnukjDqMXOFbHnzhs6vDL2EAcHB18nNZIuO+++/J89dVXH2NouP/++1Mun6usZSEPR3445Ye/vnKBP/lgfGAjr/YIB8mRbNJknTrl5NzOBEzABEzABEzABEzABEzABEzABCaSwIQaGuqOfHa6q1/rayiE0TlXp1r7Og7H6sCzx9EhJ678lV8dznE9JULGAPKk4084frUs+SMPfzbiyRBAfM41YkHpKRNptfYCxgTS1o50bJJdyyUuMvW5S0ZOqE61DB+bgAmYgAmYgAmYgAmYgAmYgAmYwEQRmJSGhjYMOvBsdKrrrR1PnXT2ODrmi+qII5eOO3tNeSAtMmQowCDQSw5GA1yvfJBHetLV6WtDQz1iATm98mAKycj8pu4yNGAYwXhhQwPU7EzABEzABEzABEzABEzABEzABCYTgYk1NLSmOgCmZ2e7YzgYL7wGKiPD4sRVOowCpGsbDOjkp5zqs5NKw1559Soza0vKQFCHk0bpyG9xnOIjh2PKy/Hipl+cPBzHBEzABEzABEzABEzABEzABEzABPpBYEINDVSg7oT3o0KWYQImYAImYAImYAImYAImYAImYAImMHEEbGiYOPbO2QRMwARMwARMwARMwARMwARMwARWOAI2NKxwl9QVMgETMAETMAETMAETMAETMAETMIGJI2BDw8Sxd84mYAImYAImYAImYAImYAImYAImsMIRsKFhhbukrpAJmIAJmIAJmIAJmIAJmIAJmIAJTBwBGxomjr1zNgETMAETMAETMAETMAETMAETMIEVjoANDSvcJXWFTMAETMAETMAETMAETMAETMAETGDiCNjQMHHsnbMJmIAJmIAJmIAJmIAJmIAJmIAJrHAEbGhY4S6pK2QCJmACJmACJmACJmACJmACJmACE0fAhoaJY++cTcAETMAETMAETMAETMAETMAETGCFI2BDwwp3SV0hEzABEzABEzABEzABEzABEzABE5g4AjY0TBx752wCJmACJmACJmACJmACJmACJmACKxwBGxpWuEvqCpmACZiACZiACZiACZiACZiACZjAxBGwoWHi2DtnEzABEzABEzABEzABEzABEzABE1jhCNjQsMJdUlfIBJYjgfmR17Sx+c2fP7+wyU2bNq2w9XJ13IGBgW6UJfXvJlxJD4aHh7PmMIR1zY+AhV2DlRSZq20CJmACi0VA+rTWo/jV77WRkZEx57XgeAMu8J6sw31sAiZgAoskEM3qaF1329e1PiItOkmu1k3ym6i9DQ0TRd75msAUJyClNkahhZ4bGh7qKjwpQnWA21Wmg8xGvMHBwdwjt/afMWNGtwFHY25oaGhM/LbMle0cXnPmzEnmM2fOLNOnT09+sCIs2U4PtgO9jT0rGy/X1wRMwASWhAC6dHgo3lOhQ3lP4WRYQL/mOyvCsSfUBnPlofegzr03ARMwgSUloLaxdA9tPekbwtjkJpPOsaFBV8V7EzCBxSaAQkPZ4WREkKKTP2G1siOccylGwokrQwNKk3DJXsB/JPKcP5JpkKH4yFmZHZwwvsANowxs4Do/ePEPpmKla1Nfl5WZnetuAiZgAosigN5Mndp5h0l/6p2n9PLnnFEM6N88Dh1MmJ0JmIAJLAmBto5R+xgZbUMDbUGc2uSTRefY0JCXxX9MwASWhADKDqXGHmWHk5KrlZ9k0kibO3duNrb41b1WgMioz5UGf20jw2GQGBlOBcovSrWxQvFXxr2ugXjACwdPNp0TzvHQvDBIxD8zXBnvFtfZBExgaQjMmzcv32O843iPoX8ZTYY+XWWVVVLn8q7DyOARZEtD2mlNwAQggD7hhySc2m1q19XtZsXDj3h1WCaewD82NEwgfGdtAlOVAIoOxcZenVzOcTI8SDnW54pPGhShNn59x5BAOH6ESy6NOW3IqqdSTFV+S1tu8RdzvVh0PjAt1rvQD2hhe8C4QBo44hR/acvh9CZgAiawMhBAf/JOY5OhgWMM6LyrFvZe0ntuZeDkOpqACfSPAG22+kc6tYvJAZ2EQ78QD31E+GRr39nQkJfJf0zABB4MASm6Oi1KD4V37733piJcbbXVuo0wOsL8AoSjYSYjhJQp4VKUGvlAHvgThzDSkMfK6sQDJhzDAib1C6hmQxxNoyDOysyu5uJjEzABE1gcAuha3mm9dC36lTC2wVgLZ3DGYNeoK/2s99bi5OU4JmACJiACtHvVZl511VW77bxs14XuwaGXpIfQNRgaJpOzoWEyXQ2XxQSmEAEUGy4bU62hojS6HnjggQxjSKl+7SGulCbKUJ1jlCnDUmnQoTQJmzkjplh0FjAkHWE4pVlZO8xiwR6nxi/HMMS/Zsu54hIHR5qVlV9DwH9NwARMYPEI8O6pDd1M5Zs3NC8NvOhave84pjMgwwTSeV9pW7zcHMsETMAEmra1dAntNbWjYSOdhD/6hT1+cvLX+UTubWiYSPrO2wSmKAE6rig19ii0elOVsnNLXzgGHxAup05v24+GHGFsKM4FRi6ErAjNfElLnJXV9WLI9fjXv/6VfNZcY80yY+aMMTxJgyECVxsiVlaGrrcJmIAJLCkB9ChGdEbsYURfY4010gjxwP0P5GgGDA3Ewfig99iS5uH4JmACKzcBdIgMB+32LmG0l9ExhGGAUHsYfzb8aeexn2hnQ8NEXwHnbwJTkACKrt6kCNnX/lQNBdhVdmEsSANFGAzwk7/SEZ/0OIURn2PkECYlOkZuplj5/sADPmxwYbQIrGbNmpUvGV0LyCgux5PlBURZ7EzABExgUhPoGLlVRuZMY2yggc/UQHSr1mqQbk3d2/nyD+8qNjsTMAETWBwCdXtNbWH8OGbjmDYfTm1h9moLEmeBH+sWJ+NlEMeGhmUA1SJNYGUhUCtDKTv8sLTS8MLxCw8KD4cSpDNMHP2qjkJU2owUfwjHoUj1yxCNOpysuJNFiWahJuAPjMSHRi+ORq/WtlCRYA4znJgrzHsTMAETMIGFE0CHsuldRWz0L+dsOsdP/rzT9JUK9C6jH+xMwARMYEkIqP3GHode6TWFQmHSSaGZcjRxJprgPzY0TPAFcPYmMNUJSAGqwUV96ADTyEIp1g0sGmF0etnLuEA6ztWQo1GGU9xaDnFlxZ0+EMaLiR8VlmWdqD8wk7GhzZow/OA4PNT52kQsVGZnAiZgAiaw+ASkZ3n/6P00XmrpXcJZhHfuvNGvUoyXxv4mYAIm0IsA7TfawOxpz2mr29uEoXfYFkdH9cpnWfrZ0LAs6Vq2CawEBFBybHIoOra6waUwGl5a4FF+7FGkcx6IYf+x+CO/yqNMcRglNCR1lZnxnfLO4pAZ6D/jEsD4AH8axVwLrs9ksnCPW3AHmIAJmMAkI1A34hlJxzk6lmO9q/QORN/amYAJmEBfCETTmk+/o1eka7o/tnVGCpMP+kdTZ9ujWvtSjqUQYkPDUsBzUhNYmQnUDSuOh+bFSIX4R+dWChFDwd///vdyxx13dL82wQiHddddt6y33nppVIAhirNWkppqgT8ykFd3mkmzsjfo7r///nLTTTeXO++8I0eNwAxWq6++etloo41yD6faEc61Ess6zMcmYAImYAILEkBn6n3Hu4hj3k0yQNx55535nuMdhm7FoY+777lVVyvTB5vpgwtKt48JmIAJjCUgnUM7l41zfnS77777ckP/MIUCfYOuUZuZr7XNXGVm93ys1Ik5s6FhYrg7VxOY8gRoZOHU8Ko/ZymlRwPszDPPLKecckq5++67Uyk+5CEPKXvttVfZbdfdysYP3bhrlECelCsyZUjAj1/jGc3AMfGkfBVnysNcggokj3jx3HLLLeVnP/tZOfroo8tDH/rQstZaa5Xbb7+9POMZzyjPfe5z09iAWMWHG8YJzhk1omu0BFk7qgmYgAmYQBBAn/LO40s/F198cTn55JPLX/7yl7L22munnl1zzTXL0572tLLLLrukLubrFCvj+8o3iwmYwJITQL8w5XVgevMFNn4kuu2227o/3KF7GLmgDb3Dhp7BT+s4TAadY0PDkl9/pzABEwgCdFjZ9MsOxxgIUHL8ssMohksuuSQ7w5/61KfGMHvLW95S9t133/LoRz+6zJ49u/vrO8oVhYphYTDWE2grSeWJMPJaGR18MCjA9jvf+U459thjx2B49rOfXV796leXxz/+8fGL2nrBdlZeI64TG0y5RisrvzGwfGICJmACS0CAd5DeSzT8L7/88nL22WeXd77znQtIQQ/vv//+Zeeddy5bb731AuH2MAETMIFeBNTWZRQD26233lquuuqqcsMNN2T7munGjGZQm3n99dcvG2+8cdlqq63KJptskl8e6yV3IvxsaJgI6s7TBFYQAnRc77v3vjIyfyQVm74MQQPsjDPOKD/60Y/KddddV84777wxNd5zzz2z4cWv73vvvXcqSCKgNDWFgikWatCNSbySnzAq4YILLig///nPy+9+97ty6qmnjiHyyEc+sjBq5EUvelGObth8880L33hnWgsGBo9kGIPLJyZgAiawSAJq+BNRRloa/ieddFI5/fTTy0UXXZTvOsJ4j+E222yzfL+97nWvy316+o8JmIAJLCYBRgLThka/YNC8/vrry6Me9aj8cQ49w9Tk888/P0cy7LDDDuVZz3pWoX3NiKrJ4mxomCxXwuUwgeVMACVF44mG0YPt0GNowDCAHD5jKTk0wL75zW+W97///Qut1eGHH16e97znpdFBc1u1wi6yKJs6xmq8qZG3UMFTJBB+1Is69qqXWMBG4UxHYcrEe97znnLttdd2G7XtKr/sZS8rr3/968tjHvOYlI8RCBmS047POdcRp+vIMX4aCUHaOoxwOxMwAROY7ARq3SadRpn5elF7kWHCFb/Wl7U/I8suvPDCHFH2pS99qVt94utdJc+vfe1rObIBA7Dec4QpD45rvap88Kv9iWdnAiaw8hD4xz/+kT8onXbaaeUTn/hE2WOPPcpLX/rSHLkwa9asnE5x7rnn5vQt2pEvf/nLy4EHHphTaScLJRsaJsuVcDlMYDkSoCFDQ4kGEQ0fdeYXpwhqHGlPQ0gNI4Z4YXj461//Wr73ve+VI444IkXSyVWnus6D4aYYGh7xiEfkiAg16mTA4Fyfx8wpFZEXZV0RGl8wgxV11Zy6mg3hLPxDvVngkeuEH0PnTjjhhHLYYYfV0ZMJcTBO4F784heXgw8+OKdQMKQOQxB5IaMXQ/z5KkjMWikD02JaSmfxdNJwXWHuKRdjkPvEBExgChBAt6nzj+7T+6XWafU7hbjEwa+tK5GFXv7nP/+ZHYBjjjkmRzXUGEhX6+L3ve99OaJh22237Y7eS32Lzo2N+Hr3IYf82fCr/es8fGwCJrDiE2AtrrPOOquceOKJ5Vvf+lY57I2HlRe84AU5qmHd9daNxcDvLH/4wx/Kn//853Lb328ru++xe9lnn31yhMNkoWNDw2S5Ei6HCSxHAjRu6k5n3ZghLEbZ51B7FUmNsAzDkx++oyOKv8LwZmGsa665pjukH2MDDmMBnWocnVU6rjgU5gEHHJCd4S222CKtsGoQkhflUkMPf/LiFyh1glPIFP1D/WRogI8MCao/e4wMxFstVi3ne+xMSbn00ktz4TGtewFPxcOgI7YMn2NaChZwhtRhbICfrrWuna4f+XBP4MScY8rRNTTEisbtX/+IY2cCJmACk5UABtSh4Vj7J/QfehadJoPsjMEZ3YWG423WPZYeRheiGznnGMdwZfTwOeeck41/jnHoVuKyJy/0Mu4lL3lJ2X333XMRZIY9o7NxxGWTLm48Q+fGVET8cQpjb2cCJrByEaDN9/vf/z6nIn/84x/P6bDPf/7zC1NkWZeBttmNN96YuoaFIFkYnDUa+GFpsjgbGibLlXA5TGB5E+gYE9oNGBpUagBRpHa4GkDqjKqhRFwWgGTtAKZNMK+MBQtrRwOMzrCMDnyGccMNNyyvetWrypOf/OTsDJM/SpJhYcRXY40GImXJ/NLKUUueesfUg7rRuYcJPDnGDwbUFX81WvULGkPoGLLLHscXJGg0i5NI8Gk1rN1vfetbc/VzGrj4EV9xdQ1Jo/JwjD/54vAfGY45x4x06DSgM8B/TMAETGAKEECfolvRqdJ50rH49Qqvq0VadKzWDbr66qvLj3/84xzJcMUVV+S8aeKjH5GPziQNexxGBhaEZP70jjvumKvDk+8CTu/kzvsNGZRNcheIbw8TMIEVmgA/3jFFlvW4jj/++PyRiUXUMTTMnr1R2XzzzcrsDWeXHR67Q3ftBnRVresmGpANDRN9BZy/CUwGAp0GjhpG2lO0doOIMPzqjqgaVYxmOP77x5f/+d//GVMr4moNBzq5yKBBxsKGONYToBHGegIbbLBBNsToZOP0azrnpCP9ZFKiWcgH+YcOPL9eUR826sdG4xKnUQ4c33zzzbnw40c+8pFy5ZVX4pWOxi9MuAY4WLMQ0F133ZXnNHKf85zn5K9p2223XY4a0fVSo5uIypc9MlQmhbHHz84ETMAEphKBWrdJr+n9RT3Qh2qcM9qgredqQwTHf/zjHwtTJvjij0bokQZ9jU6VvJrRE5/4xJwmiMGB0Xv8GqmRDcSTzudYZcOPjXP5EW5nAiawchDgM5Z3/OOOcu111xaMmiwI+ZWvfKVbeRkxn/SkJ5XHPvax+RU32oSM1MJeORmcDQ2T4Sq4DCYwgQRohOHYs6khhl+7wYVfL6c5q5dddln5yU9+UjSsX3FpUGkVXCy0NOowHLDHPfWpTy077bRT2W+//XKvuDSyZFggPg04RkPQ6NKvS8pjqu5hXnf81aCUP4YWGDA87qSfnVT+623/Naaq9TXS9aPBK7asf8G0CRYJ4isfjCAhnhqw7fSEyaksOvfeBEzABKYiAek89B46rzayLu57BcM4o8T4dfHoo48eszYDuhK9y4a+Rm/XjveVRjTsvdfe5VGPflQafSkHZaMMOORQPullwnRcy/OxCZjAik+Adty9996beocfjxgl/O1vf3uM7oECn9JltNRuu+2WIxs8dSKgqDFrBbriPyiu4eQlwHOoZ1ENmrqRs7CSKx3x+QQPi9H85je/SYsrC9fUDiOBjAd33fWvaFQNdX8JIh7zyviV59BDDy1Pf/rTyzrrrJMdZfKg4ZVbLFA4PNJMLWCxwsEZzVSKOp+pcsycYf5RL5wMDaqr6kG8u++5O9dmwIjDpyyPOuqoDKbhykuIhjNOulTXD3YKI/xd73pXOeSQQ3LhzeRaLfhIuJyuq84ll/NspIeZ3Os0iI73JmACU4UAehadiU6rRy6g1/BH/2IoIDx1Z9hc0XXSgawA/6c//Snfc9/97nfznUfdpccxGtQjGvAnrYwIxMXYwAg+OgSM3kOPo3OJQ1zJIq7y5djOBExg5SOAkYEFIVkX5p577kmjA7oCgyc/Pl188cXlBz/4QS7++MIXvjBHTfnzlp37RI1ZK9KV78FxjScHAT2D6ozyLGrrVULFJw7H2ensNIxYP+CnP/1p+dznPpcKkTmstaPxxdoApONXIdLix15yiX/kkUcWlCVrN6BgiYNllsZXHW+q6w1eFGKgulG/XvxhS+OWlYcvuOCCXH0YVnyJgtEd/HqGI60aqcinwUwcTaHgU5cvetGLCvP7MPpg/JET2zbX2p/jujHcjitZ3puACZjAZCSA/tJUvFVmrtI1mKLbpOvQoehm4uI45z2EY2ogjXq++sMaRHwBSE76l32tK0krHU1cvjzxX//1XzmVDQO7DPC1btV7UTKVh/cmYAIrF4Hbb7u9XPjHC5uvSsTCkEx/3WuvvVIv8eMTbULWiznvvPPyK2P8mMQ0CtbjmizOUycmy5VwOUxgAgjUDSwaNWxtV8fhWJ8+JC4NIvY0uo477rjy3//9393kkkcajmlwsZc8NaZoyKlRR2eYX3xojKEo6Sjj1IEm/YrgaHjCASbUjWOc6gcP+GAMwHJ95hlnli8e/cWco3f99ddnXAwwyFEjlrTIYo8fx6xCzFQVHNMmmKKy66675kJCauCSd93IVRlIk9e3M4KBeJzjlE+e+I8JmIAJTAEC6C+NaMAQW+teii/dRzzpUOKRhlF7LML79a9/PTd0M/4yQkg3Sgb6UnqSMIWTD+9JOgt8DWjTTTfFKxfc1egJ6Vpk6X2bkfzHBExgpSLA2ly//OUvcxFIvj5BO+5pT3ta/gCH4fOiiy7KqcrbbLNNefazn13233///Iqb2neTAZYNDZPhKrgMJjCJCdBAouHDhsvGT9WhpQGGZfVHP/pROeKII7o1UUNOjSz2NMoYsoosflmqG18kZMEsFrQ56KCDCsO/6CirIy1DRTeDKXxAxx4GNETrxi5s8WekAo1YvrzBcF1+QXvta187psbEZcOJoxhJvuQRB+PN5ptvnqMaeFHNnj0b70wrxrpm+FMO5CJDjV38cMo3T/zHBEzABKYAAek0ilrrMI51ThzFkz5FB19+2eXlvPPPyxFlNPzR2zimPqBv0de1flQ4cSRT4UwPZBE3Og2Pe9zjiNLVtZRD8TIg/kTp+GNnAiawkhFg2sQvfvGLbAPyuXjacM985jNzwXQWirzpppsK07he97rX5ZfbaD9vttlmqZcmCyobGibLlXA5TGCyEIi+JOsHdF3nnMZP3SAjnF/bWQmX4VsoQ4wNOBpZaqTRCKPDqsYTDTOcGmbEJU7tvvSlL8eXEp6dc1jbYcghjTq/dbqpcizDgHiKD8yoHx1/DDjwveqqq/KToUwrwWEMqA0DyBDjmrl4t5m85z3vyekpWMCJjyM98ZUeP5UD+bU/YXYmYAImMJUI9NKH+LGh42QYkG6mbvJDB7M+Dp9u5l1H4x7Hu0zvr3rxx1qe8mjnz6+PjODDoI5BmS8QDUxvRqQhux0fmXYmYAIrFwFGpDJi+G9/+1u2B1mnAV0wNMR6ZfOyPci0ZAwQbCz2zWgG6a7JQMuGhslwFVwGE5hEBNQw0q8ovRo4dEz51f26a68rp59xen7mi8VqGOaFI027E6sqyp98iIdC5FidZ+LxCceDDz64bLnllmX6QEy5iAW5MEzUHXI63L3Kpnym0l6GAuqE4xye5557bs6/u/TSS3Olc8Lgp8Yww3dhIDYyCMhwQPy2Y8HN5z//+blWw9prrz1mrYY6LnnomiifOtzHJmACJjAVCOidRllrAzX+6Ln6PdQ+x4DA5yz5ysRXv/rVMYZe9C06mzToYLm2PPnXe6a+8YlM5lPrU5ftd5rKjTw2OxMwgZWIQPzIx+fP0S+0t1nfDEMDX3nTHiMlxgWmGnNMW22yORsaJtsVcXlMYHkSYOBC1X5Rw4siqLHUqzgs1MiKtyxSyK88X/7ylzOapkWgGNVIYo8sKUA6r2rMKQ/2bPpViGFgDO/POaybbJpfmCAdsnAYJ9hIsyK4rFt8YWLGzOYTniwAya9op59+evmf//mfbhWpMxxlWKgNE7BRQ5Vw4mLpFlex5Zc01mrgU0hbbbVVNnJ7ceQatQ0N4t8rfreQPjABEzCBSUQAvSXdhV6sHXoOh05rx6Exz4KPLLT2xS9+MQ2/xJUOJg3GBtJh3JXDX/III090M3viySjBl4D23nvvnNa28UYb53tOMthTNjbStctdx/OxCZjAikdAzz41Q49gaEB3cEzbjDYdeoZ2N0YG2ns46SJ0BuET7WxomOgr4PxNYIIIoKxwtSJSw4YwGlDtxo3S3HrrrfkL+xlnnJGL0TBtAseaCshACbJxjEOWvuvLvDIpQsLIn404aoDts88+3bUa+AwYQ8EkS/FJu6I4eFA/GrC8TDAy8K12PhdaG3F0PeBUM4QDXGR4IJxjRizgzzQMLOI4DAx8eeLJT35yLgzJcDuGAMO/dnxacyg+Q6prw17XQOWo4/vYBEzABCYjAd5benf10l2EdXVbfPZXn+/FmH722WcX1mRAF/MJZ9KjC2v9yzlOeXCMn87R65oySDp0PI651qzV8NQDn5pG9emDY3UwcdnQzW39nAL8xwRMYMUkEM1zRjPI0aZDb6AHMCpIH9Cepn1He2+ttdZK/TRvbnzCN0YB4yfdJDkTsbehYSKoO08TmAQE1AiqFRF+wzH3izUaUGTdRlkoveGR5tNgNHyYL4aRgeGkrHzLCAccFlXC1WhLeXFOHvpuuRpPikMYG3npF3QUJp/+Ouyww8oB+x9QNt1s0yyPOtKTAN9SFQEubGLPHh743X777eX888/Pz4X+4Q9/6E6Z4AscsIMRe+LWTgzxI1wvJPwZalezJfxNb3pTecpTnlIe9ahH5agGGsMql667rtF453X+PjYBEzCByUpAuk3lq3UvYehE/NSAJ96VV15Zvv3tb5fPfvazqZ/vuOOObhzSSGeTjncT58jBST7HhEkucTS6jLDnPOc55S1veUuObOAcV8vmHP0rHcy5nQmYwIpNoNZX6BL0hn6IQ5+oLUw82nfE4cc89r102UTSsqFhIuk7bxOYQAIoKFx08/mTDj+UFPva0ICSm/PAnHL3PXenUsO4cPLJJ4/5ykTKCiVHWhpFdFxxKEfS4/CXgsRfcdUoIx5+cnxv/IADDigsXMg8Viy5uKne6KrrSd2pD35Ypq+95tpc94K6y8EMnjCTwYAwcVM87WuGxKnPFeclL3lJfgrpCU94Qtlyyy1z1IgayZSnTqdjyZnq/MXAexMwgZWHAPpLOg6dxibdi17lWO8nzlmb4fOf/3yuzVBTUjx0NvF4V6Kfka3OAHGQL0fe2vBTvnQOfvCDH+QIs3rUA7KQKwOF5HhvAiaw4hMYoytilJXa6OgFRqeiW7QYreJK32g/WSj1xdBAJXGqHMr5+9//fvnQhz6U/j/72c+ysyAFnp7+YwImMGkJ8EzreaaQDKP/513/zHUZrr322myAnXPOOWlsIJzGULvzjB8yUIyE4ThXwwl/nM4VBz8ds5YAQ0tZq2GLLbbIVXUxOKxoDhb6wgTfSma47nHHHZfVRG9qNAgvGBka6uvTi4f0ssLEXumZQrHrrrvmxqiGLcPYsN5663WvB+nVaCZ/HGklR3K9NwETMIGpREDvJDr7ev/Uuo3PWV599dU5sgw9/Otf/zqrJ+MAezZ0JO8q6UTO9V5r62e90xBEWpz8PvCBD+SIhq233rrM3nB2mblKo28zkv+YgAmsEATUJmvrBvzbflS4V3z8aJcRn7Zhna6WU099JR5hqZuiu860CtJJD5EXuqjWZfjJqRyS386z9ue4PkdG3wwNCJbivfDCC9PQwMrxuLahoVdB8LMzARNYfgT0zKE0asXRqwRMjeArCH/5y19ygcJPfvKTY6ItKv2YyNUJ6aQEaeipTFWUPHzNa15Tdtppp26nGEuu9Eg77uKcP9jyLo7sxYmj/KkDxgNGMlx//fXlV7/6VXn729/ebawii7jUlwYxQ271klmcfHrFaTN+3vOel8acPfbYo2y33Xa5zgbpyIc5geh1RpJQjlx8KEzrgzOahc3asnrlZz8TMAETmAwEpHdpUKPL+AIFnXrO0cPscZdffnmOMvjKV76Suvi2225Lf6XPkyX8syhd+dznPre87GUvy3ccBl8Zd5XNotIrnvcmYAKTg0CtL3h+ZYSkLacwdA4b57S15L+0NaA9jY4jL3QJhoe58+Zmuxk/bcqHstHmw3/GYIxGrkY5E4fys6mcSkfZa//2OfH6ZGhA1KhFph7RQKF/8pOf5BBdINJhYY+/Cs45m50JmMDyI8Dzh5NiQ8mgJOj48zwSLgMDK29jaLjiiivS0MBoBhwdYBQa6XBSOJKdnvFHebT9a12AolN6KTNk47bddttCR5hf4R/+8IcXfvnZaKONck6alHdGXII/KtMSJOlbVOpNw/aWW25JAwNfmWDdC0YyHH/88ZkPbOFK/dCXOI7FmvK3eWak+LOwuhHGi4dFhHAstHnggQcWFt1kZAMjR2josrAncSlrLY9jbSnAf0zABExgEhNAT0pX6t2GLkWP8b7jW/U33XRTjiqjcX7ZZZeV7373u92RDMTTu4lqShbHCuMYxzmujpMe1R/0uXQ6+cm97W1vK7vsskt5yEMekp+rmz17du4ps/S+4npvAiYwdQigD3iGo/VUBqY3feDUHWEAYNFtwnjOpRvG0yPyz5pHEz40Wx4il2OFI4/2M+fIVP5Ertt0+twv+pA+AGEzZ8SIqtFZX5mW9GzIUx6cy8mffDFqMGoCWbi+GBqUkfYXXXRRNpY/+MEP5iqY3/jGN8q+++6blWChMxq5NKKpGCBQ9JrPLRnem4AJLFsCUhIoA55Ffr1G0bCgI88kCgPjwm9/+9ty4oknFr40wdcQiEf8fjiUE/lTFvLDSTnpHD8Wh1xnnXUaRRca8L/f+d9pvKRDTIf9wTgpyweTdmnToPhp3J533vnxy9nxOUwXWqsD1QAAQABJREFUQwNDdmGBjkQnoh/ZUnlXSn1p8xdj8oID8ll8kzxf/vKX57fdMeZghCCcxYZwlEv3y9KWwelNwARMYHkQQM/pnYX+kv5Dt/HvhhtvKBdccEFhNC7GXxaBlOGBNRTQj7wbkdMPJ/2OzNrQsO6665aHPexhZcuYxrb99tunHsbIzvuY94CdCZjA1CHQS1+gc6SD1M5GD6CfiI++ob1LWK/0S1J78kIuuoNj2nfscchGr6k8ikfZ6hENyo/4dXl0rroontqqkot/XwwNdYYUlhENP/zhDwvDzvgO8cc+9rG00vKr3bUxv5tG9mqrrpZWHOIDlM3OBExg+RGQ0kBRoBwwIKCQUHSygGIY5BvirLxdOxpKxJUM7es44x2jgOr4teKjLOTNHifl15bFQoZ8nhHjQ91Qa8db2LnyXVicZRU2fWB6LqzJVyU+85nPjMkGXQhfHNdFDMRM5db5mMRLcAJnDL4yIigp33VnAU5GNuSohmiI33tf81UR4pM/ZSJ/lUVpvTcBEzCByUYAXYUuxaGz6g0/jAsYGc4666w0puMnR+Oc9xHvmQerc0lPnmqEo3v1jkWXqmzKkz3vt/322y9H8BGXeHYmYAJTh0AvfSHdo7YucWTE5HizzTYrj3zkI3Pxc2pK/F5yFkWBdDja9az/xQ+IGDLbjtEH0cTr6ibKxVa7On+O2aTLyAf9pHplXOyxIVNl6IuhgQzZKBzAMDSccMIJOWWCX+mYX41Vll9DjznmmKw0Q8Nqa267YnUlfWwCJtBfAigDnj+eO361pnPLyAA2wjhHMeH45R1l1XZSIm3/xT1PhdSKTHlo2JE/8jFmqEycKw3x6IyrsdYSs1inE6lzqAs6k7pRR+pCnetGJ3EoI3UmDmH41RwWq6I9IiGTIXOstYDTi45jjAlrr7125sP0CpURAxSGB9LKKKXykM7OBEzABCYbgdR1oUfRpehcdB17HLoO3YtuZZ2cdmdeOpi4SsPxkjryVv51HvKXPN4BvNMwalBOdC4bebNRF8pkZwImMLkJ8Kyy4Xhmea7VXuXZ1g917Hm2N9hgg5yqfPjhh5dnPvOZZauttkqdsbTPO6Nk6XszSoLRUrSv5SifdAu6iPLh6nKrHpSDrU5DWpz0mNKnZ/Wnr4YGNZSZOoGh4dRTT83h1ixy84hHPKJceumlC/x6V5XFhyZgApOcAI0yKaZaAS1psaXI2umkkFFcHNMoYyN+rejGS9+WN5XOeQFQLylv6iuDCy8mOKDIYcNxHW9x6ilmpJdcXUuxRo7iLY5MxzEBEzCBFY0AuhidiC6UruV8SZ10qd5n6Fv8OGfjWBuy0cvEIU87EzCBlY8ASw4ccsghOaK0H7W/7rrryplnnpk/GD3i4Y8oq662aordcMMNC2vAYGxFB2m0KoEYOtF36EF0EZvajYQTX7pM58Qnjhzn0pl9MzSQsawZF198cRoaTjvttPKP2/9RnvNvz8mhICwkh7VGTgVRYeTvvQmYwLInIEXBc8sziDLhOZbT80k8jjWCQIqnHV/plnSv57/Ouy1DDTPiEk9bO96SnCvfJUnTr7gq/8LqTPlQ9NQdxc910Hn9q9ySlonrza9kOEYscB1p4JKPylVfW+ITThj5sifuRPJb0jo7vgmYwMpLAJ2l91it5/CvHe84RvKh2/ilEX23NFMmatn1MfqUDT1LHnKUjY2ysslZ34qE9yYwdQjwDEvHoFN45tXe5pxNOka1+vjHP56GBowA/XB8Ke4LX/hC/si/5557pkGBKRSMmuDz5ppOsfrqqxc29B7tQtqatBM5RwdSdvwoc+2kW1VP9sSRLiNunwwNNPxHuoaGP/3pT7kY5Mknn1xYl+Hggw8u22yzTfnrX/9a+OQlBQG2HOcqpPy8NwETWLYEpBBQICgGDeEiV86lEPHXM4vy0PNaK9EHW1Ly0YYMZNcNrFquGlvEUdw6fCoeq+7UiWMpchQ7jVDVmWOcrkltCFjSeiOTa47rZThoX1fKRb6UUeXAz84ETMAEpgoBvTfQf9rwQ99J56HnpIPxQ98tja4Vm1rPk2edP3F0zrHyZU86wkij8hPHzgRMYHIT0DNPKet2E8+znmuOaV+zyX3qU59KQ8P6668vr6Xas7DtZz/72XLUUUeNkfPa1762POEJT8ip0wSQ34YP2bA8ZMOH5NpnGB3kaCdSZrUb0UWcy9X6SXpKdSROnwwNjdUGJY1jjYbjjjuu/PjHP05DA99pZ+rENddcUz7/+c+ndUTDNVCm2jKx/5iACSw3AigDnkWeXc3FJ/O6waVOrwolBSOFIv8Hs0eWFBJ7ZPajYfdgyrK806jevGxw7DV8jTUQ4C4+UuSc4/rBHjlLIk9lIR06284ETMAEphoB9BgNZjWaZUxAp/XSq0uiI8djgW5n0/tN8fDjXUtZ2FMGykPHg7385ad03puACUxuAugNjJY4nud2m4lwnv+23jniiCPKC1/4wlwUkuf+wTr0CY4RDV/72tfKRz/60cJ0Cb4kJ0f5NKLqWc96Vtl5553LXnvtVR796EeP+awucdBFjPhCh1FmlV+ytK91qHRnXwwNwCBjQcXQwCr1fBIP48LTnva0slUsbHHjjTeW73//+yqP9yZgApOAgBTieMqwVhyToLgrVRGk3Lk2Gn2wvADoRbU0L7vlVVbnYwImYAKTmQDv2QfzLlVnZDLXzWUzARNYNAEZGxemB173uteVpzzlKWXjjTfujoRYtOQFY5AXOufqq68up5xySvnWt76VUyGYFjGeO+igg8puu+2WAwP4ohs/fLExhYLPn2OoQKaMExyzKa/x5PbN0EBjFGsHjsUgMSicfvrp+ckgCsgnO5hGwfoNOAqnvY7Tw39MwASWCwEpOzqU9TOI0VAbCoTOLkZENtLUv7YvTUHJU2XgmLxw6tjiR56yovILP+VSGu0fTBnq+j6Y9EubhnpQBthSb/2ChVz8Cee68EUQGBBO/dlQ8v0ov64tecqIgR+yFUb5uB4a7UKYNtLZmYAJmMBUIIBOxUl31u8P/NC3+KHv2udLWz9kS5eiy9GvbJSpXa46L9K149XhPjYBE5i8BHi20SW04Xj+eZ7xo7OPHiCMDb0jfcRoAr4OoS9/Ef5gHPLIg343C0JicKCdh35Dp9R5Il/lUF58YpMlDx7+8IfnxmfPKRt1oB2q8iILP9VF6et9XwwNZAg8MsOxRgOjGdguuOCCOj8fm4AJmIAJmIAJmIAJmIAJmIAJmIAJLGMCjFDgU/UYPPTjFT8gLY571ateletGMNqBxXLp78tQgYEBY4MMFRgyZA+QTaAvhoa6oGTCoo/nnXdertVwySWXFL7jqcKpUFRWFh7SYHmh4ITbmYAJLDsCPGcoBp4/HCMUUDicM0SKRWBQEHxXHMsrQ6dQTDieVY0sID5yltQpf9JyrGFYyOOcPNjjKMeMwRllJBabbf/qvqT5Tsb46Ds2FDMOznBBH6q+CpPuFPN+6EpkSz5yuQbwv++++9Jf157r0Fwv1odo0ugaTUauLpMJmIAJiAC6Cj2HfkOncc67jb30qd476FX0nd5Hej8R78HqXPJGvmQjiw0/dD2j9jRyDz+F867l3ax0qo/3JmACk58Az602dA3PNhv6gGecZx49wzF+GrGKTlA80j0Yh0z0GG25W265pdx+++2Zl3Qgeo0w5FNG2p7SQ2r3rbXWWmW99dbLr0Y+//nPz2kVjLIdzyELuZSffJCH66uhQSDvuuuucscdd5R77703OzEaJqKKlOBGx4HKAcPOBExg+RLgWUWxScnoOdQzqWd5/kh8IWawGVKKQQLlhzFChoelLbXKgRzKQP448q11BGVFeRGOAlO8jDzV/4Q+HBpuFguibrwc4IKejAF149auXwxgPTzSNILzGpT41Gmc43RfZHmq8wz0HxMwAROYAgR4f/DuQp+hYzmmXYoOpVEtnUs8NvzxSycVvBS/gaE/pUORLd1NOdQop1w00tl4v7K1dfMUQO0imoAJBIH6mVc7CjDSAxyjBzhH5+AUb2Q42ubxT3oiA5fwD/qL7aqrrionnHBC+d73vpe6D32Dzus1mmHHHXdMo8IWW2yR0yQ233zzLANpON50001TL3Eu+RSLOtR1oT7URTq0r4YGlGY7AwpBobDUyHqDn50JmMDEElBnXspgUaW5/74wNEQHdNasWV2FuKg0/QxHj6DMFre8/cx7WctCb7LR2JwIp5ff0rzYJqLcztMETMAEFocA+hX9Jh1XGxrq9Go0137L6pi8aDfzTqNcHMvQsCK+55YVR8s1galMoK2b+lkXZhh84QtfKEceeeQYA8Maa6yRn7Fcd911c9TClltuWTbZZJNcT5H9dtttV7aKjzjgGDRA2xQDhfrznGvEwqLaj301NJAZm6wy/YRlWSZgAhNPgOdbDbWJL41LYAImYAImYAImYAImYAIm0CZw9VVXl49/4uNpbKjDHvvYx5Y999wzRy7wsYYNNtigYHxgmQNGLTNlgmnUuNqQgFEEg2g9YmFR/YK+GhrqSvjYBEzABEzABEzABEzABEzABEzABExg+RK47LLL0shw1FFHlZ122ilnFzAta9999y2Pf/zjy7bbbptTIhjZ0K8p0e0a2tDQJuJzEzABEzABEzABEzABEzABEzABE5iCBBh5cMUVV5RTTz01P3O59dZb52gFpmXNnj27bLzxxjl9gunQjGJYVs6GhmVF1nJNwARMwARMwARMwARMwARMwARMYDkRwMjA2og33XRTufLKK3M9BaZIrD5r9TJtYFrh6xFMlcAxHQK3rJY9sKEh8fqPCZiACZiACZiACZiACZiACZiACUxdAqybgLGBz9TfeeedOS1inXXW6S7gyDSJXoYF0mlxyl7hD4aIDQ0PhprTmIAJmIAJmIAJmIAJmIAJmIAJmMAkJICxgS/ZYDTQVyLqYrYXcsTIIENDv758Y0NDTdzHJmACJmACJmACJmACJmACJmACJjBFCWBEwKUxoUwr/JchQWH65C+GCDb8M371KeClrb4NDUtL0OlNwARMwARMwARMwARMwARMwARMYIIJYCxgNENtQMDIgN/w0HAJc0J+qh5DA3EYvdCvEQztqtvQ0CbicxMwARMwARMwARMwARMwARMwAROYYgQwKAwNDaURQZ+t1GgFDA61q0c11P79OrahoV8kLccETMAETMAETMAETMAETMAETMAEJoiA1mbAiMDaDOw1JaJdJPxxxFkWzoaGZUHVMk3ABEzABEzABEzABEzABEzABExgORLAeKBFHZkawTEjHHBMkcBvAcNC2BuYUoGb1lnTIU+W8o8NDUsJ0MlNwARMwARMwARMwARMwARMwARMYLIRwMjA1ydwg4ODXWODyqlRDTpnv4Ahog5cgmMbGpYAlqOagAmYgAmYgAmYgAmYgAmYgAmYwGQkgOGA6RM4Ri9wzobxYGDaQH6BguP5IzHyYf5INyzDI34/nQ0N/aRpWSZgAiZgAiZgAiZgAiZgAiZgAiYwAQT0hYmucSGMB92vSjA7orMcg+JRRE2nYN9PZ0NDP2lalgmYgAmYgAmYgAmYgAmYgAmYgAlMAAGNYMjPWXY+c8mikPgzhQJjAlMoFI8i5giHCNcx5/1wNjT0g6JlmIAJmIAJmIAJmIAJmIAJmIAJmMAkIKCvT2BYmDljZi4KOXfe3DQ08NnLtjEBw0O9iGQ/qmBDQz8oWoYJmIAJmIAJmIAJmIAJmIAJmIAJTAICMhxQFKZO6HzctRjqL094RMMkuIIuggmYgAmYgAmYgAmYgAmYgAmYgAmYwBgCHtEwBodPTMAETMAETMAETMAETMAETMAETMAEloaADQ1LQ89pTcAETMAETMAETMAETMAETMAETMAExhCwoWEMDp+YgAmYgAmYgAmYgAmYgAmYgAmYgAksDQEbGpaGntOagAmYgAmYgAmYgAmYgAmYgAmYgAmMIWBDwxgcPjEBEzABEzABEzABEzABEzABEzABE1gaAjY0LA09pzUBEzABEzABEzABEzABEzABEzABExhDwIaGMTh8YgImYAImYAImYAImYAImYAImYAImsDQEbGhYGnpOawImYAImYAImYAImYAImYAImYAImMIaADQ1jcPjEBEzABEzABEzABEzABEzABEzABExgaQjY0LA09JzWBEzABEzABEzABEzABEzABEzABExgDAEbGsbg8IkJmIAJmIAJmIAJmIAJmIAJmIAJmMDSELChYWnoOa0JmIAJmIAJmIAJmIAJmIAJmIAJmMAYAn0zNMwfI7Y5mdbDb0GvXikXjNX2IdWC8hf0GZtOqbQfG9qcqTxtWQtL00vOyuwnhm0GNdM2z/a50vbyl3zk6Zj4tXylH2e/sGS9smyJmd/Jd2yO9VmdAYlbZVVwJmmlGxNWZ1zHq/17HPeqQy+/HkkX10vFJL6OF1bCOmxsUcaejea/OFJHYy+ro96lU9l659qE1jVeMN7CQxeMP8ZnYdkvQvCi6jNW9PjCxoYo1Vjf0TtjTOlHT3oXphNeyxpP/qgoHy0bAgu9RMsmy/GlLuZtoGgSVN9J8st9OyKe40bupBSQ+XHQjds9iEgSOuonnyZs2miyjsgFd5GCRNNCBvmQYlTcgtHxGc1kNLyVpleU0cgLyaJO2JJZp19exx0iyyu7nmgXlfn4mJZ36RdV0k54Xaz6eIHkBOKooY7TY9F/FpArSgsEdGUtLAel7kauD+qEdVHHSVRHr8WME70bpU43btw6Ujdl66CVuJ2kFdxK7NNlQYB2/lht3b4q4+RaRxv3wjUBdVSkjRt9nKymgvdSGxoESfsFKh0vSS4U78uu48XZfUlHwLRO6tzFn4xbJxhVZ03MppvXxGhuA/7OzxdyJB+TWTfX0YNOdqMerSMEE6cjLwtfFWdsPlVAS8yDPW3kt5gtobBEPDKSLKYNRBmjPshNWhRZxV4YC+L0ClfabpniekReRB2AfZt/LxndtO2DVuSOrLHMmzStmGMFdQLzXlBE7RWzrh/AOM/yK0K9b+65+WWESB18DYjmbx23dbywfBVV+XcvTGaj0NzPnx+MKWaUsbnHm5yTTfoTrSoNkdOr8qsORy9uPDudC02KsVEan/Qb/ZOxCdGW2fCncmPkhH9zDeMepPyZC6mb/Jqj5m96pj/lGnXIGz1vpI/x6wRyCal6c783vMZWalRmV2AjrgpoDpUf+5SXZafMmcFo/Co9TxmuSVsFjMYeLU4ngwVipX/8oTILBHaFVxLHHnbEdtPqVkDWqDjF0r6W0cRSiOqktArlnuS4vh+BT7qMQ/nbTkLl3yOKghrmnFWRMn11rsjaK6iTj6LLW9HyHomSNvdjKSONR3PejqxE1V7VGC9qHZ7H4tK5hypRS3yIPMpL3g37URE9y0MCtl6BLb8s66i47pGSt6J3w5foIDPpSFwcgXV8MuJa4arnAx/uvE5IVHVUML40M3StM60i5knrz2jSVkAkUjrKkPe3PCJqdb/j27s0lX9HRLesqpfK3s1jrOwxhaqyH+Ovk6ouTZkUMHZPtG7UjCjB4dsNUBrCWv7hlZw7DPJ5IlbFRKkXEKeAel9lD/OmhbEE8lrpmwvXKrPyy7idBCpvnM7vRCekE5r7XuUf369zF+S1hNpozOZyIzl807sJU14UbzQ2Z70dcrrvp0iwpOlTap2olU3T7gnPKOTilKenPBKSRy2gAZBymzQNq7yPiJhQxrYDMl4lpiuuLn993KWBvEiYYfGHY11reVf7OEyX0Wp5eITDq/bO86gPIqGU6TJmj4jy174jMxO1ohOlzofzVnS8RiMpsE4kvyZmxh2JNjuFbdrs4dWJP8p+NJ8sQFuGZJG1ElPvHvEIb+JEfvRHxnMq80KikJTsVM5e+dXiiduNk+ma0K6f5GVE6hyZj8l/JN61wSrSwmqMPgu/LrhupnhKhgThhzfn8kPk6HGGx5+MWUVfMIZiTp19XwwNdL1GKrYCMzwcF2h4uEyfPlBmTJ8+SmVkuAzjPzBQ4s+of17MEISABS5IcwG4uXjhdG+yiDwQG/9GQu78KMj0yGvaNOSqJKNZLHjUKXg7IMoyPG9e5jM4Y0aIovx09CL/bFzzwAy2Uy31OS9nOu3Tgk0qgAcpcWjeUJk7d16ZMWOwzJg5o8wfnl/mRX2mDw7GVjOPDIQAXBy3zkeCKf9wA8E10eZZ50/wmDt3Tl7TmTNXCfnBawHXpO9oiOb65jXu+Oe1iuO4huSXVy4UEhxww0NDWYLBKH+UIjaViNBRx31ACEqUhuWA7ru4F7k3sBOkzOmRg+o7jPx54R/XdGbIz8xHZeoo7toyNDyU8QanDeZ914QpAXVhw8kvDlFguDH3NH6dOCj8jtIPwK14mTL+xPWbOzev4Yy4H2fMnNlNz7M0EvVDgU8fiPs084kU4c+9NDA96kR9ezhYwQw3PD/kRFmRgd/wSDCHY1zw6XndR+8b1TSf/Uir2igX9vUWiMsDc+bENZlfVlllZsprUjX565o1V7V5GfFk45pGDmcDcczzF2miPKOlCXxRADhwf06Pug7H/T43eKF7Zsb9v7RuKPJM+XE/DsbWNH1EQdKb2lOjJkTnTXhz1nBR2ZtLH9coolB2VWpkqKPL4vmVn3Lp7smkdsog/FClXBseH/ZD8yA3vwzGs88tlkkjEn7T4kFpkupO4Ex1aNITn1BCsqyxD+2e9yS324x45hudGxKjUuj+6ZHRNO69TBW7rltIwbtx4iDux2HuX3jDBi/KS+ufrfEYFY9Xx1th3Zw6/nXwcNw0w6FvBqOM1GFu6Mfh4D4znq3B+r2Uwhb8g2zKw93QyylvQjmeFzqG9weslka3kxfPE/c3V4T7Gzo8A+TVefzjqHIRpudjGrogztEZ3Azthh9lZcOlvNhzD3FNU8f0zIDYS+CCA3o+814MeY3uDl0kfR7XCR0/MBj6rrpWvCWyURgyG43RlGkorjO6ZzDSj7leqqiKToUX5RJQEAl5CYj4HPNgZTuhEcC9EaUkMMtCifiX/nFv85xwP9AOGhxAR0VI6LAsQud+b/IIHx7kRbnFqEsWvSOnHZ0cutWPazOf+yP205JxNyT9KHe+RyvvbBt1nlduQu53rjHvq+mLcY0XWr0o7Fyen4iEvLzfOwkoQlWMUTFcE0I6gVkf7guuUw/Hs08x1WbINkRcB/yQVG8kr6XoWPt2+Ai6LK4tbdOBzj3Cey3byMEM/3x3kzAceeEkr33ehI7+nRdlR36yjvoF9ry3eL4kYzT2Qo6UkaKQOISl7opSDcb7KN9TY0o4Xg5ZiEZSXv92PMJ5PsJlOKfBJLd4lweTGdNp5zT6pxMzz/mj+3WMVMqfG7LjmECd5/MZHnFfzEcf8ExVuiNiNlGrPX4D3Ov5L06qzKKJke8Pyon3vJA5FNchn+fwG4w6VdEb4QikPL0ckasEWexOPI6zbxBl4d2RW0tG3u6IkAwBA1TLjUTh50R7DLfqqqsGi5Af1xn2PMe8c/UObOQGg67gscKIr404pG27edF+p/1K/2OVbLu2Y3TOo6LIGu8ZVaqh0E16nlROhdV7yp7yolwUP5+LqCPlbLYmtp4fyp7PaOe5Se6hxR+Yc3/2ZVdbZdXgwj1JCLvYh7zcN5k1FwAGbFyLFMIbO07yfoOPLlJXUvg1jvtqKHgRY0bo3qVtK0juRO77ZmjoYKzwxUM41BgaMDLQwBXw7ACFgskbMsEH2c4F4xeyFMKN0FEM8+M4buVQyzQimo1zHmOUnjp9w9EJpPE0PRqOyE5Z3Fm42HGjpXDyShce+OkUv4xDcUbyYhOE8s4GToTR4Ml8QgZ5kw+NYMoynqvFt+Pkw9sULIPoUGMQwJ9ONXvyZK8GYVN8pFKg0eJTAnXMeajnxDYzyk7nH7lz582Nh6QxPJAZcvmHazo5ISEemqYByINC3pzHFpkiH0WR14W0wWhaGi1Cad1/f5Q78ou8yDPLRRoSddnECde3U/TGv8mfWNwDQ/GypExZ97hvotJZpjlhyKAcGH1Q7Cp3B0FTljjhZU29MLRwTWam0SNevPECSI7RQcn7jg65nvco0kgYZeZHh2s6jSpu1ShL/pqR92BTBYwMNHa4DnlPdDNvyp4KLQ5hqesQp1l+OFLvvN74NWA4asKTVfChvl3X1DLvgfCbGy8GFPYq8WKYEZzhh1yUEga9fBaCjfIejjoRhqFpAA6RB4VI4pEVDXKOKQp76sc+O0JRjjTcxfNEHhEl2ZIHZyPsI+EIrCKMTSXvZJNpSAdCXgpzHrg/2a22WijrSD8Uhgye7LxmkYiy6qXANcrrFOlJ29z/cW04j/JkHiEDJczzR/1Jny/5qGve77zYQr9wTyZDrkHHUa7kF/kjKy8HBQ3HeUNGARhdmk4vin8G9040HCPTJnKTrBECkzjnaYETXMin2fjbiUwm8UbJxmzcr5SbLQsSYZRfneCBqEPTr+5IY4dH7iv5nWx4Uc2jkxDnM2Yis0SjAg2NkYeGYsNwJPg3lW+e0yb7RgjlR3wULTeO4Zz3NiFx74xQxngeMELROc/7Lsqua0Fd1Ylvno2QnaDZU+5wcMic9DdOMyz4xfVKoy73QjDo6tmoIGokCxY7EjTXr8Mv/fSu6FzziJD3U+wpS8qOe4bc0Vc803PmzM17aOaMmfF8D0Yc7g3dMxSK2JFb5z5p7kMZQhq5GSWrpwqSII65ptwv4ZqGTJQ1xcWfRmyWK1NF/Cp1E97xyOsSx+hJDA3UaZW4vzHqNOWlzI1MskUSSdF95M/1I3/qz/NCXRpDesYKpJ30pIVVk7rx5z4NvzQgdfyzQkvwh6JRIcrIRvm5rs0F5JGKeyru3ewghT91wKVRJMrMvUatRkK/x91RBmm0zgidH7KG4lrNow0Q8tOgHj8EhIQ4wxA+N4SM5PtgBj8QUJAO98yA405e9R7vfA8QnA9D3A+kxVE4HhrqE9cjdQbMginsOIdzio5z/FJ2eHBfUSfCqRMGEO5nnvsZtF/iHkwX4ZQ7XcidT37kER45QqNTlPRoYlGcJh/OO3VKdRHHjW5SxCYQPpRjICJRG5hR1/nRfiNv7pm46I0ij3DeC9wnXDu2iBl+sfGsRNm4n6gr7Y3sDESbobnOTX6dImUhqAOu66f6NN7N35BF+Xj3QjPbAHDoXAfyGpM+/LNtE3v8yRuX7+DY6/2oc8qc4SE7ZSk+cgiI8PnBQCo3I+PdORizzwT5lGZoc6U6zDr3dVOeJhXPIM8yz2S3vRcyYAnTRud2cgj/vIdCcvP8RN3Qi/FPrGmXYAwADfXL4lBWGLU4qdzdSCTCac8xdc/nivZRPG9xLSkr+ROv2Y+yyCT86dLJk9E/pMkz7SMmzwvv1HDcW9wzqSsjb34kadre3GcRHv844L6h5uij/Edgp0w8R81x+EV4liV38YfzTh68CLMtiVcITRGEhz8u7+d41wxEwHSeDfLiPoh/vDnQ/6Qb6Bhf5sX7lHan3uWkwyjeGByQGRuZ4LTXQT7MEU6UyCfvNZKEY0f0zC/YwBw/7qPmfRwn4ep2GufcW3AYCLZ6BpqE0Z6J9vD90WbHzZo1K+8jjCS0AckH/TvYMYhLbvce6pSLtJQl+2Tca5Qrys79wR6nZ20o+jTkR9gqq3TaYxE/40ShOtFTRt5T+HEdMoB7MKh35OOXejXyhDXXMAK7cVJmXCeS5js67gfagTDIe6t6DlU+3g/ZfgxZ+RxG3uTPPRZ3Y7n/gXvj2s4rq0XZV0E3U3bus/pew4+NqlOm1CONH7o+jqId3tzPJMsCRiE5JFlkmPoXbT80N65duJnxbrOhIUAkpNgDp3Z5m3UCeUelC7rzO53o+sXFgx93fghBiUQiPXRcKO4WbrhwqXwjXEoBPzopgwOrxhFxIi2N57hg8dTFxYqOVuSXN2iE0ujlomWjhbRxM/BAYN3MbDOfCI+0+OevJXmzRGT2nREM84fmZCeeG5MHfdWwcuWvxnm3RNzFdCis/MWPOsoFh/vuvS8fJF4Ycbtn+bOecZNSx3zIYMsZPIIrEnhoqBM3c1ZI5aGOKO/YUjkFMxo0+WBFxykbmlnPkMI1YMuGRdSZJyL4pKEh5EyLMmVmoZSycxu/TqeLjtcQFtLIM8vEgxXHNEopO8ouryNlYuNeYE/ZqFPEb16ujTFhcPXVqVFHdnTw738gGxp5XTpKo1FEIS7Ky8uaa0GjG2bz4tpzG81AKXA/RHlTHuVAbtYxjvVjd8SNnm/en1jv6QTzEmfkQGN8QLmFEQjlHcnz3okD2KfyCvncWyg+OioBtckn6sa9jSED5lw70mbDEkEdBqP3OWVrXrT5a0eE5zWLsqTii/JPm4GRoeNofEWcfGTSKwsXckPMnAfC+DPUdALjnpgf5eCXvaxARMvsY5evy5ChxuNMRhwMdK5rCBpGTpQ/KOY9Ni1+kZkf5UgzQbClIQBiRMajEzyinlnX5tc56sp9QCHTQju9gT4y7wFSxIu6U5+hucmHa5wvAK5VlCt/5Qu/uCBNJpEH9+6ceLa5r7jG5A3b5hpwjUnaPBd5j0UY/POeCzaZR+feIy6MpwVjGHA9uZ9SD+iZ4rniuuVzktKx/GT5MnO8KGNu1CrujZTf8c8kUS/84p5htBHlGYzyz4xnKkcBAZj7kHuT5yP2atTzjDSiQ0bEaRoxTfyISvRGB8eF4Apzn6K/Zq2ObmrUK+k5xiF7OHh3MuwUPXReRwdSflhlUTIWZedlTSc1ngNGp0SZZsR9ReO20Y15FbgBGiMEe+qALog99z3xs2EAR+RnHFhTw45LjFGWDnPKGMFlOvmUTgXmzGsqnUlIEAdZQQ7CJY9GB6XcCJMhh19yMOaiX9EXqeebVNG56vzSFRkqXuoc7g/+oc+CK1lxn1C/vKaRnnPqyXnWlQ5XpOM6wy47xZ18eMZ1L2Zhu5WnGp06ELfy5zRq1GngNvHIh63rgjXl5vlIJPE3G76wzDqEjLhZaMhnhz7ueRpW6GbK02zcJ/EuiXryHORIuEifxlXiRB25IFUpu9kv6oA86bikwZpyZ/3iT6cOQw88UO6/7768zwbhHGWWXuW+TwMD1yj+cd2mrRp6KsrKe35O3M9D8I/zVWetVmJcShYnrlI8b2GojnQYnhtDYQgjb23EVIWqvTqXXBPu1eEwRvGIDMZ9Ew/uaKLheGdEY/WByAeR2RmOcnAsl/d4lE/3BqyHQz/nfdN5Rhh1MWu1WVGv1ZQMK2EYaUNXhrwZNNKDCWkpJkzG1EEZVnUgIt4YGbh/MITETRadtOAX/4bmx3shyg4f5M3kx4jYMlGUK99dcb9wzehUkTe6ERnI5QcGGvTcRzNXjeeJ91/HzQ9dMRejUIZ32gGRKsvdiUNR6/MUKgHaU/ZO+eGI477OZw/lh8ysKDoratlRilHblJ33M3UKRzj3NOVGFu0TjPeDYQBPF89OOu7JiDs3z9FHkf7/U/em31oVWf5niMyCqMwyeC/zIIMyCU4MzlM6W1nVXd1rdffrXv2m3/XqtfpNr16/1X9Fdf060xzMNB0RUEFBARWRGS7TZZ5BQATB/ny+8ZwLaWVVZXXlGw889zzPOXF27Nixp9ixIw5yXGuvOFdRte4GB5+0zqZU69yiOZ0dfRCZFV7kS/0gaNtTDany68DP9qlzLe85AUOecWCkDPeBT6S5bWpoTMGKP3+dnHDwmwAE/SOtu44uFPmS761zc70pLDizoBqwrbZpl6WjcgH2XbAjm+Cd/mnJtfrCcuqT6E3pjvwaoO4W+6EOs0JgCZ+PGWZOMOlf9KKd0aW0WVzll+gF6BU0xdk+99nm7B0Jq37rnlLcswpg8Im/CTBx0i9ygK/uEC9tT3xS7hlkgPMTEOTpchn/xuwF9U93dEDkgrqCggX4Jm0u4bM62dMXXeFkR9qXtqXQT/7wtCiCw4/QuQk2NDq7KSye31+qfapPFd9QvPOw3dTS5zxwBV0lravfqB7lQ7uSoQQeP+A7+lzsKrDM+DHQ0GQi6QvEdsBPCfBKThGRps0BnOg1zpE1IYaPLUs5ad86rjmYBx/7qAYAtJX2I/DBK7hRVvuUf5RrZEJ/JjYNnJwIk+e7DsuDu/Igr9tmbbs8X+1ZnbhULrTv3lcfKE9R5gDSRntdPCKb9iZ1tVrMGbpcJYNQnFr13YTMxSuwEbThz+jiox6U9xP5pFx0CHjIhj5jm+UbMzTiI9g//JaFPUShAVWv/Dz//s0yGhrChOYSiE8IBMGuXLrM4Pl8uXj+fGa+TXXphYCGoDBHnE0HJyrLkL3FslBZRpPa0j2KwU7LLwluxxkhM52lO8xSYcngMo1K9jLCJlwVdk+YXsG0nIdOr9ctK0N6T2axvIzYE6PuWSa3nMbIQ+X3HYZf4y9z6BgISxhdqKVk6+df4pQUrQLgQMmIm+25xKzvt99+G5ziAFoOuI1DLx3EV+b1exO594c46uAoYAqLv6PcwT+4WQPXPBw4aiSc7TbCmLYDI32AE1GNGwIBLaIUqM9HK643g+elKKrefXvzfM8ogUQFganTF5rCFLbLvokAS1+OGEv63bP42BKNqnV5ONDt179/BvkqPTMzrM+ovSRunP3gzLMOxlRCKiwNr2cHchoj8dUgmSpWBbn2UyLP0Mi0MXETB53Ri0RdvydNyj7X4PR29h2lBcjwn4rYw7rFRaUYpwAF57UoQWiqQ5sy3FeJNcse5D3hWi4HQCwnn1Va1P5tFKs8b180itPAR4wzODug0Oj4nAYh9AQn4XnIn7YzgQPKNzAjeHZmFCn4UUf4g2viIc2SCaMDAP4XcfzD69Kef8ocf3BQ6Uuc1Rha+Tf0sC/o/xYPyG3JAojiR/kjJw4gDEZcgtaiYZ9Zzn6WB2xPaGKboE/4nRLykn2oYdMJ+c62g4d92NDadgsrBlI8g1OdvW2CjilDHdbjQZHwgG2X16uxbUX2Qw9niepHHeIsyjV45Sr01+gIRhg18wp+5ncTZKjXrKXWFfypI84CfGEaofJ3XTepB6uDaduVUfkrEAAhlPQBFcrfltHnv8oZynER3Cijc6wD0T9y1CNwXCKRzANKfE/w6AfkScxvEgCPxqhBc+HbHsVRWl+zQcAVd+ksPqYSGnQxOyX82CJCAiMOTqQjvKcz79nBaQYitDcZM9DVMvKk/ev3aDThcKj31WPqJwen9ol06t2zdwZnOlGRaRH1keBX8RSOl6VNnCB/cT8OBEW+u/hdHHVl29mczNxQX4O7NGlmfJSx6qDRdmDIIxkkQ6yKN33FP+uRv8K/wZ0ghgFovktrz7f2vzU0UJfZr8GyIgq29Wi1Pk1qrjVni0bf0F9VB1Y9H7zEjf5Rzzhz5BmUgrPtbuyHpLD/dMqEZXtiZ6g4tOIh6/HazcisOl1bZx/3hV7R3eAu8Iprg91fdw7NeFb9Yv8qb9JMGyKNvj33bfn2/LfIbyuwwTVlWXzkzCvfIXfUb/BSJ787PPEjg6DvoPF3yiTYi7f9ataXfVr1LzjDR/Ks2VTKTtdH1G00DfLUBBeqHNfr3jTb4go6R6dbvk8mCbpMummntB0X0JX+7oUjLJ/cSCT5WL1nu52coMsiG9JYe5xgHLD7EWRXbm23PHURfj1/8UIGXD3RdfGJWvS/sQ35DrrN0bQlWQz2rx9umrln72lTGnnOoMR+BcfeBH5765OIPL/Dr+AYesMHoQv1e9h/8rf85GCwDwEe5dS2e08+z/PQJzzl4DpUrvQXBrXkUs43/PayR7oGeXNgZ/9qJ2yBNlZaZKArPaCdfOmgQ56KAPBbPpdftIMeBt9jO8HbvvLeLf36lf633soj1d7ZOfK87bpEn4tl7c9q5wIIHMTZfqy4gynPd31Pofpbm6cfpO8ovNhX6ORgVhqJh4dyrV0SR3WjuqXKqD4i7add8oQyqSz1A295XXrYTp+N7oT+tuXChQvo6kvh1bqEsGKXv1Zpva266/egkTYIyyIutVKmdOrVw9JW3hR3+15c1UE5eCD6iPviK22FY9nYVXB3sCkMbWrkVNm3LkCk36yT511q+S1jBr/3RSZin4EjnvJ61XvoZctbOe23zTK5mEs7644doS5pHN+Euv2egSm4mCkjrQFYgwfqGj7ibMCAIXACDWkj4C9h0ww2yE890QOZaEGPpu/ERNjI83n8+Mvoq37IRV8+ypN4ezRn+81LkVG+XQOH6B/OgtLXEn/lNPKkPo79atkl2hf7JN/wT72pL+Ehj6v7IEKeF4Yy6Mdr+paSS7w9xEP9ZGBQ3vKK/aztsO0pFbRq+eYZaZ4PFygWPCxcaa+/yoCfvtF/V8951gbZ18FZPhGvFq8Iy3v2kX2vXPuMPr08oN6V7/X7GnsX+eEZ22xfKrt+giNwqu7BZ+WebZMW8qfwbWfsEjjWa9AGWLahZ28DSdVfVe7i13I2YEGhGtgBP3G3H2x/6rQvqdc+c4km0BLgTeYOdeqvUXn6Vx9RGerb95YycOAgzjXI3AIlGj/74z8daJACV5gJvnzFGd2WcqHv0n9SiP44tG9/2bplSzl4oLOcPHkCAXZdLE6RSodZCItF0cD4dk6UhYyt8HC2Izx0RXOfs4fXIzwaRbINGoZXOJSgLgOuY8Y/BVYnIULEfZnKw9+Ku0JqfQ68vKfBVOgvEpm0Rp0LYcvI4p6BL0yUTInUGXA3/PEpVd4NR21K8POq+Bvlyl4KCFt1LC6GQa2rq03gEwUBMBVkgALLdvlduigcGsM6mFM5iCuGHqfc6GcEWXwoaxsUuusCVxEThveDV6v+LqHn2cY50WD6fCPQaT7P6aBHmUtTaBP4tlGhavVn8KUeFZ34S/s600VDQMN+MqijktWpsA3iVPGQCyrdwha53opSUkcGnQBRcLlVFQeGIm3ivn0lrew/jwRmWkpOJZsA0mWDKAzUqD+DdBSNdSXdFZytXz5p+tY2y0PVOa5094FQ1ML5oHBuoLl42O6GRtKpFq3tFJ79rJLynn3voCs8AGzbII7OQolneIWyOqvS2kpV6sLxOctIE419cGv6grPtSpuoJ/LG0zFYGjjqcrZLvhTXtBo5EUYGoDwjbI1B6EP90jHBQ3EBVnAnBSyzZFywTfapRoLqGTyaSlcdwPAEP+qgtvKqvGG9CXCkZTUC7OylQRjblzbTiDgP0EQZcBDvc5lFadHeXqlBPfvTT3ohsDViOmvSLA4V9xIUkg600X7wyIyu9ABmXb8ptVsfcM/3n5zzoNjw33rjHHOxcYKUDXGJ00m7EqjCwXOQbj+LpQ/aP8lm4FmdFT/2CmjHmElIv5tCri64BQfNs7rwR7KO7CvhGGS4xiysT1e4QS3wheEBGuGzH1WCXLNPNMwO3uw/dVafPjhQXJNmkU8qt0+llbypjlQ+MoiC53U6xMH+sq9rgJFnwSltbNHIunr2cjDaMzRxprhHd4Iy9KlwHRjJj6LvIx6RyRbuwmpgWsRC9q/9n5lMGmegwWBA1S2WqvwdWQQfg2sup1AGfDY6TBg00LoafSRM22I7PTfyqv3w3oXvLqb+AQwe1cHqmPSHaFHPjUerKXmuXq84BTv/WADayI/ypofyIY4GcOybZhbTdnnYP5FB+MBL4Sfw9BBXYVlS3KWZh+XlG/WvA2jp3pe+drCtMxeeCTIp/h/6I6/XYJPBz6qj+qDvldnzDIwcHMkjKSN+fGyDvK6jl5l32mv7b9aZBJ7O//faCXmP69ol2xX+ol8ozvNwhQFn+t5+aeSnobmNsPVx9LnY9d0bFpI2PKsPIX7NEs06wCC4Bw5NoKGpP4/6bItflG9pmyxI8I6uaTnfsQXANuiSIBXPeD++hvILnJpGXpELWFBSH6Qezl5rjrSDH9doqFzUzTRc+NfZM2dGpU+yRiijfJoBKm3NaFD/JCCDjIWf0EGZvUXmFbsmOK0MRGdBG21vBvPg3fhs2gR9DW1CRbPSvcGxwde+8Gh+5zvXxNxb4u3Ej1x7EfkRbm9+yxvqUnndPs1hP+W37ar6PM+jS+QDeUL5a/w36eCA18GLdMngloLypDLhAMdBZx6GqH+GoxVyoWr96+fwSkUm5ZUfgwvfUe8F5Kk3v13v3dBHu5MjwGu/K9eBLf/TJw5SxKfizyCS++p3/VKf12Y5sFE3JdjDc04SGOhJX9POppKuNoRWXg2xW3Sjja0CDe1vdu8q+NW+tkdMH7eIwQB9A+v0t+A8q4+qjFSf2n4QP3WPPC3/aPv6MElnOfGO/uEsrtoJ6/oWXaBOsu+jc2lz7WvrgafBKVpdRCkXnRccvMA9kFGPKXPCiS/BtcCSlsqXdOOsbVOnSOfmPg8lo0GZtz7hJzABn4hTD2TVQWMzZlBP+THYJe3NxG3kqUKgblELdqF66ClfK69NUDDyAw274QPUSb+GjvJj3asr+su2QAHb6RFdL6/wXX9Pe5ASFEj7wE362j7bIt51Nh2dz3P2bfioRWcxFG8P//JY65tn79bGhO71UgrVYg0PYEusn/vRZ+p8ZDC8yrXYIGheJ5JqOftK3QLSgSff+JFX1K3qGvnHoF1dklEH+5Fr6NP4VfH96Fufsx77X5ro36oT0iCa4DXbYDlpkwwXf6PvamadjcXGS1PwiA2SV7UFlJc/wosSiI+wbEN4i7O0l6/s13yklc9YH9fFe8rkqWX27Dll+J3DK1x4UTkS5M/9+NsEGq5obIxq1oFclu3rk/thXLPt641l6dtvl3WffFr2rt9YRk8dV0a1ja7GzUEwHe5Mg51TncfKvHZ4BCJUbliaH17nGdMIv0PRbVj9WVm2d1e5k1sz7p1VRtx1V7ntttssWDscJpJ9VfQyW5QdzGJdKjfrtR6VkbBVcJbrf2v/cvbsufLmP/9T2QO0h8eMK+1TJpdbb789TmrwVUmpfFVU/wGGaISrc++e8quPVwG9lH98dEkZOHhIHTjBgGFWFfANyjXKmN/iW4UOZUe7nCG9QPTXZ6qTWFO/tm/cWN7duqU8NPzOcs9DD8YABZ64KmC0X2Uq8l4KPfitAAg/qoTbGsRKpzrQ0Nk8dfJUWbry43KaUs/NX1AmTJ6cZywn7HpWYaDMY6xbmxUBVxysy1oVQumhw+ZZw3jy+PGyd9OW8snhziTA/uPzL5Shw4bFMbX/FHYdxp7wgeekcvN8b5xh4bm5SwJC9jP46aA5w2tg4RyR5n27O8rnX3xVznLvnsG3lvsff7bc2q9/BhdxioCpAglfaowY6Cj0DhSkVRQh/H6NupzV52YUoMr68MGDZfWf3iw7gO0CkCceeKiMmzChOu46ScAO7TlLg0bpqYRVjBo7Z1nl+BsdEPvZtm/+akNZunEDkEtZMn5sGQ3sOwYOlOBVEao06UN5JEbbQBx0MgtAJ0X40tlBuV2V3/C+Cvw8PPTZu2+Vz0+eK9OAP2nBfWXoiBFkmNwa+BpQed2UWDoREa/ZNgKKE8JZw27wTLo3fC4ufo4ePlxef29pOcm9V2fOKMNGjYrBjiMAzukry/oPhtTYxAnghwqfy9AMVGgPFUVp5zf1ugRq986d5c2168pU4D/4wvM4Yv1aRo1BILhroPo4AwRclXt0ge2AHuItD1qxRlD+dQZRuus0nDx+rKx5+92yE9iPtk8uM+6bQxo2Dgy6T6MvLfyvHKUh9Q848pMCnqEcuFMGx+0mHH8dOB2XYyeOlY5dm8rqjft9urz8xMPlrtFtgRPDRh0QI46E8KvjrqwbcVdvQQ8JRu2NQ2X19qkzIIf27SvbP11dvqGEtHn4+V8QPR+YYI/wJaoDMekuDP8aYIg+zm/gQxvXiDuDfNVZLgMB6ODj0OWz37xRNv5Yg3ePz7inTKFvzfTSmY+sQ2OhKkNNn1JF6C5dmu/KrnT3X29mgQxs7tu7t7z+pz8VF9rczWfG4kfKyFEjY9wNGlQ93iJ8YFWAypa08JD3pI/9XI0+g11o4yBq47q1ZdmWbWUA5e6bOqXcNX58uV154hBX4YuTUIVZByg4yrRdvlU3dtWTUlSlLuL+uXPnyof/9ddlE88ubh9V7po0pfRj5lS+kgfjTAVF/ngWzfqn/q6Xciv6FPyVaYv67MEDneW3H63kFzxz/4IybOTI8Lx41QAoDhq8Y11hj1Y16nZpa23qZ/WOsuCRgTP1CL9jx47yzhdflnnDBpV7H1qYAbIDu0phsfgrj1bblCX7Qp48feJ4Wbvio+jJ52bNLBOm0LsAVvZcflZpi+MLXup3nTuDkl73t/rdoAJCEWffJQE9yCTQUT4B7H07t5ZV2/YFwf/+F0+X4djAy+jfHwweUZEyazs8N4e86M+cb/iujjGo0YeBnQPRrd98U1Z8WXXwPYMGlfmPPlYGDLiVjE2ynHg+Tjt0kr9gkMipvCdfKw/RaeBtH4TneUafRhk7fOhQWf7WW+VQC6nnFlb7oS36jsxQdTDFqs65oQ3WG+RbzzVtaAYxzr7KBDt3bC9/WLk6pe5rG1ymzl6AbR0ePXaJZZvOxmpTHUwacOCh4B268M3+ySBBeQLn6E3P6OML4Lf2g3fLp0dPl4k8Oe3BypP9sK1dAyRrbuHvV4/g7tlKWkdzLZfsW+h/+szpsvSNN8s+yrz00IIyZOgwmtTyH7gmb2lP5RF5TR42YBh/QdnhIw28rz/gfftInrd85/795b8uWxGf45dPPFruuOMOfAD0tCEO/ULKN3gFa2Dl8Cyi+VmvRZt6nY99Ju9tW7+uvHPocHly1Ogyff59sX3ygrItLn78Ln76KoIMvbnmgDa6q+WD+N0BuT7C6ZMnSyc+zdsbvg46ry5aWNrHjo0OUF9VjFroNTQGXo6cgO0ZXNXRGeAA3741sKNPs2PVp2UDFmwMxR555dUyePCgBGLVB9VXBFdkMu1RYDgCCzji6qEeki/ts/QR+J8+dapsAPbqQ50p8zA6chZ+h8Gf71xGjH3M0z7boo8wcoS+fJPGucBf+jLwoZ/9faBzX/nju8vKBe6P4DMPvhnd1h4dE50nXXnM/te/Mwu67idV+UjA9kv0MrCrXDW65mrZtOHL8v6Xle6PzJhS2idMLoPQCdrUBO/Uv9DF521HUBfZVhsin1RS5ZSLKeT4pkeym1e8/tvoyPvbRpTx02aW/gNuix+j3rYOeV7+0v+yL6S57WgCodQE9GrDvK8PqM2yb48dO1r+edlyi5RfLl5UhqIjnbixv+xL263+yiel/F7h+bPKnvSujdFWyq+Wt45dW7eWt7/+JnZ74SsvJVO58WnFqQFpsMoJSnEO3/N85CF1Q5K0pwYHlI/vCeCcPX2qfP7+0rINIM8jS3fPmAHvGeAh+4t/jb6NfwCt0pZcr/Iv7tJMXyr6gLrlYyfAtB97GTd90nkwKL74yKLS1t4uI9RsBnDQX9KOwJR1DEjbY9+gnXTOZtPcc+zhssnsz4AuMXi2f9/esg1/rKNSIH//j//tfy+vvvZ3ZSxyK9joVfjN7z/3428SaICW4UPp0eK3ShcFBt9lB4PdD955t2wl4CDd5s6ZXSYxOOqLU6CAZ48EOkaGqAxaGVDmDQPzjFfiaKUOrmPYTNW7wFKDz1DeKz9eWYaPHpVOGjlyVBmIgUiEjF6KUecsEyiEGVy0hKhxECOcCJ71aBA1PP0xjicxbMuWLSsd23eUuQvml/b2dpzQO8otKEGxSpvFScZrBIff/96hMRS/jj17yhu/+z2pe7eUhx5+uAwbOrQrWmd7q+BWQ+TvCJ9Kj3YoFM3miw78L5y/EBopYN5XiLZs21ZWf/RRmT1/frnnnnsyKxU6ct/DCGjK+wy/K3wjezoOOHEcwUNcbCPPWcZI9qnTp8va9evL9u3by6NPPlkmT5rUmoWif6QG5SWJ9M9bKVBwzoLadpWR9xtDZF3SXKlyVtrHLrUAAEAASURBVEgFuJfB0ZZvNpU7hg4pixYvKiPuvDPOZYyZSonSvYHXDQZMaj/PJtAA7gYaTB/XgZatFHadUp0Lg0f7gL0bw3yws7NMmT69zJszt9w+YECUjPwIcjVSqdIATrMmXQeSagJTd/sq9HC/AhWz/CU9OzsPlC+gy5EDBxOsmknwa9LECQl0OUOaAQbwPXuoRO0D6eTg38GjdJIXu2jPLxWPvLlp0+by2aercIJuD7+Phu8HkXJll2ZWVbg0uotHxIs65AcjtSpVlWw216JOo7YqePE/e+5s+ezzz8uGtWvLhEmTy7hx48qdGJ/bbnMYZsNxhlDMdXCKow9eCb5wV/ge3RPYoHdAyHqkTdOOAwcPlQ+WLg0PLVy0qIwcMSKDYXFSMXuEe6iq4bXIL78TWQZeBtKUy5sgoKO/NV7yxc6OjvLhBx+UMeA9b9480tX725XpT/nWwZ+z8JaXJxPxhiaZrYY/7Av50PsaSmctDMR8++35cvTo0fLlV1+VTeu/LM+/8EqZjTz1YL+Ja2zcE6Mjn0GPxln2e1rEBXvaYIObZ17lfFVSMhXpnieXrnxfjgB79749ZcuWzWn/wocXlbFtbTFkCezAzyB1ndbwv9AdqMgvDa09297qTEgzZonRCzrQu3fuYuC4vUycPKUsWriwDBsyBKeKFFzoFobmQeXRfyKeQIPA8KzsR2HDqOUHTrg0AMeYA/8QwaMv1q0ru3fszNpJ+X3atLsTuFPXRqfgAEQfQAcdJPtUaqlnKu7VMZD+CRDQ1szKwZe76NNPPllVzqFvRowYWWbdM7OMHzMmAwAHCdcDCuIp6vzhLzWmztoHwLdt9i1tsn+t2wDSBuzS5598UgZBj3Hjx5f29vYyZPDgyIx0qDpRHUzfgbvZXNaZTVmRmTi/aQ1/bjgM8pwG51XA3rl5c5kxa1bkaRiDI2dPpUPlM2kcLPO0fH/j0fyyiG1KRhAyKG3VNasI9jognD1nThlFoGEAeiyZH9DcMuqMOI0AgALweNXt6gR5yIFv5MA+sbJW/T67o2NXWQP+k6ZOLffC77cRJFHepWXTnzfi+u99t232vcsDjx87VjYyYN9L/z78yCNlCvZDfhUJZ/WjEygr/up3ddst2Hzbar8bOM3gnJkNqWcQrAd7Nnj9KPZDHb8FuhtsXISuuYugpg45BEwzu/Y5uQHpwGkRvGt2kfvKr7xugOjU6TNlE3C38nGQNIUAyVxoP/D2OzK7iwYJjaS567TlD9ui7ZDmBkLMavHIcgT1M/e1Tw5i9mOTPkcHHzjQWW7D17gXvplCEN9ZcAdANSMDfEA23XXDOUBbf5q2ZGDENXWNGSRbsNer6VPfPtU+diywp5SRd46Ic2ygwcGRqcEGGnqBm7rRw9nPDHppj3yjrKZdltF207enz54pa9EF61avJnA0pUyYOJGg4KhyOxM/VY6kZD1aZM6P6zqzdU/km0OeoW5rPQLPrAb2ubNny7z75uEvDQsO0lV44iqPmR2jPdN/0jeSl9UJ3resM9GmYCvPzoiKm+3ZQ1DzHSbFBg4ZXB64//4yhAGjE1S96ZvuyE9CfJEjodU2pxe6GlO/VPTVN5V2LhM8c+Zs2Uxm75fY1jnAnsnAyMC38uehPGXwyHf1uhk9wpHmbu6rvRPH8AoyrM9gWwy4HYMuexm8bN60KUG8RcjT+HHjhBodE39OP4UrXQfwUoFnDnWDh7JddWX1aRwY7dyzu2yFbw7Am3fSnwsWLCjDnfhB1sQ7PgBgpHNsjzzCdftCPONj8j0VNtfA3eeOMqn09YYNZRu0UdbvnTe3zILnB6BrMuAHbg9gaE8rreQ+DsEBS/728JrfrbPxx9R1Hbt3l48//KicQCfcOWJkmQxftre1xRcIPPHkXzKBoZH8Yn+FT+iDxn4JU/pDnNTj4PF7gjgbGN98tGI5k4SDysSJk8q4sePix0tDdQ3GovoHqQX8gnxQzh973xZFTn3GD3XIo2fg849WrixfrFld7nvwQfp0fIJfZrJkvMRzUkYcpV0CajzfBNs8C67yVtXz9pF8b/v0Oz5mfGDb7n/gAXy94bFr3lNP24bqd7X0PbQSVnM0/evZw3ux7RTRRm7fsaO8/cc/lvHQ/EF4XtvkIT80h8+YNeSkgfS1H6zBNtW6amAgA2/qEb6TqicJrn399ddlG3r46RdeKPdMn8FTLH2i73iwiwfkMZ8JXjyvDqhZCowTtI3cU4/pAysn9v8JYO/r3F820reX0NeLliwpY9vbobWIyS/QHST9rX43UCK+4q6vJH0pBg7UB1zblTEIcn2G5YE7d+0q26CN9Rw9drzsXL2m/C//5b+Ul19+pQxvGwXfAFiSNh++/pyPv0mgIVwhFeSOrg9fekAlCLYLR27Ze++VPTt2ojz6l4ceuL9Mw7i5lshIXw00IG4wZnonsOjMGA1/XAfr9zA3HeeGT+dxFFdieFasWEGWRBuCPrG0cR6MgTDlSIZTsGTYJqOhmelRUP2nkokyhMksJ6MpeAqFjPDmO2+XnUTmHnjwoTJ+/DgUyuAEGoyEZYAR5gWObQ8BPP/bh4LsZzvO/69//ausyXxk8RIEfVjXTJ6gxE9ntMJV2OtsiBwozrbJgUazVslak7rFWefsSwRlOQOvBwliLGDgZYBExeZhW5PuA5w4E7TDSJyOeAIhGNcGB8v6ke8VTAMNx0+cLJ9+tqZsow3PPPdcmX733WmTzwAqz9pXCmJ1dmtKu3gLQ3jSwN8KZG3TTUmfPXz4SNm9p6NswAANIvjyBIEMHUVTeY18AiCzDr3hoW6khNdAQym9zGgApo7zZT5XrAccvFadue7lJM7/zp07yw4GXYcY+M649x6U4APlDvpbA5PslNAAQwtu/k6aP9diUMH0B/rkKvx5Dbh1Pf71jAadlc/WrCmHMMqu+5wL3e/GUbev4nhKR5prv6qcQgN4z+/5TZvs0/S11ygvMR30ati/hCYrP1yO8zagTBg/nsHL2DIcGknrBBpsM7CtQxloAiA6JNJFpSrN/a1j0xhQnXsN28crV5X19OvUqXdHnkYyeNFRDC+IC8+4seQ1lj2BdUu51lQ9+9CItDAtGtrz3evit5/gy5vM1qmYH3v00TKaPs2ArWXQKdhVttLIwAY40paa0aAM0P38IQs6vKx4yEs69gbW3nrrTzH4C5HXO26/ntkkz9kH0tHyMbhck+Y6JBoDz+qdZuBlKrBOnwOjw0eOlM9xojes+7K89NJrZcGceWTUsM4cIxmjA8xqfNQFVVYkWnX06WtuOnj5AUXhQP0HfndnDaAp34eOHC47CXx9vZGZEdrz5GNPlIk4inX3d+jN0jRhasgUwsvQXudbWot3LkokPtZcHbIaXBX3Pbv3lO3QZgd6bNKUqeWJxx4rI3AqfFuMdFNGK85qw/qP6sJH6TtNKnTRATJIkkADdHH2o5PZrjXo4J3AN/A5hwHArHvuLbei62vacaWxdXjIm9I8RljeEGcqC/35ncABDlr2R6G9Orgrli8vZxnUjSKoNo/0wskTxtdAAzruxkCDtEkd1uM/4NIMm5B+tc5c47qOvMsa1q5bX1Z99GEZigxNagXXdKKVEXEW1yYw6vdkNAC1BhrIaAHXtKehP7BFw/T4E6dOlg+WLS9b0MMOjCbhiA5Dx7sGPzoQWogPf8Qyh3LyLw6LcDFtAroBQ/ttN7pmKfrdNzs5MGobTTYfM17KsrqgOv41VdV6hG278om9q/pc/NUz0kfZVS+o4zfh/H/44Yoyk/7UftyGjkxwutVn/wLPf+eCus7+P8OA9BAz9+u/+KJ0dHSUp55+BkdxWnS0eGbZBPgph+qKulQGnmD/APd8yZIi9QJlQKgORCGbm4RdwkYcRp4cYOiIOpv+FPZjbFtbMr6y4SfElMra7BupHRpzoevMd7nWj5vWaV9OYD8MTm3a+E1mkqfdPa3cz8BryB2DEgBxOKecmoGhXTK4YbszKOe6ffIdAQNhKr8Geg0yGHRQBnYzYFz5yaoESm5jwmQufDNj2jRS7XuWSwz6DDTQ6i7cmzY0Z27laNpgPeqg7jwvPb9mMLriw+Xpx/ETJpTp06bHtqpnk9HADKx6pxfyob6UGJEB9E1CDAIOH8FL4B27im6Qb+zXlas/LZ8RyJiCP2AQY9To0eV2guLhMeq48eiiPTCb77bjzw55TSTgzUPo4KXoglP4ZQ8/9FDwNiApv3gIQ/7RTpsV4/f4HtgrQcj/4mkQUDsgnzswETf7yMDaG2+8UYaQ4bGE4NTwoYMTbDFz8mbKOqkhNhI0ts0agelRpf6GszSCLjbHYIZBx6/QA599+ml5cNHich/BqRr0Tg8F18Yvku4JlIC/SwQNLlpK3OURz7ZFnBNYM1ANv+svqdOUp6mTJ4lW9EzabdvzL5dTX5Cjji7a883BpR/rMIvkHIO67bt2lW/QBXv27Ckj2u4qSxbWSQJpK43FQxgGGRzsxmfhunqmqdtaLVvbwGCPAZj8f4Q+XUtW2WaCjsKbiywtYFLvtlsHEDSp/N5TODyrPhZGHZQDEDyrfZXufEQCPrGOTBJwdkC3DB15/OiRMnLk6DJzJtlT48cncGembfZZ4jGXdoaeyKx+mjylHtVPU25sh7rGOq1DXWOg4fP1X5T33nunDCFQfTc+0wRkygG7fJwUe+nAd2nfyC3V5QjOfItcATb4B74Tqb3KKSY632eicy329cGHHmYyb2KyJfoRPJUONjr+Pt+bQbP1SndloPoGVMB9+0T+0m74XX104NDB8g5jM2EtWby4yx/znmVrk+lb6UoZ8b1+lh+v84pFpJWDbCrIeTP+xhu//325m8m8JQsXZkylrIlfKAJgnzMAql70uvpEfKzfQ3/MdthO+8DrZt46NtN+fIOOf+GVV8o8glMeF+F/YVs+vi7POlbTHvp8aEMd8ogBa+tV1ixr/5tRfYSgVMeevej5DZlYcfyhP6atb8YH4p0lNeCqjdKuCUNaxbfmepXVGkB243+zys6Q5bhl67ayET18nDYcOXqsHKKuX/5P/2N56cWXym2jyc13zi4dCTAr8vMzPpBdqPGfPNyUK2ufgCNtMiMMM3UnWuuxB4J+jLAc3LuvuE71fmbXJyPoLjm4ANFlOdMC4dIwsYwsR/9UQQWYjBnGY0MmBs3Ofq9iULfq449L25gxcRTHcDbQ4CFTykyeNTwykoyuINh5snmcK+HyTyHRwbKcaXM6ir97849lO21YvOSRKKiBwFYB6UQ4u+v6IzKJK+6pNaBb3/7yqSqC7mV7x67ym9/+hgHRHeVRombDhgyN8250zYFmIxiWt90RQHBMt9GECCb3bJ/Ol3g7IFMgpM3ar74sb//hD+XRp54qDy+4n9mdvjFaPq+RrQZBOLQeGsQZgD4JNADLjqjd4bkaJI2JMzLHTpwoq5l52bO/szz/0ktlOgMYy6g0mv5T2Qk3A2Dh+huoCrdlG4dS/P3tfQ2/s8e79+xOZsDtZDQ8i+EczYDXeypKad4TuL0ERh9cvkSKKszX0022EHYVd153Rp3OdCUaynUdI/He7IwUSvAEMwGz77uvPEw0V948R7aDfGnL3Z28J/i4Dl8nzGsqFml0GRr8CDw6K5siGtTQ4dFB2MNMmgOvw9ClP+m0c+bdV6YRaJAWzqA2fa/yi3LlugqK7gwNMugFZ+ksLaULRMRhviVs+yV9unzpe6UfgbaJEydktmvkiBHpQ+lcaS09tYc6I7Sbs7CU00b5yl9dhoG6TVU89+25shxZ+oK0rhn33svAaGK5k0ySWwmYZPBqH8kr7lTOWyNuIshj+qb8I+ygCt3kFNf+2Q9VXl2a0at0Mrh44803y3miuo8w62KgQWfLtfiRS/DwsFvlGx0LeQPw9GHlHXk2nEhfaCyEr+zaR99s3VJe//WvY9geJ3A3iBR46Z0+w4mxXIIJnFtmkzN0woFI9J+K/G1AS6dHZ0UjdAGHx1mjz8lUWf/ZuvLsCy+X+bPmtgIN8CMP3YxOkVdu5uM5QhBofIc3DTQ4SDfAcFn6wVC9+/Ul6evHcvjYkbKLQMMXX35JO34szz3zbJk8bnxmFq9+jzwh2zor6h3bfIkdkK9Ae/GWOPZjqoRm8gqtjCzJk2Zj7O7YnRkjZ43GTphYHifIM4LBtA5Vk1ovgNA9T/ON+mCRHEKkYhVFuWpf054e6Hd18IHDh8qnDCx2bN4Sx3EejuKc2bOzqZ0Otvwor8nHgQNE+7bRu14X/+a3g3r7qy+6Sp29Zfu2svT998u3p06Xu3ByZzGzPnHs2NiM6OAWbCkengnGtXf9K11yD7xTb+pmgzHwd++HNcwwfvj+Uma7RpQpyKlO6FD0sHgpn/JV9GQLrvhJ457MnsszllEnCdsj7eS78nQSnN9+/72ymX59mIHLRGC7zMl7kRdgWb45AkH8mgutc20DfAX9zcqyKmlmRtybBNZM71788EKW24xOgEe9alAyuFNOCkROeC60sCcAkiCN7eGQ/s2sjkshz184X77C+X/vnXfKfGbTFjJB0K/vLbWteeKv/9PQxBlcdbyDrgMHDpQ12I+OXbvKS6++WmYzAFBPOXACG3uuhb+DO6/A1SAfaZdG0KIbs/Jib3aWAzJnAd2vwcFLx57d5Uvk9RaCI88/+1wZ29ZWLl1kI1BsRdUYwASesJsjlGpdaAYwBget4yYGXX3IRDlFMHYjmXZmNJw8fqIYaHhgwf1lEEsqvzt/MXbCJR4qazPhDDRkFo12a4f0MZzMUDeaJeGSD+vSCe3FZw/ZRx+vWsns+h6Wad5W5sydSzDgbmbVu5fvCBrCOKmjwf3Gc9MOz01brEeG6YM8Kb8bNn1TPkCenL0z42BaE2hATqWPNNZJN9DgudnALbB5RudZPqwSxlX7go+DdwelH3/6SVnDLOz0mSyhIhPDNce30gdOEGjTPLpoLmq5Uv90fb/hOl8TUDLweBgd/EcyDo4wQHrs0cfK2Pb26BmzExR09UZsKviotz38XQPvFbo8L5+Lb/w9fBmRUGb0x17/zW/Knfgaj2OfhjN47AGPGWC4xt5N2rtqIYTlN87U1fXda/zM9RvuaUsM+K4nEPARvvBjzzxTHsQPdt8T9YBPaOsqrtpUAiHYVe85yIXZqaYGLbrUBfXYDu16k9HgwEu/74UXXyzT4o85yVMHcdpRZSjoSlTq9L9088g9zxRQDp2kcNbeybwOAppmwmwjmDyira08TqB61IgR+MD41vBzbCf4ZCAG3yvv1ffAyLWu2zfSXvjqAIMY6oPDBADMoty84evY3Hks8TVoOgBZ+xZZ8011aDxoD52AEb0AnqEzsDw3MmrWoDyuXdR2y6s70C8rli8rp+CdkSxZmT2LjGr43r0T5HcHjsqq/p56NNlStCvLLOkfdb9r7SVTU6cOv7bP66uZfPgTwak7R9xZpk+fQaBhPHwzLHpSWDWQoUVu0biSm18e8Gz+CpuWtdoD8bD1vZMhtJRJ1K+gz4MLF5bJ4O2Sx1vQw5kkgi7yubS2f6Pv4RlIkv7zun3hP/s3tKOtXA6P7Tt4oPwO3OXPp594gsDd6JrRgK7J+EB6+7HxzRF4/OAcXoHe1bZUexL/U3kBn83wy5/++Idy79x55VHw70+f6luJQMMf0SXAip9n3/HRxioLFX7locaGKMtumn+azLLP168r67HfL732WrkPn8MyLsuTv/RzPeQ5YSlLdWLVe3Vcoh7W1ilvTba1Y5uD+DQGqs2460n2yNPPPlsmjB3bxRs6RtoQA8rNmEB6qC85ZTLCNjRLiLVPBhpuZXLQDCED+N9gP07gH5xkAuUUwYanX32lPIudupWMfCpCuO0k2qCPZ4f+jI+/SaAh0VaFFQJLDxmMXsURq4GGjgQalpejnZ3lDmZGFzCwMzp0GYYwHdb3zGanVDpPgQ8DC4vrfvzNz3SgP+NgwEiuAXZjnVWkFX2y4qPSDswppOiMgSEGDxrsE3G24kzDaDJFmE9OANNmcM2PHNYlcyh0lutHoOEsgYbf/OGNsg0l+Mjjj5eJEyaUBBpgzO+InLl+zCBJNxWcWPL/r+EJFbMDpwQaMGyDyJJ4nEDGUPZocAa1Eb5KA4UPWlCPhzWFJtLFtkgzDsuKt0Km42vUbw2O1h9e/3V5EsOzhMG0KVfpL9qZyCZnqWt9MWQGH4Dn7wiNgFv0TwfwU6F1g0wH7J9+/lnZyyz1S6++VmYQzVVZuMZNLFVs4iLuKgdxTT3ctZwNyaAPA6pSsym23cFJZqQ6Osrazz4vg0YML88/94sMSt0ozI3spHlP4PTwIZS9r9a0HRrGBBroBAd1zny7nj/OObQx0GD6pTNdphm6tnEedFmII+0eDeeIIJs66v4MGjeNj1TXWCSYBK4gG6V1DcWBhmLAxziQ9mgYHAg6y/jpqlUJrPWH3+ezdno6zqi0MOVLGhjN10B3KVPuyTeAyn06I/e8r6L0GbMjvL6eQMMHb/+p9MERmTh5UtLUzfZwoGAEXdr6iXMF1EpzB/7AAncdCJ1M+9sarcN6+2IELuAMffDRh+Xzjz7GOMzNcpgROBS3MrtgYESjAypAhW+uYkRJWTKAomHUYsqKGkvPOkjNhpFmaDgb2Mn61N8R+DrD/h6P4ijexQx13toCHcWtOaSFHxV6DCowgzOXajnvOpiu/G5b1DsbUd7//E//VKbPurc89chjpMCT2WQ/AduU9xqEqfRM4AK4OThH1mwch2W7Ag3g5iv3jsPvLtP67JM15ZkXXy3zZ89lFgRn9TKBBhqc1Fr5RJ7Mp8uFoDFcixMEvfHkL13D+YNsfQaQQg/tDhNY27l7V9KODTS89PyLZer4CcmWuHqJWaPLzqbBbjrPOL6X2Q8hWQX2Ob1nfyu38of9nUwWaK6z5NKJXbt2MTDaUrYwyBgzYVIyGu78SaCh8otUVXJbdBEmR37LL8A3g0eO6Ul/9unfrxwh+2g1s5jbN22OYzcPfp9L1oGD6YvIqzTPIU04or/BuyuLir5RFqxFXaEOto/VVTqjCTQw63LeQEN7W5ZOTBgzJjrA1FrLJhCQPkwN/gFD/8mT9VvVpbVd0sid5p15MFi6nMG0Kd5m8UxAxw9mgJElU9gC4TdOjnyeujjrnMh3pnzaRh0njzgYLfgOqP/07jtlE1kTi3HOJ44bX25j8GhwrQk0hFfyZP0jnn/psF71ZZPFYj/t2rsnjqI29FHsx10MkGyXusYZrgbv6IDIu6wpVaCJdOETuDyvUx6nFR2pg+wSs/XM6BiofhjclzCDfAt4q9//MoZ/Cet6rcFDnrCes2fOZInAaiYJdu3cUX753/5jlmaAEG92qDvqR4cFV1heuwZNzUaQT9U5DtrTH1Shwy8Pqf8NHh3CUdy5q6Oso29vZfnBK8wU3dXWVi4TdNMfqDwux9VPGIUflUM5t75nEEMZJbkbsHsRjD7F5Mg32I4t8PtJbMmM6TMJVD9YBt12e7kIfBDJ8g5thpuH6d47gKl9UPlbXSxsM+2EbVA8gYZb+pS9nfvLRwYa9uwu2o85pJLPJKPBQMNFBl4wTgIN6gOPqBbP+XX9T+1lcecb/dtvQP/YRlO932bPE+3ZRPylGQyO2ghQOUh087oMAMA3rwEFnJsBZ5mKPAJv2H/KsPRWNqxZvuxF315CT64A90+WL89SoanAH87srrYrmSnI2414dn0XxeuoX//OdaXXgIyBx8Ok2f+WgcvBPXuYtX8a539cZknVcwlKA9+BtXwUOQHniiP9B87qSGmvjMnr3sseBvy2vDP3v/rVr8rItrbyFDw/jCUUBta7XcPHuMxMKW2uFkK8qj5UY2aACNZ6Pbak8o82pQYHDPq5t8e6L79iGfE75dlXXs7Ej29ykc/F1wk6aaSdNZPOIKs+msGZarMd/NVgSpVhxiDQRfudVO/9+8o6Br0Gewzc3Q3t5cW8GYIy6qjQmD/BMrTle2Qsd+p1+9kPPNAEGgx+bd2xM1kHw9uhzRNPltEjRmb2V3qqywOh1V8olfBIMvCgiGUa2ysvansTTEGfHGf2uAYaNqYv7jPQQLC67y34I+jPH9Gvbj3p/hjSSX72XzJJqdTXQRpo0IKE1+ln+TQZDfDq9l07kxF32kADGV9zGfROnTQJ/mfpHLT6URkF3+7staXfp/9nP/g2rJt9A4B1ir++SGqGZvI7etZ7n6Jj/vib18tI9oZzOcx47PZQ7IdBRjNkpYzeXf1XaQ+qOSAXEP34jzP1UzB6zUmC08j7+yuWJ9Dw8OIlLC8z0DAomcnxHSkanS1v09fys3IgsOgb7jf8L2iB6yd6zz1nOjsPlF/99vVkSjm5oR9ppob+ms/pezV6OHZJJDnCf+GbWk+j27wen5VnfW6TgQYCGXMfuL88tmhx6Y9NdyDvIbw6PvjzQEL82PB59QlqvZU+zXezDly6/dm6tcn2ePkf/qEswOfwcFwm74qTh+0wyNkEGqxTv1FYBhq81yxBV7c5/jhIlqbLcL8mc60XwYFf/OL5MoHx5SVgG5wCaJ6PHaKeLh2svMMn+hXSwteWa1/d50Rd71I4x6xmM3xNEOMkdtCAyVl8m8dffL48/dTTpb+BBnBKgMFAA/5eeCKt+Xn++ZsEGqqgQIswIUKJQjNKaCqgim4rg7rl775Xjh44kDVv97cCDQ4OL+DMdNdps+NlXBUUZ2UigskXLys59cx1hYTyRhSd4f6YVLSVRInbceIMNIwdS6CBAYYdbTReplRx1EE49QhbJpcR+aECtKxM5qGAOOC9BSN/+uSJ8lscrS1ffVUWMzAyEmqKVG+EUYfFTfHEn/8cIMr/fPXnv3FYt0y6DWb+FUsnBhtoeOTRrJvWeDio0qiI10/brlHvcmrBXWMUeDC1bVRQba8zmZ99QaCBGd4nX3qxLL7/gdbaYB37GuUTtrRoBFMlpWBqOBIgsEkN4W0crVORqCwMNKwkyLNrz77y6i9/WWZOnUb31UBNSMFzN9LC76lLo0SdHtYV2kMPFYP4G5E/QKR1x44dZS2O6DBmMV8iUDIKAfyOgUuixMDuTl3def4mjQDGSFXUDRoooM4SaYg0QD9CZw2PAQjProkyJWoTgn6KNswnJc1AgxkN53Eir0E7jUPeKEAfZIaa3xoLHXqQl03ZEI+ATjcCBv6mLe4PoXEwEvox69727+ooAwhWPbDwYRzF6elvgz80OO0XX51mFZE08J/0le6pw75o3ZM2plvrwBhoeI8smz60ZeKUyWX6jGkxEPJEBvc3GBweb/EKOGI0al9ahzJkP8hn9jcReuB/B37vE0H/hH0UXCs5derUMooot4Mj5SLBKehN62mPwRdxtvUR3dAlzhDtaIIMN6H0Vbhmm+wno+E3pNKdQ7k+gbOig+sr0cxoUEFLm1AhePuzGk5JIr6ph/ZJN9vm22aUF/E30PA1g+n/+v/8U5lGNsZTzEgNIeCoLAvUwIGDL42AxsZBLI+1cJYP4aUWH0rH6vDVjAaNj4GGNRi21StXl2df/WWWThho+IFAg0GG7vKKHxGDpkEwtOF3LBFnRgfuAn+JQMEPNKdXf2a2Qe4QGQ0ats/Xfs6z3RgYvVymMuB1WcbViwYaoDntDg2gg8GKq3zkcx2gatytp+o1dZkOiM6SEfSOXbtYr76lbGaQMW7K1PLk40+U64EGQhYtfNW58r7/ctgWwfoDXhG+wTszeHrQn+4tY5BkLc7WTtIB7T33g5k7azbOEEvbCHLYL428B4yggBNaAzMBt1YZ+VJc1E/JaKCPXDrh8q/zOBajceZmz6oZDZZ1UCpfKydiqe5r6vJKlR/kqFUnp9Rnm1zaoRO/mhmRFe++m1lM+b06ioPRofUNC3HceE69Kw2ktwRxYGJddXBQZ/WE3zjewj995iyBhnfZ14NAA1kkE3FC3VulK6NB/m7R2Gc9Gvzrr/o3UlGbWOkW+b25dJA99bvf/y5O82MEGkaNHEEQg53cpQ06UUSrM3dd58qX0thP6qYt6oEqE8xiI09mipzGCXIG9m0Gdgvhl0cINJjR0Di4N+L3730Xf3lK/Wj7zgBbHb96zWcJNPz9P/53BBpmRhh15qR94+CqDx10aS/1LXSo7Xv7WpKoEwGaAYcO8kWCT534Gu4dtJYlYHcw0H2Nta93tbeXK/Cjg2mKI5JiJIXqISyPnLnY/G4GMTfhzzi7dQLczWjYzOcEvH/vvbPLYmgzmKzEi98SWENm48uop+Dr7ugo91ewJp3a8KS/1L/ocOXfpTA3M7h0M0sDDS6dMKPBQMO8+feVmSwr6UPbLpFxdhMwldGuQIOwWsj+i7ZwIbJBg2+BH5ltSXr9H+EZl2BMvnsay2LuKWPa2oKz2R7yxE2weNW2PG87WroxS1Wo2zLyeQ2CMyAFbm8C9Veg/TIy4lYwmJ45e1bsh0vvBrCcR75Rp9aj0r71owv/rt/NF87BB1ppXw9B799i+w7s3l2ewimfOH4c+wfdXt8QAV2sw0CYz8QXsqM55LlGt5gB4KEPKV+Gn7lfJ346khE3akx7eQp/b+gg9j6CEOmlH5CnZDT4NHC5UT0cAwvy4vVPHrLulKmvGnRj2LVkNi1DHzwHPz5MQLYP/S2v+6SBhhq0dIbVZSvYFtrj/UxMiS/9ErkVBQ7bqP48xUCl80Ane2aty/LHF19+OcuT5Td35m/0R1ASd46GZ8Lp8E+9yplC1f8zqNmzfIs/ZpbmFvYpk+eHt98V+2GGqb61M8JdMKzAX/CW9E4fAFkc4z/QRx7qGgMNBoiOHD9GgGR92cbgSxs9B7oswIY4KP0WWbsJGvRS38nj6kv+qc9Dc/pG+awyWq+DfAZ1Lj3QDhpoWIY/c/LIUTaBbEugYQp+vIGGC+fMEMLua7fBXR+vvlmAjWfBrTs8Z53pB87Kkhioe24heCYu2o8/kpU8+q62MoNAg/taucePfGigwSCjS9vk+FDnBlqnSYFCNWkTf8BD+OrKUyxFepexzXrGOIuwH3eTITQIn8bM5GywDp2lpZDj3/FbQPZh9ZfgLK7pL3kIV5r4jJNvnZ2d5f8Fd/2+58iyuQsfW7/I5VzKEH/qGXjNmEP46gP9PC7zqb67/e0z9rM237MZpr9nInUuk3lPEChJRgPjPrEJLWxrvgQ7gfG/fizQ2CnrzrgPWlr3BfaSccPGNQTWXOb7KoHq++fMCY6OTRrbFjvBs/af+Pjbtje6wT5Sj7mnkT6oNt4+O8i+UwaqvyBDqBv21AwhJ8e1i2ZTZK8f2hpZkfrSoXX2W3QWdUlLedVxKpWXAehz5clg71eMi90DUB/h/Omz5cnXXi7PPPVMufWu0aG74xiYkucCXAL9bI//dKBBhono86USvTKhBlaHhR4umyHoMgINx4gSDUUA7yPyNIlOc1h/mU7rBsNmUCcMO4/rzad+k9mFa23WA7NgoHvi9HxPpxlo+OiDZaVt7BgCDVPJaHDphBkNVu8735khQRnKXDJZmADO0BmT443Wy7wZ4HPN+wpaHwynbz94409vJljy0KJFSV1yh2adaJXiNQINQIEXRFBsK46p/IY/tqfroEgEHVy2MQAwgj5o8KDyhIEG1gmLh7h6xAlXSHSwxJFnZF4FJve9pyHiuorbNom/xsnU188xbG9jmB950qUTC5JmKIbCMsJHYQkaJ04hS6BBxcu1KA76RtrUoyoABdY1pkdPnMx6zB2kZb/yGoEGZl1spzS3jgoLvO1fhMV/4qbScBBkP7ohn0pB3HUSVY5G/lWA27ZvK+twREeMG5MZqeEMeC9zz1nMrHsFL7dLSlojOAlboczO+NRRgw0gosBCF2dFDH5l8yEE3YyGkyirufPvLw8/+EA2g3SXYxCK0XH27Cp9XKPd8BygxD1BGYy+7sr30od26DDGwacde3azISGD9X072JOENLeHFi8q984gLZhnLtInDtZV/I3C1xmo6cKiCa7U0aVsVTKtPnKGV1oZaHifdOm+vVoZDXcbDBgZ+jqItry0aAJVgcXdenCPui2Tdkgnvss/ztZ9D30NNKxa+j4zUrPL3ayzNV39dpxoeUYnzd2zfyT1v3dv6Am64u/A0P40uOCSCZ0AU8Vuwsk2kmtdKvJ9ZL+8/sbvs3TCddOuKc/SCfrFPgU7GSfYVoz5HdjKDJVx7+oP8GPaUPvCAYi8pRO7ccvW8ltmFyaDtxH0IYMG8px4gBs8osw6g+W17K4MntLJNsi3zQCdWkMXZ5akjel4TaDhU5aVPPPKL8t8ZkZ85WICDcBIoAFaGvySvuKas23Sc1dH2ChGCFAsGQ09mNG6ghN2iPRRDZtrVX0txSsvvMQ+BBNQTpRLRgP9eqU6ab714UcIf43NGJ3NceAv7vJG5BY6yUfqEDe/VJ46kNEtW2pGw7gpd5cnCfIMZ6+AunSiCTTIelISura4RfzTD7andc/63JxMR8U3Q7ihlMt53AxS/pjFxqqzWS/Zj0CwQUNhRJdIEmgiKA91gg6tfGVgJ84tuNsf6j35wjZs37mTzX4/KudxOkeMGFFmE0SayL4kAChX4UXL6gA2MmNf+11eEpbwoxu45qFjYZf078/r7JBxg0cf4ogOI8NjMmvK3efHGSl1qEHN8KQ0Aa76JfUBR76QJpFn6hdXa3D2Uhq63Mg9T95i6cQWZjIfWrgwgYZB8KTvzI4zRJ+JW/Dj2YoiUCqqXKlHSCb9/GdbwcUswAQakCedWQMNI0nf1UFW3gyAiLO61kNHVMDSJvqftnvofPrPyi3roNGUaNfbf4ntfu+dt7OmfBHB2FuZxYusB8FWRwbKX/dHGoF++ZZNZw/hzLl0Yhf9+9rf/325l1R7hDSyJm0yILetoJb13NLbh+nT/OOc15BRdd4AAt4GknVC9zNYN817HYG7gWxm++pLL5fR7e0E7XjlHDzTanFa8VPMm1YlgMFNJDcDmZugTQ94+gRBUmejDFQfpw2zZs0tS9gDyQ15rxDE+AHauadEfRuNe0Ox5II+EXc3A5b22lr1onZD+Aav8vpJ6tjDzPQqMoT27iXQQIB3Pnb7nhnTSx/uXWJgVAMN11mkGTD+hGXSLNsSWkFEA8kQsmwkePSH3xFooM7JZPA0gQblz0BssuLgjaSUg7N8Hl8usCo8+yc6p3Vfn6QXgYxr0PYDguwfkDExnQDGNHyCxn4oFz7zUzx/+lvEmzb5PQc8qo9wEF3zB9/IAY0ee+zxzDK6f5A8rxwK33qUEfGW/9PXtF8+F+9mYOz9DFYpW1tVktHwewLh8opLJ7QfahJF/yZ8PbkhuMmU0Avu7PrIqJSsv3OfOlMGP0F5gufXowc+XPZBefoXv4g/5gDnyhUzA8EBPMzWvIoOzR4N2ER9O/F1UNxNW9owpUShDuVJfWRQ0MCd+82Yiv0LNsebNnlSntXmRTe25FzUrE66BEz+tv6Ag2UzsKNOdbw6fDeBhq0EGrQhw5gceALaZ+mEM8LQW7oKMzoSUP62HmkcGgBXf1Bcrdnr6hllw/Xw2o8dBKrtu3sYMM4jm/JW9LMZQt2wDe7RoP8Vmyp8gIcUnF3Gp+dgwE5ZUkcrX2ZUW4/2w6V3p6hndFs7tsk9fiaEpmY01CAD9JUm4oltU8foH9wMfgkAgbcySpHIr33fD37392rsx1voYPcicb+TsePGJTPZtmg/7Dv9cmnq8VPeFkZkNDcpQzl51YwM92h4hyD7evTBokceZTnMlEx0unRCP0b+8JDfax/Yf6365H1gxQ/3PnTUbsgz+lL6QPs6O8tvwN29mp5hebV9qh9u3/isR2NbI0vCa/WltlXMu3xiYPu7ydRTHjdu3VJ+++vXM/nwBEvD3eCzZkdZkg8wgneaLWyvV1uqzmlk2kCbyx3sT8u7DMnl1Z8RaNjAGOcV7McCAw08ow6T5+VLyztWFCfbbxv8SAPxNvvJ8YyBPW2f9wxU6NN07N6TDKGrlH3hhRfDMw1scQhd5WeQVndKLtsj3aSt9Upv2+TG9AZp+zKmPHv+22TeGmxwj4YTyOtZxlJPv/ZqeZpgz8C72hSR+Dc5+13gP+PjbxJouIzjJiEz+L9Zp0ZuIZJ/kzOJV3kVFLuyEgjQKA9mhtfd2ieNG88O0jhE9IKDuezqb4fxtIIYZdcirB0V5vaL9+nUzFBj9K8hIB9/8klSo+5qa8uOsmPGEGggoCHTyBAyncyks6LAa+gb5lCIjCLLIAqXDGLnRhHi6J5k7cyb77yV9ccLWDc2iZQr16ybnprd92m7DMefilvEJ1/DI/lW0W7dr/cc0FBJnKxf/erXLMcYWJ4gYmmgwZkyDTcNiLF3MB7BwfgrfEZqEyQBlMLjjIOC2KSpOeDSgTaFyBkpo7kP4OQ+QCaJu52LThQQbY/AgLvtzowRbfGa9/2XgUEUSqtPUIAKbVdGA4Mu0w1N1ZvJhi+hb6u8z6psdVxsr6Ki4pDAOl7SOGu4qS+DCoTde6bv6yi6ed36tevKSGYXXmYPiEEEGn5ESI2iuxmhEe5eCHMAh0lqH2iAbGNjjOJM0J5u9K9reE0z3ESQQaNp2t5MBtQPEXG9Hd68hiMYgwCPgXwcxzorhSIBqG2gg7JkghFLXq9qoMEUw55EPtFW5UDHrgQa9u7qKG7m9dCihWWGTjT1XzUjI8qNKCozwjrIpqZKB2kuD+bgu/IUfqQ1cZygGR1b1hFlXcbmQ/2Jak8ilW4yUe4RDDAsI71F0r6Ttxs+bxSshKk80wrMSCiOKGReYXkN+r6/fHlSX6chp87wKlemH3uYTu7rg34k9b9vX/oQMpma6UDRrAUdL3nPV35Kk27qA/hYw2xb9pKC+dvfv0HA5WJ58sknShsz1Fk6IW2AoRzJN9IZVquGn99+N4VVOc2sGJ2MGxkFr5L30Ml086E32QPCTc4W4fwPZN20yt71txpvA3Ya6MbYQPToA+XLGQINUF3bWY20smgZN6U6dpylEwxcVn/2eXnq5dfKfDbHc8YnS21gvSbQcLPy06J91QsSmU8YqHUmSBDiwfP2WTaD7Ogo65n5ZofR8uIvXiCFfyKNArB7NBBwkN8ir+BDCliCDRJG96crW0Wa8fGIs0Uf+DpXs2zck2Qbhn8c72t+gvWYQ9Bjl+E7HVnhBkUejuPQgiH/NG2oDhJ9QO/4cTAr3xxmpugbdPwe8HcgNXPWvQQDZnXNXghK+Y+jqb6iLnkz9Xid716zT4TnPR1SnXDr2MGM1JrVq5ndOstr+IZkj4bx48aFr35EF4QmFfnIkHAqEYDbgh1ZkCggo4MkTr2RH/tOZ8U9hMyCmzRpcj5Dhg0NvSNPeYwn+G9POriV5sqr9dguvxtI81Izo+qMlzNy76J/t6CH5y+4P0vvhpCObVqwz+UDjtIgBwAA0VVXvVj/WkL+j3xQTl7dvXdfeYOlffL3o48syVtczGKx79W1NcDXShHH6Y2O4Tnri7xRmfRKQIZ2aV+EpYzozG1gML38g6UEYxcwA3s/zj+zeDwrHB3C/+ihnbK/zqNnjmJbP2M20ECDM7D38EpUbmYGVj5QXm1v+pc65Rc3bMuAEVkwqKrcqu/7klmTPaEo44aJvrnBjIYvv1hfbmfSwYy4Ee3t5tVWXwMqh87/SgO6WkYhrYofAw098TlMd/0aJ/EbaHOUDC0zGpY8vLDcwb4e2owfGVQasHavHweGDhqzjJQ2qOtDP2iejAYVKN9tg0EH5blj397yKfy+dx+BBvY2cDd49yVxDfElMkDVA050VXmsDfjX2hKesZ+kHbbfujbBi28x+aDWnMTAxXXlbW1tuSf+BtiUi2TE8Kj7CNyMncJwVN1uz7f4x3N4hjZ2M5CBz7Hsww9Z2vd2mcqgazoBkrFjx5b+A3ldLPyXZQ3iXtH+s783tufPbvgD/NXFBmTfIlviGL7k4sWL2eF/LIMX3rKCXVf/q9/VHcKShxpdYH3yq3ye+8BTJ9X7+CX4NMr6tp07yp8Ikrix+KPAd68vikZVw4zgLUU5vAgF41vkqne8xpl78WQ5NwMNU7TPEnT8gszYVQRN3VzuAWbte5Earf20L4BGGwj04NuEZ2ivMpzACAyCtQMPca6yoQzqByvH2fOEibx12A8D4s+wptzZb+/ZLvGwrRXDir/fbU7Ofm8dtQy420ZwkJfNaHBTRd8i4Ct0HyUIM5KzAy/5pdruGtwRjHhLqQSP4enA4rd62O/2jYNd23KEPv0KntwFbGV62syZ2ePHt4BlPxV0glmCoT3n2ghwA45yoy42+B07yHfbqmwlewp9tgO4vunqNJOGo/A37r2HQDX+QXf8Btumzxq9AgyDDOFDvoc3uKf+Uefn0N62cOitPPH9c3TY20xGGgQ3sOZbJ4bgx3tP2nj2E+qLtoS54fCnvJnLtEn6+OmGn+/S8nfA/QuWKC9cvIS9vqawafGw0otJJ/euSJCHPs5AWr7nX/he2tK+wAK+bWhsmfo9QX7athc9+Qf8JX3CJ1kaPhKfQLlIkAVdK04G5kE7cIWX5gBPmP4IrsCyXh9QvqrerktZf/P66+UeJpefgGfcwLxOStX+k2+UW4/4Z9BfX8bxj4FF5bkJGKj/zI4VV9864+bc8vvmzZvYI449s+aydEI9BTwDJ44TtJF+1FPiZEOkS7NHQ8O7CbCLP+0wOHQcXpHnzbS5Qpuffx5/jAkIdaDyabuDO30gvpFJ2uA1+0De0wYru/KmOlL/+Gb7lMwmNy13c1X3nDFLS332FGOcZ559hgzuttARAJIlOLf+1N8/w79/m0ADkV6DDSq9HgQXohAQe+JikORq1gV/uPSDcpSI6wAiOlMZHI1ra89AqQcDEpdQZJaBDlEY6VfOkpYvHGES7tUIGtdhBoWhL1kFF5hBcI3tqlUflxEMRF1fawTd99jGqcE4NClGMpzKQ0YVZhUalTtKUWYAZoQFBCznbKCz3+8TgfaNGW7KNH4CG70wQ9IPJ1XBddCj48cfMW1hnK+ifv13fl3/LW7WtZOZxj/gKLpB2CJS7Ju3ZcioDjpVIM16cfHU2LpsQ9yT0oiwK0AGCWxflCLlPGvcXAvkbOCc++aXOcwymnIlplGinBUM2+7zCoOObxRIC3dpXgd+9gtKTPpR5jucqeMnSSVfuy4bkj1DhN4ND3UGG0Ui3KoI6waEPp8ZQq5n/Rv8ojIQXtLUqN8e/5b01sOsse3o4K0TRCuHsW7MtK6RKHKXg2iYHcg58PStEC5d8TlVdQYW3NPopEekU+6hKKGnG0r51gkHXb514vix42wcOKPcxzIBB6XySgwP/SPuKiyA4nxXJSrvSOcePXkDBErwojNUwDcKLGyV4+49e9gc79NyAIfRdx67R8O0u+/Oc9ljotU/9rEf++kiM+YJ1PG8dAs/Umf6xPaAg/1uf2mUP16xPPIzfsK48Ly75DvYd4AgnauDAwV41r6u/IZ0YZnDt+Ag1fwX5cgzAxhEnGdA/dHKVax7+5TXfk6LctWADmBmTXpqdAyKGOXv0wdlCuHNGLIPXedGxYyLlQkcQ9qSQAOwvW5Gh28o+NNbb2ft/hIi3G4GqRwbJAsvAhfgYBWdn372t/XYjrqOteIep6ulKex9n3f24oP3l5b2MWOSfnkbm3GKtzMnGhfxTNaH8OF5aeUh3RwgKnMxEtTFzaoPuG/q2ykiz+vhx7VffFkeYdMeX8HljI/Gqlky8ednnRZ1QjgReC2+BLTZNqaDuE7SCP/R48fgm73p2x95f+QzTz6drK9rV5DHyxg2zj8SdAhv8OiP8iPPS5i8NpN2SacQzvvWy20Hv+cv8NaJvXvLDmijM9c2cXJZvGRxgpquHewKNNBO6VEDDdKlhbf9IWx/chjo9YM3R3U3ZSOybQzq9lOHvDENeXKtat34iY33KKvRVU+pw+yDil/Vs8LNLG+LT5VhnRWNv/goT6Ywnse5UK+7A/+49vbIv7JTA8dBjDZXmD4nut6zvjiHIs+Rurnv8gVlwVnGT9CRtyP/2g9fUearP8VVfSAsP3kWqGjMwKvUackqbVT2LWd6pXWY0eA+Bw68tjMo9fWTptXahn44ipXf6/NVW6UKCVy/tE61Xv5SobpYXVCXE/QgcNeJPL0V/l6M/XC38wTKoKXw1WXiZfDPlE/7S7p61AFX7RvLSgttkve1a2YGbEJPrgR/HcUF980F77rbeehLG//awyZZXBnWMVPf+XqyL6B9B4GkpxgYzUBHWq+p3uJc5bXKdWOzdZJtk/RVF8V+8N3Av28BoXnB+whvndi9u751wsH6U08+VdoZZNg3tlPS3kDef9GMG1tWBzOUV+8TxHEzSLMltm3bWo7iIE6dOp23Fi0ow9hfyaB6Ase0z4wGZckJDgeOHo2vIZ9CjNrX+Q7fQnsjCM4yfs4s6X6CsrcwqzuPCYIZDNp9lbObZ9NxXVqvQfxfa0va4R8KZDNI8Nm4aTMDr/eTzm1AdiKTJ6NGjIyMOkOqvDhQTMCM78qJOlG9Ke2UZftRnrGswOWb/szwOgHhxM/HzML6StSpDIza2trZ7Pr2+DHKU44G4Rahm5/15l/4C97ypWn2S5ctz5KVh8iwacPX8zXkDozETZ9IRpM/xbHqqgpPnol/IM7epx3i7UfbqS5x0mQpA7uRzE4vZK8ANwRX8PQ1Aje/hMdvYNCJiiU62LO/1Tute95XT3IYEDNwZ4BqzaeflCUMuu5jYNQDHPIKR4qaFSvdHcj4WlEHVvJ9Ag2izD+D9dq91M0z+iLibhDjED7Tl19tIKX8RHmcIL4bBzZyLT26PkLi2QoR7Ft94BUPb1mXhYTvZpD7Ow+wh9Du7PUzlMHoQgJrZk/VCbC6vCN0Aj+ft95Gz0Ynt+jgNftFmntdnI4y2HLiZw/wlY/J8I1LEAw0mGloxoFZPNrSAA+SFT9/6+vpc3pOU4Bp38qz+k9mCn780YfZj2vEyJEJrI0bMyZ2yT1FlE/pLW4JNDTt5+y1wEbvSg9xbmxJXlHM/XX4BEvffYe3CQ3P0u32dl+PPCSoRt7BLbTgirTx8NyQ3fON+j/9BI3MpnSPhuW8mnPDl+sJOD6YPbPM1nZZYYKB0KdrHAAu4md7pHGts9G5TLji31iPvlCVjRJ/7N333hel8sjixbyylAAJ7VTW7HvLy3/qQOGKG4Aj95F9vltenRy+BE4z6SFeDqjf5PWWBo+0T2Y0yBsJBvGc36WRv/V/rUtfTBwNHuozKBPKsXWItzbdJcgGA1x+4AbXj5GNYTDWCUz7y4xrs4iEaTtoRuhhO21CfEdope9mOyJX8Izw1QMnT57K8jvlyaXRTwF/wvhxKSu+HrYz4xueV1Y9DLxJkppVyDIMcJAvzfz0rUVWfhod7ibOOzo6yAo/QbDhaDlAhtYL//DflOee/wWZMW2hO1wcmDzE2c/P90B2Jcv/v8MH/UTQ860Kku40EssbAZiVxy3bxCvb3if9shNF4juwRzEo8nV8fXAmUJkxnM4aq0gkJ/3PGTLLERwyW2MgrE9ml4Fcs3cJ4fmK2bR1RBWHoQDb2tvzLnGdRg+ZSAOkg+ZgUEaKEuR6hKYFz7KSIsQQD8rJLG44spKlGQeJbvk+6/a2tixzUBDqoLE6ZxQPg1WMhdZijQD8l2ziejAdi/0HDpRlzCAPHDgohkfnXMOh4cxOypTTIRRn22KgQQXk78YRMDiTQAOKwDKJQPocZbYxuHBjQt/hPp0Ue4Mn0rJpe9rMb4VRGP4WRiWEtK9K0vY4YDAKbRkHXu6o7oyXswyPPPZY3m2fwS7CFk6AGCobnWJpqaLQwVQYHQA1Az/7VwXpIV4K+llmL90Q0vRUlx8swrC5sZ8G1f7soxEBlyghkNVweYh7BhfgGG4CbHiU39JUp8nXyxjl7sCpcFOyiVOmJhtp/kJGAABAAElEQVTD4FEyC8BePlWRmAIrnqEbsA2U2Jsqpgzm+W6bVT46ur7Sztnprzd8XY5zdoNFAxnjxo4JfhcIosiDDmg9e9hed+LVMPYhWGFwqS6lAHPoEUNCKzRsPrN123aclVWRn7b2tromkFlSFbRvTLGMn8YgNv2moZAYOtvSLcEenBbfhKHCdVD+PY7gWgZ1Wzd9k+UHo1kv5royZc3UwaQP2l84RaZrK7NxPqnPWVoNgpvdXCbNP6nB/LYfjObKA+7rsXzFh/TjmTKf4JcDI4NfBnIStJP3+AhfGclwkXr5Cv9Iizogsh3uzxA+TjlKUs4Z3pWsEW7DkZjNa0tNMVQOwt/QXN5tZEm+8LpG1f6TfvKRgxl5yMFZKqYynWzl3YwJX1F236LFvKFgSvjAZ82uMTsr+3lAn5z5nSVhIssnO2LzzT0a5Bk3gXSfGRWeGwIdYKPMjRu/geeuMpv2SGnH2b1CNsMPl3S2KCYdGdxb3sGum536PbDB1/aHXsAXf3lAY62s7j/QWfbs2VP27eX1ZGPH51WLA+8YSEYOAy/a2ci8cliXNGjmwBs41z+2Q5LowNeZNXlLHbmXIMOBzs44ADqKU1mC4EyoA0H7SLqKkzMUHuJW+64ls/B8rVsdZIqtxht9DXx15DfQ/MK357LjtvvkuD5Y+6A8iruY1ecr7wM8fR1dQH949kgL7Bfqj6PIPYOxa9CRA5htcTZq/PjxSYMXdoN/Y4ukt3yt4yWOftSRtsUMDM8us5HPnL0x7XilWV8JNMzNWyFu47qyoFyLl/qvZXlshai3+E6MK865b3fYAmAry+qkg8zqvPv+0tTta6MHoyszYOU5+VKdrl6R53V6fFbb2ZIs+gV6UUYZVg/ZTw7ybZO6ZMeujvLJqpXlHrK+Zt8zE7x9xW51tILcf/CP9JE21ufMjoNeMxoee+LxMgm6m72njRBn+98jOh1aNTNl9oV4OIukQyksZ8AcVEg6dal2wplS9yUxmPcQTvpI3n5gWeliub/2kGecpbbv3QPIzfZMUzcQcAz6t7ePJRtjZja6zkAdWsvn1iH97bLIF1+ku3yqDEUe1AMiQmEDs9ooNyv+Bv17EL43481Xw0mbnuiv76ENwAL7r26DDaBwE4jZvrOjfEYgWfk0W20ksuRO9h7q1wwywLvJ8jIILI/42z5oAg+W1U4pI8qBrz71zR8G7taxd9MkdMDYse1kCg2JrKlDtTt/6fi32iL6yqsy46tFDYS7N8aC+ffVDB50qHQWP+lsWXkk+iWUqgSwC7WJ6l550H6IPwXdvS7vuURgBRuLj2CTYgNrDoyEmSULlLEvQ8w0Al4WKHXYswCsZ67U317jwzNV/r7LwGv1J6t449KSMovBNCjgc1yIHwzayRh0368snaC/pZd2qPINcksdHuJrG7RffneZ7DEGXu4d4kTKo489ij/WHj50oso2iL7oqFM9grrX8qv1J+2r16Sf/W7wyH14DIDtwmcawkSbr3MdNpQND6G5PJAAEhVEZ9MQ+cSjGXyKa/OR7nWgp+7plqUe27fvKJ3QXt91PIHeKZMnJVCtX+LkgvrIBnTh6hfwE0fbb/tCEykfvA2wGmjAx+5kSQmZiOeYKPC1pWaA+nYe+0WbLn+ru8VPnpY+8lBgWnfoRoX+B7b+md/dM8vfG7AfH7BZsZueTpw4IXuJGaCyz2tgTaKKa/5KlrSjklq6BBWutspBH+Gqh9U1Ztxt3byJ8cfs0k5gbQBypu5Xl4mvPJFBOLSUrupw25L70EVbIf3U7x5mRDdBA/t1Gf6Y8uLyYe2Hsqxd0MfVsmZiBtyVsdh+YEgb22c93Aq9rNfvGa9xX9x2MXnyIeObqWSrzSfQbiDAialQQoYHvjAMjtteN/I121UeUQbE07bZFmlpv3jPuu07929yjOCbrqYQMNXfCT+07EV0ATRp6pHEDb7py/g+BuTBvUV3x1X6NIfwx7bi7/m2ocWLFjEpNrLlq1c6ClMdLn7qJuG5abg8a3v8bYan575MLMDsefWygWqXZrgM7Bxy6+bCe3Z8U/6H//l/JbOPvehGtLHZdpV5OBF8xdjPz/f4mwUaYixbdMAFjnLo3c1AwzU2kNmYQMMWGOIMHdgHBXo7s6e+OcAUwKxh8lkYTtaTYStTcxPG0DHwk9l1LoR5KKPhdMmGr6/ZuOqTMmRce3aVHUIg41aUgAysUjAq6neVoLCr0q0CIhM4YPRo0u6873WZx/XNvnLu2J7tZcb8OmPkwMvOd7BkeqECEieHZ64bOJuDEAFLwbNtqSs1VWWvknVjws8+WloGjWzP+i4jY6amm5LtIMRBruqHh/1DXXVZhUbVHd1NZVcxqiwVSnGXVipYy5uqvnrpijLt/jmsZxwfwxQjYLspEdicHVxptKymS1G0lIXtsLQDkCgtfjt4Oc36sR3beUXk8cPlgUefTtqV2QbNYF3B9RC2SlkcVQ65L62gu5/ax2SHaFB5Rgfc9rm+eceWb0gT619mMaN2G0bfTcRUYO5ibRvdOEzFooLR8TQ4oBKy3x2w2KDKB6zDQvgdXJxHkXV07Cx7d3zNLt4XyriZZKqMGxfFZqTUvtLR9dDQG4100ztpK+4qPfvVYInLHzS2KkcDQO7ToJLa3bGrnDlxrPTu24+9Q8bFCIm3DoHPuFREeDo3iRhTp8rIjRHTtyosjK5lGudMvpPXnMX88pMPyOa4VkaOnVbagT+QpQ063Xl9JnROJgTPevjd1ED71771zRdGw005VSbOMsg1si98abmVKPT+nZvLpHvmZvd9nbGbEFTfrCHTGLBTuWKRKgPBRBr1fsoc9Zl14ltJ5Bp/y/7N/bMo1dWkBR/dtafMXvRQ1sIn0IBcGmioaWk4i9YT/oBPgOFveVPevqLjBJ72NwzD3WroPPue9dXvLi1j7pmeXbellwGEGlBQH1VHszGS9q2ZKAnuAFcZlSft48Zx1Zh4XYO7B8O5fs0XZc7jC0t7e1sGncIy4JL0TvHyO213dYTYcQpPyY+YphhyB0P2jnysrCsXx4+fICj7Vblw8mxZ+MRz5U5SJC9/Z7YXARCgGFhR/nT+dON8ProLuFev6nToxIfiqcd29OIVjJeoy9f9HegkGLCF1Nfpd2fw0h8d7GBSHaqMePhMdzIl1L+hOZBcpnSVlF7xj9nTKCfQUINl9vcRZtMOH+xkreHhMmH63AS/nCVyEELh0D+6CbrKj/aLutJ6pav0lk62LfIK79pn6hxnvHZs3cJbJ46W2wffWcYQKHEPBZ/3OWW+8oPOUc0CkjfAPPIT+0EfRR+kldqY7tFJVupges1bS8vtd/Yv7ROQp/Z2Bk23BXflInqbumyH+Llhn2ffdGD9jXzaJttgoIHK2KTutgx615EBs3ndhjLvwXk1sEbwS7mTr2LTOINq4Kv/7APrTE+mb9Vl2o8W8tzweZ0213cuW/oeeuYWAncL0JGsk6eYOlS7J78kqBFd42Z2rUDDDXIjD8nrZjxIN3Vp+J227UPXfLR8JW/mYWNYHHR1uM5TRbiFD6eGRnFEwVN8b/x0lbQRrXsGPkzF3vbFxrLk+adLG46c+lX9pG33I1zLS6cEfsMnvuq3Xwbl0lq9J80aAtkf9otLhnaRtea+ITPvmVWdaOgiHza0rGS/TnPxDN6UkaduPKSrVwzOmaniZmRHD+4vQ0eQSTluAjj56s+qC6xAGVJXNnretmgLE6AGvnypbtKxt2tdXqEucnMw7dORg/vgsx5lzHgyDggGGGhoBnSWb45mYFQbVXneOkW2RT6+VJ7xGbPKNn+1jo0m7+DVkyPDp86Qahtsu3aoGcjodNe+JXPN2Tng+qpKs9DUmQatDepo9x1cmdq8FZpv/uTTMnHO7LrMFHhOmjRBOeXyOr9Ae3nRBoFv5ZnK76G+1/inXOsPOfBay6TSsb07ypwHl2TTPW21fKcv5vOWjQwBNPxjo4Hvd3GWH0IfLruU0iC6hwPyzoOHygdvvl/GTB+f5V+3UKd9qlAppuLoJ3yunIbvnHzhcnBtFWl9rw0Tur7EFV5hvLu8terD8sojj2ZgZKaYy/LMZjCwfg097sDazUPVJ40/oy2lslSvLDS+gbrUW8qN+zRs3bwxmx4uee553goxgvbWTA11QewUdSjjGZiKP3gFb/q10gTEOfT3mj6SLvo0h8ne2b5+Yxk2aQzZvfPKQDa1lR/VHX48xFm+F4K6O/42FegjWT9AA9f+8eN9A457CVQf2tfBIO370j5pehkzZkwCYcqIz0kHfS5xap6V9uIv3k05+7+Ba9DOAaRZyZs3bijnju3jDWbjkKeJzNwPy3MX8MeqH8cEXAuOulWfR38tgQ5gUm3qto3ykL2sT6sd2L6ro7z/x3fYJ2tgGTtxKnv98DpX9LA6SLqIY9WLtF2agFPDl9ZZ2wfMVh1NG8VDP9vXIO5c/3WZjv1wabU+i+MC2yxD+N02SHv1i36x+t+l0/qW3rceA+f2qzomk640Svux8qMV6YcF9z8YH1v5EJnAse9oBwyaZzIug+6VNpWnxEGa136XNC0dBP2cPPl8+cdl6vw5WfYh3/mqcIqkTuH6bHxf5NBgiJk/0sTr6iHp4F5SLqn2Wvo1evUqyxv2l2/WrCqT585nGe7o9JttlDcs2/Bwc07FaZ1oMj400EBdzX3P6mQnIN0DYvfGL0rPO4aW+whUu8GnYzPp6pHxDeWlu7yQtuHHuZmk/oWdo62UR/q55BC8zrJPyylgu0/DWSYdv8eHcPPijVt2l//r//4/y9/9/d+V0cPa2MOr1QfV47JFqfPn+uc/FWiw0aoloy++nklhQsWjkxkwIAR9ujtYu0Zqy+aynHS0z9esKcuXLS2HTl3w0UKMpwx20gKa6jgb5/KFmCPaRvAakEFxahSmE7wzee/hEyG9Q4Xm07tvDyL8V0oH1248BDkYXdfD/ZcAfAbA4nkHv28feidKYACDQmtCQWMoTx49hJN/lqUekfXgoqrsM6AHaZJXyhkLtg4HDqP7y+ws2zh5obBnbT6DOA+/83bekzoIB+m64Tp76ng5eOhUIeGxkJSUyLVqqheAegN/+2mEld8ed/IRz8P+4BhDQ7uBFAFvcLmp9L8DBYZz4Jp2BfLUyWPl1P7D5QwAefEh79wmRfa7y+Uc7RVPj0P1lL93+Rd+PU8lioEfY3PWOWTwLeW2QcMzUFb5u2nhGeBfPIETAYLC68Gzdik6q5zHrhzl2o1H2wDS+L43nTV2Oe3iZVxp97i7JycSqxOoQ+uA/tyZk+X7syyFuGGSw3rkg9M3Aua7fT56AAOysxgsvvcCF8t+D/IDYKRBd40nNVSO4hoInDl5pJw6dKbg8hdx8Bm3Bx16F1kRp0+UTjvkhkMxNknyJB/73nwYz6I2cuTAMnj4qCg8B0XfnjlVjuzYF9h9QMJl9KoeuV222mcDfnKMpALrkPa2pRfEt93iNfzOQawj9k0m8A2K9ywBnP3bOoK3vNKAk396sSxwP7GQG49R/Bg6Zng5ceBw2Uu/yH13gPwlgPelsjvHtsPzt3GV9cs4hMc6d5T9J38ozmGhx8tZmEA6SR/V20E+zWGd4iAd/OhOyDORD87DgT90zAQMhW/EMIhxruzetqsc4d5Pj4mDBrJ+9GQ59ZMb0l2a2OfC9pA3R/IZ0j4KvmGfBYh38eJ5+v9EuXDkXMpJT/H1GWW3F0jtlaCtwxZDrtCvH/3U9w5m2M+QiUNDvH7HiEr3WwgG6Sifhi8unP3/qHvv8Kuqa1F7iIAoYKEqTZogiGIBFRFFQFDAhmJv2DW2qLFrEhM1MVFjTuwFG4JdVIp0FKygoPTelSYISFe/9x2LbTzn5vmee5+Tf7J0s/dvl7nmHL3NMZfTcNTAJNBivHUAfwXfdSzn5OvSVaVmJY6ZpIzvu0Lx7AigNjghrr2q7hhVa9TCGSz6qdiDZsMajjJagHPC5/KcEqgigxqIWfCLefN2Xg34V3iX8C8ehJGypl5VtrPU3h0lR2YDQ/CbqdNj+hYajfKZF7ulE0f+rYz6V5f4Xv4vPtizanmqwxrmEVqQY+J01qRZ+d09AHR5Jo7dl5d4WYB8+p9XLWBXoRKNxJCf7vDw9cY1W5K/Gu5RlfFxnFDQGm2rvl2BsTY/+aP6bhXS2PweRhFGPv4nvXivPbnvTrtS7go/fr/2p/yO+JFva+5FT5FddyuCARh6K5d/E7MWLovSNHfhO4jwqFgV5wejYtZq7/LPS1zX2BXHZb2BwGIO3C5pDfuRrut7Q+/gHgNMo2n1isXx3bINOPuwL+veAMDF1S670W9mFSeK/HPofCX+UlIxB2WpOHbuO/LmLgRSKjN3DRWzO9+tWs7xwStDlq/FA3sy58RP6TERMbu0KP72qls8Ja/6G/nKeyk3ynKDyrtWJ1NYdEvXYFq3dnWsXboy1sIT8jdLYG3cg3GX8brEj7xM+VAJefEDE94KyJy3ZJBw343ATVV6F8FLZqLSYFyzKtasIGsDewCW5FEh7W98KGuFU+lqDj+tXLou+cM5V2WR8mF5JrVz9T2Y184pg33TAO06xv968cqkS/FeCSR51LDc6Np/OTZ/xh48trFnwsU5ueaddymTDojGrgad+unbZYs523xN6hjn6lqFRUne8PL/uBqg/8qV25ETYjYzN47S5RvKpd2qV4ldCYzp0GqcbsDAXD57VqxCsXr/xD1fLLNjkUGcv8Zf/verMmurCOy3loQBH/stf1u7bo2oUm13jOxi+9l3q76Nr+csTLnh/YW5F7172f6BjPzFGMUnhbxQHgkTx/TZ8dUPNeo3TENZp0lD2+aFWzZxzPQm9kGv2hrz+Z5XfX5YrjLbQVf99/lL7zuBy80gxFv7UD5VrcK58lXsd4WRTkBFB0cZv3bFEvamU3nCdxzJ+TsnddlKEPFLeeb76g+mmd8T5+JJHVi5+k7YRDUJABUBcJ3KteiP1egP8emcKuxG5ehOuyBD18eqpev/D5tGPnPMvD/P0pbzqs6j5u7VOFWqJjqfYz2hmZVLv8YmXJnfl/7+h4mRtFCiPz5OGEu3XvKp9/qOh99pipCqVrdpBmh0iNas/Y7xF8SmbbLFOQkXE3U/8EKd8sux+TOaVisCFZvWkv3lw9Lca/DZ7o3qQve7ZSDSIMTKZdjA8JLrhIyiXEUCtQinjcjWxbz33zGKnOK9WntUoUHvtzhexVycv1fVmrtGVfmVYJRBDeXMIhJ136zZGojEn23I1LH8vQhklGi0GAHc8KI6DG0A/wcmvpkv+B3nV61Wlditxh7QphQLnNGrC6fO/tlu8be7A9iyO7C9AIaZs0Jo/ferJveVxjcwqHJjR2TARgxpYVi7YW3ssZrpfCrHViPDvlm0PPFTgR+pr53LTlU5UpX/pi//pZQsaLE28vZH3qYYMenduyvTdqm2E71cOBkIOelvdUq/mT09ZsEUrk24SsuIUOa0Q6xbsSkW8Xfpkl68t3P3e0w98Sr97FylXFSpzgkv+AgGM9RNa8DP6sVfp3xXFvg78WR8aofKnDoBn5VUiGPtyUP+dC47soYtwH4p4HNeNerVgGaqZCWsvOpR8N+h+76HJlNW8B3H8LXPXtJO6arDC8dVnzsPsSe8/X7dOtXSXtL30EfYsHE94+J7rCeZu56eC0xKKLsGH2LUea/mUbp250U5FqdOLV2+JJYYu9WuzfG+u7JFhj+gKeXMGuy9jdAOoizHFq47OynGmMe6/yfNN8Le2AJCiXkkzLWZHb8Wi63foknqVZM9Jj/XEFBYuXhpLFFYcDWoQjIaRfbt0tWxwhv9i+sPf7orzrvgvKhbvR4yhsAa3zGU5sP//pOvf0ug4Qe8+Qw0oEQt9dC7V0iV317y/5Hz1b8i6/JeHoE2YxoZNTJ1tbeVMW4CKWZJMhJHhl/Gttz/gwmTEq61+feQTkdmRthoU5apE4EyAGHUzqyWZU+WuosKhXJGqviOjYfMYmnQGgQxopxHdn32ScxdI5lEtMaRabpPi6LsjzEzu8AYZjOMqhml07G3lEjDshQ59XOR7x5tMxFGDm1QsnDRoviAs1e99qIUvRkllbVq1c45mt0zeuZVZJzIlqPJzSbIXFZPWAJqZtj7GNnzPkVkc33uEZ8xZUrMIePh1bFVK6KnNbNBl8yjM6NHltG0bXSZ0VkimmZQDapkRo45uzYfRsSFq3N3v+noCROTwPcnouy5sSoLo5/i04yH3xXmAtuItVl8YVRkJYuotZm6zLLwvkancLMsbtwnNCrKmUe027cZZY+c+Y5x5xxdv7D35kYGNZqKCCmZPd4X785XxVOUsRLQYh4/omlXAafJ7JedgbPipSDqdGQ7jqaqTmaAiCjzcAyPHbMXiJlS75tCDcEgLoRzPvhtbhHg+x6ZpONvKevAUe/zR3Htt8/e2YG2MnhnUlk2n/NiDC8jweJAWLkg510q10zA+TaXEVc/szT8vY8/K97k3yNbtogG9esnXHzT6LqKw0oU6Uccmu0X12Y3ssoC/O5Ils+qDeFtFsL1mc2eMm16TIDvvBruunMcSDbC6GxWrjAe8YHkGeHgmPKLc9uIE2YprCd12M3Xqgb7LLhtSH76CW/j25Wr6EfxScxeNjeoJ4r9jtyPctx6md3TMJUXxa/ztgJHnO4MX+TcwIVdtqVd4SRYxI1VOzoSa6HJBWRU+xOxFqcdWrSk9LFm7scWBpvg54QF3zdib+TduZf26SlLkqb4rZ/7kF6N/ltZspQs/5dkx5awbg3HVoe1yV4pVqWUsqdmyxzTzJ4wdWuTPGEWai2l/FYluT4nL106vspnGXuJp8+YHJNnaQJG7NO0HqWsxVFsBpTMLnhaRbEXkewtdOpracdrM/Jks9F2YGOZvgabdOBpBUbJZ0yfGf2p3DBExIYbTqk4PmqTpZEslL9mkKAOjLGiKVHKLdbvRPPUCLa2iFOzhOLaMYW5fGimdiJNkKZ+s4TvRxy0Z51o1eqgDJqoQA0OlGW+Zi/FnPJBnIlTL/Eg7IV3Kevk+1mhALyWfP1NvDXyn/x0JNUnezdpwjxwzHHCdMbUB37fMRN30I/BYeG7me0kZnpck5f4sSokZTTr9hScUePG5Wf+0w1Z4NG44kl6lC6Fj3N37WaKzfxnhQFr8zspE7inr/1Oyj3m7laI8V9MjKmLFub4LRvVpzx8rzz+1QGVM85Xmk6c8RvlmQ//9p7yguXQzl9jM/kDmemcbDg66MMPc+xGRDWas++0dt26zLFcBmfdE+pcS3Mqxi6qH8RLwirvrR4wcyZBYDzCa2vNHCqH0a9e7eiJsAf7rD160O86H+lemeKcrGwo6WX1mzhVjrk2ceTYPgsrx17KVgX3tKqd9qIPhVuLctvVtsC7Y0t/hVxMluE+xVqUYW6DVBe6/cy5mFlSx0pPy5HBs5Dx09k769WG/jH2XLBqROPRSi6DhcIm15338ZsiJf/nhowGHsSpY4pv15kluJTIfj5rtj9Io7rj0Z2yr4AZL/WdWUGrijwSUlnPW3kftztAMVkZZHXQJuac+8PVm4zNT+hl9G18/MnoWIzT4HUgRumBlM27dU1Hw+15No5UxrsWca1NBCKy8kyccstC3/F7T1Vy3NJaFixaHIPHjMmx/ac9lQR7NWyY/OM6haGXdKfMzwwo44sH6dGH95AvxK8yw6yh761DZk+dMTPGAx+v/ag62bNRI4IEODPCmrnI+05IOHo/+49o15h11y4QhsJc2SYPyKfZMwScLUfWfPjhR+kwOMqh0KQN++xvIm/kJd544V8a3dkvwb99g8/khXROgKXy0m01jm9lzGKadX7AtiuvdmxBqfmz/mAO0HSeWsJv1JtWEypfDQ4L28Lm0O4RdsyGvWrlybSoP7TNlgD3qV9Ni8lLp0fzPZrS8Hlf6NHGlMW2Tu+ZfMqz1Xo+PClGeZWwZ2D52YdVDF7C0rUuJ5s688vJMWrBNl5t3gI505Cgxra5AVfnpz7VVko5K5zEKespYF/A3fdKstTXOv7q1dHI+OKuEd3ZP78HzUy1l5P3oEkvdZ24FGf+VmB4X2kl7UdowAlLWyW7zwqFGVMnx4RFhe6rV6l8tGvfMbdmapMnHTK24zmudC+cfHg5f2kv74OOl97d/sQX8jPL/QcPG5Xf9Z820Mw+lM2rm7Vl1K3fU2VnvwX5W5j6zA1zjpKND22bhBPP/uc2JXl3NlnykWMKGez4XY48nOqQOknrbo3KLDj4KuZf8KxyjGmnjWaVh3Sv3NA+8SGMhJc2/pdUKExhi6fXAQ0bZHWhW5cKuHuKR0ErwkH5a8WnY8hfwt21+Le87CVfObYydBlycsjHn+T7Bvj2RxbURvdZmeNv1U+pUxlbfSNdyK/qXj93G6Xf8d4+vI/2n/rDhqNz586NiXPn5fgdsAnsKae9lPKK7wsTH+oEYasskx8cS5jwJBrSLkv64f7O51uCo3NmzYpxswoZXA8b0Ubkbu90q5a/l09S3/H9HISBlFPyQPo34F36zTlsoyfnpSywZ89XJLrnY3N5tcMvq1+v0KvKMeHg5IShcNCn9OGW3pwv8BU/rkl5r2xP2PC5PUymT50SH02ZlWNrBXU4+ICsSN0ReeL67Bfh77URpbNMivJ6PXPWVnxnyPD4w72/J9BwftStUS8DDQ6mvCv9l4P/h/7zbwk0qBB+/g9kKS58bGf4HFFmx+1BHMdn+auO3wEo2YbsNTLzvA6hZJlYhTRqyiaiP/l8Qtzx0MMJ0h6HtYqu3Y/L85I1aiRWHSkNH4URNJtbL9wvnowN8tLRZkYyh8JKhZ6/hYC+ptP1qI8/jt79340D8RM7ckzdoXSPd7+ZxrbEIMGl08lvHTP3f0K4RemfzI3zCJPLKBoalVCKGkn2FPAIoCGjRlFpsCA6HH10HHTgQTQRIeNLudI6hJ/MIiOkUGE+Kh2dXoXLWsppZCobQ2pk2ZBEuMqoloEu+XoJnUqnchzNW/QsOCLaEGhoWL8+J3lUzTln2TzzlAm8h8BRAckpGokppGBMlapz1yhQaPndleydnzdvXnxCme+wD8bEpeefFwcQgNkFY1cHSAZWeKRC4PvCRzgko8Ns3utnYwUYKrBlXmFvgGMKhsrY90fH0LGfUvpcP47v0jmb3aUhynycizDwUpikMOU5mVOach08K8S9c1no5UfuQd+8+HrlCgzcL2LwgDdweDfESZ06RPvD20YDDHTPfd/BQAnCRoMum/9AK5a5ilsFVN4TODiuhkcqINfL3+6nn4tSfm/4sHh3dGHMXX1xL/abtYoqlEOVYa3bAZfsMcJ4Cp+8mKvjeSUueDaAIQzFifBxnUaGv+JYpz4vPB+fL1keh9auzhnbp1KmvHcqeMfQsFFgKmyLYAPjQPvChLcyyyRP7EJfErNywltHTAPEfgkfjxsfDzzZOxowVueTT4TeD409gY2Gs/Mp9hXrHOVkc1yFqYEGgzkVKu5YdE2H/1T44rQM6f4fSTMsptS0/6AB8exrfeOSE86KQ+AlHYzdyMpKj0JBQ0Ujy74bzrnKbrumckj+pCTfRWjyZuAKPGgoGhX2aNlJyIzX6OhcFrwc3Z59cnXqkK3eBeVKCTdrtsGnxpdOloa+SldHxfvKazpN+Rp618DRsbZ00HPH58yZE5/TP+blgYOjZ9dj4pADDyRLQ8UTSs7fCkN7PMiTZvLW4QRbEumxiN8Txtfp1sDw/oJOJbIjsPK1Dt0XE7+IjzjjedacRXH66afS7G4fgo7Mn0Di9+twjFA+jm+DPecsnnXetB9SvoBj5ZfQMShgKfmuZCvcpvPRR5/GW6/2j3fHDowTj+4a55x6RnFksIKXPb4bN+Kw88ukc2C+HngadMqxGd/n8uDRMmiNf3Hlw+oI956Pobnu4y+/nDxw3TmnR9cO7WNnaEulqOIuTxbPOSlblLfCQSGQhipzLjkXJdrnw21GAc25gPurb7wR742fmNUZv/71VXHoQQclP7o1ShpR5vlQhkgnGsc6ARWBlbLfffwGfnQAKmAMuxVJo1WcjSfQe8/9D2RWuxNBuxNO6pFl/9KhcsuxNpCW0ADwgQhJ3kpm4rWGi8pdWaP88W/nJG3Z32UUBst/vdgn2jRpmCdJaBBZ0upvNNKUZY7l/DWilTvewy0MrieNaMZV9sjTBk+Uxzqd8xctioEjRsRXGIvdTygajjZFFhg8U0Yb8FJnKA+cexpwjsP4/u3lezlf/s71wbc6XTbPMuj4Jl31bcLajiaDjRs3yi7mfs9qpxLenLt7+AuDtijv1cgS9861NL7Pvr+CAMl8aP5zYP8W+u+8k06MVuj5msDFslLnJCz9fgYpkYW+l/qJZ2Ehf/pdcSkMfU8ZLa7m0yzLvbijhgwmhV01TuvRI/Zr1oyAqZVgFTJQntvXkB/C2LFLOg6IgAv4C93nvZWhjp2OH+s0+eDpFy8//2IsAX49CTIc4wkelEArn93aqQ4sBxz5UeoSs8DK8DxNgvc387dydx20pWzy6LvyOoV8x0DAMHD6/JsDEz83XXFRHEWjwZ3RQdoEG6Flu+vrhKqvTJrIxm5v28TanbM2iA1kzV7b26XgO4PPNFybMSNe6vtSfDJzdmYkL73+ujiIPdHOWZ0v7XlJm8pJn73SSeWeJZ3lFhS/X7GEA9ZkybxHZD/4xFOxfy1KiNsdQWf9/ajyq83vS4GG8jmezre8Kv6kUR2h1FfcTzmQ34eHxI/3sTzZrZ0ffPJpvAnNnHjoIZzgcWA2IPYEGOVJ8T+cxe9LQS1/K34zICO+mYf0sobAr7yV9hS0tgoZP3PO7Hib/fPOqXP79rk3314s4sgtMNKIsFQv2MxS+aI893PvnWsgoM6OBsUqtiqON7JPXpw+fXp8Ou6z6P/K63HCGafEEW0OT3q0z1JhvyAbk96VJUUgV3ouX77or6LMzEo3dIu0r7z2t+Jr7tw5JGY+iffZPz9l0dy49PIrCFC1yNNV5BlhIG2mbSBd8556LgM7jJV8Jr1Ir4wnPoSTOnbNmrWFPUYvo3c+Gx+Ht9wvzjrlZBq018/vO7Y851wN+Gi78hKYo4+Aa7ENQ5wWwWNQkfD3XuLWU73G0edpcL/XYhr37nXicdENnqpZrWrKbXWaa1Tm+H1xK504UcdM2aLc5HtWdf3o98AJhAp+f6CR3rwYMGBgDBnzMaNE3HbZpXE4dkc1juDWMVuPnl7FHAw6KBtSBjowl7DQcdZucMvghs30KOI+JpW0c+y/M5FAwAMP3h9L126KToceGMcd2z2aN21Kcoc+INgYaVfkWosARo7MwMLWSxiV6Mq1FAFHfZctbFVYyXGY4+LhF1+KZpDYMaefGYe3a5d2TcIdHayMl56lceFj8NXEnfTtd6Rv6Us9KM6LbUlFYC17vn3wQQwc/UH07NA+/Q9PyrPXiIEK+aPUj0JeFcfSfiaykOMl+eD3lC2FfYXvAo1+TSLS5o790R9N924W7emL4HGYnkRkUNr5ZoKRMYWB+tP1K3eTHn+Bg0KXoFu36TKDR1OofPdUliHjPo+zundDfyBnSNLa+0X+cPtE0gy4k94LmaCkhP6ArTawl3KiCGgW/YaEpadujfvs0xj9zsAoSz+zs844neRP07RNDeao78SeQTL1SAYaWFMGNhjP+0r/KTOgQYMFrkse1iZw3oNeez4++WZTHNP6wOjU/ggaVtdHn1GFB9+pG+x/pv1RFl2kzyjNrUYvz19Mn76hQ6Jd12PjzLPOjLp7wIdZN6VF8c//XNt/6vW/DjT8cuE/batkkHjBdvGAeKd88XkMhDiXzJ+H4VEj2rZpk9lyj1ZbQ5+DjPLCOCJXY3f0Rx/HLbfcEd8w+MVdj46ep52a58dKQBKT2T6FlUSQDelQaIWzVyjQFO58pkKVIJOxGMuMh3vwBiC8736mdxxbu0Z0Pe3MaE8012NdJIZ0ZJWcPGScFB2sR0YpMYvveX+jszpedrXegHCQERVSr3P80rcLFkU3Ov+2wZjbp3nzdCg0KnIfoVa+YzNuSdgqQBQEMr3HLMo4Gt0atxrYq4DTfAMBEHTvZ56JLpzC0BkBZdWBmXsZcS0KxO/rkMjwOosuwOVoRGpcKXh1tDJgw9+Fgb49DuPiPK5rJJmRUcOGxbXX3xBtMf6rEfTwOypcBWwJJiV4lCHLLaoLwWQ2bJsBydoUku6lFa5fkOkaMnhQvPzuoGhJJPTCM06L/RBWOnF+z7HNSjDVInAEA6s0hIP3VrC6EIWj39mBOXn8z088FhPFHY1wfaXvC/HZ7EVxxRlnRI9u3aIRDX/Kw+TlUBBIzVTqKiFPqtDZE0Y6K8InDWrWoSGnak7YAUvrvqfPnh2vvPlmPMWxjF4P/P7O6NzhqKhKSeZWHEaQmiXcjqdgFkaFgnem22gHg9P3pBthpyGjQ63g+RTYPPjAAzEcQ7F7syZx7kUXRWsirhqKCuTMHG37fY4rvW+Dr0ao21DErXToMW++1ulWwbmndRiwuf7ue6M5s+l50YXR+ejOCMGG2RVdJ8hO6c5ZZeODCeb6N0vjWFcKxM0I1w2UzKZCJoJdhiDiD5yEsGjREgy5AdH7lZfikh5nc/71IdlsyUyjzrnANVMkfRpoYNqpOMyqSfNWNJSMLB1Fsw8GHW2qs3KFgYap8TpByu2Zh4EGT++oR8bLZqCb6F+gsWngQzgZjRaPGopeJacpDTBwJM1Ljz48a3zmTBql4mA8/WzvOPvss6ND27ZFsz4CNipKZVHyC4rBsf1bo6si67HkfgulwywwFRsA0XLKIxOtGHAv7ic0cRqBMTcdw6jX+ednIKMO2QWbOZmV37i+MHTlGYAOqTCGDMvDMQw2SC/5KfDSqdllZwM4G9in/Fm8+9ageHNk/+jRuVuce9oZ0aRRI6bBarGKN1N6CFdilOyU85NvlJtSpDS+lX/Ksa7KbCOzMsG1yW+7VK6UlR5Dh74XfR56MKbw/bt/dWn07NY1alatgoxChulsWmYB7flb+degljRksFBjkJe5lJRDKGjpNmHJGiZjoD/97HPx/PCRSZN3/vW+6NDu8OR7O6Yr5w28OG7KfGjUagu3iVh+bvBoM5kJ6VzHV3w7tsa0dP8Bjb/uvO66ML9wAbTe8/TT8qQK6Uuac6+58iYdduhH+nN+PjtpAxKZJQFm6oM00PmtsnoF5aLvDh8et//9oTjxkIPDk1Natz449UdhXEEX/EYspqMOjKRldZGZH9ekg1s47DrexelJGnU6ailroPfP3nsvTj333Dj88LYcEdiSsXYiW7U0jS17X6TjC68oG4WNl2MnfllH6hZoxmoT16IBtQQZP57s7guvvEwFSdM4pn377GJei6oGjS1pXKNMWAgbYerlunJgFiV+5SPxnVU4fM9L/TF5MiccEYR5Z8C7cTmOUTtOaTLQ4IkQJdqQxNXJ0oVzlDcNdpYqYDLQAH/pmIlf16numjZteowdOybe6dMndmzaJC4BNq2Bix3Y5X8DKQZqMijE2KmPSjjlPhn0AT6Gj4V18hzvq9s0/m0w2Pcv98eHWzfFr047Lc4gOGWgOo15vg93Ztlveebudh3nm3TKXM24boHnNjBfDVGPqayAXivPqTzKYI+rfOOt/vHAsy8lrO6//abo3qVzVEEOmHm1yk45rK7aibVomFo18QMyws/UF55yJHVu5n2dISspxYFr+ZwAzGNPPBEDxo+LI2rVictvuC4OQw5r96S+5DvJkBIHvxcXiU/+1FZTF3kPqzHEtadTmIzQobQaYySNHa+/++5ozfvH4hgdcQRGdMOG/LoILIojCV4cGDyVH+VVrwxucP/UL8w7gxvMOxMr0LtHyA0YOSKegibP7nh0HEECxdN8rHo1eON85Eunrl0n3uRfFyCfaheoA/2OvC9NaVOYxLJaYhJB/L793+LzrXHiMcdGi32ak+Gtk3j3+8pXYWgSQFpUjgtfcbsNXPxWXQI+yGpUoBxfPlZOTYcmPx3/Wbz9+ltx4uk94ohD/xloEDfKgLRFmZOwVNf5njBQ91tJpeNq4NP5q0IMDPu5x3tPmPBFDBs1KgNst9x6C/ZYq3QY5XPpT1kmfRWNUItqKXlFHvb9QtYUuj0TG9CVAUur+caTUNImf3bkyDiRY6AvP++82AeZ4JW2KLgU1jqozlUnyzn+iI0vv5aqk0oB5qw+Yp1mx+2JMHQ4gbW//T2+YLzbLrogTiNoWovtQ9+Bc+WLMlUbwGcrIdLuBuLaH1k9BAzEjRlfE0plmLdd+8XvtBkzo1+/V+KFt9/JSse/3/nb6Hxk+6hGcsMt2wbuvl+zLqs+Xa+/SXnAerS3tNG2R3aK8S0g133yPu+E7vOzjwms3fn731O1tiTO6NIxziCIfxDVMOrHDdjoOVdgIFzkMdejraF+kI/yb96TfqQjnWkmAV42J2yGjfkgbv/bQ/Dq7tGNpFKHDh0yYaj9K14NMDqexCL9ZJCR0TwFSDoqejTAQ8zbtSn/DNQoS2bNmxtvEFgb8tZbcdaZZ3IEN/4HCQ77cVm17NzUS1467l6uQz2UfKZ84T15IukIW8zAo0GOdNbxP/qRJGi+dzOSDx2ySW1NKmGUryW9l/DmPqk3GEuYuH5lj/aZD3Wg/J29eJAVi0mieiLLB1TzvYKfcM3Fl0R7fCcDGZ6GlhVL6FGBoo8jffssPoVDKcCmbhEPwt37lmxDE52jR46KgX1ejLJ71o9fXXRxtAKnVnqkHYD+c34GGfQTlWnyp3JYO8pLOtX/NPmojLO/i+teiU0wZuyH8eqzT8XA2UuiV9dj4sweJ8bejRoxF6JJDKEuy+Q4rw2aVQIfNvM20DBr/rwYNGhQNGi5b5xBAKQu89sKn7ECZEKR9EhiyFn8Z/7z7wk0iAuhidGDZuE1EJRaQThSK6YiMIcQgVy2ZDEZvT2i3eGHZ+n5ZhC6xgwWX03hC0PqlI4YMzZ+d90NYbHe1ScdR6ABp5SskWNKnBq6XgoRs9U/opB/wOnwWCmzs07HjI9Oe0aqmIcZZ5XPvEWL4q2hQ+L3jz4aJ+61V3Tr2TOOOuqoPANXQ8VookLVS+ZTUSp4XY6Gl4abf8hENrzzyCv3/hitsmxpIlE5FdvK+YviBIIBbczwNmueZV2WjjuWSlYBrfHopXCRuK2oMAuisSWjm4XUsPZvsy6zZ9MBnG0fjz76eHSnVLrbUR0h5sZZvqRwstGmijf3msJkMoaMqBBSqcokGlbCRaEn8etwK8jmzJkTH2MkDhkxPD6hlPH6m26KI1ofkk1zsskf8Mu5cx+FdpbVKqwZe6uODeOlAgEeafByXxm+EsaUx5eO+3w8VS0D4onXXovDCJBcf/HFsX+zZtuUAHNEWKjAZHZjeCkswKTCy0ujQp7XmNHH2Yl5l3fuu1SOrzEo3hs6LHo/+Xi8P2NW3IyQOhcB3qB2nbx3GZwqaWwrBorCuAg0UL4Pfl2T8HFMEK5kj598IKi2R5AoFKbOmhnPvdQnHuTh9dzf/hYnEETaBfytX/EttEeXeeghlaQwYR3CQ9grdDXk0igUhuBApW8moiKZc4BFpPWzuAvF9g7C8HRKgi+96urs0Ov2CDMLSXzcV+NbgaqRLp51xryn5a3yjQbiDuA2FTf3Eg+rUBCDMCYuveOO2Jsxel12RZxw3AnplLpXeP1aGvPAt46j0lSAK6zlMIMK3+NMryTI5Xm/i5d9na/Lq6iEHc0pltFz4eNxn8bIse9Hh0OPyOMB7c6bVQDMyyi/xpN6RtxpCCjEy5YrjE75KB0xaDVpCr5wLhpMpYzXWJTPdj9tITpPddOx3eIQG4NWZI88c6+wfbmoBB5URJa1eyN5Xpi5LmWBsklal7+sVtDRt5HprFmzYjTVTf/4x0Nx/gUXxzEYXA0Q8gb6pAkbzUmHPqQTZQOowyHAOQTP5QmybQHuzhUBlsZLOWCzBf6YNZdAA1mdIcB+MoGGy1BsbQiw7U7FhPdXgajobdJoGatHZ9ooUxpPixMcSJNboaU0glWc3Nf+LxuRd/PmL4zJk6bGqE/fjzYtD4wuOLy1cepspvWjD3jS7uXykjiQDv29e6G3Apfv1/MduMLxlHPOZa9GjSjp3j8N32HvDYrX7n84Rm5aGXdfdkmczvnODTHQXafVQWvgI4MV6ciCMw1GFb6Ohk5FcS8UJeuQZpVxGeTBmJs0bVo89uST8fDAQdGG5f7+scfiaGQwhBercKadqzgUf+loKI8FDO9nE0ru5ZxV3OoD8VOSe85hFPRy+xW/irk/bo5LTjkteiDjPXpXw0lnXjmlAacs1Fk2mKmj5+eAPCtM5AUdb+et4yfPupZlOKVvDnkvbnjwr3HOkUfFscccQzDAEw1qE6yBDzUG5XHmqnx3Leu4pzJfA8+5eh9laGmdNgRVjnryyOQZ0+PFt96IsW+/GWf1ujiOhCb3Z/uEBpHNqeQVdZvzUyZ6SZsSvDJGmaOckPKLShPolO/asM9KlU8J+j/9wgvRCmeue8dOWelhoEE8Su8GLYS5axWmzlVnyM/Fibj0fr5WzxhEQMBxKtO83Bo5lGPkhr43IK658dY46pA2eZqBhmzOi7G8HNsAoXJRXJTu4XfM1mkcGgAWjo7v5xOoPHoPA/pVAprVm7eIq3pdQKCBkx4ICmoHqCfUgdJJOreM67NOqLhTFvJHyl8dDOWccDFbaEO0kTh0Lz/1TLw5e2r85pzz44LTz4jGyAJpTLvCY45t9lqJtbh9QodD/jSgYDDa7KjOiltIlZvb8Z5Zrx0ITM6aNy9e4iz5P/d+Idf/9ztujRPRH2ZgxafHYBrs1TXVnknTEtr3fdckvDy1Zwv33yTtMq5BR4MByvrPJk6IfzzycLz80Zjoud+BceGvrojDsDvchiFOdU7USVZmaGdJ46XAj3JT50r8pg7kM/9OHcB6hM3QMR/EZQTX9+Kzc867MLp3Py6PThQ/yR/qDGCrDNGukG58OG8vnSbtGb7CewWvpl0FXZnNe3PI4PjHM0/GmR26cCzjsRzjd3hUZRvYZhxinSvvkbYFc5dvpEfvJ52kM8081Ym+LvBKEz3gugx+mUDi50WOD1/JFooexx2XRx57Ko6VXM5Vepa+XOci7NNvkD9rua+VUcLIKjUDD1sBzhYQaxVYBfhaWb2AIMn06dOoyBhD4uFokmEtqORjKw92g3QobE2rOze3ezhnnTnXov1ncE/cyE/yv7KhCsFcK9yKCtbFWT01/bPP43e33R7t0HvVcablfXWR2X5/45rNrgpgk1XqIx046ds1qMsMbIoPT2OTtz6mYupNaPLREUPj7KO7xJUXXBgtsVPlSfG6hman2jAGDpyrPPAj+sQxpB/HdD2uQadO+WZl1o4kptYSaLBq6m9/+EMGGv50xRVx1sknszWjelYSikN51nkrJ/2t1TTCLCs0eN91WV1q8G4r32HR9FyzKql8BhpeQIY9TMVdA+Zy1x//RNC0Aw3mK8fG74tjZdVRwkk4wCQpE12PwQuVuIG67Xfg/qxtI1WVG1wrtkFZeEob++Zbb43xc+fGhWTWzzuboOb+B2Qycy1wUb46pkF8bTDpUd+ioHsqbeFJcWyyQGBZQSFupDOPsX6P5MPNf/lzHN24aXQ5/rhoR5VQowYNEp76KSU7WDvIq8CfwWnkBGMLG/lT3Kp3MxHIa5MAM+fMjb7v9I/h4Pb8yy5POZD+B7DRxpAXtceEu7alfOi8kzZ5LX6VEUWFKA438NuVpp8eszllyhQSM59Ev9dfJ0DdLI7D5jhgv5ZRv359YL1Dbo0Vt47t0hk8xxEW6mXXJZ2KE+VB+iMEavVxTNJ++dWXMYKk2BOv9YvfXX9jHHMEzfcJYqg3k0bgaceS3pK+wZU41fb1fW/p+NJV4cMVTYOF05eTvuJkkPfizWefjrINGsX12AdtDjjw52oMf5x6A/j4W+Fqc1a328sPW6ERaUcZuT22q3LdQIPwcdvHqPffj5ceezhen70wrj21Z1xB4G6vBg2zglraEJ4smdsUFQ36L+qJ77AZZkBnbxEYqrFXwzjrrLOibgMCDa4TPGivocBY2X/29b8PNIhdIQjjFRfQ439BmgBCqM0kUjWCZpDLEfY1tlU07N1kL4ICW8jGkoUHofkAsGZnDTT88Tc3xteMcuGpJ28zFMnH8j2JU8NbSpYYymGwl+VW222BmOnOrhDQadXI0kHfTkHD93/SEIEoFzCHN5jLbx99OE5u3CS6n346WakONIOqHT+hXH4ONDCmDC1zGARwOQYaSkwp87td4ieIoALCeBPC0j1MX2FEv/Dm67FwwuQ4+ZQeHJHUJs/W1eFm8qk0NS4UIho5Xt6zYJTCMZXAFCje2wx1OUr61iMkZsycGe9/8nE88dTT0anL0dH9qI6U/+ydwQAdXY1Jla2MKzPKzAWTy+gy4rZjZGAQ4aiCE0auSafro7Fj2SIwPD7/bHz8+sYb4kiyIntQPup3xK/fT6bhXplBRFArWAw0qLxUlhrXKgONAz+rSJnij9zrCyKhQ8nSPftSn9gXw/w3l14aBzRvnqSSQklA8CKjqtwLVIu2xKWCS4HupYHH25xEAVyADfWnnK1NoGHY0Oj9+OMxmnXccdkVcQEZ3j1x6jZAT6SbMriA5sIxxGhG8WukIVX15HLehTOHUWqUFAdSR7uswgRBOG02gQYioX96/lnejXj2/gcINBwbu1JpsmUVO5JZKxMFp/6+MJ4z0CBPsI40BICx91BZZNSV+5RjDc7hMwINf7zrrngbo+X8Aw+Ky66+Og5p3Trv5fw1VMSndKKyEl8666lwkHwZLWVc555Rf569r87eSgJUBhouuvP2OIARz778yjiRjtQNCVD9gBG3gYoMTQfHkp8KAxPa434GFNZhAMyYMwuDbVKe/Ttn4fwoB1x+4h4bwe8q+HcehvQcIul16+0ZNTAmPNbPrISOl+eEW/pmnwxpx+CizcRcg+efy6urVq1MZVqpIk3BgJ90Iz0ZQPmWEsj585cE/YAYI+I+4GRlQ02Cez/RqXGnMlQ0QG/yitk7aV5lmc51PoNj6KcUWNPZrQA9f8e4M2bMwJD7MB557B9xHk7dsSi2BvXr59YlFa8GRPIldCl85NmtzOsH3q+EQtNIwbrONWkEbQe8QEzKoBlzOEJu/Dj2kg6LaUu+iWswttzqVLVadRR9ZYzRokpiGvLiM0qH3fu4YO78vN/2lNaacdFZ0XnZgoGnvJF+pP0tOPgbqSZZA+6m0KuhHk3J9t+XbU7ww4bv1yAH6YUB/n4kO7th7aqko0o7U5lUcWfonkAIfLB69VrwY2m6SoyyzKWromevM6MbjrPGzrD3Bkf/J5+J95YviHsuvSRO69Y1GlIh5PpSFmDQbWUcYZ2BNA1UaFDnPJUpBqg49jNp1mZpOo3lcKpnElB7+LFH46EBA+II7n7HI49GJwMN0N8G5FwaUPxOxZ+Gi/cA/hrLyT98T8dLJ1I6F+d+T9mjcWoG9tbLL6MfzA9x/qlnxkkYuftRCSMuDUYpY8RvKSOq8eUcpU+DG26PET+Ws+t469h7L3/zzYrl8fqQwfHrB+6Lsw49PLrjvBwJ3eyB/thE4K4ImkEvfF+HQ94ygO295Vnh5Rpco691BgxOKSNXYbAYaOjz9lvxMVUNp593QRxBUHY/qpsMRnyPcyNNCn8dF4380vqdmzASDlkFxvjO2fGdh6ckLCTQ8PEX4+NJqklaHdwqTuh4dFY02CXdSiLlezqFzNX5eamjna8Z6jQQ+UzdIV4NNuwATmH2WDJnTnw4dkwGfEeOGR433/Y7Ao+HpeOlPlCGq9PEoWP7W+nDsb2vz16/fN+1uG4dti9wpgdRKfha3+eiRssD4uqLLonWGLniPLcaML5wrWu0MAAAQABJREFUMZDp93M8llDQYJHtlX9yDzJf9X0do/LI4NXQ3KjRo+KV3s9F3ylfxI1nnx8Xn3l2NK7fIMfT2d+kzmbeO2lPQGOb1EOMtwP6AcEJT4JTcGLiAY2S/WN0MCpCS7ORjy/2eznueuqZXOMjyGIDDdUJaJp9hbGhE7iQ+W+XD9Aq/pBprkkaArk4u/C+7zN3tw4KV4Mzn5LI+fvD/xX9Phkb57Y6LM69+KJog+42mCM/QhSMYH4MGoHmsgqUIQ0MKDO1GRwrs/CsxUCMhjFIi9UEqod+8H70uu2maMQce513cZzEthXLsX9AH+nQS3te4sv5+nfKYGS/r6V9P/Nb0qR0Kg1oLyzCwXht8MC4/fF/xGWHdYiuOF5HUGG6C4GGLdC7jrM06fdLQZK8D2M5jpdOSFZ+QguuQabQrlyOXWDix0DDKgK6PQiQtGlzaDTD7tiJgABEkjgUhgsJeHi62JcTJubRc8uofPV9cVsWO20L99rIAsoSWDK4ZBLIxNISghMGNJo1bZrbSTwiWz2qY7IJmedcraTaEXmiM6Zd4G+VuxkY5HNhrVOqfKhB1r8KASjtK52zD0nQzPx4fNx1480kfg6O3atUjYp8ZjWtNhYAzUCawSEvnUfvoX0j74sa1yGPiYvdqlZLO9jg/at9XopHRg2NswiSXHbuebF/c48/h775jXQjH+msQwbcC9kIHpOfWI/rEic+8iY8a3eWkaaB++sk2/7yh7tiInP665VXxTmnnJJrW4ONrIwten8p4wkCMF8DYl7OWXqUToWJVZVbud9P0LKJHwMEHmn4/PMvxH+93C/24ze33fvXOLZ9x6gMXr7/Dn/CcXDQlDl5QXPO8SecRm2lIhAI/B0PHt2E/WqgYcfKJES4t4GGW++4PT6bOzcu6totLup1QRyCU4qioRH4d+l4svCUPYlTeEjdoUxUJqv/hHn+x32VA0Ap8WK/r0HImt/cd28cQ6DhmOOPz0CDFUKu2XENOmrPOHvhW5KN8qh2oAkT7aaS7hVm8piJzFnM+SUCDSNJ6J17yaXRrs1h0QLdVxE5uubbVYy1JeftvdR5/m4blHgu+LbEn95XGO2AvWRCeNr0afERtmo/AuGN6zck0NCJI48PxJZEMrDujfgfjpd8ug326bzznjpaXGujil//lr5NepbHnlxK3yaP9B2OrHn8lT5x5/U3YY+1Z/va7lERvnYcq7tMLFjRqOwWHuoiA2u+Zw8x9ZTBBufuPfL0QeD4FVVfQ/A/3nj68diufqO4+epr4zC2adnXwyCrtC7MhYvrFybKYGnez7QDtMHkM2nGYEMmHwg8r0ZvW1Hd78nHo9+MOXEdifGre/WKPcHpj1mxBu+JW0Z1TtshH7St1XjfM/eZBOlfe+31qMFWzHPOOTt2b9AgZZMJT7+bfjTf/U++/i2BBqOHKviMwG0T/jIMb5huibmTKKnE2flm4cLc89XqoAPInjVMBSiDWgoqgo0crcQBsIfCA3f9IY3qk9nb3P2EE/JcXQWlEWjUWTKaStp777ZTpahIK3SNIjNTClUvMy5GY83mW24n8XxNRPGt4cPijqefilNr1Y3jQGzHjh0wFOvEFoRrZoOcO1caRxCfDJmKDgYpCUGJRqP2R4TYdhDqJual0p06c1Y898ar8eXQwXHaWb3YJnJYNjWpjoBX2MksZmodU6EkUfuQOVQ0li/aMEZBriLSOPexgj4E06ZRPjru0+iD0XIQ2dHOGKEH7ktEEQdA+ClonLPMANulEvU+GkEpBGFMmcO5a4zKjCo0lf88HMXxn42L4fRR+GLc+PjVtddEu4NaZWmqWSbHt1pEBaTDq2GqAavg87QB93YLNY10o9IGPYSHGWRxZjR0DMz4+osvRE0i81edd34c0Izj0piTyiuNeX7vnDQcfg44MKp0VShN5s29FeJlFK6s8wfmsWDZ0hiFgOr33HPx4cIFcetFl8Q5J/eMupRfrl+zNraDRixLLbZR4ITinCu4pFFpKg1gBErJUNyCAWmjwbKUvtpgcfrcOdGH0s4/v/gcM2TrBNmFY47qELvj7JZlruWdj2Pxn/8b1BG+Cj9xXir18rM0XnhPeuIpafMjgjD/+PtDMXguEXSMiV4EYQ5GgAsDGw6WovziUh4pjHGErI4Q8JB+DCo4Z7PpVsWokMWHZV1Dgfuf7/lj1OPvEy65IrpSQtqUQMNWK4EwWArD0/JTDXGzCxgV0EmV6tXje/YwGsV+d9DAmPf1ophJoKECDm0GVLiDGYfV360iKLAGY4pjQMnGuNVH4yqVIWvU4E2Dk/ll0yvhBT1Jk/690S0IvFcOHvZ9YSP8dKo2E5Qwq79sFcE+rjtvuiGO6dgx6latHuVxtslpEWyEpll3HjfIevyt8BBO3sOgg8ac75cMsG9oXCdNvo+sefHlPnHm6ciBw+jrwbYMe6RIbxm8QBEoE2yYZwnhJnhzLYqlHLgpLz4Zk8mjyAkkAheRamnqtFkcmQivDsQ5WsScrsPYsgeEJ95YRaCh5RaI8V98npU+s6CxBchHcVaOIymdt96HtLSVQIMhBqPpaB79EgymAk8el1RpRwzU6rsDf3hiC3DiWIBsmMbvtmymLzMwLlueCp5yNrU1C6qxAp8xkBUCHvE6b93KuPVX18ZJ3Y6DTH+KEQTuBj/zYoxevTj+cMnFcULno6MujdTEk0Fc3RZAnoaFOJOHdSLNTIm/nx054KhzZ6DBKhcdU8+m/sfjj8UjKP52LOnae/4UHQ5vmwZFKRDgGI6r0V4yPpXrmb3QqEAmKN81bpXtdthWhrg95H0MxTtuvC4WM/Zlx50UJ+AYtWyxb8or6UBDXOcxA37cQ7Aqbw0S6NQYWDXjphyShjMAJ66Zk0HNt4YPjev+9tfo2nDvOO7kkzLQULd2nfy9tOI85CllsY/iP26Sd8J4YRyvpHWezaCYuVUvTJ09K16nmmTS+x/FCT1PikMpH9Uxsnlr6iPmqQ5x/sow+Upal8YdNY1b4FMydOU9+cBKDrc3mP1+kmxgy333TUPRKpZqjO16nZe6gCETDsobja3UT4xjwFSRljSJnBCWViv4uWW1HxqoHvxefDRtQtx58x3R/uBDi21S6IMMugBrnSPxKE59eI+SEyQ+xEVeTEKDVVx4P7Ndw4bRJwcZv/NBLeNKZPyB+7RI2KrRdOSEuZlXqxs0DoWXwRHnp0GqvnZxynmflcsauivh5w/GjIk3XnwpXp46Ia6mb9OFp54eTeo3yDXKP8pJwxU7orMZvKAV8OZRpzAbPEnQTZ2I0Wxw0PPRNUh3JYM8F53U79VX447Hn8rTEB6447bo3rkzFQ27EhikUoL5WzWjLWMgnImaCM+AuNV30o24/ZHXP7JG++YYCBfv3ncc1R4PP/5ovDVhXJzTqk2cyjawgwlYi3vhbSBGWGPlshQyveBDGtVesO+MNoE0osEvju3NY7bc8uAN/H7YWEq9kbtVuGfPXpfG8dhjzZo0TZ3v79VJ0o72iwFm5UA62OJTePOZ/xkw9/K1slX8LEF3vz5kUNzxxCNxdvOD4tiTToh2bM2w54myLefvfNH12gHyojaYPFOipXSIeF954JjSk6XeJn4M3PXD8bIXwgmdj4mW++2XW+8qUVmlrlPO6rTMA0dDoK+RQ4fGamS+jbENHlvRwA3jB2hmCy8N5Ktbpaf136/NU1s2ctLXLiQ8PNlJ/aKtpF4zASM9Z0UDuJK3fkAmS5u+Fn+AJnlVeKXOwlHajWBC4yZNssHsOJyv2Z98HrddhT2G3VSbQHVlA6roHR3OEp8L11KAyPfUv65NGvdeyjwvK4B0tsdSvfrqi33izbGj6G9wWJzd4xQqGppl1ZkZ8pwfa3f5OlsGTYS52xqV/+JUfao9KC61X5XF2qoeR/w2eu+hB/4ak7nnny6/nPFPjtoEUVYT8JQCvIfyL4O8zCeDvHwizcqvykz5VFr/iXX8CP6lfx21KdOnR9++/eKJ/m9GK35zwx/vi85Hto9K2B86xDsQZKiIjjXA63aB7INkYInfqp+UAykDhA1w0t5zi6iJA7eIfkRw5977/hxfLFoY53boFBddeCG6+6CsZDKQkfhj/QUfofeQDSnDmEs6lCJd2DF/k4baSupv4W4vgvc+GE1F3P3RpXa96IpusmqtcYOGKe/SoQavygFlo3QhD2mnK0sMhHiqjAE47UJhJTylBRM7cxbMR38MjtHv9o+ePc9EN7WLA7AlxbtbVw3qlALeTjPtImkjaUadBY/yWlpMGwT4aHd8y28XLVrENq0v4xXgXmf3WtH1qA4/VzRI81ZUSwvKmkyOJRikoSKAIWwMRBgs8FmZ5jZueeZrAoGTCQqOJknw/JuvxNXYqZ3bHh571ub4XWymrLKAb4SzvOdYyi3hY081GS3tEOjTwIGBAfnbxJK0OmXqlBjFlvn+2DQ/1KuZgYZDCeAbKPJSbysbDbRJgzk//jZBJpy8rzhUn5QpT2CZsaWjsiSFVrDuj7Aj+/ftE32nzojLCR5dRmVCM3CqLkpZXtagH7JJm4L5gLiku43cY+6iBfH662/Envu3iPOohNjVhA70mJf44He5wOKd/8h//y2BhlRgKBcRDfYLQMC8vJHZ3llEk0aTKf9m0cLMcLRu3SqaNm6UhpKnTkikZpklJp3s4Rgst1C6pDLtRXToxJNt5rV3EphBA0tWvJeK0iqGSgiVHWE6CUVCl7C8RKxXZgIhRp3jpZR6v8oeoFsefjjO2rdFdGMvpoxeix4NNl6S4GQ0/5NAfnkZkUumYVwJXSWEGMh56uBJ9NPnzImn+vWNTwe8E+ecf1ERaGi+T1RB0ClUs1QOoaCwcp4lJlRoqPAkaEv3FAAGGmwqpfBeScmuBvoY9pQ//+KLcTid1Lu0OzJa7M0Z27Vqb8sYFyWFCk8FcmZBeZZT7MquMnCOMomfK6CKQMN2sXDhwpg4cWIMI9AwZsSouC63TrRm3pamWqIIXoGHyl0YibMiaIHRwH8ypn9nhQSM7lYPAw27UXblZ5MmT44P3n8/Xnn22ahFWec1F1yQWyfEo4ZDETlH6CNEZE5xqZFbBHYKZvf+YlRFqnLYypzKYjAsXrEim231/sfD8TlH5d18kYGGU6LeHnuQeSLjIiyhLwMNO3AvrWWddDMq3l+l+hOkKr2qgDQUddrNNFriNH3ubDJS/eLPzz/LlyIe+u3v4lic3RpEidFcBBo0ClGIjCvMk2ykPf73dUFN/LkNZuJd+KskxMVHn38eD9z/1xg2d170Yl/zhSjmQw46KGnEgE06bQhv4SgNySfO20CBzlAaYcDcsl0fKjbXgrTPioYh4PRWyhibMJdTLr2c3iHdtgUaoEMc1iwNBwZpuAF/y4Atz622x+65dcLtNA///e8xkbLiFQLgX1ziJdf9Lz77375VhbMIv10rvUbccv210a1Ll6hPSd2OdgH3bWCSvArMDTZoVMlX0o6Gs8/SjAaphpewMxtlhZD7+Xu/8Fycf16v6NS2bdSvu2fSrL+RRzOwBr3vCh/Yz8By3rUEb8qCix24BxST49mrxUwjyMh+LdOpaLDp07vvvkugYVPcfP31ZEUw/rcFgdw+oeHxIc0iX+j9dIyDN+bkCv/f/pHMWNq/5br2sl/FWaeexpoiBpJR7/vAX7JHw33XXhUnd+sau1ermkfzpQPH2cGQWsory6F1VAzYKZ+VMTq+BhF9X1lmhYqyRmP0SwI8Dz/+eDxBZdkh3OvG++6LjgRNbYSnIah8UQZI9/6+MDwxBqB1jQx7V0jf7lX1c+Ho7zSgNH5HUfV18/W/jvmM/euTTo4TDTTgWOuk+Lk6ouClQgZb2eA9cusA8/fvNEC2AVZ6kWd9WNHwFkGY6x/8a3TEUDyRPZVHtT8qg71ZmQbNZNCUKapPZApLbOXZ/Bz6dDxh5j2FmTLPAIqZGcvs+77zdowbPCR6nndOHExlU5MmTaI6wQDHkM6tzNMQNRstnWusKWw0Ivk4DUTf9/Ie0rKBBstTDTQ88fzzwKNFBhqa7rUXVYaczc6ahQmaLccUByUnT6cg9QY4SQMUvSQuhLs4dfyFOGo25B1KQuHd0cPi7jt+nxUNu1iOzOc6JwLDTKw60Pd8OK70UUxXp7Tg89K8rViQdr6cNClGYEP0f+6pqEIw9hrkmM3xku+hh9xygAwz2fDPnjMEwDR6gXcGl5DNGv4azjaSNbtmUHE1Du0I5v3iw4/F2/NnxjVUM1xqRQOyQB2WSRTGthmkPRr8O7PVrEd+lhatOnJsAw8aoHYit0eD27DmUEnyQt++8TsCDV5WNBx/TJeo7tYJ7B8MlNR1P1F55/wNhLttzsCJlTEyuDRvL4jsB8HfBl9tgmq29FNw+ucH7o8BE8b/vHXCigb1p46WDpE63LENmGhMq2/Fr86YwNepcCzXsJlsvDrArXgGHaxo+DX8RMvpOJPKr+OPOz6b40kvykfHSSMdWpAHDc6pp8Stl7ZC2kv8LX2KDy/lyGIcr1cHDYg7qCq7vEOX6NipUxyMDjSwtpq5y6/SgLSvrvPZbUTSt86W95YvHVMHx/fVreLNk1Amz5wRL7zxWq69B9npfZo142hdjvFmDHWvvOPW0Dlsg3j9zTei9z/+K0/G+uWxeTnZ/59/dFUID/1br7POOSvq4XB8Dt1PGjUmbr/213EEgYZaJKwqw4+uMZ0f+MXXGdyRHrEh5St1ne+r/4SNcFQiqMeUM6VAw5NjRsfJhx8RF591Ns1V9078KXdT1vCLQk5qZ5S2DxeyUtx6f/HvwBn01UlDjiyhKfq7bHN69C9/iQnc8w8kTs6mqqwu2WmrFL105rykk6SXlAFFcNH3pSeIBscaGcnjB/COEElaNdDwYp8+8QR9s1rw3dvu+QtbJ46Knbj/d2TtDaxVAkbOTx9BmjTILZ1I1/K/tlFpq5MBBrdRQKixHpk6dhxbWe+5O75asiQuoHL13LORw/sfkHrR7aaFbcrXmV/StkTN2MKqRPPKBO3wTMRxL51LM+5WmA5E1lx99x+jPbLn5F69okvnLtGoQYOsbnLrnadlaKPKH9qO6gdxaqDBZ7ftKDd0qJWT2t1+x7XOho7lp6HQ8hnIsCMPPzz2Z+5u6bGHmzrCgF3a78xRmJR8JsGSf4MT5YFB0wzEgGt7a1ghZGXr8yTd6tTcg8BdZ7YkckpMnTqQANVPONyOoaxJPmQcZb6vpSkf6gxpQL0nbUo3BjQ9HWYCcmzEmA/ovfFm3Pirq+NoAg21SRbujP9jMpqhUo6njaGeYFznmQG7hBRoBR7CXHiIj6zGZhaTsTlGoj9ee/Lx2L5h47jzht/EoeIUHP7si/G9hAvvJf0BG/0GbpNjuTjl+3bltNtN6mEDIuPtnzfmw48IVD8fL0+aFpedeGJccc450axhw0yM/oR8F08SnkFz/syAkT0aCNHGnEULqWh4LWo1b0qg4dyoQmUwBMCDS2Ll/23/+OI/8vr3BBqS0X6xfv+WKsQQjDsFA2TIwAGxdNFieiHUyihbs6ZNUumtQ8mLaJnKxoP+ZvCoUXHsJZfkgLewv/bMs8+KFuwfc9+aJzO4v85tB/7ODLcRdZW/t1RAZgkXTOQ2DAlCpaJBtD1EsnL5Ckrp3opr7/1TXNzhyDjmmGPjUJpT1kIIarhJoDqMMpzC45fZdIWtglHmT4OMexlxdc+zLKUQcL/NY88/F+PefCd6XXNVtCUj5R4pM0bfsQdXoe++NwmvlL3X8PJ+6fACMl+rJGRGAw0VyKauI9Bg6dJoItEP41CfTDPF7h06Us5MBtbMOuNqHJacWcdWkep0GmDQqBK2Cl8fvtbpymoF5mK2a9JXk2LwyBHxDmU8v7/7nmzGYjMgjZKMgm5jwBJj6qhbgWEjLIWJgRGFjGszq6vyq0YpvcLXrRPvEWl9+ulno+WRR8RN7CM1gp4Cgc+9h4I7/2be4kCm1+CXNjLizXs7GCwB1uvIBm8HPneDnlYgvN8eMCDuv/XGmMJB6jdRMnbuqadGQ5p5uc+1DOMblS8DSnPfFe9lWbw0AyazmgQFqVAtnTixFRyUx5Az0DBt1qzojXF+H00DvXr/7cE4EaNlF3C6FnrannW6RUDaMFpbcpA02jStNJQ1iBSCClfxKkzs6Gxlxkeffha3335bjJg5O85ofVBceZ3NvFpD0zRQxNiSZqQtppS/s7GVykhDNBtdugq+oxA047KZzzdCx1a2rIK/BgwfEZfdenvU5XuXE8Q46QQb1eyVim0TDQmL0mOOs4T+Xb/GuVUL5atXS8d66KgR2TBx9oJ58SWO0P/tpSnBlLNE7Je/gRLzffGoDN1t24fmOrbFcfMdXRPycClrPe/a63e33BjdKe/fk0DDDgxQBuPc3huOY6bJFwYV0kAC58LbbGoGp8CNDp6KyP3wiyiX/eCTT+LBhx6KX/3qyjj2KLqSQzMGAaxG0QgXb46ls1UY68gb4FQBfNvEFqRyZwx/6NHGVVY2WOE0Ezh9PG4cjfEGcCTfivjtrbdxskKrpDEIO7PIBqU+/OjD6PPCszRCmhBfzF3kEjPrKWyEhXLlf3MJW2FTHAr430fS3Pc+petWgovnYHDKK6+w//V3f7o3P3rwjlvi7J4nRzWCht9hbFjZVL4cPRTQmMqSsmSQ0tmFT+VfZY+yQSNA478U5PG1wd6Jk6fEI48/EU+/NyT25Q5/euyRDNy59WQrMr4Ed3lfuCvfpU1XUhgSzJx7gaLkh5wkf+vQq8hH0Jjp15dcnIGGG846M05Gh+yHQSQudbjTkGZujqkss8rEJp9ZSp7BgcLIU5ckvUhT3IxbxDfg8i0CJNf/5b44mu1/Pdh614kS0kb1G0AnhYOFaZJ6qcjw/JBHdGlYe2/lTsnokqdLjpG0IJ/Pxik1UD2obx+2UF2TgYbMbkKTyndxJs+bRbPnibRsIFp5ouMlstV/yuLMTjlp6FM5Yj+QzyZMiMd6944DMbKsUmnRnJNQahOoZi46DdJ6KasmbtVVGmvq0hUE6c3oWKUm7MWzBrzOzTJOc5o+fVoMGT2aQMazcd8f74nO7D32uD9h6Hddu2zslFy3+k5d4RoKx5RAwLb3fF94OC8N04kkK4YPH0aQ/floxBakG668MptBGmAw2JDBBeSlzrXwV5f4e++jrZb6lLsrkw0EGMy3SdvunH61mUzgADOwbNccjQF/00UXx1UXXJh9m1YvW54VJOpvg9Tqjww04KwlrFk/gE49mJlS1qeFoIz3CLNKVC3MmzsvHu/9bPzpqd58GvHw7+/IZsU1q1VLmrNCRZrP4/gMhiuzcI5chwavulD6UxakXED2+7pKtapRBvvgK6qibv7tb2MgQdMuVKrdfu+92cn+J+D9NVsAtA2s+JBmtJdspFvaipjj53wZnzmr91wPC81tITpe7ik/+5prkvZuO+/8OI5AQwt0t/pZG+PnABvzLtG0a7HcXnqSj8Wn9KTtJE9n4In7Lfx6SbzUv3/8lq0fd55/QTpGnmphrwIdKh0jmy9Kk+6PVwl6opH3cQtC2o7SEPAwYOV9tPW0++yNMgmafOKll7L64MyTTs5AQ00C6PKIulpHpzI4st/C6ziufR//e9DpKmZ9+124sVV4W7P2r+TwDrzvhTXFqv71Jb/6KOk12/Cx2YhDibFheHipJ0ufO06j+nWiS9fjoL86Wa3y0buD4q5bbokjCB7VxNmsCP6kr5SzwEPaLmBLRSm8KY8KG4OXJT4SD/KCTZp9/0PsyFdw1p/C6T2za9e4GX7at3nztMlzLMcFt8pe5Zp85ViZEII+lPPi3nG1F5QRZZiXum0ZtuQAAg1P/P4P8THr+S2nQpxNsnBP5IxOYtq6YMygor/XvpMepKeUh7ynrFPGGASwmkT4uGVTWvBkt2eQYQ9T2cum3nj8PqrLOh2dW0pWc9y2POrpdslDzNmxrKgTttKNlUbaw1brrOd9m0BWRk5ZLbCBJsqjcBqvv/mmmLrk67jy1FOiJ0GSA/drmTLFXi0APOnQCcqjwsRkTSYR4THvk/4h8zYZmVsfeU/cfA/s+w95Ly78zU3RZpdycd61N6c9VqN+/ViLs70COVoB3s8qDOS6MsxGvgbSDaobXCjZj6X1ZBCFuxpomDFnTrwIHfcnGXkptl76H8h4t6/qOykD9GmEuZeVL44pX0lP4tuAXvKRfAqsxKmBUxtof4EcfgQ7uEGtOnEq2waVA269czwr8opAIBQObtW30om6yQCU9pPNcl0PiE6bIU8QQ4Z+Q6DaviFu3X7s3bfjb9hLXahssidJViMjW6QPEw3a0+p5md5tTPK7f/tZwibpiuA18LcniTT15ZdfxWBkfO8nnoiqTZvGPbffztaJg9LWMWhuoN25awOop7yX+jP1CvMXXBk4gh7d0qpd77P+gaePjURGvvDEo/H6xGlxJc2nr6Whe6OGBBoITGljl3xKhkob3Xm7/XgLymsOa+/38itRuc7ucc6550Sdho0SfiwADBV4+udzou0/7p//faDBJSuNIdT/Bgz/BtFQRUyntG84xtniefOyhKfNIQdnt1KVnHvQLbsXnJ6eoMM0hFLvE6+6kncirunWPc5ie4MErbFjsKEUOJCAi2iolQwoZAhMJioCDRzjw3cVmhpiEqPG3jdkv19+59247W8PxVmHHxqdiSYeTIZ9D8qCLcFNASc1MH+DCWbydbIlVhlR4ZsRQAmNe5XKuxSKUqMZqadf6hsTaXR2NvuEDyX70mzv4qhCDU/n574hx1NBa5B7Hw3NDAywfh1fMxgqS09XUBnavG4OQuRDoq2PUI1xEpnHY9nL2KBuvexercBLBkFYamQ6ltFDqz9cOx8nM3rEnWVF6QTAXH4m7L9hj5T7xT2h4K033oo72aPWjmzazpWKHg4ZCGDuwkVYp+HGnGVsDasMzDBQydAQ7l5WNMjIX1gtgYB9uu+rcXDbNuyxvTD2heEdR7xlebJwYN4aFQouGV8n2LHNNvq+wkpQaygieaMqlSgriCgOoAz7wTvuzKzwteefT9fXHlG/Tp00Cm3epfOksnStvk4lzbOXc1YQSsZGvH1WqGTzIQw0O8G/iPGvYvN6+N57svTVQMP3RNC34zcqNwW0xosKQZwZyBE+Ng39HsWfhjNKSWPLNbg/zGDAx+M+j7uoOBg7Z14cv1/zuPRKejS08rg/9nAZ4d42b2nGh/AHcOlwWFqrsnZl7hc2Mi+O3XNoMzJLeAePHB3X/PauVMq/vvCCOA6eaoIwM+O6maaAwtkJqRgcx74gO/jAcVuBEH3/w7HR9/VXspHqV7NmJwzYJBC7EAXYmReVIC8qyNLgZ7nMTxgXz4BRNOVasFeAETDelv4py++2Zwx0NMEq1kpyz33IJMtRzgaTwDOP9c4t70r24rpro0unDlG3OoEG3t8OmG+CNqQXaV0FqQxJhYRSk86lcelMfhDX8ocnurhVYSx7Dv/2+JOU6l0cR1NmWIsqGMvtpBOzrwU9WlljZhgDRQgx1vbSLPc32CBe1zJZjYjKu+zGmsqzL3t+fEZwbSAG11TKg28hgt6arRMGPsoQEbfBknsUPxv3KWVzr7CNY1KMnz4vV6nxVAG4Yf/kg50UaawKV25BNQ80K0sLN+AEeUALwJHfCf+EPbCWfgBJ/pjClYRtGX6zmXFWo+tXrC1ENz/J6wYCXKdRQWbZ7xsEG/90//35/t03/jpOp6S5RlUCDRgbVsFkbwd+qIMojA3mpIyEFqVHg4wqej/zUr4pfzUo7WPz5DO9ox902YTP/kAZaSeCjzpzOuNpnPB78aTslVfTyQbO4k85q7xVxijnhKnOrr8X7zb4/PVV18Rixr66x0nR45ST04j2++oQvy+dmBVy/pZmmiX1vgY0s6KK+bq1Le8HgJX5Zo6WI8PfHTE8bkV/HNmYY3opObYizi03qRf4nrSjQ6fhpczyrHHXYYZVHjPgorzz0iDNZ+jKOc1duCh6v/oKwd5X4woM9NZkMRsT0FBnZPaZhduMVBnmXHXiPVJVmFvGrmQpHeWo/PESF5aSeySfJxQ89cKLCY9uHTvgeNGFHwfAe5sFFtYSlLJM3VS5IkY49xYvy5evSOfcv6UZ7ylvGTixC//s2bNpCjs2Hu7zYtxLRaJbkcykCW+NcMt7veSXlGngsYAF277AjfhRFiWP8B0vAw1up/lqyuQYNWpU9CEA1qDlflQ0XBIHUaViFjMD1IxdbG+AD9gO5O/SUIQhDHR5P6vWzJDm1gbWo+ytTWBRI3oQ+uOhm27ODOzV55wbl7D9oB6ZbxMTGufZnZzvy2BpdwAn6VtdDSkm3VlNlXqRdwyYeNqOiQKz5c/16RsPvvBSrukvN90Qx3XpHDWgCw17G6jayM3+DMKj5CRJGsp68VeS8dK+wWADD7sgQ9wC8Cm69c67744RyJu2HEF5K1uRjjjsMOZNl3v0uuW/Bha2AiO3EOiAZRWP+tx7MJ5BEudvRZwwygQFgmcdjtXwD8bEZbfcmg7xtQTubIDarEnTxGPB69pEOrLwJ/cS9l5uRZKXpBH5SP4SD2lzgF9pYiHO1as4AH/p/UzcRLmxjlEzHKMqrC37M2yjMQ11qzOEya4kVryHzpf3z4wma1Bey6M6acpWadajP5+lMd532E+nsuXDE8BsfqqNqNNjVYhHbC9YtCje5bSUN9lG9+2GLTFr+apAbWWwYSdkbUX+qIDPVZKzkoLsxRLQP7zPZ4AY+86Vy3PKAGELyfB3PvisqJzkjbLYOixmHcqNWwWiOOFrOHXvRnvGkR06xx7YNp4SM+adAfGH226Ntvb4gfcqcPMMpjs4lzDV+fIeeVw4a5OXtTm80mbgu8JJh1Nn/2MC7K9TpfnimA/jZI6e/M0Vl2OP7Z3yKRMOLCDtDH/Pw6148q34Le4Hv4lzeUB6EsfMy++YnR46YkQ89+e/xjh+e0Ov8+J0Tp2oB9yV1wX/GEAr5u+Y0r2/VbCnvIdW5CXezH5RP0D32sIGqabPYnsyPRqewk71euqPf+QEhI7Jo2so8Vdnaw/LR85LuSI8pA2DMNoH2mXaSSbKrNzRR1AeGFizavhWmmfPIMh44fHd4iQy1C1btMiAnZWwzlFb0mCaQU4Dd/KUzq78rBMqLqxi8DUrTmVcEV79HptrAM70VXf8LvZluef/5nqacx+f+uPrUqCBeVvdZwBB2FiBooyQP8WGuk5YCSfXIky8xJuypt/bb+f26ivPP5cecSQ6oXkThkVFA4kweKeQLdrd+ktsB+Ad+cfAnokExzWAZKBBeaBtswrdP2HKlKyIs9F6DwJUzZo0CU+d0P5yfMezAkncOh+fCxqxooEqKug0q1qU/STGKmJnansvhP/skTLi/fej96BB8cdrr42ObQ8rmp/yO4O8hX2Bjc38tCdcRCnQkPYdcy/ZBCW6dCs3aKCiehL+x5B47lm2bjduFL+98cY4uGXLxKFySL9Cnez2E+1iXxfbJrgJv88ABi/kWRRM+gfCpmJlevyg19/Hb+rz1GMxaOq8uJCjnS879+zYuwGBho3KXOelLSm/wEOupxRoYMy5rP21N16PKg3qZVK9YaOGmcAmDJ94Lf7xxv+5178l0PDD1iIyaRRIgyUvsQtC0GAxb+qU+PD9D2LurJnp+EmcDTHMzAIbMTJDC+WkoJB53ofRL7/n7hzmfLIiGr/74KzLyEZqJaaMXkHEMpyZLDNlKjKVsgJFVteR9zcaAyVh6x7bwaPfp2Ptq3FUvd2jM43x2lDRsAfloxqBpfJWBYjK0fWIYoMB/q/CU5ClQcEaM0uN8FII6BTOnDs3XiOLOfXDj2lC1iNaH9Qq9m7aJA22ZDwNVphMwtPQ1Sn16BmdbQ0qCdFSIhWJxpeCislndH8p+8pl9Oeff5GTGw6n4oCjBPfaK2pRXqRGUzEbEJD5DFJ4P50vhWsy4jbBqoKQub23Jc7OxaPT5s6Zm47XsGGj4opLLyZjtF9RtoTgy34SzM37qCgywwecNaa8h3s6DdT4mZyp0SGzq0Ds4DyFeY8Z8wHRXI6023efOBNmbI4BrZDUUFUgaagrFdJphNENygh74S1u/MzMjPAwe2w34goIkmUY9O+PHRPPPfxoLOT7F/U8JUu97cJfKAZoQhwymM8apkZyRahKMy8+VAka5U4cMC+zXwZ8xGl/cPrSe0Pyq7decXkcCc1UR2mXAZZlmLdK2dJUDTbhrZKQNsSfGQRxq1PhWn1fupUmhdsXkybF008+FeOXLI2jGtWL0zF098eI1nESV65fvvA3OmrlEPxZ0iXvcE/pXvzaLCn3GfKbLOsCN+vByUgi9Hfc/1BAJfQiODU6HtUhGu1ZP3nPI9Qy2srvDQg5N7sv6yxv/HFrnujhvryh74+MRWREp86elzCohqFVk3R5zV0rRZXKO3EKA28wxk/wgYI7myZuJdgBnDXSNVSkyc2bN0L34BlY7EBvge3LoSTxfjchJzT4f9hOIVyJTtD0ZSA7sWwZziFfL2V/rrmoFw2O2kRtSmsrSGPAYPMvAg3yujCSd9K4AFYaG8LI7IVBSuXFchyjadOm07BxfDwzYHBcQca+basDo+puNuKyeuGfjV+lOy9pUSVtUGkjvLYR50Dn2YwdFA/PwrsoWQ2MufMXxPgJEynJHhXTln0DP9H8lEzdzmTPDAiVIyC0nqaNk1GAo0cPj5nIxnFT5uR9rEKowq6c6lUrRFWMYHswbA9dSV9r2RdsxcqO0J6KzhLwH3+iaReyNneCUl30AxZuGv6Mk9kKYKoD8RP9GcrQp2EDUZ0ly3AMFzIm39F88br4wgs41aNr8tnIEcPjhUcfI7uHs37emdH1aPrYUOFihZAG1g9GhiDozI5Al8oRIZQKmfX/7NwBmdJVov2pM2fGCy/1iwGfjou9+PDyG6/PEzl0fsSZclfH3EuZkFmXlAfwLLdRxhvQS+UNXJQhpeCyMvhDjm67moy616ltD40TTjoJx7pZjisfmfn0OY1zaNZmU+LbSyNLuanBWOqhk7KJ7+kAf4tRMXTMmLjnqaejVe1qcfQx3TnesnUeb5nyi+8pX+QpM9XqD417A6ZWDHjfkrxTLiRfKyfAj7w+n2zg6wSnRuBUn02QJLcZovsMNGRghHnZNFDDUKPOhwEF9VyOx7NyJYM08LDQT9oHrmZeJpENfPHV19giVDc6oUOaNm5MRmr35JnM+jk2v09ZA17t+K0Rps7VQFQeKtd9TpnD/cW8+7Jnz56V24VeYu63XNArm21VZb+5+PE36oaUNdCH/6kj0uFl/vYrET7CRp6SfiCw4t4YkNNmzAirfwbjXOy8d5M478wzoiVwEXcGXgx+WWmwHbLCGTlGlsXKN8BEOWxSQPnoax0Kw7OV0R8aih8QIHnhoX/EdO56FgZ0T7rN59Y75Kf87hrKIsvUEcqD7clmeaUdwLqcb0H7flysT3pR9iwEpwMHvxf/9eobGey95pILoz1GtIEGlVLqN+bDdJJGlTEauuLBscSfDrs40UgVjhlg067hvYlTp8Yj6I9PZs2Kg/eoFudddkW0omJFmWsptg7XjjyyooH1aEAnbTN+aQ2uQ3jYKA+PKh0jBiDQ+z02wbi47S8PRGM+6n5az2hPYK1RgwbFPJhXST6Wgg02zCwFrrSntEnk6RJcShUu0rOBhsE6F2wxO71t2+I48Bb7RDVgo43n5e+FhTQob6lPhIU2gfzjuOLY19K+DeFMprj1wiTBm/DTiqVfR1dKve3R0KBRw9TRbtM1EGpg3XJ/nYVRI4bGcvbhz17G0eLce3fUU5XddqYPGM2LDTaUgb+hoPLAxt+aJNu4fh10jNOBcyiNlHC2kex4Nh+FHn9i3uo5A8NbfoRnd7DxYLlYufY7jkJeF8vAfdEaPKIeFQ0HHtyWatAalKpPiU9GDo/fE6g+GHusMk5hOdaYNhjrTvsUWLg1SBrUcdP+ULZtANcm4gqbrNAFVhipGz9D7/UnmNn/i0nREb13AZVZTYGLsk5YKluV6cJa/Zakwhy1U4VzNiSEt6RxYe5v5NjcnkxwbsyHHFP4zHMxg/cu7dkjTujSOeoQxE86c3xwpywoZH1RVi/p6YylnCjh1HGZx3bwrY6q95s5Z0688tob8crwEfyCnllU4h1N9ntn1v49wVaTUqn7mVdRTVNUN2nv6jyXnF3na3DBZ3lX20e+Gk8w9iG2is6i0Xe3VgfEKVbEtWiegQYd/mzYWvwo7Ulle8kpTRpnIVYHSb/aYWUJ2OUWENbkfv5R2GP3Pvpk7Ml9T+t1NhVxHXPrnUevZ/Nv6CWrtMCFcqAyAQrnZlWac7cHlnDztTabyDEoLG4Wf7M03h42PPoMHBzndmyfJ/ztv3/L9DFWE2xTBggH/Zkic09gAHtEPa7OSzyCF+HBdLNXlzLze2Sz27cnoz/6URFu/7MuJDqbNt4r+6n4+6ywY03qbnWhFeDyojTkHKUbA4AGSpWQSVTgVrpdtlz9MScb2748ZFj0OvmkaEtipu4etWI39KdVfGljM6+kD2Cs7JMH1XXa1tKWvoZyQf9G2WsCRHqfMWNmNrt/9/X+UbFOrbjq4otpftos9ajjFduHTRYWMFB3lKevgvpPHOgXGJjU9voRVnabmU27fXbrxKfIyP79no+xC7+NHiRNTuneNZo2aIjcRRYCaysl0jdlHQZxrTzmtNzYBN4WIHsGDhgYex18QJzGVswGjRqxHnRVSiDWyX//6de/JdBgfwIJVAWioJVJoIaM+vg8h7OMP2Sf37RJk8kQLGOvb7WojdAxEmhZHz9OIpJZVUxfojjf7PtSzGWY7hgTR2H8Nm7QAGZDuUA0dtDO0yr4rU75TkQjFSBG41TKhaDi/iBLYvY/AwGW8i1fsTI+J0o84qNPU3EefHzX2B+Crl6tagpPM2epsPjNzwoOBpEvNACYba7V9WrcWYKVHUR5bUnQ/EUL4yPK0pZNnx37t6Gagej8ngRVrAxIIY4g9R6ZOYN4DQrsjDOhgLL0qQg0FEavTOl6dUYVnG5HmEdp7ScfDI9qu9cmOt8imjRuErUxFGVCSwVlLrPpCkwjoQoJS0Sdb6lBivdy/5WZLwWUTLCSkmDPWZ8+bVosnDItDkUpN2nYMLPuZkM0aGU+15APxpBhdsJQM3OucW4QyEyYQiQFPLBRiFiyZfno5IlfxOQvp0XNertT2llkAd0fqVEo3lQywlfl6XuymrjQENOh1thwXHGqg6WD5MkQq4D7lyiHkf3fjlm8d0K7ttkw0GZbCj9/X5RCMSIkYXZag1ZDV1opPTRkjParzKQpkJE0YbbDIMl7EyfFHozfpVtXSo6bRTUyKxWAgRluBbj3EbbZqRc8WBXh+nNc4CNcpBfH1xnWCDA4pOIcNah/fL5qU7TetUy0P+408No4y5alBxVCKjgmr4Fo8CazmsD6R2hDA1s8ZpMwvq/KVzlbursRh+yzLybEo/3eiBrMvfNR7dgDewhKv7aSuZg3TqP0o+KXXmyAaVmXJ04shS5mzZ9LgGsSr1fGvMVLGQVjCBKtV317eiXUiNpVaZ5XrnAWzKJp4GqkYV7lg6efr2RJ5ihsNb6wuzLTbpZ9E6X4OsPlKnB8GMfVzoLWZy4g+MZ3lm8b4fTux8YBLVpEDXhmB8bJQAOwlTYyAi9embuGuYastKSMkI/kITOAlSvvnEpzxvRpHBH5VUyh3K31EYex77hJoViAsdtaPGZMgzbpA/j4rIJX6W+AFzdA16mQoMnylGHoBHgc0WYd+a+/hpdmxlcfT4hl69bEkSccE032ahy7VCWQQQbaBqrr1q+N+QvmsmVpAhH9+RicS7Nyox7Zsd2rR+xRtUbUqFI5dmYfYDkUrJUzysc0XoF38jYBBnnGoyxBIkEAKgyAhZUW0puBps3iFaW5HXMsQ8OstRu3xqxFHCc1k+wPv3XLitfxXdlGxrnbWhgTyS58RGZkAe+f0qEdFTYHUnFWtZDXjLeZAFWunS0UIkhZKK37WrnrQ3il4cIY/l3AjsazCxbGkEEDY/TMebnHtsOpPTIzIs9rJJgRMVCn0td51nAx6/LLMeW1zL5wj1KgwTXIT+qPZ17om9tFuuzdINoexdaGhg2Sb5xj8dtf8Cvjy1fqLtducFI6dr7KDQMTrkX6WgsNec76MxhyB1TZPg44rFM0JRPotj/ll7IxA5msF4DkGAZBpENlBAPle35Xuajckz4FnYajzcLG0rdj5tTp0frgA2MfjnBsiCxwi5sZYuelLLScNB0ux2XMhD/ypGTMSaPKdxWXBq9wtSLCjNcwzkGvvmfdaEW1Xd06dbNPgeuUtlJGwZeuXX1XMu6Va8oZx/JhoKH0XT9bsXw5gerZMW3y5Jg/Z1607dyJIDgN7eDT7CXAfBN/GrD+1gUDo+RZ+Ym12fdAuax8TFhyB7duiLN58+bFhC8+jy/GfBTlalWNTp2PxSaon03f7DCvLMtTGniWHwyweT/1YGa/eA9mRjbAz7xvQFhnQHlsj4bJOHQfv/1OfAlDHHtAyzj8MJIPZuqYprThvKTtzCry7Lydl3BVdionlEEGVFybRiV/JhyXAZsJBL8mjvogy/APPaZT9gypQv8G4ezA0qSXv3fevqfs95U6VYe2LI6qsh+wpMGuYW0vIU+1GD5kcEz/enkc0Bi8HtYuGjZokFlW6a1UIWGzSas+/L33yPkyvrSd+ECWlI52VocoG9ZugJ/AaZ9X34qa/N0G/dG6NfrD7DRjaVNZ3eRYpTWU6L3UtDR7CG3Dv/fxkXYN+NbBmIBdOJ6y6brokX2Ze/N9WuSxqOJOGsugi7CAbkrBFkFkULDkSLsGBnZh6TRp+1gBMX8hJdlU5K3l5J+WR3Kseot96QbfIPHpkX2Ob/B5BU6U5dVfTvycrZirYuF3m1kt1QU1d4paBLVr70rQd0fsEo5Z/pHmxf8fde8ZXOeZJegdZhIkSJAACZJgQGBOEoMSSUkUJVGipG6ppW51Szs9HXZmd6d2x/a6Zu1yzdg1tR57POW1vbX+ueUf9nRQd0utVhYpUaSYJDHnABAASQBMABMIRjD4ec6Hq5nZv91/9FFX9+Le73vDeU8+5z2vGW1p5PO8/xWyjr6BoY7RdIYCH4acAYusHg8MbjDm6wi7gcOQLRghZy5cov7IiWjDzwSGJf8fP3Z0zFv4YG65sqjwth1fxb957bWYNwP5hHzuT9+uvWOXPnU0FjRJUA2a19kuPHT46zBQjxTm4rD06NgM/Gxa+z7b9c7G3OlTY8WKJ8g4ILMJHd571eeEtdkj8l5Bmzovzxa6WuEEEh+lg9zGQp8aZm552c0WrQ1r1qUO/70VyzNTJR1rzFEeZfs26vPp9IIH2K9rrv2QW1GhK/FEo8ttkY5fmLexHchixR+hj3n9xR+9Hg+T7SE/1Fmps8FsFtdEHph8HBp2HsoYZYQZjfJdMxmkYb/TvhB/mltbYx3p+zvOd8cSbOTVf/TTmElQTMeL0emSo0E+I4XmP2ClLSMfLfgcASZwUEeDW3CV1f7diT7l1sF3PvgkEPHx4FPLY9GixejwEwrnAvCVlyWvzTUYwEkhlRlUMKNAHm5GmbBQXqivql/K4/zOYptu2dzxxfaYP2tqLHwQvQZ68khLt24rU6VPYawN4nwtTq1+/w9Zv4XjSB3bDByDTmY06GhoJVNzx9aNbC+tjAXKptq6PE1E262UdVyyE9RJs/B18nB4VV9/rqG4Kw9O2cQ8rlzpyaztxuZjsWXzl8BkAcGBuVl/zmCL/KVkWwBwxs3A5LV870udnrcM6IkD0nQG+7jHAKhHOx/cuyd2bdmOboRd+frrHD9Zl/NmKImLNut62pf4ro5eVuapFKwdbYrjbkm+g1CwcK5OKvm1J/NIT5vfeScOwDaenj87lnMIgFuFyoYYZIM+mftdgmcZ+GH+yqBb0PFNXmbZf/bBh7Hqj1+PH7yOo6G+IfVi8cqr9J5/fEP/93s7GlwjxYGCSZVX4hOJVHoHIxhVgI9AWBtIF9oNARwlgjeAxfIYH5UInQ0SVhZB4lmFx0UAfx6lpZc0qDKY/PgZM2M0zomCqXGkHl7BaxiQFloSoTwixAiFfafRB/HJNEwj07NViu5LkD0o/RfIDHA/1KDhFIOaTDGsmsmpyKWSxtgkRolFpml08jwpQRLEKP4eKrNjpiqUtmsEwGrTGkwKvR6Y7DmMh97zXTGiuiZGjqkifZI9wHghZXIyED2XRqRtX3bdDaHpARxKW8moQEQ9ZQoMmbrRcIlSBpbjP3ea/u+QtlOR1eZHIwyE3zUQ3nNdPW5ToaCwvQbjusEcLGQ1VEUCxFb4mbakt1HPpYz2MkzkwqkT7D9nD/MQ0vOGk05cPiqNMhV/xyX1FcK9SAHyaM9exuSRW46nH33anoxEZ4cKo+M3HckjxC6d7Ygr7V1RNqEiJk9n3xj7p0qGXG5ZYawya7/TCaBBdYV1VUEaCbMbBsG7XUX6S+8g8BlG9N2jRU91tEcLKdPbmdsjY4bGw08+l4zmQlcnRmEhRGQgGsF0kNiKXZtKujhTRIN17JBGhYFZDkydh4LPIxaPH9gTl7p6wJNB4OPcGEu2hFGirP/APcJfwNJFOkBMZBQnHKuG3yDe6S6/SwNRRZixuE7nz3dGR9MB6j2Qyom2MWXRwhiH4Cnto00mh+AU54QLA2MKOEuA+2DaHQ4chJuOgR7WfCBr67YPBZzHQzU3cVrJ3qbgXISYMbkqps9bSJHPMVlQ7iZVzxWe4p9eXY3067TZy9hUEI0cn2F8rafaiOZejvM9hYIxnS0TDeP6xwyYaf14jGdwq6vrEmmkVxFqREGHYKRAgxoMV3tKkVy2MZCHWmYuKuO9icJl5sItFqIXiXYXfoEGRrRnaHR2X4u9Rw/HF8dvpIPEjAYLdC2dPYOjlWqJLsHogTuIgPLM3nXGK77dZA16ci2o+4E0TeUk8bDYp1mGw8/TI3RCnCfT4FLn6bh26WyMHFcbFZVjgTHrBpzHVFZlrQbHL/w1JoSRbd/gXeN/OFEsf7vOHrxhwzypoEy9HqECzbLnoxtFpbvjEvO7G6Nn18QoFEizTvqBDOjmGAklB18zONaFsC1wpxYfVw2wnVQ5PsYZTaOvIaz1ANcfOpNPXcfQV9CWk00inN07eptsEXmgdUxGsJ9FOu+6SGV0xqMztD/jGzCUbVikBh+iHsT2w12Jj5bnEr73TZuOgVKfeNzRdCw6T7aIvjG1blJMZi+7mSD+XUQrqNOA88I9s+LYJfixQlga8/QLax6I8BlpY9yFMoDTExo2jbntyL7oOdsTw8YMiur6OdSsGJfPqkyoIOg80PiUbxTHQRXF22xvuAoiLxUZYSGv97hLoxuu3emOtmj6ak9gkkXVLAzp8ZNwMI1MnqFzQrrOTADGqnJ7lWj4TdZZ3j5U2oKHmRqbPI8ZpxOPdo1CqPgeP3Ykjp84Cw8YGtW1OBnYxmPUP53U/C7gdTa4116p2MN38jKLhw6hfQ34kmNYhiEf1FhQaTTqdLq1KbrPXEB+jIzKqQ1RCS8Qj9Nwgx96rKBy00wJt9F0iTiMd5QGATBRXrhQRjZ9t+80yIDVxa5z0X6wmawlcGwWx2bC5wZDS66rPMxMNS//di2vQ5vXmPsI+O8o+tR40XnlXcLb6FiuKTL5dGtLXO86GwPgnxVUJR8Jj0llDFg4P/m6VzoaaCENdWjDQMVAYK6hpXEtTDRalXfKcNs/c/pUtO77iiADMmJMxLjaOey/5XQYeNaQ/jhfdQY4auaqvJD32u5NZLJzGkr/rqfV5XWi6vzQUa6zwUyni9DfReTTtY5zMWJcJbUbpueWGtv30qi+cw8ZB66VijLeYIgLhSEAAEAASURBVIw9ZAwoTcqBtccOqm/I0w0aqEc4B+VPNzpH9/H2QLWPkVUjo6q2Ab1gZDrpfUZjWr0pdRXW9xo44xzEFY/VFCcHwjS8T8WVZtO5dJ3sJY9Xu3CuA9oeyl7zSuZWpCOn80zYc7Oa2WA/AyNTxs0IlI51CLv9TsekckNdRhi59Q7IYRTfiI6Tx+NIU3viRH1Fv5j1wAocAVVJn+ph6VhLuhENcSLw0m0Cp0MHhC/7G3273tKnRpH3iV/Sczc4f4mMg8tnzpM+PCmmz78P/sV2HZ254LK4JuaoM/n5GjhpW2iOyO3UNlO3Kjk4vtZ9GNslZNf5xkbWnG2c9dNiHAU+K9E9NdTNwlFfMdjhNgO30rUdb6JfaIq2vRbVVkXthOqorxwW40cg0+DZN8lCuAfclcFmMshvxY+rPTq2oXu+E+cyEMXcNZL74xCACIA7OIOjd2CZ9ZmGRnvnpdixB+ccws31tl8z2hY9QkYDBuapDhzWu7dzFDhGVw06Ae2no0F5BAzEnZKj4Tp4I+zFzxGs5RCcaupf8iyDFeJ7JXp00hP6UvvRHeh76JFV1RR+nkINAXgBdC4v6cHwk0dZAFm5anbENdpPHs/45AfOuz90lzTUp2PJy9SzmxuPxMmDTQUPnlkb1ZOmsKZsSRQPaFenolxGJ5jz1qg1JV5+nrwNGeA4lK/ii9mlmWmIXDCy37J/d5w+1ZU4OWMRJ681NOR6GFhyrmnkAhvliM6dK9Cp/MpTM6THqzgaqtDhKqsocEwfF9Hx5LHyM7dfnAXnu050pH4+7YEFMQGHrOORxysb0JQyyq5hegXYXAU2ZcBiOO1nkAy81xGgI9BgmBkZ0pLO5UuXusDLE6lLDy0bRZbjBDIO2AY1jFOUwBNPMGNx812H7k3hA0+zNhQmatoqZo64/q6v2wGlq3J0YfH6TPtJ+E0zx3iPRfZNjrF98qMHO0a+ajBS/u5WKnVnt6bKG8sZo4Ez6VXnuk5+A1YD4ZUGO4XRZejpVs9lHiaLEhyunlIf1WQ3+IyOhJQhPCu9yu+1Da4zBzNdLaCdWQasibqvNo22h/3IV8XjLjOroYfREytiIjx41IiRqWfndmfmmhfPwjzSEdoD3V1jDqNGlMGDi+xpHQLD4IFFVnFBA+eh9dNN6PDn0FtoZMpCdI5x44u1Yv4GF+xDHNUuSicqbSOFgCk30Ie2mzzxHkGeweiw8krHYV2PU8D81L6m5HuTcfY2TJ+dWezXoKNegmZwwHSo6fiSPq8xl+vwWbOyLbT53pdb4u/+l7/JrROTptT2cbViutKJ6/VNvn4vRwPgT8Yvo/Dl3wLEd1YNxQqE5f0Q0eZ1a9fEoX3700Hg+dSTMVAkKI0EPYQSsQvtu4QqIUn4tpXpUDADmYDRLRm435djzEpo7u3ZTCXU2aT01U+fHpWk+JYjqLJNBIn3+soFAzEU/homCgqJ9RbGmQaFjMjfJFqV0REwO9PvNmzcFM2bNsVDL7yQ2QkWGCm2I6DAca+CzYlLrEaSCgUVmEBwMtX8nc+otSlwvUclYDCElXsDKVZUSdbDMtK/9FbqgVYY5PwZT3qvGad9yPQVBn5nlEOlSsHjeH2pqJu5oPKtkmuF3jW/ejNWfPfFWIgHMo3HJFjgTZM6HryKaCzSWHjlmHnnBoV7zotxeJk2nZ5y2jCzZBcpeEe/2h7P/vOfxDSYvc+U4JdOBP5O4412ha1z8nuVMOGisuElY/Maytxc0wtknrTjhdy9eTOwqY0nn3wyqnE66XlWGAkD4ZjP0YSCSGaVbZM+ruGl0m22hWlVPiMWyMz0drcT6TjR0oSw3RMLHn8+FuNV1vF1DYEkDAWOCp3j1bOZDo6El3gKbGSerAGTzXYVoHrIXR/3KO7cvi06Dh2KEWTu3P/IwzF92rQcr6ldCkEv23fNEmbAxzW3rzSgGINj9rPfqazk9iC+O8qabsPjXgFTbJhLVsuMGem4U6lWQVU4iO9G38T+wRiDRj4UMneJthulk/achwKNjzkOI/0y/c83bowv1n4cL1BHYwyKyfH2tjh4rDFOMq/Gk6dy7LNxNMwY3z9mTtTRUIETqD+CiNQ+mLxG5hCOANL5kQ45lXTW2vXXgZYRXD7fwNHQi9Lo/su78Iq7GAx3yWi4R+GGs32Ohq9OcOIDPZ7jZYzph889E0vYWzcaOrnHfPJY0Ry/az8gWojuvf+f/5+YseJR0nAfQdFw3z7R6YRfYZyKXwo66T3rLoiPKARGdL3Eq3R4MS7vLX2vEbD/wEG8z8DmR69TcXkuN1tEUyGFcAWWA/opqF1H8Jw5SbSyt4w6s5YqZUrhIcM9Bq+X8bbE7l1fkdlwOI604/XmibnoWLWkzkyunBjjK8qjAgE6GDwbyEujQ/o0UkXTOPSKlO4U8tA9Q04aH4Zzgm6jGyfPDdccegER8uUe5IPHOerwyPkUjMJWA+gFqo97jJdHyxmx7YejxtMNBvJehgLlOeTuJU+6hX+l4gePzCyxnTviOA7lMrKIZpGePA18NyVT5TFhyBpIm9JtplKzHgpzlQL5i8LdsSevYH7yTddMw7O9vT3279gRl3FAj2Hf8sw5s2LKpEn8Dv3D/wqegyJGX/Ienc+OzfaUFRp97ud2Tvbj/Qk7FB5/33fgQGx645dRQ6bMNLITalAqK3Fip3jnXvm4c87III0ORFHT6aDs8HsVEC9lUxpP4JF4r/wwPXvz5k1x9LMNsZStEFaS14gq8XCfy0wK3h2L+CRepiwCR+UB6eiSlwHzNNqUXdyvg7SdLLT3/t//HJXT58STTz+dzgedpfK57IM2fV44+neJnwknU1t9Fx6ugXxGh4T4Lq0KvyM4nH71y1/HU089EQ9zGsBQeEmOUwDmpZQBJsDDl5dtymu8z3aEid8VTmhVdBRnZK4FWc1SOMI2w5f+5Z9St2lGfi+P13GkkS3Pzp6gSWEjTkMBxTuEJeyVgdKENGvmoH2dJurpVs1W6HXExPHx0LLlKMTVGEuksHovSrUwVjZ4v4EE+bGKpoa5Bq4OWA3zHADfWSxuEHR1CfnR1NRI1slhnM+dMfv+xXH/AngSir5ZKykfgKm47NgzSoZsEk6OPGHLWO1XnBKPvFHeqBw+Q5R/39696dAagsK8+OGlMXvmzDRsrmMsFE4wTQ7wT0gIY9qTbyn3bTd1Bt5lPumgZhzWs3AUzS2kKH/2WfKpqbRbP2N67rOWv+l0MbjCouGsxohlTUs6js4C8U6YS5/KLudSyFbryZQnr/2C7IRNZBeupCaCOoHbK6WNpDvaLnBLyLikbgcp8MYTLoRFqd5LGsj2xTy8zyMLL+DQWstWiHN798WqH/8otywlXosrPKs+kzQptBMm8nh+ozu/12h17OKi8FM/0EHRSaBlF5l/O7d/mUdCd8gQuZbUjok6HA3TcDRMGEGW0B0cMBha/Vg35y/tpt7FODVWCoe3ATYjtoybe4QhFjlyjsgmvOSGhYgGEwxCzrVRoGHn3qNkUBT9dfBm1uRrf/Ivsg6GGZzqMfc8AtogArxY/HQerovtO2edi50Ygvv27IozzWyH+O4Po6GuPuEmTIW5fKyXNsQHI82Fg6vQP3QOKAPFUf8TpwBg0qD8o729A3pqjmPbtsVUMloeQ1etIjuvqINmzS9xl4wb3oWrMtF2NPRtTieq/ReRZx28dgHfAH4XqHNlJu1xXhryc9mKNg/Zap0Cg2XSt3hsO8I38a6PdnOtmb9zypoINOza0nz2rc3QhvzYumUTgZwzMaluWjTMgMfXTMJBj/ygP/HLS7ntdyV+n3yYOQnrXraBJu4Ic8cNr5CefPYAet7G/+/nMWXxwpg2a1aMxZFlwXppyDEp85Om5AG8kk75TRiUaEJ8AUGTtqVReZHbynQieHTinrXr4qFvrY66+vp0TGuwpw7t2HOtCu6iHioPkF5cAMdnFpxcUx3QPrV35PfyunPo2R++915cP38unvzWS2whx/hG1vu8J9s4boMEfJHjTtq1Nb4XTjZY9GRvzkmbRxhxP8+ZHfIuxSMXPPFEPLJ0abitSlxLuuhbQ/FTnqyuawBUY9x1FVe1DYsT3VhT2naurq0ZMWaQHzh4gEyxzfHMj17PQq+OT76R4/Rex88r/2YOpS0S6iQJF37TUSoPEa/cGnoDnUSnoXWHDqB36Bx8HNtP2U1rWcftKple0p8ZsuoVycfFU9oTHrm1meGqk7i2yu1C3+cEKOSbdl9L6/E8ne8CwZRdHa3xd//hf2frxGsxDp36HnYzjycckyb5/E2+fi9HgxN30QWI7768ZAhehZpwN/YjND9FOJzijNcq9kAvJz131vRpuajXENqZEsf9MgsXykVROGiQiPCm1WgAamipKPqdSCyxuW/0c5wAn5NKNf/++2MeyqII4V40931m4UMYiIqXz4lwyVh4tw+RUKRVMZIQ00BnHCKdhOhebivoHoOZPLJ0WRqMZmOYymo7yfAhChHQth2X/QiM7If2vaxFIXI7N5mvjMr5HkEZeuvNtzhyaWKseurJLJaZAso5ypj6xiwhqgiI1EbPJLoiCwAll3ZU3hXQMnS3WDg3mc5OYP/+u7+LpykA+NiypenEcb+RY0umwPgct3uUVQb0MFoMxe8cazJ3/p+wcyJMx3kofM4Bm01bt2al21dfez0L5sg0/N0HJRANOi+VEWEt45cJuR9YIlTJkOkJC/t0TRXYpzBqj6Hk7tqxPcayT+tFKtyartmNc8P2vc8eNCCMHsgohmCQlfYnniMFWSdXJVEB+9C77BoNwjiRsBubmhBuh+NEa0s88vgT8dQTK4lgjSyODhJvKOLinIV1MiHXi7mXjBkFn/MR9mm09q2r69N8vDU2YqyfbGmJcopaLX300ViA8aWQzXPHhQXPub4yH9dC5uz62VemXbIOepFluKb86Z1VyRcH3F+5hn1yI6EHTzRZMH9+TJk8GQcDDJi201OMIqTTQJeGpyj4vIadyrNZKQAnlWiZrOukAWL08ArZNu98+GFs3LIpXnn9tTxWb/9Rj1X9Kg4fI61t9z6XM+Zgs86c0C+mobxPIeV1BI4FI/syb5IbaJEL+KhIDCG7wb9vETk2Q4SuoRsVMb4ET4WZ7zobbt3DM85EznVfj8Ok6e1vL4y4YsNGxP/6b/+reHblE7n9ylTpFHLZj8Z9/ziAsvLrN96IOcB71cqVSafSt3gh3Yk7bqEA2egX5Yh+pXW/F/f9WzpwTRTk4q1CRLrWuN+OU3PDhvWx6sVv54kA4nPSJjhrpHkoUSpf/ZjLPawYZWMqG4xVhcMMKNO3h48iZfM29TlI1f3oo/codrcvPt/TLNRiJpOaNoVMgsrqqB5VFhXk+g2QVnjpuvDl+JPfMGdFr8pzKqQSKC9/A4ETN92iom7bixPk7kBO3sHRcJitE3uaKXzE3a22x/Xf/Pin8Z3nn4+G2trksw7eLSKDGM8o+R24d5n9iMJkIHxYfBSvOtgmsnnLlmhpbMxU1BIfTqcpMFSxUGGUnlQihJn4Jg+xboDOHlM7/V06cB18xq0rrtnRpsY8muoqGVs1FL+dO29O1JFhoUNaw9hL4872xHGre9u3Al66V1mTL6oUlLaVuaa2Lz156ss6jjyeQORn9uzZGEfULTBtnvFI345J+aAxKspaJFXjoUj7REFnXcQPZYLGcTp94Pc6Gi4QOVvzySfRiMH7KI5wHY6mL5vNJv9QKU4DiD78rLPM+h2llGj34LpNUKVGxVM+b40W11o+aWG03/zmN0StxseL1BXQGSs+SlPeK70Z5RNfhLv8RdiL78JLPHee4r1jd22lF3my8sO0+bffejMeJBV0BXxsJM5Ixw2GJc7Jqxy3WW8alM4lC5cBV9u2L/lvOh7EG/oQN6+S+dRJsTW3iRzGQfXDn/wYB+L9OJ7EiW7G6jiL455d4zSKaMe5SUc6RYR5tsuap5HH31XUhJDvNDc3xyGU0COHDkcFJx+t4kjcqegFuc8Wg1YqYeI5HmW4OCIvVl5oLAmzPMKSdVFeZf/IFzPFzmGU7sRBcpAAijUQTCF+hILSHmFttqK4mJkIPJfU6Tozbjt17mmQsQ7OQ7zUwHad3M4ofrafPhVfwW+bm5pwaA2Mx1c8EQ8uXpLb5boxyjK4QDvqBgwuccM55Jz62tdosk9x046dQ9Y3Yiw61tav/QTZNiRmzpod8xfMx7k2KfHB/cbiinWX3Hqn0SGeq2vYJj0WdMZ3ymDxSVpVBlv4U5n18SefxifvvRurOJnJkwzEmayBQt9ewlk4uHbiSMmpJo7r+HFe3qkcU58oPWNq/Bmq8v8W2dcJz/k259U31NXmODRALXRZ0jMMDPTH2ZvGovRCO/6mPihPUGarX1oI0O2rJ9rb4yNg8sH778UpDPajZ4tzehZOHBRTOcqvnq0T48ugcWOcPD8YQCjPxX/5lTQ0mLVyXvblXKQFx2EQRIe6mXvWZQCxkXPse2dp2rouxv7DZ+Msjg35+nFes3HW/sVf/k9sB3iw2J8Oj7+rAwjjRl4sbbremeWan+9msVe3Xe1APlnL69nn2N4JL0vYihN9+OB2EmlF3TYjvsKa8coPcisauJvrj2xM2PGc+ncrRpEBjmPs0a/FIbh69bNZ18yTaOTt8mlpXc7gvIsaD8g54CsfkaeobyYO0aeXsFL2nmHce9lqcYz2pYP7cXY/wFaICvh4PseYdKrKh8XpdKLCyzSAEw58l/jfhzsl2OtAco2b4AWfffopWQTnY2ptXSym7ZnMQfwzY058FJd1MqgLqSOJ7/IzXwZszEZwXsmjmY9OTevD+N22HTvjE2p4TUYHU35MnFSTfMQMEuHhnNTppXu/E6cLDspa0qfOJHmZzs2sm0D7jskgicG8tTgFd8MPHl2xIuboyCD7z3EJG/uXNwlL8VoYCR/bFSflRdKnv6fMAe7yKHWlMQRMrenwBsVSe8CLF5D9nrolTcuzHWehp8EDac9+vJSx8hTHqB6bPIZ77Y8lSHzq+4Reczje+PnP0LEfj9U4wuUDOqfUfdOWY6wMLrOeteWU26M4aULd6xq6h4Ek1yUvPnuva6D86GI9d+IcPLB/Xzz/0otZbNt5W+AzZRT3y5f8nA4qxiuOSj860w3U3iYIY5azPEp9wu3nF+GxZvScRT5t376drJb+8dIrbu+cmU40ZchF5Lq6Q2bVs07COJ3MwEOYM8h0YOjYSH4HZORk8gO3bDUqnxqPxmmcyqfYWnfuZHu8+uf/Ml5+5ZUYjSy/wxzUGx2v9PNNv35vR8M/BkCKBP5XIiKR278OUA35k48+jC6Eg3uRHkdpsSDkPRDW4icW+igWH0MFBFO4mc6pkPW6yuKrgGgAlIgy0/VkAiDnuo2fx6ccnTgXgTlv3vyora3NiJSCxReYlu34P4lBJcDFy8gxgkIG6y0uqr+LQCLsMNLGLmFM/w6h2XhIRXEFe7Vm5NF0MtBkfAp4kVkE4+VzBXLABGECfpdX3xCEjf8ppEw7a0KZ+CXRtKqqsfHsqlVEFsYlEygJiBRatkMjJcYskcpwFQ6+i8IlBdL5yvgV1grxbRil73Cm7mqE8hMoiiVD3N8cf7FWhaGZSiD9+H3JyNLZkEoTaySRS0AyYQn9DOlIG7fgaGhuiVe+/2osTGO6cN4kU2ZsqSgweDFB5uYa+pfbKRy/4xZm3l8wQ85NR3C3tbWlYNsJoY/DwfASlX/HT5rMepMaR//J1NBKivkrfHFOycgRkBpE7sFzbnl8DrBOLycMRYHiXrP97M13f75ZE8twNKx66qkYzHrf4jmZUHqYGbcCINuGYTi+4nta5jdhmF5WZ6TigsKChhxtrdRc2LAhnRgjYebLHn+MbJL7YK4oF+CyY2ZoyYgUWDZmu+KEfSmYMuXcTmjX9GPrbAxGOeDh+AqYfPjbt6IcRUrP/0IcbNVkxGSjzF0Ftpc1lOG6wkZ3hbO4mMVXwXnX0VTcFKwJTwQJAgzplUe9fb55YzxPwU6Lch1qamRf+s7Yd4RTSb7aRj9E3fHzzKwZEPUYNhMrqK6Oo0GBI+yRTjknxyP+mEoqq0yFlXvEUZ0NLDxKGDQODEoREU3pa5DsqctUmj/eFptPsTWEWy1K6PV//Q//jhM/nmLfLGNlDlBREeGzLdZHw+gXP/t5LAAmq1cR4cV5JDxTwEDfOkIKAwvhAz6WjK9UtrjPd9FcHBV/XOPEZd7lQV8wf4/Z+/ar32Pf6UNpqKQzE5gjdh0N/5yt+KLAR7GkQYVzYaiqbOLErGDLD7DavmtHvPv+79inTMHNnYfsOGbS/5wG0vtGk9JaNjhGUqFcJ8Ng8GZQn5KjkqVSTpc5ToCQ745fQSwNC9fkRbynwsC47hJF63TrRMeZ2NFKMSmaOM7L69/96Z/GKzj0LBCYx97Sh8LeLRvDoCsAkcqQ8GDiyUusp2Fa+5dffEm67NFcx4VLFufxiSoNKk6FEltsq9B4TAcP66Uy6haWVGLAT2lIOkj8oM8cA7LgKDV+1uDs7cEImlJbG/dR3GratPo0CL2XqUuMqXgp7FWik1aYN43FNZwZFho1RdrCb+kYQnlMZQ18cE3Xf7I2xpN9NAfDa/q06alEa5xxc/JCnXOJG7Q5UMWONSy1I967FPLW5GX06bpoOJ5HEf8AJ8ZBFKLHVz4Rs1BWpCnHaHvSpIqx85Cvul7DUXpgugnvO8BPvBMuOlJTdgkn/8ELjh5rRn68kXupX/zWC1n7yDFoiIvHtlvKUMgsQeBMJxJjZoeJ396fhjSwNvW1VCtIHrsHY/rtt97C0fBIrCSKOSodDaJ0sVY5B+aRESra8V2eZS2A5NPQmO07fvFSGeNyqdCdprisRRgPQbM/+ulPY+HixXoj0xmrrPC5Qn5A5zwnL3Y+9qkeoAFndL2EX45pOMaueKqj4QBjVwkdDa94XiW6rjadgcp3qTQph7bEZ+nTsVkIl0nEDZw7mdFAH47DvnLLGzDqZKvCftp23F0oo/ORe48uW86pLJVZ78jAiXxcLpDynz5y0r7Tr4aA9OMQ0mEE/ty8XWRdiTMn2jgN54ut0drSkviqg+oBjC/1hVukdZu9IZ9JHCia5C8VcL4Bxq5broP9851wt2vx3WsXwYePMYx0PMyZOyfHPxEHnrimg0VYp7HBuzSpzNAoE76+Undgjv7mHKQDkc1spjvKjzVr42P2Kz/97LNxP4GfMQSXdBIoF6RV2/fBu6y1bUlPACxrj2g8p4FGu6WMPOWmazCE+jKn0SHf/O3bbHs7Gy+9+O2s3ZUFRRlLr8Zg4getk0GQdWl4Vuedzip/t/B4ERWHS8v/0eMgxjhzsi3efv/D+DW4fgb94MjpcwmrhYiZKTiq6keT0VCGcUk64ADwB5bMmMzMLOpoOT4dhF7Ss/MS9gXM4L98zu8Zlxl7vf1J4aco5AmMjB27zgdiLo+9PM77HJxlf/XX/z4e5cSAKrNngdcdAzPoTAOZp4aRp2N5bKJZMK67x1ib3fQlGQedXZ3x0ndepoAhGXfSHeuj88zPrq8OmTJ0Ah0sLDQ9sobo0Tq9hIm4Y+RdHdK1MivLQn2Hqd3ViDOgbvr0LBpczaksV8FH6U/HoE4E5yw9lXQ0110YqNMJk2ybv73EU2XBWRwNO6lhcvTwoXxu0YMPkY34UG4tuoFRnFtb5cU8bwabeKch7BjFO+WqDi75jt9LEvZlgGYQfOIIcmnNRx/jVOiKuvqGWLyYAu0zZiQ/0aA0Q8jAn+PXDikVqE992PaAc55GJh3xz/mIU56CIf9RH1vDiSUTCIjNQR+rr6+PcdXjkiblV6k/9NGlbYorzl18EF72aZsO3HHLn/xep6NOV+lp+xdbMmNNR4MZ1TqJ5Kk+lzyV9jOzkne33Np+SYe3f/vQyS8P/doxhjz0KNKfIT+uwe9eIgOpFmesDgB1acfAcBK/pFdxI/ka45evJS92Di4m/ytW9R/e7Vd97Fe//GU8gPxQH7OosYVEsy3a8RIO4pDt6RhSXzW7xnoUtmmwynEn3PksvuuU6IR2hP1udNNXvv+DeOTBBxIfDD469hwzOOG/ktwQH7MQI3zK2nQ6BaTjMoIOTDr1X2F+HieGWQceuXkXGvk+7dcDewZKlifP0od0ZSBSvV3ZLd7kOO2a/rUxdTSwGP6ZY+sHbA3WHCAbbi+wOQtOnsXG7MTh8+0fvk7h6peigqCzchDQJy/0+W/69fs7GhKogKEPuAlRkD2/UDmFGI7s2R1rP/wgOklntMDSIwjNmRC8SlwvyOSeLPcIXiFiZdrkMJRBPc22ItGpaMm4PEImDVWQQ0GrImt0fiNEuO6zT3Of1oyZM7LA1TgMII0IF15FS4VXZVEiF9klGCNFegUlShUzlTwZid/JxEYjILtQKt6hIrJK9OOk/zTUk7pElFpiVqg5P8RxCn+JRWalQJeh+neJmchQNXI1eDWCZQ5Gtizk9Cu2fehtXLniCaL2NalgG43ycux6Ot3yUDbMLAr2tTM+FaM7MECwOYnWolYSjMJBwvI+I9oesfcxqVFPrl6dR17peZboJFbvKTEIx6kSYNRPBURi8zg2YShxOi8JXQbjHIwA6WjQkdFy4jhMcFWmkqtAqww7PtNjZSo+U0Tj3ItaRPAtHOPalhwNwsq5+Lztp6OhqTH2Mv4qBNpzeOhNl84jiVhL19MtAKZTajDfoS0Zm2jI9POlgjKQaLDfldLnreB7njXdg7JlRKoTj+ISUuwfxwlj2u0l0nl9XAYivqh0y0DEF8fuuilkGCrwMypfeNNlt+77tUZGKwXXVBQ72k6iuI6KB1EYdAjoHLtaEmx8piW+Y/1cMdcDvHYOro/OAk8nsOiMQu7mHY5YUwlmjY2mffLBhyg9A9PLatquGQ1mcAi7NEBp1zEpuNLApW3XJQ0VbnCOMkH7dM6mydUAX89RX7dhAwrLV6TsPpynJJxEGB0E//eSAbLmqy95snA0zJg0JGqrxsS4kUNxNMBMGadKsC/blwELpxSIyWlZCefJK5UYcFqHww1qCJgGP4LtBPf6E03kDKG2iz2xrbEtNp5hOw1t9WWyxt/+t38ezz25gmKJCHLSAzzKzgWWzsWzgxilb3E84Nx58+PpJ1YS4SQFPmkCgcb4VAzFNfFBWCvUMhINfXqfQso0VCPCWTSJdo1+y3+sTq8i9zkZDa9QpfuRB5bQBsYyxngFNRFGlFEUlWfNmLhHZkZ/ii9at0Hj2iyhG+C86wGhYhCNZk3vcHzZnjRE9wHbdXsPCtqYhWxaMGNQTKkYG6PQaMtBdJ0MwsEaAioNveIecHStM7UR4jVbQgGb/0R6LmmWr3GCAB9gdXcghhKenL1kNGxuJdLBb+3eyPWv/4ij655+Ko8eHo3RoHJ6k7loEOTpKvSp8S1e3fYz/FL61fN/YN8+UmuPpRKzEEVuyaJFyTeSfsAt+0lFgefFibz6PjjmTNHsm488WgVXpcObD+HgWkdE6jJ0OxmH2kJqmEyfNi0jHRqDJfrRyWDqo5EJ9/5KiwrpK0QQLiNbLMKZ9WsYf1YDxzCQR+6Ax2wjsj4O2WS7DbV1edSy7d4lC+cW8kdeoHErH3buwiCNORQZ1zQjJ3znHJNHgPvKE7O+Plq7NvbDJ81smjVzZqbwK7tSGWLOPJZKobLPcZsxJ45cB9/EGe/LdunbNdbAUFHTsWFx4DcxjkzV1FkqjxSf5eWJh4wt4SncGav0rzIuPntCRxr//K1ckH8ZFevuJsuQeSgDVYg+JkiwEJm9HGVxJAqZ9KxiLs2lQ4N2HbsOPIuolsPzdOj5nXLFq5Cxrqv8/x7K1mUKpuKggp6aGhtRFL+fW6K8z7FJo47NOSrzU2lW5rEe4rKKtrinMXoLHixPkTcOYe+sMuI4MukwvEBn8ijk+LMYvZ60oyNF5VJe7ruy1bV1rdK45t0xu0XIkRtQ0PC1H3FUZ4PFv07C44+B7y3HmmMa0dHHli/nVJZxqXwmeosj0Kxts4ApP/y+ZPSL1yrzOpD9LguveQOwVH5staA0kekqDJZFOGA8htSaQNJjkQEKX2Iu8jPX1LG7LvI4o7vCw3XWEE2HGN8NG4HRB4x279tPFtUHCdeZKM+zeE2umZQyyPG6fdECm+7hVs7SGAMr+Kfj9pg++WMPhu9V+KVt2o9HeVo07tP162P9mjWZAWpGwziCKKOQrWZiOHbbVnXO4nA07UkJIHTC/AYOl6Rb0uLM9EBDSSdY4WwYSPbUGRzhH0X3xfPxLFkq9VOIwAJHa1Ddgxen/KHNu2RvURAneZ6o4SlIt657KtcA9sOXs/5mqHEqF/J1IAGGtvb2+O0HH8cv33wnLly5EE3oNl4LSZCZUl0RDWTtTSynSDSE1a/XkwfEjgIu3peOX8Yg3JOu8zfu5V9BJ9IftXQ4duI66WX9hlEAnMy3Fvr5bAtbEmljEi958bQRFfFXf/WXsZTIexU64hBphnVwj76BCen4OvPRUT0U/NTpef7ShczK2Ibh5Xbf7373uzGPddVYu0EdpiHwcgN6WQwUvHdM6tND0bfEuStsSfFkAXVvdVWr6xuFL61zS0srgZ9G8P1Y1MCDn1zJEdMTawoDi/uUC7bpP3FQY1Z0Vo9Ko5expuzj25JeIn5KxxZI3AUPNsPUINlCtmg9TIaQWyc8ItgtieK+dK+jQb1VOBuAcXwGVMRF4Su/kDe6Mn6Wxo40NsVaApHOsba+IbNA6+tqs31pM4MAZHkIW3lNGv48Lx/yJbxtU9wv6VbymlK2zw6yMTZ8ti7thVmzC3qaOH5CoQMDU3mxLwaeOmDyLuZu2+ryjt+xu92iOJIcuwT4mZnn8eRryBDa8eXWWPnkU7kVSRlhG/J56wAJ14Q5c3AN3AIsANThpV8zBjOIBc9zLXyV5JTbYn+D407D/vnnCx1bmCp/1af6pa5YwN22hKlHZcpTbF9dw+9sz+weF90xyEM1vg8iuz/C9psBLj6+/NHkBdk+8FP/MvtNPiD/ck6ur3ZD6vKsje0J/8zSUHaB64LSDHcdajoaDEZ+7zUKgqqPgXtmTHgph3WAqa+7XVodTJtLuMn305HBWNWDk1cCH+0c++/AodkIru/AiTGA+800MABjBpuOBmlHu8/sILeXKlfE9/yefoWx+OJLTmG9IBqPMuS+W+92Y3vs2b8vLiMjL0CvZ9GxX+DEiRdx9oyZOgWGVdh2NJT06Xy+ydcfxtFQ4rkihq9EBd4hcgF2SEcDHvQu0uEnUoDj4YWLOGNURwNKAoqUtQo0kkVki88pzD26TaVKIVww78Q5GDzvMEpIpnA0wHA3oiSu37g+ptTVsl99ZtSSVju2soqFL4wPhacI6NhUMCRUDUcJvUi9hZGwoCobMkEVJhFkNA6FTlKrfktGQxNM0D2w06Y1sEfKI/AsGAKDYDyDUeAVoAp++9Dol3F4FdAolFIZuAqFSC9ij4KR6Gh441e/SuJ9GubtHqlCocXw4XnbTGYBAWj0qxzpkbZ9GW2h9GPcJTdT8SuYicxYRdJU7w+pHv9Un6NBJpTwBPEzEsEApQeVFC8JU+VRIhQOGhslYk3lk3tVAp3DKbxwKooyqxfImPCsYcfopHMtUVScg0IojXSZdXLqQhiXFGjfVY4ciMadTFbB39jUGLsxqivx8D333OpMfdVo1kiUwPthpZaxlhrsErrMyauATd8awKScpwa4TodymOR5BM5eHA2HDhxgH1Yn3talMMHlKXg8a168EU6w44R/Gso2DIwcq7ginIWjjoacEv3oFFBxdE03bdkUbSi75TA2t9wsADYWFpNBMlCEJ3yHBxWe4o6OhhTUwCpxnu7Eo3uk2avMXYcuRlZSNA4c2pn0hKMBhc0I6QLqNFjzJBk3sFEo+hIOjjePogL/xW/3eYujzjGjHPRTbOe4kwUQddyt27iB1N3t8dCjy1N4tmEQmNXgMWoff7VVSMQ8gmPTJ5WxbWLUP3E0OLd0NLDw6Wign1wX8M1Ikvhg/zJQhZnKzI0bjJkfyojYQhg4FQbGqe4bsbO1Lda1FRkNFiz0+p//638Vq1euiBropD/K5CBgoDAU56QV90u+SSrgvHnF1olx8AHXyj79vaSo6G32ewWlV0Z0ga04bwRH2LtfUDoVL1WcTCv+AnzftH5DOhqWspfUDI7r17o5dsw9m/CUm6wVgghTBgXc5xFIRrGA/71elSR6hPeYLXUdoboHr/ZaFPM9wpb9gF7TGe7c+n5RO2ZcVJcPjgryZQdiZA2iDU8bEU9MxS0cDQgygCcI7uKQkIydU86q738D+JFZgA84dvoyGnafPBVrjmN88H1pW8q/+ePX44VnV0VDbW2RCtunmA6A58iNVPh0TKVS7Tjk07yb+npg336csY2Zlmn0+2FONLCGjvheZPCAc4zbsYuThdLpoBh4viALcMGXhou4UYYS7bvG7kecrW1GQ11DQyxCAZ/Bu2taFNbFqGccEHoadol/zNkaEUZn3E/ZQ+aEtOkeeyvrC7P+OLfMdJBHfrF1Sx7zaErttLr6dGTpyHTN3BKjkSTtqIQqozQ6Na50NqSiBx9IPGcu9qNiJL/X0fDBx2ti/+6dsWLlk3nUsdlrKjven8ocsNWZ6d5ZFR0dnsJJJ4NyKxVf/s41ZmVvw+uFvRG7VhwNv2brhEfsPoN8MiJlGqjiQGNZhJDu5WUl3iX/ypRjcZIlSH7GPSrlZv+4T1/e5j1WSP/g/Xdj+WMrYgU8shxlUOeL7ZWUVZ83GGB2ig54a2QIE5VX+8z1zaUGD4ENj6Zzs72DiBH0dKypKb7DEXJLyEJSOdTRms5W2kjjHxinYx0a6A/ey+91XGt8WgzxOsYpkMyCa8NHlacTydMGDqPkWkPIInffJlNnem1dKoU6o3L8wEzdoBTVEpdzPWlLeaH+keMFX3Q22of4aFX9E8D92LGmaGk6FnPnL4iVj69A5xgDPoLvjJ9HU6aK+zqYXQO/c82FjXJbBTgdL3zu5XvxRUPjOGPftHkTdYTasuK8mR46Giw2qVKbEVf7YG2VrSrKxXaawtFg+66rtXq4IcdtlfOykUbtiu2U76LT6MibgyNg7hwLDSI/gO+tPqN0CG3CnBP/07hl9OmcpWUdDToDpKlr8DsNXeWS3/eAB59u2BDr2H73IFtklU2TiPRWgJOelsJEi0w68EJnlLLVAIzjVPboALQtx+mRvK6Bjh551yBOuDHV+x22N3Sd6ojVOo9qp5DCznYsHAd3wYOUo8D1HhkDd/tTmJNteKZHW4yuPzVxhuC4HcZxwXdxSly7hh4GDEagz7VyKsU7az+L37y/Fp3vDMdbngGCEQ+Nj6gdXxm1o4fHuDJ4EisHMqYMN3tEI0g56zjVPphOgfPAyRo3aoHSskvh1QtMb3I+Xv/hY4iUDovWzvNs/aUiPs9V87u8uG54Rfzt3/x7jlvmJAVgYW0c6Vu9VxqV3nQ03GAOOho0ej0pQ7xxP79bQ1/D8FrA2qYed4VUedrXMMqaW7zLHwxypIOKPnUyyNfcMuN66sQQZ3QCuS4tra1U1j/MNqeD6WhYjZNnSs2kNBILZ5rD0GhDhwaXNeSkfflByllxnr/9riSfpC2dm2e7SFFH9h3CKaicXcIW36XIkHKcmjoa7kGjjttsPvV2eYN6oDqOMtpsPY11ZbZtZkYn4zeIoJ7eeKw5PvlkbVzEqTMJJ8n99y/MYxlN45e/qktq7EpHBvikKQaaeq66buqu0G+J/4IB4Bc6A3hnEGi7gR/aN42+RE+eqpfFFtlOao0B9bySo03HoPMoeKQZyNA0C6IR6oll0pT1tgz6daOLfIyTfQ+BH+0PTyPTeePY02lMO45VHibc1bB0hsvfhJPyWtSTV3sVuiGj4W9fLeCMGUI6r79F1pcBK2HqmuWzTDbHTjs6Nf1eXSmdo4ybRlIOurbCSnqQh8vndKLoaFiD7J6N/uvWOwucKuMYdLZnoEC9TLvB7XfKDJ0PtiE/lB/0gueFPNVpAK2BSzqxraGgPraD4NcP/uiHBH4eyLZSf+O+hLO8g97U67TXEifoSzxUF9T+UwYznISNsJXXeyTxUeTSdhwNA+Gbr7zy3dQ5rhCUu4VOM4KxDlWXTh5pJm8hRxLW9J0OePBTPYFfszZLP+yP4TiTLiGX92J7mC14mfU9D+2eOtkWL71ORgOOhsraWhgJYyoxjWzU/31zrz+co0EOW7oSMK4c1IjytC8zGj6M86fYOoGBsHQRe6Tq6/EMw5AAdAogFsTUUPe+6smWiZYYhwLRhdN4VdAOYA2MYqusaYRtgrl+vvnzqMUJMHu2qa/T2C85Oo3hGyBFEgdIobdJRE0HAWM1fUzFIbdOMH4VR4aRUU2xUw/jOVJofsOewCMH98cqBNvMmTMokDcWQuesZohEz7ZVryH3JHadFxoaQqNEsDIoFWgRWIKUOFU8JNimFlJfSS2aWFNDatGqqGTcEoBGWXqJGUiiGOOTwPws43Pug3hxcx5BpcHrj8lQaF/iUfHbRkrau0S8VlHMZMWyZamEep9MJFO0aJfbGQ+wBzYlB4PwlmE5kRSoPGTb9u1cuonqdLCeGzZt4uz39vjxT/95LCKFVMJVQZZZOAYNdoWZiqIMxe9ksu4/k5kIL5VbB2H7MhSV/w6M26ZjEDoGb+WkifEt0oJrp0xNYZgMFBhJ5J76oPJpu8LL521LOKe6xWfbzz3iwg3B6b7pgwjMw4dIXcJz+chjj8cTpKcOx8lhGp+KnBkmue8YQemcbV+BVESrC+W9JHhUhFSOXG9TbBtbjsVnGI8tTY2cZFBJJsxKUsnvz4rCFqlUoUtlHdhrBGqEMuAUIIPoN+nGKBe000NFW09V8Sic0eOoGA2NGIH9kNTUkaSSzpoxK/ft1aDM6VxyrgoT8VpcF6YKBdfR9VXpEb/828hvHrcDTtF9fjYt+CP2lH8J3N33ZmZQK8Jo76FDOBoOxdqvvuBuHA3oN9Nr/gtHA2uPpEg6oBvaU0TJM+2/ULp8VkUjkRUcV0lI4x/B1J+MjHsqKsyr8zpFlk73xM42Uj0xbC74CNdf//mfxnMrV+TxnIMHGu0E5vy7Bw64504G/qtf/DLuY6vKs0QAxgL/pCPh0CeM7V486Adu3kNgXYTRCy+dXCowFihUwIlHeutT4DGvf+xoePl7PwgdDei34Drpl3euM34jCjQOnO+hGN8mq8GjLlUChkFfnmqgtmLNhEFlI+Iqgns/Udf1GLm7wMd3N2/OOdbSZh0K7kxOZ5k2YTTbJ+BLKCxofIXDgPmC+qnYavTfgc2mU9Z3JsdPycdsTMVb3jSYYfXDMVNkNPTGrhNE8E4UFc6L3cgR//ZPfhzfffGFPLs6lQbWRmNmOGMfRWbGPTo15V0DZgiwS4OA/uUDGnSNzMF0xOXQ0jKyhIxue1KCiqi0Ly5I+y5loWSRhghckxeq9AFz4e93Kl5WMPeY2QPQ6dtvv52yYib8fQkKeEN9Q/LxbpyGRkiNeDnXLOYE/1FOaMC4piqeOrTusVhpFKlMq4zhaDBiZI2G9es+iYkTPTJ4DkrojJhI7ZH+rFXWAemDgzxAHNJxKQ4rLzQy5EfJf5mXa59OPviAytO5813xLkbXITI+nn1udabsWgtFZ7IwVkERIAkb8MI+VL5cQB2BKr/SrZd9eIa3Cotj1wl2rLU1fo78YEbxwnPPpaPB2hMat8pL2/GzV47fOTAfHfslJ5q80rZL8sn1uY3imdkee/bmHtvvkMGzeuVKqtlDI+ChOE2D4BTzdXxMwHaLLLYiiub808lBX87V9j2lRFq7xLqdhK9s/eqrzGj4Hu0vWXh/4of3JU0zjnS69tGtfH4wODwYHJKmXQePX7yCDmDGl9Xdx1L4Ub1Bo+sgPGsrtDVyZEV8H0eGjgb1gVvQkjxX7jQU/iFPFFbCQUPF5+Xt4ozGlwZvEdUE5vwuvzhy9Cip3ofjdFtbPESk7pmnnkoH0RlowaymLNwpLvDyc8nRIK4UxpY6QcGvlacGCnSIjBxTkYX31pM1ZSq5xswD0NIi6kAon9KBD40ZnRPnrQMiLEqGhu/qOzpMNEZLjgALO5YR2NBxbZbjb3/7ZmaNLlhwX9wPrzSjwQrpl851ZabgUGhqOLJPR/4geBkgQlZDm+BFOtLAQeGDRE9YSVdlGEY6H9asWxcf/e7teGTZ8ljICRJTJ3HyC7rUbRzKd9WZRDDxx3V17sA9OwBPpFH5l44G+YsV2nXCIFhxPJZz/Ou5+OVbv4lmjNKXX/4OtErgB0NgGPR8BwNNTHd8/QbAT/pBYzBG5QfNMR/o5h4ZbTqDcTJcvdqdhV1HVlbFybOd8eHGLfH+Z5vhZyei+ewpRxGPTY1oINBRUz40OCCHrDKMOXkZa1eM26EzZl5Z1Fh4OB/e4fSsDSzB33GC3MYpZsYeZfwJ9XNiDPLrRNfl2LbneDRxxrB8Wl5cO3x0/N//8T/Eo8iXe+jCnpAwBJzPTBN4sWsrvmuMqg/I+zVWDPhsRIa4veRHf/yj3LZiAMaTpXSWsmApgzKjhCGmToP+Jf4p4yA+YO2WDHgBOO+WlgqMZ/XEYy0tbEM6EDsweKc0TItXXqKwLTqrmUk34bde8ioNYJUJt2ho+MoD1PO+pmf6EijiqPxT3fAUmTtm8OzFTlA3f+SJJ2L58mW57dGtDQYjrdxvRoZ6mXpOScfW8aLNkPoOOCg/NivR1Hoj5G6Xa25tic/WfQatnoxq9KRH0ffumz8vjXFPmJCmpBv1ITMahIsOCNdV+tUwVq/8mk8CG5Iuk3560Vm+2L4j3nuX41/HV8d8nI5zkFETx6OP0YbOBh0kmcNDH8rCjIDzWwY3gI28V5grVVxLkVV6chuV+/nX4GjYh8H7NM6duRQrzmL0jNNsQZ11aZQ7RvkAY1N+qLNa3Dt1bOYn37HppLPiQ8qp1ra2eOPN36SjxjpotThilJnOV8eCNKqOatu2KQ8w41N+qEPZd/mwMlseL26a6aGjQeN+Pzz4fXTVh5Yv52j1J77eOiF8E8aMqtgaoW6qe6SgJ+0bT7vwJK0rZKu4vm5Nkra0JXSiWx9ER/UOZMgPfvjD1MfkJwZYtV2cu7qlcy/ZU46RRhI35MkGQtTp00nDWotfjr3jDBkN2GbbdmyP/vC1V199NWbU13M634W4fvlK6vLlyMKRZJn3G4jznD7uoXNoPwkP6UlbQDry5I3MaOC7MrKpupmPtcT2ARsz5s92dkZ7S0t87yc/iZfZ8lRJoDz5I/hXLFrfurl+39DrD+NoABmhfkAAYFis5PYFxsAob+Z+xo0In3N4oavxHi9b/EDMbpiee39vstBWfpVAjGDpUVVYqTwY2UqvZ59RqvFaEC5Ml8XV0O7l3s8wdtes+YhqsjMwMBbE7Fmz8wxiC99IjCVPZe5dYpSZ0gliaEiKbCKfCGxfXqaPKipECvcBvQWhNB49HE89vSojUpUYX3pKb6j8g1Qe45meYghFp4WGss+r6Jp14CUzKXlhk/nSr0zscGNj/OznP89CMi+QdWBBKR0YKpeprEAsep5lwG43EKx6XTMtDCYO4JNJiNBpUHKv9zgfPYpG6z54hwrQODEeX7YsMydkoBKgEQoJzHUzddY9vG6J8JQP56f3z99LTMrxljzgZjSYXrRh48ZoJtXzRz/5MTUa7ksjWgdHekMZiAq1TEqPse0Ib9cjjU3GqJDzkpEURvG9PNbqbOe50ItuMZaK6rHxrRdkglMyLTaZgg+Bd3c95k+HA30pEFxDYZ/1CWhfhunfqbxC+K7JeZjFIZQ4vfNtRKZ0NJhNolC6glDXKJLp6RFNDzfwkYGVPN+OUyGhMur4jS7a7yAj8gzE4kMbPv88jsOoRiGol7O3Wdi4Rcg27jBmcb5g4KwFipBCyb48B1uBLzNUwJtKqgBickReRqWR9xXe//dwfo0pH5UedLMaJuJBL5h+nyLL89KUl8ZderKZk+0qQFx/McX1UCBbpMro0mn2vupo2IVh9O3vfCeLQVq4ZgdKgM6GT9kS4jUXH9f0icOiti+jobxv6wQN59q7HsLePlQM7Jfe83LtUw0TVxmT45Z/3AUfrcx9h5TSs9fY39dxJba0WiwWx1bfs3/7F38eLzz9ZNZ6GdBPxUBDoVC0xXcLnb35698g8OfHMyufzK0TKcDoSwUix8HnhDO85g78QWVJo8stTwWuIDjBew1UaSIL/7Fe6WhAqG3+fHO8/N3XYtmDDyeO9N5E4N+kYF8v9WbMPuAZayHcJuxxHeVa5WI4a+/RV5nRINxRmq8hMHcx3jUbPotdKHEfbtmSs5wCCtSSPT99/GiKbVZwfjsCk0ipzoY8fgo6vI3RpZNGR4PReZ0NKj930OJd9Vx5JqOyO5D3Ibw8BcNxdV1360RXrD8G/+I6n/+P+O//7F/ED15+MaZicCdPBEfuMsZhGB0VCFRhpxPR/gaSot4PmKm4eMrKbvhM0+EjyXOWYngtY9vNMBwNePiSdxXO3SLaIf14ZWYScBX28jOxQoXF/ba+j8FJNBge3AjevQO+Wwxy+syZGSnVmazCZjEvDXIrgKeixbqloxNDTJqU1ypHklYZv/CRXtPhIL+DV2vsei57zaRJMQ9Hw4xp1GjgyEpxUkeDacdCUiMp4cL4+2EQybPEF52lKhcqWdKZPECFV15jRsM77N/15KXVOAJmM34jX9K6e2rlNcJVJU5eKY6apaHiW4paFQokdCttSUTSFS/p61hrS/zil2+kAfEtiu+p/NuvWxtSkRMHHC+PiXs6gqV9lTB5mONVgZZfGsW3Tb+Xr3nk31c4kH7xs7/PPbDPI0PcVpcyG5x2v7sAcK4lg0IlVBmik9HokAqXRoVtC6eUifx2CQXfjAZTva2n8CK8ZhEGr2M1BTcjt3z2OWGeii50nllgLKb/EvYo+rd4pWOQdkeShSjdtlJ4WmeyjoYxlWPjdbZmNNTXczQbKbPoHOoRcicLuilrjcDK41XupXfb1uiy8JcGtdlwRvHdN23EeDfG+h5w/nR7eyx9fEUGCcrIUrqE0aQinCnjjN810NAU3l7KRS95nnAXzspt+xuOU20QSu3ptjbkxwaCGwfz5IfHnlgZD+Fc0wjqRj5pSJi1MwJn5TBT34GPe9lLiruGQn/mcYexW+FcnHdOyg9rGOmofuu3b7HW/dMwWsw2p7qptQmTq2ybMYvHCPgwdKzEfcYoXsqz/c8onY5AcVBZpe4hfXlcp4Efi9etJevgAfba3wcfruVoQ/Ua5VwavMAgs/gEBP0Id+ExAJqQdopoOoYr/NL2b4EPFlErx2GUjgYMo1b0sRfJopw1ja2sOBqGQtN3bpmmrsxn+wum3c07Kvqa78hr6HUo2QxmNVir4Xav9RrAK/Uc5H4LGQ0frN8S76yjiPNJTlfoOOno4rG6iGnwwxq2xo2GkQ5jrv2glXQ0OHjmLQ9Mhy/vrmmuJu9iKMPnne/pS2cD1jsT48SAQWR7McYTnd2x4wDZm2R7iyHKuWkca/yf/o+/i0cfejB15BvoWv6mw0edRD4jvmvkysuEv9t52gnOWBi9Dfz5IRHehQvmZxRdnSMLiOqoAzZZF4o1UA9QZqorejSiRy9Lo/IC27RtC0mbhXsMGt2PrNKomwp/fPWVV6K6hgKi4KPHRao7yMPkWZqLZiaI62lEqn+wrvYjbbvWOoMykAVenqVWyxdEpQ/s25u/m0m5bOnSDPRdB69v05bBGTNCpauSEatOLb8Qt+X1A3C+qk9Is+r45cgOI8itjH3tmjXR1tIS49CTnkAvsGAxxMdxjRzxzJjUEeRT8oLE9b75SLf+5iWdFk4BaJZ1Hw7eqZttxdh9953fxQQcUhbmnjHoD/+WAABAAElEQVR9Rtb4ESbKT/lMulogffU8nYTSaurc4J+073qaOXKbNXU7j5lNBljd5vv+xx/Hti2bM9ApPY0nWOvJXlegVW0Ft51n9L6P5ys/5DmuXT9pijUV7uqDLMHXOCuvPw6u/Bpe4Lq/8DyOaviAWSbCwrYzC4NnxBvtJ+W4W3N0xmbmnPQJviivXRf5tvX11PkMOuzBIfgWGaZLCT489/QqnLEj0kmkDi/dizPaOa6BfB7UoG/mw5oK+1vQjc5edSpliX1oB7q+8uFtO3fEXvjwy997leKpSxLHdHCkneHztCmea7fYnr8pc91CrQ7otki/89K+UY5pp5yGjze2thCE+CruoNf/AEf4rBnT4/oVCmgDX/mXzr/h6Kpm1iGcWetS/S/rgxTHJ6tHQvn8gwsw/qEEuLqRNYebGtPZcJosG50ap1iHl197ndoqL8VY+CXAz2cMYqZNnSP85v7v93c0MHcVlxSoLJQImNjiwslgIcUjhw/Fl1s2IUDbo4J9Vzoa5kKMevksQGRamxRgGhtYhwKJo4HFT9EmIrJAMgARUaNyGMaFShmWoRtRKc62Ln6Hl3vG7NmxCKHpyROj3C8JMorsPiuSpSCDaG6iCOnFTCbCbyK8zERmJeOScBQeQ0CK8zgafkeNg9bmY7Fi5co8SrBqbFUiroa/ETRTo0pCIJU22mbq2Z5CwUsmVRAiBASToPP8vhnP1t///d9nIZlvQeiVOGJKBrlpv6X77gIPo4k0VBj7Mm2QWOaann/+NkLiuJ2D8JTZ70Sp8Dgoo4yPko5mlNHBqXDKSErRI41NK2pnoRP60ispjB2zLxmXzKcfhGgfvRDbKQhED3oTsHnl1e+nUqEC5Dpp9PuMeOHf9sWocusCDaWXXk+ihO3LjArQIRmi52fLREx/3YFRXY5i/txzxdYJMxM80lIGxcKl0S5OlNZYBi1DlXnA15NZu84qw/YrfnWTpXIYnDyIcdfa0lJ4W4lIDa6symIwcNNUiGRQMkyZoDgic1R4yMQcs4aRqXPivMZSKkbMWQ96UaOhLYuLZQrpvPn/4GgAHgXegHPAyb3luR0GCMnsElC0k8YE4/U4PcIZaH5kOzC2bQh7TxKpGlMV88H1GTNnRA1CzrkJE4WBzjLh7rgLpgu10aZC1HmpDPu7a1pBFs0A22Zu54gYffLZujhw9Gg88/zzWT3Z6rhfbN9GYZ9DsX7L1sTb2didMycOjjq2c1SPHBKFowH8A6+EU0lgiIvij/jod158TMXUyL6OuDyPm9/MbOrFOL9J1Kej+2Zsazkd69uMPNJsPkkxyP/xv4sXSe8fjyF4l72uAzGCxTX71Ju8/+DBeA/H4Jw5c+PJFU9AT6MBp//ok7kLX1/CRTpLI5F3aV/h4DpqmHJ3GgGJNzxseuNForAKni0bv2TP3j/DmF6KMoYRiuLae7MbuF4B1vAaaMeCX2AGRnOxXaMMHFQh0DgDzKTjEe1nvtsQkm9/+D6Ohv2xYVuxdWIq9ewaJgzCiVMRkyhCNoo2exFw/VEKyzGMsjgYe32lg3/iaGDYKj7CSkVIOKvs/peOhgvX78ThU1diR9Ml6mFEnOmD7V/+6z+L115+ib3sUxk5T4L3KnoDUVBHyIsQrNkoC3kHGBkd1WsvH9iPY8o0ch2KSzAwHiYaNwQFvuRoEM6uQkYaWC/hmmmSfO96CPdcF8YrrMVfMxoGagTAIz/84IOs0VBbXx+PsNWpfubMVKBMZZSGTKUvje8evEYa0BGQjl7ap+tcTxVFjSKdJRrURr9Mv/x8/fqYNHlSFoNsqKOYF7xAulcZVcGSd9wDv4zo2KYv5yC/V1mXjzoH6UlE9zfbN73zXcZ+kP2Yq1Y9k9GuscgPnbjiq+N0nVQUMxoIvehAkY+mfFLJTYJBaeXd9TX6beTXvjw+zK0TZuc9/+xqikGOz+cyc0R8F+ryEl62I844VvmlOC/Mk5+jzGnUm5U1GGPa6xaOBjPi3vjlz+O5b70Yz616WoaKpx5DnXsL/ljQVBaWk0+5dvB14Z2RQHkSfWRaPrwrjQvG4v7eMxgYO3ftjuPHW+OZ1c/F/SjRhTFiQWgc1MxPPq7scH1VdnXSipfOT2MFpmpqFqMFiL7gxR5P1nr8eGY07GBtx42fED8gIjWxoSHH3sv4NdY1SjVcWKzc/27fg1CyU+YCI/lDOqxpN/mDcOE0q7sXLsZX8GDrerTRz4PLluNoeCbKcYzdUgmnvYHeK49B11DJN4PES/khbmS2V+JKgZu5xv7NfW3HT+TWieamxnRsPEUm5X3sWXd6PcBMvNRRNWQ4Tgbktutx7fyFVKCVi2XKeBRptP+4S8aHNSuQYHkCEoCMA3v2xu/eeRtW3ZunTixZvCT3frM4qTAj1Fh8HWAMWHwBv5UVWUwOWJs5pvKvgacxqpGh48HaHEaa132+ITbgrDZF3dpEptibYeq6Kfds07aVRxqGKRicOzTgD7eR8dKpeo38JZ35rP2gEaMy/f3N3/4WXfJEPP/c6pjeUEeKPUcowotY1IS5yNd7m7ohN3WIWBvLU75omlN+BpDR0J/9rnk/GQ5MSkUtzp5oi3c+3RC/+WhdnDx+jGOcW3kgYtlUeDE1oiaNGBZVgwnIaNwDi3+S0QC9Z0YD96tzqOvJd/MFHeQ8+c7rHrzqDvLtRr/Bcfl2/zjeeSG27+WUC8DCigWbnWLWxCnxv/3NX8djFEQsh6b6Ad979CnsdLhJs0A8nQHKKh1hBufOECSwuOoJAj8Wr0t6go9IpwbCdDCk0QwOKmtT75CfQTNDWTtxg5uBI3I41xTwIBMNHLW0tCY9eRxtLUHCly0UPWlS3OFUBPXg1P9ZzzToXEtxB3pSzvo5HWt89hQdP0tP6iXqLJ1kkbp1whoN6rSLHnwgHrJGA3isUScdmcEsP4fJqiSmnmogRhtBeGicOg8dp45deSM+Ym3H2aam+ABn78ljzVGNnmSttTkL7st2r6ALskQMg3YZrzoCWEmbOAeUU7z4MvUL5/J1dhz4ZP0tHzYjzu1lE2pqEt/r6+rIBKWGFPN2zeQzbsOTF9uHcPdd53riH+O3j1vIIudvbS4dYCBuXKMw6e+oEbdh7cc4GlZT+JvsIzKEBiMXlUe2JZ9MPk77rqvZJOmoZu1yXsAonczKPvp1GNKdToGT7e3xNvqS8HrmmVUxFdmvA8T2tB/EHRWWIdhcnkKDAMlT7fxdpys38hXYCE0rn+Tv/aUpPmtL7EYveOMXv4iHly1DPj2bhY7vwj9KVxrp4gv9Kw/FlVxD5pE6A+upXq09ojyUJytvzTq1ttguAmDq8c+RjfEgtp+Xv4tbrp84ov6pLeN87UMZrr2TRWhpT9xN3HIc4hjzlpaaWltiMzz+Fszj+zoaZs9m/swV+dxLAEkHWPIy+RkwEuapUydsRqRzRFzUaaduopN3MP12Y5ceJev5IDp2O8HaNgLwnhTzwndeya0TyivrByqXrfElbn/Trz+MowECzKilggMgCyDwJb14LGMcOXIwthDhbT3WxFnwEYvn4PVDmRsMsksYpcI6Mgqf14tlalihVCVVSBtJ7NZEcG/MEDzDRiPcG+oRWesgRJVQDYzpODGqqhD6KBOZFgUSyDiMCMj0/U6EkOjylb+reBXKrsiciwzBd2HwWtzIwn4ajHV1tZm6JCKrBBmt1GPpHmGNZbpJxcL5SzAJC4mbi94SofM+frNvU9LepYbCWLIkTN8fTfqkhJFpXDDjVFx5Vi+eURcVRJUMx6/nViNXpcy2dTSoUCbToW0Vx4OHDsfWzZtiEXumF+PFHUHWiKPROWQbMk9ha6TJduxbBcT9o77S0PYe2lW5E0ZeCpdzXZ2piCrYPE7Jk0RkOgqTkpIsEetB1KhQyJnOpdKl0aZXUgaQjgaVCvrQA6gDwq0Zp1Cq9qOgqzg9sWIFUfvxKDJ4+phnCgHmmIqL6yfjhEk4PuckXsj4ZFDCWziWBJHnNjcifJowoE+1d8SCxYsy1bsSh4bzFy7iSDpiGH8hfFhK/olHRYZAkUqXzh2+06GUxhJza4N579m7J7dhyIytSj5j+vRUlFVw02EBUzJTxchjwaRQwmBIKqK2yVuBKzgYjKTJbFWsPWZpN2eIb1j/GTrvqMT1+vq63F8uk1fwKiBU+EpbghT+4qv9AZJcj2KOGnkUx6IdjWwj5F0orFu//DKaUP6XkolhmrcMdxfRhqZjzaTd7s31n4NWNBNjWEfDeB0NGMMaR1mLQDzkLtdD/M/50rG04SX+OT/8Qwhy6E7hxH0qb3eI2tyGtjuu3Iwvm4jstfOc9/sg11/+2Z/Ek48toxp3JQq3DicUDOagoPP0h0aM3fWffRYNDdOoQvxgwsh1s1fvkTZ8lZwMtpm4wfpJr0bDxBMv8aegK9oGJyxWq+DctX1vPL3q27H4vkVJp7eJVN3tpWjoHU5QYMoq33dxgNwhzdsim5mWyeQJqGW/tjsUOvAo0r2HqNFAmrQ1MHbsO5D9TkR/aRg/MKaOHY2jYXhUDEXhUaHgNYhn5aF61BOQPOFWBnTWfBm9TEeD3/NSyVFM/eOMhgtkixzsuBhfHuUIP37r4uX1r37waqx+cmXUYnC7zcw+TFvtT4TTOjTij9cd+uulv3up2A3IYl5NjY15got0Mwen2gJepnXrNJD+xTfXWJxwzYS1tKYy4VVkA8hzdPQWRq8FEy0K3AyP3Eyk7jI8o5qI1P3U+JlWX5/4plIl7ZciOvahISYfcp2NhIpAwsJIrEaLRVPFJxU9DSYLw24D5yfQdkN9A4qchlFFKnGunUa80TQrTcvbvnY00JftOUf7y3ocrL18TGVa3mY9GOVHI0r0MpSt6dOmpfNOnqcTXNgoBzQQNeR8l9/ki+/lBzA0QZTtatCp7OpgEk9VFD/GkWzK54pHH6PYFrWJ+C0NCp5hGMm3ckx8b1+p/NteHy34mwquSpkyIKMxPKj8cCvS+8inR2j78WWeg16k7ydPZ+zSlHPVmFOGyJv9TbjYVyFHCnqT5qQ1HT1mGlrZ2+0NHcxhGbxmFjzSsTkOYeG9Ko06Y3QIKPfugouub7YtzHDap2NP2qI/YaOj2iybllYq5eN4dKujhQNrOVnB6G5pOw+NJE92/vbhuzghLupgUElMmQqcxFsjfWa9GdHbB864JbS9rS3m3b8oi0F6QoCyS35USsEtUsiL1PTSmriW4mPyIuiAjvObwjS9l/Jjt22fPJnpyY88+mhmxKlVdXV2JW6oHwgTnWAqz2ZvymelLTOzkp/zt06GkkE2nLEPIDPjEAquBW2l1clTazMrzpRps4I0hr52fDNIeYCwse10/gAHnf3CJ+UdOCi9stoZRDD6bTrztq1bcWLMyuyg8WxDytNKaCOdd8BdI0xaLclt+YH4qNGcGU3SL+ugfPB7DT5PnTjXeT4+WedRhZ2xgtOcpk4ig4eU6iEYA/3Y1iHdWKz1+i2M75vgJtsTyglI4ZfDYWIkEj4KyC0K6WlIOkvu0nZLx+n4dOu2+HjTl+h6rXHyzCmXKR6aElFPQc6pI8ti7FAimOBXZjQwl1xFl455/OOMBnHT31wvl7agQYHJt+DGbRwN19nW0U0aWuu587GVUyda+bnkaKivnhh/TnbZkvsWxGgcRkNowMi4WTjCzXaEt1xBWkoehOy7wPaJ7TgG26Gn5whaqY9Jn2lIwSMzK0hY8px6q98LL2k0M3lYW3myvCAdstyn/nQNPmXQx+PGm8CdSbW18dSTT7I9AH0M+ZCOVtZdXiX/KvF45y8N+b18XWNXXHQCGn3ij7K/Uz4AnR5vbU16cz+/x4G77VZ6Egd1Jtt2ynp4zC3GL68pIF0ART4krshDnIcZQmZJt+K4+3zDBpxTJ2Ms2QAPoBfMmjkz5y7dOH/pKefOPBIn7YuXNFpqz3fhLrxdVOt9+a5O8BnBzvFsndD2qKmZmAFD4fw1vucj4jtQ4Xl1EuGkgSvuqxNk9hTjLnQ9jFJ4sbzG4tzbt27KrUgWbh0P3K1foUxVj2cICVNxQzpVD1JuFeMvZKo46fx8T3riIZ0Q6tifMHZ1xeXLlxOwmpA8UEeEslk88RltJzPAvWzfy/VL3Of3ku7qfNQ1DfapP2tMf0Cg9j6cAJ56ZyZBOptsAFhoz/jyOx2YrkXB94ui3MLGzI60zVgP5+C4xDkzOg4ePgxeNuYRwPPnzknctiaZbfhyzRx/wtp+5Om8SrAXN8XT1I+533VxHcRJsz12Uay7F97yLNnmM6Y1JP2l0xzeqSPFLC1hrf2XugfjSzkiL+U7s+LoIh3I/YCNzg0zGqytpF5tQf3TXefYwn0q/tlP/yS+88rLBNEmwrtwOfLcMDImBqhUfsOvP4ijQTgWL9Xy0ufC0YCKCRPZH+tIXdpNGuZ5GPr0mikxtaYm0/PAMADp8mJMg2gamla+HYKX2i8VPC68SCtCZrVrImxoFPm3RpWVt9tIlTQCVg0jqcJoHwlCu+ASoszKCK7KqAqeSoAMRGT1eZHXe/WyipzJCBiTBprFWKzS3IWHa9bsWRw/WY2ywR5Y7tP4NxUqmQZ/234iMJ9FcAlCxJaJyGjdDy/Sp2Dmd7pkf04XhQn3ZFTZtFqZq5dMVUTlgWRsIrHfZXVwFWTadvuCipD3Sei+HJf3ZT/M+UR7GymYhzLluKGu7muG6u+O0XkLCy/fVRadh8pzphQB6mTg/O7aer/CS0NWJmhl1otEp5YvW04V4gkJT+HqVYylEBD+bfsydNtQWXFNHYOAsE+ZiuNSWdGpopLe3NycleI9wnHMaM7ppm0FYTKoPuEJaAVTErqpRq6nkUrf7bPk7XY8/m0xL4WPziOZ22ScXvXAxuJ1mdoOHH3WecowZchuJZHZClvXT5xy7MJe5db7ZYYyLLdmtCI0LWSkk2BqbW1MQDi4huJMriXjkMk5Xx1m1qzwb9dV2DpP4d0fJlcIo0LpUsC2dbSnwBemVVSo1zDSsWb/PuM+adfPtp2vsBXuaeAxBmlCpd8xi7tmzqRHF3iaanukqSna8LQ2gI8aemfI6hGPPIbn+MkOu425yNiZ43E0sKd4fLkZDeAcTJWGgQtw556izQJfhKPz9nKMKoPWL8i9a3zj/R7d1X8I2zjIejpz9VZs5dSJDW2kM/NboZ5EfOuxh2M2DL+CNGWVjtKpE9ySdKvg9EzuarzCszFcPMXEfoVFOhC5T5i4ttLLcPqysrxrquNL3HJ84qh4nw4JfvPyswLi2JHmmLfgISqe1+UY7hElGzIIp8dAFTQ4HnhxB8UIqcL6mUmCcoeCZWqdAl1+MKRsBEX9ItoZ76HmY7yfjqPNLdlPFfJ86vhAwR0bU6rKY+wIjD/GM4D+b1wB766yhQK8G8Q8hKjp9G6d0Nng6HMLRbbEPJiMCf7WaCi2ThBlv9Ybe090xHvNZCpwn/OVYh+9HwfwtPqsT2BapGmBKXrBF/cfujdf/LQYYT+UdLM5rBVwhaiT6a8Xznfl75MmTY4pHpPF3MVj8YwF6KPFggaFr2siDQlz+Zi8U57r9ypz4rM06x7GZviM2ySGQiu1ffSkcDdCr3LlSzh7v45M19h5CeukZd5VsLiJNUBBc+3ltYzDrIAjBw9AQ2OTTqsqq5ImNcQ0mi0eLA2poDguo6ziRknJFyb24z0lhUW+4N8pP8hWOcH4Z6MIqZybVeZ9Ccs+RY6hJi3Kf00v9dJIFX7CSFy3KG+OXzpj4eVLOsJ3khVjNsT8eXNTAfV+8SJxOFsCd4BJ0guGoTBSscu2gYN/f30BD+Hkd3yE7jtiC0ajNYQ0XBybvEQZmXAE7kw9+3L9pCXnoFyVnlxLlVbbKmiKNeA7n/WI5BMn28iiOktBxfkxCflh9N/fSpFRPzsf5bZGtX27sLm3VmeyA2ctCl6DEswaWczS7Rue7iReanTPhJeNHVNZjMcx8c85SNPygkI+F/jnWrqlRryR76tQq9k4ftfV7SGO+TTODKOxE3BgKLvLgY1GueMThuKLMHYuVmp3/UoKvHvInZsLJa7mGtC+c72A3Gtrb+NkBbJ1eHZybS2nUU1MR4u823Ydt7AF0xO2aURitLnG4tU1+hXmroltaNjRUeojpgW3trYkbmssjUWGmEkpDvm8WUk6ZMRfM0ncLuNnx6Y+o7HoOgl3aSDxn0nLMzTE3A56FAfV5ClTc0ufjmzbyDEDU8cqLJNmGZP4Igzkwa6f/WSKOv3oKCzo1IKjN6h5ciGPvR5AiNgTLSrZDsJC4mgg+ocTxcsssms4GWBzRBDLs6Cx3OBGz2UyYjBcGaVZDQwfwx9ZAK8+d6k7Dra2x97mE3Gx8zTV4M0tiFgyCV48rjLqRg2PCcOoW8GzOn3769TgH00kHMxokOqcv/gjEtina5AvPvvfHfq7jeFwizoNbLRLR8OmbWfT0VDOvVe4p4rMjaUPLY4GTtSoZivQSBy2/YDbbdbEtRdu2QMNC3/lmPzM4Erjseasxv8ggSXpyXuN4IpryoLMDAKfHXfKfj77vPRyDadBCcfNuBFnPB5Sh6NyVYfgBXSAsdQ5mDNnNs7+MeAgayUvgd4Lh5RykKKZOLvkx37vGEo8xmwq9RyBoqNU56E6h86RTowuHbY14I0F3f0tM1OlT0eccAXc0KDjk4falzj5tZwR2F68abR6nUN+HEOnuU6U2BNoauvqvtbHxF3b8CXP0vmrjmfbwiJlFOMXJ/3bl2vs+HVkqLNaJ2cXGRmVlZXQKU5q1sw6OV7yGACcn9VDpC95WOI7fTt2P4uMiffM3+9K8k/4uRW3E/3AcVszS3rRCeE4hYlrK19wrNJp2jf05bPiS7bPCJSVfJ1rIQxdkwvwmN04TZVzc1nTMQTbSnNP3uj4AWYhOwo90t/tU/6eco+/Re7MsOxbo8LhfJttAQQLab+2gfp5yA/H5jyFiON1/F7yb+EivzUwqH0gTqaOCmyUt4X+6JpoWxU6uA6wjrb2WLBwYdShc9iea+rlvIWHc3UutiUPdi5F8eJird0eK4/2/pwX/N3aGG577DjdEf2A91xkazWw9zKgpgMps1N5Vw57kqE1c5ybMJeXySOdo05IAS8k79LHFdrvgAerY1/GhrtG8PBgY2v8x//0f8arr30/JlRNRN/VcadOjL7Ev2/69QdyNGgwAUTQR8aXVM7/ie0Arjvs7doTH+HV2rxpIxVI1wfqUsyjss69SyoReG2AIwHAOI2j7AK/LZlDhHZKbSp0RnDPdJyIE4faiK6yL0qPWCdbH9CMRVFR6iKvS7z4ORA7WUldP8VIHBqO5dSxE9HOJ2yjmFw+IKbc92CmAXl84yWqC19qPpmK9nDGMaSKfYwaB7zcw9PDsUFbOi/zJMcc8ZowCkIePS6R8sYV9kteghgQaENHIBCrJ5GeWFmkB6E4G4G7dKotrvC8YBk1fkzcZM9VB/N0V5DjddydvLwaeJFsnBFIUcv5eZ8ijy3bUb1gboxGGVbwylh1fvRc6Ixb3Z2cgyxWIljxvA8uc6YQHGmTrWd7cu7YLTFj/MgUfjdB7nuFU5IUT2A/jCJRYPXAYSNi3KSpOFJGxkWMhtMHd9NusZr671Fv0+AT5ppe7id0vbzm8hpfWxNXz3aw7SW/QowyHiZeVjMuRlTCIGG+Oghkvirx1xlHz9n2uHGZY3OG40hBwBq5kIBvohScv3ovmoum4uHqUQiJKrZcdMVNzoweMAgmMrQcQx6nFEbbYBiYEf9kFjCanssX40rHcYSneFngxGDWbiCpSN1niUz3teub6zCaFyiTsFc8uV6uge+jZ9THGAzX4YxfgeRxee2HqZB8/jJzq6YKbTfrwHYTHhxOheorRCmOAaACa0iFpI2xE9kSc43jWy+xNw/LuYzU/1vdF9h73xs1Sxanpz3XG+X7IoKxc//BIPMzL998uQYum+2e41W6avhQDVENGgUzgxABUXhKwxheFVMqyAipJGJB8VAYuOt8jcj8tcuuHAycfzrL7kG4A4GjSsWh9rNf10So4i77vKkOB9O7RzQYHhpzkaOzJgzMjIYJ6WhgXW1HRQJGrEBWeAg/haYMF1mRl3JX5uvWCbMoemXI3DMgDVjSYBnrmau98UVTe6w/yf46njL7iVIF0DAGIoAgmEeas4UgixMdBrDvdQCM/kz7uThcdBMPc+75iCqK+qHYWYjrJnO+e8NIGVNhLYeMGhPVKDXumdeoO3W8NS41tSTNibv4PPA+4wzks/ih6tDKS34jbOtiMnSAggW0xg4bGyOoWXH1PLC7RM2H8eNi2LgJpG9yTKQKEzjTjZP14mkMCJ41NjB4zLi4TcbKTeByBQXnfA+/AP9KaLJuwoiYMrYiJlYMizEUgxymsAXwVy5wpNTVK3w3nO0MKoyquwh55qTjonAy+E2BM4J8ALAXhv3ISrjLSRid127FruMd8QGT0YkzhJuGlxOp7r6e9Rrq+W76wnkoTlXgKooBaYI97GG+dPhg0rv4QNInKfY4DsdUoaBdjwvnrmRmhFg1ndeEmrFEoijWdxqY87foA2nHAOZSNnZCnrph9oj42EN66PmWtrxn+KRq1rFwNniix23qX1ztYisVjZygDS/XoY7XKHjukAp4AvgqTbhmsPeoUKEhI0Gcu4nR2d11Li6DF8JCmtap4jWudgJzGBEtB8lu4m9hMZNXxcSqpJWek6fiOoBkOegHEwP49wfuw8gasGirzhHTXXu6zsSNLk+1YAzw30HwpIHATSfjVZTbjSdO00KBQ1N4Z3oJi9t9vMkxuV7y1bG1E2PytJnJx85hyJ49cDhlhHOrGonByHhvIJPIvI6hFeOQL+diF/TotXTscOiCKvHM9x48mML7RHR5AZPBI6vYCjgyRpDCboFNi9J2drTFNZQdGYv39EcQ916m5o9zRhANH1cTZ092xN6+9u3Dta1kMN7PNvev8Uze5BpPWXJfZhD0wGPOtByNntOXU1YwFLbSOCYHVRiaZsscg1HBrgr5MbU6bl46C60oTx0z9RZukSU0iEyCShz8jL0cB6MGTBcKbNvO3UmLiht5uP2Lf+d5SZ8MMX93veVjylbXmKWLsqrxyXMudZxL/Bw9eXyePuDWu5uk5bYfOJo8dEIVsgW43ey5FNdYY/mq4HA+9nuSl/g4g1fZaIw2lPneboIb/K3cFi9ncvPUBx7OCKjGj/Kjbd/2ON1tJkAxJscmftq2z8ojbF/csC/5xVgmNLK+lrpUyIj2zoSbB7kpXyYsXJB6h8r4qdYm+OYZvkXP4lUJLZZVjMFJeSkunAS+fCeuSceum7gnXITZUAZxni86+Cyu1lSPifHTZ+Uefp3kPYz9Bjh9g/VVTt9mO8I16NN5VNZPQi4PjsNHWxIuzqme14j/n7r3DrSyOvpwB6SDdEEElI6AIioqWBEEFBUbdlGxazSKNdFojCX5NDHVxN57F5TelKYCKgJKEzh0lN6rcp9nNtt8/d588Y+bN9kezj57r3e9s6b+ZtYsFJpJlk3f0P+I31HbUQ7du2PThijHvGoQuGq7HXvj6pXYERIN36xOv8W5NcYn89SJ2dhVdXHxOpC5agfAXlMf1dmHoIIAfv0yj0kvyHntVm2jHgGIQf9qfJMNS0viO4IrYxFp7cs5SRNpvg478r2MvetqVx+fkS1szWrsHvUrl4sq2igSJKWRffUL5GbhtGXYUf6tbSsADcIOrB//gV0hcsEGfscvAg3by1WJTQjovOWrY8zExTGHiVQtSzUXgq39tNFjVb520P4t80jAHcjrxhXLY/PyJfhLzrjAL9JdGpVBADCjMUmG4NoPXqnXfB+qUwD80E/f8nDyVQNetZo1wCeoTkKPHgb4klawzPl0TIxd833KibIiT1esjo9VpSY6DiBgxeYo4T2vOrzsI1Rtr/rYSEr4oYPrtnHJipQP513voAOySsLGqFsAbbbx2o79/W6LSY4CzdVpldG1jr8U/2k+v/s8zXg1wJc0AblhKf4hiyOf+tQ+a+0a5aN2431z64I+jTr2G3h+DbbGz2mbKu5BnLByQ6xjwCX8rhx67cGrFi+fryxCVQbZrlCV4Bpff9Oaldjo5VEVG1SNhKKgoPRbT4ywDn5Un1RAB2tdS3Hf3esU+HbOF1/GV/zNS1msxrgVqtXi+/AFa7kD/xBjAa8TG1CJWU37ga+yHj92LWNvXb0iduAD71ad/gCbKNuHNxcxzj68lH153mVtzMtnk0+dS90aPENF+sIsQW52vVenbpWo32I/bEx5GhfyPIBm2/iprZF22uByVEmWxU4oq2vxvz9WUXGpK2rUIhDnNBSv79jCroyr7HdDIZfhO9WhS3ViHBOBC/GBN9BjBEwJHwF9wg3UKeoWWV59vHzXy/k6fo6MTMAyKXvKnTJYl2ffo1lrfPwaCQzLN8YgG6DNllVLg17bafPKcgT6buU4BpMAcNO3m2Iy33XNW/Bq1LQBviz9atatZO74eTBYJYJ24wPttPFprT3hWdb6m4UlnJI1P/Ve8nodmuryhfXL1qYNWcN42pHiBanTB/R3n82Xz+S9a9erHA2bt82eIjxa9ohaAUCx6uuvoRvv6BiwlXY7IOcmbINr6/j/+Xrgwd/EeRecR6PzveF1R/Y+eugJs/3nj/9L/f6jAA2iOEWUSs0q+qQaNMzgL5RfTo73aeY1dvSHMXTY6P9XAqEzKQ+qCX+DPG1cG/PXyLr//aXg/d0s/Pef+ffvyvwNKYGriIEzuFmH8dKo/lhXQwaqTIM8jc8mMirEST/apUDUAVWvSPDiHtm1q9bGsh9tdObNWB6nVxFndtMKABHm/j9T/h+/cX0ks7IZCKzxBgImDcCPddVkoBo4A6K0OzEOOwiaXNcfY/4qmeo4GRVovqjxWLuUBi4/1sQdm9eedTkdASuwHYW4Ftp8+yON35hxSKxnlUDy5GqAJy3O//EqKtc2GNNWe+0G0FAz6rl1YlczyL8DDRo1pQ0dgJwVgQYNjI6YlSfuB9cLs0ojEXmcN7cc7AQMWrphG0DDYhxmKpF2zZVPp7F0TTVO/+wFJ1ItwMkEBC+b162JxRvIaPwDgzqv/20ejl+NShMrajYvXxHLeL7/+WI06bVjC8eF7hZN9qoV9XAIanGkWhVAtTLSEAfX5lXlKatrCOBYk4aMpXFgpUqxokFj6l0K8APkhd6umTlZgYbvYIZvN2+Pzwl++8+lHJq/+fny3HrTv9NVtVDh1di6URYjuZ1jJVds3JoODh/9py/v2bS2R7WSuSCoW716/X8w6v/MDXQA9uJVqSagKhkS+wwsQBf8u0f7Z4bP7+7Jf6vVMZtHlhTaLN0GWPZPjlqQlEKwV6M2GgG5WMv+7aX/5Lj/+evpaNeuCji+PVauBSD6zx/4P/6uxUclxB6V7BGAPFHGvWQLQM//cbz/7muua1XobjC98pv/GHT+d5//R94zeKpc1eaVAA0EAPNZ0x/z2psFrsK+ba/12I+FP+Lgyvc+Vj1hn/Rpln6z4r91ZP+vt9ybL1bZc4/Mem5YuSZ9DrXOj3Fp+2qyNawcQe9mgtEt6CbHNkD5MS5td6269bFB31EZtSwDsv9P46JIdGPbIOz1a1SNluiTBtgKgYbdSJIIoma0xGf0Cf59RYN6QQ0sCOwY/JlfCxV0CTRg4wpAQ3mAhjUx/ouFMYtIsRrHI28CKdYWWoUGTk6VLtVP2IU136z60Wji82fgVA2eIQj7Dn9pCUH5LozCP//T197MvXKdPfL5N1gR+SMqYMm5D1mHCoAl0nkVuuB/8peS9P/g06RfAFpQUbQVeVqwbPkPQMU/ONR/+bj6t1oltrLC7x5nvHYtxzT+l0/9/Q3t2T8ia+rgelWo3K5MSgYdv24Vx9D/fbh/+l/1GaEqFT42VF26hiTA/zKiNu0fWXbpXhu6m5DcgR+8kbkv+l/G/0f/pPathk9lsnAtyd8fiy7y2N78p0rd2pkg2Aqgu3A9lcn/4ATvvffuuKD3BTTkbALdrJTPerX8+Q8O9f+7j/8oQEOWgCOQUCS1qmfMqmrBl2nuto1uslNi2ODB8SUlNJvXbYzm+zQqbJ1QFaNUy1GipfNg2Yqd3aey7+bZgYOSWIc2bhBHdz4uTxyw5MUglao5kELK90G9LImxSYnHN2UJO/MwkFHZmy1T5xf2QBVKvCw19XinURM+zQCmZ7fONFhpx/muIKuUNRcaXLnHFySSAMhyHsseDWCtIvih3J/Z+Z77dS1NtNTH41ZsQjV0xGCyht/H8R3a0xxmv+zkasmQpVnOK/ckZelPQUwtC01jxbNliRBZf58htwjsuodlRN9SwTATlGzgmI+iOXDmIe0PjSaNGmXjGUtj3R7i5TIUSnfodl+FzCeCa0naZrKmzkGaue/JbRgGQZazeazj14w9ccL4mEWG4qSjD4827AerDfpqSailoGlAGdwSKffYWqJk2b8lrZbfWfLl2Jbl2pNBC+ueVp/Fo8zstzBi4uSoSeas12m9stSpsB/Y/WA4GNA8aYp1lqekU5Y08UT+3Wfw2bz4M8EqgFHlStlx2X3Wb/cfmIHiIa1a0IyuPaWD9fP+ljY5n9wKIX/wEmlOZJoRfbakv+PiQMgz3ijf55+WDY77aFyMnzqD3yJ69ejGsV3sQYfv5JMi/2fJnZAxl/xZHF/edizX0z1bVv3k2K4Zv8+eOzeee/G1BEUo3IgTT+QotiZN0sFwHPFMXzZ0cizn7rPwT2ef9/EXaWW5o1UdzsuspfugPUbnzQ/HZIakC42WDqas0n4XrqPr5Sg+suvnODaAEvTYxtzWUPXjPvAZs2clHb7m315tscEt6SPQiDLB7NHwH06dsIz9vwEa/CLvp3zyLCx20iFLHCG6lRU7CIa3EQwvAWiYOHcZWydoLMrXigarx1Edky8tY3PPoFtdPCVFWmR5NXP3p2WD6wgYfC7p4ge8r3Ii7T1pYsnixRyd+FWMJSNxzMHt4sD99os9dlUMqTukjbxtCaeyb1llofSU7Bv7Azeu35DbqKrSfZozVnEI4E94chu0X7jsm5g68+sYOm50ZhPOOIETa5o1z9Nw7BZfGmfTUuA580pi0mdfxILFS2Lh8gVSKGrgeNffo2LUqkIGzQZkKLzS39E0kJMtNgCzk3SIfRvWijqWblJhAkWzksEtFDolyXk+OE8PWXKLmkADN6X/RTmODuVEjyVrYiStzv32Wj/KdXSnjjRybJ///mIa292Gf5BI3REdac7FHsu9KLHO0x0YVV1tA1N1gDIrzXPPNgsvH2XZJnRQrtSPhbLxwvfcfjJ48KCYvrAAk158xqlZBq2u9XvKa64Z6yUfqxeUWZuZeS8dcdfGseX1bMzo+8iuJelfUbr9wjv98zma7VWDfho9aBrXNPeJO/ZmqoY8yk+SFPctmyX3HkVQTBm1tFcK/rClj9SN2/gmfj453ho0NMfvcTT82JqySvZxq0vVAdopdaX2SXm1747z9EhH9a+lr1mOL88zZ5/FUk516mIqDD4cQz8jnKxuhx4UrbEfbr1yS4Il7ZZlqvykif+WH+2ubi8QeVrau+VEZyppxfhuRUv+5fvS/pX+g6IZJ5oc3qEjzSP3ym17Zjalv5fjFl6FrWc+k3N0re0/YtbfrQY+o+WhbtVSzyxGnqZMmxJTSpZGF+a+X6vCfuJqAA9FneQYjuX8UrdDH1nVsmvftyz3hzVG/xhkaU+WUWo6a87X0W/U2Pz8ZRecHc0aF7YCuobSBGcm9aT/thTYoLso+1ZqKRdWmegPqIstz/ffbqX7Cp9jyMBhCaZ12L9V0qYea2rpqnOVP7xSLzCf3CLB75bdOn/trcpN/at+UfcXn/Gbb5fn9pOhH03gMxGnde0cBx/YLmpQdeOlLpcurqmXd5K3HVgaF0rF2bqTOgkbCV/6XP60WuzrufPilVefj2XrOUmL755//tm5lUPeKPCJvgzz02Y4Jt9h4LSv0trMbSpsb8l9TfA4CXXJBmTli2lfxmv9BqQe7nHskenT1GOLqvPLOTOWY6at4tm9r8+uLMmPxYaa2hVlVdrbh0GetKxdf+zjz6bGflQYHbB/u2gMv9cma6qsurbKdco8dPemylnqceZbmuYLu9mAgdnaWyo/y/pvplnu5u30u1j6Tbz5Xn/8zvVx+skn0qejfm7BrERZofyinH/Hf75ZxZHXX8+LKTPn0AdjKZnUNYwZsT/oVgMSRy0I+BsA5gg0lAbwtR+Ez+8ype/AT2sYCrq3wIul/Oyuv0MgPo7cQp/tZE43c/zmup1lON6SUydmLIkZpKkrY+g2MqxNDFs2bUw5dmWOPZ5CheL66H3GKdiPZmxHK1QJ2kPHrWPSWNqkXmC+ldlmob51+6XbR11DaV4JP9DO/a7HahJgM2fPig9G9I+SFd8DaFeMU0/itKH69XN7ZfI889TnSJ3IGD5joXxd3sYeu97Qr5BUlLX0R2xeVyF7RXiSz/sfjOWTEccdflgcTl+EOjTBldccUz3onB3DLaluHS3EAAXZUh6UI/k89+jr38M3bll1q/SHJC0nzy7J8c858fho26bNDyeq6Y8pn/mC7x1DXzf1FWMo8yZB1ElebtdJ3cZiObdZbGF84pXX82+NqnBscK/zs7eO9iflSAJwD6tyLaGX5vr3Of6usbJJOJ+X9nkKFV/ZAJDmaWqTkadRH09KeTrpuGPiIHoO2cfEsR3Pk14cy4pr75G6uTwnZMmvSXPnbSVoYZuRsuZ2pg34QTZm/ngSJzFAmwP3qRMHHtIxG9gbw2iz5RV1mDpHeqrz7fcAmyZtpUP63vBWbl3k7/YW8BjopW7vnD07Bn4wLprUqRFHdjyC2GYfjhCn8pF1d27qA+Mm5y397cFh/x7jg/Txc/4pFCk8abuhu33TShaUxBdsM/ycbQRHHdw2j2etT9+kagCo+u+uvbbVMQsXvgXz0z7ltivup12Sb5JvWSP1sc+5ZAkyNv2rGDl+Ylb2nIMP79G41bDNNoaUF3L9tMfM30aPxqbl8dPsz1Tws7FT6iKeBcWUciDhtB8z8TlGDxqWwMghyG6XY4/lZDS2S/sB+MVeFzxw8l3ynmsGr23CdtqnYeizL8U5v7wtKxoaNW6S91BWCp6/q/Ovff0oQAMrlMRMUkhXXi6GSljUbjoNz8aPHh0L583jTPby0aHdQRxvidKEkJZb20VdBrHJiOeOj/lkQjz069/E1wx4yYnd4rTTTsv9QypAz3IXaLAplKcwKBiVCGZt1JRAg0zAJdOo/DK4wCDLeAIJS5Yuo/na6Pjjs89H+5q7xWkXXROdO3fmHOn6CRYIJMiYKkEFXyVVg5JNHR4dQRWkQir/aKTTiUZJuqdz6dIl2Zn/vSFDY+WihdG9a7c4vGPH7ERrsK6gSCv3ymtkbUTjPIvBtv9WSDSizluDrEOqwGsc5s8viU+/mBL933s/Txo4iq7E+6Ng927YMJvl6IgWL+ni87pvsiqGy+dxT6xggwpH4VKB5X4rhLiEho6T2ds+etx4Kk/GxxXXXBEdDj6IPWf1s/dFzgnBdVEFJvLoUEpfBTEEG7ykm/fVqZNOWtlaABU+l47c2HFjY8Abr8Se+x0UF9J4bv9WrRIIsazSPaHpgELTFHrWS6DBQM8mdDZ48Rkge17SRwVemWOZbNwy8oMP4vXnH4/x81fGxaeeHN06d0KRGNh5lBznaTMnvy8fqEikgf/WOLqOKmueLh0V5+tzphHlp3vwBgwcGI+9yVnJfOam22+l8dqRec5zgjuMo3KWR4pGothEx/k6vk6h/Udck9yzpUJECdqMzh4gzz73THzw2ZfRveOB0evMs6MdR1NVwABnh3XkRFUn/8v78mV2LmZuyk1BgRWO7jSgK+xJs3khiPCixfHhJx/HE488TqF/xAl9Loru3brhpDdJB17ln44x4xqQKD12kNe4GDB/S9ndNNbuIxyHmXPnxHh4xOtAfORme1QAaNj9v5w6IRDC1FKpqqB1/uR733O2/CuNu8fnlSbwrQK6X1YZZX22oTw287RWF3w2f0WMpqxGs6Ka9urbp3d0OrxDNGLLQzXKHnVu7WDvfSoghzZ/0rAZuK1im5JramAnL7rehWC4dB5b+/XXszHKn8bbr74ZZ/c+j+PEDst+FzbZ1HFO3QJva0SVXfVNNr2Dn9atXksJNFsY6BtSsybo0G4EZ+xHKl0eJ5LnmDF3foz79IsYPmZsrFoKT553bhxKcFGf87WrY9zKUqK7jprfz5DnAUOGxZczZsTEGZ/nM1qlZXWRUkXxVQKi4gmGga5PS6D5/Rt52gd9KqQtcIGnKKh3lQufOenND51pM54ZQsI/25G1FVu+jy8pO5ywcH2so+/Ct7uI2+eyC+Kknj1SJsaNHUf12ZCY+tnsuO7yPpyB3SkaN9g7qkALAS97m6hv1GMaf2mvLlR2dXp0dNVfypeOonQr6PhtAF8Ayc+/EP1Hj8sy2ZvuuxunpSONBivknlwdT/dq+iDKrOugnOlQ6BgVHWvHltfVj/LrDpx/91Z+RC+g3953TyzYFnHKMR3jTE7EOfjAAzJ4sW+EIJGnVUij7GbOnHVy0/mCnrydsq8TJNP6LDYodk+u+4kHjhgZv/jt76MVC3RK7z5xBKdgNGrUKOVfHWCAJP8IRMnv7t01UHUPsnuObXKpXVHPSCtlcD3lvzYb/RrgaTDjL5w/F2elc3RAx7cBADNY34T+TrmHrsqKAbgOdHV4UN25Eln1Pektn6vTDKYFb/zuesDqL6fPjBdeez0a4sB17XR0njaQzfoYX0BNOXJOBUe90MBT/ZV6nXEE9D0/XWdd+6oeVTaWApDMxgn1lIoxzP/MCy6Iju0PjsbQRbDaMfysdsGxfH5fRQdPx9RnyD44zpt/+9I++fm5JfNiMg1ph9DraSsVT1dccnE6ovJMWcDJDJ7RBYTNqROy4SO/F/WhjqjyoZ8gQK2uE+TV6VuOz2ED3PfefCXGz1kWl/Y6NU4+4fhoghOtrZZn5D1p430EnuwRofNsGS4skvpMfnS+0sY3/bfNJd1H/N77A+KRJ5/N0uGf3nRDdOl0DP2ezG8iszi0CczxvMl8CjC8kXaIf+c4ggbcz+BCvsyGcczL9VWH/PXRR2MU+qZD84Zx+dXXRgcAZZuxZtM/eKLAN2gP7JTVLMpV0Wk3cLfpojpS2ySYpCOtrlkNWPvh+I/i7rvvjwOaN4iu3U/Ep+HUF9aVx8YPI1jUL4Auyo+2Wll3zup97ZvP5rYCe28Vg111v/7IAuzTcPyxF15+PU7t2ik6oIP3A7hrAAAmP2rzDF6U/y34AOrwgr3WZmMXy1Lez3o62+++K/DQduazYeuOWOvpOvRcePWtt+gxszFOObFHtG7J0bV16eFVBeGFnj4vRdYxj21toydMjhEffxqLS+iZQ88vr7b1yJ5TRdO0RpXYqzJAA/cpDT/aM8E14uu8CnSTXr5cN/Uu70rSfAk6SLDveZ5tvNZ/XyZWoZ/mAyhOnbciZoH28ue0c/UaNODo5IPYE147j5meN+2LuOO229N+VIQmPq1HZZclCCoEdsprIcmkbhH0tY/IRkvfmYfNUqvgJ1lB6HrY82YSOtKTEvqPnhTHH9EhLjirV7Ru3qzA7/CDIIYgj2vrGD6XE0xQyuflvfwd3emlLPk37e8yklbvDRoc9/710fzbDX0ujFOh/d40GVaGBFcF7dUH0lCZz2CPcZV1dYF+uHKk7a7AmDar1H/Sp9FWvvtuv3hxwJDccnXZHT+Low7vmD1YXAv90LRN3MPx1AGV2PLmnA1Y3aYjjbQdWks/I4/qB+nbTpr8Rfz5T3+KLxYujWP3bxmXX3V1HALQLk3cPuRSyu95TCX8X1UfGF9a2VJ3Zv8xnk3ATn4vx9yxKvQ0W5MNBsd8/En84annomOz+tHjpFMz/miyT6ME7pVT11a6ryJx6ekKNlA0RsjEKc9nfwrJL4Dnsypr/tueNPYbMr4Z9P7g6N7lyOh0bOc45JBDskmwtlqaq8PShvJdZUwQwmcymakf7uDKmICyvOOxp6sYey5jf/oFIHu//tG8cdPohg5rgw+fCT2eP3kFXSY9PanEJrXaQnlC2ifQAB1cI2mdjcC13dxLgOSrrzidcOKkeB9Q86ze5+TxrvqpAhk7eD7tqfbC5/VyHG2Idq4Yc/h3X+oJX669W3Nn4+t5UtAoGsmuI4a8GPvUnhM8bMJvzySf1zkmMErMuhn97glHFfBPPeZXO8DtYhu86xGkKE9kAu7hPZPXytOQ9/vF4GlfR5+TesTZp54KjRqnrCZfsK6umQ2j3TKqjrUZ+DroNA/7MGzosGjWvl2cefZZsU+TJilzKWspMWqGf+3rnwcaFDop6E/p4Wq4APxPRtjO3uivpk2LMR+Mim8WLWKfaXWOt2wfrZs1x0lmYVEGpf0+n1eADNhHjB8Xd9xya+6b/WmvU+J8zgQ+gGYcOj9bNsK8OBalWWGF0iy3CkPnQeb1/qkYMSIFlJWsg84AGl9DbcbobZTgXX/6S5zYunmceva50aVL52ziohNYzBopwAYxOrY2lNPgpaDKMDCwl/e1i7MCaabLrI7VGC+9/U4snT07TofZPE++NYZTxNPmgAp0Ef1U2crcMnkGjEk7KAEJCyQ1A8OpDCjbFTiRs+n6+9GkifEi56Z3RLF2o1O3VQcNOLJHR0jF53hSM4NRGNlTD1SCGv1CYFAwxgYGOow6xhqfhTTlmjKVypMPR8fQgUPitl/8PI7G6O+Bk2jVgPQwcFDIna9zV6HohKlMFGo/45p7//w3wliDjLfo7ucEqEOHDo3Xn3k+GhMoXnXRhTiKbRINFmjQ8fE7P/ASzyHNs0EVBsy5CxAYEHgPhTyBBo4b+2bF8hjMcVrP/e338eGi1XH9BedHL7IXLZs0TQOmQ6XSS16BSZO2zDdpxS8qpgQaoLtzSO7lHgZOdl22iuQVnPPfvvCSyx5PPfDrOLFb1zQwBp/yg9ij87U7sr+nA8bYGtZcX/gzHXP4Ne/Avcrh9JqFNtj97e8filGfT4uTj2gfV1x5dZ6bbrO/rYA5nmkOFJcvg0adiqQF9DWD7tr6LGnUWB9RXH9XXkRLh7CmPwe4a8N3z7z6qjj5pJOiGfJnF2sdfxWaHJ2dnPnpMad24zZ4W2En688/o0pnREyHDmPphu51ABsxm9el0SUlpYXjLVHUPBuWnOeHD/iMTjxkTAfU+eh0+b78odNg0y63UORRQypuvrsNj5QnpqKB0wAWrWHrBL0v+Dw+WV739L2OkxGOjUZUq1QkWBd4tL+CdDbAUlZcN439OteG//m7E1GulOcMLlasiLlz5sRYjM+Tjz4RF192SXSlK3LjRo0ADzh9hLnIM3Z2N5izu/YmUH0zSTYH3IER8kz4ymxfKFuRoyxpZrKJkyfKsYcQFyOmz5sfYyd9EQNHfhCL5y+J66+8PDoC3NWpWRsnl0aLlXZHn2zjTPvJgFhDyFxx+sTn4/MZNaPSyb2kdeCPCrwBHpDl+UCV0YY/tGvG3mkAkdI0oiyVDvOuL0FnCZx+rTRP3hFo8M1CRcO37F/8AvBj+GyMKTdazZ+8+t58Lcc49cp/DxgwMP76xIOxalHEnTdeH6fDMw1B6EtBb3kwAS/4S7lMg69TAt+pE3XW5CszMOkY4EyoIwUrraKZzHFUjz/1VLw4ZFjsz1h3/eVPcdwxxwAq0hgMEEAZV6dIA2VJPSPP61TYcEkBNjjzPoLL8qpH4Wm8N5LF+/Cjj+PnfX+a+54v7XlCnHveeXFQu3a5dvKYHdx1Iuxm7nOoH7ynTntB/n1As9MQnp8ZlAJi6OytAgzoN3hI9L3v13Fkg5px+vl9okvnztGYoDS/y3wdT7DNYFS9KAvQjwAAQABJREFUUIN9p2bDPI5LG6JOUd8UHAnuxdro6Npca9bcufFa//fi07Ej44wzz4mj0fHtDjwwm02txqFxy5wZFh1d7Z88L5BsUG4TQWmiDKgL1DvaQoNsT/HRPn0G7Z94/vlo27p1nHhcFxp0tcwu5oLFOrKySSGw0+GnURpjGAhoO72HAaNOlvbW8X0G31/CEV3TsX2jCUr7vdUvrrrumugEeLQ3De18fuVfnvCzOqSCQ8pi6nL9AOaozGlfpM0PjjC02oLzO33WjBj70fh45+Xno3zTVnHDNVfSlf+AqEwvDI+X9rixndgHM1AGu57uI+8o+46XPoFCIQ/ykNl0kN+thtRRFKh++cm/xQfzvonre58f5/U6I5o1apQ+gGsofxRefB97qT5wTAMW/Y7UwZAmfRBklj/l2ghOzaHK8eXXXotfAfZ6/fnOO+Lk7t2yuZg8Uwykna96zC9LI2nmv51/6nS+qw1Wj8mXrjMCFhOxrQ/+/vcx+JNJcdx+zePavjfFkVSreGyw/hKTQx9Ac+Q/aQVtXFvp5NjKrPPejgPtyR0pS/g1Nnpdi27VJ7jkxlujLc911jVXRdcux+UJIZLTDuwQImXJ+UsrnXt1rLJuAKp/Ic0NWNJeYW1SpljzhWQa+w0aEg89/lRcddpJeXLKgQccCNBQL3WYQLuBp/xQbO6nHeWG/O78zXojS4y5Yzs2nvmXQndYzbAGoGHq7DnxzEsvU4a9PM4+/TSq1trEPg3rk8WtyOcBDHx2kl9zFy2JoWM+iQEfjIsFc2fFvPlzXao4eM+IRnvWjkbVq0TdygRN3CeBBmycawNJ0rfU0kMOSZHr5m/m/iGxS5Wf5T9sDQRwo0fDhp27xcrtpWLBaiqw5n8bX7OHSRvHqNGkWXOCxKPx6/bMoPfj4UPivjt/BfhwSFRFTpV/ExrqFPVgAUwSaCiVQZ0ypD/qqwgcCYBrawWpBI8mTJwY/d99J56jZ1qvrl3iOuzf/vu2zASeukt/Q/5w6o7LQyL7Ju8EHvQWfNv3C59QDytr1fHHln7zbbzJVunrf/NAfuwueOYsjsPcB6AhA0D4Qv/UNfV3A/gM/LmP+qEAclopRzYcP9WEjKcppWwxoqemvMCaPvLmO3EQv9/wb79O+1EDPWhCKsEXxsmEIGskj0sTZU09Iw1spuhj5UkY8KiAXVYAMd54KgJ+dc89MalkYZzX6ci45PIr8McO5uP4Jujo3RBA192gXD+vMj6CPr0MULB92EV4PnWkehjaS7FN9DKyOaJAwO2/+310a9syTj751DzBozGJEwHwbfjV1Ui86Ffbe0I/O4+w5xmcr7JayOjT7BHAM4Np9ID3FYTRfrxFNfj7r7+F3TszjtllPwzWC/FLAZyQRjKrOtiG2OoHdZr2xRW1Cat0UmblHRu+L6CSRF3z7CuvxP4tW8Wp3btHy+bNs4eItNMnVSYc1/Glt8lCaa9s+Lt8ol5T7rIyi/VVV9jg3KrkUWPHxsus643XXxvHHXVkNGrQICsRE4wlNszvMmbqYseWvvCJPoKJanlE+yuP+llttjw1A54ZO2ZMHgu/HuDgpp/8JP0xG9UKFusnqleKcYz2A6KSDEfWkFftoIJsla/puDweFxso4LsCuzuGeb/63FMx8Mu5cQ2g3eXEICbT85Qk6LIdf0Odrk9RABoA0OG5jcx7DkBDP6qu9mjWmDU7Nxo2aQK1oBgyl4vOf//+M3/5l/vPjwM0QMBkMB9frZqc6i/+4/uYBgo2gq0TyzEqe8LwHQ88OFqCiCH1NMdAuCzxgglrst/b7w8fNy5u6ts3VPV9zzwtLurTJ0trzaKvoeuwzkQFjIPCqKNoxslu7sXu+cnQLKKOgMxsQC2KqaO7hMDr1f7949aHHorzOx4WJ/bsybEuR2V3ZDMzGZDCmDKFQlIMHvNJmKOMqCApOIIXGs5iZlpBNhh7GqdiwRdT45xzzuZs14OjebNmGdCb0TKzZUZTwXRMhcH5SisRXMcvOhMqqgQDdHDJusycNZNqj0/i8ccei5NOPz16HNuJY0KbJlppllGnv1D6j6DnHHc5KdBJxvU+InaOX+i0aokjho/3zEjNRBiH4lS8T+POu++7N46h3M2SXY2oIIzCLGghQpmAgvfAaGgwDAQKgk2puQ4QBkilWIGyXo+BmfjpJBD09+MlQJJWZOl+cjEZqTat08AINBTGwzHhHgqnhtqgvVjRoFIvZmHys/y9NEJeudrusRTaDBk2LJ79/f3xIf08brukT1wAfZrQDdw56ZBbjZLAEXygYTMA8tIZ0lY6V9dUBSXf+KYAS0XQZI91fOaF5+M3ZGG9Xvjdb+PUE06gkoMyweU0r4Ke8okZgByVE1FSkTOOxsl75dpyHx0hEVE4LCpXB0lFUY7DsN11990xbsbsOPHQdnHjTbfEEYA8OogbyJyX9oQVxjH0Kg37SQfXUJBLMEA025dr7H0S5dWYQle3PgwYNSouufOusCj+vCuuwLj1BGhohtwVtgPIexrCVLT8tCGnBqYS1TBrMXbj4DkV4Qx4+5OZ0/kEjX2wqy3qAzSwn1ygoUo5npx5Mbmks7yt7EnbgsHHQfNNLsmbVR3QKw0Oc3buKnbyGLGV0tKlAA1fLFoXw+dtoAESZbD5zYgHbrspeuKk169dJ0rvgP/gle3whjyR1Qqso3KZDi76pbiWKavSBrnT8fB41QXzF8SYCZ/Ew3/+S+qY4wl2mzZunNsbdITckiHib8d0eV/5Us7KYyzKYaxQKDAOE2Nbwxb2NKzbtIYmUOwp4TuzFyyOj774Kmk/b/a8uO26a9F7B8buyHJFmi3VrFYDcu0k6J5K5cDIBBremjQ2nxItmNUM9CeiAzGlfYwnKLGRPhIrKbGtB9DQtkWtqIuRLJVAgwCfq8gFGUsjlwYBAlSCuGX5yWxd2NhB0LiMhlMT5i2OtxcX3pdzvH52+02AuudkgPYGjsq99zyY79936y0g9KdlB/R1K2mKBT9WIsD1WGKDgLwna10MWDIohT8NiHxPntQB0BHVQfiCzMVTzzwdTw4cnDx511//mhUT8utqSijlbx0fdaWf1zl0PdQFHq+oXlaWBZMM0HmDJq/oAjITBjSjCEhvuPyKBKpvOPuMOIuKhv1bt0mgaC0l0lUYpwoBqkCDulzZMkBSP6p/i4GcwIVZl4LMCmiQ/QZ46wdgetMDD8YJLZvEaWefF926dsWJbkjFC53b0TOug/PTWZE9rPrSUVFHaqfcbuB6pQMMTeRJaWYgPmse26jIwPbjddmFveNYSjAPOvjgdGSXoaMt6ayKY6iuUa8ZgAqEGehasSC/C047V/WOcuZ7a5i3Rz7qKD757DPYvGPiZIGGlgANlMFL06KOdP7S2/nriDq+zpx08UQHwRzH14kTJBRw0H58SUJhBA7XS6+/Gbfe2Dc6A4ZbLWE20ee2ms719Lmdt9+Td4rOnYGq7yVQU3QUWSvXeDJbeYYNHxYvvfZGNKTRXF8A0/ZtOQKPdZTPN6/fCHi0FVkhA4psqn99Du+lI6stVcdtwhndwnG0lsCqe6sDhK/Crxg+ckQ8S9fvkYtXRN/eveNSHL6mNBHMrCV0Lmbr1QfOWX3Aj3T61WEuqGuoDnUtpLnVE57qINDw7Isvxn1PP8M3Iv7yizviNOzHHlTbZbKB7zhXbYU/+XLKoDeQXnnxI9eSNw3cBY+sEDBbOgkHXaBh4MRP48S2reLq6/tmgkPfyJMr5G8D00qAdPpO8pDP5cgG7/KJANIW7QiVO9KlDDxUjmB8IzI25MMP48Ibbspp3A4I0/Okk6NNy31TRgXsnK+ylDLLqPKS8p600J4yZ+2dDro/ddIzAEa2ln67PN6k2uM3f3s0ru91WnTpdCzZ44M54o1myfgcWwDrzC46V7dhyht5zCxrqBfmtgl9mVKlBCQ4NYExyyNv29i/a0XD1Flz4qmXX4pv58+Lc3udSRazLTp+n7Tdmzethw8BjgF+5y1eFoPYltN/+OhYUDI75iyal8/bIYGGutGwBsdb0i+nouers/Y2g/R5k4askc/Dr8yIl+/zEmjQ5vnSL5OHdsIrO1iLTaU5upAOvgsBGr4sWRaz2cKwhe9iQaNlq9bRs0fX2LtB/fiU7PqgV1+KX93xyzgKf6wWlUeVmLP+RvIJukTwFCWVczGoU98KpMnjqb+4v53tpX11fGyD4In4HO9j0x/tPzjOOb573PaTa6JtyxaczLApkxulmKvJPNeyoMMLoKWZ42L1gjfM5+Wn/K+/VJUMsc2sBRruuPfe7CtxzzVXx1kk3RqSEFPHSR/1rfKuzk1b5X18j78VqrXQbcit222c75qNVBMiT56KYDD91DPPxp/feDPtx80PPID9ODYDfrenpaxCH3WX9FCEEvRiHINhaZJAGPTRj3KBfMYq+Kr+HAsIc/sdd8TEhYviJySszj3/fI6GP5Bxd9CwFJ5B95ZlfAFrA2CBH1+pb5m/VatWgxobCNbt5H5FgHbl2jUxYOTIuP7e++MkGi6f2OOkOOaoY7KietXyFeixDVmZYZWEOkw9rJ0TSHBu+jgG1/o7Hl3rJd2VBas4vxbUpOJg0KuvxaUckXr4oYflaXlWZReBBgNvece1lY8ErPxdu+vPgs5Bn7muzF9f0CMZl9G48rMpU+MxgOoD2+zPFuIe+NgN0765dvpH2iV1ZLEyzfnJ+6ndmLOAjjSXR43JXFNtyYLFi+Lzzz8H1PwwHqVa5Xc/vy26c6Ry3Vq1srJXeRJQK9pP7cQPup31s5LPahLpIp0URn2+igBz3m/GzBkxevToeOeF52ILuuXOm29Of0xfWV2d4JY0hhbqGDWxdmU3iCCM6fsy0jb8LU/dKtKlMjwjcDcCH/vJP/8uhlMRdzVVCdde0gegoWls30RsuhmbD6+kfSutLdKvEDxD3nnNXbwwXnv9jajRdO/oje1p0Kxpzp8HkHy7LhfjX/di/aDyP3OlpuE/WvEfaAHbJwLJgiMA0774PIYOGJBAw1516kbHdgfHvo2bZAZiC86RgZrKOPehwiwjxo+Pm2CE+Xz7+l6nRp8+l7AHq3U6cmtwRF34cixYeRwKGVvDo8BnZlQlTjmKhliBNLOTjMcNFNKlywEa2Hrwsz/+Ic4HIe4B0HDUUQANe9bLjFIG68xHZjPA8JG2mL3gPR3hNKL5VCgvFKUZabzIVOqWZc2YgxJ89dX4etzHcd5FveMwqjdaNG+eDvK6tXSM5zsKopUE3sMrwQtuJDMrFAaNRcc8zwnnGTwycc7cOTEOJfgoQEP3k0+KHp06JWq2J02mVBY6D+koM6aC6Hg66YniMkc79Cr03qMokAqaSso9TJ5prqAPGjQo7vjlL+MYSq6yrIi5ZjkbY+l0CiTINjrTCrTKKY+74r6Wukl37yFLeEqIQNCnZMWHsC/7RSoD2nTsENde3CcOoOxKZaHTmcALhs0SX7NTKrtEullj/225Z9HZc+4qwdLcp/zulWMpFQ3DR42Mlx59OIYtXRF3XHZ5XHzWmZn1ttRNJSJQ4TiuqZkQwRGfQRr507V0saV7Bkr8rgL37PFZc+bEcy+9GL/GWfR69je/yWC3KqDLujVEfjyrfFh0tkrxHLKF+kj+SRGRytx/p/fmDyqZchoIHCWBhnvvvy/G4RSddEi7uOHGmzEQh2T2WKAh+d3xCUxL7Zq389jJmvr9HTq46eT+/ZkM8i2d3QDvvjdyRFx0+x3Rge+cc+VVcUKPE+gBgRJE+WWJOwaGJUThQ2v4xqyWAMbu7CFex3qMQR7ffPvtNPCT5szy1tEaLKl5g4oADZTwVxNogJfhf8EGZUV5FsTKteJ3GC/fkx4q9jSau5S8WQjloixfshnkDno0LOXUiU9LVsXwEqoI+A7hZF7/duuN0bPrcbFXLU412EZgIguSZXcdfeYsfWO9dSYMbNPJ5f4GSRo30Wv5bQXZXSsaRlvRgDxdcNHF0Q3AsWnjAtDgfAUdna9yKu/4klCus7oAIeN3jDOd17dS0bB151aammHYeH/W/EVsM5mG4zoyFs8pidtv+GkcCdBQk2xFRWUfBl6L0zKFo2c/+HBsTJs9O8ZMn5yd6vfinnXLurefqiMCCYPUHew5trHhmlVbomZdejQ02TP2qOYGC0Nr6AtNvdLB5Z8CUp42YbAh0FDWNeU57NHwDYZvAke5vQXQYBfoIohz2203gKifnWvxztvvxj33/jbH/DXVZeeSDaxLpcc6msTKgxXQpZrjLOdkbO9e+C//cPFZD3/6vutt8GVgagZ+6lfT49Enn4gnqSzryN9vJkjqQualCk5BlizKQ9xDeqtvlXv1mjrOEkbfV7Z0UnVcdGN2ErxUAeT5jnuOhF+vvvzytB99zzqDKo1zcg/vFpxoA68K8FglgGodRp2JpJ0CwDydq7ySThb30UnMIJXH8f1lK1ZEv+HD45bf/S660z+o1wUXxfHdj48GDRrQeGt1bq9xnh67ysdznikP/GZApEOmbfJ5lA2dLHlSnaMdmzkPoIGKuDfffTuuACDpAq+3Rw+79c6jFA0Sc+7SV17kJhnUMe+sFuB3x/Q956tDrd1bDdAwv2R+fIIefgpH8TAy3id36UwfhVaA7PVzydZrn6C1tsOf2mUdRnW6ZNLZ3UygruOvEy1AIvBshm3hwoKjOJy90++8PzB+fsstcSzVfLUJbHw+LyvKHMi5ydPexzmngwtdpEdyjDdj7gbB9sExuPmcbRODhgyON3DIanMM67WXXRqHtD0gagCGVsAX2AwouAOnzkC6vDzCAJmRlU6uI+PBVZlF2r5LX2o/dgfsFWj4ALv32vPPRr9pM+KnVMBcdu452UvKjGU6uLt0VDrm0pSXl7a1SGfvox6WTgZ6xWqTOWxLfIEs4L3PPJPf+dsvfhGnHn88YGO1rOCRxwXR1DnqZPlFOZb2ZnqlkUC/i+Tauj/eNShPNZUNVQUafvvHP8Ygtq2cwukTV5CtM8CQvzcANKsLyvJv6WJVg0Fkrq88z4IUecUMmy9tiralPLpsExWpQ3HQL+x7c4KVtwF+nYLP1KbFvrmtz4pUhLugZ1xT7uN6KpvaO+csEGOzWkEMqJWJmVLcQxugP/bGewPivof/FtdQctz9uK7R8bAO2StnE7Zvq1VBjJN0h4+9DCi+U+86ForABJJQhicLCWKUB9jagoFdsX5LTMGmPvf6a7Fy4cLMqndoj+/Zohl+QFlOBVsFPeWBWlHCqToDRo6J/kMBGhbMjq8Xz8t7HS7QAFjWAKChVhUSXHo13Oc/Aw0ybFKT/5ik8jdnxWPm/Hkz9ep3PMt2eHQrRx54vGUJyPGkGYtiGlsnfDrrEvZt1SaOP+4YKhrqxafw/QdvvxW/vP3OOIreSnWoDvLYYSs9CiAPeoR76iv9QHv4I4MZ1lDrYKXKZnzK75hMzdq18kSLTyZMiHcY97EBQ+MsdMwtV10J0NAyfS8r9QSr5RumD42RJniUX9O+qnP9Q8qr8sUaC8bq91XFZ3BrxnuDqIi7//48FeCea66J80j81N+zLtsc16feyqoXxtXuf4/9NojXbyrDWkpIZUB58jm2sdYCJQJrHmFvwuPRJ5+KhwFj2zGnOwB9u3funNVuK9luoNzrpwpUp5/qPXgV56yvp3+a/js3y+CdYNqg1/fGTJwQd9x5Z3y6ZGlcl0DDBQAN7RJ4XYuOL4J2VjYos1b1mPBQDwrIKv+CPPyCE4Q88EgGp/LaairLBBquvefeOL5Vs+h56hnR+Zhjo7FANfpXYM0qLbfNpj1ino6nfSgC9vlMPLe3d13UCc5bQEJd8wqJwoHoyUsvv5RjUgUaOLYSuukLC5DqS0sbq4X1ZQQ7GQB9g15Dl6qPtNdbfS6AhrIAjuvxA5cAJk+aOiWeeuml2I+TaE5HhzVv1CgTp/pDVsimrPLcaacZx7mmPnOerGXqRu7vnK1yND6zUmYxY7t1YuS4cfG3N96I+2+5GaDhKPidY3fVg9BO66BPpi50zeQhhk/aJE38JRdZ1inolyrwjLSZCgju9oS3ANlLMefbb+xLHHpgJuX0W4xf1FnSWF/S/xmdlcERUA4cP31s1tITvvQ3tB+V8Dms9hiF/Xj28YdjyJclcXmv0+NqKrZbN22WMrpzG1IIfdNep1ZgDMdDbkpDg/nLlsSrJKer7L1XXEjM2LBpU+7MxTzyeXymf/HrxwEaJIJr7H/8P0Lh/ywRgboxlXLrwWSzv6W8sj5o0uEg1lY0aPC2EwhVYMEMvP3eKhA/HcWfY4wX8u1rTzs5Lrz4YrZO7JfB7sZ1ABMIdSkQXEvLVa46pQqHmSON11rGsGzMLQ8ysyVGOncGGDqKbxLw3vW3v0Wv/UEU6f9w9NHH0BSrADQUMnSFQGJ3ggIZZCXfsfGZzp5l0zKxxsR9uwZlKhMViw6BjuLTr78e0wcPiXMJeA/HaLZC0GuARItO+tpmmSefV9hTIGQoxpSfCoEShoznsHGVc+aD2SRzEc22Pgb1ewKHxa0TJzDv/fZl6wT7Gc3+W3ZVVKhWd6j4bI5mJYjggI0hvVLJIlQKT+6N4142a7RHw7DRo2MkivBGHMWjcXDdw6Qz6bh+TwfLQF2FtWMb++d1Xnjl+jN/FYDPIboqzQUadBRtLjdq5Ih4GQXYpmPHuLbPxdGWufthn1Ha6px7LNoWHMYMKnA0/anTmEIqrfl/XsyBG3NEUUU6Ji+PD5j3Gy88GwPZG/+Lyy+PPmedFXtDl006oMyDh+XjZOegg4ovs3e8l4gma+m+W29jhkOFIiot/aWZWcZXWNN/w1n0evSXd8UJnTtHLSpT5Bd5UYOjsnaeOnipaB2QexTBnXS84JVEuHP+ZBFxmj7+7LP4w5/+GGNpHnjyYQfGVVezxxaHyHFtYmXpls70TrJH3yfNCXxZM35LA1baMledCuagM58BB/OsBkiymfm9x3peDNBwJO/1uuqqOO64rtGsSRPooHHimFHWypJIQRWPeRIpt4t2NbJ9ItmjUf6vv/lGouWfz5sjCXJ/erP6HMFIxr1O1Qp56gSEhgZWNCCfLJRAg4YnnQZoqtNV1A/qhgo05NIhX41D+T30FhgpjZH9HkdKoOGTr5fGSDba69LrVnrdd8O10aMLjXZq0XyPN8vBM9wl5V+ja8bVLRNm+qpRxsmK5DNqTPy7cpB6gBJGS73HsDf7tZdfjnN7XxhdDmevPSWMnikv35n9sJTQ+eu4uI3CoCPXnIE1A9KbVeDoJ+ZAQ4Xd2EaxiWeaMW9BjGXP9LvDRsTqhYviF9f/NI4+6MDYk8yCNNi07NsomT0nS/omfTwx5tMMcvHKZemIV4Fme9aqErvjDH/PvuPv0DOlShUaRG1at4njIUuT7dozqnMk5fdk8gQadDrkNa9iRYMgg2CD9Cvn36DVd/C3p05MWrA03qAVOK02fuhQf+P118SZVJA5v0GAAH/79UPZEfu+vjfFuWSk6lGNZsYF4chAKGWUNfbzGfCj06yk0tlPPQCPa1CVJXWAZeTuN50yfUY8/Ogj8SyGX568kszXMez7romTmqGmfAydfaQE8PJdWasQlPpc6k7XUb63qdI2nOkKBEZm7UZ+9FH0ufrqPMniutNPiXPOPZfmrW2TL7fQF2Mn87dKSIexHDpGpz2dodTH3Ic5S1Wfyd4X8pKBtTp+CQ15+wM0/IzA7hiO7Tv78p/ECcefEPs0aEAzYI53U17Rve6zdZuN9kP9q41yL2wxcNSpKQRjqDF0pnrfrRMz5qJr3n8v3h/4fvQ+hbLa7t3iUDKZ1aHN+jU0ezRrxPMLY0h/9bJ6lrcSaEMQUo/J94ImggEV0ZHuD547b158zF7SZwHC2+zbKk489ti0qw2p/NI5t/mY+kt9rtz60oF0G4trqGxJF5Rb2iTlyYo7AYMSxnYP7PBRo+KD8R/FbbfSxybnbe8HsuPMIwEznlP9VLAjNgF2W4xbNHTS4U/lCZppF6WLvSl0Tj/HwR08dEi89+JLUWb/1nH5hb3j0APakfGqDchJxRE2Ywe+hE6hwKzPIO94uY6ZiUVS1ZVWIAokqYcN1i19/Qg9MKDfu/Hy+E/iql690n6YDJHGMERWf2W1HXMzUeC56dLccmXHz2odbKN23IyvTr/O9O4AIe7BfQOg9lfYbYqR4j6ypSd160aJdBW2U2Kb4RPloth3St4v+DNUD+KDuOZui1GBCta5xtooKw9cj4kADX94GCeXbVinHdwuLr78iuiI7a7APKULBOQZoCk/6VkL32uzsdPwjvzjc7DYgLzIK4+bgAPjl4U2Ag3DxoyNS2/9WTTlYxdRYn/yiSexn79FOtECAYIx8mMldLcBr5WhZq512g2udMh1zA0Y5VQrhTJBA62WfLMs3hwwMO5/+JG4qNNR0eP4E+PoI46MPWvXjpVUO+zAV7IkXh7UTsk7hWCAyaggWFMsLKpPcIMML+tVBh1vj4alqzbEF9jUV955Jzax9fTUHidQKXhItGaLQAX4YAUVps6vWs3aMZ+mkQMBGvoBNCxc8HXMWVqSwEp7gIZ9SI41qFkAGspb0SDQoI1DvuQwedXJCC+og/WrrCTD0rtkBVni317aMTYFxLbyJGA4anjuijUx5tMFMQv3w6oz2ufFvi2bsfWFqo46ewA00Fx1zIdx5213ADS0B+ytlZUp29lL5/GwzsHsugkUeV4doi/jliD1lqXeW6F7bj1gHapi0wULx6MjX3/tlXh66Adx2jFHx3V98LGpUnEdDaDLMF31bBHk9dm0Mflinj6j8q+e9z2rbHxCwTWrhs3wvvzbB2MM792NLu59Zq+ohy8pECC4YLl+bp3iS+p1E0EMk+Caukg6pj8DH2b/IXROBQJe9c2XM2eyre+JeIzMt9tB72KLRnd0WSV0jE0FDUptcCiv2JBXPStYYmBqPJC6LH2eQvCun2cVmHy1CVmzouHu++6Nycu+iSu7d43eF12cQIOJvNXM38qg3QHFrZwS5NEmChCoF9TvyqTyaVLJuQsySB31gVvvBn4wKq6/79dxNEee92LrneBac3SNWW8z4G4By623zNsr7RxypTqz6a+6ReBBX0Y/UP2jn+fWgXkLF8brbJ0YCM/3pirrSOIEe/zYXFWZVIdJFyswjG+sHllNlbg2MOMb+Eidpz2yEayAY0WC6bXw1fwF81PXvIAv2KzBPtHzuOOobGoZ9UjSmiQoBtP6u/7bGMl5yhjOU39QkFSgUFpY0SDvCJraJHMu1RhjJkyIJwAy+lJh0/WII/A5ODYcnbc7/GKM6HZE+xnpt6cthUaCvZlo4h76efKmelgbUwWbK78JVA/o1x+g+rWoRKXazddeG4ex9c4tLwIgXn7P+eiDyH/KgakBfYW0UzwXhEvwxTVVp1UgPhA8GkvM+trLz8eASV/FxT1Pij4A1a0BDAqyVIhhBNJQVUlbfewdzLE8en/x8m8AGl6PynvXi94XXhD7NMU3Zz4CtDmX1DI5xX/Z//zzQIOPzrrm5QLzS6p+FqJMKQwKqnUywfEQgIYVNPzYm+xJp8M6pqHaCbENpDSqZRBIBX0dFQ4jCGxuJou2iNGuO/PUuPgSst/77Z+lOe5jKpSSUwaNsNjMK0vHuLcOjc7R+o00NTHIhQl0ANwPaJWAGc9ly5fHGwMHxi/+8pc4u/1BcfJpp0enTp1y64RN5axo0IlVWdnVXmO8liyYAmrZrszu8xYZuqAEMaK7nAu3TjwD03yJo26Z+lGHH1FoBklQ6vPqRBj4plDyfBopXzK5KKLKNTO+zFUlnqVv/M09tgrjJ9Dyr08+mc2eehzbmb1ShR4NFTDECpzBkc6az60wapwFGxRyM0uFcvbCXjiBA8EYje18hHwSmfXBo0bGiBEj4/Y7fxHHgIbaTFHlrkOSCpt1UhTTQeE9wYEEe1AsXqLaKhXBHR0ugwuV9+dsnxk2ZEg8DtBwJEjl9ZdfFm0BYKSjDqa01sBYrm4pfIEnoAFr4fuCDSp1lYa08m47bQSFE71kxbcxEkTx9eefjkFfz4+7rr4qLjn7nNxT7p5vaZ1ldKydmRGfw3VWEUoPL2mhftF4ugYazmLpq00QX0D53f/8C/nZx++9J04kC5BAgwaSz+uM6qQXHIzinPm4c3a+vngA+UVH8XteKq3N3Ocj6P7gQ7+LcdNnxylHHRo/+cl1NFKjooFxN62hVI8xVHhluI0Z/HRsoEkaMMY0CIJIhTWCh+RVvpIotnLQb9jwOP+mm+MQ3jvvmqujW/fu0aIZ+8eYs6VoItdMPZt4mUHQEYEwnDlcNzbCPyM++CCeee7ZLAGeOr+EUci60KOhRYM9OGO8atTmdITKZOAFC0oRFKdzxQx0vbx0hvy3tIHoBboDEumUWPK2CvkSpNgd52i3CvQDIdvs8ZafUIYm0FBwwHKoeOCWG+Kkrl2iQe26lPCjqNlmobym/LCG8p1BhY6DnbZdS0FHHVADF/lTI+QpKzNmUE6Hw/X8c8/H5VdeGV2PPDJBOxuo2t27kG0xCCWbDo/68hEMpmV3aaau20nkV6Y8DhqvUjiwm7j/jLkl7PWcHK9wOkEp7vXAz26Nw9vuH+Wg9Xp00Px5JTFz6pexkn2Pa0rmUVnCfkt4owzsUZH/VC2PY8Wyei504SxrgyP3CJJ1LIOMVyIQLo8usaMFgUqVijWYcwEk85mdu5PVWSwPr1WG3lY0bKWkWNpOXvRt9CtBp0FWkml53XbjdXEuDqFM+/57AwEaHowl/PZvVNic0/OUqI/DrXNrA98CUMozQxM/L1CV/2P+3DINtrykQ6GRtvmbgKWl5Pax+csjj8RTBBgd+GxftiJ1PvKo3IupbpRflFmG4uX/HJB74AwJCAgsuRZmMl1XQQaz1AIN/hxBQ9s+6F6PDbvm9J6UI14YB+FUsHCxdQPBKGNrQ+w3UXTU5SHvoZyqh60SyqwUzoTNhg2WtpBVW0DlVz+2af3sod9H533qxjkXX07564mxV4MGHJnFud/YkAys0cfpMEMX5VFmUQcZUBfkdxeduJ+XPGuX+Fnz5sWLBLyD+veLC3EUj+3MCQUHH5yZPKv/zJ5lWTPjOX4CDfCIo6h3tRcIBL/74jPMWzuwBrBj4cIFaT/MSLVq1jxOOq4LdGkXjRo1St1qDwh1rGC6elJVldRPmqD/oYmur6dKFSs+ClWEZWIe8/4IWbKXy7Ax4+LO238WnQGUs8ePup11Ksqoc86sETRX/2i3DCTlJdffdfbfzrsipal4xjiKAA3Dh8bbTz4d5cnaX3fl5XEoWcaadCS3WZcJi+8JSq10NHtvpk6a+z+BI8EKQSh1r1k6fwo4lAPsXM5zj8NR7P/O2/Hy6PHxEypgLsVRbAFd5ENlqCDsjMbcpYlOrJd2w6RK2vFd9Ff+dJ79jNsMM8sIUH3PU0/ndwSqex5/fNQCPMoTc3g3wXq+p/0u8D7+DHrQSj+dfnlDoFp5yq0H0LMAsn0XE0gQ/BF/xq0TPdsfQDPIn7B14rDMitrjR8Ba8HEnILVgg1UfVkvI78pEPh5rbZWdQEMRcDDIsGTdhraX3fKz2Jt5XnrFpXEqumA//A5B7230gMgMKYOYiWXhWAu2oGJfdeAFFXzplMuljg9zZTDmfRfjE2ZFw58ejou6sJ3nxJ5xLNVNNanS3Ig8GVAbNMoP2mx9G3lHcVWCrIwow+u77+2bga3nb5XQMZuoAFtCtcBnX82Kl956M9YT/J7R82QAmPZsF6LClKBzLVU+Bp5VCd7nLVoa7w4ZGW+8PzyWLSmJeUtL0DMRh9YDaGBrUX2BBno0VADJ8vjsUsxD2jkNfc7M+st3/J4v/pZ+ATKjjlFW1VcmprYgQDvKkwSgcq+EUyc+nlISU8BvrfvZUaoSQEjT6MgWyj3r1M6tTlM/Hh/33nk3WycOiRpVqOBRh7ie6kP4hegt+SftE/d1RfVjFODimpqUQMiiWq0C0DAWMPDVl1+MpweNjNM7d4rrL7s0DmzVOkGLrPIimaeR89lkEHsqMUJBxzCu4lV4UPQMH1FnGhhZBboUMHbQ0KHx9H33x8d87G6Cxt5nnhkN6PGjH6vflbrQRWSc9Om8FwOqJ4q6Pv8IzbSLPodNIQ3spgM0PPzoY/G3NwsVDT9/kIqGYztnsLueMnaz8tlMmPsU/GHmzjoojwnq8izGA+oG6aQcaavsB2EgbJXjrT//OX2MlsWVPbqzrfKSOIRqRKuNrGgwuZFZdnSWduM7bLeVPeoA+dTxlAMBAOmeMsUzuL1vDUHy+8NHxNV3/jIOqRbR++pb4qQTekTjps1iJ7IqcOd6quOlg2MaF0lr/VbH1Ebxh/Rd1Qm+J7ikL1WyaGFWbL+Hn92HJpxHoIPbtAFoYAtCIT4oJDvV71UI3rWnxjfGUIJVvqyS4mZps9WVbn+2V0vJgpKYAJhphVDTvRrGKd260dC9dW77cCz1loB6Vh9Dx9QvrL+XPpNrYKxhj6Kij63vKiBjbOOJFqM//iiefOElejRcF93wCerV3iNBnUxCsUaCroJp6gDXMuMS7iXd9eXT1+Ze/jQhqi7wmmJFHFX1rz/7XJRt3Ch+fsMN0fGgg7Ji1Mq5/B7zc55eypLVX/rdrodxlPFMGcBXFCjb77BT/KlKjWpJmw/HjImXiD8GfDItLj7tlLjsgvOiTfPm+O6sUxpM1tKx+d1tPFuhtzFAOfh5ITrwRZJdlRrUjYsuvjj2btI4ba1KTpkw+vlXv9CBcu2Pc+ViqTlSN0Fg/uHxlgINwwYNTKChUf2GcdwRNDFsQeMZmMZyUxkTbUN2vSL7BNk6MXZs9GWf4SKmdd05pyfQ0BZUzrJayztlAANr+zQoMGYdVSjcNhlGgVLZivhpmBVMFaUCa0bqjfcHxB1/+GOce0SH6ElFQ6dOnWiIVY8yeCohYChReQ2/zg8DZjVDsWrCxytkvxR0syMoFRjcfZ/Fo9UEGmaQyTwX5X00wcv+gCR21t+GAU7HBQFwHBVu8dKAGhQWFAjGmTFVgqnEoY37b20IKdDwZzpMdwQEOKlLF+jYgpK0eumQaHh06jOb4jOkIBacLAXSSwDC91NpQRNLi9DAsQCgYSJI7iCydSOY+92/vg9A6DCOf6kK4OGWC5wgjatTRrmlow/t8zkQHB1EPyPNvYeGx7mLrBtkTCSTNoBM3UOvvxU96VZ7w5WXxwGtW+dYKlMDO8eyvMvzf4tAgyBGoTM8AsffXYdULtBEJWip+hKU1PBRbJ148pEYWbIs7iKYvoiKhgYc4Sgw5feyqSTzde1cA3m1UPbtFFhL6OWjfWfQwmecfyVAkoo4g/PmfB1PPfdc3P/UM5Iwm0Ge3L07VSpV4UlKWvk8TJb8Ls8XAiUUC/eQJwulhygY7qCzq2K1PHU3M/rcb9xEtk78GqP8FUBDpw7x0+tQgocdmgGRWyd0EAUYKuFQm4V1/ung6mCxBI6rM40qL6wTSswbi2ILmPQjc3zuT/tGK+bS55orMzBq2bxF0kInlq/yXS7pww+BB/fl1WzQgIwSe3RHDI/HHn88PH1jOoGxV0tS4S0a7oUDViVqVqJSxHmYxdsJQIPMMTXIojMGXXheOPkHxysBHmRRpF6EXmcbd4ITDagWIsjZvlu5XcdbfhMjABp0wIAK8nroZ/Zo6BoN6tRLBV4WMNPLbIAgg+uaAa7yw8vn8331QOoD1ln++RaemTOHrRM0DvzrY0/ETVQcdKdHg9uQRLmVf/kmnUjGN1jU0VAWyjDHHRgPdm6k7IsO7CyN4eDlnkzLVGfNKaHJ5+R4mj4EtVcsjz/d9Ys4rNW+sRI5m40j8xUgx0wcyJrYkJpVOdUG2pdjboICFEdAT5wxqhWYbOoJOck6ETRErKPKYfGmpbEEn4RdEFGBNOmee9akoovsL3Q0QLZSRB5mKJwH9qJX8khCKoa2sU1qHSXFi5bHAICG8vydYta87rz5hjifvjLe8513+sUffvNgnk/+QN+b4qyTekYDSogtj/TvmalgTf1fXsiQQa461i7P0txGSTajzewI4K/ZWIHNqdO/ikceezwe7/deHMiX7/rrw9H12E55dKZ9DgqOOSbWoVlP1+sH4AQaqHvU+661jqHP7F7HItBg+eWVl10RS/n65TSYu/jiPpmRUj/qyOkolkU5GYRZJux6lykHl8GvZtd03DLzIv3gm91xJkqh0zbTxV1Hrt+QofFzmnkdt2+jOOeCi6NHjx5Rj6qAjWzHUd8IRusgZGMt7uPJCOoxM0o+i/rXV0HnYF/kUbNN6L3Z8McLb78Vw8iAmz0WlN2fXgSebuIWPrNdWbGSNIFA0EAyJZCp9GIzirZDPW2w4d/X4oALrllm/8TzL0Sr5s3j5K5dOY3jwGjcuHHqFCv35JdCHwzkh7GTHtxTJy6rvbiHGW91juMXtk6UjRLmPQG+HjJyZLw7dETcA9DQhYzUXpR/u7fecQw+na/6NnUi99IOCeQZhOm0m21LMAkaGoxUgF++R/bs0TBk+LB49bEno+ohB8Ut11+bQINl5DpzZgIhZPZs8GhelFjqBO+lvmU5MlixjDxLVllXwSO3JnqyzmgcxbfI8L45bmL8tPf59Gg474ceDQINgi+pE5izts6svYNmvyJoXsjoonvgTZ3/dKoZ3+Bl9tx58QKO5P1PPs13Iv56911x2oknRt3aHKnNPNUtHkUsj5gVlXekiXKW+sw1ZVx+S75MOUPPuL1NWZ+ATyDQMIDjM48/qE38BF3vKS5WLugzGTjKkdIH41YAY6QRNEnm4AcSneuqfSqDXRIwpZYrvmV7wYjRY+LKW28PIB8SBH3ijFNOyy03TCZ2EGCZPJEns9qTue4w8IJGztPgs2jvvhdgwDDoFwjkybmLCObe6P9e/PpPf40+J3TNaolORx0dNQjud+AbbsW+Kk9wTEEP+D14yIm7PTebQRLAfU81w+rVK/Ke1QlOtoKEJdDw5cx4ngqeVUuXZDPI9ge2Y9tgI8AL9v4D3pQneVSBJFDJgsXxFtsIXnh7ED0GFsWCpQu4B0DDXoWKhr3cOsH5kwINLBiBYEE3Z2JB+4Y+0mdJAM0vun7yNvwsMKtPaWn6dua+hbltL1eoaFhA1cXnMxfEzFXobb/GeUNt2jSj8uKgqEdFw4TPP4vPPhobv7nn/gQaPF5YavhTm2FSxkpWdZv3r0jAx41YF0rMtXvofgG1reo2aF+jdk3e38Zx3R/Fa6++HE+/NyzO6NolbmTrxEFtWmPK4JNc113H+yGDymraE2jv1gDX1RWQx9MPcT2ggZUBFakiXImP/e5778dvf3l3zORzv7yWrRNnnE4VXoO0oQkswC9FgMF56//J976nLdePledNRqjbN+NfCzTsjj86fdas+BtAw8OvvpE9Gm568IE4rlOnqI6PbUPD1DGM5WkkArvq3IKfU9A98p8vBSB5ERnyeaoyd09ZGE1107UEolMBn644+QSCvz556gQPnECyMqROsBrOWESfz78px+qz7JGDHG9lztJdkA0GyAoTA/b++GNX/uz2OIBTsS/56a3Z86RR4yaxDZB6ExXbgqX6AtLDaSon0lkwQbBXmujjFIAH+IwP+Xz651Y0vPIup4m8/EpceuVlHNN7WLTCzxZo8PNZPYLOcWzlVVrJP8qw+l6da/Wmg6Y/xbzLEyMIkHh6jkfRPkOftab1G+b2r7YADfXpvWEzYXld/1w/yXH1nZQH7YS2z62BJh4rso3je35fTZWdVTeuq6dOTEXHjxo7Lp55+dW49ea+0Q3btxdVMFUq0CtNAISx1CuOpe/uHO3DoN3Q7/BesqJ86bO6plkpxj++/PLLGI4f+yaVlNsbNY5fUnF3OEBDnkgG/Qz+9Vels5e0cfuKW1ytoLcyRl1bfndSM+jGDVs3JV9W36NWbiuxmfDTxB9DP5kel5x5elzBFghjs0wMAvR+hw+/U8AXWvBfNCskYs678fwlVPo//8ILUbnhnnEJMWODBo3wadDVPJ/rxJM5pX/pC95yaX6cSyFIpxCF5v9UHRJs2rQpMRKEc8G8uVG9YhUWuD17jJuygKDQOGaqLRdAZ8agdPSEiXHtfb/OSZ13fGdKX8/J0xUMQu36WgYnXwHX2bTMpyiQ3tuXzKJwZndPhFwnTMeRW+TWifcIph965rk4utFe0bPX2WydADnDsIm2WsKV2RUZAuXh2BoJ56fxV1B9SbQi0GBAUwQa3M9v1/BZY8bHaRzh6H7D1mTuReU1+rkXjTGVCEmvETVzo2C4X16kT4Ch4LQTvSFImmSBhuU4gp8hjI9RNtbh8MOzS75nK9dn7pZJmglSQSuIXjqyju0WCkEM33dsmy9KH+dg8CVh3DrxBU7oyNGjY8SET+MXNxUQP8t9dYr9vMKY32P+uV7SX9qgrc3I6YBlFgN6SJ9sxkKgYTZm8pQpMZxM4JNkeDsdfFBcgTNn2ZVrntlEPl80MgYYVpRkMCE9+N1/O1+NkGssXeyGW6Fqlfhm5YoYNmpUPPLQH6OEcWzGcmbPk7OiQeVf2Pu36/s6RZDfkmDnqOLyWSzj8hK1NNBwHXQSBRsMsF8iI/U4wZfXgz+7JZvXuT84m4UxhopaI22VDcOn4XHe/judXe7h2IUsKcYJZVUaB2kbMjMRlPgBsrpfLVwWR7RtEVdy6oQOkWjqVvaV23TIDKxHw4qkC6pZfpZbMOBv55vjel/Xid+9POrKAGbo6DHR9/5/Y7yIG889K0u93TqRvEIWUDrIA2a80dYJWpSG/lUAWdajYD+iYeK7ZFjNzE2fV+KnYx+MZIM9Kkad3StHNY5FqIDl/W7bOgJktlwQ8HoMV0Gu4UOI4MkIMFAaM4EHL5YzZdN1L4vSrWhZGkYWlzsWrdscE+cuiVGLWCs+S3ib1x1X9sluxG7BKks1Q1Y0wZfFZkCCXBpKNYDPbgCgPkigoXyhSZ5yt3z58szCjgfkeZjmdTf1viA6sad8zzp1KOukeSy0cF46/85W3jYIqoDRK1eRfeP0y9iEQyalaSCO08/z8dKxs4LK4OLjiZPjryM/iKYL58f9N90YbZs0im/nzI25Yz6INdMnxc6lWzjirnTU2qNR3i9BNcYrhzNriW6hVzXjJh+xLszFPb7rmceyzWtiycY1sYZ4ZysiXKEGGUX2GlsqqGO5AdBH58y1LQe9ixUNlu1+y2Ht05asjrGzyZDzd4qy87qewPZ0er/w4DHQTvCPPZHm7rYL+7Cfvyt6pi4LhjwyqjxWeKmvkB/0mDRW55gt8hhIfxdo0PC7FmYy3NL1FY7i0889H++MHhv1ufOdv6IfDPrM7QZ5ggiTlhbKpQ/gOqiL3b9b1APqYtdR3V7IGOHL4YBtZV7jqRC67ue/SHDqjGOPzK0TB7RpkxkFnSrlyuDUgEgH3ZMs1JPep+i8KU8ZPPJsVdIBpfcPNF1ERcPgD0fHg088FS2Y+/nX0oWfgN2tE85dMEB+UfYFBH0OgQafxUC7mK2XTr6cv7pZcFj+mrdoYXYNH0dlwJlnn8nRZO2j5b77AvhWS8Db+VqR4To4vrrdSjV1gEC3jrPbFDKrxj3V2OoyS/w9dcJGnE+/+AKnBrSI7jjnHo/ckLmrgz1BhwmlvUv6/3vbKp0ZR1AneT6VGxVFOr3IyqJFi8gaTcltVv3Hfhy3XXV5HMnc7X1UhfJQ6erzF8F1nUIdR1/qx7StfCb9B+6jPpI+lvMqv9MAp0Z8MCrefP3tqEoTzmtwyNox990JFMvjC+QpItCm2IdAOqlg0vlkrKKOFGgwk5THthGQuUdYoGE8gONbr7wQY+csjt49T2JP+Wkc59ogK0i0bwmsQyPX1mcRIDBjJngm/yvz6Xfk4vMf3rN6yr/PA5x985134y+vvs63I+7rez0n53TOZpCOY6bekmD9jB90D2sr4CI9EpiBTvpXPodZQeevYjUx8/nUqexZfzLG0V+i/d57xJXX9o3DDjk4HWUrAsykJTiFvjJTaoDqS9pD6JRP/SODIlM0bplwi4kl99+yZcMG1Hc9+Iec+3XnnBEnHt8jWjalIo57uzUjKyb4a1YfMm7RLZYX1cNZUQK/GHBZLcHqpr2SN5dSaTBg6PB49PmX44yjOsRxnY+jWvNQtk7sEZsAHXcw/5wn8pR2n+8KSklzhcweDaUZcwe6btNG+lEQGFmhsBn9vHD5mviCKsHXaUy4Y+P6OAVAsG2bfbPJYhWecTsyVw7+qUiVwPxdPRreHPwBfWiWMK8l+RytANT3BMitsztgLQhwFcD+Cmxjq6Dfhu9iJa5LLh19ySFOLQEIaCofupWwPHIq0LAN+m/kRIxtZeh/UbpcLFhF1cWMxTFzF9pLUXs0b9qYDPp+UYf+Jm6dGP/RJ/HAL++Iw/AJTDQY5Ao0WJlikk7wt6BrPFqUgIy5qW9t7skEsU8k46C6QI99bDbxt4mTPgVMfjP6jZkUXTscyilgvalSaZF+dWFLM+X5+Bn6pwkuwCsJoKmQfVJ+d/30n+RLfxdYdvulPRqsaLjzzw+nrrj2/HPjDOyKJ4n4efVsAmnKJ2MJBhT9JvVjwcd2uwN0hGYCVep4eV7dNuNr+m68+GK8MnhYNOb7P73l5jiaLQJuT9YPK/rpyqdjyz/qAeXTYNy5FoE79a9+mc8gIGoVsduTb6FH3EJQ/J4d2tMg+YI4iH4w6gAblNpDTIBaGXI93NqqvnTe6nDnzE0TnHG7UAIN8grzXwfgrj92x4MPRSswobMuviK6HNuZHg17Z58ZgfCMbRgv9RdEKIssSnW3Izt/aeAaO2dtoLFCAazewvGZi6jMGRqvDhgUV55xCsfFHkr80ZommjWS9vqO+jKqb21pgvbcSx5VD3kjq6ggUuoaPpAJsbXM263bk6dNi1feejsa12/I9uFjo3WLFqnjPXkjqxl4foEG11iaGzsIXhh7eOSqIIx0FsRYB3hhs89iFYx9FDwF7HlihJsuuYiK6kOpotwTf7LQ/Fj6KmUyhj5Fxme71tbfsxqBD6Q9UfagjfpXuzUTn2Pc2LHRDxBjR+2accsN10f7/TlCHnmRH6R12jfo6/eMD8ohX1A69ae9IQTYy5KwMcFpk9zt+GhVaxZ8ZCuEXnj2kZg0e0X0AjC9ENvdohHcqc7lJcggzVNbSG94cyf0L4W/uJBtvG/Ql6Jhuza0CbgIeyzQoJUt6BO/k8/ts/+LXj8u0MCCagjVHi4eeBDk+p5Spy9j9MiRMR2DuGnN2th3nyZpxC0JFxl2D47MqLNl1v5T0KeHXn4tSdqlXevozpnWzRo3ZqEMNEF6VN6UxqkIdeDszWCplIyWzi9jqkBcHf5bMHTOi2sl5XJjcURfGDA49yefDmJ5OOi/++E8RktFp3JlsKxuMJCpDopqqaECWsxspgMggyLkCqP9GlQEC3C4RoLKLZo8NToee3QcgIJq1rRpdsTN8hsUVAIVjC/7uL3DhotOVEG0zNsgUmHSCcrtE9xn7fp12dn7q9mzYjDH4bU+YP845IC2uberHsGRTooCrqLTESoguN/nHlcDTtFQG/H4d8GCdHR5ToVSR9dslxnezyjDnDGnJHr1Oo1tGS1ybimMfE5aeiXoAJ18wy0PCmUR4Mj782w6GSpzMz6i4V8z9qefTowhYz+Jpk0bxSmsafNGjVIRpBFgDipBBd7MkaVSKsI8BYT3s7qE9TUL4TobtNisypMbVoCMjkR5/+mlV3N+PY8+gqqZI9KwmSESaHBfvV3UbdjpWtpkzMs1kQYFwKWADKtwVFxmY8y6lyxaHAMBSQZNmJTfuf7C8/OM+OrQNdF3+GYLa6fhEXl13vJqqgf5kMv/qlzNdpcFYCjP85k10rmbNnMmgdezWUbZdI9KBBgX0DIdDL4AAEAASURBVHujZYIKKir5XWVlEzgreXwWgR0BLitqLKcvGFDuyLPw/+R/ZUOA6pMvptBg5+2cx0XHHhNdOneOvevXzxK0DVQTpAPHnG0Wmg44P5UWHZUVVPnMX7iQ4HBmGrEvWUekI6oTXJfnQ7vzs4qBLj/t2AgADQpdke7YdAsGgCgnygAtYcpEvIWgdLhcZ4+3lEYVULbleQ4/a9Z+I17FgrWbskfD6KWIF0OL73pdeEIXus3vR0ldHQJEnPxcWwwb44lqi5hryJSjlWTjDBZ1lAy8snQSfpD3DbrmzpubzRiHjhlPNcNRcfB+bdJoVieoc16urXMvGAgdXAPnKhhLmhZSzrAeR0RHH1eLNSUbSVfysmQDdVzm0KNhAhUNI6ZMi0rz5sT57D9uukftWItjvW3OzGhQvkzsRfapOrSoDO2lhzoHLym1ZlmMWBlunHzkgvLuTgyUYMNmPruOz63cvjVWbt0Yy7dwGg+Oapl6EQ0bNckMwVo6sK/n75k5dEwcnlJUf5SCP9ds3RnTF6+LibPWEExwDnRSNuLcE09gC8MROZfx4z+OflR+rZLmx3WnMeyhucdWGjoddVQ6aDyrcq48CvIUnVz1lTpY51MwURBLx8/3ZgNWvQ0Y+8nM2VGb8fvg5B60/34JNHjWtvTmo+m8qRv4Us4ptyTotOAQCdoqU47NwmQAozxs5pm/mD4jHnni6dwScjTnoJ9wQo9o0bQp48Ffjo8sCdwJUuvUJY0ZTL5IJ4WRlSl7ErglrhJgmuWk29Hxltl/9BlZnXffS6qd07VTHHXEkZSPkvrU/vC9v2eHcIyRV1fvh78pB7zSQXQ+8Jc6M0ExZHoxeth9qnO/nBIdyOy2at0q9mlEBha+lh+z/4m6l3s5b+1Hdfp+uO/Y8+u1v/K/II/0kd7qBvfbes76rLnz4v0B/cma7IMOa09Pkr2z8Z5giPNUBxcb52qTDXxtVKhts6Gk1sNAV+fWYE87r81cvoLtQCUlMfXLr2La5Gk0Kz6B4/L2za0TztHJ+Jy+1Dc6uDqhgoLaW0EIs5HS30tb6/yVRX96xPC4j8bF2HETomzd6tGje/fYlzWtxXHZVXByDXZzbzN9bAyszUip+72Pga5raYbXwFed6dHCSSOewR4NnwH29nv59VjIvY4//DD0wdGxN5k6nU0DO3WujrRyKp8UK1L4eIHG0D3BfNYh7SvPZLWEXc8tJXdr34uDh2fz1cvPOztL+Kuypvocnv5jEkVeqYK9tPQ79QH3kk+Sp+AZ19Znsa9EBdZF3tlAYuar2bPpAfEOMv1NNAUAPrP3ZZyy0gqfw6aB2GLmnWA1si4MDpenbEkbASZ9Jvncl0CD1WxpV3h/NcFAHon6ypvpL53YvTPl2IfHPnvVT1BtC/bDIFHbJ98rY1a5CH4VxxWEyYALnimUwRvc6aN9D+1XxfiJBL1sW+jcdl8qjw6K1mzLqEXW0/4M2r4i0KBPoz7Ivfbwt/y3g0qGzfC2PyF5boWwGaS6eR6Noadx4s/ID8fQVK5qHEng0owTJ+ruYSd7gn50pNvOKpAJX7pidYz7dEqMmjgl1gAyrFy9PCvpwG/pP0S2G5SeggZ67FApiLtWu3pN+nTgt0JDAx6TMDugtdwrDShyS7slLwqyez+BiS1kR9dvBXTbrWJsArCYv3JNTJjybXypLeW7G3ntXWvPaIfeqg3AOZ21/fLL6XHlJRcmECCt5Y0CzQ1w/76e3lteSd8UWgsMuIVHcA0hyzXVvrsdchqA48jhg+OjWYvjsNYtOAUFHxtd4Do6ZwNpt8YofOp1e22p2wvBLll7xtEQqK/ly9QFygU+a+pIdNjj7/RP2e3VrUvKUx2y6sXjJw1E5T99MX3X/CDvOX9tr7o5wS90ouB9eeRCn1I/9uuS+TFwyDCOA/8iaXYJQd3BVn2RGLFxs5l15Sp1CLRSh+kX+LuN4rVXJpakkzZMAEJZcu7qy2mzZscTjz0SSzD/B9StGaedeTaJzpbpwztvG8/aWFhQM3vxoCP0+bRvfD1to7KkriEh/gPPa59srv0JuuaxV9+MdhyP3LHjUdF2v/2pXqlbAEl26S+EJ+crjaziUN9s3Gz/DQFOgBLG8jnyWeRlns2qtfkLF9HMclKMmPhZHH/w/tG+PT1JWrfO/kBm5V0vfZoEfJmsgFU1EmZO3Grx7D/lCqjbXRd0pVuonLcxwsw5c2PU6A/w8epFB3pONWnUmMqsPRJMF+DJigZ0oP+WH9Xv9uPQBsk76gT1eQb1zEPgwe1L32I/BAPsxTPqsylxWje2DOKPudXeymFtkkC4sYj6xcux0l4woPZT3ijqZnnTS1DSCt2S+SVZWT9+9PjYrVrl6HX6GWwfbJrjGWMVbL/yU+BB528yQp/byhUBMpMPgkaCXilX+GjlsSMm4yZPmRoD3305ZpO16Qo4dTJbqz0OXKAXJyx1u7ypPkcj4ODoy8Ev0HYZMe8w+mp06MlR3Oefx5HQTeAd5YAbcfkkysS/8oWvomj8c5dMqwDmQNBDheC2FJZPHJVuolNjxNAhMZny+WXzF0Y9jnZzvy8qM8v6NLgCBdsIDiyn8TioyaPHoiDJnB7QMvZFEOsSTKfRtQQFZY2GQCHCzBjK0igNywgVFktqDLBcFz/vrFQezs2g0X4BVh18PfP/oe5No6u6sjzPIwYhEGKeJaEBxGAwxoBHsA14xgY77EhnOCKyKrK7V3Z1dq/M7urqrq7V1VUZVZGrs7JXrV7V9a261uqcoiIjHGFHOMITGIwxZhTzrBkNSCAhJjFIQtC/3766hPNTVlaQH3zx85Oe7jv3nH32/u/h7LPPMfYLT05ziCSWl5VHik0WiTMdD1CmbQ1aJ7gI50JH189Mg4uIK+AU49WRgll8jiB5EaOl7sxp9gR2pdkYcxUVVaSPzonvm42hwPh9jSuF0DRsuDzrq8yMkKpQtbTtryCvIFh34gKC3tLakhoOHUuli+eledWcYTuVYlgInsZDHsAI4xIFY3/s+1he/s2q9TraAmwOUpLK+6xP0UMkurOzA6O0L9UsXBIFMi244r1BQ2mLgEjzmG8IMAK6KEDSRgM6jElARdr4Pesc+F1XkFvPNqcjew4SIEjpmZdei6rKIFp8z0CFCkJ6S2tp4899OMoGlDSaHKM0tD2NOFO6rPpqwcJjJ46no3s/ZY//xFRWUZMqy8ujqJ9GsqsMg4CQhrp00GHMI50GGgSZYvop02iw52BooMfocTdO6eEjh9ORwyfg6ZTWvMBpHwsWRsq1xpVpryofjdJi+m07Ki86yk+yR6aEBEMdYFNTi5gzMzJ0BC0W9vlnv0h13SmtqJmdli9fFfveVPpWrNVIpMGoNm06l33WWFAx2sdQlDhBEWRQCekEMLE+W3BvampKJ7/YHfI2b9XD6UH2ZU+bNi0KYrptwfl3HkbDiwXw2hBt9PNZP329xt8tdtNF1kgHaa517e1hgMG14fxLD0fsCzs2zWffYfVcTk2YOpc0eYosGTyAr4eYX1+Ki6n89s9Kv76b4jgS73IEtBjC8fNkhHPX+gk0ELRrJ6BBu1mOTkobVi3lpJUq0iQn8h2ezkM1IORJHYdCDPvR8IYR9D4Ur3OpgZQ5Ro4xCzR4uomp4hY4qqs9miofmE8ADHkCl6LQG4pG4z9z9CGPDM0zRslPKJ9BtiAMUHBMJeZxpSOKGAeBBp0AOoPj1ZvaWtrTxa7zieXeNGfoRppB9crRVwbSVGhQUTolleIkTaTPxci6zoz4hSfJvGVBhgxVxC9ZSacUWaFtRkuGBwEZxmZ2Q/eta6mj50q6Du2nk343hj2Vt8iuuMnWC1f4xeAxWr5+jypsPdeoIdGBw1zP+PiUEoBxvcQeYIOXjvkkqwsn9+yLbAcz0JYtWRorRtnKAUYQ/CZto/ATfRLDXMlRtvybMhHBOnhJx9S58fmmJ16A7mfq61J3RxMYPBVDaFkcD+nqUzh0w7Ijjvuy/kMEF5k7M53E4NjCIcqIR7aNAaAh595HtzfUHvgyXSKYUrW0Oi1bvjK2l8knbpcwyKD7H1vv6Leryhpqpu8GzjMf9l0ZG2Cl1KCgtQ40Sl2Rau/sTG0dreBSf5pTWpFq5tfEilFsabJv8IQ0FHvExgEwQmzUeJbeZrWpMzXmvJx6DTGNGfXHGfTHlYsdnKe9GB1SlqYRvROHQz/RFiAW/OIqmn1ze4H9v3yxO/SINSUMwGg8Z7hNUVnGctXtEzi9ra3NZEhMSXPRfVOnTI0ghkEcZVH6W9gs2uaZrvi6ZUL8vIXMSIOoJQI9lCV1rP26zoqxR8t1dp5L3V30vWpBKistJVBNMUh0e+huaKPuoVMxbg3ZQmhkht2Q+opnKAJe4rSXW228zpIVcPzI3jBIx5XNDVnVOHclrYitVq7YG2iIUxDAFR3dwDNorvaX/vIrPYj+y6vaDCPgmz70RwvZWo111EwB4yoWPMA+/kWhWy3U6CyZou5WGPWeGRzqBfHAQKaX2Ta2L8ZLc+lvWrCBBgOetfv2pC3H6gLLXn3qMeo2LY0gvTaG7apHIm2Z8UgCeUGe99nyklwi7cRYM3ds2/lVn7Sy0njo0F62ofZSsXxBmj/fVcZZYdxK67CTDKyhCRk1eAmP8lKXZ068GAP2+sJuK2BulSlrQRgY1x6r3f1pGiLqOO+hBaT2PxTOhSegaEjHyii0iCAe/StEB+o8OJnSRBoF3wD+8k6sjBtE5Wczn3QwWk4dSmVza+DJygj2ThhncUzkhLmU5hLF/oqtaCt0HKn69PEO8tnXx/ZCajSMRa9Kl1HQ8AZ6s7P3WmrtuojN1EbQfiR6qYx+T0mTyIJU38jPBhrG4mj1XruZTjS1paON1Mzp7iID6HKaCPE9sUdzH7c6dFwlPxBjwXYZn6YRDDF4oFNiRl0/+sD5Us4L4QczyaIuEXMwmkaUqX7GcwPH43Yhiz9Ro+EygcuOdAp2B76jgO3ssRM4rWwZOnRyukBK+bmWU2nhslU8szRsCfnBtqS7AQdxU20vj1vrR1p7WpGn3Li44X5yHSTnVPpoX7Z1tKX6MydTG4tKcyorCPCsiiwSJiwLTOHw+nMEMqCROlXX0cUwsSqK6DFOs9d0GrX15HltkpAn9GpLcyM/XyP4XRM1oazBFHYe/XRODazFIhYUlt8jCMnf/NzAp+Mxw9l+W5tBp1186gLD1E9tJxvY7jAyLV35FA5vZdKPUDbFeReoxOPIagDbxDAdUYuAR+0X9LTyZcDc4J0Tp50gVojvx44dJoB1Nc2cXZEqK6tiIdJFJOnsUbq3yLax+KQFov26bblAFjgAfRyLHoK1JYLn4Vd1oIs3pxsa0sGdX6Symmqyayo4np46BMUl9+RUWyC3B7TlomYCtB5E34iLYYNr48lf9NksQeXsmnYaPkJ9w5l06cLZVDH/wVSOPM0EC9QfBhoyW4lROGTmQIwsQtb8fRB5yDFOOROfXQwTDyxeaCBD2jc31rMQWBJBhulT9D+y45vFMOfMl/ypj6N/AFECC1D+QRf5U3oH/oNl6taoIdTemhobTqeG+o708GPLWRyYHwE9gzna19cIlLsVuwT7RnvPMQ8qe9KaOb/LXGrLRyCc/jsG++B9BknOwo/d5zvY/jCZLMEHInvVQDlDz2jK/c6/utmAiQE3t+Zqh0fdEv4WddGQhMiMg/6F8JGB1Pb29lR/6liqrzvHKYNl6fHH1wQ+3GQrjP7qOBantAXcVmoA7Q7PNdDgdqZefK8dP/7r9N0f/Kv022TvV1ZXB84zu/QsSHXv//HB1/B/YJZi8ptdTopOokJu6pqX5q2FApmadIyU/E8oxHGodn/qbj+XpiNUs6ZyPB0MZwVwI5qmgOrgG6TwnG6PszF924iXKez+TYPQKLfb5IwyWYhKAW92kmF+j8ecQtR0MgUMjWKq8HPDWIE0EqbDYTERlZ8glxnGKl6dfJhV0ISJBTvB2rO/BQaLwFRXVAxXQEb4YBIdPPtrUEDm1vDS8HZlJgQaabI2gM+VeY28u6ruMyyKNoXq9pdhsgPQRyERLLPijBjOtK9hYZBBo0ZD8jpGrtsQBDENmGiXPrgiItpFihh9t29qZvt37kI3q6stqWJuOasQswNkDYpoaAiyrmKrkHyWjp9KhD/HCn9s6WA8X81WiOAHtBHsjI6aStzTfZGsjeowPlQ20l1aqoB0hBV+Bdhodi/ROz8vLSuLSKeBBAHQLgvi4VjEmLIMjE5AX4O9Zt685HGSkX7M30OxwTPus7WispFeL1ex7H84LIzHfspfGrPSR8rcYH4uYfxdpgiOBvgUVprnkNaXr6wJhAqFUXwNZX93NdBouHRyPuK0DloT+HOAjTQ6nm/myDmMvxu8yxOzCTRNmzY16KJhaNqU/GJ6Kp2KYIArjkapVVzOq+lrOlMa/bGyw5jdqyZfnmdVvAPjKVvNnBS1TSL1WwLQc8dsW0bB5QXHqOMnfV1BsH3f7Xsod3hTk9Y+qVAaMepNma2ePz8Mih5WMl1p7eTVSG0DXUaLCGp8GQTwdwMOmtzzWQKaNzeRcVAVKz4eWWmK6iB868kt3jsWmTa4wCPjyoBdRwM+HoWsYzCcv3E77Ws6nzY334pnuSPa66WVS9h2VRVpwbdu6CgzzmF+dBQXMLbPNDbGeduV5WWhYDWgRTn/7rz6WOXKAqnSxki5bUgX6a/Br7NosELjQb6worPZH6b6Xjjfk8pnlGGYzQg6ule4f4hVybvuiQXHkPlRYzhBAyV1F94epE9XMRhHNB9P5WBKOYbtNBTMFOY+Ag3yZ8gtHaOjhBPATiFARxGlxvxgNxMkiNkFc5hfXpq1/dzfO4izce1c6uKekeNQigSRRk3DMCPNpO82zhtasoSUP6+rN8CEXk43aLuQdjZlc4fEx7WeLKnlrH4Uw3tXWN3wxBYx14yrmdSuUH48mWAAg0eBvWdYgqcdyOklZNvPZyBLs9lmIVYol8q7mBBGE7QMfOJ3P3O8GnBxogdy5taDcLI0Eof5w74YZBXnxrE6UlZahhOoPBEMon9Y3FmQg3kzwK2ha0BAWdXwncgebI0VjxcWxz2a0wr5Bu00is6RutjBKpkrL1NIV55EPQQDkRowrtaF4cJcBaY5N8i0/OM4xLbAHCgYFd/hJ1HG+THAqP5wy9E1ijFWeaIJq5RiQpYZkn3f321Lg8iVuD76LuYo02KN715imgZVGEC0LUZcweD1aFRpNQ/9ZM0ddZxjV2eIA35fXSDOeuXPt+/igPwuFgS28h0ImxlszHc3Fcmb0R9zabuivDSCkh51ZgaY2WphrGM8O5Vyp0EH6SQGOUZ5JFbNeb4BTTHJ32+CpVegyYXunnDOq6urY04NtKtv7LMy6SVGib1mJBgItQBwMcZtyCnzbxBAI9asBp08V/Edqzh88WIPxuA4AhPzIjNRQ9+X+l+H1xUp+dI++1z1omznnCr7/qwRLk0cpL/L0wZoNbw9DWIi9ob7z3W+Mscl53cdL2t73IwTSW4wrxq2nkRhFko4yvK5/CK9ePmu0d3e1s4JJlexUcam6TNnxB5r5yvrt52iJ9wrv8szZmw4J2KYOjT0OXMhv4Qtg0PhCqzj6IbmrQQPDNiNpy+TcZjH43xrI3kaBDATMmUwXOfIU3wuowO0XRy/8modD/WdDoXP0AY0CGh2Sxs2gfLqiVWT4HdtOAM7FkK0vwYE3daqbWA/3dYjb0vzsdTcUFZ1og002HbmHLkYUBxp/2a3XCHjoIIV+AlkNUKG8GV8t7/K59BIHOqCQpxsVzsN5BDkvkugFw9uDM83QHGLBZUR8P9YCopevTWYmsiAaDh3IfUSaLjedzVNRexGgame1KHeEkHLUVvAG88uCntHR98xG3Dupw0vA5ZjwCSDDRFoYI516jD8kEOwHJ2Vxk2iTsPY1NJ9hUyK5lSH6jOkRlg6zSosTi8+/wzHc8+BLuinK9QxAxscqDIhruhEy4/OrfPtEYWebnOOQNkN5GRBTQ3ZROzLFyOVbTBbu8OtdWaZxaIHWKW8SetJBgShhYFY5cnAnfwfuMyzXWw5jy1pVpOBzQoWcsy+kbbisPaYWOUCkDwc2AmGifHau7oZ936mbYMl4oMBTnVwF/3u7ekOWs4moFJGhpjy7XZiswNckHFufUZgG/wiz3lpY8uP0iFsWHhGPLPv1n3rxXYzo7YX3W0ArJS+zyCDOe+nTK3Ta6Dhq1ig7IpD0XeelTvR2gZMCKfGlEQf2891pjMnT3EKVEm06xGcFlCXBh7pqo2X+zJ0MuRJPPMeed5xGFDLMmHoB+MI2kNLA1gNyOq5DhZosd89plK/RXqLQ1o00sSxhOPN/QZU/F370CCU/cgzseUZfS35yGdYfP90fSNCfyctqKqMk3DURc6V+kO8HUM9ExoMHaJNZBBHHrQApDaBx5MaoNBWsB1xyjmxn93o22MnT6TJZJtUo/+K4D0DMPZfLFQ/SXtxU9vY7X/WclBn+x3bkx+1HcUBx+WYfFendaC7DULPB989PlV6SDPnzGeoo+Sj69jR+k9+Js/pU+r7mUUfWdM8x0t9pZ0s9vSzLawHnr9D0K2SoLzZdQYYfMmP4piBBtuUxrajLal/OgWdoH1h5gtGRASVDY648NdnX+Dra4zzBmNyEfmLhrr0b/7V98lmfitVMRb5BMJEn7L/ibxf3+s3DzSgf3OFEMYTkxqamYlgBgDEgYg+fr51K8e5NZCOwtF41fNTNVkEhTIpE3pbRwRGlcFlPFP3wgnz77Sj4MjEGioq/UIMeJWcK/kC5t6DB9J2Cn2U4YAsWLQwVVZWxWkJGriuUOfOqUaOwBErnDxL5jADwt/pdICTz5BJNeIspmeGxXsUtjlONdSXX/8GwYCKMBRsK6KWMig8kAOpNNAxtl0dWmNkRhNlMoFKQ8yVYpnf42VMS/8x0ayJCNX6deso6jYrAMj2ZErZS4PdsQiiCo7fc2XQglR5hFi+VMAVQt+N5BoIOHj0aPohR7FtZE/rGupFWBzFDgsYArxRUb+jgHoJfI4hAIV7FGjbM+prEEUjUaB3JcajhPYfqE1NzOsmUpEeWLgw6ysgbNsKsZe0MkNBOihAGqe3mZcw3FXCw/f4XJ/nd8+zEtyKgX6STIWp7MdfSyqr+339jsEQVxFukw5uhNDAX/4Mo6veYwaFxovPldb227Z9XcI4bGjgCKv6+jjiagkF0VauWBHpZdJTgJVvvFd+EcgFZ4FJA8/+qdSlmXMt6Dq/QTt4vr2DM+UPHkrnedeQW0b7Kn7nUaMwVo7kTcBV5WDWhw6UfOi8uz3EZ0REnuf5T4NOA0yldOLUqbRr+/Y48sdggMA0E8dOJekcycO+pJNjkF8EfZWvcmRWTR4IE+ydX8He6PYVgPKzL76grsqJtOH119JUHLpmVieOnTwZher2nT4Zc2qaJ2TXZIp3XQJDPUtZ0J8PTs6ehhFYwqoKBles2DEXZjSMxoMex8pTtgIIBeEFBk3foIX8MtpAQ0kcwbi3MQs0aODlGQ3/3VuvpydWPky0eArfc6tKxlfSyDk7cfoMRUH/3/TYc8+l59euDSc5+GVYfqS3NPZy3NLFOVYudX40isQhHQ5pqPPgHDsfHl9Ye+hg2kdK6Jv/zT9iz+yKkCemhlTdPiq0E7giWGLl85IpFDEioDoCWvdh4Bw8dCgd/rM/Sw/x3IVLycjAnJ/AazxzNpb7NZK8xDsGFqm3yrS/sQAWQYZBoppuf8hPetHMukkfewZuprbrZJzwPsh3CuZMS8WkZQ4RaOjuw4inIO+kEjI9kIOBO3xGRsOplu609RjHRNFGFqJL6bsvvhAnzVThVJZgeBo4jlUQaGPvxC+Vu7+I09LOgJtpj47vDLIqHR99im0o8LyBWrfuhAPH95TvwEIGFnS2IcYtX4r/+bzIs0b/vdefzxJUOwzG9yLTk1DgbkerrqykR4wfHAqDBToGRkE/eTlkgb/bZh5g1DBzXjXOdYrEBXHsGLxeS3E0g9RVVVVpLm1bPEsucT5kF2XdtpyPGD/P02D3Fw0M+SOcVMYW28lo2zFcJPiyHXlqOn06Pb12XfTbwI1jUwZ9hnxo29LJfktT8UT8NJXT9M/oi7T3fmhm3+XNs+3t6c/+4i/SBIyg1zdujK0K8rjzJIaLOWKLOG//sjmgNgw4oIPnWNR1GmcaeBqg9l92FD+PshXiR//u36WX/iF7Zlc/iazgGPB5GKvSj591pB2DWQTirXwmPQwiB/3pv7wU9yBrPtQjqM/hkB4EJ882NqTX3yI9efGioIH6WpmzHXVlyCG6XkdaCjg2ZTp0KfeGAQoKOadinbzVSeCrEYw/jH4aCy+/9NJLZC6S7k9/XYFyPgP71DnD9JXXDCpK7F/rVWgf2IGRKwYzPgNKLRj/TfS7lQDVggeXUlfAE5o4q545tL/quIx34D/azIM7uV520SHSc/0bNPe5Pl96uRf6IPx+nrmdgMH9ILK0sKaGfo6MObPvYbM4Sf5HvxxDbF+CBur9kFvkSnw1gGH78r0LQmfQ1/u+/DJwe251dVqA3jbr0vbVSfKd4whHi21+6r2QSfmU9mxbebUAnDovsjLgH+s5SbcvKab3+YcfpIfZYlGzYEGaOm1qpK6r98LZlIntN/2SJ5xn8S/sAdqR3hCC/ol3vsRHdbEp1j3p4y1bUntTfVr//PPIEw4A2ycLCdrfBnsHCSAMYNgPUf+gYAw6tRBHg+95Ss4Aq+sjCDZYL8GMBj8z2HSXTMS2891pD1t+vjh4LJ0/10adga40iT5qr3qh1iIQUIn5NIdSNaWzCRZTJFYnXTtwYEAHjxVVxhT4gt4zyA4jREAnskpogzgDAWICHWMnUqehKDVTR+LLWrLqAGEzGrp4lY+bkP7R7/0uNUhIecdZtKaR8+G8xlYD+h92ELTydxnWRZNz5zrS0WPHIlD1LLRZgG0Qci//8rLQdVzyDZdz6vflSXV0bC0N+hvAyPhFnBJjm+H3+oZ6svTOpjlVVWn1k0+mGQSS4ihTvqu9q13oS2dNWXVenT+mMuwcZSJ+8dl8Lj7Kl26XPgnONCOvjmvpQw+B8cuQZbYWDfOi43cq5Bf1jo5spqcNRLlgQrALrAyMpg++u2jngmMLjuiOzz9P3eDNLIIMD9L+PPheLBQPtKWVWZ/hXBoAcNHSyzalk8+yb+GnwJf222NX5cvaw4fT+z/+cZw8tITs6yraNmNUjHLhRl7nxsAtf5YfDVRLi3guY/Q54rBYkC1cZRlOBtk/+2JHOrBze1r/yibsyPlxApHzIk7qw0gT+yjtDaxbeF6dIQYrjxLOvzk26e53tXkMLLWDkz/hVBazZl/bSMFnghnSRVpLD3VqBK55F3cdr3+XtcWC3H6T3n4p4yze45lDBDHq008pdLh89er0zJo1QTMDXl76NI5fXHAMBh61QW3fObHv2VHpkI+WnQPtW8fhc+WNgyzS7t7OEa1vfzutfGgZf79L1k22Hdj75UFlMsbC92KRjbFLO/2r0AOMyed7vy/1XmTndZxLRw8eJIBdkF7c8HKaV1mF/ieQod9Bvw2O+BKfPELaAIbzIH5Zu03fNsvsYHsW/OplYO4igS8XZuTLLhbu3Lp3mcKjb/+TP0gbX99EBlD2HO0Y20aI47tf5//dl0BDfu6rkwWXyBUhRFCZjAVShU6dDMXWDXGnIASPPfQw6c8oTu+Fwe9g4KusVMwqO1PL3UtmtoD7YhQYDRpuiIkbg2Jwr/poDCiPUtq+68v02batqaK6Ks6MXbBwASA4I8Atov0wnkyrcg7HEUZjBqNdn6lhFc48TpyML6No6BQBpLdYEfkhVYuP1tamjRzrtBDF6ZnmMo5CLuMrYyEYAJEA5H4wP8wNrXBGHSuXQGXhJ8Hd59Y1Nab/REVqo/8vvfACxlAZTJsVUFNgFGAVfe5AWhSsiKgmkkNUn0g3jC5zCyAaGwpp5vhmwYRd7JX7EYL+yuuvh+NVwvcdu6uDRlUFB7+Xg3RuKKpANKgEJZWGl2ClIAq4jq2LaOLOXbvS2bOt6S2Ok1yBoMsD0sXvK8z2X1C1XV867dLGVRINFu8XDPIAhwapkcd2jKz6uvp09PChNI1il69s2JDKWcGwAJXth3PCe6zwSCPGbfumDbtSc+H8haC1gRXbF5ikk/TRYbQK7SleXfDkSooAPo1z5FGe8omgFAYu4xWkHH/Qxb7z+y3653nQXgJXDmYCjeDQ0sxRbxhy53CQ3F/36ONPcPLI0gigWUzIVVJBWyUnULmn2gwXed+06musUhoZFaTkV40Z+6Fy1ajef+Age7o+jLThRezFXfogqedkrDi+MMAZvysVrvZqaLhiNRKe4sM4a17HSEfDuZT+ke2BPDlOQfCDzZ9wHCn75DjqcDpydOLM6bR7/7507PTptGXfnhi3e1flCl+aOgYadMLK+ENFKcWzJpVCG5QZBtddxuSc+DLwMG4sxg3P7sc4UzGE3oHWHo95l20TBchF1/VBjrfsTJ+exXij3WETKf3gf/z99OLapzkqixUPUk+LKcyosYSKiDkzsPaXf/7n6VGcoo0vb4gVAGmt/OhM5YGGkHP4RmPEFRpP13DF289Vg9JRnsmyHNijDq/KU18ScNyzfUf6ztvfTU89sRrrkJCLeMf2hb6+SwQcyFrieyVTZkSgYSQK5wo89tkXO9P73/9+HDG6dMUCjEqCDPRpLA8rhBZeKjH7qjtrkTHnXFl1zfMWBNBAHQX2jSTYMIrPNR774J/ugRupnTTPjsErZDiAuXMoCjmX46wKR6R2VqKuUcNhMnIQq8CkSV67xXa25ktpz8GuKJrmvmCvf/yd76SNzz1LRXmqVLNVDa2fbiPnkWUgTtNfeSxWqeEbletI+P8i2Rq7d+1OJ48djSDdmrXrOL/7yTQKY+0mATllSqNC2VP+NUrFEOfM9sREcZqexz3hmHFvOMi8N9bXpS927GBFqpttBDPiuEcDd1ItgrG0IYaJC8qWWC7miD06Nvnl7xpA4qMvMcfss31g+y4KPJp1VLNgYZpP27NYHbF/9jszovkiD3SODETaqqt33tNHMMfMBa8IAGCo5nh2gSDTh598kuowpNetX58WopvcrqD+CLow38qCzxEvxRL1i+NxFUYDWnooq4Fh9EHdIdaMhP6tTU0UB/6PEfD6FkfImTKvcagxJoZJB+c9nAn0mwaon6lTNabsv9im/nIOxBj1lyuO8vw+sOavkKfnMbReevbZkAfpbJ+cO9vxPtt19d16QKNYwXbvvsGEwH/udVy+lFU/M9jbRpB9F05pA8bot+C9VTjU4pttinsa3Y5BvWPgayR0BSDDEJRW9lf6SBex0r37k3DM1WWtBEd1Xvbt3cOK3Pj0xjdeT/OqqgmSgH3ggPS1/zq7XtLfMajvwtiEJw2saVhrD+jkKY+Oz22Gp8DCU6zUGWhYSr/Xr1sXzoXYKnYwyGhTPDEg5Wr8CN55aBoSE8BZ/ya/SJd8gUCF3Eybu9GrHRiiBpAeRT8tw/Hy+Zf4HlMW3xEbgqf5PPQohnroX2hoP2xfRvV7OnU+S/oePX48fb5tW6QE1yxcmJbheFWygGJ7BpG0CzTi1cUGG6Srcuv3vcx0cltZ6HM+03ZzVX8CfKvD6hGnn/zy/fQIem8pem/WbAq6MQ5lMdKGkX/77T8xwXacC9vUyM/6nc2pto/32BffXT3+GcUdO9ta4rQXj6qcyhaDQjLJbnMqjP229g+WGEXb3EZXQvvwDU72ANt7LIs4lvEY8BZZzQ67Q9t1bOn98LOd6d3N21NnezNZsmQ4MVaXZdwWyC72qNNQNottgei4aZOnkU3BaiY0U69k2aBgHP016GGQwd1qBnbC4WCszoPLKuL4bbIWbpJx0dx9Ke06cC6dgWVYv41Tfqop6vsv//k/jVMmZrgySn+dt0FWVuVP+UX9rY7P7Cm2XGDTnGVhZj9YZpbNxtc2RaFXncT86GF1n9gV9OVZQXtkVmzQJtU5ktcdk3LqfIszrjKbEXDq9CmwuIGU/PnpxRdfTDMJTnnkrrKojGrLhF1Be2EvwTvaH8qQMuWV25A+I3gWGTet/SAB/HpkSjl7mNoxFjEcD89oL4mT0W+eYTPiTARjac/7zTZUhyj3gZPyFi8DFdYyaaTvWyhO2cNzzGZYRbHRxYsW3cPBcBihS/gZtKNNoJxLJ/VU+AzQIgs0GHBE/ui3pyfoBB5AVt/54Q85faE0LX+YlH9kyu0Kfl9bUnlVb2iP6VRrU7idLBZ+6L9zy58Zk/VqshpcBn3Usz3o7w/p+8H9e9NL2MAW751GBq70zfAvW0yStj7PYKOLlkwqWw3QvciT/OJl/+VBdY9jMpunHZz887/6YdjVb33zm1Gzx/ulo30PeaVz4VjzfeXQgIizqcyLc86l8mvbSpW0d0zyxRGw5qcUUl/5GHVvWPyZBj9LQO+xdpu0N5iuzpaXndco6sx3IzuA5+hTaQM79+K0bTtW+XL33r3pS2yDt3/nd9iu8Hj05wY8KZ/5DHksxjKMK86tMmSQXf9GGRDjHLM6wUu/5HzneQLJjWn/3n3I9Oj0W+hWF7EdrzpHjJc+YqW45pwa1IotY+gjs5kHyL43+K3/Z22sGABY0U1gze1oBmHasA07ey5Ehtz61zZGPUKDYdpadJ4+6U9n/YrOfU3/d18CDXCjHBBCB7eFYCla8TOTcRpm++Kzz9IFVninUZ9hzapH0pL5CyAZnAMDwDXcjjOgAuIjq+nDWRFtVkA1bFT6MrEMZ5XXEbyQlnQbhvn8y53scd9GoKE6FJuCPllDme+4iirDy5g5SMt4PktQlCE1moIhhycxPmOSR2pUEHF6F8XWSIGx5wHXGkBWx01htU0BQkHzZ4MhCpwgkA3NPacqUwwJngHHIu2ciQ0jeynsdY0N6R3OvJ2JMn6RKLSr9mGM07DtOWb7Fg4koGS0dRT9AlnxbTg3mn/RNu86baaDe7/90VDdd+BA+viXv0zPUuzHiKLF7NAmARJh7ENvo2YytgabEVGVu6tb0scUYivDOh6I5s0oz8FwyjqIEH+BQ93e3pG+9e1vp6UYQzyY75mhkoGB9NCBVpkFHaCbbfQDBhoo9lUhdwzeJ40G6LeBhrq6unQQxTkV2mx6dWOaTaABrRIAnQOJdFQ5Sp8AQfoqza0A7yVwqehc7da50DHyGLojOKQnjx0nc6IzPUK0dR1HG45WcTA3vw5mGEQCSOFHgdBtJnQ0gmeCFCQPejlGeWoUAMZNqaOpKe1kFbMDxW9g53Haf5BggH8bEpiGQdA5sv0AOhSERDbQYBqkc6+zIp9Jeq8C5455OgJNPnr//QCwxSgeldtMaUO7d6C9YByONXLDA0IxGy02PU/lZqZE9IH5DP5iTDofMBenwFwNxeZxpC9v2shq1HQAsS7tI7J7lGyGT4YDDZWw89xSij6yUl7MsAsLMDYGrnMEZ+Ksb5xstnkgwjgAdJz+2G8DDRpfGmJeBhq8IluEz0dw323nij3X51h1P3D2Asdboui5B3Ud15/8kz9Mrzy3PpXPRE5GYAhxr2OQN1Vaxwge/eydn6YHlj2YXkCxTWdl2uCPfdBgcc40flSSOvXWhhklz8CvZjR4aZQ7N9JIg8B5V2FbQHI3gbs9n29Pr5Pd9CTKk5xNvsH4MG1v36GgHXw2oBwVlfA9FDrtXsfZ/HznzvThv/h+Ws6dS1ctxKjEEML4hXSRlhtkshWfi3waaMj4CgcaUOxjKayfvOaREWhAefJ9jQAQjK0T/amVbIq2WxwHx/duT8HZm4mRNrogXWT/cl//FWhFUAz2mTS5inTiUWQ09KZ9hy8GXfNAw//y3e+m18GgxQsWgHEYBIz7LrwYGSnMnU567qBpLMTeUfizF2W5H7rUnTwZWP04OPMESr8Aug4QaJCOyrlyL797Od4sYOE+azNtMqzMHT/HXijOIQOtGLibcdavENwrg88feeSRVF2D/qAp95Uqe2FQQzvn1DZymcxYgxv5T2yMYB79Cbmjf3fAuD30ffunn1IHaGZavHhxWsD4pxFokKfk2ZwX5IvgHebHGbf/DiecYWRKPM+w2KFCH/DgEg7ABx9/lE6Q8fHUM2vjBCKz1jSATQdVHgN/6bdOkt836MgAWIG18K2ZIBhB0DsjnUZ0prMK4M325pb0lz/8qzD+xMiy0lL4LjMspanzFc4IhDCYoKw4BlcB7be/h+xDIA11x2C/1REWuK2lcNm7GIpPPP10em7t2th+4OxJY2kohqtfpbc4ZvDJOfPoap2X6CtzH4ERMMjL1GP3E5u1totAwGkytL5JoNqz6u2bToM01wD1Ercdg2m8EkGaiZHhBPCZ9HGCndtR0JU/xNY19YdHbloz4PXXXkuV2AcWuVNXaBQHP8ILEfiFHgwrZN15lV98lpfj9CUPuy2qD2f/BLwe+8TB+OU4LmJNEQb+LZw8gzzOgd/RdhHfinR27T98AdGyRQhxgs/kFe8N3YvMtbe1pT1k2LS3tMSpR48gS8sJBnjEqnqTm2NeeeNHaGQ3aSOXTeVNfPPdKwx1+KrAeaU/ni615aOPg2c8+s5Aw/SKuaG7B9Fr9t/vhlzZXy7nO2wLZEB9K69EdgLPd560gYrQdXcZ22aCGJ/+6oO0iqP1DLDPwQEzeOYYdXZt25/tf/AN9LF959Vn216Mi+dqi2i3FYB7/JI6WWXUHuvpOpc2bnyFfdzVBOoJCahThsB3+87YbzKd/Xf5bLQLHGTogLWDZJ25fSIyGpjiwG1pwklona3t6d2PP00/+sUnOL5tqaGrNQINVSifGVNJhWb6JnDrJNi7ZAwFSAkyjinM+ETMCWee8cg7cawiZBOj80CDes8sPmYOTUHgeERRuk7/Wnoup9pjHLVM19UkLl9UlExK/9ef/Ov0NM72ZNPfoa2FGe8MGpiiQ8ylxwOqz8QRseIyuqnlbEs4Xp5O9sabb4YtLJEjYMZ3vHLbzLkNuYKn5B2DDLZnn7OgFDggvyKzgwSWDDQcR7ceO3okVeGkv/Ha62liWRmpfBQY53vaWM6rwQ/nVkzJMYLm4+/M+D2ZVv4CK6H/Zfb9799fy/aDE/RhEL55nIKjT4T9PUT7YoH8Jb/5jJBDeIbm4m/+XdvVK5OljDfN+IJ5UktDQ/oUZ/2igQb1B3p7IWOQjkNibMgQsgpuGDDSJvCoTu0kxyN9QhayJwQ9xYpCgpp0LB3ixJqf/+TH1EOYS7bEslRFQHOG/geX+C2+O3YDafKKAQV1Z25nOkfSXGzhBw0j6EA2Gv27yLbLD7dsTsfIcHrh5ZfJ+locGQ2OLcbNd4P2jMH58+ex8Ix9Ntgr5uvoeonT9jsy1uQHfm47y4k4P/tZyPTGV1+NIuHi6L3gtmPgGVFbAPrYrngiPaQ//4+XcxILx8xrzMswxhlo+OAXv0grmc8X1z+bJrItkQaD5iHr4gHtyyvKTo7zQ/C3wVL5JLCUeXcsoYN5tvaxQVdPRNuDD/LNt98mOIU9xv3qnhxDcjtAvpJeBtJjizC6zTbUd3FykrzOGBzfEH9ra8v8j9p9+2Mh6rfeeiuVYxeI3Wbg265yFScFgjnRDrwWNhF0uQ1fqX91aJ3H0MHqKX7uwyayTuCJM2dSO/6H9c8sbr1244b0Ipl3U8uRK3gveIHnOKav+/WbBRqU7fylTvPnr3Kf6IoCP0Vq0eYPPwgHbMbkqURqHyfQUBNKQacy0rSGjRDTuyxe41EieikyfbwgtmDjKRCeM4tbCoOTggmz7cRY2UVqlBkNKs75tD2DqJ/MFtEn+uB3XSGLvYIwgKDiJXDJ8F4ytIycKUSVZ0rnAKetBklgiCdYpauqJO0ZYYlgAgJju35HplPQVcL5ao9MKz3yAIApU4KaDB6rMIytvqkxvY/TOIuNf8+uWxeFYTS2cvDw3ZWWzDDEcKRN+5n3W+HUiVSI8nR/hdPVcvfAunpRy6qRhuJjK1eG0hZMNVwFQNuznyp225SmXuGQ8u44dXa9pIuAoPK8gkNqoGEXK7wdrDK8haAvYSXUCLROuEAk+NhXn5EHe4xGa7BIA8fk2AwQxJhpW6PRKKFptabq1eIETMEw3wQIuk9KY1ywDmXG9xRwFYLF/QxuZO0IlCp8U7GgDeNy1c17i4mo3oBnDsOTJ0gz7EHJrTLQ8PQzaRQG/h0V5zA/OKfSNQCRz5znMAZ59zII5tyGouL33CltamlOHqXT3tycSiZNTo+vWU1l4aWxJ85iSnQ+aBIKk7buPcNx0dcAXmgm7V019e8aaIWs6vHFdJQgwIesGJn6tmg40DCbKKgAKO1Vas5TpnQzBRoODX10xSVWW3mX/hqM9sOiWGMIwhjp3fLZtrQPxf/Us+uj3kkj4zlEUObYmdPp00P7HXpagi+0oLIszZkyPk0cy6oUdQCGSN0fjfyOtU4B/ZRPTH0LmKT/kQYHn8kP0swq3QJ24RhWOu0DrJc51QWpg2KQh9svpW1tA3+jRsMP/qc/SK8aaODYo9E43hb2s4ZF0A3auX9Xo8LslycxWDyuUp6TX0IRB4+Zmse8DivOXztx6gGCjrzsn/cE9tBfecAsHLdO7N25Iz37/LPpkeUY/+z5vUUdhNHjcTKpg8CII9AwyBYFIWwsYxxgZWEPxsiOH/xJWgztFq+geCBEmcC9cG+k52bGCJDIlzRaTZeVTq64kYCQLg1Y+JHp1+Dmszuk78EpsU+wj++0XLuYzt68TDouEXkM4n6KUw6MgU8xyAbv4NhcCpuLgCbZG3cnUNSTbSD1GpbUNHBCuf7nb70dgYZ5sZeS1FfowBJMrG47h/Yn+sS35H15TPp0nu9KR3FeWhoag2bLH1nF1okVcWyk+9ilqca+PKhsi2l5mrd/C7yFDzXKYsVCvmG+JpANZHZL/amT6f333ks3wLMaDC0rabsnUzmyuJdjkH50iY8yPBfLYi7pn3ihTCmfGsCuRIiVrlr5N7N1tn38MXuv54T+WEAQQ75RNjQWdIztm1gmPokxqjt5xH768lnuadcouEWf1C9u7fOUo48wFI9isDyG/li8aDEnPpTGqpmGZ/CY8kB/xS/pK976rnHkoMQxnUjVq/3QUTYg7+ctba2cIvFByJhZWe6dNlChnGdGfzZvth8BSGnBz5mDR5COdux/GHrKIC/HZ79c4Tty4njajFNq+v4a5GkqAajgC/qiTMSL/qg3MkfXoLL8mc2n+CgO5UF+ZVAeMEBxCbnYW7ufgO+x9CoZdytxeLM94XeicKWBCgPFeVA/HCbw0oBX1Oag72aMWQDWrCnnUn50r69b53S8DtYeiBWyVza8kuZVVcVcZjSXX+AVMNC5sl85XdSn9tdxhJ1A/9UdzrFbhgw4RqABTOxsa0sryJ56ASN6LKtlZm0aKHHlzHHnOsnv2560Nghgu9JZnpKGrk66b9jveab8PvReG0EkHZ6VFGk1lVx9kC2OZI6VmCdTyBP2PXe2nMOYA3mTf9od8j7EDQP2CFmCH/3qV7ECtwS95GtmWVlkWvQRAJKvInvBvkIfdbf08ZL/rQOiHrxK5p1Zl9LFbDj7bq2IbaR679i8JS3H3lj6wJLg98ksMAVPMEf2WzpoweU8za+hs2VI7wscYE6dT7HDYn4uLpnq/Ytf/ZJiqd3p1Vc2sO+7nOezymokFXrSpIqZ7TGeCoRMURTCdGw4EuWC/XSXVV0YyN/voquKkZWCqdMpTNuZ3vngk/TDn3yQuq90UT3+XAQalhNvLSdoO6NkLMc4s+8dm9Y6Dzr/Pi7spqAzP/sZc6xN48g8bUKd5+KB93uEM5JHcJdMoLvQz9OVLl5Jx+p601nMAjHlMq85RcXp//m//zQyGopobwj6j2A81opwy7D2nQ6OW4A8OlbHyZpT7lU3Q6gLPP4mW5GWYQur4+Q1nU2/p9zJQ/4csk6/GETwoHQXR8OmZE5jAY62tSncOmEW6B4WUKoffDB9C8drell5GsIO9BnaFAw+gjw5P4qfYpXtO4/OubwevyPH6oAxzG1P9wWyyvan08iTuOzCjyebGES5gqPtlMZWCX6KYBRtKsP21wnP+xvyxSc+x78pc47RxbzPtm6jQOKFCDSsQD8tWrAwaOD2ZmnhQlJu+4q9PlTs8nnyfowRHrfPYk/oD3mHe/ccqE2fIE/WsTFQPQc94h79zPaFnrTvmG3H9vJ+5QFVdVKmW3T+hzMJGKOZRL3ovM3btqaTRw6n9c89H1uolD+xQLvZtp0zsdHFwbCpfR7t+Jk2q7IvTfLFMY+TlKbyhfrj061bY+xrWWybRT0YBy/+ucgo/YIOtHWP5soqvKzce+U2pJgRRfl5tn6A24vdcvvZp1vSCnTHemzsCWwljm2yyjhtSj9f6ucsuCjtxYDsecq2z3H7fJZFRYAPXnYsF7DdXUh1e9wGAl+Psf3ZgHboe/vLM7K5zGhhm/KI9PLZ0sQ+GwSIsTBWFCc4eDNkyay1I/gJRWyJe+PNN1Jl9TxsEGq18Wzpb+234A31NG0p9XkQxvbNdJJXHJtz7IKtGdd9tH+qvo5tvqezml/I7nVsy+fZqvwywaRJBn3lbQHGmYo3//f1vX6zQIPjVlOY0eALgsYFE8LhTBrEgeCnWc35GEHsIvo/h1WjtaQcL2HrhBWir7ECAKfimGTGpwVfPPLPl6tUKnZBKpgRBc2sUWUawTTYwHd0eHexarEHx85AwyIilVVV7JFiJVNQC2HmXeM2TmegLZlUA0ABVJAEEZlBBavyz4FKpjwHaCvoXR0dBBpWp3nV1RFRNE3GPtkflYkGeUaM7P/h6AJ0MpxgphDJbLYZjIfwWITRwnU/Jho6beq09MKzz1F0bXoGnBiKXt5vWqjAadBEofaZPjqMw9HQxjFArzBe+IPfUaFo5BtR3Etq15OAiIEGCznlCkUHSy62LUFLofRv8Xf/wu8+w7ty4RWQ/Y5G6AUUxOdEExsaGtLb3/2d5Fn1RsY1JKWrQiwo+H0zDBRKjUY/z54juGRjcY5jrNBF48UsiTqEsZa5nVpWyv6xjVmgQbBmrH7PyrzSBk4LIyvvuwNSSXrpoEsXU++ivsW0aek6gQYNuWOs0pvC+wgrsGZ7eAa00UbnTIAWxGO++F0+ktcdl69QnHwgXTTmIFTwk4DV0NxMqvfnqZW5nYDCeZIgz0MoZ40Es1lyA1FQsiBPGAMAEoPK6O2E8ArlB/+HY8Tcm5qqPOwflidrNixk68RDy5djzJXFivw10ri95DVXF3U4XKl3BdCxRFAPo83x5ateMfeMaQwOuYrtE/i9lpTp5za8HPvW6xnHAVYyPIZz25ED0f5SAg0Vs6an8mklafpEjExWz7Hi4xhF01IRiszIhp9Gk9qgocWkh/HlPIWMBWdIOv4OL3qPLst1jMMOvOpjndfSF60UOeOJV5Efr+//4f8QgYa5M2bzOXOB6FksyFUs583sC9MkKyur0hqi6LNItc+2/qDoobE0+KqCNAtCnFBeXS1yXqV9HmjwZy95V3myHszeLz5nL93GON7pDitk18nkGE1ah4GGIcaBTYiBawCJYBGrUP291DIhBe/LP/43ifBqqnmoKk2h3RIM3kLGJek80k6MyuoMwNs+l5f1Ga6BredvcNQolc3uksWhQeu58QUEd5zjAQTgHPuP2270pCvYcVfsrzqTxYMJUzGGRtwkGwDsQB9O5DzJgbuTUlPX5XSqjUdwr6toXv8rGQ1vEFGvLC1lnnCu4FczyEZLM+7UcBE36RKry1kGkvS0mOJRDKHm+oaYgxWs8K4y0ICsG7hy5cxgj3KbHWFI32jb72YYYy+yFXdXgMOR5iGTUPAWBq1jpevnrLrVTMLAAABAAElEQVQYlF4IxizH6Z0HxiNIkXIoHoplo9ELeVDWORVnnGsrrytDmT4hiBTySuAZg82tV2ap5IGGB5Y8wPa4hQQaKCylocz3DDSI8RqDrrDKG2K4xENi4WX4mrGEM8fv4pfP1/G6BM+YSn4GI/qJNaspajs/jFAdaO9x5Mqo861MiAEirj9rlNpV8cYfIlAQH2S47FibcS7ex/ESC9diyHlcpTUg1GVilu1wN6KHfCAj8TsPNftAA8vf7YeyY1qvRpgBA3+/F2ggCOOq+tMESjwKzT5Lc+8JHUT/QhfycbTDs8QXZSl3DDLczDDZ+VUWLSy3ixWpowcPpddJ2zWjwewDcUIjVT1tSqo6w7F4tLV4advyIg/Huc8yBSw4a7FI+276v1tWmgk0HNi/PxWz0v462x7nV1WBFaSc05bPCIeXcUjSXBc6t44lgvfcE8Y5v+vQiSOecHWJQINZAcfh+fOssD/6zNOhu8ewLfQSjrBjNogl3c0akcbOccyh+tsH8rfMQWKe4EFpHc46z7HA8l703tmmxgg0PAKOPUwQRl1qUV+DXxGUohV1iG2rqww0SP+4mCR5XnkyWK1zMcpAGM8+hP54/913I6PhAfSS2xvMhHEF8Topv27z8HhpeQGmi22W2hNhX/AcVyVHQIubyLaFS60ab9+LqQavntny+fa07VcfpBUESDy6rwIHzGKD0XdOqQiepx3lySsWXqCRbWj/SaGgiY4TY4ogFWMvIkuuk+1rP3v/5+lCe1t69dVXItDgdjyPRx7pkcDwRgH0GGJ72a0htnFa3VJa89BChKQAELTo8CBB8dsU0S1mVXoMWzMvkCr90w83px+98xGn8pxPp3s7Qu88OTulahaBZk8YlyYRaCii0lvBEEEi9Jvzyv9iNsVkTwSKwTkoPuWT0HduQzLo4DsMio6g6HIBRaMJNLRxMsbJRo6GJcLAenNgd+m48enfE2hYg0M8iu8MENAZzZELhLqyQAPzYjDNI/bEMOXM02paWs9GoEEH7JsEApYR5AnMzm0L+QMMkCJ5PQD/Ls+IZ4GLdt0JGsYBccLtaW3Yv6dxjHbt+IKV3Zr026SSzyotSzd4rvyogyfOSxPlKtoMGWPstKf8O3w/l2Jhi4A/BtK6sSP3gsGnjh7LAg1PrYlAg7J/FVvNgqDaefJ5/gx50T5rrwe2wDeBZY5FmeYZ4ob9r8M+3UYNNzPiyisrOYVoeRQXVz6V7yi+GLoiC+KIh9pk4oByFVsuwSzT4g0SqDPkT7eYyat7cXQ/po5bKUGvBwg0aItpz2trhU2N/o5FFp4nzjgGnx2YRn9jYRLccjxisDivrnLblIUC3cq6d8eO9DxZyYsIgkuXWJmnnQg00JZjF3dyO8jP7av6z3FIdxfbvMyK9vL56o/NBAKck3XPrI2td3QyaCf9nKchbBgvcdf7ssCC8kttK+igTs+c+GwhzudKM7dAH2OR4GOwYDlY8OzTa+MI0mhDHqG/Ykxmq/Np8E0WoLbP8nX4Yzwz5oT7xQLtYrdVuHU7CzQcSBvffBN7jIVUnp37Ko6ZrwY/Ow7tq+BPaOU8+IywRYNfMl8r+s6YxGADVGLlCDKmXnvjG6E/LmPLeVrNeGTUouZAzt/QJYpOJgMuQDpS/szLeTXbwe1AnpJ3HLrom3WRfWTdmcsUXX7lW2+lV8C06fBoXPQ5Lhulr1/n6/4EGmCquxgBUDgmD0mKlbRAd5jnFBH0zay69DB55eytXv/kmsg6uIuQ96I4jH45OcUYDQrBACBuRkMYhjAbHBJMGNF8nlXEPm6dNo8nUxC/RCnv/nInEafqtGjhwlTJRFmdVWWsMGUMjWGJonK+rJzuqr4GmYAbzqLCPTyxEVnjRpnaKvO//OhDCtXUp2fWrYuVNAuSGbQIxoWZPeJJJaKjYvu2rcI0gmVwwMwClX4OiCpYhVjF6grs//cXf8Z+v5L0yosvhaBrAMaqEVwqUMU/33mGikIQ1KAKRc27Ve8dgy8FUwHyHusoeKLFZxRPepr9taspWGW/naeAYt69fEZmRGvAqSSGU5RoSxpqCEQ0lvbz+xRiq/m6eqHR9d3vfS+twGAxouq9gpIpoyog+2OgwYik9FaY/cxLAPGKIMwwsFhArq29LY69OoihOB0Aj4wGjGj3dYUjQj8FVX7BuMDgxBCRvvYrCtjQvHtxLVQmGDpSFcNEAlBGyc3EOLy/NnhhFStSa55cjRGd7b+VBioDA0Gex+6Xpaeg5GqaNBYEBRJXoMPYhudVRM5LfVNT2kGg4WxjY1QkX02gYTkrUkWM20CMwB2OCzTSMHLlP49E506w/VXxW91WA1qjooSiVwySOT0cqa8epWkK4IMYi2WlGIoYHzo59st5inoegKGBLo1I+dVxBejCVY7Hsdn/DPBHR2Gaj1A8h9hasumNN9h3PD2dqjsTUfsTvH9+5JDTlR4gDX/G+JGpYsakVDadKupjCdKxyj7Cl/zFmOQDuJM5x6mBPqopec85cnwFjN25UilpiOnQDuFw38IQ67x+Ox3tvJ52EWgYyd+uQmevf/mHBBrg5bmzZqciaxU4Dr7vy2DSCdLsP3z/l2kRqzlG0Kej8DNZyjI75LtwgpR1eYQ+yt+u0BlokSDKkcpeTIJo8e4KkntfXf3evX1b+s4/+C6G4CPczV7BW+QEsKJW4D5hZdFRU/XcYpWF7Ku9iSLZvweM+uM/SVU8Yf7SuWkyzzbQMNpx87OYZrDBuVMxKZOOuJ9fLoGt7VdvpI7L7Nln/pSY2wbzKPY4ccqkOILqEitBXTfPpy7itpfodhG7gCZMGwO/c7Y4AYkbV8BZLFmPfusbHJWae64lTmIKGnT5QK7/7b/6Xnpzw8ucTlMaTovBXKuqOy/MbjjcGive7jYhzyJX6RtoOMTKQsPpMyHPj+IYPbpqFenF48KQY3gRWNCQUBZj8vlM/pMXlCX5XllypSgMLZ4Zq0LI7BmU8vukSw/w7Br43VTv6srKaMeUQ2VJ51wM0LCMlFRwK3CGdi2u6jFpbjVRJ1n/R1k1MHCDud8DxmyH52ficGgoGmiYw8/irXzlsX0GHeyj8+JKogFMn2XfNXrtu3IlspqJ4Lt4cwns//SzbakFLHgKHKiqqIxVevumLIhZBkTkUeUTBgisCRmxv8yl9NMBDqwc7n9unFqR/B2KeXkK00vPvxAZDTqKtqvu87sandJUjLH/jkNHOgw8+qAz6qq1WCkW3MsCAzMOk23wAdlTa9lSs37NU1GA1nalh/OnjlSXiR/2UVkykyJOM2Jctm0asMa/9HFFUJqpC3pIGd3JFoFDtbXpLc4PfwxDUUcepohVI/vkKn8het7iweLmaDIaPW7M5zEY7IwMh/uYXwuxypMGlswKaIDmBqqnMJe/hSFaU8m2IXhoAAwO3UGfcicj9tHSL3Feo1AcV2e7kCEOa/RHFXcw9zL646BbyXj1YL889vRT6XkyGtw6cL27J2yVTM+SXQQNNcIjOEK76u/AIOdg+KU+s8/SzMLAre3taffuXcEzruq69W4FzpH8ZiBALLegr1guz4fToZ5yTqCTuOV86yCaYeCpD+PgxXGcLKFDXIs99h6BO7OmFqOz1R+emqFu8hi5CcUlYY/IQ/IfkxgBN+dW2lhTaATzMijPQHN5Rn4aSyDczwxUf/zez2PrxMPLHkpVlZWsZE5k3/LVqMIeTpD227Bt4EqsvFREMCTmn2lVpjyJyeMTPSJPh7SELQXnu7vTT957N7WcOUWg4dU0n9oSxWxfi1pAZCxE29oCFIIk+kt/s0C9a15j3V5BoOH2res4R9KRlUnGMh7+ONd1nq0TW9NP3t+SLpLRcJLtE17ry8HrOWWpdCInmhFoGMP47/IcJjJ4WgHTHvDoRayDoI9jUf5FA9go6GzGjSddYBhSh4j6LCOxXcl6a+/tSycaqZzfw65NnmeQuJLgx7/90z9Oq1etjECDWxdYGkDfoaPQE8pRBE8JeGhjWlzPgooe0boH/dSDQ/0NdLcZDXQrTq8KuwNc0sXVGfPEAPfoQ+jAFTOknGp7rg5ynv3M42e1K8xarW9oIGC+h4KKc9MbZCCVooMvIQvyo4FN+VHe1kYKG1rs4nf7G7zEz8qCl4sb2nzWL/EUrX3028wm530l9pjZiB4zrQ2rvHoCl4FvF/hsT1yRxqJj5ieQ9YtsyS/S33uUV21s+72ZxYc+9HdFVXVk8FRXVQVtYostzRjA1N4zmGFQTbl1TOKblzIlbokLsRjAID1qUTrtrt0f2wNmzZnDsbVLUhVtG6iWFz0Bx2NgxRv53T370kRdKB4r+9pBcXIVeBNbJ6G7wcHJnIyh7noP32nbr95nK+trHLu7MGpPmPkVtI1RyGuZXaNOkYY+2y0SbmcIW4zPtHW83UwFv6ud3wTP/IJFYH9+6YUXw45Up+uDRHAU2pjVZHu5LR86m989ScMArzVrzKCWRn7P1X7bFy+OorvffeedtIxsg+fXmrE9LXjBoKi63nazRYeMAfOAQmAj/ZC+0sh+h53NHPhdFzA8qnkvuvsItscbZGM+ic3h92xXG94gmrxAZ+7pWLPc/UzfRH2rznOuxWUvt6TI/xa4biRwt5+FpQECmZvINpgP75jx5QK5hXCLtTeYO4Ng2grqRGnjs6WFC8Dyt8XBpY9HWo4jGHsZnj564ngU6j/PczovdJNR1ZHe+N1/mDZt3BTFVkO38f17F2P4Ol/QB4r8JpffhrAaB5ncQxAmJgglcQD2o4ePpG2ffEJQoWs40EBmwPwFbhpKN4gQOUnuXwvHme96tJcZDQIdiBFAbttOokd7WQiykL3ZWDhpiEn7bOfOtG3bllRNpHUJgj6vel5WfRvmMcigUAt+AqFMYOqLzGZk0LRChdPf4wVj+67iHIXhcA0n4R2i/yePH6VGw0uppiar+irgeXmUSQFtuqIk48rcMpWApOKUvCp7DVMBxVUMQVcjTyE7VVeX/upH/ykKhG2gBkQZYKXhJvNGhFLa0kYACW0GiEODX/+DLnx4h5dTEeAzzJQKusW8fvHuz9J6jNC1T62hMrTV5LO2wxHk59z5zGiMYQ3d7KP3SYsIxNCHGC+fhcMAcBlF94SCEyj9t7/9nbQCZ9o2NNYEA+fVduxzGCnMhXMsgKmIYgWG9lXUuVPgM1yV9+QGFcQRVtKnsaLwMtHcSlK65Y0AI56BdR+OpkcD6rD7bP8uJQQcCJwpTX53HD57NHNiZPsgxtbxY0dJ2+tOK1Y9mp5aszocmywwBd/QvjQPsOW7wXu0L0001gWvMN4JinnMjUagRq5839BMjQZ40oJkEzje63EyeJZjzBlkcfVdx8Uqv0BdAJUAJU2dc/k9+FH60H/HoPPqHBdihFoctZYo69bNn6TxHKFoYM301LmkTHsmvOM3ICc4xxYJgZdnOSfym/13PqRVgCHjcJU2eJP3btK4Pma/+nEc9o3f+AaFtqYFKFoHxT1lX2Bcey0m0DB36qhUzQZWsxrcPsFDSO80aAC9mWvnHMLTD+jOOBgN44S2YoXPhV6wCfeZzUGqI3S8g1z38+ros0bDxbStFQOc7zHbcf3p//7P0msvv5jmkKlgzQkdYQNNKnIdE6tuv/vTn6YVK1ell9g3bcDxrtlGtJ8rk1xGfI+gAv1RHjV8IgOGcUgi5yILgFEsC2VkYSZXYPft2Jne/uZb6QkCd4F1rpCR1zoIGAz4HOZxZJFntWMeopSu44hbHPTzP/qjVEWzC5ZydjZzNdHphR4GUixwq/E1CL10iKNv9G+AjlzCwWq9SpbPVQxysO+2n/fjFBJAGAeGeULPTdq5dpvj/HpxLtlTPLvM9HwMISh35w7OBwYBX6Aexfh0mUZPdPSmvfVk79Cf846V65/+t/91+uYmjPeqSmQFfhaDmJsC5Yx+jACQ7ZdhIscpv48BB11ZqEXhW6NBTHkCfn+C/ZKFODd3cOykuzzOV2OMPksZ1dAy8CCNlX9pLv/mRkURhibMkxpp9wOMrSsY0xWVVWklBsWCmpqQ59i7LMbzDI1FgNfoEQZQFtjV0NKIiuOoNC545Uf4GQzWQXVVZBf8bZGtRTULqdZeE5l3GuUhm67o0H8dMzupA2pf89V095M6bo0iL58ppjkuDZYtW7emJgLVq3HUrQTv8csaY+oKGxJPlH3xRhzIdBb4aVv8LfDGv/HPvt8l2BCYQ/8Nav4lxbyKMTxf37QxlaM/LF7ld8Uy282DGhGoQDY12PIrjGfkzv7L4+o9DVGDBhbbsqL6e8P644V169LE4vFheGp8iimBqeIe7dq+86l8x5wy/gicMSb+HPPv516udlkE7guy7dx2863vfJeMuxVxU2Aw3/G7gVmMwfGo+00XCp3L2AEunmt6fLYwIW2KSorjOD/TyE+hlw4wt5MnTUlvUlNlXmVlOLq3weA48hh5kBLyNETJxuDcMnejMfS9rlNgUIyw32J8IZ/rXB2CLicIxnp85oqVj0SNn5IpZFGiu5zTUdoVfM/jidV1ypODcM6lhT+HHRA8y+d86FzoNOkwfkmgoe1sSxxHaDFhA9UuZJhiq940QKLOE/vE7miQ+TD7xjnEQwo8NrsD0zfade+2OuUQeu+jTz4OG8Wg2kMEA9xyo8zcZPV8LIGdcfYfmVLH0lWaH+ZNBhf0oh11k7xo8F9brZhMB+uOmMHzEcEptzg9RG0itzxOxnlW5/kSR4Kn5UNocZd+Kzuhs51X+NF5td6Ncqs8jaJPY8Zx7DEBgXcINLQiTxtihXc+thNHxtKXfo6ktH6UWyxGjQF/x5KmTaDB4KjOFnFZMI32RDAy0cwKi3nGzjtH1fd3P9mafvzBFor+sj+7/ayjTqvLU5o3e1aqmFCcphexBYz+AL5OJG0oS9wEv5jNoOVhBoY6xIGNQNGKGdq23u92NwaT6bgRWaCh9eLVdOQMx0aT0SBbXOW1mMr/3//n/yyteXRVmoAsjpQ3SG0roNngIzGNcXp0ojrU+fX4SdPgD2DXeBT3hpcpHMhWVvlEDAuZUQ6ZU1dh1ZvOqXpaedVeFRutA+J4YssxfdZx9Ljg5uZmMhrO8DqVSisq0wYy39wicAN+zLA2wy8YOfhDW1g+kXd442XYhX8yPx9oh0Sgk7F04zAewKE7c+Z0OMnLyLx9lCC+CyUGMcRe+y+vG7BTgJRJX5GBxOdmetq+QQD/+WwDvQBtajl9Or1PnYBrnMxh8dMHkaX51dXBbwYVIF/wo/IkX5qFZGBG+RQ7w4bl8/yZBWbPaNNgS7otZve+/elXBAKmoj+WLlkahSattSatDZDoG2jraWcHw4Cdsa00aIEYiYnQPwouwiPqQPF9AotiN8Hgn3/wq7Rj6+a04ZWN2HoPRI04/Q9xQH2h7aNNF8Fq6G2gIeZcutBvL/Eln+fweewL82wB0b9+5yfhnJv1VUHafgQZ5DH66TPCt6H/kfXFc7R71W/SxZ+zQp3yvDrPWmZZfRoDVEfZbvMe8rr0wYfYXkaggYxtAwvioMFS+UFeyH2Q8An4m91WTw3C49LFYKkLA+FfMWdmovSCwwYaDmOTvvX2tzn551H6wFbWwFw6ao9oyJd6RG6UNrbhvEZQArz3Mx9oP2LhGZnwNIiGlubIVrlFXzZxGsRisFJeFNdHiWHQRvujgN/Vs86B/bcPoWOlMfMo7W/ynDvQbCyBhiv03QLrR6BNF0G2Tp7l9qFN2JSb0OEzyitoIqOnNPW/4f/5w9fy+s0DDQ47m9NfEwDCAGmwJkpoqD8dRyFbbKun81yaw9nzTz/6eHpgfk0onX6IPtZJx6BklsJYcfX2FgxmG0bgg1mYJNuzRsMYQFKgLMC4GuD7W1k9/mjLR2m+ThcR+hra9tx32zOwIAMITrkR52qKvxutNGrppSOqkSSYyPyRCghw9MHMf01E7igruS+yf2b+/PnhPBpRtD0BZAwAlRulucGmEGuwCYCheBwHjKeAytQCpIJ/mkDDe+w/ngUwPf/ssxFoiIggfQ7nXOXMfQJ3GEeMW6Hxu/1E1lXERtN9nm3L4N7r3ibv2ctq0U9+9NfsKX+e1Nqncbxc4dT5tNjKcDFGaKAxpKGoUyAtBKgwQmlTcFco/V0Ac4yCSA+02YmhaAXVN0h9XU4BPgUuIrUIr8pKEJfWtuk8CCjOp38zuuhlu+5H890592zfTlakmpqb00kiopMBp2ephq8zLeiEQUsfFHS30YxE6F3VzAxdlTmBC+ZWYHLlUrpYzEYFJJh0YxyeOXMm1fGyFoSpdGtWrw4Q5JYwoE0HM9jg785zzLVzAB2y+dOoyFa+VdBGLvOTUpqgxx5W0jrOZcdbrkJpqiDGEQFV8fjSgbPfo2lTIJc2wefQQx7U8BqBMhvF8WIFGo44pv5+Haf2wMFDVA3fSlEqMhoI2C1euCgMRRUfA2S1+zpAnBWddAAZ+EkW5lTeg25+Fmzzlbm10KBH7ViT5FRjQ3qJFSOdosMYph7RdxJ67ebZXgYaFpZyPv2MKal08jgKZcEb0Mu0UuA1lJ/8K+9aHAt7NPggDzRk0V628TDuOAKKdwMNdzGmBzhNwhoNu+vb0+cdPu3X17/9o/8jbcLAtMijRoJn0btS4Pw4Z0cINPyUo6Yee+yJtOHFFyLV29ROZdp74oHDzQW9oYfzGYqBH10piVV3PtMQcmVB5XkDWXEFVsV28Ms96RsvbUqrmdcCDXKV4V0yF+6SHSFPyCfjTF9ncwd9ukY6tY7s9j/6F7F1YvGy6jQbY2Uy9meBjjXfUbY1xjzz3DbsUwQUwJfL8G5bHxkN1yMlgfapeQIfDPHM2H8M3W5jPQ5iPBtELeF4ywqO/igmy8TsoIF+VsQYm/w5atz4dLF/KB1kCW3Lsa4oBsliWlx/+HvfS29s2hDOsBW75UlXUIfI2BpJsCYcEHjMuM1VHDBXH13ddGWhluygulOnYk4exzHyuD/Tx12Nkv4Gv0z1F3PFJ+dDOmukaJgoq45Zpaz8igUaF352kkK8W8g4sE7OTIKOj9L2A4sWxapCYAsGkcG7kCEYSsNXZ1WWCzyU/3xGMXgMDUx99ax5mVIj+iAZQgfASTMo5lfPS/OqmB9qNIjp1gDRuNDJtX15yO/bbqTHQwNXdcRDeUf5E7/D8GUsZnt8um1bqq8/kx5jlU5ZtR6P9NXA0ZhTRkIeHTv/IaDR9wg+0J7vGnUGEkWx/nBYwDjo09DUlP78L/4yznR/k1WX2azO2pZyZjaaePvV/c3isLTPsVKcNItQHWXWjr+7xcx56LvRl2oPHk4/e/en6Zm161i1X8cxqWRVQRPprXyLJ8pJtMd4YiXTz3hu9FnjnTGEbmJUvnuv25A6lQsyfXRe3mJFaiU4HKv79EOeUOfYL8djEEldO8TWB5RAGIrySNRzgircQBCNLVwTOWKR77htwjoKh3G8SopL2M9Pmj0p/LeusciAcTiOYL7BvQiKMr8GTZwP+dEAlNvodNgHkM8sVZp5gvbyqQF296vXwZeXCH49sGQpK7BPxGqdNA87hb7ZXl5DJBYPoIX0d0zqZnk76MTzwunid3WK++F3s3LscYUTJ00mkEGtAxZQXA11650ru+oSwyTqPXWyfBTbG3h+bPMgW1OGdVw35E9shUL4nwdG2u6OnV/wzJGpet68CAZUkC04ZPYVgQYsq9BNrtxJb5+rc+L90uA6zp90GVfCiU6cEOZCirhldpVBuK3bt6dPP/44LV/+cOi9UhzS6QR7zLqx7QAQ+h0rf/KjLI++02kxyKMTp+Mlr/u7GQ0GG0YXFnEsXFf6+S/e50SnlvTC889xpLa23jT+DiZe7k390Ad2wWnmWMtxnILBNrw+MgLMtBjBolcR9xVz6lEhhRxHoHhDt9JuQ2t7ev/T7eknn2xjO8zZOHpOUFxRmlLlzCmpeuJ4aiewoo5cjAjbA10iwNwLNMCWPDfsMcZCz5GNLNAwEpzxNoX7DrI8iA3bT8ZbHxkNLd29af/RnnQSfxqVGvVyHqwoT//4D34f/bIyzQSTnOsheNZTJww0jIQeA2CALx1S+b2HBYJWFmeOkY5tLaFn162P1W+DxeKYgSPlx4UOV9jlGVfbDcJKfwNc2kpuxUBxMY9gNjjnZ9pjp9H9J5Gnc6y8VlZVc9z4M2GrynOxMAXvKqfh9NKeQTEd08AiaBb/eJDYIwZEYAN+8LlmNBzBxmhsbAydsAgb/uGHl1N4loLGYLl8M5b2DJAYhBU/lBPxNlvthmfos3igXRHzQh8MNFhg8Qx66T0WC6+xVWsRwZdlBNas8eNCjPIhrqtH8ndlONMfBolYRIL/xTC3q5hJYjbfGLNowEl9FW3sjz7+ME57WMKij7Iqz0trC/oW0XczFs30lD4xB8hrYCf9Vi+rn7TjDZoaHPS9RKeUufwAWaolA/eFl17MTp1g8cdFUvWGGXeOQaYXI21TGjkG/ZwsyOmWP2wrxqkOiUUE5El8aybQ8EP8A32JN1hYqiJDyCwZL1f6ldUISg3TXvySvsEz4pe6lbZcJAmMF+vFMfjKhZ9jnPzzARkTS5YuSevWrh3OaCDYRt9z3pEngs7wtd9Vx+b+kfTVFh5HANkxa0uY5aKuMjN9f+0B6igcopjwb6fHscfkRQsN28F7OijsDXldnYjtDe8baMi3cDtWdY0v/UF5vwubpr65iSLoB1I/43VLw9LFizM+1N+gncHrZApjl4mR4ZtJA+7VvpEmBnXFgwGeq566w+9mUF1jccNikKfq66MGoEGNXvTIxm+8ycmGG1nwmE2Pgil5p8G48vfhX79mb/cn0PCVQQuxsEkoJEwForyDrBwfI6Ph49R4po4UsBFp2cLFqZooNzMex8oVIWQKswpYcJOxoh1oG+9KCP8p7AKn6ZNGHzVab8Bw7lnfuW9XKseQEEDKcUinoKBDEGFapj++b9pWrtycNlerw8Gg8SgciXDE3/ldg8uij5euXI0IvYbiapzRsvKySMlS8XqvijPSZ3wGzxIE8xUvnU/7jGgGv2TOTGZsqVQF+ZbWtvQ5hSxNUX/skVWRiSFI2z+FQmBS8Pzdtkyd07nyOQLSbQBJgFIhK6Tekxl/GAX07wTG0ObNn6RHWWHUkDMdTVDQ0ddQNGgR+455hvQJsOVZCr+sbpsGGpwfHQXbNNDgGC+TFqxj10Gmyvr16wnwzKOfAB3gJ9iFMTU8d9H/4f7F2LjH+VEyVToKpvSJ6B+GSy+KUyO9sbEpjScr4FH2eM2kUI2rJ7YrXQTuQtLns/mlvzEGxsED7Lcrrs5jAYo+ot/83eddpqBLe1t7amUFoPtCd5pXUxMrRrH6zZjte6Qa8hyDACJ0gCH9iwwH7+Fv8mls06D/BhtiJYl5OIcB7ekH1rCwAKkFSudVVQWwGWS4i6HsCotRevtubQJaYN7oHv8EaVe2R2A8G1ywwI6gVYBxJGCdRo5MNy7BAKkoLY8jUT3OVQdBcL2XDsZ82u8AXPruhMa8MocR+OFZ8ot/z7JtRkWBr1oixM1tbWnVE49HYTkDJ0dPHk9NHLt2/HQdo2frBHbswjnjUiWp+bNKcDh1ggBoV+jdL6sBr7LT4YlVSb8EvYDomAMmHYZ264R+w3A/Gf/dQlZTCaCcI6NhV11b2t5G9Jiv4sLF9d9/7x+k1VQzn6RDAf8a+DFjwjnSKLGexFa2Ci1Z8mBaDc8b+VeOHLf3BH9Db3nQK3iPfogzymQEeeizfKIRFCsaPCPn9xMEvs4cOZHWPf5Uegijxef7HdQOVc5R+vIENC3g/HZXli0GeZsVpiOHD6ZT//E/pBUslD6weGGaxvBZbzOqBY/hOKHgRmqAhgGZ8WkEFFBqvRiZzVfYg3uxL93FcNKg7bvsiQEpTS/HgCfo2ccRl7eGCETw7JIpE6h6PZm0agoCYhzdZeVuFAP3qKS70La7/3Y62NqdPj59MWQ8r9Hw5sYX0jNPrSagV5alXfIcCxuqSEfCM/bRAKum6XXa9RQA01MvkpV2+uSpdLa5ObBh6dIH04MYXO7zNqApH2gMyWuBI9Bb+Qw+93ONOWTVAcX+WrGMe0Oe+Mzj/vYT4Ont7YlTUCy4FVsQdOihgXhjcEFjS76LoIBY4Jx7OR8YRB6bpUHnSqBy5PxqPJ6uq8cpPREraKaQl88ppejh5Ez2mb9YDRzmHwkmL8k3OvrKjama8of6y37HXnMNO/650mhwqgUZ8ohCjxG0flBeHDPHycB45loMiABDzC5j8Z1nZhiZ8Wlk+4Fp8qZ7ST/48KPYprKOWgFxfBj9sz23JLkKGCmvtCNmKX/SXiPdPrtS7/YEKaVjrMHuXDjGjDZ16bPPPqMuxor0KBkHE8hokL4adLkxbt/UR7bvzxqNthd0gj7qDSfXz8IQhk4GwCzYKE62kX3w3AsvRKEz2zEjUD5RtjQ4HYs6SKwc4XwAGtJZ6rggYcaXzqlBWY1/nWB1h0V5GzDm3DK5+sknSfWeheGvPIBR8LJ6W0yQb3yG/ZcnbdvMBZ1bcUGnRr1jX6SXjtxZAhlWbLcYtVk2DxJsmIyeki5wW2Be6GgcDcec7anOAmzihX+THsHnPMd2nXz70M6CjJlZPT3dOC8TOS5vUaquqgp6uAqoQ2FwNYIkPE9MC7yFbupmA12mdYth0sKtd7fgUY8NVw5cGDhEcM1nl5WWkqVZQ4BqZjgt/RjP6iOD4AYa1Fc6eREoGabHNTDBNg00FNKmdog8KRa4regAJ5V4HN/8mgUEdyojKDxpPEU76bf2UgHzZ4BEW0LmFlf4OFb9zNiLlXX6SRci4BDya5YC/ei52Ju279jBqROdcQRiFdsqJ7OlsBB5uNnn6jqOrzjA9oTRRRR3ZjZugV/qJtI9uI9FbgIG1g3ClAx63GVcree7087aw2nr/oOcTnCOFcYMFZeVkbk3fVKaj9NXyvc4PDuNBM/EbOUbUsW8mdFASZ4Ijog72d94Op+HY+0kcVlzBzRNgwRCbmK7tPCc3QdY+AC+8kDDvJnT0m+/+Y20TD0BDlmL6TZBhn5xmL6K49p9g+pT6KTOuXztCquw3VEvy0ykR8kmqcIeVvLMINHZxV2OLTBZUJaOQCf53/6GHYPeNkPOZxiIdWXdrU4GlsyyaWpqgi49cYrIihUP3wusKTvyvEEF8Uycd5uo78qDetV5VtTyQKR/E/e1PS5i69U1NMRxtz6zev58toQujG2+oePRi/bf/qpHxBUxTLwVx5RReSRvT9o7JuXAwKD643OCX9dxQOdWVkXGWllZafCdcph1LJMhm3I8vvyZxwT2KJtRBBIeimwG2lWfaLscJ5BhBlIJW5Oqq6rDB5mBPR+BBmQoy8CG+vQpCwridIpjtGlAWpyBEAJ/2JRu51MOxo1n6yGBswjGYn89/tjjqbq6KrKSxRPlJ3wcxwBtws6DqWVJ58FsELFGLJXe0j/wmec5x/m2x48+2RwBnvXr1lIQeXYsmjl2AwW2WzgckHUB0cbFd2fUtm032gfj+UrMi0QTx8SixuasMPo85nQV2ycmRgBUXgFvaU+fw/mMxUzatT11nt+XF8xokDfdUiYG6bcYANHWc5vwKfwbFw2ffe75tIR6ZY7bbIfwgWgvMhnoj8/Q5g2fg5/lFf2gCJzQb3VNBBuQLfXghYs9Ebhzce028/Qk23mqKyuDD0kkBSsJRrEAYyDpLuMIfUKbNB30N+AlfkWggeeKw9rwY/AHlC2Dgs2tbfEcC0ZfAHN+9/d+P73OMczlpRXQUgo7k2EBDP/M29f0Qj9Jmt/8UrhtKg8SKKVjRpgOeycdP3ksbfnww9infProsTQeYZjFyosOhNsOdEZMJdIohRWiYIZpfhqCglRU2NWw5f4QI75jdFPl7PM62d7QceEce9gnoXQmR+0FlZcM53E+7r/UQYzqv3xukS6FQ9BSGGQsDYxgEphSgc0MJgAXp7cJg6KPtLwKGE1HXSEV6GRagcMxaBwaDTPCF0aQtHBcvEtgGVqDJTcsHIyfGchobKinevakOFrGKKvtyl4RBBBAeUWaMX217/bB/WgWYlIQdaylg/T3Xl8ORmHtRjGfRdgNwsxiz1eABM+1//4L4aNfdlJBs30NU2l+XcUNbbwsRGUEMAcQ6WaqrZkH17l3IY7FVCLQBi+keUT1oKO0lPa+NDh9pv0U4AUZBd7fYx74TLoHvfhM2re2tQUIm0lSDNhYaFJnxBQnaW4arEEn9yX7WaxCYQgYqHDvp3s7pbnj9Dk8Pgxs9+bFWbq8T8FJn8kKpsV73F+cGUCCT7ZHz+f4XedD+kkDo542Z38zo91IN4ENnmfdkHYi/5ev9PB7cSotKye90xR+j4yjT/Q3FCe/G3QYw3M9QUKwDQUKL7qyJkhpTGgkGuF2Rcp9cTp2Xe1taTKr5qYym9khsI6BPga3IgDCeH2XVydgJDmn0tgo8r3gVVhyTD3f97lGugfpk8VpurrPx1aN0ShrVwVUfOf9/Hxv8MMDFBpcOKsolU8sSVNJKS0Gfc3S0KEdRx9iO4MyRT/sW8yq/YGG9ilSTXnuIC8hlTfoCT1w0LGS0/nrA6T2d6StLbfSBIzMm46HO+ehCGeR4TIWAxfSw+vxxYzPoN9lotzNTdTGoMp5KUcUKueOz8li6uHxLKjpeP3M/eh+pnNjOnfIJ85WyCvznBuNDlrFquN4kTTbRVWsTJsq7SogfFaAUz+CDAIaC8V0BSXElKV5GBxT4EvPqx5xYH96eu6MtBhem0hQYhxbGrIMEPYnE0BUVtw2MWhf6Z8ocIufL8LPzTjZLefJsuE0iX4iDZcxTDFNMJw4VQOH/sL1K+nq7as4jewdHO8546xO0afYksZQR0o/6EAoKHVRWPLoufNpCyd6iMGm62JCoEhnpLmsbFr7AwJEXYYoBEkfRkiLYbw0PfgOfTVt14wKsaKbValLBNaU61lzyqKorbIibolHudyIJxoMrqJL4+BH2vcSg+MYKvGI8WvkyiuXwcg2goJi6yQMbzFKIzLHAGXKc6t1XnTApjIvZgyIo86rsuTL/buuAso4BvBiaxvPcX/2qROnKHw6ARyYlabzfWkQe4OZR411Zjf4RZ1TjIMlbuksi8FhDNCm8yf/yGj+3UC4znx9cwsrsO2cw11DUbwJgRnKhMNWPjK8l5dN3aSoHlimEaqR7aqUQRSdXGmhyA46BhwlAw1ZWnML+nAoznF33H4uzmp0Bd/bJ+eMZ3l0W2QwoD801MQuec3vGJQV18RFn+cYrrDC3YnjOxH9NH1qtroaW1RsTyfXsdK+M2hGjideeHyb6erWJpIWYr984fwqjwa2TU+Wfj1ginqkat68CO6E3HGvWtN+SW91ibUVEI40jZXxsaxAmymgQxVuBnwxmhVG7QWNuD4yukKe0RX2oYjgZSm4MUEnyg7R72xl1xRj8BeesV6T4/fPOstXcUrkFzHV4EugB3/zkoctsNsHbSxaOZ4sD08pcQHAuZIO7vd2bj1NQr5w76/61TFJC/Wk7The288zBrghVjHb0Hs3rl8le3JyBNdMIxc7ndNYjYb+A8id43BbwiS3JvA3s7q0a8IesL/QREPdrXejmHsd024yERsa65n7ftqeGbbYBHjaVXOPA3dLno5iZDSAb+5HVt7U3+LTFZxaV1rddjCa4I747fO0b8xOs5aNxdp0KibAj8qkWagGGEJXwcQGRuyn7U5H/zrPOpwuAmjPwDTh0JnRauaTp+8oG7egbyOO480rvamqmqMt6YPjHg2Lx7GV8iLtR9Fc6iCMMOAAvvMJulZMZn4A5jvYTY6fhAeeRfCIdjs4AaINfNfu4ba4lpZz6sTUyamGrROl4LsnBY2i3wXQIdNc3EZ/Xeixr8qn++3D6lPADTDwmUNSRgwSW7loiK0dHuNsoGFX7YV0mjgIG8WSpZwnk362eBH6ZfpUxodOZ0z9zKv1MwI05CHobSq2c6Czqo1wNeyOdrZOXOFY0WXI08TgR3EM8EcHwA581/kYR8A47CXGrlw6Z25Z1e62vevoNKXQjDX5+SoZSL3YkmbwFI+fGAGq8QT1xAD7FBgATzqnYklke/Es5TSyhBhDLtvqVO8X3y0SrP6w2GQnATC6mSoWL6D9ObBuAc8Fi9B/2njKqkFrMWaihbHhx8B1cVJ7DNqJkdo5OskGTMdCnz6yOy+wTasfmbUOk4t6E1moEHdj1Z5+i7f+U08VY1PljrwYrE2YOdncEcEA5ImnuWXR+T7PHDY2N4RemE7b00J/UOyXfiifGc/TN9o3uCk/6thKA2mRF/aMTB76Ie21t8zI8O9m8fRe6EpzSsvQawQQueQnX2FnMz/iylexTFtSDLb/YkpGe2x5frY+gXPqJc80NjUFdmb1g8jU5nNt7LDP4VX7Khvrc7iYJVaKYRZS9z0PTOd45jP4Ly79m7O0P4dtz2VsQRITzUgQC5V9aRAPzP4X+kH6BEYyp+KKA9WX0gZXf/hdcfAmfG1w7VxbB8ddu11+CnMKDmFzyFve6xy7iOz82q62h993i5lZiGZ1G7xwcVfM9h59Tm3szq6OqD/haW+z4ccS9IdBahq8ZxO48K08uaiinnPrmEEQfQSxUpSQV8RgHjwcCCcDieCC9RnceneFs3gtIP6D//P7FHH9JsGkeQwZe4J/2ZXx5vAvX8u3+xRokDUzZtAYN4IbDgURW7nozKkTsad85xc7049+9vO492/732JuGEmIF12ezv5tN/8d/z6F+wlSU/U6pQt/x+/+59zOpo3YV97xn3Pzf8E9NZAVWUot/wXf/du+UsUNxdM5EqjbVdr7e0kXTLlID6Rm3X2/Fk8BvEqmpt6z5+87bXAh06zhHhP6uO8XGZqxosG0psb73jpBgRkonkuk4Gdxo7/zE1gIR6kjzeCsOmHRRAMNo9NcFP70cQTvSEkVhDEHwjDi11AqFnnUEEE/h+Ggg4V2RIFiQPK5Kzw2qVOtoTbEdoM7OAbnyWg40nIh7WiiCjp/pyRkKIFrtzGa7uPlnBYjT40S/u/hKqfNRRjEtzHSqjCin55fmRayAjeebQ9FHovJyKWVCldjQaWEjo+ffbdGwzXoehGH9yIrWoMFpE/jcF0jTfgWnxdijA6yTNd9s4eCmay4ImDFE4rCGCghMGUBtCKMJ13PfrZMdF+9kc5evJwaLrJlACGk+ShCNpoNzNduORN/86KmZMyPwYj7eXH4RRxb2sX7/cYZ+7lq5sQw0rovEKTwg/t4Taet6ZNGpJOX4e2/h0sswG4hIJQ5Hvf7ERWAGb5Fanfy7/O1CGEdR1D1eBvV6O9z2zY3l9f0yaS6X+JUpfvc/kLsusIJbNu61J+ycOr9fYA8rx7pvL/NRms1/N8YLfViU8d9bp/ktSRPig7tvHjEfb2W0trEubNTR2vnfdfbdrRCUERxnZXp/7YLLA5QHL5vaRljnzo11VAMsgw9ZwFfAw2R0aDzEDfz7vfUbb7zWQQavvKTSGGw6jaOyW2cwDvU8BlgIa6151Lae5gTHQBY1uzRc8NN8O7vmDQEaFJq/XuQ1QW0P2H2pHSz+3I68f9T995xWxbXovaiiYAgTVFEpQl27IqICmoMamxYsPfYEkss25IYjSXJTjfNGHtvqIgNUBRUREVQEAsiICiggFQBwfJd15rnfsF99v593zmf5w8HnvfuM2vWrDZr1sz8f0ANr/9vJXXfXH7W6btOyoG11m3KVuVLY9r/BUHTDV7SiTAb307Zx+u7q4EyQPilCfXfd40febVlm3rsjsPUPc6/yyRNbrp+CxaXXhSTv2tBQN5waqhflTP/N1JHKsAyVTENAf9d4l1ab020KjNaE+f/J7rpl7+6PI48egARMV3JrXI0FCeDf7/P6TtwNFQSkE4DglxPkx58vUb68BTwk995J0YOH85KyqPj2aeHxAo86p3w2OolM5pB77ZJVBp14HyVmQsZTedaQ7ddh/VyFEsvm56tlO3cz9FSDHRDVV2TwF0GnHqRzxH6Ojz0gmV4Fcf0UvL9bMJUps5flh27ddq2yBEw1wgw1NRQLzPQU6h3059OE5NeKp/rHRRiO00KCz1XdpIcPdQ7q4eNvkC0a9sSuJunh9eOhJ41v/VjPWzpjLGDkflbN72I5R07HXpsswPCu1/Qy3MEZz4jm/PwgLVhZLPNOu2oU22kBDgyzFRcChzIFD49l5ZlEhclf8sq5fiqL9enQyju58/7lNHRr6J9q2aMHrbmW7uO5Xuyo7NHtAd56kkWH3rzHMmpPKDWMfMWDt7PUTjO9RwvWbQg5qAYzG3DjVa1qXAVKHhS/nMorOW7pnynvOZVVlGcG7rsyJP7SM9hYSXHF9s3rocHcqMcrV5G50w8NKF9PeqV9Xvxkm1LfaQjR1JzFM92oh0MiXMExygOR6xnMV92NlsACM/G4KYta42UuWvcq8GvoZEgcswX8xqcW2LtncoA4VbikQc5KvbRrLmp0BzV6AK9t6IDvwJYpaecIwuOpW9p2cgLPa+GMAOsmWe5jlY5srKMtllOW67BaJNwzINmps5ekCCtu1ajhN0dARwpMQ/rLu1Lj/KRI46Osulhr+Z/fjhrJnlSlOgjdQfQ7uvXj41bt412a60RLejsys/1pW9+5muoYDoauHZ0QtoRNdKH2ehc0L5JZ4N0Bq+trE+Z9ViJmjUa3psxJ8ZPXZzv6JiSC9dbe51cDGlNytPYI76Eu4VCV+cZbmZH0xGBpPnSMslrtqF4NLRw0YJ5MWfJl9ESVLVqsw4dvKaJ7xwhoA7CmnIGwAt+wAt4MvpC+nftD/kfRmG0kFEKRzKp6wJGCmbN+4QFDJeng2ojytyeCIOeRBZtwuhhM6IZmvBrDNIcjUuvNzBWJG64q7WyeXU2sCV8LM1zw8WJTOC3iHaej1f+0y8WxGeMcn9GPsrMpnhS27ZtFq1Y6b0J8DSFVpqB12XIjamz5jA/cEF8wosf8tNto1Ikaxb+bMWIPts6Uu95s2bFPKJDxG7Xlo0zpFK5/jkjAF9TtrxTn5DWIl9qcirpvcghca4XX7q3TuJTeezRkeyPZ8zOPeM1urq0Y9S2jd0w16VhtJBj4toywKsjK8pdyzeqQembowVV/hyVFsIk1hxpmTp7XhoSrItJlEZ7osBaJE8bYWDe9fV8ZUk12Lw0AWyekmcCXisjz4Fd/TKXaJ/pRNyo4dq3bs76OuvlaKsjZ04ncP6/czZzFIx3lCHW21Eaf1XyHg8SR+JLXBnF9SnRJti30XYtRtpbEV3BKJZ1l2aLzuM73pU2zUMcKat8x3uOKkv3XquHGxISrf5wpHPevDmxkEbXKGrTli3YiA5Q/+QaCORhsv2Fh5McdVJvGja8GK+/OHa026kEhswri5RPtpGjZ/M/YxXtRStiHaZTtWrNVs3yNeAmzilHnjFvR5vUGx7F9xJGbA3RdmTIXWrUL/7EHVXJNR0+hXbdUcWO74YbtGN0r3nSmLRgva2zMt6RKWEWL45sKt8KvRZa4sWkxRwNFK/gy/VXPpnPblTkvTZE2ZrIFu0KiiOfwvuORroNr7rOUVPrZPSG5S5mrq4j+jmNCxisp/gXFqd3fjxjWsyS2Uid2zRjatN6iXPlrjrJARorqg1hNI3wS/OGTqtTjdYQFn8VXxn9KP6Nvpg27aNQRkrv7dcjYooICCnZ8GPhlD7Ul8JqedpR2jziX7qyfX0mPQmD+CpRaY7uzYspn8xL3LSFRNZdv12dPSZ3O3ooro1MUR42R74ZXWD0gaN7Jts4G5KDeM9EeUa2fIqsWQDi24D3lkQ3uMOHdoOypui7ojfkJ/G7NvO1zcPybAdzJ6vysyTqQAPnfdfWmfURI5NIjTboPKfDOiqsnssIuhpWjWJxZ5rFixnZZ2vlJSKatFX7erEBfNK1ZdNoj0e6OfOodDQ0SEVYK5Q8lDsleW47lWv/Klu/ggmcCvcVNOSCvt8QNfIFMnn6nIUxduKM+ICZGtIe48+0CzJ87TYZLWaE3uJFS3Dorx2tGYVvQnupt83f9srIFQrRVhRntnPKGugpbVXtOuhVetSWa0Hd5RHXqZqCDLYTbcduw45MCWa0+gvpkXfFj3RfZ3eRh7RmZI71W0R0xTJG65Xpyhh5WTkiH9ouc4gwnonCsv4dmjeMduttkJFDwlFFGPIok1EW0o9tatOlzoDmrV/+eEuaN9LC0XUjsT6G3udab551W7c1o9nrZL0TJu4pf5Uz1sW8/c6fPKkM9z1h81ooLTN3IuBdpwRP/Wh2OtQclGyPrHFbX1s14edDZZr4SbjJQ5ybn/LZcov85EbeKzQhT/jcKVHTsFXVH+u2aBLrt98g+V16V1YIk3qO7DNqt+g1IwqcnlZwmnKftjbJI5bnN/LDbGzVhbBFu5bs1NKiVU6N06arwz2QSgNG6qbekFeEnzwq2Z9tQN5lNN6aWztsENbtmTmTnXU4b9OKRcAZ7Tc6RnrxplENOfUNXKsHlT2+m5EnwKa8cVtI5YR1tVzhNqpAe2zBfOyxZV/Fejh8W6M/1C3iWJrLVMDgO674lv8FdtqvgtmjdGPB4tITbY7PWHfhU/CCn4EFrztkxJV6UTll3ZV3wia+i1xWdpb+nzCY53Lql4vf2va8p3xXlroA66c1G741MK3Xfr2MTNQiAbDEvW2WtgDt9qX3hJ3zxYsXxFvTZsWVv7osjj7uaCJLuwCxFo1f+8/6+/L3N32njgaJPR0HokYK4PcVDD3p3Xfj5RdfykVZDJvp3nWT6IjBrTBIBucomxpyb3jWG29NiAdY4GoGeO3fq2f02WsvvDydk2gqYpJpVFKpfAh7UYgoUH2uwCuddwVUETY6QUyG5730ymtx56DB4Qh43/0Pj5132ilD56twRjLKhpXg0kCiHnaiUoAoAHluknDsXDRozLxfCNCQU7eLeeb55+ODMeNij333KdvdbNwxWtJxNPzLb0p4j9M2XCMBww2G9L5CUaLNcqiHwky+8ui8UOe0vvv++zH8maHRCRy65VW3Ll1ifQzdEm7knHjagG9LKu2gMqiSz1J4K/C5aZiuisJdHiaMH5/7d4999/044agjY5stt8AgwqDDcKiMKwWZKx6D4GRIBUGG0pKXglDDTlxYpiFVzjG1XhPfxtk08rl4ZOTLsS2hcYcfdCALr3WiTYuCsqbiOHFKXnnuMX9F4XlPpi0HhCVlNwE25zi98NJLcf2td/BCxMH77B37MF/ZlbR1xKjApJUMg0Ow2KYakRpq1t06aawpsNMA1nmCwCnC5euc0+qCPI+PeCHzv+D0U2NXaKY5RrEhjSA0FZ4PNeZqQHNVUz5AnDDXHTObNNB8ZyKOuDtvuzXe+uiT6N66SZx4ylks0LVlCnDbS0PYEHONNq/dmspwVst1wUoFu7DnOhHA3Yi6fUNotQuAOdfslTFj44Yabg7ttw+LJP4wOhEiL+yGJNeDwNIYBU9psMiX0gy/+eDFLSNHvTo6ZmAQTpryUQLfnZHL7u0jOqIM2jVvHM0ZOS+OBWqqYQwdqmIMG9Sgs19XORqyY8Br4soFD9PhAB98bVugfpd/0zBmM/L+DiGyr1GcndFqRP3YQ4+A7reL9dsSQk0Hyk66MqdK2ZkFbvFN0RgRhR9KuxSjSBqbzboi7yKXxkPzE14ZG3v2+wF7vm+Z28tqpOe8YdtL+OUpMwP3aViQ/wrmaqo8zbcsVFbm/4PIDN/+8OMZ8ea7b8WE996Nd16ZGFsDz04822GjDtEVQ3lt5n41w7RsDPwNIOzVHQ3SvBKxPnG99OnTEbOCNlrO+QrgoNsE77GPPeefEiY+Y+ln8fHK+fExIm4JqKhP29jPYAYF9A2Pg8C1G64HrTSITz5jfZK5n6fCnc6IxOeIMjsopgEsyNe3wnND/wAAQABJREFU715Zz9EjRsbge+6MOdw/4aD9o0/vXcFNu6QPd/FYvgxa5KjxKk6klULv0jr1EW/8A3PcRk7yyzB4cDB5ytR4lO0qn2NO9CbAeuhxp+auLM0JWZW+lZNF7pIv3xuOa8isspHcstOF4IDmuY9sMQTYn3AYOj5uwoT441/+lk7HfbbvwbzHQ1k0rns+l4c0hszT9kx5I3zkm3BaIvf9WVg5lroI/0L014jRo+OGe+6LTeDVffc/hPVjdmIF9vUTRkMyRYVzcJU50o0d8zRooaOU7RwrfHj0njzstDBDiEeMGhXjXnk1evfdI9d16NylC44jnAKULy3Ks8JfB3sNvxasIep0OuuSHQ+MOHWhYbSGR7/3/uR4eDArpCOXd9ulZ46eqD/cjjGniJG/eacuBFca326hKDrUoYn/Km/aQBh8/2PWinjn7bfjNeTEmPenxenHHxPbbrVV6j3lrHjM78GHKY1pvk/a4TplGPkYdq0ut43U8YbXip+P6Cy6PsootsOcS0fg8EP7s0PIJrnwoTI+HRqUoaw0XNY8zNs8hFEdqx5wOqX5Of0mp5iAszlzmSf/6itx5623ZYTjwXux8GWfPrn4sNMphN0QcEP0dS5LlQ6omL+dbHEtHUpE6hP1q7KholfX7HmZxYEfe2JIOpBOG3A4C5dtn1vvJh6gd+lP/ZlTLzjX9jAsXTyYp4vyKTcNkVevqr+so/V7Gzl29913x5jJU6Ib2+ye8NPz2AGqRzpBfK5O9ufUA9fjqDoD2mjWodgdOH8wmnNXA4DxO2lAmnE75Zvuujd26NopdmJBU9cakd4T3hp85mO+OuudiuL8b+0c80l7A9jFu+/ZphSdYcviZiQDUPc+8VTs2mWj6Nm70LzrMcmVdlzV224P2RAlomxQjyTPYFtKTk7fkjfrMcXOgQfbexFOsVmEnL/7/qRwcWN3x+m9ay/WkeqK03Q9YNCRIz1ry32TO6C89dZ4Fv0eG9M/nhFT5pbh2h7tG8SGOD8602lbv1l9pv2iy5A79VPnaLPw4/v81Z2bY6FzLUl/OtOZpJTRDG7h/DUOvhVE/M5gusa4t2fFNKJr/YLZFNESOdu+czdC8VvGONp21gdT4+IzfsyaDZvFWnaEqJ91TPkKjqsBkpTFyFefiR9xJ+16rQywU9SCjqHTDFzA8J5b/hVvfLIwdtuuRxzV/1A6X514l0Ek6qcta3tqH+pgSLlGXrZttjXv2Pkvzo3Cx8p6YXEl/VfJf9C9D8Ys6nTMwT/KhWTXZdFO5a9OSdGeWppKK3fMU3qUDxQ2VYfXMnJKKu0l/evEcN7/sGeeiWEvv5pOkmNPPCF2wBZ2cEYel6e0FYU/p2ODIzPOTmNNHooPO4qWm2uwSLvA7/sT330vHrz/3ngHx2avLTdlN5+DYwvWjPBdaVF6FkbxmnBzPwuw/hBkOrCsQ75DaD1tJl6dbvWpuGENk38/ODC2bNYgdut3YOy44444Tjfga9DC9+YpHl0nTNkqn6sP0wahzKyfZcqn4ES5qgPdtYJmuZMP/DTk+Rdi7147xvbb7xCuaeSU8rTd4UcK4VNgNy/ySbxnlQrehTt/wJM2JTLOdVlSxr/3Xjz77LM4Gdqws9ROsWm37gm7W2k6UKmedrpY9hHI0+mD8js5Ur7O46J/xbX4d/FRZdls4H7rrbfitdfHxLP0nY7vf3Ds0KMH0xhbp+PV9asqvAJWQTfwl/PCC4CdybJy+jPPnUpo/2nSpEkxgn7Zg8+OiE7sUnP8scexK8umOcXHaRPKMvEjvtUfFqCscSDVo7SkjE8LBNtaeQzy4OuyEOs4dk568rGHYvLsxdFv992iH4vvd6L/4bdOGdOJYr7Zp0RWOZBnXi526Rp3w556Mnod0C8dDR3ad+SZsom2wRL0b6lwqd/38e936mhIUWlrS6gwt6OazoFx1ddXMRBmf8w6CjT8jiwK0h0joShTlDdIl/BdyGcO3r6Ro16O63/+83gPjJ51xGFx+JFHRA86ATJ2Ogw4piJEeeZ8HBYrSacF5ZkkYDscqhAZVqGrZ18CnMm+108MHRZXXv+32GfzLnHQEUfF3nvvlQvqCWsqNZmdb9MIUAhK0FaLfJIIeW5JlmFntwGCxFEk5xcqpO5/+JGYCFEfOGAAC7jsHFuwaJwLgOm55YvMx3lfLuxkx871D6ybdVJwJMziUD7ivsf5LmCIEfr6G2w3hpHenYiQPXfrlXhxxWjn+KeRxfeFIf3Wzykzy/WYTZPPi2eU/cHx1GpMTp78QbxEZ30YkSdjYPZzzjs/egP7BghAO17m6aiH85pkRh0EKiCdDCkEgVcPuqvy2ukVNxpp7lqgcHGv+kGPPhJ/eeDh2G+P3nHWKSfF1ltsnkaUgjURCsiFpYBVOrIKHBINXnjL+/xXUDlH3IUiZ+GAGcwaIH/4+RU57eD8k0+KI9nnuXuXLgg5Fkik4rmADPhWoAivAlDDvTgaUKzMT8xn3NN4FHaNJYuciLK/7a474290Lkw3/e63sT+Ll7WEll2lm0ZL3BaEA54AkxJcce4Fx2LSFNrxlgLYNHrM6/H73/42nnnjrdh7s03i/Iv+I3r33CUdCtKEdJIL+4B3M8s5sxjX8leuNk676GWtR8e7AVEMrq4Pc+X2YAqxoRha51/+yyzrotNPiWPoUNrxWgrsyxZ/nvhRIKbCkd6hSS4yD7e7fAV6eHLY0zgZ2O7nzbczn83wc2y6AdEdRDSsy8hr81pEQ7YbNJjzEgFW54OKLbNTyZPtl+BLIeu8W0d5NMC+go4qR8MXbEk2e+GSmDB5RrxIbKFRTVVn+IrzLoy99uwbG2MsNqW+LgKmESpiCp6hHQrzXBrRKMh/lCMfuIaH9DZjxke5Iv0olPJLI59nxd/DojeLTLqAlnP8VT7CnQYPbVA5G3LEiPyl8TJ3uMibRswtcfHRb6jjEkal35v6frw8jh0q3ngzXn94SHQAnu349dhgPRYWWytaMV+iOetarAGBo0JzDjUmOW9IKsAJJ4At4AdfEFTO7eV8hTAR8dFsDdZpQfl++sWS+HDpgpi2/LOYwk4QjsStwLmgHhTnsGuw/XuwAyl1YlV4iljAYpAf02GeQigDg88ZWWC5F1/8H3HEEQOS94Y8OijuvfaqmMD9K39yRvT/0X44pzZMWSFOli5xrY8yaq7RZsOKc38F7/6ttYq45x3XD1Gmjp/4dvz7ppvjjsFPRs8NWseZF1wae7DYkqNGypA6By/1l9+V8cq2dMKSaT0UvUo+FT9Gq7yc6zWQtx1J9ccvLzgv3mFk5DSU98mnnJILUWnQum6LBo8yTAABLfHtsZx5tFiopvYTBs+VCXMZuRg0ZEicf+11seemXaL/kUfHPnvvnXSjEa3+UGbZicntu2gvR6edvykva2RrBImPqqOrzDdyyZE6t6t8+Ikn4qWnH4v9+h8ZPXv1Yju2rTJqQoMz13mh7XL7XfKQpsWzFZHuNZpT/nKdtM43vqN801H9+pvj45Y774712raJ/X6wN861rXLbYPWyKJDONRbTsYncUT7qaDAP9ai6yfLMW6esstRO9RTgfvXVV9MIHTPqlbiIHVb22LVn6r2MDAB/ylfxaF6Vge7Rexp5GnPixDI9F186BDxOev/9GI2D56mhQ2MhUS+nn3RSbI8h6lxrZXyOqEHbwm3bmoew+a2dD2WtZYlj67EWTi3bU903i4Ujnx42LP55+eXxGnU79/jjYkD//tgoXfMd4Vtaw3k6GkCAdoD1EA+cJF5sUzte6WiwI2aHimd2jJ546qm47V83p6Ph55ddnFvvtmcBRmETVmVv7uQBztW1dop0bIgzBxHEofPyHVEz0sxr9Zf1e23suPjzn/8Sg3BQ9W6/flx81a+wDXYri+mBV3Wftk2ujQIe1Mc6Hqp2kI+kyzVoZ6MchFka0tGgPTb8hRfj9+S53fbbxD5sa7g7u1d17tQp28x8pANxK59KH177E2/Z4c38ix3me64eb3IHDVe9f2zI0LjmHzfEET13jB/st38u3OkOU2SaOFD32XFxVyKdDYkreEh7xC6+61u4/aULsLm1pbsyzMcZNQ1HtduzDnxsUC6O+SMc7G4TujELsuboPfVTCKjuPpwxPV56cSSdkWdjEt+Nn/Fpwri90wNpp42J6mrXtF6sxTbGdY4GaVkIOKZTPaFJbkzZot6xS6fLPZ0MnBuvhZsivgJe18v5iIGAN99ZEB8SwuTYK1WJdp07xaY9doxWjNK/ipNn9uSpcdXFF8VudEjd7rIR7VNFqeokEsfitXScFWjyqjILXgUc5YI/HVTN6HCuBJ/DRzwf//77X2Pgq+PiCLaLPufHp0UP7DGdFeq1hURSKAdcTFk7LxeQJE/5U5rVDldb5ci79rZINFHODOz8Yc8+E7de85t4g1u/OOfsGHDYobn1rvSi/FOHJO74q55L+DmmGDYfnisXHShMRwNw6HTQ0fAWDsf7738g/j3wkdiKV3969dXRF5pcty27j1D3dDryvXJAGhY/0npF7wkzdfKYtE5dyqCBEXU4YdgS/m/gZsSEt+PovfvEScianbbbHtpDf0iL5Cdiq/x0IiTg3Et7g7LzH0fr1YSoJ49LiQKZwYDeMJz41/7uD7Fz542i36GHRd++fXH4dqnjF3WkMMu3rvOgbWadqjKVMSXCqYYj3s31BlD2LjD4mINid98X+x9xCLsF7Rk7bI9Tk3WtXO9HnBTc03bSEbCn3QcevS/epRsqUNoXeeA6NfNZo2PyBx+krHn4scdifZxv+wK3C8x37sRaKbRPdtShGfVgOvHARzoakFVyhXInHSLUSbwrO13vSFkm/7lT2/ARI2LQ4CfivPPPjb679coFfF2AWBykg4qMxK3fZ+Jg3iaP5VfkjtSUfRfujsMOexw5cPdtd8X63bvFBT/9SfTcYft0wAijeFF3K1OEXd1h36Yx066MdJA+Uk5arANc9PkUHK59Mwvn0QsvsizAHbfG8xMmxekDjojjjzo6NqeP665y7u7iNqXKR/WUfKitqA28jOmzU6ZPp86PRftNu8Yxxx4dbdttzBP4BLuvPs5IOaQQGIfvafqOHA3JViCk1vgQmAQrMXxDw7nrxPN4wT77dA4j5OuwevUOsQmMpcdeZSMDa0StCeEZKvjsyJEQwk+Dwcw474jD48STT8wOtSM+elplNJWzBl2GaOMx0muUZfJNhnwh8FIgcF283cDG/49Z+OshDLlL/vjHGLDLTrH/QQfFHm7VQ4c6O6MKJg1BspPYCiOSCddlEaYiZMwsDWyFFT+J0FEZIw5uuf++eGvIczHglBNyP/nNcTSszaI6MrrCwnwTV+bJtYydAkq8JQr5A/wKE2HX+2VozvQZM1iheAyG4h0ZhfHDPn3Y27VbjsI6AltnjKcgBGZSJWA9lry5STEaBB6rsNxpU6flyu5DnhvO1onPxX/8/PLYA0dDGxwkLjDn5+JFA8Q6aDxr6FhmJVgsT3j9WZaKrxFGhYvwvIqjYTCM/ueHBsZ+e+4R5/4Yxba5iq2moPy4Vn1xb1KBkw3HQlmeW3Ye+KOTZ00E1WyMoaF4uO++4cYY+uHUuOy0H8cx/Q9Lj6KGmHShYWhbSmfm432FuQpZJWznxvY0dw1E6+nWhk0Iz31/8qS4+fbb47d33G7Rcefv/xA/2nffXIhxKQqZzFahtiYA0xynIPOrohzynDtZA97LMESOo8DNb667LoZMmBiHbbtt/PRnP8sdE6QTR2gBMvNPjzzvr3TRLoSWVJQOAoSwsOeWV6oH6NH9zV2s6DPgexreO/2SS6MjsJx2zjlxCNvgbdq1W0Y0uGquIcEqGwWheEnDhDxUMJ+hYEYx2vfYk4PjvSlT4+V3dP9FbF1zNGxEp3wdpk6sRVnZXrYPvKiDARDyaOcFFCefWxH5MqMayMd1GuxEf4WzEMLB0iKmm9+cxUvjzUkfsE4DHWXem8rPdN1FF8cB+/wgNsT4a8R39KmzbaWLXESM/FPucBDTxdHAhQBQR0ftNKbmsHDhZEZ3X0S53XPn7XHUscfH3r17524GbnNop1TlU4wiDaPC97ZJ3cKmZCnfa+zVQynVg0+lyWVEO0yeTsdr/Fi2Rxobz93/WHQBhB2gsa0Jw+zcnIgGxEYzFrJsBLHXZ5RMevGvSWp3kSHb14XNFG3iaCXnhtxRUDRpyEgv7ywgamUm21dOW76QqRBs7wYKv8HPtCajbzQII3tggT51C2i50Rps90a47oIVbKHHAjXvzFnK7hE4GsjSdOGFF2VUg+trDhs0OAZdc32MinlxzVlnxCH79YOfOiT/SMArloMbwJV/NbQAzdvAaz2UafxqbZH0hCyzY1fnaLj5pvgXZRjpcdXf/x77YhApU1xETpyb/LwmKRMnNB83xQH0QynKF3VBkaEYweBeQ2HEy6PiqgsuiNeZynA+Dsejjz02tqNTKm07Mi1+pXdTybI6L8d8IPy1HydZt+JomB8PY8j99Lpr40DyPBg5s9debr27UUYauDBlcUqBBeqR4Zh0DuTTNMSQKz6XXzU4dOqJN417wzXdUuvOhwfGYEbUjjvxpNiNDuPWlNOadQ9yuzK+z327+Uh5Zkp8mwt1UnZJjyl7yb8KuXc7SaN4xmBw3XTHHYzmbB4/YtTFbXfbE7qbuKMTkHSOTNTYqpw+PktDF/S4erZOTfGuQ1YjUaxNmzYttzgdiqP66aFD4oqrroq+u+6aW9SpD4TH98RpXaq1gfeUz+oky1FX5YACr7rwmO3mFrtDGSC4/6abYl2M/gvPOit2YNTejk8OCIBPDWU7MvJt5QTxW+V/0or0wnumCkfNcKIvZtR+KB2jO6//ewya8UGcz2jXCWyX1hUDWlklmOokv9TeKNPBlC2lI2SN5APLMLzbD5Q7dmRsY51Hdz9wf1x9400WHf/ACXNwv/1ycU0XNBMz2bkA1hLRUPSRbWE72M7aALn7EOUoc6QXHQc6IexM/+nPf4oHR70U/TbsHBde9cvoA93Y8f4cfhIv0rtwFpqGYoAv4TU/8CU+hLWK0FA3CpMhx0PolB53wc+iG9+fcCayAJvJkUydOkaoFtyCHQswga+Eme+zLamD+ctH4kk+8mVHyT/86KN4iEGCK//xjzgdh90P0Ku79OwJTbbPdk/7gsELd2lqBK8oxtNeSlsEU50ypUUqi06xI8zCbDgsl8KHM4iCeZ2Rxrsffihh7L/fARkF2rFzpxxldhs8p/TpdHAHqmeIFB321OCY/PGseJOpIrhcYpu2DWNjIn42btU41sXR0ExHAxFDGdFA4dlZtl68KydL4xUiRIedBRc+/ga6/pqfjgaj01bSgVjGC9PnzmB7XfCAyHPYQcm3YdcOsfPue+VWvq/Cr28wcn/dpZdFbyKn1kaWuJh0dpJ5V/quo1HoJGVtQqX8AiZwJlDiX3jWYgT5c3DzGhEHjw8aFNfTaTwMOfDTU09JeyztRdrIozSis8FFBtfAgaNOSl5FxlhmTtsgT2VZtquDHOB+LqPTg554PP78yyvjLZ5f+ZOz45jD+ufId+VoELhi3xVZLH0WWZ8NnLSibSYffU0VvqYMR5GlHXcCuOX22+L6++9P/XHpf/4hfth3r1xo1/zr5C/fW450nAvSatdB6+JImk0+lQ5rMlN+Ume9hD129XXXxCjsg7PY4vuYY45lcHTblB32V9RP8r56JPEF7OJYBCd/5QnXJMuqnDPuMvfJp3PiCWTk2ddeEwd17RoHHHZ49N2rb3Tu2CmdBeqI3MKUdstdGcC9ssd6lCTstAPlVbaN106HMlJlCotO30ebPnHX3XHsaaem026rrbZkoWO2A0fWpE2jTBP//KvwnnlbBjizvCySm25f2xhnr44nF4185fXX464HH2DXwI3jQAbbHADesAMj9/DgkkXKmrI7lXa2OFdz6iyt6NR8hbf0E3D2oBvUXzOQA2+OfzOes8NO/ueff37ss1vvdDQ0N+qa9tfRUPBbMJE454+4MJm3yTLTacJ9nZrSqtESz6I/HrjxxlgDB8AvLrwwdsUBYzScbaSuThiB1YyM3NHRYB/1q5Xyjv+kQ97FsHUHCSqYu2HMYxB4BH3Wu2+9JR57fVycxUDemejuLSjHdctcMDyjEOEl6UUb1C1EjRp2t57J06fFvfQZ23bZOE4+6cRou3FHGBajTbqC75RthcA4fE8T9FZHwf/HVcgmSMYjC5FTtTiCSQZ5G4/7Uwie+XPcGqd9ji516YRHnI75EghYOjEE3+0FJYphKLbDTj894TmLkK7Tzzid0e8tcq6goUeOjNlJa4xXeA2FH0bvNzRm6eiQG/9lQo0VhaBb9ug9s5E/YpeE+x4fHJegmE/afY/4Yb9+0RPF5rYuVURDiVqwc1SMfatknSojzMsUMBI/v9zWiPKs9qSpU+LGe+6OiU88F8eedUr03GnnXEHYeUyuUG3eErAjphpDEncKX5hIWFNRcNRgcRsjO7t6v1z1V2WoELz+nzcQhdE3DsDA7dqxE6NGraF55wSXUaBKEIrAShCufvR+1ewljBG8wOgTxk+IoeB+yJAn47Kf/zJ640F35CdHh4QHfIhXHTmG1Am/o+IaMwpwBborAru1ltjQWBHvTvsYy163QzDQ//3IwNit9+5x7qmnsj3gZtlWZJl0Iq0o5MRvIjMfqMAVieV+Cl2uFC4aXnaGZ8+dGyNefCEee+CheHDC2Lj8lB/HcXiKO7CziY6pykOpUDFE2GzddaJyLK0JLbWC9oxg0MFQ5mqyCCHOoTaEcE6ZOjVuueO2uPbO24Qsbr/utxjpRDSAm88XLkJIOV5RUioATq2/1coEAdUJQ170vu1R4X4USv83v74unpk0KY7dYac4AydbT5xxtucCOoTi1RHbnC7B+efzWfXb+7SHdXKV5AbwhGtJLGZU23UVFIjN2BHCVdiffu65+PHll8YujdaM/meeGT9EQWzSuUuuiOxOETqpxKveej36AJejZs0ZLVzInLxRdMYHDR4U70Lbo/mZtsPR0J1h+o1atYk2xOY3+1ZEA8odJKfHXDyoLKm3DkURJY/raPDnCP0KetJGNNSz08BKPY1wbM39fHm8+d6kmDgZZcdn72iBkX570QVxMKNq7VlPgQ8zcqI4AyiHSojbVb/aNWWKbwg3hbxGhqvRuyf4C4zC/vlPf4yTTj41+uFw7MjqyIYZmosdu0oWqLjIITuM8oPhkDAG+yl/UZvvyVPqZzu4ReXUmdMJiR0fo8eNjSH3E4HEtzvBLz06rB+dmjdlZIwdAPBpF0cDvFL7ZxnCCrcl3VuqjgYpzHm+2k5yRCMMVSQUxurXMQdnw9Rli2L6Vwvjy8Y4O1qw80RLRg1RiIuWuBUUIejIyvp4510D4zPk5aQ582LsjIXpwFiIbjWde+65ccThR6b/fPjjT8XTv7k5RsbMuOa00zEq9o2NGC1NwwT4nBogHyVegU3cC5sYLwZQPkyZpmFhp8fRUjsF4wmxv/nWW+MGdMIGlHvD3/8Z+2EoKvPUEeqFdDYkDdExAW+G38sHAJBTs9yi0k6u8lFHp7RraLgd7RGvjI5rcZp8+MWyOP6gQ+LQww6LrTffougb+L6sMF22EK7wbf09Xz3Z5pWctF4aH3MxKh4ZNjTO+d1v4rAe28cBNUe122J+zoiVjgyNFvnJTq1wy6PiTRiNpiikWDoLdQYv7WiajKF484P3xT333xM/Pf7k6I3za6seWzNitBbTCpkby/fNgMNV/OUvisk6O9pn0vhX7opLDVxpXn4z2m42I/djMOZuxtHQEyfAgXvtnY7qdshJG0/95NFR/jSiyU/Zaz3EvW24HCeaWxvaDuph1yByhH3mjBkxDj0/9Pnn4oHHH4urLr8i9kKv2onTEeWoUTqgpQ/gEbd5BG7/+Y54Ed/5jHroEGyJI0A9/ipRX488+FD86enHY98ddo5Lzzk3dthy63xfA17dpH5Wfinv7YAbfZS6MDulTplDP+GYVs/quLcs9f4SohyGP/983H/r7XHPO+PiHKJUTiayx9BX142QxpW9dnTdeUUc2UHVgHbFeOm9OI3svIAa8CTNa0w2pP7TpkxJnF+DIWr615WsLr7/Aayh0pz1qOYn3NK4+WoP2K7KGABMWhJPUoc7GjWCltRPziN3PRmn0Y0l5P/6v/41HmCL7wM7dI3zr/h57N5z15Rd7uohHdiJEjfqPG0F4bUM65ajdRi28mY1SJB1Rlc6Avn0yBFx2LlnJ+wXHnRonIgxvAX85Na3OrCkFekjaUS84DATt7a599KRAa7SGQPuzds6WT8j7h7E0XDVv/8V5/U7AKdd39hhhx2J4GmHXeQK7sqC4pwjCIxvwAU4yhF7kc1PyjdKzm0sxbnrJ6nHnToxjg7GnY88mA6c/vv/iBHYbaMTjgaN/fmsFbYGDnKdeDM+nhFPAceTD90X02mTN+k0GXexdauGsRGOho10NOC8bdYAnQMPuKaV8Ft66bTJKwUaoTL51HbzB3LTXnExyC8AeAVRe58j2D+c8wHOEBwOvGJ5rjHVmSivvvsfnNGkrzG1b8TjRHxc/ovovcMOsbYRBuSVbQeus3MEPgAl+ScJEADEiXhOOuLoiLftYEfdqIC3iQoYRQTMoHvvjk2wg884/oTYatNN8xtpw90mtPMWLkBH0HlvxhaxOktdVT8jk8gz9ThtI/1rt7pLQnPspcXs3DFw0KNxzVVXZoTpFT8+PY465BD0R/scjKtke8Fb6exWnd5sUYBP2UstrJedsWWMhq+FbdmaqIX3Jk+OG276d/wZObk59bzqP/8Y/frug02KjS280KwyWJmgLJMO5QFp2SlkacMiHz2qL6yH+NLekf5fHPNqXHLZpTF29sw470cHE+V3REZPyZd+L86TfvnGlNapgAt35lS7y73sQyALsi3A32fI4cHDn40zrr06Du3ULQ44vH/06dMnjEp2FxsHU7OTCxwUV/KkLhRoppmEVdh1kCjL3MLTe077+IDR8buIHH7qvnvjtLN/ErvgnOrWrVtOYdMxWDmOxG/imr80XZZjJhUtWytt1jXgpzVxTjllcNq0afEKAye3knf3zl3iECKEtujO7ijwh/j+jHZXtrhuQ0bxUQcdpzojpRlpJJ2ZwKtDU3mjDZjr2s2ayYDe+zFy9Oi4/f6749yzz4l9eu0WRn3paFDeyvepH6i79c0K+If/lX3tbZ9JNybtNOneqRPS+6NEJS9nN5YrLrooeiILsk8DHj2KBuWKdO9UEOl9Gdv9lshzZDrw6hywv+f0IiobLZnC685PI14cGffefgcyeHSc3v+IOOfkU2LzLl3hHSL1kGPiwags8a7T2D6Hzgp3F/kAnX87g5jrdu8cp51ySqzTpbMeRGSG9o6/IlmyQt/TP9BV1ST/f2pQGl6FoBDRMLGTnJxC9m8zJ2kIQnwOho5EuSOepG5du6bitzNlR0QFovNAgfjM8yNiwNlnJUBn4gk986wzCR/dOj7HKTGPxUJUWHrLJI2GEFQjhjIbwoflTjG2clSafGVw5yWq/BUoM2bOjAcwcC//61/iKBTaAQhAIxo2YFQnw0VTsRXG9lvLkAClXInEo9c+0ziS2NxP3lGeFODvTyKi4d54k1DJY087I+cFbrXlVjkXSK+WRhBSgnwKnygoNEI9iq8cJVHwUUgKJxSiDGnHyKkTo14vjoY+ffbA0bB3zlN1D2w9iE7HSMMK4pVhqlRgFd5VP5vda+cqWo8PCWEcZ6foORwNQ5+MSy/7ReyBk8TRXUO/3ALQJNNmxx185uhTDS/phCE/nQ+OojniovGrB91FX8aOwwgdMiTuwoDeolev+CmMuC3GioJHYyTJkPcTv4Jew49larQnzj3PXzEuHD1eg/xnMzo9hNDX267+Qzz/5Wdx6cmnxfGHHYHXdaOiFKBJQ9CEVwFosnNSRs1gfHCnIpKKi9GFBxYcNsOIXrNVy5iGALzxlpvj13fdlt/eeu1v4yBGXlrhvV9O50KhIFx+X6mDrAI3PZpSEHoNjnyZlsiOl+300muvxtW/+lWMnDolDt96mziPUftdiSbxY2leHhEvOqucj2oYowaYhelsyA4FdXDaynLwbiRDGV03omFBPKliu/yycHmwC884Ow479NCMaNBJZESD89B0fqmg0zlHnjqIGhPiv2TJ4nhx9MvxCIbD2yj4F99/N+uzDX2Y7hsx+oKDpjWOhqZ6A2wn4MxOFhcqZq+lDUVlWYAPdHGtav8Gmif6Hn8B7SNeqMM3dIbdOmEOo03jJ30Sb84q235NyVIj/nDJxXFIPxwNLISqo4FggFRy8qYRDbaBidt55rV3Kgee9GYnQAN8Kh0AFdtf//mPOA1Hww8xuDqi8KV5FU8azMAq7pUDKiDxrXzznjIrF1aj/evDH85n9LeUjv8HM6bGGJxeo1HMQx9+PLoAQ0/y2LFLx+iyNttn4TpoxGKQOhoafCuiofClo5y6GpQ21iUXE+M8iYe1G4w68N5K8DYPw3fSEvZQ/wKHH2K3YeuW0YJFbu0ALlHRIXIaO/kX77jzgucs/SLeBr4RH7G+AXqs2mziYpTvMUcfk+331MOPxj3XXBnYwPGf510QRx50MI67dmnMSO8aFiJWOha/YjnblIs8cp3yBlxlSDM4T+MCOntj4lvxjxtuiJuffioXZrr7b/+I/fbai2Zn20I6gI6ki99iDJU1G8roiKKTqCp40zEGFbaGofSl8Wfn0s7U80Q0XHLOT0JKPY/OxdHHHZdz1sVeGfECSNuPJPyFJX1aS7VnyqRyWt7VYHXqxMAhT8XZv74m+nXqHoewlk3fPn1z6oRGXBq50KJ0n3oKmaNsFsYMu6VuwqzMM4k3+UHDxTq8/+G0uH3ggzGYztFpx52coa/bMJrmVoGLMaLd6q2xPF+jQ2GUZytHg5Cap/oup35ROWWZ28QZxWPH5V+33xZbbrZZHLj3PumA6YCTRNloeKd071zubFfgSp3HMZEE/Yt7HZkaXI6qajBZl+kYuK8z2jUMp+ZDOKp/ddkvoi+OBuG2vnZ0dfCkfqOeaaxLQ6B9db1UOR/EjW3erAULPiL/xr7xRjz5+BNxyz13RLftdoxLf3pu7EhEgzxpGwmDLSjPapAWG0QdJa/aEbWzCs3wnjhz9M/z5oz0fY5uHYLz6Jarfh9PLKNzcfRxcepRx6QTP0OqeV8qN5pB2nd0TceA+ShzpfdqbYMycsy7vKMjrBH6adrUKXHHPffEL2/8l9WKf191dfQ/YP/clnIh9KQ9UCLt4HVwrfykWtkG1kkdkfehI3VetgF0oBEtzb+GE/8POEsfHjM69m7aLi67/nfRp1dvkMJUEeqWnSLyFF6TOto2MfnMTsCXOEe1A5SNdmL8uTCaunsIjoajLjg337/y2BMI7z02um6+eXxNB0KHh+0nLm0vG0Ee1A6xzdN5r80g3oQ/24ORcsrUjvgIe+xBbKWrb/pXnL3PD2Nv5MCOdI50ABVbA1kg7uF7eYpG5L92HXJYmUuZlr8Sx6cdlgZsE+xW4dpy0wkjz4iGRwemvs+IBiIGO3fpnLbmUhxMLjzalA7mzBkfxuPYhU9gu82gTuNwQmgp9GjRIDZimt6G7HSyTjM6IEQ01P+KLTKFQYwAU/I6p8oRqK0uiW1JRvmtrvP3VT0668D+ZT22VObB9LkziWhYmtG7RjRADdG5XQscDYekTerUidHDX4xfX3FF7L7jTkSm6WjQqUO9IRLx6q+0LB/XkjJAvBSHhPYY9M8/pyJpp47G5njs4YfjL08/GYcjv849hYEf2lS54XfSXBVCbx3r4xRRhjnCK43I99p5dt7lLd9XtzZlKqsRDa4Dc8V118QnwPPLM86MYxm532gDHA10usxHxIi//FFe2tkc87qGOOlV3BkR8jVOc51Ijan/O++9G//8943x14EP5ELLf7jud+iPvaMl9pgRoNkhtQzySRkM3dkBlp7k1yIbkXHgT96VZ5XB8qs4ehHcXIK99AaLxp/aZx+m3p0cOzPwY15pw/O+fCN84srkgUtS1RLqFW0GbCnK97l67dO5TPMdPjx+8ptr40frERVw7ACca0y969Qp7TwjGkpewKXiJlMHguStLItrHQzC609Boby2Drbr5GlT457HBsUgIqhOo017Y2tvSf+jDYORqxwN8hD8JNz5r7RDgk7+1X2ATxvC7c0XMGA3nQ7xq9jxtzP63nGDDeNgbOCtN98y1B/KQ53s2kc6Ssw/dSH5iW/lg/qiNK0NU/BjZa2P6z+8S7sajXjLA/fEhT85NyMaNly/PdHgDKQBi7SXNEGe/ssksgSYlLZ1Oc33PHWxXm20d959N15Ajj364MBYjN1wGRFau267XQ4C2D7ShxnZh7Kd1IeWqX0pjrJRqJvRDDoaXIDeSJvmrAviFqEjX3wh7rvjznjw1ZfjlIP7x9knnBSbd90ExzQOVmhdvWo/T7wIufpBOe6AiY6G+++7P9pvvkmcfNKJ0aJTJxQW00ssl/d4uZTPne9rQkbSat9RspFkZpVmUbwgCgZ4e/yEGE5H8KMPp7MmQPPYlnk93buxkFN6qhC+CEG9caAzR6Cff+mlOOPSSxKqUwjzPsWwrq22SiPRRftUggrBL1fQiCi0JhjRTRowLxDioMRkOtdkSEbk2k6TlbTDNgPP2WPPDIvrEFT9NukWBx5+eOyJR9GpE45gyhwKKhn8vzJhChaIxiTTpyBRaaOQl/HdfEaZJ8Is9w56JN4a/kwcdvQJsRuMrqOhNaOkKnTDobJDB64UJCp99zUXZxKzcGeILUSYJVkOTOm+1WVEioWZWLRql547xw923zPn2m/ASKOMbihizgGGmMWRdagBuwpe8rOcQu1lnQDroaNhLJ0ip60899wzce75F8RujK63o3PRAmNPZjRPceQ8Z2EX12W9gOKssE1UaDKh00jEVysEnEJw/ITxMRwBOxAP+oY4eE7DWFGxWXcNUL9No5ZvzFf4PP7X86pNrJkr/DeFnmZ++mk8OeTp+P2vr4kPuX8uxtBRdIw26dQ5hZ54MIJBASKeZXgVY6UEhFPHSDoZoBEVqJ2KJoxGGXXwDo6GW26/nfUl7iH3iL9dcSWKbZ9ogxG9AuGeBih5CFMRIwW96VzgXrYCFSnOBk7EP8m2lyZGY6D/+te/jteIWOnXbdM4/2IcDTh5XHdED7pz++wgOTqazgYUjwJdwasQFUvmbVjzlxybrMW6FBhQOr9mzfkko1QuvObqLPOC406Io48cgKNhk+y4uEaDqwObdzXSoGKww2z9F8BvL7/2SgzGCHv7A9bxeLes0bAVVtGmGzWKDjgaWjZlheGGjijSdtl+dsh1gtBa4iWVWsG32JAexIFlaESs5DtHegwvdY/xFUjyTxayONB7S+JVLC9cCoG/IdN1zL035Ht9Rq3QUAhx2hIZII5tgTxSNsXWrss9FZsjpI5kKeQ/wRhyMciXiNa489574gSmTvRlFNC5wUZWacCnoQVs0odKRw+3CoMKpaNHZ4/PxJfrrOQaDVT8c0bSJ384BUfD62novvzU8DSIdgXGXl07xSZsb9mY+QyNvnZ7y8rRoPNFs6rQv+0LxFlnStAu42maY+UNDD/+Y4A1SEfDxAUfxttLgAt92Wg9dpxg5XnDHpdCz1+hERshI5k9Tegu00bw0r817f14cTowUIR7OJsuZLrB0YT9WdgTOBr+/ttrYzb3r/sZUyoOOZR5metk9JIdPzKttbUQF1wX6MVXubaNxbX8ZIdQJ5nGxviJE+MfN94Y9w1/Fogibvnt71i8dc+c5+mCWTlVyjYU99BKxfPSjfyigqdBslzLUDbZPv6cejeSiIbzLr6ISR8Rp+79gzjuhBMYkdoGftOZBp8jq3U0CqUYTTi5qksV/Fmxqnaue7FmGCY5iE7pz4ho6Nl83Tj05OOiz557RueOHc0u6SEbCyTYYST3hN9Rd0NXlaHVehKeKy+V+fK3nd8PkMMPPjE4XsLZe/iAY6M3c497oC/VHxq53wC7Ok/HuvUtsgucULhyvJI54l269FqjeAnGkLtxjMXJc9Pdd7OS/jqxP50612hwNE2YHHXRoE1nMXArL10nQP0srOaV0XvgX0eDeJOfrLeOhjfo8A5Hfzw98vm45GcXxu7IMBdoc2FdozpShwBLdmyps3yrDDbyoGCqwEpJpT2BxdBX5/ZOnPh2jGD+7iO33BFrso7NeaefEdsDu7BmJzflOnyiLIA/hUn8+NwyxJP6PyMegMFvdNDa8ZrDSNwQbIJ//urnuUbDWUQ0nHj4kUR9dTab7MjpJNJZ7ZSwxsiGdNCDg3RE8I4dd3EkrNKp5WXkJTJ0KrgZyCjj1Uz7MP3lssvjACKE2oAb1xSRToRHWrHANEaBUT2lzEnYuU4ZwEuOrOkct26+O4bO6J/+en08NW5MsLJPXPbXP7DmSa9cW8MIBn/Sl/i381/hzA6q9Jc2A/DWik+7yWfClNEeo16Mc666IjbhhaNP+XEczHSkzbp3S9gXGZGKrPJdf1YgF4Pje3WoZdre1sFzO6lcwIPFWT5j5sfYY8/E7++4LU7YbXdsMeZ977B9LjbpOlLmJx509ACoyMnvc40o5Il4dwHIjGiAv1z3pyl2pVM+XKNBR8PAJx9PvhfnZY2Gjtg0Ze2PdDTQGTFa9GkciE8/MpAdzxbGm3PnBS7v2LpNk9iQwbEOrNHQpklxNNTD0ZC7KwGT+s2kNLL9ijzhxHbkn905200p5s/pE1/j8P0Gh8gKHMYfsfj5+Ikf544kYs+1iDq0Xit677NfLlr5Oo7BCWPejCsvuSR60dltjj2inZs8A17S0SBeqlQrV6h4zHsF59Km7dMS3Wbk44gXXohbiYx9cPy4OGTPPvGTk07OxSbFp+1lBJRTuYwosqO7nGloXyIfzFMaas6ote8qs6R520oZpDP5YwYUnxo6JK77y5/TcXLJaafFgIMPyTUaKieUNCC+bNnSvh65AeDyg8+lGWumc00bO0eUuX6Lxam1x255fBBXEX+/7IrYB1u4TavW8D9YFgc1e9I8zFbAE1c8F27hND8fyq/af8pMHb4vY49d9+tr400iGg7BqXn6mWfGLuDeiEx5yTKqvkVmbSY12G3zcs8ii82hI8C7yv+ZRFQPeWFkXHr9n2OvtdeN/XE02P/oiqyRj3QKi391lBHW0rzrZBitJ56sj/oiZQy5Wjt1qu8r4ydPmxaPEDn86GOPxnE4d3YnIk79sY5TJ7BXKkdsEiUwCbb5JoDiqoCfsBfdDVWSv46GjxnoHPfWhLgfWbZe67ZEkaA/ttiSXZ02yr5c4t7Kk4/RfUbI6XzS2axjSv5X3rizkviWJm0L9dQsIpuMsnmBgZ/bBg2Mi04/K6feFUcDUXO0jXrI+he6qAFa4d46WJlaMm8v1R/qPx0NI9EfTw5+PBaA43Np05230YFP9IU8pROELHXGVHjS1tPe1nGSzl7xDQ3YM5NOdN01MdoDG9lFih9+4IEY/PprcXS//ePEI4+KzTp3of3QF/RNHeC076FNoy0gHowMdGBw2kcz4sknn4jO22wZRx9zNFv9bsx3OJmybtbCuq1WuVodv0+H79TRIPFLDHqHUumAnK8RTq7R4PyYCUQ2LEKAde3aNTp27JgdnGIs4Z3jOwnejtUoRlDvGDw48diBv2f/x0U4JphTTsOomDPxrkKeCOSoR4ejAVZQzjWXICEkBUGyIcRSLernfVc3ffSeB1hkbkZmcwLzAnfatRcdasLtYFYdJY7WSRBFAJbiyCwZSGpMZrR8fxBPdur4zikCk95/P/5y3935UZ+1Noieh+wbm+BUcdFFGdGfncMcEYR2LEPvWXbugF3vcBorwJ/0xT2JTLzMI3R2HDsIPPzaqFxc7jCIuVPHTixe1zoZRSGigK28rTKkmfB5gZUTYc6Ms0Iu2MhIJ2k2HsV33pwQ9w57KpXDkbv0wpmxC6O7bTKiwe/MV/z481ylqoOjhKkikChPD7dOBldct+A2rDSsp3HSe+/FyIcHxbA5M7O8C447nikl3YvxQRslrMKbvwKhIAqtqYK6HNPsyI7qGgjheRjQL6I4bx/6VL67U9sN4/ATjmJOYIc01IT1CxUEbVqFBdsOJo1QsAKMCoDiKLEwBaAhaYarTkV43/3bP8aY2s67x/9w/9iTKJi1MWhcfwJiS/ikt0rlJ7lYAHl5nimB549H/mikC9ub8MfvbrstX/HPLxhd36ZHj6QLDdwUfOBVA1GHAF2IzLSMKkOP1EEcp6MB/K1BB1PlDLVlqN7IF16MB5l/aurbffPY/9BDcuR+2RKmWTACYJ5GNCTdi3/yatDINRqapqH5xhtvxEN33RZTModVf3bitBthEsyeiKZYZbBXeoDtNqSjQdLl3DUCRAL2VR5FPdXOHxtMsNUX95mKUZ8hJNciYBA+po+NeKwOcTyvpR9u3S32/9GhrEYMXcH3GdFAu6YCojA/qT6r2sNnGdGAkjAyxzZ3C91pU6bGyIcejVdYULHnmq1irwGH5NSu5jivKqVg+4gXO8p2/k0a9zoMc2Vlrh2xNyRVmrHiS6H96R9Pp11fj6dHvJI7Zrg51gB+e3beMLoTutf0G6IJvmHaCnTXUNrj2vYSeqlbqlTRSZtgJu2Cctdr7lLJNGKRP3NRVuPnfxhvfKazjLfxzDRfj7dgbfxIGCGEQMKeko1rYcyZtzKepzEdbVo99cTRMuCIw8kYR8PAR2PoqJH5+ABkwUEHHkhnl44RES52PlnHMqMqsn15S1wX3NsW5Vo6SmMF3HksRkV9QiQnx1+vv76u/LP7Hx67sXCgnd2lGLRmxKcJR3FSFWNUma6xCGOk8yKdyVybr3LIo7zyyquvxd+JCjD14DeAMPvNCAs2Xw2J7DjTpt4o+LU8C8xb+cc65Ae1owfnf+t4G/L00BiIDDYd2WuP2GW3Xkk34q2ilwJ3vpLt6LWOYGlRg0t8mL8GkfUqTs4VLIo1I/5w1604hCJ612sTux3PorZbbJ4O+uJowFWEh0n868gzP/EgfNKotJEg13Au+QiTIbmLmOb11oS34ubnh+V88FP23T+6E9nQjo6U9K7zRX2UI9Pk4q4ijh5L/6lNeSaNWpqOhsohKz/NJkz9bTpFjzz1RDqnDtt519i1167Zka/aJx0fwJs6kHbzqJFcOqhgQ/zxXLzkiBIw2alxAGPypMnx0uCn48mPP+A5kSpHHoMDf8uUiZavHZE6ClgTXoURlVfe+1wc6ISoIhu8p13g9APnH4/GUPzH449k3lvFGnH8z84Lp8Nk+wCntkFxsDFfHbg0Tq1P5sdXTlWxjBwkgD/8zgEOowE/YnG8h/90Q7z0Zdk75xAc+PtmRMPa6QCwwRL+GjWmfBE38L+4sY1LRw7M1NpV+lEfmN5+55341b//mef+ueRYFsrcbrt04Otg0CEqXNKH8s/vhF1eEn6TuEu6gV4sz7r4jotnvoD+uOe5YfneD9t3iv2OGxCdO3fCJmFLUjr0ytZiw0DTZCIPmreOHB0itrORLDp606aSjmgf+XD2bOyxW+6OV5Z9FhtRwgFEIG25TQ+mP6xTszNKx1Hocm0MYBM/5iVd2ZHmAnixPewIc78JetnohlkzZ8VbDHLdOvzJhP3wHXePLbbtEe07bJCRltaxkXoOOO1EDWftroE4a/5r2oEbXZGnBIkR0UBxyNN6khd1VQ7nD7zJ0XJglQCVevPjhs7RrzWRkMlfA7LnzGCLKe9GDKk+WO3YjfMuOFyeGvN63j2FzsuO2+/IFAYc2tRX+q1423pUyTLBkKAlLDkFl3elH5087gKmrBnLgoc333JTzKx9eCVRB9032STztRNUTTl1jRQdDe5QVTkaXDTUqRVGlNnW2qvCYJnak58y/W0EzqOHGKAw9enWPQ7q35+d3dZJOrT9fFlcKX/zCE1U5xBg4lQ6NE+3vXbrbufzW+/JU6fGv/70+xxUMv8T9+4XvZDBLiasbVq1ic9KIhf+p6MBupOuhd2jdF/tTCev2YcZh+Puz/felZ9i2sRZp/44ttp8i+RR7VrliPScwAF81oCjuC8389PMO+UMPOCzL8HTHCIpnx0yLB6faKxgxBFMv9uZ6OQNGeDwXfGYeo42SF0HASkbdDimLOAbedZdndKmkfeAOzvw9BGmMTp+0+235W5RuwBZ72OOiW6bbYrDiO3SlWHkK/6VtQX/wC/uRVEN797nZuoo7UpJXYfjPJxibzNIcO+IZ30jTsEZ1p28nXqnHJXY5W8rq5NEW0E+VQaW6CYGOijfvkzlaMioL2jGCNP3330vnnzkgZhM3vtutW3stc9e0Rbnkbs2yVtJY+StbBXWTFkJ/hSgy9G6ZOMw6IajwO+mTmF9iWeGx6PvTczPTut3QGy77TY5BVxHkLaWeEknODBaBx0/2sXueGGZUri2ntOSjQAzgnZN6m1Ew9jXxsSNDz+UeWvO/pydf9zZzWkTDXAoGlkhH1VlOBhrX2kldPTJ3E/j2VsfiL7nnhhHHDUgunbrmvZ7PWy1krKStfPv5+E7czTY7MmANJaMUQnClRDclMnMQxszJh0O7+FZcuQk58rzLphPJdWQEVEdFBLQIpwJiyBSk/N63ffYML5UjHwjLatg9BJZzhK8Zzn3kIaU6VRiwqPQ06jzPAmT7xSyC1xXAGK3PMMPmzLqoKGahAyzSGSmojhsZBK3dGyYZEqTIzwAkIavzCiRuj+vzhC7CGuRr7tBqOAlZJlQODSEVG7OA1IwLmCeo0njRYLWoSLzq0gVJhaXhM6zJeTt3srOR3O0SKWTU0/MoAZfOa0ByYV5mfJo+Xnl6xq5Gnwq7mKouT+ye0+3IpzQBSz91HccgRA/CobS2dXx4N7pxTmTbU4dc45wrQxhL0Y1jIlzxX3XF4J78dYOB4R1KAZ2zdgRMoGzHgkkjJ3HFOWJv1pNqAyvAJwjbDnfHwWhoaEQsy3b4Lm37DR6KFvcC6M/81ehVArD99Io4pkqO8N7qWsKNABROFp/jRcFREu8oG2Y1+kY1JfUCcKsYZRveb8CPzGdAPPHo8jkvyLe0zT4KFP6NRrGD3MtBoRXEzpdCmNHYBVQ1snnjjKmwqRMaUohqLGu4VmFNa+k06pB6sq4tvnCRYvTIJRfKs9qrhOiEAUWF6yplJpt6kgGmedCN+azCGNyIcJUPFdKeR22Mthw7TXYcWKNaMG6AI3pYH9Fx18YbS07QeLaaqdTh/ulI1Q6VoZ6+7MN7bDUo446Rxzt+RLB/Nni5TFj9oKYs4hIJ5456m7kQ7MmzQiRbA5OkBXAKo5zBJNyJJVUvuBFGpO+bQ/bMRNlaWwVhS4u2S8dfloM/p22ZYieUSQJP0ehT8daGulFPokbPdKO1skH8oZ8r1IyX/O3W7ac1YSXsV7GN8tQNIuWxKJpU6LD3CWxW/vWsem67HMPYbu95ZrA6dQJ8aqrwVrI8ZZdftArcLsoZBJPQleeuRika1vMIcxu3NypMYF2bNiKrU837hytNlo/GiB/FsMTLF+DMwPDCs9DA9ZqWIKFO/3TeTHzM3ZaoZQVzF/RCNG7v+4662a58z9bkB0J69VwLQwF1vtQsdphbQQ+m0uTtl0aLKUda6ya7ZByTpBNwp8HbvBfPjXEVYekMsXRltatWiZtO59Tekl5xEcpB2kLZbXtYnvzEPqGHnhPPrYY5X5lMMynQz0Xo6gxBoSrYSvj7TxrEMvHdj7lqUwVbQifN7IS+aRcr3ZLWFYCr1t9LQV22zpH9xgVlz+LoxpaqPIgb/NMegJmO/PmIa9Jk55bTzu84tm6KWPm4jh1xMSdIByRceqf+DSyqR5t3ISyaM3UR/FUs+cAAEAASURBVKkToUc7WNZfQ8iUC5xBl4Iif4nn1E/AvRQdpT5dm/xTPwkT72r4FF4VxcDlTxnBMb+H1stiWEXWWLZGpKM+toORbguldfDTArhbwE/W0W+VqaJaGZA4oB7e0xAUbmlCWS3/2pY6r31XmG2z4sCGbvKd+tEaw1n9mgYi+SijlefSickpmDre7egq+4XVcuXZYiMITxnl1fgzf8t39yj1QTobaRfrle+BA/W/+HFRPNtb+LJTQPnViBeFZL2sc3GuoYPIc8HCxYl3YWgO3Gszol45M4XTaAthd/RMItSRmW3APfNS7lSy0qPioLIp1HlOjbGdUr5LH9ab6ypZB3Fpfbzvu0mH5K2T1Pooy+yQSo+2u23hN0ZTylPqhibYVK0Y5S4hyTpbS+SDeUq/2dbkaXnSfbYntGnKzgXyQv5LfqYe6n9pxvaSFp1q6QiobaQdZZs4em9NvnGUT7zynVwlbYt3O13ZDhx1pGSnRv6mnaSX+QtYj4N3WzAVUpvQdis6HycI+X2xYnnaDUZBSGvCbEdMvdKG9W7WQfa1bcLILOFfrqtTT37ju0pWc5LwpfzwYrUkFvJN+dsOC0esLRwNyAsacQ67K3306WfIaUZIeeZ0OBcWbtK07IriGiFGea3dlCgCZNka2MnCD3BZ9xy5p7zUa5bBubhPu05c1WhAeWJd1Vs6hZLWwY34V/YYodO4sTKxVgsOtp90osMrKUk8Qz8WYkfd/BydVpYpo9JerbWpelVbWPi0rVyrRZwLm99nKbWj57VS62RnuS507nbpn0MfRgzab5DXdZoaXWtmbYj2MgpReFxMWNzk4BewSyuFXng13y59AHUBxSfeUndDY8VxVRZAdS0F668d1lTcIKP8IDvp1oFU5V3lb92qX/XM9+QhZZ287X3tDXWIdXTHPe1+5VKVj+/LT+ou2yr5gLrKN2Kqgl06zwhN8tR+EC/qj4WMLtj+ysfCC8qCVTRd4V+5mHAKiXWiffNoKT4DBnWs62Moc2xH5VAuyJ/wuU4bcoJ/FZ3waeah/FC+6RTR2anc4UHmqYPHthJmI9/NV3i1B1wLQty0xb5uS5sKj3qVD+pwmzRkbsJoef7h/L+mqi1SRgDHCmC3b+N98xe/X8IXymflmHXW/jbC2vpYtvJAG9wWz5+8B61Yns4vo9akf3d1UgY7UGKU94ZEmdumrqVmH7is8SPPFFt7JeVpW+uMVj87SN17rz6xO06nzl265HonjRs4eatWvVLTvP4+/vlOHA0KeQlJRW9SiUhk4sZOjKuRulDILEbNP8GDbSNq2Gdj8r5rO6QSpNElAr1jCi6TBOi0CgViKk7y1Pj3WqUvM6goDctVQUjAPvO79PhDEBJSUXKlo+wcbEN6ZBgN52Qq3ktKokzOyr0894prqcyjL/kfOL0lkeToMeUakibjtERRpmFrufyER6JNxkMYuDK5Atv6W7ZEK7OpBMxXeAUiRwPI1xErb6QBBmEqEDVMZGDzzXwEjpTQmuf/lGpwV49TcVATnR5OY9B48Z7KTQNGeCplJf5TcNG+rr5r+Qp0FYht4btptFAXjRVpQg+uoyIusNcCb3o6UxBo8xhRdv6/IVU5zaaGV+uTPwDMWnBZ1Uax4rk4E/dSGyI8BY64SVz6wDoKN+8Jm20nfWlMJ814H0EozlPIAacwKlT4OJWo9VCA2AGTVgxfk7YU5OalArXrk+H0fCVclpnF89ejN/PouRcF+HLkjng2z+QX6MajbamxpwAU3hSAfm4daHPLaI7BJI9k5wADPesGPDBIhrk5ioF1sKojBl+kUkmANJZ5lzawI+HIqGV4LX3axvKKcFtvpzfYaSlCkWvyNaxrja+ZI8waA80bfB3NEbaN+C7xwiioeLceSZdklLRBjsJgRZQXiQ7gVLflyDRHw15BPjxFxwMP0ufLaS/msrp1ZD1D/+lduuCXndGVdK7drYHa1NqarMlfWlUO1KrKN6scRt70mYYFH+V38pw0oDJZljRQOlvKDg3fVIjgR0NcfPid9zTsrGfKImikkn3e12laD6O0gc4E3v0ao+ht9oZ+/0//yPDjrlt0iTYN2XoJMNbkHV2hUEJxNJC/OCIHvpfey7+sEOfVURhyJwoMkYxomPxhzNp0/Vi/W6fouP12sfHWW0YzRpCWML3MqShQFvm5AC1b8eK1mbuAtT/A59qMGFiaStLOvw7BRqyR0QxD19DSL7h2FvpyYHWRS0eXG1B2E3CoWer8Q8Ck7bhI+IT+23zwLb6genYccgSUD61t1RZJ7/CEdJMdnXxGGbwnXWZbQIsaP7arownKIw2sDHlET9gOLlQrf/idNOnCf4aVuoe8HUj5ztGlLJx3Ktleu2GptZrkae0P7U7baCRZrnSms1oHty/bSnZOhF16yo4U99Jxp6ECzqyz8kjZaJJ2hM98xZF1ksaEXdlf9GrpHIp3nbp22ZrShrkCvLRFXcSHdGe5wic8tqNOUZN5Jd1zru7QkLJco6XUT+LV5lOGOgKVvMt5mSpEvYCtdMK/yMV3XezXRRA1rniUxrLRHllvywKn4iV1KzlbXwcGbPfskNOGRe8Yho2jAZjUneoTy9d4FRYrovNC49Z2TruA96UF21MDPGkLvCsbrYRbXWanGZz4LB0Wq5UrzgVaeH3feokL62/b5FbTyAid/eJF3JnMXzpRRoof8/C5Ok5jWhoU99mR0wnKN+JBvFc4V6ZYL2HIUT3aR5ox4sBOk1ufVdsxS8fW07LEVRrDlJcGP04i8aHcFn7hUF44KKMuddRV2hQW9a31s4yEQxqBd+VQ8/WX7W6bJRy0gW1PPcWfdKW+MbrPPGxX8yvtV5P1vF/xp+2b9Qah5Vy6KnpFnek985Uv1K2W7RZ0wp02BbDLS8Xu0+ZzwMbF4FgPAFqQj+z0WRdphdKApTjRpBN5IeFOOlK/axu6/pKRXURbkodz2a2XHTGTjgb5VSeN9ZT+dCZLj/XUdZTdgrZpzn0noOnsAxC+JFfqIu2Avjz61/PqiuomjHKY56jH4kygvt+Al8/RZ4tYM0cKVO/pcDdKNnerIBfxY34rcFp/sdw58LQJOCl8VHSfBSsvk/cpLZ0uti24SkgSuASK7/ymfGcbKw8tw7U2xIudI/nV/LNufKujoXLApw1lHmRs/nZ6/d6FIMWp9OF92yf5hO8tU3pRviVtCDCpyF3xV11bZj4qd/g2Lz1aZx4Ju3DndFtgdDqT76Ssh69sY/OTT+UtyxVf0qDfCWPSFrSSLUPbimDhEj7zUo75K7odZ0a2sTDyNAHyk5pNxfecZrI8Zbnve7PYOzXbExx4raPL6Gbzd4DJdUKkV98Xj9bvczqrya9c+54OCutjmcob6VKZkwNVvGN9vC/9m39LBiB9ZnuKD3k9yxBKi+IgLMqBbA/OhVne8pjveI+feFfmeJ6yDDwqU8VrFR2Yz8hTvEsflqltqfzR8ed3wlzJJGlOIOwXZd8MGpL/18RprVxT5rregwvgS0Nur1zgy5oIXoFTsD23ZM4zU+5YheqJtOgj6aWy9YRHW0fcZ3Q5Hyi/Cl0BL+8rn41AtH8i/s2yGmRzQM98tXns/+kscGqL31u439nHU7dCXejqMlBSRcTW4TxxywvkYT6tGXxdv8MGZWF6ZAFSLmExD3H7fU7fiaNB74yKVoEiQhTUhhcmbmyhGo6WLVjMKvrz04DXI56CiMc6GpKpVZowml5tjTm/M0xXT1cKNIS9jSQhS6ASqtfuKiGBSm0SgHnJfJWRUOaRea8sUNSWucZ2IhYsRrhCUBqNlVCwMa2DIK86ejfBKfQLMRUC56+GGYvvqeBkJAWDc8UUGtUuFnYazUsDyNELFaowmjQ+E1+1Dk/VuRUAFYcwq9glapkhO/IIE3GQW7UBQhFCZMb5KobL7P+bPxUjlke2mUklKw4tU4GjR1pDzqSBaOfMo4rbdvO5hqoeQHFZFIkGHEK6ZvAoDD9h/QSNQucGKgBtK9vY3UP04unp1rirBFxWoiA3FZHtkMm6cZI/KukrjrBiVqQCyg4m97P+qWA07F2YpniKZX6NxTQqecm2sNxqVVo7Wi4UZNIwEReqHQ05vcYJN3jRwLZTqoGfI1vgyw6hACnMhcufgH7rWLuXD2rPqo6VwlgjUWMnHT20t3BqbNoW/jxPmqEMw+CcWywtZQcLQe0c1XqOdqjgoUcyokMJj5CP7ZZGGXgH+RhPQEE+tGqGv7vWg0pfnEhj4kmq0Fur1zYdDTgYGtLW5qWjoeFXGK4rl2N4YXSxrRUfojkccSIj8YFwlTcSC8CWR27nkfoUCcxzkQSM+Z1IQ8AiublPp8QwhgZMA2nsvArz4lWayFDnLym7gSsiJqTmT5HUuaIDlaOtkQYZ9S4t4/PyjnRvG2qIO6qa07JcHBO+8n7ShwqmhnsVt/dMKeesI0mnjPjS2JWGxV+DbAcUEPA1Rw6tnDc/Rj83Ip498/xYl286bdY51uGdVuA0HQ0gH2iSL0SIOatcbB/PlXH8zZ93KCblwgpO6ObGp8A88d2Z8fnum0f3bbrHlnuwTSdb9xLPD8KAS0ZhlXO3xXShTVfgXJaRRQ1YvKu1iIsVjCouXVpoW0dD87VaZCTJcmhjOfhbTl1WECkjT+tocNzVyAbpSMhqgAK9NeAfJ3XndfdKKySOktbzy8SnNJiyCPpRdlYddTOy/spnZUwaVMCgnGzKWiR2zNQ9yiKdgPKUhoz84Xcqe9cmUF456mXUhm2mISeABbdWoKRi/FZX3z5apvLRtUsgvmIo4pA1H8iKVByPykDnfCq0i6MBmqIdNHIrY8a31V9FV5W2t/7Kd8OSNfatV9JX6ldoDH6XC9ZEXtRtNadcAB9FN9QcDbyjPpKWbRf1oM89L3SsEb0i5bvvVA4SYUvngjSsXuMo3pVr6WgAZxqDzktVJ+toUD/5rlEJlSFn+6kLS1g9OgO+MW9hMC+/kcdMGTrLMw1R5/YrX3zXPExVx1Y+de69ERRQA7pjUToc04AmTw1G29IBDOto/tKf55mX74gT4LYdNQwrA9g2EPeWazm2U+nA46igXrwOHRV6aUy7ZEedm5ahnaH9k3wBzHZUK7tDne235mE916jvTifIVybGfI5OlDYF0jYQLuFUFonzdDRwtA3ElzAqh5V05iWc8oM2hjSmXFDvalCLS+0adZgRMZZje0mX6mtlpHJNfMgv0rSdMfHvt9bfe+LENmthBAPlOeCT7SEsfGcS58KtfVB1YJOnuG8ZJvFaygRnymLuZ0elVucc+AFuB37Mn0+TTizDvHXyOz9eRwMfZx2TnnimlFTGVx0teSZlMfCoV10fRGeWkkdnjs4vO2CuYm8kq2WJf/nVUX3tAaOdvvrG0WFG65EV7qDUzDpSj2ww+I1CrRqJBuQ/6C/JDOsuvFV7IDF4rn70U+qbeg5bYhk/usPkj/7lvg4Ht+o0glD6VZQ4RfOLpUStkAVNkm1j+0jLtv0qfPw3jgbqajv6nvQq7sWR36xN5Kr0qjNWvEgf3hefEq+0mA5SeFyd5LeWKz7TNuXaNtI+kp/MwzLS0ZDtY5VLh19eTSyQ5+py1nPvJ7OIS1JeJy65kB7TDil8IHzStnJL2q7oTMebNpEwqz+EU/qWNnXi+Z11LXAX+k0CsBz+Z92AMaN2oQPpQvtKWMyzgs9rv5NnzLvSIdJ/Omh8TvK5Geu0quwDZUFLBt0aomOXLl0EzbvPiO8WuWddHPSSD20vZaeDgHV2LPj2mfLG+vBhwp30D62qP9ySXrpRPivHbJesIHUzeWV9lGNZL+/wLPHoO7ZH4oT74N4yJF/b0TLteItX+zHiXBllnsKrLSTezLc4n4vsV/7Ynyj6rehm9Z+RTD6rHOzqFx0Zbj2r3jby1sgD80+6q9WhRibcJfnQPx6r5xx9RxqxKsKd8g+6kV4drHKXD/P0eXFEaXfoZNPZgxMCvnTXPZ0Nld7SyaC+rWRxPe1icJ0OSvkXXPjdYmxUd5xobLnwc+pSAQSu7EvJ1LSLPJ5b0Uuf9pvsX+XQU+EVpI6SIatmLb+v6TtxNOilLURQ0CBSpU9bGt7LPkc+YZV4GdfGNPQqha4v1JKjTGnkw+RQc7lLw34N4WpwpfCiQRQIMpZlmtJjngUmpSVDSGQ2ukcFn4Thd77riBcQstp62W5M4yYFhpmRqmY1N3+mSjDmtfXKnHkgk/G9hp2KSSJu3pStgfhyOZ0hBZ8MLE4kdD/TaHD6ROYLlQtndgb5NgW4zyhIZk8hbT299j3q34AwaOd0ayyKQ5m6Dk65hqs8ZAnlTxpkq11/61Qk1cpLwcPH4sqfMPhLJcHR+glvGsgIGQW6dZOhU9CSl4JNgep3hirZdl5b/wYuhMS/5YwiaBQkLdh2wlBLdXWp3ctr8/W59zjxkMLPc8opD8WDsCLwgc3yNRoSfnCocKwcDVUkhgaViqDyxFu/VQoJoa4xBM5VPhaigLc9xYf93CIIVsHFWalKDca85jyP5VB3bh7+kl8o19e4THgqQ1NlYI6WuZKOvEcNokY154yOOCMxpG+jD/xJj+JGJ4P3qzbLzMk/FXsNfs+lRcu2kyGuiqMBpYIALY4G6qu+gXdQTbzp+zjP2F2hgfzLezQ6HViME+ggz72GLsQZjFoqlgqP514nEDyz4EyeeI0A9sdrK5EXcE7he7MiaR6g38iD1ffTn+IIloY2KenAPPxlFoBT2orsMlWOiIq+s4OEMwPqSOdldoDETXqhwaMfkkcq5YrOhX/1lO/wJwtRkfA9EQuYwDZmENYTo58ZHo+demaurdJ1y82iNcTTwi0uededJ/wYSAG7KBiru0rBZO24UyolLI4wuWPHQuhxBgh5472PY0nvLWPL7beIrfrsGR132Tli3Q6Zb34HHqVWc004aXONn6Qz7mlMJtHYhr6nQ8J7lOkdTO5caFTlqJRqCE5s++JogD7FeS1Je6VGiZByDiLzH7c0wHR8SWt+pRzPjiHPvLZ+8oTnKXc4+mZlJIt9aVy+lH9tJI1H+du8HB1Z3SmksaW8siOsweY7tr+pQJCnWV45W+1vqULeEP6Uweou3l7xVa3DVYO71Ie6Zb0S+uTXysjMUcfkiZKpf1MGkK9y3nPrI5yZP1E76rlVb9t60lU5VnJNOZoyV36VRnmeOo/78kJOrZIf/U688Q/pnnxewSbMynVJUTjEqRlVskmcCUt+j/HkteWa+NS/2Z4pO6mDbSHOEyfCVMtTOG3fUu9iAFquctlfHRzcEwYHGIRR3eHP2sspyzOSi04g75mf+BMMy7fM1LHAqOxL+SedkCzD5Hsm6cB7la4T9wmPeCfPpC/eS7lAfuYtJqWbip6VMVk+zxLXwk7SIJX2zMOOUcPaIjXO783FSAFYPWQ9SwepdIjIrODC2tJelqVsSBqnLOWyDgXLLTRf6l7VNR0X2Ex2Nuw4ZseKOqaupt7qENvSOtuO4sKf14mHlNPoAb7xeRk4UI8CKrCV7+Qf29ya5h9Pylm5mfjznm1kkl7ML9uDd7IsjjoCzMF54No08q/vpJ7lvk4BnxfZCFGaOKQc4K62WwFEe0l5wh/yLQY9dTMvPpEnMupBuuQTZYQ4TDuK567XUCJWpBWpjBJ47gKgxS7iI84poBwzV67NvFbnCg7ueLMc6p7zvbCZB7o0nerktRL6zmg+8CRcphW8gukIjPy4NgvtYHWKryTN8W3yFWUnTmt4lhZ1PonrggdwYd75nXKmyH7pXLpUpy9Hl8szFf8UOwc5D+4Kj5FHgm7eYGa1sqVRadICso0pX3hSPgl7wmNrFYx4PxP55Zn4IKUatKYokLxf/aH9HVkv/OTUpuKQ87E0lDg1g1o5yTEi0mvqmjjicdYDuEwpbykrS05aKfe8n/xA+2hjywPCVcFpXgV+6csfz2r18VlVVnXPsqr7wirOm7BVqBJ8xQqd40TXcM8yK5xZZpbLW0nD5O87FR9l34eyvCcASQvU08Em5U/TzN+1ohiVRwYpY9S5lRPEulQ6KuvAtbjKTrnnpGwBXswBG7/lueWot+04yxvprIeevVfyA4fQRfI1eVgf7Qrh1MbOgeGsK20A3OK+6HTkqvVTn2edipxWJlrnKpqmwqPwZcpGqc5rF9aDW9I4p9k2PhEmnzi42EhHHro7dQTPfK4OqTVjtpcOFMsTd8KV9aKudX0O8kqKljb5p4wVl2siu+QnnbJG82fUsHfMnO9Tj1BeRjVSN79zHEhHWhW1U7Cvni66JumZb77P6TtxNGTDggUbbPVEWydyvb8649W9qICFAHJeqQSGcIQilAjlZ+NIOHRUbZBU4jDkt0uidBuLH+1tQamc6yQ2t6r0JV5qGUZilpEMhfEjOxXeMd/V67D6eZXH/3TEVMkOnyBrBJhUYBry5qrAVomtgsvcBfj/PUnIVZ0989qvC7RerX5tfv8d5KuX9b8+rxjPr8VPCr40jLxTyvCsKvV/KkNvnsIjnUIq01qipVM4pYLiXKNLhvx2ziX3/xW6KheP1GMVOjgtAmvVG14j7IDBfFLQ1fBhWTK694sBWUpyrr/CWxq13jzlh7FXUzQQCO1nGJP3V9XckjIHJZpneVHyzBdX++Mblm8qf6t8qvcLJhRupdNVKXkVY3nnG4S1AtM6Jc/U+MP3ra+h7hm5wBfiG2qHKuGb/LcKdnOr5ZhHO4uZKuWMgFeppPGgMCQP8xMv5Tv/cgeD2W91UJhjLh7Ha9JlMf4os7ya2ee51/Id39lO8qtKxqMl1DCanegviGj4Cilsu1iETeM7iAM65/giuc5amZ/5ZsoCOCs3Sil+ZRKychQzqRDNW2WYz6rn+dK3/4gjyikONXLVYCSpTBFM5V1v0Q4ggnucC5fWItE7Lw97Jh449fTowu3uPXpEC8onJuVbjgZrJxasU6W4E6IsqkBusSqvphrJvD0PZExjdHTsuzNi0W7bxDY79iCiYffozPao9dfrQE4YCVk7eU1jo8gjHX5foWwXMZLl9AcX0qJHAfyUA9iAJxT8p27csnv2FXWyHaxa4t1XskUKLiqc5+3an/LEVvDNcrQmBdOerXqjuu+xLol32ijzBrep0Km/bSZVSqdJHwlRybXKscpjVasWCFaVXb3xv3tMyi0w1f0ttc8SpO3URwUSq1hoWMxJIiKXdpZ2qp5FPln1J41c3jO/NMKscyGqWp1tA9uUdoEPxZF6zLp5L3GTDanUKtRdoCl/LanCy+r4tmaWnTKIEQQd4Gmokocp8wUudYTlVclvKgPTehX6XUUldq5TdvGsdLirL63hqlRgqSDCgEueg4L5Tng1nP0JfeopeK8KSa1ykSZy6hm6t3Jqyacak6tSVaplISvJU1JLY7dWV9+toCu4KrLZ+5Zd6NKrCl7PSzL3FexOoANbme5InjRQOoA85Z66xtFBpc/qKfWRHRxuJp5lOssDGuV8aevSpn4njP4TRv/5joa9PKLdUeATolJXnxsBIiwm4cgQ53zunfLeypojTZtJFpQOih60TN/yPWiCtreOwiq1rUoFe74nXWnYK38yyqTOtihvm4cjteajU79hfTquJGqcbSm+jPw0uQivHYFi3MMV1LEBnYei01cvP19PWMtZgbs6F3rpWVqpcGGVUPdJ98UurOpafcURWEqmYmH1xIeVrMrblkBKZJVndvZyK1raxld9pLitktf+GEKCJsE5zac1ac2rlswvgEFc+ZOuyqh1VffVnlGZqp1Ku5IRyXpX35Y71VvCDD/Arz5P52hdmwqZjwsf2OlK/ZfPS1tXZVRX5bpcmbOpKqmc+7eWr6dV8lZ+UL7yciWDC0Yy2/lsvIYOEnWb+ow2q4OxymC1o3iq0bq4Spmb7SQ/1fTH/8PemQZbclR3PntvqVsLWli0opXNo41ubSAM9hh7cNgRdgQEmjAYjM2MP3gZ+4PXiLFNhL84wkuMCQcBhD847BkDHhzeZFvQRhgJIZgAQQASQgsNklqbpZbU6lavc34n81/33HxZd3nvvm41VHbXzcyT5/zPyVNVWZXnZVXZ2IAO2S310Xu5jZbYh2yfE8f6Eem05nMXn3Jeop730nCe4kO/37Z8/PwBw8ZjGwu434CPAHtxjGPaj6Wsy/ep17JfON9IHrgz/9BOgnvUryy7dB+M6PSYK5GutXm8snrxHe8Jy0leK1XPsr+klXsjrgMkD375mDG6ZuUAqPnExqSNNgbkY0UYSMWybISuVPYS1w0j6dqgVe2MPQQY87VA8nDmMn+0sl3kY5lo6NS8hXchYROezJbYeGv/9lrQiGOJleEWEjeOfB8nDW6d7UNWSCHIHwRNiR+XnEdrzIfcZ/O5S3AJhuBn5EcjvaMclz8LCTTkFQ2cQHaamGfYUbaf/WTCUUy2uXHhRFlrS4bde3iTvcXNlAnA58KcgQj73jBe2xkA5igshzsXaP5yw8GNIoAo2onMAWy5L130dsOh2W8w8uDpf8FxUr5R0QHn+s2KnAOoVPBVXcKBRZyE2Gi8BqCDm4MTe3Sy+CBjtuQXdhkjg58GvtiXYi+yWR1WZcv221+5WCLMRfcEnl13+iQbYxvlEVbXJXpgPsde/EN/CIqstZsBTjz86pMx9oUl+gG//bh+H7zVZrzgcMOG3/2Gz+oMpJocZHn6xvFCD/IJBXZrD0AfpfH+UCtHTydre6PcjNoFmmPO/nUJn5Lwuyer85++lH1Bn4m+wkn03AdEQ8e6vOEhXeBEc7DyI+xIo4ycqyu/mS9Svf/uX3xsNpmAX8w5JrDdzw2wkKVO2Xix3U4+qmycQT5hsJq/MNJvU7JU5tBAGG21su2rw9wQmi4PNNiFzc89dBhm9tvoqENa+88Onaw4m9K5GLNJ7nJr825kUrbV2mHx/moMsbqPKzQg4+1e9GGCQAO3opaVBEedaMXubHu2WhLswfwODgKCBAc3M9GWXzuowm/nKmMYARRu6nxMswsHN2J2V5y5MYExiPPaxzlzCG9itPfS3GKBhr9873/zdzS88sptaasFPbewKsOeY2FFg+85y0FCoy4uo/MBDmvBeZbZggm7MNlfAW172P5q+uWv3peee+M16bKrr0yvft3r0/n2nobNZ59rrfbXZ79NzceG3yywb318tL8y2HLiNWYzX+yxA80U28byFcCxhpsIBnUr8ulULopUjRpSy/c0G2NJ5ejxmo8wdhzn48L6bD5lG+M23/pfNI3JV1twg4UaxikvWNnP1/yXUcYczlVWe+UUbcrIPsaZvOvifFpWyrhuSrFD+8iPKLMbbdBG/aFkErnD3pqZMkc+rozfA1b5xYR+/jFmWr9Bkj6NnYxVusHjBgX/Zo38FlyXUh0O/uUkarYU67CeE1g42Cte9jlhDm7G7Z/5EA0eTPR6ZuSXa4Wu9VyHvecOxY9hm91ZLtvIjTaTPL838GPSJUC3reD6sWL2mTz7GR9k/tE+zGgMX9iHHhM3jJxTxl7pzuc9fL5Kwm+UM65fA3LvTGK0asP/SMBJYNcGN4vjx000G4suVxJ+/JleJglGI2ADo183Aw+NHjDp+k6j2cJxZPZ5B+iEH68gjfzgQQi3HVqeMLFffV9yHUfeajnwg37ukdjHGdabjaaU/eOdKVR8adbg/86v+Y8m3BvkBCb7kPsuO0LcHlqEizVUs91UGD/552QI1oat2d5yX8YgU3g8CGDtnAveB5tschxwD+jBDbv34BqEvB9b5V7EFZQfa+osiscE1mXd+dgXl9/LGirtTCDYD7lvshtg71nRoD7l9kL0TFzkXNN8cmt9YQyFlvdI7q3q0NjgYcPb5JyBnrxDWFe85MeIfEaf8v7HPyNkJMWDbo5vgixcxwjW5EkUXCZtDOV8hVD2hRfRzfFgUP5HAvyEPquzJ7CJew9y7oW1r122qmUanH0Je0m2H8wqjl/2DWMfwSlWqKCDCWTXT8YIs8+DL35eGb7ZnL9GY/emHEc21vKHElZgeEDOxirJswqUwCP+2FACXliADUvTyL7cBg+0SGd/SJLyaIOP/nDu5eM/843GKsMx3+JvOwCNXdjmDx9bbN+57Xls5lwlkIg++j+6tiLXGdFZl+0c0Z0F1o7DbPV/2S7Kfu5ZNWPDbLrxmd03crzxdRBPPgbpOLSjl/sLFNj+AYPx0fvJ+YsP7B9jPOeH9rGb4mAqBVs7G7O6Uf+yf6HqWnPYA91Zn4+bZYzIAen8Pj8f0ziOzQbscl9zLLkzre42ErCxR3bs3s2v0RYQwG5b2+h98lUzOgvM1DUcr9aeg6CGwf0hXeAYRAf6XBV/0DNcAg1G90cY7Yy3pm6jP8drWkygwU4CnjVhp/oSEXMkO4clb/bfBoN8EHKxXs/z4yScDR8HnDmfnOUkLNX2t+CXm4/8l9sswDJEIl7cHIClC6DfiBoWy8g5gP3EY0da2Q8aP8DtRtna80mYL0hM1hlkcsKgRnI71cYRYTyhagrcDg6aPKg3MBAyu1kKxLGlE1HfiPeDDjEGE/DLTQU+8QOekwJBS/vspUC8RAYf8CxyPpFMTjY5V/XDiaJUcFT1XCeStcmHftExvQxm/PXAAwU+oNlJbHZxM4mvfbDxgUI2Fluw2U4i/krB/uUZpvVlpQeDlS/P5xixfZMvcGMW9fcndAUmTnp85n6yAl7ywZY/B1jiAuptkov9b/nF2g/ZxJPnVbmx4fks+VgQ+bizfWMjRF7OlveNK+Snqjo90BzHdCtnwOI4Gg2Axmy+w++6afdvPgNceMn9ptVEPcqNvw3R/1oCm9W4YKLFfey3KW6J/XDh4vKAqTKs5OCWQIM/3sS5gy2uz9pg4+aBE9sRLCuJwxfznMV+cDV2QCNRp61UncbNlB0edl5iafaBDQGOHPnYmzaMOJ1PNTKK8OgKeBlQSiCMJ3Dh1K4vNWdi6SJvCmaJMc8zw9Sdl4AXIcYk7Qs7GaxsPjH/cgHxGxcDpZ9ccNnWEQkwv7Gi4dAjj6abLdDw4Z97b7rIIF9x2ZUWaLD3Q9jFb6PlrE3wiBL9N7EcfLPxBAvd4RS4gWOzf+wP02GXunTQbviftJuir9/1QDrygzeky67Zll5z3evSyy+zTzedbSsarP2w3ZTJT3bKWpkLOZB4wjazw5ZjWdHKFsT14AI7xt/rYIww239IvPIBLHvXZS4AAUa3k6kbB0yulHpMpt2M8HEYYyz5jR7HGb52J9p5XcYXxm9/pIexp7TjX78ZMRnOUf5yw3hCAJpHtFwxOOjHvpL7uMWYZf3JN8jW1pe8X41GsAqejwOmh5sid6Nh+1+ewMdWbcAYH8cQjB5QUF9Nhpta+LkusG/32uNmJF6IzONKyOAHLZlFfx4rzGe51cY8DKbPnNHc2nCsjBLHjY+VRsJ8JfioE2Rgv7gdXMfz0edSkQ6vT4ANHJu0McYwLvDXZt5hw37hmpHtyOc17W4jttJ/S+w3rjG+Ai7sY2/kp/iJ44HrCIm+uz7ze548QM947GOKHgDEJpL5lPMV3X7DZ2MNwXp4+et5vh7TIVj4sQS2tfsz4FYlEOnHjNqLvsw/6o/Luin2U/rotMYPE2h8hc+5F8nXGTECUmyKJKgmx4oEfMyjoNiArX7Mca66nF0L3Gc5AMdxBA80xiv/Q43bZ7KFL5td9Fr/fFwDDzPwBdcF4+V5at3DoJv9h3890FCOV5nstuEzXGR6PRlWvuZBzseQH3fsL9OHPRxX3FvIzS6HaZz7po+0jrEKGcNGD49LIuD7uJyT8HkynnyOoMJ76jrAd785yYEcK4+NjFXma7MHu9zXdl4ULoOlxK/OIq82fyST/yhnYxD7zmWFMhLLqKOuY9paO4bX2XHrI3k5J1wSZhig+f4sOOZjfOjHbOH3uvicZkFN/6s6K1Dz4zN+/+44gHJcmQJwtC9knNE8OZ6xoM8InJvsU+61qXOeQiOBuCQJLzbUNO9fZMhl7qd5dwr3/6wY8v5jhx2Lfm9qeplPdH6x48MDCMbP+zZYIcoflLAxr97MFnKcszF+cZ/tlqu/S80Yp8Dn/huR3TdGdz9Ym65teYwq54S1i46knw/MTYrv2AddAt/66Sumje7BUj8PmKQzNuTzg+BLnt8E2Q7ECjW501H2VMk6Rmw3vZx/Ol91XBy2fcFj8fiSz5+S8C/8wPqY4+M7/bUjxM4pxn5M8ICIjRv+x8ncaiyMC3je/lX+NJbJCUH/z3FrndCGlNtTzgvj4cW5+ItH5nx+4rL5vgQMt98Kfr3x8ct8YOci73PjHoTHylh9has47gl+IePGc+7YCc873Lg38X1JG6nrk/GQuN5YBia3fBwrugZ3u8EZj8+fhQQacB0+PUhAwXYaJygv1/GxHl9z4tuBxYnlN+B5rxiNgzYvWXfHsiO9jR2WD5I8ocnOzTdaDNL85SoPaNz0MOFzQcPzHczJ6TvSlLP3/EDDyNzOAcEB7BN87XD4+tKkNldQGPwCZrgcrF3CNkvQuGChT4MH9rhtgQe7StVlKLuNmX7E/MsgyAR3HYMgbVGdKws/rTbhw6Z20bCTnUlfwMZGr5tPvX/IFB5yt81o5CqDC5412w42CBtkffao/Wt0Tij7lwcqmHsSGL2pNCIeISDbYOD6ZddEnKJA9ttgzfHKIOUvgHJ6AHB81WvlwdhoUyCPigXDM35MQDbA1PkeuvlOSfz4n+T7Khdzp61sIpxDcPjENeJClGw3kS3ytKGXdj8f6QREUskNy1dQWFOm5EHSOUYsLtHRgLEU0TLF1Fkhm2PChk1PC7tYnMcPQ6PQ3fXo0dZxVYUaJDaXNs4nfxbQxhRepOlJvolWoJxjinO3+Nsv54aDGSTEspuZPNoEygJWW41/31O7083/8q/pf77z3YmnM19t20tsO/mcs1L6zkO+eAAscFhTsXGrud7ePbcmr7LL+NZwxOr+bkfjwdJDp9qbq5/an/7DyvfYds5129P1P/CmdNXrvj9dat/n3vxi02KfpuAN5v4HSPqMs0nc5XqkBoKNS3aeeqOPTdZHjgsFGjj27D+38fQRMatahR8lKsWpkEJRHJ5DB8Q3KFbGaWPHt9rJjYV2twt+S8h2ctZmxysXd7+Ri3zOHH7Yh1FOTZDnSfQBHLfDil7PdmQYK+cDIVedwfg5r0j01WWsDAZ09dHq3KA5Gzdm6g98tPm/cvNVxmNrsWQtFFDdgUMdJWRzkvKRGVk+Xxd93BuJGa5NYss/JwPjUPaj3Peh4dIXrsN+jvhRUlS6YSODZIJfs+E3XvU16O6K4HoHjSJdYBS/dHxuj9XG9BvR932h40eflFp/uUH3PzYUQckLG/tIPhaajWrP1GATBHUqFANJIl2OTTom6Dv9qvE75lJwu8wmZCkj5zJFEAwSVb8GwodvwbYycu4b+MQbfIuskh+n4kG26MUX2ldGXqJH8rSRxvZbqXtbYSBDTYdpBNG8ofARGPU+lH3iPjNbSLD4PjYgPz6cOvopEE4oXeoaqXe0ooPGos8D+rSbPmAiVC5HAAEpByinLNudwaYSHqHlXOeorqhCXmvj8Roea3MZy51dspBpi8naaJYvtA+QFyvt3OvYfu3GzhonylXwCsZkcrFF8jquXX+lM+LEcoGIpK7MvnC7g/F2bvr7pdDBeUzCXt+sDKuOKbVhF/zQ4bP+5/PD6oI2njGfdA2ATEjRfmHB3tljROgcp9D8+A14okOCz30ZgQIvO1djEzic0yTvE/fWJt7dv0GnsSdNakNQJpA7fhGI1zFs0fyG6xYsGn9QK587mDUWO2nKfS19cB1OzTw0IltUdvyqwx+T6J0A8oHB2+1HmJqfcPzgL9qxDRmVKWjMdDm7p+K8MT7mon7uuAo7e5H1zSDIy1CV97WBgjmWjOD22Y/rLHX0sH2XpBUHGvCb+8fyfXxKzSKkRNI2b85RHs5rX3ZnJxFRLT/viwCBCSJwBCZ4U7snAeba0v1idFhIwPiuYGeKqFwM5ItIEbfGcyNEbDA6aYxJzCWXjHhC3UmqG3s8+AK5AxRNUF3DDAVk55WTvj74SXhqm4QxqU3yyruDoBA6WQqidcQ+i0c+RsTZ7afTgZiwCkQf5JhM4V1u1qcDPNcDgxWizlh2vcYTAwsMhAh47gy5v/EYcwcEXP8rOGImq7JVmaJk5SOloM+eQKglhDlC6VTCqm3UPF4amTJOj7XIU6uHr6bJJMt5lIChR8MPrOV2J+21r3LstxeenmyPY+y3NxzffNO/pF+68afShcZzyZvemPbbZ18PP/JwOsm+gLPZ3o9wyAJx++2i95x9v/ugfdaJx6J4a/MRGzBZLbbX3oJMcGfrGaf7eLl/93/YJ5TszcZbT0l7LYC78yvfSFe85b+ka7ZvT9dc+4b0fVdsT5vOtPcu8OEYIgREJjAOI+kDZTb/BAk3W+qJ0TwxHueNv6PRqsQnUTvf1/6BCfxpSTwt+Vq25pFs5INW86ld9CgnmnjmySNOn5zj10okKLrqFcgYOfCKrrwS6/O7EGp21ZtwLSHRlAtgnhzZpkKjq035JFy3wX7GxqpawJkKUUpFU51m0SRPG7TAU7OINeaBPZLnKs+iB8BOlwREqOvSDl080MRHOdKpKxmPs1l7i6WGDGJNfrXPk0uH28GPbbIlXss64jzgxiusTswVFbrK6KScR/pCdeFcFgi5rmKiZeDxmhDIx8tZekTPwQiTJuh72DYea5sljSscSQDd1zbiWlqSmUtbMmU5mBFrLnwxB6VOatA7/6Is8Dvd6pHU8Qa6IKOtKtM2Jq+Gkk9rh034LRy1VbBdtSWjRtqmyYt3Gt8kPShxeWMSn9f5EaGlKPCreczgStYxO8ZRIbK1VIpT8pFfbcrFo/oS3sLANWesTYqVG4CwulwF2hC2umOUMjq7usoldzoMx29aSKBB3c9BrfwcM8vTFGQ7bMtHFCGETuCB5Sf4j7+eEBEiOFYn7RrlsR0a8i5GRXfCkXk1dlDEx6Dl6KgxwGmlFnakzYrTwl4krbYj2jhJj/hq+ZZMi0fyymeRa+FEuYhFWfyRLn61KRedvMUf2+ctz6Kj1hnrLXnRlE+yKWJ1QQY76bqZP8J5Yho738UwonynB8UKMORybqKMgDbYwC4gNau1jKWmrjGOUUW8YPal2FZMYqyzJ4Vtsm7BU6PJpNJsjzXY8lFe9mg+2WufavrXm/45/e//9f701p/8yXTRBRekO+64I936sY/YCoQfSJdcekk64aSt6Xlbdv6Ne+5J933jG+m00063T4/Z97BtGSLPoT5w330WcDiYLt92lX9K7GtfuTM99thD6ZVXvTZttc9l7XrsSXtEYlM68/QXp8su25auvPKatOXFtn7CVkb4mxwxEONwIwEG5ey/tXk1g43K4XDXvuSvePmt7SblaWKgAR2LTtH/YLd0QKv55rVjVvmW/lrXrFi13CTsVluLVmNS7+Ob1c7IF8stXatNi/r7+rVoG6LOGns1bJikD/2robPuV23D0dBZ2zBWrw2yRietwLA+0e66Zgq8TM5NJgpdqeUxqABQBstBd6t6yqGCkYykIo7KrRxMG4uP2F+JD9vm5Yzc+9vXp16BGRvUbbGvhp5aB7pm1dOSla19eR+26MvB7NO1HPok/bJxObgtmZauWXQgF/laOC19UabV/kKlyW7lk/ovXyif1CfhiYe6aMrVdpzlKw400F98GH3NTbgCC6xGYXUSzwYSUCDQwOMV1HkuzL83XuTBiqlv34iu/aDrgMtGQyLYosuL0KOOYNvxfCCpH0ejD7Xfp+mUbcvd/5PwI3Zt13L1TZOTztquuj4Nh3Zh1eVadgm2CfpNF38JB4QNJianlndBgSWC1k5CXnLx5k20zJWxwCibR4LreuHtUyWoleaYRip6Dljw9ICtNOCRIFbdyXLlsFEm3737CQ80fPIjH00/+1PvSJdcdFH6xI5/S//yfz+W3n7jjenKKy5PW07emp6zZxy/+OU70//7whfSyVtPtkDDKf7pN1OVvv3ww/ZIxYnpumuvts/znpBuve3WdPddd6Zt27anU150enrYAg1P77PnsTduSa985eVp+1XXpdNeZs9KEkNgs8cuPHGvSqChBBtwqT3oYbYSZGDT2hQL/mp/OnPuOL/sOt99Dmg/6qjqq5Gjg5TNyOXV/JW+Wsdy9U/Cm8d/y9E/SabPrrrf1OfhbckvglbbMKlvq6GvxlxN/XVfj6Zu6aptWM3+SufUHKOCIbWNU+UDQ4DpqGM0wG3rVnyVeqdfzOTahKQ21WWoMGbJDYPVDLyViGBDX6ChViWVi87VhdXWtwg9wqh9gO20rXYfar0rqce+rLbd0rUSPcKo+7wSzBrrWNTntb/2Q12PfQCbdumo88h7nJUXEmjQo0V6ZOeg3Rnz7gVe7rFhXY768giFVjnyFzpeKMIjFny7mFT7v65Hv6pt3v0guYg1rSwd0/imtS8HZzn2TrPjWLQvtx+z+Gwa9iwYk3yyCPxpGH36Z7W9hR9pLZyaVtdbNmVMrUAgQMCa/BIo6IIAJdjgN0SgRmRD8GoILoz9lQgNxXLPinyHLTx02OYDio0vUYW1xFTQImmm8gRIN5GVGjyC6GOeMaOHcXC/jXu8cXuDvSQIK/fbKoVHdz2cbvqnf0offN/vpfe897+niy3Q8G8WaHhw57fSL/3qr6art29L9l2ktOfpp9PnvvD5tGPHJ9NBexkpn5PbYI9PnHDySbwZMJ134QXpmqu32ae9NqRP3PzP6aaP/2W68IJLrWlremjXo7bC4oR06ukvSdu3vz5df+0N6dyzt7iN7CKCvfSJx9G5bz1kFfYedttobRv7hFVmCjQQZuCvcTngkIMOZfcZ57QE7nLTRN8X0FnxZ8Farp3T5ObVPWuf0OvYQWDSOSA7A7uTptnXtSNYKjWGsI9WPtbvzsCsvc+2Pnol3nVhCV0AoUGkTigUJrUFtiXFAN/fFsGDQCQvETZCbA9iLVbt6lEbwkUo4owYjn2p69O8BppgLVLX85ioa5blHYO0kscy/lC99g3Cjc2X/tV0wXCPzGaBBrvgdOqNspI0D05fb6L+efCi3CzYkX9aeVa85do7Tf+xaF9uX2b11Sx9mgdrmr3T2mXPPDolU+fTdEUdNW9dF3aUiWXa67pkvpvzxQQa/FEIu0FlxYJ58aC9sYxVC/42Vws2KMCAg9kxB7gZ93Zb0VACDbWT+3Zg5Is8sdzHE+nzlhd9cPTh9fVjXntfCPyL6stKfdUnP8lH89jewp9HfpIdtLXwoa9ERx8muDFFPvTluv0l3yelecm9W+LGlACAz3ooS5pc1pLb1gUYdANX6NY6lmKQgbI/Y0Vuy/mtnKfGGT1qGcNYQQXMOnkvZS55SdwrPm/vqeETRZv4YoB9eYK3ej+ya1e65VOfSv/nL/4iXXzBhemss89KX//q19I5556d3vMzP5Mu+b7vcwQ+NfYlW9Hw6dtuSw/bJzF329dlWDFx1tlnp5dfeGG69BWX2GqFV/gt5+2f/Uz6xL/+U9ptwYndu/ek3c88l8480/heflG66vLt6bL/dEV62UtP9D2AvQRFSHJn9HoOFkHJ3uRWNoYYCDLkd6Q7hP9IfkTJpeCOumlZ9Zb/V6Kjhbcsw+YU6tM7S1/gkTx5LNdmzIJXy7Tq0hHbwNYW6Uez3LIL/a1+t2gtW1uYLVqfnkn0lr5ptD7dffSW/ln7jmyNW9ejvfPgRrmVlvv01rbW9Vn0RuxYliyYjIlcbcbeZyTPaVB1gVksQEu1jQUaALJ2XzZmeNxA22d77bsTRs1/uHNVy/hp9W9WmFbPVoJX623h1zzz1PvwZrUZvj6MeexYLd5Z+zGL/kX3sw9vVpsjXyzTlxZ2izat3zUu/JEWMVWO7cJv0WiTjPiWQwOjhRMxj4fyqgQa+MoE36cl8biEf6OXCIQldoqvgLAlDj62Gjl/ItGbu5++ndcxlAJ82iDNIgdP386b1AZ+X+rD6+NfBH2Wvi5CzyIx5F/lYKsfq+1D8KNe6Y56ZQttdVJb5K95JtWjvMrwCw+aypNw5m3rw5S+li19OsCyD/z4TZd9CNRq5e/i3c2WTWu93Ao0oKlsfhOl6WqgLzkaTKOwyRVosD/NywpJ99lMO3aTkygrRZr41NbKu17Z8LbGNh/WOqJJWJf4FChBBouFeHr6mT3pKxZA+Pzn7vDx8AR79GGPBRHOfPGZ6YYbbkjnnX+e8z1vBux88MH0jfvuTTu/82B69IknDGd9Ovucc9KrXv2qdO5556at9pk9HnB47PGH0gP335u+ec8307ftyxUbNmxOZ730vHTBBRfbSobz06mnnJ5Osq91WpzC9lXuM+OuLSZzd/IVOuj0Ob/p3N6tY8ha0UBbXtGQwwx5RUP2HDJxz1l1akJGSf6PNNpEF98secRAnnrMZ8FYKU+0W/pXiil59U86yOMmPuXiV305uXRFWeGSa6Nd9CgDLdbhI0XeFo9o4stS41gt3IgtmRpDdPG2cCItlmtZYUR6XW71BcyWXX26IqZ4lMe2GrOu17wtjJpW18GIuLEc8ZGjLeZqr+kRQ/rEE2VU7sslq/a6Dl20qFP8kRbLakeW8dDfVMNNa8ckVOWSCHnHG2hdkca40VDVgeaaZ5s9fFxGaPgWlyaZOKFnEw0QJvKUhRPpEwF6GoVDc8TtYZ+bLMxop8pzgx0jgVYfMEX9qPfJrGaupu9lc7SzLtd2Rntoq+s1f6zLF6LVddEjZiz38SPX1xbl4Yv1Vhma6MqROx7TYgINNvjygkdWNBB8JdBwqHw2BKd4oMFumOuVDYdYCWFrevnW67oSiIhOnLTD1EYetyh/tMovhINA/jhafR70vLA9MM8x2XfstDCgaSkptz6jKae1jAUYVBdK0TIWYCjTVafpLC5+dfaszYfbiO03XoQ5/G9MvQP7auwhbji9R7zz4ID9jQlbRKSBMknd8e9QpvSgBRB27txpKxz4bqVN222sPMnewXDueeelM04/1UV4b+Mze55PTzz5ZHrksUfTk7t325i6Lp125hnpgosuTKdtXO/vduRFk4RZnn32qXTfN+9NjzzyWDrBHp8484yXprNedk465eT8yIQ9teaBBot5eCLQsH9/Lm+wz2WWOIihaV1I3h+w502/eX0DVHUr5hnxe+u3uPSodnq0X8oxWPZTNIL9sogU+yfMuM9FW4SueTCiXVGutifWY1kyLZxIi2XJkIPVwos8q1GWPcqjjtqeug5vpLUwalpdl76II9pq51FnLEcbZylHOyNOX1n8YDOs+zAvZuViqvNp7TW/1xGSYMlRbtcLvjjEKK0Ab1P8u5gY9++x6Kb2yrHQ/ULQeSz8H8+GPh9Eu/rKyNb7L9ZjudYDZsSt26n3yffRI57KysFTmVwb9OM5LSTQ4A7lx7yCY3KggUmIJSPwbVECwXzekscp1paIAyKHCVBYvZBcZNYf12vM5CpPko082NlKs/AgB1+NUddb+KJFPaLNI49MC0NYL7Q82ko/o//UVtMn9SHKTOJbZJt09mH27b9pci28ebBq3r56bUddb9kBbSmeFnBqgirJMOP2wIDoMUcrm90yjQUdamvQWrYYZCg0SPGmC+naTiMt6xyZhKMermV4Y9GWzC6mKtBwhEcoDuz3MY8A7H4r77Mgw36b6fO5yg22MmGjfVFi0+bN9h6GDf7uBPpjT5XZ4xeHMi+rwiwAu2HjxrTlpE3+hUriBActkGuQ6eCB59PTFozY//yBtHnTienEE7bYqx422+cvzTcmikl8NZiAg/fJbO3e1aCOGM/4YyzUIebEnobAwuFcHnUZjj6/q418nhRUj4nJzWPEGSp9eLXorPg1Xl2vcalPw56GEX0Mr2/TQFuGrJRmilGrDTiZgU2UldPWV1YbuVKNIzoDBGtVAAAgoElEQVQ5OEqxLJpkqcdyqy4Z5TVerMdyxJIO8pon8knHLHkLR1hqI1dZmLJFvKK36rFN5Rov1mO5xot64VNd5VpW+oQjPurTeCUrHarXshGnr4xMjaO68poHrLGtjxHBWRLy0cBJMoXvsOWIaZskQtss8LEb0/Dq9j78eTGn4dTtdb22S/VpdsyCMw1Duo5GHm3psz3yzGLTLDiRJ5Yn4U+zYxYcYdQ5emv5WG+VhSGbY72vLD3CUy6MVj4JS20RR+W+XDa0dB1vtIUEGupO88WJQ7YRUCCIwGMU/k4GuwPewF1wiSrQNi31cWjHIU851qG15OCBrhy+OqlNeau9pkmX8ro91sGdJS0SaxZ9y+Vp9adlu/xZ5+gVRksu2iW+SKM8Ta7mX0S9z5aWPbHPapd8tF002TepTTzCU10yykWPufQopy2WI69wlOc2cStHHo4R16g0Anda14CsQgWUtVmxw8qz4RpbWpUjEVOnwog1T2xDZlp7zYO8bzI9LuoArczqjxBUeH6fj398XWetvbPBZ/22CsJflGvBBV9SYAEB+wCPBxpYHNF1HcUlYSMbqmyhmD2aYQGewksQl0fPNlhAwkZW5yOo6y+qtLxbKIYwCTmSQLuCETzwY23RDhuj8T8bYaWYxmvZdLUDr3bKJNVzLf+qTe1RDlpszxL9v+BH/pa+Wjry1219deEq7+ODPiv+JCww1O65QJVPMmBRbTKAPsVysG0WVbEvNb/alNft1IPqrlluUK6Gui56ndeYsR7L4GmbhNGnFyzalEeMqEf0iEO7eJTDF3liuW6j3koRi/ZYj2XJRh2UxSO66uJv5VGu1R5pwoVGOdalSzk8zXIUgokUGNWsnOZYpk4KIrneYhJfT5sL9v2YgiVigbakrQ+n0Gt7+9jnxRVOH37Eg4e6eCe1CTfyQJNsXRZ/zGvZ2FaXI27dRn0erJb8Imh9NrRsj7y0qy5e1WWX6KqT1zzQxKccWiu1ZFt80GbFArOFW8urrrzWETFUVg4vqa6DpY12yjUP9JjUrpy2WI68NTb1JcxOjFLHZ3khgYa8YsFuRe2mlE11lgeTCDwcOHDAVzOwouGg3Vnzssj1FnRga+3AWf2rnahcu2FWefHPmvfpmUdfjVHrXiRWjb3oeqsv89g/jz1HU9csdrXsQW41+j+Lrk5vH3PsVGEWq/LIUvfFRWAsE1KKWY6J6HjqbIFdTS6bK5mm2XpGGmHQmjf+kq40ahclYI9II32BtpJirXetCORsNok/ZKsQDh7cb1+E2JjSRnsogW/8EhXAV4yDBFht1n9k/wFfkcAqhcQzDbiATV22YiwTMLDh02OzxGVdZcgRy0TLCxbBBR96vdHoZsbzey3oYRGIjQQ8CIbAC7gnUC0JDADKvpnNlivYUDi7ZpcrP7AvMklXH+ai9E3TE/VLp/LY1leeBX9mvAgWy33KF0WvDazri9IzCWdSf4s9NUtdb8HXXenqtbA1QKrJwuzkRFhhHvWA7fiRKPyiuG6q62KPeW1zV58k3DFFpNUryxRyldEmM5RH2hgjDdNSARG+conV9agTXWN1CcUcgAlMY/jGN1YvOBPEo6auPC9/J1gVWrbAsij8St2SvkuP8po/1vtsjTwqz4IH7zyYwl5U3qd7VtvntaPWJz3Kp+HV8i3+ebDAa2FGjCXlloAMMWY1K1dTXQdXWyck5lZeDBFOnSMCrbCNsEWTQIsR2nGcFhJoIJDAttZuUHNwIbuS9y/wGAUJOhuBCJ5Tft6WEG+0m+1Nmza5w+Vj7YTOp2roCFWh2rlqXYKjhhXmtTmdHm+oWxvKOoFGWyRNhQpAoRghjka5z8zVMKmla6Sn1bqaHsiaW1pHNi1Of0sP6J0VkSGW+0yQkSWfJCLWscGWgKJha6vVdDKdjYXZGkZtPts1jIhCK9jko0951fZlLmOxNMJr1zN1+b9LdMtccpRbvn8fj0o875+eXG+ftszJ+sfyAlIxkpVdPDqx3h6dIOjaJVzBpo4VXFY7HIHfHjvrYi7W5n9VttzZTMXhA/aJSnvugrCMj8FEGthgMJ37ntsDUNp84onJntswgWJXyYyr2FhAAULWthxkyDlspNKUK+EX+iJTNC/iLlJPn46oT+VOrwvNINkJCKEnnwGq7KAegIo8E14lQ3VWexuiR4XU9asUWvYGWsfeY1xgzRy1QFc3zsIcV3QIdgmOGpaZd2qLvOOLqDGFtlpxqIt9kgmBnRN8PI3VjXOMeZx11WumG3OiSTLH89ggY1o0tdW5wEQv+qgKRnlgUXHkmoqphq2aM3ZhUpvyDtwKsGiL9EnlWvck3mltLZuQWaQO8Fp6XEfX0BVgb6dZjJoBJoMHsFBsK14dap+pizanpcd1dA1dob+jsxg1A4wrKPeYlGsRqVG+hKEWAKRjpmLJ6pEtlktzFqEhjrk0tlLED9jCVY6oWMm9rEblwuevS2IW7TjMFxJoOGR3w2x6B4M8s3v3U/bptd3ppJNOTi96ES88yx7bZ0uKeVZ5o30jfrMFGnpT7fRexg66zTEPTgth0o7usLtCC6GfFrGXBREB+tWsWssk9cvqT8PSqToaiibJNFSMkYCr5RsqnKnmi0BNmcgwQ3kSvoubkqinryxVEY9yqEfRJeyNRl5QBTk2BbgMXRoj3bEb72cY4YDrU+YOW3qEQx432ev5CGiMvKyKFAZh4G3u7o+HrbeXIBADsFhAHgPNJywYcK9wcTrikQJ/mdcRC8QSS8Bva+xxhzX2WQorOetBe8/CEfss8CYbE/1lDIIA2HDWoMTSof3P+4qwtes3pHW2SiIHdmkwPnhJrFQgQoH3+OwE9thLG5ggrSG4YS+Y9OR+opGNrOTU7b83qy1zjP2Ke4xIJQsuIc9N6FWwQB0YNUlPbXTXt65Qc/TXaz3LgHBjDWeSaK2m36B2y1TsSQxtyNmo4PYZP6ZzrJKxW3ItWp8lNWRddzkDBLOF2+TvUzaFPgm/dcPb4m/RJqmt7a/rLltA+7CR6Wvr0z2PjGHXZrm6mjitPsnGui3oHA+Ij3e1Fhtvtc67TcYVGGUmeSy3XIVYvgKMQbRYRzSBjijLLwW7l4AsSk+fDsdfgZKIuyyYCLCk96tPmKR+Wf1pmNynYyW+rzGXY2u5x8RixAWpvDtx1KVZdHTCRajUJSo9TqbSGnPFLL0RU2XLxaZc7OSwOasa69wZnGPU8QhwHJUXFmjIX4+wm2z/Kx1D4pF03333pXvvvTedf/756aKLLu7+gkeQgfc2bLCb5Y08q9yX5Pi+9ppe9omT55Wtsfrq0jGGb5WxugmLrw9nufSmntVSNoeR0YTaxjlgJrI2dSxDWcTxHVcIy4BavR3d4wlM7ezsCplZA2JFXoKkCaVP9g2wp/uZDJiVHNN+vFpanJyDDejI1KzNy8YbabmFX0BoZGJMmUQuLL14MK9oiBxwgqnNb77EQONqJ1OMuv02sT9g21ob79ZbZAE7ur5acGGtTfZ5VOGIreo6bGVbb5AINPC5ShslHYOVAnxCksqBffstFnAobbKVDutZceAdtAb2KYEDmCx4cKC8THIdKyIINvh4a8wlIOHdJ7hhL5/0IAdvgyTYQKDC97vhGHheMTLKuY3NNBC6njgctV4/0wjkaqejqaevL10/rRDLzm8GYmPX4JU+pIoOWMXv5zI0KbJyYWkF+ARYoYg8Vy6NUUi45OXJqZFpkXHVy2adDOwMQWmxsKY5uRMo1lEv/GpyUKN1fodVjYUXksZOykcjoVpmuG1FKeVgllfG6oVvCQ2wQhRuYXVFTqNdjTVAXe+EV7cgtcrRJhOj5ppW1yMv5YgX2yLdynyZxwZZywHsGykjQCxXYN6UaSBFxGxu5M84ur4sbYl6rDytvxX78VO1jnV96wq2//AIdXkmluldXYdGatDj+eU8wrTK0T7vXf8L4cf8hKu6FCqdT+RL5TDHcifcKBS+btylTjLfy/2mR9TSUtlk1MgA07QkbPGVeoRxEnaJuOT4kHDIdTwiXPyDuCACp3ev00FDx1iMcQwarFBI1I7HtJBAQ34nAwNx9i1/qSN94QufTzfffHO67rrr0jXXXGufYDvBruM2VJeNxyi6v8i5RPXT2jsVS1fVjogyLVonsMyCMBF3XUFhKB6VA0P6ygG9zB4tTgzfyKbFoU5BWobCuA8j+jKgju0IUBncVa3QlemgOgzRyqrWubVKTE1Ie1JDgXCaMU16QeDYPX+BGYEZkDPkcUPNowlwvrWKasVDjn26+eqMjgyrVTbF2MR6gUNW4DEIAgnr7DGF9QQSjM5lMQcact+4ST1gQYKDlntwYA2vbdQNpoEYm7/3wQAJNqBhgwUb1mwoKw8OHbB3LOy1Vz4c8FVgBCJ8xQerIlidwAXQ3oHjwQY9YkGwgU9MAI6zwOJRCqvkfYb+vMmT8r0xuYhy+doEhuQeMEd0vqCAh0ZZrizg13VIkelAjW2jyclkHcWqyUyhVZoCaaxY1B/7QMOYVVTiPghl8U1yRN3pui4M5Y41CVCMq5GbcZPsa5nVosm0JVjCR4jGlnCLJsBVymuVMq+lru5TXY8yNW5fm/MRZMhjs3bCJPEINSpLYpTnMTcjxnL0PdysfMu5VSb1aaTsu6hUOryk30ZY9L1vp4OC9hPFUP4u8uz0rpgfOp9U3It0iU/iBVh8r2rx/dgeqW2q65Wpzarw1Wh1wXRN2CWi5/x0rUWy0CJZ5ZLD4eJFYqxZDZ7bj57Ng8kZ7UcCRf54y7pAA5N/H8rm7JDLBRnqh+wG99lnn007Prkj/cmf/Em68cb/mt785jenl7zkxenEE7fYOWsCODXIjTlOjh8jlkpsa8mL5nxiFlGAfXRr58DqBhX4gixFF43yVlYV+MBOtUvgKnX4IsyYRwwXMWWur0/pjLiLYpMZoauLglZ3x3ztxGUok521ccuA6t/hNfhq1BsGR1IsS33su8rKxdPKe7A06UEkwni5JePYNNjmgYZxpnyzhbQmweOnl4t7a9Y3WY+4F5ibwmI9U/h0wAINrM7yQAOPUZjd3XTex5Ks+4AFCwhK8LjDuhBosDCFxwI2WMCA8Xf/vn0WUDhon760FQusbCDZSyWf2/OM07ds2ZrW2mcxfZzCED4/wfsWWMHAqgZWOJQvX/iKBr2QklUNFmjI+ysGGeTnbLXrA7YUyLuW8V0l1u/BPDjC98EquyDq8OOPfdY+L2SJ9p/qs+ahZ0tEwPQtMkXblkjMSRCW8lp8yfWvYujrdB8d8dgXwbVotHU4XUESRyk3w6baVpkyydQWVos2BmmALUzJtdrG5K0C7yx8tZzqyEqfaDGf1Bb5+sq1beU6pVUNUl6z9cFleuRWOZ/HOEMm5+ufkDIfv3krcmIW23d9XnWYanHFqnVdKjs9XWHVVL4wgeWIo2hdvX9b86WWWS1ay+y+Xdmic83pw430KNsoR1aZ1LHFxliGEaZW/wVynOQeaNDLHAkAjL2gLHSCAAK9jn1GzunmDD6x5gEE43riicfTPffck2655Zb0wQ99KH3/G96QfvAH/7OtarjGHqG4yFF5ISRfnJC+jF8UurNtyEVZUHjEliq7TiyRrcZyxG609VfFjt8wCHhwQ+46Khz05ZemsSctWf0wN+yWZ71edDiep+4GNmvPL7gsuNbAy9r8UuF/McxwY7+OfdierT7ofqLf+a+L4sq+9ZrxYoOnYHO2z/zNZMIStvvz1vAUdm842j/FfU21i7Jrqo6GokkyTWMDUXDCUD2wjIrGJL4RMZcmytXMPfU+bLHrWFFdeZ/uGq+uS77OazyXG90gRfYxyFrOGY3oTGpUniVzjXKsRw1qGeXjrVYT5JKGOQhZ/RIBQZP737hsH8BqCxos5x91cSHO+MRtah5bFIrIHPnXXpVrMhYrsJUPGpvWAEji0QteCmHnPoEKDya4mNqNp4x1ecCyujcZkz92YXU3LvOPrMuWWqsl2gqe10e1riWbWlqXsI/oNd+oZb7SuDkj2UXhjxBHnY20Vtl1r8AA9Wm5EDbeS1R5y0xoUqV2+GtabFO5L0e2k5+mvA9kxfQ5FHfGTlE6B2TngRb2XDg9NrVwYe2wu0IPQCD3YQWWEW4k9pVnAeyTXRC9NmEOdyzbAteJIkbwrLA2Y3bsWnJ0PoPRh56vKzNqWS2f1KbLnEXpa+F32F1BWufLwV4RRMu4+UxYEfck9SvqV7Gqhd/hdoX5uyDcFUB0c7pa+0owayzZWdPr++s+nS35QGuJheYJx6ZxjTHWBh4f9S7QwMscmbxq4t+Zbx7S4ApNwQTKMUDhE1+7CeIzlnfddZcHGT796U+nj370o7Cm9773vek973lPuvrqq72+z/5yh678eUsG29GuUNABXdIHzYMMBDcMwQdeu3mm3fk5IKy81mX4GxwrK/LEfI31CzrtecKedbnN3c08/ck3+QosgMs+dhvAtX/QPIBhdOynHbtI4JGyfZRozT3Dv3zSEzv4tJ1jYnOV4CZwQrv0wkKAAQzvq9W7fUWfhjR4QB5YekipZTxf7mEzOrg7vKhydtgo1UGFQkaaxDW7rgC7oOIku1Axfvs4UrpUjlGlL0VucTVoHUkF5W7IEvBRqzBhieVxkf6Wcb7vyRrOXE0HaWdVOkQ+2j6vzIgXu9lMmeSvSW2gT+r0EsNmM6eXq6Vr0Tp6lU9oWG27tA9aemRWyw/ip00Y4j8WuexZju5W/7qDbyXAGNME79BH5o7zjddGXENplTyg3Tw4fpUcPAF2ueOH9tkE6J7Tb5JEbpsFWygrOWainpXgyJYXSO6BBiavPqnW5LYYJzpVn/SGHJomvWrba88RP/744+nOO+9MN910U9qxY0e6++67/TOW73jHO9LP/uzP2aqGHGjQpBkMJuyapIPLqJun51lH1KMJOJP7A/ZNeib9fCYTDF4yCe5Gf6Eaz0HnIADBD+g+OSfoEPT5vgyTdXR5f4oNCqZgE39hZOVG62AdwyGAEH1ZsHw1gtHVV2wiyX+UY1/FB51EAEJ+MUZMctmaz5mHn8EDgwe+Bz3AqDBrGl3JotSIOivOwDd4YPDA4IHvBQ/EkXK5/R1G2OV6bpAbPDB44PjzQPeOhmi6Jrvkmnhrgh/56vJTTz2VHnzwwXT77benD3zgA+nzn/+8r1jgL/lve9vb0s///M+n66+/3gMDyDLR7gIArA4IE37apV85E2rZAY3HL8DevHmzBxpU32SfzORlaiQCBaJrBQWrKDQ5Fza8tX7qyGMjOTJs4JAUKBAWPEqys8akXTrhF5/oLXn4axxkJS97JDvkgwcGDwweGDwweGDwwOCBwQODBwYPDB4YPDB44Fh5YEmgQZNgciUmuX1/yYcHXlYN8DjEk08+mW679bb0u7/3u76aQRjbtm1Lv/Ebv5FuuOGGdNppp3WTdQIFmkhrIi+ZmMOjCb8m3crhk73QmHiTazKOHO3Q2RQYQA4eVkKQq132CIe6b7zDwYLR0KkjQ1IdH0CTDZJ3JvtBJvYhtxM8yRy0x0TV9dpz1rVveJ8Ej3pIV5QbyoMHBg8MHhg8MHhg8MDggcEDgwcGDwweGDwweOBYeWAs0JAntWVSbbNcJrFxk5GaENNGYvLMZH3Pnj3pscceS7feeqsHFXiMAh7x/+Ef/qEHGs4///wu2ECbJt9MvNmUJCc9BCXgJTHx9vc7GL4ejeARCgURIi7y0NfxKTgzOeISGGDFA7isgoCPdmTWr7Nv3usdDqZTgQXxUIcXm8mxT4EG12d02S6dtKOLdtnvHWr8ICPftPCESduQBg8MHhg8MHhg8MDggcEDgwcGDwweGDwweGDwwAvBA0sCDZpMYxyTZG3UmdhqUzs5iUDDd77znfTVr361e3SC1Q0nnnhieu6553wy/q53vctfBvna177Wvz6xdetWn2wzmWaLjzSAGSfZmkxrck07tmGvVhLwyIQCFZrQw18HMLSKAjq48MKnvkpHXRddtmAfNMnGVR/QhIutyLCBKX7KvGiBL2YoSafq6MBe6MKQfuGId8gHDwweGDwweGDwwOCBwQODBwYPDB4YPDB4YPDAsfZAb6BBE16fDBcr9RlJJrgk8TDxZTL8la98Jf3d3/2df3Hijjvu8BUOW7Zs8ZzVBmw/9mM/lngxJF+fOOWUUzy4AB4TapIm42Ar0CA9MYeXdvFQV0ABPG3QtTIBmgIQTq/e1QBNCV2eSiAAWekXj/DApC2uUKBNKxzgp19qp42EjDBEg49NCWxwSGqr7RDvkA8eGDwweGDwwOCBwQODBwYPDB4YPDB4YPDA4IFj7QEPNDDJ1UQXg5jIxtwr9sOkl02JiW+c9PLyxz/4gz/wT1rSBi+Tf4IBqhNgeN/73pfe8IY3+Esc4aEN/XppIwEJHmOIE3H44IGmCTurKJiE65EJ6qxuQCc8WrGADPKa+KtN+FoRQb8UrMAmEjo10RceWEq0Hz5kdXMZMmzgkmhjI0GL+ihjB0l9owxdj3hQJ0V91IUDNmXZStuQBg8MHhg8MHhg8MDggcEDgwcGDwweGDwweGDwwLH0wJJAAxNXNiVNlDVp1uSW9jipZjJ+yy23+GqFhx9+uJt0a5IuPPK//du/TW9605sSj05EfQQaCBYQZFDwAH5NxDXh9hUKZuN+f/ni6N0KyMdAg4IVvDjx0KHR+x2gEzQQtgIN2AJdgQza6S+BEhJt8Kgu2+UbeKC1cifaD7L4BN9hBwl59Q15BQ6gG6JhOpv/SFduy/rEP+IaSoMHBg8MHhg8MHhg8MDggcEDgwcGDwweGDwweODYeKB7dIKJqybJMqWe1ELX5Fplcib4vPiRl0D+1m/9VvrmN7+ZTjrpJA8a0BYTn6L8sz/7M1/R8NKXvtTf4aB2Jtts0qFc7ZNyycGjSbgm4AQStKpCqxLARkb9nlUX/GDBr2BFtIv2gwfspZD23gXxwCc58Wo1g+qeW1zB1kC4XPRF1AOO+jqrzWM6hsrggcEDgwcGDwweGDwweGDwwOCBwQODBwYPDB5YRQ90gYaWjnpyzMSWyTt5TA899FD6+te/nj772c+mD33oQ+lb3/qWBxqY4LNCQRNj8Hhnw6/8j19J111/XXrNa16TzjnnHMeEJwYG+FTm3r17XZ5JuVYngKHAATRk4IV+8skn+yqJODHXCgLsBUdtXd9scr92XX50A17s1coIeJAhOMKLJimjD1tpI2ELL7sk8eJLeMBRQp90IkcbG3Js4IFPX5BVgoeVD/hafacNvfKn/CWZIR88MHhg8MDggcEDgwcGDwweGDwweGDwwOCBwQPH2gNdoIEJbB1A0MQYIxVkqCe3TJZ5CeQ//uM/pk996lOJl0A+++yz3i8myEyywWHSTE66/vrr/TOXP/7jP54uv/zybnJ+wgknuJ5nnnkmPbLrkXT/A/en+++/3yfXPErBRBw70ElAAExN+M8888x0ySWXJD6deeqpp7peTeg12Y+2a8IOD2WCGk8//XTa9fCutPPbO9Ojjz7qtoJ73rnnpbPPOds/yUkwgYQMNvCljc997nNO2759ezrrrLPSpo329Yv1+d0U8pt8S1CEr3Hs3Lkz3XXXXR6QefWrXp1edtbLPFAiPvoGPnX5kbLsphz74wYMP4MHBg8MHhg8MHhg8MDggcEDgwcGDwweGDwweOAYe2DsHQ1MXtmUFGgQnYmt2pmg075nz5502223pd/5nd9JvAwyJoIDTPKZxNfp2muvdZnXve51/j4GJtb6iz78jz32WPra177mj2MwIb/33nt9xcCLXvQi59fkm4n3I488ki644IL0+te/3gMXBBtOO+20LggBL7bDS/I+WJHHGxRo2PPsnvTEfzyR7r777vSJT3wi/fEf/7Hzvss+yYmNV111lX+Sky9lkJ55+pm065Fd6Utf+lL68Ic/nC688ML07ne/O73iFa/wVRsKsMAbAwIEGp566ikPMvBOC/QTbEGO1R2syoAf37KyAltlP2XtE3jQMaTBA4MHBg8MHhg8MHhg8MDggcEDgwcGDwweGDzwQvKABxqYvPLlhDVrx9874HRrY4LLxNYn6GY9k2NWFRAQ2P3U7vTvn/n39M53vnNJv5g0E4iAv04/+qM/mn7t137NJ/E+sTb9e57b449B8HgFur797W+nL3/5y+n2229Pf/RHf+ST7CuuuCJdcvEl6dzzzvXVA0y2CXB88pOf9JUSBBu2bduWLr74Yl/ZoBUI6NcKCGTQSeCBjTKTelZi3HPPPenv//7v0+///u+7yXyOky9lgHvllVf6JzlZafDAAw+kz3zmM+kf/uEf0sc//nF/N8Vb3/pW18ujEAoCgE9fyNGPT0m7du3yx03Q98QTT6RLL73U8Qk28H4LkniRj74HB5u1wsOZh5/BA4MHBg8MHhg8MHhg8MDggcEDgwcGDwweGDzwAvDAKNBQAgqaIGObJuJxogtdgYZ9e/elZ559xifnX/ziF/1dBbzL4MEHH0x33nmnf4UC/p/+6Z/2v9h7QMH0oIMXQV533XX+uAM8TKoJSvAXfwIU4PAIxc5v7Uw7/m1H+sVf/EXY/B0MN954Y7cKYOOGjenW225Nv/mbv+ntfM3iLW95S2LFxKte9ap0+umnO50fdNCn2Ec1eiDAXuJ43/33pY997GPpt3/7t72J1QxvfOMbfSPgQOCCd1AQ3PjgBz+YduzY4bpvuOEGf+cE/WIFAglMBQioEyCgjn7e6/D4Y4+nL37pi+mv//qv/T0NP/RDb7Z+XeaPfxBswd4+3+NL9ER8dAxp8MDggcEDgwcGDwweGDwweGDwwOCBwQODBwYPHEsPeKABA5gUk+qJaz1ZhgcaGxNhgg6sbCBAwAQaGu9s4BOW73//+2FPf/VXf+UTdSbGeu8Af43n85a8l0GBC3QzgbYPL/hjDfATbGC1wq//+q97QIO//L/97W/3VQa8TBJ53pHwkY98xFc+YBd0gg0/8iM/kl7+8pe7DdN+1E8+zfk3f/M36Rd+4Rdc5Id/+IcTG4GLyy67zPt50003pT//8z/3IMMv//Iv+yMT2EX/FWSYpk99ZMXGn/7pn/rjFz/xEz+R3va2t/nqiTPOOMN9DB642i/y1Vy6phozMAweGDwweGDwwOCBwQODBwYPDB4YPDB4YPDA4IHFeOD/AwAA//9bdIrnAABAAElEQVTsvQm0LUV1/19vZsYRVGRQQMEBJ0TBCcVZMc7TMppEo65ooibRJGTFP8nPmOU8T9Ho0kSjGDTOMxrnAXFAEBAVQUUQERSBN//rs6u/ffap233uufede+559+16r29Ne6pvV1dX71NdvWp7DmnC4Yc//GF697vfnf71X//VJH/+859Pxx9/fK+WzZs3p61bt6b169en1atXW3rLli1pw4YNCfM+85nPpBe84AXpe9/7Xrr1rW+dnvSkJ6Xjjjsu3fGOd0x77LFH+upXv2r6vva1rxkNiv7hH/4hPfWpT02HHnpo2rRpU7rqqquS9CATPWvWrDGdu+22W+Ig/Pa3v03vf//70zOe8QzLn3jiiemhD31oOuqoo9KBBx6YLrvssvSWt7wlvelNb7L6l7zkJenpT396us51rmO2/uEPf0jXXHNNuvbaaxNtINCuVatWWT1pbN5zzz2t7qc//Wl6+ctfnt74xjda/m//9m/Twx72MLN73332Tbvvsbvxin/btm2Jgzz2E0cIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBGYFgVVL4WjAIfDf//3fiYdwwqc+9al0//vfv7fNPPjr4V9E5HmI5mH905/+dHruc5+bfvSjH5mj4fGPf3y6+93vnu5yl7uk3XffPX3pS1+yB38cDRdccIGJ+Od//uf0tKc9Ld3kJjdJF154Yfr+97+ffv7zn6df/epX5sjYe++903Wve910+OGHpyOPPDLtv//+xoej4b3vfW/6i7/4C8s//OEPtwf/m93sZuYwgP9///d/03/+539aPc6UP/3TP003vvGN0xVXXJF+9rOfmbPjxz/+sTkC1C7agQPi9re/fTr66KPTwQcfbI4UHA2vfOUr0+tf/3qT98QnPtHaduyxx6Zb3vKW1j4qkIODgSDngjDCaRIhEAgEAoFAIBAIBAKBQCAQCAQCgUAgEJgFBJbE0fDd737XHA0vfelLrY2f/OQnzdHAA7J+jdfDsgeBuu3b8gKL/CM9D8/QsAqBFQ3Pe97z0nnnnZcOO+yw9Ed/9EfpmGOOSXe4wx1sdcCXv/xlW2XAygmcD6xAoJ7VD6wg+Pa3v50++tGPpg984APp4osvTre73e3SLW5xC1vlwMqIE044wR7qr3e969mKhfe9733p2c9+tpnG6gKO61//+uk3v/lN+slPfpJO+9xp6Zxzz0n3uc990mMf+9h0v/vdz1ZEnHPOOekb3/iGrYjA6fDoRz86rVu3LuFM+MQnPmFOEJwSrLS41a1uZasgsAe7caZ84QtfSL///e/TAx/4wCRnCjYR5GgAE7Cp80YUfwKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBZUZgphwNGzdutNcceJBeu3at/eKPo+Gzn/1sOumkkxIOjBvc4Abp3ve+tzkcDjjgAFs1cOaZZ5pjg9UIPKD/3d/9nTkOWH1w1llnpW9+85vplFNOSeeee266173ulR7xiEekffbZxx7scR6w2uJOd7qTrWzglYf/+Z//Sc95znPs1DzgAQ9ID37wg82hgSMBpwUOgUc96lHmEMCxcasjb5WuuPKKhIPiZS97mTkUXvGKV6THPOYx5oDABlZdvPCFL0xPeMITbKUFqxWwgVdGsIG2vf3tb08f//jHTe8b3vAGc2Dc9KY3NRzkXJCDJhwNy3zlhPpAIBAIBAKBQCAQCAQCgUAgEAgEAoFOBGbO0YBjYfWqvH/C2jVDjoZ/+qd/sod8XpW4293ulvbbbz/7ZZ+9F04//XR7LYIW8iD/93//94lXHc444wxbycBqAh72cSiwgoGVEDyw8woED/c4H+BjJQQOjlNPPTU961nPMsBwajzg/g8we774xS+mj3zkI1b+zGc+01Ys3Pa2tzVbeC2DFRyve93rrB6HAysvbnjDG9orFaxq4NWOvfbaK935zne2/R5wNLAPBc4GXu147Wtfaw4TBLz4xS9OvLbBKxbsH4GjgYCDwce0Q84Hq4g/gUAgEAgEAoFAIBAIBAKBQCAQCAQCgcAyIjBTjgb/K70eoLWi4R//8R/Td77znbTvvvuaY4C9F3AK8KoBKwlYiUDAMcCqBvZcYAUCr1Xw2sRd73pXW2HAKxNsxMirDewdwcaOBFZM8LoEezew+oHXGwj3uMc90n3ve197BQKHBq9fEKDFOXHEEUeYTewDgSztS/GgBz0o4YS45z3vaZtW4lDgNQn2asDZQDs42NCRlRysaMDRwP4QhFe/+tXmaNCqDTkTcEro9ZPYDNKgij+BQCAQCAQCgUAgEAgEAoFAIBAIBAIzhMBMOBpwMHDoV3vwIc/DtRwNrFLgV3++/PCQhzzEXo3gNQr2YMBpcNFFF9nD+re+9S1zNvAVCPZTYO8DAptHstfCQQcdZLJ//etf2woDNq0k/M3f/I0drDLAcSFHA/sw8PrEurXr0te+/jXbfwF6Vjzg0JCj4fLLLzd5vObx9a9/3VZfQPfXf/3XtvIBW3/3u9/ZKxiktZoBxwPOElZfvOXNb0nv/5/3w5Ze/7rXp4c/4uG2ySR5vTqBk4GDPI6GCIFAIBAIBAKBQCAQCAQCgUAgEAgEAoHALCEwE44GHrZ5eObBmUMP06xYoI7NIPns49lnn22bKPKAz+sHN7/5zW2TRgC95FeXpE9+6pPmLGDjRjaN5OH/F7/4Rbr66qsTKwNuc5vbpOvse520ZWt5uOcznOzjgDPj3/7t39If//Ef21ce+Lwlr0YQcGrwCgS2sGmjvjaBI4K9Hli1cKMb3cge/NlrgS9uvO1tb2sdEmxIyWoKHB98ZpONI3lVA0cDASfDJZdcYo4GZLP6gsArGLw6gWycLl2rF7TKwRjiTyAQCAQCgUAgEAgEAoFAIBAIBAKBQCAwAwjMhKOB1wE45GjQ6wF8sWHzZhwNnzZHA44BHuz5BCT7NPAQr68y8OoEey7wOsOogMOBLz+whwMP6jgQiNl08Y53vKM5HXh1Qp+3xMnAAz+2sDriXe96l4n/8z//8/TIRz4yHXXUUe1+ETg22IsBZwMbVLJ/BKsweOVCAQcCX6OQA4FPXl555ZX2Wshb3/q23IYPGumrXvUqc3Dw2UwC+lnF4J0LWvVhBPEnEAgEAoFAIBAIBAKBQCAQCAQCgUAgEJgBBGbC0cADM0EP0axo0K/4rDZgRcPzn/98W9HAqwo8qPMqBI4BNlsk4Jz44Ac/aPswWEHPnyc/+cnpuc99bl7dcNvMs8X4tm3Nqyny5pN77LGHvYaBo+EZz3iGSTjxxBPN0YBDgk0ltYfCX/3VX5lTg89UsjqBlQk4FXAu4LTA8cGrG+wr8bGPfczqEHjyySfbKxysxmBjS179ILBZJZtJypHBXg84OdiLAgcMezwQK4CPHA3CTXURBwKBQCAQCAQCgUAgEAgEAoFAIBAIBALLhcBMOBq6Go+zgQdoPln5uc99Lj3vec+z1yBYCcADOF+POProo+2hngduXoHg05DsiUBgPwY+IclKAB72cRIQcDSwHwMOgj/84Q/2WgWvZ0DHKxeXXXaZbQaJM4Jw/PHH22cs2SSSfSBYVfGhD30osaKB1ROssGDPhcsu+03+IsWHE3s/HHvssbbhJM4HnA1sSMnXLQi0g89eHn744bbxJI4GnCRsBvnKV76y/erEm9/8ZtNLewlyMoCJ9rIIR4NBE38CgUAgEAgEAoFAIBAIBAKBQCAQCARmCIGZdTTwEM3qAPZYYG8EvcoAdnymkj0acDTw6gQOAF5X+MIXvpDYDPKEE05IbOLIFx9YNfDhD384/cu//IvBzv4I7K/AKwm85sDXInA4sKcDdQRWNIieVyz48gT1rC7AefBf//Vfxv/Yxz42HXPMMYkvWVx66aXpTW96U/rnf/7nRDn7Mtw2r5rYtHmTORE++clPpi996Uu2mgJnB/L4bCWvTtBGXq9gNQObSbL5JHtE0AZWS4AFKzvY4wEng61uWN2sblhlJsefQCAQCAQCgUAgEAgEAoFAIBAIBAKBQGAmEJgpRwOrGFJ+i2L1mtX2cM3miqwI+MpXvpLe+ta3muPh0EMPTde97nVtj4NDDjnEHrp5ZYFVBqw6YAUCDgj2TjjyyCNtD4avfvWr5mw4//zzbeUCzgEcEHwdgpUQrI64/e1vb/SsbmAvhte85jX2hYr99tvPVicgE+cBn6J85zvfac4IPnHJygl04qxg1QKrJQh/9md/Zo4CVkr86Ec/sk9bbt60Od3jnvew/SV4vYK6X/7yl9Y+dLKJ5IMf/ODEpzF5NYQ9KKAhYBe65WjQqgarjD+BQCAQCAQCgUAgEAgEAoFAIBAIBAKBwIwgMFOOBn61x9mgfQt4jQFHA6sUTjvtNHMOsIKBh28etKGFh69KrMm/8O++x+7mLGC1w2GHHpb22nsve0DnYf68885LP/jBD9I555xjDgleQUA2X6J46EMfap/NRA6rC3iNAecFdvDKAjpud7vb2coGHvb//d//3RwfnENecXjKU55iDg2cBdjJppAEvfZAHqcIjgNemWA1xb777ms0F/z0AnNQ/L8X/T/Lv+AFL7CvWbDigS9VsDcEgbZyYLdeo7CK+BMIBAKBQCAQCAQCgUAgEAgEAoFAIBAIzBACM+Vo4Fd7Hqb1Kz6vKeBsuPjii23vAx66efjnYZt9DdhwEScA5bxOQB37LBx88MHt1yjAGgcCD/vI4RWH8lWL7flVhI32xQj2WeCh/oorrjA9fG7yd7/7ndmBLvSwDwOvSPD6AiskPvKRj9inLnEyPOtZzzLnAftJ8AUJXvnglQj0EHCK7L///rafBPbtsfsetmqDOl75YBPI97znPfZ5TT6ZyR4PbHKJ7nrlAq9RcBDqOiuMP4FAIBAIBAKBQCAQCAQCgUAgEAgEAoHAMiIwU44GPUDzgE3gQZ0DBwSxyoWX6MlTxz9eu8BRoZUA1OG8QIYcGZTxikZ+ZLeHda1cQB6OBDkJcF7wME8ZKyb23GtPc2zg/GDFwzve8Y506qmn2ucyH/KQh9irFbxqgfND+lCFLTqwDVupx5GCo4G9GVil8MAHPtA2lzzwwANt9YZWMPh2Cw/sQqavQ1eEQCAQCAQCgUAgEAgEAoFAIBAIBAKBQGA5EZgpR8NyAYGDgYd6vZIgB0bfQzwP+2xAycoGvmbB5pAn589Wsskjm0fOF+Bn5QNfqPjxj39sX7Jgo0k+13nAAQekvfbay0TgsCBgl1YvhKPBIIk/gUAgEAgEAoFAIBAIBAKBQCAQCAQCM4rALu9owKnAAz2x9n5gtQH5USsGeD2CjSTPPPNMe42CVyt47YG9Ffbcc097lQNHhRwEyNu2dZt9hYIVEmx0yX4QvKKBIwFHw0EHHWRfmYAHhwKvbJDGLmiQhxzqSMsxMqN9K8wKBAKBQCAQCAQCgUAgEAgEAoFAIBDYBREIR0N+cMexQJBjQY4GPdxrZYMe8Hn454EfRwAbTbLJJPkjjjjC9mLgs5XaSwJeOQj0CgeODV6bYE8H+HBMsDkkhxwT2KCvTCCLcsmBhyBay8SfQCAQCAQCgUAgEAgEAoFAIBAIBAKBQGAGENjlHQ2cAz2460EehwJOgfJwvzqvHCiOBVYiQKuVD/DibGB1A4HPa7Kvg4XsC9i6raw8kJOAculCPnoI6IXGr1CgDocE5drXAVr4OWxHitVlLwvKIwQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCs4BAOBqqs6AHeRwBHDgBWOlAYJWBynAKeMdAJcayfgWE6iWfPLI5FKgjUOb1ez3iF59iyYg4EAgEAoFAIBAIBAKBQCAQCAQCgUAgEFhOBMLRkNH3D/g6GTzo+1cqWFlAwHngX62gnAMZ5oRovnzR5wDQSgVi6HEi6DUL8siSYwGZXo7sxA6lqfc01EUIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBJYLgXA0ZOR5wFfQgzsP8jgDiHnwl6MBOq1skJNBdfpKhPZ6gFeHlwu/ZNeOBeW988Dbh37VIVtyKY8QCAQCgUAgEAgEAoFAIBAIBAKBQCAQCCw3Aru8o4GHdR7kiQk8uPOwTywngR7o5VCATmWkCeTlaNCeCsjFocABr8qhVSDt9cvRoHrJgI46HaqPOBAIBAKBQCAQCAQCgUAgEAgEAoFAIBCYJQR2eUcDJ4OHeR3kWZHAAz2BB3y/+kDlVtnUk5ZDAAeFHApFJo6E4mhgZQT1PiBfB+VydIhGjgryWlkhGfApLfqIA4FAIBAIBAKBQCAQCAQCgUAgEAgEAoHlRCAcDQ36OAX0VQm+HCGHAg/zrFTQQz3l/uEePjkKiKmXo0EnVrzKE4tHshTXNMVZUTal9I4Kz9/F6+VEOhAIBAKBQCAQCAQCgUAgEAgEAoFAIBCYFgLhaGiQ5sEdRwMP9qxo0OoBqrX5Iw/0OijXwz6xAo6G2hmhOtEhgzS6SEOvIBqfp6yLrotffBEHAoFAIBAIBAKBQCAQCAQCgUAgEAgEAsuBQDgaGtR5mNcmj6R5+PevUPSenMbHkF+AMBIcAnXAIaBAPQc6eC2CNE4NAnSUEyj3DggrdH+gC0eDAySSgUAgEAgEAoFAIBAIBAKBQCAQCAQCM4FAOBryafAP+DzA62FfKxN46CeonLzKrKL6I3ldNOLtchRINzF0taNh+zbcGdkRgS+i8WdIXmVCZAOBQCAQCAQCgUAgEAgEAoFAIBAIBAKBZUFgl3c08FDPSgYe3tesHXzGknJtArlm9Rp7wMeBQLDXKlbl1x2ah31oCTz0Q+NftYB39ZrBVyxER+z5yBNURhp5Ct4e0jgh/J4Noos4EAgEAoFAIBAIBAKBQCAQCAQCgUAgEFhOBMLRkB/atQKhfnDH0UDQA78cDTzk+9UGcg5A5x0C5GvaxZ5s5LKiYdv24uxYlR0da7IDI0IgEAgEAoFAIBAIBAKBQCAQCAQCgUAgMEsI7PKOhiU9Ge4VhyXVE8IDgUAgEAgEAoFAIBAIBAKBQCAQCAQCgRlBIBwNM3IiwoxAIBAIBAKBQCAQCAQCgUAgEAgEAoFAYCUgEI6GlXAWow2BQCAQCAQCgUAgEAgEAoFAIBAIBAKBwIwgEI6GGTkRYUYgEAgEAoFAIBAIBAKBQCAQCAQCgUAgsBIQCEfDSjiL0YZAIBAIBAKBQCAQCAQCgUAgEAgEAoFAYEYQCEfDjJyIMCMQCAQCgUAgEAgEAoFAIBAIBAKBQCAQWAkIhKNhJZzFaEMgEAgEAoFAIBAIBAKBQCAQCAQCgUAgMCMIhKNhRk5EmBEIBAKBQCAQCAQCgUAgEAgEAoFAIBAIrAQEwtGwEs5itCEQCAQCgUAgEAgEAoFAIBAIBAKBQCAQmBEEwtEwIycizAgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEVgIC4WhYCWcx2hAIBAKBQCAQCAQCgUAgEAgEAoFAIBAIzAgC4WiYkRMRZgQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCKwGBcDSshLMYbQgEAoFAIBAIBAKBQCAQCAQCgUAgEAgEZgSBcDTMyIkIMwKBQCAQCAQCgUAgEAgEAoFAIBAIBAKBlYBAOBpWwlmMNgQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCM4JAOBpm5ESEGYFAIBAIBAKBQCAQCAQCgUAgEAgEAoHAtm3b0pYtW9KqVavS2rVrLd6+fbsBQ9nOEMLRsDOcpbAxEAgEAoFAIBAIBAKBQCAQCAQCgUBgl0Bg69at6dprrzUHw2677ZZWr16dKMPJQNqHWXVAhKPBn6VIBwKBQCAQCAQCgUAgEAgEAoFAIBAIBALLgABOAw5WNOBYwKmwbu26lPIiBvKUE3A4aGUDZdCtWbNmGSzuVxmOhn5soiYQCAQCgUAgEAgEAoFAIBAIBAKBQCAQmAoCOBn0ygSOAzkTvPNBzgatbJCjYd26dS39VIydR0k4GuYBKKoDgUAgEAgEAoFAIBAIBAKBQCAQCAQCgaVGAKcBjgYCjgPC1i1bU17n0K5Y8I4GHBCsdCDWaxXeQWEClulPOBqWCfhQGwgEAoFAIBAIBAKBQCAQCAQCgUAgEAgIAe9oYBNIHAibN282JwKOB61iED31HDgnOHAyrF+/fiZWNoSjQWcp4kAgEAgEAoFAIBAIBAKBQCAQCAQCgUBgmRDAaeBXLOSFDGnrtuEVCzgbWMWwceNGcyhs2LDBrN22dVtatbpsFqlXLpapGaY2HA3LiX7oDgQCgUAgEAgEAoFAIBAIBAKBQCAQCARGIIDzgVcocCSw0mHTpk3p97//fVq7Zm3aa++92tcqRoiYelU4GqYOeSgMBAKBQCAQCAQCgUAgEAgEAoFAIBAIBBaPgD5/yYoG/4oFjohY0bB4XIMzEAgEAoFAIBAIBAKBQCAQCAQCgUAgEFhxCOA4IBD3OQ18Ofsz8CoFr1XstmE3W/mw3KDEioblPgOhPxAIBAKBQCAQCAQCgUAgEAgEAoFAIBBoEGAPBpwHV199dbrqqqvSNddcY3s34EjgdYm999knXfe617HXKNgsEocEB5tBxlcnVq2KjhQIBAKBQCAQCAQCgUAgEAgEAoFAIBAIBAIZAZwFOBlwLLAHw69//et0ySWXpN/+9rfmeFiVVqX1G9anm970punggw9O+2SHAysb1q1dl9aum41XJnQiY0WDkIg4EAgEAoFAIBAIBAKBQCAQCAQCgUAgEFgmBFjFwN4Lv/jFL9LZZ5+dLrjggvYLE1rh8Lvf/S7d6EY3Sje72c3SbW9723T4YYfbhpDLZHKv2nA09EITFYFAIBAIBAKBQCAQCAQCgUAgEAgEAoHAdBDAyXDZZZel733ve+ld73pXOuWUU9Kzn/1scyyw6eOll16aPve5z6UzzjgjXf/610+vec1r0oknnmgrG6Zj4fhawtEwPlZBGQgEAoFAIBAIBAKBQCAQCAQCgUAgEAgsCQJXXnll+sEPfpC++MUvppNOOikdf/zx6U/+5E/SkUceaY6FK664In37299OX//6183Z8Jd/+ZfpkY98ZN6v4bq2hwNGsY/DLIRwNMzCWQgbAoFAIBBYQgR434/gdydeQnUhegEIcG7ivCwAsCANBAKBQCAjEGNndIOVigCrGb785S+nj33sY+ltb3tbeupTn5oe/ehH2ysSvC7xhz/8IZ111lnp3HPPTb/85S/TUUcdle5+97un61znOmUPh7xfA46GWZhbhKNhpfbSaFcgEAhMDYFt27a1kx4/sPsH/LpckyTK2fSHvHYJ9nw0YtvWLD//041DvGrg1i2ZP//ju8kE6jmgJ960aZPF69evtzLsJaCbAxrlieu8VeY/NZ/Kpxlj2/Zt+eF8dbEdmzhoK4cCZdCuXpVvtpl2vmByM33L18jz5w0Z82HjbfA6xScZpie3I+/pZOdA597ziJa4tqOmi3wgEAgEAlNHIA9h27aX+4l0a6xSrHJiPw6qXmXKi44xkjKNqdCJhjp22Se/bt26tly6oK35VVfLR4bkigZejvq+ovqIA4GlRIAVDT/84Q/Tl770pfSqV70qXXzxxemFL3xhOuKII9INb3jDxFyO1yv23HPPdL3rXS/d4AY3MCcD1wLzydyjy3Uz/9RnKZthssPRsOQQh4JAIBBY6QhoUsJkRZMi2sxkR5MjP5mBnnJoKWdzH8p42KSMOoJkUU9ZXQ8vfHIkcJOBRvJxPGjnYuTtvvvuVi951EuG1wc/gToF9FNOmexW3TRjbODABg7ax0Gazz3x4E6gTHRgMl+gfRziUzuH2sppGUAyhA+64B+id0qpU4BW54CyNavzp6jWlnMvGmLZRBq5EQKBQCAQmCUENEb58Y2xU4e31dNQDg1B5cqrjLHYy2HcJM9YyPi5aeMmGzd5Z93zwg+tp19oPbo5uHeMc/9AZ4RAYFIIaI8GXp9gVcPrX//6VjTOhvve977pNre+TTr6znfOr1MckfbYYw+r1/Xor5uWcZkS4WhYJuBDbSAQCOy8CDCBYUDXpKdrcFcZsYIN/vlJ1eqqFQrI3LI5Oxzyr0M4DLQ6AV7qCPXDJnKYcGVRxqeJGRMvaDVZgp88cgn63jJ50VFOWhMym8jllRCSzyeTNqzfYBM7aJcr0Ba1B3sJtJMy8NDEkLTKSKtt4hfdqHZAi2z4OR/SJx7qJBcaDtFTrkP01IMvsWyjTv2IWOWitbq8KsM7OCQv4kAgEAgElhIBjVXcYxiDusYjaKjPI2B7/4BOgXodlGnM9PWeXuU+5j6ksRtZjN/cv2o+jdlmZx6DoaWMX3i1Ck481FHOfwVfZ3yNY0P1EQcC00BA/ZjPWv74xz+21yjYq0Hh0EMPTccee2w6/vjj7bWJm9zkJvZDEg4HrVwV7XLH4WhY7jMQ+gOBQGCnQ0APtkyYRv3aockVsYImMuRJKw8N30xG9m677dY6BWwy1EzauPlwUIazgPf0WM3AQzBl5LGJpXSsXkAWwTsu4IeXsH7dept8mTMh5/2DMWVXX3112rhxo8nGebHXXnuNbK8JXeI/ar+wE360CZvBQg4VTKEcHORYgIa8xwTsdMBDPe3WgU7owRQc0AENPJSrD5DnfEAvfeO+uoFe+OHlINA25KiNVhh/AoFAIBCYEgKMSRrrNObWjoJRpsDvD2R4fuoIxNQZbX6ljH8ExlN+3SXGDo3n3IvY+I6HKvFCLxrGTcZmxlLd73R/kw3QK5jexha1U3URBwJTR6Bx3F111VXpN7/5jX3i8rOf/Wx6z3veY5tB8slLwv3ud790z3veMx1zzDHpFre4Rdpvv/1s/khfn5UQjoZZORNhRyAQCOw0COhhUBOXXsObm0VfPRMaguTpAZMJEnU86BJ4uCX89re/tXf1iLn58B1lnBNyNHBTYjOgm9/85ummN72pfepo7733tgkX/MjnQVt2EzPBYnJGUDlpyjWpI489enhe7omYbKMtfkJJ+2QjNhPkWFC5JpTkfWAyC6a/+tWv7MBpA7ZMcOGhzfvss4/dyPfff3/Ded9997V3JL0cnUuwJKCfoHMKprLT421E+Y/sU17nSHI419gSIRAIBAKBaSDAeKUwNPbgC1jAUGTjKGsIOvbM4R7EGPf73//e7nPsqs/9jHfVGYsZn+HXPenGN75xOvjggxO/5PJ5P90jsRNZGrNldx3TDo3R1EGvdpLuGptrGZEPBJYKAfr8hT+7MF3084sS1wJ7MdDfmZPwOsU3v/nN9L73vc+ulePzqoanP/3p6YQTTrD5Cf2f/jt0rS6VoWPIDUfDGCAFSSAQCAQCHgEmIprI+MGcMgVfrrKuGB4mWMQ8RGryw6SHmw0TK369welw/vnnp+985zvm3WaTIG42dbjrXe+aHvKQh6Q73OEOdmNih2Itp0MPdvHQ6/VwY6Kch29ia1u1pFR2om8WHnaZeHLQFiaZwptYB7bSNrCkvd65QHs0seTmrSWK3/rWt+yb1d/97ndhnxOe+cxn2q8Hhx12WDrwwAPT9a57vbTnXnu2OmUHjOjgvBHLRv0yJ8eHMJ+jyBVgJ3zI9ufOkUQyEAgEAoGpIMBmvFpx4BX6sY9yxj2C7jWWaf5o/OWexD2O1XMcl1xySbrgggvSeeedZ/e7r371qxZ7XtInnnhiOu6449LRRx+dGItxNrC6QQ5dyccmG/ezKbyWSDkHNnm7GGPFg/z6fkFZhEBgWghc9uvL0hf+7wvptNNOSz/72c/SYx7zGPt8JXMvPmvJFyk+/vGPW8yKBj5v+cAHPtDmilxP6vf1NTkt+72ecDR4NCIdCAQCgcCYCDAp0SCuyQsxgUmK6rw40VFm9c3kR7/A1A+RPADzmSNNvpiAsWSOd/b4vnJfeNjDHma7E7OMjl98WGJ60EEH2YHTQgF7pBt7uIlRxuRPD+FM0ignyFHR1z7JnUbMAzwP37WjgfZw+Imix532kMdJwS8FrGC48MILLeaGziejPvzhD/c24c5586Xb3e52hiuOBlY3HHDAAZbnlRUmu23I53frtvLrGvYQhCt466Acmwi+zAryH+poE2Ecx4QRxp9AIBAIBJYAAY1VtWjGrqGQhzQcEnPKGyLuJ9zfGHd//vOftyv2Lr30UnM2/OQnP7H73ZDMJsN97S53uUu61a1uZQ5fduJnhRmr+bjXmXOhodU9jqxs0T2MtvhD46/iRkREgcBUEWB15Ve+8hVzJrzlLW8xJwOft+RHI64VfnD6j//4j3S3u90tPfnJT078wITDjXrmNvRvOd2maniHsnA0dIASRYFAIBAILAQB/RoiHk1ilCf2kxnymvBQrodIrRTAI81qBn7hueiii8yp8LKXvcwmZfD6gBzpIw2vD7xCwft7rHK4xz3uYZOwehKlPJMz2oIMTc6wSQ4Q6vLjcefSV69zGmnZiM3CDSyZvHLU5diE/TgnwJYlub/4xS/yrwNnpA984FS7qXu7kckqBMkD4xpb6B/60IfaO5I4INigidcruNmjHx4FnWOdK5V39QvOh4LS0BGUV33EgUAgEAjsLAhoHMOJzmsSOM2//vWvp09/+tPpU5/6VG8zuAcxJsPPGM5Y7sNRRx1lq/ge8YhHmAOCFQ66r4lWeT8GI4962UWdxm2VxZjrkY70NBBgfsKPSqyw/MY3vpFOP/10m1+wQpVPWfJa0RlnnGEOiAc84AG2epX5CteI5j/haHATqWmctNARCAQCgcDEEWh+sdGEBPmazJCm3A631NTX1xMY8jzMnnPOOen73/++PRCzmoFl/B/4wAcQOScwKUIHMb+mM4GrA+/23ec+90m3uc1t0uGHH27v8eGA4Nd4bkwKPAwz6fJ2kUY2MXpwNPB/uYKfFGIDeV/mH/CxV+3hpktgYsvNm1/SeF0CrE855RSrq/9oYoscbuL8UkDaB278d7/b3dMtbnkLw5bVDYcccojtkcHmkQqy02NJHbgKW/Y/4xdA2U099OIhHyEQCAQCgVlBQONhX6zxjTGMwEo09hjCyctKMh6meOf8Ix/5iN3vutrFOKyxmHGUBynp8/RHHnmkPXjd8Y53tAcvnA0c7FNUhy5+aLCXwH2YY93adWnd+nLvsIr4EwhMAQHmGsxPLr74YlvRilNOP/ignjTzPVb2MJfTSlXNFejHuuamYO5IFbGiYSQ8URkIBAKBQD8CTFY0YdGEylPrAVixvwnU9NDwoM+NhV922NynDtxcOKDjQLdkI0/7APh9Abhh+cAkjF99WHLHslOWnBKQhUy+krBm7ex+6UBtx6HAwWSQX7iwHwy4+cp5IlpivuSBE+bzn/98Ovnkk+09R48LsliJgCzw84HzBu7ShzzyNR0rGh70oAfZCgdw5tUKeLDL9xNkK68+IX06D+jA8bB6Tdlbou4voo84EAgEAoFpI6DxS85R9FOmQ/YwbjEGMp4xXvLwxKsS3/ve92wVWe3khVZjJvc28SMPGTgZKIeGsZ7xk7wc7Oy8z+oGVpjheDjyiCPTTQ+8qY3/2IY8greza2zFVg50cO+IEAhMEwH6NHMR+jUrW5nn0E+5BkjTJ3lViDkL8x3VEddzimna3aUrHA1dqERZIBAIBAIjEGAzLDaWImiSQuyDTWQaOk9T04mHG8q5555ry0i/9rWvpXe9612qamM98HKz4SCgR0ETNNVxA+KmVAcehnmn75a3vKW918ev8vzyI0cF9PNNxGqZ08qr7eAIHgqyl3rS3IiFNRhocssy3Ze//OViG4qZVHKDhx8sCX5iizzVe4ePF8JrKnzfGicOE13eF2bliJ+sIp+gGLmyVeXS4+t82gTEn0AgEAgEpoyAxibUdo1JfiyTaTy0s3rhzDPPtLGYVQys2mNFmQ8a05GBHsnXwxNljMnkNUZTpnseshh3cfqyX8O97nUv21NH9zd/X2D8RRa8yJNMdFK2bWveRLhx9HobIx0ILDUC9E0O+iEHc5hNG/MPKvkfcw8duk6s767Or77meSn0lMsBsdS2zic/HA3zIRT1gUAgEAhUCDCx8ZMgTYYg08MjaWgI3ASgIaieOiY5+jWeDbA++clP2u7BRpj/cDPhs0Y4IZioSY9uQqKrZYtO9ehiUoYsPompcPzxx6f73//+tnM3r1Tw7p+W4EHDDYuwJt/A7HUJ+TSGfSpGM60/tEV20U5hKwxYwcEBdtxowYpNH//v//4vnfz/nZzOOXcwsZVjgfOggDwO3aS5wft66NAFntLBr3qbNg+vhMC58KQnPSnd+973to052SgSbOFDvoL6SHvOwNjhW+/w7nklI+JAIBAIBKaBAOMpY5buY4xH7diVDdC9STE2Mebh6GWl3rvf/e7EF5N8kIObe6HGQ+qR6/UwJlPPeEwduqkXj6eX/Oc85zk2BrO8nE8/szGy/+Sz7sHwIh+ZapNkRBwILDcC9HHmNfRTvZJJ36f/cmg+QhnXEXTMb4iXO4SjYbnPQOgPBAKBnQ4BBn0GdAZxHTSCB2DqVOZj1XNTYEKD44CNHtkzgI19+PoBn6tkIqYAvx6GkVvrxYbV2QmwYUP50gEysYGJEoF6DoJkQaPAxoXs3YCTgS8psOz0Zje7mTkcRONjbCdwU0PecgTfJvTjCKDN/FKldl9z9TXpV5f8yvC9/PLLE04cdml+85vfPGSyn1CqPcKYOsmDibYLS5XDozQ02OID2N7+9rc3THHygPOtb31rm+x6OnMm5PPkV8kgF/nSqZhXW7wjwsuJdCAQCAQCS4GAxl3v+NQYpbETvYyffrk3u+ezHwMrFxiDP/jBD9omkN5GOXX9GMs9hnKCHMvoQz50pHVv5MGKAI9orSD/YUUDY7BW7XGPO+SQQyyPg0NB477GWY3tvm2ijTgQWA4E6Pf0R/o5gTz9ljIdlNOHyev6pGw5QzgalhP90B0IBAI7JQKadPnBnYYw8OtBn5sBAz0Beh5CedeOLx7Ax+Y+TLz4FjLfCsfh4AM0umEgizyyubHoBkI9dVqWzwRPkzDoNXnycuGFHlt84LvkJ5xwgr3bygMxv/6gDzla0SDdssfzL0ea9uM44RDetP/KK3+XP1P5g/TZz342fe5zn7PNNL19rESgLTpXYKJzRRkHgbbrVy5/bruwhR8bkC2Hj9dJ+vnPf356/OMfb6+sQA+tsGSZLo4G7JJeYgXKCbJT5REHAoFAILDUCDDWMgbpnsTY5McixkzGSO5rOBc4uB/96Ec/SqeeeqqNxd5Gxj2NaSpHtgLjLo4EAnI1LhKjS/WktSoQmbJTTgp4FXid7XGPe5yt4MPhgPNBzgbaQpvED4/a6NspWREHAsuFgPoocwb6KHtqsaqST2lbPl8HsxSWzNHw3ve+N73kJS+xtrJciuW5BAACiAiBQCAQCOzMCHSNZZqIMeHxgckQO23zy/ovf/lLi/kWMs6Fb3/723M2JoSXcZIHUWL4NSkjj24O0flJleiYHMkOJlvQazJFXP/6zjeY73SnO9neAuwrwCaRTMJY8s9yU16r6Nq924xY5j+0GVx/+tOf2g7NfKkDbNlw7LTTTptjHe0XhuDJQVBMWudSmEFPPXmCPyfkJYd66nR+qFPgM1SscuAdYjBlOS9fqeA8zxckz9s4H0/UBwKBQCAwDQR4JY/7G1+S0MFSb1bt8UogX/lRYAzDiaAxtG8s1linexp0jK8ag8lTxwEt5cSioxwdPvA5YvYmYqNeXm/D2bD/fvunmxxwE9tcz9Nyj+QeKn2+LtKBwFIjoH7vVzHSn7WCZ+iHitzXmefR//lSyiytelwyR8N73vOexHffCZ/4xCfM0cDFqoteA8hSn6iQHwgEAoHApBFgIqPAWKbJDQO9HuqpZ8wjzx4BfE2CSRcbYn3hC1/ofACWTGJk8qCPDP1CTlrjqLfB8ykNvxwVTJi4acnxwDiseujJa2wWP3sLaKn/IXmpKZtr3fjGN7Z9HpCFHZqEIYugG6PykoWtlFl5ho4NjRYTkIOdtAdcSeNkIX/WWWfZt9iZ1OK88YFJLbzQzReEMW1Re+DBdtWRR7evp6wrwFPTMdllOS8bR/LJUSa8woiutSZvQEa74FWgnlBjq/qIA4FAIBBYSgQYexiHOBjTGAN56OH+hHP3/B+dn874zhk2/rJSzwfGM+i5Z+g+JBkaH+vxTmOelwMNsqjz4zm2aYwmlmzpg7ZL3rOf/Wxb4cB4zGegWe0ne9CLLo4IgcCOIkD/Uz9FFnn1SZUTq45+WJcz7/F7NeiaUX+Hnj4vOSas+SNdZLvqPS1p0Y+kdfO5ProlcTScccYZtmP6a17zGrP7wx/+cLrvfe9rG1hoiROgZIxpSgukEcefQCAQCARmHAEGdw67UeQvS6xaXSY2TLj4ZeeiCy9KP7vwZzaxgoY9GFhKyi8+F1xwQTr99NPtF/j5mqkbBhM05Ggg1w1gPn5shKeMtwN+8ekmRR4dPuy33362a/fN8p4N/OqDk4Gdu/klnv0G7Jeg/IDMJ5ZwaGATN0ACefPC5zTL+bg50ha9U0uegG1qE7FvF2l/YCs2sqnYD3/4Q8NU9jOJpBwHA69LyONvSvIfjwNlXo9oFMuemk7limWb+Priul3QgSm/qrGUl53RWTXCOcKxxMG3sVlVwh4ampyDKWHL5ryMuPniiRXEn0AgEAgElhABxjrGMcZwxinGeT5TyQoy9l8g4ExnDGYvBl6XqF8FZAyGV2MxPBpDNR5rbFUdcR2gER3yfFA5sWSTlm7p8TzHHXecrTDjVQrGXZwK3KdwsuN4YDzu4vMyIh0I1AjQ73TQTzm4l9MX2cwx16Zrrr3GnGX0L/od5dRrHxT4CYpJQ8v8iTLND1WufooMlYmXOs3xKINGdbqOxAdva2/zuXOVwSM+ypCJPfBij5dBPWFijgYaIeU4Gt75znem1772tXaRvv3tb7cNWQCS94IxjAMeDt/gYlb8DQQCgUBgdhFgLOPQWMYYxsCsCRifUDzppJNGNkADsgb5kcQTrtRYzfhL0M2DmDaNCvzyc/TRR9uv8Tws43DQr0A4VAhMzrjpELgJ8eDPg7Lo5HDW2G/684033xGMR/cGsCFNjDxk4aR51ateZZ9GM+KePzonqlYbJVvl04rRL7z7dPJKBRNeVpPwHXheX6HNwg6+jdduTFu2lolGn5woDwQCgUBgUggw/jJ+8QDOeMQ4/5Of/MReiXjb2942Ug3j8HKNud4w7NCDGe2hHX2BTz8/85nPtDGYe9Yo2j4ZUb5rI8D14ve2og9xMG/UK6jsZ8Kckf7IDzY3ufFN0u577N7OwTQ/WhSSzUoD7CBwDWpuR5kOXZvk0edpoeeawWbRUS9e0vzIQ7tERx1BsaUzc5nZWdXi/gASYlBEYIMzXp144xvfaBuKsQEW7/7yjvL5559vk04NPjJG8eIsCK5AIBAIBKaHAOOXxjBNwhgDGXRZ0XDuuefa7tqjLNKYN4EheJSaidcx8eILFTgZ9MkwrVRg8y/aZSsampsWNyvdiERHngCtcFAsPIh1gDH3F/j4pewd73iH1Y1qnG6aovFyVTaLMY6GO9zhDuZw2Heffc2pQNvbFQ0ZAzAVXrPYhrApEAgEVg4CuscxBjH28HDEV5JYQYZTvSvwcKIxi5V+yFju4O83o+xhI+RHPepRNgbrB4Xltj3071wIsGKBla70Ofoa1w0H93Jb0ZDL+eGdOQ3zxqOOOiode+yxtnKUTR4ze+89Xvd+zWm6kBFNVx1l2IRe5knYpPkSNmIT/CqXLM3HuvRCw4FcDslF10RWNAhAJpEENuB63/vel3hlgh3Wn/CEJ9ik9Mwzz0yveMUrjCb+BAKBQCAQCMwOArqZeIvqGwo3Hsb7nSFwo/Nt4uZXt2fa7cAeJq4EbubYszNhOm28Ql8gEAjsXAgwxjGmMc5xMO5qj6HlaIm/B2j8p8ynZZfKlI84EJgWAi984QvTU57yFHuVkmvG99vF2MA8DUcCsfo1zo+167IDMG8WyepR5iBcq3IIQkeZyvVM3/I7R4JsYp4lJwVl8gcgl4MwEUeDDOOXLsL3v//91tHAe1xPfOITzTPIr3yvfvWrjSb+BAKBQCCwKyOwozeSHcFONwbdQJClG4YmYdzs/E1qR/QtNW+NpfJqC+30bVX9UtvVJV+6wZcgG7tooywQCAQCgVlHgHsH4xhjGuMsaZWR11g3zXZovJfDgzzPKsQ6sEcPRLKfh7MIgcC0ETj55JPTU5/6VFsl6q8j+uVCgugvv/xy20OFr3Hxg79eX2VPKDb2Zl8ozQMlX33fzwXt+T7vC8UnNL1DQjz+WpJu6kgrPzFHAxNS3sslfPe7320dDTSW74azHJQlr1rRIE/ncgxAZmT8CQQCgUBgkQgwOdEEhsGUAVr7DiCSsnqixYCsOkss0x/dAIg1/spe2kUaWxnTNTHzplLPDYdY3mu1zbfPl/ly0nUdZQsJ6CYQ60Bml9y6XLwL0TdJ2i4bu+TTf2Q7aW1KRj/Teevii7JAIBAIBCaJgL+XaUxCPmMpdRyEvvuFVU75j8ZZ7lUcjJnYR+zb4O/jmKj7XoyxUz5hK1Cdrg/mVbpG6FfMF9U/1ewXvehF6WlPe1r7BSrodN2JZiExXzj79Kc/bccPfvAD+9oZG3o/5znPSfe4xz1ss2n2imDVAisd2GAanVwrVtbMsbCVgzZQp3bIFng4CGpvPceaiKNBFy0GcCGzGeQpp5ySPvShD5lxj33sY+3VifPOOy+9+MUvNhomTdByUctAS8SfQCAQCARmHAENpBp0/WCL6Zq8aNKimwoDNQM2Yx/HcgXZ7/VT5ss1rst20UKjdlOnmwz1vtzziYe2U65xXzLni71d0CqPLB2S4fWqTDF8o+pFt5RxH0Yq93h6O8COULfX00Q6EAgEAoGlREDjJ2OpHjw0l1cd+lVPuuvBivJpBMZVDo2b9fjKuKr7EnWkoff3aN1vpmFv6Fg5COh6oP9w6B7P/Ed1au1LX/pSczTwVa9JBL4G8+53vzu94Q1vaD8/i9yHP/zh9pUVnA7oIuYLY6xw2GuvvdIee+xh17W3AXs1Z6MdXCNqE9eM2kL7KK/DRBwNXii/trBBDI6Gj33sYzbhftjDHpYOO+ww2wjyla98pQ06eEy4kHXRdxnn5UY6EAgEAoFZQUADq7eHMUzljG8MxkywvEOBiRlOiLrcy9lZ07Tf30jrdoAHBxjpplXT9OXr+4OwRpbuIX28vlx8vmzaaWGE7eov2NBXXttXY1HXRz4QCAQCgUki4McpyWW84n5G8Kv5VM84xX2wr150yx1jJ23RPYk2cXDfpl2EGHOX+yztvPr9taN+5MvUMl6dYJsBPqmqzVNFL5pxYuZYzDHPPvts2zT7da97nc271L+9DD7n+uhHP9o29z7kkEPM4cAqBxYCcO1yHbAlAjHzLDkMuV78fI46XUfIp33e9iVzNLAZ5Cc+8YnE5zv4TNeBBx5o343/4Ac/6NsZ6UAgEAgEAoFAIBAIBAKBQCAQCAQCgUBgxSDAA3eXY6Fu4OMe9zj7rPX+++8/9CP8OLxeFg4AHAEXXnhh+sxnPpM+/vGPW77rBxkcCHe7293sNQo+pc0nNlnZsM8++5iDYY/d90hHHHmEbVBJO+RswAkHL86I7dvyD0fbtpoO9GKvHA1yNkzM0SDBGMBXJ3AonHbaaelrX/uaYcBn0K688kpzPHhQIh0IBAKBQCAQCAQCgUAgEAgEAoHAciPAA1PXg9ly2xX6Vy4CvMbAqgJeX1D/43malQh6YB+n9dBysAHkxRdfnPggg8pwDpCmb2u1TpfMu9zlLvZaxUEHHWRfjTzuuOPa1UmsaoBXjgbs48BmVlIsmaMBo1GEYgIbT3z0ox9Np556ajr99NO72hFlgUAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgMGEEdt9993TNNde0zgYcDTgDxnGkHXPMMemkk05Kxx9/vK12kGlyLCALxwMHTga9KmULD/IGk/wnTGRFgzwaUvLzn//cPnHJ5o+ked+EOjweekcXQ8hzjNvoYnL8DQQCgUBgeRFgHGOQxePMobGNwVtjmwZ0LBW9xkrqOKYdZBM2Erwdskd11CutupqH9uqGBW1uVXtz0fguXk+rMuQRpKfkyt+aRnUqh0d8prvCU3XiUww/R1+96JYqlv2KsWOULbJVNORVtlQ2htxAIBAIBEBA4w1pjfWax/OAQeAhQ3N56ElDq70boFe9MUzxj+zHHsZQ2adxV/XKYydl3KvhUT2xxuApmh+qdmIE6v5Cnn6kPqY+RxPpb1xPxKpfTNPF+5vf/CadddZZtmciz9+jVjCwmuKAAw5IN7jBDdKNbnQji+HhFYp73ete6fa3v0NO723mID8xfWymr97RoBUTtd0TcTQILDMga8B7ctVVV1nMJByjVq0eTKw1OWdwYgAi1CfECuNPIBAIBAIziIAG88FkBKdpWZLG5EQTK5muB3A/2eGGM+2AHfqMUX3TG9cW2U1b/AE/dbqRCiPR6z6xo2O9dKBPeEqmr6Oe0FVWapb3r8dllCVGl++h2R1h90m1dRRP1AUCgUAgMCkENLfXGE6esVeOBh4w5HxAJ2MW90Y93PDQ4usnZdc4cjRe6l6F7diCjarzcoxua16lnd87Jw0tzyp99J430oFAFwLsY8A/Av2onhtRxbzMnpczjfqnMSzwD7wc/ND//ve/P73pTW+yTR4vvfRSK+e67AqPfOQjbVNI9lQ8/PDDrc+zSAAnBPs2YLOe53EySA/XENeJriXoOHyYiKPBC4x0IBAIBAKBQCAQCAQCgUAgEAgEAoFAIBAITBeBiy68KP37W/89vehFL0p6fQILDj300HTIIYeYIwGnw81vfnPL88EGVjPc6la3SnyNQgEHghyH+mGNOhwNa9dkB1z+gY1AHY4IK8+OOR8m4mjAEHkzMIijL3i6PpooDwQCgUBglhHAg8vAyqBae29n2e6p29Z46hn3wWnUvWExtiF30jIXY8diePQrAH1oVKCf0cboZ6NQirpAIBAIBEYjUN8vGIMZW/09BBqVx5g7Gs+o3TEEuLfT31gxM+lw/vnnp7e85S3p5S9/+ZBovjLxwAc8MO251542h731rW+djjzyyLTnnnuaLWxGuccee7Q82Kg5iOYqukaIKePwbyrU7ZmYo0EXZn3RttbmBDQcXLxxAXtkIh0IBAI7EwJ6l44BtR5UGXz9xIV2Me5RzrhHHWmO5Qr1+CtbFNf12FnbPGqsF73GfHiRWWO10PbLPsMwL0dkuaHsIO4K4qHO+GYAe2zixkwMJl14Yy/4QYfd0PW1EdoIgUAgEAjMAgIaczVekVfZtO3DBg7ZoDxjqx6gGH9lq79X943L025D6Ft5COjeTr+s5wA7cq2oz+JoeO9732tff2T/hQ0bNqS9997bViwcdthh9vlK+v8heXUDX4XsC9B0rVSgjDbI0QAdx5rV2fGwdvjHk4k4Gni/xL9/0mewwMuXfZ7x9VFFeSAQCAQCs42ATVryg67fe2aUxe3Ylyc8CipTfpqxJlVDOscYx73NnTKGBA47JzTBq0gWnV2oLV6R5/Xl00gLN27ShFG4YKdsXb0qv/c46D7TMDV0BAKBQCCw4wi4e8uOC1uYBI23cDGW6vlD46qkic6Xq0w0EQcCk0KAfqa+Vs8BVL4YXeqzF154YfrsZz6brrjyinTozQ9NN7rxjdL1r399czawZwp0zEF22223oRUMtU7Z2WUjdSrvo0PeZBwNtWWRDwQCgUAgEAgEAoFAIBAIBAKBQCAQCAQCgakhcNlll6Wzzz7bHAGsaNhvv/0Sr0V0BTk25KSwfSsn+KNGOBq6UI+yQCAQCAQCgUAgEAgEAoFAIBAIBAKBQGAnQuDqq69Ol19+uTka+EwlG0Lq1VVWMujgVQtef5CTYdTKhMU2PxwNi0Uu+AKBQCAQCAQClOn6HAAAQABJREFUgUAgEAgEAoFAIBAIBAKBGUFAeyvgSODzswo4GKjTKgbq5YCARg4IHA84ICYRwtEwCRRDRiAQCAQCgUAgEAgEAoFAIBAIBAKBQCCwjAj0rUzYnvcW27ptq1mGk0F7LFAAjxwUOBnWrc0Oigm8QhGOhmXsCKE6EAgEAoFAIBAIBAKBQCAQCAQCgUAgEJg0Alq9gFOhdUCwLerqYS+CHA04G+qVEDtiUzgadgS94A0EAoFAIBAIBAKBQCAQCAQCgUAgEAgEZgABewVia/6suu3smMxxgPNAQV9fUZ5YTghinBKe3tMtNB2OhoUiFvSBQCAQCAQCgUAgEAgEAoFAIBAIBAKBwIwh4Pdi0OsROA5q5wFOhTpAP8kQjoZJohmyAoFAIBAIBAKBQCAQCAQCgUAgEAgEAoHlQCD7D1jN4B0JcjjIHK1gwClhTohVecXDZH0MpiocDUI84kAgEAgEAoFAIBAIBAKBQCAQCAQCgUBghSAgp4J3NqiMmPJ6tcOkmh6OhkkhGXICgUAgEAgEAoFAIBAIBAKBQCAQCAQCgRlBAGcCKxfYm0GbQOJcoFyB/FKEcDQsBaohMxAIBAKBQCAQCAQCgUAgEAgEAoFAIBCYJgLNqxNSqdUL5LVyocuxgDNi0iscwtGgsxBxIBAIBAKBQCAQCAQCgUAgEAgEAoFAILCTIoCzgM9UErxDgfTqnr0YxLMtf61i9ZrVae3atRNpfTgaJgJjCAkEAoFAIBAIBAKBQCAQCAQCgUAgEAgElg8BViZs2bKl3XtBzgZiDpwKPqgMPhwUrHqYOUfDwGRSC3vPY37qgXQPzNz0/JLm8uxoido7ro07qi/4AwGPwHL0efTPSn9fjvbPStt9P4j0roXAcvR7EI6+v2v1s1lq7XL1eWFQ+r6ugKW0RjqkuS9eKhu8/qJjqTT1tawu9xbVdZH3CIyD1HKfTW/vzpWeLHJzz5UvYTeFxQfvaMBhYI6GRjxfo6Ceg3KcCsRyNkir8SizA/FEVjRgNMFDNLCpG6ru0gEXqexzcQV9aUlS7FimmPTWoXZea2oG2TqCsYtlBLkkrvh4Qbh0EQuhEWB2sY0gl8RdJhY+S4kJOubIl2KP9ByiUtlFKrYeln7GkQySugvEHtQRmHiyLlRGsA7fWEYSFlJI0FeTygaV1zR1vsvOmqbOd/HsSBnyCbK55Hbtv8LEozASny4GmEcw1SxzSEVABek5BCgY7rqlZPhvD1shkg5yIwmHZUZuaRDwp6PWMO/pGdFHJGuUfNH0xUutf175fYZF+ZIjsCP9BuN2pnOry0htHtd28fmT0VVGvS/3ac9b0pV2iOcLDcsw6SA3SA3Oy2LdDd6RsGbNmiHLWM3AAQ3BOxqGCCeU2WFHQwEGV4NSssyfBJ8u9XNLBsAWiiJv4GzQKahjJPlD+qcVq+Vz9XW10ajUhLkspaSDcRRLB3mf5BVX3odLJyZ9xDUqFfMotoq0ljSF/HJZUFDpw2YSVs0r2wh6NFXFtSzyFcmcfDOkdZzDRlotoINyxRbVgKqhHZj0kYqFuIOt/ynNEY8j2+uZRtqZt2h1fe0ayB6kFq1kUYx9li1K2IKZRmnvRGQUg7RXjH0sLdm8BEVwH5nUKm7lqqCPcQ6hGHaleHlA0CyvPjXeGp9uz0jNQEUHoSfz6VZOT8KL8umWvEtYRViT1HmRL/aBp7Vl0YnaokULWnGMNTJ1vq/BOqeqr/Mqn4V43DZNw9aCk0crpxdiYEVeWCVAo4yGCF1xihfeQr0e0bUyQXVINQ2+WQtXNZJjAo4GgaPY65PlxV2gXBfF3LIB+IV7kB8+s0jlWN2I6NLipU8qLXu8NYMyaZljzVwSkQ7HFeMotop0WM4Kzy0Il1HEHqcK0FFsFamXssTpWnOdz+oxvKO4NWy++pbQJXivy8ks2JS/jsqT+OIFpedKLexFff47ROCMkhZXJFLFIiF2ZEPpYfnicBKM0eVFsivEo5rtAc1YeFKfBiZP6tNDTDWeDWEtqyZbrvxQOxZpRHfbuiYcWZuIJ6F4pL1SBJFPj2SaaOUorXOaP4rYW1Ux9rG1ZPMSFOEiU9ynspUrgi4G1c0hVsVKj33Dc1oY+eIlgaAo4u8gNaxIJigeqpWdvrCDUGSKPbkv62Btx9CuuhYnL7Ai9PJ9urAMxpzClv9CVMnw4ieSbnV4i3x6Ilp2HiFqeoW7immIT3flK9ahU1jXTeUcj4l+3a4x2SZONrgSEA1iDWoLNHD7HDY9Ow8LKhoGfyfeoCkKnJCjoQAkuNoT0JyI7U3c4Duneb58kC4yB04GnQRiHeUkDE668nNULFFBscn/rRUN2tPUFOKabG6+YhzFVpHOlbWCS/pwmYNJF6HKauIqLzJgVFokipcH4qxdBmnQW3JDskLX6ME1P1exI5tbOUZJ27SKtsh1bTfCnK8VurxkKa5EtqwtS03Y5nNCRKvawlrcys+ParrwaVAQqWIPjif16UG/9tRN2hF2yezgmGqRM29Reke1aXiyk8Vr1oKmHVVcWztkSJMxHUMVNdeS5qVZsW+yT5sRIpJFys8hzASuTGSwkVaV4t6+2RLAORiavbxSM5BJfoiti1hMc4h9xUpPO5QMI5efNC7+HDRjPEWluNzxvHalFbdnwstpC5tERSxSxVD59Ch2iVLc0o4pwJMprbjILKNOK9+POShrK1rNi0tIacudC4ZkzyFoKVd8wjfdYaJixcKhzqvcsVqR8oqt0DMPVUjKdGNvznQ1D2sbvvc2wNTXgmeR4RWGYlE1V3pJD0oQA5t0Kvbid6b0RBwNHqgBVAWmAkaFdINQXVrnkdvvaBDM0sOKhpweGFANUqLfgdjLRowZrE5CwTDBnPYMVw+TzyGWfOSWULOrvItVdSs9HhuTmrDO1yBW+ZocXCuSKUPdaNeoNWXtyT1k+ytAZkwKmxr3IldtR1tOi6hW6vIiUSw7GwlttmWpCX1ebTdiX9GKWdmJ+Zrcglhg8OQ+Ta0n9en2nPYh2RDX8vrIp1E+ZP8OKuxq1/BkI2uriZbaAGtTVmp6auU72OAFsHdpntP0mqjOz2HIBriymhzz2uquyiECMoPT00Xeysp0Pj3nnJok92eI2JWv6KRvdE7PB+iOYNEjm3vcoGqQQpWsU9yqHyZriy1REYtUsYjrPOUVa5uvy53BEjeIK2LpUVwTQt6OPzVRJWvAu8BULRd2k+0rfHqB8ndW8rrJFd6qVkwzfdo327Mqrbilq5nnELSUU0vUJk1D8dxmq8TF8xlGvcgbo+sp+2BkmSuMa6697qbR6CXSscOOBrq0B2oAFegKYcXdreiqLWVIk0SlfR4qd9RnUOqkAFalVdcXSw30Ste0rawBAiJpq1TQJaMuq5nqfIcpHSTSuMvE88E45/zVDEKqBrPK12xVtaRMMc4W1EZNS7s1frmU50aa6qr99Qlx+XEtbVlqhjo/5GyYFugzoqfGojarBbFUzEcOVcUyf7+ew1AbsZLzTePnA3YhGCEL+vlkimAhsid8KrpMHDKnJqjzsmeIqWm/6nJcsxm5LyTtZfh0B78TPZQcYvPyRTWPHpGt/Dgj1YWPb/gQmL5invQouY1MP9OVNKlTrPJ57YSwYpIJiltZIxISobglHUeIY/LkPo08yNqHnbqyVdgQ+jy0TodV1WWj5EmWyRiHUAwrLO5qusPVV/v0fChIhOKWvkvIHKKWehdLCIgcd+E0DhqdrBKmGEFFV3vtjSN7Rmkm4mgYIO4fuAGpADWIR6Mg6mEqgNdBjU4EsTi0mkF56KYQTF2xx/9F8xxLZLbMqvOdTHMF1Wxz9Ej+LhTPi0lNUOeFVQ1mla/ZquoiBaK6wpcpLWGe1tdRrrzsG4ozgWTMUThEuDQZPWib9NaQpdHVJVVORcXQeCyr/LgWtiJqhqF8zohwCIcuQ1dY2RAOuW3khYWaWuVrFpH5uGJxfdtTufQcBle3YpOu0b7fT6297kwutt939RfZrzrFKq9iZ4XVOFS6+03NIHlDjLmwytdsVu0LSVc8Pu9JpbIrHhLRxVTrGWLokrjSynKDW1yWofGmvhjg/4KyrFHcIt/a25Z0JxyjZ/HpbsaBbuqdGIdVH2dTPsQ0YBvWXR5zCmn+O1w5j4JJVWelra3LYsCkGrJwOX3NbfEoIkWmeD5FYlfc0vcJmEPYcuxCiQxCi88OANLLKuGKRah454V64o6GARQFnLI/w2igRtcKdGKflia4ObKzQdWqWuq4Mdy7V1A5pz1ddnWVzWFsGuDKazZXtdStnVn5HpM5ePhKWkAeorp8DiPEObjycVkK4w7+lZ19YswYZ1wf3VKVt6prVJZKYSVX7ffqW5sa2ibvSSopc7KtiJqpzhthU9gyzRE3KIB0HLoBx+iU7FmMzB2xRXpHWVfZNA4L4obY5mMaIh5lzEqsy42fD58lbXZWvoz4100fMqWuBIeusiEmB1ZV7lmtyhc4NkuO4K1JfX6IbZT8XiZfMU8a+UMK56FfSPVSy14yw+dpZMarnJbhmZ6H0ac7+1ufCsfoT71Pj8E6jMw4zAh1usl6Np8uZM3vqr4CpmmFxTo2p2XfUunpw3vMc9dnltgVt3Rj6mvpd5XEEC5zUBtCYYh0qKa55HrZ+zh7GSrps5tdckdDgbYDKDCluAvbOeQiUgygEkAahobJk1C1lKFHZa/5i7XFCayb56qKdBH4Cg/VKBs8nU+P4pmvrk9OXV7na7kj6qlS8M22Ml/ZFjgq+4qCy0uQYldVi3JVoh7uliqV7RIAo0/30VEuJZJBmXhJL2eQbZUNXaaqyT72bL5JXqzKfdmc9ncSZemZSVVe16h0q6dmrPMtYdHTKxM+aBV7QsmULE/j055H6T7ePj5f7tOSp7irTmXEbVChClxe7clVQywi7Ykd2/yMDbHki1dWKEaVaKS2plW551GZj+erh7aPpi73eZ+Wvq4y1bUxRNMMACedfSDOZ8+ohqlOcYcsqfdVMsXKjKASwBhPWNVQdo75jscJbDgLO399gZWqMDP18LVkIxKOtUdHB/MQU0d9V5Fr5pxqtQ25XXS+HmZP4+t8+RwlHXyj2iFZiiVP+pRfyjjbJ3WKUefN9umW2GyqDa8MdYxetk9XHG3WsQ7ZMmn90qN4WH5rzuQTrcIe0V3Q1mUCspZV00mFyvv4REcsWl/m0/PVS4bn8XbKhrZeBQ1RE6lUZHVe5Yq9Cp8enFckDNUUVl/UQyId82LTEjYJL8+nKzpVERP6TBJdoeo+VaJRDO2w3JxTgQRZ7LUOKoy0u6oQNbLsNtQpt6NQ96yBmp0ytbSOhiHkR52BDuzmkPuToLSIFDdyVN0hdiJFTl2XKlfd01EXaIUTWOtzVf1CYRqHcFy6fk1zayYls0cOxT4MNbOu9IQLSTdCu8QN6UNmj52mztdJ2BwBjQxjyH/66imXDNFOO65sW0pzhlSNqygzjUvqoWt1jcvcMngpTRoZ1CvuIOksmo9ettW6+/h8uU93Kh9RKL0jSNRnxyHtEtM2aZSATDSqukvupMpa+5zApbSlS59TvfRAjDKAho+qHzI0Z0bRq05xzduw18WtevgmEVqBw9Ba8SgdDd8okj7znMphpX0MlA8xjSJ0dSOwNSrVK3asQ/Vd5ZRhUx9vzUN+IbSeH76lDrktXo1Po9rD79NmVk3cZ6tj9Cw+PQbrkC1DRvcxU+50k/U6fbomndrigso+7FhwoCFdcsYp76ORETtaLzmKu+RR1hVcmzyJT3exqcyxD8PTJ8AzIAS6ukzCx6n3tDV9JbvPpFrEJPNt01rllVGVMr3J2JJX9a28XG7pPkJ9Pt7UNVyeuZK7s2Qn62jQLwfWeqFTx2NCI7ZMPvecqMQRVWLbGpFW9YvKZqHjimv1o2hcpj6jnLAuUa66T8KKLZ8Xjy6ChaDhwO0S5aoXInVytF1GTU76aEmu8dMyw1SOoUwD/+gGjK4dS5fDYLS0FVTbh7/Doo9kXBScqLnjZ67cUfnj2tFHJ/umZcdIfdkI1ffZu6jyHpyXRNeYBnbhPWRPF8GYsluyRmAtqtVTV8DYw9PKHDMxUodktEQqWPlxF+S02qDoq1wsLFloLbLOS7Q/FT6t+nFjL9+n5+P3On16Pj5fX+ur86Kt5Vu+j1hMC42z0C6Rte6Fil2p9DVWdb6v3TWedb6Pb9rl47ZnKewyTEYZ0IAmkjqWTR5bpTuddRJQM4pJ5TthPDlHg4HkkRI6ikHHp+dBqyH1EsXRVaa6+TRY/SgBCMpE85H4+j6dQ+WeQcaOEzsh84lwpEOS4RunztP59JCwBWRkL7olT7EXM6pMdYrFR35UGGrvfMR9gpyQcUT4dnqRsl0yRAeNUzGEkfi7aCVPNObfqwWpEh1NXb1iWHnFsEh2G+cE/OQtNAnJrIqVbeOWry2ZP+GbUlO3dT2CrbglcnbXgnryjnVAkYV2ljeFMqWLhjrKFSNU6ZpP5Z6GdFfo4/UyPJ8v92lPQ7qrjjLCUPtUWFX4YqrqPGV9YUh+Q9SWSVAuUBISn+6TO8ny1p4eodgjmoXaJr4e0UPFkj2HRwY0sdU3xKrygpqqUiRhFaFoVO35SfeV13TkK9FDJKpTrErpV74rbm0Yh7hLAGWtkP5+1ZJITy5QUmLrvMpHxa3chsjytSBH5Ktcca8K6KETXxePrxO9F+jrKfc0vs6Xe36lfb1Pq97Hkqsyn+9qg+gwbmR9JhzHGe31tbKrRK2HPHxdccXang+Vd+mTLNHUcZd+0Yi3jn19V1plfXGts6bjQUo6fZ3KWuxV4IlymmKFWpfyXax1meSIRzJrurq8j090xH0yRDNfvWSInri2U3Wyx9P4Msq78n3yvBzSCtB7uyXTy+krq2l8XvL74i6dNa301uWLyaudfbyyvdMhAFNDgE2yq44lQzqUJ54jV8wiVmzEyuy88eQcDYZBRqsFzMMqgFSm/Ig4k7aiMplPi6su65LeVSb+hca1vi7+Ufrm1CFwTuFA6ih9qhvBPhC0wlPjYNHiVGNe5zNWktcFm69rZXYR7qJlHp8aAkHtacbFsJPOC6wIvI7ajnHzlchBx5hTMa7EXYNuSbCvoJuEjkrk2Nm+0z/KJurgE02fjNqILjrJqGmV7+KxOhkhwp54Pvmw9erokTmp4tq2UXa0dWJSQQcOIumys4O8i8zKRsnpZXIVMtEVRTIjMB+uk8Kt1lPndTK69HWViX6+uEtPV5nkdOnqKhP9qLhLT1cZMrp0dJWZPoRQqZhCnybvQp9OkfTqEcEuGte41XkPSx+GfeWedznSo9qy1PaAieFSG9GApWJiHbKJvMdUaeI2LQEw+TR5hZZYBTtnPHlHAzgYaF0IqWwEWA2Jx70vPUJKezJrmjEsMBav08voKh8lc1QdclXfJXc+vb5+FtJqi2zpaxN01Il+PjrJW0gs2aN4ZMcoGur67JuPbyXXd+HbhVNX2Ti41PLrfJeMLl1dZV28vqxPV12+GNlez86YrjFQG/qw6CsXXx3X8uu8l+fTtZylyHtbfBpdXbZ0lc1nVy0Xel8mmYq9PE+n8q4y1fm4S15XGTzjyvTyp5HGLmzus0/lfe3CxlF11BMkp+Tm8owjQ7yKa5mUd5UtRrZ07OxxV9u7MOrDrqv9XTKh6yvvkkFZnx199H3lC9W7nLr72txXXre5r61d5ePKrHXsSvku3Ea1f2fCdKFtG9XucerARsfQYNCAJnuIu9Jeh3D28RzZEuIZSXumum4nyk/Y0dDVcodUH5hiE2nOe1KlFUPu02JX7MRY0Xx58fm4lu/zSnu5Pu3l1Olx6aSj5o/84hEYB/vAfXx8azxr7ObLe021rDoPbV1Wy/fySM9XX9PX+VpfXR/5fgR2BPtxcN8R+f1Wz18j2xTDUdtS57topMnL6Sqr65Et+Yprmvny0qNYcubLe7k+Lb5Zi8e1sW7/JNqxWJnj2jwJG3cmGR5PpWus5st3tVeyqPPprnwt38sbVefpRqVr/aNo67ql1l/b5vX5tOzqKlMdcS2vLvP1kqXYy4l0N5YLwWVauKLHn9dxbVwMz7iy++iECbGlMaIplD0+Jq08MpX2cihv5fm0iBVDqFALUPlOFk/X0eDBqUEVoA2Nr1ZaMSQ+7cUq7cX5NPV1XjyKa9l9eclRLP5R8Ti0tT7JE29fvegi7kdAGHZRBK5dqPSXdWHpMfRppNT5WnItz+d9Wnyj5I2qE/84cZfecfh2dZodxX8+3CchfzEyZJdinWcvy6epr/PiUVzL8nmflizJU0x5TefzPi2dPvZyfBqaOi9Zir2cWUzPZ2fdvkm1YbFy57N3UvbtbHKEp2Ls78LKl/l0X3u9vL60eOeTN1+95PTFXn8XDfL7aJZad5der9OnZTtlKq/5R+XrOslQLPkRFwRqvBaDyzSwlY6F2lvTI8eX1fnFtL/mka3ESotGun1MWnlPJ95ajvJWL0bFEiBm8j6t+p0onpCjgRZvT1u3bU1bt25Nq1evSWvWrMnYFHS2593mOEpugNgqdpTzu8ploIU1sap8WdHE3/4gejQNtA3SvsxLYVM8r1d1lElmXcamHti5uqnwsmse8fbFnhcaz18wLLrATbSexsulnjo7aFgTPK/KJhWjRXZJ5kCzSgqN2ZWtG7RkUA5lnxyVSy55lUmD6pT38bZtWWdmGIVDzV9j7+WRtraAcRasftCW54Rs3JZptm3bllatXp1Wu3MIbVfwdtRthH5UveqsTzeZrNZsUZ10Wj7/MVwqudCgG5qajjoK4bNkTrebPJWiob+tDFfq2+XTkPi8T4vd5OU/W+2crkprcvu8Ds8jWrVRMhSrnryn8TJE62PDN2vt6k/I7OOnjtBXX2rL31qOeD1/V5mXMem09GE/aa6RNmQAKVddW+4Sarfx5nLxexxFIzanQUWdsfSKv+az6zlzokvXa03jeUtdGasoV51XLn7F1JHm6KJXveoUU6608TYC6ZOEbfnYalhnjHMZxV6HeBXDYwQ5kgwra/7A24gb0mvlDY1k1XpU7uUpDW1XKGNpxpIxsCEoNuQ5Qs4zLhK66Ky8VLbXW+Gd2zakqI6U71eUjxPUvlH0qpO+rXl8x3brW6u5Dsq1gL5iT/6biQelw5ZIJ6Wc6+3cLzImwmWYerI52Wc2ZJ3eFq+J+xidhnMIDXwE0Vu1w7wtL2SL+mu2ZU5i6VSavPoSwtFPoCu1unOZyrkPks63jYHt0DbEDXsR0vyVHBWSh87HqltIjAz6C8qRJXnI4IrQWQBz6OgHdV9ABvMKq+e6UkMQMkbw7egjh6YvmPm5ErVqA7SUb91KL075eSD3lVxJmekjncuz2RZW5XMCL2V1oJyguOQW/hfZ2938TxKKHdsMN641gtmBsYRc1qcbir46453gH3RJHzob60yDbCg0+W/+r7aoDkKrb9rl6+tyz2MKduAPsui/jI3oXFONG12isUfB22Jz6CyLPo4so8t5tUU8xJ7Py6tpqIO2pqFMh+fhuoRWR6krtuQnYOvjq3OHLqNjqd26fWtm2J7bnp+NG8sGsrOkVlhjCYMTSQYr4p08TMDRUBAA+o2brs3HprRu3bq024bdMtAM/8UBsS0POAYsnQNQc7AOlx0SJRSwNaCC7ipu1Jme0J6HJm2FPX+2GnHujJkfC9qQy4sNpcSfP2zigYUAX6PW9NoNn6qGgYjsluxUoTFrc2dYkzuWeHLVnAC93VByXOgaYZlfotVWzyw+biQcdOC1a0tXtbohudluz5zTTEq3ZTvRjfzV3IjovFVQmyj2afKtfWRyIN8VVO5tKDbmNhq2nJNiu5+Q2XnOWuHj/KNhFXg6JV426W3bc3/KCdri6cRS5ID1ABPKtmzZYmVr16zNsai7Y7Mk8zDZw14Gt7Vr1xqxyc/8SCeNk416BhKFfOsvNzbrh6usv2zevDmtyedv3dp1nXaLl1j6fRvqeuXrphTegqed/0xIm9uH8Uyg9uN8AfY1awpW4pXsbL7ZUujyYMmkgcqGkPNHsMlOjq0fZ+HeJkgNDwhzUK1oFFOXrSFqB2rVKaauUHANbk+bwDTbtG5dc25skGZChp4SKGLSw7W9RvbmMjCAhvnQ1q30jXx9ZFmetxHRGeFYpd04VhnPCNhmbSCRFZRRkJqii2LrPxl0uyYzDWUKspk8dJpAkkeujSOZwfoFDaC8ofNl1JgJRjH3T62TvKcX/1zOQkc5NJxXxvft+ZqEn2uS66BYBlUJJt/aDN+gr23NfOBoWGRexjhCH7/VWSV/stTyv+BBZU8o+vM4mm0F1+FzBq6F0SYxjQyKuPaptLEz4+3talgK/o1NjVV57AWTwZhXmyXecvaLXC+bfkXbOMXqJ9zbNuUxDCMYi6C3oTXH6rOSQUybinOVsb9gqnbCrHqUNN3XzqFdL1k/1/Ya9TFrQBZoNpV+bUUdfzJJZ2D8ZazE9rXNWKnxE6zW5uuI88+9lT4BnZ+Y2hiT6aws26Uxqdyz1fKBat1nRG920WjalGPZadfNgG1Oyq7XXAqdD6XHl7GK870pz390zmkLfaz05nKdbN2S5wxZRvkhxkuam8b2zVs2W79b39wvZO9c6v4SLBYfMXm1Qnm4ba7AOac+26g+Rx0BWtqmMU/nph6jqDcM8jjKvMgH2eHLxknDZw62bBcSydNHsNX6TI4JZiMTthzsemgayqUE9rRp7drVRrc598X22sDWfCh4O4UVdT4t2h2JOcfbmDu4MRPdzG+wjbGI/mLXTW4v9zibu2Qa6LCHeNPmTYbH+jz3pn6SQVgoRrb06vpj9MJWjTHUb82D1TXXXmu0u+2+e75my/0afuYajDHcu5G2NreL8+V1QEdAFkFxyY3/V3xgTd80rPN1qfIt/ECa+4bdD3I5QeMMaejr655y+jjBrhVLDc5Jk+1sj+rGjbETTfQJVJo9lrfZQMZtMAeGjjbCAZ3upbnA2st4ZfXIYWyif+VQ+PJ9JVONMzYZ0xh/hPHmPLfatHGTyd6wYUN7DUsE+glqK9gyrti9ONuo8i1ZzpbmXPEcRP8r10+ZfyGHeUg5J9Je2mcKrKVFXskP/y125L+NQaZ/mCRXlX8a97g+wRlsN+brkPL169enfOcyu/MMMT8Xb+QEJtq+btXgGaFYiL5GJ+MlbeIc5krOURZeWbDzZXfY0dCcD4N+89bNaXMGem2+KXJjJNjkswERuDjFPLjRkegM5SLO4DZYUl4u4ExJB2PkQo79LbHSTVE+jYQsrxFSBoniRS3chVKfFNFpwyxCVmNB3lceNBBlg6jV50Exy6eDU95O8Lh7ZSE2wFpnk+Qiz/+1rimFWNqQWnsZcXOezgodeltsshBorE1ZH3jYw2ymLzjR8qbtJrTwD3QXPJEJZVltAioYICQbYwZMLiUaigZ04sYuBkCrBQMG5YZOnNi3Nd/UsdcmXnmCaTw5b+c48/HP6PIFxoVq7W/kobd9iLE2ljw6y0AwsMsMAa98EOTEII1OziEi1pijATnQgREUDQ/2NIP3XOxLjzL5WQ50yLXBu7HX2oS0LLRIRHbWT9syXuWGVh4SSn2hK60o6FFOmw0LmK3dJEgWvEoOupIqbSn1dgpyOWXYRlwmhuBceCSy9PPSh5BN8JYXi4os6srgnxPWpxp9mcMm9TmmD8he9NKGVh62G7ZFT9GEoZkF4TnAQ9pulCrNZdCUbK5tiLdkG+Q8AlcrRxbtaGjg0/WDTCaUJg49Zg/1xX7y1ocbXlTiJBtWzY0lV+QAtu2NLuun2HjyuUNHkS9DWvNyXakHy0zU5BvsshTZa+c/k9jNP+OGdPBRAEvKbGzKMdc3D11qPKliT+Hz2GLbQJIkqgTO/tCeT2xV/wdPrv8sl0OB/mPtzNoGtkMDxYAfnoI9FQWT0v5GXpZfrgfoBvKzUP7P0Sv9itHNUcaecn8APz382nWZ7UcWqJVfbDkXzYNT07Zywyl9p+A8QIMVPbSXErsesjC7nzRliC7TQuwvWKsl8CmNEbKTMrtmcptpPw9Y2zNW2IsE+iA03D8KzqQbyzKBYZ5jnRPy5RwVC4SpdaXGKh7qTG4WRJ8i0CeLHs5HaadVdP4pbaursJ1xsFyHRa6dkyybeE12LNAOnBHtWNkYRj3XDY1WP2nnEs25MVl2bdHwrL10sibC7mI7dmVx/OVPKR+gb2Xtn0wIRlRzHnRuEdD290xsDzLNfU7tMztznY2xmR77zCmCvY0+5BUrWo1mHO3nwHgcJeCic+goF5BEP6KdHbTH8mU8QliBrLlmc0b00l3uJ3Kuci8fyEM+c0DsXrduvfXRlt7GJvBbeGCGgUx4wZxQ5GY8M46Gd+6r9E+wVj+FDpuMNydog8Z/HlrKec18TT8vbYerhKI399csUQ4v1XXF/W2bW0MJ8yLawbWsHx8ot3t/boeCxiAbo7jumyqdExxSzF/5EQM7CZBQr35mhT1/wKsvIAccfKBMPx7BKy3SVc5DdpSZIyHZj4+cI84DNq1uHA2MvdYUyujj+TyY7EaZnUeXbpILioQl/RhUih6NPczdynVGv2BeCI2uVxTZ9UrHyOxcqQVzLCv9vlwfoittRKddr825QM7iwuA+Ynqxg5DbUsbtfB/LOuz+kIspoz9hm+YCw32Fes5mqedCKW0t428WVM5PE+t8ZobFhcZObGLc59rFMagfp0rPyaKtWU3bclZjDkqtD2NPTptTiOuFvpLlcP0w92Fuy8GPRdxjuE6GHW6cuRz4kwUVTQN9VJXAOSVlJ7spG5hX+lAuzqxGlv8Sc66JN+XrkHNvc21zKNCfm+sgC+ZHeKPlHtVc37SszKnzdcv5zeVDjgbKdvIwIUdDAZu//AM4/rFcZBvep9wpOPEGF+cvDzY2cDZA0/EyQYaSSiL+5JBPngFf4VxuI4UEyjIpyRdOHig4iZqg2YCCmEJaZLu0JinQmZ6sF3lcBJiwhU6T82tz50Am3lnCbrvt1g7mCLXXJ2Do6xAYkKsLNjnt6OhsYJELbUBjssLgC2Z0SsMA2RwK8Oc8nb4dfJpG0qZyAy5qyqBaBj8GGPLmuXc2SOzC4mIDNzh70GvOMeeZc1ACNoNl+WWGm4xdgM7j3pjd0nHzx7vMgImteOiJGTxA0H4prW130LR6M6426bc+VErBDAxg95Nnzq09TOZ6dFCHt7TF3thLW0qyYI+g0lc4h2WgNirK86GHSM4v54SbQcFn0Grk0dZyLsuARRlykMlRcvlvLpTtWjkxoCv9CJr2esv9GEHlgSdPQtqHUEnk7DTpTKc20CbduLDLrqdMx3mwQT+nzcDyh1zONxjkMtqIofBybXIjyC20GwPXKLKZuBEyallvrs/nS6gwdoAd7UBOITQDS55zStuakGusjXYjy/R2g8nVOqfYQYBDbSBPe3UTo9LIGrFNlPsd1xNOMlSXa5QY2zCtjGX0GfqC+n2jO7etlY/COmBWowjbsdOwy8VM0MqEAZ2sYsoPYBm7NpQmGR5cX1syPTbOoTMFGeMsn34gjBhbVtkDpAQ1kp1Nra6ehDjBsfRvfi0sk+AyBpVziv1D5zIbWs4JmJV+0qrNoAp7fgXZsnmLtZt20UAmRa082YW8nDY+TkpPUD+g2rTYCdyert14reG3Ia/CW7d+nY1nrHqgLUxm4OOAB+mD+0bu564fWmWjG3vAhf6NvVhYJnjc6rJcT2z2FMbW+kYnDWvbhb35QDYHgbGDMZh7kMYW6M1Wj0Wul91w2/VNf20VGhMiTXZZZVLGSvoJfQx+Ya+eiB1ehAlwf3w96YIlskCkYGoXUsODfK59aIslDeb+HAy1q1BKhsbacu0k+2UJmTYGZRk8iJFvA3JzxtqA3EZcW69EpjOiPPagg1+QKVif+wtYcq5z43JRtjeL8fiDG/opNwe37zNZCpjYPaiwNxiVc8Q1Dz/t4b5qcyUELTKgy+6vuQ3YAh42NiA/n+cydmNUPho1di3bued6wJ6MWZbDmGeNwpaGHls3btxofX233TaYjmvzvIkxlOuLuVQhhmn8gHhWnNrZUvtzIbgz9oHNhvwrotkEsQI0uW0EO+/CPpdbn27oYDG2XG/37aacB5vNeZUK49eG7DhhPB/HfpPVyFBktpte1RYnOePCujy+2XzPQG/qc9TeG7kusB1+/tHXcpCji7T6EWOkbLTryfp7czIhbMLcEtXkGBENAUmbQ+RY8ybmabnTWn9Bh5Fmwu25LeAKf7lflmvNzkHGbugcNPJpP2NYrmwwYOVDXl2Y/5WxMivOQaNCyY3/tzyM5vtI7t+26rHpP/RVu26RncvKmOga3aoAgdwki7guM00jg3L6H9cIP7ByPZFvH36bVZbQLTxwb8yrpLI87GSsoQ1cS9yfCOBpcxE7x7kgG6k2GdYZtaE8zSvNAVCrk7OHsYUq+hxttJUzrp25asEBO7EXbMFGY5nhbf2mjJfgabh6DdkY+jT/ylyavmSFLf7Mk7Cfa4c5AivKuF7X5bGAVQXWSGuVFzxGutFt11+2wezNNhqWGTewLfhiHVroneUf92SC2kNdAZ0UdOUZz85hpkWOORnzubUAeyMDXOwoNTvt34k4GphOMaHKw0g7gQJMHjLoWOuYWK2ql3M1N2YAtYtEN/9ykgqinKAlDFIlNeSVRq1dzLkw22/ZfMHQ8RlQJhucIRmzrfniofOtyje2hYcsC0zVUdV5EcQNgGCDh29oKV7UX2Rmm/MdL5/HLLNtis5nI5V3lLgBGpa1bphUltNZHhehXdxDWLfCF2VqP1OWC175ZsEN1R6KhvT2cw7XYB8HbVF7oMhlYDSnn4uG+oyjTYIq3OC1G3cj1waemqZRkQdcBkJ7KPMPpZgwbgAH6x8VQ6O+Kh3O0gZrurcvjwPcKDMldq1yy8YGzAzQmSLrtkHZ9HsZmRK7TH5WYDgOuNuU6rvsb4kWmMgyvSOtE5sFiuwmB2BC0ycYa3J/NOcF7fXXcdvHCof9pX8RhuhyXn1HmAijms6YF/NHdhP7c1bOp0lkbFC7FqJi25Y8Ac1OV+4fazTmokfXzUKEjaDNK/EYb1bzEIStYMbDCc6NVpfTS18kCNOS6/jreKy24WtldrAsooirx1brzWcPF6Gub3+qxtZZt2dsxg7CbAz3g84xr4N87KIsM5+79uFSk01dBxPq99tz3ySsWl3Pa3oMtXtk7rd2LXTRgG2Db7aVeUZxXDYnivunnd9FnbhhhWDBONDei7Ne8jaudl1bud76Ta27sXdIeu6NeaLPfXRtXiZMe7fnZcP8ULCO/ITw9yo511i9qpmn+TpLYyahq2mlZuRfHpR40CmO2ZGkC6+0eVHGnn7Rh7+u2SHpahSFdcOoU319zoaEjJ+hT6JpqP9ayVwZXNd2n8m688oFC5QR7HovyaG/Rp9p5xvDhpjGzWTd+T5i/X3I/szPtWA6awy7ZDftqs8T1zb2636R83bvJl/r6xI7T9l27k8ZvzXmpMt2ci6yDjsXtS3Ior8QhrBsyub0lUxnYxP0etAFEwRMqO8g39tJ3sR7+fPhn22y66CxsUgo5w/sNZZl2Vuz02oN8/eJjDUZNz/2cb0S7Lx6m7MNFijjUJ5ClZEmILM8G5OzcV7Px/TH9nrPfLCWPyR22jARRwMrF7bkk21envbC4mIoYLOHQfZNjgGSLgaRGsrKLC7WRcfJmk+c1ItOvEMX7OLMGJuLjoZ3F532sCtjkICBPj+2VEeYZXBekG/t2lF5TjTJPCi2g+zQJKwGt+HjwgVnGxScLe0FXfeb0qcWNgj26K5M5+LP7uOCix46appF57Gb9nFke3iQMWcAgy3nPN8IGYxrHHKt0dsNEVqHkdW5P1mGYY/tNrC7uqVOcg452n4lhbnM+nPO28PiCPvzRjqlff4G1Mjh5gRmjC92Q8/l5Gmn9eOGbtJR1rEt47oanX0T2VZnbmsbRrWzJcoJcON6rM6tbjh2Hp0scABn3Vi9qDqNjOzksTDx/oxU2qt+3XHOTPG4f5Dl2glbfpgrY0keA3QjHlfcDtHlNtlDsMYebOOYp406ZzTD+uQ89Iu2sQOrLlnmKM/GaLLPBg/Y1WUWImnjUl5LQzaikMPAcjW5zK4HYU8VfazLaMfmk/DbdZILNfm3eunzxLOSxjaFnOYebdf+Atot9h2KF4CR+ns9RuU5gF0/ejDiXNCeoXOxQ0buGLOgpuvNSrA+25zzdg49hnF952AM1gWR9M7HeqTY2JPr9INHjTn14G99p0fGRIv7+nVdXuedEerH3Hvbc1Q3DPpcprlQfU9z4sZOtnNkOTUzdiafsaGjE1ufyFU1ttlhZsGtKC4FfX9HYNHHMlZ5jdkYenDs5tWNNobMa3/GZyH3i7Fs9kS1vTm/PdvGObFnHs4JNAo6R4qp00GZxvi82mzzRhNjPxa2jhLxSd7OF0/I0ZBXNOTOjWeGf4DIP4Mvg3/tNdekq6+6Kl1z9dVpY15GhwMiK26mGNlTnH+O4RcZeJr/5fJpJz0FaJPK+cnlVpX/6HQRc9FRrqX7/BLJqxsEXB14pc0znWP715w/0vyCYMvDsIvBMdfZu0s5tl/W8cQiXOWZjmVNtmQVuVRka/irUGwquTbtCRpC+/UCGRk/flnTe238sm4rG9CbBRjGWYMtbXeaDBdkZZuw0VQ0sS3LB+9GFr94a1mplgOVZUnwNwbNE80hywr51ZFylkmydIlVH2WDn4xMMd+kmm1mYf4NG8x5OM4xFxYysAk5Wq4Jky27shtW2RQJel7/gJYJzeBcIl3WIYdz1BwmB+zKRd0um8zk5d288r7XtddyoZd3qWxJGjoI/z977/Ws53Hl6zUYkHPORM4gSII5ApSYMyVqJI00dcp3vvGFr+1ylW9dNf4XbE/5zDmKlEhKzDkTJHIOG5HIORMg6ef59dcbGxxqFG9Y5Qa+/X7fG1avXrlXh5f72tcIAaetqdaRO4JPcOpZL7AsjdYenVbn1ECXMjg927Z6zqmRjQbtfp+VZ37qyG6dfhbZBLb3tf0mnLKq7DiNrE7DBL/W/g4eDRePKZ1GhXI97mmXLztyb6hhvVxo37vroN3BGxp7dB25s5oyLZLvvRnR8rxtd/otP2qboYMwlIXok3JgxZKO+7zmFDZHrtQHs9WOr7gpnvx0Oq6Yhae2oZL8kq5Tj+e83kqVmSojnquPdR7k/tYmcXdqZ+8+vTM1t8msT1YY0qFzPzwSgtUps7a1VuxJPp37Ulmu0TzapAw5TdG2aYPktff4fDZsQi7Ez/Mu55L3mdYP3TpgBE5JJXk2eALb9dLi4nIvdSx6FTp07r30VCB825+0yQs84vfwCoRCI86Id6ZddnAUcrUpPsDJ/K8yI5CKc62/0Vl865Kdet62tiVx4q0tyb3SRhgVHf5eXqzucpp84zo/29Tv0BfaevQj/KpXVU48Z11ZOsS1tvRIGQ0h+GN98k/8hVvn9vGUNAFvfYlt8Xp36UYwDwOgHuObUmMHfud8ngMV+ejeCS4p+Rq8tbHaDPEOPhwE7QZ8To+vU8p7ZVqmN8h725RlV9yXx/hj+8Qv0st1+VDlDtw9D/7CrX7VuuqHU92FM93lT533hnrNv1V+6kOV/n6vPG+DFk5VlbqV9m7i2s27DqQ8D7gKsUKrvKi6qGwGeWBYKk/rffnbA/GKW73W83u7xWOjUZYxcaJOrYU24OY/S+VHfaqdk35imWtpe34Gn5Czg5932V7bbRFf2+BHEBm8qZdy/a/+Q2XKpnCFJe/lq+f8rf1ppaJc68/9PNsTf+VJWXBfjSzHlB781o57rHjbZiHy23qst1XwZ45pb+cefQkPd7/VSJji4j3xmRwzbR+6BW+u1ev6R2knHh1Zt43AUsYrPlKc53kwR/52w+e+q4w9vOBznTr5leLp7tJ5vvs3X7wefIDD/8D1T6Uj/SZwkE76beOmGus4A9j7Ox9gRKatu53jKIy6oWVnbwZgBX/Odxfv50dayOlufLpvAL8et3s6+HZfRwd5yiUkSoZT9yOLwLUd2gaP8lacpXTonLiuxlbi3Cr2uzJme+Vp4j5iIOXPeuKfOOYZYPlotbec69mubvz+/JdGs8h7h/cVPs8Cs12XxtXWVbo3yM2XaU/bDLu2RNC2Vjmv+iMMYYp4g2vjPfW3lvA+BLwEQR5d4oPnrdeqO3be68hDZueCi+0KzbmefXBiA3yu4ql8NN1Xn0Q3s8K4X5nEvAW+T1h6NsdLl5We94YO3K9v7MQdjZ7e1uTa53Oe42WwO3bVpWq+aEB9cUlEN/8UEAt42vZGc7560j8pfvsPeHau/ekDT6ThVWaNH8XxEpxan3KlXIiDS3PUEc+JT0rDkR/RQyGELh452bGL0kIeDRw0qIwZM7b07ts/TwRGj1rr7+/W339IokFyNpb2ZIJGB1Eq+/ftK9u2bCk7urrKrp07y3kSD+nEcdUdrdOh5ihjZFArTWjkRurwOv/CahSlrVk+zyi0nRcVy+CrLx0aldCkhjudhpcA9dhtEFW0nNPhVEeisBjoxBEr4NwjTjHm1BsF1WmBa4yljoHrV7vWUcHp1FFrqkIVugTv7ou1VuHzjMkQO4h2DPv3R7C49wvaYwBjoxVsBdfzwc3fHcG0wkYXgKY0mtkmMTp77mw5e/Yc6yN7l/79+nM/cAhCNegKdjq3whf5IOuhQ+MOzG8evG4JbXQYfB84cGAMwOnTp8tpEkrSPx0EeNJtEPMUEoHRUWk1fnFUnM+9BCzeaxE/16adRVZs02CUTxq5Bstrnqsc7PC181wCUfHnuqWnkc4ztNU63bQliSi+96MTNgj4yt6pU6cTKKVTD4wGR1jSMwGlsPkIW+PJIUj4jB/x02CcR/bsGLrTrLy1bc3gx2HxW5nSoPuM13vSqn2PXlCJ7Re+8iJPfcbfgwYOKn1og7TyozG2Yya+otboLJxw1wOl0qPSyQZYn0XcpY3Fc61e4aSzzvM+1T4xqh2ZEKZTWD0auKW9wLqCYKL/gP7g/FU5fuJ4+CjPfTtNv359gdUrgZb8i3OkjtgCce58V/6VfWmWHd6RYztyOh7pYJE2Z06fSf2u2fWa619te0tqeV+cZ5xIlePwkrpTmbhzv//E12P/AQNiV6TvqZMnfQiZrRt4Xc0xcqZc88+2hA60P+jzJ7T1d2TfemqRPt7bnOfJUyexW+fhZ5/QxjrE1fucwhtbxPfYsSYv2pLU2WCKt8krN0q6GFlQf6zn5MlT4W/We0ozUQEvn/+2Ynssud65pW0UKH+lez7olYHy6TOnkb+rygDoJR8jM8pND9r0rE/42gP1KDyBn8qYdE+9tC3yBw/lrUlr4ctbS9ON/Oj8abi2Y5X7etHvJvWso9kcryhr2aiQo+dtkziLV3gE0vU8+CkTXJMcwtMXxA74u3NOee/HTuvaYQMkP/Kk8drzJpWjO8AQVuw6x5DZ5nM+H8/BO2XdmYPngNWrA9/zF/AXyoY4Ket9e/cJzuewEcKSVtIigaOwuuVe+1g7l9WWgYeyRD2ucbW9BnjKvPLbm6V8NRjkPnCLZIgjpR78zvNpQE5fdo9n0g5uELbfz8HTL/j420/jZ/ex84ybS9thVIf1Xw1OraC2yXMpHP0uDH2IdiNJIp6vNq5eq0hX/IWTp33WOusvvtFOrtla7/yKXo/2TZmUn2fwddLdmEOZ8VnlJfaTB63X89YbWeEZaW6pbcDm8pCwU39Hl/3tTuUm7rUNAwcM5Dqy+0VNzOdegfyVxTorPsQGyIfwlVMTkeJtYjVyaaOpxNjD4Nl2NJsibqGIOgA8fY06aWlt14f4EYZxh/Ym+tXR69z8F/5Jfdo6+EeF0K/ubaB/1dcNgjbS13jvS0c9pWZFPzYosgYi6qmyYPuUc/1IYjxujtwDw3uUGeXeY2ID/KvyfgE5VfdBIaXSga/f/F0xqOc7N7k+XRoap6qvTT4GETcZG8iHUwzGaQsTD4OLR3FX57T7seUdPZHuypGxonQ1vjP2lX/iraxJk+a7W7uDuPh26JPf/Ok04T+cD/rUZWIT1IK/otESJCpHns2xxvIhP3RUFrxefXb1Y+IljtpDYbsnjgMQJvJt46nTp9KeZg+UK+U+tkt5awj/Fce8qQPaGAtLY/lgTCZPYnM7Oqd8+7HERqbxNTZVZ4w9xVv+VzvCffxI/AFM4eu7xT3nlFcwlg6hRSD/5X+sXjyEJ69PEVsrA/3RV22xdYeI/JGu2iH9ib7TS9Ku6XrkGtxtnzKf87GH1TbJE2N3+1DG8uqafSfhNb/0TcyrRbSFl3MleHVu9ru4qKvuhSTeNRZGH8GzmzbcIxxh2pa0yzbAN2Mg6a6OCC97ywT3alPV5zZ4JV+lffwabQ1NABYcL0ezYpiqOoyW3vzzfwpxV/w0dD2ND9THDug/AH/a9nEyHgVneGMSymL71I20q2PnbU/zr/JBmMq8PPC3CTfl6Srw9jhvwcKyePHiMmjIcCA23BpSqeY79+cfkmiw1ZIjxGydAQjWSLNj65ay7KOPyuqVK8umdWvLVRB2+PBh2buhddB5OEKtYOc57qlCWqFE9lQmhUYBAL7M0jjt3rO77Nm1s/RDQUaOHlPGjh2bQPfcGRX/XEZ4xCadd42VsDtIR3BRZE6hxDUIUBEVBkdKT589U7Z3dZXjx46VkWPHlGHDh5cBBO0qf4RLxQWvKyCAQtdTUKVJDECw7uiOFXNfVfQry9EjR8pmaNIPZ3nNtGngjZKLDDBjFIDovSAfxRSKOKtAKlOyluDi+TYKYuM0pta9B9ps+viDMmHugnLN1Gmlr5066GcHWIeX9tNWtavhCqgoW2uLKLcCSO6jXmikQ9u5fXs5dGBfmbvo+jJ6zBiCCxw0RkHj1EbbfMZ6LKmD5yuu1RmKjwY1z4CLbRO/Y9B80+qV+X3tzbeW4SOGc75uhCMOyoy4ylvx8Yc89GgQQaXdbeK24CBfNXqnTp4ohw8eLPt37Sjjpk4vM2bNTkB+BmOrkdDJi7G4WPzesw1xHOBMxbloG5UJ+SGMI4cOlX27d5Uju7eV2bfcVabCW58/i0xKA79XknRwpD1VrjXALfFAcIKRbfzQiHrPYWDvBfYJZKdP/4Flxty5ZdSoUTHErZOdESZwDvadP5X2VU5sT5IRkQWD169wvn1jsA/s218O7dsbPAYPHVbGTZwQuZSvOnzb6XIoHqodDo4NtvqZhkkbGogZDRzXFw5AP+2Ibly/ruzfua0MHTm2jJ04EX0dF10+T+DrVHnlwHqyZAHK62zEN8GGdKNuO7lyOv0x6tAW9KGDdQierl72SfkKHk+A5tqDIUOGBKayGYLwbPRHW9OROytIG4CZInP4r03zfN/+vJ4Lx71nz56yadWKJKbGjhtfBgM7CUIe0uHVZ7UFymNNmEWekIvqtGon1WuRgU792ho70ps3rC87P/2ozLpzSRG+AZj1aheFI5/SMee7jWkyI62jrwFcYYu3nTJ1w9eLnYH269esLedIZsycP78MHjy46qFNzfM+XEsVGdtef3s9TOCo3XGE6wrwEj+LmzYePHCgbFz+SRk5fnKZOWdO6d+3X3gvHgJquMrHBNoAlCYW76l216RN7fyKd2jGNTst+/Z+XnbjS66ZPqtMnDQpOmq9Pps6AqnTFvG1eLD6+qf7PttjEG6xs6h9P7h/X/CaMmNGGTFiZGgeexNaG7jVwFT8ldNWqD71iD+MT/CgHeg/cEC+Hzx0sBxAn44dOVwGDB5SpkybHvmxTUl+YWPiQ8DJf7E8AvXTsT/yV1pY6wU7zuDej2SOSYC9yORhaO9eKGPHTyiTJk7KLIZz6QSTOIFP4Z9UAF7kSHpQl3Txmx+LSYZ0pPswW4LnjoDzjq5tSQYMRF5GjhxZhg4ZGtqHDi7a3nUAAEAASURBVMpjw7VCEFUh8QE2gC/xpv5W51ridc/uPaVr4/r4vxGjR+NfR2AnBkTXL4S3yjk2ILSHth37H/tMReqc9Usb5SWt4miiTv+0o6urHEBuRo8fH9/dj06vHQ9p4EcExVFs6wcOhA+csIg//2IHuEG7Y5LHNyocx4dsWrumXERvp86cXYYOHZpA08cu+Dozno1PUC4o3bSHZtZphVWvqBOaeMK6lB1laT/yuPqjd8rYKbPKzNn4J+o05mgj0D4hmD9bWl3Atz59inTbuWN72bZ8WZl1821l0uTJuaY+ZfYYtAkdwCOxCvbMQLsm6dRlca/66X3au8BFvoy5Thw/XnZu3ZxBpfk33hS50X+YjKwN/7NYp53SO/yBv8q8SU6TU0ePHinbN23E/g4r8xZeWwbSAXDW7EXgG/tUalpTbbO1NTvhsdqaKi8m7/SFvZGZM+B+GH3d9/nn5eDuHWXagkXEBrPK1XYksZtfd5JcDXvxayW8EN92gqP8FAt9t7JwHLrsRx6PHNifpMXM+QvL+AkTgvepEzWBrRxLX5+TrpGNAK26G33jBu/xtPc70OD3/fv3l8+JhS8yBXsgejp+0uT4PxMnxnuWHijX7zx42bnUlVtDs17g3Qf/Z4Jz584d5SADhwMHDY6N1B6YZLsIbJM8NcbWDIp7pX3aA44W/Zedq3SwxJh73LujD3Hp8RMnygZi4ZPHjqKvE8oIYhp1St1Xx1qSR/nvSfeK6bf8TTt4pSC0UV/37d1bVr//Thk0clSZNG1GGYQ9007Y+jqwV21BfKwy4hXoL9/UATuz6od097z3+axJOqvasb2rbF2xvEycPaeMg6d98X/qROK5xKW5nT9/WVFOw1t89znsyfo1q8vhPTvLgptvjz6pw+IQGQcB/aZxgnZGnYzN1JbwXVunL2swlf/4qw7jjxw9WtZ9+knpB73nL7ou/aeaaKB/IyXAJaVz8LuyXR+XIPnP2crzzoXYGWN5B5u7Vn5Wrrn2+jJj5ixoemXiVWkjvkITz1aiq/xI/4Y2+Nv2NZttTChtTI4cB/dD6JODEFPQVWcDiK/+NXD4HtjAv1RDxb/V3Y299/CxXu3CaeL1o8ePoVO7Igfzr11URtAHtC4Tj0kKgmf14/SLfJZP6NWBZaXqrLbLpKD+oy/JxS+Qp4PE8scYePNaH86p6088/Ux58MEHy4jR44DcCN4T80al787xH5ZosMl2+mWuI/y+LaBxdcu6deXtN99AUdYk2BpPImD8uLG5r1cUw06FAlcdv4yOSEBbjwo0fKBU0Y4BQ8g03hq/jZs3l7UYKA3HRIKsa66ZXIZhZBPIYRiyRwQwDYzSGbBOAPoRfgwh11MFdTbDaGf1KELw2coVZc/uXWX69BllwkQMIMbVrGJgGOjQZixnFTDR7MhEExFqi7wkSPE6uNsR1Hjt3X+gfPTxx0m6zCU4HzliRIIwBbglGiK4PFbpU6mQqbHQ+yqya9LBugwE0oG3XQa84LSNRMCny5aV6QTPs2fOxOkMJuPXJ7B1/Jam4LYn0DlADq90X/NnrnboJu46/NV0Gjdu3FBuueXWMm3KNShjnc6bWRg8oXNTLr5ZhKehy7TGjtGO0lOx7TagOowBWUFySqd06803l3EkMmpApmIb7HfkJTyEfxgsnYF41050lZ3YMbEHtrw1M3oCp/Y5wf/mTZvL+IkTy7ULFjBrYmACqRgLAguFrtG8O2nSqSO8gQbZRZvGSDuDXR2QwcQ+DGucMh3fG6HNvNmzQkBHrO04OgIhuXVwHi3WIe7KrR9x1SB7ziuOMlqPwcSOHTtIZhwmOOpX5i+Yjz6Nj7FzFCx3K88BXJ1V6/iqQZ73nkYjO7i2px+wEpwTTOzo2h4ejILmM5CdoUOHJFuvoVfe3OC10l8e2P4a8McZAN8myV9lvnUEfM5gYi324HMSYEOHDSuTCIQmTpwQfbpw3pk8Ok7ggY88Vg9MIMiTOFGupUBrCJlp5CaOhD0AQ/05dP/ggw/KF3SyppJYG08HQ51yV18dLWAi035P0NChsbQGYalT6cNvz6XTCy46HkeFtnZ1lZWfLS/asKnXXFOG0YaBzNSQR8Fd5KWuFVFaR1V9UdbVz9A/eMBfzleZZrYBgfJKbORWAujrb7ihTJ4wMe2ST7UovxVuHQ24JDudG9K+OgpZHbE4ZbQbhhw6fLh8jC2Q1+rTKOhim3ri2+DIVMWnyoq32KZ6NLySpwZRyqRO1s76bjq8y5cvLxPGTyyLFi5gpLF2tNGK0EP9kJ92IJU5aVRlsAah0sGP9Ur3yhNGvdVXOnVd23eUrVu2lBtuWFxmI5OO3qsn4uinFaDYpP9Q2j0tcBS+duYoSYZ9e/fBWzrU/L75ppvLZGRSiNJKGjY6JMj1N3ys5BDnGrTJY7IE2CAoxEUDXANEk1M70Cn1dhRB0LXXXkuwMiy2MQGKuPMRbynhMejnPCda26gHophFZuNKdqmH9sewNZs3bSo70VcTabNmzS5z+AygQ/0Fdauj6o20tr3ibZtCCyppuHs9/qnTFhMOULXsJ4GxCf96FNkxWTUTuk/CXvaG9tJOOeipl03uxb9xpNHdo7S0DEBntJVbtm0ryz9dFtszabK+ewodmBG5T956f54JLaRTSAwJDKDVL/iDrKQdnBAfZciEgm+JWklwvnXzptBlArbAGMG3ISRBIR+FS+nuvAND3P2kdBrBnaFPpvADX1oe7OiTnaBFdHbHjB6VetMx0tYAoCX8tQOWyDSwQrdO3TYkvOG3M7W0Nf7ehdy89/77ZQKdlmtJDNqZ7satg1eA/oV/5E3axVHfvwG+rkRfb7rllqpPyFQdAFDkqnxbjd978rjRTHnXHlUdqTPuRMUg/AiJgHUbNmSG4K233lomjhtXO2rw668taSr0cJbQBWTXzoVyuWrVKuzv8HLbzbeU4fgnB5bEM8kdZAvEw2fx87s0jY2BCq2jamzhUiT9eD86044a7yHJsGv3rujr7Lnzyvy5c0o/fJDtMs6DfN2lsaHB80LOde6h2ghTHeG+shyiY+HAlZ1eab1g4cL4EXlzAT8mjtLVdoSu2hIg1uc7s7oYGRas93neWQEmTbVdXcQFm4jHTPaMIMk+mwTVqFEjo6vOflAAutEHSPd30Qzil5+LHKDr/dBXR3VXr11XNgPfBMAUdHXq1KllMDM+nJXkxzaEB8Q42md5YSXRN2TbH/JAGde+hDe0WT9wAH36DHk8TqJh8pQpZSJyP4pY277ExQ7cnvgC7C8qdVbHFWX7rl3low8/LMORGe3YcHx3P2IG26h+1phcHaixR9PL6E13xVVXPddsk/B9fi3yvhz/Oo9YcvrUKRn9TswmL/n8TYV65O8pBgmWLV9Bh31vue3W24g/xkA7YCrX3CM+fo9gUFHFuep701fr104qY7bN8/p/cVOfPqQfYjL5lptuKsPQpwzacY9JcOH2hPPNtlh9eNt9rATTJzm4tgPar161siy87vrEBs5QO4M8SUPh1sekrW0BPypUP0JjrjcZV7bsrEeuuMvBSGPtXcA3frqWRMAU+n/Gu7UvIF5i5p8OnfyeJiGBwO5Zf6Ob/Zjz6NM+6LITO7wXfTVevZGZBmNHje5ONLisyvgGIMg4tOz4rGobpTXXoLdw9R143hy1NyeYvWM8uaVrG/xlWwFodWTPhvI//2//Wp56+qkyZvxkEK0602mBmH8ny9+daOjZ6gs4EYMPHa2BfaImDMQaHMJrr7xcNtMpNUCRWbNmTM9ls0JIU/3ALBkWoeBvE17PcSUC4RcVxWDa0Xkz0MuWf1befeftMnzkCAKKOWXOnNllNNlQO7kGcplCJJMNSlDOZCKBU+u5JITW4UmFwjrMshpMvPL662UjiYwbccgzZs4oYxiBHWgQjYMywx3nY7DTDTFyLMYVb+FarBDYwlcQVRYdw/PPP58A/Lbbbotx1YgboJj9b0og7Pp4PapsKlLgILSWnsGL+GvEV61dW/743O/LdYtvLDdBdztdBowaRoN9SwyPuFYiB+fQpoNr++4NLbA0CXOG2R7vffhR+fC9d8vDjz9eFsydm5HlqmTVkBnQWFflmQakFutqdPAYp4ocNLx9xsD89TffTELj0YcfjkPWQYmHcE1OJSkAZVRoDZPBqUXD5L3BN21LhaGXSzuOwNdtKPkyjOs0sqB33XFHMpXSO4YbmBo55UeK2ybpkADA83bQpI/4cPSiSQGN5GFg72C0aMP6DWU3HYz7H3ms3HzjYuB9ybUjBAcskyEQFjfbWeUNWDGwdfq/nQQTEjoxr1u3QYVOYSdGdeP69cDeVXrDy9tuv6PMmDYtsuLIRStV1muHUHlqRTYLT1r7XTmwXQNJtNhpN2m3hgSPgcPkqdPo2N1QRhNEn2ZJgtlpp2abaIjAyEg+MdjAEr/oBRU4Ito6LNal0z14+FD58KOPS9eWzck8z5zNaN2MmSz/GEh9NUgBTGYzqL9Xqed02EwW2klqiQzp3ovgROMsXk7VH8rsgujTc8+VcyxvWEDwPw26jCPIlXaOukQjQbkFCQY96bQAP3LYcbzKjh3p4EL7vCadTAS8/eqr2IGZZeH8eXQuxmRmgNd1NNzo/9wvaZRHZVtd1y5Kb2kt732mBpCVlnZ4337vvbLik0/KfQ89VOZQh51Vn/UZS6b78lzTl5zkj/wUtjhIa+EmmOM5Zcz79+AsX3z55fBKfZqQ4N8RC57vtD+85Hc9dmyC121U52inyw6RI4yOZtnGkySQNpEEePWll8scOkVL7rqLZK/BStUfnxUv4YiLz6hnBphiL1+kV47eTPGabXC664GDB8pqaL/y00/LAw8/Wm68/voEoM1GNvkDQIjRwTZwLv3xYsVDXKSr+DmqYOJuBQkkZ7A9+tjjdNZnBk+Dc3HqIFdpJRiR7RTbpJyrr95gZ8hn9E8G51u3bi3r0dcdXdvKZALce+6+J4Gi8pRRe3kLPAOWZnsS9KcO67Yi5Qo6aeeUWWyIsiHuKwlAN65dk+Dn+htvjp0fwqw7p/GaTNOGaR/zCq0O/dWjJEuggx04S9rJdW6G7ibZvi47d+8qK1asYAbV7iwfWnzjjWUunReDf21wk+UOkt14hv78qWQCss3gj/S2mPDWh68gNnj1xRfjT2fRoZs7z6TpWO77stol8eQ5cZUOepDIjbYFXLVH+sLIFnhrf/yuLTtJp/GNt94qn7z3brkVeZyFnXGQwCTHZYmGIBe0Kn/ztdrcetaq+Ud7vrJh2Gllczf69Mc//rGco56lS5aSgGHmF8kA4w0DbCEoYxZ5AMLx8/6WtuEBbUrHC5xtp/hrd5R7B1B+/etfZwTw7jtuL8MZPLFt7sHhvX9tEXWfkn4OEiyjU/f6Sy+Wh594siy+7rr4GnUz9Aa+91vab/HvnEhbbJs8UA5sn3bae5w5tY9ZcR/QqdMXPv7EE2Umdljef9ugQwX6p/+mpdaNTDqF+iS23Y7LW2+8wSDBpPIYtmwMo9QuaTMmSMcSOolt9eG1TVVGamxnbfJAfFxG6Yw4Z9wdY/RyC/q6gc70DgZpbr79dhIZN5eBJq7gMwDD10acxoWWaGi/hZ9zngAR9UUb+/mez7EF68r2bdsA9XW5/c67ksgwAas90Bf5MVbIwBj32A7bpE7qnzOYAD2MSfSp8kF5tOO2YePGsoJ42KTIOGhz3fXXJaZUn1psJG7CbCXfwbOda7+9rtw7o6E/tHEGz3sffFg+++jDMgbfMQ9dnTdvHkmeoUny6L/VRynvci3bETmFzpLB71U/wZ32+Fv62y5nk+ifXn3tNWYlHijzScBMnz49AwUmeS6YXOnQouH9lx5DO+zf+o2bygvEBuNION6A/xjLIIrr4cVDGTYxIj3VAfsxVS+rbbSu2Hno7tGibPmsPtAkz4f47Zeff67c/b3vZ+BqIDOznEpv6+X131SoSpyOklB+GdooN0899VQG9TLYRv12qtVpcRGzzILhi7/5n9JB2UYEf0+3GEh/vgv7/vwf/kAfamR56MEH0KeRaRNKIiAF4U+WUKPzpx5qHdJJOXSprEnHN4k97n34kXIPsbY0M2GoPLQijsEdXbCInzbDWFvb4sff8qrZHu3LTuLgDcwENYm39HvfQ5/mpo3diTXbLMAc840flTaVRtDN0/yJ/4We0vws+riNuGAdOtW1rSsyfx/wr2E25WlikvMkUEw0GJvaO9AXGo+It/quvTEel3TK+5XoKCczo8FlEkfg6RpswSriGmc1HCHB9vmGT8v/9L//n+UHP/xBGTluUvDkD6XhXX991/7+QxMNX7KrtQ5HwktkBfQCnTozWW+88gqjLtvIUI4o31u6NJ1SLGmMtxkzRctjFWgVpiMcIXATCgMy7gO2RrYfTt3A8N0PPyivv/ZqGUnm1kBlIUZqHCNHdpQMtExuZIYAxjYjPNQj2xQqhUsB6alHGgZ3mbfzsv/QwfL7F14o69auxincWeZkWtT4XBM+WlCDH/GS+7ZBmP7jq8d6wbpsZFXCOAfqMDj/xS9/GZrdc889ZeoURnSYmmP7XGOr0OYx/hosGmRoAA2UNIg0gvMYQ26iptwrGj7v9U8++6z8+he/KDcxqnAnTtMEjGsyVVhHfDQG0sai0uXIH+v0j53f1G+buN6Mk47tFB281wnk3n3nrfLkD35YrmekLh2c8F+6dtbIgoftFadapE3FttVt0kRjbGfdo4Z+D1MMX/jji9lE9Jlnflhm4XjaO90NoiNr4CcM8RQ3jZDFKd0GawYT+ScbpB/nXAd4gEzlxk2bywd07GbhLO+79146jaMDoxltg5bWIWpBejNMrcMbioUHNfhU7g8cOJjOxZrVqzN68fgPflDugvYGDtZrEkTZSjvhg/XpbDWywnM0wmDCrGrWV3NdJjiyL0+3dXVlNEen44yGpfd+r8xjNozttHNu8bv3ygMD10u0F5QcpXAQB5MaliFM6zYAdVThs08+Dh4mYW6/7XaC/3FkjE8mGaAR1SlfIQ9FuNMZULZ1HPLS0xpWeeD3BBM4l71kn998++0sEXDK6Lz5C8o8HMMQMunqk3Itdk7D9LfBSn86bC1okcfWYaLBUV1nNDkK5R4b6s1mgsRfIO9n6fg68j0L/DMCi2FXNmrypTq3GjgoO1UnrTgJPHkCzcw61+SPjohlLwQ7n3z6WXn5Dy9gB2aXG5hi6Cipme7gjD0LbRs86GOdwszoDvA0Ck2HaGZ3IsHnD7MU5iWSGJ9gz54gmFiILUuSBVqHlzwbB9vRf+GIF39Sv7KpXvvbZFajmee8dyfBxLO//31074dPPx2H2e5PEC4smcWfJufKkaW1S9jKvksnpI9JSztVTpVes259eR7417KM6uH77ws/Etx2YHagdLffOpVLL4cmwG36mjYgL7bX2UeOKCzDlrkE74mnni53kZTV1igj3bh21xOUASFV/2OBTILP844KHSSwVac+BvYJAogf/eifoP286L7rSrsLz2kpEwxxVLcqwQCmbQFXGWHHxSuOljkSZfC/evWq7FM0bdbs8sAD95dJyL4zNdTXJJGEDVJaW7H2EyAgm+/+5fqXZCAuKgfYN5cf7GfG1DIC3HWrVmMnTzM6fVu5E9qYdHOEV/gG/8pCbDMwpLH0VqYSTNMxls/p+HK9nqt82bYdupCM3UPHzqDcZLizv7RLdoyE0+gvwk2XqrzUNghbmpvg1x9LG0cTtQ0fMQL4wrPPZl2wI4HO9nAk0w6IQar3pnToUJco1iSR9FK+kmjAflq3U1CVq0GDBzEL5mR5/qWXyruvv1aWYCPnw9Mxnc5F7cRV3EVO/PTa/hd3QEnxygMP/BAX6W/8oX/ZsWt3+dVvflPOnjzFVNcHytTJ18Sum1C0Q4hI0EF0LTXyrV3ji7hZqryHMpyviR3pqG1r9noVM7/+7d/+jYGTOeV+AtyRw0fUoBYYVfYC6q/6Yx3qnHL9Lp3GF+kYPfXMj8odDKLYIZOeDTfb/6d0yPOxRRz1GdqR7EnCbxODzgp4nUTAAeTzJz/9aZmPvRR27MFfg3HoDmcqMuUMdWkPupjB8xJJkslTppYf//CHme14/OixxA11Pw/8A7hIpzZrMCCgXXjAD/Wg7hfAPkp0dAciM86iNCm4eg0zYYjN7iJWXXr33WXwgIFZmgGwKhedNigTnsiRr9+WcPAWaasg7GSPstXo6jYS7dZ/L53S65B5cTXeM5aRP+EB+mmR1uqvRzsu0rrFwNoc2yMvjIVXM7D06bJPojsTWA5zI8tWruGormsHehbpYUlS06M/Iv9+qUUTpy/sT+LuyLHjxHtvlk+YMTgBHV2wYGG5llhbv+uo9aXZDOyBQKzS9FP7ljiA9lZ/46AMiQYqVs9tgzMmTB49R6ztErZFJAKcjTGJTl1/lnR+ga38Ju0bjn/uqN3Tpq1cvab8ithg4sSJxVk2+u4hjNwrI9oyeeS90ko5la5VVmRwtW3yMPaCSlu/YQAzYUwYv/HOO+U57MH9DBLcyOCMex0IU9mIjf9ziH7zehiCL8fWHGSG0LPPPc/srM3lZz/758xAujzRYB0VR3H2UWUo/BTniraYVHxoT/wANlm5coDmlyQ1R44ZXX5A7DGOARRpUn0TcMWtCbnfW+HZXPvG7+DANWc6HiGuMaH84gvPl4fx3Q99//ssq+ybGQhJNICLRTzac/6OfsIH+WHS3qUoxhpu2G58o908RP9sOwnB1cTaDkw+9OijxGWLfDz+VZjt4zm/t9L8lkcJVOvnCP2c9X0amdtEPOng0uZNm7Ms8fFHHynTp0zNwIoJZgfCHHRzOwDlIkvDoKnLkK3JwViXYJjI1Gfbr7wC3K/G3hzGXi0nLljB5xhJPJdoHDq4vzz5438ujzNwO3T0eCA0ol/Cu+H/XTrSt+9w+e/AGrpCYwRYq8QxnUcJL20wnmsg5NskAnYjEEk03LuUQG4uxuN81mO5T4PLLWqioQp1I6vHYMgfBc+iMDp61JtAy2DknQ/eJ9HwCmu6RqbjsojpM06zd/2qxi9BPgJhcKHiqBoKVe2gEnhxzo/QbYcBV4yfjocsmYmGDYwY3e6oyGzWADFtWqdqh9BEQ28EzYyWiAbfYImidIQkWHfo0YRex2BnfePmLeW//ff/HmG9F6c2dcoU9q8YjpG+AvzP0JmuI44a7bSbdni0ZLo6NHd0kQZx3k91sOKvUf+IEcBf/vu/l5sJEO8mk+hUNDsvGnjxD0zuFffw0COwbUfD1aPwvUc6aRzE3w7eayQaPnjv3fL0M8+QaFgYeHZehSskcRBuNknBYclD5SS1UJGw/W47LRp679eg7Cb7r+Nxk52f/NOPMyoibhqeGEGekYcqu/XJX0c8xNWp9HZuxTW1WRe/lQWnWDndaj1ZVnF3TdqD999fRkObBMLiyMf38RqoywvX9Xq0NHqYaXWdlb+lt07F6XcmGrYQpDi102TAE9DmTmgP8bKO3UBCHqQOcPZ58RdfZdO22UHVmGqgUsDfZ6T/Fozfcjpd23A6fUi23f/gQ2UBQbTXfBZwKY5ieE6clAvrg4lcNyivjij85D7rHsyImTKx7LNPCSY+5PvZMmPO3HInCTZHMM5CNztHleboIEAMUqoTBW54bmcdnnNNmmRGA/UarPRBZ6T7a2+8nvWGExhxWUiQtZAOhomGc2dqYk248kFcTKy5IZd1GqwHX+T9a3671s03T4iTU6IHoDc7oPt///f/Vs4RjN7A6GuCFepxAx+Df5NTyo88ED9LpYuUqfLqiLTXMyuGeiSW8q5stY7RLEa8b7z+hiQx1FdLgiiOjZ+ekx+eVzYSdEAL5TQOluvKu0ks6XOMIPePJGQ/fPfd8hTJqUXQRTtj3Y6Y+Jx4hc7wVW6CGp8aENkOAyT54TPeJ738rXw5AvtbOnXi051oUH7553XvhxiBK6z2sR22qX0uQv9MN6aOmmi4kNHENXSMfg/8RdDlUdYYjoQuGbHvtFl71YIh69Km2QDraVM4g2+nPmeUObLgbInPwf0TbJlBrknNu7FntbPLiIFwg2/lofhabJf/W/GrNJP/4qGNsLXa+O0EWiYajtM5fYZEg51S19nrQ4RjoOrzaqiy6FFcg28YUQNyv9p5Uf4dVT9DAL6O0ZZVJNu30rmYRqL6wQcezOi3e5K0ERAaEJjSpP4DdYFx3pLawcGg5Rw8M2gxueaMhmV01tcTaLk/y80mlEkMDjBxx8a2gY/eiHO1T3oqE4IE08gBX2MbWvJIvG2rsi+PnNr5PtP39+7eTVA+tNyaRMP8Snv8d2gA/cVTVJuMeGxF3kh5/YK099KQocOsOEmS53/3O2YFDaojpMi8HYB0AIEvf8Q39lFkab+/k+gEkG1Ql/R9kXfq0pb1pf1nkJvnSFS/9cpLZQkduoXz57PvybjUlaVHykHwBj9xFP/QukPvznexT9UceAL4dTblDmjyi1/9qpymA+aaWpcODqRDKq5tI071W53tgMgxAT44hy60QXx9xnrki/dbx8o1a8v//f/8X6HLAwTno0g0yEc/ONLA+mv++IR1msx39PkddOkFaP+DH/+43IncmCDRzmgjTd6EFtbHeeuM75ZOFK/FFoF/s03OsPG8I5UmGl5hBHY/a6d//vN/STLZ/RPaQID3WYTWvufEN/6kNupAwqiQmAhb79JE5fJlEg1Tp80oP/nRj1gmMLqcZCmh8Z6JcDvmVZcqwCqDPYADUzm3ndp1R9QHGO/RKVq3bm1Zie92SdJS4oL7iMucqWLyOq/b5dmepeJY2+L5lmzI986N6p5yu4P9E9yrbAuwZeEDdEodXUcxsHMnoz+Z+RZ5RNagu/Zd/JMIDN+rDLbOe3iKvkqbNUk0LIvvNtHgDKRLiYY6m0iUulsADn7P7zSkxzXOi4Z+1v3PjjDj7o233yqfMqNhwoSJkcsF6JRJQ5OaDg4oJ87okQfipQ/XRiSuA15bbgVz+KUYExtC+76M/u/aZSL8dywP+JylgyyPI9EwedLE0o9O2wVtQXT/En6i292OQLtcnjrNiZ0Xr+XYyF8QCzvAcfttLOehDc4etiiXxgP6TW2UPjIxN7Ifm8Y9lQfVL0oYf3vNmTD65tdINPyeQcP7mWHj7GFnPZuMt9QYKV//4j/BH/hJNCCXvyGJr9z8y7/8vMxlnwPpqhApjtIh/S+OLU71uudjf/gSm9ftK7WstU3any4SYA54jqRv86MfPF3Gok9uFmu7hHFZMsrn+FgabTo/6oEntMcipi1w9vByZP5FZm4/SlLw4fvuK1fbfyKeTBwkLRssnrG+hpv2UDqrP9LSeNyBMeMCdUPf7WzElStXkWjYVx570tlZ14c2dvKDuzrkv/zIN+BX/gnbj8W2hOfYDvcNMalZEw1rmTGxgRh4fHmSBMC0KVPLKWyQ8bBJhquBbw0OvCXWop7oO3qrH1PGQRZGYMdEAn3qTWxzCH2SLitIaprUOHbyePZyeuQHPyqPPv5YGT7OGQ1aviAuit/Z8g9JNHx10dERR/ZlFH8jZNBE/3rx67KNjNC7b75eduEcXAe/5K47y9xZs8OoI2Sk7Khn5FaG8IElIa1UlcQ5jUJVgaiBnll/R4xO0xl6jwDxTeC7WaAzGhaR0XJ0+jQjDSYizHDbGTWzLd8UpjhPjQjwEyyhlMLXmCv8Gh07L4fIJL7A1E7Xpd1y+22Ziu0mNU790Yj2Aq++KgHtDqIiTYmiRJzr79oghBzaKNAaND+btm0tv/zVrzPrYgmZ8yms+9b4eY+Bo05f1agKYNvr6HR1QDXQMsvt/bZJmOLufQr5J0wH/s0vf5F9Au7EuOoUVFKNaYwrCpAOV9p+SfmEd9mn06ZGK52hiYY36BR9yuj3UxgQ15E2uA2/GDtgJeCCZ62z2KFKaG5HIfIDLnamLecZcdnFRmF/gPYGAj8mmJg+ZUqCGp2ADkvDKt11IrbduuzYgXjg2Mn13ig6vGib1zni8jmBkImGjwiiF7Em7UGMnx0jA8QYN2BLewN1aZ/OIPU0Ouvk7VC7Btv6NIK20XYfZBTH6dLOaHCDoEefZkYDAbrF6V3KWYw/v/3e6Ow5n28jP8KzMxSaQyOXW1hMNLjvxlamAvals/HQI4+UhXPpGIFvdogHH2EaRMVRdPCWN8qo9eQ69yvrfjcwcHNAg5WMHDPttSUa7iBJ4owGnYb6JD7qaGYuIZvWYVEnDfaFabAljSBe7nd2gw7ZNW9Oj1y7ckXdG4Mpu46KDGFzqTN0jISt8Zb28kKboq6ppyaj1M8EvODsFHJHMOXXIJ4fxCiuo1C/+uWvkmi4jlGF2bPYOHAia8oZWXT0Ox0TYMsvbY6wlBH54McOtL/ViZZosE3ywXZ9TGf3ud/8ukybypIScDeIc025zyaI5ig9TYRyiOzKT+lrnToi4YQXtKnZAdtoJ/fFV18rH7EMTIepPkkz71Ef7LQrH8pjdY3VPopfglPgKa/C9j5L+MtRvZL2z7qsBB4+yXTmyQRc3iu+3u8xcDu0sE3SR9onOaXtkubgb4InySPwll6+unjdho3MaPhd1mE+fN/90SeX0ajz3uNH/oqrNsj6tMnqvjwUbned1GMSQx1QX/cxWvEpM21MNDz65FPlLjpGfaCnyaMkXMGTL/6vBaTFm0o6J+pvr8sP2y1PvckEj/tLfApvTxDsP8lsj/ksvzPQMtmbRI6dQW6W9voSbXLFvSbzrNaAzbDAjpe2x9EyR3vXMUJak45bs1nVfd+/jyB3fOT7IrZb+mjLGnzrEOuaaOCLSHNCPn8B3mewOSYahH/4CPvYQJdN69ZnE9rF7C9xO7RxBNYEjQk7YQlTva+fug5ZeRXvqrMkHuBVfoN7luxA363Yr/eY9XUAezls5IhyEyOkTk2ttO8kGjp8A1R4Kl/9tCJPe36UNd+CYl3OuHuZKbvOwJjFyP2MmTMzpVn+RJ86MJUZY4tswqndRyeE432RUetEZoSp3XH2hft6/OHlV8o7r75cbme5iskj92zRzuW5zrMA4Ck+HiihPX8vtaBe0qZRW0alTADugia//u2z5SQbZn4fnk5lbbBvABIv9UG2qfcmqfXHlqb76oHFe9WjzEoEgezRwDnldjU8/a//9f9NTGNnd6SzQLj2ty6dqE2sbyZRB94lbnoRe/DED58pd9x8U2xes5Hxrx381EHprZ0Xf0vjsTSyLZLO4Fo+Z4NSEsqvsuTUUcaf/fznZT52WD8ufNv21xRhJwEGnZxVdpSRwG1dXeWN118rU9kY1o7RaJZOHMG+GZNdfZU7v6tN1a5VfVI2THZV2xubCb76E5ffaY+0tcZ765MYJNGwcWO5l0TAfUuWZOnESToG0kDIKRw6IlOP3/K73ZDEHe12Lbmjr1uBLSUffuzxcj1+5Evs2OHDhwLQJJq4S+O2HNPZCOqysu0AlZ156dhmOKivfl+LrXHphHRw6cQNi9nrh6Oxis83vMWrcSHHb/ntvdLM2YMmApzq/Q62YBU66/KD2bNmZ8agS3qsT99mrC3+xmT6a22o9llba3uMSeILwEf8jRncGG8A/YKdxHu/Y5nvvs/3FH33TGyBvrs/zzooCfO6ca4N+Qv+0i73ZFF+V5CE+RWDes5quoXlMBPxf85GVKdiJyGEOCrjyolyI729bpH34q+MWzzaztgT7n8T2jzPrIDvPfBAliK5qa1xo7crZ39TASdl8xA+6lkG3ZwJ888/+WmWVQZwEBGX6kM81yxX2oBNDA7cF7+Hn62cRxc6wmkbnU3ya2ZjjCLO+wGxxxgGbZ2N4KzeyOKfQD6yA52+eZR28tclBi5vWInMv87SiQeZLfHA0nuZjdevLp3oQU9pL0rKXPdR2ALvXFN2jCmkif7A2RLqlP7VpOZjxAaL6f9p308zc1nap89lK6CFdfjPOvS5zS+ledQjP7XFLdGwhX2h1rAE2llObiz+ODMmpk+ZUk6iC+c6iYb0/ahPKXHAXF8lurHVyB0oZJbjeXB3YNK41c1VDwPjM/BetX5tBhBOQu+9e3aWh5/5J5ZvPlZGM0MuD9POyrNg+Z388w9JNHxt/wWmxQArFFJWxboKAhFEdZEhfo+R7+0oiUbjHkZI5xHIuYnSqWPHuQ1lgDk+6KwGQSgMrUSxO0LhlXQsMVAmGs6QaNBhvvnGa2UYAf98RkSuI6M1jilAZoh1+O7WLPwEdOAT+EhCkiL8UBiDNOesS6PgFH1fx3eAIMLpzFs2bmCfg8Vl6rRpZRTr1Z0WlewVz17JxymdNcypWAvSTzpFflPA/SDICryGz48Zs1/SMepNe5awdMKOS9ujwQ6BdOWxCgzc4jSBURWidursPNmoCDaC7gi2xSlGn9Kh+wOZ0MVMjXSt4TCSGE5bsxjcX6Z4wG+Kl/NUnPpEwA/NSIcApdLROS34dabBL/v4o/LMT35SroP2GgIDRNsWA85vDZ73226NtXXYEfN6U3gdoTz3VYfSTAPlXgQvQ3uDjGcY4XVqqksKdGCSJIxT7riuMU3QYCBDsZMiziZb0oGhJgMhz/sKS9/GYQfgw/ffK4sInh9i5GIEo2y+FSGyDG5xLJ1AXeNh+63D73YaxdNp0rYr7YGH3uN6chMN6xjh3c0Ixv2PPsaIEVNTwdH1pXFW0MR7bYjtlrwZhaFecXY2ghtGGuTZcdERutzCurYA+7Nln2ZGQ38C9IdY92bwbxBnp0za26GQ7n4s0lne2LbagSZRIm9oj4GBU3wNnk0euXTiU3TK6YrTGVXI3iHjJySQcSRMhwXgylPwNii1ZNYH9YanOYO8AF/5dNPSwUMGsxTpUBIN69idedx4NuFMouFaAvQB6RjJU195Kd6O+NrZkgby2412lB1l08xw0hvQQ51y8zen17kU6ffP/o7pzCczW6IFK8LUQaWDAW2lj3Tyd9oDfOUxHQH0wkSD15v+Om3PdjlD6Peum54+vSxmJMoNsdz3RNgmacTfjmloATwTX8K3DcqfCRTrbPoVIaAtyuUJaP8ywfkH2MoHHno4iQY7YE7ja3CiV3E8YENd0SdwT5JHmmDfLJFXjvLYOmz/Xhzxr6GNAcTTTz1ZriGIU86q/NbkE7cGbrMDBjApwA7OtEmxVW5QPGhW5cik1IZNm8sf6TTOnjOXpUhLy+gRvLnBh8EzsgZPo1vYJ9vhRe1J5JR7UmC03/w4khOdOXUyHRbXlC8jAdadaICm0kWYwVf4whFXnhffbyudmnJd3fLNCrsJcldiK88gczr5eci9fBN+s7m2Q3ms1hUcW71WQlUGbMLOGmraaBBrItLNT1cg713o7RTg3k9ScxKBepZOuAEq8hDYALGudJGA981Eg5V8yfkL0N49GpSZtkfDJmyZa8gX33wL0+BvJfHGbAoS7foo7Yq4ygN9g/ZfmjtrTFop601PDT5NkgjbYG5L17byNjb+AMG/o8YmGlzqZKJBexN9ku7ADxnEX53sQfvIqfdwQ9M7dUE6ucHZWyQeRzBIMJOOiwm8Mfhu5UOYl+mLFXBOrgon/oPfkcVOfdZiwkf/fJz2v8L0fRN3i+i4zJ07h5kkk5LUUE+ku882PoqgYJQe62gfqxX3JBq8Dnw7Tm4S9js66seYwXY3vnsmNqFuXsf6Xny38I0x2kxD4WlPksDGxluZ7ZT+tkXcpafnpdlaYo7foa/TZ7KvBzHTKBLhV13JaL3XoacYiq9HMawc8Hc94/Gb51QP6zJx+R5LYl5BXx9hj4bbSDRoI6w3Ph5Z0Tao/8qLeCoz/rZNVikN87vDC3mujLmscT8+0CVyJhp+xCDBPBINJkqbre1GWyQtIpqT/ri8PbkEzibXtLFuXLdly9bsyzVlxszy9ONPsEHbqLzBS19k0lYcE++Br7DlgT5a+kanOzJubSZATIQr885o2ESc6swAB5fuIYH0vSX3lEH4FzsX7sklLOkYisuADt7SrudshsZH6zARoB4a06xiFHPT+nXQ6qvyGHb4OmaAfYUuOQhhXKev0A6kCNO6lFO+RDa5pq3RbssjddfXL+tnV69dUz7GRvrGmQkMWt18y80skZscuhuXdaCGJn6v1fDNSvydv5f+KK/ZowH4R1hH/jYDS6vwgeOIB5xxYCJ/BHbO+MRlyYFZQXXDFLbg42donz5Wmlv51fBU2vTu16dsZ63971jKs//z3dFXExmTiYf7wc8vsMsIYIV/Cb0e3zqYd9rRULB9oSf1mrh7jkS4/tp9p9xAe+gw3xRTkwFV7o0qKp3rTKM6E7TJvnDVH+VLfliMWfUjbzNo9SKx9t0s01p83aK87cMp9NqY6HXuvvRHjBue7ezl5+xRVHvmXnHOdtzNzIOnSYTPnj4jeNi+uuGvNFZGLuehOqmtFFd109/GrhJSnBIfw7cu9hJ7DltmouEJOtPOaKi+FZo3JAXud5HkT08bnzOeB77nm40w3s2bwPCBH5GIWUIS5l4GVAega5dvBslzyLMNiB6lTvgAnYWXGMH+CrRvPkS5P3jQGQ3b8d0rM1v28aeeTpJHONrgbpoEJ32T3rvSVRua+E67FtSlFbzFTrREw+ZtXZGbjSQGTTS4t9WMKVMzSK5M9tF287SxsoPOviTA5GDbD8y4Wnqdw9c6OJOlffju/syeOnL8RPmI2cOfrlxeTPGfoO/xOXx4/Kc/K48/+UQZMeEa6IH9AvcA4e93tfz9iYYIBM3vedQHEnjjFY10swnkBzidrRhuO/xLcJoLGb3QYV5AGGpyAQD8VwRCV7nTKVXwvG4lCAPCJzPNhCqs7zKV643XX02iwV18F9+wOMsbTsNIDa7rxcw0JbBAueKkEYoKTT5WAdR4W63CJxJ29PbZMXrj9TideQsXlKnTCISYXjSMTqkGjCeSjc5rj4IdaKYBKgywqKXVpCIl0cB1HbefjQSfv/zVL/P6pHvuYUYDnWkTDXb+4hiAoXJEKYK7RhD0NHYqTcOXc+JcFakaEacOLWdK0esYqOsZtb+VaXS+6s8gz+BGNJ3OWtcwVnyr8ayGNvhG0a2mKqfOWwMl/e2Uvkqn6MMP3i8//dnPs3RCg6BBcwRZQ20bElzQDungs9I7HS8CmODvMzoqCObIq8+75ns7Svfmm2/l/bJPkWWditNxhND7atYf8WIqlfTIpjuc97ondF6WZPzB2XPWmVkwKjRBotn/95nutgCn8zCJhuEmGmiTxcy8GJvUkJE1cKnZeA2dNJSAtik8NfigEjucjhxv69qW6VZ7GfVy+qVJniy3MTinfbaRBxJQhObUYSCiofOaAYT46hQMCu2wGDwro1u2bmPjus/KdmTHV1jZKXWPhnPwW8Nu5yz4iR1w+R9nJx90enZcYwyph0vd9UhT3wqxfMXKspx13xrPaTNnlVvpuLjPAQ8HN6euGWQqv5Z0WIBk59oOrjSpddYZAvLkqt5XlaHDh5aDdOpef5030CCXY8eOZ8PGRVk64cZJJ1ijJp98e4SIGbBW3kJrAJpo8Hf0QVpLP+RQWU6GG33YuGVL+QPBs68ic3aTb1sR97yCUqT4r4wKx49y52iRMDNKxDl1Vjr7yUgQdDIINYg00eCMhlm8zu4mRoucgTSCKc3SNp0LaGQgKv2lhZ0p63AZjPSVt8nI892g0uDQpQhmuI8jl6+y+emHb71dliy5t1wL/sIW9/PIubIoDOVPB1T3EGEGA+1JUOR5ZNJ61Qf12w2RtHvO3tjDKOMvGbUwQfgMM5AmQxeTXgnCkT31MiV0gofaBh2dxWvaL45V3ju2h7ptu53mTZu3lFdefqlMobO4lGDC/WBq8qwGCoINA6C8cLRp4q68aF9aG5RZgwg7OOqDa+3tsHzKSJ17NDxKMOGMBvXDzm46iuBqUKGMBsegbHv8pOL6l3qTaINGBlkm5lxHvmvXrrKGmXfS40GWIploED/5pe4HCr/TeeGoPIqzdNFOVtzrXgexQZwbSsdQ3XXd9GcEFCYapgL3AeyBiQY7TdmjgTpts7V0Jxo6mAfpSjjkkuqQ9yuQLY92eN10r85oWJeZKjeSaLgdfa1Lkc4EpvbADpjyaVE2rU/ZVsbVHX2esvgVUaX8Vd5NDm5mxt0bdNb3Yy9HsdP5TcyYMKmpPDvKqEzGf0h3YEsr5UketCLsBOo0xqVgVyGb8kpb8xkBokuFfE2e+6lMnTq1jMO/6gf08/oP68nz0N3AsyXZrUf6VBtHHfzwI/7a+RPYgLeA/Rkdal8P6b4q16Cv2nn9UOIBYNZEwyU5l69pi8cO7W2Lyc2vEftsxgndduzZXV7A1hyBB7ffcUc2yZxAB8ykp5tctySJcmSJXAJDmumXlB1Hp+WB7VIPknijTo/rN21mecBL5RqWZMhT3xLjoIk61WxYEM2fKuOp6E/8CU+4TZ3Wz32AjX+DUcb7SFTfRmzgUhzx8T7pE9+N/dA2qoegl2L9VmnnyvvUJzvxwnVGlbOMfKWro98HeKvWk/juuSw1a7HDn0Dv0mnp3KnMKmWBdt5krwNLe1hSuZE389ihnjxtennskYezplzf5836Wfkn/sYH4qhNVKb8btyiDdWfSnc7veqweuAeDfpuZzs6Tf02YrKld91VBrN04gzw69KJSoq6MWhDGyQ79LHy4M2X2g4HUEjcoXc76Ci66eyGNWtjN+0Y+Yahr8HhMLMptO/GLpCg2h9p3Oz+FdV/ZFAGvNV/i7bAZVou+XMq9nvE2qeZzXMNtFEup02ZEl/qrANLnurgKp3CzG7kqbeezBXUOrR3M0hnNLz17jtlBfrkq5fnYgdMOiahbFyI/cieMOCsbulP7XhZHDSzUdoVqeNGe/U3eq49g/5bmQLvUtn9e/dkjwbh619dOunGe+qRqHWjB27te9VYIXcaZqWdkjiFun0rhPo0jOTCtWwUPW7cWGLtYchFfW24NlL5VSaUF2VEvPQB2kb1IvYSfIWpDZM33mc/xKVIr734R5ZY381sR16DiL665EZdNgZIaQg35NpRtL9xLXqofUKmD9APcWP0/SwhfJgZrO5XJg7aW2U69lFFof3NfklfQYYiXNMO+4wDMrbR9qpP+gWXIr3I7OFR2N5HmMXT860WPAiQQKnAOngKwwrEL7bFtlgn5xIj8MVZdfuxAS4f9G1d6tPd7FdmjOdAqDyVZ9VvaGOqrxCUpfFVGmqnXWYT/YAv6rczHbdt481FDBK4LPexzsa28s/rgR+5gXfYMfH0n3VKM21W7L84c635ces5A372z1auJum4eXMZzb5/jxAbzJg6lcQXiWRsiwl3l0z4al1l3k3NlQcHuI0v6h49LIfG3kt/ddXXWPoa0RMk7t5jkPaDT9gPjWePnz7JazR3lKf/y/9QniQBOXTMBCjQoTs4f5cLfpTW/z3Fp9tHf9rAmQa7CsG58GXe2PAur7fcTidgABm+pSYaWKvaC+E5x/IAnTlyU5/16A+llRIxRMAizLmEEdDx4ex0HL5B4N2PPyxvAt/spBtKmWgYj1C4C7FrxxQGjZ5OQqGK4oB0RK5TTxMym2LmXWtsltiOkTMaNqIoi264HqfPZo0ERXbY++KYDIYUOKxJFQXa0sjhCRXOtijQKovf0zEi+LOztwklcWdp7ip3QRdHdAy6TMK4dEL00mkHWDNsCrRK7iimdElHg+8aEetoHXsDATcaeQUDsoDpRDcTTGj8DIQMHHzWjwoQYwVciwqZ4Bo+xIBIso6idysriBkovs5o0UdsXufSiRtYb2/nwOL9JjE01Bbhq/QWYeh0HQ21Lc1QWpcBrIbd2Sg6ZTeU0hG1RINBgXCakW1TaX22tUO6Z5o6dRnQyQ/vl+7SzBFdRwLdpO1NRpDnM6r+KMbVpRNuaiT8tFu5E2fo42//ZXST792Fa1xMO00w2UFy5HgLBspg6AAB11JGMG8hQL+aupVJaZsOI0fVRBl1RNjXS+qYDCR02OqBtHLjPddm+/ot27IVmXEn+C70aeAgZzQ8XNeU47ydCSKuPife/hNvjzGsPK8hFLby4XkNtwZR4yxPXe+2gg71OWZ+zJg1J3I5YQKJBvTCNbBOX/UVfnZ41PHoJg25WiEGT5215aqr6xp4g4pe5CQGDmGGENND3377nbJlLWveRo4jWGEzyDlsBjl0cDl/0WmW6kg1BV8T8SSpKL7Q4Es67b4O1/ZdSdDViw9/wnd38fWzcctmgpXn0Z1TvF99YZ1+yYZSjooYFIQ24Cbd/UijfDo2BnIk0PI+aWTQpdS6WZg8+JAlK8+SGFxIoHKzdoYRAG2BRbn1Y0Cd6dLoWOtsXAksO0jWJTfcPMgqst+BNfRlF2IczWvQ5tMPPyr33rW0XDvHHb2Hs3FSf3QEp8ht1fZhA6nvK+o6h9wYBF3Zx30eakfXeuxcAj4BjjJsByWJhmd/S+b8VEZFruF1grbN4j12dJUNZ2c1uc81AYG/tlg7lkCMumLbuNckg3K9YfOm8gaj0zOx7SYaRvAaMe8Xl2artE3RI2ijPKr7wkm9nJP50lAnrX03meCabF8R6dIJpwU/zLIPlwfYJhMN2hCTLqEN8iAPpbNyGd5KaD6e1tAmsKUu5d9Ox8HDBzOjYS0JAWXkwYceZAR2dnhnR0UZ1B5fgitY4UMR64A22jI7QxaDCs+5p4EBqKOM7qniMir3aLiP9fZuBpmOEDh8M3lhXaERf8W/1sVRPoC3I4xuhCpN99LJdTPIrQTR0m4R+824EdmwwUOgTR3BFJ51xC9wV2wDR+lhYKVd1HZqdxxxMdlgR92kwMatvEkE/7ePTvUYkiO3QPf58+bGVuifxI+WU7O0Bh4fcQfbXPNXdEi4nJNGdpAM2KXNcqaOvv/uO0mMzCKp6WbL7tEgHOXbUSJH7JSJyErnWWWmJWXEwfYFPnQJjbjPafDv0xl1j6hp0+vrnZ0V51Rv+ayPslBVlQ/gxE9xFHZsD8hLdstXyE6Wa3G0fqcbv8BbJ04cOlzuuONOXh89g2Uf7N+E3GoDxSn8EzdkPbICHOlv+/2d5SoclU9p40deKBtriDn+QMfLZOldwHfphHGMSUNttfCTsMjj4InMhfY8X/WUX8BtRR3zt7j5WlT3m3mXgYLvkfi6efHiJBrUVfETX5Okyox16j+TdIlPh98duHh17qnJTm2k1k0f9DkbOb9FEv/wwUPlh+xR5IyG+HroLm0leuIhcFV3hKfvTOKRc8pMEjC0iZ95Rh/lcjo3VN6CXL737ntl/NTJmc5scsqkoUV/pv0y2FduhK891l742ySLbWqJhqoX8BvaO6PBjouJhq1bt5bb7rqTgbG7yhBiwfP4FGXyK32UvCJ5fuErZ3C6aTGzXKQdtkLZTywSPsjaOuNOPevq2k6y9OOylenYVzI75RFmT7nxIQLJq9RNtDsIZSKHRvu8cRMZ9atpw5U40XTmoSEVVXtDvVdezexe8HN25Qp893vo00mS9lOmTi9tg3E76sYZiG5w1FZabLsyH73knDKpT4itkU3gI97aA/doeJfkkbMdfd31wgXXZn+JMbzuTztpx9V9ONyrwThbeWvLHh0dV5G0KdLGzq11GmOb0LS+Lezhpj7tR3YWEq+6CaqvqrdTajykjU8Mz/OSpyXIpZF1J64yrkT3lSeTxsbySYTB/zXr15cXmDHhK6md9Zxlj8TayqM6Ic6gEd1yeZxFXBs/tY/GtMq5M5rip3hO+iiX72JrXn/5pezldj34t0SDz3mvOEU/hQhcS85xXuIos9bVznlv9JijiYaXXmVGA8kY3+Li651bIl5I6qt1WDxvmyq+1e4IMz4WHnvN3/yJnTHm3dK1rbyArRmNHj1OIsPXd1cbiU5CQ32I+idtLdK2O1lK++NTuEd8lSdpYoxvPLkXfmb5IDHrbeiSG6O7f0UbBPF+7ZkAtKyWAABAAElEQVT3a9Mc+LHt4qnt106Le9oLH+2s2+ZzF5g5hS1wQHIVSzP8/iCDbjcsoh/C/UnuyFNpzX9lRkrHoHjwO3VQUey5MqgOyfMBJF1d5rCBwRMHazfRVxg/bkJmezij4TTtukDcY1ypbmoDlUPxlLfyQl5qZ6S19JJHCrobQbZEw/udRMM52njiLG/r2b+XGQ0/JWHyBBtz/v+JBllUi9zSMvH5kqlgEYoYYb1xPe9GVW+yGeSubV1lKBmfJWxWpeP5koyRm2qo0mFQWANAhZmPjErgowLCvAR8MLWty9fhGih+uOwTsufvZKfpOQTn19FxdDPIM1xz+rWCEMMBDIUsRgrjrzAqBAkuqEscdHQGztZv4uIwxvWPZP03bdrAtFTer86o+iCmgDuN3c7i1cAxGLoi1rkakAgUcFtApQNT0OoO5gganQKV1vNbocnvWZdGxdkJd8b06VlDptE7Tt2GcMmGiyv/atbYDDz4U3+MKQphQK6DNUCxIy1sGeB7xF/8wwsZ0bmRjtFYDIg7cqtgMX7cY9Hh+NFgSBdp4fewEFwSTNAGp1fGIEAnX8fnRpzLyMh9nylRbl6n8/ZZnblJAUdj5ZN1SZcGX+cvT4UbPKCj16Spvw0SfI/46yQCFDFHRaZA+5al9F4diwZQA61DCXxlR9pjPDQYdsIMWHQOrc3uzmxQsoVA4lVme8wjQfIIm3mZmdchSmMdl0Yv1AEBj06xc3aMMqQRS6AKbcQvxoq6HeXYx7TRzVu3ZBr/kWNHyhISDW4OJNxDBF4Gou5J0Bsj5EwYd8s+AS2HEgA7DdFOpbLSAv8agDqdq3cC02045OxcvZmlSAMGlodJNCyYy2aQ8Cej6tQjLdJZFDU2oVFebL8friR5IX/87kiRozkGmY4er1m9pqxevjK6OXv23LLk7ntYzz8xowpnT5MBRz/OYxh78UwvZNnASLJfSQB2EbxPn2JTK/4NGsRrWuHnuS/Y96IXo+oDrmLpxAESUx+VHZu6yuhh48pM1tjOmO5r54bRWcbhf+VbJAjoIGrv3v1jqAGdQOwr4EBEvT+4Iu+9+0ErgpZzrLHVeDMrwj1PnkWfTmO0F+BwnI49cdLEyJW2QOfl7CY7Mn78rn2Q33bMQjPIpDrLdJMpTquV9udxQr7C6nckBh2Fuokpr+55YnLIAEpbonwatBh4ZhRQ6ejwohdOODZDYkG/r/3w/QKq+kXvK8p+NgN66733y5rlq8p9d/JmnlkkYAYM5h3uJph4BlDSFddV+iDfNB5dP5UpeVcx9dTOZzL/oK6N1M4pEz7nK0n34oilzVGecbMhbZk2Sj2xwy491NXoFHKpg47cUNfXyFCSItDJkn1v4K0jmdobN3zaQGLtXUbT1Kd7kRnX4Tu6r86YJNPOKp/SqdkQdV1bpfw1W6wOKpsGI9pZ7Yy7ta9gtG7t2jXlPkYwfde3CViD/IssP/gafbQj4Ud6G5Ta9gR01JfRP0hm4Gxb7GJ+ibz6euQjjGJ+zkiRm8Bd+OIimzU+UObMmJlRaUfTtfHKSLM54hkdAvdGb88p6wYrGf2C/wMHDyQRfpa3cawtqwiy9uzeXabi95YuWZL1wcpcfE+CKXAGX3mm7GjblFVtTW0HCFuvosNpeW0ixtlTnxD4d23aEj8xl6SdG8MOxb4YKCZ4hR6RG+yg9NA2yFfls/FXGYgvQSbt8NmxcDNOl/a9SoC7F1s8buKEbDbpKCYoJLmkT5bmyk4COeCon9pOg05x1hZLF3XqgvD5ZsfIelZha3w1tXI3g2TAddcuYrbHBJaBnMnbHHy1nR04dcoR214kNl0zrr3VOynnkCT23zp6I2caI6eqaoc//nQZvntjXpU3c9qMMoWlE8PYo0H7nane0EEaGECLj3LnjCPp3xueo/1JCtKq0P0iVbt8xfp3wk9HAc8cP1nuuotBgmumZGmicmnHy2Lc0XOAQDrbSVGuTSDrh5LwFXdtGHRXtlzbvgq5efa3v+V1sfOSuDN51ItG2vFKMgP50c/Kz+avlVF/K1eRSWnVKUnOc934xrXN7sXzCbNB77n33mygJlx9pTCUE22Z+ilvbJN6JW+VSfVJmydd3C3dpNEwEvXaSd9t7yDBmyQxjh45Wn76kx/XxB001wcq3z4oT8XVWEgZ0d+pO9JFPRWW7VKk/CO/teGOkrp561vYmlGTxncnGk5i15Q5bYmyL21th7TSdzs4ZSfGjpWJau22fPacltWYxPXkJvKdKr0dP3vrHbenYzSMuOYLExd0bi58SfKxLzHcgL7lLPbnJLMHHMxy1oP8FgcT8cprNI9z+ljhb93axb5QH5TdW7ZhHweX79//QN464Z3HTtF5IXHh82gM/KttNvK7gqk0V6D4NeEMQfTp0MZOlx83l3PWo7OntAcnsGmTp0yN757GrIBz+G13yY/OgIf4WkwWZo8KaG2iOske6lYGQTh81q456n+EOOVDYC9nRoOxwiLi7NtIPI4l0WASw1k86r0yElsAD8Nr6qlJPdoBzvJZedGvug+afFZ3fZXgS6+8zAj4PjZ1n5/9WnzrhH5AOyZPkxDo2Mn40o5tsH7jVuNNfYs6baLMerRjxsar168vv2E2onJ6E4Nu06dP57XdowPzJLOf5Z2JEX2beCv7fvKmAGjspuT6M+Nxba/6oa9SP9RhO40uA7uNWSSLGIgwoeG9iePAvc70qvoZvwRdunWU9ts+9SF+Rh2xwAYHqA4hl89jazaTAPsBg3q++lq+RO/VEZ6zvaqKdQpX3DJoylllzz6CepT+DffZBvFwpsu2LvshLJ2g3/QkS33tP+mftJPyVduhvkhbeWV86hJkRDOzVOSldaq/yqE6KB7OMnKQoCUaboU2vhXJvWwCXxpTj/Ik7bUP0lXfp766d4k4ODPGN5Mol+eRH9+8dP7iF+UQdmw3y/rWbdyQpUdLljITdMF8eIk9x5dHd0QS+iiX2nxp4Klq97F30Fd/KmzfOqXvGDaCQRJwcJamccdG6D5h/MTy9GOPl+no01GSy85iGIBe99VOUZ96m1mK4Ce9TDJ4XrrYn5O/tsWlGcq9S5FcOvHJCvZUweadOH82gx6P/NM/kYB8lBk3k6oAiOx3vBBLSvW/o/i0H/kXXuqENIIqUeWpIwqvsvvzBowgklgWEQhNY2TBPRp8NVSECsclKv6T6Tp+wMXw1WwQiQCY1katNJhmqTUka8n8L2e0yz0Vrpk6lWnNs7IOSwVyBNZgqAVtPlcTFpwDnkWnHAVHYRUWFZSLWVPu9EvfHby9a1s6LnnjBPWopCpCAh3a7/SZ0KEjxGk8BNAhK9C2waAY6qCEdeqVRncPSvI2U6VVBJd9OM17+PARIZwOVbwNTBRacddgm2FUeH1e3B0VMSvsfWYJDebt/Nk+X/f3DrMCJk6ZknceO1vCZIBFIyqeinHWaqEIGmsNeMNfpZQvZpQNqjUGGgXpZad0JbxdRyLpRoyHSZLLEw3ns4GhS1B0kApIC4iEr3EUX9vQjKv1G1hr0B258NVqBmdLly5NllVDZt1pPzDkpUos/tJfmRC2CaP6ATYwDYpsqEZM2BpKN4D7kAy9+xD46s9hdPST4acOjwlEoY0Ukg9J5mBArT9BUjNaEpPr8toA6RDJhZ27d2XEyw3JpM28uXMC8wgGqj/GyU6YnRfrMDt6mhkcBh6OFGiQxNG26vjVhzgVjvLaTtHWLVuT3TaAuYOpnU7nkg5O1Y7D4r7MKKHN1RlVeddJ1CAIgw7N1LerCeYN9pSZ0wQeW1masXXDphjSKVOmMUJKgopp8AZ5Zu6dGvsFNPgafPKqHpMNBv4ESRdJBpxGLvRGzrbQ4BrIfNkLGe3D5j3QZh12YP+uvWX4oFFl4vjJ2f15yLDBBG/O3nCUuSUamL4dYy1sgk/gfA3OBjOuVe59dU00SCvX7/ZFL103/QZTDM+cP1NmMiIyGacwmjXf2g6n6iUzD8zWKeojHHCUZm10POZMuZd2OFanE1/J6JVr7BwVef2llzNzyuUq7jKf4AT5ks7pECHTiGW1VdQLaRLMKtct0fA1/FH+7ThdxF6eR2wPESh/ilPbtnFzuf36W7GRU8rAPowsMzMECxNJVB9NNPQWLs+H38DqRftpZOyM+qqdtI3pcPCkiQY3DnyL4P/Y2VPlzqX3kNAcW2WW+207aKbTGJ1SJ/ltkPM1DcjeAJxTZrxPvtgW26xM2LnoIjBfwSipr4u9kcSam5eqZ+qNU2bFxRkYjvAlYKFez9nhVb4t6mzdB6QmKZ195WtV9yLzmzZtyiyem+++C1s2NzAyNRp7+DW8MUD0Y7sBBKn4GFyAsKeSZKBRHn1NoXbFjqV27BAzbRzJNOi547Y7WNc8Mb5D/6GNNwiNn5KufISnLW6+yTriT6BV7BNH22wiQ7ibwf0gM50mTJnCjLsbsqzEtivL8XscG6+0gfLP+sRfO91mh1mP41aOYrr+9wij9iYGP9+xM0G2e4ZMpY4E58KGnsq2uAm3Bj01mJV3wksyX15AL238l9RpcO4eGerTR3RGDzFFdSTB53ySSFOnTglBTeSLpzTXN5mYugKAUqclGjhV6U9bTJBov0xY6au1mfqn5QRb6uck1pLPZQbVGGxNd6KBNtop0udJ6160246VRbtsIKlOSCN5UhMNvCoQvh46ejjvb99Fp3ciHRaTpePoFKmzddQV/ZPOfGK7gVD9C/UENnLK0bZZxP9LdDWvuAR3kzzvsy77PDYza77Hjgvd1Y0sxxIGciOx1H3lQiT1RSbebE8SDfouiuuClRl5YgzispVXXnqpzMA/mageRIfLoMig2/hDH2WSTx7r4yI3HJWjJGWAafuCPX/Ey9/Cdmmio/ZrsTeL6Sy6rEQ7FlpwjzKpLqiPJpLsqMtflCZ6U/HF5kAHZ0c4Bds9SWybHW991CckZe3APcII6VTssPBMAEoTJDu+X5okCQDexl7Kh502E4HKn+2KflCvgznaVxMFe+mMOjNg6LhRGbV3jw9xUKf1OdLWwQrbLk218bZNvpvEMcEgDT3Km0gT34+RNLFjtKNrR5ZXXsssVl/bPUh+YcvcvNVY44q+dCT6M6Wf3y5R7Qv8gdgqddZYx/qdAYeEhea9e9fp6u4Fs5YR0gM7dqez4dvAZk6fHtk6SXLc9mfGBE8Kq8a7JPIRil4mGqCfcLmoY6eTpOzCe+I/O0jbtnXhX9dkkMB9FJzdO4FZd/ruL3irkzZdW5bOON/tdCqryp5wrN89YJRB67CDaiyuDddOrl27rmwg3vPybAY3FkEbB0eEn1mY4gYP1B/xjzxxb3jI0WLHW59t59QNOPWxxiHOuPuQWQFHsMXTZrIRJDbYRIAdVvVGPkWG5Be/TUA6q9hzJkysS546m9Xv2nY7fg4ymcRzecArDBoOJsZawKCYNsE3RmnjTyGzLdGQgQjlQv5RV2Z7QBtnckovkxbG2sqi8aS0sU2r16wty0giLbpxMTMpZ6QzrT4kZkcmtL2xv+oRH3GURuoQZ6K31pnz8lcqc5/4qGMuRdq+eXNen+lsxNgu8Iod5z71Wr4ZE2pPxU+89B0mOYzx9InKv8UEnMX7drPE941X2SsH27t0yZLM7tWm2jF2loq2xSSociNatts3KllfSwjajspbN1eFN8DP7GGWJm6D9u4jdC2+7zr6OcYG3hu7Dv/sU0hH2y8/jUGU/cyUIZ7K6y2RA32VAz60KrLqayGdndy1Y3tiEF/nOn3atNDV2UdaP3UHpeJTZdM2N9n07WX8iCxylX2yTkWnBzMb0eTnjl27E3ds37aNfSvGlnvZi2cCdv44iTz3RPKNKBmcMU4CN+Md7a8yKM1SZG6qt201ie1MnpM8v2bjhrKOQewL4HaGxMnurk3lf/xf/te8dWxc94wG8P+Ol39MokEiSMwOPWSYn6v4TfegrFnlO1T/kE7dO+wCfQyDOGfCpGyi6FTo6BT3m33T4PRnZFLjgLnL9BWNkp0bFUpjUKfL106lzD3B8oZjdK41ov0Q8AGsu9IYZHoy1zXONRNMthsYAwmiEiAjwF9Qp8qcj/ijLMLRwWkIdeYHd+8uvNioDMKZ9MdIxUFx3Q5bnHzHIPjdoKsfuGssdPYqhQZPZbcj121sOsblLIJ9hA6AVQ9G+Hxl55UqHfeKv+27QD0qoMZb4TX4F45FofYjvcU9B3A1qNC4nDx2vOw9drQQErA77pCM+mosdMgGVbmf54Sh8jlF0OlATnc/gSGRNyq9RicKBA1sW/CCNscZNXJXg6EYgYE4nDgw4Eg3nxFPaZ1OCc+1GSoGMMKw4yftL8Ajv/rOZPE2AeEUzCM4frsgY1FuX494XrjgWltaRU44nhPvwciOdZ4Ed9vgNQ2GnUXvtg06Vo3ZWTr3x8BxGPiNnDA+nR3XwsfYSxieNdAX9948r9NVzG1bpiXnmsZER0FbuO9KHL/yasfrBDLpIoLRjNYPZRq59NDwysM+dh6FD2/7QJc+nIvDAHf1QBjyKAX87exJIM+dZY39aUYXdBeOS4wmeNZhK286jRjRkLUqpSPGfprhcxTGQFw+Sith2vkyQHOa5ilwNwBWpgb1uroMZ222jvUiGWINpc8Ymn3hc7TXgNBEQy9GiHvhPGgobQNdOvHCUM6+voIA82r3JDhbjh3kfecQhhWy4D0cmaTTQQB/7iva3MskgkEnDuWCiSlGU+gk93d6qEE+sFwu5ewpp55+TfClbpkI0OE5wmcQ6mr0QSRABhFI6CDlozSVNjqwdISQOTsxdQkU+HOPPMqUvjSgdo6kTaZf8pwzT/bhZHTRvunCOjOlD9rp+MVPWbQY3A4a0C8bBp1GFuwwK++xS9xzBZ1dl3sgYOUsQ8LHIYpLS2BvmTyUZFT/odyDfBlggttX8MZ2QAZmw9jBQ474cFM5C93PUneCYGivLuhkTa54HETH7ivkft/nuwoTdMvQwWyCBd3FOboEvf0mfaxDB6+dHM7aX3l4lCD4NPSLzPLbe+WTduYr6jbZcIrRhRMEMsPQkxFMf1fmtK/acUNPhcIkQBILPKscKaHSKzICDp6TP+ER13zLQmhK3aeVS84NYfbGoBG+6o+2+4yyBs7qNoLT4Sk2FNwAh6wTiPK88YbttcMoTzPSzvNOAXUvn+MnSHoDf/z4semMGjj78Zy213pshs8afCs7wj8LjbUbnouPot3REWBr+08zcmEgrC1whfJQ9MkAVF0y8KudUeiAXZGmBoDCNlhUHk+e4lmuxbbCfPmhrvrKVhNEh/fuKyd8lvPD6UQPhTbCkabxe9gY6WQRP/mibHqPiaNT8E4e6PPCJtrURssyW4UOdbM1A4cMKv1cy9+hvT5PXZI28qwf8uxbmCxJGtFGfaB2X9jirrymE8n97suy7+CRyMEg7MPwkWPoWPg6Ozp0BPX6U+1veMxzboIJ4hn11q9TCUkmAjjqgPXlKpYg2TGQMV+gT0fwTyfPXyxDGX0eOHAwdNVWqac1yVNlU7xY/wvuA9AT7aK24gIzZSBMZEp+iv9FD9o82vgFQeX+PXvD1+HQxOAxgTh4GIhbmt+LbikfwNa2Kdfx6dyrdnhOHKRPZAhenMFe7Dl8JLZm2KiRsTN29rWJxhI+r8xLf9s7kM6wm23Ld3nqUejWGd9CXXbmlQU7UCcPHirHSCwNAd5g4Iu7+l/1sbMUCZylq+3vD32Mn/Srrl9Wf6RVlS1awXd1AwOSQSST6lJhPJ2XTO1HR0w09DOBwH3aJuXTTnJ/eG5nXiIfpfOg/3aAQn1NXEhbTczT6NgmfffxL+ms8sQIEmB2lJ1poMxJC9t8NbhXPVRk6ii+9ZsYuMCSXvVOn+69Fp91FPQieJ09DQ5wY0SfgcAfhcxgg/Gb6qMzXpzZou/TLygL2nQJVUdknfVgTIZtYuaEfiqzcDBAru0+d+RY4lprHTZoOHHTEDr39W0yPiUobbs6Kv7+6wOPwDa+6SyxcJIYyiLtSrKBNpgoPMWgxfFjvG0G2FJzcMf/9VJGaJ9yWP8hZ9KIehycccaK+B7Hf57AFmaUF9jqqjz2E9+LDdaW6eEGgNNQkvjqTQaioivVFni/MZOdML9HVqk/sRo01C6a3DNOt8HOdPKtU0dJnIJpGYw89O4kIaRBYrJO51OZV7+G4X/VO6/ZaTsnXbiGuEY2fU4505Zpm13e52bU2snB7lVG3RnEkSLqBHDUJb6GNsbx2mF5eRb7ol/UY6kPzaNZh3KkXh3HTh4BryG0dyA22JFr5Vcc+B/5sv3SQ7vQ4o6z2O7QA7ws3bGx8tl51mSItkzamCx1f7qz1KUcuNxYPBJz85tK4xu0BT7vfdoC6230V/aarTJmOENC8DCBh+81G02srT7Jb2mSBnPeupPQVGbA39c6OvvoDLgpO+qY+qyvkrfxV1IKudAHHoK/w8F7OMkjeWSb4wOhb2xaYuxKL+2Mdtr4+Cv9JPdrW/Qjyrl75XhO/+XgxUlmlcXWjB6Zfo5ykDiOo7GBtsM30ZhYVO77QjP7NafwMaew40lOUac+Q5oa18k0Zf0kMnkKH8M88LwpzSXOSdxhB7Wr0t4EvfTtg/9JHM299g9N/hhLRRaRHu6GdiZhqp0+fOBQOXye13xyv+lmFiOWf/3X/6P85Gf/zODeOH41WUOAvsPl7080SAgVz4/foUc+fDfc0OCtX782GxK6aeALz/2WEUvPljKJj8wzeNzLx3F2CX6CzzeLxqE+dfkVxv7L1PGjy+CRo8No12av3bytHO/c5ligK6jFRVZZj3X8Z2UkF733YOemO2fPwFEOLEcP7i+nyNRfRQbl1Lmvy/bO9XbwGWnwbcVr4lJDsIITq2Umh2k33hAHtmsda4FqfJKLU/krrjvz69v/CHcin13fctn6Fs+cyk6yExLQbVi5Ju3/llv/01PSmC5GlMC3Dgt3Px8d2ULgDyKxc+jA3rJqx97LaCs/TXCoQK29fL2sjOWXTT7S4+x4vn/OR/mYs/j6GM3Pt24qKw7weqke9/0tX+WtsmR7xo8YWMZMmoaTPMebUTaV3d8AOJrfzJAse2mAbfhmUa7s0PZgWRnF75EQZuB4dktmepiGee/OzWXjoboR238mI9+E72/lxWBKxyDejGkxLZoO9MjxdNIHIae9yl5GGNZzvmcRj/5UdgqBPNzzQue7PB0AI5kJeplMSPvp8+eS7ONVlwYdBLpHWWOnTlr3gBEjedXVIGYPDGQpxECmmp0uK9av4sql8v25c5Kx3vnZ6nIIyg29kr0nJpP8G4xxpxHpUH1FZ6oXr7hDj87A19PHd5OwIukCUYdNmkowz1TQ818y5W7Tf7AH8tDkVqUogSbflS/5MI3PNQvnQSzeinLqRDm6b3vZysVv00uTBTW3z5ceZQbfvV8bwgvHuovyvgBbM5SZACcZLf18+57IozxVfsXpLynDuEndsH5hyl87/71oyORZ1xFwDyrnj7K/zK4j5cuDjFjy74qrGfkh+XBlP2YH4dTOHjle9p/ZXXbwXCvq5fiRQ0mAkAzFsWJUypnDR8tFOizWMfz/o+69462qrgXcQa+KICrSpAoKYgHFggVpiooNC3axa+waE02MRpNYo4klsaBYEBuCiiBILxYQBaV3KSJNQOn1fd/Y7FyTm7yX97v5J0s3+5x91p5rzjFHb3OferEnkcXtCMT5ixfEt6uXkEa6ZyqDCuPNlhQtWxwLZMj/4nLO0sI/8lBxY/96taNmnX0ykjhjwpd/N7ficHvxg3z8n+Fk8R73dzcQfvZPiF2+07Rpw6iCw245dYxfzP0fapXHuAfe/s/olI9TBrg//yhDpJNqu+sk5chioiwazktnzow5/2ogB/snlwqaLy+VBV/F64C9KImqUbug9EFTi5FP/4ynixeril/6N959Xi1eu9WtSYMpdgCFZyNG2spF3yXsfwK+/8/RduUO6cfXT79Xl99rNqAhG0q/4y9bMDemrCqszmd/x+sfcYGP/tclbgrSn47tTY2rlYk9azXkJ+aO7F48d2GO6d9+ev0rWvWearz8u/NYyqt4iWs1GtOfpQL19Sij65Z9G8tXo1TzuXxOmpBufyp7+PXfupRbe9eoFlVr1Epl/LtvZsXSNYV+QNLyv3s1hMGXrViK8oNtf7du59aqQd3Ys2ZtSgVIsf1qSvI7ZcA/4vC/8yzH++k+NWY76+1/SGbRfQd/H4v8kydJD/N2Dihcdwews/4Zk9x5z56877kH30QBnLzifzjgQcCmRt366GSlYvqn42Luzvv/3TfxRbp2rUU+r64oHxAGe1G7X7NevTQMxoz9NPknH//d1bhmGYIVW+JbhddPrn33svSkciz9ZtnfwdxblOmtOIFDx71leUa6l86dFyXXrIsqdJ3XwbMDr9e2MjgNStGPST5anODOZxzToCZG38ZYAv+uXJXM2D3qxvofVsWa79ZE7X0bkWG3NxlpbDxOjbU4rVdNn4eMW5t46cflq+MsrIpjCT49cfLc/4Wf4p6PLOq4Ox+bb/vX2C2q710ng2EGHaZOmPR3NFG8V3oXtj/lU8W/CeN/hcOHweN3230PcJ5sHJwai+fM/1+6p/LYvVKHk5/89JJnKCuk03+cv3J339Ycs4rOtJIo+KoFc2L1P+gn3JK60D/yET//Z5f4uWznH/YFcfY58ODE1TXI7y/nLfybvNiHeypVh/8jSJYic/8VT2hahd4sa8gK2Dmm/LHIy49B79gDHPGY0lkTv47FO+8pviHSUq/4Z7pI8R71geLYflbke3vxc4sDDyBoQt84TuWYSUZMUYY6bj1eZQDsj5CgcP//upQdyswixdbj50OPb5vy76sRI2Mmv3t5j/gmPvx0Xvz6Ty/nK+2qj7lHfrfmXgR7CGzoFFsHHXz77Yq/w0nvEU/+FczdG/fwH5/vs2ryqlJn73R+q2v/8P0yMpVX/q+xlK3yjeW8/t8u8dZxi/c12b0SgbbawAF6R3avmb8wdcLiHjqmcu0fcZmP/nb9dE/r8Kl8TXgKW2mtDA4zXLAE0NbGEpj0gw//Ibqdf17U2Vvs8kne+d99/Z8dDYJBD19G3ISFMMEL6hs8M5nZjNnTY8yIEdlgbt70mVGDFPXa9Aoozfe24/3aikfMiISZAkabZtPco9/wkbmBLerX5ixgTpHgSEmjN5tI/8JPiAcZzxQvvUj5Asn0oKmU64XSa22UQC+bqXN6nvXu6fmcgiI55PMvoz4U2oRGPPUb1M9Udr2kevD8jlFSO+XK9OzgrcfL5+tN9Xm+G/XI1HaEj3VcRs1XkUFg+vPXc76Jg+tSg75v06zH1bNbOK6yEMHSs+gYRoqtK/Nno7F6JYWpXljn7M/+TZgK1G+/Wxqjx4wiQlk5G2ftDWPbjfmZgrSWMhQR07n6PTMDdiGLwY7+pnx5RrzpSnrX9MAyhYSZa9T4s9/FPNJMv6BGvEWzplGP1DL7BuSRk3jnnIfea+8XVo5VDu+eEQm9ovkZ8xCGPjujbqzPtdrQzQ7p00kVMp249WGtYUB6w2VLMBG86EYLCrWMhTpShXpVDAs946ZpGV3LPWD83E89oNxjdH4lWS3ffrskJn01jeZZ9Tlqsxl9EKwDU5xiqIEremGNPrjujPwzx+Lznb/eR1Pt9H6bsm1WjbBcjUCdO49TJCbTgPGHLdH+8ENJ8W1EhIcu1ngrPQ3BuYsXzkvvrGOIE8La8h6j2cLDNEDh4T4LRynFutB5c+fG7Omz0gHSps0RUa8WfQVYn5f3ZFYG4xoNTq87+JlZK6xLuJuylVkQMCxr2Uw1c1+s25sz/5sYM3RUMs/m9WpRCnEwaWB7MCq4RWTHEiMjfM67HGsyrc21OO9CJLWw5+lpZz3pfS5XPp1va9mXiV9NigHDP8q5duEYz9b0LhA/l2Pkrt9AvV454F6RsSsZHSMjAefEDhSIciUqRantZFGwHzvo4bC9FLhZFrxh3qQ/iMlEO1dS+zguJkGzB++xO/RESiWpoHqFM0OBOaf3HZzwd1O+Ld8R/7OZGTgqvPS8G8UzwiB9i8eucRE4YwPDzZu3RfOmjbOng0c7uW+eeqG3XLyQt2R/EveX723T880eZlkF+2qEciM4JB80ppLZUMBU2I/+eHw022fvPJ61Bo6aqhUrRxnmVBIvuXS9jT1dDzPcDJzKExUtT/+JEhuAxQZ4TZK+/IX5Mgej0eloIMNgOQrN15SNzV40Ow5u2YpIfE0aEJK1BB/VcQDBFnoVMA/5g3C1yZHZMasRanIacUVaNXq7g/UYOd7Mnq6DXpcu+S4mk+ZbvvqusV/TpjSp5QQM5icf3cy+mv0iLMRP4W3dubA3emJ/GeEvHOUbvuTD8jp50XIUyfnyGnC+zWGtomnDBuCeKajiIetlXGkq+SEIIj1lvxfWJg+QF/jcpAf23miT8sP5LF++gtKi2TFjyrRUYI5rd1zUBTZMJseUP0mDPkM+K10mLQIHx7HEzWiIiOn8HdN7xQGj86aZTps+NaYsXh5NatfIRnrSk2MYgTG7wzn6HPmNMDEyn6mi7EWm0u6cc5YrsGb5jPCzf8zU6TNiyLgJqagdeVBzePH+0FOBhxvdMfoGhHJe8gBxwvl5ZTRafgTeZwSONSU8k9egQOE4nDR5ckz8ZmE6cg899uhoyKkG0oNZWu6TzhYvxyzyHtcunOQHua/8zXG9v8j7xIHFOOI9DWDB+k3RvEZ1mrodlCVveS80yNRT9qSsBVaZEgsvAcwJu5QhwF6Y++LmhKMRsvXsy2xwZiiwqYpG2Hxfmr3uw3HTyAgzhYS3+2N01LlkVI11JQ6CSzZtLgG/gzsXMn6Yf2YdEgm3nMOa2ckzZsSkWfPikOaNwJnasRf0WqlcBeiowEcKsq2AC8JMfu8JQWaASZcps1mk+GO2ioa2slb+amPpSZS57IB3NG4EH6tdm+Mw90g4K/+EpXNNIDF/8d26aGFiCV5mRSITxF3nkdFS9lcZZ7nkAvByPLBR/rmndtT3vHpLlcQ5v+93has11HZIV/8wJd4MPNfj7+Ku+2yJhsq78mnBwgWU/0wntXhDmO5ftxaObuaWacOMb/RT/UNZ4jhmkTl/57iGrBV1C7N45IvO3f0pwG4r43+P7KakYOJkMqFq0PvmYE7YqJa8iNuSzyir1PcK/ASHNFlNNltlVXlqzLqNlC+YEQE8pF9xVbrzubNmzo5JY8fHEsZof3irOOQAekkxd/m7+2TGROIda/B0hSrATb6QZQT8XRlPzDjKI5PEM3VM0qXS2WAfEEuDR0+c6lTjwhPaRzNklHu+Cr2hNPqNuoQ8cTNZOmbU2HekjD0X4J8F2USEn7LDjTswN2DAZihUBnbukw0S332nf8rufXevwnGVh1MKoWlW4GXJU6Aj0ca9kk4TL3kXZpZCqncKN1+Jv7y79+L+tFmz4r1RY516HLlfkzjowEIphP0IhIl6k/hoVNb5qBsUcdyxkv4ZxywP55TykHlI7+pNEym1GDt5ahqchx5JiQ6wEa/UdYs0Ijidq84dj3M2Km6mk3wl0/25Qb6WOMxcXKc8z+yEjymfmbdsZRzcsC49HfbNkyTMMFAfSp2Pe6U99T3lt/Sg/PIz9RvLv9xrcSdLA8BdM9xsYjhv4cIYPerj2A11pEnzFtnTwdJj6cOsSmnFbAJLEZyrzR8tWfOIdHVtaa0Ic+HrvmSGAbBdRUaqJxrMnTknWh56CCWke2ephftXKGdBt2Z8+bsZ3K5JelIOyiucu7JR+lLuWRqiTuTcpdfFOA89nUwbqOm+lpJXS91aenVOwoa3zERZi25aBTtBfuS1FD094QKtChtL1cR516jc/QHYLKHM5aspMzhpp3Hs16gRmcSF3mzuu+Mre4pRfmnReSYegTMFnJVybaxZPvUCxzfDwWaslpPPJrD43fK10bHNkdGQkjplnrzFNaZ+yDP8vrhQeBV4g3SWfd+AGX9OPMwMPjN2eLYlXTOmTImvF3CKEkGbI448Kmphh3gxZfgG82eu8nTnZHli8gJgrQywX5OSVnqQxtXRC6U1ZEcgu8WZryZPiPmrtkTLZo2jFaePVAOnzYAw8658KXCA54j74jnMnzLkgu64EpzvP3RY3Pfg7+Psc89GX2yQc2C3cn7/zf/8RxwN4EAqRaon/FjgGrzpRQecMW3q5Bg1fHjW6JRE0B/GkXaN69cvpKiBtKozbtiuGMwK/I8//zxuuv93jhSnHNeGc5jPoqNzYwgMBRhh5tmlmRbLw1TsdFJ4SXD/owwVuuNL7CrTCjmJdAVCbfDIkfHUS6/G/ruWiJPPuYQjgI7M7vEKHBmSBkSm9ICYMleJszQpzMnoeJ5MSiQUwbOxGMJNAQH1Upu/OPr1HxAffDQ8urRpHR06dMwad+vyV8IYbawischwvHxXoRNuyZiYo+Nq1Fn/JzKbMqSnVWY7DYPrxVdfi7IYZse3PT4OanZAGjAKMNOrNJhlQAoAL1NUC8a0hF8QDDKfZDY8t1hSIGNZTurXp198Gc/16h3dz+4ah9LspwZpvvYOcG6OqeIs05OhSdRyK1NJZTD+LGxkSDIV16Vh7e+LFi1COZ+Wze5Mi+zapUseeeWeZw0eOCAjkEkU5l+AvX/TMHSM4ktGoBPA/gAywTUwym9wTtkI6c23e1NjfXycSuf4FMhuC3BQoJuOZWM6Ie8eu56CMGD+MlPn/xMlVeVfoboI5vQJJ2t8+EGfmLZkXdxw5aV0oj4qdkfRSe6EwqAhB3dlZEZn7Y6rY8MxVAIVrBppNuJ0H7OPBmMLs5kYRUM/+ijeAGdkeTf94vZoicAX70z1dDwRpJBOplOoAAvpJg0MxnCcTCcDB+2RIi6q1OlYGz/hi3juT3+MBRsj2qFondP1DJoJNcp6atPaFAq+ig4MFVPxUVqUEWogijMqDuszzQycAp8r0qjwexxrQ4cPjedeeoFvRFxNoyKPCq2xx16cY49ARDiY0hqlUMJxFZem6aH4U4Lc020bmOtWjTiEhXVWZVAIULZsJrQNeitVxp4L38aADwfF4BFD46A9a8fZF54bhx96KAK9Iso9J3iwToW9jFsYK5ztfyEOmSopzssTFBa+cnukXZ7pXn81dVr8tUePmI7gOf/EDlnvu1+TJingFWwFmICLChhgrLNCgVMKetfIlX40aDbymaULpg/bmZ5FopRzhNy48fHnp3vEGSigh7c8JPat35BMmj2iHJtVgn1UmVsPzq+lh8XGUtAlc9QQKr2F8TdrUAIjft/O8x03nwV8Vq9ZlX1AxtCca9K8KdGxXQdO8MCpSV3rLjgWrcc1XValVtZkaYBo5F46t/XsiZkN/m5TyrUoRgrRbDgHLphKaa+Ot997P0pXKBWd27fPo8Z0ZGRDVHC+iDPOsFAOIYwsj9lZ+gPsxU9hpALly9/tDj192rT4jBrud0aNjZsuvZjjKluzb5RpMEnH83Lf/I6fqZwkJPhTAV/BTdYjH06lgM+tt3X/Z8yaxXHHw+K9t96JrdRS33TTzXHwAc0LTkbW5ujSp+MIFH93XsWmdCqp4pPyIvEGvuF7eWqy10LLHiE3cNCAGPLJl3EM9HTeWV05bqxB8sBMzUa51ziRV8qXffcZRUNeZUb803mYfB4cEEa7VSMzjDK6wcOGxiPPFujp8gu7cXzfCXm0oT17thMFLQdtlEieU3BWqqylcgSM7COikegDHVNZIq/XsPT3OfPmx9s0GOw3ZAQzirj3lhujDbCvhAGhEqmTIfkK++hJKcnPGc8xVHqz3ITvqeQl7wRGJZJ+CzJh4uQp8XyP52Pa4qVx3EHN4kxOHDigmc25Cs5p6chL/mejTZ1iWSvMPqTRIg2xLfJ9FXP3XVmj/DOlfsxn4+PRZ57P9XXvdma0PZImjHX3SQNQWlVmCoOcG3uq/BNvNvE8G4sRhCaajMNXXUHcYtzNGHk6JxZ+tyQGDBkWr7//QZx20vHRuuWh0azpfqkopmMNek0FHbwRTn5f5V+DVENdZ6b0JN/NCcoH+F9HpbzJxprvvN8/e81Y66sBUJNjAoXreowT90DHvrJJRx0CMWVAAVegf2EET87UYfcJPJKXCstVKOhfAvsez78QHU/oGEce2iqbvUoTwsI5iOPKaeWsL2lL2SqvFGfcY//ufYX9LpTfSK+z582NMR9/El/Pmh6XX9o9Hfka69nQGLjn8d58X15TlP/KTvHPckfnbB8d+ZelF+KPslD+N3s2nd1pTPn6633i0Nato9sZp9NYs27qduKia0x9CSRQB3OujlupshFoTrnA6SrsNUCcO1PIS55kn6dJEyehfw6NUVPmxBXdzoaXHc+JR3uwbsog2EtxnC8mjYuDzskU7Sx3YCTxEXULxzBywzXybIQxDuIdMXfB/PDEsz4ffJjPvOeGn+VRoeKGxxUnTJU7jJF8C1iV5JlQIyWT0BPjqTtuR4eyHGM92ZXqQLtVoSyP732FU/DVl3rG52T6HNa0UVxwwQXpeJQ3Om/5jUa+ZXVOlOEKzyzuIzAQCdU5lHdp/PG3qp6Ig3wbNnJ03Pvo4zn387t0jrPPOCOPEbfRX8oM5uoIKbN5RD6Lz/I54onjJ8wFUgF/irzBZt4DhgyN3u99EI2Y1znXXhVt0LU9jUicVddO2DNv+YI9QqQBDXmNZfdS/dDSRPXgDAbxHcunslHt3Hnx/Cuvxhiy505r1ybatmufR2UrA6Q3cVKYFPrGMD2n6O/AQ7jLM/koHafilHaCL7V7a/XHfzkxHnrgUQInLXLeNtmVXn9KT85RfBHn0+Bn36U1xys6fBKfgL+OL+9fTRZLNjOl99vIYUPi/PMvzD21H4XOZr/veNJiBsdYP+Ar0CnPUjbJh3XMiAc6JoSPPNMS8+/gY8rAwcNHpH58UocO2cNLx5w9BaQh1+Alz5THyoMqwhOFlyVN7qE4rE6ZeiD7bIBpNWXY3yBDJn31dfTs+35cdOop0e7oNtnDy33SoS6NyB/TOcS6fZLyx32x1CGDwHymPLKvVlG/1Mlur4XPJ0yIIcMHxqwFa+LX0NPhZDXr7NXWU5Ym7jNu2gmsxQcUSlgpQQUWym/nnnZPoj+lR9gL7ok9RjyCu+/Ij6NFowZx7eWXRXP0PXmueCccxPXU84CFct89Sx2DNVki5frkEz7Y/iKW7eroscx7FHbCQ3/6SwY4Tj+pY5zW5aTYp3Yt5A+RNRwN5UhZsteMOrx8XXyzVGM1uuo3ixfS4HZkHN6hfZx9ztlRs9Y++Qywlvf/7uv/7Ghw+QWUVfko/Fz8XfBsoo7uayKeIz4aEouJRuxNhKBDm2OiGZu7BYPzRxQrDSiJVCVWpP+Q7IdTLrvcoePyM0+Nq6+5Jg7G8LKGS2XLiJ81a1BXIoQIImJLaCK0zNSaWZsDKYDcTKOeKnh2Xn8L5fnOPzwULfcsHRf/7JdxIicO1K9Xr0DIjKUHWETIrAmQTMaUzIn5pGMDYpepZh0PAmcrRA/LTGKfMXtOOgJeQWh2P/XEOLvrOXT+PYZoLt3TiRBuhhDK6Bl3XAkSZJbYZRwqKBKRkfDNGAIKAgU0N+a7StQEIoyP/OlJ+ltsjjM4M7ZN6yPwoNOUDgatsas3X2IreuQkEhXmHBc4q2BIJH6ucS1zcNfWYDDaHNHj9R5+9LG4+frrOIb0qKiLN7GorCgUZHwK8IwCwDRyfqxDR04yABikTFJmJoFY8ypjtCHaFygT79Pc6nucRVdedGG0pMO4Y6twqPQoiBUQMk6GzDlK+M7VqwAroj6sQ8ZRCsUFzsbJIKsyU+JTIt/P9Hg6Oh3fJS7udk40Yk8TzigowsafrfdPRuE8+cy5KnCyZlj48FwVOlSxZCLu8VyY3yAcAa/36hGfz1keD9x7V3TBkWG3ZXSNQn04Y4mDriGZFDCW6SpsXI9KfGUMYBsRpREMU/WZLI6MgK/ijTdej6fAmbqg9SN/+Usci0B2rjbiMXJsLxObArkG1+LcJbzcR+adDVTZG5A2BeVWaj4VyGtQzoePHBV/+NVNMZXctLMweK+7+so4uHnz9KzqZdWYlZbEGcfV0yot87CC4cveGin02M5VMET3uixHLlpesZw63A8GfBB/fKKgrNzWvXt0BS9r16yNAoiCjdDaQLRpKykMpcsZwaQOGlwtQSbDxh+A2RYMPOBUqgyCvyxCjpRTHUdb2LMy5XeJ+QsXRd9+/aIvJVfNdqsbt//mtujUrh2KUhW87pQEgBo6KTPKoBMSmO0KPhmtUlkXZxRwztm9lDfJE6QpFZdPPp8Qv33gwfh86oy4+SKMulO6xME42KyP1QutciL2qaywubkf7on1v2Wh/TQq+Nk6S/sksBgyM1BCud0TawYPGxF33HlvXHnhudH+2ONwDDaPenuTreKoG4kOgxdryC5YDZ/8EZq207HPLIutWIZB3BuVInTczEAwG2E93YlXEB2dM38uiuKo+HLu5Dil80mctnMgUZcGmYFlDbMRs4w0MIYO2qRf4IA0pb8GPIc1qrC552swVCpgFNcirdd+IRuoeZzGEWzPvPQy+1AyzseB1IRoVDUiIx63mQiisQueM0NoH9oB1tJTKhjgqwiaTkGeKX+Xd3jPggULY8KEz2PIsOHxPEbdQ3feEZ3ZU5uKSfiut7hv7p3OBPlN8hWeJ0ykA25KfmZkzu3ZA0WtFOuZghLUt+878fKfn44tRAF/f9998MnWhQgtCq37KR9XSZdWHSfnCa1qwOjoKhozaawzb/fZ3kF2pfaknV69X403PxiGQ7Nj3AA9tWi+f8oLx7SRoTRqDa6OBuWbfKHouJInpAID3uho8HkqLFWr755OmDffeSdu+M1vmWXE3XfcEhdd0K3QgEp8JvOkvAYKdLsFHFcZtV5X+aFBrlGnoS3P9yQW38V58V9YTnFPn3sunun9Vo7f8/FH0jFofyA7hNMiJWFr2rZGtQ4TFWVhVJAZ8n/wW1pwL4CLfMb1uI4xRBjvu/++GE9WwGnHHhnXXHtdHE4quvtlpofjun9G5aQvo4wa/UW5lPjE/lcC1kaTpFF7yoj3y6iVHThkaNx4129y7nfdcm2c1vnk2J9IplEi8dh9Szxh1c5XWIMaGaTwlCvpTt1BPqHMYULQAvoCtDsHZ3Wvt9+Jx55/IS67sGt0PL4dR0IfymlEu3PUL04YnDzFU6aMaIkbxd44nlySjgYio/IZ8SiNMNaiYm0zN7MZnu35Ms6ijXEGjvaDaKRXj1Im5YT0aeZkNQzgHRgLK5Yvz6CEjgDnqjNJ5d8j18QZDQvlrXthNoVnyAv7hx96MLrStbxT27Y0xW5Mmv5eKZeMuqa85373TcVa+SzeCB8vaU76KzgglLuUs6APfUt0dNqMGTEAHW7slxPirjt+nsb0rmStFU9OYlKJ7wZklHfiTOI4fCFZJ2N5BK/8zaZ6ytndMHzUVb5G/tmk9qmnno4OHTvHdZd3Z08bY9Uqm9BdmLO46PSce9HxpA4g3erI5k/peBHfXVN+wD+LCfzYYHAgRx6/PeqzuOv6a6MbZ9TXxmBUzkH5yZOcq/1yzIDwKEGjq7mP0Jp4tB3H73ay3oRWZiqS0bAVpJs5Z1Z8OHBAvPT6GwnDx3/zmzi2TZvkdz8i9+R38rN0UPHdPP4RGi7HUZUV6NOUfIzxrdHcjgz8EeeEequ8UP7xxZdfxks47gZ+9nm0P/TguOWWW3C0t0qDK7Ns4ANZS7+Tl+UeMpN8JrCX7l2bBp4OMXFHPPDkhR/Rp/v17x+X3XBrzv3Wyy6Jq6+4giOtG8dmeIH69nbGl1/aq0CYurepewDjdHQxtu/iDd6C3A+foQ4+e968eA16+nOPnnEwrP3qO+6PTp06hlm4G6AJaci5AySegTHIM+QH8gUdZ+67Jzeo08sf5GvymNLgUVl0/UlTp8YDjz0e7w4ZFlece1qcduaZmSkrP8kgA/yAL6bj3hp9s8Hky65HY1LeIA1r4BoUKAftVtyF+gP2wyOgh4/BCX799dHh2LbRuVOnOOSgg5NepRedAcLYS8e66xBPDNJow7gP6SyF/xcaNm9Lp4n3LF+2PE9o6o9O+R66zfXX4ZxiT+twCpQZHY6tjui96r86YNQJ1uH80EEj3qo72nxVHmCGgXyyEvSokW1Qb9LXX8frffvBm8vGxQRqD9h//78dbZ/9bBijcEk5XOyBMHaPXZb9JeTt0qnZNgb0NKblS1MnT4mxH38cDz3zYtxx5eVxxkmd86QgA2PKO6GivBMmyYsZV1o1Y0QbRhmqg7AM/Qy0zcwmcTZmBNigdhiOu9f7vBJTZ62M5x5+AL3pmDxFSR4vr3TvtrHedPAAJ+dtJrSOTfVs6cd+bunAYEG6OnyO948b/3m88fpr8XzfD+IYHBi/vevOOAx9T6eXJ5eoYyujtMmSdt1Tf2c9KQOl5wSXWUQFm60kmaiOvwyd7P0PP4wrbruTOyKuuOS86H7xBTR7pTw7HQ06GOFlLFYaUu/19AkdDdowZrh8OHhQ7NN8vzgHHl5tr5qMsnN/8qk57H/lP/8RR0NiyU54KFSkZH/1x00YDlPxylo6sQRHQ3U89scf2Ya0xyYIbpQIGJobm547lEQZ2WC8Ojddd13M4/vXn3VGXHrZZdn5V2NCJQ41i+2CqYmdbJIM1weKaApeGWoqn/xZj5yMQyYow/oOIaKj4ed/eDCOrFk5zr/qJphfp2QgErfKmwqhhGcWgqlyRl4UeiKhnysspEYFEZQVOxA6GxB2MizLMl59/a144+1+RHdPi5NRhDzzvTIItXH1D6m4FUBUUJZFaJmEwkBlXIXK+bs0Yak30YiLHYUd/3McDY8//deoUrl8dDnhxDiy5WF5FJvRBedoGr/KgYxOgeuabMhmYyXHl3hU5lTeJZRUxHj3uKv5pHkPGzMmejzxZFx05RXR9qijolEjagoxqDWiJVSNdL/n/krgzs+x9CC6BuGjk0EG4xxkACoGdv7+Gub33qDBsYAMgasuvjhaHehZw9VgwERJYaZe2YWb+avsyGglSMcRP/w9meFOIVeC/faIqeUQ6SSUlVFjRsdf/vpsdD3jrLgQ5tqkQYP04PLNVNqcWxrrzF+m4SsZiBviulBSxBXMj8QfFWiPjZqHAjp0+DCORnozhk6cGQ/c/cvo0vmEbMqjo0EGmCnNbJroZqqbRp1RPOftGtwH04TLotBvJXXW1Dc7pCukPYL0rbfeiodf6R11+f6fX3gujm9zVApeI2kaL3rnEefp+fd5HnPmPhSyD9gBYcJ87cquMirOlIWR65kfPmp0PHbfHTFuyca4sHPH+NnVV8XBGLxZ9oHSozInPeWmuhZfjOf6NUhtDrVk5fKYs3BBLMJRVxY8KouH2Zr2VeD02I/HxlvvFAyXc0/snEexiTMbacJmWcsmFOutZCqULIPAkSmDEyW3E+miV8sOMhrSmYSDpEQZFOmtGNw8byvrK82JC0uWLkcJHRVjPhnF6iP+cOdvsvNvHdJHK0B3ZTEUSoBvlmApLBREjq9Dzyg9oybeS6sqFUaqdOIpQFVcPsZ7rqPhG056uPzyi+NE5n/QAS1Sydkor0FxsOkRX0z6AmHYb5Q1vm8zR8GWwpjP4BJ0I6eJLXtgdsN3lDZ8NGxk3PHr++K8M7ug/LfDO09X5DpEYLVvSUNehYHy7YqlsXDVilj8A42wGM+9KM3fdTRIbz5EivVIJ/fZ86NXk+a9GAPAI+qmfjuTI4OPjX1REGvXrp3ZHs69JPzAI+vc30zNFiehK5gkJW3Qv8oDPFFaXbNqTexeeZc8umk3atpLwJvnzpkXz778SlTbo0qch3LuKRvVKRGwkake+nSQMjuVZaMd4rh4nXwFePsuvfoM6Vv60rBfuHBRTJw4Ebwcbm8YBgAAQABJREFUFc+/0zd+fcvN0fn4tqSRFxrSmVmgMzOBK5yZr7RvNpgOJPejLM5a6VelVj4pXCrjYGIy8fXUqRw7ODD6vtkrVm0pEXf+8pdxNI6G3avuhvJO1EY+yfqkUx0OKkVCWaVNepRHytXyJfyZik6ZdDRgCIzB0dD7jdfiNRwNZ3buFNdffUW02H9/aFIcYSzG9BmJK4ysMaYirZIrL3MtKi8AiLkX1iDtlieKuYxGpu+8+27cfM992T/k17feEN3O6Rp1atWiXAVjkQapZUESSwDS2AU2OosSB8F/aZ9ZQGs4kYG7a9D5oGKqoqfB+PwLL8YTr74OXkS8+MeHo3OHDtmIWOVc3NMRLvRlBM7b+8QRnT0an/L5hI3rBV7ikfSlMTMaY/ee394bn86eF+cef3RcvdPRIKlouKTshqaywRf4KH+U/pVJRkvdT+GiYa1B4e+uSYfAchwtHwwZEtf98tfOLu69/YboesqpsV/DRnn0ropiwbATxcUZHIHikdsBDLAAUPTA1UQYJsTfdFZ7isQGePIsjtd7jWyPR3q8GJee0yVO7NApjgBvjH5vZO47cAxa7qkDT7wRDsoHcfxH+KyyUJ6Zhihj6/wVp1R8pfMvkX/P4GjYwr1dTzs1Dj2E7vQNG6bRuBbHnhFFSx1RCPIUIvUMHZzKQnm5e6xe43Ncp3Slk9Wxv/lmAbD/LJ556sk47ayzowPBjcYY60ZJ0wmF88K9S3nBd4W39OrcpU2zwvzdPZZPFp0Nynz5jCd7DRw6LEZOGBe333wTfKxlVKlYOZ2TiTPsqdlTpXA0mD1iwMOAQBqh4LryhKfFNrZDWtWIUTn/EaPDjIMRw4fHc08/Hx07d46rL7ko9cOyjCX/lT69pFLxLh2ZrMFt9Ohm+YMIW9h7f5cfFXTKb8mIG4NOM+DdftGLEra7r706zsHJU0dHA2MYaEmeyBxV+BfhsLET/HJgqrEiDkmvO/D0boP2lCfiu69tCOIFixZyXPOn8SHHt3tdf+mlcQgBFOlNB5lzcvby8aLjzoaTZVibpyYUeUEJ5KJZDep4GtN7gnPqdLPncGoYNfN9PhoUnVq3jFtvvjmNUg1n+Yl0JZ9Jox2eVtAJCjCRxwgj8Ua4iefSlnzY6LoZiu9zGtzlt9zh1OOXl18al150UeLNDvi7Bn/SDbiu3uQaCgZYAf8c1/GTjsBz3/29GByzbLDXW2/Ho8+9EEfXqUpQ79boSGacRycWSu8QcuytMsG1iEcaqtKWvF54iZdp6AFH/sz4zIE9l8t9MWVy/PHpv8S7lFnfePG5ceJJJ0VL8NJGezruLB/U8cXMMpMpeTKOE8d32joCSkK/W8BXs6XkMfIDdah1GOwjyeC57aYbyZo6Oo8/P6xlq2hQrz48vHByg7DO/XNePKvo5ORj8A/ewNx9L+rCedIOz9VYnzlrVvTHadq392tx+dXXZFZZnpZXHaemNgjrd3ztD8dQ1mn/SLc+U/rUkHY/PaXLz3V6aj8tWrgwJsJr5GU2vLzk7LPjwGb7R3WasurI8R7vFwbyXuepvppZIDxHx4a8TTrdCs54CklJAnrKmWVLyRAi02P06NHxVM9eceNl3eN0gm4NCEiKr+JJ0XnknIWLTlFtLzOatHVsjK0+k/yRz/zcrADpb/pMytqHD0WffJ3gz6J4jjKCjmR/aTeqT6euzdzdw8zmQS75HHU+907eYyDGku4ylFdpN5mtVgWnjJT4CTzy5Zd6EuD4MI5veUjc/fPbozWOhqQhdWHhDWQK895JuzxXh534n8e4Qp8pX8ATNiPxpRzZVcvp+/EBp5rcedsvwzKtay49H3q6IJo0aoi+wTyR2WZFMaGcu7DXOeS6Pd1w5ty5HDH6XtRr0Zwsl/NjF8s9WVtePPu/+fq/OxqEA8iURFyEBAxHxi8RbMPImD6VNNmxYzi6Y35UQhi1QdluRhRCoW2HX5V2cICrUNc2kvSTP979q/iCT25AIJ/drRupRc3yOTKgHWyYSoNI52NUhgoKuftSNCQKx4vJNDR2NLw09BQg7wwcGHc88sc4mqZAZ156bbRr1y7q1q6dnsdMb4VhSzBVULxFOo9oMu3SCLO/p2OD55ZAWJYgQluSzvLrcKhYg+lZvX3e6x8TB34UJ+NoOPaoozFcDqSOFESHyQknv+/cC545O/CiPEOIKrIqEiot1mNX4HeFg+l0psCv+mF1fA4Defall+lxsVdGLVpRO9aobr3YReOAfZCByNiSEBCGMg+NdxU3ezXIrTOigfKm0LD/goxs+fJlpErPiZEQYt9XX42Tz+xKWvDhHMu4H3VvNaFDj6YzlRwid+4qEbw8i970w0IkwSgYRMoNzqGoxKjAWO89beYMapCGxvTZs+NKUgBbHnAAGQ27RxUU7HQ0MK7z1SOc9WgIGufuWDI/lQJRRaGpM2YHzKnCbrvGChrMjMdTOWTYkHjmzffistNPjbO6nJwK6G5E3RXKm1RCFczM1TQ9veUFZbTgvFBJVkiqQKjIbeKZKrrlUIYWUDM2CvwdMPCD6D9uEgruzXFyx/ZRA8PIVFzHFCfTKGVcDQ17H+QeAw//83+f5/h6dWWA5fGe63H+etrUePe9fvEwyn8N1vfnZ55GuB2RBornX1tbnPXFKLjin4aLBrVeZy8NZplpSeab+M5aNnNMZHnxkj0b9fHH8cyjv4vRi1bHJSd0iGsuuzwObNqU5jYohLxUvEsTuVHpTwHjZJlnGRwhPyJwZ8+fB959FWM+Hx8v938/n1kNuG5HiG9lTptg0Cw4jD3bWUFBKFVuw8snHpetgFGIG3fLVgUiTjpgZsi/xFaY9w7mzH6oMW0rQfaGnhueva2kBgBZBbR5Tya9rdAm75zTuxHRaR2HNGsSDWrXgJ9g4BJ98KhHldzsLsx4lhGZ+qkRrHA1YmxJiccICf/tPNIo5qcYvH/442OxDt501mUXJy9ozrFdwnkDxoURTOGNNGDOGCr87EkPOjfwILF+lHPwZYtCWcUCfNkOX1iDcbEQo3HY6LFxzx8ejfZHHxEntu+Y/KDxPvWjDAJnw6ofOCJvacyYO4dI4efx6Cs9E7YV8V9SRUIdb4Il8RK/DIqoSMTEeanEb4Wnrd0IHXAvgCcjhDfuIzC2M9W38G7aL+DNF2/JalX4ZZ4aH2vW+WnE6ce3jaOIPtevUTN2I2thCUcavtT79ahLn5yuGADN9mvKOdt7Jq78QAaU0S4VHvmH46gcGRWXl5nyKh5kZgDv4pYGj/zJXirT4JNjOHf8zbffjou6d4+ObdpkLX9FBK91tNK6kSAdF/KGjIaAyzqay0LP5VFIZUTyGGnKhQuTDThhTBsdiwwZMuSj+I6MuauvujqOatUqj3mTp+tk2GJpCXSbzAx6lWd5qdTqSNNQN4vHvhvyA2FecVdqvpnPJ+PHRZ++b0fvwaPizE7t45rul5B+uW+B5hnT3i2isc4enyXOJL8Rz7mKyno+kXE1xlS6NFoXLV0SHwwaFH958OGYwr23X9U9upzcOfvlyLfKmuWCA88SRPmMPKU08FG5N1KsAq4Rp+PCd3Fd2aGhakbVrDlzo9drveNPvd/MRlr3//6+lCO74DTUMScPU9lPOQW/1HhUdij/ioqoz1QJlV9qQFo+6Nx99tjPP4/7f/e7+GT+gjjvuKPi8iuvisMwqHXqeAKS0ZwiTUmrQkSlUR5vZFCYOHY6fYFNGhY8x6jR9yhjA4YNz2wPG3bdect16WiQnlaTWfUDdc9GiTTyUllmnirQjp+8WfjxRJ22lmEKMxVE17AevJm18BuyVAbEY716xXmd28YJHU8gSHDE/zga1hOlB+7y/EyhZf7pPGN+8l9nXzDomByfqfgaybNUwGNILSt5AXra+P2KOO2UU+JI6u33a9I0cVnYqEeZbaE+kzKFeaauwLwL0UXoB0IuOhrScc1eKbs9+nXsuPHxFobLiaefHm2Zd8OG9K/A4auzQ7rTQBLNNWCAcspzsw01OpW39qMxYqpxLV1Ld37Pc+on05Pko1GjY+QX4+Kqyy+PQw9sEVWRAbvCJ8wwLbww2pm7gRrpPjML2APpUyfDWgyALcy/Ig5BjRZxfpVBAhwNI0eMiN6vvBnHd2iPYUQEFv2wMs7sMsglebk6CxPLlzRq9oj8RgTS4FL/yEwHFqiTweiq2ZT2hTJbYsA7faLXpMnxq0supYyNskp6hIlr5cB7eYswXQu9fj1jOtHUPtEDhb8GcNrETSWZw/Yd4Dyr24FMSh4MkxXvc43ctxXDfMsmouJIDvfFclJljHjtkZhQefIADV+NmR2bCnqgpky686DPHeVKxYo1K8DdytH++HZRq2Yt+AdldRwJ/elHo2Pf5nXipp9dG60POST5y1bkrk5X8cUAmbwm+QE4JF66Pp0dOtaVqcI7eSV/07liFs5gHDwP/eb+WMS9d2IUdT3tdAJXjZL+zeAR9upN6nUaeuKSaxZe6qqpN/F71s3zDGlKmlV3mr9oUbzW55149IWXol2DvePsiy+P4zAa98b55bgF/ajgdFQ/y2AKuC5v99Qkjb3MLkZOe5mVXA6dRrm9Cl4wAUfDc6/2iqlffBZnnt8tjjmuLUfEN89MEJ2a8hfHKoWOopFXWj2HuVl+pjEszhv42QKMVq/7MU+WEE9Th+IZ8rJ77rorjmhxCLygYxwFvTas34A9pSwTvbRIJ9KKNCV/NFNDR7m0U6CznHr+XDjmfRvHd5KNOG9efDhqdPR7rVdmIB1N35MWOKj2wgmjni0tuoHKJeWFhMsj2EbkBGOre6uTyzMNsPhHyy7MaFi4kIyGyV/HG++/j7FdIS6iHKYFeryOBrOj7CFh8EuZ7e951CTjWBZhQFHj16wXeZr0SiwoeZmZB0vRCb5E1x45YlT07vd+XHbeeXHy8cfTK6deln0JX/dSHU+ZJ64IB2nWOTqmOKPOpKMKccbgJWjmSN8qeKVH+g4dMTze7d8vxn49N578za+i3VFHxV7YCTrmtoHzjq8+Ix3JJx1CHc9AsyePGIgRV3SO/KjjDifyngSm1AE/GTcuXnz+uXhp2KhoBw+79bpr4zDgLq5YjmjmsO9JPIzryRE6js08gLiyeet69t7sDnEersrpM9iayJHllLMOZu5/fuSe+Ap19eoLzokLzjsnmjRsuJOesJU2oOOmvlxwgrmXBjul07k4uvsQcGx85GFxCYHYinVqp0zIBbLW/+brP+doAFjpbACRZGpwpYJggMlOm0KqzcgR8Q2G7C4oYccednh6rBVKW43SCEkISUG4ikjdiM8+jYfuvTsmAdmfdT6RDpzn45FrDnJhYLpJCGBfIlkyq2S2PB7GpwBSAVIYiLhFw85ooIa80e+3BwyI2x56JFpx2/kYjWY01K5VM8sybDbp18shPG3A5fjr8Y6qaOl8ECFzvtwjseDSj7Ic+bWOSOxSBP5X06bF26QDTxhMWu0pnYm+tqXe/qCoToOh7TA7Jllg3DxF5VtFSMaigSchS4Smz5lmaOO2Enr6QOY1azFKGH8cQvnZl1+m7qdmnABjbdX8QBwN+2Sjosy8kCEBjzQAGFPFQQYo41BhVBB5GanQAWDfAD2aeipnEO3S0fDO671JQ8PRQDSnGQ4e69KEbXpCYUgyP+frWHrjFC7JVN1G9kQPpvspI7Y5jo6DRQsXZb3h+3j+bZhy5QUXRiscDbtjrOeZ79zvM6wxk6YUAjpinL9pnhotKUR5ruPncXVEtCvvXjVW/rgmPmXegz4aFC++N5iSlVPirJNPiv0RmNU8xo99s8GdgpjJpmBOxV+84eV4Zn2kkIQxCm+zGsoi1GwO9c3iRTEEBtKv75vx0dR58bs7bo1TOrWPPak11HmkQLahadEAEj/SiFFAAA8NBBUhn1OMSMmgKuEkURmdiMDs/frr8Xjfd7OD+nNPPxFtETymXG/CEbCDuZkqLb0YCXH8TMVkXJ5QEDaMreIj07O5jAppZcY3OjJi7Nj48923xsfospd0bB83XnVVHLTf/rEVXN9EHwXnaORMmIA8BTjwc3kUwrV8f/rc2fHJBBw5o0fH+59+nPjzj/9UKkcTQ0h+JfWy//kLKxpV1evUk7rGEfCPww7YL5rQ2LIcz1yHEo8KyNFuKKbAh4/Y68KeWGcuLbgua2Hd4zQcFaT8/OmkifHbRx6JxZyb3v3CbnHCiZ3jQJQVDaK1OBitp03V333kpSFQFth4lKfOFY92dJyt8Ltt0FFJ8H0bRuoqBNw31EoOH/Nx3PvgYxi6B8fJnU6M4485Lvat1zAdDRt/4PhW+NGcBfMzi+uxXj2Z+f/Pi8WWILCx4z8A9nY0YTuh3fGxLxkX1TEiviN75QXwslq1XdLR0LRxY87ZJusAgb+BVHLxWoVKXEwagr50DlgbqfECmiafyaZH7E+RJ6msTJ06NR0NvXU0IFQ7tTk6PBvcOlIjtToQTIO0mZmGo/zAI9SM6tujx7RjlaI0QIC9GQc6flUqjIqMHDsqBn84IL4j4+Y6ouo6GurWrE2DWDo8M8Ym588ENUBT9kCrGeFlzpVQeoyy2I/EyJb5ZcqRXapWySM+P8ZB8sZbveOtEZ9RitQhfpZ1nvumsq9cqoDscGznmg5CcMmsPetLhVU6YcGZZHR87tgaAcor+wS8P+hDjrh6LL4BFW69/JI49eQToz6NeTM6pzKEwVuaqIhzV/H3uzpe16GcqrgVIkQ61shkgc//iBJtdNY05Flz58ZLOJKfeKNP0knPB/8QJ7VrF5WQDWsw1MV3jbsi/1Lm2YzX6JHGgZfyBW6TSpZyXrg7d+lgDMr5b+67N8YvWhJnH3FoXE1mYuuWOBr4/g9kzQiP0izdUj+PMjaSafBA3v83pxHjJ53yDJXQHayjFIaYxsWA4SPihnvvB74R99x8LdHp06NxvfqxEvm1ZiXHOIM7RioLeIkDAfzRWPdZGsMaGzDM/NxnlMd5ZIS04GhYEG+iGzyKo6Fr29Zx8kld4uijqD82o+FH0shpcFkSvmK2kaVVXvJzHSOZfcHQZl+oTMvfMyMOuKr82+TMVO+eb7wZ65cvZU9PieOOPiZaIAMtWVrzPc1TkVFmZpUjo6RSeYiavVCuqBeJKz7HuWrIuQzlubS4DGfl9Ok03B43Lt7oVQgSHI/8MBtRw6UYVRW+jqUcVe6pA+hoMAIsvMR/P1O++3cNF3+ev3BBfEnG4Efw/9GTxmePhsPhFbXow1ONE250HOl8Sac7dGQwwki+c9bYFic14OxFsw35VBH60jEl3mrsmt00YuhQeE1fjJa2cTFlWi0a75vBE/sYaEhZ1mdPGZ2JOjKVz+oGrkknmvPN/gE+l5dr0tlpj4ahjP0uOtMbRNjvvuRSaPbEdDpyJ8ZC0WDAMchzJpG58UyPHvH2mNG5v//uPxXK0W8CR4Pz+k9cTfZrGfs2ICMT+rLUZjiBqxYtGsbPb7kpjqCZs3qp6di+K6tYfOKLMlwj1H1TzhWboxacI3AleI0/yzdWIDcHDxsRv/rt7/OUgDvOOzfOOf2MbO5ntnDB0UBWBjjPFqazQZ1OXcYx5AHiu7gubuYz+FzHqYbY/EWL4xX4+0PPvxhHYaddePNd0b5t23Q0mJmmveD8U4eCJneoW8LfdG6ql7qmdTiuNYrlZfIY+dhG5O4q9OEJU6fEM/CyTzF8L6Rfi06qFpQj6aSQ7tWX1Ml0MgCI1JE9flc90IwKcV6d26NFN4CbllD5Eld/hJfKy+779T3RslHj6Ey2RFtkdxNoKssysVOUZ46hLizOiY86v5R/8iHxUbzVoJfmCp9ty15ts+fOi0HgWB9KZs/CuXPMkUdGCyLre9fYGxosZDSI28Vsj9S5d+K+mcnSlnq9/Tzcbx0GZjTYxHP+vHnxxaRJ8daAD+DNZeICSlkPwNGwB9mIRv6Lerw6u3jgvJy/urcOYefq+NKtGQ2b2aSy9CfSUb0UPjue3hL2QHrpg0Fx3TnnxinQrDy4KlkHyj0d7DoC5E2ZUeZYjJvZdiISeynOqGcnr+TmSgQby2Of6Wj4CBuh3/vvcBLOgvjTnb/A0XBk7A0PVuYru9fDi3Vo6HhQTvks1+G8lYOOqXNA2eTRle7pHnvDB4HFx9gJz//16eg1dly0Rc+74/rrozU4o12HMVPAFfGS8f1vC/snv9UZKW3Zyy6bzTKWeK5usKMMvJL5ryQQPHDY0Hjwrt/EDJZ51flnxQXnnJ2OBucIAFLn9j0d3/C20sikov0xl8zpt998M+ofdgiOhkui/D47HQ18lQn573/t9Z9xNAhEXwgqa2dEfA1kdh7mty2mEw0dTkrJvFkzY7eKlUGco6NFk/0QTBh4GFKwLbbU26mNwls0DGPmzl/fFXP47OqO7VFEL6H50AGMxf0wKBlrpqfyu4hm1NLvKyitDROhFGIK4/SayQgZuxIRZNMv36Qh0y0PPBRN+M5Vt98Up5x8MjXlNbMBTUYxQVIdDXpDk6HjCEivLgNqZJRXyeU/u2BvZ53lUcStz15OpOJramDfePf9GDRoaFzYqV2ciHFxRKvDsqGUBCjTlsFmWj3zlJGkZ5656mVWcdEjp/JmzZjp9SqOq1GyFpPC+MmXX8RfXuxJ6t9epBuTin0QDebovK2CaFRE5qFiLvOQAZoK6fgis8LYZ+h0MHPDTIrKeC+9fxnKv46GYaQBavSeT43QcXhwmzZtSkPIGjnPbBLD/FIRAZ4qnKYfOrbPTVpCWLsmiV4mbHMr92TO7Dk0efk83vvoo1jKPK8io6FViwOyLEPFUKMi4ZB4AIxhpo6hIyZTanl3k422KeDWwlB3YNDtUr0aka4fqL0aF4OJYD7Xd2A6Gs49hYwGhIIZDTIkhXIho4G5MlGNJKbM5bxdQ4Hp6agywiUzrAC+VIS5zkXRGjh4UPTu8Wh8vHxHPHDn7XEqDbf2SOcRwoRu0iqJCjWjmTo2xGkZl0quz3ItOpEyms6DTRWrRJ8BHQ2f08OkR4/n49nBQ/Pop55P/glHw+E5no4G91YGbtdrDV2zJQpp2AX8Fu6pzO0UkjoZ9MrbXE5FY+ioUfHgz26K8dx3EY2wbkP5P2B/MoSAydZ1dsMtKCkyVh6V8/QkhAowT0uCZn0zP8ZDw2PZv14o4l6a/l6C0LWW42WU0Z/BcPaowBJ8Z/T83XtVz+H/0G3hcz+DSyTu+D1f3q9bYWsJaLg0KXHs37YNNJjg6tb1PCILR5DR0DQaktFQmqj+ahxwziebk8G8pVlpTNhbYlJw8iDY2NuiQqS3W+fjJxO/jHseeijGz50ft51zRpyGonUIAj/pScMInqKH2x1VoS7j3jF8aXAeqcwLRxuL2AK8tjJmSeh1Kz+vBj+/gaaGjR4T9+BoOJaTSk4iQnrsUcfgaGiQGQtbMHZNlV2EcTnk49Hxqz89ysic8c25pKWoCS4JEIpwlJcRVEOI5tLyc7YYngYseV9LEIHtzP0QxmY1aNAJZ2HsKz/3H+4noJYvt34jH3md3bFDlks1rl0nHQ2ecPPsa6+Bz9vjnFO6ROMG9UlBtPwALzw4aFZNKj3iDAqPkRdpqaiAJU2l8WLNKdFx6FwHhM3lsi77k0/iZXopXHnppXEiqd4N66OsUDssL1FpKyhu1noXlLTMBGBPy/tcBTR8Jp0D0ivAkF+qwEzGWJAXvPf6S7GC1I3bbvt5tMHRUBslztNQdDpacy+9akiLL0VHqjhSGbw3q2EtNdZF413HbxXoaT38bTTy6dVXe0bfjyfG2fRouOmqK+KAJk2yXMeIi7hiRoOySdrSSS3PySg7zywaRBnlgYda3pf9MlDmzILph4J402N/yj25/cpL40wcp/vUqgUywAdQUvCAZFlNeXi5ChaMPencGlflXTrYgYfjmzm1FuefDlP74cycOzee6/FiPP123xy/96MPR5dOHdMQX0VWgDxSXlN8KXPNCFDCysMyY41vZlYcsLJ8rTSGnnxMXjOarKdf33tPTPh2aZzR6qC47oYb4/BWLXPua+H7Koo6TXcjs8RImnzZdNd0MrCPlntoDBmpMwvR5qo6T31GZjQM19Hwu5z7fTddE+ecegZKbj14QCGjIY0HnArKPx1R9nQyrdx+AhXB2yx7YI3KP2nKSJS8LksnFi2gEWR/MstejTOPPZRMktPi6DZtskTO0ol0NGCwUAGWMsVIlGm6ygpPi5GxCQMVavHIgIslJWaaWJpo49mX3no71n23OLpAT+3btqP31IFp7K6jSfU6MkqUI8qQbMoKf1LfEc8zBR/4qMf4PA2vlPHsicb07FmzYhSOhpdefilOhYe1Rzlvsm+TlN3KfWWr8AANcx9TtjKueC+9eY9XGqjA3vddkH0KyXnz59EY7wucoaNi9Fefx5XdL096ali3Xp74oSG3jX0UzzOzTzxhvKLupQNK482XZWXip/spbFatWRMTScX25J+nXusTpx13bFxGqreOhsoYRTpdMkWaucuvk06gTfdOo8oghgGJQuQUNOM+6Uta2wWn5Up48EdDhsQ7zz4bb2Ek3cPcz+3cOU+hsV+EtJM6DLDVkTEFHbUnGT8vkNHgcZ+wSgwc9En4BFBKZ+f67QRpBBYXI+gLSx7iz76KV8K6QJ7QFTzGD7gc0+MF7dPgmthhju4Dl/hM+WeG3zFHHhM1cBKVgH9sRPfrN2xQHNNsv/jVHbfjaChkNBgUMwihrNNQkpdlzwfGyKgxe6uBYgBCo66o14g/6nwrcfIMHDIsrr/nfr5B9tQ5GkbnZoBG41+noI4u4S8/ADtSHxNm1tdrzOlYMUvQ5zm+e1pwsJUloLQoXnrz7XjwuR5xMN+++jd3RacOHdLRoMEmr0zDlrFt0Oo6pFX1NfvO6LTdgD1g+VQakujE6vUajhqQn+NoeLJnzxhAJs81Z5wUJ6LHHwJsKuLEMqNB/Suj08DIUrPK0JUOPNdlCrx6QWn0STdwGy9piwRKHGJkJxMAGDV+fPzu7vvi4Hp14+Qup0aHdu3zZIut2AOWKyWv0ejnEu900klPfu58C448A2cFg97PvW/FihVZEjNw9Kjog2F5HqVOR5M9bFBPXdsx0pbh3cyLzGgA9jqpHVcHo/S5BmeHmUPKVUsky8HjN8NL58yZTUnll/H2wAHQIP2VTu0SBzQtOBo8KSZ7SMC7GIRRuXiXH5RVt2Z+ZiTphBXWZjNsAt5mfll+IK8ZR0nJ8KHD4qn3B8bt53aLU3FUN6LXzG44Hc0yKGYcZIYs48pD7HkiHyw6odTD7KyyBR7veyWznAjszZw9JwYNGRx9330jPpm5FEfDHdEOJ0xNHQ3wdeXFBgK/4rv2WcpY5lygO2iYsdQBxHGfYX8J97TaHrunLNHR8MKzf80yquNwNNx9y804Gg5M/E58FFfAm7Qpgd0WcFL+4BqUfemA8GG85EHythIEPHcl4Pk9Ac/+2Lm/+OWvYwm3XHVe17iA0yOaNGyYeph8cqvOasZXfynLq8CHCxnUZjS8QzZVHU6auvCii6LCPnWSJqRhn//ffP1nHA1FCEDAKpwikx7+1IDZ+qlfT4rhgwaT0TA7a206HX1ctADxjSxsBGnMVNAYKwOibcP4HTx6dNx0w/Uxk3Fv6HJSdO9+WRzYosXfGISKHGwpFSCVr+LmS5RufmmJH6FvpMQUeNPYVF4qoIguReC/QY+Gm373QDRijJt/dUecccbpUaNO7ViLouXRe3qu9Dg5J5XQNMRAOJ9llDm7mYJpera2MpFSpnPtII2ZTIQpM2bGK2/1IbW2f1zR5USyGk6No484MkqjWG1BmcgGXnw3FVwEWF4wFAmcR6VgkEgkFj3SehJLw0Q2MPZi0mo/4QSBJ57vgcOmHI3ITozjDm9DM0hcJkzvR9YmOpq6ZZ1lplcB2zTUmb/RDB0NGgLFVFU9/3qpPRFjzty58dGYMfEC41911ZVhVKRB/QY0gINImV8houUsC8a5PxXnnF5dHi6TyjpP4G0zr3KmHMEMZ+PE+AQnxnukBa9hX64g5ao1UZHsuk2K4xbgbrqYQlbi86WyIn2ZJSHz9TJ1zEWq0G3Hk1gBBmVGwySM9ZHM/c9/7QnjPiMu6npmNG3QIBVohVjWM7KGFJjAyvUkowU+4qsKjKvJcRlffMkeCgitbzC0Pxg4MHo991B8vCzioV/9PB0NNffkXG1wUQ95Mc1eb7yRR9W2rAFX2PFzRgGFoco5+ysTL000TWV0Ig2fnnn2mfjrgMFRn3sfe+Ypau5xNLBWU7yds4w18c71o6z7ubBX2Js6jfsYYY/Dg5eOBtdk40lTsoZS4/nAjdfGWHTii3E03KqjAaFG8XCmfDpfkAVEVgVy3jB/Jl2GfTGjYc4ijiEk68L6657U1HspXncBfXX6cQpjrlHFzM8dxfXzp1TIsH+TXmkrkhe+jVSohLgvqQCQJLtYj0I2N+9COJTD9cFRFWuxijfhfPO6rNv5cSyC55ADmlEXWIvvI3ApWdLxKOyFWWGPUaykfYCUsJcOwH/hIuxVgo1Wf/zFF3Hfw48g1GbH7URFziCTpxXKio5ABZqGlwqQOCTty7MKx0C6OgZPLZPJ8/8Wft6OgaOjYR34+S0pkoOpH73jHo4rwljs1LZdtMbp2KB23TxVYgvZJOsxZhd9920MHsN9f3wo17jXrii1lERk6+LCU3KvpQCdAsK3Ci9Rdh3TMM5cgZ+dCrpZwtRqFbM9/Gwz+76Cz3mL6rzQ3WM9U1/Pd3VcqOB6de3YHofIEWSK1I8aZOss+W4ZdZg9cVhVjgswXpo0bpgN3HT2aVTII4vKibwQEKfyVqBdo2oojuyBSpORHRv8lcF4WfnttzEOgT90xIh4pf8Hccu118ZJbY+LBvXqRUUcGcJ7A+Vj6dBkH3xPZ6bjwxCs3VbpMgKmoqWyu4O9qVSN78LbpoOr/Qd8EH1eeSZWl94l7voFPRoOPTSqs6Z0kkCvhQykQhSkANxCxpZKmEajzl2zgeQHIqeGUSUcDfKo4cinHs//NfqM/TLOPalT3EzPkxb7NcksGnmB8kK+ID46X41rFXczGvigwBd2wspoiUapjq9SwGYFjoY+GDm3/v7BPOP8npt/FuedfSaO5b2zbKQEjmpPJCmLdyKj6vBIiU/DvNhkT0U/I5c8y7Ht0WCWRmUMr5kzZ8UTNJt9svfbuedvP/F4ntCjzFyPfCo6GpKOUPhtoGgJknMvg2MgGTK8WNjLy3QwlEKGQBwYsxtjNMbufb//XYyaPT8uOO7IuOLqa3A0tEqalEdmhgcWl0Z/KfBoB7JAY5olpLwq7VgiNsqt4wsfaVX4rESxfn/IsLjqrrvTsXk3zSC7nnwqkd+GqUeo4BrtzswZ4KKCbv2yfKAypYU6xvC8JA3nOnwO+yrtWoY0e+GCeIUGar/v8UJcfsaJcUKHjvRoODwzGnROgQxRmpKnzIqQFzCu0UGj9pm+D1/R6ZjGl/IK+FvyYPmPJQKWTvTo/UasWbwgTifK2JY08gMpfZR/b8QJkxkw4I4KdHkMAvUNlIzcJ4076UnF1nGN7Gt0aMTYxX7hooU4GsbH048/Hqede06ccAynWjRpEntguPBFUtClch0KQtqPMNr5T31JZ776hw1dzX5IXYTnuCZl7zfAZeLkr2IgTp4h48bE9ddcF8eS7dhon/oY81VhLDgScdy5t5kRI57nHkK30ibPtFTLl0q/8klZUwHjxdMN7N00etSoePGV58h+OT6uuYgu/PvuW3Csw3ezrwRzNlNPx5N77P4ZKDF9XCdDQQdkcNalrmAWThmcS6uhp5Hwmfcw6F6Y9HXcTxnVhaeeSpPrOsxRTlrIZNDA38Hv9tjqiXP1GRxCYHvKL+VaWYbmqeksAIOiMr9XrorMRR6s/rFwH5gbVVgjqh40Fxwh/BN+jeyrxA3ruXcFW+jYjuvlsz2YXFe6/L0Kcu/YNm05/nsvDG+a5+HwHkKUt3XLA+OO22+LI3fSk3zF8gn5jTgjPogz6sHCRh3Srv46dtIBBo5LS+KO2Soev/n+h4Oi++138tRC6UR3AkANGzdmIsCWV5aA8rdS6hjAdhP7JY80s0Ce6D5uh1atu099FT5i2ZnO4TkLFsRLb7wVDz77QrRBYF3xy/sIvHWinKd6rMeplk404CevsR+aurZ82TVkWjy/J56y56mfgDcpC8QjXmY0PEy/sn6jx8QNNIM85bTT4tBDW9FsmsAV+FgsJ9HhbgNdHdRZbgq8TF+XnpIfgz/FrCnx9ocNa+Pb5ctiOJH7+3792zj20EPipE4ECY4+Juo2apQ0qVNQHVu+6Jx0vKb9wdx14rgX6sHKPlaRumzSE/daOuHRk+8PHxbvvvJqXHbtNXEU8qlBg4aZMeheqDv6XelPfqYubEZf0inP9ZlmSvl7oVQDxw8w3IKdMH/evJhAltDrlE6UBC8uPvN/SicyS4kx08UAfJ2zNpsyu6I8h3F1AmmNWH7ADLIs3BJiA1faCV+MnxAj0CmfePm1uPHSS6LrCfRoqF0nm3yng1HkZxx/Fj/4gQ1DR4bXayeUpXeCPEEnmxkB8oN0joMz02fNyoyGPn1ejU9m0wzy/nuj4zFkle1ePQ1zHWs6G8wQLO6lfLjAswqOE8fO52L3FRxs0Bvyz2yHz8hSef21XvF0/8HRsSXNIO+4o9CjAZxIJziySecFIyTfKeA3MAG/fYYnmpWQFliPJTSOaVCvfPWqlB6uifc+/JDARqFHw3UXdyNL9jwcDY2SRncQJNhGUNJSHmVR9kJCl1GHsfH/7G/mx/vI/lrN949u3brFLvXqQQsFHJXf/Tdf/3dHg6sHYf0nG7IB/DSiIAbjIqByTJ7yVZ46sWDO7LC221MnDmjSNAXTBtKTBaaK/S4YRhqAg/H03XLNNTGVb994BicIXHIpGQ3NM2UpDTruNbqoQipCe7kPyZRQCo1GidwqGSpfWbMqU4ABLIXI3/5gQPz8wUcDdho3/PrncSZK9N44GtaR7aAn1OiNzg/HzoggP5b0dxDQeVr/p4DXi7sZItHgVQGQ2Vhv/fLrb8U7b78bl552UnTBE3r0kUdxRByNj/Cuq0yYmqq3Lz1+KtL8l15Mvu9cres0KmKESoXXXg02lLIb8mcYpU/3eJH0zY00JDw52h/TNg7cr1kSxo+kvgo/HQz5ksATLoKqqExgmO5kjsm0IG6XlsrKwoXx0dix0QPmfe0NP8OJcTglJbXooVAllTaVWZ0vrt3xsnGXQp4xFP5+ruIlg9RZY8TFFDib2JgtYfdnO1evY9/shNv64IMYvw69CiqnAJNxakA4Tu4lc1fx8XcVCq/i302DVzEoBdPy/PPpM3FkoOQ+1+Op6NiBBqJ45/Wyul9pSAMXDVCNIxdsOp6RIp/j3K0ZVelSKZfJqqirsPuyR8EgPJVvvfp0jFm0Me7/+U1xUgf6epAFUwEvuYJevLTXgZ2TxR9xJZkE78W1sBAXkII+UwFxfHnreITCiy++EH/pPyiaE82+5+HH6Y9xWMHRAGNKg05hjOOGjSULgUgrz3O8PKcb/CiJ0NeDvgFvvAqGyldl4Gr6t033Hr/7lhi5bGtcePyxdPUu9GiQYevF1XDU215SRRyccW8VMGxg/EDE7htqdE0pNTX3pbfeyX3gZNiohlyqjK6hgsWBEZk6ywipvxGsYX9g9NAF+goCBscE97NtCDLwEX0CVBcc3MjfoaEyGBOb+MMqtK4f3W5qMTbg8Vj9A2nX3ON1FceTHYcD7MDmzbKviqc/rEVhSuOOvXW/0yEA3NO5xHcSn3iXznJP2SPpyxM5dDQ8+sRTMYlmkFede3p0wQBoiQNMmEirjgtAMsqic9BohlGDUsCoNJNXoXLBphiqJG5jzVvYl828VhCt09Fw+933R/fzukU7ovYHNWsRdWrUysj05h/pMA6N62gwo+GuJx5jhIjau4KjeAEq8GiqsojOMT7KnBEx0DidBxUBtO6sTRt1XjEFUEMHgmAiAIxjxHcwEYNUuiUDVYc6WTrMkf4Xq8Gr71GEKRmM1fnUiC5tj81u8k3qN8gjOL9bvjz+8sKLUWufmnE+ztgmjckQwli1dKHY40B4y7tUVkRmjZW/wZvJymPyFBPwQCWnrBkNixbFWPiMkcY+Q4fHrddfF50xuupSOmF6ps4Ju7/LP0z1tIGUEVlpX+PaMh/5s43KMvLNXoq31fasThlJ+ZgxdWr0p8v8O689Gz+W2i3u/MUvog0KqBkvyb/ADZUJnXf5O2M5T58hD5fnyodVIpKWWJNKV0VKJ2yqWXA0/CXeGTuxcIrL5d2JGDXJzTHTQ2eDsDeKLs7YmDQNeJ4nuvss9ykzbXh3DRrSFZF/Roz60gfl/vsfyLrp+2+/EUfDWfSv2DPT6/PUCZQTj7gU9hqLztG5ZlSRnzNtFLqQB2hYGA23bNDsrOnw4Seefiaefr3gaOjNqRMnd+yQpQzKgGL0UoeMck4ngz2KjCa5N14a7s5Z/m+mRzo68WiZnTN2/Ofx2ON/pMRsVlza8bg4/6KLaHp4SMI6HQrQkgaF8EnDQj7My7mqnBsRta+LmJyRZt7lISpzK9bQDHLosLj6rnsC8zZuvOGKOKXDidG0YcMcw7mn8a3c45K/Z2NknmmjRZ0/W8kQY5Ozptc+FgAxlV0V0jmLFsarOBoe6PFiXH8hR2cefWxmHOyFkmvWmpGoMuCemo08XgN9A2ntwle+kscrKlt5vA4esAxeDw+DX6xm7pOmTKVHwxuxetE3cSp6gU2i92+6HziOkwenZrGXhDieThFwNPeDMeTNOkPMUGHYv8l3Hk0K9Vr6NCzPVO8n//ynOPnU06PTsWRONS6cOiF9mmat4q+jXkNR+SfvMhugcKQ0mRqsQZrLi3FtNOk+f7tkSUxFvg4aMSIGjSWj5Lrr45jDDqOchy75lCZuQR7plJVPipGmcbsPwkAoqA8KZ50wNhe2j4oUYINs9bMZ06anbvDqW70IDrSIa20G2ahxOnTSwSs+wE83sQ7FgsaKTVTlE8pWnSc6GpL7MW/fNUQ8teE7dK5PMRgHDxgQfx37Sdx75ZWkkneJenXrJkydn32MlHcam1MxdHq98WY8SyQelpo8ndlHbaZQFc+A5YHibYVKNuOjvh/H/jocxhwaxNqRh9xTseKuGPacnkSJE76plHG74lmoRO+FjRvXBj1/k0drr9jnaBN0tIo+Od+ytasYYy/kpI6G2jga8gQGHIATqClv0vqQuJ5Ub49JtkTQQJrBH2WUTk31STMCjB7raNDgshRNZ4NlIcJdZ7s4uSs6ndnD/QcOikswjLzuuuTCuJgy5caNGmXgR/mnriQPK7vTCF2Lw0ya0mjOOnXo1THtAyMPSoOR8b10NLxG5tRjPV+JdvWrxUVX34xu1j6P6v0BnuL4GrpmMMib5Qka08V1yd+M1rsu9z8dKOI/+KST48tp0+LBp56OvvQLuPG80+OkLl2iFU6YgqNhXcJO49Hym+z3wfjqHwYlMjtO/AT24oCwslRWx6kNtC1hG4G++sBv/xCdjjs6OrbluFudAfXqJ19Zj+NROZeGNeuX10hnop/Od50Qxc+Uf6JnhXTUFnTtufPmR/9hw7JJ6RVXXBGHwyP3Rp/02NHCmPB0YOH3zELQ4N0ML87MJFFd3sK7c8jfWJsOCfsvLF5EmfKUKfH6e++xHxXS0WDphBltWZLiF5io+pBlyXkiDXtmbwY/tX9NrgM7RLvmx030neBnT7kwk8NTncaM/TiefuaFuKL7pZRVnhh1Ka9WXriH8jHxxpkZxNSJIS/QoE5bARing124gSoFxwB7D/7MmD07RowcEf369YrP5q6JHn+4Lzoc3SYzp5TXym1lbDqifBZ8zB4oBR3DFTFzaF9nl3KwWLpVyCzbFF+ga3+AfP3jm/3iBBxId99uM8gD02GnM1n+JfaKH65AW5LV0L9iTToW9qi+RwbgdsAz5F15dDu8bVeCHN+jh37w0eC45+e/inl859qLz41LLziPo68bpk5gNvsOGEI6Y8HJzMRiDfaVEOfmkNEwePCgqH/wgXEWWV3V6tVLOCIcE/8Z8r/2+o84GmQAlkyI+SpPMrL8mS3bjEE8Y9rUGDtqVMyYPIUj7dbGgZRNNNqnXqaFbYF4MjWT79oUUKE9dsKEuOPJJxOoHZo0jLOp57dBjemOlh+IDCK0ymYyDpiEioqX73psRQ4FqMqXBqSILbJ9j/JvQ6lX+3+Y91/dlXNgj6cTLs0V9eQZkdeL6MvxRWgRXCVXLd/n+bOEaJRNA0PvqoqKlGUDnJfxnn8xbW50aFw7Op10GsrKQZkGuAYiVTA4pkim0FcgJyFCZDJwCdEGIRKmv2vwGuVRcNhpfhJC+eEeL+fcu5/UDsPg8Ghcv0HOUWNXZreTA0Fv7gdzh/D8XCXa6G7WTTECy8ufFdZrURSsnbZz9SsYR90RCEe2ahl70UzK2q8dzEfBbnRBoS6D9nuldhK0z3TvZFwy2FwXa7NOy3UsXrgwphJpHND3vYxY30STFNO5bFBj/ZPCpTA+TgbnJqwZ06ZwrkPlPBV09kXJnXgGbEqg6FpWMne+Xtwvo/eA4XHE/vvGhZxtX5coINiQ+MLk8j1TohjCOeaWsWfCSJxJxss6xRX3QdjLBD0P+hPSpZ/pNzDhfu7JnTgV4ohMqbUDvwqWQlO8VCiqhApc0CKfL31krR50oQAqRulk3uLm1Bkz4923X48PwRmvX11zeXpZNZi3oOA6rvNWICt8VSYUQsLcTIxsfMNcjVqY0eA6pEGFtIrwuM+/iHuffjbHPhjfxhU33JbZHirP9jLQKy+cHU8mDVKkk2AHY5uiuJia4pnz5sbX06dTp/tJjrM7t1VFE2tYq3zsU2OP2LUcTB1n2Hbuz0WVKnjYFb0bcFaU4NSJihzhVZrOhNtoCgkL5zG78rOKOOnUPLsiXRAV9NuZz/coabO/XUHZxo+CN74z7MN1Kg3mDmnePOrXI223uv0CMOT4vsaPe6yCXkFBxs/2czFqwaoS1xUdllWJswo1laMvMUrve6ZHjn3ywftFW8objASquGV5CniiImejSZVXCInfFRZEodnj5HXgiIrqZh7kUbe+tnHvanjVKJTcnr370Im8OQLz2NivcROO+N0zSmG0aPSY/rvs++Uxnojh8+/1zXnsja5fEn2q3p6lokn9hlF1F4+F0oGHI47nqOjqFS+FRVMaIafSbeaKkdRy0IP0bfqfMNmqdwGcSecra4cycR5tjaUIxYUrl8aKlSi4Bl242hx4ACU1+0XtPWvwTOqbl6+I3z/z12jRsE6cdXpXlKy6Wfds+ZjNz+SHcIOkVZ1T8sXEb+Aiv0ma5WeB7/5aZiFvU/mfBK2O7N8vRqxan+U8Oo/2oklYHnXFuCpUKk4aPulMQFHWAFOZ0ePi2jT0NJrS4cmadVTbXHU2zt7hw4dFz49G5LruvOLSOJQyLZ1HgI8ZQ9/CkDll1hrv4ohzdizxRPnhu/Qvf9CQVGG3lGICzt5eZB1NxVFTp1q5uOmaG2Lf+vUTDinLwD3lU2aWAXtxRtwsGEP8yryVHdKbzxF2ju+pGcvJShuMAvpkn3459/NOOYG+JCeinFcLG3BaN210bqc7M2EuDjrX5PGMq4EuX0nc5BnyA9NtNQ7sBP/yq6/G8IlTcvw7aIDqUbo6Ma2HzvkDDWEt705nDnshf8iUXYixGKVLJVceyfMFoDj4xeTJ8esn/5pjH1mnWpx+7kXZRFRjxbGy7NH57fSauRcFpZE9AGby9uRBvCX8Gcl3GGD2aDDK+PRrb+b4Z3Vow+kKbTNDyHkbBRSm8ldlk3jnf/kM9kEDw55Qlk9o3MsL5E+mYiu/Fy4jm+SDgTHgs/HR6fAD6AVzBMb6vrE75X86Q/xe9iIQh3gGX2Fu/MdzMr2WOeps8PnuqXNRjojLHkE3DSX6kb88nxlEl3Sid9PBh6ThYpq/qdziSEbqlLHCBR6SPIefXUf29wHfc2/5m3Ldy4Z5qwiSjJ80Kf5Mn59T4DXtcNzVq1cvjQvpUGeH9/uSnpS10pIvdQvLItVDDEJ4D6vLd42DlWSMzad+ePCwoTFqzoK4GoesDdTUDWxCbeRYg45F515mbTb44yDOFUgUHF5MVz5ZKB/0yLlKaRQvACcnTsLZ3u/D2It777jtZspBCRIwQPYlAXeEqRkN0ktmT4InmZXIeM7V/Rb/vUq7D6xBHWQFPGwSxsWw116P0fytG3A5vVPHqAGv2cqY7r+yWSeIeDwP/U366z9yTGYwwIbzOqpeudiHSHxVUsXKQ9I+22wGL58njcuTkp8wX/OsNpPlqtNJeJbmB/m1OqVXqRJkHbHCDYyxir1fsnp5zFgcsZg/V2f8Aw9qlhlY69BXv5s1OT5fvT324Hu33XZjHLg/zYrBEXWtoiNcXVJ8cV7Jx3bSguVk8hZxR6PInlrSmGUxa9G9Le177MVXc06nc6rFmTQOrF+7TsFIZ1+FbSFoZHNDcY0sCnAwy5PBbfVJHQ3qeL77rNTBuXkhDqo+9Csb/NmEHP/Wi7tFG7IRq+LksGlwMXtBPUfjXB4hL5BPaOzKlW3yJ3yFmsafclu9zNLsqXPmxG1kfiGJ4qTWB8Rx7TrGfsgvHUzZ7BVdW7o1KKnupNPqb45l4FGUH+KU5SXq2TrF5PHLVq7IRtHPYZC2RGfqcBoNSpsdkCXWLkaaTn2Un4VR8kL3lvlLR5lFKU4Ab1/+XVkmnq7DwaPzbhBZAX1xzF7NqQ2HWEYMTrov3lvEZ8cugy7imPI3o+gZDGNM9TY/z2cwrs+UzlcxdzMmXgHnhdtl3S/J9P10OsH3tAOcqnq1+GIPFGVclkFyv/wknafJH9lbdErlap66hINoLnxsHGUlb478JLq0psSsQ4eoydyl+3Q0MKb6lw9JnsN+eTmXBJY4I91xHxuSvN25a/PMwzn12bhP4+UPhnp33HRhN7IRW0UVakTlSsJDPuPPcr+CrbZTx3NMYQQept3DXupo91kGE+U7nro07KNB8cHEaY4Uf/rFz8muJxsRPNnKnhYzYuV7lsaVwzbRNig2pd8FW0ieKe0ZXBEfxR1LPzwSdTTy6eGevXLso1oeEBec3TXq1a7N/dynLsbLch7xMG1AeJS0uR66soR21OBB0bbbOXFut3OjWp16LjBxqgC4HPa/8p//iKPBiLWps3rAU6EFCQrqmuk962P2zJnU9Xwck4geTib1vzzKUg0aCVUEkbNZC4gjwvt9EXzRCs6ZnTcbL/kPsVetOtGwQf3YHU+f3v1Mh8KAUGkodJYumWktplarxCrYjapL8HptRTCjhjJCiVJkW7RseSq7NsCqxrjVSNfNY+D4uwSeihRUYZdYETq7uoJ0Kr5eIpmXY8u0s8kJvzu+5/F6TNYajkmqUWNvhEXVPL1CL2sazYyXAlEE4pJQZBQykPT4MYYZDQp8EdxonoaYTNw1fQ+DXkjtdHlgtadj44V0HRpGPt+xC4ogt0O8WSfG2rNkgvmqAKlkKKwVxDIC1+xnwm0JGR8yQaM4NSiZ8FxfDVaZvHMscAtZhnssYUOgMhaZCs93D9xH73MuBdwo1KnpVV/A2D+wBhsdVYOppnOBsSxrEb7Cw88yLQk4C2vhZjTN9WVtH2vQO2yfg+wYzvrs6r2UqM53pErutXdtFBWO28Gbm3vFmDJvGZOOorx4F+eEvXN3H1NYAA/h5gkF6TiRCQAXjdKVRKVW4Cyyjq42jNXGQo6XijJrSMeRewaOKZAZOOdcFAQ6VFT2ZVD2pBBX3QfHXkHZzhIUXRtmNd+vedTZc49UZjPzAnjICNOYZsw0YIBJKp7MXyW8BIq0Bqf/5QWD1QjXcFqKsrVowfdB98oAAEAASURBVEIM741RHY9svVq1YneiSeJ2KibSFc8wEmI6m8a+TYS2M79NwNrGSCtwci0GtnMXr8jhayA7qvI6aP+q0ax+raiGk2ALhvM2YCWusGkcHQX8uduz0uGypEvrtOMD/lySlIdSpWxAtp1oG0o2VwXGMEWvFHu7fO3G+GLmghg3aQXzCcptEO7c0xRtq9beDWJXMqPKkk2S5ydT6yaMjRxr7Ip/wkt8TiUJZUXlx312T31XAfY7y1DQ5yLcyvGdPehsXAU8qyQuAl8FMV8o4EjyKPCI76j22KtBZZY3foOmWZeKq47HbeIa9EiGO8KDJquz5iQPqFF9z9hz9z1iN4QmZo6hbGCzHprmeLplS+LThQsTDtWFBa8W9XeLg5oSldyNjB/qH7by8khNPeIlNpMFxiLKm77KHH6Ax22hELg8GT6pmDF2CkN4ZUngUxnHgXPClYkTZ1MsWLk65pBJsWT5+phLholXkxrVaTJbN3mKhuw6lD3rbLWuatETZtcUsvBscLw0c9DhYvReoyuNtqR39gCFUR4mPQlj6SJpG7iL+/IzefJKlGizPoR7DQxpFU4NcEuujJaJyvICDSH5mE42lbWiMcayWTuw5ntM4m/3ej789+zr0hXL4M3boinOHcdXTclUa96dc6otzEk69ZX0z6fyWvFE+pQ/+rPzEm9U8tbg2HTuq1C6TCHep+be9BzQGVtYq81tpa013GOUJI1a5u5++IzsSg+eWmqggVrgzcAV3mk5j03U5uMs0ZCp3bABJ07USh6ffF0lxXUzZRbD/1J8QTY4VxA98cE/+xfhU9wHYWkUc6n9DJi7WRs14WV7VS/AXsXbMVSAbHLqfOU1Zjco31WQ5c/ugfw4TypgvvLzfA7fXc6a5+OY3QrPqIKBXrNmjZTdjimMpU+NLeWCcjVxQ17BPIWv6caW+rmn8nv36P9h702Ds7yyPM8LmE3siF0L2iXEbsBmszGLsbHxnkvlHlXVXT3RMzEx3yfmw8SsMRUdURU1Pd1VHV2dVZWZTqf3DRtsY8xidptVICQkJMQmAWLH7PP7nUcPiZeszI7KL46ZB7+W9L7Pe597zz3nf5Z77rnKqhhnIPUoWNZ8rA1e6Z2KxoxP5ROKWGkeEg6F7WX1EaQH/3GP7fh1g7MoK5xHnA5Sf3RGpIunimhIa4xeoM9HCIKdvYzdgf5TP6lDCqCBOCLWiysxDsYqrXxJ18vguTyorvSZ4q4/5Xt5TvvDRY624yeQmeu0P5LAGsUU0eHKq0UZYTLJGEFk61Y4frMD/L78r67Q5pBOkXnj38yhz3eF9SS6+0hHO0Gp0RRqHJ3CKNYmoo9ejtfvhj1E+9ke84HxTPlI/lJn+7799m951GdZLK+DGgenL55Lk2pqqQI/MmjuiqJ6xixNhh9jkI/yOiTOXWTY8KFbYTT8ow4HYxYb1Dnq9HPw4wnS1Q3cVxYXp1EeeS0GM3uOz5OtIBSczt/05xbZHT7HLNUhQ5S9O1nVfD4zvdk+8QB0y5XURSaomULnCfYUE7itLC6Kdj0lzBojQ5kDt/8YbJB/PTms8VgnOAPLwEpeK6eOSrUTCtNo9j8Momn56RI0N6hr8UFXWg3aOybl7hbs7r7/4EHG7tYpU890dnWq1X+3IMZFnOHTZES0nTmWdrXcSK2wAbsMUiEKYDgpgAUDOTWL+32WJLAI+ChsMufRZ5lpFHMFHV3Eku7KjHa4/CPfaUOKp/KP8yuWxXegXwe2ZBv2qpjqau2EsTi72CnZQgzy2iO38gKPD1yXn+RH58H3zIyRL8NZ5D15WIy4hP5tae9Ix9qORG2eCdgdbgfpD811iO2nNrxjMXNDDIi/GZe6W6wQO+2L26Icj/aTT3Vbm7rbLKRbBHTUrYWjCgmE46jTKfFFbDHDNFsFzzKn8mBQbvuKl2Eb8OyoFwD9DC5rB3Vi67WDxYOhv/yur2CNH+dc+dYhd8HHAHFWZJGFA/vZI5cGTpU52/fK5cLfr/G9NnyEY8eOpqKx47ELOOYdHZ3VcTBAkdmOorjFJ503aSrtgzb0YSj4arDBuTDwod3pyr68b7H0w/TdLVNlZEroP4lNkDxkImsvW2By/lwMM0vW/noMp323XYXaDD/nWVvfZ7st5QyZLcexB8eMGstC07g4HtnnOoeRDaAcQENl11fOIzKH+lQHXb7Ubghe5btihcfdmrlt4Xu3VdVVcGQ3AT51hvxt+6FLJCJXRlm6CV3kv1go4N4M+w1eqMuhibjJ/doFJ06cBIvP4T8QPKTvo5GnXA+JtwbstFmVHbdq2RYtBra5rVj+ku7ah44hAhnQyOwP2z0Jrmh7DB0+MrKqh2EPSF9P/zITSX4UP2KOGbe1YeQ3C/6/vn1n+pu//D853vKHacT4EnosADnKfKT8+i28/iiBhshoYAIiFQ/hdkIRlSCNKWSHSLvesnFj2r97TzrZ1p6KUYQW5lLpRyV3Jk1Fblqq6fN9rEtACiwzGcxqtoRGrcDEtAf6W2zL44t0kEyb37djfRo+riSNIxo7CjAbomFNPxQcQUWmt33bGMpeXVeBdUyu87xw4lCC3OQXwkBwZc5o6nmMqM937khtuzaliplUP6X9ERghmfPtd5RFhQtnDcHUaByI4SMo6iTq3Jl+iqoJp8h2Y+WGvmlAnABsPv90U3yvZspUVvizDAKNj1xIjbjbf4XJCFsUyuFvswCM/gqesaLjPTzXedA5V+E1HebIyt+8mmo4MmVS/eQAK+dHxRP7qxEO58/2dVbdn6xiu4wxfQXFbPTOSKdj5BZe3m9/UOKMaz8rWC27t6S5T303lZexCsH8C2J+PwNhqsny/btjsY88x1VnHXSBWYD0e/KA/TfIoFFrgcp9WzakASNGpwWPLKFwJNW/GbN05atBP+ff1Q6L+pgh0E+nknEbkLqO4RhgAdgI0ioAQcTMGPt3gr3iDZ98kCoXLkmzSYsrAIhdeZJPzGxRGVtELVPaWRDMYJD04n+84PFgGXmGVQvGAKEiTfZ4RwfHuTan7mOH06RFK1J1TU0UKRJcVIIqWqOaPiMCG9DEzBGdrCv07RKAGLzL5xJesHT+VSRnAbKT9P0MYMzAUnX9lEi7i8wPaK+DYFBOx03wt323oKjElYlLgKFpeI5Nuhn08IhM610YvGhpbU2HDx2gwjmKHidwdFFROM/7DzSmdZu30wKppDQ0Ch93au3AVF9WgmGEY03A4A609XLP/C367p5cwbgXxkB/lpEtUsh/kMl+WQwVo4D5jOAExPQ4pv4YWGfIaNjdcjztajwb26pPQG7+Swsrh6XKkopUOrGSFYAJ0I/sH76vMavj5HFmu7ZvSReON6cxFWyvqKrB2eF0CnjDZ2iISR8N08wIgOf4roGFvsiNcmQqnsavBrnYMYDAh8aE/NPUzAkcr/8qldTMSJXMqduKNH7kQZWndFS53cc8xlnOOCq9cWw01MxOMYvEAl8F9Fv+t0ZDZ/fptA9eeWPLVkmXxvAayWtS+dBUXzExjRk2iL6j9NhWEoEO2u8D3Vzxc35V0Gau3IDG4os83gt+0bhVPlgCihRKhIPgERlA1H9o6TqbDrElpqv7ejpE1rPXjPJS0vyq2BJUgvM/KrY6yDu2b9r2dTIqlKl+9HuwckY/opAfPON1/ATFat94J1UtnBfyNBh81oESG8UE8Ujsk45Bc/jR38VNazI4L15ip3T3M400McKjt9paWlLbnq1p8kPLSe+tDlwRy0I+aF+DTDrnq/dR6Iu+fkE66TV43PYi48Kv8E+MFIPPkpp8ClocbT6k1ZxmzluQiuB525W35IcMczLdELyG3Pkcr4vIk3RQJyjP+XgFKY3RMwRAO2n/1OGDaVRFXZo+azYnQAwP51CsE/OUCQMVBpfdOiAWe4mN1+i/RrcrStLvGnPNDTF+C4I1HzyYOhr3Akk3Uu2shaluEqt69E0c0xkJ3IAPxB373g+etO/2VR3o3lLnR92VvcS6rLiZK8LHWM0+j+Hkan0Jhl9RUXGk84rXMXe0oX6wvb7whbUXlHn1U2QUSUeeH5kbfMd50PBTrx050poO7djKto6hsagwGufblTexNGiCPGayCjGQTxSSEdSQXQNuMCVZHhkma8DC/ARJmWfuc7vhAWyP9qYDOJhVLG6Mi2LUg+wj/VX+tVJ0LFyVMrjaG550Fdq98R69fadHXuXJmCeyoZR1O3cGvtm/+/N0mRXEiXX14BHBOAK4Yrh9l/ahK7hffpYfNazD0IY28lcEBaWP+oRLefNznbhjBGz2bt6YxkPzWlZqI4Waz+Qv+dE+SXP5zFdf5tRgVfAt/dfukAecJ/W9et1bNeDP0ud2cP70sfZUT1ZkKYF5dZP8aBBfB14dqAMjT2gv9XJOaMstY9wcNUPke9t0vny2fOD3TLHe+9lOeOtceuTJp6nFND6K1+momL4tPdSByisMGhhrara8as2da+CvRr9zpL62cKN2j4G+U9hMHTivLVs/TeXzH0pzyYbyhDCL7Glv6Zyq57SN7PvAQeg5ZV26QjfrgKnHDUK6im1mhTylo9BO25+QFv7prn0xH/n/npk6OE2aMDaNMqNB4195oo08sO0WW4QoaCjSRU0K9rDJW47BIAOTGkXgsuAR2EAk+jIrnN3Q5MhpguoHU2qnGbcFjYbNq+urKW44L02knkTYasiC2/bEAx0qecV5Upakm3Nl0OAIR5FKqxE43yPJ+rMAr3YvvYj5sT9ijfw8ANrE3PKec+/RfWJ88DpthkzxRTMVnK922u/qaOeZ/dNo9GoR22eGwfO2rm6N1XDoEPoUfgg8YK69PEbS4o5x3Ke8RPv2RUyWdzyZZd/uXan7OMGykrJUXFoWdo3oGCdA0UboWuZRXWvf3X6jzhWDtYPDhuxpN3QuNNEXkAZtLCise+0lCveNSbXTZlAPYXTYXApzOIzOH21ryymnbksVq2Ixhrm2fQOw2dWzYMgfHhsvLh8Ah7e/8XqqhB/LKyujeKI6QXvZvkSWExClThMLtOXlT/lUHM3stp72nSzvhT5iuTUwdpNJd+rI4TRzwcMhT9q0XlfgB/W8PC4/6Lc4f8SkA4PMRtZ/EoNt1uwE7X3nw2CCtqKBuB3vv5lGVNalB+YvYAvHiExnc1+Mn3tCV0EP7eGwbWjHMWlLBs7ZYZ5gn4PH7Bt6XSzoIihxDj04vqwcrJkYWCTmOX7HYd/u6if6Lz+EzDJ/tm9mSs6L8o3zYkjvHAuM2sInj7bFWGrvn5WKkRexMWxK+uBY7V/oQvEL28vMSXnFrfLXsPMM8MiHbu9znNqh2nEeP95x9Gg6i609hG1idZzWNpaAnPgiDkcglrbD51IW8VvdkqdqUqfehMZm7hhYMDPeWkEGSsS0cwQ1T4I3J1jwMSh3/sqFtLmlI/31X/1l+j4ZDYVjin0KL2fN17f3+qMEGkIiggaab9lf/ka8nxQyjhvbvz9tXLcuNaH077ApeAZbJ6onlrEiBxmZgKiezaSrFN33YgFEU76MJMlcRr0VRIMMkXIC7U3RMdDgKvk2BHDDpnUEGMakyqrqVFlRmUYRgYz0JiZbB1Lj20icvfOYL1dpBE7vkRFlQsHA/rtXUfBWwLNU1o/TXoyhmexL1GEsKi6OIxkVDKXZs4x1UDR0jXQNZQuIjp3gZfuu4OVRZAVEIAvHnWe1kDb4zqpVpJX3TwsAqCKij0Y2VRy2b78EcfvoJXC5khSrJLbbA472XaBScPMVCUHgs3370yu/fjHNoSDlg7PuD5qFgmJ8d0GUvocjC00FTR3SCxS70enWkQng0rjIupABEP0yCLN+y5Y4TeJ7FKCaOqmOfruizxGdOMsGcKwiL/2ljSuTjkUQ8EhNwTkAFkF1fIKTzxN0TFXqAEA2EKASkJ9+4slUWjQBesIL0FXj0/F5r7RyzL4ELXlGg8a0dIFU0vm+QGL/POu4+2x3BGHWrnk/zVv6KDUXlsVKiKBokMOIpsrdCHlu8Ej/MB4Zl2BkHwScDFxR4hrC9OU0CvMIhpCK50hba1ry+IrYp2zhTQHGMRuEkScdrx2ULipsM2vOs7pzhtUpeUTFAWHiJV9qYLQBfA0HDgQA9ieoNW/BglRVWRHGs4CmAonVHnjQlXohytVgs2ps08JkRq/jyB74xbHqKlpV3+Kpn+/dw3aL7WF4jyCzoqSsLD7fvG1HevH1tx1wKmPgowmQ15ezEo48jGRl/RYr5VgAGcjDL1/wYF1HsQC1h7zzAjg18lF3DJtGjPBCBwMormq4wjSAQEP3tVvpwPEzqeHoBQIrKbX5UK7vzp/O0Zx1qd6sj+KJMUdmRIS8ghnuc1v13nsohpNpEoE7na6JKDaNz3xFQb4MQwvKaPA6B4J/yCRzoZEv/1ncVXlS3jxtQf7Zvmt3+tXPfw6vT06zZsyIdP+8OrvGj4aw3w9DAofOIIUGvgauvHuNLQcqTQMN4tklCk8d6zqRNuOw/M0rr8YYx/P/MfgPVUX9U9V4Iu5DUIrknPfG2dfY6Q2tLIgY28e49w5WbtSIgcaKqKIqJslTt8CnMDj44A4ZIL0LBqcLGL2HT51JB3FkzrIY3UxGg+bNQ9OnkJY7mS1Nk1N5aVkcVxUGAwEgV3siVRDeMpRsNpqBhnDIesbc3Nqa3mZf6IwHHkzLqR4/AhwMgx6eFAeVU+VeGdTYyuVVfJcflWefFxc/vVccdX+58rB73z6O1tqcllE1fs7MGYHPyqNyKF1cDReHxS/nWyy2PWv1WPPBudRZFh+kk7KlPLmVo4UghkVZb9LWMxSLm1RdnRkSyIYrKeJsBEOVV7oodsoXXhoM4UDxdzhQ4I5OtFlavn8EjG9qOhSnLk2sqYsigOPRVRaYilUjG6FDjkXjJzLa+CkmRlCUVVnpZSDSybXuioakxTVP4+x+RqZgA1tRNOoWPLI0zUdP6XhdvEghRPqdBSDNSBMrwV7oYKChPzJryrwrvbnRFzQSM6CbxquVsLdv3046bieO9DjOqZ+aKssrgsa2LV+rdzIs69E90EYd4Lw6Bmnt38pHGNkYxK6aqXM+37MvrV39fujkmppa2i4PmRKvInjOM/wpBkaGifxj/6CDMnyb4o4WJnVsPkcjz7oRHp/oljqdxu1bNqU5HIlbQ9ujh40k3b8ga5P7xR7tjFgBo12zuZxf58XCXXgx6PhMB8d9ZNEx2Li/g+y899d8kC7g4C5cuDAcwmGsxLvK7Lilj1gvLQ0I5PyuAart4XznBrPvaTh7r31yLAebm0OeauvrKSg9l3oIOHQ9fCLdxSu/H9/h94GM2Qyv4Bueq9wYRHYFVh2WOaQei3clnPJde/emvfD8MvTT9Mn10Y5zrt0TQSjayBcB/K7v2ZYBCQ13aeS8+jz74DOknbq+83RXWrdhA9kEZ9KPKGzmiTXaE8qDNoz9NiAhPtBAvOdR19oGBresO6EcayOIO3f4qb66Stsd4FZTUxPzujlNop7Ok48/FpmjBuC1mULuoZMOnL+bYaQu97K/pikbUI85l1+0GdDdctCh1iPUSHknvfHBx3F//r9n6tF17EMfQYX5fmTAiIFwW2C4iOW8+JLb1aWxnRZzwAyOIAp6sReFRM081Ra5we+XCfhe5YYL9KX19LG0qTGlk9w9ku8UkdYwa87C9NiyJ6LYt9mTOoSuUAfvSHPmQhyQlmKGq/z7Gw6kndu2hTwW4XDVgGNucZEX/b5yanBE+1FaG7zyu/bRbcbiZNTK4jOxJpw7vqO8XkCetos1bNHRrlNePZXMrN3cmXZOdfAMoMkPzrWY6KX+c17DKWZevEyX16k0K+UYiwSe7tV+uClNYbtxXd2k5OKV2yEtvqgcGsCQyvKgBW61meRDT3VR7/q5Tl9uP4tvw7DhhnBfw6FD6dcvvhi+wZzZs6MQ+TC220mPsCmhKf/FHMrP6hBpo22iHCuj6qhc5wQHQBttNvXI5h070ruvvZrmPbQoTZsyORVCI3lWWQk5oWXprLxIk+BL5lXsEisdh7+HPDtK59j2oeF57JH3P1obQeXnnns2VZaVxRh9rgWKpYcZ2PK58xovvut47Lfjs33lyTnJ5NTMQnQQMtPWwRHuH35IrbrSOOJ6LLLouANjHTe84+/S1qLk1jQTz9QbZlOFjNJnL2kfW5uhoVtkDGIcbmXxCpl1XmewTUSfwLFKB1/aw9oI/q6/IZY57warPUpb/rXPBnnFfgNfjs1AQHNrS2rE1tYfWbxsWZoMT4pTtu/cRjYE7UuH0K/0vwDdnNsW+gnyoDTTVhCThrCApOyq//bT9uHDzWSKjYktadamiW0s8LN4ZoDN7biBkehUF7wgdMiBNFeHa5e7DWyItUjQbdoH+pYeN30Qfj9L9r6n5x1v/Sz9N//zX6UXKGY/avT/H2jIOOor/zetJCLzOQDiTGDepAMHDDR8jKHVlAZA7AX3z0mTq6ujsJKrBu51lomdMNNWNSQ0FgRSRVNgkhE1OARzTI3YS6NTbFr7+q2b05qP3ifboDhNIuJUW1tLKtKoECDB1dQ2JzcCAwikCkzAtU1BQLAO5QyDafxobMk0GoqdGLjvffAB577vTffPngO4skJaXBRGptF8HSv3gLl30/4pKIKTTKvh4GqtwC0gKKRIYQBZCBjPkdFexsGwwNojFHAqIUqs4yJgajx76TDo0IZhwTgidYtxBXjB0Aq2++8UEJWYRqTvKbg7UQpvvflmeoDCU/MemBNtO17B7N5LOtu+GQw+2++GwrF9xid9vAz0KPBeCva6jZvSpk83pmfZ22fBTo13246VTMY8gL9jLD1zbDt5W/lPESP/XboICCqsY6SZfrh2bYDtC889R9pgcRhOYdgyhgAR+uFP6e2QVIzxgsrSPJwXaGG7jtubBBPB72BjY+zlnsVxiU8+tjwUpsArX/gMmS8i5HzHIedFE+XVcD746RWODnzjSpo86/nU7mltaDyYWgk4LFr2aJo7Z3YAc0T8BW15hf4Y5ZRH5UPTeX2GtI+6BbSlcxsDA1ydV/efyTM6Fx47VoBhu3jJ0lQPzzt/WfQUw4z25HnPGw7nlH7qGGl4qdQiM0QF4iChj6uogzgFw8JYn+3ZwznMu2KFzLTSsaSXfQE93H/29y++HGOugCARaKhIqYYA0EgVMoEGV+udi1v013MxPL1CzjHQ0BdnFa4OHpJ01zG28B6CLlL4NsrQNDKYMJ25eiMdPHmWQANbC6hW2B5PTenfPL08LaBacH3NJAyFImhIHQmcVDNZCghQNGMovvPeKt67kOow0JXXEgwu+VpjQYUl7eGeyPgwPTeCW8ip0WkVtv/kHSPaKg2dU1dSpP3WnTvTyxz/OgVn/AGi5wYGLeAUMul3oLtKWQPBLQRhNMCfvcE959G5ll/i2Db6coEy5G0nj6aPt21N/yvHgHlNYMpHQYbqCX1SDe2PIdDQ10CD86WxQBvuJTSYIGPC8QR04HX65z/ejbGhYpElxgKZGTK1L8AlVoEuQPcjp8+nxhOsslOj4YDxV65H4dFZ06dTpHU26dGTItCgQ2OastkSir7V3HvRj5vQyp/yXDgd8IeFnN7FSK+dPDktX7IYg47TapBHcUplrpyKq/Y5nCz5H77zHmU+AgD3YIHGijebXXKaFdjPCPJsIftr5QsvpPkPUiyV76rEMyM/c+xoJOTIQLJYZZsGOMPphSedl3Dp6LfyJEZ73GZLa2v6HKdLuXiWQn2TkScdFY+QCyyAgD5PfgtMoWeORcPN9nOD2fm2DedCg86VqMNsg2hoaKBI5f5Uzsr3EzhGJchMGGjcGysv9FPMjsJrPEf9JM5I/8B5PlcvRdFRsF5DS0PbQMPnYMHBffsj0PDgg/M5/vXBSJv3aEf5TbrfxVja8VJPaFypW135j0Az8yG/u5qnU+84Gw+TEffJJ6xInUgTiorTXE4lqa+bFDUcXBiIFSB4UqzRyM22tTDBXD5KepgRY1s6d9JFY9G+S/udzOlHH6xJheBMbU1tqqqojNo3znxsUzDlHDpIG1fT72Yf8LnzHjUfmPOQOx5o0Uj3b7tYYXGu1R+vTRvWrU0P0u9pBNEqikqj/kj0m/kNnqN/8oXYHSdUSS+uqIOCfdub53ufWHlDUvIMDe0OMnje4ASrizjTSxYvSRUENHV8xJTcXpG3bc3+Sx+DZ86n2+WUA+dAvSQt5KG7DjD3NzQdSq+/8UYsbDw8nyPexo4L54ReRvtBYNp27nyOOk5d67NC7sBTg2/+HSdKMEdimzJzjow5ncZtBO4eX7kyzSZoakBKvS1+K3tioc/wb3WVPOkV+hG+sf+Bc77PnPoc7T8DDadYAfx4/YYoTPcd5HUyOGyfvOwjXw3HR1rkWGCxWJ9rIMSgVNgx/B0ZnXxDWhkkcHvkoaamtIlAxsSqyvQM/R/PVsaLGOvKkqn5tn83oCydeaaZJvbAtHXbd9uSwQWzJSxcbBbewZaW9CL22ItvrwJfGWum4tOzkwg0sA1oJG/2v2mw1eGrRTLbIMMg5sLn0uZN4F5eiSM0eY8HRmMWj9YhvIkXcxXevsx+jfPMUcvpjvQJ28a7uNWSq2a0LX58cfrec3+SZhIw7w9tb4sr0Es7mcmIWgXypYWxHZs1Unahuzdv3BAyU052mnVASotxWHiW9NDpkghiizTK7SL5Qv0mHvhTWWDiszmG33WY3fqzcdMmgr2f4kQXpnrkaSo2n7a2OKXzZbsSAVYK/JV35CHl30frJPK/+KmOdSuz+tWtWO0EkN5AfxzraEsz0a316O+K8vKoH3MdHNaOUXeHDue7eSBD/WNAzblXD8uHd/mS5/m3z9oDBr/55htpHNk1c2Zluns4K/fKZE4Tdbd+gniiM2p2T2AXf8vj4pk87uch27Q9iHvkp41kJK5+9920aMmSNJOArIFgZT7TY04JcopMhRxIH/sZWMMcQpx4P6YnC9ypa3zPRa2TbFta/dFHUUftO9jaNZUVzF6QNvpn/6W1c6outZ/qK+kgvQzC+PJz9UjgMm0bINDebCUY/i4LnhOwlZ5+8glW7UcR1ELH067b2/1pu/Ynx0rHJZ7LM4HrtB10px/arfbH2jEdHR2h//bt3ZPmLngodLfFP/MAtYs8zp39tF/SJfAG2shTzomfBY2Yz8iqwj80EOAWi8NHWmPhzW0Ry5Y9mqZNzoKm4dzTtrpJ3rGNWKjmb/vuWBy7ATZ1uTrPhW5xYXhhYcjxAYJTO7ALGrHlx7PFxSPKDVhbG8VsBbHOdl0E1L9TjtR3DIT/cdHWbWRPfrFGiza+AQ7nXT/Bumd7WYA/R32HM5cvpq4zJ9N3//wvOKL42TSqcIINRDM02PPz2/njj5PRELSAoICgjGvFT8EDdoEqrEw2cDTexx+nNoyWYQjl0vkL43hLMxpuEA2TIcIZ6mGi26RYC9C93STHz1DyMbFZsKG/zgnC0h/FZNrLxxif7324KhWj6KdMnpImEdEaQ3qLxoSrhxa2ktloiO4o7Lz4KRMEYwuGXDrqYXB5P/13G0EXBu57a9ag2A6m+0mvr6qpTuMxEj0D3NREVzz6wvFuA8n3DgejISwaEAqgRq7G7V2DAsa0fQ3RpsMt6de/eSmchsWLHoliM6Z5ReEy+gsFYDFWXmHMMD7ptwIqaEmXWOWBwQURwU6BMdKoEOk0WyBxNQBiRoPGuane0Q++L/MLDLkBIQ2kRxim8jVtSCsjmwqTbcbqL+PyPgMNH2/ckD7duBFj5ck0HXA1Uqny8vJ+wUlZyVYnoQdtqRgEJ9sWjGJc3OtI/duxOcZjFsxZs5qx3U7f++532atcHJkW9luB1XgQWnV2/K4K2CN0pEsEPKCLv6v1pJNjlZ8uYJBY+NLCMBork+j348seZWsGUVz4JWSbPuQBgNw5NLI8YDBmAG2q9O2/T46AhsqZZ4CQpKOfjSPBItDQfiQ9hOKZyyqjzq7GOZ0K4DbI4Ly6z92VYdMvVYqOJ+srj+L34CeAcSCrClCTPf9Naev2bRFlLWCf3vLlj1HXAXAV9FEO9hdiRAAMUUIOcPn5W34zq4LGw6i1GNQ1nBZTqV0B1Di3xshnBKd279sbWyuG0P4IQFeHad3GT9N//Kdf0gNW2xn4KDLHJ1Wa0cA+adr1nHkmNuYwAg30nZAHd2cZDf0wNgw0GPxw5fhqlOTO5MBtHkA/4+M79O8UWycOdZ5LDccvk3qX0lGb4fofOIJy8bwHCVTWYVyOoy0L9ZBmS/8LCJQ0tbZyFOmq2JIwCUOlqppUb/qnA6dSUP41tqS7zvoAMwvg15uMz4KoYfQyh74njsGcMQc6MFZ837pjZ1rFqn197eQ0GyOupIh6F2zF0sCRH0IhBl8wb9Je2eeZGsmuBMTF+/ZHPjZdruV4O6e9bEz/419nBXAncNsw2LAW+3ASPD9uKAYLzNGbuTKQlO+LdtVIObQ2h4GATNn3BBpUpiETyBv4JKfevo+xYjBeJNDQduZiOnTqOPssCTRkJTLSCoKRD+BwPDR3IUHD6WkEQSxXz6ys7qkX/XDgNL7uoJivIPsIcvC0Y9bZPnioKa3C0CqvqkzL4Hm3OsnHGgrZ0bWmjMIB0Ee8ki/EGlfkxAzxwpW2kNkglDR0nzWp3kT/d36+i2MxN6enwIKFrPCKG1dwSjQeIugCnZWtLHiE8chzwjiiLRAfnuO5tOfvFn6TZzRWOru60pEjRyLQYHtPP/U0gaTa0EvuDVaHKJvqHPWIbfqS9hrOIav0RePcvmu4+AyNc09iaD7czMlL+znmeW8qr61LT4GVGv+RUQdPinVh0NG+fKEe8e8IQNzlJWSb3235Op1xT3kWaCDTA0OoiQwnM/bux0CfT0aJ+/OvXOAMenhMPghMkQ49NBFn1UH217kA3QMbo1o3RqKFk8Wegzi7ng5yCqe6pHRiWrDwIQy5yUF7T6MwlVv9Lc3VWeEIMN86tgaRNKjEr6A/tLeGgnxq8SyeGEbc2o8+jFoFdTW1qRrnaDz1myBwnIijvLpFQmNUWqM1oq/yirrRgJ2yHMa/c8scQMSo9XKGtH1POVq3jqMBCTTMmjo91VaQ0kwKrCclmEGlEyENwvBkvBqZOtzhmIrPZl3xfPvjefJXeQEasZXKQMMrr76WLnadSY+yklZVVk7WAft9+V4sQtB3nQV1hA5GOPvQxPk2Y0q9ZAqv/C+NTOXX8VKPGPBpQD+9jmM0CqPfbMeJxWxRwzFSb0Qgw7nkpfw5jiAoY9BIDx3M7z1vRh8CK6Qf8+Pzt5CpsnH9J2nFyqfIdpwVuK39lgcYtBNsK3iHttQlYpzGudilrRGnhHCPetV5Fe8MtBnI/wS74BgrgitDnupCZ+f0kKbytzrbQIJ2V66fNdbV17atUxErq4xEOhmAN7sptoSuW5fGYYs989RT1JAZz2dk/jB/Olj2WZvS4LL9vQ+b0QxYjXxpdoNnay9d4yXdDR55hOPBw4fTP/zqRY5xfiNqMVwGh72e6wk0FPZDb4CHfXkOFMlkS+rKzLzkc5HtBiQxq8EaUvEZY+zFVol+1MsxyOdpE9cBn0vcfIY6Oi1snfikgd/5rrkXbkD8MQHJP/vxn6U5rAL3o883cUYsQuy0Wp9KWyPbqseWDMYm73is4UYCg24hqaY2zYL5C1L5xDI6g9yRoRN2L7SXM1RQgWXKjX/ypvSGWPF7FHlFnuRFTz47gyL+mLY3wzOFBBcsjjiDwHQs6mFjQNjg/chYlFf4O7fl5Hmx36ws24ugGn2yzwZN3ZpoIfXXWRQ7cfJYun/W7Ag0lJeVxYkUbjk0+1k7Kewb5ZRRhO3aw5eOJ3jcuWAsbhfy2F4XQK1NswubZs3q1djvRWnW/fezpZLaOsiTmQvqEHnbgIl2vQtd2ktmfNhXccv+60AbHJPPnVd51tV35XE9AZi1q9ekpcuXUwB+WuhGM8jCCYWu0kKet3v+Lm+Kvbm8KrO2F7Y292gvyquXLlGXDdp8hA9lhsDzLLrVgJMhMzCDGCtPeCnb8ru00A/I7Wmf53N930tdZcBP3PB+Mw7eevMtMhpK0gvPPsOcFqaLZJG4Rb2A4Bgdy3hIrIFfpIlYmfGM/JLZqfZfLJVWyqsBzaMdR9MedN+OrVvToqXLOM3noVjZN6CjnmME0L9nLgMXwAPoJEY4v77CxuaZUTMBWXVrnCdKeDJWC4t5DQcaCLqfScseXY4fMiXaNRsiTvhjDmwLogcuSAj5UQwLm4lnR5Yc/bYOkMHs4YUjQ47NZtjG4lIDiwTjqQn07JMrs1OO8Pts35ftKtPqJ/FTXFGHOPeht3w043GsEegAg9zmZmDkc3hyF/7xebDrLItOp852pie+/ydpJSeojI5Ag5Lp5PZMsJP3Lbz+5YEGiBeXP504JkpFGUa6q3i3rqe9RFnXrllNoIH0k6Ej0mMPPxKBBp0Sz9M1QCEj6BwqyNdZQVOwXV1Q2SlFKmoj8XGUD2DtucH9UNIql08Q8I82rI1Aw2ScCw2W0QjKVYyJu6cBMLkaQzqmCrigGKmAMIPGrMpMBskVnuBnoMHqs+/S98bGhjQLA666tiYLNGBEGsjohZLwbG1EOgxRDSADCxqwGnIKoqsU/lR5auxqlKtEBd3DrMC+/ApHKSGYizj+zuJcBjF0gjKlkAmcjpjGj+9p2OmQSivZkF6HAe2zc6PdcRhoMBr37ttvpfm0vRBj695Ag+lU4djSlsLndwLkeubC5ymMAqsGqxjlakEWkSNlmBWj9US4d+zYxukaK+PIQaPMgo1ioRLTmJa2KnlTMn1PofNZzmk0Km0AshBOfgq+AoFbJ94mSOJ3fvzDH1K9tSQKrBnhtfiOBpdOl0+LlVFoa0Qy289JepiCz5wKihqTgquFqKTL6dNUeW5q4uirT1MVxv+ji5dESp3gZ3sai1Al+FKay3exSorykW+vkRUhPeK59F/Q7cVqh8r5FDwj+O0nUmlGw2IMhvmsMmoA6chkgAMQudQs+DGJGkgWifHZKoje9FOlapq0aesQJw3hCB2V9CGMIav+ulI6kKKIyznjeTKOkXUpPGbMdvpx/0BkRBoZaLAtI64qHnnR/sYKIzSytoUpw4L3ORTZNvbWfgbfDMOBNp3VuiFur/how8b0f//jP0mVVEMbI9E/tZW9U01xMdW4Af7LBhrMaEAx8Pyr3HODwfrPbRP9efVl3lUqTr1xhtvMtwpWehsP9u9rvLqsI9B9NTVBy06WeY72OMP/+jmOiyXgN6NuEkWOinE0SK3HiMgDDYdaWtIb8PtltiRMnjYt1SCvJfCNAbKo7M5cajAHrenEoAHImnMKtkQQiLEpp9JbhaNs6eB5/KC8tAND7sP3V1NgcBKVomcgrxgrrGI6j+49ZcgoF7cAZcauRreriu6DdTtFKDfuVU6ky7mrF1PriXaOt/w0/U9//e8lbSrERx4IJE4uSmnKxDFpAumdA+i0NHRFSwP6t05uFmgAeaGpJi595f9mWXkM5G147DZgdIvf7xhoAFvcOtHaRUbD8U7S90jRxkb0Wo6zMXPylLR4waI0e/qsqLYvTWLV0fb510vmRZ4QSuZMuQODoKmYZuDurbfeJNBQnR5bujQCDRodOi62IchoFIk5KmVxQXrILxomyrm08r7sHkeSbYPyeKnPCYBt27olPY4hNH/ug35EcOhiFjCC3mK6smrBJZ31yMawn2IW8+/8+dJ31Eg3uKah4eprSyun1rDCq854jiNOp9TVRQDQlTQAKWQ0ggDITxilyo+4yPcjs4Ex2LY6RLzRYDcQYDrzYXhSQ6iBYENReXla8fjjEWgw8CXehMEFjcQXcV4SOw6xWH2l3hK/XLFkTZPaJ4yPmigDBlM0ldX03RRYPtTQQN8vY0DPIXPtQYpTDom/I0hCezpeQdPgUMYvo4YcZiuZ1uBQN0E4MMYjldnyAx4cACPfe/89tppRh6C8gvZnccxpdXzddGYL4BkIGAQee3KFukkHXnnTwLaIn21LJ4MZFtxDQCKIKUe5qv4Bxr9FESch0/U1dRRHGxfBnch2hH81dsPZZU79rkatfKNjEDqdv+UbcdICfCjKVDBsSJxS8S6LBBvWr0sPL8LmqKdmyzi2JuI8GEAyK82BSBf5Q3oYAJexnOs4QlSwBNMshvkF8nUDjO8zeCCFvoZFVf0XyW7qPn4yPbFiBStdFDjGcTH46VyIMwbE5dNwtJjb3tBI2dFu4e1wGGM80MdiiY5B20M9Zar3a6+/xl71kvTwgoVRoHIggVGLscaX4Qn+i/76M4J00CTG47OkF5d86rjEWedF7HAFeBOG/wfM7VPPPpfmgamR9cV9wdf0UePZbV8QmsbBnx6dGKnA6GAzX6LmBCQK24FOuVgjFnhizYaNG9JpVmJfeP6FVF9dEzaE/GjfnU+DDv70DXV+tiDDc2g32x6DHuEz7RQxRLvDjAuDJEcIYKxduzaNwVl87pmng2fMVNT+jPHTrm2HvcSYfEo8T9rxvnZT6EDoZfBRfas92dDcnP7uH/8h/f2rr9893pJbs4yGIjMawA0DDfTLo4O1EnrBIzEK54D35XGzGQw2aAd6Wbj3Pv4ITOZ7IAqnUhBo4Pdu8PjIGbaJNpJpArQyTMbBCRlPfSf9m5/+OSdQzUz9KHZ+4/xZghU6kdxkMABZMNPQZ4wYNTL0lcf3rYMu1ngqq6hMix56OJVNLEu3kclrFl2GzmFjOK/wh/MRdq+dRHbMbLReiQ6X2/HUgR4lKb9r7+nsbmVex6P3pqP/ZtE3iyRe4Gh17Y9Y1IP/fUYMHcbU+ZWntFetC+V16Sp747FJnF+DGENGFUZNklfg9/b2IyzqzY7M5FLS1MUWt0fZV+0waShOypNigIFG+Zqm4BOCXejhyO5F57o448lcBiJ37d8XW5FGsR30fgIN47G1LRhvxrOYbp9tz3GIDbEwo51Pe2JvBNaQTcdioCGXM21A7aNPsIU/ZNvmCjJs3FYZzmfwNwPmp/RQ/xlwVF4NZsjbfj+TtSzTwHT8oJdj4kvayaewV9d8+FHqYqvA8889T0C2IvSnDdmP3OaO/tM/n2ZmtXa1fZUu2Sq+bhqYDe9oj6tXzLI1cPfqa2BNeXn6/gvPh/90EZ0r9nnKkTS2/96vnsjxXJtavBLDwkLg87CF4Rs/MyviPPUz9uzbnz5cs5ogzOMcFbo45jQKu9NXZVR/zi1n2sHyS/ghvK8M54EG9apH2ps5bMa7vHPydJbRsGffvij6+viKJ7DLpjGX0JdnaxNo69l/8Stb0MIvgCbSWJzxlfffrfgG8wcPGwr/3+EUjLa0j5P+9pKNUci2u+cJmtZWVESR52vYCuGLcZ+0dB6+oH6SwRvnVNpr0zh/mTxAQX4XU/tiM15GnrazZXbHnt0UhcUnIdBwgm20K77//fQkwdMJ4ycyChglKC/1v73XHyfQIC0gtpMKVQPABC2BDCsqjhf6YNW77L1qpvr3aAINiykiVxdHTV1wAzbfM6rnZN2HkeDKoWf+yqixsshnkjkLNHAmNso20swBiIvso95I8bf1FE0qmlgSxoqBBms0XL14ORS+7Wt8uiKtA2+AwPcy5sjSagTbcIKdS4RE5nO14aSBBgyhhgP70mycxRocunHFE0hhHByGaC8MrfuuY/AAfq6Oquxd/VV56RRqGKnUDJqolH2OqaoacRapcQ/Qq6+9GkbaooceIj1nXERYw4mmj5HyA8D6t45/ZoS6YoQxQhsytZWzVTbhIAlszIVT4tYH61e88fLL6aGlS9PDrIrkgQbviKihAOhXEIDM0Fep34pIrs58FCODXn7mpRPsXBnUMC3KKO7u3bviDOPppNGpzP3Ml4LsnjxXLKx1IHDnQQaNHTsZBiP3qih0ghVWvycIt3d0pDffeRv69Up/+pOfcG53SeyH1oEQlGJlnD3v0iTbz2mBJYxH5k8nQvAwYi59NLQMgDmnGkL5qshWakxY1G/pI4+kkazeC7pym7SW5gKU4OA8aEA6h7egh86tTGl6p05F7ry4gnEC8BO49xCpPILSXE500hVY23QvuiDoMwySeLaulajdOuNzYiw6vtD5FsaoFe4dk0bFCJSxxWY8vm87UdYWfg4YNCQ9RqDBrRMWWrrYfZ5+Z0EGDX+BVYNdZ0aC+y8CL/zlfmZfEF4rLNo20LB527a0jZfFkkwzdIuCAPzB+vXpr37+c75JoIHXCLpZU9k/VZeMzzIaLgPsjMl51OC/Cv098hHqEWAg0ABf9VMueJ+R4sRrcPE7suO47/NeaHwdup9Bpo5cxEk7e4moNRH9C/HY9MMnn0rzZ81Oc8jgqC4uJpjC8anwmIGGARgsB8GYl5GnC5fOp+kYE2Y1lJHppJHt0bhoQWjPDwKE0t+q8laSVtkZgFRhG5CR213FjxUv+m0gxqCDW5HWrvkwAg0zpkwjyu1pDJyewnevoyx0vvNjGHWgxSxXnTSeLaamcXUDeVAWxMhuAg0tJ46mtaQw/y///m9jkENgBQ3NOeM45qyiMBUR8BmI16JqlQ8N+mnQQjZ4mv/xmX29wwsmwjHjXvhWg8xjBA003L4DzhFo6MN4z1+/xYkT3eng0W72sVP/gmnjv7R02vQ0jfo5yx7mzHAc1kJWfi0OJgY4QRYh1EES1gugt9soNATEBh0xj2l99dVXUg00X/Hoo2GsiEPKtHigseNLp05F7PeURS8j/xqDWao3xm0PHviZKy7WETDQsJVAw/KnV0aGkEaVq+oaKbHqooHCnOrgyU86dz7PbS/irYEADWh5HcEI/BSP3edpMGAHMmUw6gUMOY/eta04xhPe0NlVZsNYUVb5XZlSlsxWcS7dxheyypSIzRorzrepnRYlPLB/fxoHhi2HNmY0iHPBHxpE9pNAg3jjuDSoXblTbxX0GIQeu3bN1ApW1HsTaBDjzMb4nH437eXoaLBr1iwCDXMeSCNZWdfQsh2zmTR0xO0cYzUY1R0ahfKSdJFPdTLcsz5kOEV8BxWQ0dAUKbXnzp1N5ZWVkTFYUVYOfdljTlDTIIM61WMO7ecN8FgnO4IyPUa5NPE5ti3tQ7cSyDDQ6WrR6lXvsrI4PNqeSjCghGBAZHkR5JHucFk8L2RTnYqM6XAqBz5bPBHfddRdRSbinwaPGJbOgwuea75p04b0yJIlaQo2x9gRFDkFFw00+H31lQ6Q/OacZk4t883vfZGf/gC7fKDOJlcu3S5AL5BhNHTE8NTO1r5f/OpXqau1LT2FYVhbUcX2u5ERCMiP4VZPievyioanfOgVWSRgoTIcATD6cgumlUb3wU+uWO9vbCRj4hWyKGvTEhYKPF0DhotUZuUlc9LpNfRQn4Z8watiPO/gTDAK3jebyIC/wU1PPPG7bmXaSKDhHZyLZ77z3bSAjDuzleQL5U36ah+p7wNvoIG0NoDn3/Zfe8MVRmUg3/Lh7zpK8uX6DRtCPn/ACl1NVVX6An7xCG5tDdtxL7bB97CRaM/sH9txJVq7DwmIebXujYEG50gnxOKwbR1g5kcfpTFkNDxPoMFtJQYgxCH53XGHTcFzIHHQxu96WWhV21Ig05F2YcJsBgPO+5ub0n/4L/8l/WfoIkcYdvJ6uo6tE8XouZ4aDX3FA3jD1sVZL0hHj/kO82iQIds6ER9F0Mqs19ssSl1Ht6n7blCkV2ugm/eOnO1Om5q7cTi438S3a73Td575Yfq3P/vzNHfG/akfp7fdIK0axsWGAsPgE7N3cttgNAEXiJn2GmiALhaKLiktS0sWPRIZDdeg+9XzWZ0Dae9iljR2kU+ax4lVvK/TaCFLFzg8jUW70tVXAxnnoe/adevSdhzqoglF6X6CDLMJPFqQ8fyprvQFz8izvsTgoA1tyv9iu4Ei9bSYG3VtwAWxYAiyOhRb4wQFc1969VXsmqbIHnbbYzE4qR0Tx6fC+7YrH4r54qQZfs5zyBlzoX6U15XXyMQgUDCQgC8KJ+3DHnv55d8ki47Omj0rjYNnrKni99UX0kX8FXd9xbZfZEYaG2gInKAdsSYKBmqD810XyDzpaN2mjWn122+nlQTu3Iqkjg97Vtl2/PCdbeROqHKWBQCxvWg3AhOM4TI+jTJkUEIbwsy4bratrFpD/YrW1vQ8gbvqiorgafWqsi6/53ggXXyubfq5z/GnNkj+t4EGtyqJGWYMuu3xZfyEiTXV6Qff+Q5+2qg4TtkAz32IjXlZ6g8eFL6NfOHvkX0FVilDBh+cW/FMHBGntfeVSbetvP3a62k5tdZW4Is4p27tU1+4eKLucDuF85ttwwHHHIfPsG1+t331tlvjLNx7A3vHk2qaW6mvhLPuFsinqK80mwwgdYKLzAaNbmPryTNm3GmDG7gQj+2rhS3Dj0Jy7fcV+uoxumKB+uQYmc/q7920P6xgaHoBW76mrCJsbfEstzfMapHm+TZ5M4f9O9tagf3L850nM6YtpDqwEP8Uedr6+U5en6WL2FanL1Oj4dSxtIJFVQMNpSVlIDlkDouVX77F1x8n0AAzCSY9VAkAB2WC0eC01MAkfUzRpOM41WNRxEsXPETqMyuwRNUvsMfUWxX2fJ+LRo8GvmCaZzTYtoapq7+u1ApovTCITOFdt4WMhk8+JNBQyp6xqZzbXU+F6THBaDKboKHBkhtyGjG+lzNxCCiMmaloJhYhVPn1CUOuk0DD+2n//r1kNFAMkkDDhJLiKByoc9IH8OuD4uiLMMpYSG4GRIxbpRMOKO/HKgVGhopYharQW4vBVO8XKVCjo7lk8eIwQD26z0CK4BfGLsLus6RTpN4AQPZZ/YaKQGnhPGksqSR8PoJrXzQkt7Da9dIvf5EW0vaiBQuiKnakRAZA3usAYHTyjAgoABKCpIaJbVlcUIDSUBIMbVsj3qKHH36yjoyG7elPfvSjdD9pdBGEgBdc4VCx59HUCFBgmOhI+AzBwxcNR3uCoqvA4ZxhnJsi1ko681vvvBOrO3/605+lEhzGL3hfh1DB1RjsBf01eoMe/HT12T2LOv8Gd2g8lGWs1sFPRqVd+bAAXL51YgpK4QnS3TxmLxxynDPbNC4vKEXFXsDK5/ncUHA6LPwtYKssfTGAeNYx9jOb9rp735509NjR9ASOy0ML5odRFQEK2g9nSLnh5R44HRiDDmE8QwuNfZWTSs6glQbRIBwXgb0RY8iMhqZDh3hveHqSbJKpOHd5RoOBBle9+sAgFuxzTIK5tNVYD9CG9BGJ5j0LQGrgWo/BY5E+xQDdQgDGLRPjKYBVUlJKP26k99euTX/5d3/HuFOq4hUZDVUDU1XJOAINWUZDL+ZX/vptoAEmhj9djR/Aal1/xqIBEv1gzK60u41CeiDRjBMeIR3ajIam7ivpQNc5qlFfSyeJEXj9xQ9+khbPX5imV1XFueYF0DxOQgDYidCl5qZD6cXfvEQQ7EyaOXtOmjptWqqsqAjax5Gj8p5YRaReo91gksEY5Ut+1XCIlHJ4yIi26XkGj4ygq4i24Bi9TVptXU09xsQstk4URzpzL8Z3i7mCjPAiHZUPxQDaVekGjjnHPNoAWBjwvN914Wxq7GhNH1PY7H/7f/5TjHEoQ+mNwXl/CYGGyuI0gdXZvtZIwPAzoOGqnQ/KArFQE3Dj6bTNmHj1gcZZ3RjoqSwyiAg0cE5obxTrBfCqhW0pjR3dHNOYUpNRBq7HWNWcRfDksUXL0rzZD6a+BGC+YGXSk3n6IEu2f5M+aFi5z9hhZqnPYsSN4PlXMOSmEOB5ilV7V6nFj7tZVtDC1SGxxPn30ilSnjR4eFvbBQOJiD9YFgYG3zFYaUV5V783b9qYnqbw7EKCpn7BjAZXeQvAqrtYyTyKnfGir7FCitzo7Bqi0dlF4EKWlC9P/jHQsJNMHoPQzz/7fJyXrSg2AABAAElEQVRVr2wakPIoZXnG9uKkA9qRrmbCiS+uPjsfEURizkN3IMNmBRh0PoS87iVFcj/poyWVlbH6PRGZCtpBN+XTgMBdTJMQYIQOk4ZvtqqMj0HwXQcG5RDOtJgvjkWgYV9DzJMZDXOZR1fWTTXOAhbwDnLmVhsvnTbnIPQd9M5Xc8RIA5piWQHGeR+CGYfAmHdXvUul/xOptKw8PYAOnITDLj4a5FEfizWRYQg2avhfx3hUDyhLGnMabGGQ0q7OkTpRA0/aG2h4/913IgBuKrbb70onFEffdS4IqYSRK+2jz/RXedJxcQ5y7JQZlVNx5zbGaH9o333xQlr14QeR0TB33vzIaCgvLonjZc1ccTXK1UX1mSS/SxP50fmAZgW9cYTFJZ53jU5c59icXhxxqHPVhmP0i1/+KnW2HEkrWcWsI53Z01os+GdKbQRI4AM6zGk8YjD4Di1CHnBsBQr/NsAjtpg1ZXaZtNJO2LV/f/oVhZxr68i4W8JWJGwn6SFmRZvwXyY3dJ7LVN3Q+/BNBFHUqciRsqmxr6MXjhFCZlbfRoJ2q15/Iz3JfuCsrgdn1TNv+WpfvsqqrpdWyoNyEMEq5jEC7NDdrULKVgT36Ye1EjwxwzT7czjQP8Jwrq3BUwe7lC91kf0PHqFPtuMEmP3juOVraSBfyqMGNXS6tEcMzOWLBGs//DCNLS2OVG9x2DFH8Iyfyg6NMnP3OmDZYkrUfID2eUaGK96ukso/+9Dbf/tzMho4PUBpgb3jemYSRysT1BjRs3WiHyQ30OALEoR8OQthj/HGDTA5Ag20STfiOOA+mLQB0fBVbzLpiFCmi3TzFPWIWk6fTesOnmaBjUZkOX6uXPGD9N//+V+keeiZvtcup5tnT6beN6h1Q6+CT9BJsQJLo0NHFQY/fU6g4ZO1H6VuAj1lFVVsYVuaSsoriBzgdOEM64grs9pbTDYdzmimo6iNoeyr8wzuEGYIXDPQYGDtHKfTmNHwKcGGMdjX97N18IFZsykijdNEISWdUudIW1ueEXscu7aZfGlgSlq7Wm3QNIIY6BEDmr1ZJOhg9fglsntbDjemWQ/ODd1dhUwVICsuGsIIYf+GzqbvypPZmmKkzw2dIjbwbBeYDKTEqj4BNDF5H/j76xd/RTX/0dSa4aQVgr4jkCnn2QwGnVPtMLEGwcowXh7iuzB92Ntij3qdH9F20JE/LODtNuL32VapMz0HHWjwVIdTfnNBTX2m7gy7l0cE3vAcP/fKbWSDfN6vnDkmn3H67Jn0NtkSLeiS7xIYnEQQJvQabQY/0w6/Br/bVrQNnf0Zz6WtsM35TPm2XW18n6O8GlB+jeBaGYGG77/wnchGNDiFMxMnXDmntmN7EZiSV5RRSBNBZPkcOvn7LfhRmRbnDGieI9tlNxkBq995Nz1B/aMVy5ax4Dcotq/dFBcZr7Z/bBenz1H/TKyhrcArPpOR9CmUUxROzIfbfo/3FIO0ppjboZ/F1p4zc2bMoxisTaCtp+2u7ySvyJPaJWKFgV/tYHlR28BtVAZ8DTS4QHm4jczkAwdi6+PYkaMjo6G6ojIyhDy62kCd/TSAYcNmtpjBp84OevMZnB/j8Lnin/PaF9tIX2YrNofBhnOcPmag4dSZzrQSX2rl00+RcVNKmyIQnYsXP76lFzSCEv+Sy2/DFB6lZmQdSQrAC2mV8yFU4z6KQX6yLp1ob4+o/KI5c1N9VXWstF5mVVwg0shSCFW+TrgvgU/pUbg1KpwoGWUAk2qEyjQvheSTzZvS6rVrokjj1KnT2Ds2JVKaZYKbGIqCay8MN0FEMFFZ6lTIiA5fQZahQ1J7xiC4uIp5sotKuB98EAUtZ5BabI2GccVFUfTJxSXPk7+PvXe9HT/CLkP4XRWmYKeTqNAZNXPly6ic0dZIIyOiaBr8L375yzCmli1dkspKy8JAN11MuigoeSpXOKGMXUNGIbGKrwLi/nHbV2gEDivtmtLseLZhnL8EuD44fz7G+XyCG4PDmHA1QSNEQ8Xv2LYGKF9hHoywYpTQb6OLEbiA7jqqAoKXYOW2kjU4n1vYN/2zP/2ziOL6nWy+MiPIe6VxDlLxrHsBkAe6quQc5C9XOc+xH/BwS2t6//33QxH9+Ac/5FicsjjqJoCSNgUPHSoVg/0UiOQfwU4DV2NF40fAQ7eH4QIDxOrocbZl7CfKuh6+VKmtxDEaS1T9Js6+/KKykmcEp/hbcOY5KjmBwuh0BFOgn+1Le09MEKSOYZTvbzyAc7Evfn+cFRcdI1dvzGiIFRdo7UpIPgbNFZ1IQZauMge8o2LmWYK5Rlc/DH8DKUafd+zcEU71ILYiPfnkk/D85GhX49zIvO1aOFDD38CMzpzOocopAg0Ik4pCFXeRNEYNjdGkEqpE3Lu7jeKEg4cMS2N4r7S0FAfxZhZo+Nu/dUpTJa9CFutqqwaQWTA+jby7dUIFCf/Th6vMxXXooUtjfYaBvPqhhJRBceI2AQVUEvyHjEhbXggiqeH9UufVm2nfyXPps+NnOSKNvavoC6//7mf/Ki1Z+HCaUlGRJrCVxECDRXn6YnQxQByjxvQSzm5395mkvE4l+FVZWRlKzH3TBhSU/9j3zfP8W6fCl45jZAr1KB6NRo1/DVJ/epb8Zmjzyku/Ydz1YWSVsW/dParZkW/wnCAF71lYUnyRFhr4PDLm0N/7iF/I8HWeeZw9eQ2tTekT6P1//ae/jzEOxy4aiPM/rbxXmlZRSY0GDDEKkN0mY8I2Iz0YR9lKx31I5WbRGwrngQbozzgM2hi8USTuBhrIaOiF3F8Ar46QJXLoBNtS2BTc3LMtZQU86n7gRx9emubdPwdHc1C6hBN+Dbzqi3LvjewwbTGmcGiQE2VV/lKuLZz0xuuvp+lU9F752GNpOGmpkb3FeCOwImaBTa7iKDtZcPm3mCX+69x4r1ggGIk951nNOUUBuG07dqZPN21ML/z4R6SSzw98VJ4i7RKeMY3/BoreuZSfVP7irBwoJtiejjSTFYE1scKaLicwUFpbW5JGuvL3DMaQGQ22obMuv6jj/Gnbyqm4aV+Vp5BRWFrMl2f8262DZju5N/jAoYNkfe2OjIby2toINJQhUwahxN2Qz54+iwPKh3Mn09BbdB4YwD91iY70nf7wE+1rLJ6ALrs/+zwdPthIv26wZ3omfDkrCtuqk8Ua33csBk3FeAMcofYZf0woP8OpE3ho3/5br8WxHWg6lFZ/sCYdxeByb/PDD7MFYeqU6N8F5kUdGJhI+z4jAhs8M1KRobVYwy04RtAKevFkxkEABSwyMOM2rQ9Xr46Tliz+Np1gQ+mEonBGzSYR39X5MRe0JT5l2Tt0nXalk3pdfnGl6zb9v8nLYwtPn+9OH+AYbdq0PtWzADGDtuuraiLV+wscF+muPHpFJoM6ReOZnxqE99HxIX2hQ4/Ou8XDrnAY+m2eYxCpDWf65ZdfSV3tR6nR8GiqQVYNVqu7nTsxOJyKHoxxldLVVvWuVfDFd59rkMFx9QXfTSNXp5sd4+k/Bhqqa2oJNCxOE6hdMaif+8nBdvBY3onMCL5rn5UbX47LZxlUNyvBz5TPuPjdy60Tn4JlH6FflyKrbp0Y6uqueCqNuT/basAcagPQZtSHYSxusbBt5ce+Ks/yZ6TF8577suMoQQINZwhUfv972YlUSEU4mdEWbQYW831tL3k59Cn9036wbZ3e7H1oCU8OxDk3WHzsGMXlMP7X0/44slgtBqk8SWv+i2e44hzOrvNA26FP4SPfs6CkVHA7mw67GSTqQQNfFmf7OQs///Dmm8wfYs/L65nJvVMdxSBHgLdunTDQYP0Og/nKrA3apveLAdf5sgUhpY9XL5yd3gR41fked9xvEEEdnJlzYHHHxaupmdXYjxo4dlph6Xnwike/m/7bP/3X6UFkuu91jhA834nTB1/C+G6b6sPcir8uzMk38tEueObTDRvYytCdyiuqOOHmEX5WOEk4R5lTJybYZ+kljoESmW0kDkAr7Q2xxsCLPKqsug3sLCnwH6/7JH2KzWQmwHR0hYEGj/szQ0iM1FnUDnaedCBhvpg75TN0Bp9JH+fa+TXgqG4Vh5paW9Jr0P0IRzhaD20mzrpBTbMVTDO/Aw0z20bXTZuPALf2EnOs3SmPuBjmeMQdF4HU3eKYGRuf7dmbfvObX6exZGnOI9BQXlYWtSa0jdwiGBgp3sgv0Ccye+AJM+I8Bc+AmphF8/Ess4Mcl77JOWiznu2Pbp1YQPbRLJxdaxQNJgspt7H9oryt76GtoQ3gHJjBoE2r7Fo80sv3xaLsj0TWXVd6h21OLc3N6YUXKK4KXfKTEwwi2qYXMwYpwQfmWF2b40LIGG2GLuQz29cf8bkekXyIdle9u4pAeEV6jlX7CWRqGgDXr7HQvfaS9FHHGRjVx1F2/NvsF/VH2GDQTvqL9eKcJ81oa+9C/21avz49zqLY8sVLgi4GGbSt9XGC9vQ95lS+cf6YC23moDFj03eKguvoPnnTjJuTyI3BADMarAsTGQ0zCDTAC+KV+kE7PnQ4bTIBYQsH3zMXmY0NN9Fv+V55VQb6D2JbPrRxwXAHde6aCfCUjC9OTz22IrZOmA1rYM1FSHEs063wPZkJ+h6RKQeuRRYLffFv+US9wgyl/iwyX7h4Pu0gQLKN19nLFyPQcOb8mfQM2dsr8RnGjBzPqJ2xbF758a29/uWBBofO/MnQMi9UjMkyqqWjbNptI0phG0Jo2o+nTsyDESYRqezL506WTBUMIFH5fiApjGygQeYyYher4H4k8/XGsesxLi5epqjRZzvSJrIahnM8X01NLUffTYqUZplYJRmRVSZZ8NAoUVH6mNh/DFOHIuJ5DCAUm0oujC0A1v32HyMgh1uaUz2rLWXlZWn02LEICulY9Fsh7MtLZlZgFGj3hQkoHo8nONlulnHAqi9jdFVPA0egsgjLaxjnAufC+QvYT85+d1apLVBjHwXvcPahk3vArXA7FCb1PY8iygIZACOgFCvJgIiKPyrp8lyPhHsf8KufkhWvsxKuzzbSqwEVq/MIhoLn8xTqMKzov+9pzEh7BUYgV4Cda8fgOeLrSKPbxT6j50m3cutELnAKlf8UsGw/dmYM6ZTLJwJKBo7wjf+kEW1rGPk8j5n0TOhNtG+xwqdXPhnFsLI97xj60EWaWQzMfrtq47ypFOQZaeMc6JxnioabeIb3ePTmqVOdUbl6+/ZtaRKBqaWPPEIB0VHRrnxjewKIz7B//pR/5OmcFhpjKnqNIUE19jdyo/vpmls4cqf5EPu/uzhyblFUOdaxsqK2fCOwBj/SngVrpJsyIL3DGYM+1iwxjdQ+8xGgSPYB77cdbScVu5G9jG3cMyD2H9fV1ES7OkFh5NNfV440uHyeSl7ayp/yhyuCkZ7aw6fy0UgMBg1eK1frdKkshhF5LSoqikCDJ4z8x3/6BS2nVI1tMJKu1ZVxMkJJUZw6caenRoMK/havK5DbQINgaYBhIM+0TgN+AGMiyMG4dc09GUFF0IdXbw0V+tlJRsOuY2fStvbzpMQSaLAZrh8//UKai3Ezo642lRWNxwhhxQ5F5xwb7DnUcjiOi70McNcTfKnFYZyIEerK5d0aDWCI+KFBIWGd7whk0r687aM0hsQio92+VEiuuFmjYfV7q1N5WRWrr9NS8XiKQYI7dwMNjM/VT7dROJcWR9WAuEbA8zqO8H3wa19W4Af2LwiD/mjnibSvuTFtxOH6GwKOXsPxsYcwbdOqBqep5aVp3DACDdS6uXMDhR5yYyoxjgO0vQ/DtzfbJ+AcsAVjA8Xfmz64rchVCKaeIbrSgHKmtg2CH6dOtJ25nJpQzCdPE2jo2ZayeA50xSF7mJMLZlIM0n3+l4m630B5ms3Qh+eInQYcpJfjc/VFY0U+E8s+xrHzOL7FixbFlpI4bUL50CCDtsqMRqC/K6saSzpFGiTKmbTy3tzI8v4L9EEjQr7cRZrhCtIj53KCjnLodgGD1K6qi/Wx8iq/M8emBxtcEzMiFZV+GJTNnWqNJYPBFpE6evQogcf9IXvLH10ex1sq/2EsM1ZxmE4Hv4hbyqunEsl3BkPEefFGnolAA23LjwYampqbo0bDEQyi0vKKtBg8KEambMfAhXR0lU9eVHbFCX8qJr6XBcFoW3YFC2LVnvaVM48RPLC/IR1tPRLt1NXUgcNT2QY2LPArAiP0z76HnrNd+T4apz0fAff4ku/tv3pX2Zc+1prZTHbTcVbvTTn21InJ9ZMCuyye6olR6lT7KbMF9vT03TmWJhpv4r46SpzROBXXxCHndNPGDbF9ydXLSQRiTIPXgbA+Ruh55ranu9GWQdFwHGnbjBMABJ6BL3FcbtEVt0/4+xkM6M0E8DRCPeK2nixKsw7cEmQKvxgvbnvJB+oHHQpp5f7hXuydH4ysqmPsgPVmkOoIZJgCbkD5/dVrON7yTJrHtkqLNQ4fwhHF8F3Mn2NnftWx8rYrodoyBr/kc58XGR/0X7qYSZVnBVxF5vZiN71FKvZEjvh7aP6COI0jFiAgtfSJjCmfAW9Ka3W7JwvJ686N+tTnqFtjLuiHulW9bsDjc+iyney1BwkwziDzywKi6oeYM9r0e145fypTOjH+9BP5Rb4xE1K+MX1YbW5KdAf8soW2zyJbjz++IuQpX6SwPe0XbQD75RzECiD8IT9G+jH9jAAvPGhwzLEozxaDPNbREbr7cxyA0ePHpSWLH4nTf3Te1M/S9bYYDC45l45JO0S8EE887cW+6+QabBDfrzOGS2SaRl0Mavz80xtvpUHw0uXMf0tPc7zlJPRNduoETiF9ug9MtfZCUIMGbVO9cXfrhGIRFOFZ3NdPQQgYgY792aYCz55Gz7Wdv5IOc7zlxsPwIW0kYHrIgAlp5eIl6QfPf5+6OVPJmCXb4zJRYQLOvYnuxfYS+q3ja6DBsYhBBxoJaqKj3L7klkeLHpaRPeWqq466OCNPShfnN+Qo8AAeoXvaTgZlzQpVznhYyK38bvr+VoJTe8Bg9VpgDTpQuymCmdJELICWQXv4LKd9BBl4tnznM+T1WBwDZ3yG2VTuh19LEKPzdCe1lbDj6yezSFARC2tXLrK6TrvqbHoV9pOk8vfIGuSzeJ+2fFc0C/7lT+1deXof29feo+bX2HFjo0aDGOxR9/KgW3XFey8D1GKwsiJWyZva78qVK9bas/JyBJrhWS+PEjbjbitbiaeAv2LkaLI+zFpW3rS3vMLZlC+5hpKhamDBQLrbftR5Otba3/K7A/K78lUnGX1mk7RjE1vjx61IYc8xNgPH0iDjQOVTGjjFfhPyOr/86uKk70kXdYD6w3ly64Q1TzZga4+GNksXL46McOXU7BeP0/anzdofbUT1wwCccWtheOypT/+trQofQC+DkW55PE5A9gABvH1gvZllCwjyiDXq6+gpfCPtc96URgY8pYXmo8/0JV+qk7TF1FEG1jy5oeMEi4YHGmK71rJHHw39Z7tipLMjx9iOPC8PSYOc72PRDX6U97UL7JDt9xdHoY2BdouInqAw/Xjm8+F5C1Il8hTtQJ/InKK/0txgVmQF0oZ8L53FuFiQ5m8DrY4zsrOQn25os5cFyd32na2JnWz3PXuxO73wk59GwGTsiHF2OuZf/fFtvv7FgQbnz+ven2Iz5mS8a2pTE1Gh7RgrTTDbdQRyWk1tFE4aiuJwxSIcOxS9kXTJKgCZqiwuO3GZIStTYgBxfxjRTJqM6IrZPhTynv17SMcpiKPsKisq2B+s05iBaih6mRkGE0Tck63DrNPn/kUZ2/5nAqgQ8uJzI35WBt3G1oDjJ46lyursxAnTrQwSCNhGzGJVHWdJplKxezav7Zn+r3OiENpmrJ4wJplbINFIPYrStKiR0GiEeBxBjCEY+Br0CobsZf81igRzgwyCo8/SANdYE6gdm//C2LVPALnfjcKBpEhaMbwO8PY8ZPvh5zpfKnGF2GcpfIKXhr6GQB4pte3oL33WyPB+FbZRXItNHkIYH1m6hFXe6qBhGEA9wifY+F1XMaSJ+8IEcY1/I8YxRwhfOL483yq/Pk/wPUEkdB+BEttYQEaGxXs06n22LwEkCxq5Z5e9ZIDTUFaKpIH1IwyGaOw7XulP54JWruacJZDRToZNA1kNJTiinqvs6QHSwT5FoAFglufsrwpBQ11ekU4aXN6j4gz6CCQ8n5tQyOc4mvM4NSaOkvZ/Nk2ZMT2CX/Kc9wet6QutcDvt63z6fWirseQpCqaLOxcF0MP5lWdcBZBm7n+1/ZOsNtORNGPGTOoQlEbfdPjCyGWojkPesb8Ct1ksznmkeENHA1PyzGUML5WHq2z+bGxqTo1kBih7/ZHR0aQaGsXe+fnu9No7q5jFlGqwx0cw3Howt7aY7QM4Drcvs7oA39iuq4pX0G/ZrGeBhgJS3dw6kdViMOshCzTIgwJ3bwyhPvSxDwazWyc+O3aaQMPFCDScUoi4ls16kPoINWzTmZqqq8oJyLGHEgMxV2ztR4/GEVzybkVlZTgY4+AbZdt0aRWO8+DWK/FDpgwDQ/agfTM35CWNIemuQZSvrLn6rSG3bet2DNtSnl+NMTE6ZJIYEARHFiPQkG03gvhRhd4UxSuX2TdKDYs+jLk/q6SDBnC8H5hxlG02+wk07GT/6D+++7ZDTMPoyAh+1lcMT/UTi9PYoWQ33cY4wcjsdduUVIJ0zJ+njPQn08AAwB20WQQb4NnevN+XNqJLDMNAg9Jyi0DDHeh7nj3B7d0EGuCjk6dvJ7YGxzV7KkcDg3EzJ1udv5otIcN5ns7JDXCM0x2YUwMD8tMt5iqTQ2RFmeTl+fYauSVlZWHgWgA22x/ag63MgfMQaMtPscBXyCOpoYhe4KdZIM4GtwTfZ6dOdMOThyKLZz4FzqZNmxIBBudKJS8W5FiSz7FGnnisEScWa6SIuWFIg8rBp9DLtNdOUzCbD4fBOQ/HqxzHNHBARxYa2LmQ/Z5+iZHW6dF5tn8aoHQ/0x3wjYaczzLYbMC0pbU1dXV1khVQTB2F++Nse+kXTiLtK6fiqoa6fCkeiLUae64sKSM+S4PIIm0GHDRMu0lLbW1pSZ0YQvZ3IjjvyQ1DMeQyA5x5AqcCc+iTOKaB7BzGCjLP1JiWJv4L2tC+z3ICjuNMaySePt0VqbZTcCyqkCsxJlYBoUfgIs+2/7EPlvb9PZxaJ9WL/nJr4Iv4bKDbMTmnu9Ahg+CrYhx1K+RbX8mVH1dJDSJJC/lGw1gj0ABpGOK0c42TNsz407CLwCmso6MnDlvfRufCY4A9uaG8uDRVQp/hg4dGvRbpLy0cp7pCne8Ruf5txfU70K0AmXF7hp1ntuLUCfew65x2Yfxv37Yt9qbXs13TjDgdsAIC49oZzmk4MIwzHC77TVvqjgh2QefMmWE+ob38qA6B28Db6xGsts6B+DWTzCwzp2wbwkVAzDmVz3yOl/xugWl1jPrTzyNoR7ve57OUVflEfm1qaortd9axqUbuxSnnMpNRH5PxpbqHqQt9JP9kcm+wk895hi+/EwsX/H4Fnu/qOh0ZfWYmPkDhVuUp+I6+eL98HfqfNlzZNcV8gGODDsqrOsr25FN1onTzc509jf5MplqibpHyNIptftpCmRPI6iHfERtkBWmgY6juM7hnsEI5cq/0fbykuc76efRuc2tLLCy9ump1GsYWmfMGz7meItBQTzHIQgK7/ZmbAc5pHmigMenDW+Gg435kWydgm9jGyfsF9H+gmUmYxWbw3UYPWIuoi70Sbec4eejMpbTjODaj/SUGVVlUk+ZMm52WP/JomlRVg87E5rnmcbViAWjOWJxT9bJHmxuU0elrw1lvOXw47C3t1KoKMuKwKcNBp+2wdZ03Xs5Z8EePfMmDsQgBHigD3B58Ka2kkfamWNBK+2JUacnEVAPfWJckeFACyA+0LY+JZdocyqrY4twZUBZjDNJqXwe/87fydwx7ZjfO6CUWCSZgUxgcdJHDjLUo7Ee74k5c0ok+yY/ytbwUfEpfHZfyKp/Jo/KEvx9uaaH49+bQ2ZPq6uKnC4ben23pRcK5Xz71uFDl1EvHUMy6gp0h5jme3K4N3cM8KE8HDzYS9N1H8dbSVF5mtkRhLEj6Xfk9aMrvYoJzJya5oCiGWzBReosT1hvzp+OJLWJ8cPZcd2yXPXXiZJo3nxNo2PaRB+fii3w35NAOc2njRPCVsdlGBMPBM+chsDLkSrqTpQHtzIxza4lbwmaSjaE8OXoDDNpmAak9PGG/DCoY2HThq5u+ZZhuXbQe/wb+VH5jUe/kKQIZR1L7kTZq3NVF9u1gfDXx336KObGYirzKP9JXHDWTxfddTAhdiUwzKaHH/Wlw1vZdKHBhzyOwHySIUVtTHW3re6pD5UPnyTHkWKn/I6bE+OE9daE8I6/Elhv8OzGhubU1isab9VE4bESaCs4XIU/6fdKFBnnF1Mac0mjo5dh+C52u9AS0I3iFLos6bzzXrFb102Fsg0YyeE4xhk6CDJ3nu9LP/uLfxvGWE0aMj/niSfbMH9/a648SaIA3QjnmtHDKfAkJmNzpIM7cxnXr0i7qBRzBiSkaNSaVAiAWZRIAI70TQYyUTr7jKrSRZufS/Z0KQm5QypSwTRiWGiCuKFtQ8VDzQZyigWksZ516tr3HTAkaAl5oADvJKwINgIvfdYuB4KeRHCAFkwkqMptAIDNeROkJrqdYeSyeiBGEMaFCF4w05BwjJk48w3Z0DocRqRRUXFW3fx7PKG3COOKnQqkCdZVHp3H7ti1BOldfRwLacaICIBoECOJmQCWgGmgwbc3frTUgQIbByH3xz/vtE23bB43//bs+S2Mx4jRC7Z9K6l6wCTDmXvuncKt8BKYAb+bF/iqUOgARaEC4VNg+X9ocbtyb5i1eHtkYPluaB+jRps+xXfvrmF2VyOfN9iNbhWcYZBLYHbsjsJaB+7LdkyaoT6VQnXu+bSd/CRwxDp6TKwjBTwBToWmsaIw7lxq6oYC41znRmHFPV3NTYxo3oTiKiEpbecR5VTHkylhlJviHQS19eflDnnQMjlWaxvzyHM9IP32mK8D7HCBcXZcVJHT8ptQ7QH+3/wJgZpxjCEFjP9NxsX+O2/mSOeRP58FUW41IwfUMwGooqhrHW2NCGthv6eg8Rf+5Qxo5pxGU4bnyvACr4e6lkpRHC3D6DDQcPdYR9TFUUAOYL3neTrSwarpz9974TgU++igMoikTyWooJqMBvrkTx1syj843fbhM83lGQ39qDBTYHj8126MWA4YXdzMu/oafIA4GPIX7mIcuChbuPtGdPjtJtJ/FjKMkgsDRqZJVGiteV1aUp/GksmrwxP5jeZrfPUHAVe/rN65R06MMI51VKGRKmA4Fz095WR4UC8Kw5r3MOMnm1CCY9A4Di/vC6WI8mSHXnvawEjh+fGnUrnB7gEoRajsIXsi6jpvzwEOHMRYNZI3bq6zQCox9evfNHAYe0IWD3XbsKEdcHk2f7t9PT1jpgG6j8H/KxgxL5WML04gCCG1Gw032BN5klZfnDOpjAUROxiHQQBehp9Kv3EFR+iCtHLN8epvnGGi4GYGG/pGue4Qim41k9ZxkkexIT0ZDybhC6DUhVZSWo0w5TYO+W7/C1cGrFCHzCWZKheElP8IrykrmeNwMLGvyGChwpqa2JoxMcUT5EIv4L+gs77s9y6CF8m7Wg4E/+Vc+DdnwZi7fk1+VhzCiD+1Ps+Y9zPxXhrwZWKNlRpcFGuR5ed92DHQou8qTRnI8Fz7RoA4Mhv9tX5w2g6qj42jw0jSOQQx5cmzgKzcFISPQQL+UJ2VJx0xs8fsRcLTDjlHZ5j6xR16yjoIZAWfBhNHQt460V/fvimP2V/rET54nhog18qZtyEeO33vVFxF8N5jFZ8qZdDlh2zh2zoc1VYqLiqPwcRZoEBOyOZAuBl7EFPneYJr4K6YEvvTQRfo4BsdioFS6uxVJ47uyiuNiJxQFbhkAkfaBi/TdcVgHySwS6WvfxZXY00xbYpWZjn6m/vR+A+2NBxtCRizOpmwrMwZxY/sC/YisuBgvv/M9g4rSSZqZmaATYjDZ47BdhY2iiuIPjsER+u4iwYjhI+M43PFjxhDkoxAlfZc+YoZ0jkA38yUG8kYEv8y2JCQT/CQ3hgMJ99i+xqh71i3weQ3no6KiimewJ9vx00cdW/snjRh48IxYlcuP9I4tCsiWK41wWNBcrFHO/Mxg8rZtn5LqXcSe7LrYrunxiBAuCx5xnzzt5RgG0/eB4LUZAQbWfIafB8bxU153TnzffnVA+/bWllSB/rDoXgQCuC/mnmd4vy8vMdOMBeVKmVC3ypPZ51mgwTlwTn2+2ycONzdTx+NcmkLq/3hWSiOrxf6KSYw5intzv0EneVLe9DPtCreFRmBJPcY9jkNZVud61K1B9uMd7WkYDlFtbW0sMDiH8mzGG+hKHAjH7v59ae/79lkslgc19nV0nUsddo971uE6yMLYzr0NyKABnxh+WjmZQAP6xuMtB9BuBBpuo0fAVrHHiyYjo0H+iBoNpJS5yqme7x8v7iPIq6q7w7NvQi9PnDiB3mxBvnYcS1GLiO5wOlthqiqrTTOmziBlewLjYO5uUFcEvWadHOdIDJBP5B+xxt9PETC1PoaZjRYbH882gRHUa9Ghi37SSekJATKa9siT/CNNYhFCOnKJNd7L3eHcKU/tbe2sUB8Neo4fVxR1DiyGHHwAr+Rti5Xq2Dg6kp8hq+CYdpn9jG0HPDvjd+x86G8dAlPUr7CVcww4MBY80Fm3uLKr0xJYDJPaviIgwNxq/8pLmTNqbzN5sE/SRt7x/uMEqPaSBTN8ZCFZQmWBwYHhjF17NvCYtnTQ5UUDU37XFv3MTBiLIctHuZ4SK7Th1HNuF2plgWYM/oc4bOF1Cznaj/BDuM8xeK/YXYg9K6a6/Vv9FPLKPY4rsN7+81wvM7cbwJouVu+nzWK7CjgWWTq049i8pH3oQvpsECbsPd4JZxp+MJjjPeqv4B9+l2b2R5lqYovfIObSYMAwt3b5j3uIRPObNOefRg19VGalnW2fp//2XXs1xy7HJ0+qFw02niRAcuJ4B9ufy1NZWVnwhYSVlgaPoh/0UXmV/lFsmvZiMQ+5vDu3PLsXCx5MTPCrwR9PQ3E71VmyG6axWGtWuDSXJ5wb25QD4l/P3/n8ye/6A/JQcA6f23cXKMWE4+BMGzjjEaPDBw9LFRPxAZk3ZTpoo/GafTO+F5k6YI00FuPUz/Kuc+URu9EXeZg+Wb/pOHjTjn4yM6PzQmc6duJC+nd/8+/S937wJ6mY4y2jT/zfvn+brz9KoEFaB70zHgylKYHwRSKv4cChBiq1f0Ahr03pN79++RvppWFsPNukoiyG/OXbdE4ykfvy+7iGqRDQ7o9Qm0J9jYkVJm1H+/mbvsPbX7tsh5q8X7pQe2k4L7dCeOTXDVd+YUrbdFvzV+/nrW+8NAXsP+tKX7tsf7SMC+N9gXK+BLCc471MvX/t9m98o5B3HW/eH9tkPSOY1M9GsiJ9jVWG06z+yK7Oi7T5r3kGt3/t8qCi0VQnd2vDWaKWXV+74/e/gRudpLP9yYQq+45tD8URKMB4M+PlBKuOPVvJsxv+gP/LUz12wpfulgbO91AEfiBFjq7jKLRpEN5zyYvO1zd9/57bwqH76j2EmdIQDLMBnLQhkF4iKHD8ni/JD76k/z83B/ZfmnyVh51TSrWxp5egDDdc/eJSzDdu7Nfu5a3fe7l6TmmqRH5EfH+cf2OYY0OF4XEBJcfuhbjyfpfy2ViINKWclFKAvRDFfNtAAwGCMHo09LnZAmqujfRnFWYQLwtCqifuQPvrBBp0gFVhYX2pGFjO6UMg0EDDnlPn0q7OC6S00jcYRBlCRaaB9xEQYWWHA7W+Nj+OZVShgQUU7LmLdAcFy3vyjvUkfZr8L61+3yVN/O698zuWv4cVjqLg3fV04QoGwlc+58/fe/n8HAvkfc5joX+c0MKqlRNewEDH8MHowQPSqEFkP7CKdpvU2VvXcWh54HCEpnRMQRpDfY6BfNbXFBGNWmbPQIOZDph2MVb5w2COuTk3CU7cxkDQuG1lBe3gyU4i6Zw6weRKW2nj5Xjluz/kkt48Peg7jJ9WD/8CpdyOMe+lnClZ0tFL3pVm+fh9L79sxz58ld+lkTI1CKxxFfJSJ0E8/vay3/fe/9W/46bf8b+8XeXMYHE/gpSBwziQ9tHXvZf9+0Pp4vfENnHM9g2cm44plp3HqITsX7vkN3n0D72kid9xe4GFi12RvwGWXUSHiP9fvezP7+J7afFlBMz0sXM6kKwqV/DN/NEo7vxqwz1/O3ffhGffNCc+L3Qruvs+g8N4dNevcGoA79tH5fWbeIS3v3b9LpxXzgoHYhvQf+l+/fylZPJOzot+z8s59fevjt/PvH7XvMv7I7A7zHj6AmP3IsGRb6J7NPLP/E9ayMP34ozvjcYmGMSqp0VmL7nqyXvSJb+cT3nA8Wgz/CGXc+ElbUZhDA8k+9Is0/PoP+2He2XJ+ZQ2v4/n5RHpZx/8/iheVCAIm8wU54sY0Sd47/e1wy1fuqS7/fV79/ZrJH8XoDv6o1/vYMy7veveeeXjuOyHMpzPd/ZuhkGO/2T+Rs9P35Pu8oF87HNzfjbQkG+dGGBGAx3qS6aYR1bq6tlXHXXdlWzrBD97Ag2uft6HTPYlC4wSH9yHo4GesxbRBb7fCd+0dJ9MW9pT6qDDTDv6MuO7b8KDb5Jj3xvEy/7r8LmNU+fWWh/S5l7e4s9/9hKjlUGfnc+ZvOA8D6XffbGH3cYr1kgf6aV9cC+dCYd96W/+vHuJWfJx3nb+ge0X4uDq6F0jo+gSAQ/77uXceP9/zTj8Xn6Jw8rrQIIueMXpBjxDfkjgsLaecmSfvor5vPWlS95zzKx9fAmf1HMjcE4HENy5Bl0u4ETa1r18y5+/93IO5SXHmWO1tLfvQ9F/Zq5cYstyNw5Xjld+HvPOT/v1zz3Ttn15OQ7ly2cOIShQQCaZ/k0HGTbfRGe/99U546245D8/z/khezfTf4XYev2o8WF9pKvnqLeEnHxT+/l3/tCfzpt0d5N5PwIkBq2u075cKRb9ofrjm+TJPjie0bwKWNg16GkWqf6lvK5sOFYv75Onpf03Xb9LP9n/Edod8Iy4cYqaDc7p//GX/3v6yU9/morGFEVzWnb/nw80SAmN2Xj5Oy8nwBU2KyH490FWjdezPWD7lq1p56ebWdm8E1scBhnFxtDQCDDS7p50V1maDpO+xt4VrzG85i56KI0xfdsMBKJbpvPEFgoAySh3pPwZ9SQ6FanjRI8IZUUUzEibvxvJM7pp9GvLho13DdWprBhOnTmbwmWkUwLKRsEcjBF8V2ZM3fSKyBi/G8myr0bWjdy7T8gqvQqRUUhPYlizeVt8p2ZQv1TNXmcr9Bq1UyEakTF6L3NmY8aR0MAlkmZ00MidEV9XmfxpipJbQuy7abjumd3acCjalw1nL5ifioie2tZFAhWmow1kZSP6B9iZCjUEhWzmgynKRiR1fh2nq+v+btTWlfkLCGnD9u2phXYredWSQjUMR9+oqwrdvhmptq+uBFnszFRTFYMrgkYu89UJVyy9z3HGKgTO9tGmprQbg8lrAq969rEVTSyLlccrpLW74uwXTKU0EhwRWehmpot9tx0jgo5B0XN7jRWLTWm3XsGWrTvvOsQr5s4h8ljI2OhrTyTReXVfmAo4Iu4Yzz7Hcbmv1Mv2Yn8y33Ol/5yRxs6TqaOpJbXJzFyjB/XiWKe5kWHh6qNturJm1NQ+m97qKrZ91Tg33S5Om+C7Rjy1JDTAIm2Q8bqf9f0enrH9+tLxkZY/lNRssx00VKz/4X5kC5zKS6al+TLLJE4LQW7cf24xNPf3ejysBeki/YvjovY3/zbUsWLBg+w3G50uE7y59cV1UvhGx8ryJY6/MoI8hDn3GM1ITft/2XvPsKuqq217IL0pUiyA9N6sqDRpggL2Huwae4klajRFTbHEFPM8SYwdewOVZqF3CypIkw7SO4iioIjveY7F9s3x/X3zJ8fxLd3c9733XnPNOeao1xxzTBBjswRWgBhvJYBcTxVxFmXiEPp5INq1XWOKZNUXaADN/hrZASBwZXE349vBaxedzxR/AuBqpHhWgo+Mi8XIdypLBknOD3+Xg1Y/8Pv30HAdbX2ydmO8t4oVHCzGOpr2anZAkaVQu87+7HFUvcMbpFqWYUVCWa1epVoek+T2qj04dlmnArrJ64lwQxOLf6nY3da0jHoXU+BLLx2sJo3qZ1HMmjVr5wqhc6ps7bJqN3xmcScDL9v6mvfUK9YsKMtKF6eVId7I7R5T8eCJnTti9tyPY8Fe0ndqXpfCbtQ+2LcmgQPOyJavozyyb9GwFevXxAezP0gFajv7Q6P9eL8qv4MlZMotuyYIZiIa4xm0aYQM7XcIn+PQl+fLFotx+wT0/XegwXElyOD2CXUtsuzWic+3fc3WCRD09dRW2Otd1atVnZVHCt1Cw8rMg/pg0l493Jp2mh3NaT5168N3nPbzFWmJfKcSq8PKuTLkSozypC7Zhh70/cySwSmV/ysiE9VYrTZNdOGcOfHB6oIwTXAQjujSJVeYdJJdYXGPPA3kyowrpYJXrkKY9ePefY9SY9SFU0r7VqC2IJoBvVuXPl9B9s3izx1+HNmyaTRr3CR5rNjuxqoOgYqymycRobftq6sg6tyEbPjd4nDue1ZWDSYdm3vqTcGd/MmsbNt/ehx5KLU66hYrTMi2Ol6+cRXRdnObC3+7SqZGLOyMWUTFaqQBijrHtHersc9jT+jHK1Zl+/U46/SYzta7qAbP4YbA03mkJFkxnkykzREc0cbls+i3tkMaukooWKiOl3bScD0gzZuTJv/Y946NGxVb6gAtzQTjH/RjkRVi/12Z8X4vVyWVhawt4ffQObsIutQxOnlu53BV7/35hTx5T7/u1B2qVy/1oMVYrXlhPRaPN8u0anSZTA8JivZp15VpbaG0dtXW2jQWHvN0nFlkzMxevNymoxFFYg5rf3QWYMx+owOKPbJ7V6vpU+7pheZmTXp6iIGhK3SpFxwLz8Yi5OrtZvjm3U9mZNsGNp2P6JCnylQjSyDtBjrYrEXnV5poa9277u/OoSuGruDpc5h1IT2dB+nk3LjPesxHn2T7/nNc6xZ5kofbBeUL287jlZlPfQRtiDKlXczCm7QhD0kfV/wzQwK963GPm2l7HnVeltCuQckxbHGpVbtWyp884qXNc5XNS56xbTMv9C0ydZnn6z+VVkYdg5cZO9YCmTl7zo8A2VHNG0S9gzxxpyZzSa0S5lGZtACb26FcsZb2rgrmHnR0oQGqRTqVJ6fBPpjNZLbPex/NTr/R5/Xq0J6MqoPSNxRE0+fi2/n9wvbzpzIp3flPupvVVmQwcmIKekD6SZ8v0GGzZ7B3fsVqG4km9KlL795pI/QbpKP3WWXe42itqTKfVem5S5cB8KIDmXOvARaDrFfUaLAYZCXUbQUcv8xoQMgYTtJNfiplNPwINPBeedopB5gmoFkG2U2gAdpvwxdehw1evHld1mjYgIgrzg1rHUR9kOrx5ZptsfzflnB6djw8a4HI7/oD6hnpY1E6+VJ5VZekvNN/wUdtnTVIlFUenjpBHee8zv1sbixYuT7HaOBzYreuWdzR1Vp9F+Xd0cnrZig4Z/K3vpg0T5+Q+9KvUdfBS06XPKceHj1pyo8B2GHQr10H7B/gXGbFyccA0vK3q75mP+zLZ8qTfojPtg/6UeoiV/edq2/R9W4fW8nq/qR5RZxQC1ZtfTj1IihiqB/gvR43b+F0j/feBZhWmUKqbks2Q8IxKWtu86lAZl0leHYnPtMXX7F4AOC5jaDv05nzfgQOO5LN0qxlG3ylWqmnzIZWHzt+M5+VVbeDSHezxKSP41Km1bvaMMiT39cf3ExdlynoeYPThrxatW1HjSx8DgLy4rsFLW3frDhX8asT8Cqz0sF2M9sOOlj3SR/eGEW7uXrZ8phNdsJa2vXqQ0btAWRAaFv1OZNX+Uedqww5d1kPjM+1GWad2H+Daz9Xt6cN5ndX9tesXElhzblJGwGO3sQJ1pVL3oO2Zp14aS+cS/WI8512lYfKIyVQTl7BNUk+9XMzdRfMmxmLNxUQ26HUqGpDBqBzpu3WdsjvZsKbJaANNwPCwZTalT9zazb87vHtguTGK9+xyGWW2JRpH/4I6Hdp2yqLD8tn2gUPGVBvqBellYUhFchSVox2JH1xeFd/Ie2uuoj0JzOi5pGd9/HywnZLgxO6Yp/IqNVmWji2Ios9ZitmTCKtoK1bibShm8l+fm3M6Pjznx6M8y+4gIyeujbBJZWctf/e6/85oyFpUBo/dHe+VGwJNMCo/BkLF8yPaZMm5xm/61euiqZ167Ff8hBSf0mDhIA5eUygVUs1TLPmz4/XXnwmVuMQH9/52OhGFdeGh9RP5z6BBhkNFZsBF4ao5NApfCUjb6Dr3tM08vTDqfLaTDD+PobnhbffiWb83ePkk+Io9udb0MbAMmsJwMwKrYo1hQaGUjFlkMs9GmyFXUconRTHCbPJC2spWjaWsS6e/nEWAWzbujUVkRsSgFbMM+kVCMERhUWHQUBD5eG9tqkA+rsOUxai4TOdGZ1HjeDCpUtjxLvvsJ99F0eNcQQQSqRBvfrQAsWJw+G4FWwVlPeU0sBs1+fpRHhlOhe/qxx0glQgFlybM39BvEc15649ekSbFi2ijsoPJ1enSfrqhMr4GhIVlwpKDeoWE9PKBCVyPDkOHSMDh90cj+YRcksorDI3QYnO7KXyLGAVYFUdeL6jUyaoIM1VICqH7Ku09tnQRMXH4xgLvyPcVfatxor3N7nf1LNuJ09+LzpSKO442jdNVqNTSpFX8eUYEHoZQkXqHDu2ohgYfMUzTaVUsfsM319HOmLuJabo6Cyq5513Uv848tD2eayhyk7+FQTT6Ps8wTPblV+cb4s76oBKM4EjwTT53C1C7jleyt61CZMmxKiPZsRBROGnn3tutIX21XFAbU/lmtXc6bc/BR6kUfIjxPiez90L6ikpOuXfoGANkgRiDKY9j/3dYRwbte276NqhLUd59qGQYn2MPKn4OCtVOV1ARb7LqtSM2YBQ5SxPb2dOV5PetWj5sli8fHlMmzkz50SggUV1gIbyAA11KQYJ0PD/qdGQQAP9KgENVZlbT56AtKkXEmhgHAas7oOuxFh+IADcxe+r2Bv78aq1MXoJwATP+iKfGtHt8E7RqlnLaNKY2gWAhK7m76KPOlrSJY86pP8GY6aP6nwpuwZ1ygSEK4Ii2tPxWrBsWQx9a2R8Ae937MRpOO3aZjqoZzqnXqFNDZx8I+01QMqv+2zlWT7Wd8NYMw4CflyzzKLYTSbFFwAgn86dH4NffzcOa9MwDj/sqGjXuj2OHLVGOFVjx3bAD/q3GdmbRdbXSyMGF6NkwCY3IFm54qADiB+E0Y5YBihwFGJxBMqrfo2GrERQAFXkphw92Qs0ZDFI7im0nn2CR6QBfd+DXH1B2yu++BpjvilWrd8ZCwvsL45s2zqNel2A0RqMfxtOurrM096tSt+YOiC169SGV6iLsdOAq3BEoChPK4IvHYvCOSrOAVcXKLeFLjJ1nNMASJFdtHBh7sedMm9enN2vXxzZvn3uC9WhVQ8qL8p5yUFTnTgf6vwMEnFE/V0ZFuz17OtKOHo6B54iYfrzJ2wZs1r32Wefwz7nZsnjyQ+oMHlF2XJriLreKwPpvXpLeXIbkTr434GGlKfPPosJ48bEhwAZx8IvJ/TqGY0POSTlUR1gOz7H/sogJT7UCdVhUYfqFGmrUg9LOz7TyRQIt6jY2CFDYiF9Oqdf3+jMHnfTbDN9GL6zaCotJeii7k6dDl8bhKWeV8/Rvm0bGKh3tAmCritWrY4x48fHux99HM1pvz8pmh3ats26RQKw9k3gX/1i/zOQkTa81GU+K1/5HsE19+jA2X+Dx/lLlsTQYUNjwYZN0fPw9tG9R8/U89JYHakMqc906KS9x25KK/fU/9gub0kb9aTP1RG1btMO7PN09nA//vKrWTD1xD5945jDjoj6pFlnUIQ8ZlFO7lKP6+CnQ1saE9NSEf2iqCRYQvvqOIMWQdX18OWU6dPjLfZx9+l0JMdhtovmjZvG/jj4hf7F2tN/nyXH2H45gWB40H7K99oLRpPP9z3H4fva3pWktI+ZOCk2Ll4UR3c7DqC92FLndkXT/KV98g43KlHyi/pG3aM8aPu0t0Ug48lShTyZnmuNkbnI1Ggc1U6du8ThtG1tB0+SEIxOPqMv2jrpnLKVNgoaSWv6J61K88vjEzT3780sIKxYvZKiZZ/FzHmzoutx3bOY98FsgbX4ZVGjChknWJROhfMPP3CvMvQ1wLfyqT3KYyQhnjJlgOMxm59j/+bNncMpElNyC+Px3bpFI3zESgCa8on84VUGu1DQ1GCW3+R92+I/5UkdowwkAIQeEWxw688nyNOkoa/HjF174id9+lDs9pg8ecR6PNqF7+izAKVA5Rp0h/Lx1tQp1GOgRhd+ls8c0LZ8tKx3UNRAz5eAhvIU6xBoEIZDXeT3oHBmNAgy/Ag0QAeBhgqAhOVBkcsSGDJ5HH/JAg0+7hoyARdtWh8TFhQr6roNR7ZoT7YoORno6p0Aq4tZXKpxSNU4+ZQB0bJp0+QPQTskKOUpgTDGoX5RB2eQuFfuUUw5D25DTnGjp+oFM6s+BTwa+tqQ2MQYTmHvf4+u3diqcXAhk7SnnMAU2aZ8pL+a8wqtEwyEt50HfXf5M3mItvzueoLpiRzbPBSeb8M89aRg+OGktqvL5DfbNlg2O8I6MvK6vqCy9eOWI8dCX7Xh8qJy6qkz8tOSlStj5OjRuT+/R48u0bZdO1LzG2agqK7Rp5GHDAy/g4bKvb6gizfqfuVKfa6vI5Apfb7mb/l1G1mcny1aFG9xXGUrdIDFUtu0bM1pdgelj2RtN30lZ71Ec31pf7efCQaoT5EtaZZjYGDqAhfNrO/yMWD79CmTU55aNec0O+guCJN8TRspl9wvX8uD2lJ1OR/k32lrfBZtagfcwimIvWol29HQBdM/+ogMw9rRtePRuVVd+6L9VSeV9K3PUmbUlwWIWaT72//CF4a7+U6h6wDGiJ+WLV2aJ7ZNe++D6NqlaxxNDTLrdEjb3KLEeJ1D+58Xf9BN+sw/+TxoxHuOQn5Rmt2qYNDtVoVZs2dRB2tqzF//RVx97jkcNd2aWJG8A9pzzMkN3CdP6GdrR2zf52WbtOdcGsC7QOCCmz79N8zt0mXL4mPoMv796cSg9eIUfI/m8Iz2yYL+Aqb6FelbM5dZs4j2lBe/o9+gPnI7qX13UU2+Lwf/b0MPfzL703jz5ddiJV/r2/mY6Nmtaxx84AEA7MwR92GV8HULOskTqjYBdBcG17LwM3nsuBhwyYXxk4E/iZrYteLyoVL0v/f6zwEN0sKXP5KJMJYoTJHJBRRk+nDqtFiG4JZFODuCarZq0jQqMKGlc0i9WSP6NSuPk6hWfOdvfhXirBf1OwGiD4x2GE5BgG9I/7fSdXGsEArNCchnO/kKULJz0Qd+hzvS+RRdlyHWI+Ajx46J3z/ySLSn/XOvvz56H89Zw/XqJQJpFfU09rSVSoI+WjjOY4l8fqJq3Cfj2eYPMLEv0+U0dgZjLw15PV4d8VZcCYjRHaN8OOO1AKUZDRokAxWFJlFDmNeA3WepuG23HAF0KnOY2RQ4jwbzMyu4fgIK+q+nn46aKI0TevWOQ1u3jUaANgZGtq0CLwXlSZa9k2JA7PjdjyQqrJOiYjEDwWsjDvmSJUti8ocfxuuvvhI/Of+CpvJYyQAAQABJREFU6AwAY0Ee98kpEQWKrUOEEkKpZEFJBNnnCNL4ucZdhevvzoV7kf25bNkyTqeYyRGJ43Nl/OJzcXBbtc56FyUkWADH/dQ6XSUnS6PppYK2HY2R/OU48IQznXpbGsxZFAGcEo+/9kacD93P5MjHJlSHzVUfjE2pFogr0aL1tlNy0m1XBZVOIXNo+wkKAPBYzdnV/AkTJ8Tw1weT3rgmfnH9tdGHyvF1SDUjtkujr/OpEuLWvT8BWaCxylcn0XG5Bcd9kxYwtRCMjo1Vxi1mOuT1ITHorbejPjJz1+9/H52gvTVM0qlFAdK57LfPcFVXhejT5FXRWxVrhaqsLMEroqO7cP49ok7n/H3m9OnH/hnTl62J03ocF5eClrZr2TIVnynL8mJqPLUeo3HsKmaBiy/p6+eMfxZ9/JD9jYM5YskrgQYi4HZNKkTL+nUpBoljCThQKgZpZfyvkT1CfS6QYvqXNRr4XVDMlcrvGMN3PFNHT11QDVrtw1x9y7PXkNEwY83mmLbyK1YeAAj32qyz+50WRx9+JNXpm1EoitNfoJ+nPHhU0rcAJZWRJTNBdrNK9BXy4rMyu0Q+oj8CeLnSq26iTx8hTw8/8s9YgtxeeOYZ0YeKzh4VKo0tHmnQJe1LoGYaOejufz/AKOkw4fgUQAP6DIbYBciwiz5t/mJrTMQY3/uXf8aAbkdidHqSCUPAfkgjDA6gKmPcxkrLcug7efoH8eenHklq0fSPuvQQ+lmnThmKntVmXr5nP9+WqEFiVPtGZaP+fnWhGXunATjMgpAZXbEsw6scykDJYfhwtgEoY8fBFWhw68SSTdvjM7ZObKGthXtRnP49e0YXnHBB4JpkHngyy1MvvsxRV1XjLE59adwAcBhH0GKWRn/ffWv9BBw1aOUJEl7KaWkFNx/Oewa5Oi3Km5+7v3rhwkUco/phPDpiePzyyqvi+K5dsy6C+kNdaFaE31dvpdPGfQa+CQzCL4INynDqGmj9LfxuNpL60wK4U6ZNibeGD41NOJh33n5HHEP19azYDy0EoQyIlC3nVhnyWVkgimcIAgpeyC8MrvgpnWnfVQsrrw8e/Gq8TQbVST17xGXnD+REg+a2nLJk3wo7UTgm6pt0kOhbOjROCpc6XrZ2H7dBl9ke61g5HvnOu/Hrvz1sjBE3XX5pnIY+q4eDmxk59Lc8vr+VwA0uYMKc5LRvyI06zGBX581gI1c3aVs96vPm44C+9Npr8TxAewfa/+ldd0W3Tp1yRUqHNZ1ydLf9VQZ0hpLniy7/6EBrS9QRAjI+Sz3pzw8o7PjQn/8UM1esjNO7d40zOIrtMEAknXtpUiTeMG76Lv0NBnyOfJE6nj4hbD86uSmv8LMO41ZWG8dNmRJ3PfiQ34pbLr84TjmhfxaWVk4FSr/Bf9AOm22nDVGj+VzBHqvue4Qu2i15NgECaCTQ8B0yvHzt6ngDujyK/Tun3/FZYdyTZQ4i40t+kW9KoK8BksCN45bP1Sm58KD88b501zmXH+Vl6w7MXbAwnoP2u3FIB5xwAkfgHUFV/abpKH9Bpp+84apuZsh9XRTCNHhQggU1bdd5TqCBOU77jY0w22EVOuR9AuonnniME6DOjh6dOmcBxprabi5X7m2HzqaO0OYZuJRstbyVwYRzqnzwuX2RZ8y4m4sPNx7avw/Q8NNLLiW4OIzUXgpXk/3kefLfUuxWfZN6EpnVUU+nX77hdwtoGhwiGQlIOZ8embjDVcC5c2PatGkcFfoipzN1i/PRw55KJmjsCWPKp76BV8pLjoORSA9e0l+vz+cloIzs6ku6Or5x48awoOZI7Os7CxfHrRdfEqej4+tS0ygBetr1ngQaQHMXLlsag158IZ4DLCulVPvz+HZVonnd2gBc6AkAamsuVEAHlgddK9l96WsflANrNHgCirKtv1oRXnc7hAtrZQC7vhdQ5/etHG+5Dr5dsnlTTFpcAA1MQfTqSIBSBwANXb1jO4Uip06OQ9o2iauvuoJFjg4pM9puM/d4RGaSKFMJMkh7nmlfpL2LD6VjKw2c7J+6bitA8kTm9OEH/pi+9pWnnx7nnnFGypM8WDpxR72g7+ucVsW2eq862UKA3+wi4KZN6S3YqM50ppStz1etisHDR8QjL78cx+5XNc675vro0b17Fnv9iq0RucpLm3Q2eV5dIL8rr4Jnso5ZT/50jjKg49mCg2awzVowP/71zDMxBn18xdmnR3fsV/v27RIg2/HlDvgRH4w2U+fgQ3lUsTTzOamP6LcAoPRIMJm2zQrVdqtr3vv443gAX+yIFm04vvZ4Tjo6Jpo2bpx6ykLyyo61EJRHdVdJvpgSnlzQIX93jvyPfvs9MxrWsNV4zJTJMeSlF4hvLkz71ARdoK8tAKK/rwxyW9omedzL9+yzQLCZ3AIGW8ky5ZesaeHfFjn3tLlho0bRzrdxFkdKtmvVitM2Dkr7rS4oLTqWbKtzaraH9sOMBm24GXxMeoLW6Q8TK1jkdS5tv/fB+/HMm8Piqp8MDP0GT2QQvDFDUBvtnDpW25EfinHstaWMRR/bTwqduSeqsmBo5vZS/LDxkybEyLdHxPQFy+OB229LYHA/+uZ8+gx/SiPBKX/PBQieqa7N7Cb8X+MmbaoLefKLz3ML8GwAzbFjxsRzb4+KTm3bxA1XXhlHtmuffnpJt6vrE4hW7zh3tJvAnXOgHuJ9/T4vARJBBk8r2sjJEWNYpH3ovgdiFZ9ddvYZcd5ZZ1LzqkGC7GYg5RGt2J9KACf6Tdo37fVOxrMCPfsui8jtj+uSMW8NAT+v5INi/os3/vv+/c8ADXJSchM/pUfpxXumJ1kM8oMpU2PdypVRi0Cr+7Gdon2LlknA7y3k5ATCDHAo6O2OeHfSxLj4hutyFfOaM06LK6+6ivPBD01FkVVfaVegQcYovWSAwkAWzGF3ckWEdjN1B4bT8RA1eoUjo265/77ozHcu+s1vYkD//hR6bMAqY5EerVHTCUrHiL6plDQmoqyl91R6MnECDQADAg0irZ8tWhyPP/tcPPnyK3ETTNb/xP5xLIii6ejfY5BtV4fCnwqiv5ecQIVfhaJQuzqksVDxifR51MoXpHN9QGrnQ//7v3EgR+qd1q9/HIEj1LRhI5QEu8y4F48kAROGho3RKcFI8MrAHcFTyRiYWpTK4MAUea+NOP+uAo6eNCkef/KJuP6aa6NXly5UGG+W1XmdX/dvlVLa7KOKVjo434IXCp+IqeNxW4lKcV+Cce9dvnhxVs0dMnJEbESR3XQFAk6KpIbL4CUdNvrxNQbINPcMIlQWts9lSqcPEjhwbnUo96AE3be9hT3Vn5Cl4tFIf3xiUFx93nlx0dln53m3zl3OH/dKc4MADbN9p9tJYwVZRaQxK62IleFzHaGyOP9rV3yO4nsrXnr26Ri3aFn88Vd3xakgoQdiFERBVXbyn+0aVPi7beZz6e/30FsUW8Vdjvb2CDSw17nqfqTxMQceMzbomUHxP4OHRGP69NfHH49eXbsUaX8oU7R/8rlgl7Ji1graj29yyUeACYILBdDA2cgEXTrOlclE2YEjMA7l9/ADf4iJrMBe2P/EuJG5dQXZ/u5h3lyJcpuBjkS2zXgcf1kcL+diKeP/mJXECaxOPPnaK/nYRvxbBx+4TZNK0aL+gQANBBIEdWWgb/IzcvcNfXXWZIDyGBy3TZSHLoq6vPo9RsNjxQxWDQCqM1dWAf8e3lzHiv/sDV/EjHVsyWHFfU3RUFx+9kCyVTpxhFEbspzq4ehWYV4ZLzQ2pV5wpxKg2w+0+SVbQxyTzksZ3ucPBrx3hYA+GExOxZm4+8EHyIhZGNdfemlxTCsBgH3+Buc/K/Yzxx6fKGhkCmAyIMbCnz77e+hdDjnFK5YBYid/f8N8rNu6Jd4dPyFuvvt3rNr3in7H941OZCG1aNwUQwjxqDi+DUfYlZPRUyfF3f/4m8TKves1+FmTJverViZq1KoZ+9XhBAic1xVrlsaeDWxralA+Dt6PbCCeW8m9FckSdsjVVtIp6ZxFIYu+2irOJnO6B/puYXvLwvVbY/bqrbEdvG4hWRJe53AefU9WWls0aky6Xw0KE62Nhx99PBodtG9cet450axRo6RlRQp2lqEQ2ne0sxMnWZ4xVVp5d4VIGum4eBn0CaSWNyDj0mFdjUGdD3A1cerU+PMLz8f9P789TsaRc7XIADEBCfmeOVI/unIkuKlhVh/k1icdT/7O7UcMkpEmsKbOnzvvsxg1ZlS88tTjsZVUxYfufwCbc2yCsRk060DA7+ryIpvOu1nBxWkweBao1QEy7TiddPqgrq9UvWqmG0+i3y88/2y8OHZCnDegf9x09dWsurSB1vSDdnXSbddnZRDCtMjyjkUH0fFlkMRP9Y6Ouqtq+7DFbSs0f+3NN+Kq396bffrNTTfGwHPOTvBH56osvFiOVxlon4AGtHHFvxT4W2xRp4V3047obGXKrLaK58zGFj8xaFD847XBcRhPuOPPfwaw7pW1BtQDpQDDvmfGAT/Vac5p4SgXK8cGFTpZUg5KZvqxz5z0/vtx5y/viveXLouL+h4fF1x0cRx95JGpy3IO99ptA39plUADdM6VTeyUhHIstq0OSgAeeZL+mwDuRowZG1cDjnj97uYb49zTz2Q7Er6EgRHyXwIaXNXW7ns5z7nazncMDF2BN1MGZsqXR7cWK6Sfx3Ovvx4PPvF4XHLagOjb63j63jFBntTz6nho7xnqzoV99TkQJ+2GWSTOpQou7SO0l/7qN09omkFV93+ySFCGsZ996mkELkdzykuLHO8XpAyXJfisBpilzrFauXNgdoD61EtekcYGAwYIOUba/4JAY/ny5awevx9/++uf4vxLLot+PcgkadYsapPVJ1+60qkyYDZ/tHmZ9UHbLgpkIAm9tYvyiZc1RdQnZhx4lvzI0aNi6txP4/abbo5uAJL12d5QhWy4nQTCOwFMzeQzA8FneGWQAS3MRnFrqcHhTvUlbQo07IeOMaNhJlly48aOjScfeyS69uoX1158cbRv2TIqIbtunSgBDXZLnyj7xyOkiwGAfGmP5X19HYMN+aca7W8iU23s2HHx+isvxasfz4jf3nBjXEBAfUjdeqlXnETbzBMo0GGfLVwQ//Pov+Jfr76co7BdqBC99wIN+1XAB2EcFeCFsvKSNjlHXPSLfxN8+w6Z9JhV7YE6QO1nyOy2X90DEhmwE2VyG9tGApClzP/kxUXmHu4kW456k6lTl4zD79kSuz0mjRkRrZGj22/7eXQmu8yFBvkkqU1bggICDemT6ffBH5mN5VRAo3ypA/jbl/p5E3zzDhkBd91xZ27OuOOii+KS8y/k2MoWKU+2rx6zbX1f9ZdbB2FKGuWhyIAF7XxOBlzoBH1V+V/fc/Hny2MQfvD9jz0WvWrvH5ff/ovoB8C2P37TlzxbGcrAUd3iHBhEYp+YwGJRTpqRneilTlbnlNNfBTjxmR/NnhUP4gu/OXFC3HLR+THg5JMzO7kSc6/tdxFIu10+s5gITvUR8PHLA16VCiVmJgxtmZtYngxTt/a4ALEVX3sc4NfPb7wxjm1/eJxyysnRvetxFE5trjOaWxTSp6ZP6WPTv1zoTE6UBEVwqs3yki/SrnGvmcer2PYxlOyjJx/5R1x79XXRvXPnaNWqddY40j80C9ZL/k5fFdr7HF/2Wd7PwrXQzeBfgOGAevV4EMAguuBjZOrFN9/MTONLzjkHcIrtffXrR8VchCHOob/ek0CDepFxmLmsbFlI1v4aH2TdH+RKu2g2rtmCM/C1J0yaHH97/oW4k0WCM4hDGhAUW6i2ivoKPSCTu4icMRhtynPqyVLQ7t/KgSemmPVUjVoxbiNdspgsEoLtV994NaZ8uiAev+++OIGM9pr7kpkFTdUv6kXtlPyjbpAvc/GWPhqjGLgnwI5PmkCDz+JlBs8nM2bE8OHD4h+vvxm9WHz49c9/Hp2OODJ9dWmH4CQYpT4UQLK/CeLLo86Hk6L+ZQ58Q9u6D88hFSd2UNB1OH2/G39mIV+7+bKL48pLLo4WTZoAgkNz6nr9ANCVWygAGpQRdbmxnRnISwGIhtG3Boe2jfMHnh/V69VNMct/GON/8/WfARqkAHP046s0I/w0NdvzUyei0NatWJnBWU/Q9gQacJK+JQBLhwaBkvHdpzJmGqsWd/4iltHkjWeexur6+QQW7TJFyKOsKpVF4TDx7gdMhFLm82+VLcyis6rCE3GVkUWlVH4KjumRr44YEbf+6Y/R/5BD4kzQ+T6k05nR4J41j0RxHDJUrujwuwGqTKhBkdllbAXSQEWD8kNFHKKyPoPVtGVL44kXXownX30tbsag9el9PKuYRxKsa/BpFKYSzdXpMpiW2bxUKCoPDaXOkgKiYOfKL0ytA2//phOU/p1AtAZjGXBC3ziW1V0DF1frdDxVHCo82/KlE+FeMQUmARf6r4DI4DotroooSFad9SzdMSDc/3rs0QQaerLS1YCsgJqABYpZOvjcp3AL6njZZgnQKKHOqVChjeN1v6ZjsobCLBytoaPe5Ui/9XENzsSR7TpwVGeNVHA6PdLY/aaOwX4VQUWhDH3PaVSh+GTp4SpJRZTTFoL2uTjQ0wyEH3si+p9xZq5Ot2zMGczQRWc2V6ScPx0X6GPWhA6RiLTt2cdUivwuXVSEgh+eFbx85coYN358DHtjSAyfPTfuv/3ncUqfvlGXOgfl8PIFu3ScpZF9TiNDZ6WDl87yj0ZI3vFtflYhsKADOFoz4vHHH4t/AmY05aO//PORUEbcg62DLCCQzn7+/L+/a4Dkue+YX5VVponhQGgoVazVUd7f8P5YgLuH7rg9pqJoL+zbN26+9ro4XHmSN3AUVKrOaSnrxblNoIUVoV0o9GUowE+Q4fHQ94khrzqkaAnta5PO37Jh5WhWr07sXwk60o99CLCVZ8PdnYjNbgfLWMsyiAQZpC7ty9d76LurSrmygQOvM7YPMvEDjsIm0lwXbP06Ptu0OQsWLtdP5rrs1FMzFfAoDGfTRo1JjSzP/n/kyZRyn0ObOce0K796OQ8aTue5CMiKlDid3Gnsa77vr3+Jr5YuiXPRBf1B/02TdC7NiFCnaFj8W0fIPZ9Yl3SG1D9eHlGalZDZwuDK0S702HbuW4nzOBra3/ngfXF2/xPjxF4941gyVVo2bhxlqW3ApMVXq0hJxmEaDW3vHfRstmfhqqN4HQh2KIZQiWPWqu3Pcb37mC66DgCE/aj4OwfXrAd/E3zzXJFxHo3cmEpIf+QB5kIAxv5XxAkQxNFJdBVt8aYvY+46jrckVWS+1Y24zuhzAmdcdwKg40SPOrXZBrY+/vH0IOpvfB/ns9Lldp46NWthlDn66Wvo8r0OZRFYKDM6f8qROkEd7NT7t/OvPjb48hJomK1NAHx+GZm66Wc3R/8ePfJ4QIFKwUTpnQE/vKmeNOBS57q65jz6ezpd0FqHqwKGXgIIVM9j9XXihAnx+qBnYgtt3ff730U3wF5XW1IO5Qto4nyq47U7+b4Ny67Mp302UDJwdGz23z3cZghNI+NuyOuvxaNvvRPn4DjffOWVbGFrVQTp6GA9KO2Tzqzj0Ln9Ud/Trv22fXWcffYoQ1fUrK1jFeoRb78d/3jgvphFd+688oo4C55v2qhhBl3lDFQEdwQG1eX0UXnPlRdtKPe4QudLW6jd07ZIV4OLeQsXxlPPPBN/A9RsxXfvfeih6Ne7d27T+gr9gEBmn3Xo1C8GL0WQgVzxd4Kx0M/AQv0oMKsuFmhyLG49uPfee2IK+v4KwKOBF1wYR7GH1zkVpDZwgahJH+2otDILJ1es6W6p0rs0t//aFvnIQHWjQMO4cXEN7Tdk6Nfc9LPMaGjdrHnqd2VVHe68OZFKvLpM2uuY7sNzKzMugQb5x8t2zWgQnF2yemW8PGx4PPTsoLiwX5/o07M3QMNRcQgprM6n93+P/yHQQAM8gY5xv3NpH0sykDzD54IMbnvMbESAhpms3D/6/PPxHUHQmaeckm03b94saxzkllDak07SpBTcmWnge4LUjqfItHEuCvvuODIbEf015cMP44Unn4xTBw6M47t0ySMr3bOuTtQP0Dn2++pHeUKb6ksap/8EX+ZqKX8beAhUe32+fFlu53lr7NiYOntm3HT9ddEVAKYuQMO+gLryt7owMxDkGXjIdkz5Zii54qdt8kQQedU6JIJjZlu4QDFLXYC8Pv7oY9GdoOUG9HDb5ig4bi6AOoi799KWZgDA366cyqP6OT7PTEF1kLRxW6IZg+qwiaTua7tfmv5x3Ivt+wm0r1vnwAzmpIXBgltg9LnmIB+PPvN0PPrG4AR89+4qiwGti9OVLC5aCT1s5t5uto/JSwIsyW3ytrzqOPn5LbSWL+TzytC0Ij+Vr+8EOfmiGX1fYagFfpduYusE5QZ8nnj1CWQDmcXkKuiXW7bHhAnj4wjk6A5WeDvBk4JnO1lVV7/wdfi/8FHlFWmSYJS0IkiUH6WLvCoflQXJ4K1Yx6Lb22PGxC333JuFB+9CVs8jG0Y9r88hEPgtvqhp35iU1DclPzVrPyA3ud0Y2utDCpaaJaOu1M4uQQc8gx/8hyeeiBP33zcuvPX26ItO8OjEL8nCMZjW79A2VcWfLXwzZVVgQ/+MZzIW+2r/y6iTeIYryOqaT6jXcv/f/zdenziBRb0z4iT05FHQpiLfsb6QY8h0fua4BMzkUbjc6/te6gvpo7603/KAumAbPuWE98lGvPPO6Hzk0dEPv6nTMcdG0yZNss/6DvbLuf0RNFbXMJ4MQvlMfavtUh+V/D9tmvrUgH3YuAJcu+qyK6M7vnar1q2jBpmLFpM0C1Zf0vsrIDs+zGdqO0oAh8G0OsFFLH9aw8FMX49+/eTTT3NB1a0mF5lV1rZdbs1wUa9UO8I+qcOUI7e6+Lv6QR2dPx2bc8k4+ELaqDXr1+aWx8nvTYuXX3qFWgIXxsnEOGZButXXOESbqr9q+wV94Xl1DXOYvgG2yHhE/e579tGMAPln/pLFMWr82Bg8dHBMm7c0nvzd76Nv165RA4BEkNc5lRZFNpbbygRJAdygkXSR79Ux6cvTfxciCt+D+hLIyydkVb9FFuUjb7wZnVikvV0g6bDD0heQ36ydYN/lP4EM+ViQXRvi54XPoP1A2ztGOiSNyrMouR379DbZ8v97510xle/eeP55cfkF5+fCzU50JB0rQC+AL3VyYaPwk9GB6kSBuWHDhkaTjkfEBdxXrX79HCtfTBuTP/9L//nPAg2FroOJoEZBx3Qq5hJIjRn5VqxdvjwOqlUneuDMqsywRAgVQAOrckWQ/R3HfGyO8aTl3M+KznyaufGMU+IsVqfbIYSmR+5G8RpcVGCy0oFDCbpybHEvmUOh1vFUCHWAMiCFIXS4TNHfCJDx6ltvxe1/+2uc0rAxRvkn0bNXLwoqHhw6WjKEXVdYEvVkTAomHJx/VxDkgEkUkCwmpCLniLk9oKwy9WKE/ClSxZ59/Y248ZRTo2f3Hrl14mDPXqXfZnBsIQBRCD1+TUWl8lCh6pxreD0bVgTRIpH48jkGwQfTDD8m9ecxHMXqPKsfAt654zHsP26eSJ8FCRXCXFFM5QFwgSCq0KWLDoXP0LBKF9t0VcRneU7sylWrYhwK5FGAjGuvujq6s+JSF7rUoNBL4dSijFU+kNM+K3wlo6Kj6DNSQe0VBhWwCsHU0VW0LRgwcvz4+Bw09zKyDkxZqkOw7nGh9lmjo0PkGKz1UA7l53Nc7dIhQpfCKygC+qDD5RGKItwCDQtYifjgo4/iuaeejO4DTo6BGB2BhqqVOU6TuTRgV7hNxRNosAiQitVUbfusgtZR3Adjlk4jY3HrgGDPCvo+acrkeGvk8BgK0HDfzTehXHvnMa3iq3g5eZa7zprKTl62j6lEnVv+dn59hitL0l+Dn44cffoE+XgCcOeR8WOjDbS7/69/ix4YNY2h9FDBaQakSyo3nufv6VTQtt/RmdX4ahBUsgyEKsU1EikdO3lyPHTLDTGNdi7q1TtuuuZaaga0gS8ohoRRMxOF5tLJ1wC4lUGeqF6DfeHQapmGaxZ7aDmG9alhb+TstgaoF2ho3rBaNKlbM2pWxhiSPVEWYE/5kIi7WLbZjSPlynNx5KZTaICIzPgb9HIOlLeyzH053k8DhDxvJi1/8RffxMLNHI20MWJJAfDHZSedgJN7FNuvDo9mDZvk3G7bsi1pZOCm8UoDofKnneJ5Or3wETIrv5fS8wXu3mMf/x//539i1/JlccbAC6LvCSdGB0AY+f1r0jczsIUG8oy9dQ+pvGmBKVcmdAbSEeBJeTH/3zL+raxELd+0pchUePgPccFpZ0Tvrl3iqEPbRROKYlXhOz/At2swLnNmzYwp8MDDw0bnKQWH01ALtufWrsLe8O+QaftdhXPi2YiycrXV7SMOakzRN/a6VqwCvzFW/UyDtMo42BXKCwoiH+z7cxVnH2SnMs5ceVKRy8DP2wQatlJ0cMNGCmrtjvlF3aU4qXuvOPoIgJBmTcguqJMF4B4HNK26c0ucjS47rHWbLHpYiRoNO7Yz12U4prNysQqik28WgFRQ9xYgKc42wcaPoCbPNuC1INMnADyTADVHvM0WM/ixb1cK/tai/gP3VlFHIYfqLTPY3MPu0X3yTW6pEEiB7jq5rl5YsFYQgMnPUxHmL1gQ08g6GP7Cy7GBSOXee+6JbkccmQ6V+kQ9k7IPLyRwyd8GKm6bM+hNkBP+T57h+zqPPi9XYJHh9z+eHm8MexNg8J04Gz3ws5/+NNo3b5FymAEtNNBOmHpuoKSMGQQLlqTuUsfRgyyYBx84BqYPu1U51rAC+w4rXc/++aGU1ztYETmV4KspW9h0oBNoYOXezAYBVAVXQF3dayDhS6df59n5SF1A24LsOpmfLV4cg557Lv6KfWrA+3984P4YgE6wOONWwGZpKMigTbV99aZzoO1Qb+22XZ7lHAsOa6/MXrN9vzON9P377vt9TFq9Kq6Bn35yEUDDYYenfKZDjBypzwrdBXDB/QKd6v8s8KX+5HcdcvWSMqyNEYjZQGrqCAKuawFhOhywT5z/k+vixJ69M9XbQCu3OfE9hgCvkFqrzfCizwmkopMqs0ouNKaOkC8NuuRa06WXrl4Zr8KPf37xhTi/Z7fo25saEEcVGQ2mX6vntd9mw2QWJm+px+h88oc+hjZEp1q+1x9wu41jsSCiNQ6efOXl+Aa5O43soY7wZPNmzXJbpf0RsLavjt+X4MOXLEpIK22kz3LlKwEMeMvnSjtrKy1bujSmkZ31+ksvRd/T8DvwsVrgY1mnwXlRbrSh2g9tlMFoFrSDn9WHyqX7zg1w/NyVZP0P+7NkmUDDx2RnjY/3586K6/ANOhH0HsTiiVtCBUWkvyvJ8n1hs9FW0hheSVAEW2ZNJf9W7zqW2gRVfsc92ZPIpHxy0KA47sR+ccPFl0TrJk1T9zqZaSWkMf8pixmEQiqB+AySsC+CGh4BqONvrZZidbpCrCJT0y2VI5HXVz75NO6m7+dC+4PRNd+w3cPAwvFr75ziuYsW4b+9EI+PGJoV5zE9eQ0AlWt9SD1sHrYbZjANf+cOV8cJ3hm32WPyn/KtDfuW/ll/RxsnEF2B9yrA+PpMboPRPu9Bzr6mzs02ApAlG1fH+NkRSGBU5NW7x9EUxGRvN/L3FRkNkye8H0dQF+q2W29jcemIDMZ3kEUi+yV9mCd/luaWt9P/UIflghj8bdBUjcUNbaCBkvUo3h47Lq4nu9frV6yinnXa6elP2q7jyboq3KsusH35VB4ysJP/5HMDRS/BJNt2vn3W0pUryRAaEn8ArD6BsQ/81a+iT69eqefdSuoqr/Jv/bIaFC13fuV5dag6ImccuqVN55kwcs6TQIP+1EwA5QfZAv365IlxAxmmJ7Ow1xEgX93ldqKUeeisXlGetOHSP+WL9xyHOkB/z7GoY3yWvLQNH3wyoOmDxCGdjjom+vbsldkSTRo19qspO8q19kKa+/JK/oQ3CjuiXAE08FztizS1bWm6FplVlz0z6AmC0UuiGxl3rbGvFnD9inm13pk20HbNylAOlE1lWNsq/Wku++5PaSOwoWwtx6eYgUwNJs4pB2/rC7tNuQ4F9T1ePnUfNNavljaZ8Qm1i0UJ9AOyKm3sKw5OtmEwbZHT1fDM7HlzYgox2uuvDo7Tzz6HRYKe0ZTtATWqc9S38w9BjRHkMe2FMi8PSgO3XJfsoLTysv/OqYs085ctjrFTJsaQkW/EBwtWxlN330O9nC4UqGYhAR2T2/u4z+yLCrznItsOfPncyo0cyjiCy8mX0CqBMXhF+6StmgHQMJK6Gy8BNrRs0zZuxffoSHavQAtdzbEbJxTxTREzOZYEyXmuW3G0jz7H8fkcgcXqNapnHDIa0Ovx398dYzHxN5x9Vlz6k3OjeaNG8DWLvfTLmiHGqvKiULV8bAbidubNOmjD0FMtuhwbF1x4YVRnIZzBJG8m0yW1/jv/+X8HGhy3/JKMVRABmVb7FRfvL5gFYs1+oTWk4Ll1ogvGu03TZlkpficERr5RzihJmE1kZyzB7h2/KDIariOF8ZJLL43DcP4VhKzuj5MlM+vEJVP7fCZRwZF5TbHSOTGwUHEoJDpfOpDrzGh4a2T8nFXMbtw28NafxwmsSjWoXz/30Ys2Zno6TIBaY1woJxwLUT4ZTgBCw2/w6KoxKoW9djAcL5+xEGZ5inSxF0DMbjjttOjVo2ccAWJWc7/9E8kspet6v+3oPOvIeenISQyDI42V/VaBqAh37OS8WFDaD8lo+OdTT3G0XfUY0KdPAg2tmjXPgOsbFJSKVCWRxkeN6KWwQxff9xk6KjphOqKuuGhk3cu4aMniGIOD/vSzz8Y1V1wB0HBMnknr+eDSVyG3XcEGX14aslwVYTw6NHn5OeNQiFyl0xlavHhxFqcZPnpUbGa8V5KlItCgYq2Gs+LlyraVjHOFm/YM4nSIfLaOUip1hFwjlsYBZVsOp8KUWrMlJk2dEoOefzH6s2JxPoFRi8aN8wQIDYsr986fgYVKRX4tmLYwyBmA8I4BhfMoP7rnuxLOlpXU34F/X3vqsRi7cWv8meyA00C4D6zBEZCi/cn/qg3+gyzSWqWaWR/87Rh8+cj/C8YQtNK+z3PrxEusdP3p7ZFxDEro1399OEEeEX6Ni4ovwYtSn+0nv2tI88W8Sg9XZeT93HvKd6qgjHdgzMdPmRT/c+tdMZYzOS7r3Seuu/JKEO42OZ/fUvhIQ2j6MM3wU3CAAfHsqji332A0lyxdDhI8i20GH8UL775FywAMvGqS1diyWY1ofkitqEXAS3pSlN29C0fevqHs8bb2ICSmKavwXfW3fYtwGQi5QuHYytF3zbTpqFLRlZ71pJnN27gt5qz9hurVFC/MsUdcd/ZpgDDHENi1JnW3LkWCqCoN+Cit/S/5WuVPe85jItLyJc/w2fKo7wliCai8RxrgPQ8+GLOXLokbAQNOY+X+SAIjv6c8yYue5KDcSqd0WqCN9FdSnSPfQwgKucNBR5HFF4x75dbt8fbEsXH7A7+M6y++NHrhTHRo0SRqV68aG1atipUL5scOVpW2rFsdC5YujUXvf8r51RR5rFUlGlapETUwSt+hDzWQWGJO6dgTW3ZsiK38uRtV4XnAfAUZqwgvkWlAsFgGpYg5xplFj/GzCml9fvUHaPSDCgt6WaNh0Rb2jLM1ZT2e7aK92SIDeh4fxxzZMZo35WSPA2oBNGyIR597IQ7a57s4D2eldRP6Ds9XreAxjTwboMFjf6XLTvnIOU2dBWUQBE2p8yztIWgG1gKdAg0zCEgnAIC9BIJ/Pc5/P9Ij67FybDCsg0VjqXNtu6A1ugaD7PzqaPlSvxuoqCudL/W7crxw4cKYQtvDX3wl1pIt8Nvf3RvdXOmir/KI/CjfKa86Czr+3qcAKPdm9ihv6kvHpIz5DLc6uao0GSfrJYKRZ6a9F+ewQncTQEOHli0LOdIG0UedCnnD1ZEiK8YAz+ei0/fqTvsinyq76uUKTP5mVmDfIOPujw/eH4v47NdXXhHnQHtTUw2wLGhWCfkoan/SNv2WBrahzZPO2hCnOlvnbx1/2zdonwcg++Qzz8bD2Kd6tP8XgQbAEoGGLRyb9uOpNj4DGkgTZVmaaDd0Bg3fsyibPM/vBlc6ftJO5/w3v/l1CDRcRarxZeiajqSoClh4MpFAko6mdtqAIoNf55h2nMPUdTzbNv3bQFLal4CG4QS7V/zht9GC719GrZwBfU5gxahJ+gZu01T3Fvq3WKkXzFHnOw+mrVtIEybi+65WkVWGjmNwsRM+WbTy83gRJ/SBZwbF+T26xsn9T45unbtQo6E2W/qwrUl/uIZnFKt+jB09Ig2KFVdohrPrXGgXlQXpLhDj2GcCtD/5yiuxm99PHTAgt1S2bNEygTW3Skgj29VeOW79m+T/vbRwdVoQQ6e8AvOVPgh9+YLFk6ymDtD++D/+ESfAL327dYs2LM4cfHBdeARHFsBCtnMepae/q9ukVSk4TXCG95wjtxGZxu7RmkvQTdPR/W+PHRufLlsaP7v22ugM0FCHYN0gxdo4Bk9msukfyTN5+RAubZ4mMmnFe7tRHoJU+5MpKVhoscbRo0fFU4Akxx3fJ66hhlBbfBp5QSlVt9uScqss+iz1g0C8z9QXUU4ze1X5hU/LAGrswwLQCnTNBECMN15+PkYuWRG/u/76DLzqk9HwLRll6hYvC03ygAQannzxuXh0+Jt5dB45Phn49+tQNVrWO4DMPfy2HwjAsO+79670ZyDI3z9A54Jv1bzYFxjBMTsOJhImQcfQf54Gbc2goMwuaZlbdlLba8PaGD+Huj58WpnB9urZia0TB2T2jHbovXcmcoJZ27jtllvjWAAqeUOfSPpIm+RH5ci55T8p7pYzg0UDPemddPFzxikIuoUsmxGjR8dP77mHFiLuufAitmkRGKHnC360oKLZZQwXHZNAXs4tD3duoXVmUjlP8HraW8fK+y4uLF25ggW31+O+Qc/kNuXLyVI+EZ+1Ltt5BLlsT/lnBtMXziwNZNixuGijz6T8SDHlyfnRv/E56o9PPpsbf37ssXhj6pS4ER/+dLYIuB1JkOwrQPzSqrRBnfMsr6Q+kWKMI7fkMS/Zb+WNz/3I4HEbQMXE6R/G737zmziC0yb6AWR0JstUYND51Care0vBcs47NJGdEiTls0Ln4o8xHr+nPlXeXMBYCV8KNLzw3KC4/MJLcjuoxSz3ox7MdwTOgrLyinMLZTMm8bm2rw0sxTbqGoidGWYCgx5nunQ5BQ8JqAezuGvG1vnogw5tPHniwNhXfcfYpbV+OR1iPM6Aeo3W+cwmi4vFMginvnfRrSJAuDWEBDEmvDc1/vnSq3HTpZfG6X36Uly8HjXFyB6mrdyWuHfM8p6LpUljxq9NNY5Q96gXinlmWyJxgsViFyxbEqMmjY/XRgzh9KKV8TRAwwlkZ9WsxtGeDDbvl5byG+NWf3hyjbTW/ufz+Dz9Zcbu4oELftaKU0d8hK55HR3851HvxgkdOsRdP7spjiE7VltT+AH6kuhE+F5d6aUO1oZrG3NOIFPGP4zLtgXYqpOZ7Yklb48bG/9Dn9/jvltYxL7qoguwT40yU1s/NWnMAlzGTNDKrMDveJ71IwQahg4dGs06dUygYV+yyVF4tCQBef0XX/8RoIH5kz9TCJIc/sNLBeW7C2aDgI0bF2sBGvYFUeuEI2+xH4sr6UjrmPm9avtWy5WFMSiOq26+Jc/rvuLkE+Kaa64hK6B9OhNfkXbokXxlcJYr4UCIoGcQANpVcgxllJweHAmDaJnQvbYqEpHE11i1+Plf/hJkw8VVP7sxTgLhbsSkGvArfDKRiikdTH6W0NBMIeVvMx1sU6XnlglCY/YeFk6wVbefgpHfHDo8rqKoUe+evbLoUzUc6G3sS7NtA2uVju3L3BlcSwM+s12dI510V6pU4Kapuhdu0+ZN8QF7r/73iSejXq2acTLBbidAm5ZNm2d7O1FgCbYgGNl3xpfKGhqk84WAS4Pc18yE6Yy64uIzVq8mnXnu3BgL0PDKK6/GZZddkkBDk8aNM9VNZzNTxfnp9CqEggklhWubPkth9lJwNW6VQap/wFmZP5+TR957L4a98058xXeuEmgASdyfarUG3F4qH1fnEzWHNrZnRoJtGXD7voopEXM+cz+ddQnMUrHC7hiE/MnBb7D6egrpkSdnRoOOkM6Pc5cGh+cYyIi8Ow6NRRoCBDqDCt7TEPsMA21TolaRAjiC2hLPPVisMj78yzvjjL4nRG0yPXbjZLlCphGwPYN8DY37RJ3PksJLpxl+LUv2jsrPzIbiWNayMWvOnBhCkbCXCbrqYyR/8cADcRwGUxqrSHPMtF9ImLTXuRJ1RYnyLEluIF+efV9FOh36kPcscOVJJROQp3/d/YcYtWNjXH5837jysssSaMgjS+Fbr5JjoqLOOUwliMEGqFi8BMM181NOa5nB8Tuj8vuH8O/+2OWWLfePZofUBmhA8QM0lPueVR4+c3+kTZchR/bfgQbbF2iwfxof6WZBqwq8KkFAuctNKGs4PnHWWiozL+cYSdpZk08FaDj3bFbrQP+bNGNVqg5IN8erQgtBKGuPaISVHfndZ8nvypT0KZQ1gZO0gX8MxN6j5smv778/3lu0KG476eQ469xzMzDS8dTBk0Z2VmPjMwrgoeBNgULl2WJfDCq/V64yq4AVOXIVrlj5xVfx1oQxcfPvbovbfnplHMdqS4u6B4AP4CRNmxaTH/hTHqm5X6tD4ktoosGpjrGtg7wfhKO8L/Il3+7CQLnGs5PHuB3li907YjP7J7bySDDXqLA/dR1q0661KaDh7h/IFPvWbIs9rDIAlkHj71m92/0tlAEE2QJwunDzFzFrA9lVLIot2Qs0nAgIdazpoY0aEETsx1GI6+Ox51+MpuTynnvqKdG6cZOoBc9XZXw/eOIELhDWPmldOILwO3pA2lszJMEkxlA4ctZxwBGBJ9esWRszAXgsLvfEkNfj59Tg6UeRsIb16ufKrRP0HTpePldWXfVSXtTrOhc6zAZ0yqlzqFPm5/sCYruCtJC5nDxxYgxne8AKwNl7WZU6jn3NZqPIi+oVHUNlX31voK4NcpUo+QKa0mx+h6+lXXHFvQq6wCOGJ0ybGoOeeDSGzP0szsNxvuGySynK27qwE/RRpyQdE3U6vCOPa0N8ZvK7vMcY5Vf7r8yabbcPztB2tk688sYbcSUAgNe9110bA884ndXNA7NIlxkN1QgA1TmuJDsGnU55O4EA+ivdlQHfK6UzOzafPxs9/ASO/9/fHBb1af8hsgP69+pN/YrKrAJiW/fysXOgRhNEUw+px9IRRy+aAm82nwG0sqUek/dgCrKePoxf/fKumIw9uQxeuuqGG3KLgNlTm8nkE0hyVc0sQe1tQRP1WQr/j3ZLcED5kgccBwRMPS/QcPlv78m09luvuTJO738SW32aspWnyM6SD6WHPKj9EQBxxdp5KIOPQMRS7MGl/wLh1WoCokMb9/YuXLE8nsfRe+Dpp+PC3sfFqSefGj26HZdHOG7dsBFdvxOQjQxA6CJPesmH8qVFrOVzM9W0p+ohwRFppu3aio1KoIFsx93btmVGQ2fo07pV68xU2Q6aKk0EX+QVa82YyWPAr87RDjgu21TGBAfsv5dB4UYCgMnYwD9hO/qT0eAqY7u2bXPf9m6e7+kLzqGrx4Jr0id1F++pPwWJ9Blk/RLwIK/63GXLWSElcBFomLV8adx83fUc/XkEpwfUzD7ab7NLpYt2zna913acXwM350XdpMwqfwKFVfANDLqmfzQ93mW70IvI67E9e8bl6OB2LVokX+nwp86VH9HJzqM8L2hk4GgQZr/5odNB+/ytrge9FmywwNp4dMHg55+M0Z9vjHv3Ag0NAWBUnt6rPVaP6c/NXbwwngD0f5RTKrS4tJy8dlyHmtHsoBqxb3nGJpiOg5hb9fhcLYRzwousSceOvOyhPcGG9LfoVwbq8Fh2UxogNzBofLUbvqYe0RL4a8K8nXn6Q3X63+24Lgk0CGq5GPcRxesatGkbN91AqjdAgwCAOquQG4M37B50sSvOoXpFvpT22vOKyKgr3glKOia2VW6Hb4YRcF36y1/xTsTd1FO5BJCnUePG8SWy6jbW6gSA+k+ZaUBb6kfl0TnwGfKgdNNfojM8U91TBHpFRsMbAHfPRkfav+rOO+JEfFaPLtXPy3mjb0WmATxJ+/qv+t6pJ2k3V9chmpmV6S/wnv6aweUn+Kt/HzQohrLt8AbaPZ0TCo5lESKPdydTWrmzoLN+krwpwKlMeRnsOgbBBuVV/ehYpJ3gjKf/TESX3UsWRgf86/4nDYhuZN21aN48aW6tM3V4xhzIpePOxRRpwxjMxioyOc3IKACD3E7I97ahC5SpkRMnxOAXX4hLL7mMmifogjatoyZZQt/DJ2ZW5+q9dGa8ZuW6DVn5MRtXuVRaE+iB5w1Y92VO1ctLlwIM4qsNIaPB7annow/aY5884W0/bKTAgrQX2Ey+pH20QOpbae88SAjpoc4xtb889mFfdOUG9MjHZGCOmzo5/vTcS/FrbPfZ/ftFAxYJtNXlEHKzfZIfudeMBlyF7J9xjZkBnnCnfZR29kU9X5WFUwuZz1+2lBp94wAaKLw+z4yGuwEauqavLYgh78nX6ld52SxhdYv6RKDB9/w7AWZ0nX6BvrjZnwrfB9M/ipeeGRT/nDQ5+rEA/MtbbsmtE45VnrQIqQCVMUlmvqIjUv8wFnWzMZDbsF2szCwIZQDeqQrQYBHNEfjHf7j73pgHH9164cC4+uKLOFq7cVEwl1hB+7rnW/kFGUGGM+MNOgg0uEg9DKC7ydFHsnXigqhRvwHzoAai44z7v/n6fwYaFMx/f0mMFADQd4/y2bPnu/hsztyYCtBgRsP+BNldWF1q27wFtRYQbJhCB96XJyBYvG40DuiFN96UZ8xe1L83QMPVWWlXwTOjYTernXDTj2eSKhw6ghpSJyRXvpgg/84AGAVrHzWuK9euycrSt/35L9GS96669SYcipM5grJBOqym6zql+eImlWxuoYAZTPWS6VSCvlR8ZWi7LOnKFjryiMc5CxbEIAL1McNGUo38vDgeJ+5IjLJAgytGKrzqOD8GLqbUZkALTQrHE2NNuwqNwuJWCYGSSlUqpWLRWRBo+PsTT7Cavl+chHI9FqChBaCNhkdjoLFR6dFgPktjryA7dhVoKkcGl0E1hsGz26XR5ytXxgzaHg/QMAIU+lwY3SMiW7dqldsnTPdJJBJBVuAUFGn0o5FRAfBMHffCCBWKqjrOuWDK4iVFRsOI0WMoBvlF/JR9pAIN7tPUGdRgcns+ozAArHrAH6ZMO48q3gQLfK7019lCcVWk/S2sEL3/4QfxLkbzmcFDSf87Jc6hUrtH2qlYrdNAh3PuEsXlQbZZcqAL3kApQrtSoKqDoHGW9ss+XxHvjh4Vg597Msau2Bh/+eUvMqPhABwt9+tapDE7L9lL//G7hsf5NWvFtg0EXC0SnCkBDRq4mbNmxysvvRgPDx8Zh0LTX5Nt0wPaO/acyux7AYg4f/Kj7RQ0I6iDZ3PVe6+zZBqs23TkpS04tROQp0fu/m1M+HpbXAbyf8Vll0aH1q2YF9KWeYkOS8+C6eFQ7oNAvAdQgaFejOH6aAZAA6m5L48YSQ8pBMmrGl9r0mC/aIgDVoOVnjK7vopKP+yMagTLlbh/HwoGWuAsgRF4LVfbkUuPDZUfkwehk6uN7l91H6tZDh7DuB50Yfb67TFj7TZSYoOjbvOxcdEpp3MaSkfS+FtF43p1Y3/AIDMipK/p+zrMFvZyHjKV38mVUWnfy7EJkPmXNHv/Y1K9AR3XLlhEAZ7zYgCgo6fEKN8GsCWnJ1F/3tNJL63QZSP03RMA9sGAlAOp3gdUn8qH8c0PrKYRPLw1cXzc8rs74mb4/Vj4/WD4qTw6bjkZIp+T2k+p1NiveX2cY1dqCKyhaVVeNXAeqvBwnQz5B1wVsIG+QxszqDZ9uy027NoMQEGgwfd/qEggvy9FIymOWoE6Dd8C+Oz+3hUv0HmYqCIG0/O2scaZ0TAfhGEm6QzrqfewpEioir6s2nTq3DkaNqhPccCqeSStp040Y8xn4Gh1bN+BzK+GmRWxBxDmWwCLPHKuIG3qKvlIlF/nJFNq/SxpXug1A9M1rObMxBGSL59+DaCBlel+PXpknRxBWB1oA0eDoirIuDpX2VeXeb9bvswsUE8KXqRDyd/7UVNEuTCjYeKECTECo71o1aq4++57ojsOaG3AWY9uVY/pVJRWmlKX0U8dFVMwBTTlJ2UNNkpeUpZMO/bUCQGSpx//Z7wxa34MPLEvzsTFCdxZA8KgLR1mVjt9jrytsyw/OS6f5aWDm2AkvwsImllmMawNa9fGqxTxuuGPD+X37r3huhh45hkcT1Y35ZxJzVV5QXr7JnlVPzrLOksFkAfQia5Xl6lzDH6zFg3f9/SYx58eFI++OTwac+8fHnog+vXqlSs+CSTTX53QTO2kj2lbfQbvp8zyLJ1bQXCfIf11ytSl6jRPLfrdb38bU1euivOOOiKuo3Bgp45H5f1fYbsLvas+LzJ1zDLJ1XPo5jNcrVZHa7PMwDBwU3blq81ffhHDx46Ly3/zm6TN3VdfHuedfma0ao4ltz86zhBFGjh2iZNbL5B3HVtpV97iAHzmKrx0qwLNDd491m4RgPLLFIr+y3PPx2Un942TThwQx3XukgH1tk2b8iSbytkn5xJpRF7lQZ+l7bc9HUd1TAbW9EWZ1hnegSP56bz58dgLL8T21aviLNK8u9B2W9KlXV3ewf3pfEN7xyw9DYrKwBsgjekAyznOZ4ICUoDvOVf6EfoGUwAaHqR42insybYSfAf0TZ169ci6oxglhW0N+nMxgHvkwxKg4Pvq4pQHxiKv+54/lZM1+Eyf4de8g0xNAhS45fob2Ld+RNTEeXchgInToaDfBcggzyTYwNj5Nfnck1zsbwZ1jEEQSFm1SOZH2JUxo0bFsFeHxKG9euapE9Y82Y8951lZn75qMwxSvAShMqihPee0tC0k/TH0JsJG8V/kgcBl1bq1eerEUPZ8v/nhnLjjisspelucSFUJ+6a8CjTI01aqn7doYTz5AkDD4MGFbPE8vMro2qRKNKrDeGGfsmSLVeeUpWrocusp7IO+szZRGXxAQXPtnVsm9JDSV4G/9mEM1s2RVtLkB/zK3XxnO0Pa8DU1GjYyf0t3ZlHG/Svsx9HlR+axeG7J2PXNjpg1aUo0bNWUwoFXcULB4VlvpkR3F4G0E/6dPi+8o+/hc9yWlX6fo6Ef/lBPuBXHYNqFn0t/8Uu+GfHLiy7kRJFLolHTprETv+FrbL9bJtRd8oZbQMwWcExus5HH5UX/9qX8lvwneWs5uvd5gOQHn34m+h5YNS644basWXYQKfylQDrpwdxm5g59kOek34/B8F6+Ebxxfh2TesZi2jMAGp4GuHvvvffjLI4BPokM4qMBlN2GYT+KrXfFFln1jFtt5B31cMYI+G7aKvuuLDsz3mc2qoGfoOnv0TXHsj35hOM5dQIb0rRxk6RF+gaMXXrrTZTkJWUp+XWvv6ZcpB+IPGt7+MwCpQsXU/QQeRr20stxyRVXZIHPptD9gDqccMMYlU9lOzMPkH/lyRoMGJPYjQ1y67P0oPMZbylX6jJPw1hE2x+y1WkIJ35UAby+AKC6bcuWqeO1r86ndM+MMukL/dU3AjHMZOpfn6282l9pUgGgoTIxx5atm/MIR+OE+x57Ou666oo4e0D/rGMjGGLhTW6gFdOp9+cAAEAASURBVC7uF6hwq501GNzSLrBhMO+4HJPyJ320f3QiPluyuAAaRg6JD2aviCdZJOjbtQAabL+kq7Sdyq2xpvNn7KhOkTf0D+Q/7bkdkZ7S3s+mMafPPf5YPPfhx3HC4YfFbTfewFakw1PPSHPBAv0J9Y4ZojkPyhXPhmoJMphNJOBmv/3cD/aB9tu3bqEY5Ltxxy9/E6v57o3nn8u2mIFZlN4MujxpCbDbRbjkc3jRU+fylJMvv8z6fsNZ0GxMvwbiL9Y8uL5P5OWVFC1+/S/89z8CNGgAfXmlkPCXFYCto7CHNLl5pLVPJMBcsWRp7MuEH8tZutYVqILgi9iK2qQiwzh86YoRK9+XM1m2dQYpZBdfclGe9U1jWVk+jwnBkcuVaZUqTKAiypVLmE7jKIMnmixDIJwypEHVSlbT3gb0+N0jj0Vb2v/J9VezdaJvOrkqD5WTQqjyUCBlKB1Ip1knSCc0lSOds3/lKrOnGKXtHiRXLeZikF9+c1h8OGZ8nHvuGXEcKyKHEbhY7GarR1hxT9UqFFCijyKbCoB1ChRKnRHbVEh01D0SS6DB1S6DIo3yJ2SHPIUjtD8rlX169sptGU0bNU6gwUKWKuxS/xXkTAFCgEuOhErU1HiDJQiXykn6rFzFEYZscZn64fSYgPHph9L2iEXTL+vVrZvtq3x0RnXodAZVzDqxidTSbw1aEfQSCkFDhckVLA3SqlUrYw6GYTSFmVZztM/57F/qAIIr0ijQUFLYqVxV9tzjvBarkEX9DTM/RIu97LPKyYKKm6GLGQ3jxo6J10aOxkHsS5GwPnnetOcSS+909FFuKpFEzWlDp8u+20+BLgGR3C8tTzr3OkS8VkAb93m+M/zNGLtoZdxzI3uDe/UIgYaiQA1Km7bzUnlyb4IMZQuD71y7+qeBd0VXI+dqpkGU782ZOy8Gv/ZqPD1mQjSkkbvuvTu64Jzbd/soLQy07LfGXAdNoyCf8LgMiL8mMNMZ07F1dVFZkJkEYaZSvO6pP/whpjP3P+nZPS5AibVu3gwHjWPJoKlHU/lSXqxRkQZMxUqA9CXtWgzy0znz2Kc7I16nAJ6XOSg8Ieqxf+KgmtWjOkcvxjebYz9i2dr7Voj9Sf3XKa8oDaRJcjZ0VRIZg/xR8CSyz7i4DaABOUbWPIJxA+mkczbsiJkbvsKp2BmrXcrnGtDzxDi8/WFxRJsW7AusxzGMzC1tynui/NJTedEglZyGTJGE31NeGaPfUd+4MvDRpxwX+9hj8d2yFXHiwHOjW/fjODqzVfKLhsm+a4Qziwl62EZWN6c9T/agoQQayqmPMCIVPPpxH4pRkS60atv23AZ291/vi5/Ck4c1bRZV4YMKyP0P7HUsh0wI1lRlruyT9M+jKQmXBBkq8dLJ9fqWtj0STRDJ2iRbcXY3ffd1bGcVbRMAz/pNgKCcZFevQWOcyMpUPDcQJCBHh5Rl3mtWAqHn3h941hZQiwVbpO2mWL2JApPmB3Md15fCtcceg7wfTGEn5oA+vsRe/iYAu/379mFf9hGpt9UpGzZtZhVXZ0eeKV6Cav6uc5KBLjIlvQzAdAZSnhjn2nXr8oisqe9TVHH423H1FZflKSueOmEGkvzgapoBmPKvI+17rlLoPGwDqDSwMqjTYS5O0jGjgf3HjG/xkiUxDUdoFEZ/4eIlcSvb8DofdWQceMCBqY+cV4Mz+dBLXlH/2LY/MyMJmU1gwO/wv2PzHHIzyywG+cqLz8bwT+fHmcjThedSQ6hVq2IFFl4pANm9QAOWQ96Rj0rOrc6JK17qRQVYWVX+XEFZTUbDcGo//PqRR7Nvt11+SZwJaNqgXr3kWYF5j27VFgqCp96EtiXaFytQZhvsPcYY/ZU1i9AVgmTzANSex7l9/p3R0Ywn3Hr3r6LncThy6DLHaT8RzGLs8HbSAL2iDrK/Kbd8RTviPKv/1fHSUgfS4y3//vDDMX3thujftmX89Br2wAK0q8cMuJ1Lx5/6jPYMpp1nx6HuN8vBIMTva3u1w15m9Xm2/Si2xNx8/4P53i0XnkehzNM4WrRFAiOpg5EnNUXKPvznZTDh6h3IYy5uuBquLfd9MxLkUs9YXwYAMHQUleCx3xeedHz07t4za5bUxlm3uJy2z8J/Op0MOGkuSCRttrNKp4bJwNi5Rh+lDWRc0kqe83SZp19+JTYvWxSnnX5WHMPWxFYtW6Zv8A1jNlgsgB19D/mmqCHiyT9WgtcmCWzItbYpvdRt7ufObZUzZ3LqxN9iwCknxfHd2DqBff33rRNOcGHrCh2c+5rThgg0MMfImGORT2R7HWnn3EyU5diAccjUWDK0rr32huh4aAcCusK2Ok8GAdpYAxVv9v6ClYoMBOdDPaB+0KamrmOM2ie3PU5he8OEoSOjedfOnKZ1YrRp2SL2r7F/8pZzaAFFwUVaTQDebFYdfgMXecZVa3UMaIdMnGnYHu+8duOGeJ/TOEaNfjeGTpvBMYhnxIDje+eRcx7NqW5XLgUGTIFegI/64pDX4lkyUv/9aoturUvGbSW2TZSFdw4+sCaFzemfmbICDPi56tkKPNttefaz4D6DKfiXV+bQQZgsgMzn30KQLwQavtlDMcgvYtrnO/KYyZrla0fbDmSj1KoNPQVYOLntvZnRoPWBcfFF58cRpHynT0pbGQjp8yFXaaucC+bUBQwnoFj1pU4Xvq8AnJ+pb5TZrejRd8aMi5t+f38O9Yazz4yL8A3ccqosS1cnUhkUnFe3ep+LejvgOX0Hx+m8etkX+UW/0MBv1dp18dqw4fF3Uuz7tWgQp57H4hV8WZstM+rfzFyVmblP/aEOSIBRGeN350WwQz4qtgUCeCCzLsDpa8+ZP59tZiNjwScz2XLTPXoe3ycOhS/d6mufvV9A0zgh6/6k7AoE41/ymbxun50gece+p/8HnVy0eI9TSv6KruncpTM6shsn3x0WhxxSP31fgUV5J20aP5Ur7YhXMSTnjTgEnlDOHJe6Qb/Dmipm3Vl4fczQYTGQOjwWt66Ljq8FGO73sz1Jw/dd4HQ82jqfp5+jflFH8sWkiUCZvqUxw5KlS6j7NRM78nbUPPDgOAv70aJpk8ykrowNzQwm24YO6hDnWBpp/5TftF881wXLYmGF+eQ+45xN0H32/HkU/f0g/vbUC3ENi6mn9O2TR92aYeRxtMW2QeQCneB2LwPpvF+ggWe6IKodUL/4PP/Tn3ebxkIypsa/N4VMm2ExY/66+Msdt7HodkyeOpEFeRmvelWfWt5Wj0gTeVOwRR1htovv+56v9K24Tx364Ucslg16KkbMWxTd27aOqy679MfT75w/vyPfy7+C1D5LHk8dB404+CvBRP92juRHdafj28C2fLO+fnHfH9mkHHH5GSfHOadTyJn6ShmXsdgjyOA4nOPc6kzfdzKXW7Efi5cv5xSYd6Idiz0CDbUPqEcrPt9rr5AVf/zX/fsfARpyvybElulxNZM0qEACESaC/2aTdvcOKwXzSL82/bAZKSENCF51omVq1bKKTOUoGuURjk/vDWik6K1XsM+laZNkUlcidBhc0ckJU8hlACY+BQ9mkQkL45ENp0NgOyqVDRjN0RPGxeS5C32LwKtLHAliWbt2LQKPIvAqKW0dimQwGM5pLiGAOgS2peK2Iqv7ixQogYLFy5bHP14anG0fU6dCdDn+VKo/kwaoEoCJbUel6bhNeVQ5WLTPS4dChavSU4hEjRUeg2ENtQV03GP77MhR+f1Tjjmcc4M7RP269bOfpnMqwD5Do5MKhbbSANOuSkNFZ1v2XeXkipTP2LRpYyxetDDGjxgVS7i/DZFft/79o3HjJlGnNqcrILjp2OCMaNxV0jK/ApZpgMy6YxIQkf45Dto3pctnbqb9pUsWxzNjJmbfzzmuc7Ru0YJVQjIO6IOGJftLP1VwOuIKfu5FZQxmamgccl75jvNdHmVQkWBDJ28eFYhHvzY09zUbAF838OxMxXYFMwMIhFswwX2ZKm+tV7aho6UnwHzmyjFOg30vfS5t1hFwfYRiHfrBjOx7zyM7RF9SvWuJMNNXV/8SgJIi9FU+tO2kO8+RHs6tY9TISzvHYXAhjy3GMLz53EvZdx9w0Yl98jiiwqgXqWLSVvrn3Mrz8jvt0XxhNEF3dfz9hlkmCRjQ1nZWnj368yXANS926MX1l10cTVB+uwlWBd/sa66A0Jg8oRy74qIidOVy85atFCHkSMLFSzC+M7Mdaazc1sTW1aiGES0D3fCFD4AkB+OUHVSjFiBANcAGGAn6lEE+fsAZU3HSPG1LCwBAlK9HJJZBps1q2IdVpj3Ix3qAhtkADTPWfwXA9k2so7ii17GHdaYuRPM4hBoCtapj/Fi917fU0XRly7FbDKzkUEtrP0tHl3kV9XZ1NzOKMCiu8D478t1su3fT+nFUl+OiQYOGqY8MRkpBaK6iQSfpY2Bi0TEkKHnJCuTloH85AvjyZA3sIZNrB97Xui+/BuRhS8+MydEZp/RwAvWK8EEt9EQz5qou/amokwptNIxOpsDsPvxdCRpVYWAVmQf7sBP6uOfXIP97CPgV923nu7v43ibqIywkS2A9JDqwQc3Ylwrf+7DRd88+6BiNJvQ5gLkwHfsbsi427iRVd/vOmEOG1aqN1OCgBoZXi0PbR3OAxRrcz/ZmAMG1MWpwkcFy9lHtKOrXEQe9YfZhC8i/vGFGg3030BV0kD46mupHDXDqGRunD/KZMrAJHbwIPTYBUHAZH/Vo1zKO69I1auGASufUIzhT/m7wbfCl7hJkcX51Ur9kddsgz7O3Uy8gYxlQ84zVq1bFjOkfxpCPCl49pztFONmuJ6hpG8qiwSxdTT4pgGr5XV2groFfGJv6IB1R+mi/3QOrI/rZ/Pkx6uXBMddxcf2MlYvmjRtBF+wEjarvcpsWNPA+A0Z/Ji/CK1mok+/k97hfuuks6QRuAkAZS/rlyE9Nvozo2PSQOP2U0/JEDu/Pfc3wmg6Ptqm00mcb9j2Davoo0OBLmgvIqJPUa2auDX/p6ZiNrHoNPL5HHMMqoFl2Bq1eOoqQBrrzkPxN+jOrjMcsobQf6FI/cg505pjydPJmzZ0TTwIela6bBp7Dqn3rpLPymU3yj/S1Pe2f9lBdpg7OTAfmRvmVZvbDXgg0fCXIw4r6q+MnZfPHND6QbMSzojFFs1zt1mam/vImLu9ze2A+lLbLMD5PjvCntjxtlA4u3/wOWRLkGYQe3sAtB/M66YwB0aZVG46YrR47sdU65qXV1rSv9g++lx4C2MmjjMfnZl/4TblQLxt06Eg+Ssad14ntm8cRRx0bDRs1StDY7Qc/2Dc+K174Q84vPKm99XM/cD7VO8k7PMhnCtpsx7f4eMbMGPbRJ9GENk7GwW3clK1OBKvaUvWAl/Ym55I2lNv0nQy0aEubZL+1k47Bscr/rvKtIavhzTdH5ErdGT06Z82syoCXrhSXnHC32Rg0OK866LbhS4/QZ0on58OgQpnT59N2L164KKa+MTRmFOwXV5zcL9ONPUpR221byqKAonzhkdhmcPqc9EWgjbKRhKPN3F5CVl1FgAZPMfuY1d2X3p1ITyLak313Yr+TMn3fwtBm3BlgKj/S2mJ34yZPiskzPfOluHhk1MWM7c+rPFhHWVi/RfOIBgc2yuw9ZigqorPL8zLDoZx9YOBMJ/RjTvlDkMHtFn6gjc6MBmjwJTUaNrEnzlOAJi3aHGt5ZFU2a9SrXy+zYL+ijs7CbYWvam+uZ/GqbavWSXP5sQAC6RBNy5Pqg9Sz8Im6yEBXG++86qcXIESx2uu2yolTp8Tr46fkQI8gGe+cK69PUFOgN3nOgXDZlvrRLM+0R/Cc/MJI8nP/Udc7T/oSVbHB68kCeuGFp2PuJgGiiItP6BUd2RJqlqlARilDBZLRJnTd23f5Sd7zc31eaaaeKNl09Zrp9wuXLIlBo8Zm2035tzf13Fq2bIku08vRJygWaCzepx3Rv5TF0n+nnzBQfs/x2AHBcRf49AcN5D3S+5W9/urFvbpHm7Zt40C2sKnH7L/6Sd2XGWvIOVOdvOo8SBf7bzsCM8qyfXCi3EK9/PPlMXTIm2n/BGQPBcTw2M9qBPMpl9yT/ZJf0FV2sQLZki4CqUuUUeMldX76NrQsgKceWg1gOmfOrBj24ac5vp9Cl8b4ewK62r8cLn01VpC2NJ1jSYDDv5AraaQtERTzOwKy2tptX21Hjy2Nye9NiI+XbIgWNSrEWWcNBHSrje+DDdUr5H4pkLYUnpfKxkn6vuotC2n7TOdYjeGln+33Pl+zKqZ+NC1GTy+s66ldjs1Fgv2qmrlqLCMoqv3QDgHuQVfb9ScTnHOjfZd2vifPpn8CnaTNnDlz4l/osdJ1NYcNtIFnirnBz+I7mT1Jf/MkGu6T75xD++rCuLFnzg1zKbDvEFwQ+QKe9DSOF8dNzuY74ov1O/mMPD1mN/6bWy4smitt5B23TlirRWhOoFtQdNQbz8UFt98T5194ATb//wcaSvMEiTBQENsiRSoYq/gWFygZBJXNZnEk0tusEsycPj02r1kXdTAUB3BMmhXRd2EAcTEgOhOJY22xqZ1M3C4YReYrz/vlYaAihYxiTxgbz1cW0RbVVbGtQmhXzJqLIGAMmrSKWuy/c6VF5M9VFCdV9FEDJzN/SbDpPtbqKMNKGj3eU3BVTCJuGkXvyXQbkNtFn34S29ZsjkPagrITdFciXdDP0+Hi+xkU02+V4Q+8vwtG1MCYUlfWtrI9V52KfXUWJvS7blvwWMmPx09EQDFeRx2eBfykqUyeyD/tpmNAGylgfEbR+EKhaTj4XBp5+T0+SMWgA6CArGYlYibVlhsS/TVq3S4DfzVNGgX6Z999nmdml8fwfo1y/BpFUJ65rMj9rrC6PUCDrvNcGAEd3KLuwdK5n8ayDdtYPWkWBwEgKdS2r5KxPypFFVYaKObgS9qWbtVxqiuhCNJQ8rmC6yjssw6EisQjkJa9Nz3714pgoQYrHCoqnZ+kiYPm0rksjs4iaGU+TPfbH+calyodrCLTA/6kbRWtbXgM3iaC50Wfr4oDeXBLitFV4R6zGuy7DqLj4EG5d/5bxp505z1XLSpDH/lWNZl9Uflwn89QWW3Hwdm8ekVsW7s56h/aNuoSoPm5KV85VumUlxJi8Ipx4xlmm1Tm2RVpX+BIpWf78m91QBkdhfWsCC/5cHKsxnc0s+DQLkfHwfXq0T5zQv9NnzOzRD51G4Hy+S10ScVMh/eF9lVQpPScuSkCdINxHS1T8j5ftjiWLVhNgRvkqWHLqH1w/XSMlrD1acbcBdnrUu/9Q5myNdYM43BejevrhO1PpoP7+XEI6ZOFsvYQHP8f9t40uM7zSsx8uYPgToIgCYAkVhIAQVJcJW4SF4nUYsm2LNmW3O50Op12Kl2pmszfVGqmJpWpTHUlqamUOx07sdubZEt2y7Ita6FIcd8pcSd2gARAEgS4AuACbvM858NVq+dv9x9X9Sdd3ot7v+9dzn7Oe97zDkfxjIReTe830CJN3aYir/eMUZGMBD/wRvftB9QQIOPg4k1ORLmTrtyVSlN6bCHObmk5x7ui4NkeMJz9sg/vZZkHKhYr57ce2g8d4lxX1eJ0s4+YQKCKQloPBwE8eMXKO4P3GDJtQLdgjFbeDOFGOpNWfEn30kLHeQpjnm5KRRUTUnF5Tew3Hwtfj6OdMSgREAicoTs4+g6yUEWiQXP9YlcaYIVEeqnln3UUhayypgAwGfEgowlGwZigPyCqwZpnoAG8O+Y7tMvWvsCxtD5Im3cY9ANo5YrOb3cL+3zJCmFhOn/G9DR2OsGWsRStIuNhNPdNIyjnCvgt2um5dT+1991N9ZzoYaCh9UaAA7k5M82YWUQ1+QKK15IdwP33cPryyNZgc1MaA46GI38f2e8o+JRgEZ5L8Kd83MPRVyeOneIEn6mpesmKcBI0CsPlYMwaCf4njUvrGuIwTASKXPELA4137/FftyAoP8KhQDZ3NdWnhkvX0vJFtam0sioCEgZvddJjuwx0Hs447T4A3+6Ttv0x9Kuhfx/a1jgOw0N9AE7VUPJYP6t7Xac/Y08v1eXXrWI1oTAMxdyqnasv4fhBG65+DjLfPmCjkzIBHMuvQSvyGd+5uhsr/cz1Wk9PutrRnm5cupImFs9IZQsWhkEZjt0QjysbDLgaQMwq1gsjEM7zY4C3zopyVGDrpDkWV8l0IC+zD/16Vxu68U4qmFeXZpTMDgMrVsmhE/WycslX1JAR9jbE7A3Sybviz/E4B+lcJ8xMjBukUF9sPAtA76UJhUVpAnp7HHLIYJ5jEHZmBimjnANKixfOO+MWb+rX0fSfC+4Ie+toKIftpxsHtotAaD6BuCkzS9gHPC0CRv5mMNA2srYzGX+X733Jmx4tOAXj2QDlQJ8F1EAe7Xu6wWhg6bGhrfWnU8+57lS6oIqA+Yw4glHbwRU85bsyUVgYMPBIwlvI8UHaVncM57dh0HscYaijAtzvAwflt/r7JjRzBt19C7YvX1KXJmNs6xgLB2lAp8BgmQG3CDYzH5dVNLDz6E+1LV6FvXCX6nWGnK/fXers4Ai/plSKkJ/3+LqwaeI3fvf+sGu0KRivdKj8NetJe2m0jUEvOgxeOtI5+rVtMyzONdanxnMXkanVaU5pWVCEzrzjlQdjSwvtu3jgaRcW6R2mrENuS+9xNDLt6PT7cstHrFjiHLrVpO1sPbBFJ6wjY4asTReRtMmUB8JIvnWrnLaY+tXq68oA9dMYxmtQTWfNFUp5bixOmNctDPqrBIXa0UXEtVMNunsSad3aRxFoQ24IXBcilJN96Bf5VUfWdscIe6Ed9AWsaUO+U1b3YnMco6r+2a5uvpTX4A9uHWmT3MdbHMO8dk7iWOfZ6CG3T0DnzJSQSMjtCDRAR8q4HA2bC2c7MjCzz3gEmPU/IPDK2llTD8cp1vckAw1AOI3Pm4wsnsrx4jPTVLbDoUChR/aak2k2Dp7SxrhD3Z3IANBWBB9m7Vzs7EydJ04G3CdPn5JKauvgKQqRI7sNEkoDABn7Cnwiqx5qr0sbyhSG5QlFI4AZAHewXCG9wTsnZCBrziELujouMUL0K2cszywuCVkmvUiTZtfYh9vY1FNX5UtkhPbCWMY9HnhZ96iPBRADmzrqrh6boXmDgFb9kf3pbm9/Kl6M7qbwcNjatGMhPvuQ5rXjw1ahv/7QR+pt5ErMA4kOPzgWeUXnUH4zZHkRB/w4CyWWHp8zvypNo1aEC13a2UpDdZX0aZq/C3AD0P0NAi5mkVlDaiL2oQuGBm3kn2gfWOonCKfWpibqbdWTYVWaSkrLM5sXAooaJrQlP7lKrty1HlM/31nDYAr2ST6yXBmZs/doPNp3vtZmMLhxbNdH6UTfQ2wHjp+ePSfG6O8GM7SpA2P0J0/JwXeBr/JNOaYMdtuLsJBHtLscv7Cyr+vYTWcOHqVGFPbJuvURzFP36k/o4BuUUKbdhx/uA+tB5q09bMDbzEu3wbuQIU9FHQPeHZCy1QBXV8e5tLehNS2anJdqVqyOTDbHYPvKwMiiZWxWnrjF+JRlNjCKZ8fwu7aIdpl4da6hu5mr9OaWhZ5zDakfRipduSQVUjjZS32W8xUie4IxKkfC3mbcBmlH8hpGe9boER4RxATHyhJloScAXmO7yLUL58Dd2FSKTTkJmnERSvknXgz4TCUTRflxDR4ZoF8XszyNJp8XIMPfwHfFjtI/ji1mzEU7vfNcW+o535byiovZEns/HUVX/Zf/+v+k16lh90+BhkDj3/2j4SjyNWgiYg5xIfIRmIpmzmlHcO/aui01s3r4ECe/em4ZqxAlOFQQJs/KXKZxyxQarnmmqJESLQJFuKkspgCrdCzoJNHlsXLo/kCFw6dEHvd9sg3HaGqqqK1N1bzc66TyzSlkjQOFgYaWY7VdlZrfqXglSD7y/xCD2wfGxXUMrQOkyna1tqTquoWppKQkTdUYwjEPRqIPK+jalEaGaesKBonWaKwKwdUbjTyFqX27IqcRN4nCap0YWh634mrA46TMTGVVT2dSQRcrRYwwGBxhomCOPbEEKByv6fjOz3Vs+/O7XJRbZ1vnqhHht5ctBfMYe11dXaRfKaQdu2PJpdRNRDEYTc2ML9pECYWhD7N52kesANG+0WGNNAWcaUpHSZU9dmhfWvMM1b+rqgIGoQhkasZkVob9CEvnA4ijDw1xr1htRgkq8CL6zx251DbTo09w7J9/r6RmQREFZzI8Ks8yJa7QDOMV2CrEHZdYpfMQNAp5hYHCWNrUmFcAefzReYIwxwl+lVRUsidyRShHfxNX0p6Gpf1pFPludDSMawSwe2pdeZWG/M6X43DOV0mhsu321tZ0FSdj2dq1aSHRcIMXpl47Pu/1sl1X9FS24iuMUuhJunElQtw7f+9zK4W00dHRkU6fPEEQqY1imOPScgoJlZeVMQaio9C3DoYBE+c61mADz4STYVvMT0VvdoHBtQcYzgY/pHtTww1INDPulrZWsgsI5k2YRHCNs5353oKQv/5gazZu/g1cxl9/908tH0kMSKWzCDRQUHA8SlwjwVViX8MxyjwOUvSHYagBZOFCDJuxBBmGI8wfQCeXCDQcv9SXDhJouMUe1htDBQs3bNic6jCe5rFtongqDo9GEyeyaGTobHZgTOzbszsNIsRLKyvTbGqvuAoh7Wn0yS++xIGGp6nI/iacpRNnJawC59zl9xlNZJ/PNDSknbs+ToXzK1NVNUGYgmlpCisok5FllKWM1WBAlQZYuhtEqbhf1CDpabK69n2Pffec8lBOFc1FU2elUnBHdRaMVJxBAQKOdXwNNIxAeesExh51vhlEPJEwMfSrdyHHGO19nrvGGM/dvJ7O9VPAi+Jij2ZNTBOKqVfAFpYHHE8grofdYjWQcVlPpo+shpZesqM4N/RSL6sI5vlx1cyvSWWlFRzvWZuqKsoIFCHnoOt8MyksYARMb0P3OnAPsEof8ooV3YAdjgs0f4DU9rnz5sVeWaP8ymcDDMJa/RD8BJ37nc6msFeOeVSbvKOCzvGSclODzq1aGhRncVw+270jbXn1m2kJqab+7rGByl3lon/r0CkDzIQyA8H2lQHyq22pY3L9OhZ57AZp8d04Lo319fRzO61dvwE4zAX3GDy0l41ZitFGJyA6RB/Sr98qyzQepBmvoC9+c1wa5y3NzamelZSuZpxGMtDWIA9mUPTLeWvMKJu8nHdsRYAulDXKNjMv+vqyCurKD2VSyDhphZcyshkZ33X+XPRbUV2d5vNyO5r6L+eoB21xvwNmaNGX8lJZEAYl/dsuZBUySMfDuZnJdPxTAu2k+05mxa2StktZHTONV3wKU1f3lPGOz+K+E6F55XPAm7nJP/KWciZ4jrmZ5SEuGhubWIA4FKttxfDq3LmlsZVQOIeTxmBDN/jOgKx+rlOornN1aTQ1F1xhEveuLulQWwzQsbjCZApxw0kC7eUVZOOUspI9i22HkwJnoSOUB/BntI0scCueNoh4C1rHmbN9adn5msWjvtXQ7Ua279u7J1KBl7DNcBb6SZqLlFvG5v3iVBmiUWlgdSI6X2NfvSddS5t+FifCW74QBwZF2wjs7v1kW5pbOS8tZR+xzltOb9im+DIAGrqQ/szoyWcRx2dNTw7eGaJfx+FLmhFWZkB43PQJahut2rCRzI3qoLmAK/d4n7wjrWep2gTUcOa1b3w+9CrGs8Fw7QHnhMgKWjJTs5dAQ8PZMwQp7qUn1q1NJcXFQSM6u9K19Bj6lD7Uterm4CfacNtIODb0o21o/+LVyu7WergATbY1N6ez0GUJtuTaNWvYyjAzYBc8A1/FtjbgLy51LqQHg/XSkvbMQ+zIbD2VvgC5zofZFu0E7TwO+re79pDJMhJn0C0R4iPQwn1ZwGHLXE5eKikm0EBWB4I1j5zo0bmMBuamLRPBVB9E74yOoLG4zWzQR4zlAXO+iefWw9aJZrZO7Gq6Gdk0HOaL7ipHz1WnVY+vILujNI0jcGJAGgCj725HdqzOjtvFdI60H5VdTciC09C8C3iFZPosWLwo6NLVerf3Wo9JSovgDZ+0QbVXwwEFtn0UhJV25H9xr0x2IUdn/Dpy6Di2duPpU2kyusFaDuXlFZHurwzU5ugjWCAN2ab2R5btmslZ5UAEj8Qr87B9A5oG7bRRLiKDd+/ala6QSTcPG15ZMBO8astas0fYOU5XsF1hlxfEh4ET6VW6V5Yp97Tp+Rb0I5flJ8bW1k5Ra+o5WPujgpSUKI4IT9m/9KgtbFAtV/sns5Nd5YaX6N+gjjJdmOTsCPuRju33ODL+yO5dqW4ZR0SjA82gUy+F7ON3aTxonXErB3WuhYF0CWEGv8qzAXP6yPGi2wndErGbrUstp0+mJ5/ZQh2lOTEWZYCyN7KQeEa5FLY2cBWmwuI625O05aUTv9PBdtz6E2FjApuLwHz/nj0Ed6ak1fghZhfqA6gFrLugLBY2j0ZBh2PBGcGYqE8Df99ClpgFNxaZLq1HPRzGoj40QOl2+OY2ClTu3pkWr1wVW+ncymLdgggyDN1rMOuRqZQEikcCZ2HlQofZZAYGc7JU2SfMzYhS/3V2dKQ2/DP15PLVa7KTQQIume2sfITz4vmMLlmEhA+ck5kx/dCsOJJe1F3CT1lkxo9Huzcha66QbVDIKWceSVxCIEM5oi0tTXtFMWfatNaPQSSLvhs0lmYjQ5N5WMttPPIzl+F4HV4zQ70Bu2AQ2dFHZmpb46fpO//u/05f+/orFOQuomWINy5n8Id7gWcg8Q+8ZIhI2QfxIl9nSUSMIBgA5aVmFNr+nbtTR0tLbKdYWrsgVZezZwhmsGK70VUzD8JxRnTks5qWT7BBJaHiCKUPo0faCsgaCVETP490zzso6304i9u2fxyOcs3ChWkJe0KLyWrQSJQQZGYFkeMUcTKnk/Y3iTZA4BdDuHQuRsAMJvSSXvzRtm2ppaE+LV22LFWUV+C4FIYhJ+RkWBWvDSrEFa5/L9AQhgQMS3sKVYWCgQZXdayS3oqB+Nbbv4zAw3oi8zNYSYsIL8QehgGDklEUCAZaZAIzIexQo8ejosLQYDDOQ6JWmGf7goeH8PuA49IWUmF1JeNX+JmGGvPnPhWQhpZt+gr80V/O2ffd9jziEwBmQQbgqSGosbKLehqHDh2IIyUX4UyHU8tcNYgZUUR/hbuCQaNIPPgKWDOInMD2XZpxYM5Bwd2NcbsPxSB8N23cGAU7xaWXeFNQ2nYYXBjNwlSY2UakCnKPQtV7zZBQMSm0VbI9l3siCHOQ8VdDM09v2BB7+9z/ZwMqgiiSyDMxXtpUwRmVVhno4Nhm1rcrqu4rzIIrly53h/Fff/YsDkx32vjss5zHvpzfKf6kUGYOCtEcwQkzxy7M7FvnQqNBfNp3FDlDiU7GKPC+ltY2iukdTZ04dnnj89Pjq7Jjl4TxA5aJVfi+S2++hIl4jTRT8G3qtq9HCDcVVKzagpJJQ4bcKcZ9GqeLQbKKNDFNY2+7mS67wcUP3vwFY4Q1/Ydn8gnQjx+LYwef37vOSgEr6iWF1AogwlsAH7v6EhkNBDSyjAazYgzIuMpHAIuMhEdkVYwZPpqgBK46NQ4G6fcSxSA/u3A17eoixo3/328MgOvFF78SxVWX18xPVcUcb4kBdgdlkcfcVeqe+f7+799H+d2kijMOM9uWZpeUBN9Iy0FbwEAaUdm4GiG/WVjoFgaFuNHZdXLSb452xK+0duT48fSb936biinMtZCtBrPIApgO3CYSVBiL3AhFC6vcRjnfI/NqGkcWDoP+DkNnH/znf5+Gkz5QMnd6KqWPEuhpCmMYJ40JTHBtcGEYlpHwCfwwhofIPFcqH/COfcz3jJ17dXMNNtyAD7qRg53Q9WUCo7eKCGLOmZ5GTyY7hNoZtwnk9PVciCDt1OmzUWojKT7Wnxou9JDeSqCBhWCvNUsfJy13QRi3dQQbishsiCKdWOAGGh7gLFklPgINLs9hdMRKMPBURjeikLdt3ZrmIwc2PvVUyGeDkdJebt+p05SHNQClV2lTftJZFfY6N/KUdK9MUIb7u3snj2HkHsDYeunllymetTLwoWGbk5fSvTQlH9mOclg5qKNi0DcMXeAadWuAv+OwfZ0ig3fHwa2BQE8hqsZQlL/VS9ICw/n80nD1WQ1PjUfHqc7zs+MOI5d3jQ8LIJquqbPe0d6eqpQ1mzZFOqWyxv5pTrDEu/IlH2NEPvd7ZYGyxmCEW+iUMXGz/9CHMvIEQUdX06TVhTikS9miY1V555uTr94rzKVpDSydXmnfZwziSH/CzMalvcABnw047sUA7WG/tVkeiwjwzMdId6uFcxU+4t6+DJROhhd0iO1XuAf/IGsi6A//+YxtRy0O7hGnu7ZvD/6srKyMs+StCcRtPJvpBPlOw88vdXgMZPNF8JrbIOI0CX4XXljtBBroA9x4fJ+prJ8dOcyYq6lLM49sqzlRwdznbddFkqi5ghz0FKNJrP5K02bK3O6/FSdcxaodv+nQ6KzBmUG7LhJ47PFtaGsdwaM5OHYGeISjOjnTDdYAyLYu+P0k5IF0IrzC5sFZFFbiPzJ+DFgxT2moHpx+QOX4soqKtOYJsmyQqeogJyo9BB8BS+ehrpDefSnj1B8G+TK8ZDB3PF5+53aII2xRPbhvb9q05VmO9F0c47Yt+/cVuhyakf/MrnExQpo2kB11EZiX8i4CDdwnX7iqblBPurS4tA7SBui9oqws4CZcfCHKCBjheMH/OkfhEAoDB0g7MSfazuCEg83YxwE7q+qfx/ivRz8dpy5TWWl5evbpp9NsaCbsH3SaDqNZfO759rkIMtCHDp1wVZ9GoEFY859y1CCDx/A1tLVGEb0fvvsbtkaO5ihuHETk+VR+m4g5OxxxIBTZ7UUwnVNy4El1n6/YOsF9FoQ0S8PMNvtTYJtVNUbh7vfOn7EYaIgaDWSYNVMMcnfzYHa85YixqZzg0qqVy9MLWzanhQQcxuu4sNXxAbgYxHEzmKN8cnU1ToagPfnQOlifYg9Lk0UEBJezVWHOnNmfO9HCmulmgQbA4/PaucJf/SjurAlg4CK2GjBe6d4Moatk8Oxn0c2t0NYVq0JGzoOn/OzzjieCjtCftCOPWyhWeWxgQbiHTc7v4kr6zWyfrEbNuc7O9PsP3k/dBJIWhpyZB0/NwYElWEzAD6KIfmIhi3lIP8qECM7xroyxfek/ZzvF6rH45bv6Rk4j2rEjiph6IstMagJNYtuyfCk9KqsMZEUmNfJReSt9GlRzK0noMeDsuL0MlAozbQMDxgeA+46Pt7Lw83hazMKeWTwW9LZd6dhLvvJ52/W5oEnmFX0DI9+VozKBcsF5uOXhGjrgQxYMW1josGB1FTIhx5/eJ7/4kscNnKj3lBW2F7Yq78oGbXDvC59BXmBc2tduqfsI3e32DQtfzmTsBmeYLbQr37iwCfFDhyPYkjSKl3AT3gM3s0CDxTatQeBWjwjIM5YBbJIrHD9/qqE+7dm5kxpYG9J6AoPjmJMngig/AH02Z2w5gwy+PC1JvrUe0aCwB27SpHJPeRq6kN8vkd1rIODs2TOREbNp8xYWSljU417nJvx8OWfhLk1GAAw8+p306kvfSvxaLDMPWQ/VE1jrIwjQkk7iv15C1hcVlaQN656kNtjcoEXhZqBV3S+sPdkw5A79yTNeypeQx/Cmx3NOZAuIelfc9RIAcpH8M7a5DKBHr5NOefnK5fTlb387vciRpIX/tHUiYPh3/whYMeMlfAG8xoopPHB4ajzlqROfEGhoJUVodFQv93hL04UU2q5wyoB9KKUBgC0Rq/TdNxnRSYmE+9wqYTaDRwrC5VGM6C5Ed+DokbSTSKLEZyR0+YoVBBpmYVz249TrOGaXhCWCjWhKxH0wiE5nGIgQhv95eZ+/u2pzGSP0Haq3nmaf35M4oxZvmknbChAJyHkb/WXC8bdtZE5k5vDZFpNDsGSR2Ixhs1RvV9aaWlsJNLwVkbRNtO+4dZgUQAoQBYdRXAWVxoOXc1BQKRCBdBB3DJ3h5wIDFtlyXh5z88aPfxzZEk+uIVKJIBFOsXIuo6CQGWHM93MjdogxnZspkRoSRh41zmKFVSONsVnIaSuC+wCBhm9Red0iReFAghOmHPMOocxn4RAZC/GD4MoIxlUfr8jE4B4FinMw0HCBCuwGeRT0X33py6myvAx4QFM8awTS+1Ri0o7fhQMPTAxAaBzZtsJV5Rr38rcRac8U7yDA47GSOz/+OK3dzNFIX3oxTcCQuUGE1FUUCVkYK7Adq/AX7gp9Ba6GmkEwU2MjiDKEE2dldFhD6ASOy3n6+TJnU69bvSrGaQTWdixs5yWOVQY5BWMwws/CyyKW9qcB4cu9hzrBCtajGM8X2c/mCpzZHpXw0zCcUZ9zC8UIlIP4j1Rc8GW0PwJGjFnTSjk4AsPHz3eIpHpNxsj2WMKjGIiHcYyslzCeyPYsUrHvEmiy0vD/+MnP4l5dEs8pnzEtn+PfKAg3koDBwPU0FWNsBvmsBeNRsgY6oF0DhNZoMP0z3jEA7dd/w8xjfKa0RobTcJQ24+4itf9o5+X0YRtGCwTqEZdeX0UAryBoswpaq2HV2YyGPujQFLUJBEUagM3bb7+dBvhuCYG12hpWRlAMwlHFkIO3fMiAMqUMTYTDBH+ppOU/4RjRfvAj/o10S0cGNf/mxz9MZQtr2W+6HEOoJE0HbqOVBRhoEcxhqg84apTcyTQZ+kMbp8P79qXt/+9/SGNaL6eS8tmpmHEX0d4UXuPAOSAI4zTYA9lpAPEeARjTDpV+w0eg/IDLoMYrN5vOLW6BKOm31F1gbl2s5F2kAGVf4Yg0aa57KEaka3eocdF/MQ2QSZEH68xiC9WjkRNSFyki7b3XU9dFji8d2jqxZdV6irwt5bzwJ9IiipF5oodH5o5g5SEPS/wRxpTHFGLCIxigMetjYHgYrOwnI+F0/dn0zq/+Ni3j+RdxXlxtUwYrDHTuMzbN+DQMW2AsrYcTCpx1sq2pogzxO/nal7xssTuzp3Z9sj19iQKAazHmNJqUA7kMhWgLYy3LNMj4V/kZBiHt+9lXGB7gVP72mR5Wptva2tJ+jknzyDfTFj133KDFLXCaG4d+i2OTjuQtjXTnZRDEIJX4cJLeE3oGx8y6PwYCjsFPl3HWKyjQt3H9elbqsuMqdaY1bOwjZBrj9LNyzkCG9CrvO263TinzY1V9aPyuAh46dDidwWCRXh/HEFqzahXp1pOhaQxXxqrBldNLtqdxKD9khTQxcDBi7VMDzHfhoxGp3jF4tJ1AgNs/ikpK4iSDeQQEcttCNFiFoWOPYIhGrryGvAnjDdo2yKQ+tW3lqGOQn/z7IM7ib6EZU5IXwNNLWSRwtU7+Uw7Lp35WrnI7QkdeyGjfVTRtgUcYierLCEBorNKXq21XmNdH0Msh5JYO16JqttwUl1BThtRX2o6VN/jQVfbINIOWJ+HM205krEH3kbIL/zlW67LgwiA4cRpp/xwO0Vu/+hWG9s30/LPPRSDc7XfShPJWGhGOjt9FFB9VV6jjhHE43dynrhG/MS/m52f7O4GB+8bP3kjlFRUEwjOacaHBdsSh7eYuYWO/0p1X1nbmgAi2CGLQj7yh3WJK8N6Dh9K2Dz9ML3/961H13rG6ECL9cht4Y0yMRxrVTghjmjkZmHJr3hj6c3UuCkEyAP8TprdxhK1rtHfffrI+LqcXXnghAneu4MY2RxoXo/Gfn3k5X2WCbWTOufPJ7MJwisCTq9iujnZ2dqaG+obAa0V5Zfoy7bvKKA1q19BEyM1BxqyiE+Y5+hN2UQuDPl1h5I1n+I77pJszLc3px2/9In3/V+9QdwiRzm9e8607hF4bbfOgcnbxSAKxJZHxNRw5PQK4WQxyFDSiQyRmVDHiGYrMvrchxmcQwi0oj5jfTY+3JGOvGf7aUZ8FGhDTBPAnpOeffyZ985WvYVfVxYlMj5CDD6Vb3nUCxasrpDpPygqdYW0ag9oGhGeXl6dVa9dEBlJsW4EGo3YTc1XfaRPlgjzZIo/2XcZbjllYCXtlTj620TXo/BPs7E8PHaKGREmaN49gP7JgesE0xgJugbd0aUBBmaB81XHTZlFnmbkjjgRp0Ck4VxZoLyt72jrOp3c4XvZiR0fwqyeelaK7x/ObW6YDoDwdtA0MDQ7kggy2KT/Y3ue05Fz4XhtWWJ06cza9/7vfxmKewVgDDcoh6UMa9D/b1lmUjpRj8ps4dD45+8GxZzyepfErSw2k7EDO/Pqtt9JTGzex8r0kAjDaJPK1i5eZk8yCizBlbAbt5HV9FHWcn21bPvQZZaUw8+8esmR//bv3Umtzc/r2H30r6ps5Ty9lpLao747Vrw1W5ORMTpfG4iO06b3ayCGDoFl5vpXsqV9x2pzbDr7GcZhFbh0E5kgrbCxolfGHzNFm5DSXR8hZeVgZb2aNwVi3a+sfWKQ1K3bIVmn0/8Xey9S7OJ4+eu+99PI3Xk9feuZpZAnZfhTHdwFT30L8iU8zJS12bThXXNheztaBW0OnCRd1l7ZaFxlIpw2uoV/1R5Rly5c8FnMTpuJWPeZCkj6FlzLNl7JGOS0feCl/hLW6RD3TR7DOujqnG+qpL9fKAvPM9Nwzz6Sq0vKw88yMkibUdV4GGpRRyhx1tPBVPoLwsI9gfLbrw6/YONausx6TJ7kdPHqYhbS76QYLQtf7rqfnmcOzLzxP9vzcaJeRDb3/4b79wzMapBCveA9y4fPQOwiGU1IjVYV34dB1tbax/y6fgm5LUy0CKgqdgQSdP6RzpHsN3OE4GgSPkTOj166eiXi3HQSCUHSDrDQ8xAgYT5oP3JX2Y6wYaHCl3kDDCgINBgMUthoMMonjc/VWxBu1g6Mxvq9ixBHY0FlEKGYXwgYmVPiNw1C8SqDhFzDgcVIMn8GYqKmpjrbdtqHQVJBFBWAZMQRslgZpW6HcIbpgemChIAlhNiQMZYB6BMdbOEamxT+9cSOFHVmlJcLM0+FgK6SNgClAZRyZX8EQK1IIKiPxgzgl9iXzKMR02j3lQmNV2Hz/u99NT9L206wyGmiwgJdMrHBUWCiZzGrIGS4yoEyioDJgEZFKQcgzmECxv0tjQKf5g+3biHLvT3/6538eBQyNahu8sX3Ho7AWtvalgJA0QqTzu4IyW+nMFI4/imvb1hDs6OxKv2NFx60pr3FG8nwMrtwcXekTJgr8EBjM2XYNPPi9Y1Boe7aw9S5cwfc3CCDdxgBtR4AcIzK/FeG9mePAvsEq6TCUziArMRr2wsK2VSIBe5xQxypuFP6mwgojjVLnpyHj7+LBKGs9kWeP8GslLeplHJf1rHh5fw+F8BRmGqT+rfCWjnwPRUNb9s1PKEGin/Rn5NcsGIWrfTU0NqZDBw+kC53n4/zeNevWpfmsLKjMjfa6hSLgyN9hqKtUgIkG5DBfASedPw3JTHjzAxkNU6JGw4EjR9O+AwcI9uEoo3CK55bGHtdtu/ak7/7ox5I2K/iki40dRVFX9oEWTkwTx/DF7ZtsH7iVJvG9mQweiaqiQjfxks4YRyi5LGogfRpFVjFZDyRP/PC6g5LsIoXhMwINe9sJOnH7tUyWp2989SWOy1uRVj22hKyoMlaShqXrwDQfOhXXjU3N6c033ki3cUxX4Ixaed1Agzyqogn+ABbiNBx2xwl9aHhJMwYZTEFWOQcdc58BrGkYU8PhGyuYf++v/3uat2hhWoVDXTqXFVJqUQzj+UGUqhFuMw+GjSXARdGY8fT7ABlzFONv93f/U8rrupSKZ89NRcx7JvOfDA481lP+c1sXLBF0YZBhkCwQU+oIp6HEoEOKTN5mXPdQWM7bQmbDCCgZfrwGji9gSJ5HqV+dPEigoSg9JOvgwrUO+BQ6RcdCPpzTDZ5GFqZetqZcuDFAwcdbqf5aoDS9tGZTWrV0RQQaFi5cgBE7lr3WVyiYw7nS0JT7d+XLR+DRIJOBhpGsPmhAucJ2AoX/s5/+JK3dsJGTEl6Mol864V46LdK0vC0Pm11kW1klecQxKxjKCuWajrGr4dK6zo2BRo0Ig1+7CDy+QNDxSZzpwCn3yjc5uZDhFVkGPJX7/qd80HAzIKFcD6MReeDf0kNvL4VqW9viGDwrsf/pn/6L2GZ2D3qQz4NGoUkvZaArfhpAU0gfphFo7RrBBjIywJ20QpcxD4v8OvbjGFnHOEbVUzwqDTRs2EDaLoEGntOJGz+eTB7aV76HjMcosk+NXGWwskGDyeJ4WENRsExjxu0hBhqkyZPAxr7WYuQ+haxxpc5xGizRGHRsykN1m+3ZtluldBit4K4slvZ9Vy6JC+9vaKI6+sdb0w304BxWSK28Pr+yMn6zP+frM8p2eciOxEUW3MlWetTv6jHvVdYJf7cImg7vtsRfvflm6KtFZGMsX74i+jEoGtsOeRfmPiuMwGrgQd2p3BhkpfPhoLg0qww5r7CB1iwS7Crge1s/YvvB9rRu/fr0WG1dmltUTACXLWLgVXkQ9ED7/chXV7THk4Xl1gyD+m6ZmECw0D3K3hu5DNgpI6g7o5PQ3tmZfvzGm6kPPfhVTmkqI0CiwyQ9CnODxtIYzcacc7TpBMJB8hdg7dyChrk/dCJw9+9jOI0/+MEPUmVVVXoW43wmNKMdxNPAGzkDL0nj0nbIe2UI3xngyDnW4tJ75Afb9vJv52u2x+/ZtvnaH/9xWkewWnyLx9w4hXeG3yBpYI8jAP2EXAwbgwAzdAImwlmQVnWA1c1m8m3fuSN1dnWmr73yCplS1VmQwQFwn3ypc2cAQ3oQx/K7I4wAFfc4V3WqGNfOecg8dVxs2+1C+3fvSeWlZekrLBLMInA3QPAk9DYyRKfFugwGh5Q1ubk7RueVjZmbaFzHX5qxtscp2v2fyLC//uWvHOnn15rykWluQUEaR3vDcAimTEA3TswnEEUbyL9hQ68gP+CvllUPOFe3gpDXFy/nGYEGZL9ZDX3otZ7bFOftuZR21WMf0KMYdqnj5c3r05/98z9NK8hGHSHfUu8AxkIOZws+8oA2SmSUMGeL7J04dZJAw4FIBy9BP67FNiguK4NHkLXwq5c8FHqQsUgffpa+wWJsEYq6IuBReznGD1/luQiDbNzGwtIRbI+5wH3ePE5+Ki9H/yEHaUc5y3TRgWbwULcFOlPOyGP+Zj/iWnpVvoh/+Uxaddtue0dH+tWv34mMhhWcNOCi3pyS2dTzwlaCn3hA4o02XBDx+ehQHNBx0DrfyTs5WhLepvQLI7c2/IpAQDEywKMqZxFoMHjr/dm2K2Q37VinSjmlPxF1DYboMrKEgKEOu9tdhY9zcjFLOb5118705g9/mJ5F9z3BYohbrLOtVFnwT/8gC/CAdPrRllRWmpWnvjP7VLniuzByDPK5OlG78W2OPW5raUn/8s/+LNUC++gfusjxeA6Xsag09L30bjApdC59eU8umKhfolxwDi3Ywz9FlhWR/fL611+NUyTk45EwPeEDmYQ5AnPGNYz6WB5ZKZyUFZp26kPtPQNVfjbwNYyXCw2XsEkOfvZp+g1+zh//y++k5597Dn5DF2FbRPaDaA26pBu6ukeDbrnQDrDug0Fo56H9auaINGR2lYt1HpVtoOEQeuQStvvr/+yP0+PLlsU9ZjHq2xmw1EbJBcAikEl/tqncFP/KhyCjoGHkKfrE2iIt585HNoYLhwXTCtKLzz2f5pVXUG+B8cFvBm1zwQYDqWPV5dCz+BSu+m5QZ2wN1S/TTs1Tp4D7G9eu4p8dTvsJ3N3E1rjJgt/A3YH0DL7IZk7emY3dGoIMuP6hX/84gQYZPl6Cg89C1gsEgonUTKBhN8dbXoCYJ5M6snrg9cyZAABAAElEQVQpxyZqrECUFoR0/1AwB8+Gw4wiV/BbyEyDX0LI0vNQBgibe6ZRgbSJIB5sciTjgfThRx/G6Q+1pKYuh8l12HVQNYbiYnwSmM5NLu3T+gv+ngnDTBiEMGTMGnbWfLhOJPGX7/6aEzOOpU1EsxSu01A4KkEjyPZv3YJwwPmsIjZiGAYDBC4DRQSR32RyGV7nVOPLVyORsreIoNveRoyhIgIk+QpW7tXQMjCisDJrQqZ2vDrC4azyvSssd0ipk8gVKIEHYDReIcbfrhh9/6/+Kj21YSPpzE9G+r2MkAUaEN5wlzCPiC59ZgYdTB1CXYWWCQ6ZyehjVJZlLM7P4j1bMSb2H9hHRsOfcOxgXTC4hoh407HXmFVwxgqXTMlLGPt8MDfw8Apjku+Eg31rJJ/v7CDQ8H4I29e+8U0qu6MwedY2NJDFp4pWwyoAHfomi9hqMNFBCGlxoHEt7C1iZgRZAXXqDCdVEGV98vnn06uslOeR6TKAQa1CsR8FVAR56M/vFEr5PG874llBr9Fg+85FZ0latap+a1trpEwbgd780ktpHY6ROO2FnsS7SiYcKtq1PQ3l2OYA3YljcW9bKkznYsDA474867upoT72Brc2N6YJ06elZ59/gf2ctcDFbTmOXVrPos0+J0wj2iyuGUMoDJzYEKwIdXEjDYzHmDDV7SCBhoOHD1H/YXyaQhS3GIFnjYaPd+9O/+1vfiS60kxIbQJbJqpKZ6TyEk6YYPV8+F0K8z3ITjkYST/Ev2LVyMKiGlvD8Aw8VUGhL094KSp0GEYDAx3uYSNGpfu8ujG+Tl++lo6z4s4iferIgtHpTzgyaC31NJYtWkwKW2ns3bsGvFVGZipZifodAoMPoQ+3UFVVVsU2KnGpYyt/iCvess/0KfwNhJlyqwHky6yVfowleU6lL8+7SrqfjIaf/uCHpI1WR7V+Aw1mNDx6gKyiVkQ4uPIHQYbhZDVYyOgOKdwGKg/89V+mkW1X08wKYAoeZ/CaQNt5jCf4jyGppPmfv6Utgiw87+7BQYIMtxjzDQKst1C2FjDyhItxY6nyAA/cAOcXkAtNyNgO7ps6DwRhoPReJ/0bVpjGbiv+5Ox2C9653YIikuwNvnKdeg1seVFKfmnFkwSBlxGoXRYBVR1gV2uVjBMJnLjNTdq0gKhIG87WCZ0T+cDv3XLzxk9+ktas35C+Bs2PwzG+xfMaCMoC+VXFrAxWkfvMADBW1uYyjyQIswjie3AmzDW4rhFoMNPmAM7Fl1HE655YFTohUrihJ40nr3Ai4JuMR+mP76VtecnvBK7fCW9PefDv66ysnMfI3UsauasM3yY7qxZ+GgSeBkp81pe0agEr9+tKQG5l0hi9gzxxxTqcMvvgkm+zc80HYl/zZ6xcWEdhPo76c89uiUCDukXjxxVJ71fmuLoifzoPZb+0EE4u/ToAC6y5+mqw2IDY5Z6edARj5ezJU8ioW+nxNWvT2tWrI/tpgLEr94S5RmUY37ShHBJv6hBlkPrE78SDcJGGIzgAjjwe9JNPtkegQcNnJYEATwtSXpuSa0DEdmQn5Y6wlr8yAzbTJ34nzNUJ0oD8pNHmcYKHDx9Jv3vn14x3XKqtqyOThlOUiktC5olbZZ99OXbpJvjLsYbOpU/4QVtAh1SH5T7yzBoNGnymev9u60dp98cfR6Bhce2CNGdmMemr46KQnnQbwRXut9q8zoYpu/qHOruUNyErC0MRWSo9erLUcFbzxozDGUCvdZHR8BOCJH3In5defCmVsnVC2QEEoFlWj3nXSVA/ZTKHsTH2L145vZtzvH2PhQ9gcxJ++gnZiDULFqTNmzZRoHWSZAdtMEAhblN8ztkS8onGtbTqd4EHYCcNCMcczuUDYbubANWHnNv+6muvpzU4XjqutqF+DX6iM2k6YA/87U5952KCOIkTJ8QJ90fGoL/7LLDrgS63kyp9kQKx36R9t624qq6MjVV0cOXYdXp9Rtpx3E5QetcGkK4i84nvDDTA8LH4pN12HgfAlXtrbrzA1kSPgvS4cOlLZ03acunBbQq5uhC2GauW0EoUZQNDMpUBFBe3rP9xqrkpfe8nP07/Ax2Su1hkTc/WTUrzZk5JExGrI7C33AZh8IwREz+nJ3j2EXMT584DSokXDBJ9jqERgxKOz8UASIrCkmTjUfm15/a91NZ7NR1sTYkSlGkKw7rGoF7fsjb9q+/8q7SSQMPdgTvpJhloHh+o4xyZlLQtnRrciSKFfD5z9gxbJ45EIe2Zs0vSSuyOstLSoGlhb2BX/SsOdAaFsbJG3ejYdKJFtIsb4sWTBlx9HUltnx7gu3PPnnSSulnWT6isqCDQXkr2FPoPGIR9hCyL5+kngtLAXxoXfdKSvGDgwaCg43ArqfawtV06L15Iv4Uer8JP6u6yMuoEsdCRz3wNNGgniD95KLY8BfXAD3ynTSt9hm1Jb8qloEVgo3PnvI6fOp3e/vmbEbxYZc0vtlfrsCqfpHfHpc0snJTnUZsC+tY2kze094WftkTIPMbhxLQ77PfjXbvSW+i/F8gIWLl0WWyjkuekc/lF+Rf6iHEqA8WhMMnqcWWZC8JFfpMPtKO1zdzSbNbZLwkKNjc2pH/+J3+SqisrQ3foNwnbgCvPKXv8Lux3+xE2vASObTlPf3fgztexebWyHenNX/w8zSJY+hqBwVnA3a1jj5Cvw9g2CcBD9g6HR0Yi/1wAGcDesT/tS+k+Cs+qA6Bd4e/cPE3hWt/NdPTk8fQugYZXv/VtjsPcEkHmvh72beJT5OqBRMYTPP4A3nKLmjLL4Zkx5bt4D3qH1gwQGdRV/zXDs0ewWQ1qvvLaa2kFAWuftVivdrpyUPzJl9K6MxZfticupSlt55BDwMdxa7uYEdXY1haZZZ40pf39Apma88rKI0DiolKczsEzYUOrd8Zh9wFXA0ra3von4lJ/xkDDKPhX2lF3XwWnR7BpjsBP19EvZjTc4rWFhc8tzz+Xps8scaS8HLGvP9zrHzfQAJEFRQsTCA1uAk6c005a507Op74EMU/DOFu7fCWBhqqIhA2iGC22YuROJhuNQIMqiJxJ2zrt2cq4Zl4QMgQPx8beobEg3mjybhTO+x/+Pqrr1tYtTI8hmN3nKeEohGUyiUpm95K4/DtbFXc/mSvAdMiQFYoKBJl9PA6EqfTvcDSnBXDWrd+QKisrqDDK9gOYy4iWR5bI3uEg2ADPO2aJVUa2vVD0Mj+fja7rkMoknsTQ0tqafv6LX8Bsw9P6p56KAIlFWwShhrjC20CISkFhIgMpmBQeGuuo6GBKmTQEB30oZCLQQF86jX/z/e+z7WNj2kh0W8dfrtVgE74yobgScwJKhyCih/wmozgfuDIyKWQUI4ymosmwKonte3azKrU/ffXVV+PIK+enMJPRnG/ME2fB9mVujZjM+HeFyllyBX7IlqBv7/c5s1EsLvc+qZ08ll4jnaistBR8ajQhMIFDGA6Mw+dUnM5FB1aYe/lun+JTR9Kx6eCrRDSEzjZQ2G/79vQYRtaLzz6XprG/2JXJaJtnbVdakY40wAyyfDHCrWGqoW5v4iMXaDDTox2H7wypr21trWn9li1pNX0ofC0AI61J645J2vCzmRPSlMa0FXANbDgOBaU4kB6trwGxxj69vXt3pyaMignTC9iz/tVUvaCOaMo9ouP94C3LyjEYo2Nk4MqVJ/uNQAOGkn0AQKEEraKYWamz2rLpYm63MU19LAp0MkXrZhQVs3XhHkWy9qT/9qMfCdo0Gx01NX9Yqq4oojBjQZqWj5IeJM3+Po6jcgBauU8FQ7sIpwDlYWBW495UewXzEJoi7W40pADngyNWJeHHHo6zrL9CwTJOnsAGSy1DgYbvfO1L6cnHSYNmdXIO+61tu5+tMBq9FmxramlJv2Or0yPgunjxYrbbVETwTqf8i4GGmAT/yKfSjeMRl5GhAoxNfZNOpCF5y2wS6ecQDuO7v3g71VRUk6K3lBTaIvZ8TqYlMw0wTpgnyKJQYrbVwQ0y91AoBhoO/tV/SsPab6TCsoJUAk/MAO953J65aXxgHNneWEeXGWgGBkzfvQkcr0D3vbf6042Bm6HMx4yimORYslxwhu4R1PCYy1MNzakZu6CQGGzeRGQIz4L+VMAK3MjheZyUcC1dw082Q6SHXWXXSDjo4OXuiS8tW5MeX+Sq8rI0v2Z+bH24gzHhiq7ZZMpgg8IPCTR4OoYniGTOgXz9IJ1BGf+cbJKVa9akl1/4UhoHzG4jIzIDXwMo4/+QXcAoB3PpXxknH2l8GuRRfrjPXkNNg8vTeSzsdxjn6OWvvRorsMKqn1U+dYcyQUHieHxGflHOKIuUM+JZJ07ZEnQHXOU3L404UzDNDLhC0OGbr78WtQLuovzdv2mbvngk+MmsMtP23fIg/YXxBqGHEQMv8SHkqFtBbjB/jf9jHD3YhkE0H5p8gRUdM+766TfSO5Vf9sE7Qw25a1+27XvIMOjRVRKdJx0idYdGTQ+B0c8+/Sw14pSqF5atfDytxoh2a5+n9ggX2/Dlv2aWhCHLX7YbMpKxC3tffqf+0kE28NDcylFmZAteZbVoFob5GurB1NbUsELKsbe9PSH3dK7FqXKEf2IO/hOwpk1lsnLO9FUHoqEVZ93zzBEyMba+/35sQ5nHqnd1dQ1Ff2cGDISNz+kA+UwEQhibelyeNV3Xlw6If+sYWATQQMM4ssE8m/z3W7eSBbOVFPJ1aZHbqGYVJ49Ks2K4dOmqmLrDjCK3Kfly/vbhQshY5AZfRHDatg00jB4P7KHtCxe7MM7fSjcwmF8gWF2Kka6sFQ4alhKasM7hVvnvJf1lsJEigTl96mjnFiTifp4zZfeXGOfzCXptwjbwWEBh4e9Ba1lzoZOkRenD2hLiIhdosJ+APfSTrcTiFPC7waVs68QHUfNk1YqV8E0W1A6+GdLTjlVa0EaSSjLdqnshmYNrbjCwoJ3looRw0wCXLrez+t1NvaLX/+iPyLirij5vwcsRaKAvxyGd2J+Q0OGI8cJf6onYsunv9BFHVjJHVzotbneOVO+jrGIWF85Kz2/ZTOG0AlYIKXqHHJc3zLSCoZDJZIIix5VP0oiywHFr8GcOjnYf82A8BjJONTWm7/30p+l7rKw7R/FjOOLlxyammiIC6mTujUZGDUP+Kc/8LeyYB8IAuWD7pJojHQkkOD/sE/BloMHTQITPPZwr53QPmX4TPddDEKGtF2fsfEqXaK+QoVxGnH17yxPp3/zFvwnH6U7/7XSN071G0XY+8t5sLuFnLQZpzToN8u7pM2cJNByKUwQKS0rSEwQdy8vKhgINZGIi07Q75CeDbMpdHa2whcFhBBqgxX6cQwNKypjRyn4DDazA7tq7NwINs6mdUFVZlSrKy2ObVgTmwJ86WDkWddfAYwRJoSXxKhxCNiNXdLTVt33IV4O12tQep2wGq3JlETZ8eVlpZPGMhfYsCqgE1h7LeB22HOIbbRcDjdKntBQMJt1Iw8xB20f74BiBhrfefIPjO0uQkauisLuZX9KeMFHfaFPL/+p/aUl6NyBgv9qA2uQxD/ujf8fiFkGzEbchJ3+NLW8NoRUESnL6xbmHrKVdPwsD2/Bz9gLpjMG24j/phN+09Z2jNus1ij2/+97vQ4e8/vq3WFUvj/E4R+nP8Zk9JS5zgTr7kEYi6AKtKgfUuc7J79W7Erlja8E3c8upgYavf+1lajQUZkf6UiPBQINZY8JlhPJvAhmf6H552XYio5sZCOuQzcCCyUTbBm1uQnPHTp9Kv33nHbY9fjW98PTTBOpYHEGnG8R1odZ2+CeKTT5CR+nX2J/yjgHGHIWP+BXuZvoKR+0CC69/ir16zm3KOOkeZS3u9E+yABr6THqHDgL29CNt27YyT3kgrIVbBEz53cK395hHQ2srJ62dSg34ClMmT6UeDIvN8hN+qKeRMGHa0cfIdNso+CQWcvlOuCpDmErAhYGH/WCQ00CDAZjjZGMImysERa7eojAl9tZz33g1PUcdjimckpRdSqFoaejvP7y3f9xAAwQsUYC5oRdCHEQ0YiBuQ4B0n+9Isygy8tQTq1M1EW6onnQwrFzukYhUDhZiuguR+NLADwIUxiApzpPlfRxG/DCYH06mStxA2oOj+8muHalg5oxUjRHk3i6r1Ur0EpOE4Gd4mRft8J39GW2SCWOVne9lTBnEvyMggLFyE4X5m9+/lxpIu3oCA7qysjIr8kKU0WFpcEZBKt8D//xLG8IgGIN+MyKkP4SYQ7B9jaZREH5LS0t6kyirTuymDRtjf2qcLsDYvC+cTZjCsakEou6F7cMYt3EsFK4qyIAV8HKurjDkFJGBhjeJ0D+1cVOk77tyqGA1bdXxuXoTYBE0fh//8ZlxK+xQwfEyyINsCYGtQICTY5Vxx549rH4fTJuffy4txYieTEqseHE/YURkFT78p1DLCdcQurRN14Ffx+G4xYWOvNscXGVsa2tPH1MARyPn60RZS0pL8ZyG0rV4WkMxBAPPjyRq7eVRlvankNfwVIGEcOI7jT6NYp34yxjODRgVB0gzLAOnm9avj7PqGVYIa4V3tM9Y7EN4aPhHFWVuygor8hu0Ky6jcjHvXlcwrpoaGynMdCoq4m7EsVhLmv1wxmjF6AiU0K60HTBhbEGjwoR+VRoRcGDsrnhKs97nsXLCvo1xH8TIaiGjYTxp+8+TqleFMfqQY7xuEmjIw+lUoN5TOUg7Gjs8p1HIROLlCoRwHkb4Wbo0zc0iPFYQ/pTaEgrAcQQFzerxyLmbwGw7uP6rn/w05jiHfwvJ5K7mLPvKkqmR0cCySxp5n2wb5iBf3DPQgJ6QlzQsPL5WeImTUHbMUzhDTdRhgEbkUcb6ACV0+Q7Fm65Qlbebwp0EGlr5yetfv/JiWk9a5YLqWhwf06A1sFBWtoWB47aS3777G7KeBlgdXYzjUp3mYhQFfegkAw+VirgNWDP34dDmI2DhqqZKjB9iZcP6A5mDSsd8Jz0baNj+3ofpsepFHLP4WBRNGofiTSPgu2EoG4we0wtvo0A53CGOrkLDp1MYaDv/8//BOWa9qbCiMM3GaC6k35E6L8w7AnziKGYJBfM9LdImTgFj7YUGL1K/phu+QD+lccQ2RrGd4ja1FoZNoOhkcRWnXAxPJ9tPplZ2OxgMn1xA7YhxFkAi7T5kHcYdNHCdVYoLNwdTG4VuL11+lE4PFYP8ysp1kdGwavUTqY59wRb189QYFbBwk4ZiGxWCwNNKPM0ik6uMF/61gvJbP/95WkwA5stk2bj/9Q54yNG05Be8qEwGzvK1R9AJW2WydKHxKcwjaKfxD0TcTy6/HqJOzqG9+0jF/np6CgNamFldPcdHIS+VrfIPegS2CjpTjvmH/9qetGif/I84GQxjpbOzMx3CQDfQ9gpBTU8usvCazpu8Zx8GWuUfnRRpVmPF+Sh7HXO0jgwLeudvVzZ0/JpamiPo2NLUlOZUVKYtm5+JuifKcGEb2VlDjpVGupdZIPZnvxrJjj1kmZ+kdV4axTpyBhqaOJFDQ9hCZGs4hWYs+skgjzJbWaDMtS/nomyUB3Q2HasrwI4/c3SRRc4R2vS3lrbWtG/fvtRNIGYacmDdmrVZUJN5u/VQINq+lzwtrgMevDvmzJDmk4QtwHnJ866SGrSyIOEnBAOsdzFv/vwoLmdWn86oNKFhr7w0aBPOLmMLGgI2EeyBrhl82BLWNTFQoBzLI/jnEcbvf7w17d7xCYWclxOcXJAq2LY0bgKb7QnCqlvjEmfQy+fp9szDMRrmGeWXyh775YMG9jDaHzN+XGQ0vPXLtzmRozdtIZhcVc6xuzguws5glONXdmdTd94ZnYgLadUfMtkofRn8R2bwjJTqPacI3P2ORY5S2n0S22O6WVXAWj3kZfu2I6wCx/xtUD36lP58CXMucSH/Cn+fM1NnHym7Fq/bDK+6HU2HRjoQhwF76EUYOR91nE67NBJHb0Mb0mecDY98kvrDPqEjA15uH9xKEP8Cq9Q6RlU11Zm9dx99zNyCJ5lz0IQwot9sXjh9Q7CLVUj4QRQYCBjN+AZYlOrAsTiLQ23dm6qKqqivNJngFOkcwMPAP+0xPrNT1DcGGpyTjoZ6NjKSwKk1JqSp0H38fgf6OdFQn/7XGz8j0ECWDZPiNMG4vlLDsX8ElQuRpflSArLqDielya+jqWVkHaIHzM3MIbPyBhn0oHVt+F3cqnvNfDAOHXiRvunzxp0Hqbvvdmq9fCUdbvv7gYbXNi1L//v/9m8jQ5cIGlu0zE5jQPxvEEa9PsjcnNN4FyEgqPoTJ9JeMkmuIC+LK8rTpqfZclNZEXaTC3rSlXaRY3Brj3aGMtwMF9uZqF0Lrh/g2Csvgm7A/+gpk9A7bJ34ZEc6ROZXUVFxbC9bWFfHAs1UYOrYdHozW0O68z8vcS092VYEI/leHEkHLtaMIdg3Bl3TjQz+W+B+/lxbchvVAvi1Eto3g+OOsgw6k0aEJx3FQlkEkpmHdpnBKeWaHYW9wVgch8e280U6C2x+8bOfxellq8n0mINTPVG4MQ4DMNKgCw3KVmWYvKXtntlP0Luw45ULHiqHfClPrYezDbi//5t3o06AGQ3ypHMPnmLuPu+l06scNpBqHz4vD+sUO35pNeNp+JypKo8vgs/fs+h2iSyqV1nUcxuxdqltCmV522CLc7FdcZllYmSBGdsw0Cvc7dv2XZhUXmgPRyYowTUDDa989auRSTIIDVBLGhgyDp69S5aLvsboifgQBgOwUQy26RuEPeU4gLkLAPYBoiIYcYVMmE9PnqCw7Xtpy7PPp+c3PR3Bqwf4bgaPAHaMW3vUhZVs+5uycihYxBi1iT/XM86ZtsX31asc0Q3dfEpWwDmCJS8ydoM8wkOcRLCJMQojaT6ykHgXFv4Wtja0ETqFeWS0wxzxk8R9fUsLxRpPxYKwWXib8aPml8FP4BUCjndtR/Fm5pR06StsDHq1D/lgDDKCGcXRnyJVWeaWmVPUtTp2lmKTvRxte62XLIdr6cVvf4tikC+lqQXItLiCc4Y+/2G+/cMDDc5boHvJSBB0DtiQHcjgjPcTJ9OOrR+lCziOU4nKr13B3unKKvaaETlEQJlWLWEamTRF7rZCHKEtw6uIbV8GV7F5DN5E0pJ1pHmI1FwUJqmjew/uS9PZp1c5ryoK43m8pUaWjKiQk/EkNtuRuRWIpqxKfBpoEpoCRAJWmfpu1MlikB8w9kbS7JdgrJRXlKfppBVphAeR0qpGeEZ0kBCd+Gz2wa/pj7b9TmdURjey6N8yjgz+NsaKZ3uvX/8U6ZdzwlgxbdYxKXwig0HiREBZM8F3lacGuPtKo0LrkFOkYaCRo9J3jqYbv/vLX6bV6ywS9kREnMWWgkZh6mdERQhEPsYlnEIohmAnCo1wF7emRJnRoDBxFdEtArtYBTx69HB6ktTOJYsXxVGCo3D+4ggmFH9OmOZWRUSCwjecd/rNKY4cfpybTGugQcHhcUcWiPsKjCdsxJU4VEFKFxolMrIrU5KhCsM5GfGMlaVQlhhC3K9ilZ6s9u4RlM0trenTo0fSTJSmisd9ngafNOakC+lGeObGptLSsPB7jX5XSTQOLQxmf9KPcMuKQTZgDJ0OR8CMhlUUI3PbhaekiDudC50pjR+FvQa1dKnAdi5uo7AfYSG87NttHH5ubW9LJxHc7e2tYfBuZEtPzXxSUwk0DJDulk+aexawwfiBNh1/KAPaE0ie86081FlU5Y9mq5IK1td1aMq9jKdwXPIx/vMJNhhwuAnMdhKU+V8//0XQSCn/FpIcU03RwUrqNExlGwW55igmzlUHdvKecvchDK3itENOogtaixUqvpIHhKfjGYljPjzmijAGVwYaGq70pzOX+1kdS6ktek3pz7/yfFqHUeypADNmFkWKuA6nRqmwaWykUjsrpHcQ4jWsYFbPz47js2iWc5c3VKziSXoIZxea0yAV1ho+8oOp6fJrlpLpigdHKdLmURTage2705LqxWnpAipLY6jkk0r4aBQ0bXlyaheY+nfzDnsCoY8iHI882v7MGg3/5f9KwxoupxmVhWkueC8E/yPoz+Mvh6NshYdzwCwDftk4DDT0M9ZLjPs8x1devMjZ4XQzlYyFEdDqhUvQJ3CtXDQf/I1JpztaOBp0IM0uZfVtCsUKx8AbpIvkcY/zfQScrrOK1n51IDVc6k5d3WQiEOv1enHlmrSSjIZVqwjkEGjw5AJp1dUuDVH5fjwrXMprjyrVUcwCVkCMMVof412MlZraBVE0yZUe003FjXwNykOmBZyhRbOXTAtVRionzGJQWevYuNomPQqPm0RWLnVTUOqzTzFy96WXWBVZhzMtn4pP5aiBBR1S5VLQg4EGLp+XR315SYsaWL4rl3W2PXVCY+UYstJUz5fYRuVxf45TY0VZEPfzt3RtsEVYCBfvkUZCT9k+sPFz0DbvN+Gn9nPtcexcK7K+CENuw/oNUURUQeXz1l9RzuQcK3ldOeB3Gky5PbuOV/34SDrhxaCi6N4JakC0NjaFY2ABVLc3TAL26g1lozI2dAlzMaNBmeO2QgvaKoPM1tExcxUoeJOBubrnvNsY++FDFJ7t6oz71q5akxYvWBD6QBktXIS/hqx4FMnO3e99CXf/U15Hxh0wEhWZoX07fXaco6l37Qw+rqisjEB+EdmI4tZL+a7Bp2wNHuc7eVe8q3NNq1Y/CSsrxsMNYQzrQPTBBx/v2JEO7t+b6thSWcfxreWzSfUmU8tCXrYdMpcxS3MgNYrhOmZhBpWwN56xM15hoat09xEwAvRu6elg+91vfvPbqNGwfv36cIqsPZGT3eo4DU3HK+3bhrShPFZ/CB8d+HBu4E2vyPpBJjjnsw2N6aOPPqSmy+zQH55IZcaEdodt2nZkrNCOvC3wA9rCnJf9hVOBfAznjN+lA3lE3B86+mnaC+yf3LiRVfOlgV/pQTgLA3WttkrYZnwXgQa+D37zHuZngFEnWn4IOmUUyoqLFHL+ZOcu3i+kl1kkqK2ez9jgOeSadKyc5TEuR5zBRpxLr/ap7nMczgFAEQxGVyJ7+mi7Db19+qS1CDgxqqo6KwZZNCvwGavQwEY+gXliHiFTaFt7z+DgTbIKXZGMvfvQjzhwOeQ2NufJxsb0E4pz/4AAz2SyF67fhZ64XqzipInigjSDFctxHNH8kKzA26w+erLaGAL7wsAxM2B4E6cRZeejD5HrEUQSVtIxY9LZMUgoD9/kpu6bZDRcvpQONqfYOjGNKV/h2Veeeiz9xb/+C/acL8GSHkXQlqDkbfiBQLHwNovW+SgvCqx5wncngMu+3bs5JeZiBBqe3ryZo13LyXyguj62UTjJ0o9wZ8zBo4xNOQhRITfY+w5tu01WOjG7jEETtJ7GcbH9sR1m/57dFFSckRYuXETNlsXRt1stXOSR5gW9dlTYx+DQPtQPQdvImJDFfKdN4G/yknqgvbMjikG2tTTFKWkL4dkqArP56OHbLKAYzLFNacJgiTo7s62hD74PGceYhUcmLzKZoW0tXR0/dTL97Vtvx9HCj2OPzYavPCXHNuK0MZ4LO8g+mLM85m8MOOYkvOVf6VJYS2vhSzAei/7uIgCz/aOP0ks4u8vIyDATwgfVRwEDxusXBk2VkW7hFcYG+ByvOJD2/aw8UmfKHerFC2R7mCHUSybul8gWrCovC/4R1s7NOasrhIE6V3tPOW9bOR3ob+onbZ6w//msfvNkGDNB38dumk4w7UWys2aBXxfTxhBkyCfL9BGLVuFv8MzI8djWBFsNAMpbyuKwyYGJWULKY2W9eLJ4a/fV3qjftJtAzDr8kI28Juoj8Nxo+MOtEcLRmg9uyeDE1/BpbEP6NkNKf8asX2WF81DfmEVzlSybLuTwCYJIbp3YQtDUE3Sco/dFwB48iksfDlkGrGILBn1mdDKUXTJ0j+POw/e7zfzPtjSTcXAmTqUqLJgep05UlpaFbpBfgq6Vw/C2L2kytjYqy+yHOYavie0ubORdEadteQ3f9WxTYzrZUJ8u9lxOXVcu4292p1f+7F+kL0NDBdP/KdAAur94hcaILzK14Uc/ZdHjFoS3lWpbGxrSPVJdF6Pw5yP8xgFsWBBkZZFByAFlDl9jIxrtdXVCApFYNPAUrlaB9uxsz3Y1ndcCN4cp6rf/0H5ODZgU0f8qDBaJIsYQxKASoC0Iz8vsAQWFf2fEmAlcCUxGdDYarhq7V4iY7SAlqhliqCF6O5eCWB5bM5H97ApTx2+gQcb2QZna/8LQlMlpy3QlCdBLh3cUTKp4MCLX3MpxfB98EErQfWOeOuERWQoNmUFB4ryjXebi958bjhK2KkPmp02Fh8aTEXvTxRRUVtp1H+ZC9gYvI0o8ga0T4aTzjMzq+LJXhrlgRPqQwZ2TgQ2DC7n0SFcBHI8rB1cxNA3yHD9xLG3A2a2rrYmiPjKvq7+uFmZGCuOnHxkMwMdccnAPYwJ8ZINgDNyTC664KnKYPfFGap8mCjpbYwKYyNwKby8Vm5fGgs8qlMNQ5G+jin4WLhpQGsLusVOg62x2IJiOUsOimADGalbJo6gRbSm0HY+w1hHWiHC8IfQQPuLEOYYiUljRl0JdmnF13VXG5ubmyFa5fuN6Wk3a6xIUss/k9v0Lj2ifuTsujUYbVmkEzdOOglbFqtNh/9KnAtliWJ5m0dF5Hidz2NCpE+U8h+APmjGSP5SKbXvAKNoXbkzMNkdiVLtq7ot/Iuhk/Yp+8OZxPr5GkKKZj6M8lTQ6he7+w0fSG++8y8hTKsMPKOBVPXdSqiqamqaNxSy/h1P58C6p9sDQnvCAPZrxES/pP4qWgpNRBDY0FO6gvJyTJ0aMYAyPbok7hgM8ezHAGsnxP8tKD/5wahdgXK8/g1FMQMtjtWZBD7nV2QxXwwlOnU/7WPV2BUReLZ1TGtuoJrHnTvi7SiP+czgM2AMX6TQz+KErYKrDJX/4WZqVe6XpE6dOp4M79qQFZTVpIXLM1Zx8UqkfUjXpPuH/4ax4PSTod4OUQ2FWCC+PAXcNGPXHf/TdNKKlJ80snprKkC2zkH9WNldK6IpIyU5TCWT1cv+CKtIN8HcRg89AQzcWaN8d9oUWQj8cBXoNI/UBCzbTZ5aBw1GplZXnO+PupXIMkQnjMNDIMhkGTjyHfTQywuyNG6yKtfSg5C71sO+WWhhkQHhtemxJWkLwZDHwrZhXGUfxhewKZZ852dKO44tsGGCUGdFsO4HXPVr0k23byAZYgEJeG/LZk318RmNK/hT+ofTpT76UxoW1dC2slZvel/Ft5sC7immKpMUm1SObNj6dVuIYuT3MSxmgkvc5lXtOhgVugX3IY3mal7/Lrzle8rSIKwRMu7ouxBFZ99ATz2wmPRId4pg0xGyXf7JxQ6DSjsZDZnBiTHCftGLgVrmUy5jw3dTgtvb2kAddnR1pTnl5HIU4i4w7HbkIZEJ/EQzU+RoiOdvXeVQe2Hdcjl9ZiZyRf4SRe1Q9RreDPpRP8wmQLGQbladOePmdss42xEMWlMkcc7M1hINGj3pPvAitTH5mhuJ5VtCOo1+7Mebc0vcEiwR1OI0GMK+RESNPGRh1mMJbWDlc28vaRHYxKfvxFcBjXOJI/eDe108JZGh8z5lbGjWQzGgQxso85x9GJf1lq+niDmeUZz02N28kBrkw8X46vmtWA92Y8m3K7j5OVjh58hiZjrXYHBXUaCiKrRMagDnYizfTtzWcLQDm/MegL7QxXNEzY9G+1ZYPEW5RB4L+OnHmdmD83wOOFpcrYWuJR5PqqOV0dgR6eZZHMxkj7qAh6VV6DIOf98Cwk47PWWaD9TG2bd+WioqL0woCSAXTpuHM4xhDY9oun/OJsAUGmY6CT/k7eIC2bC93hbMDjgxuSZfHcEqPwk+m13scnzVZ5A1lqeOxDx0t2xW2Zpva9j34TdslUsoZi3A0s+GLGQ0eH30I3dp7pTc9jW1QUVaWwY/hOGd5/fO2aUs8a6/Yt3xnAEM97JjVCYEj6Eya1S5obW5JJ8iGKcIh2vjkU5FZptPmvM1aNKBhdpL0LD503HSAnPsANqjbbSzoPZyggHRrFppbbhra29Lbv/1N+hm1oUZDR4MKfq4vk5BRVzQjFYyhfRbPhtN+Vrw343ltBO1UJCK2KzDCMuSu+AzQmB2/KCNsTLQANwMN/dRo6OVIpXaCnQcb7kYxSELiIfefX788vUqQZvGCWugJ247g9QMCAB7NqXqIthi7sFMWanu0tLamMywU9ACjKQSmVq9dix6cjd1BQWn0l3AImas9zLMhq2gr5DJ4NnvKrbFBA/yuAy4IJk6bGkGe/WwBPE5Wn1sd5s+bH9nD6sDMPjT4rAzLxpdzfsWJL4NifQNkJjDv2IoIPUUGG78pQwzcbSWDtQena8HCuqj/EAVQGfMDF2aAmXJCmo5AlbAMuQW/QEPSqXNSJrmaH7KDeSofxMBZ/JCPyQrQN7AWjIuRFqHkp6A5g6vaAI7JlH95weCXfGDgRXmvDM0gr1aBrZ0r47E2yiG2ge0nyLOGY+oXkhFn9rDy1vvjbnmIe4WFMNAmc8zKRdsSB1655wzyS++PQLa29gF4tRfb0gKfpSWzoy3HFPClDecYQUxkmAJH28i21QHCJeQCMHdeztnAj/ayeug8tvCu3XviCNknoRlrNGj75kF3LFkCIO7lvvvYjaQCxWKKMsGLKSl8ow/9IeVTBD7pW3v3MsGAptbmdOrkKXyEuihuOh7eewRM86A1A0HCwoxSA7lmjXl6gza79qx1/AwyRKCQ+xyH8s7rCguGLhLU41uqx93eXldTHb+HfHeM/K/OEBYOVfkrPFRU4ld/KBcwsG0fsTC6tlcu0HAZmpw+bXp6fOmyOLnI7aT6rdK9csf21B8ulhukEtb2K+zlVenATEjhE/8xfosVN7S0pLNkJl9m21fvwA1qZl1Jr33nOwQavkLdmX8KNIjjuIAh8Pzif9Kw6ERRIeY96qi9WaVwNJ0ms6GDz7NxXMpx7qa6WqpSQOm6ohURIdqy8IwRbKP+GnoiUaGVx4qdxXCiMAyEPQHDR2V1CiI7+tkRDKbRqYh2y8vKQilLMRKVCLcNCc92XBXw3f23EpbCIwiXYRuAUPiqDGXU66SLuV/9XEszkeGKNKt4VlSTVUDFc8zVQIPCyCsI1vFCSBovcqFjlBgVXu6jUsh66bh4fu1+IvMeA7R4ESukCHAr2jp2HW6VuQxmu656K0gcu4QcQgSF597REIrAzJUq9+2pRF0x1PjfizE0t6ycVe/5UURFha7xF8a5A6E9MagQ1KlXeMWqAkrD0ytcqTBabSpVRCsRJApIVxpNK6pvOBOV5uP4SeAmrMWnCtyaEMLRAIF4ti/nJkw0lD4XkswnhKRRTebgZ4uNncG5cK6u0hVOLwiFIE4dK41FG7bp3zQXBqFzky6Fj3gUDxrTzskIaDia3O8+Uk+GKJkzJy0j3co9sBo5ITQYXw720rOC0JUaVx9VZDpOdqhSUiB/vsrDdwo/941dQHirKBYvXx5nzzvfGzheCr0Q9owxM8xzsM+Me/twjCocX87XsTsG6VW6vUaK1aXuS9DU3TSvuibNLimBHjGWmXgWrTV1LlvtC4XGs7HyRN/hcEDj9M79OIm0rTJzT73VnBuamuOoQg2msZOmUKOhKJxgiyl9sH0nz6VUBmlPQ+fWzJ5IoGFaVqPhPqmkOLWYUKgmhTq8SlBQFrQCuPtrh40isDAeo4g2+ggsSMvjPc6Wex5yVJ2BCQaeBRqu3k5neq6RNs85z0p9rs0Y3DVVVamsvDQVzkAZMg+VPNMKHPR09wTN3OW7AgyKGcgaecrUbOlYGSB8pQ1x5oqNjpjGqcX7NMQ0ao2ua5hJR5mC1iClOBABpE/3Hkrls8ooTlqRpiDD8saRfUKg4Z6BhnwAg0N/E5jeIno9mXbHEGjswKG68P7P02SiBHMmF6ZS+pmFUTOBcbjVBM6Iwo/SA39GBoIYshjkDXDXjWPUeetm6u1newz2yBitUQIaAyjr+yi2/AmFOECc1oGxNnzm+FRVVQpcOQ4Ow+7BIPKAOY3i/mH0e+MuJwEB19MXr6WbbJvoJAPe3RPLKis4UaAqsrZmFVORG96VxxhOGBIa/8ohXzBoGKPSUvAGMFThf4ZzUUkWyXL4ye0sNyjcJpytGaAhLP06P4OlcmmkXkqHdBF8yviUk64QGbjzWXnMzIBmlPIZtvUsXbYCZ7c6cGo74m/ANEzkljog57zpVORkTgQm6VdcOmbflWE+66kLngjR3tYWAe4nTKtltcvfg++YrzIhcyaQXcgy6VY5Lh/bRhimtKvcD1lEX9KYBr6yoL29jROMegg0VKZl0PBU6v84ZrNCXH2RcXWes+1M8igGGDJLwFhPIAu8ZFlHrt7JP8pXAzDtBBkMBDje2SVzUllpKQGwKSF3zbq4grPnmCYi3zTexGjoQ+4PepN5uMSB/URf0grj7yZlt7GxMQxcs6SWLKLuSVlZwOAafatLcin3BgRsN2DFs/apLHZccYl4Lr8XV+Knnay1BoKa0rqZZZVVlcGzPuPvscpNW/K4OsVgqDA3UOCKm8EAHYlc8Dy2XaIPdRj6CCKdRH+Y+VVeXpHmFpekGVM5OYB5PACH4aw7Hp4XLlmgAb3FmN1+5qlY7IjCic4CaZ5gYH0MNEQY6FY5Pw496rTW1ZlCPiXmpcOt7pBGNDSDZ+hHvarukNojsMbv0otwUx8GXPg94AYctQ327t1D5tbMZIq6Wyp1Av3dsfseOg4eUA5KqwZvzFoJOUaf4vdznHC//Cy/mvmnLDuL/jZN3cCa9oK847i9tIUcqzJSW2G0vwN7T6yQj60LYGap+lp8yFcZHXNkKo6Rxv9NdPhS9F8RQWHhqoOdh7y3H2v1ZDya0Ydwky7U/cLMUeTGHzUahmSBKceXOCrW7UJmt5pJaWBNWI/BdjTb1JXWSwThlDGTgxfQjcgfdaOZPlbIH0fmn5dZhiAnjdLZpXjlR9hL7+3eG7/l/vkKSTwLZs1MUw3Y3iOl3MExl9wYdSYYLf0ZMMa5o/17vB4AQ24M/RQnLwV/+RXyh3v72RJxhROAzl25ng419KUrtKMLNRWRsGjJIrYqrUnlHOOsjZFtm+AG2oBo7C76twd/V7dZe+oCwUFXvl2VrQM2HqXrCW5hfw7Jx5ydIz1KQ8I8+IF38e8Ck3MT19byslC0QTgLaDeDV7fFWaehlGC+OjA3ptyiRiwAMU7bMrBhhq70qW1kJt70wulBSwZ6dVi9pxv5eIiFJbfrVc2j6Dp6W9vXxS2LwEsf2pJOXNoRhsoC56JTnuMhec+ARGZPEnxiDFoj5853RGDNdisrKuNUL9sLfUFb2oghNwb6Y3+/tpeF45UzQd/wmfoox0/CTR6URrUNzwCXk2TdzSeoWV5eHjCSp3L3e68vrxzv+jnjgczutH3pV7x40pIFDdWLLpCdPnsmtsQsWFAXR3RG4IP71RV+FgfSuPxt+xGUof1sa55bFbmPVy5bI+QFutbxmbmtLHP+C+oWRDDGhdwx+FvDWZiQ3iC3COIabDBzzOCO/Bpb++hbvegYDLJq4zKI1A9+LcrbRWaTdFnCdteK0rIo0ClOzSoai1/nGCz+r/zOBRrULQZf7iFf9EPc4udcLHrsb/oqkdFAu+fRI9LqEmRNeVlp4ES4qsukwZi7soV+pAuxYDDf75WdIRu4P4415vep+BpuU2861x7+pfUeplGjoZYFLutXRKABGexx3/ZDFwE7cSUMvbR3xIWwU+daJ8ygSvAb33nCVRu2gfUxbkBzFoPs6mxO//b//I/p5VdfSTOHAg1CfsgKi3b/EP/BdgIT/4DLh62OLpOHg4fElUlQqfzHygM10y1UtXfHznTk0OF0ZO/+NBLjpxjnTkZWAKlUNAg9issItHvR80hH1pAy0OBROTq5IUBA1DCiayOwCiJyTb+tRMt2N7XELGrYWFdau5S0XyrhotjCKHaQTFPjRabUQFQp6rhHgAMGMQ1KatG48uUcTInvxxg8wtjbaKKCV3HtPKJdUyNVXiaTYGPrBISjsNc4DuHN8xKUzKhS1uG0ErUGgGliXgYSTC3cc7oBWKW0lrPrVYrOM1Zx+F1DWgMv+mH8OeFi217WaXDFT+dIx1LGlPgNpuhYnYNRdtL+HO6tW/ZYlnEAcwnbXKDB+ToXhZCCV9gohDR87U9GNdAgwzs2xyRsxUvjEVaMAN1qHJsioqy2I0nJfL4rvBUK9uGYfdYr2qcPV0a8Yn+ZCoQ525fM2Q8jflrfHEboasZuZeJYWQrGDZTG8z6bGUkYi/yn4pFuXI0V9aHoDGgBP4scqRQUOL2kKx7jCEVhs3jtqgj2OB5/U0CIT8cujQoTvw8Dl3bonTmaOofTwZy9L5wZ4GKxqo7GM6n5BlXruXPDguo0p7Qs4KEwlM40BsWB9GlgyYwIhR4/xf7Zmzpo9GtWjVcEsrhXY8IUtmukWHV0dKfr/LaktpzicsUIfFYmMW68R0XvZ8etse67CoAOM+Gt0y9R8fcgNCTc3CrhmFpbmtLexnN2m+ZMHJHK4SfH4upmQ4ucwPc0VUg8rGbOZAINbp2A28loGMHWCcwW+F/VrlFt2MGAOMEn+vT8ZVcvhOCAhYb4L98CQwxmOE6zIYoH4PMye1frewcoBnmTs4U56tTtgTwzd1oBFfsLqAXCloXx46CrLNVWGhVfV3Gqjh89HitD87h/ZhXFqqZlKxfiUbo0ECctyou++6x7PKUTx5kLcGrMhEEArrxP+jl3ri0dbTqXymm7Yt5CsqgmhnF7n20Tg6TUDifaD3LTHe5V0UxBxuXzbG9XRxrJtouFBWPSvMlFaRbPTwcwExi/1cixN7NAAxDAdEHBMx/wcx8eGkDx9ZD9cQm+vnoHeLD1wb2Mrn9wOAeBBgIP4yfFClrHuWtp5NzEyvB8DECOYn0IjB/iJEDXRuDvkVPcA/034lgfA8WgIJHUQI+eTJHHkY3TUwGrGZNIKc2O5qJxftSBkWflLTOdPCbVvZjBF/ytLO1qb0tHurrTfMa2dOPG+E2nQJkgHYfhB39rALnyK0xDztGmWQGu0hvktR8NRw1AnXD5WFnTevZ4arxxLy2YPC7Nq0OWgX8dTB0Hcer99qNekJdguJD5jlnHQ/mvTPI+eVbeUza797gH/HzWTcSFa/PyJVH40H6Vgcoc741nh4wTaUmZI/0Z+NNgzsk4YSlzOW+DyV3nOYu7laAjX5fh2yx6nNRReC0MQPpQbogb/3Yl0cwBgwzCVrj0keptoFr5PozvIFgCIk6PbSU4cuea6tPZHst5IuMLJqfSeTVZEAY+UudcJyAgjVtJ26CAMl66lp6VX/JELuATgQYQLnzEj45B/eFPU1e0ntJ6dJTBDJ1vTyQJHKEnvRhO4DYnC21LPAobacj2IO645FVl7IXOjnS4qTXkwXzkSdni7Fg4Za3jU5c6VmW291ukS32ojIuANLrbAJh6Vx14DzgqRUxN10E+ffxwarp6J80lLjy3spbtRFPJpESmQr8+J3yFp/JNA1F8Ok7nj0ETK6nqvagwzvBHIrvcAibMensvp50nG2I+T9VWEQhgvzfzUx+FLOcPx+yUbVO4K+e9Qj/xmzTpPKUx+xSe0o0Au0jwaPuJM8nclOVLF0IzbtdUCjI06EaYChvhrZ61H+nU7/gn2nWc0o76wDFk+lk9dje11p9OJ9hCtXLOrFRaXhl952jXtmzHl3iUti0W7PgM+hgIz2wBFiXgfZ2MMNrhD+fcD22cZltMB+2sr56XCmfOCl4PWDNW56qcVUaKY5/VkfSSJ8Wtc5TG7dsXzBH4su7LFeTXkbaONJf7H1tGdhM2lfgW7+NdMOBZV8atk1JA1oMnzBgM0L4wy0p4GEySzsTFCPrOIwDu9oD6xvrUwDHE4fHjY6GS0ldqRqfamQTUiTCMxiGKcBFzFU5xOU4QoguDFoggQwQafFg48pun9uiAxVykf75368Tl/kEyGqhA39yXrtJfrs250/NT7YLFsfKe8Qu0Apy18Rx/yCTgJ+Npp8nDHpl3ua2J46FJyWdgC5YvjROTtPWUgcpL+1cmOha3Bwt38SBPWVhSmSBuHbW4UEZ7XKzBivrTx1ND9/UkFdeWzcbmmxPOuI6a43BMPPD5mJST2pXyp7b2FTJdtDXcimHQSflo++piTxbaffAwB2SntGIe9U60hXk+MpBpO0vJz+S6drXH+7odVRQ5TgMU9iGNyw90g86R//iPzxc6zqc9jS2phPur62qZO9m90Ecml5QB2APQojLLl6dJ6CtIz+q3R8gD7V55VroJOQEsXcG3+GUzdHO481JaMGFMmjt/QbadljbFp3Tsc14hM4GJMl68ihv1q3a7sJF+5QnrqNzCARV+nthy6lAmh1dXzU3Tp89A3hlg1T7PbBjbVs77n/jLZA1FG+FVdarjFffyXuhd8K3sEVCe9LEDP8HrqYXzCfIUhD2pn2XhX3Eai7Bko1oQV140MOu8xHtsWwQH4j8CDfRhX+L3OoUsL3a2pMbeO2nRrKmptKwqxqf8tRCkMk27O2wLbS14WN/LeavbB7DZpQPnI/+K52z7x4ioc3Cp81w63tkTizXraipZOJwb43LOIT+ASSb7DHiRKQFsA+eMO3Qgdry4Ec/3gLNTGkewx4yjTjJEjx07GcWyiyG9mkXLIqDs86E/ghexE4GbQQZPOwpZTJ/Ox0UtZZk6Xt8n+lZGAicLz5uJeOJSV2RPBPD55y//619SQPdbqahAK1HsKFtyUiG++oP75x8l0CDT+wrHCY6OjAAAyzolQuARkb5TadsHH6YD+/al/Tt3p1GsqHnGbzjFEFJE5xF67sFTaBw4eYan/u6qKi2J4o4hCGEaAw3DELSmJRodnTCZo04QmCorx+AqWuaoGfXCMOOKaC2/XSdy14QSzF26/DXl5bHS7577cE65T0E+CEFbVGuqRZggUAlXgRbZCQgGx+84JTCp09WpboRZB/P74lU+bVKaVjgzjGAJX+JTsDvefOZspV4JXQUegp3+JWQNlhAI9OPcTSE6cwEF+IWruoBCfChx6DqYUGJ2zpGBgHCchtNggSrHnilYDDOeV1AaSfY/jRwFWi9OwuUvtK2BU8g+7wkEbTR+Ha8Bn7uuNiJIFAYef6SDdofxKQxDmNKuQiYMFd4VDj0d51I7xzh98VpYNDNNIrqs4PVMecc8YeLkENAqF+ehglKJRVCIvgPmtK8QUimYzulet/PNzX9v7EXslZ+BwLECtXO1ArusapBHQRCMz7hV0OJEgyyyLngXJiHAoWHn4Epk/f8P7os51nAChnukeiKIbxOQiv3PGvL0KU2EMKLtEI7QuSuzBuMU9NncDNg8wkFrT20YP7mLrPhUvXhRCBeNJwWgjoh9KZRtW8fYvZ6OT5pxX55zCDr3ScbuPBV2Fri6m2uc93k4x4WsHnqEogaQRrb0Y2BHo3IKEVvxqtN9GyHpcWK9rN7187kPupdHVdYsnKfqOdRomDWFrRMYx/coLmdWg3N0DI8cX6ZwdJiNWFtPwLHrVN6F5vk6FLXmM2UOgA00z3gu40GfuHgjfXbhBnSFkz2AXOGe/FETogDmpAlke7B6hRBg72o/igtewnnVoAyDAWGu0gsZA10NINRvobDGcI/ZSM5XJ7KjqTF10m7ums2HkpqawI8yTXjKqxaGVLkZvbfy9yOc/UeMkdlpOyM3oB8c+gcUMlEq3EEOXgPn7jvNXY/z4emy8alu8sw0k4kXML7IaKABedFgA/H2wMlo2nQlRmV+B4C7feIqwYab4LifgM7/x95ZgNtVXYt6JDlxN4gRD8EtWViLAwAAQABJREFUhEJCcHcrBYoVqdwK1pZe2lKh99aF2xZa3LVQ3JOUhECgwZIAIS4n7sT9/f9Ye50c0lDve1/v91ayzt57yZxjjjl8jjnn+o3ICuDynobZOvCylOyWKXORFW0j+vRlNwycsjpkWmBWsEgntIG8XI3snA8eJsxfHm9P4z3e9X1dmyZsUdqUqL0G6Hr6cdqczTRpf3cimNUYOSDf6dAJW/I5z2uEaLTq/GvsmbqrDG0Cv8lPjiQ6kqIMWYHjPHPa1JhNmeWxPQvvNkVOWXY6fXym8QHMOn/KwqbISoNyyjJ3pVGB28fym7TuTirLlizNNNAiZFCULm3tQoaCz8kXGtL2qzzoe9KLnzqCHkuRRYsXOPKGfOCefCzPmo4rj80Y/35Mpy/KQ4O+T49uyCSe1QDjtzJHvnf+sfupa1QqG8WN8+MNnCirlTPiUP1iEMBU5dfGvlsWnZ+792IaDDSbzjb4kHjToATnjvKId/GqLJCmXYAyF2ejj6RZgy/Wo5E8p7o6xjPlofax23Zdknfsz8LN0Sg2sFKs7p26FVjtx5XIuQ+YTmh7HChQd+iAiCP5ZAqyhplONYcTGLtvXzEskw5wksCDgXB51amE9q+wl6Nx4q044STwaNmNoCuNwpHjxtWU7ZddunTObUbtF+VkpvdDZ5pn9l/D1E8V54Rn3Ip0HWvZ6PSJkyr7hWeVofNmV0f1itqSEketc6dcBNNnpRn3hRd+6cVAXA4a0OFpUEKXHt5XXyvby/d07KdUz8zAcD7Enz5kQrXtDF1iQPusMKwkiKycFz9N4AnlsH3hKJ460EB32TZ5QtpZxvS8GTNmRu1e1a7pgFPQDCe7GUEtF99UF6hjXWPFd9OYB0fyUdILMCiXy8M+0VFTd06btyCznsp7PTu0yzWD5EfTmnWYdFI11P2uU9AM+K1HR8fssCzPdjqgRL+anWK/jR834UNlt6CSXn16pR1g36fzy3M0Fl7VQWNaKHCnQ0M5NCz7Xo7MbChkQxXOUAvWLZCfRr7zXgl2fkrvuYMT/SYPSoP2lzzpOjwLF81n8V3kMKYjJC2646SdGsaOtLlNfQa4agIN0C2384RXMuOJX0rwMqPBrECfkI5BZsoH6dLRUgMNi8lmmMWCvlMXMqgyZWUs4TFtk5boaNtuOnjtw37dYcftU3YhBbI8oTAQpsxRhhXOfWH3mrmyAppXSSkzKB5witFubYs33xtfu/jYviPTQ5hSkOn20LF9ZvvkPdctcltBda007sCg8kAdm7Ro+b5DH2u/uavA2PnzP1T+AYzGu/ijNnpBA8UCpPJuDggpE2i3QWlpw0CpdelMyyPaJwkQf6aRcj7fFZcrR3sWIurZl2km1C2vlCPXwm+ZLbDXUmZRl/aSekR7X3zotFuPTr+HOmLM5Gn5vfzTl8BHa3BjsEZZKL49lTY6kGkLQ5/akdZv+7yvHpSIlMPyq0Hx+VOxh627crCEek71NKvUqc8GLtKOxMazHHWVcl4bUxngwp5mxFm+/O19aVgdsRJ+nYSMLiXZtpTduet2KbOsv7Sh1ce2RbpwjTFlmThejgwyEw6ihZeRofS7aFcmL1iALYwPVftQ/3Xt0gndXwzg6oAbJLRP69Nmp7FZl4U4SGE2mffsH5+VyQwS2w9mSU9iGiO11xz77tBXhZqLG+faWciWpEueN8NKmSCM9nsGJuD/nM5ICbbNvvee9DT9/QlR24MS772RNQYyfC55hLpyEACeaY5N4hpxyk5Pp9FpR4nD5DnaZNnqAvnhlXff/VDQYC+yRA1uGxxZT4arC5rWwyZWBmjH2x4KSP/ZtaLehKb/+4f/Hed96vzo1L4MNFRIvgYj/35f/imBBlm9OGU/jEqMbhm4CRFjHY53iZwPZm7UW6OYszdrdvTt1jP3nDb9JNOSKgzvCKUjNWMwJm544KHE5m69usQxLH7SgxQtAwtrWOxuPSnWLk5Sju7DDUlwuXo2hGUkUIbTeFMASuBJKNRjOpIr6j8JLH2oof+BB8VuOHXtcXgNNGg4KcxkBAWRDm0u/MinjqKCSObO8hSqlKGhqoDVUJiJgf0yOwK8NnFS7APz9SXl0UVnFN4So2LbVFsDCBKopwRu3SkcKVsmUnDoSAJKCpB0JlEYUwhkPP/k04RwGDXsTxo55XdBcVqOgsB3Fcp+lxmEvRWRWZnMQEaOTvCM9ev0+ttIt6NyLu44fuLEGMWI8J677pTplC76Y0qoi+mZtileXfiHIlJo+n4ufgIOjA6nMqAN4tHv6QCAS9OmxmOgv/bSyBSAhx95WK7S7cKa9pNpuLZf592RQvvWtiiMxLeIEMca0pYrzmVW69Q4GD9hQrz56sh4bc682Kdvn9iX9GS3OM05eOBK417lYX9anwa4fWd/ii+vWY9GkEc69DwrHM6DfpP5dy8QJDOD4NSDD47+u+0WrcGrgkknSkPQPlCZJGx8V0FYvvB6SD+OkmffUp8K23tTSJ16iZXRX6pkC3z29I/Hzn0wznmmUOiOYjh3DCEFrQi7wrUYHSl4T2M0o+KJH2VXVQaHfP8d+Omxhx4MHbu9u3Rh1fsj4b/OhfGD6WG6ubgw28IF1Zo2N9JPCjs0uQRemskiCZNRjNWkq44Bzx6dOTswA2iHbu2jV2UxyDqk6Ndle8sMNNBlqAHwUzhTBhzXIBfgrnQmAbBQ7gjetQpg6nJMrj7prPVReAYa/jhtQbw4eQGCPXCys1q2qNs9evRg2kJ3RlPIbNCJXoZj2AA6KBQdxjePiid5reRjlbc8IU41NnzGkSz3Sb7vgfszSHUIAcd9Bg3KqVc5AsP95HNoWUfO0/mxGwl81FvLqAGLybhNUwOCEBuYgrKeXSc2MOK1llzqpfTzHHDnvNCnn3s+gR/A3/22rRM7t+kSXWl/B+SWgYaGAAMa0uFJ/gRXZjOoe8WiYzIrGU1ZQd0rwZP7O3tPXK6CxpbQ94uBZeaKRTGeaSYGC7tuz7zalh3IIGG0iOBHQ4JK7m7BX0bRyBAh22bqYrNK2EqNU8W+W+/uxa46ZIs0bsJiRTjtjz3zeOzQplsMYiHZzqwr0LQJgQZgty+V1RrFIlPa1LnQWZaPHSHR8cxRf/kYmKV7eXYhTvz498fF2FHsIsEI3HEfK+b9u+Vh6fhbrjJEuZX8Tx0aAznqSV2m6CoPdE6t175RrppNZJqm6wK9NXZsBlLcenB7AttOT5P/5QnPDIjDJ/KtsEkbcuoyjB1lZSGDCwdeGe19M9PehwdGDh0cr7Mdyi4dtomB++/PNqdkFFXgUDgqX5XZvude7RqiBhmkOdtj/RqGyjjbXMxBZRszDPBX0B+/e2EIkEQcM3BAHADunfqznGCZOBEuDUaB9btGVikvxZfGlo6C/WLblGV+Wlc1o+RD//AHMpbGR0/KH3DEESnnHRFOh79SrvALp8a/8kZ+8nBEVQNLJ8+6y763fJ2R8ZMnx/333pvG3B4tm8f+7Dves3v3QkdSXsrDCl3Yb/KnPJaBX8oQXzplGrrSESDnPZ1iF+ocNXpM3MOaRn1aN4p92CVlh959oj2BHMsosmLgC3kD+DNDBviFU1lbl0jKRmTNehxrHX/lQxNknXTgVClT7V9jKt3Q19+MbowdDDqEdYdwXLQNciEveNopPI7Ep1OEw2tqt0cGgWi/eJAX0ojmU5yrd5Q90zGgn3n6qZR3+w/YD2e6d25zalBBfDvtUSPf99N4xsC13cKpPaWeUaerc1KP0S/CrtM2s7o6F3Qb8+bb0aN3T7YeZJ0qBxl0jjh1JKQNHayaTAGRS9llxoN1SuMFbckJxT23a57ByN7bo0fHGzNmxXEH7V/ssIH+c0qUafbSgzgwYCHdSO8OTqhbCrupcOgMZuVgDqXrcMoTE7A53mDnsNenzY49e3YPcdOV7EjT1g1aiBOdN7MVSscIRsg1Olwg0ICsePdMPMp39HvTFs1Sf40ZOyZGsObCePy9oz+2Twzov3d0YFpdA6YaOvIrzTktwGmyU8Dj6yyuOgLZ0YCgwlqjvxwn7FyHXSe6RFum/jUgoKurKXHKFYQRoC30Gv2W/2ikgQbPjck36CH4UedRPrIddeBJp7otZH2G6iUEpMloeGsGgWTKa9y0OTqoW6b2r0CHzAP3cxYujvZUdhxzt3v36J5BHvk6d1uiHOnC08P+k+ZtVw4CwhvqdXGjveQ96Ud972KsDzz1bL536F57hIskdmKqRUHDyF/wrk4SZt8pA5rym7yrEy0V5XU+lTvKNYiAqakLc92CIeC/J/cOOPJIpgfvlkFTpwIIp7ILgk7elS6knaQ87ikHlJMGLgqHkT7mus9rW02jr55gAcMJTJfs171z7Lv/Aam/GkFzytkC54V9l4EA9IdyQNiVLZbtUdqRaQvLq8AvbsZBlw8+83zswo4be/TbO3p0787OL+2zrb6XMhI6TB0l74PXpEHbIM4qv/UjbJcy1msGgueQkfM2TumTr70WA/EPdtp11+jZi6mY8KrweSo7RK40pWwWx9qq3lOeGMATH76jbrT/5RV14PQZ1ewoMziaoMsPxV7twro3ylthlP/Tzqjwv+XL9+pur1uubRLnjfHhmmP7q2ddNHI2MnLSDKcTjI23ps6OQXvuGrsyMON0Au0v6VHeTBgpw7UWzIRSHqsPDSwkz1CeWSb6gE5j0pZ1sGX+EqYCzmSKC+vqjJkyM8474bjY3YEfYDEgKn6lQ+2z1BV2BPf0ldQhmY1KPRkoF2/c87my3ydPnhIjsbVHzp7H9N+qOIkdPFzc0SxLn5Xehd0yDQbmjmS8r5zS5nCrVTM59VvVg6We1bczmPr2O2PjUWQNplUc0m/POBzcdwQ32p85ME7rzcyRd2lM4ns19a1A/8xkKvTvbrkxPv+jH8Q5553LOhlFoMEm/rsf/3CgQQRInPxPAeBv543b4U0hMMiBRbbeiaEY2+8zv6sO6dH9d9s9F2ZSMJuWmIoPojDCvRYmeIWF067+/g8z1fSEQw+IU089JXbcfnsiYQiIFezdKrFieFOx1aXAytGyVJIsMgdRyCh2qITpIaPLsgq/oWRW/M89d0d/YDuMDt1/wADmEHZKAlP4KCQkNg1X33evXQWEBpyGkkRYHBAxil7m9XmF1yQW6Xvi+edjyNA/xLHsItGvPyvk77ADqUhtClgA2TI0wmsf1qlgVXj53RFCjUfrlwFKI9otrx5mpesmMOYA4N6T+ZUu/KYBbkQtHXOFn8KNtmhcO8qoQrCMDABQsc5CGUU00OCotQb6G2+PYbuxZ2PQgQczH3fX3Olhm7Zuq4VCFi8wori1w22BasGFO2V8y9Mo8o6KQLgVXhqAU6ZOYQ7YaFbmfTmWIwhOPProFFAdEYA6I67HoPA0u8O2KNgSD9RhmZZlH9our8uoKj+3CF0E7G9hBI0Y8VI8/NKIOPnww+LoQ9gqFAFu2QoahVwaorxrVNioP4VmWyxbQWX5KgV/l6lnKsIZ1dXsJT88nrz91nibCPqXP/WpOATcb4OBm4IffEgbKk4FtvSgYrCPy4BL4oq+TQHPPYWiaX9+vkufPvEYC1ANHZqZAldcdVXsw6rFaYjTh+K77E+xodDUKARMDkrmizhfC3+sRdHTjOxf52Tb5tdeHxW3//gX8ea65XE8q/Wfc8YZsSORVuklI+cKWWBy9MP3Hf1ypK8OhpirfE9mgcUx777Hvu4spgSOPbpwtmeaUt9u7WoCDXXXVQINOEPSiqu6Gr1VMahV1hqAlL6Bty7OucEMF9RaQ/5+Bg4BvEETlBVOlYGG1wg0PDN+IcELRgwLmyCOPPTo2H3X3VmIsXd0Y3EubEEyMZaksVkGqBIlAimNUn72K/0vDTgaJk2KOvnhTWTSb265JcZB++ccelgcdtSRbKtVjIo4GqYcE4eFkYV5Sf9vWguc6xnhpn25YByac8Mm2oZbu4kVxNZVYfzj2M8iuOWuL9f+6lfwCVOjOPcmTWjndp2jO6ONnaBDAw2NAMb70oanfep8XrokaQppgMGIouWhteDIZxrAa+JxFfhcBG/PZR7llOVLYsLMDzJduRUdVB++X7sRZ43wgqMODRGFVQ0IBGyqF/MJlsxGli7Csp3Iaa0HfqxfrpbdmfnULVo2y8DjA26v14AtVA85OHp17Z7zcY3CG+ywLzVkfVejRNqXZg0GLjU7Bljls5QNtFOeVV7OJhg7loXoXCxu+CsvMx/x4zGgX7/MWlNhK5uUV/JOBhOkF3kGmWg9xT/RJGZ0upDz8JH12WfKmjdxGEcMHhzLwOG5554XexGQdbqCxpryQ6fYT//5X7hKWWFQ0nKEvTRA0iimfS5u9cZbb8ezTz0ZT/J5NEGAk9nz2q0Ny3YCGbCoQwpdkQYe5Rfr1JidpNSUbQu5VsibYh2LeeinwUOHxI9uvDlp4j/OPCOOOfzw2JasOqcgSM/qIg+NnNIAdWTWcko9JC7Eo8/a5jTQae+EKVPi0SeeiPvAza6UcfoXv8iq9v3SqZPORQaPZVmgtuawfMty62R1QDpM/BaH0qN1KPPfoF9/c/118RaG9HHIsBNPOSV22XnnlLk17YduUn7zvvrWPi4DDanvqDUNvoqeUb7psCqLho58Ja657rrYu2dnjLgjCCizejy621E+R7OxAhkpxgZRx9IWM0qETTpxTjBR9Tx1kqQnnV2+5LvTkQHPMk3yjkcejSPhhYMGDop+yBqdUhqJHGDUG2OymFKlc6Vx7vpJBAFxTMwAkWbUN+JE9Ek32gU6F26V9tCTT7IWzao4lAWCdwYvbrEn3SnjddCsIxcZ5N3S8bcTxFOBq0KviqfUNXyacj5jxowMrA1+7jmcon7sJb8HZTPAAewGVrUl7F9lYGnjCGeNbkTvZYYi/GtfeMhrOmzy6/hJE+NlnKI3sOU+yXaVuyMjDX65qJu6Eu2fOko9JX1Ii9Zr0N02JW0p0DiT9qnbvnGAwwVBXx05MgY/Dez7D4jjcUi379WrCAwCd2HPSesV/qdsRwaLbcXpZb6bpZG8TekGjVxLw2k285n68zJrYD3z+CMxnOlLBvGPxvjvzACK1op06Kit9CJ9vT95cjw95IV4GB6svRjk8TtVEWjoHK0NNKw30AB/KY+oT12H9OET2uJ7Ln5cCTRs4hkJIdfxIhPCNpiFqwOzgbYZaJjOWkRTFiyOUTMImoP3JkyD24NdfwxwmSFiIP3l1/4YezEF5ewzz2ShQRwv3tVpSxuXMq1X26+GRsCHskHeEd/KO/lBGaV9YnDTAZoXR4yI//qfX9vdcdHJJ8bhhx0WPbbrmjKqDAJKy76jjPTgZ97PfuS3dCIuMjAAjQmH8Dno9syQIezg8Vgc2G6bOPITp8d+BJXNsHX6pzRS7g6mc6/8KOVJSuasqJDxZR22yfd0XN+fOCnu/t1DMRQ9csLAj8URRx+TgQyDtga9lI/l1AnhcY68n7ZLW1PcyEfZJlEFHm2T9vAynOrXsSl/+8tf5ba4A/fdL3Zm7YUuXbqk/SWcwivPyiPKXfkx5SDfMyCMzBTf9okFy+fCbiC7moG34fTpUw8/FIcRjN0Lfu3Th6Apct6+Unek7ZIQgYMKrH4qZwo5Txs4DPZZb+o/5JBZMBOTjofQOXXjRAaW1E8tc9oZwV1poYJb37Es7Rv5Vf9DWab+Vd43xAbUuVYmqOlnzSdA8t67MZI18V4aMiyOOeWkGOQi9qwj4zoKDkbS+KxDnjfDzKkQlpsyAF5lHAa+4eSzCDTAR/SD9DmD3TVGs+bbMAKPQ//4Vnz3S1+Mgay70Fh+oT+FydO+k0bSRqBM5Yx2vVk6ntoLBiU9xJkBYesfM5Z1xp54PH7HFJ39+m4f5591VgYytPN9TrrwOdsr7G5Pa4aS1+S3htYP9OpBM9jFpTTZnOytD5Blw9FPd/7ylzGOes8lSHLqCeyW17kLgQYCW+pNgpAGZfXvpDeAJDOVjHJoaCoBxcfhlQHHHRXnnHNOdCBT/X/L8U8JNGhApIMG4jT6JCoJwJ0PVPAGGoYNHhLTJ0+O5kTpc3tLmErhW8yt1SBCAEKQGnJDWTX+y1/+SqYCXnD6KfGJT3yC1Up3SgVmNkNTlLkGvmn8EoZpTzq5heEobxUKyU6SeArhhFEBMc5buCAefuqp+M+f/Dj25v6Zl13BqsiHsTp9V5S9TpoeTeGAGh30u4LRMs2UMPovU0vYpVOp8pQQNSiMgt754INxN47jF1gx+OBDD4299toz53sVUyZQSJQlrDJ4Mj11yhQSu4yv0eEnwKfhroDy3dyFgXneN95+e2wLTEfiGPXfu19GcRWuCj2N/Ew3Q1AgO7PNGj9ZLoyh8FVI51xmjCwViILVbIapU1nPAaV8+w03xVnnnZMCZHsWzXFBPSN4RhTT4JT5gFlBa9t1eIxG+ztxb19Qt8/KgOJmInh5DUNFxeMKq+eidPoRcOpo1gGRXqOowp+GGuVlBBHGNl3ZaLSMCdJS2Ii7NKIxJkzLWsDCU6NwpgdT9m9+//u4EGPiTLaHcTtM4bG/bKcGrc6lh8a/yqtUbmoCo6D2iWXrYDQlkqsidMGyZ8nIefD222IEo1Lfu+KKOO6ww4lUkkYHXCpW+174pEUPeUDaA4DEvfh3NCEVkbjjHR0f6zFIctedd8Z1wN6Jd3987bVxIIot+5R+zBRy4ErFSTkGRQw2qHAstzQoisCGct6662Y2yxp48w8vDY9fXHV1vMquBWcefkRc8rnPZZBn+XJSuFGo0rFGRBp00AgFEsBpzDoIbTOrwdXPX3vjzXgVHD/IDiYe29HMNg3rRh+2tzSjoS1p9/VIt6/awPQJ2ufo+SYsfne/sM3JIxloQF2BFg0w58Cl8ajaoZ2b4OVNdTHUwbtrNLw1i11NqpcSjGSUWR+I46zTz46B+7JK+o59CTR0wHnG2YZPnVqSygGcS/elkZQGhgEN8KeMkscMNKjEjdC/SqbKj679BYp5Ulx8+hlxAo6RSl+6MY1PpaUR5G8VnKZlFZO0G2ykDwg0ZCodZW/YgAFPoKFuIxwuIuUrcHjmANewl1+Jr37jqoR9EH/3bc9c37YdoyvyzoyGFvBPI2CGkzKib5+qQOtWAg3SqH3JFe5Lr3IWo/sVvgNjsZig6xwcqKnQ9tRFLLoEOht3aJG7dyxdRQCPrUIbItbYEAN+Ab4q5iJvcAtRts1agKFv+J3j2MMOBrf7Ro/uXVmroVW42N3Nd7Pv+MZG8XGU5k69mWvdpn1Oh3ALT41zHXLlibgpnUdlQfJv0mYRfKsZ7aeemY6OsvDUcOT8HY/+Pr540cVxOCt1u7CYa+tI72nswCPStvwv/8qb9qEOgv3qKa/al+JNXvUY9/77MezFF+PJRx6J2cjsq676zxjYf590jKRFaU5ZYFlmMKm/JJockUHefMA0JgOHTmtT9npTVi6N81dYef0hsmAeBP7TcIo+f9FFsTPBZAPmZqalHkIW5D8+U5bzmYXw4XMauMKrge58YA+Dwu5Y8wzO4m+//4MYz7WvffazcdqJJ2TGhPpJeiiMZaZhAKP6o5S5fiqLDI4pe3SE5e1cgZ3fGo9mN919371x06OPhkvhXfv977M3+CFprJoxoZGrHMv+BFfiyL5Ud4oLnQmDAIl/7lu/7ziVQsp9BaP/e9dcE38k8+4sdgM4H9y4Y4JwSyviwrPUe7widjNwYfBCvGR77BAO+8lnDSovhh+fJhh76TXfjR1Z6+STZ382jjr08OjVrXvKX53oNGqhf0dMDYSZnZXOLvc2Eoh1cUfDDgamrEu7wWCZWzc6zekB9Pav7rgjzjvp+DiMYPs+e+xFkKd9ZkAoQ2yj9C2OLUMDVnlrIEGcl851YXfQHtokLS9cwMLDjHbdyvbAVTx/AoF2dyLq3r17yiT1nzgSVvGjDWAZ9kPaU8AnnsRNeaT9QRvMaJhRXc0OUKPipuuvj2NOOikOJctm++3ZApjRaQ/1U6nrkh65Ji9YvnXbx+pE+zjxYt2UrRPlopRjgP1Z9Ovr7Pt+Gcb/PnuwgxWOSQ5AUJbZImZ9qEdNO7bP1F/agwavxRkNzBRqnRqnSFm3Acl333svF8T+3b13xc77DIhzCYTvvH3fhCX1HPxd0pm8njygjhIX4Eq7BHCTPyUbA05OnWjJ4M5sZNhz8NNdt/w2RkydHVd+5tNxGoHBjji+DnIpS9IhoSyn942bNDHuxfm7/bFHuEfxnB4n7Nwi+nZoE62Qoa7RgOuU/YP0oe044tBavTLQwPP8yowGbSP70bIMHtuHdGLUI8gCAnIhyOnsrjRx3kIyGpn6xXNV9RrFAYMGREecNx2aVejpwWTx7M1aFJdfeilbke6JLeAUqeUFzeBAO4qvvVXaf+kYUZbBKnknBRz4F1Hqeu2/xUy5eerZ5+PSb17N/YivXPCpOOWkkzPIk3YidSsLUuPwqnQjPRSBwcKusv9tn7Zb8gfPSKNm6ZgFc/dDD8VPb70ljkF3nILzdNCBB0RrZPxiMtmERdtPGS4/WY99kU4p5SnT1X0pF+xrjhygAxafHQPdXHfzzfHksGFx5hGHxmn4Ce7+4mBCMU24gB+0W1X2tfgXTulJfSV+0nZHDzhNzoCmDzvoMnzkq/H1K78aH9tzb7JAD2edlH6Z7Wj2QGmTiW+z3AwMaFdYt21Rp4gXeSlP4Led6jDXCZtePZOg5tC497Zb4/wLLyLLZh9S+Ak0sAhhYYdiq/NsyavCL1zSfi5eSJvyPu3I6/St2dvqkkVMjXt/4gRk2eNpw5x96qmxG0FNF5U1YzjlAP0kfoVT3hfH5dQJ/Rx1mG1J+xY4XBenGZl5cxbMYyD4j/H8sD/EzQ8+HFd85mICg0eRMdGBwQ/wSRuTaShbWyW3s+WCfCAe6Fw7seA7+xa5kbyBnNAunDhtKnLstXhy8HMx9NU34lff/nYcMnBgtAJuUaA8cFBZHBnALIM8BknVdwbjtfGl79RXSb9sHGC7wZWDG/ffdVfc+sLgOJDsmkuxhfdGDiv7PDJQSnuLKX2FTHBMu+BhYOV7MYgm/gq7Q9vEXQgXM+D19AsvxPe/+c2YQlmfOfMTcR6Bwd6pn5CJDO4YIHWHHvkrpzeDV3e7cVvPKdUz4ncPPxy9+u3O4Mg5sS27GP1vOf4pgQb3Bk0lj6JRsSRFQBUShcd7KKkXBw+O2dNnsHJnqzh4wMDc09oFpTLQAAEa+ZOAlq5YRor6i3EJ+wc7l+YL550dZ9BZuzEi5RyXDTB2S9OribStIYJkpKiK0VEdPAnPzleBlgKLC4UzQ2c6P9vF4u6mM7/07atjX2JT51/9jTj6qKNZrK9broEg80pUCiNXPpe4nR/rZxFoIHJJ+Tm/BsGk86jhqrOuUebKszfceUfcSrDhyvPOj6OOOirT0VzrwbmtErsGgnAatCiFicIrR0e4rnNTBgRkFoWxxspson1uMXjtb66PbmQCHE/ZGnG9ezGf0fRLYDbwkqPrwCtjp2Lju/B76DSlYUiQIVfwxkBU4MzHEHJrpOfB/Y9/cW0y4KEHkJraB2OlkvrjvOjc7rIiaBVYaehTuMpd48jf1qt6kgGdPyZcEyaQbkxK8OM47HMIalwMI/XHWNEQsn2F0NRhKRyHMhVVRaahrFFnG1RoKhsdEEfdGxLIWErgwtWKn4PJf3LH7fH5c8+NT51xJtNtuqbhrXMpHjXCHUFReGs4C6tlGt1VeYoX+0dFbB9pNNbF2Zg1ZWo8RXBKpTBk4sT4GYr5JAzFTizIk+oPeMpMC4W+eDHQpuMrP1im7XMER4FmH8gvBrDqANc7jL7eesvN8dN778lAw/U33RyH7D8oA3UqQY808HmviDRTLrg1Wl8qfdsCUjR/qE9z2NT5lklLL2Ccf/+KK+PlJXPiPKYhfeWyS1lZeNdYiyO8cuXydOSMAFuXjpKf4rbtth3TiXPk4NU/vh5/eHlE3PbgfQlPNxzNVqyD0adr6+hJoKFdUwIKjJ57GmjQAd/A6D+qBH4sMjzWMb1Ap1ToUI/pWMunDeEnRxvXw8saaGiJmM/iA6PnkhEw2zR2nOkCDXHRORfGgfuzXejOOxIp7oDz7IJBTAkC5s1KWcOncAbkVftenJjBYb/aB/KfPDUCurnmhz9kbvl78ZnzL2DP94/H7hh0GoOrGfGxn8SHMkGca0wb065PkKGuQ6c6lgRQNjG/Y1Md2k2mwgbwshI6noXDOnT4i/H5Sy9JnB3A34EdW8ROOOtdoDFmUeRikGY0lPjQyNKMqsN0B1CchoXOKNxMz9K/Kj1+MUOD+sgS4ZdTJ2aT0TANw7l6GU4Ne2Y268BiuIy+LSGYsmrlUozj1YwKIEuat4l1tGEu+7HPwKCYyWKb75qvy3HysUfHgYP2j149e7CAWGv27p4T191ya3RcWy/OOu1Ugjs7k1bbIergEAujqYMGhqU1Za4GZsUszWv2h7yg82QfKCul45mM5oxm5PsPwwmA3Xl7fP2LlxDkODR6du+Rqd6OXDjqYx9pPOvgS0NODVIueNgXXpN+5C15zGliXIyJGKAvkFX20IO/i8ks+PojnPaDyEBqgRwvsmvAN/2WBiFlKrdtg3LIwJ90Yb9rXCk7POj+XHVfh3I4fHDvPXfHncibMxmx+Noll+SoVxm40BkSP4kPvsvvttu22HnSpvpOveIzbjUoHzvNy5TaJ0kHvvHqb8YblPJtHIszCVgbNE3ZBCBSgzgSRt+TNtVBykRlmc8lvYIL+yX5T9kADGPI4Lnlttvj1wRKPO5jxE6n13UYlrN2hqM0FFHoOOSuTqMrcZfyMDM1aI+y3QCQDqQBEPvXtg1nZPqqr18VI6dMjguOOCo++/nPR3+MfwtdRTn2qf/MQEh9IT8ph3lXXcUHNF8E9TR+bZ/t0QlYvIypPDiNF33tq9GO5778la/EqcefgP7rw7oLGJgYzPWRsU7lky5dRyQDDeDAef4bsB/qY83mFtkY5eopZZHZOS5oPXn69Ljzdw/GT264Ib5wzifjiIMOIdtjb7YZa5drOwhHYeQXuluIxYuy3ikJ4l88lDRmf9el7tXA4m4ArxNc+w1BjDrQ22nQTX9G6nr37g2uydaA3nV81DnSitMolAU6FuJMvEsrKeeB2X4uA3AuCFpdXR0vMpr2g+/9V5z9qU/FMQxw7Ejwqz36VVvGRVktx/ctswjgFTybwVRoSVqR1/I+z2SAhr6eRpbmm6NHx5Po7pFjx8RVX/1KDNi7P3PemRqBHSa8GtDqVB0TaUbHS9xYn/hZnzoJuQbd68yJI2nI7Z/fJYV8BEG7u+6+M/ruvmd8FrvJdGyDV1kGsNj+HAiiHOHToXFrPG5kEEVcqWvlPDNO6rN+T+N2bWMBNpP8dNtNv4k/vDcpvgU/nUUwuRM2zRr0jVJVB8253gY/3h0/Pm5CJl13/z2UVBzE0+OondrF9tu2jNYI3vpkNNQONBhkqOI00GCavzC428RGguzqY/8RdyYwUrE1eMBAgzsALUHPVS9dQ6BhUbw0ATrhXaRaHE7/me4OcrGRl8UzBE37wUffuvrq2G+f/knPS7An5Wl1Uu7OAB3JU+o5ZU7SCO0y0KB8AHHJo37XKTYT9FEybC66/Ips6NX/8R9xJs66Np/2jDKmPGyVtoEyTie75H/7x0OHu5Q70pHrCcyonhE34tRd86tfxjG9yMb43GcyY6Jdm7a504996sLQyipluPRteaCQdhTTDbyXdA8+vW7dCgudUqfG/YzsqUeQw5867tg4i0DGQJxSA1xLGXyS7j0MJiUfQTviy5Fp5bN1SePKUTOVDIZnsJry3a1pyEsvxZc+9x9x4ID94/hjj42PEQzo1asnbaf3KVuYC3wXsGpXqC/K69ZVymFlnIf4U4dMh18fe/65uO2318cll17OoN5+GWhwirXvaedadilzAKlG1guzslF7NeuqtFPZY1vmL5jPDhUElAmYLUE2XID/tBdOdafOnQkmNN9sK4FQ3xcm4RRv9kmxeK/87EL9+CPIToMQDdnlayX+0/CRL8dTg5+P/7ntjvgmtuTHTzgxadXsBLcgVRZ4+Fe/yC2H6cSUtylbuO8nkiinT8gzGQyDPg30vfDSi/HQE4/G8FFvxy0/+lEceeBB0ZadTaTzlL+Ua9vFTwYaqKOcslwGGqRv+1L8KfcMktC4eAX9dOctt8T1ZPUdutdeceVll8W+BJBSz/OcZYoTZYJ8sobvBkyklQykpr1X8Ll9IkPpf9ZnncDl+CGPMTXuyssvj2rufPG8c+MiTrdV1kd2YN1At1ulmsWbOFfeQxtmNZgN/wD6p+MOvTOjoX2n/5/RIIaLQ2qiI1yRVcTxJ4WZRrDKzOPd0WNiCEpq1rTpmQJ6yMBBqUh8dqMKFSSrKDRE3fv6hWEvxmUIvZm8+wVG1s/4xBnsDb5zGicb6KBWTVBwrLC/VoMQJtDp12jQISiFoMJJBaehK1wGMVxl1lW07zHQ8J2r45DO3eOTn/00wYAj2RZzO6ZlkCoNo2Y7UqgBE+W4hZNsI8NJhJZdtJWmopych6T7pHB3TvYNGBP3PfhAXH7hxXHk4UdE//5749Q3jaU42Bq4GrMysERdCqN0hpsQ3YLYNTIcabM9Kn5H1ly9df589lp+fVT8glGLXmRgHMdo2h4IkB7dumfaUo2AQuhriKv4FSJGLBV0Mo9lGpDRAKiPUySDOqqzkCjo5KlT4jkDDT/7WVxOSu2hgwZFX5ROewwtA0IuqCcuNACEMw0X8KoQsZ4sV0FDv6fS4VPjWZwZxHj99dcJNDyTGQ2fxphQ+Olsa1yJZw1MySedQvpBRWG/5pwmlJfUpELL0REe1Blu0rJFjoqMouzBONQ/uIk5TqRKZ6ABHAmHArM04HLUjvZrBIojy7dggxApuBFaAuF1laAL8kyrZDT87p574nkc0p9/69sEGo5h+xnM3fVIDnDh/C1hVzilYcXvVMaViLlOjYpIfNsfOjM6Rg0QhqPffivuvP2O+AkjOl1p4y9vLAIN4lnlLm48xKMKSNhMFc1RBOC1b9NQok0AQD9oOLt6MQvIUddgIuc/uPIb8fLC6jjnmGPjMvp2T3C/ETyo0BNuK6jgfhV0DoGwDzejYTTP9RlGvflWDBk+LH57zx0+GT14tm3jBtGbxSB7shhku2bQ26ZVAcXl6A5IwBGFibAINAABC2e6DDLgDMGX63hGOmxE39YDXlYUhRYx+MDJPBbJepuFIEfNWBhLl22MucDhcfEZ58YBA/aPPXbeKbp1QXFiUK6HVxI3lT4wwKOhK00WAbxC4Rjtl3c1hORflfLLo4pAw/T33okLzr8wTjjxxJyOZGDLjCbxL1+p4HRqU9nRmJxCwfxdlarZB/UIddcB/RArjjxBSb5WY+wMRZZdesXlgh6DOA/o0pY1GtpFJ/C3Dfhw14mGwEJiXo6oKQehphz9yukTdIqjOqplTaY8xBXP5AKSfMtAA4tTzkA2znU0vn2raN+1I8Y2o/MEYdauYRoAtOP2wO7oswJZ/f7chTGaUee5izbEBKMcHCdjrB0Az/fq1Z2dOlpnYPPXt9wSndZWZaCh3y5sldZ5O9KkiP4jp1dVeD4j88gaYRfnOhHyjb/FsX2sc+GnuJw9ezbp0u/FsBEj4lc33xhfvuSyOJbsLKc6SbPKSBfdUx4ow+QDF1rUgM455hVjMwM/8HBpkLVuTeAOXp/EqP0QAtsPI+cnVM+IH/6AQMN+A3J6gDAoV3wnZRjtlvflAflT2tB48b60kgYq+PZ7Y/mJdo/AoXvg/vvjxicfjzOPPyGuNNBAWq1t9VQe6FykbKF8+VSry7I9EieUl9uHUrEBbGVGc6Y6zSGj4SlGL2/61jdjFM9++9LLMtAgboRbI3mjsAsz/9Jwg4ayAZSlQ1C2y2vKYmFPZ4q2j33v3bjp1lvjV2Q1eGSggUC7gfBVwKFjn7CCVx3ZlFUsOiauNWR1XpSTKYMoT30i72nYKfuGvzoyvkGg4ZUpk+LCI4+OT6PH98FAp+OznTkFBt7KkSMML3FiPcJcbo0njXgtAzNc918DZLHrxTz2/LPxqa8UjtH3MBLPOOXjrHXQJzYsd1Se1cjpw01E6NyJSdiUtwZ1HKQQb1VMQaoPXVm+eJGTMtDAtynV0+MOBgh+cuNv45Lzz43DyWjov2e/aN+aRQXRfS5kJrxlsCHxLNzQufLMQxx4eE2azS3ZoAnXIHpjzOj4ze23x4oFC+PjZNztR/bQDjvsiIyqT1YCwUD0s4ER6cdR+uxvvnsoryzb/hQ/1lfqL6f6uEPFixjRP/je9+KcCy8k0HAIWyr3ZRtBRu6hZ6cmeqRzRRt8V4HvIIPledoeD7972N9em4FTNBqn7qkXno/h2B9fBe+OwLqlpAMPBhoQRjk/W12V/AgPG7AXx9aj5HedAGsop7Oos8xoeI/A4MtMobrnnnujF3beZ88/n+1rd0zeNwhT8GCh/yzA39oEpjF7mJLsYrzi3AWM1+HYuFVl8zatk5+exxG9776746lXX49vAfsnTzk1tmO0cJP0V7dCZ9p4yJqJyKXrkEk/v/PWlLYVtRMn77xt7MiK+a3rU/cGbE7aA9chlZH9nEWggWvikLMINEhfPgXMwAZzFQ4kn1XgrS51mqk3j3mBk1ij4dUJy2Iqz7IyRTiv3ilBZuo5wPP044/G7uD8mu9+l8DdvrEJvW6gQZlIw9MuU1Z62GfSR/YjuAJhElCqD+GxX7SHFkMTj5CheNEVBT9963OfjzNOPz3XPVGOK7cSepokbkt696I6Vl7OvhXnyjh+Sl8ZcEIezEK/aAt/53/I0GzeNi7+5n/GUdisLlC+mjb5Tg3tUYa8ZXBXGlIeKpe13zykf1Fbu01mCP3sul/H7we/EBchh884+5MxcMDA1Bdm+ZRyV50tv+dAHO2qg00iDwurcsypNtpJOqZmyCrfXCtnyEvD44ovfSkOP+SwOAa4+/djnYYePRInBueERZwIm3SeNizlFbDCw9SVNizyR3vCPrDNBm7lKQMNtxNo+NJlVxSBBoKObQk0FPpJHQRxUEeO4oNrbV9loXypbanc1eZWzlm2MljdMgtZ4FoB+jn6JOeTIbTnrrtmRoOZJsp05bwwp/6gRfkpjqnPckW2dofwZqAB3deErIWVixbECDL6nF7085tviW8SuDsdm6kLg1LJlwTXcopa9ho8C7zrpB1sJGG0L1zjoPhHNfqOthH7wCDkuxPGxzNDX4h7H3kwRo0dH7f++Cdx1IEHpQwWh+JG36jEvQNXlFzobvrTftHO0+6zTOGXhlqxMKxtcrDzrttui18/9iiBhn7xNeTBxwg4KGdSt2EP639Ii8pbt741+GIgz2eUNQ6QUxjNQSZzulB4PQY8ly9eSKDh6biKQMM0nvjS+efFhQQaduyNfqIv60LKZuIrK4tFfQlUo6+041YA93i2YX6IrOaO7JxxztlnR2sWZs3DBspq/8bHP57RIBLKw+8lUgoZlEgdgyP1PAtkOHXCyJRprDuhBN3/WmGgUyCzGDBYCZG8iNL54teuzDUazj35pDiNEZ2ddtiBVDeYDwXcoiEpSkTSc9sp3ncUPA1IhVOF8Y14phDjt51kxoMZAwuJOj1MFPfqX/w0BrbvHKddcF4cglLujOLR4JMJZRiJWiGUjgUEVzJgjiBRoGWnw0vdKjXrk0HHT54Ut993XzzIHN5LPnEmqWIHxR67sxUbAsy9pSV6Ry4k+jQkQFkyqAIag1pBooBaQ7tkEp00gw0av0b/NVauv/EmFkvqHEcecijz9Xbie5eMRmp8Wr4CVSHqqZBw2oWGhelF6cSDbxWHjGRWg0JLwTp9xoxw1PpnN94Yl15wQRxAlLVXD0Y3iUAb0LEct6MpM1XsdstwBMm2qxzK9PUiu4RdHmi3Uc3pBJlGjx0Tz74wOBbQjvM/+UmCTTvlKKIC0jLFd/YZbdDZSKOXOhTgtkXaMgVURSCDV9GepjC4KbWjGXEZTgT6l2QFnI8hcebJp+KIdimEmAafRhN95kKW0lum5Nm/tF2FKd2UwQb7wHYpqMT99OoZuYDaY6S9vjK7Or7DCMAxOEYaoKZBMfgM3LRdwcN7Knn7wb4znUtcKAA3BxpWMYq5Ih0NDeGx77wTDz7wQNzw+COBOIyf/einsT/GRBFoINhCmcJbpGYjsPiujpd3zGpIOqUe+9EpCUWgYWMG1twJZNiIl+LnX/lmjI7lceKgA+MzF18Uu2PQ6ZZYpsam9GjAQnYxuu8805at2qYQnFE9m/2z340XoY07UAAeHTi34ezTvUV079iuCDSwpGDDOjiadhTtX4vza9FFoIHgE4jKf9K+dSdrwmvyG8K8ClzWb0gQEaU2nzUaXp82N156f3lOoSrGsZk6ccxJyI+PkareN7p07kigoVhNXV5Sqao87VPxL89INxr7BgwM1nnoeMjfypxRTEX60U9/GuOmT43PnHJ6HHn0UQQ1d0lcOnouTUgP9oGBhuxj+d+1GriX5WFL12ekqy6nTs5q2rcMGpgJT414ZWRcc81387lB/D1wu21iF/ipE4q3HchpRkc2sC+pYx3viR+RVs+MBq6V9YEh3i6cGAtjvCyzIMi1yEBDNRkKU1lzYc6CRbGu8zbRka3HGjZhpJRAw/q1BhpIbyfQ0Bhn9oN1pNCzDdeod6pz4cgZFsjhwkUDGfnvzkJkrdsUTu9Nd94VHdbUjdOYv7vHTrtE145d0mhbjHOT6aHAnztFwL/KAOnNERcdJnlKAw4Q6RMUN8adtDZ37rzMcHrlj6/FdffdE1++mJEu0uzNlpBPlQHyuTh3lw/7UKdTPsrRUngN0kmDWdpXHvhOrsFA37u43AhkwZOPPsY+2JPiu//1Axyj/jk9QD5Svhr4K/pUvAIiNKhc1gjX+JF+SmPXsq3H9Eh3WHiV4NQjj/w+7mZU53hk8Bcvvjj1WQZBoUGZCEmQtEjTEQ5J6FmGgCvXrc9ApocLj2mwOs1sHsHkZzGcb/zZj2MS977MtJJTNOTQT8LtVmMb+DSFU7wIl/j1j3SqHrLslGfg2k9x5bOeLmR5+113xW3MBfW4/rvX0O+HREt4zhR6225x4kY5o5Nqhpx8pSyzLI075VwG9JAf6kFluL9H4ohec/W3YvTCuXH6wAPivAsvjH6keotP9WsR8MZIBgfK+IQL2knZy7UcSeW6PKxz4CEuGzZhlJIgj+nGX/rut/L6FZ+6IE4/6ZTo26t3rn4vnGY0AHxlLQVsCpx4eT0DwfBryhmQZPtsg4LUKRbYgDFtVjXpxo/G9ffeHRecekqu0bDnbruThdmaHWsINACX2SHSou+rQ5U59p0DGYkbaFN6FCfiyzVH5AuDAWMI8tyGMz1/0uQ4CZtmv/32jR0ZuZfGdYzEjdP1pJ/CSVF+FjJGukx46VvpUj2uvPcdsyGcVunUiV/88lpGpc+MwwYdED179iQziYUyMXLN5ONVZHGxQruGtHBJi+lw2B7KFG77yjZl8IiXZhEYfBcH3ExT5/R/EefrY3vuFe3ats1FomG+lIVAmLSTMlMa4V35reB72mSfI7eUD8Jh35jNM+59FonGAbgfWbP93v3ifEZgd2SAw0EN6UB8CA9FFrxFuTl6yj0lYmmbmclgqrbSwakTDkLMXTAPu2BEPP7YI/HC62/FZUzlOZWpEwYa6hl0yr4sBmEMfJjRcDMZDTc8XGT8JKHx59hezWMHMugMNDTYwBRBsxfAZwYZaFMxdcJAg/IblqfdBhvKw3Rr761b6/ph2Jj14X3O5fgsC1inYcqCZfHqbKaV8YJvHdG/mPeuXaFj+tiLQ6PfDjvF17/xjVxTRf7JbW91hOgv6THbAj3YnwIn5yu7MujgLxmb69rKBhqWQDdPP/98XPHd7ySYl599bpx8IotNQjfrKVfaqMG98NvesnzeSBlTlk/b5DNteG05bdlpM6vjDgKyP2fqxC48f/FVX4/DkJcdyLLRIU47V3sOOtQPlSZ9L2WaOKRs22bfKwX8VBuXfDD2/XEEhW6KZ14ZEWcSDDiVIMk+LGZpoGMpNqZrhSjPXKDQIF468MgzaVLZYj0G7qRLT21h7TEd2QUEBl8iaPq173w7jhlwAMGGg9OO70rAV8fToKbwSOPSsnwonr1mO8RFqWcKXYDM8Rna4yDTTIIBzyDLbruLDNwLPh37oZ969uqVtnY5SFLixUCkdoCDoJZfEzyVf+VX6rVP5Vf17YzqGZkx+BDrK5lRdSYZPDsjZ1z/Qfy6S5t4LWWsuPFIf4S2WVfiGnkgDZhJ6KK5bZjWsYApL6+99QZBmGFxI/bwJRdcGCceRXYvwSOzmXSkawcaTPrcSOfqTCsvxUu5gKl9Lu9qRxl8dAHXcZMnIuMHs/bG3TF1zpL4xTcYECZLpX2rNtA3I//gQPxJByWd+6mdrp2ZfUkfKxvFScpReMidHxROo5gq+yCLFd9O0HTADjvGpV/4Quy1627ZZ9Jb6iVwo15y2q/2jPJMflFOq3sNGshdKd+0f6g3p3CzlsqzQwbHt77znXCvGKdwn33GJ9iRqme+4zSRBnUYdENEJG1K88g4p+0tJzCe6/s98WT03XevOAv/qC3yhmqKA9j/nY9/XqBBhJRniRTknQ7q6Lffjudw7t8fMzbntxhkcMEZCadMQZIIJWaNGFd+/gGjCh67du4Sn7zgfFbG7pOM5q4TsB0nygHCViirHBV6mSJaERwqewlNBa2EVOj6eyHEMGTY8Lj/uaez/AtPwHFhFG9bIokqPQ1cDSxhk9hyjlKFGeC+bKOMKFGq2HSyqxhVVQdqCDuf8aHfPxJvTBwfh7KX7r6D9s/FIDXkVPjJWLwrriRUGVrjW9TJCV6TIfxtm/I71yzbuUfjJk6In5PCb/LySUex+M3uu+X6EjKvhi4v+UoKPgWbgsLrBmNU8OJC2HOqA4JKxaCR9AFGoCONf4QRHyOl+YT9B8XeGIjbdWErKFP8ASUFEcaWbVBYeygMneMqbtOBAw7bYL0aREao7R/TpccxcvH0o4/EdN77AlMb+tKnjuopIBV6CmTLs/wUIrTfOkVGaXTqaEgrjhaZSWKQR4Vp+uXLLw6LYe+/hwDZmVS3Y9I49z0NQ3FiuxUS4tu6PMSxdaVBw29hFkfiJBUIQsSV2kcMfynuqtDMWUw/OAj8OA3IRUkVlAooD5WgfWe/KvgsQ2FnlFWh4pZuOmg6vTpVKqXx9Omj9z4QI6qnZBlfveDi6Me8MSOo4q5SYMJvG6S/pEHuiSv72ZPKoUNwyDXIIIXfauoe9cbrzJO8Mcvuwt8vfPVKViXvlTC72JT4FBul8lWRSkVVOKZrmNfsAqqTJk+NVzEGX5v4TpZT/unPl15U3Z6CG0ISjfiu2toEOtYwiLYRkqyH7VyHi/KIwQXQxRQDnoOIN3B/BQRRl8+mANG8N5/MKViwahOjZxEvlhVVPgf02YUFpUhh7NEDxdkWxQmO6NMyWGk/ZkANHIh3FY58bFDBIKIN9ZqGmUp/LDT537+5Lks/qFufOIK52S7eKi5U7D6jopFGxLnOUUa8Uc72nbSUQaa6NsqsBNKeqdvAyAIU+hvIvoceKIIz+3Ht0C7tM9DQGTja8XwzPrFdXUayJtAAhyLX6FeuSU30NmVzsjaEh3A0Qq4J0wbORS4ESZDhPQKZU+csjSWU16EnOx0whWPZB4tiPVulNKbpDdklpKod+3PTpxPB+SjKmpElFn9aNm0Qxx17MrvkdGEhNRaDXDA/fn7tddGD26cxHalvT1ayJxvD4KvOMaClghefHipMR9gyPZpIfV5DbhXSDUNLfoBO55JGPg5+HTl8RIycPjk+cfhRsS+GVhsyEnT2lbnyj61NZeRm2AQAAEAASURBVE49/rY+KRMEJJ9ZLr8SHyUPKz+mEdR8kykxd/3hhYTh8nPOjz122SWdOPnTLB5Hi4Sl5HsfBG1ZX/IS35Vhma5dkW8GMgwAvzX67Xj4hltj7MaV0ZctqM499+zo1b17vp8AWRg0IK1YZjkqm7KNdli+7Up5BL1IY8oJAw0auC8MGRK3P/W4pcTJhx4ex5JxZ7qxGTZOHdSQM+CrQa1+k1/lYY05DWdlsbix3HQyuKfsN9A5Bdw8gIH4Mnu+e3z246fHx0jhd2E/5ViBb/BqmbwP4gu882k9iXvK58FEWGlcW7bvvkXQ9Du/ujbLhpXjvEsuTyNXWDQQ5Sv5xt9pYAKXjkk6udCKBljiw76pyG0L03HUGHPe9PUP3pvlHzdg/zgRWawtYRDcbBfXeVInK281OsugojSlFjAvrg5CSHkt7eTWkuBQB7h67px4gmyS50a9yn7vHeNwDOjdMUKV86bZu06R/Sr/STf2n/U4/1gDXtxod6iri7rdkq7YntIMnQmTJ7P+0Q2BPxnHM/VgwAEHxA59+2Y/mk4tDgrap/fsC57TFskv/qF88a7OUu4Ig7Sk7p6D7n6L6Uj3EPw6fNc9UkZ26YLuZiRPOs93gNdP8S+/GFjSpvC38Nt/8pnt8jMzF4HBIMb7E8bHsOeHxpsL58TpLC5nJmVHAoPuvKQsNQijblbmait4TdxLi4JuvfarOsDAg7pR3LuWxjgcxpEEA55+YxRrb3SIM846gwB2j9SPOibKbXlJ2vFIO4qyxE8GqXnGHxkYBF3q7jqsk1MPmplHFqmZlI/ee39uu3r0wP3TaTSoqdGvDSnepW1tz0nTpsXDjCq+NnV81lX+2YMvOzHAyGYn7DoBPdJG0EWAgfq4x+Y+mRatE+Hhh04Wzc7nGrFnZyOWkNnIu2uXV+7zzOKZRXBhAt/f4iwP9fR2HbslnS5dMCvXa/He1Uwz243BpdRf4MTgjpVkhhCyz9/i3SCXtJHrj3Bdh1QZUa6Noe3hgocGjm68/76s9uh++8SxTBHo1mW7rDedLt4VP3aq5ZV0oj5Nu1t+oK+VL9Zre9OeADkzoclHn3gqXnjjj1n+xSeSxTNgv7S1vSA95MARstgXSxkvHcpjlpv0z7PSjzab8lv6EakTpkyOm2++NcYvnhd7NG4VJ30aOc+gXkMDWGT2iZMMwsj7FVmStmWWT7+Dj5Rp/qbMtIVpo+szuNPYKHT3HQSo+lD/oSeeGruSFdC5c6eUhzq8ah9hyiAgn9KpeBG3ykPryqAj8kBh43XbpBx0GvSwl1+JR0a8GMfu0T+znnv17k0mIQuAop/kI2WAeLGv9WDsB+t0wMOgkTLGwwExF2jW7nEdnKlTp+YaSHc883QOCJ15zrk5vdqMP3k9pyVX+EndpPy1HvnXzzLwoO7zsN8N4JotZqBh9HvvMIAyIl5+5904asDAOJigpuuVuXCk09JyHQL4Sfwqi2lATmNTn9qv8rH9WtKu/a4sqAvPuqvekJdejIdfGJx1f+a003MHuXYt2VmD96SXDMwLV0W+iH/ljHSjPpKukukqOLIfXNBf3Th27Dvx8PU3xajVH6APIr75pUtiZ2SwdodyT/tFWSOd21fKx/UMzNh34sWsOH3W7FB7n4El6dGMu8VkZ43ARr6eQUOPgwmannLKSZmZpP7YRDmGRvUZlGEKCafhOMVvJe2qRs4+f8ejccKVF8aZBBo69+xWCAoFzL/58U8NNEBXKRCgIjq66GuJd8J4ItYsMjh+3Di26pkVTUmPbs3ImunSMpMdbGc1ZxsZDYUpREJfZ+XRlXRqZ6KfRre3gQENWqxexujPWpQO0ejGEIMBARlcoefogUavAQMjUblACM9YtkpEIbAUY82VTWdhRDs33NVSFayu3CoD6gTK2DWGHA1x72lHRFR61qfQ8lB4mxbkdlobabP1LEXpT5k+LZw3aRvbkcHRunnLaNHU+b8YI7TVLAMZzvQeR9N1XFzAUcWvIDViWQQAijmwjqKJUxXofAzR96dMyhHybVq3zhVNi1WxcW4YJU+lB/EqMGQwPpLnFBaeOV8POFcCg/NY0zClXA1o003nk/GxCIZpA8ztST1swTQVV5N1+oFK3W1a7AedN43jxAGC1D7MAA39pwFEtfCRgliByIgKSm0ROLHsdTzbnmkDZrc4L1qDRiFhGaWwk7Hth1IRyfQ2RgZVsDQiBUzB5OjFcvrHANICYBf/zdHonYmwiv9GCFKBsZy6fKYAQcg5ki/eVUTiQJgzSADACi1xbRtU1AYy5ixYEItYlElHcluM/o7t2keLxkQ5oQV3Q5F2FJgKPxFue/yuUnBkXYM0jStTTfmnMEzhSB3CPWM2+6Az6qWBsH337rkqtrwhbSd/8Fw6yMBmCq/36oMHp1AUzi/XaFdxopbAl9FsaX4m6djT585KB7A1DkUHjM8WGMA6yM7J1kmHWFLYGrU1OAQyUIir6DeUJ3WtxMFZioLU2Ld+tqyOZuC/PYsLtm/G/toNaDsjPQ3Y4tGUVjMiVq/BANqoEoBfgHsDxorGl7yYfcjUHftgxUpWCUe5sjdKNHM6T1MWqmIBQ9domLbaec/cQTjLh83qNcipU44y6vhpRGvYZ0ANGjayrXFv36mIxXU6l/S3v/1uP9BDaZguZCRRB8MF7lyo1ukw7eCrdOJ4RtyL32Qk6Q98qsxSucMD0tHa9RgdrM+wCStzLWm7azRaGa1ai4xaOn9hLJ46PRag/Dsx/75/x7axI3V0pt/aQZAGV0iEyCBD4ZLLMwYZilMDQ1hBIKfcrShAWXNJntiELF1KfVPA4bgPliN75sXKHbaLHrvuGE1bkc7HwmWO8TVBdpmyqPu+gmyMBWSbzFnOyM1SZOU6pjg1lg+bpCHSqAlGC0GHNbRrPlOqXHOjDYHSlsiCZuDIyPx6ssvMkJB/5fu11GMmjfxi/2oIAWDKRK+JN9ME5eul8NP06mq2ypqLnF4bvXAUu3bqnLJVR1Pcy7c6u64DobzRaJR33avcEYTS0LLvlUUZTKJ/laGupr6IEa2FyBvpwGlm8qtw2p9p0HI9HTVg9JryXRg9NBK9Z53SjP2vjFC/KG+q0R+TqqfHEvRMc+R6j85dMrtJuA3CoASTrzQu7Ct3f5FOdYYNnCrflRGF3lIOkLlBnc0INKxATphCOWH6VIKRa9jKrkfqP3XdEmScgYZG9LnTjdRf0mVmvynPaEfdKugbOCw/8Q6vKqOV1Y0wFhctWcqo0aRMm3aOfcrhFq1yWkmOIEsj8KSp++LdwKiLVCqvHC0zUGggyT5QfqUhjUx2dFp6nLtwAbBPS/3bgtH5Ljh08pMdIX59RhwlX3JNo1O9ap/kHuvUbZtSX/GsTmveg/RXIiNmLpzP6O9c5NCK6Mgo1269+qaOdWqDMli9afaibRY237VO9blGcH2Cda4fU44OaiDSOfAfcp71aqZDk/NY1Mvtcju0bptyuDnyB+QWgyLQhIa09KFjmxk7lK/M0YlT5zkAocyXRnXANLh1HD5AB84H/zoyLpbbA7uja2f2t+c9acPFraU526zhq2Erfnxf3Cm7xLtOl32b+py26lwsVv8hy5ZQTit4tT2GtXpVR0Kek3fUdcpJdZL0bIaJq/83hqcN9lueh/xgf9mnHoso24VhlyL711J3c3RvWwIMbnvdkjnfwinNOC9+JWVKy5bp+yl75SHhhQbFidl9FJ+BNYNjM9ntYwY23we0owXldUcWmC1oGYoRcWNGWtpPvGjmituT2ifylZlB6uscJCCNWRrYCB+sB5FmO06fNRNZM4/7m6JTu23IyurIOl8sEooszKAT+jvXf4G+tU9m4UytRpY3dXCIvly5cln0atUkurfHjqPcehvAoZ3EoUR1uoQUndMjvAzv0Vp0nVcL6U04PKcVKsFzgUjgNushMxpWs5r/MrY//WBtrEBf2ufq1kYs6rB+XTEdy4UqG7DV4LbQpHaTelp5kwtuQtdmPcgDHtKm/SntKR+VD+JG2SVs8q0yTjp1XRKzcMVzF6ZKunCdwaOcjgJfa0OmnQA/SR+Wq7xSdjlYJW1qszRBlkhTDqA49df6lmlPIoOle7chtU87UUcLBpfShqY/pHflvDxqYEMeLmxtZKLyXhoAT8qfAuOFHWx/z8MWHkvAdBn85K483bt2I/i1Lc+zHTQ0k1lMtMtP6VM6tUztSsuSjuRfdbnTJqRV+Up+UkctgBaknzrU7bpw2yHn22I7iRP5OXEpPnlPnjRryEwHt3V1BFw4fDZlAZ+uc6BTL+5ciLMae2AeU7lbQ2fbYhM4RdkUffEnn+baZJRh5rayUdjFvTadekmbUpnTnMwd9aL25graNHPOrNwqcbXvQAOt4alW6BbluHhP2SfvALe/lY3KBm176cDpUMKetjZlys80MqeYLaUv5y5dRL8uBk+rsuyObdpFK8o2y1wZmzsCgo/UtcBLRelMp/1F+cp/OI9v8AL1GuxRDhtMXrBsSUycXR0LV5D9Rr93adEm5bxtaIwPIi5z9yV6UDmmLVejh6Bn4ZQOi4ADtAt9WlXKEu65GcAMfFD53GvbQ+/SjnraPix8JuiRumVxcZ22NngWaqdNqMPlCQ/psI5TJ7BbV5KtNBk5NnHmjFwou2vHzmTb9YqW0IK+2XpstCoEoJlUEiDSHFiVW2Ra05/LoZ2Z2BbHnfHxOO7446Jzt675XAVVWd+/659/PNBQabmMCz8jEIqgAfhL09jucP7gpAnj2dt8QkyfNAmvb200hijttIwOgXeNA4lNI2YpRsQHa3Ba+K2SVHCq3FeRzZC7TmDEa2y5irSjDBotGlgacHWJMJmWLhMatFDYqpwNAkhIazCI3cZIIauw1UA1eOCRkXI6XUGU2RIQm4yQ8zuBVZWhUZAGGWWqMNyO84PVjO7x28X3hMVVyxVujSjbckSMROtohSPqMrp1lPXLKF5T6QtjwoRRqMFoKqDM5XoEpnZlMABmUbgZHGmCUGjI+6a6rYHxNdJS8FFnOrO2hfsKUYWHjlg9lJhlZloQ7VZ4udaEDCpeDAz4TuIFvMMJKRhUqBqgMp5GtwLVPXJt8wIiwK6srUGr0eWiMxoaRgRNRbMfchEdnhWuNBQoWxyYiZIjCgpPhLCKx20uVWwGHlRAiqVCZWvkoTCoFyTmPHGDPOLG/tA4FveuSJ0LEhJFtA+F2X4Tbu6kkrFElYyjQ8XiWwgvBYcnZTl3z6i/W2U1Q5ibQWEdwu56HgYZmhLU0PhfAc2Je0dtPexH8amSNAimwJKOM4AEbDZInsk/PMeDyTsaxhoPOhGOcmm8cQsl0yiVl7SxBpym0w2OpLHlOFbLMDKkEXeLsC8NNBQOCG3ne32EuNsbmdmygMCDW2a1ZAEbHQmVvatYA3QqHhcWVQAuJ6i3mpVy61O32Q1u9SO88lwjhnJaUma7Zg2ibXOckQZw+joCJUydcERB7sfPgj/EJ3hTmdFsgw0Ga+RHjWlxtgEBXUU6aSPSGxrQ2Hr00yKmToxfyJ7QQdkt26IIMQxo4/LFS2MZp3zoHvHSiEa4ij9XGwdvSQtAoJGVyoPvqJz8brBG5aSc0ohsQNvSUKWfpBsXrFuJw66RrrEgrahU0lhBNhgcrIJ/xJlGlQ72Bha5rENT6jZAXtCXm2hvVWMCqaweXgckrGc6g+nSm4a8EH3atYreRP870/52oLMJfVsFTpw2UUgyRzKLIEMVxi4o4zdlZjYDqkk4wd9a8CUe3a5yVd36MZvgwTT6asa4KVH/2ANjl/37R6sO7ZjCYRAEfqLN7lfNY+zZvCmWrGEKw9JV7HKwBN6oj7LtyGfEPIyf5Thc9RqzJkGLJuCexaOox8Cp0fyG9Fk9nqtDFoUyWGUtUeg8mNFgmmQ6n3YYuMigAE8YfFD2KKPtE3mrHFFtiSPehsCR04CU8RpAGtLiXF7T0FJmaOS6aKF6WjmljE/ZSJ9rUHlYtvJPppEmNHibN8VIQZ6q7B1N0mDTeKKYNLDM+LI/NYyKAHARRLU8DQ+dHGWcsk26dYXoNcDqaKz06+r7ymHlO81PQ0RD3XmYXsspHdC/wc40rHlHeaxMtNxiT3nm2GKA64wZvF6Fg7ECGaNs9n3btAZ6qwMsDeFDuCppQ4NUPBTz32k7/ZzONnyeMgX4dbqVRRpmfjfLyfKUhwayHI3W+NPgBIEJfzq9wO8Cmq1wbDx8T8Nco0hYdcSU787/F07xqtyQB5Sf4reQLfAZ7VJua6SV8lxaVtaLQ5/1HfFv4EH94/2UD/S9vC3e6zUniEimznJ0uHK3jQGyuuxNz0KWyn7hSjuCcig5aTcdZ3UYUZJ6nKb3Jqw8k/Oz4WH17EZwV68pRnXKB+CkDzewy5XbaUuPqVMoVJ5MuMQt7ZHODeCLd20OTFv4U7lgwE25Dh1Dr446ukie8moZONM2MFCVRj59nfN4wYSyzcB+EYTBSAeOnL6C3HHtk+xD+MJD+ef78lzqHPpBnjTTK2UUeBLH4lbdIqKVYY5sCoenZaibvC+uvCbdyyNeV8452FHol2K3Julcu0AdWwbXXEtAnLnIaTqk0KDlqRvX4Zw34bp07zpZyovWbdtkHeLK3ZHSeKdcBzRK+0t6kDZAZPKKo7raaRnsoa3iSDtDcNxWXf1SF6Mf44XtI3E4kTNLHSDgPQNtBo2acboo27rlpL8jxwqnQqlLGbTdPe3rN0P2MQK8Bt30weL50a11k+jdaRum7cJ50IshBAETKu0igwx1FKBesc8JY6xXKdDHwr9xLQNka9DREI/6Me1S+GENcn0pAfnqxatjysJVrJ9jJgg2FOmBjWDH9djBykCv1Uubmb4GV572URP62mCSz2hDii37U5ykXQnPqifcxlWnyAXOsy+gO22dHJSDp512oS2hk4klCL1hh0FT2mc0B8d9efJu6krKl96UrcovhEnW5zWdbZ11aUfnt+RFZbF0Ku8o14Xf3/KRgQptHvGkzMysU9qmrZM2PfJgI3ybNivyRh2u/Sfvus5EYdu0Shlj+3PQDprTvtBx12Y1AOYuCtKmtG9d6h+DbdKkqfvyq3aB9rG2t9flqVUETNYhB9Lepk3ylfaig4TqBMtTniqLPf3uKd/bRsvJayKSw+spE2w/ZWjr2of2p2Wro/zuGl62YQVt1OZqjh5Qbolf5WEGfvme25HT3/pQtmEVeFFHGjyRp7UPLU/4LS9lLr+Th/nUNvbUF/OawQf9Ezte/jWzwZ0R3NFnk0EDbikvHVgwo0f565Q0AMvMHkftfV/6Ex7ltjpBHZFre4gHnhc3GdCGbqvg2fr0KetOMwEXOpFHgGMD9LrxgxXYl9i/2KUJD7LUACXEQ5mF32KGiTLFOqQPWiRlVuos+lHc21/CpTzOvoISpMmUKXwaDLI/tRXkKW1hZXHCC35c0NHpE74rEUl7ZrSb8bEee20RgakPWBNLGJJ+6Vd19Vropw6+CE+jv5FZwF3FoJnyVYvPHbzWU56LQu49cN/Yn2y3dgRA/rcc/3Cgwc708BM8ckIkEGOVhMU1hbcR/CWksM3HiF3I/NxNdJLCLB1ACV7C43mj3Rslbl+kDI2uJAqeN9NAAWQE2o6y+4pV91EU1sVpx0tgGc2CwKV9y5X4ZEK/5zMSKV9lfh9RYGZ6GN+5zGMAUGEEGVBwkil5WAaSSFxMRgHi6PxKFJzwOhc8BYxw8qzPyeDC7Slj+dtKLSdP4LL9BQPB0PxLwcR1FYPtKR2zZCDeV6DbJstXUGsoKiSyDbTb8hUwnqXhZsO4nId4Voj46TU/LVsFlc/xnvAIr58u9Gk94kT4fV2BpfFYKnnTmUqDWUbNDAjgN4ppGTJUYWwWjrjGsQJMfJVGUFmnQGlAlNkeRd8UxmgaBcBu+6UV0440rhWqGqgasp4K0xQKfs8+RClowAKTqt+6PMq6pRmViv8S/8LOuwptM2NMa9Y48BkDJ6bpmsKs46Xi1FkBnKTXxK04rJSXSOae5XoWh5hHLUKTtlXhnjTM1Qy6gPt0ICjXI0duUMY+q9J2pNdVt+sDp0JWQ43CoBn6EfwDSAbBHAmxPzQYmmA8WYeG3jIjuhgfjRDom/EujoHH98GSI6pmEjRq6OgUwhYD3fvONcWnRsEwWtuQPYQbg/964JlAA7WmIaXnvXqt/EF/I1A3Qltm/uScPbEsPuxH4KtLFkB9gww4z3AMgQkWMaQ5s1fizNUhSo4zoeZcj0G0miwLDQT7VJqSfsS3zrB8IX41oOzdVPQi347hv58pC3gm20yfG1DQGJF2pHeVvAuGyls6Yjk1AgVaeT2fE4frgXktxrOr1UM5dBDFm5ogP1OWc0OgtmhJem5dnOdJLKY5+Z67ow2jsV1YTbgThnB7ZYavoKDsJ7lfjlQq1SNDwgAEJSXOvSrSDFwJ+2pwtYa661SRfgyu5yF7Z3N/zjsTY9vPfDIGHHtYbNu9M4EGaIXyGytPaP86Ig3EcGIV21suW+0uLCjAjWaJsCoz110HxtFi1HA0IFOlZZuWoJ6RQYKg8rKOs2nC9anPT7OEAB3Qij6VgjQuNIY0uKVv5a4tU5Z72j/KPvtHp0aelr5TVvqbMqwnjR+e8dNK0gCq9K0j0Cnnrcey7VvwbtmOXKUDJk9zXwfb+zkKB9+kcyKNU6a4NJAgLPZxHjaIX8oKy0lZQpu8aluUy1SWNFfIWPjOexX5WASlxIR9q9yBJ/nMR4TJ9vBTWP1i0CTbwG8dVg0UDbM0XIHfmq0ny+NBdacjhNaj7ko8+IwVCF/qOyACRq8qh0pZJL/IN+LSgI51iBf7UZmu0yR82QY+C6eXYL3tpXhl4mr7yDKQu/KcfShv2YfC7unzwmzZyrGsj3fEs/KZFuQ/+avQKcBNGb6XMj4xk2jKcqxX2VGfQICBX51tdWpp4BpotxxltHAV2KoUQttcVA/CzECDUkwa4SMNvHIxSB3UBuhwHQ0NWZ2e1QY0qMfV1A006ADbD7Yj+yTxAu9KZ7RPh8VglHjRFtKgtQ+ke50EHSrfM4CeNAdsyh3r0GaQL+yXDFpZU9kf4Em6U7YbuDaI7OHz8pg4E3faJeJaOaieEP/pEFK2JKj8UH4lnn2Xf9kn4K68Lk9I89J+8lHSfqEXDKSX75SZRupV4ZBmaCryn6AMuLAc69YhEQcGWmxSsVCnTiwZF9IK9dj3HsmPtM0sIr8LYcp54Pa3Z0kvDoSU7Vf+awvoYIHAIqBNnVkz19Jeos+V6/ahO5CsJtCQ9iTOQF14VNikG+ncXSHqsy6Ii0yuWbWcKRNsR9yCzA/X4kH2y1mJUJ6VZ4q1GXyTMiyfQMMG5LLfbdpGAhab1uGAIIsbEACpMuUanPnM6k1VMX8Fgd8V2BFwt85TlfLf0DN9DYHQDzqzOEjoebMvdIykG/FW0IrgFDwlDIkr4LJNtr0MkuqsS6vi1L71EMd+59EC79Ij+Pe39oWlGpSX7sQ9labsEn5q4OQfXyw3A2+8aH32rxkJli0dGGCUJpU5ZZagZafcoG9si3RnHbZNnawtYru0Q8oBGxCXcpKqUwbL8waxvS+9p2NMGdKgAULljfwmrcj31ifPKLfUabZBnpUPnHZqkK4Msll3ymHgtr3yjbhPvcV3ZXjiPXEPLsQDcFiWRynTsg2UnTKDZ21bwavI/NRFBD4IaBg88BnrMfAjzLlwIO/mdpPUJ46zjsR8VpJtEP+8mPIv5TDtsCwzNGxD1inMnPa5uDaAYfnSsPI/+wC5rzwpeN8MLfpdOoL380Q+8kDqFPkHYkvnz90UtImlGGlSeZJyFF1NYalPhUf9Im0V/VroDzCTz9RhAeu6Ou+lnDHLCz41oKEMFi+8mPQkjgubAhxBr+LEe9J7ediv1qM89tAOVj7JH/ahp/jM/qKO9A+A3euCpA2ozBBPyreEm3J8XrpPeYi8Ue5oJ+m7ZqA9YShka6lLDW7SOk76XvxqF4AXZY4wGMQBgOjI1NWevXsREC226waMrFf4/12Pf0qgQUR4EhwuGFCFCJF5TQWg4DCo4HxwDYIkGjpf4pbYJQQJOQ1UnkcSqEHzWmkkUVBBvKC8DoZ/jk5IzJbj+6iHwqlFQGGsKRQkPAWHxJcEB0x++lvYahQw12UsDwmpYEKji4ykJwMq+IGJ7+X9os6CuEzR0+lVeJWMZGkytmVZhsRWGlgqyHwf2NIAFAfCwPNWIGz5SZl+SsQKkSR+4HdkSCbmMjfFrQpZ3vdhhb5w8s/fHPm38t3RAwWHDKKA9mZZn3jx8Ld1yUyZXiVcHsKZ/3hG2DnFoW0oAxU1eBEu/ikYvKYxb8mlgrNs3998FO0RhKIO4ecNyk6AeFDoPL2U9fpDXNqnCiBuqKB8PgWdT3NN/NsPCjiVnXhSgUl7Oaecvnc0yrqkIftCg18AMnIqrqVHqtOotwJh1DhXsCZ9cz2NuFr0lTiiTMEoBSQvJ+xGzr2m0itogN+8Kw2IG0/h9Jksn3IUSvmbd21n0a5Kv/OsozbKfZ0ATyFVHFq+MKTBhoFTTBtBsMFropDm8em/yidlyYYp/GBM18NwYZx18J04SkfNdzDC6qNEGjFhFXcJoiF1cxOf0BaTZKEXAxZ8IlA30C7p2GBD8jr4yjZSXkNgauDk1XWOEFMxgh0POpZjgBl6cJuwXCmad8S5cNqXvi+f2y77SnoTEv/b5uRvrtufyZuVe0lYPOl/6cOjMNRtN2XYNzzLq+kAyLfpKAGXBpfOQ640TDs3ARCUTMSf9xI4ZQAR6rW0A1Rs07BpVKEs3xo2PIb96Cex6c1R0Y65d515tz2OTSNkhz1lMNG+sk7TcqFOZE6lf4QbiLJtfPPT58yCWAm8i+j02WQzzEWBLZgwLXb5zy/FkWecHC379KA98DtGa31eKOgbOczWnBvrkVkQBFL4Xo9+argJmUIHSd9maaxcTzCH7mvINBZjRavoa+lJeVAfbWoCC15aynNlUY6i08f2gM8ZeFACAJ6gA6+OKneBuZB1XKQd0qBGpzhOwxIcS48WIn3ziq/nIR8InwGMfAhYRE3KAvpM/iiMSN+gbq4pY+SZ5BvKy/q5CwazTIuymvzjDwAut1ZTRihrLasAhb7mi3pKGq5xkHxfuDypUxpKGZNtAafIFNsh3wiH36VPgyC+mkFwyivLlaazfcBjfcp+boMfaJ1r6VxTlzjzKIPoliX+hS1xxLP5nevqYetUFic++PRf0rM6mbI1yqV7G0v3JZxlNphtVcZbLpIjRxczgwj8GIi1f/MF/npYT+qCbKf1ljgvcGl7sg4f5pnyrs+JFHHodfFoOWA1610HTxhwSHiAua5BQPHN49kinrXNygb7OvtIfPodmtL28PnsJyvinoahBqKnGDV5KO0FQeBZR+2lCy4n/ftdGeohTuRdR9Oka0cXc4CA+gwgGZS2P4tsD52ZIsgirWZTKcvySh1lewVLetJm8Ewnmb5JHcTtwhZQ9umwqF8FmHeEl381uLcMTpHvtfLwmrSaMPB+On84UumwYRP4ZOoMXxAW3+W/Xz2t0y+OoBsg0XmTtrMGrtsP8rUOn3Sn02Cwzz4s/vF6PmzRwmYbKDgrkS8K+BL31qfeS/riPfBaOipF24trWS74sO+kUQORq8G1v7V11KtWIX17mg3rKHU9HjDY7H0D/CXfbEQXmylJbfSNI5HrkZHoO34bLLCNFaAplUYDV578okZoFJ3HSSf5JPRJvyOL6xCkqMP3jcjTbB+Zgiw0FSs2kZlI8HcjQXn5cyPbEasPqyDs3DIwcQStJkNbF4UCr3aaPKoOVEdJP+IvM2q4nms1qKd4PHEEMPmqOAbmxK00JJT8TplCucpi+0Rc8zefk2YMGvqeATXrLMDgvVKWgTNfzPr8ytsp74Hfa/KlcCdfF7VmvdYtDOoSH7Su7Ct1inApVylXOkjaKR7LuizfOjO4Z0CA7+k0cz3/8b641maSn7yftM9n8gWflun7tsN7KesAxLo9LMf/wpb0KbyULzz+9swyuZYDQfSL+kM4Eu/UX5Zl/xZ0b4GKoKIMp11oczlIJ24LW1Y7QP1lXfatNirZIZRaBI8KnatMMPhoO8zSVW8lniptSBjBoTUmnRQtyikyOa2Y3wZBtZulKdurLqVSuyvLEv/JE/CUtpy4UG45lVKZCvKKQIO/87bcU3mWNirH1De5Zhxlm1klHlJWgHsDhZsIYtRpKF0VfJ6DGhRN5+SAatIruLGvhNNAT206zIp53E9f8yg/vVZAxjVpwj6k/vK6OMrALOUa1DHLhKYm7SjnQH7KOWEo+pKSaZODTQ5IwtW5QL3yRtznM1YObqRlAw3iSj0kUEnf2Lb2ZQY7gKUO75odYeaQAxQUk0f5Wfn5b/cBnYDtf+AoXxZ3OjkSQBoBYEZbdCOdYKqi5trfe1jHX4VomwIxwPG1qioh3HoJwi0xbf348+9u/Z2/46pwS/B/4UAMAqvk+Zef/eiiYDCUXB2U0p9rue+nkv0z2PEJI+6IWVCOwvyTQ/zVhvXPY/tPXv9XXECYJUwIvjwQKon7kmayL7xTG+7i0f+7f7fE3UfV/tc999c99VF1FNctw/PD/GKfeuJMsUYDEoAHeIKRGuO3xSk/fvgtS0JVcFUDw3v0yyaMKwxTBAi/pU/T4VHCPGFv/Cnd/zNaRcF/6RAmFHB5bOaf2jSSqrEG0nTLea++eCDQ8P7QofH8T38eS18dFc3aNotOKPVtUCYNkI/FCuaWJS40sHxPA4PWc+YdmppGpnCAH40TMm7jAxTwfM657DqxEOPig/GTYt9rvh7Hn3161O3eizJNsa7QfJakWtPoJkU8z83t4sLmgyZvQCMaKvVtx0/Ftj1jr9QcW/JPzY2/7QthTcounIG/7c1/0dNb9PlfX4tY8rDXKkfqJa5jlPxzj63U9TdXoJSXD4XN3t3KgWNU0H9t+IsFhTWmGZvZykv/uktqHOmyFob/9spEnWftJv3tpWx+AwcyAdK5rHUQ5uOyo1jFdWFPA/OvgV4axJ5Ke6aW/KlV/D/w1caXGCR4gx43GPC3IYQytLdSXm8FlIqTijewlZv/+kvi2lbifte0tHatOgjS0V9jexXvmV1IP9cc4s/TWsrT3yUv2e4Sx0pRT96Xn7DBdMyI7vC4srhYUJwv+QZ3KJEAd/76C/y1NVm1VVvY0v/BwwEB+7xqa/be1soWLx4lHopf/7q/Yu7PMLW48vin81NRbPl3E7RVB7/nbzp4R2e3Ltkrmw/xtwXueC6PLcrXnvJpQk7F/S3+bqWkfGIj2aQZ4GQK5v+VQ5tBXfgnfbAZQnvJ80Mt+RM6/wt9/Xc0ZgPBRINtDdwV5kN4B7b0DbZOW5t8D4irHGD70Ht/BxC8UrS/CFDY+1tQwN9X6P/Dt/5pgQZJpDxtj4jxt0eJqPKzuFr560M1WCzf8F4lKlqrlA918YcfrVTuRU6t8xoIvFZTQU25fOHJspAPuzDl094vvpdXfOujj7I0S97qexKqRxlUKH+XIJbXi6e2+KtBWDwotJsh2lzrFi98xE+eT4axhPKsXYbXbHlxraipuPanBRZl+WRGBmsEfFle3qm8VpRYG/I/La+88lH1lfdrf/qsEJfHR+A+b/OUkVcPnWHxUPZBBh4q9+2HrfSFb5Y08eF2VMrMggt48mv+Ke/Vvl7iwge8Xj6z+bdXijfK9zZfqXm+hN0naxezBey+WdZQlmZNxdXizofbs/muBZfvKPw8avgwXy1EYhpRGlAqwazfp1C0qQz5vgVMllPgsoSM9wwClbSpEuJdnWKfEIoSDt/NI9vP1a2UXXmg8mDxZllT5WLNx9b7tLzNWyVMQsD3/EedUpplGmjDVOd74UToLuu45eJgLFy7gV07RrPC9PC77on5774bVcy77cIaJts43YfRiSqMzpwG4+iBQUCMObPoWAssRzOExCwKA7n1MfBcANJF2ci2ZSSM7S2h6UWMSi9nNHLF2PcINFyVCwrV7d2dOIPBH/CKQ1g4BOJCA8hRA81YKsl28VEePMIb2SLbaA8XJkw+idPEiBHXcoyqNj+V73/Ep7gSdx61e7O4Zk1b7eV8vvjDu75edGetL0UJxeWam9wv6irerX29uPKRf5OueDfpasv3ijKLvx9uRU15+T7vlXSpYe6xZXk+Z/Hlcx+CN1/wD1e3rM3fnMXlWu/n41v9Uzxa80I+Y9WW7b8EI2lhi9eFMeEs2+OTHlA8fZ+jen/ynvWUz+XDf8efD8NaFiCs6sGSVopatlZf+b5vbgGLt7KPKrdq8O+zlu8bxTub36xdno9VfpfvbsWBKWG1VOVBVpuwby7fe5vr8FetY6u4L+EoPz/y7VoFbe2r75fvAil1FTrca55l+Vt7d/N7NbRRU1bl+RJ2f4qjxFNZZvm+N2vD4e/yKJ/1d+3nK/drv2ZdWzzibemkeMybngWd+z0lt232etmHlaLLjwrX+gSnv9RPJS/XBoBbwlBzWKZytSJb87rvcfp+4sbfvKN+rATWHUb68KE+qcCYNwpIPvwMZWTdW7Qj6+HJGtz7Vm2YS3gts/axxfUsm2sljlKW8TvtpdrvbvFe1lXUaOklP9W+UrvWP72+RXn+zOpq1/nhEizDf0VdW3uOQkp9lXjx/drPlXXWvl5Tca3KyufKd8vf5Xtb8pPXt3ymVnHlV/osa0tcWza/xH+J+5rnKmVtcV0rxKPQ6eXDmz8LbvD+5t74P+y9CYBdRZX/f3rv7BCWQCAkYd9JIOwIDIIso6iMCC64zjg6M46OzvhzEJVxHWdfHPU/jrM4Co4gigi4sMq+BwgJCQQIBBLIQkhI0p3e/udz6p736t1+nQTo0J3OqeTe2k6dqvt9/epWfd+pKsNM67An8Ym/feYoItWfMddTDROqkfCyRRMrmd5W8rlMdf73ToH06dEab2v6BiFclLMKixpdT6USMl+tS3UYhvo3wg856fuLvqJumshzFNWTk1z6vK31hmG1/eQn8RzzSrFKrtWBpP5PSCQZyqbyRZmt1HvNRAPPDTDuACWPezp+DWD2B+kpWsKCHu+vo5qTaSxXVFcoky+C6Y94IOGktP+fRVaZBysqUqBfcg0SFeHqw2VJ1jRToDf/UlaaTkZVONWT7tV2pnilyICBqp5qQ5JwrqGql7w8J1ec6yrLeZ6XLcddD+ku42mb41f1pdK1LU4aynq9TFFlFrUm5HFTMHB5cvqJ9yuTS+S6yumvNE5FXkb1uurS340nW7Pq3ooXTN08Er2OAQU0g/qpqXxRNr0mUmmND6iuKFvRQwnkvXxeEFl3np6nlfNSPEnUl6tqqYeH56KH8imeJuJpMJte8On1CMnANN5+m+fXSLU26Fai4f477pTr/veH8vSDD0n3yhdlsm5+uJNuPNSkm1u16jVGLWra9PPrVWKAbSGbdMkCS8A48YH1+PQJZp6oyxV61G5xvZodvqy/hq3XXwvXaH2rdB+IDbopUecTC+SUv7pYzj7vXGndezr2zandLAVyqx37XGhlhm8GjZn9aS7DlirhkJ5ek+xXBn5p4Bmr6GQKEDKX51ax89yqxoH0VMvn2FfLEyrrzdtRLp+XJC+X9by8DGllmdp8G1xkKLiWip83z1Rl+vz7an/3WsLjlcIE+imoyd2cSFZjP/H0NANh4eJZfqU5tThUccrTN1az6x7Iz/SAj0VTWlUr37lMruazGiC9UriUb9FqWhJL95Tqea6AuId5hnJ+noaky9bKeQzp/i7P9fIuRV45zfM2x8/Lb6yesq5cNs/TtuTNKf8t22foZV3Q4+ipl5bprylfpFuRolyuqqLNE/ujX/t3k9WTq86SE6VMXd7OLLOcZNVy8/qRVaHK33FewGUSpeA51dKektVXE/TyVFHIlrGvtBlZ15eVq6Sh2NPLcgPFKfN6uHrt8jZ5/S7j8U3lq1zlM6FMXp6yG4sj7/pzOdJzV9bjeV7W4/i5nrychl3cRFzOE2vLVv/ac91JJpVMEinsusgv6av8PdXqqUq5BlIyPVWBVLCSVZIjt1JHRUhbUSuXvnsII5Mr9zJ5GnKb48q6vIzrJO56S2mejEglywMbQ58Cmavo0bJevMj2rFJyVnjrCg4K0bB1PXK0NhAIBAYXAe8WXSvxchdJvF46ZcrlSXNX1uPpQ+fTWibg/Mpvax/1ZWkbO+kzJx5cXzasxVYTvAZMY3Un7ntu/q1c8s//IvOuv96IAfbx30HtAnQPa70wnK01E0RPn25Gtl5PrWBSv4teGPOt1GuVXqDCcgY9nEtW627SS9a+aChiaXDeRZ+Tc84/Tybsu4/GlC7AwssYeiwjsLxIa1jNtBNWoew0iWeEaEhX+pXXPz3a5mRKndJlbREPBAKBQGBIEfA3zGvrr1zL5j5KXtumyiZZl8pLbm5tIRcIBAKBwHBEIIiG4fipRJsCga0MgXyARPjVDJRcB4/+asq/XpDRTpuAK8FgG4jptJzNmthIydh3TCCVaGhUS4SGVj3+ccN6uf36G+Rfv/JlWXjHXdK7x2SZv+g5OUCPSzvhmGNkz913k7F2xJVSCro3A0eOLZg3X+679np5Tuvaa5LIUUedKFOmTpO2HXey4zPXrVgtLzy7VBYufFIeue9+YYHEmB22lzUrXpSPXfSXct573i2T9t9PU6ufhm0saBtCKdGgyy8qJpaI5OBr1D6A4kOwEyVKImTxq+Bw/px4jHCBQCAQCAQCgUAgEAgEAkODQBANQ4N71BoIBAJbMwL5xBzCwS4lGFgb68QDlgRsBtm9Xq7/zXXyN1/4nGxYu0p2nDxV5t1xq+w36xh5+9veKjMOOUgmbjfBjgNjzfuyFcvluht/K1/94jeMQDjjlH3kiBPfKIfNmCn773egnQX/wlNL5LGHHpU77rpHfvPDH8nO++0p7RPGy8N3z5YPXnyhvOcDF8huUyEa2EI27SCRqAG27mPFuLaNZ2D7aCca8mfyz4ajAWAT/CKdMK6fiW5KjnsgEAgEAoFAIBAIBAKBQCAQREP8DQQCgUAg8EoQsAm63pRcsH0kWEug1gvpCMG0BRmEAfPwJt3JeX3HGrnuuuvl29/6Vzlo/wNkt8mT5eGHH5aONavlLWedIbMOnylT9pgibePG6lnrnfLM4mfkqmt/Jd/68X/LW9/8e7LnnnvImtUrZcquu8opJ/2OTJyws6xc9LTMu3eO3PzbW2X2ww/J0Se9QdrVKuLGG34jBx9/grz/gx+Q6bvtq8sr+Af5AT8AvaDHVullu/Y7yZC4kWSmgSDP587PIDSigefVDCNQ1OcBiYcLBAKBQCAQCAQCgUAgEAgESggE0VACJKKBQCAQCGwUATgG3X+BM7k5b1sPnC7EmaGzGZAeTKabOHZzgoTujbBm9Wq5UY+3vPqaq+S0N54qeyipcJ8ud7j3njvk0IMPllmzjpBDZ8yQHXbcQZatXCbzH1sg11z7S3nqhaXy0T/+I9l1t13kpz+9QpdJPCof/9gnZO/d9pRlSkY89sAcuf2W22RtR6ec9bazZZTu1XDllVfa3g7v/9CHZO/J+6hFhJ51r+1JR0c22DF2zUowtHNeO5ttQjL4lZqfnoWwkwyklIkGyJUgGkAmXCAQCAQCgUAgEAgEAoFAHQSCaKgDSiQFAoFAIDAgAmrJ0KcnSvTqhYUA+zP0FZPuBs6H1rRuPeqzS8kIjklas2aN3H7bbXLD9dfJYYcdKpN2mSQPPjBbHnv0UTnyyKPkcLVo2O+A/WU7PYli9drVsvCJJ+UXV/9C7p0/Wz78B3+gezPsIffdeZ+sW7Neznjz6bLbjrvKiheek4UPz5PbfnuLLFu2TE4760wZPW6MXHfDDdI6YYK88/zzZfrkvXSzyA1GfLToxpMsmOjt7ZYmJRra9LQKO0M+JxncsqHmwZVxcKuFxiLMswbRUINSRAKBQCAQCAQCgUAgEAgEahEIoqEWj4gFAoFAILAJBPi5X53ux9DVpUsT9OLwhkY9PrKlpUWXS3BORLJsYEeETt0M8u6775Zrfn6VvPTSKrUq6JEFc+fIPtP3lXe841w5YuZM2X7iRGkcpRtHtjTL2heWyU9+9lN5/x/+oex//F7yjjPPl+NmHScH6LKLXSZP0jqa5cXly+Sx+Y/qUokb5KdX/kD2PfAImaA6nleLiGNOOFnOevPvytTJ05Vm6Na2NOjZFC26ZIIDKXXjSr3b4ZY8hpML7muSiiRnBEMRIWz7NWjAwsSLcCEeXiAQCAQCgUAgEAgEAoFAIOAIBNHgSIQfCAQCgcBmIZAm3xz6aESDWi4w6cZ6gaUUTY1M45VoUMuH5oZm2yDy6acWyfx5j8oLS5+Xl19aY5P9XSdNkoMPPEim7bGHNLTp4ZVKVEiLTt7VcuFB3cPhXl1e8eLyVbLrlMly7InHybS9piuZoVYJuiSjo2OdLFv+vDy+cKEsXPSUdCrZ0dTWKmPUqmH3adNk73331b0cdrSNINO+DLYrg7arVxqVFWnRZRMYKBipAMmA83iKJUKBxAqxUGQQx0E0hAsEAoFAIBAIBAKBQCAQCATqIBBEQx1QIikQCAQCgYEQMFsFtWbo1Q0g0yRcJ9zZpLuvV/doUPKhT/NbW1p1C4dm6d7QJRs6OqRjXYd0d26QZiUj2jSvrVnzW1uVYNALp/stbNB8LCRYjsHxmQ26B0T7qHY9wKJRuru6paevS5qbGzW/R8U7pLO7y/Zh6FMSAqsKCIeWljYlM7CsSA5KQFssXaq7SbmDUa3t0qLESD+iwQvgO4+QnzyR51cEahIjEggEAoFAIBAIBAKBQCAQCKgxLD+7hQsEAoFAIBDYLAQgGrBk6O7plmZdxtDS1KJTbhYlpMk8R132GNGgmzAqodCsVg6NGRGhrEDa44DaWNmARQF7HqiC7vV6RgQExbg23WRS0zLXp+U2dKmwBtpa9dhM34MykyFIOzYYrcAyiSQEycCeErSLpRPtSkQ0c3oEwlxlB8lQZHOOhhs9IEZW2omCpw4XCAQCgUAgEAgEAoFAIBAI9EcgiIb+mERKIBAIBAIbQaBPj6zUbRY3dEprW5u067IHJxogIdwR6u1Jlg/4uJZm3cOBDSPhC1SAub7N2snUOGKUa4Af0Dw1aFALBr3IV9ekac1cCGlGb2enHavZ3K7EhFoz9Cj50a3WFj2qnI0pm40EUQJDT8GAU4b4aFLSIxEFqgM9uaNOjZNc9WuJBprGBWGBnnCBQCAQCAQCgUAgEAgEAoFAGYEgGsqIRDwQCAQCgU0g0Llhg1k1sPljqy59SMspdHKvk3j/nd+4AGUKsFCoEA0qr6sebBLPygvIBuRYxeBGDz7Jh1zwMJN+5QDMyEFtGZKxQ5fmazvYlLKB5RfKWrBko0fjKGxQVqKBIyxVMSQD5dlHgnoIc+WOuvL6qLN6kVNtQ7OWLriQfnpMMG6BQCAQCAQCgUAgEAgEAts0AkE0bNMffzx8IBAIvBoEWB4BSdCgk3sm7Jwk0aPMQRN7JOhknnzbX4FNF3WujyzOyQTKYK3Q0ckSjB4lK9p0X4U09UcUksHJBQ0aAYCPBCsqWhBCwNgIIkZ1aBvUckErYUkHfpfu30DdECLU3dOd6Av2jYCDMMWpWlPnap1gIJ5KEEr1QzBANNAOihbFNRQuEAgEAoFAIBAIBAKBQCAQSAgMCtHAr2X2i5mOZPlFz93q1avtDPkxY8bIBD3b3fMGkvdy+MjgKONhSyhurouobcqmPml5eiHaz3N9LutxBD2tX6GNJLyS8rmsq3w1dXrZ8AOBQOD1RSD1TNU6bTKurAGbPzayNEEn8MlaQfdI0D4JoqHGUUC7Sbq4DRvUAkGJhmYsHXSzR3eQECamcvSohN0RZ0PHSiJhZQboW3rNmkGtDTCbUNelxAL1tLRwtCVEAxSCLqHghAsScqfxlJt8Jxs8DVGrW33IBq5qizUSLhAIBAKBQCAQCAQCgUAgECgQGBSigYk+g2U73o0BrDoGvXPmzJH7779fDjnkEDn00ENtUzTy3JSYo9ooU3aU5con4J6GLOl+Ebdz7FXefk0s6ie97NCBc9/rpj1eX663XL5e3NuF32hmyql9m5L1fOrzdnha+IFAIDA8EUg9CFP1dJVbyUTc5u+FoIUR8oLlAq6oUrAsUMQriurn91Pv8pvbDtQWZTjxwskF9/Nm0mNz0dN7NRoMFwgEAoFAIBAIBAKBQCAQCFQQGDSiwSbaShowcWad8EsvvSS33HKLXHHFFXLmmWfKSSedJBMnTkzrmXVSDjFRQwwwkvWBrub7xN9bStwddbgj3YkC9PmknXQvk8tbOVeV1Ud6PzmvZGO+6tKaTMLL43vdXtTj7ns6st5mTws/EAgEhicC3nXgMwl3R1fivZKFc0EX8jSP43shwpU+riyocZJc1vdd0DjJeRZqXKwmjJA7D7tPermQxsskg9eDKEQDfl5Mo+ECgUAgEAgEAoFAIBAIBAIBQ2BQiIYyli+++KI89dRTRjT8+Mc/ltNPP11OPvlk2WuvvWSnnXayiTXkQA0xgK2wjlp9sl7WWS9uBIOWs3XSOmH3skzmuSAzcEzkfTLvMozOTU4DpFXSVd7Lu29K9JbL5Gn1yuf60YPLy3uZPM11hh8IBALDE4H0Ta5Owr2V+aS7IXVliQFwAS/ocfPzRMLluCdl6dpX2eYKSjb0arBMdqCWtuAqvhd3n8w8XImgmwz85BHzS4PmiqxKuEgOLxAIBAKBQCAQCAQCgUAgEKggMChEAxN6rBjYfZ2JMyTDPffcIzfeeKN8+9vflnPOOUdOO+00OfHEE2W//fYzgsFJAFriE/GcELAW6gjXrQWIlyflbGzGmuScsGBUTBlICC4vh26f3Fui3jzfSYg8fSCSoaYNjMB11F0un5f1Z0N3XrbcFq87/EAgEBi+CPCVx+GXJ/k2AdcMfBPAx3mhFEsJFaEi0yb4hbAlFek1Vg5Wg1ag9gS6F0S31oQUV5GDgkq4klaoqrTD4p5oRWpv1ja9mV9kaTgvkWfVFo5YIBAIBAKBQCAQCAQCgUAgoENJnQjn48dXhcn69etl3bp1Mnr0aCMbHnnkEbnmmmuMaLj11ltl++23N7LhggsukCOPPLJSB3srQFDgmKz7UgqfkJvFQmYNUJ6cU5bm50QDcX8k971CJxs87kRDrtfLl/O8Te67jnp+uV5kNqdcPV2RFggEAsMHAe8s8T1M65h4VywZSPBM90mz2XlR0sM5wdCvkMp6ee+msWjQ3RH6Gpr1NIiGCtlh9VOHOg+7X9VBbqaT6Mac1VUIoCx35XieF+FAIBAIBAKBQCAQCAQCgW0egUEhGjboWe4dHR0GJuTBvffeK9///vflkksusQ0gIQROPfVUufDCC+UNb3iDpWHRwGSeyyf6EAE5GeCTfhS7TP6JURYZz8sn+PnEPicN0O8uTyeNMnmdLuf6PV72y/WW42V5jyOXt9PTww8EAoHhi0BBFVTm77TUJvWaYfPvCjlQegbPNHIB4SSYrLZqCyVbBcoX6UY0WC1arkkJhma9+ls0FBIVssFa4CSFqXJ9pbZVotbIIqbhPOoynua+p4cfCAQCgUAgEAgEAoFAIBAIFAgMCtGALibNHGf53HPPyR133CH//M//LA899JBZOWDtcNyxx8mFn0tEQ3t7u+2fwNnuWCP4BP/VTLqpN79oixMW3i7Ptzw1O2ZPhzzPInrz+nXHhvoDbBfMfR2325Fyefn8l8BcthTOSZZSVkQDgUBgmCLAVN0vb6JtjljM4ftlupBbL+AXZEMqkqiGXCsUQnUer1Ku2/omtWhQqwY956eSjGx+UaWVr5AMFQXemo34XrP7JVFPdr+UHdFAIBAIBAKBQCAQCAQCgUBg0IgGoFy5cqVGnCDhAABAAElEQVQ8+eSTcustt8on/+yThi77NmDxMH36dLNoOP7442XSpEkyduxYPdu9pTK5948CUiB3PvknzS0YPN+tHyiT5zl54XJ9umFkTgagkysnIJD1utz38vjeLvx6+blsvXwv73LEnWho0vXWaVbgueEHAoHAcEWAHsov2sh82648sbYbQ0yFikQjGkiAYKiSDOSmuXsiGapWDYhqrhVXCbVoSFYNLp3KWRuSWu7qKODlrLCl2i0V1WAlUOR53P0i2YvnyXm4EAsvEAgEAoFAIBAIBAKBQCAQAIFBIxqYNK9YsUIWLVokt912m3zjG9+QJUuWVCb07N/wp3/6p3LcccfJYYcdJnvssYd9Aj7ZZnLuk2+flOeWCQizBIMlF+STN9DeDE5AWAV6Q951kkZdXp/LeHoe97CXp625HPXkcYvoLa/L0/BdT+77c9QjJ/KyEQ4EAoGhR8Dn2/geplWbZdGAYEYyMMevEg0p7HP3GosGJxmsQpVgM0hq1H6swhPkjbEwN0/MfK+AgnmYtplLibk9haW4iloxj4UfCAQCgUAgEAgEAoFAIBAI1CAwaEQDWhcuXCh33XWX3H777XLppZeahUNe2ymnnCInnHCCnH322TLjsBnS1Kzmv0oc+CSbibxP0kljEu6TefT4vg7IkJ8TDeT7BJ5w7pDln/926PUhU6mvZuBdLe06XY4cL296GeyXnBMSnuzyxE2PDtptguHPERYNDlX4gcCwRsDn2/gepsGbTzSocMWyoegHSrroUSpEg1diZIP3NepDNnhU5c25rLUsb2GR4fLuWy1e1BPxPZzyPGY+qjwhZcc9EAgEAoFAIBAIBAKBQCAQ6IfAoBINHGn5zW9+0zaCbG5uNgsEiIJ84n300UfLF7/4RdsUEisHn8AbyaBLHPjnBAN+Pkmn9S5PuJzn+ejyOpFxfeS/Eud1uY+uenXmOpH1i3Qvs6lyuY4IBwKBwPBEwOfy+TSeeXe6NNUzckFyfXJeFba+riruAlVdLtpPp+urFklgQUaYc61ENGxyRV6lDFQGuSnBfa+dki7qxT1Onrl+CZ4RfiAQCAQCgUAgEAgEAoHAto7AoBINbAL5vve9Tx5//PHK5J4Jv5MOPvm//PLLBeuG8ePHVybuPjnHd2IAKwT+k4YbaLLuZSmHc6KBvRkon1s+kIdlBC5PtwS9eV0ez/1y/S6bp5Pmz0lZ8rxduS7C9cqXZSIeCAQCwweBYrquE3T+JefWBzbvJtEui6WwBfVWJFmfpmEWYlXEC13umYWERijSWBYisbjISpSBBmqIBo17C92CgqTCJZVeEmVIJ6XW72q8Ug3COPeTeBJIOXEPBAKBQCAQCAQCgUAgEAgEahAYVKLhzjvvlI9//ON2vGVbW5tw1KVPpiEbiO+662Q9keKf5KSTTpLtt9/eNoSsaVEaOVuST9pdB8RAeVLvyykoQB0+qacMefhOKDjJ0NnZaemjRo2yMl6/1+M+deX1uRw+Mk4oUCdyXs595Orp8LKUJ7/8XJQLFwgEAsMTgUQyONHg0/Ni3t2ns/A0iy8an83KKzP3RDJANORkQ/60iDrZgF8hGxCiCi7Cldm/t8PZgLKfZKupTo+QXiUcEsngd82hQLUQwulBaSDO/RSLeyAQCAQCgUAgEAgEAoFAIGAIDArR4Mda3nrrrXLRRRfJ888/b8qZQLv1QI73xRdfLJw+ccABB8jkyZMrk3Sf1DMRh5SAEOByHRAJXD6p5zQL8pjoQ2xwkgV1uvNynuY6169fbyLjxo0Tjtp0R71+OUHgbUImJwjYmJL6qZslIF4HMrnztjrJgc9FW9DBqRy0O68nLx/hQCAQGF4IONFQnYFnc2+bmBezb+sKirASiubUgyQokwxWrHhMJPPLCAcV8DSvwtQXrAZ5OKcMaJunOSWSJFKrU9kk4ZJOL+B7XYX6/FGrDUkVutrwA4FAIBAIBAKBQCAQCAQCgQoCg0I0PPLII/LTn/5UfvOb38hvf/vbivKBArvssou87W1vs2UWRx55pJEHkAI+WWcSvm7dOnnppZfsYkLP5JxJOReTcuSRwccyAeuInXfeuYY4oAwOMgAC4OWXX7YLooG6Jk6c2G+STxknC9xSAR1OMuRkBfVDcOy0007WLuT6OR3Rc7QmpIITJ+joWN8hHZ0dMmHCBDvu05+9X/lICAQCgWGFQJq4J7qBhlUm5URsBq8paSavvoYR4GZ+SqpHNCDlDtH8crLB8jUj9WzEqChVltQ7beB5+LgkU2mWN6aOXyEaqsJePKnyhhFLlab0uAcCgUAgEAgEAoFAIBAIBAIFAoNCNMyePVv+93//V9ijgYn3Cy+8YEddumXDgQceaJP6jo4OYZIPMfHOd77Tjrs89thjjQhggu+EAHIrV66UZ555Rp588knBYgKyAZIB/UzKmfivXbvW0rFK2GGHHXRZxq6y22672cQd8gHHpB75NWvW2KkYTz31lKAfUuLggw8WSA/ICvRRf6Pu5m5TiMwygXR3tAUdTz/9tLVx9913lyOOOMIIA5fJfScteD7qWbZsmbUDbEibMmWK7LPPPoaPkw20GTIF6w3mBz29aU8J9Dr5gV70WZuz9uV1RzgQCAQGH4GqhYDPxLP5diVJZ+CVsLYht2jQKFnlK29peS5vcQoUE3svi5aUREol28K1DSiS1EuSHi8UWjSFkz1DoatakRdIPqJ50drciAUCgUAgEAgEAoFAIBAIbOMIDArRwOaPt9xyi5ECTOgXL14sTzzxhNx///02YX7zm98sU6dOrSyHYAK99957y1lnnSWHHHKIfQRMnH2ZAQQCE3EICY7KfOyxx2xyjkXAdtttZ0QCyx6YaENcPPvss2bJsMcee8jv/M7vyOmnny4QADisDpjQP/fcc/LLX/5Svvvd7xopcO6559oxm9OnT68sz3ASg3I+kWdUzjGcvrRh+fLlcv3118tPfvITueyyy+RrX/uafOADHzCSw8u5LPUS9jj5YHPNNdcYKTNmzBg5/PDDZebMmTJt2jR7NmR5/iY98rKtvY0ihpvjAxnBxbM7idLS3BKDfkMqboHA64NAlWxI9dXMuWtn8iqQ5WrQs8t+ueVeyv1yfjXumqoptWXKrXW5WqmUmtIqOf1Ve+Gax6omRigQCAQCgUAgEAgEAoFAIBDQoaJOYDc2lNwsjLA+YCLP0gQm9gsWLBA2hrziiitsEv/5iz4vs46cVbOsAYsDyAcsCnA+kWYU3rmh06wYsIxYuHCh3H333Tah96aykeQxxxwjhx56qC2bgOT4x3/8R9PzwQ9+UN71rndZHmQElhCLFi2SBx54wJZ3QF587GMfsz0i9t13X7MkYMKO7paWlgopUGmPaa3eIDUgLP7mb/7GnvOP//iP5ROf+IRZJVSl0vMQz0kG4mtWr5H5C+bL3Llz7XQOCAOsGg466CChPSyl4BNpVnLDLSkgLHCui7Y5Fk48mEDcAoFA4HVGwLvPytS8tn6yyXI/y/WSWdKAwbL2vGyt+ryiXKqqOpdIqVXt/fNUwhNdXVW8qjRCgUAgEAgEAoFAIBAIBAKBQIbAoBANTHqZDHNBNMyZM0d+9atfyfe+9z1bXvBf//Vf8sY3vtEm0UyWXZaJvS0P0AbZxJ4ReTGI9cn1qlWr5LbbbpPvf//7wrGYkBO/+7u/KyeeeKKdXMHE/IYbbpBvfOMbZlVx9NFHWz57Pxx88CE6WW8wYuCLX/yiWRNceOGF8t73vtc2osxwqARphxMPWDi482fEIoH2/PznP5f/+7//k0996lMCubHffvtVTtCgPO3PlzX4kgfX53puvPFGueeee+Qtb3mLWWJg6cF+E+Dim01CJjjJgG7SSaN9nu56ww8EAoFAIBAIBAKBQCAQCAQCgUAgEAgEhhKBQSEayg/w8MMPyy9+8Qv51re+ZZN7wixncFLB5ZmMG8Gg5IP/ek+ekwyksYkiG0x+85vfNIsElhiceeaZtkTitNNOs+UGWDz84Ac/kF//+tcyf/58YVkERMOMGTNsQs5GlZSfNGmSfP7zn5dzzjmnstTB2+I+SzGWLl1qezpAhIwfP1523HFHm9Qjs2TJErn55ptt6QTEx1/+5V/KRz7yEWHZBkse2MOBTSx9TwkIARwnU2BhwUXa8mXL5dbbbpWrr75a/uM//sMsNM4//3wjULBuYD8KrB1wroOwbyoJljkRQl64QCAQCAQCgUAgEAgEAoFAIBAIBAKBQGCoERgUogFigEkxE3McFg2QC//wD/9gmx+yJ8EZZ5xR+fXdrQMoQ5hJcz6ZZpLOhJrJOb/g86s/FgtYLrDMAOsIlk+ceuqptqkjxMZ1111n+x5ceeWVcvLJJ9tSBjaHRD8WCCzBoNyb3vQmecMb3mDHatJW8mkH9WEdsGLFCkEfyz9I33PPPW2zRzaZxLFMhLrY/JJn/Ku/+ith+QSbUbJXBfWwvIJlJJAKPBv4QHLQdjahJJ1NIVnOwZGgECQPPvignHDCCfLJT35SIFAgOOo5byskTOzNUA+hSAsEAoFAIBAIBAKBQCAQCAQCgUAgEBhKBAaNaGAy7RYLEA1YEXzhC1+wZ2MZBRN8nC8HcAsGyjHJZ8LvSymYTHPxqz55bL6ILiwXmPhjHcEeDSyTYHPI++67z5Yy4N977722jGHy5Mm2JwTlOXaT+tmU8qijjqqc8kB7yIcYwFKBtrEvxLx582wjSpZtHHDAATJr1iw7oWKvvfYyqwX0XXLJJXLttdfKZz7zGfnQhz4ko9pHyR133mEbV9JuHMQFFg4sk8AqguUV7CvBKRM8n+8dAREDXjgIFZaGYLnBPhbgBNmCczIGzNCdXyYQt0AgEAgEAoFAIBAIBAKBQCAQCAQCgUBgiBEYFKIBkoDLyQN+nf/xj39sGzjyfDnRwKkUfkylPztLDjhyEgsGP5bS85hkM7H/7Gc/a5YGTNKZiLMsYv/99zdy4qabbpJPf/rTVoTJN5N5TnRgks/FUZTvf//77UjNww47zPZAoC4cdXOyBW3kpAwIBywhmMyzDOPRRx81uYsuusjKs1zhpptuVmuGq+Sqq64ykoGlGBAWl156qbz44otGaLDPAs8EmQCRwAkcWCz84R/+oVkssLcEsmxOyaaZ3/72t60erCM4OYPnYzkG5Att5LnADbKBMHg74eC4m4K4BQKBQCAQCAQCgUAgEAgEAoFAIBAIBAJDiMCgEA3sSs4BakyAcbNnz7ZJNycz4DilgeUATIhZUsCE2X/1Z7LsSyWYVHs65dAH0cBShc997nO21IBTKtgIEusCJwTYM8EtApigT1NrAHQxied0BxxEBHsgHHzwwVaHT9axNsAy4aMf/ajJsRzjfe97n5ERWDZAQvzsZz+zPR8gASAQWB6B7rvuusvqOuWUU4xUYKNJ2vYBPe4SEgRrC4gOiBeWYzzzzDO2PwRECUstICJo349+9CP527/9W6sf0oKlHVy0FWIDOdpbXmLiRIPjbgriFggEAoFAIBAIBAKBQCAQCAQCgUAgEAgMIQKDQzSUHgCigaUFPnnmF32WLvBrPJstMmGGCGCizAUB4RN/4u6Q6+7qluuuv04uvvhim9iTxyR+p512MgsIyrHfARYF05RgYC8EJvgsTWCCD0mB49QJjr1k+QIOeTZtfOyxx2zZxde//nVLZ88FiAaIAJaAQDSwdAMy4j3veY/svvvuMnbsWNsskjzqP+6444ws+Zd/+RfbiJITJFhusef0PaW7p9vqoB4sK1iKcfzxx5sOKoTMYCPLr33ta1Y/y0JY3gFehx9+uD2jYxKWCwZR3AKBQCAQCAQCgUAgEAgEAoFAIBAIBIYxAoNCNGxq6QRLDLBowFqBX+chGSARyuV6unukq7vLCAlICSbWvnTi//2//2cTfywKZs6cKSyBOOSQQ2xTRyb7yLLsgkk5GzqyDOKOO+6wIzHBnz0eIBCwhMCxHAHLBI6WZNnEZZddZul///d/b3LsqQAJgA42a+QoS0iKmTNmyl5772XEAuTDgQceaEslOjs77ZQNNnjkJArax9IHnhlCg/ZBULAZJGSInxjBBpIc/+lEAxYRRxxxhJx11llGOLApJDjxjDhbMtGjezTosZ1BPBgkcQsEAoFAIBAIBAKBQCAQCAQCgUAgEBhGCAwK0cDkngmwb1b40EMP2fGPX/rSl2zyz9IAliQwuYY4gGTgKrt8CQVkBA55JvrsbcCmjWwGyWScUyfwieNoAxNvJvxYD0AgcCzmf//3f1s+ezx8+MMflr333tvinB6BRQIkCBtIsocCSzGQY4kFFhPoIY+lH5wyQVlIDsgKCJN/+qd/ko9//ONGTFD/D/73B/Kv3/xXIwWmT59u1ghYJbCvBFYP7B2BpQTWFv78kB3f+973xC0qWDJBGYgGjuiEWMkdZbHG4Fkdozw/woFAIBAIBAKBQCAQCAQCgUAgEAgEAoHAUCIwKERDbpnAJJhf9S+//HLxPRo4BtItGnySzETZyzkATKKZsDMJd9LCLRo+9alP2eaMTPbZLPFkPcISssGPnXQdWA8weYcg4OhICAIc5T/2sY9ViAY2YoTA+Na3vmWnVjBp56QKZM4991yzOmBPBZZfQEb8+7//u+lhWQQEAO368pe/LH/+538uv//7v28EAhs/Ui8EhG8iSaEzzzxTzj77bFtOMXXqVDvesr293fRheQEZgi4cz4V+jgPF5yhMHFiBD75bN7glh8dNMG6BQCAQCAQCgUAgEAgEAoFAIBAIBAKBwBAiMChEQ95+9mDAOoBTJ9izAIdFAHsOlCfETiyQnudBQnicCT37LLDHAlYKWDC88Y1vNGsGrARyiwY2muR4SggC9le48847ba8I2vCJT3xC/uiP/kj23XdforbHAqdZYE3ARo1s2ghp8ZGPfMQ2bGTTyaVLl5qen//85/Kd73zHyr3zne+0JQ20iyUSf/Znf2ZEA1YOtJulEP/5n/8pf/d3f2fyfnv7299ux3Fy8gSWDU4gQDRAhlyse1Dg2Cjy2GOPFTaYZGNLP4UDrKgTAgZSBEKGNOp0UsYUxC0QCAQCgUAgEAgEAoFAIBAIBAKBQCAQGEIEBo1o8F/asVhgM8grr7xSvvKVr9ijQTSwyWHZMVmmHC4nG5yAYH8DZNiMkU0ab7vtNmFJAssKWGJwzNHHyNRpU608epiIr1q1SjhJglMhOI0CIgH3mc98xpZfODHBMZtYK9x44412fOYtt9xickz4f+/3fs8sFLCMuO+++4wo4Rne9ra32bIJ9lmA1IBMOeaYY0yeZRcsp8BhkYH+5559Th6e87D8z//8jyxfvtzk3v3ud8vJarUwceJEk33qqafMooHnw7HhJPlHHXmU7LPvPhWigedzaxCIBY+Dmy/DMAVxCwQCgUAgEAgEAoFAIBAIBAKBQCAQCASGEIFBIRqY9HLx6zqOST7LJbBoeO655+zEBogGJsXI4dxiwSKlG/ssMKkeM2aMkQc33HCDLS1wooGlCBANRx99tBEPeXFOqVj6/FLbyBGLBT/5AuuDD37wg7ZfAvJu/TD3kbly7S+vtSUUpLMXxDve8Q6r+6677lYS4tfCqRks12A5AydasL8DVhOQEMSxQIAcufTSS+VP/uRP5P3vf79ZLMyfP1/uvvtu22gSHSy7uOCCC0wXm03i2AcCC4i//uu/tjgWEuxnwekUbBo5evRoSy/fIGC4wDEsGsroRDwQCAQCgUAgEAgEAoFAIBAIBAKBQGCoEBg0ooEHcPKAZQtYNLAZJBs8Xn311WbR4L/E+8O6vMedsCCOdQLLMJYsWSIQDUzg3WHVwAkSEA4HH3SwtI9qr5lsU46jJKn305/+tBX70Ic+JFgT+LIFrABoG/pZ6gEp8d3vftdkOeGCJQvUy4aSOCwizjnnHNvQkr0fOKkCyw2WTEB6YEnxzW9+05Z1/MVf/IVM3H6iLHp6kVlNYNmBlQKWEiz34HhONsZknwg2zvzhD39oZAP1sCnkW9/6VjuxAhkwg3TBh8iBXAAbsOMZ8Ms4oidcIBAIBAKBQCAQCAQCgUAgEAgEAoFAIDAUCAwK0VBuOEsS2AzSl05g3cAeDfkpCfUsG/xXeibQhLEaYP8E9lrgCEisIyAZnnzySbMaeNe73mUnNEwYP0Fa21prmsGmkCy5+Ld/+zcjDI4//nhhnwSsDzimktMfcCx3YPkCG1iyjAKy4aCDDjJLgrlz55osx1SyQSQWCUzqf/azn9meDVhYkAbhgUUDzwk5wRILTouAGKAdEC/nnXeebYjJHhFYQbC0AosHnu2KK66wujkSE8sLLCcmTJhg7UMvhAhEA0tJnIABI8iQIBkMprgFAoFAIBAIBAKBQCAQCAQCgUAgEAgMEwS2GNFw2WWXyVe/+lV7zM0lGnxvBicksBJ4/vnnbVNGliswsWayjSUAE28IgylTpph1AHH9bV/NKpJlBUsonnzqSVvewKkRl1xyiU3gOboSYoB9FnAs04BsWLZsmbAxI3spMMlvb2uX7p5uI0ewLGAPBvZ3wFoCMgFygCUPbB7Jcgj0UZa2sbEkRIm3F8KBzSVpK3sz0FY2rWRZBZYRWEKgg40msbhgU0pkIBjQg8sJBUga8t2iwQTiFggEAoFAIBAIBAKBQCAQCAQCgUAgEAgMAwS2GNHwaiwaWCLAxJpJNBeut6dX1q5ba7/kjx071ogGrASY8I8fP94sD5ygYELPhBy/r1c3T1Si4Nlnn7XJPKdWQFp89rOflQ9/+MN2zKXvBcE+CNQLSYAurBEgNHwpB/rcYYkwb948IzCwaDjqqKPM2gEiAgc5AOGAjw6OseQiDDHgDiuKm266yawgfvKTn9jxmee98zwZP2G8iWC5AAHC80B00D7S0EGc9HpWIa4//EAgEAgEAoFAIBAIBAKBQCAQCAQCgUBgKBAYUqLBf6WHYGCCj0VCQ2Pa3DCf3DPBZlLNZB1HmIk8k24IiZwQIIw8+pjgQ0hgqcDRmGzWeO+999oJFiyhmDp1qi2hMGKiKId+r4dw7pjsow+yAWsLCA+sFrBAwFLBn4eNJnkm9JQJBtIhOBYsWGDHdj67+FmZvud0YWnH4YcfbiQJ7UeOZ2luapbmlrSUhDSe1y0+DLOCWMnbGeFAIBAIBAKBQCAQCAQCgUAgEAgEAoFAYKgQGBZEAxNv/7WeSTQTf//Fnsk9F5Pu3NIBwEjD+QSfMLLr1q2ziToWEJRBN/s9sITjk5/8pJ0IcdFFFwnLKNh/gQk7F3VDdmzo2oCqyoSeCT756KZtkAdOhJAHsYAbN26cpUOCGElQECHePsrzrGvXrpXHH39cbr/9ditz5JFH2rIK9m5Alvbj+2aQhNFHecLUTZg2EeYZwwUCgUAgEAgEAoFAIBAIBAKBQCAQCAQCwwGBYUE0MFn3SXy+vACAmGAnPiFNtH0Cz4TbHWW53JKBPCbf6GIizgUZgGXDokWLLMyeCjNmzDCLBp/E+4SdCTzO467fJ/ZuUeD1QyxweRnyKUs7KOv6aA9xZNmjgU0tIUPY+wHLCNpPW5HjQgdto7w/kz83esgjTplwgUAgEAgEAoFAIBAIBAKBQCAQCAQCgcBwQGBIiQYA8Inz5oDBBJ1JNxN5JuLm1Kihp7fHJuksa2ACzuQdawAnMMxSQSfkTOTR4Xsf+J4Pm1M3Mkzsudw1NugEv+A7qIslFTwPer19tBcrBtJpk5MCpLH8graxJwRtY48I4lg2uBx6yYN08OfI20BbXgmG3vbwA4FAIBAIBAKBQCAQCAQCgUAgEAgEAoEtgcCwIhqYlHNBFuCYbDPBxmcy3dOt+UoqMIknzX/VR5bJNxNyfF9ywCSdOPL5ZDyvh3SWQuT5rs+JirwN5FEeR7qXox7IA3wIAfZWgIQg7nooQ7vJZ7NKX6JB+9AJCUKYvSXckU55L0c6z029XrfLhh8IBAKBQCAQCAQCgUAgEAgEAoFAIBAIDDUCw4pogCjgYnLNBJ2JPBNvJuY5sUCYSTZyTLqdCABMyvkE3PUgz5U75NwCgok99eQOvRAHrp986sEx8cdBJrB5Ze6cWGCvh6bmKhFBW9ySIt8gEv0QDLQ5JzzQk1/+DKRRBvnyM+XtiHAgEAgEAoFAIBAIBAKBQCAQCAQCgUAgMBQIDCuigQm0kwM+yWZCzSSfSbVbBvikP5dxciEH0fPRi8stEIijj/pw6M8JC9LIR4dP6n1ijz4sEuyf5uO8PPIQB/joc53EXV9Kg4SwopZOHaSXy6OXy5+P9kLGkAYxES4QCAQCgUAgEAgEAoFAIBAIBAKBQCAQGE4IDCuioQwMk2oun6wzgSeeWwRQhsk5zifjFiluPsGHHHCCopzvxACWE9S1Oc710h7ClLMlERpGH/WR5lfeNic+SMvTqRcSwfd08PbkREN3lx6PuaHTniW3gNicNodMIBAIBAKBQCAQCAQCgUAgEAgEAoFAILClERhSosEn2U4oMKFmYu7pTOCZlJPOhge9uj+DT+BJo1weR56rPIF3SwKIBvJ8LwUm6q6HcuTjcqKAfNdX1u16vb2UNRltq1s7eFn0cLkM7XZ9PHOeznNRjnTkiFPWiQzqJT9vmymIWyAQCAQCgUAgEAgEAoFAIBAIBAKBQCAwxAgMC6KBX/GZPDNxZrLvE2gm4u7yyTxp5FGOibj/8p9P3n2Cj6yn+8Q935uBsrlDFiKCyT3yXN4e5HK9ThSQRjmeAYdO0pwIQY4TKnzPBmT9Qo468E2fkhT5vg9YMHR0dpgMe0kgh0MWHcRpX7hAIBAIBAKBQCAQCAQCgUAgEAgEAoFAYDggMCyIBp90+8SdiTOXp/tk2ifZAEce8lz5RN0n4Mg0NkISFBNz3VOhty+VoSwuJxEoB1Hg1gPU5ReyHkYOGVy5XtKQ84m/t9HTycvb5zrJx5HnzvWgg3aRRxp1cnk7PM3LhR8IvJ4IVP9i9e/TKuZOaopZkPQiSjB31fJ5ah5OEmyuWtWb6c9Ft7VwDkMlnPB65VCUPq9NKRjg89xUscgPBAKBQGDrQ2Bz+tWsU9wc8YFAyNQMJBLpgUAgEAhsLQgMKdEASEyU3TGp5nKigQm9T/wtTa0CmG8wyfaJd17e9TAxR49bR5COvE/YPd31OKnhSyr8eEzXhy6cy7G0ApcvvSBOft4e9OM8jQ0kITvQR5ov1XD93h7i6HIiAx20HQsO0lpbWm1pBtigh7RwgcDriUD6y+4/mqp+m7U1/bNrCIfi25GJ9S+Q68vD1Wetn1rNH6Ghom/Z9NMppkAEtP2gKhL6wZ4J9svL9GRim25HSAQCgUAgsDUi4J2g+/WeQTvDSnadjpG8Osn9NVWU1GZZ2c1SUFsuYoFAIBAIDDECw4po8Ik2k2cuJtykeRwfV57AlzH0cuWJf0rHEqG61MD14zNxx4cA8LrK9bkc6U4EoBdHfRtzlOVlxP4NOJe39KIgYS7q93yyqIOLdCcWPJ63tVATXiCwxRBIf73+V0w1KYVvZ2UoZEkaS1mpLZ5Z+GTlVxKq3pNYspQg7MWThMfcr5Yb8SHDVG/m+9NmkQokWZqL9fMrwqrPw4U/UHGyS6L91EZCIBAIBAIjAgE6wvzKH0o7QusnSx1ipS8tZAfqS2tU5UJF2NXS4Rbj37xIhAOBQCAQGO4IDCuiYbiDFe0LBAIBn98mosHpBrY/ZUyUxkV6Z5yUj5t8ZloIeXbZd3xzfXnYaqCQVVQo80Lbil8Qlva4hrHeDA9SLCEh0ZCF83TLrRTQmIZ9YGx+kZcXTxoLvamI1ZmryWUiHAgEAoHAVo+Ad4L8mETYL38w7zvpCAmT7mkuo76ryZIqQYqacyH1K2lFlpMM7hfJ4QUCgUAgMNwRCKJhuH9C0b5AYJgh4OQCfhoaVUkGGx9ZIoMtbXgS0IETORrB1wkwJcpDt4qoPW/S2WiSadxVo9tk0GWpFttmboar3hwwD5SJhSpgBTRewDEr+U4yOOlAKS+Sg0ux/MrzIhwIBAKBwIhBwDtA3lb5G4sHLPrPmn6z6BjzPhRR1BTiROs7hPSqyHndmmBp+JXM+ioiNRAIBAKBYYZAEA3D7AOJ5gQCwx0BpxcYFKWhUJVoKBI0qzwgYpCkT6ZXMZwy34duPHM2rFIx1+mEQyaAsCvbFgdeBqCjBRalsEHvaerXxJHHWWLhFx+Kp/nA2eT05qo8TtG8uKeHHwgEAoHAiEIgdX66u5Y+lb+t0jup0gl6f1n2yzh4n5mn16RRV9HZYrWWO3vPqfC2+L7LcYhwIBAIbHUIBNGw1X1k0eBAYKgR8EGQ+9qeysBIB0NZcmUwxoCqyCK7fOVPVIiquB4La8VU2gtUZrhkqOS2OPACC5z7HgA4B6oS9jT85CrFKliasGbi+1UIb8TzUhsRiaxAIBAIBLZiBBKZDtGQQpANqZfUrb/Tc5UJBosXQkmi0q36a6zaB7sAIhAYTmKoRI2Q1uVkQ7VIhAKBQCAQGPYIBNEw7D+iaGAgMAwRqBAL5QGRt7UYhPlsVAdJjJvKF9I+nqqIagrh/iRDLqwSVsB9NG1DzkHLH7mydCJl+hA5ibj1iRcw8AqkSUufTwI15blkXhU55cvlwg8EAoFAYKQhQM/JP92OWx8t9Yb0gRANqafUO+SCEwxFquGAgF5e0n20eL+KCK66TDARDtWtxV2iUJbE4x4IBAKBwFaBQBANW8XHFI0MBIYZAvmGhNa00rDJx0aMsvS/D6zc96cplaoM0XzQlUZjKlVTMOlMwkXYFW4jvuPmj1sbz8HKw0m6KqvYqUtxD+OncDWPUHKem0igXNIlwg8EAoFAYOQgUCUaqn0p/WAt0cDzaipkA5mFM/5Bw04wuF/tg5MgRdyaAd9J9pTuClOsUB1eIBAIBAJbBQJBNGwVH1M0MhAYRgj4KKlMNvh4iKZaWG/6vzo880lt/2fxojW+W01U6ivKVYQ04OH+KkdkikPhA9aBHzL9KpbyvRQxwmXQ3JqBfB/uEu7/eVGSK4gG0AkXCAQCIx0BiAacEw6EUz+YWTSYQNGvqkeJ8pX32Z7nupKfLPm8B6761Xo8hHy4QCAQCAS2BgSCaNgaPqVoYyAwnBBI467qSMrbVoyzKvPYYsBFthdxUfcp4sX6CQ1UiMJeyH1XOMJ9IGHA6hePS5rDUOungat/ULV5lPQUwsl5Cc+r9xFQKr+8bPiBQCAQCIxMBOgJy72h959VotYlXNp9MMnz6mHkWvr7DQWxC83gddbTEGmBQCAQCAw/BIJoGH6fSbQoEBjeCGxsxFSMg3y5qovmD+ST1LojLwrUG0uV08rxvIIRHAaeHr0gGvBzfB1X9zVboaz9lYw0XBk+j28O0eDlvYwpjFsgEAgEAiMRAetk9ZZ3tvSg3gGqT5ZbLLhY2XdovJjHq365RIpDLiQLskQ0DFy+qilCgUAgEAgMFwSCaBgun0S0IxDYmhBgDOTjIm83I6Bi0OVZ7vvgqBBJZcs6XBh9eYEi7utdieIqulJ0m7i7JYOTDQ6hY1HfT8CmvCqVUAEsx92BR7gI12RXCnlulhDBQCAQCARGGgLeyZY7Qu9s9Xl7NZyLeTiHwrpUTSj7LuNLNNCUqvK+OogGxyj8QCAQ2PoQCKJh6/vMosWBwNAjUB50aYucCPAs93085n6/ERlP48KE85FYUSgfyLmI63Nx0ke6c6KhbNGQYzFwWEHOcQasctwBNFBLyJaiLhp+IBAIBAIjEgHvH/HpdHGES50s7z6yyfJLgzXOi5CYh13I6QU0oMNdkk2LJmLphKMSfiAQCGwtCATRsLV8UtHOQGAYIZAPhPKBVTmdQRKusnmgC5f9JJbuPgrD14IM4vJf8BFCn19eB+kj3QFbTjbkzwseOPwcwsqo1T8c8z1iRWpvBmiGahasERwovUYoIoFAIBAIbKUIeDdZZhHo+0qXkw08KcW8qHeTLk5+JexClphHymRDrgXhcIFAIBAIbB0IBNGwdXxO0cpAYNgg4MMhfL9oXJ5OPB8aOSlgQuVBmxf0Qj4KK2bMWDNANDjZQDZZTYVPeFtxjrf7/twOGXEL5wI5vnnYC1c+uaKwf3IocpeHSSvHXS78QCAQCARGCgLeXzq763HrZPUhvR8s4m7Vx+O7KGEX93AlMxcis5JRhF0/GixcSTDpuAUCgUAgMNwRCKJhuH9C0b5AYJghwNgov2iex8tNZVjEVXAG0mCCequQDcXAiXScF/BCWtBJBnwXQ1+zXvhc25IzCLMHLhBM2Hp6LuSged7G/IqyTMjTSMrDmUgEA4FAIBAYcQh431l5X2VPSF/o/WEWhmxw58U9yd5/5cx+cS9FhoYbitJWh2vyQuEHAoFAIDC8EQiiYXh/PtG6QGDYIcAwqN7lDc2HSTY20gz8Ri1lpEBl0KaS+U9APmpLwlaoTwsg7mSD60bPtko0VNgWBwPfx5+EPV2DFVdOc3kX8HgdPy9KuCziKsIPBAKBQGDEIUCnl1/+gN4REifsnWORTtRcEa+IVzI018NlnwzSvBAvPAgHj5viuAUCgUAgUEWgry91JA1OTlazhjQURMOQwh+VBwJbJwJ0Z716c86gPABK3R1jpfQPkqFJhbjsnPCKAg2YMCMoBlLFZQMrzdKoEw1eF+Ism/CLktuMM6z0afEBpFsDvFyaFTCAgJEhsxEcCatDlssd6X6R5nKZ70XcRwFhL5YOWqsWRU24QCAQCARGJALef3qH6H2lP6z2xT09PdLY2CgNTZqpcj29vdKg/XBj0Re7CopUinui67W43jzuwjnRUCnslYcfCAQCIw0BSAMnDhobivFd9pCe50m92t9w0Qc1NTE6Hj4uiIbh81lESwKBrQYBGwfpDbKBeS6EAH2hO8vXCNPTXp39MjZq1rsOw/RfcWyXFtYOKBtU0ZmqpHaUVkDL+GkTOcmAbqryPRq2qXGXA6vPb6RCV3f6AJpbEijdyjSAaZPae2Sfhw1c063AWMs7cIXvxiVeBT4uUUUpjCgXqtNnmdLjHggEAoHASESg2g+mp6N79X6w6Dqlt6dPerq70yC/Wd9MKtRtxIO+8xj0q6DrcYysrCfily8vYZXpjU7Xwq4h/EAgEBipCEAkQBzYjzpKVrqVghMM+HkacchO0iAaIByGiwuiYbh8EtGOQGBrQkAHRTZGKvzKJFUHQoyFqmOmasjHSeT6eAkfYTrTxFZ4TtLhBINlZ/gg5fpMR5Y3ooMOJw8JOD3c1PlLpacgHhp1cGtpFFCEHKTcz8Ku1n1UejjZMpCS1FAM7INoAJFwgUAgMJIR8H6QZ/Rw1nWmrlUzehn46z+6XQ3qpZMEHfRXJgMZSF7eFOaKy2HiCJcv0sMFAoHANoeAExA8eN6/ECfP8yEazMKKH++G2AXRMMQfQFQfCGyVCDDiwhUjL6a7DLT6dLcrTEf5l7KqgmnCWhRQ+SSVxlB5jG7RpNTiAR857ligWlA9d0PfhXpLXkffINWbgVTU66D19tiLpoHRLiYmjHh50dhV20bIIZyp24iPhMtQhAuiwT8/DYYLBAKBQGBEIuDdbEHp2jOm/tB7xdQX5g+frPboKVPp1G9yL5wXNb9Ousvhe3ZSkudEOBAIBEYwApAGOCMrNchIzMkEJxlsfOx9hMpi1dDV1WUkQ0tLSyprWobuFkTD0GEfNQcCWycC+SDJGIbUuekKMe0RlWhQ01Fbp1owqdY5pi6yGHYVnac9feohq5PWRCtYFksrbHTH3g5MnDU161Ar4Txt60T0FbY6IZoKEQaWBEKvxcjJwUq5yPUYEaErK9S0bvNh8w+8Cn/SvvkaaFG4QCAQCAS2RgR6dMDPnguw3ayXpp/t0X+1/S02Xsk5EYvZGZYNLBts9rWF9k5TOWd6rYiWTJ1qUqBhel3veV2v+0ko7oFAIDBSEWDZRA9LYfVL39zM1udpnI1v1gr0J3U6hG5dwtXZ2Wky7W3ttk8MZYbSBdEwlOhH3YHA1oiAj4DwGTTp+tQ+ncD26oAKPqCxSQdcbIilg7K0pIJ9GtLkOI2xiFcHUUCQiAa/F2Mu1Q/R0KgbNejwDrGqo4NNo7m6nW1VcGSFEoqOpX8QOZZpQQM5VWAS0cBn0N2rSyvUNTe22B4XFtGbv6/w83DKT9pctuq7ZDUlQoFAIBAIjDQEeLfxS6ERDbosDYqhWy/89E5L76jUU3K6khILeoeO0A0cpEXDLSxn44VoGxvlfaeG3eJM32no01KF3oQkrzotbX1zXjLlxj0QCARGGgIQDVgm4LBMwOV7MGDR4BYP+G7hgIwTDW1tbWHRAHBmEkIgXCAQCGwdCPj81tmCyjw0DblqCAAODteLf0x0CwkdSOmk1+4MnhiW6XoyvacwHEKiFhopbjtC6vDK68VntMXIy8mGrQO519xKJxr4NS2hiZEuIdAFDmw/GOACV07QJDnKkdpkcqk5QFm+TIFlqyaU5Q5hXGGxkiJxDwQCgUBgZCKAVQL9rHWUeiNGD5x6YXJSD5qIhx4jGaBy6XWVhVeiobDKM6JBMbI+tehIjWTQsL7LyIZk8As0keJVtw2+7nj8cIHANomAL5FwEgHfNofU/gILB+JYL/TqPl30QFip5oQEoDVBbhbdzFCCGBYNQ4l+1B0IbI0IMEjicqKByb47G0B5BomaUJANFOhjMKWpDNC69I4kQ7BEKzAFTv9ISanaS+ZHT1CgMGFVW9RtjmgAPUgFfk8jBFq8ZBJpkwgE7unVwxsmXcgRtsGyhiAj/GPDr0pqhA/IPkcP5xGVrAgXYRULFwgEAoHAyEQg7/9S58idV5HTvS7BGw0yl3dXqy2YUApCJwTNJqA9rb/L6ERJc5IBX7N71HOSAR8R+mdIBoyn8VUkXCAQCGwjCDjB4CSDb/JIHKLBLK0UC4iGZj1tDIvi4eaCaBhun0i0JxDYGhDQEZCOn4xDqIx8GBWRiImpZRYP4kRDn6YTZkmFDarSVBlDU34RorgPo5LpKb+7FwMyBmhpZKe+6qEvbdFhF/42NfKCKgAIJxo0qI4BL/hB2ICl/9LWwz4XupavuQHj3WTpgHyicxJ0DiEfjTn8ylUkmqc3sC4GxWmQnIrEPRAIBAKBkYmA9nu8z1ga6O+aorOE0KU3xtFF0gvzL72/ENb+V5cWpv0YNJ7WEmq6daTJZ5fjEtHAArecaIBkCKJBQQgXCGxjCORLKLBkgFBwRx5EBM4JCcK2B5f2KZASw8EF0TAcPoVoQyCwlSHA4IpTJnCNdGgEWXvarTkcsUiYwZNdmscgjf0BGKCxf4NZI6gP6aCDLgZVptN8BmosAkgG/o02OFNZBBi0caSj/cSzLRIN+uxgaYgpahZWTOyFUn2pQDR0c7Y71h9KNDQ1QzSwJZniaC753LmspKrRYrUXCZ5GwArojQI2QFY/XCAQCAQCIxIB7//od33qrw/qHSfPzDuNPtJumkEecbppIyjwSdcL3wsTJ1wQDUa+awq1+PtQgybtJAOvPUqFCwQCgW0DASwWursYO4u0trZqN6JLtxjXqcuJhJyQcKsH/OGwPUEQDdvG32o8ZSAwaAj4GIrf1PnHrgA2ANLOsKFLh0gQAQj5EYs2oNI0LBEYlDXrLNUKaJgJq3agkA10nemXHMqzAED1NiSyoYFRGAJ26Y3BGYSF/rdLvW3CQSzYpVhC3KjpnIGtLyBlE6oQIKNkD2QQkPUp2dCg6/UaFM+ByAb7zPjcuHAW1pvHPeAEg/tJOu6BQCAQCIxABLQD5N1Ff4sPeWDvHX0nOVlAGuQ67zzWRTMRwLIPQZMhSCH3i7KkcWlfSj+dX6rNHKV4XWoJf11aetwCgUBg5CKAhYIti6C70f4BwgDiwE+VIM5mjwNZOLj8cEAoiIbh8ClEGwKBrQgBBkMMobp04kmYqWuThht10tu8QXPcVJTjdwizc273Br3UZ9TUqhNirhbNx7IBwoB1ZdpxJr26BwGdq/6DwmhuUHMxhliMvLhSpg3OTJ8mjXTHYwNdZQCL1UhHh8iaNYqvYjtqFHS3CqgkpnX6AtIdgwrMNE1fWn2KbwNpZnqXLElQiTPdBKgIh18JVwKaqJII21WENRouEAgEAoGRh0DREao1Q29BNDQoidBglgm8k7QPRGS99sXr1mu/qGnsEM+xdJDA7doPjx6dyHX6TMjZ7OrV9xoke7oSeoj5RQphnPspFvdAIBAYyQhgoQCpAGHgmzzyvPWIBkgJ5Nz5coo8zfOGwg+iYShQjzoDga0UAcZUkAv8jq7TWyMG6N5adKjUojktXSrRrRd7Kig9YAOwlS+KvPii9K1fZ3szNI7Vgdf4sSLjdHI8uj3ttQDpoP+dbLAdCKjMLBrSSQqsezVHp8q/ar+a0kfw3aDgBsmyQQmbTr1eXCWy+BlZrfiOGjtGmnRQ26Evprbx46Rpt90U4wk6CNZPi1/hsDIBXycajNjRBMUQtTj3HVb37Re8JFJ735Y+gNonj1ggEAhsEwjQK2K3l86TaFS/gT4YRrxbe0jzNf7CCuld/Kw0dmk/267vNSV/NygZ3LrrLiK77apEsJIPnNVcEOu9SjboFAJtqps3G6R66qLNegG9Kl7plD2TTrnSMWs4XCAQCIxIBDhSt1sJS77uOdGwNT7skBIN1l/GYHVr/LuJNm+jCDDssrGV+hANEA58j/WwHWmHaMCCAaKhU6Wwbli+UnoXPSMvLF4sL/Pruw6Yxk0YLztMniTNu+kgbKJOhtt0ENauF5s7sqxCNeo02uqBUsCqIQ3DfIyViAaGZ0gPVwdWuXst40N0aWediIZ1nSKr1yrJsERWPzxHFj/2uLRPmCANo9rl5fXrZfvdJ8vuhx6sA9zJiikWDIqSLVtRJc1gXKTZ0hYGumk86763udpe8AZ7JHAMi5NvXtwCgUAgEBhhCHhvh91eunr0fZNsEARCnZfUOr2tUUuGhYvk2dlzpPfl9TJ23Djl1ztknVpA7HHQftK6/74iO+p7boxanLXR/1K0Ud+fEA3JooF3mSbb1ULFvFi5CNPZwj5wpdejBsIFAoHAcEfA+5B67ayOr+rl6ldfx3u9uvwKHSyP2LBhgxqwrjGLBjaFdGuFLrUY7uzsNJlRatnKVV5SUb+G1y91kIgGGN/q8PPBh2bL5ZddLl/5ylftSa7+xVXypjedLs2YlCGZ/qfBaoloQM8rdZv6wF6pvpAfwQikP9TqA5bj5NRLq5Z45aFcn4fxcfkfb55HuseRy8PE1bmKFEv3XF1dgVy4HM4K19ONOOn80JJbNJCORUObDptaMBeFaGAivFx/cX9ykTwxd548rteq9WttyDaurV0OPOIwmT5zhgi/+LCMYqz+CjRGrRsgHRSUbhvSUR+N8ot4+tekJqvNaqZatXIoxGgg4u5rsBLO00gvu43lk4fLMEoJ2T0rT9AvJPJi1afJytYLlvQ1mHWCCq5RbF9YKfLYQll8z90y+5bbpHHCOOlta1Ujh5dkj0MOkmOPPUZa99UB7sTtFdsx2gD91DDZZdmEWzMURAOHZHpb8XPn5IKfV+HPkagGj2mJrK1WvhzPldYLu3z/BvSXdtn+OfVTXN79+lJbNnVjdW8qj5ZlUFca6uXwccjkaeUynmfC2a2cXo5nols06PW6P1Bl5OP8+cry9fJzeStcupV1lLLrRr1MuT6EPc8Letz9PJ2wP4un47us+wOl5WU2Fc51bUq2nL+pspvKL+t7LXHqGshlWOZiWXK1ZC5Aqgp5En4KQzNg0dCtc/0eacY6jBcgRMOK1SLPPS+r5syV+2+7Q9ZpeLz2ueuU8O3U0vsdPlP21/5Y9t9bZIq+60Yr2dDEccRNmo99RCIaaBtEA28/LtPtRANxSAYEEMzZdRpY98E0vZ6rJ+9p7tcr92rSXou+zSlbT6Ze2pZq+6bq2lQ+7UIG90o+w1Siei/XU45XJTcvlJcfqH25TD2tm8qvV8bTvGy9uvO8V4KZl/M68Oul5fmFSClpox+VN5kyedjjeZM97H6/AiTof2hOSIWlS5fKgw8+KAsXLjSyAeIBxz4OXJMmTZLdd99d9txzT/NHs2RrmLhBIBrSwB9EWKmNP/vhB+QnEA1f/ro95tW/+Lmc/qY3SVOLrldjsIzjU4BkMJQT1P7BlH2Tt5vnpJRUKhvy1mZXi0UoEBhpCOgfP3/u5T95vhP2vfCMsl/GIX2Jqi+6Qi9iXjQPk8aFVQMX32bGPezR0NTToUsntPNDYNkKkXkLZOndD8gTs++Xp664XF7WZMZODKQOeu8FMvPEk2TU/vuJ7LyjyA46IR6j/YNOlnt1ApxOIk/TWzpab0vye7U+zlDgnjVY9Q690xZqkxI2Pk1PrQLq/OKhLK43rHExWPAukXRzJCIIScAGY8RXKoGz4HGRe+6XeffcJY9c8n2rj3HvS3pNOvFkOeQNJ8l+R80SOVDx3WUn/YC0PNYikAtsVsanpstS6LUT0ZA2I6M2HPXTer/4pKtDYj5xlWCDznCBQCAQCLweCNApufOOyn1Pdxm6pyKNt4cnW5J2tinOPcspgmy74O8210H/16ez/xYd0DfpZXsPYbH39LPS/eAjsuDue2Tur6+WtfMfFaV1pUMv+uM9z3ir7Dtrlux6yhtEDj1Azfk0V7vNXv1FcoO+wZKNRBrD0ps6n9DEi9L2OipaYCSxNow2Fu3UULhAIBDYUgjo94xvn1/+tSv7Xr33FS7vPvmeR9jLF6MouoNqJWVBzezq3CCrX14jc+fNlSt/dqXcddddMkv7lDFjxhjBsGjRIrn00ktl2rRpcu6558oZZ5whs46YpStnx6N5WLjXSDSkCQB8bxqIQrn2yANzZhvR8NUvfcMe8ppfXKkWDacp0aC/WtoRQZoMoIyqgV3/e9fvH47j7X4qYOoqt1Q6mU8XxxpXxcgsO6tTE90v5xOvVphy6+lxOfIG0uV6Biqf60g1DayrnO91up/rqldvLpfL5mEvR9pAz1XWgyzOy5af1dOTVK3eXJfL5eU93/PQkbfL8113Hvew+8gQHikOHPRK37rqg5HMxBvfnpcsvzwNP3eFrlRIMzTu+1vVK1qtLdXP965Fy3BWeFfnS9Lao2ak7fqrzZIlsvxXN8rjP7pCetevlp5li21e2qPrVrs2rJfmKYdI2067yGFvebOMPVEHYZP11x791b1Xl09s0EFYX0OLEhIMu5ScUCuJXq2oUX+JL57OKAajGWgsLm9YStly96LKmgqsfr2RpxeDTMaKJNO3kcwLxV8ucAYMJFuUAGD/MLZd6NHlDU1KBDRrGryAFWZQq+Rsn+5hUaEtnl8qPb+9VRb9WMmbJc/IumcXSG9Tm3TrqRNdGzplww67yuq+Ntn/vLfJYb97msje0wqiQfE0coF+2qghrQL6AINgiIZkIEzVaYNPHoWG6o4Z+hk0KsnByRW6o6Sm8STp89FAuEAgEAgEtgwCdEiF826+33jPBVxW/fQeS2NUkrnYYK1HTY05Mrm5UfsxI12LTAS0W6PHo//moj56Ort6u6SN/pjOm814ta+VeY/KvGt/Lc/fcouyvMtFnl8kTaPGSXfnWulubpdR20+WHQ48WA54x9tEIH7H6+DfSINW6Wlq0Yt3XeplqZc+Hpq9Vfta+ltrAI3gJaF9d3oK9cIFAoHAlkOg6DD4TnLZV1B970fctwxaoQnIcLl87iNS1uH9in2rvaAXogAZeq1+8SWZ++hcue2O2+WSH14ip5xyipx//vkyefJkO/bykbmPyNe+9jW5RfugmTNnyoUXXiinn366jNMlXMPFDQLRQEfOP35lZKV2t8xWouEKLBq+9Df2nNdcdaVaNJwmja1KNLBW2D4VRRaiobjAud6VgCKn1vFB49KHpYN1F3E/Zcc9EBh5CBS9HH1SWsGVHrHyXSDqHZZ/qUir990odOU9f1ctOgAAQABJREFU6MaIBtR4EWbSjVoPYyBtiI7M9Jf2DXqR8eSTMv/yn8iTf/3/yc5T1GJ04h46vkqnSnTqL0prnnlaXtA9Imf82Z/IXm//PZF99tINs5Sg0H0GOnQfgb7GVl2KoQNBbTNEQx9fcI0yZWfBRKIc9O6N1VqH3mkbFRya5APV9DEkMhSY7KVCQ8FLLyMUkFfBHiUUmpRMsdM/UWVLJVQIH0F2PWdwq9gtufoX8uRfXCTbTVJeZ9fp0q2Ydmnlvc2tsmrNSnn4sWWy+3veIm95z7ul4ZADRSboL2m2hEIHt0rhsCK4D0C1RVWiQavQurQmra44SURzG/oUf7VUsT6WpRcQDTbo5WnCBQKBQCCwhRHQTkm7xIqzvoiEPJFcOq/iYj/iNDatJGlXqgsglGho0sxmjgO2fqwoQ3ntpHv1wiiZC/X0clxYMzQ70bBe7fNWrZDOB+6XWy79gXRcfo3sOnMfaVf9Tfry7dHlFezB0PHcU9I4dV854MMflfHHHqvWZbuq5Z5OAJSEoB/tbWmVPm0G+0ryrmCBBgsHWYqoOdoATSXD+lwVDCsyBSNcILCFESj6EIZpfP3cMYYrssy3DqLI9KEo8vlV7qa8vPcr+KxsrRTyPo0M/covf/4FufHmm+Tyn14hP/7R/8mnPvUpueCCC2T69Om63VazPPPMM3LdddfJnDlzlMccb0TE8ccfP5KIhtSRgxAdOsbMdJXVpRPJouHaq9LSiYZWNY3WAbE52NoK0ZA6dP9A/IN1vFNuKsaHRDz5hTWDxoJoSPjEfRtAoOipUt/ENy85OsHKb/5k+hfKBdwv5O1LlL5INb2nFSvSy0UoatVrRr/OsVGtGdY8r5sULpKOOQ/K4zffKi9/9zLZ+aCd9JSv0TqZ1rVkKNRfkTp085pV85bKjue/U/aZdZQ0HHmEyAH76BKKiSqjdGVjiw60dMMb7b1tUxytrM8qpP5ENuDbrL5eI/0ZXy+fNtAJWZP6bJAJ2ZD6slqigc9Jl+mqFYj2lirUo31ig5qFqBFHMvLS/J4u3ZOcXcttYq/C/IK2Vge3enqHPPG4PKMvnuVf/TuZuN84PXFiZ9mguxND4HQrti8rMfP8I09L29mnyN5HzpLdZs2Q5v0VW6xG7AhM7YcVW84J0QUvxZX+XKy99hjan+vz2EsQRoT6rc/W1ldIBp4kXCAQCAQCWxCBOu+iTRIN2jX5+9Fblnor7dy0n2w0FsLeZClbk33tWq92el1cJOlFOe2a1VqPskVf+MIS3QBygTx3/33yyA3Xidxyo0zaay+ZoNYK7WqV1qWExFq9Vq9YJJ0Tpsjk08+WqUccIU2HHCYydZoq0/XTMBq6b1mfKu/RpjCp4YeDPiV2W/RdR+/cpxbAfUpaNDUoCa/vRGsNjQoXCAQCWw6BomtIfUi1GpLpD/Ctc8DPHGQDZfiKelmPu5jrQA8Xs+aaAv79LjJfWLJUrrvheiMafvqTK3T/rWPNYuGQQw6RKVOmyAQ2Ate5ND/Idegxu2PHjZWdd97ZllawiaRvGkk1Q+Veo0WDN9uRMfjlId2wgj0avvTVr5jAtT+/Ws7QzSBtx116U3eI6+VMEFr8chH33WwYvyhmWTbZMCUuGX4gMPIR8O8MFJ+71AHqN8KT/MuUx104fVWLHlMTy3GXw/fy5bDHvUdt0t+Alj0j6+++Qxbf/GtZs+AJaXrwNtlxxynSplYKHPeFaKMOmLr0l5m1uvasd/vdpG3CDrLzW98iE046QTfMmmKDL36Zb+TXGyMjqYhmpC6b5vC9bxiqX3ccD/dpnOGnCdpkBos6LdcrmcSSyWfDpJ2LF4t9RirU29Frg9Km0S1q8ZWgpovs6lEs9RqlL4pG6lEiQVYoybDwcVmpe168oL+kdf76Etl5h8l6QugYO9ayUwekHIbEJmNrNdy13Xa2ueZObzxFpp5wnDTvs7cIaaP0aNG+oiX6Sxl0As03S12ti+qMD9ZGNunAmYUV9sua5WimPU36ayMWLhAIBAKBLY0A/ZI76z9JqEnUOB2ZXj7gz9+PaVkC/RkCKkdHm14pGtcEI1I1T/s8usduvRCjp7PJAH0wREO3UhDaD6+6/RZ5+q475IUFj0rrogdkpx2myXb6nhujfXaXWja8rGTxqjUr5OXGNmnb8yDZ8eDDZNoZZ4kcdrhal2HWrBXoL5JWD92xOmuO9t3N2uk3Q65rfRANvDOFpR72gCYat0AgENiSCBTdhHcxGk3fPk9wP28DQuqq4/PUh1ia5VS6KNOFOP2LdTToowNwR4Zeq/T48rmPzpO7dePv22+7XS677DKTePe7322kw/7772/LKLbTsR3HYLbqj0kQDPjERwbRADgOOMBo+JEH5tmpExf/9RcMkF9e+Ss5/bQ36QBXo4zAiw/DkfaXAsKuCj8XSxuRFRJkuiBSTEbcZUFPelW+6x9InzfQ/XqVbCyvnvxgpb2SenPZgcKvtl3ow9XDMK8LmXKctNxtKj+X3Vi4rKcc31hZ8l6pfLmMl3d/oPrIxzl2WdyDZFftGZKoEW8u4D6C9ZzrLudRzst62H2X1e8683z6RX6dTx2kDsAWPyVLfvMrWfh/35PmtZ0yZtVymTBqtHKMuvJUB1+sR23QX8Q5VYITMNf2Nsvql9bLTu97n+xz8knSzEkJatUgWqaY7RY9cdEmHQxi/sp3vkF1Gj7Unz8LcRxp5XAuh8ym8pHBeR0uj+9h8hn5qm76MogGJvxggwifCV0jY0kuM8wgk76wQ608dElEw+g2aVBrWsa+iaTAcLdLmnWg2c66Cn7RWvairL/zDnn8qqtk3dMLpeXpOTJp3PYyWjfZ7VQ2u0sHpH26jqVX8e3ShqxX0+BVL62Usae8SfY6+miZcOghItOmiWyvG29SEea4DF6xUMifhTZonB/9bGlwAbNG1UH0plC6W6I/aBExMS1cjQ4YyutFvhyvVxAZly3XMVDeQOn19A+U5jry/Hppeb6HNya3qTx0lJ/T9eLXK/9K08ry5XheXx7eXLm8zEBh1+V+PTnPc39jMp6HLG5zMCzrLceTpnT3vLL+PJ7LeP3l/IHa5mUHqtP15fmbCtfT6WXyvDxcL9/T3EcetzltynXn4aSh7t3VW6ZGjGggkmdQd3HRvXr/m8R4U+qEXeU5rciW3CGAlUI3AXXW2akC10OnTRjH3mK6R4MRDbr0QubOlSeu+oUsveE63ZtonbStfl4mjB4r441o0E0e9T21XvvkNWq597L2+p36smzb/zA54l0XSOPRuoRior7jWnUZm1qgqR2gHQbUSj9bNJyqk+MB9aJtnlhpUyHi8SJa41E8zy/HEfY092sU1InkcgOFN1dvXj6vqpxejueyefi1ym1u+fz58vrz8MZ05Xkedj/XkYfL+R5332Xz+OaEvZz75TKk539DLjeQn5fPZQZKRybP87D7uY48vLH8cl45Xq7T9SJXduW0crwsn8fr4VYvLS/j4UKOEyU4wnLBggVy5ZVXyhe+8AWTgEg47bTTjGRgyQTLJY5QqymsGVhSMVysGWjsa7doAHQ6R3wIV+2P5z7wmFx++eXyxW9cqAkiv/zZb3SPhlOrRIOl6k07TmN+FFD/7NxHBJwrF2wzUuYVvgmpBEQDLHSSIDVcIDCiEeB7kRx/9el7kuJFLCWnpEI4/37k2V4OMfv1nAT/TiNoYQ3wHeQLq981vrs9Oj/tUV+nwDph1onx6tV65OJ8mXfNNfLE178mO+2zq5IM+muOZrdpmWYlBpimdquebh146e82OhFeI4ufWCzjznuv7H3CG2TyrMOlwX55n6DKtWI1Q+3TsnzFrR20xdLVt1MUii5Bo6+HoxmVX9NoC46Eoi/jFzSSuYCOT0az9Epn8vhAssIoQCIQadeVve2NSjJAUvSojz2E7nKuV9t6Hdh2aef67DJZ/pvfyOzPXSzN43t1V+ExslN7u4xmE0h9GWGF0KgDUsiGHv2c1nZ0ybJ5j0vTiafIroceKlOPP1YajpipR4rqOmHrM1WnDnSNcEiNpbl28VF3F88EO0IyDjHCPJOnaTBcIBAIBAKvCwLW72hH5H6lUhL0oo/y/pcwzmgGJQuMaNA+z95zdJiQDLxPKEhfmPdqlQpgZbmUAu5Yp++5NdIz+yF54EeXyepLLpVxB+8t7X1d2oU3al/coO869OieOaq/U1Wv17545SOPS+ee+8uMj3xUJh57vMj0qSI7bq9kboO8pJYSLWrBN5ru2BtPdTSLjtabVfTH/kyaEy4QCAS2AALWlfBF82tz6vAvpvsDlbF+RTOtkuQzrObrnldHto+zCD+7eLHcetttcvvtt8uKFSvsqEv2ZXDH3g1vfetbBQuHHXbYwYgGzxtqfwsRDY8XRMPn9fl65dorfy1nnPbGWqJBkQNcezEQroNEka0immuTHBWq+EWBgmRg8uMfVB1VkRQIjCgE+G7wrUn0mg+PUqrnmIhJpVDeieVhcv27RsfGlb5MKmVfqswnz75zOvfVARBWDa382vKSHqz42BPScd/9MufO22Xlf/+n7HrQNBnXohte6UYEbSoytk1/steaXlZ2doPqbdRf1F/e0C1LlyyW3hknynZ77St7KSE58Zij9YzGnbVutQ3QwVsvywe0nE3waY8TDXoSA31I+Vlo4pZy4GSDQSq19QXqF0QDFgCMD/P2VHFVosHfJjwDZgsI42z0q+RLiy59UF0QDSyCwO5jFELr9LC0FTrAfWyxPHvTjTL7y1+SsZNaZLsdd5EJahrH8gq6RTBiJ3XC69Vsd11Xj6zX89x7dDfORrV82OOsM2SiWo3ItGm6PEU/vGY1x+WzYySLrXC1sRauPA8imufPhRhJXOECgUAgEHi9EfCuyt4JXnnWR3lfhY+DaGC/A8o1qwWXneig5KxZNNBhkmN9IRLuNJ3NGHXPBDM1aFT5lXqyxFNPy7L7Z8vca34pfVf/WnY+bF9pVwZZ33S6lwJ7laW+GJ1Yl3XrtfLFlfJS+3ay6/G6jG3mEdJ6wjEi++2jgg2yoqNTjRtG6enOToCoAgzaeEdot2w/4OGrYttaQoPhAoFAYMshQC9g4xvGat6JlKvzriLPJ+zxsk9577C8gsK3Ya1m59XZeE7TOjo75MUVK/VaIWteflmWL18uy5Ytk/nz58vNN99sx12i+swzzpQjZh2hJzy+yY6/HDWKJQTDww0O0eCA8slo+OHCouFLX08WDddcmSwaGpln+OCa5wdkvXyyQFLumEQlkkFT7WWAX4QrdaoCrdePCKLE4Dgq2JiuTeXTis2RGZzWVrU4MHnby+3I45sTrmp/ZSHXjY8bqE0by8915OWTxld+L+vzuvP2DSST1+8ylBso7Hmu2+tCj4c9D99dvbzaOrwlVaLBU9Cch9GZpNDgF6l5LcQplWwOfLCkEnzvEGRSjc9lgvqLja6ZgN5r5gu45AXpuP1uWXrtDbL0ifnSM+dG2WXynjJKTUk7N3QIZqET2rXj4xecjg4jGlr0eK/1jN00/nLjGOnqbpV9f/99sucpp+gvPnvoURU6EW5v08m3Ftaric2z6IlZOqHBvub0XCSlhlkgu9FQGozzMH7uPN/T6slVZcjVqqukB/qLRMaFtKUqnbLSGBGiRAUR8J2/wFQHmrb2RAexXXp4eq8OWKEauFrVmsF2T9Bf0GT+MyJ3zZEndJ3eU9//Hxm7e7uM234X3XiMX9D0laS4NusvaS16Ac86NdntwCxYrR02qF3Eiofny+SPf1yJHF3CdoAuT9leLUbG8CLS+iEZwNaWUmgSD8iljh/8IJNYXUHTuXA8E4+dntafmBTC7pOfpGrTPY18L+uyedzT8HO3MZm8fq9nIHmXRbfLeJm8vjyfcC6T63A5z/e8ero9z8sMpHdjZV1HLpOn0Q6Pez3eNo/ju/O8vAx5eZwwLpf1uMuVZaxAcXOZPI1wXiYPDyRH+kBtyMsgU9ZXrw0uk+sl7LKen+tz2TzPy5TziLtzHe6TTtid6yPuMp4/UHs8nzIuQ9idp7nuXD6XIex1erhePmmuy/PxvWzue37eBs/3POI4l0kxjw9Uk5fy5VxeKvnQCx5CU+q9GMDTk/Uq0dCoRAKnIVl59e09RweKszhkhFqUMetXqwVO/FmnJMNTd98ji++8R8bc/ZDsNkNPm6AfR7+S45zcQx0cVUyf2qOd50tK+K7SPrZx3CSZcNDBssc5Z0v7LLUuGzta1lGPbpbcqlYNaDEiu1OLUi0drVo6mK9h3gzVp9J0c5QayDmejgRyZfmyDPkuX5alfJ7nZT3dy9bzcxnCuFxXSkl31+t+Pdk8r17+K0lLtab2eNs9rR4GrnugvHr55fa6jNfjfj2dlHXn+a5vU76Xcx2U9zLk5ekedxniucvTPew+coRxA+lMubX3gWQ9HWmvo6zf8/CRz/PzMq7L83N5L+cyaEk9ig2DSPasvHglrJm5jIfdp6rcUc4vrQDykB6HqTEXxRBg7Ic18MqXXpRH584TNoVs0eUSjdqvcGHV8Oijj8oDDzwgV199tZXiWMuPfOQjcuqpp9oJFJY4DG6vmWhwLPHpE/FnP/iobQb51a9+3h7x6qt+qXs0nC561HtClFQEHWx8dST5vTo1KpLKRIPJ6o2yaq7GB8WVdGggXCAwwhEovjbFU6aOMUVSTv5dKCg7+36Qnl+pTJLme8dFB5v8jHQwwWpJzPx1Kyv94uk376ln5YVf3SjPfe8yPaBgtZr7L5ftx441gqFDj2Rs0tHTOLVooDTrVlkx0KK/qNumkGqh8NKGPj2GfI1Mec975eDjjhM5ZH+R3dWqQXfQTfsd6CSaVhkrqVr0EXuVHU7f+dT29Bxb9g6yYMNllfNEmkizIBr8RYGcX7wwGHgamZ2/VWg2iWxy0ax2DHoMBZYMaOJwtDYlGhQl3jQiv71blvzoWlmtJ3p0Pv+4vnBabKOfZi1vR1FCxFSIhj7daLNTSQJ9UbWN0vXBDbJ4th599K53y2HHHS+NMw8V2XOPtBcGA2SYBMXfRrR63Fp6OI0quBiP8GzsRYZvfJPWmfpnlbFPlLLhAoFAIBAYfAS8d6Gvz51PBryfzeNVOXLppdL0POnivUFPy/I03m/ad6oMfboefqm8QpeFm9VSzN5t3Wpe0KVWZRvUqmzBY7L4xt/K02rCvGbxUtl+5Qsyeedd1aqMHl616ruwW8kGulWWsbGEzfbL0Y50vfaza9d2Se+0vWWPt79ZpugyQZmW+uHelnbt8TkByGh7Uc5ZmrFqoFE0Tq8+fYfyhuhPNGh+uEAgEBg0BFKfksbUqQdJquk/vD+qVsZ4qDreMwEXdGH3XRk+nYT+Z9xI31Y7ftQfjjSzXa/n9ISbm66/UW7Wa/nKFTJjxgw5TsfIo0ePNuuGRx55RL7zne/I008/LX/wB38gb3/7222TSDaHHC7uNRENOZYApWNRA2v2w4/IFboz5te//Ff2nFdfdY2ZczS30ltqkoFeIA/gXDUfX5FHMukexc/DXlY7Y+rnik5YQQi3DSBgXxp7zhSqxosvVA0G/tUZyPdvTppApokkGt3CgXCKc843v9gwIdZvXI8Ovl7UX9wfXSiPXXO9LP/6P8rYqbvK2Amj1SBBN6TRQl36ixB62zDx11IdnD6hA7Cm/5+98wD0rKju/3l9e196WViKgAVBUFDAKGBXgpVoElPQWGKMmvxTTFQS0Bhji4kl9lhiV6QqoIgKCshSlt6XBRZ2YfvbV/f//Zz5nd+b9/Ptssh7lMeZ3fube2fOtO9999wz5545o6/tEslMGy/YOm3Ls/rW22zuC463PZ50oM0+8hCzA/fTZHi+l+Hbvk/YJazhTJJQvu5Ezz1pwn8KBkX+c4ZDi0pkEh4viujECG3RTjcYVINRUQg0hGO7ANCyCf6xX0W7RE72UnfhFv8My+621Wf9xG551/ts+sJpNmWXXfweUFGIz20Savkyh6Mzqh2QI7LNwqq7e4rwHrY7brnGOg460hbte4DteJQUOYfqa9rOO0mARZLV8hRpfhByOyTwNp1DqmvDeFrXC7Fdy1REVoYgRUO0G3GMOeNEIBFIBMYTgdHvrMJxaqUC53CxEpfJgTNkOkFhGLFO0PEG52LCLpWCp5eykKF8GJCinCm/jOmkCMfiwQY0479/ldmKO63viivt6nPOtfvOOdO6Zm1v88RjF06XNYJ4JHVjzTAsRQMWZiga3E+D+C8L4bDMu++2m2y16Oe96BW2i7Ye3vGwQ8322VtahR53CqkNjTUWeY5Xv7vQM6t577+sGjZLIV3eEiRmSAQSgYlDoMFHmg04E9ETXkLhQuU8coKPEHuoGVekFWZUnmkRQhKOw+FGhSMVYlSOU0V/98p77ELtNnHGqafZZz//OXvxi19sr3vd67Sb2wJbr6UUt956q51zzjm222672VFHHWVPfKIspnQ+Qx/6Hi1hXBQNsD0OFA0AteSqK9yi4QMn/YuuzE4/7bSiaNB64iJcC14XspWJ2Rhf9R4oBAkxUn0EyvoNgwmXG1flBlXGiYAjwJ8Pfx+tcQ3P1vJqujgPeq7r89Z88gjRfpx7on6ibNDV+ZFH2kh+/ZdeM8Y6nRKlDOXGPoJtFjFsRNnAo1VKBPPEhaNEJqUjBelpX3efbb7xVlt16ZW2/MJLrffz/2fz9tnNpk3FxEvzWHWFLRJZ68qXHlpAAGMpBJPjAVXfKxP/TbLR79d2l11abjF9+51tx+OOtalHah3rzturnTZNvctkuF2T4S59dWfqHtNsEXgviesACvSeEOfEddhSPulBGzSUAwfSnWXBw4JQYwzNdNA7nZehtwpF0h3pFOXxZN4mxYsMCQoHQ6UCtsrbuN7stuVml19jt1x0qa36zOdsvpQDM2bP1Nj1D6GWUqonsOC7Wodw9SUXOud7Xa++st2/bq0NaqnF1CkzbfdXvNRmH/N75stT3KGmFEDq/JDMfDtd0SCplr5q64nN8vUAj27rVlrR7zj7HWkx/nYKLgyTUGMQ1+BBOnGEoOM68iMv0uprzusyQRP1tsZbo4+yNU3dN9LrEO3WNNFe0NXXcd5aLtLrMpyPVW9rWeiifB1H+Tqtrq8uxzkh6i5XI+1HOuWjvqCJPK4jP85rmrrtqKO1LPR1WtQTaXUddd1xHvk1fZzXNJFW08d50EFD2gPF0I9FQ3rUWbcX5+RHaG2H9CgbNMSt7bSmcT1WOdLHCnVfWsu15sV1TdeaxjX5kU6bcV2XIz1ot3bemhfXlC6yXYlJJ/DGox1+4XtxeGM0iJzYbFhl4ZesSxCvhl+ibCAuRLTA+0kWB9q+sks8b4qW9vm7kKUTEuZ7f32R3fHrX9udV19vm6+91ObM3tlmy4psupYHwhq9Npdr2RYYxa9Uxlqewe4TbeKtbfKfc/+G9bZK77Dh7bWE4qkH2YGvPN66sGzQssJNqqFf3zC1P4X+tVkXLxTpOIb1fmyTNTAbD9FG6bPyqvBAuAcMxIT6/kRaySl5QR9p9fWWzoM24rreaK8uC13rdV22dUxR35bqGqu+1jKt9cd1xEEf18TRXp3GefQ94i3lBy1x1BVlWtuL/KhrLLqgqfNIG+u6tc24bm030onrergmRJvlqtDEeWse6VFH0ER7Y9G20kfZKEN+lGvNi/Sx6iCNAE1dlyc2fiIv6vltupIT6RGP1FFSoIJXEIeM6DRkR6GYtzLnJSgiCc5TjjbnQNQCBZ7JWASwbuMGu1OOIC+79DL7zne/Y0uWLLGXvOQlvjSiv7/fNmnp8YYNG+w5z3mOHXHEEba9eIsrOhHAHyVhXBQNiJoAhakHiobLlwqQb8qi4aT3+zDPOO0Hrmjwr2VItM6MGwi4oqFx3ozKVCf+vOJ1MnLDmoTlhLtSJP+WjLxMBCYnAjV7q0fIo9B8TloygudFzDMb5zzDpU5SCWHVUOKSBvPDJ4Oe8kFZMmySJcPyZXafPHDfc8FFtv6GW619yZU2V5rWKZqYDkmwa5dQ1+1fzeWBW1+KNuvZ72brHVkl0BJffPjijompb3s5LJ8O6/pt4Zv+yHY6+kizxbv78onNst3v71e7bSwZQBgr1hFN3hAdfDhigArwoj14uo5ILliSWb62IQR7ZtA72hIaxQ9x/sjyCVQVKHDaB7VAFx658j4buvRyu/2cn9i6628yk4fhuTNnyrWClqCI34EvQuegYg4UDlSLr4apshYBGwTdTcK4X0efFAfrr7nVdnzHW2zPFx5r9gR9SZPSwnAapHKDvld7l5CVgK0vda4FkrNOlEJsxcb4GIYfFHCe2xxQniQCiUAiMK4IiMsoIF+W91M9yYa/xT/4a9MHDoU4YMYEeJaniUeiEZBylWV3LFDjH5lcoWiAB7eJZ3ZLLmUpml462oNZCl+ZJ98gOfbuMz4nC73tbKYs8BbMmW/TUChQufjvEFtgKhR/OSxlQ9EwLCW6+Lredx1SNKyT4vY+1b/66jus7bD97LC//Gub9Sxtd6kdKIbkj2hT21TVhqJBCnV1bbNeBUNy6tshJ0dtPRpIyrmOcf4kAhOJQLCQkH65/u1QUuvZaohHxB6ioMfNVGXxLEPRSFMESRzkEBC5+uQMcsP6DbZ8+XK74YYbfLnEFO02RmDry56eHpspuXDvvfa2vbRjWxcf9B9l4SEpGhhLmaCU10CHmCMMe8lVS2TR8C07+aQP+nDP/OH3tL3lMdLISqDVnvEFTrIAm8PJ/DqmNyRyTohsj3UnIvbM+PEbGRcZJwKTHIF4KOph1s9AfQ5NTc+5jtCmQhoHpHFVGGg8hVCwbgxhSsf61WZ33m4D1y21ZZddaSsv+Ll1rt1oU9ZusBmalHbyhR/BS+2wZKJNAhLm/NTZpUkwfgUQ8VzRoHTM9Xs6ujUZHpZj7zU2/dW/bzsffojNfPL+Zrvtoi8+M/RlR2KhFA3t7T1iwNiSMgix4taxqt4JDQFWxA0864m4d02dKKjphVHTKh0UB5tCI1/SipKB7Szb5DjMtDWlLb/bVlzwC1v65S/b3N4+m6MJf49Q61I5lNW0gbJhUBYL1MDe7Xy1Y/nENMx+hTZf08AYIbdPX8ZWXXWTTX/tK20v7ewx9SD5ali8SEKu9nR3iww8biA4d8qAV/hiWIFyh4Hx8mpYNCghQyKQCCQCDw8Czl/FQMX3yr9otlgxlPeA0nihFL3BSBykxOTBw7AMUDyg2lA0lPecc2RxPfFPFLYEGOxqvefuutM2LLnclp55uq396qk2Y7/tbK6sHeZOnQaXlB8bqYobB+IsCoawaIAnw59dISK+jIPe9eLHq2681fr23tMOPOF1tvDpWia4zyJtO7ydDbRNM+1BUf6pGygahmX219Glih998wdQypAITD4EnOcUFhAsJQZJFqE1rtOcoMFG/JyfLV3X6Zw32oZfDcnidZOWbvXrgMe4NTBVwVck2/XJF1enPtzhr4EYHzEoGlA+4DDy0RIeoqIhGD/osG4btjuiaPjXk/7dx3nmD79bKRqQXhvBlQwjtyuUDBGP3Mr6rJRFRvf7QdNxkMV5VMl1HSIv4jovziMv4khvjSM/4tZ8rreWty35Y9VZp41VP2mEGoNWuvp6S+ellof2G32JWuhTtBcxeUE3Vp+Dbktx1L2tcdQT9FtrG5o6vz6P8kETfd9a/XX5+jzqirKRR3pgFjTRTly3xnXZOK/LxDlx44AMmaoOI0qGRiUIXzyvYnb+VWXVPWwvY/ddeL7dIouGNd8/x+Yv3sFmSSHQIzpY3ABf2QWg5rB+IONxHk3BPfgKPyjBixLd7EKh5RMrV+hrz7OPtTlP2t921NeeKU95otm8+cyqVbhLfcWkVVJX7JZAxxvd5LQZaCjSW8+DKPLjOjoX18StNJFWp1Ou5ah1ILWigVUJ+F4c1A4TKAPKIhCZ60rE7ADndRukZFhhdvV1dvXPfm43fPy/bbcdZtp2u+6unTcGdegLl4DEqqFf/i6wZuDecA+HhSUY90ix4PdAyhmqZIePPp3fc+dNcrR5uC3cay/b+ahnWcehMtvddWcJ37IQEa76fqfQ6V/UZOKAJkjXivUS8wHGmGOskBNIJy1CTUda5EfcSsc15aNc5LfW+WBpxqKv24r6o924jvbruO57nEccdPX1ls6DNmLoCHXbUXasvEI9GtNIi3qifJ0eaRGTF/VHuUiLvtS0kbclWtIJrWVKavndlrzoU/ShtXxcR35dZ5SFps5vvY68qCvqiDjSiaPOKBM0dQwd+UG7tetWuqAlrkNd/5barulbz6M86XEe/Yv6okykc133r6YLmkiLOilT50V6xOTXoU6vy7XScB1tEbvQFwUamfHiitlAa9zaFi8hXh1iaX28neQfpzjr1XtNu0p0SlD3RonEL235nTa09Cpbxm4/P/mJtf/iEtvugJ1sthwbT4EnirnCgxHweZ/hJ8d3sYAJK5DKO84tLvQrozK3MLt/xS22cdpC2/lZz7HdtPtE5xFaJrj/PurbdPUL3t0tiwbVj7iMDsRfnooDD52OwpzrsUKMvwW2UaStNLRRp0WbW0sbVWHjYlvog6a1fKS3xjXdWHmkEeo+19ect9KQFiHy4po46qrTOI/2W9Pjelvygzbi1raijtZ+QVfnbemaeqPOqCNoI69OJy3qjXNiQtTDeV2mPm/N45pQ09T1t+bFdd1H0qLtKFvXRz4h8uKcuLUcaRGCvo7JizLE4hP1ileS6qM5Lsq1hqiH9OhvnHO9pQMatUv+Zlm89uuAj3TKShgH6nWA72BBxYHyAZ8NnKN4mGSKBvAo3B1FA1/mllxVlk6c/C+1ouFoWTRME3GlaAAxgRJ3NhQMsOU4D1Dre+bFKKUb0RTguWmEiMtV/iYCkxeB+qEY6++etJoGJOKauD4nb1RQYWaoHDxURASucbxwz91mv/ip3Xb6t+y2m2+z3qWXy53CLjZX23Tx8YWqkY8GNCnu0xrXTj3nM6Vl1Y6UMiWVwy0YpPJhinxDpwST5F45H1yvfcUHFu1h7ZoAL3rh823O4RLCttvOaXwLRji/BDFftIoEFn1T6sMa6nbpEsEHVWJk4JCDm3xKaXQfB5hSEbjCgFdHm8TLLuHiE/rVa80uvsyWn/lju/Paa2zDFb+2+TKNm6OXR7cGC77IsVgzbNRyFBQNXRJ6+YqGUMsLRxpkoYq/BgRfztrdomG1zID75m1nnTNm2W4veoHNPuqIsjxlmvDs6lGPVLEwlbcGNUAjukQqx4yY8eoe+SBZDgNpjYEuMyQCiUAiMK4IwGcICHwwnLj2E100kgt/KiTNc8oRKMNrhkOsDJ31pmGpVcWYe+CdYnRtOH0MRQMaAZaN3XST3XnBz+yuX1xgm5bdbLNWLLXtFuwpRa6WCYuEr4zFYkHViv+yVJDAxIB0Yt6ZWJnhOwcLhz7x0A2beq2vU0sAp860WQcdZHuc8HKzpx/slnt0cnizLNIGG8p0KmyOmYsMiUAiMKEI8Lw1eEYoGmgvkp0VRQf0iHtofUZ1HVlB2ixHRhxl+lyuOSc02i48SzKdCiLbhWKhw62Ei3KBtDiQ/cjDugHZ+tESHqJFQ8GmbMo2LCEYw7PN8tHA0olv2ynvO8XHeebpP7DnPU9LJzpYV1KYOSa+bWLMOIQDb6YdBXfA4Zw3AjFGZyMhzguVKFSoKcSPkOVZIvD4QICH5oFCPDTQNc5jAkwcz1LjASzCFl/Jo+7yYOpBk4QmAcnWyZz0lpvs/l+cb3d857O2Rl/g2+W4cOFsrV3VLgc8uQR45oCYIIoFvsDPZA9gNdanaxhjp55/nwirBzQxJAEMfw3rpGxYJ9oNPd3aBuzlts8ztYZ10SKzWXPcaZZPfPX13dpRNvCl/REIgLaVAK6uD2nQwKP8JUO60vo0/iFpqnvkSMx11Do3lkxskO+LZcvtPi2ZuOWzX7XNvas1/98snwttvoXaFMUobbg5CLKbpMRB4YCigS9p4AhvRWCGc3bpniHgco6fhg0octS5DXffadu95o9sryOeaXbAvmY7yGJkzmxJy+DJobuIosEHoXPuPUoGLW1x5TCadbqRIRFIBBKBCUcAzubcrdGSmI/zwXjbNLKDrI6DTxGLjcHaxG1liYD9lninrrvEL92CAR4H894gXsy2wkuvsaXn/Ei7TJxtPVPbbK52WZo3dborzPG/AO9FaY6CAT5Ln7Bs8CVtyuOcFBQN8G1noWoC67I+daT3ilus/VkH2d4n/pnNOFw7UGy/g9k0+c1BkT4Iz2V8OlDschpj0WmGRCARmEAE9KzBCvxoNMPjF8qC5qMIr2lejKYPNkTxIPE6uEYQ5EMOMQFiQsQ87w1RjMKxXAISlAlhsRDpxKRxoGSoFQ3kbemaPA5CXWdNT17Q1XW31gsd/Xe+5/zZU4RZtFCuH/QvGPWL0fZLs9sj4RMQr7j6Kvvut79t73/P+7y+M8863Y593rHik1g8DGitdr8c6gzKw7m+m8kBGUMsLJkOltsQMbeHfyX26vyKs5JaYirhOkMi8NhHgCfigf+ag+Ex3qCmJMFjJRIHoxyV51SlHGUb8pceRD3RfMmRkxn8JrgjQGippEvXq9bYpssutpsv+IltuPFaG7rqApve0WNTJUh1a5KKQFWeV3goWljiwklRKuCrYbMS8eGA7wZ4KRNmOsqX940SwO7XPuYrdawa6LO5z/w92+OA/W3x07SO9Qlsd7lAnRX31VIA75MmwJtVrvALOkpgpIGIJ4zbT2DJiMqoStW0xlgi0AMOAul+qAB0pA+K/22WIOqOMmHIGoOtQbC9Wl/PLrSVV19tvZdcbDMkEk+TCUOb/F106s3UI4ETHF2QFW7wTTTdVFzaG+Gc3Ak341XMP77A9elYJ2XByvvvtZ6nPMN22Wdf2/EZh1jngfKFsctOEnLlRwclDpUxQF6E9A3MlTYkRcVmtd8Or9cLrfF+EtHvGmgIVB6J8Ei2/UiMN9tMBB5rCDT4GcpTjgaX04tEbEMT9wbvr/k/78Xm0RgubAwjBYyz3Dexrl3mlBOEzVquN5V3kJabOQFMfoW2s7zhZlun7SxvvuRXtv6yi+SEVztMDG/SLhPyQQS/hX0oEJVDvNjPiUN4V89Ey3wBvk1wPuyKhjYbkFPJ9p12tzlSpi888CnW8+QDzXZfpHet+PCg6PUu7hfP7ZDSvWMKivXi28gryp/HOQL81ZW/qccfEBM19pF6YQMEUiI00VZiOSe3QaGE6srP4QNQ8q/EpRxXLhvjA0sMoUu8x/lSqbQhd4koBHOvO3iK6mjwElF4qKfx3lajHqdTFzxfaVxzzoe+UEhwPiB5m+twJEkatHU7pOF8EjoUHdTD0Uo3VvpDUjQEqKw35mtZDybVCpdLC/ydb37DPnjS+/z6rDNOl0XDsYWRI7lK0SBPFuK+4vr6olcCtUVooNS4OSO3Rul+94mDVjFvFWfxpNUZXGdIBCYbAvH3H89Dy/hqJsS5/pdQPxtx3ojhegQJQNIE6vlEZNLzKSHHJAzJeYLq0bN699123U/Os2v/8z9tevcmm6Nj3ozZmgxLCFIVtfBVhK7yRIollQmxYrqEV28UDRToZ8mABMYeKQ8w3cdZ1r0bN9qdK5fb0PaLbd5OO9vexxxrux4s01KdawGadqJgtwTF7Ibg42WQMSYGMlFhBE+E22ix2GW1ttkAHiIOACEAEoeUDfLmU/AF83tX2n2/uthu/Pap1n/XMuvetMoWzJhm0yVkDvL1TcqGbmGGdYib5VKHmvCqdc5LjR7RalEweLauWDiBgIqDTlkz6J7ev2GdDUyZK6dBM23+MUfZ9s842KYvXmQ2V1YNs2U50iNBF6sFpGIUDVg0eEMaBHhz7xx3VZ8hEUgEEoFxRwBOJqYT/NKVDTAhgvIwj4MHwZ+cFykm3Q9FQapTDxKUMUFuo5xnioDthbU9nL58Ff7cq+v1sma4/Q4buOpaW3b5lbbi5mtteNn12k5uqk2T/Mq7C0VDmT74ib/b4L+8RonjH4p1WiuKhlKGFCz9UPr2iRcPTJtlHTvubDOk9N3t8MOtYz8pfWfLQS882MelGvQOsB7x42bfy5DyNxFIBCYCAT1zzj+IeYIjeKIuGrHPPSNNyTUp5w0ZrWREJrF4FcWQ+5ALXZ6STEUgHbkbMsyt9MzDTwgx8WcyX4dIJ63OI53rUBygJIjroCOPQB5LLgh8DBw1FtIa9UDHQbkoSzukRXvQcs5BGBdFgyDxD19igx5fdsXV9o2vftU+8sFTaMPO/sGpduwxx2hioKUTDMpfGCoVGmr64v0ZDZ4X9owGgWc3iVW+Ktd6w0vh/E0EJicCoxRu/vCMjDMuGw95k2E00/XgxKMGw0K54IoGEfi10lA0kLZOW3vdtUJOse6y4Y29tvKuu+1aOYFc9fOzbYepnbZAD/10MZhumKH6RLX1AQujSlc0eA+L4IVOEmFtWJZQeNVlS8bp2pWmU5PbYX09v18MeNm6dXYf45RCYeEBT7FdFu9tCxYtsunaJ9j2WGS2s77AT5eygQZ8kMQTHNQdcc8Gv+IiQtW2D7iRR/JYhzBzJcM995otu8P677nH1ty+zFZo+6L7llxmHdrVY/pwn82TcDlNyth+KWfZPq0oGopaw2+ZVx9iLX0p+Ho3deXCsHcXs92y/XC/4n7dmI1adrJBSyM277HIpu6+q83ZZy+bI78Y8/ZcLAeRu5rNkDLHX0IjtflYYvyNIdJqhkQgEUgEJgQB56diojA85/WKnQeJATHxbioaGuchM/LuiBDyITKn3jnOlNFLcO3KdCl8UTDcqXfdzbfamututHtuudVW3nSTbVy70no2rdEuE7LcUxlEcfeT49y1NACL911/qLL6xxuRrjKF4HAFsBLCV8N6vefWiw8PTpthXVI2zN93P9th731s2l77yMJMPHi2FL8o1vkox3Di0GmGRCARmCgE9KDxUMND/Gi043xE5xFDVJ9Hd3hO6+B16cc/3CiTCT0f7kJ2hYcRkMX9EI9CRvTdvkTXWl+h3qZflAFYIjDpR5EQCoN+bd2LJQMOJnu0tS6KgjoEHWmjlAYwNAXq5AhlA3WHosIJqp9xUzQIHlaVuXJmyWVX2zf/93/tgx/5gOnbmH3jS1+zY486qoDWqzXIbHsHs9f64uLFXkTed78bumiEUeByEQmKIW1eckFQHGkl4cH/Rr1R5QPVF/RjtbS1vLHoH2zalupvTW+9rtup87Z0XtM/2POoszWu6yGPMBbWWytXSv32b5T57ZyRv5ugibimHSuN/G1Jb6XhmsDYtnTuBI38mo70+ro+J8PrI5EQcblqXjaYwsh1dKJRBkaHoAVzI7hwprpQNJC+Wub8ty2z+2662fp0vkYWDXfKKdbAHRfbjvN3trnaz7dHjFaGVPo3trKBFv0rj04aVP5VvlvMdUi7KGzsl4CnMLVrigwUpuhDuvwzqD/3yJLifjkv7F23ynrmybx0u51szl572/TddrXt99dXnz0XybJhuipXX2P4LcMrGKly8iMvaJU0Ko38Oo98QpTjnHwwrWPSIYLOheJyOeoF5XmRrgssBOT0Upsj233X32hrb7vN1l5/g22Qk83B1XfJQqTDZspUdraWl/EFbdNgn26JXgosT2G5iDpQmqIjRbTViZ/zG90r56gb6Foxu5Xxm9rvtI36irdq/Vpbh7JBS9hmPPWpNm9v7UbxxANsxuLFsm4Q944X4aiXLQ0woEYoXSiNRhrZpEeI64hJr6pwsqgnyhDXdUT6WOXIi/SoJ8pGepQnbqUhDboow3VrGCs/0lrjuix5hKg7aEvq2P0OmtayUYZ4LJo6LdqLMq15UXfkB33QRXp9HWW2REuZmj7qiHTiKFvnRT55rW3UdJFHWtRTt1fnB02dFvVH2ag76oiY9CjXSjtWGdKCri4X55Ef9ddx5BHXoaap6+a8zqNM5HMeeZzXgXRCTVtSym+dX58HTWtaXEc+cd23Or0+p1z0oT6vaeLc29BPKBlCuwoPdiWDYufHjRje5oEGOBqBU+hd7tRXRBQMpMHfqBOfDGvWmd26zFZr+dqKK5fa2hV3Wt+q5dYxa5Z1S9k7VQcKdbivXK35UYx4USdgUUZ1nHELyi98klzagfvixwGnkVzxit0oQX293rMbe9fbUOc069luZ5u95962/YEH2uzFe5ktXCjfRDOkpWBcqoc+cxBoIoKPRReRF+nQRF6kbQsNtFEu4tY0rqP+1vP6eltooK9DlKnjyI/+13mtaUELDSHyOR8rjXRCXSfXdTmu67C1eqCLulrPx6oj0rbUXrRV00X9dR7l6/S6vkinjigT9KQFbdAFTZ3HOSFoytVv/7bmt17XJaKdbWk/6mktQ32R13peX9flgj5i6AhBg8yDjyq/rjrHacg/HjcLFAzj0os0ytX8C37ly39RIOicoxF8hxpN+m2GnvkpUjCyRBha8Q2WrBZ5LKi3Hoc1AYoAtsLskNzY0VhBAJ9CycD2mCyDGGs7TGjqUCsbSKfeOLimni0tqXhIigYqpyusfUNRM6XB46/4zdX2va981T7zkX+zWWLHH/2P/7DnPu3pcmzWa2vvWu4O5Tr0FbNNX+nYrg3tMMJ0EYOpsXE0bqYzcOU3c8Bb1KOvdeXp9CpDIjC5EWhzhQBj1B/9GIFng7wgi2fFyRF69Dx1SwPJutHBfjlnFNNp0+S+S4S+hQ5Mpk+KwLXr7N4VK+z+e1ZYhwSiITmDXL/uPpn7328zNQmeLrMEBK+GGFTabLRNFO26dtSvS0q3hK1p0tbitXvdpvXumKtdX3Y65e+hW7sfMBnGX0OvDpxumRQQXdrRok1ffoZl4TB/zz1s+113sY7pU+U9XAxvbBi8J+P94yIjvKbBk6gfwZIuwJVw/Nihg7jpBNLzlK8b0i5sfSmDFA0rZcmw8g5ZNKxdY0NrV9tw7wbrHNxk0yTBzpCyYaqYd6f44MaBjbLu7ZdFQ5fuGVppuGVpkfYD6egH1yOQuF2DpxT0MX3r8D3dV+tFg3PIIb3MOubMtx4tm5i5yy42e6cdbcqsmdpBVCI1LygVHNZ94IWFOXCnXlgowItAXXqQv4lAIpAIjCcCiIDwMbb97ZSwzntGQqv4vXiWMoYUl3M4bzknhs8Vbuwn4mFSDDBZV9khPnBpgu8KA8VdvF/khHdQioYNK1fZGlmZ9a66V+/FTdYhnsuuFHA6/OIQUC7gjQyZtWyN6clFme6ncMVGD1yGVVnJuUNSFKNowC8ZygYmDwMa0yYdG/VulbcGG5JvhnY5Pe7ebgebumChTZs316ZqmWCXlgjCc3mfR92l1fxNBBKB8UQAfsNj26GHrUOCJbJP+VQDv4H3SBbiKSTWdTyPhQNBq6Ay8KriiJtJeVFE8oEJfjUgPjQgnjQontSv602Sg4cl73VoC8vtFu1hM/eRRdN8KRnZwl1OzzfL+gF/NMHZaOKBQigGUAZgeYASoNVqAXkurBLID4sE0qANeuT3UFw4Fj5IdU/pESI/6qzrGzdFg3DUlj+lyaulaDjta1+37+I1fcMqO+n//T878olPkYZ4hd1+2WW2+f77bYqUDFOkbJgigLvk5Awz6nZZOmjlhyrRbXSXnNzUOMrNdQUTN5imFMdLhZv+QAFIIIt4a/QPRPNA+dS9LTRb68MD5W2p/tb01ust1butdFsq35pe11eft9Jx/UD5rWUeLH2Uby3Xev279CXq3lLZaIOYEH+Dce6J+mmlq/MjjzT+5kdCfT6S2mB3jUoLTbSPE0Imml3aC9y3RNRkc7hvkw7t4C3GMW3adD2TephlfbRJk9/Vq2633rUbNPGUt+2pc+QIWzsciEF2axbNJLg89qWHMKFoh95wPnKUHJhTl5iY+2hQbp94waAEMXgI7Jm+qYc6MPUvX38kpdlm+XLoX3uPbbxDzrv22d7m7LbY2mTO2qfxwPhrXKmpvo7zggS5JZQejdC25kMVNKVEUJTXCnl+8MLRgRAMHp1iUAjHrmgAk0Yx8CGtU+MZlqJh3b132YY7l1kHH62mzZMfxh5pnbWHul5COIrE+WO76u0b7NULoV8WDcqXooFlEBzN+1x60eiNIq6jq3SwcVH4pffClcP41hmgL3oZDWzqt741q23zjDnWsWCBdchst0Nmu1iYdKqtzVJI9Wl3EfrTPVU7iCBxu/BdXrXRXKP1ZvNjXZNG8K6VU6evr0mu62yQjSrTSkN5ykRM/pbqrGmge6AwFn1rWut11LmldPLHyos0YsKWcIixBk1drrVM5HmFjZ+oP8oTj0XXIG9iGXVvjTbK1PHW6CMv4rpcnJMXYaw+bCmfMmPRR13RZsSRvrU4aKPNun7KcR159XWkRxx5xHWo66/rjnKRVpd5oPOocyy66OuW6q/zKV/XVefV6VtqJ/q+NdrIE7vRhB5+ioxYcHUpUZW4okF5CO+kFeUvCofScvOtoHdKmxQGzrXlG6dDR7tkTz54tcuEeLN2Uhq+9xYblMHtkFwjWM8syfcz9UGxR7y4Q76DUAhoMkAfVHcoGoi5Hh1GJh8FJcmwkm05nPPq3YrjYw76x0c6FA59Eu77+rTt5cb79J4TvSrt2lHK3/k7Ws/0mdbNl05CQ+Hhp/r5rebJaARV7fmtceQT13lcU1+kcd0a6rz6fEt0NU19Dn3rddSxpfTI31LZ1nJcE1oxaqUrVCP92VK5oCPeUh1B80D5UUfQE7f2M/KiPzVN1P9AcdRRx1GGtPq89Zo8Qmu/WssUqpHfB8ofoSxnNX2cE0eI9lvzIh26yGs9H+u6ThurHB+EkHk6XdGgp1ZEiFsoGspmXDEvbTzrdKQh+zmP0jmuzrE+5ZkfQpnJM6/d1AZRLOjD3ibVNSjFQq8KrJfj86Fpku2mdNm+zz7Gdn3mEVoavJvq1DoByXxYNgypPPLltoZQEqzTEuS7ZYmMImC2ZDocPnI+BWtkbTlPwLqBEMoCzl3RoDbZxaxWPNQ00BFC4UAetBwoGjgI46JokIG132QZeXi45rKldtY3vmlnfvM7tkkWDP/4lrfa4fs90e5adpstOe/H1nbfvTZHa6vnSUM7Sx1p09Zum6Vl9umFbhivDODkFnJG/Ryc+8vEz0ubv/UEKC9DIjDZEQh2UwSpMtpguhFHalxHmSY2MD4xBrdIEAMbFnOAkeEHoEvp7VgKiAH19m3UKqcBLW3o8jwX1sQkI6Y+P/eHMVorz2d5jkuLZTpazgu9JtxqB0EOBoXZGE6y+tWPQQleCI1MqrtgWEzflY8H7o3qyyBbZcqyYUhlZY/hfGGk5dLGxP3y6iihoFD4UuFaqEoQRDU2HZzzkoJvgRLlXBGhsw4UJBJycY47RQoFMC+7SYhRU0IFUTYgyA7JXwMCZneHvI8LE8xuh3jzVT0ZQZzUcrfjnpf7UOgFo1bFSJUjLTk7BZWXYbuWZwzZavVnnQj68JUhjDv9nrN8Q0KxyvTphchourBoUHX1PVVGhkQgEUgExhUBuBbCOyIrvJMAP4On+ocnPy8cj7TCk2tuiPzIu0XCvyqD1/aI18Kf26Q82Kz3TbveKe1SZHfq/YOSF0s/BG3qw5+CuLT1K+YdBReHV3cr5gNZmUpQmQ7v2Uhc+KMUztSnfP+6KD6uavydVxQOpQZqZgc3LPQ2qq+bdI2lmT4zSmDH2q8ePW1kSAQSgYlAANsBFJsoEuP5huegcNTCK/EF5qXiB3qmQwbi8ecJjYNyyLf847nn+W7Xc4yiolc8Ry5oZcHUYdJt2jopPPslNG4euN8OesXrbb9jjtbuM4v17Gv5RJcOWUGxuxqBdh5MuE3Lcs877zzDJ8Pee++tVRkz3HJhp512sh122KFZlX9sgg+K72HZ4DK5+o2FAgdpoaRoFtLJWIqFWvEA7UNSNL1n30QAAEAASURBVAA8R1ECSAncuL56yeV29je+ZWd++7s2cOdy+6c3/aUUDfvbbTfdYBd86r/MtLXaggXTbeGseXKwI1MxfanbLFUyEx5eBCBJR/nHzYTR+6Fzv8FqtOQW0Lmh3IJyS3WSIRGYxAiU5648AfEMMlweHRhkYUQlLo9TSRmBREKZnqlBmejzJPX0wHikafUvNsWqyNeSNszmWRolrqNJrhgN5lQwT1XJs8g/Ai3EM8g1qSWfZ5kUr8LpoPX2WTYlRQE7VrB+jDSUDL0wNjE4GqEf0LBtJowfGszO+sWYNw30ymnvgPMfmmgdJW1ORKCdwmsQbhE+4YHS+vqoi1DJC4qXDAKrj1dlGjA4fh2a5HeyDEITenfGgyAp2n5h0q+vbWwXPISll8bu8qWUsbSGkqFNZVEyuKIBTGgBbYaflVaK2F3adIFcmGEhwj+d+ouvS/VMFfbdakN3VZYNQ7ZBL5p1sm7ZILPhAfWB+8LWSyh7+P6GCfBmfOxIWOYOZ0gEEoFEYKIRQOj1f8HkC5sTR9K/lnPnyeqQ5zU6hjVYvyz29PqS47PpWjbYU3ir6qUCJvyd8GDxOVe8ohVQGFSemzmrjbCeIB3bCJQNKCtcAeL9c05Mttou7Zcz0fD+Up0oKgbFZ4vCQu9M0hsHY+Rd6ztS6P2HBQVC/LA+wm1mhyLxXuoDhwyJQCIw/gjw3PJ08Ywhu4UMRwr8BH4wKNkn5qWtMjBzUVdSRnnF1IdVABIyCkPmy8hZvciMkrs3Kl7Tu8YGOpVzq9kzTnmX7fPiF5ntsZcYh3yQdeOrQcsnGnWp+IMK1157rX3+85+36667zg499FBXNHTrI9LixYttjz32cCsHlAhTNRcnPawQohEUFBzhNJJ0lBLOk4WRK0/Fp9wCAgZbBWgID1nRAHwAxwHTRZxduuQ3rmg49etfs7YVK+397/o7O2L/A+y2m2+yn3/9Kza89Cqbu/cim6c13rP1xUzfznQLJFSrdOlmeUUUUVY3lvp1g0s7KB2CfRcmzx8DXxB9AqQ4QyIwmREIIYYJLs/ICHNsTPb1PMSkvzBN2GQ8M+XB5wljYkn58lWb9aKyJBBDjDI8T1y4NlbPH0zDn89SReM5LMIVtOWLE5lwAXhCmYRH/5DdKI8yBMY7gAmrhCwmuwh30PFFH58Mvn2jUrysfvwLkmhZbsHEGIa/UeURAmnr4QyMMBQ69I/26Q/j5R9jpL9hVss1dD4Wxbx6+OcvJHDTAQ8j9GlMmyRYomhg2Zhbc4h5S60geVi1qxJa4IBrgj5pXrvoSy0Rk9rgm94/6LiH3CuEZAnXtK2Ya/7xf0D1sRwFbHXTlSve3ugjffV7R/8eduTpf4ZEIBF4vCHgLK4adOFzhZeSzHXQEMP3eD9Gmk8IxM/goRD7hEAvJBTYnYrha14HfFIHSnfehVhBUJb10XxRDM7Kuw6eSOxHozy8kkD70Q+ui0KhqH9ZuujbxxVCWKyC2ufE6xFdnKtNauNdTZ+otNBTJkMikAhMBAI8kiEnuRysa5SDyHhYNTAHdflW5zznBJ5UDj6WO3/x8+ArutAzjiKTeSxbjPeKDwxKlt0gDrOW5cuskui90w5947ttxxe+wGzRniqinRrlt+WhKBquueYa+/KXv+zKhv3lRJ1lEigW9pEfiD333NP22GMP23nnnW2vvfYaZeFAjwnhNDIUDfBQFKDwq1BKOF8t5M35Qq14GBdFg/StrgxAsOZs6RWX2Y+0bOL072rpxC232Xvf8nY7bL/97M7bbrVLf/gDeVq/1WYv3M5m6oZM112RUYivDe9oCsojjJqbWW5q3FgmI+Tzp1BuKkoGDm4wN5oQN79c5W8i8NhHgL94/q7jgPHVk2z+9mGK0PHLdZxHWSV5cEWAC1ESmPTJHKaBMDMg5ocg4/Vw0giwUxQOBPeaLQHIlQHqDFS05RPnxpPn9euc55cQNDBvf0aVjEBHO1hJ0F+unZkrLqsCVIqCuu4Uvfiw/MDIN4SYNUIkX3yY4BPKr59O6A/9LGMp46BdxsryDfrCNVwQIbTwpMZ4m72idIRSF78oYSiMcBtKC8zy2pWBTwSEU9ccqw33zSDLBrZHKn4aaFWH6AhFnOWs9Ke8HAvfJBXlTo+sFHzFspQ6bQITB5VYjuAgFIGavwMfj9Kpj3wfk/5WELyxPEHRUFqk1gyJQCKQCEwMAnAheH4E58H6IY4j8iDjgC+7cpYMEfGu8Xem3mOkw3OLoqFY6UHDkj1fvierLefDKtMmfugT/2b7haf7O4+qVa4o2UtfaA7OW96WXImAX5Uv72fq1KFr6OgL/eKfC+/Kgy4Oyg6pz+GMkusMiUAiMHEIuEym6otkWp7fkGnhC0g+RdFQnmF6EvygyMHwhKKIpDSsw5WNkrFQNGyU1e5GPdM4guxVfetlMWDTZF06tNIOet1f2e6+dGIPFdI6ASka+mX9Oih5D1XntgaUAEz2r7rqKlcyfPzjH/+tok94whPsiGcdYfs+YV9DCbH77ru73wZkzTlz5ri1A+fsToE/B/w6cI3yIZZYtFbqPFdjgpdBQxg3RQOCtgyBVeWQXX3VEvvxd75nPz7jDLv7iivtLa9+rT1N2pP77rrLbrrol9YpD+szp05xR5DdEmhZq9xO7IzW++Vwwni5ubxguDl8ay0xN44/BW6mBGDFxaKB1AyJwORGAFZTjlqYKeJMTOSDUZbnYfRTIR7QmMiPsC0muHxNR6Dp0GQSk3nxCa+UiGcRhzakdcvcq00MpE+OAhDMyIedlEOVKyEYcfRT2covAlqh50uS+IUIinXDkE9eUTbAoPCwzXowvjZ5vVIqtKFFbbRHR1yQVL208XAFcKX/vEQYD23zwkHZyuScPvmuDIqZyI8IoKWckr2M8zKNdUjjcQGT+jQmlDgczqDVkOMuoZfdOVDsFGWMWpaigNpBv5QvSCO98q+ULL/lBck9KaFTL6wpLNvQ5ZD2cR+Sdn2z6uZ+TJXzM9rm76GY+6ku5bFUAno08gTP95ZoK0MikAgkAhODgPM42JCqD25T3jXwzPLegydzlPyG3Ah/UgL80IVieJcSfOmC+Cl5Xo/4HeWcTyoRPu5yJu8ellOgfFX6MI5xFBdlQWmptFvegdEHVdWsj16RzruTdyu8lyWI8Fr6RL3w9vDNg0IZevpVOG1ph0kKSmfqK/xdpxkSgURgQhAIOQ8Zr3CWEf5QFAwVj5HMFVTluR2R+4JPeb6e4U7JbSOKBjl/1eONooHlwh0zp2kL3WF74otfbgcecYTZrrtrJ4qptq57ihQN+HZA6iz84IEGDV9hGQTH1VdfbZ/73OfsM5/5zKhiKA4Wavtc/DTMmyc3BnPn2ixt5RtpBx98sO27774uh7N0Ypocg3OE4sF5qngjgfa45ohr5PgID1nRwLBZ2sBBk4jaSy9fIv8M37EfnfZDu2DJlfbGQw+xJ+6zt61fudJWXPhL61qz3rTqxOkpQ9foUrkphUnrssmseQHEQXucE0cZ6oApRz06zZAITGoE+PuPZ4GBOiOr4ngWRh51qEoZyhEiD8bH84ubP879S7xiD6qIDWAoQx5l8MUinifFQCmny+azGHVG33hWI5AXB88rFgoE/N3ibAuPEdDTd/pAO241RgK+ENVJ6uWIenTq18QPR4h2g9/EOBkDB/2HhvygofscpBOg4QBPyhATAhNwgafpveRzfO5NXX89fs5bQ6QRc0R7xASwxYF5uxqWnsHvPX2gTfgy/YA26tGpnzMGaAjQt9J4Rv4kAolAIjCOCNR8LHhS8NSIg7fSbE0fPAqeBt8jwE+DF3pC45o08iKferycrJdheJthwgq0OVaIPkQfI4aWOoPH+ntN1673UGK0SfVRhnY5IoSMS37QRF7GiUAiML4IxLNcP+vwBw6eY55BzuvnkTI8s8GT6pi8yKc8sm6vDuRe5G5i6RJsigSwPV/6Ctv3qQfZ9B12svX68LOxu8cGJHAPaRKvCbsoty3woQ5lwi233GLf/e537fTTT28WJC92mmgmVifPe97z7Nhjj3UrB5QVKBd2220323XXXV3xwHUoFlAycKBYqI+quodu0UBlcQMAeUAoXqEtLE/7zrft7B+car+65ipv75md7VqXMmx36wo6ubgoEwnF3JC4EcSEuIFbi6PMWOVLLfmbCExOBILdRBzPQjw/cc3oOQ+6+nmK9GCeCDwEBCEEm6ANhkoeZUIAgmGSF4G8CFG2bjfyoOOZDabMNfXEZJp6yUcw5OCcY6y6lNxM53yiA33liL7Tp7rvgUc9vug/5QiU4ZyYFwy4cx5lGDPKhsCZOuNlpFMvG3HUGfWRDj0HddaHLpt95/7SHnTgzUGgzWiXOuMgD/pop66fvAyJQCKQCEwEAsHDgufQRvDK4EnwpQjQQxv0XJMPXwu6KK+kJm3wwZj41zwRfglfDr5Zl4v+tcbQEIJnck751jpohzaJoaVvtBX9pV5CXX9Jyd9EIBGYKAR4FuOIZy/4Ss1byAs6nt3gSfGskxflOadsyLo69Wcf+S7kwO10vsN+e1jXnPlyFNlhfW7NoCWrTOQpsI0hLA7Wrl1rN910k61atcpLsqQCRQN+FsZSNrAFJtYNHJyjVMDq4TWveY0ddthhNn/+fLfyCouFUDRARyCdtiPf00QEBr9zCACpBcsu9d1uuuFmu+RXF9lNN15vK+5ZYSvvWyUP8X02TcslZmpby04pHViDPSDP8XiNV4/cFJtOFCOUxu0SkbosbUi5dcTsb+r/FHvbMlsZ0qe5YQ6lsJd9hkRgMiMAs+HPnCegnMffPFcllJR4RspzRZqvNVUpnpXiY0FLjxpLJYJJusM/PcgbNsgfrszqZ02fblOl1fSH25cuFEpZfPqyJurFn8Bwu+yZMH/wfsmUihhtJ3l+xHIHnmotEeC5Frmb7iu/TXyhnW0T1R9M9fG0jcftYbxtq952bTvR5ray1KxjuMQgUY7AQZcTFAr/kRCoAXFOwGnjkPo2qD3Y2GcZHkRP/P5o/L41mxLanV7Lv8TvcATEy6Vdu05gnsv9pE5d2ZQhOciU+/FuvDKKeYPdQI/8JmgvNbgcoGON0KEDfB1n7wrmfDLRVWX0ZVgHydytDhWjH7QDy/ftQ/Uy6NA+zkPCtJflGXJy09ktEVd9G9y0ybd6w1Eoy9qG4NO0pTKbpV2n5jhUfYZEIBFIBCYIgZrXIP0Vbo/fGPiZv+UUc+H/RECs14NC8GLo9I/3l44OOfpiFyNWPbexhE88mXfjoMri7K1fZQcorzQMlqfqI9k0mfB1awkE/BAe7/xeTHVIR+H9epfRtsrQmrm8WuRXFqppk0q9T0WrOnC0C63Wyck6UCeuVRCTFh9my80uHd2i6RS/bdfEoF/vjn7VqdxG/SqbIRFIBCYIAZ5fquYh5YjgDEcXnulxyS3XzouUCl9ykuBRLHuQiap/8dfDzvIrsRHfNnIYnkSenvN2OUbfIH8Iy1beZ2s3brC53W3ypwXP0ZOvuqJV1b7FEBN85Lxh8Zr1G9a7kmGlVhSsW7fOFQxYKZA/lqKhrvjwww93hQPWDC972cvskEMO8eUVoUwIWuriIPgYNb7oh6cpc1v67hVs6QdFhrDShEUUiu+9Z7XdftstilfYmtUCbM1qbS3Uq+0zerS1xjRpU6Tr0Z0o22b0+eQBkD2IoSI6Ix4XBYPOK4bNTQ+FA3L7Zk1AXMkgyZvzbbsVpan8TQQeqwg4+9Ojy9PiTK05kBGmSC7/ecCdEuYGnWJXNEiZgBMq1t2j5XQGIQI9eVq7P2DrxZQkFdmcWbNtmmztN+vcJ7nY9OuZlB5A60t5BpWsJJQMPIMoBWgRgazRok+WmdCWFahF0SAPDL7udQA/AXRLewp3iDd0Km5DmBvsV76OYZSR2j5HE22EMnfOpV66ogFlhfMLRQ9DUOuOd7tm/0VxwLgFE4oGzeYROouyk/vid0B0jYk+sAg7xrpBguQmYdQhszi2uERp0SEQezbLf4Liqf0oGoSBAHbFwZQOG+hi+zPxOzHcDmGPosGFbb+71I2aAcG39ANFAwFFRKEViZLg+KwX3oyioUdOhoRpr9alDEvR09MjhZLa6JeSqUM0U6R4aFeMUpjxuJdhvTRxQqnXH416G/mTCCQCicBEIFDeY/6mc05XeDDvl8L/eNf4O1A//o93ns7hy4UXFy7Few0lw2YpDdrEV1HW4jOBLeekQRU/FH/VAPC306/KB1ypLeWv6pkiXjy1b9C69Q6E5/Keg+fD76UXbiodhlUGrosM6/Kr3k/tOnjXoWrAz8OgzH6Lo0kRiueiYHdFg/j2ZpTqOrr0bkbR4LsOqX8oGfRKcJ5bFBkTgXTWmQgkAgWBhlyDcFvLOPCUxhNeksvTTpmQ9/ycH3gNSgSR4POsXYd/OG8oHYak7Ozr1/bs4kXItB2yMujumSKRe9DWrUchMKCteMU3JJ+Jm1Hjgwox4V++fLn96le/svPPP98VG729LNqA5Ynnwct0jBXe+MY32pOf/GT33zBz5kzDcSTOIpEBN+lDVFhMRDtRR61gaKaNh6JBcqkLr+q335PeDf22Vg4fN/VulIJhk/X2bpBSAa+VXTZVXitxNsftwikP+7JTyL9Ueip3kptbKitfDXWuNP6VmGsvpgjNtRi0GL9PcpScIRGY7Ai4OKNnoIg1/tg1hhwpgcDINQKKsxRiMZfwdI2jKZ4/f75UjKdvs57NgT4Z9jPZFPPrlhZxs2/3IhHKFQ2aaOq5R8GI4qIIdnoaXeJDNBTz9Mb8R+mi9cc3nmG+FKEs0KRXjJbnuHzd4QuOegLz02QcwWuzYiprc8lSeT4OmCR1wScIOn+YAuNCyVBbNKBoQejEuoG+6lc9Ki+ZQk8ZOohFg5ZMaHwDitskRPICAiC+sHUilAqobrHFTvAWwG4x0SUHjSIDKdeAS2BtVx47SDDyIb2clCPs1BHhh5LBbby4QWqrfL0r2NEH6kCwRsFL36XOsc0q14G2mL8NOf/xnSbE1BkHfw+k4wQU3swd5qCuDIlAIpAITBQCzjZb+A1cJywaxJgaTStWBtzXXw2NczKhF7drvFfEufTe4YC7ITCXdxhvLfFSMWy3bPB3IjRayiZejJIBngz345/zWNHCP+HRHot2xKLBW1SbRdkglYZPPHAA7Ltf6D1G5bx7/TWGDKsXIkp2lLyd8G36IB6PJcOgDsynGUmGRCAReDgQ0LMGMyF4xJNPCJ5TzkeeyCIVNUmQvxRcxnZZDUpxHT3HyM8sX4AXkIblEkpPnHD3ab68WVYMHVj5qkhptW6TWrceUAjA22644Qb30fCFL3xhzAJ7Ld7L9tl3HzvggAPcDwPLJZD5Fyxc4EqGqVOnulNJHEbiLJKAkQBjiA+UpI2lYCCdIHneR1muxul3SOZhmNrSMHwR84xBXXdJiC3WDIAt0NR0EzqSSG3cVFj0CEPVeZ0eNx56pHhug+JyrssMicAkR8D/7H2M5QlqPD6NUTMJ/O1QfwkZK58SzXqcQExTMc+wP496Xku+fj2fq0ZbnlQ/gw1a2EspJCFOJ41znmf/p2yvSjX5Yx35SnVlg+dCQT1BqUuVRuQroVmocT2BEd1Q9aP6roT4euY9bfTTe9XAzHH0kmW8LJ9wodGJ9ON46HWjCrCW6NQLyhUTVKhsvpqVNnStOodxhqO4i69xCihyEVK7ZBKHssAFX13zImNHCV50vu0SLzu17f2koPejqpskcum3IrrXpG70xVPV3/rviaoyJAKJQCIwMQgULlQ4UoMviUdFcDYqvhUp/i6prkt+ozR8jUOFQ1nMBW8sylEHvLZM6qHThF+TAniyv5NotxR22iJ7ljL0h/INAo+D95e2SgPRTumnykBUavP2QolCMwR4bXl/Bg4lPX8TgURgAhGIBzSaiAey8ZRHcokbT36JmlmlSKMg9XFa1QvfKaGRR7bzNlXkfKGRPWabkffbMbvloGhYunSp7zgx1vaWlDruuOPsSU96kvtfYItLfDNQloMQCoRQXJAWaoPII21rYVwUDWBCw6FYcIzUqnhjMyDslq9hzaQ8SQQSgUQgERgPBHi5wXhhuhXfHY+qs45EIBFIBBKBRCARSAQSgccWAjfeeKN9+tOftg996EO+iwTOHHfaaSc/33PPPW3BggVuqbDLLrvYjjvu6I4ix3uE46JoYM0wB9oTDpQOXGPyxd6/nq8va5iG1PkxGGigzZAIJAKJQCKwbQgEn0XRzZIJrjFpQ+Hglg3itwTSPXaTXfhyMRfeVm20F86fRCARSAQSgUQgEUgEEoHHDALXXHONffWrX7UzzzzTFi9ebCgU8LVw8MEHu98F/C8gI2LBgNVCyJUxnx+PgU6YooHOFgsHmXrpvL5mfTDrUPjwFjQp9I7H7cw6EoFE4PGEAEpcDIZxNkRA0QCvZfsiXhQRXNkLTyYBRYOO5LmBTsaJQCKQCGwbAqht02hs27BKqkQgEXhkEVi2bJldcskl8pu41hYuXGgoFjiwathuOzbTHB1cpmzM32sZcjTVg7saF0VDqyKBL2yxWm4sYXYiBvLghp3UiUAikAhMPgSCt/KCCN4Lf3ZfDYrrdXaTb/Q5okQgEUgEEoFEIBFIBBIBEEDBcPfdd/vHJ5ZJ9PT0uGzYlAU1Xx/C4XojIDeOl4KhWaeE0GJXGym/Q1xXUQu3cU6VNQ3nHLUw/Ds0m0USgUQgEXjcIwAvHRzUrhOKeXlwELh2SwZZPRDgx+TVfNkz8icRSAQSgUQgEUgEEoFEYFIhwGYMbEeJlesU7foYAfmQPByb+25jyiAt5EPiOI8yv2s8LhYNYzXuHcbATP85jyNoYxDjNZCoN+NEIBFIBCY7AvBTAvwTZcLGjRs9ZisiXigErBhQQJAfCohU7jo0+ZMIJAKJQCKQCCQCicCkRSDkROKYcyMPIheiZOBAXpw2bZrnQxcfp5AV46PVQwVoXBUN9aDoWK1EIC8ONCjsHRwDf6iDyPKJQCKQCDyeEOBlQOBlMDwkRUNvUTTwwohtiaCJJRPwWmhT0fB4+ivJsSYCiUAikAgkAonA4w2BmG8zbuQ/rkOJUJ+jaOjWtugRgoYyjzpFQwyKmEAnI8R5PYAQeCMvaDNOBBKBRCAR2DoC8FJ4LXyUOJxA8sJofTmQDz0h+O7Wa8/cRCARSAQSgUQgEUgEEoHHIgLIfZu18QL+F2LuTRpzbuTAsHJtnYN7OdERoBuPMC4WDXXHYiDR+bims0FHHgfXhLj2i/xJBBKBRCAR2DICYpvs2jMWb6VQu7YKDouxqARTOQIvl+DNkZdxIpAIJAKJQCKQCCQCicDkQCDm17WSIWRGFAjx0Yk0aGIeHvJhxOOBxrgrGuhUdJgBxMDG6jRmvQyQrdnYBz5DIpAIJAKJwAMjAN+Et0ZAgcB1OIVk+URoo0lnLR6hTo+yGScCiUAikAgkAolAIpAIPD4QCAVExMzRYy4esuN4ITFuioa6Q6FUCEUCnW4154WefI4w4ajryPNEIBFIBBKBsRGolQycc4x6YUjxUGusU9EwNo6ZmggkAolAIpAIJAKJwKRCQN+hJBk2P0iFAUCMsZYbOY/8kCWRH8PfV5T5XeNxUTRsqXHWDYdXy9rZRNAPDUrRoPUjqWgIRDJOBBKBRODBIYCCYcOGDa60hc/i3IeXBi8KDl4c8FpeOrw4QhH84FpJ6kQgEUgEEoFEIBFIBBKBRzsCoTAg5qhlwq31nTl7X1+fy4o9PT3jIi9OmKIB4Ze9O1E2oEigwy74av1wLJPAzDcsGsZLc7I1ADMvEUgEEoHJhgB89rrrrvMtLvfaay9bsGCB81XGCe/lBQM/5mUTAV6cCodAI+NEIBFIBBKBRCARSAQmBwKhYKgtXcf60AQdNKGIYE6OsgHZMbZKf6iITIiigY6jRFi/fr1/aaOzU6dof/fuLteSxGBT0fBQb1+WTwQSgcc7AitXrrTTTzvd7l5xtx133HG2ePHi5osjeC0Y8TKB5xJIR9mQIRFIBBKBRCARSAQSgURg8iEQch+KhFoejJE2FQvyleiKBW0YGR+lxutj1IQoGhgAGpEbb7zRrr/+ettll11c+GWPd7QkhBgI56FJiXPiCNBtbbDkAySh/kpHerRB+dY66vxa4I76iKNcxNGnMeOGJ/jox5g0jcS6X1uj21oeY+ag73X/t1Ym8xKBRGDyIIDSYM2aNc5nv/e973n86le/2g488EDbYYcdbObMmaMGy1ZH/QP9zhdZYpF8YxQ8eZEIJAKJQCKQCCQCicCkQSDmtAwo5t/14JhHomxgt7KOzjI/r/PH43zCFA0bN260888/3774xS/ay172Mnvuc59r8+bNc40Jg0JIZtBoWBCA2a6NCX0t/D7QhDwApD7OqSuAjDxi0mpFQ+QBsFQQDm7kk4eSxCfxjWUereXHAp5y9CPGEPW10kLHQYixxnVrmS2lUzYwpI7xMm+h3gyJQCLw2ECgt7fXFbm//vWv7ac//an95je/scMPP9ye//zn25FHHmnbb7+9DwQ+Am+Bp8HbCPCM4D+ekD+JQCKQCCQCiUAikAgkAonAOCIwLoqGUBTEJH/t2rW2bNkyO+ecc+ztb3+7vfOd77QXvOAFtv/++9uOO+7o3UfRgKDLgSAcwjACsU+wmYvLhGNbAgI0ZWqFQNRZl4eOQBsxqYcu0ulLCOSkcx59pJy3I6UI/6KOiOv2YkzRr2gz6oc2Qn1OXRHq+qIN8oKGujm4DtyjbMaJQCIw+RFAmbtkyRL7yU9+YhdccIGdffbZbjn21re+1ZdQLFq0yBWS8JLgjSgoCcGLJj9KOcJEIBFIBBKBRCARSAQSgUcCgXFRNCC8MumNL+s333yz/fznP3fh9/TTT7c5c+bYS17yEnvVq15lBx98cHOcMcmOyXNkxAQ9hOG4xvqA/1Eu8qPclmLKc9BPyoblA+1yjYdN8kjnGKte6FCOUA8hJvjQRqjHAV3QUxZBH3yChrSoK+qr88jniEDeWP2K/IwTgUTg8YUAO01gxXDuuefahRdeaD/60Y8cgFNOOcWOP/54wzEkPAjeEXzt8YVQjjYRSAQSgUQgEUgEEoFE4JFCYFwUDTFpZuKMOe/ll19up556qgvAl112mY/tta99rZ144ol22GGHWWx1CT0TfCbQ9Vd50qkzJtauIJAlgV+3tzUVBpQhrW6fxmJSTl4E6uSQMYLvegEN5aIscXz1gwarBQJ0HISw3IA20iPPCfQT6VGvt6l01r+w2wb5MT6vR5qTNo2ptR7yCBFzHnW30pKXIRFIBB5fCGA5dumll7rl2EUXXWTnnXeeA/CBD3zAXvOa19juu+/uvAZ+Ezwjzpu87vEFWY42EUgEEoFEIBFIBBKBROBhQmBcFA3RV76wrVixwlgzjG8GTHkJ+GZ40YteZK977evs0KcfarNmzXIFAV/bYhuN+osbk2ufhDcm+WEZEDSt1ygiIo1yCNFBGwJ29DFi6ChDHLTkcU19TQWBlBWh8Ii6oKmVEX7dqBgalApjLfuAjiPqpojTq42ou1HNqCjKEQf9KIK8SAQSgccdAvfff78rGn784x87z8VPA+GDH/ygnXDCCe6El+vgOcEn4U/dPekMEmwyJAKJQCKQCCQCiUAikAhMDALjqmhgO8u7777bl01gvnvDDTc0e/2c5zzH3vymN9thhx/m+7xj1cCEe6B/wK0HmMyjIOAIwTgm+CgjEJJZekA+5aCJ/KD3Cbx0AFgIkBeB9Jigx4SeMqGcoM66LvI4gpaYgzRCpEf9EUd+lK37AE30uy4fdUcdW4qpM+qHJuqIeEvlMj0RSAQmJwLr1q2zX/ziF/b973/frciwaiC84x3vcB8N++23n82YMcN6enqcd8A/nEeKBuVq8g6HK38SgUQgEUgEEoFEIBFIBCYAgXFVNGDKu3z5chd+TzrpJHcIGX1esGCBnXzyyXbEEUfYzjvv7FYN5DHZD8sCFAkIwCEQx+S/v7+/qWggf1sDQjUKCg6EasqiVCDQRp1HGu1Fm7UQDm30CTryOFoVCeRBx3jIp62oJ9qLcpFOmW0N0Q9iyvvR8FuxrXUkXSKQCEwOBFA04J/hS1/6ku8+cfXVV/vAXvziF9sxxxxjT3va09xPw8KFCz39d+E5kwOpHEUikAgkAolAIpAIJAKJwMONwLgpGpj83nHHHXbVVVfZL3/5S/vEJz5hq1ev9skweQR2oGD7tYMOOsgWySN6bZ0ATUz0WZbAdpcxkR9L0QB9fVB/szwXCuSjbAhFRigaQuAOC4NCPaJAiPxIj7qoj+ATfE30I1A/IZQkQ4Py7K7sLSkaSKeuUIBwTaj7E2209iXG7AX0E3RxnXEikAhMbgTgGziwvf32290/w0c/+lG76667jF0oCChzn/WsZ3mM893tttuuyVuCX7TylcmNWI4uEUgEEoFEIBFIBBKBRODhRmDcFA1MklEynHbaafazn/3MrRpYSsEkGsGYgBf0o48+2nAMecghh7hJ71gDrif05A8MsNtD2RUiJunQ+MRcTiKHlEdoVSSQFhN6aBGuKR/CNvkPNVBv7E0fTi5jvNEWbUQ/og+UQ4GCciR26yCNgwBdq+LEM/RDXRyE8RyLV5g/iUAi8KhGAL6BL5ylS5ca/hk+/OEPj+rvM5/5TDv00EON5WrPeMYzfKkaPImj5iucZ0gEEoFEIBFIBBKBRCARSAQmAoFxUzTQuUsuucQ+9rGP2Ve+8hXfWQKBOBQNTKaZkD/96U+39773vXbkkUfatGnTfEwxCWeSzXkIwzFRj3Qm3uTVE+36PPJjsk55QpQPwZoyhKDnPNrlPELQRUy6n7vFBZP9ohQIhQAxbYWFA2OONkln/FyHYiEE/9Z+RvvEUZ44zkmnHxyt6eRlSAQSgcmLAHzknnvusWuuucbOOOMM+8hHPjJqsFiNhaKBXX5YtgavCL4YPKPmJ6MqyItEIBFIBBKBRCARSAQSgUTgISIwroqGn//85/a2t73N2NIS64KYSPOln8k1a4pnzpxpX9SOFM9+9rNtzpw5PtmPSXgscWDizUEdTN5bg0/2laipNz+jAnm0S51jlScfQZ2YPlE/21byLwTwqBAa6iEmeKxTaEnnoDzji36ShoKFjnV3l/opS58wd6YNnLMFPXljBdqiDCH6VZchnyPyxqoj0xKBRGDyIQCfXLNmjTvb/eEPf2g43q0DSydQ6P7e7/2eKxxQNGRIBBKBRCARSAQSgUQgEUgEHk4ExkXRwMQdfwwoGt75znfaLbfc0pxIM/GOCX+ZgJt96lOfcouGVqeQ0BKYUKNoYBL9u4SYpKNAQBFBXTFJJ69V0TA8pHZFFzRbapOy9JE4An2kXN3XTZs2eXatgEBpwPihRdFQh3rckU4b9UG5un/Rl2g/ymWcCCQCkxsBeAlbCd900032g+//wN530vtGDfioo45yXzjEOIScP3++5wefgWfU/GpU4bxIBBKBRCARSAQSgUQgEUgExgGBcVE03HvvvXbjjTfahRdeaP/zP/9j11577Ra7Nm/ePPuLv/gLF4Sf/OQn26677jomLRNpJuZ8vWPCjrIABQFCNpYIKC8I0LFFJkJ0lywI6nTKQ0/5On0Qnw9a9hAWDaE8CAG86BGKMqG2mhhLOKd/oViYOnWqC/ChUKF++k2gDfpCoC8xvhD+oaOfdRvQcEBDetRFHXV6rYAgL0MikAhMXgTgIzh+dEXDD35g79VStDo8W9ZiOINkeRrOIOG58JDgNfCL4HV1uTxPBBKBRCARSAQSgUQgEUgExguBcVE0oFg488wzDTPen/zkJ82+8eWeyXF4Q4+M3Xff3U444QR79atfbQceeGAkN2OWGGAafOedd9rdd9/tE3Mm7UzqEZZDcYDiIQ4m+TvttJNtv/32vjyDdqFHKGdiH5N0JuikE5Mek3TaXLlypR/0l2sC9bIXPcI6Sz24jkAd995zr11z7TWuFGFcmCnje4I+kh9CPWW4pr/UjTO3W2+91a0bKEfdlKmVE9BzpKIhEM84EUgE4F9YkF1//fXufPf973//KFCwZIhdJ5761Kf6rhMQBD9JJcMouPIiEUgEEoFEIBFIBBKBRGACEBgXRQM+GT772c+6omHWrFk+ieYrPwcTc0x3p0+f7pNxhF2sH17xilfYO97xDsNZWR2YVLNbBQqGyy+/3M4//3yf/GMlwCSdiTiT/ilTprj5MLQoJPbdd1/3so7iAiuJ2bNnN7/idbRrGUZ7WYZB+2MpGnp7e11wZyzsR4+jNWj32Wcfr/spT3mKn9M2gb7QNkqWb33rWz7Wl770pbb//vu7YE//YklGLdijZGCSQBtnnXmWzZo9y5gYsIwEjFBqUBYFRUwMUtFQ/4XkeSLw+EYA3oMzSHjI2Wefbf/xH/8xChD4CdYM7D4BP0T5miERSAQSgUQgEUgEEoFEIBF4OBEYF0XDsmXLfKs1JtFYDqBIYIvL733vez6Wf/7nfza+rJEHDVYGfPXfb7/9bI899mhaG5AeSgDWIPPFf8mSJfbrX//aPvOZz3hdc+fOdXPgRYsW+eScCTm+Idjm7Q/+4A/suc99risvqDesCYjjnEroA5P42noARQbCO1vG/ehHP2puGUd9mB8/W+bIOFhD0UBZFCEI+hdccIG9733vsze96U32Z3/2Z66MwJKDuiOgcEDRQR8YHwqK5cuXO05YNqBI2HHHHS3GRBvQEqDn4BqLhzrQDwKKjAyJQCLw+EAgFA0oQ88666zfUjQ873nPs2OPPda3tkTxibUUZeAz8KXgLY8PtHKUiUAikAgkAolAIpAIJAKPBALjomjAcgHFQPhBuOKKK+y73/2uffSjH/UxfeMb33BrA4RcLAeYGKN0YEIeywUQfpnsM6mOL/p8+UfZgFXD29/+dq+LCTlWEPh3QHmBFQCKhvc21im/8IUvtD//8z+3gw46yAVsFBrtbVqT3JjoMzkPRQNtx5IKhHCEcdr7/ve/b3/3d3/n7VEPDtVe8IIXuOUBio7777/fFRKM6xOf+ITn43cCGpZvUA/j4aA9FCj4eujs6nRhn3xwYIkGig0sIsAGL/FPfOITbbfddmtu/Qke/X1yItlRnEimUuGReEyyzUTg0YMA/AMedN1119npp59u//Zv/zaqc89//vNHKRqw7oLXwOOwMAueN6pQXiQCiUAikAgkAolAIpAIJALjiMC4KBoQYJlMM1nm6/2Sy5fYt7/9bYu1w/huOOaYY6VY6G4qGpgwx2ScmGvqoC4UFlwzSV+1apWde+659u53v9stAHbYYQef0D/jGc9wh5J8rbv44ouNST8H4S1veYsrNthLfpdddnlAuGiTCT2Kh7vuussVDW9+85u9HFYMmCGjBOAcIf3SSy+18847z04++WT7/d//ffvTP/1TO+CAA1yxwdKRbRHkGSvju+KKK+2Tn/xv+8IXvuBWEZg9s0wDhQVKEnBgYgGtb43B5po6p41Q7DzgAJMgEUgEJg0C8A4Ulezug1IUi7E6YIHFkgl4FkpZeCA8Az4bvLWmz/NEIBFIBBKBRCARSAQSgURgvBEYF0VDa6dY7oBFw7/8y794Fo4iMeVF0GVSXybNZTtJJtKtIWiYTCNUo2j413/9V1+msOeee9pLXvISY694FAAsM2Di/wN5X7/41xfbj8/5sVf3tre9zd78pjfbvk/Y16+ZrGN5QX1YTKAUoR+0RRp9w8ICKwrq+od/+Af3/XD00UcbxyGHHOL+F9atW2df/OIXm3vXo2x4wxve4E4gyaMuFAQEviJSr1toyKpiYHDAFQe0HcoIlpl8+tOftg996ENe5i//8i8N02csG1A2QEugXsZATGDCgGJkLPycIH8SgURgUiIAD8D6C0UDy9NQwtYB3oDi9G/+5m/s+OOP9yVqWH6RHrw3+UaNWJ4nAolAIpAIJAKJQCKQCIw3AhOiaMChIhYNp5xyive3VjSQgLDLgbA7lsDL0gYEZYRjFAFYD1AXO1rstddedtxxx/kyBr7+z5w50304fPWrX7VLLrnEfvnLX3qbb33rW41JO84c77vvPrvjjjvcLwLnKBRQBjDZx1Ha4sWLvR4KomjgK+GJJ57ofWA5BAcTf5xMYmHxpS99SVYIn/R28M/AsontttvONqzfYCvuWeEKCrb8ZDJA/xHwCZzj9BH/EQsXLnRFwQ033OCOND/4wQ86DQqZ5zznOa5EoU3GRwAvMAncUGCEssIJ8icRSAQeFwigcMS3SziD/PCHPzzmuOFhr33ta513zZ4125duOQ8ZlkVUZ4crQccsmImJQCKQCCQCiUAikAgkAonAQ0Rg3BUNTKZRNOB3INYOn3HGGW7REBNjaOoJc+sYMAtmks7aYgRjFA0sw/jpT3/qSgEsGti+Da/q0OCQEauAc845xyf8+FTAbBhHjkzomcxfdNFF7uuBfmEp8KQnPckn/i9+8YuN3SIWybkkgbXPfCXEsSMBJQM0WFKg+MBhJBYP//u//+v5H/nIR+yVr3ylt4sSAuH/O9/5jitaKItCgDJYeeAA8l3vepcrStglg2UfONJknTV1Unbt2rWuRPnDP/xDVzagCAmFTGDmDedPIpAIPC4RgD9eeeWVzvfgayh1I8Bv4K8ElnTBR+B1LOnCOqr2TwNthkQgEUgEEoFEIBFIBBKBRGAiEHhYFA1nnqGlE88rSycYBBNmhGEEXSbRXMckmmuUC5gHY3mAwiH8IeD0kYk3SgT8GPDFH8sElk4w8Uf4RimAjwZ8JtAGlgUoPtiVguUcKB7YnQIFBdYPOHdEIcC65r333tvXPp966qluQcHWlVgX4FxtwYIFxtIIFAMoBXBKSTrKDvpCf/EVgdUFDiL/6I/+yE444QSvf+XKlfab3/zG3vnOd7pVBFYQ+JhAecH4cOpGWSwp2PGCgCPNY445pukYsnVSAF4RxrIKibyME4FEYHIhwK418Bl4HrwD57sRUObG8iosGv70T+Q/5okH+PKtWFIBLXStPCXqyDgRSAQSgUQgEUgEEoFEIBF4qAiMu6KBCXBYNHzgAx/w/j3Q0gkUAqF4aBV++QKHjwb8PfD1Dr8E+EtgZwaUBtBjCRATdJQI73nPe1xpwPZv7FhBeawMWLLArhSHH36413PmmWfZ5z//OadlmcWrXvUq98qO80osFVBgoEhgwt/d1W0XX3Jxc8tOvhbiCJLdL7CQYLvLz33uc/bexu4X7G1PX3BeyY4c7C7x5S9/2a0a8MGAooHtPVGmMHEAM6wyWAJC+Kd/+idXgLBUZN7ceW7q7Bn6CcUM12HtEHkZJwKJwORGAKsnrBjC6qoebShuSWPLXXbggc+gZEC5UCt0U0FZI5fniUAikAgkAolAIpAIJALjicC4KxroHMsEvv2tb9vJp5zsfT1DFg3PqywaSAyBl3O+wIWiIZZXkE5oVTSgWMD6AF8HOIJkvXIsY4D+RS96kTtnxNM6Fgksq0BxwLIF/DZg6UBZ6j377LPdPwLlsDZA2YCJMUsn/vqv/7q5jAG/CZgdYwGB1QHhT/7kT+xlL3uZe3fffffd3S8DVgj//u//XvJf/yf23KOf60I+/hsYIztaaNMImztvrs2fP9+tHWK8WGOgnMD/AwFP8lha4D+CJRbhFNIz9RPKhlQ0BCIZJwKPDwRQNPzf//2fvfGNb9zqgFE0oIxA0QDfRNlAzJEhEUgEEoFEIBFIBBKBRCARmEgEJkTRcPnll/sXN3aKILT6aGgdUCgaanPeUET09/Xbueed61tJ/uIXv3DniFgZ8KUfZQIWDrfeeqsrN2gXh43PfvazfRKPV3YcRKJwYGvKF73wRbbHnnv4cos1a9YY9WGFQEDJ8I53vMMVDXwtDCEePw8skaAdljd85StfcfrXve51vvsFSg8sJfDtgBKCbSpx1MZuEgR8MlAeHxBYL+DckeUeKA5ijNDRRyxAQtFw0kkneTnqnjljpvVM6YFsVKA8Ib9MjoIlLxKBSY3AtioacFKLQjQsGuA58NjkF5P6zyMHlwgkAolAIpAIJAKJwKMCgXFRNMTX9fhShkUDSxVC0XDaaafZscfIKqC7TK7rCTJCb2t5rA2wVJgxY4bHrEcOZ5BYD+AMEoePOFRk+QTlmdxDx5IDJv/4VCANSwEUGfhfoAw+Hviyx3IG1jdThvKU4wsgigAsGv7+7//erSBQNOD3AUXDhRde2FQ04GSNfuCfAYUHdbKjxVVXXeVj/9SnPuU3mD6yxAKFAePBLwNKB6wUOto7ijmzzBxQkrBcIxQZLBXBooElIigmfItMfYnMScKj4rnJTiQCjxgCKBq++c1v+s44dSfgv8FLSWfZxB//8R+7Fde0qdPGVFbW5fM8EUgEEoFEIBFIBBKBRCARGC8Exk3RwNKHWAbQqmhg6QLLD5isQ8fyAbl/1KQZM962UWNBUN64caMvbcBRI0oClAHsYIGvhSc84QnuS4FdJw488EBXNFABDiS//73v2ytf9cpR9XGBEoB8Aksj8JGAXwX6S5/IQznBxJ6vfjh7xLoAJ2tHH320KxSgY+lELNNAgEfRgEd3/DBQL4ElGigqvvjFL7oFA1gwHgLKBfa8R3mBwgTlAX1DqYKi4b/+67+aPhpQ0uBPAguNqVOnjlI0gFGEVDwEEhknAo8PBNxHg5am/dmfl51xYtTBf+GZBPzIYHmFHxkUqPCwDIlAIpAIJAKJQCKQCCQCicDDgcC4KRrobEx6maCzwwO7KxDYvpHJPYLwqEmySckwWs/g9OzEgLDMBBt6dp045ZRTPMYy4fjjj/etH1EOYKEQgQk+eVsLLJ848Q0nusICpQd9ZrLP1pX4UmAJBIqRk08+2W666SZ3BInfB2hwLMn2mATWPh933HFNRQNpOITEioJlGVg34OSRJRQsxYilFFhOYCHB0g8sHFz4l97g8isu950mUFAQwA5FBtYP0IWPBpQiKCboN3hyhCWJF8yfRCARmNQIuKJhDGeQ8AH4AfyBMNb2lpuHtePP5pEdfyY1UDm4RCARSAQSgUQgEUgEEoFHDIFxUTS09h5FA5P+9zZ2YMBHA4qGbZkQj1JEaDLd6gwSSwAsD/DTwFKIsAxYtWqVsUSDNckEnD6yrALBGysDfCAQyP+rv/or3xqTulFooESgb0zmoUXRwBaZBLbSxKqB5QsoHrBQYJtNtq9kSQRWFSxvQPg/79zzbMU9K1yJAT314z8CSwx2nCDgdPKlL32p0+DMkrYJ1IszyfDR8KEPfcgtGqgbywfGQWASsWnTJlc00F/KR54T5E8ikAhMagTgNSg8WRpRB5SP8AQUtQTyWeKF1VUoK+F38Fh4RiiG6zryPBFIBBKBRCARSAQSgUQgERgPBMZF0RDKgRBcW5dOuI+Gxs4N0WnKcFAmymFhwBGTbwRmLATOOecc/zoXZV/y4pfYIYceYk996lPdoqG3t9eWLVvmFg+f//zn3Q9CbGPJJJ3dJdg6koDSACdpi2QpgNXB6tWr3V8D1gwHHXSQT+RZ/4wTRwJLF1j2gW8FFBdYK3zta19zHxAvf/nLm9tUUhdKApZdHHnkkW6xwO4W+IL4zW9+4w4xWR7xmte8xl7xild4W/h2YGKw+v7VdulvLrX//u//dksQLDVwTElfY2cNMIEWfMI0GuUIR+DnHc6fRCARmNQIuKLhm1I0nDha0cCg4QXBj0888URXiO6///5uHRZLJ4LnJt+Y1H8mObhEIBFIBBKBRCARSAQeUQTGTdGAcBsWC5dddpl/ccOBI+HUU0/1yTq7LhBCyeAX+gmBlwk0E2kEYkx87199v7FzBBYEH/vYx/wcD+pM/veQc0WWUWA5cMMNN9ivfvUrd+6Ic0YsHZisc9AmPh4+85nP+IT/kEMOsWdrVwrWLOMMkgPlAZN/DuhZ6vC3f/u33j3qQsFw+OGH22GHHeaKCBQNZ555pn8txFID64p169b5jhP/+I//6OXw4YA1xJSeKXb7stt96QQKGHxLHHXUUb5umnFg9UD/L7rwIvvCF7/gSzfoB4oS2qY/LMHgCyRKk8AqsMs4EUgEJj8CoZRlpCgaxnIG2YrCG97wBnv9619vKBpQUnZ3dVubfOKkcrIVqbxOBBKBRCARSAQSgUQgERhvBMZF0UCnakGYCTWTdfwcEGpnkFyHoiEmzWHJwHV8pR8cGPRlCCgarrnmGkN5gf8EfBQgNONTYfbs2T4Bv+OOOzyPr/+uaDj4abbX3nvZ/PnzvS3yWTqxdOlSVyzglJG2UDKgsMA5Iz4TSMfCgaUOLPfA/wMTfQT7Qw891OlYusCWmJ/97GcZisevetWrXDmCQuT8839mV1+91PvJ8g36SRksElCgIPRzYO3ANX4dzjrrLHcCieUGSzKwlIjdLFAwUJ7+hqWHN5w/iUAiMCkQCH5IPJYSgPRY8gDvG2vpBPwBXhG0AMPyL5ZPoJwlj7rJhzaOSQFgDiIRSAQSgUQgEUgEEoFE4FGHwLgpGuqRsUQARcOo7S3HWDqBsEtAeYAgzUQagZgw0D9g96681+68807D/wJKBpZIMOlGWA7lBOUoz6QdKwV8NjCRZykEAVrKUAfKBpQL0R7LGhYvXuxWBigVsGygPSwMUACgJCAg2O+4446+3IGyKAS+8IUv2CWXXOI+F1iKQfm77rrLUGrcfvvttnLlSi+LogKrC7bbRBFC/1AyxDjp08c//nH75Cc/6fT/+Z//6dYfLKugbNB5Jj+xY8cWHGk26fIkEUgEHvUIwJ8I8DPOQ9Fadzx4GDE8AespfDTgkDYC5eCfQUs6fAmHkCg8g5fU7QT/jToyTgQSgUQgEUgEEoFEIBFIBMYLgUeFogHhmKP+yoZAjNNDtoYkxl8D+fFVDnpClG3XVpmYBbO8AMsEYgL1UI46mPwjpPNVkAk8eQjg0BOjRIAOBQTtQUcIiwQUGSg28MeAw0sUBFgxsBXmQU89yHbeZWdXdkBD3RzRTxQhKBxwysZuGgTaQCnzqU99SlYQV7uTSJZW4LyNtmiXfjKBYAzURZ3UHxMLryh/EoFE4DGLQPAwYp5rnvXgHXHNMx/8D4UrSyder2URdQieFjznta99rS8Hw2EtitDgO+RTV4ZEIBFIBBKBRCARSAQSgURgohB4RBUNDKpV4A0huR5wpCF0P2BA/yAZmjII64SwCmBiT2DSX4ewcECxsKU2qC+sKSiP80m27cQS4corr3SfDq985St9NwmUCXWgfsYZ/SAPhQZWGiwLYfvOWbNmuQ8IlnAwKaAfKFmIUYJQPpQMTDpqBUTdVp4nAonAYwMBeErwP845gv/wjMd1pMWoUJiyffAb3/jGSBozxokt/mDwIfPkJz/Z5s6dOyZdJiYCiUAikAgkAolAIpAIJALjjcAjqmgIIZsJdCgFEK4JESNkx7Etgw8BPWjrCX7UGe0GDekcrQJ95NO3KAsN5WOnC3aU+PrXv+5LNU444QTfNpNlElEX/QlFQ63gYJkG/iduu+02t5BgG0tMnBfMX2CdXWXLyygXCoroR/Q12oh+ZpwIJAKPDQT8WZbD23DO2Nrr+lkPHjY8NGxr1q5xx7I4o33Pe97TWmzUNfyE3XKOP/54316Y5V8ZEoFEIBFIBBKBRCARSAQSgYcDgUeNooEJOSEm/fXEnqUDrZNq8kMYj8k/15GGcM4ReZyPCg3LhzqNOlXKLSJIjzaoM4R94mgHmuXLl9v5Pz3f1q1fZ5go41wSJ5RYIUAX1hL0A0VD9ANrBnxBsJQDXxA77bRT03cD5QgxZvrBQYjyEXti/iQCicBjCgH4As80SsT6OY/nOp754ItYW2EFdeONN9rFF19sF1xwgfuJaR005YNXRN773vc+X2aBMjNDIpAIJAKJQCK0GF2kAABAAElEQVSQCCQCiUAi8HAg8MgqGhqT+hCMI2bgcY7gHEcAQl7kkxYT86CrlQVRhph8AvSUDwG/toKABuGfOOqlTNQdjiUpH7taYMrMsgz8QrAOmjiWYUBH/ZRHYRKBSQO+HsjDdwNlUERAHz4hUFbEdZQnJohMx2iFRNSdcSKQCDy6EeC5h7+EoqHmSSPP+IjTW/gJO+Jceuml9uMf/9h34fnRj37UHCS8rLVcZL773e+2E0880VLREIhknAgkAolAIpAIJAKJQCIw0Qg8oooGBhfC8bYMlEk3R4QoG4qCENrJhy7yW4X4oK8VDaFUIK12vkg9QUe9KAjYkYKwww47NJ1Ock29dd11PeTXgfb4qtnZoZ02OstOGygrUDJgIs3yCZQP1Ec6YwuLiMAhxlj3r24jzxOBRODRiQDPP0coNeEFXNfWW/GcRzoKzZ/+9Kf25S9/2a6//nrfQYfRwefgAcHvKAffiICi4Q1veIPtuuuukZRxIpAIJAKJQCKQCCQCiUAiMKEIPKKKhhCMQ+iuRxqCMzQhcEc8lmBNXky4m0K7JvEYTfjkXUI8E3UE+7qeun7a55ojQistfcU/A+nUN9bEIMoSR32Ui36Rxi4Z7R3t3p+gDxyoOwJpTBpoh/aiHuoIRUbQZpwIJAKPDQTiGec55hwexXPeyqMYDRZa+HJgm91zzz3XPvvZz/oSCpQNEYLPUBfn8IkI733ve30rTLbMJQRN5GecCCQCiUAikAgkAolAIpAIjDcCjxpFA0J2LXyH4BzKA/IQnolJC0VEKyDkh2LBlQCa0PcP9HvZnu6epvVAs63G8g2vh+UI+heBPhCiXa7/P3tnHqxXUeb/zh4gBAhbWJOwQ9jCNiyyiIiyqIPgOI464+6MWlM1NX9N1dTouMz8Skspt6qxpqbUKmdURNzADRAEZd93CXvYZCdA1nuT3/fz9Pt903m5N+u9yb3J0/ee95zT/fTTT3+7++nnPKfPOZaHeJfjlRRtGnnYyEM6x76YcNzECfr6hYroytIpz2VCT5rpqQ84eYUDy6lJy5AIJAKjF4FWZzGmrS889hnjbHx2ly/U/M///E+834VP4g4UoCUv+gLn5uc+97nyAX0K044G66XUHQOhl3GJQCKQCCQCiUAikAgkAkOBwMZ1NHQu8jGK2Rx8bgO7NYhJ6z13Pl/oY1xDh8EO7TK93R0XQuuccBkY3Ry3weW29NBxgU8a704geIWCHQ2WC2eAN3hwl7LKsaIszkmz46Clgzcy4WggmD/HLd9c0QAiGRKB0Y+A9ZB1DuPceok4Nj53e6Uenfjf//3fct999xW+eLOqgI6Bx+c///nyd3/3d+Fo4JyyXM6q8mdaIpAIJAKJQCKQCCQCiUAisK4IbFRHA0L74rytAIYwm9N5zIClw23AYLYh7r2N5zZvy9908HF+9tC3aXYC2FEBPYb/4sWLg87Lm10OdATzJN4bMvnFkEHU/ECPswIHBnQ4MNi3MiGL+ZsnLIhvt4ZtHiYCicAoQMA6h3HcGzzWoSEdpyKOhquuuqr84Ac/KKxm4OsTDi0P83Uajoa//du/jXc0WLdYV5om94lAIpAIJAKJQCKQCCQCicBQIjDiHA0Y2BjDy7UKgT9Ce0fflReJwgpnA2e+8Oa4DTgJ4Nsa46T3GttRrhibFuOe4Pw+9soGvjDR0uCIINgRYX7sKbstjzjftWzlomzioW0dFKY3H8ox3zY/8RkSgURgZCOAflvW+WoM49ebpWZse8wThy7wiobvf//74WjwiobelU1e0WVevY9OWG84PfeJQCKQCCQCiUAikAgkAonAUCMwYhwNGL8EG9g+J669uG4vqlsa07FvAzR2FLR5OfaKBfNxOl99wMnBxb7zwxN6eGHwE7baaquVHA28JFKXDGWLLbeIvNDgOGAjtI6G6lBgCfOKz15ygcBGGZTFCoeuTD18gqF+SDeN43KfCCQCIxsBxj+6xbphIGcqNbDTAEcD72j4/e9/H49O3HvvvfGJS2hwNFi3WF+hQxw++9nPlg9+8IPddzRAkzrD6OQ+EUgEEoFEIBFIBBKBRGA4ENiojgYbu3YEYCyztQGj2Bv0PEYRr3bQcW8wHXtCa3w7rs1DOvGsUMDg90oEaKojoBrrlovyHU8+x5uPjXsMf9K5SCDYoUF+aBzvC4Qg6vzAv+XNMXEEyiEv726Ap9/90Mmau0QgERhFCDCu7UhAFzCmGe8e/1SlHfuvvvpqPDrRu6IBvcJGYN/qDOJaRwNp6CDrJNIzJAKJQCKQCCQCiUAikAgkAkONwMhxNGgVQe/nHl1ZjG02G9TeO917G+nsCVyY2wA3jffQkAZfv3uBFQQY4ATSfRHA3cSB+NixMFA58CU/+biIcH7y+J0MdhRYXtNYRvauO2XY0bBo0aLg2a54aPPkcSKQCIwOBPwlGnTEeH2Ol5VU1k29+oDPW1599dXlhz/8YTw6cfPNN7+ukugIAnrDAUfDhz70obLbbrt19Uk6GoxO7hOBRCARSAQSgUQgEUgEhgOBjeto6PnqBIZ1r3FNpX0hbgAGonFaS7squpbeRnmvw6CNN327d1kDlUOa0238k5c4+JKnjW/5rnQsnwkXHwTykB9nBcd2iqxEnyeJQCIwahBgLPOoFqoQfWCdYN3BOGcjvPTSS7GiAUdD++iEK2udYB3h+C984QvhaJg+fXpEkW6epsl9IpAIJAKJQCKQCCQCiUAiMJQIbFRHAxVJg3comzN5JQKJwGhBgAt+nI5dp4K8DazqQic6HseDdSSOhj/84Q/lRz/6UaxouOmmm1aqKnTQm68T/+3f/q28//3vL/vss0+kmZ/Tc58IJAKJQCKQCCQCiUAikAgMNQLpaBhqRJNfIpAIJAKrQaDXGcDFvzeyejVVG8fLIK+55pry05/+tNxxxx3hdOgtBno7Lpz2yU9+spx77rnloIMOKlOmTCl8Lafla7rcJwKJQCKQCCQCiUAikAgkAkOFQDoahgrJ5JMIJAKJwBoi4BULkPuin30EPSkVn77Ufoy+SuN4XgbJioaLLrqo3HXXXeXaa68Ncqf3Ohgqs1LOO++8cvrpp5c5c+aUGTNmlGnTpsXKB+czXe4TgUQgEUgEEoFEIBFIBBKBoUIgHQ1DhWTySQQSgURgDRHAKcDmi33vyU68XzTbvrTx2WefLVdecWX59ne+XR544IEyd+7cKA0a82PfG970pjeV4447rpxwwgnl0EMPLbyrgfLaMnvz5HkikAgkAolAIpAIJAKJQCKwPgiko2F90Mu8iUAikAisCwLyB/CS1/Zi304C9l7xwBdvOOZLNfPmzSuXXXZZ4eWOTzzxRLfU1hmBg4L88DW/I444osyePbucccYZ5aSTToqvT3Qz50EikAgkAolAIpAIJAKJQCIwDAiko2EYQE2WiUAikAisDQKtcyE+MtN5ioLPXvIJzBdffDFWMeBo+PSnP70Sa14ACR2BT+rimCCOPYFVDPvvv395xzveUU477bSy8847R3z+JAKJQCKQCCQCiUAikAgkAsOFQDoahgvZ5JsIJAKJwBoigFOAzasQ/LUJ9osXLy5PPfVUufvuu8ull15avvrVrwZXr1pgP3HixFjFwMqHgRwNfHHine98Z7yrYccdd1xDqZIsEUgEEoFEIBFIBBKBRCARWDcE0tGwbrhlrkQgEUgEhgwBOxr8KAV7nAwEnAc8KsGXJi6//PLy9a9/PeK9agHaSZMmdR0Nfr9DEOmHRycOPPDA8ra3va2ceuqpJR0NRib3iUAikAgkAolAIpAIJALDhUA6GoYL2eSbCCQCicAgCHjlgh0LXs3AuTdnxdHw+OOPdx0N3/jGNyKJxyXsVFiVo+Goo44qBx98cDnzzLPKKaecnI4GA5v7RCARSAQSgUQgEUgEEoFhQyAdDcMGbTJOBBKBRGBgBPzSRlYleOVCr/PBOXl04sknn4xPWvKOhq997WuRxOMSvL+B4EcnOLfzIRL0c/TRR5dDDjkkXgZ58smnyNGwg5NynwgkAolAIpAIJAKJQCKQCAwLAuloGBZYk2kikAgkAoMjgDOAVQx8McKOhl5qHA+sbli4cGH3HQ08OuF3NNjRAB1fp4DWL4NseeFoqCsaziwnn5wrGlps8jgRSAQSgUQgEUgEEoFEYHgQSEfD8OCaXBOBRCARGBQBnAN2JOAg6A1Oxwnx2muvdR0Nv/vd77orGvzoBLR2Vjhfy+/II4+Mz1ueffbZenSCFQ35MsgWnzxOBBKBRCARSAQSgUQgERh6BNLRMPSYJsdEIBFIBFaLAE6BgQKOBzsM7Gjg0Qm+OoGjoX0ZpHl4Dz/ys1KC1Q2EOXPmlIMOOihfBhlo5E8ikAgkAolAIpAIJAKJwIZAIB0NGwLlLCMRSAQSgQEQsEPBL4PEscDWrnLgHQ3z5s2Ll0HiaPjmN78ZnKAxnfmQQP4tttgiPouJs+Gwww4rBxxwQHnHO95RTjvttFzRMEA7ZFQikAgkAolAIpAIJAKJwNAikI6GocUzuSUCiUAisMYI4CDAyeCXQ/I4BI4CBxwJ7csg23c0QGOnhB0N7ImbPHly5IMvn7ecPXt28aMTO+20k9nnPhFIBBKBRCARSAQSgURgFCBgW0+3mbR8ta5gtdikOfgmlM835j4dDRsT/Sw7EUgENnsEcDSw8oBJAkcDjz14hQOTxaJFi8oLL7xQ7r///vKb3/ymfPGLX1wJM2jIw94OC47hQTj++OPL4YcfXt785jfHcToaVoIvTxKBRCARSAQSgUQgERjRCGAj+saU7T7fmLIDwhUgnW0khHQ0jIRWSBkSgURgs0Kg9TxTcTsI/BUKzplQOGdFw/PPP/86RwNp0BFwUNi54LhI0M8xxxwTX50444wzykknnVTS0WBkcp8IJAKJQCKQCCQCicDIRKDXgcA5Nt5Ajgbbfl7pmo6GzhKPkQLEyOxiKVUikAhsigh4xYInBDseWA6n71F0VzjwCcslS5Z0vzpx2WWXlfPPPz8g4T0MpHnSaXEyP+J4P8Mee+xR3vfe95Wzzj6rbL/99i1pHicCiUAikAgkAolAIpAIjDAEsBVZ8do6FmzftdfPLZ1vPI2UquSKhpHSEilHIpAIbDYI9DoaXHEmEE8YHPOuBZwJPDZx/fXXlz/84Q/lO9/5TpC3jgbn9yMU8GAj8DlLJqp/+Zd/iRdC7r333t13Ozhf7hOBRCARSAQSgUQgEUgERg4C3EjCBsSpwI0nPyoREuqVDNyYIg067DzS09HQab+BPDIjp2lTkkQgEUgEhg8B9B/bSpOGisM54AmD0nEm8I6GG264oVx88cXl1ltvLaxqIDCZQNvqUk9ExC9durSbBv0//MM/lDPPPLMccsghZeeddw4nBvEZEoFEIBFIBBKBRCARSARGFgLYcjw+S+DGU9dmlJNh2fJ6MwlHA3Zg62gYSbXIFQ0jqTVSlkQgEdhsEAgHQccj7cnDKxGYMAhMLK+88kq59NJLy1e/+tUyd+7ceIyCNPLAo3U0TJgwIeKZnLyigWPCO9/5zvjyxFFHHVVmzZpVpkyZEvH5kwgkAolAIpAIJAKJQCIwchDAtsOO46aRVzSwJ/imFOe2H1tbkHjTbuwapaNhY7dAlp8IJAKbJQJMCjgB2PslkADheCYSVig899xz5Re/+EX50Ic+tBJOvY4GEnsnF3ixEf76r/86HA187nLPPfcsW221VcTnTyKQCCQCiUAikAgkAonAyEAAu803i8KZMEafPe98RMI2YruCwc4GbEo28oyURyjS0TAy+lRKkQgkApshAniqmUyYEHA2OHiSIe6ZZ54pP/7xj8snPvEJJ6/kqbYjoZvYOWDiYfPqiPe9733lnHPOiU9d7rLLLvFYRm+ePE8EEoFEIBFIBBKBRCAR2HgI2AZEAq9MIM43k7yigXTfqCIt4vvkaBi74rPn0GzMkI6GjYl+lp0IJAKbNQLhaOjXZyzHj4vJwhMKoHhSYUUDjoa///u/fx1Wph/I2YCTAQcGLxIisCLir/7qr8qhhx5apk2bViZNmvQ6fhmRCCQCiUAikAgkAolAIrDxEcBxwM0i9gScCl6pYGcEaXZAeA+t7UOON2ZIR8PGRD/LTgQSgc0SASYITxLsvfqgFwwmipdeeqn86Ec/Kh/72McimTjyOHgyaeNJ55wJCWcG4SMf+Uh5z3veUw4++OCy7bbbxmMZ5pH7RCARSAQSgUQgEUgEEoGRgwC2HDacbTrbiuwdcDSwQUN8uzrWNBtzn46GjYl+lp0IJAKbJQKeGJgUcAjYWcBEwUY6AUfBq6++Wi644ILy4Q9/OOKYRPyCRyLI68nHedk7+BhHw7vf/e5wNGy33Xa5osEA5T4RSAQSgUQgEUgEEoERhoBtOtt5iGf7sHU2EM/XKaDjpeC2KYnf2CEdDRu7BbL8RCAR2OwQaF8C6QnBDgH2diQwYfDVCVY02NEAvfN4wsH5QBx5260FFkcDj07wect0NLTI5HEikAgkAolAIpAIJAIjAwHbcbb3OGdlw8KFC2PDRsQ+5GaU7T8knzB+Qpk4aeKIWtWQjoaR0adSikQgEdiMEMBBwMThFQ0cO3BsRwRfnXjxxRfjHQ0f/ehHTRKTC3n97J4dDxC0vLoZdGBHA49O5DsaWmTyOBFIBBKBRCARSAQSgZGBADYidiB2nlexPv/88/FycGzCRYsWxeOv2IhsU6dOjY2viXHu9zi0tuHGqlk6GjYW8lluIpAIbLYI9DoamFCIs2fa6UwWvAzyJz/5SfcdDYAGHROQ83FMwMlgRwMTjGlIe9e73lXOOOOMctRRR5VZs2aVKVOmEJ0hEUgEEoFEIBFIBBKBRGCEIIAdhx3IzSRWMjz77LPloYceKk888URZsGBBxGEfErADt99++zJ9+nR9unxG2WWX6WXLLbccITXR472qzIpbaUMk1u23314uvPDC8vnPfz44XnzxxeX000+PZR4uwsWOBG+LZcp9IpAIJAIbAgEmEIIdBHwZgsnCS+FIQ0eiH5lgcDR8/OMfJ7obyAsfaOy9blc4kE48z+0RjjvuuDJnzpzylre8pZxwwgkxMXWZ5UEikAgkAolAIpAIJAKJwIhBgHd0zXtsXrnzrjvLtddeWx577LGy1157lcmTJ4f9x42oO+64o2yzzTbloIMOiptJxx57bNl6661HTB3S0TBimiIFSQRGJwJcIHNR7LvxrsVgzkRfZHOBzB//BOh9cc15rxPS/Egj9KbX2NHxG3XBxdupux0EOAbAkQCuLJWbO3du+e1vf1s++9nPRrx/qL/xMvbwIQ4+OBqgwYlB3I477hhOhy984QvlvPPOC+83vEIWM+3sB8IWOredefdky9NEIBFIBDY5BFodOZBuXFWFnZe5TjNcl5T4lld73CXKg0QgEdisEcAGvOWWW8pll11WvvjFL5YjjjiifOADHygzZswoW2yxRTxKgQPirrvuilUMH/zgB+NmEo9SjJSQjoaR0hIpRyIwChHAWOKOORegkyZN6l4kUxXiSO+9KPXFMPFOg870GFxspDk4nXOnbwqGGfVic3DdOOcZvBtuuKFccskl5bbbbgtnQy+d8xsLzjnmGT32YM3Whs997nOFyWi33XaLssHd+aEL98/YjgekyYjjA6eF+bft05DlYSKQCCQCmxQC6FXrSes96+1Wd/ZWus0HHefeoCWu3Xrz53kikAhs3gg8/fTT5aqrriq/+MUvyve+973yqU99Kl7qfeCBB8ZLvV944YVy8803lzvvvDOcDieeeGI59dRTR9Sjselo2Lz7cNY+EVgnBGwscfHJhrHEsn+MMKeZMWkE6EhrDSsfk25HA8fQEdr0iOj8DBbf0oyGY2M1UH1YMvfrX/86HkG7++67V3IYQO9grHxOmh0NOAbAtQ3/+q//Go9h7L777hFtA5oT84JHKxvtCh3PCpJGW7cytPzzOBFIBBKBTQkB60J0INvYMXpB2/j6pR/qGXqzs0INvdjOi15tZjzMi3PrUPY+Nl3uE4FEIBF45plnyk033RQrGs4///xy7rnnxvu29t9//3j8lRtSPE7B1yhY4YBdt+eee8bxSEEvHQ0jpSVSjkRgFCGAseUl+Vx0hjHVWRoajgcdjx1Xl+5TLe6q+10BPFvmxwN6qxxG2DJ9dWFZdWCQDq2Ntc3JGHv55ZfL5ZdfXr75zW+W++67rzz55JO9cHWN0zB0O6lgxKMTBNqCtmrDZz7zmcIXLHbdddc2unsMLxvU5MfJYMeCy9mc2qELTB4kAonAZo0AcxgGPfqQl62hB2PO6uhMdCUbdFwAmO51oMkpwWMUr9OjnfiW/nU0bWIeJwKJwCaNwPz58+MlkKxu/eEPf1h+97vfxTu29t1333gcdvr0XeRwmFZmz55dcD7wbgZuCFkXjQT9kY6GTbqLZuUSgeFBAOMKZUZoL0JtdBGPgvPGBetAy+59QQs9ihF68yBPG+/0iNzUfgYwPJlgLv3tpeUr53+l3HvvvfGZy95qG1/ijRvHduSAL/Ft+Pd///fy4Q9/OB6dIJ50sIaXMXa7sCcefqRlSAQSgURgc0Wgb2lfWbR4UehDHOboxlbHWn8yN/bOd62uHgy/VodDT/B+sDwZnwgkApsuAosWLirPPf9cOBuwA6+88srygx/8oFvhk08+Ob4kxv6www4L5wM3mthGiu5IR0O3ufIgEUgE1gYBX8CizDjG4CL4gtQGmFcjtPTQcW4HBOc4LExrnqZj72Dl6b3jR/M+sJM/YIzejeB68egEL4H80pe+VHh04pVXXokqtthw7I1EY24exrzFhhUNH/rQh8oee+wR0bQBd+DIw3s23H5t3va4La/lm8eJQCKQCGzqCHieQw+2+tZOBtcfnYnDgY15jcfZrFtN0+6h79WzpFuXt7R5nAgkApsHAuiP1157LW40vfTSS/GFie9+97vliiuu6AKw3Xbblfe85z3lyCOPDKfDfvvtF1+l6BJs5IN0NGzkBsjiE4HRioCNIgwhjv2SRzsLiCNtVcYVF7ltPvL20tuwAycbXd6PVux65W4xcP35VjKOhi9/+cvx6ASfMSIYbx+3GIOn2yWIB/jB0cCKBr+jwY4GSHtf6OnsNqg5j/L0jLKejsmQCCQCicAmjwA6FT2JbmbzfEfFHYdehIYLA99RRG+ysgEaz21yDXd1p3W19/CDj0N77LjcJwKJwOaDAHYg72ngM+fcfOKRWuI450WRfG3i4osvjpUM55xzTnnnO98Zj1ZMmTJlxICUjoYR0xQpSCIwehBoDS2/DwCDCsPKRpUdDqurVTgSmkcHyO/QXuASvykaXmCJcYqRyqoO48mzwJdeemn52te+Vv70pz+Vxx9/PGABg9YwBRdjY4eF8Rto/+lPf7p85CMf6ToawJjyCZRNu7WhbQO+zuZVF5tiW7T1zuNEIBFIBECgv0+P/i1d0l2ZYOcsuhLnrAM6m7uPxGHoW1ejr9lwMqA/HRzvc+jb+c/xuU8EEoHNEwFuMN1xxx2xqpVPXR5wwAHluOOOCzB4dxcvivzZz35WbrzxxnhR5Pve+75y8iknxxcpRgpi6WgYKS2RciQCowgBDCRfgNowwvgKY6pjLNnIgo5jOx5MR3XjIrlzd5y8DmGA6aWQfmGWL6RJNx08N4VAfeykaR0NPCrBioavfOUr4WhgkiGAReDTwcvYktZiy/lA4R//8R/Lu9/97sLnkTCGKdP5xo2Vk0GwtvxbHpTlrY3P40QgEUgENlUElvUvK339faF7cca2zlnrX+Y39LhfAul3OPTqUugJjmdPIL6d5yJSP6Q7j+NynwgkApsHAk899VT5/e9/H7Ygqxfe+ta3ljPOOCOcmQ899FC59dZby7e//e2y7bbblne84x3l9NNPj0copk6dOmIASkfDiGmKFCQRGH0IYAR1L1Kbxx4czx12NgwoG16+e09tMc4mjNenEjt3ecjHhmHHlycwsLgQtqEVaR3HBXGOH33IrZAYoxWM2FNXMCHgWODzljzq8MADD3Qz2NFABHiAgQ1Ut4WJSYOmDSyvO+WUU8pf/MVfhHd8m222aZODvl0ZYf5RRrPsd6VMeZIIJAKJwGaGgL8ugfOBT8u1uhm9iU5n49h6HYjQyZ7LenU4tA6mI66Nd3ruE4FEYNNGAEcDX5q48MILy09/+tNYiXreeecVHAk8QsGnLS+44ILywQ9+MJwMc+bMKTNnzlxppdXGRigdDRu7BbL8RGAUI4ARxR0c9jgSvOyfKnHRy0YaBhgvwyL4oppj4jHAMKJwLngFg40w4s0TPsQTiCfvaA42MNnbQeC7ZWA6b968eHSCFQhtaOsNJgQbsS1GxEMLf+NG3LHHHhufQnrLW94Sz/L1fuYSWjsajDN7Nof22HG5TwQSgURgU0TAOlRaMFZ8Wf/hNPfnLu1Ib+uPXkeXoodxIjtYH1tfW796b7o2vdX7Ts99IpAIbNoI8E4GVi48/PDD5YUXXoj3NGDvoRvQLRzzqNaMGTPKrFmzyi677BKfuLRNOBLQSUfDSGiFlCERGKUIYEixxJ/9VlttFQrPRhhVskHVxjm+jbPSJI2L7bF6nMJOB+JIpwwCCrTXIIuEUfRjXBDZOBDHMXfJeNEPz9/xKaMvfOELK9UMGhudNkQ5J97n5mvexDvwyARfnDj33HPLm970ppigyG+eyAHWGNVjx614Lwbxltu05pn7RCARSAQ2VQRi/tF7GvAzMP9Y/6EnbezbSYzzgWPoSOdxCo7taG8xanWqdbX30FEuW6uf2/x5nAgkAps2AugQNm4+sYKBF0Li3MTu5hy7e6eddop3MnCMnml1yEhAJx0NI6EVUoZEYBQiEBekMr4WL1kc0qPgbFxhGHHswMXziy++GAbT9ttvv1KaaXwX3UaVlWWU0zHoiOPOkA095x1texuY1Mf1dB14mdgjjzxSbrvttnL11VeXb33rW06KPfSuP0YovDgn3ucQDkRHPF5vJiaW2p155pndz1ySRoAHbUF+DGb2DpRFaOOclvtEIBFIBDZFBFalE11fHAy8uO3Pf/5zGP877rhj2XLLLVdakWed3OpP61TzadMol81zomlynwgkAps+Ah77rikOBr/Piz0buoHVVDgZ2BPIh17xO7ecf2Pt09GwsZDPchOBUYwASoyLUb5CMG78ijs8KDg8rxhLPLNKII7P8/AiG5wEPEPG82UYZtBxMesALY9QcOfI8b5rxB6lyjKx1hhz3tG0Bz826uG6+Jylcvfff3+8Rfjaa68t3/ve915XtV7HQu85GeDreLCDP4FvLPO4xAc+8IFy1llnlR122CHi/QMtbUPAeQSPDIlAIpAIbK4IxLykuQmd2jrQjQc6E7199913F3Q2y5cPP/zwsueeexa/AwcHslc29OpU62b4eT7gmHI9L/TmIT1DIpAIbKIIyFxbtnzFSlRsMhwN6AEcmOgSdAP2NqscsK23mbpNpPOFHPRI742ijYXURnU0tAp1YwGQ5SYCicDaI4CCs/HFMcrPjgEcEBhd3N156aWXuo6G22+/PS5cjzzyyFjmhdLECJs+fXqc+xlW8sMbntYRLo84FKzj117ykZGD+rARqEtbH7DjsQkM1uuvv7784Ac/6NI5DzgQzMeYtA4FeJrOBit5uNMG1v/0T/9Uzj777FjhAC3tBz08w4kkWk9U5Cf0yhqR+ZMIJAKJwCaMgPWs9SnnGPgsX0Zfs1qP56fvvPPOcvnll0un7lWOPvqocDSgb7fbbrv4wg/OdztvW51v/qFf8bJ3FpGtFC8dnSERSAQ2HwRauwvbrm+pbu51bsLZ3kZH4MREd6Bf2PM5Xl6wjl04EkI6GkZCK6QMicAoRQDlhzeVwCoFlNxyfZby4UceLpdcckn5v//7v7hjzqMTLCklHccCCpHny3hHwBve8Ia4yz5t2rS40MVzC18CitIXuxGxifzYgHR1bMByPv/l+eXe++4NR8N1111XfvjDHwYZNJ54TM85mLaOBtMQbzrKc7zL/PjHP17e/va3x2cubQibj2nYOy97+MGXLUMikAgkApsjAjgZcCzMnTs3Vp6xkgHHOo+88bUg5jJWjTHPsbqBz9EdccQR8Qk6vrI0YeKKrwuBXzsfpH7dHHtU1jkReD0CttlancBNIB6ZII7Vva1d2Npl7fHrOW/YmCFxNKAkCa4Yzxb/+Mc/Lp///Ocj/he/+EV8dgNPLgHwuJAAoJHicQnB8icR2IwQ6B23jMv2YrI33dC08RyzMgGnAcoPpwN3eVj6/6Mf/aj85Cc/cbYB93wTmO/+zp49OxwQO++8czgsUKDwRqegJzhGPp8PyGwURVIftjZQP5wqrAJhNcNFF10Ud8hY1eBgHcu+zQ9GBLchx9CY3rTQ2Ynz3ve+N767fOihh4YxPGXKlEgjj73l5tfyMV/HUdaGDK4LZQ6lDEzg3BnwS5bgb1zbcijf5yxh5NETr8YhD6Gl4dh9l3zOSxzBZfSeu51IJ09vemTOn0QgEVgrBFr90Wb0uCSOscZGXGuj+kW9fHLuySefjMcB+cb9pZde2rJ63fE///M/x9d+cD7wjiKc7X6kAuJemawzGPvWD69jugEjWvlanCzCYOmOdx7OpQHjrqzzDrZHF/NSZOwL61f4wMN8wYbrChw6PKPOfrAQZSt/hkRgJCBAf0THsO8d546nv3uzzND70VbGhccW6W0f5xgbgnR0mM+hI44yV8qrG4Q8quE06BzI24aV8jVpxLe0phsyRwPAUBkqxhJpLjBaR8Ob3/zm8L4gLCBh1GHMWoG0lcjjRCARGH4ErMxsyDAmiWNMEtemW2GgRIgnWHkxnjEIuLtzxx13xLL/G2+8MVYw8IlG09pgQ0f4IopnWHEucLG71157hTHG8YDvDViidzpoORiGheUJ5qPwp8UW8cED/MGeO2M4Zz/60Y++rmaeHGiHVqG/jnAVEc73/ve/P14GyTszaAevMiErLxXiyx/I5KV65ENOsLccqyhm2JJcd+RYXT9wXXmXiBALeuehbtQHpxYBB8/jjz/e/ZQUdB4LHiPQ0Xb0ZdLBjTuVXDw4MB6gcT91OfBg8wuaiEc+O3U8JjgnvjUm4D0SsHcdc58IjEYErDuQ3XqAOC5+mVscGJuMP8a5b5CRxqq8m268qdxw4w3l1ltvLddcc02kM/9Zp3uegy8BXcpF8yGHHFJwrPPo4NFHHx3zHemUZd3AOYGyGe/oAuuHmrJxfqmbA7gZO+KMaeDYSSOdc/KBLV8vIljHGaOIHOSH1SF//OMfy2OPPRYrQaxz4QlmlIF+5tEUbIjdd989XnJsfd6ydbnMaW07tzR5nAhsSATox4xzxglj3P2bc/o3gfh2rFm+GGsaV/wPFuDBzT/4MiYoj3PyEufN+Rkj1nm95ZLHG/L02kMhTyeectpz+A+ZowHGFM7eKxr8WTaWUHPX0gqTCrG1FVU2hY63M44qhkSDpfc6jIDRSECJEZzeu4/Ezk8YmqL3fuU0l9NyWMG/peXYPKocVYoqUeXT0vTm9bl5+HygfS9PaFZIuAIv563yrCx3bzk+r7xX8HB8LaONb+u04nhFmfWot89bFtOBksvw3mWxd1s6biB65/MeWocWl5ab62m6zX1vI4CxSrDSQMmgJBiXjiOdOPIQ3wbi58+fX55++ulwMtx7773lD3/4Q1wkm85lce78nvRN4/2JJ54YS0yPOuqoMmvWrDAuuOuDMUFAXsok9Cq7iFzLH9d/LbOtF7nxAAPqgk401ty5Ydntn/70p/Kb3/ymfOlLX4qymCSggd66kwTkh5+P40A/jhsovq3zqaeeGgbvaaedVo477ri4I9TKBR+XyZ5z2pC9t5afyx/uPWWvaUA+ZOdxHimYOAZn+i3PVbN3/+IcR8MDDzwQbUAZxh78XS4YeS7bZ5994sJht912C96UxQTPSge+7EH/dfu6vSZOkKOsuahp62Iay02ay6bc9hyaDIlAIrB6BDx2oVzVuOmls771+xjQzVdddVX55S9/WW666abXFby6OY6Ve9x0w9Gw7777hkOdF7htPXXrrh4yU8pGnlYm20SmGe79YOW1MiGDMe2Nb+WDBt2IjiSgh3HuooO5ACINXQcd26OPPlpYLXLPPfeEI8FzJbhgq1AW+plHU9j4ZDM6F91LmsvhpgXxOIzgSxmex1r5VnVMvgyJwJoiQP9zP277DvHeiKcvs/ccD3/O6bvQdfupuh/XPATi22D+xHPsdOsu8yGt1SnEs1E2weOKc2wU7CUH8npzPtKIc3A8smNvYeOY95A4GlyQ96xouOCCC8p//Md/RNSvfvWr8pa3vKULQK9w7bl55D4RSASGHwGUg0MYAfrigxUESg6lgZHFnjveNqQYs9ylwRhgmT9jnIu0G264IZQL6VzAwd8ODJfDHgVkXpRrHYCzYe+99y7HH3983AFilQOGghWW5eEcI6OVv+W/Jscuc01oh4oGw43lacYE48c48NgJ+LGaAR16xRVXRLHU0W8Zpi0wyoYynH/++YXHKLgzNFBAVozCVo6NgZ1la9scObwR7zTimDjpW55w6TNLtSrmqaefKg8++GCsvAFncIcObHkECMOXF7zBw/xcNnvHk8fPYnsZtJfvckFx5BFHlpkzZ5Ytt6qfuLNxzB1OOzDM1zwtN/H0i7Z80jIkAonAuiHQjqVeDswr6Il2XoIGXYsuYGUeDkhuojHfXXnlleFsRAfwyBmBY28RoR/GsPU75bNx0cucdtBBB4VdzIoyLpJ5x1FvYMwjm/P2pm+oc2NnHeRzyrfOIs51beVyOvqPDUypE4+f8CUqNlaKsJLP+eHFI2ysZvDnQl0m/Fo5wH/rrbeOF26CoXUr7UY573vf+wp2BTS0Dw4LNuwTyrNt4bq0566Hy/N57hOB1SHgft/2HfoWG3257c/wgo7+SaBftjQRuQ4/8LD9wzH9vi3XugWZbC9x7JsvbZHI19bF566TaeFJGuWQRhgSR0NbIEwx3niB2X/+539yWn7729+GFzdO8icRSARGFQKsWOBub+9FMctFeRcDhtd3v/vdleqEQkNZohtQnigfByugVuGh5NrAsn4+D8anGP08K+XDh+dkeXkhyyU3lYBRhNPm6quvLv/1X/8Vn0kDO4d4lEFK23d+HL8ue5Q//LioJnzqU58q4M0dNtoNQ81OIvOnXPKRvrEDxirOAeSnL3hypn8gH33JdyAxVpGdOPobtBiuXDTwhnjuTA5HeNe73lWOOeaYeLQC5wNyIRPY8pgFjgnkBVPkIxhz6odTDzroMyQCicDwIoCuRVfgUGWOQV+w54WPvIsBxySPBuJoQEc7+GKV83YOs03sOc5zFzqgDR/72MficUFWRvklyb74RWegO9DVIzWgq9DD3HRAF3vOQq95niee+Q18OQYDsMWu4FFLVi2wGnK4wic/+cly0kknhUMI/Q+utAdOCZwP6FrakTTqQzAN2OPIgD5DIjDcCND/GEP0P8aQx9O6lGvdw3hj5RB9edttt30dK8YitOwpj3LZVgoyReVmiCho2ExP3tYx4nTiLcOQOBr47n3/sv6uF6TX0cAyYB6dIFDpVjlHZP4kAonARkPARhXjsjeg+Fi6iJFF4JyNizWMru9///sxEaN0vKFcrIgcxzmBNCtQ0lBQXEyRbiMMObgQ4y7+YYcdFs+yzpgxI+4EoSyRd//994+vJQwkcxQ0in4wbnHY8F6Lm2++uXz7298Og6ytApiBHZitTwBnJjEwxzgknHfeefH1jwMPPDBWNXC3DePKF7mU6219yh6qvBj/vtOFAwzjFXwwHOkzGL7cHePiAEP2mWee6RrB1IP+w8U9hi+8wJR4QttvOSfefRfc6G/sCbSbDVc71YwTEzobRqzLfETPHHNnDQcEdzC5iEBuO3zAHP6MAy46cLK1q3mi0PxJBBKBdUIAPUFgjHkO8rhnzHHhi/OcFU1s0OP85REJ3sdA4NE29CZzYBs87q0j0BnoGTZ0hAN0lE08cuAwZ5wfe+yx3fHOxS980B28AwZdYDnNZyTs0aHghK4FN/bgSN3Qh8YCGuwHHjtBF2M7kEZ+sDSe6GEwYU8aGFv3gpk30ltMwQJ8KJONY2iQg2PfqHCbM6+hV3kXFA4eVpige5EdWcmHYwEbhDTaAPoMicBQIUAfI9BfHejr2BTssXPbNNOsyx4b5+GHH47+zAphxkEbGCtsjBXS2CMDewLHPnea87AnkA95e3lHon6GxtHQERTlQcC449EJv6OB45NPPjkUyhNPPBEVYLC7IuShIhkSgURgwyFgpcGkjsJgcmUMc8x4ZNLnYgwD4b777ot4aDHAWPbIu1faAD94kNeKyOPaY91lEs/GOeUQyNNrQBA/S+9rwCjgOXiML+hYdsqFMQZCr9FHnpEejANygrEdDTh1WCHiQHugvMGFereGFOfG1fSr24M5/GgnDCsCLzPEoeO7ajxC4SWopHsS4djtyfGGDtSdOmOsMnFisOKppx7IaOcUF+7QsCQX582ahLY9TA9PNowC44YMbAT6nQ0G51mTPe/EoE/b0YChTfl2NDC+eDs9FyCsajD+7CM0dxfWpLykSQQ2VwTkIpWVu+L5Y3CwTuXYY5wLTFYuoDc4ZkOvoGd65znyMWf1zjvoBfNDX6Cr2j35XDbzaBt4fOLggw+OC1t0L3Q4TdETXChzDi/q4zuLbf7hOrZedF2oo+N8YY6TAdzYc6EELsjLxjzDagZWj7Xz2prKazw911E+OFgnw4fzdQnMeWzgiy6mPsgKb+TGUYyjgTZg9SRlr4u+XxfZMs+micAYvYx0mW7KM0bot+gR+hWBPs7cj/Nxxp4zypSt6yNZ64sEN2VYNUTfxcbDhqZsbAtsPfo6ZbO3jYF+4pgxTBr2p8+Rh3cw8AgwfDz+0AvODw3nbHEsonUbpZG9/rgwF4Kj4cILLyzf+c534rntr3/96/HCLLzDl19+eShxKgs9m/M3LPMwEUgEhgkBxpuVAscoN5QQYxLFwrmNBY55PhVHgxURtCgiLpB6DaZeka1srBsoD8XFfqAAPTJA72OUMXEoZBQeBgGPVbDKwSscBuK1qjjLtSqa4Uqj7mzgSH24MOZuGhjzSADxBGSkfmBBO0ALFpzTPuA4FAGetD0Xt9y5YfPSfsoAe9oeuTAk2dMWyLchA/VGHq9WYDUDMiAPeCITGzTgQz+xvK2cxp84eFI38tGXyedgI4A4t4nTevfIYec5fExPPGV4gycbZbIn0K7I5HP6A29Qx7HGRQeymd71pY4cZ0gEEoFVI8DYYvx5LLZ70hir6BLsU26EQUtgPKJ30TfsCc5LPsagA3kYp+bHntDScw4NtJTJuCdAw4Wu51/0LXHoYOY49LF5R4YN/NPW1fIjApjgkOGOKfix2gNaNtebL+yw2hmdxmb9ij4jgAE4Gq+I7PyAE21AurGifEKbh7LYWjmhsQzwadMtH44c5lfKgC88rbd9zuoSHinE0QAfaDIkAmuCAP2M/s6evkOfYmzTh7xygXHgOI7pp6ecckpsOMCGIvBY0n//938X3sV19tlnh51Cv+YpA15K68cprIMYA8iHXIwPxh42Decet61cHovUsw3UmY0wJCsaYOTBS6E8OvHzn/88PMEY0DyHxsuxuDP6mc98pqu0yZchEUgERi8CXFyhTD0Be3JnT0AvePNkb12B4uXYkzv0zo+C4nh1jgzybGrBGLDHCGKPUQdOVt4cg936BHhh3OIwWlWADkOLlRc4QkZbcF9yP+yVnwkUjG30cmyjgGM7Dlo6ePW2hdNpG48HyjIv2nI04teLV54nAqMRAes7dCljFd3HePTF76rqRF7GL6Gd7+BDPPoA/dHqZXQHG3kdrLNtnGPIE3CKbi7BOFLfXl3ZYmD9CmbQEYypcWQ/EB20xKN7CS29eUVC/iQCIwyBb3zjG4V3PLGaZigCjy197WtfK/BtA+/m4gtv3Mwg8GJrnJrscUTgeGC8EbCBGE/oOwLjyWk+J87jjLhW9w2ZowHGBAS68447yyW/vKRcqTfzssT6rLPOCq8gz8xSWbwlVjZWHK3QlVP+JgKJwHAjgGKw0eMLJd89sPcSw4y4lRRHx4gif2tcWRmxJ5BG3lYBEU+6dQA6ozcdmoECMiAn+UejzkDmFnMMIeKMky9Q2zpCD4ZOGwiXdYkDw7aNKQdZiEcu9mzEtzJTluUmfkMFYwcWbATkc7CcPh9IftIcD54DYQr2rj/09E/ocKqRxkUB5dN/oePCYzDjFZnJw75tQ87d/8mP7MQROEZGr2bxhQ3p3oIwfxKBRGCNEGjHlvWE42DgcUWa9UI7r6EnuQPOGObOvPUGYxSHBWMUhy35zQ/d4OCyyNfqCox8eHh1gPOT1xu6wHKZ34bcIzsyECwHca5LixM0pLnubRrx6EICOMKLuDa4/o73eUvTe2x9Dm2LrfFDTspbXYDe8rlu7ImzDl8TeVZXTqZvPgjQj9t+ST/yWAIF0umbraPxy1/+cnnPe94Tn2wdCqS4wf8dPV3w//7f/4vHhLj5jxw8NsFqBm4gUT5fhmSlMO+L4bFk0hgTjAFsII6xWagPccje1sWytmPE43jIHQ0oTK9owNHAcjSWZ/BCFT5/9+Mf/9jy5D4RSAQ2AwRQNt5aQ2BNq+4LOis4Gylrmn9ToQPDVokPVb3gy4SxLm0zVDJsKD7Uk0lysAAWDgNhvS5tsC55LEPuE4FEYOMj4ItW5p5Wf6BPMNrtLLSkxJMHndrSmw+GOwHd4FWBm4P+NT7eU3/rx8H0LbQDpZnHmuxdxmC0q0sfLF/GJwJDjcAnPvGJuDnPo6y9emVtykIH0a8fevChuPHPl+FwarZOjV5+b3/728sJJ5wQL1vH0YBuwv7G0cqjHKx4gCdyETh2ORwPFobc0YAC5W30P/nJT+I78Lfcckt9uYWeNeNFK7z0jIDgKOA18TQOJnzGJwKJwNAjgMIIpaGbM7qX3Z3krUjWZtInD4rKdwQwpnyH2JJDY2WFTmgNM/JyJwkDjeA7Ry2N+WzKe/SlDddeY3d96w32TCi0K45i9sQRjDPnjiN9YxrFyGEsLB+yEkd/IRDfixMYkk7/80QZxPohLxu8yUv91qaO9GHz4BiM4GP52BO3qkA+510d7ar4ZFoikAgMjADjCx3AWOXY89Fg4w19AD3pdg7AmXjmNMZ1a8OaP2nQoGfQqQTSWP2AHoIXKyMcSIOezXKtjf4xn429dx2QA8xcB9ePvfUitFz4EMAJHNt2AENjDx/SnN98I/NqfuDD/AZ/twVZ4EWa+wLp7byAfKRRlvX4aorK5ERgJQTovwT3bfqc+/BKhM3JkUceGS8732bqNvF+k3ZMNGSrOKSMFeOPd6fwNAHvAKMvIwsyEFrejEUel6Dfo6dmzpwZ74riPTGz9ELU448/PlY6kI7+cl6PE9dtIMGG3NHAgORlkLyj4Wc/+1nB0ZAhEUgEEoFEIBFIBBKBRCARSAQSgUQgEUgENhwCvFyWxyRwnuEAwenQOt5WJQnvWfybv/mb+DQ3Tgg7+uz8w9nggA+AdMphIwy5owGmrFrgcxrXXXddueaaa8KTwhu07VnEa0JFeREPnydDcO5a4hGxlwQ+GRKBRGD4EEAJ2BvJuMNL2Xoq7fW0B5S7AuRBiVjBsLe3n/HtDanhhSLjzg3H5GWcM/7Nx3cROIc/ZVqJIRv5WQkFHenoDWjZSLeM64LS+uRdl/LIY9yoB+VzBwVs2BPHp4244wVmbOBJAAc28lNvr3CIxLX4cfmU7bYDb7ehsaVcMGdiwtONXNBzDr3bai2KHjJS8GJDLjZkRjYw9PsTqJ9XMIAX6S3OYE2g3u6D0JCPupPOxrH5kx9a9zvKa8eC4+FLPOnQw5d+z0QPP9rVbelJmTxtfuSgXPZO45zg9nJaROZPIpAIDIgA44RxyJhjTDMWCegKxjRjkHh0CWOQYL3A2CUw5uCBToGOeI9/aEMXTVK8/oinLHgz3tGV0Fh/wIt4eLFx7PIow+nWQYx7eLJZR4VQG/DHOIAP8iAzx5ab+iK79ZZ1l3EzFn7BHOekEagX723jOoDl2saD9uGrFqR77ieNgDyUTbA87B1IQxZkJL/L97xFfuKQG1roPF+QD9kon408tC/xxsHl5D4RGAwB+gp9j/7FWKaPEehbxBOsA9hDj23Au17YGBNtn44Ma/gDL8pkvFAefCmTeJfJeG3fKzMYaz4lz0sj3/ve95bjjj2ubD1168FIgz/lwHvCeNlQE6udNSSOBoRnM5B8Doj3MWCUUqgVCtJ58ENLHisL4jMkAonAhkOA8Ufwvi2Z8egxyfiFBqXHuO0ds6SxEe/x7fxOM2/H+9zpjoeHDRaUJPEujzycO0hCReiMaji6XZ3uNDL4uENHuRszUCdkoD7WmyFmp35g4HqTztbWPWg79XddSDfP3rq1NMYYGjCGt9OJ47gtm/O+pXqZlrBj8uqVgzwbKyCL6225XRfjZtkcz3lbB+Kd1hsPLf2edM9lYNYaAOQxpuxXSlc34+KD0FuGy3L5yEtosefc6RwTxupb3N3+XqPyNxFIBFaBAGOIeYwxx/jknPFMsA4kDb1LPMcY5MwxfC+eQBxjEz6MVXQAcfByPPRjx1VdTRqB9PbY58T1xkPfxsOXjTjrB2g2dKB85GYjtDIOFm9MSG8xJ2/vhRcX/dSPi3oC7UAgjvzGoMWc9OXLJI9gbrGxPOwtt3lZbtPAN/iIFh4D8SFPhkRgfRBwP4OH+yDH9MN27/5um5vztg8H8Rr+MFbYHnrooXLxxReXiy66KMYd+g3HGU603jBnzpz4cMMee+xRDjjggMIeGZBn1qxZ8bld9GKrA4MH1dAwoT6kUV+XT/qQOBpgyoZAbFbWKBcUCmEwoCNRPwbc57lPBBKB4UXAk63HnidU7xnLXCP19de7soznUHqa3PmLdIlIfusAjlEwpJnP6mrRlg8fG3rDfVHrclcn33Ckt8q4xarF0jqTdGM6FLIYY3i1Bp95IwPyEWhzjrnzQ3tiCCLPxsQOuQbqW8jkDZoWV84HC65Ly9N82IM9e/olWICJx4J5ko6xDLZgSnobiGeDDrla2YgnEEcwHeX63DTImI6GgCV/EoE1RoBxx9hl/DA2GU/YqT732CeuveglHlr2bPCAhrHKHTuMa4JtXuLh7/FNuWzO33tec9c5tKVzPOWxwc/6wGkbco/8BOsm18cyIDsBOR2MMefoRGi4uCGv5xHSoANz4rmIIeDY5nz8hOoU6rbVOOnVDuarwhKebbC87B2c37KT1spvOvamaePyOBFYHQLub91xo84bfXFs7Yf0K9LYiGeMs7euCtqmz66uvIHS+erEt771rXL++efHuMOWc2Bc8oJHtv322y8cCbyTYffddy+HHnpo2WuvvYKUpw8YmzgorCPRcx6vHh/IO1AYEkcDhbANBSgDCZlxiUAikAgkAolAIpAIJAKJQCKQCCQCiUAisHoEWNHwla98pXzzm99cifiQQw7pfspy9912LzvsuEOZMmVKPNrM42U4HzgntI4EnCL9fXJ+auWWnZ9x/Y8HcGA/w9CsaFhJ+jxJBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBjYIAX5v4xje+EY4GViu8+OKL4Uw499xzi79wwSMSvIdrst4zg7NgdY6Dta3IkKxoWNtCkz4RSAQSgUQgEUgEEoFEIBFIBBKBRCARSASGFgFWH/DoxK9+9avCuxN5FILHH3hEaJdddil8pGHatGmxcoH44QrpaBguZJNvIpAIJAKJQCKQCCQCiUAikAgkAolAIrCBEOA9D7z75IknnogvP/I+hT333DO+uoOjYeutty5Tp05d6Z0QEwBr+AAAQABJREFUg72jZH1FTkfD+iKY+ROBRCARSAQSgUQgEUgEEoFEIBFIBBKBjYwAjz/wLoWX579cXnjhhXghK6sXcDjwPsWBXgSOyORjIwyV4yEdDQFn/iQCiUAikAgkAolAIpAIJAKJQCKQCCQCox+B+CLOkqXx8kY7GVZVq96vYKyKdk3T0tGwpkglXSKQCCQCiUAikAgkAolAIpAIJAKJQCIwghHwygT2+rBmrGRYtrx+ThOHAoHVDaxc8N4rGoK+8xnO9a1iOhrWF8HMnwgkAolAIpAIJAKJQCKQCCQCiUAikAhsZARwGOBMaB0InPM4RV9/X0hHmtP5VKU/VznUoqejYagRTX6JQCKQCCQCiUAikAgkAolAIpAIJAKJwAZGgJdB8tgEqxV4HwPBzofly/QeBv0RcDQQoBuqdzIEw+YnHQ0NGHmYCCQCiUAikAgkAolAIpAIJAKJQCKQCIxGBPzVCZwHq3s3Aw4Igp0OQ13fdDQMNaLJLxFIBBKBRCARSAQSgUQgEUgEEoFEIBHYwAjgPMDZgPOARyJ4bIIVDn5UAgfEQI6F4XA6pKNhAzd+FpcIJAKJQCKQCCQCiUAikAgkAolAIpAIDDcC8fWJpUujmPHjx4fzoX1Uwg6GVo6BHBFt+poep6NhTZFKukQgEUgEEoFEIBFIBBKBRCARSAQSgURghCKA46D9soTPvaKBPZvjXY1IHzNWz1E4Zv336WhYfwyTQyKQCCQCiUAikAgkAolAIpAIJAKJQCKwURHwoxIIYefCQF+ViC9R6BELgh+naFc6RMJ6/qSjYT0BzOyJQCKQCCQCiUAikAgkAolAIpAIJAKJwMZGgJUKbLyngQ0nA1+fIM7vauARCtMhr1c4+JjzoQjpaBgKFJNHIpAIJAKJQCKQCCQCiUAikAgkAolAIjACEMDJsFTvZmCVAl+fYAXDkiVL4hzHQ68zAccDNF4FMRRVSEfDUKCYPBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBEYCAVzXgOGBVQ+95r4ikO/Q6IRy/tvt0NKwtYkmfCCQCiUAikAgkAolAIpAIJAKJQCKQCCQCgyKQjoZBocmERCARSAQSgUQgEUgEEoFEIBFIBBKBRCARWFsE0tGwtoglfSKQCCQCiUAikAgkAolAIpAIJAKJQCKQCAyKQDoaBoUmExKBRCARSAQSgUQgEUgEEoFEIBFIBBKBRGBtEUhHw9oilvSJQCKQCCQCiUAikAgkAolAIpAIJAKJQCIwKALpaBgUmkxIBBKBRCARSAQSgUQgEUgEEoFEIBFIBBKBtUUgHQ1ri1jSJwKJQCKQCCQCiUAikAgkAolAIpAIJAKJwKAIpKNhUGgyIRFIBBKBRCARSAQSgUQgEUgEEoFEIBFIBNYWgXQ0rC1iSZ8IJAKJQCKQCCQCiUAikAgkAolAIpAIJAKDIpCOhkGhyYREIBFIBBKBRCARSAQSgUQgEUgEEoFEIBFYWwTS0bC2iCV9IpAIJAKJQCKQCCQCiUAikAgkAolAIpAIDIpAOhoGhSYTEoFEIBFIBBKBRCARSAQSgUQgEUgEEoFEYG0RSEfD2iKW9IlAIpAIDAMCyzs8xwwD72SZCCQCiUAikAgkAolAIpAIbEgEhsTRYAMZwdvjtiIYz8NnQLvU4SphuPm3SA3H8XDKb97IPRD+Th8obVV1db7B+K4q79qkUc7ayrYm/Fv5oV/bMlYll3mvLc81kbulWZUMLd1IOjY2yLQqfFZVt9WlrSp9VWUOjJMl9h6qsQOTjoLYwbBZ2/jBqmqU1h7nwTi+Pn5DlPH6Utc9ZiB5HQfX9cXKvNaXz0A1XBPe0Kyq7IHSzddlriq/adZ235axtvwHkrktf314t3xWdby+ZQxWh/XluyqZnTZY2U5fn/1w8kau4ea/PnUfLK/bdG37+WD8NmS8ZafMVclvutXRDJS+JnnXtc7m7fy95ZPeG2faNdmb//rwWFU5QyFfK5vlbcts09v49T12Waviv771G/i6eVUlrm+tNnT+9XY0ALE3hG+PXZkKWE0ZI4qxGhRj4lQ/yzlw6Bx3EF4+phdq6BlSPfFjfb5c7Bp+LTvzijj9sI9s/HQILUYkiFeXRDTkb3h3c6wgqrmdYLbsBwk1q2pj2VQi8rsOK+IbBspUJUOkBgny6a8WVwsP/vA2/2XLVjByXOQzFKIN+eNHtOTtZGnq3uVHkvlQOjTdrI1s0FFOh3ekBF8zDwJ+giZYjvWlVuVpTIKom61iF8UG8zFlbEcesOjKUjnrt0emruydgmPXqYDSajI9tiND8OlUWTJAGWmddMSqW0fADqsg7NAECxN28F3Bp/Js210DtFuPDtdaCsLVjMFypZ9uUkcA6qK/Wg/1kc55MAgeHToLF/K5NKVJBnpOxChvpR6rvY7brNCIyDlhN1C6h+uyZTVzt75BTFzLRFI3ZcC/Vrxn36EJXhApk/tjzUM2EYWAjYSmU5lB1yWmlKZgijMNrIQI6bQP2AZAy+FL2do4jJ844ETB8ZCIA0na4FC5cao+TKRixmi8hq7kPETpyK/UyE8kpN1QT2q7VNnHjumMo5BT5KofVMuX06LI78w+MEPvazo8HaIHxKnykC2YrEiHrh2v3fbtiYcl0kAbbGDV4A+fFaFLoahOucq3UuiK0D2otN1fHZDUyV6LamlrumNg7z5Qq1ixi/YVafQn9pZZGVfKQ7zToOuE4NmIvqINqnimC1C6Jz5QRsrhT4VFe3Z05XL1F+JCB7psBNI4o7iVRXEt4RuptXDR1z9O9Wc+UDXzR5sbDmsdgkGHC2VQrjbzRSJCtz+EXFXEjqBk6cgnwk56ZCJfd/4gRrwDg4qXabp7F6YIl99N6zkwaYsLJC02kSUY8eMcEVtPFd3pSTWVijRhZd5K4180q5PNLEIN+WTAPe1KQodjp3zKaGJXpDd1sKRt/shHXkV226sSIHhs3XxRwpr/1HxVTo7hX8voxIm/sR8bE8uK+AAuarEiLo4sWyMGvANj+EffqXxrtaRDFb+8A2zIVFn2cOC0Stwo1hrnaPFBp4t7lFexk2OZctHV1Ee0OlXoFBIRZkB8lTUORKKcNS5+60/kV5ZaTodTh13ddU46bGuulU5abisYIEsE78VHCozoiOmw7WauFemedg86fJB9pSxd/o51Od2ctRydBkXLv5t3BW1QmZWjg2UP35aP6DwWyAKGhJhLO8BW24V42i1+q0Adtu5LkZGflWQbUKAu6UoHwXwFfcgiXrCrIjf4va6MjjBG2Okddl0e1pWKcL274zhk52eFDHHoejbCNhQrYlv5KZAyAs9G7qgIafEfeSsvCtERJ7JXupjWitcyXGg3L3kGCV121eZoqYINfJExgo6J7JRFbBeboOnID42zdPOST5tCN8n1CH6M/xrqvhI3iCi96nviQowO/WjdrZejAZC8VbO1nvcrfhmGsv5QquN0jrlLXFneVyaOGV9j+peUZUuWVMPInR2OQjaMcE0cdPhoDP2EcSwWAF+NcR2ME/exE3VA6Cv94gkFuXwhgxLvGl/i09/XF51mrMpkowO5E9GxmLDoM/39/ZE+dgL8qYFqhoEuAaCHPwaMZqYoUQQrBaWsCAjdBiXSmSaMn6BYEKoB+RctXhzyTpw0McoHALLDr0+ys42T3BMmTFDRlfHy/qWlb2lfyD5u3HjJ1h/yT5g8WbmQX+gvXRRyU+cx4zqTZ/DrL+PHj1Pc+LJcdabe4B50ncmbyXwZFz60ifJHYGJ0W5alwlWbaJBonNplLG0TdVNN+5aUpZIPYOEd6fDRMWGZylyGMhGe4yTL2AlbKpY0MK91XtbJayU4dqzoxtKXSlm8ZHG0wcSJk3Q2RrKobyGv+NOWyM8x5UaZgKlyartq16++05GfYi1/1FUMwJyLwejTwi76jc4ldWAdZcFSmIyTXAJTZ8ivgvrVZ1S/2BCGeoO/20D17pN8/CEbZatmyqk+Bi7alvcLF6WNR/7oj9RbvCQXFxJUkvEhDtEncNL1qU7IPEH08OxT/eizE8aPlwjigyzw7xN/2k1tG22m9Cq/mCl//5JFwWss+SQzBheXwMuWaVvOHroQXeVIPGVX7YpEBlYVUdsdzCZMUN1IVOjrq5MHcfVccqgO0NFWtDN9oh/59Ec8kILqGBk37OnXyO7xOy6wYUz1l6WLXguIxktuaOjXHI+ZyHiobYP+Wbp0afTpCRKcMVD7h+g1hkI3BHXVC2PHerwuUZstCgzHCY8xKCzhERehwiR4gEIIyU8cRDxOgj7qNVZ1AizRqWX0V8pktTwxixXTt2BxmSz9MF5t1a+xQ/8P+cerfy/XOB1D69Yiw0gVH/onsXWsaeSrrkJTbSTdoL45IfqlMtFzO+0icAInwR2YCwyl66R2qyhjhfxRhcAfAtorahZt43zCXrgi2zg1Nn2tjiP1c7UlY4UQ/VxjkHZZumRpnNMGtWxyc4gclKP2pnN19Ent98jdoYMWXVUrwVmE2n41f6QpNtq1o2eQIcqLSnDEAfVgWDGf1D5ALOM6jE0uOJQfnQD/8ejh4KNo1W3J0lr3SZMniZ760Hvpr5WvWi76I2UESNSB+um0bvpVFMcRulXsRIpNn/rmYpUzceIEjakpIlteFi96JXTFJOnAsZJJ2khbn8aB9L7kir4DPgqhQ+NYkqkuMY+oDdEFSzQekJfxNmnyFqKG1zK10YKoL5h1ZYPZOgTKDxlCf4OLsFa50QJUv9Ou46Iekkt1BW/qgFzM4WDNHIhCQe5oK8k9TjTjxjPvVbwDA8199D3mudB9oovQxbbBGyma+KBDJuFE36Evj0fPjAGXisSyvgV1zOmUOTL6vOroelDX6IuqV6SjZxRolzpX174Mb/RztWngrXiNXXQ3IjPvvy4gWxPZHrcN5Ysj2q/Om/CiDPVH6QL3dfo4GEf7KDXmFckZTSLy2naiiflTuqjTNvClfZSgXIwrxjnzC7zR6502V+zahY49JhmYg8aPY4536C8LFixAyLLllrIZ0G/SSeg25g1kDZmUjp4nxDgAR6VFUBp9i/RJkxg7lf9izR/UmfkTvipeOEsWbc5aOwqI1zG+ouOIKtjX1gidJ2aTx8MbbEpZ0r8w9N4yYTNZ5U6YoD6rtl66RP2cMUYfUaExf9LCEhl9ip6j/9NeMR6iL1UZIIvQEZB+Ax3tGW2K7CbpHimT81HhNnAKVmGnKqfmHYGrPfWtfYH5xmMv9LzKrraasqoebYjSlRd65Ii2iHEqbqor45j40KcUoLgoR3VmX0vVgULth53aKLGOL/ocofa16MNhcFgOpavskFdyAFOMVeTsYIYNDG4eu9i0lB39K2zppWXx4oUSTXOfxspE6aCx49AFBEnYGUuhJ+FLvaCNdPedjtxwhrmCy6tnNRp7Sx1SUchP3dQ/sOMkI3Y++iB0t3iEnSgdVdtZ+VUmdQosma+Yl0THHEuRS9WHov+ErS36/o6dpzzBI2xE+FZMQ292MEI69GFgHtw6eItvVFQF0FZjo9+oDgRsFpWJvoOIMmp/Vrp4EQ9PcvJPUaFjRLdM9v1iXRdxTTRuIrod/KA1jtgzyh9xbVJNR4eRFPUSY9qDPgAgkrwz3iQHAAVb5Yt+gy7DFpcO1tikbzA3xLUE4wKMCPBCfvIq0O8wE+g7gR5zgvoC9qOOFFclreRIwF8tGnT6l8smVeIE6VJm21oLOI/OMCSOBoBkAzT2AZSOpNr0pw5ddPG84LWyWBNC3yIZzxogk9VAS3WMEeQLKKCnAWyzh9JSYxIfHZpOqE32ufiqudSYDLaJW05WI44tC8Rr8eJF0cg0TFXutVPQwZhA4bNUA27FBZcaXZ0hBrky0UGgpcNwocbxpC22iH0obdFCQ/5qsNY8SDRYAJeBAmVyITAZ/uLJZLdw4cLymrBC1i0Uj7IKYToMlsggX6JBh4Jgchofg6EqkSUy7jGGUSQxcYrfVlttFduSxUvKa68xcQo/5Rkv3KgbeTCMUZZMqNSZCZfywyARLVj2aSAxMImjDNqE9glniOLggyKgXAIOFORgwkR5LFZb4wzgmLpiIIZDodMmcVEshcbAJG3rqVtrbhtfFikfjhcm21DWKjMGscoYp0E4QfjRFgsWLIxyqS/8KY881AP8yBvlSi4UM3KAIYYFachOHZBDBXSMVQ1yyc/FLpi4fPBD9rhglHLplwJiggSpieAvbCdoHwpC5aCoMbbgHY4Y5cdgwtmzXLJw0bmUNDqdwObCpeImPLjAFG8U3DjVZaImtMmTJpeJqgeGMxdo1J+JvV98ULV2Ii3ByFDdJsvZBJbUD+NrkjALY5Z8Ou9T3ZZLRiYt6Nw2tC/t/trCBdFHxuuiZqzqxpTZH04GjC8Z/eKDVhVsyjsu+GNUwhfcqpGsCwDVd5Imism6AKMfLqQ/6o9+jPagX1MX5ABjeICdL9xiUqUQaWAlqTzKrZN4TCTisoXqusUWW0b7z3/55Wh7xhiTLn2Tuk0SfvAnjr61cNHCaOeJjKdol3rxi9zVxKh1Y7xAQx0X68Jl8SIMXF3AybEwvl+TlbAYi7NBPQHtt1ztFeYFMgsfJh4cQP2Se6km3T4N7XGTZETATxe1ZN1KxrJQLgtenF/6XllQttBF40T186WLq+EzQW0wZrzaJf7U2uIFz3A0qDz2YMi4oTNtoQtF+tJSjf9xAm1LdIr26F0uEAI34cAYCd0HD9WTyZDuSJ9kz3QsAv47myJBR/UnDiMEfOCBIcR4Ih8XdmBq5yd9kHFEGB86YkKllzwxHlXf0K/w5U/8MECoE30WPUVf5aI0xpV4UU6IR7+pHSP4x5xE/xAB7QgvSOkHjGn6AnqKuNBnSgc/ZCYwtmKuUH5kYVyj+8bQlvTLaJNl0ScYs7Qv44X+RJlbTZki/aRxqjjOQ5erpK6zRXG1cEmqf2XvbKCvoOSIQ0AkFD0+LCmCslQ6h3IY29ttt13o5hdfeCH2tDljaqL0OfK/+uqr6qY4HKvug2+0lbCCp/Ua8jFeF6GnO/JuKX3KtkTt+eor4qP4cEqHZB35EXJNA3UJnGufYY6g71AumwqokKh9qD26CgNtsfoHfYdz5GQ+Y/5lLCMPzpHartXxs9WUrWJuZc5arHwLZXvAHwy6Fz8qi9CKj0rpjXMMfZ0yyI8eYO6AH3FgbGd3zA/qK9HfOvXwMVhT32gLFYx89AfmqsAcHaM64jCnL3OBHtioDgR0YG/oiBwV6R6LqHvcqSB9gAB+jLXQdWoL5gR0deg7lUcfhybGkuiRIS4CaJtou2oj1TqMka6pbcM5srNH1zNmwQb5A7cYn8jflawetg2AgD2B0UdTMZeN03jdcsutogwuLCh7/vz5kWPq1rIZlM7FTNRHcye4M+dQd3QSRWEz0P/phwR6HGOddGyxbbfdJi7CXnrpRY3b0nG2yVGvLhmrGjr5gll0GOrT2XzOXuyrxtHwlqy0LxerW2p80pcXaP5D/v6l/WUKdprqFXJId4f9RF2if+D4kz6UyDFeNP4Xq/+jZwNv1Qfe9C32BNqP8qEDC85Dd4VMtd781iPqpZP6U6viNMWTl3LAjDJDfwVqyk8fjXEhHae4GFuSM3S8cMLpV/t+zCBhj5GGTNhjMZ5VMeSOfqh2YPZBzxPoN7RP2Eo657hTxeifYd8FZS0zytU5ZbIhc9QdPFSXKFtjLfhKDvoA6YwF6wX6K7ZIxZL5rMqwzTbbhC5cKHsIXRj9UfzreBU+UY86XqkLegnMoaP9mUNUXJRp3BG91qdTL1euQ4CTAF0zTjYCsqPnbNvSR0JumKpuyI1NE04qxTE/wgYa8sacrHMwp7nRV9j8U7eZGjK9+sorBZtxBWbcoKltwJwHTtXOQGb6GzKv6HNhN6hcyiSdDdmZo1w+NyDoPxCBDRiBu+0zxil//FOtiZofxsne4boIHc41EbqdRHhWQNENVY/GzUpjKDnoAwRokcd4hc6lXRUHzURhhI3SrY/ikGucsJKgVQcLX+xllAI2FDeAJoCJ/qrjr/KjPLAA43oDUUc6x+aHH20aNonKRr9AS1mTpRfQD+N1cwKnBjewJmJzRAlwHb1hSBwNAMqmZo89jgaO8QWxf+rZJ8oDf/pTeeqJJ8pLzz2PNaY7a2ocKZV+dbzxApoJlA6GsgpHA/1DcXFFocPouLSKNsWGMgpzWA3EBRCdoRobdaDQv+i8DAZY1UFeOx1KRgxDmVXlWTsIx2wE6sNvDKyIY8qonZVOQv6gqYcd+so/svb8VH69keqw6nxxx1llkJuLqoWafDhmQmTvwYBiZoBgEFTlr3qLMXcruRBlkDB4MXRswGCwMNgZCFV50iJVaVBXsGCLC0zJEvyliBl8cWEXcqlcxaG4wohSGVF7aKTs4IMhgrFEuciL0jWeKA+UIDJQH+IZ8LXNa/2oCLCiKBjwKCcGnyfUinZVCmBJu0pI8amOBiZsZOZCjbLjohV5VRaMUTTgh4MFulDCSouJUGUjN1soJPGn35EXJYBMrhdlRrwme2SK6VXplMFqBu4Yh2KQkoi6IqbS2MAIxYP8YhJOBi4cuJizk4C07sQIreoA7zGqLw45nA3gM1blgCv1iLGhjPCgdeMiWvJjFBBHP4InTiraCCdF4CC+jKeYBHWM4iQvsoERx/R1JiQulsZJ6ZPWp6UK/RJ8TNzRQ2lWQ5D+h3HKRKddxUx1rv2BrGp38axGqC4AZNgBEhMfIe6Cqzx4BMa0k3iyUgesiYMeXOuKBvqM6q8yiKTdq4OLCyxdMMkggBbDFwxpQ3jEBCIZ6X8xcat+5KV+1Wtf+yEGNoYUdLR7tI0wIq6vX07SPhmuKp8JYXy/6oajQcdcDVYnA3vyIYWitSdZPolYzaAsUpKi0RYuDcVPkqySovQvlKGj9poY/Uk0ndUflM2F5rJxajetaBC6oTOrAazyVQ/ajLZGd+F4oc+ALX1wsrAOPSI9Eysa1McEIMJRxcgfOg9ZJV4kd2SHQCQijBQdK1/kob2EC+nawHmJVhSpuMCb/kD/Y2yF0aby4BQ6QIYG7YvDB5x7dR509AHGezjwpDdobxxwVb/T4wkhSFcGTsGBv7qvVOCPHFQOJ0A1wkRDHFwkQ9xl0TG6GNkI4BrGiOpJtZepPcKwEx+crTgaCKw0WKi6UC5GUTj4xAP+9DtCdZx05AYk1Q15JFpszAQcd+OUxgnjSIKoz4A8WC4N+SdP3jLSFqu/U07VH9XJS1twkU67YLxH+7iuilNELYgS9Q/OvqgkjTv3zE/ELV2KIxC5JR+yVCm1X5NQ6WuRyq8D+hl4c0yfpI7MRbWP1FETqxOEOXoLXWcjOpymkoWLfdqQerLRV+CJA4K8GKBxASOsqAN1auXmzCG6deekja9Rrm/VQ2MkE/zRo5TBDQ5kjDpRN8lAfbr2B+fCjIuOqge5ENQYi75N/xauKgLxcKCj44E32lw0oeNFE/Me2Ec9LHlkjRP6dxuQuqaix6peDWSVELqQ/iR+lB8Xw2r/2jaqQ4dRpIkm2kj0yMCf51KcWdQL3RW6Vu0apXb4hl2g9MrPXDvMV7lD+qqHQiZhjp7B2Y3sQIBNwdwPdnUFUbVpXBdkjjlestA/CIwPOCMzAd7GlzkAe4m+tkiOPLDCecEch6OBfTRM7OBC0N4OBh3j/A2po6rQBOIxf8OL9qXdseP6Yk6Ww0yrKJgHq9OHFau1roG97DvAowsxJ1NvnGfMXXVFU50/qUP0D6XXVYtySIgOPVXHmPJKGo4J9beKHjVR/gidfe0BtLsdY9if3BAREMocfMRLvTPsA/a0C2lx80H7sCcUQX8BosBauMMHfT9JuoV92DICOOJFzwUm9UF2+DKegB7R4EXo1iniFYvuQjbS+KO/qy/Wfl51b8WoY+fFuBKteCMntMpWdYnkgz+bHcXoc/oGdgt9g7Rq62ATdMpR2dbvMe9UgTsyVZl9oqKi60RtOpgjX+BOnTr1ZpVmnTvQH3W1IP0i5lX1E+TgHOwEgPor9lcdgwAGf/gyX3o8UNxSzdHosbpqjfmOm3N1jFAf+iCZg6/AjzkefRH84NnRSzBDaiUhS6THT72+weFNCFy6epo81kFqW/GgDjV3JOmn6hxYMhdjw9hxQTnkwXYOHUvTiQ75uYYBe5hRX9NCX9uLm2N1XhCTwDswU33DpiAvmekT0jXh5FJdww4R/7DJ0ZP6o++CNLLrp9a/U2aMC8XFzRriGLuaM8C81l5lKIROUJvtvfc+5aDZs8tOO+0U7YcdyIiFf6UM8lH5I3shUF1n4Stg0R9rhxYn4thwNGCm3XPfXeXy3/ym3Hz99eWhW28vU3XPbtfdditTt5pSttDAjaVGKBQ1yjJpo2qIK6M6NYZ6oCwxUSQoq3Ey5Lmow8hg0N/5+6vLFS89V2Yqy4EHH1B2nTGzbLPttjpT26tjLFmkiVD86+QknuLDxQyTIp1w0UJNVCqGO1Ccv8YdIOXdbvtp5RUd/+rCi8oDOj91xu5ll7320gWJ7irL+zRpi8mi18SmySIuXJF1gACvCE1yVX5jy+OPPFp+euONRT668penvlFlbh9KC9JQHpIb/siFMRurAEIBaIBRN9W/TwNwiy3lDZP8II9X3BPwPZdfUf6oC6ljlDLnrDPDkI8LfvGDp34C3lCUOo42YMAwgJpzugn0KDyOF8vD+OJzz5Xf33xLeU6833bk4WXv/fcPmuqZroqBCwLArJ736pSIcpFU5SArBCgQDALkWSTeLzzz5/LYtdeXPyp1N22nn3F62UEDEAOTyYc68DgBMsIf/MGUiwfLqGwRkLcqo9rmeG7nzX2g3H3/n8o8URyt7ZhzztEqiqnBG287S895lAI84IsyQG54xaZC1Vu7KwgwdjEUuBh65smnyi2XXl7uFN8Z2o6dc0SZtd9+YeBgFKG4wyhGcBQ3GxMD/V30KGTSKQ+jiH6K4cBFNLLdd8ed5ZK77yk7ifaw6TPKbgfsW7adtr3yVmOK5WbIjCEVYyb4V69rnTQoRUF1gY4+RRk4d/DW3/TLy8ofF7xYDhXJPocfUXbYdZeyle4W0TbgQZ8Dz/EyjmgzeCA4cjMZLJHRzUUsstOuk8SXCy7q8+yf/1wu+O1vih5qKG+bfWCZvueMKBcZ6DdMXshFp6z9hLZTHDChoJXWNRDjHPJaD+gfvX9uufiuu8se4v/GU04OPRAYqm2YODEQXpv/SsjM+I0JXPLXfi/cVRbHIIShMknjir6LnnnpxRfLzb+5vNyjtCP3mlgOPfpNMXEtV1+Z2Ke+J8/BWHlJmRrUa6TLNIHW0SW9VcdZ9B3xHTdZd8B1wbhQk/urWqL7mpalP/vCn8s1N90r7qW8742nlj332KMsmb+g9PMIhYwcJr0l9B89XjVuEpOW2kN4IXO/jDQ1iWSW4SHc6U8sWwR7LjSfenReefDGa8rN4j1b2/GnvTn0G+0VYwncFRj35EVupn2aIvbUQ3KDMT2/f5l0kkiZuOe//FKZd8+dciQ/F7jte9D+ZeZ++4d+ZnzT/TB8qDs6C50ZF/PwIi42jBYkqO1LX5qAThD/J594vNzwx+sL9y33ZTv+uDJ9l13DcEc3mocYSyY6TnTHGi9+sGWL/hTyV102WW2L3ph7zz3ll7ffVWaJZq99Z5bd9tqnTNVdTcZKPI6mPPS9alRIZuWR9SBq+qRw1hZGBP1IOGLcU6ctdeeFZd3X/fRn5XbRHqVtxvHHlm2l42nPWKlEo4k2jHClS3Rtta9046KoihMroJbL0TR2oi6ItDLoqSefLP972RVybpbyt289Pe5OLdF8xlhSRw65Y4WUximYw4o2IB0dg4FNejVgdcdfPAMX5UX2uzVnX/XE0+UQ5TvxvL/UBYDGQufCDlnXNoAjrYEOeVnj6Y/XXFceV8x7Tj6x7Kv549VXXykvP/9i1A0HB+OV+W2yHMjxWBx9Re3CPEDbcWGIzg8HlfCnzVi59/zTT5V5N91abhTv6dreqvljx+k7a2Xlwig7+oriCVEPAeP6gLsDcZzSx8apHPQBKw4fvPeecuvd95dnlXaEtiPfdpZ0jfpMx6kUBit6Xuf0O+aHGDtqb/oI/OJCSP2b8Rp9SPLTW/+s+eNm2TT3i6+0bjn1mCNibsVhuIBVJcx14kmwqLFXVPfclYFGZVHfKdLh6PmH5s4tF1zFzFr75H6nnFh21niCJ7oAXOkjbJ4/0JOhK8U3jPmoJxcl1S6hDbZQG+Hgv+OP15TLH3u8HKTUfQ89uOwkPUbZYEP7dESn+DUKjCVsLuybl156qfzwpz8PXXDafvuEntlad5qRjRVy0fclN3qJMcaYpa+HA5wLHvHCfkDvsQevmLs1F0D/+Lx55f+uvLJsp/hzzjyzTJVNgMy0Z3X6135StYqIPEjFCQdDnHMcnDmnitgpOOj7yh1XXVque176V+bpMWf8ZTjEYlWh9BgrfOkjXNjDimYLBoqLi2zvFUs7ofeZf2nf116ZX5576sly671zyzNKf/cbTyy777ln1HGJ+EZ/g50EQqSOWBHTKaiK3klDDjb4M96ffvzx8shtt8X8sZ9ojjvt1LL9jjsGf/oztFVWya0+jj5B/srHxajvozuhlB7DnsFJ+4pWpDx8153l0YfnFVniZfb++5R9Zh8c2HAXG+y5CUKIdmDPCTJqDqJczsEcWWp81WuMq6c0f/zxqmvKk6LBpjngDceVXdUn6e/Y/lUPqy+LSehzMbVepP5YA/GomnTWUm2xykA2AX2a+fK+O+8sP5euwU49XHPf7vvsKz28TVmgawdsjQmqY+guQbQS7shcG1lHNQSK1EvbeJXBSqlrLvlluUvJx+giYa9j3qC5abu4/gDgwAOdoj6PbSgAoj+HE0L2Aul2NG+59ZRIi/FLn1b+p3Xz9wfXXheF//Ubji/b7bC9+ntduRzXR5KYFTeUZUdMYE/ZHZnZ1XZWOxBPGyiR44fuubf8+sG5YXOcePbZwmxqxFcdUlc6IWPgHTpSc6nqLqKQnbGHLQmG4EG/pz7YaDh3b7ni94HN2XMOK7MPOyyc79j30c/AW+0TurUKGXwZAYx54kPPqb4USTx1xHH64vPPlyck+1XSN4S/Ou2NZQ/1GeTiBjl6hlWhy4QNXRCsmEfRM9g39BnwWa4y4maT4pdqTCyRDpyvOQ47+MG77ok+CX/Cf/znf5Rz3vlOORz2DtnQ9/CgT4z2sN6OhkCCfqiG5zg6iQyhsHyEDvF33XlHueLSS8sjugiYILK9d9+z7LX7HmXaVC1FkjGJEyAuTkRbVzNo4GBQaaMH0GRqXzFTZ9deU144GlhCzvLRG26+uVx39e/LrjNnlL3327fsIQW7/bRp0Wkon0mFDksnotHiwkV7JiH403npdCy5phLz1VEZJNuJx0vzXy6XXnaZZL+/HHfyyWX33XeLCQlaLkBiIvOErNy9gQlioEB5GNEPPfJI+cVFPynTdtihnHTySWVHKW8GAWkEJklkR64wdmUYsscQZ+JGTgYJWOB44ZwJFKWzQBfsd919d7lGF71/ceop5cg5c6qBK0xi8JFR9NGZdRzYcK6N9DA8RItSsCJBLvBjKdNzzz9XbpGj4bFHHylvPvuscoAMRfDgIpQBXI0WdQ6FWObVqVOUqULhw90OFRcXom4PDLln/vxMefTRR8u9d95Vpu06vZxy6hvLrtN30Z3PeqFOHgY4F48xKYsXoV74UpMV/YbJB2OFyQ2Hwctq00cefqQ88tCDmkCfKAfNObwcc8wxZRsZFbEsTXhTZ5QVShMsuNAHIbCJoCLwVPZpCTwx8MY7z12exzUp33rTTeWZeY+XKeJ52JFHlv333S/a1Uoy8oC5eIttDfR3Be6kM1EToh2EG7jTvvSFu3RhdN1VV5epW29bZu69V5mx58yyg/oPsiE3OJKPc8qJfo+8wgjMia+YVMyghf9kGVtMbNfp4uI2XQDsN/ugspeU3vRdpgubbUIexhNlgAvdB5GrcrXsUqZcBIiOCxv6Mkq43iUeU56QMfTbX/9aSnphOfnUN5Xddts1HA3QVb61/8ENTFVQyEr/q3XirrHkZizrzhDgqbtV2YTb3AcfKJdefEmZuc8+5bjjjotl5Uy6OIGoI3f6MV7AArkxUIkP7FSnDquYKGAaDjCl09+ff+H5cuutt5Ubrr2qnPauc8rhhx8a/aJfz9ROkHc0HA04FOQIXSZ5VYI4RE1Cb9FPGcdE8W6G5VqVsFh3FV5Z+FqZv+DV8vRzz5Tbbr2ljH11cXnbW88q++45syx+VbLKUcpdazBiXPfrfSzyaAj/ygsdAyTq4R1cpDtUN57xRUe9pvo+9uhj5cG592tyu7fseeA+0jUnl52ka5isMYDdV2hUVmOwx8EAtuwRGn2s6DCow5hQ+aQ899yz5YH755Y/q99jZO57wAGhC1gKjLzQoEsYT7RxnKvPEcQ+yoqOBG+1N+1L/4/l41rt8Yhkv/nmm3SR9UrZeefp5eADDyqz5KDC+ES3UybjG8Yxnjp8Y7JXfPyp/JibREPb049xwqAr75Jj6pYbrg99P2MvjaeZGk9yBtCHq+6FobgAhvYrHt2rRlHoePFlnCEH706gHizxZC655tprywMas7MPP7zMnDWr7LjTjvE4GwYuZQQIou80p6qBwUN19AdASiNQH0Ysj95okMe4evTxeeVnP74oLojefLqcscgtnMkP39AfwjpOKaODQ+gYtTt40f/BhH7AWKXP0070mzs1f/zqggvLCbpQP0HjiRVjtQ3FsYqFaGselI25gbnpBRlzzB8P3fencs5731MOOeigGGfoIC766Ls4nXHc2NEa/U/tQLvAB/ljNZ5oqSt3H8n/zLPPSg/LuSbeU6ZtF/199913D91EXrB18FFgrUifk05craZ+0WcyeLEJ5j7wQFywv/DMs2Vf9cejjjqyTNMFQHXCasSQT5iGjOKITgZ7eNE3fEGEzNFnGVs6JqAj77jjDjkcnixbqA8dLAN6//32j9VIGLisbrP8ti+qjOLdEd7n8KP/Q8/NCGS49777ytWX/059ZIzmj73LvrKZduk4Ghiv9PPQB5KfvfsQfZu/yrPWg2P6D/Me8xPOqRtuurHc+Puryt4HHlj2EW9uKm2rC6+Yf9Q+NbQod6IG2VEicwEXpc/o5sZll10euuYNp5xSZsrmY25CN6Kj0QfhBFdb8a6Sao+gwTrzh+Sn/zJGujpDOGA3wOOhRx4uP/nhBWW3WTPLqaeeWqbpsSTaEZN2LOMHGbXnwFg0A3dFHEfqBK4lfQ5sbrvjzvK7C/+nnP7Xf1+OPfroeLQt5MHOkz0noaKNcDagT+BA/fsUj6URjmT1E7cC8xc24oty2j2pfnOvbkCAwxvffFrZR22LDYaOo91tByB8rUdUhopEoM9yQqr1BGMr5g/G0oMPlnmyV7nxcOyxx5addeOHFafg0+3HwUL9W3EE86nRwkT1oHD6TPQbYf689MC9995THn3o4RjX++mu7sEHz9bjJFPqRbKysOo5xhR9kE31gQ/9ArHZuv1TaYyluOmkOeIR2afXXXNNma9Hy6arLx500Owyc+bMmD/CHlNfCHsmROvoXmEfY1b9iDHVpzLBkUdciUdP8pgustx2u65vfvkrOTKnl/0PPKDM2muvsMd4vIsbS/EIheoZuAJEJ5B3paDzwAtK+rt02suaP676wx/KrddfV456wwllH9k103QDlLlVhUf5VD7sbcaW8GVcs4U+EU11tGk88M4gyY6OZNUYbfTEk0+U3116WawKP+mNbwybALm4aGYFXMxpogd7ZIugnUhUbJU/6kV52sjrtgfTP82dWy750Y/LgUfOKSe+4Q1lW43VmotxWO1RdCHlRB9SAfDwRnnYadWG1COkuvkHpshPP7/t9tvLbbJXz3r3u8qcww6Pvsj1A2WEvFGYfpBPf1U2VrNXezDeH6PxQ9VIp0/SzvTJeXLC3H3XXWWZboIxnvaeNSvwjcegRROrKjvY1BVEHZtGxQU2lEm7K78q8f/Ze+8gPY/0sLORc855AnIGCBCZBAESgSBBcJm53NWu5JNvfbIl3bl85VPV+a4syXcquXy3d7pz2VoFa5e7zBGMIDJJRBI5Y2aAGYRBGCLncL/f098LDLkrW5b2H1bdC3zzft8bup9++sn9dHeeIi2voj+OEWioYZD5ELaNtlkdg/H/w5/8aXryySdTb2QawjJgBjEC9q0/fj2Bhhtg1g//44A5I9Cgr8w81h0Qw/KPPmKUoT71xHkfN2xEGjqoLHVGkJiOeotOi1R3CE5jyoyGPNU5M3lRbAhhijSlxIwG57opBNes+yytWrkiDaCDhmHkVkAQOuwhqGEmlY/MpsMh8SnYFY6Z+DJhS+ThqFO+hoqE0rVL13SKeXrvvLc0jAqN83LKNsrtXJpgDMqLshSgHI1pIq40vsD9oi0KKxXbXgyWV155JXUFLw/NnRvCWyaXscI4B85w2KN8IvsITkd/VZQyW2aOXFakW1KHQkeGvHjxUvpy65a04pNP0kxgnz5lSghvYRCsULR3IMqAyuBCaTDD9kXaI+WFIKBfZUTrvnDxQjDL5zB5zeGatPixJWksCiK/g8BTWABz4IYSHen3nowezP4NYWK5Ckb7xxGpo8eOpYMEArYSRVexLWJ0YQBKwv623TGFACY06GQar/UIuXWUpEb8DsHHfdtq1FRj5XTDV2nv3j1pH8Gj+uPH03gCAbNmzAhj6OKFi0Er0VPinP/2lfDlNmG0KEAoy9sx5QG6pfigH1MZq1HGn6PYjh6ujSyJe1HKY4jQq3Au0yceliGsngNG4Lf9ClXTDg0kUWQ+rI97bdth5PPcF/Tpqk9WRF8OHjwEQ3Fo6tOrT+BAY8UyxE8uIBuKUQ+lBf6BV7hDmJfoU9w49/UcIyPLV69KG9d+lsaMHZOGY+BqoGsoOqJT9CdQ8l2jUWM/K7bcv1khq2T8XTjy8Z3fh3FG33znHRyIS2n+Q/NSGUFBDWDpPRt/1kHL0WzSvziIslREJZwbcdcZjgWYoFOp2Xc0kndiRC99951UCV5mz5qVupPpIZ9Zvk2V94vRUHlcuGLUQhqhPUGD1mMf0IYcpWYkEbozsLaeANK6jZ+nhU88RnBqUinQwEhwEWi4k9GAUrMP4gA/NMSPhEKzUD7INm4TjksXWVjq1JmGdPjYkbRx08bU5NK19NSix9LIisHp+kVGdAk0tDQVHB5ympQR/tus6UBvco1CaIMo00S08BwogLbAjVkByrOq6qq0Z/futBeHt3zEUHD/UBhGN1XalCddCF7j9zXP5RvP2bDL5Uez6B/P0tVxRo+3IeMPH6yOcsYT0JyI0u+goegIkG0WNuqwj3M19q045h5tkE6sWw2gISSdm3Ei/vdgrKxeszqdJ3Oif9/+6Z5xE9KwysGxDoqOnTQZsoz3s+FJXZZtDZQfaA/QBTjTgM+7XoVBgY04u59C892790jDCJYOHTY09SagIa2E42VpAXvWG0iq4C9px/ZLJxQXtGSd0pf1dGD0JgLVyN9djNhNJpg5dMgQUiN7pfaMzqqTsiwEJ7wfxhx1WaYt8K/nKJz7lp0D8Vyibun64KFq9MerEQB4eMGC1Be4hSuntd8tI+hZ+qZs7wtvIWO8Jpa4FP0gn8lj4RgB9wdLl6aps2am2TNnpY7ALcwZMt7wpQBSQP/Lh6M94tX56QYD1hHg2QvPPvPc8/TruAgqWK9TfsxS0C7QYC/gVq75XR0XuKNK6cfsB+GWbtRNxzHkDiGHdxIoaYfsmj9/XirH5rBvItBQwAz8xdeM61/+7QM2E0UQc2xPYxPorO9DhzgyNXrMWPTHzNQdp1T9IWw5AA5OxZV1BNwZP+Je6uRi8K6OjOWr1+UDg0cbN25KR47UEmhonyZMnEgQZhSDMq1jJC0HGkplBdCl75yKQINXAmbLjvpZvyYGUlLaygjsx2SYtqQus0hGEBAYgIyHkDO/8m70Ly9ahnJYWA3+yTfqQuEMmcFv6cmPfXaBfl21dk1au2JFGjl6NE7dyDSQoGCsI4Letu/jaAR3vvCf/6v80XHUQH+PQPUJZM4DOEYV5RUE3DuE42DfxsAF7dBeaSE+ORdH0Dn9oXyxT0On80UZ4W/va4+9+uorqR96af6DD6We3ckwtW7v867BhhJm452MIS7ZHj6R1RAV5jtRPa8YLHMtic3YM6uWfZLmLXo0zcIucL2iG6URY/GfZUJej8ZBEeW6NO20DZOosn7KdqyyTp0gPZ8k2FvDwMyXX34ZNtIjix9No4aPiHvyUNhvAmM7Aj7OlJebzhcOf8eZJ2xzdvDIOCAIfrCmOu1m+vOBA/tTH2jlQQcJ+vQJGVHY1kEnFBD4DDxl2sm0n/Fs/1u2tF4Ed46Tvbr5iy/Sfso32DkWe2zypEkx8HOZDKQmNLy58AgvCIl/gWvgBAeC7SfkI98sX17VUZdODQqugB6/Qt70A/aJZJgOw2bSJo2MCXUSBYgXpEuc/R7fKL8Y/LQfHFzynrRjoEF9tn7zpvTBO++m3gQaRhEgMRjQCxkv7+u0AlCUJVgepVPgPkOerwcMPstHXBoIb0Dnfbj8k7Rh3edpOvbMiGHDw7dpTwAy7LESPIWt7bko/865KBMe9RBX9oN661BtbXoffrI/HoLe+zKopGKPKTnQn2+EzrYeyimOoG4L4lK0Djx5374veNz2b8fmeOO119Lo8ePSQ7PnlAKOZhiVYOH50CclGIU/7DBLLV2T5sW3v+Vdp/I6YHSezICNX2xOm8kIfwwHfeokcpOBxyC2R4YXSO1b3rXl0qp1SBsGB/StvOZt6dTgiv7i8RMnIuhoIOMW62MsfHhRGka/OkW9mLYJoCEztW9C3lNGtmGjQomfD+2EFh30kY60uxtYO+xwbV3ad/BgOoSPUFN9KNVt35d+8D/+bnr6yadS+4F9EBhZRhENy+Xw7rf5AEd2w9/z8E0/2d+4W4g0JGI8k7a1DeH30XvvpYs4eIP69U+TRo9JQwaVs8gZBjIEQ+/wn0/8gxh4LeYv21FQQCjPqAti5yz5O5rVkkCDi0yu/HRtWr7so1ReWZlGMApbWVEZkTmJK5idOmymRCRBKJiDwKjTQyIOQlaoQIRhHPPdSPkJnIs3cVwOYFjMnjMnBJQp/G0wgiN6Thk2tfgEPqJUUOPFv+WQ0XQ89yAEf/GLX0QEVMXWywAJ143GF86RjCjMpvOH8A8FlBlauDUqZW4dcMst2ngRg2sTiu3j999P9xORmzV1WrRJxpWZIyMCgANPJfwGuMCtAtPBVvBbtmUqjKxLZj9HVK6OiN8aHOrqQ9XpqWeeTeMxLMSzsIZzzvtiRjSEwueeglfcewSswO/ZQxDEicGEo4zm7MPB2LBuXeo5aEB64vHvpP79+pNmdzbKiFWafY/+QhNz1tnIDod4t01cjWvRNK7ptDg94CR9umXb1rSLbIlTKJ97Z87AKb0vjOizX50JWlQINYtRhSz0bYVCJtoGzBq2AB516KiJezMCnO/mqMiqVStTLY5Xx66d04z77kvjMUbFyXki1BrL0mJgpoQvcWJwx/aLMwW5Sk3jLhxBrjq1g5fTZkbVzQpozeq7QwmsjYGfBoAb4TNNslCWxVlciGP73c6w/8Ixc5jG39ShIdeB1LpzOKUfLPs4rTULxgAJBu4Aso+MRJvCLD+1BMYmrg/AGgW3YpeXHFSI6SXihPKlA+sT9/KUMJj6ZpT45ddfTxfOnE0PP7wwVQwaFAssKfhDydiHCnDel9Y9LKugFeGWD6gR4Y0kYI2OCBRQp2mQ23Gk33rzDQKOI9Lc2bNTj27dwyGTunyvCBpZh3SdjTVHdLOCc70X+zbwTp8AeNRv5lQDIyLrUGqfrlubHnn6iTT13sk8Cz/cyWjQGBJmaU8DJcNvP0egQTzkFmXZSHe0aNUCnXI9HT9Zn/bXHEwG7ppevp6eXfJEGjp4KPITp5VgLQigWD78B1HpNlMubpNN0xR4aSDXcsnRoSBdvEd/Q+9X6dODyJldu3amndu2p8oxI9PD8xekrhiKDA9kHgcfvmN7o81UlPmH/ovr1u89/qP8mvJpjgJW/tSRFbQePt1L2eJ15n05qNmOQMM1AmsaREKn7LYv4+Cs4SbPxkrytoOHIktI2cQ9px4YGNRYeY9gr1O1yssr0rTJU9JIM4TgF+ndOqPcqMQ/uf1hkMb3wEb0tfes1+d19uXbtRhxHxL86tO3bxqLszsafupDcNOMgwga8Kx1yPvyWKyVEnJXXoV/uO79wHfpt/TanlT6c6RevoXu+xKHei7yffiwoQSVu2GkmhkAHAXs1BHTawA/msFfeQZAc2PoHUVdZPlhuBhosL4D1QQaXns1tYIGFjw0Pw3EkHYusfJRmWs7pXMDJspe4TJA4VmsxD3kl/Je41L+inndBKrVg9tw1Jd99GGaPG16um/69HDqLFPZ4vsZuIzz+Pm3/vFpHSOzh5iWgUNajzG3lmyPbeio5154IU0ePyHgFedhCCIjhUfZGOsA0B/qH2WkRqF1K8uUG+om8eHnEkHMehzSg1VVaTOBwTYsLLx48eI0uKw82iYuRHLIX+GNhsibtiP3Q5y9VzriEQMNrQlUE2jYBU3uAjfHamvTpHunpodwvJyueR4ZYaqxGVTFKKx9KIwegSn++EuZK9tmR5KpQrzjdJgayvzs889STU1VBBomEaAah/5oh01wN6Mhios/RcmFCMi/c2XxnfqtOK+a3yQC1Uvffju1Q1eNQmeP5mMw2WlI9kvYFNIIOBZey7DPTPv1rAMkrMp56afgvbYEMgw0fLxyRVr27rtpEgMb48aOS4OQ8QYapL9wvIRa2o7D0ovvXvjVv3Xg7fcjDAws/eD9dBraeRB+Un+E3i3RuzwTMMNX4UBQovrB6/aBcl1+VW4beFH+qxdcL0FZsJvBhzfeeD31GzAwBn76YOu5XoC5czARclxZJowZUivLvzhbceOmAHPUy2VHX8+g877AcVnHCPWDCxelGfSrNp6BNEdYtTnkObMEYsFnAkvKB/GulRk12z5tMfhImvd94XbA5AADM2ZPnSGg8fx3v5vGobtdx8ERYFVp8AzANgZR2rQBxTXPwqyjq4ywn89hYx+srko79uxK24F/4JDBackjjxL07ZcunD0XMtJF8cLGk84pM8rhrzpcu6bgex1Eb6pnw14At8cYVJLe9RO0RaY9MDvNdOAHm0N6dGpiM5SQAQfLlt502qIS5RjfuQN+uO435YCBBnSHde/dvy99TEb1CWyP/gS9ZkyfkUYTABPmSwQGFaxSerTZQq0D2axMjwEZL8n7wGrgQpqXttoyQKL8WbdxQ3r3jTeDh8bApxWVFakPQQf71KMx3YvbOwf1BODFBW5JvQXuW2JPOlXo3Y8/Sp8TaH/gwQfTKOB2ELXIFpQ+xG3BqwE77RF2+dM+zItvZwfca+LFmsRjTe3h9Da8Kh8smDc/BvMiQwzYtZUE0BF8cSTo4jnq8OwhzEEvYJCz+lzc+IyyW3vslZdfShOh9QVzH0wd0LnK9zuDP7wjHPmDjpXWgdE2aYdZZvQt9cu7HtqTZjZ/BW4c+FnPQPOSp57GLpgc77meg+/58WXf51vUEcFI2qMfoA1uQF39It5dP8zryoMj+B/SzSbKv4WNtmTJ42k4gwROnTED30Fr7Q8xFBVQiT6VfS3upW3p8qq/ecRMuFgAHp41S6WaoOA+pm/XMU2utrYu1e7Yn777+/8kPf3UU6n1gF50Fnxiwa2QPMAVDbCub+nx6wk0hEUKBjzbq2LfntUOw1DeTjrwhxDzxYYzpLwOTPeOHZ+GlpWnJnTyNZzKPOLlqxApf+MjjSgd/RQHBKkx1nmxcqsAAEAASURBVDjQcBUhuOJTAg0ff5gqhg6BEUcFo/dgdEpYZKxQbrwrEWWnPM+9VblI4IXhJWNkZQTzQhBtiZSfxrh97e230j5SaOYwCjgSx643BqnZDxeJqGm0SKAKB9vxzeOXr+QnHImUmYyg/5xAQ89evdJCyu9BCrwMYvqqgkpBLWMZ1NAAlPk1wjy8ruBTmKqcVGYSuIJFYjczYAOR4vfeeivNWbgg3Y+AdWTa5zXcXJneuoRFPGSHMAuOCDRwXaWcGT+nFoorgw/OVz9cW5tWg/tDCKvnv/c9Ag1jAh+FIJEZAy+yMfUY7TO6HoyNEWP7C9xbv4eKTafuCErBjINP16xJvcoHpWeffpq0t/7pAoaceGiLEHaHB4DLn4Af+uF3VjZZQElLVB50JE6df+xI2kYEyDbo0vTdGXPmpDn3z04dGNG3v3UmItCjUQu+FUIKWtds0BgTN/a/UUqFiYaAAqUNzkMLnKND1dVp+fJPUg1925E0t/semMMI77hgibMYG5YZW9DZYHDijgCZjvLIuyNzwqyzZZDhCn2lAu7UrUvMVd0I3EvfficMk+HQuxH6MhSoCllDVFgjzRPgMivSAugoDC7O12mfChRAAi+uNKzR6xzj8wSn3mW0a+W77wH3A9GnAwk0dGJ09iqjC7ehh1YYCU3wnG/fcH4jjp4OL+/H1pL0qfXHoTNAv6iMpbmm4P74kaPpRTJ4vjpxMi1atChVlJVFZob4DP4Droig02+hKMCvR+MAQRheVkGZzTG+WxJwUQk5OrEV4/91yh81dmyaj/EvP1m3vO7oiIaZQULJTVqxDpWxMErXwXfAYnnSsdUIl1MMVPrroJtP164h0PCdHGgg2BGKh4yG5qBU+eRhkMHMLDvda/YHMyVKMkJeg24J9LTpwMgE/+qOk/Z6YG8YXE2IZr/w5DNMAxsGcaHkoQ/lmHCEQ8XzN8A7wioUGEzEdyqyUeDe5wycKBuaOWqPMXXgIIEG0mp3EmCrGD0qLSLQ0I1Aw23aFcqdsnw9eAXYNRX9KBM8x3VlMfdUfK4P4bxJHVqDR/Lpjo2bg7cfZGT9vukzUwv46QZGryMA0qHGkHTiIb8rW6TrGNmC722CO7B4TfrswGrYTYFfh07j33VbhoCT6ZOnNgo05Pmatlkw4w9nmxKfaBTfuWZf+9Gx81Hn0UoDq5Bhb5Eu7ZzmCfdMTGNxjnoi4xGSMZXKUnWwfFbZkFFA+8U7P5TN0o7B2Fx+dohbdemcrkAzL7/5Ztq0enV6BGNlxLBhydXLDQYoo+8GGqylaEJInCgrd4r107W2SHmE3LEPpOsi0EDNkaUyCAfJAIr9Iiy+qDHl1mbqQa+pI5S9frcPNMyUu8Kk02JGgRlxOikaissxdA00zCLQYJaKuMgQAo9I9hChxeG1b/yOS1xT3ntcIgClHF7z2afpC3jque+9kO5FjlleLHJJG6ULeVNaMXNF+JUDwi/uhV8Yxb3PSe+WrxF6XMfrIIE7AuGtCaA+yUjRkIoKdB5rNNBfAtgYxOK7wbDiKK75W9PGFNgW7HBloMEpiTuY3lBbVZ2mEUhehJHethOBBhxgp1K59lRk8VmLtOOQNI2LPrFf/MXJq9K7cl7HwrUo1KercCwOHjwQa+Pcy5SVCeiPtsg6p1I1QWdEOVFKoKz0LZfpj0bN4HtgP+hCPbAJh+5NsmDMThnLdJ6xyMp+/ZhWgkPq/GZxqO2hXSCWIoCvnlJPUHYxrSWcc3g7RiBpU9v2HSJT7QMyeN5/9bWYajqBkUwDDZ1L00rsL48SCjKgjREdd/lTaoA4ioOOUfYdrT8eGaanoZ0F8+dHRpywatsp2yNrFZjNbHAapPTv9aAjCtL20H7QhnJNLm5k3kCvywcGGt6EX81omDd3TopAA/SGpke/EmgoyY4SVNGHfr+DbxvG//wb7IFvQAjbTMfoS2hmA8GABxc+jBy7N+hZO0yd1JzAwnV0lDtomW3Vgg/EHbaFWWoGF5Vzli0ugg+QweoGM3gOHDiQViFnGqDPH/7Wb6Wx2GPOI5efM1gZmQVKM5CUloEVbD7842HltDhTxp/DjjRzavvuXWkTAdNKnN2nn3gy9enXL11i8FCbI9M6RaEvIuPG9ykr7AJlDTQFoUQgxT41OyVW9Kf8kwQazIL5YsOGkPuzWGvmfnjKgZWL2Eu34afmBBmaUrb6TXCVhdosVABeMh/JS143yBBZTvSxMkGHcRmBhmOkqNuv95PdO240Az+8fxF6p1K+I1eUIbZfHEPvysYIhqu85H1gda0Q5b0yxMCaMvyzjevTu0xfG1hWFrxUxrkX9rzBIvvfQFKWOUEaFFY6Sn3pL1oSf3OTlGXY+dB0BBoI9H6GPHgIG95pgwYalNVO+9Cpl1/1a5SNlqNO0YYSfuV8oWvOkh2hnWMmhu/Yz9WHD6fX33oz3lkETRqodkAodJuYjrKwVULmSyqZWAp73TLiY918t27riPf4vm337vTqSy+lKbNmpoUEGtoB90X8FMsPG1q6Fg+ULy6V8bZJ/nRBX2szIK/zr4y0nfatwbPT+AIGeT4ncLcE/8BAg+2y3QCT4QrEZuwKm4tOa2u7M5N1KA+yP2KgAZ8P+au+PIwMNpi8AZrUbn3iqSfT8MFDGNhmBzbKMJDsourSd9CL7aassBlpT16TA/pC30hDrn2krWqG6Vn8xn3w6u69+2Iq2HEy/Y/sY7D2H/8wPfmdJ1OrAWSVMNhEwczdhW+aQ4C5CRLJt/IAR2Dl73v4pp/M4VAE3yUyR0pbgBxtimu30u4tX6aPGdU5fex46tOjZ5qMITeMjAYN7xsQjHwMiYYQVVDcAMFhpCtE6EgPCVy8y0qQWqzurzB29Hs10dA1q1ak8sGD0/ARI1J5WVms0SCzSbjB5DRThlMpSXDhkAKrxC1RRh08UzCODkcbDF1H0V6FEXdv2RqBhhHDh7NwktHKFiEE3CJOx0shFRoFOH/1keso7ilIJOy9Bw+m1159NYIX85k6kecGO0cKwcF93yoY0HbISCpVjcxgUJ6wCUbCVVgyr4syyQCmEJla9DYjyHNQygYanPZhexVSKqBw9mmLsBe4KJSDiiaELXgKYUbdBSM7J/BwXW36FKV5DAfgOSPoo0aDa7YLRFGG4Uc9udU4oZQlvDK5/WK94VDTvniGZ33Htrm9WpHR8OlqAg1lA9IzRCz7EEE/c/pUKO02CKGW4BAAI6VR2lBY6ZyHsgkFBG5EON/NEpHJTdOvP3Uybdq4KW0BNw3070wE4NzZBBoQwGY4uMCLq/OH8AY6yxbJOkwqGR0K6cORBSgmHPdrwNGOkbNWfA7VVKdPSNWrOUiggSkxs3DYJ4wbC93djkVmpOFYbTnKhdYj0MAoi8LX4BK4kSkc3XUBmSvM49cB69y9W8ztN9DwDiNS0o/zDe+ZeE/MWXcEVkMlRpBRnip8ipF5wC04iHJZ7AaB7faKRukN5rlVngscdWEu8wVoYumHH6SVS99Ps+fMSeNH5WyJThhllzFyb0JnjgSATlpBGbzrmgE2xQCMoyY6uf6OERT6lZ/QM8qLMuqPHU2/gN5P1B1L81Gc5Rih7TFQpdugZx4OJ9z3+fjHs7Rhe3VA5enr0JD93AI60PC1Qml6K6P2r738chpHurGBBvkp6B24XeTVvgv+oe1Sp4aAhwawAReNUhWQxr+0H6OktME6nVbiejCfodgWkJo65Z5Jsf1QBBogtGYMswQdUqTyy6yswIPw05fxsTL7lT51lK4DwR0X9ztSf4zspv1kS3yWmqInv/f0M8xvJtBANsN1+NhgkwGiJjwbMIMHUHLHiFaZqZBviBe+UwW8ygJq0LR8Xl1dTYr6Xubw7khlZAPMg+b7MuqizLCd+Z0StMG3SmT7jmtahlyTj6zUPrhGv7fC6VKeHMOpW48c2L1lW/DHA5R9H46pfWMWm4sn8WYYo4WDaF/r1Oq8WKajRfan9B48zAudcNRbE7zbsXtXepNg6XkM6MEo+0njJqahFZXoAHYNoV+FPeAUt8JZOiwvy0ivCz59ThsiwESdTo3zvdU4u++8/AqBhgGR0TACQ9qpE9JTHg3M7+WS4Vf4R+yYASBfGeTVWAmZZqdQpnTUmhHuS0WggTnrjz3xRBpBMLygdx04ZYIwRocF3PRvVMQfz97zVHxsA3zgQp8GB6oYGXmVjAY1xTwC1Y4yhtMFL4Y85z2L8bvt9ijwIE9pFErbjnI5wmvb1BvShH2zjUCDCzlPmTkzMhpco8HAlEeAGd/+y39shu2KYCePh6F4uiGySbbAU8+gPybj9GpkmpGgHhafRbDB68ImPoTftqtT1Ik50CBOkMtcN7XWQMP+A4zwfrqWAECn9AyG6ODy8hhxt26wnPGc0fu1tkS7Gl33q7TkYpCtCQyexmDfzsCDmRiHq6rIWLs/PTxvQQSuzrC20A3oobVGJfBn3ZFpxk4s7Asrd1RWSZmDa2QKoJsMVtfU1eZAA/KgHfw1dcaMCFS3ps8vncUxImihjvYogRmdUXwvzkWjpHHrdQ0i8Wmg4XWMf4NG41ifyFHYAf37R5+cY4RaOSv9il/hDHspZEq2lcSxukSbQydKKLQLusBP4t5U7w9efzOm24ylbLMlOmFLRaYM/SWwJfAD/twSW/PLR24L8MNnLVsxdQJb462l76ZTBBwWENB04Ep4dEqULdKMhUq/hWwI24Xrtl37Q3ko/MX0V+nHgKov7oVm3iQjrh966SH0Xy8D1cDbTD6F7nV2hVf6yUGQRv0groomRAN5CrilfWWE67V8QaDXjIaHCLJPJzNLHLvLmPrPdmg7+XEainot7Fdkv2v6SN/ypHIz+IP2yOseTuurYarQGso+jW32g9/8YUzX1DEyeCTCg2ZKAN7BucA1uhZti2ezs6tj7RpC1TheO/btSRvXr0uDcXafIVW9H7LmPPLdAQi382bZoAjEBo+CHJoeeLJP7APlj22Vl8xocNv4FtD8CfrUrLJtW7eEbjGjYQbypj2ypuEkAz/owNZNsD015qRD/inJAv/gTz5yEXlv52vgnToNDGhj7j94MK1auTLVExDvzTpfM5kCNhZbFeom44xABrJF+ekIdbRDqMFLyExkdPAp5dgHOrnSvP3SgYFIj3VMd3z/rbdTWUV5ZMPJS92wO+wjy5B/sh4VHxz+CbR/HfdxS9zTJj+uqdAAfO9/8nHajMM7d54DnSPCt5FepGPtUe1Us308h2y3eOEH3/a5slJ+cOqO14Xb9+0XZc2r+AcOPD7C9ACnJ8sn0mXoC9qPlQjIJSLh/fhX0Ix9LLzUY8OKgLz8pB23Y8+e8D+mEtxZgD3WhjbJByHDoS1fi8EMyy3BrF6Un11wX3tG3pHmfU4eMTPZgZ8G6PzzjRvJ9liTFhMImMp0G9ukTgiZB0T+lteFzr4w+Cgu5C/L8nsEGnhCGlUm+PsQARgDDZvRTS07tk/fgd6HVVaGDemCwoFvaZ5y4yjBXuhGs76kFWWedrzBG3lJH8SpE66ztoOBH7MbGrARTtQeTYu/+2xa8thjTJ0YgPEINVs49h7IKhFOqa5v4enXE2iQ6ORy12mAuHOgAYFvoIHre7duJX3/g1RfW5t6oIymMH93eIWGIoyIEaoBDpUFccCS2UgHt0VGQ0HYMr9CH3YKI/PuGg2fp7WrV6ZyCME1GsrKcqAhImF0tArR8hXKEpffNfAkxqxEcZgAQWYJI546NLzaYiieMdBAhHs3QtCMhmFDh5F90DMUsavlOsrd2kADREVhf2cSkJF1zJyn8xqGYm9GFzX+u3fHMeJQ0CioZD4ZPhiCcxiwEjHGVmZm7tOWSAGSESFmnWDbYrs34Ey/iWM3x0DDDFasZXSHx8KINnNAGHyHS9SV4QcLwaAKkKLeUBYwrMyoYjYN0IVkPiNlWuX/zLPPRqBBRWgKu3CLVPGrNNF4UbjYFuspRv8sy0cdPbFsI4GOSBWBBh2YngP6E+l7IhaVOvtVA+XniGgLGLApDJnTGamOf9Gn0I51Sj+heCg7z1dXubVmJI1FLDG2ttOnJ4F9+v2zI6PBEZ4zTp1A0Kh0jHib8i4+PCuADDbYL80ZyY4+4J5OnosFmQHTEkO0pqY6rWDOd011VepAyujM++8jhZT1jinDldYNAKjQDLSFcqZct8tzz1xTOi1XuFWpYCeCAjqu7bvolDYleERGA8aWUdXhKH0XwSlzRVzKiJWrwYmjXki4UJjyjDAr+KgyXQPnBhpccNUPPRJGjI7deUYvPvj4o/QpUyemY+COGzk6DeyHoUgw4NolRr+hqSz/MLwNNjCFQoUm7sWxtHUdxW23h4C337kegQaEuKvkv/z6a+nU8ZNpXoxIDSLCzlottEWaiY8vW17AzVe+Z37J84KlMelGA10lqkFml6tgthFoeAUjegJBgAWkGnYrOZMamNI7RQVty1/SdA4klAINPBMBQOiwcNztD0ePTPk8g4LYuJk1GlhVfSFzbCPQ4LPc0wiSRjwMIBhkMGAa9Cf8tM379oBtVLGLs3YoMen06Mn6tK8axwiDSyvqhaefjdF7mDgcryu0+SqKW+Qro+jOOzix8co6ecuz5Ys7F2A10KDhWlOdAw17UZ6DKgeTFkyggVGXQvEqS3gR6APrQS+AF/zk2at0JB8DUxiMBEocgXUP7hMnCNxhCO3fuTsMn1lMQ5oxZWrIoQtksTnlBlREoEFZI+4DB9C9fWmZGoZSUQR46H+NO4MwZiDt2ruHeaTvpwvI27Ky8jRxzLg0tLwieNRsIA2IMOR4R0j97xGygDb5L+hfHqBu6dXfnZHvvvspMuxD59gig0eOGpllvDvcgHtlqPDmjwUraw2swa/oCOnS54pAg3LNQ6enNVlAF+D315mWsZU6Fj7ySKRfGpzREMlGqPQgpzc6gK1og9dzrbZH2goCCKPFETMDDS+98jKyo0VaCD/1pQ1FMEdjOAw6YPGQpnMf50LtU3GkYRfGKn0T7Qs6ynNhtzCf/z2yEV1474FZs2ItiLxTUDzKn7/rkVsiLwlDDjScDtybGfDM88+xRsP44GHT79Vfd/kzqC8qsu881IPKFdso3StrLFs54qjVMQKazs12Sk97ZPBTGIqV5eWxhsIV1kTJJd4tNwrlT5BOqTPiOz3jT3vIEdg2HduFobgVmHWM6sD/1GkzQ9Z0YdT+HMET5UExwps7jRLsPI6gfcoq9JQDKcoIP/KTo5g6davXriE4eBD50ClNI5PEjAYDDRcj0JAdiIJISuDGzzvfqav4Ln1J72bwqGMNNLz12muRdTEGvTR6zBhGMgcE7g3Gqn/U15Khh2JBfhU5lmO/+MOggo6OPCVfOz1C3C9bydSJpe+lexhhzNMyyIijbmWe/ZWPUuGlX3/7KfNvUwzu1m1aha3hVNbjR+rIaFjAGg1lkUauLhTEaLXwQutmpWnneUTwhGekFR002yP/2h75UPpxcGffgYPpZfhpYFk5qeQPse5G1wjcK7ubYN+qr4U8KKeEIKVtPujL4rv34qOsy+nzrpWziUG3lcs+SQuXPJZmGYylPyKYCS0btFSHyV+O+DsoFnoOXdeMUU11Uw40ZBxKS04b8XB09/DhWhZyXpe+IhD23HPPE2gYFYMyZrYGzDzvoSyOs38ELn77p8QVwC3/Wb7BNadOVNUeikDDVuCvIFD9+OLHmAbdLxz1yGiAZnKgAVoD54WtbonKSGFXN0kfBgV0FqX3VgTW6k+eCGd91+5d0WcTZ0yDp6Yy+k2g4dTp1ISMhrZNsbE1Cyjbvgvn3+ZQdlymzLBCbCjXtHW0DezXA9jYq7HHThF87EWgYSq6aRQDktpFTv24ZQCJ11z3TbvPs+33yGVz5oFcvfJfm/V2BM+kH6dTvk+G6UCCU9K7ZwMN2hz2Z+AxeCaKzH8KnBfnRri3TN/JgQbWaCBD6AvsjgfmzkkOdHZj6p2BAjM475QP3GEjiWvbTy0F7ftbOooAK2WbiaGcVBa4HszLyAL5+FH00wCCR+LO9hcBdX1dflriHVrJZCO1Cyt/wZnPZJ+EnuAB+3vnnt3p7TfeYHryTAZ+5kSmjmvHeQiXskn+C9qjAN/zn/C1bNEqbMmr8INsLF4cPJGW1Lk66OvBvQt9Ln6SqawEGizTwRN1moeyKvQI35W5tlMZEFOToEH70o/N8zkzHdTvtXV1ae++fbHmiVnKTp0YOrgyfA/9j4yOTOd+FzbPBsT9Lu0pF5V30qr2bxFoOA3cW9AfrpVzicDb2fMXoM0Tad6SR1mLblHqVlYmOjTwOEfD828ufVuPf1igwVbLKH7s1zjnCxrbTVpqVKS0myjusg8+jDlSvbt1TzNYtGPU4KER8acnpE6toCA6hYVOlYx9N9DAd4gkcE55OaMBwjZ1CSG6EqW84pNlqXww+5AyGlVRUREp074jEWksOoquMpK5wsCzPEHmnAmdcrknt8i8El1rFOcFIsUGGtzCZi6BBhfz6kaU26CFo/dwM+1AIOfCKPHvdsgsVJIO7GeNhp//PHUnHWrBvHks6tczmEmjSdhkCtOGCmMh0uUQ/goRr8uAUDRFIYhL8KtkJXujaeuJtppK/tDChdlQxGGksGAmnafCwc+UnIW4LQhhBS6s308WfgYa8hw1t/10bp0ZDaZ6mlrkOgRIKJxm3gHvMQqFshc1zrdS2StEMtCegIP+t50xgoKjrZDT2KnDkNi/f3/6Eqe6V//+afGjj6beONPX6e8QrpSpudMcoggHT8FiRTC3Z9ES2TDSEe0QLkcEmkADZqk4ImVK9nEM0klTp6UHSNVrR3/fQggKu/QYcILgrCipTWSDiygcnCtBhF1Kso5mOEUQWTrM3FpTAQ8fPpTaY2BNQ8iOY2FFjfk8J9DgCLBjXTbjk0e7hZ06hF9+0EiiTEe7DS7wMuXnKQIGjz4k68DUz+EsDuS8vYEoCMuMwB2vB07oh4A5yhU5HOBJA+y6Zy4Vu7uIdxebdH7nsuXL0/rVa9KE8RNijYYydonpTFow2sooAv0LzxKosNjoBIsWx+KE4IZK4AbnmHvf3Ag0wT1pk/4/Upsj6BfJjlgITZYNLItMIw0r6Va6Dlq3SHChweJv6VGjIeBHaeo0ST/yiUaLICjUt9KvL8FP40mBf5h0Zo3/zDvARk9FGrmOIu9KKNKzEfzAif3uVdpi2TmyTsobys1R1pMEqFy8bj2BhiVPPMUc2ynwPvPI4aNMg5bJB2BucuEmkSQdFGGzj4tgg8/oMNEC0mVZDIz2HEfOHDhUHYEGg07PP/VMqhhGRgNBu8s4RhEQEr28GxRHoWEMineOIsDgd3nfNkhvyqkLTJ2oOngw7d61m4yGnWkwi/HqlPZkpflbKDpxErgP0PO7lgGS4uMpflOmQQ5nHV4FfrMQNHTNaPhy0+ZURSqgPD+FudlTJzH/mKkTyudYPAl+KtY2CFoBzjvOgOVCf5AtH/qIvhU7ZjPYt7v370srGZFyG01H0SaTwTMC/WFw4lYYFbmvAkbgKw77MPczV7h81xhS17g4HiOGJRm5Gprv3q20GORQF4PsFXJP/VEYnZblNCGhszxHWKVLHQODk0GjGlyU3Ux5g9HiVK+lrKdiRpyLCTt1QlnfggBQyBJ5XTj5l4HMp6CaUlO8Ex/+eL6l3KBvzRg5WF2dfvrTn0YA4DFkZD93D1BewEeFHnP0xiPS1inA+/KZNB80BP2rA5V74lsjydEoHTKDmgbu5kIv8zAUnWObDTOBE5r/usPggHW5jWU9o//OKd+DMfrcd19IE1hE1IwpAw2tkKPynTxtfRGoLsEtvpQF0rgw289FBqDXTT0/gv5wjZ8twN+J4P2SJUvSgPKKoHfxEqjlj33V+CihPF/iXtFKH2vSghHPdm1ixNhAww7smuNHj6V7CGrOnf0A25Z2TzeB3Yy4kFvaNOpO/oUOUbbzXdyaSRJ8bDuQQeom+Uk5X32oJq3FeD7EuT2BvCnopwg0EIS/Bi9HADlKoge+BvDXeySaVjSAcyv4Ud4zwP4egTWzMIfhcI3ROSIzwO6MQRm/+B+8imvlsAGrTKX8Vs7zq5AbIYOlR/TfVdq/fPXq2NVi1Ji8/kNZWXnqRGafUw/syzi+AXe++Kv/Bp7gpxZsCezCs2+QzVd3qDrNQ74Pg1fdYUyadaSxCGRo1+ioe0QwhHb7jO2RD6RB6YcWRjtjhB1Z7NSJv/zLv8KWrExLHn0kFhMOviA/P3S1FHEH6UUjGp/5HuV6ze/iKusmbabPSfVe+uZb6fFnn05zyIRpjoy4TQDGkdAcfIY/xTcf4RPP6i/PEqs6Kf/Osli7Sno6iU3jLl2bsQ0cMV4MvbuLi31kuZYV8k9gOCw5juhjvnnmkFbzs9mBxONjCu6FtKdqf9rGGg3aZIPKy5nmOy+cUhe3Vv+1cD0rixCv6H7L87d0Q4G5QmSOuk67PtY7QOfTSQz2nCD49QVTHPZGtuWYSSwGyfpHTtfUXmoGH7EUcqzToFyTLu2C8A8oW0euyGhQf1Bp0Kyjx8q6vfv2p+X4B063Mbtm0j2TQw472BPwQ5MGR8B41lHALNzSvWUJv+VrT5v1aN/EgB8BY9u5kXWVzBruQ5DXNX5cULgH+sP5/A66xeg3skw03DlEkT9sC5+4xQMF/sN5V3eTxffeRx/FYpD3P/BABEjUH67RYL+qb+PIhQEu/SbsVlYqm1McXso6MPdtM2ByisAv8A+koSWLF8daXwKqvIrngTKjoARjLipgDvlAmUV91uk1y7Jx8pgZca+yRsN4ZKQDPwYcIzONlkrH2i0+L07Vab6oPM9wYhNk9YiNlv0bAxm224BVnsq6MQY5Hmdtg2n33ht9ZiYiWL3T6IzTKDrkrnjRF7TM2EFCmRR1IiMMPiAftGkOVlXFgCTRwPQouHHapjrbLHZ9It8VdnVIDEjRhrBfqUqcCIOyRsxJi9p6GGQxHehLdMfWbdtZaPVSLBh9ksGaWQ/OZdHi+anPoLJMd+oDCSN3BF++vcevJ9Bg+0v96lcJxW3/3FHCY8eObbHrxFHmSHVFeEwbPxFDcXBqRWe444SKU6Lzo6C4G2hQSOWig3AUBtSDSCtlNNBpdNQajJXVK5eHABxOxK+8vDwWV5TAJAgNC89Gj7Ohg5IvEZbwafDamRKZ1438+dv1DE5hKL77/nvpIEb0LEamKysqInKvkSq8GhRNoTTpQUjz+S46it+N8eOTKkKDGWY0vMp8xi6Mrs2d80AYuGYkGDQQFhVzoTy95jsaUwoYAwWivWlTMgEwOp33rwNme3k53jOCvvStt9L9MLkR9PYYuOIyDEtwIkOHUFGaAH30A3gOJw8c6BBpkHooSKzfd10Pwjm2RhQdwV9AKqAR9KzcM9wx95v3PRzxKBxC2xVOBkJGxWMjjGAqbFSkKkpHvquqq9MeBFV3pqo8yAisKWnOo7Tt7nig89YcsFXl0Rd+l+Y4S4OhdIDZvgzbgDbqVBr9V2Hu37+PQMMxtg8bn2aw8KEGi7g2UKKw8aykM8ptuygpBIm4BUxPcQbwwJcjXgYGqggwOHfsyNE65th2TBMnT4oAmMJN5QNCg+bNaNB4MXDmHEEdMf9Fn5cUMpWTsoeSQug0bUmgguCWQspV+DswTWNwZSWfwQSoekXgwrIxXbPSD/jBEQLcPo5RevAWqbrSAA0KZQ0tetYQc9ReI3frpk0xsjts8JDIaOiOwy7fufL2dWBw54OmNMCRJmk5lDLw38BJvsIosylvTak3MhmgS+nMFDJXOV7KNCqzHhxVd29io8vStsFA+1HE2m3SQ8Z1doikPS4ELwcN8d164znuiLcdjNjHKseM1D2IYpaHVU6muVuHwbnY0lHc8L7OjHQZ5dI26cayi4i4Iwfe19jSMdrAyMIXGzZhyLFGAwaLMs4RzDCqfJd/GiU3wI3BBr9LN6beFgElZQYUyT/aCO6uQGsnvmqINMaNpOrpmD/+6OLYWcHsHqdNqOxuQwvSdNC1ZVCA9CKOhNtz4dAEr4Ib6VYDt7qKXSf27E37SGWsHDo8zUMe9GF0R6WcHQahyeUF7qNci7Q/uO7tuJENOzNtqCzkgfODt7PFl2nkTtsZBz+564QZQq5pUgQefSUMKMvOYHMp46FkzQSfxoKQ6A/TDQ1sHKDcDcgZ55i6Jec9Tp2A5k1N14lQHtl+2+zHsj2Kvsy/BN97yjjq5KM+UI7pNJqaajCtvKIiVcJTbt1mMdJO9BVnsy0i0GCfQorqC9ujnMtrN8B30TDaybvKvNOMcK/E8aoG9xMZcRmC3utFMNmt23Q2hb2gZVEsfFFEo3Z4PT788Wygwe3vrP9gTU16ialCBgAeWfhwTIeRpoTbgJ80bzqodehE2iZlYEwrxGj2vnwvHp0yIb27xoNw5RHYLWEoPkCQ/cH7mV7GvRx4LgHF6b/mCBlP2abxasy5Vk4N/fs4geoJBKrVXfaJ+jWcR75rVCrDNNiLI/qSH8LiYTsKWeD2ZkdYC6aq6iD6Yzdr23TFKS3tOgEv3XF2eU98xCFiS8eda8UFznEb/nOubV6jYVfau3tXrOkzauToNB390ZMBCIMM0oI6Sj7WSVWu6xDKmx5hk8DzUr5BhgiS0JcROIPfXKDNzCkzBtuRJTeBwJq6tY2BBhyXGDGOku7+aQR+vlhqROPrylnrdMvSVQTW5GnTvYcPZ9eJAf2BLw8iyPPSR0Gbyt2s9+WbkD6liuWNbBPYr/aButsFbTdhk1UMYSorZZeXlcWWfOJdWePxq3BcKvRrpww/9YC6mDpR72KQH0Sg4f7Zs2Pb6J7wqrxghmkESqB17QnpXvi056T/TCOZ/21bBKvlP2o00G1Qdg8DP3/113+dypEBix9eGDaB/B36Ga72ny8Iv63PB+cwMPgloHG9dI/flm/bzSxwh4J3WDhwydNPxi4uMRUJXSrNm3EnjMIOwCG/dBwVBXm3Iew15E0EPkvywect20DDYdK93WXFQN0cpuG68Kx617bKLwFR6RzfM3KjLusrDp/0eWWbNs05dN/eqgNpJ1MnpMm+/fqnmWTZ9GOnKzPKzJ4M/IgXyolAQ8hNVURIxMC/VUh/XIzAmmcqiW2dt+3cEQtOOgV12OhRTOkZF/I4r38Ef6OGmpUCGFGODQA+kROBBsrlkawbuS6elB/aymY2rVyxIp3F7uuPvTFm9Fhspgp0N4FMZMttbBaQVNKnym5xnHWbstQBH8PLjj4bbNCWcORbOaltvJ5puEvffotAdfdkcM3BToMO6laDnhFo0Iku4b7Ac2CcxkibHnf6R9zzMevNdT0+XrE8bWSAY9r0GbFtdI8e3UN/BH9Kv7yvvA8/hnIyzVBaqWzv2Q/ig2KDXrym/jtMQPYN/AOPRQz8uJOID0Vg174LxS9m6VeuB4z+KpXt77juPT42JcOR2+UuYG8QhHEtsTn4T+6uV9hr4q4INCh7lIMewpZ5U7xIhznTWVst1toKXrlyZxcXFyhdxJSDyfdMjLJDBgSgwgN+KA/gAvYC1+pmZVrIaWguDh7TllUHub3l4dq6sA1uUJZbRw8bMjjoQrwLhz6C8t2qwr8BnwXOrce2yZvSTwtkt8GGWHcDOaUM3oFuOo+u+gqb+wQ21Bx21luIzOk/qCzgNegchfP+t/1ALkga/7AjiMsiSvhQlSi+HWc0br9r9w624/skHdy3LzVFaI8ZPDSVk6pHL8Sc8khZotOjc3izCDTQq+EABYBUIsFEoEHnDEHgglcqERfY2bxpQ6S+lldUhEMqQWfCx0DUaYQ4jJjZZxKXv4P4+F0wikEImcSUQNvkyuBfMUVAx+tYXW1y2zYdI6NyKqVAHQZxbPMXaPxlVP5KEuGxcAhos8S8bPknYcDdiyHq1AmnD8h0sobMKLFqRIWipAGZ0PP12xqdlGOakYLEZ3NEz3beSLvA+dpVq3B2J6cJOF9Z+dpXWeFGQ/kp01lHBBYwlPxe4M/6clvBP9dtqlFFt27bvmMn85kOpSnTprHYViV4yXO/NKb8+K44EPfWEQcFxIiO9+jjEDIwqdiTgc+fJ5WIEV4zJg4TpXc05B7m3DvKKNwCEFFT30eQ0AG5Hu5pMPiMAQMdFsv3iJ7husrzLIxeW1eX6nB6T1NPOU6LhpyOhu3UUIyAA2d/G2gIpwKaA2tBhwrDMDsw+FXm0iqFx1SHIyzs57xyt5xqTWDH6Tzl5eVBr+GQCIxKk/JVyhFooA5NFNsWoxAGGgQdYRNzAzkbxLiE4+kCotsINrgqf/9+/Rjl7RvpdJHyB1wqffnElECslZIQ1IhE8IWzRBsou9jjVzWiIrW/nTNmtkfVvv1Rrim17oTSpSPz5RHOYsBMnhs3nWsL/MhWFak0CLKhV0dSwT1nfxtsiFEXfurku2q4o5g0M90LTdqnBsgU8BFA4obNzugEI/ateOJiBDS4F7xr8WKMG/6zn6R9R3hXYERX4NDdSxS9Aziyr0yT1YCzrJg3x9lDmswGqGWJfg086YkK6Gt5Stp1y7yTBNZ2ohz27t7DQplzY2EmU6R1KCKIEFKP90H+DT45o8Fi6RPKa06RzvG1n6OPxA80cxW4nfddB252791D4OIGQcEZaRDGf9CCMoY1b24zoipfRokaR/SnR9gC4g2kaSAoW2yH7dGY0PB04VaDDbUYo/0HVaQpRP+VNc43NSgUz1uWPFI6cxH0y2PiIt+zTOkmAnrU53tORdq3d286Rh3K6KFDh7Et6tDYoUD602lRBkdZdmTpKOSupasvNOqkIwMEZjXYHteDqMNp3M2otzvduBOQW3yVseihfKkcsq/8F+XRdquwzOhL7hVHVG37fKZ0UUPwAPy0jwCVi2T169c/RndjEdEoSBxo+GR5awCdiBs4wBCV7oURfRLrolDVHVzx3RFjp5htIZvhOLgRLzp06iYd6TCyaJ9wBrzCLGDCb92evVb6cOIAR9CNN/1XC26WoVsNIM+cPi0CJPKbb2kwSbuxODB9oCMjjoIPwK8yTto3uKAzfwG563d3n7EPXPF9J/26jBG1e6CXKegn10WyP4UtAOP0dztsBfRJfb5ooMHAnfNgXTHfhWeHDxkcvC0fix8DfKapmgor3PJ/9F2031rBm7jjowwOlHF2HvhJ0rFdUPjQoZoI9t4L/K5JYtn2ZW6AZZS+Cd6vOIpm2kNm3bh6uDLS0a7DBHmc/+5ih6OYwmagWsqSH6X3/MFJhQ7Eq7JGWO8EGoSbe+p6R9Ttc+FzwcM9yIFTOI9OHRoKvVeWl8f6PRGohh7t+7/1KN36ZpOUBfZrVXV1rC8BMKSS9wb+spDDYehzLcOYdarPSOcRhABvwm65OjP2Zdbp2SaJfgUfzj02CNOH7Jry8rJw7gz4Kv/V7/n4z8BfeiKfqM1H+bQg0H6qgek22GNubzkRm6CslKauE+Li3M7nFn6D+jqC0sRlgjPiNdNQ1uX2p7JVfagzIp8o610c78233mbXiQFp9n0zUxcySuTTwLeg3EFqI/hhWsu+A2gBsA3wOu+Id21VF1ddCb/OmfcQmVkTgs5zWruBhivhxGbb0tFeA+zyOzIG3Km7lO+ZhyycfuC37xvQdKX8KujSBU/HE5wqGzQw7BZhK2w42yFIcbaAaA9//A+MRaviPrLNdO+LZPNVH0F/MH3Ctbm6de8Ruzb06NYt5F7Y5LzpObCgWkK+yAvCa9mZ5xCd0JI0HwtbgkzxcpI+3V91MNb8MmNgUGUlND+MwbdOWU9C7y4ISUMzvLxTyMcoy+qoJD7cs4HqjhwIbhqDGxsJVF+CPqTJyorKsJt0DpVlIbN9Lz4Wlr+L2+BZLqmHtBucuqL8tI/MFNOO3YYNvCps+A5sFzs0YNeuUb4WQS6KABv8CwQXWPaq/JbP4snD/vKjzeIuYA7m7SIQMxr7vbysLAY6lffxHGXab9rDhf0kzViDzrqF+5z9ID58Vr0gbRlwdmvRlawf5DMGj3r17MH37Gsobw2sB8/ynrqOx+LgZ+hzfxRwiHPbID95WFdVdU0EeQbAp5Pg1y5MzxUO37kT6LPsEq59T1p1TQYDfAFLZGvnwECBU3nFAZQ9+DfV0I47u7lQv/3lu1lPSDJZZlmufGQvePY5z9nfkb/vHgbd3R1Ou8aFRLXP3Jq6MT/pWylTwjblVTMatEc8hF/8hn63D6B3cR0Zvuhm+7Sq5lBsGev3M9RVhw3yw9/73fQd1nHqg20TiIzS/NMYujsXv1Vf4DFJ5u9/+LJk5bnxx2uOEzrytXvvjhCu27dsSadwHvt365H6dO/JIjKX0uVz52PhPRf3U1ToHLqKbKSMQ7gKFAk3BKUKlkocBUZcZcULMzvitW/XttQFAehKuCpPDVIZw9YVQlbi85BIvGb/ScjBQKXOlPhlLAWQilen1z2zzzacwnlhD3QWs2zPSLJBDlEXDA4MkUIT5WUhERVRt88UKG5MLhK58JzAmHBRQueZu/6DQQydZUeCve+7CmkZQ2Xqb+GTCcNZpw4DJDEyy30Nb6PjRb2uCL9j8wZWCh6TKsvKop5gMt6R2QN+2hqMAjPIIDJQ44Mq4zlx5juZ8G9HPW7TUstUAdOx+7IlXFtSuoz45neyEW1ZEVihPb4vbAo7zyp3DSBhEvcGSXTqHE07SzTXXSDaIFQ15jTQDcKEUwqcMrnwFx+VWKxUS7ka1fazAoweyfgICk2RBdOAcrNst8vszsJvOtQukiMcwmU6tLiQqjVCbJPnwBnXFepuf5lXnuUdnlQZXbt5PbJgaokUnzt/NtKbgyZRPBGEkd6kNeq4hfI07dDROtdn8LBN4l+FJg+BuEgzdOTLufGXaNdRDPPDKH2NUOfrdUYhu7CXTq94RoSG8tQ4ATlhzDiybbkaJeFEU64RVgWgcLtApPcV3m5begoFJA+5xoFzyju17xhOhgrrGsGOa+x6QDgG4ZkVouVIF3m9EOuB7r0CnTmtR1oNB4A5lxrRtnkYBnRXpqu4loBkdUeh8SPaQVtoQEmYZ0UXhVJw7OQA/hEQ0V/yq/19lMjw1s2b2Ot7INOcBgefasxoGBS0EUEk2iwlh4LTkAaexvxWBBo06jRmHCVtwJirwXGphebH3jM1DSLdWHqxz3K2AvSu8cTnBlGFWxk8egPYwYcpoHGmZufUNydSL++qGFU29QS96upqWQfjOo76iBghFchYS6MUaND50pC4fR3qINggHjSUlJHKFGWH8kyYpGO3odSZNuX4CGWfZi2IHv0GBe7NiNH4D4XPs/aB9C0eLAvMBp3GCAXlywv2sTzrNq6+IC4bWBCsllHYk9Th0R/cGwCTn+xb5YbKO/jJIjyoSxqQZ7PMJT0eXIcxxL1Q1Lxn6uEpRhfMDrp4/lzUO2jAoHCopRFp1v61rXdgp0+CT7mmbPCeHw9xZz22UUfQUUZXfXaRUg1Pp8WZ6eEq+dI6oIDLPDLhAp4GGswsamrQBx7yCFyD7zBQs1rhu31zG5gvphoc0vP0be++/SPTLhbjAofK9RwkEbP8o7IwoOk/AA1si3UDXwE9fyIoC97sDuldY2g7Qcc2yN2Yv8vovYto2X51gfRlu+Q/nXbpXd1lmQaZxIVOsLxpxog8Ir+LL/WIGUgbP12dho4eF8EjpzRk3rDlAJTRGvXxx//grCRvxbuPFc9xU1g0SM+j9w3cSTeORI8km6E/AVP7xfu2Qdg01HWcLMJ70r/93jgYaNcWfWz90uRZRokclTpFWrbbYFcSeOyKkRv4DlrmQQoVPr/lQIWQ56sGlwrofcLvygFtAldNNwh+imCGesLtSl0bQ2da3FqKMEjznqVzAyfSurBKswZrQ59aZonuQ3/wjoucHYUez0Pv8lrffv1jFfsc1HTamlDfPezrgJ9L3ilwkX/5XG6Vz8gPZiLW0a/ydUdkfA+cC+ldHReDLZQvbH6sS55Uzgm3sioChsCtTPVe8De0Ji7c2762ri4W3utIlmYvdGtv7DGdfnlZnotDkDjElf3tv7uwazfYJ/kJ2+F38Xfuwjm26d3NWkcn6dNhlN0Lm6lz8KpTfaRpacN+aI/8kd/NYIvgNzpOXMeaPhQY8MAXuR/QOZR/jODXik8+SL36laWJLE7a3kA19YtTZVJBEwExZSsnoUwBDXQLdzSE9pQaED99XvxpL61bsSxNnUPGwdAh8Jv8lIOAV5ki5yi2tBI2L/XJ77ZBnWAAuiVTESNrlOKFSZpUp5tGfgJarydIZVsHlg1KZnoUfBTylOc8hEUeslyh9xAG+9UyPXL7Mk4uQ5PHT1E2Hxfq7kCWZl9kpFMbQk9bDu+5tbOYsMyw06VtZRX35AXxofOsDFJma89od5hFeRTdUX/ieAzy9UR39OvfPxxqs3DMonR9qBg44b1M70BI+YWcD1nKvdw/3KMu+1qZ4RoQewh8XYcOevTsFTQpzxqM8n70YSY+cJBpP/SpuEfGixcD3+EXcNYGzVO7coZkFfL9y/WfhR07sGJwDHIYqBan2rnyhX0vYrK/UdCNpGJ9/OXZDLuPxYNhk18iSOZaAYeq96eySqb0Qe8dmP7sgKF2s/wKgkOX6BOIa+1jCos6i3aEjCnpYuWpz9o2A+Hbtm8LPIxisM3Mank9cMfZaQ6uaWM58kfQDOBZT4Er67/TJ9H7mYZ85jj8tO3LTakr/t5gZLDrIqlj5PmgOYmNw36wXgqK6wZ1XD/K+rQh5APvCbcZC9r36m4zbE5A8yPGjosdM7IdSnmUJUzqQGWw3y3DOqKegN+AgHSfA3rCoS63z+Qh/RAHaJw26w5gBo+Cn9ABwU/0qfI07C7k4J1AA2UY8JQvAy/U6zm2pKY9F9FpZiAdg19dT6WBbNZD9afTv/3TP0nPv/Bd6ukbtCAWg5fir9B9e49fa6BBt0x2yegpAg2Jxby2p4+ZU76eSPQaRhtPnzyXhvTvnS6dPkkn3kydMaLbwfjXIB5HiroxD9etbaCM2NrnqzMNEFN1ME9zFoW5fYV0Lfi2feduCI/Laf+ZvLhI0Q3lnVuwQj+pS6yWe+0Kc7yaO9rPTg0QWds27cIAVlhr4EqwXwHHudPHWZjGVZkpH0K5iSMlAR75ipTSomDOnfj07tkBw65DMPfN62yXdelckEJLFivq1KUPiy6RZg5xKZQv4myeOXk4XT1Deo27KWV/MtqivbPn7N3CW/G1P/zUkkrc6iiUPtdk2NZtO+NwU29rFnKBqGUGV8E/f+Y0K7uzZWJb0rk6gA/ac/7kWZzg4MtUY6eUDtfI7c503ZY47o40m4534RQ44vqAckbaEAaWrdCTuTQ8L50/E/NC4ROMa43A7Fhdv3wr1VO27xZHH+RbC/wPFy5qRoaFQvUqhnaLNi1Tt14Dw4B19Fq8Ghk/ffJY4LkZyrNN+87BjBfPYRyepk+KQktn0VbW3XTaNgE7EgEnHcPiFFtBdW2eOvcpjxW6VUwaFnU1u9Kpo5dTB3Y5NSofwZmSL1DfwKrn3yhf3PTuq2LECKfvr5wj6OF0WK73HdCJ+exl2bmiL3R4TtXsZes+DJpuwE2nSvcqzSvXLqSD5xohvVQPzUtdUBAtoN/mOMgunHTtbEO6zbN9h1ayeFAPaEbn50YsGHlq//6EeR3c1LwT79D311l48cxFgi/Q0jcP/IzUrR2R9mYYtDrf0N75BgwXHiwfMxK67BKCT+F8ioyL08cbUoc+XVIrAghXyU64fA06vskiaiepNdsjX6sCNFJ+3lbu/NnLiQkgYVR0r2jCLiyjEMDMGaT956GXwzWMTpzOr3cg2mjb9YuJMfzqg2cgK+ZP373dGfbv3rVz6tapO9uOwjgcl6+ygvbFsyxAVU8mlOlqvNcEY4G0ipv0+eXzt9LRu0UkYXZ2unjsAF2269Id/DK/jwWg2uJ0KD80mlwJ2akcBnEuQ/dXOV+/5raJbHOKvHDbznNnTqQqcdPo6N3BlcFBPG0z1NSE569dYoVvBpQ79u4b63O4gJmp026jdR4Zcx2nSuIDXRhdBIdaMCoCHR87einVNSrbr4P4SEniWhlENdGe7n1b4bCWM4rdkYBFTns/dfxQOnW4njKBhU/hSzVv3TydPUkmCe9+8xgEU12hry1fPpbHpJdeg3qyjkBf2oZBgDi/Ak5On65P5xrqgx5bdujEwqddgI1Rb2Rf1eFvcitlUE6PPt2CLpRf1wnUXCFQc40GdR/YL3XD+dCo9jiHoXmseh+lsaBq1540IE/zwdVB/l9JB+p+ufyyzq4aD2EheW9Au25zTJdBz01YMX5YOE5qIqeeNBAkrj9Uyxxyt+DriqFRMn6hpzMnyFQRiEZHV773Yy9rDRLbL/zOx7xy4Svm6LcnUDMgsok0li9fuYQzezg11JxNbQCnFfteO63hNgHH2+y4JK+eaFR2Bd9BHSnJzGc/B89BUvYxVaVWXZqlDt36h/5w9OMqtHoRejrXQJDlLEE96B2RGkZZkyYG51jM62SW31YBiRPEbwPNOpLrQlT2rGU7hSlnvHWk75z+oMN4liBrZL9BLOfOnCTofQkZlVJXZJ2S5xJO3Z5jd3HfH7ruwBa7Hs5TFec0P+iyZ7/uqUu3nuhX1tWgMeqPrzCgrgD/DfpQnaJubd0ub698ouZIOhYl3f1TBk/E6tzo6nYdcB55/jwyUv3aBf2hQ+mAhEabsNuvN+RT5I76jBsUxhSRi+fSifPI6btFy5KpvFfH4Gn7U92qjj/TcA291DT1KxseBrwyWEP2xIljqYFgQitkqtuJ3kRX0rDUDP37lTqKfv3mUdGtJXTXO3R/bCt4+kQ6c+km9kLX1LeisuRAAhfB3HoCLJcQiK1Y4LcJ+kmn6SZBrGtsGXzgRGPIcy2DB5LpiJ68jjN9E7o1QA2JYScgf/tV4pSRTQHeNW7N/Dlx7FAsDGwGhjjRGVAn3+Klg7S58SEXDujOTlXyBf3kWQNf+lHf9kKWKSd1DJxec7L+KPDXp669O0c/KSeVmVfON6Sj6MRGYjyqGdQWedidbBKNfpTqDRwIebMZwbqOXXvH2ig6H+6q49odRw7sSccopH9HFmeDWWLawG3oHx45duRiaoz6ASjuLgRK7LfzZ+vTRXTMFRgKdZp69K7MMhI60mEyWHzp8gXa5TpM8AZ8ehPdd0M+ga+aI4v31N6FnkupDzyNGRQ8KnUphz16Qqt9Buhss14XsJsRdLrhBDbN8XQCEe+7HWk3l9NF4PG38rvx0QXYO3RoR8CDNqkWMrtChwmnqU/qiu5rR5DM7DcHXtyK+yZ2nzIvOzA4rGEzXCOww8BJ48L53gOAO2AztWwFsBzy4cl6spb4Xj5Cp4+BG+SMtpGDLifqqpFbwAqeHYRo6mAXtHmOfq1DrX3zKO/XMewZrJmYznMTGrh0+mLqTMN6DUI/IWd0vgxw1jNt+iKOVTsCcgZqDXxeB/cGb+vxBzDJvn4g68r6dWO6EO0HcbcIlt/AKb5+6UbqOpAMTgJYpqHLy9qpVWa8wQ9duzN9FSf2OrZM69boR97fV13/9bL5VdGnI/1GB0nvBut9AqdTXHfEF2lPJyg39RHOIMeO1+zDJgAv7VlQHNu/Zas8tcvA2Zaqr2vugRTba0BF+AdhSyAk1VHyVtuO3QkA9Y4FsOUBA7ynkDXHDuN/IMJaUb59ax83Rw6eOXE2HffB0oFZRMCUwGW7ziHD7H+ZKd7hPdduMDNX/0NedbDrfMNJAlWXUrvOTAfkGWXyNZxo0JQOQdCFuUe3pwHA7tEaW7UVdte1KwyAHL+Q2lFNzwp9G7LxoEkH2s6jHy6cPck6duxmAd2AbuQYpWib8xtRlg40MpnUqz066SM4eCPvoSPBuUGHtp36QC/I9wig5Kmqp/APLp09RYBJfcmaPS3Z8Yi9wUCjAABAAElEQVR2XDlDNjVlwTJfO7rzq2M39Bz0bpnKm1P159MFrg8diE2DDDIjz8CAGQunTtXj/5wiOAmfYGO3RG+qa85jk2sTfJNfK7plvwkkhnx0rbJzp85DK81S3/Jh4d/kgTh2PSIAcuTwsdQSHDdrA33BZ7fFDb5CQwMDqV+DPP/4N//bH6cf/PAH6Is+9AnWFQEo+xHu+hVPf7su/VoCDfKBH2iLzslxOVkXWREZDTv2bE8fsc7BpnXr0oEdu1O39p1iBEDh69YyzsNydMbRAgXQgYMswlFdzV2cFD73TpkUe8fKqI4uOfqrADP65GEqk9E2o0xGr2RelaJTHzRWvKZjLpRmKGxet/6O8Te5fBCRwsEhWHIaMVEo/hURthxtj1dzy2woh+UrhPypQep3I5cniJ6u2bY9nhmEwVxJpK0HkWVHjYTDNviS5ZtyHoQkx3NYhu03+qkAlaA9G60VNleZdT7rAZx035g1ZnTgxZRfn3NOrvAaRQ5FFKWW/lCW5YmjAm8aN8Il4zn6U42w3nriVLwwslePVAleXHDQURX7R/g9Muz0AcwT0UEaFFvQcN9nve7z4siUUNtzglHDfV98mQ5HCSlNHzk8FnZzZV2NGo07n3PEKkdqc7SahsUIhXDLeMLs4egPyGMxJRbMwajdt38fi8bVxL0OPDJ7zpzUEbxcYbEihZllFiP+Mq74Eke2JQwlDTfrEE30Z7E4l6OWJ8DNh6vXRtn+GVwxgG3qhsWq3bEQINfEIWo/zrk3uWgX+kLpsE6dkxih5Gxbrc8RoNVbthWPpemjR6SBpE/F6C5ttr8CnwplnjcY5L1cOPxGWUF/GFPST7TBvkWoanju2rs3bd9bHeVXdGmRxoyfFpkE7lDhHEuNNPvAI3BC+8WzcAaO6VPxEYIX+opgHHVK79LNR8uW3wk4zZwwMQ0eMjiCeQZN7FONvOg7YDet2z3DRY4Ra9tlnfazUXOPbORmenf3kZXM//eYNKgfwaB+pdXTc9qj+PRd4Q3HMPo2l+M73I1D/NjP4XRBC47WakDsJ5pfffVGUoFPhJ86kCqr0assEi4VqnQRIwLgIUb3qCsyN7hu+zI/0TeU0ZwFcM3KOkUAsOrwrnTwcDZNRw3uw8KX5aktjorBpptkI9x22gyZDeJEg07jTqNVmtQ4c7eGyFDAIL9ploReL/10CZo+XFOX1m74LDeOv4vnLIgpLrnF4JK2QjkBkw6I7bFcD+ETfj+BPq4pN2yHRmcDc1l3bd2aahiF9+gHqY1npW63xwulx3uOsClnHNWUKA0yiWv5Rfrzu9ccNVRK2j+uPaOsPo7B+fGqTy06jgnDB6chyJpQ0lrb9FU01X7lX2z1Bl6AFviQX5zl/xj1wEpwFCN4grKVOY4wfbZ9Z5St8TFx6r0xuqdR6Ai5vOo70ovyV/KT5mIUx7aAd2WddXsos+UxFzC7jJzasmULzlp2wIdDk8OHE8gDNzrtIYNpvHWIH9eRUPZYkhlG0ot8YaXKSvWWuNHxN8jl6Nua7Tui3p78HTxudOqO/tC4k37Fv4ErZZpwutuRNONhYIKCox3+lm6lYduZA+oNqYrMvyo9MY5pw4aEbnLXl5A14DV0pX3AIY4c9YupFrTfe34KfvC6bbCOM+ik2prqtKGqJt4d3rM7i2cNhdfbRv0iQBkQMpZ6DDj7fsjEoFXugzdlQGSQlGSGwS6v1zPiuWvzl3cCieoPs2bU79avnMuGGbKM9orvLBMyzYcsEh9clzbt0zzw0CacxP179gB7dcCu3TJ/1szUlUwxM36Ugzog0oPrI4S8pM8cEbSOxvSuw1nI4OBn4Hee9XvLVmb5SdkDe3VOY8eMD7ou5G/oPAoSrrs0k3+7zom0HnwkzUIz0cbgWacWHUmrSjLSBswcOSKmK1im+JbWPOQNkRnTyJA5/jZzLsswA0GlDCN4zGu2TxnviOqOuiNRxgD+TmBKQXeyR6W/Yj60fBT8JO5L9dmXIXehVeuyr70nn3n2mgGjjWs/S4UrOGP4MEYPh8aIrZkuBqzkTQNDli+dhu6jFwubQxmtbJOegp8oW3vJaS1H0a2bmdrpMYYRyf5l5QTECDaCQ2FXf/iu/Wh7HSl23QThNhNMWonpmPS914RB+LUj6wlE7dy2LR0lCDCAQM/48eNCr8qPtlVekVZ8XryLj+AZ2qCcdUAk+oQ6lG0e9o29dZxR2h2bV6e9DVmXjejeme2Oh4eDmqciilPlKjRJe4tzFEJR0qxtkyeKfhaP7hLkcai6Ji3/ckt89888Fl90XRoPadD+92zAUlzFqLKyirqkKeWcMiueB2Dbqz3jLkeum/blFxvTvuM59NENhpp53wOx45nToMRD9Kdw84n+LX33t/Xap0Ez4CWvKZXpU3ljhtvHaz6Puv0zFZpxUWxl4UX6RR5VlwhTyASuh11Zog9hMHAjP8UCrOAwsM9Z3nXLx807994pf/Y945E1/ZG96DT4QfgybsUPcpx2q18tJNsZlMwzuUf9m/kv9Dp9vpvpYruPZYofzEDA6CnTYw2TQr5aseUG7mmHus0irNs1ypSPlmob7XfbGHzM/dNk6q7ZscsiUiVlV4waE/pDO1+eCHnAPenYcq1HvR+4plDL854j/fKH+DGj2OnkLlhYdWB/OnCqIcq/d9jQyLTSR1Df6WMJixmVBb+GXob+xatlBM3yrO0pMCMuHQBzGvMX7Fyl9unFYOW4eyaFXo3BSWUJ9KZMsy+FV7/II3AFn1moGQxmI4hr+8QMBA8zOrd/ti7Vxa+UJg+pxMYeEMFfYRV27a2wlayDj3iwbHGh3SC9Fxk54hsCi+wo3606uD9t3FdVKp31Ltg5pTsBK2EUJ0GLwBdtBjbtULOU9TXM6Hn744/Tv/7D/5VAww/J7BsQmZO+8/8HGkooFXEenouPolHBCRkTfLjFvLRt7DrxQdoPg7mgy1gEZoVzbEGkzo77TrtNpELYOVqb2eHhT//jn0dk/Km596WZs2bFXLxgiOs3iXJi/GCg+1uDSwUBRUSnyqwGGiQQ07J1GnQqVFgShos7riLQ8J+WvpfmDR+Sps26DwUxnqh6NxQLo5kwlx0socqcHqGQqCcTH0oJ4eszmdCz0SoRmpJUfbg2vQvRHELxz2fVYld0Nu1fBaZjJczCYVmZgQgQhIGYmT+UQsloVwBbh0TtPEOF4x4Y/ZUXf5HuxRCazpzZQTBLrEcBvn1GHpZBZDphFB8qNXFre2QafyPGQoEYARV/tbV1aQvbXq1je719GIvPfff5NH7kyNSla5cwZHMqEyHKKN+pBDmgoPGrUgohAwEY9AihRpu8Ht9hxGqM/204L8vffS+1LxuYnly8OA0fnB1SceGWNwoJhbbwC7sMGozKd3FhSli0jUY6+kG0iSwRtxs7w+JZm9P7776WdtedTc8+8nBaNO8htlJluzGi4O5/3QrDymkOBhtM89PwzArPQEMO8OiAUVEIgJhP1YpUUoRBdV1teu/DD9PbK9bEyNiPfvsHzK+cGIGMm2TXIC4ABdiAUbqzbFNjI1rL78IJEuc6SIUy0oCRZ3bu2ZteevGnaUPdsXRf5aA0/5HFaRTKU+dCWhGPGpthpAOrOFDJiA8XY1Q5KKx1NJxPpuGrgjB1VEdv7fqN6U/+nz+PEfJFTy1JM5iLN4C5igaurpKSaNBOgWZdWeHooqI0gc4RsfY4Ua1Zbf0WbVE42ke2wXcPQzfvfbws/ezNt9Jiyp0zd26aNHkyu770CENKughj3f7icIqO7QLEuC8/yGtRN9+9brt0tJ06s2f//vQq2wNSWXrogTmpvKws0s+lK9cWEF4Vb6EgCmdSnGiQFfRSGL1hFEBvzhE3iLF1x870KltTLYRe7sVQdFsqeVX6Fi5xL29bju+qlLIDI4856qfzRYaVKfUA78roptcfPn4kbd+/O61ZvybVHjiRfusH30tjRgyPKSitm5PFQHDjFtultSDbytRuVyT2bAq8fXsVfjAibyCuGXR4k6kY15kE60jAV1+R7vjl9rTy/VXp3c8/SovuezB998lnWCOlAhikKGhMGQIcdFmGl37zhwrf8EOMPIR8yUaLbbUdOsums69HDixltwEV8w8XP5zumzY19pTXyZU4DDQ4wiquLNNtUZ3vHAE66FpjQDyY1WXaqcjxWaf7HES+vPTyK2nFxi+T4+P/3T/9UZo0YTxrgLCPPO2OdUxUzvYrzXEb47ymAQYu+LYO6dAAowaKfKRB4WiF8mITgd4//rf/Lka0n587m0V870/SjW1zJCPLXmnDkVt4Up6B9nUQ5FVpSEM3DBjgiyAfbbBNZxgJWknA/P8F/nuGDUoPzJ7LIlSTIsijY3P5AqPRwK4saIW8MdggXdgW5YJ6ThksDLYp7/Wes990ug6SWr1s9ZpYjHX+ogVpBE6j81tdN0GYlDUtoQ8X2HT/emWCtGk75HtpVJ3nod7Q0ZGWpfe6urqYI/72m2+nCVMmpxnwqdN/8pS0PLVKfs1ZDrSBcovAXExDpEzv+5H/7E/bIO0cZfeFnTt3pPUbNqSVBE3/m+fZrpLAndMwxBugBawhw8Q1ASphVa/osEYQit8hi+mP4jlHuMS/ayJs2rghffzx8tSlX5/03aefjgWlbZv6WJ3mO+I1+pTv9qn8GrDLFsgzT6FXSs863eE8fbp+w8b09n/4j+kL7n/ngdnpsQXzWeyuVwSWdBrEIyUFPSqr4qBR1u0aOtKgqeAXlKmM0vqE2/c5Pcl1ad774MP00tKP4rUfvfAsK6RPZuSaQDh9qF4yU0NT2cUkPYcuoZBYL4j2K2fsXxcFFp9F4FD5tJOFql/8+Yvpi7qj6aEhlenBhxfFfOVYfBg5oN6RbaU7HVCdRvGkjNAoF+feN8PIPpHHdCRts4G1jejtP/mLv0rdgOux+Q+lB+bMTeWk5mt8O2rKq0ErBn3EdbY5SuOkFGx/WIH96CHtKEPNEqo/eTIt+/TT9DfYBQ+PHpnumzMnTYYuTbF2KoBTYHzfsrPNkZ0j+Twcp1KZOsPaU7ZLvhU29ceB6uq0FHvMTM8HZ7MOCHq1X//+ESBz7Rpll/aP79mHOso6TuJEe1AecKqFIU/1gPgxdd71M/bv28dippvTT9ge8LeffibdP31GTP+RV6UZdxzQMRLu0D+8H/XQh+I++jQ0LX1De3ynHTQhPPvQe6tXrWArzDfSAYZXf5NdKrTHnCop36nvpXfxGPqfd/0tPXhPfKjTpUPLzgHWppH+7bUt9OkH7Kb2/u696X52xHr28cfTkLIy7mQ+lcfVzSEDaEM4PAoy+5Hr8q112fmWJ2dJP25x6uJ2y1asSP/nT38e5f3GY4+mBXPnJNd0EKfiVjzYr7x+hx/lq4JW4w73eSgC1m6nbLZTG/SHNsc77y5N761dF+X/63/6O2kWi7EqF90OU/nbin6yvHDKOQu37yqvpNkzZKVaeWT8SIu2grO8u4M1mP7yJ/8hHb5wPT04YXR6dNGjaeSwYRHAEP7gf2HjlZAoIiAOv2RHXRtG+WNfFM+JS6emfr75i/R//PRnqYKnFyxZnGbOnBmBDANP9pOHMjUCf5yFXTs7gpjwZlGHdCWPhi3Ge/o5ddg0n6xdm/ZBlwsfW5xGjxoVa7A4BUh61NcIeSZdI3PsC3WoZ8vzI43bx/pM4twpUBfIBqrRhmf9FXc8cxq5OC9Tf5hNBRzak5bjd2WMtl4ERIFYPpPeo13UbZuyNNZ2JWsEXj1G4G7Lzp3pxQ8+Sr+x+JE0hTXxerM+WHtsImFWL0s32rbqQtuSA8FkQtK30p++n3jwOeWMWTX2g/z06ZrV6cOPPsGWbZ2+//3fSGPo0zxdmkXWwYHB+OBX7BBxkLMuss1hZqR9aXnKCX+Ld3nd+uSnZe+8mdaQVfooQYYlCxemQf37wSPYW8jdEhkEjUnl4SODJ7ePrUeWvPP222nS7PvS8+y+VFFZEff1efIhl3y7j39wRkM0P/OS9B+HSJRxNS9MAdmxY3ta/vFHpLPXpC6k40xhRfLhFZWxQNp1Ooke4wUIHafmMh24kkDAH/yrfxVR7h898Rhbizwa29HYYbFHNQIUEg4lLGGHsC0RbsF0EnLsVw6jSBQSoIKt/uSp9O4nn6Q//vOfpCWjhhMMWJxmEchwoSgjsREMgPBliFCIvKPStW4Fhdc9JF7rovIgfIWuBLeXbIwXEeB7YfYnnn8+lKZrL5iuqhDJgYbgs4AnykIwCq/tUCqFcOK3dcmMYchjxJretnnHjvTXP/0pewFPSQ/OmpmGDXYV815RloyukhXO4hAuhYaMpxEtzF5TkKvQVFq+oxBxwZwVazByCTj849/5nTQdZ7oHjK7yLZxn4RSuMMILGMGFwRgVWrFHr4pKuC3fSKZR3M+YOvPWT19Mve+ZmP6RgYwRI0m9NEXNLckMNJARAYz+Fr8ahPavOAl4qdd2KM4NSN1Cmbbu1CGdIIrryu4v/s1P0hcH69M/+8FvpOe+83gYipfOkaRE35kF08K+knkptzB6FNTWII50rE27c1RWR6YZ9NiMNL89Bw+mF195Of3k1TciS+eP/pc/YDvMWTEV4jrCu9i2UCdAGnOkOvqZemSJHBCwTVadHTuNRundujcyQvpnf/Zn6ROU/qPjx6Tnvvf9cLzaaeCIA4RVrN4PjPKJSt/oPVVhfEo7XAb2a9y/jnPHAzhjRqLbxnzij1asSr/3P/9RGkxd3/1vf5OtqeanyrIyUsENNGDoghuDDYUBHqMhPCsuboMHcXGLgMZVjCMXanUFXfnhKm0/dKguvcticX/ByvfPzn0QIxRDcRKBhh7dgyakA3EtfkWA/ecINcXE/VB69IH3VKb2s22TlxwlNRvj5XfeJq/0cmyvNwHZUVlZEaNOGprSh7BIkxpXoYCoU+NLg1xcBX54Tvo0+CAMLjZaVVWVPkchv4jSf/Z7L6TZU6fE/HyVT4xQAKMR+Kw8M91bmPQXVr/f6U8QR6ABGOiLlkwTun77Rtp/uDqt2/ZF+nDFsnSUNOPf/dHvpMkEMjqTzdXG+UUslX3zumuVGEgENnkJBeoIsIEGM3FuwDctDTQQvDDQ4AKTpnEbaNi6eVta8cGq9OqKt9N3HlqUvvfUs+xSUQlNCB3lwR9FoEE+9yPs8mQEGijnNrwQ80O9A/4cSW0NTTo/fBny+sX/68dpP/f+p3/0g7SIrZcGkPYqnchPVy9iRBPslb+VKc5bd+TJIIDt8CMtfi3QgLGj42hg7c9/8hfplQ8+SRWU/y//9z9K98+Yntpi+MT2r9B7LCwG7j3rsEcAADrSCXKbQR2uK/Sz5WscKat1rLy2Bmf3X/7zfxFBkn/2+GPpYfYHHzViOPjIWTTRZ+BC3pdfpU1p3/eljQjqUZc0BZcFL0sDjlCfJhX13U+Wpz/48Y/T/RNGsNPDo2kWhqL7jxskuQoehFmn1PKLAGQONGRn2DrDgRdP4Fxa1aFz/vm+qqr0+vsfpI3rVqXHH3+KrRPvYf2boThebAtGoELJ7rZs7tYk/8k/YRTZDr4rO/2t3nAETDo2sGcQw4VAN2EQ/Ywgybhx49L8++9LZpI4l1scqhfMDMiysYQfccBH3KmXxJXywQBKjBjRZ+qC2rratJWyV639NH20fEX6PRa2moUzXQT7wxmCbqxDXNpfwqluKOAM2V6qw/4A9XkbN2h5167dafXqVemtn7+Uuo0amf7JD3+YJo4eFfRnMEH9Y9mWIbwGHgq54G8P21Cc/e7oqHOSHVFavnJl+tmP/11a1nAu/ejpp9L3n3wylQ/oH8Z9BJ14kZ5C/kMRFKOsEh/KXeVjZO7x+xJTOQo+MKOsDTTvvPyXXn0t/fivfhb1/+G/+P00b+4DpMZ3DpqJha2RSxTKtAiMccqXz4IHKDPkMjhXP5nZpHEq7BrF9sdmAmv/97//9+kjzk9PHJ+efuF7aTJGun0qXoojZCV1iCPpxL5Wr3td/o955/SJZaqLlZ1ON1j5+br03//hH6V+FPTcC8+nR7GZhg0ZEvaM85g9xLHPSwvKWcv0KOwEz1EPOAzZSrvEax0jjW8RCPjTv/pP6fuzZqR58+en6TNm4JR2j8WULxK4sx06XTQ/4BR2v4ejLuyUbf3SgffMFFV/ONXAxUx/QaD6CvbTYsqewKCSO20YmBR2+089oVyUFsWDQS9xE8455enAqMlzoMH5+aVAAwGe9Zs2pb/52U/T98H5nJIcUH/Il8Lg1ASfLwZk7DMdsMAxTm00hL/WJ1/puNg/BtY+//wzFgpfxiLq1en3f/+fp2ksHukoqbD7vE6WfNXYbpSu+R/yXrwE3wKL7RM3LliqDF3HziCv/+IX6W8Y5V2CHfnbL7yQRg8daof9f9S9B9SV1bWoPVHpTbrwIXwfTYrYsCCIFBWs0dhr7L0nMaarURN7THJy1NhiL1FULAiCIE1BpPeOgHRUikj1f575sr3nH3fcO84YJ+OO4aubvb+933eVuWafc82V+OZ4kwZzDSXFAsaucfIW6EtHv/2ry8h73MpQn8CUGaBvvvNO/OKBP/N0xK1XXBpnYPQ2hc9kJgH3FvvuC7nPxBNeDlyO67vwo0t0EHgPuscOXsoU8WA2RuOL8IFnB7yXW5kfvuvO6NerV2aYfsX2PHlvNXQUnXQlh4mw0GFUFeefMkPHOMw/HQ0eH55OPPBmC/MaS+btQw8/HJMXL4uz+vSI888+N7qQmWyASvmUfEQFBjh72bav1O2Yi3AWH1OP55lcH+6X75vdNHjkqPjFgw/FwTx72lVXcKThsdG6oiJ1YOkt23TyXMJXWeGVuhTrbnvKllI/9u1vyoOFBAn6vz8ohhE8Ofu8c6IrRQw7dixqL5R0bOWf6yheusZm0omPBW4WGUOurYa3mZY1cZZatHDhwoXx2aTJ8a+33kq5cWxvT31pSyZMk5TP0ouwUWaWYFLQJc4Q2pdei/ko8wqerPwTn6wboz42BhvkqVdejRuvuDx6U2Beu6w2OoM4oVx2/aXvgn6KIJDP60CSbqU5HUnSkPPTwaKDdfqMGdReGRpvY39Ual4WN1xxRQaW3GaSjgNg5/jVvZRtjp9/8iXfStuNMWivyOsNKovzdYCNOD1qzMfx2rNPx7tkDl9CgPmiswn8lBvcwC5i7jotMkBNm3LGzCoFTm4jsgj4W28NiCbwpbPPORtHQ2sZamY75MLvwoXi8w/z33+Po0FGoRBwYeQTwEIGrmDWfJuOo8FTJ5YvWRIN2Ovn8ZbtUXB2Z6G2b0ZRBCkSyXk3uvDBqJFx5c9+lvv0rz3tlDgHg33/ffdNcJsBYYS6dIxUQfQFsxUBRWA9XxK5AkoF24X2JfLpQX8dRe7Xf/lL9GScZ93y8zz6yqMTM8ILwdqmhOzzIoeKVwpJpuc8ZYKpZPCHCJQ1AGhfZJw1j+OROJd24vsD45xLLo3uh3fDq7hvpgAlssqcgY1GkkShEapxIUGKuCVClKD8rig4BoMizXsJ8PtkwoR48tln8iiXvj17ESUlTbKsLMfrXmqJwzYdN8PNy/nk2vBXjpuxukb+7hz9ztMdJmLwDsVgH4Oz4aZbbokjiCxYAMXIuM8XMCiEl3MRzjJvjQC9rTI7lUfHnUoA7fqsAtwj5CT0Z1/mTF2MiusvuSQOhAHKHGzbe1yjFEAyfp79fpB8LPUtIxC3LFCko6E6m/mXI9gGD/kg/v7QHTGPrK6fXX55nM9xaa2a752Oqd0x6sRFiwp9R8RIpd+FTGYNnL3MNJABWIjU4nv8CBNgLjCC6XNmx1PPPRePMnavv9yDYMPwqk8hrW0Y6mggbAFCAChkIIB0atBuKfor/qgEu746BkpbMxR6rsQYjN177r0nhs+eGyfs1ymuwMlzOLCvhULhWH0Z/aGB79MCbXv7jiJbRXgwxaJooONGIKOpptG3FiX6/aHD4vpf35Hr/bsrLuZ8cI9ObM0+PdYQBluNyLSKrYq5yp+Kog4FFdrN9OHRi1+sWRVrvv4y1uvlRin2tJPtGMmrV6/lbHAcVGNGRZ8uXaMTRkBFBeemE8k0Qp6wFtGYezJcPkvrXs7Jn1JgMh8/iwviTxF9Xp9VwD8EH9EK41Ci6idgNHY95FD2xtZKL3ThuBO/fbYQXiJO4n/Sga0COnBL5SyjgeCQR+vNmD49RhCdfuH1/nENOHMUeCkfcC+4kbmScpbKFfjh3wkfYCT96q226Jg1HsBY+lTZQrlnU+GshfNi5PhP4u0P3qFA18a49cabOQ7TLBiyQ3A0VOW1ncIVq1atJQqxgq0WwBZDVSVchcRCucXWCYReFXgaG1V3MD33Km7aRObU/MUx9bMZ8dGk0dF9v4M5erB3OgKAKtOHX7FuKhTOI2mfNqV5ULTAc0ZccKIC3kK/BdEJIzfu8X7nnbfjkT8/EMt45o6rr4wfH3dsGl4a/dLQxq/Z4wkOKODpACcXfeZiqhj6QjEHjzyak1/yHh1f8guPdXqMjLUXBgwMVNu4/W9/jqPIOhDfzQjwZA7pNc+tZ7zSVUZ24Tc6GHV+SZfSqPy+tKYikBEjDaNbbv5pLKHta088IU49/TSO29w/oxfihYqV208S14RL8pqCH1hcMxU6cFO8lEdk/7RlhFp6ehMnzE333BNd27ZMZ8CRVLyuIEMvcdHtMDwjbPzbZ+VZKqlpGDAfDTrHoTHscW7yHvmnDvF5i8n2wDD6ePgHcda5F0RXnF/tM5JZHxzG+SFsaMvCaOKH49MQEudNJZdPCgfx3f50ZpplZsTGrIPxkyfFP+BlHTAqTiL7aB/eG8PjjQY7niJzrDDYxENxRnmnkSVPV4aqmNm+cHIe4pnHSc5AmRtOdHoAjsdf/fzn6bjTUa1zSeXN+2xDXqicsC0/F7KCjrI33wucFf6OXT7iSTgfDhkarzL2Rp07x01XoiiyphYzlGY2w4cNCBT0U8A2HQTMQfwsXc7FdbftVEThUzo0ByOb/nnfPTFw3VfxU5yOl55zTrQhk2SL4wWObu+0rk518F/JquwTJuK9PFw5vdMXXZmBJK54FKZK6RyCD8+/9FI8+Pg/cxgP3vYbjk48FqOxfmY0eDxg4guKtPqNa6wzyZdOCKP51jMwW+2bbfzOfM10cvxeYwkM3P/Qn+NtsvrO2n+/uAz50Z2tTvLZkmGnMv694Sh+AgPX0JdrrN7heviSpko6kMdVDob/Xvzr30QF911y8UVxCtFvT35R2bZ97y3whH+df9INOJ9g9x+/B+a0m18mLZg1tD2WEsXsT7bgn558Ki6Bjo465ug4jCCKafxuT0jjm/3i4tlGeEPBc4vjkOmY7wv+Jd6LS/6tTqNuZWBmKgGO5998g/o4a+PHJ5wQB+u4wzgyeGLEVllsds3XRLh1lq0ki0Jjz8BUMS/aZ10h63zp4JFWnLdHY8+aMzeGDhkU3Y84kq13nTPrS+ertG8EuYprxTr5d0E/OgKtj6U8cbwCH3xi7M7ViKn0bCFQI7wfQU9zZ82O23/z++hhpof4Dv9LfgGuuG4aa8I83+WNu9bP9oSXlzCxX2uTeerWx8i9/mSsPY1j8BQCbVf/5ILYjzUttSXuKBOkvQxy0I4449jEEXkWLI6+0Bt4N5vNSHMdHKLLV66MN+Bh19/1p+z799dfE2efdipGY5MsAisNpp7Hmjr9wqGWpkMGgoRVOntp0+K4VWqTOYWel6dusS5TkNvPPfdCPNV/QNZheuDuu+LYXr2jbk2iy+BIVfQZgxnWs9BRJZ6JF9Ki62eGnXQLQn6vKymzpFv58Wj0mTvvvjvGz1sU5x3TKy75yUVxKFtClUfyUcfvJX7YrrxMfV1ach1LV4kXiy/ikPMyej1w2PC4EflxODeec9NNceJJJ0VF61aMnb374LyXOpg6tn2IT0VNOeDB3661/dqf8FOH9nszwzw15SWcDA/BJ69FZh8FjzdDyOKU0kRpfOo0jpsHEy8dGx9TX3B9DUCYIWsWq9mWnrQjj58wdUq8gH3TpqJVnHjMMWTetmcbfLMco/PVCVsaizq98sR+xEXlkZxCWi4F35RP8huzS6exrh+NGR3/eO21uA24HIPjrinF92vCS23XdSw9Zx/q09ouDJE+dEYWDkZpVxiJ89KedOepVSM4XWPAi8/FtgZN4qfXXpuB1OIEKDJMwW/5fJH9pn2mPUINNvQV+W8GUpmDWTHi+f/KaKiJo3p9Oqqf/vO9MXjZurgSXJee2pZXZD2e7ehL4l06jFw3xrwd+G5z/NDv58z9zQFvReOKchwN50TLioqkL2lR+zInKFL8gK9/i6OhMIZMU2TFISixPxGKDztRenU0jBj6YVYibgij7HZQFzIa2lCBHWUlI6ogPYtZGYTaCpIMInJxKelQFuS44bQfx0WXXJxnbEsMRo2MIu9AqdI4Unhlai59ilgSvAhfijhIPCKiDFmjYCWpVS+jRP/8/gfi+IYN4vQrr4yjjz4aA2NvjBuKiu1S1pIxJdNmoWlExFZQl9q2XRtWUBlldAxGZmbNnxdPoFSMf/ftuODyq6JH9yNiX5wkdTC4My2YB1U8VdJkgsJMZT0VT5ifBKLi7Hjtws8S6pcYIospqDNm/KfxnyjpPUjPOb5Pn+iEYdCsqQWoLPLinsKCkB2/rxRAwKjkURdX/T7nQwdWmnZeS5cuJfNkWgzmKMzBgwfFr37z2+iFwG8Ig6qBgcBTPFN4Qh2Xyrpz0Luu4iwTkYkp5Fx/mZ73mQKpB/0zHCTvv/dePAqTUjG/+fLL40CMUonYS2YgbJOJOnfGRDP5t78ng6RBhWX2T/s7MKarUdRv+ZrV8T7K7cP3/T4WUCXm1quuigtIrW2N8u+xSCqIKvzk4KXjwc8JZ8Yqk3MdZViqXEaMSu+VYSrVatWIGXjQH//nU/GXZ59zKPEf9/8pju/XF0dDnSyUqaMBrSH7UGjqaDB1D5MxccdIpDCQ2cJ5UykVb6rQvga9gu32O/8QI+fOj5O77B/X33RzdAP2RpilDzUcx1wywDKqTDtwRuCCwoZQcv++BphHIOog8TNSGwPW/cEfxrW3/j4LCf76msvixz86JTrg6NuGwbr9my1E2ImaM2bnoTGdUW6ercK6r6eY6jQcLeMmT8gtJIuWL4PRIjh3A0e/I+KFs8IjJb/4YimMtU0qz6a6qWCJc+KlkWKxWZywiBw/JBzFFZUmlZqSY83vpAVTenX8rSLzYPqMaaTB74x9KjeMq26/JY7q1YttMXtSbMgz5VGkaM+aBK6hz6cAdU39XiTi8rOCWSPJua5ctTJmz54dIz7+OB4no+GGq69mX3aPTCWvC846dvdy2pZ8JZUI1tD3nWQgbCPyaH0Z0/VVaviSZzDsarA9if9mLCD1dfzH8dbAt+KL1d/Eb35+S3RDya1ZnUJSFB2rwbvZDHPnL8TBx755aHsRNChd8g88zqJvZGGxDp6LuXU32meJquCU3MGk1q1ZHyuWrck1KWvUIDqgPNcDH3UyWIhw5w7gDL7tAY1IkDlumlZwYdawzuC5wFHo70bhQeZ6PFuO+sILNWAHvPVGPPbAPbGAp++94fo4HYO9vKwsDWbpaCsOYqZZ0KewFtVZaxUTT9tQgBrZ1dkgjtuVjjWzAqaSfvnY44/Hc2++F+W0f/8jf4u+vXsXGQ2cRuCRr3DVzEAyC0mj2kw256CQNrvmu3SkQWHgT4E7Bb9zC9aHH4+Jn19/Q479RqILp8MLuhDJdK+ptL5dox4ZIk4wMnpygHzkH5VIDdRUhsGTdBLkLywDeLAaenoD/njdH++OI9q1iR+fenocCd60alnO4wWdFllT4ghrgLIi/vudPMy+5JdeGgtpZPM54QSs5rOf/DlSsQe/1T8uufxKorvdohPyw2PBdL7pLE03HW3ZvvSjIiraGIGyj5ISJrxUtDTWTY9dTqbKOPZlP/rPf+bxdCejKBaOhsYpfwtDna0XKsauI+snHOQHOjlUvOS/Kk3yfhXh5J+MX6Nrzpw5MQzD6GUiXr//5S/jqG7dMiNOR4MyR3gnTfLuOuTleto+c7cv6VelXDj52bGbpj2F7ZRDiXy/ytgb4GD4OfR6GGtq0TYVMh1HGkWmA6soqzRqyCVgip741z5daHGlkK3VkcvrcTQM+fDDeJ4MnjeWLI2fX3BBXMbWjzbl5QXfFh+Y++48WrUSmMm7PN0aJOK4MiOzvxi377thsOyhk4F1VimdPX9+vPDSy3Hvo0/mSP529x1x6kknREPSzDeacUf7yg3fDaJY/E58sT8hoW5UiTaFjynk0pOODfUG5ZiOhnseuD/e/HR8nEWq9DU4eXp0PZy1qZSFVoWlSrhTl0f6wbfUAfisMeC9/pbOSdrUEDGA4FaQQegEZ9NmW5685NJLwPnT8shms1HdfpA8m99SxjFGnyuyFviSq6SLpN7B32lIgqcOwm0l/xo4MO547LG4tm/f6AMf6MKWULM0NYy83N6pTNiELNwB75Enu+bOR/3Pv8WrYnssRengM/Iz93xPmTkznn39NYp9r43TcbAfQttt2+FoACdNB7fwpfqNdWMMtEwhIGIl+6/YYiu/0JjZAu/Zzlh3AhP5l1uTpAf1OWuqKJ/KKPDXsjlOaniwjmcdUVvg4cJC41wjye2UqXeow9gebSeu027qP3zntg0LodeF3g1gjSZjYhY0+8fbb4+ehx4W9QjUmfGnbJLeXU1hYTvifQk2jtsxSlvC3fXQ2Sf9msEzDp3jnTfeiL8N/iDOQJ7ecMklsV/7IutLWCvzXFcNfuWB/Fdc1CljW/LhFB/0q6NBWtABVpOtSB6Z2P+tAXHdnXfn+G6/8bo494zTooyTKnSgqNu5XSgDMeo2tCX+J87Tt3JV3kYPZAtBB2zbrIyzYSefxTHlxzPoYo+92j9aMf87//jHOK53n3Q0fIOh7hY8i6mrzxgIEt8YesGrmBMdaVGnzErZpDxhHpWrs27Q88ix4+J3t98Wn83/PC489qi45IKL4jCyeytD+6ljA9uCx9IulzxWGnVs8sVETGAuXbkurkniPvP0tIG3PxwaV995V/StUyvOvPraOB4HWNNWrSgO/lU6YuTpDljjVhxxLWxDesm1ZJ1ddx19jkP+Kr06TzManmNdddz9/JRTok+fPulcU4+XnkpOF3mCbcnn+T95uzhre+KRV+I8930HfpkB4tgnYSM88cLzOGHLCT4cR2CifdKqmczyJ5rKS/1NeeUlvtif65l47zzkk4zZrZTObQm6z8RJE2MovObRN16Pe2/5RfTFTmjakIxq6EkcybHbIM/yZ7aXc2Atkx8wEbefKC+kM9t1bVyT2WQfjRk9Kt587V+xiUKev7755ugOL5BelWdFAKlwSDlGZaGw177RBnM+yih5umM3K8arLgXLdToqP5586L54Z+EXcTWBjesuuggbtxVy18KzO+ADBr3JxEhZx1x41gxkXS+Lli2NN8mCb9KmVZx77nkUlizPwIo4moFm8OiHfv3PHQ0uOAAzvVtkh5uBCIJFUhAhtse0KToahsRKkKkBjobD9j8w9qmARcAINsMY9DyKoNZJoBEyGkbFdTden4UDbzrtlLjw4ktifwRo7tsm4uD+ZZlRHZiyioXeJaMLesf0WnqpZIlgpaikSGdxyDXrvoyX8Lbe8tBDcRkes34QS1cipR7XI2H7Khm9IrEIWwhS5gLiaYCocCXB0ybYA9KgyPObyD974QIcDS/GZPZjnn/V5dGNfUwd2SKgQZL7DbnXYkMSnuNznF4itYqdyJiKEt+riFaF+VmsS0XRlOaPOQrz4b/9ncjucUSkjo7WLdkjhWEkIWr8y2wKggb6ALVgUjoCSK2ivSRAmKKrI0EpOHR2rFi+Io/x1NEw4K0347Y//CF6IdhMA3RMwkQlNtuDEQnvUgTM3xX4CSfebdexOw7X1HoK44m2DCSd7km8lYd27x43XXpp7jl0PF4qhUbUvXwuYcznFHhMJBk4g1ZplwC/1cAG16pmRsOa+GAohP6P/4yp7JH6xRVXkkp+RrTE07oFYzrPYLZtlBSNFiNFqfzTjoKYlcv/VPYl/q0YjNtYizoN6lEdv3HMdU2ffSYeeOqpHN/Df7orju97DGnwbIchOrA7bWgQGZlKhZSuFKIlT30iBjBJpRz8doWMCGjI66UfA2z+cPfdMRqj86wjDourr7ue6PfB6XRbtwp3G4atQq4y462CUeh7Rk1ZN9s2yruFMZteqKOhkjRAxFem+NXGDTFw6LC4BkdDB0Z/4Y3XkO59fOxT3ipPBNmOo6BGFQxA0uDToGP+Io5t7FG3VqaXjv50bLw16L1YiJNh9pLFKHLWEdB5YMoyhT5XrSXTYVM0rsNJDjhmhK3KUGE4GCkrlHPx5TvW2TGL/+Jd4oz9MSdpqvRyXfQyb8AZt5xjr75O1kx0/ae/KKL3TSiQB58xpmebbjNQIVSoZeE8YCzO01UKBvGdr3JPvMrW13ihly1blmn2fyG76dIrrojjiKpbIKgOypJC0lNd9JRLI85FWtXIgCOwtsyLNTfCL+5v9YQb0qZr1mE/4R6VYs7iBTFq4rgYMOidWLji6/gtqfyHo6xUtYr2btQ2oPr+t8BuErzxA9ZnDjg29/NFKfh1vMgTv8OoSEcDFsd2jtnZSbuV4XdCb8P6b9LZ8OU3X0cd2jTFsKbj/I712+VsABuS3p16wV/4oNACl3z/jvQdBd83bKGZv3pZ3P6zX1I75RRvjvfefSdefuCPuWf9QaILZ2Kw71W/QZ64oiFUDWeJCmPukwaX5ds6G+Rm6QjYpbyl84F1VimVN8srps6YGU8+9XS8CI/E9I/H//pQ9O3VMzNrvoY/S69p1IGT1eSx4L8RZeWLjgUzO8Ckwsij75LSIo9V4bWGwq3Q0ALavoktVKecemp07tgxeZbOq4KHFUWCVa7S0AL3XGN5l7LINVU5cC1EouRHzHElBshbQ4bEzQ/eH8eRoXL8cSfk1gmPx82ixvAXGDm0ROFJnBny5XS8gSeuQdE+fTFuZZOdqegk/tPnAhTFJ195KV595cW47oqrohvGekdknynVX3/FEUXMr6Y8kGe/ZS7yT41xOZg1elSGpCFp2EtaFH9LNRrGYZQ+8eyzyLzD40dHHRVtWnPiDUqoCqeptcoeZapGSkbUmIvt2Y+/+dm+lAW+VORUxNaQKThv3jz2B4+OZ15+KX5PBPwYnCRGjJyfcJaHu79dWWy0S2Wu1K5wkUaFtziiLBBGGoQqjjrBh6HMvfHMM1EPB8PNOJO74IBxTMpedQPbcLuk9O/YVEyl2YR5gqSQUa5DKVpo6qt7ykfiIHnrxRfjJQzTG3BMXcjWiVYtCD5g8AgbU7FVl2G2kFcxNtesyB7DwQtOsvK5XUjlUePLrBuzGuYvWhSv9X8j7v3H0y5J/P3uO+IUnHp1kR/rVrOnHD6ncVQ1eTtwhlh0LGvcWb+K7hPGACd5i9l8m5D1OhqEvzWtHoSHDWBtz8BYvOLGG6Mbslu9zKMyXX8j5V7CRR1BXcO1VNlPQ4DPbh0VB/zeNVUJ9/7B6GM//elPozbPn3fhhXHKKWQ0UOtAw8X7bV8j1r81pjMDAzoVzi6q6y2NigO2V1w6oCtzbOKaeAW94HePPBK3nHxy9IIHH0T02K136jwabzqGdYY6VgnGQrrqC7mVCFwpHG3fZfFHnSX+rb7ivu9ps2fFM6//K77BgXn6iSdhdB2UR50KN9fdcalfLcG5MOyjYUQ9P4pVfL92zWoM/rrpfDHKTTmddKKaSu78hJ8GuBkfKzlpojb8sD66Tk0MJ4M3Ghcep6gT1PkLc7ezJc4oG2mvwEsRxTmpFRSwqgc9tm3fgfpY9WMszo8ZYz6Ou37zazIaDo2GyKZqtClc1HuTblgv30WUwpFeHEUqLxB2rmfuzd9FH+K0BW2HkEnyaP/+cSzO5Z8RcOvcrl3yEGFXqvEg/5KfiYvKPh0n9iOfVV/KsTNuP5sRsCcOkuXwArdO3HzvA7nUv7n2qjiHgGEZMkqjzPbkS+pHtqsun8Ef1tYgio7m5PfgexVwHK4WX3NqQyWyBWvWrhlz5s2P5597If7zldfjAHr45Z/+FH179oIvVo1i6wTBOz7ruMjxOwr7Ah+tG6QDR9pU19tMhhBu+XSMVyvpY2SY/gbHzswvVsXFx1P/6Ixz4hCcm1WRR+kAoR1x2zVwXc0oUKfQuaXeW9BPUShZ/aEI8nEfeGFmwACyp65Evz4NeJ8En+kDHzaT0gwc+bTOJmk7nb32A56l7gHc5ZeJT3xvvSFlSC34f9IYv3+OTvMMa/oGGQ1XwSO7sXWi3T6ewlQ3s75cWzAux2+bylTXQvyRhzoveWqePENfOhl8fYM+pM40acYMHNVPwxtbxBknnERh9Fa5/cxn5OfO3TpoiTbghO2pJ8hjhIWd268ZMPZZg60NztWCthMIRg6B/h7DyX4PgZm+R/SIvaCFGsDXranyD5/NNl3QXfAQJuKhfaadAm46HmFi2/KcuXPnUXfqk3gDJ8bX6D+3/+IXmdGgU9321PeEs/qo7QmjQkdl/ny23lEG4fjMCHKdpbWy5mWZ+TGccb/wxOPx0gTqE8HHrvnJT6Jj69YZfGbo2aY6rTjjthS+SBvDrIaFy5bGG8iHFp07UjvigmhUXs4CqRsUoyje6fgHfEGHLs//4PLpNLBgFiySAMxVYjH4Aga65f/naGjI0Y/dDuwS+4CglWAw3+Jo0GsqQ2jQoH4qpB+MHhVX33hDsceWYimXYJS6dcI9SOvZn/wtxgcYl2lkKhWbOeJSxUWmnp57FshpFYWO8EKBcCJPZjTg2X4ZJvhLBPOVeMz6Hnss6ak4Gth/rEKjMS7BpEAUkZlTKj+8p1Eq8TBuF98U8jy2hHlqTNuf+/mfQlGc/sHIOO/yC9k6cXh06sheUhD+qy+/StD4OVPObAvYSTwyQRFeRFeZlmA8bs/UHY190/m+IGr08Wfj4+H/wNFwwnFxoooiSNkA5g7kMwKsclVEJlQQMQhQGnXAyATduyTxVUP5kfgVljWIFmgImhalMjdo+PB45+0B8fs7/oCj4dBU3ISrMJEQFdAqjo616It1ZlYyWonbz7brvV71YRTeZ7HG93DwPIejYT8cO9dfdHE6GiRWPegpiHcxn4QzMBEW9ifDdV0cs0xRL59CYifCoioMdAVraurrU48/EhPxKP780svjvNNOi/JmZUS9UdR2CTJTvkuOhlKdgMQRxivuGpnSi/stBp4p37XxVjYk3W/u4oVsnXg27n/qqZzTX++5O07o15fjq2rENygzzADhj/ASphoaMhJemYUAfWjIZMoXcDaTwbQ99wKaLaFjbjSwMVVvFI6Gs3t0jauvvT4OO7hLOtTWcFqHbK866YA6GzRu/Rs7Lp0NrrFCf6u4hMd/ZxUUPNrU2IPjxzrS4Ad9+FHc+Ns7ozNIcv4N18aJx+JowNG3ZQPp+Rs5HhRHwx4ocmYJiYvWd9gdoV4JJ45H3H0w4qP4x1OPEu2ZkTSZQPjf/kGIfH/2xP/24//oC9VLKC6vW274aRx3VN+owAFUkynqbMjMKJQu8VsjslTMS0VMXFKZlBgUguKTBu/6kqPh03HxMGnHF1x8cToaWrWqSOPIiJGKahqcuwSxhqiRCwvRIoozlVJlBsRkC4oRVbJDcM7gCYp5HPE5csLYeHvwuzH3iy/xoLvHloyGmhxxyraJajWgabICPvn0s3j1tf4x7pPPYtb6Rbtm+d9/Y2YpCP/7T/yf77zlmhvYW3g+2BXx3jsD4uX77orxfH4IR8NZpHc2QfHdAEycey2cTfIuFUc5gE6tjGyhyGWUSJjr9AIXAWL+ZnTXdbCg1BPQ0ssDh0Zjnv0bWyf69mKPLXBejyNZ40rnQlWVIOCdhh08RN5lRFHcNvIkjUpHqdiBs/JJ1JoYRpbKzVddHbNp+1YcDWeefXZ0xlj3WEGP1VX50Dgyw8ZMLLMWfIn78pgSz7c/FV9liYaA85PXvMU2rZ/9+aE4gfV0K09PCgpblFcl1FfyMmSByguqbiqCKui2kX3QnlqRolOeacQzU+/BL7dOPPnqy/HKyy/E9WTEdccpuy8p2SqT64jQukWrFvwbxCaiytHNwNY0cPmuMrAkQ9KggaclzIC756UvIVvC6LfbwLqype+ko/qwx5atE6Spuy6bNm1k7oWcTPmjQx84+508X0VZ2aID0O99RqNOmltJZHrWrFkxZOTIeIaU7Nt+/es4ulv3lOk+K6w1cIS7939jejr0Kj06f2Hsq+RoUP4KR9eqcDRMj+HDP4w3n3k26qL033TFFelosC3hWDgO2ONei2PZaFOZYxuF80geDP2Dk/6nrFWeeIkznmWejgYyEV9Ekb7pLBwNOKotWC1MfcbtnaZOw2zTMcBg0yGiI0EeLm7oYLAuyVZemWUJP5bXzF+0ON6g2Nd9TzyTff797ttJ4z+ebVS1OA5xdRpWGkbKkNwuoaMBY0s62LZL7qkT5HYh1lL5pOKvHuFcx+NoeOgvD8ebEybGaW1axzVEA48gI074rWXPujLTdXK9/C6NdN6Vu+Kf+oHOHQ176xb4vXhjXSkVY7ey3oziz8l0cR7ZHicTKe2A8SK/1TjRUBc3jN65Dsp06amEJ5lxRN+uRxGkcSjAlD50NKiPpaMBHtOnDxFYIo0NGzXGkVZsYfAZj4ytSiRSWSrNik+OzX5qseZeHo0pDpWyYKzRMJWU6Wf6v8aWlG/j9BNOjC44MVq1bp1jdL+889Y5tWjp5/HOu+/G63/7e8zC9CQx8r99/Vf59N9+6P9yo0ff/gQcbF7WnLWdElOGfxR/uPUXuXWiCUGlGqynTgTn73xhWfm3epfrKe0nrLlH4049yuwHeYOF+zSaPoMPDH7vvbjnzbfivL7HxC+uvoYi7TiPuM81lE+lvOR5dY6SXiMvkHOpc8iDS45kMxz2QGcwE2P56lVsnXgnbvzTfTnL316no+HUdDRoHDvgasyBnYbprFEvU1faA5zIYt189jQvM8iqks2wCXxftRE9C0dA/YYNYu6CBfHPp5+J/3j5tQye3HHffZndq6PhyzXrimcZswXA1fllEOJ8YeTB76FZtzaph21ivtKrGXj2ZU0IAz+/w9EwHUfDZSf0ZdvHmXEINRp0elsEWZhne9CnxmfyNH7TySDsYBdJP2YaSHuuhfxUmvoKR8LbOEyvuuuuOKND+zgBp11PnOxmVCubpEEDosVpPQZr0EdpUyNbuixlrsiH5aGuk1F311jaXrx0aTxHoHAYWXGXXXNNdIWW2sDjdRrqZHccrqv8UR1dB6GyRRjZnmN1jbWZhH8l3nU0bML2WkdW9aRp0+ORZ56OirK94wxkX7vWbVJf8hl5gXxRHEwnD/2kncC4bE+7w3EbpBQHpWsz0oSTjobPgPtQdM2nBrwRd9z8s9w6UYZsqsVaoWWlnZgGP8/L851zyebzb/FfeKjzKRfts3SCjNmro5BN/XGCb6pRK27b5WjQgewYzQ7RRrLN1Aecg7jOeAu7TyeVwWDGD12Z0bAH9kfTZgQzgeNHI0fEyzhgXqDA9RXfOxraJJyt2eblfA2GqS9Zz8S6XAZM5y+hrgbbd8s67RM/gb82btWKm7EjWG8Wq3hlCz/cf/49jgbhyAIkhQEXXZ8qIsLI7QjTEIQjIK7VIFOT+g3jyEMOi45t22aK4A6IKAtmsahQJancm+J9Fu2cG67Ps0avJcXwsssvjwNQLmRQ26h0bzq5EaOapOvsBjHkmd4svEjhfxKSzLb0nhESkMn06qUY66++NzB++/e/4/1vF6ecfU707t07mkLo39GGCoiIIyH6vB5hlR+Vz0Q6kEVitJ/0tNO/XIM6bwAAQABJREFUaVrfwnRkFFNQtp4hPWfOsDFx9iUXpBK6/3774blj6wRM1naLCJowE4cYKy/hlUoon0V2GZhEZTE4CdFsiMUoimNIpfvPJ57AgcF+9d59iNRRlbysGcRBlB+4GIlKONC2EWMVEYWNzMrog4qZjLHIzCD120gHCs6yhQvw+I2NgR8OjaE4G36NomhGgymMVkKWQWmUlxwKjjUVCeHKfzpOHLeTkhj93TWoTXqoazX+s/HxLsLnZYoGtjvs0PT4HYjyr5IsA04GwTOlsfsus9DjKHwcv4xU5cT0b4m9ErCpQdaByv97nGryyMMPxETSyX968SVxLvtITe/aQwUFI1rjHKCm8qby7153gJB7Yh2/TKkUndpK6rkRo91p30I4M+dTd+P55+LBZ59hfhGPPXgfWQHHRh3wb8OX6wJ2nEaSUVidUHroNY7S0cD94o6OLvEQIGUGgspiNeCqITYa2Nxx553UaJgXpx12UFx7403RjX11trPp6w3Zts4A8T8VXeaen5Pm+I53o8lI1tiJcbedz9+CB/ah0B/80cj45V33RwW3XH79VfHjk06mRkrb2EYk24wGazSAdbkVwTUVFruTSVOpLsYwjoaho0bEP198JqPu01cQgft/eBWuK6a9q8+7fnt7nHTs8VGGU3J3HIzG9yuztCrkRuSMLhi9EwdzH504xbNJZ8Devc0KwtUo+HPnsr0BL/cjbEU6/7xzox+OR7OPGuN0lNGvJwqmEAL9inegpOJSja0jSJ2MqBXOJHpQMChCyGiQ/y2cPzs+/GQUjoZ3Yg779m65/uasedKoUVOiMnVRdNmWAvzHIVzfGPBOTJ4+LUbP1KzncV6ajs5ddx2VV/7tl30wi4RryYnz+5/dGhedc37i05v9X4/H/3RHzOSeB4iQ6mhoThaJe8hVPCtBUzCoNC4yjRvlSGXN7BqzgTKdHPrVsQaDB++hOR0NzM70y0f/gfd/4JDozDd3P/5I9OvTG17HCRY6nuGlWRQPfJdWsyYPeCnv0dFgtg5ElYqdxYN1Lidfk5ex3laxv+ySS2Mxbf+cImSXID86UIgTy5aU6C/5tlBS5OPSjs/qQEr+q1HjWOEx0q28GsFR8DoQYfma1fEGKfw3k6rep107tmWclftg3Trh1jmjnGY1paEL33P8ppXalo4HU/rFJ3llKZrsyRPSnc5kTxV6hlTv999EUbz0itxmtj8RfLfeeRyrvLQ6hpzzVLFzrMqHlBnMR15mB6X2k+9wj0aVxSB1NDz5/PPpeLFGg6ci7Y2TxPtUcn3OTA4V21SaaU4+XLSDMiuvkSa4Tzqyb37M+ksTwOXByI5XMdh+f+utcTQZDY0xGL3HZ5QhKnE+m9FvaNbfcj81vwt/4eTvmfYNHNPRwH1Zo+HDodGflOna+3aKmy67nKrkBxCp82Qp5ZtFJd1KUkRwHb+OsIT1LgXddU/ZzRxUsDMDErm8Cbia+vrSo4/EKwsWxi+J2l8OP2hVXp7rqdyTv2j8V+M5I7HKooSzOA18pKGd/ODWodJWodK7hlF/tpPc/9RzDiH+fudt8WO2ItWrWycDJ7aXmVG866B2q5/f+dmsBK+EiY5w1leZoaxK5wHw/IzU+nvBx3+R8n1qRXn8/I4/xOFka3ptJgNHnFOeurY6W4Sz+AZq5rqqD+gMKqq4s0bqO7Src0bDaNCIEXH+r34d+9DehcDmZBRpHQ2uofuh5bfSkIaKzqTUOWhDWJfgLaxKOOS4En6MyWKQ6mO3PcbWiT594tjjjsvtQvXJMP1uV/viDSTIuJBTrKWfcw7iE/3Yv/xd/cZLnUKjygzQieiez73xOh3uTEfDQdBSeatWOUZrFahf1UVHWbpsabxPhP89gixzliyJ6WtXx560JfQxjf+fXm2pDXJ0337RjEDJeLZyTAQ3b6NmWU8CP00Yq4YXoEvnggMTVdQXdaClU52/NeakOSPJwt3fWe7M1nPdxuCMfRU+8I9Ro+NcHA23shVpvw4dknepAytLXSO3rRR1ecAh6Sn7A895N9pR8HcMMvBKZ/Pu6AxfEhx5nT3nv/3dHVnQ/fabrosLcJyUEbQxKi5O6wQQx81KKWqU6J+Hj0Nf6jaZyQNdWR9gKwSxaQf1vwjMePz0TNLgH3n0H/EfL/3LUcTrOH1PAV671YT/075bq31eWV2J8av3iUtb0Yd0iFik1e0YIlJRQwiuyfhLWydGjfs0brvj9hjH1okrTuwX5511DvVgDkw4pGxKXqWhDo/hc554QF/ifh5ZuovWkg6gC2Em/HeiI6xaQzH6YcPiunvujRPLmsbJ55wXfY4+Cl5TkfqKxq7yTFrNrC7XFNz2kob9lHokHzRcS3qyTgMd6XMXUePnnXdjJPzmJ5dfGkdYIw4eL45n3SfhzVxdW7MR5cc6Z23X41zNOhWhlNUW/da+gsg4pYMaDdQSsJjwky+8EC0xsE897vjYr2OnPMVFh4p1crSxwIrivxw3sKU9g5lmApnJkHxmF35qWxhMXYJt+Bm2zVBsv+fZtvnLG27E0dAjWjRrGnsi+9Q5zAiSF2gXCRPhkDyFd2lAR4ProYyya+0zg06OafKUqWzdfjdexUG1O5lqv0LH7k52k8UgHbtraTA17VZGn1lZtGvDwoYFSbi5xSZrKmEr2k9t8N2spixG/8Q/4vVps+MaslSug092aE0dNGC8wyw47nW95Ltm1NTE8ZOZIrTjmr3+Rv+o27wZp/2dEy0qWuVcDGInfamD/MCvf5OjgdVwpfPiA3DR/HSxt6IITt/laFj++ZKwRkP3LofkHvE0zCAsvZsa8zayDoVoyJjRceGvf5VK8GXHHhMXXPCTOHC/zhldMvthG5FAFVGjDAoaMcHFN1VaT6uIredP4SfCSFgip4tuapGpS3c98WT0q187Trnk8vSim/qaBCDC0Y6RkHQkiGCMTAGnEiQiKrhl6k5wdxhmVZQlq4avXl0UH3oFYbVk0rT40amnxBHd/ksxSAixiKIVZlN6zlRcGKvwS2YksTiBotMi1Zt5rEVhWLAQZ8CECfE81YIP2H+/OBplYn+UrpaM3T3x1reQ0LySEBlvSbjrPJGQVCi+97JC7EYtNCznz58fH48Zwx6p4fHZp+PjyhtuiCMO7hJlCDtTyVWcJfISYcuoUrBlb8U/jjuVdcfPJcN0v6GeXvc/fsCxOAP6vxnlhxwcl5x1Vgo2GU0WupERM3ZxxrH7n8y2wAvxCILlHtdAo0Un4W540GvgQdfR8D57DR+9746YvvG7uPbcc9Pb2qZFeTomTHlzFTXcFRQ6HYSB/WWmRI4VXDISg7Fudsom8LYkPK278eKrr8R//qsQbH+9+w9x3NFHY1OS0YBgc8ecAtI+NDx9sYiZpYNumAqb+OO4+RMnCbCE+elV1zizwOf9DzwQI+YuiOP37xBXXXNdZjTY4laEptEuBbMCU09/ti2sEs4Ifd5tRwNMR4Op5ToaNmGIL2O/5LBRY+KOh/4ezej70isujpNJedunojU1GnQ0sD0HGkG0ZTQ2YSzdANuq7CFci6Nh5NhP4pW3XochLowpi5bSCpFAbJ2a8OGaVYioary7JtCNtQpE53yJw6DjNrfqobGhR4Br4kWSDvjDz7x8p3uEGmTA2m/Ha7J5C+mSnEC10d+4Ssrebb/4Jadm9I2mCM7dviVtl/urIhBMtdTolB+k8cMzub0HOCSt8rfKehoXDGAFBatmEe0aNW5c/OvlV+MkIt99uhXZR83KmrmS30cAhImXuKfjS6M06zSwFhnpYS3dzlIV2FejRoPCdOZCnBiffhKDhg+O+UvXxVVk2ZjR0LRpc2iC46BQmr9avykmTpkcAwd9ENMYy8hp47IfI4fG6GoD25o1LOAFrRl1EZeAoVfqA/y9B31KEqlMOUzgJdgTxn7PJa1s47cinRvnL58hmVjH95YRLEXvfnndTXH26WfmoryNovjow/fHMn6/48or4lQyv1pQC2Y3cC0L1+FI07CWr8hTrMYsvejpF7ftK19iPOuzm9Fd+IyDm0xGw2Pw39eHjoiD0Pdu+eP90btHj4zw6sgQpqbUihxGvVRMVXIzOg+NKnx1ZNi3BatUOOT1xdaJHZnRcMV116eSe02/vnHxZZdGF04rUT5oXJSMUMdixoLtyotTSaGfgv8IOS5pjP+TNwHoVfDht9gGeCsZcUdiEPz4zDOjd6/eecyX/LGUnediGY2SLFMG0U6JhxVKUuFokEfav3xIZ8DMuRxf/N478Rky6vTzzsujaPfFUV0Xh+cGHV/MwdotaIo5D41BI8jCwHaTFwA3hpz8s2RcmsGzHMPLEwqeJnLfpqIiju/dO3SCt2T7nU4Pj+f0SpnqtIU/f9uul8pSoczhBN41bh0BPrtoMacWQUvusfV4zptwTvXEodzYU4tQ9nWICEMvx1jAYns6vVXICyVMZ126SuzMjnMbhOn4U6eT0TDsw3gLWq1N2v7VpKZao6ERES/HoPGk46XIYHDw2VXSrHTvDBx/Opf4nM6lXTizet3a4tSJPz8Y7321IW4+95y4GPlkMUhlsnjjNhgIO7MO3BYnPprgIC76brRLl6Y4L/NTUZeXqZd4xOJbRO3//NzLOagHf3NrnAReNiA6bXE5cdxgi/JJpVS9KHUDdQ7aMKJmBNs+0G7SoDMLxDkIU4+f/PNf/xoDyGg4tkmjuPn2P0RPMil1ylscVQeXuOk6Kr9T6XUtgIewyQwE7pXP2Z5tiwMaHavXrE3H3VXsKW/L6M8//zyyKU8kE8ZTJ3A0AHPhLw7rdFA30pmbPNIxozeoxKs3ZYo5vJNh5PfqQku+WB5vkiF0zzPPxkXgS7/jj88snqak2eeawg9sQxhkMKNSYYS5vvJ18VtZ7t86lnUQqlOYbbF0KUd2o3u+Qk0ujwo+GWPUgFU5uO/ecB17zrUuWZFLMKKGsCVqMAGLeUuXxLRVq5IH6/CtXk1noXWXWO/v5Pm8870i3bn42f590X3yXqabvmfR2J8Yfr4zfHCGv/mO5WbMGHgEgr/hM//nke5tWu7Nseu9Yi9g4CljU6Cn3/3sp3EENX7cOlEDg95LOnL+DiLXlE5dv3zZG/+r+6Zzhvt1GOqU0YkwGkfDK//8Z7w4aUqcTkT9+osuyhoN4ody0xR+M7B0cFbXYEeIyIddX/sVv62fII7L9333O09Z+WLlinibuht33n1vrKJft06cd+ZpeeqEWb06FzJDCACWgjHKFGWoGQ0CSz1NXmOGgVsGt1dGdrNmrtsMHA2PPfFUPNH/LcEQz/7x7jgJfWxP6ld4WpMn/3j0tnWydJQIm7QNmJeyWWe1DibpyYw46TeLQapDc48ZDffed2+MW7Akzut9RPzk3As4oeAAxlfo2NJJvmhLPUFDOXXqXbxCnmGfro3wlJb8W/17OXg1aOTIuPWv1CVqVC+Ox4nRs2evaN2qgpnoBCycc0XNs8JOKHSOUu25kiENvGlX/i/H1EnwNZlZcxfAa6CncQMHxY/POSu6wQc6d+5M1nP9pCfHnXYMeJL6PGPUES5vkB6VjRrDyReAk/W+KkGzX4IPny9ZgvyYHK9QA6KsURMyS4+KA2i7ZcvydOpkoBP4Mdlsz/lnJhltJw+2D37Xvkrbgb+rYTupk+XWCZzgI7D93sYhcMUVV1BM+PB0NFiXpDLyJvGPsZlNYnviMyieL20o8Vw+WThgkF3ck1mOzNVi9GZUv/la/6i9d/O4+tJLC/nRgK0Z4JR47fxLNo2OaNcuG3dlJHb74CWPV4dPXIL3rMVRPQrb6dWnHo/3Fyxj68SP4rJzzsmsffm5wcfMikBmODYzRQpHA3Vn4FnzCCAPIAjbsFWLOIvsy5YVFYmbOhq0QZkII/hhX/9zRwPzzwgOCOyVHlU9nSARaAZD3UYxyGnUaPgwFs+fF9Ux+rp07Bxty8ujkouLAHcPjoxHZ4CLNhJC/xWKnNG8C3r3iJPwoFsN3cU3zUomZEq8xq4CWSXX1D2V3EwzEtlBMgWgwlhCFMFFpGUYGJ4z++ALL0XPOpXj6LMuiCO6d8egbpYCrcREjGRldXCIX0Gt0pgeUuYlMRZGDATJmKqCqFuYi6nWFn56e/DgWEO65FFH9cFBsn+0bt2aKvl10lhXwUrlQiJzWwPMMIU/YxTJTe+SEM1CKFU4lZgsxKKj4TMJhpS3zjC+7hjsnh7QlKwDmapjFyWTCCFMha5CWoai8PGS6dmfHlBfphn63WKiXUakPubYo3kYPT+mKMkhOHeaElnQWeDzyZh4VgaS7cFQhK/t+ZvtZTaIzJzxaIAYLTHN04qyo0iL+vD9D6K8y4Fx1ilERdq0AcbV8nkZhETtWPR+JpMC9qk4AnsZtX06Rz8r2Cqx5tY50NEwjIqyT5D2qBl8MW3/CGO0ZVlZpnDpOU/jgjXSS6zzIoUnayxjF+4ptJlHZbQJ25aJ+K6loIHdHyb1/PuDEoa33XR99D6iexaDLPbS0y5tmy6moNQQECaZRcETJZxKnKFdjQJTYWVcHsc3Afp4nP1dny5ZHke2ah4XXUZaMI4knRemYuvNVVUuZUuolCqUVUIZYTpGNO52MNEdtOmxnKaXK0Q1jEZ8Mi7u+fvjQZwej+mZ0e+oY9hjR0V1MxoUyBi91mgQ1mBPbp2woFllvP5fbtoYn06ZhME8NDMaJs6ezz0RjdB19qpXJRqguNSmP2xicIBtKghvDe7KjgNjUC/+FuoXWEdBo1gFVNyAQUBDrKdrqmBij2sVUmN3KszJFviKca1YvZpikFJvpNFov9de+JM4EuHZlDT+KjxXEzqphoLgTcJXgKSA34XnrqM0Je7YjlFx4e8Z7XMx6vTQD0cpOgIltOtBB0XbNq0zi8dxFkoyzkvWSJwR1+2AbnPMpke7nUvcqgUMauKcMYK0hS0UC5ctiYkzyeQaOyYWfb46zj3zbKqS70f15yZE3Igg7OZ+zU0c9TgrRo0eE7PmzomPZ01yinlefT3eG+1ZPRrXr0MES6MFQ4GCkFncEc3IQpI7tpaUb1AJWIPNwFuDwhTaXXTKeDlFE5iKHziEqnFaB7i6YOmymEFwvzHrtp5lNxZ4OQrV8cBBRW/IUE5xeeFZhxM3cob7cSijLeCRwloni8X3FM7y+eooc7VwSgnfNDbAAelGnBT2flYpEn4qBNNVFP/5TAwZN6GoG3Lz9VT1PpBilnVdvsR1FizpKRUVxuP3OiSFrzTjtjJxRzwqlOkie8v+PkZhufXOu6nrQap3t65xBkdNdSbywq3p9FThc201cFUQk4rkKb7EEybiexo48Bt/d54aZF9i7A8cMSL+QDHLnq3L45h+x8WhRBqbAxsdLzpklUmZEQHdipiFolEY7eKP48hLgPFZ2Ag3TxbyeMv3hw+PRbNnxdHU4DmIqMs+7fYhvZvTe7hHh5qwUcUSz70ca8KX+chHUwYybtvWOJMXabTZ/lTw7fl/vRblZDEcc2SPLApZVtacRr7LjDzHr3JjQTu3JTrvdCrTngq0vEwDwHZdC2WY8C/kxwQMmDExccKk+AnOncPIIvF4SxVx115lLTPpwEWp2raFk06I5AO0rRyxD3mEzyjDNVRnzLRq+PAY+Pa7Ubd1q7iQCGmXzvtCT41SxriFx5fjt13HlWsrsJlbjhs4+VvCzLXmg2vlcXyjMLxefuwfIQWef2zfOPOEEzhJZO/vcUv5n05kHhLqJQWaBpMXqHhqpBjoMGL1vezmd4+3fG/Q4Pj7q/3tOm6//uo4BnrS0WAEM/kuckk+ooNNGVIDuWlar3xImFjoUKiZzaBB5xopu6TDiWz3eBpDffTCRdGF9i//5a1ZvM4aVso1109c9nINhLfj9kqcp10VYRV4YaIxqrPAfnU0fIQD6dY//yVa89vpFPXr169f7s3W+aKxnrIaXLQP2xPuyn6/l++aaWBb0lvyAO+Fd4inS3F+WZPrOQyAfgfuHz37cDzyIUTucVDZtjqFYy5otMBncUP8cwq2n0YC7cv/bb8uxriOOyvZu3XiHRyDRtmP7qNjbf+oAH+co9lX0nRGVLl3OPj14eBBsQxda97XGwMpFS1q75FHStavWQU5h2zEy7ADBzernmMAgsxZ2oYe4cE64KS3DEgJT36Te2UROICbhZSRtxbi3QIf3UANqS+/2oRhD2+CZJXADfdqEO33PYAaDfVyW8wsjr2+m4yGQ3EKul1T+Z90xJpJgwkP+qSbhLN06vfCSH4o7jsudSvpycyl8fDIgRSge/mzSXEsPOY86th0atsmaUnA5pY4njPaXRM+oE6jnu+ztqfBr5NNXdXTUJSzGmDijXJbh+M9Tz/HbCjofuH5cebJbL1r2CB5TOo0PFvoSuh7zEdHgwawnwEOuIQTG90ni+4RQKlCNoNBIMemjv3iK/+KV4cMz/bvu+kGTozqnsWQS1llW6ErdXjtghLeiSvqp+K4zhHxRRwAUOkIV98zWDFh6rR45LFHY/rKddGzbcu46vKrs0aD+tZm1ks6sS1lWUmfEbbyFvE/g5r0U9I75KPihNkOnnr3EZlHDz/9z+jarHH0VH7gYKtoWZ7jlCtZYDXlK+OTPxkM1Nlgn9JRqTaHARXXWd7klhe3pXjqxOARI2P84CHR54Tj2IbUJTqQqeIxvkXE3u1p0A9jdR1B2fycOhNw0MmQcga83Z37PEZag/drtiV9QR23afDhgWT0NcJx0dMgKhmgZsQZLNbRq3MKIABS1hIe5bzlN8JFHi9MhHvCjmd0Pio/lpFpPhWn4Ceffhqjhg2L03BoWtOqKXygNvpFVcfhutGO62e7Pud6ChPb8ZVbWvhOGvBGaxvxSMyAD3jq3Yi334vqLZrHmaednsXoPe7VbSrJI+FJ0quXYxd/5M3CXfpNDZI+QdLMKEsHFX+uRn6MIzg74PknYwyFv8/s3StrWrUrr0gcBwL/qx1wW2eFmTpu094E/XwOD9TJ2faQLmzzZMtUeXmuTeIlXkn53w/9+rc4GjTeXCgxwcVHQwQuMI4UyTuJRkyJD1Dm58yYrsYbHcpbZ6E+jbStCEr35mpw63W2Iu6kmTPiX2+8mYy+K8XxurDf0IwDGVEqbXqJyGjQAJBgahPVtqK5hCQRFsKpSCnVW6YQlfglrDUYXp/i+R8GIbbYs3Ic0LNf7ItB7fEvIpT3puIGIsuUJWIJ2GdrSdh8LzGJ3EbTZE7ut9HoUJkzBWjkyFGxYdnS6HJEj2jdqnV6pmuT1uVYHeMGpIqEWIvvjPIVokhRWkTp9MbradfANxIoYa5j3EuWLCFtbFZ8Sir8Phjr1q0oI827HoJV+ElRtqshJePzs8JcD7afdUYkgTIOmbiRIufovSvYYzsbJXT29Gmx9gvG3vvo2KdNmywUlnvemb+KRLZPWxK8TN/iXho1CnvnJjOVyJPwWVcZ4TcoJPMXzI/J4z+lxsTkaL53U/Y2c6QPkTQJ2naS6YE2WYCNcSaR04dwdl0yisffGQnj79yzyrg1qtewZWU8RTIHctzRYjDvjF5HRm8ET2PW1LVSgdMgFHdcAwWzwkdYFEwERuJv4G0WCuL7xGNxmcssmBEYF2+M/TT/vvK0U+LgA1EEWB+NMtOl9aLraLBd21cRTIUOMKWXGvhkAbRdTNH1Bdsy8uKRqANfezEmbtgR3ZrUjeNPO5vU1HbwMxw4tC0+ZgSNMfpZmGn4Zgo7OCPsijoNCHyG7OktwkVGaDHI8UQtHnu5f+zN6Pv06xOHHdo1muNAchuSDruSwE9lzrVl3p5aYTsbtmzOk1TGT5lIRfzPY/q8RQmDcrIUy5vUiaZ1a0e96mQ/ADvpl6dR8okQyxsZq0dYahyJ25VxNKQ3nb9UgOG50A3wk7cj1PZge8hO5radymrrqB2xiLohi5awbvy8LHtlbY/pjcG+L5GdPeEPRJrAPxVy8dK1FPfMSFGBNCVbo8htTRpgiqjSurjNaemSpTF7zuyYPOqT6Nj1kOiEQDaboR5CtDBAi0waldbcMkF7GhPfOh/xlj5Vhl37utBR7RoYXcD8W4pCrli7mr13i/CkT4jlny+NviecEe3btkNpcNsEUWAcO5s48WMRRoje9gWfc+/iuTnLFvy7V50q0bxx/ShruGfUreGeXPffu18coEl6KbxQLDFMzGypLsxzDYrooTQJsqeAxB+Rzoade+hoYMsGvHPWooUxbvG3UZ/xbuZ3NxScfHRfjsU7PPnIZ9Dq8MED08Fz7lG9ohsKSzOix/IZjTmj69JMGoMoAqYry8fkXTSXdC/P1YBmsMnnSkbGAvDovXcHxtBpM+Ow+tXikJ7HRNvWOGOhJ6P/bgPzmElxxVdxQbf8psJs0VsVXulXg6a07jqRpIWps2bHC88+n07Hk/brGEf07gMfblXwKX7XuJMmVTBcV3EmnSZ0xFBz/sozeY/fyzNUjHSKboCXjoHX/OPNAdG9RdM48NDDoz14o/xQ7kin0qY8QLqSTwoTx2VWkutiH8oVvytwUiV0S26P+3wpRxijiK5dOD+69OqdhbyaU3irNvCV/zquLJTG2uZa0E7uF6VPncYaICqqKnRe0oU82AiZDm4LEw5+561osndLsqYOwQneihoNTXJNlTGFTC0in0aDVJ7TgWK/+YKeoSmVH9dC+NmHR2fOMPo6fhz7hFdE35NPjH33aZ9jkhbli65tqQ2fsY00hKHbdABIq6yN/Fr5oZPa7QHOe978efEpToxxn4yPOo3rxXFkZbVv25bic/USDhpGmzaw7YZnM3KLbJXHiozJ4zX6gJU445VrS7veq87hOeufvDMgxmCPn3jwgdEL+eFxfAiCXDN5bq4bOG+bjp9BJ66n8svYhbn1oqxxo8zU6NLY+4LtASNGjoyXhnyUUeurT/sRe6c9qtAjgAv5pmxy7L7Ec+Wy/Es+pMwT36WvlE/SNldJlzBjYsTwYTENuHeuVyt69DsB2LRByYaeWPsCBoybGQjnlG3MK+cDfqqcOw7pVaO4Nk4tjW/p/CuipBOmTIsXX3olt3Md2atH9OjZK4vmFsdb4kSmDS/by/aBcdImNOB30qs4pK5TyEZ0KHDJbNeVq1bHJwQ4PhoxKvYvbx4HditqktRjS8wGZJfPyTd0SNlP4h1rYfvqLY5RR4b0I+6LMx5PrM60ZvWqmLdgQcL+K/jsoUf1ochi+yjDMPKkCenetRI/PXXC7Z3jRg+Ptes3xZJvC4OjfcNq0QxnVlndatGgBvjuCm7HiGW9nVziFn0JU3GpcOIAcsYqbgtP5TBsAbmmowEHDHDYjlW+GT12NRk0S79YQ/0VMs2AlatUt27laN22E2nddWPhwgXx5fJVcfLpZ1BcDkcAY6Xxgo6gG/lfZunRpvho9qhzN7vLtVf/ky/piJQ/qq/Jk+fMmRPjx4yKaZNncOJBS6LqPaMCPiMPUUZ6VLpzcI97NQIA8jT1fAsky/c14NTBbCt1bvGY2bpW68AZt/N8MGhoyu0LTzou+vTskY5ko+7is0a7J0/IP3JtoU0xUl4rzgDYHLsZOdYmqVG3DrAr+PuiJUs4UnRoDJw4NVpx68lnnpaR9TqMXbxynOKF47e9avB5jXKNSJ2W4rv3Jc7zOZ0wwM/7dTTMmjc/3nv95Zi2cUf0Li+LE04+leOA22bmkVtylB0MN3FZR4Y8wvUv6dzyK/Ex8R64KGfEe2nbOgcTpkxN+VdRr1rs37137IOxXobeIey9zLqz3oiQkMc7dvViBpj8WZ1DvPPoeOGtAa9uqf3hsd2fYPTOHjs+Dux+GG13iBZkZpmZkroC8EgaZMy2Z4RduncNdG6qX6aTgzlqRyCMterZSrw5bRCdpuPJcLXAcWccX9o3BiPFD3FNPub6Jf3znW3IHZIegLmwEf7yArNKfJdK5APz5s2NyQR+PobfnHAM2RJka9eHBtw2bMa2NkHJSZpjYy7aNuKg9CyuJuyBRfJV5pf1J7hvIU7YCYx79phxUQ0HT3dka9uKVsnrlE3f27AMVh5WyCeDe+CSOAVc1CXNpNTZq73pu7LQNZ05c1Z8POSdGLtycxzdqX0c1atXVDRvDo5jD0BPIowwSLkvPOhjC/zKzOMV1F4ajXPlKJwMZ51zdjTbuxxa0jEsBvgfa/UDv/4tjgYRNrEJDEsvTwJFxqHZsYO0+Ql5rMhnRMs3f7U+9m7YOPZq0DCLOm5a92UqlnrsqhMRdEHnz50dH7NfvYx2Wh7UKfZCKdLolkGZSl6Jxc/0QhcBgb8FIletMpoKhyEqQC4EY9IBoCBJxkK7Mlr3Ec3+ZHjMop5kW55ptn+HaNCkaR5llMjEfSKWCq5FVvRkrcPRYKXQPfHiWvHXZZeRpTeNe91rI9OV2DyTe8LIMZnq3alFWTRs1pyMC9LPaEtjWWazESVd5PboR9FpHUqExFkbprENItGxkek1KnoIL73FHqVkPYAlE4gaMc2u8Ld9uh2RzElmmsVXaCMFvYRCXwqejTC89cy5Ku3Uon3HKBEpfOxLJizjWU82xqrPF8XqeYsyfa+sTXk0xrnj77ZV3F/sgU1lh7ZNa0PEJtFU4zNqdaEsCT9grXDzpUNj+ZLPY/bEaTGZMXqddlDn2JuoumMU7kV6pdGQwkhniglzjTrXsiZjhFxzHApwx+yewMqssUdwzZs1IybPmh/Luaf33nvFwd17JDO0CNpGhONWjGo6KhRe4KCX9jsYV2ZS8L0nRzCUNB6FuXglM1HZ82jRmZ+NiwnLVqdBdky7iuiwb2eMu6Kg3Fa8yaaNuQ6u63cwbRUGlQwvz71XMTHirzIqXhoREDbi2lLg/un4KXmcayfuP7DfUQiesrx3w9cIdmEA/mlQynZsn0VMPMxUdvDDjAHTsL4lsrYHbVeV8UIzCqFpOvomzcmxHNm0VrTqRLGt+g2zRkqmRDL3FM4YIxmll2pxCijgrNK8Ei1o8fJlRCpWxbK1xokjOjSIaLdX/Wher3Y0qAntkt4oTRhZVxny3XHraBAKbq3Q0SBUtm5D4OBlkNmqfG2j/x2VWF0Uj+9gzN9hhK/d+C2ZJJxyMZ89lzz1BS+vXgd0zGODqgGDzcAGDEOQQ+e71nIzc9hIv+JRbb6XwRt1UUhIG4mT4LMG1SaE+dJ5s2Pi0jXRirYrulB0DyW1CrRaMiy8z5fp367ZRhwz66EpFS2VOWl1w9dfEu2qmv2pzCj013+zPlZ/uTxmTlmWzq+TDgbfm7dk3qIhWyKo02CdA89tX7BgIYruili2kf3UjKNDjUrRgtoj5U3qRwtedasT1dyK8bhFxZsIH/9lfRrmvJGsFAsE1q2DMgIPkk5VhDSM5cXbVCS4fwd97SRrZPeqNeKrzWSZLcSgnbkmacqtEyq53dmHuj94LX+bP3t2zBk/NubzuWfTBtG5yyEIciqAI9R1rO1kDVnKpBP3cVZCETIV1S1kVYCv2R3StUqolwpF8iQ+q2yuWDg/viGKUaUOWRvl7aMhxq5Ks4qOz6nMlvBdz794Ik2mAgDNSE8qFhqT9qGxoXHk+n6+cEF8Mn5SbgtpsVed2LsjTkEcAQWfAT7Ay0ujxPs3IVe+YV3FziriKbDTHZBOMvr6nqeCn/Y1H3qauXxNnuFe1qFtNIWPqSQmbMA/eazGYjqlwb/1mX3xXTqjiuybgt7SGcAaGX0XmK7tGvBhIoriUr7pUqNyNCOyuScV+HVyKDvcb8pEEg72s5X1/Yr2xdc9oXluKLb8oDyW8D2VXOZh8ciVSxdTc2ZpYEJH665dolGTvVBYMXjp+2vkyxb4mPy2Ooa6ckvYb9ZIlLcBF2GnEsdypMxz3sruL+ERS+bMxCGwJBbwW5+K5lGBoyHr6fCMSngquIxTZVF5qkG0mZd9SD81kBlG0zSWNHasXWHkWQVtKQ6SaWPGxlzabsTroC77xV7NyFgDr1TeNIws+KexIh6ZZWMdHjMN5bGIS3gSY4ZmSvKUW1MGM7VUolcRHFhCxlYtMrU6HHpkVkR3/63KuOm1aRDRXuIn7ci/pDEd+Kazuz4a5uobVsuXf8ojdGpO/nBwfAxovXo0rhL7HtoHfAV3d8FbXBHfnYtySdmahjlwM8thq4af4wdGUALwV79yuVl/HETL581iH/qmqN1k72iEzqEDBgRIGeJiOUfxRQNAfrslcUi9gR/4LK/WMFKnsfp+NeCnk0Nesmj+3Bg7eWbK1kPo8+B+x0QTtlFJcxtx7riejl+dSbltxtRW2hQuVfjOI1mVTzqAvdJx4Hz5vB4nz2SKFY/l83689sExWI5DVkeL20WNkvpZHJOmbOEb4KEOYHE+297KPeKv66Js1QATPh51uByn5uiZMwPKIDiwfzQiMGN6v1l06/ldWS8OW5PJ1PAlSxbyPScdoUI6l46NqpEm3ihaNagRTWtVjjqV0T2Qsc7UPnTgqbsQeGR8/JPg9BfWxgmKI8AHFEoetptHQ5PRsJW/N327neyJtZz2tTpWoo96u5ll9UDWDmz18vjTr9asibXAv0lFW44ab5QGl/fpAFV3EUeyaB/wFDYbcdSZSVaPOel81qkqz1IPkHdKF4ws1sJnls+ZgtwhCMU3rfbdJxqiB1uwTxxxe4OXjon8mwnJr3S0ItpxGpBFzH3SqgE35avOQWsoaGSrwy+ePCvbbrpfu9i7wuzVqhixRfF3cUV81PmKZKYNA4HqYBjXtFud1054WBr24pdGKTQmH1kHr5k1bESsZcL1GWP5IQdEs+YtcpzyYPUw2xZO8g4z+sR5caawEdSXdJxCy8BCGeJLp4H3LF26JCaN/jSLnzbHPmgEXHTa6UROPONZ11d+pt7/Ld9v4aX8dezCE4Uq79WIhwmm7NOp4vNzCNoMWroq9uG28qb1oklrToUwQxMeb7tuYTOjIwOIwH+Dawh8qsMTPc5bvUWeJn8uOdrFBecrPU0ZMSZW8PmA8r2i8d7l6IEENxir/Mkrgy3SPXM103ET31sfo648jgFYV0+el/Ia+tjJS71Q/XUtOuCCCVOzr+btWkFPZenYs91vNuJ05D7tnFKgcRt92L7U7prKucRF6U7ZKz9zzQ2+roT/Tpo8LeWHevBBR3ZLHV7bRv7iunqvMJL3SsMbWO9N4Nue0HQdxq8jXh6mw0E8r4EcE+9XkjWwcNTY1N+lgNYHwwuQfTqC1X2FjbIt+Zjjoj/5sHIqHdS0JX82c82T4gzCaXtUQc+RLlZ8sSwWTPo4xhKxKaf9Hj0Oz2w7t06AVOlsyOApn61h9S0w2MK7dXzW40x7c9SoeIAi8+eSydG0rCUtiJleanvFe779QP8BdkDv33F934pxWogabkTMlU8U4pg8gb35b8Q00rV0EuzTooL9vk0hGoAOoph2LgNQoOidNeVbb48RAQlEQ0HvvUzdInsqPVZ+NzVQpeezSZNi1NsDokPXw6JV27bRmL1tCg8VmTSQeZf1y6RkmgoQEanwppoxUEQSTBuSgBVYIrW/W7xm7KfjYuXsObE/qUIef+cxRjJgDYp8FmPMth2/hCDipTLL/MDSRFS3htieSK3nUWZtutFSUvbefebZ2AsvWPcjjqQeAsYhS5IwZByOR8JUOJaqqm+G8UhQiYC0Y1sldPR+mZSKunPXoz920OA4jD3WnTq0zzG45CrtjjVfwIMm8iV8JMxsQ2Gz6x7fvXzWubkebhWZRAGXBTNmsteMPa2tWu0SSgVDzojBLoaiMBTmOTfmY0q9BGhftpnGIN/ruZXorRi9nEyC2UQZ9izbO7oC+0bAXcEm4adDwmd5RobJgiXzsS0jC+KNsPCsbMWCgsTtKHqvv17/dSyYPy8WIQzXr1se7Q7grPqOHCG3C5/s3/Q+Z+y6pdFAe3YjA1OQmE3gOFyb3H/FdzpXFDzL2aM4ZfKk+GLBrKjZcK/odFCXqKioSMYq3GxX/FIp9pVMdBfDEy65VYLf9dRm3QH6lnFn0Th+nz9/QUwa9VEK6RZt2kWbdu3yvHqNys3gpEqI+C79qIhk2jzjzPkzbgubuZe8YN5uHxHnYYb0oed62swZMX/evGjXeV9S4inuhBBYvnplLGY9Zi1czOgj9sXR0L7pntGS1P7GtavhCCpwBqgwLuDAuzBkodNwM0q9O2kO0ta3ZiMxVlMvd8rMgcF2NDWdDUbd0ShizQbGwRgmzS/g5V5Pr9Nxwhyw775RH8Gvc2g3BYF4yuKoJFh1eQSRyb077Rv7k7ptRJTbgEPh9Er8Zizivc+o/Iqn4p04pde/pKwkr/B31lwaU9DOwHP90ZCh0feMM6Nj+/aJE86zFkpdDXBF551ZPW5jUUBrMCjMquGV3535SbvfInwqazgCi2UIwFlzZgHbJTEVY4puogN1C8ob1o5W7LduiTOnXk2UhK0YUjgaNAjEHaMkzmHjBop2Qe91av9XR0ORnSOf2wq/3MZ9Om90NFQim2LdN6Si42gYO4Pigtyzmpf9HksU9xDSFRvhhCrwk+9dP0ClkekRolmVHhyvjpKjM0PYrV69JubPmZOGpvDrfHDXLBank07ncOI4MEjexPiFq3xe2peviBMaZ8lj+M4oQSrG9KOy/gVRmplkw63zGDmOUN2nbftoAU+oA0/UwFaxdSuFyqv4rmJnW46Px5NPywuT1zEODSM/q4z6rGs67q23Meo7pvxoVlZGVku9bFvIyEvFAWWNl2nx4pNtyieNstpPOi15d37yY7rJaPkE5N4Xs6dE50N7kGbKccQolY5zA4aOyq4KeBpSOXYVeo0InJ/MR8eVSn/yOcaceLurPz8vX7kqRhHNljd0IeXctlXebF/4eo88UUNAGpf/yRuVQ+JqyjrGmlE9DLnv+TF4uhG6X4j8mIj86NS7d3QiwqRcTQch8HD+ZmhokKZSyrjkO3SXPNjxiw/KSS/bls4ck4rlCvjkNGT3F7PmxAkXXZjbAIWhGSDfG460ndE8YSFAuYSJ404FkXE4P+8xkCA+yGN12OicmDPuk6jGfuKuPdnzzhbDlGvMXV5hJNuIl3wwgwaspTSpbExZzT3KD+V54hZj0dlolHXu3Dkxb8bU2LBicezb9ejc+698/haHdjpioItS1NZ5K/fEk3QACnt4ieMWSVyrdLIxPfHMPe7Tp06JlYvnRK0GTTC+KWBYXpHj9EhTiVYZZ7RT2ZQwoT/hmgEH4UTTjsPMRfsR53ObC/csWEg205DBOacWyL0KdSZgIw9wrtKi8HVt1WvkX8LEdpR5/JiOA9vWaaPjQPzVqWJ2zUSikqPfHBAVnTtFa3ikAQW3/riu4oxjdq2EC4POfl0P15NO+B25QB/e7/e2q/EiL1qD4T3u49GxesbMOBCdpjnBkDoEbJTv4oXw07gWAMJUR5TOruK3wkFh9Nn1kPf7WQPTY89nIvdmTP0MeYfTQYuf64CymtES2JTXrRpNa+wWdfdAvlUqHJ2OTR3TPmUAu2OAyE8d+391MmRAA7DxlIIIuQc/RzfevG1nfE4kd8JnK2IJrEV3ow71TnvWiKNPPC3atmrN2PiWfnZKq2TKMZPCOZRLLD8QZ8xi2pCwmTVjBo6ntdHjmL7Ujdk7f3OMylrxQ71RWGjkZfYY78LKbTDOQ1p2zb3kOc5Fnr+ctPklny+KpQvnRVMM4y4pJzi6HHyRVzlG6cjn7UddXZ6T/ITfkyaAgXxGOhCXEl/B4y+p2yA9LSJY9O3Gr6Pj4UdGp04dMwovz1LmuRXCGiaul+uqruIchLv4ql6YuAuOqIepn4tj4vTiJUti5IgP2Sr6VbRpz9aZilaxF45tx5Z6EO9mjxaOPvgt/bi2ZjArw8yyyowi5pMZNcxBHu9LvjFz9uwY8yZZYq0rojXZCc2aIz84CloZYf8FbHEU0J68XngpC2ks4aA+rRxQZtivurBOTbPkXJNRY8bESGou9O53TLQsL0/jWX2wuIDPrnE5X9v0ZIPU/4CTclyngdk+Dia33DA/HS3Ofw2OrjFsaTZrpSs1qtxK4JidW64dKOuYQLr8zj6l09QZaUe+qI7s+mdtB5/lu8IpsyUWwIOHwQvaH3YI2zgOST5Qkh8ZhGB8wkkHoSf4GNg1a5RlLfCcvzObl3dx0e/FHXmD9sfcuXNj/tTJ0e24E6Jdm9Y5RuGbQQnh62y4n8fzck7CJNfa7xM2Wls6bpkLvMC5edTo8iVLY+G06RSFrx6H9DiCbNeyoj4D9p5rZBBOfq68laclvBwv/XsCHYgJ7oKzfDarwTUVZz2yXbioY2/EObwRJ+c07MA/PnhfnElGQ5Ome8NHlGuKB3ULFuEHfv3PHQ27FjDhAIBFhvwPzbwKnlvQMmZMnxrDPvgglixYSOSiehyG4GzfmjQwgP8dTERHg0JEweZCeFRf6bxbBbGKvwqhOKMRBkvAU44Hjoitwm7EmNEx4sOh7GvbNxlUy4qKVBR9RgYkI0pmyGgSAUE2kVuFS+RwzBK0BpqKiMwtlXmQfw0G73D2/6+AWR0Ic23LuJuQUplClbaz+CRzTQYooTO2TG/d1Z7YIhOTARsB9zdTWGUuIpEVqQdQIbaMtLWj+/TO4llZYIrJyjhtX2Veg8+UwO/oy/PSdVYIF48lVIFwHsLHOajY2b6/T0Mgj/vk4zhslxFhZoUCIBkl4/IhCVf4+K7yrdIo7EpKgUaB96eg4j5/s8LtypUrs6bD4oUL40ccebVfp04pvFQWhbvCxbZ8Pvfpwhwdp/OxQrUKoUpWCiaYo+ug0uL3buVYDDHOpHBcPRwMfVB03SYi01ZQqliCGlmvA+RJAeYYZbQy3u9hZ5v0r/fXd9d/BQUSJ6EMzZg2NTNQDjr0sOjWFSaLcaGTQUUljwJiHAAms1GyaCbwSkFdYk6J+wp7YE+7etwtkLSA9LKPSdNaumwJEfK6cRB7uDt17JhMzLadJ/8kA5UJ1WD9VGwVekb0XEMFgnOQyVcnwqSQkhG7thMmT4kR7Dv1mKv27TtgUB/A8XotoDRPWihw3mcdXqHcFnSlo0FGLQxTGaNt8TWFPnMz/V1Hw6ekhs9C8LfHsPBUks/x1s5ki8GMOXPYr/spz6AM4RZu16xyVDRsEHtRS6CWjgbG7bzMXnBNjHwjcjLDQUeDnSuM3GPvlgmLDPkC6dLJsBXtbNvuZDTsUS1Wkb46iX11Iz+nUjb9sYU1r1uuuYrCgUdEBdHMusCNABN1XooicMJqzvz5pFV+EM1JF+zKlitTBp1lFvoE1zJ1nbVSmBTKj7RbbHkRp6R/YZOGBfiU97Eu0pPKxgQMo4/HfJJ7Kw+iroACxqhaHXiFWTeenb6ZPbxb2Mu7HVotjD6Ut924D85lJkcezYpjZT3tzZ43J8aOH0vdglkx6JNPco6t+LeMSbdq1oBjPOtRBwM+ukOnjeMqeBOEBagLXiBO1rA+BDB2nOKONKWDrchoMNyGAN2djAGcOOu+2RqzP18QE+YmywijH16Xs+/9mN59qN9BFATl3iKkLqXq0m60uWN7sd3KbTk6Gkwd1NG6jLT56Wz/WDRvQfZ/wAEHxkEUXatdg33Q4LN8LvcVC3NgKf0WeImQZh7biRJoXO9GxosOSZV019I9uSolCxYvis/I5DJqXpdMkE5EyluXV+TeTduWZjVwxGP5i4qofEV8k6/JB82q8jvXWNh4qZhZRMuj3sahzDWFv7Rr1y7KW5LN1bhRygfvE68zm442vGrp4ODZjABDbyrPKhFetl3wSruvlFXGR9P2YvDSYyrbtmmbabJmU3iSkDxWnudLxT+VNOCdpMk/JdmUNMpcc+y8e3mPSvQQeIGyzMJfFpAzJVVF1HGI+/LjkkLluFV2nY+p6eKLY9dJbuE027FhcX0tDqTpOHjGgZedibAeylFpe7JNMWW0MAeuwliZUTJaUg4lfFW4VD5VolWqC6VJI8Y5WS9i6VLOTp84IVZgxJzCccQWFvO3Uiq8tOj9jtdon2MXDsoPFT0NAi/79x5pURowyr5w4UJkx/QsRF0Hpb9fP47ExeAV3vbhHJyruCK9mJmk0SCO6LRxPZTp8oht9CMM/b4WgYAvWbeJ4OMUeIHZBPuTCdQdR3gTsjTFxdze6djoRxzQ+HctHKd9yptNf3ZtbDMNYtLDVbLdduX2lk/GjSXTbXHUYZ/+oVSO3w/dxpo9ue2SB21Hp4oOBOFQoivV0mI9gJXwR39I5wC9iXPizJTp02LIoEHJt9p26BAHwMcqKirSQHO9NEydd0lnEhb2l7jpGHHqmWlQ8E+ckMDetnVCaQBoGCmfWkKjHTgVq01bovHIcB3njrNELyU+YPsgdsp9+Yk07Trbvmtg9pYoJW91a+qwj4bHOhxJXcH3drStk6R0pLG6SdbqcL2gdbNG1D1c23XgnHPTKaE8UDdJwxeesYjMhw+HfRiD330tVrCdYd6XugwiDquoCx9uGmXVdo96u22JuruxPUN9Dx5W6EyFkZKOPMZc/AXuM2BfnkIiFmRRQ3k2fN86RIToMSV2x9GwhiKqy2IeonNP+lvO6/DyvePci6+Kgw44iL31nKoi7HcAF7ZsyIt3BybSmbCRsJybRtcXy7+IKexz/4pAylHHHJNbi6Q9nUGukby24IU4XXlWehE+0r86qWvvOurAKviOBvfODMx8vgSja9HCWLZ0KSe0NU98L3P7pXhCH6WAjyMSsX3eS/z3i+95M20rb/3bnxzTSqLkHq2u4aV+sy/6TJcuB2H0Es0A33S0etKcDn1lnbzNwJtrJ7zNovOYSnVlBVauK7xCuhJOc2j3o+HDkUWbo1Wr1qmLlbcoT5kkHiNS0VMIdtC+fFEjNHkisBBuwkX5okyS/+qglC9J0+L7BHRJ6wo0JPNlHwrVivc6NdMZwBwLmBf1QFJvVRfisj3nk/YJOqVtSxOup581+s3Afn/IkBiHDdKzT5904BvozK2KwFgcVN8FwjlXwSqvKsHb+WvL5MWPyStYM/vVLvmCrMJhjN0173nkkdR72yv7Ls2PXazoEd9jdTZja7Yjr5MnZQ0K+rFN19vZyWc3YVBPR4f74P1B0Ql9oOcR1NFAx6aDzIIRdsp616k6vMOsMXWKdC4Bc+FO1wU+AR/xzPbFSWl8DVsO3PK2YMGCOKJnz9gf+SGfEiaujXjmMzpxvHxWuDp+5WE6BLjHe4WRsBIOwn8ZsmnenLk4HqfCK6pSi6kvsrtNjm8r+or49g3b5DwiVVpSfibtQFPik5n3OwkoaavSesoAcd3xfUXwesbsWVnvYs3XX8Vq6HUdttSPLr0oTvrxKWSJNGcMxRbUws4pdAvn8EO9/n2OBoCrYGX1Ei0zowGgi6YK/JEgs2ls9WvWoXou+6FJjxNJkbpibXrYknGyaOJWaS+MBFQIKBQyBEc+Q5Qut06g7GJZxTCLDH4wKB0N+7FvSMFWHSJFE0zDS6+TVypcEJsIpaIhMYl4MsVkyCC2yOd3CnHTH9ficX73/YGxcNbsFGwWVmle1jx2gyg8Mmc7AgAsknYKxrkLYQuPHr/Rnv3ZHlhWwAhmlrDi+9kg86uvvooXtHkcDzLLoCzAYoOOKTMmeDeSsofzlaWk4lwweMnatmWMKu/5DISiMiYT/QwmOBxG1Ye2e6CwyDgVDhKXUREZk/MvCQYJDcDAdI3MckoFYxHu3CwI6R4FgO9VRHU0fDRqFGlyc9lbdE4cBOyZbDKIFEDOnT6SacmYnZftc20jMmP7yRB2zVUWUHLgeBTbnNmktaPQNWnaLE4+6SS2cjSPHQpExp/ReMZuSqZCP41919k+6FeD3RRCU7R0ALgNgonkPNYybhXcqcBmzZrV0bV7D/YQ9iRllqM4YewayBr9vnSCWTiyKilYucD0DSIlHFiK4jvmrOGHtZfzW7pwPsfdfBSLEMzWD+kGAz+A6LqCXQeL+74co15lK47b/tF+hNIAAEAASURBVO4ws/+Pu/cAsvO6DjQPcg6NDHQ30N1oZIIkCIAgEgkQYAIpUkySmDym5bU9XtmWZI9V3rW3xkGe4DCaWU+0ZcoKphgkMRMgARIZJHIgkRupu5EaOTfift/5+8GcmdqSpoTaKu1PPrx+7/3/DeeefM49V1qw6r44KN67hmlwMW6dDG1xNjjflWSRzH37LVLFusRNRO5vRTD3xVFlmwA1Ga3rWTwvSMB14C6jc8wK80z3bBm/fSicTTk9zfg+XLIk1mM4Tpx8B9Ei9oniONmAwbERpv4e6+01huGOKI+owSArL+vMthzT9gt4WRQLTp6RCJUiC8ulQsx8HJNRQ4thGelBymQ9CdQoUm6ZcxsUHxTLJiL1qzdviWX7ONmC/sgszeuPf//34v5Zs6IWXNDR0EbDHZwQL+Ufn4Izr1PkqnbEiJjJmvZk/E5TehcXE0+g1RTyjCPxnmcVdiq8qdSw/pkVA10psMU3X6ZRf4SjZdHCRTHn0cdj8u13pOBw60JHBKRKDpp0vi5fQ1AyPSNqFlW6xp5cl4dRwt+gWtb7NJ5sj7X8cNEHsZ73t5YuyzlW8+8QtM6agd1iSL+ebE3B+MbIt+J5exxZzjMzMXL8zA7816Hjq+gBSuIeukuHTubg4PS9jIJ7mayKo5w0sqMBpbSOoXKPyq3X155/Ph7meLlh1TWp3OtoUOlqJX/GABD3VKXlT61dU97tp4FaGutWr4m6rVtTAbEGyCSM0g7g52WUDYVt1rQQAMCqgDngYi4AJxUB0291sOpccNw6SaRZt75t28kJHktIlUWx6N+3X0y45bYYWTssZYGRb9coZ867l3xGnkO3SQsqPRoY8pr8nnvk8RocRsqW4xRcBI8cjCE6GodgVXV19KceRfI824YHFAo1vIY2Vc797bLKrbyIvmw7lWfalt9zW95ztKkp5iP7LLA7HpiMxElSUV6etNzMFjr5u3hnGz6XHbTMA+AmTWu4lowyv3Ps3icua5S+/sYbib+z7sYZiwNOuInPiQMam8zdsfm54Ps40GhT/qu8cLDyfZ2dboFhIHGNrXrC2+i0RbQmYkjfORVFkeixCpxt+nzyDsZhJCj7ZNp+Jw9TYSxdxRoVCryKolXtD6DkrsCJoYHxpWeexZE/jgUr5FYqgo6ZeUq3aei6plwqksLNsQvnknzNtYUnKPd379qVx2qvXrkyehAZfJxid0PA68vKbJ6VH6bscR1Zw3S60K51kzT+xdGcB2NNXsFY2jDHTt265p5cHV8b169L/WDcuPE4P+8ktZ2aQOCT8kEW6Lt6QG5PcOzCVvkkPqIoyy/aoWMQ7nJa+Vn62sl+4iXLkKu7dqZjY9pdMzLTSIPzDEqq7WqMK/MLGa7BUijOLHTCQ+eGcxBGJfi0QYfw9w1r18XrP/5RytCbMADEy/KamsQDxyauFTAuaFXYp16Q8gN+CwzTUQ3wHYd4JezbIz+u8tsC+OOCd9/Jegg3UwB3+Ijh0aM3W5fAl4zWAnOwN9fQ9dURIM5ZGNzf/S5fgERcc47FOlM0DkfDGxTDPli/L2bNnh0jh5PNB62qjyVvYfwGYrxKDgV5jPzL7RHSq3DXSNU5lvvE4WV79uzNavevvfL9aDxGUe8j8HGuSUO6wIcHRQX1Gfq0uRjdW7EtBvjI8zVszdLT8NRY1EDRcFKWmn2TfEz6AxfcXus4LnHDJay3Nh3R5dhGse8wEeUVe2MrtlAf+jtkn1VD4je/+vtxx8RJ6Whwi0g6Gji+qY1RVO4RJsIMAIG/OFFweNU3NrAFc3We3PYIgR+DG+K2ji3pU/nv/KUdL/Ff40gYaxhJ0/Iiv/c58Ud98TTydeeuutiKcaQDzxoXdxP4qSyvKO6ThzhHaQa+4pX6GetoGzq6pH/ble6Ev0ap79araMJpZNbXNnikRQ1vJTNrsrqHRqnOBfivNdnynec04HO9eV6ZZEFHXzp1lCmZ5o/+YTajuLV1+/aYiw5/Bgfh0KG1OHDG5akpYpXp/mKjWwfU99S/pFcj0OJ0Bv1Y79RZ6JvZJC+S16nLXkHPtIDhe+CkEW+PkqwZWhSUlm+VaFB6EofVR9XHMstGePFKuwQY0W3CKQ1sPnQlkHoWnePtd+fGxzgaZt17bx5LLL53xDD3lC/1KenPh5PHAmffvfze//zse8oX5wI8XRPHUd/QEG9wKov84gGKvFrUWLlSOOB4ShjTPCMt1oz5Zzs87xoKpzwC0y5pNydBu/KRC/C4DTiQ3iSQeuvEiQQxZkYPHKfiisa/9FLQYmEbqKO5pRnGdl3uJRFxv/zNzAzxyktdzG2na+HB29A77pnzQPJI10z4SYPyEP+WB4p7vhJPpd8WvNR5Kg/z8v7WynbwoH7vPrIoN8f6NWsh047x8CMPZ3aWxHf1KmMH9h6NWuBdgRMZTEZnEq/S0UA2Qzsci+KX8JZfg5BxAdm3ibaFzUGCJ4eOH01H4ewnHosHOPa4X2UF4AM2jCtxhTZ+0a8b42go8DoXMSVsSlm+BEDXrl4kqvBJLPngg9jP4vWiiu0UBPPo2mHsm8SY1oPIorN6/I9xIpGkAgqRghWl6DTUkkqAyq/77X3OtEedEIvIaFiIMjR0+LAYTVRdR4MppBZU0fgpiqYUnqqMaEh4EI+IJ1FJ7Co/BTIoOPCEo2y0R1E+SmqqZwJvRCGaPmMmht2YLK6iQWz7Ohqspi8xmw4pEZpupYesFJkWHMmEU0EonBgikC8LAf7wpZeiN17K++6ZnQquxokEI4HLSBQMCgO/08BwDgo3PYnua7sIE5ZYFJxeEpbj0ZO6EoXoLQyvzz3+GAUSp2UbRoxkII6V23gVc/fZktKbAglh4z0ynYJAuZnL9s06OHr0WCxatiR2sK3kqWeeIaV9bCFw+S2ZHM/6vIqaAsTnjMzruHBOMoHrTBF42bcRaLNUVBQ3Yex+tGxZDAZXvvTkk9TqqIxmFK3zRoOApeTn0YYaWEZK7FPFQbidUwkG8KbYybj06vtuJMMqsZ6CYTr24UMHY9KUaTEDZ0AP+j5JGp9ZBu4d9N1z69uCx7mXEEZq4T9hIVMEwZkbqCsy0XdHI50oPLt270LZ+pDtGTuiO+s6HQaro8E1OoX3UmGcrI21sw+dZ6aGmmZlhFGlTlgU6Z2FIJUW3P/oHFbC/OYiHHqR4TIax9fIESMoyDMgFQrxPT2rPO/fGlgp9GFyqTCD6wXMWX/GIyGkEGSdFGwnEQ7vvv9eHn91P0zPfaFGlDWIdTQsIDvG62bAqqNhKOn95b04dQBHg8qnwhq2mvR8DYasQqSyIojEGeek4X2FL4y2G90xQn4RWJwi0n61PZ5t5nWMQomrSWVduOtq7pU8mb0G54l/jaNFZ0UV0ZTOPOvJNRdQhBSURjw/Qej88MUXYyxRuvtQRDWMpG0H4PyL6EGxt1UFWlqSToSRMC8pBgpQf1doqAhIy6ep9bGcQn0L4WWPPfUshRPvyKiIUftW0H4rnAGgAXgIjfB+pQ0wliaB7UVqAFxifVszZnGyC+OyENBatti8M+8dHA2b4v1V63KWw3h2WMK2H/VsukbPjghP6OcqiqaOBoYUF6AfjXHHLK+5iHf9GlkPbclakE9k9JGBCG+x7RrzuQQeX0TJPXLmYuxAYd+0g3bocX8LbL/+K8/Ho1TbHzq4in7gQeCMCrVpu5eZo3NzO46ZGueYbxv+7orhdRAeuQaDbufmLWnATpk8NSajLOpAO9VCT9J8GlyJGoXSUoo8qbCb5WIHOi+s9aCTsAtZXPan82jue3PjOOmMVVXVMf2OqTEaR7VKoVushIERhVxjeTlwSRwHLl7yg3wHFn6vAazjy+r08iRPHJjHqTI1tbW5hcoq2kZgvfxdhcpnUilhTW3WdjJSwrv0qhKRCqP38p33aOiYETcP2bR108aYgqE+cviI5PHWl9BQt53MNGI80qH0L82q9JiOLswKI1JlkcuGWVeNBvFzJ07Al199JWXW/SiKnp6kU9J9uLYnj9VocHzCyf6UU8q+jLjRZIkmnKfGbEfWymcs/Gb21FtsRboTWrobHmlml1lJysqkFf6WvgpjlOdYS3mxsPDKLSEaYtxnm/JoL+nO2gVLiH6vwzh66rlfypNHHK8ptfIMcSbb47lcB8auPFEWyoSdiy/HYdvCRHqX/Rh53QovWLZ0aXRDuX2aY+QGV1dHMwqqsiiV2hbadiye6iDd9MZZoDGkI0RF2AyoVFqBl9E2C9MdzeJfW1LBda/72DE3x/QpU6MP0WfrAsjThaHvOsKU//J3AwK2pWy1P3HdbDXx3QiZDvGOXTuHxYeXwWd37NyedSamTp+eqepdUHgvnLEWBfTofMEF4V7QuzJanCzwVaXce4SNekIX+mmPvsRiZdHDH6FzdOW7W8bdRrbKzVk53vkaKXT+qWsoo2lDnEs9ALxz9VJuO0cdWvAJ4el3ZpiaUbdg8eL4AMNrLAbdzRTarq6uyeisTgTnnjoB4zIr0/WzDof4qF4i7pQcS9YlEd3NHPKPXjjtD8MDXubM+T1k2D0IrxpWMzQLuqlLuWUtDVjhAu1kNXnaP4vTTLzLY+zAz3Qq8a686Nq9R3Tt1TOza9546+348asvUoeIGgBNRUGNcf0jKvsNiGFlnaKC85y7um0COF2BJ3q6UCeCC8LWdG+LsGqYmjlZ1GWAj7EOvnQ06Gy9QJDB0yXad+0F/+wce3E0LF2+K7bA/kqOhtFExv/F//Evk4d269IN/QOxj57ZynOiWRMWNeWquORL2B+DlhrAeTMpj/L3lwj8jB09OnmMDhavXEPHAXx0NEq3iZPQU+qntOv6ijtmrbgmfXHsyp+3Au8N8LBt8GIzGmbffTfb13A00HeOB9ywjdwqR/udwTn7cz1zTewfXcA2dcCo/zl29cHDBHt0NKiPCcNxbIGegqPBAurWHGjFFpMO0DYjTxxXH9UB7RZrt8zB3JLnKVelKVPRrZXSA3xRr95RVwcPeysOQ1eDq6ridhw4nmInfVoDqwM4bFZpHn/OeM2+U/Zl1J6526b96bDRsBRv3JqhDSCvXk720dvo2NU1NeD7zVFVXZ39yt+lB3mZdCp+uqWkRLMl/uV6+MrpARv5l+vgcx55bEbDOuyPmbNmpfPIDB7lncE++1ffE67SlnzXdtXt/A4OmWtg8ylP/M515gsN4331DfFDjm23rt0jDz8clcgPe5desh3knfbNVRxc8kkHpyxRXyoZ6Y5VvU/9T34pv7EP6XsjevbrHId5G9ml9949syiAC+6lHADO2Q9j8V63qTlmZX7WkRAvEd7X0A1pLvsW7vImM9AsfLlqzeosIPkw9s2k8RPAuaIgasKTJ4RPyjXWt7QtwnV17An3Fn1E57NXd2txAL+9yFWzqXU0tAW+DwMbj/Q12/4SsGgFXNQrUlcBR4S9eq9bZNNpJZ8Dv9zi35qxJ10AN2t9WFPO08Y2od8eAPd1Npw4djxmP/lY3P/gnBiEo5GmEq8LTBDCv9jXjXc0sEgtuUhABoWAFOKNREUW4JXTC92/rE9Mn3B7jBpaCwMhqgKx5FF9IIiLJVNLjRaE9V14y5T8Q4HUCQGut+gKirupnh6duBih/OEHpOpB6KNgrrW1nJYAgxF5ZYR6NlNw0n4yRtq0XTDKfwsi510FUURVeEq8HRGcx4lIvfSjV2M1Jz3oNdPR4L5Ai+ZoNKq0tYcQW6FNZBqqhAKDEDWSyUIhKgQFqvAvfWjYZx/ct2P37ngZQncP4+yZd6fBqLGsIpQKFIqDY5YgnUM6HpiHyoCCWKHQjKOhjQYMRGhfzsN+VNhWoMT9CMPr8WeejllEXWT+KhMMOImtBIckOhWJHGKLEgeh+L0w8bJZ21eZ0TjzyE3TJHUKuLdIR4NjVqHI+/nH52V+pkk5j5IRnEyW9vOd1fBdxVLlXzywzU9IW1q6aFEMhsCf5nzZgcD9LELUomYOph3j7QwcWtGn2RteHjXKxFIgyAQ8H1mhL1NQYJiarbKycdMG0vU2xWGY1bQZd8esGTOy5seJo1Rzob2M5jI+C3EpkPKEB8er0iAgZCvMzZTIwpgjoQGjy0JaO/fsjg8+WJB1ILr37hV3IpQ9v1somuLo2DWMWah86WiQcYs/KnbJyBmrCrV9mIGgo6QHHn5rQHzMmr7xox+FVZZNxzZqVFFezpoUlYcVZDqiErb05RqI8K6b36mQiwc62ZyJRoB9udf1FMzeiNFC4P4YqfT92ctYx3zWA6sNmzfH/I+W80TELTj0R1a0IqOhT2Y0dOF8y0vgoeNXyGSHwpx2VQyAXK6rv2SEn++N9KRqw9+XECbncTBda4dC3LU7UfeLsW7H7vhwd3PgA88iPrzFH3/9d2IOAncwKX6Y2BAZPIQsFBVNFbxNW7bEd154IT3oD957X26HQaXiSegCGKosSJfSl/c73jQogL9wKzznhQfcbB4VMddLpUHDyOjFssVL4vNf/FJMwUuvwLrIVok2ZDC0wbEibUt/V8C5K7RplXHhbOQ8nTDcr4e8B3hhwbQ1OBreevetzGh4f80mpxjDmNaISpw4RC4qeneJnh3ADejtGvTsyRLisk4KnRkKZdfXtOJrOj0RbApr4c4PiWKsuAtROBpQ144A262NB2PddoQm/V13NDz/y/EYyrtHnyogFZjSlpHGcxidGr0eJcwsKcKEYQaP7A69Glkwcrxj85YU6lOnTCMad3vWoDmBM1JDy5NBfF78k0co/FXKxQ0NYlMQxUVpVVyXdlUUNcR0cP0Y4+I0UbuR7IGddscUjsZF6LMm0qu4buTKtjVM5ZH2oXIoLLzEv+JdfC8yEzxdxLVagjPzbZSh4SNHsvVuTBpdvfyNdrzXtgB14ksq9sKel3iRxp7zkJacF680spmHqeQapW/NnRsb16xKRXEUDhKdGKaD23bSozwq8dNni5frl85Ohp08iPc0CoQhY3EczruOSOyLL7+Eot0pHiQbZSBp3kYJnXvCA8XWlGMV59xix/OpjDKh5AvgE5Moxs2crHvCNnTWDAc2jjOPU37xlR/GXXPuj3tmwCM7d8UhCg/EYMotAozXqzBOVXZ1NEDHfCdtKLNcg4SL/IfvE060r7K4BCfPx8D/iziqJ+IcFObSaElOSJ8quCqM8i8XIp3utCkMnIPPaKTq/MgaFXx3gGyJHdu3x9IlS9Kp98xTT+f55OcwXDTy07nDfSqYOmSVWY5VfBYnNU7tV9knntqXToa2nTqkIVdyVB8j43EcGTZ3TZsWnvcuvreGl7m+1pIqioGJP4WxLr6In0bEci85eHsRurXIXnuMVrdm1B/A0QBcttftSOfD5KnTYvy4W7P6uo4Gsw3lJeJIyWhRZiSMW/BVGKfTgXcNDOHplh/h79GGryE/3IN+E4aRwZly5Idyxnl7rLfBFuWwMBCPvK4bFszBvnVcCX+L18lDxWvXyaMNFyBDboM/jkU+6bjTAeRv4ryXfNbtTNKfmTTiyPU1bukrM2Ro33GJz73gmcruf8Sxtnvblvj8I5+P2mr2rDM3M+d0+EqThfzDsKDwrdtexDPH29MjeDES3H5yRV4Jf+8CvDsjV82UeJ198D/GYGygMO+2ppNZM2EMiU3lfcpieO+uUdmNLZgyM3jtFZwMl6APnYmdWTeLxp21f9cEPEl6hodlJJw1MIKto8HiredF444cGc72iX1HThA42Bt7UF+IpWZGwxCyfv/83/xb5Muk1EOM2reFJ7YCVzUIrcnjfJIHQOvSs3pFA3NQH2s6eiSexXF385jRqQNrkLqCaXyC2z7rmkovckanlLzAT3xnnSgNI2Vk7z6904myfScnAuBo2MB2ocqa6vjcgw+lo8Ggj074dEozT3mNvFCdW3wRh5LXt+CMbYr/6qaO3yBgE4aWGaabMUp18E2cNjWPnNeQNyOsNbxGJ5v6nnjtqOU6l1jD3M+P810ng446dTyNe7cmud24G46kHXU7OeXh7WhiH/zgqqqYgEE6YtiIlEc6Bh27DlbhADBT10s6BVfFSYvKms3k9gkd4uJX0hP83bksxQnwGo672uHD2YZ0azoc3PbhGDzBQdmkUV/gvwYp/wEPL9fiurzms+siHXulowGHyXsENtahd0wnQ9PAklnPJUeDsHc8tpFrmuvawpOBNV+mvmN7GtryOPv23WL3e7DJvvP978d5bIIvPPEEp/tVsC7eA3zhUdo16kFKnSvgnX3k8/RnG2iRScvyUcfifHS8Owd1Jg3qt3Hi3zx+fMzCUe1JKtK8TifbSR2Xscm/rQXiJd0rT3Q26GgQHDzC/QRxgaXz1Qnu8bUfraJI++o18cTTT8VkakDYps6nhAVjdkz+LT4KT2WTPM37pAfX2ZXwPnqBR5KVRYf72La2GUfD2pWrogN24BPAxlOOzqL3WDdNp7NyU71CfUVdxEGKs+KT+h6GQeo9bqUwy6o1gZ6eOL09hW/ztm3oNZ/GfrZx7z/SRK2443HvF5/gNKUHY2B1lZNNXsKg/n9x3RhHg6BIIpV5wUXVcSF+sIgfKAaJV+jNH/84Dtc3xJDyiphpRApHwyUMhLMwSI06MwhEggswcA03K3tqFMqYVDxkViqrFh65xt4XF7HIaGiORRg/Czj3fXANjgYIsWbo0OvHVabnljEVTLUgbAnESyYoEmqkiPAlwex3ImV3ohsn8G7/EMG2gRMz7n/ooRg1clTWaJCYrJ5+iT2VVinPF+1kShtti7gZsQIw3itRy5xEcp8TvRX8u8jy+AnCzUiRUfWKco65Adm9NOaLMZrCxgkIGtFcKkYKbIWbssJdQKnoYdRIPBKyBo/Pf4RR+uoPf0gE9ktx92ccDTouZE6OIxlHwgChzjPu4fe3ElOyTxUZBYfPOSaFtfu7PsLA8P1RPIpWrJcJJJOBKReFqAqmZxslxuK7RJnETrsSaxIqY+mB4Hdu7pEyIrUCJbR/RXk8ybm3leXl7NM+ngpFwhSHUAfGpCe6OI6pcDTIBMyEuYAiYM0BNPQUOqYUm9Gwn60T7oFdC5M6huIye86Dcc/ds9LbagRWxT4ZqHBGcCp8FJ7i6XVvLXDSKGrFfDWK3HdfqqVgoZeFixZSrbsu99jm/jEUOufrvmzfxbl0WsDUVLT01KpU6xzLOg20Jw6qyOR6AjJPKRH+y4H5y9/7XhoFt026Pe68866oAvcvwwBtX4eDyof4YhvitXRQgrlr6RikCR1VCkCVaff+naF/DSONr0fYN90PY1dHwzqUjPUoAu99tMyljFvT0dCGGg29YkAPlFgcDfahs8T11xjWKUJHSc+qB7KI0gsES+WRUYHP0icwZm5XSe2/1Apn0Jnm+GTvoVixrzm3ThyzU64/+53/PR6cdTdHPw4Q6zGEoWPWSCdVydHwwt9/O/d5pqOBzKYci3gCzom7KsXOXVimoOE38VZDTtzLKCTj1jvv98LK/YZNKD2ryRBat3YNx1XOyQisY77C3tnunLzRHoeA1eHPwNcQZfAw2sTDLR2Jo6bvCm95TVfWUu/4Ogy5efPfi/UInbmrCkdDLbAzo6GmX8+o6NUtepHR0IpxtwIfhaD/XYK1mhUCYgridIYp9vxfmi5dwju/4SazRy4zpiOcOvHJvgOxdCepffx6IO+J+J3nno1H5zyQGQ0auZ1I7dUJZqr0CYpGAbQsSOvanVdRh2cbkToAPa1BEarbui3hOmny5IwuyK89hSdpBzwW/3TyyJu8hCuaicNz2CiP4CP3aGy7ZgplU0Q34uR6CR52HoPhJpxq44mUDquuyXZLfMM2xGNfOgFsT3rXGErnEvDLNUZpkn/qdLLmjbi6GOX83ddfS0eDqa+DqXfi/ljpRnzxHscqLWnwqOBmITn6KE59wXHIgJM/Mg4VDyMeKro6Gl5/+x0UluVxD44vHQ3WgjHiJ73rGDFbwvlqjEgz6SDxb3k5su4CuGf7XTVe+d2xO1/xqshoeJVodddMfbUQskfTpnKsooYjLtOBeV7ntie0yFM0to8eP8a9RB+7U5tB5U5cFndPnUlaNA15DbD/zkvfizvunZ2Ohl7deqL4cx8V8z0yWKelvLhk9CevAW6OzZfwc57+rkxx7DojxAWjl25bWfXRx/EYGWsT2AImnDPa6fzlB8A++QkLKt64vtJxid5dUz+r0NuXwQjvP6ijAeNoxbLl6Wj44he+EEOHVOV+c/mr8jidXIxVmk+cox37V4YXiju8CWXb9gAIWynhc2U9WFOMQ2SH24VOYiTdMWlKzJpBWjB9Hzl4OLfaePZ7RrMSsZkD/SRS0r7zsD/llAaMTjaL+LbDiSG+721swGBcRsGwXTivu4X0dCtZB0Zez51EGact5ZNj9OUlDNQ5nItOa41oUISxF4aLv+v4EkdN2X1/3tzMstA4GjmK7aAVFTk+YV8o+UWEVJj6rG2VjCLxyL8z/RneqyHrunrMnTS8YOHCmI+jYTy1iW4aPQbn16AWuS6spSeBIqw1NooxCvcSr3UOJdqQV3ZgrczSMYjQAK956dVXKUpYF4+xPaCW6F8n+FA7cRcY6iBLhxy1fgAu9KVfwOMoVfqRe8L+ipln0Cz47Zq2Y612YaS/veBDnA3v4ehpiO1NRzKTbjR8uAI9sJoCjQO7cMwiAqcV8gOmj1zGmUm/0qwczbpDOTOBVfzlH0mrylxpAElL/R4iuFeJyrK3ruHoKU6BaYoD7IixjYO8yjt0iW/9+2/FtNsngUM45Bk/7CQjpEUhZ6QL/WqIaTyqH2igN2J4LYOXHcJ4efrZZ8loGJO1pjR4C5gUwQyHJKzFhdR5lUl85xoU/3mHa44M5D4jzXsbiqPVP6b9WnikGaZuFTiFY03asy0QJeW//F5e6Xc6lEprLa4mTwAHLKjqNoLuGHFH4EPr1q/PLdbqxndgUN85fTrOuk5Z1LUVbXQFB1zDEo4XPNYsRubPWP1sUCa3yvJd8h/eXRG3Tsx///04S/ZCdU1NOpNrqqvTkSuepcMUmnLcZjOkw4G+cuH8zPoaULIWhPwga0QwtiweDq7qLH31Bz/gSNJhcZt13HjvS1aKupyOO3FEmtExLE/PzAZkUGbu8L1jlZ6kM9dDmeaaWlzWbSvvL/wgVmF/TJoymRoNI7MOj7aPslRdRXqRH5hJLYydk/qNbbqm6ZzJ+UASLWvg9rD2yJN6HA3f/u4/kDlygjX9IkfOE2SA1rxSpyZ4kqcwiW+8XOMMTPGnbYk3qUvRj/1JE6XsNW0dYf8hjpLh8JjpU6akXHW+Xo5bBiAu24bPqh/JH5M30P51RwMztP2sVca6quc2UjtsBRmm69asjidxVE/Gsel8zXIvZEbhbHUdzWZSxxa22kXqqX6vA0hacF5eqS/A0/YBl60UiV614qPoRjDt2WeejSp4ZTPBZWugpNzmGeU8XWZ/0piZiuqUZ5ChFjHtRE1C11xc0IHTSwcU/W/GtrFWTgNHH+8/fDhOEjB56Lmn4iG2iPc1o8FL87kYVqJifvcL+s+NcTQoODD8i8XiXa7oCwALqY0o5u+8/kYcJXpcXV4ZMyi8N3rosLiConLmxMnMaLDScApLPM4ed2WNBhkzq5TE528qv6YgXj5fGH9dWDSwNT5gP/y7c99JZNY7P4T96noUJbAU8I7CNiFimWcqRiCtiO73STAgR0aTuMfvRb5ueNBP4mgwVW8LBsFs9kiNHDkyzx3X6+9+eyuxaypLjDJ9Ca8Qki1RNZkXhKVSk0yAtiUElQG9bGY0vEKNBhWkWTNn5qkWeZQO4zqLh0+Cuc4cYEoSUokg9aZeRWhJlK0xIHTKuAaleUhQK4G9HsUHHv5c3DV1Whq49i9sHYOMmEfoQ1ZFVADmIPNyzDLHnBC/S0z+bv8alAovBdoyCHE3CtEj7IEdhxFQSs+VIadCCTx8Np+2DT74n3D361xjxqBTwksGqmFg6us2tmSsxiHQlyjdo0Qvyssr4iwCNbNI2sIonYOtAFOVR4aZSrR7yBU+prmdw/jWIWCNi6xsy7odxmC0qN+mjRsyo2HqjBkxe8ZMDAP2xIGPJUeDKpxQ0UhUGBnhNWsExEql1nYBUhpHl1injMLRvo6GJYsXRz3v3TAoJxPtuoUUUtfRbBJh4N+urYJY40Em5Vqk4c/8xVXXQIXRv4VXHlkG3qrk/uSVVzM992a2ZNyOQldeVUXEuzga1WiBbZvaKp7KxHU8OAcXO9PSwBnb9L7C+G2T0TwLFL6NEioDf4jCNIWjYU86GtahoL67dJHLFLdiN44spxhk757RvxtbAXA0WHOiaK8Q+kYXFP4aZa6HeKpBlayC7xXerpnjy+PzUPxUoc9jyBw41RybGo5Ro+F8OhrIA8nrX3/9K/HwPbOiov8gWAzeDrYCKHCFn3A0o+HFf/xHeMHIuA/nkXwglTJgmbjLWFSevRRcoo/4WHI4CJMSXLxHQSRdGbWzJon1MdYA/wfZs3fHhAnwEpQ3FNcOhIHbkCMn3jeDj5dbo+So7IIztp9HnKoM+eKz2S/nGYeFpN59fy5OnM0xb/UndhlDQTwzGqqJpJWXdYleHVBEMDjdJqIiLQzT0cDwVeNoDlgwB/5w7K5x8e6PBc0lJ2YsV3AeNOFoWL93f7xVdzWPBztS3Ba/9yu/HI/jUS9tndCw0Rvv3liVXboBxvAEeIXbJ9xPKX7up5jfehwwu3buTH57G5EFT69Q8TlNHQL5TMlBJ29xbMkHGLP83QmI++lQ45O4wg2ZEt0ahdUaP6/jCFBRHD58REy4bXwMHzo05yuPy7YYk89LKxqj4qGOJw3oTL/nu+wJHPQPDRgz4oyWLiUjbv4778bQWrZOID+qqqqIzvbJcWpMye8cn3igQ87USQv32V46GkjNcX0zA4QvPQILtkwh1c6pRL/F/uBPONJs5t0U8xqGElpGRgOwMWroOFORZVzy9VT5gYkGvIV+PX72Ktl7RRqmCqm9Ch9Ri2LCyI+XcIR3Im3/wQfmcOJSf9aG56EH6U3s00nRWjnBGJOvI9su4tC3HstVcK1dJ3CceWl86cy6dh6ZJsLwWoWD8YWXvx9T7783U197dHQfPnyEOZvSKt0Z8SrBXtzzyvaAsWuhPE3jF/j5q2ukwme1+RWcCrF+7bp4+NFHOX5wXMpm2xIupVcJX2w35TlzNyPBuXjZnhltyr08mYn3Axhd23fsyEKWXYiiGZFyXc9BxwYAjPILP5VijWRxyLGpM4h7jr/Udo6DX9sjQ7px7KzpzB+h+OuoPn7kGI6GO1J2l/UoizPMSXqRduRpACZfn1X2bV+e5LsyxG1C8sgOXQhGoAfsbdgXi6hJUrerLrcPTaK20m04YQyynD15mgkjM4CtY/Yq3tWThDUclDYdf8mhXKzBpUIJBq9UcD+cPz/XRONInWkw2YLCXdjYbPJqPicfSchkT8knhbv8Uri7tsprR6KjWt46n22DH8ydF7dNGE+q900YpDoa2KYkLvBMwZsKHmCr6XShvZLxK7yTjmnLNcmAkwYB+lIDOuTL1JfYv29PfB6lfMTQGhw8OHWY88XzZ9LJY+SwFby3VVsKX0MB0q+ZWe3UKck8QxACH+lZ5AeHmE9dfWO8u3hpvLlgEQVWd8c2skoM94weoKOBgpC9unK8ZXscDYwIHtYKHpB02PJvKbsx6QA6lg6MBgun0ku4XoYW3b7GJrg4A/HVHzsdn+7ghJQWR0MT7VX16B3//q//IqaTbu62CQshagT7ci7SgHqTa2zAQtkuTjY0NCReNh1piie/8MUszu16yCOLdWRdaT/5ArivXBLO4qbGUDoL+N2xGwQRr8yYM1iza8/uLOy3bs2aqBoxPB7//KMxAEfDWfBd3UucsG3HpyzSqBPFdQzYh5f3aKTL584SwZYGLOLp9rJ17LXPjAYMyNvRl3Q0qBOfZl5uXzWbQZYkzHVUOefUYWlTGehn8ajEpzXq5REOSmN3AY4Gab+quoaCjSPToO6Moe1cxWGDFXIA4ZC6BLBNPHQd+dutL2Y0iOvOTV3P6Lq66DIyGl5/9UdRVVWV9bKGsYXbTFCddgZ9hC8gSeeoMLA/X4kj9FrilY5dfbCkn/SgvpT62Dvvs5V16eKYSkBpDMVVPTFD2FhPSppSfxLH3O4ojGzbPtO5Zce8/Jx98rdyTPiYkVhfXx/fffEHaWu4ptVV1akry0Nc16sGFGhTc871vH5JmIw3L9oUbvJT11obovg+iNxvj7eo0TAMmM8kkNoPB4w2grBNhwpj5/GUd9ogtiiu8n+yztw6kSvDTdwo7IWXW2PMaFiJk8Gt+Y+4dQK9w9+U6dkGbSV+01iRzYFTivEl/OUt9K1DLMfD98JeelKHO8R20F27d8fHOJGsR/X0U0+no+HSGY4z52UgU3AIS2FlP9KL20Wlq5RH0IJtA53kq+JrD+S0gajN29g6gV4jP2s8dDBONB2NOU9/IT4HT+tjvTVwJwHOM/894BOyv3D/3BBHg4LVFMEkGL1DKaPBFDEI1NmMQeepEyeajkQlKc/TJ0yK6tphABMGhOGVR/ewUHonoZZ8DraRaVEZwaAN206PnUfEoXAa6elSXsG9EcsRmq+99uO4FcV/LN5W0wB7kDKVSi0InWcHg8QXW6IeGg8lw06MTnJpeU9Ch1BFelNmPMf4dfbD78CAMXVp5Ej22OK5N0XeEzNMawNDQSVa4Rm4XTofxAQFJA3xF84BkL+ojaCCC4PDCITaowEm+MJ3vpNp+6a+6iRpBxNxXJeIZMmM24K8UCbtwgyBqcjNP8ULYjGNFWxM5VllQ2YoA9Kh4B5w95Sbvj8VB4/Gp8qIBoIwLYS6ijnjp0+NLwWO7zIP+/N7CSkFdstnlXf3BX6IQmQ6nRGpcWwPsM3PCjU/iwfpOWYOJWXinzyX4ojTKwRbWzI3jKDuY4+UGQ0bSPM0o+HhhygGKQEioBReKbx90MWTSaAg249MIdeB9vxRRU6moIKjQm80Tya1c+fOVEQtOnkLCu5dZHtY6OscglMhm8da4kUHYAl7QoBZmEinQ45YJQUY2b6Gn72V1nvf7l2xdOnSaCRbpRsKlp7oW8hosGiOx7kKTxvJaBPjSuWL8WoY5b5U15d2uSGFiY4D17M7TArEzQyh98k6MOo6YsSIrIbbl3Q6PahWLU/YiIvAw/VTyfeSsbsOMlPxv7i8p7i3NbA/yZq+8957nDyxNuY8/HAW2rJGwzq2sazF6Hgb77rXOE+dGNAeR0OP6N+dvfo4GmTixQuB71pghJfOIi4Zjz4ri3bf6nnWTObbGZzsxL3tJB+UsPMYRY0nmzGGj8YHe8/+d8Ug/+obX43P33dvDB5UgeJilBy6ZstG4hxgtcrx6wi26pqazBDqTQq8fcjwi2iwikrhxDEbRdwpRQVcDwWSxqnwVuDnevCdBScVPktXLM/tPF98+pm4Ew89aS7wMeDK0Vnnz50AB1Cm2HtL+I0MAqPprC/4amylnTBhWXmj6BBFgcDzVeD3G++8GesQOgvWbhY8UUuTo6oC2PaLgd07Rk8jccxRZ0NuPwBOquCXaEfcy/aAKX8WuCVNeIlnXH6vonC1xdFwmOMt1+1tjDd3EeXjNzAyr//zn/9GfPHzFD0aXIUhQ5/A4wJ43wFcaW+WlXhSUiIS/235Wgr8jThi99TtSpjdjFFk1oF8Tk++uKaTz/GoNLpW4qXrIkAKJQhZQHM6AHJC/J18j9+3b90WCz/8INPSyysqYtqUqTGEyA7SO86Q4STdtJenJp5D8/Sh3LAtjQn70gBwLK6pmT4dVTJZ/4s4c5ejKC6GR5aKQdbUVEcflDnpWxrMwm+057izijxfi99eOmLaMfS2OSd/kNfQPzjWjsKWTceOkPq6gIKH27NGw2gMuwF9+mWh4as42h1jK5yr8qyr8AajZp4p7/eeUtSRqCw/FrJS3sF48Gon3ISXRdpe/OFL0R0Fzj22/cloQCIhhgoFukNbzrZnuwPLREE1nL++WEMVTSPEeYoR+KvjwdTYzqyVypLXFSpff4wB8P0fvRgzH34oi7B2AKfPnjgLTmB0Ea1JOSR8aVMFs2TkSktFZAq8ZC7yJPlM0hyw0mGsouiech1JnyM6fftt47PGTa4/fFZ+ViiKcleXQ2W2MKBzrWnHSvKmpRopV9b0NPjAtb+hHvmxjYr+K8kewtFAdlb/6hqqynraUZGxx+P5nGMtZTFoCLjOzkWZlAYU4zZiagZPWzIazmP8rEbB1UFijYZx1DmYeSfbSsj+AvGKtWL8ADVxUY1ZGORlp/4tjxeFaNf0evljezJgUFziJNG0BeD79u3bUs+YgtE1EdhYqPMiuk8WEeZhx1cao84em5ZvFQGIFgc2xoc8WUMscZ512Ig+Nh8erx40cuSo0Fndl+1S0pPGWBokDM02dSyWdAD7Ujkv6NcpqEO0yCp5AzR+FSXc4qcL0fdugQeoj1Uity2KzMBYT+iSMdiGgQjbdN3EmwI0yCv6TBjxhfdfAGfN3tJhdIAgwStkx+poeGjOA5xAw1akPhT+xkl9Dbmu7pCGF06Gdp16AHcMW/poDV0CbJGaNSKtWcKFr6auACz37KvH0bAi3vpwSezZWxebOZWHX2Msw67o2z6G9esVFT06UReItQQ3WgFTIJGo6j/JF1wTvpPXMJt8ye/FfdfZcZj52Kx3r0tZXGlPPY4jHJ++kcJz+I/kxdYiGjWwMv7iX/1p3EmtAp1VzqlwBID7OFGkg8RL4CacpD2N9b3oMqvILDtOFtUjOO7MzgIAzLfIlHRdNY4zC0A8kachh10PdQIzquRf6vM6JzRUO/QxmHeJYrZbKRC9IY+hHFBOjQa2L5phmlm7zFdcUgKpX6T+Ib8HVyy+6j3KXHW/jjh3k9d5n3SgPtZEUT9oaQsOMIv8maU5DdnahcwvBpIZWW6LyWBE6neuDDBlrMljbEdYtOCLeCXfcJsfaTzRuH1HFoM8xda7wVVVifPV1dV5ckMuoPTBM45d3DEo5biwWDN7NjPM4JfN3KNuLV2ko1O9gHFZIHr+u+8WxSDHjs2t257+41XozeB5wtmp40AGVrluwEU+I/zzJQSdFqiZNAEvuASf/Mnbb+ZJWvdiH3j6jE6MIqMBXOZejWUYY66z8jX1dPpOvc/vbRTcs2ioerdwLzI/2uapRa+ybVCnyJw5c6KKaHpHZKmBX3mJeGsWkHbOVT5rG2ThctpXLnrZllfKR2ApnruVhYHFdnj7C9/+dowac1PMuf8+5Co8Ur2Le64l31auFmMSp/M315JxFu3TNjgvTcmn5aP24xH1Bw8eSgfVDmyoexn7BIIb6m+p64v3XPIP10A4aUsV9keR9eH3hcOZ4AFroW7g747f2iBuRbJYsc6ez0NPFp51vYSVeoXj1Y4SFX1eeF137thfrmfB16Q16UvbzgxFT3HzRECdDI04G46wBe/exx6JB8ma7wddmU0ijpiVqd3yi37dEEeD6ZmJhAodlSAALxMQUGBoHjG1aMGCOFDfED0h/NtGj43hVdUZBXL/b7FILDJIIhGazgn6pcLlPlGRTMIz3VSl9cwJ9tgR5fE4MlYvFmLUvf/e3DxCZSSGl3uYuqEYa1iJlFaS9nmLNxr9lDBLCKWS6z3gRQ7bvkQKidFUwyMwp/kffhB7d+3inPIJUVPDtgyUOn9zv58EIzE6X53dMtR8nnE5H9uxbb2D7hXUSWA9htwvy5g83vIVUgG70d7MGTOy6qtH19B4kW7D8xpJtpnpN3x2/AoLPfcS32UcDTl+GIpOBAlGR4B71UzRcf/0bbdPjHEYADprvDcZGW0V2REaiMyDPm1XoeMlXGBJ+bfMS6JViPB1zvMETHA5kZ1PERAPouTeNGpkrlUyOog6mSptqUTI/IW1cBcmJU+3xC+T9VKYOlfn2dBQn86AbRBjPzIaZt1NFHvQoJyTsHAQRuiEuQaRzCGdMvSnEeD8aTDnk9so9CzTjZ7o0xgX9fUNFHzZk8ruMBjIRJxU4slZcERhYzFA91ul15vPrl+JGTlahY/9CHmdDSrRMh3nsBdFcQ2R70Mo0h6reSsMULx0bqnAMu7EuZZ3lWQFgNED9/OlEkHbfqfHXEHtmhqtc93cH/wRKYzuI6+uqopBwMVjg4q1JHoozGH+6cAALzMqD8xtx7290pDtOA+xXpUolQ28xa7pwqVLSK/9FOfUzEz18njLzTJGcGkRhoHXzejEwweY0YCjgYyGbihtmfkB887tU7QpjBRyGWlwnfjPNbA/t/00YzgbLejI0YwdSo4GMO7cVVJKT16I1bsPxqIG9mvT33Vj+Deej3vv4kzjfgPBLw2iApeuEl1Vmbbw00IU3YqKilxTt5soHDM1XP5E79KMMBIXfUbYGl32XdoSRzIK7mdgIh17zJTHKa0iomM63YN4/yeNH59Okqsor2dOH4O/FEavpy201tFAVK3kaGhDPzobPH5P/JfPnQbP3ae3AOfNpwjL5Z/uYHwR1eghIwfzjkE6COW2JxkNbZqhPWhI8Sm3MqPAF6BMGPt9/slX4lZeLW9yIFAUPEXAktGQjoZ9B+PNOgqPcmNL/CG+8sxTMYeCtFXlOHG4V2F/4SyOBhT/MrYZGFHLoq62B+yM2gs/i0FuZ/yNKLoqYqZiW/E8Cx5Cdyo84r7jkjc4fvFNOPgSP6Tp/E68dLBc8krpetfu3SiipKmTpmuxvolELobX1qLkFXUzjJJ1xvGbRg+P5trRXsLBfmyTd/mK0QqLxOlwlQZM3zdtdxUKhfKklnbdOuEJAv5OIzk2HReO0zkb3ZLmmUZmGrQVn2CfOoHyfiKmecY2OO32BDMm6uv3hMeh1lbXRH+MYWWZPEW+4VFtACIjh2ZgaYz4vVuqOuEo6IhznUllIVrh3UZnPP3Le+pwyL6GY60MB/4DOOD6ULzNjEDlgPy/E1t3enTtDp8kQsX83e7gVgpPgumGHILz577TZhyUKkpdWKfuOm3gGxaRW7/5k3hj3ttxx6yZ6Vhrp6P/FPuO27E3HTjK4+U7qYDBW1TAzIQr6Kdw2KdTE9C4BuKBl6mvOhosArcbGXjPffelEq0h5H1Jkyh1rr9wLnCjMLCSd3GPuOQWEI0iLzMVTcVWhtt2XV1d1vnpwnf3ko1YxbrqtC0p9yV8dPzOI8dOG35fOkZZfqBy6/qbIm8m0kl45CfQ7dbNm/MI0FHUDbmDrLIBwF55qWFf1GQhrTlpVuNZ2SCvLXDf9S05wF0DP3fu1iWNo/rG/WQKUvto965MFR7XEkBxH7lV8pWj0rvyQWXZcbuVxEt4qQgLmxKP8zvvk6/RUWwmaLJ0yeKMgNbUDs2TVgzOKKNTR4KGUwbSn+ulE0buku3QrngijDW4/N1+ClnfJmFjhPcjdDLrZSn3TLEvFV5NgwvctX1p0TYde+oEymwu8cc5CS8zPbK+EzjQmRoYTTjC5+IkOdRYz5bB6ZnR0K8PW6y4/8JZsh3BcWXLtVZEPsm+ac0pRsodKDZr6FBFHEOaOgI4DDrAV6Xji7Rd13ggFlGMd/5Hq8mm3BN1++tzLLcPCo4Y7sMpQF2ifyfW37XSOIQOcy29y/mIj8LIb4GNfKDIaPBnkJ/7rX1gLaJLrXDedOsdV5ERe6gFsXTN7tjIjhgzKPA3xLD+5fH7X/+tmIi+1gEHio4EI7s5D2RYym/xCbyULsRnT50wldzjLTXWZ8/mRA5gL6249uKz6yZ9FVHbYksTw6UdDDDatT3Xxft0gss35YPi/rYd2+PTLZupZdEQvcHz29EnPQpRnuj8XENmmWvqOFPPpC2DayVHgw4knbzii8/J533OmhLqNDuRIWZ8jrmF7XHoTNb1kH4MsOlQdK4ZJGyZe9IPY5N2xJekZ+YqXonLRv197d6zJxYtXJhBlIHlg+Dxw3AqVyKfkNM5Z/Us1oZ+1HG7oAdaY8VAXRYoFL/B89St6S8zUJmL72YsWLtixZKlaReoSw5h+4E2grC/TjO0K2yFdTp7aFP+n6e3ADfn9tmXsNfGOHH6VMzDCb58+RKK0c/IrRN9WRPHl3yM9RPWrkHyHOZBF8mHbM/5qZM6Du9XfqR89Tf6aGSL2fsL5icvupNgm3xA41bals+ItzycRr+OaNdOPiKsXVcvv/OSJ2j/pC4L35R3eiT6P7KtxKwpT50wOCY/FSd9PnkJ7TtO9bF04NC3/MstrtwMXFxbdQ3Gk6R0NbfeqI9tgZdZuNGCuTfdNCbxKXEr4enQizGpC4vrylT7Ub6oc5Sy27UV/U7e49yKU/WKUy2kp7uhp6HV1SnXhKVrq76vY4Ou0q70eWFW0Kn8S74pK9BhKr5j3yD33Q5Tt3t3Hrt6mK13h44ciQN76+PxL/+zdGj0r6ggwEMdOhpWt3GdftGvG+JogLvmIooExcXiwnZN3zT15tNPNsWC997nrPUN7Ok6HkNgUBVkBbBESsskDBdLxqEiZxX6VihTMm0jQyKcyKMXz/Sg82co1MTJExYzcgG3bt8R2xD8AzC4Ksorsqq3DEZEEilKhrNMz8/+pvIrI5H4JNIkSBDBy3tcXA3NUzC+NSiiB+rrY8Qo9kfpxEBxkYjEfsfN/ylESm2lcsIPIp6Ibvsim0To3fblc3yVZwhvWL8hiXc4TEpCdEuFzCA9rA6IZ0oGp23riLA9jSPbELFFahlLQezAEVg6/30NVN+GGGuGDYuhROpy3NzPY7wYv//RlzYLfxZCjPEpXOzfK+FD28V/LlmhxJ5AWTQt7djJ43En2R6DK8oRLO5LQxliYLl/V+cReGBvPi9Tsl+jUM5J40CGLUzSIEHwuN4eeWSBpnqYSBlMWyVd2AjjhCvrbpvOO6NRtCtMVWQcu23YpnuzvDKFjfs1OB2f+xndx6vzx+OaamtqEi/08Dt2vawJWRq1nX/K8igULhmXcCgxNfFFBua9pi5uByetkq932PTUSmDjXEvMPhmS7fJcScD4bCq1wF2DLb3HwCb7oS9hJ27u3VdPlHdrGitubejHEZMqcvk8MNDJ4HJaOdpxWl1cx5ZM3VeJAdrWFZxU7lm33x4IdhU7KwVv3bEjho/BWIRWmlBi6vcfiAbgtX3XLsEZowgWjBjUGkdDWQzo3jG6ZToCyhcCwogbM0H4m04JvFwr8VQc4r1YOQeKAKQtSMQ/CTCB8wgVEkWj/hTR1J37Y/khhAMwaqZN5/SF++6Km0ePjJ4YT9dwSOhs84dUkLhHwWkRUSszWyU4aYl+E87gufQi7Eu475oJV40V8cJ1LGiEzrhZx5v4pUKggbFn7x6cjvvgBVRTr6pKIe/e9mtXEZpXSDXE+XgVnNTJ4FnpbTHshfdFBMeVZgw08E/lXKPlHFlO9S1OnD3Q6Se799IpETTIZSgKbk3f3lFJym5vsibaAdfWOHTpJPkIU0dJ5uYWZ4PwBqI55gSU8/Qz//o9oGVMeO7ZctRE6vvGhqPxznYKv/FbUYIp4p7bx8ctrPkAThrJejitoVX6sw6GXRX4whiARxui9alcQyfygf3gxxEiUwrsqpqhUVNdlW2YxuhAxE15objseOQ5iYd8KHhjoYQJ/wKP+YH/Ve4O0+4elMUzwL8zyuFQ2i+3FgHrk5E3nnGdsh2+83mflQ/4Ugl1jcW/4h7wnrEoF+QlKfThkTrrBuBsUJHTqef4vJ9/8j2dZLRtDYScO3MhHE1FeKLH8nk6dR+r9YlyfryrKFrMsrFxH9s+hiP3BlA0sAeGOtEw+Lf82lOGxG4LjVngUydUjpf22I2TVdfdOpFyhPavsphuLfSZAzh5FqPk9kP2aeyWEb0Xzpdw4ulYUblXcXbtLpDRB0MgswZexTzSSYBM0tEpPTj2IrJfKIIa5bt1mq5dzck/QymGNTzvWLPJAABAAElEQVRlIlSXPF3+Lv9TzggnxyftqHy5FU75yld5r/zF310LukpaOwpf2VlXl1uSJhG9dX+wskIcEW8MCriWaXS2jFfeJqbbr0aBhoiXTlrX0+XSMD1KhPcgkaL9yBDPaHdLTL++xXaYXFPayIHk8iKbW8af4+MX+3bMKvHeqvxQpqr46YBRNllH6ASOpAocGCNYW08BkmerJAt7HQ7p5GQNMojC3DQ0CicQuge457oIJJXWdIaCC56KtHXbViqRH8tieRUYRdY/kFcV/LuQSUmP4iADtF3x28vvfYlDPuP6SAcaaa6ZxyB+yjY4o7h9kB06ZTUoC/gVjjWf0YBTNrumtiXf9HvXVtltv+JLaRuL8lvYm1W2eeOmqBk6FLlXkXQlHxbH5NM5RmApHMQJDS/XWcNDevQOP5do1rW+CvFp7J04dToNu+NHDsdw+PvA/n2pFI+DpiN8HGmijtXM2l1AdblIHQQYFfPGqWjfnNrQCqewWyfaE8zqgI6ZDlNgcgC9dO22uliJs/fksUNx/Ax7GbhmjmgbwyqGxMBO7ajZQDFripu3AW7paGAOCXL+SX7Au9Mr8EuDQ5pgLt7EvSKnmRlXyAa61hlZi3zYdfhELFnNCVsw4R70Z0ZD/y7d43Nz7onh6Gs9kN1uF5ZXXCC45hpLt8JIeMqjpBe3sh4ju2v3nt25LWEchrqBGS/x1vtyHH5mkOKROk46LYB1ic9JW4UsZY78Z+DDLaj7kasN+xvA9xNZO6e2dhj6GBkH3sX0vNdxyP8do8ao38lDEpf4W1yRT/u7+Cl9ySs05DxedD94qUNkCHgzrLYWR4BbE4ptOdKRa6szIJ+jHWGgfmNbBcxzOPxTrEHJCWax4s3wYOWH20/NsNFJYkF3R+7z8kBh7Fg18MVJZcsZHHvK6rbopd4nLiZdQQ++Oz63yn5KRp9OzkGDynO7qfqY8yyNS1iL71KoPEU4pHELrgoX8V3e5f3FM/Bj2rdmlvS0Z+9u6KkWR8CgDDDZhjwq15H1TNpkLAYIdPBLi45N3cu2xQF1T9u2b7/zZX0Mnb3aXmYfWUzXIrWJY44FmBcZVAV/l58UPEUeRwFe7hHO6lfil+vhGoljXvXo2TrxR5PRO27sTclLdIDLI4u1k39BpjwvPJPmGZfr4PhdH3mWuJRXC+7Yhrq7WTzy+ZtwTlWTleWcdHInDLk3x8V3wsm1S0cVfclr5GP2V+AWOOTa8ps2pcUmm44czcxqGiFrbVz0788JLIxJR5Zjd33dmqxTRv4rrIWxMsnRSnPCscAD1pb71W/cirSfbAwzGTxtSMfDEjIHv/XXfxVfevop+hmYtq9zEKbX515A4Bfy3xvraCiBAM1UVV6gXmVP3GaE2vx582IFUdgPfvJGHOG+YbyIm2A0U8m3b3+qOXcknfIoyhbH0gwewZFDpDxLjCD6waMHEAQ7Y2CfYRRd60EqGVXQT53gPNJDcfbgpTiKrlFSlk1Yqq0dTMVyqoaDOBrL1lG4xPFvzWdPc5JEr+hfXpnEYZr4iSOHMALwI0NoHbr0LNJ1WVqFvga9FUJXbfg03CM+hFffAT3w+HGMIZFOEUjFRwPSqu/HD+whfbIyBg2uzt+OYXCeOdZEth4KfafuFKUqKpmfPX4kzjcdMeM68FtlRX19Vgqanj0pFNe3Ij1fEpgMsBkhcuEcJ1wwh96DUWiIdJr+eeRAI9srqAZv0TkMCCvc+wzY2QJ7FNFdO2MzspXcj6iqGoBCphPDfe2kbV2gSBBC2Pl0wENqmrgRXOd1lnZPMv6LwCyZY9eeeY9/G9k/Ub8rGggkHeSz193jxkYf1uwoRsEBlHbkMQWceHUmFbdnP6pp98otLMmkWJMmCi4d2dUYHTBYO5X1pW1ObIC5t28xiI8eQrA1NkUjbXNLTBzF2qP86wXWgL54HsMNhaE1c+7KnuceRAllGv5+Gtw4dmBPRht7D6xO5nOWVCvTykwfPXPiWGw5dCKFOk1HJS8yJKNHeR8qUJMqyPz1KJ4+Uo8Thb2nfdtG/6oxbIPoCd50yP6PUZn63PGj6Cw4S4g6tu9YwNBikydh3tt3Nyaeu65j+3YF58QeFFaYczNjv4zR2a4jp6hgdNiuRW68LBR0GNjs3VGPMR1RVun4xQMUvLMn4vSBE3EE3nw07y7+qeGtV2+iBWX981z2QpklytGwJy6C2kPGjYqyXijZ0gPCSdyxaKGfm3k/vOtgCu3hk8aTTk1hrF27Ys3ehswk6EGw9Yp0iBp3gaKNHtOod2AE4ZcRg1rhaOiV6f3paGCMcOA0hjUHUmhglOi8UQNLppnClAGDg+7xN/qqowMCRNHlXtbzIvBsOHMhlm3bF8sOgEPMvxNKYYc2KMZU/+5J8TpTYS+iBLRrbcFLMQRnHDh9eBeFvPhE4jtR7yFpGKSDj34UfkaV9Mz7MuWvz4DKdLAYSTmwty4uneUUAyJh7aGJdhbygYcJp/NnT8VpCr0dZ/oWpuSgUU7DGIiCC1337QR+I4DRWzyr++TRk3ENeukETlq1Wy/6UZ498gn8YUA3UoC7CEJSaIkcgT+nm1ESSfs9AD/yKqedoQOBLYbR4M84Gtw+Yf0Q59AKx05rvQcSJFf6HPIvAcz/vIqr+MNbPQXjMnziMOerbWogMrjtDM7eIqOhEzp5FwbVkVff/p2i1yCOjcOZo4f/Io7do1u35ckTugxb9+webXhpcF9mDmdPH49jTedib0uPI6H76tE3o9AU9GhEyrRNafYyPKdTz77Ai6KC8E3Xxt+y6CrPm+EizFUYXE95n3Lh1PFLSa+qJdJr7yEDoTkKL+EMUYlyPS9REVrlu3vfAdRH6JaK0KlTx4tsO3Ctc3f4O/h16uSxOHlod3Tq3hclsn00Qmulsdu2ON8JB9tVGTTgEx/ayTdVSohImP7YirmpVLgd77Lp7Kyd9Q1Mf+/MurejEr3FFM+cOxOrP6kLzZYKXoPKKUzbmd8Zd8qlCxzBDE3hF8gtD13Ly6P3QPg/fEZF9Bw1Y07t2hqt8BH0RNFshfJ9Ae8DFQnAH2rZIBuP7DvHmvXIUxu641RwK51K81lwWiXJbX4ZrUYx64iToQe8Kk98OsU2gtNn4jJZK+27sp2kB3NGdp8ntfwiRpnbvI7va4xd4ATDC2WrdNVjGByHuTUD7zNH9rP+0EOfjlE2sApa6pVGvzVN5PGXziM/oF/XWqdbcQoLfBD5cmL/8dhGe17jBvYGNpW5jsqeUwfrid4T5S1rG936EGXr3JX11pmgQ4mIFnR25sSRpIWOXcuiN8qZskUD9ODmbXGCMR+hXd5Sfhgt7g4z7lbRP/FG3JJIlOPihDiXSifj1IFxFkVTRa8bdRe8ziBTzoOHDJCCYDgl4QOwpmBZ8roFFtSzP3qFkUZl8469SZo9wdPuyOsuRONL2WpmzR3YuzuOw6ukzo692XrWGwkNn7lAH02Hz8aulnZHMmamFdeQtR3g8R3BWbcXKsOFgwq7Bon6iga/81AGnmdRdDKZwqzib0TYezzx5wRRtD0Hjl0/yUfeUcVrUG0l8OaUAcbvKTOerCAf7ANsu+rYpT+NLwtgHmvYGeePixXAdVAZcqxwsijfNuw5kPoYGxpiSC+2xfGb45An2YYOGNgRxE4ggHbLenukKEYV+HoKmXrq0L64dJrCngP7UbOCU0CYl44Gt8idgh4aDh1M+deHJtSZYJfRp7oDvLw6dbHTFmxj+8DhpjPRoU9VDISmlMNnjx+OS8eAOfiOuoHewBYm9Emzz05R3HT34eOx6yB0jMNYPuI1dahO3yFRiZ7ai7oOndFnW6NHfHbrhPrSZ7dOpJHD8yVHAz+ztmY08JLnwXMud4A/sQ3JPpesoh4V+KRMkU+ggcWQvtQ+wtFslpWBMWsVHMYpnXWWuKf5HMevssZmN7ViTeUl50D6HfzmNRwe1ndQJbpaYQx2d67gzXlwTz6sbFMXcluxvPYcRvgF1lyB4mfxxAVTrz13simOnbgYh/lVfAdTY2BlP2o6leW68hH8IjoPrz5/qoltbD2ijIxD61mpv6pz6JhyKxSNpl4u37cPt7KeJwDYWFcfu22Iq4pX9ejapPXzMAG3W3gMqUc4NzXupP3u6GTUaHLetJ+66qE6YEOWSvfeqYsVtbRwmoHDJ+GR+5rY6mvjXKMB9MCqYcljnaMGqDIoM2KQT72Sj5WB683g27HUka4gp8Vf5YW6srCU32hcNuzZEZ8eg0C5qnn16o9O0L0MSDpbWDD0qdPO06k0PnvCD8yq09iV1vI+Gk9bpQVGGsaX2bp27vSROHSCDC3aEdcH9W6NLjksn/d+6fQ4gY/TCEZl88CKflFeMywDS2egA/mYJ5VI/9o5reHBXVg3+Z4dn4ROVtQXWvy4ft2Ry2WMmVE7KC4LjF84dThlZY/e/bGJ0I3gI820dxQ6tE1tCiP1ud6M6Szy9XTDkbSZ9hXNxLh+PaNqKHX5gPPRgw3o4mQ6Iocuk0l3qfkMuuuAHJdwFf/MqDkLz70ifBhOG+Auny74HrCCD18Aj/fuJyOAPkZ3bxvlQ2pTF3BsFumXDl2HTsiOEj0Ld50Pp9G3Th4+kDwuM2KAizaUc1CXaKpjOxPtKj+ky9HV2JQ48dXDz1ML5vTR+mjGNuiA4dATm68XOof89xz0pX1x/uRhahBSuws9sC38RflncENefKrpUGxB/PyP15//q2/Gs7/0HEeMVibeiDsty3D9/X985hfl841xNEBsMkAZroig1uupEf4nqW0m/XL+e/Ni+dJlHBX52k+FjTHoqqrimK7TIPK+xkIB/6kP/ow3DO3eOboj3BTSR+uJTPyMz/2st43q3zuF45GGA1EyxH/WZ3/afTL53pUDk7E3NB2/ruj8tOd+lt8HsG5lRIKNYpw8sD/2FXrEz/Loz3RPTS/OrMYAkHltPqQaeOMuFeDelYMSB3Xu7D2D5L6BV2VnIhqcdCAT3L9rbyqxN7D5GFMxEObfHgfHudh2sOlGNh34AygwMzAjDifMFPl5QAOOpKMB7psZDUQ1BlKjoRtp4nBhXkg8FCodDZmOCU9QGLu9pYhFwhHgoNZucO+ZlbrzWEu+bK/xjNC6hmLRyKkTS7buiYUgoVykHfykA+mu6J839KrGqtTRcxbBU3dSt9+Nu1DrOWVnSCpMO/duu75F4f+tB5VhoBeDURJrB3XIEz0qe3WJMrdOYIleQ/G5RKE+HQ3u/29jFgnAET4uS14Cl6vlzb/ys2zZGg1mNOho2Eihzbe2sdeeX0WHTvx+VqnaclXSaDcKC5pBdqq+4brhU/r9530f2ReDlHXWaXgUXnAjuYEGyIDyAakknMbpV3/u50H4/3mmFoXTeDRycYyoxHEX7QZd+GiiYiCGGQ7gc2zx2nsEa/tnvAZXGN2FMKE3ZZsRL53ORrCM2BvBtqiaTo5mnAxN8MkbKVkHMc6ygcgPDX4cxfuabyBgaLuQT/3Ad845P9CUTvqfETQ/9bYK7uhVWZ7y42TTQY4bvHHCT9qswYnTmeCH0bOje/bEvs/Q2k8d3M9wQ21vjmnEaDe4Ule/H0fUjbv60dTAqsGg1ZUMPtxIelLXG8LWhw4Y0zpImpB9n3Wi/7yzcEPJkMohGCvX4nDDvjR+/lfaHI0hUdmnQ4zsR3ZZTxzKWD3/VKPBrAX4LzRmtNJPmVFKX+JoOhr4jZ+gR52t8O7W0CKZDM3UkDh9rU3spubEinVkytEOvo/EaTm2f/eg7lFPMk50EDXtro/GgpXzy89/yWeqyAaxvo0OxcbDOHh+/mavt4A/NcrIqNCwPnVwf+y5eGMRfihFkrtQs0q740D9gRuKMzolh1QOSEed+tjWQzcSI4F7O05XG1hsAz6O7NOBcyOvEejZneAFjn0/Ufkbua5DLfzds3fS6t6mY9e3tN6I8YvzAym86ikzpyk0v9c9TTfwGgg9lbHtxy2O2mYHbmDbNjViENtlwPdzxw4jP/7Xx/6H/9cfxi/9s1+KoTVD4SXyDzVm9bvCkr7Bw/3/tLmf39HAcK0+nVFLgUKUUOVGCAmki+yJs5rsQmo0WLH2YMP+qB1cFUMqKpL5G6Fz4QsnBeeNwvTc1/Pi3HkJiIkjh8aUadMzvU9hcQmvtgLpCu9GSI1CmFrWFkNHz6Fc3eiNnqdMGUOwGy1WGJj+bFr7SlKFFq/dkNG8B2dMi5spYmh1etNiSq9MF7J9PFF6LlUQ1NkzAkfbekFFBY0lDcQsbocXroFUIYshNULgd7O3ctTIkXmOtCdmeESeqUhmQBgl1bvl+E2DMl1Jz1qR9m8aNkqaMLRfxuFeVIsvmuY7nz3iwwYOyOKLblfogwJjFWq9u4U3XdAVMDVSYDqoWwn0oKoE6pEs0sXcQ6sf9FocIc1/LwrQhjUrY/uJc/HQ1MkxorY2j1Q0jcwr07AYk31kihBjzEgK7RnJ9zJ10n3ADDnhZgqyeyZ37twZHy2cH+sOHov+MNpHHyvO7HV8kpEeSpVj2xYXhHemUzFerSYjKa5ppkKx3lj8RFQhRJ4/jtf2U7Io3n53QY7httEjqEA7gT2zZEroqfReonyuKRPnxULSpmsgHISx8LemgBGh3K6D59VtPKbNHUQZWLVmVazcqI8zKEx5X4wjFawrcLmi4Qe+6a23LT2nwjdrcNhvC6xM9bI/M2Bc88Rb5iYeWafjuy++nMrh8M6tY+p9n4tqivKId7Ir4eGV77RnP0ZHbM8r07Na+vJ+03S9Vy+952t7EsM7Sz/KDIW7bx8Hvo/L9HDTe6UJ04GlHVMmU1GiDbOJrC/hOdU7dtXFOoqKeizoQRQSr1FI41JGw4BuHaM7Rq8Fg1q5hoxN5VrakUavOxpa5sEUUjEz0mlsKyMKPOvz19qzRxtn1MFzl+OjnQdjwR5SF2mrpKY8MHN6jBk5Ko+Scw2T9mlH/En8TnqiyB2/ScvCii64pAfG5BpDZ1mlux6v9ZbNbNHYE9PGUFdg1Mjo3Zv98xjAPpPj5iHXzPThXA+ak79cIAJ2EeM/o4bgyDVTeIh2dSSSLW7uP9QUW0jFnb9kEYb81XjynvtjWA3bc0yFZX3EM89jbiBKtWXbVtLoDsTOxsItOZjkllq2pVThDB1IjQZPnWjLtok2wNakWfkgGJC45jhda6foS4Rx7i1/Fl/wgyz5inCCDx1m68QGtk68tY2sGu4o+W7umjKRIo638vzV2LBxU3ywbGW2M3vKHdDTeGDeLVNbmWzSZ6k/5yO+y28zLRIe55UZJMA/owmM0XRaaaOBPejz5nESAwqia/vk/ffkGdXyGfHvs+m8uX58V5qj8/M7jR7TF8XZgscVuGb/bvv5wdvv5hgmVlfGpKnTKbJbmbyjlNopbtiWjkPTd+UrrnfSVQLRx52hfEKaoy8+n8T4X7txY3y4ak10gw1ZRHYEEaR+4I18RtwyBON/Sae5TrZVtGOL3sfHjGC5Vh5x2Azv9sg3tyctXb409h49HZNvGR1jR43JlNDck6ujyfRe8E9e5SkIF8C5Y2QN7Gqs5xjNtxCg9vXTr9FDKzkBY0TU4ggb2As+SVacsvgSc73oOgIfU/tdV1NxxahL0icZD614iYE5duU911Hk3d49u2PTutWx+diZuGfi+Bg9Yjhp82wF7MKxisDX+6XRktwT7sKfrxMWpT379iVd6fDO7S2M6wAOnR3bt8Xc1esSZ5575HMxtKoqt0a5/oXMF5Mx72iwxB8Tb6QPCCD7kTfxt/OSF/i7W39MrZ47/4NUym8fNTxmoHP0R37kOqbjRh7P/KFB189CnfYrrVsoUrzzmEqxRGJTD3KN7OMwsnXl6lWxfM0mQRVzZt0Z48fdSgFoamTQntFgay+YuZVpz7yD4Enn+FaLMfBcnsYF3LPdFtgpE3ezvfCNl16O/fCmvtz30KOPZA0Tt5OIg8pqYa6slv7MlFGeCntpLWHDc8kv6bc4rci4OoYvxory45W57/Mp4r4J4zhidixbSAfkZ5+xffWVkkwzlViZqhHo+NRpvByP90hPxXPNWcTQgrgfb9keYzEyxo6/nTpYnCRB6nlB3wXnB5rAHRp39V1IcFQsdOecx1RKn81EgD1uzgj4OXjzgSPHY+ceijwumB89SdmaPmV61LA9ZwDH1YlXZi2KK57YdZS6X1t3kgK/g0wTsmJOQVNetwHQyj49o7Z3txiEQ91TJ1qRPWBGgysjjAr4FY6GxBfaLDkaQK/kvep9jvGKOh7OBh0N5pPtZcvGyk+aYht0q5ZlRl8ZzvvKCiK8yJKNWzeSJXo+HpkJn6mtzSNudeAXDo0iLV7aApFzLLm9i/5N07c/v/d3ZXziO324RbCublesWrwgPjl2NrM1HpjzuRhcbpaA+pjbMYo1U7fweXHFubrOptl7qa/mVqYW2koeSd9H0VM/IYv53RWF/Jgx4baYTE2HMtY09SDuUb+zTfUDeWaJTm039TL6cY7yAmlJPdtsN+9ze6RHJm6s2+vt8YUH7uUY1dGpR6vn+rw45uWYsx/GKCz8JudDW7ae81GeuJ78LB9Rx/7H1+GlXMP7dI3Z9xe8xsFoe6RtYR/Og7F5opLwtV9x1rakO3+3TXVse/a5U2QZbOCEqXeXf5QZ0rdNnxoeMduX7EXHqcxM3Rq6Up4Kf+VmSRcUxiVeV4yZffzQdI4NfcIT1T5evTrW1VETqLxf3Dx+Yp6m5FGc0rz8xjE7NuWIsMhtLsAm+2WM/iacMwuNti2C60kJh+TBdXUxj4BxJcGZyZPvoCBoBdstyrI2QmbsOG9eylLbKbZDw8uVsdIc8svMpatkQmbfyBZhZy28Hcjt9Wwh2H7gYNwFvozFdiqOgsY+YjxJPy3wFVY+L+3lugJq+xNmuW2Dr71HuLkVoqGBrakb1sWHmwod/ukH7qNeUnVuk5Evpqym7ULWiw/CATkELyvqRqAp0ZcwShnG+ihH3EavnNy2dWssfu3NDMjcjFN21owZMZBtzeq2rk3acYkD4Abv8nDxW1tD+fD9734vvvEn/5KMhmfZQliDHPEuKVG6Fnt+sa8b4mgQkPkSHrz8KJiuoASdJb1rC4S1fMni2LV9R3SCqU+bcHtYfZtMNFJpqSYr0vCC32RNhKWrVsZf/ek3c9F+7YlH4mEqUo/mtAcBrhF77tQZ0rTYf+peUxChQCiLlGgwFXuTJMYsnAWRe56593gsyZ69+2LuwkXx777z3RjLGL/4278Z98yenXsJRSCJUUEsQZYcGRYMkWCcmMgugSYCSYwIBrd9WJHWs4y37twRP3rjzdiDov6QR+Bx9NWYUQUT9Ex5ha8FcEyNsrCagt79iyUhrFDWWPddRiiC+vLs1v1kGXgG9mu0P2bM6JjOvtYxI0ZSgX8QkUf38WH0SujMy8EqCCRwBYwMXsNSovYsdYlPx4SOBwm0vr6e4x435lE963EIPff8r8Qk9iUNZE9xRsloUcaGnE0YOEYJzPN4LfCjMmmKv4zUvVO2afqv+2J1NKymzbde+0m8MH8he8EnxJfZi3QTjMR5+5yKh6lgPucEJGIZtExUODh2YaYCmvtoZZYKH84eP0hRmGVsy3nzzZ/ER2u3x/NPPhYP3EPxFvZsqaBbNV9nQKloqWsAlef8TduXmeT5vI4FWHk8nWfdXkHLc/903b69Mff99+K//uDlhOy/+dM/itl3z4gyFMVLp6n7ofKvssX4xHuNUpm3jFCjTbyyJoVz8zuZn6+SIrhy3fr4D9/6VizdsSuemHJ7VmC/+aaxuU6JAxIGl8y/aK9QHGXoCunEzxbGJf3ZvwzY9LODeIaXrPg4/uvf/JcY2qt13PP4L8XMu2fhsa/MgmyegW06WaaM0V4a/bxbJ6WMvfrWEfBIx3femxfbdtbFhs1bcyyjscVH5tYJ0jszo0FHA3AVb3mn+0KJVUDw2blLT17+bUZDZjUAj8JQuER68snAFRat2aN6+MK12NBIBfPdqmMI13wy4g++8s9j1l0YARjhICJ4Xzgajd7qjBM/cj8wfSgUpIeSEuO7+OQauK9P48K5fcjxg/d+/tGYjDFdUV6RWymK7ScaJdAQfVivRdqRr5wnJe4cUfKLRGzdamQxy6um2167mAX2dKBsp97C8lXrOQN7ManQ++M3nn8+Jt58C/sriTZAfzq2TqHofoISv2jZ4iwGuXjNhpylNRpqy8lsKOse/bqSko6joSM00Rmc794ZHsf7WWrUnGd+qRAwL0ErbXolfH0vPiVPdj10NFxqcTRYo0FHA1wN6BbXr/3y0/EQAthVWrx4Sbzzwxfi06NX4g9+89fjofvuZ2tUGamYHM8KbIW7WGlfwtS0cHmjfEFly3Ep9OUzJT7WBSdLR5Syzdu2xd+98EK8wlFyE8s6xLO/+bW4Y+IECu4TjWfQ0oZGpkJf5UwntuuYl7jDH372N3m86+LeS4FwBkfAitVr41t/8sexjYk9f/9s5MejMRZ+6Tjlq9KFfD5xkvWVH9ufeCrfT9EuPAUqnUlnPucILEb3/pIl8Zd/9/dxxy2j4v4Zs2PyuAlRXUHaPDBJhzSP5dzhIxqhhVFQKJ9WTu9IfxqQOvlU2lXkzrGWTaSkb925M95Z8AHOp0/gYffHtEmT41Z4QRmZCudPn0X+Fc4GHYGdupNSjSO//kRTrN++haKQb8fq5WsKOLX8K+sQXjn4z/zy9MMPxO04lW4bMYZtOoMpOokBatvAGlU9zgCfs/AsTwsxC03YulWtFds2PBqwcDS4X7lYp/qGhtjIUa0WB165dEU8/avPx+Tx46Mc2WTtF9dUeFsnSV4uTbke0pffS6ufpVedEG6T8DllV11dXRYEffftN6MVx//9GpGf8Ri8prNKgxoYGtOOUzwsHBrFfn/pJbcV0Jdyxj5NR7d/l9hipotY0+/9xV/HemD0vz3xWDzF0b4apYlr4EWuFeNQhugQ0FGRDl3mnwULUfKbqUeVxUIhNo2i3OtMavve+oaY9/778X///Q9yBf7kG1+Le2bNjL4EN5Qb53EyuCWgI2tQRop4WxZNx7VODeVU0jjtiYE6gcSXlJt855zXbfok/vbvvh3vr98YU8v7xpd/66tJTxoX0qLw9qUjT6NIGrEYZ8oO2nIthInwF/+FjTId0HDfqVi0YkX8yZ/+eQzv2R758XRMnTY1aqqqE9Y+o/ElfaRMA6bqFI5Z+ky5jp5hX+oIwlzalf48Hry+oTHeW7Q4/v6VH8Xj06fQ9rSW/dAc08pzFvn1AprU8JFuSrWvgA1E1BYHd3uyu+AUzJHsHxwNnYHhyXPNsWPfgVi9aXO8ydG1XSlWfN/sWXHb2DGMfQgnplBY07Ry4Uu7+/YfisUr1+JcXR3763dHw+HG5G8zaqixRFBnYJd20ZMjjDtegz+QndAauEpZwsi5poHPJ5oDn+UU/qbMS/sEmkFX5IOB/QvOge1YzWQ26FBctYUaDYdph6faEomuqaqE5kdGX46O3oCM2gQ9/e7vfA16vYVtg9Z3AYbQjHDUYZ2GY/Zor/zHb8Jeg0s9wHVJ2Lesi4UjV69dG++89lq8+vGaeGDypHjuySdi9PDhSUvigutGQ/lsGnTOkf506Nuu96iHaTz6t+MR16S9Rur1LF62NF75z38bmxnXV3/5uXjsoQepi9Ava3g5PvVpjWhh4n9exb90S/upM+SXwI1+rNnmuwS7bcfOeOnlV+K78DszfH//D/8Ah++0PLlNvEqDFz5WPF5qvVgnHSPyAmlXzHLMzkm5LuzkNWs3bIy///bfxWrS5x+YeEt8+flf5ejdW1O+uh1N2BTOhMLRkDUdoKt0fgP3/I05Ok+GmzB0dm6TOMTpAguXr4j/9J/+S0waOyqmz5wZEyZOpDjl4NSXbMM6Fc7DdoRt2gfIOGGVjggCSNKdYxb3pGtxTgN+z759Me/DRbHg9Tdj5uwZcdeMGTFhwsSsb+bvjj31UtbSNlwHAyvqZllPQzgw7lxf8KWdwQHsk+PwAXnw6vXrOSXrbTI1K2M2x1WOxv6QxzsGHbGFDAUG8JHET8Zf4B/bdNAztXmszdPMHOyjWw+2mcBvDNCuxhm7BD3+J/MXxG//6q/EtNsnRSW017Nbt7QR1a8dv7xGWKQDR/wRyLxcP/ss4b2yWD3c+7dswRGwcGHMwyHbpqIivoz8mECwsBc1O9RdErcZf64h78LGdjKIIt8EBsLN+grWQ3F9pDtrpAk3T6d4/eWX4ierN8ZTHAX9LPQ0rKqqRf/RntEWIPDOWKQV7YyLjFUdrh67bj7y4RYcN09hF/UbgPKnq4H7teH+CYMTpX8h/7kxjoaWqUu4ArFYdJleURV68yebYinGfQMR874wyllTp8fY4SPTK9yMkFX5SmUA4Hu29/ylS+N3f+/3opH2vvbUk/HUM8/kmcBpnLNfONOHQQazBDyGRWbhMV0aWCVHQzJaxuJnmazGusJ2N4T4E7Il/uQ//ud4YGRVPPbUc1To5agekE9lQgRKRgJCiBkSolFxkbbEwGSmihGZvMd1tYJpXWA8VjA3G+N7IPPW5R/HF555Ku5EcHq0YRdSwo9DYM4hlWLAJCNJ7GP+EqNInUJYJIdxlJwMFn3LCGxjY2ZjfPcHP4jJOBnuoQDjWBwwlRaKAul1wuTYGLdr4aUSbrtmimgEmCqXkUOwWK+dTFcmZeXWdUQWFiD0P1q+LH7na78b0ykuNgDh4GkMjktHQEbPGa9OBqtbF5EbjUXgAIySAWL02aYKjCcjGKlauXJVvPGTH8e/e+2t+Nz0afHbX34+bh1DlViVPZ4TLsmceE6iF42EueOV0CRmx6BRoMAxunOVLJZ2XTtHY9PhWPDBh/EP3/3bWPtpfXz1y78cT37+ETJnhqTSliYRjA9ESaPFc+WL9fTkAzZLCX9gZGTRfdg6Gtyn3JrPHWjfAogv/ejV+Mv/9kLC9G/++l/HQzCTXuloIN2e+eq88JWGA7BwXjJSmXYyR9bUS2aVAhrnh3v3vX/ZytXxZ9/8s1i8vS6eu3NKOnluR0nXiWaBOGEpPGSovlKQgTsalukMEw/py340bJIO+M7nPPZqHrT3lT/+Zkyi/8e/8usx54E5WfzSwp3ivPskVRh0usgI1e3aMLZeOIk0OJasWB6v4iSy6OqaTwuP8FgcDSMGtY1qjM8BOhraM1dhrCLCC/6fbbp2Mm8WtRi/woD2+ZT9yHAtqOPan0HxsxZEOhpIPdvQeCYW7dKwpbhXC0b/+Tf+RTx47+wYSLQRQKB8EjVk7uKd+C3MrCaddMTYfVfZUtF1PQqHXrv0zluxeAkC4lXo6fHnnou7p0zJitFlFkTiXnmIlzAXD3ViuPf9PJXGikKUZiagcDHXK5cJSTH67kRtNDI24zRaSEbAG3PfYw0OxTd+67cxvG5LZagDjolOGN06s9ZsXI8TZy5Rjk9j/sp12d9gnOcjBuNoALb9unTI4y3boTh1Yg5dKQzpyRXn5FWsVwknNPpLlzgHiFsgxl+Mz/X4rKNhA3so3yaMxtct90V84+tfiae+8HjypLffeie+/c2/TGfvN3//95KeBvTpg6OBSB9jaS/OSYOsgXQuDumkciuE7+JjCv4CcVMxUFgr+J3r36Bo/cM78+J2EkC++uf/NmbCJzWMhK+wd61E+uQJ9OE65hoydy/nqIKoE9M1l386DnFgEWv6R7/91djC1L/6+CPxhS99KW7BKPVKRZS2bK/Ef+Up0qX8UBoWpo5BRUYx7xwtiKbqepDowxvz58cffes/xL1kgDx87wNx16QpMbRycDoP5F3iu68sDAoU5JFetiNUOhCZFe7pWBW3cDydp31PqPgE+fHKm2/H0mXvxZeefDZmTb8rJtx6W/SilssZlL1LOJjMzjIK04kieM0Y/kfZ47pl906OMV4YH5M11rOsD3uqT8Tmuh0czRVkv3WJ8oGVGR06fORwzN9QF7/xzBMxa8rkGD/iJqrqg2wImeaTZ3gD9tB+M3A4x1q0Bud1NiiPLmNgXXc0YHCx+kU2HrBTfqzGaffB4sXx/rz58Vtf/2rMQHEqLy9OHHA9ldMaxvKphDHP5ToDg/weehMufud6uia+K7c9KWEZesGPf/Cd6FwzMr766/8PdWcZbld1LeyREHd3OefEhVA8OAlJcHcITqFFb4HS0lJaSgu0xaq0WBUJWggB4sEKEUKIEHd3I27f+46VTe/z/b390wU755y9155rzuE2x7whjjjooAzsyZcGFuTXkpNr0F39xCS/Hs9xfbaXdKjM14awYm3EyFHxl5/dF6PZan3HVVdkILwjgYZs0KkcgM4qIRgr81OaSBp1/uggXRT14U5gYjNPPsz3K0OXvubMXxCvvf5G/PLJ5/LZj//sR3HWaadksFR630GwdysBvBqswxNCTMDsom/HPmwKg7bCwXV5yozVUTk+tG4wRj4YP+mLePzXv4m3xo6PARVt49bv3RPHHXUUer5G0rtzk9bUScJzC8/bQrDB3w38aY8ox1IfoGeFjf2alDMb4ffhH3wYt9x9T/bVuvDG6+MUjtnzqDcv5e7XsGdOzjOTMn7GM9Xr4lpbySoC1+IlHgxQ2Kz6jaHD4uFn/xw3DDgJW6x/9O7dO1qwbcvAsRWs6hAgTYAH/YzuTl2rPiHQcAB6x0DDXnTEV1vojYOgq022dTMyeu7SVTGeQMMrb7Bdl2qHs5h378MPoZKHRoPIYPseKBztqzF/yQrW+Wm8M+rjWMSe+3lL52cQ9rh21aIC+6oVJeMNqxMkxKathD1TCRtB+Se6XR//gBmcHd7zpdvjqzI4K+RJcd8uPtsOr+2iP9M2Aw1rNseEGZw6sdqZIBNIxPUiyHA8sqVli2bxGU7vp0Pfi/u+d28ch0PahO0CVtP4jJSzPps1K88K2zvFZspG75E3pHNpXxxU5/eNmzbH2HHoptdei6eHj47z+54YN119dRzUrevX+Ck1LlSmlvBpljp7LfCsIsABVqARZb3r9xK/Njh+D8fpj794NExL3Hfzt+KyC85nS1jLDC7pwGnvK8+1YfJKmAkDoOZaeAk3dele1iePVQVn6pkvZ86KZ5/7S/xx0KvZN+ZXj/0qTsGGr4veN9mXDnpCs5hWgRN/lw6V684b+4j/MuGWshp7VR6DXj+Z8Fk8/MtfxPjZC+LSvsfFjd+8IXpjj8n3X8GnBsB8hi/H1FGXZ/yu8Ek8sKh83n64gzDWtCcbDL47ekzc/uDDcd5BPTiW8fQ89ri8rAw4FsE5dVSp2kA4MRKw4oWzyo/Ee/F8aJDxDR4KL0+MUda8TmBt8Esvx/kXnht9+/aNw6WbRo0zASi/Opb3+7s8qX3vXJWzRR8NSAr42AOjCk62gYZ1BCbnzJ0bY0kYvvDKK1TxdYwzBwygurRrtGIbiEciK8vEnZQgHv2ZdMl7wlbbS7rSls+qGPBfsza9HfjuosWLY/yE8fgf78ffBg+O+75ze5xEVVn7Vi0JsnPaGqMJ27RxlevMVzz6KtGf9q74VFcJK2nX5LPrnDKV072GD4vXn/tb1OhYEXfdcgs4xR5jbH0gdbP4S1sb+nTeoEzlUdAMsCjhx/u0s+ULqzls5jiGeb/w7NPx6ufT4obzz40bsSe7duzAs7EbGM+AgcMVviqyjDXrw2zB/9AnHfLOO9Gua+e47LLL6K3SBsDxPb7gOngw3/zvvv5jgQaBaHRYArJ8DbbI5iSouJjKaROjhg2LpQsWRnMcz5OO5iiSzl0yE2yzK1WPSs0tBB7vZcbowrvuTMjeft45MRCkHdi9RzaN2YKQrIKysdRTBvN7W2iyIvGaGZVBNEAVwlK6f5t1l5F8z9LU1959L36KoXtln2NjAJk6S4BaIQR1uiRKia5k4MqUZkxEtcZQllMzsM6iWyb2YYx6nv12iG8nhsFMmPGvL70Uk98ZHpfecF0GGnodSKABht2wrlTRgPEG42WjNcbMCgDIUCEqMUuYOtNm0zVSIfkMNCwl8jWWSPQfn36a47r6xSlEQ7t16ECXeJopMT/3ZCmAFHQpXHlP5tYoFT4e0efvlkZ6yUwaH96/GGL/gozUcKJ+H456P+758Y/ieMqXGoOvzOQyT7PGGiUZHNgPTw1wo5MaFY5jEMN7fL6XpaAa/wYxhr33XjxFj47jjj06brv22n8rNsbK+bLuNNRZg1FIOdNnKWxlbnGYQRqYfzvP3E3JZA2a0i2jomHosOHxq5//PFYTN7jrxm/GwAvYmkGkVSOOlFMaiRpwCiyP61NZe2XmSwcDurWhk0Ycf2UZbA0yhvUaNYiZ8+bFX/7xj3j0mT/ndx576P447eT+0bg2Wws2fZUd5x036QYBlGIWPJauQtGBU+atYPQSVgYa/PkvFNv9P3sgxi1aGpf1Piy+hVPaG+Xg9zwv2Mi1uPL7ZhC/Lmdkviq/HA+Y2Q3diHwa0MDNEzc2E8h7h6081/3gvijjxuuvvTLOOOMMTmTonEEXeUJjRGyJp+0Ydu43rQLd1MPh3gxNfUDg6eVXXyHggkFEyaXXQSQ5OzevTnl//WiBEZaBBuaQ2x+AZ4ISQe9PaRLE5u9Ol2WlrMhj/OCfrMBgTJuMelpDpVp1YuXWPRxvScPCORspKbX5nTOM+Bly4QyqVTLQAE0YZBDU0qYKWhngsXs+0+h5wg1BXRhayBgVJ3xlAGYevPrh2HHx1J+eJrhzVfQ/7lhK7Ntn6apKShmQzqy0uP+y87f4qEuTn9pUXuic2qRqzy6P3+IIUiL0GWiYNS9GE2j455D3MNY3xL13kLWnQqga81N2NOC0Dtc//vOJlLy/FZNwvsd8bv4nohzS7F4eUdakRcK2Ps4MTBaVWaOnBSiL9gBE5QJL57mFUve7Xklv/iz+KuQgN/470LA7Ji1ezakTMMv/uu6758644vJLFAzx6quvxz0//UV++tO77oiLkMMt2d+4jayI22PkJZjya740UFU4o3IP82KexYu5SQT8XXLuJlPh9qenn4m/vDssejHMfb95PPqdcHzywyac6ULG8gCfwTp1VnxPx0hZ5HhpJDLPDGIBBfGrY6XcGMPWsrtuuS3m8vU7LzgnLrzoYjpq90j60BBNRwqZngaja4EndRy/lvn8rhFRCkDo7KXTxFyWI2veHD4ifvjEr+PME46JM/udgkN9dJS1aoNziP6ABvPEBLLbXsrxahjI8qy0woQJSAAfXl5pgBF0MruxkUDbl3PmxAtkGl+mDPPb11yOsUWZPVudrJ7aTMf3vVTT2GchK2ng76Ab/u5qEQtXLI1PqSaYOX1qVEU/LiGr+NFnY2PNii0EE1rn2fFtKI1eTqDkkZffiVuvuDBOpTLooI7dolWTliowzvJeg66sFXXhe49CNdjAbHkZWFbGb2fGBpaU05r+e7IKxUzVIvSHhuIIgppvvPNefP+7d0ZfnF0dRgP94k+4JB8Ka2Dqexr+vlf6O/UqtOR74lmcW3U1eSpbeUaMjNdeeCma9+pJxut6KhrsYg6zcF9W8wFT9Y06roRbGcRx0iDlp3rE50o/8rjVeutoiDYavffCH/8QgxcuizuuGBg3DLycrF0r9O7GpD8N6RrYHGjkDPRoVKMxUkerMzwFxiCDp4FYaZdb8eB1S75nzZ0bzw96OX797D8S54/85Adx9umnEmhgrzPJAbeB7kb2adPUoUkfwKBikyAp/C69OE8NU08bMdy1AzpyG4tHukoHn02eHI8+/kS8Of6zOKNzedx0591xLIkIYSi9Cx91qFJCfSOslGsGGYRXVkeAbzOeNvEswcsAsMGHER9/HNfcc2+W9t+JfDjvvPOzVF2jvDiBonC8ErdfP8etodoEbMXArrFyUydBfvV34c+HsYQtY6/iGD2ADL7p5H7YYyfH0QR8myJrbOgo7nVID8A5r5wvVsFXeQvmMpEEr1LxtZvKnk3I2b18aKBh624a6K3jhIoZs+MV+GkdfQIMNBxz5OFUmHYh0FCdxpPrgAuBaZrEWtEwdMy/qCb6KBbMn8U2tnlpUx1MB762zerTmLc+29hobMkzAWCeOqFsUvcAXi5l0n47hvf3/5WfpykAfxt43M3Ed1UigVCVypJKbp3YREXD8phO/FYbTk3TlfmdOqAPZektYwK4ff+NN+K+7/4wEz/NcBhrgzdlfDot/BSnhc2r46vclc7RxcBCHGszqNul/fok+uRDK0zfxaH7zdvvxfl9Toz/+eb1mbSSD32JHulAOagNXMhh5CB2oJ9rV/scbksbNe1seFWadIulVSS/fPhXsYTPM9Bw/nkEGlokPgud7JiQAGO5FmlBMKYm42/tQMe0ItcTp75Cx9aoQ78iMtAz5syNp555Np565fVoxHf+8OvH47QB/fNEp9VsWfS76njn79g+z5d/S6PCS7snbb5iBbmG2ozvMz8ePz7u+eG9MZV9/Ndy+seVlw3kqNGDs3x/K05lYecZdDHh4LHkxZZAYeOLB6W8kW+Vb7lO+MB+R55C9PbI0XHzAz+LMztXxGlnn8vRjMdGGUFN8en3Hde5esqeekOetApPWk37b798lId0RE2CyFfyypwF82MQgeo3B70S11x1eRx3DInOg3ol3AySyPt+z2Cdz9EH8TlKB5tyqmsLPi5k1wEGHNEzG/iuJzyMn/R5/PWFF6NX125xNr6TJ3s1a0IfEeBmJYbjCPuSf6N9bQBE/SlctPVqMJ72QsEc2NvMfdGSxXk06xiCyc+99Wbc/53/iX4kH9q2oKIaGaXOzOPl8TdKdO881Z/qDGGs75k6nJ/agWkvWO3GHKZhX40aOTIG/e35qN+hIn545x2FPQZ9WUFTovtSQMfkhUJGGFktp10tzTh/5aXz9x7tyfWpP96Pv/7msXh38aq4iaCagYbOFRVpc8h3UrYULu1ZjehpdHBrVgzPXbgg3iAY2rKiLC4fODBalZWhY6At1meza/7h+//d13800LAbREgERg5kAN1zVfqULyfHqPeGxlKyHs3oatqHDFAPHB2oj46mmzM7JqEaXfPc2qEffRiXfO+7fDfi+xdcmEd+HNitG52U7cJKh9IqEKuCRORBVLsw6sxCS2gykQZuYcAwEaSXuReJRMJcTvZbxfZjAg3XEq3sP+DkOPLII/cHGopIIvyQxJfZLeaggFDQGogoZRgkNCN+eWQc92/F+diOALe8/O+vvByT3303Lr72egINGHM9e6VBZiDADryOa+mPzGhQwQCGhK7QlvkMLthhVSGLbs3SzA00q1uBAB+LY/K7Pz0V/U7qE6f27RvdO3SIVmR3WWkGGoSHDJ3Cm/dkRAVeBgRgFg0u3ytdGgEKtSUy+uQpMQyDa+h7w+LeH91LoOGIaETZq8Z9qeJAR9/vK9jEtYyjwC4FGizzcl0qM7/j0WpmLqaw5WMMDu/Lz78Y5RhBt15zdXwDnDqWeCkWD+5gboW3hq0CwLnrBPgcf2pE+vtXZk4RMHWaNooVVIp4tvZvfnl/zKMT7N1kuywFbEcmwnJjAw2Woxqg0k7QOXWfYyoinqXRloIQuFvVoAntnumqdGKvg+M4Y97c+Mvz/2C7zd8TbL9++AEUW79oSCR7x/pNGPYEdJinl/hMxSYtMk8JUFyn8HadwFq4CDvL8aXVTwgePfzQwzES2rn88EPihltuzdJXYeJ2nzSMhTNvQGoJE401adKXOPVSoKqoHFPhaz+Ar6C5IaPGxDU//FF05J7rr7s6zj77bKKtnQohisHsHIXpLuhSB1ZHuTJwroniXY+Ss6LhNbIfs+3hQbDQ6xsEGjo1r0VX8XrRvB7HW1rRABxTQEIbLDPn4E9LrqVLwMDlOvwXGmWeuSKWofGoMtnHUSX7MLaXf7Urxs1dHcPZOoFryZYKbuC6/7bbku7bUGljJZSVPH7XKgoFuspYxasxUNqvKZ6dkEpEw8tAjHvK586dEx/gnD359LNxDfR4soEGMtMNiFLrZErXKhphKs6EkTJH4V+dOdql2O71u3DAquJ8VQcG9mjYCRynzpwToz4aG/8c/E5Gre+5nYoGyi9rMDdxUxu6ch+8FQ1D6FcwiRLZUZ9NyzWW4Tt1bR8cb9k8WjesRUUDuDZbgJNp1Ugl5iWONPRybXwLyvr6ku6EVgEx/mX5wr4INFSnR8Ou+HzRynh3duEMl774g+/eHpdfcmHy3T/feDN+8uAjafj+9M7vxAVnnxWtaBS7S2NC+QXfQMTIMnHrM8Anz/CZzkuDLR+6H/bOIYOGrN09qhqKf35vePTg/p//7tcxoM+JmV32+DRh/XW2kC9akql89PsqelenrvEZynk/E/d+Ji+P+XRs3ENFw0zGvvOcM4uKhp4HMl22pem4yOfQXm7rcBx+lzakGXnXRdjUyYyx8gfEJ7+6nhVr18UbyJrvPvZYnH38MXFW/1PihN5UwrRszTZATsnAIFR2KR/zORgjynHHd46JP5/JM/K5fJ49GuCRrwgUzpg3j0DDm/HuP9+Kq6+7ku1xR3PkaM9oTEXDDozcA6hmqAn9CZ896D2z6FY1LFq5nBL6SbF47myyrhxbxnaAGVO+iGqrNkVZj440OmwbtZo2jsUEGl4cMibOu+A0KjLYltH9QLpct2ONlQhkbIKuCEQT6NsNMnciN5EuTJQgCevxeW6bECS7dlv5tyOPAdXY8rjHSZTVjqCiYdBbb8cPv3tXnESgoTmlpVayuHZ1Qspz8Obf4ll+LRmK4jUr5oCzst//HNsAorrJhtKvk6lrQtDo1muvza0Tli1rVJqxV8drXDqGuFaPOL70KF4d00t69XONbPfDuz1gxOhR8fzvfhNvLF4ed115RdxEsKF969bZF0mH+d+BBmQWdJ97tcUxwKhEINCAwh6eswt87Es9wrrgdZMRVsQ9/+JLbLf5Wz7/8R//IM5ki1ITnCa3gGajXKbGSAQyGB+4ewqLR5pKh9KOwV97Ehls2AmtqK/q1GWLA/AZT9b7l48+Gm8QaDinU0Xc8r3vQze9XWmsh5+UEcKoZFA7Cd6C/giIgAt1qg6lFSAZQBFWyBj5TQd1BHuyr//BvXky002XXRrnE8Tvge72frOgJVz5HIZNOZkyiHHU58pjab0kRxMn3CsPLmNr36AhQ+I+7LHbgUn/fv3Do06b4LwYaNB58ahdZkrgRdlHKbow50HaffYM8fQdMJr06Gk81UkAbN65O5as3hAT6f3w6ltvxQYCGmeecnIc0/uI6NmtKye/1MgTgsxmGphYQEXD2yPej3++S3+tJfNi7vL5KVMPI9DQrnlDToGoH83rs+2UCXhMZmVwD5DyHvGjkJW6FIUAJP/yHaam2AAohT2QJ/UQUNrKiRMb9tAMcu0GejSsilnE8GjPFFsRP+UVZQQaTqQaiEADuJ0wdGjcf3dR0dCsAT1PcP6UeVYfimO+kn+XbFwf6Odewl4cp33Ge/LLVvhpIjbHULKoj6GfLuxLoIHAXa8uXVLOJd8IY75vZj1tXvDo4rRbxK30BGNhL+y3W4GBerUOGeLl2KlvgtMHf/FILGOMB269mVLyoqLBYJa8mpc8DlwEXCHvCx2dgJR2lMvMAyzHdmjenjSW2U+fNTv+9Oyz8TRJK0Kk8fjjj8Vp/fploGHN6tXYdx5NXyt5vghAig/+Ay7So7JMPZE0yrzzfWDk3JXZH40bH3d87+6YvnxV3Hj6yejEy+JwqqfkRW340mUwxyoFEzUG8rQ3SvaXAQH1U8k2kyy0izcQ+Bs8clR866cPxNkdyuIM/JsTTjghytjO43etPM5AA/fnllb5kvftzSNO5THtceVoISFFA7wFTE2szMPHemnw2zGURMHV110dxyCDe/ToQaChQQYSPLlF+izJQ/0ut4EJf2XaXvSLfAnDJl1Z3XYAcN+EnFpGktag5rP/+Ht0u/2OlwAAQABJREFUq+gY5556WnTDjmwKr7pt0KAp3yxkvPTCVdLNrsnfPTlIJ9sAfLIKOPHkKvs2Tf1yWrxPUutPr78W9//P7QQajsOGb06ggUAIczbQII0n3vLbyLX9cBYu6Ycgq1O2A2vll8loYTMN+2rMqJHx8rN/iarlZXHf3d+No9g6qFwSFkkfELz8K90rYBzTv7Q1lHNpYxhUgqf8XFrxCFarzUej957/7RMxeAmBhvPOjeupTDAJ7Nh7qIRz7ZkcYnxp2lM6DEy7dcJAg/Z1iw7lccXAK6JVRXnKC3HAQxIXTum/+YK+BO3//XIQEWJGSedY4V+DiC1kAZKnxJjhw6loWEAWuF4cc8hhOMgdo4oIwDirBUFIqAqddUSZh338Udzx43sxmCKuufTyOI9oaI8uKDYIzUh/bTpyqyS3k631OMDq7NOvXI3majCazTX+d9RJ58CmawYBZM4VZDJfI7OuYrv8yEPilNPOyD2HRUUD2QWEh05cKnfmo2JTwGRWS2Of9zLKCxHoKBlo2Mu4WyAYzwP+cvbseMky82Gj4pyBlxeBBioa3I8pFe9kvUYoFRw6oDK5zrkC3P2rBkvMhuiAF+er43Sj0BVQNnoZ98Xn8SRC9qgjj4hTT+wTB3bpXGydYKztGi0Qbwo3flEQKYSSCZm3xF5Cdn7GPWmoMf8lS5bGlCmTYxgMM/jNwXE3gvYEAg1WNGjMOIakImx8hnMVptX5zAhfsuf+ZwujTQSQdHwVcBr0Nt37GIPln8//Ler3PChuwpj7hr0r+L5r9Ts+Qxz5U8GUGS/mrzBg6PypYLFc2v3te4FTfcoLV2OQDB0xIv78zJPx6awlcdd118bA88/H+G/F8TvspUKX2afBqgMkVfZr0HgUxwojaUTnQIPfqLk5Wc+31SiohkEyY+6ceP7ll+O3Lw1iFhF//OWD2QOiHkJnK4YCYofAFwIZweOlMNSITtbiGelwCDc+S4dexcH6NBQ1dKxSeQLH5TWM6SsOOyQGXntdHHHIwQkLlZeCX2VoJt3vCQ/fy9JrnmVWS+WjxvbZCjdfZqQ2odjfJct4/f0PxGHcMfBb34xzzj4nz6reAY40shWgItU+DR57JP3tg66NvLon/RPLLN9+K+agxCbOX8AoBBpoEdyxed1oT7fzZqWtEypvjT/orBKMc4AGOD8LusMglJmYozAwSKfS2Y0MsJTXffgZPCSIuIvXss074tPZS+N9Wm1j0qTD63N/dOO3yPIeQ0ldKzI7lPiCI/lK2Dq6sPEl7NNx5KeX9OVvygadkdWrMO5mzcpAw3M04rn6aioajjkm2rWhRwMOgNUmXsoz6ThhzQC5HxMjxpI4lY+BBlRcNKhPpURdspHgaSe8MWX67Bj54Sfx1pD3OIZwX3znWzdmoKGeOAcvllRvxHGcDF+MomHk5BkzYtjYokcD1brRoTWv5o2jXROO8uKMeI9O3Ec1hdlPAJrOWTrzLNy1l165Wtbsz/zdf/mQaRWnTkCnKzPQsIJAA44e9xUmacTt374uzj/nrITnsKHDY9Djv4/ZfH4/ZYznnHZatEHp78WAPwBYZgk5ONNAkdbMSpQCXOl8yQvQlLjO/3i+vC1u3Hf81LPPxZ+HjoiejP+z3zyRgYaa8OJGMsjiTcUv18ujlnPKT/KK/GrTPWWieysNlMq74kK5Z+bhAwzFn3337pjM2DovF196aTbONUgqPnVcfTm280kjg3ulozQM5AdwpiOac/YjXsrjVRgVrxNo+M4jv4qzaJJ5Zr+TCcgeBT22zgx09moBFgxayHCcw+xID4xSJrA2HUglRdIp98E1bNViDdDSLIyOQRiKn346Oi46/5Loja7sSlC+WYOGBHjgaeZskIk6cU52WBGrcKDW0dthweKFZM7nxEZ+NsTR2gk8NnB6Qn2OJ2zVmBOW4JO1bH2Yv3xhTF6zI47qcziGVi/27x4ZnTtRMk1wQUdu1y4Mzl0aVmRyoDWPFavBtr8iAICTRzANMABny+852gw+EScGwWdMnx6j2WP7EoGS793xnaxosKGigQblkgZzwhuYCwthLXwz2My6pJeCb92mAb2DD8+mVzdOmTKF0tfh8ebzL0Stbl3jpquuiiMx/htRFaAOcSxxqyyTV/0OI6ZcFM8eVWdFSepVn8/nzrsu399KWfCwESPjLw/eH29u2hZ3Z6DhimxY/RV6VwdA50UJ7rY7Bk857DMBDIhWVlIBhXzLhpAwW1ayoDs0qjUkX2JP+c/++Fyw4ywevO+eOK1/v2jENqtsAol94b57Aw57tDEINKinsvIOeSystkHXTIJtLWQvoZfdOHj2UjCbOv6LL+KhX/4q3vhsYpxXURa3/YDsN0Ek178e+S0chbtBcH/PYAw0ZODH5Il2iO/ZU8mfwkb4q+tN/BhouJsMbzPGu/iqKzNQ3a1Ll+Q5HUfHTlnE2Cl3hAuXuP3a0eIz4eXz1e9eBgyWY9O8hFN67x+ejDtOOyX6nHBiHHLoodms2EpPbQczrlWoAnDLrN6PstlxtkGDOwl2ATXolGxpDQ14tgFixK//alvMWbwixrF14nWapO4iYH/6gP704bFBt8ekc7zgNrbtIg/t0D938VKCDGQ8/8lWtzVLYsHqRTnH4/Bky1u0iVacONGwFsmzSuAGXq0ELSn7lKvSVzI8//JWyg51Apyf8rW4Z/990gnr3oLVsJZS2Hmr18Unk1fFbOL9DWvQpHj7Xo55bRknHnckPQ2aMv/Jseizz+OBu38Uxx12KAFHjhlHLmkHaBukk8wDfK6P9m9JNHHNO8JaHCTOxQe4svnpRCpMh5MMe+bdoXFqnxPjlquvzkCD8lp6yEohft8s/UN7uU0ZevByPIOnaZPAC8r+IrBKAB2aNHg0mLEf+dVjsZD7H7z91rjyogvYvtVy/3hF1bCTLsn4r3/CC4WZAL3wuVrbrcmVsRVs/O0ap6Arn/vLX+Ppf74VFfz904cfjlP69ola4H0jgTUro3MbHvDZjI3jpZOvfvJSFjnnZBDfA0auRTllj5WPsHfuvueemMqR1DfSqPjfgYaiR5wwFAap2xgrYc7E1EPiJfWgDiJj+bs2qwEO4bqO+b01alR8++cPxnkdy+PMCy+KPn36RPuysvRv3C6kbaGsyiAduLYaWh/EagptQCuSDfSkTFXeca8BCvl5/uLF8TKJ1I8GvxWXXXM1TTiPjG7du2cvggyOcr+6janlmK5F2aZ9ZBIUwkl+dm326wFwUQX+cwutTQs/Rw4/TaChrHmrOA+boEfnLtkYtoa6FdmVY/Pd3BLGd5SxuXUdGDtPYc0DEq8lX0cZugw95va4MeiPp18ZFN9na4MVDe2hmQY8PysapPf8upxV8JqwT/nDmEof5XwpoK1slA6Ek/24bCD6xl+fjW1U8P2Irfm90R9+V1lSNAc2AK5sJ2DEWgBTVugYiFW3KBfFp/f7DH/atHYz29A+/IieJM89Ey9TyXojFcNXX3ghsOkE+KBbvqMOMQFl1b9Cwy2J6n17+81bvCgDDW3ZBn8lAe4WHToUi4PGElFJSGLsv/f6jwQahF2WIQH4/E+Y8KqKcoB8YyrCcgyl7asp52xBRLb3QeyTq+iQmeB9ILQahH4AwJeKPAPZQMP37rk7nYvrLr08I+jdcUqhAAINlhgq8DBAiaDtwniqwh49nRqNzyReBGoqPsZ0PkaSFCwy+rJVq+NVhOAPfvv7uOTgnnEmZYA2TFEIeiqExpAKUMNKQajQUzxlNoDnM1xR4cBcbQK1VyMDxreZl6dCKARfoNRt0YQv4twLzkPhH00jsgMLg0ljB8JKB0jChfBUxhprXjKic1VR+BIeClcdVZtB2jDl04mfxR+eeZbtB0fFaX360uuiMw5Ay8zu7kBIpfHMmDJk4iWVQCE8ZCqZTqWgce24BjlkhCWLFsekLyZR0fA+ke534+4f3BMnIqSaNKELP46VTKWhoGBSeel8yXwqnMzYwRS+731GwW30ZKCgCfv8VXpuy7DhySCcurZkLG6/5po4hEirQlNYW4Lp+AomAzlGikuGkaBwQeLPe8WxUXkDDXWbNo6VwH0YgYa/PfOneH/mgvjeddfl1omKNm3TKCThl0ZcJQUnc7GiRONN5ZaReX4mfTCezq/BKismqhDAqk0jUQMNz/397/HY8887k3j2kV/E6ScPoJN1zdiybgOOFw3xmLOwcZ6uQzgLC68i0CA98Xje1wiWBnRaVVifgNNHf/WreH3KtLjy0IPj+pvZP3b4oXm/hlyWaYFTeUzDS7p0bJ1zf4rzVP5JKzyHS6GYig3HyEDDtwk0mD2+8obr43yCMG07dox9jG1ASDhDBBgjOMA8BxWcWbmaCOkNGHsfk/V/6+3BwGFufDJzpsNHLyzmjq0aRDu2TjTHya5jRQO8CIGzNpQknF8Fw1gGcq2snHkKD+fO7wjarJRhDV99tZXb9kZtgm4QU+wgQLnUQMMcAw28xbdQUXn97Pbb2TJ0YrSl63l1YFdVHs+1FspfeLh2n1QYgMX3Cloy+FJsJTLzMWf27HiftZld9/ziU447LjpUVETjJk2+pvHMvDKetC3/aIBptBmoymxJ8pLjmmkA9gbdkBNZ0fDh2Bj83jCM3q1xx7dvjKMPOSQaUiGUpYPQ+QZ4ZNJUqohGj4pJKNnhn07MybZhCAMNFWSD2zauE40oj6+Mg+/2iex2Dg0ZwPFiWknH/pRPWH7ShJ8VdxT/EveNPcBrF4qvVNHwzmy2QoGiHZCU13dv/XZccsG5Oc57774Xzz/8WFhj8TDR/4tpyNsSuGzVsVBu8b41ZdK88Hb7lJMpyQKAD70WctcZKGOsEFAuW9Hwp2eewcgdFlB53EcmYMCJJ2bmUsdOHhXWdpqWR+VVZYpyQUdR/jeonbIHelUOaeQVlVQ7wem4eIDSywms6zYMxSuvviZP02DQPMotK3/AUY7HmPJk6ZnKRR1VaVZHy3uEq2sxO70afnp12NC47Re/iLOQY2efTI8Gqg7KW7fNPfWewV3Qn1k/PHLgkPyeOONPgGE1iOhzXACYGfFtPHMTjvv0eXPjxbcGx78+GhqXXHRFHEczyF7de0QTsq67CR5U4T6R/BXG/KzpM2Lh1Gnx1YqVsXHuvNiKIVgV56s+lSduX9wBP5qJr4f8Vq+tW7Mq1m7cHOtgs2oYQU3btoku6OLyjp3I7DeBhqlGIzBRiUDfXrIFuwhsVNqHLkSPa4waSK1M5Y59Gnbt0agG5uBU2bMM3aQxN4oy+9cINPwPGSkrGmwUZmduYaChW+IjYZ6ySzzzfV9+JuzkuTTY+bsBgQDXa6PJoejtV9Ef9ahoMAPbm4yUjSarsMYsq2WNpcuxvRzX3wpeLfSLz8ngAbRTB5rejuM1fOTI+Pvjj8YrZDG/jyz4FiWs7WnQtguYS3s6lQYC8rUfd46bkgYcZ0UDCDVTaqbKv3NPOTpiLsHZl9h6dv+Tzzql+OMD98VZp5xC48e6GVhDwyeeDEBp7GegAfho5+j8Q6Bp29h/AILMZ6pf1N0GxydM+iIefuSReI0teJf17BbfuoP+Sr2PStrbzNoEhfzkL85Z+SVd53ZKs5CMa1ZWXErzaSOAh5rIsm3YLO998EFcc+dd6dQNvIaKOGRBJwINNvI2SJxOLLSRQR5wJw6Fu/PTvjEIKLy/xvP+uSifLbN/acg78YM//CG+M6B/nHTSSdkcrzk0bLm4hr/2GJRHgqDgG//22o2u2Q0d+rMyjFWNZFNV7Am8A6p/aCBKj4axk6fFi/SGggDSMTqUMvIKusLXrUNyh/cMrlclMDd/Mb0iCDS88MZQemktjXkrFuQzjm2FHG7V7utAg2EeiC31XFaicJfrkKb2peEGhKUT9N0BwFu5K9pSlvEzq17gpe1VcN7Qc3NXrY0Pxi+L2QxrD4j1O/ahg8oINPSmRwOBBuh+5sf/ip/f85M4/nD22lPZVAQaDLYSuAPOaesyD3GrjVBgmfnwPPnAuSmLxIX2lQGccWwPGMKWkseHjYjLTuqbW1l74egkXzKOstTLLLXy0CNxpZlcK/iz6tbnaUsqi5WPBtfcfrCcaqrBNFj+3UO/jKnc8/PbbslAgz0aPPFCWaQ9DUWmPSdsHCx5zJ/853O+1h/i3+A89KkemYqN/ee//i1+9+pr0YExHsR2OvWkfsX2ZGxBk5a5fRLaUeZ4SXsK3LQPmK92tnaZcwZAYAu+YHz514qGH/3kxzFxybL4NidauHXiCLZOOL9tzF9eEpalS3hrX0j3wj9xwj0lGSfs/Y52z2oSnYPpJ/btBx+KS3p0jdPOOTf9j3ZsndgDXAwY5DjMkS8kfAzAK39LW5HkN+esznMdJi7UgeJ19nz6ib39ToyiJ8wV118TR6OjDDSYMJQH1aUGFdSrBtLFQb6HHZb0jB0onp27FZf7eFZVcGqgYQXJmc/w5Z7GFq6ggu98Ag09u7L1Dv/DQJS0kkkmcOiY+jfCXZqzas3AiXa2lVD2JxAf9ieyua3VEpOnTYnROOxPDnop7sXOO/l4KxpaRn3s5ErMx6RO2gXAXjw6xxK9ijsrpZQ1JhH82yREDWxYAJqnoIwgQfDqU8/A0OVFoMGKBuDoWP/e3oVeQwaLA2Gc1V7ASVr0P+nPZ5rU9nuN2GZoMHb0+/Ro+P1v4+V5i+JmKoavJ7nRo1Mn5rtf/zgm32MAhbEEmTBw++z8xYviVSoamsP3V1x+eRFoyHvEjheAYi7/zdd/JNCgMhAxMpgEpaiDBIjmEsnZvSOmICxHg+TNa9aSfW8VvXsdHF3LKzLQsBviTOMZpLkPcdeO7THsow/jru/clo1k7r3w4tw60YPyUbRONsOqxP67NNgEPv/v24e6x3JLoYFUT0MFgSHzZaSS6LUOlASyePmyjPh9/4nfxEUHdY1zL7o0TqLsqilNR7Zt2JiEmkYVBCFq/VdBrZDRsTOymAYknxlRtqKhOlnKrTC9+77N1v391Vdj9WxOEKCE5hgMRY/e0WBYB6M6np2DJTazFxpUCnKNZrMfSlzH3cF7Cj3L12thkLh1Yt78eRlt/cPTz9A0qW+cjlLu0bFTtMIhsYx8B5lU51pSPAo9y4Z8hsIpHTsVA/hKpx2Y6Hgp9OcvmB8TP/sshhL1+wRj8XtkME5kS0kzMlK5n8hxmFdRbikDwKD8p+BQ2ClQfHYGS/bTgwZBHQwGGf1zHLo3gMuvX3sjBiBA7rrxhji0Z880EuHijNYqCBVKfk+8MXzSVSkbrVBIg4mfBnn24VjXADbLMLCHjxgZf3vqD/H+/KVxzze/GVdQ3tmpfTkCmrmSNTfLvAdnzZmLgwyYuAbmzo8UIGnIMbbbJuwVUocTAqpTor90zpz4/VN/iof+/Ge+HfHcI7+Ms0/FUESI2WNEupQ+SrQiXNKxdQFcpWi2z8rn8UDXV8rMfDxhQjxIhPu92XNi4KHfiO98/544hIoVoi8ctbUmYW51gspN+BvtVohnMyXG34kAlubFr/iU1nkQc9idUWgDDbf/+KeZkbrh2qviIqKtZZ27mJbMfeXSiwJUUOxSiPJcHhYNwZ1O2Uef0An4rTdjCkGGDyZPyTV1J3nfsW0jMu4NqGhgXzeBBgBMsIFIr/DAIDPQIBoz8KAChkd1kIVB0inPMMJr9Fi+skHXPkq36bQQSzZx2sX8VTFqEYE/noh7ldejBMDOoQRWp3cfNGnwSOUmPuVLM3FGtUFjKlLX5aWS97kGCg6AxjaSBZ4/b34GGn7/29/FxQMvZ8/6CdG9W7dowL5ADVNPiTGwo4Nl1Uhu2+FZBf/gIIEL18eCMYpp1EefhgaN2TXKvbPnzM8eDYMx5Baz//d7lI8ec+hhuVeyFjx9ALSjQnTP45vvDonP2Vo0alwRaGgFbDu3rcL58k2jZQO2TlQH79BuFXBeDctV1aVDW1SOQM8s1mn4kuSkP3FZ/Ams+UWD10DDbgI5q7YVPRrenr0jamNnbUGnev3ke3fG1Zdfyv374k2yRX+874HsGv74vffG1RdfTJNByswxIvfCpzWQ824X8ggst8xYNeYjUzYCFA23wqgoZLDBMoOKyiEDDfaZ+dPgd+No7rv7t/RoOP4EMlLVs9mWfFTIYNfFWNCT+sXfk1ahK/Hq+DqwJXoqVYaNoUv+fbfcFJ/u3JuBhuupgjkQo0Kc2m1f4MhH7rHMwCXjGxRV7ijHlMdFZVsRPFT+aCRkRQM08SqlzEWg4Yg459Qzos9Rx0QZW264CUIlAOX3mZtjqQstcU7aQ8a7LmlWw0njRbkfdYnaAcO10OTkmdPjH2xbGUaw4ZpvUmVzQl+qmw6lKW3d2It+2YkeqEwwYfGCBVkhNuuN16Jeq7ZRl/HqApN6rKGWAUOe7NYGS04tjxUf9oHYAv9thhbWMb91PL9a46bRBBnXtkPHaE1/klYVHaNxqzZRlQoHqAYdRVAHeuFryWN7aLq3e58Ojllw4INc8lpKoPoLAtUjP/woBjP3m267NQMNZWVl9HxomEGYbQTCpVJ1z35q9avJqynX+SyDSuKbuZoJrA0PeE0nAzuELN3zf/lbNKP7/vdvvjkrGsS51yb2PiurDN66PconyAfSjTwsDn2QMlRcaJAqg+rBr1sJbo0cPToGPfWneB69/b0r6NGAoVjB3KW9NGaRUwwEq2sDwIPIEMfz2ELeTXr0p5UG9vqprOzjWeLX44sH4RTd/6fncq7PPPRAXHjWGdkfYgM4VT9Vhy/c3pd2Bny+dxfynVWIOwgp7QVXZJDNQLjwyb3C4HwcgYZHnng8Xh3/WVzD1rtrvvXtOBrdLf3p2AkL6d1f8nfeV3ekwc/6vC9plTENsBX8RBIC3boXWh4yZkycdfOt6dR969qr45xzz42OOBgYBFmhmaXEzMP+JMreDNLBW3XI9iEQYis84zzUTb7EqzaH9y0iQPXyO+/ED5/8Y9xy4glxMn0Ujj7m6GhENSKKLXtXmITQ9qqKblBPqFt5HEO7JmWhZeUG4LBzGL8SPRf2UZ2zeCVHR1LR8OdBL2YDx0tx6np1s3ld89w64feqGjjOQAMVDUNHxctDRhKQWxKzFs1OXPVuGVGGU9WmUW2qcWloR0XDvp04r/CrPJz2BPj2v4SudAKOzMD6otAi6SV1H/iz78k20ub76jSiIWStmLdqXYz418KYDXnW5d7NfL1Xj+7Rv++xBOlagNtJ8fGbb8WDP3qAxM8RGXC0elK61tYqtvAWzkw65rwnbchP6RQxB/HpSxzXNOiHk/QxfWxee/GF+O3IMTFwQL/4DjaTgYYiWFQoBMfIHkisTfgz/ZQByVc8w+co3wxo+CwrIWvAfysJgg6xH9dPH4yxQOX+m79F4ud8emZhY0MjylqhJS6VudJ50p18ykt9prPLlJPW0XT0ttAppYINfp8+a1b8mYqGx9iO1J5xHn/iCRoTnkxfqRqxGX5SLxowU7Zn8ofvSzPaw6WfzjlhB9/levhMeWBV2cfjJ8RDDz8Uny5YRKDh5Ljmyqvi8IMPTv63IW/O3XmLd15+X7QLIP8uBRbStmcu6jztPWXbSuy5Iciaux76RZx7cK84GWf9GLYIZI8GdLq6wzGkJf8VCDaFVf4WSTjkF39og2wFj66hDsk8vsTa12QQ5kUq4t4e9Epce91VxdYJ7OwmOMSlQEMGALjf4IB86HY/5ZowN0hjtak0vE2+Y9tmNWyVTcjuJWyPs3rqmX/8g62d5XH+qadx/DKBBnjVrcgmIVI+SjeMpaz1WQZ5hUlV9Lv05bylVelF/8ZT+5YjByZSrT3ygw/i94MGxQN33RmnYI+1xQa1R4M4dRxxWqrmKGiVpABrsLpROaRcNwignMlqad7fh5P/BX0C36Vy6iV6NDTs0jkDDUezlVU5KF2YZJNWfIa8Jc60bXyGdC59a3vIE6Jam1uY10F/7KSiwUD1Mw/+NP65Yn3czrbtm668MjqjUw0e5ZZkxmSQgjaAC4OxzQ5ZDhxsBvna669ndbbNIFt16FDIe+RkcUEJSRP7//wv/IFek53/b5eCw1ciQouWS6WrANlJk56pk6loGD4iVlGe3xgn+9DuPXECy7L8dhfl/khM7iSjSSR6Jwpj9Kefxs333ZtnWt/Qvz97bC/OyJmZhZ1G4JF5mGsZbBD+4B5E+JLhi1cpcsat6YBl529uXkGPhjdHjIyHnv1L9G/dMM689CpKlzjzFIK2pMw9WIUDwYBARuVvdNe1KCjyvf3E5jP2QTQ2S3GvjRUN0xCCg9gTuJrS6XPOO4fz5w8PywyNQq/GSFdwN6QMViErQ5hd9zJAY18GBbcGgBlCgw06JXVR2OupaJg7fx4VDRPjueeeixMpvbS0yB4Nntdqc7bcP8YaVSjCQ9SmMoBRxE3JmNAI9gaZRub0vgUYrp8ztnuNpqCcb8ZQdE95Uxw6u+oq8HRwZWp/Nxos80k8Gj8luGdQgM9VHArBJnxfZp1AWafHW/4exXbqYYfGzVddiWLrmgzt8zVCFcxmVhRQGQgQuY7F/C0Z877cnqBwSEQQKEGxuR1mFBHFvz/yWOgG34RTdNEZnH1MRkrDnslltFWm93fhoJBVoLsmmVj4uAaDUxpyqtBadetGPY5PmwXcrWh4EmHg9egP76FPQJ8s6bI5noqASQryhIk/8w9+CJ/SlQYrz1C4CT8Vh0JNQ/FXv/pljF25Jk6raB+3fPe72eVYenP/l9UdwrpwdlGM8hvvSac+y32sBqeM4hblrwgy1uQ9Br9GkBG587Ff0+uAU1zown82Wye6QDcahTr5OtDCxFI5AzhuTbGSpj7KyZMRbFjo8Wxfzpkdn0ybnsupYLC2zWpRoVQHOFDeZ6OK3VujGntma1ShaRZ4rMZ+WQ2tNLykQWBq5YI0Iuyljww0MHeVdk2dLoyonTjDSzfTo2H+mhg2n/PlGQIzMq/7brslTu5zQrQgQm+gwexwGifAxzVII9Kb19cODe8JexWKkWvh6BFf8+fP52z7z9hz+EKcf/aZWerdFV5ttj/AYtmxl0rHsX05T5G7D+fAZ1UHTh5dtmnjGoJlG6MpW3nc4z597oIY868J8e6IkWxLWh6333wTXfK/kSWMtaGr6vD1FmD/+bSp8d6okVkJ9QnOlFdjknLt8K9acZxcE4KkdcnUVSKIUQPrqH6NuulIKi+Fpxzoq2Tqyi9Jc/+L8Jyy9xgU3U222oqGSYvIOs3ZGbpppV2nd998Y1xCcFSqfWcIpa+P/Sbsy37/rbfF5TgXjTFSV5OBtHGd21zMgGooCledPB9pJtefRdWBDhGyZj9uMmsAvKbNnBV/ITP94ugPs8rmth//MI4n82KJqMc3Khtt8qtx5Xrk2RzUf1hM9qfg82xCK+2wLmWG42uofTzhs3jg+z/IQPUVRx9JRQP9YDh1wgy8WR+HVL6U8JpGLvSRcp/35buij45SH+DxmevRAFuNjnh7zOi4D8foiPK2cf6Z59IM8hgqGtoYWckghXLMdatzNDCdvsMk5bgEeCwD5TxLR7RabdaNzF+zYV1Mmz07ewi9ZtbrwrM5jYNmkL0OisYEGnZs2kCwfnVswJBfOHtmfPnxh7Hts+nRGvHcvCnduaG72oynzpI2vg7wCUbgtofXDl5bmcgGDLJ169fGupVbKOPGyTmqVzTvcWB0pLS8XaeOzAmHBIcoOx1zjCsASDzs2kugYS+BQZy8qlQSGqxRJ2RFw7RpuRVpyIhRcf3V9JdwrLZts+pAOesRv/J9OkH8zN/56SW8DAgkjHhPmlJOWg2hjJzK2KMx5l5h73GjDhXxnRu+ub8ijqgcl6coWHKscWkAV3npJY8qBx0js6jQiu9pUEo3jZFxnqwwEmf6bxj/H/Cdmzix6HKPt2TuGdDm/gxUsE5ljbSpnHUdzlt9kVUr++kTQsqxS/ifu2BhvPn2kPj1oNdyTg/dcXucMaBfNEa3bKMiQNtJoky6Z0yPcPRvgw45pN8SJrwtHaZO52cGlbn/c2Dz2z88GUO/nBGnl7eLq2+6KfWHxv4W7JkcI4M70qCOUOGE6agqK12H/ODvrkM553flJysH1R/f/MlPg7Y8ccsF58ZpdMrvSkWMTkTqHWSBlyfOqE9KdoVBH3/fYAKHeRgIT2cJGSGuTVwsIZP5No7XIzT5vPSgntGPBnNH0l/CCtO0gxhP/eazqqIX5FEvK2Cr4gQZuDbTuYcgNzelDqzFVoitVD8sXbOJSjFOAXv9Vbae7YgLsAm6Q9stmjVhO4YNFVmrDgaBqXlLl8fQDz6JNzl1YuPapbFw2cJ8Tk/ixq2acEw32wMb1mSrKXKm+j5gwxo8Ujzz2qIpaUQ5y9zAlbNUEhqMUH4Y8ONt6yGyGeSemvWpaqgec9k6MfrTxTGT2+rxGZwVnbp2JFB0CP1NmsREAtAf0Tfip9+9I47G2W1MEMWeFdKziajUSeDKS8lfssO0L3ygOE1HiWeb8a2L7tEZG0tFwxtkjl+YODm3gN1IoN0MbMorcKNTKr/IS9KCx4NLR1npwFjKXp1dSRVGTXo3sKY9uRz5NBz59czv/hhz+Pg7V1wWF555Oplvq1QINAgP5qZtZkAgOVXeSlipyxwSHcvzdgM3kx8GG6xWqUF1loG752m4/tzb73BnxFP33RcDTjwx6vKZzU/dviZPObYJGsdLu9Wb+UN4WIUhfwonfQTfM3iqzW1w59FHH4mpa9bHJUcdGQNx/g49sFfaLh7vLK5zKOaonClwwDiugc9yqwRrM+khP6mbSrbmKpKtNlf94ZN/in70/Rhw3oVx7HFFbyh1qLpDGErn4s5Ap7oi/R3ek5/Ekb6H/VPUhY1MbvDe+nXrs/r2n1SPvzz4nbj23DOiNwHH7t27ZUVD6iVwpr5Tt4pXNRyLTxoVv0m7PCdtYe4zmad/sxH5uphAgwmRQVRsd2pHH5ET+7ClvQu82ioDpfof9ngQ3s7f8U3wfC1jgINwL1ViOGcDR1Z+LWMboH3cPsD3e46K6nu+eV2eWtSm2f8KNCCLDJgKz6R1xnKeEqa0J2yk28KPKGjRPhf+7djDqIj725D32ObYIu667dY4HJtAP0A+Keit0AvSTrE9xabChUwUf9knh+cljwF/aacBFXWb8QHGfPhh/PUnD8QYZnMtScirL7ooOpeV5bPlE19MMG1tKyM93W439LIdGlm4ZEm8w9zadOkUF/O9NhXlzAmu4J4UGjw7CZd//1uv/3ugocBzCqGksP0w8W1/3Uu270uQ/P6oUTGLbP8eDMkDO3UJy9rNjNlnQYWq8rbsSgb6lKjZvU8+mTDtT4dpHfZOFRWpxAw01MQZqVaZ5iMoNrOn7uGzm7vGhntnVMBmnzRWzCaZbdOBlHg2ShSfjovnYET3Bw+45Lw8TqlZk+L4F52vzDo5d4jDCJ/7fFTEGsEZ8WKsVKYoYiYeu6EJAw0eb+keqXcQskumzYwB/frEITgXnYhsWcK6mWiojOxeMhXtNkpujKR5xIqCzvNZC4MWIoTJ3QpiFNCj8OzRsHDxIvZ0T4/BQ4bGgQf1iCMJBHStoBkkzGjZ01bWL0PorEug0qeGrIzpeDKcTKIw9HkKdrMiZtyXLF1C5cnk+Gz82Fi8Yh3lkWfR/4GSWhjJho7ue1NwCB8zsSLXcasDU5ncvWUKyiw14r5iK0Sxj1TYz8S5+OSjD+NVtpQcXlGG4jkjOtEARyHt80sZ55Lx6Xs+T2dAPNoTwpKmvB9cSF/2EKiOIFmDM+757X8Z9EoGp87v0yejoW1wGDWsVGQKDx0Is5WOYWmdwiWrXhJmlmBTyZDC2w72fA5NqujmEXF8Z9jQGDxuPCOpOAfGsYcfxnFK7NWXBhXIwNK5Z3kx89NITgbw2eDGNSjgVbIqJ+lHJ018TwE2z0LvM3fujYP42uVkY3uhHDT0FHQai67fv7OBJM+Rt/IZ4FK8GoQTtyqhIhrrM3CAMaI/Zo/no88Pcupx7YnHRt++faJd69bwCzgDn0aDpcMdwFelq/C3THEva1hPpnAGAQbP3RYOU+YtzHGaQ2LYXlELWaixZRCjErYe1Z7RtmlN9pI2xhklOMIcNLjc159HggEDgJDrN8ptNUw2WQUOGm6VaLK4B/5YvnV3jJ23Nt6duynHL0IHEVdfcDYdoHvlnnVL932lQZ6zKqL+GrPCOSP44oZnCEeNs3REgONamvrNnTsnFefQj8fGgOOOTsXTvn27LIfzXmEujQtPadvx6mPouTd6y+YtbPP6imQ0FUdssdm8aS1bQDZEU7LDBr9mzl1IM0i68EM3OvIDL7wsT9qx1M4tKVVY4xaMtVnz58enBBgseZw6fXKuAv+N7HREfQBbz0pg7Mi9DMIuimjTlLOb69HJn/28bEnGCEHJAjvpQeNBvvAfSMR38g//zUADSnr3/q0Tk2gGOXgOWyf4jNh8Xlede1YGcTQw33//g3jqjbfy/W+fe36c2bdvNGDe6whcZSmtjiaGvvDxYUnfzMXnpzMkvfvtnBg4gg68V+du9nyO4KK8fvSUGUHOM264EkMOOV8POZOGmvzBF3XqxIPB2cJoYWk+gzEdW2NX2hffGmfyn/w7kYqJh596Ltc1oHNFnH7WWfQ56Jh8n4YDtJCGqLjl5fekIWnGZ+YzgIE87e8uwct9+OvRH++T8XrydY7M4/omcuz43kdniafGuEauz0heRH5I48pIL+lJ/YUwJgNExhi+M6vjXs2tVB9s3vpVzEcOD//woxj1+ZQ49/gjaUJ2MEHBjjRRrkegiR5GGPETCdqu+HRUNK5ZJ1oD8JbVakVjjOrawBizidHEAhBi7rxVwHI/zGjBQANJj9jDMWRla5jvcvTjSnTXVoILHU8bEB05Xq8S9GlDvZqV2OdamQatyCa3J6if9xjpV6jK26xD+bYWuli0iB4uU6bGu+M+i4FnnBqHsjWuKXo1T50AlgaLUz4q8/m6cJe3/KnutFTeoLan5Qh34ehP5dQidN+XONSjRr4fB9SrGQMvuTT7PNXkXvVdEUyQKly7wQznVugP33KbgPxbXOCbCWgnyI/2sRkOvT/89xfy44s4jeOMk/pkpWDul2bNzlF94X/aAo4tLUvX3pO6mzVKh5nR5DMfol5bRKOzMR98GC+O+SjHv/YMnOlDvpHNVbO/BDT+FfJEmNhHxJdlzdJf7hdmfOnc8bJqk/vEcepk7vOkEgN345euyKq1O269iTPiD8x5WkkgbwoDadLLI9/kGeGrTnVc12cwSHhnEzrwoq2zhiqbcV9MiScGvZooH3jcUTSvOzEqkJPi3cCeg8s7WXnAs4qqkoLm5d/SnnN1ni91u88S38tIvnyMbnlx9AfRp3kDeof0j4Oxa6ykzPnJU8zT4LeMX8Kpzpy07eknu6gwsKqqCgIw5TU6ZCvBsRXrPPaVo+NGDI892F4DqALtjBHfgmBwPbZOMJXcIrirEsezrmAek6bG8PGTY8t6tiJtWJ281AhZXAemqoOYq8397BZE/gbHTDbjKFKccNaSeh0Haw+vDEIpV4Cnes+KkD3AuJKOiTQJzVUiULAdnrIZpD0a/jVxeUxDLTI0Ue0aBKubkg2lKXH9ugQeZ2I3z49bOO3jYCoOGpKo0zkSJgXsQZ+AEIfCN+lSZ5R5gUvp3nsNjGVgGLzKa5OxyYe/+XqMWb4+eneqoAz+VI4sLE/dpl3haVfqZWWU2WrtC+ne5nc6e/bn0nYqybns68Q0pIPlVO7+a+z4eObt91xRDDx9AD0UTqRPGdVDPFvbN3mIOfss+Up5r22cNg1jGFiSbpW+VjOo/5W/ygtt7DfefidGYNd4WYF09KGHRQPtaOmNeRtM8RkGSnJM7pMvDbIZYNtOAMtAjc/QBpH5DISpWybPnBm/eeK3sZLvHNe+NRU856G7O6cvoHzPXlnYivKK9GhvB3lWB1oeSBrn2SWbVnjJr8JmPYHqTz6fFL+jkaUcMrB/n+jT1xNGWmcQXDvDppSucwtySdpRL2bFAPpDOZCn0yF3i5MQ0I/Yatrz4nop2+hGfzI23vz40zjr0J5xCNVwXQgGaMdnAB3YF3rOgIN9sehTg15XIgszT06SnqUn7XoEEcGGA7Ip+NJlS2P67NnxAU51G3o0HEHgq0tHKuEICtaC5wxsG2gwOCLNOY5yRrtVvDhfAyO+Z/N+g5QmJnxv9bo1JFLnZw+IweMmxhXoocMO7Emj+2ZpYxkMUR55lWSvyVnH0k7XL0nbHfwJ/6RL1mTFm3iaybw/HD0qBuF/tOeea667JvvbFcdbAj/mq37I3gzA0iaS0oN8JO6kWf0xnyP9pJwB7vKHPRomTPw8Bv/1+ewLdfphh8QZ/U4i+dA6+VHxq1+6F+JSP2QVFTAxiL0JflhMfwqryI88qS/bms+LduXlPBEUwHcMwC8q2//u6z8TaBAGQsYsHwLVyDuUC0EAKAyeaexDHvHe0Jj02YRYu3xFdG5X9nXzLDtpS6AKmzr1sKQhmlkLFsQHn4yJLRvXRoceB8eBZHTsXm1U3C7NtarRgBAmsAO6RK2jpIA3u+y+u5oGHkCoxKbzaLmWxOH7lkZNxmD54uMJ0b5z6+jQpXuUlZURrMB5YC5G2TVqnY9Gp5E5CUNBWCgNyvRgdolbJxcrmD3ORKykJta9Bgdm0qSJsXHBXAy2g9nnWZbNUjy5IisOUM5pXAEvGdGASFWIWsbcgaHppeAoRf7cf2rDxa0c4bmaagybfllG2q5bt+jckX229LyowzwMNGgIigaZg+nzC8qH5xo90xkWzgWiMCrEEwyVjMoabSi2BENxGQadDNu5W88891gBmvs3mYcRYpk9t1CwfuduCalbDgya+PyaGIqOqdBVOfl9gxzOfRkKwrK6qszX4Euzxo2TEZNpGUtjpxBQOu2ugywy83fOGaUGRsK4WB9rBE9+vhlDdCFZo8U4MOK3TVl5BqZ0AhUeGodZlZLCXwhx8QCFs0aQClhhKz48ike9Y1Mpn6tgX0v226N9li5ckJ3fexx+OPOvSEPPEnaIPpWKhkQGTfhmKq9EBrKC52sLKPBUbM7H56sIdcoWGSkmwLOV0rdWnbtFp06dKUdrkXB0jioAg2oa4So1FbxHqWaAjjE0PDTgDQApzB1eg8MTDoT9XPhp2uTPs3li69btoh0l0m7fEZfC21JjlYpK0r1z4sdTJ5z+ZvC6CAUza+4clNiKWLByXVYY1PAZ3CBFmREtXcfBwt064vw0aAefougPYO3M4QANLX+y3jQo0jgqhLgKT2F+AIai54jvsKKBUyfGL+R8e7ZOIEoyE+Qz+h19SBqLTch0etxcNYUwtC/ShK/rsKGlf+8FN+JUOk/lyrMNqMh/yoQN68HrwkUxYyyyoFf36AhN2lyuVIIvfKRlg2fKHh3FOsCtdh2yvZwAYaNRDRn3Bm/etI7s5IZoTKChBqW4i5atov/CnFi0cDnwrEmX9q7RFoWsrKrMHPZBt1uhrSXww4wF82MRW7oWL5wpcWOMsl6agnkpQUlYBwX/2c+gZ7eI1o3bcAweFSDslzfIgFrNIAMoyXUn4qCvpDP+8P00euEnt06s3rYnvliyhq06HHfLZ5v9Hlf/ww4uHBS+MXfGrFg1f2EcAA917NApOrKHtCZw3Q49SMOVCSLp9FgiLp1YDSMtF8Zhkjf8WRi+0rr4lW+9Vq5ejU6YGosJCNRqVCd6HnxklDs+sMys8/6Za1zoQCZBAxflg2uRzxzT/jzypzLAah4NOh2zeeB03EfDEjbtu3wjOiAnmzWl7wb/yQ86cE465TxzhFR4DvTiZ7wMNJsxTbrkXnWBz+MBsQk+9GSIz6d9RsVPM7IWnaJLWYes1MutcPIQcvbfBjjGLY6d2RD3+1tVdQB6Uh7Q8FUw2Lnd0yP4FkHT9TH5y2mxcuWcqOhwYJS3bc+xoi2yH0x17rGaYcrot6Py4q3RuXWVqGjYJlpWrpKBhlpMMfEMHuSFnLbQ430v8QTF5BsGGr4CvuuQI+uY0yL6oczn88rHHxRNu3WJfTQyVK/VrERFDQcbVpHRuReMEFhHLmIoGnTI4BFwUUatR07OmTML3M6OI485Io1QeUlZJDyEr5d4dFLSQ9oI/G5/odQfBNWVR16F8bcjeydtZMvI2jVraDq5HP6oyRan7mSm2TLI2I7l2pLHwVexdsfHiNWQ5T91XwaERTZ3i2952+1AGrxTv5wekz4Zg87aGWXd2drZuTOOJNublKfcX1R9yY1Jyfk8HswzeLbyBTmqY+GaXK/GcK6T561h3iZbFkz6Mqo3qBrl3Q/idJu2KWtqINOl2Y0EyzWQrerMZrHo2nSaoCed8jxpJuFlNYAZMk9ZKTqgr8BZnzxlciybMT3qImMOPuyIKG/XlqkWQRrn4Xxy7rzn/ISNOAEwSd/eo67UoK7LlgkNbSsU3D7m9oa5s2fFzjVLo0VZl+h2YGGP5fzgv+QbxmKIxKdVAolz+UCc52foAebvJX4KPb+bKtJNMX0mvUa+mB5lB3aOsvIO0bpN64RDZnhZp99Tiln9pvb0Qeq7GjhBleQbHCz7AykLpYeqNAvfSaOMTRyRvGLNBkqTl3CUOnZB8yZU/jSmHwnOOg12HW0vY+8GFis3bIrJ7K8eO2ch2yFXx86vNtIjA35w+gk5AjT8PAgSoFCSvlj1qUihMg360rnKrCXbXbIClveqQRdVmaonlEgbGWhg3iZH9iGDt1euHhvIUM1fszHGT1uep04YJNzEqyU2Xc+u9Ari6O4Vy5fGmtkz2Up5bFTw4HroFk9EygAUdFOiweQv/tJ+LrK+Bf15n7Au0aV2kDbxChzSBfPnxcIZUyi9bxnfOPjQaNOqVeo+4WtSLPkxbWvsPmhDQGSzTWAsP4l/nXVlWG5ZUZbBC1YGL1i4MObNnhZrF62PHscfwclr9CkjkahtKU2k7BWXCVx1N8PwtzYRZkDyqvQknWqTKIvMBBuYW0VQ02Dros8nR+N2raMn/kFF+7K0x8SD87YZrs8wwOIjtM+1Y9UT0l/KFmBjsEbrQ3q1eliZPBc7ePhrr6KfOWa6VzcqkruC7xYZlHbuOqPat46fQcWU42xFAkYZhNcGgQZ8jnIml8nv8ph2h81hp0/+LJ389p27RzdkmU3TlQHaGFZxKW/cimDPllpUqvkst0brg4gDda92YNrxzD95GXiZkPT0hmVzZ+A7HQQvteWo2KY4xMUpHEwodazrMDDu/GsyvvpoD3BThyW9ggf7v3iCzi4QspVgnv3CllN5MHv6NKpH6kSHio74Zc3zyFT9m9SrrDeD9Y7Ps7THDgAHPm8PtnlW3TL/TAAjXzw4QDve09VWr1nNFm5OfJk2Lw4+gSbFHTrwHBvBQtOA0TEyeAM+3dZg0MheX/ofeaw046mPSoFnn+/6xIPVqSuxX1fih1hJ0KGsnEAvthpw0a+x+sT+edJcBqb4ZR/PhZkkwv32gL3n8EHBs9W4+pgGxbVB9A9mTf0iFs5dFm0rWtNn5khkTVPGwzrjefKPpJBbNLBz9Js8ttXE90rWPf6tN+Kq+38WF19yUbQuL0f5IXgIgPrsnJRE9F98/ecCDdrFIEoG9CxjjUwdZMgjt068w3aC8ZTFrFy8JMoptWnHy2ycZbhG0XTYsjEIxO2ZuVsgbJ14Gc4InspGJFsBgXmbFQ2W70rU7ntaj6PcACNF48D7S/t4inmwD49xJBqVl06vSrSxDEiAwasQIEbzLJFFWUF8Osk2Q5q/eGkyfRvur48w0lGRGI1I2Wl6L4Tu3D1FQIJaiWLehUCpZ+MelS5EopGjs1w6qs3ARpbdsKbp8xaw1ErsR4LwIV5BqbFqBE3h6t406Uyh7l5Uja4mNMNryUth8RWliZpTGk7OP3EAkyn01yD0VxL8aNqkUbTAidLgULAq9HwJV3/6PZnNiLNraEhpus63GQ8b4JSCMBogmUUCBir+FcB9DUxsY6sGvMwSJaODW7VIafwUujxDptRwkutKzxc3Rj016DVYfanEjB6uZf4yZ1vKnQyoqCRdV2WYUOFabKmAVFDehUFSPNM5uI9qA+VkOj9169ZL/Gj4Kuw9TmcFZZtmotqVl0eHsrI8Hkkac61GkxVaRpf96fylHUtdDTI5X0vmNTtTFglLXq7aUqrVlMhJY37XrJnHhCbsmXvCgvH8qeA0QODYCj3PofY7DmSFji8bVjlvM4Ou0SzQvLnzIIid0RRju0NFB0ryG6QicEvAFgJWjq0RLa7MTKgEEzcqLP5WIWqwSMtgosA/+PG0jWWr18Q2vm92320GS1etIFgxn0ZGGPqbKF9jjfjC6QTrqGp++9JhPZi0TJcyKhsatKe5I1tUvJlnHYBStKqB2AM8gYIAVu5dk64Lo5z7MBrxgWMTr0U8Z+KS9fHRKp2nfwcajujZAVxRdYDBVR0HC187jTmVnGv2OE4bvtYBXi3Jphal/UWmS7jK5xpedscWnhtQ7gbXDOA0bFTwRxpQwEtaFT4qKw0T8bSartGbtmyLDm3LomXjJsByO0GGDex79fz5XXlUWjWcha92cDLKNqPghXJpRDO/OvCxVSPufdzF+rcyps0FF+I8LkfZrF6xGEsOBVYV+t5OvxLgoPEprDfwOhAx0KNLFbJqbajqIhtBZlnjWo5GDBUXMChUku6VvxV///+BhmnLNsZHMzEmuYNd4nl9o3VzqozK6D2CoYOBV6tGLbYz1E7ZtQUZo6w1G2rZ/F41PzLLR2zBSVzHOqy8cRp1lAWUhauIAUq+lDlMzX8T/tKf8BUfddkWYEmvMPb4SQO5rkejWANrHc9ehmFsdVlDtpHVRc5K146duIIPzHhaEafztXm/jDToKZ6Tf6EPDSWrvnRkM7jG/KWFDTgZ9m6wcqI2dFCP75gtThnJekoZMeX9dmTXZnST+NMgrI3TW5XIuidCmH1KGc99KaPQIzbUWgbf+F176TRirTXlOWh/E3JcGV8Dnq+KY29AIrdQsKVBWV8PONYBB7WQbfYyWkfmY+/KZVF719ZoundnNIF/W1N+3RxZ1wg41WBO8qHBAHkhrwJFiRffqQLPVEO3bWE+65A5W1A2O3lvBfQ+F9jM4tjKeeCtZgf6NeCY7dzCaUpLNkTz2g2jOSW6Grg7qVLMk5xw8GxsmlvrGFu9sY61rke/NtfAxQgV6WlkAX9xrfw2AO1lMNZqAnnCisL60IB0ofGvTBD34ti1SBvi2kt+VC5LHymT0RfyoUZ39mDhe+JVp2Y18t8xDPLVh278XbpTrqrzpA/1ps/Iqj/W4He9lCnqRI1KaUmbQp1j8GEThvFX2CLqKI93a9um2Ke8iUywstYAiv0V1EOejLURHen7zl26qUvAUr0kTbpt0GeaQVMmbReO8FOx1YTsOdsPm6C7Taaoj60EUCfrPFYzCSKM+C/5PdmNf7iEe0nuF1VrhdEtTJZjdBskbSgN80xh6TzUvVYwykvqXXnA+cuX2/ncKs86fEc47+R9s6jCyW2f2jPizazqMhJKdaCNutC2ekdbSjg6nvNyhlaGCc9lBNodu2XrNtk0Wx0njjze0kSAz0uHEZtrM4mmBUuWpIxo36YljU5x/wl67UXX796ObYhd4Na1A6rVRkZREbhXh6wIIB6AfqmCzKyC3t5NYGLrNhI72h7gYsP2XTGLRqDTCRBvWr86tm1aD/74GP5wV6B8ZaChHGHcCrJu27IGx5M2wllEA7L+3Iu+k6AN91qtZKChGuuwIaQJAeW0vGCgxAoKKxqwMGLh2s3x+cxlMY+pSHUGlBvC7/1POp5nNKd/yMbYuHplNkquW6tu2ijycPIRNCNNSgfSpJnV9QQIdvB+E3i1gXCDl6XtokKBvlPiW1oGF9K7wXYD6o0aNcGJF6fSf6CRjigAAEAASURBVNEnIJNs0K/BXhule4LDBujV77ZoRu8ubXVkvrLOJto6wEUWuAjoShvStzhXvuczmZvCKAMKvC9s3E7h6VcG8/2sLvQnXeZpZuBcGekWzlrIzobYUtLPGqomdIh1oLVJpTFlgHwmDjxFQN3PwxMuJgCdj3PQHmuAHpH/dBrVY/KlvGeQz8TS8qVLspLYbc7ZPwUYK7MNREqbXp444Hq1N0zmGPjSzpMPzHQrr5Rl6YBD8D5DXMlPBkT0U1yjc5IPPI3OJuoGiuQj5YeycgW25DboXvvd01JYXs7Z+/RvHNOfju/z1q5dnUH/Jk2oruQZ4lPZm1/kOepWzTK3aom/hctWZKKyLfqpvvYnsssAaspDKJYQauombVPt42Xwn/ZBM+S7QQDhbhWJvpxBemFfsjXXQTPzuV+d6pHkJvWU+5ksY15WqejjyBkGH6TTzZs3RotWbRi/GX4UleLofwMZyivlZFEdUFRlr+Gzdeju5srIRg1SBopXYSrdOV/pRbrR53K7unSWfglwyOQ18/dvbfoG2GnCUTt6JXa29mRlbOD2bVqn/iieb+WKW+/YOoHdrf7QNhR/67HjhK/zlwZ8X5nNkpMWDZzZAHIzcnYz87AJtP7cp7PnxqMP/SwGUp3TrHV7bi50HV/yi/tf/Pgvvf4zgQaBiByFs/gfshQuCHMJSOqeylnx7709JGYTca9WqQonJXSJ8jZtUA4YFTCQ+6iqYsAY1dI4qEXJWC2MAsdx/6PvyUAqw2oIYSRbCpO6CEydpn+NGxvvv/FK9O5H2WcHomAINRVrChAITidCQtKpVEhkIALCdY+ewkCm10hJYmDGKm5LF92SYd+Fd9nzNH/KlOh32mlRRlQ5nU+EgIJLIagRbXDCdei8SeRm5hTgBhYc2PGMxhpt9ax4hYjCzmzBK+yVa4ohdNIJJ0ZjFIQRPoVHliA7P+bm/LOCAMY0+uqYClrPxTXq6XNlRIW+TOBPGezLmTNjzLvvxNH9B7CNo1fOoaB5kWRJkJFHsjvAVqcqS1CBh0yaL+7ycxWZsHMftfdpkKyBqcaNnxDz5s2Ncy65JMuTSw6ZilcFp8D05VjCNbORMI6CtYC92RqcKQSuBr3BAUv7ZNqV7Af/grLKBhhyJ/XtS2UIiheDRMbVqPI/Aw4Gcr6eLwKSP1gZihsYaOCnEcZ7ztvnGHGfMWsm2bc5uaevKxHlXj17pqHr/J03/yeMnVupdDsNurQXDOJAMwg0/+PxKZhLwm0pOJ0yeXKs4qf78Xv06hUdKioSxtKxa/cZfs81p9Bj3o4vVpy3As0peFOxNlgKOGo4zmTen0/4DCFfKcrKy+PAAw+kAqJl4sgOuPkt6MWoekaqmSsPTcPdCKtjZ7QXg0V8mMXi4wzGbcSg+OiTT9gTPTVOsjM6AnoBjrh0tGDJ4pg8e16Or9Gl8WWgAWpMh1hDqQd9iTq1w/lpQDkdWe9qLCh7KcAnBhqscNBAYerwdIlGgAF/QxWUuxNQZMTFnDoxgT4C7y8rxi+J3WsvPjeOPpzjzzDyPLbU3iQZQWds6WnO/PkxjKY/Hbt35+SOw1MWCGuVgfhRcauoNHQ0fkvKXxgLE3lHxSmPZZBhv8KSlzeSLZj4xRfxr3++Hudff1McfGDPpNctX23CaOaEktqWCBKQYF079pgp48zomgREyLCZiTCgqmGzmznXhPeh/Ji9wK0Tk9hCMZfeKBOAIGsiOrOPQIUBBsOgSLLcBtCBAqouHapEy4Yt2YvP+6WKBuhQupFWC8JNquE93/13oGEfSq5U0TB56boYM4vqBO4wiOF1CX1fjqNXQllrKiYIMOiQKqPtJu0xfI6tMapRs5teHJmNAI6rCTbOmT0nli9bkrKioktnqnI6pdNeyu4nT/EMf0rH4iIrgJwj7/m+dLkb2Pm3hk5tjEjlkntyx4wcSZXH9ihDvrcvo0oMes9suYob40+F7mWmM6tWoDGf4aWxobzK6rGU+8hOeEIa0FGcOWsWGabJGVxtSya4davW2Usjv8xcSoGGPLqQ6Xrcmk0QlbkyzraNZJ6objEQomOZ5e2MrSFiENb9m3NnzogT+p6UlSENCHpmhRtztrGXzrq6bss28UHfDww6ex8oD3bg4G1asy6WUK01Gb7cO/XL6N6mVrSu2zAa8p2mwLIBfGwjyK/LtVNySAz7L+77N2koT8Afb+xk7ph66fiswMGYi+yYtmBFUFcTh/3PVdHrGz3ZsrA0Xv39E3HS8RfRWKx3OqBmGOVH9ZJ401kQjY6beGVO0qK8oGEnjOUleVAHXEPQQO5aDGjlzCLw25e9+Z07dUweVm9pqPodedJniANxW8Kp96TBryDhUqZK7+pgg8/qpkVkmSZNmJC8dgrjt23dKulAI7cYR6cXicMYOtu5lY3nObZOvnrDbQT+lIak+7oYlpvRT19O/5IqsS9iyZzpceQJHHF6zDFZum3ASp2vvk+9xLykM+nCcdQvyhrtBQ15S7jNyOng6IgzmWy6Nm78uFg4bxbNKpvGYUfQOb5LlzTit6AXdQClvTR6kUvKMY1ax5R3hLfBdMczg6h9YGZbx17eUJaP5FSAetBh525do7yiQ25VEC4Gx52fOin5EJg6rkEf8euVfMq9PIqrCNAIQ8uzHeNf48bFyFdejB69j6PjfTcqAMjE4zjKg8rRkmx1zgyaJePC3OCeOBffjrMV21A7RgfAjLnN/lbhML43YkQsnTc7+vfrTxPWNhnMqAZu9jB3CCbhg8RHvsJDVSiB9jh05rnXLRb0GKmk7ML2202AohLPq4yjupRxP506PT6c/CV6exG9aJZpwkY11qiOU99ZWVaGQG7ZjKqy5rUINNAvge9L/7sJNO7cgQaEp9SHnoZUXbwzhn/7U0rdRTXTNl47CDTYfWjRus3xxewVsYBAg+4rYbFoSR+eG2+8Jg7q0Y3tmeCV93ZDJ2ZtmXqOI1+krAAuXjpoywnwTIcuraA5EnrsUF5e2FXgTlSpH9VvwkjZKY/l+9hqBvjyBdzFvzazOJdeDRasWb2GXkP0kvjyy2jSuk0cT0Nvt7cYXC74UFS6wsKWM7HjRP1uURXEB8DJ58mn0os05jPMNqs/llJJoH3TkQqC7l27Qa9W+2Avseaskua78pRjOJZ0L21oa+owqgO05f1efejN5xjEWMC4k7Aj12JPNmTOPbCDK8rLUwaoj3M85mpAQDki3+SL+fm0UnLCALg869/ScGOa3MrXE9Edg195mUx2RZ7w0KZNm+zDlslCeE/+ca3SuM8wuOm883PwKk6UpRkk5dnCQPwa9NYW/uCjj2Pa2E+j3+lnZOWfNovywgSdMFf/iVv+SP5xq57vG5D8WkayLscUJsqjDCKxhuXA5O0h71DxszP60w/Pk0FMLqm7DGbp/Fu9Zh8F5YfzdyubNC/8tb2KdZlwA5/IJnFqYtVA5CwSYsPI1nfnGOWjjjwi8ZK9DkBhVmMwjrov18R4wlUqcUztsN1kapQHJj2UDykzeT8Da/iLU6ZOi8kTxsXJ557HduPuqVuEZ8l28z7llb5cLb6vXNF/SBJKMir8EANsLC63satrVhLImofOnYq9Zx++k7CNKsrK0i8TjtrxwkfiFNb6r9o1SfT7/83kLbpA+lLfuBVCGG2kz8c87L7ZwMYKlHUEVO1j+OPHHolLL70kGrZozbjFug1M8FBG+O++oBew+H+5/DbCFa8rCV1FCS3z2v834nAaQBw5dGisJoPconGT6E2ZVpfyCiLQKEQUZ5FBqp6N69ZvXJ9dquuSEdNFzBMAFGYg1UinxqfPcpuoxON2hOGjRsXwIYOjN8K1B4rNTK5MIbOIYAMLTlMjwHHqEcE2spYBDAhaoVdM2rtgRf7UUHD/zqrVq/O4rnkzZsTJOF6dOnTIiKWEnIEGmRxikzkkOA1xM3SC1cYxCmoZz74M7t32OE9LQBWUBkQ0ol988cVoDoOfNmAA+wdbEKApjFWFnoLC/XBOSmbUUTaKqlC1vE1izqY3CAerJ2QC9y77vvPTMRoB7I/t2zeOwYkwqpfinvEUGjKh83c9qeT5XaGl8GaSadAIQ5WQ7xeZPpiFjMuiJUsQgh/GEjJtV11NwzX2hRr4Ubk4b2Hgd2VAhbN/K0jS0UimxIFCEDhXDUQNGY1TM0WW4S5evDgm0LSoEcrhTM6mbYcAV/AWQgHmhQkz8ATafK7zywgveFdR+Cz/FjsKKGHoGt13OgN8GiDxeMeOnTrFN+hk3qh+w5ynuK8ODpyXkVqFpgLa54lLhkmFZMmzXcZ1FnxTmjLKbNnguLFjMypeB4V7KA5vD4RgiWaEQ8ICoZNzdECuhDvP0hCXDs1G+B3fz+AKt2lEfknAbhKlgwarysrKoyeBhrbAxsi1hqtZDpWbz5D+dKzFX2Hw4gwh8FQ0gCVf4t/n2ZvBxmhDR46MiZMmRf/TTiX635jAxmwaQn4eX86aFaPHFX0qbAxm7wAq+CkFL5xhnf72bYMu3Y3p0WBZKfznQ5hDZfDq9glPTbCiwRWbaRLGTkKxAapgPKLWRMSXbdkVY+eujGELPIOCIXh5Pfj9O+OMk/vl3r3KyAGw8XWgQVqeBl7fput9565do8/xxyevOr5wdM3SgYpZeBhA0yipA796j1tHNLRVchrk8nI6CMBRI9ntRZ+MHRcfv/9+nHv+BXkmu3uDt5NJa9SALFFdZBOw/oojeLeyGZ7wH4EmMiJkva3Eyq1drNny2QZkA2zO9xnbyoZgOE+k9HnMByNyjVhTGURpyF8NgHFtALAPEdCMnk9tW1QjiEO1UU32hlbFWINWfAlPyRCk5xjSq/+5LsGqoV0EGqqzdWJXfE6Phjfn0jyPzzDP8/rBDd+M8884PTqXsy2IOVcCvluRYQZIEBJJP9L4PnAo3Ws961SvwFix2dIi6F4HvvuBvaIXskAZrCGnTJIW82LNykmDDPKKPw3KKh/Eh/Pn48SV31fmffnl9Hj37beZy2YCDRXRBdyWtS9DfpK5hJaV6cpD8eazNJJziwpji2+dWg0O5a3ZPJ0u5Y58IF4nISMnQtc2vq3o0CHKy8vzd+cifWocOCmrDFg1fURwLqQn+M8s1zaOjNzLVpqa0K0GpAF2+VrYuF95CPJ3+rSpcWKfPtGtU2f2d6OfcLKUscqn6lQz2AR5DdUAhACjAQascp7FxOb1G2PVYrYuEWAY/8m/YtvQYdGzomm04fv1mVZ95laTV3XmB6kUeAbXXsLR30ovwWs2ZTt8Ygf/WjhgTID39sVK+g/NQ7bO37EiNpSVx7HfujkO7n0Ye1vnxTOP/C5O+X/UvQe0V9d14L3p/dHro7z3qAKBRFEBhESRUBdqliXZkmU7zZaTOInj2HGcLGeSfBlnMpNJJplMMo7HTlzVLAkQaiCBQFTRRX+U93hU0QTi0ef32/ddWf6++daaNSFrli7c92/3nrvPPrufffa54ba47ZYZzOJ1SaM/1++KE3BfGsbKbx0YcaD81/g3uC9f+V3h3DjDii5Gf1jFfDmBANPy77pnFvpjTI6/xqCZM/JsTgIAc0kzjre0VM5USY/lzLvjZDDDGcP6+vqsC7QSh70TQcn7KWY6uKoqa9YoJ521N3Ch0azOEb4MgoBzP0tLfs76SfCXRXUVUD2ZZVZGrgDuFUxw1O3cGZNuuAnc3JJr0E+gk6UJA0nynWOsnNaJMHtNPZWyX9rhX6Z004cyHdrgtTbB/DcW4Hxtia7YS5OmTGGS4KrUPyeYpNBg1sYQ7+pD6VCn0VcPYfe3zNxBhpmhp052YkNbaDW1mGY//7MMTowiwJ76g6Uc8qA2kfDJR6nzeVXnidcMFMGz8o/BHPHtqdyVypzhNQNUe+zlF55nCccE6i2MjWHDhqdN9j5GtVkTjpfw2p7PMrtEveZ42IdyjJOfwYfZEi6H037ai6yxvsvuHVvj9ltvjRFDWLaEw9+eTLCLZMKKeyOg5y4SRGRpQjMcevGAyMpAQzOycXgM/AnpO0Dooku0va2+IV5atCR+9ubi2LtrG0vqalN6ZqCXayugWXeFIOkL+cvJbktdWepbLN9oCjSQ0eDzdcKcBGjNiYjOYL8ZDurfs/x+inonZjScQrNloGHrwdiNEJZPsQxjSPfe8Qff+GrccO010adHd+QKwTMc5tPoEGUlTeb4eL184usRslTq6utiNbsYHcK+nHbzzblcT1xrC2gzeqHFEh0D6bHgMYKl/GCmpjLRQ/tHXnU8zCgx62/fvobYWbszNhFo6Mcyt9vZ+al/ZWXSSzrr9FnHUVrwWc7qGyzWvrJd+bc8fOfpOMu/BjHWM5FXu3076pMabtD6hPHjMxAmrPZYWa1ckE7kIz9ri+bknXyAnvLZ4lj7vh3+g/09D+07qbT4rbfiCLZ8H5aHjKVt6xYYxFYvyIfa2sKp3CqztHyOPJABCOUC7Ssb5A1tCYswamdaH+y5p56KAQTUxiDDqqoL/SE8OUFKH9V1mRWM7JFP1E/qnlKOyUcG45RD2mvKDf0bJzfmzGPZOc+4jYlOi1WbzZcZSvCPh8Fcx0m4zCyRzz2UWerFQrYZNNKO1enFjgUes7b31NXF0xR3dELhzttvJ3BHFivjJx9mIAS9pN6Thqxx5yFf6vPpkBuIkSazvSZb328NElh4ef3GjTGPXWVGjxsX07HHupOVkTa6uhl4xZH2p3o+s2J4tn6bvpqBaG2azKLG7/voRKp4cxLY2mFroflZbNl+LeMqDvxN3WGbhU0N/PQnZRVyy+/0SwwsS+se3mNR2czO45ku8a6F3t95ZxU6v1ncif9hsFe9rswqgkiFvWgfnBgw+Ju4Yyy118W38PhcM67LAJYZmu9u2ZzL9Q6i8/cfPhQH6vfGA597Iu67/352n+ibO9uglrMPKawSyo/vn8sUaAABID/Fh8K7YF0EjYrvUkb+X3/lZSpcH8RB6M1+8uOziKHbjpxBeKo4Nb4bT5GOyIC3xvBqDTMxTIVjAHNKzBJaGxwR95tuQRisOcYC1ky8ZqDhpdkxDsGsQ9eLAiIVMJtCR2GX2QEQl1F+B74DM0Y6EDKMwkMhmILQVyGmLxKF27ocxVB85rmfhYGGmQQahg0ZkkJUQzCZBCbMyBxC1rYlcCORHkZTFVISs9/zJ50u61L4JAXyNoSrgQZT4G8n0NCPLYByvRvPF3cKukIYKiCACaXjzJyGhOlsYCEDDBKz+LcfKTh5roJsOYw498UX2KViZtw0eTIpmzhVXCOswu+rh0yn4BcfGo+Op20Ju4ZGDi9/dGLtk8qnDuZw68N61vB/ku3Arhp9ZTKYzppCKGESHu4rT9tMxqNN38uM4k985PNo39kk11Pt2LEjlrHcpgfBl/vZu7s3gvwiuNNwFSDTJDXoIJNi1sb+oBxclytuHUMj4aUi8b304F7GW7ZsYfnBjhRoOi7joUnTsqUJU+U0TgCM2dzCeLE/ZjYoaGgQvDHeSEGDDNKpz1Wgm2a1Z+fOWLRoYdTv2hWdULgTJ98QV+F4ea9LUzzEgRzjIZ0qXM0sUIApCP1NRWo/VH6FY8SyB+AziruC2U0jyjU1NTFy1Kgwip4CEBykk0M/HWdxa2TbmTuVs8rVV5+pE6Vz530qwtY4YjqWFr5cRsbEHffcnWm7FgBajsC1EOmrS95OmPui43p1Rakxi9+RJRKm8WMNsStC6+hO4bqOOGMad5rTWZcB/OgQG3hw/bvjZ3/tZ85u8JXFvC4ynhYsbKBGw/KdB+K13czE0Y+TXO/xFxhg99w2Mypx1HWEW5nRwu8ZWccAtv7Kc888g7M7OmZOn0GgRHddVEjzJcYBFZymUQGOTQn0UDmIZ2lAPlPBSUPCakrjIQyipczWLcNouRfFNokAkmGss2fZEaI9dMi6X6YzSaHHIKLQl4GG1m002IrUd5dTJa4ZtwocCIvyWeH6Z1QczkDD4jcSDpIjgiXAFB1rxq4TZHdBN+c/eJ9igBcI4JD2Cc4NMrQmsuPzXYIG+v73Aw0sPl29Z388X0ukn/uYTMvjG7/6K01FWg00MCuCjM26AmRNyQsZaMNo0oMyfb45ucWm++8n0Lhq1arYvnUrMuVCjL/uurgWJ8NUVPHmOCfTMAbSvWNRjIf0B/z5vXylwVcETbwu98AGV7XUcnBrqhMEHwdWVcfYcWNj6JChKSfLdffyTAYbGNe2FOeUfx1tDUOdJ8fawIUGdMo4PuvoKKOXEmR4C8euL8HeK5hJGzp0aNYEUj4qfzUqlRsaRgYaLI7l2lV1Ujo2jHcLUgSUk8rL3PJRugFXbmn8wktzYx1ZK9OmT4+RGCu9SVN2aY8ZaQ6chVedXXVZnDKlE/yjAc7b7PNeCrBupiDfmuVLI8iuGzO0H4GGNlHBOLQXd7ShppUGlCpJC/5pOsR+ZiGAX7MYpLtm8E0LsgstqgwHxVHGeA+42H3x/Thc2SeueuyxuHLCWGakdsU//Ke/i+tGXhczpt6YmR7qJ4P5Bm4cPwPayhJxpAz0d+lFmaMOKgINFDXGWHQmznE+jrG1d299LEG+b0auPPjQJ2PC2KuBFJqDjw1aSCPSTaE7+IXPGox+rWHt+Hw0YKX+sh6KafS7CXq9i1O0An6tMNDwwP1RVV2dGSLep4Mijcnv0ojvfXapD32GTlpLrqNzWavAAFtX0n911pavXEHh5OWxr74+Jk6aErfefAsBom7RSL9czpgy3ak68KO8NTCV9I1ssT8a20mf0j86z73kMXBUBpnh8errr2W9iy44mpPQH2OvvpqxIuuT9t3e0AwMAwPKdp2kj/KV7fus1IE818kYDWnHy1nQ1cicOSxltYD2yCtHUzBuXPSrqpJZsMFOgVcnFQq+1HZKeMFOGu/0pTDezbIs+NhrxVdbHKUL0JCB6pexOa657noC+FenjmoPvVwABvEtrDmOEi6givPUp7wKejEWOu8GMHAygdm6No5tPXbBU88+G/W1O+LOO++IUcOGZmaBhWlJu8oaKDSDVCSI0cKMiWICw90fmpnNAJ+hFXk+zybjDs8gz5119ew4sSieenVBNOzZwSx4Lc+OqCKo0ANZ7E5AnRmiLlzeieKrnVmj3qEdMDFe8qm6xayGDATw3iUTqfvooLzZij5rP7g+3OWB51qShg5su987Hqs2UgODQIMUSDgrBnfrFX/yrW/mTjw9ujIpxjg543yWYCCDzRUOrzyMiAF+x99irNL8MmjSQMMsAmujrxzVdG2RZQCD5hJgnXfH1fE0zd1xbCR7RP0n7tPOFOe893A5kLWMtm4j8wv92r+6mp1H7og+A7A5mpxzx9CgrnJWPlSnyq/K5IJPi7ZKgBxn++VyUZcyr2BCaRPBaq83G2MKp1v6NuJwilPlqu3rwHpvCaP2jM9MPuA6ZbAyvpk6Xf8DftuO7b4AH+E4+mMAQZKx0PuwYcMYFCbQ4B8xKj7V+wYGdBaVDxBK8ljSJjSefMXVBe6ZVISfPJYSBHgBm6M/2XBjxlwVgwcPjt7ICflCWabt6GDJNx76DtpoykZtZfunU6zfIH/aH/FpYO0U/Z+NfbByyZK4Bf9jFNm3puvbR9vUXjR4qc2eOEWOOLnlcdylRwZ5CRCow0pfJ3UUYy5cu6CZZww0ILPuuvNOlgj0T9yK3+w/jTqhpp3reHo2IYznwVP6YXwnP3smzrnA7EOX2lh01EDDlQQcp7NdpVkw+nq2LTxlZoD9Fy8WS6UhaqScStvc5R0GxfTXCn9OO9laai5PPpT2mLvlffLTn46J117LeLG0kd9SFzC+jpW8Kdy+Nwitz6Evqa7KfkIAyiWXxbhES7jMaNi5k0ADE3pOCN19DwWloZm0zxkbeQdKKGxr9RX/9KGcnBKvuQQJnIquzLoWP/ShOc814Lpx0+YMwhhkcDLiEHLtrkc+GffMmhVdGQMnrIAaX4ZxaOLDHNSP6Z/LE2iw83KrB68ypsQp0/jFBlIMX4NZzGjo270n+19TrbS6hlk7hDPM5poqB8TTZs6iDFwyke4WDJGRIBhFB9eih83xGBXcnTDCZdQ3Fr+FIHk1hpPNMAKn0dTXrjgYDrgMqyFUROCNKhdGo0LU35NxeCaPTuHLS97j9xb6sHDZCxi5u7ZtT0NxcE1NBjFkdAW0EOde2hCnAkJuF15/ksB9nsJQIvd3wwAyQRoiPGPL9h3sofpMbgE2Y+rUTIHPpR0yI9c66+sMrPcLswrCV/slwyk0jKTZPw26jKxBzAovZ5MMNLzItpLTCGLcOGlyRlM1CIVF5tKgyxR7GEFm8TsFrkJDuMVDIUgKmMWPQQ53M7AwzEocDLe9uYUIt0a0cHlqCBVwaRCCF9oRW/7mUcxmOgaFcWck22uM6qqY6kh7NetgGQ51r8rKDDQYbXXphPDZP7cBc62Yws7PKXRlemB3VrMZM+pGwX2is4vSljNG1hrYvn17CpKjR4/gqI+Oa6+ZQGYNjjYCzuBFRxUVbaqAdWI1EhwzBZ94z2AMRkYLDBzT3lxCo6LQubJ67hsL5sduntG5W/eYwrheTZTe/rtsQ7pRUKk47WtGVuEVcVMqa9+r7BS+pQOsAjT6u5YMobfeeIMZqfYUuhtMyjHFI4nUO44aHd6rECyVhZF08atjZXBCupdOM+JK38S7s0YG5yysWQQaVhJomEUhoR65VGMFUeN1GO6voVQ9BuMED2D7rT7M5Hdpj1OOjrl0rpFk0AtUIMbgp48KfY2s1oxDK5i82OoLruaZKilAQC8Vho12n4EITP5oxAFqYOnEqt0H4429l6Ibwv8IgtfjT377N+IuZlb7spzGzCarJpvVoszRaTTQ8CxKfzQG7swZ00lvJIJO2wV/mFLnllfFEgcVdPYdvPEmeUxa9drEFfiWD+Q9Z8U14IygryPocge7DVyHwdKKvp2n33AUp44j/YYmz2NEUuGB8RQP8Dy0JIfZXxVOW4wBd51YgdH//Lx5sfbdjfE2RWQ9+mAnVPWuiEoKJfauaE/FcxTXSdYVgoMKHExn76DCphM+YKylLQ3eRKrvm/4pQxjtvKbMaCi2tzwQL+w4l5XOj/K7x5cf+1TMYpawhmKhbcFpBvGAvZnjhRySV03F1hk+c4H6MQSV5PMGlORSHMZ3169Lw2HKtGm5f7ep0W5tJ42nHJF/wLXvU+mDfw1LcZ2GEzB4rbQrjWY6O8/bzK4s83G8TH11HfekSZNJrR2RdO4SK+lcQ0Q5rz5Q/mq42I7GnsECAxiOuzwkTqS9cg2lS4Xm4XhVVVXnjJSBR4sPC5s04tp45ZVcpanIqpiciU6niXHtSHFiC5OaMm+/LKJlMNi6PRYum/3yvMxAmsKMzhUYK31YO+tWbMJlIFAjzraF0eicwVyf6YzTCWYq95BVsG3d+ti6dk10WPhmjLliYFSC+07c15qBl+ftk4e04Dg55olnYeYnt4g7w3lB3qfdDxjPk8iE9izv6QL8H3DRPvTHbjbYq4On+8Jj1SOGsr52X/z4ez+JjiweHz95QtYjsLbRFTgxZpWoi8ST/CzVFbPS6Ar1CXAoq9U1yjtxL/+pFzW2rBOweOnbsZlA0kNsIede9d4jXj3lxdSVtCV/FzqwMGh9rn227XSK+ewzDCb53a7dFFyj3dWr34mOBHvvYkZqCOOb2UzSMtf7LP7n/cIp7Sk7paeUvRjtXqcOVpcaXOqE4+dW0zrrG6D3IwQfJ1B8cdqNN1G8r2sGAswAclabJ6S+KHW9RCesfJ163PE2eMWAZbDBWh1tsDl2kuo9f8EC0mt3ELzoTmGxa3Aar6Q4IZXoMd5dF60cF37hVR/njCbtCa/85Ol7cVaOQQZXuGctxv9rr7ySMErrtl9TXZM6Tecg10DTrvpMnnF8PR3bDJok3goeZhCaxgXckGHkJMHrb74RbxAQm0DA8UqC4GZpqteFRd3pKa4dJ/sgn4vzcgy87qPtGmjQ4G5OgMVAw3MEMQ7U18Ud2BxXDB2SgYY2wNZIAccMjELLLVpROLljd/gKWkeHWxi4deog6IX6DAYaLAhpIMkdVrYxafLC/IXx01cWsHRiN8V59yQMYyqpydC9S/TpRD0LdvrpyH1IQJZFwKfgl48OJ4f44IXTsS/sVKQFeFTv5bImx4P+nke/XWiD7UrV352H3IUAWiXQoGRSFg8imPwnf/zNmEywtiOTbgbqZWL1ZmErF0+UeJXH4jMDDdDNYiYCDmPTPIrjdRW4dyzlJR0oeVIcGwAU98JaOlsur0na5xplnT9KP9qi0v6OHbW5XGgLQcHK6uq4FXtyEIEGdaXP995yXH3vuOYhfnhf2tqOrafXKA/MGLJA32Ic6TUEGwyITb/t9pgxbSo2HEuXsb+zqDjy3ftSl4Br9ai2q3AqT5QPjoEwS6f2wSxZP2/eti3p/QNouxp7aTRZB9rxwpTbv9KGbelg2r73SI/iy/fKaM+ibXHDU4FFHnTiZxk28KsEk/tig40adWUMGcIWqmzlySWJZ+W815uFIF/6jHJcxJnfZeYJzyt4uuiL/HcMWfPya6/FCvBzA476lSMpggs/ufzCCUgDL45lGWxUBtqeY1sGMZw0sd0y0JCyA1wZHHZZyWz8GxrIMe1H28JtoDsnNYUJNBswcaLWdnTSPXyuukaZIG587+82oC9ippIOtTtvjUB+ubxMvZqBaAD0GvFgsEFcSw/qbd+X8qEFgXDHwgkMv0s65rN1mPa5WwMZpmY0fOqJJ2ISgQZp5Ti/CUcxftJhQYvi2ok+4TZbxOxGfSF9hzJrRZqQXw5Bd7vBzdo1LJ0AnjvQH4Orq/O5wmvGjra5drX8JGzaXE4O2obyUnyrv/zs+Gj36R+408TmrRRNhpc+DDRQ0+ZOAg13z7onehIMg5lSLyTyxenH/PjXBxpEAISZZxMynJ3xdK0P5Bxr165l3fRcdpzYx57EveJ6Ag0jqgcXKdEoAdeeObtj8cG2CFYL0p3EobRZUzQLYUhNBK8lqmuKdmuUrkLqNAO9cOkSFPNrMQjh4ZpAlaZpsB4SlSmcEoLRW404HSyHTqZMQcKgKgh8D4WmwPQCHTmF4IsESXbhNN6E8LNtHTKzIiRk18Br3Dm7oBDXuZKwJfR0oiA0GUWG0riS8FS6MrICUiH49NNP53c3T5uWdQhkcAVPMi2taZAomD1LPBsRNYhh285OyRxGSGVEU6tkHNMqDTQ8TyBjOg6EGQ2mbXmvAtIosH0o8cCj0qnVwFN42HY6hMCZTEufFEIpdLhXx2spyqF2985MLRo14ooU+BpTObMFrmX8TI3iOQZJZL7sB88S3z7HZ1jsxsNCPTrbuzEUnZFahuLs1b9/PEBGg/UxyhoNCkbPIghAUIR+O2b2S6NdQ1+8uYxE5W7k31kkvzOjwUyS7eD+ADQ5DgF14w03ZI0GU181FKRd27amhgajQRMNC5058eNYmznRkpl7g2J+toCWhq6BhvnM6uzauo36Et3jxunTYyyBBg/XI9rvUuFInxoLCT+/ZwYChqo49hoNPWF2PDXIFFgrGFMdo844q8OvYPeRDDT0TSHttcIijLZbCOnCATJwpNFr4MJx8VA4eigEOyM8DTTMQTEsWbos7kTo9cCpMJV3FU7OWgTjfGZMPEZ2aR41LFYd0K0ielSQFdAKvsHR4w/PoF3gkOeQ4+ASWkqbgH7AHyoNRg66RTklHWMc8Lk5fTTQcKYMNJDRsHAfxbHg9aM4th5/8uVfZ+s56nWQEWC1bzOcVJziR/o30PAM/HQVQYCZ4L0HhjoPSgdDOWDwM2c8aEs4nM2UXpNfATaNFmCXtzRY/F76tRiaSycyVe+dlXH3vbPYq34cY4RSJKPhTONJ8O52teAcw7UZhu4Fgg2nG6EVZovb0M+24DiNIGi+FUaEjt4K8PoiqfXrNr0bb63bkH0cyAzakH4VMZCptL5dKE7IDPq5U9SNQDF3wDhuhXV7AXxcRMaKVrgyR1HaTAbN7xxZx7b4TfxmAIQAgrtOrGPXiVfYdQKTJI1bXthC7ZGYhfFeQ0BP+eqpYQvn5mnb8m7WjoHLzDzT4FHhL8EQ2sCsvbRnUHPKpMkZ3ZfeNbSExbEugpoq36ICt0Ew6VIFLKwqcZ9hVxwn8enSnYUsVzmMMjb19aabpsaV0L2BJdcja/gYWPTZytmUy9ynTJcfMnOHVzEl7/m7fKEhp7G2cMnimP3U01GDgegMrFXAc5cj6EmacpbMe4TJIIuBBmlCWaMu6tKeAsTgqpwV5IbMUjAF9xD9n4tTt4alR5ORMSOHDc/tiN0a9xzjKZ8bIJVRipkuZz1dbodT0xYaIXNsx8bNsemd1bF5w7rogmM+nkCDGQ3tgQW3PTMUymCD+og5kEzdlnZBQQZFGqVh6Pwks7qnKCR5tNGipgTxmJmt7NQb2mjNdnsRtSwDWsfGlx2vu4bskUEUWj4SqxetjJp+g9lFYjDym0AcfD0OJ1JdK+85ZokcnivOPdSzBsFzDHg2oCQvqf8cEx3a/WWgATn/CI7RtfCs46MBKt4dS2kjZRg6QbpIncHYpc6iUcdcZyefCd6lI+WbRqKZEuuwPSrQKc7ADq6qTt0uzh07DUyNWwO/RYZEQXvCqFNuUKzQX+aswUd8bkXw6D1mCl0qtBmePcba/rEsBb2JsTXQcBId73p6OglvFoGGlLB89sjJCeReyhXwYA0hKDR1VJsOBKygC2vhvElAaU/dbpZOdI9x4yekg9EWndRIwDEzYWgvU6DBsXJWPIhr29V+UF9JVwYhiucifzCCvWbdxg0x/9VXs3jp4GHDYjwObU11dbajcyA+HS/X2YsHnT8DrUU7hc6WHzyU48peeUubyOyh+QsXxlvMIF97/fWZIWTdKetaOXYA0DQG2GXYfX7XlWwHx0F+th/lIQwJCNe4vtnduVw68TzLqA7U70nHaPjgGpY8skwPfjlzSty7jRz2YdsKan31RFa1oOgqS5sY83ZmMFzA0ca+bI5ga6ssxcZrTh+3Uxx6zhuL49nX3mTJ467YuX8PPSK41t/tmtVz1GToQJ0tiyTQVjN5lvHLoAKA2g/tsHS2BFuc6PwyvhAuchSbxA6hVy4igy8C35kWbdnekoLHy3fGGmLeJE+kLK7p0jO+9Ue/H5MmjKcIJBKaNszihUya5L2QcUC38oEOlIEGZ6cXkW0nfT7xWWs8XAmOKayOjPXILAZ4x0N7UTs4nTvGUrtMfpDX7IN2ibRk4VJl5OZtW9jZhGAnkz/9kAvWx3D222enPUf/fE1Z63d8th3b87nSnafj729e69INbWlnj4V7JQFf6WwmunXmjBnQHtkOjLeBO+vfOMljmzlJBczKEWm/pHsTiHxEodOZ8EAWSL9ubfjS3LlxAhzVYCuNGz8em2ko40vAk0C1ukk4EzZwkEFT2kk9wnOcSNImzQCw13EUuCIrARrWHnNpuHWDRhHcGULbfSjULgEpi3T47a+2t1mS3lN+pw2knSb9iHPx6XufLexOTJkhtOytRTERGTOaMbWOkNlJZjQbXPfQmVUe2F7Bj8V3OtJSizjS3lH+aNt66LMY1JwDbuz7TMa0L3XQhFXbM234hE356JJo6sMAl8u2bLRcBqZNI94dj9TzPMN+KMddyvrSnNkEpq+MqSwB0y+TZr0+ZTf6JP0xxtW2cyIGHpFm7JOv9sm2vN7sDH0js8oa9jUQaFgWa9Gtj33+8xloEIfijOayPZ9TvC/oOiedGRP1iqe06FimXcZzxL3BG2ly165dsWrlKrIQWpMR90AMqanJIpH6IOJfWKQ18SoufU7h85pofzppUzlsP9SZZvi3x146iS3plvHvbt6SO07sw8c8yNKtuz/9CIGGWdEL/soDPH542PjH+LgsgQYHyjOJAlmu8jyHIawRwg9EhdbEK7PnUDV7P5VH+8TEq8YRaKihmi8EjBIz0ui2lFZWbkvaqNu4nDhFDXuQa4QrlT4E4Dqs05wWgctAg0IQof8mxterC16NSqrky+gjRlyRW19J7EackiFgHqNNGj86YRKgsysyRjI4v+uEyUDpeDDIGrpuPfICjLgTwpg6bTppgNW/kNGQ6cQSBPcbFZbwCiGLsIDxZR7xYrqa1Wxlkh44nwp4Gdq0dJdOqGxvvflmqm+TWgTjyowauJkyxOuHgQzuEWZ/95QJdY4yOkhfdaQUEDK9CsZAwwvPPZuBhiKjoQJm+nkBNeHzGSUd51iCnFSYfKnQEs5CSRRMK340YN47eiTeXLwYQb4lDcWrEYLi0mieSkqmFdH2X7z7uVQCwuwzDTR4GPTwMFKoUHdP9k0YimVGw32zCDSwjlQDVOHtYQVsq7zbRqaFgg8Z2qJtGlkawGYa+HwL5DjT6XvXrm3dtjUN0d21tTGJAM8t02dklWbXDjuOwq3SNDXbmUWdGRWd+JF+7KeBC2cvMwuB71RsBgMs9LJg/gIyGrYVGQ0EqDLQAM0dO34sceM4SZ8qSpWcgl2lIF7so4pIx8zvM+gDrdpHx8FiW8/88EesTe3K1k5jcr1hmdEgLOJfxSD9eb9BLfEvPQm/dCFPlMtlkl6hiQrWX1vozEDDW4zrbaSLWfHcAourmb1bv4UUxFUrE/djcIaHUX57UM/OZDWQyYERd541sheY3Qd59MOsD4UrvEqgAVQ10ZKzAzAwh0FEadk+aYgZaLiEsXaOpQb73j8bK2sbYmGDBRFZOkGbHn/2mwQa2KPY9PMLFA5MhaHhxr32260BnyXQMIb0+pnwa5HRUPC4Rpd4UEGkAUS7ZqaU9CTfSaMJUxrQGkWFMSO+LLi1gtkL18rf95BLJyYg44D3LIb4+0dRLifSWWzbgfXfbRkrDMkPqNdwhsrJ7ehrO3jfZSPJDxgR7wOvSybmss/7Gpyt+Ws2Zh+rWc8wtLJVVBHk6deVwlZkiFwkwNocB6CdQRv45gz0lw4qcCp+Msjg3eCRjxz2wnfFZyg3Aw0XwNXBxguxYe+xWLCFrVy54piXc/z645+O++64nUDDAAJDPoe74AFTtnVyzGpwBkjDywwhZ19Nkd1HAGApRuK7ZK6Jyxtn3Bw3TJyYGQMWvpVfPDIjCF5yzORFaVQnRjovA2rylTQs3p0B0rjbun1HvLVoYS5D6Nuvsgg0UDBMo3Q/z1Y/6MgYpPNZBs28z8N2MngEfRRFszSiitl15bvjupDxnPvsc1FVUxNXERC8giBGBhoYHwthKiM1RGAd6BNdR6BHozZnLDD9K6hngRlTVObnHnchasbznaE+jDx5ifG1OOzkyU2BBnSgGQ25DRvyS7haEIkzUCn9nT5DQIxndq1gzTtO0pZ1G2I9+9Kv27A2uq1cHtePGBD9CfKwSIARJuuKPlrJ29GWNtzy1RlUWC5hPsfvGWQArw2NOCTvN8Y+crStu9kf76aqRyU6oyJ3n9gOvK/vqYs2U8bFcAyeUydOxpG97xEkmRajR4wknfVYzoINJchwxYgRqVMLWS4khRMpL4qz1GGMs4aWcs6x9Xuv01CUbpagu50lfYSlGgYalFHKbXlZWnIcvUdZabvip2zb97br6W/Su8Eanai6uvrYhoy3yFYXZNiHgQZoxLZ1MGyzI7yqjJR2bKMIOhaTEMqltB2AN2soSFOMvcVPN2FAb92yhSrj70EzV5PqTaABeezSMwMBKUWgF/lSHepZwu6ryk856Ppn+cnxc1lJawJIu+rqUv42NNRnfSJTvV0S6l71Ol0u55Muxa+07yH8gJm6Rfz4DPvp6XXaBF5rn8xoeJ3gl0551eDBcdXYqykyV5X993qvkT+d+NFOMXjgTKD6MHU2vyk/HVcP5ZB6xbRhjegF8OqSN96I6yZNynXNWTMLPCv3vF84vUc9J5zdWNriRIA2ks6E32XbXAsoKQ8YdGRNJ2YADxNoeDEadu+ieN3NMbR6EJmIHQjiqn+YnGJMVTqtoOe2HZm0oFbDCWj4EnKhA7L6EoGGRpZYgH36hl4ELxaDrN27P+a99XbMXrCYAM+O2L5vN8FViqL2ZTtLMveqenYiu4ygNjxn0UmX58j/ApjwItfUtwXsAo6OA18gjRO8edpx5KrbW15s15lAQ5uoPXgU+3VXrEdEusjvKOfwXv3iD37/qx8GGnJrTJqBSJKemlCYNKST6rhkoAGbaTF620DDY49/5sOMhmJ3lEIva0M4pvKIQTRtAGEuAw1pU/Io6UAbzrFzF5TNWzfHGnhpC/Wh+jLhczOBAAMNOlk5SHTN19LJlReVmdqWHkX2VqH3pS/lstkSTrrtx6nT3niHpWHaJjfffXfcPG16BoIPZqABGsYu0A5zuYU+gTpD/s2AIXj1s3ZM8V2RlZu7u9Bfi2fPefFFat3URdXQodRUuSEDd9Ks9p79T53Eq3AXmZ7wsJ85s5YA42gAMvUKfbUP+g8GYbQJXiMrsQ876Y0i02vIEAINZDQ43NKzuFS2dMa3MXji57TPbA+aKWx4ZtvRhQY8S5vNcXL3mXkEGt4m8GitljGjR7Mb2KC0BbMWGg64YyY/Kjt9jgE7aUT9pixQ7jq+4ib7qq3J2Hrtrj174kUyGhw3s1TKQEP6ETzfPmqzaO8baNB/6Wz9Cw751zZlal+Vw74v+lTINwMNLmU1w3TGVIrdQ08+W1qRdsSjiHIshKf0G9JepU/ysuOqLtCeVE7Y3yLQsI+lE0Wg4dOf+1wuZbWvZoGIU+mvpE9pTX9UOEtcyDf6I4Lg+ItD+UGaKpZu18bbTKC0xKZ3N4ihQ4awi92RzJgQL17vYXeEW/vG++2LY5mBPOwmZVlBp0WRVXeb2ApNbtq6lR0BqdFw8FDsZ2nSrM8+FrOYUO1ZVZX44UYazic0vfr+43lclkDDBWYWk4ghag0m1CqzKEQAQfD5C+diPbMKptIdx8Eb0KsINAyvrqFAHIqWqJzOIlSGYGHdJIRk0OEDTklQRskZfphSYncLMfc2tu0WKMKLCP0FzEi98sZrMXBwTVbgd49tZ2LdEszolIcK3/PDKGsKFJmvILS8xoHlew0hFW1rUgFN3XqGwkmuP77l1mJXi4qmtBkJVmXUBtiE38KBClBhVkDJQApABaPG00lmC/xNo9hXcSYj/uCHP4iBKPq7mXXpTtsySxFkULDiaAKLxosCxEPHM5nVJ9CnPBPqwrlXmAiHKYMrSPOekzUabilqNDBLIfH6bGEqIsRcD/wajMlAwJaMze8ytcxTMKjClh7zQIWl2R4Wftq+sza++OtfimEYXESDcj2rDC4DFtsMEXVHUBss8TkewugJ2BwKGZQoN6QSpO19ZBqYdbCclGxrNNwHA/bFAToPkzoTmHcBh20aAFA46yimUAJXzobZtGlauWyCax0TFYUBn60UCNpMWpdLNK6dNBEhOI0ZkC5xhrQr+54KE5jsq4JCIeLsRWFsgWfxQP98Ru4+Qb+cVW2Bobtn564PazRIK2WNBp9vqrf3ZHv2nz7rlNuu78VBGs/QpfhJ5YYychxaI/AYiFi2fEXMZjmMWyZan8H1hgYavF48S785vuDAtovtn1onsOJfus10fvrg+OvYORvTDlhdo+sM7CIU/82335bFe7bW1mZl5Q0Y1q8ve1vUxxhAGVbZLaqZdTfQ0KGVysi12jgTkj7M5paDyoLmGOc5xvIa/7KfXNQco0tjW+WqIWYaeHPSuJu37RD7T56NpVsbYv7uRow7HGtOj7/86u/GrJm3RB9mmeyDfGL2gcJdpW6gQcXmTh8zp09PIzbHibETJx7iuDg1LIoxLBWe9O1pe+nwgj8Vhr9rcL+NYpv/ystx3ycejBsortqWIMCF82SgkM1wnhoVpuS2IMuCqTJmv0mtO4sjwam5ZUGwTLeFn0h9yVm2VcjGOa/Ooyjkxpj99irBiyqGqqZfRHWvbjGgewUpu9xNVoSBBqurA37CJt0V0NMn3nmmMPCVT/7zd/82ZzwuMsbnkScHM6PhvZi7FYeW39hULI/fe/LX4uH7782MBgYu121agKwNxmJXtuq1bWVYBhrgI3lLfjUFftXKlbF9y1ZwfD6uu25iXEc6trOzjThe0pv4S1kDL8kHvpcPnD2SZ4t/Qtt0ArTGgTytQn5jwYJcY1uJcTuZ9ocPG57FhN3W2FkEeU++MXBkqrX8Iq0XwRja5Hl+Fo5yjA0eOrvkmLqrRSU8NBKHbhhtG2hQDmQQAPpyJkgZ5ZJAuDWRrVx06VZLlvK5vCR3AJLG0IEITQIx7eMgssalSOs3ro8pU27MjIY+3XtSTb7YNtQMuBJGK1dfpH0DYu740QF+ZB/F2L7h3Vi/fGW8s3pldFjyVlw3vDIq0ZUGGsSWgQY5y3E2dGe1e0LHBY7pr8sm3M7yBOeeDw7HlhNnCTYliFFBE5h+UdGlMmu07EO+bsQZG3LPXeDiijiIEbR+xWqC7DNzFxfHyirZGs1mC6VhxbPFTc4EAoNHOv/gRAdXGpE3lcm+V05Z46e+vj4WE6DSYX+YpRPXjhuf463+c5xss6CGbJLv+MQfnyN/ysPqVg3b4ntojLY18vaynG/79u3YHuuiMzPqWUwYm0MHUd1VFpUTHo1aD3WmMiLbph3xmfqK5+hEmtHQnEDfe2SpuKzPQPg++jAGvTeVpRO9unfPono0lHLJIJ0OloALV8ocW7VhDnunfOTH7LdL/gxg1TKTtmjxotz20WUqzsA6gdIBh9jglIFw21UvSecpELI15VrB+T5DR9Jx8Nlel1lDXLCO4OYrL81Lp9wsHgtCDsJ59DqvtxH5s7BhmpxE8OLvGQDgWfYlZ+vgz0yf5z7H2vXRLp1YTKBh4uTJOHSjsv6UmX4GAR1TYeH2HAfHzeC8betkCnOR0VlMTKQ+5zui+rmD0/5Dh+NnLzxP/aNaZtVvZpaxikADFemRw0X9BZAKXi42I6MD+XuOpWtnuR/MEuylX9SWOY+eaklGgwFwCAhl3DK21+2NeQsNNLwVuygGuWXvzswMGk9i7MDeXZDF6DkDDWQ0XIR+zJDQ9nBsc9aYsXMCR5zZN20FCKn4nVc/p/0AbB9dOlF78EgsWrY3NnEpuXdZo2H0gJr46u/8Rky+ZgI6noCJ+gK8XTQymE3CA8oyniP9ikNtGrN4ljEJ4UTK/fc/EGOgGfGXvAfdeJ3Op06VE3COdWFLFjPv0r2TccpH7/H6dgRLlZFba3fEhk0byTLdGP3IeLuNDNlB2GPKyMywobviQ7rUHrNGjllZaTfCBwkv7UmX/M0zHWqDsdjY1pZwKZI27nU33kCG6ZSk1xPw2iVpAtyaPVbgu+CXMlDn12mPIaOT7oWDU7pqQ3BNOfAyGQd1O3ZEb2T8zbfcguM7lrbIrCIjSb9AGcDd2X7SPp/Ej6e7whS8mVd4VdK/7dsvJ37m/OxnoW5yB7DB8JT6w74ry+QZ+SUPcCDcfrL/4rq8Lp/F94VMMnDVMguXz331Fezgt2Mqk2GjGVOXTpj6b7BInBvM0a4Qqepa5ZuHY6l9pC3udcoy6VP+k9+EyYyGZ4FdvX3n7XdEZWVl/qadLB7MwhFW6xc4Nga23CLVcTDQkP4OcEor9kviV2d7qNfWYdf8lF31rp04KW4H7+4Goh8nPTtuaQ+JA2AscMUz+SyshU3MWAur/QOP/uadBj3MOlgK7s0WfJiMuOut0UD/XdYt7tQLJf3ZRtqK3O9RLjnJgIZtSpv8F/eOgzve7YBeDNw1x6Z//PHHo//w4W6jkrsjeo26WhzZdnlm1hLt+Ls4SrqS5+mf/XU7YosJG/xy9x8DDWY07K/f+2GNhu5krICMxCUd1nhLmD/Of5B9Yvhfd6gcjDY5uDqqNqgCFT1nML43rt8QS0h9ff/IUda79Yxrx1wVwwZVg0xmnTFkNdY0sqx1oIBwSC7A/Ub5PVTyDpjXWQDTBx1RAABAAElEQVTQyG4+xGg+68fmL14Ury1cEKYBjqbo3ghmuypQ+ucxcp2RaCtDM9imTisY3I8XysCpZD0iv8t4EnACDOH4akSvGUL+FELwpzgu27ZtiVtvvz0LhVUQ0fOejBwiRNvQbyg6ZxlkXmdMZbh05GE6DWY/p0HJq0JRZpEYXcP0QwINV5ANcB/rvq0Ya0oUrEWfi9m/5vTd7fFyqQNwa1jLQCloFB7gRHg9RYxjIaMqcFaufideZunHTTNmkM48KVOdHXIVUK5ra0KlCsJ7nPUw8mcUXHxpIKhsXH9sQCjxwlOMpjYQDJiHENyDUfc7v/uV6Dd2HOWP34vjMKmwyXxuoaVAtShc4ov+GzhJIcvv4kB4UvgxRsKgM1MWN1rBGqxu7Nl8LylFPfsPyGKQKkkPca3D7v3iQ6FnJFNHyL5rsHQmgiqznk2hSKosQsN0bre028a5v6EhRrM22OJDHXHczwKnQk8h4eE4FEKviIQq/IRdhe0Y5tpYXn2ubbuWtA5D8W0CJPswQjtSfHMCAtCUN2fwi6JFtM9hdFiBLqwaxz5SOixn1sWfRkwhxAi4QUcKUtP3F5D66tIJgwxDhw6lkF2fpDFxYL8BiF1JjmO8ncwx1amSTzXSNRDlJw+NC+lFId4Gx+Ycxt482l789tK4icwBC5xtQ+CuYrbaXSdeI6jnMYrbh/dvx2xol5zp6dAKvEMfYCudb/uQKfaMp/jLWTuew2PSEUx+o39ZhfsU2SwopXbgok37imjZsSIOsuvE4s0N8cr242nwAWEef/X7X4t7MHL6keJ3CQfP8Qf9SQPS0gYyA55HcY4YOZJZkWlkCnUp8KqTIG1wjUaUtC++HUuVizCmk4rR6ZiIl0JJFQpamlURLmZc52Do3kXg64aJ12dF8EuXDK6oZIGStPcLyKfzzQiu4e5dzKlvHsS9LehjVmd2fJwVAJZ1a9fEXAINK9atj2cXLs0+DkT+DGQLtepe7N/cs1t0b49zhpHZnOtzja6UB13YdHEUslY8FMgoHA4Ays/+NdBwAfl5vmUbAg3nYg3FIGdvBx5+K3H7h7/zm/Hphz6RmUNmC7kU4Dgy2+yxXjiVGtLKhAxwsJxDee+5/8B+Zo7XZ4BNg8aCWOOYwWjPLKZrptNgAL95AKO41ygR9ypeg9EasxoTOo5mCvneKtQq2Xdx6l6nRoP0PLD/wBiPkTh88JA0VqyW7tg4ZspbxzZnPTCIcqxxbjSVlLnqkTRUGHjHWwPNWdRVGCoGNC1UNQReGlxTkwWFvUYaMJAlKoVbGX6GNd6wVxqy4ubCB6zHJBDkczRIm+HAmNHQHH51h5vZzHZppN+EM1ruOlFBoEHZkuPFwBVGutkNxWycS206II+wamL3ZtIs31lDMGdZtH5jfkwY1o9AA1Xjua8Z49pIfyz26GEmQxvovMhogEb8jX6cpB/HGdM9jcdjOzx+ki51aAe/H6MK/VFm24ClO474KRyp97pUxIRHH6FS+Fhm1+tj3pyX48apM2IagRKXm2h0Fqmmwu8wGLxkTHmG4yC+lGeeykkNcsfF088WA7Mwm9tPmtGwfeu2eOChhygGOZb+FAa+dCBvO4bi3ICrRp2f5UONXOWv+sWAVg4IcKjfzXzch26qra3NbMoKZMDdd98T/auqMlBtQbCCNzQDGAXa9TVxRfulbPA7s18UWsIgXZrFY2quGQ0bN26MXTzjSnZZmYGs6U02ovJdx0VnTpzkZ54mTUofGvQZlHHsaN9nmMlgeq59uABulbemklss0wkTMxqsdeDOVU5miPWkHGSUNOqHsh/8lHLNP+kA06b85xg5wQHR55Zt2gTiV3q3rtUAs5jgB/WDukw8imNTjRNGfpNfNdbFv881sGBGn5M/fvdhoIGgoDWEDDQ4A9uvsh92R6fMxPA6bQzlRk4cyV+0Ke0YaPBVGtFmSt1Of7wH5JMhxC5gOBfPM3FSR6DBWfUhNczudnS9NTjUv0mj3N0SKFiNDjlLoEE92wbet0aDUrkZDkJLU3+g1bwJeb19d128bKBhPoGG3dtic11tTiKNgwUH9u6InqvIQEM7B5cxMssAJInuHDfHzmCmpzztX2Wul/MtvF5c5vaWjeitcyytayRzbyeBk8UrWMrJ7z246TDXX10zLH7ji7+cGXP9cFg7YX9p8+YECK+OjXji0sSTz7ICf11dHbLsnVxffguz067nd0yFJ6+HJpSV0rtt6ZQaaPB37WBpRl61XpP62OweJ2nexybaVU/NrO3boPkNGWi463acUmjGon9lfQdfdeDMPHCZAEI37SnHL53tHBu4wFdgaAH+W8IXBhpWowe3bduaGQJXjr2amlnXkO1Q1MwywNCWNi1YmMuspXkGO4tx81u5zXKpj5rlxAaBsCZ7zN0s3oQezUquYHnTbcB+5YQJCvQ4Df5hjkIvwK/KYbP3LN5q/Tgz+FpA9wbV3SFGh1E8qUs6konDjfEOwdLnnmbXCfBh/Ydq5GhP+FabWnklPWsXWthR214Z6QScdpp4UR4ZrJMP5LG045E19vEoGTxmmL7Dcs1bZlIH7Qp21UMvWD9OOlCmlDaQ45l2OjrPgJSOtu3nAczybKFjkdE8iwuYaNsdT+HfOEbWsRmEk+v4+7tBcPEhnrQ7tR+1081i1H9w4kVCV7Zp70mPjqunE17K+zUst/npj39EnbJpcddtt8GnFIjGd/Bi5YqF18U1AKRs87mOoxlb+iDSiu1n4IpXcW+71g3RR3DXojXQ/P2feIhAwzVpVyhHhNNTWavsLv2bDOJAS8o2+6x/Y/v6B9ry1m2yb4eQMztqyWhgbMXTIwTC+4N7InToe3wjrjdw7gSEOPZVvkp+5zWDNNB/0j2faRK8ECgBpqNk87mcx0CDu07sg/734UPdweTVrPvuja7Y8hfgR6WLgR3p4ON+XJZAgwIpUw5FNqcG8EUMFkgG5jyX1WQXLliQe4FDknFFzZCo6tcfuxwCQDA5e2OgwYFPp4cZIZV6OiqMkAMoM1mcTCFmtXmL7biu/QNmf5ZTDGQZsz0Dq6rS6aqimJmRsyz8wex3RmkZZNdZyzTWdrAd0yd1iBW+yShJDPIJzjBCwDQzC+u4b/POnbVhMS/3hNZ5lCFMb8eKy0BDMaNVGGCZoggObPvD+gDAr7IvnQOJWWas3bkr5rI0Y9jw4TFj6tSwGrkMJmWqBAw2qNwVIlYyza2y6LeKQ0IX74JdGO1FdFuDW6EiQ2qkL1m8OAszjWMLR40Bn+2hYSXH+1LONshoOqXCIMOqeBSUMqqGgc8RP+LywOGDhUMNozz2mcdjFH2w+JvrpjWoxJGpqQpWry9wXQSNFDJgOgWxsGThHsZIGvC5pkalA0OQqjOFPaeDm76kHH9Yo8G7m+jCXvg8+6MgFa86LsLqUgydDNvUMLX/bhm0E7xbpfkIwmro8GFZrNFUUYtZiVuRIowpxGlfgZuCHEWjANMQ8nnCK/6kH7+3z3sJXrhd0yF2WXEWaaTpdIMHJ52JAw1Zx0ADyPF1HBVc2R/GXCPefuTocJ3fp1BmXIRhEwJqJQEYjX5nokyls2ij8NhuBjzgQ3EoD6iUDB6pKKSLItBgFFp20MHW1WyGIQrPgDed6bUb1ifNdMJg2F1fn7PKRr9X0i+PEdgiwytbRHX3rtGXmZ6OTYEGjSu341LJJY1DnwYPdeKlIfFUzFRD0zzTQmsu+3G2J7cKIz34IlkN+9l1Yuk2Mhp2OeuObOBqjy8//ljciIPfq3uPFOYK8pz55H6d1+0oBytMV9fU5Ky6vC5e5HHHRsVvuqP9Vhkk34Ab73Usc8y5zntK48z36TjQj9Wkjy54/fW4cdqMGItRkbNizJK1bonxQbBBOjR4gimbs2pWHM5ipYwxDyDQ4CwsVbmhtTNcq/G2mCyRjSieBWvfzT5Wg9vqvpxki1R26xTdyGhoQZsGG3LtL/AAfBpYeUNSCiOYBFN8owHgmMrf/v0w0EC2xYHcdaIh5m5XRsM7nB6ff/ShuI3gUiUzJs7G6kDoyNODNBAM1oojJY6F8ehw4ssZtNrtO+JAw77E69AhQ2L40GG5FbHr4UEuRpFyBgA5xX3iFLxLq9JfGi3iBsUqv0r/GlwakTtqd1LZe2nScj+2ftJZH1TZH00C/UA70pRBkKQx2vI+FbTtCq8IcH2k4+/4Kgc00nyOQT75adOGjVnNewDBTGd1creSBBc5AJ3AhEkPju856hz4nWvS3bXhDIGy8yyPse1cDsT4QnCpQ95jqcFC5O/2nbVxDcZzNbOBnZGJpsFrDzs2nhl8oW2deGWxQXXrOJyl7QM4QXVbtsXOTRuiFxlqY4cPiH4Ug7RGg+NqoMGgvMNfbKtHwAb4QJ3opm2WS9HfE4xD7bH6WNPATB6/9RjYBaOJMT58Kjq37BzdBvWN96HlhnatYvhdd8TQUSOjjjF9/dX5bFV4fVyHca7Tk+np6LKP8ofy0XHNAAF4Ve4YaHD5i/rDcVB/+Nkgp5XszYRx3bdZZTOY7bKYsHThuJWyVH617ZJ/i9/PpT4pZy3Vtzmu3CsMPtctpN0+cwv6T167iUJqZqw43sKhM+2wijPvVccqZ32ehmk6WXzWxtCmEaY0enN5wMnYtWt3zngJ+9BhwzByr2OLWzLCkOM2KrzCrQOnEapjYfviTP2UMotn26b0qgEsQC7121Nfh2OxGqd6f6auj8DAHVwzOB2gDOZynW0ZQLC9hB95a588CloqvpcvUt+AF8dBHti8FZlDIKM19wwcVBVVLJHpxS4+KbOhlbThxKPGN7JSfSF89snnalf4XGWn35VjpG71Hus2rV6+IpdkGAQ3gKceFPfqLw174fC94+X7ctxsN50B2vZZcoe/ZSAfnjlEgGoBk1YH9+2NiWSUDajsS79YFoKMNJEo8cJ9p8+yU8gp4EQOW1TQTLDmFwj2EmTgadhRjC03aJ+5vevufQcoyrg+3lqxNhoaKAZ5eH/y5cTeEdW9u0X/zu2jJxWP2xOsaAGMtlFQD/gW54y5ctT3+T2gZ6CBT3ZDOetxljFz14nzFIN06cQuaqAsWXkwtvJduXRiSO/KeOSh+1j6QK2YHt1zK1zHWkco5SgPE0+p68GlfS7spQPIss05kXItkxuDq6uTthIn3G8QySCgn9Neop3SSdXZtW15SV2XtAXe6UScIhjrrKu1Q7ahr3ojg2+aMiX6sjxA+jbAUGbvyFsZaMD+9lkGpuV9nykifIHAksYMYkg/LvXYiv5zq0UzyGqgGeu/OGsv/aFaM6PhEvSSWcMJl4MNpvMsfA7luW3KD+qP1CW81mG/uDNBPfaeQUkzy8wMUAccI5Buv51Zl18dyHbg1K3itSGVJQZG3UnIDE1pXRkkzaojhN/tYl8hcGemwbDhw9j2s39OcJSZPhm84Tp32nGCTvtaPCvj7IMTcOJIu0BeU4aWjqu4WbSE5WVk4E6+YXJUV1VB69hzwEMvE6/8BRHIPmmC0/5oCzi24lt4k254peGEXfjl973IYCeW1DnKyH7UmVB3isO0F5GF6m+YMNtRdhvA0V7K+hA8wOtLezUfCM3Is/bJGnTuOnHN9ddnRrV9z3ovNKmdmz6d9IZ9o62QSzaBS7rSZpefxId6w3t9bmEvNybNbyDDxsyy6eiPMoNHOZOZ2dznGKsnikCD9F0sn3dcpVW3s9Q21pcz+Oa4eBxBztTV1acdr60zk8mtwdXVaWPYb2lDnInHMugunuUbx0O7XvyLco9C3xT2wXFowCKcteiQIwSf9DF3btwUn/u932H3pYeiJ5No8qf8XWQcMYgf8+OyBBoSByBUY0clauTvEoGGNsyeKXQ3bdlIuvGrsZI0l01r1kVFy9YYs30RHhA9V6R65F6LYHEnhdKIZsNIEpXr7TUgVfwOnMZuM5x7BYJErwFQx+yFEdeevXvlmmydC9N9dRoVTh0hJh10GdoZQYMQRtIU3BKbz1FwC7t0ocCSeSzmdZJ7LNRWx3ZHo8ZcTfusJ4RYVcYauQY9WgET4juZ2xkQmUIiUUCf5fkK2UIgig3xVEQWJdAjrGFeQzR3wIBBMWLokDR4jHoaQZRgNWgzYsaNklsqfRijIGKMzHRSWUcMc6qgDWYo4HKWiT414OxuxmkchmAdUFmZDChsMoWHAsXT/nsYcZShZUQFgtFFjTBxJWOphFQgKgSLTTawztY1UUMQsGloJQOezwCFAR7b9lkGSey9EUSZx6wFl3b4nZ81UsSTEXGNFR1yA0F7UA4dyUqwiFoFS1k0cowqc6ncm0hRucjYjpttqAx03D1s12dIC+JMY0shsRcD+gDFZBx/UwH7M+tiG/6mgBRmI84WBFWol8aQz0/8Qo8KU6/LMaJtr/H5znjtYZ2zS4VMnx5A7RBrb/i7Rqz3FcYTY+s9QKiglnaKWaUiCqvALFJ8eQa04vja/sFDh2N3bW3yTTccbtcDdobmpWPbV2F5nfdrZFoNXEO3CBbheIEPyCSFsHwlD+mOSrfWV1EA7sWwsNCkhUVdJ9jA5/eOHo+jKGaP4UyyDCOJooZAQ2XndtEpAw0Y1eCjLfhyV5ikddr3GT77PLO1Gr5tUJTO/J6Dh/kp4Vaxavieh5dOUcCr7sSZWLlzXyzZh3Joyd7SzCKDtbiqemAal2ZzaAzIG0m7PExX6zCpkBs2bkhjqBq8Z20BvpfG5BFpw8CGYy0+xauA2oZ4kMZVTH4nDj2lYcdGfqxH1mxcvzGGjxob/VmT6TOtYt6KIENLiseoIByHS1Tzb87yibYETZQ9bgN2sbEIdGrwKQ8aedZhDImGAw0Y0Iej4QMN44gh1GgY1r9VDCIrq09FW3YFwCj4fwUaslYA14pj/yYN+a74Arj9HrlT/oblkRkNBhrIaFi9m33jCTS04jpvYXfGGD6oT/Tvx449ZHK5hV4FvFqBU6wCPgCfa1Dm9rj0p9HMDe6VbjUIDiJnnKW2MQOCfZiJM5MMYQdM9JeBVulr8MgHGgLOUitHxbc1JzJTgs8MqoIpjUZhO0y7O3ftgvcbkTHdopItkrtTv0BdoNPrNR4p07jXAJ5j7XjKr+LAa/zsOGsclrxqUNYUzL119dB/2yxG65pxd51x7D2kMVtIh4q+tMRBEX5lofrkHPUzDDQUz4eeePYlTmexTsKDLj068N4hlnsMY+tXqlVLi44N8CROaFN6LAN0fClT5HKM88hI6zRcRE80Z1nXmJYXYwzF7/qTKm6gwdw4t6uUboRR49MgA+Z2opGvkiYawfv7nNsPMEtzCEOK6/v1oA8tOkUzCo22r+iZO6EcxMBbf/JoNLtyePRkq+VDPHfpipXRG/1UAz9Za0RHi0ErAg48oHRwxVeBW2eUWSqhk8D1GtTKbPGXQVSuU4aaTbKf7LcTyMthI0ZET+jdewq9AN6hE2WrBnHOSiNXNLLlR2e01JPqbOnc8SwMPrMPCDSjv5TDZt1JS4NrapKuvVaE/NzZLwK+Bm2Vm2YhCLvLV6QVYZfGldcylTCp+9RNR+Hdw9BOn8oBtF+d9C4vqOukhdQN3CudKGvEhThKGSR+oA3X7vq9ekrHXXlkIFyn6wRy1yVBvTA8tTu8ToM86cw+AJ+yxrbLlHeQkThUj9lP4S0OnUqphT3imRjYuaM2jW5nXruhX3WGbMu+e0j5Pke8OuFi4T4dD4O0jpF99Pm2p14VTwbeDKDsJPhSi2M0sKqaulvYSwRrkt95vg2XcHuz36tjUzbDi4Wdoc1XBHyEuRhzZfdFcPN+1myxLs6wYUOpY9KRPiqjGR9oIZc20e5ZIpRnLqJrOnbO+lcGsj84fhQZfAq5AV4IOGTtA2Bvhq14HB7Yvf+92LnvcJw6dpgMoCLP66aBEYOxVft1aB1d8Xg7AD8VFpLvGMrEkXJL+WlWEVAUyOM3pa/XmHWUNi4/ZaCBjIaLbdlKu1Xb2H34WCx+Z29sxlwh3yQDgEgeCg1fHf3R653Qv07CnUH2noY302EBZm0UeUq9JD843tKN/CQNVlcXuNdGluYcV2lYWky5hUxT56ZsA24degM/4jttNsYx/3HvGfTUCepaHCZoWk+WTdfu7DoD7s0k0aY+n9lc4pS+OqbQqdsjSxcZsKDdlL1N9rXwZOYXtK78d0v7fQcPFBNUwNijb++keR1+9UZuFQo8ZjaAzuyP9/EAhXPSrXaLvoI07HfahNKU+JJXLTx7lNljA85DhwxDR7G0AbjeP34i4RZmdzHLADIyJp1a2iz1Uo4vn5Ut0q846kpgURtxd31DLH9rEUFBdFP/ylxuKj85RsoTeVsZZoFAv/O35FfaEN6cHWdsczz4Lnsp3fD+NE7tDmzgQ0ffy1pcXfBdhFtbJO0f+pqBHvHD9QZ57Is2e4l/x167BvQ14a6w+cXPMXCz/t13U6aZ2dQF3a9jLwzCSmcTL9owGQQRpzxHfOeEDbSfk8L0I+2mJtx7jfLDwutrVq2ModT3GYKMtHfKbYgR+AoYzfJUlxc2IDYwz+JWHl3IG5+jjHNSIm0FZI/9UXbupRD17lqyysim7mddDHopXygrlV3iVx1b6ibtf/Ejjxj0Sd8KelQm+1D9H8fWpX0HkZNm2zRHLg+qqkq/MYPQ0jH9TpkHD+hLylviKzOWoVknbJMv5Q/g8dq0d4DJmieHsFMPEWCQvvSFdrNE8d//h29nLYh+vfqmHMnxypFIiuDdx/e4fIEGcJDEobBXRCFx2jQnosS/TVsodPYq674XsU/xT5/738LWANbGfQCmodPLfhizquFUpe7ndHbnch4k9TLjx0ycNtC/wSE7aT5gLzLTe3kPfJyg4H1sPXF52y1bG9q1SL/cdLgIBJTfX47XHsQUkEex9/jlaO0X2xhACqX7Gp84ilOFv3i5DxMYK8G7CnTXMUXM5T2GMl1C1nTUpSX0r2hbTUEbI4B1MDw6tGeXGMQ2jJ1whs9p3CKwnR1QYcP6eer8KIgNNCgyW+HEWkX8ItecQyibAaCB3bYNDgnfn8JArDtBptKOvQQaMFxQQiqO1lgax9nF4XIe8pE0/29AMglm18BJINiw/eyu/Pz/98fxxy3PeglDqH9hMciBGOluq9aljYEGFLPKEHyloE17BBzn8dFAQ0k7WipNP/OisXuB2fdzOOEHT5+PtSydmLONCD7tNCONWMV4+PgvEvZAkDN49Kg4zuzeO/WXVxArw4hVUeSzkGO8XNajP605ppdbtpdACr+UqMN+OQ+HbCh/sBtj+0caHs/7KyCS60htHUXGQSX81oExdaGicGRGA/d4fxpt8JZ856HBd4YGT3LWv0+a9q5jcYxxb9eHHYgIiDUjU4Y9oqKRnScaMPrXHz8Y27i1BUGuYxhIp+HdOFJUrs8G/y/+GdGD9fjtO8X2PQcu+9gOACeIpqhDNV1eKVMgrDf6qW37ZrH7SMmjlw+RiGKKtOGYHL8YDZe5eR3gwYP6EDjfHztl2Mt8gPKo7sb+8sfOxN5/rX76X8A2cMQQHN7zUb9t1//i1//vV4rWEowpCJIaHJj+HdtEd/yvjnjTrXEyDKir3HJCB77S5tXcM0NCJ0uPWxWorPaf13oYXKdMc+46YaBhF7tOLF5VBBpYbJDysDQbhaMfp7phF+flPtR7JP+zx0zEwcvdOO1Jk9KOMvJy61dx04vTcfq3gL2KdsW70yqXV/PRIMdQlJ/zDnXFx8v6F9MscSOr6t9czkOakU73Xs5GP9KWIQ7mr1L+Xm7YfcwgCNLA5eZDl9//UI5p4f6fHn/67/8sHnviM9Qx7Nc0cUDMjMYUI4Um/z9t+f/+fZcv0NCk3HwhhpSC1lmVDDRsJqPh1deyguecZ56K42jxKg0ZLhORClbPEqFneHOaH87znZFEbWt/w7bKk0mdLAjJZFk63B26EQVrzaznaWY5ULSlSSRDYJMVgU/uIXMu20zDjN88JA4NXtuXMRW6fidcXqfxX9Gbclk4QqdOHmdbLqKgfKcQ6kJ426KUZ09qqJWqhDccOlFGuG3XawE1AxsyUtuOzuQRFWQmtE07opvtSaOmYn/DwRN5XTf62aa1WRJEPJlF4H/Ck8V1aNPZKdvjMgqCcS0PoN5c9ttnJfw8mEBhtCHi7zroc2Q+uLuHOFA4yxAGWrB9so+ynf1P+Oi8EwTCXsxv0FfaateJCPYJcNz0W17bHjiJ+DWyTpmJHtZRGkmm7sApngdM3i9uxKenbQq3cPrZo/zO1/LwNxJfwBVF8zB03z/Gml++czy830OasX3HiQz1DO44EdGahliySXSXZ4CEc+Qqnmjqt2Mt3LaBns+6B2mIMysp/OIG8KNVB9eQMtYgRpoQNiYi8mgDcJ4uydfecCw8bJeJfdZhCQ9mPjTgSDnLcxFCZlLiQ0GU/eN6JmOYfYHeeW9//N7TfnaCvtoSYW109o6t6HyOeGvfydl4sgL4ZzT7A5xyx84+lXgVH5Bm3sNyVRwN4KcB2xZO2/fVz/bZZztOttGRIoTNcUjfh96PnmSNPTO456RzHV0v5ryiKwYYwZfhvSmUxc4TFTjDbj1rJXOj4G5v6cONWhtBxu6C9opMkeYEEww0EOJlFoJsE6L5vm9HLQIrcuvw1NOnZdt2x8J6namiH8LWBcA7tse9YmxU1B72w6HxbNeRSuzUeNAAPMMszDlm7wVb/Hq5ipJdylzxRIG8gnZst0MbKgIzO3GWXSLww5Mv2kHbzgg6g3Hkg4tp0AxhBqkrEefGRlLRjzDDZDQeJJ47exRaLNZP01zSJMmQ4NfZa/FKATJeS7wLq/NcpvaeJa3gFEEY4ZPWBkOXNX1ZG1xmNIDbVgjBluKVU6JL+tKgzRFu6j/fSyN+J21o9Baf+MtbMxrO4UkdooPr6w7Gy1sboy3fn3ZsuAQxGl27EeXg8xHS6U+CI+VDVwRMZ2YCLpJRcJx0ZOVr0hHCpzUzfTkr1hQEceZQoc2KOWYMuU6AOJte+FC8lzktUniG8VdeKce4PPHmqxkfR7lJnAwEsO7dezNjwEzykUNx7kTZzwIO8epZYkPZ5nh7evQE6e2hLQvp5Wo3vhMzDDfZaXQamE05vXBOPvUODp6duoe3jp/XlzxiEJlYWLAxSoAtQkkFnnyup9cpk8r3bRSezAQ1nqZOAcgTFz04e/btTkMX2bLvaNKWRr/4prxHyoVWtO+1GulXcdZ06xLju3aKK9qREYigcXbVjJRzws/vZqo57sQq/ZuvIlJ9bKDBgMFRxumwsyi8p+JHJk+YdXIUut97+ETs4tvttLWW06NnZS+yPIATWnJmzO0bjzUcTGOzI90iRpGyR/wLizrZ/ktX6mvJQpQ6xspbVB7jzgcOl8l3YFeVVsx4n2R25zgIUz6rQ86yMwrxx2xTWaYuUe9ySyCamU1k+SP0fPwQ23TyzM4Mfqu27LQAnzruPtOx6Y7uNlPg5LEj7AzjqPziIX6F13v8FdR/yKNJV3why8HuyZuCbrudgNGZP2nyNApRh8Q+duvoLC3LNLETlL9tQdIFcH8cZpK3s82PvvpAcQbAwuJJd1N3tUSRuRTHGWNI58PfEEmp28Ul/7OvZT98VcaUcoa5nqRpSDsP267owW4S2B2nThwjdbeQWV4v/4hg9YSHepDYJDIa/ICcEv7udLQdcvAD8CnOuhBo1sY4wYyxPJFj2AmbDB3lbOLJIyfDeQubtbvyOuyQtOP94tQxkF+EozPPJNEh8a7OVAZ4r+22Z+vjtixdVaE0op8aYVLvE/b2Ns6bk3whrM0Zi4ssTzhPB8wQldda8aASPzyi0NfQ5Tnvu0gtKYNunN7vcX0fgiB9usWgivbRuy1LQRForRgs9ZuzmkVmjLl2wOiAowzUGSl6gVHbQvtF+D2diy4CDZ3iLBm/uw4dicUrG2IzD+zK7adpSFwIm3A6XtK89Oz7rvTRbVA/OE79HBrkloK3eCXxL+t9uOuONOhWtO8fZa07vylXRI9tCav9k3Zs0+ckf/Lqc32+4yVeUfHwJ9cLAAPQkYyynKk9eSxOHyPrhMaEwTasWdyWXVOUQ8cgBHnK54K2aAOsmgR+Z/ten3KKB17ytBFtJggjM5JgOMBPHcIvCbdtCaf3SxPSjv3RztPuEhDBJBkyf7cvzIOk/cykMQWz2cGpczeWXZ0kQ5ldovjdQEhHGiGpJhuUp2zjo3zlM4VXvhNhTKgnbny2MCU/QeuNbK9qsCxB4Y/9tS1xLaw+S9vU943gw+9tw+99hvTx0cNx8vfWnaAnZI0Zfafpx3t8J7iV3Si4yVgfxm9QR2CSJZwXEaznYWJllrBo85Rj7zM8xaF9lGe6sORDgj1BJs8JDOXSf/IZdtlDODyFSfilDcdAWVjh+NGoS4L83XFV7jR3AsMaOm0pCk2G8f6Dx/iV6wGqHQa6ct86efqDPke5YJakskq4hTnpkh/VBeLLw2v9rSWzJG2o/WLdg1PvEzx/X43Gb7RR6m6vc9xw0RJHjhexXnY8LNoRXvHhc5RJyvrsA5+169qQsaCN9wHZhPZPOlMm6pMKU6nveJuwKzt9Zso4v+PkcclXmrzN0EXe+wEZkOK/ObbV8TP4eLz/0z//U5agPxYD+gzgvsLGsT/qdNv4OB+XJ9DgSH3kyFRZKK9I27sU77IO87WX58WWDe/mrOfgQVUU9eqf63MMRaQjInFgAB0n3fEdrv+bH/4wo60zpk2J61jfM2BA/zT4vSbXBkI9pvJJ4W2RhKbb+DmXWUABDpOpMwrFTPHjOtfiHMNQWkChu/8xe27cgKE3ceoMipeNYX/X7jiCCE7az6UFKAePTLmhnXQaaUPFUZz5YwYUVDClcnYZx+xXXon177wTd99+B8VbLLREXQdmw1U4wlI+Q2WkI2zKp22aYuOrh+lWrhcyTdP1d6dYK+06oq07dsTTP/lJjJkwPq6nSNSg/qRqkfroLLJ9NzW5YJ1sJuH+Oex810Sx2Qc+mg4lHNZVMD15JYXRXl2zPn71wfti7MiRmXZoIZZMKQc3WXAHfJgOrxGR6Z5IFtNdNURzDRR4NhXJdDlpwBT+feBl27Zt8frsl6LtgL7x8H33x1DWhhZKWYcc0JroqAlEBFbJYirrAu/5G9/n6CAZWoIj1zm9s8YtVJ+LLUc+iAdvnRm3z5gRPUk3PUlwReenHYpAetDYyPVg4EqaEc+OiUV/HHfXy1qkyLQyn3GODJ1ddXti/oI3Yi50oxD5/KOfjHFsg9eVIIjSJhNSuVfY0tT3jUjlfzEawv7z9/zqBZk26jM3bdkaT/3oX2LtweNxbZ+ucfcnHo5RpLFl6qDwgmvTskyxc6mHBdBcW+Y6NpdWZGoqqRzOarr8yLQ20zldS2fq3dJV78Tffv9f8qmfAjdTbrghBpAib2Ell/+4rKDkE3FhulkLsgs64+i6fazboc1fuCD2UHuitn5/tjOSsHkN/scwDPlBPTpFBQXw3D/eNcQXwa21BMSvgTgVjwhwVsl+S/NKa/kG1zkDcr5a4PC8xfUwwPaxBd+q7btjQX0x61LOHD/x4L2klY7L1PlmiXtoRNpITGfzBa/6yKaTpvMHnVdhkh9MjbMy/TrSBufMXxizbr05xo++Muu3iE+XXznLn+lw0Lb8Io4SV+BLA8tsDGtMKO/cfaINxrfBQHm2rn5vbHx3UyycNzeOnG8ev/yFX42R1BagmeyvxdEs0uea12UUxKuluNm6PXsENIagkGr662B3i14unSDQ0Bo6bMWpc6bykTYLihOjxbt08m2g6TDQIA5IHATX4BacX4DfD+M1bKw7EIu3FjuzHG66/tF77onprNNUhix86634Z3bb8Xh05m25w0ZvlunIpKLTJTkJBe3y/+dH0rn07pM58nevKdLqxeFOUkHnzXs55r5TuLPf+tIXcky95ggphfbMVHnbUGaadmpGi2Nh2rZNFxkwrLcE5+X3qQOg37Xg/W//+3cTR3dOuT4mTirWtipPTNkv9JN4S/B4/WgHBJojwS/6kXDw0CxiSLrlYrYz+87Tz1ENPuLxex+IqddMjP4s57Cehenwrum8yJkFjY0kchqklI8t6nsRPnELSneecFmW22WeQra/R+HIHcjg1xe/RfbRsbh56rToi2HcBrY5x/reQ2wx13/X2hjdb2BUwp+Z0UC7GjtJDwlzMR5ln/xeuWAw4ix0qoF7hvee0p81RFpyHgGm3aQRb8EYXMc9p2p6RW/W654B8DPnm7Gsb1QuFbJA4eLnn4o7HvwEBT+vSj5R5ZhiumPH9tQfr6zZEE8+/IkYO2pkpgkr403jFePishhX6QdDDrnl2Joyr/6QBjyTvni1bg63RO3OnbGKFNwXXpnPEsnu8Tm2wxxWU51OiWPqCEpb2hIGN9O5o3+Ote2l3lLucCjjpBnhcLmMOyVs3PhuLH5zfiysrY/bJl0fd94yg/5SlEvZIszcIyBmichnxZIXeEF4eW7aOinoVAku2aHOBrqkE4H+/QcOxWvz58cPXn49n/8ktVCmUF+gA/LFtG7pQtrVPrHQs89TzmsnqEczm6tJvpserttqSrj2gb9t3rY9vv/978VaZPO4bm3jwcd/Odcrm4qs3ZDrtnmy+k9bQ9x42kbaSMpEZW+T/eTz/axuVIevWL02vv2P/5SwPzZjakyfPj2qqA2kPsp6UsLjP+4RF+Lf8VAOaie4JMW04WQqxtL2PUzhV3e/hqz5PvbYrAljY/z48bmjjNseavc4TtYYsp/iPAOCqUeQ5dxviryH4yyuGJkMYp+hL6bk79xTTzG9V8j0OELxyJnU8BqctQXaM+7aNB7SzX7od+26DbGGel8uZdtzTDcg4treEVXUaKhmZ6U+7YtAQ8vUb4XcTzpjGUb2CHwmDngFuSlg1O/yoHgBCTjs1L7AC7rQhiVpBNX3sHRi5Yb9UUsUTSfUZJc+1IMYd/3k3Fp649atsWrFqvjtJx6PMdgE7aGR1AGOGc8obVVR6iPti6eHv+m0i29Tt6Ux6dQC0Zs2b8ktE1/atC0mDx8a9955R9RY7d5xpCH5hRuTJ8VrDijf+0/bKO1GnpNryRUAPFse1x58j+WVby9bFk8/+3wGUB+77RYKyU5h+ag2dsF3CSDtizdu/fDwvd8JM/+L3xNubbLCTtm1py7mzpsX85aviEqufeSzT1Ag+OrkffnJeiCdWVrnFpJH0fPyknQuv6jTi/R7XGX7CczFs6z5hizm34ZNW+Jv//qvE/bbx10V06ZNj2FDBqduty3HvEjJF9LCpkgbnvfCLNTZprKDf45H4pTX91k2sBTf4L/85OmYQCBs8q33xNUUwbV+jDBK79om8pbLuZSP6i1pX5nic+Unx9NTWOQT6wYoO+SnBUuWsAvfoph5/YQszDxkyJAsMi+9K/uUN8LnYdspA3hNWhLvCXeBe4BPfmrk2eJ2G/rpOewCfZqpkyenHVnBcgt5WV4XHmV3U/PAWMDq8kuXRMvHyhWvM0irXMglHMDdsLchdw376euvx6PseHYNRaV7kdXZkTFDtCTsNizkxV+HkB8Sdp/LLxKQ33nwmnABT31dfWzAjn199rw4i8j4wpNPxpXDhyfNCLd62yUpLoVxuZFLXmxPO8SgvbUAzbx1+2F3tVIGa2NrV7l0fPOWzbEUObZk6464mVpGt+J/DGRMQWpmGBY4/jlfeq9LZ8+AgwaWDb36wgtx5+c+E59iG83KyipbtwNFN4pO5fuP6x90ZkkS/4ouFDRbNOB7BlZibo4xJKI3rVkbL8+dEwcx7Hv26BHjIKBhNTW5tkWl7d64ErwVly0sNp/iWd/65jczEvfAE5+OOxCCI4YPS8XruhyFnAaFhGH71mPQ0VYgqpxTCNCuBKwylfBlItddHaT9p+a+FF/7q7+JR8deGbfPujcdr/6VlbnmR0ZXmckIKVaTjgvh6+c8aIv/2eZFnnsJAaGxJyq31O6M7xAkWT1nXnz6136ZgnTXsrbKtU+dC/iAUYNKwZEGCv1WOXlkm3yv0lQodsDI8loFj+vvjkPQqyhG9w/f/W5MvO7auG3q1BiBAOzDrgzufKEQ9N7SKc9Gyz/2Q/jzUfzhYQ6VTC+z6nTJiK8uXBgvAftXv/7VmMozerPeugNRPddkGjAo6wiUOFLYeb81HTLQoHCEETW2dMIUNDrxO3bsiJUY6E//9IfRqXpYfPmXfymupuCPDOgYAVD+F9wE0VcB9LV4yd99r8Oo8qbxaA1s+wiSzH/jzfjR978bCzbXxtd+7dfisw9/MvoD+3HWGEtjHQhZ51p56MUsEWFV8VqXQOHrOilpJAtV4bykAcPYQgnstrAlfvLU0/Gff/TjwP+Lr/3xH8XNOGQ93TkAAeoaQp/h0QRywuh7YfX46Pvys3jymcsIBPzn//Qf46X1W+IT40fHL33hizERYeU6R+lZwS9tG4m3voCBhg4dOmYWSSNZBG3oWxsMJ4WXhqindVKsMeKevS9h5H7h69/ImdcncXjvvefeGI7yadQxwom2II8KVwWsYem6MYuh9urfj5m0M7HgrUXxo6d+HFt21MYqDBOPkYS7h2CEDe5JoSxmwStwtOVHK0PLxwZfXLumn+XMCkjmrtK4ZUkVY548g6KxQr+znKdYFnGWUHgzgkKHWDO7dsfuWF5XRPz35lOJ+v7ub8WdM2+OSmteQF8tkxi4GTzmW1+9VrxDl+Upjcpn0pvfHcKw3LFje7y1bEX88Dvfi0//6udjxqRJGEM9so6H/OraYdeuup5PB0MlmbP3yhS+MQ22KLxKpXSL86GEpHeV/laM/7eXLosXfvZsHGT6+49+72sZGFSqONvQifWc4tpdYWa/+DzrJDfEm/C2x1DOwYOKjIaeGWjAgSKAZMpuGxwFxYVt6AR52FVTeYuwQvE56c1v+JGFKTi6jG9zxoggznsEGjbV7Y0V24oZySJ0FPHN3/xyPPqJh1I2Pfez5+Ov/uNf5PKsb33xSzGLitEDcTxFqcHhCxc0GB3PBKHAOW9LdVLSvReI+1x/idw2QLCeQMB3v/f9+HuUvmmeP/j7/xJ33DwjdYBraW2ydLiUw46Z9CmuDTR4QRos8K7GobzsGmWNCoOtby5dHn/w5a+kjHjkUw/FHXfdTQX2K7J99Ye0UgLcBH52wvfCWv7ml/YnHRxe3RngELL4BbLzvvLnfxEjqnvEFx/5lbj/Fiqw9x8Ul3DUlYOXyAC6gDfg7MwFyK0MOtjOJfmZ6WkWLGQdiCyMypTc+6TnHMDgWMc+9T9+4cU4xlrrR++/H/7qHe1x9I/vPxgr314cbRa8HuNGULASfrdGg4FPOcujcPd8JyXwj27qehlk0BXSOCoopuii5NOKwJ47ZxyFb3djCL57sCFWH6XGzl3TomZINXLxDEUbj7F7wLgYOngIM4HwJA75nXfeGlMmTszaAbZZX0+RSYK9r7/5Zrww5+X4xh98PaZNvD7XMetwp/MjLoVD2k15gIxAJ2vYObaOY44HY5D6l9cuyFivX49ussjrj/7bd6Ibxv/Xv/wbGRgsDFaDR4wP1ytTpZcsZsoz0kkCvjKgYftpWCLXvMeaQTojS95mxwG2DP7+4uWM6UPxuUc+GUOrqzIYo/xV3xnQ1IU2iCr8jqf0ra7Q6VW+KM9ThtIX1xRbjNiaBT/66VPxR3//HZ4e8fff+mbce8dtWVfHomO27dpqdbFrt4XdILKyR/kv/qyV4zgb+DVwpAPQEd1nf5evXhP/z7f/IuYsfyfuHT08nvytr+SOOPKMdoPteyQfgZu0k5A92kfq71w7DOzlGHitGVM6Oq6PfuXNRfEp+End94VfeiIeJMhkTY2zyEWdHMfAw/blS3lI3Mqr2oHiweckL/F9+SrsB9DdP3z+xfjGX/9NfOW+e2La1GkxnkkUa1KIG2nCYJMBxdwKFv7WzpHGbbdYT86YYuPlRAv01ZKsrdPoyaPHTsR6gvj/jO42e+PBu++OsVezK0BVVdYXOOdSPghCmaTz+sbCRTEfGtsOjW/ca45KxDUZaOgR1V3bEWgA56R6tgAG6zvYD4CSom2G07/5pulVPVHwpRmCIIdt29n9BT13oTUFtgk07D1yPNZuOUhgo7BpnK0e2r9XzLzznixKuxzbeRmBkj//wz+MKddMiI7yC9c4k+0Yifvy9KEfBhoARfDEi3CKf8fGV8fMav1zcRj/7tU34hPTp8YXP/N4jB4x3CboiJMgPgV6Q65qE9sz+dy2HGdrh2g/Seva29ySQQxpuAEZPvfll+O///lfxmru+eMv/Ep88r5Z1Fbql+MpPwuc7SWQxZviAXzVhMXieX7WRqC/uRsCfbBa/z9973vxX595Nq7m9698+9sEZacmL0gz1hHqwSSJdbkMgEpDBlgy0ADc4ssgmOOVPCt/o8vkPZ++eOWK+APwvQbH98m774xPPPRgjGdiyf5aNFN+UoaIC/td6LcimFDSgL/5nMQXfKbdID9a72rO/AXxhX/3Z3H/iJq4874Hsgjj4ME1IjAnaww4q0ek+4QJ/PhM+yHutMGVCfKENpvjaWBCXbiPmjE/eXFOPPPPP4iHP/lA3HADO7/gb/Xo0b0IXmIz2ZawZf9L+IHPfiTuhT1PhoQ+S7unwKVbqK7ZuDH+G7gfWl0dD9xxJwGYIVmTQn1dBDIKesxxpT1xZoBIOaZdJN0a3M0deZAPBtp17t1GeevWrbFw6dL47v/4bjz561+KW26Ywux+b+pEsROKshVaS7xCGfkKjB6lrZGf7ICDki9FsFR5Z9tLCMA8++PvRWPHnvHHX/taTGbCSl9SfJT+i4Eo8WjQ1d3DWsGjDAxBEvgAXrI+n7axO56Ysd4cXW8NKbfZnAM//XTRkvg8O0c88cgjMaJmsAzI3eAB20sbSBjFrbb6aWjwA/TQzrq6mDNndgwaMzIeZXeLPoOqvCqfKz36/OKVl4/pcZkCDaDvw1EGEylIQBQGlNpxy9o18SrO/TGUilVNjT4OgVB1zs5BfO49qpFgMTgLDOrs/uZv/1Y40/b1xx6NTz78cIwcNZItxBqzCBTSIZlWxs31qD4PxnGmWgaS0HTiFSJ58tlhk+gbYMQfvzg7vvKXfxUPjx4Ws5g9njptavTp35+CbRb6I9CgcLZNjhxm2/Ofr34N3BK66lsDHqlMCjDpdhgH727bFt/50Y/i3QUL41OfeyImYYyNvGJkzvyYPiSDaaAreJypU3DICCmsgM9nK7wl9nLfa4WiWzJapXspVcf/7h//EYV8U9w5fXpcYaCBgk5tcHCcTS6NYuH7hSN5zz74bWEI+C63BgNXe3fvYsZoVcxbsCBeYebl69/AUGTWxUq6wqHRo9IpjYkySpvRT3BeGo4KEg2vdDohClP/nTlfvxFHCiP0B//wTzHghknxu7/2KznjlQaoQCW4TThvAj1B5X2+JtRCXFzqeCJpWRrSiUKFh+IVoqDf+4f/Gm9s3xN/+KUvkXXwaEYUT2L8u/90CjzHELw4holvhasCDPgdA00GBiaVUnPwqUBxFnMTgYZ/YsboP/zzv/jU+O5ffjvuvnUmabIseTFjgrEvRYGgK/h+4dWbyi766o+8qviAiO2tVsSf/Ls/jVfe3RqPXHNVPInDN+naaxNGAwFZLIcxUlgrAKWdzApgtvESD5PWL3EaFNGxSIcCI6QtKZYGGmZTVfizv/2VTK373S89GQ+aTTJsWFxkuctZBKjtAw44KIw1l0hkRsP/pO494PYsqrz/SXnSgAAiLQkQQlcUUSwgSJGOCoqKCiqIotgVLIvltYFr313XXWxgQaQo6qIiHenSLKAGCEkggQABQgiQnrzf72+uuZ87D2F13/X/+fyZ57nu67qmnDlz5syZM2fKhZB3Fc0lV/y2/OD003Ko3e+nzSQmWyfokzU0TFmfA5AwNKyFoSHtTzqCp4aGMaxyaIaGlRg/zCO4ozhUQwO4mjcd0RLKoRK2YgCDEEaUBx5dVH5/21/LtTPq0r/ZybWUL/7Thxj07scMsoYGrPvQUsVffpesKTt3n7sMoWOtZ5lf/tV/Lh3bHXdMo2P7XfkOA4Ajj3lL2e8lu7FCaJOc9O7ARCBLadOLoYEDgXRK5OMAX8WHHjSdvzJjgIHlaMo7UmWFstqxXYnB9Bc//2mZjT77iY98pOz8vJ2whgOXtGutvS6DBmYLb7iu/Jw4nr5/OYe26rbmYozHpy03KhuuM7asw9rTEazpzAVS8m9EXmLLQXR4+NVujPD4i75yzwEvvMG1FCPOyhgalpS/Mvi5cRoKE3Hv6eKf9NETyluOeGPawxmsmvrgiZ9JyJc+9JHyBnjGT4ouY0Cswr/SPQbIsZqXtDf3WgehPSmjABBB+tvJqhwps6OsfPu7MTQ4I/Xd7/xn2W/vvZ0SK49wqKQNxMFnBuYUVDmZWQZko+0UYFGyM0sI/NQ9aTzw1MHGxVdeXd57zDvLXcD+yOtfnZOcn7399uFLjUaRYfKB2JNOV8sBLcVXj64Qga18I94IDdWsNDjnN+eX933u8xyOuG45/s3vKa/d7+VlvU0mF04fLMsXPMoWFfjGro/7ChrASvtB78KgPKNQPkagxKS+RAFlxdmNhWyfmzpjejn1rJ+UmbdyCjUybEu26ayFoWHeHL4ZftUVZeCSC8tO207i85bMzlG7fsZSXhBarwygr3y0LWQlA/fINu6gxFUHzJnNYjXDMJQgDQ2zkNO3zZ1V7sCIuN1RH+CArecwK7ykTL/9bhRelHe2UDzGEtJH5z9Qnrnd1nwlYusyntV0ELTMYZXKjQxezr/ssvKTX/yqfPyED5e9mfGy//DwM53tRBro7C+Uu/ZzDoAM88pACT6wX1FGr8FBtsq8P/zxj/RL55fvYxTchJnvE97/vvICBo2mNW76G+AlPfymk3+qfx0cWe/pb5I/chQ+GsvKJY3Rl/72snLGqd8tZ/5pann/Gw8vxxzxhrpiAp4TT43TURpBv+OcmgflSYlknOgbDi6VERgPKJvn+syYMQP5+ePyqW+dmjTf/dynGPQeFNotZJAQ2KRRGdVYZoVWWc+AFJkcgwC854ov26sKr4M7DQ0uP76eVYgnfv4L5Re/u6G8+tnblncf/+HyEoym0s/D7kQtci8cIm51MKAsdrCqs14ctDjgyqAUPDwA0kmC8y79bXntu9+f1J896o0MYF5ftmC1I0oBX1zh4GrwlQNtOelXoavvzsCq62i8dyVYrXMMNiBkftL/nnvvK6efi6Hh698oHzho/7L/fvuzAmnnstb6G5TFHLZZB1AaQ+BTBF+rUzKoRgZgtfJIc7cNOAhehC4394EHs7rpVIw8bus47JCDy/Oeu2OZMmVKGc+Bqs7Ga2TwmjHzzqw6ueD888rtGBpu7g56eiECavLGE9k6MbpsMGYFq4jo62mrw8krvAwtbWm2vUrJQVnibKchCbXP41pEu3+ca6WGBgx8sx6cV268+d4yjSXxo4CheWP7LTcpe+3/cj4LOrFcixHp2vN+U77ymU+X3fl833hpSRz5XrzlzVy2K2nax+PiF9pQB7YR449hpn85/ZlG8J+ddWb5KrreEfu+tHzwmLeVHbbbFpp0uhy4W6aUEdBpN8Bx9ZDOdivvKI9T8zyLkytM72P16rno+//xuX+OoeFz7zm2HHHoq7L6VkNv5LqJxFnX4R7dmlfzrUAjIeF5jPNco4A9lvakoeFkdOCvn3lWmUzUr7D64KB994sx8SHGGPbTGhBtQ8p7B5KR+eDnoFceV9fxrgsWZCrf6K5g0PjRE04oN86aVd5x4P7lTcwyvwC+EcZSaKfxTLoGb9LJz62Pk2pKBOkd+ADPGAR/J0cfov/4r4suKUd/8tPlkMkblYNf/8ay1157lU2nTIne4ApTDQrSyNl0V0hWvUrjhq2fugAPp+aH2wAAQABJREFUy6FcEHf53jT2fTNmzS6nY6j++Wk/Lm9+8xsyieqK7adhaMiKU+orKxqAL53TXnmyL0g5eFZHroNhikg+Hob4CHScQ9/sRKeGhm0xOr/6oINySPsGG26Q/tnxTfiMdp2xGXjKm5VHqjyXhtnODK4aNq0ntyfMYaLzJlZ6XMQY4dvw5ac+dDz62EvKJA6V9kBUDzi2HpUn8mQ1hFT+iZ5BWWo9WiG84NQP1LFN95e//LVcdukl5cxvnlxWbLJ5+ewJ/1R21dCA/DRc3KWhxs+6igEAAmZPj32rMs7t8G7tQVBHDrsS0ck8vyx12eW/Laed8t1y7k1/Kscedlh511veUraDRuqPTr4haYOg9LCfiPEFhleWT599V/kJRrN1Np1QDqffmbD55ogU45EX/XOuVqiU7Kn3848xNFAJVr6NDG6tVLDW7ZFpG7c603H+BexpfijL1vws3OabMVtAR/UYAzUVVxl9HQaNDrQvvOKKctT73ps9mcdjHTrq6KP4ROD2nBjsybrzMxC31jzUYxQNYTmN00bkTICNxc/aVIECEiq4dJryjLD9XNfZCO4T/u0b5eCtJpWDDzu87LHHHtnK4YBepUZmsKEr+CyNTJKGyD3CkLLyT9FgPhrOMIT/4wzEtVbeMvXWcvrPflZm/fEWvu16aHkx2z6e8YxnZkZ9AbjbyFUmIpTtfGn4zhqIt8JboZF3GolL353JsKP2O8l3TL8jg9KTv/0dPreyTzlgzz3KNgiojZiF9TvRDvCCe/BT3FX8e3XS92759PfzkxoGPIX6WpZwn49F/6qrf1c++KHjykuwoG/I4CIn6IKny4uEr1BVCVH5j8WbBupqERUrwxXClsPOaW2+Ya7C4fed/czNqWedU567687lA29jRQMdG9IArMBUZHEd9+S9Pa/SARHHqGBPb8dp3iiK99C5XHTxJeV7J3+jXD37vvJPb397OfK1h/EJvIkZSHuOQeMPc3MGX8u7gHozpNSJwsuvG8gr7o0diwAcw+Uner596inlqz/6kbmW7331y+WQAw4oayPEHiFvet8YywwTt57gA07w5L6Kv8wT2nPCLfxzFae6f/KT/6dcwQz+YaxoeNf7PsCKhudFAX2EwYvCT4GpQcB6c5uKKxnGjnZpHWeTUD5XHigIFYKuEFAgupfzEVZB/Pqii8sxzKir7n+E1R6HHHwwBqqta7tBEDaeVqldRr2pcA5jcLQWn2/ytOlLr7qy/OiM03L68Z+hr25bxssaGibzWc0J644bNDTQlrU82wWOBoZ3BTk/qdt0bJTZIY/dpiaa5eCtKHbeZBgDmtFrjsfQsLDcyCz/5Xcsy6x3bzB8/AfLy/fdp0xyRQNwe4YGIEpj67BHa+mh8u+9e7aj0z3wwNwyffqMciVKxbcYAByBQVNDwzZbbVU25FNv8vFjDBo1sIm/X6+QxzUCqmz5/e8R+PnuEk0NKWSFQZHtNNxvu/22chWGhvPO+2WZNn9R+cB73lte+Nznssd1Xb5EwucNKaMy44YbWQ7+i5/lSxnX/PUvwW1r0m+5+fAymUHmxuutgaGBzt7ZtyUoOfCanXnthORmZZLUVP7iL2/pC4x0kPhqZPBrHsuGszQUQ8NDzKhPvQvD4rQ6/Gy0PfGjH2NJ+pvCYz8+44xy3ImfDqyvsBrmzax0WO/pTyuPIcM1+JqX+5TJJi4050nqR9bqa2BHd+tA+aBM+COzIidjaPg+y+C3Ico/f/1rZd89d8/KtHzVBNq7/FpeV6a7SsQ2IKwUjw5+BcRWTrr0ULmpkcHZCGX4xVddU45927ExNHzw4APLGw4/ouzwrO2jTDzCwMv+QHjBvZJLbDt08e35URafwSOGBmTe/fPnl58zw3jcP3+JFT9rYQjH0LDPQWUCKxpYQ8lezgXsvcRgR6Ny68QK2sAwjHDDbJeAcsvEGgwoPYDR74m7tWmZ8TBUuSxz5px7ynfPOLP84dory9uOfEvZcv2Ny3i23sxn+f11V19ZxrCaYKftMDQws9JWNNRaSGnIw78qd2QT5sCyZQJK4muXzKCbessBq9SFpV5OhT2EDLmHawYrGu6duEbZ+f2fKc/f9cXM5Cwrt0+dzYB1ITyGYsXBC2txMN7T1h2frziszYqDESjEszVcYWhQUTz73F/D7+9ihdDO+fTuusxO28/5JQfzkyWUNfYT9oMuO9bIoEJqn5LVASJLRNuc6f7wRz4hx/bL75/xk7LVjs8uxx37jvJ8tm54SngGANS7q+7sX600+Sx0II86MKjyp+an8lb1Aj9V/RBLvS+4+KLy3c9+vFzEBuX3HvH6cjQr4py1Ux6q4Iv/SupMo7KrJ3snvvMemWC4MOmT8olT+NOBgMucb6NvPe2Ms8oXfvhjS1X+45MnlFcddADLgutXNhwQubQ4+gvhMRpKpE6vSs3xrlzXAL6EPOVzT1CXj2+ANl/48lfLL29gRcN2W5ZjP3BctjppSHC5s0Z2ed5BpDQWtO1RuA6GxN+BlwSTPvY1WdlHvfiVh/Mvv7y86fiPinr52GGvKa95zWvKszA0KCNVznXqf8LWL7QHfhsYmbd+1rF+5mE8yz2L7Xg/xYB04infK8fs9uJyAIOXXXfbrTwdA9Ui5O9Czt2SV+Rov/aiwXLQVSNDW1LuAMYVimuiEzjp4xcY/nDLn8sPz/kZhzg/hHHnZaxo2KFMpl7lW2WVdST/uZ3r4ksuLhf+Bnk9665yy73uQOeMhk0HyuYTJpVJnDmxHgc8jONgpqxooBxZ0aWAkN+IO5x2bOurGHb05U3OyxU5zKw1eA4bs0YMDTPvf6BcfR3b7JZynggwrJMp20wuL9pz37IRXzX6HSsabmAF1Rc/+YnoY2tS32iz0W+laQZc0LIOwOwWpJHYBK3425+r51jnfsHE92uYOf7Jj08v/3bxb8vr996rHPd2tttss014QWNA+AQYytboq8pj+EcdUH6xzcnv6nl2fLZZr9HIsLvn1BUN//H5LxdN5598x1vLGw59JfrYBPBAv+polsLyDNmCrHdpl3fuOickFpPHQnhgLIPStVkh5Fahb37nO+UbZ5+dOGd97V/LK1hx5+qw+Wy9Uw/O5ALArA3rN7SiPcl/1rt6mc/SQvylm4YG+66rmfj55Kc+VW5Aph2BTvCWo44sL3zejkw2+NnFuqJBugd/84AObaDOS8oSPcT2y5/hOtO7dftcVjS847Mnlf04u+fgI48te7HMfjKfKPcrb7Yn9W0nOpQ5GY+gb7h1K7wFrJDLbPjzfAInAq0HVz5PnTatnPmrX5cL2Lby5qOOKLsw/njmM5+ZrykJW6OmbY/OlUu8KT6QxT+Gx64urCNbXQwN0HUecmTWrNnImj+W7595Rtlm8y3KwfvuW7ZnEnWC2z7gD8c3IpexAHJcHsmKUM4YUwZk4hSYblFfgp6tPFoHHXMNtl7Mov1dzrjPT22edukl5YsYGvZhi+/GGEjWgBYaGvKFKfASt6rnSYHqcq9ktkLir6xw5a/l9UtkF51/QTn7lB+UkZM3LR8/7oNlZya8s3URUtT+JxpoyiK+I+inPd/Fqtag66SjhoGlbJXSvDaciaXxjG8efnRBxh//8cWTypV331+OwdDwvre+tWw3ZQvO1OAzmkzm2XbUI+U56TKacd1w+gonGfxsrPrW2I2fjlHrjWXSlM3TbulIsDFgeNbYQB09ld0/xtCQyu9IQSXHWfPKOw66uBmL+wUMMh/Gwjxp0sSyE3uStpwyBaZkYBcrWJ0R9nNqWuUuRFl5w7vemcM/3kPn8FYGpc/e4VmxDLm/TOVS5cFO1pnGlQghrUZWpI06y1HpQLSY2ZH6rgCR6e/EcnbOeeeX//ON/2SZ4dbl5Ye+tuzB6gDPjDC+DTuDcLU0mCnCD5g2RkuWi6Aq4AlXEDO4eAwBNAcB+/tbbiln/vzn5f7pM1ii/oqy6y4vLttvvz37jMZx/sTDga0VzbInL+7ibOcrc6vYqEDLkFpCxzHQdQuFhobb75hWrmLv2zdPOaW8dK89MTTsWbdOuKKB9HbczeIXYQ6+umAu4hbJO85w/dcEdz81dAcC6qorryoXYmi4/rprWX6poeH5WNUncIDOOhHW6bDowN1bJa7OUii8rIt8U55y+Y3bDM6A7yB6PB26cW7EWvmb835dTv/RmeUZu+5S3vOWo/hO9HaUuyohFan8VhSDX333N2iDfy2GswQ4lK4BhKwrGi4G7x+e8p1yJYP1Dx51VHn9K19Ztpy8eYwKKm75/nJXt1pbM3OEvwLGTkbFyj3LLsFehiCJNVTao4hOnXZ7OfUHPyhf+t73zLV850tfLK9gD9m65P04wp3KQoGvThLLa4N3hF56znqX6k08em6H+Vx13fXlYx/7RLl2+p3l1TvtUN5//IeyosEO04GXK35UwMVZ3D2kUt4ZiRAagRDS0KDy6SoEL/eIZwgJ/zwA3/zm0kvKez/+yexnfDt7017xspfBN1uV5aRbhsFCXhRFO5smpDVYjFlrjfIQM5iXX31VOfOcs8o0OoM/3D4zBd0a+bflxsy6r/f0sjH7V9dE6DpDlE/2iQsAXaHkXYt88iClwt8ySwcmdhDaXLS1pRDNAbGGhlFrjC8PsnXipr/eXC6bVc9omJtcS/n88ceXA/fZm5N5+e47eA9Ia/hPiktzB1TerQPLUi9u5OuzQl43FwPRdAYAV9Cevn3yd8oRR76x7Lf7S8oztt0WZWyjGKb8vJwyxU7UJYzKG9uAHaVZxsINzKyKoewOvNcer6FhGCsabi1XXnll+c0FnE1z30PlnRh4dmYb1fp8Km0NVjN4UNoj7Ne8iRVK5533K5aG31x+e9MNwW0yv5szk7YZSuEEzr9YdyyDseUo9BgarLMo89S9hxTVVmz9qeyClIXXV7pAY3FcDo/E2IChwcPR5mFouA2l4aZpKE3EZg1B3CeO+1B50+vfEBg/Yxn5x078TA4p+vLHMDTwfeenoxTMY0XYctqz293ky+THTddqodG/tQPrWxopM1T4VP6/cfI3y48wNLwA+8FxX/xy2Wu3XWOM9ZOHqIUZHGUmJ/T3nU6XfJIHsDzzwKWYbvNxD7dnloiTRrhLrrq6vI8VDTPB6dgD9y1vevObOU/gOZG9zpCKs+wQF3pRZXmJpOyeu2DoirBPe3JFw1xk+M9QWI478fNl060mlONe99Zy6EsPKBP5/CPT/2U5+S/lFMwYGhA8yzGKrByA38lwJbJ9BAVAxWBW1IEWCj18gx0hxoZhxJs+axYr4jA0XHdVOeaoo8tWGyJ/l6Ggami46vIycPGFbJ2YsFpDg2WwBPZVFsu2UFc0VEOJM8KugBjDpeLmn6tzlnDNQ6bcRzudedc9Zc4mpez60X8pL9p9N6CMKXPv4xBhVlW4xF7jIfoR7w5GWNZO/+EZPm69c+vEJVdcWX7O7Myx731P2TOGhk055wdDA/WoUdr69/K9KV2+V0WvruSLQcrKxjnQcVnrn+hX3TpxDgPSdXbcgRUrb2NFww7MiCEviCMs+7/apzIoBVdlpX2r7cXBatpN8q+wnYxwefJcjGfno+B+64SPlyvI831HvKG88bWviaFBGlauQK5gaPCwW8+dkeeUC+orDsz8zLQM6uSAEx0Oep3xVab6edOzzzmnfO7b37NI5eufPIGB0X5ZieiALgZecM/SYOpCvNVrlFWpS3jQpbZuxamz5LUdjCUv47h14gtf+nL51fU3lgPYc/Xej56QFQ32F87oygySs0dzyq2+oae6TgwN0M9+UH/paZtVnrtc+sLLryjHfOyTMQoecfRR5WUve3nOELIs1XABHHDU+GFZ6sAO2WTbwVkHXs2Js/X9OLi5reTciy4q3zz1e+VVe7yk7Lv/AVnuvfGEiWkf6g7Wq1u17ENAK3Urz4i/gxX1NrOKDkUblR8ttzx5059uLqdBe2l6KEaM7Z/JwGjSxOg90YFon8KaSd926WWXlosu+DVbJ+4qf7mXQxNwO00alRUNE9caVdYbjaFBkzh1Pty+TrrykwsiK1PAqlfWyF+EoDKr9nH0hchtDzwePmZNZMMoDoOcW668elaZSpyncTH9UTbZZkrZ4UW7lvU32KhcS5u6jZWmX/jEx9k68fyyBnWDmSZtUZlqGeTrDHzFgbI4yIZpg18N0xBRjRPj2RLoYPmqa64pZ512Wvkme/lfi6HBiZ9nb7cdEOrKFutHg7az8LngRzKKwUI+yoHO5G/7sFJcdaYeqWFt9j1zynkYY08+6YvlFsrziXe+vRz+mleVTSeyPZn2E0O5xONfJNU95A7pqGvv0s8+xL5rOXmMol5ddv+XW28r3z7llPIfrLrT/egrX4uhQVmwkAGxebi6VzqIu+1cvUPZII/6roxxcO7qTXO1rcr3yuOrMZiedNJJ5Zpp08qrd35heUf67p0GDQ2kswxJB36RpMCPnJDPKUerFwsmXAfxTnTej97xy4svLe/+3EnlgE03ZEXDEWXvvffG0DC5PCruyJG1qSPTPIqhzfGRW9s0UpiTRDPf4E4deZ6YxhcN4XPn3l/+PPXWctavfsXZUP/FwYJvzRbr7ajXp2Hs1Zip8UKesE/Tya/VyFDxNwcPcbZe2yo+dWw/g+kZbtfd9Pty2tmsJGGVz8v326/syLaMyZy1pryaz6SYZW9GfGVBNWIui7zUAKhxOdsUlBXg4PhgHH3InTE0XF4uQBb8jOsTGBpeiqFhItvC18J4qMxVF+uNb8BTGluK+std5K0F6kA/24IrJizvn25mWziG6nO+fWoZtvlm5cPvfW/ZmRV7GnVcAdPkoduylDfWl38eDGuPqgFzODr4Ivq8Rcgix2kaftdGJ5rPmPSSSy8t3/7G18slrEp+K4bYuqJhq/QX9hvSW1kr/UEwh7YiyDIxOGP2rPKj008va0xYn1UofHViiymhI42MuMrpSBUL95R1tG1q63/pGojUcxpDBxCPFQxk/gBz/vqX55a5CKAJfJt7J2b23NvjaoRqqYOWoGHHqTJyMZatt7GigdVk5egD9gvxn/Ps7cNYDsId6Krw23k40MoMAOkzoCF/96vbaWbQSDyZRLQ8jOROKvVXF11SvsCM2iEM6vY76GVY0HctkxCCClctfmFgGyIwLZOdvffaFHnAn/90/gMMQMYhGB4H71mzUd5ZAn0OSwHn3T2nHMSst1sntseiuCaKsN8LV0ApRGRoLZh26O6FVPDZASvENaSkg+V57BrMFjPYnUfa6Sgs19xwfTn1h6eVF7P/dZ89WNGwBYcb0RilZTU0iFvF3YoVbxl7qDOODdLBrgYBz1C4mj1Ml2Lk+dPNfypHHv3W8mJWNEyaNCmnjrezHOzAVRDEW/prZBCODdaBhIq8M1DCF25mFxgQ/IklV+5//K/vn1YmIryPPuLwdGzCtXMSZX/6MbXD6XcJ6wpl5w3BslRvDp31by+/nEMyf1wup6G/jUHRwdDewfQ6LBX0sNAmQNxGYYMPSYAluCzNhv7ylDyzBEGi/xhoPw5BNW3GdPbYnlW+2hka/uXTn+Kwl704kHA8A3WUZ5TAhmsbXFWhB3yQFlaurnA+65yBFZFr6dg+/enPlhswNLz8eRgajjueQenzo3hqaMgZByRqxoYMuqDUCgYry1V+wVsFdBTK70hmFDIIUyDSad3LwND29Il//mLZmvwPO/bYsj/LDLeaMoWvCCAwF9WVQHY2rvSwLlQcNDQM0B4fYG/r1df9rvziV+dmRcMNf741uG+NRrTlhLEcWLhu2WAtzhNhAOLKJC8VkhgaaIOKSAdYqVvyGDRmoCQSlnMOOBzLrRMraMsrB1DgWanx4GMLy81snbiaBRQszs42KjP+zPs/AN/vWSayXG8U8Ny7F/iE9ejMU+hPuAwlV63O0DCD9uTsxQ++e0p5HTP5eyMHttl6q7IBy3Y1lrmk2g5HC70zDNJHvlY5eRy55gFJGqiyr3qF5V6egZfZZk/gNVezHPficuvd95WjjjyqvEjDHYcojefkbpfRzmN23bZ2ycUXc3AkBxVde5VFLJtwTdpoWNmUZYMxNHA09MgV7DOlTuuqJYoFLhoaagEtoTzIFZ/KYZX3qpImbZfxOYMVrGiY9/jScvusOeX3tz+cOmgHbb7/He8srzrkEKlVLmaG94xvfrdMfeSBcuKHj+fw1leW9VAKHoSflNnjUPqcKQ3RyXOQz6G9731+0l550QwNrmj4z299p/zkokvLSzbfuLz57e8qL37hC+vBgsgTlUNhZyYamSww+abCRc5TSDg+Aww7fGe1HfiJ9yMoRFcwW/ep9x9XppHgTfvsyXLEN+bgQmehla/KeS8BhkUE3DnpJ031N7zJUhX5EbYHDA3nXnBh+SjtaavttyhvP/h15RW778O+6s1YkuyXazC8wvTNeLCsMzYwzGVQxowJ7dVPlXrqtcrZMAY6y6HP4hUMWJE7t89kmf3ZPy1Tb/5DOfLwI8pWrGoZv2IkW0oeKDdezdYJDA07brNRmZAVDcAB1yo1LX0wT39lKSyWkmwpfJEBAc+j4Zlx0NZ2aZmWYe1bTHudzyEpD/B8x+2zykzO/HzhCSeWF9Eexo5bh4EJM/ijWN1FnQ+3PMsXM3v2IJ+efjB0V/bfx+zxXzlYVdqfh5Hqze84tuxGnXrImYMb+z0H5Sp/rZ9TAfTdunWwaN3II/aP9ilerU/569SpWSH0G/ayDkdpfpvGo2c/C0MBK6BogzrhxdDAXcXRyz5KJ2wv5ZB1atnNW9w9F+oiFMXvffbT5RqivJPVDK9hmf0UFGgZIQPY8CPGEs610cC2FjNl9m/ykXJWo5dxR6sUI0Pl9yZzps+YUX7JUvIfnPydTJ586KMfKvvSf2zIarCm9Iub+otGU/sj9zynHVBf4mr4YmTrUq46oHNVTp2wuJ7B6Je/+rVywY03ld03mVDe/08fK7th5HGVj/qUeoEKv4q6HOKqi0ZjeSR5WxeUxxl+6a6fZfJb8pddfU058aR/Ltuv/7RywGsOK3vtxeF4W20lWRNPOkYuMlut4aGuEqhwQmtg66xL6974DgZdXTTjzpnl/EsvKz//4Q/LS6DJHnvuWZ7/ghdEH5OjnSgK/up8XKAW3Kvh0fBKG8vlALIeLkd7YSXGrNmzYmg4m4mfdeCZg5kc2IYtg0+H7q7ayGw88CzvXcTVMHw5K4Y0NNw6p57R8JyJa5ZN2UI4ia0T6/EpFVc0DBePrk+Tn2yBcplX2qA/cUpl2xnygAeN6m4VXOkEAYaGFcjwO+9/kPNBppe/Em99Lj5mUjakb956++eA83rFMxruYyvHpz/64bLL856XwyDRukJHecx2JQ9JB1u8fulj81brJ+2INiX9ncF1lco1v7u2/JRZ1NOuvKYcwmqyd+SMhm1DQ5IiF+QXkAaufaD6pcVysGqdtC2y5iqfyzv2AT7PZpXKRczan/qFr5Q/E/4Rzio77FWH5IyGrGiQH8Q3KIMz99DNd1x7TzDvLt3XyOuMsu7PyILTfvSj8q2zf5L3b37uxHIAg/WnsVrQfttLPVT+scz2JdJFnVq+01jh7Lp001CdHgseMI4D/d8xKfZv//av5eqpt5aDnv+8GBpeBO0HaBttVa98bRHkR+nbLhFKMQjUCCY91O/le9PezbaS31z22/JhjOsH8tnVgw55FSsa9sqKhkcZD4ifB6Ea34ORF2FkHY+Mi45hntS3f9FVaZ/i7KoA6/9++mZp8zNWbF939eXlMNrqC3baqWy9zdY51D11p5yh3MKR5o5t5Jf2HIKaj/hTkAFole1l4KahwW1aZ2K423Dd9TLh87wdn1u2gF/VjRzfSBT1VMdoC9maK67KMQf8TpgqhzwPzboQZydS3X5w112zOOfg2nIpevzF4H8M4789GDtNpO3VMxqaoaHiFvqH1ubQ56wQnH4al1yxLb/egs5xCQaMX/7kx2XFOhuWdx9zTHkhkw/jGVs5SSF+ypSF0FtDiLwvjSSCByY7TnGl2kJ0MM9W8Bw0J/Rcse2qYbfJnvGj0zig9MZyBCuGj2Lb43aMcYeR3pbjFn9p07bsWG9Z8QiMO++enRUNG2AkfjOHvm665Ra1AORbW0aTLJbsqen+IYYGrVQyrxWrlX8Vt5RvtmNx/xWD76k335JOWmV+U04tHoDIGajCFjK8hgYZUGvrV374g4B5Jr9HHveBsu2221BPCAbCW8do405DIa3CKYYG+Mzl5FooFTYqoi6JssG733z23feAyy/KZbdOL6onRx5yUHneC16YLQIqKZmVlcFgCi/Z1mb4xIZoB4LyieAeqyUVAaWydQvK1jfYj6l79S67lBfsvAsnHW8VRvULCMLMEmzyiqGBMoify4OVWjYghYiWV5UYFRcbqEJo9t2zyvXX31DOZGAyBfivgimnTN6cg2+ehtXTbRgITdK3RpjWVttd8A9S3Q/RIiTtcGX6WTT03zOgPPP8Cwtz9OWwF7+ovOhFO2epnYqiNLV+bYRedj7OXERggbfCW4XMA3gUlmaosBT+IursNgTglef/qvzqtpnB4MPMIG+3zTZRElUQ4kSqOfFrz9x7z0bhxZsH1biE3RmpazCSfP+/fpkUW8MVrz327RlMe3CiAwwHADEiQdsIkMQUCHjSgYiDiocdjfRXQdBgpbHhThSPsxEiV0y7I6kOf9lBxTMy1qXelyHsh3dC28A24Op/1i/v9RbcLUOMNzzcxHK0z/37yV1oKSd94L1lxx2eQ9tYFmXMwaWJVEDt4N1CIf1VSpdrbLBMwBkYVw+qUYAthhfsROc++EC57LeXl3Pp3HQvY5/nXvvsWzZjwLsSHqxbSrTakl7Y8KFGOiovqyPmsaLhJk7iPuWUU7N/tIckDx7E9Ix1+GbzRihIGB4YJ7GiAU8uxk/wdL07BtdKrqz1IisCuKj2fM4I/RdbQ55zgN7w0WXOjMXllzCii6373SG0p933qEr6APz4ZIaGtCUTwpvJL3dnZUWAvbCs9JhFvV59+aXlwj9PK3tssWnZ+4CDyuTJkzMDq7Lip/yktfWUmRFoIouqQPhJTleSuFrKyy9tWF9rrjkuHfedKNA3osydcv5F+RTUmzB87bTT81kSzIqGtdam/CjDzFjceuut5TJmU6+85abg1f+zOy+T0edZYc1BkCSBToxHyQs6Q7/wlbQkTPkXg2sHwOI2XvSsAOnKxDh7gwdYzbG0nIu9iLnOVdzOzE4ccuirIRnnhmBw/AWDWt0r4fVXHvyKbG17mIOqaCjZStIv61teoAE6XecvDgIAnjImvMV9KjNSJ3zpa4bEHfvqQ3LavEqBMt4BeDM0VOOx3K06X+HaNuX4zAYT3/qxDiSFhtDrkJH/9qMzAntzfo9697EsH31GZi7sp5TzDl50pul3vldp3/n25Cl9FArvQ/DE+Rgaftq1p8MPPLDsu8vuZTNmeDQ0uE3LAyA1HuTAKGjvpzA9nNU8hzGYHKAAAxDM/bcwS85y0Mjgtq3p8OSn//U/k/lRr9i3bDNpcllvJIeSzl9QpmF82Oj3N5Ydt5vY++qEqrfmpf4/ssNVejlf56FV1dBQ26PGidoK7G+kJQYacNLQMPXe6eUG2t3EtxxRnsUWx1Gj+QzfEgbQnJKvLB9huTB6zZv3INuP7o8R38H6g8zUTaNeL/3xj8v15H4ohvBdaKvrs9pOY7n4KV+VW/bF1oOXPOR7ZttoP8o3BwfyoHzjrLz9ybQ7ppXfsQ/2nJv+kNK97/DXccDnM1ESx2XgGXlIYTTmqFPY96g41vZufsh05TqyWsXTPtK8xX0exqMrgP3Nrv94zthh5fXv/EC+dGUfLy+Kkwa2RaxY0dBgOo0YKs72Geol4usBhfYlGn75D9874L0AA8mvb3J+t5TX7b1HBtQeeKjstu1q/A5+4G5+lrsaSpQ5VSfQyGDfREecPDSwSNebUaI/9rV/CWx/Tjj6LeX5DDCcWYyhARydXbRtyRfqGi5zF0Fhpy6kB/l62WYdaLpf/CG2HFxJ33rGRZcIurzmBTuVXfdiBhYjDEmCv4YDW3omR8jHfioHTHbwo1MR1/ZvnRjfgYgztqHNuf9VLr9rdtkOKPtg0Hz2c3eMPiYPpOzBH6M4dStXWxfC14mD+Dt7bT/uShIHvQuA7cGytzDx821meHXvPey1ZcoWW/AJX/Ql6kna29dZ5nvvu7dc/7tryo/Pu5j2sKp7Fq/bbl7YxsbkAEUdpnpjJMiZfo1HKQC69c5zc6qSobrKJqu3nNxkkS8rGmz3a5Y7rltQfmGEIW4L3tffYrty7R2aIEp59+tez0rg5/C59mpalK8ts3UqT0sn//SzPYEKb+Qtv4CYE1kxmJHe7TR+XeOMU39Qpgsc9ylWHWyLEUZaGF8dqbaZipyGBnVtdVLzkselnXkow5TvptPvPga8l192aTn9ot8aWvbYfuvyGvDfmBV68kj0ryTkB1jyv867eAs0dx7NXQODhgYNbNb3tOl3lB+c9PmsljD6WznQfc899qB/Gh85oq63yAkgyx1eqUYv87atjsZg6gpcZ+z1i2wkQ/lyCTz2hz/dXL783e8KOu6T6JLPQRbaLtULQnMR4V9E5dP8cW/OughfEqLclCc1NDhGcPLhpxh4dG/df++yy267Z+XwY6xwFB/1fevBgbvnmzlJqVHR+pD21vHylUyKUUbrei0Nufx5EKZy8gecg3YnsF/BNtznvWiXMnnzzWPstb+UH5Q10duJI97SybrtYU8eFq+Ob5hkgVaPMpaagwHJs3K+xxkQurfbVtEZNtlkE/ps25xfeIPNobnbSz33QCIp0x3juA1cHBw/KDfF2fGNkzVz7p3DOPH35ZdshxH3AzE2voRtK54/59d53K4W3MXNP2mfJ37rS/SdlIKCpGool/2D9TDt9tvLlReeX37+x7o99d2vfhU69rNjZFB/cIuFcnEh8l1jQ6URWcTQgKGt4z8NDOrWS1TCwGkNznrxHJvrmbD69x+cJlIxGH6Albd+XbEdINzOQXNFQx1fwB/SfdRIDvC8r3z/s58t+x///vKWo9kuyfgYhgFS7cnDZIO1kzyeaj//EENDln8qtCGOCnmPYTtq3P4XPvN22WXlzyx/nH3nXVl6q4VOxVzCe5lmTRjaBubJqdNuvw0r2Qisz+vnQKm1WMatwmGHnk+QwMwqFF5pJChuPus8c0ChF6EI4ChIxofhFmB9uuPOO1mqM7NsPnlzZi+fHguiDVlFyMFbGFfm5V+8vPobYhSDMLxKmoocgz0iKagewSAwfeadWTIzidmcpyEEPL1eGDY8D3xqyy7FJ0YTaOddg40C3PLZyJ0BSoNRoPOg0cQTTp1hX4ftDJM4WFNrnwqFwqLiLv61CZpnzxFe3/21qXrj13TcPeVaS6MHKD3CnqPNNt0se0gVorH8C5N4eSapMyIOTIUl3YN/VwYNEjZMlQHjW28KF0/Avo8ZEg//c7ZoXWgj/IqMCA06FYo6kgqmHe6D4fGF3u5Z1ZDhEk9nX+5n+dhGjHw3x5A1HuGR0oKXSpMUUAiqlEp7XSufim6Wz0F7O03p79291fMRoBob5tx7T/YjT5kyBUvrRtW4Q94Kk0rVil8bdOlpiLjqp6ulqb/Zi0z5H8ByPX369Hyrd11mMTadtAmnbmOlDt4MZsHZDtOBih0RyAGVTpS2knMbgGtpMpix24T+CsG6n3dZZhhuv/02YK5XNma23kPdxtN5OTB125GDo5yrYHlVXsSSNjTA6ogl1N8caHrXnLs5rBFrLh31w/DKs9YfX7ZC85rI9+zXGcesPnSKQkmbRkrTXjzczA4MaMDQOUixNUlbiBsc3Ye20otZHqQuM7B09jSmuQ/zKaU772YPL4c7weMLWdrtVoOJm06G9huXNZEfzPVx2ekAz7uZ4Hr0zzN5Jtt6t3y+y5MOSu+b+0C5h1mGyRheJmA9lz+kb+uMlWdebXmz/DIcfOVh78o8c17Mkv8lHJQ4Bv4x/kKePU9GWTYfy/4WW2wZujuoQ1Jm64SDkYdRJh54YC4zcPP5POJ8OuoVHAA5DqV2fNmIQzbXW2sM519QFytZ8rlSK4PlZRkoVhpLD9EosPzggEXoPqt4NzrwhmXca/lwl+2yooEvCdw2855yMwenjsCA6NaKZbRhzxmYOHESZRqWgeND4OW+z002Wp+DvCbQ4bMFzQGLOKi/SmgzIsfQ31tylyLxqZ2/cbhUtpRTrs5yBuMRDntzZsk9nutjTTH5ElbYmH9m+/GpcqDmIM9YfUtpu8rCfIYWGeOgxXZhG1EG+3m1u2fPTr8ynhVNG1Gva2NwVEY5WKkDUGgH3eSdoJ28RL+Tk+EpQygJz/KsMxi2gbtQim69Z1ZWa3mGwlYbTOTzrmPLYvgzWwFpCysG4HzKscLnkdYBVUf+bvcZRz2MpMHantN30qFgBqVO+VQZPDkDY7hnfmwmXcbyRZeVKNrw0CN3zyrPWjq3PJdtQ5twbgdzcqxosO2L8+CffZXOOrB/UqFWPsQX/pK3fVYOZok/9f8oh17NRam6ed4D5YK7HiwP77Bl2ZI2sQwryQK/T47xbxS8MsAZDZAYQ/iizHjbD7QBrbzsCiqXB2+LEdnDQ6WdyqWz6PYXvT6vk7/2dw7YVb7UAxr97Qtta676Ug676mD23bPLvWxP9ABgz1JZj9kk+3j7HwfdDgTSDwHHmSPLKT+0iyInXBxU2j30a232B5v3XfDLTGUw/dQGEyfy+cZN0m87qy+PiDvFDo7ysMttxS940xjkD/lbHsyny5AvDgJcBfIoe5TtP2YzM+0qpK35CtWmm0yK7FYGWRfSUL2iDQRqA6q82YwjaT/QS4OD/ZV6jUrygxgDNN55sOT6rIKyDdt/KLNUmk2Xfs+Kw4lr66+lhXhKoxgHeJamlkv40n4OgyO3gnnorucGOEm0NoM6B06uMFXeqbdlFpw4wvFd+lgH2ZKqLoPcMB95RqOAZXSV6V333F3uv/e+yABPx1e3UX+wrVoGIwrLwYa0Cg9Zx+AoV0tzB9Hpr6ml8D3161ev7LtnA19+2BBd0vp20KOMsZyeKeDAzv7ApeduqXMfvZNU4jlhg/XKhuutwycnx2YL22gsC8OW969osK9TCltXtb4is/DTRW6II1vlBijTIvoxv640jC9j2OfN4fOWf502gwPlMPuOUhZTDwMMfNheZ3vz3K+H0W22YDC3YSaUqgyVB0Nf8kweFhqnn5dOKSzv1qv61HqqXypQZ7K8HrI4ZfLkDNRtF9JRvjIPB14aZvMFKfxta5GZ0M8sK//jx7Nt3C+UqY/dNWt2vlAj3TdCjk2caP9Rv/AFAGKDI/dgCl/oXNWcO776+2sXo17jJ7sRFBm0PkZ7ug9+mQf+Ziw/KmvkDuvNu4PGGDWFI99QFvVp240yR1yVMdl2Q8MzjsYY4/kllKm33RZ9ZUP0JflGI4Y42T5Db9KmFCnAIM3rK7/S3cJwN748a1uQNurAGsHGQWO/wtcOzNUoKB9KX2WZdWBfpW4m3vZzUkV/249yQNmQiSHwllaPsuJqOnLGet100sTofM7ay8tVtlR+EC8RFF/rWZoFd3MwiHDh2dc6uLb/so3Nx4B3x4zpaB/DiuMbVwzXFZ2UTSMsePu1NPETovwk/0gDnVTxWZlkPfguD7kqTFnwIBNAjnE2YsXqhk9fHxqxsguZGV6AD4ieNBVWQOrV+YFk/uVNfZVr4rAyk7puP/fcFnu/7bfbFn3saeEXx2XKX+WI/G1dhSYQIBITUPZh9qEj+bqYWyaU8dLEVQ4ap4Stgc0vR62/7tPL1ltsUdZGrixl5eswYI4Bvvyo3FXuOI4VzprrIEd5/90vOFPjc58qh3Kun8ZQjWpwOznqyChXXp6SP/9rQ4PV6QyBjTYdeohJJwBzSVwb8DyUhLtnzUI43Bvh4MBGi6wzzTJvljvBaFauHaJWrswMQGythzLC4yjyUSp4dyCulawyBUybZlKFifzs4DYdKHmIkzgEN959rrMNzm4KX6smM1EwUnhTFgVGFXqrNj6rW2dDDGvjYSNZvBRmomENzq7A6MRxBkTrambTeQ7zqtxRThUXhaFlkOnCyNIQnBSC4iueChcboUqRFsEIQ8Kkt4pqBL+CuhPWlqdiN4Q1gaVrZahFUAlEIYOmwlVZUyCJg0JJelclnoaBEBRH4xlHQRjaAlccXC1iOfQzvFlxVaSEI+5uYZHmSPPgLjzLoQCuuDXsfLMT8r2Wx1t/aI1BsfmTh1xRodIsTAe2EXKWoVvOKT84ePEQSyFZ5zrTmkY8LZvL1iyDOLdOVJ5phx0pQOVt6aFhx3wqPwRcfhTQOjHvfyZqK00N7+qqKoxso+BdXNIe7HCAL6/k0FPahR1RXRLozKRdAzML3F0OqXHtMerAAwZ9HoGlVCGY59QVfpRDPhG+2yY0UijC7cisb8si/8TaDK5unfDAGloTgyYUIPIZMUAacBrB0oXhSx5nvznbBaC9n11cxqxs3Qplm+QChu0oygsljnLJ3bpYxkoMO8qVDHI8TG4EA7URKl8MwpZBtAUoY/MWsISNwfwaHJzoFgsNDaaLdZg6ppKi/Fb1mXYLXh23PIHuIXgLFy88NDpkAOSgoaO9irmXvOIqnhhHDKPOrRtTysMDKIV+biuDV/DIaiKs3Jbfco+mQ0q9ENcDrQBBeetKiEVMa7nfz3Lb1uQ3OVmajcD6M4blIONGMWvKKHIUU2cjMTAMX87sBcsZUuvs22OYJOcD15LQVmjDSAWwsy3ZdmwwlSZ+GUc6a1CQnosZVHLGX1lIT7mC9PrTs0Xp8qs/ujWUNQySqSxOTmYV02OcXg/Nx6Iwe0jSSntZ4IRn+PVfF57nt97x6Pxt83HcPKNGWaPTOCU/yOse5qeiJf387KJKgn8ST76MEg2tMrNLeW039hOPwhd+4kuZpAx24GCbsV9S9gpXnrf+zMs27yVGkZciG4xFV1/u9dYKlBKpXKykbY1k+Y7tQtjDGDiMY6/ESLYe+IWYrFpArqMdQVfIJwmpT0gVyK5+8mspwylvZLZlNL4GCeKryIxlz6oy8TGWls+7597y4IxZ5X4+K/vAzOlly9l/KTtuMrFswlksazLocU7bWm+YV4o15MmCEA0NKWJiySuVlg6PLKicswAEHwSBv957Z/nd3MfLJm8/uuzgHtbx65FepdztDSiNtnHa/gDyRTlv1g4gbRMqjk2pzqw2fKe8cvCuEmd86W0d2G58Tjr5v7VNw7k0HNmvaJQ3nTJJeWwemRnnLo2cRZNn2mfJxKUqc/aplTIq4jEUkI95t8t2Zxv1SwXhL+KpV0gtz/TJ6jbybXgqi/vpbLwo0Hjb9yhrzUtcLbNtexyTJyqYbdLAeC7Blv/kyQWscpSvxd8VFmQWWewAOwMD3s1fp/y0/CkfeSjP1ZfSN+EvnQIX3NPvSkPSGk9/aW1etoPWvwk7dUW49PC9roJYziwon1GmLemkfT2DoZ6jYZmcmHBWteoNlYYq9TYa86lyzTaugY88uXQq9cqADAbJ073a5mt7t/y1/tQ90G+oL/OybWvQkHf1zyUN8Leth/e6unXFmbQXL/v0FlYPIiSMPK0Py2l9RVchb/MVD/904Q/a5hjaGnY9zjeB/2xtWlmRBeKqnBUnOFgWrm2rSy8dCMpbaE/9ut1uIdeoMaxi5XoEA+JDCzjwEvnrzL1t0f7CrxON9NN6AHVVl1t2lsBT9tfWp/RtruLcveFfQ/yt5TC8tUt1Pfs9dWhXgWkIE5SrSx5nAG/9iav6lPk4AaVMsw7s9823XcKULzQwqcd5NolnnkhvB9TGc1BsnrYH66yHN6g1vSn4Aqvi3ecvYvxrVM6gWtypp7FM3LnlVzyVEZ7HIJ5+HcqDQeUvtyvoNF4qEyrwmkMGieBba7qWx07H+pYfQjXyti+Rh4T9OH2MtBF/6dlom0zy09FdnAk3Rh5J07aFCV+6u91LvpQmjm0sh7JFesqXj1PXyjY/jev2J3XN6GzJXzlQ27I8bk7q3d41zilns/KDd+ktT8elUPwE9e7OS4d15d9WAymfBcCgDD5uhR4Ab+VBleHSlXon38ExiD1NJbNU1hBrPaRM4KEs1VgincXR8ivHxd2xnbqWRqqstCKOKyIs81Adu6LvL65WZ4pUPepvOMkwymH+3qVZDKLSjnaYr3nAO64U99LJ7+JW5TC8RlIn82zfGpzlcVc4S4vKk8hEeN/xgRMm9iXpW5Td4O9KZCdm6KYDR/i2I+PYPlTd1LHJMIdCbzRpQtnmmdux4ootQKRPO2ViyOMBnuoOGloT/+/OxO1K3fK+eAmWORqnhgatXdR2lDIPO3mMy4ZvBRrmN2FlqAzcgCQzWGk5VwGGU3H3gDAVlljMQVcG1WouHNFHBHIpfGtHqfBTyYwSTyUSLY3eBpjOmrsCRU7VKGEDqIOIjg6SxDSJwU/3bKjMp7/Oxi3DeYKqs+EK51jGKINpbIQqTMLPoBFGS1rLQByVBMuQDjw400SIE8Ejg9vgEJQyuQ3FDjGHTZFOAWLHGYt/6FBxipAj7+YUdsHYe5+/vtLO8qc+oKmdv2VQKROnGBfIR2GtMicu4my8CGWAK3jTcBCGCjb9HbxlPy80GTQ0sHQK/OssvjxS6ZJlrFZQ5xptn/BOlKFhiJHgLz/YKagwSY8oDDTmCENw8m5nJ80V3JbbsukUeNZ9U4bXiqFBQwT4WSfAkWcUsFVpYUBGfVqulRn0OmAZxCwl4dV7ri4o48HOz3x1wtaJu5d1Jw2j5HZC1g5InKW7OKscCVKhBYB0EGOYlVFZfpwO4THaXowL0MQZVXnezi2KtnUFLYSzfDGfZFTgKvgoZ/gTuK2zE7NmaHAJOJI5hoaRDDBsOsPg+eV8kWIkg98ISweldEoAI2WoUO+UKX5BuhOYlhua03AAhB/8xLRKvXNAlqOyFYQt4JyG4czurMkMF2gz07SAZAy8bRvgDJNm8Ctor1Xozbue8eueIwR81hEWRQjaqkxIAwekGhmkT+MVW6ydvjxlHBPaRgcwigxHEXSgbDu3jCqgrtJS+Rytcgpc263xNTIsZqC2mHIt4ZmAGBs0BtnexRS1KbQdPYL0iJDBvRIYxZbBryxZr4NDAodJJ+ezu0KiANP9c1lz3SVRDJdXqGesoVzkhaGA0S1nNmA009BAzvrZ+c1ncCsu4+n0R2FU0oCxZNFj+Q49ha2zFRiDMnJm/0ZVHC1xzarRW9nYnkWhn/bSWwVUb9ue7UCF0X7AS5hRCsDZtmXbVUbpH1pi0PHgVvd2CkdDwwK2+KggaNAcz6FTKqMauJRbzdAQuQA/Sm+NVT2Hn2wacunJg/n2nOG8qKBqQBhgdZ1LHqMIsQJjHHtTRi7BqMGgK6vzmPXXIJGtE2SjsaEaGlBmwXs4J1lraLBZ+R3uYchLDQ3O3LmKaE0UOnF/lDNC7sXIMPOWW8vt7P284+Y/lvWvvKLsuO3GZVMMEmsBAHsUbVZc6yXePkXZ4q46LTdYPp1bmjJgIBYYkCcGMK5HMCTMA8nbp04ttyGKdj3pK2XXPfYs66y3EfE8KNevfGCwoV9YgaHBflujrXXjwE4ZrIzRpe+lrszSJcz2X87M2S/oaXjaE7SI8kV9ZBBv/Zoef/sH25v9iPKr8YmDGY0B9t/CyAFq1HGMTsgRZbvpomQrNABon5SBNnflm+25yl7PX6iD9xZHOW8c5buz9ZZPJ9+pJIpf443kJWHBW3yavmE/qXJuGnUZYTf9IvlYXi7ljAM8yx5DA35kHtyU05bDPLxqOare0uhne1AWqtSLt+2KAnFu0MIYMJKn5Rc/+F2+N40ySxxtc3J22kMnp8xL3E27Fu3IA/bUQ6SJxnnTZTADHBV061djnzpBcJW2Hc2SH4irY1hXjQbim4EGtJceg662dWW8qxmML46hGfGsQ5V+jb4OyAw3D3lEfdA6dTBgXpZXmmTwxV35oSwQF+GZPkZ7yhaDMH6trqV1ZA31PYAMzNeTsvwPOa+M7RkaxLzyRxg7BaGurK9wvx6+c7OcymB1TfAfiXxiT1FZifxYgIHSVX3DoYs9ukZhDQ0xRJNGGizCGLEInlRqKefCgzIjeSW37l7xtgA1azMXncZH0sf6VL4qO2NIAjcPPu4NuKgXaSe/246ciIisAqi0Sx7Si7/UlfXLpW7nYNG68Jwx+U55nBllcI9hVjroglRFU1SFr+vd66vMG9hLkfkrpDtOXcnBurxvG1EPdiWZ7UnaqoNXfcr+RWNVBV7xlv8xTtAGUs/iA57SxHt7tn3ah9im5HG3NaQPIlGlOIgIttHZe177wnmX7uLU+M6V0w7Y5X9ll0aFjEmo00pPdFH8jO8qPPO3n3FytmUnrup1tgf9qkyu4yLheunk99bmQgFprhtCe4GIvjyVYrR4vGjgWYROqdzNpyiRxfKfOk8Mkuo+ffCkkfJVGaH+Y5B1EH3auNBbvNP+6TM0QLiSwXYqLyrr5THlv3RfySq7ilclsDRq6FVkazWkXBbNhxo1XlXO22ZqvvK1fZG8YL0o5x1fCrQagas+FgMHcswJJ1kjOjPvfi1CWvj1CY0MGmKsC2VlJjEpv1u0Hf96cHfdOkGx+XNFlrRTzriKAemeCUEn8YTn+Wr2/dLIVTsWZA2+TjOgvvYUd//fGBqY4XewZCeQZd92EFSsFh5nmGVEecHBk6fXIi2ioNm4ZKoMXAiDE6pVqLIPFRQ2g/6Nk7xj4UUwIwLx7jc01A7UfG0sdj5yjDjJ1DbW+lkk1Gwq1rRh064tynjJpWuEotCfq+9iQ7eaP59FS8XFmU7D20xxFVC1wfhsR68QbgxfyyWvk0gsgBOBDl4qtwoLQ/TLaofAd7bO2SXxasJbGGZd4QgrwlXMG/JESGh/PPzSIMEphiFn1xgQqWiZp/SRfsZRAFd6CZCcSRNhCo7Gka42WBVP81bAaPiJcKFOFariYgeVpbIVSX8rioOoD/r51Pm3YhjZlQtLNfKQj40837g1HvkpRIIvYdUhSKhjB3byn8Jbp+ARf63OlsvljToPnrJDklqxhsqPwR2lC7q7pFC623GEdzr8Gvo9w4J4BmJ3b+jg12jaaGNQU4KrwK48qICyHlQ4TKPgs514LoA00MKK5gBAB7F0oCi9zu7nE0KULQYqBsVyq0IwA+NuZYCfueshKJ7yguWmx3d7gFtH8tlMRijDbKs4OFLm5gR9BKmJiVNrjzYGbatrpeY9HT3vlCEOno6f/uavIHXrhINgVSm8PXvCZaYjWDkwhkG9bqH+JHWwNNwOKAoQPIm/rkfzIc8gWYsogX3B1TbHQI16VWEJDyCbNErJN/rZyYQelil51DYcnMHVN2nloDADOBHhmUpBzlAu+QU4yRP+ckbY2SrLu5I2QmOqoDqyeFPRGuEG4OHk6T3bJTAweO8ULQATU2UCmqXti5w4Et97DA749bQ2INsOpK9XOi5hdGXo5IdqtPsNpdA4Oj15gxJSJAYYrGqwXOl4Sbcio2cHLsGaFARX0oZUteXEt+btY9cWI5N4rrJJbxJKOmgn/QWTFTvG4dn6SJ8hPjC/xl0NDfK9Co1tV0XA9q3cqf1KVUKVA7pkIZng717+BuBn3esq+vzW//ilMHlCbvPit7OH2d6ItGQFRhF4dCyGBqsq1jBh2R66avdQyBz6RF1kwAfPZ0VD2gQZ2ZiJr5xxW5LtbSzbmgKAOn94zn1l1l+mlT9jaLjuysvLuPN+XXbeesOyGTMrGhpsGfamHWcGr1DNciXMu3/VkRu1nVabJriEgMXgMh/8XdEwfeq0MovTVw/8+illj333KcPX3YACUBisEZ5JEiMCr87Ka9DMsl7kUNlRFpwAAEAASURBVAyZys4QGh5ETmWgDGz/Ks3J2XCcsj/9Be8O0jN46skHlHPkWG2HGuTIELpaBpW6nhwhD5VdYamQZzbMSPBpZLdyxjTwRfrTjglq2zdaN+Agjf1S0qukk9diFFAVZP3UGazv+heQeW8yJOxDsWKMoCzNWGvjdtZef8uiS+nBw7uDOAcD8m2MkpY/+FPW8Lwvunrv/xVAU+rFL7hr6LE5YkR2plRaq4+YXFxDA/hNfFV2e3wvfcDb+ME9ffbg6kUCKhYk9M/+3bsDAQc5GdCjkySU+rbuxK03gCKdfKOfLkaZVlZAW/eWJ/2OyJkdN/XB4E3+0kgDmf7SMnIXmNFPHAwAL/VheMpWYURfoR+zDNKUSHlWB3MwI0D73+G2a+LUSxTIy3fbp8LdjCNXm4z1nUt4ce257735517hpQOzE1OHi8GWMtov2BdQDtuy1zCMwmirYpGco0siyxTp0kJ/XWjeZelNElmG5ho2AZLYlX7qlNJsHEZTnVvWbEvSovJ7TRk6UMY68IIU8JrpRMAYqQvCpXkmNTQmkr+8bX3E0E48DRw9fjNDnbTjP9h2d5+TZyL4otbiRZuGH+wjxDFGNUHYXyMHlCXhL2BqRFHP6xCsbQ9/67K1RfXP6Kq27RCt4ljlW2eUs5zmQXWtQN8IvPjUH3yCe7v3BSWqZTEsbdn8ySdtFSOxzoO4bf+2Xcukk57qZ8ov9VFpbb/RaCQNpaUeprM80qXShrZJO9bwp38b3/T4QWTASGga7ARS4epXnfzVK6e0p/D1K2zKyLoqIDIY+rmKWZqrKzWetP9WHkeXVpZKW2Cmb6eOzMg26SWO8qEyRFmh8aEe+And0S/FX6VC2EkoisASxbggW/uy5uW96SHxo5zGl0d0GjmltXQWz/Cv9CSSqCrbMqaBpgQyyK884ARS+JkJBgoXPdtxpQeDS3MNygNs/dWtBHflr7zp2XltMm+4iCmPJQJp5WthuSWTrjfjl+HowfovdnsWwaPQ22mRgftU/vmHGBoiCLqGJOtaAQ7UrBiZxINkbPyVWaAejcjGlwYgE0HkOCo3/jZ+PBdhabIzyEoBGg+cib8NrQ4GhC1jKJB7DvARDMQbQQfBKzCqBVyWk8EVUjLVYwwWZbQMxKzwDkhDJ694tvfcWyQDfZYbEGBaW+seKpm5HmYlc5smp8OboQ7mc8mRaXOgj2W1o7VD08n0XsYHp9yJbAO2A1fwKGDMWmE0SANA4qm/P94DkR9B5bkG9cKN1MifmVYattZ9rfiLObFIC7QWxtE2rv8HJw4NJ++6lp86h36244ZbwhNr1Z/+8P4QhdxSlHOVpsxMhZYAhD+yTIvIse52wqKXlnpxRkNejYJmeA9BciM8xh38WucQ4Soi5OngxcGNn5d0H2UK0JVvdYVpQe3e8LDvagNb24rgVbqi9IJS6hFWSGUSN+xvJC+BgYd7uR2cRIClog2Ql1l6TUegxbXyN7PvzERqaLCstdVUI1KtFDPESQcvM5AnhSl5yFRfxaP7sx3UjqFNawRJXPwTof50ft46WDz28I6fcElroRTARJPt3SLhUlpnWp2pHYGgVZHVSQqdOdriHdc3I4P+zbWOxpzNM3ce211vskjbtz2pR6r72fGkfdHuUnTTWCyuNE8z7py42nd519Fk0ozzIvBgy1364ZZjxHDPsPzibLh4mbQfp0TUk4s5Ax6sK+92kl5dZv2GhtCPoFXidEAa9KDQ0ToNEGQxOCA9Kp3xs2imkv7WtB2sNFbtRZXJvYet0/PuCzBG4PHY5yxT/5Ugojc5tBQjUgZYENXZ6SgqgBSHnusnDEW3rUfpJ5Lk1dBQByw1RVjKR8KiRHRt2NksZdpqZVjLo91NH1r50OcM7/zlrV6NYAjwcMexnGMwLEsWWiFJYGFMA4PaPzYQKaMvFqLnSUTa8WMLmfUjtoZ2DfSYTMqSBZzzcfvMcsv1N5YLLzi/lHN+WvbYaoOymbPNxG2GhqpdVcUx/WoHfggn1DzJ2OyVP9XQwOGo1MlcGsHMaTPL/Rx69+pvnl5ewin9ZRxWB+lPg1zKgMjl7SNZsdEqS6OrxmQH1B7YljIDN1+0oT1p+InyiF8aSxqW5a1GJRGKIUDZbX/XnPGt1FaxLZ007cLUFaJ0EyeKuukN+3uccCCAy62jGKsQQnO/S2+eznJZa8rOngG7wR2aB+/ymbxRB6yDaPRH9bkVSXmfwT7p7MNdytsTpavLR3ypTBV2ZZQCycFfjMYEtUGJFaCBxH4lWxOUneKmsMLVNkT6Ti7Fc+gPdLUNBcdQQago/CjJtrnmsmqEeFl9ZP11Lv7gZp3E0NEC+u7Soc02xxgDPi6XlnJZsWJ997t+Qurv+5Aoq/hRXJcfyyMZPIS+lKJLg3f0yPS7+EkXaVh10g52f54Z1Ohhi9L53CL0PVuwVfyNO8QFiYa893rBCUlZjQ1yH5D4MWsn0/wk7RMcYcbrOaKsJlYvjmG2/cdYLSj9M0Ci81rKKiv1avta2WsJOrM84OoAq3xF7ZBr/Q+yQM12SIbKFc/80ZgjDOnvCij72YZrL0nnEfIKrQX0R+z81LGXM4hLX2A5lCHk48DQPsQLpboCST3wKBwrO504iFs43rO91mfjd3R1MOmkWIxTFDqfDUe+1W0ArMAT/4YXYP9eF/RbubpEwlmMQVADiTQeyUq5ftfCLeMYJrOGc85PaENRbCcaBDWyO3TQSXNXnMnPrnqOToNf5e9BvheNhkruxGn3AGrveTEyHrQNV7GpM0trjcriZR/uhIATtxUR4oKbMkmYrmwJ80RH6HLtZda9Uy+ryJmuDQKp1jNplY+rc8YZ6ob6mYt+jm/U6aWZMqnp3cqukRKr3yVzPKwEXQ9n3sMr1ZjqKoT0P9An/E2PrQkQzs/Eh1+c8LyK8Fwm2OnrHAfLd4FdM8rKY3BS82IEHHzNNDI32fv01Hb/EENDteww8IKI7q2VVAothbxUixBoLaLRS0K3irTh89czPHSdjAM6B9cqK87+dzVOZQCbtFFQmsbTeIBYClJBW586ZWTbB6gVzXGZzu+HKwg9dLJrrzWg++2v3Pbc7v0RAR/mElfDnfWjW6ZEll/2wTcMyh26OAg2XpSjhqQeuuCktRWWjdBsjQylgrQVlJHrZT4tqXjo2r35t3sNHQw3XoMeGqFsavywY0FPiRAcSSekjFNNSfY8i3IvXWR4Neq0jgSvnmt562edmKek0L+FtXsvUffw9/hbf3Yi2d/ZaCzlEYRm4IF9OhW0Ho/xXpuz1tLaoAexSezwhcjKk5UvO2wohB2dAiaHAUqsv+Ess67d61t9ly4WIYHiyyUdzU1/Z24UtK3/TFqKpv8wOoEUWRRIEMUb/FQI5YslDITkGQ0iWkWdFZAn03eAjJyzCm8K3EoKTY0AUC8ykYvFXypihkmcAfwzy9TSJYYvzZEilW5KXHidZ3AP4mnTHf3wtt3mIi/vaTkaXogiiWDPJJMfba9dsYXccySLe7K7gYa1cN8BV/3wlPb9cBs5OjIYPbh5d2ARUoGXMiWsIKI2lAAhghUEEA0NKl8DGhpQKixfyqOeQxKv5qyf+LlCwYuVI/Xi2RBnwjicD4tFTZLCkLFxQymREIh3HWnEIx0+iKXjZok1IfWLBNZjIia1j/VVXmelDwhl5UuoBExXM6QGVoM8IaLTf/Ea1+CKpWdt2K40BodUXZx2a3HzbtEsjp5EFnZXwgSvErcLa23HCCOpHHlGertSxve/o9kG9tAf85ZulkEZQk2W0Rpd9OioFgyhveF14UfF0LZW+xneRRDFJwWTefByNZKGPG0WDnrGsD1mIVtC7rvjrvLnG39fLuI74Ev4hvmuW3MgHzMoHgY5GsI4MBFA15p7T+Ja22ullxja5lUOPbchh2qC0VIGMfMQ+PdzTb/1tvIAn6097N9PLXtpaBjDF1JANfQEYGR/X4X5uWAHlvZlngtDkYkMvyN/lc36xxBq5lZaRTXPyiv7cV0bPCc8PvwQ5GBaOFVpbQHd3fAufZXRNU1wEEedeRKnF8+KFwcd6bNFAfj2t9I8DQF/lVKdusffdMIRFyMCO02tS6RfUOjeW9bdq6glfo+kvhvYIppY197rW941boi/ZZd+MSIQnlVrKtUMCIZrFNJ1dMhzP4J4uDzZ/lFEAqcLFzd5OP/kr7c/DRUHO+KgztDITewM1lrd9/oH8+HSmd7nbN8gfQwiwBWeeXouTsvD+KEtAfUwYX2qc+zYnOkcdEqLkQM1tUWiaoO3uFu9cR0Cwg0i3buvxjFuc6ZXl200rsi3hLUsAhlMYxhOhASeV34AFJ01PGad9GXSe9av9rPSvYMADOqFDBr6ROq5gO+9rf7BOMo+9ZaRCEJzWYLCZ53HQIiHA1VRVhQZfyGDYHUMvwTnGFi/dCcNCeHp19559l1HUL2MA9DUCfFWV+Ik4Kc/rPn136vuW2Hp34x0MofwYyRKBXeQQn8iWjE+e/mcd+pCXol+M1iA6EfBV35mOTv9qSsfPJPIrd7/Uycm0kQeMnuHQC03abSUc31sJ1mBQViLh/oWeoAiPE35iKt+21BYxCSg+qdGoV5c4dmOycj+reWNd8/pp/PervbuPU6EcYnbEvDumMuVB/Khs+zklHFMDA+uzjGFRgOQVvdOe3FQDZ6+a7wBMUEnXu7WRefk9t5KJLwd28nxrU8bjNee6r1Dd1XP1byl7weXjG+AX2WK+dSrygp7UmWg/ATNRS8Z8BNcW26aEiqtKXHC5JeKr5lrAKfMBGXcSmYrMMbb/mLIbhVJXWWlhPlBG2EuZDeAtBjFKl51dlEYSgNzeKq5/7WhwQIvggHda+OMwGiWOrfGFFFpw/WPipJoteZ4ss5gTBtaTykhvQRvrlWr7z5bqQouFfxaAbVBASL+GTQSECYifjqNLi23wOi/+7w6J+zm2vPQewsPXjYwhJLljdBLI6nl1S8ND4xdNqVyFUMDzKV1zZLoVxtZVz587fCd2RZKSNVlKA2Mm4YctbWGN3y8N7o1nPvD+p+HxvO9pWl5trpcQkvV8GMZ6pLIGncJwtIZoSb0FKDWRxzAbKzCFF5r3IZZNw2273+va/gZ3+d6NWxXBwUrIXWjUmNdSNdcLiHHQfncpXOF1XIQpvCrf16EgHd8IniIW6MluPfY59382r3CqclaTs2v3Zv/MpYrxwpLmxhQQRAugJZBc2cj3ENf24vtyLNCNCzQcTF7oHHBlmeZW97C7b9C/xboXeBe4UfuVp5XRmVaagfxDtcRdziXvJhM4Ms4371S4bYNYPHuOSbGixGIiNk2JSOkQRMfR3Dc0LsdhTOq5jWA5tMGxjXVqml8G5p+6LtxTKu/l8/9/NjgduRIpMTHQ7qqNKhkdUWspAKGij8FhP7wmVqbjjRuG1uGIpDlgbQhJpCoK+hHQVq/UyMP4pLU0pQD+JbbATn4UbZQt8NGaimvPGwBqnGCuMaPRth5ttKlToRIzZHOMjiUUjlxm0061g4B6aFbweyRh1xqaBjDgMs6dybAkfBwjB3DaEOC1SVN3zNYVHYyEP8uyLdVnuPR/TRaG7fJDeGHrSyOcCiC7GJ+LY/48246XcO/vXvXb/FiFEfqQVk1alRXNybAtbj1re/XfHk1vfmaJxTIXX9rwC9IDIsmz4uFIFSDQQ4dk09op9LKHO2xUmvQ0ZVtGUQzQK9xXDuCAYDZI9vMKBTcx+bNL/dxTsNf+KzjZaxoeARDwwu2nFAmsFd0DQgzinhVPapyalDdSQsNru7uzSCOvF2BNIr6930xSmEOCEVxnM/zfdBmBmc0PLDJ+HL4v36rvHT//Tg6hS87qOxSIf10bvSqn9d1+WidUbT49udxRKor7lrs6t1+jeuAwcGq7XrwIoZJCHf2TniZbFCrrt65r/ITWBWe/jEacHfwqWxseWj4sM2lQo04BDXAPMFVPGvURgP94rr0yoHGj1a0/KszWsK6MtrOhmTZe08+ADGtl++gHiff5zKx/gRGF+Je+7Ra/0a2Tpz0cHVcVhR0acRFZVcAwo7c5s0+JpMkFC79Owbg0IiwfpdDRbv8OpDBIfXWRRS6NLdehaVOptNfnM1YfHXpk8HHuu03SCSw+zFdjD48pO4quNDUcqoLOWNuO9Lg5QDFL//IKqb9e13qSD4HX8ClTvSzLMo8+b9udU0R6g+4iE4o3547/Cxn+j2x4LnpuKFvjFdGbBi2RB22Q17NwZjt6mLl1iD40p+s+esnXzqjKw5u9UQc9XI2nXFbWu+y3CKW9UvTnA/V0QPvxIPk8Ivp6gSTfKlbHX7N33vLw+fmmp/39iyg3jMP9pNLlrGtjDrIoDyStJawF2+V9DWs5tEfo/rUdqMmTT7Ut0/tuc2eD0Kv6RPen0cF9Td/TbcUPU4ZJP92Iiz5KSalse3BZqLx3XbT4pmzPKhBQv+68seVC+gDpIvOrQUdZz79l36GBIYvuBpz0H+on+kFknj8tPj6uxpWnVN6jXKLln6ZWFV3gEFIqJGh8jzyizLZJ3goomND+T4rH4AqLZIXsNKv6IePbVk57Z9tuh6C2LBIljVdffybv+Zhau/Jr3vvh0iTTxtXxtTJu7o6Rj6znZjQc6oCiITBEe9UGJ4NduUgo2lu6EWvnEpFe8aGkTPmA3ZeqMPkC1z1Qg1bfqFImFmFrI4VeJEw+D513T/E0JA9sxBTAmkNykoDmchL2qQ2qAo7GlyUAMKs2KaUyHAyo2lcvhTrdJjVRlgr1IFUVToYSIW5K2iVbJ15+Uj9Ja8wsR20gTjvEbpdgx5lB4df0DMCrsXtf25+7Z6IXVzhab0SSmbVE6gaP2jfstymtRxmZkPTGSd+eFuu2lFqkXdJVFUZHSRnORe0yGnvlEB6S+MRHu7WuY4EefM5+bXA1dxb/HYfGr/5m9TnHk0pA6gEfs8f4ZCZSYEYl0tLrOUewWBrKI0D2x/iD83X9H/LddmkEduobfxu84iQhnaVX8hAXKGTWan094QB/v7V3MVXAefKAXgwAwLDutik69UT5TGe6dIhtR4WH13KVR8zKHmCXxfWf6s51bTKM5uI9FUXtpOh+mOIMk1VKH03noqcdUHb4FkejLAWC8tn2+lEniHykQYty+KKGxd5ASWZ2LrShiLkkzw/DqyE5WWpFYTWaf1aRU2fT3saB1BhEm6VQWoBjB9KEqcpmM4S1Gi27drJglacZZZ3zBNxEO8GujcLTudKsZOlyQxvrj23u/5Dn303nZflMk/hyad1ZY+zGJ7RgAfOOjFv22jd2gJPET9wuQtHanh3dtA9evVcDSNRK9BdGMZwH65yCXDUCT4kbORLlA6O8BpM6eoWGevZ+sPKUI0Fgu/i+5D4woyhwZL5wq3+cKsQPXPDUGsBEZyyyG86/XU1pmXBMg8cbSZGyUCFwtOyn1B3phOfVS5fOtjdrb3W/CGEfCH81L3v4GndWycCs8lloEjKFIG44U2CdWbhj3Q0iXCM7wyoMDowUY7tc5RJ5vcEF0D4mmDwVl/4NVj6IGnAHYUQGiiBBwyQmN6taBqTB0GmPZoPSNgD6OPrAMjbbjTQ2j6Ga4iwwMQxjUYKgQn/ET5dOOu2O8qfOKPh0osvKrf/4udlR0I34lqbzEetwzJWZx8XVBREXTVQvEZM2qAsnn1/IShu7EbrlOGctbHyYc4iwUdD03C/oPH0DcpDfHZ6Ju9epn3Pz88qLz1w/zJ2YC2+UgJmABZ3i2hi6aqcsrjhb7zTlxmOC59yb4P8zCTRp6Uu4WPrOHVpeoCoOIcWZCQ/2EZ0GuV7fX6foaEOZu13HMzKx/Bq1z7Eo/XFUVyBL2z9q+5B/DC+/RO0Jx9XG1XZDu7kL74687aMVfbWUvluF86tyigA+CzfEd3Cx7/5GV8/8eQ/cY0nTVu7wjvOuMYxbmjVxZPWCajRaj10z0I0XZaqUw5xDV2ko8BwwaE+pr4c9IfORLDcKTvP9oFte4R+0ikrFoBlMRodO1DVz7rDI/CMYyDwGz+0d71FqEMpeo3wsnUKf13i9t2Fw39cdMv6mBYifZIPxLKeM4EDnFEcxBtbUpeuA52Uwko63lr/YoB1p79xQ2v8TA5LVZlHQK/+OrimiyON2Ld8vAdn4g3eRbajke0G4W/bCM06XjPcLS4xngBEPqxAkR3CNLy7Erf9mDmuu+W5oaifz7J7ZKI6A576i1Fm1SlkdOEOgPE1ils3bQZYIOoIrWzpo4HiosRWbuF1XXtoy2vtX7hbRFlY2M112fXwznsXIc9dhLQrdY9gTH4UoGo3lZfU35SltX33GbfA3zLojFkHsvSh0LXxvqEa2eo5Yq6e9KtXvGO08oDtAVcP4sTBcoqSV4emQU/qWjz5qvGWMBp/Nf9WzeYhvwk9ZaFLEIZ1ZzEc8vgujW2/6n/CSzr8G34B4TuXTly9fG8Xj73wFk+/OCILq7maHiqrjOJiPMRT+RCduY3FLBCuL2naTuJRSGWtrhlJrY/UBR1BzYP6yl+i8SMkZFgH0Tfj/U9cf3zTSyuvxo/qYMp+XeBDV/Pzi1vKEP0kvn1L+MdneZkyy4XC7+nXhHlwoxhbDgbXFXvuWXmGv3K5VprwutLCz/b/StHFjHH11ajpWMRc6p+IPHXdP8TQILF1Nlit1LWR2Ghp0DKRBIUJVVL9U1m3oduJq2BI/NaxC8v9YW7HMI4zti1sIXuChO9neUaRxriAjuBsQsyGOrhNgiqyEsMtsqxMwbIwTwQlXw8+HCA8cPDvovG0+uf+8BZHFo2hASlcLXAoTTRImSMHmnVQ+9P6LJPZ0TThKO0W5QBNP7mF8pfGR+eJf5YsUV73iomr+9SezNBgeMur3fF6gjOert2Nu7r4hjeY/eG1adY0/f4+G2ZHpYDJ2Qn9ETp4EY4ApopXmy/R/lsnSC9Fl3zkwTTmp9VURclnGcMlt7Xh13JUDvS5lsowO6rM6AArn9XDr5aDEIRQE6YRNmqIJK0DZMRAJ92F1lzvGSC9ZwKHPrcy6B8BSF4qA3W/YeVXw1QG0vHBA5nVUdCZGJe08JHliWIC5i5zc/Ypyjylp9uM5VQ6Wd58GcXBqjVFPQUPANlGdRGG/coPfk0IKmxzoE5EoMLUFBYUDLr09UySeKV9GsMoDixsr1X514u/riytjUpeZzGl6wDLfi2nWZgug1z8U/bOX9BBwQfc6p77/frjAEIKhPbClJf8IoRfKXD2wFl80AtMD3IULy3SrqhQjtg/ib/phOVVkfVwSMrmYJP2K97pmKi/FlOcKrVrkoaHIHQNH5+FKx7Nmdb8W3r9XabXcAgeIFIlG4GNuJ1P8jI9aYRlQuHrL/116gSD2OqjTBMmkdF625cOArpmaKRa/L57PPrCE4kfvTJTxUBSfnd1jnWsQcd+w/xbMnGyHeoyADVTEG7FSjkIVpZIN2fgVTzrzFCDMggvgIb8mLcusfkZTFX926/kWQRCKhca7PgwagbmIV4HI4nBxdcerXlDHY7yMRL6jcjIgRjmBdLt06zGN535w/3lkfnzyuw7ZpQ/ckbDb9g6ccHPzil7bbpxWdcvzTy2oAxnpQtLZzgfga/uePo+DDkMnlthH/foI2XlGmuWYXwmzdmoJXPvg3dHl7F8xcU+chEHf3qWgoRcRJ/zKKe5z5p6W1nKwdcfOe3ssu+BB5Y1R4wrCzt8Gk382o7ioZ7PQiDONq3MtSzWn3Eth23GWRtnrZ0lst+NTAVAf7wcDgxdHUw6E5aBQJfe/rXWpRxZ4XpGjvnZHoUTuLz7bFpdwyEv3bvP8ov0UC8xvbKmnitA7fIsjJzdQ1zPn9AlH8KESTbhU/3dW413nEprNZJSd2qpOEiVNKLURYuBUbrYr3geUsV2MLzFMx/1GeGry8uj6g3iq5/xpLHlULZLE7dNONOZw5G7cEAkcdLxWHWPauxpBhmj6ITX4EqTOgDDME3d6d/vjGuKVY0+Q2PV/MSx1WlNJR3pVfC3v2l5Cg+vzlVsGt497+Ta4lR8LeMyZLSEcZAgPOmnC63wiB9AmsHavdmG6YTd8lWG9LveayINhrT4Am5wkolRjIu/9deeA1dghC3Hoi0PKvsyAOv87S9cQRHjDmEVeWGpp1RQDY1e/jWY39U7QTdn2uYUqe6zl3ecQR3oeLaFe098f4grnAbLNuRL+LrzlP9ddahoU57r7Dd1jc8DLz6DsHytesSgX0B2cFsaZa6f17UVyjMaG4yifHCiSf6ynVa9R76rsibwoZ/xYjijc1EW1YGiqyU4pN66sG9ntaCHaHsQuu1oNF+9Mg/La/11KAnyf+wgc9pem5QTpnUYuB1gyyp/Rg8Ezz41rNbFkFzlCftR07UtRz57NbliHJ1ZNPzbvfl773f94cKq7+ZjP1bHcsaPbmyfw59hxvO50b72B5UXjK8TnuHyXTX69Lf/RKGn7PoT65h67cenxmg4tbdV7+ahC59yb3XnGVT2P21cIs9K69SJ8blanUg/r5QZXB3XKTtl+iqzNDQg/0gl/+jcViJnugLEzO2Ps1rLZEZQYQSGq3pNmy0lIFcpV2kj7tI4fJ5fn57a7h9oaIDgSPYoG5DFRr9Kx9JJfYmo4m3l2oHESi0jdZQ03M7aGeh0MH1h7ikVfowPwulo3xjC11RSMuGlcv0qlWSQny20g7PztAk0ODz2XH/FPtmzkZNffmEacqpZDza4DoUeXB9ao7Sx+WxjlYFVyCxz7dQrLJlbuuqvgabmV9MpKBtuf6sMqyDQvQxN02D1x21xhoY9mX9LS/WmTFEcm2ffPcKPOK3e+4L+rsdG1wgC6CPtbLgZJNtwFQgA1yBQGcG68q/Vt0+1AevbGradV6Wq4cTtqyMFROAGtGnBoq8ANUWSJZ9WOU/wr1HyazkMz9VJudBM0F08+sd0knag6UwM6AKTDhyTR3BRIfFPHrTsVWj5riHAZP0rEkwYcBVQ4strCtNBSsmL8Ce8KFmjzJOql85UQcAffDuaBKRhzZkAJ4xUjy9G99bCoIGyQdcs9nl5QqLW1hJaoww+VnS69wqtL7DvseEYHPA3a2cGbWsj4aMOLehfO6g6CJKmxONq6dpd0NaAXVCoBs0tbIxA4cUaQ2nR8h56N0bj2pZ/g9/ihlWM2LnVxbdSWvqKqT6DlzCGOtHVqeA0mL5rna8pO4hqk7rBDKJg6mVM3dB79a2/Jqu0rrN6bf+1dW/7C/sRx3iwd+KaUj4JXAO8cHnnxzC9hGs/kT6oi5OI/Ax5bd6DyOozJJJKcCuu5MlWBHJVUjj77xWGCCI8d4Tz1fhePtuG5InhKaPRyIj/hHV51hhExql+LeXTYgsfXlCm/vkv5fTTTy9z/npTOfjQQzkzo5Tbbrm5PDhrJga50WXS5Clls82n5BDJx+cvKLNZofDHyy4pzzrggPLi3Xcrcx94sJxx8sllI1Yv7LHP3mS6stxNnHvumlUenDu3bLnb7mXrZz+r3HXP3eWXl15YDnvXu8v+Bx1Uxo9YgxUclSSimKKR1nv/cnfLpmyMVGyNmThVJ7CO68SD/VxkKm3Bfq+5KKbKJ/zqVaWw9WhYrUtzrXQUrvnZTo0vTF4H03eApW1zLTdxFa5hyjJd8pFxiCRe5qczHyM68EvZ8DMfvXVGsxjC8oo/7/rpBBmYeas/4QmUXfFug4MW3CWrr6RNmXhLHubTwWvxW8QYD9BrLId9YD3TquLk72CySCXgUFcArVReFZpvxq+rUTraS+cnRgtkYXllgNcK3hc34cRMvfZBWZ1/xbRDQBjJtOYsTs31PxsafDvPNqjIKz8NpV68/rprADsYvtbcakB7zl2AXR41tO+3i9jkROLpR3yT+Njw0EM+iayzDto4zIgE1PZBChO0RNyFbRQvXbvXt5rHf+c/NMz0Gvish+jCIrk612WkHGxRenn3oShZm6GtGbDsN3WNz1u6Bsew9tyMDf1+jZ6ms8UqC0zQWqNp4w/v2z5bOxWGvJTJCZ6rjKjtPrIk7dvUdayhEUIZJQ+rc2tgqzpApLvggmfDNR7/wx9JAanJQ3zqc0D43gdLA1DaExGN21yjXf97pUv16YtKqQZhtnT94f3Ppn6y95a25iC19AllO3pUqbhqet8qrSNjwsNV7vTDyaAdKMrbJ+ZT61XoYrcq/ApldX6D8OtTg1uhABMP823jEnm2Vyc+NwDcWxq9av+iXm0A+PTKZJpqbBBQWxFN6QMsfYlJANxfisFJOXLpyl/zrrSodJbPa7r/rqyi9P93948xNEBgG0Zz6VBSEfg070Yp3h0UarWNcOssn4lHnBZdWD6393Zv/t51gu2/4tn9PFma/jh/z3ND3bj9z763PNpdP93QeEPfa6xVf40zFI4x+v36n1vqvwd2i/tUv1v+Vl7v7bm/XI1Gf+ven8bnBmv196pEJ00DPBRA/3sHpD9q/3N/1Pbc8n2y91UYwUh9eTTY/ff23OD1JUlSk7c8230wD1L34NeHQXjSogse9KzZtHbPWwtqdyO0515+NVXLKm+rhEWumKql7BKYIgJ6MPbQGEPfW8q/5z4ItcbufxduOijuLQ/DBy8NOtIo3dKQWLx23YfDNdO3y5B+eL43NzT//jSD+faqLLiYtj+d7w1+u/f7+axrafoVyxrCb0vYIvUCuofOv0XTt/95aHTfh4LqvT9ZQiI8WdDq4A/168FvAX8vMBJao63uTa4ikHFC8xR4dwm2xecxLkEoy9L2/7L3JnCXFNX5fzE7g+yLwICMIAy7IqsDwgiICxKjRkQTNWpcY6Jm0SQaNX707/JPoolRE9e4EVQQNYpoQJFFFpVdkR0UGLZhHZgNhl99T/fT99y+3ffed39nODXTb1WdOls9fW9317lV1Rpo22MbDVkQGbnDw3TxCdkg3XLt9em///u/0723Xp9e8pIXGdcvfnFhWnrr79Nmm2+Z9tpz77TXHnukDXPQ4f577k1XXfXbdOopJ6clefnDS154XFp6513p//+3f097P3mX9NLjXmB+33Dd9em3V12dea9KBx1zTDp48TPSDb//ffrit7+Vjjr+Zen5x74gBxrmF0ssskX1lbx4AJKnmTBNUz8PDfIR+F09KLYpLRWqWXmbiSb7RmsTzI1FUxtDt6VBXE32uzX01kYj06tl5BSupvRnUJ/qmofxt4lHdmizdiPkP+45t2iQtPLM0il2ueN10tDR28VWVBrsSK/0NEhNCAk/W30t+1r3qV6vO1aKVeSq3iBYD7BIqJKRf2oYYe5N+rLUYEeHaJOZN/nkab48Gr/64YjuJv1eBpvFt7NO9d6gpbfd6/ZlL+nLvRp868SUZVP++VzlumVk/EG7YWQC0lgy5azQw5OAa+s016i5YR1L4xJoKFDKIOVwURGJy48hORTH9BCmxxAZZCd8m8mQ64DKTZtfB6sf+cozpospOJakKq/T/IlUGR4lyVP3Zerw12nQm1L3qS9kxYcO6VHej19yTbnkpMfztNEko9zLTFbZ+zbRfsiW7FgOUYSy0+Ij92VhIprqXlxlcl8Wb6WwIrQUJFw2123WpWrsHcfrjL6ehaS3KYfm9dbLqis31VLkiCJ1TOuSWLaYIQSKWw58klHeke0uOTPma6dejt546ILoH76MkK8zFtgoJLwdX+62NrjWsd/hFQ299UNc8NB7AgxFLk5xkBdcBWdnuCZ/lXuJelk85PJLWuH1ZdXJq5QFpaOiuYJ0GmloxjYFBX3EavoJyFSXoyIOyIfR20dF/aEXF+wVq15veQIgKf5AWe7aWc8ExeS4Z1q7Bc6QoZ4/R5k40/hmpNtuuCmdnAMHl196YTrooAPyVM3V6bIrLk2z8nKH3fbYKz39aU9Pe+6+ew40zEn35UDD5Zdfnr785f9O222zZTouz0x44KHl6Yc/OystOeTQ9Pwjj8wzImam3+UZDZdecmk67XvfTTvttns6+NBD0023/D596QenpLf+1T+k4/7gD9JGG8xND+cZPrPzPXxu9o+gStG92ve/6l1msN6Qk4rhOf3r0IVEQe0gY0wNfHU69breuk7JdKx2KJIWBT+8vl66KEXXZKuiFoUGsnpY47RqA3uzsxLmA1Gm4hOjWqFJ+nq51AJ/p6+FdIdb2jp5gUkrX6EqN6M/V1RXXimSfRpyqtpLuYLq/tbpuqcUeanF8TcXZbXe2kav86lu/Hbvyd9my7MH+vJa3zOnPcgWmr1/vix9yuVHoV9U5Q2S5fVBHOORy4cGa6Ze7VZpY6Kxi7E4xSbT8qfG3l8gM2O6zXyPrhabkOFt09NGR6ay0caE8mFSPwckL2OypXrZLrLYyZtovr1f2av3Zems557Hl/vZ6Ncm/eLx9Sb9TTTJjjX3ttFVt6V2cpWbbEqO3Je7hNRQKpA+5WpW3mRnXaGNT6CB3mZ0mA7CYdMO89Qk22md9ZksUcjrnwg8MFXLNv/JgYe2NGywAXlOQvG4Tk2niHJv6t/ayw9lmJPMI1eb7kHyam+Tl1dt7YW8HvrEPbl53Tf1aSK8kK3Khv+wYLBsEF9b3uRbpbOjRuqKXMoq4R5C1dJT8Mp7GmuEEagtftXvyEu0notDbrTljR9kMWclhV593mSl0F7U9F3ofSj03E5lhTFaoEtSeWEV6VKDnXM4y0ADda8wtyh5m6INyltUFeZzo77vDCJJ2EAGf+WzL1d+V04W3B2p4ndr6fI5ZZ+a+iN/fe7LlXyTMI1tdAn6dim2trKhiyahMu/X5lm9DU+fbuXyYV/u0j0b/4mAv0YsYIWsz4maqvtVKVOGGUwOEnVbWpgHNRZsyJ/zpbfckn529tnp5xddkFbnyMayB+5Pdy67K+2/335p//0PSPvmZQ87L3xyDgbMTstXPJiuvOKK9I2TTkqX//aKdPDBh6TNNt0kByFmp0P2PzA9fd+n5j2OZqW777gzXXbpZenUb38n3bp0adpjr73SPfffl5bec0967ZvenI7MSyw2zHu5LH94pe1HseHcYs8S+tHpXVGbzL/9PlL+NHif6vR+Ovq1df1cjdJ+zP3a5FzdMdF9XtnJhQE6+6lTWz8V/drMJSnx/k1G2b53xdUVc01uFL43tRSw0dLWP0IqTZKVTqbsd4UN8SK3Zr+KRI4OXcsrSWuu6/ZSMKjeKWcJL+QZTOMk/vF+YJZ6mz9t9DZ367rF521knbqzqln5sObgazMlXU3tyNkno6lRgpOdt3R6pC62qOk6v4XOft+N/p2XjUG+tbdz5rtTvd7dOrZasx8di75dZeVY7nB2yqIpb/0gVgxdp2BsHZom0uMTaBDSRHvLNVkgrhkOtpQiz3Cwj0xmKTb0ygjkazfrZYr2GiLIO1JbmXPDg5txG5PjdCfOWMb6x6muVJU3mt6vQ8VRFUbiTpOpSpErdC4BpYQEvTFoqtfb622eT2XZq8uKPiiX3CC+envdfr3dPmQQa4xlVWbreY+aGkHa6nn3B1JaEfblUpmEa7pHVW1QX3S5NFJ9Bjvam0Q6rUXJu1iVhxIsmIoBUue3tYJaaCq+Dz2fzroL1ZmT/UICabSRMzuAVNStaBSo+ZtfBR2Klq6/UtpFHLKCOeSLTnUL2UMvl6+OZx1/C99VLwS9EjlF3jna8PKS6PJ1r4mW7rr7VtSDcV5J4WDxt43ueVQ2YwjUhOSE+MaS11R3qRqrnX66uwyVlcpeLpTnn5bqR2avr+QFdsj1JpqRs71IuP+xEDfr5Gtc8XMvzf/t3pYb7r/rnvTrvMTh6muvS/fnTRyX3X9/eijnixYtSk972lPTwoU7pc032zhvTpmXJubj+utuSP/34/9LV193Tdpmu23S7rvtlhbt+pS043bbp803ybs95hkKax9ekfmuTxecd3668fob8stMZqb5G2+cnrjddmm/Aw9Mu+elGDPzZoePPVxs2pUXoBYO2c6jhaf8JdEnJWi+Ljp5E3+dLh7JtelSe1Ne19HE42lD2dD3qE35UEq81T7luo1Kd26oys3yddFmrm7qAJUFc10xdS+ouvJuE53aoPYOZ1kqjeTvQXF1rTOgUKlzHSwovk085H62RlHvtPpOQUUHO/L4YAN08RV+US/8K67+qsNZlItSUSvK0tBLK/xWe5G39aWjtyqNgLWSGVRo0yknB8kP0163Ybrzn/xfZxaWfib7tcmFuhnRlXdslFZNIP8ZJCgF45EP0xHZGQmvZJT365N952AczsBwXP1hxJ1CT+GYvk1ytyfv538PcwOh1Wk1KO/2u26243fHRkey7FNdqMPaW/LCva3rFGViAg08JJHYcIWHKJZU5EMPVPYpyizMfmAXWNtgrWHH20JJ/ptV+PPjy5wLOx9mkxbfWmmYoIJZzg4UXjRZLjnGxb7XX+gtKcJ7UvvuutTUSe+sY62KtEtOvKpXTLVC1Z4LkjElVUNHoCSJTXmHoX9JGpV37JVyPZi3WKgU9LfX2tqotlRqnzskc73BTqNog6Eu0aGEMlMeKRX/OBUS0i2hN8esuOSCt1uUi8c0ONFQaPG5JMkZgmUp3jU02anEXb9U06+O/zjT8bnjWoeroCFRSBW56up1wSXMlHf0IQVVtgpdXmOl34RzS5OSJpo30lY2QyDQokCOtMkPoreoHSQ2se10Kh/V9660Vve17LsCDXWf+MTyAynLCpmKXWwwWgqVmclIb6ateWhVWnr7HenOu5alB3OA4MGHVuQd41dbUOApu+yStthiY3tThyYK3n338rx84rJ0x52357c0zU277rpL2j0HJdiRvzplWe/yO+5JN1x7bbrt1tvSwzlwsfmWW6YFO+6Yttt2u7Tx5lsUO10WG2oXHyeCDLPygZ92DSiK9T5OSl34YMzjNl7GvX6vs40+Hn6MRvdk9p0+9vPR4zReZetf/pO/d1ztSB0Xeim+tbhGNjniQWsqZxpGLJJIgUCDgg3os8acy5/Ct8KbzpWZ9o6H8Bb8xV/0iNJW7misZKpnj0JmTH87QI5JTdmt0eto9SP3mo7bNXe4s9njhHRXAPZwuM9Tp61gz8Im7/IOy+SU+vg9sQ6UhqcAe/rVOds6gToHQ/RaIp51GBwrHhXquVforzTd9HpNWoze5FtdwNe7hH3DulMen0AD/eUzkJdFsGO77eqap2baxUEg5XY2gGQnY3vlYH7gWZtf42J7OOSnI727eRB0+lFBfKbeTpx/7FfrROdl58ov4oit+Q+ccBqREkBHgD9e2YiUTC7zMG4OhYWY6nnZHZHHgIxT0QBv2RG76ffpVJeSEULdR63dfe1zh84WIy3kgV602RVdD19dwQZpLYx2P1wJvl6HRNFNpciRxlg99zaypF0M0CAtap+EHPOlGeUdLzq973DVfSq56YMV+dM5it4XMtKv3NiNWwN9WvwvdCqXnBKsXzxFL8wM/9fUIqyjQVRONjQNJI3Wr4GKx4OBjulo0ae+57yOkCQJNLApMgy8asuC8FIHk8eA+prH0ooV+Q0UK3n1c37tcX49HjMi5ubX+T5h4w3z6+QKEQINebJCWrUypXvvvT+/pm2F7bC98SZPSJtttpF5vjYHDoj/87qMx1Y8mh68/4H8qssV+f5cvBpxo402yq9H3JB3dRVK7R2NWYAICSse2TiCsl0Hcj7ZyWPjbdOn8Uht+qW7X/tYfRit7rHaHaZv8PTzTzrGO9d9rmfA453x19wmR+Gtg+TrKrvc1Oc/9jn3gQbZ7fAWFO5aOvChKBfewNvhL1pVp9ZpLWqdurSQ2wmQeTGOJh8PHd5ud1d8y3DlVn+yYtNNPkIjTTpHqKKDd1Y2ngGe4VApuEbs80iU9+MtDZffu36cPW3jhb0uOMNg32Szx7FMGBpPGMWsvKawhVzj6q02+VqnSbfyXi3rDGVcAw28w5mHJ3s3cN6ToTpHwJFBtPcyZx7edct7tZnlQKCBIMOwgYZmZDlDOpo5JoaqT0DOR3oRxCH/wZKqkThaffmmou8jcbTW12FF+2KixnpeKhe5rIJQjTSsF93nqZLSyct5dR6qxqIwaoNOj8w4khWrzxtGRmCoxurVdzX5Bl8245nggg2QOiyFlvpjX8fHLiumjT96iJI0GkUrmDoWrG6DZkY7pGadRdsE/TWThU/F344PnT5gu+a3d6dqkix5Psobu5rJVZZ4gU2x6aRoHRwmINAgFytj8qruWWbo4a2Ehi80qB1eeCI5y3PU1kn1vcybukGTzWjIgXfOLIH3+qbIXT1ASZvZ3Mbrb+3IZT46eUukQl/pg3Tx2rRyEoUFGghKlB81sRQfNOJXimERZECIyAQCBBps6UTOa/o7Siaw1ASoNzdWn1r1uwZX9KbHBY823Riq+lYVusxbpU9TL3MDpZ/96j7njSBAXYK+rUG/kSRDxZc9v9NnKku95X2vaNVdRnr65V43ZfnZkpsB2ihw8IXI34MN9MXIVUu98oVEQe94yBW7m1dX6Y4vvlTXXvpRnYPS/Egz1ExEUtdGq7vNr/J8F8hkIyOx06pzBE4a3qWisWI/ArNdrCPpc5fgWCsyLNxVH0LveGBv37tsy5+DNtNt9tr4oQ/sDgxiUt5PYdlWY/WudTX5Bl/2JiSg3LetQ+XxCTQAUj7sXaMsk8hVrg9MCbXzlAnMZihegcm7o/OLu+wnFWTy5RfmMQFZOmDAU/bJKzbPcmM9h78uJx2S9zK0ia5yrnuSxCcyry583neVvTPyXc6o7nlVVn/E42VU9rpLmg08S3qlqixUdckPmVdmKDgl1c0HPbRVjBD6J6emh3FYNaZDiup5j9YJIMjRel6awiU1NVkv2+R5K4sYlMNo5fxHgQZ/XjBa2a0KpXrVlXurPQZKQ9DzUTVTkHzOrai61zcJZTNbOeb8arPteTNP9d0Vv/pBXh75cy4p5eIGlOL1mbRwODlj0mNspktYeUUoRY3f/8kyqKunLprswlQprkusX/VqRgiYOjBUVE6vYWnpvbHlxrxyMLOV9z94M624R5bwc4/MNILx3CNnsOwBYcY6JOJstDMxIufVZTHzsGqxCl5kNkRWFxMoUn7hhMnlW3W+F+f4QeaH1JWIgTDjAidJKDT7mZnvPn5MRcqmB6YG3+piDSyGyUDdFUNdY9UwQYXSY8xSVN5kraFz3tuG5v5998LYa1IgnkG+1f3t14+KVwbLXFVrl2HlECmrrtyY3R+USJFymlUucxPnT/4edG0ICW9OYvcVo/kGlX3uyl0ulvQuGnYglEQrm8HiT53XNfUUR8JbF5bLdfqE1b1Byr5eGm0gdbnj+zsS3kqJFNTzimE9LXiwKPt62eUGUgUGcNEu2PrxIiQ+ylUSMecmn3ORKp6y0Eav89XrrX6poZ7XFfSpZ9E2t0yrb/Rlr3IM5r2aqS6PS6Ch2PQxP4vwpJOfRx7LUzsfzT+x8Ki7QX5AWZsfVjh4+8QM9mIAVAErIMeEhJQpH5OyEQqrA8pr4i3kGtfgamPXPNGXB6sbN462/tXdqdeHcaCuu14vrj5ZU09Dr/aR2h9CZfUh7hk09pqfGIqcLPN+fRSrHMl1sSunSWzKe5iMWRLkKpfFarSDNp+kUblvU9npqvQ6GkWJV7kK0jEZeXbEzDrfao4VLTB1fscqPCtlqs9Mmw5kGS12SRkqBQk5BoGSh1oyW16URanYUGe2JWcE/riUpUyQP6WGnEmioOSaEUR14utzUQEG5fTVwWRdLyFrQsaa1EBF5Zxr82TOj22QTBQg01evXp1tbJBmsz4iJ8b/2FQcnwkHqKKOOsUG7OOTG6Axm+ERiywQ6M/1TFud69yjqef/hY6cU7b9kx5Zk2l5xkW+h8/IQQ97m5RxZYYqpzyJic6QlNPxpuToYq2zOZaOvjqTFzYBT6gzT3Q9O1A339WJ0n6NVheBq8bSqxemumCPEEyTkfoYtibvqMrkvuz9lD5yXxaPaF4FgQbpVC5+lztRR81FNfi8LPtrifjkehd7JtaDDN1Gippkm9pEG4ZHvPJB9UnP5YDymgMt5BrX4GojJiIqH6xm/eIQuMprvWsh17gGVxvhzUTT39BYJ9Xrgy12vpLi7elLJphe19Bmx7FIHb6LXTltYrVcDfVcjF3MleZ1rjAugQbWihJImJEXh/LDB4lrMptLsa50Xt6Mav78jbra1uYnHzBkZoPNaDCp0fwpz5BOCCp00lrVweAFmhgH8NTFu24WTl+dzzWNuNjTrx5CVimaN9yvL76trdzHU2+mzmau2J/slhhlQzlCJY+dk5JuN1Rkct0Gr56/MCSp+rmUpYKr/Nth7iKb6SaBBlpdRef3Zq+y10/7MlR9gLfsl4l5Q6WsPdCUbNXn1OmtOyI9XXRVnH5XRC0c4jIV7g+sxi6GKlcBYVd2skVRxuBRmRZfLjjb/7boR4XUWJ75wNfYvQyNqqtsAs7koHZYxVOKmQpong5Ruu136pJZmQ84lLJd+EmX9Li8VCuOQiO14hraoZcX38qPgtPEO0zZbSo64FEjeeY2AXIr5Nbu/nSoyCqVsqqazoKzINXbxSjb1DM/vnmx7kqpquSpVDoBk5ce0TMjvCQjOXqT/oKz9690kFfXs1KnVJa5WL0SmnDP4M9lm9RnRBqKo3hFdBlosI0bH0urcqDhsXwuZs+ZY6eEwAJuEyAw8TwGItcMBrOBuqyT2RBr8mxC7rO8YprZDJhChfZ4zCstrP5I5p2d+eZlRehb+9gj2V4RjOCzZhs657aZ+ZWXBCgmPeE4SXlRK/42uVPSmtglWok1MdVpxlwSqzIFMZbadIK7rvlYFG8tR7z8rsHVSdbQUU+D/9yJsTSrquVypYvYXanE5L5vbqJVAp5xupSzw5V/OK8O1HMx+dyXa/3x4nZeM4HcRNToZRytKyjgbBgL9ToNPaI5nWK1XPrJISh3/L5oPotPDdRdauShPfN51qrsbfqy00nRcELI8/iy50HApxofTcN+9r2akZQx2ZVKQtXvrsZcafCxi2VQO8x1oy3G7Bx55R5X2WnKkWnSKV7a62VEShmaLDXoaCCJe8R5ZcdLZmKPDfma80oGJl/3OprKmd/0Ol3qb6W0NFzZwERJa1IpmmfJZcS9CrGRw2rsYjBm/pRKKoYOKZfWyTRugYZHc6CB2QvMauABCrhuu+32fNyatt1227TDDgsMPn5UKX65yQ8u+csDlsXmkYpQCEc0jCCV56avxAhVmq5h9PY1Wm/0CnGIej1HZjTOIteQpL6hqTJNm+fz5SY5R6t76nvY0406s9NjxS7hTHH1umi9DnPBXnyuKtV1xnpdjM5Ws12FFgoFnl0qfD6o3fM2lYdxs8K3jdkrlkNljkiBFMud1FhAXg0tpbeee71q87Seckd/TxMEmqVnAKvJw6OjErSWbj3SWTaNOGv0BaX58Dd/u1EVDnksZd6rKT5FXl5c8k4dU57ppYIOJyWdPeSqM0alK8l257m3kC2A8uVSrLrpFjqxogMOeVX0o8uUVWSvt2U4SqeP7fyTYcNbr+wZXLnmnaRRRy76proOPjIE5eGxJYTIUamO/F1kpgF0zWjIMwt4xpk5KwcK8m3SPna5Dkt1ZHndT4sbMHrW5tkQa9Lq/HYKZOfNzxs8ZnkmRBBo4MAsOgg6rFm9ygING2beIpRAK77mGYn8y/pm8m/G7KwGqZEn2WuSVJtyeHzZKiLWFTS5k2nIK/my2C33DU3MotWE6mJqFvto82a9WTsN9UaMNBkuaZ5dZbErb9QpZt8JCeS8qdmzjne5c+0aoNkY5V09l6zrSAlecSVVe3de2fbqpKJCwjciT100r0+CApF6neb5y2ZY+O5bU5PeQkaailrtb7tYjbGh6hTX1bimBsHhSXW9SDbqFmNj4/D2Gjmlu8W4b26UH2di3y7KGZhUHql9yfY15JTKzrD8TnSoYoN+kbx8Zb6psYkqWwASAABAAElEQVQm4UqwJLh6XaxqUoNy6WrKJVTmiBTXlmJLgUKEcUpBNTbprec8BNT0NZlcF2jjEmgAHwIIBBt4GJmXN3okXXLZ5encc85JBx9ySDrggP0NsxUrV9kSCjaMfCQ/QLF55Oy8pIINIqvkH+Aroi/ojEArTkbTjULnyEuOtewtS1fbA7falTfJqq0pH87/4bia9I+F5vviy9Ipr5SP6kKYhb3utrK4ZKuem08SVi5HfS5BaK4skXoOk2PzmnrKdT7pqtMRVFuPEkdAzmT1XRlWCB2lUUR0QPZJ+gsbZYtsKK8GpF5yHMuln20a+wWYB4i2qeyhq6u+ofi+ayqtWvJToOHRuRo0y1bwZ/Dh0EGxlKhw7ToLlWBHr0qDbeKltHXs5j7IfvU0DSep0Ml1tdhyUtJF7msFf+9feAYleuD51KNBcr69Lk/d6x2NTq9fZfRW4xiv1BpoLOyKv57DVtwn8xA/n2vbBBKZvNQQh23pIUzSTTmnRzOvfdZtCoSaOw8q+ZOXgwp5I+Y1ayzGMMs2YkZxbsizGR7JmzNskGcbzpwzFyMWaFhb6ibAsCrfg/nIzeJ1FXnDh8eynjl548d5WU9+L1SeEZGXUOR2ew2nhSB6dnTIWiY2mbv0R9g0mSv7VDXlej8Rsfd89NtsIDBAZ2W7YLWq1CFO2ecwqJ1yWzLTMLYxw1BLuj42iYi9p+/oaBKAnoXQ2dYMy0Ql/O3yFSfUCW/UaDTKS+UdpoIiYdPcaayV4BVnramr2uFBQgcshTVjdsVCa5YymqSVOzGRYOVoaDLd5R+xexrlHnqXL3XuWr20C3WQmLcDL3Xlkq/zQO+X4O/c4do5B/nWJOl9aWqHprHFaPS36RwNvclXaB7f0eiVTFv/phr7Jr+EhfKBH0x10uelsPQrFwvN1nc1KBdDUy6HnG7EmkQL/eV3UwzKpZsbr3SKtg7mYw40CBdyAg1r8kMLMxRWrVqVzjzzzPSv//rx9OY3vTE95znPTRtuOC/vjr02zZk7x4ILxVso1liQYY4PNHBapLgH1IaGfDK4GPiWYc+N5/PyPWZLQp1H8t3We6Xrcr0czRTpb26FOshyu+RYWnx/fLnwqKNZ/ivvOkkdtqJUMXVOv9fty92inRapqOeV3Q5rqQKCuF2xJImdXOVS0DF3KG0lZ8FYpKtOp1FtbbqgI2eHBqeVUFVw4qUVe1LL5bIKpw6YKZdNHf2iGmONQ8wIjzGh2ZLzjXpFL1p7/ja50ETrERwBoe5DdbUx7IVJtspNISMHRTLKMSe/Ci4xiTvnntkExFlKllmdTXXliKpcikBy55QaQQYXaOjyGvmiHwoyFPXCH8Jrxb9On9BYT952va2tLr/b2pvo3g7yvi7+0eiVrHL0MqivYPMNNOZDdpTD4v3hdw2WM/DRYc8iBHitJF/NWbwS2qIGUlzqLPUWZwsNaC+WzXCWFGhgRkJ+UaYtsbCpD0Q1dJrxgukQ+VibP6dry3UXq/L9eGWWw5f5+R6Mb6seeijNzT8EzNtwbvbz0byJ5Gp7K9SsWXnpRrZQWHQ+TkYxd8VwpOttyQOdeYy1xK5JROxdA1gY22ygq9TXxuLtSL94VZcJ1dXuZetleO3gfDYlKSvbTGem9dNtIpmhS3SAAAEqsZB3yTb5NU407HCezJ4cQHfdgS4Gz9jxu+OShIu8m7vDBV0HVPFJGprKyuEqrqC0SaLDh0ynlqUUFSoauv6a7VJxR1MXS1Xp2K9IlW+iNPE4F8VW5LiWSzq6G3trXjcy1JXD7cuqk/dL6ABDr7vg72ijNNrUq7ejCb2yPBYbHY2jLzX52UQbrYW2/jVj37HSJtfhaC/181/YN+mXXD23D1i7uaKlFJLeei5x2KzvXQxUZFWcjqamnHsxX3YspsnqFYPThXoa7blSttbNfMyBBroNNBw88DAdc9k996Sbbrop/fSnP00f/8S/pT95+cvT0c8+Ou2x+x5pu+22s4cWZjPwsKpNpmbyEFQloV4RyrPWQC9PQuey7mTKok5sb8vwlAbLlbB9FqqPVUWuCv1kYUK+jWew753bWJuOypEJKLTZ9H6rrLzHDZS4Rq9TZeU9skbotEpNPa8A7rCWqiCU3BKipSzT6kV8uWKCfxKS89Tcw0U7+3LKcnEpd44NCDQ4TnW/slOAkHXqgUgXv+yEmfXCtbKHlaYm/jpNdeVeTvrqec1sV1W8XcSGirfX0GwkdFVvfKgEMrW8hkESWTmC8qGQd0zGnTkVNDLmHu6OAi/qyt4WKqhLC/XKrpV9kEFluAotXE8LavGYUVxfCw0KM0i3cqTXxyQc6afdpTxcdLhs8Pj7sscH+lp2r88Flk6w3IGlDdTnsgcDsxbQT8pFDWyLAR7ShBn8mSl9ynfex3KQwN48yUZJeSbDGpZM5E0ZZsyZVyjizU85gLDB7Dlw5wDCoznokM1k/pl5BgPzFLhErF2zOr/FL4cTqNDhrLR4M1Su2CsrJn9GA/gYjkDQlKyxu0GXqkEidlnsFrXzUSfpfOj0oLfBbI/YaAnyGxvVAVENUtzghLFkep1VIuQmlhkaxHsFS136PPbT622MtSzfyDlPqlvHqoqzUtG8h0W5Q6mY0FoJd9orkhWgcxTfuu62jnRHk2jFkxlXTiVfLqhcS4tU5NgRxW4HZUU+wEuZJL6i1vlbp9frHc5OqYfHOSLbsuuaOgrGueRt4FtxF2o3It/aOdpbevruWNFb3PscsSx6H3tbR04ZRl8/X7E4qF1ejQSv6Yi976vvc1Wug+nqvu8qKxc+0o8++x6LwX8pK2YaS8tyoMxp0VGxl4VCdyVZfrEzt25clS4V6hrWnfqYAw2ASCLnmYSL8W+vvjqdddZZ+fhp+tZJ30h77fvUdNxxL0gv/aM/Svs97WmZI28UuXJFntUw22Y2dMHIiRTBTqqx5z8VUYQitwGPLumFH+LsZizZm4g1mu9Tky61q62w3vGwps6qkmlqG+w1lzpZ62godHbo/W105Ma7VLfb8aiwNKju/anr8nVf9jIFft34y6by6u4swbqyijEzuLLYlCPeKTtG6W3Ih+PqFuzY6Karhs7iyJx15nq97kBZh82z+rL0Y8/YfSNEqPl/XYc1Fa0q9s3ran3dl1GiuvmT6/W8nyHx9uOhTTb68aGruuJUN55MrYKehXSbrkK+tGBM4lQu695r9Ive66cklXc4i5JEOzmcenSmLEnyqneZSrlzoK0eaIDWlGSrqa2NJi/a2j19ovV7W5SxZ4eHyzUM63sxI6HAkY2U1/BWiSzcHWjIhPx5Qie7JNizRzaOrA80cM/FpxyywJPMUOSP5hmFq/JGzLPyfgpz5j/BmtauysH9PGtigzyjkCWOK7LdDXLgYW6uowPJKoSQZ1nwYwBviZphSyGzXT4uNiui4jK9k/WnCgiUXa3s4nxD0vMaTf1EKr0NOirB0sYwA+0Wd5q0d9HqPvpGdNoBk2dsMWYsZZtnl85KLDdWZTUq94KZaZi+S3S8c3y0w/tUN9LVkV7GXkqXgGnr5Snghu4PmZYG5dBVLvJCY/FNLqTU3uH0UgWP/yuflPu2ermju9PSROu0FqU2HtkkV7kuq3qbDrU35YN0IoPe4mjnbm9pstpN6+c3ern/kcZiwxSM8U8/P6V6GB54R9IXdPrPr2wpH4kuySjv528/7L1cW1k26rn3t60sneTWd8+IwnpdAjJW1mETq3LPUugvKXWGirGuXA3rTj58oCGDYA85XYP/3NH8MKS3RqzNbQ/laZcXX3xx+sY3v5n+Jx/333VX2nOvvdNznvuc9Io8s+GA/fc3dFblX01YYjGTN1XoAT3L8wAEHRL7N7DUYlb+tWUG00ot4Uh5cAGAMR+cI+PPbUwDnSmdmc5yDjbGmpkfqngzRtlUfAIQLxSbDh78aMcPduGmjTWv6MMGfaSNhD7SWuPLvwCVuqt3oZuhQvtj+SkNWZL1zz6+GdFMwzcwhN7xxljt1y/fjgp0FZroOlipB4XMZP6VH3WbTR410epyqtf11uvi6+QFh7fhy8Y3WEmhzgnWRTp1x9RxYqgSktLjtYg2jBLJWV5+roaRqz7szgfk6ra79EtxF1PmyP+7SOIbZd6kq4mGevlXL4/S9IjFOjfe7CFP4OZQ4ZX3mbL3VYa6aIPOX8v329uR3iaa2rzNwn9xk6tccHco6mknV9DT65ONfjn86JVct8V+ksO1ef2yM5420G++S6mM4F7ZKTUN53HmygJr86ugyXU/MVmn2+42WT/33yKRF4d8Ks0XdJryPYllEDO4P8xk/6PMwX2LQEG+b3HfYakjN7sZeRkF8ohxB7KU2+y+A6HcGwIGHrq555Dgn6xk/RuJwRKQYUSG1p0Z0TeMziZcsIOs8iaeNpr5iOwwxkvmEbAO1SkFbobR29aP0dIH9l8MXQaaPS2ojQJd0qrAr0O0et6kraB1fGjiKfS0t3g7HU2e2lseTlu3XD8Z2VXeLdm/hl7kpH+0OrCiOxDltjQS/fKpTRf06RJkkI/D+Cze8cynBnt60G65CYsmWhMO9c9JvV5Y7nxubczZpKiJ5pzwen1Z+n1uqnqYnLImW+sIbahAgwbONiiuPRR3BsgprcwbPS5bdnc697zz0oc+9KF0xeWXGwwLd945Pfuoo9IrX/nKdNBBB+VfUObaQ44g1ECZATw2eOCCtiYHIx7N0zsJHMyyX1WyRG4v/OGM5Hrmg5caez4UgYkcaMjBCfQzuDd61m0BCIID5cnMYpbUv6JW/H00BzhWM4U0V+dkf9m8EjF85AGOxCaWJNULv+EpDOBXYSP7l/14ND9Q8pCuQEomGR2d8BaBBtyzx7miXzmogC/oqR7wSgzQiQybdJVdUdeqOv7hjdqp++Tb2sqe35fhHya12R5GdlgbdV2tNgEdMCvFFHK9S6CkOaUVu6NNdbHLZe8MfexKZf/U96qt6GcxgOnVVlBqWJjuUl+lpwMnMjUJx9VcrHvbzDVyqnxBUmXyttTmd92/jg7f0qFKv28VrZdLLTm381PWzZm+3E6wg38XsaHSrdF76MuFYEEpJHwr17DHa6p63g1IBQfkiqeijr3gzXXOdofabLOkVmy5DqnmZNXc6LtvVT+aral1QvMmdzDYxyWJiIW6ygN9lbCz4UkD5ceRofLZO1ARhzM0sO9iaLHhycNZHD+urq7KkS7i2G2p+9IkM9R9We0+b3ZFGgvpbp7umtfly4Pset6mMlbkRVP7MLSx+jCMjX48zUg19apOk+d1DXU+WRddeUGXFnFNl9yf226P+3voeVVu62MduQ4izS3Dtdf9kxfQO+U2n7x0Py88X708Kt16hu4Slr85twGfGouzUwSrRMOLgl6UOmWje/2j7Vi9o9Og3hpo0OBbuXxlQOyTrzOb4Y477kjnnntueu9735tuvvnmivXwww9P73jHX6XDDjs0bbbZZjboVyMzERisM1BXkIE2bDNbwJKzWwzgO4P4gqEY8CtYAU2v29THQAN57KGbN12QP/zwwxZA2HD+/K7gAQEK8SFrHxU+COjO/qAfNGymQqYLC5u9kIMNyEIjwEGizqGkfoiO7xUWOVAyN+8SDg/t4KOABPrES5CBevdZkYXIA4F1GwF9W/h8q0yP4vM+nufVI9urt2jtRbyX0isblPFHoHO2ikeYdgs6Q0io3Mw9mKNZLqiBwOMBgc53bnBv+abF92kwTsERCAQCjw8ERhRo0MC4DZply5alG264IZ2XZzR85CMfsaCDBsrIfOITn0gEHHbaaae0xRZbmBoG0QyuORj4+0E5siQG2RyF/bzcgqmeZZsxuD8agGtQzoZbMxmIl/zYUwBhTt58i/qDDz5o+jfaaCObbYE6G9xnn6rlDlnelimU00gLX7of3pCRLPblM31SkAMe+oq8D6oYPxt4la/8RGbevHmVHDL4jQx+q5/oFWZmPP4EAoFAIBAIBAKBQCAQCAQCgUAgEAgEAlOIgAUaGPxqkKwBOblo8k9t1CVDzuCX/MYbb0wXXnhhOv/889OJJ56Yl1Ess0Hxaja7yum1r31tWrx4sR277rqrDZAZQJM08KYMP3QG1AyiKTNox44fnMsHZOu+MRB/jFkF+Z9kxC97GvxLv+gEPNAHvwU58DHXFTBQAAG9JPhIlQ+5ys7i8KmNvShYzgGN/sHLEhJyaJKHH98VEscGfkqOOriQjC/n6ocR408gEAgEAoFAIBAIBAKBQCAQCAQCgUAgMIUIVIEGP2jNw/a+My0ZDGtATM7AnHTJJZekb3zjG+mMM85I1157bXrggQfsV/mVK1dW+QknnJDe8pa3pEMOOcTkGHQzeNagHT3wM/hnII5uBtkcnk8+kDNYrwb5Wd7KebDPnggktaOTRMBA/JInxxdybMofghWr8nvGoeMPg3rpoYwesJMNrxe6DnjVF/pHnRkLJPQh52cmYE90fEEPdeS8/6Yg/gQCgUAgEAgEAoFAIBAIBAKBQCAQCAQC0wSBKtDAwFYBBg2W+/kIvwbRCjT84he/SB/+8IfTqaeeaoNqTfUnSMDgmIHyAQcckD7+8Y/nvRoOM/W0KWngDg39DLDlC7agkVSmHRn5Qs6BDIf0UYaOfXJ8oQ07HOiDx9uTPPyr8qvCSMwkQI6EDPrIvR3K4kFWh/TBDy7wCLdH1jBjogiAmPLyD7zIkyPPoTq+ckQKBAKBQCAQCAQCgUAgEAgEAoFAIBAIBKYTAtUeDQxgSQxmlURT3bdB84NeBsNnn312evWrX51+97vfmQiDaejkyCqocNppp6UjjzzSZgigQ3rg41BSm2jUFRigzECdNk/3g3IFFNAnHnLoJAIFCjagRzMW0AGfdMNHQk4YIKfAhQIe8NAunqY6er2P8FidME+5/wM80kObAhPYZ4YFdvHN3qzhzhe6IgUCgUAgEAgEAoFAIBAIBAKBQCAQCAQCU4lAFWioO6EBMQNdEgNfBrcc9cRA+L777rO3Tfz93/99uvrqq7tYkGGQzNIElgv8y7/8S3rmM5+ZdtxxR3sDBczYI/lBOnoZVG+44YbWNswf9CgAgE3vL20cosFH/6j72QzeDvzyCX4ODfznzZ1ng33Pj76VK1baHg3aY0L2aFMAwcuorOAFNvAH/ez1AG7oILCC/CA90hd5IBAIBAKBQCAQCAQCgUAgEAgEAoFAIDDZCDQHGvKYnwEuA1oGvyQGuhqQU/cD8DvvvDNdd911tgnk5z//+fTb3/4WlioxaOZgwMyrLV/60pemZzzjGbZ8gk0hfUIvicH8vffem5YvX554GwQHiXYdftCObwQWsEOijTK5dFJWgkb/SMhSZwkDb32AzmCfvRTQ8YQnPMGWTYiOb+giaELul1bg/qpVK41OoMHPukBeviDD60CxjX58J6Ebu8ipzwpsiMcY408gEAgEAoFAIBAIBAKBQCAQCAQCgUAgMA0RaA40ZEcZEOvAbwbUOqAzaKbOgPzXv/51+uEPf2ivtbziiivS73//+6qrDNT9AJuGBQsWpBe+8IXpNa95je3ZIGb0MthfsXKFBRluvvnmdNNNN9lgnEG794cyQRByBusEMJghsf3226dNN9k0zZ5TbFCp4AA2NMMBGgn/pZNXXN5+++3ptttus00sH374Yevblltuaa/j3HbbbdPGG29sAQdkGPyvWb3GZK789ZWmb4899kjbbLONBSDwF/34RkIGrFasWJHQfeutt9qGmfPnz09PecpTzG/6AB++4CttJPnrdaE7UiAQCAQCgUAgEAgEAoFAIBAIBAKBQCAw3RCwQIMG2zinwWzlKBMMamNaTfFHjl/mzzvvvPSud70rXXllMeCWLEEG9DEor6dnP/vZ6b3vfW+1KSTt6IOXgfhdd92VfvOb39gsCV6becstt9gMAPZR2HzzzdOmm25qeyow4KaNAAPLMZ72tKelhQsXpq232tr8ZpCOTnQz+PeBD8rQ4WHpB3rowwUXXJBOP/10m1Fw9NFH2xsy9tpzr7T9ghzEyHZJ+EhA5dJLL00nn3xy2vaJ26aXHv/StPvuu5t/zEiwlPHLIRuzgR3kmKVxzTXXpHPOOceCD3vvvXfac88905Of/GQLUoApuKEDH31ijwZmm/gghm+PciAQCAQCgUAgEAgEAoFAIBAIBAKBQCAwlQhYoIHAAQeDV35JH/RrOUsgGAwjw8CZQMPxxx/f0w8G5fyCD389veENb0h/+qd/mg488ECzySAcuwz8SSwrYDDOmywuvvji9P3vfz8tXbrUBuN77bVX2nnnnS2gwGD8wgsvTCeeeGJ6/vOfb5tMHnrooYklGSxJoJ1AA75q4I4NDh9UwUf4brzhxnTKt09J73//+80PlnnwKs799tvPZh488YlPtIAFgZD/+7//S6eccor59ra3vS296EUvSosWLUqbbLJJ1Sfsqm9gq8AGQY3LL7/c+khf99xjz7Tf0/dLzJzQkgz8IdBAgETp0UceteUdCkQMOleSizwQCAQCgUAgEAgEAoFAIBAIBAKBQCAQmAwEqkAD0/pnbJD3OZjtAg3lr/FyxAa1mcY+BgQaGJwTSGDzxwvOvyDdvexuG2ATEFCQgMH9sccea4EBBsccDMSf9KQnpf33398G7/BoMO4HznfccYfp5pf/97znPeYGsn/4h39oMxcIODAo//nPf57YhJLEazNf+cpXpoMPPtgCEQQ7NNgfJohCAOFrX/ta+qu/+qtK35IlS4pZDdkesynoN33+zGc+YwEOggxHHHFEYmYCMyvYvJJ+0ifZVmDAlOY/4HbrLbemX138q/Sd73zHln4we4I+sQSEfoExgQbwQZ7EeSIAQV3LM6wh/gQCgUAgEAgEAoFAIBAIBAKBQCAQCAQC0wABCzQwINZAX1P19cu76AQAGNyS08YAmv0ULNiQ91TQdH/oLD/43ve+lz772c9a4OHTn/60LWtgzwENvun7FltsYUsR0ItOHZUPeZkAwYszzzzTlmbw2kz2M3jJS15iMyH22WcfW96gGQ8//elPbYbFUUcdlViaweCfQfswCdv0jeAGSyHe+ta3mhh6jjnmGAtcEARgoE9gg1d0fu5zn0tvetOb0mtf+9q022672cB/JG/IIGBw2WWXWcDii1/8Ynr5y19usyLYKJO9HsABn8AMjDgoW1AolwmcRAoEAoFAIBAIBAKBQCAQCAQCgUAgEAgEphMCFmjIWwjYPgI4xsCWxMCbQS0HSQNdtUOjjcACv7j7xN4KzAr48Ic/bGT2O3jOc57jWarBMnqVmuzR9uMf/9hmGLDpJIP9E044IS1evNiWM7A84pe//KUFNggAnH322aaOpQ9//Md/bIEJgiAs8UC/AgqyyWCdAIj6QKDh1FNPTW9+85uN5QUveEHiYO8HlmssW7YsETj55Cc/ae0f/ehH05/92Z9Z0ASCbGnWAXgpWECAgFkIeosF/Ow/8a//+q/pP/7jP6imv/mbv7EZGwQumD2Bf8ihg8CDzgt1j50Jx59AIBAIBAKBQCAQCAQCgUAgEAgEAoFAYIoRKAINOMHWCLVNHxnUctBA/IHBrU9qrw942XuAPRMYhJOaAg2F3o5O6ugnZ2BNUICNHymzFwIDcAIYBBr45Z8lEgcddJAtU2CPiC984Qu2VwRLNrbeeuvEcgaWULBEg5kQvHKTNz2wNAIbvEGCtzwwQwKdepUk7ey7oEAD+y7whgwG/izDIBDxP//zPzabgb598IMfTK973etsbwV8ZYNIAh/XXnut+a9+EpAh+EDA4oADDjB+5OH7t3/7t/SpT32KqgVHluSlGvDssssu5qewoZ2y6vXzQXukQCAQCAQCgUAgEAgEAoFAIBAIBAKBQGAqEegEGsbRC97EwGD8Yx/7mGn90Y9+ZMsPqBBAYIDsB8kajDcFGliaccYZZ6S/+7u/S7w6k00eGfyzB8PTn/50CxAwi4FZBj/5yU9slgMzEGjfd999LRBxySWX2KyI//3f/7W9FZhR8KxnPcv2QTj88MNtA0kCDsw2uPfee9M3v/lNWxKBvwQZ/uAP/iBtt912NluBTRxZNsHrPFlWQTDjuc99rgUhCGSwjINlEPD89V//tdnntZnMtiBQ8hd/8RfpT/MmmGwaSXADfehiqQmbWuIbb8/4oz/6I5u1wes165gp0IB/kQKBQCAQCAQCgUAgEAgEAoFAIBAIBAKB6YTApAQa/IyG+qDZg0GbDzowU2LlypW2R8P73ve+9Ktf/cpeW3nEkiNshgH7L7C0gD0hmIXAoJ23RDD7YY899rCgwXXXXWcDeDZcvOqqqxJvpDjyyCNtQH/++eebfgb2zI7gzRIsffjGN76RXv/615trbNDIHg0EAG6++WbTQVCADSmf97zn2V4RBA0efPBBm7nxrW99K/3gBz9IH/rQ/5eXeLzMZiQwowJbBBnYf4GNJpmxsOMOO6ZVq1el66+/Pl100UXppJNOsqAKhr/yla/YWzRYPsHGkfQzNn/0n5YoBwKBQCAQCAQCgUAgEAgEAoFAIBAITEcEJi3QwGCdGQttgQYCDAo0aBNEAGNvBTaD/Md//EfbOJHlFAzSec2k3szATAeWTJDYl+Hd73637afARovs78CMAWYUsIkksrvvvrvZImDw5S9/OTFr4P15TweWZKCfAb8CDWwoSWCCoAdLOLQHxJvf9Ob04pe82N40gS8EE1gqwpsoSJ/4xCdsVsJWW21l+zrgHzMu0MNsC3wg0DBz1sx0//33J2Zd/Od//qfNpkCePRuY1YBvBBrYQwLf/EwQ+CIFAoFAIBAIBAKBQCAQCAQCgUAgEAgEAtMJgWkRaCDI4Gcy+ME0MwUY4P/lX/6l7bHAPgmHHHKI7b3AXgzsfcCSCJYlkJhl8JrXvMZeM0kAgsE9swwOPPDA9KpXvcqWXjA74O67704s6fjSl75kcizN4E0TzFxgRsMb3/hGoxNkYCNLBvoXXHBBFQh4y1veYrMnePMFwQCWTfzzP/+zBRgQJGjBLAlmO7DsgqAIszPYeJJZCuwRQeCAfhNIINBAcAHbJAIVzJrYdtvtcu0xm9FAkMJjY4zxJxAIBAKBQCAQCAQCgUAgEAgEAoFAIBCYRghMi0CDZjJoIK3AA/Xly5fbcgICDWy0uGDBAlvKYDMC8tIJButLly61vRfYH4G3Qjz/+c+3t0Dwlgr2iiAtyRsssqyC5RYM7JH5xS9+kb7+9a9b+zve8Q7bQJIAAHs0aDNI9mFAH8sWCDR89atfNf4///M/Ty972ctsRgOBg3vuuSfxek2WTRA0YJ8K0hve8Aaz/eQnPzltsskmiRkObEKJPhKBEpZrIMObLBRo4E0U7A2xww47WJCBWR42G4QdOxs25jRl8ScQCAQCgUAgEAgEAoFAIBAIBAKBQCAQmGIEpmWggbcz8AaHDTfcMLEZpJZOsEcDb384/vjj0/7775922mknm02gGQosk/jbv/1b2yRy2223tWACA34CF2wiyRsfGOyzVIFNH1mWwUAfHpZmvPrVr7YZB8yAUKCB2QxsLjl71uz0s7N/VgUuFGgg4EFwgqAIAQz2i/jsZz9reyxwbnlzBDMbCDQw8wEf9txzT3szBsED7BNMIdDAhpbYJn384x/vCjSgH0xIzGrwhxHjTyAQCAQCgUAgEAgEAoFAIBAIBAKBQCAwDRCYPoGGtY+lGTOLpQEEFx5Z80iav9F8G1wTaGAPBWYUMEhnHwY2VSTowLIEBuHMCvjud79rswzquGpALzpBgp133rna94BABIEAAgIM/H2ggaUYxx13nM0qYMaCZkiwdIKAB4EG3h7BzAoSG0ay1IM3ZRAMYDNK9oogLVy40N5EwXIMygRSmKXwwAMPWKCB/R1OPvlk4/UzGli2QR8JwJAoR6DBoIg/gUAgEAgEAoFAIBAIBAKBQCAQCAQC0wyBaRFoYHDPgJuAgH65p87yAn7FZ9D+D//wDzYYZ2DPAJ+3Rzz1qU+1TSHBlGABb5Z48Ytf3BfiV7ziFbZEgrdSMKMB/dhh0M/+DMx0YDNIAgkkZjMo0EDA48QTTzQ6wQo2a8QfZkkQ6GBDSGZLoG/5g8vT0tuX2t4R7AXBjAUSb8/gdZgESVhCQcCAfSguuTjPaPjMp6s9ID784Q/bHg1PetKTLIghXMjBKVIgEAgEAoFAIBAIBAKBQCAQCAQCgUAgMB0RmJaBBoAi+MAg/L777rOlE+9617vSjTfeaJtAMvAn0MDyCfYwYPB9xx132NsltOSB11IymGfQz4wCZiOQXvva19ryCgIE7Ofw0EMP2cwGAg0M/NFDoOHtb3+78bNHA7Ma2MSRN0cQMEAXeniLBZtB8tYJAhQEIu666y7btwF+llLcdNNNtiGl3wviJS9+SVq0+yKzR0AFWfaXYMkFwRISsxuwy4wNBWDgJYGLcpWNEH8CgUAgEAgEAoFAIBAIBAKBQCAQCAQCgSlGYFoEGhhAc8ycMTNtMKMYRDNDgSUFbADJwJ7NIJXYnPHggw5OBx18kG2uyMaObPx4zjnnpNNOO83eMMGsAZYoMOD/9re/ba+eRJ7AAbMVtt9+e1vWQCCAJQkspVi8eLH5wYaM7NlA2nvvvW0GAgN+BvV33nmnvanisMMOsxkNBDvYh4EAw+c+9zmTe+UrX2lBEJZ5EGzg1Zpf+MIX7E0XBDD+5E/+xGT0Jorbb7/dAg28AYPZGy984QstkLEkb2DJbAmwUUCBAAz+UmdvCoIsHJECgUAgEAgEAoFAIBAIBAKBQCAQCAQCgemAwLQINBBU4FBi4MyAmsH79ddfn84//3xbssBsA2Yw8IrLhXmPA4IALHdgA8bTTz89XXvttTZIJwjApossj5g5c1Y677xz0ymnnJJuuOEG25CRdgIQvHUCGQICvP6Sg8SbIwg0sIHkU57yFLNJmYMZEOzTgBxvnXj20c9OhzzjEAuKfP7zn0+8JpP0pje9KT3rWc+ygMDll19uQQ1keZPEUUcdlRZsvyDNnDXTZlAw4+Lss89O//Vf/2Wv7jz22GNtxgZ+MSNDgQaCCyz1YA8Lytq7IQINBnn8CQQCgUAgEAgEAoFAIBAIBAKBQCAQmAYITItAAzgQaGAQTYBBmzeytIEZBwzEzzvvPNsHQZsuMgDfYostbCDO0gMG8fzSv99++yXeFEFQgAE4Mwp49ST7JzDrgQMd2CPwQDACft5gweCdGQvMQCC4gX540bHXXnvZ4J+9GL74xS+mL3/5y3b6CA6ccMIJ5jObQJ511ln2Fgn6wJsv6M9tt92WttlmG1vKwb4S+MZMBRL9Y1nFe97zHqsz44E9KJglQRBFvqIP/wg6sFEmMz+Y0QAtUiAQCAQCgUAgEAgEAoFAIBAIBAKBQCAwXRCYNoEGACHQwEGAgAE1r30kSMBAndkMDKoZcNOmJQQEDBhwE3ignf0SmOnAfgu0seEjdHSxDIODoASzAtCzYMGCtO+++1pQgY0cmUWBLexqpgABjK233jotWrTI5Ah6sJcCSzJe9apXJTaGZGkFgRH2lGBzR+yS8JM+Ic9sDJZsbL755pVugigf+MAHTBf7PrD55IEHHJi23GpL89sHE+iPEr7JP9EiDwQCgUAgEAgEAoFAIBAIBAKBQCAQCASmGoFpEWhgAO0H0YBC3X69z4P0FQ+vSKtWr7KAAgNvtRMAYCAPTQNyAhEsKeDQr/3oeuQRBvxrbNYDctAYqBOgYCNI9ofABm0kggPMZICPvRSYWQDvypUrLaDAcohPfvKTtn/E2972tnTMMcfYvgwEEQhk4Dv2C9uPmH/Sg6/oJyDxq1/9KvGGCfaIYO+GhXlJCK/L5MAe/SERGME2suiJIIPBEn8CgUAgEAgEAoFAIBAIBAKBQCAQCASmGQLTKtAwXoNnBSgYlI9EJ0ELggOSQQ+BAg32OXcEIggS3HLLLemiiy5KZ+WlEhxvfOMbbQkFsxaGScyeYKbG1Vdfna666ipbTsFmlCzXwK6CJdKlQAN0Ag0Koqg98kAgEAgEAoFAIBAIBAKBQCAQCAQCgUBgOiAwLQIN4w0EwQHSSAfjyHFskP+Rcs10eD1assHSCGYYsCnkqaeeasEI3hbB8gqCBQQETEepk7ICGOjgTRkEGNCx1VZb2YEcMxmQVXBDPinogQ7f5n0zg/EnEAgEAoFAIBAIBAKBQCAQCAQCgUAgEJhCBNbLQMNY8GRgz6CepAG910c7gQJyZkywnwNvr2APiN122832YWgLNEgP+rVBJTbYV4KNH9HHcglyJflDQEEHbdA5PE0ykQcCgUAgEAgEAoFAIBAIBAKBQCAQCAQCU4VABBoc8sWgngF8EWhg9oBmIYhNA3zNMOAtFOzJQGI2AssaCB7UZxrAz5ILEoEE6izD4Hg07x8xa/Ys2weC2Qx1WWySPP2xtUVAhLdP1H005vgTCAQCgUAgEAgEAoFAIBAIBAKBQCAQCEwBAhFocKAriGAD+zy2ZxDfOGMgt63NwQgFANpmPvjAAIEF9lmAxqwF30awgkSQwQcNpJ82W87h3mSJPmZWwN9kH5lIgUAgEAgEAoFAIBAIBAKBQCAQCAQCgcBkIxCBBoe4Ag2QKCsY4Af/Yu8KAuTggU8EAZCVPG3QNKOhPmuBNpK3432p64K3CjRskAMNs4o3U0CPFAgEAoFAIBAIBAKBQCAQCAQCgUAgEAhMJQIRaKih7wMIPtjggwY1kWpPBwUKFDgQH7Logk5ZMxBUb9JNm/SgFx7Pp3avT/YiDwQCgUAgEAgEAoFAIBAIBAKBQCAQCASmCoEINAyBvA8+wF4f8LPPAklLIhRUIBhAWYEC5fDSxtIHdPnNH2mTvNolR64kHuQ9Xe2RBwKBQCAQCAQCgUAgEAgEAoFAIBAIBAJTgUAEGoZAnUG9JbJylYSCDQQMtCSCgIEG/ZpxgKyCATPyMgfJ+0DDzBl588i8HwQJfh3wkNoCDdaY/8gX1SMPBAKBQCAQCAQCgUAgEAgEAoFAIBAIBKYKgQg0DIG8Ag3KEWFzRh8c8Go08Pf8tKuudpMpN5aE5unwEmjADv8VbPB2ohwIBAKBQCAQCAQCgUAgEAgEAoFAIBAITDcEItCQz4gCAJwcP9ivTlYOBuR5Bl184hU/OljqQGp6vSX0+iwHLZmQnGZDwEtq8kuBh4Ij/gYCgUAgEAgEAoFAIBAIBAKBQCAQCAQC0wuBCDTk80EAgEG9ZhUoeFA/VX7g79vgJ1jAXg2U66+pFC92WGbBQVBh7ty5xg+dVA80QJNN5dDMz3KmA/VIgUAgEAgEAoFAIBAIBAKBQCAQCAQCgcB0QSACDflM1AMNnJyRBBvgVRCBMjMVyAk+KEBA3fMRVNBrLj1P0wdD7crR08/HJh1BCwQCgUAgEAgEAoFAIBAIBAKBQCAQCAQmA4EINGSUNYCvA64BfZ3eVkePZAg8rF69unqzBMspNNNB9sTbps/TJeNpI5H3clEOBAKBQCAQCAQCgUAgEAgEAoFAIBAIBCYKgQg0lMg2DeRpGslg3i+B0AwH0Qg0aKYDtjikW3ndB9F18n17vU08kQcCgUAgEAgEAoFAIBAIBAKBQCAQCAQCU4lABBoa0PcDepqHGdQTUPB7NCDTpEcBCPSyfEIHddpy/CGnzn4R2Xq2Dy1SIBAIBAKBQCAQCAQCgUAgEAgEAoFAIDD9EYhAQ8s58kGC4QINvHXiEdOmmQtNqgkm+LdMEGiQfmzW7aqtSVfQAoFAIBAIBAKBkSKgew1vMdJrmqWDNuj8r/jKPYbEE3kgEAgEAoFAIBAIBAKDEIhAwyCERtCuIMGg4ID4UD2IdwTmgzUQCAQCgUAgEBiIgALe3H/865jrgQXxERCHL1IgEAgEAoFAIBAIBALDIhCBhmGRCr5AIBAIBAKBQGAdR0DBBIIIMzbIy/dmzqhmLqhrBCA44IVPwXG/1E+8kQcCgUAgEAgEAoFAINCEQAQamlAJWiAQCAQCgUAgsJ4hoMCBAgnkJIIJjzzyiAUXmLlAQMEn9h/iYFmgXsvs26McCAQCgUAgEAgEAoFAHYEINNQRiXogEAgEAoFAILAeIkBAgYOk/YEINohOGTp19hKiTOCBsuoEGiIFAoFAIBAIBAKBQCAwCIEINAxCKNoDgUAgEAgEAoH1AAEFEOiKBRXKpRPWNd54VL7haPXq1WnlypUWaJg7d261j4NmQBh//AkEAoFAIBAIBAKBQKAPAhFo6ANONAUCgUAgEAgEAusLAlo6QU7QwB++jyyjINjAjAYtlVCQQbnnj3IgEAgEAoFAIBAIBAJ1BCLQUEck6oFAIBAIBAKBwPqMQDl7QZs80lUffFCdnMCCZkJoKQX0SIFAIBAIBAKBQCAQCPRDIAIN/dCJtkAgEAgEAoFAYD1HQHs0EEjgICkIoUADsxxoY0PISIFAIBAIBAKBQCAQCAxCIAINgxCK9kAgEAgEAoFAYD1GgI0eCSSw8SOBBAIPPrBQzWjIezrMnDVzPUYiuhYIBAKBQCAQCAQC44VABBrGC8nQEwgEAoFAIBAIrCMIaKkE7rIfw/Llyy3QMG/ePMuh+xkOBCOY3aAZD7RHCgQCgUAgEAgEAoFAoA2BCDS0IRP0QCAQCAQCgUBgPUSgmqGQl0Iwi+GBBx5Id911lwUSNt5447Thhhsm3jYRr7JcD09+dCkQCAQCgUAgEJgkBCLQMElAh5lAIBAIBAKBQGA6ILBixYp055132iwGlkgsW7Ys3XHHHbYvA4GGzTff3I4tttgibbLJpmnevOIVl9PB9/AhEAgEAoFAIBAIBNYNBCLQsG6cp/AyEAgEAoFAIBAYFwQIMlx22WXpqquuSrfddlu64YYb0q233mozGwgu7LPPPmnRokVp7733TjvvvHPaaqut0vz5823Gw7g4EEoCgUAgEAgEAoFAYL1HIAIN6/0pjg4GAoFAIBAIBAIprVy50mYyXH311emXv/xluuCCC9KVV15pgQaPD/swvOhFL0r77befBRzId9llF88S5UAgEAgEAoFAIBAIBPoiEIGGvvBEYyAQCAQCgUAgsH4gwOyFc845x45LL700nXfeeWmzzTZLDz30UFqzZo11kr0ZSJtssont1fDUpz41vfWtb01LlixJc+bMsbb4EwgEAoFAIBAIBAKBwCAEItAwCKFoDwQCgUAgEAgEpgECvCmCg6S3P7Cxo69Thge63hJBnTdLMHvhm9/8ZvrYxz5mMv4P+ggksGcDh0//9E//lI444oi02267pW222cY2kNTrL+WH5zcfs5vZWyPjB0ekQCAQCAQCgUAgEBgeAd3zdQ+t19GkDZ7h4RXV0ylFoGE6nY3wJRAIBAKBQCAQaEGABwwG+DxM8LYIEq+dJFHXgwgPHWtWr0kzZs6wN0do88df/epX6Stf/kr67ve+azL6gyxvmDC5PLNBDzK0s2fDqlWr0uLFi9Pb3/52CziwXwM0yUmP8sfW5kDHY2srPQQjmgIS4o88EAgEAoFAIBAIBLoR4F6s+zH3UMr6EYH7ve75PBfwYwI8zEoUvVvb1NQi0DA1uIfVQCAQCAQCgUBgRAjooUMPHjxwaGaBljVQh04ieMCDx913350IMpx99tnp5JNPTtdcc42188sH/AQMOKTPGvMfaApkQPvEJz6Rnve856Xtt9/ellWgGxn80YONHn7ko9pEl+7IA4FAIBAIBAKBQKAdAe6fugdzv6bOPZv7Kfdn3Xd179a9nzb41d5uYeJbItAw8RiHhUAgEAgEAoFAYNwQ0EMFDyAcPFDwKwZ0Zi/wcEHggUADic0fv/Wtb6UvfvGLNhOBvRp8gl8PJHpQ8e0qv/Od70yHHXZYYt+GHXfc0WSY2aBgAoEHHdKHLO1Kni5a5IFAIBAIBAKBQCDQjYDu9VB1P9d+StS5n9r9N+Wlifk/bTwDEGjYcMMNp8VMwgg0dJ/TqAUCgUAgEAgEAtMSAR4oePDQwF11Bvc8dBB0YOBPmeOh5Q+lG268wd4w8d3vfjdxVA8m+QGFJF0+AECZw9uC97nPfW7af//905FHHmmvwGTDyJkz8q8qM8qHnVIOXpL0UMZO1moPQ9QjBQKBQCAQCAQCgUAvAtwv/dHFkeP23HO57/v7Njw8AxBsmLFBfiaYUwQiumSnoBKBhikAPUwGAoFAIBAIBAIjRYCBv37NmDN7jj1seB2086Axe1aeyZDH9DfddFP6+te/nj7wgQ+krbbaKvmZDDykkPQwQ5mHFj8jAX1M0/RpwYIF6R3veIfNbNh5551Nb9PDDrJel9cR5UAgEAgEAoFAIBBoRoD7spZIzJo5y/Y80r2fHxF0//bSyJDq92PPMxXlCDRMBephMxAIBAKBQCAQGCECPEgQSCD3QQGpUaCBWQ0PP/xw4hWWn/70p20mg3jI6w8p6PMPKUy7hAcaOrFFzkF6xStekQ4//HA79thjD6Npc0itIxUvjeiabg8/5nT8CQQCgUAgEAgEphkC1b03/2LAps4kaPX7KDT9GKB7L88I8Pk9HKayexFomEr0w3YgEAgEAoFAIDBCBBjE8+sGDxP8uqGHDx46eMhYunSp7ctw0UUXpdNPPz2dc845PRaQ0aGHGjERGPAPKdjTwww82267bVqyZEl64xvfmA499FDjXb58ue0LMW/ePKmpghPo44gUCAQCgUAgEAgEAsMhoHsz9+r6PdSCDGvy2ybWFG+b4N7LvZo9GuClXpcZzur4ckWgYXzxDG2BQCAQCAQCgcCEI0BAQbMbeJjwO0zffPPN6fvf/3465ZRT0rXXXptuueUW8wc+zTRQkIEGHlg4lNSmXA87nmeHHXZIH/rQh+x1l7wCk+UarAklQKGELQ49JJFHCgQCgUAgEAgEAoHhECDIzwzFBx980PZg0n2UnA0fN9544yqosPbRtRZ4oI0NocU7nKWJ4YpAw8TgGloDgUAgEAgEAoEJRYAHEP164XeYvv7669OnPvWp9PGPf7yyT5DBBwqqhrLQr42HFQ7xKH//+99vezXsvvvu9srL+kMNQQbxSkedp+5H1AOBQCAQCAQCgUAgpdWrV9s9/o477kj8gHDPPffYjwrcz9nwcYcdd0jslbTppptWMxT1YwI80yFFoGE6nIXwIRAIBAKBQCAQGIAADxBaMsGvFQziV65cadMjeb0lDyX33XdfuuSSS9LnP/f5dPIpJ5tGllf4AT966kEAmVZAQfyerrLajj/++LR48eJ09NFHp7322kvNplvrRHnY8cEFX64EohAIBAKBQCAQCAQChoBmMdx55522qTMbO9999902o+EJT3iC5dS32267tOuuuyb2Slq4cKG95hoF3KOny702Ag3xoQ4EAoFAIBAIBNYBBJpmMDBVkjdMMKC//fbb0xVXXJF+/vOfpzPPPLPam4EHDpY0aN8FggDoUsCAdn9Ah2dQ4g0US/JeDa973evSoYsPTXPmzjERgiHoZzkHQY5IgUAgEAgEAoFAIDAcAsxU5H5+2WWX2WbOp512mr1eescdd0xbbrmlzWz48Y9/nNiHadGiRel973tfOvbYYxOvnCZFoKF8qBkO7uAKBAKBQCAQCAQCAWYhPJI3f1r7WLHvAYEDBvMM7Fm/ec0116Sf/exntgHkjTfeaFMtQY0ghIIM1E1PDgQoKcgAH4ef8aBgBLyyxxsmlPbee28LNBxyyCFpp512SuzXgE/ogB99kQKBQCAQCAQCgUBgOASYmfjrX//a7ufvfve700EHHWT3WYIK3GPvv/9+m7l49tlnp5NPPjl95jOfSS9/+cttCQX3bI7pcu+NGQ3DnfPgCgQCgUAgEAgEpgUCzDZgyQQDeXaWJshAYOEXv/hF+va3v5349cMnHjjgJREA4CGEXIlAAzzK1a5cfCzXYIkGgQ3sKy3Jsxp4EHre856Xnva0p9nmVLInnsgDgUAgEAgEAoFAYDACy5Yts5mJP/zhDy2IwBueWKrIfkibb755euihh2yjZ2Ywcjzzmc+0+y8bQ+rezh4OzHac6hSBhqk+A2E/EAgEAoFAIBAYAQI8SDDYJzjA0gSmWLJc4owzzrAgA5tGkRQUoJwXR6T8O0e1XAIaieACiaCCZjaoDo2DRBvBAw7s+6UXtPOay3e+8532wMPDjhLyyEyXX1fkV+SBQCAQCAQCgcB0RIAZC1deeWVixsJHPvIR24vh9a9/fXrKU55iyyMI+vMMwOxBfmzYZptt7LXT0P3+SNOhbxFomA5nIXwIBAKBQCAQCASGQEADf1gpEwBgyQTTJ3kg4e0Td911l2licM8BX33PBQUVyGnXryBNLogXPg6feNAh6EAw4atf/Wpakmc3sAO2DyzAwxEpEAgEAoFAIBAIBPoj4Pdo+N73vpe+9KUvmQD31oULF9oGzMwe5CD4QLDhsbX2U4Lx6Z7d38rktEagYXJwDiuBQCAQCKz3CPhBqC/7mx43w0fXPmqvZpoxs1i/Dy88JMmRi+4HrQyIRZcMcr7cpae8+aKjkSePm7k913XUeY1hHP6oXxrY45emOELjVwoSMxXUb2Q0gwAag3r8g583TbCWkwcRXmnpk+nO/MjXAw3woYNDPnnZelm8sosMZWxI9wc+8AF73SW7YPMLC8EFfPR+SE56aEceeldAIp+SNY+ssTawoM8TlbDdL+HrdEryV7n3z5enk8/hSyAQCHQQ4LvLtU/fYV0jOxzdJXhJfL85JIucvvPo0rVY94huLVEbTwTs3PnnB9Yp1G4VOjcjtct55N7Oay15XTUzFt/73vdWanbZZRdbKvGMZzzDllOwSeRmm21mzw36TI3WdmVknAoRaBgnIENNIBAIBAKPdwR0gyNXmZudDvDhAYlBMw9IDCzFKx7V/YOUBpm0iQ4/Oup2dA766YFHcuKDZj6UDwuUJyJhjz5ocE3f9FAIjV8ySMxMUL/10EE8BMxmziqWIsB777332s7UX/nKV9JJJ51ksppWSQUd2ETHWJN81Tms63vBC15gezU8+9nPTvvuu2+aP39+F4vOnR6O8Ul9o41fZfCdhM/sA8FnhSUgoncpHKcKtkjKpVafAeWiT3WOnzrwRf7V86n2M+wHAoFAMwK6B5CTuK7rukhd1yK+05S5TpLDB02BZ67JyElGgWrps4b4MyoEdA4Q1rXVK6K9PovAtyPTJOd5BpXZi4F7PMsoTj311PTZz37WZgyytILEvfbAAw9MxxxzTDrggAPSRhttNEjlpLdHoGHSIQ+DgUAgEAis2whwg226gfobMz30fOKHxsMVdR6QeIDygQfo8ECHDx4bLOeZCbxtgeQfyPTApkGwMZR/ZEt6vBwstCvPjwRdv0bIX2NwvHW62keSyy9y6cM36mBBoj+06QGTsnhph3fp0qUWZDjvvPPSWWedlS688EKa7GFUeiQD/1gTunQ+yLEhO+gmUMCDzwknnGAPP9tuu23ind/ql+R1HqDr/FG2IErutxIPzbTX6WofcZ4hYPaKMJF8P2zqnwvJTGUuf5X7/viyfISvia72yAOBQGD0COh7qFyafF1lXU/0fTR6vi5tMKO4tppsrjPrj2sf7eQcXDc1u0v3hZkz8n0iyypBJ+kaK3rkI0dAuHOudL6US5vOK/V6WxtNsv1yLZ1g/yWCCrzpiWA7sxweeOCBdPXVV6fPf/7zNuOBTZhf85rXpCOPPNJefdlP71S0RaBhKlAPm4FAIBAIrMMI6ObadGMdabcYTHLzZGDNL9fSyU2ehybqPDTpps+DFb/oK/G6x0ceLfYI4CGsnpDz/qJPNuq8/erSAc9o5Jt0o1OH9NJXkvrPQF4PmGrjl/7ly5fbrxxf+9rX0he+8AWTmaw/PPBoo0l8Eab4THrXu95lm0Pus88+iSmdJB6UOMf6pW28MDTlQ/7ROZwK20O6OO5s6jOKH0/9HncgQ2Eg0IRAnZd77gAAKFdJREFUGbz013HY9L2r57TxPeRaqOs5NJ+Q4d6nY+2j+R6W/3Ht5NqLrO5r6IjvtUdvfMqcAzDWPQ2M/eGtTAT+vHXiggsusOO2226z2QoEEthomT2YmOFw4okn2ubPLzj2BemP/+SP05K8PxLBfXyfCJ98n0dSjkDDSNAK3kAgEAgEHucIcBPjIOnGOxJIuHHzAMUDEkGD1WtW2yCUhyh+EdcNsm5Hddr9Axq60Gn68gOY6nqQk9xo/UVuPHR4PehTP6E3Jdrho2/kJDCCTpCBfRnOPffc9K1vfauaycDsAaZawk//ZYM6uIxXIqDDAy9BEAIIYI8N+cmrLo8++uj0spe9zJZQYJeAEv7gl3ipc6iOj5TpJ3xK9XMq+khz2REuyofV46fJDiszXnzCaiT66C+JfDTyI7EVvIHA4xUB/z3rh4H49F3U9Qd6vY3rvq79tOUrpc1c0HVdMroXile68QMZ/q+rSX2cSv8N+3yPUtI5M2whdprEMi753Xffbff3008/Pf3oRz9KixcvTscee2y1bOKqq65KH/zgB20vpDe/+c02m4HliuzTUPd5XBwag5IINIwBvBANBAKBQODxhoC/+Vc3XXcjHoQHg1MGnTwwMWDVYJXBpZ/RMEiP2uWPHrDQjU70aYYDPCT5K9lhcskql51hZOs86NBgmjYeEjnQSZseLqnrgRI+6PQLXvp06623pu9///v2iwZlNouCnz4zO4RE2T+Egon6YAxj+CNb+NWmlz0mvvOd76SjjjqqK2iAWQUO1Hf1e02enfJYXh7D58CfO2atMJWYPnGMNmGHpAfwkXwewE7HaO2PRQ5fdT6H1eP9RX4k/R3WRvAFAoHA6BGw72htWeCg77n/XsNLXfcVfc99PnrvplaSfumaPRWe6P40Fbbvu+++dPnll9smkGflZZHXXXddOvzwwy3QgD8EIs4///x03HHHWZBh7733TgsWLLAfAITZoM/RZPUrAg2ThXTYCQQCgUBgPUGABwClgYOXzJqHaMauBwceirgJMmjUQxJ1BrC0MXjVYBZBeFptlvr1YMVAG3kGqhqsyte6Lvmu3PP5cqttzzRkmYcA9Qe7/mFGdtSOSt/O7IF77rnHpk0SaPj3f/93mwXC8gUNwMGvLodNPXxY4yj/cE7wh4Q++Y9NDvkA/qQPfehD6YgjjkiLFi1KW221ldH4Iwz0tg3RKnq24fsNncPbr5SNoCB8Eamfc9/WpdJ9fis5feZyyEKfbf3C5esqm1zJCx905WrrspkrnsfzGn/+3PjcKg1/6n2q97lBJEiBQCAwjghwLeTaqGulrmv6Lupar+8qdB1ygzYCzbrG0s71kDR71mzj5w098PlrpGxIz7qWCxv81jVQuWjqk66X1H25qe5pnpeyUv0ciD4o13mEbyz4c69n+cRNN92UmL3AhpDcQzXrU/5tt912tjxxyy23rDaQ1mdD9+pBPk90ewQaJhrh0B8IBAKBwOMYAT0s6AasnBslD0X1RKCAmywPUNxU8xNGNTBFVjdY3cSlXw9wTTMaZEO83gfpE8+k5Pl5hl/oSfLb29XAGj/9gyO/Ylx88cXpnHPOSWeffbYdyLHTNJhpgA/N40N9PBKBBII32AFn1XkA1owL/OUcMqOB/Rme9axnpVe84hW2OSS0YZL6r3OjvigfRsdIePR5kExTve6LeCcrxyc9QE61L5PV57ATCKzLCPCd5Vqoa+OsmXmWWX6ls675fI91rVHu+6vvOd97Ngfkuqs9GhS8oI4+bKCDazL1SJOPgM6hcp2/sXjCzIbf//73tiSSpZH68YTzzv0UGvd/bOn+gD0CMn6T0LH4MFbZCDSMFcGQDwQCgUDgcY6AbqyCgZueT76dmyF1Hob0QKR2cgbMPJxxQ+VGii7f3nXzLgfs6NSAnAcwDuocrKtnUA8NPdD0yzs+9vO1qd33ayxlCwpk/7WxJX3ARz0s4KP85CGSXzfYaZr3abNu85prrknsSE1iqQH6hG3dL/qILvovHOo8w9R1zrDDgV7wFIbyHTucO/aLYN0ogYZDDjkk7bnHnmnrbbauTMGPP6QmXfJV57ISHMcCvnI0JfWLdpUtz+z2S5j7bKpduqirjG7V67na6vYrm7iWv07UjUal/HrJJrLelnT5dtEiDwQCgXFAoLwG9NPEd9JfK7l+8p2sfy+tXtOna6ndw7Ie7gFsCqlXG+v77u8R3Ovq7U32+vk83dqqa152rO2ai8/Gl7HtKrtrJwNvS2UmfuWSIwcz6EoqK9f5a8LWeEq70iU9o8m5r7MnE89E2NM9mOcjDs6/PlfY1mG8G+SAU9nf0dgeL5kINIwXkqEnEAgEAoHHIQK6sdmsw3xTGxRJrz94AZl0kPNAxUHEvm3PBvh0s9fgnJutPZSV611186Wdwx7U8sAWnehuS/Kl6k/Dg2Gb7LB0bFigIQv4B0VoHPSDgTp9IN15553piiuusCDDmWeemX72s58ZnX7wAEJChgS+6PeJNnhpg7/e7nmHLYO/9KCffuA7WDMThYegBx980NTtvPPOiVdwEXDgXd/4gqzODWX0oUP9QJBzBg904TSsf8PwyX/lktFnS7nodT7RJyuv++Pt4pv3D95+/F42yoFAIDAyBPx3Dcl6XdravocKJDS1o4vrHjnXQ+4Dpj9f1u07XRs8ootrLzxcJ+GRfuRNRg6tQ7n1eQr9lf16jktgqmeMuoviH1fc87ln42ySPhM6z5x7yrpH8tnp51/d34muR6BhohEO/YFAIBAIrMcIcFPVjZVucoPj6Jf45WVt3vQPOd0Qxc+vNsxA4CbOjVMzEsSnwSw3W91YZd9+tShNywfaeOjioKzpq7JXz+GRPtrQI1113pHWTXfZd+mmHyT6ZT7m9lmziwE39aVLl6bf/va36Ze//GUiyHDeeeelhx9+2GSYNsmvHfhHAEV69JAq38EJO9AZvPv+mdAI/6CPQAI+o4+EfulVn9RG+8EHH1wtoXjSk55k7/tGBwm/6CvnHFk9wEETXX0xgXH6I3+Vo1bnWrk35fk8fbLKTT7JNr55/yYCL9mKPBB4PCFQ/16p754uWlte/+4iy7VNSd9X8fl27mtMg1ebZODRtRMa7Vw/ja/8Vb0uI9l1IR8JvpPRH+8PuLZha3zjgD96vA3p1ZIIPj+6B3Pe9QzAZwI53UcnA5t+NiLQ0A+daAsEAoFAIBDoiwA3P7sBZi7dFJX3E+RmyKGAQhuv54OXwauWVvgZD/BpUKqBbpvOfnT1RTl9GaY//XSqDZ34SU5fOJTwHTuexp4Ml1xyie3FwCwG9mYgMSPAgjBZDxtBImP7WeQ2sBEW6PMDd2GpvpmyUfwBdw7OBfalDz+wSZ3+6KGXhyES+zU84xnPsA0i99prLws24De8CkqgQz7LNemnPl7nAl1eL3WlJhttvJKZjDx/EjMALZbyg23+JlZ9og9N/WiRDnIgEAg0IND0vR/J94prGzrY+FYDRMxA46jay2tn/Xur+4WupZKFD1kFqHXN1DW4oSvrHKkJ+8nsxEjOs/eL80Kqn0vPM6isz0ebHp173dN5HrDnoXyDeOTRYobDdPksRKBh0NmO9kAgEAgEAoFWBHRD1E1ZuRcQDznt3AAp85CkByT41e5lR1JmsMoN1/ugmz42SdiAxqCt6Vci8ShHl9cHfdQpDwaZyUHCH/xgTwpyft0ngKB0yy23pN/85jc2k+GMM86wV1kxqCdtvPHGlRx1j6mCDPSTpAdU6uq7NYzij7DAd51DfJct5f6c6kFY5o455pi0ZMmStP/++6d99tknsWu2EvL4b/pr60tpG+t5kH+y5/Mu3W7QLhlh7GUms4wfYE0Cny5/nSPwcdDexuPYoxgIBAJ9END3CRZ9n5T3EauuicPw9tPT1KbvN23yDzvelq4VdXqTvqANhwBY++TxFt3OR56VyL9+12nxt+U6r7Q3nUO16/4LD88+JJ177E+HFIGG6XAWwodAIBAIBNZhBHQDbrvxcuPTAJibHzdE0agzMF29anV6eMXD1qaZCv30Ci5swsdyAgbt8+fPt0N07NKuwa8GvrJL3uY3NpraZHusOb7hNzlBBoINHHfccYcFGH7yk5/YUokLL7zQTMEDNuovuZYfqF/eJ3wXDvCONaELvNDF+aPMQYKmA584xwR+OCQjHw477LC0JAcb2LeBYIM2/ZR/8pt8PJPsS2ebfvoGr3LK8NaDWNIzGTmfEc4xfuizLOzr9s1fpj6ML3x1M1EPBNZ7BPgucZD47jVeM2qBSS/DdxQZvrtr1rCPQhEs5DvMoYQM1xsOkr7jakcH1wDePgEv97m5c/JyufI7TrvXQRnaVF6z5Ptoc/ow1QkfhKv3hfOqc+vplHXfaGuv8zfVZZc29JCEB+e1LYmH9n58bfITQZ+0QMNznvMc858TQOenCwATAWroDAQCgUAgEOggwM2PhyQOkq7/uiHzEHbbbbel66+/3gbeDKh1o9UNnbruH9Ksmyo5v/ajd+HChfZaRV75hB7Zkozso3e6PITRL4IkLJX43e9+Z3sy8IaJiy66yA4eLkkM4NUf4VPP4VPfaANbkuSwNZaEnvo5wQ5JdMrQsMWhB2qdf2ZkPPOZzyzeRLHnnmnbbbdN22yzTdp6663TpptuWvmKnqlM+I7P6gt9V1/xy9eFr9p9m+cVXbnayH1Cj3iU064BClhHCgQCgemBgK4Rus9R5+D7SrCV4DH3uAceeKC6VymYoO+39iOiR2pDhxLXI+4FXD932mmn9MQnPrEIOOTgM/zwsnQOPvYimj1ndjVIlY6R5OjDt6lM3Bflh8/xyfumNui+3FT3NM9LmVTpzdV8Fk2fNZRtXHvBm2Mik/dH9yLs6bkFmk/qC/5XffAMU1SetEAD0yXpOMBMNxCmCPswGwgEAoHAOocANzMNvrzzdmPjPl0+l3Ct5+Ahi4cfcuT8zRFdDIQJErBM4LTTTktnnXWW8THg5td7BqCbbLKJ8dxzzz1mkgACcjyEaDNEdLHh4PHHH5/2228/W/9PsEGDc/nDQwI3ah4SyNHFUQ2Sy4cLDOXbtT1o0DetsbV+mhfD/8E32UeeMphAZ48CyrwvGwy+853vpE9/+tNdysEB/IQdOjSTATp6fKJvtENHNwkadc7BWBO4oZ9zgH0l4YoNBThoE7a00+ZldthhBztnBx10UNp9990TdTCBV1grl53R5sIJfRyqe33yDx8pc4C7L4+XP95uW1k+cv50CH8+t8JJ/eEzy3/Jodf89Z/rsu/QdW6a7KNjMvva5EPQAoHJREDXafvsl9d/2dd3i7r4uM7pOqiyXTPypsYsk+M7y6w1AscXXHCBXeN/8IMfSKUFWbm/YQ957mno5ruNrK47XI/4rhOsOOGEE9LRRx9t18vNN9/cAg+0kTTjgeszsx4s4D67DNznu5muE5UDDQVdE0b63acP+EuuZDrctUf0frl85K1F999/v+GBT1Od8EvYgK+eHcCec0XS58bKGW8l/9nx+NCOXukmb0p8JvgM0I5tEliTTKbEmLI/jKH2R/ZNrtY22mo/neMSaDADuZPa6OTSSy9NJ510UvroRz9qPvPOb81oGG0nQi4QCAQCgUBg6hFg4MqbEHgA0M2Fm6xuWuTcjLkpEkCAb9myZemhhx6qHprEizw3S3TedNNN6dxzz00XX3zxmDr5spe9LO266672yzhT8uUbN2rsYVuDMx7ECGRsscUWFtTAbz3YUd4g7xPwaN5Yad7ceWnrbbZOPNTJ95E6yZ4L4MBDwtq8hnP16lWGz6qVq9J999+X7r333nTdddcllktoqYRsMPAGT98H/CN5GnX6SJvaux5Gynb4RpP8+UY/tr19nXvRsQFNmOnBmXafDj/8cFtCsXDhwrRgwYK01VZbpc0228welMGLvvMQ7nV5+WHLsquHcrChT/qM0Cce1AlocfD55bMJD7KP5sEDU5/Vn2HtjoVPmOuc4isPuFtuuaV9bvkM6/OMj8IIvOgf+HFQB0fk4adOGbyZRRIpEAgEkgWuub8x4Oe7xHeK7yBlvoN8Z0h8f3R/I0hMMIE63znJIIcMdALnV155pb2mmNcVjyXtscceafHixYm39zC7gWuA+ZXHYavyfUX+co3Q91v+k9Pur334gs8k/EXO751jDUP8oZ8EQgj+owdbsoe4x4W6D95nl3J7EaCGD3zB6dZbb7VrMNew6ZDoF/jxxidm4G2+2eZp7ry51eBfPsLDQfLYU6dvtGV00oyZxQ8f6NM9D34l6SDXOdJnEJrpKfl9GT/b0qOPFAGKmbM6wRF4vd02Wb0xTH2SX+Qk7NZtj1ugASNSzi7ZBBo+9rGPmeHvfe97Fn3jg8IHUY4gw4HDPNCBVemryfX/U3QqQ9OfrasVGfiVdzWWFbUN0u/52nwQT5MdaIPa2+REb5KHRvI+1fl8fZhyoXHkf+WLJD32dbvwNPksvrZcun0u3jrN16McCAQCwyCgG4huLLfffrs9LPHgpBsfv7jr+q/rOw9qBBluuOEGe2vCr3/962HMTSoP96Q3vOENadGiRfbgwA0cvxlcMhhTnQcAeHiw46GOgR6J/tsDWw4ccNPmwSnf1dx9rfj1gSAC90VeU4k8iUEfSyUILrBEgtkM9QSWxf2xeOClXeejzrsu1OU7uNIv8NNnSP4z6H3xi19sv9YRBAIvglQcyHGMJmGbc8XD3Zy5xZIa/XrI54DzTc5nlnPBWz6azslobE+EDAG1vffe2wYFfP/Ak/79v/bOHtSy6orjxy+IE6tYKYjzHCMhgqioYCzUYoKNlULstLGwtRcRrEWURCtBUEHRAStBm4CgphC0UXAwEz9SiI1mCo1Rk/Pbb34va86c+96999377r3P/5o5b5/9tfZa//2x9tl3n31sM8y10I9fNLnwgyFtl4Ur/OTjUM5rrrmm4coEGD5gRR+gjfqrHTpYf8vQJzyDwEEhYDvGdXyl/9N/Tp061X344Ycdv6bTl+gT9ivHCMK1byyQ8+nhd99996DEn7ocFtzvueee9ioh4ybyIztjLvKjv+c80N/BgjSMB4wt2D0fimuhw4fNNub04+qX//yy44dmXhOBD0S5lKOtrGO+4zn5ScN447jFwg3j72uvvVaLXqv7Bx98sDt27FibOzCmgh9U9cUPFupKHNg3W9SnZ3zF7mxtbfVzjN/1i+yXkmVnHG6eGf8gB/XmIjllE0bZyoZMlG34sIiqi3GEWX/UmW3JNkK8epoHdyELDTBCeAV+//33uxdffLF78skn2yTh2Wee7W79w63t3SQnpwBb86lUC8yfIBAEgkAQWB0C/VodD8wQYzvk6vvXX3/dHpi/+OKLNuYzdjOeO1nQAGHkeLDhF4l33nmn8VjHPxxMyPuuTBSQnckOlwYTPfjV6OjRo22bK/dOKjC6GlmNOROIHcx6bDDovJfLIgOLCuYl3bfffNud+sepNjmr2CAHpE2Fnzxruk27Rx900947aRnqceONN7aJlztNvv/u++67779recVmmGcav5M72isyULfIQB158Wsci2Pr+OBQdeQ1Exa/2HbNhBFCF/FFNya07Orh4p5JPLqTHj8uh3HS/mm/4IML0QfwsxhBHng3/PqRgP+hILAJCDjGYL/a/77fO/4gP/dejOXYNcZqf5WnzTP2koa+5dhB/8C+8Qs+O/HWdXw+fvx4O8sB2RnjcMGE8QFCHwh91HFra6s9QDO2kJZwSJy4FxPj8PMjBNixE0G+lAdZD7ik5WKsIZ4LP/gzLiELY9XJkyebzWwM1vAPr2peccUVzabbLhRTffGP6Um7Mo5ddHfddVfb+c9OErCAxLZ5ZvzDjxvYMX7MQBbwBF/GehZHWERyrJc15SIX6akT2gt5qA/urVPlIx/x7JBgZ4Z6DuVe2EKDgiIkq3svvfRS99RTT7X3LR9++OFmzD755JPu+eefbxNPfhlSKPPGDQJBIAgEgdUigBHRkNR7pML4YLiYTMxD5IcwZJtK119/fdsyif0CHww4hpiHNvxMPpk4SKTj1xkWGaYhJ3NOvsAKvhpv7jeV0Mk2gB5Vt3XVS9zBfJUy2i6W0XdYtGASyWSSHSQsLliOO3ho07RjMLAON7UdRu5fDgKMzywaQLRrFnoZq23DuLRpHnDZxfDxxx+38XxWhOif8rLvOHbownM4hgz9e5VbedV78xHGRX+udsj4WVweRlnsZQxgbAA/LmxaJbADY3aDgOGiCVzFdNG89+IHluiOy/Mt1zLkefTRR9vuyssvv7yJRLugzHmJH/XffPPNtpuUXXpvv/12ez2TNw04M5EFDdoI9ekCAhhTLpf2wEUf01aZSMdCA9fYQoRpF7bQICgUyDu2r776avfGG280oDici5V3DkN55JFHdnBDiKrgTkRugkAQCAJBYCUIMJZrcBYtAMYI/hjreUjDRV74zEPIwETBScM8PC79zaXtRG9kOP2v0+2znLxjiZ+FhnkJ/ZANF15c1AVhLmTwSxThm0i1/pBfPZi4qvO8bWNZeKyLbLQBLtoD1zLJw+moE3bwME+jXXPGCHU2fNBYpizhHQTmQcCxhgelIbHDR1tEW6ZN86C8334lryEfZUEOx7yhTLv5J+Wv4cP86Dem+zDdrH4XasiHLozXLEAukxz3llnGbry1AY694D5PPe5WxuOPP9499NBDbWFnt3TTxrEz5+WXX24/7rPgxBe9oHvvvbe7+eab2y4XXqnzi0+M+Rf/6uLuyK+P7DyXWxbtyDaN7toiXhXlwFOwIIw40tEm8GsnFrLQQCEWxEID7+jwXs1f+9PDWU28/fbbO7bioOjTTz+t7HGDQBAIAkFgQxHQ2GJQmNRAjP/VIJEGIox7J2LzPkzKD57zGnoempADWbnGSJ0ojzTzyitv+ImLYWOuGBFHem0r8vLLA35+PZqG1xj/dQ1DPy708qqygosYzlvvld8F/affLui3ekKUR/3CFxkox3ZmvRNGvOkqr4O6RwYxQGbukRe5ajumfXMR5vZoZCS9epHH66DkTzlBYJMQsH8hM/0Nss/gjpH9y7hJ6Yyf1q18Z+VJXi51mKZM7DmLMYwh8+yKEDtkrWOTshA+pofxuBAyj6WbRodFpakyTSsP+nNByF/zEc74THgdn9nRcP/997dnZWy8tmZW/bWl7DrlCAPPShzDg09M33HHHd3v+09Ms5OCw645iJlzkrTHyEp7QB7ltQzcSsjKaxQ/9Yd6Eue8cCELDRREATYOzmh44YUXOj7hwkrXdddd15TgXSZ2OYSCQBAIAkEgCASBIBAEgkAQCALriAAPhbM8oK+jDquS6bBgx3PtrA/782B+35/u647/8XjbYVAXZ2blBe485H/22WfdW2+91Z04cWKHxbBO2MVw2223dUePHm2HCuPniyO8MsMiAYsMHAx61VVXted7Fz9gCC8uiTjkNhzXNYGFLTRYGC6na7PQwDfRfZeXb2Oz6OB30Gv63AeBIBAEgsDmIKABmdUAH5TRPggkMebos59JwZicGGhwnRXbMV7rHLZXW3AS80vAYtp6GmJGG2SCtxeRrv6qtlf6xAeBIHAuAvQ/H6AWPe6fW1pC1gGB4ZiLTLYDx178jLF7tQlffxx7pYUvLPG5TF7B5AHfsxO0g9NigSxcnMvAWwT8wA8vwiBkHiu/8ueAYHY1sMDwwAMPdByYjUwQ9hhbAr8qG7sd4Es69Ky0lIUGPg3DuyHPPfdc+8xJLTD3QSAIBIEgEASCQBAIAkEgCASBIBAEgsByEGAxgMUBdiiwQDDLgjNnOfBKx5133tkOBEVCeLFYAV8XGghjoYGFlqUuNFCQKyZ8UuO99/7W72b4uH1hAgE4QIQVHwRDGAUlTOHbTf4EgSAQBILA2iLAWA8x3jvmG2Y4LmHaBcf5Gl7zED4k8+6VznzIYh7Dqmu8xhaDiFzkIUzC70UYacirgea+GlnsGXGEG0c+0yuTRrn+CmIe0hgub+L4wiiHLUHN37uk3VQSE3VUj4o3YcRXfc0nXuab1YWvWMNTOWpZ8iSMeMjyV4m9siID8kDIOCaT4WNxLWP/p7Zr2h55uCDyed8C8icIbDgC9gXaNX3JPo1f+0Qa+kLtX8RzEScPoIAH4ZJxNR335jfdbm7lZzr56sdVnrH0Nd1u93VsMx38CKdMcFB+44kzXowqdoQZDi/5kX+oh7IPwy3LPLvF17TLuKdsLmWlDO49e4BnW3ByLqHuphuTCX6kUy/wBEPCONMQftuY0ub+n26M11iYmPN5S3Y0fPTRR03mKlvNx1kcvDLBDgYO/uWMBr40wkGR7LDgs5s33XRTW2gQD3jZFpyjOA9CdvCotJAdDRTKdWF/wBLfqeX01m+++aZ9O5zvXxNGwVQOwlE5XISxxQJgBL0Kl/sgEASCQBBYLwQYqzGGGjTcMcImkA6qkxPusQUHPe5bHgctYRSRQYOITshVddEmGaadU1fD1RE/PCofeFQ+5pEHLmHwcNKCoeaSF/lJ4yW/yuOw3IuxrviIqe68+npKtqdoOydpfPuJSl9bjbX1hms7Bn/aC3WzijpQDtyKi+1ETJATUvaqi/eksZ2ZDmzOO//svlx5yT9uENhEBBxTaNNcjLlc9AP6dW3r9AnTmY+0P//UP/j1/+hzPO9ccOH2InQbNkrXIf+6UB0v0EnZqr7IWnXWj2uemg+e2NCGw5nnupqHe6hHuuHFvflbeC/HplLFzYOZeY4Fi9ZGemxIw9XseD+u8gwMZsSDA3YH+vE//WHUfdyO/8yP8KSp2Fbs9sLNcZ1Fhtdff7175pln2usYPJezqDDpcM+777674zPHLCpcffVv+2fz7QOFeaWDC77YTXVDPu+RCf2U03BcaCELDRVchJE5BRCnQPgl3+XQHzcIBIEgEASCwEEg4ELDQZQ1bRkYaYz1mL2clsdhSgcWTlhWrRd1U+c1q5SnTuZWKUfKDgKbjsCs/Zr0XIzRoSAAArO2oWlQWwTPzz//vB1f8Nhjj7WvSbDDAbryyivbxxlc3Nja2mphnKN42WWXdddee207m4G0PKfT1nmu51m++oe2WfuI7Npu+8lCFhpgihAUxKr4+Wc+G4WgY8Qkj/TuZhhLk7AgEASCQBDYDAQYzyEMUigI7BcBJitOtpzA7JfnWH7KgJZZxli5ywwTOyd5+ynLXy0XwWs/ciRvEFgFArT/4Y+nk+TgOaiOWZs2piA/tKi+7sPmpuEwqX4JX8Y8xzazW7nzxH366d+7v/zlz90TTzxxVvYbbrihY/cCn7Kkro8dO9ax2MDrE+zs4fWJI0eOtDzIZrsmgLrkqm3E53n6iYsX5DN9c/uAfe/xaYL8zIai7VX/sS14FEY6KooiuRCMSz9pQkEgCASBILBcBBz2nQToTirV9MRrbMzDmP7Dv39o264xVBgh4sjjZKMaJsI12JYnL/26k8KNr26VcbfwOnEkj2WMGVT1MJ1p5W+4/h0Xq8quwd7ljAXSXXB+b+v6f+quHBN5kP0MhsgGUb5XC9jEPz0m4ACpS8UVnblqPMlrnhY5x5/Kl3vnI1WOKosyWJT1oP8gXWWsZaKD8v63n4Px/XJk5KJ90R8bAef2LtZtb8F3O8H2JBo8mCzSX3mVx4UG26plmSduEFh3BGiztGf6iv3FdqwfHWj7tHdfieDX27bz+sKLuov6beSTyP5Gfu7hTX+hTFzLnZS/H9HP6puT0sFnjCaF17QVA+SE7NPcIzf9nXTacMKlqoO23HLJQ36wM79Y8ix44UXbr0mSRiIdVyX5EWZcLbemPaj7KhPyoxdhjJHWsbIQrtyG6TI286/GV96Ec+3GQ17TuidPnmw7Gl555ZXulltu2cnGqxF8VYIFBcpkJwPXJEJnLuq9LiSoC/VOPyFux94MmC1kR0MFDP4KgHBUjh3OiiKeMOOpMK5QEAgCQSAIrDcCjPeM5YzjjNvcY2jw+5CCBozvPqgQrl0gDh7ajRpO3BiZdixumvw+qJKWfxj9MftTy5EvOhA+nFiQv4X3Cwj13XbwgFpZfXnylB9+LidspK9pDIeHabmX5KN/k1z1ERtkH9On4sE9dQBO+50rUGcQfChXeVpg/6fKRZx1Y/iYrOZdtjss2/br/IrylRm5SU+cOnLPRVyNl+8QY/yUIX/TLVvP8A8Ci0aAtmu7tx/YV+wntHcubBUX6Wv7VybynUNnFkKNq32l3p+T70xASzNYDKxp5VvDZr2nDHUmLzgoG7py3gB+HhYZHyu5iEk8cfLB75jCHMCFBfLWMQa/OuCSj0uCv4ceE1ZlM59pV+UiR5Wl6mAcrpioH2HV7iA/WEF1vkQ62h/zk52zP1qq+f6cOnWqO3HiRPvMJbsYONiRxQUOfeSgR+uN3Qv4IeVUB8Jav+nPJuFNBfLYT2gHtBXktq4rJuSVFrLQILOhi0AIjkB2XARC2CqwHXuYP/4gEASCQBBYLwQYwxnXMSpOSIZ+JCasTT768R6DpOGdRxuM2SSah69GHV20T6P8+2J//OnchQbyox8u+bFnjfr07mAgbC/ZyF8v0puPcInJB/83ncAbvdRxGn3IQzvC3c9cgXKpM+isOuv9Yl3ry/S4tV5qmsZsBX+UDX2G7Zc4LknM1Rk/+aiDWg+EEVfT4Ted/OIGgU1EgLZc2/PQT/u3XzDOQPaj2udr/yK8xlVc4E/a2sdq/CruJ8mOrD78XtTv4Bi+/u5CAzKDDXzACt0ZL8jfHpJ7P/GTMCE/eaGaxrownDhwW1cSR3XQjx6EsXvRHx/ECl3ACmL3COEeIkmYGHIPhvvV/6uvvuo++OCDViavR7DQcMkll8D+LEIOZLb9N/l7OXG5mm79QhD64KedcCEj8lciLUS6SktdaGgCnlFiR+BejvMRov/vKtakVy2qoLkPAkEgCASB1SPguI4kGsMxA0OYhtd0i5AevtoT+A2N2lRl9HZo2gUB9aWcWhb2i3/nhE8wtpPkEjvjzyrjjK7GbbqrrlXHvXQSf1zyDduS4fCRP/eWYTyu8cQZT9pJZB7T6k5Kf5DhO7L1kykntZavntWvzjv5BhgMwyuPddJbneIGgVkQGLbnMT9h9pPdeJt3t36xk2bNFol35Or7v0RYDR/q1eJ4huTRrc9nWvLrN2w4PlvGbq55a5qhDDVuHe6RWd2VRz0Ir/I7VxAbHuoh/KYj76T88p/F5VOZp0+fbvzZtcCiAIsDk2hS+VUm8roohOzqI0/Sqo9huEtdaKgF5T4IBIEgEASCQBAIAkEgCASBIBAEgkAQOPwIZKHh8NdxNAwCQSAIBIEgEASCQBAIAkEgCASBIHBgCGSh4cCgTkFBIAgEgSAQBIJAEAgCQSAIBIEgEAQOPwJZaDj8dRwNg0AQCAJBIAgEgSAQBIJAEAgCQSAIHBgC5/UHO2wfE3lgRaagIBAEgkAQCAJBIAgEgSAQBIJAEAgCQeCwIpCFhsNas9ErCASBIBAEgkAQCAJBIAgEgSAQBILAChDIQsMKQE+RQSAIBIEgEASCQBAIAkEgCASBIBAEDisCWWg4rDUbvYJAEAgCQSAIBIEgEASCQBAIAkEgCKwAgSw0rAD0FBkEgkAQCAJBIAgEgSAQBIJAEAgCQeCwIpCFhsNas9ErCASBIBAEgkAQCAJBIAgEgSAQBILAChDIQsMKQE+RQSAIBIEgEASCQBAIAkEgCASBIBAEDisCWWg4rDUbvYJAEAgCQSAIBIEgEASCQBAIAkEgCKwAgSw0rAD0FBkEgkAQCAJBIAgEgSAQBIJAEAgCQeCwIvA/kJaLIJcMFM0AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {
-      "image/png": {
-       "width": 400
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename=\"bayes_filter.png\",width=400)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "Given - A robot with a sensor to detect doorways along a hallway. Also, the robot knows how the hallway looks like but doesn't know where it is in the map. \n",
-    "\n",
-    "\n",
-    "1. Initially(first scenario), it doesn't know where it is with respect to the map and hence the belief assigns equal probability to each location in the map.\n",
-    "\n",
-    "\n",
-    "2. The first sensor reading is incorporated and it shows the presence of a door. Now the robot knows how the map looks like but cannot localize yet as map has 3 doors present. Therefore it assigns equal probability to each door present. \n",
-    "\n",
-    "\n",
-    "3. The robot now moves forward. This is the prediction step and the motion causes the robot to lose some of the information and hence the variance of the gaussians increase (diagram 4.). The final belief is **convolution** of posterior from previous step and the current state after motion. Also, the means shift on the right due to the motion.\n",
-    "\n",
-    "\n",
-    "4. Again, incorporating the measurement, the sensor senses a door and this time too the possibility of door is equal for the three door. This is where the filter's magic kicks in. For the final belief (diagram 5.), the posterior calculated after sensing is mixed or **convolution** of previous posterior and measurement. It improves the robot's belief at location near to the second door. The variance **decreases** and **peaks**.\n",
-    "\n",
-    "\n",
-    "5. Finally after series of iterations of motion and correction, the robot is able to localize itself with respect to the environment.(diagram 6.)\n",
-    "\n",
-    "Do note that the robot knows the map but doesn't know where exactly it is on the map."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Bayes and Kalman filter structure\n",
-    "\n",
-    "The basic structure and the concept remains the same as bayes filter for Kalman. The only key difference is the mathematical representation of Kalman filter. The Kalman filter is nothing but a bayesian filter that uses Gaussians.\n",
-    "\n",
-    "For a bayes filter to be a Kalman filter, **each term of belief is now a gaussian**, unlike histograms.  The basic formulation for the **bayes filter** algorithm is:\n",
-    "\n",
-    "$$\\begin{aligned} \n",
-    "\\bar {\\mathbf x} &= \\mathbf x \\ast f_{\\mathbf x}(\\bullet)\\, \\, &\\text{Prediction} \\\\\n",
-    "\\mathbf x &= \\mathcal L \\cdot \\bar{\\mathbf x}\\, \\, &\\text{Correction}\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "\n",
-    "$\\bar{\\mathbf x}$ is the *prior* \n",
-    "\n",
-    "$\\mathcal L$ is the *likelihood* of a measurement given the prior $\\bar{\\mathbf x}$\n",
-    "\n",
-    "$f_{\\mathbf x}(\\bullet)$ is the *process model* or the gaussian term that helps predict the next state like velocity\n",
-    "to track position or acceleration.\n",
-    "\n",
-    "$\\ast$ denotes *convolution*.\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Kalman Gain\n",
-    "\n",
-    "\n",
-    "$$ x = (\\mathcal L \\bar x)$$\n",
-    "\n",
-    "Where x is posterior and $\\mathcal L$ and $\\bar x$ are gaussians.\n",
-    "\n",
-    "Therefore the mean of the posterior is given by:\n",
-    "\n",
-    "$$\n",
-    "\\mu=\\frac{\\bar\\sigma^2\\, \\mu_z + \\sigma_z^2 \\, \\bar\\mu} {\\bar\\sigma^2 + \\sigma_z^2}\n",
-    "$$\n",
-    "\n",
-    "\n",
-    "$$\\mu = \\left( \\frac{\\bar\\sigma^2}{\\bar\\sigma^2 + \\sigma_z^2}\\right) \\mu_z + \\left(\\frac{\\sigma_z^2}{\\bar\\sigma^2 + \\sigma_z^2}\\right)\\bar\\mu$$\n",
-    "\n",
-    "In this form it is easy to see that we are scaling the measurement and the prior by weights: \n",
-    "\n",
-    "$$\\mu = W_1 \\mu_z + W_2 \\bar\\mu$$\n",
-    "\n",
-    "\n",
-    "The weights sum to one because the denominator is a normalization term. We introduce a new term, $K=W_1$, giving us:\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    "\\mu &= K \\mu_z + (1-K) \\bar\\mu\\\\\n",
-    "&= \\bar\\mu + K(\\mu_z - \\bar\\mu)\n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "where\n",
-    "\n",
-    "$$K = \\frac {\\bar\\sigma^2}{\\bar\\sigma^2 + \\sigma_z^2}$$\n",
-    "\n",
-    "The variance in terms of the Kalman gain:\n",
-    "\n",
-    "$$\\begin{aligned}\n",
-    "\\sigma^2 &= \\frac{\\bar\\sigma^2 \\sigma_z^2 } {\\bar\\sigma^2 + \\sigma_z^2} \\\\\n",
-    "&= K\\sigma_z^2 \\\\\n",
-    "&= (1-K)\\bar\\sigma^2 \n",
-    "\\end{aligned}$$\n",
-    "\n",
-    "\n",
-    "$K$ is the *Kalman gain*. It's the crux of the Kalman filter. It is a scaling term that chooses a value partway between $\\mu_z$ and $\\bar\\mu$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Kalman Filter - Univariate and Multivariate\n",
-    "\n",
-    "\n",
-    "<u>**Prediction**</u>\n",
-    "\n",
-    "$\\begin{array}{|l|l|l|}\n",
-    "\\hline\n",
-    "\\text{Univariate} & \\text{Univariate} & \\text{Multivariate}\\\\\n",
-    "& \\text{(Kalman form)} & \\\\\n",
-    "\\hline\n",
-    "\\bar \\mu = \\mu + \\mu_{f_x} & \\bar x = x + dx & \\bar{\\mathbf x} = \\mathbf{Fx} + \\mathbf{Bu}\\\\\n",
-    "\\bar\\sigma^2 = \\sigma_x^2 + \\sigma_{f_x}^2 & \\bar P = P + Q & \\bar{\\mathbf P} = \\mathbf{FPF}^\\mathsf T + \\mathbf Q \\\\\n",
-    "\\hline\n",
-    "\\end{array}$\n",
-    "\n",
-    "$\\mathbf x,\\, \\mathbf P$ are the state mean and covariance. They correspond to $x$ and $\\sigma^2$.\n",
-    "\n",
-    "$\\mathbf F$ is the *state transition function*. When multiplied by $\\bf x$ it computes the prior. \n",
-    "\n",
-    "$\\mathbf Q$ is the process covariance. It corresponds to $\\sigma^2_{f_x}$.\n",
-    "\n",
-    "$\\mathbf B$ and $\\mathbf u$ are model control inputs to the system.\n",
-    "\n",
-    "<u>**Correction**</u>\n",
-    "\n",
-    "$\\begin{array}{|l|l|l|}\n",
-    "\\hline\n",
-    "\\text{Univariate} & \\text{Univariate} & \\text{Multivariate}\\\\\n",
-    "& \\text{(Kalman form)} & \\\\\n",
-    "\\hline\n",
-    "& y = z - \\bar x & \\mathbf y = \\mathbf z - \\mathbf{H\\bar x} \\\\\n",
-    "& K = \\frac{\\bar P}{\\bar P+R}&\n",
-    "\\mathbf K = \\mathbf{\\bar{P}H}^\\mathsf T (\\mathbf{H\\bar{P}H}^\\mathsf T + \\mathbf R)^{-1} \\\\\n",
-    "\\mu=\\frac{\\bar\\sigma^2\\, \\mu_z + \\sigma_z^2 \\, \\bar\\mu} {\\bar\\sigma^2 + \\sigma_z^2} & x = \\bar x + Ky & \\mathbf x = \\bar{\\mathbf x} + \\mathbf{Ky} \\\\\n",
-    "\\sigma^2 = \\frac{\\sigma_1^2\\sigma_2^2}{\\sigma_1^2+\\sigma_2^2} & P = (1-K)\\bar P &\n",
-    "\\mathbf P = (\\mathbf I - \\mathbf{KH})\\mathbf{\\bar{P}} \\\\\n",
-    "\\hline\n",
-    "\\end{array}$\n",
-    "\n",
-    "$\\mathbf H$ is the measurement function.\n",
-    "\n",
-    "$\\mathbf z,\\, \\mathbf R$ are the measurement mean and noise covariance. They correspond to $z$ and $\\sigma_z^2$ in the univariate filter. \n",
-    "$\\mathbf y$ and $\\mathbf K$ are the residual and Kalman gain. \n",
-    "\n",
-    "The details will be different than the univariate filter because these are vectors and matrices, but the concepts are exactly the same: \n",
-    "\n",
-    "-  Use a Gaussian to represent our estimate of the state and error\n",
-    "-  Use a Gaussian to represent the measurement and its error\n",
-    "-  Use a Gaussian to represent the process model\n",
-    "-  Use the process model to predict the next state (the prior)\n",
-    "-  Form an estimate part way between the measurement and the prior"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### References:\n",
-    "\n",
-    "1. Roger Labbe's [repo](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python) on Kalman Filters. (Majority of text in the notes are from this)\n",
-    "\n",
-    "\n",
-    "\n",
-    "2. Probabilistic Robotics by Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press.\n"
-   ]
-  }
- ],
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diff --git a/Localization/extended_kalman_filter/extended_kalman_filter_localization.ipynb b/Localization/extended_kalman_filter/extended_kalman_filter_localization.ipynb
deleted file mode 100644
index 073fd06a16..0000000000
--- a/Localization/extended_kalman_filter/extended_kalman_filter_localization.ipynb
+++ /dev/null
@@ -1,264 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extended Kalman Filter Localization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename=\"ekf.png\",width=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![EKF](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif)\n",
-    "\n",
-    "This is a sensor fusion localization with Extended Kalman Filter(EKF).\n",
-    "\n",
-    "The blue line is true trajectory, the black line is dead reckoning\n",
-    "trajectory,\n",
-    "\n",
-    "the green point is positioning observation (ex. GPS), and the red line\n",
-    "is estimated trajectory with EKF.\n",
-    "\n",
-    "The red ellipse is estimated covariance ellipse with EKF.\n",
-    "\n",
-    "Code: [PythonRobotics/extended\\_kalman\\_filter\\.py at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/blob/master/Localization/extended_kalman_filter/extended_kalman_filter.py)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Filter design\n",
-    "\n",
-    "In this simulation, the robot has a state vector includes 4 states at time $t$.\n",
-    "\n",
-    "$$\\textbf{x}_t=[x_t, y_t, \\phi_t, v_t]$$\n",
-    "\n",
-    "x, y are a 2D x-y position, $\\phi$ is orientation, and v is velocity.\n",
-    "\n",
-    "In the code, \"xEst\" means the state vector. [code](https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_filter.py#L168)\n",
-    "\n",
-    "And, $P_t$ is covariace matrix of the state,\n",
-    "\n",
-    "$Q$ is covariance matrix of process noise, \n",
-    "\n",
-    "$R$ is covariance matrix of observation noise at time $t$ \n",
-    "\n",
-    "　\n",
-    "\n",
-    "The robot has a speed sensor and a gyro sensor.\n",
-    "\n",
-    "So, the input vecor can be used as each time step\n",
-    "\n",
-    "$$\\textbf{u}_t=[v_t, \\omega_t]$$\n",
-    "\n",
-    "Also, the robot has a GNSS sensor, it means that the robot can observe x-y position at each time.\n",
-    "\n",
-    "$$\\textbf{z}_t=[x_t,y_t]$$\n",
-    "\n",
-    "The input and observation vector includes sensor noise.\n",
-    "\n",
-    "In the code, \"observation\" function generates the input and observation vector with noise [code](https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_filter.py#L34-L50)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Motion Model\n",
-    "\n",
-    "The robot model is \n",
-    "\n",
-    "$$ \\dot{x} = vcos(\\phi)$$\n",
-    "\n",
-    "$$ \\dot{y} = vsin((\\phi)$$\n",
-    "\n",
-    "$$ \\dot{\\phi} = \\omega$$\n",
-    "\n",
-    "\n",
-    "So, the motion model is\n",
-    "\n",
-    "$$\\textbf{x}_{t+1} = F\\textbf{x}_t+B\\textbf{u}_t$$\n",
-    "\n",
-    "where\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "F=\n",
-    "\\begin{bmatrix}\n",
-    "1 & 0 & 0 & 0\\\\\n",
-    "0 & 1 & 0 & 0\\\\\n",
-    "0 & 0 & 1 & 0 \\\\\n",
-    "0 & 0 & 0 & 0 \\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "B=\n",
-    "\\begin{bmatrix}\n",
-    "cos(\\phi)dt & 0\\\\\n",
-    "sin(\\phi)dt & 0\\\\\n",
-    "0 & dt\\\\\n",
-    "1 & 0\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$\n",
-    "\n",
-    "$dt$ is a time interval.\n",
-    "\n",
-    "This is implemented at [code](https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_filter.py#L53-L67)\n",
-    "\n",
-    "Its Jacobian matrix is\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "J_F=\n",
-    "\\begin{bmatrix}\n",
-    "\\frac{dx}{dx}& \\frac{dx}{dy} & \\frac{dx}{d\\phi} &  \\frac{dx}{dv}\\\\\n",
-    "\\frac{dy}{dx}& \\frac{dy}{dy} & \\frac{dy}{d\\phi} &  \\frac{dy}{dv}\\\\\n",
-    "\\frac{d\\phi}{dx}& \\frac{d\\phi}{dy} & \\frac{d\\phi}{d\\phi} &  \\frac{d\\phi}{dv}\\\\\n",
-    "\\frac{dv}{dx}& \\frac{dv}{dy} & \\frac{dv}{d\\phi} &  \\frac{dv}{dv}\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "　=\n",
-    "\\begin{bmatrix}\n",
-    "1& 0 & -v sin(\\phi)dt &  cos(\\phi)dt\\\\\n",
-    "0 & 1 & v cos(\\phi)dt & sin(\\phi) dt\\\\\n",
-    "0 & 0 & 1 & 0\\\\\n",
-    "0 & 0 & 0 & 1\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Observation Model\n",
-    "\n",
-    "The robot can get x-y position infomation from GPS.\n",
-    "\n",
-    "So GPS Observation model is\n",
-    "\n",
-    "$$\\textbf{z}_{t} = H\\textbf{x}_t$$\n",
-    "\n",
-    "where\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "B=\n",
-    "\\begin{bmatrix}\n",
-    "1 & 0 & 0& 0\\\\\n",
-    "0 & 1 & 0& 0\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$\n",
-    "\n",
-    "Its Jacobian matrix is\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "J_H=\n",
-    "\\begin{bmatrix}\n",
-    "\\frac{dx}{dx}& \\frac{dx}{dy} & \\frac{dx}{d\\phi} &  \\frac{dx}{dv}\\\\\n",
-    "\\frac{dy}{dx}& \\frac{dy}{dy} & \\frac{dy}{d\\phi} &  \\frac{dy}{dv}\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "　=\n",
-    "\\begin{bmatrix}\n",
-    "1& 0 & 0 & 0\\\\\n",
-    "0 & 1 & 0 & 0\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Extented Kalman Filter\n",
-    "\n",
-    "Localization process using Extendted Kalman Filter:EKF is\n",
-    "\n",
-    "=== Predict ===\n",
-    "\n",
-    "$x_{Pred} = Fx_t+Bu_t$\n",
-    "\n",
-    "$P_{Pred} = J_FP_t J_F^T + Q$\n",
-    "\n",
-    "=== Update ===\n",
-    "\n",
-    "$z_{Pred} = Hx_{Pred}$ \n",
-    "\n",
-    "$y = z - z_{Pred}$\n",
-    "\n",
-    "$S = J_H P_{Pred}.J_H^T + R$\n",
-    "\n",
-    "$K = P_{Pred}.J_H^T S^{-1}$\n",
-    "\n",
-    "$x_{t+1} = x_{Pred} + Ky$\n",
-    "\n",
-    "$P_{t+1} = ( I - K J_H) P_{Pred}$\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Ref:\n",
-    "\n",
-    "- [PROBABILISTIC\\-ROBOTICS\\.ORG](http://www.probabilistic-robotics.org/)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Localization/particle_filter/particle_filter.ipynb b/Localization/particle_filter/particle_filter.ipynb
deleted file mode 100644
index 65f1e026df..0000000000
--- a/Localization/particle_filter/particle_filter.ipynb
+++ /dev/null
@@ -1,72 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true,
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "# Particle Filter Localization\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## How to calculate covariance matrix from particles\n",
-    "\n",
-    "The covariance matrix $\\Xi$ from particle information is calculated by the following equation: \n",
-    "\n",
-    "$\\Xi_{j,k}=\\frac{1}{1-\\sum^N_{i=1}(w^i)^2}\\sum^N_{i=1}w^i(x^i_j-\\mu_j)(x^i_k-\\mu_k)$\n",
-    "\n",
-    "- $\\Xi_{j,k}$ is covariance matrix element at row $i$ and column $k$.\n",
-    "\n",
-    "- $w^i$ is the weight of the $i$ th particle. \n",
-    "\n",
-    "- $x^i_j$ is the $j$ th state of the $i$ th particle. \n",
-    "\n",
-    "- $\\mu_j$ is the $j$ th mean state of particles.\n",
-    "\n",
-    "Ref:\n",
-    "\n",
-    "- [Improving the particle filter in high dimensions using conjugate artificial process noise](https://arxiv.org/pdf/1801.07000.pdf)\n"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  },
-  "pycharm": {
-   "stem_cell": {
-    "cell_type": "raw",
-    "source": [],
-    "metadata": {
-     "collapsed": false
-    }
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
diff --git a/docs/modules/appendix/Kalmanfilter_basics.rst b/docs/modules/appendix/Kalmanfilter_basics.rst
index 7325dafdea..6548377e07 100644
--- a/docs/modules/appendix/Kalmanfilter_basics.rst
+++ b/docs/modules/appendix/Kalmanfilter_basics.rst
@@ -222,9 +222,8 @@ described with two parameters, the mean (:math:`\mu`) and the variance
     
    f(x, \mu, \sigma) = \frac{1}{\sigma\sqrt{2\pi}} \exp\big [{-\frac{(x-\mu)^2}{2\sigma^2} }\big ]
 
- Range is
 
-.. math:: [-\inf,\inf] 
+Range is :math:`[-\inf,\inf]`
 
 This is just a function of mean(\ :math:`\mu`) and standard deviation
 (:math:`\sigma`) and what gives the normal distribution the
@@ -279,7 +278,8 @@ New mean is
 
 .. math:: \mu_\mathtt{new} = \frac{\sigma_z^2\bar\mu + \bar\sigma^2z}{\bar\sigma^2+\sigma_z^2}
 
- New variance is
+
+New variance is
 
 .. math::
 
@@ -336,7 +336,7 @@ of the two.
 .. math::
 
    \begin{gathered}\mu_x = \mu_p + \mu_z \\
-   \sigma_x^2 = \sigma_z^2+\sigma_p^2\, \square\end{gathered}
+   \sigma_x^2 = \sigma_z^2+\sigma_p^2\, \end{gathered}
 
 .. code-block:: ipython3
 
diff --git a/docs/modules/localization/extended_kalman_filter_localization.rst b/docs/modules/localization/extended_kalman_filter_localization.rst
index 5021563b5b..507ea972a4 100644
--- a/docs/modules/localization/extended_kalman_filter_localization.rst
+++ b/docs/modules/localization/extended_kalman_filter_localization.rst
@@ -2,14 +2,6 @@
 Extended Kalman Filter Localization
 -----------------------------------
 
-.. code-block:: ipython3
-
-    from IPython.display import Image
-    Image(filename="ekf.png",width=600)
-
-
-
-
 .. image:: extended_kalman_filter_localization_files/extended_kalman_filter_localization_1_0.png
    :width: 600px
 
@@ -18,8 +10,6 @@ Extended Kalman Filter Localization
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif
    :alt: EKF
 
-   EKF
-
 This is a sensor fusion localization with Extended Kalman Filter(EKF).
 
 The blue line is true trajectory, the black line is dead reckoning
@@ -127,7 +117,7 @@ The observation function states that
 
 Its Jacobian matrix is
 
-:math:`\begin{equation*} J_g = \begin{bmatrix} \frac{\partial x'}{\partial x} & \frac{\partial x'}{\partial y} & \frac{\partial x'}{\partial \phi} & \frac{\partial x'}{\partial v}\\ \frac{\partial y'}{\partial x}& \frac{\partial y'}{\partial y} & \frac{\partial y'}{\partial \phi} & \frac{\partial y'}{ \partialv}\\ \end{bmatrix} \end{equation*}`
+:math:`\begin{equation*} J_g = \begin{bmatrix} \frac{\partial x'}{\partial x} & \frac{\partial x'}{\partial y} & \frac{\partial x'}{\partial \phi} & \frac{\partial x'}{\partial v}\\ \frac{\partial y'}{\partial x}& \frac{\partial y'}{\partial y} & \frac{\partial y'}{\partial \phi} & \frac{\partial y'}{ \partial v}\\ \end{bmatrix} \end{equation*}`
 
 :math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ \end{bmatrix} \end{equation*}`
 
diff --git a/docs/modules/localization/localization.rst b/docs/modules/localization/localization.rst
index ddbf056c82..c665eba694 100644
--- a/docs/modules/localization/localization.rst
+++ b/docs/modules/localization/localization.rst
@@ -15,7 +15,8 @@ This is a sensor fusion localization with Unscented Kalman Filter(UKF).
 
 The lines and points are same meaning of the EKF simulation.
 
-Ref:
+References:
+~~~~~~~~~~~
 
 -  `Discriminatively Trained Unscented Kalman Filter for Mobile Robot
    Localization`_
@@ -37,9 +38,26 @@ It is assumed that the robot can measure a distance from landmarks
 
 This measurements are used for PF localization.
 
-Ref:
+How to calculate covariance matrix from particles
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The covariance matrix :math:`\Xi` from particle information is calculated by the following equation:
+
+.. math:: \Xi_{j,k}=\frac{1}{1-\sum^N_{i=1}(w^i)^2}\sum^N_{i=1}w^i(x^i_j-\mu_j)(x^i_k-\mu_k)
+
+- :math:`\Xi_{j,k}` is covariance matrix element at row :math:`i` and column :math:`k`.
+
+- :math:`w^i` is the weight of the :math:`i` th particle.
+
+- :math:`x^i_j` is the :math:`j` th state of the :math:`i` th particle.
+
+- :math:`\mu_j` is the :math:`j` th mean state of particles.
+
+References:
+~~~~~~~~~~~
 
 -  `PROBABILISTIC ROBOTICS`_
+-  `Improving the particle filter in high dimensions using conjugate artificial process noise`_
 
 Histogram filter localization
 -----------------------------
@@ -59,12 +77,14 @@ localization.
 
 Initial position is not needed.
 
-Ref:
+References:
+~~~~~~~~~~~
 
 -  `PROBABILISTIC ROBOTICS`_
 
 .. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
 .. _Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization: https://www.researchgate.net/publication/267963417_Discriminatively_Trained_Unscented_Kalman_Filter_for_Mobile_Robot_Localization
+.. _Improving the particle filter in high dimensions using conjugate artificial process noise: https://arxiv.org/pdf/1801.07000.pdf
 
 .. |2| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/unscented_kalman_filter/animation.gif
 .. |3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/particle_filter/animation.gif

From 1f4e5ed8c32249e07ba3493a5b195da33e5de77b Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 19 Nov 2021 15:54:52 +0900
Subject: [PATCH 0541/1080] clean up mapping docs (#571)

* clean up mapping docs

* clean up mapping docs

* clean up mapping docs
---
 Mapping/lidar_to_grid_map/animation.gif       | Bin 285406 -> 0 bytes
 README.md                                     |   3 +-
 .../mapping/lidar_to_grid_map_tutorial.rst    | 195 ++++++++++++++++++
 .../grid_map_example.png                      | Bin
 .../lidar_to_grid_map_tutorial_12_0.png       | Bin 0 -> 5053 bytes
 .../lidar_to_grid_map_tutorial_14_1.png       | Bin 0 -> 14082 bytes
 .../lidar_to_grid_map_tutorial_5_0.png        | Bin 0 -> 121806 bytes
 .../lidar_to_grid_map_tutorial_7_0.png        | Bin 0 -> 5025 bytes
 .../lidar_to_grid_map_tutorial_8_0.png        | Bin 0 -> 5035 bytes
 docs/modules/mapping/mapping.rst              |   8 +
 10 files changed, 204 insertions(+), 2 deletions(-)
 delete mode 100644 Mapping/lidar_to_grid_map/animation.gif
 create mode 100644 docs/modules/mapping/lidar_to_grid_map_tutorial.rst
 rename {Mapping/lidar_to_grid_map => docs/modules/mapping/lidar_to_grid_map_tutorial_files}/grid_map_example.png (100%)
 create mode 100644 docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_12_0.png
 create mode 100644 docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_14_1.png
 create mode 100644 docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_5_0.png
 create mode 100644 docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_7_0.png
 create mode 100644 docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_8_0.png

diff --git a/Mapping/lidar_to_grid_map/animation.gif b/Mapping/lidar_to_grid_map/animation.gif
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diff --git a/README.md b/README.md
index 5a06f09ca8..024c600cb1 100644
--- a/README.md
+++ b/README.md
@@ -3,9 +3,8 @@
 # PythonRobotics
 ![GitHub_Action_Linux_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/Linux_CI/badge.svg)
 ![GitHub_Action_MacOS_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/MacOS_CI/badge.svg)
-[![Build Status](https://travis-ci.org/AtsushiSakai/PythonRobotics.svg?branch=master)](https://travis-ci.org/AtsushiSakai/PythonRobotics)
-[![Documentation Status](https://readthedocs.org/projects/pythonrobotics/badge/?version=latest)](https://pythonrobotics.readthedocs.io/en/latest/?badge=latest)
 [![Build status](https://ci.appveyor.com/api/projects/status/sb279kxuv1be391g?svg=true)](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics)
+[![Documentation Status](https://readthedocs.org/projects/pythonrobotics/badge/?version=latest)](https://pythonrobotics.readthedocs.io/en/latest/?badge=latest)
 [![codecov](https://codecov.io/gh/AtsushiSakai/PythonRobotics/branch/master/graph/badge.svg)](https://codecov.io/gh/AtsushiSakai/PythonRobotics)
 [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/AtsushiSakai/PythonRobotics.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/AtsushiSakai/PythonRobotics/context:python)
 [![tokei](https://tokei.rs/b1/github/AtsushiSakai/PythonRobotics)](https://github.com/AtsushiSakai/PythonRobotics)
diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial.rst b/docs/modules/mapping/lidar_to_grid_map_tutorial.rst
new file mode 100644
index 0000000000..64dba26d4f
--- /dev/null
+++ b/docs/modules/mapping/lidar_to_grid_map_tutorial.rst
@@ -0,0 +1,195 @@
+
+This simple tutorial shows how to read LIDAR (range) measurements from a
+file and convert it to occupancy grid.
+
+Occupancy grid maps (*Hans Moravec, A.E. Elfes: High resolution maps
+from wide angle sonar, Proc. IEEE Int. Conf. Robotics Autom. (1985)*)
+are a popular, probabilistic approach to represent the environment. The
+grid is basically discrete representation of the environment, which
+shows if a grid cell is occupied or not. Here the map is represented as
+a ``numpy array``, and numbers close to 1 means the cell is occupied
+(*marked with red on the next image*), numbers close to 0 means they are
+free (*marked with green*). The grid has the ability to represent
+unknown (unobserved) areas, which are close to 0.5.
+
+.. figure:: lidar_to_grid_map_tutorial_files/grid_map_example.png
+   :alt: Example
+
+In order to construct the grid map from the measurement we need to
+discretise the values. But, first let’s need to ``import`` some
+necessary packages.
+
+.. code:: ipython3
+
+    import math
+    import numpy as np
+    import matplotlib.pyplot as plt
+    from math import cos, sin, radians, pi
+
+The measurement file contains the distances and the corresponding angles
+in a ``csv`` (comma separated values) format. Let’s write the
+``file_read`` method:
+
+.. code:: ipython3
+
+    def file_read(f):
+        """
+        Reading LIDAR laser beams (angles and corresponding distance data)
+        """
+        measures = [line.split(",") for line in open(f)]
+        angles = []
+        distances = []
+        for measure in measures:
+            angles.append(float(measure[0]))
+            distances.append(float(measure[1]))
+        angles = np.array(angles)
+        distances = np.array(distances)
+        return angles, distances
+
+From the distances and the angles it is easy to determine the ``x`` and
+``y`` coordinates with ``sin`` and ``cos``. In order to display it
+``matplotlib.pyplot`` (``plt``) is used.
+
+.. code:: ipython3
+
+    ang, dist = file_read("lidar01.csv")
+    ox = np.sin(ang) * dist
+    oy = np.cos(ang) * dist
+    plt.figure(figsize=(6,10))
+    plt.plot([oy, np.zeros(np.size(oy))], [ox, np.zeros(np.size(oy))], "ro-") # lines from 0,0 to the 
+    plt.axis("equal")
+    bottom, top = plt.ylim()  # return the current ylim
+    plt.ylim((top, bottom)) # rescale y axis, to match the grid orientation
+    plt.grid(True)
+    plt.show()
+
+
+
+.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_5_0.png
+
+
+The ``lidar_to_grid_map.py`` contains handy functions which can used to
+convert a 2D range measurement to a grid map. For example the
+``bresenham`` gives the a straight line between two points in a grid
+map. Let’s see how this works.
+
+.. code:: ipython3
+
+    import lidar_to_grid_map as lg
+    map1 = np.ones((50, 50)) * 0.5
+    line = lg.bresenham((2, 2), (40, 30))
+    for l in line:
+        map1[l[0]][l[1]] = 1
+    plt.imshow(map1)
+    plt.colorbar()
+    plt.show()
+
+
+
+.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_7_0.png
+
+
+.. code:: ipython3
+
+    line = lg.bresenham((2, 30), (40, 30))
+    for l in line:
+        map1[l[0]][l[1]] = 1
+    line = lg.bresenham((2, 30), (2, 2))
+    for l in line:
+        map1[l[0]][l[1]] = 1
+    plt.imshow(map1)
+    plt.colorbar()
+    plt.show()
+
+
+
+.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_8_0.png
+
+
+To fill empty areas, a queue-based algorithm can be used that can be
+used on an initialized occupancy map. The center point is given: the
+algorithm checks for neighbour elements in each iteration, and stops
+expansion on obstacles and free boundaries.
+
+.. code:: ipython3
+
+    from collections import deque
+    def flood_fill(cpoint, pmap):
+        """
+        cpoint: starting point (x,y) of fill
+        pmap: occupancy map generated from Bresenham ray-tracing
+        """
+        # Fill empty areas with queue method
+        sx, sy = pmap.shape
+        fringe = deque()
+        fringe.appendleft(cpoint)
+        while fringe:
+            n = fringe.pop()
+            nx, ny = n
+            # West
+            if nx > 0:
+                if pmap[nx - 1, ny] == 0.5:
+                    pmap[nx - 1, ny] = 0.0
+                    fringe.appendleft((nx - 1, ny))
+            # East
+            if nx < sx - 1:
+                if pmap[nx + 1, ny] == 0.5:
+                    pmap[nx + 1, ny] = 0.0
+                    fringe.appendleft((nx + 1, ny))
+            # North
+            if ny > 0:
+                if pmap[nx, ny - 1] == 0.5:
+                    pmap[nx, ny - 1] = 0.0
+                    fringe.appendleft((nx, ny - 1))
+            # South
+            if ny < sy - 1:
+                if pmap[nx, ny + 1] == 0.5:
+                    pmap[nx, ny + 1] = 0.0
+                    fringe.appendleft((nx, ny + 1))
+
+This algotihm will fill the area bounded by the yellow lines starting
+from a center point (e.g. (10, 20)) with zeros:
+
+.. code:: ipython3
+
+    flood_fill((10, 20), map1)
+    map_float = np.array(map1)/10.0
+    plt.imshow(map1)
+    plt.colorbar()
+    plt.show()
+
+
+
+.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_12_0.png
+
+
+Let’s use this flood fill on real data:
+
+.. code:: ipython3
+
+    xyreso = 0.02  # x-y grid resolution
+    yawreso = math.radians(3.1)  # yaw angle resolution [rad]
+    ang, dist = file_read("lidar01.csv")
+    ox = np.sin(ang) * dist
+    oy = np.cos(ang) * dist
+    pmap, minx, maxx, miny, maxy, xyreso = lg.generate_ray_casting_grid_map(ox, oy, xyreso, False)
+    xyres = np.array(pmap).shape
+    plt.figure(figsize=(20,8))
+    plt.subplot(122)
+    plt.imshow(pmap, cmap = "PiYG_r") 
+    plt.clim(-0.4, 1.4)
+    plt.gca().set_xticks(np.arange(-.5, xyres[1], 1), minor = True)
+    plt.gca().set_yticks(np.arange(-.5, xyres[0], 1), minor = True)
+    plt.grid(True, which="minor", color="w", linewidth = .6, alpha = 0.5)
+    plt.colorbar()
+    plt.show()
+
+
+.. parsed-literal::
+
+    The grid map is  150 x 100 .
+
+
+
+.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_14_1.png
+
diff --git a/Mapping/lidar_to_grid_map/grid_map_example.png b/docs/modules/mapping/lidar_to_grid_map_tutorial_files/grid_map_example.png
similarity index 100%
rename from Mapping/lidar_to_grid_map/grid_map_example.png
rename to docs/modules/mapping/lidar_to_grid_map_tutorial_files/grid_map_example.png
diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_12_0.png b/docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_12_0.png
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diff --git a/docs/modules/mapping/mapping.rst b/docs/modules/mapping/mapping.rst
index 7f30abcead..ccdd3198b1 100644
--- a/docs/modules/mapping/mapping.rst
+++ b/docs/modules/mapping/mapping.rst
@@ -17,6 +17,13 @@ This is a 2D ray casting grid mapping example.
 
 |3|
 
+Lidar to grid map
+--------------------
+
+|6|
+
+.. include:: lidar_to_grid_map_tutorial.rst
+
 k-means object clustering
 -------------------------
 
@@ -41,3 +48,4 @@ The red circle is the estimated object shape using circle fitting.
 .. |3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/raycasting_grid_map/animation.gif
 .. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/kmeans_clustering/animation.gif
 .. |5| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/circle_fitting/animation.gif
+.. |6| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/lidar_to_grid_map/animation.gif

From d34f3ca7fa84f42673f3556565b1776d3009e6a6 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 21 Nov 2021 22:39:18 +0900
Subject: [PATCH 0542/1080] clean up SLAM docs (#572)

* clean up SLAM docs

* clean up SLAM docs

* clean up SLAM docs

* clean up SLAM docs

* clean up SLAM docs

* clean up SLAM docs

* clean up SLAM docs

* clean up SLAM docs

* clean up SLAM docs

* clean up SLAM docs

* clean up SLAM docs

* clean up SLAM docs

* clean up SLAM docs

* clean up SLAM docs
---
 .../lidar_to_grid_map_tutorial.ipynb          |  318 ---
 SLAM/EKFSLAM/ekf_slam.ipynb                   | 1784 -----------------
 SLAM/FastSLAM1/FastSLAM1.ipynb                |  676 -------
 SLAM/FastSLAM1/animation.png                  |  Bin 44443 -> 0 bytes
 SLAM/GraphBasedSLAM/LaTeX/.gitignore          |    3 -
 SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib       |   30 -
 .../LaTeX/graphSLAM_formulation.tex           |  175 --
 .../graphSLAM_SE2_example.ipynb               |  332 ---
 SLAM/GraphBasedSLAM/graphSLAM_formulation.pdf |  Bin 276368 -> 0 bytes
 docs/_static/custom.css                       |   23 +
 docs/conf.py                                  |    5 +-
 ...g_started.rst => getting_started_main.rst} |    0
 docs/{index.rst => index_main.rst}            |    0
 ...igation.rst => aerial_navigation_main.rst} |    0
 .../{appendix.rst => appendix_main.rst}       |    0
 ...navigation.rst => arm_navigation_main.rst} |    0
 ...introduction.rst => introduction_main.rst} |    0
 ...localization.rst => localization_main.rst} |    0
 .../mapping/{mapping.rst => mapping_main.rst} |    0
 ...th_planning.rst => path_planning_main.rst} |    0
 ...th_tracking.rst => path_tracking_main.rst} |    0
 docs/modules/slam/FastSLAM1.rst               |   17 +-
 docs/modules/slam/ekf_slam.rst                |  593 ++++++
 .../slam/ekf_slam_files/ekf_slam_1_0.png      |  Bin
 docs/modules/slam/graphSLAM_SE2_example.rst   |  209 ++
 .../Graph_SLAM_optimization.gif               |  Bin
 .../graphSLAM_SE2_example_13_0.png            |  Bin 0 -> 32748 bytes
 .../graphSLAM_SE2_example_15_0.png            |  Bin 0 -> 30123 bytes
 .../graphSLAM_SE2_example_16_0.png            |  Bin 0 -> 29350 bytes
 .../graphSLAM_SE2_example_4_0.png             |  Bin 0 -> 52368 bytes
 .../graphSLAM_SE2_example_8_0.png             |  Bin 0 -> 31076 bytes
 .../graphSLAM_SE2_example_9_0.png             |  Bin 0 -> 49650 bytes
 docs/modules/slam/graphSLAM_doc.rst           |  557 +++++
 docs/modules/slam/graphSLAM_formulation.rst   |  216 ++
 docs/modules/slam/{slam.rst => slam_main.rst} |   34 +-
 35 files changed, 1617 insertions(+), 3355 deletions(-)
 delete mode 100644 Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb
 delete mode 100644 SLAM/EKFSLAM/ekf_slam.ipynb
 delete mode 100644 SLAM/FastSLAM1/FastSLAM1.ipynb
 delete mode 100644 SLAM/FastSLAM1/animation.png
 delete mode 100644 SLAM/GraphBasedSLAM/LaTeX/.gitignore
 delete mode 100644 SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib
 delete mode 100644 SLAM/GraphBasedSLAM/LaTeX/graphSLAM_formulation.tex
 delete mode 100644 SLAM/GraphBasedSLAM/graphSLAM_SE2_example.ipynb
 delete mode 100644 SLAM/GraphBasedSLAM/graphSLAM_formulation.pdf
 create mode 100644 docs/_static/custom.css
 rename docs/{getting_started.rst => getting_started_main.rst} (100%)
 rename docs/{index.rst => index_main.rst} (100%)
 rename docs/modules/aerial_navigation/{aerial_navigation.rst => aerial_navigation_main.rst} (100%)
 rename docs/modules/appendix/{appendix.rst => appendix_main.rst} (100%)
 rename docs/modules/arm_navigation/{arm_navigation.rst => arm_navigation_main.rst} (100%)
 rename docs/modules/{introduction.rst => introduction_main.rst} (100%)
 rename docs/modules/localization/{localization.rst => localization_main.rst} (100%)
 rename docs/modules/mapping/{mapping.rst => mapping_main.rst} (100%)
 rename docs/modules/path_planning/{path_planning.rst => path_planning_main.rst} (100%)
 rename docs/modules/path_tracking/{path_tracking.rst => path_tracking_main.rst} (100%)
 create mode 100644 docs/modules/slam/ekf_slam.rst
 rename SLAM/EKFSLAM/animation.png => docs/modules/slam/ekf_slam_files/ekf_slam_1_0.png (100%)
 create mode 100644 docs/modules/slam/graphSLAM_SE2_example.rst
 rename {SLAM/GraphBasedSLAM/images => docs/modules/slam/graphSLAM_SE2_example_files}/Graph_SLAM_optimization.gif (100%)
 create mode 100644 docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png
 create mode 100644 docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png
 create mode 100644 docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png
 create mode 100644 docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png
 create mode 100644 docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png
 create mode 100644 docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png
 create mode 100644 docs/modules/slam/graphSLAM_doc.rst
 create mode 100644 docs/modules/slam/graphSLAM_formulation.rst
 rename docs/modules/slam/{slam.rst => slam_main.rst} (75%)

diff --git a/Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb b/Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb
deleted file mode 100644
index 9e8a00f009..0000000000
--- a/Mapping/lidar_to_grid_map/lidar_to_grid_map_tutorial.ipynb
+++ /dev/null
@@ -1,318 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## LIDAR to 2D grid map example\n",
-    "\n",
-    "This simple tutorial shows how to read LIDAR (range) measurements from a file and convert it to occupancy grid.\n",
-    "\n",
-    "Occupancy grid maps (_Hans Moravec, A.E. Elfes: High resolution maps from wide angle sonar, Proc. IEEE Int. Conf. Robotics Autom. (1985)_) are a popular, probabilistic approach to represent the environment. The grid is basically discrete representation of the environment, which shows if a grid cell is occupied or not. Here the map is represented as a `numpy array`, and numbers close to 1 means the cell is occupied (_marked with red on the next image_), numbers close to 0 means they are free (_marked with green_). The grid has the ability to represent unknown (unobserved) areas, which are close to 0.5.\n",
-    "\n",
-    "![Example](grid_map_example.png)\n",
-    "\n",
-    "\n",
-    "In order to construct the grid map from the measurement we need to discretise the values. But, first let's need to `import` some necessary packages."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import math\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from math import cos, sin, radians, pi"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The measurement file contains the distances and the corresponding angles in a `csv` (comma separated values) format. Let's write the `file_read` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def file_read(f):\n",
-    "    \"\"\"\n",
-    "    Reading LIDAR laser beams (angles and corresponding distance data)\n",
-    "    \"\"\"\n",
-    "    measures = [line.split(\",\") for line in open(f)]\n",
-    "    angles = []\n",
-    "    distances = []\n",
-    "    for measure in measures:\n",
-    "        angles.append(float(measure[0]))\n",
-    "        distances.append(float(measure[1]))\n",
-    "    angles = np.array(angles)\n",
-    "    distances = np.array(distances)\n",
-    "    return angles, distances"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From the distances and the angles it is easy to determine the `x` and `y` coordinates with `sin` and `cos`. \n",
-    "In order to display it `matplotlib.pyplot` (`plt`) is used."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ang, dist = file_read(\"lidar01.csv\")\n",
-    "ox = np.sin(ang) * dist\n",
-    "oy = np.cos(ang) * dist\n",
-    "plt.figure(figsize=(6,10))\n",
-    "plt.plot([oy, np.zeros(np.size(oy))], [ox, np.zeros(np.size(oy))], \"ro-\") # lines from 0,0 to the \n",
-    "plt.axis(\"equal\")\n",
-    "bottom, top = plt.ylim()  # return the current ylim\n",
-    "plt.ylim((top, bottom)) # rescale y axis, to match the grid orientation\n",
-    "plt.grid(True)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `lidar_to_grid_map.py` contains handy functions which can used to convert a 2D range measurement to a grid map. For example the `bresenham`  gives the a straight line between two points in a grid map. Let's see how this works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import lidar_to_grid_map as lg\n",
-    "map1 = np.ones((50, 50)) * 0.5\n",
-    "line = lg.bresenham((2, 2), (40, 30))\n",
-    "for l in line:\n",
-    "    map1[l[0]][l[1]] = 1\n",
-    "plt.imshow(map1)\n",
-    "plt.colorbar()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "line = lg.bresenham((2, 30), (40, 30))\n",
-    "for l in line:\n",
-    "    map1[l[0]][l[1]] = 1\n",
-    "line = lg.bresenham((2, 30), (2, 2))\n",
-    "for l in line:\n",
-    "    map1[l[0]][l[1]] = 1\n",
-    "plt.imshow(map1)\n",
-    "plt.colorbar()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To fill empty areas, a queue-based algorithm can be used that can be used on an initialized occupancy map. The center point is given: the algorithm checks for neighbour elements in each iteration, and stops expansion on obstacles and free boundaries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from collections import deque\n",
-    "def flood_fill(cpoint, pmap):\n",
-    "    \"\"\"\n",
-    "    cpoint: starting point (x,y) of fill\n",
-    "    pmap: occupancy map generated from Bresenham ray-tracing\n",
-    "    \"\"\"\n",
-    "    # Fill empty areas with queue method\n",
-    "    sx, sy = pmap.shape\n",
-    "    fringe = deque()\n",
-    "    fringe.appendleft(cpoint)\n",
-    "    while fringe:\n",
-    "        n = fringe.pop()\n",
-    "        nx, ny = n\n",
-    "        # West\n",
-    "        if nx > 0:\n",
-    "            if pmap[nx - 1, ny] == 0.5:\n",
-    "                pmap[nx - 1, ny] = 0.0\n",
-    "                fringe.appendleft((nx - 1, ny))\n",
-    "        # East\n",
-    "        if nx < sx - 1:\n",
-    "            if pmap[nx + 1, ny] == 0.5:\n",
-    "                pmap[nx + 1, ny] = 0.0\n",
-    "                fringe.appendleft((nx + 1, ny))\n",
-    "        # North\n",
-    "        if ny > 0:\n",
-    "            if pmap[nx, ny - 1] == 0.5:\n",
-    "                pmap[nx, ny - 1] = 0.0\n",
-    "                fringe.appendleft((nx, ny - 1))\n",
-    "        # South\n",
-    "        if ny < sy - 1:\n",
-    "            if pmap[nx, ny + 1] == 0.5:\n",
-    "                pmap[nx, ny + 1] = 0.0\n",
-    "                fringe.appendleft((nx, ny + 1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This algotihm will fill the area bounded by the yellow lines starting from a center point (e.g. (10, 20)) with zeros:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAS0AAAD8CAYAAAAi9vLQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAExpJREFUeJzt3X+sX2Vhx/H3hwsVQ1WoVVPbKmyrCY1xsDSFhSVDwFjQUP/ACboNl2bNEtlg4LTMBZXtD3VRyDICuxMCGkfBHxk3pKZjCHEuA1oHMtqG9do5uWtjxy8VjEDv/eyPc8q+vfd7v99zf/R+z9P7eSUn9z7ne+5znkvKJ8/znOc8V7aJiCjFcYNuQETETCS0IqIoCa2IKEpCKyKKktCKiKIktCKiKAmtiDhqJN0m6aCkJ6b5XJL+RtKopMcl/Ua/OhNaEXE03Q5s6PH5hcCa+tgM3NyvwjmFlqQNkp6sU3LLXOqKiGOP7e8Cz/a4ZCPwFVceAk6WtKJXncfPtjGShoCbgPcAY8AOSSO2d0/3M0NLT/Lxy5bN9pYR0cehZ59l/IUXNZc63vvuk/zMs+ONrv3+4y/tAn7ZcWrY9vAMbrcSeKqjPFafOzDdD8w6tID1wKjtfQCStlKl5rShdfyyZbz1mqvmcMuI6GX/F2+ccx3PPDvOI9vf1ujaoRV7f2l73Rxu1y1ge75bOJfQ6paQZ82hvohoAQMTTCzU7caA1R3lVcD+Xj8wlzmtRgkpabOknZJ2jr/w4hxuFxELwZhXPN7omAcjwO/XTxHPBn5qe9qhIcytp9UoIevx7TDAa962OltKRBRgvnpaku4EzgWWSxoDPg2cAGD7FmAbcBEwCvwC+IN+dc4ltHYAaySdBvwPcCnw4TnUFxEtYMz4PG1ZZfuyPp8b+NhM6px1aNk+JOkKYDswBNxme9ds64uI9pjoPRc+UHPpaWF7G1X3LiKOEQbGj9XQiohj0zHb0zoafvihWwbdhBiAX73rjwbdhKgZeKXF27C3LrQiYrCMMzyMiIIYxtubWQmtiDhStSK+vVoXWpPnNibPcb33rWcsZHPiKNm+/7FBNyGmJca7vvDSDq0LrYgYrGoiPqEVEYWo1mkltCKiIBPpaUVEKdLTioiiGDHe4j8fkdCKiCkyPIyIYhjxsocG3YxpJbQi4gjV4tIMD2dtymLT/VNfqM6C04j5lYn4iCiGLcadnlZEFGQiPa2IKEU1Ed/eaGhvy6bRbbO4yfNcmeOKmL1MxEdEccazTisiSpEV8RFRnIk8PYyIUlQvTCe0jqosQI2YP0a8ktd4IqIUNllcGhElURaXRkQ5THpaEVGYTMRHRDGMsglgRJSj+hNi7Y2G9rYsIgYkf6x1weWl6ojZM1kRHxGFaXNPq71xGhEDYYsJH9foaELSBklPShqVtKXL52+T9ICkRyU9LumiXvWlpxURR6gm4ufnNR5JQ8BNwHuAMWCHpBHbuzsu+wvgbts3S1oLbANOna7OvlEp6TZJByU90XFumaT7JO2tv54yy98pIlqn2iO+ydHAemDU9j7bLwNbgY2TrjHw+vr7NwD7e1XYpKd1O/C3wFc6zm0B7rf9ubq7twX4ZIO6BqbfS9WZmI+oVBPxjee0lkva2VEetj3cUV4JPNVRHgPOmlTHZ4B/kvTHwEnABb1u2De0bH9X0qmTTm8Ezq2/vwN4kJaHVkQ0N4MV8U/bXtfj827p50nly4DbbX9R0m8CX5X0TtsT3Sqc7ZzWW2wfALB9QNKbp22xtBnYDDB0SkaREW03zyvix4DVHeVVTB3+bQI2ANj+N0knAsuBg90qPOpPD20P215ne93Q0pOO9u0iYh5McFyjo4EdwBpJp0laAlwKjEy65sfA+QCSTgdOBP53ugpn29P6iaQVdS9rBdMkYptl48CI7mx4ZWJ++jO2D0m6AtgODAG32d4l6Xpgp+0R4Brg7yX9KdXQ8aO2Jw8hXzXb0BoBLgc+V3+9Z5b1RETLVMPD+RuE2d5GtYyh89x1Hd/vBs5pWl/f0JJ0J9Wk+3JJY8CnqcLqbkmbqLp2H2x6w4hovzaviG/y9PCyaT46f57bEhEtMMMlDwsuK+IjYpL5HR7Ot4RWREyRPeIjohjV08P8CbGIKES2W46I4mR4WIDsdhpRydPDiChOnh5GRDFscSihFRElyfCwUNk4MBajzGlFRHESWhFRjKzTiojiZJ3WMSIbB8ZiYMOhedoE8GhIaEXEFBkeRkQxMqcVEcVxQisiSpKJ+Igohp05rYgoihjP08OIKEnmtCKiGHn38BiWjQPjmORqXqutEloRMUWeHkZEMZyJ+IgoTYaHi0g2DoxjQZ4eRkQx7IRWRBQmSx4ioiiZ04qIYhgxkaeHi1d2O40StbijRXvjNCIGo56Ib3I0IWmDpCcljUraMs01vyNpt6Rdkv6hV33paUXEVPPU1ZI0BNwEvAcYA3ZIGrG9u+OaNcC1wDm2n5P05l519u1pSVot6QFJe+oUvLI+v0zSfZL21l9PmcsvFxHtMY89rfXAqO19tl8GtgIbJ13zh8BNtp+r7u2DvSpsMjw8BFxj+3TgbOBjktYCW4D7ba8B7q/LEVE4AxMTanQAyyXt7Dg2T6puJfBUR3msPtfpHcA7JP2rpIckbejVvr7DQ9sHgAP19z+XtKe+6Ubg3PqyO4AHgU/2qy8iWs5A83VaT9te1+PzbhVNHnweD6yhypNVwL9Ieqft57tVOKOJeEmnAmcCDwNvqQPtcLD1HIdGRDnsZkcDY8DqjvIqYH+Xa+6x/Yrt/wKepAqxrhqHlqSlwDeBq2z/bAY/t/lw13H8hReb/lhEDJIbHv3tANZIOk3SEuBSYGTSNf8IvBtA0nKq4eK+6SpsFFqSTqAKrK/Z/lZ9+ieSVtSfrwC6Tp7ZHra9zva6oaUnNbldRAxUs0n4JhPxtg8BVwDbgT3A3bZ3Sbpe0sX1ZduBZyTtBh4A/sz2M9PV2XdOS5KAW4E9tr/U8dEIcDnwufrrPX1/g8hup1GGeVxdansbsG3Sues6vjdwdX301WSd1jnA7wH/Iemx+tyfU4XV3ZI2AT8GPtjkhhHRcgZPFPzCtO3v0f0JAMD589uciGiHgkMrIhahFr98mNBqgex2Gq2T0IqIYsxscemCS2hFxBTZBDAiylLy08OIWHyUnlZEFKP5KzoDkdCKiEmUifiIKEx6WhFRlIlBN2B6Ca0Wyl/wiYHKOq2IKE2eHkZEWVocWvm7hxFRlPS0CpCNA2OhZXgYEeUweY0nIgqTnlZElCTDw5h3WcsVR1VCKyKKktCKiFLIGR5GRGny9DAiSpKeVkSUJaEVEcXInFZEFCehFRElUTYBjKMtL1XHYpHQioipMjyMiGJkIj4iipPQikHo91J15rhiWgmtiCiFaPfTw+wRHxFH8v+/NN3vaELSBklPShqVtKXHdZdIsqR1vepLaEXEVG549CFpCLgJuBBYC1wmaW2X614H/AnwcL86+4aWpBMlPSLpB5J2Sfpsff40SQ9L2ivpLklL+v8KEVGEeQotYD0wanuf7ZeBrcDGLtf9JfAF4Jf9Kmwyp/UScJ7tFySdAHxP0reBq4EbbG+VdAuwCbi50a8RA5HdTqOpGSx5WC5pZ0d52PZwR3kl8FRHeQw464h7SWcCq23fK+nj/W7YN7RsG3ihLp5QHwbOAz5cn78D+AwJrYhjQ/PQetp2rzmobhtzvVq7pOOAG4CPNr1hozktSUOSHgMOAvcBPwSet32ovmSMKlG7/exmSTsl7Rx/4cWm7YqIQXH19LDJ0cAYsLqjvArY31F+HfBO4EFJPwLOBkZ6TcY3Ci3b47bPqG+4Hji922XT/Oyw7XW21w0tPanJ7SJi0OZvTmsHsKaeA18CXAqMvHob+6e2l9s+1fapwEPAxbZ3dq9uhk8PbT8PPEiVhidLOjy8nJyeEVGw+VryUI/GrgC2A3uAu23vknS9pItn07a+c1qS3gS8Yvt5Sa8FLgA+DzwAXEL1NOBy4J7ZNCAiWmgeV8Tb3gZsm3TuummuPbdffU2eHq4A7qjXWxxHlZT3StoNbJX0V8CjwK0N6oqItms+9BuIJk8PHwfO7HJ+H9X8VkQcQ0R2eYiIwiS0opWy22lMK6EVEUVJaEVEMbJzaUQUJ6EVpchupwHt3gQwoRURU2R4GBHlKH1xaUQsQgmtKFU2Dlx8siI+IoqjifamVkIrIo6UOa2IKE2GhxFRloRWRJQkPa2IKEtCKyKK4bzGExEFyTqtOKZk48BFwu1NrYRWREyRnlZElCOLSyOiNJmIj2NaNg489iS0IqIcJhPxEVGWTMRHRFkSWhFRiiwujUWnyW6n0WJ2NgGMiMK0N7MSWhExVYaHEVEOAxkeRkRR2ptZHDfoBkRE+8jNjkZ1SRskPSlpVNKWLp9fLWm3pMcl3S/p7b3qS2hFxBSacKOjbz3SEHATcCGwFrhM0tpJlz0KrLP9LuAbwBd61dk4tCQNSXpU0r11+TRJD0vaK+kuSUua1hURLeYZHP2tB0Zt77P9MrAV2HjE7ewHbP+iLj4ErOpV4UzmtK4E9gCvr8ufB26wvVXSLcAm4OYZ1BeLRNeNAz+UtVttVS0ubTyptVzSzo7ysO3hjvJK4KmO8hhwVo/6NgHf7nXDRj0tSauA9wFfrssCzqPqygHcAXygSV0RUYCJhgc8bXtdxzE8qSZ1qb1rIkr6XWAd8Ne9mta0p3Uj8AngdXX5jcDztg/V5TGqRO3WkM3AZoChU05peLuIGKQZ9LT6GQNWd5RXAfun3E+6APgU8Nu2X+pVYd+elqT3Awdtf7/zdJdLu/6WtocPp/DQ0pP63S4iBm1+57R2AGvqOfAlwKXASOcFks4E/g642PbBfhU26WmdA1ws6SLgRKo5rRuBkyUdX/e2uqZnRJRo/t49tH1I0hXAdmAIuM32LknXAzttj1ANB5cCX69mnvix7Yunq7NvaNm+FrgWQNK5wMdtf0TS14FLqJ4GXA7cM5dfLhaXbpPz0SLzuAmg7W3Atknnruv4/oKZ1DeXdVqfBK6WNEo1x3XrHOqKiLao/1hrk2MQZvQaj+0HgQfr7/dRrcGIiGNNtluOiKK0N7MSWhExlSba++d4EloRcSRzeOFoKyW0IuIIwvO5uHTeJbQiYqqEVkQUJaEVEcXInFZElCZPDyOiIM7wMCIKYhJaEVGY9o4OE1oRMVXWaUVEWRJaEVEMG8bbOz5MaEXEVOlpRURREloRUQwD87RH/NGQ0IqISQzOnFZElMJkIj4iCpM5rYgoSkIrIsqRF6YjoiQGsjVNRBQlPa2IKEde44mIkhicdVoRUZSsiI+IomROKyKKYefpYUQUJj2tiCiH8fj4oBsxrYRWRBwpW9NERHFavOThuEE3ICLaxYAn3OhoQtIGSU9KGpW0pcvnr5F0V/35w5JO7VVfQisijuR6E8AmRx+ShoCbgAuBtcBlktZOumwT8JztXwNuAD7fq86EVkRM4fHxRkcD64FR2/tsvwxsBTZOumYjcEf9/TeA8yVpugoXdE7r5afGnv7RVR//b2A58PRC3nsOSmorlNXektoKZbT37XOt4Oc8t/2f/Y3lDS8/UdLOjvKw7eGO8krgqY7yGHDWpDpevcb2IUk/Bd7INP+tFzS0bL8JQNJO2+sW8t6zVVJboaz2ltRWKK+9s2V7wzxW163HNHkyrMk1r8rwMCKOpjFgdUd5FbB/umskHQ+8AXh2ugoTWhFxNO0A1kg6TdIS4FJgZNI1I8Dl9feXAN+xp1+SP6h1WsP9L2mNktoKZbW3pLZCee0duHqO6gpgOzAE3GZ7l6TrgZ22R4Bbga9KGqXqYV3aq071CLSIiNbJ8DAiipLQioiiLGho9VvOP2iSbpN0UNITHeeWSbpP0t766ymDbONhklZLekDSHkm7JF1Zn29re0+U9IikH9Tt/Wx9/rT61Y299ascSwbd1sMkDUl6VNK9dbm1bV1MFiy0Gi7nH7TbgclrVLYA99teA9xfl9vgEHCN7dOBs4GP1f8929rel4DzbP86cAawQdLZVK9s3FC39zmqVzra4kpgT0e5zW1dNBayp9VkOf9A2f4uU9eHdL5icAfwgQVt1DRsH7D97/X3P6f6n2sl7W2vbb9QF0+oDwPnUb26AS1qr6RVwPuAL9dl0dK2LjYLGVrdlvOvXMD7z9ZbbB+AKiiANw+4PVPUb8WfCTxMi9tbD7ceAw4C9wE/BJ63fai+pE3/Jm4EPgEcfiv4jbS3rYvKQobWjJbqRzOSlgLfBK6y/bNBt6cX2+O2z6BaFb0eOL3bZQvbqqkkvR84aPv7nae7XDrwti5GC7m4tMly/jb6iaQVtg9IWkHVS2gFSSdQBdbXbH+rPt3a9h5m+3lJD1LNxZ0s6fi6B9OWfxPnABdLugg4EXg9Vc+rjW1ddBayp9VkOX8bdb5icDlwzwDb8qp6juVWYI/tL3V81Nb2vknSyfX3rwUuoJqHe4Dq1Q1oSXttX2t7le1Tqf6dfsf2R2hhWxcl2wt2ABcB/0k1l/Gphbx3w/bdCRwAXqHqGW6imsu4H9hbf1026HbWbf0tquHJ48Bj9XFRi9v7LuDRur1PANfV538FeAQYBb4OvGbQbZ3U7nOBe0to62I58hpPRBQlK+IjoigJrYgoSkIrIoqS0IqIoiS0IqIoCa2IKEpCKyKK8n9ajj1llwZUMgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "flood_fill((10, 20), map1)\n",
-    "map_float = np.array(map1)/10.0\n",
-    "plt.imshow(map1)\n",
-    "plt.colorbar()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's use this flood fill on real data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The grid map is  150 x 100 .\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xyreso = 0.02  # x-y grid resolution\n",
-    "yawreso = math.radians(3.1)  # yaw angle resolution [rad]\n",
-    "ang, dist = file_read(\"lidar01.csv\")\n",
-    "ox = np.sin(ang) * dist\n",
-    "oy = np.cos(ang) * dist\n",
-    "pmap, minx, maxx, miny, maxy, xyreso = lg.generate_ray_casting_grid_map(ox, oy, xyreso, False)\n",
-    "xyres = np.array(pmap).shape\n",
-    "plt.figure(figsize=(20,8))\n",
-    "plt.subplot(122)\n",
-    "plt.imshow(pmap, cmap = \"PiYG_r\") \n",
-    "plt.clim(-0.4, 1.4)\n",
-    "plt.gca().set_xticks(np.arange(-.5, xyres[1], 1), minor = True)\n",
-    "plt.gca().set_yticks(np.arange(-.5, xyres[0], 1), minor = True)\n",
-    "plt.grid(True, which=\"minor\", color=\"w\", linewidth = .6, alpha = 0.5)\n",
-    "plt.colorbar()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/SLAM/EKFSLAM/ekf_slam.ipynb b/SLAM/EKFSLAM/ekf_slam.ipynb
deleted file mode 100644
index 7634bfde9e..0000000000
--- a/SLAM/EKFSLAM/ekf_slam.ipynb
+++ /dev/null
@@ -1,1784 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# EKF SLAM"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename=\"animation.png\",width=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Simulation\n",
-    "\n",
-    "This is a simulation of EKF SLAM. \n",
-    "\n",
-    "- Black stars: landmarks\n",
-    "- Green crosses: estimates of landmark positions\n",
-    "- Black line: dead reckoning \n",
-    "- Blue line: ground truth\n",
-    "- Red line: EKF SLAM position estimation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Introduction\n",
-    "\n",
-    "EKF SLAM models the SLAM problem in a single EKF where the modeled state is both the pose $(x, y, \\theta)$ and \n",
-    "an array of landmarks $[(x_1, y_1), (x_2, x_y), ... , (x_n, y_n)]$ for $n$ landmarks. The covariance between each of the positions and landmarks are also tracked. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$\\begin{equation}\n",
-    "X = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\\\ x_1 \\\\ y_1 \\\\ x_2 \\\\ y_2 \\\\ \\dots \\\\ x_n \\\\ y_n \\end{bmatrix}\n",
-    "\\end{equation}$\n",
-    "\n",
-    "$\\begin{equation}\n",
-    "P = \\begin{bmatrix} \n",
-    "\\sigma_{xx} & \\sigma_{xy} & \\sigma_{x\\theta} & \\sigma_{xx_1} & \\sigma_{xy_1} & \\sigma_{xx_2} & \\sigma_{xy_2} & \\dots & \\sigma_{xx_n} & \\sigma_{xy_n} \\\\\n",
-    "\\sigma_{yx} & \\sigma_{yy} & \\sigma_{y\\theta} & \\sigma_{yx_1} & \\sigma_{yy_1} & \\sigma_{yx_2} & \\sigma_{yy_2} & \\dots & \\sigma_{yx_n} & \\sigma_{yy_n} \\\\\n",
-    " &  &  &  & \\vdots &  &  &  &  &  \\\\\n",
-    "\\sigma_{x_nx} & \\sigma_{x_ny} & \\sigma_{x_n\\theta} & \\sigma_{x_nx_1} & \\sigma_{x_ny_1} & \\sigma_{x_nx_2} & \\sigma_{x_ny_2} & \\dots & \\sigma_{x_nx_n} & \\sigma_{x_ny_n}\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation}$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A single estimate of the pose is tracked over time, while the confidence in the pose is tracked by the \n",
-    "covariance matrix $P$. $P$ is a symmetric square matrix whith each element in the matrix corresponding to the \n",
-    "covariance between two parts of the system. For example, $\\sigma_{xy}$ represents the covariance between the \n",
-    "belief of $x$ and $y$ and is equal to $\\sigma_{yx}$. \n",
-    "\n",
-    "The state can be represented more concisely as follows. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$\\begin{equation}\n",
-    "X = \\begin{bmatrix} x \\\\ m \\end{bmatrix}\n",
-    "\\end{equation}$\n",
-    "$\\begin{equation}\n",
-    "P = \\begin{bmatrix} \n",
-    "\\Sigma_{xx} & \\Sigma_{xm}\\\\\n",
-    "\\Sigma_{mx} & \\Sigma_{mm}\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation}$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here the state simplifies to a combination of pose ($x$) and map ($m$). The covariance matrix becomes easier to \n",
-    "understand and simply reads as the uncertainty of the robots pose ($\\Sigma_{xx}$), the uncertainty of the \n",
-    "map ($\\Sigma_{mm}$), and the uncertainty of the robots pose with respect to the map and vice versa \n",
-    "($\\Sigma_{xm}$, $\\Sigma_{mx}$).\n",
-    "\n",
-    "Take care to note the difference between $X$ (state) and $x$ (pose). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "Extended Kalman Filter SLAM example\n",
-    "original author: Atsushi Sakai (@Atsushi_twi)\n",
-    "notebook author: Andrew Tu (drewtu2)\n",
-    "\"\"\"\n",
-    "\n",
-    "import math\n",
-    "import numpy as np\n",
-    "%matplotlib notebook\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "\n",
-    "# EKF state covariance\n",
-    "Cx = np.diag([0.5, 0.5, np.deg2rad(30.0)])**2 # Change in covariance\n",
-    "\n",
-    "#  Simulation parameter\n",
-    "Qsim = np.diag([0.2, np.deg2rad(1.0)])**2  # Sensor Noise\n",
-    "Rsim = np.diag([1.0, np.deg2rad(10.0)])**2 # Process Noise\n",
-    "\n",
-    "DT = 0.1  # time tick [s]\n",
-    "SIM_TIME = 50.0  # simulation time [s]\n",
-    "MAX_RANGE = 20.0  # maximum observation range\n",
-    "M_DIST_TH = 2.0  # Threshold of Mahalanobis distance for data association.\n",
-    "STATE_SIZE = 3  # State size [x,y,yaw]\n",
-    "LM_SIZE = 2  # LM state size [x,y]\n",
-    "\n",
-    "show_animation = True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Algorithm Walkthrough\n",
-    "\n",
-    "At each time step, the following is done. \n",
-    "- predict the new state using the control functions\n",
-    "- update the belief in landmark positions based on the estimated state and measurements"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def ekf_slam(xEst, PEst, u, z):\n",
-    "    \"\"\"\n",
-    "    Performs an iteration of EKF SLAM from the available information. \n",
-    "    \n",
-    "    :param xEst: the belief in last position\n",
-    "    :param PEst: the uncertainty in last position\n",
-    "    :param u:    the control function applied to the last position \n",
-    "    :param z:    measurements at this step\n",
-    "    :returns:    the next estimated position and associated covariance\n",
-    "    \"\"\"\n",
-    "    S = STATE_SIZE\n",
-    "\n",
-    "    # Predict\n",
-    "    xEst, PEst, G, Fx = predict(xEst, PEst, u)\n",
-    "    initP = np.eye(2)\n",
-    "\n",
-    "    # Update\n",
-    "    xEst, PEst = update(xEst, PEst, u, z, initP)\n",
-    "\n",
-    "    return xEst, PEst\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1- Predict\n",
-    "**Predict State update:** The following equations describe the predicted motion model of the robot in case we provide only the control $(v,w)$, which are the linear and angular velocity repsectively. \n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "F=\n",
-    "\\begin{bmatrix}\n",
-    "1 & 0 & 0 \\\\\n",
-    "0 & 1 & 0 \\\\\n",
-    "0 & 0 & 1 \n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "B=\n",
-    "\\begin{bmatrix}\n",
-    "\\Delta t cos(\\theta) & 0\\\\\n",
-    "\\Delta t sin(\\theta) & 0\\\\\n",
-    "0 & \\Delta t\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "U=\n",
-    "\\begin{bmatrix}\n",
-    "v_t\\\\\n",
-    "w_t\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "X = FX + BU \n",
-    "\\end{equation*}$\n",
-    "\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "\\begin{bmatrix}\n",
-    "x_{t+1} \\\\\n",
-    "y_{t+1} \\\\\n",
-    "\\theta_{t+1}\n",
-    "\\end{bmatrix}=\n",
-    "\\begin{bmatrix}\n",
-    "1 & 0 & 0 \\\\\n",
-    "0 & 1 & 0 \\\\\n",
-    "0 & 0 & 1 \n",
-    "\\end{bmatrix}\\begin{bmatrix}\n",
-    "x_{t} \\\\\n",
-    "y_{t} \\\\\n",
-    "\\theta_{t}\n",
-    "\\end{bmatrix}+\n",
-    "\\begin{bmatrix}\n",
-    "\\Delta t cos(\\theta) & 0\\\\\n",
-    "\\Delta t sin(\\theta) & 0\\\\\n",
-    "0 & \\Delta t\n",
-    "\\end{bmatrix}\n",
-    "\\begin{bmatrix}\n",
-    "v_{t} + \\sigma_v\\\\\n",
-    "w_{t} + \\sigma_w\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$\n",
-    "\n",
-    "Notice that while $U$ is only defined by $v_t$ and $w_t$, in the actual calcuations, a $+\\sigma_v$ and \n",
-    "$+\\sigma_w$ appear. These values represent the error bewteen the given control inputs and the actual control \n",
-    "inputs. \n",
-    "\n",
-    "As a result, the simulation is set up as the following. $R$ represents the process noise which is added to the \n",
-    "control inputs to simulate noise experienced in the real world. A set of truth values are computed from the raw \n",
-    "control values while the values dead reckoning values incorporate the error into the estimation. \n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "R=\n",
-    "\\begin{bmatrix}\n",
-    "\\sigma_v\\\\\n",
-    "\\sigma_w\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "X_{true} = FX + B(U)\n",
-    "\\end{equation*}$\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "X_{DR} = FX + B(U + R)\n",
-    "\\end{equation*}$\n",
-    "\n",
-    "The implementation of the motion model prediciton code is shown in `motion_model`. The `observation` function \n",
-    "shows how the simulation uses (or doesn't use) the process noise `Rsim` to the find the ground truth and dead reckoning estimtates of the pose.\n",
-    "\n",
-    "**Predict covariance:** Add the state covariance to the the current uncertainty of the EKF. At each time step, the uncertainty in the system grows by the covariance of the pose, $Cx$. \n",
-    "\n",
-    "$\n",
-    "P = G^TPG + Cx\n",
-    "$\n",
-    "\n",
-    "Notice this uncertainty is only growing with respect to the pose, not the landmarks. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def predict(xEst, PEst, u):\n",
-    "    \"\"\"\n",
-    "    Performs the prediction step of EKF SLAM\n",
-    "    \n",
-    "    :param xEst: nx1 state vector\n",
-    "    :param PEst: nxn covariacne matrix\n",
-    "    :param u:    2x1 control vector\n",
-    "    :returns:    predicted state vector, predicted covariance, jacobian of control vector, transition fx\n",
-    "    \"\"\"\n",
-    "    S = STATE_SIZE\n",
-    "    G, Fx = jacob_motion(xEst[0:S], u)\n",
-    "    xEst[0:S] = motion_model(xEst[0:S], u)\n",
-    "    # Fx is an an identity matrix of size (STATE_SIZE)\n",
-    "    # sigma = G*sigma*G.T + Noise\n",
-    "    PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx\n",
-    "    return xEst, PEst, G, Fx"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def motion_model(x, u):\n",
-    "    \"\"\"\n",
-    "    Computes the motion model based on current state and input function. \n",
-    "    \n",
-    "    :param x: 3x1 pose estimation\n",
-    "    :param u: 2x1 control input [v; w]\n",
-    "    :returns: the resutling state after the control function is applied\n",
-    "    \"\"\"\n",
-    "    F = np.array([[1.0, 0, 0],\n",
-    "                  [0, 1.0, 0],\n",
-    "                  [0, 0, 1.0]])\n",
-    "\n",
-    "    B = np.array([[DT * math.cos(x[2, 0]), 0],\n",
-    "                  [DT * math.sin(x[2, 0]), 0],\n",
-    "                  [0.0, DT]])\n",
-    "\n",
-    "    x = (F @ x) + (B @ u)\n",
-    "    return x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2 - Update\n",
-    "In the update phase, the observations of nearby landmarks are used to correct the location estimate. \n",
-    "\n",
-    "For every landmark observed, it is associated to a particular landmark in the known map. If no landmark exists \n",
-    "in the position surrounding the landmark, it is taken as a NEW landmark. The distance threshold for how far a \n",
-    "landmark must be from the next known landmark before its considered to be a new landmark is set by `M_DIST_TH`.\n",
-    "\n",
-    "With an observation associated to the appropriate landmark, the **innovation** can be calculated. Innovation \n",
-    "($y$) is the difference between the observation and the observation that *should* have been made if the \n",
-    "observation were made from the pose predicted in the predict stage.\n",
-    "\n",
-    "$\n",
-    "y = z_t - h(X)\n",
-    "$\n",
-    "\n",
-    "With the innovation calculated, the question becomes which to trust more - the observations or the predictions? \n",
-    "To determine this, we calculate the Kalman Gain - a percent of how much of the innovation to add to the \n",
-    "prediction based on the uncertainty in the predict step and the update step. \n",
-    "\n",
-    "$\n",
-    "K = \\bar{P_t}H_t^T(H_t\\bar{P_t}H_t^T + Q_t)^{-1}\n",
-    "$\n",
-    "In these equations, $H$ is the jacobian of the measurement function. The multiplications by $H^T$ and $H$ \n",
-    "represent the application of the delta to the measurement covariance. \n",
-    "Intuitively, this equation is applying the following from the single variate Kalman equation but in the \n",
-    "multivariate form, i.e. finding the ratio of the uncertianty of the process compared the measurment. \n",
-    "\n",
-    "$\n",
-    "K = \\frac{\\bar{P_t}}{\\bar{P_t} + Q_t}\n",
-    "$\n",
-    "\n",
-    "If $Q_t << \\bar{P_t}$, (i.e. the measurement covariance is low relative to the current estimate), then the \n",
-    "Kalman gain will be $~1$. This results in adding all of the innovation to the estimate -- and therefore \n",
-    "completely believing the measurement. \n",
-    "\n",
-    "However, if $Q_t >> \\bar{P_t}$ then the Kalman gain will go to 0, signaling a high trust in the process \n",
-    "and little trust in the measurement.\n",
-    "\n",
-    "The update is captured in the following. \n",
-    "\n",
-    "$\n",
-    "xUpdate = xEst + (K * y)\n",
-    "$\n",
-    "\n",
-    "Of course, the covariance must also be updated as well to account for the changing uncertainty. \n",
-    "\n",
-    "$\n",
-    "P_{t} = (I-K_tH_t)\\bar{P_t}\n",
-    "$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def update(xEst, PEst, u, z, initP):\n",
-    "    \"\"\"\n",
-    "    Performs the update step of EKF SLAM\n",
-    "    \n",
-    "    :param xEst:  nx1 the predicted pose of the system and the pose of the landmarks\n",
-    "    :param PEst:  nxn the predicted covariance\n",
-    "    :param u:     2x1 the control function \n",
-    "    :param z:     the measurements read at new position\n",
-    "    :param initP: 2x2 an identity matrix acting as the initial covariance\n",
-    "    :returns:     the updated state and covariance for the system\n",
-    "    \"\"\"\n",
-    "    for iz in range(len(z[:, 0])):  # for each observation\n",
-    "        minid = search_correspond_LM_ID(xEst, PEst, z[iz, 0:2]) # associate to a known landmark\n",
-    "\n",
-    "        nLM = calc_n_LM(xEst) # number of landmarks we currently know about\n",
-    "        \n",
-    "        if minid == nLM: # Landmark is a NEW landmark\n",
-    "            print(\"New LM\")\n",
-    "            # Extend state and covariance matrix\n",
-    "            xAug = np.vstack((xEst, calc_LM_Pos(xEst, z[iz, :])))\n",
-    "            PAug = np.vstack((np.hstack((PEst, np.zeros((len(xEst), LM_SIZE)))),\n",
-    "                              np.hstack((np.zeros((LM_SIZE, len(xEst))), initP))))\n",
-    "            xEst = xAug\n",
-    "            PEst = PAug\n",
-    "        \n",
-    "        lm = get_LM_Pos_from_state(xEst, minid)\n",
-    "        y, S, H = calc_innovation(lm, xEst, PEst, z[iz, 0:2], minid)\n",
-    "\n",
-    "        K = (PEst @ H.T) @ np.linalg.inv(S) # Calculate Kalman Gain\n",
-    "        xEst = xEst + (K @ y)\n",
-    "        PEst = (np.eye(len(xEst)) - (K @ H)) @ PEst\n",
-    "    \n",
-    "    xEst[2] = pi_2_pi(xEst[2])\n",
-    "    return xEst, PEst\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_innovation(lm, xEst, PEst, z, LMid):\n",
-    "    \"\"\"\n",
-    "    Calculates the innovation based on expected position and landmark position\n",
-    "    \n",
-    "    :param lm:   landmark position\n",
-    "    :param xEst: estimated position/state\n",
-    "    :param PEst: estimated covariance\n",
-    "    :param z:    read measurements\n",
-    "    :param LMid: landmark id\n",
-    "    :returns:    returns the innovation y, and the jacobian H, and S, used to calculate the Kalman Gain\n",
-    "    \"\"\"\n",
-    "    delta = lm - xEst[0:2]\n",
-    "    q = (delta.T @ delta)[0, 0]\n",
-    "    zangle = math.atan2(delta[1, 0], delta[0, 0]) - xEst[2, 0]\n",
-    "    zp = np.array([[math.sqrt(q), pi_2_pi(zangle)]])\n",
-    "    # zp is the expected measurement based on xEst and the expected landmark position\n",
-    "    \n",
-    "    y = (z - zp).T # y = innovation\n",
-    "    y[1] = pi_2_pi(y[1])\n",
-    "    \n",
-    "    H = jacobH(q, delta, xEst, LMid + 1)\n",
-    "    S = H @ PEst @ H.T + Cx[0:2, 0:2]\n",
-    "\n",
-    "    return y, S, H\n",
-    "\n",
-    "def jacobH(q, delta, x, i):\n",
-    "    \"\"\"\n",
-    "    Calculates the jacobian of the measurement function\n",
-    "    \n",
-    "    :param q:     the range from the system pose to the landmark\n",
-    "    :param delta: the difference between a landmark position and the estimated system position\n",
-    "    :param x:     the state, including the estimated system position\n",
-    "    :param i:     landmark id + 1\n",
-    "    :returns:     the jacobian H\n",
-    "    \"\"\"\n",
-    "    sq = math.sqrt(q)\n",
-    "    G = np.array([[-sq * delta[0, 0], - sq * delta[1, 0], 0, sq * delta[0, 0], sq * delta[1, 0]],\n",
-    "                  [delta[1, 0], - delta[0, 0], - q, - delta[1, 0], delta[0, 0]]])\n",
-    "\n",
-    "    G = G / q\n",
-    "    nLM = calc_n_LM(x)\n",
-    "    F1 = np.hstack((np.eye(3), np.zeros((3, 2 * nLM))))\n",
-    "    F2 = np.hstack((np.zeros((2, 3)), np.zeros((2, 2 * (i - 1))),\n",
-    "                    np.eye(2), np.zeros((2, 2 * nLM - 2 * i))))\n",
-    "\n",
-    "    F = np.vstack((F1, F2))\n",
-    "\n",
-    "    H = G @ F\n",
-    "\n",
-    "    return H"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Observation Step\n",
-    "The observation step described here is outside the main EKF SLAM process and is primarily used as a method of\n",
-    "driving the simulation. The observations funciton is in charge of calcualting how the poses of the robots change \n",
-    "and accumulate error over time, and the theoretical measuremnts that are expected as a result of each \n",
-    "measurement. \n",
-    "\n",
-    "Observations are based on the TRUE position of the robot. Error in dead reckoning and control functions are \n",
-    "passed along here as well. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def observation(xTrue, xd, u, RFID):\n",
-    "    \"\"\"\n",
-    "    :param xTrue: the true pose of the system\n",
-    "    :param xd:    the current noisy estimate of the system\n",
-    "    :param u:     the current control input\n",
-    "    :param RFID:  the true position of the landmarks\n",
-    "    \n",
-    "    :returns:     Computes the true position, observations, dead reckoning (noisy) position, \n",
-    "                  and noisy control function\n",
-    "    \"\"\"\n",
-    "    xTrue = motion_model(xTrue, u)\n",
-    "\n",
-    "    # add noise to gps x-y\n",
-    "    z = np.zeros((0, 3))\n",
-    "\n",
-    "    for i in range(len(RFID[:, 0])): # Test all beacons, only add the ones we can see (within MAX_RANGE)\n",
-    "\n",
-    "        dx = RFID[i, 0] - xTrue[0, 0]\n",
-    "        dy = RFID[i, 1] - xTrue[1, 0]\n",
-    "        d = math.sqrt(dx**2 + dy**2)\n",
-    "        angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])\n",
-    "        if d <= MAX_RANGE:\n",
-    "            dn = d + np.random.randn() * Qsim[0, 0]  # add noise\n",
-    "            anglen = angle + np.random.randn() * Qsim[1, 1]  # add noise\n",
-    "            zi = np.array([dn, anglen, i])\n",
-    "            z = np.vstack((z, zi))\n",
-    "\n",
-    "    # add noise to input\n",
-    "    ud = np.array([[\n",
-    "        u[0, 0] + np.random.randn() * Rsim[0, 0],\n",
-    "        u[1, 0] + np.random.randn() * Rsim[1, 1]]]).T\n",
-    "\n",
-    "    xd = motion_model(xd, ud)\n",
-    "    return xTrue, z, xd, ud"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_n_LM(x):\n",
-    "    \"\"\"\n",
-    "    Calculates the number of landmarks currently tracked in the state\n",
-    "    :param x: the state\n",
-    "    :returns: the number of landmarks n\n",
-    "    \"\"\"\n",
-    "    n = int((len(x) - STATE_SIZE) / LM_SIZE)\n",
-    "    return n\n",
-    "\n",
-    "\n",
-    "def jacob_motion(x, u):\n",
-    "    \"\"\"\n",
-    "    Calculates the jacobian of motion model. \n",
-    "    \n",
-    "    :param x: The state, including the estimated position of the system\n",
-    "    :param u: The control function\n",
-    "    :returns: G:  Jacobian\n",
-    "              Fx: STATE_SIZE x (STATE_SIZE + 2 * num_landmarks) matrix where the left side is an identity matrix\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    # [eye(3) [0 x y; 0 x y; 0 x y]]\n",
-    "    Fx = np.hstack((np.eye(STATE_SIZE), np.zeros(\n",
-    "        (STATE_SIZE, LM_SIZE * calc_n_LM(x)))))\n",
-    "\n",
-    "    jF = np.array([[0.0, 0.0, -DT * u[0] * math.sin(x[2, 0])],\n",
-    "                   [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])],\n",
-    "                   [0.0, 0.0, 0.0]],dtype=object)\n",
-    "\n",
-    "    G = np.eye(STATE_SIZE) + Fx.T @ jF @ Fx\n",
-    "    if calc_n_LM(x) > 0:\n",
-    "        print(Fx.shape)\n",
-    "    return G, Fx,\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_LM_Pos(x, z):\n",
-    "    \"\"\"\n",
-    "    Calcualtes the pose in the world coordinate frame of a landmark at the given measurement. \n",
-    "\n",
-    "    :param x: [x; y; theta]\n",
-    "    :param z: [range; bearing]\n",
-    "    :returns: [x; y] for given measurement\n",
-    "    \"\"\"\n",
-    "    zp = np.zeros((2, 1))\n",
-    "\n",
-    "    zp[0, 0] = x[0, 0] + z[0] * math.cos(x[2, 0] + z[1])\n",
-    "    zp[1, 0] = x[1, 0] + z[0] * math.sin(x[2, 0] + z[1])\n",
-    "    #zp[0, 0] = x[0, 0] + z[0, 0] * math.cos(x[2, 0] + z[0, 1])\n",
-    "    #zp[1, 0] = x[1, 0] + z[0, 0] * math.sin(x[2, 0] + z[0, 1])\n",
-    "\n",
-    "    return zp\n",
-    "\n",
-    "\n",
-    "def get_LM_Pos_from_state(x, ind):\n",
-    "    \"\"\"\n",
-    "    Returns the position of a given landmark\n",
-    "    \n",
-    "    :param x:   The state containing all landmark positions\n",
-    "    :param ind: landmark id\n",
-    "    :returns:   The position of the landmark\n",
-    "    \"\"\"\n",
-    "    lm = x[STATE_SIZE + LM_SIZE * ind: STATE_SIZE + LM_SIZE * (ind + 1), :]\n",
-    "\n",
-    "    return lm\n",
-    "\n",
-    "\n",
-    "def search_correspond_LM_ID(xAug, PAug, zi):\n",
-    "    \"\"\"\n",
-    "    Landmark association with Mahalanobis distance.\n",
-    "    \n",
-    "    If this landmark is at least M_DIST_TH units away from all known landmarks, \n",
-    "    it is a NEW landmark.\n",
-    "    \n",
-    "    :param xAug: The estimated state\n",
-    "    :param PAug: The estimated covariance\n",
-    "    :param zi:   the read measurements of specific landmark\n",
-    "    :returns:    landmark id\n",
-    "    \"\"\"\n",
-    "\n",
-    "    nLM = calc_n_LM(xAug)\n",
-    "\n",
-    "    mdist = []\n",
-    "\n",
-    "    for i in range(nLM):\n",
-    "        lm = get_LM_Pos_from_state(xAug, i)\n",
-    "        y, S, H = calc_innovation(lm, xAug, PAug, zi, i)\n",
-    "        mdist.append(y.T @ np.linalg.inv(S) @ y)\n",
-    "\n",
-    "    mdist.append(M_DIST_TH)  # new landmark\n",
-    "\n",
-    "    minid = mdist.index(min(mdist))\n",
-    "\n",
-    "    return minid\n",
-    "\n",
-    "def calc_input():\n",
-    "    v = 1.0  # [m/s]\n",
-    "    yawrate = 0.1  # [rad/s]\n",
-    "    u = np.array([[v, yawrate]]).T\n",
-    "    return u\n",
-    "\n",
-    "def pi_2_pi(angle):\n",
-    "    return (angle + math.pi) % (2 * math.pi) - math.pi"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def main():\n",
-    "    print(\" start!!\")\n",
-    "\n",
-    "    time = 0.0\n",
-    "\n",
-    "    # RFID positions [x, y]\n",
-    "    RFID = np.array([[10.0, -2.0],\n",
-    "                     [15.0, 10.0],\n",
-    "                     [3.0, 15.0],\n",
-    "                     [-5.0, 20.0]])\n",
-    "\n",
-    "    # State Vector [x y yaw v]'\n",
-    "    xEst = np.zeros((STATE_SIZE, 1))\n",
-    "    xTrue = np.zeros((STATE_SIZE, 1))\n",
-    "    PEst = np.eye(STATE_SIZE)\n",
-    "\n",
-    "    xDR = np.zeros((STATE_SIZE, 1))  # Dead reckoning\n",
-    "\n",
-    "    # history\n",
-    "    hxEst = xEst\n",
-    "    hxTrue = xTrue\n",
-    "    hxDR = xTrue\n",
-    "\n",
-    "    while SIM_TIME >= time:\n",
-    "        time += DT\n",
-    "        u = calc_input()\n",
-    "\n",
-    "        xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID)\n",
-    "\n",
-    "        xEst, PEst = ekf_slam(xEst, PEst, ud, z)\n",
-    "\n",
-    "        x_state = xEst[0:STATE_SIZE]\n",
-    "\n",
-    "        # store data history\n",
-    "        hxEst = np.hstack((hxEst, x_state))\n",
-    "        hxDR = np.hstack((hxDR, xDR))\n",
-    "        hxTrue = np.hstack((hxTrue, xTrue))\n",
-    "\n",
-    "        if show_animation:  # pragma: no cover\n",
-    "            plt.cla()\n",
-    "\n",
-    "            plt.plot(RFID[:, 0], RFID[:, 1], \"*k\")\n",
-    "            plt.plot(xEst[0], xEst[1], \".r\")\n",
-    "\n",
-    "            # plot landmark\n",
-    "            for i in range(calc_n_LM(xEst)):\n",
-    "                plt.plot(xEst[STATE_SIZE + i * 2],\n",
-    "                         xEst[STATE_SIZE + i * 2 + 1], \"xg\")\n",
-    "\n",
-    "            plt.plot(hxTrue[0, :],\n",
-    "                     hxTrue[1, :], \"-b\")\n",
-    "            plt.plot(hxDR[0, :],\n",
-    "                     hxDR[1, :], \"-k\")\n",
-    "            plt.plot(hxEst[0, :],\n",
-    "                     hxEst[1, :], \"-r\")\n",
-    "            plt.axis(\"equal\")\n",
-    "            plt.grid(True)\n",
-    "            plt.pause(0.001)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " start!!\n",
-      "New LM\n",
-      "New LM\n",
-      "New LM\n"
-     ]
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "/* global mpl */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "mpl.get_websocket_type = function () {\n",
-       "    if (typeof WebSocket !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert(\n",
-       "            'Your browser does not have WebSocket support. ' +\n",
-       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "                'Firefox 4 and 5 are also supported but you ' +\n",
-       "                'have to enable WebSockets in about:config.'\n",
-       "        );\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById('mpl-warnings');\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent =\n",
-       "                'This browser does not support binary websocket messages. ' +\n",
-       "                'Performance may be slow.';\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = document.createElement('div');\n",
-       "    this.root.setAttribute('style', 'display: inline-block');\n",
-       "    this._root_extra_style(this.root);\n",
-       "\n",
-       "    parent_element.appendChild(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen = function () {\n",
-       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
-       "        fig.send_message('send_image_mode', {});\n",
-       "        if (fig.ratio !== 1) {\n",
-       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
-       "        }\n",
-       "        fig.send_message('refresh', {});\n",
-       "    };\n",
-       "\n",
-       "    this.imageObj.onload = function () {\n",
-       "        if (fig.image_mode === 'full') {\n",
-       "            // Full images could contain transparency (where diff images\n",
-       "            // almost always do), so we need to clear the canvas so that\n",
-       "            // there is no ghosting.\n",
-       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "        }\n",
-       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "    };\n",
-       "\n",
-       "    this.imageObj.onunload = function () {\n",
-       "        fig.ws.close();\n",
-       "    };\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function () {\n",
-       "    var titlebar = document.createElement('div');\n",
-       "    titlebar.classList =\n",
-       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
-       "    var titletext = document.createElement('div');\n",
-       "    titletext.classList = 'ui-dialog-title';\n",
-       "    titletext.setAttribute(\n",
-       "        'style',\n",
-       "        'width: 100%; text-align: center; padding: 3px;'\n",
-       "    );\n",
-       "    titlebar.appendChild(titletext);\n",
-       "    this.root.appendChild(titlebar);\n",
-       "    this.header = titletext;\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function () {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
-       "    canvas_div.setAttribute(\n",
-       "        'style',\n",
-       "        'border: 1px solid #ddd;' +\n",
-       "            'box-sizing: content-box;' +\n",
-       "            'clear: both;' +\n",
-       "            'min-height: 1px;' +\n",
-       "            'min-width: 1px;' +\n",
-       "            'outline: 0;' +\n",
-       "            'overflow: hidden;' +\n",
-       "            'position: relative;' +\n",
-       "            'resize: both;'\n",
-       "    );\n",
-       "\n",
-       "    function on_keyboard_event_closure(name) {\n",
-       "        return function (event) {\n",
-       "            return fig.key_event(event, name);\n",
-       "        };\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.addEventListener(\n",
-       "        'keydown',\n",
-       "        on_keyboard_event_closure('key_press')\n",
-       "    );\n",
-       "    canvas_div.addEventListener(\n",
-       "        'keyup',\n",
-       "        on_keyboard_event_closure('key_release')\n",
-       "    );\n",
-       "\n",
-       "    this._canvas_extra_style(canvas_div);\n",
-       "    this.root.appendChild(canvas_div);\n",
-       "\n",
-       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
-       "    canvas.classList.add('mpl-canvas');\n",
-       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
-       "\n",
-       "    this.context = canvas.getContext('2d');\n",
-       "\n",
-       "    var backingStore =\n",
-       "        this.context.backingStorePixelRatio ||\n",
-       "        this.context.webkitBackingStorePixelRatio ||\n",
-       "        this.context.mozBackingStorePixelRatio ||\n",
-       "        this.context.msBackingStorePixelRatio ||\n",
-       "        this.context.oBackingStorePixelRatio ||\n",
-       "        this.context.backingStorePixelRatio ||\n",
-       "        1;\n",
-       "\n",
-       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
-       "        'canvas'\n",
-       "    ));\n",
-       "    rubberband_canvas.setAttribute(\n",
-       "        'style',\n",
-       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
-       "    );\n",
-       "\n",
-       "    // Apply a ponyfill if ResizeObserver is not implemented by browser.\n",
-       "    if (this.ResizeObserver === undefined) {\n",
-       "        if (window.ResizeObserver !== undefined) {\n",
-       "            this.ResizeObserver = window.ResizeObserver;\n",
-       "        } else {\n",
-       "            var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n",
-       "            this.ResizeObserver = obs.ResizeObserver;\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n",
-       "        var nentries = entries.length;\n",
-       "        for (var i = 0; i < nentries; i++) {\n",
-       "            var entry = entries[i];\n",
-       "            var width, height;\n",
-       "            if (entry.contentBoxSize) {\n",
-       "                if (entry.contentBoxSize instanceof Array) {\n",
-       "                    // Chrome 84 implements new version of spec.\n",
-       "                    width = entry.contentBoxSize[0].inlineSize;\n",
-       "                    height = entry.contentBoxSize[0].blockSize;\n",
-       "                } else {\n",
-       "                    // Firefox implements old version of spec.\n",
-       "                    width = entry.contentBoxSize.inlineSize;\n",
-       "                    height = entry.contentBoxSize.blockSize;\n",
-       "                }\n",
-       "            } else {\n",
-       "                // Chrome <84 implements even older version of spec.\n",
-       "                width = entry.contentRect.width;\n",
-       "                height = entry.contentRect.height;\n",
-       "            }\n",
-       "\n",
-       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
-       "            // the canvas container.\n",
-       "            if (entry.devicePixelContentBoxSize) {\n",
-       "                // Chrome 84 implements new version of spec.\n",
-       "                canvas.setAttribute(\n",
-       "                    'width',\n",
-       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
-       "                );\n",
-       "                canvas.setAttribute(\n",
-       "                    'height',\n",
-       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
-       "                );\n",
-       "            } else {\n",
-       "                canvas.setAttribute('width', width * fig.ratio);\n",
-       "                canvas.setAttribute('height', height * fig.ratio);\n",
-       "            }\n",
-       "            canvas.setAttribute(\n",
-       "                'style',\n",
-       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
-       "            );\n",
-       "\n",
-       "            rubberband_canvas.setAttribute('width', width);\n",
-       "            rubberband_canvas.setAttribute('height', height);\n",
-       "\n",
-       "            // And update the size in Python. We ignore the initial 0/0 size\n",
-       "            // that occurs as the element is placed into the DOM, which should\n",
-       "            // otherwise not happen due to the minimum size styling.\n",
-       "            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n",
-       "                fig.request_resize(width, height);\n",
-       "            }\n",
-       "        }\n",
-       "    });\n",
-       "    this.resizeObserverInstance.observe(canvas_div);\n",
-       "\n",
-       "    function on_mouse_event_closure(name) {\n",
-       "        return function (event) {\n",
-       "            return fig.mouse_event(event, name);\n",
-       "        };\n",
-       "    }\n",
-       "\n",
-       "    rubberband_canvas.addEventListener(\n",
-       "        'mousedown',\n",
-       "        on_mouse_event_closure('button_press')\n",
-       "    );\n",
-       "    rubberband_canvas.addEventListener(\n",
-       "        'mouseup',\n",
-       "        on_mouse_event_closure('button_release')\n",
-       "    );\n",
-       "    rubberband_canvas.addEventListener(\n",
-       "        'dblclick',\n",
-       "        on_mouse_event_closure('dblclick')\n",
-       "    );\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband_canvas.addEventListener(\n",
-       "        'mousemove',\n",
-       "        on_mouse_event_closure('motion_notify')\n",
-       "    );\n",
-       "\n",
-       "    rubberband_canvas.addEventListener(\n",
-       "        'mouseenter',\n",
-       "        on_mouse_event_closure('figure_enter')\n",
-       "    );\n",
-       "    rubberband_canvas.addEventListener(\n",
-       "        'mouseleave',\n",
-       "        on_mouse_event_closure('figure_leave')\n",
-       "    );\n",
-       "\n",
-       "    canvas_div.addEventListener('wheel', function (event) {\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        on_mouse_event_closure('scroll')(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.appendChild(canvas);\n",
-       "    canvas_div.appendChild(rubberband_canvas);\n",
-       "\n",
-       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
-       "    this.rubberband_context.strokeStyle = '#000000';\n",
-       "\n",
-       "    this._resize_canvas = function (width, height, forward) {\n",
-       "        if (forward) {\n",
-       "            canvas_div.style.width = width + 'px';\n",
-       "            canvas_div.style.height = height + 'px';\n",
-       "        }\n",
-       "    };\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
-       "        event.preventDefault();\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus() {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function () {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var toolbar = document.createElement('div');\n",
-       "    toolbar.classList = 'mpl-toolbar';\n",
-       "    this.root.appendChild(toolbar);\n",
-       "\n",
-       "    function on_click_closure(name) {\n",
-       "        return function (_event) {\n",
-       "            return fig.toolbar_button_onclick(name);\n",
-       "        };\n",
-       "    }\n",
-       "\n",
-       "    function on_mouseover_closure(tooltip) {\n",
-       "        return function (event) {\n",
-       "            if (!event.currentTarget.disabled) {\n",
-       "                return fig.toolbar_button_onmouseover(tooltip);\n",
-       "            }\n",
-       "        };\n",
-       "    }\n",
-       "\n",
-       "    fig.buttons = {};\n",
-       "    var buttonGroup = document.createElement('div');\n",
-       "    buttonGroup.classList = 'mpl-button-group';\n",
-       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            /* Instead of a spacer, we start a new button group. */\n",
-       "            if (buttonGroup.hasChildNodes()) {\n",
-       "                toolbar.appendChild(buttonGroup);\n",
-       "            }\n",
-       "            buttonGroup = document.createElement('div');\n",
-       "            buttonGroup.classList = 'mpl-button-group';\n",
-       "            continue;\n",
-       "        }\n",
-       "\n",
-       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
-       "        button.classList = 'mpl-widget';\n",
-       "        button.setAttribute('role', 'button');\n",
-       "        button.setAttribute('aria-disabled', 'false');\n",
-       "        button.addEventListener('click', on_click_closure(method_name));\n",
-       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
-       "\n",
-       "        var icon_img = document.createElement('img');\n",
-       "        icon_img.src = '_images/' + image + '.png';\n",
-       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
-       "        icon_img.alt = tooltip;\n",
-       "        button.appendChild(icon_img);\n",
-       "\n",
-       "        buttonGroup.appendChild(button);\n",
-       "    }\n",
-       "\n",
-       "    if (buttonGroup.hasChildNodes()) {\n",
-       "        toolbar.appendChild(buttonGroup);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker = document.createElement('select');\n",
-       "    fmt_picker.classList = 'mpl-widget';\n",
-       "    toolbar.appendChild(fmt_picker);\n",
-       "    this.format_dropdown = fmt_picker;\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = document.createElement('option');\n",
-       "        option.selected = fmt === mpl.default_extension;\n",
-       "        option.innerHTML = fmt;\n",
-       "        fmt_picker.appendChild(option);\n",
-       "    }\n",
-       "\n",
-       "    var status_bar = document.createElement('span');\n",
-       "    status_bar.classList = 'mpl-message';\n",
-       "    toolbar.appendChild(status_bar);\n",
-       "    this.message = status_bar;\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function (type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function () {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
-       "        fig.send_message('refresh', {});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
-       "    var x0 = msg['x0'] / fig.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
-       "    var x1 = msg['x1'] / fig.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0,\n",
-       "        0,\n",
-       "        fig.canvas.width / fig.ratio,\n",
-       "        fig.canvas.height / fig.ratio\n",
-       "    );\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch (cursor) {\n",
-       "        case 0:\n",
-       "            cursor = 'pointer';\n",
-       "            break;\n",
-       "        case 1:\n",
-       "            cursor = 'default';\n",
-       "            break;\n",
-       "        case 2:\n",
-       "            cursor = 'crosshair';\n",
-       "            break;\n",
-       "        case 3:\n",
-       "            cursor = 'move';\n",
-       "            break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
-       "    for (var key in msg) {\n",
-       "        if (!(key in fig.buttons)) {\n",
-       "            continue;\n",
-       "        }\n",
-       "        fig.buttons[key].disabled = !msg[key];\n",
-       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
-       "    if (msg['mode'] === 'PAN') {\n",
-       "        fig.buttons['Pan'].classList.add('active');\n",
-       "        fig.buttons['Zoom'].classList.remove('active');\n",
-       "    } else if (msg['mode'] === 'ZOOM') {\n",
-       "        fig.buttons['Pan'].classList.remove('active');\n",
-       "        fig.buttons['Zoom'].classList.add('active');\n",
-       "    } else {\n",
-       "        fig.buttons['Pan'].classList.remove('active');\n",
-       "        fig.buttons['Zoom'].classList.remove('active');\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function () {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message('ack', {});\n",
-       "};\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            var img = evt.data;\n",
-       "            if (img.type !== 'image/png') {\n",
-       "                /* FIXME: We get \"Resource interpreted as Image but\n",
-       "                 * transferred with MIME type text/plain:\" errors on\n",
-       "                 * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "                 * to be part of the websocket stream */\n",
-       "                img.type = 'image/png';\n",
-       "            }\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src\n",
-       "                );\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                img\n",
-       "            );\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        } else if (\n",
-       "            typeof evt.data === 'string' &&\n",
-       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
-       "        ) {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig['handle_' + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\n",
-       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
-       "                msg\n",
-       "            );\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\n",
-       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
-       "                    e,\n",
-       "                    e.stack,\n",
-       "                    msg\n",
-       "                );\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "};\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function (e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e) {\n",
-       "        e = window.event;\n",
-       "    }\n",
-       "    if (e.target) {\n",
-       "        targ = e.target;\n",
-       "    } else if (e.srcElement) {\n",
-       "        targ = e.srcElement;\n",
-       "    }\n",
-       "    if (targ.nodeType === 3) {\n",
-       "        // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "    }\n",
-       "\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    var boundingRect = targ.getBoundingClientRect();\n",
-       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
-       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
-       "\n",
-       "    return { x: x, y: y };\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys(original) {\n",
-       "    return Object.keys(original).reduce(function (obj, key) {\n",
-       "        if (typeof original[key] !== 'object') {\n",
-       "            obj[key] = original[key];\n",
-       "        }\n",
-       "        return obj;\n",
-       "    }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event);\n",
-       "\n",
-       "    if (name === 'button_press') {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * this.ratio;\n",
-       "    var y = canvas_pos.y * this.ratio;\n",
-       "\n",
-       "    this.send_message(name, {\n",
-       "        x: x,\n",
-       "        y: y,\n",
-       "        button: event.button,\n",
-       "        step: event.step,\n",
-       "        guiEvent: simpleKeys(event),\n",
-       "    });\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function (event, name) {\n",
-       "    // Prevent repeat events\n",
-       "    if (name === 'key_press') {\n",
-       "        if (event.key === this._key) {\n",
-       "            return;\n",
-       "        } else {\n",
-       "            this._key = event.key;\n",
-       "        }\n",
-       "    }\n",
-       "    if (name === 'key_release') {\n",
-       "        this._key = null;\n",
-       "    }\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.key !== 'Control') {\n",
-       "        value += 'ctrl+';\n",
-       "    }\n",
-       "    else if (event.altKey && event.key !== 'Alt') {\n",
-       "        value += 'alt+';\n",
-       "    }\n",
-       "    else if (event.shiftKey && event.key !== 'Shift') {\n",
-       "        value += 'shift+';\n",
-       "    }\n",
-       "\n",
-       "    value += 'k' + event.key;\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
-       "    return false;\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
-       "    if (name === 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message('toolbar_button', { name: name });\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "\n",
-       "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n",
-       "// prettier-ignore\n",
-       "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";/* global mpl */\n",
-       "\n",
-       "var comm_websocket_adapter = function (comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.binaryType = comm.kernel.ws.binaryType;\n",
-       "    ws.readyState = comm.kernel.ws.readyState;\n",
-       "    function updateReadyState(_event) {\n",
-       "        if (comm.kernel.ws) {\n",
-       "            ws.readyState = comm.kernel.ws.readyState;\n",
-       "        } else {\n",
-       "            ws.readyState = 3; // Closed state.\n",
-       "        }\n",
-       "    }\n",
-       "    comm.kernel.ws.addEventListener('open', updateReadyState);\n",
-       "    comm.kernel.ws.addEventListener('close', updateReadyState);\n",
-       "    comm.kernel.ws.addEventListener('error', updateReadyState);\n",
-       "\n",
-       "    ws.close = function () {\n",
-       "        comm.close();\n",
-       "    };\n",
-       "    ws.send = function (m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function (msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        var data = msg['content']['data'];\n",
-       "        if (data['blob'] !== undefined) {\n",
-       "            data = {\n",
-       "                data: new Blob(msg['buffers'], { type: data['blob'] }),\n",
-       "            };\n",
-       "        }\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(data);\n",
-       "    });\n",
-       "    return ws;\n",
-       "};\n",
-       "\n",
-       "mpl.mpl_figure_comm = function (comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = document.getElementById(id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm);\n",
-       "\n",
-       "    function ondownload(figure, _format) {\n",
-       "        window.open(figure.canvas.toDataURL());\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element;\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error('Failed to find cell for figure', id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "    fig.cell_info[0].output_area.element.on(\n",
-       "        'cleared',\n",
-       "        { fig: fig },\n",
-       "        fig._remove_fig_handler\n",
-       "    );\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
-       "    var width = fig.canvas.width / fig.ratio;\n",
-       "    fig.cell_info[0].output_area.element.off(\n",
-       "        'cleared',\n",
-       "        fig._remove_fig_handler\n",
-       "    );\n",
-       "    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable();\n",
-       "    fig.parent_element.innerHTML =\n",
-       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "    fig.close_ws(fig, msg);\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width / this.ratio;\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] =\n",
-       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function () {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message('ack', {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () {\n",
-       "        fig.push_to_output();\n",
-       "    }, 1000);\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function () {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var toolbar = document.createElement('div');\n",
-       "    toolbar.classList = 'btn-toolbar';\n",
-       "    this.root.appendChild(toolbar);\n",
-       "\n",
-       "    function on_click_closure(name) {\n",
-       "        return function (_event) {\n",
-       "            return fig.toolbar_button_onclick(name);\n",
-       "        };\n",
-       "    }\n",
-       "\n",
-       "    function on_mouseover_closure(tooltip) {\n",
-       "        return function (event) {\n",
-       "            if (!event.currentTarget.disabled) {\n",
-       "                return fig.toolbar_button_onmouseover(tooltip);\n",
-       "            }\n",
-       "        };\n",
-       "    }\n",
-       "\n",
-       "    fig.buttons = {};\n",
-       "    var buttonGroup = document.createElement('div');\n",
-       "    buttonGroup.classList = 'btn-group';\n",
-       "    var button;\n",
-       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            /* Instead of a spacer, we start a new button group. */\n",
-       "            if (buttonGroup.hasChildNodes()) {\n",
-       "                toolbar.appendChild(buttonGroup);\n",
-       "            }\n",
-       "            buttonGroup = document.createElement('div');\n",
-       "            buttonGroup.classList = 'btn-group';\n",
-       "            continue;\n",
-       "        }\n",
-       "\n",
-       "        button = fig.buttons[name] = document.createElement('button');\n",
-       "        button.classList = 'btn btn-default';\n",
-       "        button.href = '#';\n",
-       "        button.title = name;\n",
-       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
-       "        button.addEventListener('click', on_click_closure(method_name));\n",
-       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
-       "        buttonGroup.appendChild(button);\n",
-       "    }\n",
-       "\n",
-       "    if (buttonGroup.hasChildNodes()) {\n",
-       "        toolbar.appendChild(buttonGroup);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = document.createElement('span');\n",
-       "    status_bar.classList = 'mpl-message pull-right';\n",
-       "    toolbar.appendChild(status_bar);\n",
-       "    this.message = status_bar;\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = document.createElement('div');\n",
-       "    buttongrp.classList = 'btn-group inline pull-right';\n",
-       "    button = document.createElement('button');\n",
-       "    button.classList = 'btn btn-mini btn-primary';\n",
-       "    button.href = '#';\n",
-       "    button.title = 'Stop Interaction';\n",
-       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
-       "    button.addEventListener('click', function (_evt) {\n",
-       "        fig.handle_close(fig, {});\n",
-       "    });\n",
-       "    button.addEventListener(\n",
-       "        'mouseover',\n",
-       "        on_mouseover_closure('Stop Interaction')\n",
-       "    );\n",
-       "    buttongrp.appendChild(button);\n",
-       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
-       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
-       "    var fig = event.data.fig;\n",
-       "    if (event.target !== this) {\n",
-       "        // Ignore bubbled events from children.\n",
-       "        return;\n",
-       "    }\n",
-       "    fig.close_ws(fig, {});\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function (el) {\n",
-       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.setAttribute('tabindex', 0);\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    } else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager) {\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "    }\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which === 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "};\n",
-       "\n",
-       "mpl.find_output_cell = function (html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i = 0; i < ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code') {\n",
-       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] === html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel !== null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target(\n",
-       "        'matplotlib',\n",
-       "        mpl.mpl_figure_comm\n",
-       "    );\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<img 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AAEIWEoAA9jSwpIWBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEMYDQAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQMBSAhjAlhaWtCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIYACjAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAAClhLAALa0sKQFAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAADGA1AAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACELCUAAawpYUlLQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACGMBoAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEICApQQwgC0tLGlBAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEMAARgMQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCwlgAFsaWFJCwIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABjAagAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBgKQEMYEsLS1oQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABDCA0QAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAUsJYABbWljb01q9erW88cYbXpr169eXMmXK2J4y+UEAAhCAAAQgAAEIQAACEIAABCDgMIFt27bJsmXLPALdu3eXmjVrOkyD1JMhgAGcDC36akPgmWeekauuukqbeAgEAhCAAAQgAAEIQAACEIAABCAAAQhERWDs2LEyZMiQqKZjHsMJYAAbXkBXw8cAdrXy5A0BCEAAAhCAAAQgAAEIQAACEIAABjAaSIYABnAytOirDYH3339fzjrrLC8e9aJ3wgknaBNb1IFs3rxZvvjiC2/aFi1aSPny5aMOgfk0J4BGNC+QBuGhEQ2KoHkIaETzAmkQHhrRoAiah4BGNC+QBuGhEQ2KoHkIaERk4cKFB78N/d5770nnzp01rxrh6UIAA1iXShBHUgT+7//+T9q1a+ddM2/ePDnllFOSut6mzr/++qvMnTvXS6lDhw5SuXJlm9IjlwAIoJEAIFo+BBqxvMABpIdGAoBo+RBoxPICB5AeGgkAouVDoBHLCxxAemhEBC8kACE5OgQGsKOFNz1tXvQOVZA3QdPVHH78aCR8xqbPgEZMr2D48aOR8BmbPgMaMb2C4cePRsJnbPoMaMT0CoYfPxrBAA5fZfbOgAFsb22tzgwDGAPYaoEHnBwLpYCBWjgcGrGwqAGnhEYCBmrhcGjEwqIGnBIaCRiohcOhEQuLGnBKaAQDOGBJOTUcBrBT5bYnWQxgDGB71Bx+JiyUwmds+gxoxPQKhh8/GgmfsekzoBHTKxh+/GgkfMamz4BGTK9g+PGjEQzg8FVm7wwYwPbW1urMMIAxgK0WeMDJsVAKGKiFw6ERC4sacEpoJGCgFg6HRiwsasApoZGAgVo4HBqxsKgBp4RGMIADlpRTw2EAO1Vue5LFAMYAtkfN4WfCQil8xqbPgEZMr2D48aOR8BmbPgMaMb2C4cePRsJnbPoMaMT0CoYfPxrBAA5fZfbOgAFsb22tzgwDGAPYaoEHnBwLpYCBWjgcGrGwqAGnhEYCBmrhcGjEwqIGnBIaCRiohcOhEQuLGnBKaAQDOGBJOTUcBrBT5bYnWQxgDGB71Bx+JiyUwmds+gxoxPQKhh8/GgmfsekzoBHTKxh+/GgkfMamz4BGTK9g+PGjEQzg8FVm7wwYwPbW1urMMIAxgK0WeMDJsVAKGKiFw6ERC4sacEpoJGCgFg6HRiwsasApoZGAgVo4HBqxsKgBp4RGMIADlpRTw2EAO1Vue5LFAMYAtkfN4WfCQil8xqbPgEZMr2D48aOR8BmbPgMaMb2C4cePRsJnbPoMaMT0CoYfPxrBAA5fZfbOgAFsb22tzgwDGAPYaoEHnBwLpYCBWjgcGrGwqAGnhEYCBmrhcGjEwqIGnBIaCRiohcOhEQuLGnBKaAQDOGBJOTUcBrBT5bYnWQxgDGB71Bx+JiyUwmds+gxoxPQKhh8/GgmfsekzoBHTKxh+/GgkfMamz4BGTK9g+PGjEQzg8FVm7wwYwPbW1urMMIAxgK0WeMDJsVAKGKiFw6ERC4sacEpoJGCgFg6HRiwsasApoZGAgVo4HBqxsKgBp4RGMIADlpRTw2EAO1Vue5LFAMYAtkfN4WfCQil8xqbPgEZMr2D48aOR8BmbPgMaMb2C4cePRsJnbPoMaMT0CoYfPxrBAA5fZfbOgAFsb22tzgwDGAPYaoEHnBwLpYCBWjgcGrGwqAGnhEYCBmrhcGjEwqIGnBIaCRiohcOhEQuLGnBKaAQDOGBJOTUcBrBT5bYnWQxgDGB71Bx+JiyUwmds+gxoxPQKhh8/GgmfsekzoBHTKxh+/GgkfMamz4BGTK9g+PGjEQzg8FVm7wwYwPbW1urMMIAxgK0WeMDJsVAKGKiFw6ERC4sacEpoJGCgFg6HRiwsasApoZGAgVo4HBqxsKgBp4RGMIADlpRTw2EAO1Vue5LFAMYAtkfN4WfCQil8xqbPgEZMr2D48aOR8BmbPgMaMb2C4cePRsJnbPoMaMT0CoYfPxrBAA5fZfbOgAFsb22tzgwDGAPYaoEHnBwLpYCBWjgcGrGwqAGnhEYCBmrhcGjEwqIGnBIaCRiohcOhEQuLGnBKaAQDOGBJOTUcBrBT5bYnWQxgDGB71Bx+JiyUwmds+gxoxPQKhh8/GgmfsekzoBHTKxh+/GgkfMamz4BGTK9g+PGjEQzg8FVm7wwYwPbW1urMMIAxgK0WeMDJsVAKGKiFw6ERC4sacEpoJGCgFg6HRiwsasApoZGAgVo4HBqxsKgBp4RGMIADlpRTw2EAO1Vue5LFAMYAtkfN4WfCQil8xqbPgEZMr2D48aOR8BmbPgMaMb2C4cePRsJnbPoMaMT0CoYfPxrBAA5fZfbOgAFsb22tzgwDGAPYaoEHnBwLpYCBWjgcGrGwqAGnhEYCBmrhcGjEwqIGnBIaCRiohcOhEQuLGnBKaAQDOGBJOTUcBrDF5d6yZYu8++678uGHH8qCBQtkyZIlsmnTJilVqpTUrFlTWrduLZdeeql06dJFMjIyCiTx0UcfyRlnnOGb1GWXXSYTJkzw3T+VjhjAGMCp6MbVa1gouVp5/3mjEf+sXO2JRlytvP+80Yh/Vq72RCOuVt5/3mjEPytXe6IRDGBXtR9E3hjAQVDUcIxHH31URo4cKTt37kwYXYcOHWTSpElSp06dfPtiACdEGGsH3gRjxW/E5GjEiDLFGiQaiRW/EZOjESPKFGuQaCRW/EZMjkaMKFOsQaKRWPEbMTkawQA2QqiaBokBrGlh0g1r6NChMnbsWG+YWrVqyZlnniknn3yyVK1aVXbs2CGffPKJZ/pu3brV61OvXj2ZP3++VKtWLc/UOQ3giy66SC6++OJCw1NGcsuWLdNNodDr2QF8CA9vgqFKzYrB0YgVZQw1CTQSKl4rBkcjVpQx1CTQSKh4rRgcjVhRxlCTQCOh4rVicDSCAWyFkGNKAgM4JvBhT3v11VfLsmXL5Oabb/bM38zMzDxTrlixwjv+YdGiRd5jl19+uYwfP75QA/iOO+6QO++8M+zwE46PAYwBnFAkdDhIgIUSYkhEAI0kIsTjaAQNJCKARhIR4nE0ggYSEUAjiQjxOBrBAOZZkDoBDODU2Wl9pXphrFy5csIYv/zyS2nRooXXr3Tp0rJu3Trvd86WcwcwBnBCpJF34E0wcuTGTYhGjCtZ5AGjkciRGzchGjGuZJEHjEYiR27chGjEuJJFHjAaiRy5cROiEQxg40SrUcAYwBoVI65Qjj32WPn++++96ZUh3KxZMwzguIqRwry8CaYAzbFL0IhjBU8hXTSSAjTHLkEjjhU8hXTRSArQHLsEjThW8BTSRSMpQHPsEjSCAeyY5ANNFwM4UJxmDta6dWv59NNPveDVOcBt2rTBADaolLwJGlSsmEJFIzGBN2haNGJQsWIKFY3EBN6gadGIQcWKKVQ0EhN4g6ZFIwYVK6ZQ0QgGcEzSs2JaDGAryph6Ert27ZLq1avLb7/95g3yyy+/SI0aNQo0gJs2bSpFihTxzhfevXu3d8yE+rezzjpLrrjiCl/HTqQe7aErOQP4EAveBINQlN1joBG76xtEdmgkCIp2j4FG7K5vENmhkSAo2j0GGrG7vkFkh0aCoGj3GGgEA9huhYebHQZwuHy1H33ChAnezd9Ua9mypXz22Wd5Ys55BnBhCZUtW1Yee+wxGTRoUFp5K3M3UVu4cKFcddVVXreZM2dKq1atEl1i7eObN2/2ju5QrXnz5lK+fHlrcyWx1AigkdS4uXQVGnGp2qnlikZS4+bSVWjEpWqnlisaSY2bS1ehEZeqnVquaES8b2937drVAzhv3jw55ZRTUoPJVc4RwAB2ruSHElY3fDv++OO9G7+pNnXqVOnVq1ceIsoA7tSpk2cQn3766aLODK5QoYJs3bpVlBE7ZcoUWbVq1cHrHnjgARkxYkTKZDMyMpK69sEHH5QmTZokdQ2dIQABCEAAAiYSyMrKkrVr18pXX33l/T7xxBNl3759or7Rs3Pnzly/1bd61Bn/J5xwguzfv1+y31/VGKqp3+pH/bv6dk/2T9GiRfP9b/V4fo+VKFFC1E/JkiUP/pQqVUqKFy/ujUODAAQgAAEIQAACEAiGgFrbZfstGMDBMHVlFAxgVyp9WJ7q+IbOnTvL3LlzvUd69uwp06ZNy5eG+gNyy5Yt0qhRo3wf37Nnj9x6663yyCOPeI+rPyTVC1Hbtm1ToosBnBI2LoIABCAAAQsIbNu2Tf71r3/J3r17vfdT9VVH9SGrOqPfxKZM4GxjWJnC6ttC5cqV835n/3f2/6vf6gPmihUren2SXQ+YyIeYIQABCEAAAhCAQDIEMICToUXfnAQwgB3Ug9oFNGDAAPnHP/7hZX/MMcd4XyOoVKlSWjTU0Q/PP/+8N0a3bt3kzTffTGk8joBIDhtfg0mOl4u90YiLVU8uZzSSHK90e2/YsME7S3/58uXej/rvH3/8UZYuXSqbNm1Kd3grri9WrJhUq1ZNfv7551z5jBw5Unr06CFHH320qD40fQjwOqJPLXSNBI3oWhl94kIj+tRC10jQCEdA6KpNE+LCADahSgHGqL7qqc7OHTdunDdqnTp1ZPbs2VK3bt20Z1F/pNWuXdv7Oqn6KujGjRtF7fYJo3ETuENUOQg/DIXZNSYasaueYWSDRsKgKrJjxw759ttvveOS1JEN6rf6+e9//xvOhA6Nqo6iqF+/vjRu3Nj7hpL63aJFC++4C7XjmBY9AV5Homdu2oxoxLSKRR8vGomeuWkzohFuAmeaZnWKFwNYp2qEHIsyZocNGyZjxozxZqpVq5ao833VDuCgmvoDbPHixd5w33zzjRx33HFBDZ1rHAxgDOBQhGXpoCyULC1sgGmhkfRhqvN31Q051Tdq/v3vf3u/1fuh+taNrk0dsaB+dI4xWXbKGFb3NzjppJO8exeo382aNZPSpUsnOxT9kyTA60iSwBzsjkYcLHqSKaORJIE52B2NYAA7KPvAUsYADgyl3gMp8/eaa66R0aNHe4EeddRRnvnboEGDQAM/9dRTvfN/Vfv444+lXbt2gY6fPRgGMAZwKMKydFAWSpYWNsC00EhyMJVhqs5fU0Zv9o/a4avOxA+jZWZmFmjS9uvXT7p37y5lypTJ90cZn+pbOdlmb/bvnHGqNYLKScWf/aPOIM75/+vXr/dyVf/etGlTb67sx7dv3y7q7GJ1c9iCfqvH1Nc2ldayf9TN66Jo6kZ06ga22aZwmzZtvJvnqfOJacER4HUkOJa2joRGbK1scHmhkeBY2joSGsEAtlXbUeSFARwF5ZjnONz8rVmzpmf+NmzYMPDI1NcwlyxZ4o3LDuDA8eY7IG+C0XA2eRY0YnL1ookdjRTOWRmdn3/+uXfj1Dlz5ng3aVPMgmpq12q9evW8D2XVB7TKKFXHGFx33XXe/6uzcJWwN/4NAAAgAElEQVQJHGcLWiNqbaJuMKvOQ842hNV/q+Mx1M1n16xZ4938btasWaGkrUzxk08+WU455ZSDP0ceeWQoc7kyaNAacYWbS3miEZeqnVquaCQ1bi5dhUYwgF3Se9C5YgAHTVSz8Q43f9UfN8r8VUZt0E39oabOFM4+A1i9OIf1lUt2AB+qHm+CQSvZvvHQiH01DTojNJKbqDq7V+12VWav+lHvOWpna7qtcuXK3hm16kftSFUfxCrTV52fr0xgnVvcGlHzf/HFF6J2Ii9atMg7XkP9Vj/KMA+iqRvLqW8uZZvCzZs350ZzSYCNWyNJhErXmAigkZjAGzQtGjGoWDGFikYwgGOSnhXTYgBbUcaCk1DHPjz99NNehxo1anjmrzqnN4x2+eWXy4QJE7yhu3btKm+//XYY03hjYgAfQsubYGgys2ZgNGJNKUNLxHWNqPN71bFFarepMnyV+ZvucQ7qvVbtMFUmYrbpq76Bo45gMLHpqhH1ofPatWu9m+x99tlnsmDBAu/3smXL0sasbmSratihQwfp1KmTZw6HdXPbtIPVYABdNaIBGkL4HwE0ghQSEUAjiQjxOBrBC+FZkDoBDODU2Wl/5fDhw+XJJ588aP5++OGH0qRJk6TiXrp0qUydOlWGDh0q5cuXz/da9UfyrbfeKo888sjBx8M8/xcDOHcZeBNMStJOdkYjTpY9qaRd04g6e1btJn3//fe9H3WkgzKBU23qmIbWrVt7P61atfJMwwoVKqQ6nJbXmaaRjRs3esd2KDM42xjOPqIqVcDqzGBlAiszWP2oWnOO8CGapmkkVR1wXeoE0Ejq7Fy5Eo24UunU80QjGMCpq4crMYAt1cBtt90m9913n5ed2m10//33+zJ/1R2z1TEO2U39gaxulKLOysv+Y0edU1iuXDnvZi8LFy6UKVOmyE8//XTwGjWvMoTDbOwA5g+uMPVl29gslGyraPD52K4RtUtU7QjNNnw/+OCDlM/wVTcUUzcTU7tC27dv75m+amev7c0Gjfz222/y6aefet8iUj/z588XZRSn2tSN8E4//XTp0qWL96OO9DB1h3eqDHJeZ4NGguDAGAUTQCOoIxEBNJKIEI+jEQxgngWpE8AATp2d1leqP0hmz56ddIzPP/+8DBw4MI8B7GcgtUP4r3/9qwwaNMhP97T6YABjAKclIMcuZqHkWMFTSNdGjWzfvl3UN1/eeust70ii5cuXp0BGvJuxtW3b1jN8O3bs6P132bJlUxrL5Its1Mj+/fu9s4TVmmLevHne72+//da7l0EqrW7dup4RrI7BUh+aF/TNqVTGNuEaGzViAneTYkQjJlUrnljRSDzcTZoVjWAAm6RX3WLFANatIgHFE5QBvGvXLu/cYLVL5pNPPpEff/zRu2O32jGjdgUfccQR3vmGZ511lvTv3z+yP3YwgDGAA3qqODEMCyUnypxWkrZoRJm8yvB98803PfM3lWMd1Ddc1M5eZfYq01cd56De71xvtmgkUR3VLmG13sneJayM4S1btiS6rNDHr7/+eu+YLN1v9JdWkiLervq5c+d6w6jnjrrpIQ0COQmgEfSQiAAaSUSIx9EIBjDPgtQJYACnzo4rYySAAYwBHKP8jJuahZJxJYs8YFM1snv3bu/mbcrwVcbvd999lzS77HNdO3fuLOpHHe9gu1GXNCSHzb29e/d6ZwirY0PUTzrnRV988cUyatSoXEdtpVILXa8x9XVEV542xoVGbKxqsDmhkWB52jgaGsEAtlHXUeWEARwVaeYJlAAGMAZwoIKyfDAWSpYXOID0TNKI2o2pDF91g9KZM2emtDuzRYsWntmrftRuX3WWK61wAiZpJMxaqm9GqW9FzZo1S959913vTGF1lEQyTd1ITpnBffr0kRo1aiRzqdZ90YjW5dEiODSiRRm0DgKNaF0eLYJDIxjAWgjR0CAwgA0tnOthYwBjALv+HEgmfxZKydBys6/uGlHxvf766/Lqq6/Ke++9J8qES6YdeeSR8rvf/U7OPvts72zWqlWrJnM5fR3eAZyo+Eqb6uaC77zzjvfz888/J7rk4OOZmZneTeSUGXz++edLlSpVfF+rY0fdX0d0ZOZaTGjEtYonny8aSZ6Za1egEQxg1zQfZL4YwEHSZKzICGAAYwBHJjYLJmKhZEERQ05BR42sWbNGpk+f7pm+6jzfffv2+aaQkZHh3aztnHPOkW7duona8av+jZY6AR01kno24Vypbh73zTffyEsvvST33ntvUpOoY0fUBxT9+vWT8847T0qXLp3U9Tp0RiM6VEHvGNCI3vXRITo0okMV9I4BjWAA661QvaPDANa7PkRXAAEMYAxgnhz+CbBQ8s/K1Z66aGTt2rXy8ssvy4svvuid7asMNb+tUqVK0rVrV8/07dKli3eTUlpwBHTRSHAZhT/SypUrpVWrVqJ0nUxTNyJUx0MMGDDAu5ma2ilsQkMjJlQp3hjRSLz8TZgdjZhQpXhjRCMYwPEq0OzZMYDNrp+z0WMAYwA7K/4UEmehlAI0xy6JUyPqTF+103fy5Mne8Q7J7PQ97rjjvN2SyvRt06YNN28LUbdxaiTEtCIbesOGDTJt2jTvww21o93vucH16tWTQYMGycCBA6VWrVqRxZvKRGgkFWpuXYNG3Kp3KtmikVSouXUNGsEAdkvxwWaLARwsT0aLiAAGMAZwRFKzYhoWSlaUMdQkotaIOsNX3cBNmb7qbN+dO3f6zq9ly5bSu3dv78zUJk2a+L6OjukRiFoj6UWr99XqeJNXXnnl4E53P9GqXcBqh/vgwYOle/fuUqxYMT+XRdoHjUSK28jJ0IiRZYs0aDQSKW4jJ0MjGMBGCleToDGANSkEYSRHAAMYAzg5xbjdm4WS2/X3k30UGlE7e+fMmeOZvsr82rRpk5/QvLN727Vr5xm+6qdu3bq+rqNTsASi0EiwEZsxmjomYsqUKd7z4vPPP/cVtLqpoTKCr7zySqldu7ava6LohEaioGz2HGjE7PpFET0aiYKy2XOgEQxgsxUcb/QYwPHyZ/YUCWAAYwCnKB0nL2Oh5GTZk0o6LI2oM3wXLFjgmVvqq++rV6/2FZcyfU8//XTvHNSePXuKMrxo8RIISyPxZqXX7F9//bW88MILMmnSJN/PlewMFi9eLA0bNow1ITQSK34jJkcjRpQp1iDRSKz4jZgcjWAAGyFUTYPEANa0MIRVOAEMYAxgniP+CbBQ8s/K1Z5Ba2T9+vUyceJEefbZZ+W7777zjfXkk0+WSy+9VC666CKpWbOm7+voGD6BoDUSfsTmzqB2y8+aNUvGjx/vnRu8e/du38nMnj1bOnbs6Lt/kB3RSJA07RwLjdhZ1yCzQiNB0rRzLDSCAWynsqPJCgM4Gs7MEjABDGAM4IAlZfVwLJSsLm8gyQWhEXVTq48++kjGjRsnU6dO9W1aqV2Lffv2lUsuuUQaNWoUSD4MEjyBIDQSfFT2j6huHqd2BKvn1TfffOM74eeff967cVyUDY1ESdvMudCImXWLMmo0EiVtM+dCIxjAZipXj6gxgPWoA1EkSQADGAM4Sck43Z2FktPl95V8OhrZuHGjt1NxzJgxsnTpUl/zqSMdlOGrdvuqm7qpIx9oehNIRyN6Z2ZGdOo4FbX2GTt2rLe73m9755135Oyzz/bbPa1+aCQtfE5cjEacKHNaSaKRtPA5cTEawQB2QughJYkBHBJYhg2XAAYwBnC4CrNrdBZKdtUzjGxS0ci3334rTzzxhGdGbd++PWFYFSpUkAsuuMDb7au+ol6kSJGE19BBHwKpaESf6O2KRNXiz3/+szz11FO+ElM3Txw9erRUq1bNV/9UO6GRVMm5cx0acafWqWaKRlIl5851aAQD2B21B58pBnDwTBkxAgIYwBjAEcjMmilYKFlTytAS8asRdTbpm2++KY8//rh3RmmilpmZKd26dZNBgwZ5v0uUKJHoEh7XlIBfjWgavpVhqV3Bw4YN83bfJ2qlS5eWq6++Wm6++WapUaNGou4pPY5GUsLm1EVoxKlyp5QsGkkJm1MXoREMYKcEH3CyGMABA2W4aAhgAGMAR6M0O2ZhoWRHHcPMIpFG1E2o/vCHP3jGr5929NFHyxVXXCGXX3651KpVy88l9NGcQCKNaB6+9eG9+OKL3rEqiVrJkiVl6NChcsstt4g6iiXIhkaCpGnnWGjEzroGmRUaCZKmnWOhEQxgO5UdTVYYwNFwZpaACWAAYwAHLCmrh2OhZHV5A0muII2oox2effZZefjhh2XVqlWFzqV2+/bo0cMzlzp37swRD4FURp9BeB3RpxaFRZJzfVRYP7Ubf8iQIfLHP/5RjjrqqECSQyOBYLR6EDRidXkDSQ6NBILR6kHQCAaw1QIPOTkM4JABM3w4BDCAMYDDUZado7JQsrOuQWZ1uEaKFi3qnRn66KOPytq1awudqmLFinLllVd6X0WvW7dukGExlkYEeB3RqBg+QlmxYoU8+OCD8txzz8mePXsKvKJ48eIyePBg+dOf/pT2bn004qMwjndBI44LwEf6aMQHJMe7oBEMYMefAmmljwGcFj4ujosABjAGcFzaM3FeFkomVi3amLM1onb8Lly40DN/N23aVGgQyiRWN6FSN3UrU6ZMtAEzW+QEeB2JHHkgE/7000+eEax28qujXApqygi+6qqrZMSIEVKzZs2U5kYjKWFz6iI04lS5U0oWjaSEzamL0AgGsFOCDzhZDOCAgTJcNAQwgDGAo1GaHbOwULKjjmFmoUyi2267TaZNmyZbtmxJONXf/vY3ue666yQjIyNhXzrYQYDXEbPr+PPPP8uoUaPkmWeekV27dhWYTPYZwepoiGRvFodGzNZIFNGjkSgomz0HGjG7flFEj0YwgKPQma1zYADbWlnL88IAxgC2XOKBpsdCKVCcVg22c+dOGTt2rNx///0Jj3ro3r27jBw5Utq2bWsVA5LxR4DXEX+cdO/1yy+/eGd6jxkzRnbs2FFguKVKlZJrrrnGu1lc1apVfaWFRnxhcroTGnG6/L6SRyO+MDndCY1gADv9BEgzeQzgNAFyeTwEMIAxgONRnpmzslAys25hRq2+Cj5+/Hi59957Re0MLKipHb59+vSRW2+9VZo3bx5mSIytOQFeRzQvUJLh/fe///WM4KeffrpQI1gd7zJ8+HC5+eabpUqVKoXOgkaSLIKD3dGIg0VPMmU0kiQwB7ujEQxgB2UfWMoYwIGhZKAoCWAAYwBHqTfT52KhZHoFg4t/79698sILL8jdd98tP/74Y6HG7yWXXCK33367NGnSJLgAGMlYAryOGFu6QgNfs2aNPPTQQ9653+obAQW1smXLyu9//3u58cYbpVKlSvl2QyN2aiTIrNBIkDTtHAuN2FnXILNCIxjAQerJtbEwgF2ruCX5YgBjAFsi5UjSYKEUCWatJ9m/f7+88sornqG7ePHiQmPt0aOHdyTE8ccfr3VOBBctAV5HouUd9WyrV6/2bhanjoQp7GZx2XF99tln0rJly1xhopGoq2befGjEvJpFHTEaiZq4efOhEQxg81SrT8QYwPrUgkiSIIABjAGchFyc78pCyV0J7Nu3T8466yz58MMPE0Jo1aqVqF2/l112mVSuXDlhfzq4RYDXETfqvWrVKu8DoGeffVb27NmTMOl169bJEUcc4fVDIwlxOd8BjTgvgYQA0EhCRM53QCMYwM4/CdIAgAGcBjwujY8ABjAGcHzqM29mFkrm1SyIiAcPHizPPfdcwqHOPvtsuemmmw6eA9qhQwcM4ITU3OvA64hbNV+xYoVnBKuzwtXRMYU1daxMv379MIDdkkhK2fI6khI2py5CI06VO6Vk0QgGcErC4SKPAAYwQjCSAAYwBrCRwo0paBZKMYGPadqtW7dKuXLlEs7esWNH7yZwyvBFIwlxOd8BjbgpgeXLl3uvE8oILqypm0WqI2aWLVvmdeODJDf1kihrXkcSEeJxNIIGEhFAIxjAiTTC4wUTwABGHUYSwADGADZSuDEFzUIpJvART5uVlSXTpk2T3r17FzrzySef7O3s69y5s2RkZHh90UjExTJwOjRiYNECDHnRokUJbwhZpkwZ7/Xn3HPPlU6dOvFNggD52zIUryO2VDK8PNBIeGxtGRmNYADbouU48sAAjoM6c6ZNAAMYAzhtETk0AAsl+4v9ww8/yPDhw+Xtt98uNNkxY8bIkCFDDhq/2Z3RiP0aSTdDNJIuQTuuf/jhh+WWW24pNJmjjjpKHnvssYQfRtlBhCySIcDrSDK03OyLRtysezJZoxEM4GT0Qt/cBDCAUYSRBDCAMYCNFG5MQbNQigl8BNOqszkfeughueeee2Tnzp2FzqgeL1GiRL590EgExTJ8CjRieAEDDv/WW2+VBx54IOGokyZNkr59+ybsRwc3CPA64kad08kSjaRDz41r0QgGsBtKDydLDOBwuDJqyAQwgDGAQ5aYVcOzULKqnAeT+frrr2XgwIHy2WefFZigOgtYPd6wYcNCIaAROzUSZFZoJEiadoylPlR65JFHvCNlduzYUWBSAwYMkAkTJuT55oEdFMgiGQK8jiRDy82+aMTNuieTNRrBAE5GL/TNTQADGEUYSQADGAPYSOHGFDQLpZjAhzSt2vWrvoZ95513yu7du/OdpVmzZjJ69Ghp166dryjQiC9MTndCI06Xv9Dkf/zxR7nhhhtk+vTpBfZTZwKPGzdO6tevD0iHCfA64nDxfaaORnyCcrgbGsEAdlj+aaeOAZw2QgaIgwAGMAZwHLozdU4WSqZWLm/cb731lpxzzjkFJlS2bFnvOIhrr71WihYt6jtxNOIblbMd0Yizpfed+MsvvywXXnhhgf1Lly7tHRuhXp8yMzN9j0tHewjwOmJPLcPKBI2ERdaecdEIBrA9ao4+Ewzg6JkzYwAEMIAxgAOQkTNDsFAyv9RZWVneTZXULruC2rnnnuvt+lU3YEq2oZFkibnXH424V/NkM1Ya+eCDD6RPnz6FXnrqqafK+PHjpVGjRslOQX/DCfA6YngBIwgfjUQA2fAp0AgGsOESjjV8DOBY8TN5qgQwgDGAU9WOi9exUDK76lu2bJGhQ4fK5MmT802kYsWKnjncv3//lM/YRCNmaySK6NFIFJTNniOnRooVK1botxVKliwpd999t9x4441SpEgRsxMnet8EeB3xjcrZjmjE2dL7ThyNYAD7Fgsd8xDAAEYURhLAAMYANlK4MQXNQikm8AFMu2DBArnoootk6dKl+Y520kknyWuvvZbSrt+cA6KRAIpl+RBoxPICB5De4RpRH0499dRTMmLECNm+fXu+M7Ru3VomTpwojRs3DiAChtCdAK8julco/vjQSPw10D0CNIIBrLtGdY4PA1jn6hBbgQQwgDGAeXr4J8BCyT8rXXqqIx+eeOIJ+cMf/lDgjd46d+4s7777bsq7fjGAdam2GXHwOmJGneKMsiCNLFu2TK688krveIj8mjob+G9/+5sMHjw4kNezOBkwd+EEeB1BIYkIoJFEhHgcjWAA8yxInQAGcOrsuDJGAhjAGMAxys+4qVkomVWyjz/+WNq3b19g0M2bN5eXXnop0B1zaMQsjcQRLRqJg7pZcxamEfWh1rhx4+Tmm28WdaxNfq1Xr15enypVqpiVONH6JsDriG9UznZEI86W3nfiaAQD2LdY6JiHAAYwojCSAAYwBrCRwo0paBZKMYFPYdrbbrtN7rvvvgKvvOaaa+SRRx4RdX5mkA2NBEnTzrHQiJ11DTIrPxr56aefZMiQITJz5sx8p65Zs6b8/e9/F/UNB5p9BPxoxL6sySgZAmgkGVpu9kUjGMBuKj+YrDGAg+HIKBETwADGAI5YckZPx0LJjPI9/vjjcv311+cbbIUKFWT8+PFy/vnnh5IMGgkFq1WDohGryhlKMn41onYDq9cz9Xq3bdu2fGO56aabvA/DSpQoEUqsDBoPAb8aiSc6ZtWBABrRoQp6x4BGMID1Vqje0WEA610foiuAAAYwBjBPDv8EWCj5ZxVHz927d3tGyJgxYwqcfvny5VK3bt3QwkMjoaG1ZmA0Yk0pQ0skWY0sWbJE+vbtK59++mm+MbVo0UImT54sxx57bGgxM3C0BJLVSLTRMZsOBNCIDlXQOwY0ggGst0L1jg4DWO/6EB0GcEIN8CaYEJHzHdCIvhJYvXq19OnTR+bNm1dgkMogLlasWKhJoJFQ8VoxOBqxooyhJpGKRvbs2SN33nmnPPDAA6J2Bh/e1HE3jz76qAwdOpQbxIVavWgGT0Uj0UTGLLoQQCO6VELfONAIBrC+6tQ/Mgxg/WtEhPkQYAfwISi8CfIUSUQAjSQiFM/j6mZvF1xwgaxZsybfANRjL7/8ciTBoZFIMBs9CRoxunyRBJ+ORmbPni39+/cXdUZwfq179+7y3HPPSbVq1SLJhUnCIZCORsKJiFF1I4BGdKuIfvGgEQxg/VRpTkQYwObUikhzEMAAxgDmCeGfAAsl/6yi6Kl2uY0ePdo79mHv3r15pmzSpIlMmzZN1O+oGhqJirS586ARc2sXVeTpamTjxo3eTt8pU6bkG3L16tVlwoQJ0rVr16hSYp6ACaSrkYDDYTgNCaARDYuiWUhoBANYM0kaFQ4GsFHlIthsAhjAGMA8G/wTYKHkn1XYPXfu3ClXX321Z2Lk19RN3tRj5cqVCzuUXOOjkUhxGzkZGjGybJEGHYRG1AdkEydOlGuvvVa2bt2ab/y33HKLd4O4okWLRpofk6VPIAiNpB8FI+hMAI3oXB09YkMjGMB6KNHMKDCAzayb81FjAGMAO/8kSAIAC6UkYIXYdd26ddKzZ898z/vNyMjwDI0RI0bEcs4lGgmx8JYMjUYsKWSIaQSpkR9++MG7Qdwnn3ySb8RnnHGG/POf/xS1K5hmDoEgNWJO1kSaDAE0kgwtN/uiEQxgN5UfTNYYwMFwZJSICWAAYwBHLDmjp2OhFH/5Fi1aJOecc44oU+PwVqlSJc/I6NKlS2yBopHY0BszMRoxplSxBRq0RtQN4u655x7vw7H9+/fnyatmzZreOent2rWLLWcmTo5A0BpJbnZ6m0AAjZhQpXhjRCMYwPEq0OzZMYDNrp+z0WMAYwA7K/4UEmehlAK0AC9RBsWFF16Y74jNmzeXqVOnSv369QOcMfmh0EjyzFy7Ao24VvHk8w1LI//617+83cArV67ME5Q6BuKxxx7zjtZR36Sg6U0gLI3onTXRJUMAjSRDy82+aAQD2E3lB5M1BnAwHBklYgIYwBjAEUvO6OlYKMVTPnWWZWZmZoGT9+nTxzvvt3Tp0vEEmGNWNBJ7CbQPAI1oX6LYAwxTIxs2bJB+/frJzJkz881zyJAh8sQTT0jx4sVj50AABRMIUyNwt4MAGrGjjmFmgUYwgMPUl+1jYwDbXmFL88MAxgC2VNqhpMVCKRSshQ6qvq5cpEiRAvuos37V15oLM4ijjBqNREnbzLnQiJl1izLqsDWiXlfVkRB33XWXqA/YDm8dOnSQV155RapVqxZl2syVBIGwNZJEKHTVlAAa0bQwGoWFRjCANZKjcaFgABtXMgJWBDCAMYB5JvgnwELJP6sgeu7bt0/KlSsnO3bsyHe4cePGyeDBg4OYKrAx0EhgKK0dCI1YW9rAEotKI2oXsDoSQs13eKtTp4689tpr0qJFi8DyYqDgCESlkeAiZqSoCaCRqImbNx8awQsxT7X6RIwBrE8tiCQJAhjAGMBJyMX5riyUopPA7t27va8pq3N/82tvvPGGdzM43Roa0a0i+sWDRvSriW4RRamRZcuWyXnnnSdff/11HgzqWB11vI46ZoemF4EoNaJX5kTjlwAa8UvK3X5oBAPYXfWnnzkGcPoMGSEGAhjAGMAxyM7YKVkoRVM6teO3QYMGsnr16nwnnDhxovTv3z+aYJKcBY0kCczB7mjEwaInmXLUGtmyZYsMGDBApk+fnm+kt99+u9x5553aHLWTJE4ru0etESshWp4UGrG8wAGkh0YwgAOQkbNDYAA7W3qzE8cAxgA2W8HRRs9CKXzeS5culYYNGxY40Zw5c0SdT6lrQyO6VkafuNCIPrXQNZI4NKLOBVYmrzobOL/Ws2dPUR++qWN5aPETiEMj8WdNBMkQQCPJ0HKzLxrBAHZT+cFkjQEcDEdGiZgABjAGcMSSM3o6Fkrhlm/79u1SpkyZfCdRpq869qF8+fLhBpHm6GgkTYAOXI5GHChyminGqRF17M7AgQNFvR4f3o4//nh5/fXXpX79+mlmyOXpEohTI+nGzvXREEAj0XA2eRY0ggFssn7jjh0DOO4KMH9KBDCAMYBTEo6jF7FQCq/w6tiHE044QX744Yc8kzRq1Eg+//xzUedR6t7QiO4Vij8+NBJ/DXSPIG6NfPHFF965wCtXrsyDqnLlyt7Z7J06ddIdo9Xxxa0Rq+FakhwasaSQIaaBRjCAQ5SX9UNjAFtfYjsTxADGALZT2eFkxUIpHK47d+6UXr16ibojfX5NPV6iRIlwJg94VDQSMFALh0MjFhY14JR00MjatWvlggsukLlz5+bJrkiRIvL444/LsGHDAs6c4fwS0EEjfmOlXzwE0Eg83E2aFY1gAJukV91ixQDWrSLE44sABjAGsC+h0MkjwEIpeCHs2rVLevfuLW+++Wa+g2/evNmoMyfRSPAasW1ENGJbRYPPRxeN7N69W4YPHy7PPPNMvkneeOON8vDDD3NzuOAlkHBEXTSSMFA6xEYAjcSG3piJ0QgGsDFi1TBQDGANi0JIiQlgAGMAJ1YJPbIJsFAKVgt79uyRPn36yGuvvZZnYHXm7/vvvy/FixcPdtKQR0MjIQO2YHg0YkERQ05BN42MHj1arrvuOtm7d2+ezM8//3x54YUXjDiiJ+SyRTq8bhqJNHkm80UAjfjC5HQnNIIB7PQTIM3kMYDTBPwfkpAAACAASURBVMjl8RDAAMYAjkd5Zs7KQim4uinz95JLLpFXX301z6DdunWTqVOnGnPsQ84E0EhwGrF1JDRia2WDy0tHjXz00UfekRAbNmzIk2jr1q29m8NVr149OAiMVCgBHTVCyfQigEb0qoeO0aARDGAddWlKTBjAplSKOHMRwADGAOYp4Z8ACyX/rArrqXaR9e3bV6ZMmZKn29lnn+3tCC5ZsmQwk0U8ChqJGLiB06ERA4sWcci6akTdpFN9QLd48eI8ROrVqydvvfWWNGnSJGJabk6nq0bcrIaeWaMRPeuiU1RoBANYJz2aFgsGsGkVI16PAAYwBjBPBf8EWCj5Z1VQz3379smAAQNk8uTJebqou8q/8cYbUqpUqfQnimkENBITeIOmRSMGFSumUHXWiIqtZ8+e+d4cTuGaMWOGdO/ePSZy7kyrs0bcqYLemaIRveujQ3RoBC9EBx2aGgMGsKmVczxuDGAMYMefAkmlz0IpKVx5Oivzd9CgQTJx4sQ8j5122mnejeDKlCmT3iQxX41GYi6AAdOjEQOKFHOIumtE3bxTvZbn90FeNrqsrKyYKdo9ve4asZu+GdmhETPqFGeUaAQDOE79mT43BrDpFXQ0fgxgDGBHpZ9S2iyUUsLmXaTMgGHDhsmYMWPyDNK+fXt5++23pWzZsqlPoMmVaESTQmgcBhrRuDiahGaCRtRr+p///Ge59957C6SmPvTLzMzUhKpdYZigEbuIm5cNGjGvZlFHjEYwgKPWnE3zYQDbVE2HcsEAxgB2SO5pp8pCKXWEyii455578gzQtm1beffdd6VcuXKpD67RlWhEo2JoGgoa0bQwGoVlkkbGjx8vV1xxRYH01JnvRYoU0YiuHaGYpBE7iJuXBRoxr2ZRR4xGMICj1pxN82EA21RNh3LBAMYAdkjuaafKQik1hCNGjJBRo0blubhVq1by3nvvSYUKFVIbWMOr0IiGRdEsJDSiWUE0DMc0jagbd6pzgfNrpUuXlk2bNkmxYsU0JG1uSKZpxFzS5kaORsytXVSRoxEM4Ki0ZuM8GMA2VtWBnDCAMYAdkHlgKbJQSh6l2tm7devWPBeecMIJMnv2bKlUqVLyg2p8BRrRuDiahIZGNCmExmGYqJHHH39crr/++nypduvWTV5++WVRZjAtGAImaiSYzBnFLwE04peUu/3QCAawu+pPP3MM4PQZMkIMBDCAMYBjkJ2xU7JQSq50M2fOlN/97nd5LqpXr558/PHHcuSRRyY3oAG90YgBRYo5RDQScwEMmN5UjajjfLp06ZIvYXXW+4wZM6RixYoGVED/EE3ViP5k7YkQjdhTy7AyQSMYwGFpy4VxMYBdqLKFOWIAYwBbKOvQUmKh5B/ttGnT5Pzzz8/3giVLlkiDBg38D2ZQTzRiULFiChWNxATeoGlN1sjPP/8stWrVypd2ixYtRH0wWL16dYOqoWeoJmtET6L2RYVG7Ktp0BmhEQzgoDXl0ngYwC5V26JcMYAxgC2Sc+ipsFDyh/jSSy+Vf/7zn/l2njRpkvTt29ffQAb2QiMGFi3ikNFIxMANnM50jWzbtk169erlnfF+eGvYsKH370cffbSBldEnZNM1og9JeyNBI/bWNqjM0AgGcFBacnEcDGAXq25BzhjAGMAWyDiyFFgoJUY9ffp07w///NrYsWNlyJAhiQcxuAcaMbh4EYWORiICbfA0Nmhk165d0q9fP3nllVfyVEId/zNr1iw59thjDa5SvKHboJF4Cdo/Oxqxv8bpZohGMIDT1ZDL12MAu1x9g3PHAMYANli+kYfOQqlw5L/99luB5zuOGTNGrrrqqshrFvWEaCRq4ubNh0bMq1nUEduikX379snQoUPl2WefzRfhDTfcII8++mjUeK2YzxaNWFEMTZNAI5oWRqOw0AgGsEZyNC4UDGDjSkbAigAGMAYwzwT/BFgoFcwqKytLMjMz8+3QpEkT+e677/yDNrgnGjG4eBGFjkYiAm3wNDZpRL03jBgxQh566KF8K/LOO+/I2WefbXC14gndJo3EQ9D+WdGI/TVON0M0gheSroZcvh4D2OXqG5w7BjAGsMHyjTx0FkoFI8/IyCjwQbULrCBzOPIihjwhGgkZsAXDoxELihhyCjZqZNSoUZ4RnF+bP3++tGnTJmSqdg1vo0bsqlD82aCR+GugewRoBANYd43qHB8GsM7VIbYCCWAAYwDz9PBPgIVS/qwWL14sjRs3zvfBrVu3SpkyZfxDNrwnGjG8gBGEj0YigGz4FLZq5Nprr5WnnnoqT3XKlSsnaifwKaecYnjlogvfVo1ER9D+mdCI/TVON0M0ggGcroZcvh4D2OXqG5w7BjAGsMHyjTx0Fkr5Iy9o96+Lu7rQSORPS+MmRCPGlSzygG3WiDoSaNGiRfmawDNnzpR27dpFztvECW3WiIn10DFmNKJjVfSKCY1gAOulSLOiwQA2q15E+z8CGMAYwDwZ/BNgoZSX1YcffiidOnXK80Dp0qVl27Zt/uFa0hONWFLIENNAIyHCtWRo2zUyefJk6devn6jzgXO2smXLijKBTz31VEsqGV4atmskPHLujIxG3Kl1qpmiEQzgVLXDdSIYwKjASAIYwBjARgo3pqBZKOUGv2XLFmnatKmsXLkyzx/x6jEXGxpxserJ5YxGkuPlYm8XNPLSSy9J3759RZ0Rn7OpI4Pefvtt6dChg4ul952zCxrxDYOO+RJAIwgjEQE0ggGcSCM8XjABDGDUYSQBDGAMYCOFG1PQLJRygx8+fLg8+eSTuf6xdu3asnDhQqlQoUJMVYp3WjQSL38TZkcjJlQp3hhd0ciUKVPk0ksvzdcEfuutt6Rjx47xFkLj2V3RiMYl0D40NKJ9iWIPEI1gAMcuQoMDwAA2uHguh44BjAHssv6TzZ2F0iFi8+bNk/bt2+f5Cu+sWbPyPRIiWdam9kcjplYuurjRSHSsTZ3JJY288sorcvHFF+drAr/55pty2mmnmVrGUON2SSOhgrR4cDRicXEDSg2NYAAHJCUnh8EAdrLs5ieNAYwBbL6Ko8uAhdIB1rt27ZITTzxRvvvuu1zwhw4dKqNHj46uIBrOhEY0LIpmIaERzQqiYTiuaeTVV1/1TOC9e/fmqoY6S17tBMYEzitS1zSi4dNU+5DQiPYlij1ANIIBHLsIDQ4AA9jg4rkcOgYwBrDL+k82dxZKB4jdddddcuedd+bCd9RRR8k333zj7NEP2TDQSLLPKvf6oxH3ap5sxi5qZOrUqXLRRRflMYHVmcDvvfeenHLKKclitLq/ixqxuqAhJIdGQoBq2ZBoBAPYMklHmg4GcKS4mSwoAhjAGMBBacmFcVgoiWfyqt2/e/bsyVXy1157TXr06OGCDArNEY04L4GEANBIQkTOd3BVI9OnT5c+ffrkMYHVmfIffPCBtGzZ0nlt8GEjEvBLwNXXEb986CeCRjCAeR6kTgADOHV2XBkjAQxgDOAY5Wfc1K4vlNTd2tW5v/Pnz89VO/UHu7qZD43FNBpITMD115HEhOjhskbUh4nqPeXwDxmrVKkiH330kTRt2hSBCO81iCAxAZdfRxLToYcigEYwgHkmpE4AAzh1dlwZIwEMYAzgGOVn3NSuL5R69uwp6o/znK1ixYreWcA1atQwrp5hBOy6RsJgatuYaMS2igafj+saUWcCX3jhhbJ///5ccKtXry5z5syRRo0aBQ/dsBFd14hh5YolXDQSC3ajJkUjGMBGCVazYDGANSsI4fgjgAGMAexPKfRy/ZPyv//97zJw4MA8Qnjuuedk0KBBCOR/BFhMI4VEBNBIIkI8jkZEJk2aJAMGDJCsrKxcgqhdu7Z8/PHHon673NCIy9X3lzsa8cfJ5V5oBAPYZf2nmzsGcLoEuT4WAhjAGMCxCM/QSV1eKGVkZORbNbVDq6DHDC1zWmG7rJG0wDl0MRpxqNgppopGDoAbN26cDBkyJA/FY489VubOnSvqWAhXGxpxtfL+80Yj/lm52hONYAC7qv0g8sYADoIiY0ROAAMYAzhy0Rk8oasLJXXu4hlnnJGncuoPcHUmMI3XETTgn4CrryP+CdETjRzSwOOPPy7XX399HlG0adNGZs2aJWXKlHFSMGjEybInlTQaSQqXk53RCAawk8IPKGkM4IBAMky0BDCAMW6iVZzZs7m4UFI3flN3Xv/qq6/yFO/wr+aaXd1gondRI8GQc2cUNOJOrVPNFI3kJnfffffJbbfdlgdnly5d5PXXX5fixYunitrY69CIsaWLLHA0EhlqYydCIxjAxopXg8AxgDUoAiEkTwADGAM4edW4e4WLC6UxY8bI1Vdfnafo27dvl1KlSrkrhgIyd1EjiCA5AmgkOV4u9kYjuauuPmz8/e9/L2o38OHtkksu8c4LzszMdEoqaMSpcqeULBpJCZtTF6ERDGCnBB9wshjAAQNluGgIYABjAEejNDtmcW2htHHjRmnYsKFs2LAhVwGnTp0qvXr1sqOoAWfhmkYCxufEcGjEiTKnlSQayYtPnTffv39/mTx5cp4Hhw8fLo899phT59GjkbSeYk5cjEacKHNaSaIRDOC0BOT4xRjAjgvA1PQxgDGATdVuHHG7tlBSO67UH9U525lnninvvfeeU39oJ6M11zSSDBv6HiCARlBCIgJoJH9Cu3fvlvPOO09mzpyZp8O9994rI0eOTITWmsfRiDWlDC0RNBIaWmsGRiMYwNaIOYZEMIBjgM6U6RPAAMYATl9F7ozg0kLp22+/lWbNmok6Azi7qa/Yfvnll9K0aVN3ip5kpi5pJEk0dP8fATSCFBIRQCMFE9q2bZt07txZ5s+fn6fT2LFjZciQIYnwWvE4GrGijKEmgUZCxWvF4GgEA9gKIceUBAZwTOCZNj0CGMAYwOkpyK2rXVkoqfMWu3btKu+++26uAl9zzTXy5JNPulX0JLN1RSNJYqF7DgJoBDkkIoBGCiek+HTo0EHUB5U5m/qQcsqUKdK7d+9EiI1/HI0YX8LQE0AjoSM2fgI0ggFsvIhjTAADOEb4TJ06AQxgDODU1ePela4slGbMmCE9evTIVeBKlSrJkiVLpEqVKu4VPomMXdFIEkjoehgBNIIkEhFAI4kIiaxatUpOPfVUWblyZa7OJUuWlA8//FDatm2beBCDe6ARg4sXUehoJCLQBk+DRjCADZZv7KFjAMdeAgJIhQAGMAZwKrpx9RoXFkp79uyR448/3jN7cza181ftAKYVTsAFjaCB9AigkfT4uXA1GvFX5UWLFkn79u1l/fr1uS6oWrWqfPLJJ1KvXj1/AxnYC40YWLSIQ0YjEQM3cDo0ggFsoGy1CRkDWJtSEEgyBDCAMYCT0YvrfV1YKD399NN5jF5lCH/xxRdStGhR1yWQMH8XNJIQAh0KJYBGEEgiAmgkEaFDj3/66adyxhlniDobOGdr0qSJzJs3T9S3V2xsaMTGqgabExoJlqeNo6ERDGAbdR1VThjAUZFmnkAJYABjAAcqKMsHs32htGXLFmnQoIGsXbs2VyXVWcBnnXWW5dUNJj3bNRIMJbdHQSNu199P9mjED6VDfd544w0577zzZP/+/bkuVMbwzJkzpXjx4skNaEBvNGJAkWIOEY3EXAADpkcjGMAGyFTbEDGAtS0NgRVGAAMYA5hniH8Cti+URowYIaNGjcoFpEuXLt4f0DR/BGzXiD8K9CqMABpBH4kIoJFEhPI+ro4pGj58eJ4HBg4cKOPHj5eMjIzkB9X4CjSicXE0CQ2NaFIIjcNAIxjAGstT+9AwgLUvEQHmRwADGAOYZ4Z/AjYvlNTd1NVRDzmb+oP5888/l+bNm/uH5HhPmzXieGkDSx+NBIbS2oHQSGql/f3vfy+PPfZYnovvvfdeGTlyZGqDanoVGtG0MBqFhUY0KoamoaARDGBNpWlEWBjARpSJIA8ngAGMAcyzwj8BmxdK+e2O6tevn7zwwgv+AdFTbNYI5Q2GABoJhqPNo6CR1Kq7b98+6dWrl8yYMSPPAJMnT5ZLLrkktYE1vAqNaFgUzUJCI5oVRMNw0AgGsIayNCYkDGBjSkWgOQlgAGMA84zwT8DWhdK6deukWrVqeUCsWLFC6tSp4x8QPTGA0UBCAra+jiRMnA6+CaAR36jydNy6daucdtppsmDBglyPqXOAZ82aJe3bt099cI2uRCMaFUPTUNCIpoXRKCw0ggGskRyNCwUD2LiSEbAigAGMAcwzwT8BWxdKf/rTn+TBBx/MAyIrK8s/HHp6BGzVCOUNjgAaCY6lrSOhkfQqu3r1amnTpo2sWrUq10BVqlSR+fPnezc7Nb2hEdMrGH78aCR8xqbPgEbwQkzXcJzxYwDHSZ+5UyaAAYwBnLJ4HLzQxoXShg0bpG7duqJ2TeVs7P5NTeA2aiQ1ElxVEAE0gjYSEUAjiQglfvzLL7/0dvse/t7WsGFDb/ODMoNNbmjE5OpFEzsaiYazybOgEQxgk/Ubd+wYwHFXgPlTIoABjAGcknAcvcjGhdJtt90m9913X66KDhkyRMaOHetoldNL20aNpEeEqw8ngEbQRCICaCQRIX+Pv/3229K9e3fZv39/rgvOPPNMmTlzphQtWtTfQBr2QiMaFkWzkNCIZgXRMBw0ggGsoSyNCQkD2JhSEWhOAhjAGMA8I/wTsG2hpPJRu3+3bNlyEIL6g3jJkiXev9OSJ2CbRpInwBWJCKCRRIR4HI0Ep4HRo0fLsGHD8gx43XXXyWOPPRbcRBGPhEYiBm7gdGjEwKJFHDIawQCOWHJWTYcBbFU53UkGAxgD2B21p5+pbQulO+64Q+6+++5cYK644gp59tln04fl6Ai2acTRMoaaNhoJFa8Vg6ORYMt40003yaOPPppnUPVep97zTGxoxMSqRRszGomWt4mzoREMYBN1q0vMGMC6VII4kiKAAYwBnJRgHO9s00JJnYtYu3Zt2bRp08GqFilSRBYvXiz169d3vNKpp2+TRlKnwJWFEUAj6CMRATSSiFByj+/bt0/OPfdcUUdCHN5mz54tHTt2TG5ADXqjEQ2KoHkIaETzAmkQHhrBANZAhsaGgAFsbOncDhwDGAPY7WdActnbtFB66qmn5Nprr80F4LLLLpMJEyYkB4XeuQjYpBFKGw4BNBIOV5tGRSPBV/O3336TNm3ayKJFi/IM/sEHH8gZZ5wR/KQhjohGQoRrydBoxJJChpgGGsEADlFe1g+NAWx9ie1MEAMYA9hOZYeTlS0LJbUbqkmTJrJ06dJcoL799ls59thjw4HnyKi2aMSRcsWSJhqJBbtRk6KRcMqlvuHSunVrUWbw4W337t1SrFixcCYOYVQ0EgJUy4ZEI5YVNIR00AgGcAiycmZIDGBnSm1XohjAGMB2KTrcbGxZKL322mvSs2fPXLC6desmb775ZrgAHRjdFo04UKrYUkQjsaE3ZmI0El6p3nnnHenatWueCf7whz/IQw89FN7EAY+MRgIGauFwaMTCogacEhrBAA5YUk4NhwHsVLntSRYDGAPYHjWHn4ktC6XTTjtN5syZkwvY+++/L2eeeWb4EC2fwRaNWF6mWNNDI7HiN2JyNBJumapWrSrr16/PM8kbb7wh55xzTriTBzQ6GgkIpMXDoBGLixtQamgEAzggKTk5DAawk2U3P2kMYAxg81UcXQY2LJT+85//SKtWrXJBa9asmXzxxReSkZERHUxLZ7JBI5aWRpu00Ig2pdA2EDQSbmmysrKkRo0asnbt2lwTValSxXsvrFWrVrgBBDA6GgkAouVDoBHLCxxAemgEAzgAGTk7BAaws6U3O3EMYAxgsxUcbfQ2LJT69u0rkydPzgXu+eefl4EDB0YL09LZbNCIpaXRJi00ok0ptA0EjYRfGsX4xBNPlJUrV+aarGPHjqJuClekSJHwg0hjBjSSBjxHLkUjjhQ6jTTRCAZwGvJx/lIMYOclYCYADGAMYDOVG0/Upi+UVq1aJfXq1ZO9e/ceBFi9enVZsWKFlChRIh6ols1qukYsK4eW6aARLcuiVVBoJJpyzJ8/Xzp06JDrPVHN/Mgjj8hNN90UTRApzoJGUgTn0GVoxKFip5gqGsEATlE6XCYiGMDIwEgCGMAYwEYKN6agTV8ojRgxQkaNGpWL3j333CO33XZbTETtm9Z0jdhXEf0yQiP61US3iNBIdBVRZq+6AVzOpj4Q/eyzz+T444+PLpAkZ0IjSQJzsDsacbDoSaaMRjCAk5QM3XMQwAC2WA5btmyRd999Vz788ENZsGCBLFmyRDZt2iSlSpWSmjVrSuvWreXSSy+VLl26+D5D85dffpExY8bIjBkz5Mcff5SdO3d655GpnQiDBg0SdZOmKBoGMAZwFDqzZQ6TF0o7duzwzjVUOWS3kiVLyk8//SRHHHGELSWKPQ+TNRI7PEcCQCOOFDqNNNFIGvCSvHT//v3e+l3dCDVna9mypagdwsWKFUtyxGi6o5FoOJs8CxoxuXrRxI5GMICjUZqds2AA21lXefTRR2XkyJGeQZuoKfN20qRJUqdOnUK7Tps2zTN5lYlcULvyyitl9OjRoZ9BhgGMAZxI1zxuh0aee+45GTx4cK5yqteZZ555hhIHSOCrr77yzpVUTd1Ub9myZQnfEwKcnqEMIMAfXAYUKeYQ0Ui0BVAfhJ5wwgny22+/5Zr4z3/+s9x1113RBuNzNjTiE5TD3dCIw8X3mToawQD2KRW65UMAA9hSWQwdOlTGjh3rZad2z5155ply8sknS9WqVUXtqPvkk08803fr1q1eH3W+ptoxUK1atXyJqB0G3bp1kz179niPn3POOdKjRw8pU6aMt7v42Weflc2bN3uPRWHOYADbYe5Z+vTTLi1TF0rqjuctWrQQZU7mbOr/1R+9NP8EXvn2FenZpKcUzSya70X33nuv3H777Qcf+8tf/iI33nij9/979++V6d9PlwuOu8D/hPS0joCpryPWFULjhNBI9MWZOHGiXHbZZbkmVjeCU+vkVq1aRR9QghnRiHYl0S4gNKJdSbQLCI1gAGsnSoMCwgA2qFjJhHr11Vd7O7huvvlmz/zNzMzMc7m6gZL6+tiiRYu8xy6//HIZP358nn5qF3Hjxo0P3nH4iSeekGuvvTZXv8WLF3vHP6xZs8b7d3X0xFlnnZVMyEn1xQA+hIs3waSk42RnUzUyZ86cPMfKnH766d6xNjT/BO786E65a/ZdcnHTi+WFXi/kawKrDwjV2ZHZrW3btp6BoMzf/tP6y4tfvyh3nHaH3Hn6nf4npqdVBEx9HbGqCJong0aiL5D6oPT888+X6dOn55q8SZMm3gYNdeybTg2N6FQNPWNBI3rWRaeo0AgGsE56NC0WDGDTKuYzXvXCWLly5YS9v/zyS2+HnWqlS5eWdevWeb9ztqeeeuqg4XvuuefK66+/nu+4U6dOld69e3uPZZsHCQNIsQMG8CFwvAmmKCKHLjNVI3369JFXXnklV6XU60yvXr0cql56qaqdv31e7nNwkPxM4JUrV8rRRx+dZ6Iflv8gIz8b6Zm/2e3lPi+zEzi9khh7tamvI8YCNzBwNBJP0dauXStNmzb11vA52w033OAdCadTQyM6VUPPWNCInnXRKSo0ggGskx5NiwUD2LSKhRDvscceK99//703sjKEmzVrlmuWjh07yty5c71/Uzvv1A68/Jq6IcUxxxzj3RxOteXLl0vdunVDiJgXvZxQeRMMRWJWDWqiRlatWuW9fuzbt+9gLdQ55T/88IMULZr/MQZWFS2gZHLu4M0ess+xfeT5c58/uBP48ccfl1tuuSX3jJkize9qLl/u+/Lgv2dfV6JYCWoQUH1MGsbE1xGT+NoQKxqJr4rqPh1qJ/DhTX2TRt3rQ5eGRnSphL5xoBF9a6NLZGgEL0QXLZoYBwawiVULOObWrVvLp59+6o2qzgFu06bNwRm2bNkiFStWFGXulitXTjZu3FjoDd7U0RNjxozxrn/66adF/X8YjR3Ah6jyJhiGwuwa00SN3H333XLHHXfkKsSDDz4of/zjH+0qTgTZ5GcCy0IRmSYi+/MJQJ0YpDZZ5zxmOUf/Cy+8UF566aUIImcKnQiY+DqiEz8XYkEj8VZ5wIAB8sILL+QKolGjRt7mjpIlS8Yb3P9mRyNalEHrINCI1uXRIjg0ggGshRANDQID2NDCBRX2rl27pHr16gfvIPzLL79IjRo1Dg6vbhanjnNQTe0Enj17dqFTqzOEr7jiCq/PsGHDRB0fEUbDAMYADkNXto5p2kJJ7fpVN6ZUdzjPbsWLF5eff/5ZjjjiCFvLFGpe23dulwoDK8jeY/cemic/EziB+asuVh8Grl+/XlRNaO4QMO11xJ3K6JMpGom3Fps2bfJukKq+QZOz/elPf5L7778/3uD+Nzsa0aIMWgeBRrQujxbBoREMYC2EaGgQGMCGFi6osCdMmODd/E21li1b5roJkPq3nHcXVncZVv0La+qIiE6dOnld1M3n3n///aRDVeZuorZw4UK56qqrvG4zZ87U8k7HiXII6vHNmzd7uztUa968uZQvXz6ooRnHEgKmaeS9996Tiy++OBf9Cy64QMaOHWtJRaJPQ33Lo2u3roXu7BUf5m925K6/7kZfQf8zZqxbJ8VffVWKLFkiWeXKydZ2Z8iaBu1k/ZaS8uuvGfLbb5mydavItm0ZsnXrgR/139n/v2OHyJ49GaJOX9mzR6TC9l+k6eb58pMcJQv2NpLSe7ZIzWK/ScXMrVIkc78UKSKSkSGyP7OorChznOwqXlZKlBApUyZLSpdWPyKlSh347wP/pj5EyJLqRdZJnU0LpVSN8lKszQlSqbLq5z9PeupHwLT3Gv0Iph+RugnzJZdckmugIkWKeOvxw494S3+25EdAI8kzc+0KNOJaxZPPF42I9+3trl27evDmzZsnp5xySvIgucJJAhjATpb9QNLqZhHHH3/8RgrnYgAAIABJREFUwZtG5HdzJXU25PXXX+/1v+mmm+SRRx4plNhXX33lmZCqnXTSSfKf//wnacIZ6i/JJJr6Wri62zENAhCwg4DaqfTvf/87VzL33nuvd5MbWuoE/vWvf8mYZ8bI1rO25j3eQd1AvmfBxz5kz6o+YBoyZIi0b98+9UC4Mm0C+/eLbNpUQn79tZSsX19SNq4rJkd/+6m0XzxN2m6YJcUkx05vEdkmpeU1OU/+LpfJ+9JZ9kuRhDHUleVyq9wvA2VCnvEKuni3FJMl0lC+lyaySBrLf6W67JbisleKSg1ZI3XlR+/nGPlB6sqKg8O8J51lkIyXtcVrStmyu6V8+d1SufJOqVRpp/c7+6dKlQP/XaHCLilSJCthDnSAgIsE/vrXv+b5xp76Vs3DDz/M+e0uCoKcIQAB6wio+zeNGDHCywsD2LryhpoQBnCoePUdfPfu3dK5c+eDN3fr2bOnqBtIHN6UETNy5Ejvn9VvZcIU1pYsWSLqvDHV1O9FixYlDQEDOGlkXAABawior3UNHjzYO3c8u9WsWdM7TibZ1wZroASYiPqKsDKB5x85P7cJfPgc+RwPoXYXqG9eqHPhaeESyMoS2bKlmKxZU1Z++aWMrFlTWn75paz3e8OGUvLrryVl3z61ZVukq7wtY+UqqSOHjkwpLLr1UkX+T07xTNhZcqbMltNkjnSUdVLVM2dPks+km7wl/eUF38ZvEDR+k/Lye/mbTJQBCQ3qzMwsOeKI7VK9evbPNqlRQ/33gd/lyu32diXTIOAiAbU77tprrxX1O2fr37+/9O7d20Uk5AwBCEDAKgIYwFaVM9JkMIAjxa3HZMpYUTeK+Mc//uEFdMwxx3hfI6hUqVKeAHMawLfddpvcc889hSYRhAHMERDJ6YSvwSTHy8XeJmnkL3/5S56zCu+66y7vj1laMASysrLk1WmvyvAPhsvuxrvzDnqY+VulShV56KGHRH1QSAuWwN69IsuWZcr33xfxfhYvLiLLlhWR5cszZfPmTCknm6WxLJIqskE2S3nZKJXkV6ksa6WaZMp+uUPukj9L4e/LfiPeLOWkvGzx2z20fiukjrdbOUOyvBwXygkyRS6UjVK5kDmzpLr8VxrIUiklO2RV6UZSokFNadxkvzRuvM/7adRon9Ste+DIClo4BEx6rwmHgD6jvvrqq963NXK2EiVKeDuDGzZsGFugaCQ29MZMjEaMKVVsgaIRjoCITXwWTIwBbEERk0lB/eGvdnCNGzfOu6xOnTreYrBu3br5DhPHERB+8uEmcIcocRC+H8W43ccUjagPp+rXry8rVhz6anixYsW8m79VrVrV7SKGkP39o+6XkTsPfMMjV1N+4r5D/zJq1Ci55ZZbQojAnSHVjt6VK0XUce1ffy3y33+vkD1ffiv7flot1ff9LDVltRwpv8guKSE7paQcJT9LE/lejpLV+ULaImWlnGx1BuAOKSlvy+88Psrg3SdFZLnUk3KyRVrJp9JQluTh8atUks/lRHlPzpJ3pIt8Kc2lePEMadtgvXSsuVROqvSDHF98qRy1c6mUWrVURN04q04dkccfFzn5ZGfYBpmoKe81Qeas61hqvd+jRw954403coWoju9R6/7MzAPfIIi6oZGoiZs3Hxoxr2ZRR4xGuAlc1JqzaT4MYJuqmSAXtRgcNmyYjBkzxutZq1Yt+eijj7wdwAW1OG4C56ckGMCHKPEm6EcxbvcxRSPvvPPOwRsaZFfswgsvlJdeesntAoaQ/d79e6XG1TVkQ80NeUc/bAdw27Ztxc83M0II08ghldm76qcs+WrOJln+0Qr5dcGPsnvxj7J1mzqLt4xcIK9IF3k3tNyWFm0ib1W/XP7d4FI5puQqabfxTWmzeJJU3PRjanOWLSvSubPsrF5dflm5UnaXLSu1TjxRytSqJZJtIqmk1Qc3CxeKqKOfFi8W2b49z3xZGRmyt0Yt2Vn9aNlctYGUXvyFVFrxRWpxJXmVOvqimOyRCpL7a/GHD7OndHlZN32eHNn5eI6RSJKxKe81SaZlbPdVq1bJcccdJ1u25N7Zr45UUn8PxNHQSBzUzZoTjZhVrziiRSMYwHHozpY5MYBtqWSCPJT5e80118jo0aO9nkcddZRn/jZo0KDQKz/55BNRf/yr1rFjxzw3lTj84vHjx8sVV1zh/bNaXKpFZhgNA/gQVd4Ew1CYXWOaopELLrhA1NdWczZ15/IzzzzTroLEnI0yf8+fdL7MWD6j4EgOM4HVrmz1jRHn2+rVIh9+KKLeF//34enWrSL//niPrJ70gZT4+AM55qeP5Ji93yc0GoNmua9mLcn8+wTJOLOT5HEu1Znac+eKvPaauluIyIIFInv2FB5ClSoiQ4eK3HCDSJUqktTriJpvzRp1kLHI7t0HftTZ0bVrixQvnnveb74RGTRI5LAbPwbNJ5nxfpSj5Zwqn0iDU6tLu3bi/ahNwaVKJTOKe32T0oh7eGLJeOzYsTJUPY9ztLJly8o333wTy2s6GolFBkZNikaMKlcswaIRDOBYhGfJpBjAlhSysDQON3/VDZWU+evnDDC1a0Dd8Ed9NbtcuXKyceNGKVLIAXpXX331wR3GTz/9tKj/D6NhAGMAh6ErW8c0YaG0Zs0aqV27tuxVh6L+r6lvJyxevDi2r6raqAdl/vaf1l9e/PrFQ+kps3e6iKgjfk/IkXUOE1idzXzjjTfaiMRfTsrQHDVK5PbbRfYdOB9j7gnD5E+Zo6TMwvny9P6r5BhZ5m+sMHp17iwyebKI36NSduw4cBaFMrTXrj1gDm/apO7eKnLSSQd+1AfEOb4mHurriNpBrGJ48kkR9ZV1FZ8G7SoZI19LU/lGjpdtRSvKiSceMIPVz6mnqg/TNQhSoxBC1YhGeZoUilq/d+rUKc8GjoJu/hx2bmgkbMLmj49GzK9h2BmgEQzgsDVm8/gYwDZXV0QON3+PPPJIz/xtpP7I89nUzt+56g8zEe/a0047Ld8r1SJT7Shevny597j6XdDZwj6nLrAbBvAhNLwJpqsm+683QSN//etf8xiMDzzwgIwYMcL+AkWUYYHm7zQR2S9So2YNKd2vtCwrncPI/J8J3La1Q8dAfPCByAsviKijD844Q/57ZAvZc/VwqfXlWxFVSg4Yr+3bixx3nEiTJgd2zqqtxuvXi8yZIzJjhogypVW/W28VufNOCfvuZpG9jiiDfedOkRIlDhjUzz4r8uKLBw5RLlnywDbcjRtFdu06UI9q1Q44surGVupHXaeOovjsM8n6+GPJULuPC2m7pZiskKOloSwttN9Kqe2ZwernK2kmb0k3qdqosnTqJN7P6af799+jE1K0M0WmkWjTMn42dYPmZs2ayU71vMrRZsyYId27d480PzQSKW4jJ0MjRpYt0qDRCAZwpIKzbDIMYMsKeng66tgHtRNXtRo1angGbuPGjZPKWl2vxlHt3HPPlddffz3f66dOnSq9e/f2Hgv7zEgM4EMl4E0wKTk72dkEjbRq1Ur+85//HKyP+qaBOr9QvW7R0ieQn/mb+U2m7H91v5QqUUpuuOEGOfHEEyWzaKZM3DpRXvvhtYOTqn7P/O4ZueLyA8f7WNuUUahudvfYY6GnmFW9umQo47Z1a5GaNUX+8Q+Re+45sLv4ggtE7rjjwG7cgtrPP4t8/rlIs2YHblwWQdPqdUSZv999d+A4CWWQF3RDq/9n7zqgo6q26E6DgJTQQRCkBZAgSJOO9Ca9qvQiHT4qCEgVQUVAQelVQRDpiFRBWpRepEgXkCagSE8ISf66M5LkZSaZ9vrbdy3W/87ce8o+e9682bnv3IcPgR07gE2b7Due06Wzte6IerEALgYUwKF7BbDr4gs4eCQAW/ekRmq4v/P4L2RFDWzFCYTFoV+smF0MrlYNEH8rF10vrDR0xRErAe9Grp988gmGDBkimSk2aYhWEKlTp3bDgjxTyBF5cDSzFXLEzNWVJzdyhAKwPEyyphUKwCaue9++ffGVeJzyP/H3559/RmHxQ8nDIXYMCNH4sth9A/GE5ldxgvAzU2J3gdgpLB7jFmPLli2oKR5JVWhQAI4Hll+CCpHMRGb1zhFx/Uj8VEKdOnWwceNGE1VBu1Scib9twtqgb86+mDVjFkaPHm1r8fPsSY/yFcuj/47+kjYRYv7CpgsR6B+oXSIKeRYbaX9fcgQhg7oh57X4P0J46+5+QAgu5q6MgEIFkfGVPMhS9kUE5Hre3nv36FGgRAl7z9vnnpO6EG0PhJApdrDqcOj9OuIrZDEjRsF/zGiPzIhdwY2wFneQAbeRGY8QX1PRLUu0iahf3/4vLMyxNbNHzgww2ewcMUAJkgzxyZMnKFGiBH4XfzhJMIYOHYqxY8eqlho5ohrUhnVEjhi2dKoFTo5QAFaNbCZ0RAHYhEUVKQ0bNizuhs7Pzw/jxo1zS/wtWbKk00MhxEFM9evXR9R/h8aIR8YaNWqE5557DocOHcKcOXNw9+5dG5rdunXDrFmzFEWWAnA8vPwSVJRqpjCud458+OGHGCl2PCYYX3/9Ndq3b28K/LVMIinxN7GYm5gj6ULSOfQKNpMILLop/PQTcHz+flTYOALVn7j/x4bzyOe032/E83kRMH0qghrUVrwdgxac0vt1xGdMRPsqsXX30iWvTEXDHz+hJraiBh4gDa4gF35FedxGFpu9XLnixWBxrqXoMGK2YXqOGLxg4inAaoLjCUZQUBB+++03t34jyJE+OSIHiua2QY6Yu75yZEeOUACWg0dWtUEB2KSVf+211xwOfHAn1fnz56Njx45Op65atQqdO3fGv+KgmCSGEH+nT5+e7EFx7sThag4F4HiE+CXoii18X88cEX3KixQpgtOnT8cVKjg4GH/99RfSice1OXxCYPnJ5Wi5rGWcjaREXGcccSYeL2u5DC1eauFTTFotFg+xiA5G4oyxG1tPYMTT4WgG0QDZ/TEz5H0cavYRWmTchsqbPkDwsQP2vrS9egEffui4q9d907qfqefriGzgid7Con9wYKC9tcb58/b/Fu0jjh9H7PHj8Pvvj93u+jyNUOxGJYSjou3fGYQiRQo/W8/gZs2AJk2AbNnctabveZbgiL5L4DI68YfVhaLHeYIhROGtW7dCbBhRepAjSiNsfPvkiPFrqHQG5AgFYKU5Zmb7FIBNWl0lBGAB1fXr120Crzg44uLFi7YDJcTBcpUqVUKXLl2SPCBObpgpAMcjyi9BudllPnt65oh4gqBUqVIS0Fu0aIFly5aZrxAaZTRq+yiM3jEaye3gTYojCUXgkVVHYtRrozTKwju3YlPnihXAtm+vI/jIrzYJrgJ+wavYC3/EJml0GVrgEwxGvTS70SjDTmTLHI3g9/og25uJWhuJQ9lEy4a0ab0L0ECr9HwdUQ3G2Fjgo4+AESO8dnkLmfELKuAISuAYiuEP5MPzr76Amm0yo1lzP9t5f0Yd5Ij+Kyf+uCrawSXezPHtt9/izTffVDwBckRxiA3vgBwxfAkVT4AcoQCsOMlM7IACsImLa+bUKABTADYzv+XOTc83SgMHDsSECRMkKYsDJZs2bSo3DJa2J3YCNyncJMkevslxRIjAq0+tNszO34vhV3Foxj78te0EMlw7jrLYh3z4w63630RWLA0bg+jO3VCzlh+KFjV/31a3gAGg5+uIuznIMk+IwH37AlOnymLumZHHCMYpFMalzKURXKk0igyoizxV8sjqQ2lj5IjSCMtjX2zk6CWeWkgwsmXLhlOnTiFE4ZMLyRF5amhmK+SImasrT27kCAVgeZhkTSsUgK1Zd8NnTQGYArDhSaxiAnq9UYqJibH1HL969WocGunTp7cdJinaQHCoh4BeOeIuAleuAGtmXMeL0wah3p1vk93d68zmnedy4lrHocg/phOCM6Ry162l5hmdI7IWS4jAu3bZD/V78gT4+29g/Xr7f8s0RE/hUQW+Rbb+bdC6NZDF3kpY14Mc0XV54oKLjo5GuXLlcOCA9NDLPn364Msvv1Q0CXJEUXhNYZwcMUUZFU2CHKEArCjBTG6cArDJC2zW9CgAUwA2K7eVyEuvN0o7duyAaFeTcHTq1Anz5s1TAgbaTAYBvXIkuaLdv29v77DkmygU/fkrjMJIpMN9j+r8NCQTAoYNhV+vnkAqCr/JgWdEjnhEBl8nC1H44kXg5k37IYCHDgHh4fZ/opewF+Mu0qEAzuFOQBbUrg289Za9Z/Bzz3lhTIUl5IgKIMvk4uDBgyhTpgxEH/5nw9/fH/v27XNoyySTS5sZckRONM1pixwxZ13lzIocoQAsJ5+sZosCsNUqbpJ8KQDHF5JfgiYhtYJp6JUj3bt3x6xZsySZb9myBTVrJuqzqiA2NG1HQK8cSVyfp0+BLVsAcYbRjlX/oHXEAvTEdBTEObdLGZMxE/wrVgBq1AA6dQJ42KBb2BmFI24lo/akGzeAX36JF4TFTuGICLeiWIAOmInuyIh/cBuZcSFVGGo3fQ7ivF5BYX9/t8yoMokcUQVm2ZyIHb9TE7UyKVu2LMQ9thCDlRjkiBKomssmOWKueiqRDTlCAVgJXlnFJgVgq1TaZHlSAKYAbDJKK5qOHm+Unj59CtFzUMT2bIj/Fu0gAsTuOQ5VEdAjRxICcO4cMHs28PXXQIa/fsdAfIY3sASpkLyI9tQvEPefL4TU1cohZfWKQIUKQGgoG/t6wS69c8SLlLRbEh0NCFKfPg38+Scenf4Tf/1yDqlOHkL2x8n3q46BH84jv+0AucMZayJVz45o1z21Lg6PI0e0o5Q3nsVBcOJAOHEwXMKxaNEivCW2myswyBEFQDWZSXLEZAVVIB1yhAKwArSyjEkKwJYptbkSpQBMAdhcjFY2Gz3eKO3cuRNVq1aVJN6vXz9MnjxZWTBo3SkCeuRIZCSwejUgNolv2wakx78YhVHog68QiOgkK/nEPyVONxyIF4e8gbSvFABSpGDVZUBAjxyRIS3dmYjs+x5SfjXR7biOoyhaYRlerFcEXboADRtqR3lyxO2y6WaiEHvbtWsniSdXrlw4ffo0UqdOLXuc5IjskJrOIDliupLKnhA5QgFYdlJZyCAFYAsV20ypUgCmAGwmPiudix5vlAYOHIgJEyZIUv/5558degIrjQ3t2xHQE0fOnLHv9l2wALh9G0iBSHTHTAzHGGTB7WRL9qRmfaSYPhkoUICllRkBPXFE5tT0Ze7uXaBgQeDWLbfjikIgvkF7LENLHM9cDW90TIkePYD8+d02IctEckQWGFU1InoAly9fHnv37pX4/fDDDzF8+HDZYyFHZIfUdAbJEdOVVPaEyBEKwLKTykIGKQBbqNhmSpUCMAVgM/FZ6Vz0eKMkHjsVO4yejZCQENy8eRNBQUFKw0H7ThDQmiPiifi1awFxAP2vPz9GfpxHHlxCCRxBV8xBXlxMsm6x/v7we/11oG9fe1NUPz/WWAEEtOaIAinp16Q43bBVKyAmxuMYz6IA+uJLbPari/r17R+LWrXU6RVMjnhcLl0s2LNnj00ETjjE7t8zZ84gZ86cssZIjsgKpymNkSOmLKusSZEjFIBlJZTFjFEAtljBzZIuBWAKwGbhshp56O1G6ezZswgVfVgTjDfeeAOLFy9WAw760JEAfOcOMG8eMGtKBKpdXoBemIYwHIc/4k+mT6pgsRkzwq9nT+Dtt4HcuVlXhRHQ23VE4XS1Ny92ZIreJ6InepYsQEgIcPEiYo/+hgfhR5D23JFkY1yJpngXE3EReW1tr3v3hu3gOCXPPCRHtKeNtxG8+eabWLJkiWR5hw4dsEA8iiHjIEdkBNOkpsgRkxZWxrTIEQrAMtLJcqYoAFuu5OZImAIwBWBzMFmdLPR2o/T555/jnXfekSQvxF8hAnNog4DaHDl1CpgyBVix4D7aPZ5pE6py4IZ7yYvT6Xv1AkaPBjJmdG8NZ/mMgNoc8Tlgsxv45hvE9uwJv0ePksw0Gv7YjNqYjp74AY2QJg3Qvr19V3DhwvIDRI7Ij6laFi9fvoxChQohIkJ6sOaBAwdQqlQp2cIgR2SD0rSGyBHTlla2xMgRCsCykcmChigAW7DoZkiZAjAFYDPwWK0c9Haj5JfoEf2AgADcunULGTJkUAsS+kmEgBociY0FtmwBJk4Ejmz+Cz0xHf0wBRlxx/16iBOuxo0DwsLcX8OZsiCgBkdkCdRKRvbvB+rUAcRWehdjKnrZDlAE7C1SGjcGBg0CKlRwtdL998kR97HS40zR8/ejjz6ShFapUiWIQ1sTf297Gz854i1y1llHjlin1t5mSo5QAPaWO1wHUAAmCwyJAAVgCsCGJK5GQevpRkkIvVmzZnVAQhxEw6EdAkpyRPT3XbX0CX4e8TPCzq+29fUtg/0IRLT7CVesCHzyCVCpkvtrOFNWBJTkiKyBWs3Yb7/ZReAb7u2gX47m+A0vYw664jqeh/hoCSFYtNEWm+t9GeSIL+hpv/bBgwe29kzXr1+XBLNs2TK0aNFClgDJEVlgNLURcsTU5ZUlOXKEArAsRLKoEQrAFi280dOmAEwB2OgcVjN+Pd0oiR+SrcThRglGiRIlcPjwYTUhoa9ECMjGkadPgSNHgJs3EXXxKs58fxhRew6hUORRpIL00eKkihBbqxb8atYE8ucHUqcGChSw/+PhbpryVjaOaJqFSZ3fuwd8/73983LpEjB2LPDwYbLJPkYwZqI7PsX7uIEctpYQAwcCb70FpEzpHU7kiHe46WnV/Pnz0blzZ0lIefPmxcmTJxEcHOxzqOSIzxCa3gA5YvoS+5wgOUIB2GcSWdgABWALF9/IqVMApgBsZP6qHbuebpR69+6NadOmSSDYt28fypQpozYs9JcAAVk4cv48UK8ecPasx9jG+vnBr1kzYMgQQMZ+kx4HwgVJIiALR4ivOgj8/Tfw7bdA//4u/QkheAZ64DMMtO0IzpHDviO4e3cgVSqXyyUTyBHP8NLj7JiYGJQuXdrhj7KffvopBgli+DjIER8BtMBycsQCRfYxRXKEArCPFLL0cgrAli6/cZOnAEwB2LjsVT9yPd0oiUNmzpw5EwdCunTp8O+//8rWX1B9dM3h0WeO/Psvol8tj4AzpzwCJDYgAH5iy+HgwUCRIh6t5WR1EfCZI+qGS28CAXHaovjj2oMHLvGIQiCWoSW+RF/sQTlky+ZnE4J79LBvLHZnkCPuoKT/OaLnb9WqVSWBhoSE4MKFCz736idH9F9/rSMkR7SugP79kyMUgPXPUv1GSAFYv7VhZMkgQAGYAjA/IO4joJcbJXHKeJ48eSSBt2/fHl9//bX7yXCmIgj4wpH7d57iZtkGyH9us1uxRYRkR8oyL8Ovdi378+ZiyyGH7hHwhSO6T87MAR48CHz2GXDuHPDvv8CffwJPniSb8UGUxOcYgG/xFrJmjReCn3sueaDIEfMQqXnz5li5cqUkocGDB+Pjjz/2KUlyxCf4LLGYHLFEmX1KkhyhAOwTgSy+mAKwxQlg1PQpAFMANip3tYhbLzdKc+fORdeuXSUQLFy4EG3bttUCFvpMgIA3HHlw7R62/2818q6cgKLRx5LE8wpy4s/MJZG9fkm82KMu/F4t6/tpU6ye6gh4wxHVg6RD1whcuwZ8+ikwcyYQGZns/O2oimrYbpuTJYu9R3CvXkBSQjA54hp+o8w4ffo0ihYtimhxiud/I1WqVDh37hyef/55r9MgR7yGzjILyRHLlNrrRMkRCsBek4cLQQGYJDAkAhSAKQAbkrgaBa2XG6U2bdpg6dKlEhSuXbuGHNwBqhEzvLuOPA4/iD97jEXo8VVJxv0biqEPpiK0YSH0HJmVbX01r7DvAejlOuJ7JrRgQ8BNIfgsCth2Ay/Gm7iLEGTLBowYAXTrBgQFSbEkR8zFrbfffhuzZ8+WJNWjRw9Mnz7d60TJEa+hs8xCcsQypfY6UXKEArDX5OFCCsDkgDERoADsnXBjzGozal8R0MONkjhYJlu2bLh9+3ZcOmFhYTh2LOmdo77mzfXuI+AOR6Kv/YWr9boi92/rkjW8CXWw+M11GDoiEIUKuR8DZ+obAXc4ou8MGJ1TBG7eBGbNAoSoJ0ThZMYH+AjT0RN3kBH58wMffQS0ahW/oZ8cMRfHrl69igIFCiAiIiIusYCAAPz+++8oWLCgV8mSI17BZqlF5Iilyu1VsuQIBWCviMNFNgS4A5hEMCQCFIApABuSuBoFrYcbpcOHD6NkyZISBAYMGIBJkyZphArdJkQgOY7ExgIblz9AvnYVUCgyecH+SI56SL1mCULLpCfAJkNAD9cRk0Gqr3Siouy9gj/4wGVcxxCGYfgIa9EYr7wCfPIJUKsWcOfOP9i1a5dtfeXKlZExY0aXtjhB3wi8//77GD9+vCTI1q1b47vvvvMqcF5HvILNUovIEUuV26tkyREKwF4Rh4soAJMDxkWAAjAFYOOyV/3I9XCjJH5Aih+SCcf69etRr1499QGhRwcEkuLI3r3AoIGx6LOrFVpieZLIXU9TAFHjJiB3n0aAnx8RNiECeriOmBBW/aW0aBHQvz/wzz8uY5uNrhiAz/EQaVC9OjBkyF08fGjvGUwB2CV8hpggPvf58uXD3bt3JfEePXoUL7/8ssc58DriMWSWW0COWK7kHidMjlAA9pg0XBCHAHcAkwyGRIACMAVgQxJXo6D1cKNUp04dbN68OQ6BoKAg3LlzB8+5OlZeI8ys5jYxR+7cyYghQ4C/lu3AGAxHFdh39Tkb1wZOwvMf9QZSpLAabJbKVw/XEUsBrmWy4pH/NWuANm3ciuIMCiISKbEPZbGxXHfU63YHTZqU4Q5gt9DT/6SPP/4YQ4cOlQTauHFjrF692uPgeR3xGDLLLSBHLFdyjxMmRygAe0waLqAATA4YGwEKwBSAjc1gdaPX+kZJnCKeIUMG3L9/Py7xKlWqYMeOHeoCQW9JIvCMI48fB+Jf3YfmAAAgAElEQVTXX6rhxPT9GPx0DGpiq9M1m7K1R6blM1G6UjBRtQgCWl9HLAKzvtIUvR3EX4I8GPeQFh8EjkO69zpi2Ig0SJXKg8WcqksEHj58aNsFfFP0i04w9u/fj9KlS3sUM68jHsFlycnkiCXL7lHS5AgFYI8Iw8kSBLgDmIQwJAIUgCkAG5K4GgWt9Y3SiRMnIA58SziGDBmCcePGaYQI3SZG4Pbtf/DJqFOImH8cXR9NQwkcTRKkO4XLIeTwdvgFpySQFkJA6+uIhaDWT6qiAfgvv9j/PXkCREcDokXE2bMuY9yMWuj7whqMmZAKLVuyM4xLwHQ+4YsvvoDo259w1K1bFxs2bPAocl5HPILLkpPJEUuW3aOkyREKwB4RhpMpAJMDxkeAAjAFYOOzWL0MtL5Rmjt3Lrp27SpJeM2aNWjUqJF6INBTkggc3Xgd+7t8hUbXZiMrbiWLVGypUvBbtw7Inp2IWgwBra8jFoNbv+k+eAC8+y4wa5bLGGegO3piBipXBr74Akh0DqjL9ZygHwQiIiKQP39+XLt2TRLU7t27UbFiRbcD5XXEbagsO5EcsWzp3U6cHKEA7DZZONEBAe4AJikMiQAFYArAhiSuRkFrfaPUrVs3zJkzR5L9jRs3kC1bNo0QoVuBgHiad0Oz2Wgd3hfBiEwWlJi8+eA/cgTw1ltAYCABtCACWl9HLAi5vlNeuxbo1Qu4ejXZOA/hFSxEO8zG2+jQ6zmMHQuEhOg7NUbnHIHp06ejl6h5glGtWjVs27bNbch4HXEbKstOJEcsW3q3EydHKAC7TRZOpABMDpgDAQrAFIDNwWR1stD6RqlYsWI4fvx4XLJ58+bFhQsX1EmeXhwQiIkBZs8Gjg2Yh68ed0kWoegXXkTA8KFAx45AUBDRtDACWl9HLAy9flOPigIuXwaefx74809E1G6A4EvnnMZ7HvlQC1vwKFs+fP65/Xw5Pz/9psbIHBF48uQJQkNDcenSJcmbW7duRfXq1d2CjNcRt2Cy9CRyxNLldyt5coQCsFtE4SSnCHAHMIlhSAQoAFMANiRxNQpayxule/fuISQkBLGil+R/44033sDixYs1QsPCbqOi8PsvdzCu/19ocnQUmmNlkmDcK1kV6Yb1B0SbjoAAC4PG1J8hoOV1hFUwBgJ3Tp9G2hIlEBgR4TTga8iBOtiE4yiGGjWAadOA0FBj5MYo7QjMmzcPXbpI/3BYvnx5hIeHw88NRZ/XETLJFQLkiCuE+D45QgGYnwLvEaAA7D12XKkhAhSAKQBrSD/DudbyRknsDKpZs6YEs8mTJ6Nfv36Gw9HIAUcuXoGI7v2Q/oG0f2PinPYWaYjQqe8iQ7WqRk6XsSuAgJbXEQXSoUkFEBAcuTRiBF6ZOjVJ63cQgkZYi92ojBQpgMGDgSFDgOBgBQKiSdkRePr0KYoUKYJz56Q7vTdv3oxatWq59MfriEuILD+BHLE8BVwCQI5QAHZJEk5IEgEKwCSHIRGgAEwB2JDE1ShoLW+Uxo4di2HDhkky37t3L8qWLasRGhZze+0a/uwyEi9slPZgTozCo5Qh2DV8JCLC8qJy5crImDGjxYBiuq4Q0PI64io2vq8PBJ5xJO3Fiyh3/TpSh4cDu3c7DW4nKmM8BuFHvI4CBexnylWrpo88GEXyCHz77bdo27atZJI4CG7Xrl0udwHzOkJ2uUKAHHGFEN8nRygA81PgPQIUgL3Hjis1RIACMAVgDelnONda3ig1bNgQ69ati8MsZcqUEG0hUoitXxzKICCa/P74I558MQ2BP2+Gf2xMsn6isuXEo2/mYfvjx7Z5FICVKYvRrWp5HTE6dlaJ3ylHRMPfd95JEoIOWIBv0MH2fo8ewPjxQNq0VkHMmHlGR0fjpZdewpkzZyQJuNMLmNcRY9ZczajJETXRNqYvcoQCsDGZq4+oKQDrow6MwkMEKABTAPaQMpaertWNkuj7my1bNty6dSsOf9Er8JdffrF0PRRL/v59YP58YMoU4Px5l24i0mRCyhGD4de7F/6JiLDt3qIA7BI2y07Q6jpiWcANmHiSHFmwABB9Y8Ufp5yMMBzDCYTZ3smd274buE4dAwJgoZAXLlyI9u3bSzKuUqUKduzYkSwKvI5YiCRepkqOeAmchZaRIxSALUR32VOlACw7pDSoBgIUgCkAq8Ezs/jQ6kbpwoULyJ8/vwTGAQMGYNKkSWaBVh95iJ27I0bYVZN791zGdCRnA+TvWx9pe7YF0qWzzdeKIy6D5QTdIECO6KYUug0kWY788APQuTNw+7ZD/JFIgRH4EBPxLqIRaHu/Uydg4kQgQwbdpmvpwJLqBbx9+3ZUrZp0D3leRyxNG7eSJ0fcgsnSk8gRCsCW/gD4mDwFYB8B5HJtEKAATAFYG+YZ06tWN0qrVq1Cs2bNJKAtWbIEbdq0MSaQeo26QQNg/fpko4tCIL5P3w05v/0MrzV4zmGuVhzRK6SMyxEBcoSscIWAS448emRvBzFzplNTq9AEzbECsfC3vZ8jh31qw4auPPN9LRBYsGABOgmlPsGoVq0atm3blmQ4LjmiRSL0qSsEyBFdlUOXwZAjFIB1SUyDBEUB2CCFYphSBCgAx+PBL0F+OlwhoBVHPvzwQ4wcOVIS3okTJ2y9AzlkQkC018iaNUljx1EUc/y6IXOfN/De+KwIDnY+VSuOyIQCzaiAADmiAsgGd+E2RwYMAL74wmm2g/ExPsVgyXti47CYzt7A+iJIVFQUChcuDPG0T8Kxc+dOWy95Z8NtjugrVUajIgLkiIpgG9QVOUIB2KDU1UXYFIB1UQYG4SkCFIApAHvKGSvP1+pGqWXLlli+fHkc9OLgtwcPHiAoKMjK5ZAv98ePcbn1e8j9wzQHmz/gdXyB/+F64epY8LUfypZN3q1WHJEPDFpSGgFyRGmEjW/fbY7ExgKiL/D//ufQtuYpAlAZu7AH5SWA5MsHLFwIVKhgfJzMlMHcuXPRtWtXSUqlS5fG/v37KQCbqdAq5uL2dUTFmOhKXwiQIxSA9cVIY0VDAdhY9WK0/yFAAZgCMD8M7iOg1Y1SoUKFJKeEFy9eHEeOHHE/cM50jkBEBJ5+OA6Rn0/FcxH/OMwRO+g+8x+MQYMAsQE7qV2/CRdqxRGW2DgIkCPGqZVWkXrMkT//BFq1AvbskYR8E1nwNmZhDZpIXvf3B4YOtbc8598Rtaqy1K/YBVywYEFcunRJ8saiRYvw1ltvOQTpMUf0kSajUBEBckRFsA3qihyhAGxQ6uoibArAuigDg/AUAQrAFIA95YyV52txoxQZGYnUqVMjJsGp723btoU4OZzDBwTOnMHjRq2Q6vTRJI3Uy38GoxcXdLnrlwKwD3Ww4FItriMWhNnQKXvFkWvXgOLFnR4OdxAlsQl1cBNZsQW1cBJFbfiULg0sWgQUKmRouEwT/KxZs9C9e3eHfGLFTu9EwyuOmAYpJuIOAuSIOyhZew45QgHY2p8A37KnAOwbflytEQIUgCkAa0Q9Q7rV4kZJ9PoNCwuT4DVu3DgMGTLEkBhqHnRsLGIXfYuobj2RIvJBkuHsKj0AZXZNcmvXLwVgzatqqAC0uI4YCiAGC685Ig6xFIdZuhh78CrmoTMWoS2QKjUmTQKE7ujn52ol31cSgSdPniBlypQOLs6fP498ondHguE1R5RMgLZ1hQA5oqty6DIYcoQCsC6JaZCgKAAbpFAMU4oABWAKwPxMuI+AFjdKK1asQIsWLSRBrly5Ek2bNnU/cM60I3DhAiK79kLKnzcliUh4yupIM34Eiver6hVqWnDEq0C5SDMEyBHNoDeMY5848vnnwHvvAQmeGkkq8dMIRVXswF/IjpYtgdmzgfTpDQOTKQMVLZ5+++03SW4DBgzAJKHSJxg+ccSUyDGpxAiQI+SEKwTIEQrArjjC95NGgAIw2WFIBCgAx5eNX4KGpLCqQWvBkbFjx2LYsGGSPE+ePIkiRYqomrvhne3ejae16yHwsfNdv0vRCgfrDsPQJcUQEuJ9tlpwxPtouVILBMgRLVA3lk+fObJ3L9CxI3DqlFuJZ8TfuIOMEJtMv/8eKFXKrWWcpAACd+/eRUiiL6F06dLhypUrSJs2bZxHnzmiQOw0qS8EyBF91UOP0ZAjFID1yEujxEQB2CiVYpwSBCgAUwDmR8J9BLS4UWrXrh3EITDPRkBAAB49eoQUKVK4H7jFZ8Zcu4FHhUogzYO/HJB4hFR4L/grVJjVCW+19fP5EWgtOGLx8houfXLEcCVTPWBZOBIRAYwaBVt/h6ioZHO4gpyoga04g0IQXy0TJwK9e7MlhOqF/89hnz59MHXqVIn7L7/8EuL1Z0MWjmiVIP2qggA5ogrMhnZCjlAANjSBNQ6eArDGBaB77xCgAEwB2DvmWHOVFjdKZcqUwYEDB+IADw0NxenTp61ZAC+y/vuvp7j6Ui28/M92h9WH8AomFf8GY1aFIW9eL4w7WaIFR+SJnFbUQoAcUQtp4/qRlSP//APcuAEEBopfuvadwU7GZbyAigjHFbxge7dZM2DuXPj0RIRxK6Bt5OI7vnDhwpIgChYsiFOnTsHf39/2uqwc0TZdelcIAXJEIWBNZJYcoQBsIjqrngoFYNUhp0M5EKAAHI8ivwTlYJS5bajNEXHyt3j088GD+LYFjRo1wpo1a8wNtEzZHf7hCi63GYTGj5ZILEbDHwMxAemH9cUHIwNtuohcQ22OyBU37aiHADmiHtZG9aQoR86eBWrVAi5dcoDndxRGZezC38hse0/8YUy0hChd2qhIGjfu+vXrY8OGDZIE1q9fj3r16lEANm5ZVY1c0euIqpnQmVIIkCMUgJXilhXsUgC2QpVNmCMFYArAJqS1YimpfaN09epV5MqVS5LPoEGD8OmnnyqWoxkMx97+G0dafIQiO6YjGJEOKY1NPRavrhmKmjXlz1ZtjsifAS0qjQA5ojTCxrevOEcePQKGD7e3h0g09qEMamEL7sF+GlzKlMCMGUluHDY+2DrNYOPGjXFi77MQ69SpA/G6GIpzRKe4MCz3ESBH3MfKqjPJEQrAVuW+HHlTAJYDRdpQHQEKwPGQ80tQdfoZzqHaHBE7fZs0aSLBad68eejUqZPhsFMr4IjTl3C/VFVkeei4u03EsDdDXbzw2494Ppf9MVq5h9ockTt+2lMeAXJEeYyN7kE1jowdCyQ6ZFRg9wdeRHt8g92oHAdlr17A55/D1iOYQ3kEYmJi8NJLLzm0fBJtIAoVKkQBWPkSGN6DatcRwyNl3QTIEQrA1mW/75lTAPYdQ1rQAAEKwPGg80tQAwIazKXaHPHz83NAaNu2bahWrZrBkFMn3Os/nUB0g0bI9eSCU4c3MxVGxuO7EJjd/nizEkNtjiiRA20qiwA5oiy+ZrCuGkdiY4F33gG++MIBthj4YTwGYSjGIRb2P5hVrAgsXw5kz24GlPWfw7Rp09BbnMaXYLz77ruYMGECBWD9l0/zCFW7jmieKQPwFgFyhAKwt9zhOoACMFlgSAQoAMeXjV+ChqSwqkGrzRFnAvCRI0dQvHhxVfPWvbOrV3G9x2hkWTcPgYh2CPcRUuF6i37IP2cIkN7+WLNSQ22OKJUH7SqHADmiHLZmsawqR2Ji7P0dFi50Ct8wjMFYDIt7L0cOYMUKoHx5s6Ct3zxE///nn38e9+/fjwsyU6ZMuHLlCh49eoRdu3bZXq9cuTIyZsyo30QYmSYIqHod0SRDOvUVAXKEArCvHLLyegrAVq6+gXOnABxfPH4JGpjIKoWuNkdSpUqFiIgISXbisVBnwrBKEOjLzdOniB01Gk/HT0RQ1GOnsa3N1BElN36MXKXV2bKmNkf0VRBG4w4C5Ig7KFl7juociYoC3nsPmDLFKfAZ8A/+RYa494KCgK++At5+29p1UiP7nj17YoZowpxgLF68GKIfMAVgNSpgXB+qX0eMC5VlIydHKABblvwyJE4BWAYQaUJ9BCgAUwBWn3XG9aj2jdJrr72GHTt2SACLFY/sctgQePrOQAR+PiFJNNYVHYQa+z9FqlTqAaY2R9TLjJ7kQoAckQtJ89rRjCObNgHNmgHikLgE4wGeQ2ssxXrUBxDfmqhfP2DiRCAw0Ly10Dqzw4cPo2TJkpIwxL3BihUrKABrXRyd+9fsOqJzXBgef/sm5AC1EH4ivEWAArC3yHGdpgjwoscvQU0JaDDnat9Mi4Nezpw5E4eSeMxz586dBkNNmXBv3wYCc2RGyNO/HRz8gww42nQ0XlveB37+jn2UlYnIblVtjiiZC20rgwA5ogyuZrKqKUf27QNefdUpnBFIiXBUxAGUxgS8h9vIgnr1gO++A9KlM1MF9JVLmTJlcODAAUlQe/bswY0bN2yvsQWEvuqll2g0vY7oBQTGkSwC5Ah3APMj4j0CFIC9x44rNUSAAjAFYA3pZzjXat8opU2bFqIH4LPRunVrfCd+aVt5REbij01nsL7DUvT+d6wDEpOD30eJJe+japP4x5XVhEttjqiZG33JgwA5Ig+OZraiOUfq1gXEbuBkxiXkRm1sxhkUQlgYsG4dkCePmauiXW5z5sxBt27dJAGIw+Fq1aple40CsHa10bNnza8jegaHsdkQIEcoAPOj4D0CFIC9x44rNUSAAnA8+PwS1JCIBnGtJkcePnyINGnSSJDp378/vnByWrtB4PM9zNu3ca9yA6Q7tc+prRlZh6P2rx8iXz7fXXlrQU2OeBsj12mLADmiLf5G8K45R27eBDp1AtavTxauq3gexXAMd5ARWbMCa9YA5coZAWFjxejsMDhx6NvMmTMRFBREAdhY5VQtWs2vI6plSkfeIkCOUAD2ljtcB1AAJgsMiQAFYArAhiSuRkGreaN08eJF5M2bV5Lp2LFjMXToUI2y197tH+XfRN49S5wGcj8oA/wvXcRzObR9DllNjmhfEUbgDQLkiDeoWWuNbjgidgG/8w5w8mSSBZiOHuiF6bb3U6YEFiwA2rSxVr3UyNbZYXDvvPMOqlSpQgFYjQIY0IduriMGxM4qIZMjFICtwnUl8qQArASqtKk4AhSAKQArTjITOVDzRmn//v0oW7asBL1Zs2Y5PAZqIniTTEWce7eg7U/otNj+uGviERUYjID16+Bfq4bmcKjJEc2TZQBeIUCOeAWbpRbpiiNPnwKzZwMjRgCi+XqiEQM/lMF+HEKpuHfGjQMGDwb81G3BbmqOODsMLiwsDB999BEFYFNX3vvkdHUd8T4NrlQQAXKEArCC9DK9aQrApi+xOROkAEwB2JzMViYrNW+UfvzxR7z++uuSRFavXo3GjRsrk5xOrT55AnRrH4mhS19GIcQfiPcs3KdBwQhc/wNQs6YuMlCTI7pImEF4jAA54jFkllugS45ERgLHjwPjxwPffy+pyV2kw0x0xxT0w1Xksr3Xpw8gOhYFBFiufIolLP4oLP44nHBMnToVbdq0gWgJwUEEEiKgy+sIS6QrBMgRCsC6IqTBgqEAbLCCMVw7AhSA45nAL0F+KlwhoCZHFixYgE6iB2OCER4ejgoVKrgK0zTvi/PvWjWNQoOf/ofemOaQ16UW7yLPxz2AAgV0k7OaHNFN0gzEIwTIEY/gsuRkXXPk7l2gcGHgxg2H2kQhEF+hD4ZjDB4iDVq0ABYuBIKDLVlG2ZN2dhic+KPwvHnzKADLjrbxDer6OmJ8eE2RATlCLcQURNYoCQrAGgFPt74hQAGYArBvDLLWajVvlCZMmICBAwdKAD5z5gwKFixoCdBv3QLernEe7x97C+Ww11H8nbkRed6uozss1OSI7pJnQG4hQI64BZOlJ+meI99+C7Rtm2SNLiAv6mEDzqAQqlYFVq8GQkIsXVJZknd2GFzatGlx+vRp5MiRQxYfNGIeBHR/HTEP1IbNhByhAGxY8uogcArAOigCQ/AcAQrAFIA9Z411V6h5ozR8+HBbb7+E4+bNm8iSJYvpC3D51CMsrfwVetweg7R44JBvRMOWCF4rfQRZL6CoyRG95Mw4PEOAHPEMLyvO1j1HRGN20eh3+HBA/H8n4w+8iHLYg5vIhmLFgA0bgJw5rVhNeXN2dhiceGKoQ4cO8jqiNcMjoPvriOERNn4C5AgFYOOzWLsMKABrhz09+4AABWAKwD7Qx3JL1bxR+t///ofJkydLMH78+DGCTf4s7R+zf0Jgz654IfqSU37F5HkR/r/+Auh0t5OaHLHcB9AkCZMjJimkgmkYhiPnzgGTJgHz5wMREQ6IHEAp1MRPuIsQ5M4NbNpk7x7B4T0Chw4dQqlS8QfuCUsNGjTAunXrvDfKlaZEwDDXEVOib4ykyBEKwMZgqj6jpACsz7owKhcIUACmAMwPifsIqHmj1KVLF1tfv2cjMDAQT548gZ9Zj1V/+hTXW/8POVZOTbIgMQ0awn/+XEDHu6DV5Ij7zOVMPSFAjuipGvqMxXAcET17hgwB5s51APQoXkYdbMJfyG67dG/ZAhQvrk/cjRBVbGwsihUrhhMnTsSFGxQUhBs3brAPsBEKqGKMhruOqIgNXdkRIEcoAPOz4D0CFIC9x44rNUSAAjAFYA3pZzjXat4otWrVCsuWLYvDKEOGDLYbNVOOmBhcq9MRz/+00Gl6UYHBCPhiEvx79QB0LoCryRFTcsECSZEjFiiyjykaliMjRgBjxjhkfxF50B7fYBeq2HoBb9wIvPqqjyBZePknn3yCIUJwTzBmzJiB7t27WxgVpp4YAcNeR1hK1RAgRygAq0Y2EzqiAGzColohJQrAFICtwHO5clTzRqlu3brYJJ6X/W/kyZMHFy9elCsV/dh58ABX63dFzl1LncZ0+uUWKLRsLBAaqp+Yk4lETY4YAhAG6YAAOUJSuELAsBwR/YC7dLG3hEg0YuCHKeiHoRiHgDSpIToWiAPiODxH4PLlyxD3BAlHpUqVsGvXLs+NcYVpETDsdcS0FdFfYuQIBWD9sdI4EVEANk6tGGkCBCgAx4PBL0F+NFwhoCZHKlasiF9++SUupLCwMBw7dsxViMZ6/+RJ3KvTAumu/O4QtzhA6Oz7c1H7k+qGyklNjhgKGAYbhwA5QjK4QsDQHImOBnr3BmbOdJrmGRS09QW+FZwbq1YBdeu6QoPvO0NACL7h4eGSty5cuIC8efMSMCJgQ8DQ1xHWUBUEyBEKwKoQzaROKACbtLBmT4sCMAVgs3NczvzUvFF6+eWXJYJv+fLlJYKwnHlpYmv5cjxt2wGBkY8c3O/3K4Pbi7egXpv0moTmi1M1OeJLnFyrHQLkiHbYG8Wz4TkidgKPHOm0HYSoQTgqoBJ2IyjID0uXAk2bGqUy+olzypQp6N+/vySgMWPGYNiwYfoJkpFoioDhryOaomcN5+QIBWBrMF2ZLCkAK4MrrSqMAAVgCsAKU8xU5tW8UXrxxRdx6dKlOPzq1KmDjaJxohnGli2IqVMX/rExDtkc9CuNu99vQvUWGQ2ZqZocMSRADJq7ssgBlwiY5joi2hiJlhBXrzrkXBk7sRuVERAALFwIvPGGS1g4IQECf/zxBwoVKoSoqKi4V0NDQ3Hq1CnzHhZLBniEgGmuIx5lzcmeIECOUAD2hC+cK0WAAjAZYUgEKABTADYkcTUKWs0bpUyZMkkOfWvRooXkUDiNIPDN7Z9/Aj/+iCf/G4gUkQ8cbM0LfBsFfpyMKrWDffOj4Wo1OaJhmnTtAwLkiA/gWWSpqTjy779AkybAjh2S6p1CIdTAVlxDTvj7A4sXA61bW6TAMqQpONKwYUOHJ4P27duHMmXKyOCBJoyOgKmuI0Yvhk7jJ0coAOuUmoYIiwKwIcrEIBMjQAGYAjA/Fe4joNaNUmxsLFKmTCnZ2dOpUyfMmzfP/WD1NFPsZH77bWDzZqdRPUIq/C94JjpubYcKFfQUuOexqMURzyPjCr0gQI7opRL6jcN0HImMBLJnB4QYnGicRz58ivcxL+BtLFkCtGyp37roKTLBkYkTJ2LcuHGSsPr164fJkyfrKVTGohECpruOaISjmd2SIxSAzcxvpXOjAKw0wrSvCAIUgCkAK0IskxpV60YpIiICqVKlMsePuj/+AKpVAxK0s0iYWBQC0STNVoz+uQpKlzY+cdTiiPGRsm4G5Ih1a+9u5qbkiFB333wzSQhqYTN+DqiF778HmjVzFynrzhMc2bZtGzp37oz79+/HAZElSxZcvXoVQUFB1gWHmdsQMOV1hLWVFQFyhAKwrISymDEKwBYruFnSpQBMAdgsXFYjD7VulG7duoWsWbNKUhIHu4gDXgw1zp4FatQAROsHJyMGfuiXZj667e6A4sUNlVmSwarFEXOgZc0syBFr1t2TrE3LkZ49gRkznEKxGo3RFKsRGAgsXw40buwJYtab+4wjM2bMcDgf4Mcff0T9+vWtBwozliBg2usI6ywbAuQIBWDZyGRBQxSALVh0M6RMAZgCsBl4rFYOat0oicPfxCFwCcfHH3+MwYMHq5Wq736OHQNq1QL++suprWMIw6B0MzF+dwUUK+a7O71YUIsjesmXcXiOADniOWZWW2FajkRE2Bv9rl3rUNInCEIOXMc/yASxeXXFCqBhQ6tV3v18n3FEHPqW+N7gjTfewGLRVJnD0giY9jpi6arKmzw5QgFYXkZZyxoFYGvV2zTZUgCmAGwaMquQiFo3SqdPn0bhwoUlGX3xxRfo37+/ClnK4OLAAaBOHfH8oYOxpWiFjzAMV9KHYdvPfnjlFRn86ciEWhzRUcoMxUMEyBEPAbPgdNNz5MwZ+xbfU6ck1e2B6ZiJHrbXUqSw68Tiq4TDEYFnHBFnBgwYMAAXL16MmyRaSN24cQPp0qUjdBZGwPTXEQvXVq7UyREKwHJxyYp2KABbseomyJkCMAVgE9BYtRTUulE6evQoSpQoIclLPObZvXt31XL12tGuXUCDBkCCnoTPbA3Gx/gUgyF+k/70E2DGg8rV4sqHIgQAACAASURBVIjX9eFCzREgRzQvge4DsARHRGugPHmA2Ni4ehzFy3gVexGJYNtrqVMDW7bA8IeDKkG4hBwJDw/HZ599JnGzaNEivPXWW0q4pk2DIGCJ64hBaqHXMMkRCsB65aYR4qIAbIQqMUYHBCgAUwDmx8J9BNS6UdqzZw/Kly8vCWzBggXo0KGD+8FqMXPfPuC114DHjx2898UUfIW+SJMG2LwZSJSeFtEq4lMtjigSPI2qggA5ogrMhnZiGY6IA0K3b5fUag0aoT2+wT2kt70eEgLs2AG8/LKhSyp78Ak5kiNHDrz66qsSH40bN8bq1atl90uDxkHAMtcR45REd5GSIxSAdUdKAwVEAdhAxWKo8QhQAKYAzM+D+wiodaO0fft2VBM/jBOM7777Dq1F70Q9D3GS22+/SSKMhj+6YTbmo7NtN9eGDUCVKnpOwrfY1OKIb1FytZYIkCNaom8M35bhiOhTm8QuVdEnvhlW4hwKIls2YPduoEABY9RPjSgTc6R69eoQTw89GylSpIA4UJZtINSohj59WOY6ok/4DREVOUIB2BBE1WmQFIB1WhiGlTwCFIApAPMz4j4Cat0obdy4EfXq1ZMEtmbNGjRq1Mj9YNWc+eQJ8PnngJND6lphKZahFYKDgR9/BKpXVzMw9X2pxRH1M6NHuRAgR+RC0rx2LMORmBigZUtg5UqnxTyFQiiJQ3iM1BDnogoROGdO89bdk8wSc0S0ifrggw8kJhYuXIi2bdt6YpZzTYSAZa4jJqqZ2qmQIxSA1eacmfxRADZTNS2UCwVgCsAWorvPqap1o7R27VqIxzcTjvXr1zuIwj4nJIcBIf7WqGH/ZZ5ohKMCKiEcAQHAqlXWONFdLY7IUTra0AYBckQb3I3k1VIciYgA6ta193lwMr5EH/TDl7Z3XnoJ2LkTyJTJSNVUJtbEHPn7778RGhoqcSb+aCz+eMxhTQQsdR2xZol9zpocoQDsM4ksbIACsIWLb+TUKQBTADYyf9WOXa0bJdG3r2nTppL0Nm3ahNq1a6udsmt/CxcC7ds7ndccy7ESzTF/PtCxo2tTZpihFkfMgJVVcyBHrFp59/O2HEfu3gWqVgUStDBIiFYdbMRm1LG9JFrdbttmPyDOysMZR1555RUcOXIkDhbRBuLmzZtIn97eT5nDWghY7jpirfLKki05QgFYFiJZ1AgFYIsW3uhpUwCOryC/BI3OZuXjV4sjK1euRPPmzSUJ/fTTT6ghdtrqbYidymvXSqJ6hFQYjE/wJfpBHEz+3nt6C1q5eNTiiHIZ0LLSCJAjSiNsfPuW5MjDh8DkyUCiNgaimteQA8VwDP/AvvVXfO2sWAHb0yVWHc44Mm7cOIc2EIsXL8Ybb7xhVZgsnbclryOWrrjnyZMjFIA9Zw1XPEOAAjC5YEgEKABTADYkcTUKWq0bpWXLlqFVq1aSLLdt2+ZwMJxGMMS7ffAAsZkzwy8yMu61MyiIWtiCy8iDQYOATz/VPEpVA1CLI6omRWeyIkCOyAqnKY1ZmiP37gHiQNGLFyW1XYuGeBOL8RBpbK/37g18+SXg52dKCrhMyhlHzp4969AGQhweKw6R5bAeApa+jliv3F5lTI5QAPaKOFxkQ4ACMIlgSAQoAFMANiRxNQparRulpUuXok2bNpIsd+zYgSpVqmiUeRJuly0DEgnVwzAGYzEMnTsDc+ZY78e5WhzRFxEYjScIkCOeoGXNuZbnyK5d9pYQsbESAlxEHnTHzLh2EBMmAO++S45UrlwZGTNmtAFRtGhRnDx5Mg6UtGnT4tatW0iZMqU1gbJw1pa/jli49u6mTo5QAHaXK5zniAAFYLLCkAhQAKYAbEjiahS0WjdKS5YswZtvvinJcteuXahUqZJGmTtxe+cObpeogcyXD0vefAknENr4JSxfDgQG6idctSJRiyNq5UM/8iNAjsiPqdkskiMA3n8fGD/eaWlnoyt6YRqeIgjffw+0bGk2BrjOJymOfPDBBxCtIBKODRs2oK44aI/DUgjwOmKpcnuVLDlCAdgr4nCRDQEKwCSCIRGgAEwB2JDE1ShotW6UFi1ahHbt2kmyDA8PR4UKFTTKPJHb27dxr3xtpDsnFX9/R2F0evV3Sx/QoxZH9EEERuENAuSIN6hZaw05AkC0FhK7gPfudVr8fphs6zMvNrb+9BOgp7+PqsHWpDiyb98+vCpOykswevTogenTp6sRFn3oCAFeR3RUDJ2GQo5QANYpNQ0RFgVgQ5SJQSZGgAIwBWB+KtxHQK0bpW+++QYdOnSQBLZnzx6HH3XuRy7jzNu3EVGhOoLPHnMwOjLTV+h9sjeyZpXRn8FMqcURg8HCcBMgQI6QDq4QIEf+Q0j0AxaniM6e7QDZNlRDDWyzvS66H/z6KxAa6gpZ87yfFEdiYmLwwgsv4Nq1a3HJ5siRA1euXIG/v795AGAmLhHgdcQlRJafQI5QALb8h8AHACgA+wAel2qHAAVgCsDasc94ntW6UZo/fz46iya6CYbY1VOmTBnNQXvcoh1SrVjkEMeSoPYocWgeioRZ+Fh2AGpxRHMiMACvESBHvIbOMgvJkUSl/vlnoHp1h/qXxV7sR1nb60L8FZuFQ0KsQZPkONKrVy+HHb+6+SOyNcqjiyx5HdFFGXQdBDlCAVjXBNV5cBSAdV4ghuccAQrAFID52XAfAbVulKZMmYL+/ftLAjtw4ABKlSrlfrAKzIy4chuBuXMgMPapxPpcv67I/9NMvFadu4vU4ogC5aVJlRAgR1QC2sBuyBEnxRM7gSdOlLxxC5lREeE4C/vW3zp1gHXrrNF/PjmObN68GXUEGAnGiBEjMHr0aAN/Khi6pwjwOuIpYtabT45QALYe6+XLmAKwfFjSkooIUACmAKwi3QzvSq0bJbHTVwi+Ccd3332H1q1ba4ahOIx9aZkJaHNwoCSGeeiEwPlz0L4jxV8BjFoc0YwIdOwzAuSIzxCa3gA54qTEly8DhQoBEREOb36JPvgAY3Ef6TBgADBpkukpkux3zZMnT5AxY0Y8fPgwDoiyZctibxL9lM2PljUz5HXEmnX3JGtyhAKwJ3zhXCkCFIDJCEMiQAGYArAhiatR0GrdKNWrVw8bN26UZPnDDz/g9ddf1yhzYN7IS2j5YRjS4kFcDDHwwxcDLuOdSbk0i0tvjtXiiN7yZjzuI0COuI+VVWeSI0lUftUqoEULICbGYYI4hLQ6tuEGcmDuXCBRFyXTUckVRxo1agRx3/Bs+Pn54ebNm8icObPpsGBCzhFwxRHiRgTIEQrA/BR4jwAFYO+x40oNEaAATAFYQ/oZzrVaN0pz5sxBt27dJPiIz2q5cuU0wUycsH6xdjd0jZ0j8X80e228fG0T/Pw0CUuXTtXiiC6TZ1BuIUCOuAWTpSeRI8mUf+ZMoEcPpxNOI9QmAt8KygnRNrhiRfPSyBVHpk2bht69e0sAWLx4Md544w3zgsLMJAi44gjhIgLkCAVgfgq8R4ACsPfYcaWGCFAApgCsIf0M51qtG6UlS5bgzTfflOCzc+dOVK5cWXXMzp8HmpS6jGN380h8R/gFI2bPfqQuG6Z6THp2qBZH9IwBY0seAXKEDHGFADniAqFRo4Ak+tmeQ36bCByZNTf27wdy53aFtjHfd8WRCxcuIH/+/JLk2rdvj6+//tqYCTNqjxFwxRGPDXKB6RAgRygAm47UKiZEAVhFsOlKPgQoAFMAlo9N5rek1o3SihUr0EI85ppg/PTTT6hRo4aqIN+/D1Qv+wDhpzIiBaIkvu/2GoL0U8epGo8RnKnFESNgwRidI0COkBmuECBHXCAkmtKPHw8MHux04mGUQBnsR/GSgQgPB4KDXSFuvPfd4UhoaCjOnj0bl1y2bNlw7do1+PuzZ7/xKu55xO5wxHOrXGEmBMgRCsBm4rPauVAAVhtx+pMFAQrAFIBlIZJFjKh1o7R27Vo0btxYguqGDRtQt25d1ZAWv69bN4tCp9WNUA/SfsTRwakRcPkikCWLavEYxZFaHDEKHozTEQFyhKxwhQA54gqh/94XX1Tvvw989pnDgsZYjbVoDNFNadYsN+0ZaJo7HOnXrx++/PJLSVaHDh3CK6+8YqBMGaq3CLjDEW9tc505ECBHKACbg8naZEEBWBvc6dVHBCgAUwD2kUKWWq7WjZI4AE4cBJdwrFmzBuJQF7XGxAmxyDCwCzpjvqPLd94BJk5UKxRD+VGLI4YChcFKECBHSAhXCJAjrhBK8L4QgYcPB8aOlSzajFqog8221+bNAzp18sCmAaa6w5H169ejQYMGkmzGjRuHIUOGGCBDhugrAu5wxFcfXG9sBMgRCsDGZrC20VMA1hZ/evcSAQrA8cDxS9BLEllomVoc2bp1K2rWrClBdvny5WjevLlyaD99Cjx5AqROjd07onGw2rvoHzvZwV9szpzwO3oUyJRJuVgMbFktjhgYIsuHTo5YngIuASBHXEIknSBE4JdfBo4fl7weitM4i1BbC4hffwVKlPDQro6nu8ORhw8fIlOmTIiMjIzLpEqVKtixY4eOM2NociHgDkfk8kU7xkSAHKEAbEzm6iNqCsD6qAOj8BABCsAUgD2kjKWnq3WjtGvXLogfaQmHYqd3x8QAgwYBkycDT58iJnNW4PYt+CPWodYxRcPgH74bSJ/e0jxILnm1OMICGBcBcsS4tVMrcnLEC6RnzAB69pQsnIae6I1pttfy5QMOHAAyZPDCtg6XuMuR2rVrY8uWLXEZBAYG4vbt20jP73EdVlXekNzliLxeac1ICJAjFICNxFe9xUoBWG8VYTxuIUABmAKwW0ThJBsCat0o7dmzB+XLl5egvmDBAnTo0EH+SkyaBLz7rku7T8JKIsWvO4A0aVzOtfIEtThiZYyNnjs5YvQKKh8/OeIFxg8eAM8/D4jTS/8bMfBDVezAblS2vdKwIbB6NWCGM9Dc5cjEiRPx3nvvSQBduXIlmjZt6gXIXGIkBNzliJFyYqzyIkCOUACWl1HWskYB2Fr1Nk22FIApAJuGzCokotaNkjikpVSpUpKMZs2ahW7iNBs5x8mTQMmSQILHQ52Zf5TtRaQ+8iuQPbuc3k1pSy2OmBI8iyRFjlik0D6kSY54Cd7//md/miXBuI7sNhFYtIIQY9w4wAwtcN3lyIkTJxAWFibBpHv37pghdkxzmBoBdzliahCYXLIIkCMUgPkR8R4BCsDeY8eVGiJAATgefH4JakhEg7hWiyMnT55E0aJFJahMmTIFffv2lRep+vWBDRuStXkl56vItXOx/flZDpcIqMURl4Fwgm4RIEd0WxrdBEaOeFmKf/4BihUDrl2TGLiCnKiCnfgD+RAQAOzaBSR6yMZLh9otc5cjsbGxyJ07N65cuRIXbJ48efDHH3/Az89PuwToWXEE3OWI4oHQgW4RIEcoAOuWnAYIjAKwAYrEEB0RoABMAZifC/cRUOtG6eLFi8ibN68ksE8++QTvv/+++8G6mimaIZYp4zDrLArgMVLhFApjf8ke+GRvNQQE8keiKzifva8WR9yNh/P0hwA5or+a6C0icsSHimzeDNSp42DgJIqgLPbhIdIgTx7gyBEgJMQHPxov9YQj4umhOXPmSCIWf2guUqSIxlnQvZIIeMIRJeOgbf0iQI5QANYvO/UfGQVg/deIETpBgAIwBWB+MNxHQK0bpVu3biFr1qySwEaMGIHRo0e7H6yrmY0bA2vXSmb1wZeYij6213LmBA4fBrJkcWWI7ydEQC2OEHXjIkCOGLd2akVOjviI9JQpQP/+DkYW4S20w0IAfmjZEli6FDDqJlhPOLJixQq0aNFCgsdXX32F3r17+wg0l+sZAU84ouc8GJtyCJAjFICVY5f5LVMANn+NTZkhBWAKwKYktkJJqXWj9PDhQ6RJdNjau+++iwkTJviemTgoR/RJnDtXYusqnkd+nEckgm2PyO7YAVSs6Ls7q1lQiyNWw9VM+ZIjZqqmMrmQIzLgOn484OSpme6YgVnobnMgNsV26SKDLw1MeMIRMTdz5swQ7SCejZYtW+L777/XIHK6VAsBTziiVkz0oy8EyBEKwPpipLGioQBsrHox2v8QoABMAZgfBvcRUOtGKSYmBgFChU0wevbsiWnTprkfrLOZjx4BVaoABw86vNsfX2AK7Dumxo4Fhg71zZVVV6vFEavia4a8yREzVFHZHMgRmfB9+21g9myJsUikQAX8gkMohdSpAdENyYidEDzlSIkSJXD06NE4LMRTRjdu3GAfYJmopkcznnJEjzkwJmURIEcoACvLMHNbpwBs7vqaNjsKwBSATUtuBRJT80YpderUePz4cVwWHTp0wIIFC3zL6sMPgZEjHWz8gRdRFCfwGKlRtSqwdStsu4A5PEdATY54Hh1X6AEBckQPVdB3DOSITPWJiLCf9iYa/iYYF5AXr+Aw7iE9Xn4Z2LsXCA6WyadKZjzlSP/+/SEOk0042AdYpWJp5MZTjmgUJt1qiAA5QgFYQ/oZ3jUFYMOX0JoJUACmAGxN5nuXtZo3SolP565QoQLCw8O9C1ysioyE7eSbv/6S2DiNUDTGGpxGYWTIAIgNQi+84L0bq69UkyNWx9qo+ZMjRq2cenGTIzJiff48ULIkcO+exOh09EAvTLe9JtoFf/GFjD5VMOUpR1auXInmzZtLMZg+HT169FAhWrrQAgFPOaJFjPSpLQLkCAVgbRlobO8UgI1dP8tGTwGYArBlye9F4mreKCUWgEW4Cfv3eRz+Rx8Bw4dLlm1EHTTHCjzCc7bXly0DEp0T47Ebqy9QkyNWx9qo+ZMjRq2cenGTIzJjvXIlkEj8FB6qYjt2oqrN2bZtQLVqMvtV0JynHLl9+zayJDrVtU2bNliyZImCUdK0lgh4yhEtY6VvbRAgRygAa8M8c3ilAGyOOlouCwrAFIAtR3ofElbzRklWAfjHH4HXX3fIvDT24yBK214XB+GIA3E4fENATY74FilXa4UAOaIV8sbxS44oUKvevYFEffSvIzsqIhx/IJ/tAZnffgPSpVPAtwImveFIsWLFcPz48bhosmfPjmvXrrEPsAL10YNJbziih7gZg3oIkCMUgNVjm/k8UQA2X00tkREFYArAliC6TEmqeaMkmwAsfuyJHogPHkhQCEcFVIK9pURoqP1cuDRpZALKwmbU5IiFYTZ06uSIocunSvDkiAIw378PhIUBly9LjJ9DfpsIfBPZDPWHUG840qdPH0ydOlWS/+nTpxEqbgI4TIeANxwxHQhMKFkEyBEKwPyIeI8ABWDvseNKDRGgAEwBWEP6Gc61mjdKlStXxu7du+MwSpcuHe7evesZZqLvb6lSwIkTDusaYzXWorHtsLc9e4DS9o3AHD4ioCZHfAyVyzVCgBzRCHgDuSVHFCrW5s1AnToOxg+jBCpht60d0rp1QIMGCvmX0aw3HFm+fDlatmwpiWLWrFno1q2bjJHRlF4Q8IYjeomdcaiDADlCAVgdppnTCwVgc9bV9FlRAI4vMb8ETU93nxNUkyNdunTBvHnz4mIOCAhAVFSUZ49qDh0KfPyxQ96jMBKjMcr2+ogRwOjRPkNDA/8hoCZHCLoxESBHjFk3NaMmRxREW5z2NmCAg4MRGI0xGIHs2QHx4EymTArGIINpbzhy8+ZNZMuWTeL9rbfewqJFi2SIiCb0hoA3HNFbDoxHWQTIEQrAyjLM3NYpAJu7vqbNjgIwBWDTkluBxNS8UXr//fcxfvx4SRZ37txBSEiIe5nt3QtUqADExEjmt8M3WIR2ttdKlADEtBQp3DPJWa4RUJMjrqPhDD0iQI7osSr6iokcUbgeTv44ehkvIC/+QAwC0KoVsHSpwjH4aN5bjrz00kv4/fff47znz58f586d8zEaLtcjAt5yRI+5MCZlECBHKAArwyxrWKUAbI06my5LCsAUgE1HagUTUvNG6bPPPsOgQYMk2Zw9exYFChRwnWF0NFC8uEPrh+/QGm/gO9v6oCBg/377NA75EFCTI/JFTUtqIkCOqIm2MX2RIwrXLTYWaNoUWLNG4qgL5mAeutheW7YMaNFC4Th8MO8tRzp37oz58+dLPN+6dQuZM2f2IRou1SMC3nJEj7kwJmUQIEcoACvDLGtYpQBsjTqbLksKwPEl5Zeg6egte0JqckT8QBM/1BIO8XktV66c67y2bweqVZPM+wtZURQn8DfsP/LGjAGGDXNtijM8Q0BNjngWGWfrBQFyRC+V0G8c5IgKtdm2DahRQ+IoBn7ohtk2EVi0gjh5EsiQQYVYvHDhLUdEz9/u3btLPK5btw4NjND42AucrLzEW45YGTOr5U6OUAC2GuflzJcCsJxo0pZqCFAApgCsGtlM4EjNG6UffvgBjRo1kqAmXnv99dddI9mjBzBzpmReCyzDCti3M4lz4X791b4LmENeBNTkiLyR05paCJAjaiFtXD/kiAq1E+2R8uUDLl1ycPYpBmEIPkaXrv6YPVuFWLxw4S1Hjh49ihKi/1OCMXz4cHz44YdeRMElekbAW47oOSfGJi8C5AgFYHkZZS1rFICtVW/TZEsBOL6U/BI0Da0VS0RNjiT8bD5LSOwK7tixY/L5RUUBOXIAf/8dN+86siMXrth6G4p+v4cOAUWLKgaTpQ2ryRFLA23g5MkRAxdPpdDJEZWAFi0gmjRx6mwaeqI3puHnn4HXXlMpHg/ceMuRp0+fIn369Hj06FGct9q1a2PTpk0eeOdUIyDgLUeMkBtjlAcBcoQCsDxMsqYVCsDWrLvhs6YATAHY8CRWMQE1b5TOnDmDQoUKSbITfYHfe++95DMWP+Lq1pXMmYK+6I8pttc++gj44AMVQbOYKzU5YjFoTZMuOWKaUiqWCDmiGLSOhlesAES7pXv3HN4rhQO4X7AUjh4FUqVSMSY3XPnCkapVq2Lnzp1xXsThsn///Tf8/f3d8MwpRkHAF44YJUfG6RsC5AgFYN8YZO3VFIBNXP/o6GjbibkHDhzAwYMHbf8rHqF6/PixLesOHTpgwYIFLhHYvn07qiXqy5ncInftunSczAQKwPHg8EvQFyZZY62aHLlz5w4yZswoAXbgwIEYP3588mCLg+M++0wypwLC8Ssq2Hb9it2/YhcwhzIIqMkRZTKgVaURIEeURtj49skRlWt4+jQgeuCePy9xPB8d0RnzMWQIMG6cyjG5cOcLR8QBs+IPygnHyZMnUaRIEX0lyWh8QsAXjvjkmIsNgwA5QgHYMGTVYaAUgHVYFLlCat68OVauXJmkOXeFWgrAclVEGTv8ElQGVzNZVZMjsbGxCA4OxpMnT+IgbNeuHb755pvkIW3cGFi7Nm7OPaRFCP5FLPwRHg5UqGCmiugvFzU5or/sGZE7CJAj7qAELD+5HE0KN0Ggf6B7CxLMehrzFKtPrUaLl+x9z402yBENKnb7tr030s2bcc4jkBI5cB33AzLg4EGgeHEN4krCpS8cEb9pxG+bhKNUqVK2DS4c5kHAF46YBwVmkhwC5AgFYH5CvEeAArD32Ol+ZZMmTbBG9An7b4hdeZkyZcLZs2dtr3gjALdu3Rpt2rRJNvfcuXOjZMmSiuLDHcDx8PJLUFGqmcK42hzJkycPLl++HIddrVq1sHnz5mSxjC3yEvxO/R435yBKojQOomdPYNo0U5RB10mozRFdg8HgnCJAjrgmxqjtozB6x2i0CWuDhU0XOhWBxbUxnzjE679x4cIFiPsmIf62W9UO3x3/DiOrjsSo10a5dqizGeSIRgUZPtzeJynB6Iy5mI/OKFvWfniqXrok+MIR8fslNDTUAWTxh2cO8yDgC0fMgwIzSQ4BcoQCMD8h3iNAAdh77HS/cty4cbh//z7EX8fFv7x589paPnTq1MkWuzcC8MiRIzFqlPY/SigAx9OPX4K6/yhqHqDaHHn11Vexb9++uLyLFSuG3377LWkcIiPx9Ll0CIyO3zX8HVrjnRzf4fffgfTpNYfQ9AGozRHTA2rCBMmR5Isqdv62XNYyblJSIvCkSZPw7rvvxs2bOHEi+v2vX5z4++yNZS2XGW4nMDmi0Qf/4kUgb16J859QA7Xwk+21OXOALl00ii2RW1854ufnRwFYH6VULApfOaJYYDSsGwTIEQrAuiGjAQOhAGzAovkSMgVgX9DT51p+CeqzLnqKSm2ONG7cGGsTtHPIkiULbiZ4PFWCzT//ILJBM6Tcs0Py8hgMQ5FlY9DCmE9C66n8bsWiNkfcCoqTdIUAOZJ8ORLu4H0205kIXK5cOezduzfO2KvlX0Xed/Padv4mt05XZEgiGHJEwyqJPkliq+9/IwZ+KIZjOImiyJwZEO2CE7Xn1yRYXzniTACOiYmBs9c1SZBOfUbAV474HAAN6B4BcoQCsO5JquMAKQDruDhKhEYBWAlUtbXJL0Ft8TeCd7U50qNHD8ycOTMOGvHDLDIyEkFBQVK4btwAqlYFzpxxgPHjUssxeH9zONnsYwTIDRej2hwxHEAMGOSIaxK4EoFF+wfRIidu+ANoCqBY/EvJtY9wHYG2M8gRDfH/6iugb19JAMvRHC2x3PZar17A1Kkaxvefa1850q1bN8wRW5oTjD///BO5cuXSPjlGIAsCvnJEliBoRNcIkCMUgHVNUJ0HRwFY5wWSOzwKwHIjqr09fglqXwO9R6A2R0SbmNGjR0tguXLlCnLmzCmFqmVLYLn9x2nCsdevHLKf2o48oSn1Dq1p4lObI6YBzkKJkCPuFVuIwG1XtMXSk0vjFrQs0hLzG87HtK+mYdCgQfbXnYi/z+Y9O0AuICAAgYGeHybnXqTyzyJH5MfUbYv379vbQPz9t2RJSRzEYZS0/TFVnJWm8BEdLsP1lSNff/01OnbsKPGzfv161KtXz6VvTjAGAr5yxBhZMkpfECBHKAD7wh+rr6UAbDEG+CoAh4WFQfwgEYeWPHnyBOJgOfGaOOSpS5cuph2SFgAAIABJREFUtv9WY7AHcDzK/BJUg3HG9qE2R2bMmIGe4vS2BEOc0i16kceNlSuBRKd5i/fWoBFODv0WQ8amMTboBotebY4YDB6GC3AHsAcsaNGqBVbErJDs7MUxAKsAxDgXfyXv/+erVatWWLo0Xkj2IARNpvI6ogns8U4nTAAGDpQEsR1VUQ0/A/BDuXJAeLi2B8L5ypGDBw+idOnSkhxFH+133nlHY/DpXi4EfOWIXHHQjn4RIEcoAOuXnfqPjAKw/mska4S+CsDJBZMmTRpMnjwZnTt3ljVmZ8YoAMejwi9BxelmeAdqc2T16tVo2lQ81xw/RE/ghg0b2l9YvRpo1gxIdHL3NeRA5Tx/4vjvAUiVyvCwGyoBtTliKHAYrA0BcsQ9Iog/jmfKlAkPHj1waO9gE3lXA2gibfvgTPwV3tKmTYvbt28jRYoU7jnXeBY5onEBHj0C8ucHRHulBKMtFuJbtLW9Mm8e8N9Z0JoE6ytHHjx4YPtcJBxvv/22pO2UJonRqWwI+MoR2QKhId0iQI5QANYtOQ0QGAVgAxRJzhC9FYCrV6+OkiVL4rXXXkORIkWQPn16iJuwY8eO4fvvv4d4vPvZ+PjjjzF48GCvwxbirqsh/Hbv3t02bePGjShTpoyrJaZ9/969ezh69Kgtv+LFiyNdunSmzZWJeYeA2hw5fPgwatasKQn2008/RdeuXYHISIS88gr8//pL8n4UAlEFO9Hz6zC8/nqUd4lyldcIqM0RrwPlQs0QIEfcg37//v2oW7eufbKTNg8OVhLuDHbiwkj3OOSIexxRclbKr7/Gc4l2w95ANhTGKdxFCDJnjsH+/f9Cq1tFOTginjy8fv16HIyVKlXCmjVrlISVtlVEQA6OqBguXWmAADkCJLzX+OWXX1C+fHkNKkGXRkSAArARq+ZDzN4IwOIm6/79+wgNDXXqOSoqCkOHDsUE8eiZeMjMzw/iQiROuvZmeHqS7yeffILChQt744priAARUACBu3fvokOHDhLLTZo0sfXte2HbNpScMkXy3gM8hxZYjhvFS2LUqF958JsCNaFJIkAE1ENg9+7dth2J4t4pWRE4GfFX/DFX7GwU4hYHEXAbgehoVBk8GBnOnpUs+RJ90A9f2l5r3vwM2rX73W2Teps4fPhw2waUZyNDhgyYP3++3sJkPESACBABxRA4depU3IY7CsCKwWxKwxSATVnWpJPyRgB2FyLR+uHZDVj9+vXx448/urtUMo8CsFewcRER0A0CsbGxaN26ta1P+LNRsWJFDHzvPbw2YADSX7woibUatmFXQFVMnvwzcuV6oJs8GAgRIAJEwFsE/v33X5sIbHuqKQDAcCeWxgCIdnxd7OQRTzmFhIR4657rLIxA+nPnUHXQIPjFiIbT9hENf5TBftuBcEFB0Zg2bSuyZHlsSJTEOQNiZ3zCsXjxYqROndqQ+TBoIkAEiICnCFAA9hQxzn+GAAVgi3FBSQH46tWreOGFFyDEn5QpU+LOnTtI5UUjT7aA8IyUfAzGM7ysOFsLjognAM4m2IEkDoDbNmwY0iXqDbwTlVEVO9G792N8+KExf4yagVNacMQMuFkpB3LE82qL+6EVq1ag77a+eFIo/g9icZYS7QAWvYPHjx8P8cSEEQc5op+qpR40CMFz50oC+hXlUAG/2A6Ea9EiEjNnPlQ9YDk4IgTgDz74QBL71q1bUaJECdXzoUP5EZCDI/JHRYt6QoAcYQsIPfHRaLFQADZaxXyMV0kBWIRWqFAhnDlzxhbliRMn8NJLL/kYsfPlPAQuHhc2wleEYqYyqgVH6tWrJ9mhkytbNvwpDjL6808Jtk2xErszN8W5c0D69KaC3VDJaMERQwHEYHkInBcceBrzFO1WtcN3x79LenUCEVj0Sh80aJAXnvSxhNcRfdTBFsW//4qbcuDmTUlQr+MH/IjXba/t2weofYSGHBzZsGEDxJOGCce3336LN998U0cFYCjeIiAHR7z1zXXGQIAc4SFwxmCqPqOkAKzPuigWldICsHjMW/ShESM8PBwVKlRQJBcKwPGw8ktQEYqZyqgWHOnRo0fcqdxpAJwE8EIiVM8jH0JxBpO/DECfPqaC3HDJaMERw4Fk8YDJEc8I4FT8FWLvagBig2+xBPb+E4HLlS1nbxlh0EGO6Kxw33wDJOrHfxglUAoHEQt/VK4M7Nghzu5QL245OHL+/HkUKFBAEvSIESMwevRo9RKhJ8UQkIMjigVHw7pAgByhAKwLIho0CArABi2ct2ErLQCLg+KePfbNHcDeVsmzdfwS9AwvK87WgiOfffZZ3E62iQDecQJ8P0zGhgL9cOIEIDYHc2iHgBYc0S5bevYGAXLEfdSSFH9XARBtWf0BNHUuAl/64xJy587tvjMdzSRHdFQMEYroAVy8OHD8uCSwrpiNuehqe23lSiBRZyZFk5CDI9HR0bYWc+IQ6mdDbEARhy9yGB8BOThifBSYQXIIkCMUgPkJ8R4BCsDeY2fIlUoKwFeuXLH9aHnWA1hcnJU6kIE7gOPpxy9BQ34UVQ1aC46sW7cODRs2RBEARwEEJcr4LtIhF65gwfK0aN5cVTjozAkCWnCEhTAWAuSIe/VyKf4+M5OECDy+/HgMfHege850Nosc0VlBRDirVgHNmkkCe4jUKIt9OImiEBtpT54EghJ/SSuUilwccXZgtPj9wWF8BOTiiPGRYAZJIUCOUADmp8N7BCgAe4+dIVcqKQB36tQJwr4YdevWhejRpdSgAEwBWClumdGuFjdK586dQ6GCBfEzgCpOQO2Nr3CkQm+IDTtqPn5qxvrKkZMWHJEjbtpQDwFyxDXWzsTfTNcy4e85f9t3/v43xOPrffv2xZSvpuD8y+cl7SAyXc+EG9NuINA/0LVDnc0gR3RWkP+zdyXgNlXv+73meVaEyBApRElkiEsJSUIhQ8aQWZkyC4kImUVIMiRJylzUH5F5yhiFDD/zPNz/sw+Os+90prX3Xmvtdz9Pj+7Za33f+73fe/ZZ9737rG3AMUzRkiWB9etN4I4gB0ri/3AM2TBxItC8uT3YRWkkNgP4xo0bSGyXk20PXa7MIkojriTPJUVTIzSAXSJ1S8qkAWwJrfIGDdYANkycb7/9FsZ+nmnSpIm1MOMrWD169MCwYcO8563c/9dIQgOYBrC87zL5kDmxUDK+otktaVJ8cvt2DELuP4Tmt98Ai7YJl68JkiNyQiOSU0J40RigRuKXRGzm71tPvYXI85Fo06oNDHPK+Np6z5490aVLFyRNmhTXrl3D0GFD0X97f9wu+OBaacyb8foM5UxgakTSy8bevcAzzwCXL5sAbsdTKIM1SJ09HfbtA5Ilsx6/KI3EZgAvXrw4xsPhrK+IGUQzIEojonExnjwMUCP0QuRRo3pIaACr17OAER86dAhTpkwxjd+2bRsWLVrkea1w4cKer2j7HsWKFUNNn6+KbdmyBUWLFvX8olKhQgUUL14cjz32GFKnTo1Lly5h+/btmDNnDo4ePeoN89FHH3kMYSsPGsA0gK3Ul26xHVkonTqFC1myII2xB6HP8S7GYQLe9Xwjdf583ZhWtx5HNKIuXa5ETo3E3/Z5u+ah9tza3kG+Ju6GDRswduxYz0OqcubMGSPQgUMHUHFsRRxOddh7bm7tuahVsJZSWqNGJG7XzJlAgwYxAI5HS7TCeIwcCbRvbz1+URpJlSoVLkcztJcvX47IyEjri2AGSxkQpRFLQTK4owxQIzSAHRWg4slpACvewPjgr169GuXLlw+qwkaNGnm3cTAm3jeAAwli3CE8YsQINGnSJJDhYY2hAfyAPn4IhiUlV0y2XSM3bgCVKgG//mridxki8DKuIyJBYs+eg/nzu4J+JYq0XSNKsEKQvgxQI/710Hd1X/T7pR9CuYPX9w7iPuX6oO+Lff0nlGwENSJZQ6LDGTgQ6NXL9KqxH/BDOIlUD6XEwYNAypTW1iBKI/Pnz0etWuY/kHz33Xd47bXXrC2A0S1nQJRGLAfKBI4xQI3QAHZMfBokpgGsQRPjKkGEAXz9+nUYcdatW4f169fj8OHDOHPmDM6ePeu5KzhTpkwoUqQIKlWqhAYNGsS5TYRommkA0wAWrSmd49m6UDL2G2zaFJg61UTpLQAFAezDPjRqlBf3tgvXmXalarNVI0oxQ7D3GaBGAtOCcSdwjQI1Qtq+wTCBv9vznXJ3/lIjgWnD8VHG57Nxk0a0D+C6mIXZqItBg4Du3a1FKeo6snnzZhjfWvQ9xowZgzZt2lhbAKNbzoAojVgOlAkcY4AaoQHsmPg0SEwDWIMmurEEGsA0gN2o+1BrtnWhNGMG0LBhDKgDARj3HSVI8D327XsVuXOHWg3nWcGArRqxogDGtJwBasRyipVPQI0o0MLdu4GCxp9jHxw/4WW8gp+QLh1w6BA8/1p1iNLIqVOn8NBDD5lgdu3aFUOGDLEKOuPaxIAojdgEl2kcYIAaoQHsgOy0SUkDWJtWuqsQGsA0gN2l+PCqtXWh9PrrwHffmQAvAPCG8TByAMWLD8aGDd3CK4izhTNgq0aEo2dAOxigRuxgWe0c1Igi/Sta1NjjzQS2GwZjKD5Azw8TYMAA6+oQpZGoqCgkS5bM83DF+0f9+vUx09jrmIfSDIjSiNIkEHy8DFAjNID5FgmdARrAoXPHmQ4yQAOYBrCD8lMuta0LpZdeApYt83J0EoBxs+/9Z49Xr14XCxfOUo5D3QHbqhHdydS0PmpE08YKLIsaEUimlaE++QT44IMYGYagKwalHoK//wbSp7cGgEiNGA+lNramu39UrFgRy3zWH9ZUwKhWMyBSI1ZjZXxnGKBGaAA7ozw9stIA1qOPrquCBjANYNeJPoyCbV0oVawIrFjhRbsXSVAAD+7QefLJJ7Fjx44wquFUKxiwVSNWFMCYljNAjVhOsfIJqBFFWnjs2N1tIM6fjwE4H/5Cw/75oj8rTlhhIjXy3HPP4Y8//vBiM55JYjy8mofaDIjUiNpMEH1cDFAjNID57gidARrAoXPHmQ4yQAOYBrCD8lMuta0LpQoVgFWrvBztRhoUxAXvzwkTJsTly5c9D5HkIQ8DtmpEnrKJJAgGqJEgyHLpUGpEocYvWQK88QZw9aoJ9Ah0wMAMIzx3AadKJb4ekRqpVq0aFi9e7AWZNWtWHDPMbR5KMyBSI0oTQfBxMkCN0ADm2yN0BmgAh84dZzrIAA1gGsAOyk+51LYulJ57DvC5I2cnHsJTMDaCeHAYT+9++umnleNRZ8C2akRnIjWujRrRuLmCSqNGBBFpV5idO4GnnjJlO4e0yI5/0G9YKnTuLB6ISI00adIEU6dO9YJMlCiRZ0/giIgI8cAZ0TYGRGrENtBMZCsD1AgNYFsFp1kyGsCaNdQt5dAApgHsFq2LqNO2hdLq1UD58ibIv+JJlMNO02tffvklGjZsKKI0xhDEgG0aEYSXYexngBqxn3PVMlIjqnUMQNu2wJgxJuBV8QP+zFIVhw4ByZKJrUmkRrp164aPP/7YBNCIn96qDYzFUsFocTAgUiMkWU8GqBEawHoq256qaADbwzOzCGaABjANYMGS0jqcLQulO3eAYsWArVtNXHZEb4xEf9NrL774Ilb5bBOhNfmKFGeLRhThgjBjZ4AaoTL8MUCN+GNIwvNr1gBly5qANcZUfInG+PxzoHVrsZhFamT48OHo0qWLCeCePXuQP39+saAZzVYGRGrEVuBMZhsD1AgNYNvEpmEiGsAaNtUNJdEApgHsBp2LqtGWhdLatUCZMibIB5AbBbETN5A8RilRUVGiymMcAQzYohEBOBnCOQaoEee4VyUzNaJKp3xwbtgAlChhAt4KYzEerfDoo8D+/UDixOLqEqmRGTNmxPg20a+//ooy0dYi4tAzkh0MiNSIHXiZw34GqBEawParTp+MNID16aWrKqEBTAPYVYIPs1jLF0r//QfUrg0YdxL5HLUwFwnr1MKcOTH347tz5w736QuzryKnW64RkWAZyxEGqBFHaFcqKTWiVLvugjUc3nz5TMB3oiCKYCtuIxGmTQMaNRJXl0iN/Pzzz6hcubIJ3Pz581GzZk1xgBnJdgZEasR28ExoCwPUCA1gW4SmaRIawJo2VveyaADTANZd4yLrs3ShtHAhYPyyZWwB4XNcRCpkxBms/zMJihWLaQAfOHAAuXPnFlkmY4XBgKUaCQMXp8rDADUiTy9kRUKNyNqZeHAZ38YxHgS3a5dpUGt8jnFojcKFgS1bAFHPVROpEeOBssWMrad8jnHjxuHdd99VsBGEfJ8BkRohq3oyQI3QANZT2fZURQPYHp6ZRTADNIBpAAuWlNbhLFsoXb0Kz3dET5+Owd/3eBXjX/keP/4IfPbZZ+jQoYNpzOzZs/Hmm29qzbtKxVmmEZVIINZ4GaBGKBB/DFAj/hiS9PzixUC1aiZwp5ERebEf55EOy5cDkZFisIvUyN9//41cuXKZgA0ZMgRdu3YVA5ZRHGFApEYcKYBJLWeAGqEBbLnINE5AA1jj5upcGg1gGsA661t0bZYtlKZMAZo1ixXuexiNN399z7MtsHG3b968eU3jOnXqBOMBLjzkYMAyjchRHlEIYIAaEUCi5iGoEUUbbNwFbGylsHSpqYCNeAbV8T2KVnkEhkcs4hCpESNWxowZTbB69OiBjz76SARUxnCIAZEacagEprWYAWqEBrDFEtM6PA1grdurb3E0gGkA66tu8ZVZslAyfmEsUgTYvj0G4OtIgjeL7MWCzbk8Xxs1HviWKVMmGDjuH8ZDWoyHtfCQgwFLNCJHaUQhiAFqRBCRGoehRhRu7o4ddz/To23ntB958Cw24v92pcMTT4Rfn0iN3Lx5E0mSJDGBeu+99zB69OjwgTKCYwyI1IhjRTCxpQxQIzSALRWY5sFpAGveYF3LowFMA1hXbVtRlyULpXXrgJIlY8A19v7thiEoPasN6tZ9cNp4UIvxwJb7R4oUKXD+/HkkSpTIipIZM0gGLNFIkBg4XG4GqBG5+yMDOmpEhi6EgaF1a2DcuBgBJqEZNraYhAkTwoh9b6pojRhriavGdlT3jkaNGmGa8eQ6HsoyIFojyhJB4HEyQI3QAObbI3QGaACHzh1nOsgADeAH5PND0EEhKpLaEo00bw5Mnmxi4E3Mxg+ohnSPpMThw0DixA9O9+rVCwMHDjSN37ZtGwoVKqQIi3rDtEQjelPmuuqoEde1POiCqZGgKZNrwqVLQJUqwJo1Jlw3kQg5k/6HrUczIHPm8CCL1sjDDz+MkydPekG9/vrr+Pbbb8MDydmOMiBaI44Ww+SWMECN0AC2RFguCUoD2CWN1q1MGsA0gHXTtJX1CF8oGb8kZs0KGP/eO07gYeTAUdxCYhjb7/XoYa5o1KhRaN++venFAgUKYPfu3VaWztgBMiBcIwHm5TB1GKBG1OmVU0ipEaeYF5j31i3glVfgefKbz/EC1uLlfi+gd+/wconWSL58+bB//34vqMjISCyPhj08xJxtNwOiNWI3fuazngFqhAaw9SrTNwMNYH17q3VlNIAftJcfglpLXUhxQjVy7RrwxhvAjz+asH2MD9ANHyNZMuDoUSBTJjP0BQsWoGbNmjHqMfYH5uE8A0I14nw5RGABA9SIBaRqFpIa0aShxh20xue8z1EVP2Bz1qr4+2/zt3uCrVi0Rp555hn8+eefXhjFixfHhg0bgoXF8RIxIFojEpVGKIIYoEZoAAuSkivD0AB2ZdvVL5oGMA1g9VVsXwXCFkqGWWv8UrhgQQzwBbAbe1EAxs4QEyfGrC22h7UYo2gA26eD+DIJ04gc5RCFBQxQIxaQqllIakSThhoPaC1XzlRMPXyFr1EP8+bF8IaDKlq0RsqXL4/Vq1d7MfCbRUG1Q8rBojUiZZEEFRYD1AgN4LAE5PLJNIBdLgBVy6cBTANYVe06gVvYQmnbtrtPCY92zER9NMBMz6vGg8SffDL2KiMiImKc2Lp1KwoXLuwELczpw4AwjZBVbRmgRrRtrbDCqBFhVDobKJbP+tb4HOPQGpGRMXaHCAqraI1Ur14dixYt8mJ45JFH8O+//waFiYPlYkC0RuSqjmhEMECN0AAWoSO3xqAB7NbOK143DWAawIpL2Fb4whZKc+cCdeqYsJ9GRs/ev9eQHBUqACtWxF1abAZw1apV8cMPP9jKB5PFZECYRkiutgxQI9q2Vlhh1IgwKp0N9M8/QI4cJgxj0AZtMcbz2t69wOOPhwZRtEbefvttfPXVV14wqVKlwsWLF0MDx1lSMCBaI1IURRBCGaBGaAALFZTLgtEAdlnDdSmXBjANYF20bEcdwhZKxv4OkyebIL+En7EML3lemzMHqF077oo++ugjfPjhh6YBTZs2xeRoMe3ghDnMDAjTCInVlgFqRNvWCiuMGhFGpbOB7twBsmUDTpzw4jiEXMiNgwAi0LEj8OmnoUEUrZHWrVtj3LhxJjC3b99GggQJQgPIWY4zIFojjhdEAMIZoEZoAAsXlYsC0gB2UbN1KpUGMA1gnfRsdS1CFko3bgAZMwKXLnnhGnf/Zsc/uI5keOihuw9/S5Ik7mqM/X59fymrWLEifvzxRyROnNhqChjfDwNCNEKWtWaAGtG6vUKKo0aE0ChHkGbNgClTTFjyYh8OIC/SpweMXRaSJw8eqmiNdO3aFUOHDjUBOX/+PNKkSRM8OM6QggHRGpGiKIIQygA1QgNYqKBcFowGsMsarku5NIBpAOuiZTvqELJQ2rgRKF7cBHco3kdX3P3Fq1s3YPBg/9UYJvC6detgPBSuRIkSSJo0qf9JHGE5A0I0YjlKJnCSAWrESfbVyE2NqNGngFAaX+l5803T0Dr4BnNxdxuoqVOBxo0DimQaJFojsX2z6MiRI8gRbQuL4JFyhlMMiNaIU3Uwr3UMUCM0gK1Tl/6RaQDr32MtK6QBTANYS2FbVJSQhdLnnwPvvWdCWAlLsRyVPK8dOADkzm1RAQxrOQNCNGI5SiZwkgFqxEn21chNjajRp4BQ7t8P5MtnGjoI3dETgzyvlSgBrFsXUCRLDeCRI0eio7Enhc+xZ88e5M+fP3hwnCEFA7yOSNEGqUFQIzSApRao5OBoAEveIMKLnQEawA944Ycg3yX+GBCikYYNgRkzTKnS4SzOIx1eegn4+Wd/KHheZgaEaETmAoktbAaokbAp1D4ANaJRi419gNOlA6I9UM33D787dwIFCwZXs2iNTJgwAe+++64JxObNm/H0008HB4yjpWFAtEakKYxAhDFAjdAAFiYmFwaiAezCputQMg1gGsA66NiuGsJeKEVF3X3kt3FH0L1jNwqgIHZ7fpo/H6hZ065qmMcKBsLWiBWgGFMqBqgRqdohJRhqRMq2hA6qTBlg7VrT/DuIQD3Mwjd4Cx98AHz8cXDhRWtk+vTpaNSokQnE77//jpIlSwYHjKOlYUC0RqQpjECEMUCN0AAWJiYXBqIB7MKm61AyDWAawDro2K4awl4oLV8OVLq71cP940s0RGN8iSxZgCNHAD7Hza5uWpMnbI1YA4tRJWKAGpGoGZJCoUYkbUyosHr2BAbd3fLB9ziHtCiE7bidNYfn8z9RosATiNbIvHnzULt2bROAFStWoEKFCoGD4kipGBCtEamKIxghDFAjNICFCMmlQWgAu7TxqpdNA/hBB/khqLqarccflkaMu3/Llo1xF9CbmI05eDOkO4Csr5gZgmUgLI0Em4zjlWSAGlGybbaCpkZspdv6ZCdO3P3j744dMXJtQjFUxHJ8tTg9qlQJHIpojSxevBjVqlUzAfjhhx9QtWrVwEFxpFQMiNaIVMURjBAGqBEawEKE5NIgNIBd2njVy6YBTANYdQ3biT+shdLKlUBkpAnuX8iHgtiF20jk+b3wySftrIa5rGAgLI1YAYgxpWOAGpGuJdIBokaka0n4gK5fB8aMAbp0iRFrLFphde2xmDMn8DSiNbJy5UpERlujzJkzJ8ZdwYEj5EinGRCtEafrYX7xDFAjNIDFq8o9EWkAu6fXWlVKA5gGsFaCtriYkBdKxt2/5coBa9aYEDbEl5iBhnjmGWDjRovBM7wtDISsEVvQMYkMDFAjMnRBbgzUiNz9CQvdJ5/A85Ufn2MLiqBEki04fhzIkCGw6KI14vv7wH0EX375JRoaD67loSQDojWiJAkEHS8D1AgNYL5FQmeABnDo3HGmgwzQAKYB7KD8lEsd8kJp82agWDFTvfuQF09gt+fu388+A9q1U44OAo6FgZA1QjZdwwA14ppWh1woNRIydfJPvH07xma/x5EFj+A4Pv8caN06sBJEa2TLli0oWrSoKfn48ePRsmXLwABxlHQMiNaIdAUSUNgMUCM0gMMWkYsD0AB2cfNVLp0GMA1glfVrN/aQF0qjR8dweBtjKr5EY89DX/79F3joIburYT4rGAhZI1aAYUwpGaBGpGyLVKCoEanaIR5M06bAF194495CQiTBDRR/LgHWrw8snWiN7N27FwUKFDAlHzFiBDp06BAYII6SjgHRGpGuQAIKmwFqhAZw2CJycQAawC5uvsql0wCmAayyfu3GHvJCqUkTYOpUE9y0OIcLSItXXwW+/97uSpjPKgZC1ohVgBhXOgaoEelaIh0gakS6logF1KMHMHiwKWYmnMIZZMLBg8Bjj/lPJ1ojR48exaOPPmpKPGjQIHTv3t0/GI6QkgHRGpGySIIKiwFqhAZwWAJy+WQawC4XgKrl0wCmAayqdp3AHdJC6dq1u7f3XrzohXwAuZEXBzw/z5sHvPGGE9UwpxUMhKQRK4AwprQMUCPStkYaYNSINK2wBsjIkUDHjqbYBbETu1HQ4wt36+Y/rWiNnD59GpkzZzYl7tWrF/r37+8fDEdIyYBojUhZJEGFxQA1QgM4LAG5fDINYJcLQNXyaQDTAFZVu07gDmmhNH060KiRCe48vIEfnB4aAAAgAElEQVTamId06YATJ4CkSZ2ohjmtYCAkjVgBhDGlZYAakbY10gCjRqRphTVAvv4aqFfPFLs8VmI1yuPppwHjsQH+DtEauXz5MlKlSmVK27lzZwwbNswfFJ6XlAHRGpG0TMIKgwFqhAZwGPJx/VQawK6XgJoE0ACmAaymcp1BHdJCyXiC9owZJsDVsRCLUB2NG8fYGcKZwphVGAMhaURYdgZSgQFqRIUuOYuRGnGWf8uzL1kCVKliSnP/uQDGi3v2APnzx49CtEZu3ryJJEmSmJK2a9cOnxlPqeWhJAOiNaIkCQQdLwPUCA1gvkVCZ4AGcOjccaaDDNAApgHsoPyUSx3SQum112Js8huBOwAi8OOPwCuvKEcDAcfDQEgaIaOuYoAacVW7QyqWGgmJNnUmLV8OVKoUA++r+B4/4FX06wf07u28Ady+fXuMNLar4KEkA7yOKNk2W0FTIzSAbRWcZsloAGvWULeUQwOYBrBbtC6izpAWSuXLA6tXe9PvxeMogL2e7R/++w+IdsONCJiM4SADIWnEQbxMbT8D1Ij9nKuWkRpRrWNB4jX2eChWLMak08iIHDiKx55Ijp07gYiIuOOK1siNGzeQNNp+VDSAg+yrZMNFa0Sy8ghHAAPUCA1gATJybQgawK5tvdqF0wCmAay2gu1FH9JC6ZlngD//9AL9A8/iOfyBd94BvvjCXvzMZj0DIWnEeljMIBED1IhEzZAUCjUiaWNEwbpzByhUCNi1K0bEBpiOmWiAbdvuDonrEK2R2AzgDh06YMSIEaKqZhybGRCtEZvhM50NDFAjNIBtkJm2KWgAa9tavQujAUwDWG+Fi60upIVSvnzA/v1eIMsRiUpYzu0fxLZGmmghaUQa9ARiBwPUiB0sq52DGlG7fwGhP30aqF8fWLrUNHwNSqMs1qBXL6B/f/sM4OvXryNZsmSmhDSAA+qktIN4HZG2NdIAo0ZoAEsjRgWB0ABWsGmEzIuerwb4Ich3hD8GgtbIrVuIypgRERcueEMvQA00Tb8AJ05w+wd/fKt4PmiNqFgkMYfFADUSFn2umEyNuKLNd4s0toIwtoTwOZ7ALiQp/AS2bnXWAO7YsSM+/fRTFzVDr1J5HdGrn1ZUQ43QC7FCV26JSQPYLZ3WrE7eAfygofwQ1EzcFpQTtEZWrAAqVjQh+RQdseOdT7n9gwX9kSFk0BqRATQx2MoANWIr3Uomo0aUbFtooKdNg2dPKJ+jL/qgH/ri4EHgscdiDytaI9euXUPy5MlNyWgAh9ZSWWaJ1ogsdRGHOAaoERrA4tTkvkg0gN3Xcy0qpgFMA1gLIdtURNALJeNR3n37mtD1Ry8880N/VK1qE2imsZWBoDViKzomk4EBakSGLsiNgRqRuz9C0V25AmTODBj/3jsWoRqqYxFGjgTat3fOAO7UqROGDx8utFwGs48BXkfs41rVTNQIDWBVtSsDbhrAMnSBGIJmgAYwDeCgRePiCUEvlJo2jfGkt1pJvsfM868i2lZ7LmZVr9KD1ohe5bOaABigRgIgyeVDqBGXCaBUKcOF8BZ9FNnxKI6ifHlg5Up7DOCrV68iRYoUpmQ0gNXWIa8javfPDvTUCA1gO3Smaw4awLp2VvO6aADTANZc4kLLC3ahdLluM6ScPcWEofHLxzHtpyxCcTGYPAwEqxF5kBOJXQxQI3YxrW4eakTd3oWEvHVrYNw409RMOIVzCTPh5EkgQ4aYUUVr5PLly0iVKpUp0fvvv4+hQ4eGVBInOc+AaI04XxERiGaAGqEBLFpTbopHA9hN3daoVhrANIA1krPlpQS1UIqKAhIkMGE6gNxYPfkAjBuDeejJQFAa0ZMCVuWHAWqEEvHHADXijyHNzk+cCLRsaSqqPmZiFupjxgzg7betN4DPnTuH9OnTmxJ1794dgwYN0oxs95TD64h7eh1qpdQIDeBQtcN5AA1gqkBJBmgA0wBWUrgOgQ54oTR5MtCtG3DmjAnpDLyNSsdnIAtvAHaog9anDVgj1kNhBkkZoEYkbYxEsKgRiZphB5QdO4BChUyZ1qEESmIdatUC5s613gA+ffo0Mht7EfscvXv3Rj/jWQY8lGSA1xEl22YraGqEBrCtgtMsGQ1gzRrqlnJoANMAdovWRdQZ0ELpp5+AV16JNd3HOcei6+FWIqAwhqQMBKQRSbETlj0MUCP28KxyFmpE5e6FiL1kSWDdOtPknDiMs6lzev6WnDixOa5ojRw/fhyPPPKIKUn//v3Rq1evEAviNKcZEK0Rp+thfvEMUCM0gMWryj0RaQC7p9daVUoDmAawVoK2uJiAFkrGnb8ffxwDyU0kwvhO+9B2eC6LUTK8kwwEpBEnATK34wxQI463QHoA1Ij0LRIPcMwYoG1bU9yy+AVrUBa//gqUKWOtAXzkyBHkzJnTlGTw4MHoZqxpeCjJAK8jSrbNVtDUCA1gWwWnWTIawJo11C3l0ACmAewWrYuoM6CFUhwGcDNMQtstzVCkiAgkjCErAwFpRFbwxGULA9SILTQrnYQaUbp9oYGfPx+e/R58jipYjCWogg8/BAYMsNYAPnjwIPLkyWNK8sknn6BLly6h1cNZjjPA64jjLZAeADVCA1h6kUoMkAawxM0htLgZoAFMA5jvj8AZCGihZOyX17evKWhuHMD1R3Ljn3+AiIjA83GkegwEpBH1yiJigQxQIwLJ1DQUNaJpY+Mra/hwIJrZWhhbsR2FUbw4sGGDtQbw3r17UaBAAVOSkSNHon379i5shh4l8zqiRx+trIIaoQFspb50j00DWPcOa1ofDWAawJpK25KyAlooffQRPLfr+BzZcRQVG2XHtGmWwGJQiRgISCMS4SUU+xmgRuznXLWM1IhqHROAt1UrYPx4U6CUuIQrSOn5w/GpU0DGjNatWXfu3ImnnnrKlH/MmDFo06aNgOIYwgkGeB1xgnW1clIjNIDVUqxcaGkAy9UPogmQARrA1i2mA2wBhynEQEALpR49gMGDTVWlx/8wZmZ61K+vULGEGhIDAWkkpMicpAsD1IgunbSuDmrEOm6ljVyxIrBihRfecWTBIzju/Xn2bODNN61bs27ZsgVFixY10TNhwgS0aNFCWsoILH4GeB2hQvwxQI3QAPanEZ6PmwEawFSHkgzQALZuMa2kIAg6XgYCWigZX5ccNcoUJwmu4+iJJHj4YRKsOwMBaUR3Elhf+NcRcuhqBngdcWH7CxUCduwwFV4T87EANT2vNWkCTJli3Zp148aNKG7sNeFzTJkyBU2MxDyUZIDXESXbZitoaoQGsK2C0ywZDWDNGuqWcmgAW7eYdouG3FSn34VSVBSinnoKEbt2eWm5imR4vvBVbN3qJqbcW6tfjbiXGlZ+jwFqhFLwxwA14o8hDc83bAjMmGEq7DJSoBx+wSY8i2zZgKNHHzxHQLRGfH8fuA9i+vTpaNCggYZku6Mk0RpxB2vuqpIaoQHsLsWLrZYGsFg+Gc0mBmgA0wC2SWpapPG7UPr5Z6ByZVOtS1EJP3daCuP5Ljz0Z8CvRvSngBX6YYAaoUT8MUCN+GNIw/PHjsHztDfjX5/jHNKiCn7E/6EUdu8G7j+nTbRGVq9ejfLly5tyz549G2/67juhIe06lyRaIzpz5dbaqBEawG7Vvoi6aQCLYJExbGeABjANYNtFp3BCvwulevWAr782VVgVP6DtkqrRfWGFWSD0+BjwqxHS53oGqBHXS8AvAdSIX4r0HLBpE1CmDHD1qqm+Q8iFgtiFUROTo3nzu6dEa+THH39E1apVTXkXLVqEatWq6cm1C6oSrREXUOa6EqkRGsCuE73AgmkACySToexjgAYwDWD71KZ+pngXSlFRwCOPACdOeAvdh7wolHgv/ncuAVKkUL9+VuCfAS6m/XPk9hHUiNsV4L9+asQ/R9qOmDcPqFMHMNYUPofxx+T09ati5kxrDOC5c+eijpHX51ixYgUqVKigLdW6F8briO4dDr8+aoQGcPgqcm8EGsDu7b3SldMApgGstIBtBh/vQmnv3gffzbyHaxTaYl6ZUfj1V5uBMp1jDHAx7Rj1yiSmRpRplWNAqRHHqJcj8dChQNeuJiyD0Q1jsw/GkSN39wEWrZEvv/wSjRs3NuU0fkd4/vnn5eCEKIJmQLRGggbACdIzQI3QAJZepBIDpAEscXMILW4GaADTAOb7I3AG4lwonTkDZMoUI9AbmIcCPd7ARx8FnoMj1WaAi2m1+2cHemrEDpbVzkGNqN2/sNHfvg2kTw9cvOgNtQalURZrcPAg8Nhj4g3gcePGoXXr1ibo27ZtQ6FChcIuhwGcYYDXEWd4VykrNUIDWCW9yoaVBrBsHSGegBigAUwDOCChcJCHgVgXSjduAGXLAuvXx2ApM05i+o+Z8corJNAtDHAx7ZZOh14nNRI6d26ZSY24pdPx1FmlCrBkiXfANSRFSlzGF9MSolEj8Qbw8OHD0aVLFxOg/fv3I0+ePGyGogzwOqJo42yETY3QALZRbtqlogGsXUvdURANYBrA7lC6mCpjXSi1aweMHh0jwTYUwtMR23D2LJA2rZj8jCI/A1xMy98jpxFSI053QP781Ij8PbIcYe/ewIABpjQ5cASV3smBL74QbwAPGDAAvY2cPsexY8eQNWtWy0tlAmsY4HXEGl51ikqN0ADWSc9210ID2G7GmU8IAzSAaQALEZJLgsRYKG3dCsTxgJRamIv9RWphyxaXkMMyPQxwMU0h+GOAGvHHEM9TI9QApkwBmjUzEVEaa3AiT2ns3y/+s6Z79+4YMmSIKd+5c+eQln/BVlaMvI4o2zrbgFMjNIBtE5uGiWgAa9hUN5REA5gGsBt0LqrGGAulwYOBYcNM4S8hJcriV2xGMbz3Xqw3B4uCwzgSMsDFtIRNkQwSNSJZQySEQ41I2BS7IS1fDlSqZMr6NmbgK7yN48eBJEn+hzVr1njOlylTBhkyZAgLYbt27TA62reZbty4gcSJE4cVl5OdY4DXEee4VyUzNUIDWBWtyoiTBrCMXSEmvwzQAKYB7FckHOBlIMZC6fPPgWhfmXwK27ETT3nmfPMNUKcOCXQTA1xMu6nbodVKjYTGm5tmUSNu6nYcte7aBTz5pOlke4zEKLTHwoVA6dJiDeBmzZphinHX8b3DMH4NA5iHugzwOqJu7+xCTo3QALZLazrmoQGsY1ddUBMNYBrALpC5sBJjLJR+/hmoV88UvzKW4GdU9rz277/AI48IS89ACjDAxbQCTXIYIjXicAMUSE+NKNAkqyEeOADkzWvK0gWfYDi6oGdPoFMnsQZwvXr18PXXX3vzGVs/GFtA8FCXAV5H1O2dXcipERrAdmlNxzw0gHXsqgtqogFMA9gFMhdWYoyF0t69QKlSpvjvYhwm4F08+ijw99/CUjOQIgxwMa1IoxyESY04SL4iqakRRRplJcyjR+FZSPgc3TEIQ9AdFSsa3zASawDXqFEDC41bi+8dxsPfjIfA8VCXAV5H1O2dXcipERrAdmlNxzw0gHXsqgtqogFMA9gFMhdWommhVLo0MrRsCcyfb4pfF7MwG3VRs2aMU8JwMJC8DHAxLW9vZEFGjcjSCXlxUCPy9sY2ZCdOAFmzmtL1QV/0Rx8Yz2Xbv/9/+O03cXsAv/TSS1i2bJk3X548ebDfeNocD2UZ4HVE2dbZBpwaoQFsm9g0TEQDWMOmuqEkGsA0gN2gc1E1+i6UKm3dihR9+sQInQXH8R+ywHg+XLduojIzjioMcDGtSqecw0mNOMe9KpmpEVU6ZSHO//0PyJjRlGAgeqIXBnpeW7fuHE6c+MXz/yIeAle6dGn89ttv3nyFChXCtm3bLCyQoa1mgNcRqxlWPz41QgNYfRU7VwENYOe4Z+YwGKABTAM4DPm4bqrvQqlq9+5ItHu3iYM/URTP4E/Pa8aNNMbXNHm4iwEupt3V71CqpUZCYc1dc6gRd/U71movXgTSpDGdGor30RVDPa99/vklZMu2QpgBXLRoUWzZssWb7/nnn4fxOwIPdRngdUTd3tmFnBqhAWyX1nTMQwNYx666oCYawDSAXSBzYSX6M4Db4TOMRjtPPuPmnfTphaVmIEUY4GJakUY5CJMacZB8RVJTI4o0ykqY168DyZKZMoxAB3TCCM9rTZtew6uv/izMAM6fPz/++usvb74KFSpgxYq7BjMPNRngdUTNvtmJmhqhAWyn3nTLRQNYt466pB4awDSAXSJ1IWX6LpQqz5yJpPPmmeKmxCVcQUrPg7v37ROSkkEUY4CLacUa5gBcasQB0hVLSY0o1jAr4N65AyRMaIr8Nd5CPXztea1YsVvo3XuxMAM4R44c+Oeff7z5qlWrhkWLFllRGWPaxACvIzYRrXAaaoQGsMLydRw6DWDHW0AAoTBAA5gGcCi6cesc0x7AmzcjRb9+JiqexR/YhGdRty4wa5ZbWXJ33VxMu7v/gVRPjQTCkrvHUCPu7r+3+kSJgNu3TWTUxhzMQ22kSBGFGTO+93jEIvYAzpAhA86ePevNVadOHXzzzTdshMIM8DqicPNsgk6N0AC2SWpapqEBrGVb9S+KBjANYP1VLq5C34VS+Vu3kKZWLVPwppiML9AUw4cDnTqJy8tI6jDAxbQ6vXIKKTXiFPPq5KVG1OmVpUhTpACuXjWl+A8PITv+wS0kxtixy/HII5fDNoDv3LmDxIkTw/j3/tGiRQtMmDDB0vIY3FoGeB2xll8dolMjNIB10LFTNdAAdop55g2LARrANIDDEpDLJvsulMo9/DDSlSxpYqA3+mEAemPlSqB8eZeRw3I9DHAxTSH4Y4Aa8ccQz1Mj1ICHgeLFgY0bY5BRCr/h/1AKH3ywAaVKHQ/bAD537hzSR3toQbdu3TB48GA2QmEGeB1RuHk2QadGaADbJDUt09AA1rKt+hdFA5gGsP4qF1eh70Ip8u+/kap9e1PwRpiG6WiEkyeBzJnF5WUkdRjgYlqdXjmFlBpxinl18lIj6vTKUqR//AE891yMFL3QHwPRC3Xq7EW9envCNoAPHjyIPHnymPIMHToU77//vqXlMbi1DPA6Yi2/OkSnRmgA66Bjp2qgAewU88wbFgM0gGkAhyUgl032XSi98sUXSPL99yYGsuIYoh7OihMnXEYMy/UywMU0xeCPAWrEH0M8T41QA14GrlwBUqY0EbIGpVEWa/Dcc8fRo8eGsA3gjRs3orhxt7HPMWnSJDRr1oyNUJgBXkcUbp5N0KkRGsA2SU3LNDSAtWyr/kXRAKYBrL/KxVV4f6EUcfs2qr3zDhJcuOANvgVFUBRbEBkJLF8uLicjqcUAF9Nq9csJtNSIE6yrlZMaUatflqN9+WVg6VJTmqewHaceegwTJy4P2wBeunQpXjZy+Bzz589HzZo1LS+NCaxjgNcR67jVJTI1QgNYFy07UQcNYCdYZ86wGaABTAM4bBG5KMD9hVKagwdRPtpT3sajJVphPDp0AEaMcBEpLNXEABfTFIQ/BqgRfwzxPDVCDZgYGDcOaN3a9NIIdEAnjMBXXy1G5colkSFDhpBJmz17NurWrWuav2rVKrz44oshx+RE5xngdcT5HsiOgBqhASy7RmXGRwNY5u4QW5wM0ACmAcy3R+AM3F8opT1wAC927myaOBlN0RyTMXky0LRp4DE5Ui8GuJjWq59WVEONWMGqXjGpEb36GXY1ly8DOXIAZ896Q21CMTyLTRg8eA1atHgyLAN47NixaNOmjQnm1q1bUbhw4bChM4BzDPA64hz3qmSmRmgAq6JVGXHSAJaxK8TklwEawDSA/YqEA7wM3F8oJbhxA1WbNkWCixe957bjKRTGdqxfH+szW8iiSxjgYtoljQ6jTGokDPJcMpUacUmjgymzVi1g/nzvjNtIgHQ4h8bv7Ue/fjnDMoAHDhyIXr16mdAcPXoU2bNnDwYhx0rGAK8jkjVEQjjUCA1gCWWpDCQawMq0ikB9GaABTAOY74jAGTA9BG70aCRZscI7+TQyIjNOw/CEU6UKPCZH6sUAF9N69dOKaqgRK1jVKyY1olc/hVQzciTQsaMpVCUsRZqauTBpUsawDOBOnTphRLS9qy5fvowUKVIIgc4gzjDA64gzvKuUlRqhAaySXmXDSgNYto4QT0AM0ACmARyQUDjIw4B3oRQVhddef93EyklkRvFHT+Lvv0mWmxngYtrN3Q+sdmokMJ7cPIoacXP346h92TLgpZdMJ9/GDBx4vgIWL04WlgHcqFEjTJ8+3Rs7SZIkuHbtGiIiItgIhRngdUTh5tkEnRqhAWyT1LRMQwNYy7bqXxQNYBrA+qtcXIX3F0oPbdqEkgMGmAKfwMN4O/IEli8Xl4+R1GOAi2n1emY3YmrEbsbVy0eNqNczyxEbDxho3tyU5kWswsEcxbBly62wDOAqVapgyZIl3tjZsmXDP//8Y3lJTGAtA7yOWMuvDtGpERrAOujYqRpoADvFPPOGxQANYBrAYQnIZZPvL5SKjhqFR1euNFW/D3kxvOU+jB/vMlJYrokBLqYpCH8MUCP+GOJ5aoQaiMHA++8Dw4aZXs6KYzid6CEcO3YemTNnCJm04sWLY+PGjd75Tz/9NDZv3hxyPE6UgwFeR+Tog8woqBEawDLrU3ZsNIBl7xDxxcoADWAawHxrBM7A/YVSxZYtkfK//0wTh6EzIoYNQ+fOgcfjSP0Y4GJav56KrogaEc2ofvGoEf16GnZF77wDTJtmCpMMV3EdybBp0zkUK5Yu5BQ5c+bEkSNHvPMrVaqEpUuXhhyPE+VggNcROfogMwpqhAawzPqUHRsNYNk7RHw0gP1ogB+CfJP4Y8DQyB8LFuDlZs1iDE2Em/h2YSJUr+4vCs/rzACvIzp3V0xt1IgYHnWOQo3o3N0Qaxs0COjZ0zS5HFbjV5TDnDkXUbt26pACR0VFIWXKlLh69ap3fv369TFz5syQ4nGSPAzwOiJPL2RFQo3QAJZVmyrgogGsQpeIMQYDvAP4ASX8EOQbxB8DhkYODhyIZ6M9LbshvsQMNMTOnUDBgv6i8LzODPA6onN3xdRGjYjhUeco1IjO3Q2xtt9/B154wTS5D/qiP/pg0KDL6N49ZUiBL126hNSpzeZxhw4dMCLaOiek4JzkKAO8jjhKvxLJqREawEoIVVKQNIAlbQxhxc8ADWAawHyPBM6AsVA6X68eHvv5Z9OkXDiEIxG5cOUKkCxZ4PE4Uj8GuJjWr6eiK6JGRDOqXzxqRL+ehl3RjRtA0qSmMD/iFVTFj2jV6hrGjg1t8XHo0CHkzp3bFHfQoEHo3r172JAZwFkGeB1xln8VslMjNIBV0KmsGGkAy9oZ4oqXARrANID5FgmcAWOhlLhIEaT2eTr2EeRATvyNnDkjcPhw4LE4Uk8GuJjWs68iq6JGRLKpZyxqRM++hlXVyZPAww+bQixEddTAQlSrdgOLFiUJKbzv7wH3A0yaNAnNYtnqKqQEnOQYA7yOOEa9MompERrAyohVQqA0gCVsCiH5Z4AGMA1g/yrhiPsMnN27F+kLFDARMgNvoyFm4MUXgVWryJXbGeBi2u0K8F8/NeKfI7ePoEbcroBY6jcWGBUqmE50xRAMRVcUKXILW7YkCom0efPmoXbt2qa5P/74I1555ZWQ4nGSPAzwOiJPL2RFQo3QAJZVmyrgogGsQpeIMQYDNIBpAPNtETgDF6dNQ2rjSdw+R3NMxGQ0R4MGwPTpgcfiSD0Z4GJaz76KrIoaEcmmnrGoET37GlZVy5cDlSrFCFEBK7A904s4dSpBSOFHjRqF9u3bm+Zu3boVhQsXDikeJ8nDAK8j8vRCViTUCA1gWbWpAi4awCp0iRhpAMejAX4I8g3ij4FrLVsi2cSJpmH5sQd/IT969AA++shfBJ7XnQFeR3TvcPj1USPhc6h7BGpE9w6HUN/KlUBkZIyJl5ASj+Mv7L/8CFKkCD5u165dMXToUNPEU6dOIVOmTMEH4wypGOB1RKp2SAmGGqEBLKUwFQFFA1iRRhGmmQHeAfyAD34I8t3hj4FbxYsj0caN3mEn8DCy4jiACIwdC7Rq5S8Cz+vOAK8junc4/PqokfA51D0CNaJ7h0Ooz3jIwGOPxTqxMpZg5O7KiLZDVUBJGjRogJkzZ3rHJkmSBNeuXUNERERA8zlIXgZ4HZG3N7Igo0ZoAMuiRRVx0ABWsWvEDBrANID5NgicgVvFiiHR5s3eCXvxOApgr+fnRYuAatUCj8WRejLAxbSefRVZFTUikk09Y1EjevY17Kp69QIGDowRphR+Q5+fSuHll4PPUKFCBazyeYBBrly5cOjQoeADcYZ0DPA6Il1LpANEjdAAlk6UCgGiAaxQswj1AQM0gGkA8/0QOAPXa9dG0nnzvBNuISFS4ApuIgm2bAGKFAk8FkfqyQAX03r2VWRV1IhINvWMRY3o2VchVTVqFOOBAzlwBL0m5ECLFsFnKFCgAPbuvfuHbON44YUXsHbt2uADcYZ0DPA6Il1LpANEjdAAlk6UCgGiAaxQswiVBnBsGuCHIN8Z/hi40rMnUgwaZBpWEDuxGwVx5gyQIYO/CDyvOwO8juje4fDro0bC51D3CNSI7h0Oo74mTYCpU70BbiMBkuI6evVNhD59go+bOnVqXLp0yTuxdu3amDNnTvCBOEM6Bngdka4l0gGiRmgASydKhQDRAFaoWYRKA5gGMN8FoTBwtXNnJP/0U9PUOvgGi1PUgfH7E7fMC4VVveZwMa1XP62ohhqxglW9YlIjevVTaDVvvQV884035BUkR0pcRqtWd59FEMxx8eJFpEmTxjSlQ4cOGDFiRDBhOFZSBngdkbQxEsGiRmgASyRH5aDQAFauZQRsMMAtIB7ogB+CfE/4Y+BakyZI5nPnjTF+B57E63l3YN8+f7N53g0M8Drihi6HVyM1Eh5/bphNjbihyyHW2K0b8PHHpsnZ8A+er5kN8+cHF3MJu6oAACAASURBVHPPnj144oknTJM++eQTdOnSJbhAHC0lA7yOSNkWqUBRI/RCpBKkYmBoACvWMMK9ywANYBrAfC8EzsDV9u2RfNQo04SNeAbtSm7E778HHocj9WWAi2l9eyuqMmpEFJP6xqFG9O1t2JUZf4Q2toHwOSpiGa69UBHBbt27cuVKREZGmmJ99dVXqFevXtgwGcB5Bngdcb4HsiOgRuiFyK5RmfHRAJa5O8QWJwM0gGkA8+0ROAMXZ85E6gYNTBN+QVmMeO0XfPdd4HE4Ul8GuJjWt7eiKqNGRDGpbxxqRN/ehl3Z//0fUKqUKUxHfIrF+Trir7+Ci/7ll1+icePGpkmrV69GuXLlggvE0VIywOuIlG2RChQ1QgNYKkEqBoYGsGINI9y7DNAApgHM90LgDJxfvhxpK1UyTXgPo3G92XuYNCnwOBypLwNcTOvbW1GVUSOimNQ3DjWib2/Drsx44ICxb29UlDfUTNRHmzQzcf58cNH79u2Lfv36mSYdOnQIuXLlCi4QR0vJAK8jUrZFKlDUCL0QqQSpGBgawIo1jHBpAEfXAD8E+a7wx8DV999H8mHDTMP6oC9udu+DQYP8zeZ5NzDA64gbuhxejdRIePy5YTY14oYuh1FjwYLA7t3eALtRAAWxG1evAsmSBR63UaNGmD59undCokSJcPXqVRj/8lCfAV5H1O+h1RVQIzSArdaYzvFpAOvcXY1r4x3AD5rLD0GNhS6itMOHEZU3LyJu3zZFa43Pke/T1ujYUUQSxlCdAV5HVO+g9fipEes5Vj0DNaJ6By3Gb2xFNXOmN8kdRCANLmDn4VTImTPw3GXKlMFan42Dc+fOjQMHDgQegCOlZoDXEanbIwU4aoQGsBRCVBQEDWBFG+d22DSAaQC7/T0QcP1ffQW8/XaM4alwEeNnpIrtVMChOVAfBriY1qeXVlVCjVjFrD5xqRF9emlJJUOGAN27m0LnxgHM3ZgbzzwTeMbs2bPj33//9U6oWLEili1bFngAjpSaAV5HpG6PFOCoERrAUghRURA0gBVtnNth0wCmAez290DA9a9aBVSoYBo+GU3RHJPx00/Ayy8HHIkDNWaAi2mNmyuoNGpEEJEah6FGNG6uiNI+/hjo1s0UKScOY+qKnNGXKXFmu3btGpInT24637x5c0ycOFEEQsaQgAFeRyRoguQQqBEawJJLVGp4NIClbg/BxcUADWAawHx3BMjAlStAypSmwdPQCO9gGv74A3j22QDjcJjWDHAxrXV7hRRHjQihUesg1IjW7Q2/OOOhAz17muJkwz8Y8202vP56YOH37t2LAgUKmAYPHjwY3aIZy4FF4ygZGeB1RMauyIWJGqEBLJci1UJDA1itfhHtPQZoANMA5pshQAaiohCVMCEifJ68PRHN0RITsXcv8PjjAcbhMK0Z4GJa6/YKKY4aEUKj1kGoEa3bG35x/fsDffqY4jyMExjyxcN4553Awi9ZsgRVqlQxDZ49ezbefPPNwAJwlPQM8DoifYscB0iN0AB2XIQKA6ABrHDz3AydBjANYDfrP6jaL18GUqUyTRmGzngfw3D8OJAlS1DROFhTBriY1rSxAsuiRgSSqWkoakTTxooqq3dvYMAAU7RMOIUPR2RChw6BJRk7dizatGljGrxhwwYUL148sAAcJT0DvI5I3yLHAVIjNIAdF6HCAGgAK9w8N0OnAUwD2M36D6r2yZOB5s1NU3qjHwagNy5dirE7RFChOVgfBriY1qeXVlVCjVjFrD5xqRF9emlJJe++C0yYYAqdDFfRvW+y6DcGx5m+S5cuGD58uOn8qVOnkClTJksgM6j9DPA6Yj/nqmWkRmgAq6ZZmfDSAJapG8QSMAM0gGkABywWNw88cQIw9so7f97EQg0swKIENXDrFhAR4WaCWPt9BriYphb8MUCN+GOI56kRaiBeBoynzi5dahpSF7OQtWNdfPppYNy98cYb+Pbbb72DU6dOjfPnzyOCi5nACFRgFK8jCjTJYYjUCA1ghyWodHoawEq3z73gaQDTAHav+oOofPRooF0704RDyIUC2IPkaZPi3LkgYnGo1gxwMa11e4UUR40IoVHrINSI1u0Nv7gaNYCFC01xVqI8vmqyElOmBBa+aNGi2LJli3dw4cKFsXXr1sAmc5QSDPA6okSbHAVJjdAAdlSAiienAax4A90KnwYwDWC3aj+oukeOBDp2NE2pj5mYhfrInh04ejSoaBysMQNcTGvcXEGlUSOCiNQ4DDWicXNFlBbLFhCz8SbmvTEb8+b5TxAVFQXjjt/LxrMN7h01atTAggUL/E/mCGUY4HVEmVY5BpQaoQHsmPg0SEwDWIMmurEEGsA0gN2o+6BrnjMHiPZk7JfxE5biZRQsCOzcGXRETtCUAS6mNW2swLKoEYFkahqKGtG0saLKql8fmDXLFO0V/IhE1V7BokX+kxw7dgzZsmUzDezatSuGDBnifzJHKMMAryPKtMoxoNQIDWDHxKdBYhrAGjQxrhJu376N3bt3Y+PGjdi0aZPnX+NrUlevXvVMadSoEaZNmxYUA8ePH8f48eOxaNEiHD58GNeuXUOWLFlQpkwZNGnSBOXKlQsqXqiDaQDTAA5VO66a99tvQOnSppKbYAqmogmeew5Yv95VbLDYeBjgYpry8McANeKPIZ6nRqiBeBkoWRJYt8475AhyIBcOI7JiAixb5p+7VatWoUKFCqaBU6ZM8fz+wUMfBngd0aeXVlVCjdAAtkpbbohLA1jjLkd/UEL0UoM1gI2vWBmLrHPxbBzavHlzjBs3DgkTJrSUWRrANIAtFZguwX//HXjhBVM1H2IAPsKHKFMG+PVXXQplHeEywMV0uAzqP58a0b/H4VZIjYTLoObzjYXH2rXeInfgSRTCjoDXIxMmTMC7xjYSPsfatWvxQrR1juYsKl/evF3zUKNADSRKkCjWWuK7jty6cwvf7fkOtQrWUp4HFhA6A/ysoQEcuno4kwawxhow9sVa6POwhQwZMiBjxozYt2+fp+pgDODly5ejSpUquHnzpmdu1apVUb16daRMmRJ//vknJk+ejAsXLnjOGSbwxIkTLWWWBvADevkhaKnU1A1+6xY8t/lu3myq4X0MxTC8D+MmmhUr1C2PyMUywOuIWD51jEaN6NhVsTVRI2L51C5aZCSwcqW3rL14HAWwF8WLAxs2+K+2U6dOGDFihGngyZMnkTlzZv+TOUIKBvqu7ot+v/TDW0+9hRmvz4jVBN62bRuMh/0ZR0REBA4ePIhHH30UhvnbYEEDzN4xG33K9UHfF/tKURNB2M8AP2toANuvOn0y0gDWp5cxKhk0aBAuXryIZ555xvPfY4895tny4Z133vGMDdQANrZ5yJ8/P44cOeKZN3r0aLz33numfH/99Zdn+4cTJ054Xl+6dCkqVapkGbs0gB9Qyw9By2SmduBVq+BxeX2O80iDJ7Abx/EIKlcGlixRu0SiF8cAryPiuNQ1EjWia2fF1UWNiONSy0jGwuPnn72lHUIu5MYhFC4MbN3qv+Jq1aph8eLF3oHp06fHmTNnPCYhD/kZMO78rT23thdoXCbwwIED0atXL++44cOHo12Hdl7z9/6JubXn8k5g+dtuCUJ+1tAAtkRYLglKA9gljb5fZigG8Oeff+41fF999VV8//33sbL27bffwth2wjief/55GCatVQcN4AfM8kPQKpUpHvfTT4HOnU1FvIMvMA13/wBUvTrg8wUBxYsl/HAZ4HUkXAb1n0+N6N/jcCukRsJlUPP5xsLD52lv/yAbcuAf5M8P7Nnjv/Z8+fJh//793oElSpTAOp89hf1H4AgnGfC9g/c+jthM4Geffdbz7Jr7R4mSJfBY58c8d/7GN8/J2pjbXgb4WUMD2F7F6ZWNBrBe/fRbTSgGcNmyZbFmzRpPbOMBDC+++GKsee7cuYM8efJ4Hg5nHIcOHUKuXLn8YgplAA3gB6zxQzAUBblgzvDhQJcupkJz4wAOIbfntVq1gLlzXcADSwyIAV5HAqLJ1YOoEVe3P6DiqZGAaHLvIOMmkW+/9db/Hx5CFvyHnDmBe786xMnN9evXPdvOGQ+4vn80aNAA06dPdy+fClbuzwQ2vm2a0xDE/SMBgNcBFHrwUnzbRyhICSGHwAA/a2gAhyAbTrnHAA1gl0khWAPY2EIiXbp0MMzd1KlT4+zZs/E+4K1Vq1YYP368h9WxY8fC+NmKgwbwA1b5IWiFwjSIGYsBnAf7cRB5PMXVrQvMmqVBnSxBCAO8jgihUesg1IjW7RVSHDUihEZ9gxgLj9kP7uI0Co1AFLJkAY4fj79sY1/YIkWKmAYNHjwY3bp105cvTSszTOC357+Nb3Z9462w9hO1MfXVqRg7Ziw++OCDu6/HYv7eH3f/AXLGQ8cTJYr9YXKa0seyAPCzhgYw3wihM0ADOHTulJwZrAG8fv16z3YOxmHcCfzLL7/EW/cXX3yBpk2besa0bt0axvYRVhw0gB+wyg9BKxSmQUzjDzHR/gDzLP7AJjzrKa5RI2DaNA3qZAlCGOB1RAiNWgehRrRur5DiqBEhNOobpGFDYMYMU301MR+/P1wT9x4hEmfts2bNQv369U3nFy1aBGNfYB7qMVCrTi3MvzPfdGcvtgNYAOBO7Oav6fy9kuvUqYNvvnlgJKvHBBGHwgA/a2gAh6IbzrnLAA1glykhWAPY+GqV8bC4u4ZRI89D5OI7jC0iKtx78FRkZCSWL19uCcM0gB/Qyg9BSySmflBjr+7XXjPVUR0LsQjVPa81awZMmqR+maxADAO8jojhUeco1IjO3RVTGzUihkdtozRvDkyebCrvf0iPpx46hWP/JYy37B49esC449f3sHKrOW17IEFhN27cQMaMGXHpyqUY2zt4TN7vANQwb/sQm/lrlGJ8O/X06dNIkiSJBJURgl0M8LOGBrBdWtMxDw1gHbsaT03BGsCjRo1C+/btPRE7d+6MYcOGxcuY71e0nnnmGWzcuDFohgN5eNz27dvRsmVLT+yffvoJxYsXDzqPLhMuXLiArfcen2x8PS5NmjS6lMY6wmAg4ZYtSBsZaYrQCmMxHne3ZWnQ4BpGjrwSRgZO1YkBXkd06qY1tVAj1vCqU1RqRKduiq8lyezZSNWmTYzAuTJfxJ97bsSb0Lj711jv3z+M/YCNZ44kSGDsE8BDJQb++OMPVK5c+S7kWLZ5iFGL753BsRTq9t8DVeq9KKz8rAF830e///47SpYsKYpextGcARrAmjc4ennBGsCDBg1Cz549PWGMfwcOHBgvY/v27cPjjz/uGWP8u3fv3qAZjoiICGrOkCFDUKBAgaDmcDAZ0J2BpP/7Hyo3aWIqcwi6ojuGeF6LjPwbbdtu0Z0G1kcGyAAZIANkgAxIwECCmzfxau3aJiRHkR2F0+3DtGk/x4vQuOnjv//+847Jly8fPvnkEwmqIoRQGFi7di0mTJgA41kz8ZrA8Zi/xg0vLVq0QOnSpUOBwDlkQGkG9uzZ490DnQaw0q20HTwNYNspdzZhOAbwhx9+iAEDBsRbAA1gZ/vL7GTAy8CdO6harx4SXbvmfWklyiMSKz0/V6hwBO3abSZhZIAMkAEyQAbIABmwngFjXVK3LhJdv+7NtQjV0Cj9XEydGrcBfPXqVdQ1HiDncxjbzLVt29Z6zMxgGQPnzp3zmMCeb34aO4D0iiWV8Wvn7ZivG3c7Gn8UMB5UzoMMuJEBGsBu7LqYmmkAi+FRmSjBGsDcAkL+1vJrMPL3yCmEaV54AYn27PGm34e8eBz7PD+/+eZ1jB172SlozCsZA7yOSNYQCeFQIxI2RTJI1IhkDZENztWryJA9uwnVGLTBwIdHYdeuc3Gi3bRpE1566SXTeeMbia2iPehWtnKJxz8DUVFRmL9gPtqubIsb+WPZBiTaHcDG3sFDhw5FjRrGJsE83MoAP2u4BYRbtS+ibhrAIlhUKEawBjAfAid/c7kRvvw9cgzhww8DJ096069GOZTHas/Pb78d42HcjsFkYucZ4HXE+R7IjoAakb1DzuOjRpzvgewI7qRJgwTG1/7vHfNRE+9lmY/jx+NGPnXqVDSJtqXV0qVLUalSJdnLJT4/DNy6cwsNFjTA7B2z4x7pYwJ//PHH+OCDD8iryxngZw0fAufyt0BY5dMADos+9SYHawCvX78ezz//vKfQsmXL4pdffom36C+++AJNmzb1jGndujU+//xzS0gyvi5UqlQpT2y373vDD0FLJKZ+UGOvvCxZTHWMQlu0xyjPa/XqAV99pX6ZrEAMA7yOiOFR5yjUiM7dFVMbNSKGR52j3HriCdM3kzagOF7LsiFeA7hTp04YMWKEiZZjx44ha9asOlOlfW2xmr+G2fsdAOMG30I+FNwzgZ9/7vm7W0bwcDUD/KyhAezqN0CYxdMADpNA1aYHawAbm/Mb+yvduXMHqVOnxtmzZ5EwobFRU+yH8XWs8ePHe06OHTvWsq9n0QB+wD8/BFV7F9qEd/164N4fb+5nbInxmIiWnh/fegv4+mubsDCN9AzwOiJ9ixwHSI043gLpAVAj0rfIcYA3KlZEkhUrvDiOIwtK5DiOI0fihmbs97ty5d3nFxhHhgwZcPr0aQT70GjHiycALwNxmr8LANxB7A+Gu2cC/33obzz66KNk08UM8LOGBrCL5R926TSAw6ZQrQDBGsBGdcadv2vWrPEUunr1apQrVy7Wog2TOG/evDh06JDnvPFvrly5LCGIBvADWvkhaInE1A+6apXxpDdTHXXwDeaijuc140Hcc+aoXyYrEMMAryNieNQ5CjWic3fF1EaNiOFR5yg3y5dH4tV3t6IyjmtIikJ5rmLf/ohYyzb2iDX2fTVuQLl/VKhQASt8TGSd+dKxNr/m7/2iEwB4PeadwENLDsX7nd/XkRrWFCAD/KyhARygVDgsFgZoALtMFqEYwMadvG3atPEw9eqrr+L777+PlbVvv/0Wb7zxhuecsW2ElV/RoQH8oAX8EHTZmzjQcn/8Eaha1TS6GhZhMap5XqteHVi4MNBgHKc7A7yO6N7h8OujRsLnUPcI1IjuHQ6zvkOHEJUnDyKioryBtqIw3n5qK7Ybd3fGchw5cgQ5c+Y0nencuTOGDRsWJhhOd4KB2MzfjMcy4szkM3fv/L13GNt7VK1aFStWrcChpw+ZtoPIeDwjTow9gUQJEjlRAnNKwAA/a2gASyBDZSHQAFa2daEBD8UAvnbtGvLnzw9jEWYcY8aM8RrC91Hs27fPc6fwiRMnPC8tW7YMFStWDA1kALNoAD8giR+CAQjGjUPmzbt7m6/PEYnlWIlIzyvGs1OWLnUjMaw5NgZ4HaEu/DFAjfhjiOepEWogXga6dgWGDjUN6YAR+O3ZDvjjj9hnLly4EDVqGBvCPjhmzJiBt40n2fJQioHYzN+3nnoLkecj0aZVG9y4cQPJkydHx44dUbRoUSROnBjFixfH5C8mo//2/rhd8La3XmPejNdn0ARWSgHiwPKzhgawODW5LxINYI17bmzBMGXKFFOF27Ztw6JFizyvFS5c2HNHr+9RrFgx1KxZMwYry5cvR5UqVXDz5k3PuWrVqqF69epImTIl/vzzT0yePBnnz5/3nGvevDkmTpxoKbM0gB/Qyw9BS6WmbvBZs4D69U3422IUxqCt57UXXgDWrlW3PCIXywCvI2L51DEaNaJjV8XWRI2I5VOraNeuAdmzA2fOeMu6jBTIhn9RqHQ63NtpLkbJffv2Rb9+/Uyv79ixA08++aRW9LihmHm75qH23Ac3JviauBs2bPA8O8botfHMmftbD5YpU8az5/OBQwdQcWxFHE512EvV3NpzUatgLTdQxxqjMcDPGhrAfFOEzgAN4NC5k36msV9v+fLlg8LZqFEjGHcJx3YsWLAATZo0wblz5+KMaZi/48aNi/dBcUEBimMwDeAHxPBDUISiNIyxdSvw9NMxCkuOK7iG5ChaFPjzTw3rZkkhMcDrSEi0uWoSNeKqdodULDUSEm3umGTcfGLsPeVzTERztMREGF8YXLYsdhqMu3+Nu4DvH8mSJYPxgOpEifj1fxWF03d1X/T7pR/iu4M3ruuI7x3Efcr1Qd8X+6pIATELYICfNTSABcjItSFoAGvcetEGsEHV8ePHPQavcRfx4cOHYWwPYezTVLp0aTRt2jTOB8SJppkGMA1g0ZrSLp6xx14C4wka5uNVfI8f8CoKFAB279auahYUIgNcTIdInIumUSMuanaIpVIjIRLnhmmjRwPt2pkqLYXf8H8ohWrVgHtfTozBhLH/7/0t6IyTxpYAxt2iPNRlwLgTuEaBGnFu3xDfdcQwgb/b8x3v/FW3/UKQ87OGBrAQIbk0CA1glzZe9bJpANMAVl3DtuBv2RKIth3LMHTG+xiGRx8F/v7bFhRMogADXEwr0CSHIVIjDjdAgfTUiAJNcgriyJFAx46m7I9jL/bhcRjPjzYeWxD9MPSUMWNG08stWrTAhAkTnKqCeW1ggNcRG0hWPAU1QgNYcQk7Cp8GsKP0M3moDNAApgEcqnZcNW/GDKBhQ1PJZfEL1qAsMmcGTp50FRssNh4GuJimPPwxQI34Y4jnqRFqIE4Ghg8HunQxnc6D/TiIPGjQAJg+PebMlStXIjLy7oNr7x/GtxDfffddEq0xA7yOaNxcQaVRIzSABUnJlWFoALuy7eoXTQOYBrD6Krahgi+/BBo3NiUqgXXYgBJIkQK4fNkGDEyhBANcTCvRJkdBUiOO0q9EcmpEiTY5A3LoUKBrV1PuXDiEv5ELbdsCo0bFhDV8+HB0iWYar1u3DiVKlHCmBma1hQFeR2yhWekk1AgNYKUF7DB4GsAON4DpQ2OABjAN4NCU47JZxgMd33nHVPRzWI8/8JzntZs3AT5HxWWaiKNcLqapA38MUCP+GOJ5aoQaiJOBIUOA7t1Np3PjAA4hNz78EBgwIObMOnXqYO7cud4TCRMmxIULF5DC+As2D20Z4HVE29YKK4waoQEsTEwuDEQD2IVN16FkGsA0gHXQseU1GN+pbNTIlOb+FhDGi6dOAZkyWY6CCRRggItpBZrkMERqxOEGKJCeGlGgSU5BjOUP0q/hO3yP1zBsGNC5c0xg0R8A9/TTT2Pz5s1OVcC8NjHA64hNRCuchhqhAaywfB2HTgPY8RYQQCgM0ACmARyKblw3Z/VqoHx5U9lNMAVT0cTz2l9/AfnyuY4VFhwLA1xMUxb+GKBG/DHE89QINRAnA7t2AU8+aTo9CN3RE4MwaRLQrJl55vHjx/HII4+YXmzZsiXGjx9PkjVngNcRzRssoDxqhAawABm5NgQNYNe2Xu3CaQDTAFZbwTahP3YMyJbNlGwIuqI7hnheW78eeO7ubhA8XM4AF9MuF0AA5VMjAZDk8iHUiMsFEF/5d+4A6dMDFy54R61EeURiJYxdHmrVMk/+7rvv8Prrr5tenDp1KhpHe64BGdePAV5H9Oup6IqoERrAojXlpng0gN3UbY1qpQFMA1gjOVtXSlQUkDq16Wlv+5AXz2ATLiINliwBKle2Lj0jq8MAF9Pq9MoppNSIU8yrk5caUadXjiDNnRs4dMibehOK4VlswrJlQMWKZkTdu3fHEGPfYJ9j165deOKJJxyBzqT2McDriH1cq5qJGqEBrKp2ZcBNA1iGLhBD0AzQAKYBHLRo3DqhTBlg7VpT9fPwBmpjLmbNikDdum4lhnX7MsDFNPXgjwFqxB9DPE+NUANxMnDgAJA3r+n0dDRAI0zHhg1A8eLmmeXLl8dqYxure0fatGlh6CtBggQkWXMGeB3RvMECyqNGaAALkJFrQ9AAdm3r1S6cBjANYLUVbCP6774Don2N0sjeCmNR6PNWaN3aRixMJS0DXExL2xppgFEj0rRCWiDUiLStcR7Y558D771nwlEXszAbdWM8j+D27dtIly4dLl265B1fqVIlLF261Pk6iMByBngdsZxi5RNQIzSAlRexgwXQAHaQfKYOnQEawDSAQ1eP+2Zea94cySZPNhW+GwUwf8BufPih+/hgxTEZ4GKaqvDHADXijyGep0aogTgZaNsWGDPGdDoTTuEMMuH8eSBNmgentm/fjsKFC5vGfvjhhxgwYAAJdgEDvI64oMlhlkiN0AAOU0Kunk4D2NXtV7d4GsA0gNVVr/3I/3fiBJI/URDJz531Jt+LxzG23V589pn9eJhRPga4mJavJ7IhokZk64h8eKgR+XoiDaJOnYARI0xwkuEqIpIlw5UrQETEg1OTJk1CixYtTGMXLVqEatWqSVMOgVjHAK8j1nGrS2RqhAawLlp2og4awE6wzpxhM0ADmAZw2CJyUYD/HT+OdI8+igS3bnmrnotamFt7LubMcRERLDVOBriYpjj8MUCN+GOI56kRaiBOBnr2BAYNMp3OgDNIkzMDDh82z2rWrBmmTJlievHkyZPInDkzCXYBA7yOuKDJYZZIjdAADlNCrp5OA9jV7Ve3eBrANIDVVa/9yM+vWoW0FSqYEq9GOfQqvRpr1tiPhxnlY4CLafl6IhsiakS2jsiHhxqRryfSIBo4EOjVywQnG/5BjhLZsG6dGWWhQoWwY8cO74u5c+fGAeMhcjxcwQCvI65oc1hFUiM0gMMSkMsn0wB2uQBULZ8GMA1gVbXrBO6L06Yh9TvvxEidN08U9u93AhFzysYAF9OydUQ+PNSIfD2RDRE1IltHJMLTty/Qr58J0EP4DyWrP4SFCx+8fOHCBc8D4KKiorwv1q1bF7NmzZKoGEKxkgFeR6xkV4/Y1AgNYD2U7EwVNICd4Z1Zw2SABjAN4DAl5Krpsd0BfBCPoVCKgzAesu27956riGGxXga4mKYY/DFAjfhjiOepEWogTga6dAGGDzedTo0LqNciNSZMePDykiVLUKVKFdO4kSNHon379iTXJQzwOuKSRodRJjVCAzgM+oFFpAAAIABJREFU+bh+Kg1g10tATQJoANMAVlO5zqA+e+gQ0ufObUr+DergLXwT4+nbziBkVqcZ4GLa6Q7In58akb9HTiOkRpzugMT5a9cG5s3zAryC5EiJy+jVKwL9+z/A3a1bN3z88cemQv78808ULVpU4uIITSQDvI6IZFPPWNQIDWA9lW1PVTSA7eGZWQQzQAOYBrBgSWkd7uLcuUhdp46pxrYYhTFoiz17gPz5tS6fxQXAABfTAZDk8iHUiMsFEED51EgAJLl1SLFiwObN3uq34ykUxnaMGQO0afOAlJIlS2Kdz6bAadOmxZkzZ5AwYUK3Mue6unkdcV3Lgy6YGqEBHLRoOMHLAA1gikFJBmgA0wBWUrgOgb7Srx9SGPvv+RzP4/+wHs9j5UqgfHmHgDGtNAxwMS1NK6QFQo1I2xppgFEj0rRCLiDGfr5p0wIXL3pxLUAN1MQCz/6/1avfffny5cue/X9v3brlHVetWjUsWrRIrnqIxlIGeB2xlF4tglMjNIC1ELJDRdAAdoh4pg2PARrANIDDU5C7Zl8eORIpO3Y0Fd0On2E02mH6dKBBA3fxwWpjMsDFNFXhjwFqxB9DPE+NUAOxMmA8bCB1atOpz9AOHfAZtmwBihS5e2rZsmV46aWXTOM++eQTdDH2D+bhGgZ4HXFNq0MulBqhARyyeDgRNIApAiUZoAFMA1hJ4ToE+uyOHUhXpAgi7tzxItiEYngWmzx77/Xq5RAwppWGAS6mpWmFtECoEWlbIw0wakSaVsgFxFh7JEoEGHcC3ztm4G00xAycPQukS3f3xQ8//BAfffSRCfuGDRtQvHhxueohGksZ4HXEUnq1CE6N0ADWQsgOFUED2CHimTY8BmgA0wAOT0Humm0slG5WrIiHffbfMxgohG0o/k4hfPGFu/hgtTEZ4GKaqvDHADXijyGep0aogVgZOH/+gct7b8BUNEb71FNw4UIC75QyZcpg7dq13p9Tp04NQ1OJDPOYh2sY4HXENa0OuVBqhAZwyOLhRN4BTA2oyQANYBrAairXGdTGQungoEF4dvhwE4Bh6IzFLw7DqlXO4GJWeRjgYlqeXsiKhBqRtTPy4KJG5OmFVEhWrAAqVjRBMh5Eu7JgK+zcedfcvXr1qmf/3xs3bnjHVa5cGUuWLJGqFIKxngFeR6znWPUM1AgNYNU17CR+3gHsJPvMHTIDNIBpAIcsHhdONBZKGxYtQuXGjU3VH0V2lMl5FIcPu5AUlmxigItpCsIfA9SIP4Z4nhqhBmJlYPBgoEcP06kSWIf0LxfFTz8l8by+atUqVKhQwTRm8ODB6NatG0l1GQO8jris4SGUS43QAA5BNpxyjwEawJSCkgzQAKYBrKRwHQJtLJT2f/IJnhsyJAaCRBG3cfV6AiRO7BA4ppWCAS6mpWiD1CCoEanbIwU4akSKNsgHolYtYP58L64bSIw0uIAGzYBJk5J5Xu/bty/69etnwv7777+jZMmS8tVDRJYywOuIpfRqEZwaoQGshZAdKoIGsEPEM214DNAApgEcnoLcNdtYKO2cOBFlunePUXgC3Mb+AwmQO7e7OGG1Zga4mKYi/DFAjfhjiOepEWogVgZeew34/nvvqStI7jGAe/e7gd69U3hej77/b4oUKXDu3Dkk5l+nXScqXkdc1/KgC6ZGaAAHLRpO8DJAA5hiUJIBGsA0gJUUrkOgPQulX37Bi506IW20/R5exCr0Wv4iIiMdAse0UjDAxbQUbZAaBDUidXukAEeNSNEG+UD06QP072/CVQyb0H5aPjRqlBrnz59HxowZcfv2be+Yl19+GT/99JN8tRCR5QzwOmI5xconoEZoACsvYgcLoAHsIPlMHToDNIBpAIeuHvfNvL9QyrpuXYxtICahGaImTEKLFu7jhRXzOkINBM4Af+EKnCu3jqRG3Np5P3X//DNQubJp0HsYjXq/NUCpUmmxYMEC1KxZ03T+008/RceOHUmoCxngdcSFTQ+yZGqEBnCQkuFwHwZoAFMOSjJAA5jGjZLCdQi0d6F0+zaqvNsGiU+d8CI5i3QY1PYEPhmV1CF0TCsDA1xMy9AFuTFQI3L3RwZ01IgMXZAQw8GDQJ48JmDT0BgvHxuOrFkz4N1338WECRNM53fu3ImCBQtKWAwhWc0AryNWM6x+fGqEBrD6KnauAhrAznHPzGEwQAOYBnAY8nHdVN+F0ks//Yzk48eZOHiv1J8Y81tR1/HCgnkdoQYCZ4C/cAXOlVtHUiNu7byfuvv2BaI94G1a8mao/s/HSJ8+PXLnzo3DPttTZc+eHUeOHEFERAQJdSEDvI64sOlBlkyN0AAOUjIc7sMADWDKQUkGaADTuFFSuA6B9l0oRf77L1K1aWNC8nqW/8OC4887hI5pZWCAi2kZuiA3BmpE7v7IgI4akaELkmG4c+fu3b8+Bu8tJET9J5Zj3NrCOH36NPLnz28C3bRpU0yePFmyQgjHLgZ4HbGLaXXzUCM0gNVVr/PIaQA73wMiCIEBGsA0gEOQjWunmAzgI0eQql07ExfF8Qd+ufwsUtx9GDcPFzLAxbQLmx5kydRIkIS5cDg14sKm+yt5zRqgbFnTqK9QD9+82h/TpqXHV199hXbR1iRz5sxB7dq1/UXmeU0Z4HVE08YKLIsaoQEsUE6uC0UD2HUt16NgGsA0gPVQsj1VmAzgf/5BqvfeMyUuhd8wdnMpPP20PXiYRT4GuJiWryeyIaJGZOuIfHioEfl64jiihg2BGTNMMF7GT8j9bhZ89FEONGzYEIsXL/aeT5AgAU6dOoUMGTI4Dp0AnGGA1xFneFcpKzVCA1glvcqGlQawbB0hnoAYoAFMAzggoXCQhwHfhVKFK1eQul49EzPVsRB1Z1VH3bokzK0McDHt1s4HXjc1EjhXbh1Jjbi183HUfeIE8OijwM2b3gHHkQU5cBR9B6xH06a5kTdvXly5csV7vkSJEli3bh2JdDEDvI64uPkBlk6N0AAOUCocFgsDNIApCyUZoAFMA1hJ4ToE2nQH8MmTSNWihQlJBaxAmd4Voj+jxSG0TOsEA1xMO8G6WjmpEbX65QRaasQJ1iXOaTz4zXgAnM/RF//P3pmASVWdW3s1NKAyIyAqogElCEQUMTKIgjgPV8U44Rh/ESNKNA5Rr3qjRuMUjXPUOOAYjUMcAiqIoIBEHBgcACWiCAKiTGKYuvt/qmzbLrubOnXq7HPO3vut57mP16q9v/19a71dbFeK6v/TFfqDHnjgJbVosUZHHHFEzuuXX365rvjJL4xL8YS0ZkAB3kcMiOpYSRghAHYM6VjHIQCOVW4Oi0oBAmAC4KhY8qFO9YvSvu+9p81+8h9X3fS+ugzupqef9kENZqxNAS7TcJFPARjJpxCvwwgMVCmwbt33n/5dvPjHp9RAHfS51jRvoZEjX9a4ceN066235og2adIk9e3bFyE9VoD3EY/NDzg6jBAAB0SFZbUoQAAMFlYqQABMAGwluAk1/cNFqd769TrovPNU//PPczppoWVq3amFPvkkoQY5NnEFuEwnbkHqG4CR1FuUeIMwkrgF6Wlg/Hhp4MCcflapiZpplXr0WKIrrnhTl112mWbOnFm1pnnz5lq6dKlKS0vTMwedxK4A7yOxS27dgTBCAGwdtClqmAA4RWbQSnAFCIAJgIPTwsofLkodn39ev7j//hxBJmhPDdCE7HPLl0vNm6OXjwpwmfbR9cJmhpHC9PJxNYz46HodM9cSAC9Xc7XUch122Cc64ohJOuWUU3I2H3nkkXrqqacQ0XMFeB/xHIAA48MIAXAATFhShwIEwKBhpQIEwATAVoKbUNPZi9Lrr2u///f/tOk33+R00V+va6L6Z5+bMEHac8+EmuTYRBXgMp2o/FYcDiNW2JRokzCSqPzpOnzBAql9+5yexmsvDdR4/fa37+iVV07URx99lPP6Pffco6FDh6ZrDrqJXQHeR2KX3LoDYYQA2DpoU9QwAXCKzKCV4AoQABMAB6eFlZmL0qSxY3XIsceqpKKiSpC56qjtNbfq32+5RRoxAr18VIDLtI+uFzYzjBSml4+rYcRH1+uY+YYbpAsvzHnxPN2om3Sebr75NZ177t41Nn722WfqkPneYB5eK8D7iNf2BxoeRgiAA4HColoVIAAGDCsVIAAmALYS3ISa/uGi1P/3v1er2bNzuthdU/SWds8+l/nbmA88kFCTHJuoAlymE5XfisNhxAqbEm0SRhKVP12HH3CA9PLLVT1tUH1trQVa1qCtHnroWR133JE1+q2o9j9Qp2sYuolTAd5H4lTbzrNghADYTnLT0TUBcDp8oIsCFSAAJgAuEBmvl/9wUdp6wgT1uvnmHC0e0fE6UY9kn+vRQ5o2zWupvB2ey7S31gceHEYCS+XtQhjx1vqag++yS86FYqp66Zeaqm7dNujYY6/N/gK46o999tlHY8aMQUAUEO8jQJBPARghAM7HCK/XrQABMHRYqQABMAGwleAm1PQPF6WS9et1yFlnqd7ixVWdrFMDbavPtEhbqkED6dtvpYYNE2qUYxNTgMt0YtJbczCMWGNVYo3CSGLSp+/gzFc5zJ9f1ddoHaCDNFpHH71Wa9Ycreeffz6n59dff139+3//+wh4+K0A7yN++x9kehghAA7CCWtqV4AAGDKsVIAAmADYSnATarr6RWnff/9bm/3pTzmdXKHL9QddkX3u7belXXdNqFGOTUwBLtOJSW/NwTBijVWJNQojiUmfvoObNJFWr67q61EN0Ql6VNddt1q33NJZCxcurHqtVatWWrx4sUpLS9M3Bx3FrgDvI7FLbt2BMEIAbB20KWqYADhFZtBKcAUIgAmAg9PCyuoXpT1//nO1zHzXw7p1VcIsVlt10Odap0a6/XZp+HA0800BLtO+OV74vDBSuGa+7YAR3xyvY94VK6QWLXJevEUjdI5u0YMPvqNTTumV89oJJ5yghx9+GPFQIKsA7yOAkE8BGCEAzscIr9etAAEwdFipAAEwAbCV4CbUdI2L0u9+J40cmdPNUN2jv2moTjhB4r/DEjIqwWO5TCcoviVHw4glRiXYJowkKH6ajp48WerXL6ejs3Sb/tboLF100VW64orLc1574okndPTRR6dpAnpJUAHeRxIU35KjYYQA2BJUU9kmAXAqbaGpfAoQABMA52OE1zfCyLx5Nb7n4XNtox30sbbp1EiffIJ6vinAZdo3xwufF0YK18y3HTDim+N1zHv99dLvf5/z4kCN09o+A1Wv3h6aNGlS1WuZr31YunSpmjdvjngokFWA9xFAyKcAjBAA52OE1+tWgAAYOqxUgACYANhKcBNqutaL0qBB0rhxOR2drVt1u87WkiVSmzYJNcuxiSjAZToR2a06FEassiuRZmEkEdnTd+i++0pjx1b1lflls630jU48Y73uvbeNysrKql7be++99eqrr6ZvBjpKTAHeRxKT3pqDYYQA2BpYU9goAXAKTaGl/AoQABMA56eEFT8oUOtF6c03pb59c0RapC3USXP19+cb69BD0c8nBbhM++R2uFlhJJxuPu2CEZ/crmPWVau+/1+Q166tWjBBe2qAJuissx7X7bcPydl4880365xzzkE4FKhSgPcRYMinAIwQAOdjhNfrVoAAGDqsVIAAmADYSnATarrOi1JJSY2OMt/T1+zis3TNNQk1y7GJKMBlOhHZrToURqyyK5FmYSQR2dN16G23SSNG5PR0qa7S1bpUhx46RC+88HjOa5988ok6deqUrhnoJlEFeB9JVH4rDocRAmArQE1pkwTAKTWGtjauAAEwATA/I8EVqPOi1Lix9N13OYUe0ol6cOBDP/12iOCHsdJKBbhMW2lbrE3DSKxyW3kYjFhpW3RNl5dLnTtLc+fm1Oys2VrW+mfasKGtli9fXvXaDjvsoDlz5kR3PpWcUID3ESdsNDoEjBAAGwXM8eIEwI4b7Op4BMAEwK6ybWKuOi9KBx0kjR6dc+TpuluPbna6Mv+N1qCBiW6omUYFuEyn0ZV09QQj6fIjjd3ASBpdibGnMWOk/fbLOfBFHaxD9aL22GOMJk7Mfe3ss8/WrbfeGmODHGWDAryP2OBSsj3CCAFwsgTafToBsN3+eds9ATABsLfwhxi8zovSb38r/eQ/vjbXUn2jzTV5stSnT4jD2GKlAlymrbQt1qZhJFa5rTwMRqy0Lbqmr71WuvjinHr76hWN1b7q1+83mjTprzmvvfTSS9p///2jO59KTijA+4gTNhodAkYIgI0C5nhxAmDHDXZ1PAJgAmBX2TYxV50Xpdtvl84+O+fIfpqoyeqnq6+WLrnERDfUTKMCXKbT6Eq6eoKRdPmRxm5gJI2uxNjTH/4gXXFFzoFttVhfaXNtvvnW+vrrxVWvtWrVSrNnz1br1q1jbJCjbFCA9xEbXEq2RxghAE6WQLtPJwC22z9vuycAJgD2Fv4Qg9d5UXr5ZemAA3Iqfq5ttKM+Ut99Givztzl5+KEAl2k/fC5mShgpRj0/9sKIHz7XOWXm07+ZTwFXe7TQMpU3fV+rVvXPef6ggw7Sww8/rEwQzAMFqivA+wg85FMARgiA8zHC63UrQAAMHVYqQABMAGwluAk1XedF6fPPpW23rdHV7RquCze9XcuWSY0aJdQ0x8aqAJfpWOW28jAYsdK2WJuGkVjlTt9h//M/0gsv5PS1mVZry46X6j//uTnn+SuvvFLDhw8nAE6fi4l3xPtI4hakvgEYIQBOPaQpbpAAOMXm0FrdChAAEwDz8xFcgY1elPr2zdwicorN0s+1o2bp9del/rkf2gl+KCutUoDLtFV2JdIsjCQiu1WHwohVdkXb7IIF0nbbSRs2VNX9TB20neapZcufadmyz6qeb9q0qR588EENGDCAADhaF5yoxvuIEzYaHQJGCICNAuZ4cQJgxw12dTwCYAJgV9k2MVedF6UvvpC6dpVWrco59kPtqG76MPtVfpdfbqIjaqZNAS7TaXMkff3ASPo8SVtHMJI2R2Ls5847peHDcw68TFfqjzpIUq+c5wcNGqSzzz5b/fv3JwCO0SJbjuJ9xBankusTRgiAk6PP/pMJgO330MsJCIAJgL0EP+TQdV6U/u//pCuvrFF1gbZSey3QwIHSuHEhD2WbVQpwmbbKrkSahZFEZLfqUBixyq5om838QtnML5at9thaX2hpwzu1bt01Oc9feuml6tWrFwFwtA44U433EWesNDYIjBAAG4PLg8IEwB6Y7OKIBMAEwC5ybWqmOi9KZ5wh3X13jWMXq63aabEaNSzXN8vqabPNTHVG3bQowGU6LU6ktw8YSa83aekMRtLiRMx9rFsnbbWV9PXXVQd/rVZqra/VpMmO+vbbWVXPN2nSRPfff78aNmxIAByzTbYcx/uILU4l1yeMEAAnR5/9JxMA2++hlxMQABMAewl+yKFrvSi9/77Uu7e0enWNqlfocv1BV2SfH3XnPB34m+1Cnsw2WxTgMm2LU8n1CSPJaW/LyTBii1MR93needJNN+UUfVO91Vf3SeqW8/zgwYN10kknZZ/jKyAi9sGRcryPOGKkwTFghADYIF7OlyYAdt5iNwckACYAdpNsM1PVuCi1bCn16iW9+26NA5eruVpqmaSS7Gu/bfuY/rLoOKnk+3/n4aYCXKbd9DXKqWAkSjXdrAUjbvq60amWLZO22EJavz5n2em6W/dqiaTLcp7PfPq3VatWBMAeohJ0ZN5Hgirl7zoYIQD2l/7iJycALl5DKiSgAAEwAXAC2Fl7ZI2L0qxZUr9+tc4zX+3VQfOrXuuij/TR5OVSnz7Wzk/j+RXgMp1fI99XwIjvBOSfH0bya+Tcir//XTruuJyxXtL+Okij1LBRL61d+17Va5tssolmz56t9977/jk+AewcDZEMxPtIJDI6XQRGCICdBtzwcATAhgWmvBkFCIAJgM2Q5WbVGhelG26Qrr22zmG30ef6QttUvT7vN9dp2zt/76Y4TJVVgMs0IORTAEbyKcTrMOIhAyecID36aM7gO+pDzdImkjrmPH/YYYdlv//3jTfeIAD2EJWgI/M+ElQpf9fBCAGwv/QXPzkBcPEaUiEBBQiACYATwM7aI2tclC66SLr33jrnOVaP6wkdW/X63f0f0emvn2Dt/DSeXwEu0/k18n0FjPhOQP75YSS/Rk6tKCuT2rbN/C+IVWPN0Q76ueZI+rOk83PGHTlypA455BACYKcgiH4Y3kei19S1ijBCAOwa03HOQwAcp9qcFZkCBMAEwJHB5EGhQj8BfLuG62zdXqXMEZ3f1zOzu3uglL8jcpn21/ugk8NIUKX8XQcjnnk/aZK0xx41ht5E/9W6kkGqqJhc9VppaamWLFmiiooKAmDPMCl0XN5HClXMv/UwQgDsH/XRTUwAHJ2WVIpRAQJgAuAYcbP+qEK+Azgz7P/on3pBh2XnLlG5Bu22UmPeamG9DgxQtwJcpqEjnwIwkk8hXocRzxgYOVI65ZQaQ5+km/Wwzs15fr/99tPLL7/M1w15hkiYcXkfCaOaX3tghADYL+KjnZYAOFo9qRaTAgTABMAxoebEMTUuSi1bSr16Se++W+t8N+o8va/u2l8va98eS9T6vbFSSYkTWjBE7QpwmYaMfArASD6FeB1GPGNg9mypS5caQ5+hwbpbz+Q8/9e//lXDhg0jAPYMkTDj8j4SRjW/9sAIAbBfxEc7LQFwtHpSLSYFCIAJgGNCzYljar0offDB9391c/nyumds0UKaOFHq1s0JHRiibgW4TENHPgVgJJ9CvA4jHjLQrJm0alXO4F3UTbP1QdVzma9/+PLLL9W6dWsCYA8RKXRk3kcKVcy/9TBCAOwf9dFNTAAcnZZUilEBAmAC4Bhxs/6oOi9K77///V/ffOedmjPuuquU+eudhL/W+x9kAC7TQVTyew2M+O1/kOlhJIhKDq357jtVtG2rktWrq4b6UJ3UTXNzhjzwwAM1atSo7HMw4pD/hkaBEUPCOlQWRgiAHcI59lEIgGOXnAOjUIAAmAA4Co58qbHRi1JFhTRlivT889//Ju9WraTDDpN2352vffAFEP6j3COnw4/Kf3CF186XnTDii9OSysqko4+Wnsn9qoertJcu14QcIUaOHKmTTjqJANgjPIoZlfeRYtTzYy+MEAD7QbqZKQmAzehKVcMKEAATABtGzKnyXJScstPIMDBiRFanisKIU3YaGQZGjMiavqKZ/+F4+HDprrtq9NZDnTSj2ieAGzVqpCVLlqhZ5qsi+B8b0+dlCjvifSSFpqSsJRghAE4Zkla1QwBslV00+4MCBMAEwPw0BFeAi1JwrXxdCSO+Oh98bhgJrpWvK2HEE+cfe0w6/vgawz6sg3SSvv+qhx8egwcP1tNPP1317zDiCSNFjAkjRYjnyVYYIQD2BHUjYxIAG5GVoqYVIAAmADbNmEv1uSi55KaZWWDEjK4uVYURl9w0MwuMmNE1dVV/9SupWqib6e9Tbacdtb/W6u6cdp988kkdddRRBMCpMzG9DfE+kl5v0tIZjBAAp4VFG/sgALbRNXoWATABMD8GwRXgohRcK19XwoivzgefG0aCa+XrShjxxPljjpGefDJn2CP1mJ7RmZKWVz3fqlUrLVy4UJmvgfjhASOeMFLEmDBShHiebIURAmBPUDcyJgGwEVkpaloBAmACYNOMuVSfi5JLbpqZBUbM6OpSVRhxyU0zs8CIGV1TV/WPf5QuuyynrR66RjN0Sc5zI0aM0C233JLzHIykzs3UNQQjqbMkdQ3BCAFw6qC0qCECYIvMotUfFSAAJgDm5yG4AlyUgmvl60oY8dX54HPDSHCtfF0JI544f/HF0rXX5gzbRntoqSbmPDd9+nTttNNOBMCeYBHVmLyPRKWku3VghADYXbrNT0YAbF5jTjCgAAEwAbABrJwtyUXJWWsjGwxGIpPS2UIw4qy1kQ0GI5FJme5CJ58sPfRQVY/r1ECbaL0qqnXdq1cvTZ06tcYcMJJua9PQHYykwYV09wAjBMDpJjTd3REAp9sfuqtDAQJgAmB+OIIrwEUpuFa+roQRX50PPjeMBNfK15Uw4ofzZXvvo/qvvVo17Dw118+0Imf4u+66S2eccQYBsB9IRDol7yORyulkMRghAHYS7JiGIgCOSWiOiVYBAmAC4GiJcrsaFyW3/Y1iOhiJQkW3a8CI2/5GMR2MRKFi+mus3Karmn3xUVWjk9VQ/bSu6t833XRTffnll2revDkBcPrtTF2HvI+kzpLUNQQjBMCpg9KihgiALTKLVn9UgACYAJifh+AKcFEKrpWvK2HEV+eDzw0jwbXydSWMeOD8ihVat3k7NSxbUzXsPyQdXW30E088UQ9V+4qI6qrAiAeMFDkijBQpoAfbYYQA2APMjY1IAGxMWgqbVIAAmADYJF+u1eai5Jqj0c8DI9Fr6lpFGHHN0ejngZHoNU1bxf9ecb02/cPvc9q6StLl1Z4ZP3689tprr1pbh5G0OZq+fmAkfZ6krSMYIQBOG5M29UMAbJNb9FqlAAEwATA/DsEV4KIUXCtfV8KIr84HnxtGgmvl60oYcdz5deu0uu3P1HjFwqpByyV1ljS38pmuXbvq/fffV0lJCQGw4ziYGo/3EVPKulMXRgiA3aE5/kkIgOPXnBMjUIAAmAA4Aoy8KcFFyRurQw8KI6Gl82YjjHhjdehBYSS0dHZsHDlSOuWUnF6fknRUtWfuuOMOnXnmmXXOAyN2WJ1klzCSpPp2nA0jBMB2kJrOLgmA0+kLXeVRgACYAJgfkuAKcFEKrpWvK2HEV+eDzw0jwbXydSWMuO386t6D1Pjf43KG3F3SW5XPNG3aVAsWLFDmn3U9YMRtRqKYDkaiUNHtGjBCAOw24WanIwA2qy/VDSlAAEwAbAgtJ8tyUXLS1kiHgpFI5XSyGIw4aWukQ8FIpHKmq9jKldrQsrVKy9dX9TU1nEcwAAAgAElEQVRZUr9qXZ511lm67bbbNto3jKTL1jR2AyNpdCVdPcEIAXC6iLSrGwJgu/yi20oFCIAJgPlhCK4AF6XgWvm6EkZ8dT743DASXCtfV8KIu86XjXxE9U85MWfACyTdWO2Zjz76SF26dCEAdheDWCbjfSQWma0+BEYIgK0GOOHmCYATNoDjwylAAEwAHI4cP3dxUfLT90KmhpFC1PJzLYz46XshU8NIIWrZtXZpj73VesZrOU1not7Zlc8MGjRIY8eOzTsUjOSVyPsFMOI9AnkFgBEC4LyQsKBOBQiAgcNKBQiACYCtBDehprkoJSS8RcfCiEVmJdQqjCQkvEXHwohFZhXS6tNPS7/6Vc6OKZL6VHvm2Wef1eGHH563Kozklcj7BTDiPQJ5BYARAuC8kLCAABgG3FKAAJgA2C2izU7DRcmsvi5UhxEXXDQ7A4yY1deF6jDigos/mWH+fJX/oofqrViW88Lpku6tfKZDhw6aO3euSktL8woAI3kl8n4BjHiPQF4BYIQAOC8kLCAAhgG3FCAAJgB2i2iz03BRMquvC9VhxAUXzc4AI2b1daE6jLjgYrUZKiqk/feXxozJGWyWpJ0lra189pprrtHFF18caHgYCSST14tgxGv7Aw0PIwTAgUBhUa0K8BUQgGGlAgTABMBWgptQ01yUEhLeomNhxCKzEmoVRhIS3qJjYcQis4K0+sQT0rHH5qxcJ2l3SdMqn23YsKG++OILtWnTJkhFwUggmbxeBCNe2x9oeBghAA4ECosIgGHAHQUIgAmA3aHZ/CRclMxrbPsJMGK7g+b7hxHzGtt+AozY7mC1/leulLp0kb78MmeoCyTdWO2ZE088UQ899FDgwWEksFTeLoQRb60PPDiMEAAHhoWFNRTgE8BAYaUCBMAEwFaCm1DTXJQSEt6iY2HEIrMSahVGEhLeomNhxCKz8rV6xhnS3XfnrHpHjfRLrVV5tWffe+897bxz5gshgj1gJJhOPq+CEZ/dDzY7jBAAByOFVbUpQAAMF1YqQABMAGwluAk1zUUpIeEtOhZGLDIroVZhJCHhLToWRiwya2OtvviidOihOSvKVaLeqtDUas8OGjRIY8eOLWhoGClILi8Xw4iXthc0NIwQABcEDItzFCAABggrFSAAJgC2EtyEmuailJDwFh0LIxaZlVCrMJKQ8BYdCyMWmVVXq+vXS926SR9/nLPiLm2lM7Uw57nRo0frgAMOKGhoGClILi8Xw4iXthc0NIwQABcEDIsJgGHAfgUIgAmA7ac4vgm4KMWnta0nwYitzsXXN4zEp7WtJ8GIrc5V6/vOO6Xhw3MGmaMO6qnPtbras927d9eMGTNUUlJS0NAwUpBcXi6GES9tL2hoGCEALggYFhMAw4D9ChAAEwDbT3F8E3BRik9rW0+CEVudi69vGIlPa1tPghFbnavse+1aqUMHacmSnEH21QCN1fic5+6//379+te/LnhgGClYMu82wIh3lhc8MIwQABcMDRuqFOArIIDBSgUIgAmArQQ3oaa5KCUkvEXHwohFZiXUKowkJLxFx8KIRWbV1uqECdKAATmvvKS9dKAmSiqrer5du3aaN2+eGjVqVPDAMFKwZN5tgBHvLC94YBghAC4YGjYQAMOA3QoQABMA201wvN1zUYpXbxtPgxEbXYu3ZxiJV28bT4MRG12r1vNtt0kjRuQMMUBHaYL+kfPc1VdfrUsuuSTUsDASSjavNsGIV3aHGhZGCIBDgcOmrAJ8AhgQrFSAAJgA2EpwE2qai1JCwlt0LIxYZFZCrcJIQsJbdCyMWGRWba0OHSr97W85rzRTE63St1XPbbbZZpo/f75atWoValgYCSWbV5tgxCu7Qw0LIwTAocBhEwEwDNirAAEwAbC99MbfORel+DW37UQYsc2x+PuFkfg1t+1EGLHNsZ/0e+ih0osvVj25UE21tVblLDrrrLN0W+aTwiEfMBJSOI+2wYhHZoccFUYIgEOiwzY+AQwDtipAAEwAbCu7SfTNRSkJ1e06E0bs8iuJbmEkCdXtOhNG7PKrRrf9+0sTM9/3+/3jU9VTR5VX/Xu9evU0Z84cderUKfSgMBJaOm82wog3VoceFEYIgEPDw0a+AgIG7FSAAJgA2E5yk+mai1Iyutt0KozY5FYyvcJIMrrbdCqM2OTWT3pdskQV7durZP36qhc+kNS92rITTzxRDz30UFFDwkhR8nmxGUa8sLmoIWGEALgogDzfzHcAew6AreMTABMA28puEn1zUUpCdbvOhBG7/EqiWxhJQnW7zoQRu/zK6facc6Rbbsl56g5JZ1U+k/n074cffqif//znRQ0JI0XJ58VmGPHC5qKGhBEC4KIA8nwzAbDnANg6PgEwAbCt7CbRNxelJFS360wYscuvJLqFkSRUt+tMGLHLr6puP/tMFZ07q2Tduqqn1krqLOnzymeGDBmiRx99tOgBYaRoCZ0vACPOW1z0gDBCAFw0RB4XIAD22HybRycAJgC2md+4e+eiFLfi9p0HI/Z5FnfHMBK34vadByP2eZbt+Pjjpccey2k+81ngcyqfKSkp0fvvv6+uXbsWPSCMFC2h8wVgxHmLix4QRgiAi4bI4wIEwB6bb/PoBMAEwDbzG3fvXJTiVty+82DEPs/i7hhG4lbcvvNgxD7P9O23UtOmNRpvK+mrymePPvpoPfHEE5EMByORyOh0ERhx2t5IhoMRAuBIQPK0CAGwp8bbPjYBMAGw7QzH2T8XpTjVtvMsGLHTtzi7hpE41bbzLBixzLeyMunAA6UxY3Iaz3z9wybVnpk5c6a6d6/+6+DCzwkj4bXzZSeM+OJ0+DlhhAA4PD3sJACGASsVIAAmALYS3ISa5qKUkPAWHQsjFpmVUKswkpDwFh0LIxaZlWn1vvuk006r0fTvJV1f+eyRRx6pp556KrLBYCQyKZ0tBCPOWhvZYDBCABwZTB4WIgD20HQXRiYAJgB2geO4ZuCiFJfS9p4DI/Z6F1fnMBKX0vaeAyOWebfbbtLbb+c0/YikkyRVVD47bdo09ejRI7LBYCQyKZ0tBCPOWhvZYDBCABwZTB4WIgD20HQXRiYAJgB2geO4ZuCiFJfS9p4DI/Z6F1fnMBKX0vaeAyMWeTdtmrTLLjkNT5HUX9KGymcPP/xwPfvss5EOBSORyulkMRhx0tZIh4IRAuBIgfKsGAGwZ4a7Mi4BMAGwKyzHMQcXpThUtvsMGLHbvzi6h5E4VLb7DBixxL/Md/8ef7z0k1/sNkTS49VGeOedd9SzZ89Ih4KRSOV0shiMOGlrpEPBCAFwpEB5VowA2DPDXRmXAJgA2BWW45iDi1IcKtt9BozY7V8c3cNIHCrbfQaMWODfmjXfh7/PPJPT7DeStpKU+QVwmUfU3/37w2EwYgEjCbcIIwkbYMHxMEIAbAGmqW2RADi11tDYxhQgACYA5ickuAJclIJr5etKGPHV+eBzw0hwrXxdCSMWOP+730k331yj0ZsknVf5bGlpqT744AN17tw58oFgJHJJnSsII85ZGvlAMEIAHDlUHhUkAPbI7GJGHTBggCZMmBC4xKeffqrtttsu8PpCFxIAEwAXyozP67ko+ex+sNlhJJhOPq+CEZ/dDzY7jATTKbFVq1ZJbdpIa3/4nO/3nXwkqbeklZWNDR8+XLfffruRNmHEiKxOFYURp+w0MgyMEAAbAcuTogTAnhhd7JgEwMUqaG4/fwia09aVyjDiipPm5oARc9q6UhlGXHHS3BwwYk7bSCo/8oh04ok5pT6UtIekZZXPNmnSRHPnzlXbtm0jOfKnRWDEiKxOFYURp+w0MgyMEAAbAcuTogTAnhhd7JjVA+AgvxF4v/3202abbVbssXXu5xPAP0rDH4LGMHOmMIw4Y6WxQWDEmLTOFIYRZ6w0NgiMGJO2+MIVFdLuu0tTp+bU2knSzGrPXHXVVbr00kuLP6+OCjBiTFpnCsOIM1YaGwRGCICNweVBYQJgD0yOYsTqAXBF5hKZ8IMAmAA4YQStOp6LklV2JdIsjCQiu1WHwohVdiXSLIwkInuwQ998U+rbN2ftm5KqP7Plllvq448/VuPGjYPVDLEKRkKI5tkWGPHM8BDjwggBcAhs2FKpAAEwKARSgAA4kEyJLOIPwURkt+pQGLHKrkSahZFEZLfqUBixyq5EmoWRRGQPduhJJ0kPP5yz9lhJT1R75p577tHQoUOD1Qu5CkZCCufRNhjxyOyQo8IIAXBIdNgmiQAYDAIpQAAcSKZEFvGHYCKyW3UojFhlVyLNwkgislt1KIxYZVcizcJIIrLnP3TdOlW0aaOSlT/8mjfpC0k/k7ShcveOO+6oGTNmqLS0NH+9IlbASBHiebIVRjwxuogxYYQAuAh8vN9KAOw9AsEEIAAOplMSq/hDMAnV7ToTRuzyK4luYSQJ1e06E0bs8iuJbmEkCdUDnPnSS9KBB+YsvFbSxdWeef7553XooYcGKFbcEhgpTj8fdsOIDy4XNyOMEAAXR5DfuwmA/fY/8PTVA+CDDz5Y7733nr766qvs94RttdVW6tu3r4YMGaKBAwcGrlnMQr4D+Ef1+EOwGJL82AsjfvhczJQwUox6fuyFET98LmZKGClGPXN71586TA0euCfngN0lvVX5zN57762xY8eqpKTEXBOVlWHEuMTWHwAj1ltofAAYIQA2DpnDBxAAO2xulKNVD4A3VjdziXzkkUeU+UUSJh8EwATAJvlyrTYXJdccjX4eGIleU9cqwohrjkY/D4xEr2nRFcvK9G3zrdRk9ZKqUpmvf+ggKfMrnevXr6/p06erW7duRR8VpACMBFHJ7zUw4rf/QaaHEQLgIJywpnYFCIAhI5ACmQA4891g++67r3r16qWtt946e2lcsGCBxo0bp9GjR6u8vDxba9ttt9WUKVPUrl27QLV/uigT7uZ7zJw5U8OGDcsue+mll7Tbbrvl2+Ls6ytXrsxe3jOPHj16qFmzZs7OymDhFICRcLr5tAtGfHI73KwwEk43n3bBSPrcXn/Z9drizutyGrtN0ojKZzJ36WuuuSa2xmEkNqmtPQhGrLUutsZhRJo6daoOOOCArOaTJ09Wnz59YtOfg+xWgADYbv9i6z4Tyu66665q2LBhrWe+++67OvLIIzVv3rzs6wceeKBGjRoVqr9C/wratddeqy5duoQ6i00ogAIogAIogAIogAIo4JoCHV59Vbvclol7cx+ZL2sbL6l58+a644471KRJE9dGZx4UQAEUcFqBWbNm6aKLLsrOSADstNWRD0cAHLmk/hacM2eOdtppJ61duzYrwltvvRXqk7kEwP4yxOQogAIogAIogAIogALFKdBy1iz1u+R/Vb+8LKfQJEl7VD4zfPjw7N/s44ECKIACKGCXAgTAdvmVpm4JgNPkhgO9nH766br33nuzk1x++eW64oorCp6Kr4AoTDL+Gkxhevm4GkZ8dL2wmWGkML18XA0jPrpe2MwwUpheJlc3OeR/1PDNTNz74yPzd/R6S1osaeedd9aYMWNUr149k23UqA0jscpt5WEwYqVtsTYNI3wFRKzAOXYYAbBjhiY9zuOPP64hQ4Zk2zjqqKP05JNPGmmJXwL3o6x8Eb4RxJwqCiNO2WlkGBgxIqtTRWHEKTuNDAMjRmQtvOjs2dJPvhrtG0n9JM2qrJa5R/funYmD433ASLx623gajNjoWrw9wwi/BC5e4tw6jQDYLT8TnybzaYL99tsv20fmr5W98sorRnoiACYANgKWo0W5KDlqbIRjwUiEYjpaCkYcNTbCsWAkQjHDlpo1S+tO+LUavjMlp8JQSX+rfObkk0/Wgw8+GPaEovbBSFHyebEZRrywuaghYYQAuCiAPN9MAOw5AFGP/9hjj+n444/PluUTwFGrW3s9/hCMR2ebT4ERm92Lp3cYiUdnm0+BEZvdi6d3GIlH51pPKS+X/vQn6corpXXrcpaslLSVpNWSmjZtqtmzZ2vLLbdMpFkYSUR2qw6FEavsSqRZGCEATgQ8Rw4lAHbEyLSMMXToUP3tb99/xuDSSy/VVVddZaQ1PgH8o6z8IWgEMaeKwohTdhoZBkaMyOpUURhxyk4jw8CIEVmDFb366szFu9a1N0k6r/KVG264Qeeff36wmgZWwYgBUR0rCSOOGWpgHBghADaAlTclCYC9sdr8oJlPFPTo0UNr167NHjZlyhTtvvvuRg4mACYANgKWo0W5KDlqbIRjwUiEYjpaCkYcNTbCsWAkQjELKZX59G/79tKXX9bYlfkitkMlZT4T3LlzZ82cOVMNGzYspHqka2EkUjmdLAYjTtoa6VAwQgAcKVCeFSMA9szwMOPeeuut6tWrl/r27Vvn9vfee0+DBw/WvHmZ3zGs7PcAv/zyy2GOC7SHAJgAOBAoLMoqwEUJEPIpACP5FOJ1GIGBfArASD6FDL3+xhvSnnvmFF8h6UJJ90qqqHxl7NixGjRokKEmgpWFkWA6+bwKRnx2P9jsMEIAHIwUVtWmAAEwXORV4PDDD9dzzz2nTp06aZ999lH37t21+eabq379+lq4cKFeffVVjRo1SuWZTyBI2nbbbTV58mRttVXmG8fMPAiACYDNkOVmVS5Kbvoa5VQwEqWabtaCETd9jXIqGIlSzQJqnX22dPvtORv2lTS22jMnnXSSRo4cWUBRM0thxIyuLlWFEZfcNDMLjBAAmyHLj6oEwH74XNSUPwTAQYrsv//+uv/++42Gv5k+CIAJgIPwyJrvFeCiBAn5FICRfArxOozAQD4FYCSfQgZe//hjVfTooZL//req+JLKX/pWVvlM5kMbs2bNUuvWrQ00UFhJGClMLx9Xw4iPrhc2M4yQhRRGDKurK0AADA95FZg7d67Gjx+f/U7f6dOn66uvvtLSpUuz3/XbvHlzbbfddurTp4+GDBmi3r17560XxQICYALgKDjypQYXJV+cDj8njITXzpedMOKL0+HnhJHw2oXaWVb2/Vc/TJ6cs/1OScOrPZP55G/mE8BpeMBIGlxIdw8wkm5/0tAdjBAAp4FDW3sgALbVOc/7JgAmAPb8R6Cg8bkoFSSXl4thxEvbCxoaRgqSy8vFMBKz7ffeK51+es6h30rqLumzymcz3/k7ZswYlZSUxNxc7cfBSCpsSHUTMJJqe1LRHIwQAKcCREubIAC21Djf2yYAJgD2/WegkPm5KBWilp9rYcRP3wuZGkYKUcvPtTASr+8Ve+2lktdfzzn0DEl3V3tmzpw52mGHHeJtbCOnwUhqrEhtIzCSWmtS0xiMEACnBkYLGyEAttA0WuZNrzoD/CHIT0Q+BWAkn0K8DiMwkE8BGMmnEK/DSIwMrFmjDU1bqHTD2qpD35TU9yctVFRUxNhU/qNgJL9Gvq+AEd8JyD8/jJCF5KeEFXUpQAAMG1YqwCeAf7SNPwStRDjWpmEkVrmtPAxGrLQt1qZhJFa5rTwMRuKzbdEjY9XuxH1zDrxE0p+qPfPRRx+pS5cu8TUV4CQYCSCS50tgxHMAAowPIwTAATBhSR0KEACDhpUKEAATAFsJbkJNc1FKSHiLjoURi8xKqFUYSUh4i46FkXjMKv/qa336s4HqtHpmzoF7SJpU7Zm0ffo30xqMxMOIzafAiM3uxdM7jBAAx0Oam6cQALvpq/NTEQATADsPeYQDclGKUExHS8GIo8ZGOBaMRCimo6VgJAZjly7VVz32UZuF03MOWyapnaR1lc+mMfwlAI6BDweO4H3EARMNjwAjBMCGEXO6PAGw0/a6OxwBMAGwu3RHPxkXpeg1da0ijLjmaPTzwEj0mrpWEUYMO/rFF1oz6CBtMif3k7+ZU8+X9OfK45cvX67mzZsbbiZceRgJp5tPu2DEJ7fDzQojBMDhyGFXRgECYDiwUgECYAJgK8FNqGkuSgkJb9GxMGKRWQm1CiMJCW/RsTBi0KxJk1Rx5JEqWby4xiF/k3S6pMyve3v88cd17LHHGmykuNIwUpx+PuyGER9cLm5GGCEALo4gv3cTAPvtv7XTEwATAFsLbwKNc1FKQHTLjoQRywxLoF0YSUB0y46EEUOG3Xef9JvfSOvX1zjgbkm/qQx/L774Yl1zzTWGmoimLIxEo6PLVWDEZXejmQ1GCICjIcnPKgTAfvpu/dQEwATA1kMc4wBclGIU29KjYMRS42JsG0ZiFNvSo2DEgHFPPSUddVSthe+QdHZl+HvvvffqtNNOM9BAtCVhJFo9XawGIy66Gu1MMEIAHC1RflUjAPbLb2emJQAmAHYG5hgG4aIUg8iWHwEjlhsYQ/swEoPIlh8BIxEbOGOG1KNHjaLlki6SdEPlKxdccIGuv/76iA83Uw5GzOjqUlUYcclNM7PACAGwGbL8qEoA7IfPzk1JAEwA7BzUBgfiomRQXEdKw4gjRhocA0YMiutIaRiJ0MiVK6XNN5c2bMgpukxS5ht+X6l8dr/99tOoUaNUv379CA83VwpGzGnrSmUYccVJc3PACAGwObrcr0wA7L7HTk5IAEwA7CTYhobiomRIWIfKwohDZhoaBUYMCetQWRiJ0Mwbb5QuuKBGwV0kTat8tmPHjpo6dapatWoV4cFmS8GIWX1dqA4jLrhodgYYIQA2S5jb1QmA3fbX2ekIgAmAnYXbwGBclAyI6lhJGHHMUAPjwIgBUR0rCSMRGVpRIdWrV2uxkspnGzdurClTpqh79+4RHRpPGRiJR2ebT4ERm92Lp3cYIQCOhzQ3TyEAdtNX56ciACYAdh7yCAfkohShmI6WghFHjY1wLBiJUExHS8FIRMb+5S/SuefWKHaCpEcrn3366ac1ePDgiA6MrwyMxKe1rSfBiK3Oxdc3jBAAx0ebeycRALvnqRcTEQATAHsBekRDclGKSEiHy8CIw+ZGNBqMRCSkw2VgJAJzM5/+bdNG+vrrnGL3SBpW+cyll16qq666KoLD4i8BI/FrbtuJMGKbY/H3CyMEwPFT586JBMDueOnVJATABMBeAV/ksFyUihTQg+0w4oHJRY4II0UK6MF2GInA5PJy6Se/0O09Sb0lrZN02GGH6ZlnnlG9Or4iIoIOjJaAEaPyOlEcRpyw0egQMEIAbBQwx4sTADtusKvjEQATALvKtom5uCiZUNWtmjDilp8mpoERE6q6VRNGivfznbcr1GW3zdRYa6qKPSbpeEk777yz3njjDTVp0qT4gxKqACMJCW/RsTBikVkJtQojBMAJoefEsQTATtjo3xAEwATA/lEffmIuSuG182UnjPjidPg5YSS8dr7shJHinM5868PdHf9Xl6y8JqfQM5KGt9tSU6e+pfbt2xd3SMK7YSRhAyw4HkYsMCnhFmGEADhhBK0+ngDYavv8bZ4AmADYX/oLn5yLUuGa+bYDRnxzvPB5YaRwzXzbASPhHS8rk67u+ZgunXG86v2kzGWlpTrszTfVq1ev8AekZCeMpMSIFLcBIyk2JyWtwQgBcEpQtLINAmArbaNpAmACYH4KgivARSm4Vr6uhBFfnQ8+N4wE18rXlTAS3vmHh/xLxzx+qBqqIqfIW5K+ePRRDR4yJHzxFO2EkRSZkdJWYCSlxqSoLRghAE4Rjta1QgBsnWU0nFGAAJgAmJ+E4ApwUQqula8rYcRX54PPDSPBtfJ1JYyEc/7f143XThftq021IafAl5L+ceGFGnHddeEKp3AXjKTQlJS1BCMpMySF7cAIWUgKsbSmJQJga6yi0eoKEAATAPMTEVwBLkrBtfJ1JYz46nzwuWEkuFa+roSRwp2fM3WFOv+yRY2N30q6fr8DdMVLo1RSUlJ44ZTugJGUGpOitmAkRWaktBUYIQBOKZpWtEUAbIVNNPlTBQiACYD5qQiuABel4Fr5uhJGfHU++NwwElwrX1fCSGHOf/PZKpV37KDW5ctrbPxNl+76y7S31ahRo8KKpnw1jKTcoBS0ByMpMCHlLcAIAXDKEU11ewTAqbaH5upSgACYAJifjuAKcFEKrpWvK2HEV+eDzw0jwbXydSWMBHd+/ez/aP4uB6jjfz+uselvjZvpsE8/UZs2bYIXtGQljFhiVIJtwkiC4ltyNIwQAFuCairbJABOpS00lU8BAmAC4HyM8DqMwEBwBbhMB9fK15Uw4qvzweeGkYBazZmjFTv3V/P/LqmxYWZJPdV/e6q69uwZsJhdy2DELr+S6BZGklDdrjNhhADYLmLT1S0BcLr8oJuAChAAE+4FRIVlkrgogUE+BWAkn0K8DiMwkE8BGMmnkKRJk/TtwUeryYqFNRaPl1T2979r0DHHBChk5xIYsdO3OLuGkTjVtvMsGCEAtpPcdHRNAJwOH+iiQAUIgAmAC0TG6+VclLy2P9DwMBJIJq8XwYjX9gcaHkY2ItOGDdIf/6iKq65SSXl5jYV/lbThzzfrrN+dE0hrWxfBiK3Oxdc3jMSnta0nwQgBsK3spqFvAuA0uEAPBStAAEwAXDA0Hm/gouSx+QFHh5GAQnm8DEY8Nj/g6DBSh1Dz50vHHitNnlzrgt9JWnXambr33jsCKm3vMhix17u4OoeRuJS29xwYIQC2l97kOycATt4DOgihAAEwAXAIbLzdwkXJW+sDDw4jgaXydiGMeGt94MFhpBapZsyQDjxQWljzKx8yq8+TNG3vQ/Xyy8+otLQ0sNa2LoQRW52Lr28YiU9rW0+CEQJgW9lNQ98EwGlwgR4KVoAAmAC4YGg83sBFyWPzA44OIwGF8ngZjHhsfsDRYeQnQo0fLx12mLRyZQ0FF0k6WdL8HXfX1KmvqnHjxgFVtnsZjNjtXxzdw0gcKtt9BowQANtNcLLdEwAnqz+nh1SAAJgAOCQ6Xm7jouSl7QUNDSMFyeXlYhjx0vaChoaRanI99ZR0/PHSunU1NPyXpF9LKmm7vd5/f7LatGlTkM42L4YRm92Lp3cYiUdnm0+BEQJgm/lNuncC4KQd4PxQChAAE+XRTFwAACAASURBVACHAsfTTVyUPDW+gLFhpACxPF0KI54aX8DYMFIp1ty50vbb16rcRZKuk9S4cVtNnz5ZnTp1KkBh+5fCiP0emp4ARkwrbH99GCEAtp/i5CYgAE5Oe04uQgECYALgIvDxbisXJe8sL3hgGClYMu82wIh3lhc8MIxUSnbOOdItt9TQ7wZJF0oqLW2syZPHa7fdehWsse0bYMR2B833DyPmNbb9BBghALad4ST7JwBOUn3ODq0AATABcGh4PNzIRclD0wscGUYKFMzD5TDioekFjuw9I+Xl0vXXSxdfXKtyvSW9VVJfzz33gg499MAC1XVjufeMuGGj0SlgxKi8ThSHEQJgJ0BOaAgC4ISE59jiFCAAJgAujiC/dnNR8svvMNPCSBjV/NoDI375HWZa7xkZNky6555apRsjKRP53nL7fRo+/NQw8jqxx3tGnHDR7BAwYlZfF6rDCAGwCxwnNQMBcFLKc25RChAAEwAXBZBnm7koeWZ4iHFhJIRonm2BEc8MDzGu14w8/LB00km1qnZb5Vc/DD/vCt144+UhlHVni9eMuGOj0UlgxKi8ThSHEQJgJ0BOaAgC4ISE59jiFCAAJgAujiC/dnNR8svvMNPCSBjV/NoDI375HWZabxmZM0fq2VNavTpHtqWSfi3pRUmdO++kWbOmqaSkJIy0zuzxlhFnHDQ/CIyY19j2E2CEANh2hpPsnwA4SfU5O7QCBMAEwKHh8XAjFyUPTS9wZBgpUDAPl8OIh6YXOLKXjKxdK/XpI733Xo5aH0jaV9KXlc+uX79epaWlBSrq3nIvGXHPRqMTwYhReZ0oDiMEwE6AnNAQBMAJCc+xxSlAAEwAXBxBfu3mouSX32GmhZEwqvm1B0b88jvMtF4y8vvff/+L36o9vpO0m6QPK5/77rvvtOmmm4aR1Lk9XjLinItmB4IRs/q6UB1GCIBd4DipGQiAk1Kec4tSgACYALgogDzbzEXJM8NDjAsjIUTzbAuMeGZ4iHG9Y2TSJFX076+SiooctU6TdJ+kHXb4pd55Z6yaNm0aQk03t3jHiJs2Gp0KRozK60RxGCEAdgLkhIYgAE5IeI4tTgECYALg4gjyazcXJb/8DjMtjIRRza89MOKX32Gm9YqRb7/V+u47q8Fnc3OkelLSMZLat/+Fpk8fr1atWoWR0tk9XjHirItmB4MRs/q6UB1GCIBd4DipGQiAk1Kec4tSgACYALgogDzbzEXJM8NDjAsjIUTzbAuMeGZ4iHF9YmTViWeq6SN35ai0UFJ3SaVtdtCMGa+rXbt2IVR0e4tPjLjtpLnpYMSctq5UhhECYFdYTmIOAuAkVOfMohUgACYALhoijwpwUfLI7JCjwkhI4TzaBiMemR1yVC8YWb1aK8+/Us3+mvu9vxnJDpI0ufk2mjFjojp06BBSRbe3ecGI2xYanw5GjEts/QEwQgBsPcQJDkAAnKD4HB1eAQJgAuDw9Pi3k4uSf54XOjGMFKqYf+thxD/PC53YaUYy3/N7000q/8MVqvftqhrS3CNpmKTZs2erc+fOhUrnzXqnGfHGRbODwohZfV2oDiMEwC5wnNQMBMBJKc+5RSlAAEwAXBRAnm3mouSZ4SHGhZEQonm2BUY8MzzEuM4ysn69NGyY9MADtaryH0k9Mr/47YkndPTRR4dQzp8tzjLij4XGJ4UR4xJbfwCMEABbD3GCAxAAJyg+R4dXgACYADg8Pf7t5KLkn+eFTgwjhSrm33oY8c/zQid2kpFvv5WOOkp66aVa5VgnaZ9MAHzW2brttlsLlcy79U4y4p2LZgeGEbP6ulAdRgiAXeA4qRkIgJNSnnOLUoAAmAC4KIA828xFyTPDQ4wLIyFE82wLjHhmeIhxnWNk0SLp4IOld9+tVY0pks6v31A3TZ6oX/5ytxCK+bfFOUb8s9D4xDBiXGLrD4ARAmDrIU5wAALgBMXn6PAKEAATAIenx7+dXJT887zQiWGkUMX8Ww8j/nle6MROMZL55O9uu0mzZtWQ4T1Jx0v6dJNWmjRprHr23KVQqbxd7xQj3rpodnAYMauvC9VhhADYBY6TmoEAOCnlObcoBQiACYCLAsizzVyUPDM8xLgwEkI0z7bAiGeGhxjXKUb+8Q+plu/zzXwRxFGSyjdrnQ1/d9458+2/PIIq4BQjQYdmXUEKwEhBcnm5GEYIgL0EP6KhCYAjEpIy8SpAAEwAHC9xdp/GRclu/+LoHkbiUNnuM2DEbv/i6N4ZRsrKtG7zdmq4YmmObP+WtIekRo3baMqUcerevXscsjp1hjOMOOVKuoaBkXT5kcZuYIQAOI1c2tITAbAtTtFnjgIEwATA/EgEV4CLUnCtfF0JI746H3xuGAmula8rnWDkq6+06NChavfv52rY2FnSoqZbZMPfrl27+mpzUXM7wUhRCrA5nwIwkk8hXocRAmB+CsIrQAAcXjt2JqgAATABcIL4WXc0FyXrLIu9YRiJXXLrDoQR6yyLvWHrGXn0Ua0ZNkKbrP6mhnbTJQ1s3k6Tp7ymLl26xK6tKwdaz4grRqR4DhhJsTkpaQ1GCIBTgqKVbRAAW2kbTRMAEwDzUxBcAS5KwbXydSWM+Op88LlhJLhWvq60mZGK225XyYiza7XuA0nHNW+rp956Q507Zz4HzCOsAjYzEnZm9hWmAIwUppePq2GEANhH7qOamQA4KiWpE6sCBMAEwLECZ/lhXJQsNzCG9mEkBpEtPwJGLDcwhvZtZaRs/Buq2HtvlVZsqKHSBEmnbb61Rk8Zr+233z4GFd0+wlZG3HYlXdPBSLr8SGM3MEIAnEYubemJANgWp+gzRwECYAJgfiSCK8BFKbhWvq6EEV+dDz43jATXyteVVjGyfr30+OMqe+hR1X/1lRqWrZd0vaQH2m+vcZNeVYcOHXy1NdK5rWIk0skpFlQBGAmqlL/rYIQA2F/6i5+cALh4DamQgAIEwATACWBn7ZFclKy1LrbGYSQ2qa09CEastS62xq1hZNYs6cQTpbffrlWbpZIGSVrXpYfGj39ZW2yxRWwaun6QNYy4bkSK54ORFJuTktZghAA4JSha2QYBsJW20TQBMAEwPwXBFeCiFFwrX1fCiK/OB58bRoJr5evK1DNSXi7ddpt00UXSmjW12lQmaX9Jy3ftozFj/qWWLVv6aqeRuVPPiJGpKVqIAjBSiFp+roURAmA/yY9magLgaHSkSswKEAATAMeMnNXHcVGy2r5YmoeRWGS2+hAYsdq+WJpPNSOLF0snnCCNHbtRLS6R9OaAffTCC8+qSZMmsejm0yGpZsQnI1I8K4yk2JyUtAYjBMApQdHKNgiArbSNpgmACYD5KQiuABel4Fr5uhJGfHU++NwwElwrX1emlpG33pKOOEJauLBWa9ZKmi7pEUmLfnW0Hn7kITVq1MhXG43OnVpGjE5N8UIUgJFC1PJzLYwQAPtJfjRTEwBHoyNVYlaAAJgAOGbkrD6Oi5LV9sXSPIzEIrPVh8CI1fbF0nwqGVm6VNp+e2nFihoarJI0QtKDla+ce+7vdOONN6hevXqx6OXjIalkxEcjUjwzjKTYnJS0BiMEwClB0co2CICttI2mCYAJgPkpCK4AF6XgWvm6EkZ8dT743DASXCtfV6aSkTvvlIYPr2HJBEmnSJpX+cpNN92kc88911frYps7lYzENj0HBVEARoKo5PcaGCEA9vsnoLjpCYCL04/dCSlAAEwAnBB6Vh7LRclK22JtGkZildvKw2DESttibTp1jHz4odStWw0NbpZ0vqRySQ0aNNTDDz+kY445JlatfD0sdYz4akSK54aRFJuTktZghAA4JSha2QYBsJW20TQBMAEwPwXBFeCiFFwrX1fCiK/OB58bRoJr5evKVDHy6KPa8P9OV+na73LsyHwRRIvKZ5o2ba7nn/+nBgwY4Ktlsc+dKkZin54DgygAI0FU8nsNjBAA+/0TUNz0BMDF6cfuhBQgACYATgg9K4/lomSlbbE2DSOxym3lYTBipW2xNp0KRtasUcU556rk7r/WOvt1ki6StOWWW+vll0frF7/4Rawa+X5YKhjx3YSUzw8jKTcoBe3BCAFwCjC0tgUCYGut87txAmACYL9/AgqbnotSYXr5uBpGfHS9sJlhpDC9fFydOCOffqqyI36l+tPfrVX+uyWdJWmHHbvq5Zdf0jbbbOOjTYnOnDgjiU7P4UEUgJEgKvm9BkYIgP3+CShuegLg4vRjd0IKEAATACeEnpXHclGy0rZYm4aRWOW28jAYsdK2WJtOhJGKCmn0aOn006UFC2qdd7WkMyQ9ImngwIF6+umn1bJly1i14bDvFUiEEcS3SgEYscquRJqFEQLgRMBz5FACYEeM9G0MAmACYN+YL2ZeLkrFqOfHXhjxw+dipoSRYtTzY2/sjJSXS+eeK916a50CfyTpV5I+zHz696yzdNNNN6lBgwZ+GJLCKWNnJIUa0NLGFYARCMmnAIwQAOdjhNfrVoAAGDqsVIAAmADYSnATapqLUkLCW3QsjFhkVkKtwkhCwlt0bKyMbNggnXaaNHJknQo9LmmopHUNGuiOO+7Q0KGZf+ORpAKxMpLkoJwdWgEYCS2dNxthhADYG9gNDEoAbEBUSppXgACYANg8Ze6cwEXJHS9NTQIjppR1py6MuOOlqUliY2TtWunYY6V//rPWUdZJOkfSXZJat26tZ555Rv379zc1NnULUCA2RgroiaXpUgBG0uVHGruBEQLgNHJpS08EwLY4RZ85ChAAEwDzIxFcAS5KwbXydSWM+Op88LlhJLhWvq40zsgP3/d78cXSjBm1yvyxpCGS3pbUo0cPPffcc9p22219tSR1cxtnJHUT01ChCsBIoYr5tx5GCID9oz66iQmAo9OSSjEqQABMABwjbtYfxUXJeguNDwAjxiW2/gAYsd5C4wMYZWTuXCnzFQ6vvVZn8Hu6pAmSKiQdeeSRGjlypBo3bmx8bg4IroBRRoK3wcoUKwAjKTYnJa3BCAFwSlC0sg0CYCtto2kCYAJgfgqCK8BFKbhWvq6EEV+dDz43jATXyteVRhjJ/KK3u+6SLrxQ+u67WqUdL+l/JK2qfPUPf/iDLrvsMtWrV89XK1I7txFGUjstjYVRAEbCqObXHhghAPaL+GinJQCOVk+qxaQAATABcEyoOXEMFyUnbDQ6BIwYldeJ4jDihI1Gh4ickcxXPmQ+9XvffXX2/WzlVz6skdSiRQs98sgjOvjgg43OSfHwCkTOSPhW2JlSBWAkpcakqC0YIQBOEY7WtUIAbJ1lNJxRgACYAJifhOAKcFEKrpWvK2HEV+eDzw0jwbXydWWkjJSVSUOGSE8+WaucH0j6X0nPVb6688476+mnn1bHjh19ld+KuSNlxIqJabJQBWCkUMX8Ww8jZCH+UR/dxATA0WlJpRgVIAAmAI4RN+uP4qJkvYXGB4AR4xJbfwCMWG+h8QEiY+Srr1QxZIhKxo6t0fNqSRdK+quk8spXf/3rX+uOO+7QpptuanxGDihOgcgYKa4NdqdYARhJsTkpaQ1GCIBTgqKVbRAAW2kbTRMAEwDzUxBcAS5KwbXydSWM+Op88LlhJLhWvq6MhJE331TZkUep/pcLasi4WFJfSf+p9sppp52me+65RyUlJb7KbtXckTBi1cQ0W6gCMFKoYv6thxECYP+oj25iAuDotKRSjAoQABMAx4ib9UdxUbLeQuMDwIhxia0/AEast9D4AMUyUvH+B9rQa3c1WJv5nG/uo0zSPpIyv/Dth8dhhx2mf/7zn8bn4oDoFCiWkeg6oVJaFYCRtDqTnr5ghAA4PTTa1wkBsH2e0THfAZzDAH8I8iORTwEYyacQr8MIDORTAEbyKcTrxTCyYH651nbvqY4rp9cQ8mNJIyS9VPlK+/btNXXqVLVr1w7RLVOgGEYsG5V2QyoAIyGF82gbjBAAe4R75KMSAEcuKQXjUIBPAP+oMn8IxkGc3WfAiN3+xdE9jMShst1nwIjd/sXRfRhGKiqksWc/px3uHKHtKj6v0eaVkq6o/L7fXXfdVffdd5969OgRxzicYUCBMIwYaIOSKVYARlJsTkpagxEC4JSgaGUbBMBW2kbTBMAEwPwUBFeAi1JwrXxdCSO+Oh98bhgJrpWvKwtl5POpi/XpIWdrryX/qFWyiyRdJ2V/udsf//hHjRgxQqWlpb7K68TchTLixNAMUZACMFKQXF4uhhECYC/Bj2hoAuCIhKRMvAoQABMAx0uc3adxUbLbvzi6h5E4VLb7DBix2784ug/KyIb1FXrllMe0+2NnaXMtr7W1GZJ6Shq4zz66++671bFjxzhG4AzDCgRlxHAblE+xAjCSYnNS0hqMEACnBEUr2yAAttI2miYAJgDmpyC4AlyUgmvl60oY8dX54HPDSHCtfF1ZJyPl5dIbb0ijR2v93ferwfKvNirRm5KOkHTtAw/o5JNPVklJia+SOjc37yPOWRr5QDASuaTOFYQRAmDnoI5xIALgGMXmqOgUIAAmAI6OJvcrcVFy3+NiJ4SRYhV0fz+MuO9xsRPWysi8edLRR0tTp+Yt/4Gk8yt/2dvChQu15ZZb5t3DArsU4H3ELr+S6BZGklDdrjNhhADYLmLT1S0BcLr8oJuAChAAEwAHRIVlkrgogUE+BWAkn0K8DiMwkE+BGozMnq2Kww9XyZIlG926XtKfJF0t6YwRI3TLLbfkO4rXLVWA9xFLjYuxbRiJUWxLj4IRAmBL0U1F2wTAqbCBJgpVgACYALhQZnxez0XJZ/eDzQ4jwXTyeRWM+Ox+sNmrMzJo8WJtdvYI1Vu3dqOb35F0ZqNNdMQf/k/nnnuuGjVqFOwwVlmpAO8jVtoWa9MwEqvcVh4GIwTAVoKbkqYJgFNiBG0UpgABMAFwYcT4vZqLkt/+B5keRoKo5PcaGPHb/yDTZxiZOH68tnvwH9rphb9vdMuKyk/9LjzuOF193XXaZpttghzBGssV4H3EcgNjaB9GYhDZ8iNghADYcoQTbZ8AOFH5OTysAgTABMBh2fFxHxclH10vbGYYKUwvH1fDiI+uFzbz4ukfacU+x6rz0hk1NpZVfr/vbZLaSdqm5266+Y5b1bt378IOYbXVCvA+YrV9sTQPI7HIbPUhMEIAbDXACTdPAJywARwfTgECYALgcOT4uYuLkp++FzI1jBSilp9rYcRP3zc69bJl0i23qGLePH35wdfa6u0Xa12+StKxkkZJ2mKL9rrpput07LHHql69eojqmQK8j3hmeIhxYSSEaJ5tgRECYM+Qj3RcAuBI5aRYXAoQABMAx8WaC+dwUXLBRbMzwIhZfV2oDiMuuBjhDMuXS336SLNmbbToZ5IOkTSn4aa6+OLf68ILL9Bmm20WYSOUskkB3kdsciuZXmEkGd1tOhVGCIBt4jVtvRIAp80R+gmkAAEwAXAgUFiUVYCLEiDkUwBG8inE6zACA1UKlJfr230OU5PXav/E7w/r/iXpVNXTIaeerquuukxbbbUVInquAO8jngMQYHwYCSCS50tghADY8x+BosYnAC5KPjYnpQABMAFwUuzZeC4XJRtdi7dnGIlXbxtPgxEbXYu+5y/nrtab+5+qwXOf3GjxqyT9+6CjdfNf/qgddtgh+kaoaKUCvI9YaVusTcNIrHJbeRiMEABbCW5KmiYATokRtFGYAgTABMCFEeP3ai5KfvsfZHoYCaKS32tgxGP/Kyq0cswUvfWbK/XL/4xRM2V+pVvtj+WSrm+znfZ94n4NHDjQY9EYvTYFeB+Bi3wKwEg+hXgdRgiA+SkIrwABcHjt2JmgAgTABMAJ4mfd0VyUrLMs9oZhJHbJrTsQRqyzLJKGV70+VV8dfow6Lvu0znoTJO0vafv2O+uoU4/Qzj17qH///mrVqlUkPVDEHQV4H3HHS1OTwIgpZd2pCyMEwO7QHP8kBMDxa86JEShAAEwAHAFG3pTgouSN1aEHhZHQ0nmzEUa8sTo76FcfL9LrB52sQz95RQ03Mvp8SQdsvr0uve167bffnpo4cWJ2NQGwX7wEnZb3kaBK+bsORvz1PujkMEIAHJQV1tVUgAAYKqxUgACYANhKcBNqmotSQsJbdCyMWGRWQq3CSELCx3zs5x8v0ehDfq2j5oxWK1Vs9PQ3SjfVvMuu0nGX/FalpaX8wtGYvbLxON5HbHQt3p5hJF69bTwNRgiAbeQ2LT0TAKfFCfooSAECYALggoDxfDEXJc8BCDA+jAQQyfMlMOI2ALNnL9L9R5yl33z0jLbbSPC7WNKTjVqp+Tnna8gfL8gGvz88YMRtRqKYDkaiUNHtGjDitr9RTAcjBMBRcORrDQJgX523fG4CYAJgyxGOtX0uSrHKbeVhMGKlbbE2DSOxyh3PYeXlevuB5zXm/27Ubgsma5+NBL/fSPrfxh210zXXa9hZR6hevXo1eoSReGyz+RQYsdm9eHqHkXh0tvkUGCEAtpnfpHsnAE7aAc4PpQABMAFwKHA83cRFyVPjCxgbRgoQy9OlMOKO8RUrVui9k0Zo2xf/rs3L1210sA2SHt+0vRpe8xcd/dvBKikpqXM9jLjDiKlJYMSUsu7UhRF3vDQ1CYwQAJtiy4e6BMA+uOzgjATABMAOYm1sJC5KxqR1pjCMOGOlsUFgxJi08RRet05rZszUuAuu1+4TntbmFWV5z31p061V79pbtN+II/OuzSyAkUAyeb0IRry2P9DwMBJIJq8XwQgBsNc/AEUOTwBcpIBsT0YBAmAC4GTIs/NULkp2+hZn1zASp9p2ngUjFvq2dq309NNaffMt2uSdqapfsfFf6vbDhHMbtNDX19yhX54/pKChYaQgubxcDCNe2l7Q0DBSkFxeLoYRAmAvwY9oaALgiISkTLwKEAATAMdLnN2ncVGy2784uoeROFS2+wwYsci/775T2ZVXat0dd2nTb1cGarxc0sebtFGjU4Zqu5svkzbZJNC+6otgpGDJvNsAI95ZXvDAMFKwZN5tgBECYO+gj3BgAuAIxaRUfAoQABMAx0eb/SdxUbLfQ9MTwIhphe2vDyN2ePjhy6+o6wH7B2o2E/o+riaa3fN4/b/7L9W2PdoH2lfXIhgpSj4vNsOIFzYXNSSMFCWfF5thhADYC9ANDUkAbEhYyppVgACYANgsYW5V56Lklp8mpoERE6q6VRNG0uvn4sWL9eid92mTm2/RmauWBGr0uXqb690jLtd59w9Xs2b1A+3JtwhG8inE6zACA/kUgJF8CvE6jBAA81MQXgEC4PDasTNBBQiACYATxM+6o7koWWdZ7A3DSOySW3cgjKTLsrVr1+r551/QX/4yUvUmj9JTKtcWG2nx35KeUwN90Wxv7X7mORp21QEqLY12JhiJVk8Xq8GIi65GOxOMRKuni9VghADYRa7jmokAOC6lOSdSBQiACYAjBcrxYlyUHDc4gvFgJAIRHS8BIwkavGqV9OCDqpg+XSvef1+rZ81R+crlqqioUEtJTfO01lvbS7ufpz/9aYgGDmxmbBAYMSatM4VhxBkrjQ0CI8akdaYwjBAAOwNzAoMQACcgOkcWrwABMAFw8RT5U4GLkj9eh50URsIq588+GEnG64pvvtF/e/fWZh9/XFADqyXd2qCnFp10g/73moFq27akoP1hFsNIGNX82gMjfvkdZloYCaOaX3tghADYL+KjnZYAOFo9qRaTAgTABMAxoebEMVyUnLDR6BAwYlReJ4rDSHw2Zj7ZO3P6dD3z6GM64Nbb1HvdmsCHl0l6tsXeKr36Hh1yeqfIv+ZhY43ASGCbvF0II95aH3hwGAkslbcLYYQA2Fv4IxicADgCESkRvwIEwATA8VNn74lclOz1Lq7OYSQupe09B0bMepcJfadPn65x9z+gxg89osErvlGbAo9cX7+RvrrjH9pq2KEF7oxmOYxEo6PLVWDEZXejmQ1GotHR5SowQgDsMt+mZyMANq0w9Y0oQABMAGwELEeLclFy1NgIx4KRCMV0tBSMRG/shqVLNe2FFzR23GuaMeoVHf3NYmWi2/obOWqRpHmSvlZDVWgrbd6qvX7WqYXa7tha9X57ttSzZ/SNBqwIIwGF8ngZjHhsfsDRYSSgUB4vgxECYI/xL3p0AuCiJaRAEgoQABMAJ8GdrWdyUbLVufj6hpH4tLb1JBiJxrllX36p+eedp1ajR6v98uUFFX1apTpax6rdVsdq+PD9dOqpDdSuXUEljC6GEaPyOlEcRpyw0egQMGJUXieKwwgBsBMgJzQEAXBCwnNscQoQABMAF0eQX7u5KPnld5hpYSSMan7tgZFwfme+2mHatGkaPXq0xrzwL102ZbL2DlHqzfo76qnTJ+m4U1tq112lEvO/063gLmGkYMm82wAj3lle8MAwUrBk3m2AEQJg76CPcGAC4AjFpFR8ChAAEwDHR5v9J3FRst9D0xPAiGmF7a8PI8E9XL58ucaMGZMNfV98cbS++mqRGkt6VNJhActsUImWqKXWtuqg0n59tdXIP6l+y2YBdyezDEaS0d2mU2HEJreS6RVGktHdplNhhADYJl7T1isBcNocoZ9AChAAEwAHAoVFWQW4KAFCPgVgJJ9CvA4jdTPwwy9wGzVqlEb9a5Q+efNNNaooV6mkrpL2lHSCpC0DYPQftdOE7ueo1Xmna79jWmrTTQNsSskSGEmJESluA0ZSbE5KWoORlBiR4jZghAA4xXimvjUC4NRbRIO1KUAATADMT0ZwBbgoBdfK15Uw4qvzweeGkVytFixYoFdffVWvvjpOk158Sbt8s1j7S9n/2yagrAtUXzeqn9Sop3beaTt1OuQX6nnuXtqs6cZ+DVzA4gksg5EERLfsSBixzLAE2oWRBES37EgYIQC2DNlUtUsAnCo77Gjmueee08MPP6ypU6dq8eLFatasmTp16qQjjjhCw4YNU/PmzY0PQgBMAGwcMocO4KLkO0waiAAAIABJREFUkJmGRoERQ8I6VNZ3Rr7++mu99tpr2cB39OhX9dlnc9RL0u8lHS5lP+1byOP10t568uRxOuyYTTVggNSgQSG707nWd0bS6Uq6uoKRdPmRxm5gJI2upKsnGCEATheRdnVDAGyXX4l2u2rVKg0ZMkQvvvhinX20b99eTzzxhPr27Wu0VwJgAmCjgDlWnIuSY4YaGAdGDIjqWEkvGPnuO2nuXKlpUy1r3FjvvPCC5jw/SlOnTtPnC+dmP9nbXVK3yn8G/aTvT1FYdPgwtXnoJtVvuplTlHjBiFOOxT8MjMSvuW0nwohtjsXfL4wQAMdPnTsnEgC746XRSTZs2KCDDjoo+0tNMo8ttthCQ4cOVdeuXbPfL/r4449r0qRJ2ddatGihiRMnqlu3zH8imXkQABMAmyHLzapclNz0NcqpYCRKNd2s5TIjFe+/r4WXXKKWL7wgU5FsefMWqjdgL+l3v5P2zHwrsHsPlxlxz61kJoKRZHS36VQYscmtZHqFEQLgZMhz41QCYDd8ND7FXXfdpTPPPDN7Tib0HTduXDYErv44//zz9ec//zn7VL9+/bIhsKkHATABsCm2XKzLRclFV6OdCUai1dPFai4xsmLFCr355tua8thL2uP5R7XPii8js2xho221oOv+ar17R3X4WX3Vb9VC6t07c3mS6tWL7Jw0FnKJkTTq60JPMOKCi2ZngBGz+rpQHUYIgF3gOKkZCICTUt6ic8vKypT5aodFixZlu37nnXfUs2fPGhNk1vXq1UvTpk3LvvbSSy9p//0zvw4l+gcBMAFw9FS5W5GLkrveRjUZjESlpLt1bGVk5crVeuWV9/Xqq+/qrbf+rblz31L5io/0v5LOkdQoAstWNtlSi44aoa3PHqzGO+8glZREUNW+ErYyYp/S9nYMI/Z6F1fnMBKX0vaeAyMEwPbSm3znBMDJe5D6DjKf9h00aFC2z7322kvjx4+vs+cHHnhAp556avb1k08+WQ8++KCR+QiACYCNgOVoUS5Kjhob4VgwEqGYjpZKMyMVFdLSpRV67bXZevvtj/X229P18cfTtWTJDK1b97GkiqwrbSSdJek8SY2L8Om7Ri30307d1aR3dzXaew/pV7+SGkURJRfRVAq2ppmRFMhDC1L2a+PeeOONrBb9+/dXq1at0AUFchSAEYDIpwCMEADnY4TX61aAABg68ipw4YUX6oYbbsiuu+6665T597oeixcvVrt27bIvt23bVpl/N/EgAP5RVf4QNEGYWzVhxC0/TUwDIyZUdatmkoxs2CAtWSLNn79e06Z9rhkz/qPZsz/VZ5/9R1999alWrHgyK3Ym1O1T+X8tJK2v/L/M53Ez37rbT9LGvoRhmaT7Ja2UtK3aal3DrlrVeR/9vFcX7dhpg7bbpaUa7NxN2morbz/luzGqk2TErZ82d6eBEXe9jWoyGIlKSXfrwAgBsLt0m5+MANi8xtafcOCBB2a/ziHzyHwaeODAgRudqUOHDpo/f352zZIlS9SmTeYzN9E+CIAJgKMlyu1qXJTc9jeK6WAkChXdrhElI5lAd/nXZVo+b7m+/fwbfTd3gcqmvaWSebO0ZH0DjS/tpFe+WanZn1ytevW2Vnl5J+2gTzRYX2pnVWitpIWSMt/c+1nl/z9c0hBJDUPYsFL19KeSlvrbJs21U58zdMYZ56tPnxK1bx+imMdbomTEYxmdHh1GnLY3kuFgJBIZnS4CIwTATgNueDgCYMMCu1C+Y8eO+vTTT7OjZP653XbbbXSszNdEvP7669k1mb/mtccee0QuAwEwAXDkUDlckIuSw+ZGNBqMRCSko2XefnuBzj77dh306URtuW6l6jdspLKS+tpQUaGykhJtKK9QmUq0vqJc68vLtbJcmlC/peZWNND69WtVVrZOWr9GQzYs0qnlS/VzrVVLlW9UraWSJklaLamHpG6GtJ0/YIga3nKTttgp9xfbGjrO6bK8jzhtbyTDwUgkMjpdBEactjeS4WCEADgSkDwtQgDsqfGFjJ35fq5lyzJ/MVJatWqVmjRpstHtgwcP1rPPPptd88ILL+iQQw4p5Dhlwt18j5kzZ2rYsGHZZZlPJ++22275tjj7+sqVKzV9+vTsfD169FCzZs2cnZXBwikAI+F082kXjPjkduGz/v3vMzR8+EBNrPwahaAVMl8YNaVyz+mSfhZ0YwzrNuyyi/577rlaf/DBMZzmxxG8j/jhczFTwkgx6vmxF0b88LmYKWFEmjp1qg444ICsjJMnT1afPpkvwOKBAvkVIADOr5H3Kxo2bKj16zPfpKfsP0tLSzeqyfHHH6/HHnssuybzz+OOO64gDUsK/O3Z1157rbp06VLQGSxGARRAARRAARQIpsCECYt1883DsmHu7sG2pG5Vef36Wtq9uxbtvru+/OUvtaZ169T1SEMogAIogAIogAIokE+BWbNm6aKLLsouIwDOpxavV1eAABge8ipAAJxXIhagAAqgAAqggLMKTJz4lW68caimSuqV8JQbsv8jcYlKK2p+hcSGhg31n0MP1Ze9e2vVNtuopLxcJWVlqldWpvWNG6u8QYOEu+d4FEABFEABFEABFChOAQLg4vTzeTcBsM/uB5ydr4AIKFRCy/hrMAkJb9GxMGKRWQm1CiMJCW/JsS+8MEunnNJP70raJYKe/92wud5dt0LfSNn/K22znRp23U0bfradfqHvtMviedrqP5+o6SefZMPbisaNtW6ffbT+kEOy/1STJir55hvV+/JL1Z85U6UzZ6qiWTOtOekkVWy9dQQdUiKMAryPhFHNrz0w4pffYaaFkTCq+bUHRvgKCL+Ij3ZaAuBo9XSyGr8ELt228kX46fYnDd3BSBpcSHcPMJJuf5Lubs2aDZo58zPNeGuyMl8C1eeXu6hNyxZqUK9CpSXK/rNexfefttXll2e+/6lmyw0bSvvuK114obTnnsFG+u476euvpbZtpUaNgu1hVWIK8D6SmPTWHAwj1liVWKMwkpj01hwMI/wSOGtgTWGjBMApNCVtLR144IHZX7SWeYwbN04DBw7caIsdOnTQ/Pnzs2uWLFmiNm3aRD5S5hfF9e3bN1vX9++94Q/ByPFyriCMOGdp5APBSOSSOlewIEamTJFmzJAyvzNgk02kzTeX+vXLfnKXh7sKFMSIuzIw2UYUgBHwyKcAjORTiNdhhACYn4LwChAAh9fOm50XXnihbrjhhuy81113nTL/Xtdj8eLFateuXfbltm3bKvPvJh4EwD+qyh+CJghzqyaMuOWniWlgxISqbtWEEbf8NDENjJhQ1a2aMOKWnyamgRETqrpVE0YIgN0iOt5pCIDj1dvK0zKf+h00aFC29wEDBui1116rc44HHnhAp556avb1U045RZl/N/EgACYANsGVqzW5KLnqbHRzwUh0WrpaCUZcdTa6uWAkOi1drQQjrjob3VwwEp2WrlaCEQJgV9mOYy4C4DhUtvyMsrIytW/fXosWLcpO8s4776hnz541psqs69Wrl6ZNm5Z9LfO1Efvvv7+R6QmACYCNgOVoUS5Kjhob4VgwEqGYjpaCEUeNjXAsGIlQTEdLwYijxkY4FoxEKKajpWCEANhRtGMZiwA4FpntP+Suu+7SmWeemR2kW7du2e8CznzFQ/XHBRdcoBtvvDH7VL9+/TRx4kRjgxMAEwAbg8vBwlyUHDQ14pFgJGJBHSwHIw6aGvFIMBKxoA6WgxEHTY14JBiJWFAHy8EIAbCDWMc2EgFwbFLbfdCGDRt00EEHacyYMdlBMt/zO3ToUHXt2lWZN+HHH3+8KvBt3ry5Jk2alA2KTT0IgAmATbHlYl0uSi66Gu1MMBKtni5WgxEXXY12JhiJVk8Xq8GIi65GOxOMRKuni9VghADYRa7jmokAOC6lHThn1apVGjJkiF588cU6p8l8VcQTTzyhvn37Gp2YAJgA2ChgjhXnouSYoQbGgREDojpWEkYcM9TAODBiQFTHSsKIY4YaGAdGDIjqWEkYIQB2DOlYxyEAjlVuNw577rnn9NBDD2nq1KlasmSJmjZtqk6dOmnw4MEaNmyYMp8ANv0gACYANs2YS/W5KLnkpplZYMSMri5VhRGX3DQzC4yY0dWlqjDikptmZoERM7q6VBVGCIBd4jnuWQiA41ac8yJRgACYADgSkDwpwkXJE6OLGBNGihDPk60w4onRRYwJI0WI58lWGPHE6CLGhJEixPNkK4wQAHuCupExCYCNyEpR0woQABMAm2bMpfpclFxy08wsMGJGV5eqwohLbpqZBUbM6OpSVRhxyU0zs8CIGV1dqgojBMAu8Rz3LATAcSvOeZEoQABMABwJSJ4U4aLkidFFjAkjRYjnyVYY8cToIsaEkSLE82QrjHhidBFjwkgR4nmyFUYIgD1B3ciYBMBGZKWoaQUIgAmATTPmUn0uSi65aWYWGDGjq0tVYcQlN83MAiNmdHWpKoy45KaZWWDEjK4uVYURAmCXeI57FgLguBXnvEgUIAAmAI4EJE+KcFHyxOgixoSRIsTzZCuMeGJ0EWPCSBHiebIVRjwxuogxYaQI8TzZCiMEwJ6gbmRMAmAjslLUtAIEwATAphlzqT4XJZfcNDMLjJjR1aWqMOKSm2ZmgREzurpUFUZcctPMLDBiRleXqsIIAbBLPMc9CwFw3IpzXiQKEAATAEcCkidFuCh5YnQRY8JIEeJ5shVGPDG6iDFhpAjxPNkKI54YXcSYMFKEeJ5shRECYE9QNzImAbARWSlqWgECYAJg04y5VJ+LkktumpkFRszo6lJVGHHJTTOzwIgZXV2qCiMuuWlmFhgxo6tLVWGEANglnuOehQA4bsU5LxIFCIAJgCMByZMiXJQ8MbqIMWGkCPE82QojnhhdxJgwUoR4nmyFEU+MLmJMGClCPE+2wggBsCeoGxmTANiIrBQ1rQABMAGwacZcqs9FySU3zcwCI2Z0dakqjLjkpplZYMSMri5VhRGX3DQzC4yY0dWlqjBCAOwSz3HPQgAct+KcF4kCBMAEwJGA5EkRLkqeGF3EmDBShHiebIURT4wuYkwY+f/t3Qm4dVVdP/AlkyEBxhSQCVIWAiooUwqiCZqK+CLiQA6pOaFlUfFPzYAkfHjUzApUUgmpmEQUUUzICQcImSvRBHFiUEEleUAG+T+/bfd635c7nHP2XmeftfZnP48Pvu89Z+21Pr/ve+6+v7uHFngDeauMDKTQLZYpIy3wBvJWGdEAHkjUsyxTAzgLq0FzC2gAawDnzlhN4ztQqqmaedYiI3lcaxpVRmqqZp61yEge15pGlZGaqplnLTKSx7WmUWVEA7imPE97LRrA0xa3v04ENIA1gDsJ0kAGcaA0kEK3WKaMtMAbyFtlZCCFbrFMGWmBN5C3yshACt1imTLSAm8gb5URDeCBRD3LMjWAs7AaNLeABrAGcO6M1TS+A6WaqplnLTKSx7WmUWWkpmrmWYuM5HGtaVQZqamaedYiI3lcaxpVRjSAa8rztNeiATxtcfvrREADWAO4kyANZBAHSgMpdItlykgLvIG8VUYGUugWy5SRFngDeauMDKTQLZYpIy3wBvJWGdEAHkjUsyxTAzgLq0FzC2gAawDnzlhN4ztQqqmaedYiI3lcaxpVRmqqZp61yEge15pGlZGaqplnLTKSx7WmUWVEA7imPE97LRrA0xa3v04ENIA1gDsJ0kAGcaA0kEK3WKaMtMAbyFtlZCCFbrFMGWmBN5C3yshACt1imTLSAm8gb5URDeCBRD3LMjWAs7AaNLeABrAGcO6M1TS+A6WaqplnLTKSx7WmUWWkpmrmWYuM5HGtaVQZqamaedYiI3lcaxpVRjSAa8rztNeiATxtcfvrREADWAO4kyANZBAHSgMpdItlykgLvIG8VUYGUugWy5SRFngDeauMDKTQLZYpIy3wBvJWGdEAHkjUsyxTAzgLq0FzC2gAawDnzlhN4ztQqqmaedYiI3lcaxpVRmqqZp61yEge15pGlZGaqplnLTKSx7WmUWVEA7imPE97LRrA0xa3v04ENIA1gDsJ0kAGcaA0kEK3WKaMtMAbyFtlZCCFbrFMGWmBN5C3yshACt1imTLSAm8gb5URDeCBRD3LMjWAs7AaNLeABrAGcO6M1TS+A6WaqplnLTKSx7WmUWWkpmrmWYuM5HGtaVQZqamaedYiI3lcaxpVRjSAa8rztNeiATxtcfvrREADWAO4kyANZBAHSgMpdItlykgLvIG8VUYGUugWy5SRFngDeauMDKTQLZYpIy3wBvJWGdEAHkjUsyxTAzgLq0FzC2gAawDnzlhN4ztQqqmaedYiI3lcaxpVRmqqZp61yEge15pGlZGaqplnLTKSx7WmUWVEA7imPE97LRrA0xa3v04ENIA1gDsJ0kAGcaA0kEK3WKaMtMAbyFtlZCCFbrFMGWmBN5C3yshACt1imTLSAm8gb5URDeCBRD3LMjWAs7AaNLeABrAGcO6M1TS+A6WaqplnLTKSx7WmUWWkpmrmWYuM5HGtaVQZqamaedYiI3lcaxpVRjSAa8rztNeiATxtcfvrREADWAO4kyANZBAHSgMpdItlykgLvIG8VUYGUugWy5SRFngDeauMDKTQLZYpIy3wBvJWGdEAHkjUsyxTAzgLq0FzC2gAawDnzlhN4ztQqqmaedYiI3lcaxpVRmqqZp61yEge15pGlZGaqplnLTKSx7WmUWVEA7imPE97LRrA0xa3v04ENIA1gDsJ0kAGcaA0kEK3WKaMtMAbyFtlZCCFbrFMGWmBN5C3yshACt1imTLSAm8gb5URDeCBRD3LMjWAs7AaNLeABrAGcO6M1TS+A6WaqplnLTKSx7WmUWWkpmrmWYuM5HGtaVQZqamaedYiI3lcaxpVRjSAa8rztNeiATxtcfvrREADWAO4kyANZBAHSgMpdItlykgLvIG8VUYGUugWy5SRFngDeauMDKTQLZYpIy3wBvJWGdEAHkjUsyxTAzgLq0FzC2gAawDnzlhN4ztQqqmaedYiI3lcaxpVRmqqZp61yEge15pGlZGaqplnLTKSx7WmUWVEA7imPE97LRrA0xa3v04ENIA1gDsJ0kAGcaA0kEK3WKaMtMAbyFtlZCCFbrFMGWmBN5C3yshACt1imTLSAm8gb5URDeCBRD3LMjWAs7AaNLeABrAGcO6M1TS+A6WaqplnLTKSx7WmUWWkpmrmWYuM5HGtaVQZqamaedYiI3lcaxpVRjSAa8rztNeiATxtcfvrREADWAO4kyANZBAHSgMpdItlykgLvIG8VUYGUugWy5SRFngVvfUD//2BtGr7VWmdtda5z6pWysjdP707fejqD6Vn7fCsikQsZRyBlTIyzlheW6eAjGgA15ns6axKA3g6zvbSsYAGsAZwx5GqejgHSlWXt5PFyUgnjFUPIiNVl7eTxclIJ4xFD3Lkp49MR33mqPTcnZ6bTj7w5Ps0ga+88sq0yy67NGu83/3ul6699tr04Ac/uPlzNH9fcNYL0qn/eWo6Yp8j0pGPP7JoC5OfTMDnyGRuQ3qXjGgADynvXa9VA7hrUeNNRUADWAN4KkGrZCcOlCopZMZlyEhG3EqGlpFKCplxGTKSEbeAoePM34PPOHh+pos1gY8++uj0xje+cf41b3vb29Jhhx22WvN37otnHHyGM4ELqHvXU/Q50rVofePJiAZwfame3oo0gKdnbU8dCmgAawB3GKfqh3KgVH2JWy9QRloTVj+AjFRf4tYLlJHWhEUPsPAM3rmFrNkE3nXXXdMll1wyv84999wzXfD5C+bP/F3qfUXDmPxYAj5HxuIa5ItlRAN4kMHvaNEawB1BGma6AhrAGsDTTVzZe3OgVHb9pjF7GZmGctn7kJGy6zeN2cvINJRnex/LNYGv//b1aZtttll9AWuldMCJB6Szv372/N8vdfuI2V652XUl4HOkK8l6x5ERDeB6051/ZRrA+Y3tIYOABrAGcIZYVTukA6VqS9vZwmSkM8pqB5KRakvb2cJkpDPKoge648470gs/9MJ0xpfPmF/HwQ87OD3qukel1/2/1/18bWullA5MKT38538Vrzvx6Sc29w5ee+210zrr3PdBckXjmPyKAj5HViQa/AtkRAN48P8IWgBoALfA89b+BDSANYD7S195e3agVF7Npj1jGZm2eHn7k5HyajbtGcvItMVnc3/Pfvaz0xlnnnGf5m66KqV0VkrppymlRZq/q309pRTjnHbaabO5SLPKJuBzJBttNQPLiAZwNWHuYSEawD2g22V7AQ1gDeD2KRrOCA6UhlPrSVcqI5PKDed9MjKcWk+6UhmZVK6e9915551p0003TT/+8Y+XbvJ+KKW0avUzf9ds/obIhhtumL7//e+n9dZbrx4gK1lRwOfIikSDf4GMaAAP/h9BCwAN4BZ43tqfgAawBnB/6Stvzw6UyqvZtGcsI9MWL29/MlJezaY9YxmZtvjs7W/h8Xkzu8XO9F1z2gvPDF7jazFePCjONhwBnyPDqfWkK5URDeBJs+N9KWkAS0GRAhrAGsBFBrenSTtQ6gm+oN3KSEHF6mmqMtITfEG7lZGCipVxqqeffno69NBD08033/yzvSzXBF6i+bvZZpul4447rrkNhG1YAj5HhlXvSVYrIxrAk+TGe34moAEsCUUKaABrABcZ3J4m7UCpJ/iCdisjBRWrp6nKSE/wBe1WRgoqVuapfve7322awGeeeebP9rR2SumNi+z0TSmle1b/+4MOOigdf/zxaYsttsg8S8PPooDPkVmsymzNSUY0gGcrkWXNRgO4rHqZ7f8JaABrAPvHMLqAA6XRrYb6ShkZauVHX7eMjG411FfKyFArv/i677333tScDfyaQ9Mt+9yS0sMXed2CM4Cd9Ss/IeBzRA5WEpARDeCVMuLrSwtoAEtHkQIawBrARQa3p0k7UOoJvqDdykhBxeppqjLSE3xBu5WRgoo1pane/dO7067H7JquuOeKpff4f03gY998bDr88MOnNDO7mVUBnyOzWpnZmZeMaADPThrLm4kGcHk1M+PkQ29hCHwT9E9iJQEZWUnI12VEBlYSkJGVhHxdRmRgoUA0f19w1gvSqf956s//Opq9H0oprUqrnxF8VUp7XL9HuvALF0IcuIDPkYEHYITly4heyAgx8ZIlBDSARaNIAWcA/7xsvgkWGeGpTlpGpspd5M5kpMiyTXXSMjJV7iJ3JiNFli3LpJds/p6VUvrpEg+Guyqla956Tdpu2+2yzMmgZQj4HCmjTn3OUkY0gPvMX+n71gAuvYIDnb8GsAbwQKM/0bIdKE3ENqg3ycigyj3RYmVkIrZBvUlGBlXuJRe7YvN37p1rpZQOXP1M4J3X2Tld/LqL0zprrQNzoAI+RwZa+DGWLSMawGPExUvXENAAFokiBTSANYCLDG5Pk3ag1BN8QbuVkYKK1dNUZaQn+IJ2KyMFFSvTVEdp/m611VbpaU97WvrkJz+Zrr3u2vs0gZ+703PTyQeerAmcqUazPqzPkVmvUP/zkxEN4P5TWO4MNIDLrd2gZ64BrAE86H8AYy7egdKYYAN8uYwMsOhjLllGxgQb4MtlZIBFX7DkxZq/uz9g93TZGy9Ld/3krrT++uunP/7jP0677LJLWnfdddNuu+2WTjzxxHT0MUenO55yx2r3BNYEHm6WfI4Mt/ajrlxGNIBHzYrX3VdAA1gqihTQANYALjK4PU3agVJP8AXtVkYKKlZPU5WRnuAL2q2MFFSsDFP9wH9/IB18xsHzI881cS/90qXp+OOPT0cddVTacMMN0wUXXNC8Zu+9906bbLJJ+sY3vpHeeMQb042PuTGdd8N58+8/4+Az0rN2eFaGmRpylgV8jsxydWZjbjKiATwbSSxzFhrAZdZt8LPWANYAHvw/gjEAHCiNgTXQl8rIQAs/xrJlZAysgb5URgZa+AXLPvLTR6ajPnNUWuoM3uUysvAM4iP2OSId+fgjgQ5QwOfIAIs+5pJlRAN4zMh4+QIBDWBxKFJAA1gDuMjg9jRpB0o9wRe0WxkpqFg9TVVGeoIvaLcyUlCxMk41zgRetf2qRe/hu1JGogn8oas/5MzfjPWZ9aFXysisz9/88gvIiAZw/pTVuwcN4HprW/XKNIA1gKsOeMeLc6DUMWiFw8lIhUXteEky0jFohcPJSIVF7XhJMtIxaIXDyUiFRe14STKiAdxxpAY1nAbwoMpdz2I1gDWA60lz/pU4UMpvXPoeZKT0Cuafv4zkNy59DzJSegXzz19G8huXvgcZKb2C+ecvIxrA+VNW7x40gOutbdUr0wDWAK464B0vzoFSx6AVDicjFRa14yXJSMegFQ4nIxUWteMlyUjHoBUOJyMVFrXjJcmIBnDHkRrUcBrAgyp3PYvVANYArifN+VfiQCm/cel7kJHSK5h//jKS37j0PchI6RXMP38ZyW9c+h5kpPQK5p+/jGgA509ZvXvQAK63tlWvTANYA7jqgHe8OAdKHYNWOJyMVFjUjpckIx2DVjicjFRY1I6XJCMdg1Y4nIxUWNSOlyQjGsAdR2pQw2kAD6rc9SxWA1gDuJ4051+JA6X8xqXvQUZKr2D++ctIfuPS9yAjpVcw//xlJL9x6XuQkdIrmH/+MqIBnD9l9e5BA7je2la9Mg1gDeCqA97x4hwodQxa4XAyUmFRO16SjHQMWuFwMlJhUTtekox0DFrhcDJSYVE7XpKMaAB3HKlBDacBPKhy17NYDWAN4HrSnH8lDpTyG5e+BxkpvYL55y8j+Y1L34OMlF7B/POXkfzGpe9BRkqvYP75y4gGcP6U1bsHDeB6a1v1yjSANYCrDnjHi3Og1DFohcPJSIVF7XhJMtIxaIXDyUiFRe14STLSMWiFw8lIhUXteEkyogHccaQGNZwG8KDKXc9iNYA1gOtJc/6VOFDKb1z6HmSk9Armn7+M5DerlpPsAAAgAElEQVQufQ8yUnoF889fRvIbl74HGSm9gvnnLyMawPlTVu8eNIDrrW3VK9MA1gCuOuAdL86BUsegFQ4nIxUWteMlyUjHoBUOJyMVFrXjJclIx6AVDicjFRa14yXJiAZwx5Ea1HAawIMqdz2L1QDWAK4nzflX4kApv3Hpe5CR0iuYf/4ykt+49D3ISOkVzD9/GclvXPoeZKT0Cuafv4xoAOdPWb170ACut7ZVr0wDWAO46oB3vDgHSh2DVjicjFRY1I6XJCMdg1Y4nIxUWNSOlyQjHYNWOJyMVFjUjpckIxrAHUdqUMNpAA+q3PUsVgNYA7ieNOdfiQOl/Mal70FGSq9g/vnLSH7j0vcgI6VXMP/8ZSS/cel7kJHSK5h//jKiAZw/ZfXuQQO43tpWvTINYA3gqgPe8eIcKHUMWuFwMlJhUTtekox0DFrhcDJSYVE7XpKMdAxa4XAyUmFRO16SjGgAdxypQQ2nATyoctezWA1gDeB60px/JQ6U8huXvgcZKb2C+ecvI/mNS9+DjJRewfzzl5H8xqXvQUZKr2D++cuIBnD+lNW7Bw3gemtb9co0gDWAqw54x4tzoNQxaIXDyUiFRe14STLSMWiFw8lIhUXteEky0jFohcPJSIVF7XhJMqIB3HGkBjWcBvCgyl3PYjWANYDrSXP+lThQym9c+h5kpPQK5p+/jOQ3Ln0PMlJ6BfPPX0byG5e+BxkpvYL55y8jGsD5U1bvHjSA661t1SvTANYArjrgHS/OgVLHoBUOJyMVFrXjJclIx6AVDicjFRa14yXJSMegFQ4nIxUWteMlyYgGcMeRGtRwGsCDKnc9i9UA1gCuJ835V+JAKb9x6XuQkdIrmH/+MpLfuPQ9yEjpFcw/fxnJb1z6HmSk9Armn7+MaADnT1m9e9AArre2Va9MA1gDuOqAd7w4B0odg1Y4nIxUWNSOlyQjHYNWOJyMVFjUjpckIx2DVjicjFRY1I6XJCMawB1HalDDaQAPqtz1LFYDWAO4njTnX4kDpfzGpe9BRkqvYP75y0h+49L3ICOlVzD//GUkv3Hpe5CR0iuYf/4yogGcP2X17kEDuN7aVr0yDWAN4KoD3vHiHCh1DFrhcDJSYVE7XpKMdAxa4XAyUmFRO16SjHQMWuFwMlJhUTtekoxoAHccqUENpwE8qHLXs1gNYA3getKcfyUOlPIbl74HGSm9gvnnLyP5jUvfg4yUXsH885eR/Mal70FGSq9g/vnLiAZw/pTVuwcN4HprW/XKzj///LTffvs1a3z3u9+dHv7wh1e93uUWd+utt6bLL7+8ecnOO++cNtpoo8FaWPjiAjIiGSsJyMhKQr4uIzKwkoCMrCTk6zIiAysJyMhKQr4uIyldddVV6RWveEUThvPOOy/tu+++gkFgJAEN4JGYvGjWBE444YT5D71Zm5v5ECBAgAABAgQIECBAgAABAgRyCsTJcC9/+ctz7sLYFQloAFdUzCEtRQN4SNW2VgIECBAgQIAAAQIECBAgQGChgAawPIwjoAE8jpbXzozA9ddfn84555xmPtttt13aYIMNZmZu057IwktAhn47jGnbl7I/GSmlUv3NU0b6sy9lzzJSSqX6m6eM9Gdfyp5lpJRK9TdPGenPvpQ9y0hKt912W7r22mubku2///5p6623LqV85tmzgAZwzwWwewJtBTwQr61g/e+Xkfpr3HaFMtJWsP73y0j9NW67QhlpK1j/+2Wk/hq3XaGMtBWs//0yUn+NrTCfgAZwPlsjE5iKgG+CU2EueicyUnT5pjJ5GZkKc9E7kZGiyzeVycvIVJiL3omMFF2+qUxeRqbCXPROZKTo8pl8zwIawD0XwO4JtBXwTbCtYP3vl5H6a9x2hTLSVrD+98tI/TVuu0IZaStY//tlpP4at12hjLQVrP/9MlJ/ja0wn4AGcD5bIxOYioBvglNhLnonMlJ0+aYyeRmZCnPRO5GRoss3lcnLyFSYi96JjBRdvqlMXkamwlz0TmSk6PKZfM8CGsA9F8DuCbQV8E2wrWD975eR+mvcdoUy0law/vfLSP01brtCGWkrWP/7ZaT+GrddoYy0Faz//TJSf42tMJ+ABnA+WyMTmIqAb4JTYS56JzJSdPmmMnkZmQpz0TuRkaLLN5XJy8hUmIveiYwUXb6pTF5GpsJc9E5kpOjymXzPAhrAPRfA7gm0FfBNsK1g/e+Xkfpr3HaFMtJWsP73y0j9NW67QhlpK1j/+2Wk/hq3XaGMtBWs//0yUn+NrTCfgAZwPlsjE5iKgG+CU2EueicyUnT5pjJ5GZkKc9E7kZGiyzeVycvIVJiL3omMFF2+qUxeRqbCXPROZKTo8pl8zwIawD0XwO4JtBXwTbCtYP3vl5H6a9x2hTLSVrD+98tI/TVuu0IZaStY//tlpP4at12hjLQVrP/9MlJ/ja0wn4AGcD5bIxMgQIAAAQIECBAgQIAAAQIECBAgQKBXAQ3gXvntnAABAgQIECBAgAABAgQIECBAgAABAvkENIDz2RqZAAECBAgQIECAAAECBAgQIECAAAECvQpoAPfKb+cECBAgQIAAAQIECBAgQIAAAQIECBDIJ6ABnM/WyAQIECBAgAABAgQIECBAgAABAgQIEOhVQAO4V347J0CAAAECBAgQIECAAAECBAgQIECAQD4BDeB8tkYmQIAAAQIECBAgQIAAAQIECBAgQIBArwIawL3y2zkBAgQIECBAgAABAgQIECBAgAABAgTyCWgA57M1MgECBAgQIECAAAECBAgQIECAAAECBHoV0ADuld/OCRAgQIAAAQIECBAgQIAAAQIECBAgkE9AAzifrZEJECBAgAABAgQIECBAgAABAgQIECDQq4AGcK/8dk5gcoFbb701XXrppemSSy5JX/rSl5r/fu1rX0v33ntvM+inPvWp9PjHP36kHfze7/1eOumkk0Z67bhjjzyoF3Yu0GVGFk7u05/+dHrf+96XPve5z6Ubbrghrb/++mnbbbdNBxxwQHrFK16Rttpqq87XYsD+BK677rr0kIc8ZOQJ7LPPPikyYqtD4MMf/nA6+eST08UXX5xuuummtNFGG6Vf+7VfSwceeGDz733jjTeuY6FWMbZAHGN85jOfGfl9X//615vvFbbyBe6555705S9/ef74M45Dr7jiinT77bc3i3vRi16U/umf/mmshcbxxLve9a70kY98JMX3nTvuuCNtueWWae+9904veclLUnxvsZUj0FVG4njiCU94wsgLnyR7Iw/uhZ0K/O///m/6xCc+0fzMGj/T/s///E/64Q9/2PxcsfXWW6fdd989HXLIIenJT35yut/97jfSvn2OjMTkRQMW0AAecPEtvVyBH/3oR+mXfumX5pu9i61EA7jc+nYx864zEnO6++670ytf+cr03ve+d8kpRi5PPPHE9IxnPKOLZRhjBgQ0gGegCD1MIX4wix+8zjnnnCX3/qAHPSiddtpp6TGPeUwPM7TLvgU0gPuuQH/7P+igg9IHP/jBJScwbhPurLPOapq80fxZanvZy16W3vnOd6a11167v4Xb88gCXWVEA3hk8qJe+Dd/8zfpDW94Q/OLnpW2+CXQP//zP6cHP/jBy77U58hKkr5OICUNYCkgUKBAHCBHo21ui9+KxhlZN998c/rBD37Q/PWkDeB3v/vdaYsttlhWZa+99kqbbbZZgXLDmXLXGQm5+OEsmruxxVl/L33pS9OjHvWodNttt6Wzzz47ffSjH22+tt5666WPf/zjY52xMZzKlLfShQ3gOAvnD//wD5ddRHw2xGeErVyB+GXPU5/61HTeeec1i/jlX/7lFM2XHXbYId1yyy3plFNOSZ///Oebrz3wgQ9srgbYcccdy12wmU8ksLABHD94r7Q96UlPSg94wANWepmvFyCwatWqFFcHzG2bbLJJ2nTTTZsz+GIbpwF8/vnnN583d911V/Pepz3tac0VRRtssEFzVuB73vOeFFc0xRafQyeccEIBQqbYVUYWNoCf85znpOc+97nL4kaTMI5NbbMtECeUxM+cscUvk5/4xCemXXfdNW2++ebNlQQXXXRR0/T98Y9/3LwmrkS78MILl/wZ1efIbNfb7GZHQAN4dmphJgRGFohvhnEQ/OhHP7r5XxzoRENu4Q9jkzaAXaI5chlm+oVdZ+Tcc89tfkCLLW7xEJf9PvShD13N4O///u/nm4NxoHb11Vc3zWBb2QILG8Dj/FBf9qqHPfs4y+7QQw9tEKLp+8lPfrJpAi/c/vRP/zS97W1va/7qsY99bNMEtg1LYOExx9ztp4YlMNzVHnPMMSmuEpg7Do3v+XHLhxe/+MUNyqjfK+Lsv9/8zd9M3/zmN5v3xXHEa17zmtVgv/rVrza3f7jxxhubv49Lxvfbb7/h4hey8q4ysrABfMQRR6QjjzyyEAHTXE7gVa96Vbr22mtTHEtE83ettda6z8u/8Y1vNLd/+MpXvtJ8LT5f4hZ0a24+R2SNwOgCGsCjW3klgZkX0ACe+RL1PsFJM7Lbbrs19/qL7cwzz0zPfOYzF11LnLUT9++LLe7lF/cItZUtoAFcdv3GnX3ctzHOxplrtsT95Rc7mypeF2frXH755c0u4qz/+EHNNhwBDeDh1HqUlU7SAD7uuOPmG75Pf/rTm6uJFtvidhNxS4HY9txzz/TFL35xlCl5zYwJTJIRDeAZK2JH04mrieLKgZW2uLf4zjvv3LwsriD53ve+d58rSXyOrKTo6wR+LqABLA0EKhKYtLm38CFwzgCuKBCLLGWSjMRv6OMWI7HFWT7XXHPNkg9jiDPPf/u3f7t5rYeB1ZElDeA66jjqKuJs3zgbZ5R/w3FLmLg1TGyjnvE36jy8bvYFNIBnv0bTnOEkzb3HPe5x6YILLmimudyVaz/96U+b45D4fhSbY9VpVra7fU2SEQ3g7vxLHelhD3tYc1VhbNEQfsQjHrHaUnyOlFpZ8+5DQAO4D3X7JJBJYJLmXkxFAzhTQWZw2Ekycvzxx6dXv/rVzWrikq3481Jb3Ds07k8dt6CIy7niYXS/+Iu/OIMSpjSqgAbwqFJ1vO7www9Pb3nLW5rFHHvssSn+vNR20003pS233LL5ctw7Pv5sG46ABvBwaj3KSsdt7sUtJOIe4tHc3XDDDZtnWCz3gLc4/ogri2KL45D4s60sgXEzEqvTAC6rxjlmu/vuu6eLL764GTruA7zHHnvM78bnSA5xY9YsoAFcc3WtbXACkzT3AmlhAzjuqxb3Woof5O9///s3932Mb7TPetazmodyxAPnbOUKTJKRhT90xb235u7xt5TCwt/E/8d//EeK20fYyhVY2ACOM7Dikr140E88/C/+/2/8xm80Z33Hfcl/5Vd+pdyFmnkj8JSnPKW5nUNscTZwPPhvuS0euPOtb32recl3v/vd5gEutmEILPx+Eg/uuuyyy5rLc+PhXVtvvXV6zGMekw455JAVMzQMrfpXOW5zLx7yFLdziC2OG+LZAsttcfwRD5+NLe5RHpd928oSGDcjsbqFDeCddtqp+SVBXJl25513Nscg8Xfxs0tkY5RbCpQlZrY/+clPmp9F44SS2G644Yb5XzzHn32OyAiB8QQ0gMfz8moCMy0wSXMvFrSwAbzcAuMeTKeeemrzwA5bmQKTZCSae3FpZmyjPFwwLgV///vf37z+5JNPTs9//vPLxDLrRmBhA3g5knXXXbd5OMvrXvc6vygqODvbbbddc3l1bKNcZh23evnsZz/bvD4u5d5rr70KXr2pjyOw8PvJcu+L7yHxNPd4gKitXoFxm3txnBDHC7GNcguZhbeYitvUnH/++fViVrqycTMSDAsbwMuxxNVm73jHO+ZvS1Qp4eCWtTAz8TyCeC7Bws3nyOAiYcEtBTSAWwJ6O4FZEpikuRfzjwbwBz7wgea+j3GZzbbbbpvWW2+95izg+IH+rLPOSnfddVez1Li8//Of/3yK+zHZyhOYJCNxwBVndsV21VVXNWdbLLcddthh6e1vf3vzksWe6F2e2rBnPNcAjn/z++67b9pxxx2bs2xuv/325p5s8XCeuSc0h1Q8+G/uMt1hy5W5+qhtXIodW1xaudItXOKBkPE9IrZ4AOT+++9f5sLNemyB+H5y5ZVXNmffxQMB4wqAODvvO9/5TnP2+Lnnnttc3h/bNtts01y6O3fLkLF35g0zLzBuc+/v/u7v0mtf+9pmXX/yJ3+S3vrWty67xsjaIx/5yOY1j370o+cfTDvzMCY4LzBuRuKN0QCOXyLFsWh85sSxyMYbb9zcaiyOSU8//fT07W9/e34fb37zm9Of//mfU69AIK4oiWPO+G9scbx54IEHrrYynyMVFNoSpiqgATxVbjsjkFdgkuZezOhLX/pS2n777Zf8QT8utYpbQMw1AeObcRyIxz1ebWUJTJKRuMQ/LvmPLf7767/+68su+g1veEM65phjmtfEf+OMUFu5AtEEjLrHD1+Lbffee2/627/92+YH+Pj/sZ122mnp2c9+drmLHvDM45d/c7/wi/+us846y2r87u/+bvrXf/3X5jXx3+c973kD1hvW0r/4xS82jbjIzGLbpZdemg466KD5B3fF7UU+9rGPDQtpQKsdt7kXxwdxvBBb/Pfoo49eViu+D8XxSGzx34W/eBwQc9FLHTcjsdi45D+OQ+ZqvyZAfJ96/etfP/8LhLhV3Re+8IX524sUDTbgycctPuKkg7mHRK5atWr+l80LWXyODDgklj6RgAbwRGzeRGB5gVEvixzFMZ6yHmfojrJN0twbZdx4Tfz2Nc78jHs8xha/cT/44INHfbvXrSFQUkYWNoC/9rWvNU/iXm7TAJ5u3PvK0pqr/Ku/+qt0xBFHNH+9ww47pP/6r/+aLoS9dSKgAdwJo0H+T+CrX/1q88T2uI9jbO4LX280xm3uLWzc/MVf/EV605vetCyOBnD52Rk3I+Os+CUveUmKn5lie+pTn5o++tGPjvN2r50hgbhy5IUvfGH6l3/5l2ZW8XNHPAQurkJdc/M5MkOFM5UiBDSAiyiTSZYm0FdDJmcDOGqw8JtsfGM+6aSTSivNzMy3pIy4BcTMxGbRifSVpTUnc8cddzQP6rj11lubL11zzTUp7idrK0vALSDKqlcJs335y1+e/vEf/7GZ6l/+5V+mo446qoRpm+OYAuM291y6PSZwBS8fNyPjLDluPfOrv/qrzZVI8RDruJXR+uuvP84QXjsDAlG/uJXY3PeMeNBsPCAybk+42OZzZAaKZgpFCWgAF1Uuky1F4IQTTmieUNvFFmfZxiWWo2y5G8BxuWc81Tu23XbbrTmTxzaZQEkZ8RC4yWo8rXf1laXF1vfkJz85feITn2i+FGffxFk4trIEPASurHqVMNtTTjklHXLIIc1U45gmriCy1ScwbnPPw5vqy8BKKxo3IyuNt+bX4yHVcdVBbHEVUlyNZCtHIJq/hx566PxzJB70oAc194Be7spDnyPl1NdMZ0NAA3g26mAWBDoRyN0AXnj53UMf+tD5g6xOJm+QqQhMkpFXvepV8wdj73vf+9KLX/ziZef6uMc9bv6eXS73nUpZZ2YnC+8HG5fuzTV9ZmaCJrKiQNyn9eMf/3jzuniQ1xOe8IRl3xNn53zrW99qXhO3CNp8881X3IcXDEvgvPPOS0960pOaRccD4+Z+STQshfpXO25z76KLLpq/T2scN8RZfsttcfzx0pe+tHlJNImOO+64+lErW+G4GRl3+Y997GOb+//GFg+snjtpZdxxvH76AtH8ffWrX53e+c53NjuPh4pG83el5474HJl+reyxbAEN4LLrZ/YEVhOYpLk3DmEcVMXBVWzOAB5HbnZeO0lGjj/++OagLLZoBsefl9ruvvvu5h5d8XTmeEhg3A5ggw02mB0AM8kqEE2eaPbE5gzgrNTZBj/88MPTW97ylmb8Y489NsWfl9puuummtOWWWzZf3mKLLVL82UZgTYF4OGD8cig2ZwDXm49xm3vxYK8HPvCBKe73ueGGGzaX7K+99tpLAi38ZXQch8SfbWUJjJuRcVe38JkVzgAeV6+/16/Z/N16662b5m+cbLTS5nNkJSFfJ7C6gAawRBCoSGCS5t44y//rv/7rFA/qiO35z39+Ovnkk8d5u9fOgMAkGYnbmcxdfvWQhzykubdrPGV5se1Tn/pUiltGxBb7ij/bhiFw++23N/cAjoPx2EZ5YOAwZMpaZZz1+8QnPnGkf8PxwJ148E5s8bDSuQfwlLVis80t8LKXvSy95z3vaXYzysO+cs/H+HkEJmnuLbxiKBo+++yzz6KTiyZxnAn49a9/vfl6/Hepe4LmWZ1RuxCYJCOj7vfb3/52iitS5u4BfMstt6QHPOABo77d63oSWLP5u9VWWzXN32jmj7r5HBlVyusIpKQBLAUEKhKYpLk36vLj0t6ddtopfe9732vecuqpp6bnPOc5o77d62ZEYNKM7L777s0TeGM788wz0zOf+cxFV3TAAQekj3zkI83X3vWudzUPcrANQ+DII4+cf7jT9ttvn7785S8PY+GVrfKee+5Jcd+9G2+8sVnZJZdckuJBkGtu8bpdd901XX755c2X4rYRcQ9oG4GFAl/5ylfSIx/5yPSTn/yk+esLL7ww7bHHHpAqFJikubfwCqOnP/3p6eyzz15U5oMf/GA66KCDmq/tueeeKZ5JYStPYJKMjLrKuD1ZjB/b7/zO76Rzzz131Ld6XY8CcYXh3JWFcUVRNH/jXs7jbD5HxtHy2qELaAAPPQHWX5XAJM29k046qTlrL35wX+qszjjTIg68L7vsssbrYQ97WLrqqquWvVSvKtiKFjNJRmL5cSA990Cv+O38Zz/72fvcl+sf/uEf0h/8wR80WnGm8NVXX53WW2+9ivSGt5S4lceb3/zm9NrXvra5xH+xLc7eeMc73pEOO+yw5syb2OKS7+c973nDA6tkxXEPvrjHZmw77rhjcy/gNev/Z3/2Z+mtb31r85q4NdDnPve5SlZvGaMIxJPX4xcAy91jM44Z4peF1113XTNk3CLm3/7t30YZ3msKFJikuXfHHXc0zZ5vfvObzYrjOGLullNzBPH8iTjDb+6XUnGboX333bdAIVMeNyNxJVE0/1/5ylemjTbaaFHAu+66K73+9a+f/34UL3L/3zKyFj8zxL/52KL5G1cNxgkE424+R8YV8/ohC2gAD7n61l60QBwQXXrppautIW7JMHcQHbdo2GabbVb7ejw8IxpzC7c/+qM/apo3cb+l+OHsEY94RNMQXnfddZsH+lxwwQXprLPOSnfeeWfztri/a/xdNAVssy3QVUbmVhmXes9d4r3xxhun3//932/ODLztttuas3bOOeec5qXR9I2zAVd6eNRs65ldCPzwhz9s/s3HfRn33nvv5syruAw36h+3fIiz++KM8Gj2z21xufcJJ5wAsGCBuJd3/MJn7n7O8YNZ1DWeqB6X1Z5yyinzDd/IQvyw7XtCwQWfYOqrVq1KH/7wh5vbA0UzLq4Q2nTTTZvPiuuvvz79+7//e/rYxz7W3N81tjgeiecIxLGGrXyBODHgve9972oLufLKK+evAIpjyTijd+EWxwuLXT10/vnnN5830ciLbf/9909xNVE8PyCOc+P2IT/60Y+ar/n+Uk52ushIXGGyyy67pPvf//7N7cXi+SPxc0zcMzp+QR0no5x++unzDyINnbhdXTSEbbMtELcDilrFFicgHXPMMSM1f+NzJG71sebmc2S26212syOgATw7tTATAmMJxP0W4+zdcbb4zWqcAbpwm2sAjzJOHHi9//3vH+kb9CjjeU1ega4yMjfLaArFLR3iSdxLbdEsjCbxM57xjLyLM/pUBOYawKPsLH5Ai9tAxEPD4gGAtrIF4l7OhxxyyPwvdhZbTdwq4rTTTvOk9bJLPdHs5xrAo7w5rjCK7xuav6NolfGauEx73F/yvuhFL5q/RH/NVcaJBvFL5vies9QWzd+4OmG5B8WVoTeMWXaRkbkG8ChicYbw29/+9vn70o/yHq/pT2DhFYnjzCJ+xoifbxbbfI6MI+m1QxXQAB5q5a27eIGumnvf+c53mktu4r58cblmXGJ38803N2d1xsFU/IAf9+uLJ3fHWT5L3SaieNAKF9BVRtakiYP6OPMnzvq74YYb0i/8wi80D2OJM3biMr24RYStDoE4ey9u9xGfDxdddFHzYLfvf//7zVmg66yzTtpkk02aM//izJzI2+abb17Hwq1iXiDO8oxf/MU9wOOqkDjzKs76jDP54hdCcQawbXgC8TDQ+F4Qnw1XXHFF83yA+GyIe/1GJuJ7wm/91m81v0SIKwdsdQl00dxbUySOJ6LBG88RiNuGxGXdcTyx1157pbiCbakHxNUlW89qushIfJ7Mfc7EMUjkIn5G+cEPftCcFbzZZps19xjfb7/90gte8IIlbxNRj2o9K8nRAA4dnyP1ZMRK8ghoAOdxNSoBAgQIECBAgAABAgQIECBAgAABAgR6F9AA7r0EJkCAAAECBAgQIECAAAECBAgQIECAAIE8AhrAeVyNSoAAAQIECBAgQIAAAQIECBAgQIAAgd4FNIB7L4EJECBAgAABAgQIECBAgAABAgQIECBAII+ABnAeV6MSIECAAAECBAgQIECAAAECBAgQIECgdwEN4N5LYAIECBAgQIAAAQIECBAgQIAAAQIECBDII6ABnMfVqAQIECBAgAABAgQIECBAgAABAgQIEOhdQAO49xKYAAECBAgQIECAAAECBAgQIECAAAECBPIIaADncTUqAQIECBAgQIAAAQIECBAgQIAAAQIEehfQAO69BCZAgAABAgQIECBAgAABAgQIECBAgACBPAIawHlcjUqAAAECBAgQIECAAAECBAgQIECAAIHeBTSAey+BCRAgQIAAAQIECBAgQIAAAQIECBAgQCCPgAZwHlejEuTIFT4AAAbiSURBVCBAgAABAgQIECBAgAABAgQIECBAoHcBDeDeS2ACBAgQIECAAAECBAgQIECAAAECBAgQyCOgAZzH1agECBAgQIAAAQIECBAgQIAAAQIECBDoXUADuPcSmAABAgQIECBAgAABAgQIECBAgAABAgTyCGgA53E1KgECBAgQIECAAAECBAgQIECAAAECBHoX0ADuvQQmQIAAAQIECBAgQIAAAQIECBAgQIAAgTwCGsB5XI1KgAABAgQIECBAgAABAgQIECBAgACB3gU0gHsvgQkQIECAAAECBAgQIECAAAECBAgQIEAgj4AGcB5XoxIgQIAAAQIECBAgQIAAAQIECBAgQKB3AQ3g3ktgAgQIECBAgAABAgQIECBAgAABAgQIEMgjoAGcx9WoBAgQIECAAAECBAgQIECAAAECBAgQ6F1AA7j3EpgAAQIECBAgQIAAAQIECBAgQIAAAQIE8ghoAOdxNSoBAgQIECBAgAABAgQIECBAgAABAgR6F9AA7r0EJkCAAAECBAgQIECAAAECBAgQIECAAIE8AhrAeVyNSoAAAQIECBAgQIAAAQIECBAgQIAAgd4FNIB7L4EJECBAgAABAgQIECBAgAABAgQIECBAII+ABnAeV6MSIECAAAECBAgQIECAAAECBAgQIECgdwEN4N5LYAIECBAgQIAAAQIECBAgQIAAAQIECBDII6ABnMfVqAQIECBAgAABAgQIECBAgAABAgQIEOhdQAO49xKYAAECBAgQIECAAAECBAgQIECAAAECBPIIaADncTUqAQIECBAgQIAAAQIECBAgQIAAAQIEehfQAO69BCZAgAABAgQIECBAgAABAgQIECBAgACBPAIawHlcjUqAAAECBAgQIECAAAECBAgQIECAAIHeBTSAey+BCRAgQIAAAQIECBAgQIAAAQIECBAgQCCPgAZwHlejEiBAgAABAgQIECBAgAABAgQIECBAoHcBDeDeS2ACBAgQIECAAAECBAgQIECAAAECBAgQyCOgAZzH1agECBAgQIAAAQIECBAgQIAAAQIECBDoXUADuPcSmAABAgQIECBAgAABAgQIECBAgAABAgTyCGgA53E1KgECBAgQIECAAAECBAgQIECAAAECBHoX0ADuvQQmQIAAAQIECBAgQIAAAQIECBAgQIAAgTwCGsB5XI1KgAABAgQIECBAgAABAgQIECBAgACB3gU0gHsvgQkQIECAAAECBAgQIECAAAECBAgQIEAgj4AGcB5XoxIgQIAAAQIECBAgQIAAAQIECBAgQKB3AQ3g3ktgAgQIECBAgAABAgQIECBAgAABAgQIEMgjoAGcx9WoBAgQIECAAAECBAgQIECAAAECBAgQ6F1AA7j3EpgAAQIECBAgQIAAAQIECBAgQIAAAQIE8ghoAOdxNSoBAgQIECBAgAABAgQIECBAgAABAgR6F9AA7r0EJkCAAAECBAgQIECAAAECBAgQIECAAIE8AhrAeVyNSoAAAQIECBAgQIAAAQIECBAgQIAAgd4FNIB7L4EJECBAgAABAgQIECBAgAABAgQIECBAII+ABnAeV6MSIECAAAECBAgQIECAAAECBAgQIECgdwEN4N5LYAIECBAgQIAAAQIECBAgQIAAAQIECBDII6ABnMfVqAQIECBAgAABAgQIECBAgAABAgQIEOhdQAO49xKYAAECBAgQIECAAAECBAgQIECAAAECBPIIaADncTUqAQIECBAgQIAAAQIECBAgQIAAAQIEehfQAO69BCZAgAABAgQIECBAgAABAgQIECBAgACBPAIawHlcjUqAAAECBAgQIECAAAECBAgQIECAAIHeBTSAey+BCRAgQIAAAQIECBAgQIAAAQIECBAgQCCPgAZwHlejEiBAgAABAgQIECBAgAABAgQIECBAoHcBDeDeS2ACBAgQIECAAAECBAgQIECAAAECBAgQyCOgAZzH1agECBAgQIAAAQIECBAgQIAAAQIECBDoXUADuPcSmAABAgQIECBAgAABAgQIECBAgAABAgTyCGgA53E1KgECBAgQIECAAAECBAgQIECAAAECBHoX0ADuvQQmQIAAAQIECBAgQIAAAQIECBAgQIAAgTwCGsB5XI1KgAABAgQIECBAgAABAgQIECBAgACB3gU0gHsvgQkQIECAAAECBAgQIECAAAECBAgQIEAgj4AGcB5XoxIgQIAAAQIECBAgQIAAAQIECBAgQKB3AQ3g3ktgAgQIECBAgAABAgQIECBAgAABAgQIEMgjoAGcx9WoBAgQIECAAAECBAgQIECAAAECBAgQ6F1AA7j3EpgAAQIECBAgQIAAAQIECBAgQIAAAQIE8gj8f3EUwM2McYjeAAAAAElFTkSuQmCC\" width=\"639.9999861283738\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "New LM\n"
-     ]
-    }
-   ],
-   "source": [
-    "%matplotlib notebook\n",
-    "%matplotlib notebook\n",
-    "main()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/SLAM/FastSLAM1/FastSLAM1.ipynb b/SLAM/FastSLAM1/FastSLAM1.ipynb
deleted file mode 100644
index 60faed6e88..0000000000
--- a/SLAM/FastSLAM1/FastSLAM1.ipynb
+++ /dev/null
@@ -1,676 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## FastSLAM1.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename=\"animation.png\",width=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Simulation\n",
-    "\n",
-    "This is a feature based SLAM example using FastSLAM 1.0.\n",
-    "\n",
-    "The blue line is ground truth, the black line is dead reckoning, the red\n",
-    "line is the estimated trajectory with FastSLAM.\n",
-    "\n",
-    "The red points are particles of FastSLAM.\n",
-    "\n",
-    "Black points are landmarks, blue crosses are estimated landmark\n",
-    "positions by FastSLAM.\n",
-    "\n",
-    "\n",
-    "![gif](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Introduction\n",
-    "\n",
-    "FastSLAM algorithm implementation is based on particle filters and belongs to the family of probabilistic SLAM approaches. It is used with feature-based maps (see gif above) or  with occupancy grid maps.\n",
-    "\n",
-    "As it is shown, the particle filter differs from EKF by representing the robot's estimation through a set of particles. Each single particle has an independent belief, as it holds the pose $(x, y, \\theta)$ and an array of landmark locations $[(x_1, y_1), (x_2, y_2), ... (x_n, y_n)]$ for n landmarks.\n",
-    "\n",
-    "* The blue line is the true trajectory\n",
-    "* The red line is the estimated trajectory\n",
-    "* The red dots represent the distribution of particles\n",
-    "* The black line represent dead reckoning tracjectory\n",
-    "* The blue x is the observed and estimated landmarks\n",
-    "* The black x is the true landmark\n",
-    "\n",
-    "I.e. Each particle maintains a deterministic pose and n-EKFs for each landmark and update it with each measurement."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Algorithm walkthrough\n",
-    "\n",
-    "The particles are initially drawn from a uniform distribution the represent the initial uncertainty.\n",
-    "At each time step we do:\n",
-    "\n",
-    "* Predict the pose for each particle by using $u$ and the motion model (the landmarks are not updated). \n",
-    "* Update the particles with observations $z$, where the weights are adjusted based on how likely the particle to have the correct pose given the sensor measurement\n",
-    "* Resampling such that the particles with the largest weights survive and the unlikely ones with the lowest weights die out.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1- Predict\n",
-    "The following equations and code snippets we can see how the particles distribution evolves in case we provide only the control $(v,w)$, which are the linear and angular velocity respectively. \n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "F=\n",
-    "\\begin{bmatrix}\n",
-    "1 & 0 & 0 \\\\\n",
-    "0 & 1 & 0 \\\\\n",
-    "0 & 0 & 1 \n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "B=\n",
-    "\\begin{bmatrix}\n",
-    "\\Delta t cos(\\theta) & 0\\\\\n",
-    "\\Delta t sin(\\theta) & 0\\\\\n",
-    "0 & \\Delta t\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "X = FX + BU \n",
-    "\\end{equation*}$\n",
-    "\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "\\begin{bmatrix}\n",
-    "x_{t+1} \\\\\n",
-    "y_{t+1} \\\\\n",
-    "\\theta_{t+1}\n",
-    "\\end{bmatrix}=\n",
-    "\\begin{bmatrix}\n",
-    "1 & 0 & 0 \\\\\n",
-    "0 & 1 & 0 \\\\\n",
-    "0 & 0 & 1 \n",
-    "\\end{bmatrix}\\begin{bmatrix}\n",
-    "x_{t} \\\\\n",
-    "y_{t} \\\\\n",
-    "\\theta_{t}\n",
-    "\\end{bmatrix}+\n",
-    "\\begin{bmatrix}\n",
-    "\\Delta t cos(\\theta) & 0\\\\\n",
-    "\\Delta t sin(\\theta) & 0\\\\\n",
-    "0 & \\Delta t\n",
-    "\\end{bmatrix}\n",
-    "\\begin{bmatrix}\n",
-    "v_{t} + \\sigma_v\\\\\n",
-    "w_{t} + \\sigma_w\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following snippets playsback the recorded trajectory of each particle.\n",
-    "\n",
-    "To get the insight of the motion model change the value of $R$ and re-run the cells again. As R is the parameters that indicates how much we trust that the robot executed the motion commands.\n",
-    "\n",
-    "It is interesting to notice also that only motion will increase the uncertainty in the system as the particles start to spread out more. If observations are included the uncertainty will decrease and particles will converge to the correct estimate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# CODE SNIPPET #\n",
-    "import numpy as np\n",
-    "import math\n",
-    "from copy import deepcopy\n",
-    "# Fast SLAM covariance\n",
-    "Q = np.diag([3.0, np.deg2rad(10.0)])**2\n",
-    "R = np.diag([1.0, np.deg2rad(20.0)])**2\n",
-    "\n",
-    "#  Simulation parameter\n",
-    "Qsim = np.diag([0.3, np.deg2rad(2.0)])**2\n",
-    "Rsim = np.diag([0.5, np.deg2rad(10.0)])**2\n",
-    "OFFSET_YAWRATE_NOISE = 0.01\n",
-    "\n",
-    "DT = 0.1  # time tick [s]\n",
-    "SIM_TIME = 50.0  # simulation time [s]\n",
-    "MAX_RANGE = 20.0  # maximum observation range\n",
-    "M_DIST_TH = 2.0  # Threshold of Mahalanobis distance for data association.\n",
-    "STATE_SIZE = 3  # State size [x,y,yaw]\n",
-    "LM_SIZE = 2  # LM srate size [x,y]\n",
-    "N_PARTICLE = 100  # number of particle\n",
-    "NTH = N_PARTICLE / 1.5  # Number of particle for re-sampling\n",
-    "\n",
-    "class Particle:\n",
-    "\n",
-    "    def __init__(self, N_LM):\n",
-    "        self.w = 1.0 / N_PARTICLE\n",
-    "        self.x = 0.0\n",
-    "        self.y = 0.0\n",
-    "        self.yaw = 0.0\n",
-    "        # landmark x-y positions\n",
-    "        self.lm = np.zeros((N_LM, LM_SIZE))\n",
-    "        # landmark position covariance\n",
-    "        self.lmP = np.zeros((N_LM * LM_SIZE, LM_SIZE))\n",
-    "\n",
-    "def motion_model(x, u):\n",
-    "    F = np.array([[1.0, 0, 0],\n",
-    "                  [0, 1.0, 0],\n",
-    "                  [0, 0, 1.0]])\n",
-    "\n",
-    "    B = np.array([[DT * math.cos(x[2, 0]), 0],\n",
-    "                  [DT * math.sin(x[2, 0]), 0],\n",
-    "                  [0.0, DT]])\n",
-    "    x = F @ x + B @ u\n",
-    "        \n",
-    "    x[2, 0] = pi_2_pi(x[2, 0])\n",
-    "    return x\n",
-    "    \n",
-    "def predict_particles(particles, u):\n",
-    "    for i in range(N_PARTICLE):\n",
-    "        px = np.zeros((STATE_SIZE, 1))\n",
-    "        px[0, 0] = particles[i].x\n",
-    "        px[1, 0] = particles[i].y\n",
-    "        px[2, 0] = particles[i].yaw\n",
-    "        ud = u + (np.random.randn(1, 2) @ R).T  # add noise\n",
-    "        px = motion_model(px, ud)\n",
-    "        particles[i].x = px[0, 0]\n",
-    "        particles[i].y = px[1, 0]\n",
-    "        particles[i].yaw = px[2, 0]\n",
-    "\n",
-    "    return particles\n",
-    "\n",
-    "def pi_2_pi(angle):\n",
-    "    return (angle + math.pi) % (2 * math.pi) - math.pi\n",
-    "\n",
-    "# END OF SNIPPET\n",
-    "\n",
-    "N_LM = 0 \n",
-    "particles = [Particle(N_LM) for i in range(N_PARTICLE)]\n",
-    "time= 0.0\n",
-    "v = 1.0  # [m/s]\n",
-    "yawrate = 0.1  # [rad/s]\n",
-    "u = np.array([v, yawrate]).reshape(2, 1)\n",
-    "history = []\n",
-    "while SIM_TIME >= time:\n",
-    "    time += DT\n",
-    "    particles = predict_particles(particles, u)\n",
-    "    history.append(deepcopy(particles))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3f279cf1dcaf4ec886c01942c36e601d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(IntSlider(value=0, description='t', max=499), Output()), _dom_classes=('widget-interact'…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# from IPython.html.widgets import *\n",
-    "from ipywidgets import *\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "\n",
-    "# playback the recorded motion of the particles\n",
-    "def plot_particles(t=0):\n",
-    "    x = []\n",
-    "    y = []\n",
-    "    for i in range(len(history[t])):\n",
-    "        x.append(history[t][i].x)\n",
-    "        y.append(history[t][i].y)\n",
-    "    plt.figtext(0.15,0.82,'t = ' + str(t))\n",
-    "    plt.plot(x, y, '.r')\n",
-    "    plt.axis([-20,20, -5,25])\n",
-    "\n",
-    "interact(plot_particles, t=(0,len(history)-1,1));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2- Update\n",
-    "\n",
-    "For the update step it is useful to observe a single particle and the effect of getting a new measurements on the weight of the particle.\n",
-    "\n",
-    "As mentioned earlier, each particle maintains $N$ $2x2$ EKFs to estimate the landmarks, which includes the EKF process described in the EKF notebook. The difference is the change in the weight of the particle according to how likely the measurement is.\n",
-    "\n",
-    "The weight is updated according to the following equation: \n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "w_i = |2\\pi Q|^{\\frac{-1}{2}} exp\\{\\frac{-1}{2}(z_t - \\hat z_i)^T Q^{-1}(z_t-\\hat z_i)\\}\n",
-    "\\end{equation*}$\n",
-    "\n",
-    "Where, $w_i$ is the computed weight, $Q$ is the measurement covariance, $z_t$ is the actual measurment and $\\hat z_i$ is the predicted measurement of particle $i$.\n",
-    "\n",
-    "To experiment this, a single particle is initialized then passed an initial measurement, which results in a relatively average weight. However, setting the particle coordinate to a wrong value to simulate wrong estimation will result in a very low weight. The lower the weight the less likely that this particle will be drawn during resampling and probably will die out."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial weight 1.0\n",
-      "weight after landmark intialization 1.0\n",
-      "weight after update  0.023098460073039763\n",
-      "weight after wrong prediction 7.951154575772496e-07\n"
-     ]
-    }
-   ],
-   "source": [
-    "# CODE SNIPPET #\n",
-    "def observation(xTrue, xd, u, RFID):\n",
-    "\n",
-    "    # calc true state\n",
-    "    xTrue = motion_model(xTrue, u)\n",
-    "\n",
-    "    # add noise to range observation\n",
-    "    z = np.zeros((3, 0))\n",
-    "    for i in range(len(RFID[:, 0])):\n",
-    "\n",
-    "        dx = RFID[i, 0] - xTrue[0, 0]\n",
-    "        dy = RFID[i, 1] - xTrue[1, 0]\n",
-    "        d = math.sqrt(dx**2 + dy**2)\n",
-    "        angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])\n",
-    "        if d <= MAX_RANGE:\n",
-    "            dn = d + np.random.randn() * Qsim[0, 0]  # add noise\n",
-    "            anglen = angle + np.random.randn() * Qsim[1, 1]  # add noise\n",
-    "            zi = np.array([dn, pi_2_pi(anglen), i]).reshape(3, 1)\n",
-    "            z = np.hstack((z, zi))\n",
-    "\n",
-    "    # add noise to input\n",
-    "    ud1 = u[0, 0] + np.random.randn() * Rsim[0, 0]\n",
-    "    ud2 = u[1, 0] + np.random.randn() * Rsim[1, 1] + OFFSET_YAWRATE_NOISE\n",
-    "    ud = np.array([ud1, ud2]).reshape(2, 1)\n",
-    "\n",
-    "    xd = motion_model(xd, ud)\n",
-    "\n",
-    "    return xTrue, z, xd, ud\n",
-    "\n",
-    "def update_with_observation(particles, z):\n",
-    "    for iz in range(len(z[0, :])):\n",
-    "\n",
-    "        lmid = int(z[2, iz])\n",
-    "\n",
-    "        for ip in range(N_PARTICLE):\n",
-    "            # new landmark\n",
-    "            if abs(particles[ip].lm[lmid, 0]) <= 0.01:\n",
-    "                particles[ip] = add_new_lm(particles[ip], z[:, iz], Q)\n",
-    "            # known landmark\n",
-    "            else:\n",
-    "                w = compute_weight(particles[ip], z[:, iz], Q)\n",
-    "                particles[ip].w *= w\n",
-    "                particles[ip] = update_landmark(particles[ip], z[:, iz], Q)\n",
-    "\n",
-    "    return particles\n",
-    "\n",
-    "def compute_weight(particle, z, Q):\n",
-    "    lm_id = int(z[2])\n",
-    "    xf = np.array(particle.lm[lm_id, :]).reshape(2, 1)\n",
-    "    Pf = np.array(particle.lmP[2 * lm_id:2 * lm_id + 2])\n",
-    "    zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q)\n",
-    "    dx = z[0:2].reshape(2, 1) - zp\n",
-    "    dx[1, 0] = pi_2_pi(dx[1, 0])\n",
-    "\n",
-    "    try:\n",
-    "        invS = np.linalg.inv(Sf)\n",
-    "    except np.linalg.linalg.LinAlgError:\n",
-    "        print(\"singuler\")\n",
-    "        return 1.0\n",
-    "\n",
-    "    num = math.exp(-0.5 * dx.T @ invS @ dx)\n",
-    "    den = 2.0 * math.pi * math.sqrt(np.linalg.det(Sf))\n",
-    "    w = num / den\n",
-    "\n",
-    "    return w\n",
-    "\n",
-    "def compute_jacobians(particle, xf, Pf, Q):\n",
-    "    dx = xf[0, 0] - particle.x\n",
-    "    dy = xf[1, 0] - particle.y\n",
-    "    d2 = dx**2 + dy**2\n",
-    "    d = math.sqrt(d2)\n",
-    "\n",
-    "    zp = np.array(\n",
-    "        [d, pi_2_pi(math.atan2(dy, dx) - particle.yaw)]).reshape(2, 1)\n",
-    "\n",
-    "    Hv = np.array([[-dx / d, -dy / d, 0.0],\n",
-    "                   [dy / d2, -dx / d2, -1.0]])\n",
-    "\n",
-    "    Hf = np.array([[dx / d, dy / d],\n",
-    "                   [-dy / d2, dx / d2]])\n",
-    "\n",
-    "    Sf = Hf @ Pf @ Hf.T + Q\n",
-    "\n",
-    "    return zp, Hv, Hf, Sf\n",
-    "\n",
-    "def add_new_lm(particle, z, Q):\n",
-    "\n",
-    "    r = z[0]\n",
-    "    b = z[1]\n",
-    "    lm_id = int(z[2])\n",
-    "\n",
-    "    s = math.sin(pi_2_pi(particle.yaw + b))\n",
-    "    c = math.cos(pi_2_pi(particle.yaw + b))\n",
-    "\n",
-    "    particle.lm[lm_id, 0] = particle.x + r * c\n",
-    "    particle.lm[lm_id, 1] = particle.y + r * s\n",
-    "\n",
-    "    # covariance\n",
-    "    Gz = np.array([[c, -r * s],\n",
-    "                   [s, r * c]])\n",
-    "\n",
-    "    particle.lmP[2 * lm_id:2 * lm_id + 2] = Gz @ Q @ Gz.T\n",
-    "\n",
-    "    return particle\n",
-    "\n",
-    "def update_KF_with_cholesky(xf, Pf, v, Q, Hf):\n",
-    "    PHt = Pf @ Hf.T\n",
-    "    S = Hf @ PHt + Q\n",
-    "\n",
-    "    S = (S + S.T) * 0.5\n",
-    "    SChol = np.linalg.cholesky(S).T\n",
-    "    SCholInv = np.linalg.inv(SChol)\n",
-    "    W1 = PHt @ SCholInv\n",
-    "    W = W1 @ SCholInv.T\n",
-    "\n",
-    "    x = xf + W @ v\n",
-    "    P = Pf - W1 @ W1.T\n",
-    "\n",
-    "    return x, P\n",
-    "\n",
-    "def update_landmark(particle, z, Q):\n",
-    "\n",
-    "    lm_id = int(z[2])\n",
-    "    xf = np.array(particle.lm[lm_id, :]).reshape(2, 1)\n",
-    "    Pf = np.array(particle.lmP[2 * lm_id:2 * lm_id + 2, :])\n",
-    "\n",
-    "    zp, Hv, Hf, Sf = compute_jacobians(particle, xf, Pf, Q)\n",
-    "\n",
-    "    dz = z[0:2].reshape(2, 1) - zp\n",
-    "    dz[1, 0] = pi_2_pi(dz[1, 0])\n",
-    "\n",
-    "    xf, Pf = update_KF_with_cholesky(xf, Pf, dz, Q, Hf)\n",
-    "\n",
-    "    particle.lm[lm_id, :] = xf.T\n",
-    "    particle.lmP[2 * lm_id:2 * lm_id + 2, :] = Pf\n",
-    "\n",
-    "    return particle\n",
-    "\n",
-    "# END OF CODE SNIPPET #\n",
-    "\n",
-    "\n",
-    "\n",
-    "# Setting up the landmarks\n",
-    "RFID = np.array([[10.0, -2.0],\n",
-    "                [15.0, 10.0]])\n",
-    "N_LM = RFID.shape[0]\n",
-    "\n",
-    "# Initialize 1 particle\n",
-    "N_PARTICLE = 1\n",
-    "particles = [Particle(N_LM) for i in range(N_PARTICLE)]\n",
-    "\n",
-    "xTrue = np.zeros((STATE_SIZE, 1))\n",
-    "xDR = np.zeros((STATE_SIZE, 1))\n",
-    "\n",
-    "print(\"initial weight\", particles[0].w)\n",
-    "\n",
-    "xTrue, z, _, ud = observation(xTrue, xDR, u, RFID)\n",
-    "# Initialize landmarks\n",
-    "particles = update_with_observation(particles, z)\n",
-    "print(\"weight after landmark intialization\", particles[0].w)\n",
-    "particles = update_with_observation(particles, z)\n",
-    "print(\"weight after update \", particles[0].w)\n",
-    "\n",
-    "particles[0].x = -10\n",
-    "particles = update_with_observation(particles, z)\n",
-    "print(\"weight after wrong prediction\", particles[0].w)\n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3- Resampling\n",
-    "\n",
-    "In the reseampling steps a new set of particles are chosen from the old set. This is done according to the weight of each particle. \n",
-    "\n",
-    "The figure shows 100 particles distributed uniformly between [-0.5, 0.5] with the weights of each particle distributed according to a Gaussian funciton. \n",
-    "\n",
-    "The resampling picks \n",
-    "\n",
-    "$i \\in 1,...,N$ particles with probability to pick particle with index $i ∝ \\omega_i$, where $\\omega_i$ is the weight of that particle\n",
-    "\n",
-    "\n",
-    "To get the intuition of the resampling step we will look at a set of particles which are initialized with a given x location and weight. After the resampling the particles are more concetrated in the location where they had the highest weights. This is also indicated by the indices \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# CODE SNIPPET #\n",
-    "def normalize_weight(particles):\n",
-    "\n",
-    "    sumw = sum([p.w for p in particles])\n",
-    "\n",
-    "    try:\n",
-    "        for i in range(N_PARTICLE):\n",
-    "            particles[i].w /= sumw\n",
-    "    except ZeroDivisionError:\n",
-    "        for i in range(N_PARTICLE):\n",
-    "            particles[i].w = 1.0 / N_PARTICLE\n",
-    "\n",
-    "        return particles\n",
-    "\n",
-    "    return particles\n",
-    "\n",
-    "\n",
-    "def resampling(particles):\n",
-    "    \"\"\"\n",
-    "    low variance re-sampling\n",
-    "    \"\"\"\n",
-    "\n",
-    "    particles = normalize_weight(particles)\n",
-    "\n",
-    "    pw = []\n",
-    "    for i in range(N_PARTICLE):\n",
-    "        pw.append(particles[i].w)\n",
-    "\n",
-    "    pw = np.array(pw)\n",
-    "\n",
-    "    Neff = 1.0 / (pw @ pw.T)  # Effective particle number\n",
-    "    # print(Neff)\n",
-    "\n",
-    "    if Neff < NTH:  # resampling\n",
-    "        wcum = np.cumsum(pw)\n",
-    "        base = np.cumsum(pw * 0.0 + 1 / N_PARTICLE) - 1 / N_PARTICLE\n",
-    "        resampleid = base + np.random.rand(base.shape[0]) / N_PARTICLE\n",
-    "\n",
-    "        inds = []\n",
-    "        ind = 0\n",
-    "        for ip in range(N_PARTICLE):\n",
-    "            while ((ind < wcum.shape[0] - 1) and (resampleid[ip] > wcum[ind])):\n",
-    "                ind += 1\n",
-    "            inds.append(ind)\n",
-    "\n",
-    "        tparticles = particles[:]\n",
-    "        for i in range(len(inds)):\n",
-    "            particles[i].x = tparticles[inds[i]].x\n",
-    "            particles[i].y = tparticles[inds[i]].y\n",
-    "            particles[i].yaw = tparticles[inds[i]].yaw\n",
-    "            particles[i].w = 1.0 / N_PARTICLE\n",
-    "\n",
-    "    return particles, inds\n",
-    "# END OF SNIPPET #\n",
-    "\n",
-    "\n",
-    "\n",
-    "def gaussian(x, mu, sig):\n",
-    "    return np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.)))\n",
-    "N_PARTICLE = 100\n",
-    "particles = [Particle(N_LM) for i in range(N_PARTICLE)]\n",
-    "x_pos = []\n",
-    "w = []\n",
-    "for i in range(N_PARTICLE):\n",
-    "    particles[i].x = np.linspace(-0.5,0.5,N_PARTICLE)[i]\n",
-    "    x_pos.append(particles[i].x)\n",
-    "    particles[i].w = gaussian(i, N_PARTICLE/2, N_PARTICLE/20)\n",
-    "    w.append(particles[i].w)\n",
-    "    \n",
-    "\n",
-    "# Normalize weights\n",
-    "sw = sum(w)\n",
-    "for i in range(N_PARTICLE):\n",
-    "    w[i] /= sw\n",
-    "\n",
-    "particles, new_indices = resampling(particles)\n",
-    "x_pos2 = []\n",
-    "for i in range(N_PARTICLE):\n",
-    "    x_pos2.append(particles[i].x)\n",
-    "    \n",
-    "# Plot results\n",
-    "fig, ((ax1,ax2,ax3)) = plt.subplots(nrows=3, ncols=1)\n",
-    "fig.tight_layout()\n",
-    "ax1.plot(x_pos,np.ones((N_PARTICLE,1)), '.r', markersize=2)\n",
-    "ax1.set_title(\"Particles before resampling\")\n",
-    "ax1.axis((-1, 1, 0, 2))\n",
-    "ax2.plot(w)\n",
-    "ax2.set_title(\"Weights distribution\")\n",
-    "ax3.plot(x_pos2,np.ones((N_PARTICLE,1)), '.r')\n",
-    "ax3.set_title(\"Particles after resampling\")\n",
-    "ax3.axis((-1, 1, 0, 2))\n",
-    "fig.subplots_adjust(hspace=0.8)\n",
-    "plt.show()\n",
-    "\n",
-    "plt.figure()\n",
-    "plt.hist(new_indices)\n",
-    "plt.xlabel(\"Particles indices to be resampled\")\n",
-    "plt.ylabel(\"# of time index is used\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### References\n",
-    "\n",
-    "http://www.probabilistic-robotics.org//n",
-    "\n",
-    "http://ais.informatik.uni-freiburg.de/teaching/ws12/mapping/pdf/slam10-fastslam.pdf"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
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diff --git a/SLAM/GraphBasedSLAM/LaTeX/.gitignore b/SLAM/GraphBasedSLAM/LaTeX/.gitignore
deleted file mode 100644
index b0b343402c..0000000000
--- a/SLAM/GraphBasedSLAM/LaTeX/.gitignore
+++ /dev/null
@@ -1,3 +0,0 @@
-# Ignore LaTeX generated files
-graphSLAM_formulation.*
-!graphSLAM_formulation.tex
diff --git a/SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib b/SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib
deleted file mode 100644
index 4d3b71ae50..0000000000
--- a/SLAM/GraphBasedSLAM/LaTeX/graphSLAM.bib
+++ /dev/null
@@ -1,30 +0,0 @@
-@article{blanco2010tutorial,
-  title={A tutorial on ${SE}(3)$ transformation parameterizations and on-manifold optimization},
-  author={Blanco, Jose-Luis},
-  journal={University of Malaga, Tech. Rep},
-  volume={3},
-  year={2010},
-  publisher={Citeseer}
-}
-
-@article{grisetti2010tutorial,
-  title={A tutorial on graph-based {SLAM}},
-  author={Grisetti, Giorgio and Kummerle, Rainer and Stachniss, Cyrill and Burgard, Wolfram},
-  journal={IEEE Intelligent Transportation Systems Magazine},
-  volume={2},
-  number={4},
-  pages={31--43},
-  year={2010},
-  publisher={IEEE}
-}
-
-@article{thrun2006graph,
-  title={The graph {SLAM} algorithm with applications to large-scale mapping of urban structures},
-  author={Thrun, Sebastian and Montemerlo, Michael},
-  journal={The International Journal of Robotics Research},
-  volume={25},
-  number={5-6},
-  pages={403--429},
-  year={2006},
-  publisher={SAGE Publications}
-}
diff --git a/SLAM/GraphBasedSLAM/LaTeX/graphSLAM_formulation.tex b/SLAM/GraphBasedSLAM/LaTeX/graphSLAM_formulation.tex
deleted file mode 100644
index 25f02cd3bd..0000000000
--- a/SLAM/GraphBasedSLAM/LaTeX/graphSLAM_formulation.tex
+++ /dev/null
@@ -1,175 +0,0 @@
-\documentclass{article}
-
-\usepackage{amsfonts}
-\usepackage{amsmath,amssymb,amsfonts}
-\usepackage{textcomp}
-\usepackage{fullpage}
-\usepackage{setspace}
-\usepackage{float}
-\usepackage{cite}
-\usepackage{graphicx}
-\usepackage{caption}
-\usepackage{subcaption}
-\usepackage[pdfborder={0 0 0}, pdfpagemode=UseNone, pdfstartview=FitH]{hyperref}
-
-\DeclareMathOperator*{\argmax}{arg\,max}
-\DeclareMathOperator*{\argmin}{arg\,min}
-
-\def\keyterm{\textit}
-
-\newcommand{\transp}{{\scriptstyle{\mathsf{T}}}}
-
-
-
-\begin{document}
-
-\title{Graph SLAM Formulation}
-\author{Jeff Irion}
-\date{}
-
-\maketitle
-\vspace{3em}
-
-
-\section{Problem Formulation}
-
-Let a robot's trajectory through its environment be represented by a sequence of $N$ poses: $\mathbf{p}_1, \mathbf{p}_2, \ldots, \mathbf{p}_N$.  Each pose lies on a manifold: $\mathbf{p}_i \in \mathcal{M}$.  Simple examples of manifolds used in Graph SLAM include 1-D, 2-D, and 3-D space, i.e., $\mathbb{R}$, $\mathbb{R}^2$, and $\mathbb{R}^3$.  These environments are \keyterm{rectilinear}, meaning that there is no concept of orientation.  By contrast, in $SE(2)$ problem settings a robot's pose consists of its location in $\mathbb{R}^2$ and its orientation $\theta$.  Similarly, in $SE(3)$ a robot's pose consists of its location in $\mathbb{R}^3$ and its orientation, which can be represented via Euler angles, quaternions, or $SO(3)$ rotation matrices.  
-
-As the robot explores its environment, it collects a set of $M$ measurements $\mathcal{Z} = \{\mathbf{z}_j\}$.  Examples of such measurements include odometry, GPS, and IMU data.  Given a set of poses $\mathbf{p}_1, \ldots, \mathbf{p}_N$, we can compute the estimated measurement $\hat{\mathbf{z}}_j(\mathbf{p}_1, \ldots, \mathbf{p}_N)$.  We can then compute the \keyterm{residual} $\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j)$ for measurement $j$.  The formula for the residual depends on the type of measurement.  As an example, let $\mathbf{z}_1$ be an odometry measurement that was collected when the robot traveled from $\mathbf{p}_1$ to $\mathbf{p}_2$.  The expected measurement and the residual are computed as
-%
-\begin{align*}
-    \hat{\mathbf{z}}_1(\mathbf{p}_1, \mathbf{p}_2) &= \mathbf{p}_2 \ominus \mathbf{p}_1 \\
-    \mathbf{e}_1(\mathbf{z}_1, \hat{\mathbf{z}}_1) &= \mathbf{z}_1 \ominus \hat{\mathbf{z}}_1 = \mathbf{z}_1 \ominus (\mathbf{p}_2 \ominus \mathbf{p}_1),
-\end{align*}
-%
-where the $\ominus$ operator indicates inverse pose composition.  We model measurement $\mathbf{z}_j$ as having independent Gaussian noise with zero mean and covariance matrix $\Omega_j^{-1}$; we refer to $\Omega_j$ as the \keyterm{information matrix} for measurement $j$.  That is,
-\begin{equation}
-    p(\mathbf{z}_j \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) = \eta_j \exp \left( (-\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^\transp \Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j) \right), \label{eq:observation_probability}
-\end{equation}
-where $\eta_j$ is the normalization constant.
-
-The objective of Graph SLAM is to find the maximum likelihood set of poses given the measurements $\mathcal{Z} = \{\mathbf{z}_j\}$; in other words, we want to find 
-%
-\begin{equation*}
-    \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \ p(\mathbf{p}_1, \ldots, \mathbf{p}_N \ | \ \mathcal{Z}) 
-\end{equation*}
-%
-Using Bayes' rule, we can write this probability as
-%
-\begin{align}
-    p(\mathbf{p}_1, \ldots, \mathbf{p}_N \ | \ \mathcal{Z}) &= \frac{p( \mathcal{Z} \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) p(\mathbf{p}_1, \ldots, \mathbf{p}_N) }{ p(\mathcal{Z}) } \notag \\
-    &\propto p( \mathcal{Z} \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N), \label{eq:bayes}
-\end{align}
-%
-since $p(\mathcal{Z})$ is a constant (albeit, an unknown constant) and we assume that $p(\mathbf{p}_1, \ldots, \mathbf{p}_N)$ is uniformly distributed \cite{thrun2006graph}.  Therefore, we can use \eqref{eq:observation_probability} and \eqref{eq:bayes} to simplify the Graph SLAM optimization as follows:
-%
-\begin{align*}
-    \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \ p(\mathbf{p}_1, \ldots, \mathbf{p}_N \ | \ \mathcal{Z}) &= \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \ p( \mathcal{Z} \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) \\
-    &= \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \prod_{j=1}^M p(\mathbf{z}_j \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) \\
-    &= \argmax_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \prod_{j=1}^M \exp \left( -(\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^\transp \Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j) \right) \\
-    &= \argmin_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \sum_{j=1}^M (\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^\transp \Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j).
-\end{align*}
-%
-We define
-%
-\begin{equation*}
-    \chi^2 := \sum_{j=1}^M (\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^\transp \Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j),
-\end{equation*}
-%
-and this is what we seek to minimize.
-
-
-\section{Dimensionality and Pose Representation}
-
-Before proceeding further, it is helpful to discuss the dimensionality of the problem.  We have:
-\begin{itemize}
-  \item A set of $N$ poses $\mathbf{p}_1, \mathbf{p}_2, \ldots, \mathbf{p}_N$, where each pose lies on the manifold $\mathcal{M}$
-  \begin{itemize}
-    \item Each pose $\mathbf{p}_i$ is represented as a vector in (a subset of) $\mathbb{R}^d$.  For example:
-    \begin{itemize}
-      \item[$\circ$] An $SE(2)$ pose is typically represented as $(x, y, \theta)$, and thus $d = 3$.
-      \item[$\circ$] An $SE(3)$ pose is typically represented as $(x, y, z, q_x, q_y, q_z, q_w)$, where $(x, y, z)$ is a point in $\mathbb{R}^3$ and $(q_x, q_y, q_z, q_w)$ is a \keyterm{quaternion}, and so $d = 7$.  For more information about $SE(3)$ parameterizations and pose transformations, see \cite{blanco2010tutorial}.
-    \end{itemize}
-    \item We also need to be able to represent each pose compactly as a vector in (a subset of) $\mathbb{R}^c$.
-    \begin{itemize}
-      \item[$\circ$] Since an $SE(2)$ pose has three degrees of freedom, the $(x, y, \theta)$ representation is again sufficient and $c=3$.  
-      \item[$\circ$] An $SE(3)$ pose only has six degrees of freedom, and we can represent it compactly as $(x, y, z, q_x, q_y, q_z)$, and thus $c=6$.
-    \end{itemize}
-    \item We use the $\boxplus$ operator to indicate pose composition when one or both of the poses are represented compactly.  The output can be a pose in $\mathcal{M}$ or a vector in $\mathbb{R}^c$, as required by context.
-  \end{itemize}
-  \item A set of $M$ measurements $\mathcal{Z} = \{\mathbf{z}_1, \mathbf{z}_2, \ldots, \mathbf{z}_M\}$
-  \begin{itemize}
-    \item Each measurement's dimensionality can be unique, and we will use $\bullet$ to denote a ``wildcard'' variable.
-    \item Measurement $\mathbf{z}_j \in \mathbb{R}^\bullet$ has an associated information matrix $\Omega_j \in \mathbb{R}^{\bullet \times \bullet}$ and residual function $\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j) = \mathbf{e}_j(\mathbf{z}_j, \mathbf{p}_1, \ldots, \mathbf{p}_N) \in \mathbb{R}^\bullet$.
-    \item A measurement could, in theory, constrain anywhere from 1 pose to all $N$ poses.  In practice, each measurement usually constrains only 1 or 2 poses.  
-  \end{itemize}
-\end{itemize}
-
-
-\section{Graph SLAM Algorithm}
-
-The ``Graph'' in Graph SLAM refers to the fact that we view the problem as a graph.  The graph has a set $\mathcal{V}$ of $N$ vertices, where each vertex $v_i$ has an associated pose $\mathbf{p}_i$.  Similarly, the graph has a set $\mathcal{E}$ of $M$ edges, where each edge $e_j$ has an associated measurement $\mathbf{z}_j$.  In practice, the edges in this graph are either unary (i.e., a loop) or binary.  (Note: $e_j$ refers to the edge in the graph associated with measurement $\mathbf{z}_j$, whereas $\mathbf{e}_j$ refers to the residual function associated with $\mathbf{z}_j$.)  For more information about the Graph SLAM algorithm, see \cite{grisetti2010tutorial}.
-
-We want to optimize
-%
-\begin{equation*}
-    \chi^2 = \sum_{e_j \in \mathcal{E}} \mathbf{e}_j^\transp \Omega_j \mathbf{e}_j.
-\end{equation*}
-%
-Let $\mathbf{x}_i \in \mathbb{R}^c$ be the compact representation of pose $\mathbf{p}_i \in \mathcal{M}$, and let
-%
-\begin{equation*}
-    \mathbf{x} := \begin{bmatrix} \mathbf{x}_1 \\ \mathbf{x}_2 \\ \vdots \\ \mathbf{x}_N \end{bmatrix} \in \mathbb{R}^{cN}
-\end{equation*}
-%
-We will solve this optimization problem iteratively.  Let
-%
-\begin{equation}
-    \mathbf{x}^{k+1} := \mathbf{x}^k \boxplus \Delta \mathbf{x}^k = \begin{bmatrix} \mathbf{x}_1 \boxplus \Delta \mathbf{x}_1 \\ \mathbf{x}_2 \boxplus \Delta \mathbf{x}_2 \\ \vdots \\ \mathbf{x}_N \boxplus \Delta \mathbf{x}_2 \end{bmatrix} \label{eq:update}
-\end{equation}
-%
-The $\chi^2$ error at iteration $k+1$ is
-\begin{equation}
-    \chi_{k+1}^2 = \sum_{e_j \in \mathcal{E}} \underbrace{\left[ \mathbf{e}_j(\mathbf{x}^{k+1}) \right]^\transp}_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\mathbf{e}_j(\mathbf{x}^{k+1})}_{\bullet \times 1}.  \label{eq:chisq_at_kplusone}
-\end{equation}
-%
-We will linearize the residuals as:
-%
-\begin{align}
-    \mathbf{e}_j(\mathbf{x}^{k+1}) &= \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k) \notag \\
-    &\approx \mathbf{e}_j(\mathbf{x}^{k}) + \frac{\partial}{\partial \Delta \mathbf{x}^k} \left[ \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k) \right] \Delta \mathbf{x}^k \notag \\
-    &= \mathbf{e}_j(\mathbf{x}^{k}) + \left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right) \frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k} \Delta \mathbf{x}^k.  \label{eq:linearization}
-\end{align}
-%
-Plugging \eqref{eq:linearization} into \eqref{eq:chisq_at_kplusone}, we get:
-%
-\small
-\begin{align}
-    \chi_{k+1}^2 &\approx \ \ \ \ \ \sum_{e_j \in \mathcal{E}} \underbrace{[ \mathbf{e}_j(\mathbf{x}^k)]^\transp}_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\mathbf{e}_j(\mathbf{x}^k)}_{\bullet \times 1} \notag \\
-    &\hphantom{\approx} \ \ \ + \sum_{e_j \in \mathcal{E}} \underbrace{[ \mathbf{e}_j(\mathbf{x^k}) ]^\transp }_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN} \underbrace{\Delta \mathbf{x}^k}_{cN \times 1} \notag \\
-    &\hphantom{\approx} \ \ \ + \sum_{e_j \in \mathcal{E}} \underbrace{(\Delta \mathbf{x}^k)^\transp}_{1 \times cN} \underbrace{ \left( \frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k} \right)^\transp}_{cN \times dN} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)^\transp}_{dN \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN} \underbrace{\Delta \mathbf{x}^k}_{cN \times 1} \notag \\
-    &= \chi_k^2 + 2 \mathbf{b}^\transp \Delta \mathbf{x}^k + (\Delta \mathbf{x}^k)^\transp H \Delta \mathbf{x}^k,  \notag
-\end{align}
-\normalsize
-%
-where
-%
-\begin{align*}
-    \mathbf{b}^\transp &= \sum_{e_j \in \mathcal{E}} \underbrace{[ \mathbf{e}_j(\mathbf{x^k}) ]^\transp }_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN} \\
-    H &= \sum_{e_j \in \mathcal{E}} \underbrace{ \left( \frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k} \right)^\transp}_{cN \times dN} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)^\transp}_{dN \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN}.
-\end{align*}
-%
-Using this notation, we obtain the optimal update as
-%
-\begin{equation}
-    \Delta \mathbf{x}^k = -H^{-1} \mathbf{b}.  \label{eq:deltax}
-\end{equation}
-%
-We apply this update to the poses via \eqref{eq:update} and repeat until convergence.
-
-
-
-\bibliographystyle{acm}
-\bibliography{graphSLAM}{}
-
-\end{document}
diff --git a/SLAM/GraphBasedSLAM/graphSLAM_SE2_example.ipynb b/SLAM/GraphBasedSLAM/graphSLAM_SE2_example.ipynb
deleted file mode 100644
index cd3d9fd5db..0000000000
--- a/SLAM/GraphBasedSLAM/graphSLAM_SE2_example.ipynb
+++ /dev/null
@@ -1,332 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from graphslam.graph import Graph\n",
-    "from graphslam.load import load_g2o_se2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Introduction\n",
-    "\n",
-    "For a complete derivation of the Graph SLAM algorithm, please see [graphSLAM_formulation.pdf](./graphSLAM_formulation.pdf).  \n",
-    "\n",
-    "This notebook illustrates the iterative optimization of a real-world $SE(2)$ dataset.  The code can be found in the `graphslam` folder.  For simplicity, numerical differentiation is used in lieu of analytic Jacobians.  This code originated from the [python-graphslam](https://github.com/JeffLIrion/python-graphslam) repo, which is a full-featured Graph SLAM solver.  The dataset in this example is used with permission from Luca Carlone and was downloaded from his [website](https://lucacarlone.mit.edu/datasets/).  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# The Dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of edges:    1483\n",
-      "Number of vertices: 1228\n"
-     ]
-    }
-   ],
-   "source": [
-    "g = load_g2o_se2(\"data/input_INTEL.g2o\")\n",
-    "\n",
-    "print(\"Number of edges:    {}\".format(len(g._edges)))\n",
-    "print(\"Number of vertices: {}\".format(len(g._vertices)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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anx3L270xnj/PzOXPZWV87eZUtQ7gzgAg6uc26OOBfjKXJO5wiLjT+P0Vg74800+TTzGo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RpstvfdV/V9PP00ricqbH1fJyPiOuzxcKkiC4aLDrx8uh4uDpJJeniy8JHG8+35PCRVD2fMJiaa\nM3JPPRVRIQJMmjWDOUFw81vBLzZWHiv+qqq05detK/c3bgwbu7Anezz4fwx5eHPjHC5/zMNrHxRF\nKyoG97tqucMavgDzuUo+j1M8Ie1QUgaXkjOs7UUACbPJiV9czDz/Sp3/L9XN3RRzYXBr/Le4r2co\nLxSSqXCp4uLnrtHhQGRMYF9+CQXCbEpy8krqFY7YOUYuPjdZj5oBLfw3zZpJpk5m5qIiuQKcrEhA\nC1ah4Vcl27cDwIcPN0/06elYdZx9trynevWkk79ZMziCTRmu1RDvKp3LDO/7uV4eDsRUECtvCbg4\nukJnvxMTdpnq4n5xkX1F+GOM46aMQissm+2kkJ6dKKkB/D9BnnsOLbZmjXn/uedCyzHa3YcPZ3bH\nm7MqWVV5+RkFPDGKw28iudlmq542lJmJ+/jiC/n5J2ocDgcNqACt+VfqPCmk2ezciWNLSrBEF1EY\nmaTz8wn5Jrvx+9N03r4dDk+7HQNaaJgXX8z8xBMScFwuaYYRNmArABrBQXzXpAnzhY0wCLOdEkCH\nNdF5surmCxvp/PbgyApBxpDISQatTWje41U4ViemePjVOubnEkUojCaTyaqbp/ZD5I04x0PD9Aiy\nNaOUlwMLzjyz4upRmaSH8zGE9hg2A2VitbJvH87n8zE/e5XOc9X8sL3dGObr0SStsTHpSLwTofEn\nJMC088svsO8vWMB8Q6wnFA2GyWP2cJ33769+n69IxIQl4uFF9JeiAOCN+Q7iu8aNoQhVCfy6zjuu\ncvNLSVEm4uOJlTccGwwy73hZZ59DrtqsGn6AFMlwK8bs3zi71ig1gP8nyODBACqjyaWkBAPuuuvk\nvmAQjs3/a+0Ja5QC7UDKBE3OpzlM9UqNRVQqEp9PcssEgyDvEgADJ6PCPhsSbDZtkoNu9mx5jvx8\nM9WsVfN5pIGbg0HQHwtbbe3aEmBGjIDpwGaTGZtdu5pD5qylD3s7dJ5qd4fL7UVbXlv3rQ35JsQA\nXK9mcMApNbytC2GzzyRMEmNDZpfKwiqNE0Sp6gqbhrqrOi9q6+YJveDwi4lB2OKuXZW/j2AQNRH6\n9zf7L4xAIhyKayjD1CaC4Ovpp6Gd++8y2//DWqfi5O6qbgL5aJtwmtvtiEX/7lnwuQsz0XgV5qHE\nRJgfwxXaToD4fMDXmTOR42AkMRPtIu49NRWr4mjXP/CuzqUhO30ZOdlvO8Gx8oZJ4PBhlDoU9Y6h\nHFjI0oiw9PybSw3gn2A5cACd+OabzfuXLkUrGhkzv/nGCGhKONwuqGJ5LwDoh3PyTfVKRWRKZSKy\na4cOxcTTuTMcoefUNnOmCK2mRQvYVnv1kudYt47DJprEROO9SmASJG5lZbJcolFzP+ccaHHNmyPE\n0GZjvjXBwx86cvgZyuP3KIevsnlY06pnZ78nwR1hd11MuSbgG0Me7qGhZizrOrOu865r3Xxl2+hh\nlYvi8rlEkYXKJ5+NmrTGOrGC4dM0OfXG84k6sNddZ66qVZnoOlZG4eLlFvAWRcej1QcYcxr8BsKc\nI1ZsXtJ40WluXrQI5kRj/kVFm6qa28OvaPxcO6wwhB+meXP4AKqKCPs9cuwYkqwmTADtcrSJKjYW\nLJ/lRToHZ7m56G7d7PTWNGgnf7ZNPTQJBNfo7I+pZQqfDtus/uZSA/gnWITd/JNPzPuvuQYd11jg\n+IknLIONzCGZvhB74ks36GHwubJtxfZI475rOgPc1q5lfn8afrtsqs4L0uX1AqSEaXBvvllq8nv2\n4FTBIEBJ2NLj4xHWaQSmNbVzwpcOrtF5+VlyYhJFwx+sC+fpOecwfzzKbH4xgnQ0B1q202yK6aHp\n/OxVZtPKhY30sBnmsQ4eXnS1DMm7qqM5YmNQis49beZzZpLOZ7t0XjPIzaPb6GFT0ogRMrbcCkBC\nUyZCG4nqc04nnNjVBf5Vq5jPq6tzmcWhfoRcEddNTJTXFFEkZQZ6Di9pPIY8rKrgK/rf/0B69uKL\nkrE1GugbSx16yc5rLgf3U+fO5uc980zm1auPZzQcvxQXQ1kZP96cwGdVBqY18rDfWYGd/mRIXl5k\nQ9Zo+P89wB84EBqttepQ8+Yw9Rjl2i4YtF7VyaxpJpusyPIbQx7unyA7e4ni4iGpOg9KMe8b3kzn\nkS11LjEMiu6qztP7y+O8Nhc/mWEheyOF/Q0b845LC8L99okn5D3ec4/sz1ddxXyEXKZ7LCeVj9pq\n86H6LblclVWAopleosVEi7+igLZwIBtD5PrESh+DuJfLT9X568vcfL0LtvjeDsSvVxVWGZzl5uJi\n1BwV2ruR72fcOJhehP+jSRNUJjOGllpZQkVUUWoqaBgEtfPVV3PY+VqZHDnC/FXjHNMEuLpWToVF\nQux2rNam2s2mHT8pXG5zcW+HHl4NjBhhLqqyfTsct+3ambVpY8iniOt3u0FaJriDhMnlgguQbHUy\n5JdfYGacFW9ehfjv+htw2uTlyYrx/wCwZ64B/BMq+/dDAxNFxIV8/XUkkB5dYYjrdThQYahO3Ygs\n24nk5pmxZl6WV89wR4RTPtfOzc+0NmvIU+1unuaM1JqFCYTJrGU/7Crg+Hgk0wjZtQvAkJgIp/Pa\npJyoGro1I3h5XG6E6eR21c3j1ega/kNtPGFN7p4EN49qpUcAXWys2czSXTVr79FMQAsc+WFtPhBj\n1gQXLpQmE6NdPTUVju7lyyUlQ6NGAH5BxaAoMIMZcwiE0zkmBhwzAvivuqp6wP/bGTl8jFy8lHJ4\n8mSYO154AbkF0ZgfpTlIKgoBVWPvDDdPmiRNMpoGW73VCRsMgve9detIM5dwHMfEYBF49dXANrtd\nUstcd510KP9Zsv8dnV9ojyiZUgWO1H8Dp81fJTWAfwJlwQK0lJWq+P77sd/IY1LY10xHy243+y4x\nh2d6ycaZpPOINBkXX6qGCLNCWbqmJW1onwDAVffonBOP3wY1JIxsmR9ZYFn83W5PD5OcGVP0BwyQ\nfCW7dzO/S+AF92u2iAnKuN2rFsgMU5uLc+LhiJ2Y4mFfnxzmvDwu7gp+k0zS+bnT3OGMUYeDeeZA\nM6VBZdq7LwRSptBFxcVZoUzXieTm7qrOb7xhfjc//yxDE4nM/oerrkKkyAcfyISuBg1A5SAiSmw2\nFPMwxsvXr28OSdU0fM7Pj85lY5SDB0EbQYSkJlGEJBhEGOWQIWan9xjysJck7bKPiAOaxpyRwSUl\nUD4ExYCmYdIqLo68btlK6cwvJSf3dugmpkpRr1hU7apVC/vi47EKtHL5nAj5+SX5vsttLvbN8fwj\nY9//TlID+CdQzjknMuySGdEI7dvLz7sX67zAkc/lihMOpxDrptfuCmXZEu+o0yEMdHFxzPedr/MX\nFwH85s8PnagCG/5UO0wcBQUYlNuexnG/vKpzkybMWaSbooIEWDxgl2ad+Hg4fB96CFzvYv/SpdLM\n817dyPwB4zlXaDnhkMpMwv2kpgK4F7aSYXBPjzcD+Tm1q2ag9JLGcyjfZIt/aJjOcXHMZ7vkRNGx\nI+gUjJrxjTea308ggKgRAfbGEMq6daU/ZuVKWWKybl38b8wf6NHDbO6JjZXgLPIQbDbYp3/6qfK+\ntGSJnDhmzIgMw922DRQGxhWc9T3saZ7BBw6AzOyWW+QqwWZD0poJ+A2FePx2B9/aXQ/XOhaRVMIE\n1KaNLB4jaC6aNkU4clXJgNUSXefNeW5+0pYfoRTVyB+TGsA/QbJvH/rkpEnm/QcPYtBMmBDaoctw\nMr/dIVn43G7ExYfA7NOhbhNIPf44JpIePQAe0bQ0ZoATEcL4YmIACsxgT2zSBCRT40/XTc4+JmLO\ny+OSEgCUqAUbjRM+IwOapiA725GdxyXkZH8ULX9j/wL+5GEAdVZo8pp8ttn30D8hEsgnKW6+P9ls\nxlraA4lMRoDPCvkJjPb9Dh3gL3E4JLWDqkIrF05IIgCWNdpp06aKa7OOHi3jwletQvIQERyhxmgY\nUW5RgLsR/MVf8d3YsZFF543yv//JyKczzpAUEkY5ukJnr+aMOuGWkY3tdqwK3nwTJp0bbjAD/9ix\noWLz7sgV57ffymAD8axGu39yslzpiLbu3Bm01L9X/Ktl0lyZ4uTAv4ia+O8gJwXwiWgmEX1JRBuJ\naDkRNQztV4joESL6PvR95+qc7+8I+KI4ibUOqKgsJTh19l6QLwumWLI1gy5Zw3T3qAIT4BQV4bCN\nGwEq11wT/T4Ec2SfPhjYP/1kBvsNG5jXn2+O/Q5qWngwDRsmM18PHIDt+YUXZFUjsQkwi4lhntJH\nArEIE/QTccDhYK/mrFRLf6yRm1+6QYYZlmkuvqYzgFzQCx8jF/e06UjeSZGUCcY6pkZTjM0mWTFF\npIcwP4wYYTZxPPywuUJTaSk0cOOziuvUqWOOUlm9Wq4eateW2q64F+HsrFdP2vdFu4l71jRQEmzf\nXnHfeu01XNvhAE2B38/8ycM6v5Xp5j6xMNFZE4GCRLxeM8fzx8bi+VesQP8RKxmbjfneXD20wlQ5\naLObskeLi3Fd4bhOSjIXJnE48Cx2uzRtDRoUvcZyZeLzMb96xl8QbvkfkpMF+LUN/19PRHND/w8k\nomUh4M8kok+qc76/I+D36SPrwxrl8ssBBD4f7KRl5JCamKWykJWLe0zItj2R3HzgXXnctddigFkn\nF2YZuqcozLfeGgn2zHCEGbM7jeXyBOkbEYpXCBErByJEbtx2GxgFhbkmk3RTPDyHQN9vyCe4JyE6\nT8msWcys67y0B75ftYp51iwkOd2X5Oa7h0hwHz5car0CaI2gZjXHEMnC74JT31q9q1kzaMDGd1dY\naK4eZZxQ8vLM4bW6LssyivKQTqcEReEQPussRGoZHb3ir6bBdv/DD1E6l67zkcluvq0HVjEiVFWU\n3ds+ycNBm9mfwo0bcyAAErMJEyJXLklJ6JvDh8v7GacgUsdPalSN2utF/xBJdC6XOXFOPE+tWpJK\nYvx4jiikEk0OvafzE83gnC23uaSpswboT6icdJMOEU0ioidC/3uI6BLDd9uIqEFV5/i7Af7evejc\nU6aY9wcC0O4uvhifl/U2LJsVGQMflhxzyOJayggPbGOESXExQKx7dzNIffONHMxJSeCzadwYYL9x\no/lSd1v42IVGd/AgQDMuDqYAIcE1AN9M0vn885ndg6sOuZTUvwqXKAgXHNseGulcJT/soBU8OCWz\nPdykCUIGvV5QUzRrBkC64QbJed64MbhahKlBRI9EA33B2S9MD+PGgeLXqIGLLSODZe1hxgpnyJDo\nE0pSEiYFo6xbh7BcoybfqJGclBQFGHbnnSjMYq3RK9hCR43CRC3a3ecwt7MxXyFMJVBQwAEFq6ug\nqP5ukW+/ha/Cet26dbGCM52XiL2DcqP292AQtQWGDpWrFGtbEkH5UFX0pZkzK04YPPSeDCf22kNV\n5Gq0+j9FThrgE9EsItpJRJuJKDW07x0i6mE4ppCIulTw+3FE9BkRfda0adM/vWGORwRnzJdfmvd/\n+imHNeWSQp2fic3nMqOj1tqhPZ7waLFywnhJ4ydbuPnVm3Xef6ub356kR2jho0bJwTZxYsVgz8y8\nh1INYZSKySF245k6T3PCbDJrViQ/fibpphhwL2k8M9bNV9ujh1yKzFFjGb1ScnAm6RGkVOuywSH0\nfcsc5uRkLhuWxxdeiGfq00fazjUNzuMzzjADlxFwjDVLVVXaridMgJlEAJ+xzq3QxI2v5vnnzSYM\no+Z/4YWRQPbppzBpGEG8Z0+50hCT1vvvIzFu1KjIsMss0vnxxm5eEZ8bXiX5SOOf8t28e7qHvQpi\n5ksUFy/IFIRvZjt+RGy4wcm/dy+ix9q2xbUEQZixznKQiF8624PaDRXI9u1gwhRt3aBBJG+QaK+G\nDcECGjahhRguX0yscc6eLDlhgE9EH4TA3LqdZzluEhFN5+MEfOP2uzX8vDxZbklUBj8Bkp2NyAWr\nOUdUgipeKh1RJkdtNPF4mHNyeP89HhlnrSJpanKq5IApVZFhOjBJ59KpGMSiwEXDhtAsrWAfCMDu\n/EtSmwhALh5fwC+8wPzgRZHgfm+ixalKbj64DCGgQU0e160btPViLTkiXPM3So4IB51D+RE8OH5S\nIqbXzBcAACAASURBVIArmJfH8+ZhjqxbF/HfIiu4b18ORyMJDdoI9MbC5cbtmmtAB/H44+bCIHFx\n8vPgwXDkMsOXIUwZRDBbiIichATmd9+NfJWffy41fiJo+ldeadaGu3aFGefoCp239MrnRfH5YYpl\nEUkltjLFwTfFye/Kyc5jyBNhwxcn9ycl848v6Lx8OfPbk3Qu12TfGdde51NPNdI04z1+T80jVpkO\nB0BdsGxGk0OHENEl6JFTUiLNbSJB7fTTmX8YhhWJLxQG6rfXOGdPhvwVJp2mRLQ59P/JM+nkRYYQ\nltZt/Ic71549AJo774z8LiMD6egHJ0RGQBjlYH4BlzZNN2lk27fj8EyCFsQ6eEREJI/PEJboIy1M\npyw0qpQUgH0gANPIgxfpfFctmGRMRG2GgU3EEZr7063dpph/kUX7/PMc1hhfvlEmSdntkSXlgkRc\nMjSPfx5oBvy5Sj6/4zAngQUM/4dPmpzMzKg3KwpuXHmlLE2XmAjna506EtytRVdEwpRxGzkSvpXD\nh2GOM7J4XnIJzqso+P/bb9GW991nrtIl/AJEMP9EA8UNGyS5HBFWFp7LdZ4ZK/0fpQbfjnB+G9vB\nTwrPt+fzwlZuWRtZVXn3eflcpjgi2lvE5FdUVP3JFm4eNoz55Y4Gqg1V4+IGbUzvY33j3PDzOhwI\n76wM+P1+FDLv0QO/EZO0sd3zNTP3fpjio8aM86fLyXLatjT8fx0RvRb6/1yL03Z9dc73uwA/OVLr\nDBJxKTn5qo46v9LPw791zuHA3OPjtn70UZxyyxbz/r17AQgLxuj8XgvQ70Yz5QQsjloB+lu3ygES\n5mUxAG/Q5eKfBkSyPGYRzDFrLvfwi6e7OSc+Mqb9O2oWEcL3detc3rCB2feROXkr26nDZBEC9xUz\nQnQHhmzc8nKEQtrtMlLlltoe9tWvDzVY2JN1nb0qknsCDie/Pw335lPtHCAkmpWRI9I0YbBHl5Qg\n65MIdMs5ObKdxo6ViUEiI9QI9PHxkaaTIUNkuOXu3ZigxXe9e8M5LUIpx4yBpv/VV2YnaPv2UpuN\ni2NTclcwyPz9Ip23jnLz4yN0TkrisOlE0BgYNXQBgMJkA+BWw8BtnUyXOqXZz08Kf04deb+aHA6T\nDagabx/n5t2LEQUWoUVbE/g8HnNyga7zr7/CUW40fd12GyghKpNPP8Xv7lEKeBul8+xYRJ5ZfT1+\nUrn43hq7/cmQkwX4i0PmnS+JaAkRNQrtV4jocSL6gYi+qo45h38v4IfIjkxAEhokVrbFe9I8/PR4\nnbeenc8loyqvVt+zJ5yMVnnmmRDXeYhS1atWYMpJt1S7CjHubdwoAUWQmTGzOdlK1zkYIwt4LKZc\nLiUHW4t3rFcywlphQNV4x1Vu9jaWoF9Odl4xw5y8tWqAjG1fvFh+VVYG0HQ4zOGML78s71dEa0Rb\n9ex9U+cpmptnDsT1Jk9GO60eiIIkmaTzQ/Xc/L6aw/vVZD50XnSe8TfewEomLg7avsCoVq1gftA0\n6dRt3twcE2/NL+jUyZwp+uyzZnB78EFERonnvvFGAL+YeATQ9+snqR+GNdF50CDmc5PNGaxjQ5w1\n4p37SOX59nyThl9GTr7WCTI5NxXwMsoJV7SyVl5a2jSfvaHaq36ni/M7hEJa1SiRLhVxz1j3V3Dc\n7t0IQBBtExMDX1FlwL9vjFmheYbyeIk9N2LfMXKxn/7Z5QP/CfLfSrzKy4sw6AadTt6XZrYjr6UM\n8wBUnLy4v4e3Xe7mQw9ITeSXXwBuM2ZEXmrYMOZZcVGiKaxi0fD9PXox63qYmpjITHPAzOEBeXSF\nzkV36/xcfH4YVEyTB8komSCRJB0PDaizYgBO3RQ9wr/33Xe4dmwsTBpGEVq1keo5GIQ9Wti1bTaA\n5e7FerjEnGi3m2/GrXz9Ncwkubn4/Prr0n7eqhUsOfXqRQ8/ZQboCsqDwYOlbVxE9TRpAuCPi8Nf\nY/m9+HizIzYhwcxp/9tvZlNE69ZgQh09GvdaqxbznMt0XnmOrFNrrDVQSk5kFMfkR/QtUw6E3R6e\nvI/1z+V9aRn89mAP9+8faV/P76DzjAHgD/IrMvlsaj+d996Itg0EMEH1dug8w+Xm5dP1E05r/Msv\ncFaLiT0mBhP30aOhA3Sdy+508xMjdf5WMSs08NFoXE52/kTJCE9oxmIzq9rm/+OLhf9d5b8F+EJ0\nHdq20Lgt0THbO+ZGLLEFk6BYgpbbXPx0dzjMii82a+5eLwDk/1pj6R5Qosc1h6WggA8lNOZy0sLk\nUF88Lu3ih94DaJYU6rx+tjkhKZN0vl2zpNcrSmgCUUya5OZGObzzDgm8oqaowwHt1Crt2gFE4+PN\nceeFhQC3p1qaB+Wqe/QwcyURczdFhNupoYlHZa/dxcum6tzbofP8NDeXrdT5yBHYwvvW0vmrSxEd\npKpwPg9KgYnqi8ejt53PB+e4qoLWQlToIkLY6vVdcU9DUnFP59fX+Q6bXL00bmwmZHv00dDKRdc5\ncJebb8rEcWPJw2spgwsTcnnaOSFneQjcy8nOcyg/gpRuMeVGOKqP/n975x4fVXXt8d8+ZzKTAVPe\nDwUMLx+oFKEQRCuNVxrFT2mAWq+KpVYRo4iCj4iKor069VO15WoLDl5UQKuVjy8qvkCLFU/Eii+8\n+EIEkQuC4aEgyWTmrPvHmj37nDOPzIQkM5L9/Xzmk+TMa5+TmbXXXnut36oYz58FwX0PPqqspquu\nIvrt0W7jPqm/RU+NCPHnweswxCf8Xct5opa6Nueeq0KLH3+swlNnn53CaWgGvvySaMIEd0eteb+x\nOI0YoHqY9K9S996Z7OwWgUlhs8q1Oc0rUtU3uN4XJPsNbfSbk7Zp8FMRz46hcJi/UH7l4ceM5E00\n1g5XolX1RoBevcOivS9atPWXVbQWJ8YbjoPsLPpeersY7e0/JOE1OhUhnYJhUWHSxktCibh7QpC9\nqorfr6qKyO9PNG2+zKeyfCJFQao+VenIX9UuzJ6gYzk/ezYluk69flaIPghbtHQp0WNXWq5soXP6\nWEneqHej0HZ80Z2bzfsRpNOKk+WeT4qLnjkf99BUiz74gGjjoyoziYjHvPGSEF3XMUw3GqrRiXfv\nwtvpKp2EsxQkk+mj3pqFOviSjHsUIvF5kMekPv0B+CkGQTEIem9sNT10sluK+Gd+ixb0C1HUuxpM\nJZCXgp07ObRy2GFsfM85h/cZGhpYB8nv59XKU08183cmzqZNRI/35Tj9JvR2rzJHj+Z9qYEDeYUd\nz+xq8AfpiS5O8TuDXkAFPYnxrmK9G0WIwhemCUVpckYb/HQ4VwHhcKKE0gY4nUwUuVYBUQiaB3cs\n1pVp4i2ySvF+McN0PXcRJiV1I9p8VlX6zbdG4rP7ZyuPMQKTnjHcsdTlg6sT6aMHDNbdb0zErAEm\nPXpCiP5xsvu1QyXOqtp4eqFhUKw4SNsnVlFMqHN67qchWjrMI6VshlJK9nrHM7tHOK5NlLzBeYPn\n+SvMCtffc/whmu1T/YQbwKmiXsO92WHEeMWHpHRSabSinlXVLHAc3nmdl/nURmvMMFX4Ipf/awp2\n7mQtJ5n++Otfs+Fft071E77ggvQ6TE3GE5Z0rWj8Han22jT7AxY3EI8Z/Hm7BO5eDQfgpxCqE5Oj\nV95akzva4GeL/KDKWHQ47FoFRMwAPd7JnW3h3CSOFvlZ1tj5Wt4Pb0mJ68uyA53pJFj8QffKIDfF\n47FU7nzUH6QP2rmN1qdioMuQP/bjEM3xu9P2/m865+DbQc+moMNg1Zvcmco0KSFN/Op5YdcXPZ20\nc8xgQ37HLywa19W9uhnX1UqaAN/toYx4IoVRmDTbDNHYjlaideF+BOmakrCSmRZBmtDTSurgtQm9\nXf9DG6CPe45O8vCv6ximekM1Eo/CoKg/SAu7KwN1wOAmNCsMd1bKGpRRQ1EOm6o58s03RDfdpOoR\nzj6bawLmzOH9jSOOSF030GQ8iQfeWwMM1lSakiJpwXHOdXOU0yCTKbwN3muGVtGeWdrbbyra4B8M\n3r0AyyLbn5wTLT3K24o5JlzvC1JMpMhI8NQKLMIkAriXZ7MtaR1fsNj97hS/10+pTjLEshArAs4C\nyWic4sd2LrOoXTulV9O9O2/AuhpwZHj+fedzSGbmTN4HeHxIiG75OR/71RE8nqi8fo7VFwGJjekN\nSywaNIjDOvcdwSsD2XJxQX/eZ+jRg+h74e7g9T0CrlVaPYq4+GxgmPadUEaxyvE0fTiP5Wd+lqy+\ndzB33arsbtFDDxG9crtFf+7O7zloENENXd3XWWok/aVXiHY823KGq7aW6wuk4Z84kbVwpBDexRdT\nxirabIl5PrcNXbsl1VMkwl6mn6JT02S+edKOXz22yhVK5Wy0AK+khEn2wKN4E+YH0m2qENAGv7mR\nk8Do0UTHHUe2z8fLdn+Q/vBLNgTOsMLTZSFauTKe/x4K8T5C58706UmTEguFhoYWHG84TF8cw2l/\nFRWUZIj/+U82krN9IQqNy9443XILj12W2WdS+PQSjXJ3LdNUchEPP8xeK8A9YGeBUzsbGhxj9miw\n7N/PufkAyyEHg+5Uzb59iZbD01qwpIJOFqz3M19U0egiNu6yknfmTDaS8vyE4KyYV1/lJtwAb0Av\nX060ZInKDKruFKYXUUHXdwrT/PmqaKyoiOjee1umQbiktpbo5ptVrUBlJQu1GQbXEzj1g3JFKlzG\nZKWv1IhyhEBtJLfujPkDqdOUPSEfCgZZI8gsojW9xrsznBwTijb62aENfkuTIsc5Vsweap3JhU3O\nuHSkKEifPGzRk9ey+mQNynIuBsuV99/n/3CXLsn3NTTw8dJSTpnMdvL59lv27GXFa0kJGxiv3lA6\n9u5lnZeOHbliuX17zjx54AF+HVlQ9ctfNt5t6YknOGuqfXs2tIbBxs8wOLa9HBX0vRGk9aUVVFTE\nqxFnA22vRECPHmzMly9XhVwTJ/JE9dhjysiPGcMqo04DLyecK6/kPH6Z4TJkSOPdsA6WXbs4rCMV\nLkePVgVk06Y50iqzZN8KzrRKqXDpnIRl8oBINvyNJjR4JoAkVVB5i9evaDKjDX4+cG6k7idae3Zy\nJ6cGRwYQAY1m+RwMsZgyRM6G15ILL1T3exUiMzFvHj9HShYUF7MwWbbe7Oef82QzYAAb4SFDOD30\npZd4AunQgQ3m6NGs8pmJL75QKpl9+/LPPn34Z//+fH5durC2jkxnPOEEt4yCLN6Sq5ZTT+W+t3Ly\nGTCAFTbr6ljqQap7XnABT1Z33aWOycf/7W9qUvT5WLqhWbpGZWD3bk5llYa/f381HqfefyZ2LstR\n4TK+8rUDgWSP3zAz60s5CYcTHyjt4eeONviFgCN2GfMHaGdJX5cXYwO0rbSMXn+dhdhaIkVNap+s\nWJF837PP8n2BAHul2RKJcAFVhw7K6AOsVJktq1axIZTdqq64go+/9x4LxBUX8/1DhngqktOM58Yb\n+XW6deMxHXEETx7FxaqL18yZbHiDQbUqkGOXxVpCKM336dNVs/P27VmDnogN66xZ/Di/n/sTbNrE\nxlZOoEJw5e6MGcrbP+44JY/ckuzeTXTbbUoOQ47p6qszrJosizZXhWhRuyYqXErDb5pJ8X3b78/O\n8Muw6Ykn6hh+jmiDXyjEP8RR09N6EKqIxxn6qTOD9PhVFq1erQqzDmYSkH1rZ81Kvk+2Puzfnwux\ncvFAn3pKGcsOHdjwHXlkbk2vZTcxmVootWq2bOF4uWw4MmBAmgYiHl55hY24z8fGvl07JcomQxyy\nlaOUYx440F2kLX+XKZA9evC+g7zvr39V7/fllxwzF4LlIO6+m8d+xRVqIuzRg/cppOyDabKGvFO+\noqXYs4ffyyn7XFqqevlK7DdU0V8dAhQ7GIXLcJioqChlfN+WWtbNqGirYbTBLyC+nuiuykwYfMOg\nr59hAa6oI23Sm5de7+MK3chruU8A69bx240cmfr+iROVJ5jtsp+IwzennOLuMQuklqPIxJVXKkPU\nqZOKd+/dy83jAbY7PXsqSeNM7NypNOtlmKW8nCeOww7jSaBDB25R+cADHMcPBNxyC85wj1wFHHec\nCv2ce647fPX++0Rjx6qJZckSngzGjFGvefrpfK7ytY8+OvdWgU1l716i2293y0tfeCFnie25PkTL\neimvvlnaD0pv3+9PkgVJfP7Lypr3JNs42uAXCC/fZrmKeRJxh/Hj3amQjrTJncssev/c5Pi/cwL4\n+CHWV2ksxzsWY6P8ox+lHt+SJZSIX191VW7nZlnqlKRMcCDAxi5bGhrY4fP52BifcoraQI5EWMlS\nhiU6dFA9hDNh2xxrLypSGkDDhinJ4y5d+Ofll3OIRXa/6tXL3cxb/t6tm+ryJNsaHnNMcurjypUq\nRDV0KIfR3nhDTRo+HxtauddgGJyh1KLZWg727uWsnkDAXa18AAFqMP3N334wYfgDLmOf+F3n3Dcb\n2uAXAMsrWXNHejm2EGx90+Uqe5UNHbnLG8+oSqwCZHVqRQk3YInGc+n3vpg6l142/k6ld75rFxui\ngQM5bJrrxuLEiWy4jj6aDZsQXAmaC7t3swGVHuhNN6n7bJtPRXr6xcVEy5Zl97rvvEMJXaGiIvbi\nJ092t0YcMoQ3Xh9/nA27z+desTi9/hEj+Pzk5m779vweTmIx7qQlQzhnnMEicTfdpLz7khLWOJIT\nSr9+2Wc5NQer/mDRq0VK2KzFm4pbFlGXLklVzfXlFdroNxPa4OcTy6IPTnGX8pNhsCubywc8Re4y\nmdwH98U5Fi39SbJEQWX3uOpifBL47mWLpk5lj+6D81J/oceMUYqUb76Z26l+8okyZNIwA9l54k4+\n/ZQNrWxO4s0hf/RRNrTFxbwQeuih7F73u+/Yq5YThmnyZqwsHisuZsO9eDGHg2QjdbkK8N5KS9We\ng1wFPPBA8vvW1RHdcw+fk+xnu2qVKo6SKwcZSpLN6SOR3K5bLuzbp9paRh2yGK3SjSq+HPRW6mrZ\n5OZBG/x8UVrq+lAnvt1SLvdgSLEKkFII0UCQllzO1avetoUTeqrle6ov2F//SomQw3XX5T6sqip+\n/skns8wwwN2fct2YXLFCNcfu2ZObzTh57TWeEKSHfddd2b/2o4/y68rnVlZyRaoMF8m49r59RP/4\nhwrvSMVK57/SNFnWwJnH743rS3bt4mSTQIBv117LGUWGocbSubN6jz59iN5+O7fr1igW7xNN6OmW\nsGiSE3KQ46CqKqKyMorFVxe6123zoA1+Phg0KClWmbCkLZVvnyYUJCeBhVO4iMY5CdzfN0QLfseS\nxbHVrP8vjXS/fmny6Z2qox6+/loVKlmW0r6/777cT+cvf1F24Mwzk0NMH33EMXCZBVNdnX3+/4YN\nqmpWCA73zJ/PmTSGwccGDeLwyp49RJdeyo+VGTveHrrHHkv0q1+pv3v1Si9psHkzh5OEYAM/c6YK\nN8nVhFPHf/p0XiUcLLXPKd2i70WQPr4mnJVSZ4uSTlBO02S0wc8HhpFs7LMtPGlO0lQBxwze8L2t\nd9glWTzvNxYdfzwlPMCPH/KMN+zWjGmYF056nxtu4NMdO5Y9dRlCaYqC42WXKcOXyovfvt3dePyi\ni7Lf+Kyv50lCzsPBIKeHnn02JRZigQAfs22WoBgwgO/z+911B/I1ZsxQm8OmyRXA6Xj3XdVoprRU\nZff07OlurwjwnkhNTe7XjyyLYneE6JnrLbqtOEWznmYSczsoCmEMhxDa4OcDj4dPpaX5HpHC+QUL\nhch2NE2/USSngTq/iNHhbvXNNSijCwaoDk1yw/guk7XT915enfB8zzkn96FGIly5KwQbUG/eOBHr\n6VRWKuNYWelu5tIYL73EKxEZSpk+nfcFZBctgNv+7d3L73XttfxY6YU7vXGAN39lkRvAef6ZJruX\nXuLnAOzpd+/Or/+LX7gLwgDWDcq6vsGyKOpXSqR/PCqcrMqqOeTQBj9fDBrEa/9Bg/I9kvR4ltS7\nn7do5enusE+4X4j+PsOifbNDtKPY3fzig4Hj6f5S9+NfEx6p4RnViVDI2rW5D7G2lkM3hsFx7VQS\nC9Eop5JKw3jqqY1LMTjZvp2zZeTzy8q4mra8nBJhn9JSbtpNxBOP3HQ1Td7w9Xr7kya5WwQuXJg+\n8ykW481iKQUhK4JPPFE1PpGv3bkz72GkJS5D/MYQdyMd+44C8eg1LYo2+JrMpAj7ODXqr+vIYZ+Y\ndwNaCCV45ehyVNu+l7uy0uejj8dXE8ChiqaoRq5frzZUJ0xI/xpz56qhnXACG/JsicU4bCRj+J06\nccXun/7E4R3DYEM+dy6/f309Syj4fGol4NTRAXgMctxyIsk06R04wGOQGUqBAIeP7ryT6Jpr3GGk\nCROSm4vHVrM0t8ypjxgtkFOvKWi0wdfkzLzfWInwzor/CHHPXu8mdO/e6gmOSWNLuVs7Xd7+p2s1\nnQRuCt4U4/PCC8pjvv/+9I97+mk2ktIr37gxt/d56y3laRsGG9t169h4S2N71llK+3/dOrUBLI2+\nzLqR3r4UT5MSzJdd5ukd4KG2lg283682iE89lSt5nSuRQIDoxTl87d8PWzTvSHczmxbNqdcUJNrg\na3JmzRpKxJQn9bc4JODdhE4laGVZVCcCFAMSKwL5nM3orSSifUH65h9WziGGe+5RRvTDDzOPX+a9\nd+6cnRSDk717ec9BnurYsaqvrJx0uncnWr2aHx+Nsn6OnGiEUPn98tatm9vod+3KefuZCty++IJD\nQ04Dv3AhT0rduyf39L3mR2GKpOq0pWkzaIOvyZlYjOPIgwcTTUFY9ayVlmfSpJTP23u+WyvI+fv2\nY0cnSUQkCsP8Qapflbo62IltE513Hg+hd2/eRE3H558r6YJ27XLTB5Lv9fDDKs30iCN4klm9WsXY\nhWDNIGm0P/vMvWFbWurO0TdNNWHIDdmRIxvPt1+7Vkk/A0R3lIZp6+AKWlGieuc2wKS6OTpO39bR\nBl/TJKZN4xZ/9fC54/YZmrV/28m9qUsnnsiubnW1qy4gFgjS2yOqXBPAbF+Ipg2zqM7gtNF0lZf1\n9Sq8UlmZ+RxqazluLlcF2UoxOPnkE65LkK/x8MMsTSE7dQFcaCb3C2Ix1ZVRGvlTT3V7+zJM068f\n5/4Lwbn+qXoVSGyb6LnniK4p8aTGCjPh4VcNseizz3I/R82hgzb4mtyxLPrsohA9ifEUdRrwDFXC\ndoW7lSABWbW3k5u9fz7Hork9Qi6P9ZUxIfry78ke644dqrnHvHmZT6WujnV+5Hy1cGHul6OuTrVS\nBLjhSX099xGQWjwlJZxiKdmyxa2SOWSI0tVx3jp14spe0+TCqwULMod5Yj93N0yPwqD/HV1FT1db\nNKa9RTf7QvTYlVaryC5rCg9t8DWZcRjhnTt5E5CF2ITbgJtmxirhaEA1C08EnHN4b/m3bA95wAjS\nFIRdcf+PHlTKoFuvCNHJwiLDyBzPJ2IDeu21amhNreB/9lmVgjlgABv1HTu4VkC+9pVXquIv2yZ6\n5BFVjBUI8MrAK9FgGLw/IVcCI0aoJitJOCZWAigK0DxUUdisojr4E97+7/uEafNZVRRzNhR3tiXU\nYZ9DEm3wNS5iZWVEPh9Fjh5Em8+qonojQNF4pe0UhOkFVFDMu0HbSCiHiJIMUZObWzgmgd3VoSRl\n0F904bTRmODWeyfBoooSixp+37gBky0ZAQ5ZNSVFdOtWlYMfCHCYyLZZmkHq5B9zDE8Gkn1XVNM+\n8zDahwAtwiQ67TR3hbC8zZzJE0TPnnzJp071hHksi2xheDx8QQcQcOnNN8BICPbZAB2An67vHI7v\nmRjxRiQGRQNB2nJLmHb9ZxXvv7yhJ4AfOtrgaxI0xCtl7YSxgMtI1MOXONYkwbeKCg5eN1cnI6c0\ndHGQlt9s0cPHugu9FvqrMgrCeVm+XKVNTpzYNA36aJRlFLyTx8aNShfH72ehNqquTkpRXYRJ1L49\nb/h6K3VHjmQjP3MmL6o6d+Y01GiUaN9NIYpCJIy9DdC+n4zmFEyojlINwkcxqGVEFIJeQIWSVkgz\nMdQJP62ZEqb6W0OqOXk+JEE0TUYbfE2CmM/n8g5dRsIoUrroTgskRIs2WG+UNMqgMYPDPE/1qEqq\nDL7lFqKVt1u0f3Zqr//dd1Xs/ZRTcpNicLJypSqsOv54llCIRt3hox3FvVzX0wboO9+PEvdPmKD2\nGOStpIRTMtet4wbuANHkoyz6e+cqOgA/2cLgOJBjM5xMk2ePqir+f8VnEhsg2++nA/eGyS4Okh3X\neYrCSDkx1KPIlZVlA7x00Ub/B4E2+BpFWVmy9y4bS8vUEq8UZCE2kPZs/saKlfbP74616GTh6OIk\ngnT3ryxasoTowwdYTIwsi7Zs4VRLGYJJp27ZGLt2cTISwJfv1Vf5+Jo1RBUlFkXiWU4uA2oYZN1j\nJVI2O3bkjmNyI1rennySVw4v3erQNxIB+v63Hq87VSqmlCCuyhDDj08MiRWfMCmaatIHuDObpuDR\nBl/jJh7Dp0GDUhuJUIiNfBoJ5ILEY/AO3BKimFDZPnP8blG4A0aQ7jvfooULiSYebtFaDKH9CFBD\n955NOmfbJrr+emUb7zvfoujtIXp5QJUrjJLYG4mrVe7b55ZVvuACoquvdtvZM88k2j7DHca6rThE\n8+c3UwN058TgmPSTHAPde/YHgTb4mraHRxQu+rpF2650N4ifbfIk0OCIYctb/V/CTSpgeu01otOK\nk3vExgz+/QD8FIFJETPg8rxfekkVaHXtyqEiZ6etRZiU2JSNFQfp8qEWAdw3N9fOZFldu1DIXeIL\n/HAm/zaONviatkmahjByEmiYF6ZdZRVJoQsboDdRljDaDf4gffE3ixqurVZFZBnYdZ27lkDq2ex+\n3qLz+1k0DxyHjwm3/IGzXgAgCo2z6N8jqmgtTnRNRh8OnUTrFlj0zq9DNK4rG/6LL+b00GYn2wsv\nSQAAC6VJREFUQ7MbTWGiDb5GI3HEsO1gcq2BvD2J8a4Qyj/hlnxOZ/Q/f8SixzqyQY96DLrksR87\nWgumaOv38stEp7ez6AAC7veM/9yGrvHJyKAIfPT7PmEyTaIx7S16/xRH3r2WWGiTaIOv0XjYNzvk\n2pxMVBH37ElvXRJObPpGBTeK31PizrShgQOTXrPmTyqUEysKpE9ntFg+ogEG2b6ilN5zw3+FXNkz\nToP/Ofq6sqnqUURTEObsnfixiCiiBlFEMQgeizb6bYZsDb4BjaYN8Fn1AqwPPQMCQAAEAGEYwG23\nAdu2YcSCqbhr9Sic6XsFs+m/sPTkudhaPACIPx4AMHGiesGaGnw17Q9YP2sx/IjAhxgMOwoceSQw\nalTyAEaNwrZZc2HDgB2NATNmADU1rof4Ti8Hmb7EGBPv7fPhsNtvgGEaibH7RAwzej2JIjTwuQDw\ncTAKBgiioR6LxyzGlCnA8vELEOvcFeT3A2ec0UxXVPODJJtZobVu2sPXtAQr/9MtPGYb8Xz2FKGX\nDRs4tJKI5QuTJTqd4RzLokgR31+HAMVks9vGCsBCKfrLelhfXqW8fMPgWLp8zXBYdWUJBvnvuKyn\nPC/nqmAeqmgKwkmhKzrqKC2zcIiBLD18X74nHI2mJdn/45EoX/dvAOwFEwAxfDgwfjxQXp7kjQ8Y\nACy7ehX8t7PXbgsTuPxy4IYbEo95885VGN7A95smIC66hD37FK/norwcVORHtKEehjAgunRJesj2\nw4diAEwUGTZEIADceqt6zalTgcGDgVWr1HsNHgwsXgwBAEOHAtOnAw0NQFERfjZvMiruvBXYwOeO\n+Pnbn30GcemlIBgQfh/ERRcBkydnHrvm0CCbWaGxG4BrwJ+lrvG/BYB7AWwA8AGAYdm8jvbwNc3J\n1/3LkjZAs0k1fPPiMNWjiOP9Dq99/0qLnhga4ti50bSGIztDYaqHj+Px3udmEedvFO+mbTjs2rNI\nV3GtG6f8sEFrefhCiD4AKgB86Tg8FsBR8dtIAPPjPzWaVqPzpncAKO8WQgD338+echp2P1+DwQtn\nwEAMhs8A5s4FRo3CV0tr0Pmc0zEBEVT6/DDvmwuxu7Zxr95DV9QiCoIPNqiuDmLxYvX8VavgsyMw\nYYNIALW1uZ/0qFHu8cTPVdx4I7BnD2DbfB1sGzb42hggIBLhlYP28g9pmmPT9s8AquHY2wJQCWBx\nfPJ5E0BHIcThzfBeGk3W+IYPcx8YMSKjsQeAF2etim/C2hxJr63FE08AC3+zKrE566cIzN21HObJ\n1UCWlwM+3pgFEfDgg2rztrwcUZiIQUCYJj+2OZg6FfjmGyAaBd54A7j9dohwGEZVFYeNTBPw+5vv\n/TQFy0F5+EKISgBbieh9IYTzrl4Atjj+/ip+bFuK15gKYCoAHHnkkQczHI3GzZo1ECNHAu+8Awwb\nBqxZk/Hhq++uwZ51X4JM/lqQz4cV//Ml/ryxBscfVw5jox9oiByccRw1Cnsn/A4dl4ZhgoBYLOFZ\nb94M9IDgFYn7+9R8eFcAkye79wQ0hzSNGnwhxEoAPVPcdROAG8HhnCZDRAsALACA4cOHUyMP12hy\noxEjL/lqaQ2GXXc6TkIEpmli96hxCL72PP5j4wMo9y2Ccf8rMHyvNItx7DJzMuqfWgSK1cN0bN7u\nn78YRYhwiCUabZ0Qi3cC0BzSNGrwiWhMquNCiMEA+gGQ3n1vAO8IIcoAbAXQx/Hw3vFjGk3BEY0C\ny69bhYvjIRs7YuPT1/4PP0EMPsQAigCrVzUthJOKUaMQ+eNcBK6ZBkRj8M2YAQAY+PpDMECcSdSc\nIR2NJk6TY/hEtI6IuhNRXyLqCw7bDCOi7QCWAZgsmJMA7CWipHCORlMIzDmjBvbmLyEMWdhE+Il4\nF6bfbLH4dkl9LUy5eRuJILb0SRgU5SIqIYCLLtKet6bZaak8/OcBnAVOy/wewO9a6H00moMifGEN\nbn71NPgRgW0L2BBsiA0byDa/vimUl0MU+xGtq4cgA98Wd0N7CMRgwCwOcGxdo2lmms3gx718+TsB\nmNZcr63RNDs1Ndi5dBV6LnkLAdTH5QkIZJp8v9/fssVIo0bB+O+5iFVNA+woOj73KGIQMHy+RCqo\nRtPc6EpbTdtjwQLQtCvQORrDWXBnwxjjxgFlZa2TtVLLYR0Zt/eBQGQ3Lf9eo8kCbfA1bYuaGtiX\nVkGAwL68ATJMCLIhioqA6urW867Ly4GAH7G6ehiwEQNg6s1aTQui1TI1bYvLL4cAJXR1DEEw5s8D\n7rij9StN42EdO77KMAAuxtJoWgjt4WvaFhs3uv4UwWCj1bctSm0tzITJBwufaYkDTQuhPXxN22Lc\nuIRxFQAwYUIeBwPO1gFcGvgIh/M3Hs0hjfbwNW2LRx7hny+8AIwdq/7OF6NGqYYs8tiWLekfr9Ec\nBNrga9oe+TbyHoxBxwIffaQOHHNM/gajOaTRIR2NJt+sXw8MGgQYBv9cvz7fI9IcomgPX6MpBLSR\n17QC2sPXaDSaNoI2+BqNRtNG0AZfo9Fo2gja4Gs0Gk0bQRt8jUajaSNog6/RaDRtBEEFJNYkhNgJ\nYHO+xwGgK4Bv8j2IDBT6+IDCH6Me38FT6GMs9PEBzTfGUiLq1tiDCsrgFwpCiLeJaHi+x5GOQh8f\nUPhj1OM7eAp9jIU+PqD1x6hDOhqNRtNG0AZfo9Fo2gja4KdmQb4H0AiFPj6g8Meox3fwFPoYC318\nQCuPUcfwNRqNpo2gPXyNRqNpI2iDr9FoNG0EbfDjCCF+LYT4XyGELYQY7rnvBiHEBiHEJ0KIM/I1\nRidCiFuFEFuFEO/Fb2fle0wAIIQ4M36dNgghZuV7PKkQQmwSQqyLX7e3C2A8DwohdgghPnQc6yyE\nWCGE+Cz+s1MBjrFgPoNCiD5CiH8KIdbHv8dXxY8XxHXMML5WvYY6hh9HCDEIgA0gDOBaIno7fvw4\nAI8BKANwBICVAI4moli+xhof160A9hHR3fkchxMhhAngUwA/B/AVgH8DOI+ICkrsXQixCcBwIiqI\nohwhxGgA+wAsJqIT4sf+CGAXEd0Znzg7EdH1BTbGW1Egn0EhxOEADieid4QQJQDWAhgP4EIUwHXM\nML5z0IrXUHv4cYjoIyL6JMVdlQAeJ6J6IvoCwAaw8dckUwZgAxFtJKIIgMfB10+TASL6F4BdnsOV\nABbFf18ENg55I80YCwYi2kZE78R//w7ARwB6oUCuY4bxtSra4DdOLwDOrtJfIQ//qDRcIYT4IL7c\nzuuSP04hXysnBOBlIcRaIcTUfA8mDT2IaFv89+0AeuRzMBkotM8ghBB9AQwFsAYFeB094wNa8Rq2\nKYMvhFgphPgwxa0gvdBGxjsfwAAAJwLYBuCevA72h8VPiWgYgLEApsXDFQULcdy1EGOvBfcZFEIc\nBuBJADOI6FvnfYVwHVOMr1WvYZvqaUtEY5rwtK0A+jj+7h0/1uJkO14hxAMAnmvh4WRD3q5VLhDR\n1vjPHUKIp8GhqH/ld1RJfC2EOJyItsXjvzvyPSAvRPS1/L0QPoNCiCKwMX2UiJ6KHy6Y65hqfK19\nDduUh99ElgE4VwgREEL0A3AUgLfyPCa5CSSZAODDdI9tRf4N4CghRD8hhB/AueDrVzAIIdrHN80g\nhGgPoAKFce28LAPw2/jvvwXwbB7HkpJC+gwKIQSAhQA+IqI/Oe4qiOuYbnytfQ11lk4cIcQEAPcB\n6AZgD4D3iOiM+H03AbgIQBS8FHshbwONI4RYAl4GEoBNAC51xCrzRjytbC4AE8CDRHRHnofkQgjR\nH8DT8T99AP6W7zEKIR4DUA6Wyv0awBwAzwB4AsCRYMnwc4gob5umacZYjgL5DAohfgrgdQDrwNl2\nAHAjOE6e9+uYYXznoRWvoTb4Go1G00bQIR2NRqNpI2iDr9FoNG0EbfA1Go2mjaANvkaj0bQRtMHX\naDSaNoI2+BqNRtNG0AZfo9Fo2gj/D1KYU/J75gHUAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f58b45e2438>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "g.plot(title=r\"Original ($\\chi^2 = {:.0f}$)\".format(g.calc_chi2()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Each edge in this dataset is a constraint that compares the measured $SE(2)$ transformation between two poses to the expected $SE(2)$ transformation between them, as computed using the current pose estimates.  These edges can be classified into two categories:\n",
-    "\n",
-    "1. Odometry edges constrain two consecutive vertices, and the measurement for the $SE(2)$ transformation comes directly from odometry data.\n",
-    "2. Scan-matching edges constrain two non-consecutive vertices.  These scan matches can be computed using, for example, 2-D LiDAR data or landmarks; the details of how these contstraints are determined is beyond the scope of this example.  This is often referred to as *loop closure* in the Graph SLAM literature.\n",
-    "\n",
-    "We can easily parse out the two different types of edges present in this dataset and plot them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def parse_edges(g):\n",
-    "    \"\"\"Split the graph `g` into two graphs, one with only odometry edges and the other with only scan-matching edges.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    g : graphslam.graph.Graph\n",
-    "        The input graph\n",
-    "\n",
-    "    Returns\n",
-    "    -------\n",
-    "    g_odom : graphslam.graph.Graph\n",
-    "        A graph consisting of the vertices and odometry edges from `g`\n",
-    "    g_scan : graphslam.graph.Graph\n",
-    "        A graph consisting of the vertices and scan-matching edges from `g`\n",
-    "\n",
-    "    \"\"\"\n",
-    "    edges_odom = []\n",
-    "    edges_scan = []\n",
-    "    \n",
-    "    for e in g._edges:\n",
-    "        if abs(e.vertex_ids[1] - e.vertex_ids[0]) == 1:\n",
-    "            edges_odom.append(e)\n",
-    "        else:\n",
-    "            edges_scan.append(e)\n",
-    "\n",
-    "    g_odom = Graph(edges_odom, g._vertices)\n",
-    "    g_scan = Graph(edges_scan, g._vertices)\n",
-    "\n",
-    "    return g_odom, g_scan"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of odometry edges:      1227\n",
-      "Number of scan-matching edges:  256\n",
-      "\n",
-      "χ^2 error from odometry edges:             0.232\n",
-      "χ^2 error from scan-matching edges:  7191686.151\n"
-     ]
-    }
-   ],
-   "source": [
-    "g_odom, g_scan = parse_edges(g)\n",
-    "\n",
-    "print(\"Number of odometry edges:      {:4d}\".format(len(g_odom._edges)))\n",
-    "print(\"Number of scan-matching edges: {:4d}\".format(len(g_scan._edges)))\n",
-    "\n",
-    "print(\"\\nχ^2 error from odometry edges:       {:11.3f}\".format(g_odom.calc_chi2()))\n",
-    "print(\"χ^2 error from scan-matching edges:  {:11.3f}\".format(g_scan.calc_chi2()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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yZfp370hhD5BKYf1hEiidfD04ZxyJwZUESmcPaMmkcsGJF7CoYjtevettcsoh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\nAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f587b80bc50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "g_odom.plot(title=\"Odometry edges\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Jzn1WrGAcfP/+FC+rDMEgj9mtG7NgS0tFjjqKnHXffaw3q89br56lpVNdVFSI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\nFTs2RGNhsDdQwXa9ICp46ZiyZb7fH++ePWdmZ1MQUK/5nme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ILDMMDI1YvWlrqUU4hRsj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\nQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f587b80b668>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "g_scan.plot(title=\"Scan-matching edges\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Optimization\n",
-    "\n",
-    "Initially, the pose estimates are consistent with the collected odometry measurements, and the odometry edges contribute almost zero towards the $\\chi^2$ error.  However, there are large discrepancies between the scan-matching constraints and the initial pose estimates.  This is not surprising, since small errors in odometry readings that are propagated over time can lead to large errors in the robot's trajectory.  What makes Graph SLAM effective is that it allows incorporation of multiple different data sources into a single optimization problem."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Iteration                chi^2        rel. change\n",
-      "---------                -----        -----------\n",
-      "        0         7191686.3825\n",
-      "        1       320031728.8624          43.500234\n",
-      "        2       125083004.3299          -0.609154\n",
-      "        3          338155.9074          -0.997297\n",
-      "        4             735.1344          -0.997826\n",
-      "        5             215.8405          -0.706393\n",
-      "        6             215.8405          -0.000000\n"
-     ]
-    }
-   ],
-   "source": [
-    "g.optimize()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Graph_SLAM_optimization.gif](images/Graph_SLAM_optimization.gif)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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+QwlrklvTY+kKDiCGkUoPMfvUmSSIMJAgXgdO5GmSgAF8bOzLPc553MVY0mm4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\nBL6Dg4NDK+H/A7aRY5b1tCw8AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f587c25a0b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "g.plot(title=\"Optimized\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "χ^2 error from odometry edges:       142.189\n",
-      "χ^2 error from scan-matching edges:   73.652\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"\\nχ^2 error from odometry edges:       {:7.3f}\".format(g_odom.calc_chi2()))\n",
-    "print(\"χ^2 error from scan-matching edges:  {:7.3f}\".format(g_scan.calc_chi2()))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Wm4l+8IHIUUfpR+rEAbaeTZhm0Qyk3tCDryFDRP4T62Az4fXgG/y2Yl36ZnV1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\nRqPRVAj/D0Fj/txvoohWAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f587c00dba8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "g_odom.plot(title=\"Odometry edges\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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kWku1JLBlLdUyAkd69hQ5q0dUPqFPXl1oqalpu0pZZQbtHWkLbO97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\nXvgUCWjQIb10hNcDHOIvddM6Jvn0CsVauqSGn8Gbb5K/qjT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-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f587c7e3cf8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "g_scan.plot(title=\"Scan-matching edges\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/SLAM/GraphBasedSLAM/graphSLAM_formulation.pdf b/SLAM/GraphBasedSLAM/graphSLAM_formulation.pdf
deleted file mode 100644
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diff --git a/docs/_static/custom.css b/docs/_static/custom.css
new file mode 100644
index 0000000000..51c98d21ce
--- /dev/null
+++ b/docs/_static/custom.css
@@ -0,0 +1,23 @@
+/*
+ * Necessary parts from
+ * Sphinx stylesheet -- basic theme
+ * absent from sphinx_rtd_theme
+ * (see https://github.com/readthedocs/sphinx_rtd_theme/issues/301)
+ * Ref https://github.com/PyAbel/PyAbel/commit/7e4dee81eac3f0a6955a85a4a42cf04a4e0d995c
+ */
+
+/* -- math display ---------------------------------------------------------- */
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+    visibility: hidden;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
diff --git a/docs/conf.py b/docs/conf.py
index 64b32d5047..de28ce4752 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -42,7 +42,6 @@
     'sphinx.ext.autodoc',
     'sphinx.ext.mathjax',
     'sphinx.ext.viewcode',
-    'sphinx.ext.viewcode',
     'IPython.sphinxext.ipython_console_highlighting',
 ]
 
@@ -53,7 +52,7 @@
 # You can specify multiple suffix as a list of string:
 #
 # source_suffix = ['.rst', '.md']
-source_suffix = '.rst'
+source_suffix = '_main.rst'
 
 # The master toctree document.
 master_doc = 'index'
@@ -100,6 +99,8 @@
 # so a file named "default.css" will overwrite the builtin "default.css".
 html_static_path = ['_static']
 
+html_css_files = ['custom.css']
+
 # Custom sidebar templates, must be a dictionary that maps document names
 # to template names.
 #
diff --git a/docs/getting_started.rst b/docs/getting_started_main.rst
similarity index 100%
rename from docs/getting_started.rst
rename to docs/getting_started_main.rst
diff --git a/docs/index.rst b/docs/index_main.rst
similarity index 100%
rename from docs/index.rst
rename to docs/index_main.rst
diff --git a/docs/modules/aerial_navigation/aerial_navigation.rst b/docs/modules/aerial_navigation/aerial_navigation_main.rst
similarity index 100%
rename from docs/modules/aerial_navigation/aerial_navigation.rst
rename to docs/modules/aerial_navigation/aerial_navigation_main.rst
diff --git a/docs/modules/appendix/appendix.rst b/docs/modules/appendix/appendix_main.rst
similarity index 100%
rename from docs/modules/appendix/appendix.rst
rename to docs/modules/appendix/appendix_main.rst
diff --git a/docs/modules/arm_navigation/arm_navigation.rst b/docs/modules/arm_navigation/arm_navigation_main.rst
similarity index 100%
rename from docs/modules/arm_navigation/arm_navigation.rst
rename to docs/modules/arm_navigation/arm_navigation_main.rst
diff --git a/docs/modules/introduction.rst b/docs/modules/introduction_main.rst
similarity index 100%
rename from docs/modules/introduction.rst
rename to docs/modules/introduction_main.rst
diff --git a/docs/modules/localization/localization.rst b/docs/modules/localization/localization_main.rst
similarity index 100%
rename from docs/modules/localization/localization.rst
rename to docs/modules/localization/localization_main.rst
diff --git a/docs/modules/mapping/mapping.rst b/docs/modules/mapping/mapping_main.rst
similarity index 100%
rename from docs/modules/mapping/mapping.rst
rename to docs/modules/mapping/mapping_main.rst
diff --git a/docs/modules/path_planning/path_planning.rst b/docs/modules/path_planning/path_planning_main.rst
similarity index 100%
rename from docs/modules/path_planning/path_planning.rst
rename to docs/modules/path_planning/path_planning_main.rst
diff --git a/docs/modules/path_tracking/path_tracking.rst b/docs/modules/path_tracking/path_tracking_main.rst
similarity index 100%
rename from docs/modules/path_tracking/path_tracking.rst
rename to docs/modules/path_tracking/path_tracking_main.rst
diff --git a/docs/modules/slam/FastSLAM1.rst b/docs/modules/slam/FastSLAM1.rst
index 052f767489..6e26aef72f 100644
--- a/docs/modules/slam/FastSLAM1.rst
+++ b/docs/modules/slam/FastSLAM1.rst
@@ -2,14 +2,6 @@
 FastSLAM1.0
 -----------
 
-.. code-block:: ipython3
-
-    from IPython.display import Image
-    Image(filename="animation.png",width=600)
-
-
-
-
 .. image:: FastSLAM1_files/FastSLAM1_1_0.png
    :width: 600px
 
@@ -31,8 +23,6 @@ positions by FastSLAM.
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif
    :alt: gif
 
-   gif
-
 Introduction
 ~~~~~~~~~~~~
 
@@ -547,6 +537,9 @@ indices
 References
 ~~~~~~~~~~
 
-http://www.probabilistic-robotics.org/
+-  `PROBABILISTIC ROBOTICS`_
+
+-  `FastSLAM Lecture`_
 
-http://ais.informatik.uni-freiburg.de/teaching/ws12/mapping/pdf/slam10-fastslam.pdf
+.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
+.. _FastSLAM Lecture: http://ais.informatik.uni-freiburg.de/teaching/ws12/mapping/pdf/slam10-fastslam.pdf
diff --git a/docs/modules/slam/ekf_slam.rst b/docs/modules/slam/ekf_slam.rst
new file mode 100644
index 0000000000..df520fbdaf
--- /dev/null
+++ b/docs/modules/slam/ekf_slam.rst
@@ -0,0 +1,593 @@
+EKF SLAM
+--------
+
+This is an Extended Kalman Filter based SLAM example.
+
+The blue line is ground truth, the black line is dead reckoning, the red
+line is the estimated trajectory with EKF SLAM.
+
+The green crosses are estimated landmarks.
+
+|4|
+
+Simulation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This is a simulation of EKF SLAM.
+
+-  Black stars: landmarks
+-  Green crosses: estimates of landmark positions
+-  Black line: dead reckoning
+-  Blue line: ground truth
+-  Red line: EKF SLAM position estimation
+
+Introduction
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+EKF SLAM models the SLAM problem in a single EKF where the modeled state
+is both the pose :math:`(x, y, \theta)` and an array of landmarks
+:math:`[(x_1, y_1), (x_2, x_y), ... , (x_n, y_n)]` for :math:`n`
+landmarks. The covariance between each of the positions and landmarks
+are also tracked.
+
+:math:`\begin{equation} X = \begin{bmatrix} x \\ y \\ \theta \\ x_1 \\ y_1 \\ x_2 \\ y_2 \\ \dots \\ x_n \\ y_n \end{bmatrix} \end{equation}`
+
+:math:`\begin{equation} P = \begin{bmatrix} \sigma_{xx} & \sigma_{xy} & \sigma_{x\theta} & \sigma_{xx_1} & \sigma_{xy_1} & \sigma_{xx_2} & \sigma_{xy_2} & \dots & \sigma_{xx_n} & \sigma_{xy_n} \\ \sigma_{yx} & \sigma_{yy} & \sigma_{y\theta} & \sigma_{yx_1} & \sigma_{yy_1} & \sigma_{yx_2} & \sigma_{yy_2} & \dots & \sigma_{yx_n} & \sigma_{yy_n} \\  & & & & \vdots & & & & & \\ \sigma_{x_nx} & \sigma_{x_ny} & \sigma_{x_n\theta} & \sigma_{x_nx_1} & \sigma_{x_ny_1} & \sigma_{x_nx_2} & \sigma_{x_ny_2} & \dots & \sigma_{x_nx_n} & \sigma_{x_ny_n} \end{bmatrix} \end{equation}`
+
+A single estimate of the pose is tracked over time, while the confidence
+in the pose is tracked by the covariance matrix :math:`P`. :math:`P` is
+a symmetric square matrix which each element in the matrix corresponding
+to the covariance between two parts of the system. For example,
+:math:`\sigma_{xy}` represents the covariance between the belief of
+:math:`x` and :math:`y` and is equal to :math:`\sigma_{yx}`.
+
+The state can be represented more concisely as follows.
+
+:math:`\begin{equation} X = \begin{bmatrix} x \\ m \end{bmatrix} \end{equation}`
+:math:`\begin{equation} P = \begin{bmatrix} \Sigma_{xx} & \Sigma_{xm}\\ \Sigma_{mx} & \Sigma_{mm}\\ \end{bmatrix} \end{equation}`
+
+Here the state simplifies to a combination of pose (:math:`x`) and map
+(:math:`m`). The covariance matrix becomes easier to understand and
+simply reads as the uncertainty of the robots pose
+(:math:`\Sigma_{xx}`), the uncertainty of the map (:math:`\Sigma_{mm}`),
+and the uncertainty of the robots pose with respect to the map and vice
+versa (:math:`\Sigma_{xm}`, :math:`\Sigma_{mx}`).
+
+Take care to note the difference between :math:`X` (state) and :math:`x`
+(pose).
+
+.. code:: ipython3
+
+    """
+    Extended Kalman Filter SLAM example
+    original author: Atsushi Sakai (@Atsushi_twi)
+    notebook author: Andrew Tu (drewtu2)
+    """
+    
+    import math
+    import numpy as np
+    %matplotlib notebook
+    import matplotlib.pyplot as plt
+    
+    
+    # EKF state covariance
+    Cx = np.diag([0.5, 0.5, np.deg2rad(30.0)])**2 # Change in covariance
+    
+    #  Simulation parameter
+    Qsim = np.diag([0.2, np.deg2rad(1.0)])**2  # Sensor Noise
+    Rsim = np.diag([1.0, np.deg2rad(10.0)])**2 # Process Noise
+    
+    DT = 0.1  # time tick [s]
+    SIM_TIME = 50.0  # simulation time [s]
+    MAX_RANGE = 20.0  # maximum observation range
+    M_DIST_TH = 2.0  # Threshold of Mahalanobis distance for data association.
+    STATE_SIZE = 3  # State size [x,y,yaw]
+    LM_SIZE = 2  # LM state size [x,y]
+    
+    show_animation = True
+
+Algorithm Walk through
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+At each time step, the following is done. - predict the new state using
+the control functions - update the belief in landmark positions based on
+the estimated state and measurements
+
+.. code:: ipython3
+
+    def ekf_slam(xEst, PEst, u, z):
+        """
+        Performs an iteration of EKF SLAM from the available information. 
+        
+        :param xEst: the belief in last position
+        :param PEst: the uncertainty in last position
+        :param u:    the control function applied to the last position 
+        :param z:    measurements at this step
+        :returns:    the next estimated position and associated covariance
+        """
+        S = STATE_SIZE
+    
+        # Predict
+        xEst, PEst, G, Fx = predict(xEst, PEst, u)
+        initP = np.eye(2)
+    
+        # Update
+        xEst, PEst = update(xEst, PEst, u, z, initP)
+    
+        return xEst, PEst
+
+
+1- Predict
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+**Predict State update:** The following equations describe the predicted
+motion model of the robot in case we provide only the control
+:math:`(v,w)`, which are the linear and angular velocity respectively.
+
+:math:`\begin{equation*} F= \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix} \end{equation*}`
+
+:math:`\begin{equation*} B= \begin{bmatrix} \Delta t cos(\theta) & 0\\ \Delta t sin(\theta) & 0\\ 0 & \Delta t \end{bmatrix} \end{equation*}`
+
+:math:`\begin{equation*} U= \begin{bmatrix} v_t\\ w_t\\ \end{bmatrix} \end{equation*}`
+
+:math:`\begin{equation*} X = FX + BU \end{equation*}`
+
+:math:`\begin{equation*} \begin{bmatrix} x_{t+1} \\ y_{t+1} \\ \theta_{t+1} \end{bmatrix}= \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix}\begin{bmatrix} x_{t} \\ y_{t} \\ \theta_{t} \end{bmatrix}+ \begin{bmatrix} \Delta t cos(\theta) & 0\\ \Delta t sin(\theta) & 0\\ 0 & \Delta t \end{bmatrix} \begin{bmatrix} v_{t} + \sigma_v\\ w_{t} + \sigma_w\\ \end{bmatrix} \end{equation*}`
+
+Notice that while :math:`U` is only defined by :math:`v_t` and
+:math:`w_t`, in the actual calculations, a :math:`+\sigma_v` and
+:math:`+\sigma_w` appear. These values represent the error between the
+given control inputs and the actual control inputs.
+
+As a result, the simulation is set up as the following. :math:`R`
+represents the process noise which is added to the control inputs to
+simulate noise experienced in the real world. A set of truth values are
+computed from the raw control values while the values dead reckoning
+values incorporate the error into the estimation.
+
+:math:`\begin{equation*} R= \begin{bmatrix} \sigma_v\\ \sigma_w\\ \end{bmatrix} \end{equation*}`
+
+:math:`\begin{equation*} X_{true} = FX + B(U) \end{equation*}`
+
+:math:`\begin{equation*} X_{DR} = FX + B(U + R) \end{equation*}`
+
+The implementation of the motion model prediciton code is shown in
+``motion_model``. The ``observation`` function shows how the simulation
+uses (or doesn’t use) the process noise ``Rsim`` to the find the ground
+truth and dead reckoning estimates of the pose.
+
+**Predict covariance:** Add the state covariance to the the current
+uncertainty of the EKF. At each time step, the uncertainty in the system
+grows by the covariance of the pose, :math:`Cx`.
+
+:math:`P = G^TPG + Cx`
+
+Notice this uncertainty is only growing with respect to the pose, not
+the landmarks.
+
+.. code:: ipython3
+
+    def predict(xEst, PEst, u):
+        """
+        Performs the prediction step of EKF SLAM
+        
+        :param xEst: nx1 state vector
+        :param PEst: nxn covariance matrix
+        :param u:    2x1 control vector
+        :returns:    predicted state vector, predicted covariance, jacobian of control vector, transition fx
+        """
+        S = STATE_SIZE
+        G, Fx = jacob_motion(xEst[0:S], u)
+        xEst[0:S] = motion_model(xEst[0:S], u)
+        # Fx is an an identity matrix of size (STATE_SIZE)
+        # sigma = G*sigma*G.T + Noise
+        PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx
+        return xEst, PEst, G, Fx
+
+.. code:: ipython3
+
+    def motion_model(x, u):
+        """
+        Computes the motion model based on current state and input function. 
+        
+        :param x: 3x1 pose estimation
+        :param u: 2x1 control input [v; w]
+        :returns: the resulting state after the control function is applied
+        """
+        F = np.array([[1.0, 0, 0],
+                      [0, 1.0, 0],
+                      [0, 0, 1.0]])
+    
+        B = np.array([[DT * math.cos(x[2, 0]), 0],
+                      [DT * math.sin(x[2, 0]), 0],
+                      [0.0, DT]])
+    
+        x = (F @ x) + (B @ u)
+        return x
+
+2 - Update
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In the update phase, the observations of nearby landmarks are used to
+correct the location estimate.
+
+For every landmark observed, it is associated to a particular landmark
+in the known map. If no landmark exists in the position surrounding the
+landmark, it is taken as a NEW landmark. The distance threshold for how
+far a landmark must be from the next known landmark before its
+considered to be a new landmark is set by ``M_DIST_TH``.
+
+With an observation associated to the appropriate landmark, the
+**innovation** can be calculated. Innovation (:math:`y`) is the
+difference between the observation and the observation that *should*
+have been made if the observation were made from the pose predicted in
+the predict stage.
+
+:math:`y = z_t - h(X)`
+
+With the innovation calculated, the question becomes which to trust more
+- the observations or the predictions? To determine this, we calculate
+the Kalman Gain - a percent of how much of the innovation to add to the
+prediction based on the uncertainty in the predict step and the update
+step.
+
+:math:`K = \bar{P_t}H_t^T(H_t\bar{P_t}H_t^T + Q_t)^{-1}`
+In these equations, :math:`H` is the jacobian of the
+measurement function. The multiplications by :math:`H^T` and :math:`H`
+represent the application of the delta to the measurement covariance.
+Intuitively, this equation is applying the following from the single
+variate Kalman equation but in the multivariate form, i.e. finding the
+ratio of the uncertainty of the process compared the measurement.
+
+K = :math:`\frac{\bar{P_t}}{\bar{P_t} + Q_t}`
+
+If :math:`Q_t << \bar{P_t}`, (i.e. the measurement covariance is low
+relative to the current estimate), then the Kalman gain will be
+:math:`~1`. This results in adding all of the innovation to the estimate
+– and therefore completely believing the measurement.
+
+However, if :math:`Q_t >> \bar{P_t}` then the Kalman gain will go to 0,
+signaling a high trust in the process and little trust in the
+measurement.
+
+The update is captured in the following.
+
+:math:`xUpdate = xEst + (K * y)`
+
+Of course, the covariance must also be updated as well to account for
+the changing uncertainty.
+
+:math:`P_{t} = (I-K_tH_t)\bar{P_t}`
+
+.. code:: ipython3
+
+    def update(xEst, PEst, u, z, initP):
+        """
+        Performs the update step of EKF SLAM
+        
+        :param xEst:  nx1 the predicted pose of the system and the pose of the landmarks
+        :param PEst:  nxn the predicted covariance
+        :param u:     2x1 the control function 
+        :param z:     the measurements read at new position
+        :param initP: 2x2 an identity matrix acting as the initial covariance
+        :returns:     the updated state and covariance for the system
+        """
+        for iz in range(len(z[:, 0])):  # for each observation
+            minid = search_correspond_LM_ID(xEst, PEst, z[iz, 0:2]) # associate to a known landmark
+    
+            nLM = calc_n_LM(xEst) # number of landmarks we currently know about
+            
+            if minid == nLM: # Landmark is a NEW landmark
+                print("New LM")
+                # Extend state and covariance matrix
+                xAug = np.vstack((xEst, calc_LM_Pos(xEst, z[iz, :])))
+                PAug = np.vstack((np.hstack((PEst, np.zeros((len(xEst), LM_SIZE)))),
+                                  np.hstack((np.zeros((LM_SIZE, len(xEst))), initP))))
+                xEst = xAug
+                PEst = PAug
+            
+            lm = get_LM_Pos_from_state(xEst, minid)
+            y, S, H = calc_innovation(lm, xEst, PEst, z[iz, 0:2], minid)
+    
+            K = (PEst @ H.T) @ np.linalg.inv(S) # Calculate Kalman Gain
+            xEst = xEst + (K @ y)
+            PEst = (np.eye(len(xEst)) - (K @ H)) @ PEst
+        
+        xEst[2] = pi_2_pi(xEst[2])
+        return xEst, PEst
+
+
+.. code:: ipython3
+
+    def calc_innovation(lm, xEst, PEst, z, LMid):
+        """
+        Calculates the innovation based on expected position and landmark position
+        
+        :param lm:   landmark position
+        :param xEst: estimated position/state
+        :param PEst: estimated covariance
+        :param z:    read measurements
+        :param LMid: landmark id
+        :returns:    returns the innovation y, and the jacobian H, and S, used to calculate the Kalman Gain
+        """
+        delta = lm - xEst[0:2]
+        q = (delta.T @ delta)[0, 0]
+        zangle = math.atan2(delta[1, 0], delta[0, 0]) - xEst[2, 0]
+        zp = np.array([[math.sqrt(q), pi_2_pi(zangle)]])
+        # zp is the expected measurement based on xEst and the expected landmark position
+        
+        y = (z - zp).T # y = innovation
+        y[1] = pi_2_pi(y[1])
+        
+        H = jacobH(q, delta, xEst, LMid + 1)
+        S = H @ PEst @ H.T + Cx[0:2, 0:2]
+    
+        return y, S, H
+    
+    def jacobH(q, delta, x, i):
+        """
+        Calculates the jacobian of the measurement function
+        
+        :param q:     the range from the system pose to the landmark
+        :param delta: the difference between a landmark position and the estimated system position
+        :param x:     the state, including the estimated system position
+        :param i:     landmark id + 1
+        :returns:     the jacobian H
+        """
+        sq = math.sqrt(q)
+        G = np.array([[-sq * delta[0, 0], - sq * delta[1, 0], 0, sq * delta[0, 0], sq * delta[1, 0]],
+                      [delta[1, 0], - delta[0, 0], - q, - delta[1, 0], delta[0, 0]]])
+    
+        G = G / q
+        nLM = calc_n_LM(x)
+        F1 = np.hstack((np.eye(3), np.zeros((3, 2 * nLM))))
+        F2 = np.hstack((np.zeros((2, 3)), np.zeros((2, 2 * (i - 1))),
+                        np.eye(2), np.zeros((2, 2 * nLM - 2 * i))))
+    
+        F = np.vstack((F1, F2))
+    
+        H = G @ F
+    
+        return H
+
+Observation Step
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The observation step described here is outside the main EKF SLAM process
+and is primarily used as a method of driving the simulation. The
+observations function is in charge of calculating how the poses of the
+robots change and accumulate error over time, and the theoretical
+measurements that are expected as a result of each measurement.
+
+Observations are based on the TRUE position of the robot. Error in dead
+reckoning and control functions are passed along here as well.
+
+.. code:: ipython3
+
+    def observation(xTrue, xd, u, RFID):
+        """
+        :param xTrue: the true pose of the system
+        :param xd:    the current noisy estimate of the system
+        :param u:     the current control input
+        :param RFID:  the true position of the landmarks
+        
+        :returns:     Computes the true position, observations, dead reckoning (noisy) position, 
+                      and noisy control function
+        """
+        xTrue = motion_model(xTrue, u)
+    
+        # add noise to gps x-y
+        z = np.zeros((0, 3))
+    
+        for i in range(len(RFID[:, 0])): # Test all beacons, only add the ones we can see (within MAX_RANGE)
+    
+            dx = RFID[i, 0] - xTrue[0, 0]
+            dy = RFID[i, 1] - xTrue[1, 0]
+            d = math.sqrt(dx**2 + dy**2)
+            angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
+            if d <= MAX_RANGE:
+                dn = d + np.random.randn() * Qsim[0, 0]  # add noise
+                anglen = angle + np.random.randn() * Qsim[1, 1]  # add noise
+                zi = np.array([dn, anglen, i])
+                z = np.vstack((z, zi))
+    
+        # add noise to input
+        ud = np.array([[
+            u[0, 0] + np.random.randn() * Rsim[0, 0],
+            u[1, 0] + np.random.randn() * Rsim[1, 1]]]).T
+    
+        xd = motion_model(xd, ud)
+        return xTrue, z, xd, ud
+
+.. code:: ipython3
+
+    def calc_n_LM(x):
+        """
+        Calculates the number of landmarks currently tracked in the state
+        :param x: the state
+        :returns: the number of landmarks n
+        """
+        n = int((len(x) - STATE_SIZE) / LM_SIZE)
+        return n
+    
+    
+    def jacob_motion(x, u):
+        """
+        Calculates the jacobian of motion model. 
+        
+        :param x: The state, including the estimated position of the system
+        :param u: The control function
+        :returns: G:  Jacobian
+                  Fx: STATE_SIZE x (STATE_SIZE + 2 * num_landmarks) matrix where the left side is an identity matrix
+        """
+        
+        # [eye(3) [0 x y; 0 x y; 0 x y]]
+        Fx = np.hstack((np.eye(STATE_SIZE), np.zeros(
+            (STATE_SIZE, LM_SIZE * calc_n_LM(x)))))
+    
+        jF = np.array([[0.0, 0.0, -DT * u[0] * math.sin(x[2, 0])],
+                       [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])],
+                       [0.0, 0.0, 0.0]],dtype=object)
+    
+        G = np.eye(STATE_SIZE) + Fx.T @ jF @ Fx
+        if calc_n_LM(x) > 0:
+            print(Fx.shape)
+        return G, Fx,
+    
+    
+
+
+.. code:: ipython3
+
+    def calc_LM_Pos(x, z):
+        """
+        Calculates the pose in the world coordinate frame of a landmark at the given measurement.
+    
+        :param x: [x; y; theta]
+        :param z: [range; bearing]
+        :returns: [x; y] for given measurement
+        """
+        zp = np.zeros((2, 1))
+    
+        zp[0, 0] = x[0, 0] + z[0] * math.cos(x[2, 0] + z[1])
+        zp[1, 0] = x[1, 0] + z[0] * math.sin(x[2, 0] + z[1])
+        #zp[0, 0] = x[0, 0] + z[0, 0] * math.cos(x[2, 0] + z[0, 1])
+        #zp[1, 0] = x[1, 0] + z[0, 0] * math.sin(x[2, 0] + z[0, 1])
+    
+        return zp
+    
+    
+    def get_LM_Pos_from_state(x, ind):
+        """
+        Returns the position of a given landmark
+        
+        :param x:   The state containing all landmark positions
+        :param ind: landmark id
+        :returns:   The position of the landmark
+        """
+        lm = x[STATE_SIZE + LM_SIZE * ind: STATE_SIZE + LM_SIZE * (ind + 1), :]
+    
+        return lm
+    
+    
+    def search_correspond_LM_ID(xAug, PAug, zi):
+        """
+        Landmark association with Mahalanobis distance.
+        
+        If this landmark is at least M_DIST_TH units away from all known landmarks, 
+        it is a NEW landmark.
+        
+        :param xAug: The estimated state
+        :param PAug: The estimated covariance
+        :param zi:   the read measurements of specific landmark
+        :returns:    landmark id
+        """
+    
+        nLM = calc_n_LM(xAug)
+    
+        mdist = []
+    
+        for i in range(nLM):
+            lm = get_LM_Pos_from_state(xAug, i)
+            y, S, H = calc_innovation(lm, xAug, PAug, zi, i)
+            mdist.append(y.T @ np.linalg.inv(S) @ y)
+    
+        mdist.append(M_DIST_TH)  # new landmark
+    
+        minid = mdist.index(min(mdist))
+    
+        return minid
+    
+    def calc_input():
+        v = 1.0  # [m/s]
+        yawrate = 0.1  # [rad/s]
+        u = np.array([[v, yawrate]]).T
+        return u
+    
+    def pi_2_pi(angle):
+        return (angle + math.pi) % (2 * math.pi) - math.pi
+
+.. code:: ipython3
+
+    def main():
+        print(" start!!")
+    
+        time = 0.0
+    
+        # RFID positions [x, y]
+        RFID = np.array([[10.0, -2.0],
+                         [15.0, 10.0],
+                         [3.0, 15.0],
+                         [-5.0, 20.0]])
+    
+        # State Vector [x y yaw v]'
+        xEst = np.zeros((STATE_SIZE, 1))
+        xTrue = np.zeros((STATE_SIZE, 1))
+        PEst = np.eye(STATE_SIZE)
+    
+        xDR = np.zeros((STATE_SIZE, 1))  # Dead reckoning
+    
+        # history
+        hxEst = xEst
+        hxTrue = xTrue
+        hxDR = xTrue
+    
+        while SIM_TIME >= time:
+            time += DT
+            u = calc_input()
+    
+            xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID)
+    
+            xEst, PEst = ekf_slam(xEst, PEst, ud, z)
+    
+            x_state = xEst[0:STATE_SIZE]
+    
+            # store data history
+            hxEst = np.hstack((hxEst, x_state))
+            hxDR = np.hstack((hxDR, xDR))
+            hxTrue = np.hstack((hxTrue, xTrue))
+    
+            if show_animation:  # pragma: no cover
+                plt.cla()
+    
+                plt.plot(RFID[:, 0], RFID[:, 1], "*k")
+                plt.plot(xEst[0], xEst[1], ".r")
+    
+                # plot landmark
+                for i in range(calc_n_LM(xEst)):
+                    plt.plot(xEst[STATE_SIZE + i * 2],
+                             xEst[STATE_SIZE + i * 2 + 1], "xg")
+    
+                plt.plot(hxTrue[0, :],
+                         hxTrue[1, :], "-b")
+                plt.plot(hxDR[0, :],
+                         hxDR[1, :], "-k")
+                plt.plot(hxEst[0, :],
+                         hxEst[1, :], "-r")
+                plt.axis("equal")
+                plt.grid(True)
+                plt.pause(0.001)
+
+.. code:: ipython3
+
+    %matplotlib notebook
+    main()
+
+
+.. parsed-literal::
+
+     start!!
+    New LM
+    New LM
+    New LM
+
+.. image:: ekf_slam_files/ekf_slam_1_0.png
+
+References:
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+-  `PROBABILISTIC ROBOTICS`_
+
+.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
+
+.. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/EKFSLAM/animation.gif
diff --git a/SLAM/EKFSLAM/animation.png b/docs/modules/slam/ekf_slam_files/ekf_slam_1_0.png
similarity index 100%
rename from SLAM/EKFSLAM/animation.png
rename to docs/modules/slam/ekf_slam_files/ekf_slam_1_0.png
diff --git a/docs/modules/slam/graphSLAM_SE2_example.rst b/docs/modules/slam/graphSLAM_SE2_example.rst
new file mode 100644
index 0000000000..bcc797ba3e
--- /dev/null
+++ b/docs/modules/slam/graphSLAM_SE2_example.rst
@@ -0,0 +1,209 @@
+Graph SLAM for a real-world SE(2) dataset
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: ipython3
+
+    from graphslam.graph import Graph
+    from graphslam.load import load_g2o_se2
+
+Introduction
+^^^^^^^^^^^^
+
+For a complete derivation of the Graph SLAM algorithm, please see
+`Graph SLAM Formulation`_.
+
+This notebook illustrates the iterative optimization of a real-world
+:math:`SE(2)` dataset. The code can be found in the ``graphslam``
+folder. For simplicity, numerical differentiation is used in lieu of
+analytic Jacobians. This code originated from the
+`python-graphslam <https://github.com/JeffLIrion/python-graphslam>`__
+repo, which is a full-featured Graph SLAM solver. The dataset in this
+example is used with permission from Luca Carlone and was downloaded
+from his `website <https://lucacarlone.mit.edu/datasets/>`__.
+
+The Dataset
+^^^^^^^^^^^^
+
+.. code:: ipython3
+
+    g = load_g2o_se2("data/input_INTEL.g2o")
+    
+    print("Number of edges:    {}".format(len(g._edges)))
+    print("Number of vertices: {}".format(len(g._vertices)))
+
+
+.. parsed-literal::
+
+    Number of edges:    1483
+    Number of vertices: 1228
+
+
+.. code:: ipython3
+
+    g.plot(title=r"Original ($\chi^2 = {:.0f}$)".format(g.calc_chi2()))
+
+
+
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png
+
+
+Each edge in this dataset is a constraint that compares the measured
+:math:`SE(2)` transformation between two poses to the expected
+:math:`SE(2)` transformation between them, as computed using the current
+pose estimates. These edges can be classified into two categories:
+
+1. Odometry edges constrain two consecutive vertices, and the
+   measurement for the :math:`SE(2)` transformation comes directly from
+   odometry data.
+2. Scan-matching edges constrain two non-consecutive vertices. These
+   scan matches can be computed using, for example, 2-D LiDAR data or
+   landmarks; the details of how these constraints are determined is
+   beyond the scope of this example. This is often referred to as *loop
+   closure* in the Graph SLAM literature.
+
+We can easily parse out the two different types of edges present in this
+dataset and plot them.
+
+.. code:: ipython3
+
+    def parse_edges(g):
+        """Split the graph `g` into two graphs, one with only odometry edges and the other with only scan-matching edges.
+    
+        Parameters
+        ----------
+        g : graphslam.graph.Graph
+            The input graph
+    
+        Returns
+        -------
+        g_odom : graphslam.graph.Graph
+            A graph consisting of the vertices and odometry edges from `g`
+        g_scan : graphslam.graph.Graph
+            A graph consisting of the vertices and scan-matching edges from `g`
+    
+        """
+        edges_odom = []
+        edges_scan = []
+        
+        for e in g._edges:
+            if abs(e.vertex_ids[1] - e.vertex_ids[0]) == 1:
+                edges_odom.append(e)
+            else:
+                edges_scan.append(e)
+    
+        g_odom = Graph(edges_odom, g._vertices)
+        g_scan = Graph(edges_scan, g._vertices)
+    
+        return g_odom, g_scan
+
+.. code:: ipython3
+
+    g_odom, g_scan = parse_edges(g)
+    
+    print("Number of odometry edges:      {:4d}".format(len(g_odom._edges)))
+    print("Number of scan-matching edges: {:4d}".format(len(g_scan._edges)))
+    
+    print("\nχ^2 error from odometry edges:       {:11.3f}".format(g_odom.calc_chi2()))
+    print("χ^2 error from scan-matching edges:  {:11.3f}".format(g_scan.calc_chi2()))
+
+
+.. parsed-literal::
+
+    Number of odometry edges:      1227
+    Number of scan-matching edges:  256
+    
+    χ^2 error from odometry edges:             0.232
+    χ^2 error from scan-matching edges:  7191686.151
+
+
+.. code:: ipython3
+
+    g_odom.plot(title="Odometry edges")
+
+
+
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png
+
+
+.. code:: ipython3
+
+    g_scan.plot(title="Scan-matching edges")
+
+
+
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png
+
+
+Optimization
+^^^^^^^^^^^^
+
+Initially, the pose estimates are consistent with the collected odometry
+measurements, and the odometry edges contribute almost zero towards the
+:math:`\chi^2` error. However, there are large discrepancies between the
+scan-matching constraints and the initial pose estimates. This is not
+surprising, since small errors in odometry readings that are propagated
+over time can lead to large errors in the robot’s trajectory. What makes
+Graph SLAM effective is that it allows incorporation of multiple
+different data sources into a single optimization problem.
+
+.. code:: ipython3
+
+    g.optimize()
+
+
+.. parsed-literal::
+
+    
+    Iteration                chi^2        rel. change
+    ---------                -----        -----------
+            0         7191686.3825
+            1       320031728.8624          43.500234
+            2       125083004.3299          -0.609154
+            3          338155.9074          -0.997297
+            4             735.1344          -0.997826
+            5             215.8405          -0.706393
+            6             215.8405          -0.000000
+
+
+.. figure:: graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
+   :alt: Graph_SLAM_optimization.gif
+
+.. code:: ipython3
+
+    g.plot(title="Optimized")
+
+
+
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png
+
+
+.. code:: ipython3
+
+    print("\nχ^2 error from odometry edges:       {:7.3f}".format(g_odom.calc_chi2()))
+    print("χ^2 error from scan-matching edges:  {:7.3f}".format(g_scan.calc_chi2()))
+
+
+.. parsed-literal::
+
+    
+    χ^2 error from odometry edges:       142.189
+    χ^2 error from scan-matching edges:   73.652
+
+
+.. code:: ipython3
+
+    g_odom.plot(title="Odometry edges")
+
+
+
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png
+
+
+.. code:: ipython3
+
+    g_scan.plot(title="Scan-matching edges")
+
+
+
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png
+
diff --git a/SLAM/GraphBasedSLAM/images/Graph_SLAM_optimization.gif b/docs/modules/slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
similarity index 100%
rename from SLAM/GraphBasedSLAM/images/Graph_SLAM_optimization.gif
rename to docs/modules/slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png b/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png
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diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png b/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png
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diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png b/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png
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diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png b/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png
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diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png b/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png
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diff --git a/docs/modules/slam/graphSLAM_doc.rst b/docs/modules/slam/graphSLAM_doc.rst
new file mode 100644
index 0000000000..b17c3b18a2
--- /dev/null
+++ b/docs/modules/slam/graphSLAM_doc.rst
@@ -0,0 +1,557 @@
+
+Graph SLAM
+~~~~~~~~~~~~
+
+.. code:: ipython3
+
+    import copy
+    import math
+    import itertools
+    import numpy as np 
+    import matplotlib.pyplot as plt
+    from graph_based_slam import calc_rotational_matrix, calc_jacobian, cal_observation_sigma, \
+                                 calc_input, observation, motion_model, Edge, pi_2_pi
+    
+    %matplotlib inline
+    np.set_printoptions(precision=3, suppress=True)
+    np.random.seed(0)
+
+Introduction
+^^^^^^^^^^^^
+
+In contrast to the probabilistic approaches for solving SLAM, such as
+EKF, UKF, particle filters, and so on, the graph technique formulates
+the SLAM as an optimization problem. It is mostly used to solve the full
+SLAM problem in an offline fashion, i.e. optimize all the poses of the
+robot after the path has been traversed. However, some variants are
+available that uses graph-based approaches to perform online estimation
+or to solve for a subset of the poses.
+
+GraphSLAM uses the motion information as well as the observations of the
+environment to create least square problem that can be solved using
+standard optimization techniques.
+
+Minimal Example
+^^^^^^^^^^^^^^^
+
+The following example illustrates the main idea behind graphSLAM. A
+simple case of a 1D robot is considered that can only move in 1
+direction. The robot is commanded to move forward with a control input
+:math:`u_t=1`, however, the motion is not perfect and the measured
+odometry will deviate from the true path. At each time step the robot can
+observe its environment, for this simple case as well, there is only a
+single landmark at coordinates :math:`x=3`. The measured observations
+are the range between the robot and landmark. These measurements are
+also subjected to noise. No bearing information is required since this
+is a 1D problem.
+
+To solve this, graphSLAM creates what is called as the system
+information matrix :math:`\Omega` also referred to as :math:`H` and the
+information vector :math:`\xi` also known as :math:`b`. The entries are
+created based on the information of the motion and the observation.
+
+.. code:: ipython3
+
+    R = 0.2
+    Q = 0.2
+    N = 3
+    graphics_radius = 0.1
+    
+    odom = np.empty((N,1))
+    obs = np.empty((N,1))
+    x_true = np.empty((N,1))
+    
+    landmark = 3
+    # Simulated readings of odometry and observations
+    x_true[0], odom[0], obs[0] = 0.0, 0.0, 2.9
+    x_true[1], odom[1], obs[1] = 1.0, 1.5, 2.0
+    x_true[2], odom[2], obs[2] = 2.0, 2.4, 1.0
+    
+    hxDR = copy.deepcopy(odom)
+    # Visualization
+    plt.plot(landmark,0, '*k', markersize=30)
+    for i in range(N):
+        plt.plot(odom[i], 0, '.', markersize=50, alpha=0.8, color='steelblue')
+        plt.plot([odom[i], odom[i] + graphics_radius],
+                 [0,0], 'r')
+        plt.text(odom[i], 0.02, "X_{}".format(i), fontsize=12)
+        plt.plot(obs[i]+odom[i],0,'.', markersize=25, color='brown')
+        plt.plot(x_true[i],0,'.g', markersize=20)
+    plt.grid()    
+    plt.show()
+    
+    
+    # Defined as a function to facilitate iteration
+    def get_H_b(odom, obs):
+        """
+        Create the information matrix and information vector. This implementation is 
+        based on the concept of virtual measurement i.e. the observations of the
+        landmarks are converted into constraints (edges) between the nodes that
+        have observed this landmark.
+        """
+        measure_constraints = {}
+        omegas = {}
+        zids = list(itertools.combinations(range(N),2))
+        H = np.zeros((N,N))
+        b = np.zeros((N,1))
+        for (t1, t2) in zids:
+            x1 = odom[t1]
+            x2 = odom[t2]
+            z1 = obs[t1]
+            z2 = obs[t2]
+            
+            # Adding virtual measurement constraint
+            measure_constraints[(t1,t2)] = (x2-x1-z1+z2)
+            omegas[(t1,t2)] = (1 / (2*Q))
+            
+            # populate system's information matrix and vector
+            H[t1,t1] += omegas[(t1,t2)]
+            H[t2,t2] += omegas[(t1,t2)]
+            H[t2,t1] -= omegas[(t1,t2)]
+            H[t1,t2] -= omegas[(t1,t2)]
+    
+            b[t1] += omegas[(t1,t2)] * measure_constraints[(t1,t2)]
+            b[t2] -= omegas[(t1,t2)] * measure_constraints[(t1,t2)]
+    
+        return H, b
+    
+    
+    H, b = get_H_b(odom, obs)
+    print("The determinant of H: ", np.linalg.det(H))
+    H[0,0] += 1 # np.inf ?
+    print("The determinant of H after anchoring constraint: ", np.linalg.det(H))
+    
+    for i in range(5):
+        H, b = get_H_b(odom, obs)
+        H[(0,0)] += 1
+        
+        # Recover the posterior over the path
+        dx = np.linalg.inv(H) @ b
+        odom += dx
+        # repeat till convergence
+    print("Running graphSLAM ...")    
+    print("Odometry values after optimzation: \n", odom)
+    
+    plt.figure()
+    plt.plot(x_true, np.zeros(x_true.shape), '.', markersize=20, label='Ground truth')
+    plt.plot(odom, np.zeros(x_true.shape), '.', markersize=20, label='Estimation')
+    plt.plot(hxDR, np.zeros(x_true.shape), '.', markersize=20, label='Odom')
+    plt.legend()
+    plt.grid()
+    plt.show()
+
+
+
+.. image:: graphSLAM_doc_files/graphSLAM_doc_2_0.png
+
+
+.. parsed-literal::
+
+    The determinant of H:  0.0
+    The determinant of H after anchoring constraint:  18.750000000000007
+    Running graphSLAM ...
+    Odometry values after optimzation: 
+     [[-0. ]
+     [ 0.9]
+     [ 1.9]]
+
+
+
+.. image:: graphSLAM_doc_files/graphSLAM_doc_2_2.png
+
+
+In particular, the tasks are split into 2 parts, graph construction, and
+graph optimization. ### Graph Construction
+
+Firstly the nodes are defined
+:math:`\boldsymbol{x} = \boldsymbol{x}_{1:n}` such that each node is the
+pose of the robot at time :math:`t_i` Secondly, the edges i.e. the
+constraints, are constructed according to the following conditions:
+
+-  robot moves from :math:`\boldsymbol{x}_i` to
+   :math:`\boldsymbol{x}_j`. This edge corresponds to the odometry
+   measurement. Relative motion constraints (Not included in the
+   previous minimal example).
+-  Measurement constraints, this can be done in two ways:
+
+   -  The information matrix is set in such a way that it includes the
+      landmarks in the map as well. Then the constraints can be entered
+      in a straightforward fashion between a node
+      :math:`\boldsymbol{x}_i` and some landmark :math:`m_k`
+   -  Through creating a virtual measurement among all the node that
+      have observed the same landmark. More concretely, robot observes
+      the same landmark from :math:`\boldsymbol{x}_i` and
+      :math:`\boldsymbol{x}_j`. Relative measurement constraint. The
+      “virtual measurement” :math:`\boldsymbol{z}_{ij}`, which is the
+      estimated pose of :math:`\boldsymbol{x}_j` as seen from the node
+      :math:`\boldsymbol{x}_i`. The virtual measurement can then be
+      entered in the information matrix and vector in a similar fashion
+      to the motion constraints.
+
+An edge is fully characterized by the values of the error (entry to
+information vector) and the local information matrix (entry to the
+system’s information vector). The larger the local information matrix
+(lower :math:`Q` or :math:`R`) the values that this edge will contribute
+with.
+
+Important Notes:
+
+-  The addition to the information matrix and vector are added to the
+   earlier values.
+-  In the case of 2D robot, the constraints will be non-linear,
+   therefore, a Jacobian of the error w.r.t the states is needed when
+   updated :math:`H` and :math:`b`.
+-  The anchoring constraint is needed in order to avoid having a
+   singular information matri.
+
+Graph Optimization
+^^^^^^^^^^^^^^^^^^
+
+The result from this formulation yields an overdetermined system of
+equations. The goal after constructing the graph is to find:
+:math:`x^*=\underset{x}{\mathrm{argmin}}~\underset{ij}\Sigma~f(e_{ij})`,
+where :math:`f` is some error function that depends on the edges between
+to related nodes :math:`i` and :math:`j`. The derivation in the references
+arrive at the solution for :math:`x^* = H^{-1}b`
+
+Planar Example:
+^^^^^^^^^^^^^^^
+
+Now we will go through an example with a more realistic case of a 2D
+robot with 3DoF, namely, :math:`[x, y, \theta]^T`
+
+.. code:: ipython3
+
+    #  Simulation parameter
+    Qsim = np.diag([0.01, np.deg2rad(0.010)])**2 # error added to range and bearing
+    Rsim = np.diag([0.1, np.deg2rad(1.0)])**2 # error added to [v, w]
+    
+    DT = 2.0  # time tick [s]
+    SIM_TIME = 100.0  # simulation time [s]
+    MAX_RANGE = 30.0  # maximum observation range
+    STATE_SIZE = 3  # State size [x,y,yaw]
+    
+    # TODO: Why not use Qsim ? 
+    # Covariance parameter of Graph Based SLAM
+    C_SIGMA1 = 0.1
+    C_SIGMA2 = 0.1
+    C_SIGMA3 = np.deg2rad(1.0)
+    
+    MAX_ITR = 20  # Maximum iteration during optimization
+    timesteps = 1
+    
+    # consider only 2 landmarks for simplicity
+    # RFID positions [x, y, yaw]
+    RFID = np.array([[10.0, -2.0, 0.0],
+    #                  [15.0, 10.0, 0.0],
+    #                  [3.0, 15.0, 0.0],
+    #                  [-5.0, 20.0, 0.0],
+    #                  [-5.0, 5.0, 0.0]
+                     ])
+    
+    # State Vector [x y yaw v]'
+    xTrue = np.zeros((STATE_SIZE, 1))
+    xDR = np.zeros((STATE_SIZE, 1))  # Dead reckoning
+    xTrue[2] = np.deg2rad(45)
+    xDR[2] = np.deg2rad(45)
+    # history initial values
+    hxTrue = xTrue
+    hxDR = xTrue
+    _, z, _, _ = observation(xTrue, xDR, np.array([[0,0]]).T, RFID)
+    hz = [z]
+    
+    for i in range(timesteps):
+        u = calc_input()
+        xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID)
+        hxDR = np.hstack((hxDR, xDR))
+        hxTrue = np.hstack((hxTrue, xTrue))
+        hz.append(z)
+    
+    # visualize
+    graphics_radius = 0.3
+    plt.plot(RFID[:, 0], RFID[:, 1], "*k", markersize=20)
+    plt.plot(hxDR[0, :], hxDR[1, :], '.', markersize=50, alpha=0.8, label='Odom')
+    plt.plot(hxTrue[0, :], hxTrue[1, :], '.', markersize=20, alpha=0.6, label='X_true')
+    
+    for i in range(hxDR.shape[1]):
+        x = hxDR[0, i]
+        y = hxDR[1, i]
+        yaw = hxDR[2, i]
+        plt.plot([x, x + graphics_radius * np.cos(yaw)],
+                 [y, y + graphics_radius * np.sin(yaw)], 'r')
+        d = hz[i][:, 0]
+        angle = hz[i][:, 1]
+        plt.plot([x + d * np.cos(angle + yaw)], [y + d * np.sin(angle + yaw)], '.',
+                 markersize=20, alpha=0.7)
+        plt.legend()
+    plt.grid()    
+    plt.show()
+
+
+
+.. image:: graphSLAM_doc_files/graphSLAM_doc_4_0.png
+
+
+.. code:: ipython3
+
+    # Copy the data to have a consistent naming with the .py file
+    zlist = copy.deepcopy(hz)
+    x_opt = copy.deepcopy(hxDR)
+    xlist = copy.deepcopy(hxDR)
+    number_of_nodes = x_opt.shape[1]
+    n = number_of_nodes * STATE_SIZE
+
+After the data has been saved, the graph will be constructed by looking
+at each pair for nodes. The virtual measurement is only created if two
+nodes have observed the same landmark at different points in time. The
+next cells are a walk through for a single iteration of graph
+construction -> optimization -> estimate update.
+
+.. code:: ipython3
+
+    # get all the possible combination of the different node
+    zids = list(itertools.combinations(range(len(zlist)), 2))
+    print("Node combinations: \n", zids)
+    
+    for i in range(xlist.shape[1]):
+        print("Node {} observed landmark with ID {}".format(i, zlist[i][0, 3]))
+
+
+.. parsed-literal::
+
+    Node combinations: 
+     [(0, 1)]
+    Node 0 observed landmark with ID 0.0
+    Node 1 observed landmark with ID 0.0
+
+
+In the following code snippet the error based on the virtual measurement
+between node 0 and 1 will be created. The equations for the error is as follows:
+:math:`e_{ij}^x = x_j + d_j cos(\psi_j +\theta_j) - x_i - d_i cos(\psi_i + \theta_i)`
+
+:math:`e_{ij}^y = y_j + d_j sin(\psi_j + \theta_j) - y_i - d_i sin(\psi_i + \theta_i)`
+
+:math:`e_{ij}^x = \psi_j + \theta_j - \psi_i - \theta_i`
+
+Where :math:`[x_i, y_i, \psi_i]` is the pose for node :math:`i` and
+similarly for node :math:`j`, :math:`d` is the measured distance at
+nodes :math:`i` and :math:`j`, and :math:`\theta` is the measured
+bearing to the landmark. The difference is visualized with the figure in
+the next cell.
+
+In case of perfect motion and perfect measurement the error shall be
+zero since :math:`x_j + d_j cos(\psi_j + \theta_j)` should equal
+:math:`x_i + d_i cos(\psi_i + \theta_i)`
+
+.. code:: ipython3
+
+    # Initialize edges list
+    edges = []
+    
+    # Go through all the different combinations
+    for (t1, t2) in zids:
+        x1, y1, yaw1 = xlist[0, t1], xlist[1, t1], xlist[2, t1]
+        x2, y2, yaw2 = xlist[0, t2], xlist[1, t2], xlist[2, t2]
+        
+        # All nodes have valid observation with ID=0, therefore, no data association condition
+        iz1 = 0
+        iz2 = 0
+        
+        d1 = zlist[t1][iz1, 0]
+        angle1, phi1 = zlist[t1][iz1, 1], zlist[t1][iz1, 2]
+        d2 = zlist[t2][iz2, 0]
+        angle2, phi2 = zlist[t2][iz2, 1], zlist[t2][iz2, 2]
+        
+        # find angle between observation and horizontal 
+        tangle1 = pi_2_pi(yaw1 + angle1)
+        tangle2 = pi_2_pi(yaw2 + angle2)
+        
+        # project the observations 
+        tmp1 = d1 * math.cos(tangle1)
+        tmp2 = d2 * math.cos(tangle2)
+        tmp3 = d1 * math.sin(tangle1)
+        tmp4 = d2 * math.sin(tangle2)
+        
+        edge = Edge()
+        print(y1,y2, tmp3, tmp4)
+        # calculate the error of the virtual measurement
+        # node 1 as seen from node 2 throught the observations 1,2
+        edge.e[0, 0] = x2 - x1 - tmp1 + tmp2
+        edge.e[1, 0] = y2 - y1 - tmp3 + tmp4
+        edge.e[2, 0] = pi_2_pi(yaw2 - yaw1 - tangle1 + tangle2)
+    
+        edge.d1, edge.d2 = d1, d2
+        edge.yaw1, edge.yaw2 = yaw1, yaw2
+        edge.angle1, edge.angle2 = angle1, angle2
+        edge.id1, edge.id2 = t1, t2
+        
+        edges.append(edge)
+        
+        print("For nodes",(t1,t2))
+        print("Added edge with errors: \n", edge.e)
+        
+        # Visualize measurement projections
+        plt.plot(RFID[0, 0], RFID[0, 1], "*k", markersize=20)
+        plt.plot([x1,x2],[y1,y2], '.', markersize=50, alpha=0.8)
+        plt.plot([x1, x1 + graphics_radius * np.cos(yaw1)],
+                 [y1, y1 + graphics_radius * np.sin(yaw1)], 'r')
+        plt.plot([x2, x2 + graphics_radius * np.cos(yaw2)],
+                 [y2, y2 + graphics_radius * np.sin(yaw2)], 'r')
+        
+        plt.plot([x1,x1+tmp1], [y1,y1], label="obs 1 x")
+        plt.plot([x2,x2+tmp2], [y2,y2], label="obs 2 x")
+        plt.plot([x1,x1], [y1,y1+tmp3], label="obs 1 y")
+        plt.plot([x2,x2], [y2,y2+tmp4], label="obs 2 y")
+        plt.plot(x1+tmp1, y1+tmp3, 'o')
+        plt.plot(x2+tmp2, y2+tmp4, 'o')
+    plt.legend()
+    plt.grid()
+    plt.show()
+
+
+.. parsed-literal::
+
+    0.0 1.427649841628278 -2.0126109674819155 -3.524048014922737
+    For nodes (0, 1)
+    Added edge with errors: 
+     [[-0.02 ]
+     [-0.084]
+     [ 0.   ]]
+
+
+
+.. image:: graphSLAM_doc_files/graphSLAM_doc_9_1.png
+
+
+Since the constraints equations derived before are non-linear,
+linearization is needed before we can insert them into the information
+matrix and information vector. Two jacobians
+
+:math:`A = \frac{\partial e_{ij}}{\partial \boldsymbol{x}_i}` as
+:math:`\boldsymbol{x}_i` holds the three variabls x, y, and theta.
+Similarly, :math:`B = \frac{\partial e_{ij}}{\partial \boldsymbol{x}_j}`
+
+.. code:: ipython3
+
+    # Initialize the system information matrix and information vector
+    H = np.zeros((n, n))
+    b = np.zeros((n, 1))
+    x_opt = copy.deepcopy(hxDR)
+    
+    for edge in edges:
+        id1 = edge.id1 * STATE_SIZE
+        id2 = edge.id2 * STATE_SIZE
+        
+        t1 = edge.yaw1 + edge.angle1
+        A = np.array([[-1.0, 0, edge.d1 * math.sin(t1)],
+                      [0, -1.0, -edge.d1 * math.cos(t1)],
+                      [0, 0, -1.0]])
+    
+        t2 = edge.yaw2 + edge.angle2
+        B = np.array([[1.0, 0, -edge.d2 * math.sin(t2)],
+                      [0, 1.0, edge.d2 * math.cos(t2)],
+                      [0, 0, 1.0]])
+        
+        # TODO: use Qsim instead of sigma
+        sigma = np.diag([C_SIGMA1, C_SIGMA2, C_SIGMA3])
+        Rt1 = calc_rotational_matrix(tangle1)
+        Rt2 = calc_rotational_matrix(tangle2)
+        edge.omega = np.linalg.inv(Rt1 @ sigma @ Rt1.T + Rt2 @ sigma @ Rt2.T)
+    
+        # Fill in entries in H and b
+        H[id1:id1 + STATE_SIZE, id1:id1 + STATE_SIZE] += A.T @ edge.omega @ A
+        H[id1:id1 + STATE_SIZE, id2:id2 + STATE_SIZE] += A.T @ edge.omega @ B
+        H[id2:id2 + STATE_SIZE, id1:id1 + STATE_SIZE] += B.T @ edge.omega @ A
+        H[id2:id2 + STATE_SIZE, id2:id2 + STATE_SIZE] += B.T @ edge.omega @ B
+    
+        b[id1:id1 + STATE_SIZE] += (A.T @ edge.omega @ edge.e)
+        b[id2:id2 + STATE_SIZE] += (B.T @ edge.omega @ edge.e)
+       
+    
+    print("The determinant of H: ", np.linalg.det(H))
+    plt.figure()
+    plt.subplot(1,2,1)
+    plt.imshow(H, extent=[0, n, 0, n])
+    plt.subplot(1,2,2)
+    plt.imshow(b, extent=[0, 1, 0, n])
+    plt.show()    
+    
+    # Fix the origin, multiply by large number gives same results but better visualization
+    H[0:STATE_SIZE, 0:STATE_SIZE] += np.identity(STATE_SIZE)
+    print("The determinant of H after origin constraint: ", np.linalg.det(H))    
+    plt.figure()
+    plt.subplot(1,2,1)
+    plt.imshow(H, extent=[0, n, 0, n])
+    plt.subplot(1,2,2)
+    plt.imshow(b, extent=[0, 1, 0, n])
+    plt.show()
+
+
+.. parsed-literal::
+
+    The determinant of H:  0.0
+    The determinant of H after origin constraint:  716.1972439134893
+
+
+
+.. image:: graphSLAM_doc_files/graphSLAM_doc_11_1.png
+
+
+
+.. image:: graphSLAM_doc_files/graphSLAM_doc_11_2.png
+
+
+.. code:: ipython3
+
+    # Find the solution (first iteration)
+    dx = - np.linalg.inv(H) @ b
+    for i in range(number_of_nodes):
+        x_opt[0:3, i] += dx[i * 3:i * 3 + 3, 0]
+    print("dx: \n",dx)
+    print("ground truth: \n ",hxTrue)
+    print("Odom: \n", hxDR)
+    print("SLAM: \n", x_opt)
+    
+    # performance will improve with more iterations, nodes and landmarks.
+    print("\ngraphSLAM localization error: ", np.sum((x_opt - hxTrue) ** 2))
+    print("Odom localization error: ", np.sum((hxDR - hxTrue) ** 2))
+
+
+.. parsed-literal::
+
+    dx: 
+     [[-0.   ]
+     [-0.   ]
+     [ 0.   ]
+     [ 0.02 ]
+     [ 0.084]
+     [-0.   ]]
+    ground truth: 
+      [[0.    1.414]
+     [0.    1.414]
+     [0.785 0.985]]
+    Odom: 
+     [[0.    1.428]
+     [0.    1.428]
+     [0.785 0.976]]
+    SLAM: 
+     [[-0.     1.448]
+     [-0.     1.512]
+     [ 0.785  0.976]]
+    
+    graphSLAM localization error:  0.010729072751057656
+    Odom localization error:  0.0004460978857535104
+
+
+The references:
+^^^^^^^^^^^^^^^
+
+-  http://robots.stanford.edu/papers/thrun.graphslam.pdf
+
+-  http://ais.informatik.uni-freiburg.de/teaching/ss13/robotics/slides/16-graph-slam.pdf
+
+-  http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf
+
+N.B. An additional step is required that uses the estimated path to
+update the belief regarding the map.
+
diff --git a/docs/modules/slam/graphSLAM_formulation.rst b/docs/modules/slam/graphSLAM_formulation.rst
new file mode 100644
index 0000000000..c673bdcbfb
--- /dev/null
+++ b/docs/modules/slam/graphSLAM_formulation.rst
@@ -0,0 +1,216 @@
+.. _Graph SLAM Formulation:
+
+Graph SLAM Formulation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Author Jeff Irion
+
+Problem Formulation
+^^^^^^^^^^^^^^^^^^^
+
+Let a robot’s trajectory through its environment be represented by a
+sequence of :math:`N` poses:
+:math:`\mathbf{p}_1, \mathbf{p}_2, \ldots, \mathbf{p}_N`. Each pose lies
+on a manifold: :math:`\mathbf{p}_i \in \mathcal{M}`. Simple examples of
+manifolds used in Graph SLAM include 1-D, 2-D, and 3-D space, i.e.,
+:math:`\mathbb{R}`, :math:`\mathbb{R}^2`, and :math:`\mathbb{R}^3`.
+These environments are *rectilinear*, meaning that there is no concept
+of orientation. By contrast, in :math:`SE(2)` problem settings a robot’s
+pose consists of its location in :math:`\mathbb{R}^2` and its
+orientation :math:`\theta`. Similarly, in :math:`SE(3)` a robot’s pose
+consists of its location in :math:`\mathbb{R}^3` and its orientation,
+which can be represented via Euler angles, quaternions, or :math:`SO(3)`
+rotation matrices.
+
+As the robot explores its environment, it collects a set of :math:`M`
+measurements :math:`\mathcal{Z} = \{\mathbf{z}_j\}`. Examples of such
+measurements include odometry, GPS, and IMU data. Given a set of poses
+:math:`\mathbf{p}_1, \ldots, \mathbf{p}_N`, we can compute the estimated
+measurement
+:math:`\hat{\mathbf{z}}_j(\mathbf{p}_1, \ldots, \mathbf{p}_N)`. We can
+then compute the *residual*
+:math:`\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j)` for measurement
+:math:`j`. The formula for the residual depends on the type of
+measurement. As an example, let :math:`\mathbf{z}_1` be an odometry
+measurement that was collected when the robot traveled from
+:math:`\mathbf{p}_1` to :math:`\mathbf{p}_2`. The expected measurement
+and the residual are computed as
+
+.. math::
+
+   \begin{aligned}
+       \hat{\mathbf{z}}_1(\mathbf{p}_1, \mathbf{p}_2) &= \mathbf{p}_2 \ominus \mathbf{p}_1 \\
+       \mathbf{e}_1(\mathbf{z}_1, \hat{\mathbf{z}}_1) &= \mathbf{z}_1 \ominus \hat{\mathbf{z}}_1 = \mathbf{z}_1 \ominus (\mathbf{p}_2 \ominus \mathbf{p}_1),\end{aligned}
+
+where the :math:`\ominus` operator indicates inverse pose composition.
+We model measurement :math:`\mathbf{z}_j` as having independent Gaussian
+noise with zero mean and covariance matrix :math:`\Omega_j^{-1}`; we
+refer to :math:`\Omega_j` as the *information matrix* for measurement
+:math:`j`. That is,
+
+.. math::
+    p(\mathbf{z}_j \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) = \eta_j \exp (-\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^{\mathsf{T}}\Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j)
+    :label: infomat
+
+where :math:`\eta_j` is the normalization constant.
+
+The objective of Graph SLAM is to find the maximum likelihood set of
+poses given the measurements :math:`\mathcal{Z} = \{\mathbf{z}_j\}`; in
+other words, we want to find
+
+.. math:: \mathop{\mathrm{arg\,max}}_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \ p(\mathbf{p}_1, \ldots, \mathbf{p}_N \ | \ \mathcal{Z})
+
+Using Bayes’ rule, we can write this probability as
+
+.. math::
+    \begin{aligned}
+       p(\mathbf{p}_1, \ldots, \mathbf{p}_N \ | \ \mathcal{Z}) &= \frac{p( \mathcal{Z} \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) p(\mathbf{p}_1, \ldots, \mathbf{p}_N) }{ p(\mathcal{Z}) } \notag \\
+       &\propto p( \mathcal{Z} \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N)
+    \end{aligned}
+    :label: bayes
+
+since :math:`p(\mathcal{Z})` is a constant (albeit, an unknown constant)
+and we assume that :math:`p(\mathbf{p}_1, \ldots, \mathbf{p}_N)` is
+uniformly distributed `PROBABILISTIC ROBOTICS`_. Therefore, we
+can use Eq. :eq:`infomat` and and Eq. :eq:`bayes` to simplify the Graph SLAM
+optimization as follows:
+
+.. math::
+
+   \begin{aligned}
+       \mathop{\mathrm{arg\,max}}_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \ p(\mathbf{p}_1, \ldots, \mathbf{p}_N \ | \ \mathcal{Z}) &= \mathop{\mathrm{arg\,max}}_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \ p( \mathcal{Z} \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) \\
+       &= \mathop{\mathrm{arg\,max}}_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \prod_{j=1}^M p(\mathbf{z}_j \ | \ \mathbf{p}_1, \ldots, \mathbf{p}_N) \\
+       &= \mathop{\mathrm{arg\,max}}_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \prod_{j=1}^M \exp \left( -(\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^{\scriptstyle{\mathsf{T}}}\Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j) \right) \\
+       &= \mathop{\mathrm{arg\,min}}_{\mathbf{p}_1, \ldots, \mathbf{p}_N} \sum_{j=1}^M (\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^{\scriptstyle{\mathsf{T}}}\Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j).\end{aligned}
+
+We define
+
+.. math:: \chi^2 := \sum_{j=1}^M (\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j))^{\scriptstyle{\mathsf{T}}}\Omega_j \mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j),
+
+and this is what we seek to minimize.
+
+Dimensionality and Pose Representation
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Before proceeding further, it is helpful to discuss the dimensionality
+of the problem. We have:
+
+-  A set of :math:`N` poses
+   :math:`\mathbf{p}_1, \mathbf{p}_2, \ldots, \mathbf{p}_N`, where each
+   pose lies on the manifold :math:`\mathcal{M}`
+
+   -  Each pose :math:`\mathbf{p}_i` is represented as a vector in (a
+      subset of) :math:`\mathbb{R}^d`. For example:
+
+      -  An :math:`SE(2)` pose is typically represented as
+         :math:`(x, y, \theta)`, and thus :math:`d = 3`.
+
+      -  An :math:`SE(3)` pose is typically represented as
+         :math:`(x, y, z, q_x, q_y, q_z, q_w)`, where :math:`(x, y, z)`
+         is a point in :math:`\mathbb{R}^3` and
+         :math:`(q_x, q_y, q_z, q_w)` is a *quaternion*, and so
+         :math:`d = 7`. For more information about :math:`SE(3)`
+         parameterization and pose transformations, see
+         [blanco2010tutorial]_.
+
+   -  We also need to be able to represent each pose compactly as a
+      vector in (a subset of) :math:`\mathbb{R}^c`.
+
+      -  Since an :math:`SE(2)` pose has three degrees of freedom, the
+         :math:`(x, y, \theta)` representation is again sufficient and
+         :math:`c=3`.
+
+      -  An :math:`SE(3)` pose only has six degrees of freedom, and we
+         can represent it compactly as :math:`(x, y, z, q_x, q_y, q_z)`,
+         and thus :math:`c=6`.
+
+   -  We use the :math:`\boxplus` operator to indicate pose composition
+      when one or both of the poses are represented compactly. The
+      output can be a pose in :math:`\mathcal{M}` or a vector in
+      :math:`\mathbb{R}^c`, as required by context.
+
+-  A set of :math:`M` measurements
+   :math:`\mathcal{Z} = \{\mathbf{z}_1, \mathbf{z}_2, \ldots, \mathbf{z}_M\}`
+
+   -  Each measurement’s dimensionality can be unique, and we will use
+      :math:`\bullet` to denote a “wildcard” variable.
+
+   -  Measurement :math:`\mathbf{z}_j \in \mathbb{R}^\bullet` has an
+      associated information matrix
+      :math:`\Omega_j \in \mathbb{R}^{\bullet \times \bullet}` and
+      residual function
+      :math:`\mathbf{e}_j(\mathbf{z}_j, \hat{\mathbf{z}}_j) = \mathbf{e}_j(\mathbf{z}_j, \mathbf{p}_1, \ldots, \mathbf{p}_N) \in \mathbb{R}^\bullet`.
+
+   -  A measurement could, in theory, constrain anywhere from 1 pose to
+      all :math:`N` poses. In practice, each measurement usually
+      constrains only 1 or 2 poses.
+
+Graph SLAM Algorithm
+^^^^^^^^^^^^^^^^^^^^
+
+The “Graph” in Graph SLAM refers to the fact that we view the problem as
+a graph. The graph has a set :math:`\mathcal{V}` of :math:`N` vertices,
+where each vertex :math:`v_i` has an associated pose
+:math:`\mathbf{p}_i`. Similarly, the graph has a set :math:`\mathcal{E}`
+of :math:`M` edges, where each edge :math:`e_j` has an associated
+measurement :math:`\mathbf{z}_j`. In practice, the edges in this graph
+are either unary (i.e., a loop) or binary. (Note: :math:`e_j` refers to
+the edge in the graph associated with measurement :math:`\mathbf{z}_j`,
+whereas :math:`\mathbf{e}_j` refers to the residual function associated
+with :math:`\mathbf{z}_j`.) For more information about the Graph SLAM
+algorithm, see [grisetti2010tutorial]_.
+
+We want to optimize
+
+.. math:: \chi^2 = \sum_{e_j \in \mathcal{E}} \mathbf{e}_j^{\scriptstyle{\mathsf{T}}}\Omega_j \mathbf{e}_j.
+
+Let :math:`\mathbf{x}_i \in \mathbb{R}^c` be the compact representation
+of pose :math:`\mathbf{p}_i \in \mathcal{M}`, and let
+
+.. math:: \mathbf{x} := \begin{bmatrix} \mathbf{x}_1 \\ \mathbf{x}_2 \\ \vdots \\ \mathbf{x}_N \end{bmatrix} \in \mathbb{R}^{cN}
+
+We will solve this optimization problem iteratively. Let
+
+.. math:: \mathbf{x}^{k+1} := \mathbf{x}^k \boxplus \Delta \mathbf{x}^k = \begin{bmatrix} \mathbf{x}_1 \boxplus \Delta \mathbf{x}_1 \\ \mathbf{x}_2 \boxplus \Delta \mathbf{x}_2 \\ \vdots \\ \mathbf{x}_N \boxplus \Delta \mathbf{x}_2 \end{bmatrix}
+    :label: update
+
+The :math:`\chi^2` error at iteration :math:`k+1` is
+
+.. math:: \chi_{k+1}^2 = \sum_{e_j \in \mathcal{E}} \underbrace{\left[ \mathbf{e}_j(\mathbf{x}^{k+1}) \right]^{\scriptstyle{\mathsf{T}}}}_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\mathbf{e}_j(\mathbf{x}^{k+1})}_{\bullet \times 1}.
+    :label: chisq_at_kplusone
+
+We will linearize the residuals as:
+
+.. math::
+    \begin{aligned}
+        \mathbf{e}_j(\mathbf{x}^{k+1}) &= \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k) \\
+        &\approx \mathbf{e}_j(\mathbf{x}^{k}) + \frac{\partial}{\partial \Delta \mathbf{x}^k} \left[ \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k) \right] \Delta \mathbf{x}^k \\
+        &= \mathbf{e}_j(\mathbf{x}^{k}) + \left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right) \frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k} \Delta \mathbf{x}^k.
+    \end{aligned}
+    :label: linearization
+
+Plugging :eq:`linearization` into :eq:`chisq_at_kplusone`, we get:
+
+.. math::
+
+   \begin{aligned}
+       \chi_{k+1}^2 &\approx \ \ \ \ \ \sum_{e_j \in \mathcal{E}} \underbrace{[ \mathbf{e}_j(\mathbf{x}^k)]^{\scriptstyle{\mathsf{T}}}}_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\mathbf{e}_j(\mathbf{x}^k)}_{\bullet \times 1} \notag \\
+       &\hphantom{\approx} \ \ \ + \sum_{e_j \in \mathcal{E}} \underbrace{[ \mathbf{e}_j(\mathbf{x^k}) ]^{\scriptstyle{\mathsf{T}}}}_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN} \underbrace{\Delta \mathbf{x}^k}_{cN \times 1} \notag \\
+       &\hphantom{\approx} \ \ \ + \sum_{e_j \in \mathcal{E}} \underbrace{(\Delta \mathbf{x}^k)^{\scriptstyle{\mathsf{T}}}}_{1 \times cN} \underbrace{ \left( \frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k} \right)^{\scriptstyle{\mathsf{T}}}}_{cN \times dN} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)^{\scriptstyle{\mathsf{T}}}}_{dN \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN} \underbrace{\Delta \mathbf{x}^k}_{cN \times 1} \notag \\
+       &= \chi_k^2 + 2 \mathbf{b}^{\scriptstyle{\mathsf{T}}}\Delta \mathbf{x}^k + (\Delta \mathbf{x}^k)^{\scriptstyle{\mathsf{T}}}H \Delta \mathbf{x}^k,  \notag\end{aligned}
+
+where
+
+.. math::
+
+   \begin{aligned}
+       \mathbf{b}^{\scriptstyle{\mathsf{T}}}&= \sum_{e_j \in \mathcal{E}} \underbrace{[ \mathbf{e}_j(\mathbf{x^k}) ]^{\scriptstyle{\mathsf{T}}}}_{1 \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN} \\
+       H &= \sum_{e_j \in \mathcal{E}} \underbrace{ \left( \frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k} \right)^{\scriptstyle{\mathsf{T}}}}_{cN \times dN} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)^{\scriptstyle{\mathsf{T}}}}_{dN \times \bullet} \underbrace{\Omega_j}_{\bullet \times \bullet} \underbrace{\left( \left. \frac{\partial \mathbf{e}_j(\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)} \right|_{\Delta \mathbf{x}^k = \mathbf{0}} \right)}_{\bullet \times dN} \underbrace{\frac{\partial (\mathbf{x}^k \boxplus \Delta \mathbf{x}^k)}{\partial \Delta \mathbf{x}^k}}_{dN \times cN}.\end{aligned}
+
+Using this notation, we obtain the optimal update as
+
+.. math:: \Delta \mathbf{x}^k = -H^{-1} \mathbf{b}.  \label{eq:deltax}
+
+We apply this update to the poses via :eq:`update` and repeat until convergence.
+
+
+.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
diff --git a/docs/modules/slam/slam.rst b/docs/modules/slam/slam_main.rst
similarity index 75%
rename from docs/modules/slam/slam.rst
rename to docs/modules/slam/slam_main.rst
index 981c45ada5..41210fee4e 100644
--- a/docs/modules/slam/slam.rst
+++ b/docs/modules/slam/slam_main.rst
@@ -21,32 +21,12 @@ Ref:
 
 -  `Introduction to Mobile Robotics: Iterative Closest Point Algorithm`_
 
-EKF SLAM
---------
 
-This is an Extended Kalman Filter based SLAM example.
+.. include:: ekf_slam.rst
 
-The blue line is ground truth, the black line is dead reckoning, the red
-line is the estimated trajectory with EKF SLAM.
-
-The green crosses are estimated landmarks.
-
-|4|
-
-Ref:
-
--  `PROBABILISTIC ROBOTICS`_
 
 .. include:: FastSLAM1.rst
 
-|5|
-
-Ref:
-
--  `PROBABILISTIC ROBOTICS`_
-
--  `SLAM simulations by Tim Bailey`_
-
 FastSLAM 2.0
 ------------
 
@@ -56,7 +36,8 @@ The animation has the same meanings as one of FastSLAM 1.0.
 
 |6|
 
-Ref:
+References
+~~~~~~~~~~
 
 -  `PROBABILISTIC ROBOTICS`_
 
@@ -77,6 +58,10 @@ The black stars are landmarks for graph edge generation.
 
 |7|
 
+.. include:: graphSLAM_doc.rst
+.. include:: graphSLAM_formulation.rst
+.. include:: graphSLAM_SE2_example.rst
+
 Ref:
 
 -  `A Tutorial on Graph-Based SLAM`_
@@ -85,9 +70,12 @@ Ref:
 .. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
 .. _SLAM simulations by Tim Bailey: http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm
 .. _A Tutorial on Graph-Based SLAM: http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf
+.. _FastSLAM Lecture: http://ais.informatik.uni-freiburg.de/teaching/ws12/mapping/pdf/slam10-fastslam.pdf
+
+.. [blanco2010tutorial] Blanco, J.-L.A tutorial onSE(3) transformation parameterization and on-manifold optimization.University of Malaga, Tech. Rep 3(2010)
+.. [grisetti2010tutorial] Grisetti, G., Kummerle, R., Stachniss, C., and Burgard, W.A tutorial on graph-based SLAM.IEEE Intelligent Transportation Systems Magazine 2, 4 (2010), 31–43.
 
 .. |3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/iterative_closest_point/animation.gif
-.. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/EKFSLAM/animation.gif
 .. |5| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif
 .. |6| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM2/animation.gif
 .. |7| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/GraphBasedSLAM/animation.gif

From 413011ba9720f7ba9ff7f57653b7b28dd35d1643 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 23 Nov 2021 07:07:28 +0900
Subject: [PATCH 0543/1080] Bump matplotlib from 3.4.3 to 3.5.0 (#575)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.4.3 to 3.5.0.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.4.3...v3.5.0)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index e11ba13b8d..84f663cb83 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 1.21.4
 scipy == 1.7.2
 pandas == 1.3.4
-matplotlib == 3.4.3
+matplotlib == 3.5.0
 cvxpy == 1.1.17
 pytest == 6.2.5 # For unit test
 pytest-xdist == 2.4.0 # For unit test

From 094a413dcd9ca7cbbc2db4b7c44f84294cd80714 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 25 Nov 2021 23:05:25 +0900
Subject: [PATCH 0544/1080] Add how to contribute docs (#573)

* clean up SLAM docs

* clean up SLAM docs

* clean up SLAM docs

* clean up SLAM docs

* add contribution doc

* add contribution doc

* add contribution doc

* add contribution doc

* add contribution doc
---
 README.md                          |  20 ++---
 docs/README.md                     |   3 +
 docs/_static/img/doc_ci.png        | Bin 0 -> 51875 bytes
 docs/getting_started_main.rst      |  53 +++++++++---
 docs/how_to_contribute_main.rst    | 129 +++++++++++++++++++++++++++++
 docs/index_main.rst                |   1 +
 docs/modules/introduction_main.rst |   3 +-
 7 files changed, 187 insertions(+), 22 deletions(-)
 create mode 100644 docs/_static/img/doc_ci.png
 create mode 100644 docs/how_to_contribute_main.rst

diff --git a/README.md b/README.md
index 024c600cb1..195eb1bd4f 100644
--- a/README.md
+++ b/README.md
@@ -96,17 +96,17 @@ See this paper for more details:
 
 For running each sample code:
 
-- Python 3.9.x
-
-- numpy
-
-- scipy
-
-- matplotlib
-
-- pandas
+- [Python 3.9.x](https://www.python.org/)
+ 
+- [NumPy](https://numpy.org/)
+ 
+- [SciPy](https://scipy.org/)
+ 
+- [Matplotlib](https://matplotlib.org/)
+ 
+- [pandas](https://pandas.pydata.org/)
 
-- [cvxpy](https://www.cvxpy.org/index.html) 
+- [cvxpy](https://www.cvxpy.org/) 
 
 For development:
   
diff --git a/docs/README.md b/docs/README.md
index 728a81290e..5c4145e8e3 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -24,3 +24,6 @@ if you want to building each time a file is changed:
 sphinx-autobuild . _build/html
 ```
 
+#### Check the generated doc
+
+Open the index.html file under docs/_build/
diff --git a/docs/_static/img/doc_ci.png b/docs/_static/img/doc_ci.png
new file mode 100644
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diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst
index 83565eb872..d907ab8669 100644
--- a/docs/getting_started_main.rst
+++ b/docs/getting_started_main.rst
@@ -1,4 +1,4 @@
-.. _getting_started:
+.. _`getting started`:
 
 Getting Started
 ===============
@@ -21,27 +21,60 @@ See this paper for more details:
 
 - PythonRobotics: a Python code collection of robotics algorithms: https://arxiv.org/abs/1808.10703
 
+.. _`Requirements`:
 
 Requirements
 -------------
 
--  Python 3.9.x
--  numpy
--  scipy
--  matplotlib
--  pandas
+-  `Python 3.9.x`_
+-  `NumPy`_
+-  `SciPy`_
+-  `Matplotlib`_
+-  `pandas`_
 -  `cvxpy`_
 
-.. _cvxpy: http://www.cvxpy.org/en/latest/
+For development:
+
+-  pytest (for unit tests)
+-  pytest-xdist (for parallel unit tests)
+-  mypy (for type check)
+-  sphinx (for document generation)
+-  pycodestyle (for code style check)
+
+.. _`Python 3.9.x`: https://www.python.org/
+.. _`NumPy`: https://numpy.org/
+.. _`SciPy`: https://scipy.org/
+.. _`Matplotlib`: https://matplotlib.org/
+.. _`pandas`: https://pandas.pydata.org/
+.. _`cvxpy`: https://www.cvxpy.org/
 
 
 How to use
 ----------
 
-1. Install the required libraries. You can use environment.yml with
-   conda command.
+1. Clone this repo and go into dir.
+
+.. code-block::
+
+    >$ git clone https://github.com/AtsushiSakai/PythonRobotics.git
+
+    >$ cd PythonRobotics
+
+
+2. Install the required libraries.
+
+using conda :
+
+.. code-block::
+
+    >$ conda env create -f environment.yml
+
+using pip :
+
+.. code-block::
+
+    >$ pip install -r requirements.txt
 
-2. Clone this repo.
 
 3. Execute python script in each directory.
 
diff --git a/docs/how_to_contribute_main.rst b/docs/how_to_contribute_main.rst
new file mode 100644
index 0000000000..e00078541a
--- /dev/null
+++ b/docs/how_to_contribute_main.rst
@@ -0,0 +1,129 @@
+How to contribute
+=================
+
+This document describes how to contribute this project.
+
+Adding a new algorithm example
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+This is a step by step manual to add a new algorithm example.
+
+Step 1: Choose an algorithm to implement
+-----------------------------------------
+
+Before choosing an algorithm, please check the :ref:`getting started` doc to
+understand this project's philosophy and setup your development environment.
+
+If an algorithm is widely used and successful, let's create an issue to
+propose it for our community.
+
+If some people agree by thumbs up or posting positive comments, let go to next step.
+
+It is OK to just create an issue to propose adding an algorithm, someone might implement it.
+
+In that case, please share any papers or documentations to implement it.
+
+
+Step 2: Implement the algorithm with matplotlib based animation
+----------------------------------------------------------------
+
+When you implement an algorithm, please keep the following items in mind.
+
+1. Use only Python. Other language code is not acceptable.
+
+2. This project only accept codes for python 3.9 or higher.
+
+3. Use matplotlib based animation to show how the algorithm works.
+
+4. Only use current :ref:`Requirements` libraries, not adding new dependencies.
+
+5. Keep simple your code. The main goal is to make it easy for users to understand the algorithm, not for practical usage.
+
+
+Step 3: Add a unittest
+----------------------
+If you add a new algorithm sample code, please add a unit test file under `tests dir`_.
+
+This sample test code might help you : `test_a_star.py`_.
+
+At the least, try to run the example code without animation in the unit test.
+
+
+.. _`how to write doc`:
+
+Step 4: Write a document about the algorithm
+----------------------------------------------
+Please add a document to describe the algorithm details, mathematical backgrounds and show graphs and animation gif.
+
+This project is using `Sphinx`_ as a document builder, all documentations are written by `reStructuredText`_.
+
+You can add a new rst file under the subdirectory in `doc modules dir`_ and the top rst file can include it.
+
+Please check other documents for details.
+
+You can build the doc locally based on `doc README`_.
+
+
+.. _`submit a pull request`:
+
+Step 5: Submit a pull request and fix codes based on review
+------------------------------------------------------------
+
+Let's submit a pull request when your code, test, and doc are ready.
+
+At first, please fix all CI errors before code review.
+
+You can check your PR doc from the CI panel.
+
+After the "ci/circleci: build_doc" CI is succeeded,
+you can access you PR doc with clicking the [Details] of the "ci/circleci: build_doc artifact" CI.
+
+.. image:: /_static/img/doc_ci.png
+
+After that, I will start the review.
+
+Note that this is my hobby project; I appreciate your patience during the review process.
+
+
+Reporting and fixing a defect
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Reporting and fixing a defect is also great contribution.
+
+When you report an issue, please provide these information:
+
+- A clear and concise description of what the bug is.
+- A clear and concise description of what you expected to happen.
+- Screenshots to help explain your problem if applicable.
+- OS version
+- Python version
+- Each library versions
+
+If you want to fix any bug, you can find reported issues in `bug labeled issues`_.
+
+If you fix a bug of existing codes, please add a test function
+in the test code to show the issue was solved.
+
+This doc `submit a pull request`_ can be helpful to submit a pull request.
+
+
+Adding missed documentations for existing examples
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Adding the missed documentations for existing examples is also great contribution.
+
+If you check the `Python Robotics Docs`_, you can notice that some of the examples
+only have a simulation gif or short overview descriptions,
+but no detailed algorithm or mathematical description.
+
+This doc `how to write doc`_ can be helpful to write documents.
+
+.. _`Python Robotics Docs`: https://pythonrobotics.readthedocs.io/en/latest/
+.. _`bug labeled issues`: https://github.com/AtsushiSakai/PythonRobotics/issues?q=is%3Aissue+is%3Aopen+label%3Abug
+.. _`tests dir`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/tests
+.. _`test_a_star.py`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/tests/test_a_star.py
+.. _`Sphinx`: https://www.sphinx-doc.org/
+.. _`reStructuredText`: https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html
+.. _`doc modules dir`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/docs/modules
+.. _`doc README`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/docs/README.md
+
diff --git a/docs/index_main.rst b/docs/index_main.rst
index 978c216f11..7cdbddc71f 100644
--- a/docs/index_main.rst
+++ b/docs/index_main.rst
@@ -43,6 +43,7 @@ See this paper for more details:
    modules/arm_navigation/arm_navigation
    modules/aerial_navigation/aerial_navigation
    modules/appendix/appendix
+   how_to_contribute
 
 
 Indices and tables
diff --git a/docs/modules/introduction_main.rst b/docs/modules/introduction_main.rst
index e9fce33443..60cb085a29 100644
--- a/docs/modules/introduction_main.rst
+++ b/docs/modules/introduction_main.rst
@@ -2,5 +2,4 @@
 Introduction
 ============
 
-Ref
----
+TBD
\ No newline at end of file

From 9d759d13c6481d001da72470a2eab67a740490f1 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 26 Nov 2021 21:46:14 +0900
Subject: [PATCH 0545/1080] update docs (#578)

* update docs

* update docs
---
 .github/ISSUE_TEMPLATE/bug_report.md |  3 ++-
 .github/pull_request_template.md     | 13 +++++++------
 README.md                            | 12 +++++++-----
 docs/how_to_contribute_main.rst      | 28 ++++++++++++++++++++++++++++
 4 files changed, 44 insertions(+), 12 deletions(-)

diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
index a643b49571..4f13bd074f 100644
--- a/.github/ISSUE_TEMPLATE/bug_report.md
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -17,5 +17,6 @@ A clear and concise description of what you expected to happen.
 If applicable, add screenshots to help explain your problem.
 
 **Desktop (please complete the following information):**
- - Python version (This repo only supports Python 3.7.x or higher).
+ - Python version (This repo only supports Python 3.9.x or higher).
  - Each library version
+ - OS version
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index fb412d571c..c541063156 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -1,11 +1,7 @@
 <!-- 
 Thanks for contributing a pull request! 
-Please ensure that your PR satisfies the checklist before submitting:
-- [] This project only accept codes for python 3.9 or higher.
-- [] If you add a new algorithm sample code, please add a unit test file under `test` dir.
-     This sample test code might help you : https://github.com/AtsushiSakai/PythonRobotics/blob/master/tests/test_a_star.py
-- [] If you fix a bug of existing code please add a test function in the test code to show the issue was solved.
-- [] Please fix all issues on CI (All CI should be green), before code review.
+Please check this document before submitting:
+- [How to contribute](https://pythonrobotics.readthedocs.io/en/latest/how_to_contribute.html#adding-a-new-algorithm-example)
 
 Note that this is my hobby project; I appreciate your
 patience during the review process.
@@ -21,3 +17,8 @@ Again, thanks for contributing!
 
 #### Additional information
 <!--Any additional information you think is important.-->
+
+#### CheckList
+-[] Did you add an unittest for your new example or defect fix?
+-[] Did you add documents for your new example?
+-[] All CIs are green? (You can check it after submitting)
diff --git a/README.md b/README.md
index 195eb1bd4f..f297b6fc12 100644
--- a/README.md
+++ b/README.md
@@ -603,11 +603,13 @@ If this project helps your robotics project, please let me know with creating an
 
 Your robot's video, which is using PythonRobotics, is very welcome!!
 
-This is a list of other user's comment and references:[users\_comments](https://github.com/AtsushiSakai/PythonRobotics/blob/master/users_comments.md)
+This is a list of user's comment and references:[users\_comments](https://github.com/AtsushiSakai/PythonRobotics/blob/master/users_comments.md)
 
 # Contribution
 
-Any contribution is welcome!!
+Any contribution is welcome!! 
+
+Please check this document:[How to contribute](https://pythonrobotics.readthedocs.io/en/latest/how_to_contribute.html#)
 
 # Citing
 
@@ -615,7 +617,7 @@ If you use this project's code for your academic work, we encourage you to cite
 
 If you use this project's code in industry, we'd love to hear from you as well; feel free to reach out to the developers directly.
 
-# Support
+# Supporting this project
 
 If you or your company would like to support this project, please consider:
 
@@ -627,9 +629,9 @@ If you or your company would like to support this project, please consider:
 
 If you would like to support us in some other way, please contact with creating an issue.
 
-# Sponsors
+## Sponsors
 
-## [JetBrains](https://www.jetbrains.com/)
+### [JetBrains](https://www.jetbrains.com/)
 
 They are providing a free license of their IDEs for this OSS development.   
 
diff --git a/docs/how_to_contribute_main.rst b/docs/how_to_contribute_main.rst
index e00078541a..c761fd9928 100644
--- a/docs/how_to_contribute_main.rst
+++ b/docs/how_to_contribute_main.rst
@@ -48,6 +48,8 @@ This sample test code might help you : `test_a_star.py`_.
 
 At the least, try to run the example code without animation in the unit test.
 
+If you want to run the test suites locally, you can use the `runtests.sh` script by just executing it.
+
 
 .. _`how to write doc`:
 
@@ -118,6 +120,27 @@ but no detailed algorithm or mathematical description.
 
 This doc `how to write doc`_ can be helpful to write documents.
 
+Supporting this project
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Supporting this project financially is also a great contribution!!.
+
+If you or your company would like to support this project, please consider:
+
+- `Sponsor @AtsushiSakai on GitHub Sponsors`_
+
+- `Become a backer or sponsor on Patreon`_
+
+- `One-time donation via PayPal`_
+
+If you would like to support us in some other way, please contact with creating an issue.
+
+Sponsors
+---------
+
+1. `JetBrains`_ : They are providing a free license of their IDEs for this OSS development.
+
+
 .. _`Python Robotics Docs`: https://pythonrobotics.readthedocs.io/en/latest/
 .. _`bug labeled issues`: https://github.com/AtsushiSakai/PythonRobotics/issues?q=is%3Aissue+is%3Aopen+label%3Abug
 .. _`tests dir`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/tests
@@ -126,4 +149,9 @@ This doc `how to write doc`_ can be helpful to write documents.
 .. _`reStructuredText`: https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html
 .. _`doc modules dir`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/docs/modules
 .. _`doc README`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/docs/README.md
+.. _`JetBrains`: https://www.jetbrains.com/
+.. _`Sponsor @AtsushiSakai on GitHub Sponsors`: https://github.com/sponsors/AtsushiSakai
+.. _`Become a backer or sponsor on Patreon`: https://www.patreon.com/myenigma
+.. _`One-time donation via PayPal`: https://www.paypal.me/myenigmapay/
+
 

From eb1fec748f9f5464e036b9d555b624a4d9c01c9f Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 26 Nov 2021 23:14:23 +0900
Subject: [PATCH 0546/1080] Clean up Path planning docs (#579)

* update docs

* update docs
---
 .../quintic_polynomials_planner.ipynb         | 143 ------------------
 PathPlanning/RRTStar/Figure_1.png             | Bin 119836 -> 0 bytes
 PathPlanning/RRTStar/rrt_star.ipynb           |  74 ---------
 .../path_planning/path_planning_main.rst      |  35 +----
 .../quintic_polynomials_planner.rst           | 106 +++++++++++++
 docs/modules/path_planning/rrt_star.rst       |  25 ++-
 6 files changed, 120 insertions(+), 263 deletions(-)
 delete mode 100644 PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb
 delete mode 100644 PathPlanning/RRTStar/Figure_1.png
 delete mode 100644 PathPlanning/RRTStar/rrt_star.ipynb
 create mode 100644 docs/modules/path_planning/quintic_polynomials_planner.rst

diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb
deleted file mode 100644
index f094ae2c15..0000000000
--- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.ipynb
+++ /dev/null
@@ -1,143 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Quintic polynomials planner"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quintic polynomials for one dimensional robot motion\n",
-    "\n",
-    "We assume a one-dimensional robot motion $x(t)$ at time $t$ is formulated as a quintic polynomials based on time as follows:\n",
-    "\n",
-    "$x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4+a_5t^5$ --(1)\n",
-    "\n",
-    "$a_0, a_1. a_2, a_3, a_4, a_5$ are parameters of the quintic polynomial.\n",
-    "\n",
-    "It is assumed that terminal states (start and end) are known as boundary conditions.\n",
-    "\n",
-    "Start position, velocity, and acceleration are $x_s, v_s, a_s$ respectively.\n",
-    "\n",
-    "End position, velocity, and acceleration are $x_e, v_e, a_e$ respectively.\n",
-    "\n",
-    "So, when time is 0.\n",
-    "\n",
-    "$x(0) = a_0 = x_s$ -- (2)\n",
-    "\n",
-    "Then, differentiating the equation (1) with t, \n",
-    "\n",
-    "$x'(t) = a_1+2a_2t+3a_3t^2+4a_4t^3+5a_5t^4$ -- (3)\n",
-    "\n",
-    "So, when time is 0,\n",
-    "\n",
-    "$x'(0) = a_1 = v_s$ -- (4)\n",
-    "\n",
-    "Then, differentiating the equation (3) with t again, \n",
-    "\n",
-    "$x''(t) = 2a_2+6a_3t+12a_4t^2$ -- (5)\n",
-    "\n",
-    "So, when time is 0,\n",
-    "\n",
-    "$x''(0) = 2a_2 = a_s$ -- (6)\n",
-    "\n",
-    "so, we can calculate $a_0$, $a_1$, $a_2$ with eq. (2), (4), (6) and boundary conditions.\n",
-    "\n",
-    "$a_3, a_4, a_5$ are still unknown in eq(1).\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We assume that the end time for a maneuver is $T$, we can get these equations from eq (1), (3), (5):\n",
-    "\n",
-    "$x(T)=a_0+a_1T+a_2T^2+a_3T^3+a_4T^4+a_5T^5=x_e$ -- (7)\n",
-    "\n",
-    "$x'(T)=a_1+2a_2T+3a_3T^2+4a_4T^3+5a_5T^4=v_e$ -- (8)\n",
-    "\n",
-    "$x''(T)=2a_2+6a_3T+12a_4T^2+20a_5T^3=a_e$ -- (9)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From eq (7), (8), (9), we can calculate $a_3, a_4, a_5$ to solve the linear equations.\n",
-    "\n",
-    "$Ax=b$\n",
-    "\n",
-    "$\\begin{bmatrix} T^3 & T^4 & T^5 \\\\ 3T^2 & 4T^3 & 5T^4 \\\\ 6T & 12T^2 & 20T^3 \\end{bmatrix}\n",
-    "\\begin{bmatrix} a_3\\\\ a_4\\\\ a_5\\end{bmatrix}=\\begin{bmatrix} x_e-x_s-v_sT-0.5a_sT^2\\\\ v_e-v_s-a_sT\\\\ a_e-a_s\\end{bmatrix}$\n",
-    "\n",
-    "We can get all unknown parameters now"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quintic polynomials for two dimensional robot motion (x-y)\n",
-    "\n",
-    "If you use two quintic polynomials along x axis and y axis, you can plan for two dimensional robot motion in x-y plane.\n",
-    "\n",
-    "$x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4+a_5t^5$ --(10)\n",
-    "\n",
-    "$y(t) = b_0+b_1t+b_2t^2+b_3t^3+b_4t^4+b_5t^5$ --(11)\n",
-    "\n",
-    "It is assumed that terminal states (start and end) are known as boundary conditions.\n",
-    "\n",
-    "Start position, orientation, velocity, and acceleration are $x_s, y_s, \\theta_s, v_s, a_s$ respectively.\n",
-    "\n",
-    "End position, orientation, velocity, and acceleration are $x_e, y_e. \\theta_e, v_e, a_e$ respectively.\n",
-    "\n",
-    "Each velocity and acceleration boundary condition can be calculated with each orientation.\n",
-    "\n",
-    "$v_{xs}=v_scos(\\theta_s), v_{ys}=v_ssin(\\theta_s)$\n",
-    "\n",
-    "$v_{xe}=v_ecos(\\theta_e), v_{ye}=v_esin(\\theta_e)$\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.5"
-  },
-  "pycharm": {
-   "stem_cell": {
-    "cell_type": "raw",
-    "metadata": {
-     "collapsed": false
-    },
-    "source": []
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/PathPlanning/RRTStar/Figure_1.png b/PathPlanning/RRTStar/Figure_1.png
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diff --git a/PathPlanning/RRTStar/rrt_star.ipynb b/PathPlanning/RRTStar/rrt_star.ipynb
deleted file mode 100644
index 20efa46878..0000000000
--- a/PathPlanning/RRTStar/rrt_star.ipynb
+++ /dev/null
@@ -1,74 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Simulation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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TAVD6AZ51DbYG5v1nHpWNlYxNGstj4x/j0LZDZ2XfgcZAnp/2PM9Pe56vjn/Fr9b8imOVziquWlHQocOoN1JlqXILg/OGzOPm4Tczre80n9YOFd5ptDV6DHSl5lJKG04LeeYy7cO7LWsz16IoSof7iInOk12VzVdZXxEXEse8oadWwfBUAfS2Inc6X5qAtQqglwHQoTj4If8HAJLCkygxlxBsDGZC8oQ2t7M2W1lzbA2A23kL/yABsAtQ+wGmV3ieCqa+qZ4aS42M3vKRoij89Mufsq9kH/Gh8bw0/SVG/PNkhfUABBmDiA2OZUDMAAbFDqJPVB/iQ+OJCY4hJjiG6KBo7esQU0ibH/xX9b+Kow8dpbC2kIfXPcyy9GXYFTsKihYuQk2hGHQGqixVvJv2Lu+mvUtMcAxzB8+VMNiOZkczFQ0VLcJcUV0Re/L28PZnb1PeWK49Xt9U7/NrRARGEB8aT3xoPBGBERwoOUBBXQEAE1Mm8scpf6TUXEqVpYqqxirPf3p4rL6pnsrHKyU4dpLff/t7ntv8HHbFzqTUSW5BSA17rpM+u40C9qUPoA+DQNQAmByU7NW+D5UeotpSTagplKL6IsA5EjjQGNjmdl9nf019Uz1J4UmMSRrj1WuJ7kMCYBeg9svwVAFcfXQ1sz6ZxZikMey8b+f5PrQu7a8//pWlB5di1Bt5f+773LHsDrfvW5otFNQVUFBXwHe537W5rwBDQItQ6Prb9fHHL3+cP1z5B5anL+elrS9RaakE0JqJTToT/aL7Ud5YTkVjRYswOH/4fKb2ndqtw6CiKNRYa7QKXHtVuoqGirY/jCtbPhRgCNACnfo7NjiWsIAwgo3BBBgCMOqdl0i7w069rV5baeGb7G9osjehQ0dkUCR7i/Yy7cP2+1u1xmwzt9tZX5wbAYYAbcWfykb3N0p7TcC+8KUC6GsTsNr8e1nKZXyb8y3gXfOvuvrHnMFzOlzZFF2XBMAuwHUuwNObmNSqny8Tkp4rB0sPEhYQRkpECga9obMPp00bj2/kN1/9BoC/XPUXntv8HPm1+YSaQkk1pVKjr6G8sdzrwRlN9iaK64spri/26Th06AgzhdHkaNJey6bYtGWgYoJj6BXei9zqXCobK7UwGB0UzQ1Db+DmYTd3mTBoaba03uzq4bc3za6udOiIC4kjLiSO6OBoIgIiCDYGU1lSSe/U3tq/UbOjmSZ7E/VN9VRbqqmyVHGg5ABVlioabA0+vaaCs3+g6zFEBkUSHRRNdHC0+5+eHjv5Z7DR83RD4tyLDorWvm4RANtpAu5IH8D2KoDlDeWUN5QDkBSY5NW+1fn/JiZP5NXtrwLtDwCxO+ysyFgBwLwh0vzrjyQAdgEDYgag1+mptdZSYi5xm2okNTIVcC5L1GhrbHXeunPN2mxlxP85m0+rflN1Qa8lmVWVdWrQx8i7+DLjS37I/4GooCjW37aeoj1FzJw5E5PJhKIo7C/Zz7L0ZXyV9RWHyw5TZany+TVNehNBxiAMegMOxUFTcxMWuwUFhXpb682RlY2VLT6UAKosVbyz9x3e2fsOBp2B+NB4+kX3Y3DsYC0AtVaFPFuVJrvDTkVjy2bX1n7XNdX5/BqhplCigqIIDwwnxBhCoDEQo96IXqfHoThodjRjabbQYGug1lpLtaWasoayljuqbvlQazyFOJPBxO7C3dq+p/Sews8u+RkJYQluz4sIjLjgb36Eu+hg9wDoepPtsQLYwVHA3lYA1Zae1IhUggxBXu1brQAmhCVQY60hMjCSS3pe0uY2205so9RcSmRgJFf0ucKr1xHdiwTALiDQGEifqD5kVWWRXp7uFgCjg6IJNYVitpnJr833aeHws8n1w/1CbsoyN5mZu3QuVZYqxiaNpaiuiG9zvyU8IJx1t69jdMJoiijSnq/T6RiZOJKRiSP5/ZTfA84L+f6S/aw+tprvcr/jYMlBShtK23xdm8OGrclzRSvIGERSWBLxofFEB0cTYgqhoLaAfSX7aGxubPec7IqdovoiiuqLtI7gbTHqjcQExRBgDyClPMUtIIYGhGoVCteAZbaZqbHUUNZwqjm2vKHcpz5QAAadwdnEagom0BCohSW7w47NYaPR1ojZZtaqoWab2dk07mN21KEjKiiK6KBodFYdfRL6EBMS02YVTv0zMijSrcrzyYFPuH/V/dQ11REdFM27c96V+dK6EdcKYJO9ya053uM0MLpz2wdQ7f/n7QogeTV55NXkYdAZtMrhlD5T2r0RUZt/Zw2aJbMO+KluGwA3b97Miy++yO7duykqKmLZsmXMneu8aNtsNp5++mnWrFlDVlYWkZGRXHXVVfzpT38iKcm7kvv5NiRuCFlVWWSUZzClzxTtcZ1OR2pkKkfKj5BXk9d5AdDq/IQOMgZp/aYuNIqicO+X93Kg9AAJoc7KzYasDYSYQlhz+xrGJ4/HZmu/2dFkMHFp0qVcmnSp9pi5ycze4r1syt7ENznfcKDkABWNFV4dl6XZQlZ1FlnVWS2+Z8BAgDHAYxCMDY7l1hG3khiayLc537LtxDa3aqJJb9KqV9ZmK9XWapodzTQ7mrXAeuLECa+OsS0GnQGDzoBe7wxNiqLQ7GjW+lW5sit2aqw11Fhr2t2vFuJcApoaVNsLchGBEeh1emw2G2vWrNEqur4wN5l5aO1DvJf2HuCcX+3jGz72aoJw0XWc3lpR2Vh5KgC2MxG0L7QKYDsBUO3/Nyh2EDS3v1+1+je652jtBrC9/n+KosjqH6L7BkCz2czIkSO55557uPHGG92+19DQwJ49e3jmmWcYOXIkVVVV/M///A+zZ89m165dnXTEbRscO5g1x9Z4XBO4d1RvLQB2FrUCGB4Q3mnH0J4Xt77Ip4c+xag3MqzHMDZkbSDIGMTKW1cyKXXSGe07NCCUSamTmJQ6iWeueAaAioYKdhXuYmv+VjblbGJfyT5qrbXt7ivQEIjJYKLR1ohdsbdaBaxorGDJjiUARAZG0je6Lza7jbKGMqosVdgctnYrk2eDXbE7w56j9eeo090EGAIIMYUQGhBKREAEUUFRxAbHEhcaR0JoAknhSSRHJNM7qjcpESlEBUV1yujYtOI0bvn8FjIqMtDr9Dw9+WmeueKZC/bmRnSc2gSsQ4eCQmVjpda1pr2JoH3pA6hNBN1OE7BbBbCk/f1qA0CSL+PtPW8D7QfAQ2WHOF51nEBDINcOuLb9FxHdUre9ms2YMYMZM2Z4/F5kZCQbN250e+z1119n3Lhx5OXlkZqaej4O0SeuA0FOlxrhPN7ODIDqtBrhgRdmAFyfuZ4nv34SgEt6XsKmnE2Y9CaWLVjm1Wi5jogNieWaAddwzYBreJZnURSFE7Un2FGwgx0FO9iSt4W9RXux2N1XkbDarVjtVrfHdCd/OVpJWTXWGg6UHjgn56HX6YkKiiIiMILwgHCt+dakN2lVP4fDgc1hw9pspbG50Tk1kbWGqsYqlJO/bA4bNocNs83suZ+eBwadoUV/Rk8jrU9/LDo4ukNhTVEU3tj5Bos2LMJqt5IUnsTHN3zsVnUXXcM32d/wXtp7jEoYxaMTH231eWoTsBryXPvcntVRwF5OBO0aABtL2u8CogbAHqE9aGxuJD40nuE9hre5jdr8O73/9Au6y444t7ptAPRVTU0NOp2OqKgLc/DC4LiTcwGWt5wLUL1bPZsB8I0db/Bd7nfcNfIurul7TbvPV5uAL8QK4PHK49zy31twKA4GxQ5iR8EOjHojn9382Tm5+7U2W936ypWZyygxl1BYV0hBbQFF9UWUmcuoaKzw2EzqiRqizja9Tk+PkB4MjhvMuKRxDI4dTN/ovlqIig6KJjwwvMPNXg7FQZ21jsrGSqosVdqgFvV3VePJxywtH29sdlZAXUdF+iIiMILooGgMTQZeq36N2JDYNqfqQQe//fq3rDq6CnD2jXpvznvEhcR16NxF58qtzuWj/R9R0q+kzQDoqQlY1e4o4I6sBdxGE3CTvUlbx3dQ7CD2sc/t+y9seQGH4uCOi++gT1QfbVoicK7pC87qX3shVVb/ECABEACLxcITTzzBbbfdRkRERKvPs1qtWK2nKjO1tc7mPJvN5lXfsTPRP7I/ADnVOdQ31rtN8JkU5uy3mFude9aO48f8H/ns8GdcmngpU5OdFbK29l3V4BwZG2YKO+f/Fp58n/c9iWGJDIgZ4PZ4fVM9c5bOodpSTUJoAkcrjqLX6Xl/9vvM7D+zxbGqf3d93KE4qGyspNRcSn5NPjk1OeTX5lNYX0iJuYTyhnKqGquotdZSb6u/4Nb1jQmKYUT8CJodzWRVZ2kTxYLz3ErMJZSYS9icuxlwNkEPjBlI/+j+2u9+0f3oH92/Q1P8hBhCCAkLITnMu0ltVY22Ri00uk6irIZF9etqS7VbwFT7F9Zaa7Um96yclv0r2xIREMHxiuPM/WTuqQpkkDMUxwTFEBUU1aL6qPY77Cye3rvdkbfn2TeyLwBHK462+txXd7xKQW2BNpAOoKy+THt+YW0hAHnVedpjDvupKryt2ftrv7XZ+dmh9k31JL08HbtiJywgjB6BPVqc55IdSyiqL+KKlCvoFdqLzTmbUVAYEDOAbfnbAJiSOqXNY8qryWNP0R70Oj0z+s24IN4vnfHevRDOu7P5fQC02WzccsstOBwO/v73v7f53MWLF/Pss8+2eHzTpk2EhIScq0MEnE0QIfoQGhwNvLfiPVKDTzVTF9Y7L1JHCo+wZs0at+1ezX0Vm2LjF8m/IMzofam/uNA5n93BIwfZWOlsLj+92dzV1oqtADTWNLY4hnNNURQeSn+IE9YTPNHnCSZETdAefzHnRQ7VHCJQH0iJuQQdOh5KeYjQnFDW5KzhaP1RPiz+kHBDOBaHhTp7HfX2ehoPNmJxWLA5bNjxrkrnDR06AnWBhBhCCDWEEm4MJ8oYRYQxgjBjGGGGMEINoYQZXL42hmHAQIGlgMzGTDIbMjnWcIwym3fNqJWWSr7L+w4dOnoH9ea2hNuID4xHh45D5kMcrj9MkbVIO0+r3crBsoMcLDvYYl9GnZH4gHh6BvYkMSDR7c/4gHivVjnoKCNGepz8pQk8+Tvy1EN2xY7ZbqbeXk99c73z//Tkn2a7mbpm5/9xvb2eWlstxU3F1Nrd+2bWNtVSW9F+f01XevSn/u+MYYQbwrX/U/V3uDFc+7p3cG9CDGf/utHWz2l30t55Vtucc//k1eSxbNUyAvUtV8V44eALVDVXEWU8VQX8Me1HkoqcN9W7c3YDsDNrp3ZdO1h+6udiy5YtnAj2biBV5olMAHKP57KmwfM1clu1M8QlGhL56quvgFPnWWWroqi+CD16CvcWsmb/Gj4s/BCAZCWZLSe2OHeSDWsKW78GrypzVriHhAxh53cX1uIB5/O929Dg25yf3ZFfB0Cbzcb8+fPJzs7mm2++abP6B/Dkk0/yyCOPaH+vra0lJSWFK6+8ktjY2HN9uAwrHcauol0kDE9g5pCZ2uNDq4fydObTVNoruXbGtW5ViPl/nk+TvYn373iflAjvRy+uXruaryu/pt+AfkyfMJ2NGzcyffr0VkdSZu3Mgnzol9yPmTNnenzOubK3eC8n9p0gyBjEozc8SmSQMw38Zetf2LpvK3qdHqvDeff99xl/56ejfwo4JwR+/K3HOVp/1OfXDDAEEGwMJiwgTGtq7BHSg/iQeJIikugZ1lOrDkUFRaFr1JFzJIesjCyKi4spLCykoKDA+bWtkHx7PgaDAZPJRGJiIr169SIpKYmgnkEkD0/mkksuISYmxu0YSupL2FW0y/m70PlnWyOPFRRyLDnkWHK0xwZEDWBCvwkM7zEcPXoOlR1iQ9YGqq2nJs4L0DsHbphtZmwOG4XWQgqthS32r8M5Ir1flLNa2D+mv/PrmP70j+pPaECoz//OZ8pms7X63j1Re4KFXy7kaJ7z///OEXfy3JTnaHI0UdnYsrJYaal0W8ZN/XulpZIGWwMOHNTZ66iz14EXReBv7vjmjAcfeXuu3Ym356koCg9nPkyNtYaB4wZyUfxFLZ6TWpRKVUkVUaFRVNc43/M9Unswc5rzGvZW/VtkZWbRO6G3dl0rTiuGk5lv0qRJjEwY6dVxr1xfq8pwAAAgAElEQVSzEsph6OChzJzk+Rp5cOtByIFx/ccxfbr7dXfd8XVwyNk0fMP1NwDw4gcvAjBywEi+3fktvSN7c8/ce9psAn7l41cAWDhhITPHn99rdWs6472rtuD5M78NgGr4O3bsGJs2bfIqwAUGBhIY2PIu0mQynZc37dAeQ9lVtIvM6ky31+sT0wcdOqx2K9VN1SSEJWjfCzAE0GRvQtEpPh2j2sTswKFt19Z5NjQ776YigyLP+4fPfw7/B4DZg2cTF+7sr7Uucx3PfOscjauu4/n6jNf5xbhfOI/X1sAtX9zC0Urnh3+YKYz40HgiAyNxmB0M7T301IjUyN70juqtzdMXagptt49NYWEhq1at4tONn7Jt2zZtuhW9Xo/BYMDhcGC3e64sHjt2DIPBOa2K3W7H4XAef3JyMhMmTGD69OnMmjWL5KRkkqOTmTvM2Y9HURSyq7PZUbCDnQU72VG4g92Fu9ucSzCzOpPM6kw+P/K59lhscCyjE0djd9jJqs6ivqmeJqsz0cQExTAxZSIDYwdi0BnIqs7ieOVxMiszMdvM5NbkkluTy6bcTS1eKzEskf7R/RkQM4ABMQO0r/vH9Hf2wTuHTn/vrsxYycIVC7UpP/7vuv/jjovvaGMPbbM2W1v0cdT6N6q/Le6P94zseU5+Vs7X9aizeXOeg2IHsbNwJ9m12YzuNbrF93uG92RfyT63LjXV1mptvxNTJrImcw29o3prjxkNpz42jUaj1//WaoU9yBTE7pLdHCo9xOieo90mbD5WdQxwXutPv+7uL90PwKVJl2IymbA0W9hZ5KzgmZudzdfT+k4jIKD1QSYVDRVsyXNWCm8cfuMF9z45n+/dC+3cO0O3DYD19fVkZmZqf8/OziYtLY2YmBiSkpK46aab2LNnD6tWrcJut1Nc7GzyjImJafMHqDO1NhI4wBBAUngSBXUF5NXkuQVAtUnO12W11KkKvN2us6aBsTvs/PvgvwG4Y4TzAzyzMpNb/3urWwftF6e/yIPjHgScF8HrP7meH0/8SJAxiE9u/ESbC+tM5o3Ly8vjgw8+4IsvvmDv3r0AGAwGt6DncDi0QNfmedntLQLiiRMnWLZsGZ9/7gxro0eP5oYbbuCuu+4iNTUVnU5Hv+h+9Ivuxy0X3QI4q5yHyw5rfTq35m9tNRCq02BUNFa0qCTqdXp06Ki0VLLq2Co4BnEhcdw09CYeGPsAk1MnU9lYSWZlJpmVmRyvOu72Z2VjpbZUnqfJqqODoukf098tGKpfJ4YlnrWpYKzNVh7f+Div7XgNcI4IX3rjUgbGDjyj/QYaA0kMS3SbpF10PjUAHqs45vH7CaHOa6Vrv1Z1bW5AW1nJ9WfmbKwF/Pnhz/nrj39l0WWL3AKgOshPXf/d1Z7iPQDa83cV7qLJ3kR8aDx7i53Xm/ZmNFh1dBV2xc7FCRfTL7pfh85DdB/dNgDu2rWLK6+8Uvu72nR799138/vf/54vv/wSgFGjRrltt2nTJqZMmXLejtMX6khgdakgV6mRqVoAHNtrrPa4OuWArwMT1Ck01ItWe7RRwOd5Gphvsr+huL6Y2GDnlCt11jrmLp3rtj7rH6/8I4smLgKcA2Wu/fha0svTiQqKOuM5AB0OBxs3bmTJkiWsXr0avV7vFvBaq/J1lOv+0tLS2L9/P7/73e+YNWsWDz74IFdddZU2NQs4/x8vTriYixMu5v4x9wOwNX8rz377LBuzNrqFZE8jGkNMIdjsNo83AuUN5fxj9z/4x+5/EGpyzoN498i7uX3E7W5VEoCqxqpTobDyOJlVmVrlsKi+iCpLlbMZu7DlPJwhppBT1UKXquGAmAE+DUrJKM/glv/eQlpxGgD/b8L/Y/G0xW7VH9G9qBPjH63w3M1DDeyu07y4jgIOMTn7aLquEX021gKuanQOmnNdhk5RlLYDYJF7AFSnfxnfa7w2cr29ALg8wzn9i4z+FdCNA+CUKVPa/OH05Qf3QuFaAXRdrxKcAfDHEz+2mApGq+R5GeS07XysHKorUJzvOaU+3O/sBL1g+AJMehO3rbiNQ2WHtO//dtJvefonTwNwoOQA1358LYV1hSRHJLPu9nUMj297vqzWOBwOPv30U5566imysrIwGo0oinLWA19bXF9v7dq1rFy5kn79+vH8888zf/58tyDoamLKRFbdsopPvvyE3NhcluxaQqnZ84TRrh984AyU4QHh2Ow2t1VHzDYz64+vZ/3x9dz+xe0khiUyIXkC1w28jtE9RzO8x3DGJI1hTNKYFq9hbjKTVZXlsXKYV5NHg62BA6UHPM5zaNKb6Bvd1z0cnvyzT1Qf9OhRFIUP9n/Aw+sfxmwzExcSx/tz32fmwAuj/5M4dwbGOCu7aleP06kVQLWbCLQfADu6FrDrRNBqldF1GbpScyk11hp06JyzGbh8RFU2VpJTnQPAqERn0UINgHEhcSgoDI0bSs/wnm6vWVhXSM+wnuh0OhpsDazPXA/AvKHzOnQOonvptgGwOxoYOxAdOqot1ZSaS92aetW5AHNrct226WgF0NfgeC7mAaxvqm+zv525ycwXR74A4M6Rd7L4+8X898h/te8/MuERnpv6HADf5XzHnKVzqLHWMLzHcNbdsY7kCN+mJQFn6Nq4cSOLFi3iwIEDWshqbvZizaZzSH39nJwcbr31VhYvXsyLL77I9OnTW/33izRG8sTlT/DkT55kefpy/vbj3/jxxI8enxtqCsWhOGhsdk7N4irYEExwQDANtgYszRYUFIrqi1iWvkybb0yv0zMsbhijeo5iVMIoRiaOZFTiKOJC4ggNCGVEwghGJIxo8bpN9iZyqnO0aqFrOMyuzqbJ3sTRiqMeKzx6nZ5e4b2oa6ijep+zInxR/EW8fM3LXJZ8mff/uKLL8rYC6DrxersBsKNrAdtPTQStVgBd+76q1b++0X0JMga5TVOyt8jZxNs/uj9RQVE4FIfWlUKdhP/06p9DcXDJm5cQZAxi/R3rOVJ+hMbmRnpH9vZ64Iro3iQAdiFBxiD6RPUhuzqbjIoMjwGwRQWwo30AfdxO6wN4FpuAr/7wanYX7Wb5guXMGNhyVZcVGSsw28z0j+5PRUMFT3/ztPa9X435FS9d/RI6nY7PD3/O7V/cTpO9icmpk1lxywq3phdvFRQUcN9997F27VoMBmezozf9+c4n9XgOHTrENddcw4wZM/jnP/9Jr169Wt3GqDdy07CbuGnYTaQVp/Ha9tf4aP9Hbv/36hxpoaZQJqdOJjEskfSKdPYW7aXR3khjo3u/wgBDAM2OZq2y4lAc2tQyH/GR9rxe4b2cYTBhFKMSncFwQMwArZktwBDAoNhBHte4tjvsnKg9oQVCtwpi5XHMNjP5tflu2xwsPcj0D6cDaPNGempePteDUsT5ofbtLDWXUm2pbjHps3oNbbSdev/62gTsUBw02ZtwKA7t+Z64NQFbWjYBt9X8q/bxG93TOZDlUOkhqi3VhJpCtYmgp/Wd5rbN9hPbKTGXEBEYQd/oviz+fjEA84bM65TlFcWFRwJgFzM4brAzAJZn8JPeP9Eeby0AnnEF8ORFy+aw8fmRz9Hr9Sy4aEGL55+LCmBhXSFN9qZWP4w/2u8MEjMGzGDB5wu0u/F7R93L6zNfR6fT8caON3ho7UMoKMwbMo+Pb/hY69jtLUVReP/993nooYe0oHM+m3o7Qj2+DRs2MGTIEJYsWcJdd93V7oV/VOIo3p3zLn+Z/hfe3vM2r21/zW3iaLPN7JyOArg85XI+u/kzeob3ZFfhLm3k8eGyw+2+3/Q6PQ7FQUFdAQV1Baw5dmreslBTKBcnXMzIBGeVcFTiKEYkjGjx4WrQG+gd5Ryh7Vr9cCgOXv7xZZ78+klsDhvhhnDmDptLs9KshUTXQSlqU5qr8zUoRZxbEYERJIYlUlxfzLGKY279o+FUBVC9gQWwNFtosDUQYgrR3nNug0BOawL+Nudbpn0wjYviL+LAL1tfjtF1EIjWB9ClCVgd3Kd29XGl9f9LdO//d0nPS9iStwUdOq7oc4XbNupybzMHzkSv07Py6EoAbcCbEBIAu5jBsYNZl7muxUjg3pG9AQ8VwDPsA9jscDYtNjoauWvZXQDMHz6/xQfg2a4AKopCYZ1zrrmk8KQW3y+pL2HD8Q0AfHHkC61CdcvwW3jr+rfQoeOpr5/ihe9fAOCXY37J6zNe93kVi8rKSu68807WrFmDTqfrcn1H7XY7ZrOZhQsX8umnn/LRRx8RHd1+9TMuJI4nJj3BoomLWJ6+nFe3v9oiKP2Q/wOzl84mNjiWX4//NX+66k/0CO1BfVM9e4r2aIFwZ8FOsquz3bZ17XNl0pvoEdqDQEMgBbUFmG1mfjzxo1tztA4dg2IHOauELsHw9DBWZi5j4YqFWqCcO3guN5luYv71891GdXsalKJ+fb4GpYjzY1DsIGcArGwZANU+gKf3da1srCTEFEKwMbjF909vAlYHzKnXytacSQVQDXPq8X6ff3L93xDnpOiX9LykxY3yiowVAMwZPIctuVuobKwkNjiWy1Mvb/M4hf+QANjFqBeH09cEViuAZQ1lNNoatSrX2eoD6HrX61AcGHTuH3JtVQDf3PUm+0r2sWD4ghZ3qa2paKzQLpieptZYenCptmSSuhLKdQOv48MbPsShOLhv5X28l/Ye4BwF/NTkp3yu2hw5coSZM2eSn+9sRuxq4U+lHvf69eu55JJLWLNmDQMGDGhnKyfX5uF9xft4fcfrfLT/I7c+UxWNFfzvt//Ls989y4wBM/jN5b9hcupktwp1mbmMnYU72Vmwk20F29iav1Vbps3msGlhX4eOHiE9GBA9gLjQOGosNWRUZFBiLiGjIoOMigz+c+g/2n57hPTQwqBRb+TtPW9T1lBGoCGQV659hXsvvpe1a9e2OK/o4GjGBHs/KEX9+kwHpciI4/NvYMxANudu9tgPMDo4GpPepF1r1Mp0ZWMlyRHJ7TYBA94HQPVaqtNp/fZcK4CtBcCC2gKtAjk+eTwAW3K3uB3X6f3/0svTyajIwKQ3MWPADH636XeAc65U9XiFkHdCF9PaXIBRQVGEBYRR31RPfm2+1mfqbPUBdA2AdsWOgdMCYBsVwHXH17E8fTkXJ1zsdQBUA0F8aLwWRl19dMDZ/KteSCelTOKLBV9gbbYy//P5rDm2Br1Oz5uz3uRnl/zMq9d0tXr1am6//XasVusF39zrLbvdTn5+PmPGjOHf//63z9uPTBzJ27Pf5s9X/Zm397zN6ztep6CuQPu+Q3Gw+thqVh9bTUpECosmLmLhqIVEBEbQI7QHMwfO1EbeKopCbk0uP+b/yOeHP2dr/laKzcUoKJQ1lFHWcGqZu8jASKb2mUpSeBImg4lqS7X2AVfWUMbGrI1szDq1hJQOHf2j+7OrcBdWmxVzvZnLLZcTZ4rz6jy9GZSiVQ5dQmJWVRY2h63NQSkpESluofDSpEvbnbpDnJm2BoLodXoSwhI4Ueu+nJvaD7C9UcCK4nsFUF0TGND6JFqaLdoo39MDoLokY1xwHMPjh5NXk0d+bT569BwuPwy07P+3It1Z/ZvadyoRgRGnpn+R5l/hQgJgF6POBZhdlY212apVFHQ65zJch8sOk1udq130zlYF0LXi59p8B86LYFsVQPVi6kvH+raaf9PL092a5ob1GMbGuzZSa63lun9fx46CHQQbg/nPTf/h+sHXe/2aqo0bN2rrQnfVql9r7HY7jY2NzJs3jwceeKBDy/bFhsTym0m/4dGJj/Jlxpe8tv01vsv9zu05+bX5PLzuYRZtWMT84fN59LJHtQ7s4Hy/9onqQ5+oPtw64lbA2RH/X2n/4uMDH7OzcKf2nq2x1vBNzjdu++8T1Yd5Q+YRHhDOhqwN2vvFqDc6J78uP6x9OAI89ben6Bfdz635eGTCSFIjU32qDHszKMXTdDaZlZk02Bq0lVK+zv4agBuH3igB8BxrbyRwQuipAKhe2zwFQHXqrdPfL75WAC3NFsB5Y6N2FzhWcQwFhaigKK1ZV7W7yLke8VX9rwJO9f8bHj+cA6UHMOqNLeYyVQPfnMFz2Fu8l7yaPEJMIUzvN73NYxT+RQJgF9MzrKdW6TtedZxhPYZp31MDoGs/wLM1D+DpTcCurHYrdsVZJfNUAfTU4bk9bQXAxzY+pn2dGJbIzvt2UlRXxLUfX8vRiqPEBMew6tZVXJbi+1Qfb731Fm+88YbP23UlaqhdsmQJQ4YM4YEHHujQfox6IzcMvYEbht7A/pL9vL79dT7c/6Fb87DNYePjAx/z8YGPGRo3lMcvf5wFwxd4HIgTbArml2N/yS/H/pImexNfZX3F0oNLWZa+TKv0qnKqc7SKiSolIoVp/abRL6ofgcZAaiw17CvZx/ac7ZTbysmqyiKrKkubmgacFZjTQ+GwHsM61FTrOihlGu4VGUVRKDGXtKgcXp4i/bHONdcAePr8qeC5i8npAVAd6RtoDHQfBdyBPoBqc25r/f9OP74dhTsA54TPcCoAJoQmcIADTEie4LbOdlFdEdtPbAecTb7/2PUPAK4dcK3PA+BE9yYBsIvR6XQMiRvCrsJdZJRnuAfAiJYjgc9aH0Bd6wFQrf6BcwTn6c6oAhjmHgAVReHrLGf1RIeOQ786xNGKo8z4eAbF9cWkRqay/o71HjtSt+edd97hwQcf9Hm7ruzBBx8kODiYe++994z2c3HCxfxz9j/501V/4p297/DGjjfIq3UfkHSk/Aj3rLiHB9Y8wD2j7uHX43/tsZIGzvet2mSshsFPD33K8vTl1Fhr3J6r9tvKr83nX2n/0h4PNAQyMmEk4yPHc+2l1xIeFE55QzkHSg+QVpzG4bLDVFuq+S73O7cKplFvZFiPYS2CYWxI++uFt0an02lLxZ3JyjPCd/2j+6NDR11TXYv5U+HUwArXvoDqNcs1MDU2NxJoDGwxCtjXCqA65Yw3/f8URWFHgTMATkieAJwKgGqQPL35d+XRlSgojE0aS6+IXrL6h2iVBMAuaHDsYGcAPH0kcNTJkcAuH7xnqw+gnlN3vS0C4Mn+fyGmEI8jID2NeGtPaxXA7/O+1y58T09+mrTiNOYunUtdUx0j4kew7o51HquG7Vm9ejX33Xefz9t1Bz/72c9ISEjguuuuO+N9xYbE8vjlj/PIZY+wMmMlr+14jW9zvnV7ToOtgTd2vsEbO99gXK9xPDbxMeYMnuOxrye4h8G9RXuZ9595bhOeq+/HmOAY+kf3B5xNatXWaq16snr9asDZ7DYmaQwzB87kqclPERkUSVFdEWnFaewr2UdacRpVlir2l+xnf8l+baUZgOSI5BajkPtF92sxKMBf5FTn0CeqT2cfRrsCjYHa/KlHK462CIBqBTDAEKBd7yoanGthm/QmDDoDdsVOg62BqKAo91HAPvQBVG/CPVUAW5sCpqSphPLGckx6E6MSR1HVWKXN+6duc3oXAnX079whc8mszORg6UEMOgOzBs1q8/iE/5EA2AW1NhDE01yA57MC6Kn/n7XZqnWgPht9AF/e9jLg7JPYN7ov1350LTaHjSt6X8HyW5a3mOjVG0eOHGH+/Pk+b9edLFiwgJ07dzJ06NCzsj+j3si8ofOYN3QeB0oOaKOHXedTA9hRsIObP7uZqKAofjXmV/xizC9IiUxpsT9FUXh7z9s8vO5hGpsbiQ+N553Z76BDx6eHP2VF+goqGyu1yk1SeBKzh8wmJTyFfen7qAiqYG/xXmqsNXyd/bXWBw+c3SrG9RrHtL7TeOLyJ+gZ3pOc6hzSitNIK0ljX/E+jlcd50TtCU7UntDWXQXn0ocXJ1zstrrJRfEXtTkhcFenKAq3f3E7Sw8uZcs9W7rEtCIDYwdqAXBy78lu31MDoevNq/o+0ul0hJhCqGuq065jrY0Cbq+bjRoufRkBfLTB2W9xdM/RBBmD+DrraxQUekf2Jrcml2BjsFYZBOe1+KusrwBn/z91+pgpfaZ0aPJ70b1JAOyC2psK5nz3AWxrBLBa/dOhIyIwwuvX9xQArc1WbX63ftH9uPdLZ7PlTcNu4sN5HxJkDPJ6/6rKykpmzpyJ1WrtdgM+vKUoChaLhZkzZ7Jnzx6v5gn0xYiEEbx1/VvO5uE97/DGzjdaLFlYbanmhe9fYPH3i7mq31U8ctkjXN3/avQ6PdWWan6+8ud8dvgzAK7ufzUfzP1A++C+btB1WJutbMzayGeHP2N5+nIK6wr5YN8HAMSYYritz228MPUFIoIi2F24mx0FO9hZuJODpQcpqi9iRcYKrXICzqlDxvYay09Sf8Kjlz1K/+j+HKs8xr7ifVq18EDpAeqb6tmav5Wt+Vu1bfU6fatzFnYHOp2OsIAwFBQWf7+YVbetan+jTjYoZhAbjm/wOBDEYx9Ai/tqIK4B0G0UsC99AE9eg81NzjlL1RtiRVG0m/kWAdDsPN7T+/8lhiWSW5PL5N6TtZt8gPXH19Nkb2JAzACG9RjG/avuB5yrfwhxOgmAXZA6EjijPMOtU7MaAPNr8nEoDvQ6PQH6s1MBdG0Ctjvcp0XxZgRwdHC0T01l6vQirgFwefpybYDBscpjADw49kFeufaVDk2+qygKd955J/n5+d1mqpeOUqeIueOOO1i1atU5WekiJjiGxy5/zNk8fHQlr+94nW+y3Uf3KijatC6JYYnMGzKP1UdXk1ebh1Fv5Pmpz7No4qIW76VAYyCzBs1i1qBZWhj89NCnrMhYQaW1kiW7lrBk1xJ6hffipmE3sXDUQt68/k0szRb2Fu3VAuGOgh0crzrOscpjHKs8xr8POKfLMegMjEgYwbikcYxPHs9D4x9iUOwgsqqytFCYVpJGWnEapeZS0svTSS9PZ+nBpdoxxofGO8OgS7VwUOygLjkv2+OXP847e99h9bHV7Cvex8jEC3ttWW0gSGXLAKj2AXS9rrW1HNzpPxuuk+Y32ZvcApmr1iqAhXWF1DfVY9AZ6Bfdz20btQKoBcCTE0Cr18Gpfdybf9WK35zBcyg1l2o3JrMHz/Z4TMK/db0rj2BgzEB06KiyVFHeUE6PUOe0Ab3Ce6FDh9VupdRcSmJYYosl3bzVogLYVhNwWxXADowAtjvsFNcXA+4B8KWtL7k9b/G0xfzm8t90OKy8//77rFmzpv0n+gm73c6aNWv44IMPuPvuu8/Z6xj0BuYOmcvcIXM5WHpQGz18evNwcX0x/7fr/wDn4KK/Xv1Xfn7pz9v9/3YNg/WN9Sz+bDG5obmsPLaSgroCXt3+Kq9uf1ULgzcPu5mHJzyshcqKhgp2Fe5yC4Ul5hJnyCtO4609bwHOYHBJz0sYlzSOsb3Gcv+Y++kb1Vd77r7ifVooPFpxlFJzKRuOb9BWsAHn+t4XxV/kthbyxQkX+1Qt95aiKLy/731mDZpFXIh3cyK2ZkDMAOYPn8/Sg0v50w9/4pMbPzlLR3lutDUVjFoBdL1JbisAnr4WsBrg7YqdoroirS/26dSbafV6qTbJqi05/WP6u4VHa7OVrMYswDkAxNJs0QaEZFZmAjCt36kBIDa7jdXHnH1d5w6Zy5cZX6KgMCZpjMduFUJIAOyCgk3BpEamkluTS0ZFhhYATQYTSeFJFNQVkFeTR2JY4lnrAwhonaF96QPYkRHApeZSrYIZHxoPOKuau4qcc//p0PHunHdZOGqh9yd0moKCAh566KEuubzbuaTT6XjwwQe56qqr6NWr1zl/vYviL+LN699k8VWLeXfvu7yx840W07uAcw3iX6z+BS/9+BL/M/5/uHPknV6FpEBjIOMix/H7mb/HoXOw4fgGPjv8GSsyVngMg/OHz2dC8gSuGXAN1wy4BnB+yJ+oPeEWCHcV7qKuqY7v8753WyIvNjiWsb3GMjZpLON6jWPhqIUkhCXQYGvgYOlBt2C4r3gfZpvZ45Jz/aP7O6uELsEwJSLljCqzizYs4m/b/sbcIXP5Yv4XZ1zlfeLyJ1h6cCmfHvqUP0z5AwNjB57R/s4lNQAerzyO3WF3azFQuxK43iS7BkB1JLA6ere1UcCA2xRIp1P3r14v1Zvi1vr/7SvZR7PSTFxwHP2i+/FD/g802ZuIDoqmylJFVFAUoxNPza25OXcz1ZZqeoT04LLky1j8/WJAmn9F6yQAdlFD4oaQW5NLenm627QSqZGpWgAc12vcqUreGfYBBOedr8cA2EoF8K9b/8qr218FOjYCODEsEYPeQKm5lInvTNS+//bst88o/CmKwn333YfFYpHwdxpFUWhsbOS+++47r9XRmOAYFk1cxLAew7jtv7e1mOpFlVmZyYNrH+TRDY9y+4jbeXDcg24TTLcl0BjI9YOv5/rB12NptpwKg+ktw+DNw27m5uE3MyF5gnMFj8gUUiJTuHHYjYCzCp5RnqEFwp2FO0krTqOisYJ1metYl7lOe93UyFQtEI5NGsstF91CRGAEDsVBVlWWVllURyGfqD3B8arjHK86zhdHvtD2Ex0U7dav8Kp+V9ErwvuQfufIO3l9x+ssT1/OO3vf6dAKOa5GJo7kuoHXsfrYav7yw1/45+x/ntH+zqXUyFQCDAFY7Vbya/PdRi9HBkYSaAh0C2/eNgG79gEE91U+XCmKovURVN/bp1cAh8S6B0B1BPu4XuPQ6XTajUav8F5UWaqY0meKW5BV+7BeP+h6zDazNhhEVv8QrZEA2EUNjh3M+uPryShvORXMjyd+1AaCnM0KoNr0cXoAVPu0nF4BzKnOIb/WuY5uR0cAH688zjUfXcOJOudM/UlhSdw7+szmrNu4caPH9WGFk91uZ+3atWzcuJHp08/PygFN9iae/uZpXtz6IgAj4kfw3JXPsTZzLR/s/8BtKS5wVlreTXuXd9PeZVTiKB4e/3CrE0x7EmQMYvbg2cwePNtjGHxl+yu8sv0VkiOSuWmoszI4Pnm89jOg1+kZ2mMoQ3sM5bGxGwQAACAASURBVK6RdzmPqdnKgdIDbpXCI2VHyKvJI68mj/8e+S/grCANiRvCuF7j+Envn3Dv6Hu5adhN2rFVNFRoYVD983DZYaosVWzK2cSmnE0AfH7z51og9caoxFE8P/V5Hv/qcR5e9zBX9L7ijKt2T056ktXHVvP+vvf5/ZTf+xRIzyeD3kD/6P4cKT/C0YqjbgFQp9OREJbgNniuwdaApdmCSW8i0BCoPQatjwIGsNgtHl/fdYCIuga2WgHUpoCJc58CZnuBczLnsUljgVMDQJoczmu5a/8/RVFO9f8bMod1metosjcxMGYgQ+POzsh+0f1IAOyitIEgp08Fc9pk0GqQ8zkAtlIBBO+ngVErg9CxVUAabA2M+ecYqi3V2vcenfio1/vxxOFwsGjRIgwGg98P/GiLwWBg0aJF7N27F73+3M5zl1WVxS2f38LOwp0A/GrMr3jp6pcINgUze8hsXpj2Au+lvceSHUv43yv+l7yaPF7f8bq2XnBacRr3rLiHh9Y+xM9G/4xfjPlFiw/TtngKg58e+pQvM77kRO0JtzB487CbuXnYzW5hUBVoDGRM0hjGJI3RHquz1rG7aDc7C3ayo3AHOwp2kFeTx5HyIxwpP0JWVVaLG5rYkFim9p3qNr+btdnKkfIjbtVCbyufrh6d+ChrM9eyKWcTt39xOz/c+0Or8y964/LUy/lJ75+wOXczf/3xr/ztmr91eF/n2qDYQVoAvLr/1W7fSwxLdAuA4GzBeHrT0yRHJAN4HgWsuFcAm5o9X2ddr6Pq9Uy9KW6tCVj9eRiXNA6H4uCH/B8AyK12jqB3fX+kFaeRX5uvLff20y9/Cjibf8/FgC7RPUgA7KK8nQtQrQD6PAjEhwqgGvTCAsI8Pg4dqwAeLnOu5RoVGEW1tRq9Ts/PL/251/vx5NNPP+XAgQNntA9/YLfb2b9/P5999hkLFiw4Z6+z9OBS7l91P7XWWqKConh39rvMG+reZyk6OJpHLnuEh8c/jE6nQ6/T8+TkJ1mZsZKXt73MlrwtgLMSrYa1yamT+fX4XzOzn29rHZ8eBtdnrtf6DJ6oPcHL217m5W0vkxKRovUZHN9rfKsfsuGB4UzpM4UpfaZoj5XUl7CzcCc7C3ZqP6/tCTQGatPJnAm9Ts/7c9/n4n9czM7Cnfzhuz/wx6l/PKN9PjnpSTbnbubN3W/y1OSnzmjFlHOprYEg6khgOLWetLq8pdpNpLVRwK7NsK31AXS9AVcDYHRwNOYms9ZK4joJdJm5jKxq5wCQsUljOVR6iGpLNUGGICx2CwmhCW6rQKnVv6v7X41Bb3AbDCJEa/xzCvtuQL1bPF553O3ion6gqPOsqZW8c1oBbKUPoOsScR2pAKocDufrTUye2CJk+sLhcPDUU0+d84pWd6HX6/ntb3+r/fufTeYmMz9d8VNu/e+t1FpruTzlctLuT2sR/lwZ9AbtPahONL35ns2kP5DOQ+MeIsR4avLlLXlbuPmzm0l9LZWPiz7WphXyRZAxiDlD5vDRDR9R9lgZyxcs57YRtxEWEEZ+bT4vb3uZy965jN6v9Oa5zc95vd+EsARmDZrFs1c+y08v+anPx3WmUiJTeHPWmwC88P0LboNYTrcpe5O2rmxrrul/DaMTR9Nga+C17a+d1WM9m9QAqE4h5cp1LkC1wqfgDH5qEFRHqZ++FrDr31vrA+h6I62tBBIUrYXRuJA4t+CsNv8mByYTFRSl/R/1DO8JOKt/rkFUW/1j8Fw2ZW+i1lpLYlgi45PHezweIUACYJeVFJ5EWEAYdsVOVlWW9vj5qACqF0SVN03A3lYAX9v+GptzN2t/jwyMpNbm7DPz1OSnvD18j7766iuysrLOSaDpjhwOB1lZWXz99dftP9kH+0v2M+afY3g37V106Hh68tN8u/DbVqfPaM/guMG8NuM1Sh8r5a1Zb7n1eaporOCzks946ceXtFGcHaGGwY9v+JjSRaUsW7DMLQye3nx4oZs/fD53jbwLh+LgzmV3UmNpOejm7T1vM/WDqdy38r42JznW6XQ8OelJAF7f8brbjd+FxJupYMAl+J2cF1D901MT8OlaDYAnr78GnUHbR2RQZKvNv2roHhTqPGZ1/j/1/8F1/d/sqmz2lexDr9Nz3aDrWJa+DHDOBeivyxQK78i7o4vS6XTaBc11IIgaAMsbymmwNZyVPoBqE4ja1NGhCqCXo4Df2v2W22St6muHmcK0aTk6asmSJRiN0uvBF0ajkSVLlpy1/TkUB7d/cTvp5ekkhSfx9V1f88epfzwrkyGHBoRy36X3cehXh9h671ZuGX6L9oH7xq43SH45mcc3Pu52w9QRwaZg5g6Z6xYGHxj7wBkf//n2+ozX6RvVl5zqHB5a+1CL788bMo+Y4BgOlB7g7zv/3ua+bhh6A4NiB1FlqeLN3W+eq0M+IwNjnANecqpzWgQ11yZgNfCpN79q6PI4Cvjk9UkNha01Aav7Ut/nkUGR6HX6VkcAbyvYBsCgEOc1fkuus5uD2jri2v9Prf5NTp1MTHCM21rAQrRFAmAX5qkfYFRQlFaJy6/JP1UB9HUaGJeO4WrFT10NpCODQLypACqK0uLDWd3H3CFzz6gzc15eHqtWraK5ue3lmoS75uZmVq5cSX5+/lnZn16n57057zF3yFzS7k/jyr5XnpX9utLpdFyWchmf3PQJub/O5Y6ed5AakUplYyUvbn2RAa8NYObHM1l9dHWLVW18pYbBC30lDE8iAiP4cN6H6HV6Ptz/If85+B+378eGxPLg2AcBeGbTM5TUl7S6L4PewG8u/w0Af/vxb1iaPY+G7UyJYYmEBYRp0++c/j2VWgFUw5xavWttFDCcCoWttbRoFcCTN9Hq9dDTCGCH4tAmfB4UMoi8mjzya/PR45yGq29UX/pG/3/2zjs6qqqL4r8p6Z0ESAUSAqGHUEIAKYKI9CaiUgQV/UCKIDZQxAqiohQFREWK2JEOUqRIL6GGEloIkARII71M+f54vDtvkkmDIARmr+WSmXnlziQ5s+85Z+8TKI5XEr79V/eTkJGAq52rGUm0wgpLsBLACgxLM4FVKpVZGfhOewBBMQ7uLvcAJmQkFJoGIQfj99u/X9qlW8TixYutvX+3CbVazeLFi8vtes18m/HXgL+EgfndRBWnKjxZ9UnOjDzDqqdX0blmZ4wYWX9uPd1/7k7w7GA+3fkpiVmJd30t9yNaV2stWiv+t/Z/XL5pIvo3Mm8IW5603DTe2vJWsdca1GgQ/q7+xGfEs+jIoru36NuEsmpSsAwsm0Ery7tyy4CcLSxqFrDyufPJ5y3eW46hcka6oAn0lbQrXM+8Lp5Ly03DQetAdYfq7LoiqX9lU3wlsUvKShItM71CevHXKan827VW1yJH0t0OrH6pDyas34gVGKVRAt9pD6Dy3LL4ABqNRuF3BaXLAJ5PMQVPZZANcA0gqFKQpVNKjeXLl1t7/24Ter2e5cuXl3zgfQyNWkOPkB5sGLSBs6PPMj5iPB72HsSkxvDWlrfwn+HPkL+GsPfK3ofuy+7dtu8S7hdOak4qQ1YMEVnRyk6VGdFshDjuxyM/sufyniKvY6uxZULLCQBM3z292L7Be4WiCKAyAyhDJnwFM4CWKhEyEdxy0XK/bMEY6uHgIZmJ34rdM/fNFFlJuf+vqU9TNCqNmOdrQIpfSgK49uxaDEYDjao2ooZ7DdH/V57TP/L1+YTND+Odf965b/s7rbg9WAlgBYbwAky0TAAv3bx02z2Ayp6sEjOAuYUzgLn6XDOxSGl6AJW7ZyUBHd5keFmWXghxcXEcPnz4oftiL09ERkYSHx9/r5dRLgiuFMwXnb/gyvgr/NDzB5r6NCVXn8uSY0to+X1Lmi1oxs/H7+/ZtuUJG40NP/X9CScbJ7bFbOOLPV+I16a0n4Kfi8ncedT6UUWWzY1GI7U9a+OgdeBCygV+i/rtrq+9rKhdybISWO4BlIkcQEZ+htkxFlXABWJKUaRXjqEyefSw9yD2ZqxZqbxhlYaASQEc7hcOmAQgNzIl30slARTmzyG9OJV4irPJZ7HV2PJE8BMW13E7+PnEzxy9dpQFkQvKpVfXivsHVgJYgSE3NSdlJ5mVsCxmAMvYAyiXKqD4DKDRaDSVgBUZQOVO0U5jh4O25AkNygyg/CWjQsVrLe/M/HnNmjVWM9RywJo1a+71EsoVjjaODAsbxoHhB9j34j6eC30OO40dkfGRwoT3YUFwpWBmPiGNbXznn3eIjI8EpE2d/DxAZHwkCyItj3yLS4+jx889BFGaunNqoc3i3YLOoONQ3KESew+LygA62zqLkW8yUnNSzcqoxamA5eeKJIAFKjAe9h5mG/fanrVxsnUCYO8VSQAS7htOhi6DqBtRgERO61WuJ7KV2fnZ/H3+b0Dq/5PJYMfAjhbnZOsNev4+93eZMrMGo4FpO6cBMD5ifKkn7VhRMWAlgBUYTrZOguxZUgLfSQ+gSqUq5AVoiQBm67LFY6VHn9kUEAePUhEwMwJ4K3vYsEpDHG0dizqlVNi0aZO1/+8OodFo2LRp071exl2BSqUi3C+cH3v/yNXxV5n+2HSz0ufDgufDnqdPnT7kG/IZuHygIDx96/alay2TofbELRNJykoqdL6fqx+DQwcDUgXhxPUTrI1e+5+svd7X9Wi2oJkQTxQFefRdQQKoUqnMlMAgzQNWbmqLmgWsfK4oclUw/lZyqGTWux1aVRIRZeZlcvy6ZFQf7hvO6UzpGDc7N8Dc/mXzhc1k5WcR4BpAmHeYIIBFlX+3XNzCEz89Qdj8sFJXQ1aeXsmpxFO42bkxovnD9zfxoMP6rVjBYakPsLqb5Kd2Jz2AUNgLUPgAKkpAykyfvIMt+HxpPQAtNVBPantn3n8Ae/futY59u0Po9Xr27t17r5dx1+Hp6MnrrV+/4xm5FREqlYoFPRbg4+zD6cTTvL7xdfH87C6zsdfaA5CSk8LELRMtXmPiIxNRq9SCCH2y85P/pPVCVmHvit1V7HFy1SQ+I75QP1vBPsDk7GSztpZiVcC3MoBFlccLVmA8HDwsEsCDcQcxGA34ufjh7+rPqcxTZudZsn/pFdKLq+lXORB3ABUqeoT0sLiGhUcWAtCuertSbciNRiOf7PwEgNHhoy1mFa2o2LASwAoOQQAtZAAvp10WtgNlzQBC4WkglnwAlWPglIHxduYAKzOAIAXV/vX6l3ndSiQnJ3PlypU7uoYVEi5fvkxycvK9XoYVdxGejp4s6i0peL85+I3I4AV5BPFu23fFcd9GfsvBuIOFzq/lWYunGzwNSERp75W9bL+0vdh7Xky5yLv/vMsPh3+47XW3DmgNIOblFgUPBw8qO0rq80J9gM6FM4BK0lPULGAoOQMox1A5dnrYe5ht2uURf3L/nzzB42SmNA7zZu5N1Co17aq3Y+XplfT+pTe/n/wdgF51erHytEQGWwa0tChoSclOEQrhYY2HWVxjQWy+sJmDcQdxtHFkbMTYUp1jRcWClQBWcAgrmCTTbtLXxRe1Sk2ePo/MvEyg7D2AYMoAykFNDnJmBLAoD8AyZgDTctMKWXF4Onrece/eoUOH7uh8K8wRGRl5r5dgxV1Gp5qdGBcxDoDnVz0v7EkmtJpgNrFi1LpRFnv8JrWZhAqVeG3qzqnF3m/zhc189O9HzNgz47azhTIB3H15d4l9h0UqgZ3MiVNGXgbONqa2luJUwCIDaCw+AygIoIMHpxJN2T05gykTwAi/CHJ0OZzNMpHUJj5N8HDwYMWZFaw8s5K03DTc7NxoV71dierfX078Qq4+l4ZVGtLEp4nFYwpCzv691OQlvBy9SnWOFRULVgJYwWFJCWyjscHXxRdAkKryyABaMoIu0gMwr2xTQCxNZ2gV0KqMKy6MEydOWPv/yglqtZoTJ07c62VY8R/gk46f0LBKQ65nXuf5lc9jNBqx1dgyt9tcccy+q/v48ciPhc6tV7ke/er1AyRitPH8Rg7FFb0Re6r+U9hr7Ym6EcWh+NvbsDX2boyjjSMpOSlmpVVLEDOBk4rPAALYae3Ev2VfwIKzgMFECossAcsZwFtWVLZqWxIyEgDJvF9WWssCkBb+LTgUfwidUSdK7x1qdMBoNLLh3AZx3W61u5GRl8G2mG1A0dM/5PLvsMbDSrWp3n15N9titmGjtuG1VncmwrPi/oX1m7GCQy4Bn085b5blU46EgzvrAdTppQygJRGIJQ9AKJABtC+FB6CF/j+5L+ZOEBcXh0ajKflAK0qERqMhLi7uXi/Div8A9lp7lvVbhp3GjrVn1zLv4DwA2tdoz+BGg8Vxb256k5TslELnv9PmHcBEkIrLArrZu9G3bl8Ai4SyNLDR2AjbFEt9gDcyb7D02FJ+OPyD6ANUjpwE8x5AefOrVAFn5mdiNBotloAt9UcrIcbKGaVYejPXNHu5iU8TVCoVV9KuEJceh0aloalPU1HOlu/XMagjR68dJSEjQTzXO6Q3a8+uRW/U06BKA4IrBRe6d9T1KA7EHUCr1jKo0SCL6ysI+ec1JHQI/q7+pTrHioqHB5YA7tixgx49euDr64tKpWLFihVmrxuNRqZMmYKvry8ODg60b9+eqKioe7Ta24efqx+ONo7oDDoupl4Uz8sEUC7flEsG0AIBtOQBCGXPABbs/wPTe7gTxMXFWQ2gywkGg8FKAB8iNKjSgE8f+xSA1za+JjJrnz/+Oe727gAkZicyeevkQueGeofSK6SXeLz81PJiM3PPhT4HwLLjywrN6QVgyhT48EPLJ3/4IUyZUmwfYHRSNIP/Gsx7294rehqIQgUsxzql753BaCDfkH97JWCD+VzhG1k3xGvyRlfO/jWs2hAnWyfxPrJ12diobWgd0Fpk/4wYhd+fXP7tHVJ89q977e6lmr5z7Nox1kSvQa1Si/F+VjyYeGAJYGZmJqGhoUUOsp8+fTozZsxgzpw5HDhwAG9vbzp16kR6esVyOler1CILqAyw1Vwl8nQtU5rfeSc9gMUZQVvyAISy9wBaygCWBwG8cuWKVQFcTtDr9RVeUKM36Bm6Yij/Xvr3Xi+lQmB0i9E8XvNxsnXZPPvns+Tp86jiVIVpHaeJY74+8DVHE44WOlcpGjFi5NNdnxZ5n46BHfFz8SMlJ4U10Rb8JjUamDy5MAn88EPpeY2mWAJYv0p9QBq55uPiA0gEUNlzqMwAyiSvoOI3Kz/LYglYZABL2QMYl27aSMkEUJ4A0sKvBQajgd1XdotjIvwjcLJ1Miv/dgzsiFatFc9ZKv/m6/NZcmwJAENDh1pcW0HIvn/96/V/KNXwDxMeWALYpUsXPvroI/r27VvoNaPRyFdffcWkSZPo27cvDRo0YNGiRWRlZbFs2bJ7sNo7g6U+wOrukhWM3GdSnhlAZZArVQawBBXwryd+FUFKifIggAkJCXd8DStMqOif56Kji1h0dBFtf2xL/9/7czHlYsknPcRQq9T82OtHPB08OZxwmHf/kUjd8KbDifCPACQSNGr9qEICjqa+TekS3EU8XnpsKbE3Yy3eR6PWMCR0CAA/Hv2x8AHvvgsffACTJ6P+4AMwGlF//LFE/j74AN59l5YBLVGh4lzyOa5lXDM73d3eXfRF5+mkWJiak2omPFMSQLlqUND0OSs/y6IRdFFTkmQoW3DUKrVZz3MhAYh/BFHXoyTl762v6I6BHUnLTTMjt71Cepl5AVoSd6w/t57rmdep4lTFzMuxKJxLPsevUb8C8PYjb5d4vBUVGw/lXJeLFy+SkJDA448/Lp6zs7OjXbt27N69m5dfftniebm5ueTmmsoTaWnSrNv8/Hzy88ueYSsvBLtLfR+nbpwS6/B1koJdXJq008w35JOXl1cmVa1c/sjOy0aNWgS5vPw8cZ/U7FQAnLROZp/BzWxTj4urrWuxn8/pG6fF9AAlvB287/hzzcsrO/G1omjk5eXd09/1skJeq/z/zoGdGR42nO+PfM8fJ/9g1ZlVjAkfw1ut3qrwPmcF32t5wcvei3ld59H/z/58tvszOgV2ol31dszqPIuIHyIwGA3sjN3JoiOLGNhgoNm5b7d6m/Xn1qNChc6gY/rO6Xz5+JcW7/Ns/WeZunMq68+u50rKlcKijLfe4oOtk/nwo4/oDmgA/XvvYXjrLcjPx0njRL3K9Yi6EcWOmB2FSqL1vOoRlx7HmcQzVHOtRmxaLKeun8LdXypnV7IzVSoKlmxlpGWniZ5ogHydFPuVIhBLn392nim+udu7c+K6JKbSqDTUcq9FVk6WsNVpUrUJ2y9K1jlqlRqD0UDbgLb8fdZ8ikeXml2YvE0qv/es3ROdrrAFzfeR34vPFkPJveDT/p2GwWiga3BX6nnW+8/+1u/W725p7vkw46EkgHIWo2pV8wBTtWpVLl26VOR5U6dO5f333y/0/NatW3F0vLNpFXeCrBTJnmDvub2sW7cOgEvZ0vuISY4Rx61etxqtqvQ/8sw0yULmYORBwt3CyUiTBB/7DuxDf0bKAh67egyA61eui3sDnLlkykaeijyF4UzRfXiWynHOGme2by7eP6w0yMjIKPkgK0qNjIwMs59zRYFyikk3ulG/dn0WXl3I0YyjfL7ncxYcWMBAn4F0rNTRbAxiRcTdmNhigw2dKnViU/Imnv7taWaGzMRZ60x3r+6surEKgHFrx2F30Q5HjXksbOTciGMZUpxYcGgBzbOb427jbvE+IY4hnMk6w+Q/JtOrSq9Crzv7tgO2owHy1LA+LAwUv4/+Bn+iiOKnHT9he97W7FyHDGmM2doDa/EweBBLLH9s/YMUT5OIxV5lT44xR5R246+Zz7/++5+/zTKA+/fvJ+9UHvp8KR7m5OZY/Ps4dv2Y6bPU2XAhWcoAett4s/nvzVzIukC2LhtHtSPn953nt0vSHGWdUYed2o6kY0l8d/U7cY3ajrU5uP0gy6OWS9dJ8S5039T8VOHjGHgz0Oz1C1kX2JayjW5e3ahqJ30PJuUlseiU5AHZVtX2nvyd/5fThrKysv6ze92veCgJoIyC2TCj0Vhshuztt99m/Pjx4nFaWhoBAQE8+uijeHp63rV1lgSfBB9m/DCDG4YbdO0qpflTc1J59cyrpOtNpdjHHn+s0LzL4vBZ4mdEZ0VTv2F9iAV3d3fIhqZNm4pywtr1a+EGNKrTiK5tTCWG737/Dm7F1W4duonGa0v4dNGnkCLZzBiQiGKwV7B4L3cCZ2dnEhMTSz7QilLB2dm5XH4u/xXy8/PZtGkTnTp1wsbGxuy1EcYRrDu3jje2vMHZ5LN8c/kb/s39l88f+5xHazx6j1Z8+yjuvZYH2ua1Jfz7cM6lnGOlfiVLeyylTV4bGs5vSFxGHKn6VPY77Gf6Y9PNznOJdaHj0o6oUJFnzOOU2yk+bC/18uXocvhq31f8fup3dgzZwRjvMbyy4RX25+9nXpd55vE4OZmegySbGJ0KbA3Q/fBhDJNM04KSjyfz9+q/SbBNKPR7mnAkgdXrVpPtnE1EQARHI4/iEOBA1/am4/xi/DifaupH9qjkAYq28GYRzaSJR7f2t82bN6dTUCfsou1Iz0pHa6u1+Pdxcs9JuNX2V9m9MtduSCXqdrXa0bVrV+Yfmg/R0Kp6K7p3687YOSbj5TbV2tCzW09e+9pkxzIkfAhufm6kH03Hw96D1/q/ZiZYAZi5fyb6KD1NfZoyop/5GLdhq4ax6sYqnKo4sairRPpe3/w6OqOONgFtGN9/PP8l7vbvriXIFbyHGQ8lAfT2lno9EhIS8PHxEc9fv369UFZQCTs7O+zs7Ao9b2Nj85/90lpC/apSg3NidiLpunQqOVTCS+uFi62LWS+eUW0s0zpttdIO2qCSSJk8CUStUYvrZOqkLKG7g7vZtdPyTH9cVVyqFHvfmJsxgOS5JZeCa3jUKJfP1NbWtuSDrCg1bG1t7+nv+u2iqL/R3vV60zWkK3MPzGXK9ikcv36czss60yukF591+qxCNsHfrXjkYePBT/1+otX3rfj91O/0COnB4NDBzOoyiyd/fxKAWftn8WLTF4XoAqBDzQ60rd6WHZd2ADD30FzebvM2bvZuaLQaFh1bxPmU8/x55k+eDX2W8ZvGE3UjihNJJ0Rf243MG6hah+GVkcENR6g2Dibshg/ff1+yeXpX6k1sF9gOgMj4SHTocLBxEOto5N0IgJOJJ+lWuxsguQ8oPysfVx8zAphnNG8hyTPm4WFj6mnWarXY2NiIrLHBaLD42csbWzCJ6wCa+jXFxsaGgwlS+TfCP4L4rHgup102fX6BHTh38xyxaab+yb71+vLtoW8B6BHSAwc70/sEKZmx+NhiQJrxrFxTQkYCv52UMozjWo7DxsaGxKxEFhxeAEjjN+/V3/h/+V1aEeNYeeOBFYEUh8DAQLy9vc3SzXl5eWzfvp1Wre7cfPi/hpOtk/BqkoUgKpWqkIiirEpgoQIujQ1MARXwzRxTD2BxNjAGo0FY1ShLb7KK+U4hk30rygcP4udpq7FlbMRYzo0+x+jw0WhUGlaeWUn9b+rz2t+vkZqTeq+XeN8g3C+cKe2nAPDKule4mHKRvnX7CrGHAQOj1hUWhMiKYBUq0nLTmHtQMpRWq9S83FTquZ57cC7u9u5Czar0BMyb9DZe56VybN8BkGMDH7WDdx/FTB0c6B6It7M3+Yb8QqPq6lWuB0gKXFkQUpwVDJimfygfm6mAb71POXYVNclE2XunjJ9CAawQgOyM3Wl2zQ41Opipf2t71ibEM4QVZyRrM0vTPyLjIzl+/Th2GjueafCM2WvfHvqWfEM+Ef4RNPdrDsDsfbPJys+iiU8THq/5eKHrWfFg4oElgBkZGRw5coQjR44AkvDjyJEjxMbGolKpePXVV/nkk0/466+/OHHiBEOHDsXR0ZFnn332Hq/89iBmAicVVgLLPStlVQLLKmC58djSJBBhBF1AmgtpRgAAIABJREFUBSwbnTraOBYqTQBsj9nOY4sf49X1rwpVsfK65aEABvD397caQZcTNBoN/v4Primsp6Mns7rM4viI43QJ7kK+IZ8Ze2dQa3Yt5h6YW+Sc14cNbz/yNq0DWpOel87gvwajN+qZ03UOdhqpOrLt0jYxp1ZGx8CORPhHiN66L/d+KSZrDG08FFuNLYfiD3Ew7iBDGw8FJE9AOWb5uvpy00GKY9NCJ4jrftQOSQV8y+pJpVIVaQfjZu8mNsoyUTubfNYs7hSco2uJACp7APP1+by9+W0RB4tUASs230rBW6h3KCnZpukl4X7hoidab9TjpHEitGqoGQHsHdKbY9ePEZMag4PWwSJhk73/etfpbbYBz9PnCfI9JnwMIG3iZ+2fBcDERybe8fhNKyoOHlgCePDgQcLCwggLCwNg/PjxhIWFMXmypJp64403ePXVVxk5ciTNmjXj6tWrbNy4ERcXl+Iue99CzAS24AUo7yTLOg2kPHwA3ezcLF77YupFtlzcwoG4A+K5XL1JYV1eBNDX19c6Cq6coFar8fX1vdfLuOuoW7ku6wauY/3A9dT1qktiViIj142k8bzG/H3u73u9vHsOjVrDkj5LcLF1YdflXUzbOY0gjyAz379xf48TpAgkYqbMAl7PvM4Ph38AoLJTZfrX6w/A3ANz6RTUCV8XX5Kyk4SIQfXBBxzuIsVy45/m5JJ335WMom+hWD/AylJpOjk7Ga1aS44uh6tpV8XrBTOASj9TkMibkiBl67KZtmuaaHkpjQ2MnFH2sPfAy9FLxMAgjyAqO1Vm5+Wd4tiGzg3J0eWIUW8Aver0YsVpKfvXObhzob7uHF0Oy45LdmbDGg8ze+33qN9JyEjA18WXJ+tJZft5B+eRmpNKHa869KlreZawFQ8mHthvxvbt22M0Ggv99+OPPwJSQJoyZQrx8fHk5OSwfft2GjRocG8XfQewlAGUSZQcsG43A1jIB9BQsg9gZv6t3kB7y2q/5Oxk6ZoKcqb0FyxPAmg1gi4f6PX6h4IAyngi+AmOjTjGnC5z8HTwJOpGFE/89ATdlnUrcd7sg45Aj0C+7vo1AFO2TWH/1f1MaDWB2pUksVdcehwf7/jY7JwuwV1o6tNUZAGn754uNpf/a/Y/AH4+8TPpeekMaiiNLFN6AlYaOByAhvsvYatIxqreN89YPVLtEUCaZ1uQkDWoIsX404mnCfIIAszLwAUzgMqRbVC4BFwwKyy/t4JQZgBlAiiXpOUJIBH+EaRkpwiLGJAI4PbY7SIGV3WqSgu/FsVO/1h1ZhUpOSn4u/rzWNBjprUZjczcNxOAEc1GYKOxIUeXwxd7vgDgrdZvFTK+tuLBhvWn/YDAkhl0efcAyiKQkjKABqOBHF0OIJXVLEEmgEWRs/IigA0aNLCOgisnGAyGCr1JKg7Lji9jxekVhXq4tGotr4S/wtnRZxkXMQ6tWsu6s+to8E0DxqwfQ1JW0j1a8b3HoEaDGFB/AHqjnoHLB5JvyGde93ni9c92f2ZGrgpmAWNvxvLziZ8BKWvXoEoDsnXZLD66mOcaS6Ph1p1dJ3qEG/R6kXhXNW65sCD76SLX1di7MY42jiRnJ5vFQzBlAKNuRFkcCVfQe1CZxQTLJWAlStMDKG/EZSNtuf+vhV8Ldl+Wpn/I92jk0oiN5zeKc3vU7sGlm5c4du0YGpWG7rW7F7qXXP4d0miIiNnyfQ7EHcBOYyf6LhceXsi1zGtUc6vGsw0rZvuTFbcPKwF8QCCXgM8lnxO7UplEyYTttjOAxZWALWQAM/Myxb+9HLwsXlsmgAV7bED60i1kAnubaNq0ablcxwoJTZoUnjZQ0ZGVn8W4v8fR59c+hM0PY/mp5YUyRx4OHszoPIOokVH0DOmJ3qhn9v7Z1Jpdi1n7Zt3WqMWKDpVKxdxucwlwDeBc8jnGbRjHo4GPiuyd3qgvJAjpGdKTRlUbiUzZtJ2S8bBKpWJEM8mqZN7BedT1qku4Xzg6g06UM9UaLdFtpKxZ5V1Fe4TaaGwI9wsHCpeBZXVy1I0oka0sLgMI5uPgMvMyzUrAcqyVCVtRBNBS7A33C8doNIoRcBH+EfwbK/X/GTHi7eSNv50/f583tR30rtObladXAtC2ettCG+yraVcFYZR7KWXI2b9nGj5DZafK5Ovzmb5bsux5vdXrZupkKx4OWAngAwJ/V38ctA7kG/LFeCuZAMql1TL3ABZRApa/HI1Go9ghO9s6i/OU1jNeTpYJYEqOZBKYmpta6HwfZ59yK0VUqlTpgRYu/JcICAigUqWS5zpXNOgNel4MexEXWxeOXjtKv9/60XheY/44+UchIljbszYrn17J5sGbaVilISk5KYzdMJaGcxuyNnptkQTgQYWHgweL+yxGhYrvDn/HX6f+4vPHPxdTVTZd2MTKMyvF8SqVinfavCMen0o8JQjNoEaDcLJx4lTiKXZc2iFm1yrVwC7PSs+FH0tAoygefLTjI7N1FdUHKJdd5T44gOhkRQZQ0QMoxz8HrcliJS03zSw25RkkYic/V2QJ2ELsDa0ayvmU8yRlJ2GrsSW0aqhQAAO0r9GehLwEYUvjZONEx6COpvKvhdm/S44twWA08Ei1R8wsjK6mXeWPk38AJvHHLyd+ISY1hipOVXgh7AWL67biwYaVAN7HyMzL5Ni1Y6KcWhzUKrUoach9gH6ufubBqqwZwBJEIJn5mSLgKUvAysbpSvaWCYOcAUzLTSt0fg33GmVaZ0mIiIiwKoHvEBqNhoiIiHu9jLsCFzsXPu74MTGvxvBOm3dwtXPl+PXj9P+9P6HzQvkt6rdCRLBjUEcOv3yY+d3nU9mxMmeSztD95+50XtrZrIfrYUD7Gu15vdXrAAxfPRy9Uc+nj30qXh+1bpRQ/AL0q9ePul51xeNPdn6C0WjE1c6VgQ2lUXJzD85lQIMB2GpsOXrtKEcSJDeHRk++QqKTCs8sI4PiTRu7d7eaBCigIICx5gTQ2daZ6m6SO4JcHj2bdFa8rqw8yO4FSpFFWl6aWQm4YAawNCpgkIR5wZWCRfYvzDsMI0YzUVyHGh2ITIsUj58IfsJsHnCvEPNJKUajUZR/C4o/5h6UVOxtqrUhzCcMg9HAtF3TABgXMc7ML9GKhwdWAngfo+asmoTOC+XkjZOlOr5gH6BWrcXPxU+8XuYewBIygDLRU6vUZkFSmQGs5FA8AZSPlS0kANGcXV7o1KmTtQ/wDqHX6+nUqdO9XsZdRSWHSnzY4UNixsYwue1k3OzcOHH9BAP+GEDDuQ355cQvZgIojVrDS01f4uzos7zR6g1sNbZsurCJ0HmhjFgzghuZN+7hu/lv8WGHDwnzDiMpO4lhK4fxYpMXae4recxdTb/KtJ3TxLFqlZpJbUzTOw7GHWTLxS2ASQyy/NRy8vX5guQsOiJNq9Da2hPVUspsDbta9OzmlgEtUaHibPJZ0UMoQy4Dy6T0QsoFERvttfY4as1VtfZae/Hv9Nx0sxJwwc1xaUQgIFVsNGqNmQDkYNxBs016++rtOZx+WDzuFdKLNdFrMBgNNPFpImy+ZOy5sofopGgcbRyFqhokVfD8Q/MBGNtCmjCy6swqTt44iZudmyi9W/HwwUoA72PUrFQTkPr6SoM6nhasYBRiitvOAN4igHLgkwmgsvyrDIrXMq6JfxdlAi0TQPlayvPLSwAio3v37g9dae5uoHv3wg3nDyI8HDx4/9H3iXk1hintpuBm58bJGyd55s9naDC3AcuOLzMjgm72bnza6VNOvXKKfnX7YTAamHdoHsGzg/l89+fk6nKLuduDAVuNLT/1/Ql7rT0bz2/k6/1fM7/7fJEZm7pzKhdSLojjBzQYQHClYPH4k38/IU+fx6Kji9CoNOQb8vnh8A+ij23p8aUiftk9JRkb19l5GlUR+zp3e3dB9GRhhQxZCHIl7QqONo7ojXoupl4Ur8t9dTKZU/bGpeelW1QBl+SdV7CKI69BKQBRzkQP8gjC29mbo2lHASlj2K12t2LVvwsPS9m//vX6m/Vk/3z8ZxKzEqnmVo1edXphNBr55N9PABgVPgo3e8tWXVY8+LASwPsYcoA8n3y+hCMliAygBSsYuP0eQJ3+lhG0bANzq6dQ3lkX9KHac2WP+HdJGUAZygBZ3gTQ19eXsLAwq8HpHaBJkyZmYxMfBrjbu/Ne+/eIeTWG99u/j7u9O6cTTzNw+UDqf1OfpceWmtmABHkE8cdTf7DtuW2EeYeRlpvG65tep/439S0qjB801K1cly8elyxF3tz8Jlq1ljEtpH6zfEM+o9eNFsdq1VqzLODWmK0cijvE4YTDIr58G/ktHQM74u3sTWJWIuvPrgeg4YCxpNpD1XQDrUwT0wrZwchlYGVfHZjI18nEk9SqJGUTlUKQKk5VAJPdlXJCUUZehrkKuEB1pKifcUGxW6uAVuTockRpO8I/wsz/r0ONDuy8vJN8pOu3rd5WyjCfl6ZXFfTry8zL5NeoXwHz8q/RaBQmz680fwWtWiv8Vx20DiIjaMXDCSsBvI8R7CERwNJmAIvzAoQ7zwAWnARy4KrUr1KQzCmtEzzsC2cA9Qa92ag4MPkGFlxzeaFv375WQ+jbhEajoW/fvvd6GfcM7vbuTG43mZixMXz46Id42HtwJukMg/8aTL2v67H46GIzItiuRjsOvnSQhb0W4u3szfmU8/T5tQ8dFncQX/gPKkY0G0G3Wt3I1ecycPlAJrWZJAjVunPrhLEzwMCGA836faftmsZHj5rEHDGpMWy5uEWoihcdlcrAtg7O7Gso9ep9nBJW5FqKEoIIJfB1y1YwshJYTEAqTgV8a3MsRsHdyhqujV7LgkMLxHWV0z9A6ps8HH+YfEM+lR0rU82tmlm/YofADmb2L73r9GbDuQ3k6nOp6VFTkFgZy08tJz0vnSCPINpWbyue/zf2X44kHMFB68CLTV4EENm/l5q+RGWnypY/PCseCli/Ee9jyBnAcymlI4ByMLueeZ3LN6Wt8Z0QwLj0OLP7F/QBzNJJu1o5U1hw3WA5A3gz92ahXhmldczdIIBDhgyx9gHeJgwGA0OGDLnXy7jncLN345227xDzagwfd/iYSg6VOJt8ludWPEfdr+vy45EfzUjD0MZDOTv6LJPaTMJea8+2mG00md+E4auGk5CRcI/fzd2BSqXi+57fU8WpCsevH2fqzqnCMBpgxNoRIttvo7Hh7UfeFq+tOrMKDwcPOtfsLJ6be3Cu8ARcHb1a9FVeaymRu5rbj1FE2x2tq0nHHIo7ZCZCkQUoN7JuiNFwSgIoxsVZuHBBI+iCGUD5lDkH5vDSmpdE+bkgAWxUtZGp/OvfgpM3TpqZTncI7MDacyay3CvENP2jd53ehaoZsvhjaOhQs9dm7ZOyf4MbDaaSQyX2XN7D1pit2KhteK3la4XenxUPF6wE8D5GWXsAXexcxE5Unh1pVgIuowhEJpGyrUxBEYiccWzs3djsPGV2z1IPoJwxlNfqoHEwmwIS4BpQpnWWBtWqVaN79+5otYXnEltRNLRaLT169CAgoPx/JhUVrnauTGwzkZixMUztOBVPB0/OJZ9j2MphhMwJ4YfDP4i/NWdbZz7q8BGnXznN0w2exoiR7w5/R63ZtZi2c1qpFP4VDVWdq7Kwl0RIvtz7Ja62rmIixeW0y3y+63Nx7HOhzwnCBZIv4IePfiger41ei4utC818m6Ez6IRxtFPLPmTYgn+KnmZxpnt7f2Ty8Qt0D8Tb2Zt8Qz4H4w6K551snQh0DwTATiuJz84mm5TABeOPUtmbo8uxqAKWN8cyaZRLvk42TuI8Gc62zrjYuZgEIH4m/z+QStS5+lyxpgaVG+Dr4sua6DUA9KljXv69mHKRrTFbUaESZBngUuol0TM4uoVUfp+6cyogEcIAN+vf9MMOKwG8jyFn0uLS48wyZMVBbui9kCo1XMt+XIDZLrg0kMmjl6Pk5VeQAMq72oIWAkr1o6UMoBgDd+t6lRxNx7jYuvDPxX94efXLYsdbXhg1ahQ6na7kA60Q0Ol0jBo16l4v476Ei50Lbz3yFjGvxvDpY59S2bEyF1Iu8MKqFwiZE8J3kd8JIljdvTo/9/uZXc/vItwvnIy8DN7e8jZ1v67L71G/P3D9gV1rdWVks5EADFs1jKkdpopKwQc7PiD2ZiwgEbA3W78pzvvlxC94OnoK9a8RIwsiF/BcqERsZE9ArYMLR5tIxHHMSdN4wmt6kwBNpVKVWAbO00lVEWUG0MfFvNdVWd4vOAtY/vkqJ26AqaIh90crCaBMMJUZQGWfYsfAjmIDD5LgY1vMNm7m3qSKUxUxQUSGXBrvGNTRbMP/zYFvMBgNdAzsSIMqDTh+7Tiro1ejQsWbj7yJFVZYCeB9jEoOlUQPnVJBVxx61ZYCp71Gsi5QWgX8E/NPme7fqGojwFQSKUQAbxFKpVEqSKUV+Xilv5+MlOwUs+vIBBMk78KtMVv5NvJbM1VceeCxxx4jKCjI2gtYSqjVaoKCgujYseO9XspdwZroNbT7sR0LDy8sNPKrLHC2deaN1m9wcexFPuv0GVWcqnAx9SLDVw+n1uxaLDi0QLRftApoxZ4X9rCkzxL8XPyISY3hqT+eou2Pbc2yVA8CPnv8M+p41SEuPY6pu6aKMXD5hnxGrTNtKl4Ie0H03emNej7b9RkfPPqBeH3ewXk8WfdJbNQ2HE44zLHrxwAw9JGUsG2ibxRdBi6KAN7qoZMN6a+kXRGkTWkGDeatM7m6XPMSsEwAVUVkAG2lDKBMNAEaVmnItYxrxKTGoEJFc9/mZgSwQ2AHke0DaXqKvBnuFdLLjGwajAZBipXij6z8LBZELgAQQhzZ969//f6iXciKhxvWb8L7HEIJnFI6JXCwp/nx1VxNO8LEzMQy3dtWYwuYAmBpM4CJWdJ9HLQOFpW3Yg7wrbKvMktYw72GKH0onezLA2q1mo8//tjaC1hKGAwGPvnkkweWMC88spAdl3bw/Krn8f7cm6ErhrI9ZnuRZr4lwcnWiQmtJnBx7EW+ePwLqjpV5dLNS7y05iVqza7F/IPzydPnoVapGdRoEGdGnWFKuyk4aB3YGbuT5gua89yK57iadrWc3+m9gaONI8v6LsNGbcPyU8up4lRFiD5WR68WilYHGwfeaPWGOG/hkYVUdqwsvOySspP4N/Zfeob0BGDpsaUA1B/yOtlaqHEjn4amxB87duwQ/5b7AHdf3m32c21QRZppfT7lvIg/cqtNwXFwyuxdrj7XrAQsTwKRTaNlyKI2OQOYqzdZAbWu1lpk/+pWrsvN3JtcTpPabdQqNS0DWrLpgvTZeGg9aFi5ISvOmPr/lNgWs41LNy/hZudmVhpeemwpKTkpBLoH0q1WN84nn+eXE78AmPVdWvFw48GM7A8QhBCklH2ANT2kvkGZAKpUKmFsWtb5ujIBLGoSSFEZQHlXrRzvpkRB1bCt2lb8O7hSsHDmV4pJygtPPfUUDRs2tE4GKQEajYbQ0FD69+9f8sEVFF91/oqPO3xMcKVgMvMzWXR0Ee0XtSd4VjAfbP+AmNSY27quo40j41uO58LYC3zZ+Uu8nb2JvRnL/9b+j+BZwcw9MJdcXS5Otk681/49okdHM7jRYAAWH11M7Tm1+XD7hxbnZFc0hPmE8VEHSdn72sbXeL/9++K14auHi83ly81eFnPDc/W5fLn3S7Ms4PTd00UZeNmJZeiMOlw8fTjaWCJr/U6Z7tluazvT/b3DcNA6kJydLAzywZQBjLpeeCZwwTipFHDk6fMszgIuSAAL9gAqCWDnmp3FBJCC/n9NfZpy6sYpQTpbubciMiGSuPQ4nG2d6RDYwew+svjj6QZPi4240WgU4o/R4aPRqDVM3zUdg9FA11pdC/VsW/HwwkoA73PIhK7UBPCWcETpHehqL/UBypm50qKoDKDsjyUygAUIYGqONN+3KIPRpOwks8c5evP+GLncLXt0lSfUajWff/45er2+5IMfYuj1ej777LMHNvsHEOAWwMQ2E4keFc3OYTt5IewFXGxduJh6kfe2vUfgzEA6LOrAkqNLSt2Dq4SjjSOvRrzKhTEXmPnETHycfbicdpmR60YSPDuYr/d/TY4uB39Xfxb3Wcy+F/fRKqAVWflZTN42mZA5ISw7vqzC9we+1vI12tdoT2Z+Jl8f+Jp+dfsBcOnmJb7c8yUgfVYTWk0Q58w9OJeqTlXFsQfjDhLoEUgVpypcz7ouRqTpektZwWej7bAEG40N4X7hgHkZuI5XHdQqNUnZSUIMIVceZNsaGUoinq/Pt6gCLpQBLNADqDQDr+1Zm71XTRNACpZ/151dJx63cGvBymhpVnLXWl3NppLczLnJnyf/BMzLv/9c/IeoG1E42TgxLGwYV9Ou8uPRHwFr9s8Kczy40f0Bwe1mAK9lXhN9TXIfodybV1oUJIByn0uhDGCBErA8Is7d3t3idVdHrzZ7LM8DBmnHnG/Ix1Zja6YOLE889thjeHl5lXzgQwwvL68HfvSbDJVKRetqrfmu53fEvxbPkj5LRKZla8xWhqwYgs8XPry46kV2xu4sMyFzsHFgTIsxXBh7gdldZuPn4seVtCuMWj+KmrNqMnvfbHJ0OYT7hbNz2E5+6fcL1d2qcyXtCgOXD6TVD62EYrQiQqPWsLj3Ytzt3dl/dT/V3KqJTePkbZNFyXtk85EiVmXkZTBn/xymd5ourjNy7UjhCbg1eSsADZ5/mzwN1IrPpXYR+1tLfYAONg5i5KTcpyxnAG01tmajKZUwYhTef2CqjiitsAxGg9gcyz2AcqbQXmuPwWgQHqot/FqYKYA7Bnbkz1MSqXOycaKecz1WnpEIYMHpH79F/Ua2Lpu6XnUFyQWE8fPQxkNxt3dnxp4Z5OnzaFOtDY9Ue8Tyh2TFQwkrAbzPUVYC6OHgIYKobN8iiyzk0mxpIQigoYQewAIZwIx8iXjKJZ2CSM4ylYA1Ko1ZSVjuC6zpUbOQsq68sHnzZhITy5YNfdiQmJjIpk2b7vUy/nM42ToxqNEgtgzZQsxYaQJIoHsg6XnpfH/4e9osbEPtObX5eMfHwiaptLDX2jMqfBTnxpzj665f4+/qT1x6HGM2jCFoZhAz984kR5fDgAYDOPXKKT7u8DFONk7svbKXlt+3ZODygUI9W17I0+f9J56EAW4BzOs2D4CZ+2aK+bN5+jwhCHGxc2FcxDhxzsx9M6nqVJXqrpKQbWfsTgY0GADAgbQDJGUl4e5Tg6P1pTjTTzEyXTkVRO4DVBotg6kMLMczpRJYbl8pmNkD85KwIICKcXFZeaaMoZwBVPY7n048TXpeOk42Tvi6+BJ1I0q6htqGII8g0b7zRM0nSMhN4EzSGWzUNnSt1dVsHXL5d1jjYaIsfT75PKvPSBvs0eGjScpKEnOAJ7aZWOi9WPFww0oA73PIBPBy2uVSzxQVZeBbgURWtRWcvlESShSBFJEBlAOgpeAJ4Otism3wcPAwI4Ry6aS8BSAyDAYDEyZMsPYAlgCNRsOECRMeasFMdffqTG43mXNjzrHtuW0MbTwUJxsnziWf452t71D9q+o8vuRxlh1fViaLJXutPSObj+Tc6HPM7TaXANcA4jPiefXvVwmaFcSXe77EiJGJbSZydvRZnm/8PCpULDu+jJA5IUzeOvmOVMsyDEYDL6x6gRbfteDUjVMln3CHGNBgAIMbDcZgNPDnqT+FIfOKMyvYHrMdkPzq5IxcUnYSCyIX8H2v7wEp+/bvpX9pXLUxOqOOX09Ko8+ye3YBYPA5h4K3BKClf0tAKvHK4yvBRADlCoSSAMrtK8oxcDKUcVjO7BWcFyyj4JjMGm41RDa3mW8zIQYBaBnQkh2XTAKW/vX6s++m9HqHwA5mLTWnE0+z58oeNCoNgxoNEs9/feBrjBh5IvgJQrxCmL1/Npn5mYR5h5kZbFthBVgJ4H2PKk5VcLZ1xmA0lLopXQhBbvUB+rn6Aebj1kqDUquAFRnAHF0OBqTXiyo5X88yBWEfZx+RMVSr1CJAy2Pwyhu//fYbx48ft/YAlgC9Xs+xY8f4/fff7/VS7jnUKjXtarRjYa+FJExIYGGvhbSr3g4jRjZd2MTA5QPx/sKbl1e/zJ7Le0pdIrbT2vG/Zv/j3JhzzO8+n+pu1UnISGD8xvEEzQzii91f4Grnyve9vufgSwdpW70tObocPtzxIbVn12bRkUW3rVgGSYy1/+p+Ym/G8sjCR9hzeU/JJ90h5nSdQw33Gly6eYkgjyChqB26cig6gw53e3dejXhVHP/57s9pU70NQe5SuXbG3hkMaSRNpVlyfAkA9V54G70K6sZmU8NCkcPDwUOQPXkyB5i8AOWJR0nZSSRlSf3Jng6eAGaKXxlmGcBbPYBKIZtMKO219qhVarPfB39Xf5P/n19h/z95nq9GpeHxoMcFASyo/pWtX54IfkL4FqbnSllqgDHhY0jPTRdikIltJlpnoVtRCFYCeJ9DpVKVWQgi97YIK5hb5qB5+jwzU9OScDs2MMosY6BHoMXrygEXJNGH7J3l5eglxs7djQygwWBg0qRJD7SwoTyhVquZOHHiQ50FLAhnW2eGNh7KtqHbOD/mPJPbTqa6W3XSctP4NvJbWv3Qirpf12XazmmltnOx1djyUtOXiB4dzYIeC6jhXoNrmdeYsGkCQbOC+GzXZ4R4hrDtuW38+dSfBHkEEZ8Rz9CVQwlfEG5GIsoCL0cvdj2/ixZ+LUjOTqbj4o6ifFga3I44xdXOlaV9lqJWqVl7di0dAyWPyZjUGL7a8xUAr0a8KtSzV9OvsuToEt565C1Aih21PWujVWk5FH+IE9dP4FWjLkfrSP3GfRWJzJ6Le4p/iz5ARRlYJoWnk07j5yJtkoUQxFESglgaB6fM9goCqDURQDk7K7+HI/Gm+c+danYyTQDxjzBTALet3pZSAwuSAAAgAElEQVTtl6RMaAv/FqTlphGdJWUlZWNskLKOi48uBszFH4uPLiYtN43anrXpHNyZ+Yfmk5KTQohnSKHpIVZYAVYCWCFwp1Yw1d1MZtAFLViKw+3YwCjnWXYKKiwiSMtNMytfyYEXpN3x3bSA2bx5MxcuXLASmlLCYDBw4cIFtmzZcq+Xcl8iyCOI9x99nwtjL7BlyBYGNxqMg9aBM0lneHvL21T7qhpdfurCryd+LdXIN1uNLS82eZHoUdF83/N7At0DuZ55nTc2v0GNmTWYvms6j9d8nJMjTzL9sem42LpwKP4QbRa24anfn+Ji6sUyvwcvRy+2DNlC11pdydZl0/vX3nwX+V2J532x+wue+fMZ4QhQWsgGxbKly4G4A2Ja0aStk7iWcY1KDpUYFW4yiv5016cMbjRYKGC/2PsFTV2bArDoiDQFI72bFGuUfYCrL5rIrOgDVAhBQrxCUKvUpOakCsN8Of7IWTVLGValpYu8oVaKRuQSsFz+lT39VKjoWrOr6PkL9Q5lf9x+cawatfg9GRI6hM/3SCPzmvs0N5tOsvH8RuIz4vF08KRHSA+xTln8MTp8NHn6PL7Y8wUAbz3y1l3rp7aiYsNKACsAykwAb/UAnk48zbaYbWb9KcoxbSXhdjKAsgUMmNTHSiizf2DuuVXDrcZdtYCZM2eOdRZwGaHVapkzZ869XsZ9DbVKTYfADizus5iECQl81+M7Hqn2CAajgQ3nNvD0n0/j84UPI9eO5MDVAyVmzmw0Njwf9jxnRp3hh54/UNOjJolZiby15S1qfFWDGXtmiNLxy01fRq1S8/vJ32k0vxFL4paYqepLAydbJ1YMWMGwxsMwGA0MXz2cD7d/WOQ6zyad5e0tb/Nr1K+MWT+mTJnAKdumsOjoIjac20CYdxg3c28KtX+ePo8RayVxyPiW48U0o7PJZ1kVvYqn6j0FwPZL22ntLhG6JceWoDPoqP2iNNqs1RXwtfD25QzgofhDbLmwha4/dWXchnEitsqxSu4DlNckxzplL6CyBCwrgpX2LHIfs6wAlkmnt7M3Z5LPYDAaCHANIC49ThDIttXairm9ICl+l0UtAwqP05TFHwMbDhQxeuP5jUQnReNq58pzoc/x45EfSchIIMA1gGcbPlv4A7HCCqwEsEKgrNNA5Azg5ZuXeXTRo2YGqGXxApQHpRfyAbylaLOYAVSUgD0cSiaAypJ0Veeqd80CJjY2ljVr1lhnAZcROp2O1atXc/ly2RSvDytc7Vx5ockL/DvsX6JHRTOpzST8Xf1JzUll7sG5hH8XTsO5Dfl89+clqm9tNDYMCxvG6VGn+bHXjwRXCiYpO4mJ/0ykxswafBf5HdM7TefIy0foGNiRXH0uf17/k/rz6vNd5Hdlys7ZaGz4vuf3TGozCZDsWUauHWnxGrU8a7GkzxJUqPjm4Dd8sP2DQscUhXfavkO9yvWIz4jHYDTgZOPEyRsnxcSiv07/xa7YXVRxqsKI5iPEeVN3TuWD9tJ9DEYDsdmxVHaszLXMa2w8vxGfkKYcqymJR/pY0LMEeQRR1akqefo8Tlw/wfpz6/n7/N+iDCxnyKKTJQIoZwTlWKcUtClLwDpjYQIoZwDlEvCJGycACPEMMZv/qyz/dgzqKAhgsEcwthpbMTJzTPgYcVxSVhKrzqwCYFiYqfw7c99MAJ5v/DwONg58uutTAF5v9bogiVZYURBWAlgBUNYMoJ+rH3YaO9G/oiRlBQlYcShoA1PIB/A2MoAF+6KUptByGUW2gOn6U1dafd9KeGbdCRYvXmzt/btNqNVqFi9efK+XUeFQy7MWH3X4iJixMWwctJFnGjyDvdaeqBtRvL7pdfxn+NN9WXf+PPlnsQp/rVrLc42f49Qrp1jcezG1PWuTnJ3MpH8mUeOrGqw8s5I/+v/B8v7LqWJThWuZ1xi+ejhNv23K1otbS71elUrFRx0+4uuuX6NCxbxD83jy9yctKpwHNBjAnK5SZnjK9il8c+CbUt3D1c6VlU+vxMPeg6PXjhLqHQpIcUneYA5aPgi9QS+Rl1viiiMJRziZeJKGVRoCsCFpAwPqSZYwsiAiuWt7wHwqiGwHI3s9gskO62LqRSEukd+jnAGU+6hlKAmgspwv9wDKm2UobAIdlybF3JYBLc0EIDtiTYrfMO8wLt28BEif7ZroNRgxEmAXYNZKs+z4MvL0eTT2biwmepxJPMOGcxtQoWJU+Ch+OfELMakxVHaszAtNXsAKK4qC9RuxAkDO6F1MvVgqEYdapTYTYFzPvC7UbGXpEypYApZVZMX1AJoRwBIygI42jmIGJpiCaS3PWhiNRnZf3s2eK3vMguvtYvny5dbev9uEXq9n+fLl93oZFRYatYZONTuxrN8y4l+LZ373+UT4R6A36ll7di1P/v4kvjN8GbN+DJHxkUWWVLVqLYNDB3Ny5EmW9llKiGcIKTkpvLv1XQJnBXIk4QjVHUz9vkevHaXD4g70+bVPqTePIBky/97/d+w0dqw4vYLHlz4uslEFj3uv3XsAjFo3it+ifivV9YMrBfPrk7+iVqnZfXk3Das0RGfUiQ1jzM0YZu6diY+LDy81fUmcN3XnVKa0nwJAhj6DOl51AFh5ZiXJ2cnUfOF1ANpeAi8LhgdyGTgyPlLEVDm2yK0x0UnRGI1G8boMpYK24GQQAAeNKQYKEYitEzdzbopJR12CuwgBSLhfuMgAutq5mv18hoQOYflp6e+tlXsrs3Uovf9kzN4/G4DutbsT6BHItJ3TABgXMa6QDY0VVihhJYAVAHJGT2fQldoIVhnALqdfFlm6K2lXSn3f2+kBVPpsWZoEcjXdlAH0cvQyywjKE0SCPYJJyEjgZu5N1Co1tT1rl3rNlhAXF8fhw4cr/Eite4nIyEji4+Pv9TIqPNzt3Xmp6UvseWEPp145xZut38TH2Yfk7GRm759N02+b0nh+Y77c86XZ35ISGrWGgY0GEjUyimV9l1HXqy6pOal88O8HHEs/Jnw/Zaw4vYK6c+oyYeMEsw1acehXrx8bB2/Ezc6NnbE7abOwjUXj6/favceIZiMwYmTQ8kFsvrC5VNfvVLMTXzwuiRSirkdRyaESSdlJYjM58Z+JJGYl8kbrN9CqpOzbv7H/4uXoJXwCvzv8HXYaO/L0eSw5uoSA0DacrOaIxgi9Txe+p0wAd1/eTROfJoCJzJ1POY9GpSErP4u49Lhi56YreyzlErFZD2C+KQO44dwG8by/qz8JGQloVBrstfbiuI6BHVlyTLK0qeRQCT8XP3FehHuEOP9owlEOJxzGRm0j+vpu5twUGdAxLcaw+sxqom5E4WrnysjmI4t8D1ZYAVYCWCGgVqmFsKOsSmCA2Juxwtk+Pr30X+IyATQYDeiN+lKpgK9lXhNrlntglChoAaP8kpP9AWt51uJUolTHCXQPNAuut4M1a9ZYPbDKAWvWrLnXS3igUMerDtMem0bsuFjWPbuOp+o/ha3GlmPXjjF+43j8ZvjR+5ferDi9QmSalNCoNTzT8BmOjzjOL/1+oZ5XPXKNuVzLvIaD1gF3O9MGTGfU8cWeLwieFczcA3NLVUloW70t/w77V0yraPVDK6KuR5kdo1KpmN1lNv3r9SffkE/vX3qXumVjbIuxPBf6HAYM5OmkTaa8qczV5zJ81XAC3AJ4Pux5cc6nuz5laOhQQMpwyrFBVsBee0IadaZUA9u/Lx0T5hOGvdaepOwkYY11Oe0yWrWW9Lx0MQP4n4v/oFVrzWb+KtXASgKoHPEmQ4hAbJwEIXazc+NAnPS5hHqHcjDuoDi+Q2AH8fiJmk+w4dwGcnQ5BLkHUcO+hjhOJno9Q3qK6U4LjywkMz+TepXr0aFGBz7Z+QkAo5qPKnIWuxVWyLASwAoCIQRJLp0QRNnDcin1Em52UjCQCVppoGwe1hl1pcoAyqUUe629RdKlzADWqlRLZCTstfZidF1wpWAxmaBu5bqlXm9R2LRpk7X/7w6h0WgeytFw/wW0ai1danXh1yd/Jf61eL7u+jXNfZujM+hYeWYlfX7tg98MP8ZtGMexa8cKna9RaxjQYACRwyN5vcbr1K9cn2xdNqm5qdhp7HDUmsqASdlJjFw3ksbzGrPx/MYS19awakP2vLCHOl51uJJ2hUcWPlLId1Cj1rCkzxIeC3qMzPxMuvzUhdOJFlJwBaBSqZjXfR4t/FqQkZ8h1K5y39+KMyvYe3kvbz3yFupbX1Xrzq6jZ62eqFBhxCh6+C6kXODkjZNUf14aJdfxIrjfal3MReqvtNXYmmbm3ioGHE44LBwH5Gzg3+f/FsfLUBLmtLzCBFAZA5UZwEPxhwCo4V6DfVdM/X/y5BOQTKdla5n/NfufEIP0CuklYmiePo+lx5cCpvKv3qAX5d8x4WPYGrOV/Vf346B1YGzE2KI/eCusuAXrt2IFgTwZo6xWMCAFJNlrSym6KAnFEUCj0WgxAyj7DFrK/oG5CKRBlQYi8Hk5eplZwMhfIPK4qDvB3r17rZM/7hB6vZ69e/fe62U88KjkUImRzUeyf/h+jo84zoSWE6jqVJUbWTf4at9XhM4Lpcn8JszeN7uQol+tUtPavTWHXjzEH/3/oFHVRuTqc8nSZWGjtsFGbbKDiroRReelnem+rHuJZK2aWzV2DttJS/+WpOak0mlJJ1acXmF2jJ3WjuVPLaeZbzOSspPovLRzqdpN7LX2LB+wXJTBXWxdyDPkiZ7lp/98muru1RnSeIg459vD31LbUWoLkVW7IClhg1o8QbSvHTYG6HGGQpDLwPGZUiXkQsoFYTov9yDKRFDZPye3wQCk5VgggIoYqMwAyj3XYd5hZgKQrTGSOMfTwZMNZ6Vyr53GjuZ+zVkdLfkX9g4xTf9YG72WxKxEfJx96BwsjXRbd3YdF1Iu4GHvwaBGg0T278UmL4psphVWFAcrAawgECXglLKVgOVAKpeAyzIPWPmFoSSAeoOePH2eUBkrd7+ywk6+nxIGo8GsBBzsaTJ7ruJYhXxDPnYaOwLcAkQJ+E4JYHJyMleulL7v0YqicfnyZZKTS28kbsWdoUGVBnz2+GdcGX+F1c+spl/dftiobTiccJgxG8bg+4Uv/X7rx5roNWYZKrVKTb96/Tj88mGWP7Wcxt6NyTfkk2/IR6vWmo03W3t2LQ2+acDY9WOLNYn3dPRk85DN9AzpSY4uh36/9WPewXlmx7jYubDu2XXU9qxN7M1YOi/tXCrjeV8XX1Y8vQI7jR3peeloVBoRWy7dvMSXe75k4iMTxbr/PP0nXTyl+b+Z+ZnCQubn4z+jN+i5+lgLwFwNvGOHpLiVCeDBuIMEuktCObk6Ivf9JWYlcjj+sMUYBpLNi7wW+XNXkkU5A+hg4yDibbsa7UQ2sJpbNTEm87Ggx9h4QcrEhvuFsy1mG2m5aXg7e9PCr4W4piz+GNxosFAky9YvLzZ5kePXj4vS9YRWE4r9vK2wQoaVAFYQlNUKJtAjUJRJwLRDLcsQeZVKJUigzmCeAVSaoVqaBCIHVSUSsxJF0zSAm63pGLn8E+QRhFqlNhHAOywBHzp06I7Ot8IckZGR93oJDx20ai3da3fnj6f+IO61OGY9MYsw7zDyDfksP7WcHj/3wH+GP29ueZPYbJNITK1S06duHyJfimTFgBWEeYehM+gwYjQzNtYb9czaP4ugmUHM2jfLYr8hSCTnz6f+ZHiT4RiMBkasHcF7W98zE1dVdqrMxkEb8XXx5eSNk3Rf1l1kxIpDuF843/b4VqwHTJvXSf9MwsvRSwgfDEYDJzJP4OXgZXZ8el46f5//G79hkm9e53PgfMtdp93WdgC0CpBUtdFJ0TSo0kBcD0yb139j/2XG3hkihil7AcE8hloigFl5UgYxOTtZxN9qbtXI0eXgbu/OlXTThrR1QGsSMiU/yOfDnmf5KUn926dOH3HfhIwE1p1dB5i8/6KuR7Hl4hbUKjWvNH+FqTunAhJBlPsbrbCiJFgJYAWBsgewNAPg7bX2+LmaxqzJu8bSjKRSQi4D64w6Mx9AufyrQmVWKpaVvCVZwGjVWhH4wBRAa3nW4mbOTXHsnWYAT5w4Ye3/Kyeo1WpOnDhxr5fxUMPL0YvRLUYT+XIkR14+wriIccIQ+ct9XzLmzBhaLmzJNwe+Edk3lUpFrzq9OPTSIVY9vYqmPk0FaVKSm5u5Nxm7YSz1v6nP2ui1FlXzWrWW+d3nC/uXD3Z8wEurXzLLQFZ3r87GQRvxsPdgz5U99P+9f5GkUokhoUMYHzFerEsmT7n6XIauGCpMqgG2Jm9lYIOBgNRXLCuFP9v9GbXa9uFiZRvs9dAt2vweHg4ewvxZdilIypLaYpQl6/PJ5/F09Cz0GYE5AZSNspUEMCNfel1uabHT2Ime5hZ+Ldh2cZs4VrbBUqvUPFXvKVaeWQlgNrt32Yll6I16IvwjhPXNrH2S6KV3nd6k56Wz6swqVKh4s/WbBT9WK6woEg/tN6NOp+Odd94hMDAQBwcHgoKC+OCDD+5br7hqbtXQqrXk6nNLbeasVALLwVRv1Jv5WJUEJQG0lAF0sHFApVKRmpNKji5HlD8Kji8C8/4/F1sXERTBtJMO9ggWPUk+zj53rGSLi4tDo7HOwSwPaDQa4uJKbyRuxd1FqHcoMzrP4Mr4K6wYsIIetXugQcOh+EO8su4VfL7wYcAfA1h/dj06gw6VSkWPkB4cGH6ANc+soblvc7GZVJaFzyafpfvP3f/P3nmHR1G1ffjekt5DeiGhBAKEGjoIiDRDCL13sSCK3Vcsr4ogFj4VFYQXUQSRXgRCL1KlKC30JIQQkgBJSO/bvj+GmeymbiAQQva+Li93Z+fMnlk2Z5/zlN9DnxV9SlT9gmBQftrjUxb1X4RcJmfJmSUMWTPEYF1p5taM8DHhWCmt2BG9g8mbJxu1cf2q91f0adAHrU5rYHhtidxCZkEmw5sOB0CDBpVWJW1KPW2FXrmHbhwiozCTG88IvYKHltIVRAwDi5vYqNQozORm5Kpypfe8knLF4DPRJ1eVKxVniOuWfrhY/BxEyS5PW0+D/L9914Xe2h62HuyKFgpOGjg14NStUyTlJOFo6UgP/x4A6HQ6lkUI/Y7F4o/UvFRJNub1Dq9Lun/Dmg6jsUvjMj5ZEyZKUmsNwK+++opFixYxf/58Ll++zNdff83cuXP58ccfq3tqpaKUK6WclfuRgtFX869MOzjRAFTpVEUGINoSBSDdlnbD6nMrafFztXYtcS19w9Xd1t2gtZ0YOtaXgKmKCuDExMTH1qivaWi1WpMB+BhirjBnYOBANgzbwC/NfmFur7k0d2tOoaaQtRfXErIyBL95fszYO0MwbGQy+jfqz4nnT7B9zHY6eHeQNoj67I3ZS/OFzXk5/OVSe4i/1PYlNozYgKXSkq2RW+m1vJfkTQMh3Lp+xHqUciV/nP+Dt3a9VaEWp1KuZPXQ1TR0bljCYBy+briBF/DXc7/Sza8bAHdyBXUDrU7Lb2d/w22C0EYuJAqs7tVviF1BxI4gYueNa2nXpLVSXLfS8tOknL3ic9Y3dMszAMVWf83cmkkGYFO3ptL79qrXi0spgl7NkCZDpPBvWOMwqX97VG4Ul1MuY6W0YmQzofPJktNLyFPn0dK9Jd523qy6sAqA97u+X/YHa8JEKdRaA/DYsWMMHDiQ/v374+/vz7Bhw+jTpw///vtvxYOriUprAepVAutX/96PAVieBxCKwiLiglhaFZq+BIyfgx830m9Iz8XFUl8C5u+bf3Pm1hmj51oa8fHxpgrgKkKj0ZgKah5zHM0ceb3965ybeo7TL55mevvpOFs5k5iVyFdHv6LJgiZ0+qUT//v3f2QUZPBswLMcm3KMnWN30smnU4nr6dCx6NQi6n1fj2/+/sagGhaEEOSe8XtwtHTkWPwxui7tavB3HRIQwtKBQgHD9ye+l7xV5eFk5cTmUZslwWeRm5k32X1tN6EBoYCQziJKuBRqCiUj7McTP9Kk7zjinRTYqKBvMeUs0QN47s45KV9OLADRz10W17Tihqh+/rMYSjfIAbxnAIp6gW082kgt5vR7K3vaekrr5esdXpe6fwwJHCKdsz91v3CsyRAcLB1Qa9Us+GeBNGbu33PR6rQ82/BZWnu2Lv5RmjBRLrXWAOzatSv79u0jMlL4wzx37hxHjhwhJCSkmmdWNpWWgtHzAOrr/91PP+ASBmAxD6DYAF3E086zxLX031fUFQMh/CT+aAQ4B0jN0/PV+QZ5jPfD7du3Kz7JhNGYPs+agUwmo7Vna3549gcS30pk/fD1hDYKRSFTcDz+OFO3TcXzG0/GbBjDnpg99Krfi6PPHWX3uN1SoYQ+Oaoc3tnzDgE/BvDnlT8NvGJd63blyOQj+Nj7cCXlCp1/7cz5O+el18e1GMd3fb8DhA4fS04vqXD+TV2b8seQP0oc/3D/h7zR7g3p+eqLq/Gy8wKQ8gBj0mOITI0iuofQZ1hfFBqEQjN3G3cKNYVSVEVc50oTfy7uHc1X55eoArYxtzF4HYqMQy97YX4NnRtKreAATt8WCqrqWNXhVvYt4jLisDazpk+DPoAQtTmUJlQvi+HfzVc2E5cRh4u1C939u0vVwSbvn4n7QVnxKU8m7733HhkZGQQGBqJQKNBoNHz++eeMHj26zDEFBQUUFBQ1bc/MFHZ4KpUKlariJOcHpZ6DsFhFpkQa9X517YqqwW5n35aqgqNSolDVM26+UhWwTo2Z9t5jjZqsfMHgs1RaolKppOIPEWcL5xJzjM8o8h41d23O0jPC4mWltCJXnYuFwgIPaw/px8Pe3B5HM8cH+mwLCwsrPsmE0RQWFj6S73pVIc61Js35finrXuXICQsIIywgjFvZt1h1YRXLIpZxOeUyqy6sYtWFVfjY+TC2+VgmtJjAX+P+Yn/sfmYfns3R+KMG14rLiGPwmsF09O7Ij/1+pKW7YGQ1cmrEoQmHCF0dyqWUSzy19Ck2DNsghWhfCX6FW1m3+Prvr3kp/CUczB0MdO5Ko1/9fnzW/TM+Pvhx0T1qVcw9Npc2dm04nXWazIJMyeOWXlDU5m7u0bm8MmoybDrNgEgwV0OhEpp824SI6RF08unEn1f/lAw/0dsnFnCUR4G66DdALG5Zd2GddEy/0E4ukxOfLqx77b3aSzl/3nbeUmeQ7nW7S+P71u+LEiUqlVDhnavNpa59Xbr6dEWlUvH98XvSL62eZ/6J+RRqCuni04WOXh1r9He8Ov5Oa/LnVVXUWgNwzZo1rFixgpUrV9KsWTPOnj3LG2+8gZeXFxMnTix1zBdffMHMmTNLHP/rr7+wtn74TbfTMgSZgrNxZ9m+fXuF52epDY0ypUyJSqfi4NmD1E+uX8YoQ/JzhMVMrVMTGxkLQHxCPEdyhW4ABdkFbNm2RRJ0Fok8F8n2GMM5XogvqiAtiC6QFl0zBMPSVenKlm1bJM+gu8KdHTt2GDXPssjONl72xkTFZGdnG/Xde9yoTV1MKrrXQAKZ4z2HaOdo9qfu51DaIeKz4vnq76/46u+vaGLThJ7OPZnmOI2+Fn1Zc3sNF3MMi0GOJxyn3S/t6OHYg0nek3A0EypqP/D4gDl5c7iUc4l+K/vxlt9bdHYUPIqddJ3o5dyLval7GbtxLB/X/5jmds3LnWtzXXO6OHbhaHqRIbrr+i6m+UzjdJbgQdPP8bWWW5OrzeWPiD/o17QPHnYyPLN0PBMDOxrBlawrbN++HacsQaXgxm0h8hB9R4iq3M6q2MOdk58jeUBFL9+Ws1uk17Pzi9YcG7kNOyKENcwi1YKo1CgAfGW+HC8QvIHBmmAWnloIgH++v/T3Ne/aPOFzs+rEzh07icmN4fDNwyhQ4JniyXvRQsVvT/OeNfJvsjQe5d9pbq7xxZBPKjJdRVm5Tyi+vr7MmDGDV155RTo2e/ZsVqxYwZUrpSvjl+YB9PX15datW9SpU+ehz/nq3as0/19zbM1tufv2XaP627p96ya1W7M3tyezMJORTUfy+6DfjXrPzks78++tf/mo3kc413fmrX1vMTRwKIMaD2L85vF0r9udNUPX4PGdh8G4k1NO0sq9lcExl/9zkdooZf0nC7uvhRyfBk4NuJZ2jdCAUGZ2n0nwEqGC7/lWz/NTyE9GzbMsGjVqRGxs7ANdw0QR9erV4+rVUlosPKaoVCr27NlD7969MTMzq3hADeZ+7zVfnc/WyK0sj1jOnut7pNCnldKKwYGDmdhC2BDPOTKHg3EHS4y3UFgwo/MM3u70NpZKS/JUeUzYPIHNkZuRIWNen3m83FYoylBr1YzaOIotkVuwM7dj77i9tPYoP3ctpzCH7su7E5FU1AbPWelMkGcQh24eMjhXIVMUGWUjt2D/9vt0336RX1rD8wOFcwo/KORkwkm6LuuKk6WTpP9nJjdDpa3YK2SpsEStUxtI34QGhBIeFS59HuKGuJ1nO6LToknLT+OnZ39i2o5pgODd3BmzE3OFOf9M+YeWi1tiJjcj8Y1EHCwdiMuII2BBADp0XHzhIgGuAbwQ/gLLIpYxoukIGjk3YvaR2bR0b8nJ507W+F7n1fF3mpmZiYuLCxkZGdjb2z+S93zcqLUewNzc3BL6cAqFotyKUQsLCywsLEocNzMzeyRf2gCXAOQyOdmF2aQVpkmJy+UhyguA0K6JQkjOTTZ6vhZK4X7VOjVmyntjZEJVMIC1uTUFOmGxU8qVRUUgtm4G71GoKZSMPwuFBWmFadJrYh5hozqNiE4vym9s7tH8gT9Xc3Pzik8yYTTm5uY10pB6VH+jjwOVvVczMzPGtBzDmJZjSMhMYEXECpaeXcrVu1dZeWElKy+sxM/Bj4ktJzK13VQWn1ostTIDQadv5uGZzP93PgtCFjCi2Qg2jNzAq9tfZdGpRby++3Xu5N5hds/ZmMnMWDN8Df1W9OPgjYMMWDOAo8fex6QAACAASURBVM8dlXROS8PRzJGtY7bSelFrUvMFbcNUdSoBdQIkA1COHC1aNDqNZAR+c/wbvhszGba/w8Ar8FIoaBTC/bbzbYel0pK0/DS8bL1IzE7Ex96H6+nXDQT0S0OtVVNcIUY/B1C/UKaxa2P+ufUPFgoLYtJjpOMXUwSPaiuPVoRHC4Zjr/q9cLETxK1XXVqFDh1BtkEEuAaQXpjO6ourAXgh+AVGrBsBwIdPffhErXGP8u+0tqwH5VFri0AGDBjA559/zrZt24iNjWXTpk18++23DB48uOLB1YSF0gJfe1/g/iqBxXy+qq4CFvP/LORFxrEosioiVvmCUOUn9sgEQVYG7knA6GkDNnVtavQ8y8LDw6Pik0wYjenzfLLxtvfmva7vcfmVyxybcowX27yIvYU9NzJu8Nmhzxi9YTQanYb3u75Pd7/uBmPv5t1l1IZRBC0M4sytM/zU/ydmPT0LELyHU7ZMQaVRYam0ZPOozbTyaEVSThJ9fu/Draxb5c6rrkNd/hz1p0GRxrKIZXTwEtqliWsIIEmoHI47TN0B47hrLcMlD7rfK06WzRTE69t7twfAw074Touao3YWRdXHpWkBqnXqEsf0e5/rG4+uVoKsTBvPNlLLNx97HynNZVzzcWy6sgkoEn/W6XT8dvY3AJ5xfgaAxacWU6ApoK1XW87ePktafhqN6jRiSJOiimETJipLrTUAf/zxR4YNG8a0adNo0qQJ77zzDi+99BKzZs2q7qmVS2VbwulXAouLpxjyMIZSdQCLVQGLFcCShxDDRRRKikBfTC7KKRKbqwc4BxgcrwoD0MfHxyQEXUUoFAp8fHyqexomHgEymYyOPh3534D/cfvt26wcspLe9XsjQ8ahG4f44sgX/Jv4LyEBIbTxbGMw9lLyJdotaUf/lf15rvVz/DzgZ+QyOUvPLmXQmkHkFObgYOnAzrE7aeDUgOvp1+n3Rz8pVaUsnvJ7igXPLpCeq7Vq8jUlOxuJRRhanZblF1dyqbMgFVO8GliUgxERoz/WyqJ87rJCq8UNQ30PoD6iMH4H7w7S5tbbzlsyEnvV68W/if8iQ+jWAoLhei3tGnbmdnRy6IRKo+Knf4VUmJfbvsy3x74F4L0u76GQm9Y2E/dPrTUA7ezsmDdvHjdu3CAvL49r164xe/bsx96dLrWES7tWwZkC+gaguEsuLtlSHhV6AJVFHkBRld9CYVGifZK+BIyNmQ2RKUU9mvQ1ACPuCHk+1kprSd3/QfDy8jK1gqsi5HI5Xl5e1T0NE48YKzMrRjcfze7xu7nxxg1mPz2bhs4NyVHlsD1qO6dvncbLzkuSVBHZEb0D3+98ibgdwZqha7BSWrE9ajs9l/ckJTcFd1t3do/fjYetBxF3IghbFWYgWF8aU9tNZWLzoiK9c3fOEegcWOI8c7mwbv148kcsRwot4wZfAZleho9oAN7JFiSyUvKEyIh+bp+xKfL6/dBFrM2spfQbD1sPKb9QbNHna+8reQWf8ntK0k4VpV2GNRmGpcKSjVc2kpiViLuNO7mqXG5l38LH3odxLcYZNbfHAa1Oy7iN41gRsaK6p2JCD9MvYw2j0h5AvRBwoVrITamSVnAqvRBwMYOytMVQXwS6h38PSRhVvLaFwgIvOy+pf2Yjl0ZVktjs5eVlEoKuIjQajckArOX4OvjyYbcPiXw1ksOTDzOl9RRszW1JzEqU0jr00z+0Oi0//vMjE/6cwMttX8bJ0omTCSfp8msXrqddp75TfXaO3Ym9hT2H4w4zcv1IAwOsNBaELKCueZHEVVxWXIlzRGPrevp1bJ4NI8MSPLOhs9B6F9lMGZ18BeFrcW0SN6n6cjLl5QLqY6m0LHHMx86Hc3fOAUghbhkyrqcJn1NoQGgJ8efswmzWXRQkYcTimwX/Cl7PF4NflLx/73Z+16AHOwjG6of7PuTs7bNGzfl+0Ol093X9/+7/L3+c/4MpW6ZILfJMVD8mA7CGUVkDsL5TkdyLWIRRqCk0qi8nGBqAokFWlgdQXCxLC4foewDHNB8jCT+LRmV9p/rczLwpLdzFK4jvl6CgIFMruCpCq9USFBRU3dMw8Rggk8noWrcrS8KWcPvt2ywftJye9XoCSKFcMSIAQveMb49/i0wmw9Xalci7kXT+tTNnb5+lpUdLto7eKrWUe2HrC+V63swV5nwW8JlkdOWqcqljZajCoG+4fXdqAec7CN5J/d7AzlbOUpqJi7WLdO2KDNDSsFRalggLe9t7o9aqcbdxl/QU3WzcpBzC4UHDORInyGkNChQ0EdddXEeOKodGdRrRyacTUblRHE84jpncDHdbd66nX8fF2oXn2zxfYg77r+9nzpE5dP6lMxn5GZW+B2P4/PDnBC8O5n///s/oMb+f+505R+YAsDh0sdR9xUT1YzIAaxhiSNdYA9Dbzlsq/tCvTjN2gSjNA6jRaQw8gKKen7hoF2/hBEhJzyAsjEk5SQbXL14AEuRWNYZGcHBwlVzHhECbNm0qPslErcLG3IbxLcezb8I+rr9+nZk9ZlLPsZ4kx6JPal4qybnJmMvNuZ19m25Lu7H/+n66+XVjzbA1KGQKfjv7GzP2zij3PR3NHNk7bq/0XL/VpYhogK6+uBrF0OEADLkM+k49MQzsZCnoAoqGYGURW2LqI16zg08HLiYJuc0WCqFQztrMmui70Wh1WoI9g/Fz9AOKwr+TWk5CJpMRnixUCI9sNpKF/whagW92fNOg9ZzIF0e+AGBK6ylSQUtV8uuZX/nvX/9Fq9MabSQfiTvC81sFY/X9ru8zsVXpGrsmqgeTAVjDED16aflpUi5JeSjkCgMvoIixlcCSAahVSwnHJTyA90LA4oJf2uITmx4LCCEQdxt3slTCGDFXp6FTQy6nVG0FMICzs7OpcKGK8PX1xdnZubqnYeIxxt/Rn4+7f0z0a9EcmHiASa0mGVTIihRqhc1oVmEWfZb3Yc2FNYQ1DuPnAT8D8PXfX/N/f/9fue/V3qs9n3b/VHpe3AMnrkfZhdmkPBVMtjn4ZUDbe8GI2YdmSwag2N1DXI8qS2khYPGajZwaSetlcm4yAJ19O0vVv2Ilb3RqNIfjDiOXyRnfcjy3sm9JAtitPFtxMfki9hb2TGs3rcR7/ZPwD/uu70MpV/JO53fu6x7KY1vkNl7c+iIgGHKvtH+lghEQkxbD4DWDKdQUMqTJEGb3nF3l8zLxYJgMwBqGjbmN1PvyWqqRhSB6eYAi+jl55WHgAaR8GRix0bm48zV4v3tVwHWs66CQK6TFUSkXpCgD6lR9BbBIx44dTZXAD4hCoaBjx47VPQ0TNYCU3BRS81KFXrUDl3L7ndssHbi0hGyMiAYNozaM4tu/v2Vy68l81esrAN7d8y7Lzi4r970+6fGJVCxWWr6eaBTOPTOfiGBhIyhWA//3r//Spa5gACZmC1ah2ApO9NQZS2kh4PPJQktLUbtQjlxaN0c2G8neGMGDKRqAovRL7/q98bH34efTP6PWqeno3ZG1F9cC8Eq7V0pIbEGR929s87GSN7GqOJlwkhHrR6DRaZjQcgKf9/y8wjEZ+RkMWDWAlNwU2ni2Yfmg5SUKA01UP6Z/kRrIg0jBiIjFFhVRYRGIngdQDAs4W5X0Et3JESrtvO28yVPlSYu1uEsPcA6QkostFBb4OvgaNT9j6N27tykP8AHRaDT07t27uqdhogbw0f6PqP99fT47+BlZBVnYmtsyqdUkDkw6QPT0aD7u9jEeNiX1JN/e8zYtf2rJmx3f5O1ObwMwZcsUtl7dWu77xb4WKz0ubriJ68zRm0cpGBgK3MsDvGcrNnBqIOTl3Vu77uYKoeTKyqtYKi0NxKHN5ebEZcQhQ8aFZKEFpq25LSAYpTJkqLQqmrg0IdAlEI1Ww7JzgrE7udVkCtQFLD69GICn/Z/mZMJJLJWWvNHxjRLvfSn5EpuubEKGjPe6vFepeVdE1N0o+q/sT64ql74N+rJkwJIKi/PUWjUj14/kUvIlvOy82DJqS5kyOSaqF5MBWANp6HT/BqCYF3Mj44ZRY8vUAVSXrAIWPYCuNq4G18gqyJLO93f0N8gHFPMHGzg1IOqu0CezgXODKt0thoaGGi3nYKJsQkNDq3sKJh5z1Fo1Z26fIaswi08OfEKDHxrw/fHvJY9/A+cGzHx6JglvJ7Bvwj6eqfeMwfiI5AjMZ5szuPFgJrScgEanYcT6EVKxRGmYm5mzYpAgL6Kf56yPVqfln1au5CkhIBWaC/tRZDKZFAZ2sHCQDMZ8VUl9wfIo7gEUOyg1c2sm5TaLRmZAnQB2XtsJFIk/77++n/jMeBwtHRkYOJC1F9eSlJtEHbM6HIs/BsDzrZ+XpGL0+eqo4DEdFDiIJq5NKjXvn0/9zIHYA6W+dif7Dn1X9CUlN4Vgz2DWj1gviWyXx5s732TXtV1Ym1mzdfRWvO29KzUnE48OkwFYAxFDutFplZeCEQ0rfWHm8jDGAygacaI3z8PWcHevXwHs5+BnEOrV6DRYKCwwV5pLoqmtPKqmAljEy8uL1q1b1/h+mdVJmzZt8PR8cF1GE082SrmSY1OOsXroagKcA0jOTeaNXW/QaH4jlp5ZKhlBcpmcnvV6snfCXjJmZPBS8EsGBtQXR77gp5CfCG0USr46n9CVoZJGaGmMbTmWgY0HokNXqgwVwMIrv3OulbA2idXAsplFBqBYyGEmNzPoLGIMxd9TXDdbuLWQNsi5akF+a2CjgWyP2g4UhX/F4o8xQWOwUFjw/YnvAWhr35YDNw6Umdt3I/0GK8+vBITcvMqwL2YfL297md6/9y4h7ZJVkEXIyhCup1+ngVMDto3ZJnkwy2PByQXM/2c+AL8P/r2ESLiJx4ta2wu4JvMgIWBxka2o9ZJIWQagqLavnwMo7p6LCzjrG4A+9j6cv3Pe4PX6TvW5mnJVet7CrYVRc6sMQ4YMISIiwqQJeB8oFAqGDDG1nKptzDwwk0Nxh7A2sy76T2lt+LyU/6zMrAioE8CGERvYFrWNecfnEZcRx3NbnmPu33OZ3XM2gwMHo9aq0aHD3sKeRaGLWBS6iKd+eYqj8UfZFr2NEetHsHzQcgauHsjRm0fpt6IfR587io9t6UVdn/f8nC1Xt0jRhuLEpseS0nck/LuGoZfg06eF42IeYGaBIJNla25bYbek4vmGxYtAxNcLNILnU7+/sKe9J7mqXOo61KWNZxvS89OlgpDJrSdzLP4Yp26dwkJhQXKhUDQyrsU4PO1KbsD+7+//Q61V80y9Z2jn3a7cOesTkxYj5fWNbz6elu4tpddUGhXD1g3j9K3TuFq7snPcTqP6zu+K3sXrO18H4ItnvjC1qasBmAzAGkhlDUB/R3/psbizFavRKqJUGRitptQcQJHiC5V+wYm3vTd/XvnT4HUvO6+HUgGsz4QJE/j444+r/Lq1Aa1Wy4QJE6p7GiYeMRFJEey/vr9Kr3k55TJD1w5FLpMjR45Gp2Fok6GsHb4WmUzG4SmH2RW9i8FrBrM9ajsj1o9g1dBVhKwM4ULSBfqs6MOB8QdKvXYzt2aMbzme5eeWYyY3kzRF9dncSEMfBQQlQ6MUiHQR+vRaKi0lgfzS5GsqwlJpaSAvI7a3FEWfzeRmFGoLsTO34+wtwds2JHAIMpmM1RdWk6/OJ8gtiGDPYEZtGAVAvwb92By5GRky3un0DsGLg2nn1Y4ve32Jm40bSTlJLDmzBKic9y+7MJuBqweSmpdKO692/C/0f1J0RKfT8fzW59l9bTfWZtaEjwmXfm/K41LyJcmgnNhyYpXnIpp4OJgMwBqI6NFLykkiqyCrRN/d4liZWeFh48HtnNtSCMbYfsAVtoLT8wCKFBdlLe4BLF6A0tytOZeSixp1PgwDsG7duoSGhrJjxw7U6soLvdZWlEolISEh+PpWXVGOiZrBO53eYViTYeSqcqX/8tR50uOM/Azu5t0lLT+NzPxMsguzyVHlkK/Op1BTKHn4SkOr00qb0fWX19NjWQ++6fMNbb3a0rdhX3aO20n/lf3Zf30/ozaMYt2wdfT7ox/RqdGErg7lP27/KfW6M3vMZNX5VaUafwCrE3byYrM6tIu4y9BL8EU38J3jSzufdhyOOwxQYj0zBkulpUHYWK1TY2tuy5W7V4Ai2Zue9XqyNVIoahncRMj/E8O/k1tNJiErgQ2XNghj7uUzDg4czOlbp7mQdIGEzAS+6/sdAN8f/558dT7tvdtLItwVodVpmbBpAheSLuBh68GmkZsMNAw/2PcBy88tRyFTsH74etp7t6/wmsk5yYSuDCWzIJOn6j5lYFCaeLwx5QDWQBwsHXC1FgotjO0JHFAnwOB5pYWgi+sAluMBdLIylIEx8ADaeUu9f0UauzSWclCUMqWBx7IqefXVV03GXyVRq9W8+uqr1T0NE48QtVZNQmYCCrkCS6Uleeo8ErMSuZh8kSNxR9gauZXl55az4vwKdkTv4Hj8cS6lXCIuM467eXfJUeWg0qok408uk+Nl50Urj1b0adCHwYGDae7W3CDn79CNQ7T7uR3jNo4jLiOObn7d2Dt+L46Wjvx982/GbhrLmmFrcLV25eyds3xx/QspDUUff0d/pradChRJTOmTXZhNdA8h3CnmASZpk6Q8QGsza2ne+p1MilO8qKw0HcAWbi1KtN1s6tqUtPw0XKxd6OLbhUvJlziZcBKlXMm4FuP46Z+f0Og0tPdqL/UJfqvDW8w8OBMQWsA5WDqQkZ/Bgn+EFnHvd33faINr1sFZbLqyCXOFORtHbDQo0Jh/cj5fHv0SgJ8H/MyzAc9WeL0CdQFD1g7herrQ1m/jyI1SAYyJxx+TB7CG0sC5Acm5yUSnRhtVNNHQuaG0wwWkgouKuB8PYHEdQFEEGoQQcHHjM8A5gCspwk7Z39G/0hIMxtKrVy/q169PbGysSRbGCORyOf7+/jzzzDMVn2zisUen05FZkElCVgIJmQkkZCWQmJUoPRaP38m5Y3SrSHsLe7ztvPG29xb+b+eNl51X0XN7b9xt3Ev9m45Nj+WTA5+w/Nxy6dgf5/9g/aX1vN3pbWZ0ncGBiQfo/XtvTt86zZQtU/h9yO8MXzuc89nnmbB5AutHrC9x7Q+f+pBfz/xa5hq3zD+dETIIvgX+aRDrdC8P8CgG6gOVCQVbmVmV8HaWVowi5j/nFApzW3pG8P71D+iPnbkdi08J0i+OVo5odBpa27XmQvIFrqVdw9XalekdpgOw8N+FZBRk0NS1KWGNw4ya46bLm/j04KcALOq/SOqFDLDh0gZe2/EaALOensXk1pMrvJ5Op+PF8Bc5EncEBwsHwkeH33cnFRPVg8kArKE0dG7I8fjj960FKMoyVERlqoBF7C3sDZ7HpQvNv23NbSWPgj7utu5SK6eWHi15WMjlcj7//HNGjx790N7jSUKr1TJnzhzkclOgoKYzcPVA9sXsM3rjp5Qr8bT1NDTm9Aw98bgxlaFl4e/oz7JBy/hP5//w0V8fSbnBBZoC5hyZw+LTi5n19Cz2TdhHvz/6cTH5ItO3T+enkJ+YvHkyf179k2nbprEodJGBB8zd1p03O77J7MOzkcvkJYzZPVlnOdvYgeArGQy5DN92BvV1ITIgrmUKmaJSBqC4TuojGkNiAUgz12b8FfsXAO2926PVafk94ndACP+uurCKu3l38bbzlqRZBrkOkvrozug6A1tzW/JUeXx3XAgDv9flPaMks87fOc/4TeMBeK39awYG3uEbhxm7cSw6dEwNnsqHT31o1D1/eeRLKVy8dvjaSkvQmKh+TAZgDaXSWoDFuoFodBpUGlWFuk4GOoB6nUDEHBulXClVuonPi+/IRZV9dxt36b31z9c3IKtaAqY4I0aMYM6cOVy8eNHkBSwHhUJBUFAQw4cPr+6pmKgCCjWFkvHnaOlYodfO1dq1yj3xGfkZ2FvYlwhXNnNrxqaRmzgef5wZe2dw8MZBQOgo8vK2lwl0CeSzpz9j1sFZRKVG8dFfHzHZazJLEpaw+PRi3GzcmNVzlsE13+n8Dj/9+1Op7TK1Oi3/dqpL8JXzDL0kGICDjw+miUsTqRjNTGGGRl22AVjcqLyUdKnEOTcyBa1V0TPY3ru9JIH1UtuX2Bm9kzs5d3CzcePZhs/SbolQxdvAuQEJWQl09ulMfH48cZlxeNp68nLblwEhZzApJwk/Bz9GB1W8mb2be5eBqweSo8qhZ72e/F+fohZ7F5MuErY6jAJNAYMCBzE/ZL5R4eSNlzfywf4PAPjh2R/o06BPhWNMPH6YDMAailiZZWwOYGndQO7m3S2h2Vec0jyA+o3AizcFL6HEr9NJ6vp1HepK1XEi7jbuBhIwD6MARB+5XM7cuXPp16/fQ32fmo5Go2Hu3Lkm798Twvf9vkeGDG97b6zNrKtlDp1+6URMWgx1Heri5+iHn4Of8NjBDz9H4fGucbs4eOMgM/bO4MztMwBcSbnC81uep7NvZ0AQsV+fs54Pu37I7COzmX14Nq42rrzW4TXpvRwsHXi/6/u8u+ddAwkWkZ99U3gJ6BwPXpmQaA9dfLtIBmBp+YXlsff6XoPndazqcPrWaYNjYu6zQqYgrFGY5JEb13wcx+KPEXEnAiulFacSTwHwZoc3eXGz0H/3w6c+xMrMCrVWzdy/5wKCkVvRBl7synE9/Tr1HOuxdthaaUx8Zjz9/uhHen46nX07s3LISqOM/lOJpxi3cRwA09tPL7U3sYmagckArKFUWguwtH7AmQkGBmBmQSZH444ik8no11AwkMqSgRGRhF2Ro0Vb4sclJTdF8vjVd6ovVcXp38fDrgA2YaK206hOo2p9f51OR0JWAgWaAqJSo4hKjSr1PBkyvOy8qOtQly6+XbiYdJH0gnQA/r75NwCOFo6kFaSx6NQiprWbxk///MTrO1/HxdqFMc3HSNd6pd0rzDs+r9S+56fktzhb34ZWMTkMvgwLOgh5gEvOLClTQsbgfooZlCcTTho897bzJiIpQjI+bc1spY4ewZ7B5KpypWrgya0n8/FfgkRVU9emnLp1ipbuLbmefp00dRp17evyfJvnAVh9YTWx6bG42bgxpfWUcucI8O7ud9l3fR82ZjZsHrWZOtaCQkN6fjrP/vEs8ZnxBLoEsnX0VoNq4LJIyEwgbHUYeeo8+jXsx7d9v61wjInHF5MBWEMRDbr4zHjyVHkV/vE6WTphY2ZjkAN0Le0awV7B0vPY9FhCVobgZuPGnXeEXkmlegB1auk18XoKuQKtVouNmWHPR30JmLoOdTkZb7hQNndrLu305TJ5qZ7KqkSr1fLuu+8il8tNIeBykMvlvPPOO5w5c8bkBSyD1NRUTp06xYULF0hMTCQxMZH4+Hhu375NYWEhGo0GhUKBubk5Hh4e+Pj44OXlhZeXF0FBQQQHB+PsXLJv9uNGVd1nyrspxGfGcyPjBnEZcdxIv8GNjBvS87iMOPLV+VJBSlmIBmFKXgo/n/qZFm4tiEiKYMImQatyVNAo5DI5VmZWfNL9E14Mf7HU6/zVxolWMTkMvWcATt4s5MVVVgdQhowLSRcMjonrpmgodvbtLFX1vtT2Jf44/wdqrZq2Xm2xNbdl89XNQNGG/q1Ob/HObqHzxwddP8BCaYFWp+XLI0KV7hsd3qhwzf/t7G/MOzEPgOWDl9PcvTkgeDcHrh7IhaQLeNl5sXPszlL7txcnpzCHsNVhJGYl0tS1KauHri610tpEzcH0r1dDqWNVBwcLBzIKMohJi6GZW7Nyz5fJZPg5+hl422LTYg3OEb13YoEH6OUAalVSeED0AOoXgIiSDsU1CfUNQG877xI9PZu5NZNU8H3tfY3qNfkgrF27lvPnz1d8Yi1Hq9USERHBunXrGDlyZHVP57EgMTGR8PBw9uzZw/Hjx4mPF3pay+VyFAphA1RWp5nIyEgUCgVyuRyNRiNtPnx8fOjYsSO9e/cmNDQULy+vR3Y/ZfEo7rNHyx4lxup0OpJykgTjMOMGN9JvSI9j02OJvBtZooBMpVURkSS0iNPoNIzdOJbJmydLoWUfex+crZxLzQVc7J/Cm0C3G+CSAyk2SALLlUGHTipiE8koMFQ6EDfNMmQMbTKUbr91A4TijwUnF6DVaQlwDiAqNYoA5wDiM+NJzk3G3dyd8c2FUPHWq1u5mHwRewv7CsOux+OP81L4SwB80v0TqSuHRqth/KbxHLpxCHsLe3aM3YGfo1+F96jVaRm/aTynb53GxdqF8NHhOFg6GPHpmHicMRmANRSZTEZD54acunWK6NToCg1AgMA6gQYG4M3Mmwavi7IF+otsqSHgeztkAwmYe3nDjpaOBtfU38n72PuUCP34OfhJRqJSrmT3td0PLaFYq9Xy4Ycfmrx/RiKXy/nggw8YPnx4rfUCxsXFsXz5cjZu3MiZM4KnWqFQGBhAWq3WqO+TRqMpYTjFx8ezadMm1q9fD0Dr1q0ZMmQIEyZMoG7dulV4J+XzONynTCbD3dYdd1v3MtuaJWQm8NmBz/jlzC9oMHwPpVyJWqumUFNIdGp0hekxV2zzOe0lp02ilkFXYEkwdPLpJHnjSqsgLoviudBiBxARUf6lhXsLrqVdI+JOBBYKC8IahdF8keCZE43I6e2n88mBTwAY5TEKM4UZOp2OL458AcC0ttPKNb4SsxIZsmYIhZpCBgUO4uPuQnhZp9Px5q43WX9pPWZyMzaN3EQLd+Pabn6470NJP/DPkX9Sz6meUeNMPN7UzlX9CaGyhSDF84BuZRv2AxZDCmqtGpVGyIEpLwfQGBFoAw+gvbfBcxBCx2KY5FraNTZf2WzUvdwPe/fuJSYmxmT8GYlWqyUmJoZ9+/ZV91QeKVqtll27djFgwAD8/f359NNPOXv2rPR6VfeT1r/e2bNn+fTTT/H39ycsLIzdu3c/tO9rTbxPb3tv5j87n1+DfmVa8DQDsWa1FeNx5QAAIABJREFUVo2lQhBkdrV25ds+3zLr6VlMaT0FO/PSuyWtayK859B7++JLUUUbZGONv9IQPX4gbLzF1ptTWk+RtP8GBQ4iPCqc9Px0XK1dSc1Lxcfeh6ScJNLy02hcpzHdnARP4YHYA5xIOIGl0pI3Or5R5vvmq/MZvGYwt7Jv0cy1GcsHLZfW7bl/z+XHkz8CQkjY2O4hy84ukwSifwn7ReqdbKLmYzIAazAPWghyJ+eOwXP9Ag7RC1iRB1Cs8BV3wMXbwN1IvyE99rbzNgjFKGQK0vIMW9IFuQUZdS/3w/z581EqTU7vyqBUKpk/f351T+ORoNVqWb16NQEBAfTr14+dO3ei0+nQaDQlOj88LPTfb8eOHfTt25eAgABWr15dZYbgk3CfDkoH5vWdx7XXrjGyWVGKQr5GqN5Nzk1mwT8LmNp2KkvClrB2+NpSr7PxnnTdM9fBMQ+itEKEQl6FP436UZGhTYay8sJKACa1nMQPJ34AiozNaW2n8cNJ4djHT30sGbii9++5Vs/hbute6vvodDqmhk/lZMJJnCyd2Dxqs5SS8/u533lvr9Cf99s+3zIqaJRRcz984zAvbH0BECqRx7UYZ/yNm3jsMRmANRixYOJ+xaBTcw3zYiwUFlIun5gHWJYQNAgeQDFfRjwmtqgTuZ5+XbqOo6WjQX6hu6271AFE5GEZgHFxcYSHh5tawVUStVrN1q1buXnzZsUn11B0Oh27d++mVatWjB49mtjYWIBq/66I7x8bG8vo0aNp3bo1u3fvvm8j7Um8Tz9HP1YPW03E1Ag8bT0NXruWdo0WC1uQkptC3wZ96e7XvcT4SBe47KHATAth99Soivf1fVBi0oXe54EugRy9eVTy9CGDyymXsVBYcDfvLi7WLtzNu0tmQSYt3FswtMlQAE7fOs2emD0oZAre7fJume/z/YnvWXZuGXKZnLXD10ob/j3X9vDclucAeLvT27zZ6U2j5n0t9RqD1wxGpVUxrOkwPnv6swf5GEw8hpgMwBrMg3oAxWo6EZlMJvW0FHtY6huAokCouEPX9wCKFNcVjM8UEshdrFyQyWQG8gr1HetLFcAixuQy3g/Lly+vtXlsD4pcLmf58uUVn1gDSUhIoH///vTt25dLl4Tw3+OWIiDO5+LFi/Tt25f+/fuTkFB2lWxpPOn32dy9OfFvxTMkcIjB8VvZt/D8P08Wn1rMnJ5zSh37ZzPByyaGgdt5lZ5/eD/YmNlIm+RJLSex9KwQ/p3QYoLUy1fspvJ8m+dZ9O8iAGb2mCltuL/6+ysAxjQfU2af9L0xe3l799sAfNPnG3rV7wUIxuOQtUNQa9WMDhrN172/Nmre6fnphK4K5W7eXdp6tWXZoGVGdRwxUbMw/YvWYEQD8EbGDQo1hRWe723nbZAzU7yFG+hVAhsTAlZakZ5vaER62RlWMYqLn5edFzqdzkA/K8gtyEA+wcvOyyg5gvth48aNj90PXk1Bo9GwcePG6p5GlaLT6fjtt98IDAxk925BnqOqc96qGnF+u3fvJjAwkGXLllXoJast9wlC0cb6EUIfYX3UOjVTt01l1IZRdPLpVGLcykbC2tnnGtgWYNAz/UHxcyiqsO3boC+7ru0C4Gn/p9kWuQ0Qij/szO3IzM8kR5VDsGcwAxsPBCA+P54/rwot8t7r8l6p7xGdGs2IdSPQ6rRMajWJ1zu8DkBMWgwhf4SQXZhNz3o9WTpwqVFGnFqrZsS6EVxJuYK3nTebR22uNvFwEw8XkwFYg/Gw9cDazBqtTmuQa1cWCrlCascGpavdi4UgpYaAKRYCNrMircAwh8/bzlt6rNKoyCwQOn/4O/mXqDpu7t6cuIw46Xkz14fj/UtMTOTMmTOPLL/pSeT06dPcunWr4hNrAKmpqYSGhjJ58mRycnIee4OoOBqNhpycHCZNmkRoaChpaWmlnldb7lMfmUzG3N5z+aT7JyVeu5l5UxJj1ueCG0TXkWGpgf6RVTJ1iZS8FEAQwd95bSdanZaudbsSHhWODp2UHzih5QR+PfsrALOeniVFWzYmbUSHjoGNB5YaHckqyGLg6oGk5afRwbsDC/svRCaTkZKbQr8V/biTc4eW7i3ZNHITFkqLEuOLo9PpeG3Ha+yJ2YO1mTVbR28tsak38eRgMgBrMKIUDBgfBtYPIag0qhJGkSgFUzwEDEWGn34OoGjgiegnKN/Ovi15/Bo4NuDA9QMG59pb2BuEhB9W/l94eLhR/S1NlE94eHh1T+GBuXz5MsHBwezaJXhiauqmQJz3rl27aNOmDZcvXzZ4vbbcZ2nIZDI+7fEpX/cyLtyJDDY2FdaHoRVfvlT0Iyv6iBGQMUFjpPDvqGaj+PWMYOyl56djqbQkV5VLvjqfzr6dpS5McRlxHEwV+iK/3/X9EtcWtfkuJV/C09aTjSM3Yqm0JKcwh9CVoUSlRuHn4Mf2sduxt7A36j7mn5zPwn8XIkPGH0P+oLVn68p9ECZqFCYDsIZT2UKQJi5NpMc6dAadQaDsEDAUhX71PYCSDuA99GVg9CVffBx8OJFwwuDc4tpZD8sA3LNnjyn/7wFRKBTs2bOnuqfxQISHh9O2bVtu3rxZ47xhZaHRaLh58yZt27Zl+/btAGzbtq1W3GdFvNvlXRaELChxvDRjbW2gsKaFRIFVxdk0JSgttOphU5QPHewVTOTdSKzNrMlV5ZJVmCWttSOajeCP838Aht6/eSfmoUFDD78edPDpUOL6Mw/MZPPVzZgrzNk0chNedl6otWpGbRjFiYQTOFs5s3PcTqM9eDujd/LGLkFi5steXzIocFDlPgQTNQ7Tr2INp7IewOJhhIRMwyTrskLAUKT/p+8BLK4D6GBRJFCqLwLtbefNxeSL0nOFTEFyTrLB2IdlAB4/fvyJ+SGsLjQaDcePH6/uadw3S5cuJSwsjLy8vCfuu6DRaMjLy2Pw4MH88MMPDBky5Im/z7179xo1Zlq7aSVy37Q6bYmK4VNeEOsANiroa5ysqgGl9Q4WtQC97bylfL9hTYbx8+mfASHKopQrKVQXUqgp5Gn/pyVtvuScZH45+wsA/+n8nxLX3nBpA58dEqpyF4cupoNPB0kGJjwyHEulJVtHbyXQJdCo+V9IuiDlEU5uNZl3O5ddbWziycFkANZwJAMwzTgDUDxfpLiIdPEQsEKmkKRhRI+dGNa1UlpJ54GwC9Zv5WbgAbT34WJSkQHoZuPGuTvnDN67qWtTo+6hMqSmpkqtrEw8GDdv3iQ1tWRLrcednTt38tJLLwlFSDU0FFoR4r3t37+/Vtzn/PnzWbx4sVFjJrWaxMohKyXPnw4dKbkpKNDzBMqKNAGHXirlIveBqJAwJHAIay6uAYQ1Lio1SuqhG9YojHWX1gGC90/khxM/kKfOo6FVQ57xf8bguhF3Ipj450RA6Ak8sZXweObBmfxy5hfkMjmrh66ms29no+aZlJPEgFUDyCrMoptfNxaFLjKlzNQSTAZgDUfqBpJq3La1uBRM8ZZFxUPAMplM8gKKIWDxx8XKzKrUvsEiNzOKij687b0NvIX+jv6cu33O4Lkoh1CVnDp1qsqvWZs5ffp0dU+hUixdupRFixZV9zRMPAReffVVfv31V6POHRk0kk0jN2EmFzaoKq2qRFrIhnv7zwGRYP6A0ohmcjNpo+zn6EdWYRb1nerzV+xfgLCZliFDrVOj0Wno26Cv1GEjIz9DEoMe6j7UwBhLyU1h4OqB5Khy6FW/F3P7zAVg8anFzDw4E4CfQn5iYOBAo+Ypdg6JTY+lgVMDNo7YWGIdN/HkYjIAaziiARiTFiOFaMujnqNhD8cbGYbVw2IIWN+zJ1UC3/MA6oeA9SuJLRSGVWaiAKoMGe427gYSDE1dm0qvw8ML/164cMGU/1dFyOVyLly4UPGJjwnbtm1j6tSp1T0NEw+R559/nm3bthl17oDGA9g2ZpvULq542PaYDyTagkMBPBNT2hWMx8bMBhAiHdujhZzF/g37SzIwAL0b9CY8Uiis0vf+TfxzIpkFmfjY+dDBoSj3T6VRMWLdCGLTY6nvVJ81w9aglCvZcnULL297GYD/dvsvL7V9yag56nQ6Xtj6An/f/BsHCwfCx4RTx7pOxQNNPDGYfhlrON523pgrzFFpVSVkVkrDyszKoC9m8d68kgewFM+eaGBKIWAzKwP9weJaUaI0jaOlIwq5gtiMWOm1hk4NDYzMINeHYwAmJiaiUJReoWeicigUChITEys+8THg8uXLjBgxorqnYeIRMHLkSKOqg0EwunaP342tWclog04Om8Qw8H1WA4tkFgrqCL38e7H/+n5kyCThfTGlRqfTodVpCWscRjtvQXz6wp0LbLm6BYCuvl0Nchff3v02f8X+ha25LVtGbcHZypljN48xav0otDotz7V6jpk9Zho9xzmH57AiYgUKmYL1I9YbnS9o4smhVhuACQkJjBs3jjp16mBtbU2rVq1qXMhQIVdQ36k+YHwhiKddUQL0nWzDfsBiDqAYAgZDLUDQCwErDQ1AG3Mbg2vdyhZ04zxsPdBoNQYhYTOlmcG5D8sDmJiYaBKAriK0Wm2NMABTU1MJCQmhoKDgic2FMyGg0+nIz88nJCTEKJ1AgKf8nmL/xP0GBWsiG+4ZgAOvgKISNTTFK4vFKImoivBU3afYeFkQU9eho5NPJ/bECFX1n/UQijny1fmM2TgGHTqCXIP4OfRn6Xq/nP6FH0/+CMDvg3+nmVszrqZcZcCqAeSp8wgJCKlU7t66i+v46K+PAJgfMl/qHGKidlFrDcC0tDS6dOmCmZkZO3bs4NKlS3zzzTc4OjpWPPgxo7KVwPph4JTcFIPXiheBQJEBKBpSogfQTGFm0NnD3txQa0pMgvax9yEhK0HKIQRDDyM8vBZw8fHxT1w1ZHWh0Wge+4IanU7H+PHjnygJFBPlI0rEjBs3zmiDv513Ow5NPkQdK8OQ5yE/SLEClzzoXrG2voR+8Zu4hjpZOrEtWghP+9j7kKPKkbx/CrlgMA5vOpyWHi0BmLF3BueTzuNm48beCXsl4eZj8cekEO/MHjMZFDiIW1m36LuiL3fz7tLeuz1rh601mEN5/JPwj1RE8nqH15na1pQmUVuptQbgV199ha+vL0uXLqV9+/b4+/vzzDPP0KBBg4oHP2Y0dKpcIYh+tW1avuGuubwQcHHdPnExExFV7QFyCnMkL2IDpwbEpBUl1ShkCuLSizqAyJA9tPDD7du3H8p1ayuP++e5bNkytm/fbjL+ahkajYbt27dXqmd1C/cWHH3uKG7WbkXXUcCf95aiylQD6+dCF2gKAGjr1ZbY9FjszO04evMoIGyeW7i34EjcEeQyuRSy3Rm9k+9PfA/A0oFLJUH9lMIURmwYgUqrYkiTIXzU7SMyCzJ59o9nuZFxg4bODQkfHV4i+lIWNzNuErY6TPIaftPnG+Nv0sQTh7K6J1BdbNmyhb59+zJ8+HAOHjyIt7c306ZN44UXXihzTEFBAQUFBdLzzEwhz0OlUqFSldSBelTUcxA8epF3I42aR5BLUbg1syDTYIy5XDD2cgpzpONi5Vy+yrB1nFpjaBA6WDhIY26kFW2f/Rz8iEqJkp67WLtw+nZRNWmAcwAKneKhfIaFhfeh6mqiTAoLC6v1u14eCQkJTJ8+HZlMZgr91kJkMhmvvvoq3bt3x9vbu+IBQH2H+hyedJgOv3SQcvQ2NIXnz8DgK/BqiJAbWBnE8K8YFu7o3ZE914tE1JUy4Wd3VLNRNHRsSHxaPBM3CR65V9q+Qm//3qhUKjJzM/ny+pfcybtDkGsQS/ovIa8gj8FrBnPuzjncrN0IHxmOo7mjUX+T2YXZDFg1gNvZt2nm2ozlYcvRarRoNdWfIiPO/1GuLY/rOvYoqbUGYExMDAsXLuStt97igw8+4OTJk7z22mtYWFgwYcKEUsd88cUXzJxZMsn2r7/+wtq6+pplp2YK2mxn484apZJ/N+eu9Di7INtgTGxSLADRN6Kl4/k5guF35uwZg+ucizDU8ctJyZHGnM86Lx1PupbE6fwig89eZ8+VpCvS8zqaOkar+1eW7Ozsh3Ld2kp2dvZ9/1vpdDo0Gg0KhaLKdcZ0Oh2zZs0iNzfXZPzVUnQ6Hbm5uQwdOpSPP/64UmO/rv81L15+EYB99SDdAjyzofNNOOpn/HVkyNChw1Jmyf6Y/QDE3C6KfniYe3D69mnkyHlK8xTbtm1j9vXZJOUmUdeyLt0Lu7N9+3Z0Oh3z4uYRnReNncKO6S7TObDnAPPi5nEo7RCWckv+4/Mfrhy7whWulDUdCa1Oy5fXv+Rc5jkclA685vIaR/YdqdRn9Ch4lN2GcnNzKz7pCafWGoBarZa2bdsyZ84cAFq3bs3FixdZuHBhmQbg+++/z1tvvSU9z8zMxNfXl6effpo6daqvfL5RaiNmLppJsjqZfs/2K7UtkT4dcjvwn3mCurxapyYkJER67eapm/yW+BtObk7S8Tl35hCTF0PToKaglxfTrFkz0EsJaxvYlpDuwpiMixlwLyId+lQoi08vhnv1JsH+wURdKvII9mzek5BuRXOoSmxtbUlJSan4RBNGYWtra/B9KQ2dTkdERASnTp3i9OnTnDx5kitXrpCfX+RBtrS0JDAwkPbt29OmTRuCg4Np0aLFfRuGe/bsqXEahSaqHq1Wy+nTpzE3N6dXr8oVNlgEWDBxy0RUStjaGMZHCNXAlTIA73mfm7g14cydM/g7+HMtoyg1x8nOidt3bzOx5USm9J/Cgn8WcOrcKSwUFvw57k+pGO67E99x8NxB5Aiizr0b9mbGvhkcSjuEUq5kw4gN9K7f2+h5vb//fU5mnsRCYcGWMVsMJLkeB1QqFXv27KF3796YmRmXy/igiBG82kytNQA9PT1p2tSw80STJk3YsGFDmWMsLCywsLAocdzMzOyRfWlLo6FLQxQyBXnqPFLyU/C2Lz/84W7vjkKmQKPToEOHXCGXkpJtLQV5hAJNgXRPYjKyVmYYKtDJDD0t3vbe0pjbOUW5Yn5OfgadSorPT4fuoX1+5uYmUdOqxNzcvMx/q8zMTH7//Xd++OEHIiMjAeFvo7RQS35+PmfPnuXixYtSR4dGjRrx2muvMX78eOztjWteD8KP/owZM1AoFKbcPxMoFApmzJjBmTNnKqUBOr7VeF7b9RoZBRlsaCIYgEMuw1t9ASP3JWL4V9QYdLVxleSv3KzduHz3MnKZnE96fMKV1CvM2D8DgLm959LauzUAu6J38f7+9wF4zvs5ejfszYJTC/j2xLcA/Br2KyGNjd8wLz2zlG+OC7l+vw78lW71uhk99lHzKH9Lq/M3+3Gh1haBdOnShatXrxoci4yMxM+vEtu9xwQzhRn+jv5AydZupSGTyXCydJKeJ+UkSY/LqwIWZWBE9KViAIOm4/odRrztvQ2LQIrp8l1PN+xGUpV4eHhUfJIJoynt80xLS2P69Om4u7szffp0oqKKvLsV5dnovx4VFWVwHWNlPdauXcv58+dNxp8JQCgIiYiIYN26dZUaJ5PJWDV0FQC7GkK2GfhlQNtKKh9ZKi25kHwBOXIibkdIx7MLhXSUBk4NcLNxY8zGMRRoCggJCOHV9q8CEHU3ilEbBF2/SS0n0d+lP+sureOtXULk6YtnvmB8y/FGz+Vg7EFeCheEof/b7b+MaT6mcjdj4omm1hqAb775JsePH2fOnDlER0ezcuVKFi9ezCuvvFLdU7svKisF421X5IWLulv0g128FRyUFIIWySrIMnju4+AjPRaNOmsza9RatUG1sSgPI9LVtyvAQ8nd8vHxMQlBVxEKhQIfHx+DY+Hh4TRu3JiFCxeSn5//QH1oxbH5+fksXLiQwMDACrs8aLVaPvzwQ1O3FxMGyOVyPvjgg0prgD4b8KzQ4cgMtgcIx4ytBhZVEbxshY1wQJ0ACrRC0aCDhQO5amFT/UvYL7y39z0uJF3A3cadpQOXIpPJyCzIZODqgaTnp9PRpyM/9v2RC9kXmLx1Mjp0vNruVd7r8p7R9xKdGs2QtUNQaVWMaDaCT3t8avRYE7WDWrtqtmvXjk2bNrFq1SqCgoKYNWsW8+bNY+zYsdU9tfuisgZgfef60mN9r6HYCk5fBkbUl1JpDL052SrDAgt9o1LsSuJi7UJseqx0XC6TE3k30mCcqIM18c+JtFzUkm2RxrV2MgYvLy+TcVBFyOVyvLyEH7eMjAzGjx/PgAEDuHv3bpV73zQaDSkpKYSGhjJhwgQyMjJKPW/v3r3ExMSYxL5NGKDVaomJiWHfvn2VHvttXyHUKvYGHnoZMGJPI2qiitXEyTnJ0muiTEwz12ZkFWZJos6/DfoNNxs3tDot4zaO43LKZbztvNk4YiNXU6/yxfUvKNQUMrTJUOb1m2d0jmxaXhqhK0NJzUulvXd7fhv4W4W54SZqH7X6GxEaGsr58+fJz8/n8uXL5UrAPCo0Wk0JkWRjaOAk6BcaawA2cy0SXtbvB1xuCLiYDmBWvqEH0NnKWXoshpW97QzDvy5WLgYeRyjqAnIs/hgRdyKMFjQ1Bi8vL1NosIrQaDR4eXlx584dunbtyqpVQrjsYRlf4nVXrlxJ165dSUpKKnHO/PnzUSprbSqziXJQKpXMnz+/0uOmtp2KHDnbAyBfAQGp0PxOxeNAkMxKzUvF1tyW1HxBncFKaSVpA/5f7/9j8ubJgCDC3K9hPwA++esTtkZuxUJhwaaRm1BpVYStDiNXm0tX366sGLJCytOuCJVGxfB1w7l69yo+9j78OfJPaWNvwoQ+tdoAfNz44cQPeH7jybzj8yo9trIewDaebaTH+qLM5YWAizdPzyosMgBlyKRiEZ1OR1qeEPL1c/AzMAB97H24k1O0mjpYOOBg6UBqXqo097ZebY26B2MICgoyeYeqCK1Wi7e3N126dOHy5cuPzLDWaDRcvnyZzp07GxiBcXFxhIeHo1aryxltoraiVqvZunUrN29W3CO9OM+1fo5sCyEXEIzvDSyK4duZFfVbFzfOrdxb8f3J70nKSaKFewu+7PUlILRlm314NgA/D/iZBs4N6LeiH4nZidS1rMuGYRuwVFoa9f46nY7pO6az7/o+bMxs2Dp6q0HrTxMm9DEZgI8R5gpzknOT2Xx1c6XHigbgtbRrRuVgtfZoLT3WL8IoLQQsGYB6IWAZMgMDUH93mpqXKrV9C6gTYGAAetp5GrSPE+f9b+K/0nN9T+KDEhwcXGXXMgEff/wxsbGxj9yrqtFoiI2N5ZlnnpHCwcuXLzeF902Ui1wur1R3EJHv+wldOcTewMbmAYob31s5Qh90M7mZtHHu27AvO6N3Yqm0ZOWQlVgqLTl7+yyTNk8C4O1ObzOs6TDCVoVJoeD/1v+v1E/YGH448QP/O/U/ZMhYOXQlrTxaGT3WRO3DtHo+RoQ1DgPgRMIJbmXdqtTYek71kCEkEhfv71savg6+0mMxXw/KCAHLS3oArZRWBgagaCQCJGQlSI/9HPwMvJLFe2+K3r5/Ev4BoJ1XuwrnXhmcnZ1LFC6YuD+sra2JioqqtpC66AmcPn06ABs3bjR5d02Ui0ajYePGjZUeZ21uTTvPdmxtDCo5BCVDowqWVblMjlqnxtGiqCWmKAvT3K053x3/DhDCwM3cmpGck8yg1YPIVeXSp0EfPu/5OWM3juXozaM4WDiwdeRWXM1djZ7z9qjtvLVbqBb+uvfX0u+JCRNlYTIAHyO87Lz+n73zjquq/v/48w42iCBLligi4gYHrjKVoYg7R86vlTYkbdiy8te3UuubLSW1YVmamjNTcWu5BwIKTnCBbET2vvf+/jjew73AhQtuvc8ePYTL5yzGua/zHq+3KIC2Xtpar21N5aaiqNMnDSyTysSi4OzibPF1dQq4VFEq3rzU4q5MUSZuI5fJRVsD9fHVpORX+ia4NnLl4s1Kux31PtX0dOsJwImUE8DdF4AA3bt3N3QC3yFSqZSioqIHXk+pUChYsWIFv/32G9HR0YapHwbqJCoqitTU+j1QA3w/6HtyzITJIFB3FFB9D8wrFQyGpRKpmAkpriimTFFGaKtQXu36qlinp57nu3rEat7c+SabLmzCWGbM5rGbxdpofYhNj2XsesE+5gXfF3irx1v1vl4DTx4GAfiQMdR7KECD0sD1bQQxlQk3rMLyQvE1zWJhdRpYFIDKMtHqoKC0QEsAqoUjQHJeZQTQ2cpZq8YwKVe7Hqejo9ABrI4AdnPppte514fAwEBDpOgOUSqVd318W0ORSqXizF8DBvRh69b6PVADdHXpirWJtXY3cC0UlRchlUhRon2vcbZyJiE7ASdLJ34Z8gsSiYQ3dr7Bv9f/xcrYis1jN7P01FIWRy5GgoQ/RvxBH48+ep9nekE6g1cPJr8sn2c8nmHxoMWGvw0DemEQgA8ZQ1sLAnDPlT1aAksf6tsIYm1qDUCForKIXp0ChspGEE0bGPWNxUhmpJUmtjSyFD/W7CqWS+WigbRUIiU+u7IDWIKE1natSc5LJrUgFZlEhm/TytrEu0VoaKghUnQXeFi+h0qlkvz8/LoXGjCA4F/Z0Bmz7/V+j79ag0ICnVPBQ4c3ufrBWC6Vi5+rsx3qjMhvw37D3sKen079xPcnvxfF3onkE3yw7wNAqD18ts2zep9fSUUJw/8czvXc63jZerFh9AatchwDBmrDIAAfMtrat6WFTQtKFaXsuryrXtuKAvCWfgJQPblD84lVJpWJN5BqEUBFGTKJkEpt1riZ6G0F0Mi0cnTX5WzBV1AulVczfU4pqEwP25vbYyI34WSKEP1r69BWK5J4t3B2dsbX19fwVPyY8bAIUgMPNwqFgmPHjjVo21k9ZpFlAQduD4gaoSMKqC6NKVOUab1uJBUent/o/gZBnkEcTjzM9Ahh2MCnfT9FLpXz4t8vAvBOz3d4zf81vc9NpVK6Pb46AAAgAElEQVTxwt8vcPTGURqbNmbruK13tYHOwOOPQQA+ZEgkkgangcVO4Oy6x8GBILhqomojiKYAVNe0yCQy0dsK0Cp8Vhs/25rainMwoXr9n7edNwAnku9d/Z+aESNGGDpGDRh4QklKSiI7O7vuhVWQy+Q80+yZOruBFSoFckmlH6Xa6aBcWU5Hx47M7z+fpNwkcTLHqDajCGwRyKh1o1CoFEzoMIH5AfPrdW6fHfiMVbGrkEvlrB+1nlZNWtX7+hqCSqVi26VtrD279r4cz8C9w/CO+BCiFoBbL22tZr5cG/VNAfdxr6wzyS2unLRQ1QtQUwCqz0eKlLKKyqddzSfP1AKh4NrR0lHLAqYq3ZyFej91BPBe1P+pmTRpkqEO0ICBJ5ioqKgGbffj4B/ZeFsA9rwBznk1r6s6Kx2ExpBVI1ehUCkY9ucw0QPww6c+JHR1KIXlhQS2CGTZkGX1mtSx9uxa5vwzB4DFIYvp36J/va+rIcSkxRC4IpDQ1aFMj5hObknNE3oMPBoYBOBDSC/3Xtia2ZJdnM3hxMOAfumuFjbCeLebxTdFP6ra0LQJWHe+cnB6VS9AtQ1MmaKyCcTMyEzLFsbO3E78WG1D42btVqsY7eXeC6VKKXoA3ssIoLu7O6GhoYapEQYMPIFIpVLi4uIatK1XEy+KHRpz+LZz1nA9TaEBvg76Gh87H6ZumUpUahRNzJqwbMgyRqwdQWZRJr5OvvWu2zuRfILJf00GhNTy1M73foJVcl4yUzZPwe8HP/Ze3YuxzJjnOz1vGC/3iGP46T2EyKVyBnkNAmB5zHJCV4Xy1K9P1bmdpbElTpZOgPZ8X13YWVSKtt2XK4ukdaWASysqU76WJpZahs4OFg6A4Hqv9gf0bOzJ+Uzdd8sOjh1IyE4gpyQHU7lpvWwPGkJYWJhhaoQBA08gMpmMlJSUuhfq4LO+n1WmgfUUgEO8h/Byl5dZcGSBmKpdMXwFr2x7hcu3LtO8cXMixkdgZWJV985uk5ibyJDVQyipKGGQ1yC+DPyyAVejPwVlBczZPwevRV4sj1mOChVj243lYthFvgj8ol7nbuDhwyAAH1LUaeD91/azLX4bh5MOa/nr6aK+aWB17d4on1HiazpTwMoyUfSpi5vVqMcNpRWkVZ5Lk5Y6U8ByqZzmNs1F+xdfJ9+7OgO4JgICAmjRooWhFtCAgScMpVJ5RwLw1a6vimngp6+DfR0GDU6WTiwbsoydl3fy7p53ASEauPDEQiJTIrEzt2PHhB3iA7s+5JfmM3j1YNIL02nv0J7VI1frPR+4viiUCn6O+hmvRV58euBTiiuK6eXWi2MvHGP1yNV4NPa4J8c1cH8xvBM+pAS3DMZEZsL13OtiZGxHwo46t6tvI0inpsKooHKVxpSPKilgtTDTjACq7Q7UOFsKHcWaItXRwlEciF4VZ0tnpBKpWP93L9O/aqRSKXPnzjXUAhow8IShUCi4ceNGg7eXSCRct4HIpiBTwbALta//fdjvZBdnM3b9WFSoeNH3RaJSo9iRsAMzuRlbn9tar6YNhVLB+I3jOZN+BgcLB7Y8t+WeRd92Juyk0w+dmLplKmkFaXjaeLJh9AYOTjmIv6v/PTmmgQeDQQA+pFgaW4qFvQ7mQnp1e8L2OrdraVM/Kxh7c2HUUGZRpviazhSwRtdv1QigegqJpgBUW8ZApU+Wmrb2Qgey2AHscu8FIMDo0aNp3769YTJIHchkMoNtjoHHirS0tLoX6SD+puBfqo8p9Az/GXRz6caQ1UPILc2ll1sv7MztWH56OTKJjLWj1tZbSL235z22XNqCicyEzWM306xxs4Zeik7iMuIYsHIAA/4YQFxGHDamNnwT/A3npp9jhM8Iw/3gMcQgAB9i1Gng9MJ0AHZd3kW5ory2TfC0rd80ELUA1JwfrCsFrBkBpEpPiru1O6A96UPTJ1BVZYMebj0oV5QTnRYN3J8IIAhRwAULFjzwkWYPOwqFwuCzZ+CxoqysrO5FOvj74t8AYh1gv6vQuLj6uiZmTZjfbz7jN47n4s2LuDZyZXCrwXx++HMAloYuJbRVaL2O/UvMLyw4ugCA5cOW0921e4OvoybSCtKY+vdUOi7tyM7LOzGSGvFG9zdImJHA691fNxhLP8YYBOBDzOBWgwE4m3kWW1Nb8krzOJJ0pNZt6lsDaG9xOwJYqBEBrNoFXEMEsCqNTAQjaPWkDwkSbpXo7kTu4daDs5lnKakowdrEGq8mXnqd790gMDCQgQMHGqKAOpDJZAQHBz/o0zBg4K5yJw99BxMPAhBvB7EOYKSEIRerr9s3eR+fHfyMbfHbMJWbMqPbDN7f+z4A/33mv7zo96Lex1SpVMTmxxK2IwyAj/t8zNh2Yxt8DVUpKi/i038/peXClvwc/TNKlZJhrYfxZeCXfBX0lcFU+gnAIAAfYppaNcXfRUgVqAVSRHxE7dtYVjZjaEb1dKFXCljDBkaNpgWMVCIV0wPqpg8rEyuu3rqq87jtHNox98BcAFwbud5XOwGJRMJPP/2EmZmZIa1RBYlEgpmZGT///PODPhUDBu4qd/LAdyb9jPixLlPogOYBnM88z/xDgqHzOz3f4aP9H6FCxVS/qXz09Ef1OuZ//v4Pn1z5hAplBWPbjWVOnzkNPn9NlColy2OW47XIizn/zKGwvJAOjh0Y6TOSfVf38frO1zmefPyuHMvAw41BAD7kqNPA6vRrRELtAtDZylmst9O0dtGF2r9PUwDWZgStpkxZ+bFmQ8iNPKHQ2t7cngs3a66UNpWb4mjhKN5k6tMJd7dwcXFh0aJFhjRnFVQqFeHh4bi6umJqavqgT8eAgbuGsXHDU5nJ+cnix+o6wKDLYHk7KSKVSJnXbx5TNk8B4PlOz7PoxCJKFaUM8R7C4kGL6/WwGX4inNVnV1OuKsfHzodfhvxyVx5W913dR+cfOzNl8xRS8lOwN7enjX0bzqSfYcP5DeSV5uFp46mXj6yBRx+DAHzIGdpaEIDnsoTHzbiMOK06u6pIJBI6OHYA0C8CWFMKWF5zClhT9GnWImrWiKjrFZ2tnLmYVUOOBGhmLRQwqyeG9Gver87zvBdMnjyZkJAQQyr4NjKZjJCQECZNmgSAj4/PAz4jAwbuHk5ODXvQLCgt0Hr4jXOAS7ZgqoAQoeKFqb5TeXb9sxRXFPOMxzPsvrKbWyW36OHag9UjV1dzTaiN4zeOM2P7DADMpebsHLdTLMtpKOczzxO6KpT+v/cnJi0GE5kJVsZWZBZlci7zHFKJlKHeQ9k5YSeXXrtEoGfgHR3PwKOBQQA+5PjY+eBp46l1A6qrGzjIMwiAC1l1eBWgIwVspJ0CVtvAaIo+zfMxlVdGitRPju7W7mI0sCrtHdoTfzNeHCs3qs2oGtfdayQSCStWrMDNze2JF4FyuRw3NzdWrlwpRhr8/f0xMrq33owGDNwPZDIZrq6uDdq2pqxLxe13TvNyMJGZcDbzLIm5ibRo3IKMwgyS8pLwbuLNlue2iBkVfcgszCRgRQAqVMgkMj5r+dkdZUgyCjN4ddurtF/Snm3x25Dc/q9UUUp+WT6OFo58+NSHXJ15lXWj1lFSUcLItSPp+lNXQ3bkCcAgAB9yJBKJmAZWU1cdoK+TL4DYYVsb6ghgdnE2CqVQJK0rBawpADUbQtTri8qLxNddG7lqRQw16e3em/Xn1wNC+ljduPIgsLW1JSIiAhMTkye2HlAikWBiYkJERAQ2Njbi6507d6a8vPaucwMGHgWkUinOzs4N2nbPlT1anwddhjZZkGcs1AN2dOrIoaRDWBlbYWdhx7nMczhZOrFjwg6amDfR+zgVygp6/dKLgjLBZfq3ob/RwrxFg875es51glcE4/K1C0sil6BQCfd21e3/+jTrw5qRa0h8I5Gx7cay8PhC3L5xY+iaofx14S9i0mKIzYht0LENPDoYBOAjgDoNrGb35d3alixV8G0qCMDT6adFUaeLJmbCDUqpUopdu7p8ADUbPzQFoJWxYEiq6QFoLtf91NvNpRv7ru4DBEPoBy28fHx8WLt27QM9hwfN2rVrq6V8u3a9P9Y8BgzcaxQKRYMF4PEb2g0Rrx8T/v3FF8osTEQv045OHTmRfAIrYyu2j99e72kZY9ePFV0U3u75NqPbjK73uZ5IPsGkTZPwXOjJriu7xCwLCPfp6V2nE/dKHJvGbCK7OJvev/Sm3ZJ2fHX0K9IL03GwcOCtHm8R90qcWEpk4PFF/8IEAw+Mnm49sTW1FadqFFUUcSjxkGgUXRUvWy/MjcwpKi8iITsBbztvnfs2khlhY2rDrZJbZBZmYmdup1cEUDMFrLaA0RSASpXuaRvtHNoRlyEMZldPInnQDBo0iJ9++okXX9TfpuFx4eeffyYkJKTa6x06dKBVq1bEx8cb0kEGHmmUSiXt2jVs1rjmXPXWmTAwAZTAIv/Ke2Ivt14cSjyEkdSITWM20cmpfve1r458xYbzGwDo59GPLwK+0HtueUlFCWvPrmXh8YWcSj1V7evt7NsR1i2MMW3HcCLlBJ8d/IxN5zeJD/FyqZzQVqFM6TSFgS0H6j2SU6VScSHrAj72hlrhRxVDBPARQC6VM9h7sNZrtaWBZVKZ+PSmTxq4aiewLh9AzafJsopKAdjYVJgnrFnzp05jVKWRSSMsjC3EYwW1CKrz/MoUZQ0WIHuv7CW9IF2vtS+88ALh4eENOs6jSnh4OM8//3yNX5NIJMyYMeM+n5EBA/cGPz+/em+jVCopLC8UP595O/q3uTVctZWgRElHx44cTjoMCEbNuh7MdfHv9X95e/fbALg3cmfHhB16ZUWScpOYvXc2zl85M/mvyVriz0hqxPj24zk05RAbx2wkKS+J9kvbE7wymDVxayhVlNLBsQPfBH9D8pvJbBqziSHeQ+oUfyqVijPpZ/hw34d4h3vTZnEbnfPeDTz8GATgI0LVOsBt8dtqXS/WAabqXweo7gTW5QOopDKqpxkBVKeRNecPqzt8q9K8cXNOpZwSI4QjfUbWeX6Lji/C4zsPfoj8oc61mhSUFTBq3Sg8vvMgKjVKr22mTZtGWFgYEonkgaem7xXqawsLC2PatGm1rp04cSImJib36cwMGLg3uLm5YWtbf2NjTVFlWwSTTgsff9tdqKdrZt2M0+nCi18Gfsm49uPqtf+0/DQGrByAChVmcjMip0XWKsJUKhX7r+5n+J/DafZtM+Yfmq9luO/WyI35/edzIewC/Zr34/2979MqvBVzD87lRt4NbExtCOsaxqlpp4h5KYbXu7+Og4VDreeoUqmIy4hjzv45+HzvQ8elHZl7cC7x2fGYyk2JSYup1zUbeHgwpIAfEYI8gzCRmYhh+4s3L3Ll1hVa2NRcJKwWgDHpdf9xVu0E1pUC1kSzwUMdQVTXr5jKTbl085LO81KnOkxkJjhZ1d3htuH8BhJzE7VqEPXhx1M/cqvkFl62XnR07Kj3dgEBAQQEBDBu3DhKS0sfq7FxMpkMU1NTVq1apVdUtVGjRrz44ossWbLkofs+SCQSQ2raQJ3IZDK6d2/Y+LQtl7aIH089BeYVEO0EB5qBtYm1WPbyuv/rvNXjrXrtu0JRQdslbSmpKEGChH2T94kP41UpKCtgxekVfHv822r3VgkSBrQcwCtdXsHC2ILfT/9OhyUdxMilVCIl2DOYKZ2mMMR7CCZy/R7ozmWeY+3Ztaw9u5bzWZXDj01kJgz0GsjoNqMJbRWKlYlVva7bwMODQQA+IlgYWxDoGcjWS1vF17bHb2d6t+k1rlfXoESnRqNSqWqNZlWdB6wrBayJZgTQ0dIRgOu51wGwMbXRmRZ4qtlT/BwlTJlQzw+ujeS8ZI7eOArACJ8Rda5XU1pRyldHvwLgnV7vIJPWz+YlJCSEyMhIQkJCSEpKeujET0OQyWS4ubkRERFBy5YtiYiovZtczSeffMKff/7JzZs3USp113beL6RSKRYWFhQU1FxmYMCAJgqFgsDAhvna7b4imOnLFRAm9HrwTXdBVJUryylXljO67Wi+Cv6qXhmDvVf2MvzP4eSX5QMQHhJe44zf5JJk3tj5Br+e+VXMyKhpbNqYaX7TGOw9mP1X9/P6zte17rutmrRiSqcpTOwwEZdGLnqd14WsC6LoO5t5VnzdWGbMgJYDGN1mNIO9B4t13wYebQwC8BFiqPdQbQGYoFsAtndsj0wiI7Mok5T8lFpvAHWlgGtKSWjWA6p9qlLzhbSvo4WjzshjV+euYr1LF+cuOs9JzcbzGwGhEcbZSv8uvhVnVpCSn4KzlTMTO0zUeztNfHx8iIqKYsKECURERDyyESf1eQcHB7Ny5UpsbGzqZe9iY2PDsmXLGDJkyD08S/1RKpWEh4czefLkB30qBh4RQkNDG7Td2QxBBD17DlzzIc0C/mwHliaW5JXm8YzHM/w+7He9R1lGpUbx3p73RGEJ0MGxA690eUX8XKFUEBEfwTdHv2H/9f3V9tHNuRsvdXkJqUTKH7F/8OWRL1Eh3JesjK0Y03YMU3yn0MO1h16i9NLNS6w7u46159ZqjbwzkhoR3DKY0W1GM8R7CNam1npdY20oVUouZl2ktV3rx7bE5lHCIAAfIUJbad/E9l7ZS3F5cY0u8aZyU3zsfYjLiCM6LbpWAVi1CaRqClgqkSJDhoLKKFh5RaWAcG0kGKyqI4i25rprbTxtPckuFrqZQ7yqd55WRZ0u1qdWUI1CqeB/h/8HwFs93tI75VETNjY2bN26ld9//52wsDCKi4sfqWigTCbDzMyM8PBwJk2a1OCb7uDBg5kwYQKrV69+oNcvk8kYN24ckyZN4ttvvyUmJuaRFOUG7h9+fn40bdq03tsplAohQqeCN4QkBIu7gtzUnLzSPNo7tOevMX9hJDPi66NfM9Vvqs50aEJ2Ah/u+5A/z/4JCOLqpc4vMb79eLq7CZG/m0U3+SX6F749/q2WowIIadeJHSbydLOnOZh4kBnbZ2g1p/T16MuUTlMY4TMCC2OLOq8tITtBFH2aNXxyqZzAFoGMaTuGoa2Hig1+DaW4vJjIlEgOJx3mUOIhjt44SnZxNgmvJeBp63lH+zZw5xgE4COEk6UT3V27c+yG0IpWoijh3+v/MqDlgBrXd3LqRFxGHDFpMdXEoyZVawCrpoBBuDFoegpqikFXK1dUKhV5pXmAUBtTE3bmdhxOPCx+XrWxpSrpBekcTDwI1E8Abjy/kfjseGzNbJnWufYmB32QSCRMnjyZgIAApk6dyvbt25HJZA+1EFSfX3BwMD/++CMuLvqlgGojPDycmJgYzp8//0CuXS6X4+Pjw6JFiwAYMWIEZ86ceah/DgYeLDKZjBEj9C8d0eT//vk/AHokQbcUKJHBD10EGy63Rm5sH78dcyNzJm2axB+xf7A9YTs7J+zUigamFaTxyb+f8FPUT2LWZFz7cXza91Oxfjs6NZrvjn3HqrhV1eqcmxo35eXuLyORSlh+ejk/R/8sfs3cyJy3e77N5I6TaW7TvM7ruXLriij6NJviZBIZAS0CGN12NMNaD8PWrP7NMmoyCjM4nHiYw0nC/6dSTlW7JjO5GfHZ8QYB+BBgEICPGEO9h4oCEAQ7GF0C0NfJl5VnVtZpBVNbClgdXZFL5JRSs/m0s5Uzt0puiW7z6u2rIpfKWRq5FAALI4s6i4f/uvAXSpWSLs5daNa4Wa1r1ahUKuYfmg/Aa91ew9LYUq/t9MHFxYVt27axZ88eZs2axZkzZ5BKpQ9FXZwa9fm0bduWBQsWNLj2qSasra3Zu3cvPXv25Nq1a/dVeMlkMpo1a8aePXuwthYeMCZNmsScOXPu2zkYePRQKpXibOv6EJkSKd5H1MbPf3SADEuhxnnHhB00Nm3M0DVD2Z6wHblUzn86/kcUf7kluXx55Eu+OfaNWEozoOUA5vefTyenTpQpylgTt4Yvj3xZzaFAJpExuNVgOjh0YHP0Zv578L/VfFUbGTdiWudpfPzMx7Vex/Wc66w7t461Z9dyMuWk1jH6Ne8nij51Fqg+qH0A1WLvUOIhErITqq1ratmUXu696OUm/N/JqZPeXoMG7i0GAfiIMdR7KO/vfV/8PCI+goUDF9a4Vl8rGF1dwCpUlCnKkCJFLtH9q2JlYqVVMKxrSklaQZromq+re1mThqR/d13eRXRaNOZG5rzW7TW9t9MXiURCYGAg0dHRrFu3jtmzZ3PlyhXkcrnexq33AnXEz8PDg3nz5jFq1Cik0rvv8uTg4MCRI0fo37//fYsEymQy2rRpw549e3BwqLSscHd3JzQ0lO3btz/Q772BhxO5XE5ISAhubm712q6grIBxG8ahVClxz4GRtxtgv/MXSmv+fu5vnCydCFwRyNEbRzGTm7Fh9AYGeg2kpKKExScXM+/gPG4W3wSEyUdfBHzBMx7PkJKfwpz9cwg/Ea5l3wLCfXikz0hKFaVsOL+Bvy7+pfV1mUTGwJYDedHvRQZ6DayxOQ8Ef0C16DueXDnFRCqR0tejL6PbjmZ46+E6O451UVJRwsnkk6LgO5J0RCznUSNBQluHtqLY6+3eG4/GHoZ6v4cUgwC8zfz585k9ezYzZ87k22+/fdCno5PWdq1padOShFvCk9blW5eJvxmPVxOvamvVncBXc66SU5Kjs55DfSPIKspCpVJp1RQWVxRjIbPQKQClSJFIJCTnJYuv6fIABMQbRg/XHrVdJjeLborj4uojANVP7dP8puk1hzMhO6FBs4ilUiljxoxh1KhR7Nmzh/DwcLZu3YpUKr3vkTGlUsmgQYMICwujf//+90T4aeLg4MChQ4d47bXXWLFixT2Lgqr3O378eBYuXChG/jQJCwtjy5YtNWxt4EmnoqKCsLCwem/3+o7XRUursBMgU8He5hDrBBtHrKJ54+Y8/evTnM08S2PTxmwbtw1/F3+Wxyzn//75PxJzEwHwbuLNvP7zGOY9jCM3jjB8zXD+vvR3tWheD9cetLFvw4HrB1h6amm182lr35aXu7zM2HZjdUbqkvOSRdGndk0AQZD18ejD6DajGdlmZJ2ef5pkFmYKYu92SvdU6ikt9wcQsj3+rv6i4Ovh1uOO6wYN3D8MAhA4efIkP/74Ix06PPyzDyUSCcNaD2PB0QXiaxHxEcxsMrPaWhszG5pZN+N67nVOp52mj0efGvepvqmUKcrIL8vHytgKqUSKUqWkqLyoVgGotle5lnNNfE1zIkhViiqEdMgQb+2O0r8u/MVPp35iauepDGs9jL8v/o1CpaCDY4caxW1NHE06yr/X/8VIasRbPev25Iq/GU+7Je0Y0HIAq0euFiOf9UEqlRIUFERQUBCJiYmsWLGCjRs3EhUlpHXudq2g5v78/PwYMWIEkyZNqneU406xtrbm999/Z/To0Tz//PNkZ2ff9ets0qQJv/zyC4MGDdK5LiAggBYtWnDt2rWHKhVv4MEilUrx8PCgf//6TeVYf249y6KXIUGCeamKqbd9oL/pDt+HfE9bh7b0/KUnibmJOFs5s2P8Dq7mXKXj0o5iFsTFyoWPn/mY0W1H82fcn3iHe4sP7GrM5eYEeAaQV5LHwcSDWqINwNbMljFtxpCXnMdNi5v0b96/mvhLyU9hw7kNrD23lkOJh8TXJUh4qtlTouhTuzTUhkql4uLNixxKPCSKPrUI1sTJ0kkUe73ce+Hr5GtI5z7CPPECsKCggPHjx/PTTz/x2WefPejT0YuhrYdqC8CECGZ2ry4AAXyb+nI99zrRadE6BaC5kbk4OzizMJNGJo0wNzKnoKxAaAQxBSNJzX/k6g7bizcvAkKaIq0wrca1jhaOpBcKY9kCW2jXpq0/t56IhAh2Xd7F6VdONyj9+/nhzwGY2GGi2JmsC5VKxWvbX6NMUUZpRanOusX64O7uzgcffMAHH3xAamoqW7duZdeuXRw/fpykpCRAeGNSR+1qE0wymUyMJqqFjZubG/7+/gQFBREaGtqgzsa7TWhoKBcvXmTOnDksW7aMkpISgAZ15qrTRKamprzwwgt88skn2NjY1LqNVCpl7ty5PPfcc/U/eQOPLUqlknnz5tUrGp6Um8TULVMBwXd10okCGpdCvC3YPzuJbi7d6PVLL7KKsvCy9WJuv7m8su0VcQycjakN7/d+n0Feg1h6aikzt88UH3jVeDb2xNPWk+PJx/n74t9aX5NL5QxsOZDWdq05l3mOn6N/Fhso/M/68/EzH5NWkCaKvoPXD4r2LwC93XuLoq8uy6ySihKhOzexMp2rTllr0ta+Lb3de4uCr3nj5oZ07mPEEy8Ap0+fzqBBgwgICHhkBGAP1x40MWsi/sHuv7qfwrLCGtv/fZ18+evCX3U3gpjbcz33OplFmXjaemImNxME4G0rGF0RQLVwUhuQNjJpVK22RY2pzBQQDEyN5ZX1K0qVUhxtZyI3wUhqJPpkPdvm2VrPW01cRhx/X/wbCRLe6fVOnes3nN/Azss7MZGZEB4Sftdvak2bNmXq1KlMnSq8oWRnZxMVFUVsbCwpKSmkpqZy48YN0tLSKCsrQ6FQIJPJMDY2xsnJCVdXV5ydnXF2dqZdu3b4+fk1aJRVfUgrSNMrWlAVGxsbFi1axNy5c1m5ciXfffcdly4J0wqMjIxq9RzU/HqrVq2YMWMGEyZMoFEj/Y1mR48ezbx58zh37pyhI9gAMpmMdu3aMWrUKL23USgVTNw0kZySHCyNLSksKRDn/i70h/EdJ9L3t74UlBXgY+eDayNXRq8fDQh1gTO7zaSzc2fCT4bzzh7t+49MIsOvqR9ZRVlczrnM5ZzLWl/v4NABfxd/UgpS2Hl5p9b0EXdTd0Z1HIUECX1/68u/1/7VEn3tHdrTzLoZs5+aTQ833WU1mYWZHEk6Ikb4dKVzu7l0E8VeD9ce2JjV/gBm4NHmiRaAa9asISoqipMnT9a9GCgtLaW0tLLBIS9PsKBznQEAACAASURBVD0pLy+vl7Hu3SDUK5TfzvwmHF9Zzu6E3Qzyqp4qa2fXDhAaQWo7RztzO67nXic1L5Xy8nJR2OUV51FuWa5TAJobmVNeXk5SrhDhsjK20ikA1b5VXrZeWucSlRpFTkkOAP4u/niHe6NCRSvbVng19tLrezv/oFD7N7z1cFpYt6h1m/zSfF7f8ToAs3rMoplVM3F91X/vFlZWVvTp04dDskPccLvBL4N/QS6t35/f3TwnzetUqpR8f/J7PvjnA9aNXEewZ3CD9mlmZsbUqVN58cUXiY2N5dSpU0RFRXHixAnOnz9PaWmpOJXGxMQEHx8funXrhp+fH507d6Z9+/aiEK/vtX7++ee1pooNPDkoFArmz5+PQqHQ+4HgiyNf8O/1f5FL5RSUFfBiNLTKhhwTsHwxjEGrB1GmKMPJwokLWRc4n3UemUTGuLbjcLd2Z9npZXxx5Autfdqa2uJo6Uh8drxWBy4ID9w9XXtSVF7EgcQDnMmoNGBuZduKEK8QzGRmbDuzjW9OfKNVN+jeyB1zI3Nu5N8gNiOW2IxYBngOoIuTYKyvTucevXGUIzeOcOTGkRrTuY4WjvRw7UFP1570cutFR8eO1RpL7tf72r267+pzzCeZJ1YAJiUlMXPmTHbt2oWpqale28yfP5///ve/1V7fv38/5ub1rx+7E5rmaaf/ftj3A5L46lGs7DKh6eJcxjk2b92MkbTmVK6qQHiq3HdsH9J4KcpS4Yaz/9B+Mi0zdYuVUoiIiOD6zevi57rIKRZEXtPyplpjyNalrRM/zsvOE59wOxp11GtcWXppOmvOrwGgp7JnndssT15Ocn4yjsaOdMjrUOP63bt3V9/wDvkr4y+WpywHoEVRC7pZd7vrx6gvayPWsjBxIdH5QoT4u93fobh4d6Jojo6ODBw4kIEDBwLCG5M60qkWejnlOTQ2asyNGze4cUN37WhdqFQq/Pz8iImJMdQCPsFIpVI6depEWVmZ3qMO44vi+fjSx4Aw4WjIBVhye+DSVz3h89hwQKitU5e3dLTsiLHUmFVxq7Q8UQHsjewpUBSQXZJNdklll6wcOZ7mnhhJjLhUdInNlzaLX3M2caZLoy6YSc24UHiBhccXoqTy99hCZkG5spwyVRmJeYni65YyS3wsfIg8E8mJ0yc4X3ieC4UXyFfkV7tOd1N3Wlu0xsfCh9YWrXEydhL+Dm9C5s1M9rBHr+/XveRe3Hd1UVRUVPeixxyJ6gm10f/rr78YPnw4MlnljFiFQoFEIkEqlVJaWqr1Nag5Aujm5kZqaipNmtTdcXo3KSwrxPEbRzGM79bIjYTpCdVSmSqVCudvnblZfJNjU47h19Svxv09v+V5VsauZF7feczqMYtuy7oRkx7DljFb6OfeD/8l/sQVxAHCjVAt0nq69uSfSf9g86UNheWF+Dr51plu/nfiv1rpil6/9uJkqvCE3KJxC67kCOnkEy+coJNjpzq/FzN2zGBp1FICmgcQ8VztN/2zmWfpuqwrFcoKNo/ezMCWA7W+Xl5ezu7duwkMDMTI6O4VN/9w6gde2ynY0nz89MfM7j37ru27IZSXlzN//XyWpi8lqygLU7kp/+v/P17ye+m+1PhcvHmR9/a9x6mUU5x9+exdGSifnJxM+/btKSwsNEwHeQKRSCRYWFgQGxurt/F5QVkB3ZZ1I+FWgtAxe1XF9pVgqoBVfsZMCC1DpVFG2KZJG8qUZdWaOkxlpliZWIlWWpq0tGmJjakNZ7POas3zbdG4BaGtQrE2sebYjWPsv75fa8SmXCrX+hyEDEs3l264WLmgVClJyE4gKi2qWjrXVG5K16Zd6eEmRPi6u3S/I4Pne829uu/WRl5eHnZ2duTm5tar5ORx4omNAPbv35/Y2Fit16ZMmULr1q159913q4k/ABMTE0xMqo8VMzIyum+/tGoaGzUm2DNYrBdJyksiITeBNvZtqq3t5NSJvVf3EpcVh7+7f437c7R0BCC7JBsjIyPMjYWIZpmqDCMjI60UsGYNiq2ZLRKZREzvqpQ1v/Gayk0pqShBgoTezXuLhqk5JTmcShNa7bybeIvNJB6NPeji0qVOMZJekM7yM8sBmP3U7Fp/DiqVipm7ZlKhrGBY62EM8dE92/Zu/kx/i/lNFH/v936fOc/MeaCF1MXlxczaN4slV5cAwizSVSNW0dah7T0/9s2im3zy7ycsjlxMhbICmUTGkZQjtU6q0RcPDw8WLVrElClT7sKZGnjUUKlUhIeH4+Hhofc2b0W8RcKtBKQSKX43lPy9WhB/W9sYMWlQpfhztHCkoKyAczfPaW1vbWJNYXkhJYoSSopKxNdtTW1xaeTC5VuXtcSiR2MPhnoPxcbMhuM3jrMkckm1SRlqKpQVyJFjb2FPsaIYG1MbjGXG7L26t9paBwuHymYNt174NvXV6RP4MHM/30vv93v2w8gTKwCtrKxo166d1msWFhY0adKk2usPK0O9h2oVDG+P316jAPR18mXv1b21RuZ0zgMur70JpIl5EzIKM8TP1bV8VbE0sqSkogQ7czutUUl7r+wV61tcG7mKAnBUm1F6iaTvjn9HSUUJ/i7+POPxTK1rV55ZyYHrBzA3Mufb4Pvj9bj27Fqe//t5AGZ0m8HcfnMfqPiLTY/luQ3PiZYVM7rO4IugLzCV61cG0VDKFGUsPrmYT/79RKwRHeQ1iAVBC2ht1/quHWfy5MmsW7eOnTt3GhpCniBkMhnBwcH1mvqx9uxafo35FQDvdCXbV4JVmeD59+zwchQyYWZvhbJCdC8AoanDSGZESUUJuaW54uvGMmNcrFzIKMzQSv+6W7sztNVQmpg34WDiQcJPhItTk6pdh0SGtak1xeXFFFcUU0EFqYWCr6rmvbWNfRstOxZPG09Dd66BevPECsDHgapRk63xW2v0v/NtensiSC0CsNo8YI1xcKBbANqb22uZQN8sqW4lAIgpirb22lGmiPjKlK3mAHR9un9zS3L5/uT3gBBZq+0GmFOSw6zdswD46OmP9B4tl1WU1aAxSQBbLm5h/MbxKFVKXvR9kW8HfPvAbtIqlYqFxxfy7p53KVWU4mjhyEuOL/Fh4IcYye/dk7BKpeLvi3/z9u63xUL09g7t+SroKwI9796YOjUSiYQVK1bQuXNnkpKSDCLwCUAul+Pq6srKlSv1/vtKzE1k2hZhTnizW7BrBdgVwwlnGDYWSm//SWhG54ykRpQry1GoFCgqKn+v7M3tRceEqzlXAeFhdkirIZgZmbH/2n6+j/y+mgG0GitjK4rLi6lQVaBQKapN15BJZHg18aKfRz9CvELo4dbjoU7nGnh0MAhADf75558HfQr1wtFS6OJSm4gevH6QvNI8Gplo1zOoR8KdTjuNQqkQzZs1qToPWIwA3raB0eUD6GDhoGX8nF9WvfgYKjuANaN0KpVKjGBaGllyPkuYueRs6UxX5666LltkSeQS8krzaGPfhsHeg2td+9G+j8gozKC1XWve7PFmnfsGYUpI15+6EtY1jI+f+bjG75sudl/ezbPrnqVCWcH49uNZGrr0gYm/9IJ0/rP5P+xI2AEIDw5LBy4l8t/Ie3rcmLQY3tz5Jvuv7QeE35VP+37KC74v1Ot7WV9sbW2JiIigS5cuFBcXG+oBH2PUHeURERF1ekaqUSgVTNg4gdzSXBwKYPcKcM2Hs/YwcAIUVKnyUZvia4pBCyMLFEoFJYoS8aHZ2cqZ4BbBFJYXcjLlJEsil2iVy6gxlhlTrigXv1b1nmluZE4nx04EtQjCLNWM6SOmY2Fa3eLLgIE7xSAAH3GGtR4mCkCFSsHeK3sZ7jNca02rJq0wk5tRWF7I5VuXadWkVbX9VIsA3h4HV1cK2NnKWRRutaFOeYxqW+nNdS7znHi81vatiUyJFNfUJZaKy4v55tg3ALzb612ttHJVolKjWBy5GBDc/PWpjSkqL2Lk2pHklOSw79o+5qjmIEM/0XLg+gGGrhlKmaKMET4jWD5s+T0VPLWx7dI2pmyeQmZRJqZyUxYELuDVrq/e09m5qfmpfLD3A5afXo4KFSYyE97s8Sbv9X6v2sPJvcLHx4e1a9cyeHDtDwYGHn3Wrl2Lj4+P3us/P/Q5BxMPYl0MO1eAVzZcs4agiZB928xBs9FNHbmTS+RIpVLKFGXiA62DhQOdHDuRW5rLhawL/Hr612rHkyLV6uit2rDhYOGAv4s/g1sNpm/zvmI6t7y8nIiIiEeyls/Ao4FBAD7iDPUeyrt73hU/j4iPqCYAZVIZHRw7cDz5ONGp0TULQI15wKB/CtilkYtYlKxu9KiK+mYqk8jwsau8UasjUlApNEG/9O+vMb+SUZhBM+tmPNdO9xQIpUrJK9teQalS8ly75+jXvJ94PM2Zx5qoVCrCdoRxJv0MDhYOrH12rd7jjk4kn2DQqkEUVxQT4hXC6pGr6+33dzcoLi/mnd3vEH5SsLBo79Ce1SNX39NGj6LyIr4++jVzD8ylRCH8HoxpO4bPAz7Ho7HHHe8/vSBdbFbSh0GDBrF06VJeeumlOz62gYeTn3/+mZCQEL3Xb7m4hY/2f4RZGWxdBZ3SIc0CAiZBisaziWbkTp36rVBVgEIwu29m3YysoixSC1LZdWVXrcfUFH9SiZQWNi3o06wPQ7yH0Mutl14zyw0YuBcYBOAjjredNy1tWoqdZlsubRHNdjXxdfIVBGBaNGPajam2H3UEsKCsgJKKkmopYF0ixtnKmes5ggegmdysRgFobmROYXkhTpZOWuf114W/AEEgqqOIduZ29HTrWes1Vygr+PLIlwDM6jmrVnH2c9TPnEg+gZWxFV8FfSUed8b2GSwbsqzGOrRdN3ex8sZKpBIpa0auwaWRfpYSMWkxBK8MpqCsgH7N+7F+1PoH8vQemx7LuI3jiMsQbHtm+s/k84DP71mjh1KlZHXsambtnkVaQeUYwE5OnVjz7Jo73v+5zHPMOziPP8/+SdS0KNo7ttd72ylTphAdHc3SpUvv+DwMPFyEh4fz/PPP67X2VvEtZm6fyYrYFRhVwPq10DsJbpkKkb/LVTSYOu0LQh2gmdwMS2NLsoqyyCvNIzYjtoajVMdMbkZbh7YEewYzyGsQfk39xPGZDSGnJIcjSUc4nHiYQ0mHGOo9VO+SFgMGqmIQgI8Bw32Gi4IovTCd2IxYOjh20FrTyUnw04tJi6lxH41MGolPupmFmWIEsK4UsI2pjdi8oUvsqG+kHR07iq8VlhVyLFmYtdTMuhnXcq8BQvdvbelcgD/j/uRazjXsze153lf3G0BWURbv730fgE/7fkpTq6ZkFWXx0taXyCjMYN/VfdUEYGRKJD8l/wTAMO9hekeczmeeJ3BFIDklOfR068nmsZs5m3mWbZe2MafP/bF9UalULDqxiHd2vyM2eiwftpwBLQfUe1+RKZFEpkTycpeXa113OPEwM3fM5FTqKa3Xg1oE4e9as+WQvkSlRjH34Fw2nt8ovrY9YXu9BCDAgAED8PPzEyOBj2NNoPr3q2/fvuzfL9RcPs7XOX36dKZNm1bn+oLSAqZtncafZ/9EqVIiVcLvmyAkAYrkMGgcxNYw/VCpUiKXylEoFahQUVxRLD4M10YTsyZ0ce7CEO8hBLYIpKVtywb/7atUKtJL01kVt4pjycc4nHSYuIw4reikpbGlQQAaaDAGAfgYMNR7qCgAQUgDVxWAmp3ANUUIJRIJduZ2pBakklmUKaZH1SlgXU0g1qbWZBULaWOFsuaOS3VUMMAzQHztn2v/iCanmqnYutK/SpWSzw9/DsDr3V8XI5U18d6e98guzqajY0emd5sOwGvbXyOjMIO29m35+JmPtdbfLLrJ2I1jqVBV0M25G5subGLXlV3EvRJXa9dwQnYC/X/vT1ZRFp2bdiZiXARxGXEErwwmrzSPplZNmda57jerOyG9IJ0pm6ewPWE7IFis/DL0FxwsHOq1n1vFt/hw34csiVyCTCrjKfenakwbX711lXf2vMP6c+u1Xvdr6oe5kTm7ruxi79W9jG8/Hm8773qdw+HEw8w9OFe8FoCRPiOZ/dRsnUbmdfGf//wHZ2dnRo8eTWlp6WPVHSyTyTA1NWXVqlWoVCpmzJjBuHHjHvvrrI30gnTGbxzPvqv7KgWTCsIjYOxZKJPC8LFw1F33PqqaMFdFKpHibu1Ob7feDG89nD4efe4onVuhrCA2PZZDiYc4lHSIw4mHSc5Phiol1l62XqLn39PNnm7w8QwYMAjAx4Durt2xMbUR/dX+vvg37/V+T2tNe4f2SCVSMgozSC1IxdnKudp+7C3sBQFYmFk9BVxDBFCCBGOZMXmlwkzkmtK/UFlPM6pNZQPI1vit4scJ2UL62trEus4b2rZL24jLiMPK2IpXu76qc92RpCMsi14GwOJBi5FL5aw/t541cWuQSWQsH7ZcKxWjUCoYv3E8iXmJ2BvZcy7rHCpUTOwwsVbxl5ibSP/f+5NakEo7h3bsnLCT2IxYQv4IIb8sn6fcn6q1RvFuEBEfwZTNU8gozNBq9KhP5EGlUrHyzEpm7Z4l+jqOaTum2htaXmkecw/M5etjX2u9Qbpbu9PStiX7ru4DBOuKaZ2n0di0sd7H33t1L3MPzuWfa/8AwhvsuPbjeL/3+zX6W9aXQYMGERkZSUhIyGNjESOTyXBzcyMiIoKWLVsSERFBSEjIE3GdNXEs6RgvbHmBc5nnqn3ts33wSiQogQkjYFfL+p2DkdSINvZt6N+8PyN8RtDFucsdpXMLygo4fuM4h5MOcyjxEEdvHKWgrEBrjQwZnZ0709u9N73de9PTrWe96mANGKgNgwB8DJBJZQxvPZxfYn4B4PiN4+SU5Gi9+ZoZmdHarjXnMs8RkxZTswDU6AQWU8C1CECZREZxebHY1aZLAIKQHnZt5Cp+/vfFvwHB/qWgXLjpjfQZWWvDhEqlYv6h+QC80uUVneKiQlnBq9sEcfh8p+fp6daTjMIMXtn2CiB4BnZx7qK1zacHPmXn5Z2YyoQ6uYKyAp5yf4pvB+g2jE7NT6X/7/1JzE2kVZNW7J64m9iMWEJXhVJYXkhfj75seW4LFsb3xsKhpKKEd3a/w6ITi4CGN3qczTjLqxGvcuD6AQBa27Vmcchi+jbvK66pUFawLGoZ7+99X3zQAEG0d3HuwsHrB0nMFWaUPtvmWeb2m1tjs1FVVCoVWy9tZe7BuRxPPg4Ib7T/6fQf3u31Lp62nvW6lrrw8fEhKiqKCRMmEBERgUQieSRTperzDg4OZuXKldjY2GgNt39SrhOErtolJ5fw2YHPxGxEVd46DB8cFD5+ORTW6eH1b2FkQSenTgzzHsaQ1kPwsvW6o1KO1PxUUewdSjxETFpMNUPoRiaN6OnWk95uvfF39ic7NpvhocPvaGrFreJbRKVGcSr1FKdSTxGZEsmxF46JjX8GnlwMAvAxYWjroaIAVKJk9+XdWpYrIDSCnMs8R3RqNCFe1TvnNDuB1UajtXUBG8uMtcybK1TVUybqeZZujdzE1xKyE8TtHCwdKLglCMCq51uVA9cPcPTGUUxkJrzR4w2d6xafXMzp9NPYmNrwecDnqFQqXt32KllFWXRw7MBHfT7SWr89fjuf/PsJIFjmnMk4g1sjN9aP1t3EkVmYScCKABKyE/Bo7MHeSXuJy4hjyOohFFcUE+QZxKYxm2pNUd8JcRlxPLfhuTtq9ChRlPD+vvf57sR3VCgrMJObMafPHN7s8abWde+6vIvXIl7jUvYl8TW5VE5P157EpMeIXeBPN3ua/wX8T6/aP4VSwYbzG5h7cC5n0s8AQhf5NL9pzOo5Czdrtzr2UDfXcq4hVVWvJ7WxsWHr1q38/vvvhIWFUVxc/EhFyWQyGWZmZoSHhzNp0iSdouRxv86k3CQ++OcD1p9bX+O9R83zUbBgt/DxuwHwU5ea19mZ29HbrTej2o4iyDOowQbwIJSqXMi6IIq9w0mHuXLrSrV17tbuYjq3t3tv2tq3FS2jysvLiThb+2zzquSU5BCVGkVkSqQg+FJOcfnW5WrrTqWeqndtcGFZIaWKUoMJ9WOEQQA+JgS0CMBYakyZUojGbYvfVqMA/CP2D50TQcQIYGGmGK2rrQnEVG6qNQWkJiQIN23N2q2dCTvFj1PzhTFHFkYWokWLLtTRvymdpuBkWUPl9u39fbjvQwA+D/gcewt71sStYcP5DcilcpYPXa4lbq7lXGPCpgmoUOHr5Et0WjTGEmPWP7teZ/1cTkkOwSuDOZd5DhcrF1H8DVszjFJFKSFeIWwYveGedN2qVCrCT4Tz9u63KVWU4mDhwPKhyxnoNbBe+9h8cTNhF8LIKhciJkO9h/LdgO+00t3nM88zc8dMdl/ZrbV9F+cuJOclcyBRiBi2c2jHFwFfMLDlwDojJOWKclbFrmL+ofni2D9LY0umd53OG93fuOP0lkKpYOflnSw+uZiI+Ahe93+dPvSptk4ikTB58mQCAgKYOnUq27dvRyaTPdQCSX1+wcHB/Pjjj7i41N2d/rhdpzpi/MaFN7gec73OfY08Cz/enpb5eS/4X+/bx5DIcLd2J9gzmDHtxtDDtccdpXNLKkqITIkUxd7hxMNakXIQ7oUdnTqKYq+XW687etDJLcnVEnuRKZE1ij2A5o2b09m5M12adqGzc2f8XWp/SMsozCAmLYbo1Ghi0oV/L928xNs93+aLwC8afM4GHi4MAvAxwdzInOCWweJkjS0XtwhdbxodtXWNhNOcB1w1BSyTVDcytjC2EMd76ULtnj+wZaVA2XB+AyDcENX7H9Z6WK2WKdGp0ey8vBOpRMrbvd7WuW7W7lnkl+XTzaUbL/q9SFpBGtMjhAaQD5/6UPwegHDTfnbts2QXZ9PCpoX4fQlzDxOnp2hyOu00p9NOs+TUEqLTonGwcGDPpD2czTjLs+uepUxRxlDvofz57J939Gaii6qNHiFeIfw69Nd6NXpcuXWFGdtnsC1+GwAe1h4sHLhQa5JKVlEWc/bPYWnkUq2OQy9bLxQqhWjY7dbIjU/7fsqEDhPqNLouqShhecxyvjj8BddyrgFCB/lM/5m85v/aHUcVsoqy+CX6F5ZGLhXHcQHClJpafhQuLi5s27aNPXv2MGvWLM6cOYNUKkWprHls14NAfT5t27ZlwYIFBAbWf4Teo36dBaUFfHbgM5aeWqo1f7c2Ai7Dqg0gU8GPfrB0pDv/9XuBUW1G0dqu9R2lc28W3RSF3qGkQ0SmRFYzeDY3MsffxV+s3+vu2r3BRuhqsaeZxlXXTlfFo7EHXZy70LlpZzo37YxfUz+dzSlKlZIrt64QkxYjCL60aGLSYrQyO5qo3RoMPB4YBOBjxPDWw0UBmF2STXRqNJ2dO4tfV1vBXLl1hdySXKxNrbW216oBNKrbCNrK2IoLWRcAQSDqGnAOiObUpRWlHE46LBzPwl5sOBjddnSt16bu/B3bbiwtbFrUuGbf1X2sil2FBAmLQxYjQcLLW18muzgbXydfZj81W2v9zO2CfUkjk0biDe/N7m/ydEn1RpRLNy8RuCJQnFxiY2rD7om7OZd5jjHrx1ChrODZNs+yasQqvU2j64Nmo4eJzIQFQQuY3nW63m9ipRWlfHnkS+YenEtJRQlGUiOG2Q/jp0k/YW1uLa5ZdGIRH//zsTjpAIRJBU3MmohejTamNsx+ajZh3cLqjHIWlhXyw6kfWHBkAakFqeL+ZvWYxctdXsbKxKoh3w5AiAYdTz7O4pOLWXt2LaWKUgAamzZmSqcpvNzlZZo3aq6zYUCNRCIhMDCQ6Oho1q1bx+zZs7ly5QpyufyeTkypC3UkzMPDg3nz5jFq1Cik0totkmrjUbzOM2lneHXbqxy+cbhe++yRBH+tAWMlrG0DLis3c81nSIPOT6VScfnWZUHs3Y7w1TT9yNHCUSud28mpU4PuBXmleUSlRnHixgm2XtvKrKWzahV7aqHXxblLrWKvtKJUKAG6LfKi06I5nXa6xvGdEiR4NfHC18mXTk6dxP91ZV4MPJoYBOBjRGirUK0RRtsTtmsJQFszW9yt3UnMTeR0+ulqHbea84DFLuDbKWB1KlcTGzMbruQIdS21CUBzI3OxYeNQ4iHxSbmwTBAZpnJTgjyDdF5X/M140W7k3V7v1rimTFEmRvpe7foqnZ07s/LMSjZf3IyR1Ijfhv2mdTNeHrOcH6N+FI+fV5pHsGcwc5+Zy84dO7X2nZibSP/f+oviz8rYil0Td3E+8zzjN45HoVLwXLvn+H3473d96kdJRQnv7n6XhScWAkK6dfXI1bRz0KOK/Ta7L+9mesR0MVrbr3k/vg38lisnrmBuZI5KpWLThU28vuN1kvKSxO3M5Ga0tG1JbEasKDxn+s/k3V7vokJVq/jLLckl/EQ43x7/Vpwu49rIlXd7vcsLvi/onMKiD0XlRayKXcXik4u1otl+Tf2Y3nU6Y9uNFX9/qzYM1IZUKmXMmDGMGjWKPXv2EB4eztatW5FKpfc1ZSqTyVAqlQwaNIiwsDD69+9/R8KvKg/7dabkp/Dhvg/54/QflKnK6tyPBAmOFo70bd6XSR0nsWHNf/nij2NYlMNOT3DctIs+rfSPmpYryolJixHF3qHEQ6QXpldb52PnI4q93u69aWHTot5RxbzSPKJToytr9lJPcenmpRrXNrNuppXG9Wvqp7NOMackh9Npp7Wieucyz2nNM1ZjIjOhvWN7Ojl2wrepIPg6OHbA0tiyXtdi4NHDIAAfI+wt7Onm0k3sptx0fhMfPv2h1hpfJ18ScxOJTo2uLgBr6QKuSQA2MWvCjdwbdZ6X5hgwdYQSEKNMoV6htYqJ/x3+H0qVkkFeg6r5G6r55ug3XMi6gIOFA5/1+4yU/BRe2/4aAB8/87GWefDptNNiR7CHtQfXcq/R0rYlq0eurpbKTC9Ip/9v/bmRL1ynmdyMNiOMhAAAIABJREFU7eO3cyHrApP/moxSpWRih4n8OvTXuz7vNy4jjnEbxolTB2Z0m8EXgV/oXVuYkp/Cmzvf5M+zfwLgZOnE10FfM7bdWCoqKrjCFaJSo3h99+scu3FM3E4qkdLGrg3nMs8RmxGLVCJlcsfJfNznYy7cvMDAVQORSWQcfv5wtTe8rKIsvj32LeEnwsVUnaeNJ+/3fp+JHSfqPRkl/mY835/8nrd7vi1OYrmYdZGlkUv5NeZXcd8mMhPGthvLq11fpatz1ztK6+WV5rHx/EaMpEaMDxpPUFAQiYmJrFixgo0bNxIVFQVw12voNPfn5+fHiBEjmDRpEm5ud94IUxtSqZSgoKAHfp1mTczYmbCTfr/142DiQa3RabpoLG/Mcx2e45Vur9DOoR05qVdJiFhJ3Osv8uk/ydiWwBFXaLRlNz1aBdS6r7zSPI4mHRXF3vHk42LmQ42xzJguzl3o7VZpx1Jfzz+12NNM4+oSe+7W7vg5+WGRZ8FzfZ7D382/RrGnUqlIzk8WavU0xJ5mGYQmNqY2gshzFCJ6vk198W7ifU+yFgYefgwC8DFjpM9IUQBGpUWRVZSldePo5NSJzRc3E5NefSKIZhdw1RRwTW+sTcybcDr9NECN0T/1OCXNgmP1+DczuZkoLmsaTacmOS+Z307/Bgj2LTWRmJvIJweELt4vA7/E2sSa8RvHk1OSQxfnLrzT6x1xbU5JDiPXjqSkokScQGJpbMnmsZuxMdO2mLhVfIugFUHimD0TmQlbnttCfHY8z29+HhUqXvB9gR9Cf7ir4k+lUvH9ye+ZtWtWgxo9KpQVhJ8IZ87+OeSX5SOVSAnrGsYnfT8R0/7J+cl8fe1rDsQc0NrWy9aLpLwk4jKF7uLBrQYzr/88bhbdZPym8RxKPAQIP7/47HjR6iUlP4UFRxbww6kfxN+ZtvZtmf3UbEa3Ha1XZFSpUrLr8i4WHl8o1jlaGFnQ2bkzSyKXsOfKHnFtC5sWvNLlFaZ0mnJH5rvlinJ2Xd7FijMr2HxxMyUVJbS0bcm49uOQSCS4u7vzwQcf8MEHH5CamsrWrVvZtWsXx48fJylJiJZKpVIxmlWbYJLJZGKUTV175+bmhr+/P0FBQYSGhtK0adMGX8udcD+v86mAp4jMi+SHUz/w2e+f1WofpUlX5678OvRXWjZqwfrFc3E9nsj/s3fe8VUUaPf/3pLc9N57D0lIKCH0KjUUQRAQXMVVUUFxURbXtmvbXVfYtYFdsQVURIEAoSq9JLQUIKSS3nu//ffHeCe5yU0Iru/v9dWczyefJHPnzsytc+Z5zjlP/dt3kn+lgOAKFXFA3E/rprmDctd3TInoSf6KG4uN4lgyqjLEaUUGOFo4Ms5vnFjhG+E14pZMXc3KZi5XXOZi2UUulF/gYplQ2euqqTXAz96vRxvX1dpVcAEnJTEjaAZmZmZodBqya7ONzBmpFalihb07/O39BZL3Uxt3mOcwfO18/79MJRrA/w0MEMDfGOYPms9TRzoJz6G8QyyPXi7+bzA3XC7vaQQxVADr2uvESk1fLWBXK1fR6WaKABq+VOeFCQaDkqYSChsF557BVGImNTMyiHTH62dfR61TM8FvAuP8xplcZ+2BtbSp25jgN4F7Yu7hs9TPSMpJwlxmzucLPhfJh06vY8WuFeTV5+Fs6SweS8IdCT2ChltULcz+ajbpVenicX635Dvy6vN4eK8wUuyR2Ed4Z847Nx1ddyuoaq3ij7v/SFKOoFubHTqbLbdv6bc79kzxGVbtWyVGq4zyHsV7c94TzS+tqlY2nN7Av07/y0i07mnjSZu6TWwTj/YZzWvTXsNMasbaA2vFqBeFTMGqEat4evzTuNu4U9BQwGunXmNL6hZxe7GesTw34TnmD5rfr+emSdnE56mfsyllk5GpKNQplA8vfkjNKeEEJ0HC3LC5rI5bzYzgGT/7edfr9aSUppCQnsDXV782OoGGO4dzT8w9qLSqHkYeT09PVq5cycqVKwGoq6vj0qVLXLlyhbKyMsrKyigpKaGiogKVSoVWq0Umk2Fubo6Hhwc+Pj54eXnh5eXF4MGDGT58OE5ONze/5NTmkJSTxMKIhb9IPM7N8Es/ztCoUA6XHea9C+/xWOVjqL64eVvXAHOpOX8Z+iiz61zoOPkjbe9MoS27hrvbexKpG65mFEZ68419ER73r+WF2IVodVquVl81auca8ip7w+E/HOa2oNv6/f5qUbX0aONm1WSZJHu+dr5GbdxYz1iTeXxt6jYulV7iQM0B9ibtJb06nYzKDJPj6GQSGZGukaJOb5jHMIZ4DBmIaxnATTFAAH9jCHMOI9AhUGwB7MneY0wAfyICV6uvotQojU5yTpZOoobQQPzUOnWvI5Hcrd17JNebgiFz8EDuAXGZIfx5ZvDMXoOS69rr+ODiB0Dv1b+knCR2Xt+JTCLj3TnvUtJUwtqDawFh/m9XYrfh9AYSsxKRS+Xi9JKXJr/E/EHzjbap0qm489s7xVnFUomUbYu2UdBQwGP7HwOEduybs978Ra+m9+fs577d94l6u43TN/LYyMf6tY+athqePvK0OP3E0cKR16a9xgPDHxArsV+mfcm6Q+uoba8V72dnbodCrhANGmHOYbw69VX87Px44fgLIhE1k5rx4PAHeXbCs/jY+ZBVk8VfjvyFhPQEkfyP9xvP8xOeZ0bwjH4dc1ZNFptTNvNZ2mfi+8jazBovWy/y6/NFMuhq5cqDwx/kodiHjOQEt4rculy2pm8lISPBSFTvZu3GjKAZKOQKtHotz018rl/bc3JyYtq0aUyb1neL8VbRoengROEJknKS2JezTzxWM5lZn9Nv/qdwq4+zRdXCxxc/5oOLH5B7NhfN6VswmOghuF7C1EpLRhXpGV2kI7zsDWTduFSbGWQF2dM4LBKrSdMIjl9OoP8g3NVtUJrC6aLTxG+N50zxGfGzftPHaenESO+ReNp69kr+DGSvaxu3N7LnY+dj5MaN9Yo16divbq02at9erhAiV8SqZBeVjbWZNUM8hnRW9TyGEeUW9T8SOTWA3z4GCOBvEIsjF7PhzAZAGJ2m1WnFFqWvna84Nu5q9VWjfD6ZVIaTpRO17bVGLlADGewOe0v7Hq2T7rBT2Ikk89ur3wKdrWGAZdG9j0nbnLKZVnUrQ9yHmAwtbVe3izq/J0Y/QZRrFLO2zqJJ2cRon9GsG7NOXPfHGz/y3I/Cid3KzIomZRN3DLqjh0ZSo9Pw74J/k9KUAghVp88XfE5xYzFPHhKGrq8bs46N0zf+YuTPlNFj28JtRrrF3qDT69hyeQt/OfIX6trrAGH6iSEDEYQA7dX7VnO1+qp4P3OpOdZSa+pV9aAS9IEvTnqRkd4jeeXEK+y8vhMQqgsrhqzgr5P+SoBDAGkVaaw7tI5vr34rnvRmBM/guQnP9WsuqU6vY3/OfjalbOJgXqfZxt3aHYlEQkVLhUj8xvmOY3XcahZFLPrZsTo1bTUkVSfx6uevitIIEN4D04Om42jhyMXyiyRkJADC6/33KX8XdYf/v1DUWMT+nP0k5SZxJP+IkQbNTGrGRP+JJqf3/BrQomxh45mNfJH2BcVNxX2mAXSHQg2x5TC9wopxJVJi8ltxb9EDxhq8EkcZRZHeqEfF4XTbPHLaLZg3fyH1qnpOF53mm+sfcerwKS6VX7rpDF8QJAzDPYcz0nuk+BPoEGj0mW5RtZBakWrUxr1ec71Xste1jWuK7On1evLq8npErpQ2m85Sdbd2x1vqzdTBUxnhPYKhHkMJcQr5RTsOA/h9Y4AA/gaxYNACkQA2q5q5UHZBnM4gkUgY5jmMH2/8SGpFqhEBBEEHWNteS1NH51Vzu6bdJNkxFQ3THaFOoYBArAzBwRYyC9o0bcgkMuaGzTV5v1ZVK28nC4To6fFPm9z/a6dfI78+H29bb16Y/AIfX/qYQ3mHsJBb8Nn8z0TSW9pUyl077kKn1+Fi5UJNWw2D3Qbz+YLPjb5MdXodK/euFMkfwPtz36esuYy/HBHcx8+Mf4Z/3PaPX4z8dTd6rBm5htemvdYvl2xqRSqr9q0SDRzRbtG8N+c9sVWeV5fH2oNr2ZvdOXdZggRPG0/KWspQ6VTYmtvyl3F/IT4kno1nN7Jq3yr06JEgYXn0cl6Y9AKhzqEklyTz+P7HjUw888Pn89yE54jzjuNmaOxo5NPUT3nn/DtiRUuCBD97PypbKkWXpbWZNX+I+QOrRqxiiMeQfj6LxmhXt7Mnew8J6Qnsz90vEgKpRMpEv4lCBbM2i91Zu8X7mEnNmBkyk2WDl+Fo6fiz9nsrUGvVnC05K1b5DBNdDPCy9WJ2yGxmh85mWtC0/you55dGXWsdzx97np3Xd1LVWnXTi8Cu8GqCscVCTMv4EilDy3SY66Ar4VPJIMvfhrohYSgmTCFg9jJ8wmPx1uvJrs3m+I3jfHPuC55677leg4+7QoKEaPdoRnoJRC/OO44o1ygj40OrqpUzxWeM2riZ1ZkmyZ63rXePNm53iYZKq+rU6hkiVyrTeq1GhjqFiuYMgxPXWeEszHaeMvu/GgU3gAH0hgEC+BvEKJ9ROCgcaFA2AEIVsOt4rmEeAgG8XH4ZuuUdu1q5cp3r1LTXYCG3oEPT0cMRZ0B/2r9jfccCwnxig9i7TSNsb3LA5F6DUT+69BG17bUEOwZzZ+SdPW7PrcvlX6eEbMA3Zr5BbVutWKH7523/JNwlHBC+iBd/u5jqtmocLRypaavB0cKRXUt3GZ1U9Xo9j+9/nK1XtorL3pj5BlWtVfz1qDA67oVJL/DCpBd+EfJnMHqsP7yeDk0HbtZufDr/U5Mj+rqjSdnE347+jU0pm9DpddiY2/Dy5JdZM2oNcqmcho4GXjr2Em+nvG10cna3dqeytZKyljKB8DjNZNW0VTz1w1M8f7SzEnpn5J28OOlFIl0jOV54nNVJq0UDhgQJSwcv5dnxz/arQplZncnmlM18nva5WFU2xAKVNZeJOswIlwhWx63mnph7euRT9gdanZbjhcdJSE9gx7UdRtlm/hb+xAXGUdFawYmiE+JzIkHClMApLBu8jIURC3+WZsowX7c/74nKlkoO5B4gKTeJg7kHjQKNpRIpY3zGMDtUIH1D3If8asT6ubW5PLL3Ec6VnqNN3WaSFJmCXAtDKgTCN7YYxpSAv1GGs/A6VNlIyY/woGPkMJynziN0+lKibRxQapRcKr/El0U/curyy5wsPNljuoYpBNgHMMpnFHFecYz0Hslwz+FGMpNWVSsppSlGbdzrNddNElkvW68ebdzuWXiNHY2cKDxhVNW7WnXVZOSKucycaLdoI3NGjHuMSYJ/KxFGAxjAz8EAAfwNQiqRcsegO/g07VMAdmTu4OXbXhZvF40gJiaCdHUCW5lZ0aHpEITHJr7ze2tddMXCQQuBTvdvV/wh5g8m76PSqvjP2f8A8NS4p3o4SPV6PWv2r0GpVTIjeAYLIxYyI2EGLaoWxvuN5/FRj4vrrj+0nrMlZ7GQWVDfUY9UImX74u0EOwUbbfP5H5/nnfPviP+/POll6trreOXEKwD8fcrfe9WGbU3fypTAKf1u0VW1VnH/7vvFaRzxIfF8Ov/Tmxo99Ho9269u54mDT4iavSVRS3h9xut423kL7t/kzTzz4zNG5NzBwoFmZbNYZVsevZwlg5awds9a5nw9R1xvkv8k3pz1JkPch3Ag9wAP7X2IM8VnAGH27z0x9/D0+KdF529v0Oq0JOUksSllk9EYORcrF5QaJc2qZtrUbcilcu4YdAer41YzyX/SzyI8GZUZfJn+Jdsythm9H33tfBnmMYyGjgbOFJ+hMLNzbNhon9EsG7yMxZGL8bS9ddetSqviWMExErMSScxK5Os7vxYvdLpCp9dxoewCSTlJJOUkcb7svNHtzpbOxIfGMztkNjNDZv5qRPuZ1Zl8kf4Fh/MOk1mT2esFYHe4tAqVvTElAuGLKwWrbt1YrQSyfSypjglGPn4ivrOW4hMzHjeplPr2es4Un+Gr5A3CBWrF5R7TNbrDydKJUd6jxDZunFeckamiTd1GcmkyVyqviG3czJpMk2TP08ZTJHuGNm5XsqfX6ylrLmNv9l4jsmdqxi8In7uuWr2hHkMZ5DJoIHJlAL8aDBDA3ygWRi4UCWBmTSaVLZUiwTBMBEmrTOsxLs7F8qdxcK2dWYC9xTSUNZkeF2SABAkTAwRdmEFTZiY1Q61TI5VIuT3cdDJ/QnoCJU0leNp4smLIih6377y+kwO5BzCXmbM5fjMfXPyAH2/8iKXc0iiP7+srX4u6OsOM5I3TNzItyFjMvuH0Bv556p/i/4vcFtGsambj2Y3C7dM2mBw/p9VpeeaHZ9h4ZiOxnrGc/OPJm7ZuD+Qe4L5d91HZWnlLRo/s2mweTXpUrMSFOIXwzux3mBE8A71eT1JOEo/ue9RoVJOl3BKdXkdDh1AJnh40nVUjVvHqqVdZ8O0CcT07hR0vTXqJNaPWsDtrNw8kPsClciELTiFT8MCwB3hq3FNGc4JNoaGjgS2Xt/DO+XfEk6IECe427lS2VIpuWy9bLx6OfZgHhz/4s3RtJU0lfJXxFQkZCaLbGcBeYc9I75FodBrOlZwjMTtRvC3aLZrl0ctZGrWUQMfAW95nXXsd+3P2k5idyP6c/UYVxj1Ze0QCWN9ez6G8QyTlJrE/Z78YHm5ArGesWOWL84r7xbMjbxU6vY4zRWdIyEjgeMFx8hvyb0q6AKQ6iKzuUt0rhrC6nuvVW0rIDXehdcQQ7KfEEzprORFOHgzS6yloKOB48Wl+2PMAxwqOiSMCe4NCpiDWK5aRniORV8pZOWcloS6h4menTd3G6aLT7M3ey5niM+TU5fQ6Ns7TxlNs3xpIX9eLAa1OS3ZtNkdvHDUyZ/QWueJn72ccueIxDD97v19NFXcAAzCFAQL4G8XUwKmYy8zFL/ODeQe5d8i9AIS7hGMht6BF1UJeXR6hzqHi/cRpIN3GwZlq+xjCkXuDs5UzUomUqtYqUatjaIuM8RljsuKh1WnZcFrQLz455ske4v8WVQt/OvAnAJ4a+xQyqYz1hwVy9tq01whxCgHgWvU1Hkx8EEBsZd8Tcw9PjH7CaHsfXPhA1PcBrIlbQ8GNApH8vTHzDdaOXtvjOFtULdz9/d0kZgkkY3bo7D6NCh2aDp4+8jRvJb8FCBl5Xy366qZt1HZ1O/88+U82nNkgRJPIFDw34TnWj1uPhdyCK1VXWL1vNSeLTor3kUlkWMgtxJbrMI9hrB29lo8ufsTC7QvF9RQSBc9Pep6/jP8L269uJ+b9GK5VXwMELd4jIx5h3Zh1N62SXau+xqbkTXyR/oVYLbKUW2IuM6dR2UhFSwUgvCdXx61mXti8W66CNCmb+O7adyRkJHD0xlHx/WgmNSPOKw6FXMHF8otGFccQpxCWRCzBs9aThxc+fMs6qry6PKHKl53IycKTRuYGDxsP5oXNY17YPNxt3PnXqX+RlJPEmeIzRuvZKeyYETyD2SGzmRUy62dVHH9JqLQq9mXvY/vV7ZwtOUtJU0m/TBv27TCqVCB6Y4thdAnYmeCJuR4KyqIDkI4Zi9esOwkYOYM4mRyNTkN6ZTofZG1lf85+LpRd6HOmrwQJYc5hTPCbwCgfocIX6RqJXCpHrVazc+9OcmpzePv825wrOUduXW6f25vkP4nJAZPFNm7XC482dRsZlRkkZiWKZC+9Mr3XyJVAh0D8HfyJD4lnmOcwhrgP+a+yKAcwgP8tDBDA3ygszSy5LfA2MXrlu2vfiQRQLpUT4x5DSmkKlysuGxPALtNAxHFwmvYeLRMJEvHE3hsGuQwChCzC7jAcS3fsur6LrNosHCwceDj24R63v3L8FUqaSgh0COTp8U8ze9ts2tRtTPKfxKMjhVFwzcpmFm1fRKu6FRtzG1pULYzwGsEHcz8wuiL/KuMrHtn3iPj/yuEr0eq07KkRjA6b4zeL2+yKosYibv/qdtIq01DIFGyZv8Uoaqc7rlZdZfn3y8Vq1WNxj7Fh+oY+q4XlzeWcLzvP2gNrxUif+JB4NsVvItgpmKrWKlbtW8VnqZ8Z3c/W3JZmVTOt6lYCHQJZN2Yd32d+z4pdnZVUS7kla0etZUjjEOot6xn0ziCxYmevsOfxUY/zp1F/6vOkptVp2Zu9l7dT3ubHGz+Kyx0sHGhRtdCuaadd0469wp77ht7HIyMeEd8P/YVaq+ZA7gESMhJIzEo0qkTHuMXgaOnI1eqrnCk5Iy73sfNhadRSlg1exnDP4Wg0mpvOAjZAp9eRUpoitna7uqZBcGfPD5/PtKBp1HfUcyDnAKuTVlPSZHwhFOUaJVb5xvmO+19t+TUpm9h+dTs7r+/kYtlFqlqrbq7h00NorbF2L6oKuntPm80hJ8SJptgobCbPIGTW3YR4BRKCcIF0pvgMrx9cy7GCY2TXZpvUxBngbu3OGN8xTPCbwEjvkQzzGCbq9trV7Ry9cZT3L7xPckkyuXW5NKmaIN30tizllvg7+DPCcwSzQmYxPXi66MitaashtSKVrelbSa0UTBpZtVkmW8KG7dgr7NHqtNS01VDUWERufS7tmnaO3Hukx30GMID/SxgggL9hLI1aKhLAw/mH0eg0op5uqPtQgQCWX2ZJ1BLxPl3nAYvj4NTtPcYzyaXyXtshBkzynwQIRAswmlO8YNCCHuvr9XpePfUqIJCk7sLoa9XXeP3c6wC8Hf82n1z+hBOFJ7A2s2bL/C1IJVL0ej0PJD7A9ZrrYpXT3dqdnUt3GhGuPVl7+MP3nRrEu6PvRoqUdy++iwQJ78a/yyMjH6E7kkuSmf/1fCpbK3GzdmPX0l2M8R1j8vHr9XrePf8ufz78Zzo0HbhaufLp/E+ZEzbH5PoguKVfOf4K/zj5D7Ey42Pnw1uz3uKOQXeg1Cr554l/8vKJl1FqleL9DES3WdWMhdyC5yc8z8mik6zZv0Z8zs1l5qwZuYa/Tfwbn1z6hEdTHqX2ipAJ6GLlwpOjn2R13Oo+TRh17XV8cukT3r3wrtiykyDB3sKeho4Gsd081GMoj8Y9yrLBy3rNeeztOUsuTRZCmq98bZRZGOAQgI+dD/l1+WJAt+HYF0cuZtngZYzzG3dLMRlt6jZ+yP+B3Vm72Zu912jmq0wiY1LAJG4Pu53BboPJqMogKSeJjWc2GrVJLeWWTA2ayuyQ2cSHxv9XWYX/LWpUNWw8s5ED+QfIqMwQjWB9wUol6PUM2r0xxeBiIvmp0EVOcZQfutEjcZ9+ByETFzDcTAiML20q5evsPew5tuemRNPKzIpot2imBk5lvN944rzjxGlFbao2frjxA38+9GfOlZ4jvy5fIHu9HbuZFQEOAcR5xQlkL2g6zlbO6PV6bjTcILUilU3Jm0Sy15tu2dHCEU9bTxQyBW3qNkqbSmlRt3C95nqPdZ0snQh1Du2RozqAAfxfwwAB/A1jbtjczmBnTTvJJcliRIghELr7SLiuFUDxS1ndhkZvrOY2tPf6wuLIxej0Oo4XHgcQTwixnqYDUY/kH+Fi+UUs5ZZGRg4QiMHqfavR6DTcHn474c7hLPlWIK4bp28kyDEIgLeS3+Lba98ilUjp0HSIEzx87HzEbR29cZRF2xeJpPaOQXdgJjXjg0sfIEHCY76P8cCwB3oc39dXvua+Xfeh1CqJdotmz7I9veriqlqreCDxATGCpT9Gj5OFJ1m6Y6lo8JAgYd2Ydbww+QWszazZfnU7jx94nKrWKvE+hpF6LaoWMV+xQ9PBX4/+1ahN+tDwh3hx8ot8cvkTQjaFiNo0Lxsv1o9bz8rhK/skahmVGWxK2URCeoLYGrOQW6DX61FqlTR0NGAuM2dp1FJWx61mlPeoW9I/5dTmsDVjKwnpCUbRHs6WzoQ4hVDeUk5BQ4FIOu0Udtwx6A6WDV7GbYG33VKVrbKlkr3Ze0nMTuRw3mGjVp+dwo74kHjiQ+KxNrfmZOFJNp/fbBQcDcIoujmhc5gdOptJ/pP6FdvTF5qUTZwsPMnRgqOsGbnmpnpLED4T16qv8UXaFxzOP0xWTZbgsL/W153Ar9FYuze0AuTduFq7HLID7agfNgjLiVMJil+Of9Bg/BEqpedKzvHukfWcKDxBdl12r0YRmURGgEMA43zHER8az2if0fjb+yORSGhXt3Mo7xDP/vAsKaUp5NfnG2kru8PazJpAx0BiPWJxbXTlqYVP4WrnikqrIrM6k8sVl3nlxCtiG7e3yBUPGw8cLYSon/qOeipaKqjvqO/hMJZL5US4RBDjHiP+RLtF42XrNaDtG8BvAgME8DcMFysXhnoMFd2+iVmJnQSwl5FwXSuAfvZ+gKBfU+uN2zcWcguj6kx3yCQyYtxjuFR+yShUGjBp7ADE6t/K4St7jEfalrGN44XHsZRb8vqM11mxawXtmnamBU3jkRFCpe5U0SlRD2iI53hn9jtGI+SSS5KZs22O2I6KD4nHUm7JZ2mfIZVI2TJvCw5FDkb71uv1vHjsRXHe8NywuWxbuK3XbLaDuQdZsWuFaPTYMH0Da0au6fWkUddex4qdK9ib05nXF+oUyo4lO8RW/UN7HhLnLoNwctLoNEJG408kv2sbS48euVTOvTH38sptr/DBhQ8I3RwqVugC7AOIt41nw/IN2FjamDwujU5DYlYim1I2cazgmLjcUG00tGQDHALEubymxlr1hurWar65+g0J6QlGIc0WcgsiXCJoVDaSX59PbWmtuHxe2DyWDV5GfGh8v6cfGIjS/vz97M7aTXJJslF1ys/ej/nh8xntM5q69joO5R1iddLqHmHMkwImidl8Yc5h/xUJaFG1cKroFEdvHOVowVEull8UX79I10juH3Z/j/vo9DpOFp5ka8ZWThSeIL8+v8+2KoC5BoaXd2r3xhaDl4n0pnJ7GTcivVCPjMVl2u2ETl0Gz7RUAAAgAElEQVTMkJ/eFw0dDXx95Wv2JT/D5fLLlLeU95r952LlQoxbDLNCZjErZBYRrhHIpXLa1e3sz9nPP078QyB7Dfl9xkjZmNsQ5BBEnHccc0LniFmITcomLpZcZNvRbaz/YT1pVWm9Rq6YSc1wt3HHUm6JUqukoqUClVZFRUtFD/mKl62XQPLcfiJ67tEMchkkjsS8FSg1SrJrsylsLOw153QAA/g1YIAA/saxfPBykQDuyNzBa9NfAyDaPRqpREplayXlzeWiON1Q9atpq+lsAWvae6TrG27rDW7WbkgkEr7P/L7HbQsjFvZYllySzNGCo8ilctaNXWd0W0NHA+sOCcuen/g8u7N2c7r4NLbmtnw872MkEgmVLZUs+XaJ2ObW6DSsGrGKlbErxe1kVGYw/cvpYsVncsBkbMxt2HZlGzKJjG2LtnFH2B0kFXVqxtrV7dy3+z62X90OwJ/H/Jl/TfuXSfdmh6aDZ448w5vJbwI3N3ro9Xo2JW/iqSNPie1cC7kFG6dv5NG4RylpKmHhNwtFBzUIVUGpRGr0enRvtUklUu6KuouXJr/Eh5c+JHxzuHiyHeQyiGfGP8Od4Xdy+OBhky2s2rZaPr70Me9eeFecmyqVSFHIFGK1UYKE2aGzWR23mpnBM/vtZm1Tt7Enaw9fpn/JwbyD4uOQICHcJRytTktOXY74npVL5cwMFgKabw+/vd+ByBqdhlNFp9iVuYtvMr+hIs34hK+QKVBqlQQ5BnFnxJ0k5QqxNV1hCGOeEzaHqYFT/6swZoND9WiBQPjOl57vYb4IcQphsv9kUSup1CjZk72Hb699y9nis5Q2l940dNmjuZPsjSmBEWWg6ObxUEsh28+amiGhmE2YTMCsu/CKGoUngrbzXMk51h97jhOFJ8ipy+lxAWeApdySIMcgxvuOZ2HEQsb7j8fKzIp2dTv7svfxxrk3OF92noKGgj7Jnq25LUGOQYzyHsWc0DlMDZqKlZkV5S3lYpDy1oytpFakGgc/d+nmWplZ4WwpmM4alY00dDSg1ql76DMt5ZYMdhtsVNGLdo8Wv/duBS0qoUV8rfoamdWZXKsRfufV54npCm3Ptg20iQfwq8UAAfyNY0HEAtYfEapi+fX5lDaV4m3njZWZFeHO4WTWZJJakSoSQEMLWKvXIpMIJ/U2dRsqvbHl72YEMNpNID3fZX5ntDzKNcrkmK1/nRZCnf8Q8wex8mjA347+jcrWSsKdw5kbOpeRH48E4PWZr+Pv4I9Gp+Gu7+6ivKVcdD5P8JvAm7PeFLeRW5fLlM+niC2mUd6jsFfY8+21bzGTmvHNnd9wR8QdRuGr5c3lzP96PufLziOXynl/zvs8MLxnaxgEfeKy75b1avRoUjbx7A/PMiN4BreH305GZQYLty80ai0uCF/AlvlbMJOZ8fSRp/nP2f8YkQRDhE5vrk2pRMqCQQt4cdKLfHjxQ6LfjxardEPch/DchOdYGLEQmVRmMmQ2rSKNTSmb2JqxVbyfucwcjVaDTq+jXdOOs6UzDw5/kIdjH+53nIpWp+VYwTESMhL47tp3xiHN9v6Yy8zJq88T9VYSJEwOmMyywctYFLmo3/l4TcomDuYeJDE7kX3Z+4xaelKJ1Ig4Gwh3fn2+ODWnaxjznNA5xLjH/OwqX7u6nbMlZ8UKX0ppSo8qVaBDIJMDJjMlYAqTAyZjb2HPVxlf8crxV7hUfonqtuo+DRsyLcRUdmr3xhZDoAnJX7W1hPwId9pHDMPxttmEzVxOlJ0Ter2e4sZivs9OJGnrS1yuuExlS6XJfRrifIa6D2VW6CyWRi7Fw9aDVlUre7P3su3KNp489CQFDQW9EkYQWuzBjsGM9h4tEmszmZlA+ssvc7r4NO9ceIfL5Zd7ROgYYK+wR6aTITOTUdtei06vo03d1qMNHeQY1KOqF+wYfMvRO3XtdQLBq75GZk3nb8PFUW/HGOkaSW177a92hN8ABjBAAH/jCHEKwdfOl+KmYgD25+znwVghHmWY5zAyawTtTHxoPAAKuUJ0khpOfu2a9h4t4JtprqYGTaWho4Hs2myj5fcNva/Huteqr7Hr+i4kSHhq7FNGt10uvywGNL89621W7l2JUqtkZvBMUaf33A/PcazgGDKJDJVWha+dLzuW7BDbNyVNJUz6dJLYsh7iPgRnS2d2Z+3GXGbOjsU7mBc+z2i/qZWpLPx2ISVNJThZOvHdku+YHDC5x7Hr9Xreu/Ae6w6t69XosSl5Ey8ce4H6jnp2Xd/F9qvb2ZrROXHE29abbxd/y0jvkXya+inrDq0z0i8ZiJ9apzYy0hhgqMb9deJf+ejSR8R+GCuSjdE+o3l+wvPMDp1tksxodBp2X9vNppRNnCg8IS43EGmD2WGMzxhWx63mzsg7+916TatIIyE9gW1XtlHW3JkZ6WLpgqOlIzcaboiTQEAg5csGL2NJ1JJ+x6UUNxaLUS1Hbxw1IlmGSjAI7VNT1TMXKxdmhcxiTugcZgTP+NlhzEqNknMl58QK37mScz3y9HztfJkSOEUkfHKJnC/Sv+CDix+wZv+am2pqndqE+BWDdm9UKVh34/FaCeR6WVAZE4R03AR8Zy7Gb/gUXH8KWj5bcpb3TjzLyaKT5NXlGRmJusLKzIoQxxDG+41nadRSxvuNp03dxu7s3RzMPciHFz+koKGgz5Boe4U9IU4hjPYezdywudwWeBtavZaMqgxSK1LZm72Xv5/8O+mV6Sa3I0GCo6UjcqmcZmWzWLkXnyd153666/QGuw2+pYqtXq+noqWik+B1qeh1NQZ1h7u1OxGuEUS6RAq/XSOJcInAw8ZjQCc4gF89Bgjg7wBLo5by77P/BuCbq990EkCPYWzL2NZjIoirtSvNqmbxhNmuaUepMz5R3CxKYknUEg7nHe6xnqmxbq+dFtrSCwYtIMI1Qlyu0+tYtW8VOr2OpVFLuVxxmZTSFOwV9nx8u9D63Zm5U6zgaPVaLOQW7Lprl2gyqW6tZvJnkylrEQjIIOdBuFq7kpSbhEKmYNddu5gVMsvoeJIbk3n7i7dpVbcS7hzO3uV7xXzBrqhureb+xPtFo8eskFl8Ov9TcXpAYUMh8VvjyazJBASnYV17nUj+5FI5L0x8gWcmPMOJwhOEbw43anHJJXI0eo0R8ev+fE4LnMYz45/hk9RPGLtlrPiaTQmYwvMTn2dKwBSTJ6Kathp2VO7gsXceE/McJUiQSCTo9Doj8vLg8Af5aN5HPbZhCiVNJWzL2EZCeoI43xgEAb+HjQelzaXUtNdQ0y44yKPdolk2eBl3Db6rXxVFvV7P5YrLYlRL9/duV9LXXbZggCGMeU7oHEZ4jfhZYcwqrYqU0hSxwne25GyPwHQJEpZFL+O2gNuYHDCZFlULX6Z/yVvn3mL1vtUmc+bE++ogosZYuzfIhOS20QKyw1xojY3GeuJ0CuRuLFh6L35oSK1I5auC4xz68h+kVaRR12Eiqfmn43S3dmeo51DmhM5hSeQS7C3sSatIY82BNWy7sg2lRtnn8TpYOBDqFMpon9HMC5vHlIApNCobRUNGQkYC6w6v63XkmpnUDFuFLVqdViR4evTUtXces0wiI9wlnMGugzGvN+fOCXcyzGsYvna+/SZbOr2OosYikxU9gz7WFPzs/Yhw6SR4ka4C4fu1TG8ZwAB+DgYI4O8Ad0beKRLAE4UnUGvVmMnMxIkgqRU9ncD59fliq7Fd3S5O0jBAozV9cgXhyzzAIYA/7f+T0fIQp5AeERmFDYVsy9gGwDPjnzG6bcvlLSSXJmNrbsvDsQ8za6tA1N6c9SY+dj7k1OZw3+77jO9z+xaGew4HBO3gbZ/fJpKqQIdA3KzdOJJ/BEu5JYnLEo2mguj1ev599t/868a/0KNnWtA0tt+5HUdLxx6PsavRw1xmzoZpG1gzao0YRfPsD8+y4cwG8WRnLjM3aklOCZjCV4u+olHZyPQvp3O04Kh4m8HNq9FreiV+UomUJ0Y/QX59PlO/nCounx06m+cmPGdyNBkIFdVNKZvYlrFNrP4YSJMevWiekUlkLI5azOMjH2e0z2iT2zKgsaOR7zK/IyE9gWMFx8RjlUvleNt6U91WTau6VXwdgh2DRdIX5RbV57ZBqK6Jo9eyE3vougzPF5gmfVZSK2aFzWJu2FziQ+N7zHLtD9RaNRfKLogVvtNFp/skRCDEhRiCzVftW9WnYcO2A0aWdmr3xhSDg4niXL67OaWD/WHMGLxmLCJw7GyGSyRk1mRyrugcCae/ZP07r1DUWNTrRZq5zJxQp1Am+k9kUcQihnsOJ6s2i0vll7hYdpFPLn/ClaorJp9LCRIcLBwIcw5jjM8Y5oXPY6LfRIqbisVpGZtSNnF/4v09XicDLOQWYtyK4TlR69RGZM/d2r1HVS/CNQILuQVqtZqkpCRmh8zuNdxbo9OQV5dnRPCuVV/jes31XquWUomUYMfgHhW9QS6DsDE3bZQyhcaORppVzUbJAwMYwK8RAwTwd4A47zjsFHY0KZtQ6VScLj7N5IDJohM4ty6XJmUTdgo7oNMJbCB5pghgX+OivO280ev1RqQG4N6YnuHP/z7zbzQ6DVMDpxLnHScur2mrESd0/G3i31h/eD0qrYo5oXNYMWQFrapWFm1fRJOySSQA68euZ1n0MkDQLc78ciZXqq8Ix2TrjYeNByeKhNzAfcv3MSlgkrg/pUbJI/seEYOVHxn+CG/PfrtHq1upUfL0kadFo0ekayRfLfqKGPcYAFLLU5n71Vwxb0yCxOj5crZ05ss7vmSUzyiePvI0H1/6uMeJurubtyskSJgePB2lRinOS5YgYWHEQmaHziYxKxF7hXGOn1qrZuf1nbyd/Dani0+Ly6VI0aEzOtF72XrxxKgn+OOwP/YZBK3SqoSQ5nQhpLlrK9HL1otmZTPNqmaxxett6y0ENEcvI9Yz9qYVm9q2WpJykkjMTuRA7gEjE0H3NripipIhjHlG4AwarzRy+5zbb2kSiEan4VL5JbHCd6roVJ/aNgAnCyfRqdqobKS2vZZvrn7Tc0U9BNcZa/cGV4GsG19rNYPsEAcah0ViPWk6wfF3E+gTgrypmJTSFHaXnODYJy+TUZnR4/PZFY4WjsR6xhIfGs9E/4m0qdtIrUjlYvlF1h5cS2Z1pkldqbOlMzq9Dku5JTOCZ3Df0PsY7T2a67XXSa1IJbUilZePv0xqRWqv7WsrMyv0er0RWe7QdIjVUoVMQZRblKjVi3aPJtot+qZzsbtuK7s2u0dFr6/gaTOpGeEu4T0qeqHOof2WN7Sr28mrzyO7Npuc2hyya7PJrhP+rmytZFbILPbfvb9f2xrAAP63MEAAfweQSqTMCZ3DV1eEQOYd13YwOWAyzlbOoj4wrSKNCf4TgE4nsOELtE3T1kMD2Jf2Z5jHMK5WX+2R6XXX4LuM/q9qreLjyx8DPat/zxx5hrr2OqLdomnXtHOx/CKOFo58OO9DAB7Z9wgZVRki+ZsZPJNXpwoxMkqNknnb5pFSlgKAm5UbXrZenC05i625Lfvv3m8UDVPTVsPCbxZysugkUomUB7we4O1ZPcnfteprLP9uuRjH8mjco2ycvhFLM0vUWjUr96zki7QvOitgP7VwDa/B2lFreXnKy7x34T0WbV900wpSV0iQMMFvAkqtUpysIpPIuGvwXcR5xfHVla94IFHQRDpZOrFl/haqWqv46OJHvHfhPSNCajg+Qw6iBAmzQmbxxOgnmBo0tdcgZb1ez7mScySkJ/DN1W+MYoAM1a5GZaOo93O2dBYCmqOXMd5v/E0DmnPrcsXW7qmiU0akpGuVz1Rly0JmwbTgaWJMiyFHT61Wk3T15pNAtDotqRWpHC04yrGCY5woPNFnJh0IhM/CzIImZRMtqhbqOupMtlkt1IIb16DdG1sMbiY+PsVOcoqivNGMGon7tAWETFmIv7aN86XnOVmawsljj3K+9Hyf4c4SJKK5ZKL/RGzMbShsLORi+UU+uvQRfz70Z5PPn7u1O7FesQz3GM5wz+HEesVir7AnoypDdOI+cfAJrlZfNXnxZ3CJK7VKI0Le9XvC397fqKIX4x5DqHOoGE7fF5qVzVyvuU5mTSYZlRmcyD/Bn9/7M/kN+b06o63MrIhwiehR0QtyDOrXPjU6DQUNBQK5MxC9OuHv4sbiPmUw9e31vd7WG/R6PQ0dDSY7DgMYwP8EftcE8NVXX+X777/n+vXrWFpaMnbsWF577TXCw8P/tw+tB/R6/X8lKr47+m6RAO66vovNszcDwsSG4qZiLldcFgmgwQms1AhVHVMVwDZN7wRwZshMvr36rdEyXztfo5FzAG8nv02HpoMRXiO4LfA2cfm5knMiMXxyzJM8tOchADbFb8LL1ov3L7xPQnoCIFR/QpxC+GrRV8ikMjQ6DUu+XcKPBcJ4MgcLB7xsvThfdh57hT0H/nDAqKV5rfoac7fN5UbDDewUdmy7Yxua68atr+5GDxcrFz6d/6mY8XU47zBLdiwRNURiZe0n8hfrGcuOxTtIq0wjZFOIUQaZKVNHd4zyHkW7up0TRYJJw1xmzh+iBbf0F+lfiJpChUzBvUPuZWbwTFbsWsHXV74WT9YGAtV1XzYyGx4f/TirR6426cw2ILs2m63pW0nISBBHxoGg6zO0tg3tO1tzW+6IEAKaDQ7P3qDVaUkuTRZJn0Eraeq5MXWS7xrGPDlgcr+rN4btpVemc/TGUY4VHuN4wfGbmjAcLBwwl5rTrBIMCXUdddDRcz2fRmPt3rAKMOt2+EoZZAXaUj8kHMXEKQTGL8clIJziistcLk3hTPEuzmx+qtfJFQYoZAqGew5ngt8E7MztOJtxFomjhBNFJ9iSusXkfbxtvQWS5xnLcM/homQirTKN1IpUtl3ZxlNHnuoRfm2AXCpHgsSowmZwiYOQ4de1ohfjHsNgt8E4WDiY3F5X1LbVklmT2aOiZzCxmYKDhYOxNu+n3772vje96NDpdZQ2lZJTl9NJ9H76O78+v1cdqWG/Yc5hhDmHEeoUSphzGN623mK81obTG4hwiTAymGl0Goobi8mvzyevPs/4d10ejcpG2p5t+6+DxQcwgP7gd00Ajx8/zqOPPkpcXBwajYbnnnuOGTNmcO3aNayt+z++6n8Ser2eDy5+wP7c/excuvOWxlx1xdSgqaKbtLS5lKLGIvzs/RjmMYw92XuMdIAGAmj4Qu/QdPSYBNJd8N4ViyMXM2HLBKNlf4jpHLtW0FDAjqs72JwikNBnxj8jkluNTsOqfasAuCfmHt5Kfgu1Ts2CQQtYHr2clNIU/nSgU1toY27D7rt242jpiE6v44+7/0hidqJ4m7etN6mVqThaOHLonkOM8Boh3vdA7gGW7lhKk7KJIMcg9izbQ6hDKEnXOytG1a3VPJD4AHuyhfnAM4Nn8tmCz/Cw8aBN3caibxZxIO+AuL4EiVhZszW35cN5HxLhEsGi7Yu4VHHJaD1T2r6uGOo+VJjg8lNIsqXcknti7sHSzJKE9ASxAudk6cTDwx/G38Gfz9M+56NLnYYNUyHRY33H8sTIJ5DmSpk3aV6P1qherycpN4ms6iy+ufYNKaUp4m1mUjNszG2o76inVd1Kq7oVC7kFc8PmCgHNIfF9nrxaVa0cyT9CYlYie3P2Gk016Y7uz41cImdy4OSfFcas0+vIqMzgWMExscrXffJDd9gr7JFJZDSrmlHr1CZNAmYaYZKGQbs3thh8TQygqLCTciPCE+XIWFymziXotjuRtJWQW5rC2ZKznDy4lNy63Jvm/Pnb+xPsFEyrshVLM0tq22vJqMrgbMnZzpVqjdfvSvai3aKpaq0iuy6b1IpUNp/fTGpFaq+vQ1dTjQGG/6USKaFOoUYVvRj3GPwd/Pv8ntLr9ZS3lBuRPAPR6+v94G7tTqRrJOFO4egqdSyatIgYzxjcrd37fB/o9Xpq2mqMyJ3h75zanD4r8ZZyS0KdBXIX5hRGiFMIHjYeSCVSatpqyKvPI68+j0N5h3j3/Ls9HMPDPIaxP3e/sF5dHoWNhX2SShBmjYe7/PqKEAP47eF3TQAPHDhg9P+nn36Km5sbFy9eZOLEif9LR2WMkqYS1h1aR5u6jTfPvcmTY578WduxkFswzm+cONFhT9YeHh35qDgSrqub0qABbFUJmqc2dc8WcG9ZdJZyS8xl5lyvNZ6heXf03eLfb517S9TQDXIZZDQX+L3z75FaIRA2d2t3vqz4EmdLZ96f8z617bUs/naxUQsq4Y4EIl0j0ev1rElaI1YGLeWWeNt6c7X6Ks6Wzhy594hoetHr9WxO2czag2vR6XVM8JvA90u/x8XKxSgf71DeIVbsWkFFSwXmMnNem/Yaj496HKlEyhdpX/DI3kfEk0dXQidBwoqhK/jrxL/y9OGn+TbTuBoKfbuoI10jaVW1imP6bM1tWTZ4Ge2adj5P+1zU2wU6BPLg8AdpU7ex5fIWcYScqf1Ym1nz4PAHeWzkY4Q4hQit0Tzj1mh6ZTovHX+JA7kHjFp3XWf9qnVq6jvqkUvlzAieIQY0G/SjplDRUsHe7L3sztrNkfwjfV48dIe3rTezQwXCdythzHq9nsyaTH7I+4Gvb3zN/W/eb2QyMAVbc1skEgmtqla0eq3JiqBri7F2b0QZWHY7n2ukkO1jRfWQEMzGTcQ3filaH29Kyi9wrvQcxws+4Mpba3qNYDHAzcqNwW6DsTG3oa6jjtKmUsqay4yic7pDipQJfhN4buJzhDqHUtZcRlpFGmmVaXx8+WNxlJ7J+3Zps4uP5Sey4mzpzBCPIUZEL9I1Eiszq163p9PrKGwoNBmt0le11c/er0dFr6vj1mACmRIwxejipUnZJOrxuhO9vhy+cqmcYMdggej9RPKcrJyEUOmORrFKdzDvIO+cf+emleKuuFxxuYdTXSFTEOgYSLBjMEGOQZ2/nYIJcAjo8zntDf9th2gAv0/8rglgdzQ2Ch9sJyfT1n6lUolS2fml3dQkXOqr1WqTwbq/BDysPPjPtP+wav8qnvnhGSb4TmCo+9Cfta27B98tEsCE9AQeGvYQUc6CC/Nq1VVaO1oxl5njqBA0KAbhvalZwL3Bz86PgzkHjU4k7tbuhDqEolYLVZSuFao/j/4zWo0WLVoqWip4/ujzADw07CH+kyyYHN6a+Rb2Zvbc/s3tRuGrf5vwN2YHz0atVvO3Y3/j3QvvAkKL1NPGk6zaLFytXDmw/ABRzlHC66RV8+ThJ/ng0geAYEx5Z9Y7KOQK8XVU69Q8efBJNl8UKpQRLhF8Mf8LhrgPobyxnPnfzOdixUWjx20gWqFOoXw5/0t2Ze1i0OZBNx3V1RXBjsG0qdq4Vi0Mc3W0cGRB+ALKW8r56NJH4j5GeI7g9rDbyazJ5MVjL/a5jyjXKNaNXsedEZ35fV3fr6X1pWxM2cj2a9vFaBYDFDIFKq0KPYI2SYKEiX4TWRq1lAXhC4ymJ3R9/+v1eq5WX2Vvzl725uwVtZj9gQQJY3zGEB8Sz6zgWcS4GYcx9/Y50+v1ZNdlc6LwBMcKj3Gs8FivQcIG2JjZoENHu7odPfoemj+pTjBndNXuhZgoGtZZScgJd6V1RAz2k2bhNH4m+e0FpJSlcLzwOJf3fX5TPaGtmS2hTqFYmlnSqGykqrWK2vZaUcpg8vjNbQiwD2Co+1CGuA9BIVVwJv0MHRYdrNq3ynhqRj+g0+swk5oR4RIhZulFuwmmDA9r07l2hs9UXn0e12sFjZ5Bq9fXjGCpREqQYxARLhEMch4kkDyXCMKdw3t13KrVajo0HWRVZ3Gu4Rzpp9K50XhDqOTV5fSZ1ydBgp+9H6FOoYQ4hhDkEIS9pT0SJLSqWylsKCS3PpcDuQfIb8i/pYsUU3C2dCbIMYgghyACHQSyF+gQSJBjEF62Xn1WSA3v8a6/NToNZc1lFDUWUdhUSHFjMUWNRRQ3FVPYWEh5SznlT5T3S9v4a0T3x/z/c5+/Z0j0htyH3zn0ej3z58+nvr6ekydPmlznxRdf5KWXXuqxfNu2bVhZ3fpV260c278K/kVyYzI+Ch/+E/4fFNJbHy/UpGni3iudTtyPIj7CxdyFe67cQ4u2hdfDXifIKojs1myeynkKO5kdTdomAiwCkEgk3Gi/cdN9THKYRIe+g+TGztmu81zm8YCPYFDYVbWLz8o+A8DFzIX3It7DTCpcxb9R+AbH648TbBGMGjVFHUWMtR/L+oD1fF3xNd9UdjoqR9uP5qmAp5BKpOys3Mnn5Z8DQgXE2cyZanU1jnJHXg55GV8LXwBaNC1sLNhIWksaEiTc63UvC1wXGJ3YijuK+U/BfyjoKAAg3iWe+7zuQyFV8H3l92wt34qWntVPGTIe8H4AhVTBltIttOr6dox2hauZK0qdkiatcEFhL7NnmN0wSjtKyWnPEdeLtY0lyCqItOY0stuye9sccomcCQ4TmO82nwDLgB63d2g7SKpJ4lDtISpUxiPSTGkSQ61CmeAwgXEO43A2N+0M1ug1ZLZkktyYzPmm81Sqej8Zd4et1JZY+1hi7WIZajsUW/nNq3x6vZ4KVQVXWq6Q0ZJBenM6DZreqzwACokCHboe1WwDHNqFoGUD2RtVCrbd/A46INvdjPxgN+rCI9AMjqPczYac9lyutlwluy37pschQ4aruSsKiYIOfQfNmmbadL1raqVIsZPb4aXwIsQqBC+FF3q9nlJlKTfab1DQUUCrtv/vNwOczZwJsAzA38Iff0t/AiwC8LbwRi4xTSJUOhWlylJKOkoo7igWfiuLKVeW93qBKJfI8VJ44Wvhi4/CR/ht4YO3wlv83HeHVq+lSlVFmbLM+KejjBp1TZ/Vcwe5A14KL7wUXriZu2EptUQqkaLUKalWV1OuLKdCWUGVqkqUavwcSJHiau6Kh7kHHgoP3M3d8VB44GHugbvCHWvZragTKHUAACAASURBVEmIOrQdVKurqVb99PPT31WqKmpUNdSqa296vB9FfoSref9ncf/e0dbWxvLly2lsbMTOrvcOxm8ZAwTwJzz66KPs27ePU6dO4eNjOr/JVAXQ19eX8vJynJ17j8z4JVDTVkPsx7GUt5QLESWz3v5Z2xn64VCu1QgVpidGPsFr015jxtYZHCs8xkdzPmLFkBXk1+cz6L1B4jSIYIdgVB0qijt6F2IbkLAggcf2P2bkVEy+P5lhHsNQa9WEvRNGaYsgbH99+us8FvcYAMcLjzN963ShfRqzgs/SP8PVypXUlalcKL/A/O3zxe1FuUZxcsVJbMxt+Pjyx6zevxoQqgoe1h6UtZThbevNweUHCXMOAyCnLoc7tt9Bdl021mbWfD7/c24Pu13cpl6v58NLH7L+h/WC0cPShQ/nfsjc0Lnk1uUy9+u55Dd0GiC6Y5TXKGo7ansVzpuCp40nrepWceqHj60Po71Hc7HiIjcaBLJtLjNn4aCFOCgc2Jm1s88qh7+9P+tGr+PuwXf3aJXq9Dp2ZO7gjeQ3uFxxuUerr3v7L8o1iqWRS1kSuYQgxyCT+2vsaORg/kH25uzlQN6BPtts3THcYzjxIfHEB8cT6xnbrzDmgoYCjhce51jhMX4s+NFky7srLOQWaHVakxVSiQ7Cao21e1EmCoZNCsgJcaJxeBRWE6aiiYvlqrqUU0WnOFNyhhsNN/okJFKkuFq7YiGzoE3TRpOyqc/Wr4XcAi8bL6JcoxjqMRQfOx9qWmvIqM4grTKN7NrsXuUXvcHKzIrBroOFip5rtFjd6y3EuFnZLFbzMmsyuV57nes117nRcKNXjaK1mTXhzkK0yiCXQWJlrzfHrU6vo6y5TKze5dblCjEqdTncaLjRp07OXmGPm8yN4f7D8Xfwx9rcGplERrumndLmUvLq8shvyL+pgeZmMMwoNlTughyCxP/97PxuOg3JAL1eT3VbNUWNRRQ1FRlV7gx/d3XU9wYzqRm+dr742vniZ++Hr50v/vb+4t/9dTf/GqFWqzl8+DDTp0+/pbim/wZNTU24uLj8rgng/813yy+MNWvWkJiYyIkTJ3olfwAKhQKFomflzczM7H/8Tetp78nnCz5nRsIM3r/0PnPC54gu1FvBPTH38MyPQuTKzuydvB7/OsM8h3Gs8Bjp1emYmZnhZS/MrjRo7Tq0Heh0/btajvGIMSJ/TpZOxPnEIZFI2HF9h0j+nC2deWjEQ5iZmaHSqvjTIcHYsTBiIV9mfAnAe3PeQ4WKFbtXiNtztHAUTB/Wjnyd8bVI/iRIcLVypaylDD97P36890eCnYIBOFZwjIXfLKS+ox5fO18SlyWKekAQjB4P7nmQxCzBPDLUdii7/7gbXwdf1h5Yy6aUTb2e5B0tHPGy9SK5LNnk7abgauVKm7pNJDABDgFEu0VzpvgMO67vAMBB4cCCQQtoVAohy721eWUSGfPC5rF+3HrG+Izp0aY7W3yWl0+8zNEbR3sQj+4u2yDHIDGgebDbYJP7K2woZE/2HhKzhNFr/ZUG2JrbMjNkJnNC5zArZFa/wpiLG4vF4OUj+Ud6DRY2QCFToNFpRILUtY1nrRSClg1kb3QJOJvQ/t9wNaM0yg/t6FFIx42jzMeB0+XnOF54nMyKf6DeffO2kZWZFTKJjBZVCzp0vZJ2O4UdgQ6BDPUYSqRrJI4WjuTU5ZBakUpyWTJ7cvbcdF9dIUGCh7kHIwNGMtRzqKjVC3IMMtlyrG2r7TEN41r1tT6fZ0cLR5Ojz0w5bg3mC1MO2/6aL0IcQ/Cx88HG3AaJREK7up2ypjIuFlzkSMGRfhGnvuBt602wUzctnmMwwU7BOFs690tXp9KqKG0qFQldYUNh598//e5PS9lOYScSOn97f3xsfajNr2X+pPkEOQfhYePxs6bW/F/C/49zadd9/d7xuyaAer2eNWvWsHPnTo4dO0ZgYP+G2/9vYXrwdJ4c/SSvn3ud+3ffT/qq9FuearA4arFIAAsaCtDoNGIgtEGsbGNuI2Z6geAGlvfjrWJrbsvurN1GyxYOWohEIhGmbJz5t7j8T6P+hLW50CZ589ybXKu+houlCxlVGWj1WpYNXsacsDmM3zJeJJRSiZTti7cT7BTMvux9LP9+ubg9ZytnKlsrCXAI4OiKo+LEkY8vfcyqfavQ6DSM9B7J7rt2Gz1nh/MOc++ue0Wjxz+n/JOg6iCKGoqI+ySuV1eiBAnRbtFkVGXc1E1qgJOFE63qVlGbFuoUip+9H6eKToni/AD7ACb4T+Bq1VU+S/us1225WrnyxOgnWBm70kiLBwJJe+n4S+y8vrNHVa4r6dOjx8vGi6WDl4p5gt1PeHq9novlF8WoFkMGYn8Q6RLJnLA5zAmdw1jfsTetmJQ1l4nBy4fzDxvpPU3BXGaOWqsWH49IcPUQ0GCs3RtS2TNouc0MsoPsaRgWAWPGUD9kEJd1Jfx440fSKhNpubgNLnLL6K57kyDBxcqFcJdwhroPJdgxGK1ey5XqK6RVpPHVla/6DFY3BQcLBzFqxUD0whzDOHHkBLNnd07IMDhuRRNGF6LXl0bSw8aj04DRZfSZKcdtk7KJy+WXTRK9m5kvghyDCHUKxcPGAxtzG6QSKW3qNqpaq8ivz+dg3sGbBnD3BXOpOcFOAqELcggyInuBjoH9ig5qUjZR2GBM6AobC8VlZc1lN41yAqHi7+/gb0Ty/Oz9xGX/j73zjo+iTv/4e2a2Zlt6D6GHEjpSROzlBCn2drbTu7OcnnqW06uWn+X0PK+oh57l1MOGcHDSQUVAiiCCAUILpPdeNlvn98ewk2yyu9mQhCL7gX3tzHenbnZ3PvN5nufz2AwdDNxdLpbVLmNK+pQIWYmgT3BaE8B77rmH+fPns3jxYiwWC2VlSj6UzWbDaDw5fZieueAZ1h5ey87yndy2+DaW3rC0W9Ywg2IHkWRKUlWJhXsXqpXAO8t24pW9iIIStvIpAS2uFkxC1zktg2IG8fHuj/3G7hiv9B1eX7BetUExaU38YpIS+i2sL+SJdUpe5aS0SSw7uIwkUxJ/v/Tv3Lf8PraXtl2BX7zoRS4ceCHrjqxjzodz1B/dWGMsVS1VDIoZxBe3fEGGLQOP18Mjqx/hpc0vAYoJ9Vuz31ItShxuB4+vfVx9fUTCCOZfMZ/BtsFc8volbNzZ1jGjIwbYBlDaXMquil1dvie+83V4HKpJcFZcFjaDjW+Kv+FAjZLjNyZpDINiBrGhcAPv7Xov4HYEBM7JPIfHpz/eybC50dHIC1+/wDvfvdPJM60j6YszxnHFsCvo19CPh65+CIPe/yLY6m7li8NfqK3XfMbOXcFnxjxzyEwuHXypasbccdubCjfx+eHPWXloJdP7TafJ2cTqvNVq2DsYOlqS+EiT3gUTStvI3tQiSGnqvH5RjETB8DTsk8bROCGbA+lRfFG8gW0l26is3QxfhnWaISEJEqmWVLITs8lOyCbBnEBNS41K9jYUbOjW9jSihqy4LL+2aKOTRpNmSfMjYl7Zy8Gqg2yr30bu5lz21exTiZ4vxSAQMm2ZfhW3g2MHo5W0FDcUs6dyD3uq9jBZP5mpGVM5VHOITYWbOhG99r6WHSEgkGHLYHDsYJJMSX7h2srmSvJq81idt7rbBLg9EqIS1CpaH8nzqXkplpSQv49e2Ut5U3lI9S6c1Aa9pKefrZ9K7DKj/UleujUdvab7edsRRNCXOK0J4GuvvQbAueee6zf+9ttvc+uttx7/AwoDeo2e+VfOZ8LrE1hxcAX/2PoP7pt8X7e2ceXwK9WK2Xnb5rHyppXoJT2NzkbyavMYHDuYhKg2Atjqbg3rx+uMtDN4a0eb+axVZ2VS2iQAtW0ZwF0T71Ld7u9feT8trhZGJ41W/fTmXTaPJfuW+FUL3zT6Ju6fcj/bSrZx8fsXq2E+m95Gjb2GrLgs1t68ljRrGo2ORm5YeAOf7f8MgD+e80d+f87v1Qvm3sq93LDwBtX78O6Jd/PCxS+wJm8NZ719ll/bsfaw6CxIgsTh+q6LYUDJ53K6naqCMTx+uLL/dobH0zKmoZf0fFXwVVB1zaq3cueEO7lv8n1+hs1ur5t3vnuHv275K7srdgdVIWRkzDozlw9TDJovHHgheGHZsmVqSKmqpYplB5axeN9iVhxYEdLouz0GRA/gsqGXMXPITM7pf04nRcXXQ9dH+DYXbfYLZ39T8k3QbUuC5Jfv5iN/KQ3+uXsTSkDXIUPBKcH+fiYqxwxhT2I08kWT2Sjks6lwE/n1i+HQYuhekWwn+Hpej0kaw+DYwegkHaVNpXxf8T1f5X/F8oPdawWWbE7upOoNix/m993zVdwuyl3kZ6uSW5XbFlLt8PGUBIlBsYP8iN6A6AEIgsCRuiOqKrjswDIO1BzolGO4dP9SbvnvLSFVriRTEgNjBpJoSlSVPLvbTlVzFYfrDvPF4S/CUskCQRIk+kf3V5W7/rb+1ObVctUFVzE0YWhICyKH26Hk2wUgdvl1+RQ2FIZFPmMMMSHVuwRTwjF7tEYQwYnCaU0AT9X6lxEJI/jzxX/mnmX38MjqR3B5XeTV5PHKzFfCWv+WsbeoBPDrwq/RiBpGJY1iW8k2dpTuUAigyb+aLJzE8wxrht9yM4fORBAE9lfvV/PrtKKWB6Y+AChGzAv3LkRE8dvyyl5uGn0TmdGZXLvgWnU7E1MnMu+yeeyt2ss575yj/mCbtWbqHfWMSBjB2pvXkmxOJr8un1kfzOL7iu8xaAy8M+cdrs1WtuUz1X5w5YPY3Xbio+J5c/abnNv/XGb+ZyZf5n8Z8Lx8+YUVLcFNatvDVzzjy/sZHj+cBkeDSvy0opap6VOpslf59ebtiPHJ43l8+uPMzpqthk9lWWbt4bU8/dXTbCzc2KWp7OXDLufGUTcyY8gMP4Nml9dFcWsxL21+iaUHl7KhYENYF2iNoOGc/ucwc8hMZg6dyZDYIZ2UqJ1lO/n88OesOLSCDQUbwrbU8HVQ8cEje9B4YExZG9mbWgj9A9iwVZgF8oYlUzM2i/LRg9ia4uGriq3sq1JSCtj9ZVjHEAwGyUBWfBajEkeRaErE7XVT1FDEropdLNi7oHvb0hgYmTDST9EblTjK7ztnd9nZX72fhXsX+uXo7a/eH/RvrpN0pGhTmDRgEiMTRzIiYQSZ0Zm4PW4O1h5kT+UecipymP/9fArqC8ImZL4bGKvOysCYgSSZkzBpTW0kr6WKI3VH/M2ouwmLzsLg2MF+OXi+6Qxbhl9xg8vlYlnNMkYnjqbZ08x3Zd+phE4Nzx4leaHUSR9EQSTNkuZH6NqTvH62fmH7T0YQwamE05oAnsq4a+JdLDuwjKUHlvLQqocAOLf/uVw98uou152YOhGDxqA0Zfe0crj2MOOSx7GtZBvflX3H1SOv7pRXFg4B3Fnmr2DdOeFOZFnm5//7uTp269hbSbWk0upu5RfLlDDwuJRxbC/dToo5hSfOfYIL3r1AzedKMiWx6NpFlDaVcuabZ6r5VUaNkSZXE6OTRrPmpjUkmBLYVLiJuR/NpaK5gmRzMouvW6wqkFUtVdy+5HaViF486GLemfMOS/YtIeGFhKAqQJwhjurW6rDIny9E6dtWVlwWZU1lKvGz6W2MTBxJblWu2tatIwwaAzePvpmHznzIr3VeblUuf/jyDyzdv7TLnKhYQyyzs2bz+3N+z4CYtrxWj9fD5qLNLNm3hP/m/pf9NfshN8SGjiLZnMysobOYOWQm5w843+9iKMsyeyv3qoRv3ZF1XXre+dDRcsaLl7hmf7I3qRiiOvAdjwAH0gyUZPenfPRAdg+xsdp7kO8rc7C7v4SaLyG053NImLQmRiWNYmjcUExaEy3OFg7XHWZn+c5u5UCCUuDTUdUbHDtYVV4bHA3kVuWy9MBSP0UvrzYvKEEzaU0MTxjOgOgBmHVmZGQuG3IZcYY4Fny5AI1Fw1cFX/HattdCVo0Hgl7U0z+6P0nmJL/Ciyq7QvK+K/8OurdJFemWdL/wbHuSF2uM7ZRb6PF6KG0qZUvRFr+8u/y6fHYX76Z2T21YnzWjxhhSvUu1pIZd0RsMsixTY6+huFEx6/aZdvvm35j1BknmpB7tI4IIehsRAniKQhAE3przFqNeG6UWKty97G7O6X8OiabEkOuKgsjFgy5WydDftvytUyGIrx2czx6kKwIYY4jxU9CitFFM6zeNhXsX+o0/Mu0RAJ7f8DyHag8RZ4zj21IlN3DeZfP45YpfqrlgWlHLp9d8ioDAlH9NUR349ZIeu9vOuORxrL5pNXFRcfxn13+4fcntODwOxiaPZcl1S8iwKf5/HQs9nrvgOa4acRWXvH8J31d8H/B8DJIBl9dFdWvXVYa+UKXb60ZAYGDMQAobCtlXvQ9Qkr+TzcnsKt/F14VfB9zGkNghPHbWY1w/6no1jFrdUs3/ffV/zM+Z3+WF3KKzcMmgS/j9Ob9nVNIodbzZ2cyqQ6tYsn8J/9v3v7CqJgUEzsw4U1X5RiWOUi/OsiyTV5unhHQPrmTt4bVhF8F02o9XZmSlf9/coQGIW60RDgyJo3hUJnnDklkb18CWhj3UtOYCudADt49ovVJMkWZVfOnqHfUcqD7A1qKtbC7aHPZ2LDpLpzy97MRsNTxZ1VLFnso9fHnkS17b9pqq6HVVcTsiYQRZcVmqZUtFcwV7Kvewv3o/20q2qcu+8907bSt24dYkIhIfFY9RY0QWZGRZVuxmWqooqC9gX80+9tXsC/vcfTBIhs4VtUfn+0f375Qe0OJqobC+kO2l2wMWWRQ1FHWpcIPyW+UjdP2sHVS86Mywq3mDocXVEpDUdSR7oex98uvzIwQwgpMOEQJ4CiPRlMg7c95hxvwZgHKRuXvp3Xxy9Sdd/uD9dNxPVQL48e6PWXCNEsbqSAAlQcIre7s0ITVpTRQ1tl3MLhp4EZIo8eDKttZ1lw25jMGxgzlUc4hnNzwLgFbSIiNz29jbyKnIUXvuArwy4xWy4rMYN2+cWrGoE3U4PA7OSD2DlT9eic1g43ef/46n1z8NwJysObx/xfuYdWYcbge/+fw3av7h8Pjh/OeK//BhzodkvpwZUGEREJCQaPV0HbZsT44lQSLNkkZhQ6HagcFXiXyk7khAzzqtqOXyYZfz+PTHGZM8BlBylv6y6S+8tu01tUAkGIwaI+f2P5ffTP8N0/pNU8dLGkv4bP9nLNm3hNWHVuP0hpfjdNnQy5gxZAYXD7rYzyOuuKGYL458wcqDK1mVtypkv9ZQsLbC5KK2/L0pRWALcM08mKTl8IhU8oensClDYJWUT1FLCVANHuDYdk9iVCLDE4YTZ1Q8O6vsVeRWBldjA0EURIbGDe2k6vWz9QOU935P5R62lWzj3Z3vquHbqpaqoNtMMaeo1ipp1jQkQaKsqYxdFbvYXbGbTUWbuuwRHAgWnQWTzoSAQKu7FbvLrlg64e2kaHcsHAqGhKgEv1Bte5KXYk7xu1GotlcrreAq97Li4IpO1bNddWoBRVVPt6a3FVfYMkkzp1G2r4wrL7ySQfGDjql1Gig5peVN5Z3IXEeC1x1/yzhjHGnWNNIsaaRaUtVn3+cjgghOJkQI4CmOS4dcyn2T7uNvWxVj6E/3fsrHuz9W896C4aJBF6kEpqSphOHxwxEQKGsqo6ypTM1H8iU2d5UvWdbsn2tz98S72VW+i4KGNiuPp85/ClmWuW/FfTg8DjKsGRQ2FJJuTWdO1hwu/+hyddm7Jt7FtdnXMmHeBFUl0YganF4nU9OnsvzG5WglLdcuuJYFexTy+siZj/Dshc8iCiK5Vblc/+n1aqHHXRPv4tYxt3Lx+xcHvRj7rG/chFYdfKFLr+xFI2qIMyoWNL5zHRwzmIqWiqB9V1PNqTx05kPcMf4OLHoLDreDRXsX8fzG59lWsi2k2qoVtZyZcSaPTHuEGUMU4i/LMrvKd7Fk3xIW5y5mW+m2oOu3x0DDQK6feD2zsmYxMXWiX0HIgj0LWHlwJSsOrejSfy8gZBhS7d83d2QFdEyTb9LB3oFWDg9P5ruBUayMq2OHMx+ZfCAfQrfLDYpUcyqDYgdh1popLS/FbXSzv3o/6/LXhb2N+Kh4xiSN8VP1hscPR6/RtxVPVO5lYe5C9lYqpsmhKm77R/dXizCGxA7BqDFS3FSs5t5uKNjQ7WpYvaTHqDEiiiIuj4sWZ4vaqabR2Rh2ON4HX8FFIJI3IHqAGv53e92q992RuiOsO7KuU5FFsDZw7WHWmduqZo+qd+0raVPMKZ2871wuF8tKlzEsflhAexRZVloXFjcWh1TtypvLwybWUdqoNlJnTSPVfPS5HclLsaSEZSsTQQQnCyIE8AeA5y96ns+PfE5ORQ4Ady+9m3P7nxsy5KDX6BmfPF4lC8sPLicrPovcqly+K/tOVQB96EoBbB+q0Ut6zh94PtPfmq6OTU2fytjksSzau4hlB5YhCZKqOjx34XPcvuR2VZGb3m86z1zwDNPfns7BWqWzhiRIuL1upvebztIbltLobGTOe3PYVrINrajl9Vmvc+vYW5VCj23zeGDlA9jdduKMcfxr1r/4eM/HTH5zcsBj9+XuhQrhQBvxk5HRiTqitFHUOeooby5XlApLOgUNBeoxt4coiFw48EJ+f/bvOTPjTADe2vEWT331FPn1+SH3KwkSE1ImcP+U+7ku+zoEQcDlcbE2b62Sz7fvv1165oGi0l46+FJmDp3JBZkX8O1X3zLj7Bm0eFpYdmAZqw6tYtnBZeTVBu94EgxGJ5xR0pa7N7UIEgJc/4/ESewfGs/ewTa+THWy0lCMXWgAjhKnY3ADSbekk2HLwKgx0uJqUbzZmkooaWpnXxO4sBtQiidGJIzwU/VGJY0i1hDLodpDarh21aFVaq/bYIUtkiAxOHawn1lyoimRI7VH+PzI53xb+i3r8teFRY58EBAwaAxIooTH68Hpcao3CQ6Po8vPbUf4Ci4CFV2kW9PRiBqanE1+tijflHzjVz1b3FgcFnlKNif75911sEiJNkR3Kzxrd9nJr81nd9NuGnc3UmGvUEheU1sotqSxJKTJdHtIgkSyObkTmfMje5ZUbHpbj8LI3YFX9tLgaEDwHp/9RXD6IkIAfwAwaAzMv2I+Z7xxhuo3d9fSu5T8uRA/WneMv4NtSxUC+Mo3rzA2eSy5VbnsKN3B9EyFvPl+5Ltj4XB25tnU2evYXNyWQ/X8hc/T7GzmlyuUjh9mnVLBe/u42/n7lr+ruWnp1nTmXzmfS/9zKbvKFZ89URDxyB7O638e/7v+f+yr3sesD2ZR0lhCnDGORdcuYnrmdKpaqrhjyR2qGfVFAy/ip+N+yg0Lbwh5QQgnz8j3HuglvVr96HQ4idJEYdKZqGyp5Ej9kU7rxBpiueeMe7hvyn3ER8WzIX8DE16fwM7ynSEvoAIC2YnZ3DXxLn46/qdoJA11rXV8tPsjluxbwmf7PwtL3RkaO5S5w+YyY8gM1Yy5xdXCusPreKf4HX4979fsq97XPYsOGfrV++fujSkHbYfTadXA7kwjuUNi2dxPYGlcDYf1LShVBMdWSZBhzSDJnKTk67XWc7j2MEWNRX7pB12t37H6NsOaQV5dnqro/XP7P9lbuZcDNQeCfjb0kp6s+Kw2k+T44fSz9WNv5V6WH1zOd2XfsebQGppdzWG/txpBgyRIyILsZ3AtI4dNaHxIs6QFJXkxhhgqmiv8CN3i3MV+1bM19q6raHSSTmlH1sHzzkfy0q3pYStiHq+HiuaKoEUUvme/4+qi62KsMTYoqfNNJ5oS+6y7hizL1DvqqbHXUGOvobqlum3aHmS6pZra1lq8spc3Zr5BApHevhH0HSIE8CTG/O/noxE1XDPymi6XHZU0iucvfJ77V94PwKLcRXyQ8wE3jLoh6DpXj7yaO5feCcA3xd8wO2s2H+Z8yI6yHVw+XAnHhnOX39Gg954z7lGJHigk5Kx+Z/HY2scobChUyV+mLRONqGFLsdJGzaAxsPCahfxk8U/UBHwBAa/s5aKBF/Hf6/7LioMr+PHCH2N32xkeP5zPbviMgTEDWZO3hpsX3UxpUyk6SceT5z3Jsv3LuObTwO9dxwrUrmDQGPxUQqveqoTc3C0BPfMmp03mD+f8gUsGX0JRfRG3/PcW1uSt6TLENyR2CD8Z9xPun3w/Bq2BI3VHeHXbqyzOXcy6/HVdFuPoJT0XDLiAWVmzVDNmp8fJlqItPPXVUyzZt4Scipxu9ZPVuWFcqb/3XloA7llsFdg92Mr2AQY+T2xhXVwjLo2dY63SyLBmqLmIlc2VlDSVUNhQGFa+mq+SNzs+G7FS5NrzrmVQ3CDKmspURe/9Xe+zt2ovh2uD9/M168xKN4yjit6w+GGYdWYO1R5ie8l29lTuYX3+en+1MQQEBERBRJblTqq6W3aH3VrPoDEwIHoAQ+KGqGHaTGsmhTsLuW7mddS56vwKK1YdWkXBjjarlHBUw2hDdEj1Lsmc1KX3nY8EBSN1vrGyprKwP5MGjYFoMZrBSYNJt6UHJHgp5hQ/26OeQJZlGhwNAclaKEJXa6/tdt/m9qh31EcIYAR9iggBPEnxffn33Lb4NpweJ/ur9/Ob6b/pMgRx7+R7WX5wOSsPrQTgnqX3cF7/80ixpARcPtYYS5oljeLGYpxeJ0kmJWTcPgQczg9Ye/KnETRcMPACrvz4SnXsxYtfJLcqVy3G8Bkt3zjqRp7Z8Iy63Juz3+T5Dc+zOm810EbSZgyZwYKrF/CXzX/hN5//BoBLBl3CR1d9hEFj4KFVD6nbHhY/jBuzb+S3n/82oHrTPowbDgwaAw63Qw35RRuiqW+tD5jrZdKauG3sbTx61qPEGmN5eNXDXLfgOhqcwfPCAPrZ+vHjUT/mkWmPYNFb2F6ynf9b/38s3LuQPVV7ujzGdEs6lw+7nJlDFTNmjajhJpXPewAAIABJREFU29JveX/X+yzZv4QdpTuC9hIOhKRG/9y9CSVg6PAxcImwO03LroEm1qd5WJHUSJFNBuqPPrqHNEsaNoMNp9tJaVMpza7msMiegMDg2MF+il6aNY0WVwu5VbnsLt/N+vr1LF28lOLG4EQ01hjrZ5ScbE6muqWaA9UH2F25m40FG1m4dyHNzvAVvUCQkcMmBbHGWIbGDWVw7GBFwTtK9BJMCbS6WiloaCN0Gwo28J+6/7C/Yj+/+PMvujxGAUEtTghmkRLKYBmUgiVfyDVUvl244W5REEkyJQUsomgfojVJJpYvX+7X8i4cyLJMo7OxW2qcb7onRC5KG0WsMZY4YxyxxtjO01Gdx2OMMUiyxLJly455vxFE0BUiBPAkxYiEEdxzxj38ZfNf+N0Xv2Nf9T7+NetfITtyiILIO3PfYdRro6hqqaLOUcfPP/s5i69bHJQ83jz6Zp7dqFTkfnHkCwAO1BxAI2o6dWIIB1PSp/Dy5pfV9ZJMScwcMpML37sQt9eNUWPE7rZz3cjr1DZsAA9NfYg1h9bwae6n6piMzOys2bw39z1+/tnP1RZp9066l5cueYmDNQe54dMb1MrlW8feyqbCTfzuy98FPb5wL96+ghAf8TPrzDQ5mwJWBI5IGMFvp/+WK4ZfwT+3/ZPJb0zuUhFKMiVx9Yir+c3ZvyHaEM3avLU8svoRFuUu6rI6UhIkpvWbxuXDLmfGkBkMihnE7srdrM5bzdPrn+ab4m/CzguTPDCqwr9v7sAARY+VUbBjgIGt/TSsSWpma6qMXecCwq+QBIV4JJuTMWlNNDobqWiuQEZWCEQIggZKpfKY5DGMTlSIXpIlCWTIq81jb9Ve1uSt4e9b/95lxa2P6A2KGYTD46CsqYwD1QfIq8vjkz2f0OBoCEv57q6K3BGiIJJhzWBY/DA1VDsgegBWvRUBgcqWSjUku710Owv3LqSgvkC1QwoFg8bQSb1rT/bSrGnoJF3Adb2yl8rmSg7WHAxZRBGOpZAP0YboLosoksxJfobPweB0OrF77BypO0KjO3xCV2OvCTvdIxCMGmNQwhaK1B1rYYjLFf5NWwQRHAsE+VRth3ESoKGhAZvNRlVVFXFxcX2yj3nb5nHPsnvwyB6mZUxj0bWLOnXp6Ij/7fsfsz+crc6/O/ddbhpzU8BlD9ceZuDfBgKKxYNO0lHcWMz629Zz5cdXdtvy4/3L3+fnn/1cNSv+99x/oxE13LjwRpVQZtoyEQVR9fu7ZNAlDI8fzstbXvbb1pXDr+TlH73MtQuu5evCr5EEib9f+nfunHgnr29/3a/Q40eDfsT8nPk9uiCDUmXrU8tEQUQSpIDqmU7ScfWIq/nDOX8grzaPX636FXsq94Tcf6wxltlDFYNms87M0gNL+W+uEtbuirDFG+OZkzWHWVmzOH/A+ZQ2lbImbw2L9i5iU9GmLs2hfYhpUexXfOrepGIwdzg9L7AnWWRbpo51qU7Wp3s5FAt0MyddQCDRlIhGVPIXwz1GjahhWPwwxiSNITsxW8nTOmqNkludq+bqhcqB7B/dX+ltGz2Yovwi4tLi1Mrs4sZi6lrrekQGwoVJa2JgzECV5GVYM/xapRXWF/pVzxbWF4al1sYZ4zp536WZ0yjMKeT6S68n1ZYa8KavwdHQmdR1KKIobSoN+73RS/pOCl0g5S6QVYssyzS7mlXyFlB9aw1M7nrytzNoDJ3JmiG4Gud79FZIOVy4XC6WLVvWbaXzVMSJOFff9bu+vh6rNbTa/UNFRAE8yfHziT9nUOwgrvr4KjYWbmTKm1P47PrPGJ4wPOg6s7JmcdfEu3htm9Lr+BfLf8EFAy8g1ZLaadkBMQOI0kbR4mqhsqWSGYNnUNxYrIaBu0MAJUFCI2jUC71ZZ2bmkJlkv5YNtIWT+9n6sb5gvbL/6AGMSx7Hcxuf89vWddnX8ei0R5n+9nSO1B3BprfxydWfMC5lHJd/dLla6HFWxlnkVuXyn5z/hH2cwY7dI3tweV1ISHjwKP6HHZSgfrZ+PDrtUc7KOIsHVj3AiFdHhLwYWXQWLhl8Cb8/+/foJB2L9y3mugXXsbVka8jjERAYnzKeK4dfycyhM7HqrKw9spY3vn2DW/57S1gqkOCFYVX+uXvDA4hj9Xr4pp+Gr9Nl1qd72JIGjQYvEF4LN9/xxkXFIcsytfZavHiRkbs0sE4yJTEueRwjE0YqZFHS0OBoYH/1fnIqcvh076dhVdzGG+OVAqiWGsqayyhpLGFt3lqWeY6G0I7RO7ArCAgkmBLIis1iaNxQUq2pmHVm9cahqqVKLa74Kv+rsDpzSIJEmjUtoHrna01m1pn91nF6nBTUFnDk+yOsL1hPeUt5QOUuWJ/rQOeVZE7qROo6Ej1fjmazq7kTecutymVj4caQ4dXupCZ0hF7Sq4StO+HV403kIojgZEWEAJ4CuHDghWy6fROXfXAZebV5TH1zKguuWcCFAy8Mus6LF7/IF4e/ILc6lwZHAz9d8lM+u+GzgKrArKGz+Gj3RwBqCGZH6Y5O7eC6wtjksfxq9a/U+SfPfZKnvnqKsqYytVBker/pKvkzSAauGXlNJ/J30+ibuHrE1Zz99tk0OhsZFDOIz274jKKGIka/NprSplK0opbxKePZULihW8fYEb4wnkf2qMfo81HzQUTkR4N/xCPTHuHt797mkdWPhFSzfAbNj531GACLcxcz64NZXdq9WHQWZgyZwdxhcxmTNIZvy75l4Z6F/HnTn8MKt5kdiqLnU/emFEFMAO60L15gc7rAhgwvX2fA3niQxfAVFQEBq86Ky+tSC2Bk5JChV72kJzsxW1X0tKIWu9tOfn0+eyr3sObwmi4rbvvZ+qk3GDUtNVS0VCidK6q737WiO9CJOtKt6YyIH0GKJQWz3oxW1OL2umlwNqh+eB/t+SgsghWljerkfdee5KVaUtXvoVf2UtVSpSp16/PXc6DmAHk1eRQ2FlLeVE6Nvcb/8xjaPxyr3hqwIjbVnIpBY8CgMWDRWwLmyx0uOByQ0HXXv9Dv/ZV0KjlTCZshuBpn1Vr55qtvmDtzLjpd4DB2X8LpcarvS7W9Ovjz0elPrv6EkYkjj/txRhBBV4gQwFMEwxOGs+WOLVz+0eVsKNjAj97/Ea/MeIWfT/x5wOWjtFHMv3I+k/41CbfXzbKDy3h357vcMvaWTsveP+V+lQD6WqPtKNvBoNhB3TrGWUNn8cd1fwTaDIsfWq30KXZ73aRZ0thQ0EbYshOzeX7j837buG3sbWQnZjP3o7l4ZS/n9j+X+VfM56VNL/HiphcB6G/rT3lzuVo93BPIyKohdkcCkhCVwN1n3I3H62He9nmc++9zg27Hd773TroXQRBYtHcRM+fP7NKqZVj8MK4afhXTM6fT0NrAon2LeGDlA103sZdhYK1/7t6oCpA6RKCbtfBNmsDGDJlN6bA5HapNsrKBMGHUGP2852Rk6p3BFcgMawajEkeRbE5Gr1FyKUsbS8mtyuXdne+GrLgdEjsEq96KV/bS6GhUCcb35d+rtkB9AZ2kw6gxIghK14xWdytmnZkfDfqRapmy/NDysHJiE02Jfp0r2qt3mbZMte9tk7NJDbseqj3Ekn1LOFx3mKKGIoXYtdbQ6GjsVlpDgjZBqY61pqtKXZI5CZPWpHb1qXPUqYbvZU1lrC9YT1lTGfl1+T1S5LSilriouG6pcbHGWKK0Ud3y2HO5XOwSd/XYl89X3RsOifOR3eqW6m6ba5c3lzOSCAGM4ORDhACeQoiPimfNTWu443938P6u97lz6Z3sq97HCxe9ENDLalzKOJ694FkeXv0wAL9Y9gsuHHghadY0v+Ump01WQ6A+Q+GcihzOSD2jW8f339z/qtP3T7mfB1Y+oIZQBQQaHA3qxSzFnML20u1+6/90/E9Bhl+tUlTEO8bdwb2T72Xm/JlqoUe6NT2g315P0DHMO9w0nCvHXcmHuz/kiXVPBF1PRGRC6gRuGXMLAgKf7v2Uaz65JqRptkFj4Pz+53PZ0Muw6q18fvhz/r3z32oru6DruZRqXJ+6N7UIkgKIkIejYVM6fJ2hPHYlgacjKwwBnaDDKfurOcE86EwaE8MThpNuTceoNSohz+Yq9tfsZ9nB4NWLscZYUswpaqFNo6ORekc9jY5G9e/cF9BL+qCm306Ps5OK1eRsYsHeBX5jGlET0vsuw5qBRtRQ2lRKQX0BuZW57K/Zz4aCDRQ3Kt0nauw1NDmawrZ8CQST1kS0IVp9WHQWpSNIrUhUTBQVLRXsr95PWVMZlS2V3WolJwoiCVEJ3Q6vdpfI9SacHmfYilxv5BIKCMQYY4gzxqmkV33uMObrsx5BBCcbIgTwBMDj9bC+YD3nZJ7T7R9MvUbPu3PfJSsui9998Tv+svkvHKw5yPwr53fKCwJ4cOqDLD+4nM8Pf06Tq4nbl9zO8huX++1XEATGJY9jW6nShsyis3T7LndY/DC+K1faromCSD9rPzYVbVJDrEmmJLVdnEEyUNZU5qds/HT8TzlYc5AvjnyBgMALF72AWWdmyr+mYHfbsegsNDubj60tWRgwa83MzprNvqp97CjbwdMbghOy7MRsZg+djUbQ8MneT/jF8l+E3LZG1DAifgRnZ56Ny+ti1aFVLD+4PKSyk1bvn7s3rhR0Ha7hDgm2pyhEb1OGQvxKe5jL3JH8gXKx62frR//o/ph1ZtxeNzX2GvJq89hWui1o67n4qHil0wMCdredRkcjzc62XLHehoiIRtIgy3JAJSucrhkGjYE0S5pqudI/un9b7p21HxpRQ15tHrsrd3Og5gC5VbmsyVtDRXMFta21NDmbjjkcqhE1mHVmrHorNr0Nq96KWWdGL+nxyB7sbruiWLVUU9ZUFrxyOkC+oyiIJJoSSTYnk2JOIdmcrD588xadhWRLMkmmpBNG5Hy+gaHIXFVLFfsK9/HEW0+oZC7c3MZAMGqMXZK49s+xxliiDdF9ZiAdQQTHC5Eq4B7gWKuAF+xZwNWfXM3E1Ik8ftbjzBk2p0tD1UD4ePfH3PLfW2h1tzImaQz/u/5/ZNgyOi1X3FDMyFdHqoUDb85+k5+M+0mnbV27QOkfnGBMoNJeyY2jbuQ/34dXXDEmYQw7K3cCcM2Ia1h7eK2atxajj6HWUasu6wu5+nDb2NtYn7+eg7UHMevMzLtsHp/s+URVFM1aM02uY/+BD4VhccOIMcawo2xH0GIDgMGxgzm739kgw5L9S6iyB893A8X2YlzSOOKj4tlVsYsDNQeCqjAaD4wta6fuFUK/ANaBpeY2Ze/rDPg2BZy9fAtn1VnJsCkGzF7ZS11rHQX1BUFvCAQENbHe41VISrOzuUehxFDwfU+6o2i1R7QhmnRLOoNiBzEkdoiafxdjiFHanzUUcKD6AEfqjlDSWEJ5czm1rbU0O5u73XLNB4NkwKQzYdFbsOoUUheljUIn6XDLbpqdzdTaa6loqQiZRxkIFp1FIXGWFBKjEmmtbGVK9hTSbGl+BC8+Kv64ExaH2xFSkfORt/bjPfHcEwWRGENMWGTOp2DGGeNO2qKQSBVw3yJSBRxRAE8IihuKMWqMbCvZxhUfX8Hw+OH8+qxfc3329Wil8D/814y8hkxbJrM/nM3O8p1M/tdklly/hImpE/2WS7Om8fact7ni4ysAuHf5vVw08CI/sjgna446XedQvN2qW8L3+dpV2ZafpZf0fkUL7clfR2/B67OvZ9HeRdQ56uhn68dvp/+Wh1c/TEljibpsb5M/raAlKy6LosYicqtzgy6Xbk1nfPJ4HG4HXx75krdq3gq6rIBApi2TDFsGlS2VHKg+wBf5XwRcNqFJUfZ8uXtnlICxQyTKLcDO5KPq3tGQbn403bZiCQZJkLBJNtJj0xFFUbUHaXA2sLtyd8DlLXoLGlGD0+Ok1d2K0+NERlb+1t3rVBYSofz1QhE/ERGL3kK0IZoobZRSaOKyU9lQyQ3jbuD8AedT3lzO8gPLWXt4LSsPrUSWZfU8ugNREDFoDJi0Jsw6M2adGZPWhF6jVwtEfEpWeXO5GoIMB77+tIFUOnXekkKSKQmTzqSup15Ez+zdi6hX9lLfWh+cxAXIl6tuqQ7b9icQorRRQcOr0fpoCvcXct6U80iyJKmvRxuij+lGOoIITldEFMAeoCc+gJXNlfx1y1/5x9Z/qMpcpi2Th898mJ+M+0m37krz6/K57IPLyKnIwagx8t7l73HliCs7LXfHkjt4c8ebAJzX/zzW3rzWL9ST+udUSptK1fnRiaPZVdF14n2MIYbaVoXkTUyZ6BcSFBHVnLiO5G/mkJmsOLgCj+xhctpkJqRM4LVtryEjoxE0PcqRCoQ4QxwyMjWtwcOPcYY4EoQENCYNOVU5IbcXpYkizZqm5E7WFQQ8XtELIyv8c/eGBNh9tdE/d++bNGjppQJHq95KrDEWnaSjydFEeXN5UJVFI2owaozIKP1oj1X16gvoRB0mnUK4dJJOLRSpc/Sep58kSBg0BowaI0atEa2oRRCUdoROjxO7247dZcfhcXRbhYw2RIcmdUfn46LijonIhKOitLpbu0XifK8fq+IKSnGIJEgkmBIYmzy2kzLnyyFsT/ZCmSefLsrY6XKeEFEATxQiBLAH6A0j6AZHA6998xovbX5J9dxLNCXywJQHuGviXdgMtrC3c92C61h+cDkAz5z/DL8+69d+BK/J2UT2q9mqHcm8mfP42cSfqa8/ve5pvy4avu4XXaE9sRsRP0JtX9a+R3B7IggwNX0qm4o2AUr1cFFDUZ8VAFh1VhqdwaspTRoTmdGZilrTEtynTUDAZrChl/RBPcxsdsV+xZe7N7kIrAFSwnIS2nL3vs6A/XH0WN3TCBqiDdEYtAbsLjs19pqg56wRNYiyCCK4PK4eG2j3BgwaA1EaJTTqq8ZtdDb2mNwJCMRHxZNiTiHaEE2ru5WDNQfRa/RqhbMvdO3wOLr1XmhFbVBSl2Jpm04yJfVqqNEXnm9P1soby/ly25dY0ixU2CuobK6kuqWaOkcdAgLV9uqw27IFgiiIah/jYwnT/nj0j3nv8veOef8+nC7E6HQ5T4gQwBOFCAHsAXqzE4jdZeft797mTxv/pBI0m97GPWfcwy+n/JJEU2KX23B73Ty48kH+vvXvgNIabd5l8/xaPm0r2cbkf03GK3sxSAZyf5FLZnQmADUtNcS9cOznkWxO7tq+BMiKy1K92+ZkzWF13uoeXZgCQStoVTPnQJCQMGgNXYapNIKGKG0Udre9M+GTYWi1f+5edoAubg062NJO3duSBvU95AImrQmDxoDT4wxZsCMJEqIg4vF6QlYn9zUEBAwaAzpJp5ok2932HpM7jaghPiqeOGMcZp0ZraTF6XbS4mqh0dlIo6ORhtYG3BzbfvSSHoveQqwxlmRzMv2s/RgYM5CsuCzGJo9leMLwHhdM2F12vxw4n+9fUUMRZU1lagi51l5Lg6OBZldzyHzV4wmjxkiMMYYYQ0zn5w5jA2MGMiJhRI/3eSoQI7dXye1scjapj0Zno998+8eQ2CHcPv52v22cCufZW4gQwBODCAHsAfqiFZzL4+LDnA95dsOz7K3aCyg/sneMv4OHznyIfrZ+XW7jla2vcN+K+/DKXs7OPJuF1ywkLqrt+J5Z/wy/+fw3gKLEbfzJRvUiZnrG1C0y1j5fyygZsXtCJ4Mlm5Ipay7DIBkYnTyarcWhO2J0Fx2Vxo4Ip7+xhJIs39EQ2uRQ8vWmtgvnxgU43QOx/rl7uxPBe4ypSRpBg16jD2hd0h4+ZeZEKXmSICmhPlFSw6XHmszvg1FjJNoQjVVvxaAxICNjdylKne9ieqznKwoiJq2JGGMMyaZk0q3pDIgZwNC4oYxMGMnAmIFKC7puFE54ZS+19lq1uKGquYqSphIK6wspbSqlvKmcqpYqaltrqXfU0+Rswu6y9/h96i34cljbE7dYYywxhhiiDdFKSFzSEqWJYlTSKEVtPsY+tz1Bb5MFt9cdlJg1OgKTtlBkrtHZ2G2CfungS1l2o791UoQA9i0iBDBCAHuEvuwF7JW9LM5dzDMbnmFbiZJTpxE13DT6Jh6d9ihZ8Vkh119xcAXXfHINjc5GBscOZukNSxkaNxRQbGjOfPNMtR3ZPy79B/dMugeAqz6+ik/3fqpup31v3FDwebqFgklrotnVTKwhFkmUqGwJIJedAAhHY69+ZEKGzDr/3L0xZaDp8G2xa+Cb1DZ1b3M6VHZ24wkLOkmHx+sJSQhCFUj0JSQkRFFhsW6vu8fHYNaaMelMRGmjkEQJp8epEDtHI07vsXeVAOV7EmOIIcGUQKopFbFR5Pwx5zMsYRgZtgzSrenER8WHzLNrr8r5FLmC+gK1MriypZKalhrFv9DZ2KeVz8eKQJ+VFHMKE1ImYNQaMWqM6CU9oiiqSqzdbae+tZ4GRwP1jnp1usHRoH4uB0QPIO+Xecf9fEC5Qa5trmXJyiVMOmsSDq8jLGIWirD1pZrqK57yFQr5Hhad/9jIhJERBTBCAI87IgSwB+hLAuiDLMusPbyWZ9Y/wxdHlKpSAYErR1zJY2c9xviU8UHXzanIYdYHszhSd4QYQwyfXvMp5w04D4DC+kKy/pGF3W1HK2rZ94t9DIgZwP6q/WS90kYuwyF24cCnvCWaEqlsrjwpcs7aQ++C8aX+3nspAdIfC6z+uXs7k8DVzVr6E63WBYKEBIJy49GT4xIFkShNFAaNQc0BbXW39lolt17SK8TOkkp/W38GxQ4i05ZJujVdffg6bQC0Olr55LNPGD9tPPXOeoobiymsL6SosUg1Sa5uqaautY4GRwMtzhZaPa0n1d/mZIIoiPSP7s+h+w51uazL4+qamHVU2Fyh1be+LkoSBVG9ITyW78KT5z3JTaNvUkmeL5/1WBAhgH2LCAGMEMAe4XgQwPbYXLSZZzc8y5J9S9SxSwZdwuPTH2d6v+kBf2gqmiuY++FcNhVtQiNqmHfZPNUD8MOcD7n+0+sBFCPon21DFEQ0T2rUu/1wqnHDVaWiDdHUtdaFfb59iZSGNqJ3ZqFC/vQdhDenCDtS2tS9TelQHF5NzkmHgCpnN6ERNegkHRpBgxcvra7WXq3UNmvNpFhS1NZpGdaMTsTO7XFTba+muKGYgoYCiuqLKG0qpaK5Qq1YrXfU0+RQlJ0Tmfd4ssGoMWIz2FSj6fbTJq1JUQQ1erSSFo2gQRIl9TdFRsbj9eD0OEOGPtvP96Q/cDjwKbjH42bKZ9Ld3vKno6pn0pq4Lvs6pmZM7ZV9Rghg3yJCACMEsEc43gTQh5yKHJ7b8Bwf5HygFjmcmXEmj5/1ODOGzOhEBFvdrfxk8U/4IOcDAB4+82Geu/A5REHkyo+vZOHehQC8cNELPHTmQ4x6dRQ5laEtULqL9hXBxxsaD4wu9++b2z9AK9tyk3/u3vZUaP1h/+6q0IgatVdsX4QyYwwxpFvTybBlkGZJI84Yh0VnQRIlXB6lIMRXhV3RVKEWPTS7mnG4Hb1uCXQqwVd9btPbsOgsWA1WlbD5Cmt0kg4BgaLCIjL7ZSKKitm6R/bg8rhwepy0uFuCKnB9Hb7ujRuQcPdj1plVu6BwSJvffJD1eqLkHSsiBLBvESGAEQLYI5woAuhDXm0eL2x8gbe+e0u92x6dNJrHznqMq0dc7ZfALssyT6x7Qu1tO3fYXN6//H28spcBfx1Atb0aSZDYf+9+dlfsZvaHs3vlGE9Ezlpcs2LFcmY7o2VTh+ubR1D65PrI3qYMyIuh14yWTyb0dcjZ5+nm8+dDAI/HQ4urhWZ3M82OZlrcLTg9zl7JHzyVoJf0aku3KG2UQtq0BrSiVi2aEQURQRCQZVklbb7exA63gxZXi5/K1tc3UsfrO2vUGLskXoHGDJKBPd/t4fxp5xMdFe23rlFjPGFt7HobEQLYt4gQwAgB7BFONAH0obSxlL9s/guvbXtN9e0bFDOIR6c9ys1jbkav0avLzv9+Prctvg2nx8n4lPEsuW4J+fX5THtrGqC0Rsu5OwfNUz1vEnM8LiSCF0ZU+ufuZQVouFBrUMieL3dvaxo06Tsv90NHd/8mBsmg3kj4ClR8JOWHDpNWKVIxapViCZ2kU5RSUULkaPgRWVVNfcbZre5WpVrZ1XzCVO/ehFbUdiZi7QmbtpskTmfCpDUdc2u604UYnS7nCRECeKIQaQX3A0CKJYU/XfQnfn3Wr/nH1n/w1y1/5VDtIX722c/447o/8qupv+JnE36GWWfmhlE30D+6P3M/nMu3pd8y6V+T+N/1/+P+Kffz8uaXya3O5al1TxEfFd/tvqQd0Rfkz9IKk4vb1L3JRRAdIC98T3wb2duUDrnxIP+Au0RpRE1YBr3d/Zu0elrhFON6WlGLXqNHLyn5bC6HC7PRjCAeDUPKMm6vG6fHicvrwuF24HA7OuULNruae9TO7HhDFEQMggFblC04YdN2He7suF57H9EIIojgh4OIAtgDnCwKYEc0OZt4Y/sb/HnTnyluLAYg1hjLfZPu497J9xJrjOVw7WEu++Ay9lTuIUobxXuXv8fDqx8mrzYPEZE7xt3B6zteP7EnIsPgGv/cvewK6MjjmrRtRsub0hUrltqoE3LEvQqfibNX9va4OvdUQfvQqHD0nxevqjq6ve5TUlUTEIjSRqmKom+600PTxevaKKpbqnF6nAyKHcSA6AEqWZNkieXLl//gFaPTRRk7Xc4TIgrgiUJEAfwBwqwz88DUB7j7jLt5b9d7PL/xeQ7WHOSP6/7Ii5te5M4Jd/Lg1Af5+idfc82Ca1h1aBVXfXwVj571KH/a8Ce8eFl2cFnXO+plGJ0wscTfey8hgCf1oRj/3L3vE8FzbNGkkxoeObQn4MkEAUG10PAR1WMhrS6v67j76fn6/pq0SmgySqfk6oUiYaFeD0Tg9JK+z3PTXK6Ty4cwgq4aQEEqAAAgAElEQVThU+3dXreaYuH2utGKWgzi8TfZjuD0QoQA/oCh1+i5Y/wd3Db2NhbsWcAzG55hV/kuXtz0In/b+jduG3sbf7/077y8+WVe2/Yaz214jilpU9hcvJmixqKwumYcM2TIqPfP3RtbBtoOrh2tEmxLbSN7m9Kh3NI3h9QTtFerTkfIHFt/2FDQSTr0kh6DxtBG0tqFMi16i5qnF4yE6QQdOTtyOHfauViN1k4EzqAx/GCKBk4m+MLsHtmDx+tRp31EJ9h8OMs6XA6+qfuGpj1NSsFRiGXD2WenZXvpOMNZNliryptG38Sbl715nP9qEZxuiBDA0wCSKHFt9rVcM/Ialh9czjPrn2Fj4UbmbZ/HG9++wXUjr+ORMx/hxU0vsrl4M1adlQZnQ69e0LVuGFfm3zc3PUAL22KLv+/ejhRwngKfUpmTy9i5LyEJElpJ65dr56eitcst8/nMRRui1c4fXSlovUnMXC4X0kGJqelTT2gYzSt71fC1y+Nqm/a2TYd6LZzxVlcr31d8z55Ne/Di7Xofcs/25/Iq9jI19hoADBqDSm6Oy3fhSN/v4kThVExziODUwylwae1bvPrqq7zwwguUlpYycuRIXn75ZaZPn36iD6tPIAgCM4bMYMaQGazPX88zG55hxcEVzM+ZD8CktEl8X/49Dc6GHlfwJjUqyp4vd29iCRg68Em30Ga07AvpFtr4QVqxHA/4wrCSqPTl9Rk3GzQG9RGljVJVs/rKeoYPGE50VDTR+miiDdFqD1izzhwwlGnQGEK2UOsOfGShPclweV1UtVR1ST7CJSytToUU7d60258UBSM4PdxfsPFgSk+foOT47ao9wm2pJgkSkighCZJaVe3zoQw233G6vraexPhENJIm5LJ+rwUYD2ffJ2JZSZBwuyMkMIK+xWlNAD/66CPuv/9+Xn31VaZNm8a8efO49NJL2bNnD/369TvRh9fryK/LJ9WSilbSMj1zOsszl/Nt6bc8t+E5FuxZwNZipTewXtLz6zUOPCI8fU7n7fx2HUheeELpKofkUYoz2ufuDartvF5lVBvR+zpDCe3aT6MCQ0mQOlVddqy4NGqM7K/az6G6QyQYExgQM0C1zfC1lzLpTGovUaPWiE7SIYlSQEIVjMg4XA52N+0mxZKCjEyTs4na1loO1BzosUoULkE6rorpCSJFXcFH1LWS8qwRNepYuOO+10REKsoqyMzIVC1rerLd9uPBXhMQKG0qRa/R09/WH62kDUnCfJ6HPcHpVBwRQQR9idOaAL700kvcfvvt3HHHHQC8/PLLrFy5ktdee41nn332BB9d76LZ2Uz/v/ZHFERSLalk2jLJjM4k05bJBQMu4OJBF7P8wHKW7F+Cw6OQv6eU1sN+JPC365Txd0fDk5+3WbGYO+Sfe4GcRP++uQdjOWXUvfZhTq2kRScq3RbaX+B8fnCCoOT/Kf/bOh74jH19bbTaE6J6Rz1VLVWqf5xvvD3yavPYUrKlb0+0tG83312IgthjwiIJkqoMIUNlWSUZaRloNVrl7+b72x1VS/3mhbZxURD9xgQERFFUpyVBaZXmIzWCICifBwT1M+EbhzaFVkZWPye+PDDfw+NV5ucOm0tmdGa33rsTRYxGM/q47SuCCCLoPZy2BNDpdLJ9+3Z+/etf+41ffPHFfP311yfoqPoOpU2l6CQdTo+TooYiihqK2Fi4MeCyBsnA/53jAGSe+gLim2FXMtz9DUw4Shhu3uW/Tr1esV/5up3RcsMpXMTmkT143B5aCS+s1ZcQEdvISgeiok4fJSbq4+g6oiiqxMNHXHzkpLGxEZvV1kZg8Ccr7ZUaH8FFphPRVSG3zfsIcPtlvLK3bawdOW5vc9ORCPkeTo9T6e0b4LWOJCogTo4W1GFjWPywbhPACCKIIILu4LQlgFVVVXg8HpKSkvzGk5KSKCsrC7iOw+HA4WhzHW5oaACUO++T3YIh05JJwyMNlDeXU1hfSH59PgUNBRTUFyjT9QUUNBTQ4GhQzH9pU/58SmB77Ivzz93bmwDeH7DR8omEzwfP7XH3vinziee3JwydlDoEPzLsm/apvO0JdMfXfGPtl1VVQ990h221f7RfXxRFcspzaHY0+x0r0Dl8epSQA3jcHnbU78Cd60aSJHWdtkXlgGMdoY61I/uyLPvdAPieZVkmr07xDh2ZMJJkczJmnbnTTYX6TxBodDTikT1Y9BZ0YlsOSKDQcMfjBXC5XTS5m6hsrPRTOgMtG+42u7NssBB2T/YfaDmXy4VbdtPS2oLWow26rIBwzF1VThb4rp/H8zp6sl+zjwdOWwLoQ8cvkyzLQb/gzz77LE888USn8S+++IKoqFPLediEif6e/rhb3DS1NFHjrEHj7fxxePoc+MOXoJGVoo251ylKX7Xp+B9zBBH0JtqrlCdbAXcwdT4sHO694zipkXOiD+A4YWfol0ebR/Pk4CePz7H0MVavXn3c9tXSEsBk9jTDaUsA4+PjkSSpk9pXUVHRSRX04bHHHuPBBx9U5xsaGsjIyOC88847qTqBBILL4yKnModtJdvYWrKVb0q+YW/V3k4KgCiIZCdkkxWXxdrDa7l7ZQ0aGRwS6D2KlcvSrBN0ErTlUWlEDTpRp1an+gokojRRROmOVqv6wqKyiEtuy7PzFSU0O5tpdDaCrNwI+Dy6Ghob0EfpVQ8vn0eYL1fPI3twy8qyp4v1SwQ/XARTxMJF5DtwYhEfH8+MGTNO9GH0CC6Xi9WrV3PRRRcd104gpztOWwKo0+mYMGECq1ev5vLLL1fHV69ezZw5cwKuo9fr0ev1nca1Wu1JVY0myzL59flsKdrC1uKtbCnewrel32J32zstm2nLZFLaJCalTWJy2mTGp4wnvz6fC9+9kLtX1vDUF/C78xQl0FcAAvDsuX1oEh0CPsNhj8eDw+Og0RXATDAANKIGvaTHpDMRrY8mPiqeJHMSY8xjSDWnkmHLINmcjFVrZeeWnVzxoyuIt8R3aXni83drX8jRfrrja3217NIDS6lrrcOkNSEKot9yTo8Tl8fVZxdqAUE1bdZr9Mq0Ro9BMqg+gQaNMm3QGDBIBgxaZd4oGdFr9KrFjFFjxKA1tBVehAjRhhrzhVQ9Hg/bt23njDPOQKvRdsqD7Bja7Y2x7hxnb5yjb9rtcrNixQpmzpiJTqvrlNfZ15BlmVZ3K7WttdS11lFrV57rWuv8xmpba5XXHHXUt9arr9W31vf4MyogYDPYsOltxBhisBnantVpvY1oo2J95POo9L2m13Tu2BKoW2rA0HkvL+d0OjuRokDLaUTNSXX96QmO57X0h/Ke9QSnLQEEePDBB7npppuYOHEiU6dO5fXXX6egoIA777zzRB9at1Brr2Vr8VblUbKVLUVbqGyp7LScTW/zI3tnpJ1BsjlZfV2WZV7Z+goPr3mYh9a2+pE/UJ5HJWbz1Ec5PHnu7yn45S08uvpRFuxd0CUZFOiZr2BP4avAbXY1U9Fcwf6a/SGXv2vvXYDSjSJKE4VFbyHWGEtcVByJUYkkm5NJt6aTbE4m1hjr90g0JZ6UOTkOt4MmZxONzkZqm2tZtW4VoyeOxu6xK+OORvV1dd4VZNzZRLNLyVGTkXF4HDg8DnD2zrEaNUZF1dVbVHU34HyHcbPW7DdvEAy4LC4uHXQpOt0P23PIJbvaKqFPwOdPEASMWsUQPNWS2u31vbKXBkdDUOLom69pqeFA4QG0Vi31jnr1Nbvbjoysrptfn9/tY9BJOmIMMUQb2nwxow3R6lio12wGm1J53ktwSS7MGjMxxpgIWYmgT3BaE8Brr72W6upqnnzySUpLS8nOzmbZsmVkZp4a1Xe5VbnM/mA2B2oOdHpNK2oZmzxWJXuT0iYxJG5ISEWr1d3KA6sewO11I3nhD+cJPH2OjCiInBd9Hmtr17LuprO5ZuQ1CB4PmdGZfHj1hzQ5m3jz2zd5ev3TVLVUBdx2e/Jn0yt3462uVipbKo+ZGLYPXfUVuXR6nDg9TuocdRQ2FIa9nl7SY9FZsOqtxBhjFMXRlESqJTUgaYw1xhJjjOnVC0inY9IoCl1cVBxppjQKTAVcOODCY764eLweWlwtnYhhyPkQhLLR2ah2QLC77djd9oA3MscC6XvpmAhlsHmzznxSkvxTGaIgquQqFILZ3TjcjpDEsavXfNXm5c3llDeXH9M5WHSWbhNH37xJa4q0JYzguOK0JoAAd999N3ffffeJPoxjQro1nUO1hwAYHDtYJXqT0yYzJnkMBk33fFiMWiO3jrmV5QeXk/3qyzy48kFoKGRc0jgmGiaytnYtXxV8Bb/73m89s87ML6f8knsn38vyA8t5dsOzIZPY6x311DvqlXW1ZiamTmR00mhq7DXsLN9JXm2eqi6FQijS5/Pv04paZGTcHrdiI3KcevU6PA4cdgdV9qpuWZAYNAb1IhJrjCUhKoFkczKJpkTio+KDEkeddPzVLUmUsOgtWPS905xZlmWcHmdIAhn0tSDjLS4l0dsje/w+d70Bo8bYa4TSoreglzqHHyMIH3qNniRzEknmwDncoSDLbWboQYnj0bB1IIWyydkEQKNT+Yx252bRB42oaSOJR0PV9lo7ny37jNio2KDE0fc4Eb8BEZzaEORACQkRhIWGhgZsNhtVVVUnrAjkq/yvyE7MJtYY22vblGWZans1CS8kAPDPGf/EWGDklpxbAKh6uIq4qNDnm1ORw183/5V/7/x3J4PjYBAFkezEbG7MvpHzB5xPjb2Grwq+YmPhRvZW7qWiuaLHSp8oiJi0JiVko7dh0BowSkaaXE1UtVRRa6+l2dV8yiW2GySDqjbGGeNIMieREJVAXFQccca4TqTRorWw9cutzLlszg86vNTqaGXR0kVMPXcqDq8jfLUyxHJ9lfuqETWqungshNIgGvh207fMvGgmMaYY1Y6lPdr3JG5vTu6RPV2OH8s6vTbe/jg8bkorSomJjfFr79d+3WRzMmtuXtMnf6dAcHlcfuHo7qiQta21vdL7N0obFVb4+vwB5590HpMnwsTcd/2ur6/HarUel32ebDjtFcBTHWdnnt3r2xQEgT9//WdA6Yhx8+ibWVWyimFxw8itzmV9wXrmDpsbchvZidm8MfsNnr3wWV7f/jp/2/K3LsMqXtnLrvJd7CpXXKaTTEnMGjqLP5zzB6ZlTMPldZFTkcP2ku2sy1/Ht6Xf/n97dx7V5J3vD/z9ZF8gwSRARARRQeoKVdtbxa3Wleq009bRttarnel0jtY6nXH02plqe446dWrV0baW8Yy3vXOcen+tta3W2zJqtR13FhdUFEVUIobNELIvz++PmEcCAUESH0w+r3M4wEMIn6/B8M53xZVbV9odMP0/g3uVjjuv0oWMEGnd0jAqZRSEJiEeG/gYNEoNRAIRaqw1uFR/CRW3KmAwG1BtrYbJbvLNeesi7B477FY7jFZjh75PWiKFWqbmehv9oVEj1wQNjv43uVgeppaEhn/DaKfHCQaM7yg9YQzUMnWnQonb64bN7ZszaXVa0ejy9TJanVZY3VZYXVbf8LXLBrvbHvDm9DjhcDt80wq8Tm5xjj9Qur1uLiB0xvzz80PxT9j1tbH+K5Q9ve0hFoqhU+igU+g6/L0sy8LqsrYIh9WN1ThUeAhJvZNgdplbDZUNDt9qVqvL9/tXaa5s8+d9+Ysvu1wAJPygAEiC+ueZfwIABicO5ualjU4ZjfO153Gw4uBdA6CfTqHDslHL8PsRv8fnZz/HuiPrcMJwIuhttXItNwQIADctN7GlaAu2FG2BVChFTkoOXhj0Ap7p/wx+M9y3SMPj9aCsrgzFVcU4WnkUh64dwtnqs9x93I1/haWH9d1PWV0ZAGDXD7u428hFcqRr05GhzcDwpOHI0GYgQ5uBZFUyHB4HKhsqcb3hOkprS1FWV4arpquoaqxCjbUGZqe59dMpmvEfP8eA4eYjhbsn0uFxwGgxwmgx4jzOt/v7pEIp4mRx0Mq10Cl00Cq0uGG+gdPG05CJZOgm6+Y7Qu/28XligRhSoRQioYgbnhcyQm6rnYBephD0ILX4dzsdvB2k4/ynzzQ987fpm0QogVjge+wlQgkkIgm3ElwilHArvWVCGeRiOcQCMXdmcNNzg5ueJdz0a2CBklMleDj7YcgksqDfIxd17RcoTTEM4zvvW6JED1UP7rrL5YLmmgZTR7XdK+bxenyLZ9o5fJ2ijrxz7sm9oQBIWqi11nIr6F55+BXuek5KDvKK8nCw4mCH71MilOD5Qc9j1sBZOHz9MNYfWY/Pz34e8Ie61lbru61Agn66fmhwNHB1ODwO7C3fi73lewEAGdoMzOg/A09lPoXs7tnop+uHXwz8BXdfVY1VKK4qRuGNQhy+fhhFN4pgMBtaBAP/tjIBtTISqOQqeFkvTHYTbG5bQM9kUxq5hguEGZoMPJ35NDK0Geir6QulRAkv60WNtQaVDZWoNFfiqukqyurKcLn+Mq41XMPNxpuos9XB5rZxx8/djVKshFwkh0AguHNMmssOp7flEtx4RbxvZaTtFtxs54eZ/BweR6uT5S0uC/dYdkXNA0dboaNT18Nxn21chxc4/O/DGDNqDBghA6fHCZvbBofbAbvbDpvLxi2usTp9vUVWtxUWpwWNrsY2h8abbiHlP3KvIz3vbVGKldwwtsPtgMFsgEQogT5Gj1hJLLdFi1ah9c2Flfu2Z0pWJSO1Wyq6ybtBJVXddcumSCUUCNFN3g3d5N34LoU8YGgOYCd0hTmA4fDHfX/Eyh9XQsAIYF1mhYAV4Ntvv8XgnMHovak3BIwA9UvqoZJ2bt5Exa0KfHD8A+QV5HFDNgJGENBjlhSbhGHdh8FoNaLwRiGcnpYhRy1VIzc9F8/0fwYTek9odVFCo7MRp26e4oLh0cqjKK0pbdcfMgYMdAod1DI1WJaFyWFqdcWzX7IqmQuGXEjUZqBXXC+IhYGv6G0uGwxmAyrNlVxY9L/3XzeYDUHbH4xEKEGcLA4p6hSka9LRPaY7dAodYiQxkIqkgBc4eeokktOTUWevQ7W1GlWNVbjReAPVlmo0Ohvx8ZMfo9ZWixprDWqttaix+d7ftNxEjaUGdfY62N1d8yw5fzgSC8SAB1ApVJCL5VBKlIgR+8KGSqri9oHTKDRcb2a8Ih6JykSoZWrESGKgECseiMUZ4ZxH1XTj9FCt+G5vz3h7CRgBtx+lQqyAUqKESqLiFlTpFDokKH1bOHWP7Q6NXMPtSKCWqqGSqrrUym4+5sXxheYA8oN6AEkL/iHapzOfhlQk5c5MTFYlo3e33rhcfxmHrh3C5L6TO/VzUuNSsWbCGrw15i18evJTbDi6ARdq7+zPJxaIYTAb8LX5awDAqJRReLTHozCYDdhTtgf19noAvvk+285sw7Yz2yBgBHi0x6N4rv9zyM3IRYY2g7u/GEkMRvQcgRE9R3DX3F43SmtKUVxVjOKqYhyrPIYT10/A6g08JogFi2prdYttSeQiOXqqeiJOFgeGYdDobERVYxVqbbW43nAd1xuuY1/5voDvEQlE6N2td9BwOCplVKthw8t6UWutDRoSm35cZ6uD0+PkhnZbG3IHgPi6ePRQ9UCP2B5IVafiseTHuM/97zVyTas12Vw2LiRyQdFaE3itaYi01rS5wvs/h/wnPKwHDfYGNDgbfPsNOi2wuq3cXDqX585pLq2FiKabhQNAY2Njqz+zPfynz/iHOP2bVsvFcijFSsRKfKuhuUB5e66kVqH1za1UxkMlVXGLPOQi+QMRKv1EAhG3mXIo+DeNbh4MC28UIv9yPmwuG6QiKept9TA5TDA7zbC6fL8DDrcj6DC/l/VyvyP3OgdQLBBDJpJBIVZwLxK6yXwLq+KV8UhQJrQIjk3fq6SqsG7lREgoUQ9gJ0RiD2CttRb6tXpfMFpQigxtRsCrs1e+fQX/Xfzf+K+c/8Kq8atC+rO9rBf/V/Z/WH9kPfIv3zkTUiVRwew0c0/4sZJYzBgwAzkpObhQcwH/e/Z/ue1wmusR2wM/f+jnmJYxDaNTR/t6v9rgcrmwe/duDBk1BCW1JSi6UYTiqmKcuHECV01X29WOz575DKNTR6PCVIELtRdavAU7kcVPKVZy8w2bh8P2DvHY3XZfr2EbIbEjvYkykQxJsUkBoTDY53f7t21aX9CgaK3F4pGLO7x9kdPjhNlhRrWlGlWWKlRbqlFjq0GdtQ41lhqcLjsNVbyK65GyuCzcYg3/4oymx/zdL0JGyM2H9J+S4j/KsGkvpX8Fpz9Q6hQ66OS6OyuBb78XeAXYs2dPxPcY+Z+PHp/4OBrdjbhhvoFrDddQaa5ElbnK10NtrUGdrY5bJOHfEsjhdoR0KkQw/hOHuD0Bbw9ftxUcm78XCUTUAxhm1ANIAbBTIjEAbincgl998ytk6bNQ9OsiAIH/Of9x5h+Y9/U8jOw5Ej/N+ylsdZwxnsFfj/4V/3Pqf7hhxhhJDMQCMdfzBwCZukzMy5qHsb3G4mjlUXx25jMcvn44aM+QTCTDE72fwM/6/QxT06cGPa2grScik92EUzdPoajKFwqLqopwxngm6BYOYoEYAxIGIFufjSx9FrL0WRiSOASx0lgYzIagwfBy/eU2A4hOoQuYb+j/uK+mb4dX5TqdTmz/ZjseevQhGG3GVsNiR+by6RQ6LB+zHAseWdChWsKpo39YWNZ3qonZYYbJYUK1pRpGi5ELq7XWWt+xZQ4T10tpcVlgcVp8gdJlh8PjW+l7vwMlAwYCCHzH8Ykk3CIL/1nZMZIYLlA2DSbxinh0k3drsQVNjCQGEqGky/VUdjYsuDwu34kitjru7WbjTRjMhjsvIG5vCXXLcYt70XA/pzvIRDKoJCqIPCIka5MRJ/dtW9XeAKmWqltMM+nKKADyg/qqSYDD1w4DAGb0nxH06/5tZ45VHoPNZQvbdiADEwYib1oeVo1fhbyCPHxw/AMYzAYAvnCVqk7FdfN1nK85jz/86w8QMkJMTZ+K34/4PXJ65mBv+V58fu5zfHvxW24zYLvbjl0XdmHXBd8K34HxA/FU5lPIzcjF8KThd53/o5apMSp1FEaljuKuOT1OnKs+xw0hF9/0vb9lv8VdayotLg3Z3bORlegLhbnpuUhWJYNhGLg8LpTfKg8aDivNlVxP2aFrhwLuM14RD+Pijm3/wjAMVCIVhiQOafMJt2lvYos5ik0+dnqcqLHWPPDDXwzD+M4rFskQr4xHX03fTt2ff4sPs9PsC5V2E2psNVzIqLXVcqs2GxwNXG+Vv5ey6RYydwuULFh44PFtR+O2tnq7jmDAcKu5pUIp5CI5FBLFnYUbtxdpNO2l1Cl0iJPFIUYSg2prNRRiBfpp+yFBmYBYaSzvvyNioRgJygQkKBM69H0erwe37LcCgmPTt2pLNW5ab6LaUs31PprsJjQ6Gzu8mt//uAOAwWDo0Pf6yUXy1kNiO4MkbS4d2agHsBMisQeQZVmcNp7mJksDga/ORCIRktclw2A2YP+c/Rjba+x9qcvpcQbdRqafth8YMDhfe2cLk3hFPGYPno252XPRT9sPBysO4qvSr/D52c9xo/FG0PuPk8XhyfQnMbnPZKAMmDF9xj2/EmVZFhWmCi4A+nsMWxtC1sq1XC9hlj4L2XrfquamfygbnY0oqytrEQxLa0vRP74//j2v9ZNXggnlK27/xuGVDZXQx+jv6SSGcIm0YTQv6+UWY/hPRrllu4UaWw2qzFU4evIodD11aHA2wGQ3weQ03ZlH2WzY2+lxhnwhRnv4tzuSCWWQiX3D3kqJkuuhVEvV6CbvBo3MN49Sp9RBLVX7Tp2RxEIqkOLEv09g+qTp0MRoeA+Ud+NlvTA7zFxQrLXVthoi/S/y6mx1MNlNYR+uvhuZSNapAKmWqts1NYR6APlBAbATIjEABtP8P+esL2bhszOf4e2xb+OtMW/d11pYluW2kfni3BfcHzD/atfTxtMwWu70hg1PGo552fMwc+BMqKVqnDaexlfnv8KOcztQfLM46M9gwGBk8khMy5yG3PRc9I/vH5JhsDpbHU5WneQCYXFVMc5Wnw3aqyMVSjEocRCyErN8PYb6LAxOHIwYSUyLfw+b2waFWNGhWiItGLUmWtoJ3Ftb3V53wDF7AaHydg+lvzfL30vZ9Ji9pgszXF4XL4GSAcMt3Gg6986/T2WcLI4Lj/45k/5FO8GudZVA6XQ6sWPXDgwfPRxmt7lFWKy13g6S9mYh0lITdEuo9mDg25PQf7RdKEiF0rv2RMaIY1BRWoGc4TnQKrXcqmz/HqPhQAGQAmCnRGsA3HxiM36z+zcYnzb+vh631Jx/G5m/Ff6NOz0hVhKLx9Meh81lw74r+7j5eTKRDM889AzmZs3FuLRxEDACXG+4jl0XdmHn+Z3YV76v1e1gUlQpeDLjSUxNn4rH0x4P6bC33W1HibGkxRBysCdgBgz6avoGDCFn6bPQPbZ7h39utASjaGkn0DXa6l+Q49/qpWmwrLfX+3q3rL7Q4p9L6b+9fx6lf+/C+7ERejBSobRFSGx69F5b4TGUgbIzj6fNZWu1l5F7szcJkbff9DF6lC0s4zaXNjlMvp7ktt638rX2bsbflicznsQ3s77p9P0EQwGQAmCnRGsAPFt9FgM+HAC5SI5bS2/xPk/E4rTg05OfYv3R9dw2MgJGgMl9JyMtLg37r+zH2eqz3O1T1amYmzUXc7LmoFdcLwCA2WHGd5e+w85zO7Hz3E5YPL6tShgwAX+EZCIZHk97HLnpuchNzw3LkUpe1ovL9ZdbDCH750A2l6hMbDGE3FfTt805jV0hLNwP0dJOIPLa2nRBTtMwWW+tx8EjB6FKVeHYjWOwu+3QKrQwWoyotdb65t7dDpZd4UxvmUh2Jzx2IFDKhXKcPnEaE8ZOgEapuS89lC6PK2SLRzxeD8xO37zXu4XJels9ym2cAuAAABLRSURBVK6VQaqS+qYv3P7a9H7T8enTn4aknuYoAFIA7JRoDYAsyyLhvQTUWGtw+OXD+I/k/+C7RAC+4PRd2XdYf3Q9vr/0PXc9W5+N2YNno7S2FP8880/u7EwAGJ82HvOy5+HpzKchF8vhcrnwze5voB6oxu5Lu5GgTMDAhIHYfWE3dl/cjWsN1wJ+5oD4Ab4wmJGLET1HhPXJ2WgxthhCLq0tDTrsphArMDhxMLY/uz3o0U+RFhZaEy3tBKKnre1tp5f1+s7Uvb3gptpaHfBxsGv+BWMdJRKIIGAE3DGOHtYTtuHwYD2UAb2OzQNmsNDZxYa8W3tMWZYN2yp0CoC0CpjcA4ZhsH7SesQr4zE4cTDf5XAEjABT0qdgSvoUlBhL8Nejf8Wnpz5FUVURFj66EJuf3Iz3J72PL899ib8X/x37yvdxx8uppWrMGjgLLw16CQIIMDp1NMb3Hc/d95MZT4JlWZwxnsHui74weOjaIZRUl6CkugRrDq1BnCwOk/pMQm56LqakT7mng+HbkqBMwIQ+EzChzwTumtVlxRnjGW6/wuKbxTh18xSsLiuOVx5HvCI+pDUQ8qAQMAJuQ+5+6Neu77G6rL4w2DwoWqoDQqP/Wp2tDizYoFtBtUUilPiOdBTLIRVKIRFKIBQIuREHt9cNl9sFk9UEVsii0dnITVFxeBxwWB13PYmovTobKNVSNR6KfygktTTX1bYgijQUAMk9eWHwC3yX0KYBCQPw8bSPsXL8Smwt2oqZA2cC8PWMvTD4Bbww+AWU15fjk5OfYGvxVlw1XcXmgs3YXLAZKbIUXNRdxJzsOQFbRTAMg0GJgzAocRCW5ixFna0O35V9h90Xd2NP2R7U2eqwvWQ7tpdsBwMGjyY/yg0VZ+mzwvJkphAr8EiPR/BIj0e4ax6vBxfrLqKsrixs2/QQEokUYgVS1ClBe82D8Xg9vi1gggXF2xuSN7/m3yfS6XEG7GnaKpcvzPr3a4yT+fYEVIqVUIgVvn0fhRKIBCIuQHq8Htg99hbD500X/Pg3gu9soIyTxaF+STvaQbocCoAkoukUOiweuTjo19K6pWHF2BV4a8xb2F++H38v/ju+OPsFrtqv4g97/4Bl+5fh+UHP45OnPgn6/Rq5BrMGzcKsQbPg8XpwtPIoN1R88uZJHLl+BEeuH8Gf9v8JSbFJmNp3KnIzcvFE7ydarOYNJaFAiExdJjJ1mWH7GYQQ3/+1eKXvqL/2YFlfb16rvYtNQ6OlGjcabsDiscDLeoMeRdmWGEkM4hXxvvOtlfHI0GZwZ13rFDrEyeOgFCkhE8kgEUkAFi3Ob24aIP1fax4o1dLQHA9I7j8KgCTqCRgBxvcej/G9x2PdE+vw1v97CwWeApy4cQJiQfvmUgkFQu6c4ZXjV+J6w3V8e/Fb7L64G/+6/C8YzAZsKdqCLUVbIBFKMCZ1DDd3sLObDRNCHgwMw/iGUKWxSOuW1uZt/fPiJkyagAZ3Q8ug6J/DGOSaf3ufRmcjym+Vt6s2kUAUEBDjlfHQyX3vM3WZd641uc2DdNoIaYkCICFNdJN3wxTdFGycuhGl9aUdPpfWL1mVjFeGvoJXhr4Cu9uOA1cOcHMHL9dfRv7lfORfzsei7xYhQ5vBDRWPSh3F+6pqQkjXIRaKoZfpuY3574ZlWZgcppZBsY0FMGanGW6vG1WNVahqrGp3bWqpOiAUBoRH/7UmH8dIYmheXxdCAZCQVgxMGBiS+5GJZJjUdxIm9Z2EDZM3oLS2lBsq/vHqj9ypHuuOrEOsJBYT+kxAbnoupqZPbfeTPiGEAL5eRv9G2O0dXbC77ai11gYPis0WwFRbqlFrq4WX9XL7AJbVlbXr50iFUi4gbpi8gTtalPCDAiAh9xHDMNz8vN+N+B1MdhPyL+dj98Xd+PbitzBajNhxbgd2nNsBABjafSg3VDwsaRgEjIDnFhBCIo1MJEMPVQ/0UPVo1+29rBf1tvp2b69TY62B1WWFw+PgzhHn49QYEogCICE8UsvUeLb/s3i2/7Pwsl4UGAq4oeIThhMouFGAghsFeOfgO0hQJmBK3ynITc/FxD4ToZbR5GtCyP0nYATQKrTQKrQd2mKnaSgckjgkzFWSu6EASEgXIWAEGN5jOIb3GI4VY1egqrEKey7uwe6Lu/H9pe9htBjxyclP8MnJTyASiJCTksPNHczUZdLcGkJIl6UQK5AalxqW05PIvaEASEgXpY/RY272XMzNngunx4mfrv7EzR0srS3FD1d+wA9XfsDi/MVIi0vD1PSpyE3Pxbi0cfe8eIUQQkh0oAlFhDwAJEIJHk97HGsnrcX5BedR9loZNkzegIl9JkIilKD8Vjk+OP4Bpm6bCs27Gpy6eYrvkgkhhHRh1ANIyAOoj6YPFj66EAsfXQiL04K95Xu53kGz04yHdOE5mokQQkhkoABIyANOKVFier/pmN5vOliWxbWGa7RBKyGEkDbREDAhEYRhmHafY0oIISR6UQAkhBBCCIkyFAAJIYQQQqIMBUBCCCGEkChDAZAQQgghJMpQACSEEEIIiTJRGQCvXLmCl19+GWlpaZDL5ejTpw+WL18Op9PJd2mEEEIIIWEXlfsAnj9/Hl6vFx9//DH69u2LM2fO4Fe/+hUsFgvee+89vssjhBBCCAmrqAyAkydPxuTJk7nPe/fujdLSUnz00UcUAAkhhBAS8aIyAAZjMpmg0WjavI3D4YDD4Qj4HgCoq6sLa218c7lcsFqtqK2thVgc2SdMREtbqZ2RJ1raSu2MPHy01Ww2AwBYlr0vP68rogAI4NKlS9i4cSPWrl3b5u1Wr16Nt99+u8X1jIyMcJVGCCGEkDAxm81Qq9V8l8ELho2g+LtixYqgAa2p48ePY9iwYdznBoMBY8aMwZgxY7Bly5Y2v7d5D+CtW7eQmpqKq1evRvQvUENDA3r27Ilr165BpVLxXU5YRUtbqZ2RJ1raSu2MPHy0lWVZmM1mJCUlQSCIyvWwkdUDuGDBAsycObPN2/Tq1Yv72GAwYNy4cXjssceQl5d31/uXSqWQSqUtrqvV6oj/DwoAKpUqKtoJRE9bqZ2RJ1raSu2MPPe7rZHccdMeERUAdToddDpdu25bWVmJcePGYejQodi6dWvUvgIghBBCSPSJqADYXgaDAWPHjkVKSgree+89VFdXc1/T6/U8VkYIIYQQEn5RGQC///57lJWVoaysDMnJyQFf68iUSKlUiuXLlwcdFo4k0dJOIHraSu2MPNHSVmpn5ImmtnYlEbUIhBBCCCGE3B1NfCOEEEIIiTIUAAkhhBBCogwFQEIIIYSQKEMBkBBCCCEkylAADIErV67g5ZdfRlpaGuRyOfr06YPly5fD6XTyXVpIfPjhh0hLS4NMJsPQoUPx448/8l1SSK1evRrDhw9HbGwsEhIS8NRTT6G0tJTvssJu9erVYBgGixYt4ruUsKisrMSLL74IrVYLhUKBrKwsFBQU8F1WSLndbvzxj3/knnt69+6Nd955B16vl+/SOu3gwYOYNm0akpKSwDAMdu7cGfB1lmWxYsUKJCUlQS6XY+zYsSgpKeGp2nvXVjtdLheWLFmCQYMGQalUIikpCS+99BIMBgOPFd+7uz2mTf36178GwzBYv379fawwulAADIHz58/D6/Xi448/RklJCdatW4fNmzdj2bJlfJfWadu3b8eiRYvw5ptvoqioCKNGjcKUKVNw9epVvksLmQMHDmD+/Pk4cuQI8vPz4Xa7MXHiRFgsFr5LC5vjx48jLy8PgwcP5ruUsKivr8fIkSMhFouxZ88enD17FmvXrkVcXBzfpYXUu+++i82bN2PTpk04d+4c1qxZg7/85S/YuHEj36V1msViwZAhQ7Bp06agX1+zZg3ef/99bNq0CcePH4der8eECRNgNpvvc6Wd01Y7rVYrCgsL8ac//QmFhYXYsWMHLly4gOnTp/NQaefd7TH127lzJ44ePYqkpKT7VFmUYklYrFmzhk1LS+O7jE575JFH2FdffTXgWmZmJrt06VKeKgo/o9HIAmAPHDjAdylhYTab2fT0dDY/P58dM2YM+/rrr/NdUsgtWbKEzcnJ4buMsMvNzWXnzZsXcO3nP/85++KLL/JUUXgAYL/88kvuc6/Xy+r1evbPf/4zd81ut7NqtZrdvHkzHyWGRPN2BnPs2DEWAFtRUXGfqgqP1tp6/fp1tkePHuyZM2fY1NRUdt26dTxUFx2oBzBMTCYTNBoN32V0itPpREFBASZOnBhwfeLEiTh06BBPVYWfyWQCgAf+8WvN/PnzkZubiyeeeILvUsLm66+/xrBhw/Dcc88hISEB2dnZ+Nvf/sZ3WSGXk5ODvXv34sKFCwCAkydP4qeffsLUqVN5riy8ysvLUVVVFfDcJJVKMWbMmIh+bgJ8z08Mw0RcbzYAeL1ezJ49G4sXL8aAAQP4LifiReVJIOF26dIlbNy4EWvXruW7lE6pqamBx+NBYmJiwPXExERUVVXxVFV4sSyLN954Azk5ORg4cCDf5YTcZ599hsLCQhw/fpzvUsLq8uXL+Oijj/DGG29g2bJlOHbsGBYuXAipVIqXXnqJ7/JCZsmSJTCZTMjMzIRQKITH48HKlSsxa9YsvksLK//zT7DnpoqKCj5Kui/sdjuWLl2K559/HiqViu9yQu7dd9+FSCTCwoUL+S4lKlAAbMOKFSvw9ttvt3mb48ePY9iwYdznBoMBkydPxnPPPYdf/vKX4S7xvmAYJuBzlmVbXIsUCxYswKlTp/DTTz/xXUrIXbt2Da+//jq+//57yGQyvssJK6/Xi2HDhmHVqlUAgOzsbJSUlOCjjz6KqAC4fft2/OMf/8C2bdswYMAAFBcXY9GiRUhKSsKcOXP4Li/soum5yeVyYebMmfB6vfjwww/5LifkCgoKsGHDBhQWFkbsY9jVUABsw4IFCzBz5sw2b9OrVy/uY4PBgHHjxuGxxx5DXl5emKsLP51OB6FQ2KK3z2g0tnjlHQlee+01fP311zh48GCLM6IjQUFBAYxGI4YOHcpd83g8OHjwIDZt2gSHwwGhUMhjhaHTvXt39O/fP+DaQw89hC+++IKnisJj8eLFWLp0Kfc8NWjQIFRUVGD16tURHQD1ej0AX09g9+7dueuR+tzkcrkwY8YMlJeXY9++fRHZ+/fjjz/CaDQiJSWFu+bxePC73/0O69evx5UrV/grLkJRAGyDTqeDTqdr120rKysxbtw4DB06FFu3boVA8OBPr5RIJBg6dCjy8/Px9NNPc9fz8/Pxs5/9jMfKQotlWbz22mv48ssv8cMPPyAtLY3vksJi/PjxOH36dMC1uXPnIjMzE0uWLImY8AcAI0eObLGVz4ULF5CamspTReFhtVpbPNcIhcKI2AamLWlpadDr9cjPz0d2djYA35zlAwcO4N133+W5utDyh7+LFy9i//790Gq1fJcUFrNnz24xL3nSpEmYPXs25s6dy1NVkY0CYAgYDAaMHTsWKSkpeO+991BdXc19zf9K9UH1xhtvYPbs2Rg2bBjXs3n16lW8+uqrfJcWMvPnz8e2bdvw1VdfITY2luvxVKvVkMvlPFcXOrGxsS3mNSqVSmi12oib7/jb3/4WI0aMwKpVqzBjxgwcO3YMeXl5EdEz39S0adOwcuVKpKSkYMCAASgqKsL777+PefPm8V1apzU2NqKsrIz7vLy8HMXFxdBoNEhJScGiRYuwatUqpKenIz09HatWrYJCocDzzz/PY9Ud11Y7k5KS8Oyzz6KwsBC7du2Cx+Phnp80Gg0kEglfZd+Tuz2mzcOtWCyGXq9Hv3797nep0YHfRciRYevWrSyAoG+R4IMPPmBTU1NZiUTCPvzwwxG3PUprj93WrVv5Li3sInUbGJZl2W+++YYdOHAgK5VK2czMTDYvL4/vkkKuoaGBff3119mUlBRWJpOxvXv3Zt98803W4XDwXVqn7d+/P+j/yzlz5rAs69sKZvny5axer2elUik7evRo9vTp0/wWfQ/aamd5eXmrz0/79+/nu/QOu9tj2hxtAxNeDMuybNhTJiGEEEII6TIe/IlqhBBCCCGkQygAEkIIIYREGQqAhBBCCCFRhgIgIYQQQkiUoQBICCGEEBJlKAASQgghhEQZCoCEEEIIIVGGAiAhhBBCSJShAEgIIYQQEmUoABJCCCGERBkKgIQQQgghUYYCICGEEEJIlKEASAghhBASZSgAEkIIIYREGQqAhBBCCCFRhgIgIYQQQkiUoQBICCGEEBJlKAASQgghhEQZCoCEEEIIIVGGAiAhhBBCSJShAEgIIYQQEmUoABJCCCGERBkKgIQQQgghUYYCICGEEEJIlKEASAghhBASZSgAEkIIIYREGQqAhBBCCCFRhgIgIYQQQkiUoQBICCGEEBJlKAASQgghhEQZCoCEEEIIIVHm/wNgwQxX62yotwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename=\"Figure_1.png\",width=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![gif](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTstar/animation.gif)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Ref\n",
-    "\n",
-    "- [Sampling-based Algorithms for Optimal Motion Planning](https://arxiv.org/pdf/1105.1186.pdf)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/docs/modules/path_planning/path_planning_main.rst b/docs/modules/path_planning/path_planning_main.rst
index 60651dd5ff..50ed9bdbcf 100644
--- a/docs/modules/path_planning/path_planning_main.rst
+++ b/docs/modules/path_planning/path_planning_main.rst
@@ -91,6 +91,9 @@ Ref:
 -  `Optimal rough terrain trajectory generation for wheeled mobile
    robots <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
 
+
+
+
 State Lattice Planning
 ----------------------
 
@@ -179,27 +182,8 @@ This is a simple path planning code with Rapidly-Exploring Random Trees
 Black circles are obstacles, green line is a searched tree, red crosses
 are start and goal positions.
 
-.. _rrt*:
-
-RRT\*
-~~~~~
-
-|10|
-
-This is a path planning code with RRT\*
-
-Black circles are obstacles, green line is a searched tree, red crosses
-are start and goal positions.
-
 .. include:: rrt_star.rst
 
-Ref:
-
--  `Incremental Sampling-based Algorithms for Optimal Motion
-   Planning <https://arxiv.org/abs/1005.0416>`__
-
--  `Sampling-based Algorithms for Optimal Motion
-   Planning <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.419.5503&rep=rep1&type=pdf>`__
 
 RRT with dubins path
 ~~~~~~~~~~~~~~~~~~~~
@@ -367,20 +351,9 @@ Ref:
    Autonomous
    Vehicles <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.294.6438&rep=rep1&type=pdf>`__
 
-Quintic polynomials planning
-----------------------------
 
-Motion planning with quintic polynomials.
-
-|2|
-
-It can calculate 2D path, velocity, and acceleration profile based on
-quintic polynomials.
-
-Ref:
+.. include:: quintic_polynomials_planner.rst
 
--  `Local Path Planning And Motion Control For Agv In
-   Positioning <http://ieeexplore.ieee.org/document/637936/>`__
 
 Dubins path planning
 --------------------
diff --git a/docs/modules/path_planning/quintic_polynomials_planner.rst b/docs/modules/path_planning/quintic_polynomials_planner.rst
new file mode 100644
index 0000000000..2fab8a51ab
--- /dev/null
+++ b/docs/modules/path_planning/quintic_polynomials_planner.rst
@@ -0,0 +1,106 @@
+
+Quintic polynomials planning
+----------------------------
+
+Motion planning with quintic polynomials.
+
+|2|
+
+It can calculate 2D path, velocity, and acceleration profile based on
+quintic polynomials.
+
+
+
+Quintic polynomials for one dimensional robot motion
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+We assume a one-dimensional robot motion :math:`x(t)` at time :math:`t` is
+formulated as a quintic polynomials based on time as follows:
+
+.. math:: x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4+a_5t^5
+    :label: eq1
+
+:math:`a_0, a_1. a_2, a_3, a_4, a_5` are parameters of the quintic polynomial.
+
+It is assumed that terminal states (start and end) are known as boundary conditions.
+
+Start position, velocity, and acceleration are :math:`x_s, v_s, a_s` respectively.
+
+End position, velocity, and acceleration are :math:`x_e, v_e, a_e` respectively.
+
+So, when time is 0.
+
+.. math:: x(0) = a_0 = x_s
+    :label: eq2
+
+Then, differentiating the equation :eq:`eq1` with t,
+
+.. math:: x'(t) = a_1+2a_2t+3a_3t^2+4a_4t^3+5a_5t^4
+    :label: eq3
+
+So, when time is 0,
+
+.. math:: x'(0) = a_1 = v_s
+    :label: eq4
+
+Then, differentiating the equation :eq:`eq3` with t again,
+
+.. math:: x''(t) = 2a_2+6a_3t+12a_4t^2
+    :label: eq5
+
+So, when time is 0,
+
+.. math:: x''(0) = 2a_2 = a_s
+    :label: eq6
+
+so, we can calculate :math:`a_0, a_1, a_2` with eq. :eq:`eq2`, :eq:`eq4`, :eq:`eq6` and boundary conditions.
+
+:math:`a_3, a_4, a_5` are still unknown in eq :eq:`eq1`.
+
+We assume that the end time for a maneuver is :math:`T`, we can get these equations from eq :eq:`eq1`, :eq:`eq3`, :eq:`eq5`:
+
+.. math:: x(T)=a_0+a_1T+a_2T^2+a_3T^3+a_4T^4+a_5T^5=x_e
+    :label: eq7
+
+.. math:: x'(T)=a_1+2a_2T+3a_3T^2+4a_4T^3+5a_5T^4=v_e
+    :label: eq8
+
+.. math:: x''(T)=2a_2+6a_3T+12a_4T^2+20a_5T^3=a_e
+    :label: eq9
+
+From eq :eq:`eq7`, :eq:`eq8`, :eq:`eq9`, we can calculate :math:`a_3, a_4, a_5` to solve the linear equations: :math:`Ax=b`
+
+.. math:: \begin{bmatrix} T^3 & T^4 & T^5 \\ 3T^2 & 4T^3 & 5T^4 \\ 6T & 12T^2 & 20T^3 \end{bmatrix}\begin{bmatrix} a_3\\ a_4\\ a_5\end{bmatrix}=\begin{bmatrix} x_e-x_s-v_sT-0.5a_sT^2\\ v_e-v_s-a_sT\\ a_e-a_s\end{bmatrix}
+
+We can get all unknown parameters now.
+
+Quintic polynomials for two dimensional robot motion (x-y)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If you use two quintic polynomials along x axis and y axis, you can plan for two dimensional robot motion in x-y plane.
+
+.. math:: x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4+a_5t^5
+    :label: eq10
+
+.. math:: y(t) = b_0+b_1t+b_2t^2+b_3t^3+b_4t^4+b_5t^5
+    :label: eq11
+
+It is assumed that terminal states (start and end) are known as boundary conditions.
+
+Start position, orientation, velocity, and acceleration are :math:`x_s, y_s, \theta_s, v_s, a_s` respectively.
+
+End position, orientation, velocity, and acceleration are :math:`x_e, y_e. \theta_e, v_e, a_e` respectively.
+
+Each velocity and acceleration boundary condition can be calculated with each orientation.
+
+:math:`v_{xs}=v_scos(\theta_s), v_{ys}=v_ssin(\theta_s)`
+
+:math:`v_{xe}=v_ecos(\theta_e), v_{ye}=v_esin(\theta_e)`
+
+References:
+~~~~~~~~~~~
+
+-  `Local Path Planning And Motion Control For Agv In
+   Positioning <http://ieeexplore.ieee.org/document/637936/>`__
+
+
diff --git a/docs/modules/path_planning/rrt_star.rst b/docs/modules/path_planning/rrt_star.rst
index 5b8d62e7e1..cf136d529f 100644
--- a/docs/modules/path_planning/rrt_star.rst
+++ b/docs/modules/path_planning/rrt_star.rst
@@ -1,27 +1,22 @@
+RRT\*
+~~~~~
 
-Simulation
-^^^^^^^^^^
-
-.. code-block:: ipython3
-
-    from IPython.display import Image
-    Image(filename="Figure_1.png",width=600)
+.. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTstar/animation.gif
+   :alt: gif
 
+This is a path planning code with RRT\*
 
+Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions.
 
+Simulation
+^^^^^^^^^^
 
 .. image:: rrt_star_files/rrt_star_1_0.png
    :width: 600px
 
 
-
-.. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTstar/animation.gif
-   :alt: gif
-
-   gif
-
 Ref
 ^^^
+-  `Sampling-based Algorithms for Optimal Motion Planning <https://arxiv.org/pdf/1105.1186.pdf>`__
+-  `Incremental Sampling-based Algorithms for Optimal Motion Planning <https://arxiv.org/abs/1005.0416>`__
 
--  `Sampling-based Algorithms for Optimal Motion
-   Planning <https://arxiv.org/pdf/1105.1186.pdf>`__

From 50ba79a3f53cb96c34002d54375be022a9bec5c9 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 27 Nov 2021 20:10:45 +0900
Subject: [PATCH 0547/1080] Fix clean up other docs (#580)

* update docs

* update docs

* update docs

* update docs
---
 .../rocket_powered_landing/figure.png         | Bin 688548 -> 0 bytes
 .../rocket_powered_landing.ipynb              | 482 ------------------
 .../Planar_Two_Link_IK.ipynb                  | 423 ---------------
 PathTracking/cgmres_nmpc/Figure_1.png         | Bin 33823 -> 0 bytes
 PathTracking/cgmres_nmpc/Figure_2.png         | Bin 30942 -> 0 bytes
 PathTracking/cgmres_nmpc/Figure_3.png         | Bin 25270 -> 0 bytes
 PathTracking/cgmres_nmpc/Figure_4.png         | Bin 19078 -> 0 bytes
 PathTracking/cgmres_nmpc/cgmres_nmpc.ipynb    | 208 --------
 ...redictive_speed_and_steering_control.ipynb | 333 ------------
 PathTracking/rear_wheel_feedback/Figure_2.png | Bin 39590 -> 0 bytes
 .../aerial_navigation_main.rst                |   6 +-
 .../rocket_powered_landing.rst                |   8 +-
 .../arm_navigation/arm_navigation_main.rst    |  11 +-
 ...Two_Link_IK.rst => planar_two_link_ik.rst} |   5 +-
 .../Bezier_path_planning}/Figure_1.png        | Bin
 .../Bezier_path_planning}/Figure_2.png        | Bin
 .../path_planning/path_planning_main.rst      |   5 +-
 ..._predictive_speed_and_steering_control.rst |   2 +-
 docs/modules/path_tracking/cgmres_nmpc.rst    |  39 --
 .../path_tracking/path_tracking_main.rst      |  23 +-
 20 files changed, 19 insertions(+), 1526 deletions(-)
 delete mode 100644 AerialNavigation/rocket_powered_landing/figure.png
 delete mode 100644 AerialNavigation/rocket_powered_landing/rocket_powered_landing.ipynb
 delete mode 100644 ArmNavigation/two_joint_arm_to_point_control/Planar_Two_Link_IK.ipynb
 delete mode 100644 PathTracking/cgmres_nmpc/Figure_1.png
 delete mode 100644 PathTracking/cgmres_nmpc/Figure_2.png
 delete mode 100644 PathTracking/cgmres_nmpc/Figure_3.png
 delete mode 100644 PathTracking/cgmres_nmpc/Figure_4.png
 delete mode 100644 PathTracking/cgmres_nmpc/cgmres_nmpc.ipynb
 delete mode 100644 PathTracking/model_predictive_speed_and_steer_control/Model_predictive_speed_and_steering_control.ipynb
 delete mode 100644 PathTracking/rear_wheel_feedback/Figure_2.png
 rename docs/modules/arm_navigation/{Planar_Two_Link_IK.rst => planar_two_link_ik.rst} (99%)
 rename {PathPlanning/BezierPath => docs/modules/path_planning/Bezier_path_planning}/Figure_1.png (100%)
 rename {PathPlanning/BezierPath => docs/modules/path_planning/Bezier_path_planning}/Figure_2.png (100%)

diff --git a/AerialNavigation/rocket_powered_landing/figure.png b/AerialNavigation/rocket_powered_landing/figure.png
deleted file mode 100644
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diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.ipynb b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.ipynb
deleted file mode 100644
index 308512bfd7..0000000000
--- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.ipynb
+++ /dev/null
@@ -1,482 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Simulation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "    div#notebook-container    { width: 95%; }\n",
-       "    div#menubar-container     { width: 65%; }\n",
-       "    div#maintoolbar-container { width: 99%; }\n",
-       "</style>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename=\"figure.png\",width=600)\n",
-    "from IPython.display import display, HTML\n",
-    "\n",
-    "display(HTML(data=\"\"\"\n",
-    "<style>\n",
-    "    div#notebook-container    { width: 95%; }\n",
-    "    div#menubar-container     { width: 65%; }\n",
-    "    div#maintoolbar-container { width: 99%; }\n",
-    "</style>\n",
-    "\"\"\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![gif](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/rocket_powered_landing/animation.gif)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Equation generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sympy as sp\n",
-    "import numpy as np\n",
-    "from IPython.display import display\n",
-    "sp.init_printing(use_latex='mathjax')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# parameters\n",
-    "# Angular moment of inertia\n",
-    "J_B = 1e-2 * np.diag([1., 1., 1.])\n",
-    "\n",
-    "# Gravity\n",
-    "g_I = np.array((-1, 0., 0.))\n",
-    "\n",
-    "# Fuel consumption\n",
-    "alpha_m = 0.01\n",
-    "\n",
-    "# Vector from thrust point to CoM\n",
-    "r_T_B = np.array([-1e-2, 0., 0.])\n",
-    "\n",
-    "\n",
-    "def dir_cosine(q):\n",
-    "        return np.matrix([\n",
-    "            [1 - 2 * (q[2] ** 2 + q[3] ** 2), 2 * (q[1] * q[2] +\n",
-    "                                                   q[0] * q[3]), 2 * (q[1] * q[3] - q[0] * q[2])],\n",
-    "            [2 * (q[1] * q[2] - q[0] * q[3]), 1 - 2 *\n",
-    "             (q[1] ** 2 + q[3] ** 2), 2 * (q[2] * q[3] + q[0] * q[1])],\n",
-    "            [2 * (q[1] * q[3] + q[0] * q[2]), 2 * (q[2] * q[3] -\n",
-    "                                                   q[0] * q[1]), 1 - 2 * (q[1] ** 2 + q[2] ** 2)]\n",
-    "        ])\n",
-    "\n",
-    "def omega(w):\n",
-    "        return np.matrix([\n",
-    "            [0, -w[0], -w[1], -w[2]],\n",
-    "            [w[0], 0, w[2], -w[1]],\n",
-    "            [w[1], -w[2], 0, w[0]],\n",
-    "            [w[2], w[1], -w[0], 0],\n",
-    "        ])\n",
-    "\n",
-    "def skew(v):\n",
-    "    return np.matrix([\n",
-    "            [0, -v[2], v[1]],\n",
-    "            [v[2], 0, -v[0]],\n",
-    "            [-v[1], v[0], 0]\n",
-    "        ])\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "f = sp.zeros(14, 1)\n",
-    "\n",
-    "x = sp.Matrix(sp.symbols(\n",
-    "    'm rx ry rz vx vy vz q0 q1 q2 q3 wx wy wz', real=True))\n",
-    "u = sp.Matrix(sp.symbols('ux uy uz', real=True))\n",
-    "\n",
-    "g_I = sp.Matrix(g_I)\n",
-    "r_T_B = sp.Matrix(r_T_B)\n",
-    "J_B = sp.Matrix(J_B)\n",
-    "\n",
-    "C_B_I = dir_cosine(x[7:11, 0])\n",
-    "C_I_B = C_B_I.transpose()\n",
-    "\n",
-    "f[0, 0] = - alpha_m * u.norm()\n",
-    "f[1:4, 0] = x[4:7, 0]\n",
-    "f[4:7, 0] = 1 / x[0, 0] * C_I_B * u + g_I\n",
-    "f[7:11, 0] = 1 / 2 * omega(x[11:14, 0]) * x[7: 11, 0]\n",
-    "f[11:14, 0] = J_B ** -1 * \\\n",
-    "    (skew(r_T_B) * u - skew(x[11:14, 0]) * J_B * x[11:14, 0])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$$\\left[\\begin{matrix}- 0.01 \\sqrt{ux^{2} + uy^{2} + uz^{2}}\\\\vx\\\\vy\\\\vz\\\\\\frac{- 1.0 m - ux \\left(2 q_{2}^{2} + 2 q_{3}^{2} - 1\\right) - 2 uy \\left(q_{0} q_{3} - q_{1} q_{2}\\right) + 2 uz \\left(q_{0} q_{2} + q_{1} q_{3}\\right)}{m}\\\\\\frac{2 ux \\left(q_{0} q_{3} + q_{1} q_{2}\\right) - uy \\left(2 q_{1}^{2} + 2 q_{3}^{2} - 1\\right) - 2 uz \\left(q_{0} q_{1} - q_{2} q_{3}\\right)}{m}\\\\\\frac{- 2 ux \\left(q_{0} q_{2} - q_{1} q_{3}\\right) + 2 uy \\left(q_{0} q_{1} + q_{2} q_{3}\\right) - uz \\left(2 q_{1}^{2} + 2 q_{2}^{2} - 1\\right)}{m}\\\\- 0.5 q_{1} wx - 0.5 q_{2} wy - 0.5 q_{3} wz\\\\0.5 q_{0} wx + 0.5 q_{2} wz - 0.5 q_{3} wy\\\\0.5 q_{0} wy - 0.5 q_{1} wz + 0.5 q_{3} wx\\\\0.5 q_{0} wz + 0.5 q_{1} wy - 0.5 q_{2} wx\\\\0\\\\1.0 uz\\\\- 1.0 uy\\end{matrix}\\right]$$"
-      ],
-      "text/plain": [
-       "⎡                                  _________________                          \n",
-       "⎢                                 ╱   2     2     2                           \n",
-       "⎢                         -0.01⋅╲╱  ux  + uy  + uz                            \n",
-       "⎢                                                                             \n",
-       "⎢                                     vx                                      \n",
-       "⎢                                                                             \n",
-       "⎢                                     vy                                      \n",
-       "⎢                                                                             \n",
-       "⎢                                     vz                                      \n",
-       "⎢                                                                             \n",
-       "⎢            ⎛    2       2    ⎞                                              \n",
-       "⎢-1.0⋅m - ux⋅⎝2⋅q₂  + 2⋅q₃  - 1⎠ - 2⋅uy⋅(q₀⋅q₃ - q₁⋅q₂) + 2⋅uz⋅(q₀⋅q₂ + q₁⋅q₃)\n",
-       "⎢─────────────────────────────────────────────────────────────────────────────\n",
-       "⎢                                      m                                      \n",
-       "⎢                                                                             \n",
-       "⎢                              ⎛    2       2    ⎞                            \n",
-       "⎢    2⋅ux⋅(q₀⋅q₃ + q₁⋅q₂) - uy⋅⎝2⋅q₁  + 2⋅q₃  - 1⎠ - 2⋅uz⋅(q₀⋅q₁ - q₂⋅q₃)     \n",
-       "⎢    ────────────────────────────────────────────────────────────────────     \n",
-       "⎢                                     m                                       \n",
-       "⎢                                                                             \n",
-       "⎢                                                      ⎛    2       2    ⎞    \n",
-       "⎢    -2⋅ux⋅(q₀⋅q₂ - q₁⋅q₃) + 2⋅uy⋅(q₀⋅q₁ + q₂⋅q₃) - uz⋅⎝2⋅q₁  + 2⋅q₂  - 1⎠    \n",
-       "⎢    ─────────────────────────────────────────────────────────────────────    \n",
-       "⎢                                      m                                      \n",
-       "⎢                                                                             \n",
-       "⎢                     -0.5⋅q₁⋅wx - 0.5⋅q₂⋅wy - 0.5⋅q₃⋅wz                      \n",
-       "⎢                                                                             \n",
-       "⎢                      0.5⋅q₀⋅wx + 0.5⋅q₂⋅wz - 0.5⋅q₃⋅wy                      \n",
-       "⎢                                                                             \n",
-       "⎢                      0.5⋅q₀⋅wy - 0.5⋅q₁⋅wz + 0.5⋅q₃⋅wx                      \n",
-       "⎢                                                                             \n",
-       "⎢                      0.5⋅q₀⋅wz + 0.5⋅q₁⋅wy - 0.5⋅q₂⋅wx                      \n",
-       "⎢                                                                             \n",
-       "⎢                                      0                                      \n",
-       "⎢                                                                             \n",
-       "⎢                                   1.0⋅uz                                    \n",
-       "⎢                                                                             \n",
-       "⎣                                   -1.0⋅uy                                   \n",
-       "\n",
-       "⎤\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎥\n",
-       "⎦"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display(sp.simplify(f)) # f"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$$\\left[\\begin{array}{cccccccccccccc}0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\\frac{ux \\left(2 q_{2}^{2} + 2 q_{3}^{2} - 1\\right) + 2 uy \\left(q_{0} q_{3} - q_{1} q_{2}\\right) - 2 uz \\left(q_{0} q_{2} + q_{1} q_{3}\\right)}{m^{2}} & 0 & 0 & 0 & 0 & 0 & 0 & \\frac{2 \\left(q_{2} uz - q_{3} uy\\right)}{m} & \\frac{2 \\left(q_{2} uy + q_{3} uz\\right)}{m} & \\frac{2 \\left(q_{0} uz + q_{1} uy - 2 q_{2} ux\\right)}{m} & \\frac{2 \\left(- q_{0} uy + q_{1} uz - 2 q_{3} ux\\right)}{m} & 0 & 0 & 0\\\\\\frac{- 2 ux \\left(q_{0} q_{3} + q_{1} q_{2}\\right) + uy \\left(2 q_{1}^{2} + 2 q_{3}^{2} - 1\\right) + 2 uz \\left(q_{0} q_{1} - q_{2} q_{3}\\right)}{m^{2}} & 0 & 0 & 0 & 0 & 0 & 0 & \\frac{2 \\left(- q_{1} uz + q_{3} ux\\right)}{m} & \\frac{2 \\left(- q_{0} uz - 2 q_{1} uy + q_{2} ux\\right)}{m} & \\frac{2 \\left(q_{1} ux + q_{3} uz\\right)}{m} & \\frac{2 \\left(q_{0} ux + q_{2} uz - 2 q_{3} uy\\right)}{m} & 0 & 0 & 0\\\\\\frac{2 ux \\left(q_{0} q_{2} - q_{1} q_{3}\\right) - 2 uy \\left(q_{0} q_{1} + q_{2} q_{3}\\right) + uz \\left(2 q_{1}^{2} + 2 q_{2}^{2} - 1\\right)}{m^{2}} & 0 & 0 & 0 & 0 & 0 & 0 & \\frac{2 \\left(q_{1} uy - q_{2} ux\\right)}{m} & \\frac{2 \\left(q_{0} uy - 2 q_{1} uz + q_{3} ux\\right)}{m} & \\frac{2 \\left(- q_{0} ux - 2 q_{2} uz + q_{3} uy\\right)}{m} & \\frac{2 \\left(q_{1} ux + q_{2} uy\\right)}{m} & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & - 0.5 wx & - 0.5 wy & - 0.5 wz & - 0.5 q_{1} & - 0.5 q_{2} & - 0.5 q_{3}\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.5 wx & 0 & 0.5 wz & - 0.5 wy & 0.5 q_{0} & - 0.5 q_{3} & 0.5 q_{2}\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.5 wy & - 0.5 wz & 0 & 0.5 wx & 0.5 q_{3} & 0.5 q_{0} & - 0.5 q_{1}\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.5 wz & 0.5 wy & - 0.5 wx & 0 & - 0.5 q_{2} & 0.5 q_{1} & 0.5 q_{0}\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\end{array}\\right]$$"
-      ],
-      "text/plain": [
-       "⎡                                  0                                    0  0  \n",
-       "⎢                                                                             \n",
-       "⎢                                  0                                    0  0  \n",
-       "⎢                                                                             \n",
-       "⎢                                  0                                    0  0  \n",
-       "⎢                                                                             \n",
-       "⎢                                  0                                    0  0  \n",
-       "⎢                                                                             \n",
-       "⎢   ⎛    2       2    ⎞                                                       \n",
-       "⎢ux⋅⎝2⋅q₂  + 2⋅q₃  - 1⎠ + 2⋅uy⋅(q₀⋅q₃ - q₁⋅q₂) - 2⋅uz⋅(q₀⋅q₂ + q₁⋅q₃)         \n",
-       "⎢────────────────────────────────────────────────────────────────────   0  0  \n",
-       "⎢                                  2                                          \n",
-       "⎢                                 m                                           \n",
-       "⎢                                                                             \n",
-       "⎢                           ⎛    2       2    ⎞                               \n",
-       "⎢-2⋅ux⋅(q₀⋅q₃ + q₁⋅q₂) + uy⋅⎝2⋅q₁  + 2⋅q₃  - 1⎠ + 2⋅uz⋅(q₀⋅q₁ - q₂⋅q₃)        \n",
-       "⎢─────────────────────────────────────────────────────────────────────  0  0  \n",
-       "⎢                                   2                                         \n",
-       "⎢                                  m                                          \n",
-       "⎢                                                                             \n",
-       "⎢                                                 ⎛    2       2    ⎞         \n",
-       "⎢2⋅ux⋅(q₀⋅q₂ - q₁⋅q₃) - 2⋅uy⋅(q₀⋅q₁ + q₂⋅q₃) + uz⋅⎝2⋅q₁  + 2⋅q₂  - 1⎠         \n",
-       "⎢────────────────────────────────────────────────────────────────────   0  0  \n",
-       "⎢                                  2                                          \n",
-       "⎢                                 m                                           \n",
-       "⎢                                                                             \n",
-       "⎢                                  0                                    0  0  \n",
-       "⎢                                                                             \n",
-       "⎢                                  0                                    0  0  \n",
-       "⎢                                                                             \n",
-       "⎢                                  0                                    0  0  \n",
-       "⎢                                                                             \n",
-       "⎢                                  0                                    0  0  \n",
-       "⎢                                                                             \n",
-       "⎢                                  0                                    0  0  \n",
-       "⎢                                                                             \n",
-       "⎢                                  0                                    0  0  \n",
-       "⎢                                                                             \n",
-       "⎣                                  0                                    0  0  \n",
-       "\n",
-       "0  0  0  0          0                        0                             0  \n",
-       "                                                                              \n",
-       "0  1  0  0          0                        0                             0  \n",
-       "                                                                              \n",
-       "0  0  1  0          0                        0                             0  \n",
-       "                                                                              \n",
-       "0  0  0  1          0                        0                             0  \n",
-       "                                                                              \n",
-       "                                                                              \n",
-       "            2⋅(q₂⋅uz - q₃⋅uy)        2⋅(q₂⋅uy + q₃⋅uz)        2⋅(q₀⋅uz + q₁⋅uy\n",
-       "0  0  0  0  ─────────────────        ─────────────────        ────────────────\n",
-       "                    m                        m                             m  \n",
-       "                                                                              \n",
-       "                                                                              \n",
-       "                                                                              \n",
-       "            2⋅(-q₁⋅uz + q₃⋅ux)  2⋅(-q₀⋅uz - 2⋅q₁⋅uy + q₂⋅ux)       2⋅(q₁⋅ux + \n",
-       "0  0  0  0  ──────────────────  ────────────────────────────       ───────────\n",
-       "                    m                        m                             m  \n",
-       "                                                                              \n",
-       "                                                                              \n",
-       "                                                                              \n",
-       "            2⋅(q₁⋅uy - q₂⋅ux)   2⋅(q₀⋅uy - 2⋅q₁⋅uz + q₃⋅ux)   2⋅(-q₀⋅ux - 2⋅q₂\n",
-       "0  0  0  0  ─────────────────   ───────────────────────────   ────────────────\n",
-       "                    m                        m                             m  \n",
-       "                                                                              \n",
-       "                                                                              \n",
-       "0  0  0  0          0                     -0.5⋅wx                       -0.5⋅w\n",
-       "                                                                              \n",
-       "0  0  0  0        0.5⋅wx                     0                           0.5⋅w\n",
-       "                                                                              \n",
-       "0  0  0  0        0.5⋅wy                  -0.5⋅wz                          0  \n",
-       "                                                                              \n",
-       "0  0  0  0        0.5⋅wz                   0.5⋅wy                       -0.5⋅w\n",
-       "                                                                              \n",
-       "0  0  0  0          0                        0                             0  \n",
-       "                                                                              \n",
-       "0  0  0  0          0                        0                             0  \n",
-       "                                                                              \n",
-       "0  0  0  0          0                        0                             0  \n",
-       "\n",
-       "                           0                   0        0        0   ⎤\n",
-       "                                                                     ⎥\n",
-       "                           0                   0        0        0   ⎥\n",
-       "                                                                     ⎥\n",
-       "                           0                   0        0        0   ⎥\n",
-       "                                                                     ⎥\n",
-       "                           0                   0        0        0   ⎥\n",
-       "                                                                     ⎥\n",
-       "                                                                     ⎥\n",
-       " - 2⋅q₂⋅ux)   2⋅(-q₀⋅uy + q₁⋅uz - 2⋅q₃⋅ux)                           ⎥\n",
-       "───────────   ────────────────────────────     0        0        0   ⎥\n",
-       "                           m                                         ⎥\n",
-       "                                                                     ⎥\n",
-       "                                                                     ⎥\n",
-       "                                                                     ⎥\n",
-       "q₃⋅uz)        2⋅(q₀⋅ux + q₂⋅uz - 2⋅q₃⋅uy)                            ⎥\n",
-       "──────        ───────────────────────────      0        0        0   ⎥\n",
-       "                           m                                         ⎥\n",
-       "                                                                     ⎥\n",
-       "                                                                     ⎥\n",
-       "                                                                     ⎥\n",
-       "⋅uz + q₃⋅uy)       2⋅(q₁⋅ux + q₂⋅uy)                                 ⎥\n",
-       "────────────       ─────────────────           0        0        0   ⎥\n",
-       "                           m                                         ⎥\n",
-       "                                                                     ⎥\n",
-       "                                                                     ⎥\n",
-       "y                       -0.5⋅wz             -0.5⋅q₁  -0.5⋅q₂  -0.5⋅q₃⎥\n",
-       "                                                                     ⎥\n",
-       "z                       -0.5⋅wy             0.5⋅q₀   -0.5⋅q₃  0.5⋅q₂ ⎥\n",
-       "                                                                     ⎥\n",
-       "                         0.5⋅wx             0.5⋅q₃   0.5⋅q₀   -0.5⋅q₁⎥\n",
-       "                                                                     ⎥\n",
-       "x                          0                -0.5⋅q₂  0.5⋅q₁   0.5⋅q₀ ⎥\n",
-       "                                                                     ⎥\n",
-       "                           0                   0        0        0   ⎥\n",
-       "                                                                     ⎥\n",
-       "                           0                   0        0        0   ⎥\n",
-       "                                                                     ⎥\n",
-       "                           0                   0        0        0   ⎦"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display(sp.simplify(f.jacobian(x)))# A "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$$\\left[\\begin{matrix}- \\frac{0.01 ux}{\\sqrt{ux^{2} + uy^{2} + uz^{2}}} & - \\frac{0.01 uy}{\\sqrt{ux^{2} + uy^{2} + uz^{2}}} & - \\frac{0.01 uz}{\\sqrt{ux^{2} + uy^{2} + uz^{2}}}\\\\0 & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 0\\\\\\frac{- 2 q_{2}^{2} - 2 q_{3}^{2} + 1}{m} & \\frac{2 \\left(- q_{0} q_{3} + q_{1} q_{2}\\right)}{m} & \\frac{2 \\left(q_{0} q_{2} + q_{1} q_{3}\\right)}{m}\\\\\\frac{2 \\left(q_{0} q_{3} + q_{1} q_{2}\\right)}{m} & \\frac{- 2 q_{1}^{2} - 2 q_{3}^{2} + 1}{m} & \\frac{2 \\left(- q_{0} q_{1} + q_{2} q_{3}\\right)}{m}\\\\\\frac{2 \\left(- q_{0} q_{2} + q_{1} q_{3}\\right)}{m} & \\frac{2 \\left(q_{0} q_{1} + q_{2} q_{3}\\right)}{m} & \\frac{- 2 q_{1}^{2} - 2 q_{2}^{2} + 1}{m}\\\\0 & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 0\\\\0 & 0 & 1.0\\\\0 & -1.0 & 0\\end{matrix}\\right]$$"
-      ],
-      "text/plain": [
-       "⎡     -0.01⋅ux              -0.01⋅uy              -0.01⋅uz       ⎤\n",
-       "⎢────────────────────  ────────────────────  ────────────────────⎥\n",
-       "⎢   _________________     _________________     _________________⎥\n",
-       "⎢  ╱   2     2     2     ╱   2     2     2     ╱   2     2     2 ⎥\n",
-       "⎢╲╱  ux  + uy  + uz    ╲╱  ux  + uy  + uz    ╲╱  ux  + uy  + uz  ⎥\n",
-       "⎢                                                                ⎥\n",
-       "⎢         0                     0                     0          ⎥\n",
-       "⎢                                                                ⎥\n",
-       "⎢         0                     0                     0          ⎥\n",
-       "⎢                                                                ⎥\n",
-       "⎢         0                     0                     0          ⎥\n",
-       "⎢                                                                ⎥\n",
-       "⎢      2       2                                                 ⎥\n",
-       "⎢- 2⋅q₂  - 2⋅q₃  + 1    2⋅(-q₀⋅q₃ + q₁⋅q₂)    2⋅(q₀⋅q₂ + q₁⋅q₃)  ⎥\n",
-       "⎢───────────────────    ──────────────────    ─────────────────  ⎥\n",
-       "⎢         m                     m                     m          ⎥\n",
-       "⎢                                                                ⎥\n",
-       "⎢                            2       2                           ⎥\n",
-       "⎢ 2⋅(q₀⋅q₃ + q₁⋅q₂)    - 2⋅q₁  - 2⋅q₃  + 1    2⋅(-q₀⋅q₁ + q₂⋅q₃) ⎥\n",
-       "⎢ ─────────────────    ───────────────────    ────────────────── ⎥\n",
-       "⎢         m                     m                     m          ⎥\n",
-       "⎢                                                                ⎥\n",
-       "⎢                                                  2       2     ⎥\n",
-       "⎢ 2⋅(-q₀⋅q₂ + q₁⋅q₃)    2⋅(q₀⋅q₁ + q₂⋅q₃)    - 2⋅q₁  - 2⋅q₂  + 1 ⎥\n",
-       "⎢ ──────────────────    ─────────────────    ─────────────────── ⎥\n",
-       "⎢         m                     m                     m          ⎥\n",
-       "⎢                                                                ⎥\n",
-       "⎢         0                     0                     0          ⎥\n",
-       "⎢                                                                ⎥\n",
-       "⎢         0                     0                     0          ⎥\n",
-       "⎢                                                                ⎥\n",
-       "⎢         0                     0                     0          ⎥\n",
-       "⎢                                                                ⎥\n",
-       "⎢         0                     0                     0          ⎥\n",
-       "⎢                                                                ⎥\n",
-       "⎢         0                     0                     0          ⎥\n",
-       "⎢                                                                ⎥\n",
-       "⎢         0                     0                    1.0         ⎥\n",
-       "⎢                                                                ⎥\n",
-       "⎣         0                    -1.0                   0          ⎦"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sp.simplify(f.jacobian(u)) # B"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Ref\n",
-    "\n",
-    "- Python implementation of 'Successive Convexification for 6-DoF Mars Rocket Powered Landing with Free-Final-Time' paper\n",
-    "by Michael Szmuk and Behçet Açıkmeşe.\n",
-    "\n",
-    "- inspired by EmbersArc/SuccessiveConvexificationFreeFinalTime: Implementation of \"Successive Convexification for 6-DoF Mars Rocket Powered Landing with Free-Final-Time\" https://github.com/EmbersArc/SuccessiveConvexificationFreeFinalTime\n",
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/ArmNavigation/two_joint_arm_to_point_control/Planar_Two_Link_IK.ipynb b/ArmNavigation/two_joint_arm_to_point_control/Planar_Two_Link_IK.ipynb
deleted file mode 100644
index a96f0ea2f4..0000000000
--- a/ArmNavigation/two_joint_arm_to_point_control/Planar_Two_Link_IK.ipynb
+++ /dev/null
@@ -1,423 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Two joint arm to point control\n",
-    "\n",
-    "![TwoJointArmToPointControl](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/two_joint_arm_to_point_control/animation.gif)\n",
-    "\n",
-    "This is two joint arm to a point control simulation.\n",
-    "\n",
-    "This is a interactive simulation.\n",
-    "\n",
-    "You can set the goal position of the end effector with left-click on the ploting area.\n",
-    "\n",
-    "\n",
-    "\n",
-    "### Inverse Kinematics for a Planar Two-Link Robotic Arm\n",
-    "\n",
-    "A classic problem with robotic arms is getting the end-effector, the mechanism at the end of the arm responsible for manipulating the environment, to where you need it to be. Maybe the end-effector is a gripper and maybe you want to pick up an object and maybe you know where that object is relative to the robot - but you cannot tell the end-effector where to go directly. Instead, you have to determine the joint angles that get the end-effector to where you want it to be. This problem is known as inverse kinematics.\n",
-    "\n",
-    "Credit for this solution goes to: https://robotacademy.net.au/lesson/inverse-kinematics-for-a-2-joint-robot-arm-using-geometry/\n",
-    "\n",
-    "First, let's define a class to make plotting our arm easier.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "from math import cos, sin\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "class TwoLinkArm:\n",
-    "    def __init__(self, joint_angles=[0, 0]):\n",
-    "        self.shoulder = np.array([0, 0])\n",
-    "        self.link_lengths = [1, 1]\n",
-    "        self.update_joints(joint_angles)\n",
-    "        \n",
-    "    def update_joints(self, joint_angles):\n",
-    "        self.joint_angles = joint_angles\n",
-    "        self.forward_kinematics()\n",
-    "        \n",
-    "    def forward_kinematics(self):\n",
-    "        theta0 = self.joint_angles[0]\n",
-    "        theta1 = self.joint_angles[1]\n",
-    "        l0 = self.link_lengths[0]\n",
-    "        l1 = self.link_lengths[1]\n",
-    "        self.elbow = self.shoulder + np.array([l0*cos(theta0), l0*sin(theta0)])\n",
-    "        self.wrist = self.elbow + np.array([l1*cos(theta0 + theta1), l1*sin(theta0 + theta1)])\n",
-    "        \n",
-    "    def plot(self):\n",
-    "        plt.plot([self.shoulder[0], self.elbow[0]],\n",
-    "                 [self.shoulder[1], self.elbow[1]],\n",
-    "                 'r-')\n",
-    "        plt.plot([self.elbow[0], self.wrist[0]],\n",
-    "                 [self.elbow[1], self.wrist[1]],\n",
-    "                 'r-')\n",
-    "        plt.plot(self.shoulder[0], self.shoulder[1], 'ko')\n",
-    "        plt.plot(self.elbow[0], self.elbow[1], 'ko')\n",
-    "        plt.plot(self.wrist[0], self.wrist[1], 'ko')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's also define a function to make it easier to draw an angle on our diagram."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from math import sqrt\n",
-    "\n",
-    "def transform_points(points, theta, origin):\n",
-    "    T = np.array([[cos(theta), -sin(theta), origin[0]],\n",
-    "                  [sin(theta), cos(theta), origin[1]],\n",
-    "                  [0, 0, 1]])\n",
-    "    return np.matmul(T, np.array(points))\n",
-    "\n",
-    "def draw_angle(angle, offset=0, origin=[0, 0], r=0.5, n_points=100):\n",
-    "        x_start = r*cos(angle)\n",
-    "        x_end = r\n",
-    "        dx = (x_end - x_start)/(n_points-1)\n",
-    "        coords = [[0 for _ in range(n_points)] for _ in range(3)]\n",
-    "        x = x_start\n",
-    "        for i in range(n_points-1):\n",
-    "            y = sqrt(r**2 - x**2)\n",
-    "            coords[0][i] = x\n",
-    "            coords[1][i] = y\n",
-    "            coords[2][i] = 1\n",
-    "            x += dx\n",
-    "        coords[0][-1] = r\n",
-    "        coords[2][-1] = 1\n",
-    "        coords = transform_points(coords, offset, origin)\n",
-    "        plt.plot(coords[0], coords[1], 'k-')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Okay, we now have a TwoLinkArm class to help us draw the arm, which we'll do several times during our derivation. Notice there is a method called <i>forward_kinematics</i> - forward kinematics specifies the end-effector position <i>given</i> the joint angles and link lengths. Forward kinematics is easier than inverse kinematics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "arm = TwoLinkArm()\n",
-    "\n",
-    "theta0 = 0.5\n",
-    "theta1 = 1\n",
-    "\n",
-    "arm.update_joints([theta0, theta1])\n",
-    "arm.plot()\n",
-    "\n",
-    "def label_diagram():\n",
-    "    plt.plot([0, 0.5], [0, 0], 'k--')\n",
-    "    plt.plot([arm.elbow[0], arm.elbow[0]+0.5*cos(theta0)],\n",
-    "             [arm.elbow[1], arm.elbow[1]+0.5*sin(theta0)],\n",
-    "             'k--')\n",
-    "    \n",
-    "    draw_angle(theta0, r=0.25)\n",
-    "    draw_angle(theta1, offset=theta0, origin=[arm.elbow[0], arm.elbow[1]], r=0.25)\n",
-    "    \n",
-    "    plt.annotate(\"$l_0$\", xy=(0.5, 0.4), size=15, color=\"r\")\n",
-    "    plt.annotate(\"$l_1$\", xy=(0.8, 1), size=15, color=\"r\")\n",
-    "    \n",
-    "    plt.annotate(r\"$\\theta_0$\", xy=(0.35, 0.05), size=15)\n",
-    "    plt.annotate(r\"$\\theta_1$\", xy=(1, 0.8), size=15)\n",
-    "\n",
-    "label_diagram()\n",
-    "\n",
-    "plt.annotate(\"Shoulder\", xy=(arm.shoulder[0], arm.shoulder[1]), xytext=(0.15, 0.5),\n",
-    "    arrowprops=dict(facecolor='black', shrink=0.05))\n",
-    "plt.annotate(\"Elbow\", xy=(arm.elbow[0], arm.elbow[1]), xytext=(1.25, 0.25),\n",
-    "    arrowprops=dict(facecolor='black', shrink=0.05))\n",
-    "plt.annotate(\"Wrist\", xy=(arm.wrist[0], arm.wrist[1]), xytext=(1, 1.75),\n",
-    "    arrowprops=dict(facecolor='black', shrink=0.05))\n",
-    "\n",
-    "plt.axis(\"equal\")\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It's common to name arm joints anatomically, hence the names <i>shoulder</i>, <i>elbow</i>, and <i>wrist</i>. In this example, the wrist is not itself a joint, but we can consider it to be our end-effector. If we constrain the shoulder to the origin, we can write the forward kinematics for the elbow and the wrist.\n",
-    "\n",
-    "$elbow_x = l_0\\cos(\\theta_0)$    \n",
-    "$elbow_y = l_0\\sin(\\theta_0)$  \n",
-    "\n",
-    "$wrist_x = elbow_x + l_1\\cos(\\theta_0+\\theta_1) = l_0\\cos(\\theta_0) + l_1\\cos(\\theta_0+\\theta_1)$  \n",
-    "$wrist_y = elbow_y + l_1\\sin(\\theta_0+\\theta_1) = l_0\\sin(\\theta_0) + l_1\\sin(\\theta_0+\\theta_1)$  \n",
-    "\n",
-    "Since the wrist is our end-effector, let's just call its coordinates <i>$x$</i> and <i>$y$</i>. The forward kinematics for our end-effector is then\n",
-    "\n",
-    "$x = l_0\\cos(\\theta_0) + l_1\\cos(\\theta_0+\\theta_1)$   \n",
-    "$y = l_0\\sin(\\theta_0) + l_1\\sin(\\theta_0+\\theta_1)$ \n",
-    "\n",
-    "A first attempt to find the joint angles $\\theta_0$ and $\\theta_1$ that would get our end-effector to the desired coordinates $x$ and $y$ might be to try solving the forward kinematics for $\\theta_0$ and $\\theta_1$, but that would be the wrong move. An easier path involves going back to the geometry of the arm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from math import pi\n",
-    "\n",
-    "arm.plot()\n",
-    "label_diagram()\n",
-    "\n",
-    "plt.plot([0, arm.wrist[0]],\n",
-    "         [0, arm.wrist[1]],\n",
-    "         'k--')\n",
-    "\n",
-    "plt.plot([arm.wrist[0], arm.wrist[0]],\n",
-    "         [0, arm.wrist[1]],\n",
-    "         'b--')\n",
-    "plt.plot([0, arm.wrist[0]],\n",
-    "         [0, 0],\n",
-    "         'b--')\n",
-    "\n",
-    "plt.annotate(\"$x$\", xy=(0.6, 0.05), size=15, color=\"b\")\n",
-    "plt.annotate(\"$y$\", xy=(1, 0.2), size=15, color=\"b\")\n",
-    "plt.annotate(\"$r$\", xy=(0.45, 0.9), size=15)\n",
-    "plt.annotate(r\"$\\alpha$\", xy=(0.75, 0.6), size=15)\n",
-    "\n",
-    "alpha = pi-theta1\n",
-    "draw_angle(alpha, offset=theta0+theta1, origin=[arm.elbow[0], arm.elbow[1]], r=0.1)\n",
-    "\n",
-    "plt.axis(\"equal\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The distance from the end-effector to the robot base (shoulder joint) is $r$ and can be written in terms of the end-effector position using the Pythagorean Theorem.\n",
-    "\n",
-    "$r^2$ = $x^2 + y^2$\n",
-    "\n",
-    "Then, by the law of cosines, $r$<sup>2</sup> can also be written as:\n",
-    "\n",
-    "$r^2$ = $l_0^2 + l_1^2 - 2l_0l_1\\cos(\\alpha)$\n",
-    "\n",
-    "Because $\\alpha$ can be written as $\\pi - \\theta_1$, we can relate the desired end-effector position to one of our joint angles, $\\theta_1$.\n",
-    "\n",
-    "$x^2 + y^2$ = $l_0^2 + l_1^2 - 2l_0l_1\\cos(\\alpha)$  \n",
-    "  \n",
-    "$x^2 + y^2$ = $l_0^2 + l_1^2 - 2l_0l_1\\cos(\\pi-\\theta_1)$  \n",
-    "  \n",
-    "$2l_0l_1\\cos(\\pi-\\theta_1) = l_0^2 + l_1^2 - x^2 - y^2$  \n",
-    "  \n",
-    "$\\cos(\\pi-\\theta_1) = \\frac{l_0^2 + l_1^2 - x^2 - y^2}{2l_0l_1}$  \n",
-    "$~$  \n",
-    "$~$  \n",
-    "$\\cos(\\pi-\\theta_1) = -cos(\\theta_1)$ is a trigonometric identity, so we can also write\n",
-    "\n",
-    "$\\cos(\\theta_1) = \\frac{x^2 + y^2 - l_0^2 - l_1^2}{2l_0l_1}$  \n",
-    "\n",
-    "which leads us to an equation for $\\theta_1$ in terms of the link lengths and the desired end-effector position!\n",
-    "\n",
-    "$\\theta_1 = \\cos^{-1}(\\frac{x^2 + y^2 - l_0^2 - l_1^2}{2l_0l_1})$  \n",
-    "\n",
-    "This is actually one of two possible solutions for $\\theta_1$, but we'll ignore the other possibility for now. This solution will lead us to the \"arm-down\" configuration of the arm, which is what's shown in the diagram. Now we'll derive an equation for $\\theta_0$ that depends on this value of $\\theta_1$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from math import atan2\n",
-    "\n",
-    "arm.plot()\n",
-    "plt.plot([0, arm.wrist[0]],\n",
-    "         [0, arm.wrist[1]],\n",
-    "         'k--')\n",
-    "\n",
-    "p = 1 + cos(theta1)\n",
-    "plt.plot([arm.elbow[0], p*cos(theta0)],\n",
-    "         [arm.elbow[1], p*sin(theta0)],\n",
-    "         'b--', linewidth=5)\n",
-    "plt.plot([arm.wrist[0], p*cos(theta0)],\n",
-    "         [arm.wrist[1], p*sin(theta0)],\n",
-    "         'b--', linewidth=5)\n",
-    "\n",
-    "beta = atan2(arm.wrist[1], arm.wrist[0])-theta0\n",
-    "draw_angle(beta, offset=theta0, r=0.45)\n",
-    "\n",
-    "plt.annotate(r\"$\\beta$\", xy=(0.35, 0.35), size=15)\n",
-    "plt.annotate(\"$r$\", xy=(0.45, 0.9), size=15)\n",
-    "plt.annotate(r\"$l_1sin(\\theta_1)$\",xy=(1.25, 1.1), size=15, color=\"b\")\n",
-    "plt.annotate(r\"$l_1cos(\\theta_1)$\",xy=(1.1, 0.4), size=15, color=\"b\")\n",
-    "\n",
-    "label_diagram()\n",
-    "\n",
-    "plt.axis(\"equal\")\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Consider the angle between the displacement vector $r$ and the first link $l_0$; let's call it $\\beta$. If we extend the first link to include the component of the second link in the same direction as the first, we form a right triangle with components $l_0+l_1cos(\\theta_1)$ and $l_1sin(\\theta_1)$, allowing us to express $\\beta$ as\n",
-    "  \n",
-    "$\\beta = \\tan^{-1}(\\frac{l_1\\sin(\\theta_1)}{l_0+l_1\\cos(\\theta_1)})$  \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now have an expression for this angle $\\beta$ in terms of one of our arm's joint angles. Now, can we relate $\\beta$ to $\\theta_0$? Yes!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "arm.plot()\n",
-    "label_diagram()\n",
-    "plt.plot([0, arm.wrist[0]],\n",
-    "         [0, arm.wrist[1]],\n",
-    "         'k--')\n",
-    "\n",
-    "plt.plot([arm.wrist[0], arm.wrist[0]],\n",
-    "         [0, arm.wrist[1]],\n",
-    "         'b--')\n",
-    "plt.plot([0, arm.wrist[0]],\n",
-    "         [0, 0],\n",
-    "         'b--')\n",
-    "\n",
-    "gamma = atan2(arm.wrist[1], arm.wrist[0])\n",
-    "draw_angle(beta, offset=theta0, r=0.2)\n",
-    "draw_angle(gamma, r=0.6)\n",
-    "\n",
-    "plt.annotate(\"$x$\", xy=(0.7, 0.05), size=15, color=\"b\")\n",
-    "plt.annotate(\"$y$\", xy=(1, 0.2), size=15, color=\"b\")\n",
-    "plt.annotate(r\"$\\beta$\", xy=(0.2, 0.2), size=15)\n",
-    "plt.annotate(r\"$\\gamma$\", xy=(0.6, 0.2), size=15)\n",
-    "\n",
-    "plt.axis(\"equal\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our first joint angle $\\theta_0$ added to $\\beta$ gives us the angle between the positive $x$-axis and the displacement vector $r$; let's call this angle $\\gamma$.\n",
-    "\n",
-    "$\\gamma = \\theta_0+\\beta$  \n",
-    "\n",
-    "$\\theta_0$, our remaining joint angle, can then be expressed as \n",
-    "\n",
-    "$\\theta_0 = \\gamma-\\beta$  \n",
-    "\n",
-    "We already know $\\beta$. $\\gamma$ is simply the inverse tangent of $\\frac{y}{x}$, so we have an equation of $\\theta_0$! \n",
-    "\n",
-    "$\\theta_0 = \\tan^{-1}(\\frac{y}{x})-\\tan^{-1}(\\frac{l_1\\sin(\\theta_1)}{l_0+l_1\\cos(\\theta_1)})$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now have the inverse kinematics for a planar two-link robotic arm. If you're planning on implementing this in a programming language, it's best to use the <i>atan2</i> function, which is included in most math libraries and correctly accounts for the signs of $y$ and $x$. Notice that $\\theta_1$ must be calculated before $\\theta_0$.\n",
-    "\n",
-    "$\\theta_1 = \\cos^{-1}(\\frac{x^2 + y^2 - l_0^2 - l_1^2}{2l_0l_1})$  \n",
-    "$\\theta_0 = atan2(y, x)-atan2(l_1\\sin(\\theta_1), l_0+l_1\\cos(\\theta_1))$"
-   ]
-  }
- ],
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diff --git a/PathTracking/cgmres_nmpc/Figure_1.png b/PathTracking/cgmres_nmpc/Figure_1.png
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diff --git a/PathTracking/cgmres_nmpc/cgmres_nmpc.ipynb b/PathTracking/cgmres_nmpc/cgmres_nmpc.ipynb
deleted file mode 100644
index 04fa940a8f..0000000000
--- a/PathTracking/cgmres_nmpc/cgmres_nmpc.ipynb
+++ /dev/null
@@ -1,208 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Nonlinear Model Predictive Control with C-GMRES"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename=\"Figure_4.png\",width=600)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(filename=\"Figure_1.png\",width=600)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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ylkN0eq7pKWN8bnhLFafTicmTJ6O+vh5WqzWs+44WcTsCGNBy2Brw/XXWui1g7ty5eOihh4LLdrsdubm5yM/PD/sPkCRJKCgowLhx42AwGMK670ikZb7TVRUFeyqxZPUh7KlwYHW5gPWVOtw0LAd3XJyL/pnJYX9Pfn9jW7jzVVUVTZKMBreMBpcXDe7Wr5Nrd3p8I3FaMOpFmPQijDrf1KT3HdYz6ETodQL0ouCbFwX/cot5nQhDi3aDToBO9PfRCf51ofM6MXReJwgQRQTnBUGATgREQWixPrAOoW1i28OTsteL9evXY9Soy4Pf43YPh3ZwqFRoZx3aXSe02U/z7jte1952aJVD6HYdrPPPtPcz3XL8qOUIaHNbi/Wt+oX0PUE/nSiE/RBw4AhePIvbAjA9PR06nQ4VFRUh7ZWVlcjMzGx3G5PJBJPJ1KbdYDB02YdaV+47EmmV78ShPTHhvBys2V+F1//9C348cgzLfizBsh9LcGFeKqZc3BtXD8oK+z9C/P7Gtpb5qqoKl6SgvklCXZMH9U4J9U1tX3Ut2u1NEur8U68S3hEQQQAsBh0SjHokGEVYDHokGHWw+F9mQ2De327QIcHoe5n1OpgM/mLOoINJL0IHBVs2bcBvxoyGxWyEySDCpNcFiz5RjK3RQEmSsMcM9Olujduf6WgWCzl0VtwWgEajEcOHD0dBQQFuvPHGYHtBQQFuuOEGDSMjrQiCgLEDMjB2QAY2H67FX78/jFW7f8Xmw7XYfLgW6UlGXHteNiYNy8HQnrYOR4opfrgkGVUON2oaPahpcKO6wY3qBg+q7E34ab+IZb/+iFqnhJpGX8HnkTs3+iYKQJJJj2SzAUkmPZLM+uA02dRi3mzwLbdYn2TSBws6i9FXmIXzZ1iSJJT9DJzVPZEfrkRRIG4LQAB46KGHMGXKFIwYMQKXXHIJ3n77bRQXF2PatGlah0YauzAvFRfmpaKi3oWPNhfj75uLUeVw428bivC3DUXIS0/E9UOzcfWgLJzbI5nFYIxQFBV1TRJqGtyoanCjpiFQ2HlQ0+ibVrdob/TIx9mbCNS0ffqMThRgSzAgJcEAa4LBN2/xTdt7pViMsCUYYE3QI8Gg488aEYVFXBeAt956K2pqajB//nyUl5dj8ODBWLFiBXr37q11aBQhsmxmPDSuP2Zc2Q/rf6nGF9tKsWrXrzhc3YhXvj2AV749gGybGb85NxO/OTcDF+WlIcGo0zpsasElyc1FWztFXHC50YPaRg/kUzzUatSLSE80Ij3ZhLREI9KSTOiWoEfV0YO4fMRQZNgsSE00IsXiK+YSjSziiEh7cV0AAsD999+P+++/X+swKMIZdGLw8HCj24tVuyuw4ucKrDtQhbJ6Fz7YdAQfbDoCo07E+bkpuPisVFzcNw0X9OoGs4EFYTgpior6JqndYq7KP61pbG4/nUf/pVgMwWKue5IJaUlGpCX6pulJJqQn+dalJxmRZNK3KegkScKKFb9gwvnZPBxKRBEp7gtAolOVaNLjxmE9ceOwnnBJMjYcrEbB7kqs2VeJ8noXNhfVYnNRLV799y/QiwIGZCXjvJ42nNczBUNybOifmcybY7TikmTUtijaAufStSzmAsu1jZ5TviDCqBODxVugmEtPNiLdX9QFirn0JBO6WYy86SwRxTwWgESdYDbocOU5mbjynEyoqoojNU5sPFSDTYdqsPFgDSodbuwqs2NXmR1/33wUgO8csN6pCUiURewxHED/LCv6pCeiZ0oC0pNMMXG1ZJNHRk2jr5irbfSg0u7EhjIBO7/ej7omL2obPb4LJxrdqG3wnOBcuvbZEgy+os5fzLU3QpfmPzSb3M4oHRFRPGMBSBQmgiCgT3oi+qQn4rYLe0FVVZTVu/BzSR12lNTj55J6/FRSB7vLi0PVTgAifv7ucMg+jDoRPVLMyElJQA9bgr+wMSI10eSf+s4lSzTpkWjUw2wI75WcAOCVFTglGU0e38vpkeH0eGF3BW5N4g3eoqS+SWrTXtvoQZPUXkGnA44Udfi+Bp0QHJkLFHPdQ0bs/AVdkgmpiRylIyLqDBaARF1EEATkpCQgJyUB1wzuAcB3L7hf7W7sLa/DP9ZshrF7bxyqduJorRMVdhc8soIjNU4cqXGe5HsAiUY9Ek2+23v4bo4rBKeifyoIAhRFDT4+yzf1PWZLVnz3p3N6vHBJSqdvVRIQOOyammhEN4sBTXVVGHJ2H3S3JgSLWd96X0FnNXOUjojoTGEBSHQGCYKALJsZaZY01O9TMWHCwOBFApKsoKLehbK6JpTWNaG83uU/hBq4z5zvcGp9kxQcYVNVBJ/yALiP886nThQAi1EPs0GHRJMOVrMheDsSW4IBVrPvNiaBW5lYzb72VH9x1/LiCN9FESswYcI5vCiCiCgCsAAkihAGnYjcVAtyU0/8XGlFUeGUZDjdXjR6ZDS6vWiS5JAH38uKAq/c/DD74OigzveIq8BIoU4UYDa0fPKDbz7cNwomIqLIwQKQKAqJouB7woOJv8JERHTq+OnRCYEHXXfFQ6UlSYLT6YTdbo+LQ2bMN7Yx39gXbzkz3+gW+NwOfI7HIxaAneBwOAAAubm5GkdCREREp8rhcMBms2kdhiYENZ7L305SFAVlZWVITg7/s2Dtdjtyc3Nx9OhRWK3WsO47EjHf2MZ8Y1+85cx8o5uqqnA4HMjOzoYoxuctpTgC2AmiKKJnz55d+h5WqzUmftlOFvONbcw39sVbzsw3esXryF9AfJa9RERERHGMBSARERFRnNHNmzdvntZBUPt0Oh3GjBkDvT4+jtQz39jGfGNfvOXMfCma8SIQIiIiojjDQ8BEREREcYYFIBEREVGcYQFIREREFGdYABIRERHFGRaAEWjp0qXIy8uD2WzG8OHDsW7dOq1DCosFCxZg5MiRSE5ORkZGBiZNmoR9+/aF9HG73ZgxYwbS09ORmJiI66+/HiUlJRpFHF4LFiyAIAiYOXNmsC3W8i0tLcXtt9+OtLQ0WCwWnH/++SgsLAyuV1UV8+bNQ3Z2NhISEjBmzBjs2rVLw4g7x+v14rHHHkNeXh4SEhJw1llnYf78+VAUJdgnmnP+7rvvcN111yE7OxuCIOCLL74IWX8yuR07dgxTpkyBzWaDzWbDlClTUFdXdybTOGnHy1eSJDzyyCMYMmQIEhMTkZ2djTvuuANlZWUh+4iVfFv74x//CEEQsHjx4pD2aMqXQrEAjDDLli3DzJkz8eijj2Lbtm0YNWoUxo8fj+LiYq1D67S1a9di+vTp2LRpEwoKCuD1epGfn4/GxsZgn5kzZ2L58uX4+OOPsX79ejQ0NODaa6+FLMsaRt55W7Zswdtvv43zzjsvpD2W8j127Bguu+wyGAwG/Otf/8Lu3bvx8ssvIyUlJdjnhRdewMKFC7FkyRJs2bIFWVlZGDduXPC52tHm+eefx5tvvoklS5Zgz549eOGFF/Diiy/itddeC/aJ5pwbGxsxdOhQLFmypN31J5Pb5MmTsX37dqxcuRIrV67E9u3bMWXKlDOVwik5Xr5OpxNbt27F448/jq1bt+Lzzz/H/v37cf3114f0i5V8W/riiy/www8/IDs7u826aMqXWlEpolx44YXqtGnTQtrOOeccdc6cORpF1HUqKytVAOratWtVVVXVuro61WAwqB9//HGwT2lpqSqKorpy5Uqtwuw0h8Ohnn322WpBQYE6evRo9cEHH1RVNfbyfeSRR9TLL7+8w/WKoqhZWVnqc889F2xzuVyqzWZT33zzzTMRYthNnDhRvfvuu0PabrrpJvX2229XVTW2cgagLl++PLh8Mrnt3r1bBaBu2rQp2Gfjxo0qAHXv3r1nLvjT0Drf9mzevFkFoB45ckRV1djMt6SkRM3JyVF37typ9u7dW120aFFwXTTnS6rKEcAI4vF4UFhYiPz8/JD2/Px8bNiwQaOouk59fT0AIDU1FQBQWFgISZJC8s/OzsbgwYOjOv/p06dj4sSJuOqqq0LaYy3fr776CiNGjMDNN9+MjIwMDBs2DH/5y1+C6w8fPoyKioqQfE0mE0aPHh2V+QLA5Zdfjm+//Rb79+8HAOzYsQPr16/HhAkTAMRmzgEnk9vGjRths9lw0UUXBftcfPHFsNlsUZ8/4Ps3TBCE4Ch3rOWrKAqmTJmC2bNnY9CgQW3Wx1q+8Ya38+4ERVFQVlaG5ORkCILQ6f2Vl5dDlmUkJSXBbrcH2202G0pLS0Paop2qqpgxYwYuvvhi9OrVC3a7HYcOHYLBYIBOpwvJNS0tDcXFxVGZ/6effootW7ZgzZo1sNvt8Hq9cLvdMZnvwYMHsXTpUjzwwAP47LPPUFhYiBkzZkBRFNx22204ePAgAMBisYTk1q1bNxw9ejTq8gWA++67D5WVlRgwYAB0Oh1kWcYTTzyBiRMnwm63x1zOTqczGPPJ5FZUVIS0tLQ2eaalpaGoqCji82+Zb2sulwuzZ8/GzTffDAAxme/LL78MAJg6dSrsdjsURYHL5Qr2ieZ8VVWFw+FAdnY2RDE+x8L4JJBOKCkpQW5urtZhEBER0Wk4evQoevbsqXUYmuAIYCckJycD8P0AWa3WsO5bkiSsWrUK+fn5MBgMYd13JGK+sY35xr54y5n5Rje73Y7c3Nzg53g8YgHYCYHDvlartUsKQKdowa8uEQPTwrvvSCRJEiwWC6xWa0z843IizDe2xVu+QPzlzHxjQzhO34pW8XngOwooiopntukw4bUNWL23El5ZOfFGRERERCeBBWCEqnV6oMD3l8ldf9uCO/+6GYrC0zWJiIio81gARqiKenfI8ve/1GDfr5F/41giIiKKfCwAI1RJXRMA4LyeVvTtnggAqG+StAyJiIiIYgQLwAi13z/ad3ZGEixG37U6To9Xy5CIiIgoRrAAjFAlx3wjgHlpibAYdQAApyf6ng9LREREkYcFYISqbfQd7k1NNDYXgG4WgERERNR5LAAj0JaiWqw9UA0ASE00wGLyHQJu5CFgIiIiCgMWgBFonb/4A3wjgIk8BExERERhFBFPAmlsbMTatWtRXFwMj8cTsu5Pf/qTRlFpp5ul+S7rqYlGJJt9y7wKmIiIiMJB8wJw27ZtmDBhApxOJxobG5Gamorq6mpYLBZkZGTEZQGYYNAF51MtRqQlGQEANQ2ejjYhIiIiOmmaHwKeNWsWrrvuOtTW1iIhIQGbNm3CkSNHMHz4cLz00ktah6eJls/7SDLpkJ5oAgDUNrrb34CIiIjoFGheAG7fvh1//vOfodPpoNPp4Ha7kZubixdeeAH/9V//pXV4muidZgnOC4KA1ET/CGAjRwCJiIio8zQvAA0GAwTB98zbzMxMFBcXAwBsNltwPt5cclYaHp94Dh4Y6LvoI8V/TqCd5wASERFRGGh+DuCwYcPw448/on///hg7diyeeOIJVFdX44MPPsCQIUO0Dk8TgiDgjot7YUXtTgCANcFfALp4GxgiIiLqPM1HAJ999ln06NEDAPD0008jLS0N9913HyorK/H2229rHF1ksJqbRwBVVT1BbyIiIqLj03wEcMSIEcH57t27Y8WKFRpGE5msCb5vk1dR0STJwWcDExEREZ0OzUcA6cQSDDroRd95kvYmHgYmIiKiztF8KGnYsGHBi0BaEgQBZrMZ/fr1w9SpUzF27FgNoosMgiDAmmBAbaMHdpeELJtZ65CIiIgoimk+AnjNNdfg0KFDSExMxNixYzFmzBgkJSXh4MGDGDlyJMrLy3HVVVfhyy+/1DpUTVnNvlqdTwMhIiKiztK8AKyursaf//xnrFu3Di+//DIWLlyI7777Dg8//DAaGxuxatUqPPbYY3j66adPuK+lS5ciLy8PZrMZw4cPx7p16zrs+7e//Q2CILR5uVyucKYXNsErgVkAEhERUSdpXgB+8sknuO2229q0/+53v8Mnn3wCALjtttuwb9++4+5n2bJlmDlzJh599FFs27YNo0aNwvjx4497L0Gr1Yry8vKQl9kcmYdXg1cCu1gAEhERUedoXgCazWZs2LChTfuGDRuCxZiiKDCZTMfdz8KFC3HPPffg3nvvxbnnnovFixcjNzcXb7zxRofbCIKArKyskFekClwJzItAiIiL9LNFAAAgAElEQVSIqLM0vwhkxowZmDZtGgoLCzFy5EgIgoDNmzfjnXfeCT4K7uuvv8awYcM63IfH40FhYSHmzJkT0p6fn99ucRnQ0NCA3r17Q5ZlnH/++Xj66aeP+z5utxtud/PzeO12OwBAkiRIUnhH5gL7C0yTjDoAwLFGd9jfKxK0zjfWMd/YFm/5AvGXM/ONbrGSR2cIagTcWfjDDz/EkiVLgod5BwwYgBkzZmDy5MkAgKampuBVwe0pKytDTk4Ovv/+e1x66aXB9meffRbvvfdeu4ePN23ahF9++QVDhgyB3W7HK6+8ghUrVmDHjh04++yz232fefPm4amnnmrT/tFHH8FisbSzRfh8WSTi3+UixvZQMKmP0qXvRUREFMucTicmT56M+vp6WK1WrcPRREQUgJ0VKAA3bNiASy65JNj+zDPP4IMPPsDevXtPuA9FUXDBBRfgiiuuwKuvvtpun/ZGAHNzc1FdXR32HyBJklBQUIBx48bBYDBg6ZpDWPTtL7h5eA6enTQorO8VCVrnG+uYb2yLt3yB+MuZ+UY3u92O9PT0uC4ANT8EDAB1dXX49NNPcejQITz88MNITU3F1q1bkZmZiZycnBNun56eDp1Oh4qKipD2yspKZGZmnlQMoihi5MiROHDgQId9TCZTu+ciGgyGLvuFCOy7W5LvfRvcckz88nWkK7+WkYj5xrZ4yxeIv5yZb3SKhRw6S/OLQH766Sf0798fzz//PF588UXU1dUBAJYvX465c+ee1D6MRiOGDx+OgoKCkPaCgoKQQ8LHo6oqtm/fHnwucaThVcBEREQULpoXgA899BCmTp2KAwcOhJzjN378eHz33XentJ933nkH7777Lvbs2YNZs2ahuLgY06ZNAwDccccdIQXlU089ha+//hqHDh3C9u3bcc8992D79u3B/pGGVwETERFRuGh+CHjLli1466232rTn5OS0OaR7PLfeeitqamowf/58lJeXY/DgwVixYgV69+4NACguLoYoNte7dXV1+MMf/oCKigrYbDYMGzYM3333HS688MLOJ9UFOAJIRERE4aJ5AWg2m4O3U2lp37596N69+ynt6/7778f999/f7ro1a9aELC9atAiLFi06pf1riU8CISIionDR/BDwDTfcgPnz5wfvySMIAoqLizFnzhz8x3/8h8bRRY7mEUAvYuDCbSIiItKQ5gXgSy+9hKqqKmRkZKCpqQmjR49Gv379kJycjGeeeUbr8CKGzT8CKCsqnB5Z42iIiIgomml+CNhqtWL9+vX497//ja1btwbvx3fVVVdpHVpEMRtEGHQCJFmF3SUh0aT5t46IiIiiVMRUEVdeeSWuvPJKrcOIWIIgwGo2oKbRA3uTFz1sWkdERERE0SoiCsDNmzdjzZo1qKyshKKEPuZs4cKFGkUVeawJ/gKQVwITERFRJ2heAD777LN47LHHMGDAAGRmZkIQhOC6lvMEWM2BewGyACQiIqLTp3kB+Morr+Ddd9/F1KlTtQ4l4gVvBcMRQCIiIuoEza8CFkURl112mdZhRIXgrWD4NBAiIiLqBM0LwFmzZuH111/XOoyo0Pw4OI4AEhER0enT/BDwww8/jIkTJ6Jv374YOHAgDAZDyPrPP/9co8giDx8HR0REROGgeQE4Y8YMrF69GmPHjkVaWhov/DiOwDmA9RwBJCIiok7QvAB8//338dlnn2HixIlahxLxmq8C5jmAREREdPo0PwcwNTUVffv21TqMqMCrgImIiCgcNC8A582bhyeffBJOp1PrUCIezwEkIiKicND8EPCrr76KgwcPIjMzE3369GlzEcjWrVs1iizyNF8FzEPAREREdPo0LwAnTZqkdQhRgyOAREREFA6aF4BPPvmk1iFEjeA5gE0SVFXlFdNERER0WjQvAAMKCwuxZ88eCIKAgQMHYtiwYVqHFHECI4CKCjR6ZCSZIubbR0RERFFE8wqisrISv/vd77BmzRqkpKRAVVXU19dj7Nix+Pjjj9G9e3etQ4wYZoMIg06AJKuwN0ksAImIiOi0aH4V8IwZM2C327Fr1y7U1tbi2LFj2LlzJ+x2O/70pz9pHV5EEQQhOArIm0ETERHR6dJ8CGnlypX45ptvcO655wbbBg4ciNdffx35+fkaRhaZUiwG1DR6cMzp0ToUIiIiilKajwAqitLm1i8AYDAYoCiKBhFFtvQkEwCguoEFIBEREZ0ezQvAK6+8Eg8++CDKysqCbaWlpZg1axZ+85vfaBhZZOqe7CsAqxxujSMhIiKiaKV5AbhkyRI4HA706dMHffv2Rb9+/ZCXlweHw4HXXntN6/AiTvMIIAtAIiIiOj2anwOYm5uLrVu3oqCgAHv37oWqqhg4cCCuuuoqrUOLSIERwGqOABIREdFp0rwADBg3bhzGjRvX4fohQ4ZgxYoVyM3NPYNRRZ7u/hHAKo4AEhER0WnS/BDwySoqKoIk8dYngRHASjsLQCIiIjo9UVMAkk+PFDMAoKy+SeNIiIiIKFpFzCFgOjk9u1kAAHVOCQ6XhGRz21vohJvDJeH7X6pReOQYDlQ2oNLuhssrQycISDTp0cNmRpbNjJyUBPTLSMI5WVZkWk18VjEREVGEiqkCcOnSpXjxxRdRXl6OQYMGYfHixRg1alSH/T/77DM8/vjjOHjwIPr27YtnnnkGN9544xmM+NQlmfToZjHgmFPC0domDMzuugJwX4UDS9f8gpU7K+D2dnxPxu1H27bZEgwYkJmMAVnJ6J+VjAGZyeifmYQUi7HL4iUiIqKTEzMF4LJlyzBz5kwsXboUl112Gd566y2MHz8eu3fvRq9evdr037hxI2699VY8/fTTuPHGG7F8+XLccsstWL9+PS666CINMjh5uakWHHPWo+SYEwOzrWHff22jB//9f7uxfHspVNXXdlZ6Ii7pm4bBOTZkWc1IMOqgKCrsLgkV9S6U210oqW3Cvl8dOFzdiPomCZuLarG5qDZk3xnJJl9R6C8I+3ZPQm6qBSkmno1ARER0psRMAbhw4ULcc889uPfeewEAixcvxtdff4033ngDCxYsaNN/8eLFGDduHObOnQsAmDt3LtauXYvFixfj73//+xmN/VTlplrwU0k9imoaw77v1XsrMfvTHcEnjUwYkoVpo/tiSI7tpA/puiQZB6sasK/CgX0VDuz/1YH9vzagtK4JlQ43Kh1urDtQHbKNQScgxaDDJ5WFyLIlIC3JiNRE3yst0QhrggEJBh0STXpYjDokGHWwGHTQ68JTOCqKCklR4JVVeOXmeUlW4FVUeGUFkqzCq/insgJZUSG1WtfeNgCQZNbDatYj2WxAkkmPJKMAiQ+6ISIijWheAM6fP/+465944gkAwFtvvYXMzMx2+3g8HhQWFmLOnDkh7fn5+diwYUO722zcuBGzZs0Kabv66quxePHikw1dMwMyk/FPlGNvhSNs+1RVFe9+X4T//uduqCrQPzMJL/x2KM7PTTnlfZkNOgzKtmFQti2k3eGScKCyAfsrfAXh/l8dKKppRHm9C5KsokoWUHWw5pTey6ATIAoCdKIAnSBAFAXoRd9UJwhQoUJRfQWerKqQFRWqCsj+ZdXfpqinnGYY6PHMz6uRafWdQ9k71YK89ETkdU/CWemJyE5JgE7keZSkPUlW4PTIcEkyPF4Fkv+PG0lW4JEVSF4FTR4Ju44JMOyuhAzB30eBR1YhBbfxL/u3abnslRXf76rq+x1VVNX/8v37pATbAssqFMXfHyfRp8U+W/66C0Dwj9vAb1vgb13B39L6b19BEABVhd2uw1tFGyEIQofbCC0WArsRBd8+BACiIACCvw0CRNE3FUL6+OZ9/xwI/mXfti37BfqE7rf5fQT/dkKrNtEfX/Ny6D4FAYCi4GCxiP3f/gKdThe6T7TYr9Ai/hZ9Al+3lvkE+iAYdzv7AzAw29rm84Q6T/MCcPny5SHLkiTh8OHD0Ov16Nu3b7AAnDx5cof7qK6uhizLbQrEzMxMVFRUtLtNRUXFKfUHALfbDbe7+fYrdrs9GHO4b1ET2F97+z27u+9CkD1l9rC8r1dW8N8r9uHDzb6T+W4d0ROPTzwHJr0Y1rzMOmBIjyQM6ZEU0i7JCkprG/DlN98jq98gHHN6ccwpobbRg1qnB7WNEhwuL5okGU6PjCZJhuyv2HwjbF1Tven8xaReJ8AgitDrAssiDP52vSjCoPO1ddRXVVU0umU43F44XL5cjjkluL0KjjklHHNK7RbzJr2I/plJODcrGef2SMa5Wb5zKpNMmv/anrLj/TzHIi3z9cqK/2et1cstodHd/DvU5JHhDExbtrWYb5J8r8BI9onpgL3buzS/yCKg1Bm+P8Qjn4hVpYfO+Lv+aWxf9Pd/7oVLvPxbdDyaf5Js27atTZvdbsfUqVNP+YKM1ocoVVU97mHLU+2/YMECPPXUU23aV61aBYslvD+cAQUFBW3aalwAoMf+X+346v9WQN+Jo6AuL/C3AyL21IkQoOL63gou0Rfh21VFp7/T03S2DUDVTiQD6AUAFv8rPbSfqgKyCrhlQFIABfD/5e9/+fsERvWCfy2jedq6TdfqFVgfNsltc3B6gXoJqPcIqHMDVS4BlU2+aZULcHsV/Fxqx8+l9pBt080qchNV9ExUkZsI9ExUkdj1F4OHRXs/z7GsM/l6FaDRCzRIQKNXQKMXaJQCbb5lpxdo8gpokn2/y00y4FG6btRYgAq9COgFQBeYCoBebP7d8c2rwT46wddP32JeFzLv69v6d1JoOcWJ17U3bbe/oAZH4tTg/4Dm1hbrWkyb+7Xt03rdyfRRWiwH4gjMq+rx29ts37J/y2V/g9KqvXl7IWTbltu0bu8ojpD5Ey23Wgc1NLaWX4+Wy8eO7seKFfsQTk6nM6z7i0aaF4DtsVqtmD9/Pq699lpMmTLlhP3T09Oh0+najN5VVlZ2eNg4KyvrlPoDvvMEH3rooeCy3W5Hbm4u8vPzYbWG92IMSZJQUFCAcePGwWAI/XRXVRWv71+LmkYPegy+BCP7dDut9yivd+EPH2zF3roGmA0iFv72PIwbmBGO8E/Z8fKNRYF8J01oP19ZUVFc68TeCgf2lDuwp8L3+tXuRrVLQLVLwLYWR8tzUswYlG3FoB5WDMpOxuBsK9L8T42JBPH6/W2Zr0uSUelwo7rBg0qHG7WNHhxzSqhzevwjwf5powfHmnyjdZ2RYBBhNRuQZNYj2X8OaqJR7zt/1qhDgkEXOt9qub0+xuP8tcnvcWyLtXwDR/DiWUQWgABQV1eH+vr6k+prNBoxfPhwFBQUhIwaFhQU4IYbbmh3m0suuQQFBQUh5wGuWrUKl156aYfvYzKZYDK1/VA1GAxd9gvR0b4v7puGf/5Ujs1H6nDp2adetO04Wof/7/0fUelwo3uyCf/vzhE4r+epn+8Xbl35tYxEHeVrANC/hxH9e6Tg+mHN7TUNbuwqs2NXmR07S+uxs6weR2qcKK1zobTOhVW7K4N9s6xmDM6xYnCODYOzbRjS04aMZG3vzxhr319VVVHnlFDV4Eal3Y1KhwtVDjcq6puwY7+Ijyq2o6rBgyqHGw6X95T3rxMFdLMY0M1i9L0SDUhNNAaXUywGWBMM/gLPAKvZN59k1sMQpgukTlWsfY9PhPlGp1jIobM0LwBfffXVkGVVVVFeXo4PPvgA11xzzUnv56GHHsKUKVMwYsQIXHLJJXj77bdRXFyMadOmAQDuuOMO5OTkBK8IfvDBB3HFFVfg+eefxw033IAvv/wS33zzDdavXx++5LrQqH7p+OdP5Vi5swIzr+p/Stt+taMMs/93B9xeBQMyk/HuXSORk5LQRZFSOKUlmXBF/+64on/3YFt9k4RdZfXYVWrHzrJ6/Fxaj8PVjaiwu1Bhd+GbPc1FYXqSCYNzrBiS47tIZ3COFTkpCbxpdyuSrKDK4UaV/6p139TVYt6NKrsLVQ3u45wfJwI1x0JaTHoRGVYTuieZkJ5k8hVzicZgkde8bESqxYhksx4iLwQioi6geQG4aNGikGVRFNG9e3fceeedwVu0nIxbb70VNTU1mD9/PsrLyzF48GCsWLECvXv3BgAUFxdDFJv/Ir700kvx8ccf47HHHsPjjz+Ovn37YtmyZRF/D8CAawZn4fEvd2JvhQM7S+sxOOfEV0i5JBkvfb0P76w/DAC46twMLP7dsKi8qICa2RIMuLRvOi7t23yyZIPbiz3ldvxc4hsl3FVqx4FKB6ob3Fizrwpr9lUF+yab9Mjrnui7ArnFq096Iqxn4EkzZ0rL0bpAcVflcAeXA6N3VQ43jjlP7QTxFIsBGckmdE82ISPZjLREA6qPHsQVF56PHimJvnarCckmPYttIooImn/yHz58OGz7uv/++3H//fe3u27NmjVt2n7729/it7/9bdje/0xKsRhxzeAe+MeOMrzy7QG8PWX4cT9YCo/U4tHlO4NXm/7xirPwn9ecw9uMxKgkkx4j+6RiZJ/UYFuTR8aeCjt2lfpGCXeW2rH/Vwccbi9+KqnHTyVtT7mwmvXITklAD5sZPVIS0MPqm2Ykm4L3aUxNNMJs0J3J9KCqKlySgromD441Bs6f859T5796vM5/Xl1to+8QbPVxR+va0osCugeLOhO6J5uD88Fiz2pGepIRJn1o/pIkYcWKXzDhvB481EREEUnzApBO3wNj++FfP5ejYPeveH/jEdx5aZ82fXaX2fHG2oP4x44yAEB6khHP3XQerhrY8cUuFJsSjDpc0KsbLujVfNGQ2yvjSI0Th6sbfa8q3/RQdSOqG9ywu7ywVzhOeM9Ji1GHbv5DlglGHRKNvht2J5p8y0YROFIkYufX+2HQ6yD679koCr6rEQP3gQvcW863rKLR40WjW0aD24tG/6vB7UWjp/lWQKcqxWJA9yRTsLgLzPsOzfqKvPQk32FYHn4loljFAjCKDchKxkP5/fHCyn148qtd2HiwBqMHdIdeFHC4uhHfHajCTv8tRAQBuGV4LmZfMwDpEXR1KGnLpNf5H8uX3GZdg9uL8romlNW7Qqbl9S5UN7hR0+jBsUYPvIoKp0eG09N0gncTsbq8KKzxBy6SSPGfM5fiP5cuJdGA1OCFE8bgyF1aO6N1RETxiAVglJt2RV80eWS89u9fsHJXBVbuCr21jU4UMH5wFu4b05d3UqdTkmTS4+zMZJzdTnEYoKoq7C4vjvkPuza6vf5i0D/133i40e3BLwcPoU+fPEAQg09kkBXVf8NtEQa97ybaBp3vRtoGnYAEox5JJt+IYpJJj0T/yzevQxLPqSMiOi0sAKOcKAr4c/4AXHteNj7bWoJ9FQ4oqopsWwKG9+mG35yTEVH3g6PYIggCbAkG2BIM6IPEDvsFz4kbP4DnxBERRQAWgDFiQFYy/mvCuVqHQURERFGABWAnqP5n1XTFHcUlSYLT6YTdbo+LERPmG9uYb+yLt5yZb3QLfG4HPsfjEQvATnA4fFdG5ubmahwJERERnSqHwwGbLT7PjxfUeC5/O0lRFJSVlSE5OTnsJ6IHnjN89OjRsD9nOBIx39jGfGNfvOXMfKObqqpwOBzIzs4OeUhEPOEIYCeIooiePXt26XtYrdaY+GU7Wcw3tjHf2BdvOTPf6BWvI38B8Vn2EhEREcUxFoBEREREcUY3b968eVoHQe3T6XQYM2YM9Pr4OFLPfGMb84198ZYz86VoxotAiIiIiOIMDwETERERxRkWgERERERxhgUgERERUZxhAUhEREQUZ1gARqClS5ciLy8PZrMZw4cPx7p167QOKSwWLFiAkSNHIjk5GRkZGZg0aRL27dsX0sftdmPGjBlIT09HYmIirr/+epSUlGgUcXgtWLAAgiBg5syZwbZYy7e0tBS333470tLSYLFYcP7556OwsDC4XlVVzJs3D9nZ2UhISMCYMWOwa9cuDSPuHK/Xi8ceewx5eXlISEjAWWedhfnz50NRlGCfaM75u+++w3XXXYfs7GwIgoAvvvgiZP3J5Hbs2DFMmTIFNpsNNpsNU6ZMQV1d3ZlM46QdL19JkvDII49gyJAhSExMRHZ2Nu644w6UlZWF7CNW8m3tj3/8IwRBwOLFi0PaoylfCsUCMMIsW7YMM2fOxKOPPopt27Zh1KhRGD9+PIqLi7UOrdPWrl2L6dOnY9OmTSgoKIDX60V+fj4aGxuDfWbOnInly5fj448/xvr169HQ0IBrr70WsixrGHnnbdmyBW+//TbOO++8kPZYyvfYsWO47LLLYDAY8K9//Qu7d+/Gyy+/jJSUlGCfF154AQsXLsSSJUuwZcsWZGVlYdy4ccHnakeb559/Hm+++SaWLFmCPXv24IUXXsCLL76I1157LdgnmnNubGzE0KFDsWTJknbXn0xukydPxvbt27Fy5UqsXLkS27dvx5QpU85UCqfkePk6nU5s3boVjz/+OLZu3YrPP/8c+/fvx/XXXx/SL1bybemLL77ADz/8gOzs7DbroilfakWliHLhhReq06ZNC2k755xz1Dlz5mgUUdeprKxUAahr165VVVVV6+rqVIPBoH788cfBPqWlpaooiurKlSu1CrPTHA6HevbZZ6sFBQXq6NGj1QcffFBV1djL95FHHlEvv/zyDtcriqJmZWWpzz33XLDN5XKpNptNffPNN89EiGE3ceJE9e677w5pu+mmm9Tbb79dVdXYyhmAunz58uDyyeS2e/duFYC6adOmYJ+NGzeqANS9e/eeueBPQ+t827N582YVgHrkyBFVVWMz35KSEjUnJ0fduXOn2rt3b3XRokXBddGcL6kqRwAjiMfjQWFhIfLz80Pa8/PzsWHDBo2i6jr19fUAgNTUVABAYWEhJEkKyT87OxuDBw+O6vynT5+OiRMn4qqrrgppj7V8v/rqK4wYMQI333wzMjIyMGzYMPzlL38Jrj98+DAqKipC8jWZTBg9enRU5gsAl19+Ob799lvs378fALBjxw6sX78eEyZMABCbOQecTG4bN26EzWbDRRddFOxz8cUXw2azRX3+gO/fMEEQgqPcsZavoiiYMmUKZs+ejUGDBrVZH2v5xhvezrsTFEVBWVkZkpOTIQhCp/dXXl4OWZaRlJQEu90ebLfZbCgtLQ1pi3aqqmLGjBm4+OKL0atXL9jtdhw6dAgGgwE6nS4k17S0NBQXF0dl/p9++im2bNmCNWvWwG63w+v1wu12x2S+Bw8exNKlS/HAAw/gs88+Q2FhIWbMmAFFUXDbbbfh4MGDAACLxRKSW7du3XD06NGoyxcA7rvvPlRWVmLAgAHQ6XSQZRlPPPEEJk6cCLvdHnM5O53OYMwnk1tRURHS0tLa5JmWloaioqKIz79lvq25XC7Mnj0bN998MwDEZL4vv/wyAGDq1Kmw2+1QFAUulyvYJ5rzVVUVDocD2dnZEMX4HAvjk0A6oaSkBLm5uVqHQURERKfh6NGj6Nmzp9ZhaIIjgJ2QnJwMwPcDZLVaw7pvSZKwatUq5Ofnw2AwhHXfkYj5xjbmG/viLWfmG93sdjtyc3ODn+Px6IwUgEePHsWTTz6Jd99990y83RkTOOxrtVq7pAC0WCywWq0x8ct2Isw3tjHf2BdvOTPf2BCO07ei1RkpAGtra/Hee+/FXAFIkcUrKyg51oSimkaUHGtCTYMHNY1u1DR4UNvogVOS4ZZkNEkymjwyPLKCjk6AMOgEGHQijHrRN9WJMOhFmHQiDHoBRv86o14Hk9433zz1tZlatbXso4OKIw5gb4UDFrOxzbZGnQhRjK5/mFRVhaICXkWBrKghL5dHwjE3UHKsCYLogVdRoagqvLJ/qqiQFQWyErq9oqqQFbSYb56GrvdvowKKokIO9PXPN7chZHu55fqQvmjTpsK3rar6pmi1rKrwveDbrrZWh3eP/gAIAtRW/drdHv7t/V9HFWrw57O9n9OWZ++oIe2BNrVNW8u+ofts2zd0n23fK2Sf/gVJ0uGxbf9uswO1Vb/W+49WXq8Ocwu/1TqMM0arfKeP7YfpY/ud8feNdWEpAL/66qvjrj906FA43oYoSFZU/FRSh23FdfippA4/ldajuMYJrxJNHyt6LNy5scO1Bp0QLByNOhE6UYAgADpRgCj45wXfvCgKEAWEzOv8fdorMALzwcIFCBYpgcIjsI2iBAq05pfXX1x5WxRb8gm/9npga2zc1PzkCEBDvdZBnGECIHu1DuIMEuDxRN89O0+fNvlKsnLiTnTKwlIATpo0CYL/r9yOxPMwK4WHS5Lx9a4KfLOnEt/tr0J9k9Smj0kvok9aInJTLeiebERaoglpSUakJhphMeqRYNAhwSjCbPCNtLX3c6mqvgLT41XgkZXgVApMZQVur7/d2zzv9sptl2UFbkkJTt2yArd/JLK+oRGiwdS8TasRSUlWIclewN2VX9UzQycKgKrAqNdBJ/qKWb3oK1b1ogBdy5cQuiwKze2iiHbamrfxzaNNm77lNsF5hGwviq33iRb79G0Df5EtABBFQICvyBYEX9EtwDeVZRnbtm3FiOHDYdDrIfi3C9leCGzbvF1wPwIAf1tA4Ge15U9syx9fwb+mvX9qT9QvMC+02HtzW3v7adtP9nqxdu1ajB49OuQQodBm2+PHGi0kr4Q1q9dgzNgxMOhj55BoR7TM12qO/a+vFsJSAPbo0QOvv/46Jk2a1O767du3Y/jw4eF4K4pDv1Q68P/WF+H/dpTB4W4eXbAlGDCidzcMzU3BeT1tGJCVjMxkc1QcOpUkCStWrMCECWOCH5aqqkKSVX+xGFo8erxKi0OgvtE5peW8/7ChEnKo1LdPIVBooFXR4S9EhNYFSaDNP6/XCb6irVVhpm81L4rtFHX+bbxerz/fq2Pq/KGOSJIE+YiKq87NiIt8AV/OexKAvPTEuMhZkiSkmYHcbhbmS1EpLAXg8OHDsXXr1g4LwBONDhK152BVA15etQ//2lkRHCTZ+l4AACAASURBVBnr2S0BN5yfjSvPycDQninQ62Ln/k2CIMCoF2DUi0gy8QJ9IiLqOmH5lJk9e3bI81xb69evH1avXh2Ot4ob636pxgs7dOg11I5hfdK0DueMckkyXv32AP6y7hAk2Vf55Q/MxNTL+uDivLSoGOEjIiKKZGEpAEeNGnXc9YmJiRg9enRwuaSkJK7vvn0y7n5vKwAB936wFYWPj9M6nDPmSK0TDy77CbvKfHeQHzugO+aMPxcDsuL3Xk1EREThpslxpoEDB2L79u0466yztHj7qGJ3tb3QIVYdsgOPvbEJDpcX3SwGLLjpPFw9KJMXEBEREYWZJgUgzwek1jYeqsEbe3TwKF5c0CsFr//+AvSwJWgdFhERUUzimeYRLnAOXCzb/6sD93+0Ax5FwBVnp+GtKSORYNRpHRYREVHM4kl4UcDpid0bqzo9Xvzh/R/R4Paib7KKpZOHsfgjIiLqYiwAo8Chqo6vsI52z/1rL4pqnOhhM+OeATJMev5IEhERdTVNPm15Uv+pidVTJneV1eP9jUcAAM9OGoRE3luUiIjojNCkAORFIKdGjtGv1+JvDgAArhuajcv7xde9DomIiLSkyUUgu3fvRnZ2thZvHZVkJfYKwJ9L6lGw+1eIAjDzqrO1DoeIiCiuhHUEcOzYsbjyyis7fAXk5uZCpwv/if5Lly5FXl4ezGYzhg8fjnXr1h23/2effYaBAwfCZDJh4MCBWL58edhjCgclBkcAXy7YBwCYNCwHfbsnaRwNERFRfAlrAXj++edj6NChwdfAgQPh8XiwdetWDBkyJJxv1cayZcswc+ZMPProo9i2bRtGjRqF8ePHo7i4uN3+GzduxK233oopU6Zgx44dmDJlCm655Rb88MMPXRrnyXjt2wMhyzuO1mkUSdf4Ylsp1uyrgl4U8KcrOfpHRER0poX1EPCiRYvabZ83bx4aGhrC+VZtLFy4EPfccw/uvfdeAMDixYvx9ddf44033sCCBQva9F+8eDHGjRuHuXPnAgDmzp2LtWvXYvHixfj73//epbGeSOtz/v77n3tw76joe2qKJCtwemRUOdyotLuw/1cH1v9Sg2/2/AoA+OPos9AnPVHjKImIiOLPGTkH8Pbbb8eFF16Il156qUv27/F4UFhYiDlz5oS05+fnY8OGDe1us3HjRsyaNSuk7eqrr8bixYs7fB+32w232x1cttt9z6uVJAmSFL5HtimK0qbtrr/6RiYFCAhcRC3Ad0W1qqpQ4btaWIXqnwJQfYePW67z/xfcRlGbL8pRW60L2V+beV8H3z5UeGUVLq8ClyTD41Xg8irHPXfx9xfmYsaYs4Jft9bTWMd8Y1u85QvEX87MN7rFSh6dcUYKwI0bN8JsNnfZ/qurqyHLMjIzM0PaMzMzUVFR0e42FRUVp9QfABYsWICnnnqqTfuqVatgsVhOI/L2/VIiAAg9R3L1vuqw7f//b+/e46Iq8z+Af84MM9yCQSFFFJGsMC+pYZm3vJRsXjI1zVQgjXbXNIUsVv2ZZnZBbSVLFtt2f9pVcbdV89eaRoUWqalcLDU1EQURJU0B5TbMPL8/cAYGUEEOnJkzn/frxQvOc555zvcLA359nnNpaW5aAYMe8HMTCPQU6OUr0E6bjR3bs+v0TU5OViBC5TBfdXO2fAHny5n5OqaSkhKlQ1CcrAXg+PHjbbaFEMjPz8eBAwewaNEiOQ9Vr9r3FxRC3PCeg43tv2DBAsydO9e6XVRUhMDAQISFhcHb2/sWo67r1M6T2JZ7wqbtjce7VsdpjbdqVs4yK1g1Iwig1rZGklDVLFnbLLOH1a+p3tZI1a+HZNuv5utQY2wXrQQ3Fy3cdBq4umjhqtNYt910N7/gx2g0Ijk5GcOHD4dOp/4bAjJfdXO2fAHny5n5OjbLCp4zk7UANBgMNtsajQYhISFYunQpwsLC5DyUDT8/P2i12jqzdwUFBXVm+Sz8/f0b1R8AXF1d4erqWqddp9PJ+gvh4mJbMN1xuyem9guWbXx7Jvf30t4xX3VztnwB58uZ+TomNeTQVLIWgOvWrZNzuAbT6/UIDQ1FcnIyxo0bZ21PTk7G448/Xu9r+vXrh+TkZJvzAL/66iv079+/2eO9mdqTkHotH49GRERE8lHkRtDNYe7cuYiIiECfPn3Qr18/vP/++8jJycGMGTMAAJGRkWjfvr31iuDo6Gg89NBDWL58OR5//HF8/vnn+Prrr5GamqpkGgCqLvSoSccCkIiIiGSkmgJw0qRJuHjxIpYuXYr8/Hx0794d27ZtQ1BQEAAgJycHGk11IdW/f38kJSXh5ZdfxqJFi9C5c2ds3LgRffv2VSoFK02tGcDXxnZXJhAiIiJSJdUUgAAwc+ZMzJw5s959O3furNM2YcIETJgwoZmjarzaS8C9An2UCYSIiIhUiWuLdqj2EjARERGRnGQtAOubZaPGu8GdaIiIiIiaTNYC8NFHH0Xnzp3x+uuvIzc3V86hncqN7kVIRERE1FSyFoBnz55FdHQ0Nm3ahODgYPzhD3/Av/71L1RUVMh5GNVj+UdERETNSdYCsHXr1pgzZw7S09Nx4MABhISEYNasWWjXrh3mzJmDgwcPynk41ap9FTARERGRnJrtIpBevXph/vz5mDVrFq5evYq1a9ciNDQUgwYNwuHDh5vrsKrAJWAiIiJqTrIXgEajEZ999hlGjhyJoKAg7NixAwkJCTh//jyys7MRGBiIiRMnyn1YVWH9R0RERM1J1vsAzp49Gxs2bAAAhIeHY8WKFejevfomxp6enli2bBk6deok52FVhzOARERE1JxkLQCPHDmC1atX44knnoBer6+3T0BAAFJSUuQ8rOqw/CMiIqLmJGsB+M0339z8gC4uGDx4sJyHVZ2aE4ArJ/RQLhAiIiJSJdkfBZeXl4cffvgBBQUFMJvNNvvmzJkj9+FUSVOjAgxs7a5gJERERKRGshaA69atw4wZM6DX6+Hr62tzLpskSSwAG6jmErCG5wMSERGRzGQtABcvXozFixdjwYIF0Gj4mOFbVbPm07IAJCIiIpnJWqWVlJTgqaeeYvHXRLYzpwoGQkRERKoka6UWFRWFf//733IO6ZS4BExERETNSdYl4Li4OIwePRrbt29Hjx49oNPpbPbHx8fLeTjVqjkDyMfCERERkdxkLQDffPNN7NixAyEhIQBqL2WykmmomkWfhhUgERERyUzWAjA+Ph5r167FtGnT5BzW6dSslbkETERERHKT9RxAV1dXDBgwQM4hnZIELgETERFR85G1AIyOjsbq1avlHNIpSVwCJiIiomYk6xLwvn378O233+KLL75At27d6lwEsmnTJjkPp1q8CISIiIiak6wFoI+PD8aPHy/nkE6Jt4EhIiKi5iT7o+Co6TQ2M4AsAImIiEhefGSHHbK9Cli5OIiIiEidZJ0BvHjxIhYvXoyUlBQUFBTAbDbb7P/999/lPJxqcQmYiIiImpOsBWB4eDiysrIQFRWFtm3b8ubPt4gXgRAREVFzkrUATE1NRWpqKnr27CnnsE6NRTQRERHJTdZzALt06YLS0lI5h3RKQgjr11pOARIREZHMZC0AExMTsXDhQuzatQsXL15EUVGRzQc1Hus/IiIikpvs9wEsLCzEsGHDbNqFEJAkCSaTSc7DqZao8TWXgImIiEhushaAU6dOhV6vx/r163kRSBPUWAHmDCARERHJTtYC8NChQ8jIyEBISIicwzodUWMOUMsimoiIiGQm6zmAffr0QW5urpxDOqWaM4CcRSUiIiK5yToDOHv2bERHRyM2NhY9evSATqez2X/vvffKeTjVqnkOIJeAiYiISG6yFoCTJk0CADzzzDPWNkmSeBFII9W8DQyfBEJERERyk3UJODs7u87HyZMnrZ+bw6lTpxAVFYXg4GC4u7ujc+fOeOWVV1BRUXHD1w0ZMgSSJNl8PPXUU80SY1NoOAVIREREMpN1BjAoKEjO4Rrk6NGjMJvN+Pvf/44777wThw4dwh//+EdcvXoVf/3rX2/42j/+8Y9YunSpddvd3b25wyUiIiJSnKwF4EcffXTD/ZGRkXIeDgDw6KOP4tFHH7Vu33HHHTh27BjWrFlz0wLQw8MD/v7+ssfUVDUvAiEiIiKSm6wFYHR0tM220WhESUkJ9Ho9PDw8mqUArE9hYSFat259036ffvopPvnkE7Rt2xYjRozAK6+8Ai8vrxaI8MbMrACJiIioGclaAF66dKlO26+//ornnnsOsbGxch7qurKysrB69WqsXLnyhv2mTp2K4OBg+Pv749ChQ1iwYAEOHjyI5OTk676mvLwc5eXl1m3L4+2MRiOMRqM8CQA2F8vIOa49s+TJfNWJ+aqfs+XMfB2bWvJoCkmI5p9uOnDgAMLDw3H06NEGv2bJkiV49dVXb9hn//796NOnj3X77NmzGDx4MAYPHox//vOfjYoxLS0Nffr0QVpaGu67775GxbR+/Xp4eHg06ng3su83CZ+e0AIA3ulXKdu4REREBJSUlGDKlCkoLCyEt7e30uEookUKwIyMDAwePNg6Y9YQFy5cwIULF27Yp1OnTnBzcwNQVfwNHToUffv2xQcffACNpnEXOAsh4Orqio8//th6O5va6psBDAwMxIULF2R9A23KyMO8TYcBAEcWD61zP0U1MhqNSE5OxvDhw5mvCjFf9XO2nJmvYysqKoKfn59TF4CyLgFv3brVZlsIgfz8fCQkJGDAgAGNGsvPzw9+fn4N6puXl4ehQ4ciNDQU69ata3TxBwCHDx+G0WhEu3btrtvH1dUVrq6uddp1Op2svxAajbbZxrZ3zFfdmK/6OVvOzNcxqSGHppK1ABw7dqzNtiRJuP322zFs2LCbnpN3q86ePYshQ4agY8eO+Otf/4rffvvNus9yhW9eXh4efvhhfPTRR3jggQeQlZWFTz/9FCNHjoSfnx+OHDmCF198Eb179250odoceAkIERERNSdZC0Cz2SzncA3y1Vdf4cSJEzhx4gQ6dOhgs8+yum00GnHs2DGUlJQAAPR6Pb755hu88847uHLlCgIDAzFq1Ci88sor0Gq1dY7R4lgBEhERUTOStQBUwrRp0zBt2rQb9unUqZPN49UCAwOxa9euZo7s1glWgERERNSMmlwAzp07t8F94+Pjm3o4p8DbABIREVFzanIBmJGRYbOdlpYGk8mEkJAQAMDx48eh1WoRGhra1EM5DdZ/RERE1JyaXACmpKRYv46Pj4eXlxc+/PBDtGrVCkDVzaGnT5+OQYMGNfVQTuPhLm0AAO3cWQoSERGR/Bp/v5QbWLlyJeLi4qzFHwC0atUKr7/+erNdBaxGbbzdkL5wKGJ7mm7emYiIiKiRZC0Ai4qKcP78+TrtBQUFKC4ulvNQquflpoNWUjoKIiIiUiNZrwIeN24cpk+fjpUrV+LBBx8EAOzduxexsbEYP368nIeyC5YrixvzhJOGMhqNKCkpQVFRkVPcsJL5qhvzVT9ny5n5OjbLv9st8DA0uyXro+BKSkrw0ksvYe3atdYHLbu4uCAqKgpvvfUWPD095TqUXThz5gwCAwOVDoOIiIhuQW5ubp17CDuLZnkW8NWrV5GVlQUhBO68807VFX4WZrMZZ8+ehZeXFyRJ3vVay3OGc3NzneI5hcxX3Ziv+jlbzszXsQkhUFxcjICAgFt6fKwaNMuNoD09PXHvvfc2x9B2RaPRNPv/HLy9vVXxy9ZQzFfdmK/6OVvOzNdxGQwGpUNQlHOWvUREREROjAUgERERkZPRLlmyZInSQVD9tFothgwZAhcXh39kc4MwX3VjvurnbDkzX3JkzXIRCBERERHZLy4BExERETkZFoBEREREToYFIBEREZGTYQFIRERE5GRYANqhxMREBAcHw83NDaGhofj++++VDkkWcXFxuP/+++Hl5YU2bdpg7NixOHbsmE2f8vJyzJ49G35+fvD09MSYMWNw5swZhSKWV1xcHCRJQkxMjLVNbfnm5eUhPDwcvr6+8PDwQK9evZCWlmbdL4TAkiVLEBAQAHd3dwwZMgSHDx9WMOKmqaysxMsvv4zg4GC4u7vjjjvuwNKlS2E2m619HDnn7777Do899hgCAgIgSRK2bNlis78huV26dAkREREwGAwwGAyIiIjA5cuXWzKNBrtRvkajEfPmzUOPHj3g6emJgIAAREZG4uzZszZjqCXf2v785z9DkiSsWrXKpt2R8iVbLADtzMaNGxETE4OFCxciIyMDgwYNwogRI5CTk6N0aE22a9cuzJo1C3v37kVycjIqKysRFhaGq1evWvvExMRg8+bNSEpKQmpqKq5cuYLRo0fDZDIpGHnT7d+/H++//36dJ+SoKd9Lly5hwIAB0Ol0+PLLL3HkyBGsXLkSPj4+1j4rVqxAfHw8EhISsH//fvj7+2P48OEoLi5WMPJbt3z5crz33ntISEjAL7/8ghUrVuCtt97C6tWrrX0cOeerV6+iZ8+eSEhIqHd/Q3KbMmUKMjMzsX37dmzfvh2ZmZmIiIhoqRQa5Ub5lpSUID09HYsWLUJ6ejo2bdqE48ePY8yYMTb91JJvTVu2bMGPP/6IgICAOvscKV+qRZBdeeCBB8SMGTNs2rp06SLmz5+vUETNp6CgQAAQu3btEkIIcfnyZaHT6URSUpK1T15entBoNGL79u1KhdlkxcXF4q677hLJycli8ODBIjo6WgihvnznzZsnBg4ceN39ZrNZ+Pv7i2XLllnbysrKhMFgEO+9915LhCi7UaNGiWeeecambfz48SI8PFwIoa6cAYjNmzdbtxuS25EjRwQAsXfvXmufPXv2CADi6NGjLRf8Laidb3327dsnAIjTp08LIdSZ75kzZ0T79u3FoUOHRFBQkHj77bet+xw5XxKCM4B2pKKiAmlpaQgLC7NpDwsLw+7duxWKqvkUFhYCAFq3bg0ASEtLg9FotMk/ICAA3bt3d+j8Z82ahVGjRuGRRx6xaVdbvlu3bkWfPn0wceJEtGnTBr1798Y//vEP6/7s7GycO3fOJl9XV1cMHjzYIfMFgIEDB+Kbb77B8ePHAQAHDx5EamoqRo4cCUCdOVs0JLc9e/bAYDCgb9++1j4PPvggDAaDw+cPVP0NkyTJOsuttnzNZjMiIiIQGxuLbt261dmvtnydDW/n3QRmsxlnz56Fl5cXJElq8nj5+fkwmUy47bbbUFRUZG03GAzIy8uzaXN0QgjMnj0bDz74IDp27IiioiKcPHkSOp0OWq3WJldfX1/k5OQ4ZP6fffYZ9u/fj507d6KoqAiVlZUoLy9XZb5ZWVlITEzE888/j//85z9IS0vD7NmzYTabMXnyZGRlZQEAPDw8bHJr1aoVcnNzHS5fAHjuuedQUFCAkJAQaLVamEwmLF68GKNGjUJRUZHqci4pKbHG3JDcTp06BV9f3zp5+vr64tSpU3aff818aysrK0NsbCwmTpwIAKrMd+XKlQCAadOmoaioCGazGWVlZdY+jpyvEALFxcUICAiARuOcc2F8EkgTnDlzBoGBgUqHQURERLcgNzcXHTp0UDoMRSg2A1haWoq0tDS0bt0aXbt2tdlXVlaGf/3rX4iMjFQouobx8vICUPUG8vb2lnVso9GIr776CmFhYdDpdLKObY+Yr7oxX/VztpyZr2MrKipCYGCg9d9xZ6RIAXj8+HGEhYUhJycHkiRh0KBB2LBhA9q1aweg6ryK6dOn230BaFn29fb2bpYC0MPDA97e3nV+2U4UXMF/f8qHp6sWPdob0MXfGwaP6/9CCiFQZjSjqMyI4jIjCksrUVxmRFHZtc+llSgzmqCRJGg1gEYjQStJ0Gok6LQauGgl6DRVn120Gug0VfvMQsAsAJNZXPtawGwGTEJACAGTGTXaq/qar004i2tfi2tfm0wmHL3kiYL0C9BoNNZ2AQE556hlWKmHhKYPYjKbcOJ3T5xL+w1arfaWx9Fe+1loJenazw3QajXXfn6ARpLgopWu/WwluGiqv9Zc27a+tsa+qjbUGrtqf73tmlqvuzaO5XfkRu/nGxE13i+iRpu5xnvDLKo/mwUAUf2+E7DdX/OzqNXPOm7NdmH7XjVfe29XtdUcqzoms6jKN7vcEwfyy6HRVNYYu3oc63Z9sUHUP76w/T0y14q5Tmw1vofNzWQy48SF25C7rwBarfqX1Jhvy+nX2Rf9O/s1y9hynL7lqBQpAC33Ujpw4AAuX76MuXPnYsCAAdi5cyc6duyoREgO49i5YoxJSEV5pdmm3ddTj9aeerjrtTCZBUxmgfJKM4pKjSgqM8JocoSVfi2Qc0LpIFqQFsjNUjqIZiVJsBaIwqRF7P6vrZWcpYCrWdhVf61EtHLTAkczlA6ihWnwVd5JpYNoQcy3JWg1UrMVgM5MkQJw9+7d+Prrr+Hn5wc/Pz9s3boVs2bNwqBBg5CSkgJPT08lwnIICSknUF5pRufbPdH59ttwKK8QZwvLcPFqBS5erbjhazUS4O2ug5ebC7xcdfB2d4GXW9W2u05bNWtwbTbPJKqKyEqTgNFkRqX52meTQKXZDJNZXJsNkqCRYJ09qvqAdfbHMiOkkaq2NRIgoWobUtVsmiQBQpiRd+YMOgYGXvvfZVW7RpJnxg2oKjiaPIZMhYnJbEZOTg46dux4yycgixozrpai3yQETKaqz2azQKXl52n52ixs+9d4ndn62XZW19LHMttbs+/Nvh9CAJWWKSlIQK3/uLQEjYTq954kWd9/1m3J8v6sbgcs+2F9XwOofi+j+jWWsa19JAmSJFBUWIRWPgZoNBqbsSXUPWbNcarHrv4dsO1jOX7171HNsTUa2zyvdWmQpsyGmMxmnD51CkGdOkEr80n1Qgi7m6lpznztkZL59gz0uXknajRFCsDS0lK4uNge+m9/+xs0Gg0GDx6M9evXKxGW3TOZBb755TwAYOWTvdDr2i9FcZkRub+X4nJpRY2lXAmuLlp4u7vA4K6Dl5sOnnqt3f0RtTAajdi2LQcjR3ZTxfklN1OV7ymMHNnVofMVomYBCet/Hsxm2/byigp8m5KCYUOHQqfTWZfiLcWQ9V1Zo+C3tFvesxKql/BrFl02RRSkOgWTEqp+vtswcuSDDv3zbYyqnE9i5MguTpEz8yVHp0gB2KVLFxw4cAD33HOPTfvq1ashhKhzZ3WqkvXbFZRUmOChrzr3z8LLTYeuAfyFpJYnXTvH8GZ/SIxGLVq7AgE+7vzHg4jIDigybz1u3Dhs2LCh3n0JCQmYPHlyi5247EgO5lY9X7F7ewO0GvucySMiIiL7p0gBuGDBAmzbtu26+xMTE20epn7mzBmbbWeV9VvVM3O7tpP3imMiIiJyLg5x5mrXrl1x6tQppcNQXH5hKQAgwMdN4UiIiIjIkTlEAcjl4Cr5hWUAAH+Du8KREBERkSNziAKQqlhnAA2cASQiIqJbxwLQQZjNAueuzQC28+EMIBEREd06FoAO4veSChhNApIEtPFyVTocIiIicmAOUQDa682LW9LlEiMAwNtNB50TPHeSiIiImo9DVBK8CAQoLK16zJuPB2+iS0RERE2jyJNAGuvIkSMICAhQOgxFWWYAfdxZABIREVHTKFoADh069IbLu99++y0AIDAwsKVCsluWAtDgoVc4EiIiInJ0ihaAvXr1stk2Go3IzMzEoUOH8PTTTysUlX26VHJtCZgzgERERNREihaAb7/9dr3tS5YswZUrV1o4GvtWWHptCZjnABIREVET2eVFIOHh4Vi7dq3SYShm4/4cTPrHPnx7tnp5nOcAEhERkVzssgDcs2cP3Nyc92kX5wrLkZ5zGb+V1SgAS3kOIBEREclD0SXg8ePH22wLIZCfn48DBw5g0aJFCkWlPL1LVV1eaa5uu1JWVQB6uTrEhdtERERkxxStJgwGg822RqNBSEgIli5dirCwMIWiUp5OWzXzZykAf79agZRjvwEAPFy1SoVFREREKqFoAbhu3TolD2+3XC0zgNfuf/2njw5Y93noWQASERFR09jlOYDOrvYS8IHTl6z7PPRcAiYiIqKmUVUBmJiYiODgYLi5uSE0NBTff//9dft+8MEHkCSpzkdZWVkLRlw/fa0ZwJo4A0hERERNpZoCcOPGjYiJicHChQuRkZGBQYMGYcSIEcjJybnua7y9vZGfn2/zYQ9XH+u1VUWeyVz3KSmcASQiIqKmUk0BGB8fj6ioKDz77LO45557sGrVKgQGBmLNmjXXfY0kSfD397f5sAecASQiIqLmpOh00s6dOzFkyJAmj1NRUYG0tDTMnz/fpj0sLAy7d+++7uuuXLmCoKAgmEwm9OrVC6+99hp69+593f7l5eUoLy+3bhcVFQGoeoSd0WhsYhbVtKg6+a/SjDrj6jVC1mPZC0tOasytPsxX3ZwtX8D5cma+jk0teTSFJISoZ56pZbi5uaF9+/aYPn06nn76aQQGBt7SOGfPnkX79u3xww8/oH///tb2N998Ex9++CGOHTtW5zV79+7FiRMn0KNHDxQVFeGdd97Btm3bcPDgQdx11131HmfJkiV49dVX67SvX78eHh4etxR7fX4tlJBwRIu27gL/08uE6D3VdfrKvpVwUc28LRERUcsrKSnBlClTUFhYCG9vb6XDUYSiBeDvv/+OTz75BB988AF++uknPPzww4iKisLYsWOh1zf8iReWAnD37t3o16+ftf2NN97Axx9/jKNHj950DLPZjPvuuw8PPfQQ3n333Xr71DcDGBgYiAsXLsj6BsrIuYwn/7EPvq4C388bhq5LU6z7fn1NnfdHNBqNSE5OxvDhw6HTqf9xd8xX3ZwtX8D5cma+jq2oqAh+fn5OXQAqugTcunVrzJkzB3PmzEFmZibWrl2LWbNm4bnnnsPUqVMRFRWFnj173nQcPz8/aLVanDt3zqa9oKAAbdu2bVAsGo0G999/P3799dfr9nF1fVo/zAAAGBdJREFUdYWrq2uddp1OJ+svhIdbVfFbKVBnXDX84t2I3N9Le8d81c3Z8gWcL2fm65jUkENT2c1iYq9evTB//nzMmjULV69exdq1axEaGopBgwbh8OHDN3ytXq9HaGgokpOTbdqTk5NtloRvRAiBzMxMtGvX7pZzkEt9j4IjIiIikoviBaDRaMRnn32GkSNHIigoCDt27EBCQgLOnz+P7OxsBAYGYuLEiTcdZ+7cufjnP/+JtWvX4pdffsELL7yAnJwczJgxAwAQGRmJBQsWWPu/+uqr2LFjB06ePInMzExERUUhMzPT2l9Jeu31rwImIiIiaipFl4Bnz56NDRs2AADCw8OxYsUKdO/e3brf09MTy5YtQ6dOnW461qRJk3Dx4kUsXboU+fn56N69O7Zt24agoCAAQE5ODjSa6nr38uXL+NOf/oRz587BYDCgd+/e+O677/DAAw/Im+QtqD0DqJEAM4tBIiIikomiBeCRI0ewevVqPPHEE9e96CMgIAApKSn17qtt5syZmDlzZr37du7cabP99ttv4+23325UvC3FUgCahAQhBMK6+mP74XP446BghSMjIiIiNVC0APzmm29u2sfFxQWDBw9ugWjsh05bPVNZYRIQqJr+6+TnqVRIREREpCKKP1csLy8PP/zwAwoKCmA22171MGfOHIWiUpZrjRv9VVSarcu/Gqnuo+GIiIiIGkvRAnDdunWYMWMG9Ho9fH19IdUocCRJctoCUG8zA2iG5VaNGtZ/REREJANFC8DFixdj8eLFWLBggc0FGs5Oo5HgopFQaRY2M4ASZwCJiIhIBopWXSUlJXjqqadY/NXDciFIhckMs3UGkAUgERERNZ2ilVdUVBT+/e9/KxmC3bIsAxttzgFUMCAiIiJSDUWXgOPi4jB69Ghs374dPXr0qPNolvj4eIUiU55lBrC8suY5gKwAiYiIqOkULQDffPNN7NixAyEhIQBQ5yIQZ+ZaowC0LAE7+beEiIiIZKJoARgfH4+1a9di2rRpSoZhl9x1WgBAmdEEwYtAiIiISEaKngPo6uqKAQMGKBmC3XLTVf1oyiprXgSiZERERESkFooWgNHR0Vi9erWSIdgtN8sMYIWJN4ImIiIiWSm6BLxv3z58++23+OKLL9CtW7c6F4Fs2rRJociUVz0DaOKNoImIiEhWihaAPj4+GD9+vJIh2C1Xl6oZwFIjbwRNRERE8lL8UXBUP8tFIOVGE28ETURERLLiIzjslHUJ2MgbQRMREZG8FJ0BvHjxIhYvXoyUlBQUFBTAbDbb7P/9998Vikx5bja3geEMIBEREclH0QIwPDwcWVlZiIqKQtu2bXmOWw313QaG3x4iIiKSg6IFYGpqKlJTU9GzZ08lw7BLbi7VM4CWiVEWyERERCQHRc8B7NKlC0pLS5UMwW656S3nAJp4I2giIiKSlaIFYGJiIhYuXIhdu3bh4sWLKCoqsvlwZtUzgNXnRfIcQCIiIpKD4vcBLCwsxLBhw2zahRCQJAkmk0mhyJRnOQewtMYMIOs/IiIikoOiBeDUqVOh1+uxfv16XgRSi+Uq4PJKMx8FR0RERLJStAA8dOgQMjIyEBISomQYdsnN+iQQ3giaiIiI5KXoOYB9+vRBbm6ukiHYLb1LVbFnNJkheCNoIiIikpGiM4CzZ89GdHQ0YmNj0aNHD+h0Opv99957r0KRKU+nrarNjZWixjmArACJiIio6RQtACdNmgQAeOaZZ6xtkiTxIhAALtqqYq/SbOZtYIiIiEhWihaA2dnZSh7eruk012YATYIXgRAREZGsFC0Ag4KClDy8XbMuAZvM1sKP9R8RERHJQdEC8KOPPrrh/sjIyBaKxP5ULwELa5ulKCQiIiJqCkULwOjoaJtto9GIkpIS6PV6eHh4OHcBeO2Ev0qTQOW1hwG7X7s3IBEREVFTKDqldOnSJZuPK1eu4NixYxg4cCA2bNigZGiK07lULwGXGqsuhnHXswAkIiKiprO7NcW77roLy5YtqzM76Gx012YAS40mmK4tA1tuDk1ERETUFHZXAAKAVqvF2bNnlQ5DUS7XzvercQog3PR2+eMiIiIiB6PoOYBbt2612RZCID8/HwkJCRgwYIBCUdkHndb2kl+NBOh5EQgRERHJQNECcOzYsTbbkiTh9ttvx7Bhw7By5UqForIPLhrbYs9Np+WTQIiIiEgWihaA5mtXt1JdtWcA3XgFMBEREcmEa4p2qvY9/3w99QpFQkRERGrT4jOAc+fObXDf+Pj4Ro2dmJiIt956C/n5+ejWrRtWrVqFQYMGXbf/f/7zHyxatAhZWVno3Lkz3njjDYwbN65Rx2wuWo0EF0mgUlTNBAb5eigcEREREalFixeAGRkZNttpaWkwmUwICQkBABw/fhxarRahoaGNGnfjxo2IiYlBYmIiBgwYgL///e8YMWIEjhw5go4dO9bpv2fPHkyaNAmvvfYaxo0bh82bN+PJJ59Eamoq+vbte+sJykivASqrbgGIO9t4KRsMERERqUaLLwGnpKRYPx577DEMGTIEZ86cQXp6OtLT05Gbm4uhQ4di1KhRjRo3Pj4eUVFRePbZZ3HPPfdg1apVCAwMxJo1a+rtv2rVKgwfPhwLFixAly5dsGDBAjz88MNYtWqVHGnKorN39T1gHuvZTsFIiIiISE0UPQdw5cqViIuLQ6tWraxtrVq1wuuvv96oq4ArKiqQlpaGsLAwm/awsDDs3r273tfs2bOnTv8//OEP1+2vhNEdzegdaMC7k3ujW4BB6XCIiIhIJRS9CrioqAjnz59Ht27dbNoLCgpQXFzc4HEuXLgAk8mEtm3b2rS3bdsW586dq/c1586da1R/ACgvL0d5eblN/EDVM4yNRmOD420Io9EIfw/g08fvg06nk318e2PJT+15WjBfdXO2fAHny5n5Oja15NEUihaA48aNw/Tp07Fy5Uo8+OCDAIC9e/ciNjYW48ePb/R4te+TJ4S44b3zGts/Li4Or776ap32LVu2wMOjeS7S+Pzzz5tlXHvFfNWN+aqfs+XMfB1TSUkJgKp/952VogXge++9h5deegnh4eHWatzFxQVRUVF46623GjyOn58ftFptndm7goKCOrN8Fv7+/o3qDwALFiywuYo5Ly8PXbt2xbPPPtvgWImIiMg+FBcXw2BwzlOsJGEH5e/Vq1eRlZUFIQTuvPNOeHp6NnqMvn37IjQ0FImJida2rl274vHHH0dcXFyd/pMmTUJxcTG2bdtmbRsxYgR8fHywYcOGBh3TbDbj7Nmz8PLykv0pHUVFRQgMDERubi68vb1lHdseMV91Y77q52w5M1/HJoRAcXExAgICoNE45y2RFZ0BtPD09MS9997bpDHmzp2LiIgI9OnTB/369cP777+PnJwczJgxAwAQGRmJ9u3bW4vB6OhoPPTQQ1i+fDkef/xxfP755/j666+Rmpra4GNqNBp06NChSXHfjLe3typ+2RqK+aob81U/Z8uZ+TouZ535s7CLAlAOkyZNwsWLF7F06VLk5+eje/fu2LZtG4KCggAAOTk5NlV+//79kZSUhJdffhmLFi1C586dsXHjRru5ByARERFRc1FNAQgAM2fOxMyZM+vdt3PnzjptEyZMwIQJE5o5KiIiIiL7ol2yZMkSpYOg+mm1WgwZMgQuLqqq06+L+aob81U/Z8uZ+ZIjs4uLQIiIiIio5TjnpS9ERERETowFIBEREZGTYQFIRERE5GRYABIRERE5GRaAdigxMRHBwcFwc3NDaGgovv/+e6VDkkVcXBzuv/9+eHl5oU2bNhg7diyOHTtm06e8vByzZ8+Gn58fPD09MWbMGJw5c0ahiOUVFxcHSZIQExNjbVNbvnl5eQgPD4evry88PDzQq1cvpKWlWfcLIbBkyRIEBATA3d0dQ4YMweHDhxWMuGkqKyvx8ssvIzg4GO7u7rjjjjuwdOlSmM1max9Hzvm7777DY489hoCAAEiShC1bttjsb0huly5dQkREBAwGAwwGAyIiInD58uWWTKPBbpSv0WjEvHnz0KNHD3h6eiIgIACRkZE4e/aszRhqybe2P//5z5AkCatWrbJpd6R8yRYLQDuzceNGxMTEYOHChcjIyMCgQYMwYsQI5OTkKB1ak+3atQuzZs3C3r17kZycjMrKSoSFheHq1avWPjExMdi8eTOSkpKQmpqKK1euYPTo0TCZTApG3nT79+/H+++/X+eJN2rK99KlSxgwYAB0Oh2+/PJLHDlyBCtXroSPj4+1z4oVKxAfH4+EhATs378f/v7+GD58OIqLixWM/NYtX74c7733HhISEvDLL79gxYoVeOutt7B69WprH0fO+erVq+jZsycSEhLq3d+Q3KZMmYLMzExs374d27dvR2ZmJiIiIloqhUa5Ub4lJSVIT0/HokWLkJ6ejk2bNuH48eMYM2aMTT+15FvTli1b8OOPPyIgIKDOPkfKl2oRZFceeOABMWPGDJu2Ll26iPnz5ysUUfMpKCgQAMSuXbuEEEJcvnxZ6HQ6kZSUZO2Tl5cnNBqN2L59u1JhNllxcbG46667RHJyshg8eLCIjo4WQqgv33nz5omBAwded7/ZbBb+/v5i2bJl1raysjJhMBjEe++91xIhym7UqFHimWeesWkbP368CA8PF0KoK2cAYvPmzdbthuR25MgRAUDs3bvX2mfPnj0CgDh69GjLBX8Laudbn3379gkA4vTp00IIdeZ75swZ0b59e3Ho0CERFBQk3n77bes+R86XhOAMoB2pqKhAWloawsLCbNrDwsKwe/duhaJqPoWFhQCA1q1bAwDS0tJgNBpt8g8ICED37t0dOv9Zs2Zh1KhReOSRR2za1Zbv1q1b0adPH0ycOBFt2rRB79698Y9//MO6Pzs7G+fOnbPJ19XVFYMHD3bIfAFg4MCB+Oabb3D8+HEAwMGDB5GamoqRI0cCUGfOFg3Jbc+ePTAYDDaP2HzwwQdhMBgcPn+g6m+YJEnWWW615Ws2mxEREYHY2Fh069atzn615etseDtvO3LhwgWYTCa0bdvWpr1t27Y4d+6cQlE1DyEE5s6di4EDB6J79+4AgHPnzkGv16NVq1Y2fR05/6SkJKSnp2P//v119qkt35MnT2LNmjWYO3cu/ud//gf79u3DnDlz4OrqisjISGtO9b2/T58+rUTITTZv3jwUFhaiS5cu0Gq1MJlMeOONNzB58mQAUGXOFg3J7dy5c2jTpk2d17Zp08Yh3+M1lZWVYf78+ZgyZQq8vb0BqC/f5cuXw8XFBXPmzKl3v9rydTYsAO2QJEk220KIOm2O7vnnn8dPP/2E1NTUm/Z11Pxzc3MRHR2Nr776Cm5ubg1+naPmazab0adPH7z55psAgN69e+Pw4cNYs2YNIiMjrf3U9P7euHEjPvnkE6xfvx7dunVDZmYmYmJiEBAQgKefftraT00513az3OrL09HzNxqNeOqpp2A2m5GYmGizTy35pqWl4Z133kF6evoNY1dLvs6IS8B2xM/PD1qtts7/nAoKCur8L9uRzZ49G1u3bkVKSgo6dOhgbff390dFRQUuXbpk099R809LS0NBQQFCQ0Ph4uICFxcX7Nq1C++++y5cXFzQtm1bVeXbrl07dO3a1abtnnvusV7A5O/vDwCqen/HxsZi/vz5eOqpp9CjRw9ERETghRdeQFxcHAB15mzRkNz8/f1x/vz5Oq/97bffHDZ/o9GIJ598EtnZ2UhOTrbO/gHqyvf7779HQUEBOnbsaP37dfr0abz44ovo1KkTAHXl64xYANoRvV6P0NBQJCcn27QnJyejf//+CkUlHyEEnn/+eWzatAnffvstgoODbfaHhoZCp9PZ5J+fn49Dhw45ZP4PP/wwfv75Z2RmZlo/+vTpg6lTp1q/VlO+AwYMqHNbn+PHjyMoKAgAEBwcDH9/f5t8KyoqsGvXLofMF6i6MlSjsf0zqtVqrbeBUWPOFg3JrV+/figsLMS+ffusfX788UcUFhY6ZP6W4u/XX3/F119/DV9fX5v9aso3IiICP/30k83fr4CAAMTGxmLHjh0A1JWvU1Lo4hO6jqSkJKHT6cT//u//iiNHjoiYmBjh6ekpTp06pXRoTfbcc88Jg8Egdu7cKfLz860fJSUl1j4zZswQHTp0EF9//bVIT08Xw4YNEz179hSVlZUKRi6fmlcBC6GufPft2ydcXFzEG2+8IX799Vfx6aefCg8PD/HJJ59Y+yxbtkwYDAaxadMm8fPPP4vJkyeLdu3aiaKiIgUjv3VPP/20aN++vfjiiy9Edna22LRpk/Dz8xN/+ctfrH0cOefi4mKRkZEhMjIyBAARHx8vMjIyrFe9NiS3Rx99VNx7771iz549Ys+ePaJHjx5i9OjRSqV0QzfK12g0ijFjxogOHTqIzMxMm79h5eXl1jHUkm99al8FLIRj5Uu2WADaob/97W8iKChI6PV6cd9991lvk+LoANT7sW7dOmuf0tJS8fzzz4vWrVsLd3d3MXr0aJGTk6Nc0DKrXQCqLd//+7//E927dxeurq6iS5cu4v3337fZbzabxSuvvCL8/f2Fq6ureOihh8TPP/+sULRNV1RUJKKjo0XHjh2Fm5ubuOOOO8TChQttCgJHzjklJaXe39mnn35aCNGw3C5evCimTp0qvLy8hJeXl5g6daq4dOmSAtnc3I3yzc7Ovu7fsJSUFOsYasm3PvUVgI6UL9mShBCiJWYaiYiIiMg+8BxAIiIiIifDApCIiIjIybAAJCIiInIyLACJiIiInAwLQCIiIiInwwKQiIiIyMmwACQiIiJyMiwAiYiIiJwMC0AiUoWdO3dCkiRcvny5xY8tSRIkSYKPj0+D+ltilSQJY8eObeboiIjqYgFIRA5nyJAhiImJsWnr378/8vPzYTAYFIlp3bp1OH78eIP6WmJ98sknmzkqIqL6sQAkIlXQ6/Xw9/eHJEmKHN/Hxwdt2rRpUF9LrO7u7s0cFRFR/VgAEpFDmTZtGnbt2oV33nnHuox66tSpOkvAH3zwAXx8fPDFF18gJCQEHh4emDBhAq5evYoPP/wQnTp1QqtWrTB79myYTCbr+BUVFfjLX/6C9u3bw9PTE3379sXOnTsbHefBgwcxdOhQeHl5wdvbG6GhoThw4IBc3wYioiZxUToAIqLGeOedd3D8+HF0794dS5cuBQDcfvvtOHXqVJ2+JSUlePfdd5GUlITi4mKMHz8e48ePh4+PD7Zt24aTJ0/iiSeewMCBAzFp0iQAwPTp03Hq1CkkJSUhICAAmzdvxqOPPoqff/4Zd911V4PjnDp1Knr37o01a9ZAq9UiMzMTOp1Olu8BEVFTsQAkIodiMBig1+vh4eEBf3//G/Y1Go1Ys2YNOnfuDACYMGECPv74Y5w/fx633XYbunbtiqFDhyIlJQWTJk1CVlYWNmzYgDNnziAgIAAA8NJLL2H79u1Yt24d3nzzzQbHmZOTg9jYWHTp0gUAGlU8EhE1NxaARKRaHh4e1uIPANq2bYtOnTrhtttus2krKCgAAKSnp0MIgbvvvttmnPLycvj6+jbq2HPnzsWzzz6Ljz/+GI888ggmTpxoEwsRkZJYABKRatVecpUkqd42s9kMADCbzdBqtUhLS4NWq7XpV7NobIglS5ZgypQp+O9//4svv/wSr7zyCpKSkjBu3LhbyISISF4sAInI4ej1epsLN+TSu3dvmEwmFBQUYNCgQU0e7+6778bdd9+NF154AZMnT8a6detYABKRXeBVwETkcDp16oQff/wRp06dwoULF6wzeE119913Y+rUqYiMjMSmTZuQnZ2N/fv3Y/ny5di2bVuDxyktLcXzzz+PnTt34vTp0/jhhx+wf/9+3HPPPbLESUTUVCwAicjhvPTSS9BqtejatStuv/125OTkyDb2unXrEBkZiRdffBEhISEYM2YMfvzxRwQGBjZ4DK1Wi4sXLyIyMhJ33303nnzySYwYMQKvvvqqbHESETWFJIQQSgdBROTIJEnC5s2bG/1Yt2nTpuHy5cvYsmVLM0VGRFQ/zgASEclg8uTJ6NChQ4P6fv/997jtttvw6aefNnNURET14wwgEVETnThxAkDV0m9wcPBN+5eWliIvLw9A1dXFN7ufIRGR3FgAEhERETkZLgETERERORkWgEREREROhgUgERERkZNhAUhERETkZFgAEhERETkZFoBEREREToYFIBEREZGTYQFIRERE5GT+H4MRGFd53qHCAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(filename=\"Figure_2.png\",width=600)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {
-      "image/png": {
-       "width": 600
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(filename=\"Figure_3.png\",width=600)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![gif](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/cgmres_nmpc/animation.gif)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Mathematical Formulation\n",
-    "\n",
-    "Motion model is\n",
-    "\n",
-    "$$\\dot{x}=vcos\\theta$$\n",
-    "\n",
-    "$$\\dot{y}=vsin\\theta$$\n",
-    "\n",
-    "$$\\dot{\\theta}=\\frac{v}{WB}sin(u_{\\delta})$$ (tan is not good for optimization)\n",
-    "\n",
-    "$$\\dot{v}=u_a$$\n",
-    "\n",
-    "Cost function is \n",
-    "\n",
-    "$$J=\\frac{1}{2}(u_a^2+u_{\\delta}^2)-\\phi_a d_a-\\phi_\\delta d_\\delta$$\n",
-    "\n",
-    "Input constraints are\n",
-    "\n",
-    "$$|u_a| \\leq u_{a,max}$$\n",
-    "\n",
-    "$$|u_\\delta| \\leq u_{\\delta,max}$$\n",
-    "\n",
-    "So, Hamiltonian　is\n",
-    "\n",
-    "$$J=\\frac{1}{2}(u_a^2+u_{\\delta}^2)-\\phi_a d_a-\\phi_\\delta d_\\delta\\\\ +\\lambda_1vcos\\theta+\\lambda_2vsin\\theta+\\lambda_3\\frac{v}{WB}sin(u_{\\delta})+\\lambda_4u_a\\\\ \n",
-    "+\\rho_1(u_a^2+d_a^2+u_{a,max}^2)+\\rho_2(u_\\delta^2+d_\\delta^2+u_{\\delta,max}^2)$$\n",
-    "\n",
-    "Partial differential equations of the Hamiltonian are:\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "\\frac{\\partial H}{\\partial \\bf{x}}=\\\\ \n",
-    "\\begin{bmatrix}\n",
-    "\\frac{\\partial H}{\\partial x}= 0&\\\\\n",
-    "\\frac{\\partial H}{\\partial y}= 0&\\\\\n",
-    "\\frac{\\partial H}{\\partial \\theta}= -\\lambda_1vsin\\theta+\\lambda_2vcos\\theta&\\\\\n",
-    "\\frac{\\partial H}{\\partial v}=-\\lambda_1cos\\theta+\\lambda_2sin\\theta+\\lambda_3\\frac{1}{WB}sin(u_{\\delta})&\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\\\\n",
-    "\\end{equation*}$\n",
-    "\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "\\frac{\\partial H}{\\partial \\bf{u}}=\\\\ \n",
-    "\\begin{bmatrix}\n",
-    "\\frac{\\partial H}{\\partial u_a}= u_a+\\lambda_4+2\\rho_1u_a&\\\\\n",
-    "\\frac{\\partial H}{\\partial u_\\delta}= u_\\delta+\\lambda_3\\frac{v}{WB}cos(u_{\\delta})+2\\rho_2u_\\delta&\\\\\n",
-    "\\frac{\\partial H}{\\partial d_a}= -\\phi_a+2\\rho_1d_a&\\\\\n",
-    "\\frac{\\partial H}{\\partial d_\\delta}=-\\phi_\\delta+2\\rho_2d_\\delta&\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\\\\n",
-    "\\end{equation*}$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Ref\n",
-    "\n",
-    "- [Shunichi09/nonlinear\\_control: Implementing the nonlinear model predictive control, sliding mode control](https://github.com/Shunichi09/nonlinear_control)\n",
-    "\n",
-    "- [非線形モデル予測制御におけるCGMRES法をpythonで実装する \\- Qiita](https://qiita.com/MENDY/items/4108190a579395053924)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/PathTracking/model_predictive_speed_and_steer_control/Model_predictive_speed_and_steering_control.ipynb b/PathTracking/model_predictive_speed_and_steer_control/Model_predictive_speed_and_steering_control.ipynb
deleted file mode 100644
index 244f63c8c1..0000000000
--- a/PathTracking/model_predictive_speed_and_steer_control/Model_predictive_speed_and_steering_control.ipynb
+++ /dev/null
@@ -1,333 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Model predictive speed and steering control\n",
-    "\n",
-    "![Model predictive speed and steering control](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/model_predictive_speed_and_steer_control/animation.gif?raw=true)\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "\n",
-    "\n",
-    "code:\n",
-    "\n",
-    "[PythonRobotics/model\\_predictive\\_speed\\_and\\_steer\\_control\\.py at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py)\n",
-    "\n",
-    "This is a path tracking simulation using model predictive control (MPC).\n",
-    "\n",
-    "The MPC controller controls vehicle speed and steering base on linealized model.\n",
-    "\n",
-    "This code uses cvxpy as an optimization modeling tool.\n",
-    "\n",
-    "- [Welcome to CVXPY 1\\.0 — CVXPY 1\\.0\\.6 documentation](http://www.cvxpy.org/)"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### MPC modeling\n",
-    "\n",
-    "State vector is:\n",
-    "\n",
-    "$$ z = [x, y, v,\\phi]$$\n",
-    "\n",
-    "x: x-position, y:y-position, v:velocity, φ: yaw angle\n",
-    "\n",
-    "Input vector is:\n",
-    "\n",
-    "$$ u = [a, \\delta]$$\n",
-    "\n",
-    "a: accellation, δ: steering angle\n",
-    "\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "The MPC cotroller minimize this cost function for path tracking:\n",
-    "\n",
-    "$$min\\ Q_f(z_{T,ref}-z_{T})^2+Q\\Sigma({z_{t,ref}-z_{t}})^2+R\\Sigma{u_t}^2+R_d\\Sigma({u_{t+1}-u_{t}})^2$$\n",
-    "\n",
-    "z_ref come from target path and speed."
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "subject to:\n",
-    "\n",
-    "- Linearlied vehicle model\n",
-    "\n",
-    "$$z_{t+1}=Az_t+Bu+C$$\n",
-    "\n",
-    "- Maximum steering speed\n",
-    "\n",
-    "$$|u_{t+1}-u_{t}|<du_{max}$$\n",
-    "\n",
-    "- Maximum steering angle\n",
-    "\n",
-    "$$|u_{t}|<u_{max}$$\n",
-    "\n",
-    "- Initial state\n",
-    "\n",
-    "$$z_0 = z_{0,ob}$$\n",
-    "\n",
-    "- Maximum and minimum speed\n",
-    "\n",
-    "$$v_{min} < v_t < v_{max}$$\n",
-    "\n",
-    "- Maximum and minimum input\n",
-    "\n",
-    "$$u_{min} < u_t < u_{max}$$\n",
-    "\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "This is implemented at \n",
-    "\n",
-    "[PythonRobotics/model\\_predictive\\_speed\\_and\\_steer\\_control\\.py at f51a73f47cb922a12659f8ce2d544c347a2a8156 · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/blob/f51a73f47cb922a12659f8ce2d544c347a2a8156/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py#L247-L301)"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### Vehicle model linearization\n",
-    "\n",
-    "\n",
-    "\n",
-    "Vehicle model is \n",
-    "\n",
-    "$$ \\dot{x} = vcos(\\phi)$$\n",
-    "\n",
-    "$$ \\dot{y} = vsin((\\phi)$$\n",
-    "\n",
-    "$$ \\dot{v} = a$$\n",
-    "\n",
-    "$$ \\dot{\\phi} = \\frac{vtan(\\delta)}{L}$$\n",
-    "\n",
-    "\n",
-    "\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "ODE is\n",
-    "\n",
-    "$$ \\dot{z} =\\frac{\\partial }{\\partial z} z = f(z, u) = A'z+B'u$$\n",
-    "\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "where\n",
-    "\n",
-    "$\\begin{equation*}\n",
-    "A' =\n",
-    "\\begin{bmatrix}\n",
-    "\\frac{\\partial }{\\partial x}vcos(\\phi) & \n",
-    "\\frac{\\partial }{\\partial y}vcos(\\phi) & \n",
-    "\\frac{\\partial }{\\partial v}vcos(\\phi) &\n",
-    "\\frac{\\partial }{\\partial \\phi}vcos(\\phi)\\\\\n",
-    "\\frac{\\partial }{\\partial x}vsin(\\phi) & \n",
-    "\\frac{\\partial }{\\partial y}vsin(\\phi) & \n",
-    "\\frac{\\partial }{\\partial v}vsin(\\phi) &\n",
-    "\\frac{\\partial }{\\partial \\phi}vsin(\\phi)\\\\\n",
-    "\\frac{\\partial }{\\partial x}a& \n",
-    "\\frac{\\partial }{\\partial y}a& \n",
-    "\\frac{\\partial }{\\partial v}a&\n",
-    "\\frac{\\partial }{\\partial \\phi}a\\\\\n",
-    "\\frac{\\partial }{\\partial x}\\frac{vtan(\\delta)}{L}& \n",
-    "\\frac{\\partial }{\\partial y}\\frac{vtan(\\delta)}{L}& \n",
-    "\\frac{\\partial }{\\partial v}\\frac{vtan(\\delta)}{L}&\n",
-    "\\frac{\\partial }{\\partial \\phi}\\frac{vtan(\\delta)}{L}\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\\\\n",
-    "　=\n",
-    "\\begin{bmatrix}\n",
-    "0 & 0 & cos(\\bar{\\phi}) & -\\bar{v}sin(\\bar{\\phi})\\\\\n",
-    "0 & 0 & sin(\\bar{\\phi}) & \\bar{v}cos(\\bar{\\phi}) \\\\\n",
-    "0 & 0 & 0 & 0 \\\\\n",
-    "0 & 0 &\\frac{tan(\\bar{\\delta})}{L} & 0 \\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$\n",
-    "\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "$\\begin{equation*}\n",
-    "B' =\n",
-    "\\begin{bmatrix}\n",
-    "\\frac{\\partial }{\\partial a}vcos(\\phi) &\n",
-    "\\frac{\\partial }{\\partial \\delta}vcos(\\phi)\\\\\n",
-    "\\frac{\\partial }{\\partial a}vsin(\\phi) &\n",
-    "\\frac{\\partial }{\\partial \\delta}vsin(\\phi)\\\\\n",
-    "\\frac{\\partial }{\\partial a}a &\n",
-    "\\frac{\\partial }{\\partial \\delta}a\\\\\n",
-    "\\frac{\\partial }{\\partial a}\\frac{vtan(\\delta)}{L} &\n",
-    "\\frac{\\partial }{\\partial \\delta}\\frac{vtan(\\delta)}{L}\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\\\\n",
-    "　=\n",
-    "\\begin{bmatrix}\n",
-    "0 & 0 \\\\\n",
-    "0 & 0 \\\\\n",
-    "1 & 0 \\\\\n",
-    "0 & \\frac{\\bar{v}}{Lcos^2(\\bar{\\delta})} \\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$\n",
-    "\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "You can get a discrete-time mode with Forward Euler Discretization with sampling time dt.\n",
-    "\n",
-    "$$z_{k+1}=z_k+f(z_k,u_k)dt$$\n",
-    "\n",
-    "Using first degree Tayer expantion around zbar and ubar\n",
-    "$$z_{k+1}=z_k+(f(\\bar{z},\\bar{u})+A'z_k+B'u_k-A'\\bar{z}-B'\\bar{u})dt$$\n",
-    "\n",
-    "$$z_{k+1}=(I + dtA')z_k+(dtB')u_k + (f(\\bar{z},\\bar{u})-A'\\bar{z}-B'\\bar{u})dt$$\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "So, \n",
-    "\n",
-    "$$z_{k+1}=Az_k+Bu_k +C$$\n",
-    "\n",
-    "where,\n",
-    "\n",
-    "$$\\begin{equation*}\n",
-    "A = (I + dtA')\\\\\n",
-    "=\n",
-    "\\begin{bmatrix} \n",
-    "1 & 0 & cos(\\bar{\\phi})dt & -\\bar{v}sin(\\bar{\\phi})dt\\\\\n",
-    "0 & 1 & sin(\\bar{\\phi})dt & \\bar{v}cos(\\bar{\\phi})dt \\\\\n",
-    "0 & 0 & 1 & 0 \\\\\n",
-    "0 & 0 &\\frac{tan(\\bar{\\delta})}{L}dt & 1 \\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$$"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "$$\\begin{equation*}\n",
-    "B = dtB'\\\\\n",
-    "=\n",
-    "\\begin{bmatrix} \n",
-    "0 & 0 \\\\\n",
-    "0 & 0 \\\\\n",
-    "dt & 0 \\\\\n",
-    "0 & \\frac{\\bar{v}}{Lcos^2(\\bar{\\delta})}dt \\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$$"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "$$\\begin{equation*}\n",
-    "C = (f(\\bar{z},\\bar{u})-A'\\bar{z}-B'\\bar{u})dt\\\\\n",
-    "= dt(\n",
-    "\\begin{bmatrix} \n",
-    "\\bar{v}cos(\\bar{\\phi})\\\\\n",
-    "\\bar{v}sin(\\bar{\\phi}) \\\\\n",
-    "\\bar{a}\\\\\n",
-    "\\frac{\\bar{v}tan(\\bar{\\delta})}{L}\\\\\n",
-    "\\end{bmatrix}\n",
-    "-\n",
-    "\\begin{bmatrix} \n",
-    "\\bar{v}cos(\\bar{\\phi})-\\bar{v}sin(\\bar{\\phi})\\bar{\\phi}\\\\\n",
-    "\\bar{v}sin(\\bar{\\phi})+\\bar{v}cos(\\bar{\\phi})\\bar{\\phi}\\\\\n",
-    "0\\\\\n",
-    "\\frac{\\bar{v}tan(\\bar{\\delta})}{L}\\\\\n",
-    "\\end{bmatrix}\n",
-    "-\n",
-    "\\begin{bmatrix} \n",
-    "0\\\\\n",
-    "0 \\\\\n",
-    "\\bar{a}\\\\\n",
-    "\\frac{\\bar{v}\\bar{\\delta}}{Lcos^2(\\bar{\\delta})}\\\\\n",
-    "\\end{bmatrix}\n",
-    ")\\\\\n",
-    "=\n",
-    "\\begin{bmatrix} \n",
-    "\\bar{v}sin(\\bar{\\phi})\\bar{\\phi}dt\\\\\n",
-    "-\\bar{v}cos(\\bar{\\phi})\\bar{\\phi}dt\\\\\n",
-    "0\\\\\n",
-    "-\\frac{\\bar{v}\\bar{\\delta}}{Lcos^2(\\bar{\\delta})}dt\\\\\n",
-    "\\end{bmatrix}\n",
-    "\\end{equation*}$$"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "This equation is implemented at \n",
-    "\n",
-    "[PythonRobotics/model\\_predictive\\_speed\\_and\\_steer\\_control\\.py at eb6d1cbe6fc90c7be9210bf153b3a04f177cc138 · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/blob/eb6d1cbe6fc90c7be9210bf153b3a04f177cc138/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py#L80-L102)\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### Reference\n",
-    "\n",
-    "- [Vehicle Dynamics and Control \\| Rajesh Rajamani \\| Springer](http://www.springer.com/us/book/9781461414322)\n",
-    "\n",
-    "- [MPC Course Material \\- MPC Lab @ UC\\-Berkeley](http://www.mpc.berkeley.edu/mpc-course-material)\n"
-   ],
-   "metadata": {}
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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diff --git a/PathTracking/rear_wheel_feedback/Figure_2.png b/PathTracking/rear_wheel_feedback/Figure_2.png
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diff --git a/docs/modules/aerial_navigation/aerial_navigation_main.rst b/docs/modules/aerial_navigation/aerial_navigation_main.rst
index e6a8881616..e072707acc 100644
--- a/docs/modules/aerial_navigation/aerial_navigation_main.rst
+++ b/docs/modules/aerial_navigation/aerial_navigation_main.rst
@@ -8,11 +8,7 @@ Drone 3d trajectory following
 
 This is a 3d trajectory following simulation for a quadrotor.
 
-|3|
-
-rocket powered landing
------------------------------
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/drone_3d_trajectory_following/animation.gif
 
 .. include:: rocket_powered_landing.rst
 
-.. |3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/drone_3d_trajectory_following/animation.gif
diff --git a/docs/modules/aerial_navigation/rocket_powered_landing.rst b/docs/modules/aerial_navigation/rocket_powered_landing.rst
index da7546e8f0..6c90d2b44e 100644
--- a/docs/modules/aerial_navigation/rocket_powered_landing.rst
+++ b/docs/modules/aerial_navigation/rocket_powered_landing.rst
@@ -1,3 +1,5 @@
+Rocket powered landing
+-----------------------------
 
 Simulation
 ~~~~~~~~~~
@@ -32,8 +34,6 @@ Simulation
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/rocket_powered_landing/animation.gif
    :alt: gif
 
-   gif
-
 Equation generation
 ~~~~~~~~~~~~~~~~~~~
 
@@ -145,8 +145,8 @@ Equation generation
 
 
 
-Ref
-~~~
+References
+~~~~~~~~~~
 
 -  Python implementation of ‘Successive Convexification for 6-DoF Mars
    Rocket Powered Landing with Free-Final-Time’ paper by Michael Szmuk
diff --git a/docs/modules/arm_navigation/arm_navigation_main.rst b/docs/modules/arm_navigation/arm_navigation_main.rst
index 81dea01b0e..4399ae13e3 100644
--- a/docs/modules/arm_navigation/arm_navigation_main.rst
+++ b/docs/modules/arm_navigation/arm_navigation_main.rst
@@ -3,7 +3,7 @@
 Arm Navigation
 ==============
 
-.. include:: Planar_Two_Link_IK.rst
+.. include:: planar_two_link_ik.rst
 
 
 N joint arm to point control
@@ -14,9 +14,9 @@ N joint arm to a point control simulation.
 This is a interactive simulation.
 
 You can set the goal position of the end effector with left-click on the
-ploting area.
+plotting area.
 
-|n_joints_arm|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/n_joint_arm_to_point_control/animation.gif
 
 In this simulation N = 10, however, you can change it.
 
@@ -25,7 +25,4 @@ Arm navigation with obstacle avoidance
 
 Arm navigation with obstacle avoidance simulation.
 
-|obstacle|
-
-.. |n_joints_arm| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/n_joint_arm_to_point_control/animation.gif
-.. |obstacle| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/arm_obstacle_navigation/animation.gif
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/arm_obstacle_navigation/animation.gif
diff --git a/docs/modules/arm_navigation/Planar_Two_Link_IK.rst b/docs/modules/arm_navigation/planar_two_link_ik.rst
similarity index 99%
rename from docs/modules/arm_navigation/Planar_Two_Link_IK.rst
rename to docs/modules/arm_navigation/planar_two_link_ik.rst
index c5643ced6d..2da2b0dc30 100644
--- a/docs/modules/arm_navigation/Planar_Two_Link_IK.rst
+++ b/docs/modules/arm_navigation/planar_two_link_ik.rst
@@ -5,14 +5,11 @@ Two joint arm to point control
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/two_joint_arm_to_point_control/animation.gif
    :alt: TwoJointArmToPointControl
 
-   TwoJointArmToPointControl
-
 This is two joint arm to a point control simulation.
 
 This is a interactive simulation.
 
-You can set the goal position of the end effector with left-click on the
-ploting area.
+You can set the goal position of the end effector with left-click on the plotting area.
 
 Inverse Kinematics for a Planar Two-Link Robotic Arm
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/PathPlanning/BezierPath/Figure_1.png b/docs/modules/path_planning/Bezier_path_planning/Figure_1.png
similarity index 100%
rename from PathPlanning/BezierPath/Figure_1.png
rename to docs/modules/path_planning/Bezier_path_planning/Figure_1.png
diff --git a/PathPlanning/BezierPath/Figure_2.png b/docs/modules/path_planning/Bezier_path_planning/Figure_2.png
similarity index 100%
rename from PathPlanning/BezierPath/Figure_2.png
rename to docs/modules/path_planning/Bezier_path_planning/Figure_2.png
diff --git a/docs/modules/path_planning/path_planning_main.rst b/docs/modules/path_planning/path_planning_main.rst
index 50ed9bdbcf..d284c4e06d 100644
--- a/docs/modules/path_planning/path_planning_main.rst
+++ b/docs/modules/path_planning/path_planning_main.rst
@@ -337,13 +337,13 @@ A sample code of Bezier path planning.
 
 It is based on 4 control points Beier path.
 
-|Bezier1|
+.. image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/docs/modules/path_planning/Bezier_path_planning/Figure_1.png?raw=True
 
 If you change the offset distance from start and end point,
 
 You can get different Beizer course:
 
-|Bezier2|
+.. image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/docs/modules/path_planning/Bezier_path_planning/Figure_2.png?raw=True
 
 Ref:
 
@@ -439,7 +439,6 @@ Ref:
 .. |B-Spline| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/BSplinePath/Figure_1.png?raw=True
 .. |eta3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Eta3SplinePath/animation.gif
 .. |Bezier1| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/BezierPath/Figure_1.png?raw=True
-.. |Bezier2| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/BezierPath/Figure_2.png?raw=True
 .. |2| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/QuinticPolynomialsPlanner/animation.gif
 .. |dubins| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DubinsPath/animation.gif?raw=True
 .. |RSPlanning| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true
diff --git a/docs/modules/path_tracking/Model_predictive_speed_and_steering_control.rst b/docs/modules/path_tracking/Model_predictive_speed_and_steering_control.rst
index fdb6573d08..59a7166674 100644
--- a/docs/modules/path_tracking/Model_predictive_speed_and_steering_control.rst
+++ b/docs/modules/path_tracking/Model_predictive_speed_and_steering_control.rst
@@ -15,7 +15,7 @@ AtsushiSakai/PythonRobotics <https://github.com/AtsushiSakai/PythonRobotics/blob
 This is a path tracking simulation using model predictive control (MPC).
 
 The MPC controller controls vehicle speed and steering base on
-linealized model.
+linearized model.
 
 This code uses cvxpy as an optimization modeling tool.
 
diff --git a/docs/modules/path_tracking/cgmres_nmpc.rst b/docs/modules/path_tracking/cgmres_nmpc.rst
index a6959e7262..6f6a102618 100644
--- a/docs/modules/path_tracking/cgmres_nmpc.rst
+++ b/docs/modules/path_tracking/cgmres_nmpc.rst
@@ -2,60 +2,21 @@
 Nonlinear Model Predictive Control with C-GMRES
 -----------------------------------------------
 
-.. code-block:: ipython3
-
-    from IPython.display import Image
-    Image(filename="Figure_4.png",width=600)
-
-
-
-
 .. image:: cgmres_nmpc_files/cgmres_nmpc_1_0.png
    :width: 600px
 
-
-
-.. code-block:: ipython3
-
-    Image(filename="Figure_1.png",width=600)
-
-
-
-
 .. image:: cgmres_nmpc_files/cgmres_nmpc_2_0.png
    :width: 600px
 
-
-
-.. code-block:: ipython3
-
-    Image(filename="Figure_2.png",width=600)
-
-
-
-
 .. image:: cgmres_nmpc_files/cgmres_nmpc_3_0.png
    :width: 600px
 
-
-
-.. code-block:: ipython3
-
-    Image(filename="Figure_3.png",width=600)
-
-
-
-
 .. image:: cgmres_nmpc_files/cgmres_nmpc_4_0.png
    :width: 600px
 
-
-
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/cgmres_nmpc/animation.gif
    :alt: gif
 
-   gif
-
 Mathematical Formulation
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
diff --git a/docs/modules/path_tracking/path_tracking_main.rst b/docs/modules/path_tracking/path_tracking_main.rst
index 7788866ad3..3f86da0b07 100644
--- a/docs/modules/path_tracking/path_tracking_main.rst
+++ b/docs/modules/path_tracking/path_tracking_main.rst
@@ -8,7 +8,7 @@ move to a pose control
 
 This is a simulation of moving to a pose control
 
-|2|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif
 
 Ref:
 
@@ -21,7 +21,7 @@ Pure pursuit tracking
 Path tracking simulation with pure pursuit steering control and PID
 speed control.
 
-|3|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/pure_pursuit/animation.gif
 
 The red line is a target course, the green cross means the target point
 for pure pursuit control, the blue line is the tracking.
@@ -37,7 +37,7 @@ Stanley control
 Path tracking simulation with Stanley steering control and PID speed
 control.
 
-|4|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif
 
 Ref:
 
@@ -53,7 +53,7 @@ Rear wheel feedback control
 Path tracking simulation with rear wheel feedback steering control and
 PID speed control.
 
-|5|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif
 
 Ref:
 
@@ -68,7 +68,7 @@ Linear–quadratic regulator (LQR) steering control
 Path tracking simulation with LQR steering control and PID speed
 control.
 
-|6|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_steer_control/animation.gif
 
 Ref:
 
@@ -82,7 +82,7 @@ Linear–quadratic regulator (LQR) speed and steering control
 
 Path tracking simulation with LQR speed and steering control.
 
-|7|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif
 
 Ref:
 
@@ -92,16 +92,5 @@ Ref:
 
 .. include:: Model_predictive_speed_and_steering_control.rst
 
-Ref:
-
--  `notebook <https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.ipynb>`__
-
-
 .. include:: cgmres_nmpc.rst
 
-.. |2| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif
-.. |3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/pure_pursuit/animation.gif
-.. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif
-.. |5| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif
-.. |6| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_steer_control/animation.gif
-.. |7| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif

From 049709fd8b38817842fd1d93e02a1c745c0b8dca Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 27 Nov 2021 22:13:55 +0900
Subject: [PATCH 0548/1080] fix #581 (#582)

* update docs

* update docs
---
 SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb       | 682 ------------------
 docs/how_to_contribute_main.rst               |   2 +-
 .../Figure_1.png                              | Bin
 .../Figure_2.png                              | Bin
 .../path_planning/bspline_path}/Figure_1.png  | Bin
 .../closed_loop_rrt_star_car}/Figure_1.png    | Bin
 .../closed_loop_rrt_star_car}/Figure_3.png    | Bin
 .../closed_loop_rrt_star_car}/Figure_4.png    | Bin
 .../closed_loop_rrt_star_car}/Figure_5.png    | Bin
 .../path_planning/cubic_spline}/Figure_1.png  | Bin
 .../path_planning/cubic_spline}/Figure_2.png  | Bin
 .../path_planning/cubic_spline}/Figure_3.png  | Bin
 .../lookuptable.png                           | Bin
 .../path_planning/path_planning_main.rst      |  92 +--
 .../quintic_polynomials_planner.rst           |   2 +-
 .../modules/path_planning/rrt}/figure_1.png   | Bin
 .../rrt_star_reeds_shepp}/figure_1.png        | Bin
 .../state_lattice_pathplanner}/Figure_1.png   | Bin
 .../state_lattice_pathplanner}/Figure_2.png   | Bin
 .../state_lattice_pathplanner}/Figure_3.png   | Bin
 .../state_lattice_pathplanner}/Figure_4.png   | Bin
 .../state_lattice_pathplanner}/Figure_5.png   | Bin
 .../state_lattice_pathplanner}/Figure_6.png   | Bin
 .../path_tracking/path_tracking_main.rst      |   2 +-
 24 files changed, 32 insertions(+), 748 deletions(-)
 delete mode 100644 SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb
 rename docs/modules/path_planning/{Bezier_path_planning => bezier_path_planning}/Figure_1.png (100%)
 rename docs/modules/path_planning/{Bezier_path_planning => bezier_path_planning}/Figure_2.png (100%)
 rename {PathPlanning/BSplinePath => docs/modules/path_planning/bspline_path}/Figure_1.png (100%)
 rename {PathPlanning/ClosedLoopRRTStar => docs/modules/path_planning/closed_loop_rrt_star_car}/Figure_1.png (100%)
 rename {PathPlanning/ClosedLoopRRTStar => docs/modules/path_planning/closed_loop_rrt_star_car}/Figure_3.png (100%)
 rename {PathPlanning/ClosedLoopRRTStar => docs/modules/path_planning/closed_loop_rrt_star_car}/Figure_4.png (100%)
 rename {PathPlanning/ClosedLoopRRTStar => docs/modules/path_planning/closed_loop_rrt_star_car}/Figure_5.png (100%)
 rename {PathPlanning/CubicSpline => docs/modules/path_planning/cubic_spline}/Figure_1.png (100%)
 rename {PathPlanning/CubicSpline => docs/modules/path_planning/cubic_spline}/Figure_2.png (100%)
 rename {PathPlanning/CubicSpline => docs/modules/path_planning/cubic_spline}/Figure_3.png (100%)
 rename {PathPlanning/ModelPredictiveTrajectoryGenerator => docs/modules/path_planning/model_predictive_trajectry_generator}/lookuptable.png (100%)
 rename {PathPlanning/RRT => docs/modules/path_planning/rrt}/figure_1.png (100%)
 rename {PathPlanning/RRTStarReedsShepp => docs/modules/path_planning/rrt_star_reeds_shepp}/figure_1.png (100%)
 rename {PathPlanning/StateLatticePlanner => docs/modules/path_planning/state_lattice_pathplanner}/Figure_1.png (100%)
 rename {PathPlanning/StateLatticePlanner => docs/modules/path_planning/state_lattice_pathplanner}/Figure_2.png (100%)
 rename {PathPlanning/StateLatticePlanner => docs/modules/path_planning/state_lattice_pathplanner}/Figure_3.png (100%)
 rename {PathPlanning/StateLatticePlanner => docs/modules/path_planning/state_lattice_pathplanner}/Figure_4.png (100%)
 rename {PathPlanning/StateLatticePlanner => docs/modules/path_planning/state_lattice_pathplanner}/Figure_5.png (100%)
 rename {PathPlanning/StateLatticePlanner => docs/modules/path_planning/state_lattice_pathplanner}/Figure_6.png (100%)

diff --git a/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb b/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb
deleted file mode 100644
index 53364e3276..0000000000
--- a/SLAM/GraphBasedSLAM/graphSLAM_doc.ipynb
+++ /dev/null
@@ -1,682 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import copy\n",
-    "import math\n",
-    "import itertools\n",
-    "import numpy as np \n",
-    "import matplotlib.pyplot as plt\n",
-    "from graph_based_slam import calc_rotational_matrix, calc_jacobian, cal_observation_sigma, \\\n",
-    "                             calc_input, observation, motion_model, Edge, pi_2_pi\n",
-    "\n",
-    "%matplotlib inline\n",
-    "np.set_printoptions(precision=3, suppress=True)\n",
-    "np.random.seed(0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Introduction\n",
-    "\n",
-    "In contrast to the probabilistic approaches for solving SLAM, such as EKF, UKF, particle filters, and so on, the graph technique formulates the SLAM as an optimization problem.  It is mostly used to solve the full SLAM problem in an offline fashion, i.e. optimize all the poses of the robot after the path has been traversed. However, some variants are availble that uses graph-based approaches to perform online estimation or to solve for a subset of the poses.\n",
-    "\n",
-    "GraphSLAM uses the motion information as well as the observations of the environment to create least square problem that can be solved using standard optimization techniques.\n",
-    "\n",
-    "### Minimal Example\n",
-    "The following example illustrates the main idea behind graphSLAM. \n",
-    "A simple case of a 1D robot is considered that can only move in 1 direction. The robot is commanded to move forward with a control input $u_t=1$, however, the motion is not perfect and the measured odometry will deviate from the true path. At each timestep the robot can observe its environment, for this simple case as well, there is only a single landmark at coordinates $x=3$. The measured observations are the range between the robot and landmark. These measurements are also subjected to noise. No bearing information is required since this is a 1D problem.\n",
-    "\n",
-    "To solve this, graphSLAM creates what is called as the system information matrix $\\Omega$ also referred to as $H$ and the information vector $\\xi$ also known as $b$. The entries are created based on the information of the motion and the observation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": "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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "text": [
-      "The determinant of H:  0.0\n",
-      "The determinant of H after anchoring constraint:  18.750000000000007\n",
-      "Running graphSLAM ...\n",
-      "Odometry values after optimzation: \n",
-      " [[-0. ]\n",
-      " [ 0.9]\n",
-      " [ 1.9]]\n"
-     ],
-     "output_type": "stream"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": "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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "R = 0.2\n",
-    "Q = 0.2\n",
-    "N = 3\n",
-    "graphics_radius = 0.1\n",
-    "\n",
-    "odom = np.empty((N,1))\n",
-    "obs = np.empty((N,1))\n",
-    "x_true = np.empty((N,1))\n",
-    "\n",
-    "landmark = 3\n",
-    "# Simulated readings of odometry and observations\n",
-    "x_true[0], odom[0], obs[0] = 0.0, 0.0, 2.9\n",
-    "x_true[1], odom[1], obs[1] = 1.0, 1.5, 2.0\n",
-    "x_true[2], odom[2], obs[2] = 2.0, 2.4, 1.0\n",
-    "\n",
-    "hxDR = copy.deepcopy(odom)\n",
-    "# Visualization\n",
-    "plt.plot(landmark,0, '*k', markersize=30)\n",
-    "for i in range(N):\n",
-    "    plt.plot(odom[i], 0, '.', markersize=50, alpha=0.8, color='steelblue')\n",
-    "    plt.plot([odom[i], odom[i] + graphics_radius],\n",
-    "             [0,0], 'r')\n",
-    "    plt.text(odom[i], 0.02, \"X_{}\".format(i), fontsize=12)\n",
-    "    plt.plot(obs[i]+odom[i],0,'.', markersize=25, color='brown')\n",
-    "    plt.plot(x_true[i],0,'.g', markersize=20)\n",
-    "plt.grid()    \n",
-    "plt.show()\n",
-    "\n",
-    "\n",
-    "# Defined as a function to facilitate iteration\n",
-    "def get_H_b(odom, obs):\n",
-    "    \"\"\"\n",
-    "    Create the information matrix and information vector. This implementation is \n",
-    "    based on the concept of virtual measurement i.e. the observations of the\n",
-    "    landmarks are converted into constraints (edges) between the nodes that\n",
-    "    have observed this landmark.\n",
-    "    \"\"\"\n",
-    "    measure_constraints = {}\n",
-    "    omegas = {}\n",
-    "    zids = list(itertools.combinations(range(N),2))\n",
-    "    H = np.zeros((N,N))\n",
-    "    b = np.zeros((N,1))\n",
-    "    for (t1, t2) in zids:\n",
-    "        x1 = odom[t1]\n",
-    "        x2 = odom[t2]\n",
-    "        z1 = obs[t1]\n",
-    "        z2 = obs[t2]\n",
-    "        \n",
-    "        # Adding virtual measurement constraint\n",
-    "        measure_constraints[(t1,t2)] = (x2-x1-z1+z2)\n",
-    "        omegas[(t1,t2)] = (1 / (2*Q))\n",
-    "        \n",
-    "        # populate system's information matrix and vector\n",
-    "        H[t1,t1] += omegas[(t1,t2)]\n",
-    "        H[t2,t2] += omegas[(t1,t2)]\n",
-    "        H[t2,t1] -= omegas[(t1,t2)]\n",
-    "        H[t1,t2] -= omegas[(t1,t2)]\n",
-    "\n",
-    "        b[t1] += omegas[(t1,t2)] * measure_constraints[(t1,t2)]\n",
-    "        b[t2] -= omegas[(t1,t2)] * measure_constraints[(t1,t2)]\n",
-    "\n",
-    "    return H, b\n",
-    "\n",
-    "\n",
-    "H, b = get_H_b(odom, obs)\n",
-    "print(\"The determinant of H: \", np.linalg.det(H))\n",
-    "H[0,0] += 1 # np.inf ?\n",
-    "print(\"The determinant of H after anchoring constraint: \", np.linalg.det(H))\n",
-    "\n",
-    "for i in range(5):\n",
-    "    H, b = get_H_b(odom, obs)\n",
-    "    H[(0,0)] += 1\n",
-    "    \n",
-    "    # Recover the posterior over the path\n",
-    "    dx = np.linalg.inv(H) @ b\n",
-    "    odom += dx\n",
-    "    # repeat till convergence\n",
-    "print(\"Running graphSLAM ...\")    \n",
-    "print(\"Odometry values after optimzation: \\n\", odom)\n",
-    "\n",
-    "plt.figure()\n",
-    "plt.plot(x_true, np.zeros(x_true.shape), '.', markersize=20, label='Ground truth')\n",
-    "plt.plot(odom, np.zeros(x_true.shape), '.', markersize=20, label='Estimation')\n",
-    "plt.plot(hxDR, np.zeros(x_true.shape), '.', markersize=20, label='Odom')\n",
-    "plt.legend()\n",
-    "plt.grid()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In particular, the tasks are split into 2 parts, graph construction, and graph optimization. \n",
-    "### Graph Construction\n",
-    "\n",
-    "Firstly the nodes are defined $\\boldsymbol{x} = \\boldsymbol{x}_{1:n}$  such that each node is the pose of the robot at time $t_i$\n",
-    "Secondly, the edges i.e. the constraints, are constructed according to the following conditions:\n",
-    "\n",
-    "* robot moves from $\\boldsymbol{x}_i$ to $\\boldsymbol{x}_j$. This edge corresponds to the odometry measurement. Relative motion constraints (Not included in the previous minimal example).\n",
-    "* Measurement constraints, this can be done in two ways:\n",
-    "    * The information matrix is set in such a way that it includes the landmarks in the map as well. Then the constraints can be entered in a straightforward fashion between a node $\\boldsymbol{x}_i$ and some landmark $m_k$\n",
-    "    * Through creating a virtual measurement among all the node that have observed the same landmark. More concretely, robot observes the same landmark from $\\boldsymbol{x}_i$ and $\\boldsymbol{x}_j$. Relative measurement constraint. The \"virtual measurement\" $\\boldsymbol{z}_{ij}$, which is the estimated pose of $\\boldsymbol{x}_j$ as seen from the node $\\boldsymbol{x}_i$. The virtual measurement can then be entered in the information matrix and vector in a similar fashion to the motion constraints.\n",
-    "\n",
-    "An edge is fully characterized by the values of the error (entry to information vector) and the local information matrix (entry to the system's information vector). The larger the local information matrix (lower $Q$ or $R$) the values that this edge will contribute with.\n",
-    "\n",
-    "Important Notes:\n",
-    "\n",
-    "* The addition to the information matrix and vector are added to the earlier values.\n",
-    "* In the case of 2D robot, the constraints will be non-linear, therefore, a Jacobian of the error w.r.t the states is needed when updated $H$ and $b$.\n",
-    "* The anchoring constraint is needed in order to avoid having a singular information matri.\n",
-    "\n",
-    "\n",
-    "### Graph Optimization\n",
-    "\n",
-    "The result from this formulation yields an overdetermined system of equations.\n",
-    "The goal after constructing the graph is to find: $x^* = \\underset{x}{\\mathrm{argmin}} ~ \\underset{ij} \\Sigma ~ f(e_{ij}) $, where $f$ is some error function that depends on the edges between to related nodes $i$ and $j$. The derivation in the references arrive at the solution for $x^* = H^{-1}b$ \n",
-    "\n",
-    "\n",
-    "### Planar Example:\n",
-    "\n",
-    "Now we will go through an example with a more realistic case of a 2D robot with 3DoF, namely, $[x, y, \\theta]^T$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#  Simulation parameter\n",
-    "Qsim = np.diag([0.01, np.deg2rad(0.010)])**2 # error added to range and bearing\n",
-    "Rsim = np.diag([0.1, np.deg2rad(1.0)])**2 # error added to [v, w]\n",
-    "\n",
-    "DT = 2.0  # time tick [s]\n",
-    "SIM_TIME = 100.0  # simulation time [s]\n",
-    "MAX_RANGE = 30.0  # maximum observation range\n",
-    "STATE_SIZE = 3  # State size [x,y,yaw]\n",
-    "\n",
-    "# TODO: Why not use Qsim ? \n",
-    "# Covariance parameter of Graph Based SLAM\n",
-    "C_SIGMA1 = 0.1\n",
-    "C_SIGMA2 = 0.1\n",
-    "C_SIGMA3 = np.deg2rad(1.0)\n",
-    "\n",
-    "MAX_ITR = 20  # Maximum iteration during optimization\n",
-    "timesteps = 1\n",
-    "\n",
-    "# consider only 2 landmarks for simplicity\n",
-    "# RFID positions [x, y, yaw]\n",
-    "RFID = np.array([[10.0, -2.0, 0.0],\n",
-    "#                  [15.0, 10.0, 0.0],\n",
-    "#                  [3.0, 15.0, 0.0],\n",
-    "#                  [-5.0, 20.0, 0.0],\n",
-    "#                  [-5.0, 5.0, 0.0]\n",
-    "                 ])\n",
-    "\n",
-    "# State Vector [x y yaw v]'\n",
-    "xTrue = np.zeros((STATE_SIZE, 1))\n",
-    "xDR = np.zeros((STATE_SIZE, 1))  # Dead reckoning\n",
-    "xTrue[2] = np.deg2rad(45)\n",
-    "xDR[2] = np.deg2rad(45)\n",
-    "# history initial values\n",
-    "hxTrue = xTrue\n",
-    "hxDR = xTrue\n",
-    "_, z, _, _ = observation(xTrue, xDR, np.array([[0,0]]).T, RFID)\n",
-    "hz = [z]\n",
-    "\n",
-    "for i in range(timesteps):\n",
-    "    u = calc_input()\n",
-    "    xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID)\n",
-    "    hxDR = np.hstack((hxDR, xDR))\n",
-    "    hxTrue = np.hstack((hxTrue, xTrue))\n",
-    "    hz.append(z)\n",
-    "\n",
-    "# visualize\n",
-    "graphics_radius = 0.3\n",
-    "plt.plot(RFID[:, 0], RFID[:, 1], \"*k\", markersize=20)\n",
-    "plt.plot(hxDR[0, :], hxDR[1, :], '.', markersize=50, alpha=0.8, label='Odom')\n",
-    "plt.plot(hxTrue[0, :], hxTrue[1, :], '.', markersize=20, alpha=0.6, label='X_true')\n",
-    "\n",
-    "for i in range(hxDR.shape[1]):\n",
-    "    x = hxDR[0, i]\n",
-    "    y = hxDR[1, i]\n",
-    "    yaw = hxDR[2, i]\n",
-    "    plt.plot([x, x + graphics_radius * np.cos(yaw)],\n",
-    "             [y, y + graphics_radius * np.sin(yaw)], 'r')\n",
-    "    d = hz[i][:, 0]\n",
-    "    angle = hz[i][:, 1]\n",
-    "    plt.plot([x + d * np.cos(angle + yaw)], [y + d * np.sin(angle + yaw)], '.',\n",
-    "             markersize=20, alpha=0.7)\n",
-    "    plt.legend()\n",
-    "plt.grid()    \n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Copy the data to have a consistent naming with the .py file\n",
-    "zlist = copy.deepcopy(hz)\n",
-    "x_opt = copy.deepcopy(hxDR)\n",
-    "xlist = copy.deepcopy(hxDR)\n",
-    "number_of_nodes = x_opt.shape[1]\n",
-    "n = number_of_nodes * STATE_SIZE"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After the data has been saved, the graph will be constructed by looking at each pair for nodes. The virtual measurement is only created if two nodes have observed the same landmark at different points in time. The next cells are a walk through for a single iteration of graph construction -> optimization -> estimate update. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "text": [
-      "Node combinations: \n",
-      " [(0, 1)]\n",
-      "Node 0 observed landmark with ID 0.0\n",
-      "Node 1 observed landmark with ID 0.0\n"
-     ],
-     "output_type": "stream"
-    }
-   ],
-   "source": [
-    "# get all the possible combination of the different node\n",
-    "zids = list(itertools.combinations(range(len(zlist)), 2))\n",
-    "print(\"Node combinations: \\n\", zids)\n",
-    "\n",
-    "for i in range(xlist.shape[1]):\n",
-    "    print(\"Node {} observed landmark with ID {}\".format(i, zlist[i][0, 3]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the following code snippet the error based on the virtual measurement between node 0 and 1 will be created.\n",
-    "The equations for the error is as follows: \n",
-    "$e_{ij}^x = x_j + d_j cos(\\psi_j + \\theta_j) - x_i - d_i cos(\\psi_i + \\theta_i) $\n",
-    "\n",
-    "$e_{ij}^y = y_j + d_j sin(\\psi_j + \\theta_j) - y_i - d_i sin(\\psi_i + \\theta_i) $\n",
-    "\n",
-    "$e_{ij}^x = \\psi_j + \\theta_j - \\psi_i - \\theta_i $\n",
-    "\n",
-    "Where $[x_i, y_i, \\psi_i]$ is the pose for node $i$ and similarly for node $j$, $d$ is the measured distance at nodes $i$ and $j$, and $\\theta$ is the measured bearing to the landmark. The difference is visualized with the figure in the next cell.\n",
-    "\n",
-    "In case of perfect motion and perfect measurement the error shall be zero since $ x_j + d_j cos(\\psi_j + \\theta_j)$ should equal $x_i + d_i cos(\\psi_i + \\theta_i)$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "text": [
-      "0.0 1.427649841628278 -2.0126109674819155 -3.524048014922737\n",
-      "For nodes (0, 1)\n",
-      "Added edge with errors: \n",
-      " [[-0.02 ]\n",
-      " [-0.084]\n",
-      " [ 0.   ]]\n"
-     ],
-     "output_type": "stream"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": 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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Initialize edges list\n",
-    "edges = []\n",
-    "\n",
-    "# Go through all the different combinations\n",
-    "for (t1, t2) in zids:\n",
-    "    x1, y1, yaw1 = xlist[0, t1], xlist[1, t1], xlist[2, t1]\n",
-    "    x2, y2, yaw2 = xlist[0, t2], xlist[1, t2], xlist[2, t2]\n",
-    "    \n",
-    "    # All nodes have valid observation with ID=0, therefore, no data association condition\n",
-    "    iz1 = 0\n",
-    "    iz2 = 0\n",
-    "    \n",
-    "    d1 = zlist[t1][iz1, 0]\n",
-    "    angle1, phi1 = zlist[t1][iz1, 1], zlist[t1][iz1, 2]\n",
-    "    d2 = zlist[t2][iz2, 0]\n",
-    "    angle2, phi2 = zlist[t2][iz2, 1], zlist[t2][iz2, 2]\n",
-    "    \n",
-    "    # find angle between observation and horizontal \n",
-    "    tangle1 = pi_2_pi(yaw1 + angle1)\n",
-    "    tangle2 = pi_2_pi(yaw2 + angle2)\n",
-    "    \n",
-    "    # project the observations \n",
-    "    tmp1 = d1 * math.cos(tangle1)\n",
-    "    tmp2 = d2 * math.cos(tangle2)\n",
-    "    tmp3 = d1 * math.sin(tangle1)\n",
-    "    tmp4 = d2 * math.sin(tangle2)\n",
-    "    \n",
-    "    edge = Edge()\n",
-    "    print(y1,y2, tmp3, tmp4)\n",
-    "    # calculate the error of the virtual measurement\n",
-    "    # node 1 as seen from node 2 throught the observations 1,2\n",
-    "    edge.e[0, 0] = x2 - x1 - tmp1 + tmp2\n",
-    "    edge.e[1, 0] = y2 - y1 - tmp3 + tmp4\n",
-    "    edge.e[2, 0] = pi_2_pi(yaw2 - yaw1 - tangle1 + tangle2)\n",
-    "\n",
-    "    edge.d1, edge.d2 = d1, d2\n",
-    "    edge.yaw1, edge.yaw2 = yaw1, yaw2\n",
-    "    edge.angle1, edge.angle2 = angle1, angle2\n",
-    "    edge.id1, edge.id2 = t1, t2\n",
-    "    \n",
-    "    edges.append(edge)\n",
-    "    \n",
-    "    print(\"For nodes\",(t1,t2))\n",
-    "    print(\"Added edge with errors: \\n\", edge.e)\n",
-    "    \n",
-    "    # Visualize measurement projections\n",
-    "    plt.plot(RFID[0, 0], RFID[0, 1], \"*k\", markersize=20)\n",
-    "    plt.plot([x1,x2],[y1,y2], '.', markersize=50, alpha=0.8)\n",
-    "    plt.plot([x1, x1 + graphics_radius * np.cos(yaw1)],\n",
-    "             [y1, y1 + graphics_radius * np.sin(yaw1)], 'r')\n",
-    "    plt.plot([x2, x2 + graphics_radius * np.cos(yaw2)],\n",
-    "             [y2, y2 + graphics_radius * np.sin(yaw2)], 'r')\n",
-    "    \n",
-    "    plt.plot([x1,x1+tmp1], [y1,y1], label=\"obs 1 x\")\n",
-    "    plt.plot([x2,x2+tmp2], [y2,y2], label=\"obs 2 x\")\n",
-    "    plt.plot([x1,x1], [y1,y1+tmp3], label=\"obs 1 y\")\n",
-    "    plt.plot([x2,x2], [y2,y2+tmp4], label=\"obs 2 y\")\n",
-    "    plt.plot(x1+tmp1, y1+tmp3, 'o')\n",
-    "    plt.plot(x2+tmp2, y2+tmp4, 'o')\n",
-    "plt.legend()\n",
-    "plt.grid()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since the constraints equations derived before are non-linear, linearization is needed before we can insert them into the information matrix and information vector. Two jacobians \n",
-    "\n",
-    "$A = \\frac{\\partial e_{ij}}{\\partial \\boldsymbol{x}_i}$ as $\\boldsymbol{x}_i$ holds the three variabls x, y, and theta.\n",
-    "Similarly, \n",
-    "$B = \\frac{\\partial e_{ij}}{\\partial \\boldsymbol{x}_j}$ "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "text": [
-      "The determinant of H:  0.0\n",
-      "The determinant of H after origin constraint:  716.1972439134893\n"
-     ],
-     "output_type": "stream"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 2 Axes>",
-      "image/png": "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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": "<Figure size 432x288 with 2 Axes>",
-      "image/png": "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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Initialize the system information matrix and information vector\n",
-    "H = np.zeros((n, n))\n",
-    "b = np.zeros((n, 1))\n",
-    "x_opt = copy.deepcopy(hxDR)\n",
-    "\n",
-    "for edge in edges:\n",
-    "    id1 = edge.id1 * STATE_SIZE\n",
-    "    id2 = edge.id2 * STATE_SIZE\n",
-    "    \n",
-    "    t1 = edge.yaw1 + edge.angle1\n",
-    "    A = np.array([[-1.0, 0, edge.d1 * math.sin(t1)],\n",
-    "                  [0, -1.0, -edge.d1 * math.cos(t1)],\n",
-    "                  [0, 0, -1.0]])\n",
-    "\n",
-    "    t2 = edge.yaw2 + edge.angle2\n",
-    "    B = np.array([[1.0, 0, -edge.d2 * math.sin(t2)],\n",
-    "                  [0, 1.0, edge.d2 * math.cos(t2)],\n",
-    "                  [0, 0, 1.0]])\n",
-    "    \n",
-    "    # TODO: use Qsim instead of sigma\n",
-    "    sigma = np.diag([C_SIGMA1, C_SIGMA2, C_SIGMA3])\n",
-    "    Rt1 = calc_rotational_matrix(tangle1)\n",
-    "    Rt2 = calc_rotational_matrix(tangle2)\n",
-    "    edge.omega = np.linalg.inv(Rt1 @ sigma @ Rt1.T + Rt2 @ sigma @ Rt2.T)\n",
-    "\n",
-    "    # Fill in entries in H and b\n",
-    "    H[id1:id1 + STATE_SIZE, id1:id1 + STATE_SIZE] += A.T @ edge.omega @ A\n",
-    "    H[id1:id1 + STATE_SIZE, id2:id2 + STATE_SIZE] += A.T @ edge.omega @ B\n",
-    "    H[id2:id2 + STATE_SIZE, id1:id1 + STATE_SIZE] += B.T @ edge.omega @ A\n",
-    "    H[id2:id2 + STATE_SIZE, id2:id2 + STATE_SIZE] += B.T @ edge.omega @ B\n",
-    "\n",
-    "    b[id1:id1 + STATE_SIZE] += (A.T @ edge.omega @ edge.e)\n",
-    "    b[id2:id2 + STATE_SIZE] += (B.T @ edge.omega @ edge.e)\n",
-    "   \n",
-    "\n",
-    "print(\"The determinant of H: \", np.linalg.det(H))\n",
-    "plt.figure()\n",
-    "plt.subplot(1,2,1)\n",
-    "plt.imshow(H, extent=[0, n, 0, n])\n",
-    "plt.subplot(1,2,2)\n",
-    "plt.imshow(b, extent=[0, 1, 0, n])\n",
-    "plt.show()    \n",
-    "\n",
-    "# Fix the origin, multiply by large number gives same results but better visualization\n",
-    "H[0:STATE_SIZE, 0:STATE_SIZE] += np.identity(STATE_SIZE)\n",
-    "print(\"The determinant of H after origin constraint: \", np.linalg.det(H))    \n",
-    "plt.figure()\n",
-    "plt.subplot(1,2,1)\n",
-    "plt.imshow(H, extent=[0, n, 0, n])\n",
-    "plt.subplot(1,2,2)\n",
-    "plt.imshow(b, extent=[0, 1, 0, n])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "text": [
-      "dx: \n",
-      " [[-0.   ]\n",
-      " [-0.   ]\n",
-      " [ 0.   ]\n",
-      " [ 0.02 ]\n",
-      " [ 0.084]\n",
-      " [-0.   ]]\n",
-      "ground truth: \n",
-      "  [[0.    1.414]\n",
-      " [0.    1.414]\n",
-      " [0.785 0.985]]\n",
-      "Odom: \n",
-      " [[0.    1.428]\n",
-      " [0.    1.428]\n",
-      " [0.785 0.976]]\n",
-      "SLAM: \n",
-      " [[-0.     1.448]\n",
-      " [-0.     1.512]\n",
-      " [ 0.785  0.976]]\n",
-      "\n",
-      "graphSLAM localization error:  0.010729072751057656\n",
-      "Odom localization error:  0.0004460978857535104\n"
-     ],
-     "output_type": "stream"
-    }
-   ],
-   "source": [
-    "# Find the solution (first iteration)\n",
-    "dx = - np.linalg.inv(H) @ b\n",
-    "for i in range(number_of_nodes):\n",
-    "    x_opt[0:3, i] += dx[i * 3:i * 3 + 3, 0]\n",
-    "print(\"dx: \\n\",dx)\n",
-    "print(\"ground truth: \\n \",hxTrue)\n",
-    "print(\"Odom: \\n\", hxDR)\n",
-    "print(\"SLAM: \\n\", x_opt)\n",
-    "\n",
-    "# performance will improve with more iterations, nodes and landmarks.\n",
-    "print(\"\\ngraphSLAM localization error: \", np.sum((x_opt - hxTrue) ** 2))\n",
-    "print(\"Odom localization error: \", np.sum((hxDR - hxTrue) ** 2))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The references:\n",
-    "\n",
-    "\n",
-    "* http://robots.stanford.edu/papers/thrun.graphslam.pdf\n",
-    "\n",
-    "* http://ais.informatik.uni-freiburg.de/teaching/ss13/robotics/slides/16-graph-slam.pdf\n",
-    "\n",
-    "* http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf\n",
-    "\n",
-    "N.B.\n",
-    "An additional step is required that uses the estimated path to update the belief regarding the map."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "pycharm": {
-     "is_executing": false
-    }
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  },
-  "pycharm": {
-   "stem_cell": {
-    "cell_type": "raw",
-    "source": [],
-    "metadata": {
-     "collapsed": false
-    }
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/docs/how_to_contribute_main.rst b/docs/how_to_contribute_main.rst
index c761fd9928..a4ac3c819b 100644
--- a/docs/how_to_contribute_main.rst
+++ b/docs/how_to_contribute_main.rst
@@ -1,4 +1,4 @@
-How to contribute
+How To Contribute
 =================
 
 This document describes how to contribute this project.
diff --git a/docs/modules/path_planning/Bezier_path_planning/Figure_1.png b/docs/modules/path_planning/bezier_path_planning/Figure_1.png
similarity index 100%
rename from docs/modules/path_planning/Bezier_path_planning/Figure_1.png
rename to docs/modules/path_planning/bezier_path_planning/Figure_1.png
diff --git a/docs/modules/path_planning/Bezier_path_planning/Figure_2.png b/docs/modules/path_planning/bezier_path_planning/Figure_2.png
similarity index 100%
rename from docs/modules/path_planning/Bezier_path_planning/Figure_2.png
rename to docs/modules/path_planning/bezier_path_planning/Figure_2.png
diff --git a/PathPlanning/BSplinePath/Figure_1.png b/docs/modules/path_planning/bspline_path/Figure_1.png
similarity index 100%
rename from PathPlanning/BSplinePath/Figure_1.png
rename to docs/modules/path_planning/bspline_path/Figure_1.png
diff --git a/PathPlanning/ClosedLoopRRTStar/Figure_1.png b/docs/modules/path_planning/closed_loop_rrt_star_car/Figure_1.png
similarity index 100%
rename from PathPlanning/ClosedLoopRRTStar/Figure_1.png
rename to docs/modules/path_planning/closed_loop_rrt_star_car/Figure_1.png
diff --git a/PathPlanning/ClosedLoopRRTStar/Figure_3.png b/docs/modules/path_planning/closed_loop_rrt_star_car/Figure_3.png
similarity index 100%
rename from PathPlanning/ClosedLoopRRTStar/Figure_3.png
rename to docs/modules/path_planning/closed_loop_rrt_star_car/Figure_3.png
diff --git a/PathPlanning/ClosedLoopRRTStar/Figure_4.png b/docs/modules/path_planning/closed_loop_rrt_star_car/Figure_4.png
similarity index 100%
rename from PathPlanning/ClosedLoopRRTStar/Figure_4.png
rename to docs/modules/path_planning/closed_loop_rrt_star_car/Figure_4.png
diff --git a/PathPlanning/ClosedLoopRRTStar/Figure_5.png b/docs/modules/path_planning/closed_loop_rrt_star_car/Figure_5.png
similarity index 100%
rename from PathPlanning/ClosedLoopRRTStar/Figure_5.png
rename to docs/modules/path_planning/closed_loop_rrt_star_car/Figure_5.png
diff --git a/PathPlanning/CubicSpline/Figure_1.png b/docs/modules/path_planning/cubic_spline/Figure_1.png
similarity index 100%
rename from PathPlanning/CubicSpline/Figure_1.png
rename to docs/modules/path_planning/cubic_spline/Figure_1.png
diff --git a/PathPlanning/CubicSpline/Figure_2.png b/docs/modules/path_planning/cubic_spline/Figure_2.png
similarity index 100%
rename from PathPlanning/CubicSpline/Figure_2.png
rename to docs/modules/path_planning/cubic_spline/Figure_2.png
diff --git a/PathPlanning/CubicSpline/Figure_3.png b/docs/modules/path_planning/cubic_spline/Figure_3.png
similarity index 100%
rename from PathPlanning/CubicSpline/Figure_3.png
rename to docs/modules/path_planning/cubic_spline/Figure_3.png
diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable.png b/docs/modules/path_planning/model_predictive_trajectry_generator/lookuptable.png
similarity index 100%
rename from PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable.png
rename to docs/modules/path_planning/model_predictive_trajectry_generator/lookuptable.png
diff --git a/docs/modules/path_planning/path_planning_main.rst b/docs/modules/path_planning/path_planning_main.rst
index d284c4e06d..8033e1a875 100644
--- a/docs/modules/path_planning/path_planning_main.rst
+++ b/docs/modules/path_planning/path_planning_main.rst
@@ -11,7 +11,7 @@ This is a 2D navigation sample code with Dynamic Window Approach.
 -  `The Dynamic Window Approach to Collision
    Avoidance <https://www.ri.cmu.edu/pub_files/pub1/fox_dieter_1997_1/fox_dieter_1997_1.pdf>`__
 
-|DWA|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif
 
 Grid based search
 -----------------
@@ -22,7 +22,7 @@ Dijkstra algorithm
 This is a 2D grid based shortest path planning with Dijkstra's
 algorithm.
 
-|Dijkstra|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Dijkstra/animation.gif
 
 In the animation, cyan points are searched nodes.
 
@@ -33,7 +33,7 @@ A\* algorithm
 
 This is a 2D grid based shortest path planning with A star algorithm.
 
-|astar|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/AStar/animation.gif
 
 In the animation, cyan points are searched nodes.
 
@@ -46,7 +46,7 @@ D\* algorithm
 
 This is a 2D grid based shortest path planning with D star algorithm.
 
-|dstar|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStar/animation.gif
 
 The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm.
 
@@ -59,7 +59,7 @@ Potential Field algorithm
 
 This is a 2D grid based path planning with Potential Field algorithm.
 
-|PotentialField|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/PotentialFieldPlanning/animation.gif
 
 In the animation, the blue heat map shows potential value on each grid.
 
@@ -79,12 +79,12 @@ This algorithm is used for state lattice planner.
 Path optimization sample
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-|4|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/kn05animation.gif
 
 Lookup table generation sample
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-|5|
+.. image:: model_predictive_trajectry_generator/lookuptable.png
 
 Ref:
 
@@ -92,8 +92,6 @@ Ref:
    robots <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
 
 
-
-
 State Lattice Planning
 ----------------------
 
@@ -114,24 +112,24 @@ Ref:
 Uniform polar sampling
 ~~~~~~~~~~~~~~~~~~~~~~
 
-|6|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/UniformPolarSampling.gif
 
 Biased polar sampling
 ~~~~~~~~~~~~~~~~~~~~~
 
-|7|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/BiasedPolarSampling.gif
 
 Lane sampling
 ~~~~~~~~~~~~~
 
-|8|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif
 
 .. _probabilistic-road-map-(prm)-planning:
 
 Probabilistic Road-Map (PRM) planning
 -------------------------------------
 
-|PRM|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ProbabilisticRoadMap/animation.gif
 
 This PRM planner uses Dijkstra method for graph search.
 
@@ -151,7 +149,7 @@ Ref:
 Voronoi Road-Map planning
 -------------------------
 
-|VRM|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/VoronoiRoadMap/animation.gif
 
 This Voronoi road-map planner uses Dijkstra method for graph search.
 
@@ -174,7 +172,7 @@ Rapidly-Exploring Random Trees (RRT)
 Basic RRT
 ~~~~~~~~~
 
-|9|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRT/animation.gif
 
 This is a simple path planning code with Rapidly-Exploring Random Trees
 (RRT)
@@ -188,7 +186,7 @@ are start and goal positions.
 RRT with dubins path
 ~~~~~~~~~~~~~~~~~~~~
 
-|PythonRobotics|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTDubins/animation.gif
 
 Path planning for a car robot with RRT and dubins path planner.
 
@@ -197,7 +195,7 @@ Path planning for a car robot with RRT and dubins path planner.
 RRT\* with dubins path
 ~~~~~~~~~~~~~~~~~~~~~~
 
-|AtsushiSakai/PythonRobotics|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarDubins/animation.gif
 
 Path planning for a car robot with RRT\* and dubins path planner.
 
@@ -206,7 +204,7 @@ Path planning for a car robot with RRT\* and dubins path planner.
 RRT\* with reeds-sheep path
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-|11|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarReedsShepp/animation.gif
 
 Path planning for a car robot with RRT\* and reeds sheep path planner.
 
@@ -215,7 +213,7 @@ Path planning for a car robot with RRT\* and reeds sheep path planner.
 Informed RRT\*
 ~~~~~~~~~~~~~~
 
-|irrt|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/InformedRRTStar/animation.gif
 
 This is a path planning code with Informed RRT*.
 
@@ -232,7 +230,7 @@ Ref:
 Batch Informed RRT\*
 ~~~~~~~~~~~~~~~~~~~~
 
-|irrt2|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BatchInformedRRTStar/animation.gif
 
 This is a path planning code with Batch Informed RRT*.
 
@@ -249,7 +247,7 @@ Closed Loop RRT\*
 
 A vehicle model based path planning with closed loop RRT*.
 
-|CLRRT|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ClosedLoopRRTStar/animation.gif
 
 In this code, pure-pursuit algorithm is used for steering control,
 
@@ -275,7 +273,7 @@ This is a path planning simulation with LQR-RRT*.
 
 A double integrator motion model is used for LQR local planner.
 
-|LQRRRT|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif
 
 Ref:
 
@@ -297,14 +295,14 @@ with cubic spline.
 
 Heading angle of each point can be also calculated analytically.
 
-|12|
-|13|
-|14|
+.. image:: cubic_spline/Figure_1.png
+.. image:: cubic_spline/Figure_2.png
+.. image:: cubic_spline/Figure_3.png
 
 B-Spline planning
 -----------------
 
-|B-Spline|
+.. image:: bspline_path/Figure_1.png
 
 This is a path planning with B-Spline curse.
 
@@ -321,7 +319,7 @@ Ref:
 Eta^3 Spline path planning
 --------------------------
 
-|eta3|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Eta3SplinePath/animation.gif
 
 This is a path planning with Eta^3 spline.
 
@@ -360,7 +358,7 @@ Dubins path planning
 
 A sample code for Dubins path planning.
 
-|dubins|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DubinsPath/animation.gif?raw=True
 
 Ref:
 
@@ -372,7 +370,7 @@ Reeds Shepp planning
 
 A sample code with Reeds Shepp path planning.
 
-|RSPlanning|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true
 
 Ref:
 
@@ -390,12 +388,12 @@ LQR based path planning
 
 A sample code using LQR based path planning for double integrator model.
 
-|RSPlanning2|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRPlanner/animation.gif?raw=true
 
 Optimal Trajectory in a Frenet Frame
 ------------------------------------
 
-|15|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/animation.gif
 
 This is optimal trajectory generation in a Frenet Frame.
 
@@ -412,35 +410,3 @@ Ref:
 -  `Optimal trajectory generation for dynamic street scenarios in a
    Frenet Frame <https://www.youtube.com/watch?v=Cj6tAQe7UCY>`__
 
-.. |DWA| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif
-.. |Dijkstra| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Dijkstra/animation.gif
-.. |astar| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/AStar/animation.gif
-.. |dstar| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStar/animation.gif
-.. |PotentialField| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/PotentialFieldPlanning/animation.gif
-.. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/kn05animation.gif
-.. |5| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable.png?raw=True
-.. |6| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/UniformPolarSampling.gif
-.. |7| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/BiasedPolarSampling.gif
-.. |8| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif
-.. |PRM| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ProbabilisticRoadMap/animation.gif
-.. |VRM| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/VoronoiRoadMap/animation.gif
-.. |9| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRT/animation.gif
-.. |10| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTstar/animation.gif
-.. |PythonRobotics| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTDubins/animation.gif
-.. |AtsushiSakai/PythonRobotics| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarDubins/animation.gif
-.. |11| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarReedsShepp/animation.gif
-.. |irrt| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/InformedRRTStar/animation.gif
-.. |irrt2| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BatchInformedRRTStar/animation.gif
-.. |CLRRT| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ClosedLoopRRTStar/animation.gif
-.. |LQRRRT| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif
-.. |12| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/CubicSpline/Figure_1.png?raw=True
-.. |13| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/CubicSpline/Figure_2.png?raw=True
-.. |14| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/CubicSpline/Figure_3.png?raw=True
-.. |B-Spline| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/BSplinePath/Figure_1.png?raw=True
-.. |eta3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Eta3SplinePath/animation.gif
-.. |Bezier1| image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/PathPlanning/BezierPath/Figure_1.png?raw=True
-.. |2| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/QuinticPolynomialsPlanner/animation.gif
-.. |dubins| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DubinsPath/animation.gif?raw=True
-.. |RSPlanning| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true
-.. |RSPlanning2| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRPlanner/animation.gif?raw=true
-.. |15| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/animation.gif
diff --git a/docs/modules/path_planning/quintic_polynomials_planner.rst b/docs/modules/path_planning/quintic_polynomials_planner.rst
index 2fab8a51ab..2bced07034 100644
--- a/docs/modules/path_planning/quintic_polynomials_planner.rst
+++ b/docs/modules/path_planning/quintic_polynomials_planner.rst
@@ -4,7 +4,7 @@ Quintic polynomials planning
 
 Motion planning with quintic polynomials.
 
-|2|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/QuinticPolynomialsPlanner/animation.gif
 
 It can calculate 2D path, velocity, and acceleration profile based on
 quintic polynomials.
diff --git a/PathPlanning/RRT/figure_1.png b/docs/modules/path_planning/rrt/figure_1.png
similarity index 100%
rename from PathPlanning/RRT/figure_1.png
rename to docs/modules/path_planning/rrt/figure_1.png
diff --git a/PathPlanning/RRTStarReedsShepp/figure_1.png b/docs/modules/path_planning/rrt_star_reeds_shepp/figure_1.png
similarity index 100%
rename from PathPlanning/RRTStarReedsShepp/figure_1.png
rename to docs/modules/path_planning/rrt_star_reeds_shepp/figure_1.png
diff --git a/PathPlanning/StateLatticePlanner/Figure_1.png b/docs/modules/path_planning/state_lattice_pathplanner/Figure_1.png
similarity index 100%
rename from PathPlanning/StateLatticePlanner/Figure_1.png
rename to docs/modules/path_planning/state_lattice_pathplanner/Figure_1.png
diff --git a/PathPlanning/StateLatticePlanner/Figure_2.png b/docs/modules/path_planning/state_lattice_pathplanner/Figure_2.png
similarity index 100%
rename from PathPlanning/StateLatticePlanner/Figure_2.png
rename to docs/modules/path_planning/state_lattice_pathplanner/Figure_2.png
diff --git a/PathPlanning/StateLatticePlanner/Figure_3.png b/docs/modules/path_planning/state_lattice_pathplanner/Figure_3.png
similarity index 100%
rename from PathPlanning/StateLatticePlanner/Figure_3.png
rename to docs/modules/path_planning/state_lattice_pathplanner/Figure_3.png
diff --git a/PathPlanning/StateLatticePlanner/Figure_4.png b/docs/modules/path_planning/state_lattice_pathplanner/Figure_4.png
similarity index 100%
rename from PathPlanning/StateLatticePlanner/Figure_4.png
rename to docs/modules/path_planning/state_lattice_pathplanner/Figure_4.png
diff --git a/PathPlanning/StateLatticePlanner/Figure_5.png b/docs/modules/path_planning/state_lattice_pathplanner/Figure_5.png
similarity index 100%
rename from PathPlanning/StateLatticePlanner/Figure_5.png
rename to docs/modules/path_planning/state_lattice_pathplanner/Figure_5.png
diff --git a/PathPlanning/StateLatticePlanner/Figure_6.png b/docs/modules/path_planning/state_lattice_pathplanner/Figure_6.png
similarity index 100%
rename from PathPlanning/StateLatticePlanner/Figure_6.png
rename to docs/modules/path_planning/state_lattice_pathplanner/Figure_6.png
diff --git a/docs/modules/path_tracking/path_tracking_main.rst b/docs/modules/path_tracking/path_tracking_main.rst
index 3f86da0b07..725e4f04b4 100644
--- a/docs/modules/path_tracking/path_tracking_main.rst
+++ b/docs/modules/path_tracking/path_tracking_main.rst
@@ -1,6 +1,6 @@
 .. _path_tracking:
 
-Path tracking
+Path Tracking
 =============
 
 move to a pose control

From f8f10a3ec8421bd9fd2b826716fbff97f5c2f32f Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 28 Nov 2021 08:13:28 +0900
Subject: [PATCH 0549/1080] Adding all gifs to the doc (#584)

* update docs

* update docs
---
 docs/index_main.rst                                    |  1 +
 .../aerial_navigation/aerial_navigation_main.rst       | 10 ++--------
 .../drone_3d_trajectory_following.rst                  |  6 ++++++
 .../rocket_powered_landing.rst                         |  0
 docs/modules/bipedal/bipedal_main.rst                  |  7 +++++++
 .../bipedal/bipedal_planner/bipedal_planner.rst        |  6 ++++++
 6 files changed, 22 insertions(+), 8 deletions(-)
 create mode 100644 docs/modules/aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following.rst
 rename docs/modules/aerial_navigation/{ => rocket_powered_landing}/rocket_powered_landing.rst (100%)
 create mode 100644 docs/modules/bipedal/bipedal_main.rst
 create mode 100644 docs/modules/bipedal/bipedal_planner/bipedal_planner.rst

diff --git a/docs/index_main.rst b/docs/index_main.rst
index 7cdbddc71f..128a50a58e 100644
--- a/docs/index_main.rst
+++ b/docs/index_main.rst
@@ -42,6 +42,7 @@ See this paper for more details:
    modules/path_tracking/path_tracking
    modules/arm_navigation/arm_navigation
    modules/aerial_navigation/aerial_navigation
+   modules/bipedal/bipedal
    modules/appendix/appendix
    how_to_contribute
 
diff --git a/docs/modules/aerial_navigation/aerial_navigation_main.rst b/docs/modules/aerial_navigation/aerial_navigation_main.rst
index e072707acc..ea6c4c8e30 100644
--- a/docs/modules/aerial_navigation/aerial_navigation_main.rst
+++ b/docs/modules/aerial_navigation/aerial_navigation_main.rst
@@ -3,12 +3,6 @@
 Aerial Navigation
 =================
 
-Drone 3d trajectory following
------------------------------
-
-This is a 3d trajectory following simulation for a quadrotor.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/drone_3d_trajectory_following/animation.gif
-
-.. include:: rocket_powered_landing.rst
+.. include:: drone_3d_trajectory_following/drone_3d_trajectory_following.rst
+.. include:: rocket_powered_landing/rocket_powered_landing.rst
 
diff --git a/docs/modules/aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following.rst b/docs/modules/aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following.rst
new file mode 100644
index 0000000000..c3bdc33cdc
--- /dev/null
+++ b/docs/modules/aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following.rst
@@ -0,0 +1,6 @@
+Drone 3d trajectory following
+-----------------------------
+
+This is a 3d trajectory following simulation for a quadrotor.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/drone_3d_trajectory_following/animation.gif
diff --git a/docs/modules/aerial_navigation/rocket_powered_landing.rst b/docs/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing.rst
similarity index 100%
rename from docs/modules/aerial_navigation/rocket_powered_landing.rst
rename to docs/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing.rst
diff --git a/docs/modules/bipedal/bipedal_main.rst b/docs/modules/bipedal/bipedal_main.rst
new file mode 100644
index 0000000000..bab316b0d9
--- /dev/null
+++ b/docs/modules/bipedal/bipedal_main.rst
@@ -0,0 +1,7 @@
+.. _bipedal:
+
+Bipedal
+=================
+
+.. include:: bipedal_planner/bipedal_planner.rst
+
diff --git a/docs/modules/bipedal/bipedal_planner/bipedal_planner.rst b/docs/modules/bipedal/bipedal_planner/bipedal_planner.rst
new file mode 100644
index 0000000000..2ee5971e7a
--- /dev/null
+++ b/docs/modules/bipedal/bipedal_planner/bipedal_planner.rst
@@ -0,0 +1,6 @@
+Bipedal Planner
+-----------------------------
+
+Bipedal Walking with modifying designated footsteps
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Bipedal/bipedal_planner/animation.gif

From c99716d6920300b1ae0f49652f06db97d0621d63 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 28 Nov 2021 16:00:02 +0900
Subject: [PATCH 0550/1080] Adding all gifs to the doc 2 (#585)

* update docs

* update docs

* update docs
---
 .github/workflows/Linux_CI.yml                |   2 +-
 .github/workflows/MacOS_CI.yml                |   2 +-
 .../inverted_pendulum_mpc_control.py          |   0
 {InvertedPendulumCart => Control}/__init__.py |   0
 docs/index_main.rst                           |   1 +
 docs/modules/control/control_main.rst         |   7 ++
 .../inverted_pendulum_mpc_control.rst         |   6 ++
 docs/modules/introduction_main.rst            |  39 +++++++-
 .../ensamble_kalman_filter_localization.rst   |   7 ++
 .../extended_kalman_filter_localization.rst   |   1 -
 .../histogram_filter_localization.rst         |  22 +++++
 .../localization/localization_main.rst        |  88 +-----------------
 .../particle_filter_localization.rst          |  37 ++++++++
 .../unscented_kalman_filter_localization.rst  |  13 +++
 .../mapping/circle_fitting/circle_fitting.rst |  13 +++
 .../gaussian_grid_map/gaussian_grid_map.rst   |   6 ++
 .../k_means_object_clustering.rst             |   6 ++
 .../grid_map_example.png                      | Bin
 .../lidar_to_grid_map_tutorial.rst            |  17 ++--
 .../lidar_to_grid_map_tutorial_12_0.png       | Bin
 .../lidar_to_grid_map_tutorial_14_1.png       | Bin
 .../lidar_to_grid_map_tutorial_5_0.png        | Bin
 .../lidar_to_grid_map_tutorial_7_0.png        | Bin
 .../lidar_to_grid_map_tutorial_8_0.png        | Bin
 docs/modules/mapping/mapping_main.rst         |  51 ++--------
 .../ray_casting_grid_map.rst                  |   6 ++
 .../rectangle_fitting/rectangle_fitting.rst   |   7 ++
 tests/test_inverted_pendulum_mpc_control.py   |   2 +-
 28 files changed, 193 insertions(+), 140 deletions(-)
 rename {InvertedPendulumCart => Control}/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py (100%)
 rename {InvertedPendulumCart => Control}/__init__.py (100%)
 create mode 100644 docs/modules/control/control_main.rst
 create mode 100644 docs/modules/control/inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst
 create mode 100644 docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst
 rename docs/modules/localization/{ => extended_kalman_filter_localization_files}/extended_kalman_filter_localization.rst (99%)
 create mode 100644 docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst
 create mode 100644 docs/modules/localization/particle_filter_localization/particle_filter_localization.rst
 create mode 100644 docs/modules/localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst
 create mode 100644 docs/modules/mapping/circle_fitting/circle_fitting.rst
 create mode 100644 docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst
 create mode 100644 docs/modules/mapping/k_means_object_clustering/k_means_object_clustering.rst
 rename docs/modules/mapping/{lidar_to_grid_map_tutorial_files => lidar_to_grid_map_tutorial}/grid_map_example.png (100%)
 rename docs/modules/mapping/{ => lidar_to_grid_map_tutorial}/lidar_to_grid_map_tutorial.rst (90%)
 rename docs/modules/mapping/{lidar_to_grid_map_tutorial_files => lidar_to_grid_map_tutorial}/lidar_to_grid_map_tutorial_12_0.png (100%)
 rename docs/modules/mapping/{lidar_to_grid_map_tutorial_files => lidar_to_grid_map_tutorial}/lidar_to_grid_map_tutorial_14_1.png (100%)
 rename docs/modules/mapping/{lidar_to_grid_map_tutorial_files => lidar_to_grid_map_tutorial}/lidar_to_grid_map_tutorial_5_0.png (100%)
 rename docs/modules/mapping/{lidar_to_grid_map_tutorial_files => lidar_to_grid_map_tutorial}/lidar_to_grid_map_tutorial_7_0.png (100%)
 rename docs/modules/mapping/{lidar_to_grid_map_tutorial_files => lidar_to_grid_map_tutorial}/lidar_to_grid_map_tutorial_8_0.png (100%)
 create mode 100644 docs/modules/mapping/ray_casting_grid_map/ray_casting_grid_map.rst
 create mode 100644 docs/modules/mapping/rectangle_fitting/rectangle_fitting.rst

diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index 4e2e57f8e4..ef773ff71a 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -41,7 +41,7 @@ jobs:
         mypy -p AerialNavigation
         mypy -p ArmNavigation
         mypy -p Bipedal
-        mypy -p InvertedPendulumCart
+        mypy -p Control
         mypy -p Localization
         mypy -p Mapping
         mypy -p PathPlanning
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index 9bfb4b6fd3..f01d85e62b 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -48,7 +48,7 @@ jobs:
           mypy -p AerialNavigation
           mypy -p ArmNavigation
           mypy -p Bipedal
-          mypy -p InvertedPendulumCart
+          mypy -p Control
           mypy -p Localization
           mypy -p Mapping
           mypy -p PathPlanning
diff --git a/InvertedPendulumCart/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py b/Control/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py
similarity index 100%
rename from InvertedPendulumCart/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py
rename to Control/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py
diff --git a/InvertedPendulumCart/__init__.py b/Control/__init__.py
similarity index 100%
rename from InvertedPendulumCart/__init__.py
rename to Control/__init__.py
diff --git a/docs/index_main.rst b/docs/index_main.rst
index 128a50a58e..79980c2bd9 100644
--- a/docs/index_main.rst
+++ b/docs/index_main.rst
@@ -43,6 +43,7 @@ See this paper for more details:
    modules/arm_navigation/arm_navigation
    modules/aerial_navigation/aerial_navigation
    modules/bipedal/bipedal
+   modules/control/control
    modules/appendix/appendix
    how_to_contribute
 
diff --git a/docs/modules/control/control_main.rst b/docs/modules/control/control_main.rst
new file mode 100644
index 0000000000..7e13ab1eb6
--- /dev/null
+++ b/docs/modules/control/control_main.rst
@@ -0,0 +1,7 @@
+.. _control:
+
+Control
+=================
+
+.. include:: inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst
+
diff --git a/docs/modules/control/inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst b/docs/modules/control/inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst
new file mode 100644
index 0000000000..328f9f6b24
--- /dev/null
+++ b/docs/modules/control/inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst
@@ -0,0 +1,6 @@
+Inverted Pendulum MPC Control
+-----------------------------
+
+Bipedal Walking with modifying designated footsteps
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/InvertedPendulumCart/animation.gif
diff --git a/docs/modules/introduction_main.rst b/docs/modules/introduction_main.rst
index 60cb085a29..f9fb487297 100644
--- a/docs/modules/introduction_main.rst
+++ b/docs/modules/introduction_main.rst
@@ -2,4 +2,41 @@
 Introduction
 ============
 
-TBD
\ No newline at end of file
+TBD
+
+Definition Of Robotics
+----------------------
+
+TBD
+
+History Of Robotics
+----------------------
+
+TBD
+
+Application Of Robotics
+------------------------
+
+TBD
+
+Software for Robotics
+----------------------
+
+TBD
+
+Software for Robotics
+----------------------
+
+TBD
+
+Python for Robotics
+----------------------
+
+TBD
+
+Learning Robotics Algorithms
+----------------------------
+
+TBD
+
+
diff --git a/docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst b/docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst
new file mode 100644
index 0000000000..2543d1186a
--- /dev/null
+++ b/docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst
@@ -0,0 +1,7 @@
+Ensamble Kalman Filter Localization
+-----------------------------------
+
+.. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/ensamble_kalman_filter/animation.gif
+
+This is a sensor fusion localization with Ensamble Kalman Filter(EnKF).
+
diff --git a/docs/modules/localization/extended_kalman_filter_localization.rst b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst
similarity index 99%
rename from docs/modules/localization/extended_kalman_filter_localization.rst
rename to docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst
index 507ea972a4..9777258e3e 100644
--- a/docs/modules/localization/extended_kalman_filter_localization.rst
+++ b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst
@@ -8,7 +8,6 @@ Extended Kalman Filter Localization
 
 
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif
-   :alt: EKF
 
 This is a sensor fusion localization with Extended Kalman Filter(EKF).
 
diff --git a/docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst b/docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst
new file mode 100644
index 0000000000..afe689b9a5
--- /dev/null
+++ b/docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst
@@ -0,0 +1,22 @@
+Histogram filter localization
+-----------------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/histogram_filter/animation.gif
+
+This is a 2D localization example with Histogram filter.
+
+The red cross is true position, black points are RFID positions.
+
+The blue grid shows a position probability of histogram filter.
+
+In this simulation, x,y are unknown, yaw is known.
+
+The filter integrates speed input and range observations from RFID for
+localization.
+
+Initial position is not needed.
+
+References:
+~~~~~~~~~~~
+
+-  `PROBABILISTIC ROBOTICS`_
diff --git a/docs/modules/localization/localization_main.rst b/docs/modules/localization/localization_main.rst
index c665eba694..b1769df163 100644
--- a/docs/modules/localization/localization_main.rst
+++ b/docs/modules/localization/localization_main.rst
@@ -3,89 +3,11 @@
 Localization
 ============
 
-.. include:: extended_kalman_filter_localization.rst
-
-
-Unscented Kalman Filter localization
-------------------------------------
-
-|2|
-
-This is a sensor fusion localization with Unscented Kalman Filter(UKF).
-
-The lines and points are same meaning of the EKF simulation.
-
-References:
-~~~~~~~~~~~
-
--  `Discriminatively Trained Unscented Kalman Filter for Mobile Robot
-   Localization`_
-
-Particle filter localization
-----------------------------
-
-|3|
-
-This is a sensor fusion localization with Particle Filter(PF).
-
-The blue line is true trajectory, the black line is dead reckoning
-trajectory,
-
-and the red line is estimated trajectory with PF.
-
-It is assumed that the robot can measure a distance from landmarks
-(RFID).
-
-This measurements are used for PF localization.
-
-How to calculate covariance matrix from particles
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The covariance matrix :math:`\Xi` from particle information is calculated by the following equation:
-
-.. math:: \Xi_{j,k}=\frac{1}{1-\sum^N_{i=1}(w^i)^2}\sum^N_{i=1}w^i(x^i_j-\mu_j)(x^i_k-\mu_k)
-
-- :math:`\Xi_{j,k}` is covariance matrix element at row :math:`i` and column :math:`k`.
-
-- :math:`w^i` is the weight of the :math:`i` th particle.
-
-- :math:`x^i_j` is the :math:`j` th state of the :math:`i` th particle.
-
-- :math:`\mu_j` is the :math:`j` th mean state of particles.
-
-References:
-~~~~~~~~~~~
-
--  `PROBABILISTIC ROBOTICS`_
--  `Improving the particle filter in high dimensions using conjugate artificial process noise`_
-
-Histogram filter localization
------------------------------
-
-|4|
-
-This is a 2D localization example with Histogram filter.
-
-The red cross is true position, black points are RFID positions.
-
-The blue grid shows a position probability of histogram filter.
-
-In this simulation, x,y are unknown, yaw is known.
-
-The filter integrates speed input and range observations from RFID for
-localization.
-
-Initial position is not needed.
-
-References:
-~~~~~~~~~~~
-
--  `PROBABILISTIC ROBOTICS`_
+.. include:: extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst
+.. include:: ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst
+.. include:: unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst
+.. include:: histogram_filter_localization/histogram_filter_localization.rst
+.. include:: particle_filter_localization/particle_filter_localization.rst
 
 .. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
-.. _Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization: https://www.researchgate.net/publication/267963417_Discriminatively_Trained_Unscented_Kalman_Filter_for_Mobile_Robot_Localization
-.. _Improving the particle filter in high dimensions using conjugate artificial process noise: https://arxiv.org/pdf/1801.07000.pdf
 
-.. |2| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/unscented_kalman_filter/animation.gif
-.. |3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/particle_filter/animation.gif
-.. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/histogram_filter/animation.gif
diff --git a/docs/modules/localization/particle_filter_localization/particle_filter_localization.rst b/docs/modules/localization/particle_filter_localization/particle_filter_localization.rst
new file mode 100644
index 0000000000..55b44e166b
--- /dev/null
+++ b/docs/modules/localization/particle_filter_localization/particle_filter_localization.rst
@@ -0,0 +1,37 @@
+Particle filter localization
+----------------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/particle_filter/animation.gif
+
+This is a sensor fusion localization with Particle Filter(PF).
+
+The blue line is true trajectory, the black line is dead reckoning
+trajectory,
+
+and the red line is estimated trajectory with PF.
+
+It is assumed that the robot can measure a distance from landmarks
+(RFID).
+
+This measurements are used for PF localization.
+
+How to calculate covariance matrix from particles
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The covariance matrix :math:`\Xi` from particle information is calculated by the following equation:
+
+.. math:: \Xi_{j,k}=\frac{1}{1-\sum^N_{i=1}(w^i)^2}\sum^N_{i=1}w^i(x^i_j-\mu_j)(x^i_k-\mu_k)
+
+- :math:`\Xi_{j,k}` is covariance matrix element at row :math:`i` and column :math:`k`.
+
+- :math:`w^i` is the weight of the :math:`i` th particle.
+
+- :math:`x^i_j` is the :math:`j` th state of the :math:`i` th particle.
+
+- :math:`\mu_j` is the :math:`j` th mean state of particles.
+
+References:
+~~~~~~~~~~~
+
+-  `PROBABILISTIC ROBOTICS`_
+- `Improving the particle filter in high dimensions using conjugate artificial process noise <https://arxiv.org/pdf/1801.07000.pdf>`_
diff --git a/docs/modules/localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst b/docs/modules/localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst
new file mode 100644
index 0000000000..bb6b5b664b
--- /dev/null
+++ b/docs/modules/localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst
@@ -0,0 +1,13 @@
+Unscented Kalman Filter localization
+------------------------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/unscented_kalman_filter/animation.gif
+
+This is a sensor fusion localization with Unscented Kalman Filter(UKF).
+
+The lines and points are same meaning of the EKF simulation.
+
+References:
+~~~~~~~~~~~
+
+- `Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization <https://www.researchgate.net/publication/267963417_Discriminatively_Trained_Unscented_Kalman_Filter_for_Mobile_Robot_Localization>`_
diff --git a/docs/modules/mapping/circle_fitting/circle_fitting.rst b/docs/modules/mapping/circle_fitting/circle_fitting.rst
new file mode 100644
index 0000000000..1892d1f8f7
--- /dev/null
+++ b/docs/modules/mapping/circle_fitting/circle_fitting.rst
@@ -0,0 +1,13 @@
+Object shape recognition using circle fitting
+---------------------------------------------
+
+This is an object shape recognition using circle fitting.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/circle_fitting/animation.gif
+
+The blue circle is the true object shape.
+
+The red crosses are observations from a ranging sensor.
+
+The red circle is the estimated object shape using circle fitting.
+
diff --git a/docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst b/docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst
new file mode 100644
index 0000000000..f78f2c95d8
--- /dev/null
+++ b/docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst
@@ -0,0 +1,6 @@
+Gaussian grid map
+-----------------
+
+This is a 2D Gaussian grid mapping example.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/gaussian_grid_map/animation.gif
diff --git a/docs/modules/mapping/k_means_object_clustering/k_means_object_clustering.rst b/docs/modules/mapping/k_means_object_clustering/k_means_object_clustering.rst
new file mode 100644
index 0000000000..e098ca5409
--- /dev/null
+++ b/docs/modules/mapping/k_means_object_clustering/k_means_object_clustering.rst
@@ -0,0 +1,6 @@
+k-means object clustering
+-------------------------
+
+This is a 2D object clustering with k-means algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/kmeans_clustering/animation.gif
diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial_files/grid_map_example.png b/docs/modules/mapping/lidar_to_grid_map_tutorial/grid_map_example.png
similarity index 100%
rename from docs/modules/mapping/lidar_to_grid_map_tutorial_files/grid_map_example.png
rename to docs/modules/mapping/lidar_to_grid_map_tutorial/grid_map_example.png
diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial.rst b/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst
similarity index 90%
rename from docs/modules/mapping/lidar_to_grid_map_tutorial.rst
rename to docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst
index 64dba26d4f..2e9470ff6c 100644
--- a/docs/modules/mapping/lidar_to_grid_map_tutorial.rst
+++ b/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst
@@ -1,3 +1,7 @@
+Lidar to grid map
+--------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/lidar_to_grid_map/animation.gif
 
 This simple tutorial shows how to read LIDAR (range) measurements from a
 file and convert it to occupancy grid.
@@ -12,8 +16,7 @@ a ``numpy array``, and numbers close to 1 means the cell is occupied
 free (*marked with green*). The grid has the ability to represent
 unknown (unobserved) areas, which are close to 0.5.
 
-.. figure:: lidar_to_grid_map_tutorial_files/grid_map_example.png
-   :alt: Example
+.. figure:: lidar_to_grid_map_tutorial/grid_map_example.png
 
 In order to construct the grid map from the measurement we need to
 discretise the values. But, first let’s need to ``import`` some
@@ -65,7 +68,7 @@ From the distances and the angles it is easy to determine the ``x`` and
 
 
 
-.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_5_0.png
+.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_5_0.png
 
 
 The ``lidar_to_grid_map.py`` contains handy functions which can used to
@@ -86,7 +89,7 @@ map. Let’s see how this works.
 
 
 
-.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_7_0.png
+.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_7_0.png
 
 
 .. code:: ipython3
@@ -103,7 +106,7 @@ map. Let’s see how this works.
 
 
 
-.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_8_0.png
+.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_8_0.png
 
 
 To fill empty areas, a queue-based algorithm can be used that can be
@@ -160,7 +163,7 @@ from a center point (e.g. (10, 20)) with zeros:
 
 
 
-.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_12_0.png
+.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_12_0.png
 
 
 Let’s use this flood fill on real data:
@@ -191,5 +194,5 @@ Let’s use this flood fill on real data:
 
 
 
-.. image:: lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_14_1.png
+.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_14_1.png
 
diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_12_0.png b/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_12_0.png
similarity index 100%
rename from docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_12_0.png
rename to docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_12_0.png
diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_14_1.png b/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_14_1.png
similarity index 100%
rename from docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_14_1.png
rename to docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_14_1.png
diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_5_0.png b/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_5_0.png
similarity index 100%
rename from docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_5_0.png
rename to docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_5_0.png
diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_7_0.png b/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_7_0.png
similarity index 100%
rename from docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_7_0.png
rename to docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_7_0.png
diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_8_0.png b/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_8_0.png
similarity index 100%
rename from docs/modules/mapping/lidar_to_grid_map_tutorial_files/lidar_to_grid_map_tutorial_8_0.png
rename to docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_8_0.png
diff --git a/docs/modules/mapping/mapping_main.rst b/docs/modules/mapping/mapping_main.rst
index ccdd3198b1..7d2c4cfe67 100644
--- a/docs/modules/mapping/mapping_main.rst
+++ b/docs/modules/mapping/mapping_main.rst
@@ -3,49 +3,10 @@
 Mapping
 =======
 
-Gaussian grid map
------------------
+.. include:: gaussian_grid_map/gaussian_grid_map.rst
+.. include:: ray_casting_grid_map/ray_casting_grid_map.rst
+.. include:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst
+.. include:: k_means_object_clustering/k_means_object_clustering.rst
+.. include:: circle_fitting/circle_fitting.rst
+.. include:: rectangle_fitting/rectangle_fitting.rst
 
-This is a 2D Gaussian grid mapping example.
-
-|2|
-
-Ray casting grid map
---------------------
-
-This is a 2D ray casting grid mapping example.
-
-|3|
-
-Lidar to grid map
---------------------
-
-|6|
-
-.. include:: lidar_to_grid_map_tutorial.rst
-
-k-means object clustering
--------------------------
-
-This is a 2D object clustering with k-means algorithm.
-
-|4|
-
-Object shape recognition using circle fitting
----------------------------------------------
-
-This is an object shape recognition using circle fitting.
-
-|5|
-
-The blue circle is the true object shape.
-
-The red crosses are observations from a ranging sensor.
-
-The red circle is the estimated object shape using circle fitting.
-
-.. |2| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/gaussian_grid_map/animation.gif
-.. |3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/raycasting_grid_map/animation.gif
-.. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/kmeans_clustering/animation.gif
-.. |5| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/circle_fitting/animation.gif
-.. |6| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/lidar_to_grid_map/animation.gif
diff --git a/docs/modules/mapping/ray_casting_grid_map/ray_casting_grid_map.rst b/docs/modules/mapping/ray_casting_grid_map/ray_casting_grid_map.rst
new file mode 100644
index 0000000000..cc5a1a1c5b
--- /dev/null
+++ b/docs/modules/mapping/ray_casting_grid_map/ray_casting_grid_map.rst
@@ -0,0 +1,6 @@
+Ray casting grid map
+--------------------
+
+This is a 2D ray casting grid mapping example.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/raycasting_grid_map/animation.gif
\ No newline at end of file
diff --git a/docs/modules/mapping/rectangle_fitting/rectangle_fitting.rst b/docs/modules/mapping/rectangle_fitting/rectangle_fitting.rst
new file mode 100644
index 0000000000..c0949ac4c3
--- /dev/null
+++ b/docs/modules/mapping/rectangle_fitting/rectangle_fitting.rst
@@ -0,0 +1,7 @@
+Object shape recognition using rectangle fitting
+------------------------------------------------
+
+This is an object shape recognition using rectangle fitting.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/rectangle_fitting/animation.gif
+
diff --git a/tests/test_inverted_pendulum_mpc_control.py b/tests/test_inverted_pendulum_mpc_control.py
index d88d4d7915..9519b45ec9 100644
--- a/tests/test_inverted_pendulum_mpc_control.py
+++ b/tests/test_inverted_pendulum_mpc_control.py
@@ -2,7 +2,7 @@
 import sys
 if 'cvxpy' in sys.modules:  # pragma: no cover
 
-    from InvertedPendulumCart.InvertedPendulumMPCControl \
+    from Control.InvertedPendulumMPCControl \
         import inverted_pendulum_mpc_control as m
 
     def test1():

From d183a00a1c03c448ba8ae7ab75f3b9bd2331be9a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 29 Nov 2021 00:01:06 +0900
Subject: [PATCH 0551/1080] Adding all gifs to the doc (#586)

* update docs

* update docs

* update docs

* update docs
---
 Control/move_to_pose/__init__.py              |   0
 .../move_to_pose/move_to_pose.py              |   0
 docs/modules/control/control_main.rst         |   1 +
 .../move_to_a_pose_control.rst                |  11 ++
 .../{ => cgmres_nmpc}/cgmres_nmpc.rst         |   8 +-
 .../cgmres_nmpc_1_0.png                       | Bin
 .../cgmres_nmpc_2_0.png                       | Bin
 .../cgmres_nmpc_3_0.png                       | Bin
 .../cgmres_nmpc_4_0.png                       | Bin
 .../lqr_speed_and_steering_control.rst        |  13 +++
 .../lqr_steering_control.rst                  |  14 +++
 ...predictive_speed_and_steering_control.rst} |   0
 .../path_tracking/path_tracking_main.rst      |  97 ++----------------
 .../pure_pursuit_tracking.rst                 |  16 +++
 .../rear_wheel_feedback_control.rst           |  12 +++
 .../stanley_control/stanley_control.rst       |  16 +++
 .../slam/{ => FastSLAM1}/FastSLAM1.rst        |  22 ++--
 .../FastSLAM1_12_0.png                        | Bin
 .../FastSLAM1_12_1.png                        | Bin
 .../FastSLAM1_1_0.png                         | Bin
 docs/modules/slam/FastSLAM2/FastSLAM2.rst     |  16 +++
 docs/modules/slam/{ => ekf_slam}/ekf_slam.rst |   8 +-
 .../ekf_slam_1_0.png                          | Bin
 .../Graph_SLAM_optimization.gif               | Bin 114966 -> 0 bytes
 .../graphSLAM_SE2_example.rst                 |  15 ++-
 .../graphSLAM_SE2_example_13_0.png            | Bin
 .../graphSLAM_SE2_example_15_0.png            | Bin
 .../graphSLAM_SE2_example_16_0.png            | Bin
 .../graphSLAM_SE2_example_4_0.png             | Bin
 .../graphSLAM_SE2_example_8_0.png             | Bin
 .../graphSLAM_SE2_example_9_0.png             | Bin
 .../slam/{ => graph_slam}/graphSLAM_doc.rst   |  21 ++--
 .../graphSLAM_formulation.rst                 |   4 +-
 docs/modules/slam/graph_slam/graph_slam.rst   |  24 +++++
 .../iterative_closest_point_matching.rst      |  16 +++
 docs/modules/slam/slam_main.rst               |  78 +-------------
 tests/test_move_to_pose.py                    |   2 +-
 37 files changed, 186 insertions(+), 208 deletions(-)
 create mode 100644 Control/move_to_pose/__init__.py
 rename {PathTracking => Control}/move_to_pose/move_to_pose.py (100%)
 create mode 100644 docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst
 rename docs/modules/path_tracking/{ => cgmres_nmpc}/cgmres_nmpc.rst (91%)
 rename docs/modules/path_tracking/{cgmres_nmpc_files => cgmres_nmpc}/cgmres_nmpc_1_0.png (100%)
 rename docs/modules/path_tracking/{cgmres_nmpc_files => cgmres_nmpc}/cgmres_nmpc_2_0.png (100%)
 rename docs/modules/path_tracking/{cgmres_nmpc_files => cgmres_nmpc}/cgmres_nmpc_3_0.png (100%)
 rename docs/modules/path_tracking/{cgmres_nmpc_files => cgmres_nmpc}/cgmres_nmpc_4_0.png (100%)
 create mode 100644 docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst
 create mode 100644 docs/modules/path_tracking/lqr_steering_control/lqr_steering_control.rst
 rename docs/modules/path_tracking/{Model_predictive_speed_and_steering_control.rst => model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.rst} (100%)
 create mode 100644 docs/modules/path_tracking/pure_pursuit_tracking/pure_pursuit_tracking.rst
 create mode 100644 docs/modules/path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control.rst
 create mode 100644 docs/modules/path_tracking/stanley_control/stanley_control.rst
 rename docs/modules/slam/{ => FastSLAM1}/FastSLAM1.rst (97%)
 rename docs/modules/slam/{FastSLAM1_files => FastSLAM1}/FastSLAM1_12_0.png (100%)
 rename docs/modules/slam/{FastSLAM1_files => FastSLAM1}/FastSLAM1_12_1.png (100%)
 rename docs/modules/slam/{FastSLAM1_files => FastSLAM1}/FastSLAM1_1_0.png (100%)
 create mode 100644 docs/modules/slam/FastSLAM2/FastSLAM2.rst
 rename docs/modules/slam/{ => ekf_slam}/ekf_slam.rst (98%)
 rename docs/modules/slam/{ekf_slam_files => ekf_slam}/ekf_slam_1_0.png (100%)
 delete mode 100644 docs/modules/slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
 rename docs/modules/slam/{ => graph_slam}/graphSLAM_SE2_example.rst (90%)
 rename docs/modules/slam/{ => graph_slam}/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png (100%)
 rename docs/modules/slam/{ => graph_slam}/graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png (100%)
 rename docs/modules/slam/{ => graph_slam}/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png (100%)
 rename docs/modules/slam/{ => graph_slam}/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png (100%)
 rename docs/modules/slam/{ => graph_slam}/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png (100%)
 rename docs/modules/slam/{ => graph_slam}/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png (100%)
 rename docs/modules/slam/{ => graph_slam}/graphSLAM_doc.rst (95%)
 rename docs/modules/slam/{ => graph_slam}/graphSLAM_formulation.rst (97%)
 create mode 100644 docs/modules/slam/graph_slam/graph_slam.rst
 create mode 100644 docs/modules/slam/iterative_closest_point_matching/iterative_closest_point_matching.rst

diff --git a/Control/move_to_pose/__init__.py b/Control/move_to_pose/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/PathTracking/move_to_pose/move_to_pose.py b/Control/move_to_pose/move_to_pose.py
similarity index 100%
rename from PathTracking/move_to_pose/move_to_pose.py
rename to Control/move_to_pose/move_to_pose.py
diff --git a/docs/modules/control/control_main.rst b/docs/modules/control/control_main.rst
index 7e13ab1eb6..9eeba434d2 100644
--- a/docs/modules/control/control_main.rst
+++ b/docs/modules/control/control_main.rst
@@ -4,4 +4,5 @@ Control
 =================
 
 .. include:: inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst
+.. include:: move_to_a_pose_control/move_to_a_pose_control.rst
 
diff --git a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst
new file mode 100644
index 0000000000..c5bf15d970
--- /dev/null
+++ b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst
@@ -0,0 +1,11 @@
+Move to a pose control
+----------------------
+
+This is a simulation of moving to a pose control
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif
+
+Ref:
+
+-  `P. I. Corke, "Robotics, Vision and Control" \| SpringerLink
+   p102 <https://link.springer.com/book/10.1007/978-3-642-20144-8>`__
diff --git a/docs/modules/path_tracking/cgmres_nmpc.rst b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.rst
similarity index 91%
rename from docs/modules/path_tracking/cgmres_nmpc.rst
rename to docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.rst
index 6f6a102618..5092fa8e2f 100644
--- a/docs/modules/path_tracking/cgmres_nmpc.rst
+++ b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.rst
@@ -2,16 +2,16 @@
 Nonlinear Model Predictive Control with C-GMRES
 -----------------------------------------------
 
-.. image:: cgmres_nmpc_files/cgmres_nmpc_1_0.png
+.. image:: cgmres_nmpc/cgmres_nmpc_1_0.png
    :width: 600px
 
-.. image:: cgmres_nmpc_files/cgmres_nmpc_2_0.png
+.. image:: cgmres_nmpc/cgmres_nmpc_2_0.png
    :width: 600px
 
-.. image:: cgmres_nmpc_files/cgmres_nmpc_3_0.png
+.. image:: cgmres_nmpc/cgmres_nmpc_3_0.png
    :width: 600px
 
-.. image:: cgmres_nmpc_files/cgmres_nmpc_4_0.png
+.. image:: cgmres_nmpc/cgmres_nmpc_4_0.png
    :width: 600px
 
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/cgmres_nmpc/animation.gif
diff --git a/docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_1_0.png b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_1_0.png
similarity index 100%
rename from docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_1_0.png
rename to docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_1_0.png
diff --git a/docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_2_0.png b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_2_0.png
similarity index 100%
rename from docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_2_0.png
rename to docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_2_0.png
diff --git a/docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_3_0.png b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_3_0.png
similarity index 100%
rename from docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_3_0.png
rename to docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_3_0.png
diff --git a/docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_4_0.png b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_4_0.png
similarity index 100%
rename from docs/modules/path_tracking/cgmres_nmpc_files/cgmres_nmpc_4_0.png
rename to docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_4_0.png
diff --git a/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst b/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst
new file mode 100644
index 0000000000..68ea9c88b2
--- /dev/null
+++ b/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst
@@ -0,0 +1,13 @@
+.. _linearquadratic-regulator-(lqr)-speed-and-steering-control:
+
+Linear–quadratic regulator (LQR) speed and steering control
+-----------------------------------------------------------
+
+Path tracking simulation with LQR speed and steering control.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif
+
+References:
+~~~~~~~~~~~
+
+-  `Towards fully autonomous driving: Systems and algorithms <http://ieeexplore.ieee.org/document/5940562/>`__
diff --git a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control.rst b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control.rst
new file mode 100644
index 0000000000..bf6d6b5854
--- /dev/null
+++ b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control.rst
@@ -0,0 +1,14 @@
+.. _linearquadratic-regulator-(lqr)-steering-control:
+
+Linear–quadratic regulator (LQR) steering control
+-------------------------------------------------
+
+Path tracking simulation with LQR steering control and PID speed
+control.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_steer_control/animation.gif
+
+References:
+~~~~~~~~~~~
+-  `ApolloAuto/apollo: An open autonomous driving platform <https://github.com/ApolloAuto/apollo>`_
+
diff --git a/docs/modules/path_tracking/Model_predictive_speed_and_steering_control.rst b/docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.rst
similarity index 100%
rename from docs/modules/path_tracking/Model_predictive_speed_and_steering_control.rst
rename to docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.rst
diff --git a/docs/modules/path_tracking/path_tracking_main.rst b/docs/modules/path_tracking/path_tracking_main.rst
index 725e4f04b4..2a572ef6df 100644
--- a/docs/modules/path_tracking/path_tracking_main.rst
+++ b/docs/modules/path_tracking/path_tracking_main.rst
@@ -3,94 +3,11 @@
 Path Tracking
 =============
 
-move to a pose control
-----------------------
-
-This is a simulation of moving to a pose control
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif
-
-Ref:
-
--  `P. I. Corke, "Robotics, Vision and Control" \| SpringerLink
-   p102 <https://link.springer.com/book/10.1007/978-3-642-20144-8>`__
-
-Pure pursuit tracking
----------------------
-
-Path tracking simulation with pure pursuit steering control and PID
-speed control.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/pure_pursuit/animation.gif
-
-The red line is a target course, the green cross means the target point
-for pure pursuit control, the blue line is the tracking.
-
-Ref:
-
--  `A Survey of Motion Planning and Control Techniques for Self-driving
-   Urban Vehicles <https://arxiv.org/abs/1604.07446>`__
-
-Stanley control
----------------
-
-Path tracking simulation with Stanley steering control and PID speed
-control.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif
-
-Ref:
-
--  `Stanley: The robot that won the DARPA grand
-   challenge <http://robots.stanford.edu/papers/thrun.stanley05.pdf>`__
-
--  `Automatic Steering Methods for Autonomous Automobile Path
-   Tracking <https://www.ri.cmu.edu/pub_files/2009/2/Automatic_Steering_Methods_for_Autonomous_Automobile_Path_Tracking.pdf>`__
-
-Rear wheel feedback control
----------------------------
-
-Path tracking simulation with rear wheel feedback steering control and
-PID speed control.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif
-
-Ref:
-
--  `A Survey of Motion Planning and Control Techniques for Self-driving
-   Urban Vehicles <https://arxiv.org/abs/1604.07446>`__
-
-.. _linearquadratic-regulator-(lqr)-steering-control:
-
-Linear–quadratic regulator (LQR) steering control
--------------------------------------------------
-
-Path tracking simulation with LQR steering control and PID speed
-control.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_steer_control/animation.gif
-
-Ref:
-
--  `ApolloAuto/apollo: An open autonomous driving
-   platform <https://github.com/ApolloAuto/apollo>`__
-
-.. _linearquadratic-regulator-(lqr)-speed-and-steering-control:
-
-Linear–quadratic regulator (LQR) speed and steering control
------------------------------------------------------------
-
-Path tracking simulation with LQR speed and steering control.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif
-
-Ref:
-
--  `Towards fully autonomous driving: Systems and algorithms - IEEE
-   Conference
-   Publication <http://ieeexplore.ieee.org/document/5940562/>`__
-
-.. include:: Model_predictive_speed_and_steering_control.rst
-
-.. include:: cgmres_nmpc.rst
+.. include:: pure_pursuit_tracking/pure_pursuit_tracking.rst
+.. include:: stanley_control/stanley_control.rst
+.. include:: rear_wheel_feedback_control/rear_wheel_feedback_control.rst
+.. include:: lqr_steering_control/lqr_steering_control.rst
+.. include:: lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst
+.. include:: model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.rst
+.. include:: cgmres_nmpc/cgmres_nmpc.rst
 
diff --git a/docs/modules/path_tracking/pure_pursuit_tracking/pure_pursuit_tracking.rst b/docs/modules/path_tracking/pure_pursuit_tracking/pure_pursuit_tracking.rst
new file mode 100644
index 0000000000..5c7bcef85f
--- /dev/null
+++ b/docs/modules/path_tracking/pure_pursuit_tracking/pure_pursuit_tracking.rst
@@ -0,0 +1,16 @@
+Pure pursuit tracking
+---------------------
+
+Path tracking simulation with pure pursuit steering control and PID
+speed control.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/pure_pursuit/animation.gif
+
+The red line is a target course, the green cross means the target point
+for pure pursuit control, the blue line is the tracking.
+
+References:
+~~~~~~~~~~~
+
+-  `A Survey of Motion Planning and Control Techniques for Self-driving
+   Urban Vehicles <https://arxiv.org/abs/1604.07446>`_
diff --git a/docs/modules/path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control.rst b/docs/modules/path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control.rst
new file mode 100644
index 0000000000..70875fdc6c
--- /dev/null
+++ b/docs/modules/path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control.rst
@@ -0,0 +1,12 @@
+Rear wheel feedback control
+---------------------------
+
+Path tracking simulation with rear wheel feedback steering control and
+PID speed control.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif
+
+References:
+~~~~~~~~~~~
+-  `A Survey of Motion Planning and Control Techniques for Self-driving
+   Urban Vehicles <https://arxiv.org/abs/1604.07446>`__
diff --git a/docs/modules/path_tracking/stanley_control/stanley_control.rst b/docs/modules/path_tracking/stanley_control/stanley_control.rst
new file mode 100644
index 0000000000..fe325b0102
--- /dev/null
+++ b/docs/modules/path_tracking/stanley_control/stanley_control.rst
@@ -0,0 +1,16 @@
+Stanley control
+---------------
+
+Path tracking simulation with Stanley steering control and PID speed
+control.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif
+
+References:
+~~~~~~~~~~~
+
+-  `Stanley: The robot that won the DARPA grand
+   challenge <http://robots.stanford.edu/papers/thrun.stanley05.pdf>`_
+
+-  `Automatic Steering Methods for Autonomous Automobile Path
+   Tracking <https://www.ri.cmu.edu/pub_files/2009/2/Automatic_Steering_Methods_for_Autonomous_Automobile_Path_Tracking.pdf>`_
diff --git a/docs/modules/slam/FastSLAM1.rst b/docs/modules/slam/FastSLAM1/FastSLAM1.rst
similarity index 97%
rename from docs/modules/slam/FastSLAM1.rst
rename to docs/modules/slam/FastSLAM1/FastSLAM1.rst
index 6e26aef72f..f1e9dfd8aa 100644
--- a/docs/modules/slam/FastSLAM1.rst
+++ b/docs/modules/slam/FastSLAM1/FastSLAM1.rst
@@ -2,8 +2,7 @@
 FastSLAM1.0
 -----------
 
-.. image:: FastSLAM1_files/FastSLAM1_1_0.png
-   :width: 600px
+.. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif
 
 
 
@@ -12,6 +11,9 @@ Simulation
 
 This is a feature based SLAM example using FastSLAM 1.0.
 
+.. image:: FastSLAM1/FastSLAM1_1_0.png
+   :width: 600px
+
 The blue line is ground truth, the black line is dead reckoning, the red
 line is the estimated trajectory with FastSLAM.
 
@@ -20,8 +22,6 @@ The red points are particles of FastSLAM.
 Black points are landmarks, blue crosses are estimated landmark
 positions by FastSLAM.
 
-.. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif
-   :alt: gif
 
 Introduction
 ~~~~~~~~~~~~
@@ -527,19 +527,13 @@ indices
 
 
 
-.. image:: FastSLAM1_files/FastSLAM1_12_0.png
-
-
-
-.. image:: FastSLAM1_files/FastSLAM1_12_1.png
+.. image:: FastSLAM1/FastSLAM1_12_0.png
+.. image:: FastSLAM1/FastSLAM1_12_1.png
 
 
 References
 ~~~~~~~~~~
 
--  `PROBABILISTIC ROBOTICS`_
-
--  `FastSLAM Lecture`_
+- `PROBABILISTIC ROBOTICS <http://www.probabilistic-robotics.org/>`_
 
-.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
-.. _FastSLAM Lecture: http://ais.informatik.uni-freiburg.de/teaching/ws12/mapping/pdf/slam10-fastslam.pdf
+-  `FastSLAM Lecture <http://ais.informatik.uni-freiburg.de/teaching/ws12/mapping/pdf/slam10-fastslam.pdf>`_
diff --git a/docs/modules/slam/FastSLAM1_files/FastSLAM1_12_0.png b/docs/modules/slam/FastSLAM1/FastSLAM1_12_0.png
similarity index 100%
rename from docs/modules/slam/FastSLAM1_files/FastSLAM1_12_0.png
rename to docs/modules/slam/FastSLAM1/FastSLAM1_12_0.png
diff --git a/docs/modules/slam/FastSLAM1_files/FastSLAM1_12_1.png b/docs/modules/slam/FastSLAM1/FastSLAM1_12_1.png
similarity index 100%
rename from docs/modules/slam/FastSLAM1_files/FastSLAM1_12_1.png
rename to docs/modules/slam/FastSLAM1/FastSLAM1_12_1.png
diff --git a/docs/modules/slam/FastSLAM1_files/FastSLAM1_1_0.png b/docs/modules/slam/FastSLAM1/FastSLAM1_1_0.png
similarity index 100%
rename from docs/modules/slam/FastSLAM1_files/FastSLAM1_1_0.png
rename to docs/modules/slam/FastSLAM1/FastSLAM1_1_0.png
diff --git a/docs/modules/slam/FastSLAM2/FastSLAM2.rst b/docs/modules/slam/FastSLAM2/FastSLAM2.rst
new file mode 100644
index 0000000000..9e79b496a3
--- /dev/null
+++ b/docs/modules/slam/FastSLAM2/FastSLAM2.rst
@@ -0,0 +1,16 @@
+FastSLAM 2.0
+------------
+
+This is a feature based SLAM example using FastSLAM 2.0.
+
+The animation has the same meanings as one of FastSLAM 1.0.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM2/animation.gif
+
+References
+~~~~~~~~~~
+
+- `PROBABILISTIC ROBOTICS <http://www.probabilistic-robotics.org/>`_
+
+- `SLAM simulations by Tim Bailey <http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm>`_
+
diff --git a/docs/modules/slam/ekf_slam.rst b/docs/modules/slam/ekf_slam/ekf_slam.rst
similarity index 98%
rename from docs/modules/slam/ekf_slam.rst
rename to docs/modules/slam/ekf_slam/ekf_slam.rst
index df520fbdaf..a79c2737ae 100644
--- a/docs/modules/slam/ekf_slam.rst
+++ b/docs/modules/slam/ekf_slam/ekf_slam.rst
@@ -8,7 +8,7 @@ line is the estimated trajectory with EKF SLAM.
 
 The green crosses are estimated landmarks.
 
-|4|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/EKFSLAM/animation.gif
 
 Simulation
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -581,13 +581,11 @@ reckoning and control functions are passed along here as well.
     New LM
     New LM
 
-.. image:: ekf_slam_files/ekf_slam_1_0.png
+.. image:: ekf_slam/ekf_slam_1_0.png
 
 References:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
--  `PROBABILISTIC ROBOTICS`_
+- `PROBABILISTIC ROBOTICS <http://www.probabilistic-robotics.org/>`_
 
-.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
 
-.. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/EKFSLAM/animation.gif
diff --git a/docs/modules/slam/ekf_slam_files/ekf_slam_1_0.png b/docs/modules/slam/ekf_slam/ekf_slam_1_0.png
similarity index 100%
rename from docs/modules/slam/ekf_slam_files/ekf_slam_1_0.png
rename to docs/modules/slam/ekf_slam/ekf_slam_1_0.png
diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif b/docs/modules/slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
deleted file mode 100644
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diff --git a/docs/modules/slam/graphSLAM_SE2_example.rst b/docs/modules/slam/graph_slam/graphSLAM_SE2_example.rst
similarity index 90%
rename from docs/modules/slam/graphSLAM_SE2_example.rst
rename to docs/modules/slam/graph_slam/graphSLAM_SE2_example.rst
index bcc797ba3e..ef5d05bf22 100644
--- a/docs/modules/slam/graphSLAM_SE2_example.rst
+++ b/docs/modules/slam/graph_slam/graphSLAM_SE2_example.rst
@@ -44,7 +44,7 @@ The Dataset
 
 
 
-.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png
+.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png
 
 
 Each edge in this dataset is a constraint that compares the measured
@@ -122,7 +122,7 @@ dataset and plot them.
 
 
 
-.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png
+.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png
 
 
 .. code:: ipython3
@@ -131,7 +131,7 @@ dataset and plot them.
 
 
 
-.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png
+.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png
 
 
 Optimization
@@ -165,8 +165,7 @@ different data sources into a single optimization problem.
             6             215.8405          -0.000000
 
 
-.. figure:: graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
-   :alt: Graph_SLAM_optimization.gif
+.. figure:: graph_slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
 
 .. code:: ipython3
 
@@ -174,7 +173,7 @@ different data sources into a single optimization problem.
 
 
 
-.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png
+.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png
 
 
 .. code:: ipython3
@@ -196,7 +195,7 @@ different data sources into a single optimization problem.
 
 
 
-.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png
+.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png
 
 
 .. code:: ipython3
@@ -205,5 +204,5 @@ different data sources into a single optimization problem.
 
 
 
-.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png
+.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png
 
diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png b/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png
similarity index 100%
rename from docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png
rename to docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png
diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png b/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png
similarity index 100%
rename from docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png
rename to docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png
diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png b/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png
similarity index 100%
rename from docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png
rename to docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png
diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png b/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png
similarity index 100%
rename from docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png
rename to docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png
diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png b/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png
similarity index 100%
rename from docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png
rename to docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png
diff --git a/docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png b/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png
similarity index 100%
rename from docs/modules/slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png
rename to docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png
diff --git a/docs/modules/slam/graphSLAM_doc.rst b/docs/modules/slam/graph_slam/graphSLAM_doc.rst
similarity index 95%
rename from docs/modules/slam/graphSLAM_doc.rst
rename to docs/modules/slam/graph_slam/graphSLAM_doc.rst
index b17c3b18a2..4367f770c3 100644
--- a/docs/modules/slam/graphSLAM_doc.rst
+++ b/docs/modules/slam/graph_slam/graphSLAM_doc.rst
@@ -142,7 +142,7 @@ created based on the information of the motion and the observation.
 
 
 
-.. image:: graphSLAM_doc_files/graphSLAM_doc_2_0.png
+.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_2_0.png
 
 
 .. parsed-literal::
@@ -157,7 +157,7 @@ created based on the information of the motion and the observation.
 
 
 
-.. image:: graphSLAM_doc_files/graphSLAM_doc_2_2.png
+.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_2_2.png
 
 
 In particular, the tasks are split into 2 parts, graph construction, and
@@ -289,7 +289,7 @@ robot with 3DoF, namely, :math:`[x, y, \theta]^T`
 
 
 
-.. image:: graphSLAM_doc_files/graphSLAM_doc_4_0.png
+.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_4_0.png
 
 
 .. code:: ipython3
@@ -420,7 +420,7 @@ zero since :math:`x_j + d_j cos(\psi_j + \theta_j)` should equal
 
 
 
-.. image:: graphSLAM_doc_files/graphSLAM_doc_9_1.png
+.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_9_1.png
 
 
 Since the constraints equations derived before are non-linear,
@@ -494,12 +494,9 @@ Similarly, :math:`B = \frac{\partial e_{ij}}{\partial \boldsymbol{x}_j}`
 
 
 
-.. image:: graphSLAM_doc_files/graphSLAM_doc_11_1.png
-
-
-
-.. image:: graphSLAM_doc_files/graphSLAM_doc_11_2.png
+.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_1.png
 
+.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_2.png
 
 .. code:: ipython3
 
@@ -546,11 +543,11 @@ Similarly, :math:`B = \frac{\partial e_{ij}}{\partial \boldsymbol{x}_j}`
 The references:
 ^^^^^^^^^^^^^^^
 
--  http://robots.stanford.edu/papers/thrun.graphslam.pdf
+-  `The GraphSLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures <http://robots.stanford.edu/papers/thrun.graphslam.pdf>`_
 
--  http://ais.informatik.uni-freiburg.de/teaching/ss13/robotics/slides/16-graph-slam.pdf
+-  `Introduction to Mobile Robotics Graph-Based SLAM <http://ais.informatik.uni-freiburg.de/teaching/ss13/robotics/slides/16-graph-slam.pdf>`_
 
--  http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf
+-  `A Tutorial on Graph-Based SLAM <http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf>`_
 
 N.B. An additional step is required that uses the estimated path to
 update the belief regarding the map.
diff --git a/docs/modules/slam/graphSLAM_formulation.rst b/docs/modules/slam/graph_slam/graphSLAM_formulation.rst
similarity index 97%
rename from docs/modules/slam/graphSLAM_formulation.rst
rename to docs/modules/slam/graph_slam/graphSLAM_formulation.rst
index c673bdcbfb..43f007e7bf 100644
--- a/docs/modules/slam/graphSLAM_formulation.rst
+++ b/docs/modules/slam/graph_slam/graphSLAM_formulation.rst
@@ -213,4 +213,6 @@ Using this notation, we obtain the optimal update as
 We apply this update to the poses via :eq:`update` and repeat until convergence.
 
 
-.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
+.. [blanco2010tutorial] Blanco, J.-L.A tutorial onSE(3) transformation parameterization and on-manifold optimization.University of Malaga, Tech. Rep 3(2010)
+.. [grisetti2010tutorial] Grisetti, G., Kummerle, R., Stachniss, C., and Burgard, W.A tutorial on graph-based SLAM.IEEE Intelligent Transportation Systems Magazine 2, 4 (2010), 31–43.
+
diff --git a/docs/modules/slam/graph_slam/graph_slam.rst b/docs/modules/slam/graph_slam/graph_slam.rst
new file mode 100644
index 0000000000..a1248a01cc
--- /dev/null
+++ b/docs/modules/slam/graph_slam/graph_slam.rst
@@ -0,0 +1,24 @@
+Graph based SLAM
+----------------
+
+This is a graph based SLAM example.
+
+The blue line is ground truth.
+
+The black line is dead reckoning.
+
+The red line is the estimated trajectory with Graph based SLAM.
+
+The black stars are landmarks for graph edge generation.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/GraphBasedSLAM/animation.gif
+
+.. include:: graph_slam/graphSLAM_doc.rst
+.. include:: graph_slam/graphSLAM_formulation.rst
+.. include:: graph_slam/graphSLAM_SE2_example.rst
+
+References:
+~~~~~~~~~~~
+
+- `A Tutorial on Graph-Based SLAM <http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf>`_
+
diff --git a/docs/modules/slam/iterative_closest_point_matching/iterative_closest_point_matching.rst b/docs/modules/slam/iterative_closest_point_matching/iterative_closest_point_matching.rst
new file mode 100644
index 0000000000..a30b1fc99b
--- /dev/null
+++ b/docs/modules/slam/iterative_closest_point_matching/iterative_closest_point_matching.rst
@@ -0,0 +1,16 @@
+.. _iterative-closest-point-(icp)-matching:
+
+Iterative Closest Point (ICP) Matching
+--------------------------------------
+
+This is a 2D ICP matching example with singular value decomposition.
+
+It can calculate a rotation matrix and a translation vector between
+points to points.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/iterative_closest_point/animation.gif
+
+References
+~~~~~~~~~~
+
+- `Introduction to Mobile Robotics: Iterative Closest Point Algorithm <https://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf>`_
diff --git a/docs/modules/slam/slam_main.rst b/docs/modules/slam/slam_main.rst
index 41210fee4e..f4d10feb9a 100644
--- a/docs/modules/slam/slam_main.rst
+++ b/docs/modules/slam/slam_main.rst
@@ -5,77 +5,9 @@ SLAM
 
 Simultaneous Localization and Mapping(SLAM) examples
 
-.. _iterative-closest-point-(icp)-matching:
+.. include:: iterative_closest_point_matching/iterative_closest_point_matching.rst
+.. include:: ekf_slam/ekf_slam.rst
+.. include:: FastSLAM1/FastSLAM1.rst
+.. include:: FastSLAM2/FastSLAM2.rst
+.. include:: graph_slam/graph_slam.rst
 
-Iterative Closest Point (ICP) Matching
---------------------------------------
-
-This is a 2D ICP matching example with singular value decomposition.
-
-It can calculate a rotation matrix and a translation vector between
-points to points.
-
-|3|
-
-Ref:
-
--  `Introduction to Mobile Robotics: Iterative Closest Point Algorithm`_
-
-
-.. include:: ekf_slam.rst
-
-
-.. include:: FastSLAM1.rst
-
-FastSLAM 2.0
-------------
-
-This is a feature based SLAM example using FastSLAM 2.0.
-
-The animation has the same meanings as one of FastSLAM 1.0.
-
-|6|
-
-References
-~~~~~~~~~~
-
--  `PROBABILISTIC ROBOTICS`_
-
--  `SLAM simulations by Tim Bailey`_
-
-Graph based SLAM
-----------------
-
-This is a graph based SLAM example.
-
-The blue line is ground truth.
-
-The black line is dead reckoning.
-
-The red line is the estimated trajectory with Graph based SLAM.
-
-The black stars are landmarks for graph edge generation.
-
-|7|
-
-.. include:: graphSLAM_doc.rst
-.. include:: graphSLAM_formulation.rst
-.. include:: graphSLAM_SE2_example.rst
-
-Ref:
-
--  `A Tutorial on Graph-Based SLAM`_
-
-.. _`Introduction to Mobile Robotics: Iterative Closest Point Algorithm`: https://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf
-.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
-.. _SLAM simulations by Tim Bailey: http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm
-.. _A Tutorial on Graph-Based SLAM: http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf
-.. _FastSLAM Lecture: http://ais.informatik.uni-freiburg.de/teaching/ws12/mapping/pdf/slam10-fastslam.pdf
-
-.. [blanco2010tutorial] Blanco, J.-L.A tutorial onSE(3) transformation parameterization and on-manifold optimization.University of Malaga, Tech. Rep 3(2010)
-.. [grisetti2010tutorial] Grisetti, G., Kummerle, R., Stachniss, C., and Burgard, W.A tutorial on graph-based SLAM.IEEE Intelligent Transportation Systems Magazine 2, 4 (2010), 31–43.
-
-.. |3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/iterative_closest_point/animation.gif
-.. |5| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif
-.. |6| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM2/animation.gif
-.. |7| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/GraphBasedSLAM/animation.gif
diff --git a/tests/test_move_to_pose.py b/tests/test_move_to_pose.py
index c6a6b8bc76..05cfa269d7 100644
--- a/tests/test_move_to_pose.py
+++ b/tests/test_move_to_pose.py
@@ -1,5 +1,5 @@
 import conftest  # Add root path to sys.path
-from PathTracking.move_to_pose import move_to_pose as m
+from Control.move_to_pose import move_to_pose as m
 
 
 def test_1():

From 4b9d05dde18daaa113a30aaef857265f2c94846b Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 Nov 2021 07:18:13 +0900
Subject: [PATCH 0552/1080] Bump scipy from 1.7.2 to 1.7.3 (#587)

Bumps [scipy](https://github.com/scipy/scipy) from 1.7.2 to 1.7.3.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.7.2...v1.7.3)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 84f663cb83..63bdc966cb 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.21.4
-scipy == 1.7.2
+scipy == 1.7.3
 pandas == 1.3.4
 matplotlib == 3.5.0
 cvxpy == 1.1.17

From 2c077225a7728a18152b2cf63fffc329069af989 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 Nov 2021 07:18:43 +0900
Subject: [PATCH 0553/1080] Bump ipython from 7.29.0 to 7.30.0 (#588)

Bumps [ipython](https://github.com/ipython/ipython) from 7.29.0 to 7.30.0.
- [Release notes](https://github.com/ipython/ipython/releases)
- [Commits](https://github.com/ipython/ipython/compare/7.29.0...7.30.0)

---
updated-dependencies:
- dependency-name: ipython
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 63bdc966cb..2a0135cf44 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,4 +7,4 @@ pytest == 6.2.5 # For unit test
 pytest-xdist == 2.4.0 # For unit test
 sphinx < 4.0 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
-IPython == 7.29.0 # For sphinx documentation
+IPython == 7.30.0 # For sphinx documentation

From 4d60c3c0b152eca35a6a244819d7d6e7664dfd05 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 1 Dec 2021 23:00:39 +0900
Subject: [PATCH 0554/1080] update docs (#589)

* update docs

* update docs
---
 .../Figure_1.png                              | Bin
 .../Figure_2.png                              | Bin
 .../path_planning/bezier_path/bezier_path.rst |  20 +
 .../bspline_path/bspline_path.rst             |  14 +
 .../path_planning/bugplanner/bugplanner.rst   |   8 +
 .../coverage_path/coverage_path.rst           |  34 ++
 .../cubic_spline/cubic_spline.rst             |  13 +
 .../path_planning/dubins_path/dubins_path.rst |  11 +
 .../dynamic_window_approach.rst               |   9 +
 .../path_planning/eta3_spline/eta3_spline.rst |  13 +
 .../frenet_frame_path/frenet_frame_path.rst   |  20 +
 .../grid_base_search/grid_base_search.rst     |  93 ++++
 .../path_planning/hybridastar/hybridastar.rst |   6 +
 .../path_planning/lqr_path/lqr_path.rst       |   6 +
 .../lookup_table.png}                         | Bin
 .../model_predictive_trajectory_generator.rst |  22 +
 .../path_planning/path_planning_main.rst      | 426 +-----------------
 .../path_planning/prm_planner/prm_planner.rst |  19 +
 .../quintic_polynomials_planner.rst           |   0
 .../reeds_shepp_path/reeds_shepp_path.rst     |  17 +
 docs/modules/path_planning/rrt/rrt.rst        | 118 +++++
 .../path_planning/{ => rrt}/rrt_star.rst      |   3 +-
 .../rrt_star}/rrt_star_1_0.png                | Bin
 .../rrt_star_reeds_shepp/figure_1.png         | Bin
 .../Figure_1.png                              | Bin
 .../Figure_2.png                              | Bin
 .../Figure_3.png                              | Bin
 .../Figure_4.png                              | Bin
 .../Figure_5.png                              | Bin
 .../Figure_6.png                              | Bin
 .../state_lattice_planner.rst                 |  33 ++
 .../path_planning/vrm_planner/vrm_planner.rst |  17 +
 32 files changed, 493 insertions(+), 409 deletions(-)
 rename docs/modules/path_planning/{bezier_path_planning => bezier_path}/Figure_1.png (100%)
 rename docs/modules/path_planning/{bezier_path_planning => bezier_path}/Figure_2.png (100%)
 create mode 100644 docs/modules/path_planning/bezier_path/bezier_path.rst
 create mode 100644 docs/modules/path_planning/bspline_path/bspline_path.rst
 create mode 100644 docs/modules/path_planning/bugplanner/bugplanner.rst
 create mode 100644 docs/modules/path_planning/coverage_path/coverage_path.rst
 create mode 100644 docs/modules/path_planning/cubic_spline/cubic_spline.rst
 create mode 100644 docs/modules/path_planning/dubins_path/dubins_path.rst
 create mode 100644 docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach.rst
 create mode 100644 docs/modules/path_planning/eta3_spline/eta3_spline.rst
 create mode 100644 docs/modules/path_planning/frenet_frame_path/frenet_frame_path.rst
 create mode 100644 docs/modules/path_planning/grid_base_search/grid_base_search.rst
 create mode 100644 docs/modules/path_planning/hybridastar/hybridastar.rst
 create mode 100644 docs/modules/path_planning/lqr_path/lqr_path.rst
 rename docs/modules/path_planning/{model_predictive_trajectry_generator/lookuptable.png => model_predictive_trajectory_generator/lookup_table.png} (100%)
 create mode 100644 docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst
 create mode 100644 docs/modules/path_planning/prm_planner/prm_planner.rst
 rename docs/modules/path_planning/{ => quintic_polynomials_planner}/quintic_polynomials_planner.rst (100%)
 create mode 100644 docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path.rst
 create mode 100644 docs/modules/path_planning/rrt/rrt.rst
 rename docs/modules/path_planning/{ => rrt}/rrt_star.rst (90%)
 rename docs/modules/path_planning/{rrt_star_files => rrt/rrt_star}/rrt_star_1_0.png (100%)
 rename docs/modules/path_planning/{ => rrt}/rrt_star_reeds_shepp/figure_1.png (100%)
 rename docs/modules/path_planning/{state_lattice_pathplanner => state_lattice_planner}/Figure_1.png (100%)
 rename docs/modules/path_planning/{state_lattice_pathplanner => state_lattice_planner}/Figure_2.png (100%)
 rename docs/modules/path_planning/{state_lattice_pathplanner => state_lattice_planner}/Figure_3.png (100%)
 rename docs/modules/path_planning/{state_lattice_pathplanner => state_lattice_planner}/Figure_4.png (100%)
 rename docs/modules/path_planning/{state_lattice_pathplanner => state_lattice_planner}/Figure_5.png (100%)
 rename docs/modules/path_planning/{state_lattice_pathplanner => state_lattice_planner}/Figure_6.png (100%)
 create mode 100644 docs/modules/path_planning/state_lattice_planner/state_lattice_planner.rst
 create mode 100644 docs/modules/path_planning/vrm_planner/vrm_planner.rst

diff --git a/docs/modules/path_planning/bezier_path_planning/Figure_1.png b/docs/modules/path_planning/bezier_path/Figure_1.png
similarity index 100%
rename from docs/modules/path_planning/bezier_path_planning/Figure_1.png
rename to docs/modules/path_planning/bezier_path/Figure_1.png
diff --git a/docs/modules/path_planning/bezier_path_planning/Figure_2.png b/docs/modules/path_planning/bezier_path/Figure_2.png
similarity index 100%
rename from docs/modules/path_planning/bezier_path_planning/Figure_2.png
rename to docs/modules/path_planning/bezier_path/Figure_2.png
diff --git a/docs/modules/path_planning/bezier_path/bezier_path.rst b/docs/modules/path_planning/bezier_path/bezier_path.rst
new file mode 100644
index 0000000000..c630d54345
--- /dev/null
+++ b/docs/modules/path_planning/bezier_path/bezier_path.rst
@@ -0,0 +1,20 @@
+Bezier path planning
+--------------------
+
+A sample code of Bezier path planning.
+
+It is based on 4 control points Beizer path.
+
+.. image:: Bezier_path/Figure_1.png
+
+If you change the offset distance from start and end point,
+
+You can get different Beizer course:
+
+.. image:: Bezier_path/Figure_2.png
+
+Ref:
+
+-  `Continuous Curvature Path Generation Based on Bezier Curves for
+   Autonomous
+   Vehicles <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.294.6438&rep=rep1&type=pdf>`__
diff --git a/docs/modules/path_planning/bspline_path/bspline_path.rst b/docs/modules/path_planning/bspline_path/bspline_path.rst
new file mode 100644
index 0000000000..a8043122fa
--- /dev/null
+++ b/docs/modules/path_planning/bspline_path/bspline_path.rst
@@ -0,0 +1,14 @@
+B-Spline planning
+-----------------
+
+.. image:: bspline_path/Figure_1.png
+
+This is a path planning with B-Spline curse.
+
+If you input waypoints, it generates a smooth path with B-Spline curve.
+
+The final course should be on the first and last waypoints.
+
+Ref:
+
+-  `B-spline - Wikipedia <https://en.wikipedia.org/wiki/B-spline>`__
diff --git a/docs/modules/path_planning/bugplanner/bugplanner.rst b/docs/modules/path_planning/bugplanner/bugplanner.rst
new file mode 100644
index 0000000000..72cc46709f
--- /dev/null
+++ b/docs/modules/path_planning/bugplanner/bugplanner.rst
@@ -0,0 +1,8 @@
+Bug planner
+-----------
+
+This is a 2D planning with Bug algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BugPlanner/animation.gif
+
+- `ECE452 Bug Algorithms <https://sites.google.com/site/ece452bugalgorithms/>`_
diff --git a/docs/modules/path_planning/coverage_path/coverage_path.rst b/docs/modules/path_planning/coverage_path/coverage_path.rst
new file mode 100644
index 0000000000..828342a294
--- /dev/null
+++ b/docs/modules/path_planning/coverage_path/coverage_path.rst
@@ -0,0 +1,34 @@
+Coverage path planner
+---------------------
+
+Grid based sweep
+~~~~~~~~~~~~~~~~
+
+This is a 2D grid based sweep coverage path planner simulation:
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/GridBasedSweepCPP/animation.gif
+
+Spiral Spanning Tree
+~~~~~~~~~~~~~~~~~~~~
+
+This is a 2D grid based spiral spanning tree coverage path planner simulation:
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/SpiralSpanningTreeCPP/animation1.gif
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/SpiralSpanningTreeCPP/animation2.gif
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/SpiralSpanningTreeCPP/animation3.gif
+
+- `Spiral-STC: An On-Line Coverage Algorithm of Grid Environments by a Mobile Robot <https://ieeexplore.ieee.org/abstract/document/1013479>`_
+
+
+Wavefront path
+~~~~~~~~~~~~~~
+
+This is a 2D grid based wavefront coverage path planner simulation:
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation1.gif
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation2.gif
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation3.gif
+
+- `Planning paths of complete coverage of an unstructured environment by a mobile robot <http://pinkwink.kr/attachment/cfile3.uf@1354654A4E8945BD13FE77.pdf>`_
+
+
diff --git a/docs/modules/path_planning/cubic_spline/cubic_spline.rst b/docs/modules/path_planning/cubic_spline/cubic_spline.rst
new file mode 100644
index 0000000000..b1973856e8
--- /dev/null
+++ b/docs/modules/path_planning/cubic_spline/cubic_spline.rst
@@ -0,0 +1,13 @@
+Cubic spline planning
+---------------------
+
+A sample code for cubic path planning.
+
+This code generates a curvature continuous path based on x-y waypoints
+with cubic spline.
+
+Heading angle of each point can be also calculated analytically.
+
+.. image:: cubic_spline/Figure_1.png
+.. image:: cubic_spline/Figure_2.png
+.. image:: cubic_spline/Figure_3.png
diff --git a/docs/modules/path_planning/dubins_path/dubins_path.rst b/docs/modules/path_planning/dubins_path/dubins_path.rst
new file mode 100644
index 0000000000..5e14820f8f
--- /dev/null
+++ b/docs/modules/path_planning/dubins_path/dubins_path.rst
@@ -0,0 +1,11 @@
+Dubins path planning
+--------------------
+
+A sample code for Dubins path planning.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DubinsPath/animation.gif?raw=True
+
+Ref:
+
+-  `Dubins path -
+   Wikipedia <https://en.wikipedia.org/wiki/Dubins_path>`__
diff --git a/docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach.rst b/docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach.rst
new file mode 100644
index 0000000000..0ece6e0054
--- /dev/null
+++ b/docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach.rst
@@ -0,0 +1,9 @@
+Dynamic Window Approach
+-----------------------
+
+This is a 2D navigation sample code with Dynamic Window Approach.
+
+-  `The Dynamic Window Approach to Collision
+   Avoidance <https://www.ri.cmu.edu/pub_files/pub1/fox_dieter_1997_1/fox_dieter_1997_1.pdf>`__
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif
diff --git a/docs/modules/path_planning/eta3_spline/eta3_spline.rst b/docs/modules/path_planning/eta3_spline/eta3_spline.rst
new file mode 100644
index 0000000000..ffc3cc6451
--- /dev/null
+++ b/docs/modules/path_planning/eta3_spline/eta3_spline.rst
@@ -0,0 +1,13 @@
+.. _eta^3-spline-path-planning:
+
+Eta^3 Spline path planning
+--------------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Eta3SplinePath/animation.gif
+
+This is a path planning with Eta^3 spline.
+
+Ref:
+
+-  `\\eta^3-Splines for the Smooth Path Generation of Wheeled Mobile
+   Robots <https://ieeexplore.ieee.org/document/4339545/>`__
diff --git a/docs/modules/path_planning/frenet_frame_path/frenet_frame_path.rst b/docs/modules/path_planning/frenet_frame_path/frenet_frame_path.rst
new file mode 100644
index 0000000000..e9d9041e0e
--- /dev/null
+++ b/docs/modules/path_planning/frenet_frame_path/frenet_frame_path.rst
@@ -0,0 +1,20 @@
+Optimal Trajectory in a Frenet Frame
+------------------------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/animation.gif
+
+This is optimal trajectory generation in a Frenet Frame.
+
+The cyan line is the target course and black crosses are obstacles.
+
+The red line is predicted path.
+
+Ref:
+
+-  `Optimal Trajectory Generation for Dynamic Street Scenarios in a
+   Frenet
+   Frame <https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf>`__
+
+-  `Optimal trajectory generation for dynamic street scenarios in a
+   Frenet Frame <https://www.youtube.com/watch?v=Cj6tAQe7UCY>`__
+
diff --git a/docs/modules/path_planning/grid_base_search/grid_base_search.rst b/docs/modules/path_planning/grid_base_search/grid_base_search.rst
new file mode 100644
index 0000000000..ac4ea2f6fd
--- /dev/null
+++ b/docs/modules/path_planning/grid_base_search/grid_base_search.rst
@@ -0,0 +1,93 @@
+Grid based search
+-----------------
+
+Breadth First Search
+~~~~~~~~~~~~~~~~~~~~
+
+This is a 2D grid based path planning with Breadth first search algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BreadthFirstSearch/animation.gif
+
+In the animation, cyan points are searched nodes.
+
+Depth First Search
+~~~~~~~~~~~~~~~~~~~~
+
+This is a 2D grid based path planning with Depth first search algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DepthFirstSearch/animation.gif
+
+In the animation, cyan points are searched nodes.
+
+Dijkstra algorithm
+~~~~~~~~~~~~~~~~~~
+
+This is a 2D grid based shortest path planning with Dijkstra's algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Dijkstra/animation.gif
+
+In the animation, cyan points are searched nodes.
+
+.. _a*-algorithm:
+
+A\* algorithm
+~~~~~~~~~~~~~
+
+This is a 2D grid based shortest path planning with A star algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/AStar/animation.gif
+
+In the animation, cyan points are searched nodes.
+
+Its heuristic is 2D Euclid distance.
+
+Bidirectional A\* algorithm
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This is a 2D grid based shortest path planning with bidirectional A star algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BidirectionalAStar/animation.gif
+
+In the animation, cyan points are searched nodes.
+
+.. _D*-algorithm:
+
+D\* algorithm
+~~~~~~~~~~~~~
+
+This is a 2D grid based shortest path planning with D star algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStar/animation.gif
+
+The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm.
+
+Ref:
+
+-  `D* search Wikipedia <https://en.wikipedia.org/wiki/D*>`__
+
+D\* lite algorithm
+~~~~~~~~~~~~~~~~~~
+
+This is a 2D grid based path planning and replanning with D star lite algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStarLite/animation.gif
+
+Ref:
+
+- `Improved Fast Replanning for Robot Navigation in Unknown Terrain <http://www.cs.cmu.edu/~maxim/files/dlite_icra02.pdf>`_
+
+
+Potential Field algorithm
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This is a 2D grid based path planning with Potential Field algorithm.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/PotentialFieldPlanning/animation.gif
+
+In the animation, the blue heat map shows potential value on each grid.
+
+Ref:
+
+-  `Robotic Motion Planning:Potential
+   Functions <https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf>`__
+
diff --git a/docs/modules/path_planning/hybridastar/hybridastar.rst b/docs/modules/path_planning/hybridastar/hybridastar.rst
new file mode 100644
index 0000000000..a9876fa3d4
--- /dev/null
+++ b/docs/modules/path_planning/hybridastar/hybridastar.rst
@@ -0,0 +1,6 @@
+Hybrid a star
+---------------------
+
+This is a simple vehicle model based hybrid A\* path planner.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/HybridAStar/animation.gif
diff --git a/docs/modules/path_planning/lqr_path/lqr_path.rst b/docs/modules/path_planning/lqr_path/lqr_path.rst
new file mode 100644
index 0000000000..788442ff63
--- /dev/null
+++ b/docs/modules/path_planning/lqr_path/lqr_path.rst
@@ -0,0 +1,6 @@
+LQR based path planning
+-----------------------
+
+A sample code using LQR based path planning for double integrator model.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRPlanner/animation.gif?raw=true
diff --git a/docs/modules/path_planning/model_predictive_trajectry_generator/lookuptable.png b/docs/modules/path_planning/model_predictive_trajectory_generator/lookup_table.png
similarity index 100%
rename from docs/modules/path_planning/model_predictive_trajectry_generator/lookuptable.png
rename to docs/modules/path_planning/model_predictive_trajectory_generator/lookup_table.png
diff --git a/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst b/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst
new file mode 100644
index 0000000000..07a7ee9b59
--- /dev/null
+++ b/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst
@@ -0,0 +1,22 @@
+Model Predictive Trajectory Generator
+-------------------------------------
+
+This is a path optimization sample on model predictive trajectory
+generator.
+
+This algorithm is used for state lattice planner.
+
+Path optimization sample
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/kn05animation.gif
+
+Lookup table generation sample
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: model_predictive_trajectory_generator/lookup_table.png
+
+Ref:
+
+-  `Optimal rough terrain trajectory generation for wheeled mobile
+   robots <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
diff --git a/docs/modules/path_planning/path_planning_main.rst b/docs/modules/path_planning/path_planning_main.rst
index 8033e1a875..61f8ea2113 100644
--- a/docs/modules/path_planning/path_planning_main.rst
+++ b/docs/modules/path_planning/path_planning_main.rst
@@ -3,410 +3,22 @@
 Path Planning
 =============
 
-Dynamic Window Approach
------------------------
-
-This is a 2D navigation sample code with Dynamic Window Approach.
-
--  `The Dynamic Window Approach to Collision
-   Avoidance <https://www.ri.cmu.edu/pub_files/pub1/fox_dieter_1997_1/fox_dieter_1997_1.pdf>`__
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif
-
-Grid based search
------------------
-
-Dijkstra algorithm
-~~~~~~~~~~~~~~~~~~
-
-This is a 2D grid based shortest path planning with Dijkstra's
-algorithm.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Dijkstra/animation.gif
-
-In the animation, cyan points are searched nodes.
-
-.. _a*-algorithm:
-
-A\* algorithm
-~~~~~~~~~~~~~
-
-This is a 2D grid based shortest path planning with A star algorithm.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/AStar/animation.gif
-
-In the animation, cyan points are searched nodes.
-
-Its heuristic is 2D Euclid distance.
-
-.. _D*-algorithm:
-
-D\* algorithm
-~~~~~~~~~~~~~
-
-This is a 2D grid based shortest path planning with D star algorithm.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStar/animation.gif
-
-The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm.
-
-Ref:
-
--  `D* search Wikipedia <https://en.wikipedia.org/wiki/D*>`__
-
-Potential Field algorithm
-~~~~~~~~~~~~~~~~~~~~~~~~~
-
-This is a 2D grid based path planning with Potential Field algorithm.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/PotentialFieldPlanning/animation.gif
-
-In the animation, the blue heat map shows potential value on each grid.
-
-Ref:
-
--  `Robotic Motion Planning:Potential
-   Functions <https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf>`__
-
-Model Predictive Trajectory Generator
--------------------------------------
-
-This is a path optimization sample on model predictive trajectory
-generator.
-
-This algorithm is used for state lattice planner.
-
-Path optimization sample
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ModelPredictiveTrajectoryGenerator/kn05animation.gif
-
-Lookup table generation sample
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. image:: model_predictive_trajectry_generator/lookuptable.png
-
-Ref:
-
--  `Optimal rough terrain trajectory generation for wheeled mobile
-   robots <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
-
-
-State Lattice Planning
-----------------------
-
-This script is a path planning code with state lattice planning.
-
-This code uses the model predictive trajectory generator to solve
-boundary problem.
-
-Ref:
-
--  `Optimal rough terrain trajectory generation for wheeled mobile
-   robots <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
-
--  `State Space Sampling of Feasible Motions for High-Performance Mobile
-   Robot Navigation in Complex
-   Environments <http://www.frc.ri.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf>`__
-
-Uniform polar sampling
-~~~~~~~~~~~~~~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/UniformPolarSampling.gif
-
-Biased polar sampling
-~~~~~~~~~~~~~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/BiasedPolarSampling.gif
-
-Lane sampling
-~~~~~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif
-
-.. _probabilistic-road-map-(prm)-planning:
-
-Probabilistic Road-Map (PRM) planning
--------------------------------------
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ProbabilisticRoadMap/animation.gif
-
-This PRM planner uses Dijkstra method for graph search.
-
-In the animation, blue points are sampled points,
-
-Cyan crosses means searched points with Dijkstra method,
-
-The red line is the final path of PRM.
-
-Ref:
-
--  `Probabilistic roadmap -
-   Wikipedia <https://en.wikipedia.org/wiki/Probabilistic_roadmap>`__
-
-　　
-
-Voronoi Road-Map planning
--------------------------
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/VoronoiRoadMap/animation.gif
-
-This Voronoi road-map planner uses Dijkstra method for graph search.
-
-In the animation, blue points are Voronoi points,
-
-Cyan crosses mean searched points with Dijkstra method,
-
-The red line is the final path of Vornoi Road-Map.
-
-Ref:
-
--  `Robotic Motion
-   Planning <https://www.cs.cmu.edu/~motionplanning/lecture/Chap5-RoadMap-Methods_howie.pdf>`__
-
-.. _rapidly-exploring-random-trees-(rrt):
-
-Rapidly-Exploring Random Trees (RRT)
-------------------------------------
-
-Basic RRT
-~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRT/animation.gif
-
-This is a simple path planning code with Rapidly-Exploring Random Trees
-(RRT)
-
-Black circles are obstacles, green line is a searched tree, red crosses
-are start and goal positions.
-
-.. include:: rrt_star.rst
-
-
-RRT with dubins path
-~~~~~~~~~~~~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTDubins/animation.gif
-
-Path planning for a car robot with RRT and dubins path planner.
-
-.. _rrt*-with-dubins-path:
-
-RRT\* with dubins path
-~~~~~~~~~~~~~~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarDubins/animation.gif
-
-Path planning for a car robot with RRT\* and dubins path planner.
-
-.. _rrt*-with-reeds-sheep-path:
-
-RRT\* with reeds-sheep path
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarReedsShepp/animation.gif
-
-Path planning for a car robot with RRT\* and reeds sheep path planner.
-
-.. _informed-rrt*:
-
-Informed RRT\*
-~~~~~~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/InformedRRTStar/animation.gif
-
-This is a path planning code with Informed RRT*.
-
-The cyan ellipse is the heuristic sampling domain of Informed RRT*.
-
-Ref:
-
--  `Informed RRT\*: Optimal Sampling-based Path Planning Focused via
-   Direct Sampling of an Admissible Ellipsoidal
-   Heuristic <https://arxiv.org/pdf/1404.2334.pdf>`__
-
-.. _batch-informed-rrt*:
-
-Batch Informed RRT\*
-~~~~~~~~~~~~~~~~~~~~
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BatchInformedRRTStar/animation.gif
-
-This is a path planning code with Batch Informed RRT*.
-
-Ref:
-
--  `Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the
-   Heuristically Guided Search of Implicit Random Geometric
-   Graphs <https://arxiv.org/abs/1405.5848>`__
-
-.. _closed-loop-rrt*:
-
-Closed Loop RRT\*
-~~~~~~~~~~~~~~~~~
-
-A vehicle model based path planning with closed loop RRT*.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ClosedLoopRRTStar/animation.gif
-
-In this code, pure-pursuit algorithm is used for steering control,
-
-PID is used for speed control.
-
-Ref:
-
--  `Motion Planning in Complex Environments using Closed-loop
-   Prediction <http://acl.mit.edu/papers/KuwataGNC08.pdf>`__
-
--  `Real-time Motion Planning with Applications to Autonomous Urban
-   Driving <http://acl.mit.edu/papers/KuwataTCST09.pdf>`__
-
--  `[1601.06326] Sampling-based Algorithms for Optimal Motion Planning
-   Using Closed-loop Prediction <https://arxiv.org/abs/1601.06326>`__
-
-.. _lqr-rrt*:
-
-LQR-RRT\*
-~~~~~~~~~
-
-This is a path planning simulation with LQR-RRT*.
-
-A double integrator motion model is used for LQR local planner.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif
-
-Ref:
-
--  `LQR-RRT\*: Optimal Sampling-Based Motion Planning with Automatically
-   Derived Extension
-   Heuristics <http://lis.csail.mit.edu/pubs/perez-icra12.pdf>`__
-
--  `MahanFathi/LQR-RRTstar: LQR-RRT\* method is used for random motion
-   planning of a simple pendulum in its phase
-   plot <https://github.com/MahanFathi/LQR-RRTstar>`__
-
-Cubic spline planning
----------------------
-
-A sample code for cubic path planning.
-
-This code generates a curvature continuous path based on x-y waypoints
-with cubic spline.
-
-Heading angle of each point can be also calculated analytically.
-
-.. image:: cubic_spline/Figure_1.png
-.. image:: cubic_spline/Figure_2.png
-.. image:: cubic_spline/Figure_3.png
-
-B-Spline planning
------------------
-
-.. image:: bspline_path/Figure_1.png
-
-This is a path planning with B-Spline curse.
-
-If you input waypoints, it generates a smooth path with B-Spline curve.
-
-The final course should be on the first and last waypoints.
-
-Ref:
-
--  `B-spline - Wikipedia <https://en.wikipedia.org/wiki/B-spline>`__
-
-.. _eta^3-spline-path-planning:
-
-Eta^3 Spline path planning
---------------------------
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/Eta3SplinePath/animation.gif
-
-This is a path planning with Eta^3 spline.
-
-Ref:
-
--  `\\eta^3-Splines for the Smooth Path Generation of Wheeled Mobile
-   Robots <https://ieeexplore.ieee.org/document/4339545/>`__
-
-Bezier path planning
---------------------
-
-A sample code of Bezier path planning.
-
-It is based on 4 control points Beier path.
-
-.. image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/docs/modules/path_planning/Bezier_path_planning/Figure_1.png?raw=True
-
-If you change the offset distance from start and end point,
-
-You can get different Beizer course:
-
-.. image:: https://github.com/AtsushiSakai/PythonRobotics/raw/master/docs/modules/path_planning/Bezier_path_planning/Figure_2.png?raw=True
-
-Ref:
-
--  `Continuous Curvature Path Generation Based on Bezier Curves for
-   Autonomous
-   Vehicles <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.294.6438&rep=rep1&type=pdf>`__
-
-
-.. include:: quintic_polynomials_planner.rst
-
-
-Dubins path planning
---------------------
-
-A sample code for Dubins path planning.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DubinsPath/animation.gif?raw=True
-
-Ref:
-
--  `Dubins path -
-   Wikipedia <https://en.wikipedia.org/wiki/Dubins_path>`__
-
-Reeds Shepp planning
---------------------
-
-A sample code with Reeds Shepp path planning.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true
-
-Ref:
-
--  `15.3.2 Reeds-Shepp
-   Curves <http://planning.cs.uiuc.edu/node822.html>`__
-
--  `optimal paths for a car that goes both forwards and
-   backwards <https://pdfs.semanticscholar.org/932e/c495b1d0018fd59dee12a0bf74434fac7af4.pdf>`__
-
--  `ghliu/pyReedsShepp: Implementation of Reeds Shepp
-   curve. <https://github.com/ghliu/pyReedsShepp>`__
-
-LQR based path planning
------------------------
-
-A sample code using LQR based path planning for double integrator model.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRPlanner/animation.gif?raw=true
-
-Optimal Trajectory in a Frenet Frame
-------------------------------------
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/animation.gif
-
-This is optimal trajectory generation in a Frenet Frame.
-
-The cyan line is the target course and black crosses are obstacles.
-
-The red line is predicted path.
-
-Ref:
-
--  `Optimal Trajectory Generation for Dynamic Street Scenarios in a
-   Frenet
-   Frame <https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf>`__
-
--  `Optimal trajectory generation for dynamic street scenarios in a
-   Frenet Frame <https://www.youtube.com/watch?v=Cj6tAQe7UCY>`__
-
+.. include:: dynamic_window_approach/dynamic_window_approach.rst
+.. include:: bugplanner/bugplanner.rst
+.. include:: grid_base_search/grid_base_search.rst
+.. include:: model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst
+.. include:: state_lattice_planner/state_lattice_planner.rst
+.. include:: prm_planner/prm_planner.rst
+.. include:: vrm_planner/vrm_planner.rst
+.. include:: rrt/rrt.rst
+.. include:: cubic_spline/cubic_spline.rst
+.. include:: bspline_path/bspline_path.rst
+.. include:: eta3_spline/eta3_spline.rst
+.. include:: bezier_path/bezier_path.rst
+.. include:: quintic_polynomials_planner/quintic_polynomials_planner.rst
+.. include:: dubins_path/dubins_path.rst
+.. include:: reeds_shepp_path/reeds_shepp_path.rst
+.. include:: lqr_path/lqr_path.rst
+.. include:: hybridastar/hybridastar.rst
+.. include:: frenet_frame_path/frenet_frame_path.rst
+.. include:: coverage_path/coverage_path.rst
diff --git a/docs/modules/path_planning/prm_planner/prm_planner.rst b/docs/modules/path_planning/prm_planner/prm_planner.rst
new file mode 100644
index 0000000000..0628719176
--- /dev/null
+++ b/docs/modules/path_planning/prm_planner/prm_planner.rst
@@ -0,0 +1,19 @@
+.. _probabilistic-road-map-(prm)-planning:
+
+Probabilistic Road-Map (PRM) planning
+-------------------------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ProbabilisticRoadMap/animation.gif
+
+This PRM planner uses Dijkstra method for graph search.
+
+In the animation, blue points are sampled points,
+
+Cyan crosses means searched points with Dijkstra method,
+
+The red line is the final path of PRM.
+
+Ref:
+
+-  `Probabilistic roadmap -
+   Wikipedia <https://en.wikipedia.org/wiki/Probabilistic_roadmap>`__
diff --git a/docs/modules/path_planning/quintic_polynomials_planner.rst b/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner.rst
similarity index 100%
rename from docs/modules/path_planning/quintic_polynomials_planner.rst
rename to docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner.rst
diff --git a/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path.rst b/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path.rst
new file mode 100644
index 0000000000..81e565888e
--- /dev/null
+++ b/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path.rst
@@ -0,0 +1,17 @@
+Reeds Shepp planning
+--------------------
+
+A sample code with Reeds Shepp path planning.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true
+
+Ref:
+
+-  `15.3.2 Reeds-Shepp
+   Curves <http://planning.cs.uiuc.edu/node822.html>`__
+
+-  `optimal paths for a car that goes both forwards and
+   backwards <https://pdfs.semanticscholar.org/932e/c495b1d0018fd59dee12a0bf74434fac7af4.pdf>`__
+
+-  `ghliu/pyReedsShepp: Implementation of Reeds Shepp
+   curve. <https://github.com/ghliu/pyReedsShepp>`__
diff --git a/docs/modules/path_planning/rrt/rrt.rst b/docs/modules/path_planning/rrt/rrt.rst
new file mode 100644
index 0000000000..1afa17b144
--- /dev/null
+++ b/docs/modules/path_planning/rrt/rrt.rst
@@ -0,0 +1,118 @@
+.. _rapidly-exploring-random-trees-(rrt):
+
+Rapidly-Exploring Random Trees (RRT)
+------------------------------------
+
+Basic RRT
+~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRT/animation.gif
+
+This is a simple path planning code with Rapidly-Exploring Random Trees
+(RRT)
+
+Black circles are obstacles, green line is a searched tree, red crosses
+are start and goal positions.
+
+.. include:: rrt/rrt_star.rst
+
+
+RRT with dubins path
+~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTDubins/animation.gif
+
+Path planning for a car robot with RRT and dubins path planner.
+
+.. _rrt*-with-dubins-path:
+
+RRT\* with dubins path
+~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarDubins/animation.gif
+
+Path planning for a car robot with RRT\* and dubins path planner.
+
+.. _rrt*-with-reeds-sheep-path:
+
+RRT\* with reeds-sheep path
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarReedsShepp/animation.gif
+
+Path planning for a car robot with RRT\* and reeds sheep path planner.
+
+.. _informed-rrt*:
+
+Informed RRT\*
+~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/InformedRRTStar/animation.gif
+
+This is a path planning code with Informed RRT*.
+
+The cyan ellipse is the heuristic sampling domain of Informed RRT*.
+
+Ref:
+
+-  `Informed RRT\*: Optimal Sampling-based Path Planning Focused via
+   Direct Sampling of an Admissible Ellipsoidal
+   Heuristic <https://arxiv.org/pdf/1404.2334.pdf>`__
+
+.. _batch-informed-rrt*:
+
+Batch Informed RRT\*
+~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BatchInformedRRTStar/animation.gif
+
+This is a path planning code with Batch Informed RRT*.
+
+Ref:
+
+-  `Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the
+   Heuristically Guided Search of Implicit Random Geometric
+   Graphs <https://arxiv.org/abs/1405.5848>`__
+
+.. _closed-loop-rrt*:
+
+Closed Loop RRT\*
+~~~~~~~~~~~~~~~~~
+
+A vehicle model based path planning with closed loop RRT*.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ClosedLoopRRTStar/animation.gif
+
+In this code, pure-pursuit algorithm is used for steering control,
+
+PID is used for speed control.
+
+Ref:
+
+-  `Motion Planning in Complex Environments using Closed-loop
+   Prediction <http://acl.mit.edu/papers/KuwataGNC08.pdf>`__
+
+-  `Real-time Motion Planning with Applications to Autonomous Urban
+   Driving <http://acl.mit.edu/papers/KuwataTCST09.pdf>`__
+
+-  `[1601.06326] Sampling-based Algorithms for Optimal Motion Planning
+   Using Closed-loop Prediction <https://arxiv.org/abs/1601.06326>`__
+
+.. _lqr-rrt*:
+
+LQR-RRT\*
+~~~~~~~~~
+
+This is a path planning simulation with LQR-RRT*.
+
+A double integrator motion model is used for LQR local planner.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif
+
+Ref:
+
+-  `LQR-RRT\*: Optimal Sampling-Based Motion Planning with Automatically
+   Derived Extension
+   Heuristics <http://lis.csail.mit.edu/pubs/perez-icra12.pdf>`__
+
+-  `MahanFathi/LQR-RRTstar: LQR-RRT\* method is used for random motion planning of a simple pendulum in its phase plot <https://github.com/MahanFathi/LQR-RRTstar>`__
\ No newline at end of file
diff --git a/docs/modules/path_planning/rrt_star.rst b/docs/modules/path_planning/rrt/rrt_star.rst
similarity index 90%
rename from docs/modules/path_planning/rrt_star.rst
rename to docs/modules/path_planning/rrt/rrt_star.rst
index cf136d529f..546e9bf6dd 100644
--- a/docs/modules/path_planning/rrt_star.rst
+++ b/docs/modules/path_planning/rrt/rrt_star.rst
@@ -2,7 +2,6 @@ RRT\*
 ~~~~~
 
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTstar/animation.gif
-   :alt: gif
 
 This is a path planning code with RRT\*
 
@@ -11,7 +10,7 @@ Black circles are obstacles, green line is a searched tree, red crosses are star
 Simulation
 ^^^^^^^^^^
 
-.. image:: rrt_star_files/rrt_star_1_0.png
+.. image:: rrt/rrt_star/rrt_star_1_0.png
    :width: 600px
 
 
diff --git a/docs/modules/path_planning/rrt_star_files/rrt_star_1_0.png b/docs/modules/path_planning/rrt/rrt_star/rrt_star_1_0.png
similarity index 100%
rename from docs/modules/path_planning/rrt_star_files/rrt_star_1_0.png
rename to docs/modules/path_planning/rrt/rrt_star/rrt_star_1_0.png
diff --git a/docs/modules/path_planning/rrt_star_reeds_shepp/figure_1.png b/docs/modules/path_planning/rrt/rrt_star_reeds_shepp/figure_1.png
similarity index 100%
rename from docs/modules/path_planning/rrt_star_reeds_shepp/figure_1.png
rename to docs/modules/path_planning/rrt/rrt_star_reeds_shepp/figure_1.png
diff --git a/docs/modules/path_planning/state_lattice_pathplanner/Figure_1.png b/docs/modules/path_planning/state_lattice_planner/Figure_1.png
similarity index 100%
rename from docs/modules/path_planning/state_lattice_pathplanner/Figure_1.png
rename to docs/modules/path_planning/state_lattice_planner/Figure_1.png
diff --git a/docs/modules/path_planning/state_lattice_pathplanner/Figure_2.png b/docs/modules/path_planning/state_lattice_planner/Figure_2.png
similarity index 100%
rename from docs/modules/path_planning/state_lattice_pathplanner/Figure_2.png
rename to docs/modules/path_planning/state_lattice_planner/Figure_2.png
diff --git a/docs/modules/path_planning/state_lattice_pathplanner/Figure_3.png b/docs/modules/path_planning/state_lattice_planner/Figure_3.png
similarity index 100%
rename from docs/modules/path_planning/state_lattice_pathplanner/Figure_3.png
rename to docs/modules/path_planning/state_lattice_planner/Figure_3.png
diff --git a/docs/modules/path_planning/state_lattice_pathplanner/Figure_4.png b/docs/modules/path_planning/state_lattice_planner/Figure_4.png
similarity index 100%
rename from docs/modules/path_planning/state_lattice_pathplanner/Figure_4.png
rename to docs/modules/path_planning/state_lattice_planner/Figure_4.png
diff --git a/docs/modules/path_planning/state_lattice_pathplanner/Figure_5.png b/docs/modules/path_planning/state_lattice_planner/Figure_5.png
similarity index 100%
rename from docs/modules/path_planning/state_lattice_pathplanner/Figure_5.png
rename to docs/modules/path_planning/state_lattice_planner/Figure_5.png
diff --git a/docs/modules/path_planning/state_lattice_pathplanner/Figure_6.png b/docs/modules/path_planning/state_lattice_planner/Figure_6.png
similarity index 100%
rename from docs/modules/path_planning/state_lattice_pathplanner/Figure_6.png
rename to docs/modules/path_planning/state_lattice_planner/Figure_6.png
diff --git a/docs/modules/path_planning/state_lattice_planner/state_lattice_planner.rst b/docs/modules/path_planning/state_lattice_planner/state_lattice_planner.rst
new file mode 100644
index 0000000000..3ef0c7eca2
--- /dev/null
+++ b/docs/modules/path_planning/state_lattice_planner/state_lattice_planner.rst
@@ -0,0 +1,33 @@
+State Lattice Planning
+----------------------
+
+This script is a path planning code with state lattice planning.
+
+This code uses the model predictive trajectory generator to solve
+boundary problem.
+
+
+Uniform polar sampling
+~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/UniformPolarSampling.gif
+
+Biased polar sampling
+~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/BiasedPolarSampling.gif
+
+Lane sampling
+~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif
+
+Ref:
+
+-  `Optimal rough terrain trajectory generation for wheeled mobile
+   robots <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
+
+-  `State Space Sampling of Feasible Motions for High-Performance Mobile
+   Robot Navigation in Complex
+   Environments <http://www.frc.ri.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf>`__
+
diff --git a/docs/modules/path_planning/vrm_planner/vrm_planner.rst b/docs/modules/path_planning/vrm_planner/vrm_planner.rst
new file mode 100644
index 0000000000..92e729ab29
--- /dev/null
+++ b/docs/modules/path_planning/vrm_planner/vrm_planner.rst
@@ -0,0 +1,17 @@
+Voronoi Road-Map planning
+-------------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/VoronoiRoadMap/animation.gif
+
+This Voronoi road-map planner uses Dijkstra method for graph search.
+
+In the animation, blue points are Voronoi points,
+
+Cyan crosses mean searched points with Dijkstra method,
+
+The red line is the final path of Vornoi Road-Map.
+
+Ref:
+
+-  `Robotic Motion Planning <https://www.cs.cmu.edu/~motionplanning/lecture/Chap5-RoadMap-Methods_howie.pdf>`__
+

From c70add32a15ad7e662185fa816ba152d244be002 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 4 Dec 2021 20:56:54 +0900
Subject: [PATCH 0555/1080] add visibility road map path doc (#592)

* add visibility road map path doc

* add visibility road map path doc

* add visibility road map path doc

* add visibility road map path doc

* add visibility road map path doc

* add visibility road map path doc

* add visibility road map path doc
---
 PathPlanning/VisibilityRoadMap/geometry.py    |   3 +-
 .../VisibilityRoadMap/visibility_road_map.py  |  21 +++---
 .../grid_base_search/grid_base_search.rst     |   2 +
 .../path_planning/path_planning_main.rst      |   1 +
 .../visibility_road_map_planner/step0.png     | Bin 0 -> 322560 bytes
 .../visibility_road_map_planner/step1.png     | Bin 0 -> 289363 bytes
 .../visibility_road_map_planner/step2.png     | Bin 0 -> 795677 bytes
 .../visibility_road_map_planner/step3.png     | Bin 0 -> 791560 bytes
 .../visibility_road_map_planner.rst           |  71 ++++++++++++++++++
 9 files changed, 87 insertions(+), 11 deletions(-)
 create mode 100644 docs/modules/path_planning/visibility_road_map_planner/step0.png
 create mode 100644 docs/modules/path_planning/visibility_road_map_planner/step1.png
 create mode 100644 docs/modules/path_planning/visibility_road_map_planner/step2.png
 create mode 100644 docs/modules/path_planning/visibility_road_map_planner/step3.png
 create mode 100644 docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner.rst

diff --git a/PathPlanning/VisibilityRoadMap/geometry.py b/PathPlanning/VisibilityRoadMap/geometry.py
index c4d73837c9..b15cdb8a43 100644
--- a/PathPlanning/VisibilityRoadMap/geometry.py
+++ b/PathPlanning/VisibilityRoadMap/geometry.py
@@ -1,4 +1,5 @@
 class Geometry:
+
     class Point:
         def __init__(self, x, y):
             self.x = x
@@ -40,4 +41,4 @@ def orientation(p, q, r):
         if (o4 == 0) and on_segment(p2, q1, q2):
             return True
 
-        return False
\ No newline at end of file
+        return False
diff --git a/PathPlanning/VisibilityRoadMap/visibility_road_map.py b/PathPlanning/VisibilityRoadMap/visibility_road_map.py
index a677804440..6458cd882c 100644
--- a/PathPlanning/VisibilityRoadMap/visibility_road_map.py
+++ b/PathPlanning/VisibilityRoadMap/visibility_road_map.py
@@ -23,14 +23,14 @@
 
 class VisibilityRoadMap:
 
-    def __init__(self, robot_radius, do_plot=False):
-        self.robot_radius = robot_radius
+    def __init__(self, expand_distance, do_plot=False):
+        self.expand_distance = expand_distance
         self.do_plot = do_plot
 
     def planning(self, start_x, start_y, goal_x, goal_y, obstacles):
 
-        nodes = self.generate_graph_node(start_x, start_y, goal_x, goal_y,
-                                         obstacles)
+        nodes = self.generate_visibility_nodes(start_x, start_y,
+                                               goal_x, goal_y, obstacles)
 
         road_map_info = self.generate_road_map_info(nodes, obstacles)
 
@@ -48,7 +48,8 @@ def planning(self, start_x, start_y, goal_x, goal_y, obstacles):
 
         return rx, ry
 
-    def generate_graph_node(self, start_x, start_y, goal_x, goal_y, obstacles):
+    def generate_visibility_nodes(self, start_x, start_y, goal_x, goal_y,
+                                  obstacles):
 
         # add start and goal as nodes
         nodes = [DijkstraSearch.Node(start_x, start_y),
@@ -129,8 +130,8 @@ def calc_offset_xy(self, px, py, x, y, nx, ny):
         offset_vec = math.atan2(math.sin(p_vec) + math.sin(n_vec),
                                 math.cos(p_vec) + math.cos(
                                     n_vec)) + math.pi / 2.0
-        offset_x = x + self.robot_radius * math.cos(offset_vec)
-        offset_y = y + self.robot_radius * math.sin(offset_vec)
+        offset_x = x + self.expand_distance * math.cos(offset_vec)
+        offset_y = y + self.expand_distance * math.sin(offset_vec)
         return offset_x, offset_y
 
     @staticmethod
@@ -184,7 +185,7 @@ def main():
     sx, sy = 10.0, 10.0  # [m]
     gx, gy = 50.0, 50.0  # [m]
 
-    robot_radius = 5.0  # [m]
+    expand_distance = 5.0  # [m]
 
     obstacles = [
         ObstaclePolygon(
@@ -209,8 +210,8 @@ def main():
         plt.axis("equal")
         plt.pause(1.0)
 
-    rx, ry = VisibilityRoadMap(robot_radius, do_plot=show_animation).planning(
-        sx, sy, gx, gy, obstacles)
+    rx, ry = VisibilityRoadMap(expand_distance, do_plot=show_animation)\
+        .planning(sx, sy, gx, gy, obstacles)
 
     if show_animation:  # pragma: no cover
         plt.plot(rx, ry, "-r")
diff --git a/docs/modules/path_planning/grid_base_search/grid_base_search.rst b/docs/modules/path_planning/grid_base_search/grid_base_search.rst
index ac4ea2f6fd..1644ed00cc 100644
--- a/docs/modules/path_planning/grid_base_search/grid_base_search.rst
+++ b/docs/modules/path_planning/grid_base_search/grid_base_search.rst
@@ -19,6 +19,8 @@ This is a 2D grid based path planning with Depth first search algorithm.
 
 In the animation, cyan points are searched nodes.
 
+.. _dijkstra:
+
 Dijkstra algorithm
 ~~~~~~~~~~~~~~~~~~
 
diff --git a/docs/modules/path_planning/path_planning_main.rst b/docs/modules/path_planning/path_planning_main.rst
index 61f8ea2113..c3c5c389ec 100644
--- a/docs/modules/path_planning/path_planning_main.rst
+++ b/docs/modules/path_planning/path_planning_main.rst
@@ -9,6 +9,7 @@ Path Planning
 .. include:: model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst
 .. include:: state_lattice_planner/state_lattice_planner.rst
 .. include:: prm_planner/prm_planner.rst
+.. include:: visibility_road_map_planner/visibility_road_map_planner.rst
 .. include:: vrm_planner/vrm_planner.rst
 .. include:: rrt/rrt.rst
 .. include:: cubic_spline/cubic_spline.rst
diff --git a/docs/modules/path_planning/visibility_road_map_planner/step0.png b/docs/modules/path_planning/visibility_road_map_planner/step0.png
new file mode 100644
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diff --git a/docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner.rst b/docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner.rst
new file mode 100644
index 0000000000..97e083389e
--- /dev/null
+++ b/docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner.rst
@@ -0,0 +1,71 @@
+Visibility Road-Map planner
+---------------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/VisibilityRoadMap/animation.gif
+
+`[Code] <https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning/VisibilityRoadMap/visibility_road_map.py>`_
+
+This visibility road-map planner uses Dijkstra method for graph search.
+
+In the animation, the black lines are polygon obstacles,
+
+red crosses are visibility nodes, and blue lines area collision free visibility graphs.
+
+The red line is the final path searched by dijkstra algorithm frm the visibility graphs.
+
+Algorithms
+~~~~~~~~~~
+
+In this chapter, how does the visibility road map planner search a path.
+
+We assume this planner can be provided these information in the below figure.
+
+- 1. Start point (Red point)
+- 2. Goal point (Blue point)
+- 3. Obstacle polygons (Black lines)
+
+.. image:: visibility_road_map_planner/step0.png
+   :width: 400px
+
+
+Step1: Generate visibility nodes based on polygon obstacles
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The nodes are generated by expanded these polygons vertexes like the below figure:
+
+.. image:: visibility_road_map_planner/step1.png
+   :width: 400px
+
+Each polygon vertex is expanded outward from the vector of adjacent vertices.
+
+The start and goal point are included as nodes as well.
+
+Step2: Generate visibility graphs connecting the nodes.
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+When connecting the nodes, the arc between two nodes is checked to collided or not to each obstacles.
+
+If the arc is collided, the graph is removed.
+
+The blue lines are generated visibility graphs in the figure:
+
+.. image:: visibility_road_map_planner/step2.png
+   :width: 400px
+
+
+Step3: Search the shortest path in the graphs using Dijkstra algorithm
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The red line is searched path in the figure:
+
+.. image:: visibility_road_map_planner/step3.png
+   :width: 400px
+
+You can find the details of Dijkstra algorithm in :ref:`dijkstra`.
+
+References
+^^^^^^^^^^
+
+- `Visibility graph - Wikipedia <https://en.wikipedia.org/wiki/Visibility_graph>`_
+
+

From d5aca66e1b61692e239e95c928884ede6f2c94cd Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 7 Dec 2021 07:23:13 +0900
Subject: [PATCH 0556/1080] Bump ipython from 7.30.0 to 7.30.1 (#594)

Bumps [ipython](https://github.com/ipython/ipython) from 7.30.0 to 7.30.1.
- [Release notes](https://github.com/ipython/ipython/releases)
- [Commits](https://github.com/ipython/ipython/compare/7.30.0...7.30.1)

---
updated-dependencies:
- dependency-name: ipython
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 2a0135cf44..09bd9f50ad 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,4 +7,4 @@ pytest == 6.2.5 # For unit test
 pytest-xdist == 2.4.0 # For unit test
 sphinx < 4.0 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
-IPython == 7.30.0 # For sphinx documentation
+IPython == 7.30.1 # For sphinx documentation

From bf4e68245f4af09af65a579a9a8c7c3df0f644c9 Mon Sep 17 00:00:00 2001
From: Muhammad-Yazdian <93126501+Muhammad-Yazdian@users.noreply.github.com>
Date: Sun, 12 Dec 2021 13:32:59 +0100
Subject: [PATCH 0557/1080] Add speed limitation of the robot (#595)

* Added speed limitation of the robot

* Removed leading underscores for global vars

* Added unit test for robot speed limitation

* Modified x/abs(x) to np.sign(x); fixed code style

* Removed 'random' from test func header comment
---
 Control/move_to_pose/move_to_pose.py | 10 ++++++++++
 tests/test_move_to_pose.py           | 11 +++++++++++
 2 files changed, 21 insertions(+)

diff --git a/Control/move_to_pose/move_to_pose.py b/Control/move_to_pose/move_to_pose.py
index 2ae0090dda..4548a6bb8a 100644
--- a/Control/move_to_pose/move_to_pose.py
+++ b/Control/move_to_pose/move_to_pose.py
@@ -19,6 +19,10 @@
 Kp_beta = -3
 dt = 0.01
 
+# Robot specifications
+MAX_LINEAR_SPEED = 15
+MAX_ANGULAR_SPEED = 7
+
 show_animation = True
 
 
@@ -63,6 +67,12 @@ def move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal):
         if alpha > np.pi / 2 or alpha < -np.pi / 2:
             v = -v
 
+        if abs(v) > MAX_LINEAR_SPEED:
+            v = np.sign(v) * MAX_LINEAR_SPEED
+
+        if abs(w) > MAX_ANGULAR_SPEED:
+            w = np.sign(w) * MAX_ANGULAR_SPEED
+
         theta = theta + w * dt
         x = x + v * np.cos(theta) * dt
         y = y + v * np.sin(theta) * dt
diff --git a/tests/test_move_to_pose.py b/tests/test_move_to_pose.py
index 05cfa269d7..8bc11a8d24 100644
--- a/tests/test_move_to_pose.py
+++ b/tests/test_move_to_pose.py
@@ -7,5 +7,16 @@ def test_1():
     m.main()
 
 
+def test_2():
+    """
+    This unit test tests the move_to_pose.py program for a MAX_LINEAR_SPEED and
+    MAX_ANGULAR_SPEED
+    """
+    m.show_animation = False
+    m.MAX_LINEAR_SPEED = 11
+    m.MAX_ANGULAR_SPEED = 5
+    m.main()
+
+
 if __name__ == '__main__':
     conftest.run_this_test(__file__)

From 6e4d43962a12913ffb2be5ceb35bee58f58178bc Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 14 Dec 2021 07:35:03 +0900
Subject: [PATCH 0558/1080] Bump pytest-xdist from 2.4.0 to 2.5.0 (#597)

Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 2.4.0 to 2.5.0.
- [Release notes](https://github.com/pytest-dev/pytest-xdist/releases)
- [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v2.4.0...v2.5.0)

---
updated-dependencies:
- dependency-name: pytest-xdist
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 09bd9f50ad..4763ee4253 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,7 +4,7 @@ pandas == 1.3.4
 matplotlib == 3.5.0
 cvxpy == 1.1.17
 pytest == 6.2.5 # For unit test
-pytest-xdist == 2.4.0 # For unit test
+pytest-xdist == 2.5.0 # For unit test
 sphinx < 4.0 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
 IPython == 7.30.1 # For sphinx documentation

From 528050ec373ebcfb9c64419b46f4139202e983ad Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 14 Dec 2021 08:06:05 +0900
Subject: [PATCH 0559/1080] Bump pandas from 1.3.4 to 1.3.5 (#598)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.3.4 to 1.3.5.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/master/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.3.4...v1.3.5)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 4763ee4253..7069c53c93 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.21.4
 scipy == 1.7.3
-pandas == 1.3.4
+pandas == 1.3.5
 matplotlib == 3.5.0
 cvxpy == 1.1.17
 pytest == 6.2.5 # For unit test

From 54be632f716f9913a89a166b7d8a722e156da7d0 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 14 Dec 2021 08:49:57 +0900
Subject: [PATCH 0560/1080] Bump matplotlib from 3.5.0 to 3.5.1 (#599)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.5.0 to 3.5.1.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.5.0...v3.5.1)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 7069c53c93..407f161b60 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 1.21.4
 scipy == 1.7.3
 pandas == 1.3.5
-matplotlib == 3.5.0
+matplotlib == 3.5.1
 cvxpy == 1.1.17
 pytest == 6.2.5 # For unit test
 pytest-xdist == 2.5.0 # For unit test

From 196c756706bb4fa2610e70808fdaf59df36e1bb4 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 25 Dec 2021 13:53:27 +0900
Subject: [PATCH 0561/1080] try fix appveyor CI (#605)

---
 appveyor.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/appveyor.yml b/appveyor.yml
index 16a5d7751d..e9535e4e3c 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -1,4 +1,4 @@
-os: Visual Studio 2019
+os: Visual Studio 2022
 
 environment:
   global:

From 4e1f644007b8da56e8926d77242c4ad479d0b701 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sat, 25 Dec 2021 14:14:41 +0900
Subject: [PATCH 0562/1080] Bump numpy from 1.21.4 to 1.21.5 (#602)

Bumps [numpy](https://github.com/numpy/numpy) from 1.21.4 to 1.21.5.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt)
- [Commits](https://github.com/numpy/numpy/compare/v1.21.4...v1.21.5)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 407f161b60..966b566000 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.21.4
+numpy == 1.21.5
 scipy == 1.7.3
 pandas == 1.3.5
 matplotlib == 3.5.1

From 680ecdafb21a798eb47649175e0eba4e72ef356d Mon Sep 17 00:00:00 2001
From: Muhammad-Yazdian <93126501+Muhammad-Yazdian@users.noreply.github.com>
Date: Sat, 25 Dec 2021 13:42:32 +0100
Subject: [PATCH 0563/1080] Add a Robot class for Move to Pose Algorithm 
 (#596)

* Added speed limitation of the robot

* Removed leading underscores for global vars

* Added unit test for robot speed limitation

* Modified x/abs(x) to np.sign(x); fixed code style

* Removed 'random' from test func header comment

* Added Robot class for move to pose

* Revert

* Added Robot class for move to pose

* Added a type annotation for Robot class

* Fixed the annotaion comment

* Moved instance varaible outside of the Robot class

* Fixed code style Python 3.9 CI

* Removed whitespaces from the last line

* Applied PR #596 change requests

* Fixed typos

* Update Control/move_to_pose/move_to_pose_robot_class.py

Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>

* Moved PathFinderController class to move_to_pose

* Fixed issue #600

* Added update_command() to PathFinderController

* Removed trailing whitespaces

* Updated move to pose doc

* Added code and doc comments

* Updated unit test

* Removed trailing whitespaces

* Removed more trailing whitespaces

Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>
---
 Control/move_to_pose/move_to_pose.py          | 105 ++++++--
 Control/move_to_pose/move_to_pose_robot.py    | 240 ++++++++++++++++++
 .../move_to_a_pose_control.rst                |  95 ++++++-
 tests/test_move_to_pose_robot.py              |  14 +
 4 files changed, 424 insertions(+), 30 deletions(-)
 create mode 100644 Control/move_to_pose/move_to_pose_robot.py
 create mode 100644 tests/test_move_to_pose_robot.py

diff --git a/Control/move_to_pose/move_to_pose.py b/Control/move_to_pose/move_to_pose.py
index 4548a6bb8a..73604ccd7f 100644
--- a/Control/move_to_pose/move_to_pose.py
+++ b/Control/move_to_pose/move_to_pose.py
@@ -3,7 +3,8 @@
 Move to specified pose
 
 Author: Daniel Ingram (daniel-s-ingram)
-        Atsushi Sakai(@Atsushi_twi)
+        Atsushi Sakai (@Atsushi_twi)
+        Seied Muhammad Yazdian (@Muhammad-Yazdian)
 
 P. I. Corke, "Robotics, Vision & Control", Springer 2017, ISBN 978-3-319-54413-7
 
@@ -13,10 +14,77 @@
 import numpy as np
 from random import random
 
+
+class PathFinderController:
+    """
+    Constructs an instantiate of the PathFinderController for navigating a
+    3-DOF wheeled robot on a 2D plane
+
+    Parameters
+    ----------
+    Kp_rho : The linear velocity gain to translate the robot along a line
+             towards the goal
+    Kp_alpha : The angular velocity gain to rotate the robot towards the goal
+    Kp_beta : The offset angular velocity gain accounting for smooth merging to
+              the goal angle (i.e., it helps the robot heading to be parallel
+              to the target angle.)
+    """
+
+    def __init__(self, Kp_rho, Kp_alpha, Kp_beta):
+        self.Kp_rho = Kp_rho
+        self.Kp_alpha = Kp_alpha
+        self.Kp_beta = Kp_beta
+
+    def calc_control_command(self, x_diff, y_diff, theta, theta_goal):
+        """
+        Returns the control command for the linear and angular velocities as
+        well as the distance to goal
+
+        Parameters
+        ----------
+        x_diff : The position of target with respect to current robot position
+                 in x direction
+        y_diff : The position of target with respect to current robot position
+                 in y direction
+        theta : The current heading angle of robot with respect to x axis
+        theta_goal: The target angle of robot with respect to x axis
+
+        Returns
+        -------
+        rho : The distance between the robot and the goal position
+        v : Command linear velocity
+        w : Command angular velocity
+        """
+
+        # Description of local variables:
+        # - alpha is the angle to the goal relative to the heading of the robot
+        # - beta is the angle between the robot's position and the goal
+        #   position plus the goal angle
+        # - Kp_rho*rho and Kp_alpha*alpha drive the robot along a line towards
+        #   the goal
+        # - Kp_beta*beta rotates the line so that it is parallel to the goal
+        #   angle
+        #
+        # Note:
+        # we restrict alpha and beta (angle differences) to the range
+        # [-pi, pi] to prevent unstable behavior e.g. difference going
+        # from 0 rad to 2*pi rad with slight turn
+
+        rho = np.hypot(x_diff, y_diff)
+        alpha = (np.arctan2(y_diff, x_diff)
+                 - theta + np.pi) % (2 * np.pi) - np.pi
+        beta = (theta_goal - theta - alpha + np.pi) % (2 * np.pi) - np.pi
+        v = self.Kp_rho * rho
+        w = self.Kp_alpha * alpha - controller.Kp_beta * beta
+
+        if alpha > np.pi / 2 or alpha < -np.pi / 2:
+            v = -v
+
+        return rho, v, w
+
+
 # simulation parameters
-Kp_rho = 9
-Kp_alpha = 15
-Kp_beta = -3
+controller = PathFinderController(9, 15, 3)
 dt = 0.01
 
 # Robot specifications
@@ -27,14 +95,6 @@
 
 
 def move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal):
-    """
-    rho is the distance between the robot and the goal position
-    alpha is the angle to the goal relative to the heading of the robot
-    beta is the angle between the robot's position and the goal position plus the goal angle
-
-    Kp_rho*rho and Kp_alpha*alpha drive the robot along a line towards the goal
-    Kp_beta*beta rotates the line so that it is parallel to the goal angle
-    """
     x = x_start
     y = y_start
     theta = theta_start
@@ -52,20 +112,8 @@ def move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal):
         x_diff = x_goal - x
         y_diff = y_goal - y
 
-        # Restrict alpha and beta (angle differences) to the range
-        # [-pi, pi] to prevent unstable behavior e.g. difference going
-        # from 0 rad to 2*pi rad with slight turn
-
-        rho = np.hypot(x_diff, y_diff)
-        alpha = (np.arctan2(y_diff, x_diff)
-                 - theta + np.pi) % (2 * np.pi) - np.pi
-        beta = (theta_goal - theta - alpha + np.pi) % (2 * np.pi) - np.pi
-
-        v = Kp_rho * rho
-        w = Kp_alpha * alpha + Kp_beta * beta
-
-        if alpha > np.pi / 2 or alpha < -np.pi / 2:
-            v = -v
+        rho, v, w = controller.calc_control_command(
+            x_diff, y_diff, theta, theta_goal)
 
         if abs(v) > MAX_LINEAR_SPEED:
             v = np.sign(v) * MAX_LINEAR_SPEED
@@ -104,8 +152,9 @@ def plot_vehicle(x, y, theta, x_traj, y_traj):  # pragma: no cover
     plt.plot(x_traj, y_traj, 'b--')
 
     # for stopping simulation with the esc key.
-    plt.gcf().canvas.mpl_connect('key_release_event',
-            lambda event: [exit(0) if event.key == 'escape' else None])
+    plt.gcf().canvas.mpl_connect(
+        'key_release_event',
+        lambda event: [exit(0) if event.key == 'escape' else None])
 
     plt.xlim(0, 20)
     plt.ylim(0, 20)
diff --git a/Control/move_to_pose/move_to_pose_robot.py b/Control/move_to_pose/move_to_pose_robot.py
new file mode 100644
index 0000000000..d5c51caa12
--- /dev/null
+++ b/Control/move_to_pose/move_to_pose_robot.py
@@ -0,0 +1,240 @@
+"""
+
+Move to specified pose (with Robot class)
+
+Author: Daniel Ingram (daniel-s-ingram)
+        Atsushi Sakai (@Atsushi_twi)
+        Seied Muhammad Yazdian (@Muhammad-Yazdian)
+
+P.I. Corke, "Robotics, Vision & Control", Springer 2017, ISBN 978-3-319-54413-7
+
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import copy
+from move_to_pose import PathFinderController
+
+# Simulation parameters
+TIME_DURATION = 1000
+TIME_STEP = 0.01
+AT_TARGET_ACCEPTANCE_THRESHOLD = 0.01
+SHOW_ANIMATION = True
+PLOT_WINDOW_SIZE_X = 20
+PLOT_WINDOW_SIZE_Y = 20
+PLOT_FONT_SIZE = 8
+
+simulation_running = True
+all_robots_are_at_target = False
+
+
+class Pose:
+    """2D pose"""
+
+    def __init__(self, x, y, theta):
+        self.x = x
+        self.y = y
+        self.theta = theta
+
+
+class Robot:
+    """
+    Constructs an instantiate of the 3-DOF wheeled Robot navigating on a
+    2D plane
+
+    Parameters
+    ----------
+    name : (string)
+        The name of the robot
+    color : (string)
+        The color of the robot
+    max_linear_speed : (float)
+        The maximum linear speed that the robot can go
+    max_angular_speed : (float)
+        The maximum angular speed that the robot can rotate about its vertical
+        axis
+    path_finder_controller : (PathFinderController)
+        A configurable controller to finds the path and calculates command
+        linear and angular velocities.
+    """
+
+    def __init__(self, name, color, max_linear_speed, max_angular_speed,
+                 path_finder_controller):
+        self.name = name
+        self.color = color
+        self.MAX_LINEAR_SPEED = max_linear_speed
+        self.MAX_ANGULAR_SPEED = max_angular_speed
+        self.path_finder_controller = path_finder_controller
+        self.x_traj = []
+        self.y_traj = []
+        self.pose = Pose(0, 0, 0)
+        self.pose_start = Pose(0, 0, 0)
+        self.pose_target = Pose(0, 0, 0)
+        self.is_at_target = False
+
+    def set_start_target_poses(self, pose_start, pose_target):
+        """
+        Sets the start and target positions of the robot
+
+        Parameters
+        ----------
+        pose_start : (Pose)
+            Start postion of the robot (see the Pose class)
+        pose_target : (Pose)
+            Target postion of the robot (see the Pose class)
+        """
+        self.pose_start = copy.copy(pose_start)
+        self.pose_target = pose_target
+        self.pose = pose_start
+
+    def move(self, dt):
+        """
+        Moves the robot for one time step increment
+
+        Parameters
+        ----------
+        dt : (float)
+            time step
+        """
+        self.x_traj.append(self.pose.x)
+        self.y_traj.append(self.pose.y)
+
+        rho, linear_velocity, angular_velocity = \
+            self.path_finder_controller.calc_control_command(
+                self.pose_target.x - self.pose.x,
+                self.pose_target.y - self.pose.y,
+                self.pose.theta, self.pose_target.theta)
+
+        if rho < AT_TARGET_ACCEPTANCE_THRESHOLD:
+            self.is_at_target = True
+
+        if abs(linear_velocity) > self.MAX_LINEAR_SPEED:
+            linear_velocity = (np.sign(linear_velocity)
+                               * self.MAX_LINEAR_SPEED)
+
+        if abs(angular_velocity) > self.MAX_ANGULAR_SPEED:
+            angular_velocity = (np.sign(angular_velocity)
+                                * self.MAX_ANGULAR_SPEED)
+
+        self.pose.theta = self.pose.theta + angular_velocity * dt
+        self.pose.x = self.pose.x + linear_velocity * \
+            np.cos(self.pose.theta) * dt
+        self.pose.y = self.pose.y + linear_velocity * \
+            np.sin(self.pose.theta) * dt
+
+
+def run_simulation(robots):
+    """Simulates all robots simultaneously"""
+    global all_robots_are_at_target
+    global simulation_running
+
+    robot_names = []
+    for instance in robots:
+        robot_names.append(instance.name)
+
+    time = 0
+    while simulation_running and time < TIME_DURATION:
+        time += TIME_STEP
+        robots_are_at_target = []
+
+        for instance in robots:
+            if not instance.is_at_target:
+                instance.move(TIME_STEP)
+            robots_are_at_target.append(instance.is_at_target)
+
+        if all(robots_are_at_target):
+            simulation_running = False
+
+        if SHOW_ANIMATION:
+            plt.cla()
+            plt.xlim(0, PLOT_WINDOW_SIZE_X)
+            plt.ylim(0, PLOT_WINDOW_SIZE_Y)
+
+            # for stopping simulation with the esc key.
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
+
+            plt.text(0.3, PLOT_WINDOW_SIZE_Y - 1,
+                     'Time: {:.2f}'.format(time),
+                     fontsize=PLOT_FONT_SIZE)
+
+            plt.text(0.3, PLOT_WINDOW_SIZE_Y - 2,
+                     'Reached target: {} = '.format(robot_names)
+                     + str(robots_are_at_target),
+                     fontsize=PLOT_FONT_SIZE)
+
+            for instance in robots:
+                plt.arrow(instance.pose_start.x,
+                          instance.pose_start.y,
+                          np.cos(instance.pose_start.theta),
+                          np.sin(instance.pose_start.theta),
+                          color='r',
+                          width=0.1)
+                plt.arrow(instance.pose_target.x,
+                          instance.pose_target.y,
+                          np.cos(instance.pose_target.theta),
+                          np.sin(instance.pose_target.theta),
+                          color='g',
+                          width=0.1)
+                plot_vehicle(instance.pose.x,
+                             instance.pose.y,
+                             instance.pose.theta,
+                             instance.x_traj,
+                             instance.y_traj, instance.color)
+
+            plt.pause(TIME_STEP)
+
+
+def plot_vehicle(x, y, theta, x_traj, y_traj, color):
+    # Corners of triangular vehicle when pointing to the right (0 radians)
+    p1_i = np.array([0.5, 0, 1]).T
+    p2_i = np.array([-0.5, 0.25, 1]).T
+    p3_i = np.array([-0.5, -0.25, 1]).T
+
+    T = transformation_matrix(x, y, theta)
+    p1 = T @ p1_i
+    p2 = T @ p2_i
+    p3 = T @ p3_i
+
+    plt.plot([p1[0], p2[0]], [p1[1], p2[1]], color+'-')
+    plt.plot([p2[0], p3[0]], [p2[1], p3[1]], color+'-')
+    plt.plot([p3[0], p1[0]], [p3[1], p1[1]], color+'-')
+
+    plt.plot(x_traj, y_traj, color+'--')
+
+
+def transformation_matrix(x, y, theta):
+    return np.array([
+        [np.cos(theta), -np.sin(theta), x],
+        [np.sin(theta), np.cos(theta), y],
+        [0, 0, 1]
+    ])
+
+
+def main():
+    pose_target = Pose(15, 15, -1)
+
+    pose_start_1 = Pose(5, 2, 0)
+    pose_start_2 = Pose(5, 2, 0)
+    pose_start_3 = Pose(5, 2, 0)
+
+    controller_1 = PathFinderController(5, 8, 2)
+    controller_2 = PathFinderController(5, 16, 4)
+    controller_3 = PathFinderController(10, 25, 6)
+
+    robot_1 = Robot("Yellow Robot", "y", 12, 5, controller_1)
+    robot_2 = Robot("Black Robot", "k", 16, 5, controller_2)
+    robot_3 = Robot("Blue Robot", "b", 20, 5, controller_3)
+
+    robot_1.set_start_target_poses(pose_start_1, pose_target)
+    robot_2.set_start_target_poses(pose_start_2, pose_target)
+    robot_3.set_start_target_poses(pose_start_3, pose_target)
+
+    robots: list[Robot] = [robot_1, robot_2, robot_3]
+
+    run_simulation(robots)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst
index c5bf15d970..81c64f5485 100644
--- a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst
+++ b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst
@@ -1,11 +1,102 @@
 Move to a pose control
 ----------------------
 
-This is a simulation of moving to a pose control
+This is a simulation of moving to a pose control.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif
 
-Ref:
 
+PathFinderController class
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Constructor
+~~~~~~~~~~~
+
+.. code-block:: ipython3
+   PathFinderController(Kp_rho, Kp_alpha, Kp_beta)
+
+Constructs an instantiate of the PathFinderController for navigating a 3-DOF wheeled robot on a 2D plane.
+
+Parameters:
+- Kp_rho: The linear velocity gain to translate the robot along a line towards the goal
+- Kp_alpha: The angular velocity gain to rotate the robot towards the goal
+- Kp_beta: The offset angular velocity gain accounting for smooth merging to the goal angle (i.e., it helps the robot heading to be parallel to the target angle.)
+
+
+Member function(s)
+~~~~~~~~~~~~~~~~~~
+
+.. code-block:: ipython3
+   calc_control_command(x_diff, y_diff, theta, theta_goal)
+
+Returns the control command for the linear and angular velocities as well as the distance to goal
+
+Parameters:
+- x_diff : The position of target with respect to current robot position in x direction
+- y_diff : The position of target with respect to current robot position in y direction
+- theta : The current heading angle of robot with respect to x axis
+- theta_goal : The target angle of robot with respect to x axis
+
+Returns:
+- rho : The distance between the robot and the goal position
+- v : Command linear velocity
+- w : Command angular velocity
+
+Move to a Pose Robot (Class)
+----------------------------
+This program (move_to_pose_robot.py) provides a Robot class to define different robots with different specifications. 
+Using this class, you can simulate different robots simultaneously and compare the effect of your parameter settings.
+
+.. image:: https://user-images.githubusercontent.com/93126501/145834505-a8df8311-5445-413f-a96f-00460d47991c.png
+
+Note: A gif animation will be added soon.
+
+Note: The robot Class is based on PathFinderController class in 'the move_to_pose.py'.
+
+Robot Class
+~~~~~~~~~~~
+
+Constructor
+~~~~~~~~~~~
+
+.. code-block:: ipython3
+    Robot(name, color, max_linear_speed, max_angular_speed, path_finder_controller)
+
+Constructs an instantiate of the 3-DOF wheeled Robot navigating on a 2D plane
+
+Parameters:
+- name : (string) The name of the robot
+- color : (string) The color of the robot
+- max_linear_speed : (float) The maximum linear speed that the robot can go
+- max_angular_speed : (float) The maximum angular speed that the robot can rotate about its vertical axis
+- path_finder_controller : (PathFinderController) A configurable controller to finds the path and calculates command linear and angular velocities.
+
+Member function(s)
+~~~~~~~~~~~~~~~~~~
+
+.. code-block:: ipython3
+    set_start_target_poses(pose_start, pose_target)
+
+Sets the start and target positions of the robot.
+
+Parameters:
+- pose_start : (Pose) Start postion of the robot (see the Pose class)
+- pose_target : (Pose) Target postion of the robot (see the Pose class)
+
+.. code-block:: ipython3
+    move(dt)
+
+Move the robot for one time step increment
+
+Parameters:
+- dt: <float> time increment
+
+See Also 
+--------
+- PathFinderController class
+
+
+Ref:
+----
 -  `P. I. Corke, "Robotics, Vision and Control" \| SpringerLink
    p102 <https://link.springer.com/book/10.1007/978-3-642-20144-8>`__
diff --git a/tests/test_move_to_pose_robot.py b/tests/test_move_to_pose_robot.py
new file mode 100644
index 0000000000..a93b44d198
--- /dev/null
+++ b/tests/test_move_to_pose_robot.py
@@ -0,0 +1,14 @@
+import conftest  # Add root path to sys.path
+from Control.move_to_pose import move_to_pose as m
+
+
+def test_1():
+    """
+    This unit test tests the move_to_pose_robot.py program
+    """
+    m.show_animation = False
+    m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)

From 7034d5ff9eb19eff561f84c806ec591b2d0c8e26 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 25 Dec 2021 23:06:37 +0900
Subject: [PATCH 0564/1080] Add rng provide function for PRM planner (#607)

* Add rng provide function

* fix CI error

* remove unused import
---
 .../probabilistic_road_map.py                 | 60 ++++++++++++-------
 tests/test_probabilistic_road_map.py          |  3 +-
 2 files changed, 41 insertions(+), 22 deletions(-)

diff --git a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
index 8681420e2d..7eac40a886 100644
--- a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
+++ b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
@@ -6,11 +6,10 @@
 
 """
 
-import random
 import math
 import numpy as np
 import matplotlib.pyplot as plt
-from scipy.spatial import cKDTree
+from scipy.spatial import KDTree
 
 # parameter
 N_SAMPLE = 500  # number of sample_points
@@ -36,19 +35,35 @@ def __str__(self):
                str(self.cost) + "," + str(self.parent_index)
 
 
-def prm_planning(sx, sy, gx, gy, ox, oy, rr):
-
-    obstacle_kd_tree = cKDTree(np.vstack((ox, oy)).T)
+def prm_planning(start_x, start_y, goal_x, goal_y,
+                 obstacle_x_list, obstacle_y_list, robot_radius, *, rng=None):
+    """
+    Run probabilistic road map planning
+
+    :param start_x:
+    :param start_y:
+    :param goal_x:
+    :param goal_y:
+    :param obstacle_x_list:
+    :param obstacle_y_list:
+    :param robot_radius:
+    :param rng:
+    :return:
+    """
+    obstacle_kd_tree = KDTree(np.vstack((obstacle_x_list, obstacle_y_list)).T)
 
-    sample_x, sample_y = sample_points(sx, sy, gx, gy,
-                                       rr, ox, oy, obstacle_kd_tree)
+    sample_x, sample_y = sample_points(start_x, start_y, goal_x, goal_y,
+                                       robot_radius,
+                                       obstacle_x_list, obstacle_y_list,
+                                       obstacle_kd_tree, rng)
     if show_animation:
         plt.plot(sample_x, sample_y, ".b")
 
-    road_map = generate_road_map(sample_x, sample_y, rr, obstacle_kd_tree)
+    road_map = generate_road_map(sample_x, sample_y,
+                                 robot_radius, obstacle_kd_tree)
 
     rx, ry = dijkstra_planning(
-        sx, sy, gx, gy, road_map, sample_x, sample_y)
+        start_x, start_y, goal_x, goal_y, road_map, sample_x, sample_y)
 
     return rx, ry
 
@@ -88,13 +103,13 @@ def generate_road_map(sample_x, sample_y, rr, obstacle_kd_tree):
 
     sample_x: [m] x positions of sampled points
     sample_y: [m] y positions of sampled points
-    rr: Robot Radius[m]
+    robot_radius: Robot Radius[m]
     obstacle_kd_tree: KDTree object of obstacles
     """
 
     road_map = []
     n_sample = len(sample_x)
-    sample_kd_tree = cKDTree(np.vstack((sample_x, sample_y)).T)
+    sample_kd_tree = KDTree(np.vstack((sample_x, sample_y)).T)
 
     for (i, ix, iy) in zip(range(n_sample), sample_x, sample_y):
 
@@ -122,11 +137,11 @@ def dijkstra_planning(sx, sy, gx, gy, road_map, sample_x, sample_y):
     """
     s_x: start x position [m]
     s_y: start y position [m]
-    gx: goal x position [m]
-    gy: goal y position [m]
-    ox: x position list of Obstacles [m]
-    oy: y position list of Obstacles [m]
-    rr: robot radius [m]
+    goal_x: goal x position [m]
+    goal_y: goal y position [m]
+    obstacle_x_list: x position list of Obstacles [m]
+    obstacle_y_list: y position list of Obstacles [m]
+    robot_radius: robot radius [m]
     road_map: ??? [m]
     sample_x: ??? [m]
     sample_y: ??? [m]
@@ -215,7 +230,7 @@ def plot_road_map(road_map, sample_x, sample_y):  # pragma: no cover
                      [sample_y[i], sample_y[ind]], "-k")
 
 
-def sample_points(sx, sy, gx, gy, rr, ox, oy, obstacle_kd_tree):
+def sample_points(sx, sy, gx, gy, rr, ox, oy, obstacle_kd_tree, rng):
     max_x = max(ox)
     max_y = max(oy)
     min_x = min(ox)
@@ -223,9 +238,12 @@ def sample_points(sx, sy, gx, gy, rr, ox, oy, obstacle_kd_tree):
 
     sample_x, sample_y = [], []
 
+    if rng is None:
+        rng = np.random.default_rng()
+
     while len(sample_x) <= N_SAMPLE:
-        tx = (random.random() * (max_x - min_x)) + min_x
-        ty = (random.random() * (max_y - min_y)) + min_y
+        tx = (rng.random() * (max_x - min_x)) + min_x
+        ty = (rng.random() * (max_y - min_y)) + min_y
 
         dist, index = obstacle_kd_tree.query([tx, ty])
 
@@ -241,7 +259,7 @@ def sample_points(sx, sy, gx, gy, rr, ox, oy, obstacle_kd_tree):
     return sample_x, sample_y
 
 
-def main():
+def main(rng=None):
     print(__file__ + " start!!")
 
     # start and goal position
@@ -280,7 +298,7 @@ def main():
         plt.grid(True)
         plt.axis("equal")
 
-    rx, ry = prm_planning(sx, sy, gx, gy, ox, oy, robot_size)
+    rx, ry = prm_planning(sx, sy, gx, gy, ox, oy, robot_size, rng=rng)
 
     assert rx, 'Cannot found path'
 
diff --git a/tests/test_probabilistic_road_map.py b/tests/test_probabilistic_road_map.py
index aa58e28bce..a830cc5e0c 100644
--- a/tests/test_probabilistic_road_map.py
+++ b/tests/test_probabilistic_road_map.py
@@ -1,10 +1,11 @@
 import conftest  # Add root path to sys.path
+import numpy as np
 from PathPlanning.ProbabilisticRoadMap import probabilistic_road_map
 
 
 def test1():
     probabilistic_road_map.show_animation = False
-    probabilistic_road_map.main()
+    probabilistic_road_map.main(rng=np.random.default_rng(1234))
 
 
 if __name__ == '__main__':

From 88671e4ba10072efc2099f24fdbdc4cf937479d8 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 26 Dec 2021 10:55:21 +0900
Subject: [PATCH 0565/1080] add doc policy (#609)

---
 docs/how_to_contribute_main.rst | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/docs/how_to_contribute_main.rst b/docs/how_to_contribute_main.rst
index a4ac3c819b..703143f6e2 100644
--- a/docs/how_to_contribute_main.rst
+++ b/docs/how_to_contribute_main.rst
@@ -65,6 +65,10 @@ Please check other documents for details.
 
 You can build the doc locally based on `doc README`_.
 
+Note that the `reStructuredText`_ based doc should only focus on the mathematics and the algorithm of the example.
+
+Documentations related codes should be in the python script as the header comments of the script or docstrings of each function.
+
 
 .. _`submit a pull request`:
 

From ea7a52a835612961251770bae3990174a8a59615 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 26 Dec 2021 11:10:33 +0900
Subject: [PATCH 0566/1080] add requirements.txt under docs dir. (#611)

---
 docs/requirements.txt | 3 +++
 1 file changed, 3 insertions(+)
 create mode 100644 docs/requirements.txt

diff --git a/docs/requirements.txt b/docs/requirements.txt
new file mode 100644
index 0000000000..5006af8a8e
--- /dev/null
+++ b/docs/requirements.txt
@@ -0,0 +1,3 @@
+sphinx < 4.0 # For sphinx documentation
+sphinx_rtd_theme == 1.0.0
+IPython == 7.30.1 # For sphinx documentation

From d08a044f75808d0c8eca5f76697e4d11359bd346 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 28 Dec 2021 08:00:15 +0900
Subject: [PATCH 0567/1080] Update sphinx requirement from <4.0 to <5.0 (#612)

Updates the requirements on [sphinx](https://github.com/sphinx-doc/sphinx) to permit the latest version.
- [Release notes](https://github.com/sphinx-doc/sphinx/releases)
- [Changelog](https://github.com/sphinx-doc/sphinx/blob/4.x/CHANGES)
- [Commits](https://github.com/sphinx-doc/sphinx/compare/v0.1.61611...v4.3.2)

---
updated-dependencies:
- dependency-name: sphinx
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 docs/requirements.txt | 2 +-
 requirements.txt      | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/requirements.txt b/docs/requirements.txt
index 5006af8a8e..3d564ac8d9 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,3 +1,3 @@
-sphinx < 4.0 # For sphinx documentation
+sphinx < 5.0 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
 IPython == 7.30.1 # For sphinx documentation
diff --git a/requirements.txt b/requirements.txt
index 966b566000..8a2591dc4a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,6 +5,6 @@ matplotlib == 3.5.1
 cvxpy == 1.1.17
 pytest == 6.2.5 # For unit test
 pytest-xdist == 2.5.0 # For unit test
-sphinx < 4.0 # For sphinx documentation
+sphinx < 5.0 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
 IPython == 7.30.1 # For sphinx documentation

From 29bb0197f9d6c40eb3d1da791da00163ec0cff1c Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 28 Dec 2021 08:29:45 +0900
Subject: [PATCH 0568/1080] Update requirements.txt (#613)

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 8a2591dc4a..debc39ea17 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,6 +5,6 @@ matplotlib == 3.5.1
 cvxpy == 1.1.17
 pytest == 6.2.5 # For unit test
 pytest-xdist == 2.5.0 # For unit test
-sphinx < 5.0 # For sphinx documentation
+sphinx == 4.3.1 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
 IPython == 7.30.1 # For sphinx documentation

From a67ecab1eef7fc858823433727e58798b9f012f4 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 4 Jan 2022 06:51:32 +0900
Subject: [PATCH 0569/1080] Bump sphinx from 4.3.1 to 4.3.2 (#614)

Bumps [sphinx](https://github.com/sphinx-doc/sphinx) from 4.3.1 to 4.3.2.
- [Release notes](https://github.com/sphinx-doc/sphinx/releases)
- [Changelog](https://github.com/sphinx-doc/sphinx/blob/4.x/CHANGES)
- [Commits](https://github.com/sphinx-doc/sphinx/compare/v4.3.1...v4.3.2)

---
updated-dependencies:
- dependency-name: sphinx
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index debc39ea17..2636263e6c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,6 +5,6 @@ matplotlib == 3.5.1
 cvxpy == 1.1.17
 pytest == 6.2.5 # For unit test
 pytest-xdist == 2.5.0 # For unit test
-sphinx == 4.3.1 # For sphinx documentation
+sphinx == 4.3.2 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
 IPython == 7.30.1 # For sphinx documentation

From d24ae2ac1abe2d8bd70e1c8ffa6a6b05a960356e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 4 Jan 2022 06:53:44 +0900
Subject: [PATCH 0570/1080] Bump numpy from 1.21.5 to 1.22.0 (#616)

Bumps [numpy](https://github.com/numpy/numpy) from 1.21.5 to 1.22.0.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt)
- [Commits](https://github.com/numpy/numpy/compare/v1.21.5...v1.22.0)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 2636263e6c..3387ce5a61 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.21.5
+numpy == 1.22.0
 scipy == 1.7.3
 pandas == 1.3.5
 matplotlib == 3.5.1

From 79f7107cd1543ff354049a8da311e718b430ff2e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 4 Jan 2022 08:41:50 +0900
Subject: [PATCH 0571/1080] Bump cvxpy from 1.1.17 to 1.1.18 (#615)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.1.17 to 1.1.18.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Changelog](https://github.com/cvxpy/cvxpy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.1.17...v1.1.18)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 3387ce5a61..396a80e9a3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.22.0
 scipy == 1.7.3
 pandas == 1.3.5
 matplotlib == 3.5.1
-cvxpy == 1.1.17
+cvxpy == 1.1.18
 pytest == 6.2.5 # For unit test
 pytest-xdist == 2.5.0 # For unit test
 sphinx == 4.3.2 # For sphinx documentation

From 0dfa274be3eaddb270b2bcee197f7d34acbc1363 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 6 Jan 2022 20:26:24 +0900
Subject: [PATCH 0572/1080] add diff style check test (#617)

* add diff style check test

* add diff style check test

* add diff style check test

* add diff style check test

* add license

* add license
---
 .github/workflows/Linux_CI.yml  |   6 --
 .github/workflows/MacOS_CI.yml  |  13 +---
 docs/how_to_contribute_main.rst |   3 +
 requirements.txt                |   1 +
 tests/test_diff_codestyle.py    | 115 ++++++++++++++++++++++++++++++++
 5 files changed, 120 insertions(+), 18 deletions(-)
 create mode 100644 tests/test_diff_codestyle.py

diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index ef773ff71a..d8cad21172 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -32,10 +32,6 @@ jobs:
       run: pip install coverage
     - name: install mypy
       run: pip install mypy
-    - name: install pycodestyle
-      run: pip install pycodestyle
-    - name: install pytest-xdist
-      run: pip install pytest-xdist
     - name: mypy check
       run: |
         mypy -p AerialNavigation
@@ -47,8 +43,6 @@ jobs:
         mypy -p PathPlanning
         mypy -p PathTracking
         mypy -p SLAM
-    - name: do diff style check
-      run: bash rundiffstylecheck.sh
     - name: do all unit tests
       run: bash runtests.sh
 
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index f01d85e62b..1921799157 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -39,10 +39,6 @@ jobs:
         run: pip install coverage
       - name: install mypy
         run: pip install mypy
-      - name: install pycodestyle
-        run: pip install pycodestyle
-      - name: install pytest-xdist
-        run: pip install pytest-xdist
       - name: mypy check
         run: |
           mypy -p AerialNavigation
@@ -54,12 +50,5 @@ jobs:
           mypy -p PathPlanning
           mypy -p PathTracking
           mypy -p SLAM
-      - name: do diff style check
-        run: bash rundiffstylecheck.sh
-
       - name: do all unit tests
-        run: bash runtests.sh
-
-
-
-
+        run: bash runtests.sh
\ No newline at end of file
diff --git a/docs/how_to_contribute_main.rst b/docs/how_to_contribute_main.rst
index 703143f6e2..4b39089e7b 100644
--- a/docs/how_to_contribute_main.rst
+++ b/docs/how_to_contribute_main.rst
@@ -50,6 +50,8 @@ At the least, try to run the example code without animation in the unit test.
 
 If you want to run the test suites locally, you can use the `runtests.sh` script by just executing it.
 
+The `test_diff_codestyle.py`_ check code style for your PR's codes.
+
 
 .. _`how to write doc`:
 
@@ -153,6 +155,7 @@ Sponsors
 .. _`reStructuredText`: https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html
 .. _`doc modules dir`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/docs/modules
 .. _`doc README`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/docs/README.md
+.. _`test_diff_codestyle.py`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/tests/test_diff_codestyle.py
 .. _`JetBrains`: https://www.jetbrains.com/
 .. _`Sponsor @AtsushiSakai on GitHub Sponsors`: https://github.com/sponsors/AtsushiSakai
 .. _`Become a backer or sponsor on Patreon`: https://www.patreon.com/myenigma
diff --git a/requirements.txt b/requirements.txt
index 396a80e9a3..c0de8f226a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,6 +5,7 @@ matplotlib == 3.5.1
 cvxpy == 1.1.18
 pytest == 6.2.5 # For unit test
 pytest-xdist == 2.5.0 # For unit test
+flake8 == 4.0.1 # For unit test
 sphinx == 4.3.2 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
 IPython == 7.30.1 # For sphinx documentation
diff --git a/tests/test_diff_codestyle.py b/tests/test_diff_codestyle.py
new file mode 100644
index 0000000000..3dbe11a11a
--- /dev/null
+++ b/tests/test_diff_codestyle.py
@@ -0,0 +1,115 @@
+"""
+Diff based code style checker with flake8
+
+This code come from:
+https://github.com/scipy/scipy/blob/main/tools/lint_diff.py
+
+Scipy's licence: https://github.com/scipy/scipy/blob/main/LICENSE.txt
+Copyright (c) 2001-2002 Enthought, Inc. 2003-2022, SciPy Developers.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+
+1. Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above
+   copyright notice, this list of conditions and the following
+   disclaimer in the documentation and/or other materials provided
+   with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived
+   from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""
+import conftest
+import subprocess
+
+
+def rev_list(branch, num_commits):
+    """List commits in reverse chronological order.
+    Only the first `num_commits` are shown.
+    """
+    res = subprocess.run(
+        [
+            'git',
+            'rev-list',
+            '--max-count',
+            f'{num_commits}',
+            '--first-parent',
+            branch
+        ],
+        stdout=subprocess.PIPE,
+        encoding='utf-8',
+    )
+    res.check_returncode()
+    return res.stdout.rstrip('\n').split('\n')
+
+
+def find_branch_point(branch):
+    """Find when the current branch split off from the given branch.
+    It is based off of this Stackoverflow post:
+    https://stackoverflow.com/questions/1527234/finding-a-branch-point-with-git#4991675
+    """
+    branch_commits = rev_list('HEAD', 1000)
+    main_commits = set(rev_list(branch, 1000))
+    for branch_commit in branch_commits:
+        if branch_commit in main_commits:
+            return branch_commit
+
+    # If a branch split off over 1000 commits ago we will fail to find
+    # the ancestor.
+    raise RuntimeError(
+        'Failed to find a common ancestor in the last 1000 commits')
+
+
+def find_diff(sha):
+    """Find the diff since the given sha."""
+    files = ['*.py']
+    res = subprocess.run(
+        ['git', 'diff', '--unified=0', sha, '--'] + files,
+        stdout=subprocess.PIPE,
+        encoding='utf-8'
+    )
+    res.check_returncode()
+    return res.stdout
+
+
+def run_flake8(diff):
+    """Run flake8 on the given diff."""
+    res = subprocess.run(
+        ['flake8', '--diff'],
+        input=diff,
+        stdout=subprocess.PIPE,
+        encoding='utf-8',
+    )
+    return res.returncode, res.stdout
+
+
+def test():
+    branch_commit = find_branch_point("origin/master")
+    diff = find_diff(branch_commit)
+    rc, errors = run_flake8(diff)
+    if errors:
+        print(errors)
+    else:
+        print("No lint errors found.")
+    assert rc == 0
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)

From c05a4fdada59fd97332417c4b99515118bfef45c Mon Sep 17 00:00:00 2001
From: Trung Kien <letrungkien.k53.hut@gmail.com>
Date: Sat, 8 Jan 2022 06:44:42 +0700
Subject: [PATCH 0573/1080] Fix ModuleNotFoundError when executing test in the
 tests folder and little improve MPC controller (#619)

* Fix ModuleNotFoundError when executing test in the tests folder

Signed-off-by: Trung Kien <letrungkien.k53.hut@gmail.com>

* Improve model_predictive_speed_and_steer_control

- Fix typo
- Using @ for matrix multiplication instead of * with have been deprecated in CVXPY1.1
- Fix missing conftest module in test file

Signed-off-by: Trung Kien <letrungkien.k53.hut@gmail.com>
---
 .../Eta3SplineTrajectory/eta3_spline_trajectory.py    |  3 ++-
 .../lqr_speed_steer_control.py                        |  4 +++-
 PathTracking/lqr_steer_control/lqr_steer_control.py   |  5 ++++-
 .../model_predictive_speed_and_steer_control.py       | 11 +++++++----
 PathTracking/stanley_controller/stanley_controller.py |  5 ++++-
 .../test_model_predictive_speed_and_steer_control.py  |  1 +
 6 files changed, 21 insertions(+), 8 deletions(-)

diff --git a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
index 01a687679f..bd211046c7 100644
--- a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
+++ b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
@@ -17,7 +17,8 @@
 from matplotlib.collections import LineCollection
 import sys
 import os
-sys.path.append(os.path.relpath("../Eta3SplinePath"))
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../Eta3SplinePath")
 
 try:
     from eta3_spline_path import Eta3Path, Eta3PathSegment
diff --git a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
index bd2e54bf91..db5a755008 100644
--- a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
+++ b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
@@ -7,12 +7,14 @@
 """
 import math
 import sys
+import os
 
 import matplotlib.pyplot as plt
 import numpy as np
 import scipy.linalg as la
 
-sys.path.append("../../PathPlanning/CubicSpline/")
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../../PathPlanning/CubicSpline/")
 
 try:
     import cubic_spline_planner
diff --git a/PathTracking/lqr_steer_control/lqr_steer_control.py b/PathTracking/lqr_steer_control/lqr_steer_control.py
index 6f01e5a840..5596ea0fdf 100644
--- a/PathTracking/lqr_steer_control/lqr_steer_control.py
+++ b/PathTracking/lqr_steer_control/lqr_steer_control.py
@@ -10,7 +10,10 @@
 import math
 import numpy as np
 import sys
-sys.path.append("../../PathPlanning/CubicSpline/")
+import os
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../../PathPlanning/CubicSpline/")
 
 try:
     import cubic_spline_planner
diff --git a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
index 26e8e74f8b..ac8b883bd9 100644
--- a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
+++ b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
@@ -10,7 +10,10 @@
 import math
 import numpy as np
 import sys
-sys.path.append("../../PathPlanning/CubicSpline/")
+import os
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../../PathPlanning/CubicSpline/")
 
 try:
     import cubic_spline_planner
@@ -175,9 +178,9 @@ def update_state(state, a, delta):
     state.yaw = state.yaw + state.v / WB * math.tan(delta) * DT
     state.v = state.v + a * DT
 
-    if state. v > MAX_SPEED:
+    if state.v > MAX_SPEED:
         state.v = MAX_SPEED
-    elif state. v < MIN_SPEED:
+    elif state.v < MIN_SPEED:
         state.v = MIN_SPEED
 
     return state
@@ -272,7 +275,7 @@ def linear_mpc_control(xref, xbar, x0, dref):
 
         A, B, C = get_linear_model_matrix(
             xbar[2, t], xbar[3, t], dref[0, t])
-        constraints += [x[:, t + 1] == A * x[:, t] + B * u[:, t] + C]
+        constraints += [x[:, t + 1] == A @ x[:, t] + B @ u[:, t] + C]
 
         if t < (T - 1):
             cost += cvxpy.quad_form(u[:, t + 1] - u[:, t], Rd)
diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py
index 2af3989fcc..3a67d6268e 100644
--- a/PathTracking/stanley_controller/stanley_controller.py
+++ b/PathTracking/stanley_controller/stanley_controller.py
@@ -12,7 +12,10 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import sys
-sys.path.append("../../PathPlanning/CubicSpline/")
+import os
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
+                "/../../PathPlanning/CubicSpline/")
 
 try:
     import cubic_spline_planner
diff --git a/tests/test_model_predictive_speed_and_steer_control.py b/tests/test_model_predictive_speed_and_steer_control.py
index 9a50e48044..904cd2920e 100644
--- a/tests/test_model_predictive_speed_and_steer_control.py
+++ b/tests/test_model_predictive_speed_and_steer_control.py
@@ -1,4 +1,5 @@
 import sys
+import conftest
 
 if 'cvxpy' in sys.modules:  # pragma: no cover
 

From a13ef29dc4a28e48e31dd9d6c65cee40ac4de960 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 8 Jan 2022 17:06:29 +0900
Subject: [PATCH 0574/1080] enable MPC test (#620)

* enable_mpc_test

* enable_mpc_test

* enable_mpc_test

* enable_mpc_test

* enable_mpc_test

* enable_mpc_test

* enable_mpc_test

* enable_mpc_test
---
 .../rocket_powered_landing.py                 | 51 ++++++++++---------
 .../probabilistic_road_map.py                 | 16 +++---
 ...odel_predictive_speed_and_steer_control.py | 19 +++----
 tests/test_probabilistic_road_map.py          |  2 +-
 tests/test_rocket_powered_landing.py          | 17 +++++--
 5 files changed, 58 insertions(+), 47 deletions(-)

diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
index eb9d7ba4a7..37dca15b98 100644
--- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
+++ b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
@@ -43,7 +43,7 @@ class Rocket_Model_6DoF:
     A 6 degree of freedom rocket landing problem.
     """
 
-    def __init__(self):
+    def __init__(self, rng):
         """
         A large r_scale for a small scale problem will
         ead to numerical problems as parameters become excessively small
@@ -92,7 +92,7 @@ def __init__(self):
         # Vector from thrust point to CoM
         self.r_T_B = np.array([-1e-2, 0., 0.])
 
-        self.set_random_initial_state()
+        self.set_random_initial_state(rng)
 
         self.x_init = np.concatenate(
             ((self.m_wet,), self.r_I_init, self.v_I_init, self.q_B_I_init, self.w_B_init))
@@ -102,29 +102,32 @@ def __init__(self):
         self.r_scale = np.linalg.norm(self.r_I_init)
         self.m_scale = self.m_wet
 
-    def set_random_initial_state(self):
+    def set_random_initial_state(self, rng):
+        if rng is None:
+            rng = np.random.default_rng()
+
         self.r_I_init = np.array((0., 0., 0.))
-        self.r_I_init[0] = np.random.uniform(3, 4)
-        self.r_I_init[1:3] = np.random.uniform(-2, 2, size=2)
+        self.r_I_init[0] = rng.uniform(3, 4)
+        self.r_I_init[1:3] = rng.uniform(-2, 2, size=2)
 
         self.v_I_init = np.array((0., 0., 0.))
-        self.v_I_init[0] = np.random.uniform(-1, -0.5)
-        self.v_I_init[1:3] = np.random.uniform(
-            -0.5, -0.2, size=2) * self.r_I_init[1:3]
+        self.v_I_init[0] = rng.uniform(-1, -0.5)
+        self.v_I_init[1:3] = rng.uniform(-0.5, -0.2,
+                                         size=2) * self.r_I_init[1:3]
 
         self.q_B_I_init = self.euler_to_quat((0,
-                                              np.random.uniform(-30, 30),
-                                              np.random.uniform(-30, 30)))
+                                              rng.uniform(-30, 30),
+                                              rng.uniform(-30, 30)))
         self.w_B_init = np.deg2rad((0,
-                                    np.random.uniform(-20, 20),
-                                    np.random.uniform(-20, 20)))
+                                    rng.uniform(-20, 20),
+                                    rng.uniform(-20, 20)))
 
     def f_func(self, x, u):
         m, rx, ry, rz, vx, vy, vz, q0, q1, q2, q3, wx, wy, wz = x[0], x[1], x[
             2], x[3], x[4], x[5], x[6], x[7], x[8], x[9], x[10], x[11], x[12], x[13]
         ux, uy, uz = u[0], u[1], u[2]
 
-        return np.matrix([
+        return np.array([
             [-0.01 * np.sqrt(ux**2 + uy**2 + uz**2)],
             [vx],
             [vy],
@@ -149,7 +152,7 @@ def A_func(self, x, u):
             2], x[3], x[4], x[5], x[6], x[7], x[8], x[9], x[10], x[11], x[12], x[13]
         ux, uy, uz = u[0], u[1], u[2]
 
-        return np.matrix([
+        return np.array([
             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
             [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
             [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
@@ -177,7 +180,7 @@ def B_func(self, x, u):
             2], x[3], x[4], x[5], x[6], x[7], x[8], x[9], x[10], x[11], x[12], x[13]
         ux, uy, uz = u[0], u[1], u[2]
 
-        return np.matrix([
+        return np.array([
             [-0.01 * ux / np.sqrt(ux**2 + uy**2 + uz**2),
              -0.01 * uy / np.sqrt(ux ** 2 + uy**2 + uz**2),
              -0.01 * uz / np.sqrt(ux**2 + uy**2 + uz**2)],
@@ -219,14 +222,14 @@ def euler_to_quat(self, a):
         return q
 
     def skew(self, v):
-        return np.matrix([
+        return np.array([
             [0, -v[2], v[1]],
             [v[2], 0, -v[0]],
             [-v[1], v[0], 0]
         ])
 
     def dir_cosine(self, q):
-        return np.matrix([
+        return np.array([
             [1 - 2 * (q[2] ** 2 + q[3] ** 2), 2 * (q[1] * q[2]
                                                    + q[0] * q[3]), 2 * (q[1] * q[3] - q[0] * q[2])],
             [2 * (q[1] * q[2] - q[0] * q[3]), 1 - 2
@@ -236,7 +239,7 @@ def dir_cosine(self, q):
         ])
 
     def omega(self, w):
-        return np.matrix([
+        return np.array([
             [0, -w[0], -w[1], -w[2]],
             [w[0], 0, w[2], -w[1]],
             [w[1], -w[2], 0, w[0]],
@@ -304,7 +307,7 @@ def get_constraints(self, X_v, U_v, X_last_p, U_last_p):
         ]
 
         # linearized lower thrust constraint
-        rhs = [U_last_p[:, k] / cvxpy.norm(U_last_p[:, k]) * U_v[:, k]
+        rhs = [U_last_p[:, k] / cvxpy.norm(U_last_p[:, k]) @ U_v[:, k]
                for k in range(X_v.shape[1])]
         constraints += [
             self.T_min <= cvxpy.vstack(rhs)
@@ -460,11 +463,11 @@ def __init__(self, m, K):
         # x_t+1 = A_*x_t+B_*U_t+C_*U_T+1*S_*sigma+zbar+nu
         constraints += [
             self.var['X'][:, k + 1] ==
-            cvxpy.reshape(self.par['A_bar'][:, k], (m.n_x, m.n_x)) *
+            cvxpy.reshape(self.par['A_bar'][:, k], (m.n_x, m.n_x)) @
             self.var['X'][:, k] +
-            cvxpy.reshape(self.par['B_bar'][:, k], (m.n_x, m.n_u)) *
+            cvxpy.reshape(self.par['B_bar'][:, k], (m.n_x, m.n_u)) @
             self.var['U'][:, k] +
-            cvxpy.reshape(self.par['C_bar'][:, k], (m.n_x, m.n_u)) *
+            cvxpy.reshape(self.par['C_bar'][:, k], (m.n_x, m.n_u)) @
             self.var['U'][:, k + 1] +
             self.par['S_bar'][:, k] * self.var['sigma'] +
             self.par['z_bar'][:, k] +
@@ -606,9 +609,9 @@ def plot_animation(X, U):  # pragma: no cover
         plt.pause(0.5)
 
 
-def main():
+def main(rng=None):
     print("start!!")
-    m = Rocket_Model_6DoF()
+    m = Rocket_Model_6DoF(rng)
 
     # state and input list
     X = np.empty(shape=[m.n_x, K])
diff --git a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
index 7eac40a886..294c389023 100644
--- a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
+++ b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
@@ -40,14 +40,14 @@ def prm_planning(start_x, start_y, goal_x, goal_y,
     """
     Run probabilistic road map planning
 
-    :param start_x:
-    :param start_y:
-    :param goal_x:
-    :param goal_y:
-    :param obstacle_x_list:
-    :param obstacle_y_list:
-    :param robot_radius:
-    :param rng:
+    :param start_x: start x position
+    :param start_y: start y position
+    :param goal_x: goal x position
+    :param goal_y: goal y position
+    :param obstacle_x_list: obstacle x positions
+    :param obstacle_y_list: obstacle y positions
+    :param robot_radius: robot radius
+    :param rng: (Optional) Random generator
     :return:
     """
     obstacle_kd_tree = KDTree(np.vstack((obstacle_x_list, obstacle_y_list)).T)
diff --git a/tests/test_model_predictive_speed_and_steer_control.py b/tests/test_model_predictive_speed_and_steer_control.py
index 904cd2920e..0ed520dcbe 100644
--- a/tests/test_model_predictive_speed_and_steer_control.py
+++ b/tests/test_model_predictive_speed_and_steer_control.py
@@ -1,18 +1,19 @@
 import sys
 import conftest
 
-if 'cvxpy' in sys.modules:  # pragma: no cover
+from PathTracking.model_predictive_speed_and_steer_control \
+    import model_predictive_speed_and_steer_control as m
 
-    from PathTracking.model_predictive_speed_and_steer_control \
-        import model_predictive_speed_and_steer_control as m
 
-    def test_1():
-        m.show_animation = False
-        m.main()
+def test_1():
+    m.show_animation = False
+    m.main()
+
+
+def test_2():
+    m.show_animation = False
+    m.main2()
 
-    def test_2():
-        m.show_animation = False
-        m.main2()
 
 if __name__ == '__main__':
     conftest.run_this_test(__file__)
diff --git a/tests/test_probabilistic_road_map.py b/tests/test_probabilistic_road_map.py
index a830cc5e0c..6c5eb540b1 100644
--- a/tests/test_probabilistic_road_map.py
+++ b/tests/test_probabilistic_road_map.py
@@ -5,7 +5,7 @@
 
 def test1():
     probabilistic_road_map.show_animation = False
-    probabilistic_road_map.main(rng=np.random.default_rng(1234))
+    probabilistic_road_map.main(rng=np.random.default_rng(1233))
 
 
 if __name__ == '__main__':
diff --git a/tests/test_rocket_powered_landing.py b/tests/test_rocket_powered_landing.py
index 7cf69567e1..52b812eeda 100644
--- a/tests/test_rocket_powered_landing.py
+++ b/tests/test_rocket_powered_landing.py
@@ -1,13 +1,20 @@
 import conftest  # Add root path to sys.path
 import sys
+import numpy as np
+from numpy.testing import suppress_warnings
 
-if 'cvxpy' in sys.modules:  # pragma: no cover
+from AerialNavigation.rocket_powered_landing import rocket_powered_landing as m
 
-    from AerialNavigation.rocket_powered_landing import rocket_powered_landing as m
 
-    def test1():
-        m.show_animation = False
-        m.main()
+def test1():
+    m.show_animation = False
+    with suppress_warnings() as sup:
+        sup.filter(UserWarning,
+                   "You are solving a parameterized problem that is not DPP"
+                   )
+        sup.filter(UserWarning,
+                   "Solution may be inaccurate")
+        m.main(rng=np.random.default_rng(1234))
 
 
 if __name__ == '__main__':

From 82d97cef940679d9739ab81ed3eedeec9d73ab1b Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 9 Jan 2022 00:01:29 +0900
Subject: [PATCH 0575/1080] add mypy unit test (#621)

* add mypy unit test

* add mypy unit test

* add mypy unit test
---
 .github/workflows/Linux_CI.yml        | 15 --------
 .github/workflows/MacOS_CI.yml        | 15 --------
 PathPlanning/DStarLite/d_star_lite.py | 14 ++++----
 requirements.txt                      |  1 +
 tests/test_mypy_type_check.py         | 50 +++++++++++++++++++++++++++
 5 files changed, 58 insertions(+), 37 deletions(-)
 create mode 100644 tests/test_mypy_type_check.py

diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index d8cad21172..d0cdd2996c 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -28,21 +28,6 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         python -m pip install -r requirements.txt
-    - name: install coverage
-      run: pip install coverage
-    - name: install mypy
-      run: pip install mypy
-    - name: mypy check
-      run: |
-        mypy -p AerialNavigation
-        mypy -p ArmNavigation
-        mypy -p Bipedal
-        mypy -p Control
-        mypy -p Localization
-        mypy -p Mapping
-        mypy -p PathPlanning
-        mypy -p PathTracking
-        mypy -p SLAM
     - name: do all unit tests
       run: bash runtests.sh
 
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index 1921799157..7fc1854386 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -35,20 +35,5 @@ jobs:
           python -m pip install --upgrade pip
           pip install numpy # cvxpy install workaround
           pip install -r requirements.txt
-      - name: install coverage
-        run: pip install coverage
-      - name: install mypy
-        run: pip install mypy
-      - name: mypy check
-        run: |
-          mypy -p AerialNavigation
-          mypy -p ArmNavigation
-          mypy -p Bipedal
-          mypy -p Control
-          mypy -p Localization
-          mypy -p Mapping
-          mypy -p PathPlanning
-          mypy -p PathTracking
-          mypy -p SLAM
       - name: do all unit tests
         run: bash runtests.sh
\ No newline at end of file
diff --git a/PathPlanning/DStarLite/d_star_lite.py b/PathPlanning/DStarLite/d_star_lite.py
index 603c053dab..c250e38f91 100644
--- a/PathPlanning/DStarLite/d_star_lite.py
+++ b/PathPlanning/DStarLite/d_star_lite.py
@@ -64,15 +64,15 @@ def __init__(self, ox: list, oy: list):
                           for x, y in zip(ox, oy)]
         self.start = Node(0, 0)
         self.goal = Node(0, 0)
-        self.U = list()
+        self.U = list()  # type: ignore
         self.km = 0.0
         self.kold = 0.0
-        self.rhs = list()
-        self.g = list()
-        self.detected_obstacles = list()
+        self.rhs = list()  # type: ignore
+        self.g = list()  # type: ignore
+        self.detected_obstacles = list()  # type: ignore
         if show_animation:
-            self.detected_obstacles_for_plotting_x = list()
-            self.detected_obstacles_for_plotting_y = list()
+            self.detected_obstacles_for_plotting_x = list()  # type: ignore
+            self.detected_obstacles_for_plotting_y = list()  # type: ignore
 
     def create_grid(self, val: float):
         grid = list()
@@ -248,7 +248,7 @@ def compare_paths(self, path1: list, path2: list):
                 return False
         return True
 
-    def display_path(self, path: list, colour: str, alpha: int = 1):
+    def display_path(self, path: list, colour: str, alpha: float = 1.0):
         px = [(node.x + self.x_min_world) for node in path]
         py = [(node.y + self.y_min_world) for node in path]
         drawing = plt.plot(px, py, colour, alpha=alpha)
diff --git a/requirements.txt b/requirements.txt
index c0de8f226a..8b6a5ecbfe 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,6 +5,7 @@ matplotlib == 3.5.1
 cvxpy == 1.1.18
 pytest == 6.2.5 # For unit test
 pytest-xdist == 2.5.0 # For unit test
+mypy == 0.931 # For unit test
 flake8 == 4.0.1 # For unit test
 sphinx == 4.3.2 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
diff --git a/tests/test_mypy_type_check.py b/tests/test_mypy_type_check.py
new file mode 100644
index 0000000000..07afb40afd
--- /dev/null
+++ b/tests/test_mypy_type_check.py
@@ -0,0 +1,50 @@
+import os
+import subprocess
+
+import conftest
+
+SUBPACKAGE_LIST = [
+    "AerialNavigation",
+    "ArmNavigation",
+    "Bipedal",
+    "Control",
+    "Localization",
+    "Mapping",
+    "PathPlanning",
+    "PathTracking",
+    "SLAM",
+]
+
+
+def run_mypy(dir_name, project_path, config_path):
+    res = subprocess.run(
+        ['mypy',
+         '--config-file',
+         config_path,
+         '-p',
+         dir_name],
+        cwd=project_path,
+        stdout=subprocess.PIPE,
+        encoding='utf-8')
+    return res.returncode, res.stdout
+
+
+def test():
+    project_dir_path = os.path.dirname(
+        os.path.dirname(os.path.abspath(__file__)))
+    print(f"{project_dir_path=}")
+
+    config_path = os.path.join(project_dir_path, "mypy.ini")
+    print(f"{config_path=}")
+
+    for sub_package_name in SUBPACKAGE_LIST:
+        rc, errors = run_mypy(sub_package_name, project_dir_path, config_path)
+        if errors:
+            print(errors)
+        else:
+            print("No lint errors found.")
+        assert rc == 0
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)

From e24e2b781d270c37a71ae49ff62e3a25b8d69af6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 11 Jan 2022 07:20:58 +0900
Subject: [PATCH 0576/1080] Bump ipython from 7.30.1 to 7.31.0 (#624)

Bumps [ipython](https://github.com/ipython/ipython) from 7.30.1 to 7.31.0.
- [Release notes](https://github.com/ipython/ipython/releases)
- [Commits](https://github.com/ipython/ipython/compare/7.30.1...7.31.0)

---
updated-dependencies:
- dependency-name: ipython
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 docs/requirements.txt | 2 +-
 requirements.txt      | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/requirements.txt b/docs/requirements.txt
index 3d564ac8d9..9d921cb0fc 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,3 +1,3 @@
 sphinx < 5.0 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
-IPython == 7.30.1 # For sphinx documentation
+IPython == 7.31.0 # For sphinx documentation
diff --git a/requirements.txt b/requirements.txt
index 8b6a5ecbfe..87d71b96a7 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,4 +9,4 @@ mypy == 0.931 # For unit test
 flake8 == 4.0.1 # For unit test
 sphinx == 4.3.2 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
-IPython == 7.30.1 # For sphinx documentation
+IPython == 7.31.0 # For sphinx documentation

From 727354c5397f3af857fd33ad6f333724a6892e5d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 18 Jan 2022 12:48:04 +0900
Subject: [PATCH 0577/1080] Bump numpy from 1.22.0 to 1.22.1 (#626)

Bumps [numpy](https://github.com/numpy/numpy) from 1.22.0 to 1.22.1.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt)
- [Commits](https://github.com/numpy/numpy/compare/v1.22.0...v1.22.1)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 87d71b96a7..e44291157d 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.22.0
+numpy == 1.22.1
 scipy == 1.7.3
 pandas == 1.3.5
 matplotlib == 3.5.1

From cc30a6bbe928c1c4043a157cfd54da953a7ec6ac Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 18 Jan 2022 19:34:13 +0900
Subject: [PATCH 0578/1080] Bump ipython from 7.31.0 to 8.0.0 (#627)

Bumps [ipython](https://github.com/ipython/ipython) from 7.31.0 to 8.0.0.
- [Release notes](https://github.com/ipython/ipython/releases)
- [Commits](https://github.com/ipython/ipython/compare/7.31.0...8.0.0)

---
updated-dependencies:
- dependency-name: ipython
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 docs/requirements.txt | 2 +-
 requirements.txt      | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/requirements.txt b/docs/requirements.txt
index 9d921cb0fc..f38287f015 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,3 +1,3 @@
 sphinx < 5.0 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
-IPython == 7.31.0 # For sphinx documentation
+IPython == 8.0.0 # For sphinx documentation
diff --git a/requirements.txt b/requirements.txt
index e44291157d..dbd81424ac 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,4 +9,4 @@ mypy == 0.931 # For unit test
 flake8 == 4.0.1 # For unit test
 sphinx == 4.3.2 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
-IPython == 7.31.0 # For sphinx documentation
+IPython == 8.0.0 # For sphinx documentation

From 30dae5691f5639ca5ab0ad953d53e68d2d733f32 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 18 Jan 2022 19:34:27 +0900
Subject: [PATCH 0579/1080] Bump sphinx from 4.3.2 to 4.4.0 (#625)

Bumps [sphinx](https://github.com/sphinx-doc/sphinx) from 4.3.2 to 4.4.0.
- [Release notes](https://github.com/sphinx-doc/sphinx/releases)
- [Changelog](https://github.com/sphinx-doc/sphinx/blob/4.x/CHANGES)
- [Commits](https://github.com/sphinx-doc/sphinx/compare/v4.3.2...v4.4.0)

---
updated-dependencies:
- dependency-name: sphinx
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index dbd81424ac..306ac1e1c1 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -7,6 +7,6 @@ pytest == 6.2.5 # For unit test
 pytest-xdist == 2.5.0 # For unit test
 mypy == 0.931 # For unit test
 flake8 == 4.0.1 # For unit test
-sphinx == 4.3.2 # For sphinx documentation
+sphinx == 4.4.0 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
 IPython == 8.0.0 # For sphinx documentation

From 88c7447d292685ec849a09b717db510807e7f19e Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 18 Jan 2022 21:28:07 +0900
Subject: [PATCH 0580/1080] Update requirements.txt

---
 docs/requirements.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/requirements.txt b/docs/requirements.txt
index f38287f015..b093728994 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,3 +1,3 @@
-sphinx < 5.0 # For sphinx documentation
+sphinx = 4.3.2 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
-IPython == 8.0.0 # For sphinx documentation
+IPython == 7.30.1 # For sphinx documentation

From d2645da2c7ff6ecbb4b62b7c6612712380ba91e1 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 18 Jan 2022 21:31:10 +0900
Subject: [PATCH 0581/1080] Update requirements.txt

---
 docs/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/requirements.txt b/docs/requirements.txt
index b093728994..6614e6b8db 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,3 +1,3 @@
-sphinx = 4.3.2 # For sphinx documentation
+sphinx == 4.3.2 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
 IPython == 7.30.1 # For sphinx documentation

From 87a535ae752a50674e26db0aadd5005bbcef7f45 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 18 Jan 2022 21:39:47 +0900
Subject: [PATCH 0582/1080] Rename requirements.txt to doc_requirements.txt

---
 docs/{requirements.txt => doc_requirements.txt} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename docs/{requirements.txt => doc_requirements.txt} (100%)

diff --git a/docs/requirements.txt b/docs/doc_requirements.txt
similarity index 100%
rename from docs/requirements.txt
rename to docs/doc_requirements.txt

From 7c3dbb01e0d1fd64f6eb9c37799161930b9d664d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sat, 22 Jan 2022 07:36:51 +0900
Subject: [PATCH 0583/1080] Bump ipython from 7.30.1 to 7.31.1 in /docs (#628)

Bumps [ipython](https://github.com/ipython/ipython) from 7.30.1 to 7.31.1.
- [Release notes](https://github.com/ipython/ipython/releases)
- [Commits](https://github.com/ipython/ipython/compare/7.30.1...7.31.1)

---
updated-dependencies:
- dependency-name: ipython
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 docs/doc_requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/doc_requirements.txt b/docs/doc_requirements.txt
index 6614e6b8db..65670d346e 100644
--- a/docs/doc_requirements.txt
+++ b/docs/doc_requirements.txt
@@ -1,3 +1,3 @@
 sphinx == 4.3.2 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
-IPython == 7.30.1 # For sphinx documentation
+IPython == 7.31.1 # For sphinx documentation

From 1280ace8e03cf68b28cc551af15cd4f0ed518459 Mon Sep 17 00:00:00 2001
From: Muhammad-Yazdian <93126501+Muhammad-Yazdian@users.noreply.github.com>
Date: Sat, 22 Jan 2022 00:12:59 +0100
Subject: [PATCH 0584/1080] Fix format to show contents properly (#608)

* Fix format to show contents properly

Lots of contents were not displayed correctly. I empty lines and :math: labels and fixed it.

* Rewrote one subscript as _{rho}

This is just a try to see if subscript renders properly.

* Added a gif animation for move to pose robot

* Added gif; Removed png

* Added the into

* Changed Param. Def. style to bold text

* Added algorithm logic and math

* Update docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst

Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>

* Update docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst

Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>

* Changed Eqs 1 and 2 color to black

* Added cross-reference labels for Equations 1 and 2

Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>
---
 .../move_to_a_pose_control.rst                | 108 ++++++++++++++----
 1 file changed, 84 insertions(+), 24 deletions(-)

diff --git a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst
index 81c64f5485..c8d844d053 100644
--- a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst
+++ b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst
@@ -1,11 +1,23 @@
-Move to a pose control
+Position Control of non-Holonomic Systems
+-----------------------------------------
+
+This section explains the logic of a position controller for systems with constraint (non-Holonomic system).
+
+The position control of a 1-DOF (Degree of Freedom) system is quite straightforward. We only need to compute a position error and multiply it with a proportional gain to create a command. The actuator of the system takes this command and drive the system to the target position. This controller can be easily extended to higher dimensions (e.g., using Kp_x and Kp_y gains for a 2D position control). In these systems, the number of control commands is equal to the number of degrees of freedom (Holonomic system). 
+
+To describe the configuration of a car on a 2D plane, we need three DOFs (i.e., x, y, and theta). But to drive a car we only need two control commands (theta_engine and theta_steering_wheel). This difference is because of a constraint between the x and y DOFs. The relationship between the delta_x and delta_y is governed by the theta_steering_wheel.
+
+Note that a car is normally a non-Holonomic system but if the road is slippery, the car turns into a Holonomic system and thus it needs three independent commands to be controlled.
+
+Move to a Pose Control
 ----------------------
 
-This is a simulation of moving to a pose control.
+In this section, we present the logic of PathFinderController that drives a car from a start pose (x, y, theta) to a goal pose. A simulation of moving to a pose control is presented below.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif
 
 
+
 PathFinderController class
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -13,45 +25,87 @@ Constructor
 ~~~~~~~~~~~
 
 .. code-block:: ipython3
+
    PathFinderController(Kp_rho, Kp_alpha, Kp_beta)
 
 Constructs an instantiate of the PathFinderController for navigating a 3-DOF wheeled robot on a 2D plane.
 
 Parameters:
-- Kp_rho: The linear velocity gain to translate the robot along a line towards the goal
-- Kp_alpha: The angular velocity gain to rotate the robot towards the goal
-- Kp_beta: The offset angular velocity gain accounting for smooth merging to the goal angle (i.e., it helps the robot heading to be parallel to the target angle.)
+
+- | **Kp_rho** : The linear velocity gain to translate the robot along a line towards the goal
+- | **Kp_alpha** : The angular velocity gain to rotate the robot towards the goal
+- | **Kp_beta** : The offset angular velocity gain accounting for smooth merging to the goal angle (i.e., it helps the robot heading to be parallel to the target angle.)
 
 
 Member function(s)
 ~~~~~~~~~~~~~~~~~~
 
 .. code-block:: ipython3
+
    calc_control_command(x_diff, y_diff, theta, theta_goal)
 
 Returns the control command for the linear and angular velocities as well as the distance to goal
 
 Parameters:
-- x_diff : The position of target with respect to current robot position in x direction
-- y_diff : The position of target with respect to current robot position in y direction
-- theta : The current heading angle of robot with respect to x axis
-- theta_goal : The target angle of robot with respect to x axis
+
+- | **x_diff** : The position of target with respect to current robot position in x direction
+- | **y_diff** : The position of target with respect to current robot position in y direction
+- | **theta** : The current heading angle of robot with respect to x axis
+- | **theta_goal** : The target angle of robot with respect to x axis
 
 Returns:
-- rho : The distance between the robot and the goal position
-- v : Command linear velocity
-- w : Command angular velocity
+
+- | **rho** : The distance between the robot and the goal position
+- | **v** : Command linear velocity
+- | **w** : Command angular velocity
+
+How does the Algorithm Work
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+The distance between the robot and the goal position, :math:`\rho`, is computed as
+
+.. math::
+ \rho = \sqrt{(x_{robot} - x_{target})^2 + (y_{robot} - y_{target})^2}.
+
+The distance :math:`\rho` is used to determine the robot speed. The idea is to slow down the robot as it gets closer to the target.
+
+.. math::
+ v = K_P{_\rho} \times \rho\qquad
+ :label: eq1
+
+Note that for your applications, you need to tune the speed gain, :math:`K_P{_\rho}` to a proper value.
+
+To turn the robot and align its heading, :math:`\theta`, toward the target position (not orientation),  :math:`\rho \vec{u}`, we need to compute the angle difference :math:`\alpha`. 
+
+.. math::
+ \alpha = (\arctan2(y_{diff}, x_{diff}) - \theta + \pi) mod (2\pi) - \pi
+
+The term :math:`mod(2\pi)` is used to map the angle to :math:`[-\pi, \pi)` range.
+
+Lastly to correct the orientation of the robot, we need to compute the orientation error, :math:`\beta`, of the robot.
+
+.. math::
+ \beta = (\theta_{goal} - \theta - \alpha + \pi) mod (2\pi) - \pi
+
+Note that to cancel out the effect of :math:`\alpha` when the robot is at the vicinity of the target, the term 
+
+:math:`-\alpha` is included.
+
+The final angular speed command is given by
+
+.. math::
+ \omega = K_P{_\alpha} \alpha - K_P{_\beta} \beta\qquad
+ :label: eq2
+ 
+The linear and angular speeds (Equations :eq:`eq1` and :eq:`eq2`) are the output of the algorithm.
 
 Move to a Pose Robot (Class)
 ----------------------------
 This program (move_to_pose_robot.py) provides a Robot class to define different robots with different specifications. 
 Using this class, you can simulate different robots simultaneously and compare the effect of your parameter settings.
 
-.. image:: https://user-images.githubusercontent.com/93126501/145834505-a8df8311-5445-413f-a96f-00460d47991c.png
-
-Note: A gif animation will be added soon.
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/move_to_pos_robot_class/animation.gif
 
-Note: The robot Class is based on PathFinderController class in 'the move_to_pose.py'.
+Note: The robot class is based on PathFinderController class in 'the move_to_pose.py'.
 
 Robot Class
 ~~~~~~~~~~~
@@ -60,36 +114,42 @@ Constructor
 ~~~~~~~~~~~
 
 .. code-block:: ipython3
+
     Robot(name, color, max_linear_speed, max_angular_speed, path_finder_controller)
 
 Constructs an instantiate of the 3-DOF wheeled Robot navigating on a 2D plane
 
 Parameters:
-- name : (string) The name of the robot
-- color : (string) The color of the robot
-- max_linear_speed : (float) The maximum linear speed that the robot can go
-- max_angular_speed : (float) The maximum angular speed that the robot can rotate about its vertical axis
-- path_finder_controller : (PathFinderController) A configurable controller to finds the path and calculates command linear and angular velocities.
+
+- | **name** : (string) The name of the robot
+- | **color** : (string) The color of the robot
+- | **max_linear_speed** : (float) The maximum linear speed that the robot can go
+- | **max_angular_speed** : (float) The maximum angular speed that the robot can rotate about its vertical axis
+- | **path_finder_controller** : (PathFinderController) A configurable controller to finds the path and calculates command linear and angular velocities.
 
 Member function(s)
 ~~~~~~~~~~~~~~~~~~
 
 .. code-block:: ipython3
+
     set_start_target_poses(pose_start, pose_target)
 
 Sets the start and target positions of the robot.
 
 Parameters:
-- pose_start : (Pose) Start postion of the robot (see the Pose class)
-- pose_target : (Pose) Target postion of the robot (see the Pose class)
+
+- | **pose_start** : (Pose) Start postion of the robot (see the Pose class)
+- | **pose_target** : (Pose) Target postion of the robot (see the Pose class)
 
 .. code-block:: ipython3
+
     move(dt)
 
 Move the robot for one time step increment
 
 Parameters:
-- dt: <float> time increment
+
+- | **dt** : <float> time increment
 
 See Also 
 --------

From 2878c4a84cdac66b0f0c471758809a385b83546c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sat, 22 Jan 2022 18:57:04 +0900
Subject: [PATCH 0585/1080] Bump ipython from 8.0.0 to 8.0.1 (#629)

Bumps [ipython](https://github.com/ipython/ipython) from 8.0.0 to 8.0.1.
- [Release notes](https://github.com/ipython/ipython/releases)
- [Commits](https://github.com/ipython/ipython/compare/8.0.0...8.0.1)

---
updated-dependencies:
- dependency-name: ipython
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 docs/doc_requirements.txt | 2 +-
 requirements.txt          | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/doc_requirements.txt b/docs/doc_requirements.txt
index 65670d346e..9319f7729f 100644
--- a/docs/doc_requirements.txt
+++ b/docs/doc_requirements.txt
@@ -1,3 +1,3 @@
 sphinx == 4.3.2 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
-IPython == 7.31.1 # For sphinx documentation
+IPython == 8.0.1 # For sphinx documentation
diff --git a/requirements.txt b/requirements.txt
index 306ac1e1c1..557aab4368 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,4 +9,4 @@ mypy == 0.931 # For unit test
 flake8 == 4.0.1 # For unit test
 sphinx == 4.4.0 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
-IPython == 8.0.0 # For sphinx documentation
+IPython == 8.0.1 # For sphinx documentation

From 058addd2ba90a16e2697333ba5be2f430944f417 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 22 Jan 2022 22:18:22 +0900
Subject: [PATCH 0586/1080] add requirements dir (#630)

* add requirements dir

* add requirements dir

* add requirements dir
---
 .circleci/config.yml                          |  2 +-
 .github/dependabot.yml                        |  2 +-
 .github/workflows/Linux_CI.yml                |  2 +-
 .github/workflows/MacOS_CI.yml                |  4 +-
 README.md                                     |  4 +-
 appveyor.yml                                  |  2 +-
 docs/doc_requirements.txt                     |  2 +-
 docs/getting_started_main.rst                 |  4 +-
 .../move_to_a_pose_control.rst                | 39 +++++++++----------
 .../environment.yml                           |  0
 .../requirements.txt                          |  3 --
 rundiffstylecheck.sh                          | 15 -------
 ...odel_predictive_speed_and_steer_control.py |  3 +-
 tests/test_rocket_powered_landing.py          |  1 -
 14 files changed, 30 insertions(+), 53 deletions(-)
 rename environment.yml => requirements/environment.yml (100%)
 rename requirements.txt => requirements/requirements.txt (65%)
 delete mode 100755 rundiffstylecheck.sh

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 4866240bd4..0a1962ad0b 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -14,7 +14,7 @@ jobs:
           command: |
             python -m venv venv
             . venv/bin/activate
-            pip install -r requirements.txt
+            pip install -r docs/doc_requirements.txt
             cd docs;make html
       - store_artifacts:
           path: docs/_build/html/
diff --git a/.github/dependabot.yml b/.github/dependabot.yml
index 9117f1216c..07f1bc80e0 100644
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@@ -1,7 +1,7 @@
 version: 2
 updates:
 - package-ecosystem: pip
-  directory: "/"
+  directory: "/requirements"
   schedule:
     interval: weekly
     time: "20:00"
diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index d0cdd2996c..c706a50ed6 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -27,7 +27,7 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        python -m pip install -r requirements.txt
+        python -m pip install -r requirements/requirements.txt
     - name: do all unit tests
       run: bash runtests.sh
 
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index 7fc1854386..4088c4900f 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -33,7 +33,7 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install numpy # cvxpy install workaround
-          pip install -r requirements.txt
+          #pip install numpy # cvxpy install workaround
+          pip install -r requirements/requirements.txt
       - name: do all unit tests
         run: bash runtests.sh
\ No newline at end of file
diff --git a/README.md b/README.md
index f297b6fc12..9aff69ab72 100644
--- a/README.md
+++ b/README.md
@@ -141,11 +141,11 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation
 
 using conda :
 
-> conda env create -f environment.yml
+> conda env create -f requirements/environment.yml
  
 using pip :
 
-> pip install -r requirements.txt
+> pip install -r requirements/requirements.txt
 
 
 3. Execute python script in each directory.
diff --git a/appveyor.yml b/appveyor.yml
index e9535e4e3c..c35e76ae4a 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -37,7 +37,7 @@ install:
   - SET PATH=%PYTHON%;%PYTHON%\Scripts;%PYTHON%\Library\bin;%PATH%
   - SET PATH=%PATH%;C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin
   - "python -m pip install --upgrade pip"
-  - "python -m pip install -r requirements.txt"
+  - "python -m pip install -r requirements/requirements.txt"
   - "python -m pip install pytest-xdist"
 
   # Check that we have the expected version and architecture for Python
diff --git a/docs/doc_requirements.txt b/docs/doc_requirements.txt
index 9319f7729f..65670d346e 100644
--- a/docs/doc_requirements.txt
+++ b/docs/doc_requirements.txt
@@ -1,3 +1,3 @@
 sphinx == 4.3.2 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
-IPython == 8.0.1 # For sphinx documentation
+IPython == 7.31.1 # For sphinx documentation
diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst
index d907ab8669..a7b9ff3607 100644
--- a/docs/getting_started_main.rst
+++ b/docs/getting_started_main.rst
@@ -67,13 +67,13 @@ using conda :
 
 .. code-block::
 
-    >$ conda env create -f environment.yml
+    >$ conda env create -f requirements/environment.yml
 
 using pip :
 
 .. code-block::
 
-    >$ pip install -r requirements.txt
+    >$ pip install -r requirements/requirements.txt
 
 
 3. Execute python script in each directory.
diff --git a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst
index c8d844d053..8bb40f2201 100644
--- a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst
+++ b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst
@@ -1,5 +1,12 @@
+Move to a Pose Control
+----------------------
+
+In this section, we present the logic of PathFinderController that drives a car from a start pose (x, y, theta) to a goal pose. A simulation of moving to a pose control is presented below.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif
+
 Position Control of non-Holonomic Systems
------------------------------------------
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 This section explains the logic of a position controller for systems with constraint (non-Holonomic system).
 
@@ -9,20 +16,11 @@ To describe the configuration of a car on a 2D plane, we need three DOFs (i.e.,
 
 Note that a car is normally a non-Holonomic system but if the road is slippery, the car turns into a Holonomic system and thus it needs three independent commands to be controlled.
 
-Move to a Pose Control
-----------------------
-
-In this section, we present the logic of PathFinderController that drives a car from a start pose (x, y, theta) to a goal pose. A simulation of moving to a pose control is presented below.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif
-
-
-
 PathFinderController class
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Constructor
-~~~~~~~~~~~
+^^^^^^^^^^^
 
 .. code-block:: ipython3
 
@@ -38,7 +36,7 @@ Parameters:
 
 
 Member function(s)
-~~~~~~~~~~~~~~~~~~
+^^^^^^^^^^^^^^^^^^
 
 .. code-block:: ipython3
 
@@ -99,7 +97,8 @@ The final angular speed command is given by
 The linear and angular speeds (Equations :eq:`eq1` and :eq:`eq2`) are the output of the algorithm.
 
 Move to a Pose Robot (Class)
-----------------------------
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
 This program (move_to_pose_robot.py) provides a Robot class to define different robots with different specifications. 
 Using this class, you can simulate different robots simultaneously and compare the effect of your parameter settings.
 
@@ -108,10 +107,10 @@ Using this class, you can simulate different robots simultaneously and compare t
 Note: The robot class is based on PathFinderController class in 'the move_to_pose.py'.
 
 Robot Class
-~~~~~~~~~~~
+^^^^^^^^^^^^
 
 Constructor
-~~~~~~~~~~~
+^^^^^^^^^^^^
 
 .. code-block:: ipython3
 
@@ -128,7 +127,7 @@ Parameters:
 - | **path_finder_controller** : (PathFinderController) A configurable controller to finds the path and calculates command linear and angular velocities.
 
 Member function(s)
-~~~~~~~~~~~~~~~~~~
+^^^^^^^^^^^^^^^^^^^
 
 .. code-block:: ipython3
 
@@ -151,12 +150,10 @@ Parameters:
 
 - | **dt** : <float> time increment
 
-See Also 
---------
-- PathFinderController class
 
 
-Ref:
-----
+References
+~~~~~~~~~~~~
+- PathFinderController class
 -  `P. I. Corke, "Robotics, Vision and Control" \| SpringerLink
    p102 <https://link.springer.com/book/10.1007/978-3-642-20144-8>`__
diff --git a/environment.yml b/requirements/environment.yml
similarity index 100%
rename from environment.yml
rename to requirements/environment.yml
diff --git a/requirements.txt b/requirements/requirements.txt
similarity index 65%
rename from requirements.txt
rename to requirements/requirements.txt
index 557aab4368..f052ea51fc 100644
--- a/requirements.txt
+++ b/requirements/requirements.txt
@@ -7,6 +7,3 @@ pytest == 6.2.5 # For unit test
 pytest-xdist == 2.5.0 # For unit test
 mypy == 0.931 # For unit test
 flake8 == 4.0.1 # For unit test
-sphinx == 4.4.0 # For sphinx documentation
-sphinx_rtd_theme == 1.0.0
-IPython == 8.0.1 # For sphinx documentation
diff --git a/rundiffstylecheck.sh b/rundiffstylecheck.sh
deleted file mode 100755
index 8a19842c6e..0000000000
--- a/rundiffstylecheck.sh
+++ /dev/null
@@ -1,15 +0,0 @@
-#!/bin/bash
-echo "$(basename $0) start!"
-VERSION=v0.1.6
-wget https://github.com/AtsushiSakai/DiffSentinel/archive/${VERSION}.zip
-unzip ${VERSION}.zip
-./DiffSentinel*/starter.sh HEAD origin/master
-check_result=$?
-rm -rf ${VERSION}.zip DiffSentinel*
-if [[ ${check_result} -ne 0 ]];
-then
-    echo "Error: Your changes contain pycodestyle errors."
-    exit 1
-fi
-echo "$(basename $0) done!"
-exit 0
diff --git a/tests/test_model_predictive_speed_and_steer_control.py b/tests/test_model_predictive_speed_and_steer_control.py
index 0ed520dcbe..9bc8ccf31c 100644
--- a/tests/test_model_predictive_speed_and_steer_control.py
+++ b/tests/test_model_predictive_speed_and_steer_control.py
@@ -1,5 +1,4 @@
-import sys
-import conftest
+import conftest  # Add root path to sys.path
 
 from PathTracking.model_predictive_speed_and_steer_control \
     import model_predictive_speed_and_steer_control as m
diff --git a/tests/test_rocket_powered_landing.py b/tests/test_rocket_powered_landing.py
index 52b812eeda..2f294c74cf 100644
--- a/tests/test_rocket_powered_landing.py
+++ b/tests/test_rocket_powered_landing.py
@@ -1,5 +1,4 @@
 import conftest  # Add root path to sys.path
-import sys
 import numpy as np
 from numpy.testing import suppress_warnings
 

From c066a9420e9373e85bc2214cbb44e3a2fd5cc593 Mon Sep 17 00:00:00 2001
From: Trung Kien <letrungkien.k53.hut@gmail.com>
Date: Sun, 23 Jan 2022 17:35:54 +0700
Subject: [PATCH 0587/1080] InvertedPendulumMPCControl: fixed deprecated waring
 about using * for matrix multiplication (#631)

---
 .../InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Control/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py b/Control/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py
index e7b3077740..3764f29558 100644
--- a/Control/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py
+++ b/Control/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py
@@ -77,7 +77,7 @@ def mpc_control(x0):
     for t in range(T):
         cost += cvxpy.quad_form(x[:, t + 1], Q)
         cost += cvxpy.quad_form(u[:, t], R)
-        constr += [x[:, t + 1] == A * x[:, t] + B * u[:, t]]
+        constr += [x[:, t + 1] == A @ x[:, t] + B @ u[:, t]]
 
     constr += [x[:, 0] == x0[:, 0]]
     prob = cvxpy.Problem(cvxpy.Minimize(cost), constr)

From 550baa1e4929aa98e69d4da97205809943cce213 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 23 Jan 2022 20:07:34 +0900
Subject: [PATCH 0588/1080] fix duplicated math lables (#633)

---
 .../move_to_a_pose_control.rst                |  6 ++--
 .../quintic_polynomials_planner.rst           | 34 +++++++++----------
 2 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst
index 8bb40f2201..77ec682a30 100644
--- a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst
+++ b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst
@@ -68,7 +68,7 @@ The distance :math:`\rho` is used to determine the robot speed. The idea is to s
 
 .. math::
  v = K_P{_\rho} \times \rho\qquad
- :label: eq1
+ :label: move_to_a_pose_eq1
 
 Note that for your applications, you need to tune the speed gain, :math:`K_P{_\rho}` to a proper value.
 
@@ -92,9 +92,9 @@ The final angular speed command is given by
 
 .. math::
  \omega = K_P{_\alpha} \alpha - K_P{_\beta} \beta\qquad
- :label: eq2
+ :label: move_to_a_pose_eq2
  
-The linear and angular speeds (Equations :eq:`eq1` and :eq:`eq2`) are the output of the algorithm.
+The linear and angular speeds (Equations :eq:`move_to_a_pose_eq1` and :eq:`move_to_a_pose_eq2`) are the output of the algorithm.
 
 Move to a Pose Robot (Class)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner.rst b/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner.rst
index 2bced07034..feec345bae 100644
--- a/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner.rst
+++ b/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner.rst
@@ -18,7 +18,7 @@ We assume a one-dimensional robot motion :math:`x(t)` at time :math:`t` is
 formulated as a quintic polynomials based on time as follows:
 
 .. math:: x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4+a_5t^5
-    :label: eq1
+    :label: quintic_eq1
 
 :math:`a_0, a_1. a_2, a_3, a_4, a_5` are parameters of the quintic polynomial.
 
@@ -31,44 +31,44 @@ End position, velocity, and acceleration are :math:`x_e, v_e, a_e` respectively.
 So, when time is 0.
 
 .. math:: x(0) = a_0 = x_s
-    :label: eq2
+    :label: quintic_eq2
 
-Then, differentiating the equation :eq:`eq1` with t,
+Then, differentiating the equation :eq:`quintic_eq1` with t,
 
 .. math:: x'(t) = a_1+2a_2t+3a_3t^2+4a_4t^3+5a_5t^4
-    :label: eq3
+    :label: quintic_eq3
 
 So, when time is 0,
 
 .. math:: x'(0) = a_1 = v_s
-    :label: eq4
+    :label: quintic_eq4
 
-Then, differentiating the equation :eq:`eq3` with t again,
+Then, differentiating the equation :eq:`quintic_eq3` with t again,
 
 .. math:: x''(t) = 2a_2+6a_3t+12a_4t^2
-    :label: eq5
+    :label: quintic_eq5
 
 So, when time is 0,
 
 .. math:: x''(0) = 2a_2 = a_s
-    :label: eq6
+    :label: quintic_eq6
 
-so, we can calculate :math:`a_0, a_1, a_2` with eq. :eq:`eq2`, :eq:`eq4`, :eq:`eq6` and boundary conditions.
+so, we can calculate :math:`a_0, a_1, a_2` with eq. :eq:`quintic_eq2`, :eq:`quintic_eq4`, :eq:`quintic_eq6` and boundary conditions.
 
-:math:`a_3, a_4, a_5` are still unknown in eq :eq:`eq1`.
+:math:`a_3, a_4, a_5` are still unknown in eq :eq:`quintic_eq1`.
 
-We assume that the end time for a maneuver is :math:`T`, we can get these equations from eq :eq:`eq1`, :eq:`eq3`, :eq:`eq5`:
+We assume that the end time for a maneuver is :math:`T`, we can get these equations from eq :eq:`quintic_eq1`, :eq:`quintic_eq3`, :eq:`quintic_eq5`:
 
 .. math:: x(T)=a_0+a_1T+a_2T^2+a_3T^3+a_4T^4+a_5T^5=x_e
-    :label: eq7
+    :label: quintic_eq7
 
 .. math:: x'(T)=a_1+2a_2T+3a_3T^2+4a_4T^3+5a_5T^4=v_e
-    :label: eq8
+    :label: quintic_eq8
 
 .. math:: x''(T)=2a_2+6a_3T+12a_4T^2+20a_5T^3=a_e
-    :label: eq9
+    :label: quintic_eq9
 
-From eq :eq:`eq7`, :eq:`eq8`, :eq:`eq9`, we can calculate :math:`a_3, a_4, a_5` to solve the linear equations: :math:`Ax=b`
+From eq :eq:`quintic_eq7`, :eq:`quintic_eq8`, :eq:`quintic_eq9`, we can calculate :math:`a_3, a_4, a_5` to solve the linear equations: :math:`Ax=b`
 
 .. math:: \begin{bmatrix} T^3 & T^4 & T^5 \\ 3T^2 & 4T^3 & 5T^4 \\ 6T & 12T^2 & 20T^3 \end{bmatrix}\begin{bmatrix} a_3\\ a_4\\ a_5\end{bmatrix}=\begin{bmatrix} x_e-x_s-v_sT-0.5a_sT^2\\ v_e-v_s-a_sT\\ a_e-a_s\end{bmatrix}
 
@@ -80,10 +80,10 @@ Quintic polynomials for two dimensional robot motion (x-y)
 If you use two quintic polynomials along x axis and y axis, you can plan for two dimensional robot motion in x-y plane.
 
 .. math:: x(t) = a_0+a_1t+a_2t^2+a_3t^3+a_4t^4+a_5t^5
-    :label: eq10
+    :label: quintic_eq10
 
 .. math:: y(t) = b_0+b_1t+b_2t^2+b_3t^3+b_4t^4+b_5t^5
-    :label: eq11
+    :label: quintic_eq11
 
 It is assumed that terminal states (start and end) are known as boundary conditions.
 

From 702551748cdfa64c2baa3debc6c84e05181e3416 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 25 Jan 2022 07:26:33 +0900
Subject: [PATCH 0589/1080] Bump pandas from 1.3.5 to 1.4.0 in /requirements
 (#634)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.3.5 to 1.4.0.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/main/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.3.5...v1.4.0)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index f052ea51fc..90e98e4f5b 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.22.1
 scipy == 1.7.3
-pandas == 1.3.5
+pandas == 1.4.0
 matplotlib == 3.5.1
 cvxpy == 1.1.18
 pytest == 6.2.5 # For unit test

From 2c245d9d81ecd9efeb53f567401c15731e34f359 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 27 Jan 2022 21:37:19 +0900
Subject: [PATCH 0590/1080] enhance histogram filter doc (#623)

* enhance histogram filter doc

* enhance histogram filter doc

* enhance histogram filter doc

* enhance histogram filter doc

* enhance histogram filter doc

* enhance histogram filter doc

* Update docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst

* Update docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst

* fix duplicated math lables

* fix duplicated math lables

* fix duplicated math lables

* update doc

* update doc

* update doc

* update doc

* update doc

* update doc
---
 .../histogram_filter/histogram_filter.py      |  15 +--
 .../extended_kalman_filter_localization.rst   |   4 +-
 .../histogram_filter_localization/1.png       | Bin 0 -> 57183 bytes
 .../histogram_filter_localization/2.png       | Bin 0 -> 198077 bytes
 .../histogram_filter_localization/3.png       | Bin 0 -> 98927 bytes
 .../histogram_filter_localization/4.png       | Bin 0 -> 51007 bytes
 .../histogram_filter_localization.rst         | 102 +++++++++++++++++-
 .../gaussian_grid_map/gaussian_grid_map.rst   |   2 +
 8 files changed, 110 insertions(+), 13 deletions(-)
 create mode 100644 docs/modules/localization/histogram_filter_localization/1.png
 create mode 100644 docs/modules/localization/histogram_filter_localization/2.png
 create mode 100644 docs/modules/localization/histogram_filter_localization/3.png
 create mode 100644 docs/modules/localization/histogram_filter_localization/4.png

diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py
index 7f453d69dd..a4aeb419cf 100644
--- a/Localization/histogram_filter/histogram_filter.py
+++ b/Localization/histogram_filter/histogram_filter.py
@@ -71,7 +71,7 @@ def calc_gaussian_observation_pdf(grid_map, z, iz, ix, iy, std):
     d = math.hypot(x - z[iz, 1], y - z[iz, 2])
 
     # likelihood
-    pdf = (1.0 - norm.cdf(abs(d - z[iz, 0]), 0.0, std))
+    pdf = norm.pdf(d - z[iz, 0], 0.0, std)
 
     return pdf
 
@@ -88,7 +88,7 @@ def observation_update(grid_map, z, std):
     return grid_map
 
 
-def calc_input():
+def calc_control_input():
     v = 1.0  # [m/s]
     yaw_rate = 0.1  # [rad/s]
     u = np.array([v, yaw_rate]).reshape(2, 1)
@@ -113,6 +113,7 @@ def motion_model(x, u):
 
 def draw_heat_map(data, mx, my):
     max_value = max([max(i_data) for i_data in data])
+    plt.grid(False)
     plt.pcolor(mx, my, data, vmax=max_value, cmap=plt.cm.get_cmap("Blues"))
     plt.axis("equal")
 
@@ -197,6 +198,7 @@ def motion_update(grid_map, u, yaw):
         grid_map.dx -= x_shift * grid_map.xy_resolution
         grid_map.dy -= y_shift * grid_map.xy_resolution
 
+    # Add motion noise
     grid_map.data = gaussian_filter(grid_map.data, sigma=MOTION_STD)
 
     return grid_map
@@ -230,9 +232,9 @@ def main():
 
     while SIM_TIME >= time:
         time += DT
-        print("Time:", time)
+        print(f"{time=:.1f}")
 
-        u = calc_input()
+        u = calc_control_input()
 
         yaw = xTrue[2, 0]  # Orientation is known
         xTrue, z, ud = observation(xTrue, u, RF_ID)
@@ -249,8 +251,9 @@ def main():
             plt.plot(xTrue[0, :], xTrue[1, :], "xr")
             plt.plot(RF_ID[:, 0], RF_ID[:, 1], ".k")
             for i in range(z.shape[0]):
-                plt.plot([xTrue[0, :], z[i, 1]], [
-                    xTrue[1, :], z[i, 2]], "-k")
+                plt.plot([xTrue[0, 0], z[i, 1]],
+                         [xTrue[1, 0], z[i, 2]],
+                         "-k")
             plt.title("Time[s]:" + str(time)[0: 4])
             plt.pause(0.1)
 
diff --git a/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst
index 9777258e3e..d2cd52cf12 100644
--- a/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst
+++ b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst
@@ -120,10 +120,10 @@ Its Jacobian matrix is
 
 :math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ \end{bmatrix} \end{equation*}`
 
-Extented Kalman Filter
+Extended Kalman Filter
 ~~~~~~~~~~~~~~~~~~~~~~
 
-Localization process using Extendted Kalman Filter:EKF is
+Localization process using Extended Kalman Filter:EKF is
 
 === Predict ===
 
diff --git a/docs/modules/localization/histogram_filter_localization/1.png b/docs/modules/localization/histogram_filter_localization/1.png
new file mode 100644
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diff --git a/docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst b/docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst
index afe689b9a5..891de94135 100644
--- a/docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst
+++ b/docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst
@@ -9,14 +9,106 @@ The red cross is true position, black points are RFID positions.
 
 The blue grid shows a position probability of histogram filter.
 
-In this simulation, x,y are unknown, yaw is known.
+In this simulation, we assume the robot's yaw orientation and RFID's positions are known,
+but x,y positions are unknown.
+
+The filter uses speed input and range observations from RFID for localization.
+
+Initial position information is not needed.
+
+Filtering algorithm
+~~~~~~~~~~~~~~~~~~~~
+
+Histogram filter is a discrete Bayes filter in continuous space.
+
+It uses regular girds to manage probability of the robot existence.
+
+If a grid has higher probability, it means that the robot is likely to be there.
+
+In the simulation, we want to estimate x-y position, so we use 2D grid data.
+
+There are 4 steps for the histogram filter to estimate the probability distribution.
+
+Step1: Filter initialization
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Histogram filter does not need initial position information.
+
+In that case, we can initialize each grid probability as a same value.
+
+If we can use initial position information, we can set initial probabilities based on it.
+
+:ref:`gaussian_grid_map` might be useful when the initial position information is provided as gaussian distribution.
+
+Step2: Predict probability by motion
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+In histogram filter, when a robot move to a next grid,
+all probability information of each grid are shifted towards the movement direction.
+
+This process represents the change in the probability distribution as the robot moves.
+
+After the robot has moved, the probability distribution needs reflect
+the estimation error due to the movement.
+
+For example, the position probability is peaky with observations:
+
+.. image:: histogram_filter_localization/1.png
+   :width: 400px
+
+But, the probability is getting uncertain without observations:
+
+.. image:: histogram_filter_localization/2.png
+   :width: 400px
+
+
+The `gaussian filter <https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter.html>`_
+is used in the simulation for adding noize.
+
+Step3: Update probability by observation
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+In this step, all probabilities are updated by observations,
+this is the update step of bayesian filter.
+
+The probability update formula is different by the used sensor model.
+
+This simulation uses range observation model.
+
+The probability of each grid is updated by this formula:
+
+.. math:: p_t=p_{t-1}*h(z)
+
+.. math:: h(z)=\frac{\exp \left(-(d - z)^{2} / 2\right)}{\sqrt{2 \pi}}
+
+- :math:`p_t` is the probability at the time `t`.
+
+- :math:`h(z)` is the observation probability with the observation `z`.
+
+- :math:`d` is the known distance from the RD-ID to the grid center.
+
+When the `d` is 3.0, the `h(z)` distribution is:
+
+.. image:: histogram_filter_localization/4.png
+   :width: 400px
+
+The observation probability distribution looks a circle when a RF-ID is observed:
+
+.. image:: histogram_filter_localization/3.png
+   :width: 400px
+
+Step4: Estimate position from probability
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+In each time step, we can calculate the final robot position from the current probability distribution.
+There are two ways to calculate the final positions:
+
+1. Using the maximum probability grid position.
+
+2. Using the average of probability weighted grind position.
 
-The filter integrates speed input and range observations from RFID for
-localization.
 
-Initial position is not needed.
 
 References:
 ~~~~~~~~~~~
 
--  `PROBABILISTIC ROBOTICS`_
+- `PROBABILISTIC ROBOTICS`_
+- `Robust Vehicle Localization in Urban Environments Using Probabilistic Maps <http://driving.stanford.edu/papers/ICRA2010.pdf>`_
diff --git a/docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst b/docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst
index f78f2c95d8..b0f112a871 100644
--- a/docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst
+++ b/docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst
@@ -1,3 +1,5 @@
+.. _gaussian_grid_map:
+
 Gaussian grid map
 -----------------
 

From 040e12dbcb88ac5a3cfa7097ceca8a5e5562ee6d Mon Sep 17 00:00:00 2001
From: Trung Kien <letrungkien.k53.hut@gmail.com>
Date: Sat, 29 Jan 2022 14:16:34 +0700
Subject: [PATCH 0591/1080] Add inverted_pendulum_lqr_control (#635)

* Add inverted_pendulum_lqr_control

* reorganize document of inverted pendulum control module

* Refactor inverted_pendulum_lqr_control.py

* Add doccument for inverted pendulum control

* Corrected inverted pedulum LQR control doccument

* Refactor inverted pendulum control by mpc and lqr

* Add unit test for inverted_pendulum_lqr_control.py
---
 .../inverted_pendulum_lqr_control.py          | 192 ++++++++++++++++++
 .../inverted_pendulum_mpc_control.py          |  30 ++-
 docs/modules/control/control_main.rst         |   3 +-
 .../inverted-pendulum.png                     | Bin 0 -> 6127 bytes
 .../inverted_pendulum_control.rst             |  97 +++++++++
 .../inverted_pendulum_mpc_control.rst         |   6 -
 tests/test_inverted_pendulum_lqr_control.py   |  11 +
 tests/test_inverted_pendulum_mpc_control.py   |  12 +-
 8 files changed, 328 insertions(+), 23 deletions(-)
 create mode 100644 Control/inverted_pendulum/inverted_pendulum_lqr_control.py
 rename Control/{InvertedPendulumMPCControl => inverted_pendulum}/inverted_pendulum_mpc_control.py (84%)
 create mode 100644 docs/modules/control/inverted_pendulum_control/inverted-pendulum.png
 create mode 100644 docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst
 delete mode 100644 docs/modules/control/inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst
 create mode 100644 tests/test_inverted_pendulum_lqr_control.py

diff --git a/Control/inverted_pendulum/inverted_pendulum_lqr_control.py b/Control/inverted_pendulum/inverted_pendulum_lqr_control.py
new file mode 100644
index 0000000000..fb68c91c45
--- /dev/null
+++ b/Control/inverted_pendulum/inverted_pendulum_lqr_control.py
@@ -0,0 +1,192 @@
+"""
+Inverted Pendulum LQR control
+author: Trung Kien - letrungkien.k53.hut@gmail.com
+"""
+
+import math
+import time
+
+import matplotlib.pyplot as plt
+import numpy as np
+from numpy.linalg import inv, eig
+
+# Model parameters
+
+l_bar = 2.0  # length of bar
+M = 1.0  # [kg]
+m = 0.3  # [kg]
+g = 9.8  # [m/s^2]
+
+nx = 4  # number of state
+nu = 1  # number of input
+Q = np.diag([0.0, 1.0, 1.0, 0.0])  # state cost matrix
+R = np.diag([0.01])  # input cost matrix
+
+delta_t = 0.1  # time tick [s]
+sim_time = 5.0  # simulation time [s]
+
+show_animation = True
+
+
+def main():
+    x0 = np.array([
+        [0.0],
+        [0.0],
+        [0.3],
+        [0.0]
+    ])
+
+    x = np.copy(x0)
+    time = 0.0
+
+    while sim_time > time:
+        time += delta_t
+
+        # calc control input
+        u = lqr_control(x)
+
+        # simulate inverted pendulum cart
+        x = simulation(x, u)
+
+        if show_animation:
+            plt.clf()
+            px = float(x[0])
+            theta = float(x[2])
+            plot_cart(px, theta)
+            plt.xlim([-5.0, 2.0])
+            plt.pause(0.001)
+
+    print("Finish")
+    print(f"x={float(x[0]):.2f} [m] , theta={math.degrees(x[2]):.2f} [deg]")
+    if show_animation:
+        plt.show()
+
+
+def simulation(x, u):
+    A, B = get_model_matrix()
+    x = A @ x + B @ u
+
+    return x
+
+
+def solve_DARE(A, B, Q, R, maxiter=150, eps=0.01):
+    """
+    Solve a discrete time_Algebraic Riccati equation (DARE)
+    """
+    P = Q
+
+    for i in range(maxiter):
+        Pn = A.T @ P @ A - A.T @ P @ B @ \
+            inv(R + B.T @ P @ B) @ B.T @ P @ A + Q
+        if (abs(Pn - P)).max() < eps:
+            break
+        P = Pn
+
+    return Pn
+
+
+def dlqr(A, B, Q, R):
+    """
+    Solve the discrete time lqr controller.
+    x[k+1] = A x[k] + B u[k]
+    cost = sum x[k].T*Q*x[k] + u[k].T*R*u[k]
+    # ref Bertsekas, p.151
+    """
+
+    # first, try to solve the ricatti equation
+    P = solve_DARE(A, B, Q, R)
+
+    # compute the LQR gain
+    K = inv(B.T @ P @ B + R) @ (B.T @ P @ A)
+
+    eigVals, eigVecs = eig(A - B @ K)
+    return K, P, eigVals
+
+
+def lqr_control(x):
+    A, B = get_model_matrix()
+    start = time.time()
+    K, _, _ = dlqr(A, B, Q, R)
+    u = -K @ x
+    elapsed_time = time.time() - start
+    print(f"calc time:{elapsed_time:.6f} [sec]")
+    return u
+
+
+def get_numpy_array_from_matrix(x):
+    """
+    get build-in list from matrix
+    """
+    return np.array(x).flatten()
+
+
+def get_model_matrix():
+    A = np.array([
+        [0.0, 1.0, 0.0, 0.0],
+        [0.0, 0.0, m * g / M, 0.0],
+        [0.0, 0.0, 0.0, 1.0],
+        [0.0, 0.0, g * (M + m) / (l_bar * M), 0.0]
+    ])
+    A = np.eye(nx) + delta_t * A
+
+    B = np.array([
+        [0.0],
+        [1.0 / M],
+        [0.0],
+        [1.0 / (l_bar * M)]
+    ])
+    B = delta_t * B
+
+    return A, B
+
+
+def flatten(a):
+    return np.array(a).flatten()
+
+
+def plot_cart(xt, theta):
+    cart_w = 1.0
+    cart_h = 0.5
+    radius = 0.1
+
+    cx = np.array([-cart_w / 2.0, cart_w / 2.0, cart_w /
+                   2.0, -cart_w / 2.0, -cart_w / 2.0])
+    cy = np.array([0.0, 0.0, cart_h, cart_h, 0.0])
+    cy += radius * 2.0
+
+    cx = cx + xt
+
+    bx = np.array([0.0, l_bar * math.sin(-theta)])
+    bx += xt
+    by = np.array([cart_h, l_bar * math.cos(-theta) + cart_h])
+    by += radius * 2.0
+
+    angles = np.arange(0.0, math.pi * 2.0, math.radians(3.0))
+    ox = np.array([radius * math.cos(a) for a in angles])
+    oy = np.array([radius * math.sin(a) for a in angles])
+
+    rwx = np.copy(ox) + cart_w / 4.0 + xt
+    rwy = np.copy(oy) + radius
+    lwx = np.copy(ox) - cart_w / 4.0 + xt
+    lwy = np.copy(oy) + radius
+
+    wx = np.copy(ox) + bx[-1]
+    wy = np.copy(oy) + by[-1]
+
+    plt.plot(flatten(cx), flatten(cy), "-b")
+    plt.plot(flatten(bx), flatten(by), "-k")
+    plt.plot(flatten(rwx), flatten(rwy), "-k")
+    plt.plot(flatten(lwx), flatten(lwy), "-k")
+    plt.plot(flatten(wx), flatten(wy), "-k")
+    plt.title(f"x: {xt:.2f} , theta: {math.degrees(theta):.2f}")
+
+    # for stopping simulation with the esc key.
+    plt.gcf().canvas.mpl_connect(
+        'key_release_event',
+        lambda event: [exit(0) if event.key == 'escape' else None])
+
+    plt.axis("equal")
+
+
+if __name__ == '__main__':
+    main()
diff --git a/Control/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py b/Control/inverted_pendulum/inverted_pendulum_mpc_control.py
similarity index 84%
rename from Control/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py
rename to Control/inverted_pendulum/inverted_pendulum_mpc_control.py
index 3764f29558..6b966b13b4 100644
--- a/Control/InvertedPendulumMPCControl/inverted_pendulum_mpc_control.py
+++ b/Control/inverted_pendulum/inverted_pendulum_mpc_control.py
@@ -17,14 +17,16 @@
 m = 0.3  # [kg]
 g = 9.8  # [m/s^2]
 
-Q = np.diag([0.0, 1.0, 1.0, 0.0])
-R = np.diag([0.01])
 nx = 4  # number of state
 nu = 1  # number of input
+Q = np.diag([0.0, 1.0, 1.0, 0.0])  # state cost matrix
+R = np.diag([0.01])  # input cost matrix
+
 T = 30  # Horizon length
 delta_t = 0.1  # time tick
+sim_time = 5.0  # simulation time [s]
 
-animation = True
+show_animation = True
 
 
 def main():
@@ -36,8 +38,10 @@ def main():
     ])
 
     x = np.copy(x0)
+    time = 0.0
 
-    for i in range(50):
+    while sim_time > time:
+        time += delta_t
 
         # calc control input
         opt_x, opt_delta_x, opt_theta, opt_delta_theta, opt_input = \
@@ -49,7 +53,7 @@ def main():
         # simulate inverted pendulum cart
         x = simulation(x, u)
 
-        if animation:
+        if show_animation:
             plt.clf()
             px = float(x[0])
             theta = float(x[2])
@@ -57,10 +61,14 @@ def main():
             plt.xlim([-5.0, 2.0])
             plt.pause(0.001)
 
+    print("Finish")
+    print(f"x={float(x[0]):.2f} [m] , theta={math.degrees(x[2]):.2f} [deg]")
+    if show_animation:
+        plt.show()
+
 
 def simulation(x, u):
     A, B = get_model_matrix()
-
     x = np.dot(A, x) + np.dot(B, u)
 
     return x
@@ -85,7 +93,7 @@ def mpc_control(x0):
     start = time.time()
     prob.solve(verbose=False)
     elapsed_time = time.time() - start
-    print("calc time:{0} [sec]".format(elapsed_time))
+    print(f"calc time:{elapsed_time:.6f} [sec]")
 
     if prob.status == cvxpy.OPTIMAL:
         ox = get_numpy_array_from_matrix(x.value[0, :])
@@ -165,8 +173,12 @@ def plot_cart(xt, theta):
     plt.plot(flatten(rwx), flatten(rwy), "-k")
     plt.plot(flatten(lwx), flatten(lwy), "-k")
     plt.plot(flatten(wx), flatten(wy), "-k")
-    plt.title("x:" + str(round(xt, 2)) + ",theta:" +
-              str(round(math.degrees(theta), 2)))
+    plt.title(f"x: {xt:.2f} , theta: {math.degrees(theta):.2f}")
+
+    # for stopping simulation with the esc key.
+    plt.gcf().canvas.mpl_connect(
+        'key_release_event',
+        lambda event: [exit(0) if event.key == 'escape' else None])
 
     plt.axis("equal")
 
diff --git a/docs/modules/control/control_main.rst b/docs/modules/control/control_main.rst
index 9eeba434d2..d6d36a726b 100644
--- a/docs/modules/control/control_main.rst
+++ b/docs/modules/control/control_main.rst
@@ -3,6 +3,7 @@
 Control
 =================
 
-.. include:: inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst
+.. include:: inverted_pendulum_control/inverted_pendulum_control.rst
+
 .. include:: move_to_a_pose_control/move_to_a_pose_control.rst
 
diff --git a/docs/modules/control/inverted_pendulum_control/inverted-pendulum.png b/docs/modules/control/inverted_pendulum_control/inverted-pendulum.png
new file mode 100644
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diff --git a/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst b/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst
new file mode 100644
index 0000000000..5a447f7cd4
--- /dev/null
+++ b/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst
@@ -0,0 +1,97 @@
+Inverted Pendulum Control
+-----------------------------
+
+An inverted pendulum on a cart consists of a mass :math:`m` at the top of a pole of length :math:`l` pivoted on a
+horizontally moving base as shown in the adjacent.
+
+The objective of the control system is to balance the inverted pendulum by applying a force to the cart that the pendulum is attached to.
+
+Modeling
+~~~~~~~~~~~~
+
+.. image:: inverted_pendulum_control/inverted-pendulum.png
+    :align: center
+
+- :math:`M`: mass of the cart
+- :math:`m`: mass of the load on the top of the rod
+- :math:`l`: length of the rod
+- :math:`u`: force applied to the cart
+- :math:`x`: cart position coordinate
+- :math:`\theta`: pendulum angle from vertical
+
+Using Lagrange's equations:
+
+.. math::
+    & (M + m)\ddot{x} - ml\ddot{\theta}cos{\theta} + ml\dot{\theta^2}\sin{\theta} = u \\
+    & l\ddot{\theta} - g\sin{\theta} = \ddot{x}\cos{\theta}
+
+See this `link <https://en.wikipedia.org/wiki/Inverted_pendulum#From_Lagrange's_equations>`__ for more details.
+
+So
+
+.. math::
+    & \ddot{x} =  \frac{m(gcos{\theta} - \dot{\theta}^2l)sin{\theta} + u}{M + m - mcos^2{\theta}} \\
+    & \ddot{\theta} = \frac{g(M + m)sin{\theta} - \dot{\theta}^2lmsin{\theta}cos{\theta} + ucos{\theta}}{l(M + m - mcos^2{\theta})}
+
+
+Linearlied model when :math:`\theta` small, :math:`cos{\theta} \approx 1`, :math:`sin{\theta} \approx \theta`, :math:`\dot{\theta}^2 \approx 0`.
+
+.. math::
+    & \ddot{x} =  \frac{gm}{M}\theta + \frac{1}{M}u\\
+    & \ddot{\theta} = \frac{g(M + m)}{Ml}\theta + \frac{1}{Ml}u
+
+State space:
+
+.. math::
+    & \dot{x} = Ax + Bu \\
+    & y = Cx + Du
+
+where
+
+.. math::
+    & x = [x, \dot{x}, \theta,\dot{\theta}]\\
+    & A = \begin{bmatrix}  0 & 1 & 0 & 0\\0 & 0 & \frac{gm}{M} & 0\\0 & 0 & 0 & 1\\0 & 0 & \frac{g(M + m)}{Ml} & 0 \end{bmatrix}\\
+    & B = \begin{bmatrix}  0 \\ \frac{1}{M} \\ 0 \\ \frac{1}{Ml} \end{bmatrix}
+
+If control only \theta
+
+.. math::
+    & C = \begin{bmatrix} 0 & 0 & 1 & 0 \end{bmatrix}\\
+    & D = [0]
+
+If control x and \theta
+
+.. math::
+    & C = \begin{bmatrix} 1 & 0 & 0 & 0\\0 & 0 & 1 & 0 \end{bmatrix}\\
+    & D = \begin{bmatrix} 0 \\ 0 \end{bmatrix}
+
+LQR control
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The LQR cotroller minimize this cost function defined as:
+
+.. math::  J = x^T Q x + u^T R u
+the feedback control law that minimizes the value of the cost is:
+
+.. math::  u = -K x
+where:
+
+.. math::  K = (B^T P B + R)^{-1} B^T P A
+and :math:`P` is the unique positive definite solution to the discrete time `algebraic Riccati equation <https://en.wikipedia.org/wiki/Inverted_pendulum#From_Lagrange's_equations>`__  (DARE):
+
+.. math::  P = A^T P A - A^T P B ( R + B^T P B )^{-1} B^T P A + Q
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/InvertedPendulumCart/animation_lqr.gif
+
+MPC control
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+The MPC cotroller minimize this cost function defined as:
+
+.. math:: J = x^T Q x + u^T R u
+
+subject to:
+
+- Linearlied Inverted Pendulum model
+- Initial state
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/InvertedPendulumCart/animation.gif
diff --git a/docs/modules/control/inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst b/docs/modules/control/inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst
deleted file mode 100644
index 328f9f6b24..0000000000
--- a/docs/modules/control/inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-Inverted Pendulum MPC Control
------------------------------
-
-Bipedal Walking with modifying designated footsteps
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/InvertedPendulumCart/animation.gif
diff --git a/tests/test_inverted_pendulum_lqr_control.py b/tests/test_inverted_pendulum_lqr_control.py
new file mode 100644
index 0000000000..cbbabb93b1
--- /dev/null
+++ b/tests/test_inverted_pendulum_lqr_control.py
@@ -0,0 +1,11 @@
+import conftest
+from Control.inverted_pendulum import inverted_pendulum_lqr_control as m
+
+
+def test_1():
+    m.show_animation = False
+    m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/tests/test_inverted_pendulum_mpc_control.py b/tests/test_inverted_pendulum_mpc_control.py
index 9519b45ec9..800aefd7d5 100644
--- a/tests/test_inverted_pendulum_mpc_control.py
+++ b/tests/test_inverted_pendulum_mpc_control.py
@@ -1,13 +1,11 @@
 import conftest
-import sys
-if 'cvxpy' in sys.modules:  # pragma: no cover
 
-    from Control.InvertedPendulumMPCControl \
-        import inverted_pendulum_mpc_control as m
+from Control.inverted_pendulum import inverted_pendulum_mpc_control as m
 
-    def test1():
-        m.show_animation = False
-        m.main()
+
+def test1():
+    m.show_animation = False
+    m.main()
 
 
 if __name__ == '__main__':

From 1d8c252fef3798d502dc3187e0c9a360a26fa1df Mon Sep 17 00:00:00 2001
From: Daniel Ingram <ingramds92@gmail.com>
Date: Sun, 6 Feb 2022 08:18:54 -0500
Subject: [PATCH 0592/1080] Clothoidal path planner (#551)

* Add clothoidal path planner

* Code quality/style fixes

* Open up possibilities a bit by allowing control over theta1 values

* Get line length under 80 chars for code style checks
---
 .../ClothoidalPaths/clothoidal_paths.py       | 144 ++++++++++++++++++
 1 file changed, 144 insertions(+)
 create mode 100644 PathPlanning/ClothoidalPaths/clothoidal_paths.py

diff --git a/PathPlanning/ClothoidalPaths/clothoidal_paths.py b/PathPlanning/ClothoidalPaths/clothoidal_paths.py
new file mode 100644
index 0000000000..6fbf46ed2a
--- /dev/null
+++ b/PathPlanning/ClothoidalPaths/clothoidal_paths.py
@@ -0,0 +1,144 @@
+"""
+Clothoidal Path Planner
+Author: Daniel Ingram (daniel-s-ingram)
+Reference paper: https://www.researchgate.net/publication/237062806
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy.integrate as integrate
+from collections import namedtuple
+from scipy.optimize import fsolve
+from math import atan2, cos, hypot, pi, sin
+from matplotlib import animation
+
+Point = namedtuple("Point", ["x", "y"])
+
+
+def draw_clothoids(
+    theta1_vals,
+    theta2_vals,
+    num_clothoids=75,
+    num_steps=100,
+    save_animation=False
+):
+    p1 = Point(0, 0)
+    p2 = Point(10, 0)
+    clothoids = []
+    for theta1 in theta1_vals:
+        for theta2 in theta2_vals:
+            clothoid = get_clothoid_points(p1, p2, theta1, theta2, num_steps)
+            clothoids.append(clothoid)
+
+    fig = plt.figure(figsize=(10, 10))
+    x_min, x_max, y_min, y_max = get_axes_limits(clothoids)
+    axes = plt.axes(xlim=(x_min, x_max), ylim=(y_min, y_max))
+
+    axes.plot(p1.x, p1.y, 'ro')
+    axes.plot(p2.x, p2.y, 'ro')
+    lines = [axes.plot([], [], 'b-')[0] for _ in range(len(clothoids))]
+
+    def animate(i):
+        for line, clothoid in zip(lines, clothoids):
+            x = [p.x for p in clothoid[:i]]
+            y = [p.y for p in clothoid[:i]]
+            line.set_data(x, y)
+
+        return lines
+
+    anim = animation.FuncAnimation(
+        fig,
+        animate,
+        frames=num_steps,
+        interval=25,
+        blit=True
+    )
+    if save_animation:
+        anim.save('clothoid.gif', fps=30, writer="imagemagick")
+    plt.show()
+
+
+def get_clothoid_points(p1, p2, theta1, theta2, num_steps=100):
+    dx = p2.x - p1.x
+    dy = p2.y - p1.y
+    r = hypot(dx, dy)
+
+    phi = atan2(dy, dx)
+    phi1 = normalize_angle(theta1 - phi)
+    phi2 = normalize_angle(theta2 - phi)
+    delta = phi2 - phi1
+
+    try:
+        A = solve_for_root(phi1, phi2, delta)
+        L = compute_length(r, phi1, delta, A)
+        curv = compute_curvature(delta, A, L)
+        curv_rate = compute_curvature_rate(A, L)
+    except Exception as e:
+        print(f"Failed to generate clothoid points: {e}")
+        return None
+
+    points = []
+    for s in np.linspace(0, L, num_steps):
+        try:
+            x = p1.x + s*X(curv_rate*s**2, curv*s, theta1)
+            y = p1.y + s*Y(curv_rate*s**2, curv*s, theta1)
+            points.append(Point(x, y))
+        except Exception as e:
+            print(f"Skipping failed clothoid point: {e}")
+
+    return points
+
+
+def X(a, b, c):
+    return integrate.quad(lambda t: cos((a/2)*t**2 + b*t + c), 0, 1)[0]
+
+
+def Y(a, b, c):
+    return integrate.quad(lambda t: sin((a/2)*t**2 + b*t + c), 0, 1)[0]
+
+
+def solve_for_root(theta1, theta2, delta):
+    initial_guess = 3*(theta1 + theta2)
+    return fsolve(lambda x: Y(2*x, delta - x, theta1), [initial_guess])
+
+
+def compute_length(r, theta1, delta, A):
+    return r / X(2*A, delta - A, theta1)
+
+
+def compute_curvature(delta, A, L):
+    return (delta - A) / L
+
+
+def compute_curvature_rate(A, L):
+    return 2 * A / (L**2)
+
+
+def normalize_angle(angle_rad):
+    return (angle_rad + pi) % (2 * pi) - pi
+
+
+def get_axes_limits(clothoids):
+    x_vals = [p.x for clothoid in clothoids for p in clothoid]
+    y_vals = [p.y for clothoid in clothoids for p in clothoid]
+
+    x_min = min(x_vals)
+    x_max = max(x_vals)
+    y_min = min(y_vals)
+    y_max = max(y_vals)
+
+    x_offset = 0.1*(x_max - x_min)
+    y_offset = 0.1*(y_max - y_min)
+
+    x_min = x_min - x_offset
+    x_max = x_max + x_offset
+    y_min = y_min - y_offset
+    y_max = y_max + y_offset
+
+    return x_min, x_max, y_min, y_max
+
+
+if __name__ == "__main__":
+    theta1_vals = [0]
+    theta2_vals = np.linspace(-pi, pi, 75)
+    draw_clothoids(theta1_vals, theta2_vals, save_animation=False)

From a5feb75dcefc71802abfc369f23e278dd0329202 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 8 Feb 2022 07:30:42 +0900
Subject: [PATCH 0593/1080] Bump numpy from 1.22.1 to 1.22.2 in /requirements
 (#639)

Bumps [numpy](https://github.com/numpy/numpy) from 1.22.1 to 1.22.2.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt)
- [Commits](https://github.com/numpy/numpy/compare/v1.22.1...v1.22.2)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 90e98e4f5b..7d830a846d 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.22.1
+numpy == 1.22.2
 scipy == 1.7.3
 pandas == 1.4.0
 matplotlib == 3.5.1

From 30fe42c7b43265eff12836122231d3283daf52a3 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 8 Feb 2022 07:31:49 +0900
Subject: [PATCH 0594/1080] Bump pytest from 6.2.5 to 7.0.0 in /requirements
 (#640)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 6.2.5 to 7.0.0.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/6.2.5...7.0.0)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 7d830a846d..b1b5e9c679 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,7 +3,7 @@ scipy == 1.7.3
 pandas == 1.4.0
 matplotlib == 3.5.1
 cvxpy == 1.1.18
-pytest == 6.2.5 # For unit test
+pytest == 7.0.0 # For unit test
 pytest-xdist == 2.5.0 # For unit test
 mypy == 0.931 # For unit test
 flake8 == 4.0.1 # For unit test

From c7ee9337e9ce789741d163f548680249e3522fa1 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 15 Feb 2022 07:25:28 +0900
Subject: [PATCH 0595/1080] Bump pytest from 7.0.0 to 7.0.1 in /requirements
 (#642)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.0.0 to 7.0.1.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/7.0.0...7.0.1)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index b1b5e9c679..de00ea5370 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,7 +3,7 @@ scipy == 1.7.3
 pandas == 1.4.0
 matplotlib == 3.5.1
 cvxpy == 1.1.18
-pytest == 7.0.0 # For unit test
+pytest == 7.0.1 # For unit test
 pytest-xdist == 2.5.0 # For unit test
 mypy == 0.931 # For unit test
 flake8 == 4.0.1 # For unit test

From e494ed6eaecb00fdf13fb361db947498c70d898c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 15 Feb 2022 07:25:48 +0900
Subject: [PATCH 0596/1080] Bump pandas from 1.4.0 to 1.4.1 in /requirements
 (#643)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.4.0 to 1.4.1.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/main/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.4.0...v1.4.1)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index de00ea5370..fb62322132 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.22.2
 scipy == 1.7.3
-pandas == 1.4.0
+pandas == 1.4.1
 matplotlib == 3.5.1
 cvxpy == 1.1.18
 pytest == 7.0.1 # For unit test

From 9da650e31bc7879ac2a5d59bf46415b0232377d6 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 21 Feb 2022 17:19:22 +0900
Subject: [PATCH 0597/1080] Update LICENSE

---
 LICENSE | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/LICENSE b/LICENSE
index 2867142fc7..c0c52a4321 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,7 @@
 The MIT License (MIT)
 
-Copyright (c) 2016 - 2021 Atsushi Sakai
+Copyright (c) 2016 - 2022 Atsushi Sakai and other contributors:
+https://github.com/AtsushiSakai/PythonRobotics/contributors
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

From b1885a056490ef0904cea80aa3389811903dbe76 Mon Sep 17 00:00:00 2001
From: i-aki-y <aki.y.ishikawa@gmail.com>
Date: Tue, 1 Mar 2022 21:33:31 +0900
Subject: [PATCH 0598/1080] Fix the broken gif link in the "Lidar to grid map"
 (#645)

The link for gif animation is broken now. I think this is the right URL.
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 9aff69ab72..8972bae5fa 100644
--- a/README.md
+++ b/README.md
@@ -217,7 +217,7 @@ This is a 2D ray casting grid mapping example.
 
 This example shows how to convert a 2D range measurement to a grid map.
 
-![2](Mapping/lidar_to_grid_map/animation.gif)
+![2](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/lidar_to_grid_map/animation.gif)
 
 ## k-means object clustering
 

From 5f4b62d87a0e9bed2d0279b32841d4bbe323fdf3 Mon Sep 17 00:00:00 2001
From: Hrushikesh Budhale <hruhnb@gmail.com>
Date: Sat, 12 Mar 2022 04:38:25 -0500
Subject: [PATCH 0599/1080] Fix missing 'delta t' in EKF documentation (#647)

Reference issue
typo

What does this implement/fix?
Update motion function in localization/EKF documentation. Add missing delta t.
---
 .../extended_kalman_filter_localization.rst                     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst
index d2cd52cf12..a9c2939678 100644
--- a/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst
+++ b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst
@@ -89,7 +89,7 @@ This is implemented at
 
 The motion function is that
 
-:math:`\begin{equation*} \begin{bmatrix} x' \\ y' \\ w' \\ v' \end{bmatrix} = f(\textbf{x}, \textbf{u}) = \begin{bmatrix} x + v\cos(\phi)\Delta t \\ y + v\sin(\phi) \\ \phi + \omega \Delta t \\ v \end{bmatrix} \end{equation*}`
+:math:`\begin{equation*} \begin{bmatrix} x' \\ y' \\ w' \\ v' \end{bmatrix} = f(\textbf{x}, \textbf{u}) = \begin{bmatrix} x + v\cos(\phi)\Delta t \\ y + v\sin(\phi)\Delta t \\ \phi + \omega \Delta t \\ v \end{bmatrix} \end{equation*}`
 
 Its Jacobian matrix is
 

From 9c5f981bd90b5871a4ac3c596f729689ad029067 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 15 Mar 2022 07:13:46 +0900
Subject: [PATCH 0600/1080] Bump mypy from 0.931 to 0.941 in /requirements
 (#651)

Bumps [mypy](https://github.com/python/mypy) from 0.931 to 0.941.
- [Release notes](https://github.com/python/mypy/releases)
- [Commits](https://github.com/python/mypy/compare/v0.931...v0.941)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index fb62322132..42ef7586f1 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ matplotlib == 3.5.1
 cvxpy == 1.1.18
 pytest == 7.0.1 # For unit test
 pytest-xdist == 2.5.0 # For unit test
-mypy == 0.931 # For unit test
+mypy == 0.941 # For unit test
 flake8 == 4.0.1 # For unit test

From a03a07296cab0669ac664042e31d420f55ebed4b Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 15 Mar 2022 07:14:13 +0900
Subject: [PATCH 0601/1080] Bump numpy from 1.22.2 to 1.22.3 in /requirements
 (#650)

Bumps [numpy](https://github.com/numpy/numpy) from 1.22.2 to 1.22.3.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt)
- [Commits](https://github.com/numpy/numpy/compare/v1.22.2...v1.22.3)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 42ef7586f1..b173a6fab5 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.22.2
+numpy == 1.22.3
 scipy == 1.7.3
 pandas == 1.4.1
 matplotlib == 3.5.1

From 762d1a4d428fb60be68106359c6456b37c7cef20 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 15 Mar 2022 07:51:50 +0900
Subject: [PATCH 0602/1080] Bump pytest from 7.0.1 to 7.1.0 in /requirements
 (#649)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.0.1 to 7.1.0.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/7.0.1...7.1.0)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index b173a6fab5..2de832ce3e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,7 +3,7 @@ scipy == 1.7.3
 pandas == 1.4.1
 matplotlib == 3.5.1
 cvxpy == 1.1.18
-pytest == 7.0.1 # For unit test
+pytest == 7.1.0 # For unit test
 pytest-xdist == 2.5.0 # For unit test
 mypy == 0.941 # For unit test
 flake8 == 4.0.1 # For unit test

From a3753272043c0e9adb810c33f1386c26e1333719 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 15 Mar 2022 08:21:29 +0900
Subject: [PATCH 0603/1080] Bump cvxpy from 1.1.18 to 1.2.0 in /requirements
 (#648)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.1.18 to 1.2.0.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.1.18...v1.2.0)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 2de832ce3e..6f6be66470 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.22.3
 scipy == 1.7.3
 pandas == 1.4.1
 matplotlib == 3.5.1
-cvxpy == 1.1.18
+cvxpy == 1.2.0
 pytest == 7.1.0 # For unit test
 pytest-xdist == 2.5.0 # For unit test
 mypy == 0.941 # For unit test

From 62eee8162ef561234ef6d79943334b344daa0339 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 15 Mar 2022 14:36:50 +0900
Subject: [PATCH 0604/1080] Bump scipy from 1.7.3 to 1.8.0 in /requirements
 (#641)

Bumps [scipy](https://github.com/scipy/scipy) from 1.7.3 to 1.8.0.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.7.3...v1.8.0)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 6f6be66470..e8dfbcbba8 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.22.3
-scipy == 1.7.3
+scipy == 1.8.0
 pandas == 1.4.1
 matplotlib == 3.5.1
 cvxpy == 1.2.0

From fb6965a34fc01633aaf441964ca174a4eff605c4 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 22 Mar 2022 07:21:11 +0900
Subject: [PATCH 0605/1080] Bump pytest from 7.1.0 to 7.1.1 in /requirements
 (#652)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.1.0 to 7.1.1.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/7.1.0...7.1.1)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index e8dfbcbba8..f671321704 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,7 +3,7 @@ scipy == 1.8.0
 pandas == 1.4.1
 matplotlib == 3.5.1
 cvxpy == 1.2.0
-pytest == 7.1.0 # For unit test
+pytest == 7.1.1 # For unit test
 pytest-xdist == 2.5.0 # For unit test
 mypy == 0.941 # For unit test
 flake8 == 4.0.1 # For unit test

From 840248e38e47bb669f32075d9794cdbdd0248e26 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 29 Mar 2022 07:21:50 +0900
Subject: [PATCH 0606/1080] Bump mypy from 0.941 to 0.942 in /requirements
 (#653)

Bumps [mypy](https://github.com/python/mypy) from 0.941 to 0.942.
- [Release notes](https://github.com/python/mypy/releases)
- [Commits](https://github.com/python/mypy/compare/v0.941...v0.942)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index f671321704..bdf1571465 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ matplotlib == 3.5.1
 cvxpy == 1.2.0
 pytest == 7.1.1 # For unit test
 pytest-xdist == 2.5.0 # For unit test
-mypy == 0.941 # For unit test
+mypy == 0.942 # For unit test
 flake8 == 4.0.1 # For unit test

From 7ac2a17d23a840cf530ed9962a5236d1b25d6033 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 5 Apr 2022 07:25:46 +0900
Subject: [PATCH 0607/1080] Bump pandas from 1.4.1 to 1.4.2 in /requirements
 (#654)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.4.1 to 1.4.2.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/main/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.4.1...v1.4.2)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index bdf1571465..050904a674 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.22.3
 scipy == 1.8.0
-pandas == 1.4.1
+pandas == 1.4.2
 matplotlib == 3.5.1
 cvxpy == 1.2.0
 pytest == 7.1.1 # For unit test

From 38261ec67efd65256f44cd92b88dba58502fc2f7 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 10 Apr 2022 19:30:31 +0900
Subject: [PATCH 0608/1080] clean up clothoidal_paths.py (#638)

* clean up clothoidal_paths.py

* add unit tests for clothoid_paths

* add unit tests for clothoid_paths

* add unit tests for clothoid_paths

* code clean up

* code clean up

* code clean up

* code clean up

* code clean up

* code clean up

* code clean up

* code clean up
---
 .../ClothoidPath/clothoid_path_planner.py     | 192 ++++++++++++++++++
 .../ClothoidalPaths/clothoidal_paths.py       | 144 -------------
 .../inverted_pendulum_control.rst             |  14 +-
 .../clothoid_path/clothoid_path.rst           |  80 ++++++++
 .../path_planning/path_planning_main.rst      |   1 +
 tests/test_clothoidal_paths.py                |  11 +
 6 files changed, 293 insertions(+), 149 deletions(-)
 create mode 100644 PathPlanning/ClothoidPath/clothoid_path_planner.py
 delete mode 100644 PathPlanning/ClothoidalPaths/clothoidal_paths.py
 create mode 100644 docs/modules/path_planning/clothoid_path/clothoid_path.rst
 create mode 100644 tests/test_clothoidal_paths.py

diff --git a/PathPlanning/ClothoidPath/clothoid_path_planner.py b/PathPlanning/ClothoidPath/clothoid_path_planner.py
new file mode 100644
index 0000000000..5e5fc6e9a3
--- /dev/null
+++ b/PathPlanning/ClothoidPath/clothoid_path_planner.py
@@ -0,0 +1,192 @@
+"""
+Clothoid Path Planner
+Author: Daniel Ingram (daniel-s-ingram)
+        Atsushi Sakai (AtsushiSakai)
+Reference paper: Fast and accurate G1 fitting of clothoid curves
+https://www.researchgate.net/publication/237062806
+"""
+
+from collections import namedtuple
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy.integrate as integrate
+from scipy.optimize import fsolve
+from math import atan2, cos, hypot, pi, sin
+from matplotlib import animation
+
+Point = namedtuple("Point", ["x", "y"])
+
+show_animation = True
+
+
+def generate_clothoid_paths(start_point, start_yaw_list,
+                            goal_point, goal_yaw_list,
+                            n_path_points):
+    """
+    Generate clothoid path list. This function generate multiple clothoid paths
+    from multiple orientations(yaw) at start points to multiple orientations
+    (yaw) at goal point.
+
+    :param start_point: Start point of the path
+    :param start_yaw_list: Orientation list at start point in radian
+    :param goal_point: Goal point of the path
+    :param goal_yaw_list: Orientation list at goal point in radian
+    :param n_path_points: number of path points
+    :return: clothoid path list
+    """
+    clothoids = []
+    for start_yaw in start_yaw_list:
+        for goal_yaw in goal_yaw_list:
+            clothoid = generate_clothoid_path(start_point, start_yaw,
+                                              goal_point, goal_yaw,
+                                              n_path_points)
+            clothoids.append(clothoid)
+    return clothoids
+
+
+def generate_clothoid_path(start_point, start_yaw,
+                           goal_point, goal_yaw, n_path_points):
+    """
+    Generate a clothoid path list.
+
+    :param start_point: Start point of the path
+    :param start_yaw: Orientation at start point in radian
+    :param goal_point: Goal point of the path
+    :param goal_yaw: Orientation at goal point in radian
+    :param n_path_points: number of path points
+    :return: a clothoid path
+    """
+    dx = goal_point.x - start_point.x
+    dy = goal_point.y - start_point.y
+    r = hypot(dx, dy)
+
+    phi = atan2(dy, dx)
+    phi1 = normalize_angle(start_yaw - phi)
+    phi2 = normalize_angle(goal_yaw - phi)
+    delta = phi2 - phi1
+
+    try:
+        # Step1: Solve g function
+        A = solve_g_for_root(phi1, phi2, delta)
+
+        # Step2: Calculate path parameters
+        L = compute_path_length(r, phi1, delta, A)
+        curvature = compute_curvature(delta, A, L)
+        curvature_rate = compute_curvature_rate(A, L)
+    except Exception as e:
+        print(f"Failed to generate clothoid points: {e}")
+        return None
+
+    # Step3: Construct a path with Fresnel integral
+    points = []
+    for s in np.linspace(0, L, n_path_points):
+        try:
+            x = start_point.x + s * X(curvature_rate * s ** 2, curvature * s,
+                                      start_yaw)
+            y = start_point.y + s * Y(curvature_rate * s ** 2, curvature * s,
+                                      start_yaw)
+            points.append(Point(x, y))
+        except Exception as e:
+            print(f"Skipping failed clothoid point: {e}")
+
+    return points
+
+
+def X(a, b, c):
+    return integrate.quad(lambda t: cos((a/2)*t**2 + b*t + c), 0, 1)[0]
+
+
+def Y(a, b, c):
+    return integrate.quad(lambda t: sin((a/2)*t**2 + b*t + c), 0, 1)[0]
+
+
+def solve_g_for_root(theta1, theta2, delta):
+    initial_guess = 3*(theta1 + theta2)
+    return fsolve(lambda A: Y(2*A, delta - A, theta1), [initial_guess])
+
+
+def compute_path_length(r, theta1, delta, A):
+    return r / X(2*A, delta - A, theta1)
+
+
+def compute_curvature(delta, A, L):
+    return (delta - A) / L
+
+
+def compute_curvature_rate(A, L):
+    return 2 * A / (L**2)
+
+
+def normalize_angle(angle_rad):
+    return (angle_rad + pi) % (2 * pi) - pi
+
+
+def get_axes_limits(clothoids):
+    x_vals = [p.x for clothoid in clothoids for p in clothoid]
+    y_vals = [p.y for clothoid in clothoids for p in clothoid]
+
+    x_min = min(x_vals)
+    x_max = max(x_vals)
+    y_min = min(y_vals)
+    y_max = max(y_vals)
+
+    x_offset = 0.1*(x_max - x_min)
+    y_offset = 0.1*(y_max - y_min)
+
+    x_min = x_min - x_offset
+    x_max = x_max + x_offset
+    y_min = y_min - y_offset
+    y_max = y_max + y_offset
+
+    return x_min, x_max, y_min, y_max
+
+
+def draw_clothoids(start, goal, num_steps, clothoidal_paths,
+                   save_animation=False):
+
+    fig = plt.figure(figsize=(10, 10))
+    x_min, x_max, y_min, y_max = get_axes_limits(clothoidal_paths)
+    axes = plt.axes(xlim=(x_min, x_max), ylim=(y_min, y_max))
+
+    axes.plot(start.x, start.y, 'ro')
+    axes.plot(goal.x, goal.y, 'ro')
+    lines = [axes.plot([], [], 'b-')[0] for _ in range(len(clothoidal_paths))]
+
+    def animate(i):
+        for line, clothoid_path in zip(lines, clothoidal_paths):
+            x = [p.x for p in clothoid_path[:i]]
+            y = [p.y for p in clothoid_path[:i]]
+            line.set_data(x, y)
+
+        return lines
+
+    anim = animation.FuncAnimation(
+        fig,
+        animate,
+        frames=num_steps,
+        interval=25,
+        blit=True
+    )
+    if save_animation:
+        anim.save('clothoid.gif', fps=30, writer="imagemagick")
+    plt.show()
+
+
+def main():
+    start_point = Point(0, 0)
+    start_orientation_list = [0.0]
+    goal_point = Point(10, 0)
+    goal_orientation_list = np.linspace(-pi, pi, 75)
+    num_path_points = 100
+    clothoid_paths = generate_clothoid_paths(
+        start_point, start_orientation_list,
+        goal_point, goal_orientation_list,
+        num_path_points)
+    if show_animation:
+        draw_clothoids(start_point, goal_point,
+                       num_path_points, clothoid_paths,
+                       save_animation=False)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/PathPlanning/ClothoidalPaths/clothoidal_paths.py b/PathPlanning/ClothoidalPaths/clothoidal_paths.py
deleted file mode 100644
index 6fbf46ed2a..0000000000
--- a/PathPlanning/ClothoidalPaths/clothoidal_paths.py
+++ /dev/null
@@ -1,144 +0,0 @@
-"""
-Clothoidal Path Planner
-Author: Daniel Ingram (daniel-s-ingram)
-Reference paper: https://www.researchgate.net/publication/237062806
-"""
-
-import matplotlib.pyplot as plt
-import numpy as np
-import scipy.integrate as integrate
-from collections import namedtuple
-from scipy.optimize import fsolve
-from math import atan2, cos, hypot, pi, sin
-from matplotlib import animation
-
-Point = namedtuple("Point", ["x", "y"])
-
-
-def draw_clothoids(
-    theta1_vals,
-    theta2_vals,
-    num_clothoids=75,
-    num_steps=100,
-    save_animation=False
-):
-    p1 = Point(0, 0)
-    p2 = Point(10, 0)
-    clothoids = []
-    for theta1 in theta1_vals:
-        for theta2 in theta2_vals:
-            clothoid = get_clothoid_points(p1, p2, theta1, theta2, num_steps)
-            clothoids.append(clothoid)
-
-    fig = plt.figure(figsize=(10, 10))
-    x_min, x_max, y_min, y_max = get_axes_limits(clothoids)
-    axes = plt.axes(xlim=(x_min, x_max), ylim=(y_min, y_max))
-
-    axes.plot(p1.x, p1.y, 'ro')
-    axes.plot(p2.x, p2.y, 'ro')
-    lines = [axes.plot([], [], 'b-')[0] for _ in range(len(clothoids))]
-
-    def animate(i):
-        for line, clothoid in zip(lines, clothoids):
-            x = [p.x for p in clothoid[:i]]
-            y = [p.y for p in clothoid[:i]]
-            line.set_data(x, y)
-
-        return lines
-
-    anim = animation.FuncAnimation(
-        fig,
-        animate,
-        frames=num_steps,
-        interval=25,
-        blit=True
-    )
-    if save_animation:
-        anim.save('clothoid.gif', fps=30, writer="imagemagick")
-    plt.show()
-
-
-def get_clothoid_points(p1, p2, theta1, theta2, num_steps=100):
-    dx = p2.x - p1.x
-    dy = p2.y - p1.y
-    r = hypot(dx, dy)
-
-    phi = atan2(dy, dx)
-    phi1 = normalize_angle(theta1 - phi)
-    phi2 = normalize_angle(theta2 - phi)
-    delta = phi2 - phi1
-
-    try:
-        A = solve_for_root(phi1, phi2, delta)
-        L = compute_length(r, phi1, delta, A)
-        curv = compute_curvature(delta, A, L)
-        curv_rate = compute_curvature_rate(A, L)
-    except Exception as e:
-        print(f"Failed to generate clothoid points: {e}")
-        return None
-
-    points = []
-    for s in np.linspace(0, L, num_steps):
-        try:
-            x = p1.x + s*X(curv_rate*s**2, curv*s, theta1)
-            y = p1.y + s*Y(curv_rate*s**2, curv*s, theta1)
-            points.append(Point(x, y))
-        except Exception as e:
-            print(f"Skipping failed clothoid point: {e}")
-
-    return points
-
-
-def X(a, b, c):
-    return integrate.quad(lambda t: cos((a/2)*t**2 + b*t + c), 0, 1)[0]
-
-
-def Y(a, b, c):
-    return integrate.quad(lambda t: sin((a/2)*t**2 + b*t + c), 0, 1)[0]
-
-
-def solve_for_root(theta1, theta2, delta):
-    initial_guess = 3*(theta1 + theta2)
-    return fsolve(lambda x: Y(2*x, delta - x, theta1), [initial_guess])
-
-
-def compute_length(r, theta1, delta, A):
-    return r / X(2*A, delta - A, theta1)
-
-
-def compute_curvature(delta, A, L):
-    return (delta - A) / L
-
-
-def compute_curvature_rate(A, L):
-    return 2 * A / (L**2)
-
-
-def normalize_angle(angle_rad):
-    return (angle_rad + pi) % (2 * pi) - pi
-
-
-def get_axes_limits(clothoids):
-    x_vals = [p.x for clothoid in clothoids for p in clothoid]
-    y_vals = [p.y for clothoid in clothoids for p in clothoid]
-
-    x_min = min(x_vals)
-    x_max = max(x_vals)
-    y_min = min(y_vals)
-    y_max = max(y_vals)
-
-    x_offset = 0.1*(x_max - x_min)
-    y_offset = 0.1*(y_max - y_min)
-
-    x_min = x_min - x_offset
-    x_max = x_max + x_offset
-    y_min = y_min - y_offset
-    y_max = y_max + y_offset
-
-    return x_min, x_max, y_min, y_max
-
-
-if __name__ == "__main__":
-    theta1_vals = [0]
-    theta2_vals = np.linspace(-pi, pi, 75)
-    draw_clothoids(theta1_vals, theta2_vals, save_animation=False)
diff --git a/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst b/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst
index 5a447f7cd4..4c1f672ba0 100644
--- a/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst
+++ b/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst
@@ -34,7 +34,7 @@ So
     & \ddot{\theta} = \frac{g(M + m)sin{\theta} - \dot{\theta}^2lmsin{\theta}cos{\theta} + ucos{\theta}}{l(M + m - mcos^2{\theta})}
 
 
-Linearlied model when :math:`\theta` small, :math:`cos{\theta} \approx 1`, :math:`sin{\theta} \approx \theta`, :math:`\dot{\theta}^2 \approx 0`.
+Linearized model when :math:`\theta` small, :math:`cos{\theta} \approx 1`, :math:`sin{\theta} \approx \theta`, :math:`\dot{\theta}^2 \approx 0`.
 
 .. math::
     & \ddot{x} =  \frac{gm}{M}\theta + \frac{1}{M}u\\
@@ -68,16 +68,20 @@ If control x and \theta
 LQR control
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The LQR cotroller minimize this cost function defined as:
+The LQR controller minimize this cost function defined as:
 
 .. math::  J = x^T Q x + u^T R u
+
 the feedback control law that minimizes the value of the cost is:
 
 .. math::  u = -K x
+
 where:
 
 .. math::  K = (B^T P B + R)^{-1} B^T P A
-and :math:`P` is the unique positive definite solution to the discrete time `algebraic Riccati equation <https://en.wikipedia.org/wiki/Inverted_pendulum#From_Lagrange's_equations>`__  (DARE):
+
+and :math:`P` is the unique positive definite solution to the discrete time
+`algebraic Riccati equation <https://en.wikipedia.org/wiki/Inverted_pendulum#From_Lagrange's_equations>`__  (DARE):
 
 .. math::  P = A^T P A - A^T P B ( R + B^T P B )^{-1} B^T P A + Q
 
@@ -85,13 +89,13 @@ and :math:`P` is the unique positive definite solution to the discrete time `alg
 
 MPC control
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
-The MPC cotroller minimize this cost function defined as:
+The MPC controller minimize this cost function defined as:
 
 .. math:: J = x^T Q x + u^T R u
 
 subject to:
 
-- Linearlied Inverted Pendulum model
+- Linearized Inverted Pendulum model
 - Initial state
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/InvertedPendulumCart/animation.gif
diff --git a/docs/modules/path_planning/clothoid_path/clothoid_path.rst b/docs/modules/path_planning/clothoid_path/clothoid_path.rst
new file mode 100644
index 0000000000..1e8cee694a
--- /dev/null
+++ b/docs/modules/path_planning/clothoid_path/clothoid_path.rst
@@ -0,0 +1,80 @@
+.. _clothoid-path-planning:
+
+Clothoid path planning
+--------------------------
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ClothoidPath/animation1.gif
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ClothoidPath/animation2.gif
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ClothoidPath/animation3.gif
+
+This is a clothoid path planning sample code.
+
+This can interpolate two 2D pose (x, y, yaw) with a clothoid path,
+which its curvature is linearly continuous.
+In other words, this is G1 Hermite interpolation with a single clothoid segment.
+
+This path planning algorithm as follows:
+
+Step1: Solve g function
+~~~~~~~~~~~~~~~~~~~~~~~
+
+Solve the g(A) function with a nonlinear optimization solver.
+
+.. math::
+
+    g(A):=Y(2A, \delta-A, \phi_{s})
+
+Where
+
+* :math:`\delta`: the orientation difference between start and goal pose.
+* :math:`\phi_{s}`: the orientation of the start pose.
+* :math:`Y`: :math:`Y(a, b, c)=\int_{0}^{1} \sin \left(\frac{a}{2} \tau^{2}+b \tau+c\right) d \tau`
+
+
+Step2: Calculate path parameters
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+We can calculate these path parameters using :math:`A`,
+
+:math:`L`: path length
+
+.. math::
+
+        L=\frac{R}{X\left(2 A, \delta-A, \phi_{s}\right)}
+
+where
+
+* :math:`R`: the distance between start and goal pose
+* :math:`X`: :math:`X(a, b, c)=\int_{0}^{1} \cos \left(\frac{a}{2} \tau^{2}+b \tau+c\right) d \tau`
+
+
+- :math:`\kappa`: curvature
+
+.. math::
+
+        \kappa=(\delta-A) / L
+
+
+- :math:`\kappa'`: curvature rate
+
+.. math::
+
+        \kappa^{\prime}=2 A / L^{2}
+
+
+Step3: Construct a path with Fresnel integral
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The final clothoid path can be calculated with the path parameters and Fresnel integrals.
+
+.. math::
+        \begin{aligned}
+        &x(s)=x_{0}+\int_{0}^{s} \cos \left(\frac{1}{2} \kappa^{\prime} \tau^{2}+\kappa \tau+\vartheta_{0}\right) \mathrm{d} \tau \\
+        &y(s)=y_{0}+\int_{0}^{s} \sin \left(\frac{1}{2} \kappa^{\prime} \tau^{2}+\kappa \tau+\vartheta_{0}\right) \mathrm{d} \tau
+        \end{aligned}
+
+
+References
+~~~~~~~~~~
+
+-  `Fast and accurate G1 fitting of clothoid curves <https://www.researchgate.net/publication/237062806>`__
diff --git a/docs/modules/path_planning/path_planning_main.rst b/docs/modules/path_planning/path_planning_main.rst
index c3c5c389ec..5c70592afc 100644
--- a/docs/modules/path_planning/path_planning_main.rst
+++ b/docs/modules/path_planning/path_planning_main.rst
@@ -14,6 +14,7 @@ Path Planning
 .. include:: rrt/rrt.rst
 .. include:: cubic_spline/cubic_spline.rst
 .. include:: bspline_path/bspline_path.rst
+.. include:: clothoid_path/clothoid_path.rst
 .. include:: eta3_spline/eta3_spline.rst
 .. include:: bezier_path/bezier_path.rst
 .. include:: quintic_polynomials_planner/quintic_polynomials_planner.rst
diff --git a/tests/test_clothoidal_paths.py b/tests/test_clothoidal_paths.py
new file mode 100644
index 0000000000..0b038c0338
--- /dev/null
+++ b/tests/test_clothoidal_paths.py
@@ -0,0 +1,11 @@
+import conftest
+from PathPlanning.ClothoidPath import clothoid_path_planner as m
+
+
+def test_1():
+    m.show_animation = False
+    m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)

From af2061a1e784a6779bdc08976a43157ddae17884 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 11 Apr 2022 23:06:13 +0900
Subject: [PATCH 0609/1080] fix doc artifact link in CI (#660)

---
 PathPlanning/HybridAStar/car.py           | 14 +++++++-------
 PathPlanning/HybridAStar/hybrid_a_star.py |  6 +++---
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py
index cc7529b9c7..b378447fcd 100644
--- a/PathPlanning/HybridAStar/car.py
+++ b/PathPlanning/HybridAStar/car.py
@@ -12,14 +12,14 @@
 import numpy as np
 from scipy.spatial.transform import Rotation as Rot
 
-WB = 3.  # rear to front wheel
-W = 2.  # width of car
+WB = 3.0  # rear to front wheel
+W = 2.0  # width of car
 LF = 3.3  # distance from rear to vehicle front end
 LB = 1.0  # distance from rear to vehicle back end
 MAX_STEER = 0.6  # [rad] maximum steering angle
 
-W_BUBBLE_DIST = (LF - LB) / 2.0
-W_BUBBLE_R = sqrt(((LF + LB) / 2.0) ** 2 + 1)
+BUBBLE_DIST = (LF - LB) / 2.0  # distance from rear to center of vehicle.
+BUBBLE_R = np.hypot((LF + LB) / 2.0, W / 2.0)  # bubble radius
 
 # vehicle rectangle vertices
 VRX = [LF, LF, -LB, -LB, LF]
@@ -28,10 +28,10 @@
 
 def check_car_collision(x_list, y_list, yaw_list, ox, oy, kd_tree):
     for i_x, i_y, i_yaw in zip(x_list, y_list, yaw_list):
-        cx = i_x + W_BUBBLE_DIST * cos(i_yaw)
-        cy = i_y + W_BUBBLE_DIST * sin(i_yaw)
+        cx = i_x + BUBBLE_DIST * cos(i_yaw)
+        cy = i_y + BUBBLE_DIST * sin(i_yaw)
 
-        ids = kd_tree.query_ball_point([cx, cy], W_BUBBLE_R)
+        ids = kd_tree.query_ball_point([cx, cy], BUBBLE_R)
 
         if not ids:
             continue
diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
index 9679e3979b..4ef0c1dd21 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -20,7 +20,8 @@
 try:
     from dynamic_programming_heuristic import calc_distance_heuristic
     import reeds_shepp_path_planning as rs
-    from car import move, check_car_collision, MAX_STEER, WB, plot_car
+    from car import move, check_car_collision, MAX_STEER, WB, plot_car,\
+        BUBBLE_R
 except Exception:
     raise
 
@@ -28,7 +29,6 @@
 YAW_GRID_RESOLUTION = np.deg2rad(15.0)  # [rad]
 MOTION_RESOLUTION = 0.1  # [m] path interpolate resolution
 N_STEER = 20  # number of steer command
-VR = 1.0  # robot radius
 
 SB_COST = 100.0  # switch back penalty cost
 BACK_COST = 5.0  # backward penalty cost
@@ -276,7 +276,7 @@ def hybrid_a_star_planning(start, goal, ox, oy, xy_resolution, yaw_resolution):
 
     h_dp = calc_distance_heuristic(
         goal_node.x_list[-1], goal_node.y_list[-1],
-        ox, oy, xy_resolution, VR)
+        ox, oy, xy_resolution, BUBBLE_R)
 
     pq = []
     openList[calc_index(start_node, config)] = start_node

From b53fdf75f66ccb63b5cfaadaa81253d43f01805a Mon Sep 17 00:00:00 2001
From: Chris Mower <cmower@users.noreply.github.com>
Date: Tue, 19 Apr 2022 04:53:24 +0100
Subject: [PATCH 0610/1080] Add optional robot radius to RRT/RRTStar path
 planners (#655)

* Add optional robot radius to RRT/RRTStar path planners.
* update __init__ and check_collision to include radius
* during animation, if a robot radius is given then it is drawn

* Add test for robot radius

* Correct import error

* Correct missing robot_radius errors

* Address "expected 2 blank lines, found 1" error

* Address "line too long" errors

* Add missing argument description.

* Remove collision_check_with_xy and replace with check_collision

* Fix "missing whitespace after ','" error

* Update PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py

Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>

Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>
---
 .../closed_loop_rrt_star_car.py               | 23 +++++++------------
 PathPlanning/LQRRRTStar/lqr_rrt_star.py       |  8 +++++--
 PathPlanning/RRT/rrt.py                       | 18 +++++++++++----
 PathPlanning/RRT/rrt_with_sobol_sampler.py    | 20 +++++++++++-----
 PathPlanning/RRTDubins/rrt_dubins.py          |  6 ++++-
 PathPlanning/RRTStar/rrt_star.py              | 21 +++++++++++------
 PathPlanning/RRTStarDubins/rrt_star_dubins.py |  8 +++++--
 .../RRTStarReedsShepp/rrt_star_reeds_shepp.py | 11 ++++++---
 tests/test_rrt_star.py                        | 13 +++++++++++
 tests/test_rrt_star_reeds_shepp.py            |  2 +-
 10 files changed, 88 insertions(+), 42 deletions(-)

diff --git a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
index c0c8c356e8..dc9a575694 100644
--- a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
+++ b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
@@ -37,11 +37,13 @@ class ClosedLoopRRTStar(RRTStarReedsShepp):
 
     def __init__(self, start, goal, obstacle_list, rand_area,
                  max_iter=200,
-                 connect_circle_dist=50.0
+                 connect_circle_dist=50.0,
+                 robot_radius=0.0
                  ):
         super().__init__(start, goal, obstacle_list, rand_area,
                          max_iter=max_iter,
                          connect_circle_dist=connect_circle_dist,
+                         robot_radius=robot_radius
                          )
 
         self.target_speed = 10.0 / 3.6
@@ -127,7 +129,11 @@ def check_tracking_path_is_feasible(self, path):
             print("path is too long")
             find_goal = False
 
-        if not self.collision_check_with_xy(x, y, self.obstacle_list):
+        tmp_node = self.Node(x, y, 0)
+        tmp_node.path_x = x
+        tmp_node.path_y = y
+        if not self.check_collision(
+                tmp_node, self.obstacle_list, self.robot_radius):
             print("This path is collision")
             find_goal = False
 
@@ -151,19 +157,6 @@ def get_goal_indexes(self):
 
         return fgoalinds
 
-    @staticmethod
-    def collision_check_with_xy(x, y, obstacle_list):
-
-        for (ox, oy, size) in obstacle_list:
-            for (ix, iy) in zip(x, y):
-                dx = ox - ix
-                dy = oy - iy
-                d = dx * dx + dy * dy
-                if d <= size ** 2:
-                    return False  # collision
-
-        return True  # safe
-
 
 def main(gx=6.0, gy=7.0, gyaw=np.deg2rad(90.0), max_iter=100):
     print("Start" + __file__)
diff --git a/PathPlanning/LQRRRTStar/lqr_rrt_star.py b/PathPlanning/LQRRRTStar/lqr_rrt_star.py
index 177131ac96..28d332aa00 100644
--- a/PathPlanning/LQRRRTStar/lqr_rrt_star.py
+++ b/PathPlanning/LQRRRTStar/lqr_rrt_star.py
@@ -35,7 +35,8 @@ def __init__(self, start, goal, obstacle_list, rand_area,
                  goal_sample_rate=10,
                  max_iter=200,
                  connect_circle_dist=50.0,
-                 step_size=0.2
+                 step_size=0.2,
+                 robot_radius=0.0,
                  ):
         """
         Setting Parameter
@@ -44,6 +45,7 @@ def __init__(self, start, goal, obstacle_list, rand_area,
         goal:Goal Position [x,y]
         obstacleList:obstacle Positions [[x,y,size],...]
         randArea:Random Sampling Area [min,max]
+        robot_radius: robot body modeled as circle with given radius
 
         """
         self.start = self.Node(start[0], start[1])
@@ -58,6 +60,7 @@ def __init__(self, start, goal, obstacle_list, rand_area,
         self.curvature = 1.0
         self.goal_xy_th = 0.5
         self.step_size = step_size
+        self.robot_radius = robot_radius
 
         self.lqr_planner = LQRPlanner()
 
@@ -75,7 +78,8 @@ def planning(self, animation=True, search_until_max_iter=True):
             nearest_ind = self.get_nearest_node_index(self.node_list, rnd)
             new_node = self.steer(self.node_list[nearest_ind], rnd)
 
-            if self.check_collision(new_node, self.obstacle_list):
+            if self.check_collision(
+                    new_node, self.obstacle_list, self.robot_radius):
                 near_indexes = self.find_near_nodes(new_node)
                 new_node = self.choose_parent(new_node, near_indexes)
                 if new_node:
diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py
index dfbd4b1b7f..55092e6e00 100644
--- a/PathPlanning/RRT/rrt.py
+++ b/PathPlanning/RRT/rrt.py
@@ -50,7 +50,8 @@ def __init__(self,
                  path_resolution=0.5,
                  goal_sample_rate=5,
                  max_iter=500,
-                 play_area=None
+                 play_area=None,
+                 robot_radius=0.0,
                  ):
         """
         Setting Parameter
@@ -60,6 +61,7 @@ def __init__(self,
         obstacleList:obstacle Positions [[x,y,size],...]
         randArea:Random Sampling Area [min,max]
         play_area:stay inside this area [xmin,xmax,ymin,ymax]
+        robot_radius: robot body modeled as circle with given radius
 
         """
         self.start = self.Node(start[0], start[1])
@@ -76,6 +78,7 @@ def __init__(self,
         self.max_iter = max_iter
         self.obstacle_list = obstacle_list
         self.node_list = []
+        self.robot_radius = robot_radius
 
     def planning(self, animation=True):
         """
@@ -93,7 +96,8 @@ def planning(self, animation=True):
             new_node = self.steer(nearest_node, rnd_node, self.expand_dis)
 
             if self.check_if_outside_play_area(new_node, self.play_area) and \
-               self.check_collision(new_node, self.obstacle_list):
+               self.check_collision(
+                   new_node, self.obstacle_list, self.robot_radius):
                 self.node_list.append(new_node)
 
             if animation and i % 5 == 0:
@@ -103,7 +107,8 @@ def planning(self, animation=True):
                                       self.node_list[-1].y) <= self.expand_dis:
                 final_node = self.steer(self.node_list[-1], self.end,
                                         self.expand_dis)
-                if self.check_collision(final_node, self.obstacle_list):
+                if self.check_collision(
+                        final_node, self.obstacle_list, self.robot_radius):
                     return self.generate_final_course(len(self.node_list) - 1)
 
             if animation and i % 5:
@@ -173,6 +178,8 @@ def draw_graph(self, rnd=None):
             lambda event: [exit(0) if event.key == 'escape' else None])
         if rnd is not None:
             plt.plot(rnd.x, rnd.y, "^k")
+            if self.robot_radius > 0.0:
+                self.plot_circle(rnd.x, rnd.y, self.robot_radius, '-r')
         for node in self.node_list:
             if node.parent:
                 plt.plot(node.path_x, node.path_y, "-g")
@@ -225,7 +232,7 @@ def check_if_outside_play_area(node, play_area):
             return True  # inside - ok
 
     @staticmethod
-    def check_collision(node, obstacleList):
+    def check_collision(node, obstacleList, robot_radius):
 
         if node is None:
             return False
@@ -235,7 +242,7 @@ def check_collision(node, obstacleList):
             dy_list = [oy - y for y in node.path_y]
             d_list = [dx * dx + dy * dy for (dx, dy) in zip(dx_list, dy_list)]
 
-            if min(d_list) <= size**2:
+            if min(d_list) <= (size+robot_radius)**2:
                 return False  # collision
 
         return True  # safe
@@ -262,6 +269,7 @@ def main(gx=6.0, gy=10.0):
         rand_area=[-2, 15],
         obstacle_list=obstacleList,
         # play_area=[0, 10, 0, 14]
+        robot_radius=0.8
         )
     path = rrt.planning(animation=show_animation)
 
diff --git a/PathPlanning/RRT/rrt_with_sobol_sampler.py b/PathPlanning/RRT/rrt_with_sobol_sampler.py
index b88e51c680..8045d8f764 100644
--- a/PathPlanning/RRT/rrt_with_sobol_sampler.py
+++ b/PathPlanning/RRT/rrt_with_sobol_sampler.py
@@ -62,7 +62,8 @@ def __init__(self,
                  expand_dis=3.0,
                  path_resolution=0.5,
                  goal_sample_rate=5,
-                 max_iter=500):
+                 max_iter=500,
+                 robot_radius=0.0):
         """
         Setting Parameter
 
@@ -70,6 +71,7 @@ def __init__(self,
         goal:Goal Position [x,y]
         obstacle_list:obstacle Positions [[x,y,size],...]
         randArea:Random Sampling Area [min,max]
+        robot_radius: robot body modeled as circle with given radius
 
         """
         self.start = self.Node(start[0], start[1])
@@ -83,6 +85,7 @@ def __init__(self,
         self.obstacle_list = obstacle_list
         self.node_list = []
         self.sobol_inter_ = 0
+        self.robot_radius = robot_radius
 
     def planning(self, animation=True):
         """
@@ -99,7 +102,8 @@ def planning(self, animation=True):
 
             new_node = self.steer(nearest_node, rnd_node, self.expand_dis)
 
-            if self.check_collision(new_node, self.obstacle_list):
+            if self.check_collision(
+                    new_node, self.obstacle_list, self.robot_radius):
                 self.node_list.append(new_node)
 
             if animation and i % 5 == 0:
@@ -109,7 +113,8 @@ def planning(self, animation=True):
                                       self.node_list[-1].y) <= self.expand_dis:
                 final_node = self.steer(self.node_list[-1], self.end,
                                         self.expand_dis)
-                if self.check_collision(final_node, self.obstacle_list):
+                if self.check_collision(
+                        final_node, self.obstacle_list, self.robot_radius):
                     return self.generate_final_course(len(self.node_list) - 1)
 
             if animation and i % 5:
@@ -183,6 +188,8 @@ def draw_graph(self, rnd=None):
             lambda event: [sys.exit(0) if event.key == 'escape' else None])
         if rnd is not None:
             plt.plot(rnd.x, rnd.y, "^k")
+            if self.robot_radius >= 0.0:
+                self.plot_circle(rnd.x, rnd.y, self.robot_radius, '-r')
         for node in self.node_list:
             if node.parent:
                 plt.plot(node.path_x, node.path_y, "-g")
@@ -214,7 +221,7 @@ def get_nearest_node_index(node_list, rnd_node):
         return minind
 
     @staticmethod
-    def check_collision(node, obstacle_list):
+    def check_collision(node, obstacle_list, robot_radius):
 
         if node is None:
             return False
@@ -224,7 +231,7 @@ def check_collision(node, obstacle_list):
             dy_list = [oy - y for y in node.path_y]
             d_list = [dx * dx + dy * dy for (dx, dy) in zip(dx_list, dy_list)]
 
-            if min(d_list) <= size**2:
+            if min(d_list) <= (size+robot_radius)**2:
                 return False  # collision
 
         return True  # safe
@@ -249,7 +256,8 @@ def main(gx=6.0, gy=10.0):
         start=[0, 0],
         goal=[gx, gy],
         rand_area=[-2, 15],
-        obstacle_list=obstacle_list)
+        obstacle_list=obstacle_list,
+        robot_radius=0.8)
     path = rrt.planning(animation=show_animation)
 
     if path is None:
diff --git a/PathPlanning/RRTDubins/rrt_dubins.py b/PathPlanning/RRTDubins/rrt_dubins.py
index 8ab06c7323..a43362b340 100644
--- a/PathPlanning/RRTDubins/rrt_dubins.py
+++ b/PathPlanning/RRTDubins/rrt_dubins.py
@@ -46,6 +46,7 @@ def __init__(self, x, y, yaw):
     def __init__(self, start, goal, obstacle_list, rand_area,
                  goal_sample_rate=10,
                  max_iter=200,
+                 robot_radius=0.0
                  ):
         """
         Setting Parameter
@@ -54,6 +55,7 @@ def __init__(self, start, goal, obstacle_list, rand_area,
         goal:Goal Position [x,y]
         obstacleList:obstacle Positions [[x,y,size],...]
         randArea:Random Sampling Area [min,max]
+        robot_radius: robot body modeled as circle with given radius
 
         """
         self.start = self.Node(start[0], start[1], start[2])
@@ -67,6 +69,7 @@ def __init__(self, start, goal, obstacle_list, rand_area,
         self.curvature = 1.0  # for dubins path
         self.goal_yaw_th = np.deg2rad(1.0)
         self.goal_xy_th = 0.5
+        self.robot_radius = robot_radius
 
     def planning(self, animation=True, search_until_max_iter=True):
         """
@@ -82,7 +85,8 @@ def planning(self, animation=True, search_until_max_iter=True):
             nearest_ind = self.get_nearest_node_index(self.node_list, rnd)
             new_node = self.steer(self.node_list[nearest_ind], rnd)
 
-            if self.check_collision(new_node, self.obstacle_list):
+            if self.check_collision(
+                    new_node, self.obstacle_list, self.robot_radius):
                 self.node_list.append(new_node)
 
             if animation and i % 5 == 0:
diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py
index bef0086095..e2c05456de 100644
--- a/PathPlanning/RRTStar/rrt_star.py
+++ b/PathPlanning/RRTStar/rrt_star.py
@@ -42,7 +42,8 @@ def __init__(self,
                  goal_sample_rate=20,
                  max_iter=300,
                  connect_circle_dist=50.0,
-                 search_until_max_iter=False):
+                 search_until_max_iter=False,
+                 robot_radius=0.0):
         """
         Setting Parameter
 
@@ -53,7 +54,8 @@ def __init__(self,
 
         """
         super().__init__(start, goal, obstacle_list, rand_area, expand_dis,
-                         path_resolution, goal_sample_rate, max_iter)
+                         path_resolution, goal_sample_rate, max_iter,
+                         robot_radius=robot_radius)
         self.connect_circle_dist = connect_circle_dist
         self.goal_node = self.Node(goal[0], goal[1])
         self.search_until_max_iter = search_until_max_iter
@@ -77,7 +79,8 @@ def planning(self, animation=True):
                 math.hypot(new_node.x-near_node.x,
                            new_node.y-near_node.y)
 
-            if self.check_collision(new_node, self.obstacle_list):
+            if self.check_collision(
+                    new_node, self.obstacle_list, self.robot_radius):
                 near_inds = self.find_near_nodes(new_node)
                 node_with_updated_parent = self.choose_parent(
                     new_node, near_inds)
@@ -128,7 +131,8 @@ def choose_parent(self, new_node, near_inds):
         for i in near_inds:
             near_node = self.node_list[i]
             t_node = self.steer(near_node, new_node)
-            if t_node and self.check_collision(t_node, self.obstacle_list):
+            if t_node and self.check_collision(
+                    t_node, self.obstacle_list, self.robot_radius):
                 costs.append(self.calc_new_cost(near_node, new_node))
             else:
                 costs.append(float("inf"))  # the cost of collision node
@@ -156,7 +160,8 @@ def search_best_goal_node(self):
         safe_goal_inds = []
         for goal_ind in goal_inds:
             t_node = self.steer(self.node_list[goal_ind], self.goal_node)
-            if self.check_collision(t_node, self.obstacle_list):
+            if self.check_collision(
+                    t_node, self.obstacle_list, self.robot_radius):
                 safe_goal_inds.append(goal_ind)
 
         if not safe_goal_inds:
@@ -219,7 +224,8 @@ def rewire(self, new_node, near_inds):
                 continue
             edge_node.cost = self.calc_new_cost(new_node, near_node)
 
-            no_collision = self.check_collision(edge_node, self.obstacle_list)
+            no_collision = self.check_collision(
+                edge_node, self.obstacle_list, self.robot_radius)
             improved_cost = near_node.cost > edge_node.cost
 
             if no_collision and improved_cost:
@@ -264,7 +270,8 @@ def main():
         goal=[6, 10],
         rand_area=[-2, 15],
         obstacle_list=obstacle_list,
-        expand_dis=1)
+        expand_dis=1,
+        robot_radius=0.8)
     path = rrt_star.planning(animation=show_animation)
 
     if path is None:
diff --git a/PathPlanning/RRTStarDubins/rrt_star_dubins.py b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
index 26367eecea..21cc9b2bc5 100644
--- a/PathPlanning/RRTStarDubins/rrt_star_dubins.py
+++ b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
@@ -46,7 +46,8 @@ def __init__(self, x, y, yaw):
     def __init__(self, start, goal, obstacle_list, rand_area,
                  goal_sample_rate=10,
                  max_iter=200,
-                 connect_circle_dist=50.0
+                 connect_circle_dist=50.0,
+                 robot_radius=0.0,
                  ):
         """
         Setting Parameter
@@ -55,6 +56,7 @@ def __init__(self, start, goal, obstacle_list, rand_area,
         goal:Goal Position [x,y]
         obstacleList:obstacle Positions [[x,y,size],...]
         randArea:Random Sampling Area [min,max]
+        robot_radius: robot body modeled as circle with given radius
 
         """
         self.start = self.Node(start[0], start[1], start[2])
@@ -69,6 +71,7 @@ def __init__(self, start, goal, obstacle_list, rand_area,
         self.curvature = 1.0  # for dubins path
         self.goal_yaw_th = np.deg2rad(1.0)
         self.goal_xy_th = 0.5
+        self.robot_radius = robot_radius
 
     def planning(self, animation=True, search_until_max_iter=True):
         """
@@ -84,7 +87,8 @@ def planning(self, animation=True, search_until_max_iter=True):
             nearest_ind = self.get_nearest_node_index(self.node_list, rnd)
             new_node = self.steer(self.node_list[nearest_ind], rnd)
 
-            if self.check_collision(new_node, self.obstacle_list):
+            if self.check_collision(
+                    new_node, self.obstacle_list, self.robot_radius):
                 near_indexes = self.find_near_nodes(new_node)
                 new_node = self.choose_parent(new_node, near_indexes)
                 if new_node:
diff --git a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
index 6ea66dc729..323bc76be2 100644
--- a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
+++ b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
@@ -45,7 +45,8 @@ def __init__(self, x, y, yaw):
 
     def __init__(self, start, goal, obstacle_list, rand_area,
                  max_iter=200,
-                 connect_circle_dist=50.0
+                 connect_circle_dist=50.0,
+                 robot_radius=0.0
                  ):
         """
         Setting Parameter
@@ -54,6 +55,7 @@ def __init__(self, start, goal, obstacle_list, rand_area,
         goal:Goal Position [x,y]
         obstacleList:obstacle Positions [[x,y,size],...]
         randArea:Random Sampling Area [min,max]
+        robot_radius: robot body modeled as circle with given radius
 
         """
         self.start = self.Node(start[0], start[1], start[2])
@@ -63,6 +65,7 @@ def __init__(self, start, goal, obstacle_list, rand_area,
         self.max_iter = max_iter
         self.obstacle_list = obstacle_list
         self.connect_circle_dist = connect_circle_dist
+        self.robot_radius = robot_radius
 
         self.curvature = 1.0
         self.goal_yaw_th = np.deg2rad(1.0)
@@ -82,7 +85,8 @@ def planning(self, animation=True, search_until_max_iter=True):
             nearest_ind = self.get_nearest_node_index(self.node_list, rnd)
             new_node = self.steer(self.node_list[nearest_ind], rnd)
 
-            if self.check_collision(new_node, self.obstacle_list):
+            if self.check_collision(
+                    new_node, self.obstacle_list, self.robot_radius):
                 near_indexes = self.find_near_nodes(new_node)
                 new_node = self.choose_parent(new_node, near_indexes)
                 if new_node:
@@ -117,7 +121,8 @@ def try_goal_path(self, node):
         if new_node is None:
             return
 
-        if self.check_collision(new_node, self.obstacle_list):
+        if self.check_collision(
+                new_node, self.obstacle_list, self.robot_radius):
             self.node_list.append(new_node)
 
     def draw_graph(self, rnd=None):
diff --git a/tests/test_rrt_star.py b/tests/test_rrt_star.py
index 5327f4ca20..232995ecb4 100644
--- a/tests/test_rrt_star.py
+++ b/tests/test_rrt_star.py
@@ -19,5 +19,18 @@ def test_no_obstacle():
     assert path is not None
 
 
+def test_no_obstacle_and_robot_radius():
+    obstacle_list = []
+
+    # Set Initial parameters
+    rrt_star = m.RRTStar(start=[0, 0],
+                         goal=[6, 10],
+                         rand_area=[-2, 15],
+                         obstacle_list=obstacle_list,
+                         robot_radius=0.8)
+    path = rrt_star.planning(animation=False)
+    assert path is not None
+
+
 if __name__ == '__main__':
     conftest.run_this_test(__file__)
diff --git a/tests/test_rrt_star_reeds_shepp.py b/tests/test_rrt_star_reeds_shepp.py
index 0a4663056a..cb6b586b33 100644
--- a/tests/test_rrt_star_reeds_shepp.py
+++ b/tests/test_rrt_star_reeds_shepp.py
@@ -1,5 +1,5 @@
 import conftest  # Add root path to sys.path
-import rrt_star_reeds_shepp as m
+from PathPlanning.RRTStarReedsShepp import rrt_star_reeds_shepp as m
 
 
 def test1():

From 4f8c923589af8738b97fe821938ea5f51d55b096 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 26 Apr 2022 07:15:19 +0900
Subject: [PATCH 0611/1080] Bump pytest from 7.1.1 to 7.1.2 in /requirements
 (#661)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.1.1 to 7.1.2.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/7.1.1...7.1.2)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 050904a674..25d15e780c 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,7 +3,7 @@ scipy == 1.8.0
 pandas == 1.4.2
 matplotlib == 3.5.1
 cvxpy == 1.2.0
-pytest == 7.1.1 # For unit test
+pytest == 7.1.2 # For unit test
 pytest-xdist == 2.5.0 # For unit test
 mypy == 0.942 # For unit test
 flake8 == 4.0.1 # For unit test

From 28d95730448ff25bbe7ec6d2e7d9240800e2ac4a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 2 May 2022 21:01:46 +0900
Subject: [PATCH 0612/1080] fix doc artifact link in CI (#659)

* fix doc artifact link in CI

* fix doc artifact link in CI

* fix doc artifact link in CI

* fix doc artifact link in CI
---
 .circleci/artifact_path                             |  1 -
 .circleci/config.yml                                |  2 +-
 .github/workflows/circle_artifacts.yml              | 13 +++++++++++++
 .github/workflows/circleci-artifacts-redirector.yml |  2 +-
 4 files changed, 15 insertions(+), 3 deletions(-)
 delete mode 100644 .circleci/artifact_path
 create mode 100644 .github/workflows/circle_artifacts.yml

diff --git a/.circleci/artifact_path b/.circleci/artifact_path
deleted file mode 100644
index d2c64880b1..0000000000
--- a/.circleci/artifact_path
+++ /dev/null
@@ -1 +0,0 @@
-0/html/index.html
diff --git a/.circleci/config.yml b/.circleci/config.yml
index 0a1962ad0b..8df4fab9e4 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -6,7 +6,7 @@ orbs:
 jobs:
   build_doc:
     docker:
-      - image: circleci/python:3.9
+      - image: cimg/python:3.9
     steps:
       - checkout
       - run:
diff --git a/.github/workflows/circle_artifacts.yml b/.github/workflows/circle_artifacts.yml
new file mode 100644
index 0000000000..56ac78ed15
--- /dev/null
+++ b/.github/workflows/circle_artifacts.yml
@@ -0,0 +1,13 @@
+on: [status]
+jobs:
+  circleci_artifacts_redirector_job:
+    runs-on: ubuntu-20.04
+    name: Run CircleCI artifacts redirector
+    steps:
+      - name: GitHub Action step
+        uses: larsoner/circleci-artifacts-redirector-action@master
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          artifact-path: 0/html-scipyorg/index.html
+          circleci-jobs: build_docs
+          job-title: build_doc artifact
\ No newline at end of file
diff --git a/.github/workflows/circleci-artifacts-redirector.yml b/.github/workflows/circleci-artifacts-redirector.yml
index 951e3a5947..d988d2c38e 100644
--- a/.github/workflows/circleci-artifacts-redirector.yml
+++ b/.github/workflows/circleci-artifacts-redirector.yml
@@ -6,7 +6,7 @@ jobs:
     name: Run CircleCI artifacts redirector
     steps:
       - name: run-circleci-artifacts-redirector
-        uses: larsoner/circleci-artifacts-redirector-action@0.2.0
+        uses: larsoner/circleci-artifacts-redirector-action@0.3.1
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           artifact-path: 0/html/index.html

From 163cc5ea58d68ee994f1546d118333dc01217552 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 2 May 2022 21:06:33 +0900
Subject: [PATCH 0613/1080] Update dubins_path.rst (#663)

* Update dubins_path.rst

* Update dubins_path.rst
---
 .../path_planning/dubins_path/dubins_path.rst   | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/docs/modules/path_planning/dubins_path/dubins_path.rst b/docs/modules/path_planning/dubins_path/dubins_path.rst
index 5e14820f8f..6765a84617 100644
--- a/docs/modules/path_planning/dubins_path/dubins_path.rst
+++ b/docs/modules/path_planning/dubins_path/dubins_path.rst
@@ -5,7 +5,18 @@ A sample code for Dubins path planning.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DubinsPath/animation.gif?raw=True
 
-Ref:
+Dubins path
+~~~~~~~~~~~~
+Dubims path is a analyrical path planning algorithm for a simple car model.
 
--  `Dubins path -
-   Wikipedia <https://en.wikipedia.org/wiki/Dubins_path>`__
+It can generates a shortest path between 2D poses (x, y, yaw) with maximum curvture comstraint and tangent(yaw angle) constraint.
+
+
+
+
+
+Reference
+~~~~~~~~~~~~~~~~~~~~
+
+-  `Dubins path - Wikipedia <https://en.wikipedia.org/wiki/Dubins_path>`__
+-  `15.3.1 Dubins Curves <http://planning.cs.uiuc.edu/node821.html>`__

From 462a8cd6c0fd48434af126204bf5b1eca7ac7357 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 3 May 2022 07:23:49 +0900
Subject: [PATCH 0614/1080] Bump mypy from 0.942 to 0.950 in /requirements
 (#665)

Bumps [mypy](https://github.com/python/mypy) from 0.942 to 0.950.
- [Release notes](https://github.com/python/mypy/releases)
- [Commits](https://github.com/python/mypy/compare/v0.942...v0.950)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 25d15e780c..46f56702ea 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ matplotlib == 3.5.1
 cvxpy == 1.2.0
 pytest == 7.1.2 # For unit test
 pytest-xdist == 2.5.0 # For unit test
-mypy == 0.942 # For unit test
+mypy == 0.950 # For unit test
 flake8 == 4.0.1 # For unit test

From 32b545fe7c35b57f280cd9d570f62839886f2e4b Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 7 May 2022 13:19:30 +0900
Subject: [PATCH 0615/1080] Enhance dubins path docs (#664)

* Engance dubins path docs

* Update dubins_path.rst

* fix doc artifact link in CI

* wip

* wip

* wip

* Update dubins_path.rst

* wip

* wip

* wip

* wip

* wip
---
 .circleci/config.yml                          |   1 +
 ...ath_planning.py => dubins_path_planner.py} | 150 ++++++++++--------
 PathPlanning/HybridAStar/car.py               |   2 +-
 PathPlanning/RRTDubins/rrt_dubins.py          |  10 +-
 PathPlanning/RRTStarDubins/rrt_star_dubins.py |  10 +-
 docs/conf.py                                  |   5 +-
 docs/doc_requirements.txt                     |   1 +
 .../modules/path_planning/dubins_path/RLR.jpg | Bin 0 -> 10220 bytes
 .../modules/path_planning/dubins_path/RSR.jpg | Bin 0 -> 10245 bytes
 .../path_planning/dubins_path/dubins_path.rst |  28 +++-
 tests/test_diff_codestyle.py                  |   4 +-
 tests/test_dubins_path_planning.py            |  12 +-
 tests/test_utils.py                           |  21 +++
 utils/__init__.py                             |   0
 utils/angle.py                                |  77 +++++++++
 15 files changed, 235 insertions(+), 86 deletions(-)
 rename PathPlanning/DubinsPath/{dubins_path_planning.py => dubins_path_planner.py} (72%)
 create mode 100644 docs/modules/path_planning/dubins_path/RLR.jpg
 create mode 100644 docs/modules/path_planning/dubins_path/RSR.jpg
 create mode 100644 tests/test_utils.py
 create mode 100644 utils/__init__.py
 create mode 100644 utils/angle.py

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 8df4fab9e4..275ad3bc7a 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -14,6 +14,7 @@ jobs:
           command: |
             python -m venv venv
             . venv/bin/activate
+            pip install -r requirements/requirements.txt
             pip install -r docs/doc_requirements.txt
             cd docs;make html
       - store_artifacts:
diff --git a/PathPlanning/DubinsPath/dubins_path_planning.py b/PathPlanning/DubinsPath/dubins_path_planner.py
similarity index 72%
rename from PathPlanning/DubinsPath/dubins_path_planning.py
rename to PathPlanning/DubinsPath/dubins_path_planner.py
index f8e6d7b1c1..69c213ddf4 100644
--- a/PathPlanning/DubinsPath/dubins_path_planning.py
+++ b/PathPlanning/DubinsPath/dubins_path_planner.py
@@ -5,64 +5,82 @@
 author Atsushi Sakai(@Atsushi_twi)
 
 """
-import math
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
 
-import matplotlib.pyplot as plt
+import math
 import numpy as np
-from scipy.spatial.transform import Rotation as Rot
+from utils.angle import angle_mod, create_2d_rotation_matrix
 
 show_animation = True
 
 
-def dubins_path_planning(s_x, s_y, s_yaw, g_x, g_y, g_yaw, curvature,
-                         step_size=0.1):
+def plan_dubins_path(s_x, s_y, s_yaw,
+                     g_x, g_y, g_yaw,
+                     curvature,
+                     step_size=0.1):
     """
-    Dubins path planner
-
-    :param s_x: x position of start point [m]
-    :param s_y: y position of start point [m]
-    :param s_yaw: yaw angle of start point [rad]
-    :param g_x: x position of end point [m]
-    :param g_y: y position of end point [m]
-    :param g_yaw: yaw angle of end point [rad]
-    :param curvature: curvature for curve [1/m]
-    :param step_size: (optional) step size between two path points [m]
-    :return:
-        x_list: x positions of a path
-        y_list: y positions of a path
-        yaw_list: yaw angles of a path
-        modes: mode list of a path
-        lengths: length of path segments.
-    """
-
-    g_x -= s_x
-    g_y -= s_y
+    Path dubins path
+
+    Parameters
+    ----------
+    s_x : float
+        x position of the start point [m]
+    s_y : float
+        y position of the start point [m]
+    s_yaw : float
+        yaw angle of the start point [rad]
+    g_x : float
+        x position of the goal point [m]
+    g_y : float
+        y position of the end point [m]
+    g_yaw : float
+        yaw angle of the end point [rad]
+    curvature : float
+        curvature for curve [1/m]
+    step_size : float (optional)
+        step size between two path points [m]. Default is 0.1
+
+    Returns
+    -------
+    x_list: array
+        x positions of the path
+    y_list: array
+        y positions of the path
+    yaw_list: array
+        yaw angles of the path
+    modes: array
+        mode list of the path
+    lengths: array
+        length list of the path segments.
 
-    l_rot = Rot.from_euler('z', s_yaw).as_matrix()[0:2, 0:2]
-    le_xy = np.stack([g_x, g_y]).T @ l_rot
-    le_yaw = g_yaw - s_yaw
+    """
+    # calculate local goal x, y, yaw
+    l_rot = create_2d_rotation_matrix(s_yaw)
+    le_xy = np.stack([g_x - s_x, g_y - s_y]).T @ l_rot
+    local_goal_x = le_xy[0]
+    local_goal_y = le_xy[1]
+    local_goal_yaw = g_yaw - s_yaw
 
     lp_x, lp_y, lp_yaw, modes, lengths = dubins_path_planning_from_origin(
-        le_xy[0], le_xy[1], le_yaw, curvature, step_size)
+        local_goal_x, local_goal_y, local_goal_yaw, curvature, step_size)
 
-    rot = Rot.from_euler('z', -s_yaw).as_matrix()[0:2, 0:2]
+    # Convert a local coordinate path to the global coordinate
+    rot = create_2d_rotation_matrix(-s_yaw)
     converted_xy = np.stack([lp_x, lp_y]).T @ rot
     x_list = converted_xy[:, 0] + s_x
     y_list = converted_xy[:, 1] + s_y
-    yaw_list = [pi_2_pi(i_yaw + s_yaw) for i_yaw in lp_yaw]
+    yaw_list = angle_mod(np.array(lp_yaw) + s_yaw)
 
     return x_list, y_list, yaw_list, modes, lengths
 
 
-def mod2pi(theta):
-    return theta - 2.0 * math.pi * math.floor(theta / 2.0 / math.pi)
-
-
-def pi_2_pi(angle):
-    return (angle + math.pi) % (2 * math.pi) - math.pi
+def _mod2pi(theta):
+    return angle_mod(theta, zero_2_2pi=True)
 
 
-def left_straight_left(alpha, beta, d):
+def _LSL(alpha, beta, d):
     sa = math.sin(alpha)
     sb = math.sin(beta)
     ca = math.cos(alpha)
@@ -76,9 +94,9 @@ def left_straight_left(alpha, beta, d):
     if p_squared < 0:
         return None, None, None, mode
     tmp1 = math.atan2((cb - ca), tmp0)
-    t = mod2pi(-alpha + tmp1)
+    t = _mod2pi(-alpha + tmp1)
     p = math.sqrt(p_squared)
-    q = mod2pi(beta - tmp1)
+    q = _mod2pi(beta - tmp1)
 
     return t, p, q, mode
 
@@ -96,9 +114,9 @@ def right_straight_right(alpha, beta, d):
     if p_squared < 0:
         return None, None, None, mode
     tmp1 = math.atan2((ca - cb), tmp0)
-    t = mod2pi(alpha - tmp1)
+    t = angle_mod(alpha - tmp1, zero_2_2pi=True)
     p = math.sqrt(p_squared)
-    q = mod2pi(-beta + tmp1)
+    q = _mod2pi(-beta + tmp1)
 
     return t, p, q, mode
 
@@ -116,8 +134,8 @@ def left_straight_right(alpha, beta, d):
         return None, None, None, mode
     p = math.sqrt(p_squared)
     tmp2 = math.atan2((-ca - cb), (d + sa + sb)) - math.atan2(-2.0, p)
-    t = mod2pi(-alpha + tmp2)
-    q = mod2pi(-mod2pi(beta) + tmp2)
+    t = _mod2pi(-alpha + tmp2)
+    q = _mod2pi(-_mod2pi(beta) + tmp2)
 
     return t, p, q, mode
 
@@ -135,8 +153,8 @@ def right_straight_left(alpha, beta, d):
         return None, None, None, mode
     p = math.sqrt(p_squared)
     tmp2 = math.atan2((ca + cb), (d - sa - sb)) - math.atan2(2.0, p)
-    t = mod2pi(alpha - tmp2)
-    q = mod2pi(beta - tmp2)
+    t = _mod2pi(alpha - tmp2)
+    q = _mod2pi(beta - tmp2)
 
     return t, p, q, mode
 
@@ -153,13 +171,13 @@ def right_left_right(alpha, beta, d):
     if abs(tmp_rlr) > 1.0:
         return None, None, None, mode
 
-    p = mod2pi(2 * math.pi - math.acos(tmp_rlr))
-    t = mod2pi(alpha - math.atan2(ca - cb, d - sa + sb) + mod2pi(p / 2.0))
-    q = mod2pi(alpha - beta - t + mod2pi(p))
+    p = _mod2pi(2 * math.pi - math.acos(tmp_rlr))
+    t = _mod2pi(alpha - math.atan2(ca - cb, d - sa + sb) + _mod2pi(p / 2.0))
+    q = _mod2pi(alpha - beta - t + _mod2pi(p))
     return t, p, q, mode
 
 
-def left_right_left(alpha, beta, d):
+def _LRL(alpha, beta, d):
     sa = math.sin(alpha)
     sb = math.sin(beta)
     ca = math.cos(alpha)
@@ -170,9 +188,9 @@ def left_right_left(alpha, beta, d):
     tmp_lrl = (6.0 - d * d + 2.0 * c_ab + 2.0 * d * (- sa + sb)) / 8.0
     if abs(tmp_lrl) > 1:
         return None, None, None, mode
-    p = mod2pi(2 * math.pi - math.acos(tmp_lrl))
-    t = mod2pi(-alpha - math.atan2(ca - cb, d + sa - sb) + p / 2.0)
-    q = mod2pi(mod2pi(beta) - alpha - t + mod2pi(p))
+    p = _mod2pi(2 * math.pi - math.acos(tmp_lrl))
+    t = _mod2pi(-alpha - math.atan2(ca - cb, d + sa - sb) + p / 2.0)
+    q = _mod2pi(_mod2pi(beta) - alpha - t + _mod2pi(p))
 
     return t, p, q, mode
 
@@ -184,13 +202,13 @@ def dubins_path_planning_from_origin(end_x, end_y, end_yaw, curvature,
     D = math.hypot(dx, dy)
     d = D * curvature
 
-    theta = mod2pi(math.atan2(dy, dx))
-    alpha = mod2pi(- theta)
-    beta = mod2pi(end_yaw - theta)
+    theta = _mod2pi(math.atan2(dy, dx))
+    alpha = _mod2pi(- theta)
+    beta = _mod2pi(end_yaw - theta)
 
-    planning_funcs = [left_straight_left, right_straight_right,
+    planning_funcs = [_LSL, right_straight_right,
                       left_straight_right, right_straight_left,
-                      right_left_right, left_right_left]
+                      right_left_right, _LRL]
 
     best_cost = float("inf")
     bt, bp, bq, best_mode = None, None, None, None
@@ -319,6 +337,7 @@ def generate_local_course(total_length, lengths, modes, max_curvature,
 
 def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r",
                ec="k"):  # pragma: no cover
+    import matplotlib.pyplot as plt
     if not isinstance(x, float):
         for (i_x, i_y, i_yaw) in zip(x, y, yaw):
             plot_arrow(i_x, i_y, i_yaw)
@@ -330,6 +349,7 @@ def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r",
 
 def main():
     print("Dubins path planner sample start!!")
+    import matplotlib.pyplot as plt
 
     start_x = 1.0  # [m]
     start_y = 1.0  # [m]
@@ -341,13 +361,13 @@ def main():
 
     curvature = 1.0
 
-    path_x, path_y, path_yaw, mode, lengths = dubins_path_planning(start_x,
-                                                                   start_y,
-                                                                   start_yaw,
-                                                                   end_x,
-                                                                   end_y,
-                                                                   end_yaw,
-                                                                   curvature)
+    path_x, path_y, path_yaw, mode, lengths = plan_dubins_path(start_x,
+                                                               start_y,
+                                                               start_yaw,
+                                                               end_x,
+                                                               end_y,
+                                                               end_yaw,
+                                                               curvature)
 
     if show_animation:
         plt.plot(path_x, path_y, label="final course " + "".join(mode))
diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py
index b378447fcd..14b7f956b7 100644
--- a/PathPlanning/HybridAStar/car.py
+++ b/PathPlanning/HybridAStar/car.py
@@ -6,7 +6,7 @@
 
 """
 
-from math import sqrt, cos, sin, tan, pi
+from math import cos, sin, tan, pi
 
 import matplotlib.pyplot as plt
 import numpy as np
diff --git a/PathPlanning/RRTDubins/rrt_dubins.py b/PathPlanning/RRTDubins/rrt_dubins.py
index a43362b340..f1bd03ca02 100644
--- a/PathPlanning/RRTDubins/rrt_dubins.py
+++ b/PathPlanning/RRTDubins/rrt_dubins.py
@@ -20,7 +20,7 @@
 
 try:
     from rrt import RRT
-    import dubins_path_planning
+    import dubins_path_planner
 except ImportError:
     raise
 
@@ -130,15 +130,15 @@ def draw_graph(self, rnd=None):  # pragma: no cover
         plt.pause(0.01)
 
     def plot_start_goal_arrow(self):  # pragma: no cover
-        dubins_path_planning.plot_arrow(
+        dubins_path_planner.plot_arrow(
             self.start.x, self.start.y, self.start.yaw)
-        dubins_path_planning.plot_arrow(
+        dubins_path_planner.plot_arrow(
             self.end.x, self.end.y, self.end.yaw)
 
     def steer(self, from_node, to_node):
 
         px, py, pyaw, mode, course_lengths = \
-            dubins_path_planning.dubins_path_planning(
+            dubins_path_planner.plan_dubins_path(
                 from_node.x, from_node.y, from_node.yaw,
                 to_node.x, to_node.y, to_node.yaw, self.curvature)
 
@@ -160,7 +160,7 @@ def steer(self, from_node, to_node):
 
     def calc_new_cost(self, from_node, to_node):
 
-        _, _, _, _, course_length = dubins_path_planning.dubins_path_planning(
+        _, _, _, _, course_length = dubins_path_planner.plan_dubins_path(
             from_node.x, from_node.y, from_node.yaw,
             to_node.x, to_node.y, to_node.yaw, self.curvature)
 
diff --git a/PathPlanning/RRTStarDubins/rrt_star_dubins.py b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
index 21cc9b2bc5..e2a456d75f 100644
--- a/PathPlanning/RRTStarDubins/rrt_star_dubins.py
+++ b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
@@ -20,7 +20,7 @@
                 "/../RRTStar/")
 
 try:
-    import dubins_path_planning
+    import dubins_path_planner
     from rrt_star import RRTStar
 except ImportError:
     raise
@@ -136,15 +136,15 @@ def draw_graph(self, rnd=None):
         plt.pause(0.01)
 
     def plot_start_goal_arrow(self):
-        dubins_path_planning.plot_arrow(
+        dubins_path_planner.plot_arrow(
             self.start.x, self.start.y, self.start.yaw)
-        dubins_path_planning.plot_arrow(
+        dubins_path_planner.plot_arrow(
             self.end.x, self.end.y, self.end.yaw)
 
     def steer(self, from_node, to_node):
 
         px, py, pyaw, mode, course_lengths = \
-            dubins_path_planning.dubins_path_planning(
+            dubins_path_planner.plan_dubins_path(
                 from_node.x, from_node.y, from_node.yaw,
                 to_node.x, to_node.y, to_node.yaw, self.curvature)
 
@@ -166,7 +166,7 @@ def steer(self, from_node, to_node):
 
     def calc_new_cost(self, from_node, to_node):
 
-        _, _, _, _, course_lengths = dubins_path_planning.dubins_path_planning(
+        _, _, _, _, course_lengths = dubins_path_planner.plan_dubins_path(
             from_node.x, from_node.y, from_node.yaw,
             to_node.x, to_node.y, to_node.yaw, self.curvature)
 
diff --git a/docs/conf.py b/docs/conf.py
index de28ce4752..892e2863be 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -13,8 +13,8 @@
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
 import os
-# import sys
-# sys.path.insert(0, os.path.abspath('.'))
+import sys
+sys.path.insert(0, os.path.abspath('../'))
 
 
 # -- Project information -----------------------------------------------------
@@ -42,6 +42,7 @@
     'sphinx.ext.autodoc',
     'sphinx.ext.mathjax',
     'sphinx.ext.viewcode',
+    'sphinx.ext.napoleon',
     'IPython.sphinxext.ipython_console_highlighting',
 ]
 
diff --git a/docs/doc_requirements.txt b/docs/doc_requirements.txt
index 65670d346e..b8702ee750 100644
--- a/docs/doc_requirements.txt
+++ b/docs/doc_requirements.txt
@@ -1,3 +1,4 @@
 sphinx == 4.3.2 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
 IPython == 7.31.1 # For sphinx documentation
+sphinxcontrib-napoleon == 0.7 # For auto doc
diff --git a/docs/modules/path_planning/dubins_path/RLR.jpg b/docs/modules/path_planning/dubins_path/RLR.jpg
new file mode 100644
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diff --git a/docs/modules/path_planning/dubins_path/dubins_path.rst b/docs/modules/path_planning/dubins_path/dubins_path.rst
index 6765a84617..b612a82ef2 100644
--- a/docs/modules/path_planning/dubins_path/dubins_path.rst
+++ b/docs/modules/path_planning/dubins_path/dubins_path.rst
@@ -7,12 +7,37 @@ A sample code for Dubins path planning.
 
 Dubins path
 ~~~~~~~~~~~~
-Dubims path is a analyrical path planning algorithm for a simple car model.
+Dubins path is a analytical path planning algorithm for a simple car model.
 
 It can generates a shortest path between 2D poses (x, y, yaw) with maximum curvture comstraint and tangent(yaw angle) constraint.
 
+The path consist of 3 segments of maximum curvature curves or a straight line segment.
 
+Each segment type can is categorized by 3 type: 'Right turn (R)' , 'Left turn (L)', and 'Straight (S).' 
 
+Possible path will be at least one of these six types: RSR, RSL, LSR, LSL, RLR, LRL. 
+
+For example, one of RSR Dubins paths is:
+
+.. image:: dubins_path/RSR.jpg
+   :width: 400px
+
+one of RLR Dubins paths is:
+
+.. image:: dubins_path/RLR.jpg
+   :width: 200px
+
+Dubins path planner can output three types and distances of each course segment.
+
+You can generate a path from these information and the maximum curvature information.
+
+In the example code, a path which is minimum course length one among 6 course type is selected and then a path is constructed.
+
+Code
+~~~~~~~~~~~~~~~~~~~~
+
+.. automodule:: PathPlanning.DubinsPath.dubins_path_planner
+    :members:
 
 
 Reference
@@ -20,3 +45,4 @@ Reference
 
 -  `Dubins path - Wikipedia <https://en.wikipedia.org/wiki/Dubins_path>`__
 -  `15.3.1 Dubins Curves <http://planning.cs.uiuc.edu/node821.html>`__
+-  `A Comprehensive, Step-by-Step Tutorial to Computing Dubin’s Paths <https://gieseanw.wordpress.com/2012/10/21/a-comprehensive-step-by-step-tutorial-to-computing-dubins-paths/>`__
diff --git a/tests/test_diff_codestyle.py b/tests/test_diff_codestyle.py
index 3dbe11a11a..89c228e285 100644
--- a/tests/test_diff_codestyle.py
+++ b/tests/test_diff_codestyle.py
@@ -92,7 +92,9 @@ def find_diff(sha):
 def run_flake8(diff):
     """Run flake8 on the given diff."""
     res = subprocess.run(
-        ['flake8', '--diff'],
+        ['flake8', '--diff', '--ignore',
+         'E402'  # top level import for sys.path.append
+         ],
         input=diff,
         stdout=subprocess.PIPE,
         encoding='utf-8',
diff --git a/tests/test_dubins_path_planning.py b/tests/test_dubins_path_planning.py
index 18492ccc59..99c266bc6f 100644
--- a/tests/test_dubins_path_planning.py
+++ b/tests/test_dubins_path_planning.py
@@ -1,7 +1,7 @@
 import numpy as np
 
 import conftest
-from PathPlanning.DubinsPath import dubins_path_planning
+from PathPlanning.DubinsPath import dubins_path_planner
 
 np.random.seed(12345)
 
@@ -33,7 +33,7 @@ def test_1():
 
     curvature = 1.0
 
-    px, py, pyaw, mode, lengths = dubins_path_planning.dubins_path_planning(
+    px, py, pyaw, mode, lengths = dubins_path_planner.plan_dubins_path(
         start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
 
     check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x,
@@ -42,8 +42,8 @@ def test_1():
 
 
 def test_2():
-    dubins_path_planning.show_animation = False
-    dubins_path_planning.main()
+    dubins_path_planner.show_animation = False
+    dubins_path_planner.main()
 
 
 def test_3():
@@ -61,8 +61,8 @@ def test_3():
         curvature = 1.0 / (np.random.rand() * 5.0)
 
         px, py, pyaw, mode, lengths = \
-            dubins_path_planning.dubins_path_planning(
-            start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
+            dubins_path_planner.plan_dubins_path(
+                start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
 
         check_edge_condition(px, py, pyaw, start_x, start_y, start_yaw, end_x,
                              end_y, end_yaw)
diff --git a/tests/test_utils.py b/tests/test_utils.py
new file mode 100644
index 0000000000..b397479809
--- /dev/null
+++ b/tests/test_utils.py
@@ -0,0 +1,21 @@
+import conftest  # Add root path to sys.path
+from utils import angle
+from numpy.testing import assert_allclose
+import numpy as np
+
+
+def test_angle_mod():
+    assert_allclose(angle.angle_mod(-4.0), 2.28318531)
+    assert(isinstance(angle.angle_mod(-4.0), float))
+    assert_allclose(angle.angle_mod([-4.0]), [2.28318531])
+    assert(isinstance(angle.angle_mod([-4.0]), np.ndarray))
+
+    assert_allclose(angle.angle_mod([-150.0, 190.0, 350], degree=True),
+                    [-150., -170., -10.])
+
+    assert_allclose(angle.angle_mod(-60.0, zero_2_2pi=True, degree=True),
+                    [300.])
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/utils/__init__.py b/utils/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/utils/angle.py b/utils/angle.py
new file mode 100644
index 0000000000..741304f00e
--- /dev/null
+++ b/utils/angle.py
@@ -0,0 +1,77 @@
+import numpy as np
+from scipy.spatial.transform import Rotation as Rot
+
+
+def create_2d_rotation_matrix(angle):
+    """
+    Create 2D totation matrix from an angle
+
+    Parameters
+    ----------
+    angle :
+
+    Returns
+    -------
+
+    """
+    return Rot.from_euler('z', angle).as_matrix()[0:2, 0:2]
+
+
+def angle_mod(x, zero_2_2pi=False, degree=False):
+    """
+    Angle modulo operation
+    Default angle modulo range is [-pi, pi)
+
+    Parameters
+    ----------
+    x : float or array_like
+        A angle or an array of angles. This array is flattened for
+        the calculation. When an angle is provided, a float angle is returned.
+    zero_2_2pi : bool, optional
+        Change angle modulo range to [0, 2pi)
+        Default is False.
+    degree : bool, optional
+        If True, then the given angles are assumed to be in degrees.
+        Default is False.
+
+    Returns
+    -------
+    ret : float or ndarray
+        an angle or an array of modulated angle.
+
+    Examples
+    --------
+    >>> angle_mod(-4.0)
+    2.28318531
+
+    >>> angle_mod([-4.0])
+    np.array(2.28318531)
+
+    >>> angle_mod([-150.0, 190.0, 350], degree=True)
+    array([-150., -170.,  -10.])
+
+    >>> angle_mod(-60.0, zero_2_2pi=True, degree=True)
+    array([300.])
+
+    """
+    if isinstance(x, float):
+        is_float = True
+    else:
+        is_float = False
+
+    x = np.asarray(x).flatten()
+    if degree:
+        x = np.deg2rad(x)
+
+    if zero_2_2pi:
+        mod_angle = x % (2 * np.pi)
+    else:
+        mod_angle = (x + np.pi) % (2 * np.pi) - np.pi
+
+    if degree:
+        mod_angle = np.rad2deg(mod_angle)
+
+    if is_float:
+        return mod_angle.item()
+    else:
+        return mod_angle

From d74a91e062732ed9bc9e85fe708cd615bfc0bc7a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 7 May 2022 15:21:03 +0900
Subject: [PATCH 0616/1080] Re-architecture document structure (#669)

* Rearchitecture document structure

* Rearchitecture document structure

* Rearchitecture document structure

* Rearchitecture document structure

* Rearchitecture document structure

* Rearchitecture document structure

* Rearchitecture document structure
---
 docs/index_main.rst                           |   2 +-
 .../aerial_navigation_main.rst                |   8 ++-
 ...=> drone_3d_trajectory_following_main.rst} |   0
 ...ng.rst => rocket_powered_landing_main.rst} |   0
 ...s_2.rst => Kalmanfilter_basics_2_main.rst} |   0
 ...asics.rst => Kalmanfilter_basics_main.rst} |   0
 docs/modules/appendix/appendix_main.rst       |   7 ++-
 .../arm_navigation/arm_navigation_main.rst    |  28 +++--------
 .../n_joint_arm_to_point_control_main.rst     |  13 +++++
 ...obstacle_avoidance_arm_navigation_main.rst |   6 +++
 ...ink_ik.rst => planar_two_link_ik_main.rst} |   0
 docs/modules/bipedal/bipedal_main.rst         |   6 ++-
 ...l_planner.rst => bipedal_planner_main.rst} |   0
 docs/modules/control/control_main.rst         |   7 ++-
 ...rst => inverted_pendulum_control_main.rst} |   2 +-
 ...ol.rst => move_to_a_pose_control_main.rst} |   0
 ...amble_kalman_filter_localization_main.rst} |   0
 ...ended_kalman_filter_localization_main.rst} |   2 +-
 ...=> histogram_filter_localization_main.rst} |  10 ++--
 .../localization/localization_main.rst        |  15 +++---
 ... => particle_filter_localization_main.rst} |   2 +-
 ...ented_kalman_filter_localization_main.rst} |   0
 ...le_fitting.rst => circle_fitting_main.rst} |   0
 ...rid_map.rst => gaussian_grid_map_main.rst} |   0
 ...rst => k_means_object_clustering_main.rst} |   0
 ...st => lidar_to_grid_map_tutorial_main.rst} |  12 ++---
 docs/modules/mapping/mapping_main.rst         |  15 +++---
 ..._map.rst => ray_casting_grid_map_main.rst} |   0
 ...fitting.rst => rectangle_fitting_main.rst} |   0
 .../{bezier_path.rst => bezier_path_main.rst} |   4 +-
 ...bspline_path.rst => bspline_path_main.rst} |   2 +-
 .../{bugplanner.rst => bugplanner_main.rst}   |   0
 ...othoid_path.rst => clothoid_path_main.rst} |   0
 ...verage_path.rst => coverage_path_main.rst} |   0
 ...cubic_spline.rst => cubic_spline_main.rst} |   6 +--
 .../{dubins_path.rst => dubins_path_main.rst} |   4 +-
 ...h.rst => dynamic_window_approach_main.rst} |   2 +
 .../{eta3_spline.rst => eta3_spline_main.rst} |   0
 ...me_path.rst => frenet_frame_path_main.rst} |   0
 ...e_search.rst => grid_base_search_main.rst} |   0
 .../{hybridastar.rst => hybridastar_main.rst} |   0
 .../{lqr_path.rst => lqr_path_main.rst}       |   0
 ..._predictive_trajectory_generator_main.rst} |   2 +-
 .../path_planning/path_planning_main.rst      |  46 ++++++++++--------
 .../{prm_planner.rst => prm_planner_main.rst} |   0
 ...t => quintic_polynomials_planner_main.rst} |   0
 ...epp_path.rst => reeds_shepp_path_main.rst} |   0
 .../rrt/{rrt.rst => rrt_main.rst}             |   2 +-
 docs/modules/path_planning/rrt/rrt_star.rst   |   2 +-
 ...ner.rst => state_lattice_planner_main.rst} |   0
 ...t => visibility_road_map_planner_main.rst} |   8 +--
 .../{vrm_planner.rst => vrm_planner_main.rst} |   0
 .../{cgmres_nmpc.rst => cgmres_nmpc_main.rst} |   8 +--
 ...> lqr_speed_and_steering_control_main.rst} |   0
 ...trol.rst => lqr_steering_control_main.rst} |   0
 ...ctive_speed_and_steering_control_main.rst} |   0
 .../path_tracking/path_tracking_main.rst      |  17 ++++---
 ...ing.rst => pure_pursuit_tracking_main.rst} |   0
 ...t => rear_wheel_feedback_control_main.rst} |   0
 ...y_control.rst => stanley_control_main.rst} |   0
 .../{FastSLAM1.rst => FastSLAM1_main.rst}     |  12 ++---
 .../{FastSLAM2.rst => FastSLAM2_main.rst}     |   0
 .../{ekf_slam.rst => ekf_slam_main.rst}       |   2 +-
 .../slam/graph_slam/graphSLAM_SE2_example.rst |  14 +++---
 .../Graph_SLAM_optimization.gif               | Bin 0 -> 114966 bytes
 .../modules/slam/graph_slam/graphSLAM_doc.rst |  12 ++---
 .../graphSLAM_doc_11_1.png                    | Bin 0 -> 3931 bytes
 .../graphSLAM_doc_11_2.png                    | Bin 0 -> 3931 bytes
 .../graphSLAM_doc_files/graphSLAM_doc_2_0.png | Bin 0 -> 7764 bytes
 .../graphSLAM_doc_files/graphSLAM_doc_2_2.png | Bin 0 -> 7924 bytes
 .../graphSLAM_doc_files/graphSLAM_doc_4_0.png | Bin 0 -> 9308 bytes
 .../graphSLAM_doc_files/graphSLAM_doc_9_1.png | Bin 0 -> 8521 bytes
 .../slam/graph_slam/graphSLAM_formulation.rst |   2 +-
 .../{graph_slam.rst => graph_slam_main.rst}   |   6 +--
 ...iterative_closest_point_matching_main.rst} |   0
 docs/modules/slam/slam_main.rst               |  13 +++--
 76 files changed, 167 insertions(+), 132 deletions(-)
 rename docs/modules/aerial_navigation/drone_3d_trajectory_following/{drone_3d_trajectory_following.rst => drone_3d_trajectory_following_main.rst} (100%)
 rename docs/modules/aerial_navigation/rocket_powered_landing/{rocket_powered_landing.rst => rocket_powered_landing_main.rst} (100%)
 rename docs/modules/appendix/{Kalmanfilter_basics_2.rst => Kalmanfilter_basics_2_main.rst} (100%)
 rename docs/modules/appendix/{Kalmanfilter_basics.rst => Kalmanfilter_basics_main.rst} (100%)
 create mode 100644 docs/modules/arm_navigation/n_joint_arm_to_point_control_main.rst
 create mode 100644 docs/modules/arm_navigation/obstacle_avoidance_arm_navigation_main.rst
 rename docs/modules/arm_navigation/{planar_two_link_ik.rst => planar_two_link_ik_main.rst} (100%)
 rename docs/modules/bipedal/bipedal_planner/{bipedal_planner.rst => bipedal_planner_main.rst} (100%)
 rename docs/modules/control/inverted_pendulum_control/{inverted_pendulum_control.rst => inverted_pendulum_control_main.rst} (98%)
 rename docs/modules/control/move_to_a_pose_control/{move_to_a_pose_control.rst => move_to_a_pose_control_main.rst} (100%)
 rename docs/modules/localization/ensamble_kalman_filter_localization_files/{ensamble_kalman_filter_localization.rst => ensamble_kalman_filter_localization_main.rst} (100%)
 rename docs/modules/localization/extended_kalman_filter_localization_files/{extended_kalman_filter_localization.rst => extended_kalman_filter_localization_main.rst} (98%)
 rename docs/modules/localization/histogram_filter_localization/{histogram_filter_localization.rst => histogram_filter_localization_main.rst} (94%)
 rename docs/modules/localization/particle_filter_localization/{particle_filter_localization.rst => particle_filter_localization_main.rst} (94%)
 rename docs/modules/localization/unscented_kalman_filter_localization/{unscented_kalman_filter_localization.rst => unscented_kalman_filter_localization_main.rst} (100%)
 rename docs/modules/mapping/circle_fitting/{circle_fitting.rst => circle_fitting_main.rst} (100%)
 rename docs/modules/mapping/gaussian_grid_map/{gaussian_grid_map.rst => gaussian_grid_map_main.rst} (100%)
 rename docs/modules/mapping/k_means_object_clustering/{k_means_object_clustering.rst => k_means_object_clustering_main.rst} (100%)
 rename docs/modules/mapping/lidar_to_grid_map_tutorial/{lidar_to_grid_map_tutorial.rst => lidar_to_grid_map_tutorial_main.rst} (92%)
 rename docs/modules/mapping/ray_casting_grid_map/{ray_casting_grid_map.rst => ray_casting_grid_map_main.rst} (100%)
 rename docs/modules/mapping/rectangle_fitting/{rectangle_fitting.rst => rectangle_fitting_main.rst} (100%)
 rename docs/modules/path_planning/bezier_path/{bezier_path.rst => bezier_path_main.rst} (85%)
 rename docs/modules/path_planning/bspline_path/{bspline_path.rst => bspline_path_main.rst} (88%)
 rename docs/modules/path_planning/bugplanner/{bugplanner.rst => bugplanner_main.rst} (100%)
 rename docs/modules/path_planning/clothoid_path/{clothoid_path.rst => clothoid_path_main.rst} (100%)
 rename docs/modules/path_planning/coverage_path/{coverage_path.rst => coverage_path_main.rst} (100%)
 rename docs/modules/path_planning/cubic_spline/{cubic_spline.rst => cubic_spline_main.rst} (68%)
 rename docs/modules/path_planning/dubins_path/{dubins_path.rst => dubins_path_main.rst} (96%)
 rename docs/modules/path_planning/dynamic_window_approach/{dynamic_window_approach.rst => dynamic_window_approach_main.rst} (92%)
 rename docs/modules/path_planning/eta3_spline/{eta3_spline.rst => eta3_spline_main.rst} (100%)
 rename docs/modules/path_planning/frenet_frame_path/{frenet_frame_path.rst => frenet_frame_path_main.rst} (100%)
 rename docs/modules/path_planning/grid_base_search/{grid_base_search.rst => grid_base_search_main.rst} (100%)
 rename docs/modules/path_planning/hybridastar/{hybridastar.rst => hybridastar_main.rst} (100%)
 rename docs/modules/path_planning/lqr_path/{lqr_path.rst => lqr_path_main.rst} (100%)
 rename docs/modules/path_planning/model_predictive_trajectory_generator/{model_predictive_trajectory_generator.rst => model_predictive_trajectory_generator_main.rst} (90%)
 rename docs/modules/path_planning/prm_planner/{prm_planner.rst => prm_planner_main.rst} (100%)
 rename docs/modules/path_planning/quintic_polynomials_planner/{quintic_polynomials_planner.rst => quintic_polynomials_planner_main.rst} (100%)
 rename docs/modules/path_planning/reeds_shepp_path/{reeds_shepp_path.rst => reeds_shepp_path_main.rst} (100%)
 rename docs/modules/path_planning/rrt/{rrt.rst => rrt_main.rst} (99%)
 rename docs/modules/path_planning/state_lattice_planner/{state_lattice_planner.rst => state_lattice_planner_main.rst} (100%)
 rename docs/modules/path_planning/visibility_road_map_planner/{visibility_road_map_planner.rst => visibility_road_map_planner_main.rst} (91%)
 rename docs/modules/path_planning/vrm_planner/{vrm_planner.rst => vrm_planner_main.rst} (100%)
 rename docs/modules/path_tracking/cgmres_nmpc/{cgmres_nmpc.rst => cgmres_nmpc_main.rst} (92%)
 rename docs/modules/path_tracking/lqr_speed_and_steering_control/{lqr_speed_and_steering_control.rst => lqr_speed_and_steering_control_main.rst} (100%)
 rename docs/modules/path_tracking/lqr_steering_control/{lqr_steering_control.rst => lqr_steering_control_main.rst} (100%)
 rename docs/modules/path_tracking/model_predictive_speed_and_steering_control/{model_predictive_speed_and_steering_control.rst => model_predictive_speed_and_steering_control_main.rst} (100%)
 rename docs/modules/path_tracking/pure_pursuit_tracking/{pure_pursuit_tracking.rst => pure_pursuit_tracking_main.rst} (100%)
 rename docs/modules/path_tracking/rear_wheel_feedback_control/{rear_wheel_feedback_control.rst => rear_wheel_feedback_control_main.rst} (100%)
 rename docs/modules/path_tracking/stanley_control/{stanley_control.rst => stanley_control_main.rst} (100%)
 rename docs/modules/slam/FastSLAM1/{FastSLAM1.rst => FastSLAM1_main.rst} (98%)
 rename docs/modules/slam/FastSLAM2/{FastSLAM2.rst => FastSLAM2_main.rst} (100%)
 rename docs/modules/slam/ekf_slam/{ekf_slam.rst => ekf_slam_main.rst} (99%)
 create mode 100644 docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
 create mode 100644 docs/modules/slam/graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_1.png
 create mode 100644 docs/modules/slam/graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_2.png
 create mode 100644 docs/modules/slam/graph_slam/graphSLAM_doc_files/graphSLAM_doc_2_0.png
 create mode 100644 docs/modules/slam/graph_slam/graphSLAM_doc_files/graphSLAM_doc_2_2.png
 create mode 100644 docs/modules/slam/graph_slam/graphSLAM_doc_files/graphSLAM_doc_4_0.png
 create mode 100644 docs/modules/slam/graph_slam/graphSLAM_doc_files/graphSLAM_doc_9_1.png
 rename docs/modules/slam/graph_slam/{graph_slam.rst => graph_slam_main.rst} (78%)
 rename docs/modules/slam/iterative_closest_point_matching/{iterative_closest_point_matching.rst => iterative_closest_point_matching_main.rst} (100%)

diff --git a/docs/index_main.rst b/docs/index_main.rst
index 79980c2bd9..151759284d 100644
--- a/docs/index_main.rst
+++ b/docs/index_main.rst
@@ -31,7 +31,7 @@ See this paper for more details:
 
 .. toctree::
    :maxdepth: 2
-   :caption: Guide
+   :caption: Contents
 
    getting_started
    modules/introduction
diff --git a/docs/modules/aerial_navigation/aerial_navigation_main.rst b/docs/modules/aerial_navigation/aerial_navigation_main.rst
index ea6c4c8e30..b2ccb071af 100644
--- a/docs/modules/aerial_navigation/aerial_navigation_main.rst
+++ b/docs/modules/aerial_navigation/aerial_navigation_main.rst
@@ -3,6 +3,10 @@
 Aerial Navigation
 =================
 
-.. include:: drone_3d_trajectory_following/drone_3d_trajectory_following.rst
-.. include:: rocket_powered_landing/rocket_powered_landing.rst
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
+
+   drone_3d_trajectory_following/drone_3d_trajectory_following
+   rocket_powered_landing/rocket_powered_landing
 
diff --git a/docs/modules/aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following.rst b/docs/modules/aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following_main.rst
similarity index 100%
rename from docs/modules/aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following.rst
rename to docs/modules/aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following_main.rst
diff --git a/docs/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing.rst b/docs/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing_main.rst
similarity index 100%
rename from docs/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing.rst
rename to docs/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing_main.rst
diff --git a/docs/modules/appendix/Kalmanfilter_basics_2.rst b/docs/modules/appendix/Kalmanfilter_basics_2_main.rst
similarity index 100%
rename from docs/modules/appendix/Kalmanfilter_basics_2.rst
rename to docs/modules/appendix/Kalmanfilter_basics_2_main.rst
diff --git a/docs/modules/appendix/Kalmanfilter_basics.rst b/docs/modules/appendix/Kalmanfilter_basics_main.rst
similarity index 100%
rename from docs/modules/appendix/Kalmanfilter_basics.rst
rename to docs/modules/appendix/Kalmanfilter_basics_main.rst
diff --git a/docs/modules/appendix/appendix_main.rst b/docs/modules/appendix/appendix_main.rst
index 8840603bca..8a29d84676 100644
--- a/docs/modules/appendix/appendix_main.rst
+++ b/docs/modules/appendix/appendix_main.rst
@@ -3,7 +3,10 @@
 Appendix
 ==============
 
-.. include:: Kalmanfilter_basics.rst
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
 
-.. include:: Kalmanfilter_basics_2.rst
+   Kalmanfilter_basics
+   Kalmanfilter_basics_2
 
diff --git a/docs/modules/arm_navigation/arm_navigation_main.rst b/docs/modules/arm_navigation/arm_navigation_main.rst
index 4399ae13e3..bbbc872c58 100644
--- a/docs/modules/arm_navigation/arm_navigation_main.rst
+++ b/docs/modules/arm_navigation/arm_navigation_main.rst
@@ -3,26 +3,10 @@
 Arm Navigation
 ==============
 
-.. include:: planar_two_link_ik.rst
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
 
-
-N joint arm to point control
-----------------------------
-
-N joint arm to a point control simulation.
-
-This is a interactive simulation.
-
-You can set the goal position of the end effector with left-click on the
-plotting area.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/n_joint_arm_to_point_control/animation.gif
-
-In this simulation N = 10, however, you can change it.
-
-Arm navigation with obstacle avoidance
---------------------------------------
-
-Arm navigation with obstacle avoidance simulation.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/arm_obstacle_navigation/animation.gif
+   planar_two_link_ik
+   n_joint_arm_to_point_control
+   obstacle_avoidance_arm_navigation
diff --git a/docs/modules/arm_navigation/n_joint_arm_to_point_control_main.rst b/docs/modules/arm_navigation/n_joint_arm_to_point_control_main.rst
new file mode 100644
index 0000000000..cc6279f681
--- /dev/null
+++ b/docs/modules/arm_navigation/n_joint_arm_to_point_control_main.rst
@@ -0,0 +1,13 @@
+N joint arm to point control
+----------------------------
+
+N joint arm to a point control simulation.
+
+This is a interactive simulation.
+
+You can set the goal position of the end effector with left-click on the
+plotting area.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/n_joint_arm_to_point_control/animation.gif
+
+In this simulation N = 10, however, you can change it.
diff --git a/docs/modules/arm_navigation/obstacle_avoidance_arm_navigation_main.rst b/docs/modules/arm_navigation/obstacle_avoidance_arm_navigation_main.rst
new file mode 100644
index 0000000000..899a64a5cd
--- /dev/null
+++ b/docs/modules/arm_navigation/obstacle_avoidance_arm_navigation_main.rst
@@ -0,0 +1,6 @@
+Arm navigation with obstacle avoidance
+--------------------------------------
+
+Arm navigation with obstacle avoidance simulation.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/arm_obstacle_navigation/animation.gif
\ No newline at end of file
diff --git a/docs/modules/arm_navigation/planar_two_link_ik.rst b/docs/modules/arm_navigation/planar_two_link_ik_main.rst
similarity index 100%
rename from docs/modules/arm_navigation/planar_two_link_ik.rst
rename to docs/modules/arm_navigation/planar_two_link_ik_main.rst
diff --git a/docs/modules/bipedal/bipedal_main.rst b/docs/modules/bipedal/bipedal_main.rst
index bab316b0d9..fc5b933055 100644
--- a/docs/modules/bipedal/bipedal_main.rst
+++ b/docs/modules/bipedal/bipedal_main.rst
@@ -3,5 +3,9 @@
 Bipedal
 =================
 
-.. include:: bipedal_planner/bipedal_planner.rst
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
+
+   bipedal_planner/bipedal_planner
 
diff --git a/docs/modules/bipedal/bipedal_planner/bipedal_planner.rst b/docs/modules/bipedal/bipedal_planner/bipedal_planner_main.rst
similarity index 100%
rename from docs/modules/bipedal/bipedal_planner/bipedal_planner.rst
rename to docs/modules/bipedal/bipedal_planner/bipedal_planner_main.rst
diff --git a/docs/modules/control/control_main.rst b/docs/modules/control/control_main.rst
index d6d36a726b..cee2aa9e8e 100644
--- a/docs/modules/control/control_main.rst
+++ b/docs/modules/control/control_main.rst
@@ -3,7 +3,10 @@
 Control
 =================
 
-.. include:: inverted_pendulum_control/inverted_pendulum_control.rst
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
 
-.. include:: move_to_a_pose_control/move_to_a_pose_control.rst
+   inverted_pendulum_control/inverted_pendulum_control
+   move_to_a_pose_control/move_to_a_pose_control
 
diff --git a/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst b/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control_main.rst
similarity index 98%
rename from docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst
rename to docs/modules/control/inverted_pendulum_control/inverted_pendulum_control_main.rst
index 4c1f672ba0..e41729fd61 100644
--- a/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control.rst
+++ b/docs/modules/control/inverted_pendulum_control/inverted_pendulum_control_main.rst
@@ -9,7 +9,7 @@ The objective of the control system is to balance the inverted pendulum by apply
 Modeling
 ~~~~~~~~~~~~
 
-.. image:: inverted_pendulum_control/inverted-pendulum.png
+.. image:: inverted-pendulum.png
     :align: center
 
 - :math:`M`: mass of the cart
diff --git a/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst b/docs/modules/control/move_to_a_pose_control/move_to_a_pose_control_main.rst
similarity index 100%
rename from docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst
rename to docs/modules/control/move_to_a_pose_control/move_to_a_pose_control_main.rst
diff --git a/docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst b/docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst
similarity index 100%
rename from docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst
rename to docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst
diff --git a/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst
similarity index 98%
rename from docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst
rename to docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst
index a9c2939678..0ec920c9df 100644
--- a/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst
+++ b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst
@@ -2,7 +2,7 @@
 Extended Kalman Filter Localization
 -----------------------------------
 
-.. image:: extended_kalman_filter_localization_files/extended_kalman_filter_localization_1_0.png
+.. image:: extended_kalman_filter_localization_1_0.png
    :width: 600px
 
 
diff --git a/docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst b/docs/modules/localization/histogram_filter_localization/histogram_filter_localization_main.rst
similarity index 94%
rename from docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst
rename to docs/modules/localization/histogram_filter_localization/histogram_filter_localization_main.rst
index 891de94135..fafd578333 100644
--- a/docs/modules/localization/histogram_filter_localization/histogram_filter_localization.rst
+++ b/docs/modules/localization/histogram_filter_localization/histogram_filter_localization_main.rst
@@ -53,12 +53,12 @@ the estimation error due to the movement.
 
 For example, the position probability is peaky with observations:
 
-.. image:: histogram_filter_localization/1.png
+.. image:: 1.png
    :width: 400px
 
 But, the probability is getting uncertain without observations:
 
-.. image:: histogram_filter_localization/2.png
+.. image:: 2.png
    :width: 400px
 
 
@@ -88,12 +88,12 @@ The probability of each grid is updated by this formula:
 
 When the `d` is 3.0, the `h(z)` distribution is:
 
-.. image:: histogram_filter_localization/4.png
+.. image:: 4.png
    :width: 400px
 
 The observation probability distribution looks a circle when a RF-ID is observed:
 
-.. image:: histogram_filter_localization/3.png
+.. image:: 3.png
    :width: 400px
 
 Step4: Estimate position from probability
@@ -110,5 +110,5 @@ There are two ways to calculate the final positions:
 References:
 ~~~~~~~~~~~
 
-- `PROBABILISTIC ROBOTICS`_
+- `_PROBABILISTIC ROBOTICS: <http://www.probabilistic-robotics.org>`_
 - `Robust Vehicle Localization in Urban Environments Using Probabilistic Maps <http://driving.stanford.edu/papers/ICRA2010.pdf>`_
diff --git a/docs/modules/localization/localization_main.rst b/docs/modules/localization/localization_main.rst
index b1769df163..db6651f6b8 100644
--- a/docs/modules/localization/localization_main.rst
+++ b/docs/modules/localization/localization_main.rst
@@ -3,11 +3,14 @@
 Localization
 ============
 
-.. include:: extended_kalman_filter_localization_files/extended_kalman_filter_localization.rst
-.. include:: ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization.rst
-.. include:: unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst
-.. include:: histogram_filter_localization/histogram_filter_localization.rst
-.. include:: particle_filter_localization/particle_filter_localization.rst
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
+
+   extended_kalman_filter_localization_files/extended_kalman_filter_localization
+   ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization
+   unscented_kalman_filter_localization/unscented_kalman_filter_localization
+   histogram_filter_localization/histogram_filter_localization
+   particle_filter_localization/particle_filter_localization
 
-.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
 
diff --git a/docs/modules/localization/particle_filter_localization/particle_filter_localization.rst b/docs/modules/localization/particle_filter_localization/particle_filter_localization_main.rst
similarity index 94%
rename from docs/modules/localization/particle_filter_localization/particle_filter_localization.rst
rename to docs/modules/localization/particle_filter_localization/particle_filter_localization_main.rst
index 55b44e166b..c8652ce8a7 100644
--- a/docs/modules/localization/particle_filter_localization/particle_filter_localization.rst
+++ b/docs/modules/localization/particle_filter_localization/particle_filter_localization_main.rst
@@ -33,5 +33,5 @@ The covariance matrix :math:`\Xi` from particle information is calculated by the
 References:
 ~~~~~~~~~~~
 
--  `PROBABILISTIC ROBOTICS`_
+- `_PROBABILISTIC ROBOTICS: <http://www.probabilistic-robotics.org>`_
 - `Improving the particle filter in high dimensions using conjugate artificial process noise <https://arxiv.org/pdf/1801.07000.pdf>`_
diff --git a/docs/modules/localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst b/docs/modules/localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst
similarity index 100%
rename from docs/modules/localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization.rst
rename to docs/modules/localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst
diff --git a/docs/modules/mapping/circle_fitting/circle_fitting.rst b/docs/modules/mapping/circle_fitting/circle_fitting_main.rst
similarity index 100%
rename from docs/modules/mapping/circle_fitting/circle_fitting.rst
rename to docs/modules/mapping/circle_fitting/circle_fitting_main.rst
diff --git a/docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst b/docs/modules/mapping/gaussian_grid_map/gaussian_grid_map_main.rst
similarity index 100%
rename from docs/modules/mapping/gaussian_grid_map/gaussian_grid_map.rst
rename to docs/modules/mapping/gaussian_grid_map/gaussian_grid_map_main.rst
diff --git a/docs/modules/mapping/k_means_object_clustering/k_means_object_clustering.rst b/docs/modules/mapping/k_means_object_clustering/k_means_object_clustering_main.rst
similarity index 100%
rename from docs/modules/mapping/k_means_object_clustering/k_means_object_clustering.rst
rename to docs/modules/mapping/k_means_object_clustering/k_means_object_clustering_main.rst
diff --git a/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst b/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst
similarity index 92%
rename from docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst
rename to docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst
index 2e9470ff6c..1f62179efd 100644
--- a/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst
+++ b/docs/modules/mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst
@@ -16,7 +16,7 @@ a ``numpy array``, and numbers close to 1 means the cell is occupied
 free (*marked with green*). The grid has the ability to represent
 unknown (unobserved) areas, which are close to 0.5.
 
-.. figure:: lidar_to_grid_map_tutorial/grid_map_example.png
+.. figure:: grid_map_example.png
 
 In order to construct the grid map from the measurement we need to
 discretise the values. But, first let’s need to ``import`` some
@@ -68,7 +68,7 @@ From the distances and the angles it is easy to determine the ``x`` and
 
 
 
-.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_5_0.png
+.. image:: lidar_to_grid_map_tutorial_5_0.png
 
 
 The ``lidar_to_grid_map.py`` contains handy functions which can used to
@@ -89,7 +89,7 @@ map. Let’s see how this works.
 
 
 
-.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_7_0.png
+.. image:: lidar_to_grid_map_tutorial_7_0.png
 
 
 .. code:: ipython3
@@ -106,7 +106,7 @@ map. Let’s see how this works.
 
 
 
-.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_8_0.png
+.. image:: lidar_to_grid_map_tutorial_8_0.png
 
 
 To fill empty areas, a queue-based algorithm can be used that can be
@@ -163,7 +163,7 @@ from a center point (e.g. (10, 20)) with zeros:
 
 
 
-.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_12_0.png
+.. image:: lidar_to_grid_map_tutorial_12_0.png
 
 
 Let’s use this flood fill on real data:
@@ -194,5 +194,5 @@ Let’s use this flood fill on real data:
 
 
 
-.. image:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_14_1.png
+.. image:: lidar_to_grid_map_tutorial_14_1.png
 
diff --git a/docs/modules/mapping/mapping_main.rst b/docs/modules/mapping/mapping_main.rst
index 7d2c4cfe67..0f7ec549dc 100644
--- a/docs/modules/mapping/mapping_main.rst
+++ b/docs/modules/mapping/mapping_main.rst
@@ -2,11 +2,14 @@
 
 Mapping
 =======
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
 
-.. include:: gaussian_grid_map/gaussian_grid_map.rst
-.. include:: ray_casting_grid_map/ray_casting_grid_map.rst
-.. include:: lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial.rst
-.. include:: k_means_object_clustering/k_means_object_clustering.rst
-.. include:: circle_fitting/circle_fitting.rst
-.. include:: rectangle_fitting/rectangle_fitting.rst
+   gaussian_grid_map/gaussian_grid_map
+   ray_casting_grid_map/ray_casting_grid_map
+   lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial
+   k_means_object_clustering/k_means_object_clustering
+   circle_fitting/circle_fitting
+   rectangle_fitting/rectangle_fitting
 
diff --git a/docs/modules/mapping/ray_casting_grid_map/ray_casting_grid_map.rst b/docs/modules/mapping/ray_casting_grid_map/ray_casting_grid_map_main.rst
similarity index 100%
rename from docs/modules/mapping/ray_casting_grid_map/ray_casting_grid_map.rst
rename to docs/modules/mapping/ray_casting_grid_map/ray_casting_grid_map_main.rst
diff --git a/docs/modules/mapping/rectangle_fitting/rectangle_fitting.rst b/docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst
similarity index 100%
rename from docs/modules/mapping/rectangle_fitting/rectangle_fitting.rst
rename to docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst
diff --git a/docs/modules/path_planning/bezier_path/bezier_path.rst b/docs/modules/path_planning/bezier_path/bezier_path_main.rst
similarity index 85%
rename from docs/modules/path_planning/bezier_path/bezier_path.rst
rename to docs/modules/path_planning/bezier_path/bezier_path_main.rst
index c630d54345..fbba6a4496 100644
--- a/docs/modules/path_planning/bezier_path/bezier_path.rst
+++ b/docs/modules/path_planning/bezier_path/bezier_path_main.rst
@@ -5,13 +5,13 @@ A sample code of Bezier path planning.
 
 It is based on 4 control points Beizer path.
 
-.. image:: Bezier_path/Figure_1.png
+.. image:: Figure_1.png
 
 If you change the offset distance from start and end point,
 
 You can get different Beizer course:
 
-.. image:: Bezier_path/Figure_2.png
+.. image:: Figure_2.png
 
 Ref:
 
diff --git a/docs/modules/path_planning/bspline_path/bspline_path.rst b/docs/modules/path_planning/bspline_path/bspline_path_main.rst
similarity index 88%
rename from docs/modules/path_planning/bspline_path/bspline_path.rst
rename to docs/modules/path_planning/bspline_path/bspline_path_main.rst
index a8043122fa..d3523a1918 100644
--- a/docs/modules/path_planning/bspline_path/bspline_path.rst
+++ b/docs/modules/path_planning/bspline_path/bspline_path_main.rst
@@ -1,7 +1,7 @@
 B-Spline planning
 -----------------
 
-.. image:: bspline_path/Figure_1.png
+.. image:: Figure_1.png
 
 This is a path planning with B-Spline curse.
 
diff --git a/docs/modules/path_planning/bugplanner/bugplanner.rst b/docs/modules/path_planning/bugplanner/bugplanner_main.rst
similarity index 100%
rename from docs/modules/path_planning/bugplanner/bugplanner.rst
rename to docs/modules/path_planning/bugplanner/bugplanner_main.rst
diff --git a/docs/modules/path_planning/clothoid_path/clothoid_path.rst b/docs/modules/path_planning/clothoid_path/clothoid_path_main.rst
similarity index 100%
rename from docs/modules/path_planning/clothoid_path/clothoid_path.rst
rename to docs/modules/path_planning/clothoid_path/clothoid_path_main.rst
diff --git a/docs/modules/path_planning/coverage_path/coverage_path.rst b/docs/modules/path_planning/coverage_path/coverage_path_main.rst
similarity index 100%
rename from docs/modules/path_planning/coverage_path/coverage_path.rst
rename to docs/modules/path_planning/coverage_path/coverage_path_main.rst
diff --git a/docs/modules/path_planning/cubic_spline/cubic_spline.rst b/docs/modules/path_planning/cubic_spline/cubic_spline_main.rst
similarity index 68%
rename from docs/modules/path_planning/cubic_spline/cubic_spline.rst
rename to docs/modules/path_planning/cubic_spline/cubic_spline_main.rst
index b1973856e8..6de0297a05 100644
--- a/docs/modules/path_planning/cubic_spline/cubic_spline.rst
+++ b/docs/modules/path_planning/cubic_spline/cubic_spline_main.rst
@@ -8,6 +8,6 @@ with cubic spline.
 
 Heading angle of each point can be also calculated analytically.
 
-.. image:: cubic_spline/Figure_1.png
-.. image:: cubic_spline/Figure_2.png
-.. image:: cubic_spline/Figure_3.png
+.. image:: Figure_1.png
+.. image:: Figure_2.png
+.. image:: Figure_3.png
diff --git a/docs/modules/path_planning/dubins_path/dubins_path.rst b/docs/modules/path_planning/dubins_path/dubins_path_main.rst
similarity index 96%
rename from docs/modules/path_planning/dubins_path/dubins_path.rst
rename to docs/modules/path_planning/dubins_path/dubins_path_main.rst
index b612a82ef2..0b8c8fb603 100644
--- a/docs/modules/path_planning/dubins_path/dubins_path.rst
+++ b/docs/modules/path_planning/dubins_path/dubins_path_main.rst
@@ -19,12 +19,12 @@ Possible path will be at least one of these six types: RSR, RSL, LSR, LSL, RLR,
 
 For example, one of RSR Dubins paths is:
 
-.. image:: dubins_path/RSR.jpg
+.. image:: RSR.jpg
    :width: 400px
 
 one of RLR Dubins paths is:
 
-.. image:: dubins_path/RLR.jpg
+.. image:: RLR.jpg
    :width: 200px
 
 Dubins path planner can output three types and distances of each course segment.
diff --git a/docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach.rst b/docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach_main.rst
similarity index 92%
rename from docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach.rst
rename to docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach_main.rst
index 0ece6e0054..d645838597 100644
--- a/docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach.rst
+++ b/docs/modules/path_planning/dynamic_window_approach/dynamic_window_approach_main.rst
@@ -1,3 +1,5 @@
+.. _dynamic_window_approach:
+
 Dynamic Window Approach
 -----------------------
 
diff --git a/docs/modules/path_planning/eta3_spline/eta3_spline.rst b/docs/modules/path_planning/eta3_spline/eta3_spline_main.rst
similarity index 100%
rename from docs/modules/path_planning/eta3_spline/eta3_spline.rst
rename to docs/modules/path_planning/eta3_spline/eta3_spline_main.rst
diff --git a/docs/modules/path_planning/frenet_frame_path/frenet_frame_path.rst b/docs/modules/path_planning/frenet_frame_path/frenet_frame_path_main.rst
similarity index 100%
rename from docs/modules/path_planning/frenet_frame_path/frenet_frame_path.rst
rename to docs/modules/path_planning/frenet_frame_path/frenet_frame_path_main.rst
diff --git a/docs/modules/path_planning/grid_base_search/grid_base_search.rst b/docs/modules/path_planning/grid_base_search/grid_base_search_main.rst
similarity index 100%
rename from docs/modules/path_planning/grid_base_search/grid_base_search.rst
rename to docs/modules/path_planning/grid_base_search/grid_base_search_main.rst
diff --git a/docs/modules/path_planning/hybridastar/hybridastar.rst b/docs/modules/path_planning/hybridastar/hybridastar_main.rst
similarity index 100%
rename from docs/modules/path_planning/hybridastar/hybridastar.rst
rename to docs/modules/path_planning/hybridastar/hybridastar_main.rst
diff --git a/docs/modules/path_planning/lqr_path/lqr_path.rst b/docs/modules/path_planning/lqr_path/lqr_path_main.rst
similarity index 100%
rename from docs/modules/path_planning/lqr_path/lqr_path.rst
rename to docs/modules/path_planning/lqr_path/lqr_path_main.rst
diff --git a/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst b/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst
similarity index 90%
rename from docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst
rename to docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst
index 07a7ee9b59..0fc997898e 100644
--- a/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst
+++ b/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst
@@ -14,7 +14,7 @@ Path optimization sample
 Lookup table generation sample
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. image:: model_predictive_trajectory_generator/lookup_table.png
+.. image:: lookup_table.png
 
 Ref:
 
diff --git a/docs/modules/path_planning/path_planning_main.rst b/docs/modules/path_planning/path_planning_main.rst
index 5c70592afc..a3237f16f1 100644
--- a/docs/modules/path_planning/path_planning_main.rst
+++ b/docs/modules/path_planning/path_planning_main.rst
@@ -3,24 +3,28 @@
 Path Planning
 =============
 
-.. include:: dynamic_window_approach/dynamic_window_approach.rst
-.. include:: bugplanner/bugplanner.rst
-.. include:: grid_base_search/grid_base_search.rst
-.. include:: model_predictive_trajectory_generator/model_predictive_trajectory_generator.rst
-.. include:: state_lattice_planner/state_lattice_planner.rst
-.. include:: prm_planner/prm_planner.rst
-.. include:: visibility_road_map_planner/visibility_road_map_planner.rst
-.. include:: vrm_planner/vrm_planner.rst
-.. include:: rrt/rrt.rst
-.. include:: cubic_spline/cubic_spline.rst
-.. include:: bspline_path/bspline_path.rst
-.. include:: clothoid_path/clothoid_path.rst
-.. include:: eta3_spline/eta3_spline.rst
-.. include:: bezier_path/bezier_path.rst
-.. include:: quintic_polynomials_planner/quintic_polynomials_planner.rst
-.. include:: dubins_path/dubins_path.rst
-.. include:: reeds_shepp_path/reeds_shepp_path.rst
-.. include:: lqr_path/lqr_path.rst
-.. include:: hybridastar/hybridastar.rst
-.. include:: frenet_frame_path/frenet_frame_path.rst
-.. include:: coverage_path/coverage_path.rst
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
+
+   dynamic_window_approach/dynamic_window_approach
+   bugplanner/bugplanner
+   grid_base_search/grid_base_search
+   model_predictive_trajectory_generator/model_predictive_trajectory_generator
+   state_lattice_planner/state_lattice_planner
+   prm_planner/prm_planner
+   visibility_road_map_planner/visibility_road_map_planner
+   vrm_planner/vrm_planner
+   rrt/rrt
+   cubic_spline/cubic_spline
+   bspline_path/bspline_path
+   clothoid_path/clothoid_path
+   eta3_spline/eta3_spline
+   bezier_path/bezier_path
+   quintic_polynomials_planner/quintic_polynomials_planner
+   dubins_path/dubins_path
+   reeds_shepp_path/reeds_shepp_path
+   lqr_path/lqr_path
+   hybridastar/hybridastar
+   frenet_frame_path/frenet_frame_path
+   coverage_path/coverage_path
diff --git a/docs/modules/path_planning/prm_planner/prm_planner.rst b/docs/modules/path_planning/prm_planner/prm_planner_main.rst
similarity index 100%
rename from docs/modules/path_planning/prm_planner/prm_planner.rst
rename to docs/modules/path_planning/prm_planner/prm_planner_main.rst
diff --git a/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner.rst b/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst
similarity index 100%
rename from docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner.rst
rename to docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst
diff --git a/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path.rst b/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path_main.rst
similarity index 100%
rename from docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path.rst
rename to docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path_main.rst
diff --git a/docs/modules/path_planning/rrt/rrt.rst b/docs/modules/path_planning/rrt/rrt_main.rst
similarity index 99%
rename from docs/modules/path_planning/rrt/rrt.rst
rename to docs/modules/path_planning/rrt/rrt_main.rst
index 1afa17b144..5a3a30dcff 100644
--- a/docs/modules/path_planning/rrt/rrt.rst
+++ b/docs/modules/path_planning/rrt/rrt_main.rst
@@ -14,7 +14,7 @@ This is a simple path planning code with Rapidly-Exploring Random Trees
 Black circles are obstacles, green line is a searched tree, red crosses
 are start and goal positions.
 
-.. include:: rrt/rrt_star.rst
+.. include:: rrt_star.rst
 
 
 RRT with dubins path
diff --git a/docs/modules/path_planning/rrt/rrt_star.rst b/docs/modules/path_planning/rrt/rrt_star.rst
index 546e9bf6dd..d36eaac70e 100644
--- a/docs/modules/path_planning/rrt/rrt_star.rst
+++ b/docs/modules/path_planning/rrt/rrt_star.rst
@@ -10,7 +10,7 @@ Black circles are obstacles, green line is a searched tree, red crosses are star
 Simulation
 ^^^^^^^^^^
 
-.. image:: rrt/rrt_star/rrt_star_1_0.png
+.. image:: rrt_star/rrt_star_1_0.png
    :width: 600px
 
 
diff --git a/docs/modules/path_planning/state_lattice_planner/state_lattice_planner.rst b/docs/modules/path_planning/state_lattice_planner/state_lattice_planner_main.rst
similarity index 100%
rename from docs/modules/path_planning/state_lattice_planner/state_lattice_planner.rst
rename to docs/modules/path_planning/state_lattice_planner/state_lattice_planner_main.rst
diff --git a/docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner.rst b/docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst
similarity index 91%
rename from docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner.rst
rename to docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst
index 97e083389e..3c9b7c008c 100644
--- a/docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner.rst
+++ b/docs/modules/path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst
@@ -24,7 +24,7 @@ We assume this planner can be provided these information in the below figure.
 - 2. Goal point (Blue point)
 - 3. Obstacle polygons (Black lines)
 
-.. image:: visibility_road_map_planner/step0.png
+.. image:: step0.png
    :width: 400px
 
 
@@ -33,7 +33,7 @@ Step1: Generate visibility nodes based on polygon obstacles
 
 The nodes are generated by expanded these polygons vertexes like the below figure:
 
-.. image:: visibility_road_map_planner/step1.png
+.. image:: step1.png
    :width: 400px
 
 Each polygon vertex is expanded outward from the vector of adjacent vertices.
@@ -49,7 +49,7 @@ If the arc is collided, the graph is removed.
 
 The blue lines are generated visibility graphs in the figure:
 
-.. image:: visibility_road_map_planner/step2.png
+.. image:: step2.png
    :width: 400px
 
 
@@ -58,7 +58,7 @@ Step3: Search the shortest path in the graphs using Dijkstra algorithm
 
 The red line is searched path in the figure:
 
-.. image:: visibility_road_map_planner/step3.png
+.. image:: step3.png
    :width: 400px
 
 You can find the details of Dijkstra algorithm in :ref:`dijkstra`.
diff --git a/docs/modules/path_planning/vrm_planner/vrm_planner.rst b/docs/modules/path_planning/vrm_planner/vrm_planner_main.rst
similarity index 100%
rename from docs/modules/path_planning/vrm_planner/vrm_planner.rst
rename to docs/modules/path_planning/vrm_planner/vrm_planner_main.rst
diff --git a/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.rst b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst
similarity index 92%
rename from docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.rst
rename to docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst
index 5092fa8e2f..a1980ba17e 100644
--- a/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.rst
+++ b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst
@@ -2,16 +2,16 @@
 Nonlinear Model Predictive Control with C-GMRES
 -----------------------------------------------
 
-.. image:: cgmres_nmpc/cgmres_nmpc_1_0.png
+.. image:: cgmres_nmpc_1_0.png
    :width: 600px
 
-.. image:: cgmres_nmpc/cgmres_nmpc_2_0.png
+.. image:: cgmres_nmpc_2_0.png
    :width: 600px
 
-.. image:: cgmres_nmpc/cgmres_nmpc_3_0.png
+.. image:: cgmres_nmpc_3_0.png
    :width: 600px
 
-.. image:: cgmres_nmpc/cgmres_nmpc_4_0.png
+.. image:: cgmres_nmpc_4_0.png
    :width: 600px
 
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/cgmres_nmpc/animation.gif
diff --git a/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst b/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst
similarity index 100%
rename from docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst
rename to docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst
diff --git a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control.rst b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst
similarity index 100%
rename from docs/modules/path_tracking/lqr_steering_control/lqr_steering_control.rst
rename to docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst
diff --git a/docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.rst b/docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst
similarity index 100%
rename from docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.rst
rename to docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst
diff --git a/docs/modules/path_tracking/path_tracking_main.rst b/docs/modules/path_tracking/path_tracking_main.rst
index 2a572ef6df..504ba08795 100644
--- a/docs/modules/path_tracking/path_tracking_main.rst
+++ b/docs/modules/path_tracking/path_tracking_main.rst
@@ -3,11 +3,14 @@
 Path Tracking
 =============
 
-.. include:: pure_pursuit_tracking/pure_pursuit_tracking.rst
-.. include:: stanley_control/stanley_control.rst
-.. include:: rear_wheel_feedback_control/rear_wheel_feedback_control.rst
-.. include:: lqr_steering_control/lqr_steering_control.rst
-.. include:: lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst
-.. include:: model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.rst
-.. include:: cgmres_nmpc/cgmres_nmpc.rst
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
 
+   pure_pursuit_tracking/pure_pursuit_tracking
+   stanley_control/stanley_control
+   rear_wheel_feedback_control/rear_wheel_feedback_control
+   lqr_steering_control/lqr_steering_control
+   lqr_speed_and_steering_control/lqr_speed_and_steering_control
+   model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control
+   cgmres_nmpc/cgmres_nmpc
diff --git a/docs/modules/path_tracking/pure_pursuit_tracking/pure_pursuit_tracking.rst b/docs/modules/path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst
similarity index 100%
rename from docs/modules/path_tracking/pure_pursuit_tracking/pure_pursuit_tracking.rst
rename to docs/modules/path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst
diff --git a/docs/modules/path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control.rst b/docs/modules/path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst
similarity index 100%
rename from docs/modules/path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control.rst
rename to docs/modules/path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst
diff --git a/docs/modules/path_tracking/stanley_control/stanley_control.rst b/docs/modules/path_tracking/stanley_control/stanley_control_main.rst
similarity index 100%
rename from docs/modules/path_tracking/stanley_control/stanley_control.rst
rename to docs/modules/path_tracking/stanley_control/stanley_control_main.rst
diff --git a/docs/modules/slam/FastSLAM1/FastSLAM1.rst b/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst
similarity index 98%
rename from docs/modules/slam/FastSLAM1/FastSLAM1.rst
rename to docs/modules/slam/FastSLAM1/FastSLAM1_main.rst
index f1e9dfd8aa..f09125dff0 100644
--- a/docs/modules/slam/FastSLAM1/FastSLAM1.rst
+++ b/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst
@@ -11,7 +11,7 @@ Simulation
 
 This is a feature based SLAM example using FastSLAM 1.0.
 
-.. image:: FastSLAM1/FastSLAM1_1_0.png
+.. image:: FastSLAM1_1_0.png
    :width: 600px
 
 The blue line is ground truth, the black line is dead reckoning, the red
@@ -46,8 +46,8 @@ an array of landmark locations
 I.e. Each particle maintains a deterministic pose and n-EKFs for each
 landmark and update it with each measurement.
 
-Algorithm walkthrough
-~~~~~~~~~~~~~~~~~~~~~
+Algorithm walk through
+~~~~~~~~~~~~~~~~~~~~~~~
 
 The particles are initially drawn from a uniform distribution the
 represent the initial uncertainty. At each time step we do:
@@ -75,7 +75,7 @@ which are the linear and angular velocity repsectively.
 
 :math:`\begin{equation*} \begin{bmatrix} x_{t+1} \\ y_{t+1} \\ \theta_{t+1} \end{bmatrix}= \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix}\begin{bmatrix} x_{t} \\ y_{t} \\ \theta_{t} \end{bmatrix}+ \begin{bmatrix} \Delta t cos(\theta) & 0\\ \Delta t sin(\theta) & 0\\ 0 & \Delta t \end{bmatrix} \begin{bmatrix} v_{t} + \sigma_v\\ w_{t} + \sigma_w\\ \end{bmatrix} \end{equation*}`
 
-The following snippets playsback the recorded trajectory of each
+The following snippets playback the recorded trajectory of each
 particle.
 
 To get the insight of the motion model change the value of :math:`R` and
@@ -527,8 +527,8 @@ indices
 
 
 
-.. image:: FastSLAM1/FastSLAM1_12_0.png
-.. image:: FastSLAM1/FastSLAM1_12_1.png
+.. image:: FastSLAM1_12_0.png
+.. image:: FastSLAM1_12_1.png
 
 
 References
diff --git a/docs/modules/slam/FastSLAM2/FastSLAM2.rst b/docs/modules/slam/FastSLAM2/FastSLAM2_main.rst
similarity index 100%
rename from docs/modules/slam/FastSLAM2/FastSLAM2.rst
rename to docs/modules/slam/FastSLAM2/FastSLAM2_main.rst
diff --git a/docs/modules/slam/ekf_slam/ekf_slam.rst b/docs/modules/slam/ekf_slam/ekf_slam_main.rst
similarity index 99%
rename from docs/modules/slam/ekf_slam/ekf_slam.rst
rename to docs/modules/slam/ekf_slam/ekf_slam_main.rst
index a79c2737ae..5a23b4c887 100644
--- a/docs/modules/slam/ekf_slam/ekf_slam.rst
+++ b/docs/modules/slam/ekf_slam/ekf_slam_main.rst
@@ -581,7 +581,7 @@ reckoning and control functions are passed along here as well.
     New LM
     New LM
 
-.. image:: ekf_slam/ekf_slam_1_0.png
+.. image:: ekf_slam_1_0.png
 
 References:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/docs/modules/slam/graph_slam/graphSLAM_SE2_example.rst b/docs/modules/slam/graph_slam/graphSLAM_SE2_example.rst
index ef5d05bf22..491320512b 100644
--- a/docs/modules/slam/graph_slam/graphSLAM_SE2_example.rst
+++ b/docs/modules/slam/graph_slam/graphSLAM_SE2_example.rst
@@ -44,7 +44,7 @@ The Dataset
 
 
 
-.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png
 
 
 Each edge in this dataset is a constraint that compares the measured
@@ -122,7 +122,7 @@ dataset and plot them.
 
 
 
-.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png
 
 
 .. code:: ipython3
@@ -131,7 +131,7 @@ dataset and plot them.
 
 
 
-.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png
 
 
 Optimization
@@ -165,7 +165,7 @@ different data sources into a single optimization problem.
             6             215.8405          -0.000000
 
 
-.. figure:: graph_slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
+.. figure:: graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
 
 .. code:: ipython3
 
@@ -173,7 +173,7 @@ different data sources into a single optimization problem.
 
 
 
-.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png
 
 
 .. code:: ipython3
@@ -195,7 +195,7 @@ different data sources into a single optimization problem.
 
 
 
-.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png
 
 
 .. code:: ipython3
@@ -204,5 +204,5 @@ different data sources into a single optimization problem.
 
 
 
-.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png
+.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png
 
diff --git a/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif b/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
new file mode 100644
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diff --git a/docs/modules/slam/graph_slam/graphSLAM_doc.rst b/docs/modules/slam/graph_slam/graphSLAM_doc.rst
index 4367f770c3..5297604809 100644
--- a/docs/modules/slam/graph_slam/graphSLAM_doc.rst
+++ b/docs/modules/slam/graph_slam/graphSLAM_doc.rst
@@ -142,7 +142,7 @@ created based on the information of the motion and the observation.
 
 
 
-.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_2_0.png
+.. image:: graphSLAM_doc_files/graphSLAM_doc_2_0.png
 
 
 .. parsed-literal::
@@ -157,7 +157,7 @@ created based on the information of the motion and the observation.
 
 
 
-.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_2_2.png
+.. image:: graphSLAM_doc_files/graphSLAM_doc_2_2.png
 
 
 In particular, the tasks are split into 2 parts, graph construction, and
@@ -289,7 +289,7 @@ robot with 3DoF, namely, :math:`[x, y, \theta]^T`
 
 
 
-.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_4_0.png
+.. image:: graphSLAM_doc_files/graphSLAM_doc_4_0.png
 
 
 .. code:: ipython3
@@ -420,7 +420,7 @@ zero since :math:`x_j + d_j cos(\psi_j + \theta_j)` should equal
 
 
 
-.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_9_1.png
+.. image:: graphSLAM_doc_files/graphSLAM_doc_9_1.png
 
 
 Since the constraints equations derived before are non-linear,
@@ -494,9 +494,9 @@ Similarly, :math:`B = \frac{\partial e_{ij}}{\partial \boldsymbol{x}_j}`
 
 
 
-.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_1.png
+.. image:: graphSLAM_doc_files/graphSLAM_doc_11_1.png
 
-.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_2.png
+.. image:: graphSLAM_doc_files/graphSLAM_doc_11_2.png
 
 .. code:: ipython3
 
diff --git a/docs/modules/slam/graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_1.png b/docs/modules/slam/graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_1.png
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z@+8nlngESj67_|gh}af;BYS?SJLUosMt?E3@%?~lvAhx#wLqRD+k^IaGoMUnjdmZ_
z7h;B~_b+?50<->ELi?V-X#H$DE4_PJjM8#ym`vs_ZOL;EpsqF17?5Iy^yrD9@iucY
zdNYmDu?l5((ng7W`1yzQ^orw6qqvrj`;jIu`SOdkdJbz7QORqzz~>3F4?hcVSf{u@
z9#R_ivG9;nBPRKm`$$zlh5o#p^hhP+KGX0W;6nE~y#g&zT78!NhXcsuGnex=1G2a&
zuh@@@#FIiD#kX!0MqZN4x$_>E$AV$lRIa-)xfa`r)rZj3x6C{Gxav)eDo%rWxpKXL
p^a7q$@b^3UzhxGG%FT}lAZ8I3v?i+?5m+XJp0+W(@`~fV{{miSrxE}F

literal 0
HcmV?d00001

diff --git a/docs/modules/slam/graph_slam/graphSLAM_formulation.rst b/docs/modules/slam/graph_slam/graphSLAM_formulation.rst
index 43f007e7bf..4978596c10 100644
--- a/docs/modules/slam/graph_slam/graphSLAM_formulation.rst
+++ b/docs/modules/slam/graph_slam/graphSLAM_formulation.rst
@@ -70,7 +70,7 @@ Using Bayes’ rule, we can write this probability as
 
 since :math:`p(\mathcal{Z})` is a constant (albeit, an unknown constant)
 and we assume that :math:`p(\mathbf{p}_1, \ldots, \mathbf{p}_N)` is
-uniformly distributed `PROBABILISTIC ROBOTICS`_. Therefore, we
+uniformly distributed. Therefore, we
 can use Eq. :eq:`infomat` and and Eq. :eq:`bayes` to simplify the Graph SLAM
 optimization as follows:
 
diff --git a/docs/modules/slam/graph_slam/graph_slam.rst b/docs/modules/slam/graph_slam/graph_slam_main.rst
similarity index 78%
rename from docs/modules/slam/graph_slam/graph_slam.rst
rename to docs/modules/slam/graph_slam/graph_slam_main.rst
index a1248a01cc..987eed385c 100644
--- a/docs/modules/slam/graph_slam/graph_slam.rst
+++ b/docs/modules/slam/graph_slam/graph_slam_main.rst
@@ -13,9 +13,9 @@ The black stars are landmarks for graph edge generation.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/GraphBasedSLAM/animation.gif
 
-.. include:: graph_slam/graphSLAM_doc.rst
-.. include:: graph_slam/graphSLAM_formulation.rst
-.. include:: graph_slam/graphSLAM_SE2_example.rst
+.. include:: graphSLAM_doc.rst
+.. include:: graphSLAM_formulation.rst
+.. include:: graphSLAM_SE2_example.rst
 
 References:
 ~~~~~~~~~~~
diff --git a/docs/modules/slam/iterative_closest_point_matching/iterative_closest_point_matching.rst b/docs/modules/slam/iterative_closest_point_matching/iterative_closest_point_matching_main.rst
similarity index 100%
rename from docs/modules/slam/iterative_closest_point_matching/iterative_closest_point_matching.rst
rename to docs/modules/slam/iterative_closest_point_matching/iterative_closest_point_matching_main.rst
diff --git a/docs/modules/slam/slam_main.rst b/docs/modules/slam/slam_main.rst
index f4d10feb9a..86befa6e35 100644
--- a/docs/modules/slam/slam_main.rst
+++ b/docs/modules/slam/slam_main.rst
@@ -5,9 +5,12 @@ SLAM
 
 Simultaneous Localization and Mapping(SLAM) examples
 
-.. include:: iterative_closest_point_matching/iterative_closest_point_matching.rst
-.. include:: ekf_slam/ekf_slam.rst
-.. include:: FastSLAM1/FastSLAM1.rst
-.. include:: FastSLAM2/FastSLAM2.rst
-.. include:: graph_slam/graph_slam.rst
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
 
+   iterative_closest_point_matching/iterative_closest_point_matching
+   ekf_slam/ekf_slam
+   FastSLAM1/FastSLAM1
+   FastSLAM2/FastSLAM2
+   graph_slam/graph_slam

From b9345da036bb9655c84b8081199f99d17b25afd6 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 7 May 2022 16:04:39 +0900
Subject: [PATCH 0617/1080] try to use python3.10 (#561)

* try to use python3.10

* try python3.10

* try python3.10
---
 .circleci/config.yml           | 3 ++-
 .github/workflows/Linux_CI.yml | 3 ++-
 .github/workflows/MacOS_CI.yml | 4 ++--
 README.md                      | 2 +-
 appveyor.yml                   | 2 +-
 docs/getting_started_main.rst  | 4 ++--
 requirements/environment.yml   | 2 +-
 7 files changed, 11 insertions(+), 9 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 275ad3bc7a..075a613be0 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -6,12 +6,13 @@ orbs:
 jobs:
   build_doc:
     docker:
-      - image: cimg/python:3.9
+      - image: cimg/python:3.10
     steps:
       - checkout
       - run:
           name: doc_build
           command: |
+            python --version
             python -m venv venv
             . venv/bin/activate
             pip install -r requirements/requirements.txt
diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index c706a50ed6..73f8aa5c0d 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.9']
+        python-version: ['3.10']
 
     name: Python ${{ matrix.python-version }} CI
 
@@ -26,6 +26,7 @@ jobs:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
       run: |
+        python --version
         python -m pip install --upgrade pip
         python -m pip install -r requirements/requirements.txt
     - name: do all unit tests
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index 4088c4900f..5c796120a5 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -16,7 +16,7 @@ jobs:
     runs-on: macos-latest
     strategy:
       matrix:
-        python-version: [ '3.9' ]
+        python-version: [ '3.10' ]
     name: Python ${{ matrix.python-version }} CI
     steps:
       - uses: actions/checkout@v2
@@ -32,8 +32,8 @@ jobs:
 
       - name: Install dependencies
         run: |
+          python --version
           python -m pip install --upgrade pip
-          #pip install numpy # cvxpy install workaround
           pip install -r requirements/requirements.txt
       - name: do all unit tests
         run: bash runtests.sh
\ No newline at end of file
diff --git a/README.md b/README.md
index 8972bae5fa..f54b880dc0 100644
--- a/README.md
+++ b/README.md
@@ -96,7 +96,7 @@ See this paper for more details:
 
 For running each sample code:
 
-- [Python 3.9.x](https://www.python.org/)
+- [Python 3.10.x](https://www.python.org/)
  
 - [NumPy](https://numpy.org/)
  
diff --git a/appveyor.yml b/appveyor.yml
index c35e76ae4a..4964bab3da 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -8,7 +8,7 @@ environment:
     CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd"
 
   matrix:
-     - PYTHON_DIR: C:\Python39-x64
+     - PYTHON_DIR: C:\Python310-x64
 
 branches:
   only:
diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst
index a7b9ff3607..4a19126686 100644
--- a/docs/getting_started_main.rst
+++ b/docs/getting_started_main.rst
@@ -26,7 +26,7 @@ See this paper for more details:
 Requirements
 -------------
 
--  `Python 3.9.x`_
+-  `Python 3.10.x`_
 -  `NumPy`_
 -  `SciPy`_
 -  `Matplotlib`_
@@ -41,7 +41,7 @@ For development:
 -  sphinx (for document generation)
 -  pycodestyle (for code style check)
 
-.. _`Python 3.9.x`: https://www.python.org/
+.. _`Python 3.10.x`: https://www.python.org/
 .. _`NumPy`: https://numpy.org/
 .. _`SciPy`: https://scipy.org/
 .. _`Matplotlib`: https://matplotlib.org/
diff --git a/requirements/environment.yml b/requirements/environment.yml
index 1441b79080..109fc57789 100644
--- a/requirements/environment.yml
+++ b/requirements/environment.yml
@@ -2,7 +2,7 @@ name: python_robotics
 channels:
   - conda-forge
 dependencies:
-  - python=3.9
+  - python=3.10
   - pip
   - scipy
   - numpy

From e18799cbb745d3cc3ff5cad707f401ee1f218834 Mon Sep 17 00:00:00 2001
From: Drake <1024507980@qq.com>
Date: Sat, 7 May 2022 16:20:20 +0800
Subject: [PATCH 0618/1080] modify end node's state (#656)

---
 PathPlanning/DStar/dstar.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/DStar/dstar.py b/PathPlanning/DStar/dstar.py
index e01508b3df..f8954d776c 100644
--- a/PathPlanning/DStar/dstar.py
+++ b/PathPlanning/DStar/dstar.py
@@ -160,7 +160,7 @@ def run(self, start, end):
         rx = []
         ry = []
 
-        self.open_list.add(end)
+        self.insert(end, 0.0)
 
         while True:
             self.process_state()

From a3fd04f68f3b1bada4c6393e5b9dda3704eb36bd Mon Sep 17 00:00:00 2001
From: jsbyysheng <jsbyysheng@gmail.com>
Date: Sat, 7 May 2022 17:15:01 +0800
Subject: [PATCH 0619/1080] fix error: 'numpy.ndarray' object has no attribute
 'append' (#662)

* Update grid_map_lib.py

fix error: 'numpy.ndarray' object has no attribute 'append'

* Update test_grid_map_lib.py

* Update test_grid_map_lib.py

Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>
---
 Mapping/grid_map_lib/grid_map_lib.py | 4 ++--
 tests/test_grid_map_lib.py           | 3 +++
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/Mapping/grid_map_lib/grid_map_lib.py b/Mapping/grid_map_lib/grid_map_lib.py
index 10651cc673..a93ea309d8 100644
--- a/Mapping/grid_map_lib/grid_map_lib.py
+++ b/Mapping/grid_map_lib/grid_map_lib.py
@@ -120,8 +120,8 @@ def set_value_from_polygon(self, pol_x, pol_y, val, inside=True):
 
         # making ring polygon
         if (pol_x[0] != pol_x[-1]) or (pol_y[0] != pol_y[-1]):
-            pol_x.append(pol_x[0])
-            pol_y.append(pol_y[0])
+            np.append(pol_x, pol_x[0])
+            np.append(pol_y, pol_y[0])
 
         # setting value for all grid
         for x_ind in range(self.width):
diff --git a/tests/test_grid_map_lib.py b/tests/test_grid_map_lib.py
index ffdb72b1ae..ed4303d4dc 100644
--- a/tests/test_grid_map_lib.py
+++ b/tests/test_grid_map_lib.py
@@ -1,4 +1,5 @@
 from Mapping.grid_map_lib.grid_map_lib import GridMap
+import numpy as np
 
 
 def test_position_set():
@@ -19,6 +20,8 @@ def test_polygon_set():
     grid_map = GridMap(600, 290, 0.7, 60.0, 30.5)
 
     grid_map.set_value_from_polygon(ox, oy, 1.0, inside=False)
+    grid_map.set_value_from_polygon(np.array(ox), np.array(oy),
+                                    1.0, inside=False)
 
 
 if __name__ == '__main__':

From 3d209faefc5d0cedf9ba347d189e99caf0e5265b Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 7 May 2022 22:43:31 +0900
Subject: [PATCH 0620/1080] add gh-pages action (#671)

---
 .github/workflows/gh-pages.yml | 29 +++++++++++++++++++++++++++++
 1 file changed, 29 insertions(+)
 create mode 100644 .github/workflows/gh-pages.yml

diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml
new file mode 100644
index 0000000000..e51197cb37
--- /dev/null
+++ b/.github/workflows/gh-pages.yml
@@ -0,0 +1,29 @@
+name: Pages
+on:
+  push:
+    branches:
+    - master
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Setup python
+      uses: actions/setup-python@v2
+    - name: Checkout
+      uses: actions/checkout@master
+      with:
+        fetch-depth: 0 # otherwise, you will fail to push refs to dest repo
+    - name: Install dependencies
+      run: |
+        python --version
+        python -m pip install --upgrade pip
+        python -m pip install -r requirements/requirements.txt
+    - name: Build and Commit
+      uses: sphinx-notes/pages@v2
+      with:
+        requirements_path: ./docs/doc_requirements.txt
+    - name: Push changes
+      uses: ad-m/github-push-action@master
+      with:
+        github_token: ${{ secrets.GITHUB_TOKEN }}
+        branch: gh-pages
\ No newline at end of file

From f74f0c1da2030794cede57709df076713be04269 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 May 2022 14:11:21 +0900
Subject: [PATCH 0621/1080] enhance_github pages doc (#672)

* enhance_github pages doc

* enhance_github pages doc
---
 docs/_templates/layout.html | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 docs/_templates/layout.html

diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html
new file mode 100644
index 0000000000..7cd969e52f
--- /dev/null
+++ b/docs/_templates/layout.html
@@ -0,0 +1,8 @@
+{% extends "!layout.html" %}
+
+{%- block extrahead %}
+{{ super() }}
+<script async
+        src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-9612347954373886"
+        crossOrigin="anonymous"></script>
+{% endblock %}

From 785e62175fe71c277d8501018347554c5edda836 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 May 2022 14:23:11 +0900
Subject: [PATCH 0622/1080] enhance_github pages doc (#673)

---
 docs/_templates/layout.html | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html
index 7cd969e52f..89c38a39f9 100644
--- a/docs/_templates/layout.html
+++ b/docs/_templates/layout.html
@@ -6,3 +6,19 @@
         src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-9612347954373886"
         crossOrigin="anonymous"></script>
 {% endblock %}
+
+{% block sidebarsearch %}
+{{ super() }}
+<script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-9612347954373886"
+     crossorigin="anonymous"></script>
+<!-- PythonRoboticsDoc -->
+<ins class="adsbygoogle"
+     style="display:block"
+     data-ad-client="ca-pub-9612347954373886"
+     data-ad-slot="1579532132"
+     data-ad-format="auto"
+     data-full-width-responsive="true"></ins>
+<script>
+     (adsbygoogle = window.adsbygoogle || []).push({});
+</script>
+{% endblock %}

From 20220fdfd27b277d8ff5fac22680b47ef3ce7149 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 May 2022 14:29:48 +0900
Subject: [PATCH 0623/1080] enhance_github pages doc (#674)

---
 docs/_templates/layout.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html
index 89c38a39f9..6593bd00c0 100644
--- a/docs/_templates/layout.html
+++ b/docs/_templates/layout.html
@@ -7,7 +7,7 @@
         crossOrigin="anonymous"></script>
 {% endblock %}
 
-{% block sidebarsearch %}
+{% block footer %}
 {{ super() }}
 <script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-9612347954373886"
      crossorigin="anonymous"></script>

From ec19f6373d1aeae40ace9bf6100f49da4b09a6cd Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 May 2022 14:39:01 +0900
Subject: [PATCH 0624/1080] enhance_github pages doc (#675)

---
 docs/_templates/layout.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html
index 6593bd00c0..c0cedb2330 100644
--- a/docs/_templates/layout.html
+++ b/docs/_templates/layout.html
@@ -7,7 +7,7 @@
         crossOrigin="anonymous"></script>
 {% endblock %}
 
-{% block footer %}
+{% block content %}
 {{ super() }}
 <script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-9612347954373886"
      crossorigin="anonymous"></script>

From b01128a8f65605d75016ef980009711e1f49c917 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 May 2022 14:49:20 +0900
Subject: [PATCH 0625/1080] enhance_github pages doc (#676)

---
 docs/_templates/layout.html | 9 +--------
 1 file changed, 1 insertion(+), 8 deletions(-)

diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html
index c0cedb2330..4b5b278932 100644
--- a/docs/_templates/layout.html
+++ b/docs/_templates/layout.html
@@ -1,13 +1,6 @@
 {% extends "!layout.html" %}
 
-{%- block extrahead %}
-{{ super() }}
-<script async
-        src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-9612347954373886"
-        crossOrigin="anonymous"></script>
-{% endblock %}
-
-{% block content %}
+{% block sidebartitle %}
 {{ super() }}
 <script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-9612347954373886"
      crossorigin="anonymous"></script>

From f5d80ecb1cf17193b9061627215ee18348e3b6dc Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 8 May 2022 15:56:51 +0900
Subject: [PATCH 0626/1080] remove old doc links (#677)

* remove old doc links

* add github link

* add github link

* add github link
---
 .github/pull_request_template.md |  2 +-
 README.md                        |  5 ++---
 docs/conf.py                     | 11 +++++++++++
 docs/how_to_contribute_main.rst  |  2 +-
 users_comments.md                |  2 +-
 5 files changed, 16 insertions(+), 6 deletions(-)

diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index c541063156..c8790ad9ec 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -1,7 +1,7 @@
 <!-- 
 Thanks for contributing a pull request! 
 Please check this document before submitting:
-- [How to contribute](https://pythonrobotics.readthedocs.io/en/latest/how_to_contribute.html#adding-a-new-algorithm-example)
+- [How to contribute](https://atsushisakai.github.io/PythonRobotics/how_to_contribute.html#adding-a-new-algorithm-example)
 
 Note that this is my hobby project; I appreciate your
 patience during the review process.
diff --git a/README.md b/README.md
index f54b880dc0..2d0ee8d653 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,6 @@
 ![GitHub_Action_Linux_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/Linux_CI/badge.svg)
 ![GitHub_Action_MacOS_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/MacOS_CI/badge.svg)
 [![Build status](https://ci.appveyor.com/api/projects/status/sb279kxuv1be391g?svg=true)](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics)
-[![Documentation Status](https://readthedocs.org/projects/pythonrobotics/badge/?version=latest)](https://pythonrobotics.readthedocs.io/en/latest/?badge=latest)
 [![codecov](https://codecov.io/gh/AtsushiSakai/PythonRobotics/branch/master/graph/badge.svg)](https://codecov.io/gh/AtsushiSakai/PythonRobotics)
 [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/AtsushiSakai/PythonRobotics.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/AtsushiSakai/PythonRobotics/context:python)
 [![tokei](https://tokei.rs/b1/github/AtsushiSakai/PythonRobotics)](https://github.com/AtsushiSakai/PythonRobotics)
@@ -126,7 +125,7 @@ This README only shows some examples of this project.
 
 If you are interested in other examples or mathematical backgrounds of each algorithm, 
 
-You can check the full documentation online: [https://pythonrobotics.readthedocs.io/](https://pythonrobotics.readthedocs.io/)
+You can check the full documentation online: [Welcome to PythonRobotics’s documentation\! — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/index.html)
 
 All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation gifs of PythonRobotics](https://github.com/AtsushiSakai/PythonRoboticsGifs)
 
@@ -609,7 +608,7 @@ This is a list of user's comment and references:[users\_comments](https://github
 
 Any contribution is welcome!! 
 
-Please check this document:[How to contribute](https://pythonrobotics.readthedocs.io/en/latest/how_to_contribute.html#)
+Please check this document:[How To Contribute — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/how_to_contribute.html)
 
 # Citing
 
diff --git a/docs/conf.py b/docs/conf.py
index 892e2863be..899f961359 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -95,6 +95,17 @@
     'display_version': False,
 }
 
+# replace "view page source" with "edit on github" in Read The Docs theme
+#  * https://github.com/readthedocs/sphinx_rtd_theme/issues/529
+html_context = {
+    'display_github': True,
+    'github_user': 'AtsushiSakai',
+    'github_repo': 'PythonRobotics',
+    'github_version': 'master',
+    "conf_py_path": "/docs/",
+    "source_suffix": source_suffix,
+}
+
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
diff --git a/docs/how_to_contribute_main.rst b/docs/how_to_contribute_main.rst
index 4b39089e7b..8a2f73e9cd 100644
--- a/docs/how_to_contribute_main.rst
+++ b/docs/how_to_contribute_main.rst
@@ -147,7 +147,7 @@ Sponsors
 1. `JetBrains`_ : They are providing a free license of their IDEs for this OSS development.
 
 
-.. _`Python Robotics Docs`: https://pythonrobotics.readthedocs.io/en/latest/
+.. _`Python Robotics Docs`: https://atsushisakai.github.io/PythonRobotics
 .. _`bug labeled issues`: https://github.com/AtsushiSakai/PythonRobotics/issues?q=is%3Aissue+is%3Aopen+label%3Abug
 .. _`tests dir`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/tests
 .. _`test_a_star.py`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/tests/test_a_star.py
diff --git a/users_comments.md b/users_comments.md
index 43ffc7cc36..66e8b4022e 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -6,7 +6,7 @@ This is an electric wheelchair control demo by [Katsushun89](https://github.com/
 
 [WHILL Model CR](https://whill.jp/model-cr) is the control target, [M5Stack](https://m5stack.com/) is used for the controller, and [toio](https://toio.io/) is used for the control input device.
 
-[move-to-a-pose-control](https://pythonrobotics.readthedocs.io/en/latest/modules/path_tracking.html#move-to-a-pose-control) is used for its control algorithm ([code link](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/move_to_pose/move_to_pose.py)).
+[Move to a Pose Control — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/modules/control/move_to_a_pose_control/move_to_a_pose_control.html) is used for its control algorithm ([code link](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/move_to_pose/move_to_pose.py)).
 
 ![1](https://github.com/AtsushiSakai/PythonRoboticsGifs/blob/master/Users/WHILL_Model_CR_with_move_to_a_pose/WHLL_Model_CR_with_move_to_a_pose.gif)
 

From 6961d84bbb896ee330f0d86d00e7efe2ee8ba26d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 10 May 2022 07:23:38 +0900
Subject: [PATCH 0627/1080] Bump matplotlib from 3.5.1 to 3.5.2 in
 /requirements (#678)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.5.1 to 3.5.2.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.5.1...v3.5.2)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 46f56702ea..33c43404ec 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 1.22.3
 scipy == 1.8.0
 pandas == 1.4.2
-matplotlib == 3.5.1
+matplotlib == 3.5.2
 cvxpy == 1.2.0
 pytest == 7.1.2 # For unit test
 pytest-xdist == 2.5.0 # For unit test

From 2c44c736090b49798db7474d1b6bdd6e277521dc Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 17 May 2022 07:15:43 +0900
Subject: [PATCH 0628/1080] Bump cvxpy from 1.2.0 to 1.2.1 in /requirements
 (#680)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.2.0 to 1.2.1.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.2.0...v1.2.1)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 33c43404ec..d404da757e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.22.3
 scipy == 1.8.0
 pandas == 1.4.2
 matplotlib == 3.5.2
-cvxpy == 1.2.0
+cvxpy == 1.2.1
 pytest == 7.1.2 # For unit test
 pytest-xdist == 2.5.0 # For unit test
 mypy == 0.950 # For unit test

From 31178c8e377a7a4939a9ca91d903a18863214198 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 17 May 2022 21:57:07 +0900
Subject: [PATCH 0629/1080] Enhance dubins path docs (#679)

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs

* Enhance dubins path docs
---
 .../DubinsPath/dubins_path_planner.py         | 374 +++++++-----------
 docs/conf.py                                  |   1 +
 .../path_planning/dubins_path/dubins_path.jpg | Bin 0 -> 22927 bytes
 .../dubins_path/dubins_path_main.rst          |  50 ++-
 tests/test_dubins_path_planning.py            |  19 +
 tests/test_utils.py                           |   6 +
 utils/angle.py                                |  10 +-
 utils/plot.py                                 |  49 +++
 8 files changed, 275 insertions(+), 234 deletions(-)
 create mode 100644 docs/modules/path_planning/dubins_path/dubins_path.jpg
 create mode 100644 utils/plot.py

diff --git a/PathPlanning/DubinsPath/dubins_path_planner.py b/PathPlanning/DubinsPath/dubins_path_planner.py
index 69c213ddf4..4d4ee07291 100644
--- a/PathPlanning/DubinsPath/dubins_path_planner.py
+++ b/PathPlanning/DubinsPath/dubins_path_planner.py
@@ -7,21 +7,20 @@
 """
 import sys
 import os
+
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
 
-import math
+from math import sin, cos, atan2, sqrt, acos, pi, hypot
 import numpy as np
-from utils.angle import angle_mod, create_2d_rotation_matrix
+from utils.angle import angle_mod, rot_mat_2d
 
 show_animation = True
 
 
-def plan_dubins_path(s_x, s_y, s_yaw,
-                     g_x, g_y, g_yaw,
-                     curvature,
-                     step_size=0.1):
+def plan_dubins_path(s_x, s_y, s_yaw, g_x, g_y, g_yaw, curvature,
+                     step_size=0.1, selected_types=None):
     """
-    Path dubins path
+    Plan dubins path
 
     Parameters
     ----------
@@ -41,6 +40,11 @@ def plan_dubins_path(s_x, s_y, s_yaw,
         curvature for curve [1/m]
     step_size : float (optional)
         step size between two path points [m]. Default is 0.1
+    selected_types : a list of string or None
+        selected path planning types. If None, all types are used for
+        path planning, and minimum path length result is returned.
+        You can select used path plannings types by a string list.
+        e.g.: ["RSL", "RSR"]
 
     Returns
     -------
@@ -53,21 +57,49 @@ def plan_dubins_path(s_x, s_y, s_yaw,
     modes: array
         mode list of the path
     lengths: array
-        length list of the path segments.
-
+        arrow_length list of the path segments.
+
+    Examples
+    --------
+    You can generate a dubins path.
+
+    >>> start_x = 1.0  # [m]
+    >>> start_y = 1.0  # [m]
+    >>> start_yaw = np.deg2rad(45.0)  # [rad]
+    >>> end_x = -3.0  # [m]
+    >>> end_y = -3.0  # [m]
+    >>> end_yaw = np.deg2rad(-45.0)  # [rad]
+    >>> curvature = 1.0
+    >>> path_x, path_y, path_yaw, mode, _ = plan_dubins_path(
+                start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature)
+    >>> plt.plot(path_x, path_y, label="final course " + "".join(mode))
+    >>> plot_arrow(start_x, start_y, start_yaw)
+    >>> plot_arrow(end_x, end_y, end_yaw)
+    >>> plt.legend()
+    >>> plt.grid(True)
+    >>> plt.axis("equal")
+    >>> plt.show()
+
+    .. image:: dubins_path.jpg
     """
+    if selected_types is None:
+        planning_funcs = _PATH_TYPE_MAP.values()
+    else:
+        planning_funcs = [_PATH_TYPE_MAP[ptype] for ptype in selected_types]
+
     # calculate local goal x, y, yaw
-    l_rot = create_2d_rotation_matrix(s_yaw)
+    l_rot = rot_mat_2d(s_yaw)
     le_xy = np.stack([g_x - s_x, g_y - s_y]).T @ l_rot
     local_goal_x = le_xy[0]
     local_goal_y = le_xy[1]
     local_goal_yaw = g_yaw - s_yaw
 
-    lp_x, lp_y, lp_yaw, modes, lengths = dubins_path_planning_from_origin(
-        local_goal_x, local_goal_y, local_goal_yaw, curvature, step_size)
+    lp_x, lp_y, lp_yaw, modes, lengths = _dubins_path_planning_from_origin(
+        local_goal_x, local_goal_y, local_goal_yaw, curvature, step_size,
+        planning_funcs)
 
     # Convert a local coordinate path to the global coordinate
-    rot = create_2d_rotation_matrix(-s_yaw)
+    rot = rot_mat_2d(-s_yaw)
     converted_xy = np.stack([lp_x, lp_y]).T @ rot
     x_list = converted_xy[:, 0] + s_x
     y_list = converted_xy[:, 1] + s_y
@@ -80,276 +112,179 @@ def _mod2pi(theta):
     return angle_mod(theta, zero_2_2pi=True)
 
 
-def _LSL(alpha, beta, d):
-    sa = math.sin(alpha)
-    sb = math.sin(beta)
-    ca = math.cos(alpha)
-    cb = math.cos(beta)
-    c_ab = math.cos(alpha - beta)
+def _calc_trig_funcs(alpha, beta):
+    sin_a = sin(alpha)
+    sin_b = sin(beta)
+    cos_a = cos(alpha)
+    cos_b = cos(beta)
+    cos_ab = cos(alpha - beta)
+    return sin_a, sin_b, cos_a, cos_b, cos_ab
 
-    tmp0 = d + sa - sb
 
+def _LSL(alpha, beta, d):
+    sin_a, sin_b, cos_a, cos_b, cos_ab = _calc_trig_funcs(alpha, beta)
     mode = ["L", "S", "L"]
-    p_squared = 2 + (d * d) - (2 * c_ab) + (2 * d * (sa - sb))
-    if p_squared < 0:
+    p_squared = 2 + d ** 2 - (2 * cos_ab) + (2 * d * (sin_a - sin_b))
+    if p_squared < 0:  # invalid configuration
         return None, None, None, mode
-    tmp1 = math.atan2((cb - ca), tmp0)
-    t = _mod2pi(-alpha + tmp1)
-    p = math.sqrt(p_squared)
-    q = _mod2pi(beta - tmp1)
-
-    return t, p, q, mode
+    tmp = atan2((cos_b - cos_a), d + sin_a - sin_b)
+    d1 = _mod2pi(-alpha + tmp)
+    d2 = sqrt(p_squared)
+    d3 = _mod2pi(beta - tmp)
+    return d1, d2, d3, mode
 
 
-def right_straight_right(alpha, beta, d):
-    sa = math.sin(alpha)
-    sb = math.sin(beta)
-    ca = math.cos(alpha)
-    cb = math.cos(beta)
-    c_ab = math.cos(alpha - beta)
-
-    tmp0 = d - sa + sb
+def _RSR(alpha, beta, d):
+    sin_a, sin_b, cos_a, cos_b, cos_ab = _calc_trig_funcs(alpha, beta)
     mode = ["R", "S", "R"]
-    p_squared = 2 + (d * d) - (2 * c_ab) + (2 * d * (sb - sa))
+    p_squared = 2 + d ** 2 - (2 * cos_ab) + (2 * d * (sin_b - sin_a))
     if p_squared < 0:
         return None, None, None, mode
-    tmp1 = math.atan2((ca - cb), tmp0)
-    t = angle_mod(alpha - tmp1, zero_2_2pi=True)
-    p = math.sqrt(p_squared)
-    q = _mod2pi(-beta + tmp1)
-
-    return t, p, q, mode
+    tmp = atan2((cos_a - cos_b), d - sin_a + sin_b)
+    d1 = _mod2pi(alpha - tmp)
+    d2 = sqrt(p_squared)
+    d3 = _mod2pi(-beta + tmp)
+    return d1, d2, d3, mode
 
 
-def left_straight_right(alpha, beta, d):
-    sa = math.sin(alpha)
-    sb = math.sin(beta)
-    ca = math.cos(alpha)
-    cb = math.cos(beta)
-    c_ab = math.cos(alpha - beta)
-
-    p_squared = -2 + (d * d) + (2 * c_ab) + (2 * d * (sa + sb))
+def _LSR(alpha, beta, d):
+    sin_a, sin_b, cos_a, cos_b, cos_ab = _calc_trig_funcs(alpha, beta)
+    p_squared = -2 + d ** 2 + (2 * cos_ab) + (2 * d * (sin_a + sin_b))
     mode = ["L", "S", "R"]
     if p_squared < 0:
         return None, None, None, mode
-    p = math.sqrt(p_squared)
-    tmp2 = math.atan2((-ca - cb), (d + sa + sb)) - math.atan2(-2.0, p)
-    t = _mod2pi(-alpha + tmp2)
-    q = _mod2pi(-_mod2pi(beta) + tmp2)
-
-    return t, p, q, mode
+    d1 = sqrt(p_squared)
+    tmp = atan2((-cos_a - cos_b), (d + sin_a + sin_b)) - atan2(-2.0, d1)
+    d2 = _mod2pi(-alpha + tmp)
+    d3 = _mod2pi(-_mod2pi(beta) + tmp)
+    return d2, d1, d3, mode
 
 
-def right_straight_left(alpha, beta, d):
-    sa = math.sin(alpha)
-    sb = math.sin(beta)
-    ca = math.cos(alpha)
-    cb = math.cos(beta)
-    c_ab = math.cos(alpha - beta)
-
-    p_squared = (d * d) - 2 + (2 * c_ab) - (2 * d * (sa + sb))
+def _RSL(alpha, beta, d):
+    sin_a, sin_b, cos_a, cos_b, cos_ab = _calc_trig_funcs(alpha, beta)
+    p_squared = d ** 2 - 2 + (2 * cos_ab) - (2 * d * (sin_a + sin_b))
     mode = ["R", "S", "L"]
     if p_squared < 0:
         return None, None, None, mode
-    p = math.sqrt(p_squared)
-    tmp2 = math.atan2((ca + cb), (d - sa - sb)) - math.atan2(2.0, p)
-    t = _mod2pi(alpha - tmp2)
-    q = _mod2pi(beta - tmp2)
-
-    return t, p, q, mode
+    d1 = sqrt(p_squared)
+    tmp = atan2((cos_a + cos_b), (d - sin_a - sin_b)) - atan2(2.0, d1)
+    d2 = _mod2pi(alpha - tmp)
+    d3 = _mod2pi(beta - tmp)
+    return d2, d1, d3, mode
 
 
-def right_left_right(alpha, beta, d):
-    sa = math.sin(alpha)
-    sb = math.sin(beta)
-    ca = math.cos(alpha)
-    cb = math.cos(beta)
-    c_ab = math.cos(alpha - beta)
-
+def _RLR(alpha, beta, d):
+    sin_a, sin_b, cos_a, cos_b, cos_ab = _calc_trig_funcs(alpha, beta)
     mode = ["R", "L", "R"]
-    tmp_rlr = (6.0 - d * d + 2.0 * c_ab + 2.0 * d * (sa - sb)) / 8.0
-    if abs(tmp_rlr) > 1.0:
+    tmp = (6.0 - d ** 2 + 2.0 * cos_ab + 2.0 * d * (sin_a - sin_b)) / 8.0
+    if abs(tmp) > 1.0:
         return None, None, None, mode
-
-    p = _mod2pi(2 * math.pi - math.acos(tmp_rlr))
-    t = _mod2pi(alpha - math.atan2(ca - cb, d - sa + sb) + _mod2pi(p / 2.0))
-    q = _mod2pi(alpha - beta - t + _mod2pi(p))
-    return t, p, q, mode
+    d2 = _mod2pi(2 * pi - acos(tmp))
+    d1 = _mod2pi(alpha - atan2(cos_a - cos_b, d - sin_a + sin_b) + d2 / 2.0)
+    d3 = _mod2pi(alpha - beta - d1 + d2)
+    return d1, d2, d3, mode
 
 
 def _LRL(alpha, beta, d):
-    sa = math.sin(alpha)
-    sb = math.sin(beta)
-    ca = math.cos(alpha)
-    cb = math.cos(beta)
-    c_ab = math.cos(alpha - beta)
-
+    sin_a, sin_b, cos_a, cos_b, cos_ab = _calc_trig_funcs(alpha, beta)
     mode = ["L", "R", "L"]
-    tmp_lrl = (6.0 - d * d + 2.0 * c_ab + 2.0 * d * (- sa + sb)) / 8.0
-    if abs(tmp_lrl) > 1:
+    tmp = (6.0 - d ** 2 + 2.0 * cos_ab + 2.0 * d * (- sin_a + sin_b)) / 8.0
+    if abs(tmp) > 1.0:
         return None, None, None, mode
-    p = _mod2pi(2 * math.pi - math.acos(tmp_lrl))
-    t = _mod2pi(-alpha - math.atan2(ca - cb, d + sa - sb) + p / 2.0)
-    q = _mod2pi(_mod2pi(beta) - alpha - t + _mod2pi(p))
+    d2 = _mod2pi(2 * pi - acos(tmp))
+    d1 = _mod2pi(-alpha - atan2(cos_a - cos_b, d + sin_a - sin_b) + d2 / 2.0)
+    d3 = _mod2pi(_mod2pi(beta) - alpha - d1 + _mod2pi(d2))
+    return d1, d2, d3, mode
+
 
-    return t, p, q, mode
+_PATH_TYPE_MAP = {"LSL": _LSL, "RSR": _RSR, "LSR": _LSR, "RSL": _RSL,
+                  "RLR": _RLR, "LRL": _LRL, }
 
 
-def dubins_path_planning_from_origin(end_x, end_y, end_yaw, curvature,
-                                     step_size):
+def _dubins_path_planning_from_origin(end_x, end_y, end_yaw, curvature,
+                                      step_size, planning_funcs):
     dx = end_x
     dy = end_y
-    D = math.hypot(dx, dy)
-    d = D * curvature
+    d = hypot(dx, dy) * curvature
 
-    theta = _mod2pi(math.atan2(dy, dx))
-    alpha = _mod2pi(- theta)
+    theta = _mod2pi(atan2(dy, dx))
+    alpha = _mod2pi(-theta)
     beta = _mod2pi(end_yaw - theta)
 
-    planning_funcs = [_LSL, right_straight_right,
-                      left_straight_right, right_straight_left,
-                      right_left_right, _LRL]
-
     best_cost = float("inf")
-    bt, bp, bq, best_mode = None, None, None, None
+    b_d1, b_d2, b_d3, b_mode = None, None, None, None
 
     for planner in planning_funcs:
-        t, p, q, mode = planner(alpha, beta, d)
-        if t is None:
+        d1, d2, d3, mode = planner(alpha, beta, d)
+        if d1 is None:
             continue
 
-        cost = (abs(t) + abs(p) + abs(q))
-        if best_cost > cost:
-            bt, bp, bq, best_mode = t, p, q, mode
-            best_cost = cost
-    lengths = [bt, bp, bq]
+        cost = (abs(d1) + abs(d2) + abs(d3))
+        if best_cost > cost:  # Select minimum length one.
+            b_d1, b_d2, b_d3, b_mode, best_cost = d1, d2, d3, mode, cost
 
-    x_list, y_list, yaw_list, directions = generate_local_course(sum(lengths),
-                                                                 lengths,
-                                                                 best_mode,
-                                                                 curvature,
-                                                                 step_size)
+    lengths = [b_d1, b_d2, b_d3]
+    x_list, y_list, yaw_list = _generate_local_course(lengths, b_mode,
+                                                      curvature, step_size)
 
     lengths = [length / curvature for length in lengths]
 
-    return x_list, y_list, yaw_list, best_mode, lengths
+    return x_list, y_list, yaw_list, b_mode, lengths
 
 
-def interpolate(ind, length, mode, max_curvature, origin_x, origin_y,
-                origin_yaw, path_x, path_y, path_yaw, directions):
+def _interpolate(length, mode, max_curvature, origin_x, origin_y,
+                 origin_yaw, path_x, path_y, path_yaw):
     if mode == "S":
-        path_x[ind] = origin_x + length / max_curvature * math.cos(origin_yaw)
-        path_y[ind] = origin_y + length / max_curvature * math.sin(origin_yaw)
-        path_yaw[ind] = origin_yaw
+        path_x.append(origin_x + length / max_curvature * cos(origin_yaw))
+        path_y.append(origin_y + length / max_curvature * sin(origin_yaw))
+        path_yaw.append(origin_yaw)
     else:  # curve
-        ldx = math.sin(length) / max_curvature
+        ldx = sin(length) / max_curvature
         ldy = 0.0
         if mode == "L":  # left turn
-            ldy = (1.0 - math.cos(length)) / max_curvature
+            ldy = (1.0 - cos(length)) / max_curvature
         elif mode == "R":  # right turn
-            ldy = (1.0 - math.cos(length)) / -max_curvature
-        gdx = math.cos(-origin_yaw) * ldx + math.sin(-origin_yaw) * ldy
-        gdy = -math.sin(-origin_yaw) * ldx + math.cos(-origin_yaw) * ldy
-        path_x[ind] = origin_x + gdx
-        path_y[ind] = origin_y + gdy
-
-    if mode == "L":  # left turn
-        path_yaw[ind] = origin_yaw + length
-    elif mode == "R":  # right turn
-        path_yaw[ind] = origin_yaw - length
-
-    if length > 0.0:
-        directions[ind] = 1
-    else:
-        directions[ind] = -1
+            ldy = (1.0 - cos(length)) / -max_curvature
+        gdx = cos(-origin_yaw) * ldx + sin(-origin_yaw) * ldy
+        gdy = -sin(-origin_yaw) * ldx + cos(-origin_yaw) * ldy
+        path_x.append(origin_x + gdx)
+        path_y.append(origin_y + gdy)
 
-    return path_x, path_y, path_yaw, directions
-
-
-def generate_local_course(total_length, lengths, modes, max_curvature,
-                          step_size):
-    n_point = math.trunc(total_length / step_size) + len(lengths) + 4
+        if mode == "L":  # left turn
+            path_yaw.append(origin_yaw + length)
+        elif mode == "R":  # right turn
+            path_yaw.append(origin_yaw - length)
 
-    p_x = [0.0 for _ in range(n_point)]
-    p_y = [0.0 for _ in range(n_point)]
-    p_yaw = [0.0 for _ in range(n_point)]
-    directions = [0.0 for _ in range(n_point)]
-    ind = 1
+    return path_x, path_y, path_yaw
 
-    if lengths[0] > 0.0:
-        directions[0] = 1
-    else:
-        directions[0] = -1
 
-    ll = 0.0
+def _generate_local_course(lengths, modes, max_curvature, step_size):
+    p_x, p_y, p_yaw = [0.0], [0.0], [0.0]
 
-    for (m, length, i) in zip(modes, lengths, range(len(modes))):
+    for (mode, length) in zip(modes, lengths):
         if length == 0.0:
             continue
-        elif length > 0.0:
-            dist = step_size
-        else:
-            dist = -step_size
 
         # set origin state
-        origin_x, origin_y, origin_yaw = p_x[ind], p_y[ind], p_yaw[ind]
-
-        ind -= 1
-        if i >= 1 and (lengths[i - 1] * lengths[i]) > 0:
-            pd = - dist - ll
-        else:
-            pd = dist - ll
-
-        while abs(pd) <= abs(length):
-            ind += 1
-            p_x, p_y, p_yaw, directions = interpolate(ind, pd, m,
-                                                      max_curvature,
-                                                      origin_x,
-                                                      origin_y,
-                                                      origin_yaw,
-                                                      p_x, p_y,
-                                                      p_yaw,
-                                                      directions)
-            pd += dist
-
-        ll = length - pd - dist  # calc remain length
-
-        ind += 1
-        p_x, p_y, p_yaw, directions = interpolate(ind, length, m,
-                                                  max_curvature,
-                                                  origin_x, origin_y,
-                                                  origin_yaw,
-                                                  p_x, p_y, p_yaw,
-                                                  directions)
-
-    if len(p_x) <= 1:
-        return [], [], [], []
-
-    # remove unused data
-    while len(p_x) >= 1 and p_x[-1] == 0.0:
-        p_x.pop()
-        p_y.pop()
-        p_yaw.pop()
-        directions.pop()
-
-    return p_x, p_y, p_yaw, directions
-
-
-def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r",
-               ec="k"):  # pragma: no cover
-    import matplotlib.pyplot as plt
-    if not isinstance(x, float):
-        for (i_x, i_y, i_yaw) in zip(x, y, yaw):
-            plot_arrow(i_x, i_y, i_yaw)
-    else:
-        plt.arrow(x, y, length * math.cos(yaw), length * math.sin(yaw), fc=fc,
-                  ec=ec, head_width=width, head_length=width)
-        plt.plot(x, y)
+        origin_x, origin_y, origin_yaw = p_x[-1], p_y[-1], p_yaw[-1]
+
+        current_length = step_size
+        while abs(current_length + step_size) <= abs(length):
+            p_x, p_y, p_yaw = _interpolate(current_length, mode, max_curvature,
+                                           origin_x, origin_y, origin_yaw,
+                                           p_x, p_y, p_yaw)
+            current_length += step_size
+
+        p_x, p_y, p_yaw = _interpolate(length, mode, max_curvature, origin_x,
+                                       origin_y, origin_yaw, p_x, p_y, p_yaw)
+
+    return p_x, p_y, p_yaw
 
 
 def main():
     print("Dubins path planner sample start!!")
     import matplotlib.pyplot as plt
+    from utils.plot import plot_arrow
 
     start_x = 1.0  # [m]
     start_y = 1.0  # [m]
@@ -370,12 +305,9 @@ def main():
                                                                curvature)
 
     if show_animation:
-        plt.plot(path_x, path_y, label="final course " + "".join(mode))
-
-        # plotting
+        plt.plot(path_x, path_y, label="".join(mode))
         plot_arrow(start_x, start_y, start_yaw)
         plot_arrow(end_x, end_y, end_yaw)
-
         plt.legend()
         plt.grid(True)
         plt.axis("equal")
diff --git a/docs/conf.py b/docs/conf.py
index 899f961359..8a3ba1ac2d 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -39,6 +39,7 @@
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions = [
+    'matplotlib.sphinxext.plot_directive',
     'sphinx.ext.autodoc',
     'sphinx.ext.mathjax',
     'sphinx.ext.viewcode',
diff --git a/docs/modules/path_planning/dubins_path/dubins_path.jpg b/docs/modules/path_planning/dubins_path/dubins_path.jpg
new file mode 100644
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diff --git a/docs/modules/path_planning/dubins_path/dubins_path_main.rst b/docs/modules/path_planning/dubins_path/dubins_path_main.rst
index 0b8c8fb603..516638d83d 100644
--- a/docs/modules/path_planning/dubins_path/dubins_path_main.rst
+++ b/docs/modules/path_planning/dubins_path/dubins_path_main.rst
@@ -9,40 +9,68 @@ Dubins path
 ~~~~~~~~~~~~
 Dubins path is a analytical path planning algorithm for a simple car model.
 
-It can generates a shortest path between 2D poses (x, y, yaw) with maximum curvture comstraint and tangent(yaw angle) constraint.
+It can generates a shortest path between two 2D poses (x, y, yaw) with maximum curvature constraint and tangent(yaw angle) constraint.
 
-The path consist of 3 segments of maximum curvature curves or a straight line segment.
+Generated paths consist of 3 segments of maximum curvature curves or a straight line segment.
 
 Each segment type can is categorized by 3 type: 'Right turn (R)' , 'Left turn (L)', and 'Straight (S).' 
 
-Possible path will be at least one of these six types: RSR, RSL, LSR, LSL, RLR, LRL. 
+Possible path will be at least one of these six types: RSR, RSL, LSR, LSL, RLR, LRL.
 
-For example, one of RSR Dubins paths is:
+Dubins path planner can output each segment type and distance of each course segment.
+
+For example, a RSR Dubins path is:
 
 .. image:: RSR.jpg
    :width: 400px
 
-one of RLR Dubins paths is:
+Each segment distance can be calculated by:
+
+:math:`\alpha = mod(-\theta)`
+
+:math:`\beta = mod(x_{e, yaw} - \theta)`
+
+:math:`p^2 = 2 + d ^ 2 - 2\cos(\alpha-\beta) + 2d(\sin\alpha - \sin\beta)`
+
+:math:`t = atan2(\cos\beta - \cos\alpha, d + \sin\alpha - \sin\beta)`
+
+:math:`d_1 = mod(-\alpha + t)`
+
+:math:`d_2 = p`
+
+:math:`d_3 = mod(\beta - t)`
+
+where :math:`\theta` is tangent and d is distance from :math:`x_s` to :math:`x_e`
+
+A RLR Dubins path is:
 
 .. image:: RLR.jpg
    :width: 200px
 
-Dubins path planner can output three types and distances of each course segment.
+Each segment distance can be calculated by:
+
+:math:`t = (6.0 - d^2 + 2\cos(\alpha-\beta) + 2d(\sin\alpha - \sin\beta)) / 8.0`
+
+:math:`d_2 = mod(2\pi - acos(t))`
+
+:math:`d_1 = mod(\alpha - atan2(\cos\beta - \cos\alpha, d + \sin\alpha - \sin\beta) + d_2 / 2.0)`
+
+:math:`d_3 = mod(\alpha - \beta - d_1 + d_2)`
 
 You can generate a path from these information and the maximum curvature information.
 
-In the example code, a path which is minimum course length one among 6 course type is selected and then a path is constructed.
+A path type which has minimum course length among 6 types is selected,
+and then a path is constructed based on the selected type and its distances.
 
-Code
+API
 ~~~~~~~~~~~~~~~~~~~~
 
-.. automodule:: PathPlanning.DubinsPath.dubins_path_planner
-    :members:
+.. autofunction:: PathPlanning.DubinsPath.dubins_path_planner.plan_dubins_path
 
 
 Reference
 ~~~~~~~~~~~~~~~~~~~~
-
+-  `On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents <https://www.jstor.org/stable/2372560?origin=crossref>`__
 -  `Dubins path - Wikipedia <https://en.wikipedia.org/wiki/Dubins_path>`__
 -  `15.3.1 Dubins Curves <http://planning.cs.uiuc.edu/node821.html>`__
 -  `A Comprehensive, Step-by-Step Tutorial to Computing Dubin’s Paths <https://gieseanw.wordpress.com/2012/10/21/a-comprehensive-step-by-step-tutorial-to-computing-dubins-paths/>`__
diff --git a/tests/test_dubins_path_planning.py b/tests/test_dubins_path_planning.py
index 99c266bc6f..aceb0b752a 100644
--- a/tests/test_dubins_path_planning.py
+++ b/tests/test_dubins_path_planning.py
@@ -69,5 +69,24 @@ def test_3():
         check_path_length(px, py, lengths)
 
 
+def test_path_plannings_types():
+    dubins_path_planner.show_animation = False
+    start_x = 1.0  # [m]
+    start_y = 1.0  # [m]
+    start_yaw = np.deg2rad(45.0)  # [rad]
+
+    end_x = -3.0  # [m]
+    end_y = -3.0  # [m]
+    end_yaw = np.deg2rad(-45.0)  # [rad]
+
+    curvature = 1.0
+
+    _, _, _, mode, _ = dubins_path_planner.plan_dubins_path(
+        start_x, start_y, start_yaw, end_x, end_y, end_yaw, curvature,
+        selected_types=["RSL"])
+
+    assert mode == ["R", "S", "L"]
+
+
 if __name__ == '__main__':
     conftest.run_this_test(__file__)
diff --git a/tests/test_utils.py b/tests/test_utils.py
index b397479809..36861c52b4 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -4,6 +4,12 @@
 import numpy as np
 
 
+def test_rot_mat_2d():
+    assert_allclose(angle.rot_mat_2d(0.0),
+                    np.array([[1., 0.],
+                              [0., 1.]]))
+
+
 def test_angle_mod():
     assert_allclose(angle.angle_mod(-4.0), 2.28318531)
     assert(isinstance(angle.angle_mod(-4.0), float))
diff --git a/utils/angle.py b/utils/angle.py
index 741304f00e..c9e02c5f2e 100644
--- a/utils/angle.py
+++ b/utils/angle.py
@@ -2,9 +2,9 @@
 from scipy.spatial.transform import Rotation as Rot
 
 
-def create_2d_rotation_matrix(angle):
+def rot_mat_2d(angle):
     """
-    Create 2D totation matrix from an angle
+    Create 2D rotation matrix from an angle
 
     Parameters
     ----------
@@ -12,6 +12,12 @@ def create_2d_rotation_matrix(angle):
 
     Returns
     -------
+    A 2D rotation matrix
+
+    Examples
+    --------
+    >>> angle_mod(-4.0)
+
 
     """
     return Rot.from_euler('z', angle).as_matrix()[0:2, 0:2]
diff --git a/utils/plot.py b/utils/plot.py
new file mode 100644
index 0000000000..2c70a60bbe
--- /dev/null
+++ b/utils/plot.py
@@ -0,0 +1,49 @@
+"""
+Matplotlib based plotting utilities
+"""
+import math
+import matplotlib.pyplot as plt
+
+
+def plot_arrow(x, y, yaw, arrow_length=1.0,
+               origin_point_plot_style="xr",
+               head_width=0.1, fc="r", ec="k", **kwargs):
+    """
+    Plot an arrow or arrows based on 2D state (x, y, yaw)
+
+    All optional settings of matplotlib.pyplot.arrow can be used.
+    - matplotlib.pyplot.arrow:
+    https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.arrow.html
+
+    Parameters
+    ----------
+    x : a float or array_like
+        a value or a list of arrow origin x position.
+    y : a float or array_like
+        a value or a list of arrow origin y position.
+    yaw : a float or array_like
+        a value or a list of arrow yaw angle (orientation).
+    arrow_length : a float (optional)
+        arrow length. default is 1.0
+    origin_point_plot_style : str (optional)
+        origin point plot style. If None, not plotting.
+    head_width : a float (optional)
+        arrow head width. default is 0.1
+    fc : string (optional)
+        face color
+    ec : string (optional)
+        edge color
+    """
+    if not isinstance(x, float):
+        for (i_x, i_y, i_yaw) in zip(x, y, yaw):
+            plot_arrow(i_x, i_y, i_yaw, head_width=head_width,
+                       fc=fc, ec=ec, **kwargs)
+    else:
+        plt.arrow(x, y,
+                  arrow_length * math.cos(yaw),
+                  arrow_length * math.sin(yaw),
+                  head_width=head_width,
+                  fc=fc, ec=ec,
+                  **kwargs)
+        if origin_point_plot_style is not None:
+            plt.plot(x, y, origin_point_plot_style)

From dc879da7b245cb70f229ca1f2bd1239dfb7bf831 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 22 May 2022 16:53:14 +0900
Subject: [PATCH 0630/1080] Using utility rot_mat_2d (#683)

---
 .../ensemble_kalman_filter.py                     | 11 +++++++----
 .../extended_kalman_filter.py                     | 11 +++++++----
 Localization/particle_filter/particle_filter.py   | 13 ++++++++-----
 .../unscented_kalman_filter.py                    | 10 +++++++---
 Mapping/rectangle_fitting/rectangle_fitting.py    | 11 ++++++++---
 Mapping/rectangle_fitting/simulator.py            | 15 ++++++---------
 .../grid_based_sweep_coverage_path_planner.py     | 15 ++++++++-------
 PathPlanning/HybridAStar/car.py                   |  9 +++++++--
 PathPlanning/InformedRRTStar/informed_rrt_star.py |  9 ++++++---
 SLAM/GraphBasedSLAM/graph_based_slam.py           |  9 ++++++---
 10 files changed, 70 insertions(+), 43 deletions(-)

diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
index 8ef2089b01..1e8d1fc6d6 100644
--- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
+++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
@@ -10,11 +10,15 @@
 
 """
 
-import math
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
 
+import math
 import matplotlib.pyplot as plt
 import numpy as np
-from scipy.spatial.transform import Rotation as Rot
+
+from utils.angle import rot_mat_2d
 
 #  Simulation parameter
 Q_sim = np.diag([0.2, np.deg2rad(1.0)]) ** 2
@@ -168,8 +172,7 @@ def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
     x = [a * math.cos(it) for it in t]
     y = [b * math.sin(it) for it in t]
     angle = math.atan2(eig_vec[1, big_ind], eig_vec[0, big_ind])
-    rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2]
-    fx = np.stack([x, y]).T @ rot
+    fx = np.stack([x, y]).T @ rot_mat_2d(angle)
 
     px = np.array(fx[:, 0] + xEst[0, 0]).flatten()
     py = np.array(fx[:, 1] + xEst[1, 0]).flatten()
diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py
index 51e8f22f4d..a5dea19fd8 100644
--- a/Localization/extended_kalman_filter/extended_kalman_filter.py
+++ b/Localization/extended_kalman_filter/extended_kalman_filter.py
@@ -6,11 +6,15 @@
 
 """
 
-import math
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
 
+import math
 import matplotlib.pyplot as plt
 import numpy as np
-from scipy.spatial.transform import Rotation as Rot
+
+from utils.angle import rot_mat_2d
 
 # Covariance for EKF simulation
 Q = np.diag([
@@ -149,8 +153,7 @@ def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
     x = [a * math.cos(it) for it in t]
     y = [b * math.sin(it) for it in t]
     angle = math.atan2(eigvec[1, bigind], eigvec[0, bigind])
-    rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2]
-    fx = rot @ (np.array([x, y]))
+    fx = rot_mat_2d(angle) @ (np.array([x, y]))
     px = np.array(fx[0, :] + xEst[0, 0]).flatten()
     py = np.array(fx[1, :] + xEst[1, 0]).flatten()
     plt.plot(px, py, "--r")
diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py
index 002718f028..1c45687ed1 100644
--- a/Localization/particle_filter/particle_filter.py
+++ b/Localization/particle_filter/particle_filter.py
@@ -5,12 +5,16 @@
 author: Atsushi Sakai (@Atsushi_twi)
 
 """
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
 
 import math
 
 import matplotlib.pyplot as plt
 import numpy as np
-from scipy.spatial.transform import Rotation as Rot
+
+from utils.angle import rot_mat_2d
 
 # Estimation parameter of PF
 Q = np.diag([0.2]) ** 2  # range error
@@ -189,10 +193,9 @@ def plot_covariance_ellipse(x_est, p_est):  # pragma: no cover
     x = [a * math.cos(it) for it in t]
     y = [b * math.sin(it) for it in t]
     angle = math.atan2(eig_vec[1, big_ind], eig_vec[0, big_ind])
-    rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2]
-    fx = rot.dot(np.array([[x, y]]))
-    px = np.array(fx[0, :] + x_est[0, 0]).flatten()
-    py = np.array(fx[1, :] + x_est[1, 0]).flatten()
+    fx = rot_mat_2d(angle) @ np.array([[x, y]])
+    px = np.array(fx[:, 0] + x_est[0, 0]).flatten()
+    py = np.array(fx[:, 1] + x_est[1, 0]).flatten()
     plt.plot(px, py, "--r")
 
 
diff --git a/Localization/unscented_kalman_filter/unscented_kalman_filter.py b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
index 7f80bccf51..4f42cfd5d1 100644
--- a/Localization/unscented_kalman_filter/unscented_kalman_filter.py
+++ b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
@@ -6,13 +6,18 @@
 
 """
 
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+
 import math
 
 import matplotlib.pyplot as plt
 import numpy as np
-from scipy.spatial.transform import Rotation as Rot
 import scipy.linalg
 
+from utils.angle import rot_mat_2d
+
 # Covariance for UKF simulation
 Q = np.diag([
     0.1,  # variance of location on x-axis
@@ -182,8 +187,7 @@ def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
     x = [a * math.cos(it) for it in t]
     y = [b * math.sin(it) for it in t]
     angle = math.atan2(eigvec[1, bigind], eigvec[0, bigind])
-    rot = Rot.from_euler('z', angle).as_matrix()[0:2, 0:2]
-    fx = rot @ np.array([x, y])
+    fx = rot_mat_2d(angle) @ np.array([x, y])
     px = np.array(fx[0, :] + xEst[0, 0]).flatten()
     py = np.array(fx[1, :] + xEst[1, 0]).flatten()
     plt.plot(px, py, "--r")
diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py
index 078916f3b8..6d3e63f0f3 100644
--- a/Mapping/rectangle_fitting/rectangle_fitting.py
+++ b/Mapping/rectangle_fitting/rectangle_fitting.py
@@ -12,11 +12,17 @@
 
 """
 
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+
+
 import matplotlib.pyplot as plt
 import numpy as np
 import itertools
 from enum import Enum
-from scipy.spatial.transform import Rotation as Rot
+
+from utils.angle import rot_mat_2d
 
 from Mapping.rectangle_fitting.simulator \
     import VehicleSimulator, LidarSimulator
@@ -104,8 +110,7 @@ def _rectangle_search(self, x, y):
         min_cost = (-float('inf'), None)
         for theta in np.arange(0.0, np.pi / 2.0 - d_theta, d_theta):
 
-            rot = Rot.from_euler('z', theta).as_matrix()[0:2, 0:2]
-            c = X @ rot
+            c = X @ rot_mat_2d(theta)
             c1 = c[:, 0]
             c2 = c[:, 1]
 
diff --git a/Mapping/rectangle_fitting/simulator.py b/Mapping/rectangle_fitting/simulator.py
index c7c3141fa9..e75738c3b1 100644
--- a/Mapping/rectangle_fitting/simulator.py
+++ b/Mapping/rectangle_fitting/simulator.py
@@ -5,12 +5,16 @@
 author: Atsushi Sakai
 
 """
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
 
 import numpy as np
 import matplotlib.pyplot as plt
 import math
 import random
-from scipy.spatial.transform import Rotation as Rot
+
+from utils.angle import rot_mat_2d
 
 
 class VehicleSimulator:
@@ -41,8 +45,7 @@ def plot(self):
         plt.plot(gx, gy, "--b")
 
     def calc_global_contour(self):
-        rot = Rot.from_euler('z', self.yaw).as_matrix()[0:2, 0:2]
-        gxy = np.stack([self.vc_x, self.vc_y]).T @ rot
+        gxy = np.stack([self.vc_x, self.vc_y]).T @ rot_mat_2d(self.yaw)
         gx = gxy[:, 0] + self.x
         gy = gxy[:, 1] + self.y
 
@@ -137,11 +140,5 @@ def ray_casting_filter(theta_l, range_l, angle_resolution):
         return rx, ry
 
 
-def main():
-    print("start!!")
-
-    print("done!!")
-
-
 if __name__ == '__main__':
     main()
diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
index c0c8fc4319..2f4d32b187 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -4,15 +4,18 @@
 author: Atsushi Sakai
 """
 
-import math
-import os
 import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+
+import math
 from enum import IntEnum
 
 import numpy as np
-from scipy.spatial.transform import Rotation as Rot
 import matplotlib.pyplot as plt
 
+from utils.angle import rot_mat_2d
+
 sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
                 "/../../Mapping/")
 
@@ -148,16 +151,14 @@ def convert_grid_coordinate(ox, oy, sweep_vec, sweep_start_position):
     tx = [ix - sweep_start_position[0] for ix in ox]
     ty = [iy - sweep_start_position[1] for iy in oy]
     th = math.atan2(sweep_vec[1], sweep_vec[0])
-    rot = Rot.from_euler('z', th).as_matrix()[0:2, 0:2]
-    converted_xy = np.stack([tx, ty]).T @ rot
+    converted_xy = np.stack([tx, ty]).T @ rot_mat_2d(th)
 
     return converted_xy[:, 0], converted_xy[:, 1]
 
 
 def convert_global_coordinate(x, y, sweep_vec, sweep_start_position):
     th = math.atan2(sweep_vec[1], sweep_vec[0])
-    rot = Rot.from_euler('z', -th).as_matrix()[0:2, 0:2]
-    converted_xy = np.stack([x, y]).T @ rot
+    converted_xy = np.stack([x, y]).T @ rot_mat_2d(-th)
     rx = [ix + sweep_start_position[0] for ix in converted_xy[:, 0]]
     ry = [iy + sweep_start_position[1] for iy in converted_xy[:, 1]]
     return rx, ry
diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py
index 14b7f956b7..870dd18535 100644
--- a/PathPlanning/HybridAStar/car.py
+++ b/PathPlanning/HybridAStar/car.py
@@ -6,11 +6,16 @@
 
 """
 
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+
 from math import cos, sin, tan, pi
 
 import matplotlib.pyplot as plt
 import numpy as np
-from scipy.spatial.transform import Rotation as Rot
+
+from utils.angle import rot_mat_2d
 
 WB = 3.0  # rear to front wheel
 W = 2.0  # width of car
@@ -45,7 +50,7 @@ def check_car_collision(x_list, y_list, yaw_list, ox, oy, kd_tree):
 
 def rectangle_check(x, y, yaw, ox, oy):
     # transform obstacles to base link frame
-    rot = Rot.from_euler('z', yaw).as_matrix()[0:2, 0:2]
+    rot = rot_mat_2d(yaw)
     for iox, ioy in zip(ox, oy):
         tx = iox - x
         ty = ioy - y
diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index baf7dcce91..10af3a81bd 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -9,15 +9,19 @@
 https://arxiv.org/pdf/1404.2334.pdf
 
 """
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
 
 import copy
 import math
 import random
 
 import matplotlib.pyplot as plt
-from scipy.spatial.transform import Rotation as Rot
 import numpy as np
 
+from utils.angle import rot_mat_2d
+
 show_animation = True
 
 
@@ -308,8 +312,7 @@ def plot_ellipse(xCenter, cBest, cMin, e_theta):  # pragma: no cover
         t = np.arange(0, 2 * math.pi + 0.1, 0.1)
         x = [a * math.cos(it) for it in t]
         y = [b * math.sin(it) for it in t]
-        rot = Rot.from_euler('z', -angle).as_matrix()[0:2, 0:2]
-        fx = rot @ np.array([x, y])
+        fx = rot_mat_2d(-angle) @ np.array([x, y])
         px = np.array(fx[0, :] + cx).flatten()
         py = np.array(fx[1, :] + cy).flatten()
         plt.plot(cx, cy, "xc")
diff --git a/SLAM/GraphBasedSLAM/graph_based_slam.py b/SLAM/GraphBasedSLAM/graph_based_slam.py
index 9335dbe6a1..0827732cd8 100644
--- a/SLAM/GraphBasedSLAM/graph_based_slam.py
+++ b/SLAM/GraphBasedSLAM/graph_based_slam.py
@@ -10,6 +10,9 @@
 (http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf)
 
 """
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
 
 import copy
 import itertools
@@ -63,7 +66,7 @@ def cal_observation_sigma():
     return sigma
 
 
-def calc_rotational_matrix(angle):
+def calc_3d_rotational_matrix(angle):
     return Rot.from_euler('z', angle).as_matrix()
 
 
@@ -82,8 +85,8 @@ def calc_edge(x1, y1, yaw1, x2, y2, yaw2, d1,
     edge.e[1, 0] = y2 - y1 - tmp3 + tmp4
     edge.e[2, 0] = 0
 
-    Rt1 = calc_rotational_matrix(tangle1)
-    Rt2 = calc_rotational_matrix(tangle2)
+    Rt1 = calc_3d_rotational_matrix(tangle1)
+    Rt2 = calc_3d_rotational_matrix(tangle2)
 
     sig1 = cal_observation_sigma()
     sig2 = cal_observation_sigma()

From a1782fdea2787cb25105b04e9b6772c2b615c34b Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 24 May 2022 07:34:32 +0900
Subject: [PATCH 0631/1080] Bump numpy from 1.22.3 to 1.22.4 in /requirements
 (#685)

Bumps [numpy](https://github.com/numpy/numpy) from 1.22.3 to 1.22.4.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst.txt)
- [Commits](https://github.com/numpy/numpy/compare/v1.22.3...v1.22.4)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index d404da757e..7041485cef 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.22.3
+numpy == 1.22.4
 scipy == 1.8.0
 pandas == 1.4.2
 matplotlib == 3.5.2

From ddc396f402044dad00d57da022d675ace544c482 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 24 May 2022 08:10:20 +0900
Subject: [PATCH 0632/1080] Bump scipy from 1.8.0 to 1.8.1 in /requirements
 (#686)

Bumps [scipy](https://github.com/scipy/scipy) from 1.8.0 to 1.8.1.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.8.0...v1.8.1)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 7041485cef..75d9fc4249 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.22.4
-scipy == 1.8.0
+scipy == 1.8.1
 pandas == 1.4.2
 matplotlib == 3.5.2
 cvxpy == 1.2.1

From 4fffd61e70e2a18bcdb1f597fe4f533cc0928191 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 31 May 2022 07:26:05 +0900
Subject: [PATCH 0633/1080] Bump mypy from 0.950 to 0.960 in /requirements
 (#689)

Bumps [mypy](https://github.com/python/mypy) from 0.950 to 0.960.
- [Release notes](https://github.com/python/mypy/releases)
- [Commits](https://github.com/python/mypy/compare/v0.950...v0.960)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 75d9fc4249..d07012f977 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ matplotlib == 3.5.2
 cvxpy == 1.2.1
 pytest == 7.1.2 # For unit test
 pytest-xdist == 2.5.0 # For unit test
-mypy == 0.950 # For unit test
+mypy == 0.960 # For unit test
 flake8 == 4.0.1 # For unit test

From 71de5d038f348d347d7b5dc00c914d523cd59f92 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 7 Jun 2022 07:35:05 +0900
Subject: [PATCH 0634/1080] Bump mypy from 0.960 to 0.961 in /requirements
 (#691)

Bumps [mypy](https://github.com/python/mypy) from 0.960 to 0.961.
- [Release notes](https://github.com/python/mypy/releases)
- [Commits](https://github.com/python/mypy/compare/v0.960...v0.961)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index d07012f977..ac5f3be2d3 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ matplotlib == 3.5.2
 cvxpy == 1.2.1
 pytest == 7.1.2 # For unit test
 pytest-xdist == 2.5.0 # For unit test
-mypy == 0.960 # For unit test
+mypy == 0.961 # For unit test
 flake8 == 4.0.1 # For unit test

From 8a0aab89a966ce411034da199ab0ac7042414d97 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 28 Jun 2022 07:22:03 +0900
Subject: [PATCH 0635/1080] Bump numpy from 1.22.4 to 1.23.0 in /requirements
 (#692)

Bumps [numpy](https://github.com/numpy/numpy) from 1.22.4 to 1.23.0.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/HOWTO_RELEASE.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.22.4...v1.23.0)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index ac5f3be2d3..9d3ca6847e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.22.4
+numpy == 1.23.0
 scipy == 1.8.1
 pandas == 1.4.2
 matplotlib == 3.5.2

From 8a8f4f7434bf786a5f190139cc3cef19c4756cdd Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 28 Jun 2022 20:41:25 +0900
Subject: [PATCH 0636/1080] Bump pandas from 1.4.2 to 1.4.3 in /requirements
 (#693)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.4.2 to 1.4.3.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/main/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.4.2...v1.4.3)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 9d3ca6847e..87e09cd9c0 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.23.0
 scipy == 1.8.1
-pandas == 1.4.2
+pandas == 1.4.3
 matplotlib == 3.5.2
 cvxpy == 1.2.1
 pytest == 7.1.2 # For unit test

From 6d97a8f7f0bfe70e312fadbe593739c6ecfe2f3c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 12 Jul 2022 07:31:57 +0900
Subject: [PATCH 0637/1080] Bump numpy from 1.23.0 to 1.23.1 in /requirements
 (#694)

Bumps [numpy](https://github.com/numpy/numpy) from 1.23.0 to 1.23.1.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.23.0...v1.23.1)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 87e09cd9c0..9130a07b6a 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.23.0
+numpy == 1.23.1
 scipy == 1.8.1
 pandas == 1.4.3
 matplotlib == 3.5.2

From c6bdd48715adcbe17c4146b7cae3b0fc569f7bde Mon Sep 17 00:00:00 2001
From: Tim Gates <tim.gates@iress.com>
Date: Tue, 19 Jul 2022 08:22:09 +1000
Subject: [PATCH 0638/1080] docs: Fix a few typos (#695)

There are small typos in:
- ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
- ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
- docs/modules/slam/FastSLAM1/FastSLAM1_main.rst
- docs/modules/slam/ekf_slam/ekf_slam_main.rst

Fixes:
- Should read `configuration` rather than `configuation`.
- Should read `trajectory` rather than `tracjectory`.
- Should read `prediction` rather than `prediciton`.

Signed-off-by: Tim Gates <tim.gates@iress.com>
---
 .../arm_obstacle_navigation/arm_obstacle_navigation.py          | 2 +-
 .../arm_obstacle_navigation/arm_obstacle_navigation_2.py        | 2 +-
 docs/modules/slam/FastSLAM1/FastSLAM1_main.rst                  | 2 +-
 docs/modules/slam/ekf_slam/ekf_slam_main.rst                    | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
index f3bd25577c..75b1f9d2c4 100644
--- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
+++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
@@ -105,7 +105,7 @@ def astar_torus(grid, start_node, goal_node):
 
     Args:
         grid: An occupancy grid (ndarray)
-        start_node: Initial joint configuation (tuple)
+        start_node: Initial joint configuration (tuple)
         goal_node: Goal joint configuration (tuple)
 
     Returns:
diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
index 429cd4d211..40e6746bfc 100644
--- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
+++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
@@ -136,7 +136,7 @@ def astar_torus(grid, start_node, goal_node):
 
     Args:
         grid: An occupancy grid (ndarray)
-        start_node: Initial joint configuation (tuple)
+        start_node: Initial joint configuration (tuple)
         goal_node: Goal joint configuration (tuple)
 
     Returns:
diff --git a/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst b/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst
index f09125dff0..f19527c453 100644
--- a/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst
+++ b/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst
@@ -39,7 +39,7 @@ an array of landmark locations
 -  The blue line is the true trajectory
 -  The red line is the estimated trajectory
 -  The red dots represent the distribution of particles
--  The black line represent dead reckoning tracjectory
+-  The black line represent dead reckoning trajectory
 -  The blue x is the observed and estimated landmarks
 -  The black x is the true landmark
 
diff --git a/docs/modules/slam/ekf_slam/ekf_slam_main.rst b/docs/modules/slam/ekf_slam/ekf_slam_main.rst
index 5a23b4c887..70a7d131ae 100644
--- a/docs/modules/slam/ekf_slam/ekf_slam_main.rst
+++ b/docs/modules/slam/ekf_slam/ekf_slam_main.rst
@@ -151,7 +151,7 @@ values incorporate the error into the estimation.
 
 :math:`\begin{equation*} X_{DR} = FX + B(U + R) \end{equation*}`
 
-The implementation of the motion model prediciton code is shown in
+The implementation of the motion model prediction code is shown in
 ``motion_model``. The ``observation`` function shows how the simulation
 uses (or doesn’t use) the process noise ``Rsim`` to the find the ground
 truth and dead reckoning estimates of the pose.

From def289b723e9216830c2a7b2577cb31b55710167 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 24 Jul 2022 14:58:14 +0900
Subject: [PATCH 0639/1080] enhance cubic spline path doc (#698)

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cublic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc

* enhance cubic spline path doc
---
 .../CubicSpline/cubic_spline_planner.py       | 276 ++++++++++++++----
 PathPlanning/CubicSpline/spline_continuity.py |  55 ++++
 .../frenet_optimal_trajectory.py              |   2 +-
 docs/conf.py                                  |   3 +-
 .../path_planning/cubic_spline/Figure_1.png   | Bin 26172 -> 0 bytes
 .../cubic_spline/cubic_spline_1d.png          | Bin 0 -> 25173 bytes
 ...re_3.png => cubic_spline_2d_curvature.png} | Bin
 .../cubic_spline/cubic_spline_2d_path.png     | Bin 0 -> 28480 bytes
 .../{Figure_2.png => cubic_spline_2d_yaw.png} | Bin
 .../cubic_spline/cubic_spline_main.rst        | 214 +++++++++++++-
 .../path_planning/cubic_spline/spline.png     | Bin 0 -> 22116 bytes
 .../cubic_spline/spline_continuity.png        | Bin 0 -> 48278 bytes
 12 files changed, 478 insertions(+), 72 deletions(-)
 create mode 100644 PathPlanning/CubicSpline/spline_continuity.py
 delete mode 100644 docs/modules/path_planning/cubic_spline/Figure_1.png
 create mode 100644 docs/modules/path_planning/cubic_spline/cubic_spline_1d.png
 rename docs/modules/path_planning/cubic_spline/{Figure_3.png => cubic_spline_2d_curvature.png} (100%)
 create mode 100644 docs/modules/path_planning/cubic_spline/cubic_spline_2d_path.png
 rename docs/modules/path_planning/cubic_spline/{Figure_2.png => cubic_spline_2d_yaw.png} (100%)
 create mode 100644 docs/modules/path_planning/cubic_spline/spline.png
 create mode 100644 docs/modules/path_planning/cubic_spline/spline_continuity.png

diff --git a/PathPlanning/CubicSpline/cubic_spline_planner.py b/PathPlanning/CubicSpline/cubic_spline_planner.py
index d2ef8622ad..7cc7bedc79 100644
--- a/PathPlanning/CubicSpline/cubic_spline_planner.py
+++ b/PathPlanning/CubicSpline/cubic_spline_planner.py
@@ -9,87 +9,133 @@
 import bisect
 
 
-class Spline:
+class CubicSpline1D:
     """
-    Cubic Spline class
+    1D Cubic Spline class
+
+    Parameters
+    ----------
+    x : list
+        x coordinates for data points. This x coordinates must be
+        sorted
+        in ascending order.
+    y : list
+        y coordinates for data points
+
+    Examples
+    --------
+    You can interpolate 1D data points.
+
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> x = np.arange(5)
+    >>> y = [1.7, -6, 5, 6.5, 0.0]
+    >>> sp = CubicSpline1D(x, y)
+    >>> xi = np.linspace(0.0, 5.0)
+    >>> yi = [sp.calc_position(x) for x in xi]
+    >>> plt.plot(x, y, "xb", label="Data points")
+    >>> plt.plot(xi, yi , "r", label="Cubic spline interpolation")
+    >>> plt.grid(True)
+    >>> plt.legend()
+    >>> plt.show()
+
+    .. image:: cubic_spline_1d.png
+
     """
 
     def __init__(self, x, y):
-        self.b, self.c, self.d, self.w = [], [], [], []
 
+        h = np.diff(x)
+        if np.any(h < 0):
+            raise ValueError("x coordinates must be sorted in ascending order")
+
+        self.a, self.b, self.c, self.d = [], [], [], []
         self.x = x
         self.y = y
-
         self.nx = len(x)  # dimension of x
-        h = np.diff(x)
 
-        # calc coefficient c
+        # calc coefficient a
         self.a = [iy for iy in y]
 
         # calc coefficient c
         A = self.__calc_A(h)
-        B = self.__calc_B(h)
+        B = self.__calc_B(h, self.a)
         self.c = np.linalg.solve(A, B)
-        #  print(self.c1)
 
         # calc spline coefficient b and d
         for i in range(self.nx - 1):
-            self.d.append((self.c[i + 1] - self.c[i]) / (3.0 * h[i]))
-            tb = (self.a[i + 1] - self.a[i]) / h[i] - h[i] * \
-                (self.c[i + 1] + 2.0 * self.c[i]) / 3.0
-            self.b.append(tb)
+            d = (self.c[i + 1] - self.c[i]) / (3.0 * h[i])
+            b = 1.0 / h[i] * (self.a[i + 1] - self.a[i]) \
+                - h[i] / 3.0 * (2.0 * self.c[i] + self.c[i + 1])
+            self.d.append(d)
+            self.b.append(b)
 
-    def calc(self, t):
+    def calc_position(self, x):
         """
-        Calc position
+        Calc `y` position for given `x`.
 
-        if t is outside of the input x, return None
+        if `x` is outside the data point's `x` range, return None.
 
+        Returns
+        -------
+        y : float
+            y position for given x.
         """
-
-        if t < self.x[0]:
+        if x < self.x[0]:
             return None
-        elif t > self.x[-1]:
+        elif x > self.x[-1]:
             return None
 
-        i = self.__search_index(t)
-        dx = t - self.x[i]
-        result = self.a[i] + self.b[i] * dx + \
+        i = self.__search_index(x)
+        dx = x - self.x[i]
+        position = self.a[i] + self.b[i] * dx + \
             self.c[i] * dx ** 2.0 + self.d[i] * dx ** 3.0
 
-        return result
+        return position
 
-    def calcd(self, t):
+    def calc_first_derivative(self, x):
         """
-        Calc first derivative
+        Calc first derivative at given x.
+
+        if x is outside the input x, return None
 
-        if t is outside of the input x, return None
+        Returns
+        -------
+        dy : float
+            first derivative for given x.
         """
 
-        if t < self.x[0]:
+        if x < self.x[0]:
             return None
-        elif t > self.x[-1]:
+        elif x > self.x[-1]:
             return None
 
-        i = self.__search_index(t)
-        dx = t - self.x[i]
-        result = self.b[i] + 2.0 * self.c[i] * dx + 3.0 * self.d[i] * dx ** 2.0
-        return result
+        i = self.__search_index(x)
+        dx = x - self.x[i]
+        dy = self.b[i] + 2.0 * self.c[i] * dx + 3.0 * self.d[i] * dx ** 2.0
+        return dy
 
-    def calcdd(self, t):
+    def calc_second_derivative(self, x):
         """
-        Calc second derivative
+        Calc second derivative at given x.
+
+        if x is outside the input x, return None
+
+        Returns
+        -------
+        ddy : float
+            second derivative for given x.
         """
 
-        if t < self.x[0]:
+        if x < self.x[0]:
             return None
-        elif t > self.x[-1]:
+        elif x > self.x[-1]:
             return None
 
-        i = self.__search_index(t)
-        dx = t - self.x[i]
-        result = 2.0 * self.c[i] + 6.0 * self.d[i] * dx
-        return result
+        i = self.__search_index(x)
+        dx = x - self.x[i]
+        ddy = 2.0 * self.c[i] + 6.0 * self.d[i] * dx
+        return ddy
 
     def __search_index(self, x):
         """
@@ -112,30 +158,82 @@ def __calc_A(self, h):
         A[0, 1] = 0.0
         A[self.nx - 1, self.nx - 2] = 0.0
         A[self.nx - 1, self.nx - 1] = 1.0
-        #  print(A)
         return A
 
-    def __calc_B(self, h):
+    def __calc_B(self, h, a):
         """
         calc matrix B for spline coefficient c
         """
         B = np.zeros(self.nx)
         for i in range(self.nx - 2):
-            B[i + 1] = 3.0 * (self.a[i + 2] - self.a[i + 1]) / \
-                h[i + 1] - 3.0 * (self.a[i + 1] - self.a[i]) / h[i]
+            B[i + 1] = 3.0 * (a[i + 2] - a[i + 1]) / h[i + 1]\
+                - 3.0 * (a[i + 1] - a[i]) / h[i]
         return B
 
 
-class Spline2D:
+class CubicSpline2D:
     """
-    2D Cubic Spline class
-
+    Cubic CubicSpline2D class
+
+    Parameters
+    ----------
+    x : list
+        x coordinates for data points.
+    y : list
+        y coordinates for data points.
+
+    Examples
+    --------
+    You can interpolate a 2D data points.
+
+    >>> import matplotlib.pyplot as plt
+    >>> x = [-2.5, 0.0, 2.5, 5.0, 7.5, 3.0, -1.0]
+    >>> y = [0.7, -6, 5, 6.5, 0.0, 5.0, -2.0]
+    >>> ds = 0.1  # [m] distance of each interpolated points
+    >>> sp = CubicSpline2D(x, y)
+    >>> s = np.arange(0, sp.s[-1], ds)
+    >>> rx, ry, ryaw, rk = [], [], [], []
+    >>> for i_s in s:
+    ...     ix, iy = sp.calc_position(i_s)
+    ...     rx.append(ix)
+    ...     ry.append(iy)
+    ...     ryaw.append(sp.calc_yaw(i_s))
+    ...     rk.append(sp.calc_curvature(i_s))
+    >>> plt.subplots(1)
+    >>> plt.plot(x, y, "xb", label="Data points")
+    >>> plt.plot(rx, ry, "-r", label="Cubic spline path")
+    >>> plt.grid(True)
+    >>> plt.axis("equal")
+    >>> plt.xlabel("x[m]")
+    >>> plt.ylabel("y[m]")
+    >>> plt.legend()
+    >>> plt.show()
+
+    .. image:: cubic_spline_2d_path.png
+
+    >>> plt.subplots(1)
+    >>> plt.plot(s, [np.rad2deg(iyaw) for iyaw in ryaw], "-r", label="yaw")
+    >>> plt.grid(True)
+    >>> plt.legend()
+    >>> plt.xlabel("line length[m]")
+    >>> plt.ylabel("yaw angle[deg]")
+
+    .. image:: cubic_spline_2d_yaw.png
+
+    >>> plt.subplots(1)
+    >>> plt.plot(s, rk, "-r", label="curvature")
+    >>> plt.grid(True)
+    >>> plt.legend()
+    >>> plt.xlabel("line length[m]")
+    >>> plt.ylabel("curvature [1/m]")
+
+    .. image:: cubic_spline_2d_curvature.png
     """
 
     def __init__(self, x, y):
         self.s = self.__calc_s(x, y)
-        self.sx = Spline(self.s, x)
-        self.sy = Spline(self.s, y)
+        self.sx = CubicSpline1D(self.s, x)
+        self.sy = CubicSpline1D(self.s, y)
 
     def __calc_s(self, x, y):
         dx = np.diff(x)
@@ -148,35 +246,70 @@ def __calc_s(self, x, y):
     def calc_position(self, s):
         """
         calc position
+
+        Parameters
+        ----------
+        s : float
+            distance from the start point. if `s` is outside the data point's
+            range, return None.
+
+        Returns
+        -------
+        x : float
+            x position for given s.
+        y : float
+            y position for given s.
         """
-        x = self.sx.calc(s)
-        y = self.sy.calc(s)
+        x = self.sx.calc_position(s)
+        y = self.sy.calc_position(s)
 
         return x, y
 
     def calc_curvature(self, s):
         """
         calc curvature
+
+        Parameters
+        ----------
+        s : float
+            distance from the start point. if `s` is outside the data point's
+            range, return None.
+
+        Returns
+        -------
+        k : float
+            curvature for given s.
         """
-        dx = self.sx.calcd(s)
-        ddx = self.sx.calcdd(s)
-        dy = self.sy.calcd(s)
-        ddy = self.sy.calcdd(s)
+        dx = self.sx.calc_first_derivative(s)
+        ddx = self.sx.calc_second_derivative(s)
+        dy = self.sy.calc_first_derivative(s)
+        ddy = self.sy.calc_second_derivative(s)
         k = (ddy * dx - ddx * dy) / ((dx ** 2 + dy ** 2)**(3 / 2))
         return k
 
     def calc_yaw(self, s):
         """
         calc yaw
+
+        Parameters
+        ----------
+        s : float
+            distance from the start point. if `s` is outside the data point's
+            range, return None.
+
+        Returns
+        -------
+        yaw : float
+            yaw angle (tangent vector) for given s.
         """
-        dx = self.sx.calcd(s)
-        dy = self.sy.calcd(s)
+        dx = self.sx.calc_first_derivative(s)
+        dy = self.sy.calc_first_derivative(s)
         yaw = math.atan2(dy, dx)
         return yaw
 
 
 def calc_spline_course(x, y, ds=0.1):
-    sp = Spline2D(x, y)
+    sp = CubicSpline2D(x, y)
     s = list(np.arange(0, sp.s[-1], ds))
 
     rx, ry, ryaw, rk = [], [], [], []
@@ -190,14 +323,30 @@ def calc_spline_course(x, y, ds=0.1):
     return rx, ry, ryaw, rk, s
 
 
-def main():  # pragma: no cover
-    print("Spline 2D test")
+def main_1d():
+    print("CubicSpline1D test")
+    import matplotlib.pyplot as plt
+    x = np.arange(5)
+    y = [1.7, -6, 5, 6.5, 0.0]
+    sp = CubicSpline1D(x, y)
+    xi = np.linspace(0.0, 5.0)
+
+    plt.plot(x, y, "xb", label="Data points")
+    plt.plot(xi, [sp.calc_position(x) for x in xi], "r",
+             label="Cubic spline interpolation")
+    plt.grid(True)
+    plt.legend()
+    plt.show()
+
+
+def main_2d():  # pragma: no cover
+    print("CubicSpline1D 2D test")
     import matplotlib.pyplot as plt
     x = [-2.5, 0.0, 2.5, 5.0, 7.5, 3.0, -1.0]
     y = [0.7, -6, 5, 6.5, 0.0, 5.0, -2.0]
     ds = 0.1  # [m] distance of each interpolated points
 
-    sp = Spline2D(x, y)
+    sp = CubicSpline2D(x, y)
     s = np.arange(0, sp.s[-1], ds)
 
     rx, ry, ryaw, rk = [], [], [], []
@@ -209,8 +358,8 @@ def main():  # pragma: no cover
         rk.append(sp.calc_curvature(i_s))
 
     plt.subplots(1)
-    plt.plot(x, y, "xb", label="input")
-    plt.plot(rx, ry, "-r", label="spline")
+    plt.plot(x, y, "xb", label="Data points")
+    plt.plot(rx, ry, "-r", label="Cubic spline path")
     plt.grid(True)
     plt.axis("equal")
     plt.xlabel("x[m]")
@@ -235,4 +384,5 @@ def main():  # pragma: no cover
 
 
 if __name__ == '__main__':
-    main()
+    # main_1d()
+    main_2d()
diff --git a/PathPlanning/CubicSpline/spline_continuity.py b/PathPlanning/CubicSpline/spline_continuity.py
new file mode 100644
index 0000000000..ea85b37f7c
--- /dev/null
+++ b/PathPlanning/CubicSpline/spline_continuity.py
@@ -0,0 +1,55 @@
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy import interpolate
+
+
+class Spline2D:
+
+    def __init__(self, x, y, kind="cubic"):
+        self.s = self.__calc_s(x, y)
+        self.sx = interpolate.interp1d(self.s, x, kind=kind)
+        self.sy = interpolate.interp1d(self.s, y, kind=kind)
+
+    def __calc_s(self, x, y):
+        self.ds = np.hypot(np.diff(x), np.diff(y))
+        s = [0.0]
+        s.extend(np.cumsum(self.ds))
+        return s
+
+    def calc_position(self, s):
+        x = self.sx(s)
+        y = self.sy(s)
+        return x, y
+
+
+def main():
+    x = [-2.5, 0.0, 2.5, 5.0, 7.5, 3.0, -1.0]
+    y = [0.7, -6, -5, -3.5, 0.0, 5.0, -2.0]
+    ds = 0.1  # [m] distance of each interpolated points
+
+    plt.subplots(1)
+    plt.plot(x, y, "xb", label="Data points")
+
+    for (kind, label) in [("linear", "C0 (Linear spline)"),
+                          ("quadratic", "C0 & C1 (Quadratic spline)"),
+                          ("cubic", "C0 & C1 & C2 (Cubic spline)")]:
+        rx, ry = [], []
+        sp = Spline2D(x, y, kind=kind)
+        s = np.arange(0, sp.s[-1], ds)
+        for i_s in s:
+            ix, iy = sp.calc_position(i_s)
+            rx.append(ix)
+            ry.append(iy)
+        plt.plot(rx, ry, "-", label=label)
+
+    plt.grid(True)
+    plt.axis("equal")
+    plt.xlabel("x[m]")
+    plt.ylabel("y[m]")
+    plt.legend()
+    plt.show()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
index ad3da20835..ef20af22f0 100644
--- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
+++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
@@ -248,7 +248,7 @@ def frenet_optimal_planning(csp, s0, c_speed, c_d, c_d_d, c_d_dd, ob):
 
 
 def generate_target_course(x, y):
-    csp = cubic_spline_planner.Spline2D(x, y)
+    csp = cubic_spline_planner.CubicSpline2D(x, y)
     s = np.arange(0, csp.s[-1], 0.1)
 
     rx, ry, ryaw, rk = [], [], [], []
diff --git a/docs/conf.py b/docs/conf.py
index 8a3ba1ac2d..24b5518b6f 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -44,6 +44,7 @@
     'sphinx.ext.mathjax',
     'sphinx.ext.viewcode',
     'sphinx.ext.napoleon',
+    'sphinx.ext.imgconverter',
     'IPython.sphinxext.ipython_console_highlighting',
 ]
 
@@ -64,7 +65,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = "en"
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
diff --git a/docs/modules/path_planning/cubic_spline/Figure_1.png b/docs/modules/path_planning/cubic_spline/Figure_1.png
deleted file mode 100644
index 13eea2122d8f0281c25ee8ed33d463e98fa9d853..0000000000000000000000000000000000000000
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diff --git a/docs/modules/path_planning/cubic_spline/Figure_2.png b/docs/modules/path_planning/cubic_spline/cubic_spline_2d_yaw.png
similarity index 100%
rename from docs/modules/path_planning/cubic_spline/Figure_2.png
rename to docs/modules/path_planning/cubic_spline/cubic_spline_2d_yaw.png
diff --git a/docs/modules/path_planning/cubic_spline/cubic_spline_main.rst b/docs/modules/path_planning/cubic_spline/cubic_spline_main.rst
index 6de0297a05..33471f7c85 100644
--- a/docs/modules/path_planning/cubic_spline/cubic_spline_main.rst
+++ b/docs/modules/path_planning/cubic_spline/cubic_spline_main.rst
@@ -1,13 +1,213 @@
 Cubic spline planning
 ---------------------
 
-A sample code for cubic path planning.
+Spline curve continuity
+~~~~~~~~~~~~~~~~~~~~~~~~
 
-This code generates a curvature continuous path based on x-y waypoints
-with cubic spline.
+Spline curve smoothness is depending on the which kind of spline model is used.
+
+The smoothness of the spline curve is expressed as :math:`C_0, C_1`, and so on.
+
+This representation represents continuity of the curve.
+For example, for a spline curve in two-dimensional space:
+
+- :math:`C_0` is position continuous
+- :math:`C_1` is tangent vector continuous
+- :math:`C_2` is curvature vector continuous
+
+as shown in the following figure:
+
+.. image:: spline_continuity.png
+
+Cubic spline can generate a curve with :math:`C_0, C_1, C_2`.
+
+1D cubic spline
+~~~~~~~~~~~~~~~~~~~
+
+Cubic spline interpolation is a method of smoothly
+interpolating between multiple data points when given
+multiple data points, as shown in the figure below.
+
+.. image:: spline.png
+
+It separates between each interval between data points.
+
+The each interval part is approximated by each cubic polynomial.
+
+The cubic spline uses the cubic polynomial equation for interpolation:
+
+:math:`S_j(x)=a_j+b_j(x-x_j)+c_j(x-x_j)^2+d_j(x-x_j)^3`
+
+where :math:`x_j < x < x_{j+1}`, :math:`x_j` is the j-th node of the spline,
+:math:`a_j`, :math:`b_j`, :math:`c_j`, :math:`d_j` are the coefficients
+of the spline.
+
+As the above equation, there are 4 unknown parameters :math:`(a,b,c,d)` for
+one interval, so if the number of data points is :math:`N`, the
+interpolation has :math:`4N` unknown parameters.
+
+The following five conditions are used to determine the :math:`4N`
+unknown parameters:
+
+Constraint 1: Terminal constraints
+===================================
+
+:math:`S_j(x_j)=y_j`
+
+This constraint is the terminal constraint of each interval.
+
+The polynomial of each interval will pass through the x,y coordinates of
+the data points.
+
+Constraint 2: Point continuous constraints
+============================================
+
+:math:`S_j(x_{j+1})=S_{j+1}(x_{j+1})=y_{j+1}`
+
+This constraint is a continuity condition for the boundary of each interval.
+
+This constraint ensures that the boundary of each interval is continuous.
+
+Constraint 3: Tangent vector continuous constraints
+====================================================
+
+:math:`S'_j(x_{j+1})=S'_{j+1}(x_{j+1})`
+
+This constraint is a continuity condition for the first derivative of
+the boundary of each interval.
+
+This constraint makes the vectors of the boundaries of each
+interval continuous.
+
+
+Constraint 4: Curvature continuous constraints
+==============================================
+
+:math:`S''_j(x_{j+1})=S''_{j+1}(x_{j+1})`
+
+This constraint is the continuity condition for the second derivative of
+the boundary of each interval.
+
+This constraint makes the curvature of the boundaries of each
+interval continuous.
+
+
+Constraint 5: Terminal curvature constraints
+========================================================
+
+:math:`S''_0(0)=S''_{n+1}(x_{n})=0`.
+
+The constraint is a boundary condition for the second derivative of the starting and ending points.
+
+Our sample code assumes these terminal curvatures are 0, which is well known as Natural Cubic Spline.
+
+How to calculate the unknown parameters :math:`a_j, b_j, c_j, d_j`
+===================================================================
+
+Step1: calculate :math:`a_j`
++++++++++++++++++++++++++++++
+
+Spline coefficients :math:`a_j` can be calculated by y positions of the data points:
+
+:math:`a_j = y_i`.
+
+Step2: calculate :math:`c_j`
++++++++++++++++++++++++++++++
+
+Spline coefficients :math:`c_j` can be calculated by solving the linear equation:
+
+:math:`Ac_j = B`.
+
+The matrix :math:`A` and :math:`B` are defined as follows:
+
+.. math::
+	A=\left[\begin{array}{cccccc}
+	1 & 0 & 0 & 0 & \cdots & 0 \\
+	h_{0} & 2\left(h_{0}+h_{1}\right) & h_{1} & 0 & \cdots & 0 \\
+	0 & h_{1} & 2\left(h_{1}+h_{2}\right) & h_{2} & \cdots & 0 \\
+	0 & 0 & h_{2} & 2\left(h_{2}+h_{3}\right) & \cdots & 0 \\
+	0 & 0 & 0 & h_{3} & \ddots & \\
+	\vdots & \vdots & & & & \\
+	0 & 0 & 0 & \cdots & 0 & 1
+	\end{array}\right]
+
+.. math::
+	B=\left[\begin{array}{c}
+	0 \\
+	\frac{3}{h_{1}}\left(a_{2}-a_{1}\right)-\frac{3}{h_{0}}\left(a_{1}-a_{0}\right) \\
+	\vdots \\
+	\frac{3}{h_{n-1}}\left(a_{n}-a_{n-1}\right)-\frac{3}{h_{n-2}}\left(a_{n-1}-a_{n-2}\right) \\
+	0
+	\end{array}\right]
+
+where :math:`h_{i}` is the x position distance between the i-th and (i+1)-th data points.
+
+Step3: calculate :math:`d_j`
++++++++++++++++++++++++++++++
+
+Spline coefficients :math:`d_j` can be calculated by the following equation:
+
+:math:`d_{j}=\frac{c_{j+1}-c_{j}}{3 h_{j}}`
+
+Step4: calculate :math:`b_j`
++++++++++++++++++++++++++++++
+
+Spline coefficients :math:`b_j` can be calculated by the following equation:
+
+:math:`b_{i}=\frac{1}{h_i}(a_{i+1}-a_{i})-\frac{h_i}{3}(2c_{i}+c_{i+1})`
+
+How to calculate the first and second derivatives of the spline curve
+======================================================================
+
+After we can get the coefficients of the spline curve, we can calculate
+
+the first derivative by:
+
+:math:`y^{\prime}(x)=b+2cx+3dx^2`
+
+the second derivative by:
+
+:math:`y^{\prime \prime}(x)=2c+6dx`
+
+These equations can be calculated by differentiating the cubic polynomial.
+
+API
+===
+
+This is the 1D cubic spline class API:
+
+.. autoclass:: PathPlanning.CubicSpline.cubic_spline_planner.CubicSpline1D
+	:members:
+
+2D cubic spline
+~~~~~~~~~~~~~~~~~~~
+
+Data x positions needs to be mono-increasing for 1D cubic spline.
+
+So, it cannot be used for 2D path planning.
+
+2D cubic spline uses two 1D cubic splines based on path distance from
+the start point for each dimension x and y.
+
+This can generate a curvature continuous path based on x-y waypoints.
+
+Heading angle of each point can be calculated analytically by:
+
+:math:`\theta=\tan ^{-1} \frac{y’}{x’}`
+
+Curvature of each point can be also calculated analytically by:
+
+:math:`\kappa=\frac{y^{\prime \prime} x^{\prime}-x^{\prime \prime} y^{\prime}}{\left(x^{\prime2}+y^{\prime2}\right)^{\frac{2}{3}}}`
+
+API
+===
+
+.. autoclass:: PathPlanning.CubicSpline.cubic_spline_planner.CubicSpline2D
+	:members:
+
+References
+~~~~~~~~~~
+-  `Cubic Splines James Keesling <https://people.clas.ufl.edu/kees/files/CubicSplines.pdf>`__
+-  `Curves and Splines <http://www.cs.cmu.edu/afs/cs/academic/class/15462-s10/www/lec-slides/lec06.pdf>`__
 
-Heading angle of each point can be also calculated analytically.
 
-.. image:: Figure_1.png
-.. image:: Figure_2.png
-.. image:: Figure_3.png
diff --git a/docs/modules/path_planning/cubic_spline/spline.png b/docs/modules/path_planning/cubic_spline/spline.png
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QknroM)^W`;b(_%t0uq{|egFUf

literal 0
HcmV?d00001


From bccf816cca09afd075aca4613f2984f0ba28f07e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 26 Jul 2022 07:35:53 +0900
Subject: [PATCH 0640/1080] Bump mypy from 0.961 to 0.971 in /requirements
 (#699)

Bumps [mypy](https://github.com/python/mypy) from 0.961 to 0.971.
- [Release notes](https://github.com/python/mypy/releases)
- [Commits](https://github.com/python/mypy/compare/v0.961...v0.971)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 9130a07b6a..ee0f1a4090 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ matplotlib == 3.5.2
 cvxpy == 1.2.1
 pytest == 7.1.2 # For unit test
 pytest-xdist == 2.5.0 # For unit test
-mypy == 0.961 # For unit test
+mypy == 0.971 # For unit test
 flake8 == 4.0.1 # For unit test

From 76d06e0b06e6e2344a73fdcc8f4388635cf06593 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 2 Aug 2022 07:17:28 +0900
Subject: [PATCH 0641/1080] Bump scipy from 1.8.1 to 1.9.0 in /requirements
 (#702)

Bumps [scipy](https://github.com/scipy/scipy) from 1.8.1 to 1.9.0.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.8.1...v1.9.0)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index ee0f1a4090..49f028f7d0 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.23.1
-scipy == 1.8.1
+scipy == 1.9.0
 pandas == 1.4.3
 matplotlib == 3.5.2
 cvxpy == 1.2.1

From 48a31af8e2a7d947683f4f14c3b5b853fbe1a69c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 2 Aug 2022 07:17:47 +0900
Subject: [PATCH 0642/1080] Bump flake8 from 4.0.1 to 5.0.2 in /requirements
 (#703)

Bumps [flake8](https://github.com/pycqa/flake8) from 4.0.1 to 5.0.2.
- [Release notes](https://github.com/pycqa/flake8/releases)
- [Commits](https://github.com/pycqa/flake8/compare/4.0.1...5.0.2)

---
updated-dependencies:
- dependency-name: flake8
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 49f028f7d0..abf9dc31fb 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ cvxpy == 1.2.1
 pytest == 7.1.2 # For unit test
 pytest-xdist == 2.5.0 # For unit test
 mypy == 0.971 # For unit test
-flake8 == 4.0.1 # For unit test
+flake8 == 5.0.2 # For unit test

From 7716bdd2485dd0e39a388f5f5d417e70e0f3cefe Mon Sep 17 00:00:00 2001
From: Jeff Irion <JeffLIrion@users.noreply.github.com>
Date: Wed, 3 Aug 2022 05:21:58 -0700
Subject: [PATCH 0643/1080] Fix `upper_triangular_matrix_to_full_matrix` (#704)

---
 SLAM/GraphBasedSLAM/graphslam/util.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/SLAM/GraphBasedSLAM/graphslam/util.py b/SLAM/GraphBasedSLAM/graphslam/util.py
index 8f7c8e174b..619aff20af 100644
--- a/SLAM/GraphBasedSLAM/graphslam/util.py
+++ b/SLAM/GraphBasedSLAM/graphslam/util.py
@@ -68,11 +68,10 @@ def upper_triangular_matrix_to_full_matrix(arr, n):
 
     """
     triu0 = np.triu_indices(n, 0)
-    triu1 = np.triu_indices(n, 1)
     tril1 = np.tril_indices(n, -1)
 
     mat = np.zeros((n, n), dtype=float)
     mat[triu0] = arr
-    mat[tril1] = mat[triu1]
+    mat[tril1] = mat.T[tril1]
 
     return mat

From f40b750ccab627d66570086fa2fd3a86c91b90d7 Mon Sep 17 00:00:00 2001
From: jsbyysheng <jsbyysheng@gmail.com>
Date: Sun, 7 Aug 2022 13:19:47 +0800
Subject: [PATCH 0644/1080] fix bugs in check a position inside the polygon
 (#701)

* fix bugs in check a position inside the polygon

It is possible that the position `(iox, ioy)` to be checked with `iox == min_x`. This will cause a incorrect inside check.

* add a boundary condition for gridmap
---
 Mapping/grid_map_lib/grid_map_lib.py | 6 +++---
 tests/test_grid_map_lib.py           | 4 ++--
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/Mapping/grid_map_lib/grid_map_lib.py b/Mapping/grid_map_lib/grid_map_lib.py
index a93ea309d8..85dd76e71d 100644
--- a/Mapping/grid_map_lib/grid_map_lib.py
+++ b/Mapping/grid_map_lib/grid_map_lib.py
@@ -194,7 +194,7 @@ def check_inside_polygon(iox, ioy, x, y):
                 min_x, max_x = x[i2], x[i1]
             else:
                 min_x, max_x = x[i1], x[i2]
-            if not min_x < iox < max_x:
+            if not min_x <= iox < max_x:
                 continue
 
             tmp1 = (y[i2] - y[i1]) / (x[i2] - x[i1])
@@ -226,8 +226,8 @@ def plot_grid_map(self, ax=None):
 
 
 def test_polygon_set():
-    ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0]
-    oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0]
+    ox = [0.0, 4.35, 20.0, 50.0, 100.0, 130.0, 40.0]
+    oy = [0.0, -4.15, -20.0, 0.0, 30.0, 60.0, 80.0]
 
     grid_map = GridMap(600, 290, 0.7, 60.0, 30.5)
 
diff --git a/tests/test_grid_map_lib.py b/tests/test_grid_map_lib.py
index ed4303d4dc..8ae142b003 100644
--- a/tests/test_grid_map_lib.py
+++ b/tests/test_grid_map_lib.py
@@ -14,8 +14,8 @@ def test_position_set():
 
 
 def test_polygon_set():
-    ox = [0.0, 20.0, 50.0, 100.0, 130.0, 40.0]
-    oy = [0.0, -20.0, 0.0, 30.0, 60.0, 80.0]
+    ox = [0.0, 4.35, 20.0, 50.0, 100.0, 130.0, 40.0]
+    oy = [0.0, -4.15, -20.0, 0.0, 30.0, 60.0, 80.0]
 
     grid_map = GridMap(600, 290, 0.7, 60.0, 30.5)
 

From 51e5a66b4d0c9e1529af1737e546becb80179c8c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Aug 2022 08:12:04 +0900
Subject: [PATCH 0645/1080] Bump flake8 from 5.0.2 to 5.0.4 in /requirements
 (#706)

Bumps [flake8](https://github.com/pycqa/flake8) from 5.0.2 to 5.0.4.
- [Release notes](https://github.com/pycqa/flake8/releases)
- [Commits](https://github.com/pycqa/flake8/compare/5.0.2...5.0.4)

---
updated-dependencies:
- dependency-name: flake8
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index abf9dc31fb..2268e0472b 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ cvxpy == 1.2.1
 pytest == 7.1.2 # For unit test
 pytest-xdist == 2.5.0 # For unit test
 mypy == 0.971 # For unit test
-flake8 == 5.0.2 # For unit test
+flake8 == 5.0.4 # For unit test

From cdf5637a8dd40603f78ff0791fb555947a216c77 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 16 Aug 2022 07:15:49 +0900
Subject: [PATCH 0646/1080] Bump numpy from 1.23.1 to 1.23.2 in /requirements
 (#708)

Bumps [numpy](https://github.com/numpy/numpy) from 1.23.1 to 1.23.2.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.23.1...v1.23.2)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 2268e0472b..85ff9f8d73 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.23.1
+numpy == 1.23.2
 scipy == 1.9.0
 pandas == 1.4.3
 matplotlib == 3.5.2

From ba0a5106d9c35600504bc338cfc4746247485956 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 16 Aug 2022 07:16:16 +0900
Subject: [PATCH 0647/1080] Bump matplotlib from 3.5.2 to 3.5.3 in
 /requirements (#707)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.5.2 to 3.5.3.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.5.2...v3.5.3)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 85ff9f8d73..d401546e6c 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 1.23.2
 scipy == 1.9.0
 pandas == 1.4.3
-matplotlib == 3.5.2
+matplotlib == 3.5.3
 cvxpy == 1.2.1
 pytest == 7.1.2 # For unit test
 pytest-xdist == 2.5.0 # For unit test

From 46b6981f34dd783132ea5a23b46af69dc6888024 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 Aug 2022 07:31:42 +0900
Subject: [PATCH 0648/1080] Bump scipy from 1.9.0 to 1.9.1 in /requirements
 (#714)

Bumps [scipy](https://github.com/scipy/scipy) from 1.9.0 to 1.9.1.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.9.0...v1.9.1)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index d401546e6c..6aee1a3d9e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.23.2
-scipy == 1.9.0
+scipy == 1.9.1
 pandas == 1.4.3
 matplotlib == 3.5.3
 cvxpy == 1.2.1

From def001ea209388e6dd106c7fdf329f9a32e37188 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 6 Sep 2022 07:16:50 +0900
Subject: [PATCH 0649/1080] Bump pytest from 7.1.2 to 7.1.3 in /requirements
 (#717)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.1.2 to 7.1.3.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/7.1.2...7.1.3)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 6aee1a3d9e..2d5ecbc470 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,7 +3,7 @@ scipy == 1.9.1
 pandas == 1.4.3
 matplotlib == 3.5.3
 cvxpy == 1.2.1
-pytest == 7.1.2 # For unit test
+pytest == 7.1.3 # For unit test
 pytest-xdist == 2.5.0 # For unit test
 mypy == 0.971 # For unit test
 flake8 == 5.0.4 # For unit test

From 1ff3095c14817835450d6788b9adc09b4dc4cfa2 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 6 Sep 2022 07:17:15 +0900
Subject: [PATCH 0650/1080] Bump pandas from 1.4.3 to 1.4.4 in /requirements
 (#718)

Bumps [pandas](https://github.com/pandas-dev/pandas) from 1.4.3 to 1.4.4.
- [Release notes](https://github.com/pandas-dev/pandas/releases)
- [Changelog](https://github.com/pandas-dev/pandas/blob/main/RELEASE.md)
- [Commits](https://github.com/pandas-dev/pandas/compare/v1.4.3...v1.4.4)

---
updated-dependencies:
- dependency-name: pandas
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 2d5ecbc470..615377611b 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.23.2
 scipy == 1.9.1
-pandas == 1.4.3
+pandas == 1.4.4
 matplotlib == 3.5.3
 cvxpy == 1.2.1
 pytest == 7.1.3 # For unit test

From 76b0d04a3ce61f6a1935e496ea43c809fb247097 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 11 Sep 2022 07:21:37 +0900
Subject: [PATCH 0651/1080] using pathlib based local module import (#722)

* using pathlib based local module import

* remove unused import
---
 .../drone_3d_trajectory_following/__init__.py |  5 ++--
 ArmNavigation/n_joint_arm_3d/__init__.py      |  4 +--
 .../n_joint_arm_to_point_control.py           |  2 +-
 .../rrt_star_seven_joint_arm_control.py       | 13 ++++-----
 .../ensemble_kalman_filter.py                 |  5 ++--
 .../extended_kalman_filter.py                 |  5 ++--
 .../particle_filter/particle_filter.py        |  4 +--
 .../unscented_kalman_filter.py                |  4 +--
 Mapping/rectangle_fitting/__init_.py          |  5 ++--
 .../rectangle_fitting/rectangle_fitting.py    |  5 ++--
 Mapping/rectangle_fitting/simulator.py        |  4 +--
 .../closed_loop_rrt_star_car.py               | 24 +++++----------
 .../ClosedLoopRRTStar/pure_pursuit.py         |  6 +++-
 .../DubinsPath/dubins_path_planner.py         |  5 ++--
 .../eta3_spline_trajectory.py                 | 12 +++-----
 .../frenet_optimal_trajectory.py              | 18 ++++--------
 .../grid_based_sweep_coverage_path_planner.py | 17 +++--------
 PathPlanning/HybridAStar/car.py               |  7 +++--
 PathPlanning/HybridAStar/hybrid_a_star.py     | 18 ++++--------
 .../InformedRRTStar/informed_rrt_star.py      |  4 +--
 .../{LQRplanner.py => lqr_planner.py}         |  0
 PathPlanning/LQRRRTStar/lqr_rrt_star.py       | 16 ++++------
 ...y_generator.py => trajectory_generator.py} |  7 +++--
 PathPlanning/RRT/rrt_with_pathsmoothing.py    | 13 +++------
 PathPlanning/RRT/rrt_with_sobol_sampler.py    |  5 ++--
 PathPlanning/RRTDubins/rrt_dubins.py          | 29 +++++++------------
 PathPlanning/RRTStar/rrt_star.py              | 12 +++-----
 PathPlanning/RRTStarDubins/rrt_star_dubins.py | 27 ++++++-----------
 .../RRTStarReedsShepp/rrt_star_reeds_shepp.py | 16 +++-------
 .../state_lattice_planner.py                  | 23 +++++++--------
 .../VisibilityRoadMap/visibility_road_map.py  | 10 +++----
 PathPlanning/VoronoiRoadMap/__init__.py       |  4 ---
 .../VoronoiRoadMap/voronoi_road_map.py        |  6 +++-
 .../lqr_speed_steer_control.py                | 12 ++------
 .../lqr_steer_control/lqr_steer_control.py    | 14 +++------
 ...odel_predictive_speed_and_steer_control.py | 12 ++------
 .../stanley_controller/stanley_controller.py  | 12 ++------
 SLAM/GraphBasedSLAM/graph_based_slam.py       |  4 +--
 tests/test_LQR_planner.py                     |  2 +-
 39 files changed, 144 insertions(+), 247 deletions(-)
 rename PathPlanning/LQRPlanner/{LQRplanner.py => lqr_planner.py} (100%)
 rename PathPlanning/ModelPredictiveTrajectoryGenerator/{model_predictive_trajectory_generator.py => trajectory_generator.py} (95%)

diff --git a/AerialNavigation/drone_3d_trajectory_following/__init__.py b/AerialNavigation/drone_3d_trajectory_following/__init__.py
index 087dab646e..2194d4c303 100644
--- a/AerialNavigation/drone_3d_trajectory_following/__init__.py
+++ b/AerialNavigation/drone_3d_trajectory_following/__init__.py
@@ -1,4 +1,3 @@
-import os
 import sys
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent))
diff --git a/ArmNavigation/n_joint_arm_3d/__init__.py b/ArmNavigation/n_joint_arm_3d/__init__.py
index ba5dac65f6..2194d4c303 100644
--- a/ArmNavigation/n_joint_arm_3d/__init__.py
+++ b/ArmNavigation/n_joint_arm_3d/__init__.py
@@ -1,3 +1,3 @@
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent))
diff --git a/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py b/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py
index 36093df08d..498ef41c1f 100644
--- a/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py
+++ b/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py
@@ -5,11 +5,11 @@
         Atsushi Sakai (@Atsushi_twi)
 """
 
-import numpy as np
 import sys
 from pathlib import Path
 sys.path.append(str(Path(__file__).parent.parent.parent))
 
+import numpy as np
 from ArmNavigation.n_joint_arm_to_point_control.NLinkArm import NLinkArm
 
 # Simulation parameters
diff --git a/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py b/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py
index 0de4c7edb8..fca55184da 100755
--- a/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py
+++ b/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py
@@ -3,18 +3,15 @@
 Author: Mahyar Abdeetedal (mahyaret)
 """
 import math
-import os
-import sys
 import random
 import numpy as np
 from mpl_toolkits import mplot3d
 import matplotlib.pyplot as plt
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../n_joint_arm_3d/")
-try:
-    from NLinkArm3d import NLinkArm
-except ImportError:
-    raise
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
+
+from n_joint_arm_3d.NLinkArm3d import NLinkArm
 
 show_animation = True
 verbose = False
diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
index 1e8d1fc6d6..fc8280e7bf 100644
--- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
+++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
@@ -9,10 +9,9 @@
 (https://rmets.onlinelibrary.wiley.com/doi/10.1256/qj.05.135)
 
 """
-
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import math
 import matplotlib.pyplot as plt
diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py
index a5dea19fd8..c2fe8e3a87 100644
--- a/Localization/extended_kalman_filter/extended_kalman_filter.py
+++ b/Localization/extended_kalman_filter/extended_kalman_filter.py
@@ -5,10 +5,9 @@
 author: Atsushi Sakai (@Atsushi_twi)
 
 """
-
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import math
 import matplotlib.pyplot as plt
diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py
index 1c45687ed1..17051623ca 100644
--- a/Localization/particle_filter/particle_filter.py
+++ b/Localization/particle_filter/particle_filter.py
@@ -6,8 +6,8 @@
 
 """
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import math
 
diff --git a/Localization/unscented_kalman_filter/unscented_kalman_filter.py b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
index 4f42cfd5d1..dbcdeef015 100644
--- a/Localization/unscented_kalman_filter/unscented_kalman_filter.py
+++ b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
@@ -7,8 +7,8 @@
 """
 
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import math
 
diff --git a/Mapping/rectangle_fitting/__init_.py b/Mapping/rectangle_fitting/__init_.py
index 087dab646e..2194d4c303 100644
--- a/Mapping/rectangle_fitting/__init_.py
+++ b/Mapping/rectangle_fitting/__init_.py
@@ -1,4 +1,3 @@
-import os
 import sys
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent))
diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py
index 6d3e63f0f3..124e48fe76 100644
--- a/Mapping/rectangle_fitting/rectangle_fitting.py
+++ b/Mapping/rectangle_fitting/rectangle_fitting.py
@@ -13,9 +13,8 @@
 """
 
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
-
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import matplotlib.pyplot as plt
 import numpy as np
diff --git a/Mapping/rectangle_fitting/simulator.py b/Mapping/rectangle_fitting/simulator.py
index e75738c3b1..faca2cb79e 100644
--- a/Mapping/rectangle_fitting/simulator.py
+++ b/Mapping/rectangle_fitting/simulator.py
@@ -6,8 +6,8 @@
 
 """
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import numpy as np
 import matplotlib.pyplot as plt
diff --git a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
index dc9a575694..01ab8349a9 100644
--- a/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
+++ b/PathPlanning/ClosedLoopRRTStar/closed_loop_rrt_star_car.py
@@ -5,27 +5,17 @@
 author: AtsushiSakai(@Atsushi_twi)
 
 """
-import os
-import sys
-
 import matplotlib.pyplot as plt
 import numpy as np
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-
-import pure_pursuit
-import unicycle_model
-
-sys.path.append(os.path.dirname(
-    os.path.abspath(__file__)) + "/../ReedsSheppPath/")
-sys.path.append(os.path.dirname(
-    os.path.abspath(__file__)) + "/../RRTStarReedsShepp/")
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-try:
-    import reeds_shepp_path_planning
-    from rrt_star_reeds_shepp import RRTStarReedsShepp
-except ImportError:
-    raise
+from ClosedLoopRRTStar import pure_pursuit
+from ClosedLoopRRTStar import unicycle_model
+from ReedsSheppPath import reeds_shepp_path_planning
+from RRTStarReedsShepp.rrt_star_reeds_shepp import RRTStarReedsShepp
 
 show_animation = True
 
diff --git a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
index ca763f0d4f..982ebeca06 100644
--- a/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
+++ b/PathPlanning/ClosedLoopRRTStar/pure_pursuit.py
@@ -10,7 +10,11 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-import unicycle_model
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
+
+from ClosedLoopRRTStar import unicycle_model
 
 Kp = 2.0  # speed propotional gain
 Lf = 0.5  # look-ahead distance
diff --git a/PathPlanning/DubinsPath/dubins_path_planner.py b/PathPlanning/DubinsPath/dubins_path_planner.py
index 4d4ee07291..a7e8a100cc 100644
--- a/PathPlanning/DubinsPath/dubins_path_planner.py
+++ b/PathPlanning/DubinsPath/dubins_path_planner.py
@@ -6,9 +6,8 @@
 
 """
 import sys
-import os
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 from math import sin, cos, atan2, sqrt, acos, pi, hypot
 import numpy as np
diff --git a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
index bd211046c7..d034cb8a32 100644
--- a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
+++ b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
@@ -16,14 +16,10 @@
 import matplotlib.pyplot as plt
 from matplotlib.collections import LineCollection
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../Eta3SplinePath")
-
-try:
-    from eta3_spline_path import Eta3Path, Eta3PathSegment
-except ImportError:
-    raise
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
+
+from Eta3SplinePath.eta3_spline_path import Eta3Path, Eta3PathSegment
 
 show_animation = True
 
diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
index ef20af22f0..b7d1b8e642 100644
--- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
+++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
@@ -19,18 +19,12 @@
 import copy
 import math
 import sys
-import os
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../QuinticPolynomialsPlanner/")
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../CubicSpline/")
-
-try:
-    from quintic_polynomials_planner import QuinticPolynomial
-    import cubic_spline_planner
-except ImportError:
-    raise
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
+
+from QuinticPolynomialsPlanner.quintic_polynomials_planner import \
+    QuinticPolynomial
+from CubicSpline import cubic_spline_planner
 
 SIM_LOOP = 500
 
diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
index 2f4d32b187..31dc917566 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -4,25 +4,16 @@
 author: Atsushi Sakai
 """
 
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
-
 import math
 from enum import IntEnum
-
 import numpy as np
 import matplotlib.pyplot as plt
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 from utils.angle import rot_mat_2d
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../../Mapping/")
-
-try:
-    from grid_map_lib.grid_map_lib import GridMap
-except ImportError:
-    raise
+from Mapping.grid_map_lib.grid_map_lib import GridMap
 
 do_animation = True
 
diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py
index 870dd18535..0367ab2775 100644
--- a/PathPlanning/HybridAStar/car.py
+++ b/PathPlanning/HybridAStar/car.py
@@ -7,8 +7,9 @@
 """
 
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+import pathlib
+root_dir = pathlib.Path(__file__).parent.parent.parent
+sys.path.append(str(root_dir))
 
 from math import cos, sin, tan, pi
 
@@ -76,7 +77,7 @@ def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
 def plot_car(x, y, yaw):
     car_color = '-k'
     c, s = cos(yaw), sin(yaw)
-    rot = Rot.from_euler('z', -yaw).as_matrix()[0:2, 0:2]
+    rot = rot_mat_2d(-yaw)
     car_outline_x, car_outline_y = [], []
     for rx, ry in zip(VRX, VRY):
         converted_xy = np.stack([rx, ry]).T @ rot
diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
index 4ef0c1dd21..b87fe706c2 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -8,22 +8,16 @@
 
 import heapq
 import math
-import os
-import sys
-
 import matplotlib.pyplot as plt
 import numpy as np
 from scipy.spatial import cKDTree
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__))
-                + "/../ReedsSheppPath")
-try:
-    from dynamic_programming_heuristic import calc_distance_heuristic
-    import reeds_shepp_path_planning as rs
-    from car import move, check_car_collision, MAX_STEER, WB, plot_car,\
-        BUBBLE_R
-except Exception:
-    raise
+from dynamic_programming_heuristic import calc_distance_heuristic
+from ReedsSheppPath import reeds_shepp_path_planning as rs
+from car import move, check_car_collision, MAX_STEER, WB, plot_car, BUBBLE_R
 
 XY_GRID_RESOLUTION = 2.0  # [m]
 YAW_GRID_RESOLUTION = np.deg2rad(15.0)  # [rad]
diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index 10af3a81bd..25c50a0cd2 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -10,8 +10,8 @@
 
 """
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import copy
 import math
diff --git a/PathPlanning/LQRPlanner/LQRplanner.py b/PathPlanning/LQRPlanner/lqr_planner.py
similarity index 100%
rename from PathPlanning/LQRPlanner/LQRplanner.py
rename to PathPlanning/LQRPlanner/lqr_planner.py
diff --git a/PathPlanning/LQRRRTStar/lqr_rrt_star.py b/PathPlanning/LQRRRTStar/lqr_rrt_star.py
index 28d332aa00..e3f18a722a 100644
--- a/PathPlanning/LQRRRTStar/lqr_rrt_star.py
+++ b/PathPlanning/LQRRRTStar/lqr_rrt_star.py
@@ -7,21 +7,15 @@
 """
 import copy
 import math
-import os
 import random
-import sys
-
 import matplotlib.pyplot as plt
 import numpy as np
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../LQRPlanner/")
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../RRTStar/")
-
-try:
-    from LQRplanner import LQRPlanner
-    from rrt_star import RRTStar
-except ImportError:
-    raise
+from LQRPlanner.lqr_planner import LQRPlanner
+from RRTStar.rrt_star import RRTStar
 
 show_animation = True
 
diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/model_predictive_trajectory_generator.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py
similarity index 95%
rename from PathPlanning/ModelPredictiveTrajectoryGenerator/model_predictive_trajectory_generator.py
rename to PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py
index 3ba1dd9fc2..a44e1ac956 100644
--- a/PathPlanning/ModelPredictiveTrajectoryGenerator/model_predictive_trajectory_generator.py
+++ b/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py
@@ -7,11 +7,14 @@
 """
 
 import math
-
 import matplotlib.pyplot as plt
 import numpy as np
+import sys
+import pathlib
+path_planning_dir = pathlib.Path(__file__).parent.parent
+sys.path.append(str(path_planning_dir))
 
-import motion_model
+import ModelPredictiveTrajectoryGenerator.motion_model as motion_model
 
 # optimization parameter
 max_iter = 100
diff --git a/PathPlanning/RRT/rrt_with_pathsmoothing.py b/PathPlanning/RRT/rrt_with_pathsmoothing.py
index 9826dba072..616c73a697 100644
--- a/PathPlanning/RRT/rrt_with_pathsmoothing.py
+++ b/PathPlanning/RRT/rrt_with_pathsmoothing.py
@@ -7,18 +7,13 @@
 """
 
 import math
-import os
 import random
-import sys
-
 import matplotlib.pyplot as plt
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent))
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-
-try:
-    from rrt import RRT
-except ImportError:
-    raise
+from rrt import RRT
 
 show_animation = True
 
diff --git a/PathPlanning/RRT/rrt_with_sobol_sampler.py b/PathPlanning/RRT/rrt_with_sobol_sampler.py
index 8045d8f764..73488210a7 100644
--- a/PathPlanning/RRT/rrt_with_sobol_sampler.py
+++ b/PathPlanning/RRT/rrt_with_sobol_sampler.py
@@ -28,12 +28,11 @@
 
 import math
 import random
-from sobol import sobol_quasirand
-import sys
-
 import matplotlib.pyplot as plt
 import numpy as np
 
+from sobol import sobol_quasirand
+
 show_animation = True
 
 
diff --git a/PathPlanning/RRTDubins/rrt_dubins.py b/PathPlanning/RRTDubins/rrt_dubins.py
index f1bd03ca02..f938419f35 100644
--- a/PathPlanning/RRTDubins/rrt_dubins.py
+++ b/PathPlanning/RRTDubins/rrt_dubins.py
@@ -6,23 +6,17 @@
 """
 import copy
 import math
-import os
 import random
-import sys
-
-import matplotlib.pyplot as plt
 import numpy as np
+import matplotlib.pyplot as plt
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))  # root dir
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../DubinsPath/")
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../RRT/")
-
-try:
-    from rrt import RRT
-    import dubins_path_planner
-except ImportError:
-    raise
+from RRT.rrt import RRT
+from DubinsPath import dubins_path_planner
+from utils.plot import plot_arrow
 
 show_animation = True
 
@@ -130,10 +124,8 @@ def draw_graph(self, rnd=None):  # pragma: no cover
         plt.pause(0.01)
 
     def plot_start_goal_arrow(self):  # pragma: no cover
-        dubins_path_planner.plot_arrow(
-            self.start.x, self.start.y, self.start.yaw)
-        dubins_path_planner.plot_arrow(
-            self.end.x, self.end.y, self.end.yaw)
+        plot_arrow(self.start.x, self.start.y, self.start.yaw)
+        plot_arrow(self.end.x, self.end.y, self.end.yaw)
 
     def steer(self, from_node, to_node):
 
@@ -214,6 +206,7 @@ def generate_final_course(self, goal_index):
 
 
 def main():
+
     print("Start " + __file__)
     # ====Search Path with RRT====
     obstacleList = [
diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py
index e2c05456de..a7aad49d60 100644
--- a/PathPlanning/RRTStar/rrt_star.py
+++ b/PathPlanning/RRTStar/rrt_star.py
@@ -7,17 +7,12 @@
 """
 
 import math
-import os
 import sys
-
 import matplotlib.pyplot as plt
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../RRT/")
-
-try:
-    from rrt import RRT
-except ImportError:
-    raise
+from RRT.rrt import RRT
 
 show_animation = True
 
@@ -59,6 +54,7 @@ def __init__(self,
         self.connect_circle_dist = connect_circle_dist
         self.goal_node = self.Node(goal[0], goal[1])
         self.search_until_max_iter = search_until_max_iter
+        self.node_list = []
 
     def planning(self, animation=True):
         """
diff --git a/PathPlanning/RRTStarDubins/rrt_star_dubins.py b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
index e2a456d75f..7c52879b7c 100644
--- a/PathPlanning/RRTStarDubins/rrt_star_dubins.py
+++ b/PathPlanning/RRTStarDubins/rrt_star_dubins.py
@@ -4,26 +4,19 @@
 author: AtsushiSakai(@Atsushi_twi)
 
 """
-
 import copy
 import math
-import os
 import random
-import sys
-
 import matplotlib.pyplot as plt
 import numpy as np
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))  # root dir
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../DubinsPath/")
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../RRTStar/")
-
-try:
-    import dubins_path_planner
-    from rrt_star import RRTStar
-except ImportError:
-    raise
+from DubinsPath import dubins_path_planner
+from RRTStar.rrt_star import RRTStar
+from utils.plot import plot_arrow
 
 show_animation = True
 
@@ -136,10 +129,8 @@ def draw_graph(self, rnd=None):
         plt.pause(0.01)
 
     def plot_start_goal_arrow(self):
-        dubins_path_planner.plot_arrow(
-            self.start.x, self.start.y, self.start.yaw)
-        dubins_path_planner.plot_arrow(
-            self.end.x, self.end.y, self.end.yaw)
+        plot_arrow(self.start.x, self.start.y, self.start.yaw)
+        plot_arrow(self.end.x, self.end.y, self.end.yaw)
 
     def steer(self, from_node, to_node):
 
diff --git a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
index 323bc76be2..66185017a0 100644
--- a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
+++ b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
@@ -7,23 +7,15 @@
 """
 import copy
 import math
-import os
 import random
 import sys
-
+import pathlib
 import matplotlib.pyplot as plt
 import numpy as np
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../ReedsSheppPath/")
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../RRTStar/")
-
-try:
-    import reeds_shepp_path_planning
-    from rrt_star import RRTStar
-except ImportError:
-    raise
+from ReedsSheppPath import reeds_shepp_path_planning
+from RRTStar.rrt_star import RRTStar
 
 show_animation = True
 
diff --git a/PathPlanning/StateLatticePlanner/state_lattice_planner.py b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
index b5fec697a4..fb80e1f582 100644
--- a/PathPlanning/StateLatticePlanner/state_lattice_planner.py
+++ b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
@@ -4,11 +4,16 @@
 
 author: Atsushi Sakai (@Atsushi_twi)
 
-- lookuptable.csv is generated with this script: https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable_generator.py
+- lookuptable.csv is generated with this script:
+https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning
+/ModelPredictiveTrajectoryGenerator/lookuptable_generator.py
 
 Ref:
 
-- State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.8210&rep=rep1&type=pdf
+- State Space Sampling of Feasible Motions for High-Performance Mobile Robot
+Navigation in Complex Environments
+http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.8210&rep=rep1
+&type=pdf
 
 """
 import sys
@@ -17,17 +22,11 @@
 import numpy as np
 import math
 import pandas as pd
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__))
-                + "/../ModelPredictiveTrajectoryGenerator/")
-
-
-try:
-    import model_predictive_trajectory_generator as planner
-    import motion_model
-except ImportError:
-    raise
-
+import ModelPredictiveTrajectoryGenerator.trajectory_generator as planner
+import ModelPredictiveTrajectoryGenerator.motion_model as motion_model
 
 table_path = os.path.dirname(os.path.abspath(__file__)) + "/lookuptable.csv"
 
diff --git a/PathPlanning/VisibilityRoadMap/visibility_road_map.py b/PathPlanning/VisibilityRoadMap/visibility_road_map.py
index 6458cd882c..e9f8733c83 100644
--- a/PathPlanning/VisibilityRoadMap/visibility_road_map.py
+++ b/PathPlanning/VisibilityRoadMap/visibility_road_map.py
@@ -6,17 +6,15 @@
 
 """
 
-import os
 import sys
 import math
 import numpy as np
 import matplotlib.pyplot as plt
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-from PathPlanning.VisibilityRoadMap.geometry import Geometry
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../VoronoiRoadMap/")
-from dijkstra_search import DijkstraSearch
+from VisibilityRoadMap.geometry import Geometry
+from VoronoiRoadMap.dijkstra_search import DijkstraSearch
 
 show_animation = True
 
diff --git a/PathPlanning/VoronoiRoadMap/__init__.py b/PathPlanning/VoronoiRoadMap/__init__.py
index 087dab646e..e69de29bb2 100644
--- a/PathPlanning/VoronoiRoadMap/__init__.py
+++ b/PathPlanning/VoronoiRoadMap/__init__.py
@@ -1,4 +0,0 @@
-import os
-import sys
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
diff --git a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
index d184230fd5..0a1f6f5526 100644
--- a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
+++ b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
@@ -9,8 +9,12 @@
 import math
 import numpy as np
 import matplotlib.pyplot as plt
-from dijkstra_search import DijkstraSearch
 from scipy.spatial import cKDTree, Voronoi
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
+
+from VoronoiRoadMap.dijkstra_search import DijkstraSearch
 
 show_animation = True
 
diff --git a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
index db5a755008..7e57abb80e 100644
--- a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
+++ b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
@@ -7,19 +7,13 @@
 """
 import math
 import sys
-import os
-
 import matplotlib.pyplot as plt
 import numpy as np
 import scipy.linalg as la
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../../PathPlanning/CubicSpline/")
-
-try:
-    import cubic_spline_planner
-except ImportError:
-    raise
+from PathPlanning.CubicSpline import cubic_spline_planner
 
 # === Parameters =====
 
diff --git a/PathTracking/lqr_steer_control/lqr_steer_control.py b/PathTracking/lqr_steer_control/lqr_steer_control.py
index 5596ea0fdf..95a1b92ce5 100644
--- a/PathTracking/lqr_steer_control/lqr_steer_control.py
+++ b/PathTracking/lqr_steer_control/lqr_steer_control.py
@@ -10,16 +10,10 @@
 import math
 import numpy as np
 import sys
-import os
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../../PathPlanning/CubicSpline/")
-
-try:
-    import cubic_spline_planner
-except:
-    raise
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
+from PathPlanning.CubicSpline import cubic_spline_planner
 
 Kp = 1.0  # speed proportional gain
 
@@ -29,7 +23,7 @@
 
 # parameters
 dt = 0.1  # time tick[s]
-L = 0.5  # Wheel base of the vehicle [m]
+L = 0.5  # Wheelbase of the vehicle [m]
 max_steer = np.deg2rad(45.0)  # maximum steering angle[rad]
 
 show_animation = True
diff --git a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
index ac8b883bd9..59e69aa999 100644
--- a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
+++ b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
@@ -10,16 +10,10 @@
 import math
 import numpy as np
 import sys
-import os
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../../PathPlanning/CubicSpline/")
-
-try:
-    import cubic_spline_planner
-except:
-    raise
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
+from PathPlanning.CubicSpline import cubic_spline_planner
 
 NX = 4  # x = x, y, v, yaw
 NU = 2  # a = [accel, steer]
diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py
index 3a67d6268e..9c475cbaf2 100644
--- a/PathTracking/stanley_controller/stanley_controller.py
+++ b/PathTracking/stanley_controller/stanley_controller.py
@@ -12,16 +12,10 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import sys
-import os
-
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
-                "/../../PathPlanning/CubicSpline/")
-
-try:
-    import cubic_spline_planner
-except:
-    raise
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
+from PathPlanning.CubicSpline import cubic_spline_planner
 
 k = 0.5  # control gain
 Kp = 1.0  # speed proportional gain
diff --git a/SLAM/GraphBasedSLAM/graph_based_slam.py b/SLAM/GraphBasedSLAM/graph_based_slam.py
index 0827732cd8..5510a41110 100644
--- a/SLAM/GraphBasedSLAM/graph_based_slam.py
+++ b/SLAM/GraphBasedSLAM/graph_based_slam.py
@@ -11,8 +11,8 @@
 
 """
 import sys
-import os
-sys.path.append(os.path.dirname(os.path.abspath(__file__)) + "/../utils/")
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import copy
 import itertools
diff --git a/tests/test_LQR_planner.py b/tests/test_LQR_planner.py
index b535b99baf..be12195eac 100644
--- a/tests/test_LQR_planner.py
+++ b/tests/test_LQR_planner.py
@@ -1,5 +1,5 @@
 import conftest  # Add root path to sys.path
-from PathPlanning.LQRPlanner import LQRplanner as m
+from PathPlanning.LQRPlanner import lqr_planner as m
 
 
 def test_1():

From cd5a50b2aa2bd80fd74fceb654d54d168c9f59da Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 13 Sep 2022 06:45:54 +0900
Subject: [PATCH 0652/1080] Bump numpy from 1.23.2 to 1.23.3 in /requirements
 (#723)

Bumps [numpy](https://github.com/numpy/numpy) from 1.23.2 to 1.23.3.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.23.2...v1.23.3)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 615377611b..61a73fd2c6 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.23.2
+numpy == 1.23.3
 scipy == 1.9.1
 pandas == 1.4.4
 matplotlib == 3.5.3

From e40b4d9dec729b02a78dd98062e736f7477d6ad1 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 27 Sep 2022 21:49:53 +0900
Subject: [PATCH 0653/1080] Enhance bspline code and doc clean up (#720)

* Start bspline code and doc cluean up

* code clean up

* code clean up

* code clean up

* code clean up

* improve doc

* improve doc

* improve doc

* improve doc

* improve doc

* improve doc

* improve doc

* improve doc

* improve codes.

* improve codes.

* improve codes.
---
 .github/workflows/Linux_CI.yml                |   2 +-
 .github/workflows/MacOS_CI.yml                |   2 +-
 PathPlanning/BSplinePath/bspline_path.py      | 150 +++++++++++++-----
 docs/index_main.rst                           |   1 +
 .../path_planning/bspline_path/Figure_1.png   | Bin 22882 -> 0 bytes
 .../bspline_path/approx_and_curvature.png     | Bin 0 -> 43661 bytes
 .../bspline_path/approximation1.png           | Bin 0 -> 28603 bytes
 .../bspline_path/basis_functions.png          | Bin 0 -> 70364 bytes
 .../bspline_path/bspline_path_main.rst        | 140 +++++++++++++++-
 .../bspline_path/bspline_path_planning.png    | Bin 0 -> 34312 bytes
 .../bspline_path/interp_and_curvature.png     | Bin 0 -> 37021 bytes
 .../bspline_path/interpolation1.png           | Bin 0 -> 27476 bytes
 docs/modules/utils/plot/curvature_plot.png    | Bin 0 -> 33206 bytes
 docs/modules/utils/plot/plot_main.rst         |  16 ++
 docs/modules/utils/utils_main.rst             |  12 ++
 tests/test_bspline_path.py                    |  74 +++++++++
 utils/plot.py                                 |  37 +++++
 17 files changed, 387 insertions(+), 47 deletions(-)
 delete mode 100644 docs/modules/path_planning/bspline_path/Figure_1.png
 create mode 100644 docs/modules/path_planning/bspline_path/approx_and_curvature.png
 create mode 100644 docs/modules/path_planning/bspline_path/approximation1.png
 create mode 100644 docs/modules/path_planning/bspline_path/basis_functions.png
 create mode 100644 docs/modules/path_planning/bspline_path/bspline_path_planning.png
 create mode 100644 docs/modules/path_planning/bspline_path/interp_and_curvature.png
 create mode 100644 docs/modules/path_planning/bspline_path/interpolation1.png
 create mode 100644 docs/modules/utils/plot/curvature_plot.png
 create mode 100644 docs/modules/utils/plot/plot_main.rst
 create mode 100644 docs/modules/utils/utils_main.rst
 create mode 100644 tests/test_bspline_path.py

diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index 73f8aa5c0d..83c55b17f1 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.10']
+        python-version: [ '3.10.6' ] # For mypy error Ref: https://github.com/python/mypy/issues/13627
 
     name: Python ${{ matrix.python-version }} CI
 
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index 5c796120a5..a3459f2560 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -16,7 +16,7 @@ jobs:
     runs-on: macos-latest
     strategy:
       matrix:
-        python-version: [ '3.10' ]
+        python-version: [ '3.10.6' ] # For mypy error Ref: https://github.com/python/mypy/issues/13627
     name: Python ${{ matrix.python-version }} CI
     steps:
       - uses: actions/checkout@v2
diff --git a/PathPlanning/BSplinePath/bspline_path.py b/PathPlanning/BSplinePath/bspline_path.py
index d4b24c7645..a2a396efaa 100644
--- a/PathPlanning/BSplinePath/bspline_path.py
+++ b/PathPlanning/BSplinePath/bspline_path.py
@@ -5,57 +5,114 @@
 author: Atsushi Sakai (@Atsushi_twi)
 
 """
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import numpy as np
 import matplotlib.pyplot as plt
-import scipy.interpolate as scipy_interpolate
+import scipy.interpolate as interpolate
 
+from utils.plot import plot_curvature
 
-def approximate_b_spline_path(x: list, y: list, n_path_points: int,
-                              degree: int = 3) -> tuple:
-    """
-    approximate points with a B-Spline path
 
-    :param x: x position list of approximated points
-    :param y: y position list of approximated points
-    :param n_path_points: number of path points
-    :param degree: (Optional) B Spline curve degree
-    :return: x and y position list of the result path
+def approximate_b_spline_path(x: list,
+                              y: list,
+                              n_path_points: int,
+                              degree: int = 3,
+                              s=None,
+                              ) -> tuple:
     """
-    t = range(len(x))
-    x_tup = scipy_interpolate.splrep(t, x, k=degree)
-    y_tup = scipy_interpolate.splrep(t, y, k=degree)
+    Approximate points with a B-Spline path
+
+    Parameters
+    ----------
+    x : array_like
+        x position list of approximated points
+    y : array_like
+        y position list of approximated points
+    n_path_points : int
+        number of path points
+    degree : int, optional
+        B Spline curve degree. Must be 2<= k <= 5. Default: 3.
+    s : int, optional
+        smoothing parameter. If this value is bigger, the path will be
+        smoother, but it will be less accurate. If this value is smaller,
+        the path will be more accurate, but it will be less smooth.
+        When `s` is 0, it is equivalent to the interpolation. Default is None,
+        in this case `s` will be `len(x)`.
+
+    Returns
+    -------
+    x : array
+        x positions of the result path
+    y : array
+        y positions of the result path
+    heading : array
+        heading of the result path
+    curvature : array
+        curvature of the result path
 
-    x_list = list(x_tup)
-    x_list[1] = x + [0.0, 0.0, 0.0, 0.0]
-
-    y_list = list(y_tup)
-    y_list[1] = y + [0.0, 0.0, 0.0, 0.0]
+    """
+    distances = _calc_distance_vector(x, y)
 
-    ipl_t = np.linspace(0.0, len(x) - 1, n_path_points)
-    rx = scipy_interpolate.splev(ipl_t, x_list)
-    ry = scipy_interpolate.splev(ipl_t, y_list)
+    spl_i_x = interpolate.UnivariateSpline(distances, x, k=degree, s=s)
+    spl_i_y = interpolate.UnivariateSpline(distances, y, k=degree, s=s)
 
-    return rx, ry
+    sampled = np.linspace(0.0, distances[-1], n_path_points)
+    return _evaluate_spline(sampled, spl_i_x, spl_i_y)
 
 
-def interpolate_b_spline_path(x: list, y: list, n_path_points: int,
+def interpolate_b_spline_path(x, y,
+                              n_path_points: int,
                               degree: int = 3) -> tuple:
     """
-    interpolate points with a B-Spline path
+    Interpolate x-y points with a B-Spline path
+
+    Parameters
+    ----------
+    x : array_like
+        x positions of interpolated points
+    y : array_like
+        y positions of interpolated points
+    n_path_points : int
+        number of path points
+    degree : int, optional
+        B-Spline degree. Must be 2<= k <= 5. Default: 3
+
+    Returns
+    -------
+    x : array
+        x positions of the result path
+    y : array
+        y positions of the result path
+    heading : array
+        heading of the result path
+    curvature : array
+        curvature of the result path
 
-    :param x: x positions of interpolated points
-    :param y: y positions of interpolated points
-    :param n_path_points: number of path points
-    :param degree: B-Spline degree
-    :return: x and y position list of the result path
     """
-    ipl_t = np.linspace(0.0, len(x) - 1, len(x))
-    spl_i_x = scipy_interpolate.make_interp_spline(ipl_t, x, k=degree)
-    spl_i_y = scipy_interpolate.make_interp_spline(ipl_t, y, k=degree)
+    return approximate_b_spline_path(x, y, n_path_points, degree, s=0.0)
 
-    travel = np.linspace(0.0, len(x) - 1, n_path_points)
-    return spl_i_x(travel), spl_i_y(travel)
+
+def _calc_distance_vector(x, y):
+    dx, dy = np.diff(x), np.diff(y)
+    distances = np.cumsum([np.hypot(idx, idy) for idx, idy in zip(dx, dy)])
+    distances = np.concatenate(([0.0], distances))
+    distances /= distances[-1]
+    return distances
+
+
+def _evaluate_spline(sampled, spl_i_x, spl_i_y):
+    x = spl_i_x(sampled)
+    y = spl_i_y(sampled)
+    dx = spl_i_x.derivative(1)(sampled)
+    dy = spl_i_y.derivative(1)(sampled)
+    heading = np.arctan2(dy, dx)
+    ddx = spl_i_x.derivative(2)(sampled)
+    ddy = spl_i_y.derivative(2)(sampled)
+    curvature = (ddy * dx - ddx * dy) / np.power(dx * dx + dy * dy, 2.0 / 3.0)
+    return np.array(x), y, heading, curvature,
 
 
 def main():
@@ -63,17 +120,28 @@ def main():
     # way points
     way_point_x = [-1.0, 3.0, 4.0, 2.0, 1.0]
     way_point_y = [0.0, -3.0, 1.0, 1.0, 3.0]
-    n_course_point = 100  # sampling number
+    n_course_point = 50  # sampling number
 
-    rax, ray = approximate_b_spline_path(way_point_x, way_point_y,
-                                         n_course_point)
-    rix, riy = interpolate_b_spline_path(way_point_x, way_point_y,
-                                         n_course_point)
+    plt.subplots()
+    rax, ray, heading, curvature = approximate_b_spline_path(
+        way_point_x, way_point_y, n_course_point, s=0.5)
+    plt.plot(rax, ray, '-r', label="Approximated B-Spline path")
+    plot_curvature(rax, ray, heading, curvature)
 
-    # show results
+    plt.title("B-Spline approximation")
     plt.plot(way_point_x, way_point_y, '-og', label="way points")
-    plt.plot(rax, ray, '-r', label="Approximated B-Spline path")
+    plt.grid(True)
+    plt.legend()
+    plt.axis("equal")
+
+    plt.subplots()
+    rix, riy, heading, curvature = interpolate_b_spline_path(
+        way_point_x, way_point_y, n_course_point)
     plt.plot(rix, riy, '-b', label="Interpolated B-Spline path")
+    plot_curvature(rix, riy, heading, curvature)
+
+    plt.title("B-Spline interpolation")
+    plt.plot(way_point_x, way_point_y, '-og', label="way points")
     plt.grid(True)
     plt.legend()
     plt.axis("equal")
diff --git a/docs/index_main.rst b/docs/index_main.rst
index 151759284d..67bd9889f2 100644
--- a/docs/index_main.rst
+++ b/docs/index_main.rst
@@ -44,6 +44,7 @@ See this paper for more details:
    modules/aerial_navigation/aerial_navigation
    modules/bipedal/bipedal
    modules/control/control
+   modules/utils/utils
    modules/appendix/appendix
    how_to_contribute
 
diff --git a/docs/modules/path_planning/bspline_path/Figure_1.png b/docs/modules/path_planning/bspline_path/Figure_1.png
deleted file mode 100644
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diff --git a/docs/modules/path_planning/bspline_path/bspline_path_main.rst b/docs/modules/path_planning/bspline_path/bspline_path_main.rst
index d3523a1918..e734352e34 100644
--- a/docs/modules/path_planning/bspline_path/bspline_path_main.rst
+++ b/docs/modules/path_planning/bspline_path/bspline_path_main.rst
@@ -1,14 +1,146 @@
 B-Spline planning
 -----------------
 
-.. image:: Figure_1.png
+.. image:: bspline_path_planning.png
 
-This is a path planning with B-Spline curse.
+This is a B-Spline path planning routines.
 
 If you input waypoints, it generates a smooth path with B-Spline curve.
 
-The final course should be on the first and last waypoints.
+This codes provide two types of B-Spline curve generations:
 
-Ref:
+1. Interpolation: generate a curve that passes through all waypoints.
+
+2. Approximation: generate a curve that approximates the waypoints. (Not passing through all waypoints)
+
+Bspline basics
+~~~~~~~~~~~~~~
+
+BSpline (Basis-Spline) is a piecewise polynomial spline curve.
+
+It is expressed by the following equation.
+
+:math:`\mathbf{S}(x)=\sum_{i=k-p}^k \mathbf{c}_i B_{i, p}(x)`
+
+here:
+
+* :math:`S(x)` is the curve point on the spline at x.
+* :math:`c_i` is the representative point generating the spline, called the control point.
+* :math:`p+1` is the dimension of the BSpline.
+* :math:`k` is the number of knots.
+* :math:`B_{i,p}(x)` is a function called Basis Function.
+
+The the basis function can be calculated by the following `De Boor recursion formula <https://en.wikipedia.org/wiki/De_Boor%27s_algorithm>`_:
+
+:math:`B_{i, 0}(x):= \begin{cases}1 & \text { if } \quad t_i \leq x<t_{i+1} \\ 0 & \text { otherwise }\end{cases}`
+
+:math:`B_{i, p}(x):=\frac{x-t_i}{t_{i+p}-t_i} B_{i, p-1}(x)+\frac{t_{i+p+1}-x}{t_{i+p+1}-t_{i+1}} B_{i+1, p-1}(x)`
+
+here:
+
+* :math:`t_i` is each element of the knot vector.
+
+This figure shows the BSpline basis functions for each of :math:`i`:
+
+.. image:: basis_functions.png
+
+Note that when all the basis functions are added together, summation is 1.0 for any x value.
+
+This means that the result curve is smooth when each control point is weighted addition by this basis function,
+
+This code is for generating the upper basis function graph using `scipy <https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.BSpline.html>`_.
+
+.. code-block:: python
+
+	from scipy.interpolate import BSpline
+
+	def B_orig(x, k, i, t):
+		if k == 0:
+			return 1.0 if t[i] <= x < t[i + 1] else 0.0
+		if t[i + k] == t[i]:
+			c1 = 0.0
+		else:
+			c1 = (x - t[i]) / (t[i + k] - t[i]) * B(x, k - 1, i, t)
+
+		if t[i + k + 1] == t[i + 1]:
+			c2 = 0.0
+		else:
+			c2 = (t[i + k + 1] - x) / (t[i + k + 1] - t[i + 1]) * B(x, k - 1, i + 1, t)
+		return c1 + c2
+
+
+	def B(x, k, i, t):
+		c = np.zeros_like(t)
+		c[i] = 1
+		return BSpline(t, c, k)(x)
+
+
+	def main():
+		k = 3  # degree of the spline
+		t = [0, 1, 2, 3, 4, 5]  # knots vector
+
+		x = np.linspace(0, 5, 1000, endpoint=False)
+		t = np.r_[[np.min(t)]*k, t, [np.max(t)]*k]
+
+		n = len(t) - k - 1
+		for i in range(n):
+			y = np.array([B(ix, k, i, t) for ix in x])
+			plt.plot(x, y, label=f'i = {i}')
+
+		plt.title(f'Basis functions (k = {k}, knots = {t})')
+		plt.show()
+
+Bspline interpolation planning
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+:meth:`PathPlanning.BSplinePath.bspline_path.interpolate_b_spline_path` generates a curve that passes through all waypoints.
+
+This is a simple example of the interpolation planning:
+
+.. image:: interpolation1.png
+
+This figure also shows curvatures of each path point using :ref:`utils.plot.plot_curvature <plot_curvature>`.
+
+The default spline degree is 3, so curvature changes smoothly.
+
+.. image:: interp_and_curvature.png
+
+API
+++++
+
+.. autofunction:: PathPlanning.BSplinePath.bspline_path.interpolate_b_spline_path
+
+
+Bspline approximation planning
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+:meth:`PathPlanning.BSplinePath.bspline_path.approximate_b_spline_path`
+generates a curve that approximates the waypoints, which means that
+the curve might not pass through waypoints.
+
+Users can adjust path smoothness by the smoothing parameter `s`. If this
+value is bigger, the path will be smoother, but it will be less accurate.
+If this value is smaller, the path will be more accurate, but it will be
+less smooth.
+
+This is a simple example of the approximation planning:
+
+.. image:: approximation1.png
+
+This figure also shows curvatures of each path point using :ref:`utils.plot.plot_curvature <plot_curvature>`.
+
+The default spline degree is 3, so curvature changes smoothly.
+
+.. image:: approx_and_curvature.png
+
+API
+++++
+
+.. autofunction:: PathPlanning.BSplinePath.bspline_path.approximate_b_spline_path
+
+
+References
+~~~~~~~~~~
 
 -  `B-spline - Wikipedia <https://en.wikipedia.org/wiki/B-spline>`__
+-  `scipy.interpolate.UnivariateSpline <https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.UnivariateSpline.html>`__
\ No newline at end of file
diff --git a/docs/modules/path_planning/bspline_path/bspline_path_planning.png b/docs/modules/path_planning/bspline_path/bspline_path_planning.png
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HcmV?d00001

diff --git a/docs/modules/utils/plot/plot_main.rst b/docs/modules/utils/plot/plot_main.rst
new file mode 100644
index 0000000000..b5fef0c4a1
--- /dev/null
+++ b/docs/modules/utils/plot/plot_main.rst
@@ -0,0 +1,16 @@
+.. _plot_utils:
+
+Plotting Utilities
+-------------------
+
+This module contains a number of utility functions for plotting data.
+
+.. _plot_curvature:
+
+plot_curvature
+~~~~~~~~~~~~~~~
+
+.. autofunction:: utils.plot.plot_curvature
+
+.. image:: curvature_plot.png
+
diff --git a/docs/modules/utils/utils_main.rst b/docs/modules/utils/utils_main.rst
new file mode 100644
index 0000000000..ff79a26205
--- /dev/null
+++ b/docs/modules/utils/utils_main.rst
@@ -0,0 +1,12 @@
+.. _utils:
+
+Utilities
+==========
+
+Common utilities for PythonRobotics.
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
+
+   plot/plot
diff --git a/tests/test_bspline_path.py b/tests/test_bspline_path.py
new file mode 100644
index 0000000000..4918c9810f
--- /dev/null
+++ b/tests/test_bspline_path.py
@@ -0,0 +1,74 @@
+import conftest
+import numpy as np
+import pytest
+from PathPlanning.BSplinePath import bspline_path
+
+
+def test_list_input():
+    way_point_x = [-1.0, 3.0, 4.0, 2.0, 1.0]
+    way_point_y = [0.0, -3.0, 1.0, 1.0, 3.0]
+    n_course_point = 50  # sampling number
+
+    rax, ray, heading, curvature = bspline_path.approximate_b_spline_path(
+        way_point_x, way_point_y, n_course_point, s=0.5)
+
+    assert len(rax) == len(ray) == len(heading) == len(curvature)
+
+    rix, riy, heading, curvature = bspline_path.interpolate_b_spline_path(
+        way_point_x, way_point_y, n_course_point)
+
+    assert len(rix) == len(riy) == len(heading) == len(curvature)
+
+
+def test_array_input():
+    way_point_x = np.array([-1.0, 3.0, 4.0, 2.0, 1.0])
+    way_point_y = np.array([0.0, -3.0, 1.0, 1.0, 3.0])
+    n_course_point = 50  # sampling number
+
+    rax, ray, heading, curvature = bspline_path.approximate_b_spline_path(
+        way_point_x, way_point_y, n_course_point, s=0.5)
+
+    assert len(rax) == len(ray) == len(heading) == len(curvature)
+
+    rix, riy, heading, curvature = bspline_path.interpolate_b_spline_path(
+        way_point_x, way_point_y, n_course_point)
+
+    assert len(rix) == len(riy) == len(heading) == len(curvature)
+
+
+def test_degree_change():
+    way_point_x = np.array([-1.0, 3.0, 4.0, 2.0, 1.0])
+    way_point_y = np.array([0.0, -3.0, 1.0, 1.0, 3.0])
+    n_course_point = 50  # sampling number
+
+    rax, ray, heading, curvature = bspline_path.approximate_b_spline_path(
+        way_point_x, way_point_y, n_course_point, s=0.5, degree=4)
+
+    assert len(rax) == len(ray) == len(heading) == len(curvature)
+
+    rix, riy, heading, curvature = bspline_path.interpolate_b_spline_path(
+        way_point_x, way_point_y, n_course_point, degree=4)
+
+    assert len(rix) == len(riy) == len(heading) == len(curvature)
+
+    rax, ray, heading, curvature = bspline_path.approximate_b_spline_path(
+        way_point_x, way_point_y, n_course_point, s=0.5, degree=2)
+
+    assert len(rax) == len(ray) == len(heading) == len(curvature)
+
+    rix, riy, heading, curvature = bspline_path.interpolate_b_spline_path(
+        way_point_x, way_point_y, n_course_point, degree=2)
+
+    assert len(rix) == len(riy) == len(heading) == len(curvature)
+
+    with pytest.raises(ValueError):
+        bspline_path.approximate_b_spline_path(
+            way_point_x, way_point_y, n_course_point, s=0.5, degree=1)
+
+    with pytest.raises(ValueError):
+        bspline_path.interpolate_b_spline_path(
+            way_point_x, way_point_y, n_course_point, degree=1)
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/utils/plot.py b/utils/plot.py
index 2c70a60bbe..3499694821 100644
--- a/utils/plot.py
+++ b/utils/plot.py
@@ -3,6 +3,7 @@
 """
 import math
 import matplotlib.pyplot as plt
+import numpy as np
 
 
 def plot_arrow(x, y, yaw, arrow_length=1.0,
@@ -47,3 +48,39 @@ def plot_arrow(x, y, yaw, arrow_length=1.0,
                   **kwargs)
         if origin_point_plot_style is not None:
             plt.plot(x, y, origin_point_plot_style)
+
+
+def plot_curvature(x_list, y_list, heading_list, curvature,
+                   k=0.01, c="-c", label="Curvature"):
+    """
+    Plot curvature on 2D path. This plot is a line from the original path,
+    the lateral distance from the original path shows curvature magnitude.
+    Left turning shows right side plot, right turning shows left side plot.
+    For straight path, the curvature plot will be on the path, because
+    curvature is 0 on the straight path.
+
+    Parameters
+    ----------
+    x_list : array_like
+        x position list of the path
+    y_list : array_like
+        y position list of the path
+    heading_list : array_like
+        heading list of the path
+    curvature : array_like
+        curvature list of the path
+    k : float
+        curvature scale factor to calculate distance from the original path
+    c : string
+        color of the plot
+    label : string
+        label of the plot
+    """
+    cx = [x + d * k * np.cos(yaw - np.pi / 2.0) for x, y, yaw, d in
+          zip(x_list, y_list, heading_list, curvature)]
+    cy = [y + d * k * np.sin(yaw - np.pi / 2.0) for x, y, yaw, d in
+          zip(x_list, y_list, heading_list, curvature)]
+
+    plt.plot(cx, cy, c, label=label)
+    for ix, iy, icx, icy in zip(x_list, y_list, cx, cy):
+        plt.plot([ix, icx], [iy, icy], c)

From 955ad60ee3cab7911fd15e7db6318233674b60f7 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 30 Sep 2022 09:14:27 +0900
Subject: [PATCH 0654/1080] Remove pandas dependency (#725)

* Remove pandas dependency

* Remove pandas dependency
---
 .../lookup_table_generator.py                 | 110 +++++++++++++++++
 .../lookuptable_generator.py                  | 114 ------------------
 .../StateLatticePlanner/lookup_table.csv      |  82 +++++++++++++
 .../StateLatticePlanner/lookuptable.csv       |  25 ----
 .../state_lattice_planner.py                  |  62 +++++-----
 README.md                                     |   2 -
 docs/getting_started_main.rst                 |   2 -
 requirements/environment.yml                  |   1 -
 requirements/requirements.txt                 |   1 -
 9 files changed, 226 insertions(+), 173 deletions(-)
 create mode 100644 PathPlanning/ModelPredictiveTrajectoryGenerator/lookup_table_generator.py
 delete mode 100644 PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable_generator.py
 create mode 100644 PathPlanning/StateLatticePlanner/lookup_table.csv
 delete mode 100644 PathPlanning/StateLatticePlanner/lookuptable.csv

diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/lookup_table_generator.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/lookup_table_generator.py
new file mode 100644
index 0000000000..6cb226adef
--- /dev/null
+++ b/PathPlanning/ModelPredictiveTrajectoryGenerator/lookup_table_generator.py
@@ -0,0 +1,110 @@
+"""
+
+Lookup Table generation for model predictive trajectory generator
+
+author: Atsushi Sakai
+
+"""
+import sys
+import pathlib
+path_planning_dir = pathlib.Path(__file__).parent.parent
+sys.path.append(str(path_planning_dir))
+
+from matplotlib import pyplot as plt
+import numpy as np
+import math
+
+from ModelPredictiveTrajectoryGenerator import trajectory_generator,\
+    motion_model
+
+
+def calc_states_list(max_yaw=np.deg2rad(-30.0)):
+
+    x = np.arange(10.0, 30.0, 5.0)
+    y = np.arange(0.0, 20.0, 2.0)
+    yaw = np.arange(-max_yaw, max_yaw, max_yaw)
+
+    states = []
+    for iyaw in yaw:
+        for iy in y:
+            for ix in x:
+                states.append([ix, iy, iyaw])
+    print("n_state:", len(states))
+
+    return states
+
+
+def search_nearest_one_from_lookup_table(tx, ty, tyaw, lookup_table):
+    mind = float("inf")
+    minid = -1
+
+    for (i, table) in enumerate(lookup_table):
+
+        dx = tx - table[0]
+        dy = ty - table[1]
+        dyaw = tyaw - table[2]
+        d = math.sqrt(dx ** 2 + dy ** 2 + dyaw ** 2)
+        if d <= mind:
+            minid = i
+            mind = d
+
+    # print(minid)
+
+    return lookup_table[minid]
+
+
+def save_lookup_table(file_name, table):
+    np.savetxt(file_name, np.array(table),
+               fmt='%s', delimiter=",", header="x,y,yaw,s,km,kf", comments="")
+
+    print("lookup table file is saved as " + file_name)
+
+
+def generate_lookup_table():
+    states = calc_states_list(max_yaw=np.deg2rad(-30.0))
+    k0 = 0.0
+
+    # x, y, yaw, s, km, kf
+    lookup_table = [[1.0, 0.0, 0.0, 1.0, 0.0, 0.0]]
+
+    for state in states:
+        best_p = search_nearest_one_from_lookup_table(
+            state[0], state[1], state[2], lookup_table)
+
+        target = motion_model.State(x=state[0], y=state[1], yaw=state[2])
+        init_p = np.array(
+            [np.hypot(state[0], state[1]), best_p[4], best_p[5]]).reshape(3, 1)
+
+        x, y, yaw, p = trajectory_generator.optimize_trajectory(target,
+                                                                k0, init_p)
+
+        if x is not None:
+            print("find good path")
+            lookup_table.append(
+                [x[-1], y[-1], yaw[-1], float(p[0]), float(p[1]), float(p[2])])
+
+    print("finish lookup table generation")
+
+    save_lookup_table("lookup_table.csv", lookup_table)
+
+    for table in lookup_table:
+        x_c, y_c, yaw_c = motion_model.generate_trajectory(
+            table[3], table[4], table[5], k0)
+        plt.plot(x_c, y_c, "-r")
+        x_c, y_c, yaw_c = motion_model.generate_trajectory(
+            table[3], -table[4], -table[5], k0)
+        plt.plot(x_c, y_c, "-r")
+
+    plt.grid(True)
+    plt.axis("equal")
+    plt.show()
+
+    print("Done")
+
+
+def main():
+    generate_lookup_table()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable_generator.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable_generator.py
deleted file mode 100644
index c57a05da57..0000000000
--- a/PathPlanning/ModelPredictiveTrajectoryGenerator/lookuptable_generator.py
+++ /dev/null
@@ -1,114 +0,0 @@
-"""
-
-Lookup Table generation for model predictive trajectory generator
-
-author: Atsushi Sakai
-
-"""
-from matplotlib import pyplot as plt
-import numpy as np
-import math
-import model_predictive_trajectory_generator as planner
-import motion_model
-import pandas as pd
-
-
-def calc_states_list():
-    maxyaw = np.deg2rad(-30.0)
-
-    x = np.arange(10.0, 30.0, 5.0)
-    y = np.arange(0.0, 20.0, 2.0)
-    yaw = np.arange(-maxyaw, maxyaw, maxyaw)
-
-    states = []
-    for iyaw in yaw:
-        for iy in y:
-            for ix in x:
-                states.append([ix, iy, iyaw])
-    print("nstate:", len(states))
-
-    return states
-
-
-def search_nearest_one_from_lookuptable(tx, ty, tyaw, lookuptable):
-    mind = float("inf")
-    minid = -1
-
-    for (i, table) in enumerate(lookuptable):
-
-        dx = tx - table[0]
-        dy = ty - table[1]
-        dyaw = tyaw - table[2]
-        d = math.sqrt(dx ** 2 + dy ** 2 + dyaw ** 2)
-        if d <= mind:
-            minid = i
-            mind = d
-
-    # print(minid)
-
-    return lookuptable[minid]
-
-
-def save_lookup_table(fname, table):
-    mt = np.array(table)
-    print(mt)
-    # save csv
-    df = pd.DataFrame()
-    df["x"] = mt[:, 0]
-    df["y"] = mt[:, 1]
-    df["yaw"] = mt[:, 2]
-    df["s"] = mt[:, 3]
-    df["km"] = mt[:, 4]
-    df["kf"] = mt[:, 5]
-    df.to_csv(fname, index=None)
-
-    print("lookup table file is saved as " + fname)
-
-
-def generate_lookup_table():
-    states = calc_states_list()
-    k0 = 0.0
-
-    # x, y, yaw, s, km, kf
-    lookuptable = [[1.0, 0.0, 0.0, 1.0, 0.0, 0.0]]
-
-    for state in states:
-        bestp = search_nearest_one_from_lookuptable(
-            state[0], state[1], state[2], lookuptable)
-
-        target = motion_model.State(x=state[0], y=state[1], yaw=state[2])
-        init_p = np.array(
-            [math.sqrt(state[0] ** 2 + state[1] ** 2), bestp[4], bestp[5]]).reshape(3, 1)
-
-        x, y, yaw, p = planner.optimize_trajectory(target, k0, init_p)
-
-        if x is not None:
-            print("find good path")
-            lookuptable.append(
-                [x[-1], y[-1], yaw[-1], float(p[0]), float(p[1]), float(p[2])])
-
-    print("finish lookup table generation")
-
-    save_lookup_table("lookuptable.csv", lookuptable)
-
-    for table in lookuptable:
-        xc, yc, yawc = motion_model.generate_trajectory(
-            table[3], table[4], table[5], k0)
-        plt.plot(xc, yc, "-r")
-        xc, yc, yawc = motion_model.generate_trajectory(
-            table[3], -table[4], -table[5], k0)
-        plt.plot(xc, yc, "-r")
-
-    plt.grid(True)
-    plt.axis("equal")
-    plt.show()
-
-    print("Done")
-
-
-def main():
-    generate_lookup_table()
-
-
-if __name__ == '__main__':
-    main()
diff --git a/PathPlanning/StateLatticePlanner/lookup_table.csv b/PathPlanning/StateLatticePlanner/lookup_table.csv
new file mode 100644
index 0000000000..2c48a11a18
--- /dev/null
+++ b/PathPlanning/StateLatticePlanner/lookup_table.csv
@@ -0,0 +1,82 @@
+x,y,yaw,s,km,kf
+1.0,0.0,0.0,1.0,0.0,0.0
+9.975352133559392,0.0482183513545736,0.5660837104214496,10.00000000000002,0.0015736624607596847,0.31729782170754367
+15.021899857204536,0.023109001221800096,0.541061424167431,15.128443053611093,0.0006480950273134919,0.20847475849103875
+20.062147834064536,0.0406648159648112,0.5374967866814861,20.205553097986094,0.000452860235044122,0.15502921850050788
+24.924468605496358,-0.04047324014767662,0.5146575311501209,25.16775431470035,6.940620839146646e-05,0.12259452810198132
+9.971782095506931,1.9821448683146543,0.5206511572266477,10.287478424823748,0.05861430948618236,0.07036494964262185
+15.00170010872385,2.0003864283110473,0.5236741185892617,15.245993376540827,0.02657730439557895,0.09933479864250763
+19.991716537639487,1.9940629519465154,0.5226444441451559,20.277923997037238,0.015108540596275507,0.09478988637993524
+24.946447973869596,2.0815930190993206,0.5306354765686239,25.20115925294013,0.010036251429787917,0.08505936469481987
+10.033694822745312,3.9724800521928056,0.5349578864544497,11.087694168363686,0.10279429366023954,-0.12501011715795404
+15.010712586144749,4.0046776414868095,0.5234972445048012,15.729582155835587,0.05010930398580602,-0.0008557723200034717
+19.9175798257299,4.053680042954521,0.5265397234296523,20.52466275843717,0.029584390559990882,0.035276591227371874
+24.98769016158626,3.991699950598091,0.5229000018897194,25.543297770996556,0.018800715817231053,0.04750751098144048
+10.018665105170687,6.004814533505462,0.5183921334245007,12.249438857228967,0.13207408248643182,-0.2742892277307502
+14.988626131330372,5.991226410357179,0.5248160422552801,16.53593823576699,0.06924423592936522,-0.08634227857486092
+20.014420271646646,6.006767110727591,0.5233060851224174,21.23271362632659,0.041402041787912916,-0.01770377839603589
+24.998338724667267,5.997352722087869,0.5235621854299422,26.009046544833613,0.027285850882345728,0.011507045054418165
+10.040118020822444,8.017131894913126,0.5076867575242261,13.8061261785273,0.14561700178565884,-0.3527538468214878
+14.96397914886416,7.974972375534203,0.5303378183744862,17.667338175004062,0.08318912494381935,-0.15372672981944802
+20.045725182938817,8.023486945646207,0.5201839069343577,22.126364299043573,0.05173969669894265,-0.06557547083017647
+25.004687466358227,8.00036398460779,0.5234938146870878,26.740089158520917,0.034867425244601645,-0.02199309906456302
+10.065138949829214,10.03244363616002,0.49375882493214895,15.701940360254637,0.14847998727328912,-0.39355037236614626
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+20.03544756240551,17.98685790169768,-0.005300968094156033,29.75070206477736,0.07340450527104486,-0.29182757725382324
+24.960019173190652,17.98909417109214,0.011594018486178026,33.0995680641525,0.05634561447882407,-0.22402297280749597
diff --git a/PathPlanning/StateLatticePlanner/lookuptable.csv b/PathPlanning/StateLatticePlanner/lookuptable.csv
deleted file mode 100644
index 85eff9b932..0000000000
--- a/PathPlanning/StateLatticePlanner/lookuptable.csv
+++ /dev/null
@@ -1,25 +0,0 @@
-x,y,yaw,s,km,kf
-1.0,0.0,0.0,1.0,0.0,0.0
-0.9734888894493215,-0.009758406565994977,0.5358080146312756,0.9922329557399788,-0.10222538550473198,3.0262632253145982
-10.980728996433243,-0.0003093605787364978,0.522622783944529,11.000391678142623,0.00010296091030877934,0.2731556687244648
-16.020309241920156,0.0001292339008200291,0.5243399938698222,16.100019813021202,0.00013263212395994706,0.18999138959173634
-20.963495745193626,-0.00033031017429944326,0.5226120033275024,21.10082901143343,0.00011687467551566884,0.14550546012583987
-6.032553475650599,2.008504211720188,0.5050517859971599,6.400329805864408,0.1520002249689879,-0.13105940607691127
-10.977487445230075,2.0078696810700034,0.5263634407901872,11.201040572298973,0.04895863722280565,0.08356555007223682
-15.994057699325753,2.025659106131227,0.5303858891065698,16.200300421483128,0.0235708657178127,0.10002225103921249
-20.977228843605943,2.0281289825388513,0.5300376140865567,21.20043308669372,0.013795675421657671,0.09331700188063087
-25.95453914157977,1.9926432818499131,0.5226203078411618,26.200880299840527,0.00888830054451281,0.0830622000626594
-0.9999999999999999,0.0,0.0,1.0,0.0,0.0
-5.999999999670752,5.231312388722274e-05,1.4636120911014667e-05,5.996117185283419,4.483756968024291e-06,-3.4422519205046243e-06
-10.999749470720566,-0.011978787043239986,0.022694802592583763,10.99783855994015,-0.00024209503125174496,0.01370491008661795
-15.999885224357776,-0.018937230084507616,0.011565580878015763,15.99839381389597,-0.0002086399372890716,0.005267247862667496
-20.999882688881286,-0.030304200126461317,0.009117836885732596,20.99783120184498,-0.00020013159571184735,0.0034529188783636866
-25.999911270030413,-0.025754431694664327,0.0074809531598503615,25.99674782258235,-0.0001111138115390646,0.0021946603965658368
-10.952178818975062,1.993067260835455,0.0045572480669707136,11.17961498195845,0.04836813285436623,-0.19328716251760758
-16.028306009258,2.0086286208315407,0.009306594796759554,16.122411866381054,0.02330689045417979,-0.08877002085985948
-20.971603115419715,1.9864158336174966,0.007016819403167119,21.093006725076872,0.013439123130720928,-0.05238318300611623
-25.997019676818372,1.9818581321430093,0.007020172975955249,26.074021794586585,0.00876496148602802,-0.03362579291686346
-16.017903482982604,4.009490840390534,-5.293114796312698e-05,16.600937712976638,0.044543450568614244,-0.17444651314466567
-20.98845988414163,3.956600199823583,-0.01050744134070728,21.40149119463485,0.02622674388276059,-0.10625681152519345
-25.979448381017914,3.9968223055054977,-0.00012819253386682928,26.30504721211744,0.017467093413146118,-0.06914750106424669
-25.96268055563514,5.9821266846168,4.931311239565056e-05,26.801388563459287,0.025426008913226557,-0.10047663078001536
diff --git a/PathPlanning/StateLatticePlanner/state_lattice_planner.py b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
index fb80e1f582..e26fb8288c 100644
--- a/PathPlanning/StateLatticePlanner/state_lattice_planner.py
+++ b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
@@ -4,9 +4,9 @@
 
 author: Atsushi Sakai (@Atsushi_twi)
 
-- lookuptable.csv is generated with this script:
+- plookuptable.csv is generated with this script:
 https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning
-/ModelPredictiveTrajectoryGenerator/lookuptable_generator.py
+/ModelPredictiveTrajectoryGenerator/lookup_table_generator.py
 
 Ref:
 
@@ -21,27 +21,25 @@
 from matplotlib import pyplot as plt
 import numpy as np
 import math
-import pandas as pd
 import pathlib
 sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
 import ModelPredictiveTrajectoryGenerator.trajectory_generator as planner
 import ModelPredictiveTrajectoryGenerator.motion_model as motion_model
 
-table_path = os.path.dirname(os.path.abspath(__file__)) + "/lookuptable.csv"
+TABLE_PATH = os.path.dirname(os.path.abspath(__file__)) + "/lookup_table.csv"
 
 show_animation = True
 
 
-def search_nearest_one_from_lookuptable(tx, ty, tyaw, lookup_table):
+def search_nearest_one_from_lookup_table(t_x, t_y, t_yaw, lookup_table):
     mind = float("inf")
     minid = -1
 
     for (i, table) in enumerate(lookup_table):
-
-        dx = tx - table[0]
-        dy = ty - table[1]
-        dyaw = tyaw - table[2]
+        dx = t_x - table[0]
+        dy = t_y - table[1]
+        dyaw = t_yaw - table[2]
         d = math.sqrt(dx ** 2 + dy ** 2 + dyaw ** 2)
         if d <= mind:
             minid = i
@@ -50,24 +48,22 @@ def search_nearest_one_from_lookuptable(tx, ty, tyaw, lookup_table):
     return lookup_table[minid]
 
 
-def get_lookup_table():
-    data = pd.read_csv(table_path)
-
-    return np.array(data)
+def get_lookup_table(table_path):
+    return np.loadtxt(table_path, delimiter=',', skiprows=1)
 
 
 def generate_path(target_states, k0):
     # x, y, yaw, s, km, kf
-    lookup_table = get_lookup_table()
+    lookup_table = get_lookup_table(TABLE_PATH)
     result = []
 
     for state in target_states:
-        bestp = search_nearest_one_from_lookuptable(
+        bestp = search_nearest_one_from_lookup_table(
             state[0], state[1], state[2], lookup_table)
 
         target = motion_model.State(x=state[0], y=state[1], yaw=state[2])
         init_p = np.array(
-            [math.sqrt(state[0] ** 2 + state[1] ** 2), bestp[4], bestp[5]]).reshape(3, 1)
+            [np.hypot(state[0], state[1]), bestp[4], bestp[5]]).reshape(3, 1)
 
         x, y, yaw, p = planner.optimize_trajectory(target, k0, init_p)
 
@@ -82,18 +78,28 @@ def generate_path(target_states, k0):
 
 def calc_uniform_polar_states(nxy, nh, d, a_min, a_max, p_min, p_max):
     """
-    calc uniform state
 
-    :param nxy: number of position sampling
-    :param nh: number of heading sampleing
-    :param d: distance of terminal state
-    :param a_min: position sampling min angle
-    :param a_max: position sampling max angle
-    :param p_min: heading sampling min angle
-    :param p_max: heading sampling max angle
-    :return: states list
-    """
+    Parameters
+    ----------
+    nxy :
+        number of position sampling
+    nh :
+        number of heading sampleing
+    d :
+        distance of terminal state
+    a_min :
+        position sampling min angle
+    a_max :
+        position sampling max angle
+    p_min :
+        heading sampling min angle
+    p_max :
+        heading sampling max angle
+
+    Returns
+    -------
 
+    """
     angle_samples = [i / (nxy - 1) for i in range(nxy)]
     states = sample_states(angle_samples, a_min, a_max, d, p_max, p_min, nh)
 
@@ -312,11 +318,11 @@ def lane_state_sampling_test1():
         plt.close("all")
 
     for table in result:
-        xc, yc, yawc = motion_model.generate_trajectory(
+        x_c, y_c, yaw_c = motion_model.generate_trajectory(
             table[3], table[4], table[5], k0)
 
         if show_animation:
-            plt.plot(xc, yc, "-r")
+            plt.plot(x_c, y_c, "-r")
 
     if show_animation:
         plt.grid(True)
diff --git a/README.md b/README.md
index 2d0ee8d653..28b932baf2 100644
--- a/README.md
+++ b/README.md
@@ -103,8 +103,6 @@ For running each sample code:
  
 - [Matplotlib](https://matplotlib.org/)
  
-- [pandas](https://pandas.pydata.org/)
-
 - [cvxpy](https://www.cvxpy.org/) 
 
 For development:
diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst
index 4a19126686..c2be93261b 100644
--- a/docs/getting_started_main.rst
+++ b/docs/getting_started_main.rst
@@ -30,7 +30,6 @@ Requirements
 -  `NumPy`_
 -  `SciPy`_
 -  `Matplotlib`_
--  `pandas`_
 -  `cvxpy`_
 
 For development:
@@ -45,7 +44,6 @@ For development:
 .. _`NumPy`: https://numpy.org/
 .. _`SciPy`: https://scipy.org/
 .. _`Matplotlib`: https://matplotlib.org/
-.. _`pandas`: https://pandas.pydata.org/
 .. _`cvxpy`: https://www.cvxpy.org/
 
 
diff --git a/requirements/environment.yml b/requirements/environment.yml
index 109fc57789..202c2693d9 100644
--- a/requirements/environment.yml
+++ b/requirements/environment.yml
@@ -6,6 +6,5 @@ dependencies:
   - pip
   - scipy
   - numpy
-  - pandas
   - cvxpy
   - matplotlib
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 61a73fd2c6..42225b5fbe 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,5 @@
 numpy == 1.23.3
 scipy == 1.9.1
-pandas == 1.4.4
 matplotlib == 3.5.3
 cvxpy == 1.2.1
 pytest == 7.1.3 # For unit test

From 3814341a10e0d7b97db4d5f5c8057e1d4cb17a2d Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 30 Sep 2022 09:24:03 +0900
Subject: [PATCH 0655/1080] Remove pandas dependency (#727)

---
 README.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/README.md b/README.md
index 28b932baf2..97a7742dca 100644
--- a/README.md
+++ b/README.md
@@ -632,6 +632,11 @@ If you would like to support us in some other way, please contact with creating
 
 They are providing a free license of their IDEs for this OSS development.   
 
+### [1Password](https://github.com/1Password/1password-teams-open-source)
+
+They are providing a free license of their 1Password team license for this OSS project.   
+
+
 # Authors
 
 - [Contributors to AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRobotics/graphs/contributors)

From aebf8dc3ff6d82af7e02b3ed1cd3167c9aae6683 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 2 Oct 2022 11:14:25 +0900
Subject: [PATCH 0656/1080] clean up circle_artifact (#728)

* clean up circle_artifact

* Update circleci-artifacts-redirector.yml
---
 .github/workflows/circle_artifacts.yml              | 13 -------------
 .github/workflows/circleci-artifacts-redirector.yml |  2 +-
 2 files changed, 1 insertion(+), 14 deletions(-)
 delete mode 100644 .github/workflows/circle_artifacts.yml

diff --git a/.github/workflows/circle_artifacts.yml b/.github/workflows/circle_artifacts.yml
deleted file mode 100644
index 56ac78ed15..0000000000
--- a/.github/workflows/circle_artifacts.yml
+++ /dev/null
@@ -1,13 +0,0 @@
-on: [status]
-jobs:
-  circleci_artifacts_redirector_job:
-    runs-on: ubuntu-20.04
-    name: Run CircleCI artifacts redirector
-    steps:
-      - name: GitHub Action step
-        uses: larsoner/circleci-artifacts-redirector-action@master
-        with:
-          repo-token: ${{ secrets.GITHUB_TOKEN }}
-          artifact-path: 0/html-scipyorg/index.html
-          circleci-jobs: build_docs
-          job-title: build_doc artifact
\ No newline at end of file
diff --git a/.github/workflows/circleci-artifacts-redirector.yml b/.github/workflows/circleci-artifacts-redirector.yml
index d988d2c38e..149a087a5d 100644
--- a/.github/workflows/circleci-artifacts-redirector.yml
+++ b/.github/workflows/circleci-artifacts-redirector.yml
@@ -3,7 +3,7 @@ on: [status]
 jobs:
   circleci_artifacts_redirector_job:
     runs-on: ubuntu-latest
-    name: Run CircleCI artifacts redirector
+    name: Run CircleCI artifacts redirector!!
     steps:
       - name: run-circleci-artifacts-redirector
         uses: larsoner/circleci-artifacts-redirector-action@0.3.1

From b607af70c3431bb8e97d17a4805aaadd6ddea857 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 4 Oct 2022 07:27:02 +0900
Subject: [PATCH 0657/1080] Bump matplotlib from 3.5.3 to 3.6.0 in
 /requirements (#729)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.5.3 to 3.6.0.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.5.3...v3.6.0)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 42225b5fbe..2befd0a9e4 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.23.3
 scipy == 1.9.1
-matplotlib == 3.5.3
+matplotlib == 3.6.0
 cvxpy == 1.2.1
 pytest == 7.1.3 # For unit test
 pytest-xdist == 2.5.0 # For unit test

From 33f2fa39dc796ab5a9d5e227cece04ffc8297794 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 4 Oct 2022 07:27:22 +0900
Subject: [PATCH 0658/1080] Bump mypy from 0.971 to 0.982 in /requirements
 (#730)

Bumps [mypy](https://github.com/python/mypy) from 0.971 to 0.982.
- [Release notes](https://github.com/python/mypy/releases)
- [Commits](https://github.com/python/mypy/compare/v0.971...v0.982)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 2befd0a9e4..a85aa121e6 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.6.0
 cvxpy == 1.2.1
 pytest == 7.1.3 # For unit test
 pytest-xdist == 2.5.0 # For unit test
-mypy == 0.971 # For unit test
+mypy == 0.982 # For unit test
 flake8 == 5.0.4 # For unit test

From 1c165262f738b41d91acdb03ca546e82c7313fcc Mon Sep 17 00:00:00 2001
From: Abinash Satapathy <iamabinash98@gmail.com>
Date: Mon, 10 Oct 2022 02:15:43 +0200
Subject: [PATCH 0659/1080] Update SECURITY.md (#733)

Typo correction
---
 SECURITY.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/SECURITY.md b/SECURITY.md
index 5047a6fc3d..53dcafa450 100644
--- a/SECURITY.md
+++ b/SECURITY.md
@@ -10,4 +10,4 @@ In this project, we are only support latest code.
 
 ## Reporting a Vulnerability
 
-If you find any sequrity related problem, let us know by creating an issue about it.
+If you find any security related problem, let us know by creating an issue about it.

From 028a0a4d8d9eb53be1291fd0db3930d0c8e351c4 Mon Sep 17 00:00:00 2001
From: Abinash Satapathy <iamabinash98@gmail.com>
Date: Mon, 10 Oct 2022 02:16:31 +0200
Subject: [PATCH 0660/1080] Update pull_request_template.md (#735)

---
 .github/pull_request_template.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index c8790ad9ec..b6ac52efa2 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -19,6 +19,6 @@ Again, thanks for contributing!
 <!--Any additional information you think is important.-->
 
 #### CheckList
--[] Did you add an unittest for your new example or defect fix?
--[] Did you add documents for your new example?
--[] All CIs are green? (You can check it after submitting)
+- [ ] Did you add an unittest for your new example or defect fix?
+- [ ] Did you add documents for your new example?
+- [ ] All CIs are green? (You can check it after submitting)

From ebdc2d45003c9ce86eaebb1ebfd09d527c7e2eaa Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 11 Oct 2022 06:46:55 +0900
Subject: [PATCH 0661/1080] Bump matplotlib from 3.6.0 to 3.6.1 in
 /requirements (#736)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.6.0 to 3.6.1.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.6.0...v3.6.1)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index a85aa121e6..67637bb3e5 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.23.3
 scipy == 1.9.1
-matplotlib == 3.6.0
+matplotlib == 3.6.1
 cvxpy == 1.2.1
 pytest == 7.1.3 # For unit test
 pytest-xdist == 2.5.0 # For unit test

From 001efc5935c4c5a3d25c2d5441db838817fa44b3 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 13 Oct 2022 20:13:00 +0900
Subject: [PATCH 0662/1080] Bump scipy from 1.9.1 to 1.9.2 in /requirements
 (#737)

Bumps [scipy](https://github.com/scipy/scipy) from 1.9.1 to 1.9.2.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.9.1...v1.9.2)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 67637bb3e5..e2fa5d7546 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.23.3
-scipy == 1.9.1
+scipy == 1.9.2
 matplotlib == 3.6.1
 cvxpy == 1.2.1
 pytest == 7.1.3 # For unit test

From bc88e70f7ed53226d9c200a2087ab361a2d8bf6b Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 18 Oct 2022 07:02:25 +0900
Subject: [PATCH 0663/1080] Bump numpy from 1.23.3 to 1.23.4 in /requirements
 (#738)

Bumps [numpy](https://github.com/numpy/numpy) from 1.23.3 to 1.23.4.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.23.3...v1.23.4)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index e2fa5d7546..160c043818 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.23.3
+numpy == 1.23.4
 scipy == 1.9.2
 matplotlib == 3.6.1
 cvxpy == 1.2.1

From 381634821511054f50dad6743ae0700d75525c72 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 25 Oct 2022 07:59:09 +0900
Subject: [PATCH 0664/1080] Bump scipy from 1.9.2 to 1.9.3 in /requirements
 (#739)

Bumps [scipy](https://github.com/scipy/scipy) from 1.9.2 to 1.9.3.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.9.2...v1.9.3)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 160c043818..1170b24088 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.23.4
-scipy == 1.9.2
+scipy == 1.9.3
 matplotlib == 3.6.1
 cvxpy == 1.2.1
 pytest == 7.1.3 # For unit test

From 39472e66d217b6980d8ec18431302219b5fe2412 Mon Sep 17 00:00:00 2001
From: Abinash Satapathy <iamabinash98@gmail.com>
Date: Sun, 30 Oct 2022 06:28:58 +0100
Subject: [PATCH 0665/1080] Update README.md (#732)

* Update README.md

Updated the sponsors correctly

* Update README.md

Remove the tag.

* Update README.md

Updated development references and styled the single line codes in "How to use" section

* Update README.md

Styled single-line comments using syntax highlighting for markdown
---
 README.md | 35 +++++++++++++++++++++--------------
 1 file changed, 21 insertions(+), 14 deletions(-)

diff --git a/README.md b/README.md
index 97a7742dca..b46195f35e 100644
--- a/README.md
+++ b/README.md
@@ -70,8 +70,9 @@ Python codes for robotics algorithm.
    * [Contribution](#contribution)
    * [Citing](#citing)
    * [Support](#support)
-   * [Sponsors](#Sponsors)
+   * [Sponsors](#sponsors)
       * [JetBrains](#JetBrains)
+      * [1Password](#1password)
    * [Authors](#authors)
 
 # What is this?
@@ -107,15 +108,15 @@ For running each sample code:
 
 For development:
   
-- pytest (for unit tests)
+- [pytest](https://pytest.org/) (for unit tests)
   
-- pytest-xdist (for parallel unit tests)
+- [pytest-xdist](https://pypi.org/project/pytest-xdist/) (for parallel unit tests)
   
-- mypy (for type check)
+- [mypy](http://mypy-lang.org/) (for type check)
   
-- sphinx (for document generation)
+- [sphinx](https://www.sphinx-doc.org/) (for document generation)
   
-- pycodestyle (for code style check)
+- [pycodestyle](https://pypi.org/project/pycodestyle/) (for code style check)
 
 # Documentation
 
@@ -131,18 +132,24 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation
 
 1. Clone this repo.
 
-> git clone https://github.com/AtsushiSakai/PythonRobotics.git
+   ```terminal
+   git clone https://github.com/AtsushiSakai/PythonRobotics.git
+   ```
 
 
 2. Install the required libraries.
 
-using conda :
+- using conda :
 
-> conda env create -f requirements/environment.yml
+  ```terminal
+  conda env create -f requirements/environment.yml
+  ```
  
-using pip :
+- using pip :
 
-> pip install -r requirements/requirements.txt
+  ```terminal
+  pip install -r requirements/requirements.txt
+  ```
 
 
 3. Execute python script in each directory.
@@ -614,7 +621,7 @@ If you use this project's code for your academic work, we encourage you to cite
 
 If you use this project's code in industry, we'd love to hear from you as well; feel free to reach out to the developers directly.
 
-# Supporting this project
+# <a id="support"></a>Supporting this project
 
 If you or your company would like to support this project, please consider:
 
@@ -626,9 +633,9 @@ If you or your company would like to support this project, please consider:
 
 If you would like to support us in some other way, please contact with creating an issue.
 
-## Sponsors
+## <a id="sponsors"></a>Sponsors
 
-### [JetBrains](https://www.jetbrains.com/)
+### <a id="JetBrains"></a>[JetBrains](https://www.jetbrains.com/)
 
 They are providing a free license of their IDEs for this OSS development.   
 

From 2b877369a4a9335e5b28e2b8cabfe03c8cbb7405 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 1 Nov 2022 06:15:31 +0900
Subject: [PATCH 0666/1080] Bump pytest-xdist from 2.5.0 to 3.0.2 in
 /requirements (#743)

Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 2.5.0 to 3.0.2.
- [Release notes](https://github.com/pytest-dev/pytest-xdist/releases)
- [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v2.5.0...v3.0.2)

---
updated-dependencies:
- dependency-name: pytest-xdist
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 1170b24088..82598ddd69 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,6 +3,6 @@ scipy == 1.9.3
 matplotlib == 3.6.1
 cvxpy == 1.2.1
 pytest == 7.1.3 # For unit test
-pytest-xdist == 2.5.0 # For unit test
+pytest-xdist == 3.0.2 # For unit test
 mypy == 0.982 # For unit test
 flake8 == 5.0.4 # For unit test

From 2d0fa60fd40398244317491655f6a51462cb73c6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 2 Nov 2022 23:23:52 +0900
Subject: [PATCH 0667/1080] Bump pytest from 7.1.3 to 7.2.0 in /requirements
 (#742)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.1.3 to 7.2.0.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/7.1.3...7.2.0)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 82598ddd69..c8660ebd16 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.23.4
 scipy == 1.9.3
 matplotlib == 3.6.1
 cvxpy == 1.2.1
-pytest == 7.1.3 # For unit test
+pytest == 7.2.0 # For unit test
 pytest-xdist == 3.0.2 # For unit test
 mypy == 0.982 # For unit test
 flake8 == 5.0.4 # For unit test

From 768df6ffabddc030fdaef3c1668c06ab30d25ba0 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 8 Nov 2022 07:27:05 +0900
Subject: [PATCH 0668/1080] Bump matplotlib from 3.6.1 to 3.6.2 in
 /requirements (#745)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.6.1 to 3.6.2.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.6.1...v3.6.2)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index c8660ebd16..ffaef30d19 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.23.4
 scipy == 1.9.3
-matplotlib == 3.6.1
+matplotlib == 3.6.2
 cvxpy == 1.2.1
 pytest == 7.2.0 # For unit test
 pytest-xdist == 3.0.2 # For unit test

From 8cd5b05fb43aea4f91faf2007b039667b68681a6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 8 Nov 2022 08:04:42 +0900
Subject: [PATCH 0669/1080] Bump cvxpy from 1.2.1 to 1.2.2 in /requirements
 (#746)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.2.1 to 1.2.2.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.2.1...v1.2.2)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index ffaef30d19..cc6ca162c2 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 1.23.4
 scipy == 1.9.3
 matplotlib == 3.6.2
-cvxpy == 1.2.1
+cvxpy == 1.2.2
 pytest == 7.2.0 # For unit test
 pytest-xdist == 3.0.2 # For unit test
 mypy == 0.982 # For unit test

From 85e00de888e8bc51ce2af0a0370e4a77d53a56b9 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 10 Nov 2022 21:04:55 +0900
Subject: [PATCH 0670/1080] Bump mypy from 0.982 to 0.990 in /requirements
 (#744)

Bumps [mypy](https://github.com/python/mypy) from 0.982 to 0.990.
- [Release notes](https://github.com/python/mypy/releases)
- [Commits](https://github.com/python/mypy/compare/v0.982...v0.990)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index cc6ca162c2..889d182f78 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.6.2
 cvxpy == 1.2.2
 pytest == 7.2.0 # For unit test
 pytest-xdist == 3.0.2 # For unit test
-mypy == 0.982 # For unit test
+mypy == 0.990 # For unit test
 flake8 == 5.0.4 # For unit test

From b50a1180d819b0d45d9c565326ba18cbd12c3e5d Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 10 Nov 2022 21:29:11 +0900
Subject: [PATCH 0671/1080] fic mypy ci (#747)

---
 Mapping/rectangle_fitting/simulator.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/Mapping/rectangle_fitting/simulator.py b/Mapping/rectangle_fitting/simulator.py
index faca2cb79e..aa32ae1b1f 100644
--- a/Mapping/rectangle_fitting/simulator.py
+++ b/Mapping/rectangle_fitting/simulator.py
@@ -138,7 +138,3 @@ def ray_casting_filter(theta_l, range_l, angle_resolution):
                 ry.append(range_db[i] * np.sin(t))
 
         return rx, ry
-
-
-if __name__ == '__main__':
-    main()

From 8f14488ec2f243e48222bc87923604a7d46c45af Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 15 Nov 2022 07:25:44 +0900
Subject: [PATCH 0672/1080] Bump mypy from 0.990 to 0.991 in /requirements
 (#751)

Bumps [mypy](https://github.com/python/mypy) from 0.990 to 0.991.
- [Release notes](https://github.com/python/mypy/releases)
- [Commits](https://github.com/python/mypy/compare/v0.990...v0.991)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 889d182f78..fa20768c46 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.6.2
 cvxpy == 1.2.2
 pytest == 7.2.0 # For unit test
 pytest-xdist == 3.0.2 # For unit test
-mypy == 0.990 # For unit test
+mypy == 0.991 # For unit test
 flake8 == 5.0.4 # For unit test

From bcb2c863eb45ec8c11c1dcfdadcef98ef9e85b6b Mon Sep 17 00:00:00 2001
From: KohMat <KohMat@users.noreply.github.com>
Date: Sun, 20 Nov 2022 13:19:42 +0900
Subject: [PATCH 0673/1080] Fix the start state to use the current accel in
 frent planner(#755) (#756)

The robot didn't reach the target velocity when running frenet_optimal_trajectory.py.

To fix this, I changed the constraints for determining polynomials that generate longitudinal trajectories.

Expressly, I set the initial acceleration to the current acceleration.
---
 .../frenet_optimal_trajectory.py                     | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
index b7d1b8e642..331df36309 100644
--- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
+++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
@@ -116,7 +116,7 @@ def __init__(self):
         self.c = []
 
 
-def calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0):
+def calc_frenet_paths(c_speed, c_accel, c_d, c_d_d, c_d_dd, s0):
     frenet_paths = []
 
     # generate path to each offset goal
@@ -139,7 +139,7 @@ def calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0):
             for tv in np.arange(TARGET_SPEED - D_T_S * N_S_SAMPLE,
                                 TARGET_SPEED + D_T_S * N_S_SAMPLE, D_T_S):
                 tfp = copy.deepcopy(fp)
-                lon_qp = QuarticPolynomial(s0, c_speed, 0.0, tv, 0.0, Ti)
+                lon_qp = QuarticPolynomial(s0, c_speed, c_accel, tv, 0.0, Ti)
 
                 tfp.s = [lon_qp.calc_point(t) for t in fp.t]
                 tfp.s_d = [lon_qp.calc_first_derivative(t) for t in fp.t]
@@ -225,8 +225,8 @@ def check_paths(fplist, ob):
     return [fplist[i] for i in ok_ind]
 
 
-def frenet_optimal_planning(csp, s0, c_speed, c_d, c_d_d, c_d_dd, ob):
-    fplist = calc_frenet_paths(c_speed, c_d, c_d_d, c_d_dd, s0)
+def frenet_optimal_planning(csp, s0, c_speed, c_accel, c_d, c_d_d, c_d_dd, ob):
+    fplist = calc_frenet_paths(c_speed, c_accel, c_d, c_d_d, c_d_dd, s0)
     fplist = calc_global_paths(fplist, csp)
     fplist = check_paths(fplist, ob)
 
@@ -274,6 +274,7 @@ def main():
 
     # initial state
     c_speed = 10.0 / 3.6  # current speed [m/s]
+    c_accel = 0.0  # current acceleration [m/ss]
     c_d = 2.0  # current lateral position [m]
     c_d_d = 0.0  # current lateral speed [m/s]
     c_d_dd = 0.0  # current lateral acceleration [m/s]
@@ -283,13 +284,14 @@ def main():
 
     for i in range(SIM_LOOP):
         path = frenet_optimal_planning(
-            csp, s0, c_speed, c_d, c_d_d, c_d_dd, ob)
+            csp, s0, c_speed, c_accel, c_d, c_d_d, c_d_dd, ob)
 
         s0 = path.s[1]
         c_d = path.d[1]
         c_d_d = path.d_d[1]
         c_d_dd = path.d_dd[1]
         c_speed = path.s_d[1]
+        c_accel = path.s_dd[1]
 
         if np.hypot(path.x[1] - tx[-1], path.y[1] - ty[-1]) <= 1.0:
             print("Goal")

From 644ce8a5fda39cf8a7fcff4537fe10b3f4bad538 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 22 Nov 2022 10:46:42 +0900
Subject: [PATCH 0674/1080] Bump numpy from 1.23.4 to 1.23.5 in /requirements
 (#757)

Bumps [numpy](https://github.com/numpy/numpy) from 1.23.4 to 1.23.5.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.23.4...v1.23.5)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index fa20768c46..5e3b4d8caf 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.23.4
+numpy == 1.23.5
 scipy == 1.9.3
 matplotlib == 3.6.2
 cvxpy == 1.2.2

From 405f3e79341356c4fa3f78dbe37bcd87f12d155b Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 28 Nov 2022 21:27:22 +0900
Subject: [PATCH 0675/1080] Update gh-pages.yml (#758)

---
 .github/workflows/gh-pages.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml
index e51197cb37..bf46598633 100644
--- a/.github/workflows/gh-pages.yml
+++ b/.github/workflows/gh-pages.yml
@@ -8,7 +8,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - name: Setup python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v3
     - name: Checkout
       uses: actions/checkout@master
       with:
@@ -26,4 +26,4 @@ jobs:
       uses: ad-m/github-push-action@master
       with:
         github_token: ${{ secrets.GITHUB_TOKEN }}
-        branch: gh-pages
\ No newline at end of file
+        branch: gh-pages

From 727f32cb8b1a144ae1b6b167f2c2d2ebee61851b Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 6 Dec 2022 07:29:38 +0900
Subject: [PATCH 0676/1080] Bump pytest-xdist from 3.0.2 to 3.1.0 in
 /requirements (#760)

Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.0.2 to 3.1.0.
- [Release notes](https://github.com/pytest-dev/pytest-xdist/releases)
- [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.0.2...v3.1.0)

---
updated-dependencies:
- dependency-name: pytest-xdist
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 5e3b4d8caf..b5e9d37b81 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,6 +3,6 @@ scipy == 1.9.3
 matplotlib == 3.6.2
 cvxpy == 1.2.2
 pytest == 7.2.0 # For unit test
-pytest-xdist == 3.0.2 # For unit test
+pytest-xdist == 3.1.0 # For unit test
 mypy == 0.991 # For unit test
 flake8 == 5.0.4 # For unit test

From 5ac66d71dd3e4cf06d7b32782af9d2e911633a07 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 20 Dec 2022 07:30:26 +0900
Subject: [PATCH 0677/1080] Bump numpy from 1.23.5 to 1.24.0 in /requirements
 (#763)

Bumps [numpy](https://github.com/numpy/numpy) from 1.23.5 to 1.24.0.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.23.5...v1.24.0)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index b5e9d37b81..47d9eefab5 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.23.5
+numpy == 1.24.0
 scipy == 1.9.3
 matplotlib == 3.6.2
 cvxpy == 1.2.2

From 6d27738e3d243cf758d6eaa16844504bb6eae2ee Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 Dec 2022 08:02:56 +0900
Subject: [PATCH 0678/1080] Bump numpy from 1.24.0 to 1.24.1 in /requirements
 (#765)

Bumps [numpy](https://github.com/numpy/numpy) from 1.24.0 to 1.24.1.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.24.0...v1.24.1)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 47d9eefab5..3f68d9ab4c 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.24.0
+numpy == 1.24.1
 scipy == 1.9.3
 matplotlib == 3.6.2
 cvxpy == 1.2.2

From af39afe05cfec0ef202cc1542dfe0103c130ed3b Mon Sep 17 00:00:00 2001
From: Lingkang Zhang <36938500+zlingkang@users.noreply.github.com>
Date: Fri, 30 Dec 2022 15:38:43 -0800
Subject: [PATCH 0679/1080] add axes3d to figure (#766)

---
 Bipedal/bipedal_planner/bipedal_planner.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/Bipedal/bipedal_planner/bipedal_planner.py b/Bipedal/bipedal_planner/bipedal_planner.py
index 9757fc42d6..8ca5ffcf78 100644
--- a/Bipedal/bipedal_planner/bipedal_planner.py
+++ b/Bipedal/bipedal_planner/bipedal_planner.py
@@ -51,6 +51,7 @@ def walk(self, t_sup=0.8, z_c=0.8, a=10, b=1, plot=False):
         if plot:
             fig = plt.figure()
             ax = Axes3D(fig)
+            fig.add_axes(ax)
             com_trajectory_for_plot = []
 
         px, py = 0.0, 0.0  # reference footstep position

From 1d683435c6d7c9eea2895b3f1441f00772f5ab32 Mon Sep 17 00:00:00 2001
From: Christian Clauss <cclauss@me.com>
Date: Sat, 31 Dec 2022 07:51:17 +0100
Subject: [PATCH 0680/1080] Undefined name: import sys for line 188 (#764)

---
 PathPlanning/RRT/rrt_with_sobol_sampler.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/PathPlanning/RRT/rrt_with_sobol_sampler.py b/PathPlanning/RRT/rrt_with_sobol_sampler.py
index 73488210a7..06e0c04c68 100644
--- a/PathPlanning/RRT/rrt_with_sobol_sampler.py
+++ b/PathPlanning/RRT/rrt_with_sobol_sampler.py
@@ -28,6 +28,7 @@
 
 import math
 import random
+import sys
 import matplotlib.pyplot as plt
 import numpy as np
 

From 2916273e6239c70a12b1e46845a26bd49a727133 Mon Sep 17 00:00:00 2001
From: Andrey Bozhko <andybozhko@gmail.com>
Date: Sun, 1 Jan 2023 05:44:34 -0600
Subject: [PATCH 0681/1080] Update loop conditions to be more idiomatic; fix
 missing imports (#768)

* missing import

* use while true
---
 Mapping/rectangle_fitting/rectangle_fitting.py                  | 2 +-
 PathPlanning/AStar/a_star.py                                    | 2 +-
 PathPlanning/BidirectionalAStar/bidirectional_a_star.py         | 2 +-
 .../bidirectional_breadth_first_search.py                       | 2 +-
 PathPlanning/BreadthFirstSearch/breadth_first_search.py         | 2 +-
 PathPlanning/DepthFirstSearch/depth_first_search.py             | 2 +-
 PathPlanning/Dijkstra/dijkstra.py                               | 2 +-
 PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py  | 2 +-
 PathPlanning/HybridAStar/dynamic_programming_heuristic.py       | 2 +-
 tests/test_grid_map_lib.py                                      | 1 +
 10 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py
index 124e48fe76..f54ada1e3a 100644
--- a/Mapping/rectangle_fitting/rectangle_fitting.py
+++ b/Mapping/rectangle_fitting/rectangle_fitting.py
@@ -162,7 +162,7 @@ def _adoptive_range_segmentation(self, ox, oy):
             S.append(C)
 
         # Merge cluster
-        while 1:
+        while True:
             no_change = True
             for (c1, c2) in list(itertools.permutations(range(len(S)), 2)):
                 if S[c1] & S[c2]:
diff --git a/PathPlanning/AStar/a_star.py b/PathPlanning/AStar/a_star.py
index eb2672a9ce..6d20350432 100644
--- a/PathPlanning/AStar/a_star.py
+++ b/PathPlanning/AStar/a_star.py
@@ -71,7 +71,7 @@ def planning(self, sx, sy, gx, gy):
         open_set, closed_set = dict(), dict()
         open_set[self.calc_grid_index(start_node)] = start_node
 
-        while 1:
+        while True:
             if len(open_set) == 0:
                 print("Open set is empty..")
                 break
diff --git a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
index ecb875f410..5387cca1ad 100644
--- a/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
+++ b/PathPlanning/BidirectionalAStar/bidirectional_a_star.py
@@ -75,7 +75,7 @@ def planning(self, sx, sy, gx, gy):
         current_B = goal_node
         meet_point_A, meet_point_B = None, None
 
-        while 1:
+        while True:
             if len(open_set_A) == 0:
                 print("Open set A is empty..")
                 break
diff --git a/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py b/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py
index dd4282573e..60a8c5e170 100644
--- a/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py
+++ b/PathPlanning/BidirectionalBreadthFirstSearch/bidirectional_breadth_first_search.py
@@ -76,7 +76,7 @@ def planning(self, sx, sy, gx, gy):
 
         meet_point_A, meet_point_B = None, None
 
-        while 1:
+        while True:
             if len(open_set_A) == 0:
                 print("Open set A is empty..")
                 break
diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
index 406e5eb51f..ff662170e7 100644
--- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py
+++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
@@ -67,7 +67,7 @@ def planning(self, sx, sy, gx, gy):
         open_set, closed_set = dict(), dict()
         open_set[self.calc_grid_index(nstart)] = nstart
 
-        while 1:
+        while True:
             if len(open_set) == 0:
                 print("Open set is empty..")
                 break
diff --git a/PathPlanning/DepthFirstSearch/depth_first_search.py b/PathPlanning/DepthFirstSearch/depth_first_search.py
index 523225ee42..6922b8cbad 100644
--- a/PathPlanning/DepthFirstSearch/depth_first_search.py
+++ b/PathPlanning/DepthFirstSearch/depth_first_search.py
@@ -67,7 +67,7 @@ def planning(self, sx, sy, gx, gy):
         open_set, closed_set = dict(), dict()
         open_set[self.calc_grid_index(nstart)] = nstart
 
-        while 1:
+        while True:
             if len(open_set) == 0:
                 print("Open set is empty..")
                 break
diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py
index 9956dff326..004e49f15a 100644
--- a/PathPlanning/Dijkstra/dijkstra.py
+++ b/PathPlanning/Dijkstra/dijkstra.py
@@ -71,7 +71,7 @@ def planning(self, sx, sy, gx, gy):
         open_set, closed_set = dict(), dict()
         open_set[self.calc_index(start_node)] = start_node
 
-        while 1:
+        while True:
             c_id = min(open_set, key=lambda o: open_set[o].cost)
             current = open_set[c_id]
 
diff --git a/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py b/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py
index f2416fba98..b259beb870 100644
--- a/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py
+++ b/PathPlanning/GreedyBestFirstSearch/greedy_best_first_search.py
@@ -67,7 +67,7 @@ def planning(self, sx, sy, gx, gy):
         open_set, closed_set = dict(), dict()
         open_set[self.calc_grid_index(nstart)] = nstart
 
-        while 1:
+        while True:
             if len(open_set) == 0:
                 print("Open set is empty..")
                 break
diff --git a/PathPlanning/HybridAStar/dynamic_programming_heuristic.py b/PathPlanning/HybridAStar/dynamic_programming_heuristic.py
index cc635260d6..8a78b15d5f 100644
--- a/PathPlanning/HybridAStar/dynamic_programming_heuristic.py
+++ b/PathPlanning/HybridAStar/dynamic_programming_heuristic.py
@@ -65,7 +65,7 @@ def calc_distance_heuristic(gx, gy, ox, oy, resolution, rr):
     open_set[calc_index(goal_node, x_w, min_x, min_y)] = goal_node
     priority_queue = [(0, calc_index(goal_node, x_w, min_x, min_y))]
 
-    while 1:
+    while True:
         if not priority_queue:
             break
         cost, c_id = heapq.heappop(priority_queue)
diff --git a/tests/test_grid_map_lib.py b/tests/test_grid_map_lib.py
index 8ae142b003..92ca67e297 100644
--- a/tests/test_grid_map_lib.py
+++ b/tests/test_grid_map_lib.py
@@ -1,4 +1,5 @@
 from Mapping.grid_map_lib.grid_map_lib import GridMap
+import conftest
 import numpy as np
 
 

From e7710546c18f8f4cd076c030939e77afc86d43c9 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 3 Jan 2023 07:52:07 +0900
Subject: [PATCH 0682/1080] Bump cvxpy from 1.2.2 to 1.2.3 in /requirements
 (#771)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.2.2 to 1.2.3.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.2.2...v1.2.3)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 3f68d9ab4c..9a990856f1 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 1.24.1
 scipy == 1.9.3
 matplotlib == 3.6.2
-cvxpy == 1.2.2
+cvxpy == 1.2.3
 pytest == 7.2.0 # For unit test
 pytest-xdist == 3.1.0 # For unit test
 mypy == 0.991 # For unit test

From 3dc96997da60116ae5bc825d2e67adefe5ff7dc8 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 3 Jan 2023 22:27:55 +0900
Subject: [PATCH 0683/1080] Adding point cloud sampling examples (#770)

---
 .../point_cloud_sampling.py                   | 168 ++++++++++++++++++
 docs/modules/mapping/mapping_main.rst         |   1 +
 .../farthest_point_sampling.png               | Bin 0 -> 253175 bytes
 .../point_cloud_sampling_main.rst             |  70 ++++++++
 .../poisson_disk_sampling.png                 | Bin 0 -> 265198 bytes
 .../voxel_point_sampling.png                  | Bin 0 -> 250443 bytes
 tests/test_point_cloud_sampling.py            |  15 ++
 7 files changed, 254 insertions(+)
 create mode 100644 Mapping/point_cloud_sampling/point_cloud_sampling.py
 create mode 100644 docs/modules/mapping/point_cloud_sampling/farthest_point_sampling.png
 create mode 100644 docs/modules/mapping/point_cloud_sampling/point_cloud_sampling_main.rst
 create mode 100644 docs/modules/mapping/point_cloud_sampling/poisson_disk_sampling.png
 create mode 100644 docs/modules/mapping/point_cloud_sampling/voxel_point_sampling.png
 create mode 100644 tests/test_point_cloud_sampling.py

diff --git a/Mapping/point_cloud_sampling/point_cloud_sampling.py b/Mapping/point_cloud_sampling/point_cloud_sampling.py
new file mode 100644
index 0000000000..df7cde41c0
--- /dev/null
+++ b/Mapping/point_cloud_sampling/point_cloud_sampling.py
@@ -0,0 +1,168 @@
+"""
+Point cloud sampling example codes. This code supports
+- Voxel point sampling
+- Farthest point sampling
+- Poisson disk sampling
+
+"""
+import matplotlib.pyplot as plt
+import numpy as np
+import numpy.typing as npt
+from collections import defaultdict
+
+do_plot = True
+
+
+def voxel_point_sampling(original_points: npt.NDArray, voxel_size: float):
+    """
+    Voxel Point Sampling function.
+    This function sample N-dimensional points with voxel grid.
+    Points in a same voxel grid will be merged by mean operation for sampling.
+
+    Parameters
+    ----------
+    original_points :  (M, N) N-dimensional points for sampling.
+                        The number of points is M.
+    voxel_size : voxel grid size
+
+    Returns
+    -------
+    sampled points (M', N)
+    """
+    voxel_dict = defaultdict(list)
+    for i in range(original_points.shape[0]):
+        xyz = original_points[i, :]
+        xyz_index = tuple(xyz // voxel_size)
+        voxel_dict[xyz_index].append(xyz)
+    points = np.vstack([np.mean(v, axis=0) for v in voxel_dict.values()])
+    return points
+
+
+def farthest_point_sampling(orig_points: npt.NDArray,
+                            n_points: int, seed: int):
+    """
+    Farthest point sampling function
+    This function sample N-dimensional points with the farthest point policy.
+
+    Parameters
+    ----------
+    orig_points :  (M, N) N-dimensional points for sampling.
+                    The number of points is M.
+    n_points : number of points for sampling
+    seed : random seed number
+
+    Returns
+    -------
+    sampled points (n_points, N)
+
+    """
+    rng = np.random.default_rng(seed)
+    n_orig_points = orig_points.shape[0]
+    first_point_id = rng.choice(range(n_orig_points))
+    min_distances = np.ones(n_orig_points) * float("inf")
+    selected_ids = [first_point_id]
+    while len(selected_ids) < n_points:
+        base_point = orig_points[selected_ids[-1], :]
+        distances = np.linalg.norm(orig_points[np.newaxis, :] - base_point,
+                                   axis=2).flatten()
+        min_distances = np.minimum(min_distances, distances)
+        distances_rank = np.argsort(-min_distances)  # Farthest order
+        for i in distances_rank:  # From the farthest point
+            if i not in selected_ids:  # if not selected yes, select it
+                selected_ids.append(i)
+                break
+    return orig_points[selected_ids, :]
+
+
+def poisson_disk_sampling(orig_points: npt.NDArray, n_points: int,
+                          min_distance: float, seed: int, MAX_ITER=1000):
+    """
+    Poisson disk sampling function
+    This function sample N-dimensional points randomly until the number of
+    points keeping minimum distance between selected points.
+
+    Parameters
+    ----------
+    orig_points :  (M, N) N-dimensional points for sampling.
+                    The number of points is M.
+    n_points : number of points for sampling
+    min_distance : minimum distance between selected points.
+    seed : random seed number
+    MAX_ITER : Maximum number of iteration. Default is 1000.
+
+    Returns
+    -------
+    sampled points (n_points or less, N)
+    """
+    rng = np.random.default_rng(seed)
+    selected_id = rng.choice(range(orig_points.shape[0]))
+    selected_ids = [selected_id]
+    loop = 0
+    while len(selected_ids) < n_points and loop <= MAX_ITER:
+        selected_id = rng.choice(range(orig_points.shape[0]))
+        base_point = orig_points[selected_id, :]
+        distances = np.linalg.norm(
+            orig_points[np.newaxis, selected_ids] - base_point,
+            axis=2).flatten()
+        if min(distances) >= min_distance:
+            selected_ids.append(selected_id)
+        loop += 1
+    if len(selected_ids) != n_points:
+        print("Could not find the specified number of points...")
+
+    return orig_points[selected_ids, :]
+
+
+def plot_sampled_points(original_points, sampled_points, method_name):
+    fig = plt.figure()
+    ax = fig.add_subplot(projection='3d')
+    ax.scatter(original_points[:, 0], original_points[:, 1],
+               original_points[:, 2], marker=".", label="Original points")
+    ax.scatter(sampled_points[:, 0], sampled_points[:, 1],
+               sampled_points[:, 2], marker="o",
+               label="Filtered points")
+    plt.legend()
+    plt.title(method_name)
+    plt.axis("equal")
+
+
+def main():
+    n_points = 1000
+    seed = 1234
+    rng = np.random.default_rng(seed)
+
+    x = rng.normal(0.0, 10.0, n_points)
+    y = rng.normal(0.0, 1.0, n_points)
+    z = rng.normal(0.0, 10.0, n_points)
+    original_points = np.vstack((x, y, z)).T
+    print(f"{original_points.shape=}")
+    print("Voxel point sampling")
+    voxel_size = 20.0
+    voxel_sampling_points = voxel_point_sampling(original_points, voxel_size)
+    print(f"{voxel_sampling_points.shape=}")
+
+    print("Farthest point sampling")
+    n_points = 20
+    farthest_sampling_points = farthest_point_sampling(original_points,
+                                                       n_points, seed)
+    print(f"{farthest_sampling_points.shape=}")
+
+    print("Poisson disk sampling")
+    n_points = 20
+    min_distance = 10.0
+    poisson_disk_points = poisson_disk_sampling(original_points, n_points,
+                                                min_distance, seed)
+    print(f"{poisson_disk_points.shape=}")
+
+    if do_plot:
+        plot_sampled_points(original_points, voxel_sampling_points,
+                            "Voxel point sampling")
+        plot_sampled_points(original_points, farthest_sampling_points,
+                            "Farthest point sampling")
+        plot_sampled_points(original_points, poisson_disk_points,
+                            "poisson disk sampling")
+        plt.show()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/docs/modules/mapping/mapping_main.rst b/docs/modules/mapping/mapping_main.rst
index 0f7ec549dc..14a7cce70d 100644
--- a/docs/modules/mapping/mapping_main.rst
+++ b/docs/modules/mapping/mapping_main.rst
@@ -9,6 +9,7 @@ Mapping
    gaussian_grid_map/gaussian_grid_map
    ray_casting_grid_map/ray_casting_grid_map
    lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial
+   point_cloud_sampling/point_cloud_sampling
    k_means_object_clustering/k_means_object_clustering
    circle_fitting/circle_fitting
    rectangle_fitting/rectangle_fitting
diff --git a/docs/modules/mapping/point_cloud_sampling/farthest_point_sampling.png b/docs/modules/mapping/point_cloud_sampling/farthest_point_sampling.png
new file mode 100644
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diff --git a/docs/modules/mapping/point_cloud_sampling/point_cloud_sampling_main.rst b/docs/modules/mapping/point_cloud_sampling/point_cloud_sampling_main.rst
new file mode 100644
index 0000000000..cbb5652f56
--- /dev/null
+++ b/docs/modules/mapping/point_cloud_sampling/point_cloud_sampling_main.rst
@@ -0,0 +1,70 @@
+.. _point_cloud_sampling:
+
+Point cloud Sampling
+----------------------
+
+This sections explains point cloud sampling algorithms in PythonRobotics.
+
+Point clouds are two-dimensional and three-dimensional based data
+acquired by external sensors like LIDAR, cameras, etc.
+In general, Point Cloud data is very large in number of data.
+So, if you process all the data, computation time might become an issue.
+
+Point cloud sampling is a technique for solving this computational complexity
+issue by extracting only representative point data and thinning the point
+cloud data without compromising the performance of processing using the point
+cloud data.
+
+Voxel Point Sampling
+~~~~~~~~~~~~~~~~~~~~~~~~
+.. figure:: voxel_point_sampling.png
+
+Voxel grid sampling is a method of reducing point cloud data by using the
+`Voxel grids <https://en.wikipedia.org/wiki/Voxel>`_ which is regular grids
+in three-dimensional space.
+
+This method determines which each point is in a grid, and replaces the point
+clouds that are in the same Voxel with their average to reduce the number of
+points.
+
+API
+=====
+
+.. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.voxel_point_sampling
+
+
+Farthest Point Sampling
+~~~~~~~~~~~~~~~~~~~~~~~~~
+.. figure:: farthest_point_sampling.png
+
+Farthest Point Sampling is a point cloud sampling method by a specified
+number of points so that the distance between points is as far from as
+possible.
+
+This method is useful for machine learning and other situations where
+you want to obtain a specified number of points from point cloud.
+
+API
+=====
+
+.. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.farthest_point_sampling
+
+Poisson Disk Sampling
+~~~~~~~~~~~~~~~~~~~~~~~~~
+.. figure:: poisson_disk_sampling.png
+
+Poisson disk sample is a point cloud sampling method by a specified
+number of points so that the algorithm selects points where the distance
+from selected points is greater than a certain distance.
+
+Although this method does not have good performance comparing the Farthest
+distance sample where each point is distributed farther from each other,
+this is suitable for real-time processing because of its fast computation time.
+
+API
+=====
+
+.. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.poisson_disk_sampling
+
+
+
diff --git a/docs/modules/mapping/point_cloud_sampling/poisson_disk_sampling.png b/docs/modules/mapping/point_cloud_sampling/poisson_disk_sampling.png
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QvA~aloRVyz^y_#32jv8rPXGV_

literal 0
HcmV?d00001

diff --git a/tests/test_point_cloud_sampling.py b/tests/test_point_cloud_sampling.py
new file mode 100644
index 0000000000..8f6447c69c
--- /dev/null
+++ b/tests/test_point_cloud_sampling.py
@@ -0,0 +1,15 @@
+import conftest  # Add root path to sys.path
+from Mapping.point_cloud_sampling import point_cloud_sampling as m
+
+
+def test_1(capsys):
+    m.do_plot = False
+    m.main()
+    captured = capsys.readouterr()
+    assert "voxel_sampling_points.shape=(27, 3)" in captured.out
+    assert "farthest_sampling_points.shape=(20, 3)" in captured.out
+    assert "poisson_disk_points.shape=(20, 3)" in captured.out
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)

From ad4067df891b2d65c22f4cbf75cc0d16f5584612 Mon Sep 17 00:00:00 2001
From: U S A M A H <39458672+usamah1@users.noreply.github.com>
Date: Sun, 8 Jan 2023 13:04:51 +0000
Subject: [PATCH 0684/1080] Change 'Path is find!' to 'Path found!' (#773)

Minor - correct grammatical error :)
---
 PathPlanning/AStar/a_star_searching_from_two_side.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/AStar/a_star_searching_from_two_side.py b/PathPlanning/AStar/a_star_searching_from_two_side.py
index 0318970cc8..822f5ea500 100644
--- a/PathPlanning/AStar/a_star_searching_from_two_side.py
+++ b/PathPlanning/AStar/a_star_searching_from_two_side.py
@@ -267,7 +267,7 @@ def draw_control(org_closed, goal_closed, flag, start, end, bound, obstacle):
         if node_intersect:  # a path is find
             path = get_path(org_closed, goal_closed, node_intersect[0])
             stop_loop = 1
-            print('Path is find!')
+            print('Path found!')
             if show_animation:  # draw the path
                 plt.plot(path[:, 0], path[:, 1], '-r')
                 plt.title('Robot Arrived', size=20, loc='center')

From 98d5851c4da12bdb7c500682e32cfb5277adc782 Mon Sep 17 00:00:00 2001
From: Kadir Haspalamutgil <31936408+kadirhas@users.noreply.github.com>
Date: Sun, 8 Jan 2023 15:46:46 +0100
Subject: [PATCH 0685/1080] Utilize numpy to reduce calculation time (#767)

* Utilize numpy to reduce calculation time

Change-Id: I6e421a1c2524a3d8f8875121a1a6d2ed832c3150

* styling updates to follow flake8

Change-Id: I909d49b40e6792efcb796846c97a0d984471d3c9

* improves readabilty for d_star_lite

Change-Id: Ic329c2140f2200b49feeb03c588d7a805b96cbdc

* removes self.detected_obstacles

Change-Id: I4a0f2a424f17f44de3b444cb9c2e8e715d761d34

* styling for flake8

Change-Id: Id55c5972cbe76a2f6a69527dbf770f9bdf059109

Co-authored-by: Kadir Haspalamutgil <kadir.haspalamutgil@togethertech.com>
---
 PathPlanning/DStarLite/d_star_lite.py | 95 +++++++++++++++++----------
 1 file changed, 62 insertions(+), 33 deletions(-)

diff --git a/PathPlanning/DStarLite/d_star_lite.py b/PathPlanning/DStarLite/d_star_lite.py
index c250e38f91..86e777d7b7 100644
--- a/PathPlanning/DStarLite/d_star_lite.py
+++ b/PathPlanning/DStarLite/d_star_lite.py
@@ -12,6 +12,7 @@
 import math
 import matplotlib.pyplot as plt
 import random
+import numpy as np
 
 show_animation = True
 pause_time = 0.001
@@ -62,32 +63,40 @@ def __init__(self, ox: list, oy: list):
         self.y_max = int(abs(max(oy) - self.y_min_world))
         self.obstacles = [Node(x - self.x_min_world, y - self.y_min_world)
                           for x, y in zip(ox, oy)]
+        self.obstacles_xy = np.array(
+            [[obstacle.x, obstacle.y] for obstacle in self.obstacles]
+        )
         self.start = Node(0, 0)
         self.goal = Node(0, 0)
         self.U = list()  # type: ignore
         self.km = 0.0
         self.kold = 0.0
-        self.rhs = list()  # type: ignore
-        self.g = list()  # type: ignore
-        self.detected_obstacles = list()  # type: ignore
+        self.rhs = self.create_grid(float("inf"))
+        self.g = self.create_grid(float("inf"))
+        self.detected_obstacles_xy = np.empty((0, 2))
+        self.xy = np.empty((0, 2))
         if show_animation:
             self.detected_obstacles_for_plotting_x = list()  # type: ignore
             self.detected_obstacles_for_plotting_y = list()  # type: ignore
+        self.initialized = False
 
     def create_grid(self, val: float):
-        grid = list()
-        for _ in range(0, self.x_max):
-            grid_row = list()
-            for _ in range(0, self.y_max):
-                grid_row.append(val)
-            grid.append(grid_row)
-        return grid
+        return np.full((self.x_max, self.y_max), val)
 
     def is_obstacle(self, node: Node):
-        return any([compare_coordinates(node, obstacle)
-                    for obstacle in self.obstacles]) or \
-               any([compare_coordinates(node, obstacle)
-                    for obstacle in self.detected_obstacles])
+        x = np.array([node.x])
+        y = np.array([node.y])
+        obstacle_x_equal = self.obstacles_xy[:, 0] == x
+        obstacle_y_equal = self.obstacles_xy[:, 1] == y
+        is_in_obstacles = (obstacle_x_equal & obstacle_y_equal).any()
+
+        is_in_detected_obstacles = False
+        if self.detected_obstacles_xy.shape[0] > 0:
+            is_x_equal = self.detected_obstacles_xy[:, 0] == x
+            is_y_equal = self.detected_obstacles_xy[:, 1] == y
+            is_in_detected_obstacles = (is_x_equal & is_y_equal).any()
+
+        return is_in_obstacles or is_in_detected_obstacles
 
     def c(self, node1: Node, node2: Node):
         if self.is_obstacle(node2):
@@ -139,13 +148,16 @@ def initialize(self, start: Node, goal: Node):
         self.start.y = start.y - self.y_min_world
         self.goal.x = goal.x - self.x_min_world
         self.goal.y = goal.y - self.y_min_world
-        self.U = list()  # Would normally be a priority queue
-        self.km = 0.0
-        self.rhs = self.create_grid(math.inf)
-        self.g = self.create_grid(math.inf)
-        self.rhs[self.goal.x][self.goal.y] = 0
-        self.U.append((self.goal, self.calculate_key(self.goal)))
-        self.detected_obstacles = list()
+        if not self.initialized:
+            self.initialized = True
+            print('Initializing')
+            self.U = list()  # Would normally be a priority queue
+            self.km = 0.0
+            self.rhs = self.create_grid(math.inf)
+            self.g = self.create_grid(math.inf)
+            self.rhs[self.goal.x][self.goal.y] = 0
+            self.U.append((self.goal, self.calculate_key(self.goal)))
+            self.detected_obstacles_xy = np.empty((0, 2))
 
     def update_vertex(self, u: Node):
         if not compare_coordinates(u, self.goal):
@@ -167,26 +179,33 @@ def compare_keys(self, key_pair1: tuple[float, float],
 
     def compute_shortest_path(self):
         self.U.sort(key=lambda x: x[1])
-        while (len(self.U) > 0 and
-               self.compare_keys(self.U[0][1],
-                                 self.calculate_key(self.start))) or \
-                self.rhs[self.start.x][self.start.y] != \
-                self.g[self.start.x][self.start.y]:
+        has_elements = len(self.U) > 0
+        start_key_not_updated = self.compare_keys(
+            self.U[0][1], self.calculate_key(self.start)
+        )
+        rhs_not_equal_to_g = self.rhs[self.start.x][self.start.y] != \
+            self.g[self.start.x][self.start.y]
+        while has_elements and start_key_not_updated or rhs_not_equal_to_g:
             self.kold = self.U[0][1]
             u = self.U[0][0]
             self.U.pop(0)
             if self.compare_keys(self.kold, self.calculate_key(u)):
                 self.U.append((u, self.calculate_key(u)))
                 self.U.sort(key=lambda x: x[1])
-            elif self.g[u.x][u.y] > self.rhs[u.x][u.y]:
-                self.g[u.x][u.y] = self.rhs[u.x][u.y]
+            elif (self.g[u.x, u.y] > self.rhs[u.x, u.y]).any():
+                self.g[u.x, u.y] = self.rhs[u.x, u.y]
                 for s in self.pred(u):
                     self.update_vertex(s)
             else:
-                self.g[u.x][u.y] = math.inf
+                self.g[u.x, u.y] = math.inf
                 for s in self.pred(u) + [u]:
                     self.update_vertex(s)
             self.U.sort(key=lambda x: x[1])
+            start_key_not_updated = self.compare_keys(
+                self.U[0][1], self.calculate_key(self.start)
+            )
+            rhs_not_equal_to_g = self.rhs[self.start.x][self.start.y] != \
+                self.g[self.start.x][self.start.y]
 
     def detect_changes(self):
         changed_vertices = list()
@@ -196,7 +215,12 @@ def detect_changes(self):
                    compare_coordinates(spoofed_obstacle, self.goal):
                     continue
                 changed_vertices.append(spoofed_obstacle)
-                self.detected_obstacles.append(spoofed_obstacle)
+                self.detected_obstacles_xy = np.concatenate(
+                    (
+                        self.detected_obstacles_xy,
+                        [[spoofed_obstacle.x, spoofed_obstacle.y]]
+                    )
+                )
                 if show_animation:
                     self.detected_obstacles_for_plotting_x.append(
                         spoofed_obstacle.x + self.x_min_world)
@@ -210,14 +234,19 @@ def detect_changes(self):
         # Allows random generation of obstacles
         random.seed()
         if random.random() > 1 - p_create_random_obstacle:
-            x = random.randint(0, self.x_max)
-            y = random.randint(0, self.y_max)
+            x = random.randint(0, self.x_max - 1)
+            y = random.randint(0, self.y_max - 1)
             new_obs = Node(x, y)
             if compare_coordinates(new_obs, self.start) or \
                compare_coordinates(new_obs, self.goal):
                 return changed_vertices
             changed_vertices.append(Node(x, y))
-            self.detected_obstacles.append(Node(x, y))
+            self.detected_obstacles_xy = np.concatenate(
+                (
+                    self.detected_obstacles_xy,
+                    [[x, y]]
+                )
+            )
             if show_animation:
                 self.detected_obstacles_for_plotting_x.append(x +
                                                               self.x_min_world)

From 0f50d84ce6493ac192de9a743f6b51485dff0bd5 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 10 Jan 2023 07:04:10 +0900
Subject: [PATCH 0686/1080] Bump scipy from 1.9.3 to 1.10.0 in /requirements
 (#775)

Bumps [scipy](https://github.com/scipy/scipy) from 1.9.3 to 1.10.0.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.9.3...v1.10.0)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 9a990856f1..b613447ba7 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.24.1
-scipy == 1.9.3
+scipy == 1.10.0
 matplotlib == 3.6.2
 cvxpy == 1.2.3
 pytest == 7.2.0 # For unit test

From c8b0698a88a7b72f7d3e0725136acb30f514ed52 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 10 Jan 2023 07:05:36 +0900
Subject: [PATCH 0687/1080] Bump cvxpy from 1.2.3 to 1.3.0 in /requirements
 (#776)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.2.3 to 1.3.0.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.2.3...v1.3.0)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index b613447ba7..3f44e47f21 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 1.24.1
 scipy == 1.10.0
 matplotlib == 3.6.2
-cvxpy == 1.2.3
+cvxpy == 1.3.0
 pytest == 7.2.0 # For unit test
 pytest-xdist == 3.1.0 # For unit test
 mypy == 0.991 # For unit test

From 5a16a113c703c9e9a88daf1c681ececb67f7f95c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 17 Jan 2023 07:28:31 +0900
Subject: [PATCH 0688/1080] Bump matplotlib from 3.6.2 to 3.6.3 in
 /requirements (#778)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.6.2 to 3.6.3.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.6.2...v3.6.3)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 3f44e47f21..a2b9663b2d 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.24.1
 scipy == 1.10.0
-matplotlib == 3.6.2
+matplotlib == 3.6.3
 cvxpy == 1.3.0
 pytest == 7.2.0 # For unit test
 pytest-xdist == 3.1.0 # For unit test

From 15ab19688b2f6c03ee91a853f1f8cc9def84d162 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 17 Jan 2023 07:28:49 +0900
Subject: [PATCH 0689/1080] Bump pytest from 7.2.0 to 7.2.1 in /requirements
 (#779)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.2.0 to 7.2.1.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/7.2.0...7.2.1)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index a2b9663b2d..944c1ce217 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.24.1
 scipy == 1.10.0
 matplotlib == 3.6.3
 cvxpy == 1.3.0
-pytest == 7.2.0 # For unit test
+pytest == 7.2.1 # For unit test
 pytest-xdist == 3.1.0 # For unit test
 mypy == 0.991 # For unit test
 flake8 == 5.0.4 # For unit test

From 489ee5c0e367a64f19b6cf3875ea6282e78bf889 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 26 Jan 2023 21:56:42 +0900
Subject: [PATCH 0690/1080] fix github scanning alerts (#784)

---
 .../two_joint_arm_to_point_control.py                       | 2 +-
 Bipedal/bipedal_planner/bipedal_planner.py                  | 1 -
 PathPlanning/HybridAStar/dynamic_programming_heuristic.py   | 2 +-
 PathPlanning/InformedRRTStar/informed_rrt_star.py           | 5 ++---
 PathPlanning/LQRPlanner/lqr_planner.py                      | 2 +-
 PathPlanning/LQRRRTStar/lqr_rrt_star.py                     | 2 +-
 PathPlanning/RRT/rrt_with_pathsmoothing.py                  | 6 +++---
 PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py    | 6 +++---
 8 files changed, 12 insertions(+), 14 deletions(-)

diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
index 9f835c8b32..012a7f72f1 100644
--- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
+++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
@@ -45,7 +45,7 @@ def two_joint_arm(GOAL_TH=0.0, theta1=0.0, theta2=0.0):
             if x is not None and y is not None:
                 x_prev = x
                 y_prev = y
-            if np.sqrt(x**2 + y**2) > (l1 + l2):
+            if np.hypot(x, y) > (l1 + l2):
                 theta2_goal = 0
             else:
                 theta2_goal = np.arccos(
diff --git a/Bipedal/bipedal_planner/bipedal_planner.py b/Bipedal/bipedal_planner/bipedal_planner.py
index 8ca5ffcf78..c34357df67 100644
--- a/Bipedal/bipedal_planner/bipedal_planner.py
+++ b/Bipedal/bipedal_planner/bipedal_planner.py
@@ -47,7 +47,6 @@ def walk(self, t_sup=0.8, z_c=0.8, a=10, b=1, plot=False):
             return
 
         # set up plotter
-        com_trajectory_for_plot, ax = None, None
         if plot:
             fig = plt.figure()
             ax = Axes3D(fig)
diff --git a/PathPlanning/HybridAStar/dynamic_programming_heuristic.py b/PathPlanning/HybridAStar/dynamic_programming_heuristic.py
index 8a78b15d5f..09bcd556a6 100644
--- a/PathPlanning/HybridAStar/dynamic_programming_heuristic.py
+++ b/PathPlanning/HybridAStar/dynamic_programming_heuristic.py
@@ -150,7 +150,7 @@ def calc_obstacle_map(ox, oy, resolution, vr):
             y = iy + min_y
             #  print(x, y)
             for iox, ioy in zip(ox, oy):
-                d = math.sqrt((iox - x) ** 2 + (ioy - y) ** 2)
+                d = math.hypot(iox - x, ioy - y)
                 if d <= vr / resolution:
                     obstacle_map[ix][iy] = True
                     break
diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index 25c50a0cd2..289e33fe4b 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -192,7 +192,7 @@ def get_path_len(path):
 
     @staticmethod
     def line_cost(node1, node2):
-        return math.sqrt((node1.x - node2.x) ** 2 + (node1.y - node2.y) ** 2)
+        return math.hypot(node1.x - node2.x, node1.y - node2.y)
 
     @staticmethod
     def get_nearest_list_index(nodes, rnd):
@@ -222,8 +222,7 @@ def rewire(self, newNode, nearInds):
         for i in nearInds:
             nearNode = self.node_list[i]
 
-            d = math.sqrt((nearNode.x - newNode.x) ** 2
-                          + (nearNode.y - newNode.y) ** 2)
+            d = math.hypot(nearNode.x - newNode.x, nearNode.y - newNode.y)
 
             s_cost = newNode.cost + d
 
diff --git a/PathPlanning/LQRPlanner/lqr_planner.py b/PathPlanning/LQRPlanner/lqr_planner.py
index ba01526a2c..0f58f93ea3 100644
--- a/PathPlanning/LQRPlanner/lqr_planner.py
+++ b/PathPlanning/LQRPlanner/lqr_planner.py
@@ -47,7 +47,7 @@ def lqr_planning(self, sx, sy, gx, gy, show_animation=True):
             rx.append(x[0, 0] + gx)
             ry.append(x[1, 0] + gy)
 
-            d = math.sqrt((gx - rx[-1]) ** 2 + (gy - ry[-1]) ** 2)
+            d = math.hypot(gx - rx[-1], gy - ry[-1])
             if d <= self.GOAL_DIST:
                 found_path = True
                 break
diff --git a/PathPlanning/LQRRRTStar/lqr_rrt_star.py b/PathPlanning/LQRRRTStar/lqr_rrt_star.py
index e3f18a722a..0ed08123ea 100644
--- a/PathPlanning/LQRRRTStar/lqr_rrt_star.py
+++ b/PathPlanning/LQRRRTStar/lqr_rrt_star.py
@@ -179,7 +179,7 @@ def sample_path(self, wx, wy, step):
         dx = np.diff(px)
         dy = np.diff(py)
 
-        clen = [math.sqrt(idx ** 2 + idy ** 2) for (idx, idy) in zip(dx, dy)]
+        clen = [math.hypot(idx, idy) for (idx, idy) in zip(dx, dy)]
 
         return px, py, clen
 
diff --git a/PathPlanning/RRT/rrt_with_pathsmoothing.py b/PathPlanning/RRT/rrt_with_pathsmoothing.py
index 616c73a697..2ed6a366d1 100644
--- a/PathPlanning/RRT/rrt_with_pathsmoothing.py
+++ b/PathPlanning/RRT/rrt_with_pathsmoothing.py
@@ -23,7 +23,7 @@ def get_path_length(path):
     for i in range(len(path) - 1):
         dx = path[i + 1][0] - path[i][0]
         dy = path[i + 1][1] - path[i][1]
-        d = math.sqrt(dx * dx + dy * dy)
+        d = math.hypot(dx, dy)
         le += d
 
     return le
@@ -36,7 +36,7 @@ def get_target_point(path, targetL):
     for i in range(len(path) - 1):
         dx = path[i + 1][0] - path[i][0]
         dy = path[i + 1][1] - path[i][1]
-        d = math.sqrt(dx * dx + dy * dy)
+        d = math.hypot(dx, dy)
         le += d
         if le >= targetL:
             ti = i - 1
@@ -67,7 +67,7 @@ def line_collision_check(first, second, obstacleList):
         return False
 
     for (ox, oy, size) in obstacleList:
-        d = abs(a * ox + b * oy + c) / (math.sqrt(a * a + b * b))
+        d = abs(a * ox + b * oy + c) / (math.hypot(a, b))
         if d <= size:
             return False
 
diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
index 5bc04c8ee3..1577d048d3 100644
--- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
+++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
@@ -57,13 +57,13 @@ def straight_left_straight(x, y, phi):
         xd = - y / math.tan(phi) + x
         t = xd - math.tan(phi / 2.0)
         u = phi
-        v = math.sqrt((x - xd) ** 2 + y ** 2) - math.tan(phi / 2.0)
+        v = math.hypot(x - xd, y) - math.tan(phi / 2.0)
         return True, t, u, v
     elif y < 0.0 < phi < math.pi * 0.99:
         xd = - y / math.tan(phi) + x
         t = xd - math.tan(phi / 2.0)
         u = phi
-        v = -math.sqrt((x - xd) ** 2 + y ** 2) - math.tan(phi / 2.0)
+        v = -math.hypot(x - xd, y) - math.tan(phi / 2.0)
         return True, t, u, v
 
     return False, 0.0, 0.0, 0.0
@@ -103,7 +103,7 @@ def straight_curve_straight(x, y, phi, paths, step_size):
 
 
 def polar(x, y):
-    r = math.sqrt(x ** 2 + y ** 2)
+    r = math.hypot(x, y)
     theta = math.atan2(y, x)
     return r, theta
 

From 732ed3ea6be5856b10727aac0e0f3724dad02d5a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 26 Jan 2023 22:46:00 +0900
Subject: [PATCH 0691/1080] clean up informed_rrt_star.py (#785)

* clean up informed_rrt_star.py

* clean up informed_rrt_star.py
---
 .../InformedRRTStar/informed_rrt_star.py      | 261 +++++++++---------
 1 file changed, 126 insertions(+), 135 deletions(-)

diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index 289e33fe4b..4f0ec31988 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -11,6 +11,7 @@
 """
 import sys
 import pathlib
+
 sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import copy
@@ -27,18 +28,17 @@
 
 class InformedRRTStar:
 
-    def __init__(self, start, goal,
-                 obstacleList, randArea,
-                 expandDis=0.5, goalSampleRate=10, maxIter=200):
+    def __init__(self, start, goal, obstacle_list, rand_area, expand_dis=0.5,
+                 goal_sample_rate=10, max_iter=200):
 
         self.start = Node(start[0], start[1])
         self.goal = Node(goal[0], goal[1])
-        self.min_rand = randArea[0]
-        self.max_rand = randArea[1]
-        self.expand_dis = expandDis
-        self.goal_sample_rate = goalSampleRate
-        self.max_iter = maxIter
-        self.obstacle_list = obstacleList
+        self.min_rand = rand_area[0]
+        self.max_rand = rand_area[1]
+        self.expand_dis = expand_dis
+        self.goal_sample_rate = goal_sample_rate
+        self.max_iter = max_iter
+        self.obstacle_list = obstacle_list
         self.node_list = None
 
     def informed_rrt_star_search(self, animation=True):
@@ -46,110 +46,109 @@ def informed_rrt_star_search(self, animation=True):
         self.node_list = [self.start]
         # max length we expect to find in our 'informed' sample space,
         # starts as infinite
-        cBest = float('inf')
-        solutionSet = set()
+        c_best = float('inf')
+        solution_set = set()
         path = None
 
         # Computing the sampling space
-        cMin = math.sqrt(pow(self.start.x - self.goal.x, 2)
-                         + pow(self.start.y - self.goal.y, 2))
-        xCenter = np.array([[(self.start.x + self.goal.x) / 2.0],
-                            [(self.start.y + self.goal.y) / 2.0], [0]])
-        a1 = np.array([[(self.goal.x - self.start.x) / cMin],
-                       [(self.goal.y - self.start.y) / cMin], [0]])
+        c_min = math.hypot(self.start.x - self.goal.x,
+                           self.start.y - self.goal.y)
+        x_center = np.array([[(self.start.x + self.goal.x) / 2.0],
+                             [(self.start.y + self.goal.y) / 2.0], [0]])
+        a1 = np.array([[(self.goal.x - self.start.x) / c_min],
+                       [(self.goal.y - self.start.y) / c_min], [0]])
 
         e_theta = math.atan2(a1[1], a1[0])
         # first column of identity matrix transposed
         id1_t = np.array([1.0, 0.0, 0.0]).reshape(1, 3)
-        M = a1 @ id1_t
-        U, S, Vh = np.linalg.svd(M, True, True)
-        C = np.dot(np.dot(U, np.diag(
-            [1.0, 1.0, np.linalg.det(U) * np.linalg.det(np.transpose(Vh))])),
-                   Vh)
+        m = a1 @ id1_t
+        u, s, vh = np.linalg.svd(m, True, True)
+        c = u @ np.diag(
+            [1.0, 1.0,
+             np.linalg.det(u) * np.linalg.det(np.transpose(vh))]) @ vh
 
         for i in range(self.max_iter):
-            # Sample space is defined by cBest
-            # cMin is the minimum distance between the start point and the goal
-            # xCenter is the midpoint between the start and the goal
-            # cBest changes when a new path is found
+            # Sample space is defined by c_best
+            # c_min is the minimum distance between the start point and
+            # the goal x_center is the midpoint between the start and the
+            # goal c_best changes when a new path is found
 
-            rnd = self.informed_sample(cBest, cMin, xCenter, C)
+            rnd = self.informed_sample(c_best, c_min, x_center, c)
             n_ind = self.get_nearest_list_index(self.node_list, rnd)
-            nearestNode = self.node_list[n_ind]
+            nearest_node = self.node_list[n_ind]
             # steer
-            theta = math.atan2(rnd[1] - nearestNode.y, rnd[0] - nearestNode.x)
-            newNode = self.get_new_node(theta, n_ind, nearestNode)
-            d = self.line_cost(nearestNode, newNode)
+            theta = math.atan2(rnd[1] - nearest_node.y,
+                               rnd[0] - nearest_node.x)
+            new_node = self.get_new_node(theta, n_ind, nearest_node)
+            d = self.line_cost(nearest_node, new_node)
 
-            noCollision = self.check_collision(nearestNode, theta, d)
+            no_collision = self.check_collision(nearest_node, theta, d)
 
-            if noCollision:
-                nearInds = self.find_near_nodes(newNode)
-                newNode = self.choose_parent(newNode, nearInds)
+            if no_collision:
+                near_inds = self.find_near_nodes(new_node)
+                new_node = self.choose_parent(new_node, near_inds)
 
-                self.node_list.append(newNode)
-                self.rewire(newNode, nearInds)
+                self.node_list.append(new_node)
+                self.rewire(new_node, near_inds)
 
-                if self.is_near_goal(newNode):
-                    if self.check_segment_collision(newNode.x, newNode.y,
+                if self.is_near_goal(new_node):
+                    if self.check_segment_collision(new_node.x, new_node.y,
                                                     self.goal.x, self.goal.y):
-                        solutionSet.add(newNode)
-                        lastIndex = len(self.node_list) - 1
-                        tempPath = self.get_final_course(lastIndex)
-                        tempPathLen = self.get_path_len(tempPath)
-                        if tempPathLen < cBest:
-                            path = tempPath
-                            cBest = tempPathLen
+                        solution_set.add(new_node)
+                        last_index = len(self.node_list) - 1
+                        temp_path = self.get_final_course(last_index)
+                        temp_path_len = self.get_path_len(temp_path)
+                        if temp_path_len < c_best:
+                            path = temp_path
+                            c_best = temp_path_len
             if animation:
-                self.draw_graph(xCenter=xCenter,
-                                cBest=cBest, cMin=cMin,
+                self.draw_graph(x_center=x_center, c_best=c_best, c_min=c_min,
                                 e_theta=e_theta, rnd=rnd)
 
         return path
 
-    def choose_parent(self, newNode, nearInds):
-        if len(nearInds) == 0:
-            return newNode
+    def choose_parent(self, new_node, near_inds):
+        if len(near_inds) == 0:
+            return new_node
 
-        dList = []
-        for i in nearInds:
-            dx = newNode.x - self.node_list[i].x
-            dy = newNode.y - self.node_list[i].y
+        d_list = []
+        for i in near_inds:
+            dx = new_node.x - self.node_list[i].x
+            dy = new_node.y - self.node_list[i].y
             d = math.hypot(dx, dy)
             theta = math.atan2(dy, dx)
             if self.check_collision(self.node_list[i], theta, d):
-                dList.append(self.node_list[i].cost + d)
+                d_list.append(self.node_list[i].cost + d)
             else:
-                dList.append(float('inf'))
+                d_list.append(float('inf'))
 
-        minCost = min(dList)
-        minInd = nearInds[dList.index(minCost)]
+        min_cost = min(d_list)
+        min_ind = near_inds[d_list.index(min_cost)]
 
-        if minCost == float('inf'):
+        if min_cost == float('inf'):
             print("min cost is inf")
-            return newNode
+            return new_node
 
-        newNode.cost = minCost
-        newNode.parent = minInd
+        new_node.cost = min_cost
+        new_node.parent = min_ind
 
-        return newNode
+        return new_node
 
-    def find_near_nodes(self, newNode):
+    def find_near_nodes(self, new_node):
         n_node = len(self.node_list)
         r = 50.0 * math.sqrt((math.log(n_node) / n_node))
-        d_list = [(node.x - newNode.x) ** 2 + (node.y - newNode.y) ** 2
-                  for node in self.node_list]
+        d_list = [(node.x - new_node.x) ** 2 + (node.y - new_node.y) ** 2 for
+                  node in self.node_list]
         near_inds = [d_list.index(i) for i in d_list if i <= r ** 2]
         return near_inds
 
-    def informed_sample(self, cMax, cMin, xCenter, C):
-        if cMax < float('inf'):
-            r = [cMax / 2.0,
-                 math.sqrt(cMax ** 2 - cMin ** 2) / 2.0,
-                 math.sqrt(cMax ** 2 - cMin ** 2) / 2.0]
-            L = np.diag(r)
-            xBall = self.sample_unit_ball()
-            rnd = np.dot(np.dot(C, L), xBall) + xCenter
+    def informed_sample(self, c_max, c_min, x_center, c):
+        if c_max < float('inf'):
+            r = [c_max / 2.0, math.sqrt(c_max ** 2 - c_min ** 2) / 2.0,
+                 math.sqrt(c_max ** 2 - c_min ** 2) / 2.0]
+            rl = np.diag(r)
+            x_ball = self.sample_unit_ball()
+            rnd = np.dot(np.dot(c, rl), x_ball) + x_center
             rnd = [rnd[(0, 0)], rnd[(1, 0)]]
         else:
             rnd = self.sample_free_space()
@@ -179,16 +178,15 @@ def sample_free_space(self):
 
     @staticmethod
     def get_path_len(path):
-        pathLen = 0
+        path_len = 0
         for i in range(1, len(path)):
             node1_x = path[i][0]
             node1_y = path[i][1]
             node2_x = path[i - 1][0]
             node2_y = path[i - 1][1]
-            pathLen += math.sqrt((node1_x - node2_x)
-                                 ** 2 + (node1_y - node2_y) ** 2)
+            path_len += math.hypot(node1_x - node2_x, node1_y - node2_y)
 
-        return pathLen
+        return path_len
 
     @staticmethod
     def line_cost(node1, node2):
@@ -196,20 +194,20 @@ def line_cost(node1, node2):
 
     @staticmethod
     def get_nearest_list_index(nodes, rnd):
-        dList = [(node.x - rnd[0]) ** 2
-                 + (node.y - rnd[1]) ** 2 for node in nodes]
-        minIndex = dList.index(min(dList))
-        return minIndex
+        d_list = [(node.x - rnd[0]) ** 2 + (node.y - rnd[1]) ** 2 for node in
+                  nodes]
+        min_index = d_list.index(min(d_list))
+        return min_index
 
-    def get_new_node(self, theta, n_ind, nearestNode):
-        newNode = copy.deepcopy(nearestNode)
+    def get_new_node(self, theta, n_ind, nearest_node):
+        new_node = copy.deepcopy(nearest_node)
 
-        newNode.x += self.expand_dis * math.cos(theta)
-        newNode.y += self.expand_dis * math.sin(theta)
+        new_node.x += self.expand_dis * math.cos(theta)
+        new_node.y += self.expand_dis * math.sin(theta)
 
-        newNode.cost += self.expand_dis
-        newNode.parent = n_ind
-        return newNode
+        new_node.cost += self.expand_dis
+        new_node.parent = n_ind
+        return new_node
 
     def is_near_goal(self, node):
         d = self.line_cost(node, self.goal)
@@ -217,21 +215,21 @@ def is_near_goal(self, node):
             return True
         return False
 
-    def rewire(self, newNode, nearInds):
+    def rewire(self, new_node, near_inds):
         n_node = len(self.node_list)
-        for i in nearInds:
-            nearNode = self.node_list[i]
+        for i in near_inds:
+            near_node = self.node_list[i]
 
-            d = math.hypot(nearNode.x - newNode.x, nearNode.y - newNode.y)
+            d = math.hypot(near_node.x - new_node.x, near_node.y - new_node.y)
 
-            s_cost = newNode.cost + d
+            s_cost = new_node.cost + d
 
-            if nearNode.cost > s_cost:
-                theta = math.atan2(newNode.y - nearNode.y,
-                                   newNode.x - nearNode.x)
-                if self.check_collision(nearNode, theta, d):
-                    nearNode.parent = n_node - 1
-                    nearNode.cost = s_cost
+            if near_node.cost > s_cost:
+                theta = math.atan2(new_node.y - near_node.y,
+                                   new_node.x - near_node.x)
+                if self.check_collision(near_node, theta, d):
+                    near_node.parent = n_node - 1
+                    near_node.cost = s_cost
 
     @staticmethod
     def distance_squared_point_to_segment(v, w, p):
@@ -251,45 +249,44 @@ def distance_squared_point_to_segment(v, w, p):
     def check_segment_collision(self, x1, y1, x2, y2):
         for (ox, oy, size) in self.obstacle_list:
             dd = self.distance_squared_point_to_segment(
-                np.array([x1, y1]),
-                np.array([x2, y2]),
-                np.array([ox, oy]))
+                np.array([x1, y1]), np.array([x2, y2]), np.array([ox, oy]))
             if dd <= size ** 2:
                 return False  # collision
         return True
 
-    def check_collision(self, nearNode, theta, d):
-        tmpNode = copy.deepcopy(nearNode)
-        end_x = tmpNode.x + math.cos(theta) * d
-        end_y = tmpNode.y + math.sin(theta) * d
-        return self.check_segment_collision(tmpNode.x, tmpNode.y, end_x, end_y)
+    def check_collision(self, near_node, theta, d):
+        tmp_node = copy.deepcopy(near_node)
+        end_x = tmp_node.x + math.cos(theta) * d
+        end_y = tmp_node.y + math.sin(theta) * d
+        return self.check_segment_collision(tmp_node.x, tmp_node.y,
+                                            end_x, end_y)
 
-    def get_final_course(self, lastIndex):
+    def get_final_course(self, last_index):
         path = [[self.goal.x, self.goal.y]]
-        while self.node_list[lastIndex].parent is not None:
-            node = self.node_list[lastIndex]
+        while self.node_list[last_index].parent is not None:
+            node = self.node_list[last_index]
             path.append([node.x, node.y])
-            lastIndex = node.parent
+            last_index = node.parent
         path.append([self.start.x, self.start.y])
         return path
 
-    def draw_graph(self, xCenter=None, cBest=None, cMin=None, e_theta=None,
+    def draw_graph(self, x_center=None, c_best=None, c_min=None, e_theta=None,
                    rnd=None):
         plt.clf()
         # for stopping simulation with the esc key.
         plt.gcf().canvas.mpl_connect(
-            'key_release_event',
-            lambda event: [exit(0) if event.key == 'escape' else None])
+            'key_release_event', lambda event:
+            [exit(0) if event.key == 'escape' else None])
         if rnd is not None:
             plt.plot(rnd[0], rnd[1], "^k")
-            if cBest != float('inf'):
-                self.plot_ellipse(xCenter, cBest, cMin, e_theta)
+            if c_best != float('inf'):
+                self.plot_ellipse(x_center, c_best, c_min, e_theta)
 
         for node in self.node_list:
             if node.parent is not None:
                 if node.x or node.y is not None:
-                    plt.plot([node.x, self.node_list[node.parent].x], [
-                        node.y, self.node_list[node.parent].y], "-g")
+                    plt.plot([node.x, self.node_list[node.parent].x],
+                             [node.y, self.node_list[node.parent].y], "-g")
 
         for (ox, oy, size) in self.obstacle_list:
             plt.plot(ox, oy, "ok", ms=30 * size)
@@ -301,13 +298,13 @@ def draw_graph(self, xCenter=None, cBest=None, cMin=None, e_theta=None,
         plt.pause(0.01)
 
     @staticmethod
-    def plot_ellipse(xCenter, cBest, cMin, e_theta):  # pragma: no cover
+    def plot_ellipse(x_center, c_best, c_min, e_theta):  # pragma: no cover
 
-        a = math.sqrt(cBest ** 2 - cMin ** 2) / 2.0
-        b = cBest / 2.0
+        a = math.sqrt(c_best ** 2 - c_min ** 2) / 2.0
+        b = c_best / 2.0
         angle = math.pi / 2.0 - e_theta
-        cx = xCenter[0]
-        cy = xCenter[1]
+        cx = x_center[0]
+        cy = x_center[1]
         t = np.arange(0, 2 * math.pi + 0.1, 0.1)
         x = [a * math.cos(it) for it in t]
         y = [b * math.sin(it) for it in t]
@@ -331,18 +328,12 @@ def main():
     print("Start informed rrt star planning")
 
     # create obstacles
-    obstacleList = [
-        (5, 5, 0.5),
-        (9, 6, 1),
-        (7, 5, 1),
-        (1, 5, 1),
-        (3, 6, 1),
-        (7, 9, 1)
-    ]
+    obstacle_list = [(5, 5, 0.5), (9, 6, 1), (7, 5, 1), (1, 5, 1), (3, 6, 1),
+                     (7, 9, 1)]
 
     # Set params
-    rrt = InformedRRTStar(start=[0, 0], goal=[5, 10],
-                          randArea=[-2, 15], obstacleList=obstacleList)
+    rrt = InformedRRTStar(start=[0, 0], goal=[5, 10], rand_area=[-2, 15],
+                          obstacle_list=obstacle_list)
     path = rrt.informed_rrt_star_search(animation=show_animation)
     print("Done!!")
 

From ffd8602e25d16f7801421310ca6892f08531042e Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 29 Jan 2023 22:22:11 +0900
Subject: [PATCH 0692/1080] support python3.11 (#741)

* support python3.11

* try to fix error

* try to fix error

* try to fix error

* try to fix error

* try to fix error

* try to fix error

* try to fix error

* try to fix error

* try to fix error

* add docs

* fix conflict

* fix conflict

* fix conflict

* using python3.10 for appveyors

* add windows CI

* roll back appveyor settings

* fix windows CI
---
 .circleci/config.yml             |  2 +-
 .github/workflows/Linux_CI.yml   |  2 +-
 .github/workflows/MacOS_CI.yml   |  2 +-
 .github/workflows/Windows_CI.yml | 36 ++++++++++++++++++++++++++++++++
 README.md                        |  2 +-
 docs/getting_started_main.rst    |  4 ++--
 requirements/environment.yml     |  2 +-
 7 files changed, 43 insertions(+), 7 deletions(-)
 create mode 100644 .github/workflows/Windows_CI.yml

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 075a613be0..d8f1e89dc4 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -6,7 +6,7 @@ orbs:
 jobs:
   build_doc:
     docker:
-      - image: cimg/python:3.10
+      - image: cimg/python:3.11
     steps:
       - checkout
       - run:
diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index 83c55b17f1..a7b5aba5fe 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ '3.10.6' ] # For mypy error Ref: https://github.com/python/mypy/issues/13627
+        python-version: [ '3.11' ]
 
     name: Python ${{ matrix.python-version }} CI
 
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index a3459f2560..6f0849a43d 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -16,7 +16,7 @@ jobs:
     runs-on: macos-latest
     strategy:
       matrix:
-        python-version: [ '3.10.6' ] # For mypy error Ref: https://github.com/python/mypy/issues/13627
+        python-version: [ '3.11' ]
     name: Python ${{ matrix.python-version }} CI
     steps:
       - uses: actions/checkout@v2
diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml
new file mode 100644
index 0000000000..ed267045ca
--- /dev/null
+++ b/.github/workflows/Windows_CI.yml
@@ -0,0 +1,36 @@
+# This is a basic workflow to help you get started with Actions
+
+name: Windows_CI
+
+# Controls when the action will run. Triggers the workflow on push or pull request
+# events but only for the master branch
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+
+
+jobs:
+  build:
+    runs-on: windows-latest
+    strategy:
+      matrix:
+        python-version: [ '3.11' ]
+    name: Python ${{ matrix.python-version }} CI
+    steps:
+      - uses: actions/checkout@v2
+      - run: git fetch --prune --unshallow
+
+      - name: Setup python
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: |
+          python --version
+          python -m pip install --upgrade pip
+          pip install -r requirements/requirements.txt
+      - name: do all unit tests
+        run: bash runtests.sh
\ No newline at end of file
diff --git a/README.md b/README.md
index b46195f35e..4ad19353e3 100644
--- a/README.md
+++ b/README.md
@@ -96,7 +96,7 @@ See this paper for more details:
 
 For running each sample code:
 
-- [Python 3.10.x](https://www.python.org/)
+- [Python 3.11.x](https://www.python.org/)
  
 - [NumPy](https://numpy.org/)
  
diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst
index c2be93261b..0e90d672b7 100644
--- a/docs/getting_started_main.rst
+++ b/docs/getting_started_main.rst
@@ -26,7 +26,7 @@ See this paper for more details:
 Requirements
 -------------
 
--  `Python 3.10.x`_
+-  `Python 3.11.x`_
 -  `NumPy`_
 -  `SciPy`_
 -  `Matplotlib`_
@@ -40,7 +40,7 @@ For development:
 -  sphinx (for document generation)
 -  pycodestyle (for code style check)
 
-.. _`Python 3.10.x`: https://www.python.org/
+.. _`Python 3.11.x`: https://www.python.org/
 .. _`NumPy`: https://numpy.org/
 .. _`SciPy`: https://scipy.org/
 .. _`Matplotlib`: https://matplotlib.org/
diff --git a/requirements/environment.yml b/requirements/environment.yml
index 202c2693d9..13dfa29f66 100644
--- a/requirements/environment.yml
+++ b/requirements/environment.yml
@@ -2,7 +2,7 @@ name: python_robotics
 channels:
   - conda-forge
 dependencies:
-  - python=3.10
+  - python=3.11
   - pip
   - scipy
   - numpy

From 15a91900608199e9bccdf68b04786b30f6772ad5 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 30 Jan 2023 21:33:49 +0900
Subject: [PATCH 0693/1080] using ruff instead of flake8 (#787)

* using ruff instead of flake8

* using ruff instead of flake8

* using ruff instead of flake8
---
 docs/conf.py                                  |  1 -
 docs/getting_started_main.rst                 | 15 ++++---
 requirements/requirements.txt                 |  2 +-
 ruff.toml                                     | 18 ++++++++
 ...st_diff_codestyle.py => test_codestyle.py} | 45 ++++++++++++++-----
 5 files changed, 62 insertions(+), 19 deletions(-)
 create mode 100644 ruff.toml
 rename tests/{test_diff_codestyle.py => test_codestyle.py} (78%)

diff --git a/docs/conf.py b/docs/conf.py
index 24b5518b6f..e7c64d07b2 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 #
 # Configuration file for the Sphinx documentation builder.
 #
diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst
index 0e90d672b7..497b85a23a 100644
--- a/docs/getting_started_main.rst
+++ b/docs/getting_started_main.rst
@@ -34,17 +34,22 @@ Requirements
 
 For development:
 
--  pytest (for unit tests)
--  pytest-xdist (for parallel unit tests)
--  mypy (for type check)
--  sphinx (for document generation)
--  pycodestyle (for code style check)
+-  `pytest`_ (for unit tests)
+-  `pytest-xdist`_ (for parallel unit tests)
+-  `mypy`_ (for type check)
+-  `sphinx`_ (for document generation)
+-  `ruff`_ (for code style check)
 
 .. _`Python 3.11.x`: https://www.python.org/
 .. _`NumPy`: https://numpy.org/
 .. _`SciPy`: https://scipy.org/
 .. _`Matplotlib`: https://matplotlib.org/
 .. _`cvxpy`: https://www.cvxpy.org/
+.. _`pytest`: https://docs.pytest.org/en/latest/
+.. _`pytest-xdist`: https://github.com/pytest-dev/pytest-xdist
+.. _`mypy`: https://mypy-lang.org/
+.. _`sphinx`: https://www.sphinx-doc.org/en/master/index.html
+.. _`ruff`: https://github.com/charliermarsh/ruff
 
 
 How to use
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 944c1ce217..a3a337a198 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.0
 pytest == 7.2.1 # For unit test
 pytest-xdist == 3.1.0 # For unit test
 mypy == 0.991 # For unit test
-flake8 == 5.0.4 # For unit test
+ruff == 0.0.237 # For unit test
diff --git a/ruff.toml b/ruff.toml
new file mode 100644
index 0000000000..fd02032f48
--- /dev/null
+++ b/ruff.toml
@@ -0,0 +1,18 @@
+line-length = 88
+
+select = ["F", "E", "W", "UP"]
+ignore = ["E501", "E741"]
+exclude = [
+]
+
+# Assume Python 3.11
+target-version = "py311"
+
+[per-file-ignores]
+
+[mccabe]
+# Unlike Flake8, default to a complexity level of 10.
+max-complexity = 10
+
+[pydocstyle]
+convention = "numpy"
\ No newline at end of file
diff --git a/tests/test_diff_codestyle.py b/tests/test_codestyle.py
similarity index 78%
rename from tests/test_diff_codestyle.py
rename to tests/test_codestyle.py
index 89c228e285..2ad3816ea1 100644
--- a/tests/test_diff_codestyle.py
+++ b/tests/test_codestyle.py
@@ -1,8 +1,8 @@
 """
-Diff based code style checker with flake8
+Diff code style checker with ruff
 
 This code come from:
-https://github.com/scipy/scipy/blob/main/tools/lint_diff.py
+https://github.com/scipy/scipy/blob/main/tools/lint.py
 
 Scipy's licence: https://github.com/scipy/scipy/blob/main/LICENSE.txt
 Copyright (c) 2001-2002 Enthought, Inc. 2003-2022, SciPy Developers.
@@ -37,9 +37,30 @@
 OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 """
 import conftest
+import os
 import subprocess
 
 
+CONFIG = os.path.join(
+    os.path.abspath(os.path.dirname(os.path.dirname(__file__))),
+    'ruff.toml',
+)
+
+ROOT_DIR = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
+
+
+def run_ruff(files, fix):
+    if not files:
+        return 0, ""
+    args = ['--fix'] if fix else []
+    res = subprocess.run(
+        ['ruff', f'--config={CONFIG}'] + args + files,
+        stdout=subprocess.PIPE,
+        encoding='utf-8'
+    )
+    return res.returncode, res.stdout
+
+
 def rev_list(branch, num_commits):
     """List commits in reverse chronological order.
     Only the first `num_commits` are shown.
@@ -89,23 +110,23 @@ def find_diff(sha):
     return res.stdout
 
 
-def run_flake8(diff):
-    """Run flake8 on the given diff."""
+def diff_files(sha):
+    """Find the diff since the given SHA."""
     res = subprocess.run(
-        ['flake8', '--diff', '--ignore',
-         'E402'  # top level import for sys.path.append
-         ],
-        input=diff,
+        ['git', 'diff', '--name-only', '--diff-filter=ACMR', '-z', sha, '--',
+         '*.py', '*.pyx', '*.pxd', '*.pxi'],
         stdout=subprocess.PIPE,
-        encoding='utf-8',
+        encoding='utf-8'
     )
-    return res.returncode, res.stdout
+    res.check_returncode()
+    return [os.path.join(ROOT_DIR, f) for f in res.stdout.split('\0') if f]
 
 
 def test():
     branch_commit = find_branch_point("origin/master")
-    diff = find_diff(branch_commit)
-    rc, errors = run_flake8(diff)
+    files = diff_files(branch_commit)
+    print(files)
+    rc, errors = run_ruff(files, fix=False)
     if errors:
         print(errors)
     else:

From 7c1a92a8e5d2c9e0a961728e2cb008c3a4747e58 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 7 Feb 2023 07:21:09 +0900
Subject: [PATCH 0694/1080] Bump ruff from 0.0.237 to 0.0.241 in /requirements
 (#788)

Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.237 to 0.0.241.
- [Release notes](https://github.com/charliermarsh/ruff/releases)
- [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.237...v0.0.241)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index a3a337a198..1e8a407a20 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.0
 pytest == 7.2.1 # For unit test
 pytest-xdist == 3.1.0 # For unit test
 mypy == 0.991 # For unit test
-ruff == 0.0.237 # For unit test
+ruff == 0.0.241 # For unit test

From f9b2fb62afb5f974cbd2ee90434d6ab8c4f6140f Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 7 Feb 2023 07:24:15 +0900
Subject: [PATCH 0695/1080] Bump numpy from 1.24.1 to 1.24.2 in /requirements
 (#790)

Bumps [numpy](https://github.com/numpy/numpy) from 1.24.1 to 1.24.2.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.24.1...v1.24.2)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 1e8a407a20..0bff2c7b5a 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.24.1
+numpy == 1.24.2
 scipy == 1.10.0
 matplotlib == 3.6.3
 cvxpy == 1.3.0

From c627f881a88c47ed6b90c90a9353e38c76d31d7c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 7 Feb 2023 21:49:10 +0900
Subject: [PATCH 0696/1080] Bump mypy from 0.991 to 1.0.0 in /requirements
 (#789)

Bumps [mypy](https://github.com/python/mypy) from 0.991 to 1.0.0.
- [Release notes](https://github.com/python/mypy/releases)
- [Commits](https://github.com/python/mypy/compare/v0.991...v1.0.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 0bff2c7b5a..01bcab20f6 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.6.3
 cvxpy == 1.3.0
 pytest == 7.2.1 # For unit test
 pytest-xdist == 3.1.0 # For unit test
-mypy == 0.991 # For unit test
+mypy == 1.0.0 # For unit test
 ruff == 0.0.241 # For unit test

From 3c1162561ae4ae95b4f946fd75f84d198b7ff8d2 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sat, 11 Feb 2023 10:48:51 +0900
Subject: [PATCH 0697/1080] Bump ipython from 7.31.1 to 8.10.0 in /docs (#791)

Bumps [ipython](https://github.com/ipython/ipython) from 7.31.1 to 8.10.0.
- [Release notes](https://github.com/ipython/ipython/releases)
- [Commits](https://github.com/ipython/ipython/compare/7.31.1...8.10.0)

---
updated-dependencies:
- dependency-name: ipython
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 docs/doc_requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/doc_requirements.txt b/docs/doc_requirements.txt
index b8702ee750..c01b596539 100644
--- a/docs/doc_requirements.txt
+++ b/docs/doc_requirements.txt
@@ -1,4 +1,4 @@
 sphinx == 4.3.2 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
-IPython == 7.31.1 # For sphinx documentation
+IPython == 8.10.0 # For sphinx documentation
 sphinxcontrib-napoleon == 0.7 # For auto doc

From 06bb1d82121a7c4c9d906ced5f60adc14c12810e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 14 Feb 2023 20:07:02 +0900
Subject: [PATCH 0698/1080] Bump ruff from 0.0.241 to 0.0.246 in /requirements
 (#792)

Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.241 to 0.0.246.
- [Release notes](https://github.com/charliermarsh/ruff/releases)
- [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.241...v0.0.246)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 01bcab20f6..2f1aa41f29 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.0
 pytest == 7.2.1 # For unit test
 pytest-xdist == 3.1.0 # For unit test
 mypy == 1.0.0 # For unit test
-ruff == 0.0.241 # For unit test
+ruff == 0.0.246 # For unit test

From ebc1d3d2f3bd3c273da94f74da383390cdb35ce9 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 14 Feb 2023 20:07:31 +0900
Subject: [PATCH 0699/1080] Bump pytest-xdist from 3.1.0 to 3.2.0 in
 /requirements (#793)

Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.1.0 to 3.2.0.
- [Release notes](https://github.com/pytest-dev/pytest-xdist/releases)
- [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.1.0...v3.2.0)

---
updated-dependencies:
- dependency-name: pytest-xdist
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 2f1aa41f29..ed3e084c58 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,6 +3,6 @@ scipy == 1.10.0
 matplotlib == 3.6.3
 cvxpy == 1.3.0
 pytest == 7.2.1 # For unit test
-pytest-xdist == 3.1.0 # For unit test
+pytest-xdist == 3.2.0 # For unit test
 mypy == 1.0.0 # For unit test
 ruff == 0.0.246 # For unit test

From 5641a491b95c4a5f90315b71cd01792319af0032 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 21 Feb 2023 07:25:57 +0900
Subject: [PATCH 0700/1080] Bump ruff from 0.0.246 to 0.0.249 in /requirements
 (#794)

Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.246 to 0.0.249.
- [Release notes](https://github.com/charliermarsh/ruff/releases)
- [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.246...v0.0.249)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index ed3e084c58..b3dcf0458a 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.0
 pytest == 7.2.1 # For unit test
 pytest-xdist == 3.2.0 # For unit test
 mypy == 1.0.0 # For unit test
-ruff == 0.0.246 # For unit test
+ruff == 0.0.249 # For unit test

From fa254376f6017a6f62701bf56758431bc574cf89 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 21 Feb 2023 07:26:14 +0900
Subject: [PATCH 0701/1080] Bump scipy from 1.10.0 to 1.10.1 in /requirements
 (#797)

Bumps [scipy](https://github.com/scipy/scipy) from 1.10.0 to 1.10.1.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.10.0...v1.10.1)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index b3dcf0458a..8e537c44bc 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.24.2
-scipy == 1.10.0
+scipy == 1.10.1
 matplotlib == 3.6.3
 cvxpy == 1.3.0
 pytest == 7.2.1 # For unit test

From f79bc4e139a70d42f4317846c1d5b1254e0761a5 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 21 Feb 2023 08:45:48 +0900
Subject: [PATCH 0702/1080] Bump mypy from 1.0.0 to 1.0.1 in /requirements
 (#795)

Bumps [mypy](https://github.com/python/mypy) from 1.0.0 to 1.0.1.
- [Release notes](https://github.com/python/mypy/releases)
- [Commits](https://github.com/python/mypy/compare/v1.0.0...v1.0.1)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 8e537c44bc..05b7c9aa13 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.6.3
 cvxpy == 1.3.0
 pytest == 7.2.1 # For unit test
 pytest-xdist == 3.2.0 # For unit test
-mypy == 1.0.0 # For unit test
+mypy == 1.0.1 # For unit test
 ruff == 0.0.249 # For unit test

From 9269e190c27f7e146190b4a3c082d04ccf32bc94 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 21 Feb 2023 12:45:30 +0900
Subject: [PATCH 0703/1080] Bump matplotlib from 3.6.3 to 3.7.0 in
 /requirements (#796)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.6.3 to 3.7.0.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.6.3...v3.7.0)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 05b7c9aa13..afda81a29a 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.24.2
 scipy == 1.10.1
-matplotlib == 3.6.3
+matplotlib == 3.7.0
 cvxpy == 1.3.0
 pytest == 7.2.1 # For unit test
 pytest-xdist == 3.2.0 # For unit test

From 3f5b973d6cb508faf707f18cfcb76b4a4c324215 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 7 Mar 2023 19:06:13 +0900
Subject: [PATCH 0704/1080] Bump ruff from 0.0.249 to 0.0.254 in /requirements
 (#798)

Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.249 to 0.0.254.
- [Release notes](https://github.com/charliermarsh/ruff/releases)
- [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.249...v0.0.254)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index afda81a29a..5eb5d69364 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.0
 pytest == 7.2.1 # For unit test
 pytest-xdist == 3.2.0 # For unit test
 mypy == 1.0.1 # For unit test
-ruff == 0.0.249 # For unit test
+ruff == 0.0.254 # For unit test

From 6e2f3df1d88731730028a78c834daf41c54669c8 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 7 Mar 2023 20:49:11 +0900
Subject: [PATCH 0705/1080] Bump matplotlib from 3.7.0 to 3.7.1 in
 /requirements (#800)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.7.0 to 3.7.1.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.7.0...v3.7.1)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 5eb5d69364..141c14198b 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.24.2
 scipy == 1.10.1
-matplotlib == 3.7.0
+matplotlib == 3.7.1
 cvxpy == 1.3.0
 pytest == 7.2.1 # For unit test
 pytest-xdist == 3.2.0 # For unit test

From 1f96f4eb55c5860d00bbefd83a28b59e0b07e128 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 8 Mar 2023 07:57:25 +0900
Subject: [PATCH 0706/1080] Bump pytest from 7.2.1 to 7.2.2 in /requirements
 (#799)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.2.1 to 7.2.2.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/7.2.1...7.2.2)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 141c14198b..35798d7e86 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.24.2
 scipy == 1.10.1
 matplotlib == 3.7.1
 cvxpy == 1.3.0
-pytest == 7.2.1 # For unit test
+pytest == 7.2.2 # For unit test
 pytest-xdist == 3.2.0 # For unit test
 mypy == 1.0.1 # For unit test
 ruff == 0.0.254 # For unit test

From cb08c39a9388c90290b797014f0663c0ed53d2f7 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 11 Mar 2023 17:54:18 +0900
Subject: [PATCH 0707/1080] Add Normal vector estimation (#781)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* add normal vector calculation routine.

* add normal vector calculation routine.

* add normal vector estimation

* fix unittests in not matplotlib frontend

* fix lint ci

* add ransac based normal distribution estimation

* add normal_vector_estimation_main.rst

* normal_vector_estimation_main.rst を更新

* update normal_vector_estimation_main.rst

* update normal_vector_estimation_main.rst

* normal_vector_estimation_main.rst

* normal_vector_estimation_main.rst を更新

* add normal_vector_estimation_main.rst

* add normal_vector_estimation_main.rst

* add normal_vector_estimation_main.rst

* add normal_vector_estimation_main.rst

* add normal_vector_estimation_main.rst

* add normal_vector_estimation_main.rst

* add normal_vector_estimation_main.rst

* add normal_vector_estimation_main.rst

* add normal_vector_estimation_main.rst
---
 .../rrt_star_seven_joint_arm_control.py       |   6 +-
 .../normal_vector_estimation.py               | 177 ++++++++++++++++++
 .../dynamic_window_approach.py                |   3 +-
 PathPlanning/RRTStar/rrt_star.py              |   4 +-
 .../VisibilityRoadMap/visibility_road_map.py  |   5 +-
 docs/modules/mapping/mapping_main.rst         |   2 +-
 .../normal_vector_calc.png                    | Bin 0 -> 96411 bytes
 .../normal_vector_estimation_main.rst         |  74 ++++++++
 .../ransac_normal_vector_estimation.png       | Bin 0 -> 282297 bytes
 ruff.toml                                     |   2 +-
 tests/test_normal_vector_estimation.py        |  19 ++
 utils/plot.py                                 |  79 ++++++++
 12 files changed, 360 insertions(+), 11 deletions(-)
 create mode 100644 Mapping/normal_vector_estimation/normal_vector_estimation.py
 create mode 100644 docs/modules/mapping/normal_vector_estimation/normal_vector_calc.png
 create mode 100644 docs/modules/mapping/normal_vector_estimation/normal_vector_estimation_main.rst
 create mode 100644 docs/modules/mapping/normal_vector_estimation/ransac_normal_vector_estimation.png
 create mode 100644 tests/test_normal_vector_estimation.py

diff --git a/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py b/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py
index fca55184da..0f449bbe51 100755
--- a/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py
+++ b/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py
@@ -5,7 +5,6 @@
 import math
 import random
 import numpy as np
-from mpl_toolkits import mplot3d
 import matplotlib.pyplot as plt
 import sys
 import pathlib
@@ -79,8 +78,9 @@ def __init__(self, start, goal, robot, obstacle_list, rand_area,
         self.obstacle_list = obstacle_list
         self.connect_circle_dist = connect_circle_dist
         self.goal_node = self.Node(goal)
-        self.ax = plt.axes(projection='3d')
         self.node_list = []
+        if show_animation:
+            self.ax = plt.axes(projection='3d')
 
     def planning(self, animation=False, search_until_max_iter=False):
         """
@@ -176,7 +176,7 @@ def search_best_goal_node(self):
 
     def find_near_nodes(self, new_node):
         nnode = len(self.node_list) + 1
-        r = self.connect_circle_dist * math.sqrt((math.log(nnode) / nnode))
+        r = self.connect_circle_dist * math.sqrt(math.log(nnode) / nnode)
         # if expand_dist exists, search vertices in
         # a range no more than expand_dist
         if hasattr(self, 'expand_dis'):
diff --git a/Mapping/normal_vector_estimation/normal_vector_estimation.py b/Mapping/normal_vector_estimation/normal_vector_estimation.py
new file mode 100644
index 0000000000..996ba3ffee
--- /dev/null
+++ b/Mapping/normal_vector_estimation/normal_vector_estimation.py
@@ -0,0 +1,177 @@
+import numpy as np
+from matplotlib import pyplot as plt
+
+from utils.plot import plot_3d_vector_arrow, plot_triangle, set_equal_3d_axis
+
+show_animation = True
+
+
+def calc_normal_vector(p1, p2, p3):
+    """Calculate normal vector of triangle
+
+    Parameters
+    ----------
+    p1 : np.array
+        3D point
+    p2 : np.array
+        3D point
+    p3 : np.array
+        3D point
+
+    Returns
+    -------
+    normal_vector : np.array
+        normal vector (3,)
+
+    """
+    # calculate two vectors of triangle
+    v1 = p2 - p1
+    v2 = p3 - p1
+
+    # calculate normal vector
+    normal_vector = np.cross(v1, v2)
+
+    # normalize vector
+    normal_vector = normal_vector / np.linalg.norm(normal_vector)
+
+    return normal_vector
+
+
+def sample_3d_points_from_a_plane(num_samples, normal):
+    points_2d = np.random.normal(size=(num_samples, 2))  # 2D points on a plane
+    d = 0
+    for i in range(len(points_2d)):
+        point_3d = np.append(points_2d[i], 0)
+        d += normal @ point_3d
+    d /= len(points_2d)
+
+    points_3d = np.zeros((len(points_2d), 3))
+    for i in range(len(points_2d)):
+        point_2d = np.append(points_2d[i], 0)
+        projection_length = (d - normal @ point_2d) / np.linalg.norm(normal)
+        points_3d[i] = point_2d + projection_length * normal
+
+    return points_3d
+
+
+def distance_to_plane(point, normal, origin):
+    dot_product = np.dot(normal, point) - np.dot(normal, origin)
+    if np.isclose(dot_product, 0):
+        return 0.0
+    else:
+        distance = abs(dot_product) / np.linalg.norm(normal)
+        return distance
+
+
+def ransac_normal_vector_estimation(points_3d, inlier_radio_th=0.7,
+                                    inlier_dist=0.1, p=0.99):
+    """
+    RANSAC based normal vector estimation
+
+    Parameters
+    ----------
+    points_3d : np.array
+        3D points (N, 3)
+    inlier_radio_th : float
+        Inlier ratio threshold. If inlier ratio is larger than this value,
+        the iteration is stopped. Default is 0.7.
+    inlier_dist : float
+        Inlier distance threshold. If distance between points and estimated
+        plane is smaller than this value, the point is inlier. Default is 0.1.
+    p : float
+         Probability that at least one of the sets of random samples does not
+         include an outlier. If this probability is near 1, the iteration
+         number is large. Default is 0.99.
+
+    Returns
+    -------
+    center_vector : np.array
+        Center of estimated plane. (3,)
+    normal_vector : np.array
+        Normal vector of estimated plane. (3,)
+
+    """
+    center = np.mean(points_3d, axis=0)
+
+    max_iter = int(np.floor(np.log(1.0-p)/np.log(1.0-(1.0-inlier_radio_th)**3)))
+
+    for ite in range(max_iter):
+        # Random sampling
+        sampled_ids = np.random.choice(points_3d.shape[0], size=3,
+                                       replace=False)
+        sampled_points = points_3d[sampled_ids, :]
+        p1 = sampled_points[0, :]
+        p2 = sampled_points[1, :]
+        p3 = sampled_points[2, :]
+        normal_vector = calc_normal_vector(p1, p2, p3)
+
+        # calc inlier ratio
+        n_inliner = 0
+        for i in range(points_3d.shape[0]):
+            p = points_3d[i, :]
+            if distance_to_plane(p, normal_vector, center) <= inlier_dist:
+                n_inliner += 1
+        inlier_ratio = n_inliner / points_3d.shape[0]
+        print(f"Iter:{ite}, {inlier_ratio=}")
+        if inlier_ratio > inlier_radio_th:
+            return center, normal_vector
+
+    return center, None
+
+
+def main1():
+    p1 = np.array([0.0, 0.0, 1.0])
+    p2 = np.array([1.0, 1.0, 0.0])
+    p3 = np.array([0.0, 1.0, 0.0])
+
+    center = np.mean([p1, p2, p3], axis=0)
+    normal_vector = calc_normal_vector(p1, p2, p3)
+    print(f"{center=}")
+    print(f"{normal_vector=}")
+
+    if show_animation:
+        fig = plt.figure()
+        ax = fig.add_subplot(projection='3d')
+        set_equal_3d_axis(ax, [0.0, 2.5], [0.0, 2.5], [0.0, 3.0])
+        plot_triangle(p1, p2, p3, ax)
+        ax.plot(center[0], center[1], center[2], "ro")
+        plot_3d_vector_arrow(ax, center, center + normal_vector)
+        plt.show()
+
+
+def main2(rng=None):
+    true_normal = np.array([0, 1, 1])
+    true_normal = true_normal / np.linalg.norm(true_normal)
+    num_samples = 100
+    noise_scale = 0.1
+
+    points_3d = sample_3d_points_from_a_plane(num_samples, true_normal)
+    # add random noise
+    points_3d += np.random.normal(size=points_3d.shape, scale=noise_scale)
+
+    print(f"{points_3d.shape=}")
+
+    center, estimated_normal = ransac_normal_vector_estimation(
+        points_3d, inlier_dist=noise_scale)
+
+    if estimated_normal is None:
+        print("Failed to estimate normal vector")
+        return
+
+    print(f"{true_normal=}")
+    print(f"{estimated_normal=}")
+
+    if show_animation:
+        fig = plt.figure()
+        ax = fig.add_subplot(projection='3d')
+        ax.plot(points_3d[:, 0], points_3d[:, 1], points_3d[:, 2], ".r")
+        plot_3d_vector_arrow(ax, center, center + true_normal)
+        plot_3d_vector_arrow(ax, center, center + estimated_normal)
+        set_equal_3d_axis(ax, [-3.0, 3.0], [-3.0, 3.0], [-3.0, 3.0])
+        plt.title("RANSAC based Normal vector estimation")
+        plt.show()
+
+
+if __name__ == '__main__':
+    # main1()
+    main2()
diff --git a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
index 74ff064e86..8664ec1745 100644
--- a/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
+++ b/PathPlanning/DynamicWindowApproach/dynamic_window_approach.py
@@ -300,8 +300,7 @@ def main(gx=10.0, gy=10.0, robot_type=RobotType.circle):
     if show_animation:
         plt.plot(trajectory[:, 0], trajectory[:, 1], "-r")
         plt.pause(0.0001)
-
-    plt.show()
+        plt.show()
 
 
 if __name__ == '__main__':
diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py
index a7aad49d60..f875d3a76d 100644
--- a/PathPlanning/RRTStar/rrt_star.py
+++ b/PathPlanning/RRTStar/rrt_star.py
@@ -186,7 +186,7 @@ def find_near_nodes(self, new_node):
                     radius r
         """
         nnode = len(self.node_list) + 1
-        r = self.connect_circle_dist * math.sqrt((math.log(nnode) / nnode))
+        r = self.connect_circle_dist * math.sqrt(math.log(nnode) / nnode)
         # if expand_dist exists, search vertices in a range no more than
         # expand_dist
         if hasattr(self, 'expand_dis'):
@@ -280,7 +280,7 @@ def main():
             rrt_star.draw_graph()
             plt.plot([x for (x, y) in path], [y for (x, y) in path], 'r--')
             plt.grid(True)
-    plt.show()
+            plt.show()
 
 
 if __name__ == '__main__':
diff --git a/PathPlanning/VisibilityRoadMap/visibility_road_map.py b/PathPlanning/VisibilityRoadMap/visibility_road_map.py
index e9f8733c83..5f7ffadd16 100644
--- a/PathPlanning/VisibilityRoadMap/visibility_road_map.py
+++ b/PathPlanning/VisibilityRoadMap/visibility_road_map.py
@@ -62,8 +62,9 @@ def generate_visibility_nodes(self, start_x, start_y, goal_x, goal_y,
             for (vx, vy) in zip(cvx_list, cvy_list):
                 nodes.append(DijkstraSearch.Node(vx, vy))
 
-        for node in nodes:
-            plt.plot(node.x, node.y, "xr")
+        if self.do_plot:
+            for node in nodes:
+                plt.plot(node.x, node.y, "xr")
 
         return nodes
 
diff --git a/docs/modules/mapping/mapping_main.rst b/docs/modules/mapping/mapping_main.rst
index 14a7cce70d..1c02c75cb2 100644
--- a/docs/modules/mapping/mapping_main.rst
+++ b/docs/modules/mapping/mapping_main.rst
@@ -13,4 +13,4 @@ Mapping
    k_means_object_clustering/k_means_object_clustering
    circle_fitting/circle_fitting
    rectangle_fitting/rectangle_fitting
-
+   normal_vector_estimation/normal_vector_estimation
diff --git a/docs/modules/mapping/normal_vector_estimation/normal_vector_calc.png b/docs/modules/mapping/normal_vector_estimation/normal_vector_calc.png
new file mode 100644
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diff --git a/docs/modules/mapping/normal_vector_estimation/normal_vector_estimation_main.rst b/docs/modules/mapping/normal_vector_estimation/normal_vector_estimation_main.rst
new file mode 100644
index 0000000000..a4d1bf0df2
--- /dev/null
+++ b/docs/modules/mapping/normal_vector_estimation/normal_vector_estimation_main.rst
@@ -0,0 +1,74 @@
+Normal vector estimation
+-------------------------
+
+
+Normal vector calculation of a 3D triangle
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+A 3D point is as a vector:
+
+.. math:: p = [x, y, z]
+
+When there are 3 points in 3D space, :math:`p_1, p_2, p_3`,
+
+we can calculate a normal vector n of a 3D triangle which is consisted of the points.
+
+.. math:: n = \frac{v1 \times v2}{|v1 \times v2|}
+
+where
+
+.. math:: v1 = p2 - p1
+
+.. math:: v2 = p3 - p1
+
+This is an example of normal vector calculation:
+
+.. figure:: normal_vector_calc.png
+
+API
+=====
+
+.. autofunction:: Mapping.normal_vector_estimation.normal_vector_estimation.calc_normal_vector
+
+Normal vector estimation with RANdam SAmpling Consensus(RANSAC)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Consider the problem of estimating the normal vector of a plane based on a
+set of N 3D points where a plane can be observed.
+
+There is a way that uses all point cloud data to estimate a plane and
+a normal vector using the `least-squares method <https://stackoverflow.com/a/44315221/8387766>`_
+
+However, this method is vulnerable to noise of the point cloud.
+
+In this document, we will use a method that uses
+`RANdam SAmpling Consensus(RANSAC) <https://en.wikipedia.org/wiki/Random_sample_consensus>`_
+to estimate a plane and a normal vector.
+
+RANSAC is a robust estimation methods for data set with outliers.
+
+
+
+This RANSAC based normal vector estimation method is as follows:
+
+#. Select 3 points randomly from the point cloud.
+
+#. Calculate a normal vector of a plane which is consists of the sampled 3 points.
+
+#. Calculate the distance between the calculated plane and the all point cloud.
+
+#. If the distance is less than a threshold, the point is considered to be an inlier.
+
+#. Repeat the above steps until the inlier ratio is greater than a threshold.
+
+
+
+This is an example of RANSAC based normal vector estimation:
+
+.. figure:: ransac_normal_vector_estimation.png
+
+API
+=====
+
+.. autofunction:: Mapping.normal_vector_estimation.normal_vector_estimation.ransac_normal_vector_estimation
+
diff --git a/docs/modules/mapping/normal_vector_estimation/ransac_normal_vector_estimation.png b/docs/modules/mapping/normal_vector_estimation/ransac_normal_vector_estimation.png
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XA~_G;K8>F^;GdS7o@%4AZS?;EdtcVN

literal 0
HcmV?d00001

diff --git a/ruff.toml b/ruff.toml
index fd02032f48..578864b33e 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -1,7 +1,7 @@
 line-length = 88
 
 select = ["F", "E", "W", "UP"]
-ignore = ["E501", "E741"]
+ignore = ["E501", "E741", "E402"]
 exclude = [
 ]
 
diff --git a/tests/test_normal_vector_estimation.py b/tests/test_normal_vector_estimation.py
new file mode 100644
index 0000000000..7612f22aa7
--- /dev/null
+++ b/tests/test_normal_vector_estimation.py
@@ -0,0 +1,19 @@
+import conftest  # Add root path to sys.path
+from Mapping.normal_vector_estimation import normal_vector_estimation as m
+import random
+
+random.seed(12345)
+
+
+def test_1():
+    m.show_animation = False
+    m.main1()
+
+
+def test_2():
+    m.show_animation = False
+    m.main2()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
diff --git a/utils/plot.py b/utils/plot.py
index 3499694821..d4bbe29ffd 100644
--- a/utils/plot.py
+++ b/utils/plot.py
@@ -4,6 +4,10 @@
 import math
 import matplotlib.pyplot as plt
 import numpy as np
+from mpl_toolkits.mplot3d import art3d
+from matplotlib.patches import FancyArrowPatch
+from mpl_toolkits.mplot3d.proj3d import proj_transform
+from mpl_toolkits.mplot3d import Axes3D
 
 
 def plot_arrow(x, y, yaw, arrow_length=1.0,
@@ -84,3 +88,78 @@ def plot_curvature(x_list, y_list, heading_list, curvature,
     plt.plot(cx, cy, c, label=label)
     for ix, iy, icx, icy in zip(x_list, y_list, cx, cy):
         plt.plot([ix, icx], [iy, icy], c)
+
+
+class Arrow3D(FancyArrowPatch):
+
+    def __init__(self, x, y, z, dx, dy, dz, *args, **kwargs):
+        super().__init__((0, 0), (0, 0), *args, **kwargs)
+        self._xyz = (x, y, z)
+        self._dxdydz = (dx, dy, dz)
+
+    def draw(self, renderer):
+        x1, y1, z1 = self._xyz
+        dx, dy, dz = self._dxdydz
+        x2, y2, z2 = (x1 + dx, y1 + dy, z1 + dz)
+
+        xs, ys, zs = proj_transform((x1, x2), (y1, y2), (z1, z2), self.axes.M)
+        self.set_positions((xs[0], ys[0]), (xs[1], ys[1]))
+        super().draw(renderer)
+
+    def do_3d_projection(self, renderer=None):
+        x1, y1, z1 = self._xyz
+        dx, dy, dz = self._dxdydz
+        x2, y2, z2 = (x1 + dx, y1 + dy, z1 + dz)
+
+        xs, ys, zs = proj_transform((x1, x2), (y1, y2), (z1, z2), self.axes.M)
+        self.set_positions((xs[0], ys[0]), (xs[1], ys[1]))
+
+        return np.min(zs)
+
+
+def _arrow3D(ax, x, y, z, dx, dy, dz, *args, **kwargs):
+    '''Add an 3d arrow to an `Axes3D` instance.'''
+    arrow = Arrow3D(x, y, z, dx, dy, dz, *args, **kwargs)
+    ax.add_artist(arrow)
+
+
+def plot_3d_vector_arrow(ax, p1, p2):
+    setattr(Axes3D, 'arrow3D', _arrow3D)
+    ax.arrow3D(p1[0], p1[1], p1[2],
+               p2[0]-p1[0], p2[1]-p1[1], p2[2]-p1[2],
+               mutation_scale=20,
+               arrowstyle="-|>",
+               )
+
+
+
+def plot_triangle(p1, p2, p3, ax):
+    ax.add_collection3d(art3d.Poly3DCollection([[p1, p2, p3]], color='b'))
+
+
+def set_equal_3d_axis(ax, x_lims, y_lims, z_lims):
+    """Helper function to set equal axis
+
+    Args:
+        ax (Axes3DSubplot): matplotlib 3D axis, created by
+        `ax = fig.add_subplot(projection='3d')`
+        x_lims (np.array): array containing min and max value of x
+        y_lims (np.array): array containing min and max value of y
+        z_lims (np.array): array containing min and max value of z
+    """
+    x_lims = np.asarray(x_lims)
+    y_lims = np.asarray(y_lims)
+    z_lims = np.asarray(z_lims)
+    # compute max required range
+    max_range = np.array([x_lims.max() - x_lims.min(),
+                          y_lims.max() - y_lims.min(),
+                          z_lims.max() - z_lims.min()]).max() / 2.0
+    # compute mid-point along each axis
+    mid_x = (x_lims.max() + x_lims.min()) * 0.5
+    mid_y = (y_lims.max() + y_lims.min()) * 0.5
+    mid_z = (z_lims.max() + z_lims.min()) * 0.5
+
+    # set limits to axis
+    ax.set_xlim(mid_x - max_range, mid_x + max_range)
+    ax.set_ylim(mid_y - max_range, mid_y + max_range)
+    ax.set_zlim(mid_z - max_range, mid_z + max_range)

From 5b14e91bc46a1bb7a32c736c534498e723ab24bc Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 14 Mar 2023 21:41:34 +0900
Subject: [PATCH 0708/1080] Bump mypy from 1.0.1 to 1.1.1 in /requirements
 (#803)

Bumps [mypy](https://github.com/python/mypy) from 1.0.1 to 1.1.1.
- [Release notes](https://github.com/python/mypy/releases)
- [Commits](https://github.com/python/mypy/compare/v1.0.1...v1.1.1)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 35798d7e86..8ed129f345 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.7.1
 cvxpy == 1.3.0
 pytest == 7.2.2 # For unit test
 pytest-xdist == 3.2.0 # For unit test
-mypy == 1.0.1 # For unit test
+mypy == 1.1.1 # For unit test
 ruff == 0.0.254 # For unit test

From 9b70a6e1c22f68f8702e013962256a52b56e2d02 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 15 Mar 2023 08:25:06 +0900
Subject: [PATCH 0709/1080] Bump ruff from 0.0.254 to 0.0.255 in /requirements
 (#805)

Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.254 to 0.0.255.
- [Release notes](https://github.com/charliermarsh/ruff/releases)
- [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.254...v0.0.255)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 8ed129f345..3efc1c87fc 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.0
 pytest == 7.2.2 # For unit test
 pytest-xdist == 3.2.0 # For unit test
 mypy == 1.1.1 # For unit test
-ruff == 0.0.254 # For unit test
+ruff == 0.0.255 # For unit test

From 3dbf2a4eb5b6ddaa113548d126429475128631ae Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 15 Mar 2023 10:36:48 +0900
Subject: [PATCH 0710/1080] Bump pytest-xdist from 3.2.0 to 3.2.1 in
 /requirements (#804)

Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.2.0 to 3.2.1.
- [Release notes](https://github.com/pytest-dev/pytest-xdist/releases)
- [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.2.0...v3.2.1)

---
updated-dependencies:
- dependency-name: pytest-xdist
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 3efc1c87fc..fc90ceb9d5 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,6 +3,6 @@ scipy == 1.10.1
 matplotlib == 3.7.1
 cvxpy == 1.3.0
 pytest == 7.2.2 # For unit test
-pytest-xdist == 3.2.0 # For unit test
+pytest-xdist == 3.2.1 # For unit test
 mypy == 1.1.1 # For unit test
 ruff == 0.0.255 # For unit test

From 1ce148e4be6e0c2cb619bc2c865f41b55a32dcc0 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 21 Mar 2023 07:11:27 +0900
Subject: [PATCH 0711/1080] Bump cvxpy from 1.3.0 to 1.3.1 in /requirements
 (#806)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.3.0 to 1.3.1.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.3.0...v1.3.1)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index fc90ceb9d5..001836e72e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 1.24.2
 scipy == 1.10.1
 matplotlib == 3.7.1
-cvxpy == 1.3.0
+cvxpy == 1.3.1
 pytest == 7.2.2 # For unit test
 pytest-xdist == 3.2.1 # For unit test
 mypy == 1.1.1 # For unit test

From 53eae53b5a78a08b7ce4c6ffeed727c1d6a0ab2e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 21 Mar 2023 07:11:48 +0900
Subject: [PATCH 0712/1080] Bump ruff from 0.0.255 to 0.0.257 in /requirements
 (#807)

Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.255 to 0.0.257.
- [Release notes](https://github.com/charliermarsh/ruff/releases)
- [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.255...v0.0.257)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 001836e72e..59a34902f4 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.1
 pytest == 7.2.2 # For unit test
 pytest-xdist == 3.2.1 # For unit test
 mypy == 1.1.1 # For unit test
-ruff == 0.0.255 # For unit test
+ruff == 0.0.257 # For unit test

From 7d1365d08d272aef76d7cb88a8044711bacf3461 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 22 Mar 2023 22:47:12 +0900
Subject: [PATCH 0713/1080] Clean up README badge. (#808)

---
 README.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 4ad19353e3..bfde2f9295 100644
--- a/README.md
+++ b/README.md
@@ -3,10 +3,9 @@
 # PythonRobotics
 ![GitHub_Action_Linux_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/Linux_CI/badge.svg)
 ![GitHub_Action_MacOS_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/MacOS_CI/badge.svg)
+![GitHub_Action_Windows_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/Windows_CI/badge.svg)
 [![Build status](https://ci.appveyor.com/api/projects/status/sb279kxuv1be391g?svg=true)](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics)
 [![codecov](https://codecov.io/gh/AtsushiSakai/PythonRobotics/branch/master/graph/badge.svg)](https://codecov.io/gh/AtsushiSakai/PythonRobotics)
-[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/AtsushiSakai/PythonRobotics.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/AtsushiSakai/PythonRobotics/context:python)
-[![tokei](https://tokei.rs/b1/github/AtsushiSakai/PythonRobotics)](https://github.com/AtsushiSakai/PythonRobotics)
 
 Python codes for robotics algorithm.
 

From 5093342d35b914c125f1a857d254083887574444 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 28 Mar 2023 07:36:24 +0900
Subject: [PATCH 0714/1080] Bump ruff from 0.0.257 to 0.0.259 in /requirements
 (#809)

Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.257 to 0.0.259.
- [Release notes](https://github.com/charliermarsh/ruff/releases)
- [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.257...v0.0.259)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 59a34902f4..bc27a2cb7d 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.1
 pytest == 7.2.2 # For unit test
 pytest-xdist == 3.2.1 # For unit test
 mypy == 1.1.1 # For unit test
-ruff == 0.0.257 # For unit test
+ruff == 0.0.259 # For unit test

From 32ecd546a45587e7634c0d321d3441057e1ebdf0 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sat, 8 Apr 2023 19:15:17 +0900
Subject: [PATCH 0715/1080] Bump ruff from 0.0.259 to 0.0.260 in /requirements
 (#813)

Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.259 to 0.0.260.
- [Release notes](https://github.com/charliermarsh/ruff/releases)
- [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.259...v0.0.260)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index bc27a2cb7d..c8b9725def 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.1
 pytest == 7.2.2 # For unit test
 pytest-xdist == 3.2.1 # For unit test
 mypy == 1.1.1 # For unit test
-ruff == 0.0.259 # For unit test
+ruff == 0.0.260 # For unit test

From f97fc0a26da22ac3eb51472cca5b2858bf9067fb Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 11 Apr 2023 08:17:07 +0900
Subject: [PATCH 0716/1080] Bump pytest from 7.2.2 to 7.3.0 in /requirements
 (#814)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.2.2 to 7.3.0.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/7.2.2...7.3.0)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index c8b9725def..4019b31e0c 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.24.2
 scipy == 1.10.1
 matplotlib == 3.7.1
 cvxpy == 1.3.1
-pytest == 7.2.2 # For unit test
+pytest == 7.3.0 # For unit test
 pytest-xdist == 3.2.1 # For unit test
 mypy == 1.1.1 # For unit test
 ruff == 0.0.260 # For unit test

From cc6245cdcfcd6e35f5cb789a7aafe7fe57fe05c0 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 11 Apr 2023 09:34:13 +0900
Subject: [PATCH 0717/1080] Bump ruff from 0.0.260 to 0.0.261 in /requirements
 (#815)

Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.260 to 0.0.261.
- [Release notes](https://github.com/charliermarsh/ruff/releases)
- [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.260...v0.0.261)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 4019b31e0c..ad78be529a 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.1
 pytest == 7.3.0 # For unit test
 pytest-xdist == 3.2.1 # For unit test
 mypy == 1.1.1 # For unit test
-ruff == 0.0.260 # For unit test
+ruff == 0.0.261 # For unit test

From 90189c4711eba64d5ce902170277b9887a5f7cf5 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 11 Apr 2023 12:20:02 +0900
Subject: [PATCH 0718/1080] Bump mypy from 1.1.1 to 1.2.0 in /requirements
 (#816)

Bumps [mypy](https://github.com/python/mypy) from 1.1.1 to 1.2.0.
- [Release notes](https://github.com/python/mypy/releases)
- [Commits](https://github.com/python/mypy/compare/v1.1.1...v1.2.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index ad78be529a..9d43d13eb2 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.7.1
 cvxpy == 1.3.1
 pytest == 7.3.0 # For unit test
 pytest-xdist == 3.2.1 # For unit test
-mypy == 1.1.1 # For unit test
+mypy == 1.2.0 # For unit test
 ruff == 0.0.261 # For unit test

From 2394e776f628cc0bba35b176e74e65345bb870b2 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 25 Apr 2023 14:56:19 +1000
Subject: [PATCH 0719/1080] Bump numpy from 1.24.2 to 1.24.3 in /requirements
 (#820)

Bumps [numpy](https://github.com/numpy/numpy) from 1.24.2 to 1.24.3.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.24.2...v1.24.3)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 9d43d13eb2..9f88bd9bfb 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.24.2
+numpy == 1.24.3
 scipy == 1.10.1
 matplotlib == 3.7.1
 cvxpy == 1.3.1

From 1efac6795ad75e7f28a04b049deab7038712efd9 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 26 Apr 2023 21:23:42 +0900
Subject: [PATCH 0720/1080] Bump ruff from 0.0.261 to 0.0.263 in /requirements
 (#822)

Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.261 to 0.0.263.
- [Release notes](https://github.com/charliermarsh/ruff/releases)
- [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.261...v0.0.263)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 9f88bd9bfb..e05a591dc5 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.1
 pytest == 7.3.0 # For unit test
 pytest-xdist == 3.2.1 # For unit test
 mypy == 1.2.0 # For unit test
-ruff == 0.0.261 # For unit test
+ruff == 0.0.263 # For unit test

From 28004c7b9c6cb0050e9b6e01a7c284ea631e9c9a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 26 Apr 2023 21:49:36 +0900
Subject: [PATCH 0721/1080] Bump pytest from 7.3.0 to 7.3.1 in /requirements
 (#818)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.3.0 to 7.3.1.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/7.3.0...7.3.1)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index e05a591dc5..501723248f 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.24.3
 scipy == 1.10.1
 matplotlib == 3.7.1
 cvxpy == 1.3.1
-pytest == 7.3.0 # For unit test
+pytest == 7.3.1 # For unit test
 pytest-xdist == 3.2.1 # For unit test
 mypy == 1.2.0 # For unit test
 ruff == 0.0.263 # For unit test

From 80124f20e814a03baa440481999847bdc850be62 Mon Sep 17 00:00:00 2001
From: RyderCRD <39330401+RyderCRD@users.noreply.github.com>
Date: Thu, 27 Apr 2023 22:04:39 +0800
Subject: [PATCH 0722/1080] Fix the rewire() of rrt* path planning (#550) 
 (#812)

* Update rrt_star_reeds_shepp.py to fix #550

Add step_size attribute to RRTStarReedsShepp, and a method set_random_seed() to set the random seed, with two test cases.

* Update rewire() of rrt_star.py

Update rewire() of rrt_star.py to fix #550.  Since the old version didn't assign path_yaw of edge_node to near_node, the old rewire() doesn't fit rrt_star_reeds_shepp.py

* Update reeds_shepp_path_planning.py

Limit the range of phi for the sake of planning speed, and simplify the the calculation process in straight_left_straight().

* Update reeds_shepp_path_planning.py

* Remove unnecessary cost calculation

Cost of edge_node is calculated in line 221, and self.node_list[i] is replaced to edge_node, so no need to update.

* Update reeds_shepp_path_planning.py
---
 PathPlanning/RRTStar/rrt_star.py              | 12 +++---
 .../RRTStarReedsShepp/rrt_star_reeds_shepp.py | 14 ++++---
 .../reeds_shepp_path_planning.py              | 13 +++----
 tests/test_rrt_star_reeds_shepp.py            | 37 +++++++++++++++++++
 4 files changed, 56 insertions(+), 20 deletions(-)

diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py
index f875d3a76d..b2884fe21b 100644
--- a/PathPlanning/RRTStar/rrt_star.py
+++ b/PathPlanning/RRTStar/rrt_star.py
@@ -225,13 +225,11 @@ def rewire(self, new_node, near_inds):
             improved_cost = near_node.cost > edge_node.cost
 
             if no_collision and improved_cost:
-                near_node.x = edge_node.x
-                near_node.y = edge_node.y
-                near_node.cost = edge_node.cost
-                near_node.path_x = edge_node.path_x
-                near_node.path_y = edge_node.path_y
-                near_node.parent = edge_node.parent
-                self.propagate_cost_to_leaves(new_node)
+                for node in self.node_list:
+                    if node.parent == self.node_list[i]:
+                        node.parent = edge_node
+                self.node_list[i] = edge_node
+                self.propagate_cost_to_leaves(self.node_list[i])
 
     def calc_new_cost(self, from_node, to_node):
         d, _ = self.calc_distance_and_angle(from_node, to_node)
diff --git a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
index 66185017a0..c4c3e7c8a8 100644
--- a/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
+++ b/PathPlanning/RRTStarReedsShepp/rrt_star_reeds_shepp.py
@@ -36,7 +36,7 @@ def __init__(self, x, y, yaw):
             self.path_yaw = []
 
     def __init__(self, start, goal, obstacle_list, rand_area,
-                 max_iter=200,
+                 max_iter=200, step_size=0.2,
                  connect_circle_dist=50.0,
                  robot_radius=0.0
                  ):
@@ -55,6 +55,7 @@ def __init__(self, start, goal, obstacle_list, rand_area,
         self.min_rand = rand_area[0]
         self.max_rand = rand_area[1]
         self.max_iter = max_iter
+        self.step_size = step_size
         self.obstacle_list = obstacle_list
         self.connect_circle_dist = connect_circle_dist
         self.robot_radius = robot_radius
@@ -63,6 +64,9 @@ def __init__(self, start, goal, obstacle_list, rand_area,
         self.goal_yaw_th = np.deg2rad(1.0)
         self.goal_xy_th = 0.5
 
+    def set_random_seed(self, seed):
+        random.seed(seed)
+
     def planning(self, animation=True, search_until_max_iter=True):
         """
         planning
@@ -147,8 +151,8 @@ def plot_start_goal_arrow(self):
     def steer(self, from_node, to_node):
 
         px, py, pyaw, mode, course_lengths = reeds_shepp_path_planning.reeds_shepp_path_planning(
-            from_node.x, from_node.y, from_node.yaw,
-            to_node.x, to_node.y, to_node.yaw, self.curvature)
+            from_node.x, from_node.y, from_node.yaw, to_node.x,
+            to_node.y, to_node.yaw, self.curvature, self.step_size)
 
         if not px:
             return None
@@ -169,8 +173,8 @@ def steer(self, from_node, to_node):
     def calc_new_cost(self, from_node, to_node):
 
         _, _, _, _, course_lengths = reeds_shepp_path_planning.reeds_shepp_path_planning(
-            from_node.x, from_node.y, from_node.yaw,
-            to_node.x, to_node.y, to_node.yaw, self.curvature)
+            from_node.x, from_node.y, from_node.yaw, to_node.x,
+            to_node.y, to_node.yaw, self.curvature, self.step_size)
         if not course_lengths:
             return float("inf")
 
diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
index 1577d048d3..c55d2629e5 100644
--- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
+++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
@@ -53,17 +53,14 @@ def mod2pi(x):
 
 def straight_left_straight(x, y, phi):
     phi = mod2pi(phi)
-    if y > 0.0 and 0.0 < phi < math.pi * 0.99:
+    # only take phi in (0.01*math.pi, 0.99*math.pi) for the sake of speed.
+    # phi in (0, 0.01*math.pi) will make test2() in test_rrt_star_reeds_shepp.py
+    # extremely time-consuming, since the value of xd, t will be very large.
+    if math.pi * 0.01 < phi < math.pi * 0.99 and y != 0:
         xd = - y / math.tan(phi) + x
         t = xd - math.tan(phi / 2.0)
         u = phi
-        v = math.hypot(x - xd, y) - math.tan(phi / 2.0)
-        return True, t, u, v
-    elif y < 0.0 < phi < math.pi * 0.99:
-        xd = - y / math.tan(phi) + x
-        t = xd - math.tan(phi / 2.0)
-        u = phi
-        v = -math.hypot(x - xd, y) - math.tan(phi / 2.0)
+        v = np.sign(y) * math.hypot(x - xd, y) - math.tan(phi / 2.0)
         return True, t, u, v
 
     return False, 0.0, 0.0, 0.0
diff --git a/tests/test_rrt_star_reeds_shepp.py b/tests/test_rrt_star_reeds_shepp.py
index cb6b586b33..20d4916f96 100644
--- a/tests/test_rrt_star_reeds_shepp.py
+++ b/tests/test_rrt_star_reeds_shepp.py
@@ -6,6 +6,43 @@ def test1():
     m.show_animation = False
     m.main(max_iter=5)
 
+obstacleList = [
+    (5, 5, 1),
+    (4, 6, 1),
+    (4, 8, 1),
+    (4, 10, 1),
+    (6, 5, 1),
+    (7, 5, 1),
+    (8, 6, 1),
+    (8, 8, 1),
+    (8, 10, 1)
+]  # [x,y,size(radius)]
+
+start = [0.0, 0.0, m.np.deg2rad(0.0)]
+goal = [6.0, 7.0, m.np.deg2rad(90.0)]
+
+def test2():
+    step_size = 0.2
+    rrt_star_reeds_shepp = m.RRTStarReedsShepp(start, goal,
+                                             obstacleList, [-2.0, 15.0],
+                                             max_iter=100, step_size=step_size)
+    rrt_star_reeds_shepp.set_random_seed(seed=8)
+    path = rrt_star_reeds_shepp.planning(animation=False)
+    for i in range(len(path)-1):
+        # + 0.00000000000001 for acceptable errors arising from the planning process
+        assert m.math.dist(path[i][0:2], path[i+1][0:2]) < step_size + 0.00000000000001
+
+def test3():
+    step_size = 20
+    rrt_star_reeds_shepp = m.RRTStarReedsShepp(start, goal,
+                                             obstacleList, [-2.0, 15.0],
+                                             max_iter=100, step_size=step_size)
+    rrt_star_reeds_shepp.set_random_seed(seed=8)
+    path = rrt_star_reeds_shepp.planning(animation=False)
+    for i in range(len(path)-1):
+        # + 0.00000000000001 for acceptable errors arising from the planning process
+        assert m.math.dist(path[i][0:2], path[i+1][0:2]) < step_size + 0.00000000000001
+
 
 if __name__ == '__main__':
     conftest.run_this_test(__file__)

From 033e301498a12a039f95a990fa9b831c5015eaf3 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 2 May 2023 20:54:45 +0900
Subject: [PATCH 0723/1080] Cleanup cvxpy related codes (#823)

* add normal_vector_estimation_main.rst

* add normal_vector_estimation_main.rst

* cleanup cvxpy related codes

* cleanup cvxpy related codes
---
 .../rocket_powered_landing.py                   | 17 +++++++++--------
 .../model_predictive_speed_and_steer_control.py |  6 ++++--
 2 files changed, 13 insertions(+), 10 deletions(-)

diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
index 37dca15b98..239f3629c1 100644
--- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
+++ b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
@@ -12,13 +12,12 @@
 - EmbersArc/SuccessiveConvexificationFreeFinalTime: Implementation of "Successive Convexification for 6-DoF Mars Rocket Powered Landing with Free-Final-Time" https://github.com/EmbersArc/SuccessiveConvexificationFreeFinalTime
 
 """
-
+import warnings
 from time import time
 import numpy as np
 from scipy.integrate import odeint
 import cvxpy
 import matplotlib.pyplot as plt
-from mpl_toolkits import mplot3d
 
 # Trajectory points
 K = 50
@@ -123,7 +122,7 @@ def set_random_initial_state(self, rng):
                                     rng.uniform(-20, 20)))
 
     def f_func(self, x, u):
-        m, rx, ry, rz, vx, vy, vz, q0, q1, q2, q3, wx, wy, wz = x[0], x[1], x[
+        m, _, _, _, vx, vy, vz, q0, q1, q2, q3, wx, wy, wz = x[0], x[1], x[
             2], x[3], x[4], x[5], x[6], x[7], x[8], x[9], x[10], x[11], x[12], x[13]
         ux, uy, uz = u[0], u[1], u[2]
 
@@ -148,7 +147,7 @@ def f_func(self, x, u):
         ])
 
     def A_func(self, x, u):
-        m, rx, ry, rz, vx, vy, vz, q0, q1, q2, q3, wx, wy, wz = x[0], x[1], x[
+        m, _, _, _, _, _, _, q0, q1, q2, q3, wx, wy, wz = x[0], x[1], x[
             2], x[3], x[4], x[5], x[6], x[7], x[8], x[9], x[10], x[11], x[12], x[13]
         ux, uy, uz = u[0], u[1], u[2]
 
@@ -176,7 +175,7 @@ def A_func(self, x, u):
             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
 
     def B_func(self, x, u):
-        m, rx, ry, rz, vx, vy, vz, q0, q1, q2, q3, wx, wy, wz = x[0], x[1], x[
+        m, _, _, _, _, _, _, q0, q1, q2, q3, _, _, _ = x[0], x[1], x[
             2], x[3], x[4], x[5], x[6], x[7], x[8], x[9], x[10], x[11], x[12], x[13]
         ux, uy, uz = u[0], u[1], u[2]
 
@@ -534,7 +533,9 @@ def get_variable(self, name):
     def solve(self, **kwargs):
         error = False
         try:
-            self.prob.solve(verbose=verbose_solver,
+            with warnings.catch_warnings():  # For User warning from solver
+                warnings.simplefilter('ignore')
+                self.prob.solve(verbose=verbose_solver,
                             solver=solver)
         except cvxpy.SolverError:
             error = True
@@ -569,10 +570,10 @@ def axis3d_equal(X, Y, Z, ax):
 def plot_animation(X, U):  # pragma: no cover
 
     fig = plt.figure()
-    ax = fig.gca(projection='3d')
+    ax = fig.add_subplot(projection='3d')
     # for stopping simulation with the esc key.
     fig.canvas.mpl_connect('key_release_event',
-            lambda event: [exit(0) if event.key == 'escape' else None])
+                           lambda event: [exit(0) if event.key == 'escape' else None])
 
     for k in range(K):
         plt.cla()
diff --git a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
index 59e69aa999..3015313198 100644
--- a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
+++ b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
@@ -225,8 +225,9 @@ def predict_motion(x0, oa, od, xref):
 
 def iterative_linear_mpc_control(xref, x0, dref, oa, od):
     """
-    MPC contorl with updating operational point iteraitvely
+    MPC control with updating operational point iteratively
     """
+    ox, oy, oyaw, ov = None, None, None, None
 
     if oa is None or od is None:
         oa = [0.0] * T
@@ -406,10 +407,11 @@ def do_simulation(cx, cy, cyaw, ck, sp, dl, initial_state):
         oa, odelta, ox, oy, oyaw, ov = iterative_linear_mpc_control(
             xref, x0, dref, oa, odelta)
 
+        di, ai = 0.0, 0.0
         if odelta is not None:
             di, ai = odelta[0], oa[0]
+            state = update_state(state, ai, di)
 
-        state = update_state(state, ai, di)
         time = time + DT
 
         x.append(state.x)

From b877a9d5ca15b794a72221c33d52d13bf3bf8434 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sun, 14 May 2023 17:15:10 +0900
Subject: [PATCH 0724/1080] Bump ruff from 0.0.263 to 0.0.265 in /requirements
 (#826)

Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.263 to 0.0.265.
- [Release notes](https://github.com/charliermarsh/ruff/releases)
- [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.263...v0.0.265)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 501723248f..4522f6c7ca 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.1
 pytest == 7.3.1 # For unit test
 pytest-xdist == 3.2.1 # For unit test
 mypy == 1.2.0 # For unit test
-ruff == 0.0.263 # For unit test
+ruff == 0.0.265 # For unit test

From 18a7d1b75871d9f12fe5fd20a93f5c50f059e614 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 16 May 2023 07:27:24 +0900
Subject: [PATCH 0725/1080] Bump mypy from 1.2.0 to 1.3.0 in /requirements
 (#835)

Bumps [mypy](https://github.com/python/mypy) from 1.2.0 to 1.3.0.
- [Commits](https://github.com/python/mypy/compare/v1.2.0...v1.3.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 4522f6c7ca..30c5330b90 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.7.1
 cvxpy == 1.3.1
 pytest == 7.3.1 # For unit test
 pytest-xdist == 3.2.1 # For unit test
-mypy == 1.2.0 # For unit test
+mypy == 1.3.0 # For unit test
 ruff == 0.0.265 # For unit test

From 57943ae6f598ca14a0aadfe706bd7762c94ff36e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 16 May 2023 20:08:06 +0900
Subject: [PATCH 0726/1080] Bump pytest-xdist from 3.2.1 to 3.3.0 in
 /requirements (#837)

Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.2.1 to 3.3.0.
- [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.2.1...v3.3.0)

---
updated-dependencies:
- dependency-name: pytest-xdist
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 30c5330b90..616e3c3fc0 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,6 +3,6 @@ scipy == 1.10.1
 matplotlib == 3.7.1
 cvxpy == 1.3.1
 pytest == 7.3.1 # For unit test
-pytest-xdist == 3.2.1 # For unit test
+pytest-xdist == 3.3.0 # For unit test
 mypy == 1.3.0 # For unit test
 ruff == 0.0.265 # For unit test

From 29e0bab4e7ab380624c4c6f7fe068e67a5c71718 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 16 May 2023 20:39:26 +0900
Subject: [PATCH 0727/1080] Bump ruff from 0.0.265 to 0.0.267 in /requirements
 (#836)

Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.265 to 0.0.267.
- [Release notes](https://github.com/charliermarsh/ruff/releases)
- [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.265...v0.0.267)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 616e3c3fc0..6efd6fcb98 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.1
 pytest == 7.3.1 # For unit test
 pytest-xdist == 3.3.0 # For unit test
 mypy == 1.3.0 # For unit test
-ruff == 0.0.265 # For unit test
+ruff == 0.0.267 # For unit test

From dd6d046bf9b6458a8bcdd947045806af8c86e788 Mon Sep 17 00:00:00 2001
From: Erkam Kavak <70749779+erkamkavak@users.noreply.github.com>
Date: Tue, 16 May 2023 15:42:18 +0300
Subject: [PATCH 0728/1080] fix added for checking if a neighbor is inside
 openList (#832)

---
 PathPlanning/HybridAStar/hybrid_a_star.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
index b87fe706c2..03db0dc167 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -311,7 +311,7 @@ def hybrid_a_star_planning(start, goal, ox, oy, xy_resolution, yaw_resolution):
             neighbor_index = calc_index(neighbor, config)
             if neighbor_index in closedList:
                 continue
-            if neighbor not in openList \
+            if neighbor_index not in openList \
                     or openList[neighbor_index].cost > neighbor.cost:
                 heapq.heappush(
                     pq, (calc_cost(neighbor, h_dp, config),

From 874ebff495b088e0aee48ad29e352548dee65e8d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Yangshen=E2=9A=A1Deng?= <yangshen.d@outlook.com>
Date: Tue, 16 May 2023 20:43:57 +0800
Subject: [PATCH 0729/1080] fix: set rrt_star to find the shortest path (#834)

* fix: set rrt_star to find the shortest path

* fix: code style white space
---
 PathPlanning/RRTStar/rrt_star.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/PathPlanning/RRTStar/rrt_star.py b/PathPlanning/RRTStar/rrt_star.py
index b2884fe21b..dcb1a066eb 100644
--- a/PathPlanning/RRTStar/rrt_star.py
+++ b/PathPlanning/RRTStar/rrt_star.py
@@ -163,9 +163,13 @@ def search_best_goal_node(self):
         if not safe_goal_inds:
             return None
 
-        min_cost = min([self.node_list[i].cost for i in safe_goal_inds])
-        for i in safe_goal_inds:
-            if self.node_list[i].cost == min_cost:
+        safe_goal_costs = [self.node_list[i].cost +
+                           self.calc_dist_to_goal(self.node_list[i].x, self.node_list[i].y)
+                           for i in safe_goal_inds]
+
+        min_cost = min(safe_goal_costs)
+        for i, cost in zip(safe_goal_inds, safe_goal_costs):
+            if cost == min_cost:
                 return i
 
         return None

From 7be69c13cce0f518d3ce5521b4bafcc56bce1445 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 23 May 2023 07:23:34 +0900
Subject: [PATCH 0730/1080] Bump pytest-xdist from 3.3.0 to 3.3.1 in
 /requirements (#838)

Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.3.0 to 3.3.1.
- [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.3.0...v3.3.1)

---
updated-dependencies:
- dependency-name: pytest-xdist
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 6efd6fcb98..389107f0bb 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,6 +3,6 @@ scipy == 1.10.1
 matplotlib == 3.7.1
 cvxpy == 1.3.1
 pytest == 7.3.1 # For unit test
-pytest-xdist == 3.3.0 # For unit test
+pytest-xdist == 3.3.1 # For unit test
 mypy == 1.3.0 # For unit test
 ruff == 0.0.267 # For unit test

From 390c1cdd9fce482f13280724d762c9f9000baaba Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 23 May 2023 12:42:52 +0900
Subject: [PATCH 0731/1080] Bump ruff from 0.0.267 to 0.0.269 in /requirements
 (#839)

Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.267 to 0.0.269.
- [Release notes](https://github.com/charliermarsh/ruff/releases)
- [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.267...v0.0.269)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 389107f0bb..649b331e1d 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.1
 pytest == 7.3.1 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.3.0 # For unit test
-ruff == 0.0.267 # For unit test
+ruff == 0.0.269 # For unit test

From ca82a4b1dff20dba0c852f4e0ac41501cba6e9ab Mon Sep 17 00:00:00 2001
From: Faizan Alam <51821426+Faizan-Alam-1@users.noreply.github.com>
Date: Sat, 27 May 2023 17:00:02 +0530
Subject: [PATCH 0732/1080] Add copy button in the documentation code snippets
 (#840)

---
 docs/conf.py              | 1 +
 docs/doc_requirements.txt | 1 +
 2 files changed, 2 insertions(+)

diff --git a/docs/conf.py b/docs/conf.py
index e7c64d07b2..0cbf3471f9 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -45,6 +45,7 @@
     'sphinx.ext.napoleon',
     'sphinx.ext.imgconverter',
     'IPython.sphinxext.ipython_console_highlighting',
+    'sphinx_copybutton',
 ]
 
 # Add any paths that contain templates here, relative to this directory.
diff --git a/docs/doc_requirements.txt b/docs/doc_requirements.txt
index c01b596539..04c30e6037 100644
--- a/docs/doc_requirements.txt
+++ b/docs/doc_requirements.txt
@@ -2,3 +2,4 @@ sphinx == 4.3.2 # For sphinx documentation
 sphinx_rtd_theme == 1.0.0
 IPython == 8.10.0 # For sphinx documentation
 sphinxcontrib-napoleon == 0.7 # For auto doc
+sphinx-copybutton

From 5ddf70615ea35c366199ef6837ea88eba94ba6c8 Mon Sep 17 00:00:00 2001
From: Faizan Alam <51821426+Faizan-Alam-1@users.noreply.github.com>
Date: Sun, 28 May 2023 13:52:35 +0530
Subject: [PATCH 0733/1080] Add dark mode feature in  documentation (#842)

---
 docs/conf.py              | 3 ++-
 docs/doc_requirements.txt | 1 +
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/conf.py b/docs/conf.py
index 0cbf3471f9..df9b8fce22 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -46,6 +46,7 @@
     'sphinx.ext.imgconverter',
     'IPython.sphinxext.ipython_console_highlighting',
     'sphinx_copybutton',
+    'sphinx_rtd_dark_mode',
 ]
 
 # Add any paths that contain templates here, relative to this directory.
@@ -86,7 +87,7 @@
 if on_rtd:
     html_theme = 'default'
 else:
-    html_theme = 'sphinx_rtd_theme'
+    html_theme = 'sphinx_rtd_light_them'
 
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the
diff --git a/docs/doc_requirements.txt b/docs/doc_requirements.txt
index 04c30e6037..a8cd2bde40 100644
--- a/docs/doc_requirements.txt
+++ b/docs/doc_requirements.txt
@@ -3,3 +3,4 @@ sphinx_rtd_theme == 1.0.0
 IPython == 8.10.0 # For sphinx documentation
 sphinxcontrib-napoleon == 0.7 # For auto doc
 sphinx-copybutton
+sphinx-rtd-dark-mode
\ No newline at end of file

From e4fe460e02f9f29ea2553e579e7599a652ca5207 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 May 2023 07:56:26 +0900
Subject: [PATCH 0734/1080] Bump ruff from 0.0.269 to 0.0.270 in /requirements
 (#844)

Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.269 to 0.0.270.
- [Release notes](https://github.com/charliermarsh/ruff/releases)
- [Changelog](https://github.com/charliermarsh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.269...v0.0.270)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 649b331e1d..4499e715a9 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.1
 pytest == 7.3.1 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.3.0 # For unit test
-ruff == 0.0.269 # For unit test
+ruff == 0.0.270 # For unit test

From 6bade5000dca75b91cc9de94f797e5d558082855 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 13 Jun 2023 20:46:56 +0900
Subject: [PATCH 0735/1080] Bump ruff from 0.0.270 to 0.0.272 in /requirements
 (#849)

Bumps [ruff](https://github.com/charliermarsh/ruff) from 0.0.270 to 0.0.272.
- [Release notes](https://github.com/charliermarsh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/charliermarsh/ruff/compare/v0.0.270...v0.0.272)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 4499e715a9..93a1b8bba1 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.1
 pytest == 7.3.1 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.3.0 # For unit test
-ruff == 0.0.270 # For unit test
+ruff == 0.0.272 # For unit test

From 3be53ab0e0802e908544e274278ed0390ea98597 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 13 Jun 2023 20:47:31 +0900
Subject: [PATCH 0736/1080] Bump pytest from 7.3.1 to 7.3.2 in /requirements
 (#850)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.3.1 to 7.3.2.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/7.3.1...7.3.2)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 93a1b8bba1..470180c1f7 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.24.3
 scipy == 1.10.1
 matplotlib == 3.7.1
 cvxpy == 1.3.1
-pytest == 7.3.1 # For unit test
+pytest == 7.3.2 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.3.0 # For unit test
 ruff == 0.0.272 # For unit test

From c06e4909c865cf3912cdee4610ab91afcd3b111c Mon Sep 17 00:00:00 2001
From: Yichao <111082392+YichaoDeng@users.noreply.github.com>
Date: Mon, 19 Jun 2023 17:02:16 +0800
Subject: [PATCH 0737/1080] fix: [ValueError] setting an array element with a
 sequence in [fast_slam2] (#851)

---
 SLAM/FastSLAM2/fast_slam2.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py
index 7cd708df81..4c6c103325 100644
--- a/SLAM/FastSLAM2/fast_slam2.py
+++ b/SLAM/FastSLAM2/fast_slam2.py
@@ -405,8 +405,8 @@ def main():
 
             for iz in range(len(z[:, 0])):
                 landmark_id = int(z[2, iz])
-                plt.plot([xEst[0], RFID[landmark_id, 0]], [
-                    xEst[1], RFID[landmark_id, 1]], "-k")
+                plt.plot([xEst[0][0], RFID[landmark_id, 0]], [
+                    xEst[1][0], RFID[landmark_id, 1]], "-k")
 
             for i in range(N_PARTICLE):
                 plt.plot(particles[i].x, particles[i].y, ".r")

From 94a8072b11fb2198b03ae193c4a3499d7a004a0f Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 Jun 2023 07:01:39 +0900
Subject: [PATCH 0738/1080] Bump mypy from 1.3.0 to 1.4.1 in /requirements
 (#854)

Bumps [mypy](https://github.com/python/mypy) from 1.3.0 to 1.4.1.
- [Commits](https://github.com/python/mypy/compare/v1.3.0...v1.4.1)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 470180c1f7..bbd3764998 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.7.1
 cvxpy == 1.3.1
 pytest == 7.3.2 # For unit test
 pytest-xdist == 3.3.1 # For unit test
-mypy == 1.3.0 # For unit test
+mypy == 1.4.1 # For unit test
 ruff == 0.0.272 # For unit test

From a0bb72e000bfcbc2442303c0bc39e4e6696f793f Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 Jun 2023 07:20:25 +0900
Subject: [PATCH 0739/1080] Bump pytest from 7.3.2 to 7.4.0 in /requirements
 (#857)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.3.2 to 7.4.0.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/7.3.2...7.4.0)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index bbd3764998..580a4ba731 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.24.3
 scipy == 1.10.1
 matplotlib == 3.7.1
 cvxpy == 1.3.1
-pytest == 7.3.2 # For unit test
+pytest == 7.4.0 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.4.1 # For unit test
 ruff == 0.0.272 # For unit test

From 81c75f75ab987645c733952bbf277d349fefcec2 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 Jun 2023 13:25:12 +0900
Subject: [PATCH 0740/1080] Bump ruff from 0.0.272 to 0.0.275 in /requirements
 (#856)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.272 to 0.0.275.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.0.272...v0.0.275)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 580a4ba731..9db6898463 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.1
 pytest == 7.4.0 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.4.1 # For unit test
-ruff == 0.0.272 # For unit test
+ruff == 0.0.275 # For unit test

From d7fdcade2f0a86cbe263d31530667fadc9ec1e61 Mon Sep 17 00:00:00 2001
From: neriya shulman <45920392+neriyashul@users.noreply.github.com>
Date: Fri, 30 Jun 2023 15:13:09 +0300
Subject: [PATCH 0741/1080] Fix broken links in README.md for ipynb files
 (#859)

---
 README.md | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index bfde2f9295..bc0198f78e 100644
--- a/README.md
+++ b/README.md
@@ -161,7 +161,9 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation
 
 <img src="https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif" width="640" alt="EKF pic">
 
-Documentation: [Notebook](https://github.com/AtsushiSakai/PythonRobotics/blob/master/Localization/extended_kalman_filter/extended_kalman_filter_localization.ipynb)
+Ref:
+
+- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.html)
 
 ## Particle filter localization
 
@@ -532,7 +534,7 @@ Path tracking simulation with iterative linear model predictive speed and steeri
 
 Ref:
 
-- [notebook](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/model_predictive_speed_and_steer_control/Model_predictive_speed_and_steering_control.ipynb)
+- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.html)
 
 - [Real\-time Model Predictive Control \(MPC\), ACADO, Python \| Work\-is\-Playing](http://grauonline.de/wordpress/?page_id=3244)
 
@@ -544,7 +546,7 @@ A motion planning and path tracking simulation with NMPC of C-GMRES
 
 Ref:
 
-- [notebook](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/cgmres_nmpc/cgmres_nmpc.ipynb)
+- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.html)
 
 
 # Arm Navigation
@@ -584,7 +586,7 @@ This is a 3d trajectory generation simulation for a rocket powered landing.
 
 Ref:
 
-- [notebook](https://github.com/AtsushiSakai/PythonRobotics/blob/master/AerialNavigation/rocket_powered_landing/rocket_powered_landing.ipynb)
+- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing.html)
 
 # Bipedal
 

From 67edaf8b026bee1c502b09d92249c10ce760e22a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 1 Jul 2023 08:29:34 +0900
Subject: [PATCH 0742/1080] Enhance rectangle fitting docs (#848)

* enhance rectangle fitting docs

* enhance rectangle fitting docs

* Update rectangle_fitting_main.rst

* Update rectangle_fitting_main.rst

* Update rectangle_fitting_main.rst

* Update rectangle_fitting_main.rst

* Update rectangle_fitting_main.rst

* Update rectangle_fitting_main.rst

* Update rectangle_fitting_main.rst

* Update rectangle_fitting_main.rst

* enhance rectangle fitting docs
---
 .../rectangle_fitting/rectangle_fitting.py    | 108 +++++++++++-------
 .../rectangle_fitting_main.rst                |  62 ++++++++++
 .../modules/slam/FastSLAM1/FastSLAM1_main.rst |   2 +-
 3 files changed, 128 insertions(+), 44 deletions(-)

diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py
index f54ada1e3a..177f078871 100644
--- a/Mapping/rectangle_fitting/rectangle_fitting.py
+++ b/Mapping/rectangle_fitting/rectangle_fitting.py
@@ -30,6 +30,11 @@
 
 
 class LShapeFitting:
+    """
+    LShapeFitting class. You can use this class by initializing the class and
+    changing the parameters, and then calling the fitting method.
+
+    """
 
     class Criteria(Enum):
         AREA = 1
@@ -37,15 +42,35 @@ class Criteria(Enum):
         VARIANCE = 3
 
     def __init__(self):
-        # Parameters
+        """
+        Default parameter settings
+        """
+        #: Fitting criteria parameter
         self.criteria = self.Criteria.VARIANCE
-        self.min_dist_of_closeness_criteria = 0.01  # [m]
-        self.d_theta_deg_for_search = 1.0  # [deg]
-        self.R0 = 3.0  # [m] range segmentation param
-        self.Rd = 0.001  # [m] range segmentation param
+        #: Minimum distance for closeness criteria parameter [m]
+        self.min_dist_of_closeness_criteria = 0.01
+        #: Angle difference parameter [deg]
+        self.d_theta_deg_for_search = 1.0
+        #: Range segmentation parameter [m]
+        self.R0 = 3.0
+        #: Range segmentation parameter [m]
+        self.Rd = 0.001
 
     def fitting(self, ox, oy):
+        """
+        Fitting L-shape model to object points
+
+        Parameters
+        ----------
+        ox : x positions of range points from an object
+        oy : y positions of range points from an object
+
+        Returns
+        -------
+        rects: Fitting rectangles
+        id_sets: id sets of each cluster
 
+        """
         # step1: Adaptive Range Segmentation
         id_sets = self._adoptive_range_segmentation(ox, oy)
 
@@ -60,56 +85,53 @@ def fitting(self, ox, oy):
 
     @staticmethod
     def _calc_area_criterion(c1, c2):
-        c1_max = max(c1)
-        c2_max = max(c2)
-        c1_min = min(c1)
-        c2_min = min(c2)
-
+        c1_max, c1_min, c2_max, c2_min = LShapeFitting._find_min_max(c1, c2)
         alpha = -(c1_max - c1_min) * (c2_max - c2_min)
-
         return alpha
 
     def _calc_closeness_criterion(self, c1, c2):
-        c1_max = max(c1)
-        c2_max = max(c2)
-        c1_min = min(c1)
-        c2_min = min(c2)
+        c1_max, c1_min, c2_max, c2_min = LShapeFitting._find_min_max(c1, c2)
 
         # Vectorization
-        D1 = np.minimum(c1_max - c1, c1 - c1_min)
-        D2 = np.minimum(c2_max - c2, c2 - c2_min)
-        d = np.maximum(np.minimum(D1, D2), self.min_dist_of_closeness_criteria)
+        d1 = np.minimum(c1_max - c1, c1 - c1_min)
+        d2 = np.minimum(c2_max - c2, c2 - c2_min)
+        d = np.maximum(np.minimum(d1, d2), self.min_dist_of_closeness_criteria)
         beta = (1.0 / d).sum()
 
         return beta
 
     @staticmethod
     def _calc_variance_criterion(c1, c2):
-        c1_max = max(c1)
-        c2_max = max(c2)
-        c1_min = min(c1)
-        c2_min = min(c2)
+        c1_max, c1_min, c2_max, c2_min = LShapeFitting._find_min_max(c1, c2)
 
         # Vectorization
-        D1 = np.minimum(c1_max - c1, c1 - c1_min)
-        D2 = np.minimum(c2_max - c2, c2 - c2_min)
-        E1 = D1[D1 < D2]
-        E2 = D2[D1 >= D2]
-        V1 = - np.var(E1) if len(E1) > 0 else 0.
-        V2 = - np.var(E2) if len(E2) > 0 else 0.
-        gamma = V1 + V2
+        d1 = np.minimum(c1_max - c1, c1 - c1_min)
+        d2 = np.minimum(c2_max - c2, c2 - c2_min)
+        e1 = d1[d1 < d2]
+        e2 = d2[d1 >= d2]
+        v1 = - np.var(e1) if len(e1) > 0 else 0.
+        v2 = - np.var(e2) if len(e2) > 0 else 0.
+        gamma = v1 + v2
 
         return gamma
 
+    @staticmethod
+    def _find_min_max(c1, c2):
+        c1_max = max(c1)
+        c2_max = max(c2)
+        c1_min = min(c1)
+        c2_min = min(c2)
+        return c1_max, c1_min, c2_max, c2_min
+
     def _rectangle_search(self, x, y):
 
-        X = np.array([x, y]).T
+        xy = np.array([x, y]).T
 
         d_theta = np.deg2rad(self.d_theta_deg_for_search)
         min_cost = (-float('inf'), None)
         for theta in np.arange(0.0, np.pi / 2.0 - d_theta, d_theta):
 
-            c = X @ rot_mat_2d(theta)
+            c = xy @ rot_mat_2d(theta)
             c1 = c[:, 0]
             c2 = c[:, 1]
 
@@ -129,8 +151,8 @@ def _rectangle_search(self, x, y):
         sin_s = np.sin(min_cost[1])
         cos_s = np.cos(min_cost[1])
 
-        c1_s = X @ np.array([cos_s, sin_s]).T
-        c2_s = X @ np.array([-sin_s, cos_s]).T
+        c1_s = xy @ np.array([cos_s, sin_s]).T
+        c2_s = xy @ np.array([-sin_s, cos_s]).T
 
         rect = RectangleData()
         rect.a[0] = cos_s
@@ -151,28 +173,28 @@ def _rectangle_search(self, x, y):
     def _adoptive_range_segmentation(self, ox, oy):
 
         # Setup initial cluster
-        S = []
+        segment_list = []
         for i, _ in enumerate(ox):
-            C = set()
-            R = self.R0 + self.Rd * np.linalg.norm([ox[i], oy[i]])
+            c = set()
+            r = self.R0 + self.Rd * np.linalg.norm([ox[i], oy[i]])
             for j, _ in enumerate(ox):
                 d = np.hypot(ox[i] - ox[j], oy[i] - oy[j])
-                if d <= R:
-                    C.add(j)
-            S.append(C)
+                if d <= r:
+                    c.add(j)
+            segment_list.append(c)
 
         # Merge cluster
         while True:
             no_change = True
-            for (c1, c2) in list(itertools.permutations(range(len(S)), 2)):
-                if S[c1] & S[c2]:
-                    S[c1] = (S[c1] | S.pop(c2))
+            for (c1, c2) in list(itertools.permutations(range(len(segment_list)), 2)):
+                if segment_list[c1] & segment_list[c2]:
+                    segment_list[c1] = (segment_list[c1] | segment_list.pop(c2))
                     no_change = False
                     break
             if no_change:
                 break
 
-        return S
+        return segment_list
 
 
 class RectangleData:
diff --git a/docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst b/docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst
index c0949ac4c3..50a50141b2 100644
--- a/docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst
+++ b/docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst
@@ -5,3 +5,65 @@ This is an object shape recognition using rectangle fitting.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/rectangle_fitting/animation.gif
 
+This example code is based on this paper algorithm:
+
+- `Efficient L\-Shape Fitting for Vehicle Detection Using Laser Scanners \- The Robotics Institute Carnegie Mellon University <https://www.ri.cmu.edu/publications/efficient-l-shape-fitting-for-vehicle-detection-using-laser-scanners>`_
+
+The algorithm consists of 2 steps as below.
+
+Step1: Adaptive range segmentation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In the first step, all range data points are segmented into some clusters.
+
+We calculate the distance between each range data and the nearest range data, and if this distance is below a certain threshold, it is judged to be in the same cluster. 
+This distance threshold is determined in proportion to the distance from the sensor.
+This is taking advantage of the general model of distance sensors, which tends to have sparser data distribution as the distance from the sensor increases.
+
+The threshold range is calculated by:
+
+.. math:: r_{th} = R_0 + R_d * r_{origin}
+
+where
+
+- :math:`r_{th}`: Threashold range
+- :math:`R_0, R_d`: Constant parameters
+- :math:`r_{origin}`: Distance from the sensor for a range data.
+
+Step2: Rectangle search
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In the second step, for each cluster calculated in the previous step, rectangular fittings will be applied.
+In this rectangular fitting, each cluster's distance data is rotated at certain angle intervals.
+It is evaluated by one of the three evaluation functions below, then best angle parameter one is selected as the rectangle shape.
+
+1. Rectangle Area Minimization criteria
+=========================================
+
+This evaluation function calculates the area of the smallest rectangle that includes all the points, derived from the difference between the maximum and minimum values on the x-y axis for all distance data points. 
+This allows for fitting a rectangle in a direction that encompasses as much of the smallest rectangular shape as possible.
+
+
+2. Closeness criteria
+======================
+
+This evaluation function uses the distances between the top and bottom vertices on the right side of the rectangle and each point in the distance data as evaluation values. 
+If there are points on the rectangle edges, this evaluation value decreases.
+
+3. Variance criteria
+=======================
+
+This evaluation function uses the squreed distances between the edges of the rectangle (horizontal and vertical) and each point. 
+Calculating the squared error is the same as calculating the variance.
+The smaller this variance, the more it signifies that the points fit within the rectangle.
+
+API
+~~~~~~
+
+.. autoclass:: Mapping.rectangle_fitting.rectangle_fitting.LShapeFitting
+	:members:
+
+References
+~~~~~~~~~~
+
+- `Efficient L\-Shape Fitting for Vehicle Detection Using Laser Scanners \- The Robotics Institute Carnegie Mellon University <https://www.ri.cmu.edu/publications/efficient-l-shape-fitting-for-vehicle-detection-using-laser-scanners>`_
diff --git a/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst b/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst
index f19527c453..a2aa521216 100644
--- a/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst
+++ b/docs/modules/slam/FastSLAM1/FastSLAM1_main.rst
@@ -65,7 +65,7 @@ represent the initial uncertainty. At each time step we do:
 
 The following equations and code snippets we can see how the particles
 distribution evolves in case we provide only the control :math:`(v,w)`,
-which are the linear and angular velocity repsectively.
+which are the linear and angular velocity respectively.
 
 :math:`\begin{equation*} F= \begin{bmatrix} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix} \end{equation*}`
 

From 4d714706312735ce46a118bb6782c2aef0ba333f Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 1 Jul 2023 15:30:32 +0900
Subject: [PATCH 0743/1080] upgrade numpy to 1.25.0 and fix bugs (#861)

---
 .../drone_3d_trajectory_following.py          | 13 ++++---
 .../two_joint_arm_to_point_control.py         | 11 +++---
 .../inverted_pendulum_lqr_control.py          |  6 +--
 .../inverted_pendulum_mpc_control.py          |  6 +--
 .../histogram_filter/histogram_filter.py      |  5 ++-
 .../unscented_kalman_filter.py                |  4 +-
 Mapping/circle_fitting/circle_fitting.py      |  6 +--
 .../batch_informed_rrtstar.py                 |  6 +--
 .../InformedRRTStar/informed_rrt_star.py      |  4 +-
 .../lookup_table_generator.py                 |  2 +-
 .../motion_model.py                           | 39 +++++++++++--------
 .../trajectory_generator.py                   |  6 +--
 .../state_lattice_planner.py                  |  2 +-
 SLAM/FastSLAM1/fast_slam1.py                  |  2 +-
 SLAM/FastSLAM2/fast_slam2.py                  |  2 +-
 SLAM/GraphBasedSLAM/graph_based_slam.py       |  6 +--
 requirements/requirements.txt                 |  2 +-
 tests/test_codestyle.py                       |  2 +-
 18 files changed, 66 insertions(+), 58 deletions(-)

diff --git a/AerialNavigation/drone_3d_trajectory_following/drone_3d_trajectory_following.py b/AerialNavigation/drone_3d_trajectory_following/drone_3d_trajectory_following.py
index e00b3fff48..029e82be62 100644
--- a/AerialNavigation/drone_3d_trajectory_following/drone_3d_trajectory_following.py
+++ b/AerialNavigation/drone_3d_trajectory_following/drone_3d_trajectory_following.py
@@ -8,7 +8,6 @@
 import numpy as np
 from Quadrotor import Quadrotor
 from TrajectoryGenerator import TrajectoryGenerator
-from mpl_toolkits.mplot3d import Axes3D
 
 show_animation = True
 
@@ -128,7 +127,7 @@ def calculate_position(c, t):
     Calculates a position given a set of quintic coefficients and a time.
 
     Args
-        c: List of coefficients generated by a quintic polynomial 
+        c: List of coefficients generated by a quintic polynomial
             trajectory generator.
         t: Time at which to calculate the position
 
@@ -143,7 +142,7 @@ def calculate_velocity(c, t):
     Calculates a velocity given a set of quintic coefficients and a time.
 
     Args
-        c: List of coefficients generated by a quintic polynomial 
+        c: List of coefficients generated by a quintic polynomial
             trajectory generator.
         t: Time at which to calculate the velocity
 
@@ -158,7 +157,7 @@ def calculate_acceleration(c, t):
     Calculates an acceleration given a set of quintic coefficients and a time.
 
     Args
-        c: List of coefficients generated by a quintic polynomial 
+        c: List of coefficients generated by a quintic polynomial
             trajectory generator.
         t: Time at which to calculate the acceleration
 
@@ -168,7 +167,7 @@ def calculate_acceleration(c, t):
     return 20 * c[0] * t**3 + 12 * c[1] * t**2 + 6 * c[2] * t + 2 * c[3]
 
 
-def rotation_matrix(roll, pitch, yaw):
+def rotation_matrix(roll_array, pitch_array, yaw):
     """
     Calculates the ZYX rotation matrix.
 
@@ -180,6 +179,8 @@ def rotation_matrix(roll, pitch, yaw):
     Returns
         3x3 rotation matrix as NumPy array
     """
+    roll = roll_array[0]
+    pitch = pitch_array[0]
     return np.array(
         [[cos(yaw) * cos(pitch), -sin(yaw) * cos(roll) + cos(yaw) * sin(pitch) * sin(roll), sin(yaw) * sin(roll) + cos(yaw) * sin(pitch) * cos(roll)],
          [sin(yaw) * cos(pitch), cos(yaw) * cos(roll) + sin(yaw) * sin(pitch) *
@@ -190,7 +191,7 @@ def rotation_matrix(roll, pitch, yaw):
 
 def main():
     """
-    Calculates the x, y, z coefficients for the four segments 
+    Calculates the x, y, z coefficients for the four segments
     of the trajectory
     """
     x_coeffs = [[], [], [], []]
diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
index 012a7f72f1..66d0ebeb23 100644
--- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
+++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
@@ -14,9 +14,10 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+import math
 
 
-# Similation parameters
+# Simulation parameters
 Kp = 15
 dt = 0.01
 
@@ -50,15 +51,15 @@ def two_joint_arm(GOAL_TH=0.0, theta1=0.0, theta2=0.0):
             else:
                 theta2_goal = np.arccos(
                     (x**2 + y**2 - l1**2 - l2**2) / (2 * l1 * l2))
-            tmp = np.math.atan2(l2 * np.sin(theta2_goal),
+            tmp = math.atan2(l2 * np.sin(theta2_goal),
                                 (l1 + l2 * np.cos(theta2_goal)))
-            theta1_goal = np.math.atan2(y, x) - tmp
+            theta1_goal = math.atan2(y, x) - tmp
 
             if theta1_goal < 0:
                 theta2_goal = -theta2_goal
-                tmp = np.math.atan2(l2 * np.sin(theta2_goal),
+                tmp = math.atan2(l2 * np.sin(theta2_goal),
                                     (l1 + l2 * np.cos(theta2_goal)))
-                theta1_goal = np.math.atan2(y, x) - tmp
+                theta1_goal = math.atan2(y, x) - tmp
 
             theta1 = theta1 + Kp * ang_diff(theta1_goal, theta1) * dt
             theta2 = theta2 + Kp * ang_diff(theta2_goal, theta2) * dt
diff --git a/Control/inverted_pendulum/inverted_pendulum_lqr_control.py b/Control/inverted_pendulum/inverted_pendulum_lqr_control.py
index fb68c91c45..c4380530b8 100644
--- a/Control/inverted_pendulum/inverted_pendulum_lqr_control.py
+++ b/Control/inverted_pendulum/inverted_pendulum_lqr_control.py
@@ -50,14 +50,14 @@ def main():
 
         if show_animation:
             plt.clf()
-            px = float(x[0])
-            theta = float(x[2])
+            px = float(x[0, 0])
+            theta = float(x[2, 0])
             plot_cart(px, theta)
             plt.xlim([-5.0, 2.0])
             plt.pause(0.001)
 
     print("Finish")
-    print(f"x={float(x[0]):.2f} [m] , theta={math.degrees(x[2]):.2f} [deg]")
+    print(f"x={float(x[0, 0]):.2f} [m] , theta={math.degrees(x[2, 0]):.2f} [deg]")
     if show_animation:
         plt.show()
 
diff --git a/Control/inverted_pendulum/inverted_pendulum_mpc_control.py b/Control/inverted_pendulum/inverted_pendulum_mpc_control.py
index 6b966b13b4..9a5fa2ab41 100644
--- a/Control/inverted_pendulum/inverted_pendulum_mpc_control.py
+++ b/Control/inverted_pendulum/inverted_pendulum_mpc_control.py
@@ -55,14 +55,14 @@ def main():
 
         if show_animation:
             plt.clf()
-            px = float(x[0])
-            theta = float(x[2])
+            px = float(x[0, 0])
+            theta = float(x[2, 0])
             plot_cart(px, theta)
             plt.xlim([-5.0, 2.0])
             plt.pause(0.001)
 
     print("Finish")
-    print(f"x={float(x[0]):.2f} [m] , theta={math.degrees(x[2]):.2f} [deg]")
+    print(f"x={float(x[0, 0]):.2f} [m] , theta={math.degrees(x[2, 0]):.2f} [deg]")
     if show_animation:
         plt.show()
 
diff --git a/Localization/histogram_filter/histogram_filter.py b/Localization/histogram_filter/histogram_filter.py
index a4aeb419cf..17cfc2e14c 100644
--- a/Localization/histogram_filter/histogram_filter.py
+++ b/Localization/histogram_filter/histogram_filter.py
@@ -15,6 +15,7 @@
 import math
 
 import matplotlib.pyplot as plt
+import matplotlib as mpl
 import numpy as np
 from scipy.ndimage import gaussian_filter
 from scipy.stats import norm
@@ -114,7 +115,7 @@ def motion_model(x, u):
 def draw_heat_map(data, mx, my):
     max_value = max([max(i_data) for i_data in data])
     plt.grid(False)
-    plt.pcolor(mx, my, data, vmax=max_value, cmap=plt.cm.get_cmap("Blues"))
+    plt.pcolor(mx, my, data, vmax=max_value, cmap=mpl.colormaps["Blues"])
     plt.axis("equal")
 
 
@@ -194,7 +195,7 @@ def motion_update(grid_map, u, yaw):
     y_shift = grid_map.dy // grid_map.xy_resolution
 
     if abs(x_shift) >= 1.0 or abs(y_shift) >= 1.0:  # map should be shifted
-        grid_map = map_shift(grid_map, int(x_shift), int(y_shift))
+        grid_map = map_shift(grid_map, int(x_shift[0]), int(y_shift[0]))
         grid_map.dx -= x_shift * grid_map.xy_resolution
         grid_map.dy -= y_shift * grid_map.xy_resolution
 
diff --git a/Localization/unscented_kalman_filter/unscented_kalman_filter.py b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
index dbcdeef015..4af748ec71 100644
--- a/Localization/unscented_kalman_filter/unscented_kalman_filter.py
+++ b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
@@ -69,8 +69,8 @@ def motion_model(x, u):
                   [0, 0, 1.0, 0],
                   [0, 0, 0, 0]])
 
-    B = np.array([[DT * math.cos(x[2]), 0],
-                  [DT * math.sin(x[2]), 0],
+    B = np.array([[DT * math.cos(x[2, 0]), 0],
+                  [DT * math.sin(x[2, 0]), 0],
                   [0.0, DT],
                   [1.0, 0.0]])
 
diff --git a/Mapping/circle_fitting/circle_fitting.py b/Mapping/circle_fitting/circle_fitting.py
index c331d56796..2eba550127 100644
--- a/Mapping/circle_fitting/circle_fitting.py
+++ b/Mapping/circle_fitting/circle_fitting.py
@@ -40,9 +40,9 @@ def circle_fitting(x, y):
 
     T = np.linalg.inv(F).dot(G)
 
-    cxe = float(T[0] / -2)
-    cye = float(T[1] / -2)
-    re = math.sqrt(cxe**2 + cye**2 - T[2])
+    cxe = float(T[0, 0] / -2)
+    cye = float(T[1, 0] / -2)
+    re = math.sqrt(cxe**2 + cye**2 - T[2, 0])
 
     error = sum([np.hypot(cxe - ix, cye - iy) - re for (ix, iy) in zip(x, y)])
 
diff --git a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
index b5eabbb39b..92c3a58761 100644
--- a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
+++ b/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
@@ -23,7 +23,7 @@
 show_animation = True
 
 
-class RTree(object):
+class RTree:
     # Class to represent the explicit tree created
     # while sampling through the state space
 
@@ -132,7 +132,7 @@ def node_id_to_real_world_coord(self, nid):
             self.node_id_to_grid_coord(nid))
 
 
-class BITStar(object):
+class BITStar:
 
     def __init__(self, start, goal,
                  obstacleList, randArea, eta=2.0,
@@ -189,7 +189,7 @@ def setup_planning(self):
                             [(self.start[1] + self.goal[1]) / 2.0], [0]])
         a1 = np.array([[(self.goal[0] - self.start[0]) / cMin],
                        [(self.goal[1] - self.start[1]) / cMin], [0]])
-        eTheta = math.atan2(a1[1], a1[0])
+        eTheta = math.atan2(a1[1, 0], a1[0, 0])
         # first column of identity matrix transposed
         id1_t = np.array([1.0, 0.0, 0.0]).reshape(1, 3)
         M = np.dot(a1, id1_t)
diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index 4f0ec31988..b6f9d234dd 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -58,7 +58,7 @@ def informed_rrt_star_search(self, animation=True):
         a1 = np.array([[(self.goal.x - self.start.x) / c_min],
                        [(self.goal.y - self.start.y) / c_min], [0]])
 
-        e_theta = math.atan2(a1[1], a1[0])
+        e_theta = math.atan2(a1[1, 0], a1[0, 0])
         # first column of identity matrix transposed
         id1_t = np.array([1.0, 0.0, 0.0]).reshape(1, 3)
         m = a1 @ id1_t
@@ -136,7 +136,7 @@ def choose_parent(self, new_node, near_inds):
 
     def find_near_nodes(self, new_node):
         n_node = len(self.node_list)
-        r = 50.0 * math.sqrt((math.log(n_node) / n_node))
+        r = 50.0 * math.sqrt(math.log(n_node) / n_node)
         d_list = [(node.x - new_node.x) ** 2 + (node.y - new_node.y) ** 2 for
                   node in self.node_list]
         near_inds = [d_list.index(i) for i in d_list if i <= r ** 2]
diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/lookup_table_generator.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/lookup_table_generator.py
index 6cb226adef..4c3b32f280 100644
--- a/PathPlanning/ModelPredictiveTrajectoryGenerator/lookup_table_generator.py
+++ b/PathPlanning/ModelPredictiveTrajectoryGenerator/lookup_table_generator.py
@@ -81,7 +81,7 @@ def generate_lookup_table():
         if x is not None:
             print("find good path")
             lookup_table.append(
-                [x[-1], y[-1], yaw[-1], float(p[0]), float(p[1]), float(p[2])])
+                [x[-1], y[-1], yaw[-1], float(p[0, 0]), float(p[1, 0]), float(p[2, 0])])
 
     print("finish lookup table generation")
 
diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py
index f6c78d3bc1..531bf91ef5 100644
--- a/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py
+++ b/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py
@@ -1,10 +1,10 @@
 import math
 import numpy as np
-import scipy.interpolate
+from scipy.interpolate import interp1d
 
 # motion parameter
 L = 1.0  # wheel base
-ds = 0.1  # course distanse
+ds = 0.1  # course distance
 v = 10.0 / 3.6  # velocity [m/s]
 
 
@@ -22,7 +22,6 @@ def pi_2_pi(angle):
 
 
 def update(state, v, delta, dt, L):
-
     state.v = v
     state.x = state.x + state.v * math.cos(state.yaw) * dt
     state.y = state.y + state.v * math.sin(state.yaw) * dt
@@ -33,18 +32,20 @@ def update(state, v, delta, dt, L):
 
 
 def generate_trajectory(s, km, kf, k0):
-
     n = s / ds
     time = s / v  # [s]
-    
-    if isinstance(time, type(np.array([]))): time = time[0]
-    if isinstance(km, type(np.array([]))): km = km[0]
-    if isinstance(kf, type(np.array([]))): kf = kf[0]
-    
+
+    if isinstance(time, type(np.array([]))):
+        time = time[0]
+    if isinstance(km, type(np.array([]))):
+        km = km[0]
+    if isinstance(kf, type(np.array([]))):
+        kf = kf[0]
+
     tk = np.array([0.0, time / 2.0, time])
     kk = np.array([k0, km, kf])
     t = np.arange(0.0, time, time / n)
-    fkp = scipy.interpolate.interp1d(tk, kk, kind="quadratic")
+    fkp = interp1d(tk, kk, kind="quadratic")
     kp = [fkp(ti) for ti in t]
     dt = float(time / n)
 
@@ -64,18 +65,22 @@ def generate_trajectory(s, km, kf, k0):
 
 
 def generate_last_state(s, km, kf, k0):
-
     n = s / ds
     time = s / v  # [s]
-    
-    if isinstance(time,  type(np.array([]))): time = time[0]
-    if isinstance(km, type(np.array([]))): km = km[0]
-    if isinstance(kf, type(np.array([]))): kf = kf[0]
-    
+
+    if isinstance(n, type(np.array([]))):
+        n = n[0]
+    if isinstance(time, type(np.array([]))):
+        time = time[0]
+    if isinstance(km, type(np.array([]))):
+        km = km[0]
+    if isinstance(kf, type(np.array([]))):
+        kf = kf[0]
+
     tk = np.array([0.0, time / 2.0, time])
     kk = np.array([k0, km, kf])
     t = np.arange(0.0, time, time / n)
-    fkp = scipy.interpolate.interp1d(tk, kk, kind="quadratic")
+    fkp = interp1d(tk, kk, kind="quadratic")
     kp = [fkp(ti) for ti in t]
     dt = time / n
 
diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py
index a44e1ac956..97c0ad8b80 100644
--- a/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py
+++ b/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py
@@ -106,7 +106,7 @@ def show_trajectory(target, xc, yc):  # pragma: no cover
 
 def optimize_trajectory(target, k0, p):
     for i in range(max_iter):
-        xc, yc, yawc = motion_model.generate_trajectory(p[0], p[1], p[2], k0)
+        xc, yc, yawc = motion_model.generate_trajectory(p[0, 0], p[1, 0], p[2, 0], k0)
         dc = np.array(calc_diff(target, xc, yc, yawc)).reshape(3, 1)
 
         cost = np.linalg.norm(dc)
@@ -135,7 +135,7 @@ def optimize_trajectory(target, k0, p):
     return xc, yc, yawc, p
 
 
-def test_optimize_trajectory():  # pragma: no cover
+def optimize_trajectory_demo():  # pragma: no cover
 
     #  target = motion_model.State(x=5.0, y=2.0, yaw=np.deg2rad(00.0))
     target = motion_model.State(x=5.0, y=2.0, yaw=np.deg2rad(90.0))
@@ -155,7 +155,7 @@ def test_optimize_trajectory():  # pragma: no cover
 
 def main():  # pragma: no cover
     print(__file__ + " start!!")
-    test_optimize_trajectory()
+    optimize_trajectory_demo()
 
 
 if __name__ == '__main__':
diff --git a/PathPlanning/StateLatticePlanner/state_lattice_planner.py b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
index e26fb8288c..f4b1461b7a 100644
--- a/PathPlanning/StateLatticePlanner/state_lattice_planner.py
+++ b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
@@ -70,7 +70,7 @@ def generate_path(target_states, k0):
         if x is not None:
             print("find good path")
             result.append(
-                [x[-1], y[-1], yaw[-1], float(p[0]), float(p[1]), float(p[2])])
+                [x[-1], y[-1], yaw[-1], float(p[0, 0]), float(p[1, 0]), float(p[2, 0])])
 
     print("finish path generation")
     return result
diff --git a/SLAM/FastSLAM1/fast_slam1.py b/SLAM/FastSLAM1/fast_slam1.py
index c4beaba257..7b89f52df4 100644
--- a/SLAM/FastSLAM1/fast_slam1.py
+++ b/SLAM/FastSLAM1/fast_slam1.py
@@ -194,7 +194,7 @@ def compute_weight(particle, z, Q_cov):
         print("singular")
         return 1.0
 
-    num = math.exp(-0.5 * dx.T @ invS @ dx)
+    num = np.exp(-0.5 * (dx.T @ invS @ dx))[0, 0]
     den = 2.0 * math.pi * math.sqrt(np.linalg.det(Sf))
 
     w = num / den
diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py
index 4c6c103325..aa77aa956a 100644
--- a/SLAM/FastSLAM2/fast_slam2.py
+++ b/SLAM/FastSLAM2/fast_slam2.py
@@ -193,7 +193,7 @@ def compute_weight(particle, z, Q_cov):
     except np.linalg.linalg.LinAlgError:
         return 1.0
 
-    num = math.exp(-0.5 * dz.T @ invS @ dz)
+    num = np.exp(-0.5 * dz.T @ invS @ dz)[0, 0]
     den = 2.0 * math.pi * math.sqrt(np.linalg.det(Sf))
 
     w = num / den
diff --git a/SLAM/GraphBasedSLAM/graph_based_slam.py b/SLAM/GraphBasedSLAM/graph_based_slam.py
index 5510a41110..4d66ef6732 100644
--- a/SLAM/GraphBasedSLAM/graph_based_slam.py
+++ b/SLAM/GraphBasedSLAM/graph_based_slam.py
@@ -117,9 +117,9 @@ def calc_edges(x_list, z_list):
             for iz2 in range(len(z_list[t2][:, 0])):
                 if z_list[t1][iz1, 3] == z_list[t2][iz2, 3]:
                     d1 = z_list[t1][iz1, 0]
-                    angle1, phi1 = z_list[t1][iz1, 1], z_list[t1][iz1, 2]
+                    angle1, _ = z_list[t1][iz1, 1], z_list[t1][iz1, 2]
                     d2 = z_list[t2][iz2, 0]
-                    angle2, phi2 = z_list[t2][iz2, 1], z_list[t2][iz2, 2]
+                    angle2, _ = z_list[t2][iz2, 1], z_list[t2][iz2, 2]
 
                     edge = calc_edge(x1, y1, yaw1, x2, y2, yaw2, d1,
                                      angle1, d2, angle2, t1, t2)
@@ -188,7 +188,7 @@ def graph_based_slam(x_init, hz):
         for i in range(nt):
             x_opt[0:3, i] += dx[i * 3:i * 3 + 3, 0]
 
-        diff = dx.T @ dx
+        diff = (dx.T @ dx)[0, 0]
         print("iteration: %d, diff: %f" % (itr + 1, diff))
         if diff < 1.0e-5:
             break
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 9db6898463..4f226facb1 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.24.3
+numpy == 1.25.0
 scipy == 1.10.1
 matplotlib == 3.7.1
 cvxpy == 1.3.1
diff --git a/tests/test_codestyle.py b/tests/test_codestyle.py
index 2ad3816ea1..a7d11d270f 100644
--- a/tests/test_codestyle.py
+++ b/tests/test_codestyle.py
@@ -126,7 +126,7 @@ def test():
     branch_commit = find_branch_point("origin/master")
     files = diff_files(branch_commit)
     print(files)
-    rc, errors = run_ruff(files, fix=False)
+    rc, errors = run_ruff(files, fix=True)
     if errors:
         print(errors)
     else:

From e0b7ea5a0ace961fdada3bf524080208e36875ee Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 1 Jul 2023 18:40:03 +0900
Subject: [PATCH 0744/1080] upgrade scipy and cvxpy (#864)

---
 requirements/requirements.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 4f226facb1..30ccc4765d 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 1.25.0
-scipy == 1.10.1
+scipy == 1.11.1
 matplotlib == 3.7.1
-cvxpy == 1.3.1
+cvxpy == 1.3.2
 pytest == 7.4.0 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.4.1 # For unit test

From 5264c68542306e901fc0585219ade6f6f573d2a9 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 2 Jul 2023 22:12:25 +0900
Subject: [PATCH 0745/1080] Update dependabot.yml (#868)

---
 .github/dependabot.yml | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/.github/dependabot.yml b/.github/dependabot.yml
index 07f1bc80e0..7d7e6723cb 100644
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@@ -6,3 +6,8 @@ updates:
     interval: weekly
     time: "20:00"
   open-pull-requests-limit: 10
+
+- package-ecosystem: 'github-actions'
+    directory: '/'
+    schedule:
+      interval: 'weekly'

From c14bdda136d76193e8fdf2f0322579c0e0c71571 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 2 Jul 2023 22:25:55 +0900
Subject: [PATCH 0746/1080] Update dependabot.yml

---
 .github/dependabot.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/dependabot.yml b/.github/dependabot.yml
index 7d7e6723cb..c6fecb796f 100644
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@@ -7,7 +7,7 @@ updates:
     time: "20:00"
   open-pull-requests-limit: 10
 
-- package-ecosystem: 'github-actions'
-    directory: '/'
+- package-ecosystem: github-actions
+    directory: "/"
     schedule:
-      interval: 'weekly'
+      interval: weekly

From d5a0512568edbb12c7020e578d2b484ec48f8f3b Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 2 Jul 2023 22:27:45 +0900
Subject: [PATCH 0747/1080] Update dependabot.yml

---
 .github/dependabot.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/dependabot.yml b/.github/dependabot.yml
index c6fecb796f..6ffedf6f3b 100644
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@@ -8,6 +8,6 @@ updates:
   open-pull-requests-limit: 10
 
 - package-ecosystem: github-actions
-    directory: "/"
-    schedule:
-      interval: weekly
+  directory: "/"
+  schedule:
+    interval: weekly

From 64c7624760a450a7163723b37a3f2dd7da09a4e0 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sun, 2 Jul 2023 23:12:22 +0900
Subject: [PATCH 0748/1080] Bump actions/checkout from 2 to 3 (#869)

Bumps [actions/checkout](https://github.com/actions/checkout) from 2 to 3.
- [Release notes](https://github.com/actions/checkout/releases)
- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)
- [Commits](https://github.com/actions/checkout/compare/v2...v3)

---
updated-dependencies:
- dependency-name: actions/checkout
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/Linux_CI.yml   | 2 +-
 .github/workflows/MacOS_CI.yml   | 2 +-
 .github/workflows/Windows_CI.yml | 2 +-
 .github/workflows/codeql.yml     | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index a7b5aba5fe..a92d022e96 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -17,7 +17,7 @@ jobs:
     name: Python ${{ matrix.python-version }} CI
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
     - run: git fetch --prune --unshallow
 
     - name: Setup python
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index 6f0849a43d..f983cbbab3 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -19,7 +19,7 @@ jobs:
         python-version: [ '3.11' ]
     name: Python ${{ matrix.python-version }} CI
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
       - run: git fetch --prune --unshallow
 
       - name: Update bash
diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml
index ed267045ca..5ca29fc632 100644
--- a/.github/workflows/Windows_CI.yml
+++ b/.github/workflows/Windows_CI.yml
@@ -19,7 +19,7 @@ jobs:
         python-version: [ '3.11' ]
     name: Python ${{ matrix.python-version }} CI
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
       - run: git fetch --prune --unshallow
 
       - name: Setup python
diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index 148260a835..ad173f14bf 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -16,7 +16,7 @@ jobs:
 
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v2
+      uses: actions/checkout@v3
       with:
         # We must fetch at least the immediate parents so that if this is
         # a pull request then we can checkout the head.

From 38639fd9c21999b1c45f0f2e3e0aa6e74d4c6ed8 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sun, 2 Jul 2023 23:12:44 +0900
Subject: [PATCH 0749/1080] Bump github/codeql-action from 1 to 2 (#870)

Bumps [github/codeql-action](https://github.com/github/codeql-action) from 1 to 2.
- [Release notes](https://github.com/github/codeql-action/releases)
- [Changelog](https://github.com/github/codeql-action/blob/main/CHANGELOG.md)
- [Commits](https://github.com/github/codeql-action/compare/v1...v2)

---
updated-dependencies:
- dependency-name: github/codeql-action
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/codeql.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index ad173f14bf..d33545c90f 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -24,7 +24,7 @@ jobs:
 
     # Initializes the CodeQL tools for scanning.
     - name: Initialize CodeQL
-      uses: github/codeql-action/init@v1
+      uses: github/codeql-action/init@v2
       with:
         config-file: ./.github/codeql/codeql-config.yml
       # Override language selection by uncommenting this and choosing your languages
@@ -34,7 +34,7 @@ jobs:
     # Autobuild attempts to build any compiled languages  (C/C++, C#, or Java).
     # If this step fails, then you should remove it and run the build manually (see below)
     - name: Autobuild
-      uses: github/codeql-action/autobuild@v1
+      uses: github/codeql-action/autobuild@v2
 
     # ℹ️ Command-line programs to run using the OS shell.
     # 📚 https://git.io/JvXDl
@@ -48,4 +48,4 @@ jobs:
     #   make release
 
     - name: Perform CodeQL Analysis
-      uses: github/codeql-action/analyze@v1
+      uses: github/codeql-action/analyze@v2

From 46d9809df367979c3d8bec8d44a63eb3e529fb23 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sun, 2 Jul 2023 23:12:57 +0900
Subject: [PATCH 0750/1080] Bump actions/setup-python from 2 to 4 (#871)

Bumps [actions/setup-python](https://github.com/actions/setup-python) from 2 to 4.
- [Release notes](https://github.com/actions/setup-python/releases)
- [Commits](https://github.com/actions/setup-python/compare/v2...v4)

---
updated-dependencies:
- dependency-name: actions/setup-python
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/Linux_CI.yml   | 2 +-
 .github/workflows/MacOS_CI.yml   | 2 +-
 .github/workflows/Windows_CI.yml | 2 +-
 .github/workflows/gh-pages.yml   | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index a92d022e96..a152972753 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -21,7 +21,7 @@ jobs:
     - run: git fetch --prune --unshallow
 
     - name: Setup python
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v4
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index f983cbbab3..c8bfbbcb7e 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -26,7 +26,7 @@ jobs:
         run: brew install bash
 
       - name: Setup python
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
 
diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml
index 5ca29fc632..13fdcea8a1 100644
--- a/.github/workflows/Windows_CI.yml
+++ b/.github/workflows/Windows_CI.yml
@@ -23,7 +23,7 @@ jobs:
       - run: git fetch --prune --unshallow
 
       - name: Setup python
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
 
diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml
index bf46598633..d5af3b87c6 100644
--- a/.github/workflows/gh-pages.yml
+++ b/.github/workflows/gh-pages.yml
@@ -8,7 +8,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - name: Setup python
-      uses: actions/setup-python@v3
+      uses: actions/setup-python@v4
     - name: Checkout
       uses: actions/checkout@master
       with:

From 49dd1a93f37a47a7e5c91376947b49282cceb3da Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 4 Jul 2023 07:35:39 +0900
Subject: [PATCH 0751/1080] Bump ruff from 0.0.275 to 0.0.276 in /requirements
 (#872)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.275 to 0.0.276.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.0.275...v0.0.276)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 30ccc4765d..2fab6f69c8 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.2
 pytest == 7.4.0 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.4.1 # For unit test
-ruff == 0.0.275 # For unit test
+ruff == 0.0.276 # For unit test

From 0fc769421f66a6403f750781dc46534d84e7c424 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 6 Jul 2023 23:12:43 +0900
Subject: [PATCH 0752/1080] Adding NDT mapping script and doc (#867)

* Adding ndt mapping script and doc

* Adding ndt mapping script and doc

* Adding ndt mapping script and doc

* Adding ndt mapping script and doc

* Adding ndt mapping script and doc

* Adding ndt mapping script and doc

* Adding ndt mapping script and doc

* Adding ndt mapping script and doc
---
 .../extended_kalman_filter.py                 |  28 +---
 Mapping/grid_map_lib/grid_map_lib.py          | 125 ++++++++++------
 Mapping/ndt_map/ndt_map.py                    | 135 ++++++++++++++++++
 .../grid_based_sweep_coverage_path_planner.py |  31 ++--
 docs/modules/mapping/mapping_main.rst         |   1 +
 .../mapping/ndt_map/grid_clustering.png       | Bin 0 -> 33111 bytes
 docs/modules/mapping/ndt_map/ndt_map1.png     | Bin 0 -> 23081 bytes
 docs/modules/mapping/ndt_map/ndt_map2.png     | Bin 0 -> 21736 bytes
 docs/modules/mapping/ndt_map/ndt_map_main.rst |  53 +++++++
 .../mapping/ndt_map/raw_observations.png      | Bin 0 -> 18305 bytes
 tests/test_grid_map_lib.py                    |  11 ++
 utils/plot.py                                 |  71 ++++++++-
 12 files changed, 372 insertions(+), 83 deletions(-)
 create mode 100644 Mapping/ndt_map/ndt_map.py
 create mode 100644 docs/modules/mapping/ndt_map/grid_clustering.png
 create mode 100644 docs/modules/mapping/ndt_map/ndt_map1.png
 create mode 100644 docs/modules/mapping/ndt_map/ndt_map2.png
 create mode 100644 docs/modules/mapping/ndt_map/ndt_map_main.rst
 create mode 100644 docs/modules/mapping/ndt_map/raw_observations.png

diff --git a/Localization/extended_kalman_filter/extended_kalman_filter.py b/Localization/extended_kalman_filter/extended_kalman_filter.py
index c2fe8e3a87..d9ece6c6f3 100644
--- a/Localization/extended_kalman_filter/extended_kalman_filter.py
+++ b/Localization/extended_kalman_filter/extended_kalman_filter.py
@@ -7,13 +7,14 @@
 """
 import sys
 import pathlib
+
 sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import math
 import matplotlib.pyplot as plt
 import numpy as np
 
-from utils.angle import rot_mat_2d
+from utils.plot import plot_covariance_ellipse
 
 # Covariance for EKF simulation
 Q = np.diag([
@@ -135,29 +136,6 @@ def ekf_estimation(xEst, PEst, z, u):
     return xEst, PEst
 
 
-def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
-    Pxy = PEst[0:2, 0:2]
-    eigval, eigvec = np.linalg.eig(Pxy)
-
-    if eigval[0] >= eigval[1]:
-        bigind = 0
-        smallind = 1
-    else:
-        bigind = 1
-        smallind = 0
-
-    t = np.arange(0, 2 * math.pi + 0.1, 0.1)
-    a = math.sqrt(eigval[bigind])
-    b = math.sqrt(eigval[smallind])
-    x = [a * math.cos(it) for it in t]
-    y = [b * math.sin(it) for it in t]
-    angle = math.atan2(eigvec[1, bigind], eigvec[0, bigind])
-    fx = rot_mat_2d(angle) @ (np.array([x, y]))
-    px = np.array(fx[0, :] + xEst[0, 0]).flatten()
-    py = np.array(fx[1, :] + xEst[1, 0]).flatten()
-    plt.plot(px, py, "--r")
-
-
 def main():
     print(__file__ + " start!!")
 
@@ -202,7 +180,7 @@ def main():
                      hxDR[1, :].flatten(), "-k")
             plt.plot(hxEst[0, :].flatten(),
                      hxEst[1, :].flatten(), "-r")
-            plot_covariance_ellipse(xEst, PEst)
+            plot_covariance_ellipse(xEst[0, 0], xEst[1, 0], PEst)
             plt.axis("equal")
             plt.grid(True)
             plt.pause(0.001)
diff --git a/Mapping/grid_map_lib/grid_map_lib.py b/Mapping/grid_map_lib/grid_map_lib.py
index 85dd76e71d..d08d8ec5ba 100644
--- a/Mapping/grid_map_lib/grid_map_lib.py
+++ b/Mapping/grid_map_lib/grid_map_lib.py
@@ -5,22 +5,42 @@
 author: Atsushi Sakai
 
 """
-
+from functools import total_ordering
 import matplotlib.pyplot as plt
 import numpy as np
 
 
+@total_ordering
+class FloatGrid:
+
+    def __init__(self, init_val=0.0):
+        self.data = init_val
+
+    def get_float_data(self):
+        return self.data
+
+    def __eq__(self, other):
+        if not isinstance(other, FloatGrid):
+            return NotImplemented
+        return self.get_float_data() == other.get_float_data()
+
+    def __lt__(self, other):
+        if not isinstance(other, FloatGrid):
+            return NotImplemented
+        return self.get_float_data() < other.get_float_data()
+
+
 class GridMap:
     """
     GridMap class
     """
 
     def __init__(self, width, height, resolution,
-                 center_x, center_y, init_val=0.0):
+                 center_x, center_y, init_val=FloatGrid(0.0)):
         """__init__
 
         :param width: number of grid for width
-        :param height: number of grid for heigt
+        :param height: number of grid for height
         :param resolution: grid resolution [m]
         :param center_x: center x position  [m]
         :param center_y: center y position [m]
@@ -35,8 +55,9 @@ def __init__(self, width, height, resolution,
         self.left_lower_x = self.center_x - self.width / 2.0 * self.resolution
         self.left_lower_y = self.center_y - self.height / 2.0 * self.resolution
 
-        self.ndata = self.width * self.height
-        self.data = [init_val] * self.ndata
+        self.n_data = self.width * self.height
+        self.data = [init_val] * self.n_data
+        self.data_type = type(init_val)
 
     def get_value_from_xy_index(self, x_ind, y_ind):
         """get_value_from_xy_index
@@ -49,7 +70,7 @@ def get_value_from_xy_index(self, x_ind, y_ind):
 
         grid_ind = self.calc_grid_index_from_xy_index(x_ind, y_ind)
 
-        if 0 <= grid_ind < self.ndata:
+        if 0 <= grid_ind < self.n_data:
             return self.data[grid_ind]
         else:
             return None
@@ -101,7 +122,7 @@ def set_value_from_xy_index(self, x_ind, y_ind, val):
 
         grid_ind = int(y_ind * self.width + x_ind)
 
-        if 0 <= grid_ind < self.ndata:
+        if 0 <= grid_ind < self.n_data and isinstance(val, self.data_type):
             self.data[grid_ind] = val
             return True  # OK
         else:
@@ -138,6 +159,27 @@ def calc_grid_index_from_xy_index(self, x_ind, y_ind):
         grid_ind = int(y_ind * self.width + x_ind)
         return grid_ind
 
+    def calc_xy_index_from_grid_index(self, grid_ind):
+        y_ind, x_ind = divmod(grid_ind, self.width)
+        return x_ind, y_ind
+
+    def calc_grid_index_from_xy_pos(self, x_pos, y_pos):
+        """get_xy_index_from_xy_pos
+
+        :param x_pos: x position [m]
+        :param y_pos: y position [m]
+        """
+        x_ind = self.calc_xy_index_from_position(
+            x_pos, self.left_lower_x, self.width)
+        y_ind = self.calc_xy_index_from_position(
+            y_pos, self.left_lower_y, self.height)
+
+        return self.calc_grid_index_from_xy_index(x_ind, y_ind)
+
+    def calc_grid_central_xy_position_from_grid_index(self, grid_ind):
+        x_ind, y_ind = self.calc_xy_index_from_grid_index(grid_ind)
+        return self.calc_grid_central_xy_position_from_xy_index(x_ind, y_ind)
+
     def calc_grid_central_xy_position_from_xy_index(self, x_ind, y_ind):
         x_pos = self.calc_grid_central_xy_position_from_index(
             x_ind, self.left_lower_x)
@@ -156,39 +198,40 @@ def calc_xy_index_from_position(self, pos, lower_pos, max_index):
         else:
             return None
 
-    def check_occupied_from_xy_index(self, xind, yind, occupied_val=1.0):
+    def check_occupied_from_xy_index(self, x_ind, y_ind, occupied_val):
 
-        val = self.get_value_from_xy_index(xind, yind)
+        val = self.get_value_from_xy_index(x_ind, y_ind)
 
         if val is None or val >= occupied_val:
             return True
         else:
             return False
 
-    def expand_grid(self):
-        xinds, yinds = [], []
+    def expand_grid(self, occupied_val=FloatGrid(1.0)):
+        x_inds, y_inds, values = [], [], []
 
         for ix in range(self.width):
             for iy in range(self.height):
-                if self.check_occupied_from_xy_index(ix, iy):
-                    xinds.append(ix)
-                    yinds.append(iy)
-
-        for (ix, iy) in zip(xinds, yinds):
-            self.set_value_from_xy_index(ix + 1, iy, val=1.0)
-            self.set_value_from_xy_index(ix, iy + 1, val=1.0)
-            self.set_value_from_xy_index(ix + 1, iy + 1, val=1.0)
-            self.set_value_from_xy_index(ix - 1, iy, val=1.0)
-            self.set_value_from_xy_index(ix, iy - 1, val=1.0)
-            self.set_value_from_xy_index(ix - 1, iy - 1, val=1.0)
+                if self.check_occupied_from_xy_index(ix, iy, occupied_val):
+                    x_inds.append(ix)
+                    y_inds.append(iy)
+                    values.append(self.get_value_from_xy_index(ix, iy))
+
+        for (ix, iy, value) in zip(x_inds, y_inds, values):
+            self.set_value_from_xy_index(ix + 1, iy, val=value)
+            self.set_value_from_xy_index(ix, iy + 1, val=value)
+            self.set_value_from_xy_index(ix + 1, iy + 1, val=value)
+            self.set_value_from_xy_index(ix - 1, iy, val=value)
+            self.set_value_from_xy_index(ix, iy - 1, val=value)
+            self.set_value_from_xy_index(ix - 1, iy - 1, val=value)
 
     @staticmethod
     def check_inside_polygon(iox, ioy, x, y):
 
-        npoint = len(x) - 1
+        n_point = len(x) - 1
         inside = False
-        for i1 in range(npoint):
-            i2 = (i1 + 1) % (npoint + 1)
+        for i1 in range(n_point):
+            i2 = (i1 + 1) % (n_point + 1)
 
             if x[i1] >= x[i2]:
                 min_x, max_x = x[i2], x[i1]
@@ -211,27 +254,26 @@ def print_grid_map_info(self):
         print("center_y:", self.center_y)
         print("left_lower_x:", self.left_lower_x)
         print("left_lower_y:", self.left_lower_y)
-        print("ndata:", self.ndata)
+        print("n_data:", self.n_data)
 
     def plot_grid_map(self, ax=None):
-
-        grid_data = np.reshape(np.array(self.data), (self.height, self.width))
+        float_data_array = np.array([d.get_float_data() for d in self.data])
+        grid_data = np.reshape(float_data_array, (self.height, self.width))
         if not ax:
             fig, ax = plt.subplots()
         heat_map = ax.pcolor(grid_data, cmap="Blues", vmin=0.0, vmax=1.0)
         plt.axis("equal")
-        # plt.show()
 
         return heat_map
 
 
-def test_polygon_set():
+def polygon_set_demo():
     ox = [0.0, 4.35, 20.0, 50.0, 100.0, 130.0, 40.0]
     oy = [0.0, -4.15, -20.0, 0.0, 30.0, 60.0, 80.0]
 
     grid_map = GridMap(600, 290, 0.7, 60.0, 30.5)
 
-    grid_map.set_value_from_polygon(ox, oy, 1.0, inside=False)
+    grid_map.set_value_from_polygon(ox, oy, FloatGrid(1.0), inside=False)
 
     grid_map.plot_grid_map()
 
@@ -239,24 +281,27 @@ def test_polygon_set():
     plt.grid(True)
 
 
-def test_position_set():
+def position_set_demo():
     grid_map = GridMap(100, 120, 0.5, 10.0, -0.5)
 
-    grid_map.set_value_from_xy_pos(10.1, -1.1, 1.0)
-    grid_map.set_value_from_xy_pos(10.1, -0.1, 1.0)
-    grid_map.set_value_from_xy_pos(10.1, 1.1, 1.0)
-    grid_map.set_value_from_xy_pos(11.1, 0.1, 1.0)
-    grid_map.set_value_from_xy_pos(10.1, 0.1, 1.0)
-    grid_map.set_value_from_xy_pos(9.1, 0.1, 1.0)
+    grid_map.set_value_from_xy_pos(10.1, -1.1, FloatGrid(1.0))
+    grid_map.set_value_from_xy_pos(10.1, -0.1, FloatGrid(1.0))
+    grid_map.set_value_from_xy_pos(10.1, 1.1, FloatGrid(1.0))
+    grid_map.set_value_from_xy_pos(11.1, 0.1, FloatGrid(1.0))
+    grid_map.set_value_from_xy_pos(10.1, 0.1, FloatGrid(1.0))
+    grid_map.set_value_from_xy_pos(9.1, 0.1, FloatGrid(1.0))
 
     grid_map.plot_grid_map()
 
+    plt.axis("equal")
+    plt.grid(True)
+
 
 def main():
     print("start!!")
 
-    test_position_set()
-    test_polygon_set()
+    position_set_demo()
+    polygon_set_demo()
 
     plt.show()
 
diff --git a/Mapping/ndt_map/ndt_map.py b/Mapping/ndt_map/ndt_map.py
new file mode 100644
index 0000000000..f4f3299662
--- /dev/null
+++ b/Mapping/ndt_map/ndt_map.py
@@ -0,0 +1,135 @@
+"""
+Normal Distribution Transform (NDTGrid) mapping sample
+"""
+import matplotlib.pyplot as plt
+import numpy as np
+from collections import defaultdict
+
+from Mapping.grid_map_lib.grid_map_lib import GridMap
+from utils.plot import plot_covariance_ellipse
+
+
+class NDTMap:
+    """
+    Normal Distribution Transform (NDT) map class
+
+    :param ox: obstacle x position list
+    :param oy: obstacle y position list
+    :param resolution: grid resolution [m]
+    """
+
+    class NDTGrid:
+        """
+        NDT grid
+        """
+
+        def __init__(self):
+            #: Number of points in the NDTGrid grid
+            self.n_points = 0
+            #: Mean x position of points in the NDTGrid cell
+            self.mean_x = None
+            #: Mean y position of points in the NDTGrid cell
+            self.mean_y = None
+            #: Center x position of the NDT grid
+            self.center_grid_x = None
+            #: Center y position of the NDT grid
+            self.center_grid_y = None
+            #: Covariance matrix of the NDT grid
+            self.covariance = None
+            #: Eigen vectors of the NDT grid
+            self.eig_vec = None
+            #: Eigen values of the NDT grid
+            self.eig_values = None
+
+    def __init__(self, ox, oy, resolution):
+        #: Minimum number of points in the NDT grid
+        self.min_n_points = 3
+        #: Resolution of the NDT grid [m]
+        self.resolution = resolution
+        width = int((max(ox) - min(ox))/resolution) + 3  # rounding up + right and left margin
+        height = int((max(oy) - min(oy))/resolution) + 3
+        center_x = np.mean(ox)
+        center_y = np.mean(oy)
+        self.ox = ox
+        self.oy = oy
+        #: NDT grid index map
+        self.grid_index_map = self._create_grid_index_map(ox, oy)
+
+        #: NDT grid map. Each grid contains NDTGrid object
+        self._construct_grid_map(center_x, center_y, height, ox, oy, resolution, width)
+
+    def _construct_grid_map(self, center_x, center_y, height, ox, oy, resolution, width):
+        self.grid_map = GridMap(width, height, resolution, center_x, center_y, self.NDTGrid())
+        for grid_index, inds in self.grid_index_map.items():
+            ndt = self.NDTGrid()
+            ndt.n_points = len(inds)
+            if ndt.n_points >= self.min_n_points:
+                ndt.mean_x = np.mean(ox[inds])
+                ndt.mean_y = np.mean(oy[inds])
+                ndt.center_grid_x, ndt.center_grid_y = \
+                    self.grid_map.calc_grid_central_xy_position_from_grid_index(grid_index)
+                ndt.covariance = np.cov(ox[inds], oy[inds])
+                ndt.eig_values, ndt.eig_vec = np.linalg.eig(ndt.covariance)
+                self.grid_map.data[grid_index] = ndt
+
+    def _create_grid_index_map(self, ox, oy):
+        grid_index_map = defaultdict(list)
+        for i in range(len(ox)):
+            grid_index = self.grid_map.calc_grid_index_from_xy_pos(ox[i], oy[i])
+            grid_index_map[grid_index].append(i)
+        return grid_index_map
+
+
+def create_dummy_observation_data():
+    ox = []
+    oy = []
+    # left corridor
+    for y in range(-50, 50):
+        ox.append(-20.0)
+        oy.append(y)
+    # right corridor 1
+    for y in range(-50, 0):
+        ox.append(20.0)
+        oy.append(y)
+    # right corridor 2
+    for x in range(20, 50):
+        ox.append(x)
+        oy.append(0)
+    # right corridor 3
+    for x in range(20, 50):
+        ox.append(x)
+        oy.append(x/2.0+10)
+    # right corridor 4
+    for y in range(20, 50):
+        ox.append(20)
+        oy.append(y)
+    ox = np.array(ox)
+    oy = np.array(oy)
+    # Adding random noize
+    ox += np.random.rand(len(ox)) * 1.0
+    oy += np.random.rand(len(ox)) * 1.0
+    return ox, oy
+
+
+def main():
+    print(__file__ + " start!!")
+
+    ox, oy = create_dummy_observation_data()
+    grid_resolution = 10.0
+    ndt_map = NDTMap(ox, oy, grid_resolution)
+
+    # plot raw observation
+    plt.plot(ox, oy, ".r")
+
+    # plot grid clustering
+    [plt.plot(ox[inds], oy[inds], "x") for inds in ndt_map.grid_index_map.values()]
+
+    # plot ndt grid map
+    [plot_covariance_ellipse(ndt.mean_x, ndt.mean_y, ndt.covariance, color="-k") for ndt in ndt_map.grid_map.data if ndt.n_points > 0]
+
+    plt.axis("equal")
+    plt.show()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
index 31dc917566..ee192e9200 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -13,7 +13,7 @@
 sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 from utils.angle import rot_mat_2d
-from Mapping.grid_map_lib.grid_map_lib import GridMap
+from Mapping.grid_map_lib.grid_map_lib import GridMap, FloatGrid
 
 do_animation = True
 
@@ -41,8 +41,7 @@ def move_target_grid(self, c_x_index, c_y_index, grid_map):
         n_y_index = c_y_index
 
         # found safe grid
-        if not grid_map.check_occupied_from_xy_index(n_x_index, n_y_index,
-                                                     occupied_val=0.5):
+        if not self.check_occupied(n_x_index, n_y_index, grid_map):
             return n_x_index, n_y_index
         else:  # occupied
             next_c_x_index, next_c_y_index = self.find_safe_turning_grid(
@@ -51,19 +50,20 @@ def move_target_grid(self, c_x_index, c_y_index, grid_map):
                 # moving backward
                 next_c_x_index = -self.moving_direction + c_x_index
                 next_c_y_index = c_y_index
-                if grid_map.check_occupied_from_xy_index(next_c_x_index,
-                                                         next_c_y_index):
+                if self.check_occupied(next_c_x_index, next_c_y_index, grid_map):
                     # moved backward, but the grid is occupied by obstacle
                     return None, None
             else:
                 # keep moving until end
-                while not grid_map.check_occupied_from_xy_index(
-                        next_c_x_index + self.moving_direction,
-                        next_c_y_index, occupied_val=0.5):
+                while not self.check_occupied(next_c_x_index + self.moving_direction, next_c_y_index, grid_map):
                     next_c_x_index += self.moving_direction
                 self.swap_moving_direction()
             return next_c_x_index, next_c_y_index
 
+    @staticmethod
+    def check_occupied(c_x_index, c_y_index, grid_map):
+        return grid_map.check_occupied_from_xy_index(c_x_index, c_y_index, FloatGrid(0.5))
+
     def find_safe_turning_grid(self, c_x_index, c_y_index, grid_map):
 
         for (d_x_ind, d_y_ind) in self.turing_window:
@@ -72,17 +72,14 @@ def find_safe_turning_grid(self, c_x_index, c_y_index, grid_map):
             next_y_ind = d_y_ind + c_y_index
 
             # found safe grid
-            if not grid_map.check_occupied_from_xy_index(next_x_ind,
-                                                         next_y_ind,
-                                                         occupied_val=0.5):
+            if not self.check_occupied(next_x_ind, next_y_ind, grid_map):
                 return next_x_ind, next_y_ind
 
         return None, None
 
     def is_search_done(self, grid_map):
         for ix in self.x_indexes_goal_y:
-            if not grid_map.check_occupied_from_xy_index(ix, self.goal_y,
-                                                         occupied_val=0.5):
+            if not self.check_occupied(ix, self.goal_y, grid_map):
                 return False
 
         # all lower grid is occupied
@@ -168,7 +165,7 @@ def search_free_grid_index_at_edge_y(grid_map, from_upper=False):
 
     for iy in x_range:
         for ix in y_range:
-            if not grid_map.check_occupied_from_xy_index(ix, iy):
+            if not SweepSearcher.check_occupied(ix, iy, grid_map):
                 y_index = iy
                 x_indexes.append(ix)
         if y_index:
@@ -185,7 +182,7 @@ def setup_grid_map(ox, oy, resolution, sweep_direction, offset_grid=10):
 
     grid_map = GridMap(width, height, resolution, center_x, center_y)
     grid_map.print_grid_map_info()
-    grid_map.set_value_from_polygon(ox, oy, 1.0, inside=False)
+    grid_map.set_value_from_polygon(ox, oy, FloatGrid(1.0), inside=False)
     grid_map.expand_grid()
 
     x_inds_goal_y = []
@@ -203,7 +200,7 @@ def setup_grid_map(ox, oy, resolution, sweep_direction, offset_grid=10):
 def sweep_path_search(sweep_searcher, grid_map, grid_search_animation=False):
     # search start grid
     c_x_index, c_y_index = sweep_searcher.search_start_grid(grid_map)
-    if not grid_map.set_value_from_xy_index(c_x_index, c_y_index, 0.5):
+    if not grid_map.set_value_from_xy_index(c_x_index, c_y_index, FloatGrid(0.5)):
         print("Cannot find start grid")
         return [], []
 
@@ -235,7 +232,7 @@ def sweep_path_search(sweep_searcher, grid_map, grid_search_animation=False):
         px.append(x)
         py.append(y)
 
-        grid_map.set_value_from_xy_index(c_x_index, c_y_index, 0.5)
+        grid_map.set_value_from_xy_index(c_x_index, c_y_index, FloatGrid(0.5))
 
         if grid_search_animation:
             grid_map.plot_grid_map(ax=ax)
diff --git a/docs/modules/mapping/mapping_main.rst b/docs/modules/mapping/mapping_main.rst
index 1c02c75cb2..a98acaaf50 100644
--- a/docs/modules/mapping/mapping_main.rst
+++ b/docs/modules/mapping/mapping_main.rst
@@ -7,6 +7,7 @@ Mapping
    :caption: Contents
 
    gaussian_grid_map/gaussian_grid_map
+   ndt_map/ndt_map
    ray_casting_grid_map/ray_casting_grid_map
    lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial
    point_cloud_sampling/point_cloud_sampling
diff --git a/docs/modules/mapping/ndt_map/grid_clustering.png b/docs/modules/mapping/ndt_map/grid_clustering.png
new file mode 100644
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diff --git a/docs/modules/mapping/ndt_map/ndt_map_main.rst b/docs/modules/mapping/ndt_map/ndt_map_main.rst
new file mode 100644
index 0000000000..bfc3936314
--- /dev/null
+++ b/docs/modules/mapping/ndt_map/ndt_map_main.rst
@@ -0,0 +1,53 @@
+.. _ndt_map:
+
+Normal Distance Transform (NDT) map
+------------------------------------
+
+This is a NDT mapping example.
+
+Normal Distribution Transform (NDT) is a map representation that uses normal distribution for observation point modeling.
+
+Normal Distribution
+~~~~~~~~~~~~~~~~~~~~~
+
+Normal distribution consists of two parameters: mean :math:`\mu` and covariance :math:`\Sigma`.
+
+:math:`\mathbf{X} \sim \mathcal{N}(\boldsymbol{\mu}, \boldsymbol{\Sigma})`
+
+In the 2D case, :math:`\boldsymbol{\mu}` is a 2D vector and :math:`\boldsymbol{\Sigma}` is a 2x2 matrix.
+
+In the matrix form, the probability density function of thr normal distribution is:
+
+:math:`X=\frac{1}{\sqrt{(2 \pi)^2|\Sigma|}} \exp \left\{-\frac{1}{2}^t(x-\mu) \sum^{-1}(x-\mu)\right\}`
+
+Normal Distance Transform mapping steps
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~　
+
+NDT mapping consists of two steps:
+
+When we have a new observation like this:
+
+.. figure:: raw_observations.png
+
+First, we need to cluster the observation points.
+This is done by using a grid based clustering algorithm.
+
+The result is:
+
+.. figure:: grid_clustering.png
+
+Then, we need to fit a normal distribution to each grid cluster.
+
+Black ellipse shows each NDT grid like this:
+
+.. figure:: ndt_map1.png
+
+.. figure:: ndt_map2.png
+
+API
+~~~~~
+
+.. autoclass:: Mapping.ndt_map.ndt_map.NDTMap
+    :members:
+    :class-doc-from: class
+
diff --git a/docs/modules/mapping/ndt_map/raw_observations.png b/docs/modules/mapping/ndt_map/raw_observations.png
new file mode 100644
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literal 0
HcmV?d00001

diff --git a/tests/test_grid_map_lib.py b/tests/test_grid_map_lib.py
index 92ca67e297..670b357ad3 100644
--- a/tests/test_grid_map_lib.py
+++ b/tests/test_grid_map_lib.py
@@ -25,5 +25,16 @@ def test_polygon_set():
                                     1.0, inside=False)
 
 
+def test_xy_and_grid_index_conversion():
+    grid_map = GridMap(100, 120, 0.5, 10.0, -0.5)
+
+    for x_ind in range(grid_map.width):
+        for y_ind in range(grid_map.height):
+            grid_ind = grid_map.calc_grid_index_from_xy_index(x_ind, y_ind)
+            x_ind_2, y_ind_2 = grid_map.calc_xy_index_from_grid_index(grid_ind)
+            assert x_ind == x_ind_2
+            assert y_ind == y_ind_2
+
+
 if __name__ == '__main__':
     conftest.run_this_test(__file__)
diff --git a/utils/plot.py b/utils/plot.py
index d4bbe29ffd..eb5aa7ad04 100644
--- a/utils/plot.py
+++ b/utils/plot.py
@@ -9,6 +9,69 @@
 from mpl_toolkits.mplot3d.proj3d import proj_transform
 from mpl_toolkits.mplot3d import Axes3D
 
+from utils.angle import rot_mat_2d
+
+
+def plot_covariance_ellipse(x, y, cov, chi2=3.0, color="-r", ax=None):
+    """
+    This function plots an ellipse that represents a covariance matrix. The ellipse is centered at (x, y) and its shape, size and rotation are determined by the covariance matrix.
+
+    Parameters:
+    x : (float) The x-coordinate of the center of the ellipse.
+    y : (float) The y-coordinate of the center of the ellipse.
+    cov : (numpy.ndarray) A 2x2 covariance matrix that determines the shape, size, and rotation of the ellipse.
+    chi2 : (float, optional) A scalar value that scales the ellipse size. This value is typically set based on chi-squared distribution quantiles to achieve certain confidence levels (e.g., 3.0 corresponds to ~95% confidence for a 2D Gaussian). Defaults to 3.0.
+    color : (str, optional) The color and line style of the ellipse plot, following matplotlib conventions. Defaults to "-r" (a red solid line).
+    ax : (matplotlib.axes.Axes, optional) The Axes object to draw the ellipse on. If None (default), a new figure and axes are created.
+
+    Returns:
+    None. This function plots the covariance ellipse on the specified axes.
+    """
+    eig_val, eig_vec = np.linalg.eig(cov)
+
+    if eig_val[0] >= eig_val[1]:
+        big_ind = 0
+        small_ind = 1
+    else:
+        big_ind = 1
+        small_ind = 0
+    a = math.sqrt(chi2 * eig_val[big_ind])
+    b = math.sqrt(chi2 * eig_val[small_ind])
+    angle = math.atan2(eig_vec[1, big_ind], eig_vec[0, big_ind])
+    plot_ellipse(x, y, a, b, angle, color=color, ax=ax)
+
+
+def plot_ellipse(x, y, a, b, angle, color="-r", ax=None, **kwargs):
+    """
+    This function plots an ellipse based on the given parameters.
+
+    Parameters
+    ----------
+    x : (float) The x-coordinate of the center of the ellipse.
+    y : (float) The y-coordinate of the center of the ellipse.
+    a : (float) The length of the semi-major axis of the ellipse.
+    b : (float) The length of the semi-minor axis of the ellipse.
+    angle : (float) The rotation angle of the ellipse, in radians.
+    color : (str, optional) The color and line style of the ellipse plot, following matplotlib conventions. Defaults to "-r" (a red solid line).
+    ax : (matplotlib.axes.Axes, optional) The Axes object to draw the ellipse on. If None (default), a new figure and axes are created.
+    **kwargs: Additional keyword arguments to pass to plt.plot or ax.plot.
+
+    Returns
+    ---------
+    None. This function plots the ellipse based on the specified parameters.
+    """
+
+    t = np.arange(0, 2 * math.pi + 0.1, 0.1)
+    px = [a * math.cos(it) for it in t]
+    py = [b * math.sin(it) for it in t]
+    fx = rot_mat_2d(angle) @ (np.array([px, py]))
+    px = np.array(fx[0, :] + x).flatten()
+    py = np.array(fx[1, :] + y).flatten()
+    if ax is None:
+        plt.plot(px, py, color, **kwargs)
+    else:
+        ax.plot(px, py, color, **kwargs)
+
 
 def plot_arrow(x, y, yaw, arrow_length=1.0,
                origin_point_plot_style="xr",
@@ -132,7 +195,6 @@ def plot_3d_vector_arrow(ax, p1, p2):
                )
 
 
-
 def plot_triangle(p1, p2, p3, ax):
     ax.add_collection3d(art3d.Poly3DCollection([[p1, p2, p3]], color='b'))
 
@@ -163,3 +225,10 @@ def set_equal_3d_axis(ax, x_lims, y_lims, z_lims):
     ax.set_xlim(mid_x - max_range, mid_x + max_range)
     ax.set_ylim(mid_y - max_range, mid_y + max_range)
     ax.set_zlim(mid_z - max_range, mid_z + max_range)
+
+
+if __name__ == '__main__':
+    plot_ellipse(0, 0, 1, 2, np.deg2rad(15))
+    plt.axis('equal')
+    plt.show()
+

From 41fec85cb974d650d2ec7965346a1f64f3015518 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 11 Jul 2023 08:02:59 +0900
Subject: [PATCH 0753/1080] Bump numpy from 1.25.0 to 1.25.1 in /requirements
 (#874)

Bumps [numpy](https://github.com/numpy/numpy) from 1.25.0 to 1.25.1.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.25.0...v1.25.1)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 2fab6f69c8..c5643beee8 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.25.0
+numpy == 1.25.1
 scipy == 1.11.1
 matplotlib == 3.7.1
 cvxpy == 1.3.2

From 4527785222d3bb885c4dfd97df54dfdd66e10595 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 11 Jul 2023 20:10:50 +0900
Subject: [PATCH 0754/1080] Bump matplotlib from 3.7.1 to 3.7.2 in
 /requirements (#876)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.7.1 to 3.7.2.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.7.1...v3.7.2)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index c5643beee8..27bc6b32d3 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.25.1
 scipy == 1.11.1
-matplotlib == 3.7.1
+matplotlib == 3.7.2
 cvxpy == 1.3.2
 pytest == 7.4.0 # For unit test
 pytest-xdist == 3.3.1 # For unit test

From 32e5e6fd2c891b1f56061f56e73b5d3f1386f1ba Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 11 Jul 2023 20:54:22 +0900
Subject: [PATCH 0755/1080] Bump ruff from 0.0.276 to 0.0.277 in /requirements
 (#875)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.276 to 0.0.277.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.0.276...v0.0.277)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 27bc6b32d3..44742ed34b 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.2
 pytest == 7.4.0 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.4.1 # For unit test
-ruff == 0.0.276 # For unit test
+ruff == 0.0.277 # For unit test

From 8c9eb0187e83d0abff9e3d3d5d3af4add6e2c59e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 18 Jul 2023 09:29:15 +0900
Subject: [PATCH 0756/1080] Bump ruff from 0.0.277 to 0.0.278 in /requirements
 (#879)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.277 to 0.0.278.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.0.277...v0.0.278)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 44742ed34b..2f6a441d3c 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.2
 pytest == 7.4.0 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.4.1 # For unit test
-ruff == 0.0.277 # For unit test
+ruff == 0.0.278 # For unit test

From ba5a1dda753671e5e54ac264c113a6b8f1142640 Mon Sep 17 00:00:00 2001
From: Emmanuel Ferdman <emmanuelferdman@gmail.com>
Date: Wed, 19 Jul 2023 16:04:35 +0300
Subject: [PATCH 0757/1080] docs: update the code style test script (#880)

Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>
---
 docs/how_to_contribute_main.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/how_to_contribute_main.rst b/docs/how_to_contribute_main.rst
index 8a2f73e9cd..48895d6fda 100644
--- a/docs/how_to_contribute_main.rst
+++ b/docs/how_to_contribute_main.rst
@@ -50,7 +50,7 @@ At the least, try to run the example code without animation in the unit test.
 
 If you want to run the test suites locally, you can use the `runtests.sh` script by just executing it.
 
-The `test_diff_codestyle.py`_ check code style for your PR's codes.
+The `test_codestyle.py`_ check code style for your PR's codes.
 
 
 .. _`how to write doc`:
@@ -155,7 +155,7 @@ Sponsors
 .. _`reStructuredText`: https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html
 .. _`doc modules dir`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/docs/modules
 .. _`doc README`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/docs/README.md
-.. _`test_diff_codestyle.py`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/tests/test_diff_codestyle.py
+.. _`test_codestyle.py`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/tests/test_codestyle.py
 .. _`JetBrains`: https://www.jetbrains.com/
 .. _`Sponsor @AtsushiSakai on GitHub Sponsors`: https://github.com/sponsors/AtsushiSakai
 .. _`Become a backer or sponsor on Patreon`: https://www.patreon.com/myenigma

From d6d761bd8a6289f43f6f763dd876018541439765 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 25 Jul 2023 11:59:47 +0900
Subject: [PATCH 0758/1080] Bump ruff from 0.0.278 to 0.0.280 in /requirements
 (#881)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.278 to 0.0.280.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.0.278...v0.0.280)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 2f6a441d3c..a1999d4854 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.2
 pytest == 7.4.0 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.4.1 # For unit test
-ruff == 0.0.278 # For unit test
+ruff == 0.0.280 # For unit test

From 9be188feca26a78fed1e638c8ca32ac8dca7d8b6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 1 Aug 2023 19:16:32 +0900
Subject: [PATCH 0759/1080] Bump numpy from 1.25.1 to 1.25.2 in /requirements
 (#882)

Bumps [numpy](https://github.com/numpy/numpy) from 1.25.1 to 1.25.2.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.25.1...v1.25.2)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index a1999d4854..26e0eb8db3 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.25.1
+numpy == 1.25.2
 scipy == 1.11.1
 matplotlib == 3.7.2
 cvxpy == 1.3.2

From bd0254997d82a3e40bb230db1f137c5a96c8e68e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 1 Aug 2023 19:21:48 +0900
Subject: [PATCH 0760/1080] Bump ruff from 0.0.280 to 0.0.281 in /requirements
 (#883)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.280 to 0.0.281.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.0.280...v0.0.281)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 26e0eb8db3..33bb44ee8f 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.2
 pytest == 7.4.0 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.4.1 # For unit test
-ruff == 0.0.280 # For unit test
+ruff == 0.0.281 # For unit test

From e7d0a219c6a4c3277a61b3e441f260d036a41479 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 8 Aug 2023 22:09:14 +0900
Subject: [PATCH 0761/1080] Bump sphinx-notes/pages from 2 to 3 (#884)

Bumps [sphinx-notes/pages](https://github.com/sphinx-notes/pages) from 2 to 3.
- [Release notes](https://github.com/sphinx-notes/pages/releases)
- [Commits](https://github.com/sphinx-notes/pages/compare/v2...v3)

---
updated-dependencies:
- dependency-name: sphinx-notes/pages
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/gh-pages.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml
index d5af3b87c6..9cb661ae52 100644
--- a/.github/workflows/gh-pages.yml
+++ b/.github/workflows/gh-pages.yml
@@ -19,7 +19,7 @@ jobs:
         python -m pip install --upgrade pip
         python -m pip install -r requirements/requirements.txt
     - name: Build and Commit
-      uses: sphinx-notes/pages@v2
+      uses: sphinx-notes/pages@v3
       with:
         requirements_path: ./docs/doc_requirements.txt
     - name: Push changes

From 5104e2c692ad8c8ffab6eb04bfa96cd9e75dc1d3 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 8 Aug 2023 22:09:42 +0900
Subject: [PATCH 0762/1080] Bump ruff from 0.0.281 to 0.0.282 in /requirements
 (#885)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.281 to 0.0.282.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.0.281...v0.0.282)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 33bb44ee8f..281a4c7662 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.2
 pytest == 7.4.0 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.4.1 # For unit test
-ruff == 0.0.281 # For unit test
+ruff == 0.0.282 # For unit test

From 801d43dd25c5bf3f553960eecfbaff7741550031 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 15 Aug 2023 11:43:28 +0900
Subject: [PATCH 0763/1080] Bump ruff from 0.0.282 to 0.0.284 in /requirements
 (#887)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.282 to 0.0.284.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.0.282...v0.0.284)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 281a4c7662..d24bb676c6 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.2
 pytest == 7.4.0 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.4.1 # For unit test
-ruff == 0.0.282 # For unit test
+ruff == 0.0.284 # For unit test

From de141eaf14cd3e71ef224eaa81ab4f76336a37b2 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 15 Aug 2023 12:40:52 +0900
Subject: [PATCH 0764/1080] Update gh-pages.yml (#889)

---
 .github/workflows/gh-pages.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml
index 9cb661ae52..072c60c32e 100644
--- a/.github/workflows/gh-pages.yml
+++ b/.github/workflows/gh-pages.yml
@@ -6,6 +6,8 @@ on:
 jobs:
   build:
     runs-on: ubuntu-latest
+    permissions:
+      id-token: write
     steps:
     - name: Setup python
       uses: actions/setup-python@v4

From bfc91b1bc33372c697081221aefca65e7c7342b5 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 15 Aug 2023 13:04:17 +0900
Subject: [PATCH 0765/1080] Update gh-pages.yml

---
 .github/workflows/gh-pages.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml
index 072c60c32e..f2630df162 100644
--- a/.github/workflows/gh-pages.yml
+++ b/.github/workflows/gh-pages.yml
@@ -8,6 +8,7 @@ jobs:
     runs-on: ubuntu-latest
     permissions:
       id-token: write
+      pages: write
     steps:
     - name: Setup python
       uses: actions/setup-python@v4

From 35f4f61e44ad9c3f8fc6584e3d0d7ab7889bd866 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 15 Aug 2023 13:16:21 +0900
Subject: [PATCH 0766/1080] Bump mypy from 1.4.1 to 1.5.0 in /requirements
 (#888)

Bumps [mypy](https://github.com/python/mypy) from 1.4.1 to 1.5.0.
- [Commits](https://github.com/python/mypy/compare/v1.4.1...v1.5.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index d24bb676c6..7ae3c54134 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.7.2
 cvxpy == 1.3.2
 pytest == 7.4.0 # For unit test
 pytest-xdist == 3.3.1 # For unit test
-mypy == 1.4.1 # For unit test
+mypy == 1.5.0 # For unit test
 ruff == 0.0.284 # For unit test

From ff9e48852d7da400768099f54dce90f3cbab104a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 22 Aug 2023 07:38:09 +0900
Subject: [PATCH 0767/1080] Bump ruff from 0.0.284 to 0.0.285 in /requirements
 (#893)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.284 to 0.0.285.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.0.284...v0.0.285)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 7ae3c54134..fed24446c5 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.2
 pytest == 7.4.0 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.5.0 # For unit test
-ruff == 0.0.284 # For unit test
+ruff == 0.0.285 # For unit test

From 637269e0af6128acf5469c057fe6b0564e4d4768 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 22 Aug 2023 20:42:01 +0900
Subject: [PATCH 0768/1080] Bump scipy from 1.11.1 to 1.11.2 in /requirements
 (#894)

Bumps [scipy](https://github.com/scipy/scipy) from 1.11.1 to 1.11.2.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.11.1...v1.11.2)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index fed24446c5..24f5dd8341 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.25.2
-scipy == 1.11.1
+scipy == 1.11.2
 matplotlib == 3.7.2
 cvxpy == 1.3.2
 pytest == 7.4.0 # For unit test

From 3fce78a41e56b3bdb183e581bede2b19ea96a5df Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 22 Aug 2023 20:43:32 +0900
Subject: [PATCH 0769/1080] Bump mypy from 1.5.0 to 1.5.1 in /requirements
 (#895)

Bumps [mypy](https://github.com/python/mypy) from 1.5.0 to 1.5.1.
- [Commits](https://github.com/python/mypy/compare/v1.5.0...v1.5.1)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 24f5dd8341..a0eb41aa73 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.7.2
 cvxpy == 1.3.2
 pytest == 7.4.0 # For unit test
 pytest-xdist == 3.3.1 # For unit test
-mypy == 1.5.0 # For unit test
+mypy == 1.5.1 # For unit test
 ruff == 0.0.285 # For unit test

From 19c0fb379c070329c0e60e347a8b41179833140d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sat, 9 Sep 2023 07:01:58 +0900
Subject: [PATCH 0770/1080] Bump actions/checkout from 3 to 4 (#900)

Bumps [actions/checkout](https://github.com/actions/checkout) from 3 to 4.
- [Release notes](https://github.com/actions/checkout/releases)
- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)
- [Commits](https://github.com/actions/checkout/compare/v3...v4)

---
updated-dependencies:
- dependency-name: actions/checkout
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/Linux_CI.yml   | 2 +-
 .github/workflows/MacOS_CI.yml   | 2 +-
 .github/workflows/Windows_CI.yml | 2 +-
 .github/workflows/codeql.yml     | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index a152972753..b9a102c714 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -17,7 +17,7 @@ jobs:
     name: Python ${{ matrix.python-version }} CI
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - run: git fetch --prune --unshallow
 
     - name: Setup python
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index c8bfbbcb7e..2acabd8b44 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -19,7 +19,7 @@ jobs:
         python-version: [ '3.11' ]
     name: Python ${{ matrix.python-version }} CI
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - run: git fetch --prune --unshallow
 
       - name: Update bash
diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml
index 13fdcea8a1..3df2e6265b 100644
--- a/.github/workflows/Windows_CI.yml
+++ b/.github/workflows/Windows_CI.yml
@@ -19,7 +19,7 @@ jobs:
         python-version: [ '3.11' ]
     name: Python ${{ matrix.python-version }} CI
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - run: git fetch --prune --unshallow
 
       - name: Setup python
diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index d33545c90f..d3e888f09e 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -16,7 +16,7 @@ jobs:
 
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
       with:
         # We must fetch at least the immediate parents so that if this is
         # a pull request then we can checkout the head.

From 4b3544e74ddfd20107de280295e799e0b0385433 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 11 Sep 2023 15:55:49 +0900
Subject: [PATCH 0771/1080] Bump pytest from 7.4.0 to 7.4.1 in /requirements
 (#901)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.4.0 to 7.4.1.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/7.4.0...7.4.1)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index a0eb41aa73..b0d3e4d4f0 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.25.2
 scipy == 1.11.2
 matplotlib == 3.7.2
 cvxpy == 1.3.2
-pytest == 7.4.0 # For unit test
+pytest == 7.4.1 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.5.1 # For unit test
 ruff == 0.0.285 # For unit test

From c9145d348c68be78acb2652ec83587ce90d4a51f Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 11 Sep 2023 20:38:43 +0900
Subject: [PATCH 0772/1080] Bump ruff from 0.0.285 to 0.0.287 in /requirements
 (#902)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.285 to 0.0.287.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.0.285...v0.0.287)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index b0d3e4d4f0..e4792a31bf 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.2
 pytest == 7.4.1 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.5.1 # For unit test
-ruff == 0.0.285 # For unit test
+ruff == 0.0.287 # For unit test

From 8a427a92abc7fe241dd67f69d17c80c54cd272c2 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 12 Sep 2023 07:35:26 +0900
Subject: [PATCH 0773/1080] Bump ruff from 0.0.287 to 0.0.288 in /requirements
 (#905)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.287 to 0.0.288.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.0.287...v0.0.288)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index e4792a31bf..9eeb1f024f 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.2
 pytest == 7.4.1 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.5.1 # For unit test
-ruff == 0.0.287 # For unit test
+ruff == 0.0.288 # For unit test

From 8956c67c21055b9a2c44c1e0d3d20b194e4dfd95 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 13 Sep 2023 17:33:46 +0900
Subject: [PATCH 0774/1080] Bump pytest from 7.4.1 to 7.4.2 in /requirements
 (#904)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.4.1 to 7.4.2.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/7.4.1...7.4.2)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 9eeb1f024f..0c616fc6d3 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.25.2
 scipy == 1.11.2
 matplotlib == 3.7.2
 cvxpy == 1.3.2
-pytest == 7.4.1 # For unit test
+pytest == 7.4.2 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.5.1 # For unit test
 ruff == 0.0.288 # For unit test

From 644d70d0fa3d83ae4434725ff0f82b4f7d4ec46f Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 19 Sep 2023 06:51:53 +0900
Subject: [PATCH 0775/1080] Bump ruff from 0.0.288 to 0.0.290 in /requirements
 (#908)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.288 to 0.0.290.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.0.288...v0.0.290)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 0c616fc6d3..6392fd2c33 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.2
 pytest == 7.4.2 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.5.1 # For unit test
-ruff == 0.0.288 # For unit test
+ruff == 0.0.290 # For unit test

From ba09b4dd90fc49d8873771782c7dc82f0a213534 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 19 Sep 2023 06:59:38 +0900
Subject: [PATCH 0776/1080] Bump matplotlib from 3.7.2 to 3.8.0 in
 /requirements (#909)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.7.2 to 3.8.0.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.7.2...v3.8.0)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 6392fd2c33..d1c0592e14 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.25.2
 scipy == 1.11.2
-matplotlib == 3.7.2
+matplotlib == 3.8.0
 cvxpy == 1.3.2
 pytest == 7.4.2 # For unit test
 pytest-xdist == 3.3.1 # For unit test

From a7513699bf7f6f3d6112e48464deddf7e8c22f68 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 19 Sep 2023 12:36:29 +0900
Subject: [PATCH 0777/1080] Bump numpy from 1.25.2 to 1.26.0 in /requirements
 (#910)

Bumps [numpy](https://github.com/numpy/numpy) from 1.25.2 to 1.26.0.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.25.2...v1.26.0)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index d1c0592e14..836f8b0f54 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.25.2
+numpy == 1.26.0
 scipy == 1.11.2
 matplotlib == 3.8.0
 cvxpy == 1.3.2

From 57ee946537a13af397bafbe8cf54507988006873 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 27 Sep 2023 20:23:08 +0900
Subject: [PATCH 0778/1080] Bump ruff from 0.0.290 to 0.0.291 in /requirements
 (#911)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.290 to 0.0.291.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.0.290...v0.0.291)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 836f8b0f54..d983e22916 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.2
 pytest == 7.4.2 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.5.1 # For unit test
-ruff == 0.0.290 # For unit test
+ruff == 0.0.291 # For unit test

From 6a765964409bb3f449d94bd55eb0dd8998730c71 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 3 Oct 2023 21:36:37 +0900
Subject: [PATCH 0779/1080] Bump scipy from 1.11.2 to 1.11.3 in /requirements
 (#914)

Bumps [scipy](https://github.com/scipy/scipy) from 1.11.2 to 1.11.3.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.11.2...v1.11.3)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index d983e22916..b67f904ea5 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.26.0
-scipy == 1.11.2
+scipy == 1.11.3
 matplotlib == 3.8.0
 cvxpy == 1.3.2
 pytest == 7.4.2 # For unit test

From 2c4d74507777993d44cbb33e238689d776221243 Mon Sep 17 00:00:00 2001
From: Yuki Onishi <yuki.st16@gmail.com>
Date: Sun, 15 Oct 2023 10:25:22 +0900
Subject: [PATCH 0780/1080] Revert a parameter in
 grid_based_sweep_coverage_path_planner to correct (#917)

the planning results
---
 .../grid_based_sweep_coverage_path_planner.py               | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
index ee192e9200..10ba98cd35 100644
--- a/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
+++ b/PathPlanning/GridBasedSweepCPP/grid_based_sweep_coverage_path_planner.py
@@ -50,7 +50,7 @@ def move_target_grid(self, c_x_index, c_y_index, grid_map):
                 # moving backward
                 next_c_x_index = -self.moving_direction + c_x_index
                 next_c_y_index = c_y_index
-                if self.check_occupied(next_c_x_index, next_c_y_index, grid_map):
+                if self.check_occupied(next_c_x_index, next_c_y_index, grid_map, FloatGrid(1.0)):
                     # moved backward, but the grid is occupied by obstacle
                     return None, None
             else:
@@ -61,8 +61,8 @@ def move_target_grid(self, c_x_index, c_y_index, grid_map):
             return next_c_x_index, next_c_y_index
 
     @staticmethod
-    def check_occupied(c_x_index, c_y_index, grid_map):
-        return grid_map.check_occupied_from_xy_index(c_x_index, c_y_index, FloatGrid(0.5))
+    def check_occupied(c_x_index, c_y_index, grid_map, occupied_val=FloatGrid(0.5)):
+        return grid_map.check_occupied_from_xy_index(c_x_index, c_y_index, occupied_val)
 
     def find_safe_turning_grid(self, c_x_index, c_y_index, grid_map):
 

From 5eed0ec1a6a8751e448a476c950f7342d864510d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sun, 15 Oct 2023 15:34:15 +0900
Subject: [PATCH 0781/1080] Bump ruff from 0.0.291 to 0.0.292 in /requirements
 (#913)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.291 to 0.0.292.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/BREAKING_CHANGES.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.0.291...v0.0.292)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index b67f904ea5..cd38b9cff7 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.2
 pytest == 7.4.2 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.5.1 # For unit test
-ruff == 0.0.291 # For unit test
+ruff == 0.0.292 # For unit test

From 4adb77dcf71d3dced8e152f861106de1d192adf6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 18 Oct 2023 21:25:55 +0900
Subject: [PATCH 0782/1080] Bump ruff from 0.0.292 to 0.1.0 in /requirements
 (#920)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.0.292 to 0.1.0.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.0.292...v0.1.0)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index cd38b9cff7..acc76577c8 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.3.2
 pytest == 7.4.2 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.5.1 # For unit test
-ruff == 0.0.292 # For unit test
+ruff == 0.1.0 # For unit test

From 38a8be94bab0baa900a82fafa774b196bd4f1396 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 18 Oct 2023 21:26:26 +0900
Subject: [PATCH 0783/1080] Bump numpy from 1.26.0 to 1.26.1 in /requirements
 (#922)

Bumps [numpy](https://github.com/numpy/numpy) from 1.26.0 to 1.26.1.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.26.0...v1.26.1)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index acc76577c8..a5875a67ec 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.26.0
+numpy == 1.26.1
 scipy == 1.11.3
 matplotlib == 3.8.0
 cvxpy == 1.3.2

From 8f1306ac01055743a456b08cf2e0032f1942f96c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 18 Oct 2023 22:01:34 +0900
Subject: [PATCH 0784/1080] Bump cvxpy from 1.3.2 to 1.4.1 in /requirements
 (#923)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.3.2 to 1.4.1.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.3.2...v1.4.1)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index a5875a67ec..46ad6feb23 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 1.26.1
 scipy == 1.11.3
 matplotlib == 3.8.0
-cvxpy == 1.3.2
+cvxpy == 1.4.1
 pytest == 7.4.2 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.5.1 # For unit test

From d0ff8cc165ce97c473e090e78003a8e5703a7b6d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 25 Oct 2023 20:15:46 +0900
Subject: [PATCH 0785/1080] Bump ruff from 0.1.0 to 0.1.1 in /requirements
 (#926)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.0 to 0.1.1.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.1.0...v0.1.1)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 46ad6feb23..0bda50edc0 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.1
 pytest == 7.4.2 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.5.1 # For unit test
-ruff == 0.1.0 # For unit test
+ruff == 0.1.1 # For unit test

From bde8bda7536449522f3f2bfaa200a450dff41c92 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 25 Oct 2023 23:01:04 +0900
Subject: [PATCH 0786/1080] Bump mypy from 1.5.1 to 1.6.1 in /requirements
 (#924)

Bumps [mypy](https://github.com/python/mypy) from 1.5.1 to 1.6.1.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.5.1...v1.6.1)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 0bda50edc0..1a89183ba6 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.8.0
 cvxpy == 1.4.1
 pytest == 7.4.2 # For unit test
 pytest-xdist == 3.3.1 # For unit test
-mypy == 1.5.1 # For unit test
+mypy == 1.6.1 # For unit test
 ruff == 0.1.1 # For unit test

From 3a8d93b2a54928d258362b34b31fd8adf04a7d05 Mon Sep 17 00:00:00 2001
From: Jeff Irion <JeffLIrion@users.noreply.github.com>
Date: Wed, 25 Oct 2023 07:02:14 -0700
Subject: [PATCH 0787/1080] Fix SE(2) inverse (#925)

* Fix SE(2) inverse

* Add missing ]
---
 SLAM/GraphBasedSLAM/graphslam/pose/se2.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/SLAM/GraphBasedSLAM/graphslam/pose/se2.py b/SLAM/GraphBasedSLAM/graphslam/pose/se2.py
index e89317a06f..2a32e765f7 100644
--- a/SLAM/GraphBasedSLAM/graphslam/pose/se2.py
+++ b/SLAM/GraphBasedSLAM/graphslam/pose/se2.py
@@ -136,8 +136,8 @@ def inverse(self):
 
         """
         return PoseSE2([-self[0] * np.cos(self[2]) - self[1] * np.sin(self[2]),
-                        self[0] * np.sin(self[2]) - self[1] * np.cos(
-                            [self[2]])], -self[2])
+                        self[0] * np.sin(self[2]) - self[1] * np.cos(self[2])],
+                        -self[2])
 
     # ======================================================================= #
     #                                                                         #

From f374393b551b781f9c9564bd8b0e273342c8e3a0 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 31 Oct 2023 20:55:50 +0900
Subject: [PATCH 0788/1080] Bump ruff from 0.1.1 to 0.1.3 in /requirements
 (#928)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.1 to 0.1.3.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.1.1...v0.1.3)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 1a89183ba6..26a481cbe8 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.1
 pytest == 7.4.2 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.6.1 # For unit test
-ruff == 0.1.1 # For unit test
+ruff == 0.1.3 # For unit test

From fc3ec3cd66f64fc889c3382c4ae41aca26adaf4e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 1 Nov 2023 08:38:39 +0900
Subject: [PATCH 0789/1080] Bump pytest from 7.4.2 to 7.4.3 in /requirements
 (#929)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.4.2 to 7.4.3.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/7.4.2...7.4.3)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 26a481cbe8..4335e42aca 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.26.1
 scipy == 1.11.3
 matplotlib == 3.8.0
 cvxpy == 1.4.1
-pytest == 7.4.2 # For unit test
+pytest == 7.4.3 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.6.1 # For unit test
 ruff == 0.1.3 # For unit test

From 8be35b6e70aeb466efcfc76f480a9d734f92f34d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 7 Nov 2023 07:03:23 +0900
Subject: [PATCH 0790/1080] Bump matplotlib from 3.8.0 to 3.8.1 in
 /requirements (#931)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.8.0 to 3.8.1.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.8.0...v3.8.1)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 4335e42aca..3515fbdf9f 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.26.1
 scipy == 1.11.3
-matplotlib == 3.8.0
+matplotlib == 3.8.1
 cvxpy == 1.4.1
 pytest == 7.4.3 # For unit test
 pytest-xdist == 3.3.1 # For unit test

From dcf8eded0647e1f6d8695cbf98fae690e29fa6df Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 7 Nov 2023 08:15:50 +0900
Subject: [PATCH 0791/1080] Bump ruff from 0.1.3 to 0.1.4 in /requirements
 (#932)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.3 to 0.1.4.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.1.3...v0.1.4)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 3515fbdf9f..e65db6c92a 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.1
 pytest == 7.4.3 # For unit test
 pytest-xdist == 3.3.1 # For unit test
 mypy == 1.6.1 # For unit test
-ruff == 0.1.3 # For unit test
+ruff == 0.1.4 # For unit test

From 9eac2c6f2acc63925085242d9bf895b0084fad3b Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 14 Nov 2023 21:40:32 +0900
Subject: [PATCH 0792/1080] Bump pytest-xdist from 3.3.1 to 3.4.0 in
 /requirements (#933)

Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.3.1 to 3.4.0.
- [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.3.1...v3.4.0)

---
updated-dependencies:
- dependency-name: pytest-xdist
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index e65db6c92a..6f7253b4a7 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,6 +3,6 @@ scipy == 1.11.3
 matplotlib == 3.8.1
 cvxpy == 1.4.1
 pytest == 7.4.3 # For unit test
-pytest-xdist == 3.3.1 # For unit test
+pytest-xdist == 3.4.0 # For unit test
 mypy == 1.6.1 # For unit test
 ruff == 0.1.4 # For unit test

From 3fa8f3e56cf5c742ec19548167bde266ee5e7aa8 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 15 Nov 2023 13:18:18 +0900
Subject: [PATCH 0793/1080] Bump numpy from 1.26.1 to 1.26.2 in /requirements
 (#934)

Bumps [numpy](https://github.com/numpy/numpy) from 1.26.1 to 1.26.2.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.26.1...v1.26.2)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 6f7253b4a7..f8439d2eb7 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.26.1
+numpy == 1.26.2
 scipy == 1.11.3
 matplotlib == 3.8.1
 cvxpy == 1.4.1

From 58df4be95f7ad4fa67b70cac657fcc2c07e85756 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 15 Nov 2023 14:57:46 +0900
Subject: [PATCH 0794/1080] Bump mypy from 1.6.1 to 1.7.0 in /requirements
 (#936)

Bumps [mypy](https://github.com/python/mypy) from 1.6.1 to 1.7.0.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.6.1...v1.7.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index f8439d2eb7..f6e237366f 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.8.1
 cvxpy == 1.4.1
 pytest == 7.4.3 # For unit test
 pytest-xdist == 3.4.0 # For unit test
-mypy == 1.6.1 # For unit test
+mypy == 1.7.0 # For unit test
 ruff == 0.1.4 # For unit test

From f7dadb8157cd8ecebb2ef3eeb2e8dc1ae5bfb664 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 15 Nov 2023 17:08:33 +0900
Subject: [PATCH 0795/1080] Bump ruff from 0.1.4 to 0.1.5 in /requirements
 (#935)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.4 to 0.1.5.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.1.4...v0.1.5)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index f6e237366f..a17a6bc7cb 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.1
 pytest == 7.4.3 # For unit test
 pytest-xdist == 3.4.0 # For unit test
 mypy == 1.7.0 # For unit test
-ruff == 0.1.4 # For unit test
+ruff == 0.1.5 # For unit test

From f5f0253e14e97856f4f6d4016cf4864fce8d46bb Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 21 Nov 2023 12:57:31 +0900
Subject: [PATCH 0796/1080] Bump scipy from 1.11.3 to 1.11.4 in /requirements
 (#938)

Bumps [scipy](https://github.com/scipy/scipy) from 1.11.3 to 1.11.4.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.11.3...v1.11.4)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index a17a6bc7cb..3fcdd9ad19 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.26.2
-scipy == 1.11.3
+scipy == 1.11.4
 matplotlib == 3.8.1
 cvxpy == 1.4.1
 pytest == 7.4.3 # For unit test

From df932b0eb25349fd43f28c1f28a92616b9e709b8 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 21 Nov 2023 18:59:57 +0900
Subject: [PATCH 0797/1080] Bump matplotlib from 3.8.1 to 3.8.2 in
 /requirements (#940)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.8.1 to 3.8.2.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.8.1...v3.8.2)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 3fcdd9ad19..788e900d13 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.26.2
 scipy == 1.11.4
-matplotlib == 3.8.1
+matplotlib == 3.8.2
 cvxpy == 1.4.1
 pytest == 7.4.3 # For unit test
 pytest-xdist == 3.4.0 # For unit test

From 9093d709b5ff74c085f50ab3a1d57928ee6a5b13 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 21 Nov 2023 19:52:11 +0900
Subject: [PATCH 0798/1080] Bump ruff from 0.1.5 to 0.1.6 in /requirements
 (#939)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.5 to 0.1.6.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.1.5...v0.1.6)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 788e900d13..b3b8509b68 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.1
 pytest == 7.4.3 # For unit test
 pytest-xdist == 3.4.0 # For unit test
 mypy == 1.7.0 # For unit test
-ruff == 0.1.5 # For unit test
+ruff == 0.1.6 # For unit test

From 0dc6ab42bab4d3e2fb06fc25d86b1abe41d4da2a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 28 Nov 2023 19:59:15 +0900
Subject: [PATCH 0799/1080] Bump pytest-xdist from 3.4.0 to 3.5.0 in
 /requirements (#942)

Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.4.0 to 3.5.0.
- [Release notes](https://github.com/pytest-dev/pytest-xdist/releases)
- [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.4.0...v3.5.0)

---
updated-dependencies:
- dependency-name: pytest-xdist
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index b3b8509b68..afc244c1a1 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,6 +3,6 @@ scipy == 1.11.4
 matplotlib == 3.8.2
 cvxpy == 1.4.1
 pytest == 7.4.3 # For unit test
-pytest-xdist == 3.4.0 # For unit test
+pytest-xdist == 3.5.0 # For unit test
 mypy == 1.7.0 # For unit test
 ruff == 0.1.6 # For unit test

From e9ae79f9204df88e768de39443781a208d66ecd5 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sun, 3 Dec 2023 21:01:12 +0900
Subject: [PATCH 0800/1080] Bump mypy from 1.7.0 to 1.7.1 in /requirements
 (#943)

Bumps [mypy](https://github.com/python/mypy) from 1.7.0 to 1.7.1.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.7.0...v1.7.1)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index afc244c1a1..f073524c51 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.8.2
 cvxpy == 1.4.1
 pytest == 7.4.3 # For unit test
 pytest-xdist == 3.5.0 # For unit test
-mypy == 1.7.0 # For unit test
+mypy == 1.7.1 # For unit test
 ruff == 0.1.6 # For unit test

From 5676da661f09d08fb0ef188f31f71f17be0ab44a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 3 Dec 2023 21:23:50 +0900
Subject: [PATCH 0801/1080] Update gh-pages.yml

---
 .github/workflows/gh-pages.yml | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml
index f2630df162..42ec54f4e1 100644
--- a/.github/workflows/gh-pages.yml
+++ b/.github/workflows/gh-pages.yml
@@ -25,8 +25,7 @@ jobs:
       uses: sphinx-notes/pages@v3
       with:
         requirements_path: ./docs/doc_requirements.txt
-    - name: Push changes
-      uses: ad-m/github-push-action@master
-      with:
-        github_token: ${{ secrets.GITHUB_TOKEN }}
-        branch: gh-pages
+    - name: Deploy GitHub Pages site
+      id: deployment
+      uses: actions/deploy-pages@main
+            

From 7585316933cee2febfc0a2cd68a13336020adbbc Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 3 Dec 2023 21:35:30 +0900
Subject: [PATCH 0802/1080] Update gh-pages.yml

---
 .github/workflows/gh-pages.yml | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml
index 42ec54f4e1..17fa1d76ca 100644
--- a/.github/workflows/gh-pages.yml
+++ b/.github/workflows/gh-pages.yml
@@ -6,6 +6,9 @@ on:
 jobs:
   build:
     runs-on: ubuntu-latest
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
     permissions:
       id-token: write
       pages: write
@@ -25,7 +28,9 @@ jobs:
       uses: sphinx-notes/pages@v3
       with:
         requirements_path: ./docs/doc_requirements.txt
-    - name: Deploy GitHub Pages site
-      id: deployment
-      uses: actions/deploy-pages@main
+    - name: Push changes
+      uses: ad-m/github-push-action@master
+      with:
+        github_token: ${{ secrets.GITHUB_TOKEN }}
+        branch: gh-pages
             

From 6dca76ac4b566958080dac7f87f44b15e44a84aa Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 3 Dec 2023 21:41:48 +0900
Subject: [PATCH 0803/1080] Update gh-pages.yml

---
 .github/workflows/gh-pages.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml
index 17fa1d76ca..c50a9f1865 100644
--- a/.github/workflows/gh-pages.yml
+++ b/.github/workflows/gh-pages.yml
@@ -1,4 +1,4 @@
-name: Pages
+name: GitHub Pages update
 on:
   push:
     branches:

From 467f27d178f05474b619f841a93b027707b421ac Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 3 Dec 2023 21:46:56 +0900
Subject: [PATCH 0804/1080] Update gh-pages.yml

---
 .github/workflows/gh-pages.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml
index c50a9f1865..ccc0f5a835 100644
--- a/.github/workflows/gh-pages.yml
+++ b/.github/workflows/gh-pages.yml
@@ -1,4 +1,4 @@
-name: GitHub Pages update
+name: GitHub Pages site update
 on:
   push:
     branches:

From ef287d7a857c52a3f34e7b86765c3467858ad2b6 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 3 Dec 2023 21:53:12 +0900
Subject: [PATCH 0805/1080] Update gh-pages.yml

---
 .github/workflows/gh-pages.yml | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml
index ccc0f5a835..d2c7847807 100644
--- a/.github/workflows/gh-pages.yml
+++ b/.github/workflows/gh-pages.yml
@@ -24,13 +24,7 @@ jobs:
         python --version
         python -m pip install --upgrade pip
         python -m pip install -r requirements/requirements.txt
-    - name: Build and Commit
+    - name: Build and Deploy
       uses: sphinx-notes/pages@v3
       with:
-        requirements_path: ./docs/doc_requirements.txt
-    - name: Push changes
-      uses: ad-m/github-push-action@master
-      with:
-        github_token: ${{ secrets.GITHUB_TOKEN }}
-        branch: gh-pages
-            
+        requirements_path: ./docs/doc_requirements.txt            

From 331f0877f6ade42901c0762fee41abcce83c7e29 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 3 Dec 2023 21:56:04 +0900
Subject: [PATCH 0806/1080] Update copyright for doc update test

---
 docs/conf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/conf.py b/docs/conf.py
index df9b8fce22..46f69cf17b 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -19,7 +19,7 @@
 # -- Project information -----------------------------------------------------
 
 project = 'PythonRobotics'
-copyright = '2018-2021, Atsushi Sakai'
+copyright = '2018-2023, Atsushi Sakai'
 author = 'Atsushi Sakai'
 
 # The short X.Y version

From a141cf41807c508e58691c3c42919387e5ff9ded Mon Sep 17 00:00:00 2001
From: Fabiha Tasneem <55595104+fabihatasneem@users.noreply.github.com>
Date: Sun, 3 Dec 2023 19:12:00 +0600
Subject: [PATCH 0807/1080] Fixed Inappropriate Logical Expression (#945)

---
 .../arm_obstacle_navigation/arm_obstacle_navigation_2.py        | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
index 40e6746bfc..591cd401eb 100644
--- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
+++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
@@ -34,7 +34,7 @@ def main():
     goal = (58, 56)
     grid = get_occupancy_grid(arm, obstacles)
     route = astar_torus(grid, start, goal)
-    if len(route) >= 0:
+    if route:
         animate(grid, arm, route)
 
 

From ff66f3babe27d1ae3775f594ffd05264ca173d6a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 12 Dec 2023 22:34:49 +0900
Subject: [PATCH 0808/1080] Bump actions/setup-python from 4 to 5 (#951)

Bumps [actions/setup-python](https://github.com/actions/setup-python) from 4 to 5.
- [Release notes](https://github.com/actions/setup-python/releases)
- [Commits](https://github.com/actions/setup-python/compare/v4...v5)

---
updated-dependencies:
- dependency-name: actions/setup-python
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/Linux_CI.yml   | 2 +-
 .github/workflows/MacOS_CI.yml   | 2 +-
 .github/workflows/Windows_CI.yml | 2 +-
 .github/workflows/gh-pages.yml   | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index b9a102c714..b83954f17e 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -21,7 +21,7 @@ jobs:
     - run: git fetch --prune --unshallow
 
     - name: Setup python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index 2acabd8b44..19c6612b2f 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -26,7 +26,7 @@ jobs:
         run: brew install bash
 
       - name: Setup python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
 
diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml
index 3df2e6265b..906edcfcdb 100644
--- a/.github/workflows/Windows_CI.yml
+++ b/.github/workflows/Windows_CI.yml
@@ -23,7 +23,7 @@ jobs:
       - run: git fetch --prune --unshallow
 
       - name: Setup python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
 
diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml
index d2c7847807..e08c6106c0 100644
--- a/.github/workflows/gh-pages.yml
+++ b/.github/workflows/gh-pages.yml
@@ -14,7 +14,7 @@ jobs:
       pages: write
     steps:
     - name: Setup python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
     - name: Checkout
       uses: actions/checkout@master
       with:

From d25fb355f3c7a1d1e2ee895d009d0d639a37c18b Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 12 Dec 2023 22:35:02 +0900
Subject: [PATCH 0809/1080] Bump ruff from 0.1.6 to 0.1.7 in /requirements
 (#952)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.6 to 0.1.7.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.1.6...v0.1.7)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index f073524c51..2c2a6d47ff 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.1
 pytest == 7.4.3 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.7.1 # For unit test
-ruff == 0.1.6 # For unit test
+ruff == 0.1.7 # For unit test

From 313ce196215f2625c1f6b20e49722b51631bdb37 Mon Sep 17 00:00:00 2001
From: Xavier Dias <93143666+Xavi007@users.noreply.github.com>
Date: Mon, 18 Dec 2023 05:42:47 +0530
Subject: [PATCH 0810/1080] Xavi007: #903  There's an error in the
 transformation_matrix of Quadrotor renamed "yaw" to "roll" (#906)

---
 AerialNavigation/drone_3d_trajectory_following/Quadrotor.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py b/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py
index 413a8625a5..c379e5eda0 100644
--- a/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py
+++ b/AerialNavigation/drone_3d_trajectory_following/Quadrotor.py
@@ -56,7 +56,7 @@ def transformation_matrix(self):
             [[cos(yaw) * cos(pitch), -sin(yaw) * cos(roll) + cos(yaw) * sin(pitch) * sin(roll), sin(yaw) * sin(roll) + cos(yaw) * sin(pitch) * cos(roll), x],
              [sin(yaw) * cos(pitch), cos(yaw) * cos(roll) + sin(yaw) * sin(pitch)
               * sin(roll), -cos(yaw) * sin(roll) + sin(yaw) * sin(pitch) * cos(roll), y],
-             [-sin(pitch), cos(pitch) * sin(roll), cos(pitch) * cos(yaw), z]
+             [-sin(pitch), cos(pitch) * sin(roll), cos(pitch) * cos(roll), z]
              ])
 
     def plot(self):  # pragma: no cover

From 3efbc94f9cb5532b7a046d4fe79d3fe62dc18bd4 Mon Sep 17 00:00:00 2001
From: flyingmars <55879494+2892510130@users.noreply.github.com>
Date: Mon, 18 Dec 2023 21:53:03 +0800
Subject: [PATCH 0811/1080] fix d_star (#898)

---
 PathPlanning/DStar/dstar.py | 19 +++++++++++++++----
 1 file changed, 15 insertions(+), 4 deletions(-)

diff --git a/PathPlanning/DStar/dstar.py b/PathPlanning/DStar/dstar.py
index f8954d776c..cc1d88ed66 100644
--- a/PathPlanning/DStar/dstar.py
+++ b/PathPlanning/DStar/dstar.py
@@ -9,6 +9,7 @@
 """
 import math
 
+
 from sys import maxsize
 
 import matplotlib.pyplot as plt
@@ -103,7 +104,7 @@ def process_state(self):
                 if y.h <= k_old and x.h > y.h + x.cost(y):
                     x.parent = y
                     x.h = y.h + x.cost(y)
-        elif k_old == x.h:
+        if k_old == x.h:
             for y in self.map.get_neighbors(x):
                 if y.t == "new" or y.parent == x and y.h != x.h + x.cost(y) \
                         or y.parent != x and y.h > x.h + x.cost(y):
@@ -116,7 +117,7 @@ def process_state(self):
                     self.insert(y, x.h + x.cost(y))
                 else:
                     if y.parent != x and y.h > x.h + x.cost(y):
-                        self.insert(y, x.h)
+                        self.insert(x, x.h)
                     else:
                         if y.parent != x and x.h > y.h + x.cost(y) \
                                 and y.t == "close" and y.h > k_old:
@@ -173,6 +174,8 @@ def run(self, start, end):
         s.set_state("e")
         tmp = start
 
+        AddNewObstacle(self.map) # add new obstacle after the first search finished
+
         while tmp != end:
             tmp.set_state("*")
             rx.append(tmp.x)
@@ -195,6 +198,15 @@ def modify(self, state):
             if k_min >= state.h:
                 break
 
+def AddNewObstacle(map:Map):
+    ox, oy = [], []
+    for i in range(5, 21):
+        ox.append(i)
+        oy.append(40)
+    map.set_obstacle([(i, j) for i, j in zip(ox, oy)])
+    if show_animation:
+        plt.pause(0.001)
+        plt.plot(ox, oy, ".g")
 
 def main():
     m = Map(100, 100)
@@ -217,7 +229,6 @@ def main():
     for i in range(0, 40):
         ox.append(40)
         oy.append(60 - i)
-    print([(i, j) for i, j in zip(ox, oy)])
     m.set_obstacle([(i, j) for i, j in zip(ox, oy)])
 
     start = [10, 10]
@@ -234,7 +245,7 @@ def main():
     rx, ry = dstar.run(start, end)
 
     if show_animation:
-        plt.plot(rx, ry, "-r")
+        # plt.plot(rx, ry, "-r")
         plt.show()
 
 

From 1801cd0dfff86de899285d02f4a1593d2df970e9 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 19 Dec 2023 07:04:43 +0900
Subject: [PATCH 0812/1080] Bump github/codeql-action from 2 to 3 (#955)

Bumps [github/codeql-action](https://github.com/github/codeql-action) from 2 to 3.
- [Release notes](https://github.com/github/codeql-action/releases)
- [Changelog](https://github.com/github/codeql-action/blob/main/CHANGELOG.md)
- [Commits](https://github.com/github/codeql-action/compare/v2...v3)

---
updated-dependencies:
- dependency-name: github/codeql-action
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/codeql.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index d3e888f09e..878a4a4435 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -24,7 +24,7 @@ jobs:
 
     # Initializes the CodeQL tools for scanning.
     - name: Initialize CodeQL
-      uses: github/codeql-action/init@v2
+      uses: github/codeql-action/init@v3
       with:
         config-file: ./.github/codeql/codeql-config.yml
       # Override language selection by uncommenting this and choosing your languages
@@ -34,7 +34,7 @@ jobs:
     # Autobuild attempts to build any compiled languages  (C/C++, C#, or Java).
     # If this step fails, then you should remove it and run the build manually (see below)
     - name: Autobuild
-      uses: github/codeql-action/autobuild@v2
+      uses: github/codeql-action/autobuild@v3
 
     # ℹ️ Command-line programs to run using the OS shell.
     # 📚 https://git.io/JvXDl
@@ -48,4 +48,4 @@ jobs:
     #   make release
 
     - name: Perform CodeQL Analysis
-      uses: github/codeql-action/analyze@v2
+      uses: github/codeql-action/analyze@v3

From 7cca7aa6ac4c6d66cbb46011cc642b2543e5756e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 19 Dec 2023 07:05:11 +0900
Subject: [PATCH 0813/1080] Bump ruff from 0.1.7 to 0.1.8 in /requirements
 (#956)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.7 to 0.1.8.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.1.7...v0.1.8)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 2c2a6d47ff..68d8b2dcff 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.1
 pytest == 7.4.3 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.7.1 # For unit test
-ruff == 0.1.7 # For unit test
+ruff == 0.1.8 # For unit test

From b2f866a5cd7bbf3f70c9c247e13f19d4a8858c92 Mon Sep 17 00:00:00 2001
From: Nivid Patel <66813410+nivid26@users.noreply.github.com>
Date: Tue, 19 Dec 2023 08:03:55 -0500
Subject: [PATCH 0814/1080] Update car.py (#957)

Changed comment at line 62 and 64
---
 PathPlanning/HybridAStar/car.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py
index 0367ab2775..d7631133c7 100644
--- a/PathPlanning/HybridAStar/car.py
+++ b/PathPlanning/HybridAStar/car.py
@@ -59,9 +59,9 @@ def rectangle_check(x, y, yaw, ox, oy):
         rx, ry = converted_xy[0], converted_xy[1]
 
         if not (rx > LF or rx < -LB or ry > W / 2.0 or ry < -W / 2.0):
-            return False  # no collision
+            return False  # collision
 
-    return True  # collision
+    return True  # no collision
 
 
 def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):

From e3f7979c3c08de87d702e1d2006ffb55440514a5 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 22 Dec 2023 12:13:59 +0900
Subject: [PATCH 0815/1080] Update dstar.py (#959)

---
 PathPlanning/DStar/dstar.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/DStar/dstar.py b/PathPlanning/DStar/dstar.py
index cc1d88ed66..b62b939f54 100644
--- a/PathPlanning/DStar/dstar.py
+++ b/PathPlanning/DStar/dstar.py
@@ -245,7 +245,7 @@ def main():
     rx, ry = dstar.run(start, end)
 
     if show_animation:
-        # plt.plot(rx, ry, "-r")
+        plt.plot(rx, ry, "-r")
         plt.show()
 
 

From 87b3aacb8b41e1924319eaae85ef88c39e3062b1 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 26 Dec 2023 23:36:29 +0900
Subject: [PATCH 0816/1080] Bump mypy from 1.7.1 to 1.8.0 in /requirements
 (#960)

Bumps [mypy](https://github.com/python/mypy) from 1.7.1 to 1.8.0.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.7.1...v1.8.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 68d8b2dcff..ae1aa2d749 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.8.2
 cvxpy == 1.4.1
 pytest == 7.4.3 # For unit test
 pytest-xdist == 3.5.0 # For unit test
-mypy == 1.7.1 # For unit test
+mypy == 1.8.0 # For unit test
 ruff == 0.1.8 # For unit test

From 748b9be4e76fa2f0d55b507a30bb578b74d6c921 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 27 Dec 2023 08:33:04 +0900
Subject: [PATCH 0817/1080] Bump ruff from 0.1.8 to 0.1.9 in /requirements
 (#961)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.8 to 0.1.9.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.1.8...v0.1.9)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index ae1aa2d749..bb78ed4d2f 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.1
 pytest == 7.4.3 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.8.0 # For unit test
-ruff == 0.1.8 # For unit test
+ruff == 0.1.9 # For unit test

From cc3fd0c55e8df8637ffa1753c6a3d973db9ed2a1 Mon Sep 17 00:00:00 2001
From: Videh Patel <66770479+videh25@users.noreply.github.com>
Date: Tue, 2 Jan 2024 19:09:48 +0530
Subject: [PATCH 0818/1080] Using util.angle_mod in all codes. #684 (#946)

* switched to using utils.angle_mod()

* switched to using utils.angle_mod()

* renamed mod2pi to pi_2_pi

* Removed linting errors

* switched to using utils.angle_mod()

* switched to using utils.angle_mod()

* renamed mod2pi to pi_2_pi

* Removed linting errors

* annotation changes and round precision

* Reverted to mod2pi

---------

Co-authored-by: Videh Patel <videh.patel@fluxauto.xyz>
---
 .../n_joint_arm_to_point_control.py           |  5 ++--
 .../two_joint_arm_to_point_control.py         |  3 ++-
 Control/move_to_pose/move_to_pose.py          | 19 +++++++--------
 .../ensemble_kalman_filter.py                 |  3 ++-
 .../ClosedLoopRRTStar/unicycle_model.py       |  3 ++-
 .../motion_model.py                           |  3 ++-
 .../trajectory_generator.py                   |  2 +-
 .../reeds_shepp_path_planning.py              |  9 ++++----
 .../lqr_speed_steer_control.py                |  4 ++--
 .../lqr_steer_control/lqr_steer_control.py    |  3 ++-
 ...odel_predictive_speed_and_steer_control.py |  9 ++------
 .../rear_wheel_feedback.py                    | 23 ++++++++-----------
 .../stanley_controller/stanley_controller.py  | 13 ++++-------
 SLAM/EKFSLAM/ekf_slam.py                      |  3 ++-
 SLAM/FastSLAM1/fast_slam1.py                  |  3 ++-
 SLAM/FastSLAM2/fast_slam2.py                  |  3 ++-
 SLAM/GraphBasedSLAM/graph_based_slam.py       |  3 ++-
 17 files changed, 51 insertions(+), 60 deletions(-)

diff --git a/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py b/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py
index 498ef41c1f..a237523336 100644
--- a/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py
+++ b/ArmNavigation/n_joint_arm_to_point_control/n_joint_arm_to_point_control.py
@@ -11,6 +11,7 @@
 
 import numpy as np
 from ArmNavigation.n_joint_arm_to_point_control.NLinkArm import NLinkArm
+from utils.angle import angle_mod
 
 # Simulation parameters
 Kp = 2
@@ -159,9 +160,9 @@ def ang_diff(theta1, theta2):
     """
     Returns the difference between two angles in the range -pi to +pi
     """
-    return (theta1 - theta2 + np.pi) % (2 * np.pi) - np.pi
+    return angle_mod(theta1 - theta2)
 
 
 if __name__ == '__main__':
     # main()
-    animation()
\ No newline at end of file
+    animation()
diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
index 66d0ebeb23..c2227f18e3 100644
--- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
+++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
@@ -15,6 +15,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import math
+from utils.angle import angle_mod
 
 
 # Simulation parameters
@@ -110,7 +111,7 @@ def plot_arm(theta1, theta2, target_x, target_y):  # pragma: no cover
 
 def ang_diff(theta1, theta2):
     # Returns the difference between two angles in the range -pi to +pi
-    return (theta1 - theta2 + np.pi) % (2 * np.pi) - np.pi
+    return angle_mod(theta1 - theta2)
 
 
 def click(event):  # pragma: no cover
diff --git a/Control/move_to_pose/move_to_pose.py b/Control/move_to_pose/move_to_pose.py
index 73604ccd7f..cc29666297 100644
--- a/Control/move_to_pose/move_to_pose.py
+++ b/Control/move_to_pose/move_to_pose.py
@@ -13,7 +13,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from random import random
-
+from utils.angle import angle_mod
 
 class PathFinderController:
     """
@@ -71,9 +71,8 @@ def calc_control_command(self, x_diff, y_diff, theta, theta_goal):
         # from 0 rad to 2*pi rad with slight turn
 
         rho = np.hypot(x_diff, y_diff)
-        alpha = (np.arctan2(y_diff, x_diff)
-                 - theta + np.pi) % (2 * np.pi) - np.pi
-        beta = (theta_goal - theta - alpha + np.pi) % (2 * np.pi) - np.pi
+        alpha = angle_mod(np.arctan2(y_diff, x_diff) - theta)
+        beta = angle_mod(theta_goal - theta - alpha)
         v = self.Kp_rho * rho
         w = self.Kp_alpha * alpha - controller.Kp_beta * beta
 
@@ -173,16 +172,14 @@ def transformation_matrix(x, y, theta):
 def main():
 
     for i in range(5):
-        x_start = 20 * random()
-        y_start = 20 * random()
-        theta_start = 2 * np.pi * random() - np.pi
+        x_start = 20.0 * random()
+        y_start = 20.0 * random()
+        theta_start: float = 2 * np.pi * random() - np.pi
         x_goal = 20 * random()
         y_goal = 20 * random()
         theta_goal = 2 * np.pi * random() - np.pi
-        print("Initial x: %.2f m\nInitial y: %.2f m\nInitial theta: %.2f rad\n" %
-              (x_start, y_start, theta_start))
-        print("Goal x: %.2f m\nGoal y: %.2f m\nGoal theta: %.2f rad\n" %
-              (x_goal, y_goal, theta_goal))
+        print(f"Initial x: {round(x_start, 2)} m\nInitial y: {round(y_start, 2)} m\nInitial theta: {round(theta_start, 2)} rad\n")
+        print(f"Goal x: {round(x_goal, 2)} m\nGoal y: {round(y_goal, 2)} m\nGoal theta: {round(theta_goal, 2)} rad\n")
         move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal)
 
 
diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
index fc8280e7bf..2bab3b49c1 100644
--- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
+++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
@@ -16,6 +16,7 @@
 import math
 import matplotlib.pyplot as plt
 import numpy as np
+from utils.angle import angle_mod
 
 from utils.angle import rot_mat_2d
 
@@ -179,7 +180,7 @@ def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
 
 
 def pi_2_pi(angle):
-    return (angle + math.pi) % (2 * math.pi) - math.pi
+    return angle_mod(angle)
 
 
 def main():
diff --git a/PathPlanning/ClosedLoopRRTStar/unicycle_model.py b/PathPlanning/ClosedLoopRRTStar/unicycle_model.py
index 5a0fd17a4e..c05f76c84e 100644
--- a/PathPlanning/ClosedLoopRRTStar/unicycle_model.py
+++ b/PathPlanning/ClosedLoopRRTStar/unicycle_model.py
@@ -8,6 +8,7 @@
 
 import math
 import numpy as np
+from utils.angle import angle_mod
 
 dt = 0.05  # [s]
 L = 0.9  # [m]
@@ -39,7 +40,7 @@ def update(state, a, delta):
 
 
 def pi_2_pi(angle):
-    return (angle + math.pi) % (2 * math.pi) - math.pi
+    return angle_mod(angle)
 
 
 if __name__ == '__main__':  # pragma: no cover
diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py
index 531bf91ef5..5ef6d2e23f 100644
--- a/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py
+++ b/PathPlanning/ModelPredictiveTrajectoryGenerator/motion_model.py
@@ -1,6 +1,7 @@
 import math
 import numpy as np
 from scipy.interpolate import interp1d
+from utils.angle import angle_mod
 
 # motion parameter
 L = 1.0  # wheel base
@@ -18,7 +19,7 @@ def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0):
 
 
 def pi_2_pi(angle):
-    return (angle + math.pi) % (2 * math.pi) - math.pi
+    return angle_mod(angle)
 
 
 def update(state, v, delta, dt, L):
diff --git a/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py b/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py
index 97c0ad8b80..6084fc1a07 100644
--- a/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py
+++ b/PathPlanning/ModelPredictiveTrajectoryGenerator/trajectory_generator.py
@@ -18,7 +18,7 @@
 
 # optimization parameter
 max_iter = 100
-h = np.array([0.5, 0.02, 0.02]).T  # parameter sampling distance
+h: np.ndarray = np.array([0.5, 0.02, 0.02]).T  # parameter sampling distance
 cost_th = 0.1
 
 show_animation = True
diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
index c55d2629e5..4d8fb7d9b8 100644
--- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
+++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
@@ -9,6 +9,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+from utils.angle import angle_mod
 
 show_animation = True
 
@@ -40,6 +41,9 @@ def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
         plt.plot(x, y)
 
 
+def pi_2_pi(x):
+    return angle_mod(x)
+
 def mod2pi(x):
     # Be consistent with fmod in cplusplus here.
     v = np.mod(x, np.copysign(2.0 * math.pi, x))
@@ -50,7 +54,6 @@ def mod2pi(x):
             v -= 2.0 * math.pi
     return v
 
-
 def straight_left_straight(x, y, phi):
     phi = mod2pi(phi)
     # only take phi in (0.01*math.pi, 0.99*math.pi) for the sake of speed.
@@ -296,10 +299,6 @@ def interpolate(dist, length, mode, max_curvature, origin_x, origin_y,
     return x, y, yaw, 1 if length > 0.0 else -1
 
 
-def pi_2_pi(angle):
-    return (angle + math.pi) % (2 * math.pi) - math.pi
-
-
 def calc_paths(sx, sy, syaw, gx, gy, gyaw, maxc, step_size):
     q0 = [sx, sy, syaw]
     q1 = [gx, gy, gyaw]
diff --git a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
index 7e57abb80e..a70e30d378 100644
--- a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
+++ b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
@@ -11,6 +11,7 @@
 import numpy as np
 import scipy.linalg as la
 import pathlib
+from utils.angle import angle_mod
 sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 from PathPlanning.CubicSpline import cubic_spline_planner
@@ -52,8 +53,7 @@ def update(state, a, delta):
 
 
 def pi_2_pi(angle):
-    return (angle + math.pi) % (2 * math.pi) - math.pi
-
+    return angle_mod(angle)
 
 def solve_dare(A, B, Q, R):
     """
diff --git a/PathTracking/lqr_steer_control/lqr_steer_control.py b/PathTracking/lqr_steer_control/lqr_steer_control.py
index 95a1b92ce5..4078ea56db 100644
--- a/PathTracking/lqr_steer_control/lqr_steer_control.py
+++ b/PathTracking/lqr_steer_control/lqr_steer_control.py
@@ -11,6 +11,7 @@
 import numpy as np
 import sys
 import pathlib
+from utils.angle import angle_mod
 sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 from PathPlanning.CubicSpline import cubic_spline_planner
@@ -61,7 +62,7 @@ def PIDControl(target, current):
 
 
 def pi_2_pi(angle):
-    return (angle + math.pi) % (2 * math.pi) - math.pi
+    return angle_mod(angle)
 
 
 def solve_DARE(A, B, Q, R):
diff --git a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
index 3015313198..c55018cf26 100644
--- a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
+++ b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
@@ -11,6 +11,7 @@
 import numpy as np
 import sys
 import pathlib
+from utils.angle import angle_mod
 sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 from PathPlanning.CubicSpline import cubic_spline_planner
@@ -69,13 +70,7 @@ def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0):
 
 
 def pi_2_pi(angle):
-    while(angle > math.pi):
-        angle = angle - 2.0 * math.pi
-
-    while(angle < -math.pi):
-        angle = angle + 2.0 * math.pi
-
-    return angle
+    return angle_mod(angle)
 
 
 def get_linear_model_matrix(v, phi, delta):
diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
index ff72454f34..1702bd47dd 100644
--- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
+++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
@@ -8,6 +8,7 @@
 import matplotlib.pyplot as plt
 import math
 import numpy as np
+from utils.angle import angle_mod
 
 from scipy import interpolate
 from scipy import optimize
@@ -51,21 +52,21 @@ def __init__(self, x, y):
         self.ddY = self.Y.derivative(2)
 
         self.length = s[-1]
-    
+
     def calc_yaw(self, s):
         dx, dy = self.dX(s), self.dY(s)
         return np.arctan2(dy, dx)
-    
+
     def calc_curvature(self, s):
         dx, dy   = self.dX(s), self.dY(s)
         ddx, ddy   = self.ddX(s), self.ddY(s)
         return (ddy * dx - ddx * dy) / ((dx ** 2 + dy ** 2)**(3 / 2))
-    
+
     def __find_nearest_point(self, s0, x, y):
         def calc_distance(_s, *args):
             _x, _y= self.X(_s), self.Y(_s)
             return (_x - args[0])**2 + (_y - args[1])**2
-        
+
         def calc_distance_jacobian(_s, *args):
             _x, _y = self.X(_s), self.Y(_s)
             _dx, _dy = self.dX(_s), self.dY(_s)
@@ -76,7 +77,7 @@ def calc_distance_jacobian(_s, *args):
 
     def calc_track_error(self, x, y, s0):
         ret = self.__find_nearest_point(s0, x, y)
-        
+
         s = ret[0][0]
         e = ret[1]
 
@@ -96,13 +97,7 @@ def pid_control(target, current):
     return a
 
 def pi_2_pi(angle):
-    while(angle > math.pi):
-        angle = angle - 2.0 * math.pi
-
-    while(angle < -math.pi):
-        angle = angle + 2.0 * math.pi
-
-    return angle
+    return angle_mod(angle)
 
 def rear_wheel_feedback_control(state, e, k, yaw_ref):
     v = state.v
@@ -170,7 +165,7 @@ def simulate(path_ref, goal):
             plt.plot(path_ref.X(s0), path_ref.Y(s0), "xg", label="target")
             plt.axis("equal")
             plt.grid(True)
-            plt.title("speed[km/h]:{:.2f}, target s-param:{:.2f}".format(round(state.v * 3.6, 2), s0))
+            plt.title(f"speed[km/h]:{round(state.v * 3.6, 2):.2f}, target s-param:{s0:.2f}")
             plt.pause(0.0001)
 
     return t, x, y, yaw, v, goal_flag
@@ -184,7 +179,7 @@ def calc_target_speed(state, yaw_ref):
     if switch:
         state.direction *= -1
         return 0.0
-    
+
     if state.direction != 1:
         return -target_speed
 
diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py
index 9c475cbaf2..08aa049395 100644
--- a/PathTracking/stanley_controller/stanley_controller.py
+++ b/PathTracking/stanley_controller/stanley_controller.py
@@ -13,6 +13,7 @@
 import matplotlib.pyplot as plt
 import sys
 import pathlib
+from utils.angle import angle_mod
 sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 from PathPlanning.CubicSpline import cubic_spline_planner
@@ -26,7 +27,7 @@
 show_animation = True
 
 
-class State(object):
+class State:
     """
     Class representing the state of a vehicle.
 
@@ -38,7 +39,7 @@ class State(object):
 
     def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0):
         """Instantiate the object."""
-        super(State, self).__init__()
+        super().__init__()
         self.x = x
         self.y = y
         self.yaw = yaw
@@ -106,13 +107,7 @@ def normalize_angle(angle):
     :param angle: (float)
     :return: (float) Angle in radian in [-pi, pi]
     """
-    while angle > np.pi:
-        angle -= 2.0 * np.pi
-
-    while angle < -np.pi:
-        angle += 2.0 * np.pi
-
-    return angle
+    return angle_mod(angle)
 
 
 def calc_target_index(state, cx, cy):
diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py
index bb648ce9d9..a624e8765b 100644
--- a/SLAM/EKFSLAM/ekf_slam.py
+++ b/SLAM/EKFSLAM/ekf_slam.py
@@ -8,6 +8,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+from utils.angle import angle_mod
 
 # EKF state covariance
 Cx = np.diag([0.5, 0.5, np.deg2rad(30.0)]) ** 2
@@ -192,7 +193,7 @@ def jacob_h(q, delta, x, i):
 
 
 def pi_2_pi(angle):
-    return (angle + math.pi) % (2 * math.pi) - math.pi
+    return angle_mod(angle)
 
 
 def main():
diff --git a/SLAM/FastSLAM1/fast_slam1.py b/SLAM/FastSLAM1/fast_slam1.py
index 7b89f52df4..17f6d5e572 100644
--- a/SLAM/FastSLAM1/fast_slam1.py
+++ b/SLAM/FastSLAM1/fast_slam1.py
@@ -10,6 +10,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+from utils.angle import angle_mod
 
 # Fast SLAM covariance
 Q = np.diag([3.0, np.deg2rad(10.0)]) ** 2
@@ -321,7 +322,7 @@ def motion_model(x, u):
 
 
 def pi_2_pi(angle):
-    return (angle + math.pi) % (2 * math.pi) - math.pi
+    return angle_mod(angle)
 
 
 def main():
diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py
index aa77aa956a..2b57e3bcc3 100644
--- a/SLAM/FastSLAM2/fast_slam2.py
+++ b/SLAM/FastSLAM2/fast_slam2.py
@@ -10,6 +10,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+from utils.angle import angle_mod
 
 # Fast SLAM covariance
 Q = np.diag([3.0, np.deg2rad(10.0)]) ** 2
@@ -346,7 +347,7 @@ def motion_model(x, u):
 
 
 def pi_2_pi(angle):
-    return (angle + math.pi) % (2 * math.pi) - math.pi
+    return angle_mod(angle)
 
 
 def main():
diff --git a/SLAM/GraphBasedSLAM/graph_based_slam.py b/SLAM/GraphBasedSLAM/graph_based_slam.py
index 4d66ef6732..edd20a959c 100644
--- a/SLAM/GraphBasedSLAM/graph_based_slam.py
+++ b/SLAM/GraphBasedSLAM/graph_based_slam.py
@@ -21,6 +21,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from scipy.spatial.transform import Rotation as Rot
+from utils.angle import angle_mod
 
 #  Simulation parameter
 Q_sim = np.diag([0.2, np.deg2rad(1.0)]) ** 2
@@ -249,7 +250,7 @@ def motion_model(x, u):
 
 
 def pi_2_pi(angle):
-    return (angle + math.pi) % (2 * math.pi) - math.pi
+    return angle_mod(angle)
 
 
 def main():

From 49ae54ab70f1c30ca848fef215bdbc172a8e692a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 2 Jan 2024 22:40:08 +0900
Subject: [PATCH 0819/1080] Bump pytest from 7.4.3 to 7.4.4 in /requirements
 (#963)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.4.3 to 7.4.4.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/7.4.3...7.4.4)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index bb78ed4d2f..8b3c4c1739 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.26.2
 scipy == 1.11.4
 matplotlib == 3.8.2
 cvxpy == 1.4.1
-pytest == 7.4.3 # For unit test
+pytest == 7.4.4 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.8.0 # For unit test
 ruff == 0.1.9 # For unit test

From d7060f60289cc0241f3411fea85764fb20f9cac0 Mon Sep 17 00:00:00 2001
From: Videh Patel <66770479+videh25@users.noreply.github.com>
Date: Mon, 8 Jan 2024 18:06:49 +0530
Subject: [PATCH 0820/1080] Reopen: Added missing path types (#949)

* added missing RS path types

* modified assert condition in test3

* removed linting errors

* added sign check to avoid non RS paths

* Mathematical description of RS curves

* Undid debugging changes

* Added large step_size consideration

* renamed test3 to test_too_big_step_size

---------

Co-authored-by: Videh Patel <videh.patel@fluxauto.xyz>
---
 .../reeds_shepp_path_planning.py              | 320 +++++++++------
 .../path_planning/reeds_shepp_path/LR_L.png   | Bin 0 -> 280028 bytes
 .../path_planning/reeds_shepp_path/LR_LR.png  | Bin 0 -> 381584 bytes
 .../path_planning/reeds_shepp_path/LSL.png    | Bin 0 -> 239825 bytes
 .../reeds_shepp_path/LSL90xR.png              | Bin 0 -> 301712 bytes
 .../path_planning/reeds_shepp_path/LSR.png    | Bin 0 -> 257435 bytes
 .../reeds_shepp_path/LSR90_L.png              | Bin 0 -> 339492 bytes
 .../reeds_shepp_path/L_R90SL.png              | Bin 0 -> 316723 bytes
 .../reeds_shepp_path/L_R90SL90_R.png          | Bin 0 -> 335979 bytes
 .../reeds_shepp_path/L_R90SR.png              | Bin 0 -> 291754 bytes
 .../path_planning/reeds_shepp_path/L_RL.png   | Bin 0 -> 277432 bytes
 .../path_planning/reeds_shepp_path/L_RL_R.png | Bin 0 -> 323010 bytes
 .../path_planning/reeds_shepp_path/L_R_L.png  | Bin 0 -> 271666 bytes
 .../reeds_shepp_path_main.rst                 | 375 ++++++++++++++++++
 tests/test_rrt_star_reeds_shepp.py            |   6 +-
 15 files changed, 586 insertions(+), 115 deletions(-)
 create mode 100644 docs/modules/path_planning/reeds_shepp_path/LR_L.png
 create mode 100644 docs/modules/path_planning/reeds_shepp_path/LR_LR.png
 create mode 100644 docs/modules/path_planning/reeds_shepp_path/LSL.png
 create mode 100644 docs/modules/path_planning/reeds_shepp_path/LSL90xR.png
 create mode 100644 docs/modules/path_planning/reeds_shepp_path/LSR.png
 create mode 100644 docs/modules/path_planning/reeds_shepp_path/LSR90_L.png
 create mode 100644 docs/modules/path_planning/reeds_shepp_path/L_R90SL.png
 create mode 100644 docs/modules/path_planning/reeds_shepp_path/L_R90SL90_R.png
 create mode 100644 docs/modules/path_planning/reeds_shepp_path/L_R90SR.png
 create mode 100644 docs/modules/path_planning/reeds_shepp_path/L_RL.png
 create mode 100644 docs/modules/path_planning/reeds_shepp_path/L_RL_R.png
 create mode 100644 docs/modules/path_planning/reeds_shepp_path/L_R_L.png

diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
index 4d8fb7d9b8..8e134ff38b 100644
--- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
+++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
@@ -3,6 +3,7 @@
 Reeds Shepp path planner sample code
 
 author Atsushi Sakai(@Atsushi_twi)
+co-author Videh Patel(@videh25) : Added the missing RS paths
 
 """
 import math
@@ -54,21 +55,6 @@ def mod2pi(x):
             v -= 2.0 * math.pi
     return v
 
-def straight_left_straight(x, y, phi):
-    phi = mod2pi(phi)
-    # only take phi in (0.01*math.pi, 0.99*math.pi) for the sake of speed.
-    # phi in (0, 0.01*math.pi) will make test2() in test_rrt_star_reeds_shepp.py
-    # extremely time-consuming, since the value of xd, t will be very large.
-    if math.pi * 0.01 < phi < math.pi * 0.99 and y != 0:
-        xd = - y / math.tan(phi) + x
-        t = xd - math.tan(phi / 2.0)
-        u = phi
-        v = np.sign(y) * math.hypot(x - xd, y) - math.tan(phi / 2.0)
-        return True, t, u, v
-
-    return False, 0.0, 0.0, 0.0
-
-
 def set_path(paths, lengths, ctypes, step_size):
     path = Path()
     path.ctypes = ctypes
@@ -90,18 +76,6 @@ def set_path(paths, lengths, ctypes, step_size):
     return paths
 
 
-def straight_curve_straight(x, y, phi, paths, step_size):
-    flag, t, u, v = straight_left_straight(x, y, phi)
-    if flag:
-        paths = set_path(paths, [t, u, v], ["S", "L", "S"], step_size)
-
-    flag, t, u, v = straight_left_straight(x, -y, -phi)
-    if flag:
-        paths = set_path(paths, [t, u, v], ["S", "R", "S"], step_size)
-
-    return paths
-
-
 def polar(x, y):
     r = math.hypot(x, y)
     theta = math.atan2(y, x)
@@ -110,117 +84,200 @@ def polar(x, y):
 
 def left_straight_left(x, y, phi):
     u, t = polar(x - math.sin(phi), y - 1.0 + math.cos(phi))
-    if t >= 0.0:
+    if 0.0 <= t <= math.pi:
         v = mod2pi(phi - t)
-        if v >= 0.0:
-            return True, t, u, v
+        if 0.0 <= v <= math.pi:
+            return True, [t, u, v], ['L', 'S', 'L']
+
+    return False, [], []
+
+
+def left_straight_right(x, y, phi):
+    u1, t1 = polar(x + math.sin(phi), y - 1.0 - math.cos(phi))
+    u1 = u1 ** 2
+    if u1 >= 4.0:
+        u = math.sqrt(u1 - 4.0)
+        theta = math.atan2(2.0, u)
+        t = mod2pi(t1 + theta)
+        v = mod2pi(t - phi)
+
+        if (t >= 0.0) and (v >= 0.0):
+            return True, [t, u, v], ['L', 'S', 'R']
 
-    return False, 0.0, 0.0, 0.0
+    return False, [], []
 
 
-def left_right_left(x, y, phi):
-    u1, t1 = polar(x - math.sin(phi), y - 1.0 + math.cos(phi))
+def left_x_right_x_left(x, y, phi):
+    zeta = x - math.sin(phi)
+    eeta = y - 1 + math.cos(phi)
+    u1, theta = polar(zeta, eeta)
 
     if u1 <= 4.0:
-        u = -2.0 * math.asin(0.25 * u1)
-        t = mod2pi(t1 + 0.5 * u + math.pi)
-        v = mod2pi(phi - t + u)
+        A = math.acos(0.25 * u1)
+        t = mod2pi(A + theta + math.pi/2)
+        u = mod2pi(math.pi - 2 * A)
+        v = mod2pi(phi - t - u)
+        return True, [t, -u, v], ['L', 'R', 'L']
 
-        if t >= 0.0 >= u:
-            return True, t, u, v
+    return False, [], []
 
-    return False, 0.0, 0.0, 0.0
 
+def left_x_right_left(x, y, phi):
+    zeta = x - math.sin(phi)
+    eeta = y - 1 + math.cos(phi)
+    u1, theta = polar(zeta, eeta)
 
-def curve_curve_curve(x, y, phi, paths, step_size):
-    flag, t, u, v = left_right_left(x, y, phi)
-    if flag:
-        paths = set_path(paths, [t, u, v], ["L", "R", "L"], step_size)
+    if u1 <= 4.0:
+        A = math.acos(0.25 * u1)
+        t = mod2pi(A + theta + math.pi/2)
+        u = mod2pi(math.pi - 2*A)
+        v = mod2pi(-phi + t + u)
+        return True, [t, -u, -v], ['L', 'R', 'L']
 
-    flag, t, u, v = left_right_left(-x, y, -phi)
-    if flag:
-        paths = set_path(paths, [-t, -u, -v], ["L", "R", "L"], step_size)
+    return False, [], []
 
-    flag, t, u, v = left_right_left(x, -y, -phi)
-    if flag:
-        paths = set_path(paths, [t, u, v], ["R", "L", "R"], step_size)
 
-    flag, t, u, v = left_right_left(-x, -y, phi)
-    if flag:
-        paths = set_path(paths, [-t, -u, -v], ["R", "L", "R"], step_size)
+def left_right_x_left(x, y, phi):
+    zeta = x - math.sin(phi)
+    eeta = y - 1 + math.cos(phi)
+    u1, theta = polar(zeta, eeta)
 
-    # backwards
-    xb = x * math.cos(phi) + y * math.sin(phi)
-    yb = x * math.sin(phi) - y * math.cos(phi)
+    if u1 <= 4.0:
+        u = math.acos(1 - u1**2 * 0.125)
+        A = math.asin(2 * math.sin(u) / u1)
+        t = mod2pi(-A + theta + math.pi/2)
+        v = mod2pi(t - u - phi)
+        return True, [t, u, -v], ['L', 'R', 'L']
+
+    return False, [], []
+
+
+def left_right_x_left_right(x, y, phi):
+    zeta = x + math.sin(phi)
+    eeta = y - 1 - math.cos(phi)
+    u1, theta = polar(zeta, eeta)
+
+    # Solutions refering to (2 < u1 <= 4) are considered sub-optimal in paper
+    # Solutions do not exist for u1 > 4
+    if u1 <= 2:
+        A = math.acos((u1 + 2) * 0.25)
+        t = mod2pi(theta + A + math.pi/2)
+        u = mod2pi(A)
+        v = mod2pi(phi - t + 2*u)
+        if ((t >= 0) and (u >= 0) and (v >= 0)):
+            return True, [t, u, -u, -v], ['L', 'R', 'L', 'R']
+
+    return False, [], []
+
+
+def left_x_right_left_x_right(x, y, phi):
+    zeta = x + math.sin(phi)
+    eeta = y - 1 - math.cos(phi)
+    u1, theta = polar(zeta, eeta)
+    u2 = (20 - u1**2) / 16
+
+    if (0 <= u2 <= 1):
+        u = math.acos(u2)
+        A = math.asin(2 * math.sin(u) / u1)
+        t = mod2pi(theta + A + math.pi/2)
+        v = mod2pi(t - phi)
+        if (t >= 0) and (v >= 0):
+            return True, [t, -u, -u, v], ['L', 'R', 'L', 'R']
 
-    flag, t, u, v = left_right_left(xb, yb, phi)
-    if flag:
-        paths = set_path(paths, [v, u, t], ["L", "R", "L"], step_size)
+    return False, [], []
 
-    flag, t, u, v = left_right_left(-xb, yb, -phi)
-    if flag:
-        paths = set_path(paths, [-v, -u, -t], ["L", "R", "L"], step_size)
 
-    flag, t, u, v = left_right_left(xb, -yb, -phi)
-    if flag:
-        paths = set_path(paths, [v, u, t], ["R", "L", "R"], step_size)
+def left_x_right90_straight_left(x, y, phi):
+    zeta = x - math.sin(phi)
+    eeta = y - 1 + math.cos(phi)
+    u1, theta = polar(zeta, eeta)
 
-    flag, t, u, v = left_right_left(-xb, -yb, phi)
-    if flag:
-        paths = set_path(paths, [-v, -u, -t], ["R", "L", "R"], step_size)
+    if u1 >= 2.0:
+        u = math.sqrt(u1**2 - 4) - 2
+        A = math.atan2(2, math.sqrt(u1**2 - 4))
+        t = mod2pi(theta + A + math.pi/2)
+        v = mod2pi(t - phi + math.pi/2)
+        if (t >= 0) and (v >= 0):
+           return True, [t, -math.pi/2, -u, -v], ['L', 'R', 'S', 'L']
 
-    return paths
+    return False, [], []
 
 
-def curve_straight_curve(x, y, phi, paths, step_size):
-    flag, t, u, v = left_straight_left(x, y, phi)
-    if flag:
-        paths = set_path(paths, [t, u, v], ["L", "S", "L"], step_size)
+def left_straight_right90_x_left(x, y, phi):
+    zeta = x - math.sin(phi)
+    eeta = y - 1 + math.cos(phi)
+    u1, theta = polar(zeta, eeta)
 
-    flag, t, u, v = left_straight_left(-x, y, -phi)
-    if flag:
-        paths = set_path(paths, [-t, -u, -v], ["L", "S", "L"], step_size)
+    if u1 >= 2.0:
+        u = math.sqrt(u1**2 - 4) - 2
+        A = math.atan2(math.sqrt(u1**2 - 4), 2)
+        t = mod2pi(theta - A + math.pi/2)
+        v = mod2pi(t - phi - math.pi/2)
+        if (t >= 0) and (v >= 0):
+            return True, [t, u, math.pi/2, -v], ['L', 'S', 'R', 'L']
 
-    flag, t, u, v = left_straight_left(x, -y, -phi)
-    if flag:
-        paths = set_path(paths, [t, u, v], ["R", "S", "R"], step_size)
+    return False, [], []
 
-    flag, t, u, v = left_straight_left(-x, -y, phi)
-    if flag:
-        paths = set_path(paths, [-t, -u, -v], ["R", "S", "R"], step_size)
 
-    flag, t, u, v = left_straight_right(x, y, phi)
-    if flag:
-        paths = set_path(paths, [t, u, v], ["L", "S", "R"], step_size)
+def left_x_right90_straight_right(x, y, phi):
+    zeta = x + math.sin(phi)
+    eeta = y - 1 - math.cos(phi)
+    u1, theta = polar(zeta, eeta)
 
-    flag, t, u, v = left_straight_right(-x, y, -phi)
-    if flag:
-        paths = set_path(paths, [-t, -u, -v], ["L", "S", "R"], step_size)
+    if u1 >= 2.0:
+        t = mod2pi(theta + math.pi/2)
+        u = u1 - 2
+        v = mod2pi(phi - t - math.pi/2)
+        if (t >= 0) and (v >= 0):
+            return True, [t, -math.pi/2, -u, -v], ['L', 'R', 'S', 'R']
 
-    flag, t, u, v = left_straight_right(x, -y, -phi)
-    if flag:
-        paths = set_path(paths, [t, u, v], ["R", "S", "L"], step_size)
+    return False, [], []
 
-    flag, t, u, v = left_straight_right(-x, -y, phi)
-    if flag:
-        paths = set_path(paths, [-t, -u, -v], ["R", "S", "L"], step_size)
 
-    return paths
+def left_straight_left90_x_right(x, y, phi):
+    zeta = x + math.sin(phi)
+    eeta = y - 1 - math.cos(phi)
+    u1, theta = polar(zeta, eeta)
 
+    if u1 >= 2.0:
+        t = mod2pi(theta)
+        u = u1 - 2
+        v = mod2pi(phi - t - math.pi/2)
+        if (t >= 0) and (v >= 0):
+            return True, [t, u, math.pi/2, -v], ['L', 'S', 'L', 'R']
+
+    return False, [], []
+
+
+def left_x_right90_straight_left90_x_right(x, y, phi):
+    zeta = x + math.sin(phi)
+    eeta = y - 1 - math.cos(phi)
+    u1, theta = polar(zeta, eeta)
 
-def left_straight_right(x, y, phi):
-    u1, t1 = polar(x + math.sin(phi), y - 1.0 - math.cos(phi))
-    u1 = u1 ** 2
     if u1 >= 4.0:
-        u = math.sqrt(u1 - 4.0)
-        theta = math.atan2(2.0, u)
-        t = mod2pi(t1 + theta)
+        u = math.sqrt(u1**2 - 4) - 4
+        A = math.atan2(2, math.sqrt(u1**2 - 4))
+        t = mod2pi(theta + A + math.pi/2)
         v = mod2pi(t - phi)
+        if (t >= 0) and (v >= 0):
+            return True, [t, -math.pi/2, -u, -math.pi/2, v], ['L', 'R', 'S', 'L', 'R']
+
+    return False, [], []
+
 
-        if t >= 0.0 and v >= 0.0:
-            return True, t, u, v
+def timeflip(travel_distances):
+    return [-x for x in travel_distances]
 
-    return False, 0.0, 0.0, 0.0
+
+def reflect(steering_directions):
+    def switch_dir(dirn):
+        if dirn == 'L':
+            return 'R'
+        elif dirn == 'R':
+            return 'L'
+        else:
+            return 'S'
+    return[switch_dir(dirn) for dirn in steering_directions]
 
 
 def generate_path(q0, q1, max_curvature, step_size):
@@ -231,11 +288,52 @@ def generate_path(q0, q1, max_curvature, step_size):
     s = math.sin(q0[2])
     x = (c * dx + s * dy) * max_curvature
     y = (-s * dx + c * dy) * max_curvature
+    step_size *= max_curvature
 
     paths = []
-    paths = straight_curve_straight(x, y, dth, paths, step_size)
-    paths = curve_straight_curve(x, y, dth, paths, step_size)
-    paths = curve_curve_curve(x, y, dth, paths, step_size)
+    path_functions = [left_straight_left, left_straight_right,                          # CSC
+                      left_x_right_x_left, left_x_right_left, left_right_x_left,        # CCC
+                      left_right_x_left_right, left_x_right_left_x_right,               # CCCC
+                      left_x_right90_straight_left, left_x_right90_straight_right,      # CCSC
+                      left_straight_right90_x_left, left_straight_left90_x_right,       # CSCC
+                      left_x_right90_straight_left90_x_right]                           # CCSCC
+
+    for path_func in path_functions:
+        flag, travel_distances, steering_dirns = path_func(x, y, dth)
+        if flag:
+            for distance in travel_distances:
+                if (0.1*sum([abs(d) for d in travel_distances]) < abs(distance) < step_size):
+                    print("Step size too large for Reeds-Shepp paths.")
+                    return []
+            paths = set_path(paths, travel_distances, steering_dirns, step_size)
+
+        flag, travel_distances, steering_dirns = path_func(-x, y, -dth)
+        if flag:
+            for distance in travel_distances:
+                if (0.1*sum([abs(d) for d in travel_distances]) < abs(distance) < step_size):
+                    print("Step size too large for Reeds-Shepp paths.")
+                    return []
+            travel_distances = timeflip(travel_distances)
+            paths = set_path(paths, travel_distances, steering_dirns, step_size)
+
+        flag, travel_distances, steering_dirns = path_func(x, -y, -dth)
+        if flag:
+            for distance in travel_distances:
+                if (0.1*sum([abs(d) for d in travel_distances]) < abs(distance) < step_size):
+                    print("Step size too large for Reeds-Shepp paths.")
+                    return []
+            steering_dirns = reflect(steering_dirns)
+            paths = set_path(paths, travel_distances, steering_dirns, step_size)
+
+        flag, travel_distances, steering_dirns = path_func(-x, -y, dth)
+        if flag:
+            for distance in travel_distances:
+                if (0.1*sum([abs(d) for d in travel_distances]) < abs(distance) < step_size):
+                    print("Step size too large for Reeds-Shepp paths.")
+                    return []
+            travel_distances = timeflip(travel_distances)
+            steering_dirns = reflect(steering_dirns)
+            paths = set_path(paths, travel_distances, steering_dirns, step_size)
 
     return paths
 
@@ -252,7 +350,7 @@ def calc_interpolate_dists_list(lengths, step_size):
 
 
 def generate_local_course(lengths, modes, max_curvature, step_size):
-    interpolate_dists_list = calc_interpolate_dists_list(lengths, step_size)
+    interpolate_dists_list = calc_interpolate_dists_list(lengths, step_size * max_curvature)
 
     origin_x, origin_y, origin_yaw = 0.0, 0.0, 0.0
 
@@ -307,7 +405,7 @@ def calc_paths(sx, sy, syaw, gx, gy, gyaw, maxc, step_size):
     for path in paths:
         xs, ys, yaws, directions = generate_local_course(path.lengths,
                                                          path.ctypes, maxc,
-                                                         step_size * maxc)
+                                                         step_size)
 
         # convert global coordinate
         path.x = [math.cos(-q0[2]) * ix + math.sin(-q0[2]) * iy + q0[0] for
@@ -354,6 +452,9 @@ def main():
                                                              curvature,
                                                              step_size)
 
+    if not xs:
+        assert False, "No path"
+
     if show_animation:  # pragma: no cover
         plt.cla()
         plt.plot(xs, ys, label="final course " + str(modes))
@@ -368,9 +469,6 @@ def main():
         plt.axis("equal")
         plt.show()
 
-    if not xs:
-        assert False, "No path"
-
 
 if __name__ == '__main__':
     main()
diff --git a/docs/modules/path_planning/reeds_shepp_path/LR_L.png b/docs/modules/path_planning/reeds_shepp_path/LR_L.png
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diff --git a/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path_main.rst b/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path_main.rst
index 81e565888e..6d456417fe 100644
--- a/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path_main.rst
+++ b/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path_main.rst
@@ -5,6 +5,381 @@ A sample code with Reeds Shepp path planning.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true
 
+Mathematical Description of Individual Path Types
+=================================================
+Here is an overview of mathematical derivations of formulae for individual path types.
+
+In all the derivations below, radius of curvature of the vehicle is assumed to be of unit length and start pose is considered to be at origin.  (*In code we are removing the offset due to start position and normalising the lengths before passing the values to these functions.*)
+
+Also, (t, u, v) respresent the measure of each motion requried. Thus, in case of a turning maneuver, they represent the angle inscribed at the centre of turning circle and in case of straight maneuver, they represent the distance to be travelled. 
+
+1. **Left-Straight-Left**
+
+.. image:: LSL.png
+
+We can deduce the following facts using geometry.
+
+- AGHC is a rectangle.
+- :math:`∠LAC = ∠BAG = t`
+- :math:`t + v = φ`
+- :math:`C(x - sin(φ), y + cos(φ))`
+- :math:`A(0, 1)`
+- :math:`u, t = polar(vector<AC>)`
+
+Hence, we have:
+
+- :math:`u, t = polar(x - sin(φ), y + cos(φ) - 1)`
+- :math:`v = φ - t`
+
+
+2. **Left-Straight-Right**
+
+.. image:: LSR.png
+
+With followng notations:
+
+- :math:`∠MBD = t1`
+- :math:`∠BDF = θ`
+- :math:`BC = u1`
+
+We can deduce the following facts using geometry.
+
+- D is mid-point of BC and FG.
+- :math:`t - v = φ`
+- :math:`C(x + sin(φ), y - cos(φ))`
+- :math:`A(0, 1)`
+- :math:`u1, t1 = polar(vector<AC>)`
+- :math:`\frac{u1^2}{4} = 1 + \frac{u^2}{4}`
+- :math:`BF = 1` [Radius Of Curvature]
+- :math:`FD = \frac{u}{2}`
+- :math:`θ = arctan(\frac{BF}{FD})`
+- :math:`t1 + θ = t`
+
+Hence, we have:
+
+- :math:`u1, t1 = polar(x + sin(φ), y - cos(φ) - 1)`
+- :math:`u = \sqrt{u1^2 - 4}`
+- :math:`θ = arctan(\frac{2}{u})`
+- :math:`t = t1 + θ`
+- :math:`v = t - φ`
+
+3. **LeftxRightxLeft**
+
+.. image:: L_R_L.png
+
+With followng notations:
+
+- :math:`∠CBD = ∠CDB = A` [BCD is an isoceles triangle]
+- :math:`∠DBK = θ`
+- :math:`BD = u1`
+
+We can deduce the following facts using geometry.
+
+- :math:`t + u + v = φ`
+- :math:`D(x - sin(φ), y + cos(φ))`
+- :math:`B(0, 1)`
+- :math:`u1, θ = polar(vector<BD>)`
+- :math:`A = arccos(\frac{BD/2}{CD})`
+- :math:`u = (π - 2*A)`
+- :math:`∠ABK = \frac{π}{2}`
+- :math:`∠KBD = θ`
+- :math:`t = ∠ABK + ∠KBD + ∠DBC`
+
+Hence, we have:
+
+- :math:`u1, θ = polar(x - sin(φ), y + cos(φ) - 1)`
+- :math:`A = arccos(\frac{u1/2}{2})`
+- :math:`t = \frac{π}{2} + θ + A`
+- :math:`u = (π - 2*A)`
+- :math:`v = (φ - t - u)`
+
+4. **LeftxRight-Left**
+
+.. image:: L_RL.png
+
+With followng notations:
+
+- :math:`∠CBD = ∠CDB = A` [BCD is an isoceles triangle]
+- :math:`∠DBK = θ`
+- :math:`BD = u1`
+
+We can deduce the following facts using geometry.
+
+- :math:`t + u - v = φ`
+- :math:`D(x - sin(φ), y + cos(φ))`
+- :math:`B(0, 1)`
+- :math:`u1, θ = polar(vector<BD>)`
+- :math:`A = arccos(\frac{BD/2}{CD})`
+- :math:`u = (π - 2*A)`
+- :math:`∠ABK = \frac{π}{2}`
+- :math:`∠KBD = θ`
+- :math:`t = ∠ABK + ∠KBD + ∠DBC`
+
+Hence, we have:
+
+- :math:`u1, θ = polar(x - sin(φ), y + cos(φ) - 1)`
+- :math:`A = arccos(\frac{u1/2}{2})`
+- :math:`t = \frac{π}{2} + θ + A`
+- :math:`u = (π - 2*A)`
+- :math:`v = (-φ + t + u)`
+
+5. **Left-RightxLeft**
+
+.. image:: LR_L.png
+
+With followng notations:
+
+- :math:`∠CBD = ∠CDB = A` [BCD is an isoceles triangle]
+- :math:`∠DBK = θ`
+- :math:`BD = u1`
+
+We can deduce the following facts using geometry.
+
+- :math:`t - u - v = φ`
+- :math:`D(x - sin(φ), y + cos(φ))`
+- :math:`B(0, 1)`
+- :math:`u1, θ = polar(vector<BD>)`
+- :math:`BC = CD = 2` [2 * radius of curvature]
+- :math:`cos(2π - u) = \frac{BC^2 + CD^2 - BD^2}{2 * BC * CD}` [Cosine Rule]
+- :math:`\frac{sin(A)}{BC} = \frac{sin(u)}{u1}` [Sine Rule]
+- :math:`∠ABK = \frac{π}{2}`
+- :math:`∠KBD = θ`
+- :math:`t = ∠ABK + ∠KBD - ∠DBC`
+
+Hence, we have:
+
+- :math:`u1, θ = polar(x - sin(φ), y + cos(φ) - 1)`
+- :math:`u = arccos(1 - \frac{u1^2}{8})`
+- :math:`A = arcsin(\frac{sin(u)}{u1}*2)`
+- :math:`t = \frac{π}{2} + θ - A`
+- :math:`v = (t - u - φ)`
+
+6. **Left-RightxLeft-Right**
+
+.. image:: LR_LR.png
+
+With followng notations:
+
+- :math:`∠CLG = ∠BCL = ∠CBG = ∠LGB = A = u` [BGCL is an isoceles trapezium]
+- :math:`∠KBG = θ`
+- :math:`BG = u1`
+
+We can deduce the following facts using geometry.
+
+- :math:`t - 2u + v = φ`
+- :math:`G(x + sin(φ), y - cos(φ))`
+- :math:`B(0, 1)`
+- :math:`u1, θ = polar(vector<BG>)`
+- :math:`BC = CL = LG = 2` [2 * radius of curvature]
+- :math:`CG^2 = CL^2 + LG^2 - 2*CL*LG*cos(A)` [Cosine rule in LGC]
+- :math:`CG^2 = CL^2 + LG^2 - 2*CL*LG*cos(A)` [Cosine rule in LGC]
+- From the previous two equations: :math:`A = arccos(\frac{u1 + 2}{4})`
+- :math:`∠ABK = \frac{π}{2}`
+- :math:`t = ∠ABK + ∠KBG + ∠GBC`
+
+Hence, we have:
+
+- :math:`u1, θ = polar(x + sin(φ), y - cos(φ) - 1)`
+- :math:`u = arccos(\frac{u1 + 2}{4})`
+- :math:`t = \frac{π}{2} + θ + u`
+- :math:`v = (φ - t + 2u)`
+
+7. **LeftxRight-LeftxRight**
+
+.. image:: L_RL_R.png
+
+With followng notations:
+
+- :math:`∠GBC = A` [BGCL is an isoceles trapezium]
+- :math:`∠KBG = θ`
+- :math:`BG = u1`
+
+We can deduce the following facts using geometry.
+
+- :math:`t - v = φ`
+- :math:`G(x + sin(φ), y - cos(φ))`
+- :math:`B(0, 1)`
+- :math:`u1, θ = polar(vector<BG>)`
+- :math:`BC = CL = LG = 2` [2 * radius of curvature]
+- :math:`CD = 1` [radius of curvature]
+- D is midpoint of BG
+- :math:`BD = \frac{u1}{2}`
+- :math:`cos(u) = \frac{BC^2 + CD^2 - BD^2}{2*BC*CD}` [Cosine rule in BCD]
+- :math:`sin(A) = CD*\frac{sin(u)}{BD}` [Sine rule in BCD]
+- :math:`∠ABK = \frac{π}{2}`
+- :math:`t = ∠ABK + ∠KBG + ∠GBC`
+
+Hence, we have:
+
+- :math:`u1, θ = polar(x + sin(φ), y - cos(φ) - 1)`
+- :math:`u = arccos(\frac{20 - u1^2}{16})`
+- :math:`A = arcsin(2*\frac{sin(u)}{u1})`
+- :math:`t = \frac{π}{2} + θ + A`
+- :math:`v = (t - φ)`
+
+
+8. **LeftxRight90-Straight-Left**
+
+.. image:: L_R90SL.png
+
+With followng notations:
+
+- :math:`∠FBM = A` [BGCL is an isoceles trapezium]
+- :math:`∠KBF = θ`
+- :math:`BF = u1`
+
+We can deduce the following facts using geometry.
+
+- :math:`t + \frac{π}{2} - v = φ`
+- :math:`F(x - sin(φ), y + cos(φ))`
+- :math:`B(0, 1)`
+- :math:`u1, θ = polar(vector<BF>)`
+- :math:`BM = CB = 2` [2 * radius of curvature]
+- :math:`MD = CD = 1` [CGDM is a rectangle]
+- :math:`MC = GD = u` [CGDM is a rectangle]
+- :math:`MF = MD + DF = 2`
+- :math:`BM = \sqrt{BF^2 - MF^2}` [Pythagoras theorem on BFM]
+- :math:`tan(A) = \frac{MF}{BM}`
+- :math:`u = MC = BM - CB` 
+- :math:`t = ∠ABK + ∠KBF + ∠FBC`
+
+Hence, we have:
+
+- :math:`u1, θ = polar(x - sin(φ), y + cos(φ) - 1)`
+- :math:`u = arccos(\sqrt{u1^2 - 4} - 2)`
+- :math:`A = arctan(\frac{2}{\sqrt{u1^2 - 4}})`
+- :math:`t = \frac{π}{2} + θ + A`
+- :math:`v = (t - φ + \frac{π}{2})`
+
+
+9. **Left-Straight-Right90xLeft**
+
+.. image:: LSR90_L.png
+
+With followng notations:
+
+- :math:`∠MBH = A` [BGCL is an isoceles trapezium]
+- :math:`∠KBH = θ`
+- :math:`BH = u1`
+
+We can deduce the following facts using geometry.
+
+- :math:`t - \frac{π}{2} - v = φ`
+- :math:`H(x - sin(φ), y + cos(φ))`
+- :math:`B(0, 1)`
+- :math:`u1, θ = polar(vector<BH>)`
+- :math:`GH = 2` [2 * radius of curvature]
+- :math:`CM = DG = 1` [CGDM is a rectangle]
+- :math:`CD = MG = u` [CGDM is a rectangle]
+- :math:`BM = BC + CM = 2`
+- :math:`MH = \sqrt{BH^2 - BM^2}` [Pythagoras theorem on BHM]
+- :math:`tan(A) = \frac{HM}{BM}`
+- :math:`u = MC = BM - CB` 
+- :math:`t = ∠ABK + ∠KBH - ∠HBC`
+
+Hence, we have:
+
+- :math:`u1, θ = polar(x - sin(φ), y + cos(φ) - 1)`
+- :math:`u = arccos(\sqrt{u1^2 - 4} - 2)`
+- :math:`A = arctan(\frac{2}{\sqrt{u1^2 - 4}})`
+- :math:`t = \frac{π}{2} + θ - A`
+- :math:`v = (t - φ - \frac{π}{2})`
+
+
+10. **LeftxRight90-Straight-Right**
+
+.. image:: L_R90SR.png
+
+With followng notations:
+
+- :math:`∠KBG = θ`
+- :math:`BG = u1`
+
+We can deduce the following facts using geometry.
+
+- :math:`t - \frac{π}{2} - v = φ`
+- :math:`G(x + sin(φ), y - cos(φ))`
+- :math:`B(0, 1)`
+- :math:`u1, θ = polar(vector<BG>)`
+- :math:`BD = 2` [2 * radius of curvature]
+- :math:`DG = EF = u` [DGFE is a rectangle]
+- :math:`DG = BG - BD = 2`
+- :math:`∠ABK = \frac{π}{2}`
+- :math:`t = ∠ABK + ∠KBG`
+
+Hence, we have:
+
+- :math:`u1, θ = polar(x + sin(φ), y - cos(φ) - 1)`
+- :math:`u = u1 - 2`
+- :math:`t = \frac{π}{2} + θ`
+- :math:`v = (t - φ - \frac{π}{2})`
+
+
+11. **Left-Straight-Left90xRight**
+
+.. image:: LSL90xR.png
+
+With followng notations:
+
+- :math:`∠KBH = θ`
+- :math:`BH = u1`
+
+We can deduce the following facts using geometry.
+
+- :math:`t + \frac{π}{2} + v = φ`
+- :math:`H(x + sin(φ), y - cos(φ))`
+- :math:`B(0, 1)`
+- :math:`u1, θ = polar(vector<BH>)`
+- :math:`GH = 2` [2 * radius of curvature]
+- :math:`DC = BG = u` [DGBC is a rectangle]
+- :math:`BG = BH - GH`
+- :math:`∠ABC= ∠KBH`
+
+Hence, we have:
+
+- :math:`u1, θ = polar(x + sin(φ), y - cos(φ) - 1)`
+- :math:`u = u1 - 2`
+- :math:`t = θ`
+- :math:`v = (φ - t - \frac{π}{2})`
+
+
+12. **LeftxRight90-Straight-Left90xRight**
+
+.. image:: L_R90SL90_R.png
+
+With followng notations:
+
+- :math:`∠KBH = θ`
+- :math:`∠HBM = A`
+- :math:`BH = u1`
+
+We can deduce the following facts using geometry.
+
+- :math:`t - v = φ`
+- :math:`H(x + sin(φ), y - cos(φ))`
+- :math:`B(0, 1)`
+- :math:`u1, θ = polar(vector<BH>)`
+- :math:`GF = ED = 1` [radius of curvature]
+- :math:`BD = GH = 2` [2 * radius of curvature]
+- :math:`FN = GH = 2` [ENMD is a rectangle]
+- :math:`NH = GF = 1` [FNHG is a rectangle]
+- :math:`MN = ED = 1` [ENMD is a rectangle]
+- :math:`DO = EF = u` [DOFE is a rectangle]
+- :math:`MH = MN + NH = 2`
+- :math:`BM = \sqrt{BH^2 - MH^2}` [Pythagoras theorem on BHM]
+- :math:`DO = BM - BD - OM`
+- :math:`tan(A) = \frac{MH}{BM}`
+- :math:`∠ABC = ∠ABK + ∠KBH + ∠HBM`
+
+Hence, we have:
+
+- :math:`u1, θ = polar(x + sin(φ), y - cos(φ) - 1)`
+- :math:`u = /sqrt{u1^2 - 4} - 4`
+- :math:`A = arctan(\frac{2}{u1^2 - 4})`
+- :math:`t = \frac{π}{2} + θ + A`
+- :math:`v = (t - φ)`
+
+
 Ref:
 
 -  `15.3.2 Reeds-Shepp
diff --git a/tests/test_rrt_star_reeds_shepp.py b/tests/test_rrt_star_reeds_shepp.py
index 20d4916f96..11157aa57a 100644
--- a/tests/test_rrt_star_reeds_shepp.py
+++ b/tests/test_rrt_star_reeds_shepp.py
@@ -32,16 +32,14 @@ def test2():
         # + 0.00000000000001 for acceptable errors arising from the planning process
         assert m.math.dist(path[i][0:2], path[i+1][0:2]) < step_size + 0.00000000000001
 
-def test3():
+def test_too_big_step_size():
     step_size = 20
     rrt_star_reeds_shepp = m.RRTStarReedsShepp(start, goal,
                                              obstacleList, [-2.0, 15.0],
                                              max_iter=100, step_size=step_size)
     rrt_star_reeds_shepp.set_random_seed(seed=8)
     path = rrt_star_reeds_shepp.planning(animation=False)
-    for i in range(len(path)-1):
-        # + 0.00000000000001 for acceptable errors arising from the planning process
-        assert m.math.dist(path[i][0:2], path[i+1][0:2]) < step_size + 0.00000000000001
+    assert path is None
 
 
 if __name__ == '__main__':

From 9efd4c07a78b52a03c37c221055db1ddf4401667 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Jan 2024 07:29:57 +0900
Subject: [PATCH 0821/1080] Bump numpy from 1.26.2 to 1.26.3 in /requirements
 (#964)

Bumps [numpy](https://github.com/numpy/numpy) from 1.26.2 to 1.26.3.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.26.2...v1.26.3)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 8b3c4c1739..417ab84425 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.26.2
+numpy == 1.26.3
 scipy == 1.11.4
 matplotlib == 3.8.2
 cvxpy == 1.4.1

From 1de7ac951041787d2a02053931ef9a618d1bf037 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Jan 2024 21:23:11 +0900
Subject: [PATCH 0822/1080] Bump ruff from 0.1.9 to 0.1.11 in /requirements
 (#965)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.9 to 0.1.11.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.1.9...v0.1.11)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 417ab84425..172a88423e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.1
 pytest == 7.4.4 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.8.0 # For unit test
-ruff == 0.1.9 # For unit test
+ruff == 0.1.11 # For unit test

From f8515d109e38d4f9cfbc665fe57f4a13b715e86d Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 18 Jan 2024 23:14:51 +0900
Subject: [PATCH 0823/1080] Update doc_requirements.txt versions (#968)

* Update doc_requirements.txt

* Update doc_requirements.txt

* Update doc_requirements.txt
---
 docs/doc_requirements.txt | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/doc_requirements.txt b/docs/doc_requirements.txt
index a8cd2bde40..b29f4ba289 100644
--- a/docs/doc_requirements.txt
+++ b/docs/doc_requirements.txt
@@ -1,6 +1,6 @@
-sphinx == 4.3.2 # For sphinx documentation
-sphinx_rtd_theme == 1.0.0
-IPython == 8.10.0 # For sphinx documentation
+sphinx == 7.2.6 # For sphinx documentation
+sphinx_rtd_theme == 2.0.0
+IPython == 8.20.0 # For sphinx documentation
 sphinxcontrib-napoleon == 0.7 # For auto doc
 sphinx-copybutton
-sphinx-rtd-dark-mode
\ No newline at end of file
+sphinx-rtd-dark-mode

From 4fcab6aa578c60d4bc594e47671a140e450c7cda Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 18 Jan 2024 23:15:25 +0900
Subject: [PATCH 0824/1080] Bump ruff from 0.1.11 to 0.1.13 in /requirements
 (#967)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.11 to 0.1.13.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.1.11...v0.1.13)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 172a88423e..127b89682d 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.1
 pytest == 7.4.4 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.8.0 # For unit test
-ruff == 0.1.11 # For unit test
+ruff == 0.1.13 # For unit test

From cfe1e3035746454adfc02f66c6190fe58bb7101e Mon Sep 17 00:00:00 2001
From: Ryohei Sasaki <rsasaki0109@gmail.com>
Date: Mon, 22 Jan 2024 22:02:22 +0900
Subject: [PATCH 0825/1080] Addition of velocity scale factor estimation to EKF
 (#937)

* Addition of velocity scale factor estimation to EKF

* Format

* Add a scale factor motion model in the Jacobian function description

* Fix Jacobian function description

* New script: 'ekf_with_velocity_correction.py'

* Add doc

* Fix doc

* Correct the parts where the first letter of the sentence is lowercase

* Fix doc title

* Fix script title

* Do grouping

* Fix wrong motion function in ekf doc

* Update docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst

---------

Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>
---
 .../ekf_with_velocity_correction.py           | 198 ++++++++++++++++++
 .../ekf_with_velocity_correction_1_0.png      | Bin 0 -> 40148 bytes
 ...tended_kalman_filter_localization_main.rst | 105 ++++++++++
 3 files changed, 303 insertions(+)
 create mode 100644 Localization/extended_kalman_filter/ekf_with_velocity_correction.py
 create mode 100644 docs/modules/localization/extended_kalman_filter_localization_files/ekf_with_velocity_correction_1_0.png

diff --git a/Localization/extended_kalman_filter/ekf_with_velocity_correction.py b/Localization/extended_kalman_filter/ekf_with_velocity_correction.py
new file mode 100644
index 0000000000..5dd97830fc
--- /dev/null
+++ b/Localization/extended_kalman_filter/ekf_with_velocity_correction.py
@@ -0,0 +1,198 @@
+"""
+
+Extended kalman filter (EKF) localization with velocity correction sample
+
+author: Atsushi Sakai (@Atsushi_twi)
+modified by: Ryohei Sasaki (@rsasaki0109)
+
+"""
+import sys
+import pathlib
+
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
+
+import math
+import matplotlib.pyplot as plt
+import numpy as np
+
+from utils.plot import plot_covariance_ellipse
+
+# Covariance for EKF simulation
+Q = np.diag([
+    0.1,  # variance of location on x-axis
+    0.1,  # variance of location on y-axis
+    np.deg2rad(1.0),  # variance of yaw angle
+    0.4,  # variance of velocity
+    0.1  # variance of scale factor
+]) ** 2  # predict state covariance
+R = np.diag([0.1, 0.1]) ** 2  # Observation x,y position covariance
+
+#  Simulation parameter
+INPUT_NOISE = np.diag([0.1, np.deg2rad(5.0)]) ** 2
+GPS_NOISE = np.diag([0.05, 0.05]) ** 2
+
+DT = 0.1  # time tick [s]
+SIM_TIME = 50.0  # simulation time [s]
+
+show_animation = True
+
+
+def calc_input():
+    v = 1.0  # [m/s]
+    yawrate = 0.1  # [rad/s]
+    u = np.array([[v], [yawrate]])
+    return u
+
+
+def observation(xTrue, xd, u):
+    xTrue = motion_model(xTrue, u)
+
+    # add noise to gps x-y
+    z = observation_model(xTrue) + GPS_NOISE @ np.random.randn(2, 1)
+
+    # add noise to input
+    ud = u + INPUT_NOISE @ np.random.randn(2, 1)
+
+    xd = motion_model(xd, ud)
+
+    return xTrue, z, xd, ud
+
+
+def motion_model(x, u):
+    F = np.array([[1.0, 0, 0, 0, 0],
+                  [0, 1.0, 0, 0, 0],
+                  [0, 0, 1.0, 0, 0],
+                  [0, 0, 0, 0, 0],
+                  [0, 0, 0, 0, 1.0]])
+
+    B = np.array([[DT * math.cos(x[2, 0]) * x[4, 0], 0],
+                  [DT * math.sin(x[2, 0]) * x[4, 0], 0],
+                  [0.0, DT],
+                  [1.0, 0.0],
+                  [0.0, 0.0]])
+
+    x = F @ x + B @ u
+
+    return x
+
+
+def observation_model(x):
+    H = np.array([
+        [1, 0, 0, 0, 0],
+        [0, 1, 0, 0, 0]
+    ])
+    z = H @ x
+
+    return z
+
+
+def jacob_f(x, u):
+    """
+    Jacobian of Motion Model
+
+    motion model
+    x_{t+1} = x_t+v*s*dt*cos(yaw)
+    y_{t+1} = y_t+v*s*dt*sin(yaw)
+    yaw_{t+1} = yaw_t+omega*dt
+    v_{t+1} = v{t}
+    s_{t+1} = s{t}
+    so
+    dx/dyaw = -v*s*dt*sin(yaw)
+    dx/dv = dt*s*cos(yaw)
+    dx/ds = dt*v*cos(yaw)
+    dy/dyaw = v*s*dt*cos(yaw)
+    dy/dv = dt*s*sin(yaw)
+    dy/ds = dt*v*sin(yaw)
+    """
+    yaw = x[2, 0]
+    v = u[0, 0]
+    s = x[4, 0]
+    jF = np.array([
+        [1.0, 0.0, -DT * v * s * math.sin(yaw), DT * s * math.cos(yaw), DT * v * math.cos(yaw)],
+        [0.0, 1.0, DT * v * s * math.cos(yaw), DT * s * math.sin(yaw), DT * v * math.sin(yaw)],
+        [0.0, 0.0, 1.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0, 1.0, 0.0],
+        [0.0, 0.0, 0.0, 0.0, 1.0]])
+    return jF
+
+
+def jacob_h():
+    jH = np.array([[1, 0, 0, 0, 0],
+                   [0, 1, 0, 0, 0]])
+    return jH
+
+
+def ekf_estimation(xEst, PEst, z, u):
+    #  Predict
+    xPred = motion_model(xEst, u)
+    jF = jacob_f(xEst, u)
+    PPred = jF @ PEst @ jF.T + Q
+
+    #  Update
+    jH = jacob_h()
+    zPred = observation_model(xPred)
+    y = z - zPred
+    S = jH @ PPred @ jH.T + R
+    K = PPred @ jH.T @ np.linalg.inv(S)
+    xEst = xPred + K @ y
+    PEst = (np.eye(len(xEst)) - K @ jH) @ PPred
+    return xEst, PEst
+
+
+def main():
+    print(__file__ + " start!!")
+
+    time = 0.0
+
+    #  State Vector [x y yaw v s]'
+    xEst = np.zeros((5, 1))
+    xEst[4, 0] = 1.0 #  Initial scale factor
+    xTrue = np.zeros((5, 1))
+    true_scale_factor = 0.9 #  True scale factor
+    xTrue[4, 0] = true_scale_factor
+    PEst = np.eye(5)
+
+    xDR = np.zeros((5, 1))  # Dead reckoning
+
+    # history
+    hxEst = xEst
+    hxTrue = xTrue
+    hxDR = xTrue
+    hz = np.zeros((2, 1))
+
+    while SIM_TIME >= time:
+        time += DT
+        u = calc_input()
+
+        xTrue, z, xDR, ud = observation(xTrue, xDR, u)
+
+        xEst, PEst = ekf_estimation(xEst, PEst, z, ud)
+
+        # store data history
+        hxEst = np.hstack((hxEst, xEst))
+        hxDR = np.hstack((hxDR, xDR))
+        hxTrue = np.hstack((hxTrue, xTrue))
+        hz = np.hstack((hz, z))
+        estimated_scale_factor = hxEst[4, -1]
+        if show_animation:
+            plt.cla()
+            # for stopping simulation with the esc key.
+            plt.gcf().canvas.mpl_connect('key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.plot(hz[0, :], hz[1, :], ".g")
+            plt.plot(hxTrue[0, :].flatten(),
+                     hxTrue[1, :].flatten(), "-b")
+            plt.plot(hxDR[0, :].flatten(),
+                     hxDR[1, :].flatten(), "-k")
+            plt.plot(hxEst[0, :].flatten(),
+                     hxEst[1, :].flatten(), "-r")
+            plt.text(0.45, 0.85, f"True Velocity Scale Factor: {true_scale_factor:.2f}", ha='left', va='top', transform=plt.gca().transAxes)
+            plt.text(0.45, 0.95, f"Estimated Velocity Scale Factor: {estimated_scale_factor:.2f}", ha='left', va='top', transform=plt.gca().transAxes)
+            plot_covariance_ellipse(xEst[0, 0], xEst[1, 0], PEst)
+            plt.axis("equal")
+            plt.grid(True)
+            plt.pause(0.001)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/docs/modules/localization/extended_kalman_filter_localization_files/ekf_with_velocity_correction_1_0.png b/docs/modules/localization/extended_kalman_filter_localization_files/ekf_with_velocity_correction_1_0.png
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diff --git a/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst
index 0ec920c9df..da214b6de5 100644
--- a/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst
+++ b/docs/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst
@@ -2,6 +2,9 @@
 Extended Kalman Filter Localization
 -----------------------------------
 
+Position Estimation Kalman Filter
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 .. image:: extended_kalman_filter_localization_1_0.png
    :width: 600px
 
@@ -9,6 +12,7 @@ Extended Kalman Filter Localization
 
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif
 
+
 This is a sensor fusion localization with Extended Kalman Filter(EKF).
 
 The blue line is true trajectory, the black line is dead reckoning
@@ -22,9 +26,26 @@ The red ellipse is estimated covariance ellipse with EKF.
 Code: `PythonRobotics/extended_kalman_filter.py at master ·
 AtsushiSakai/PythonRobotics <https://github.com/AtsushiSakai/PythonRobotics/blob/master/Localization/extended_kalman_filter/extended_kalman_filter.py>`__
 
+Kalman Filter with Speed Scale Factor Correction
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+
+.. image:: ekf_with_velocity_correction_1_0.png
+   :width: 600px
+
+This is a Extended kalman filter (EKF) localization with velocity correction.
+
+This is for correcting the vehicle speed measured with scale factor errors due to factors such as wheel wear.
+
+Code: `PythonRobotics/extended_kalman_ekf_with_velocity_correctionfilter.py
+AtsushiSakai/PythonRobotics <https://github.com/AtsushiSakai/PythonRobotics/blob/master/Localization/extended_kalman_filter/extended_kalman_ekf_with_velocity_correctionfilter.py>`__
+
 Filter design
 ~~~~~~~~~~~~~
 
+Position Estimation Kalman Filter
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 In this simulation, the robot has a state vector includes 4 states at
 time :math:`t`.
 
@@ -61,9 +82,28 @@ In the code, “observation” function generates the input and observation
 vector with noise
 `code <https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_filter.py#L34-L50>`__
 
+Kalman Filter with Speed Scale Factor Correction
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+In this simulation, the robot has a state vector includes 5 states at
+time :math:`t`.
+
+.. math:: \textbf{x}_t=[x_t, y_t, \phi_t, v_t, s_t]
+
+x, y are a 2D x-y position, :math:`\phi` is orientation, v is
+velocity, and s is a scale factor of velocity.
+
+In the code, “xEst” means the state vector.
+`code <https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_ekf_with_velocity_correctionfilter.py#L163>`__
+
+The rest is the same as the Position Estimation Kalman Filter.
+
 Motion Model
 ~~~~~~~~~~~~
 
+Position Estimation Kalman Filter
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 The robot model is
 
 .. math::  \dot{x} = v \cos(\phi)
@@ -97,9 +137,50 @@ Its Jacobian matrix is
 
 :math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & -v \sin(\phi) \Delta t & \cos(\phi) \Delta t\\ 0 & 1 & v \cos(\phi) \Delta t & \sin(\phi) \Delta t\\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \end{bmatrix} \end{equation*}`
 
+
+Kalman Filter with Speed Scale Factor Correction
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The robot model is
+
+.. math::  \dot{x} = v s \cos(\phi)
+
+.. math::  \dot{y} = v s \sin(\phi)
+
+.. math::  \dot{\phi} = \omega
+
+So, the motion model is
+
+.. math:: \textbf{x}_{t+1} = f(\textbf{x}_t, \textbf{u}_t) = F\textbf{x}_t+B\textbf{u}_t
+
+where
+
+:math:`\begin{equation*} F= \begin{bmatrix} 1 & 0 & 0 & 0 & 0\\ 0 & 1 & 0 & 0 & 0\\ 0 & 0 & 1 & 0  & 0\\ 0 & 0 & 0 & 0  & 0\\ 0 & 0 & 0 & 0  & 1\\ \end{bmatrix} \end{equation*}`
+
+:math:`\begin{equation*} B= \begin{bmatrix} cos(\phi) \Delta t s & 0\\ sin(\phi) \Delta t s & 0\\ 0 & \Delta t\\ 1 & 0\\ 0 & 0\\ \end{bmatrix} \end{equation*}`
+
+:math:`\Delta t` is a time interval.
+
+This is implemented at
+`code <https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_filter.py#L61-L76>`__
+
+The motion function is that
+
+:math:`\begin{equation*} \begin{bmatrix} x' \\ y' \\ w' \\ v' \end{bmatrix} = f(\textbf{x}, \textbf{u}) = \begin{bmatrix} x + v s \cos(\phi)\Delta t \\ y + v s \sin(\phi)\Delta t \\ \phi + \omega \Delta t \\ v \end{bmatrix} \end{equation*}`
+
+Its Jacobian matrix is
+
+:math:`\begin{equation*} J_f = \begin{bmatrix} \frac{\partial x'}{\partial x}& \frac{\partial x'}{\partial y} & \frac{\partial x'}{\partial \phi} & \frac{\partial x'}{\partial v} & \frac{\partial x'}{\partial s}\\ \frac{\partial y'}{\partial x}& \frac{\partial y'}{\partial y} & \frac{\partial y'}{\partial \phi} & \frac{\partial y'}{\partial v} & \frac{\partial y'}{\partial s}\\ \frac{\partial \phi'}{\partial x}& \frac{\partial \phi'}{\partial y} & \frac{\partial \phi'}{\partial \phi} & \frac{\partial \phi'}{\partial v} & \frac{\partial \phi'}{\partial s}\\ \frac{\partial v'}{\partial x}& \frac{\partial v'}{\partial y} & \frac{\partial v'}{\partial \phi} & \frac{\partial v'}{\partial v} & \frac{\partial v'}{\partial s} \\ \frac{\partial s'}{\partial x}& \frac{\partial s'}{\partial y} & \frac{\partial s'}{\partial \phi} & \frac{\partial s'}{\partial v} & \frac{\partial s'}{\partial s} \end{bmatrix} \end{equation*}`
+
+:math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & -v s \sin(\phi) \Delta t & s \cos(\phi) \Delta t & \cos(\phi) v \Delta t\\ 0 & 1 & v s \cos(\phi) \Delta t & s \sin(\phi) \Delta t & v \sin(\phi) \Delta t\\ 0 & 0 & 1 & 0  & 0 \\ 0 & 0 & 0 & 1 & 0 \end{bmatrix} \end{equation*}`
+
+
 Observation Model
 ~~~~~~~~~~~~~~~~~
 
+Position Estimation Kalman Filter
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 The robot can get x-y position infomation from GPS.
 
 So GPS Observation model is
@@ -120,6 +201,30 @@ Its Jacobian matrix is
 
 :math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ \end{bmatrix} \end{equation*}`
 
+Kalman Filter with Speed Scale Factor Correction
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The robot can get x-y position infomation from GPS.
+
+So GPS Observation model is
+
+.. math:: \textbf{z}_{t} = g(\textbf{x}_t) = H \textbf{x}_t
+
+where
+
+:math:`\begin{equation*} H = \begin{bmatrix} 1 & 0 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 & 0  \\ \end{bmatrix} \end{equation*}`
+
+The observation function states that
+
+:math:`\begin{equation*} \begin{bmatrix} x' \\ y' \end{bmatrix} = g(\textbf{x}) = \begin{bmatrix} x \\ y \end{bmatrix} \end{equation*}`
+
+Its Jacobian matrix is
+
+:math:`\begin{equation*} J_g = \begin{bmatrix} \frac{\partial x'}{\partial x} & \frac{\partial x'}{\partial y} & \frac{\partial x'}{\partial \phi} & \frac{\partial x'}{\partial v} & \frac{\partial x'}{\partial s}\\ \frac{\partial y'}{\partial x}& \frac{\partial y'}{\partial y} & \frac{\partial y'}{\partial \phi} & \frac{\partial y'}{ \partial v} & \frac{\partial y'}{ \partial s}\\ \end{bmatrix} \end{equation*}`
+
+:math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0 & 0\\ 0 & 1 & 0 & 0 & 0\\ \end{bmatrix} \end{equation*}`
+
+
 Extended Kalman Filter
 ~~~~~~~~~~~~~~~~~~~~~~
 

From 13137952eeee39438bf6f0eb030527ab804a9481 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 23 Jan 2024 08:07:58 +0900
Subject: [PATCH 0826/1080] Bump scipy from 1.11.4 to 1.12.0 in /requirements
 (#969)

Bumps [scipy](https://github.com/scipy/scipy) from 1.11.4 to 1.12.0.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.11.4...v1.12.0)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 127b89682d..5af5be1794 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.26.3
-scipy == 1.11.4
+scipy == 1.12.0
 matplotlib == 3.8.2
 cvxpy == 1.4.1
 pytest == 7.4.4 # For unit test

From ae0676bfc0b5548ab643c8db73959edc5e68a324 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 23 Jan 2024 20:18:49 +0900
Subject: [PATCH 0827/1080] Bump ruff from 0.1.13 to 0.1.14 in /requirements
 (#971)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.13 to 0.1.14.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.1.13...v0.1.14)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 5af5be1794..5848b32aad 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.1
 pytest == 7.4.4 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.8.0 # For unit test
-ruff == 0.1.13 # For unit test
+ruff == 0.1.14 # For unit test

From 73d1189e824c38af70f724d3ba5b0322e55f142d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 23 Jan 2024 20:40:15 +0900
Subject: [PATCH 0828/1080] Bump cvxpy from 1.4.1 to 1.4.2 in /requirements
 (#970)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.4.1 to 1.4.2.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.4.1...v1.4.2)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 5848b32aad..ceee1d03c0 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 1.26.3
 scipy == 1.12.0
 matplotlib == 3.8.2
-cvxpy == 1.4.1
+cvxpy == 1.4.2
 pytest == 7.4.4 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.8.0 # For unit test

From 0874d5023cc79cd2d9ad535f6d008c749ce4d257 Mon Sep 17 00:00:00 2001
From: Yi-Chen Zhang <chris7462@gmail.com>
Date: Sat, 27 Jan 2024 23:59:02 -0500
Subject: [PATCH 0829/1080] Fix predict covariance (#973)

* Fix predict covariance

* Remove trailing whitespace and unused return parameters
---
 SLAM/EKFSLAM/ekf_slam.py                     |   9 +-
 docs/modules/slam/ekf_slam/ekf_slam_main.rst | 181 +++++++++----------
 2 files changed, 91 insertions(+), 99 deletions(-)

diff --git a/SLAM/EKFSLAM/ekf_slam.py b/SLAM/EKFSLAM/ekf_slam.py
index a624e8765b..5c4417d7c3 100644
--- a/SLAM/EKFSLAM/ekf_slam.py
+++ b/SLAM/EKFSLAM/ekf_slam.py
@@ -29,10 +29,9 @@
 
 def ekf_slam(xEst, PEst, u, z):
     # Predict
-    S = STATE_SIZE
-    G, Fx = jacob_motion(xEst[0:S], u)
-    xEst[0:S] = motion_model(xEst[0:S], u)
-    PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx
+    G, Fx = jacob_motion(xEst, u)
+    xEst[0:STATE_SIZE] = motion_model(xEst[0:STATE_SIZE], u)
+    PEst = G.T @ PEst @ G + Fx.T @ Cx @ Fx
     initP = np.eye(2)
 
     # Update
@@ -120,7 +119,7 @@ def jacob_motion(x, u):
                    [0.0, 0.0, DT * u[0, 0] * math.cos(x[2, 0])],
                    [0.0, 0.0, 0.0]], dtype=float)
 
-    G = np.eye(STATE_SIZE) + Fx.T @ jF @ Fx
+    G = np.eye(len(x)) + Fx.T @ jF @ Fx
 
     return G, Fx,
 
diff --git a/docs/modules/slam/ekf_slam/ekf_slam_main.rst b/docs/modules/slam/ekf_slam/ekf_slam_main.rst
index 70a7d131ae..b27971225e 100644
--- a/docs/modules/slam/ekf_slam/ekf_slam_main.rst
+++ b/docs/modules/slam/ekf_slam/ekf_slam_main.rst
@@ -63,27 +63,27 @@ Take care to note the difference between :math:`X` (state) and :math:`x`
     original author: Atsushi Sakai (@Atsushi_twi)
     notebook author: Andrew Tu (drewtu2)
     """
-    
+
     import math
     import numpy as np
     %matplotlib notebook
     import matplotlib.pyplot as plt
-    
-    
+
+
     # EKF state covariance
     Cx = np.diag([0.5, 0.5, np.deg2rad(30.0)])**2 # Change in covariance
-    
+
     #  Simulation parameter
     Qsim = np.diag([0.2, np.deg2rad(1.0)])**2  # Sensor Noise
     Rsim = np.diag([1.0, np.deg2rad(10.0)])**2 # Process Noise
-    
+
     DT = 0.1  # time tick [s]
     SIM_TIME = 50.0  # simulation time [s]
     MAX_RANGE = 20.0  # maximum observation range
     M_DIST_TH = 2.0  # Threshold of Mahalanobis distance for data association.
     STATE_SIZE = 3  # State size [x,y,yaw]
     LM_SIZE = 2  # LM state size [x,y]
-    
+
     show_animation = True
 
 Algorithm Walk through
@@ -97,23 +97,22 @@ the estimated state and measurements
 
     def ekf_slam(xEst, PEst, u, z):
         """
-        Performs an iteration of EKF SLAM from the available information. 
-        
+        Performs an iteration of EKF SLAM from the available information.
+
         :param xEst: the belief in last position
         :param PEst: the uncertainty in last position
-        :param u:    the control function applied to the last position 
+        :param u:    the control function applied to the last position
         :param z:    measurements at this step
         :returns:    the next estimated position and associated covariance
         """
-        S = STATE_SIZE
-    
+
         # Predict
-        xEst, PEst, G, Fx = predict(xEst, PEst, u)
+        xEst, PEst = predict(xEst, PEst, u)
         initP = np.eye(2)
-    
+
         # Update
-        xEst, PEst = update(xEst, PEst, u, z, initP)
-    
+        xEst, PEst = update(xEst, PEst, z, initP)
+
         return xEst, PEst
 
 
@@ -170,26 +169,25 @@ the landmarks.
     def predict(xEst, PEst, u):
         """
         Performs the prediction step of EKF SLAM
-        
+
         :param xEst: nx1 state vector
         :param PEst: nxn covariance matrix
         :param u:    2x1 control vector
         :returns:    predicted state vector, predicted covariance, jacobian of control vector, transition fx
         """
-        S = STATE_SIZE
-        G, Fx = jacob_motion(xEst[0:S], u)
-        xEst[0:S] = motion_model(xEst[0:S], u)
+        G, Fx = jacob_motion(xEst, u)
+        xEst[0:STATE_SIZE] = motion_model(xEst[0:STATE_SIZE], u)
         # Fx is an an identity matrix of size (STATE_SIZE)
         # sigma = G*sigma*G.T + Noise
-        PEst[0:S, 0:S] = G.T @ PEst[0:S, 0:S] @ G + Fx.T @ Cx @ Fx
-        return xEst, PEst, G, Fx
+        PEst = G.T @ PEst @ G + Fx.T @ Cx @ Fx
+        return xEst, PEst
 
 .. code:: ipython3
 
     def motion_model(x, u):
         """
         Computes the motion model based on current state and input function. 
-        
+
         :param x: 3x1 pose estimation
         :param u: 2x1 control input [v; w]
         :returns: the resulting state after the control function is applied
@@ -197,11 +195,11 @@ the landmarks.
         F = np.array([[1.0, 0, 0],
                       [0, 1.0, 0],
                       [0, 0, 1.0]])
-    
+
         B = np.array([[DT * math.cos(x[2, 0]), 0],
                       [DT * math.sin(x[2, 0]), 0],
                       [0.0, DT]])
-    
+
         x = (F @ x) + (B @ u)
         return x
 
@@ -261,22 +259,21 @@ the changing uncertainty.
 
 .. code:: ipython3
 
-    def update(xEst, PEst, u, z, initP):
+    def update(xEst, PEst, z, initP):
         """
         Performs the update step of EKF SLAM
-        
+
         :param xEst:  nx1 the predicted pose of the system and the pose of the landmarks
         :param PEst:  nxn the predicted covariance
-        :param u:     2x1 the control function 
         :param z:     the measurements read at new position
         :param initP: 2x2 an identity matrix acting as the initial covariance
         :returns:     the updated state and covariance for the system
         """
         for iz in range(len(z[:, 0])):  # for each observation
             minid = search_correspond_LM_ID(xEst, PEst, z[iz, 0:2]) # associate to a known landmark
-    
+
             nLM = calc_n_LM(xEst) # number of landmarks we currently know about
-            
+
             if minid == nLM: # Landmark is a NEW landmark
                 print("New LM")
                 # Extend state and covariance matrix
@@ -285,14 +282,14 @@ the changing uncertainty.
                                   np.hstack((np.zeros((LM_SIZE, len(xEst))), initP))))
                 xEst = xAug
                 PEst = PAug
-            
+
             lm = get_LM_Pos_from_state(xEst, minid)
             y, S, H = calc_innovation(lm, xEst, PEst, z[iz, 0:2], minid)
-    
+
             K = (PEst @ H.T) @ np.linalg.inv(S) # Calculate Kalman Gain
             xEst = xEst + (K @ y)
             PEst = (np.eye(len(xEst)) - (K @ H)) @ PEst
-        
+
         xEst[2] = pi_2_pi(xEst[2])
         return xEst, PEst
 
@@ -302,7 +299,7 @@ the changing uncertainty.
     def calc_innovation(lm, xEst, PEst, z, LMid):
         """
         Calculates the innovation based on expected position and landmark position
-        
+
         :param lm:   landmark position
         :param xEst: estimated position/state
         :param PEst: estimated covariance
@@ -315,19 +312,19 @@ the changing uncertainty.
         zangle = math.atan2(delta[1, 0], delta[0, 0]) - xEst[2, 0]
         zp = np.array([[math.sqrt(q), pi_2_pi(zangle)]])
         # zp is the expected measurement based on xEst and the expected landmark position
-        
+
         y = (z - zp).T # y = innovation
         y[1] = pi_2_pi(y[1])
-        
+
         H = jacobH(q, delta, xEst, LMid + 1)
         S = H @ PEst @ H.T + Cx[0:2, 0:2]
-    
+
         return y, S, H
-    
+
     def jacobH(q, delta, x, i):
         """
         Calculates the jacobian of the measurement function
-        
+
         :param q:     the range from the system pose to the landmark
         :param delta: the difference between a landmark position and the estimated system position
         :param x:     the state, including the estimated system position
@@ -337,17 +334,17 @@ the changing uncertainty.
         sq = math.sqrt(q)
         G = np.array([[-sq * delta[0, 0], - sq * delta[1, 0], 0, sq * delta[0, 0], sq * delta[1, 0]],
                       [delta[1, 0], - delta[0, 0], - q, - delta[1, 0], delta[0, 0]]])
-    
+
         G = G / q
         nLM = calc_n_LM(x)
         F1 = np.hstack((np.eye(3), np.zeros((3, 2 * nLM))))
         F2 = np.hstack((np.zeros((2, 3)), np.zeros((2, 2 * (i - 1))),
                         np.eye(2), np.zeros((2, 2 * nLM - 2 * i))))
-    
+
         F = np.vstack((F1, F2))
-    
+
         H = G @ F
-    
+
         return H
 
 Observation Step
@@ -370,17 +367,17 @@ reckoning and control functions are passed along here as well.
         :param xd:    the current noisy estimate of the system
         :param u:     the current control input
         :param RFID:  the true position of the landmarks
-        
-        :returns:     Computes the true position, observations, dead reckoning (noisy) position, 
+
+        :returns:     Computes the true position, observations, dead reckoning (noisy) position,
                       and noisy control function
         """
         xTrue = motion_model(xTrue, u)
-    
+
         # add noise to gps x-y
         z = np.zeros((0, 3))
-    
+
         for i in range(len(RFID[:, 0])): # Test all beacons, only add the ones we can see (within MAX_RANGE)
-    
+
             dx = RFID[i, 0] - xTrue[0, 0]
             dy = RFID[i, 1] - xTrue[1, 0]
             d = math.sqrt(dx**2 + dy**2)
@@ -390,12 +387,12 @@ reckoning and control functions are passed along here as well.
                 anglen = angle + np.random.randn() * Qsim[1, 1]  # add noise
                 zi = np.array([dn, anglen, i])
                 z = np.vstack((z, zi))
-    
+
         # add noise to input
         ud = np.array([[
             u[0, 0] + np.random.randn() * Rsim[0, 0],
             u[1, 0] + np.random.randn() * Rsim[1, 1]]]).T
-    
+
         xd = motion_model(xd, ud)
         return xTrue, z, xd, ud
 
@@ -409,32 +406,30 @@ reckoning and control functions are passed along here as well.
         """
         n = int((len(x) - STATE_SIZE) / LM_SIZE)
         return n
-    
-    
+
+
     def jacob_motion(x, u):
         """
-        Calculates the jacobian of motion model. 
-        
+        Calculates the jacobian of motion model.
+
         :param x: The state, including the estimated position of the system
         :param u: The control function
         :returns: G:  Jacobian
                   Fx: STATE_SIZE x (STATE_SIZE + 2 * num_landmarks) matrix where the left side is an identity matrix
         """
-        
+
         # [eye(3) [0 x y; 0 x y; 0 x y]]
         Fx = np.hstack((np.eye(STATE_SIZE), np.zeros(
             (STATE_SIZE, LM_SIZE * calc_n_LM(x)))))
-    
+
         jF = np.array([[0.0, 0.0, -DT * u[0] * math.sin(x[2, 0])],
                        [0.0, 0.0, DT * u[0] * math.cos(x[2, 0])],
                        [0.0, 0.0, 0.0]],dtype=object)
-    
+
         G = np.eye(STATE_SIZE) + Fx.T @ jF @ Fx
         if calc_n_LM(x) > 0:
             print(Fx.shape)
         return G, Fx,
-    
-    
 
 
 .. code:: ipython3
@@ -442,68 +437,68 @@ reckoning and control functions are passed along here as well.
     def calc_LM_Pos(x, z):
         """
         Calculates the pose in the world coordinate frame of a landmark at the given measurement.
-    
+
         :param x: [x; y; theta]
         :param z: [range; bearing]
         :returns: [x; y] for given measurement
         """
         zp = np.zeros((2, 1))
-    
+
         zp[0, 0] = x[0, 0] + z[0] * math.cos(x[2, 0] + z[1])
         zp[1, 0] = x[1, 0] + z[0] * math.sin(x[2, 0] + z[1])
         #zp[0, 0] = x[0, 0] + z[0, 0] * math.cos(x[2, 0] + z[0, 1])
         #zp[1, 0] = x[1, 0] + z[0, 0] * math.sin(x[2, 0] + z[0, 1])
-    
+
         return zp
-    
-    
+
+
     def get_LM_Pos_from_state(x, ind):
         """
         Returns the position of a given landmark
-        
+
         :param x:   The state containing all landmark positions
         :param ind: landmark id
         :returns:   The position of the landmark
         """
         lm = x[STATE_SIZE + LM_SIZE * ind: STATE_SIZE + LM_SIZE * (ind + 1), :]
-    
+
         return lm
-    
-    
+
+
     def search_correspond_LM_ID(xAug, PAug, zi):
         """
         Landmark association with Mahalanobis distance.
-        
-        If this landmark is at least M_DIST_TH units away from all known landmarks, 
+
+        If this landmark is at least M_DIST_TH units away from all known landmarks,
         it is a NEW landmark.
-        
+
         :param xAug: The estimated state
         :param PAug: The estimated covariance
         :param zi:   the read measurements of specific landmark
         :returns:    landmark id
         """
-    
+
         nLM = calc_n_LM(xAug)
-    
+
         mdist = []
-    
+
         for i in range(nLM):
             lm = get_LM_Pos_from_state(xAug, i)
             y, S, H = calc_innovation(lm, xAug, PAug, zi, i)
             mdist.append(y.T @ np.linalg.inv(S) @ y)
-    
+
         mdist.append(M_DIST_TH)  # new landmark
-    
+
         minid = mdist.index(min(mdist))
-    
+
         return minid
-    
+
     def calc_input():
         v = 1.0  # [m/s]
         yawrate = 0.1  # [rad/s]
         u = np.array([[v, yawrate]]).T
         return u
-    
+
     def pi_2_pi(angle):
         return (angle + math.pi) % (2 * math.pi) - math.pi
 
@@ -511,53 +506,53 @@ reckoning and control functions are passed along here as well.
 
     def main():
         print(" start!!")
-    
+
         time = 0.0
-    
+
         # RFID positions [x, y]
         RFID = np.array([[10.0, -2.0],
                          [15.0, 10.0],
                          [3.0, 15.0],
                          [-5.0, 20.0]])
-    
+
         # State Vector [x y yaw v]'
         xEst = np.zeros((STATE_SIZE, 1))
         xTrue = np.zeros((STATE_SIZE, 1))
         PEst = np.eye(STATE_SIZE)
-    
+
         xDR = np.zeros((STATE_SIZE, 1))  # Dead reckoning
-    
+
         # history
         hxEst = xEst
         hxTrue = xTrue
         hxDR = xTrue
-    
+
         while SIM_TIME >= time:
             time += DT
             u = calc_input()
-    
+
             xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID)
-    
+
             xEst, PEst = ekf_slam(xEst, PEst, ud, z)
-    
+
             x_state = xEst[0:STATE_SIZE]
-    
+
             # store data history
             hxEst = np.hstack((hxEst, x_state))
             hxDR = np.hstack((hxDR, xDR))
             hxTrue = np.hstack((hxTrue, xTrue))
-    
+
             if show_animation:  # pragma: no cover
                 plt.cla()
-    
+
                 plt.plot(RFID[:, 0], RFID[:, 1], "*k")
                 plt.plot(xEst[0], xEst[1], ".r")
-    
+
                 # plot landmark
                 for i in range(calc_n_LM(xEst)):
                     plt.plot(xEst[STATE_SIZE + i * 2],
                              xEst[STATE_SIZE + i * 2 + 1], "xg")
-    
+
                 plt.plot(hxTrue[0, :],
                          hxTrue[1, :], "-b")
                 plt.plot(hxDR[0, :],
@@ -587,5 +582,3 @@ References:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 - `PROBABILISTIC ROBOTICS <http://www.probabilistic-robotics.org/>`_
-
-

From 6326e8c21205ca9c2d8e809c2dc2eaab60940bf0 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 Jan 2024 20:19:54 +0900
Subject: [PATCH 0830/1080] Bump pytest from 7.4.4 to 8.0.0 in /requirements
 (#974)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 7.4.4 to 8.0.0.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/7.4.4...8.0.0)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index ceee1d03c0..f0729fb76e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.26.3
 scipy == 1.12.0
 matplotlib == 3.8.2
 cvxpy == 1.4.2
-pytest == 7.4.4 # For unit test
+pytest == 8.0.0 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.8.0 # For unit test
 ruff == 0.1.14 # For unit test

From bd253e81060c6a11a944016706bd1d87ef72bded Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 6 Feb 2024 19:07:36 +0900
Subject: [PATCH 0831/1080] Bump ruff from 0.1.14 to 0.2.0 in /requirements
 (#975)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.1.14 to 0.2.0.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.1.14...v0.2.0)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index f0729fb76e..1d2072b01e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.2
 pytest == 8.0.0 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.8.0 # For unit test
-ruff == 0.1.14 # For unit test
+ruff == 0.2.0 # For unit test

From e4ddf10ad651dffa7c04f344cd13e878637fb8ca Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 13 Feb 2024 20:21:31 +0900
Subject: [PATCH 0832/1080] Bump larsoner/circleci-artifacts-redirector-action
 from 0.3.1 to 1.0.0 (#976)

Bumps [larsoner/circleci-artifacts-redirector-action](https://github.com/larsoner/circleci-artifacts-redirector-action) from 0.3.1 to 1.0.0.
- [Release notes](https://github.com/larsoner/circleci-artifacts-redirector-action/releases)
- [Commits](https://github.com/larsoner/circleci-artifacts-redirector-action/compare/0.3.1...v1.0.0)

---
updated-dependencies:
- dependency-name: larsoner/circleci-artifacts-redirector-action
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/circleci-artifacts-redirector.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/circleci-artifacts-redirector.yml b/.github/workflows/circleci-artifacts-redirector.yml
index 149a087a5d..504a3a1004 100644
--- a/.github/workflows/circleci-artifacts-redirector.yml
+++ b/.github/workflows/circleci-artifacts-redirector.yml
@@ -6,7 +6,7 @@ jobs:
     name: Run CircleCI artifacts redirector!!
     steps:
       - name: run-circleci-artifacts-redirector
-        uses: larsoner/circleci-artifacts-redirector-action@0.3.1
+        uses: larsoner/circleci-artifacts-redirector-action@v1.0.0
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           artifact-path: 0/html/index.html

From 0dcb68a8020a5dd8f5cad53f69dffbcca3f03273 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 13 Feb 2024 21:49:03 +0900
Subject: [PATCH 0833/1080] Bump numpy from 1.26.3 to 1.26.4 in /requirements
 (#977)

Bumps [numpy](https://github.com/numpy/numpy) from 1.26.3 to 1.26.4.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.26.3...v1.26.4)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 1d2072b01e..dd5adf0409 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.26.3
+numpy == 1.26.4
 scipy == 1.12.0
 matplotlib == 3.8.2
 cvxpy == 1.4.2

From b1bdff203c483533c32a7357a124945b74d88aa1 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 13 Feb 2024 22:57:15 +0900
Subject: [PATCH 0834/1080] Bump ruff from 0.2.0 to 0.2.1 in /requirements
 (#978)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.2.0 to 0.2.1.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.2.0...v0.2.1)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index dd5adf0409..6b65ae5b8c 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.2
 pytest == 8.0.0 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.8.0 # For unit test
-ruff == 0.2.0 # For unit test
+ruff == 0.2.1 # For unit test

From e6215ace2e62c350dcc174641fb6804d3bfd4d55 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 20 Feb 2024 20:43:17 +0900
Subject: [PATCH 0835/1080] Bump matplotlib from 3.8.2 to 3.8.3 in
 /requirements (#979)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.8.2 to 3.8.3.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.8.2...v3.8.3)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 6b65ae5b8c..be43a14788 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.26.4
 scipy == 1.12.0
-matplotlib == 3.8.2
+matplotlib == 3.8.3
 cvxpy == 1.4.2
 pytest == 8.0.0 # For unit test
 pytest-xdist == 3.5.0 # For unit test

From 47eb01f88f0083929f1948620b313019e44b96f1 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 20 Feb 2024 20:44:06 +0900
Subject: [PATCH 0836/1080] Bump ruff from 0.2.1 to 0.2.2 in /requirements
 (#980)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.2.1 to 0.2.2.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.2.1...v0.2.2)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index be43a14788..e18459efdb 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.2
 pytest == 8.0.0 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.8.0 # For unit test
-ruff == 0.2.1 # For unit test
+ruff == 0.2.2 # For unit test

From bf6acf3e31934dab4a496190ca74191cee672ea4 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 20 Feb 2024 21:34:06 +0900
Subject: [PATCH 0837/1080] Bump pytest from 8.0.0 to 8.0.1 in /requirements
 (#981)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.0.0 to 8.0.1.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/8.0.0...8.0.1)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index e18459efdb..e40542015f 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.26.4
 scipy == 1.12.0
 matplotlib == 3.8.3
 cvxpy == 1.4.2
-pytest == 8.0.0 # For unit test
+pytest == 8.0.1 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.8.0 # For unit test
 ruff == 0.2.2 # For unit test

From e2412a6667d12a8b640f2dc5c6e15e22b1e36753 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 24 Feb 2024 18:26:59 +0900
Subject: [PATCH 0838/1080] Update circleci-artifacts-redirector.yml (#983)

* Update circleci-artifacts-redirector.yml

* Update circleci-artifacts-redirector.yml

* Update circleci-artifacts-redirector.yml
---
 .github/workflows/circleci-artifacts-redirector.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/circleci-artifacts-redirector.yml b/.github/workflows/circleci-artifacts-redirector.yml
index 504a3a1004..78eb479aa1 100644
--- a/.github/workflows/circleci-artifacts-redirector.yml
+++ b/.github/workflows/circleci-artifacts-redirector.yml
@@ -9,5 +9,6 @@ jobs:
         uses: larsoner/circleci-artifacts-redirector-action@v1.0.0
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
+          api-token: ${{ secrets.CIRCLECI_TOKEN }}
           artifact-path: 0/html/index.html
           circleci-jobs: build_doc

From 6e743ef31e8f99a5e78bbb11343228d433693034 Mon Sep 17 00:00:00 2001
From: William L Thomson Jr <wlt@o-sinc.com>
Date: Sat, 24 Feb 2024 04:35:13 -0500
Subject: [PATCH 0839/1080] PathPlanning/InformedRRTStar/informed_rrt_star.py:
 Fix hard coded graph axis (#982)

- Use self.min_rand, self.max_rand variable values in place of -2, 15
---
 PathPlanning/InformedRRTStar/informed_rrt_star.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index b6f9d234dd..c37326e490 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -293,7 +293,7 @@ def draw_graph(self, x_center=None, c_best=None, c_min=None, e_theta=None,
 
         plt.plot(self.start.x, self.start.y, "xr")
         plt.plot(self.goal.x, self.goal.y, "xr")
-        plt.axis([-2, 15, -2, 15])
+        plt.axis([self.min_rand, self.max_rand, self.min_rand, self.max_rand])
         plt.grid(True)
         plt.pause(0.01)
 

From ffd28c2d635975ba9294c6fa6603a211d074ef1b Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 Feb 2024 20:27:30 +0900
Subject: [PATCH 0840/1080] Bump pytest from 8.0.1 to 8.0.2 in /requirements
 (#984)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.0.1 to 8.0.2.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/8.0.1...8.0.2)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index e40542015f..b464481003 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.26.4
 scipy == 1.12.0
 matplotlib == 3.8.3
 cvxpy == 1.4.2
-pytest == 8.0.1 # For unit test
+pytest == 8.0.2 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.8.0 # For unit test
 ruff == 0.2.2 # For unit test

From 9a22fb34ba4a205b254b8d469d62c73188771bf8 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 6 Mar 2024 08:05:05 +0900
Subject: [PATCH 0841/1080] Bump ruff from 0.2.2 to 0.3.0 in /requirements
 (#985)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.2.2 to 0.3.0.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.2.2...v0.3.0)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index b464481003..9d8ed2ac33 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.2
 pytest == 8.0.2 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.8.0 # For unit test
-ruff == 0.2.2 # For unit test
+ruff == 0.3.0 # For unit test

From 707f12fb363316ab687566a0b9819c2545eb89b8 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 12 Mar 2024 07:47:43 +0900
Subject: [PATCH 0842/1080] Bump pytest from 8.0.2 to 8.1.1 in /requirements
 (#986)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.0.2 to 8.1.1.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/8.0.2...8.1.1)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 9d8ed2ac33..e3eee34e5e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.26.4
 scipy == 1.12.0
 matplotlib == 3.8.3
 cvxpy == 1.4.2
-pytest == 8.0.2 # For unit test
+pytest == 8.1.1 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.8.0 # For unit test
 ruff == 0.3.0 # For unit test

From 1477c34cc717428ad6ae9b234451e679d8d0bc11 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 12 Mar 2024 12:19:59 +0900
Subject: [PATCH 0843/1080] Bump ruff from 0.3.0 to 0.3.2 in /requirements
 (#987)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.3.0 to 0.3.2.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.3.0...v0.3.2)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index e3eee34e5e..2cdfeaf126 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.2
 pytest == 8.1.1 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.8.0 # For unit test
-ruff == 0.3.0 # For unit test
+ruff == 0.3.2 # For unit test

From 655fbc71d9adc324276985693c3cac773791f1f1 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 12 Mar 2024 12:57:17 +0900
Subject: [PATCH 0844/1080] Bump mypy from 1.8.0 to 1.9.0 in /requirements
 (#988)

Bumps [mypy](https://github.com/python/mypy) from 1.8.0 to 1.9.0.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.8.0...1.9.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 2cdfeaf126..1021562d1c 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.8.3
 cvxpy == 1.4.2
 pytest == 8.1.1 # For unit test
 pytest-xdist == 3.5.0 # For unit test
-mypy == 1.8.0 # For unit test
+mypy == 1.9.0 # For unit test
 ruff == 0.3.2 # For unit test

From a79cc374f3eb3f39ae1a82e7738b988c43791595 Mon Sep 17 00:00:00 2001
From: guangwu <guoguangwu@magic-shield.com>
Date: Wed, 13 Mar 2024 20:20:01 +0800
Subject: [PATCH 0845/1080] fix: typo (#989)

---
 Control/move_to_pose/move_to_pose_robot.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Control/move_to_pose/move_to_pose_robot.py b/Control/move_to_pose/move_to_pose_robot.py
index d5c51caa12..fe9f0d06b3 100644
--- a/Control/move_to_pose/move_to_pose_robot.py
+++ b/Control/move_to_pose/move_to_pose_robot.py
@@ -79,9 +79,9 @@ def set_start_target_poses(self, pose_start, pose_target):
         Parameters
         ----------
         pose_start : (Pose)
-            Start postion of the robot (see the Pose class)
+            Start position of the robot (see the Pose class)
         pose_target : (Pose)
-            Target postion of the robot (see the Pose class)
+            Target position of the robot (see the Pose class)
         """
         self.pose_start = copy.copy(pose_start)
         self.pose_target = pose_target

From 1656cab0d229d294ec151ec9d4cbe086b271e1cf Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 14 Mar 2024 22:01:51 +0900
Subject: [PATCH 0846/1080] Support python 3.12 (#954)

* Update Linux_CI.yml

* Update MacOS_CI.yml

* Update Windows_CI.yml

* Delete .lgtm.yml

* Update lqr_planner.py

* Update lqr_planner.py

* Update config.yml

* Update environment.yml

* Update ruff.toml

* Update README.md

* Update getting_started_main.rst

* Update doc_requirements.txt
---
 .circleci/config.yml             | 2 +-
 .github/workflows/Linux_CI.yml   | 2 +-
 .github/workflows/MacOS_CI.yml   | 4 ++--
 .github/workflows/Windows_CI.yml | 4 ++--
 .lgtm.yml                        | 4 ----
 README.md                        | 2 +-
 docs/getting_started_main.rst    | 2 +-
 requirements/environment.yml     | 2 +-
 ruff.toml                        | 4 ++--
 9 files changed, 11 insertions(+), 15 deletions(-)
 delete mode 100644 .lgtm.yml

diff --git a/.circleci/config.yml b/.circleci/config.yml
index d8f1e89dc4..9f803ece4a 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -6,7 +6,7 @@ orbs:
 jobs:
   build_doc:
     docker:
-      - image: cimg/python:3.11
+      - image: cimg/python:3.12
     steps:
       - checkout
       - run:
diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index b83954f17e..7b3dc14751 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ '3.11' ]
+        python-version: [ '3.12' ]
 
     name: Python ${{ matrix.python-version }} CI
 
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index 19c6612b2f..5ea15ac72e 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -16,7 +16,7 @@ jobs:
     runs-on: macos-latest
     strategy:
       matrix:
-        python-version: [ '3.11' ]
+        python-version: [ '3.12' ]
     name: Python ${{ matrix.python-version }} CI
     steps:
       - uses: actions/checkout@v4
@@ -36,4 +36,4 @@ jobs:
           python -m pip install --upgrade pip
           pip install -r requirements/requirements.txt
       - name: do all unit tests
-        run: bash runtests.sh
\ No newline at end of file
+        run: bash runtests.sh
diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml
index 906edcfcdb..b9c8dea649 100644
--- a/.github/workflows/Windows_CI.yml
+++ b/.github/workflows/Windows_CI.yml
@@ -16,7 +16,7 @@ jobs:
     runs-on: windows-latest
     strategy:
       matrix:
-        python-version: [ '3.11' ]
+        python-version: [ '3.12' ]
     name: Python ${{ matrix.python-version }} CI
     steps:
       - uses: actions/checkout@v4
@@ -33,4 +33,4 @@ jobs:
           python -m pip install --upgrade pip
           pip install -r requirements/requirements.txt
       - name: do all unit tests
-        run: bash runtests.sh
\ No newline at end of file
+        run: bash runtests.sh
diff --git a/.lgtm.yml b/.lgtm.yml
deleted file mode 100644
index b06edf3510..0000000000
--- a/.lgtm.yml
+++ /dev/null
@@ -1,4 +0,0 @@
-extraction:
-  python:
-    python_setup:
-      version: 3
diff --git a/README.md b/README.md
index bc0198f78e..7a01ae361a 100644
--- a/README.md
+++ b/README.md
@@ -95,7 +95,7 @@ See this paper for more details:
 
 For running each sample code:
 
-- [Python 3.11.x](https://www.python.org/)
+- [Python 3.12.x](https://www.python.org/)
  
 - [NumPy](https://numpy.org/)
  
diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst
index 497b85a23a..88f218545e 100644
--- a/docs/getting_started_main.rst
+++ b/docs/getting_started_main.rst
@@ -26,7 +26,7 @@ See this paper for more details:
 Requirements
 -------------
 
--  `Python 3.11.x`_
+-  `Python 3.12.x`_
 -  `NumPy`_
 -  `SciPy`_
 -  `Matplotlib`_
diff --git a/requirements/environment.yml b/requirements/environment.yml
index 13dfa29f66..afbb3fb8ce 100644
--- a/requirements/environment.yml
+++ b/requirements/environment.yml
@@ -2,7 +2,7 @@ name: python_robotics
 channels:
   - conda-forge
 dependencies:
-  - python=3.11
+  - python=3.12
   - pip
   - scipy
   - numpy
diff --git a/ruff.toml b/ruff.toml
index 578864b33e..5823ca3db7 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -6,7 +6,7 @@ exclude = [
 ]
 
 # Assume Python 3.11
-target-version = "py311"
+target-version = "py312"
 
 [per-file-ignores]
 
@@ -15,4 +15,4 @@ target-version = "py311"
 max-complexity = 10
 
 [pydocstyle]
-convention = "numpy"
\ No newline at end of file
+convention = "numpy"

From 4bdf9036b60eb40899079b777b208870d2c20bfe Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 19 Mar 2024 20:15:00 +0900
Subject: [PATCH 0847/1080] Bump ruff from 0.3.2 to 0.3.3 in /requirements
 (#993)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.3.2 to 0.3.3.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.3.2...v0.3.3)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 1021562d1c..f6e64cc5ab 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.2
 pytest == 8.1.1 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.9.0 # For unit test
-ruff == 0.3.2 # For unit test
+ruff == 0.3.3 # For unit test

From f8b340be53ebb6ed4b6b0d2579394b58fe8074af Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 26 Mar 2024 07:30:04 +0900
Subject: [PATCH 0848/1080] Bump ruff from 0.3.3 to 0.3.4 in /requirements
 (#996)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.3.3 to 0.3.4.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.3.3...v0.3.4)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index f6e64cc5ab..6e7c77d836 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.2
 pytest == 8.1.1 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.9.0 # For unit test
-ruff == 0.3.3 # For unit test
+ruff == 0.3.4 # For unit test

From 61d5da8576c43c59f69655cf6324fa959031623f Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 2 Apr 2024 07:31:43 +0900
Subject: [PATCH 0849/1080] Bump ruff from 0.3.4 to 0.3.5 in /requirements
 (#999)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.3.4 to 0.3.5.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.3.4...v0.3.5)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 6e7c77d836..c1edf2951c 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.2
 pytest == 8.1.1 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.9.0 # For unit test
-ruff == 0.3.4 # For unit test
+ruff == 0.3.5 # For unit test

From c6562d787f797f56561e362ea2633ad7e3b4ee3c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Apr 2024 08:00:57 +0900
Subject: [PATCH 0850/1080] Bump matplotlib from 3.8.3 to 3.8.4 in
 /requirements (#1001)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.8.3 to 3.8.4.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.8.3...v3.8.4)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index c1edf2951c..fe97f01e24 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.26.4
 scipy == 1.12.0
-matplotlib == 3.8.3
+matplotlib == 3.8.4
 cvxpy == 1.4.2
 pytest == 8.1.1 # For unit test
 pytest-xdist == 3.5.0 # For unit test

From 84237d6007254c2a644adcabe61673da11f39eb4 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Apr 2024 20:25:11 +0900
Subject: [PATCH 0851/1080] Bump scipy from 1.12.0 to 1.13.0 in /requirements
 (#1002)

Bumps [scipy](https://github.com/scipy/scipy) from 1.12.0 to 1.13.0.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.12.0...v1.13.0)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index fe97f01e24..7a94111985 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.26.4
-scipy == 1.12.0
+scipy == 1.13.0
 matplotlib == 3.8.4
 cvxpy == 1.4.2
 pytest == 8.1.1 # For unit test

From 808e98133d57426b1e6fbbc2bdc897a196278d7d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 16 Apr 2024 20:56:21 +0900
Subject: [PATCH 0852/1080] Bump ruff from 0.3.5 to 0.3.7 in /requirements
 (#1003)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.3.5 to 0.3.7.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.3.5...v0.3.7)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 7a94111985..6ba7ea1c6d 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.2
 pytest == 8.1.1 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.9.0 # For unit test
-ruff == 0.3.5 # For unit test
+ruff == 0.3.7 # For unit test

From d27f7dca1d2645915d03d087bc12c2ad281bb114 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 23 Apr 2024 21:28:01 +0900
Subject: [PATCH 0853/1080] Bump ruff from 0.3.7 to 0.4.1 in /requirements
 (#1005)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.3.7 to 0.4.1.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.3.7...v0.4.1)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 6ba7ea1c6d..7dfadddb48 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.2
 pytest == 8.1.1 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.9.0 # For unit test
-ruff == 0.3.7 # For unit test
+ruff == 0.4.1 # For unit test

From c12faa6a2ee2ac92102467487c4b168119c6cf07 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sat, 27 Apr 2024 16:20:45 +0900
Subject: [PATCH 0854/1080] Bump cvxpy from 1.4.2 to 1.4.3 in /requirements
 (#1006)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.4.2 to 1.4.3.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.4.2...v1.4.3)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 7dfadddb48..8e4d166c4e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 1.26.4
 scipy == 1.13.0
 matplotlib == 3.8.4
-cvxpy == 1.4.2
+cvxpy == 1.4.3
 pytest == 8.1.1 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.9.0 # For unit test

From 8f84f0f5518797c1c11f4cee204c0a5c6bffbe82 Mon Sep 17 00:00:00 2001
From: noob3-3 <xiaoshan3920@gmail.com>
Date: Sat, 27 Apr 2024 20:51:19 +0800
Subject: [PATCH 0855/1080] fix::Control PathFinderController bug (#1000)

---
 Control/move_to_pose/move_to_pose.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Control/move_to_pose/move_to_pose.py b/Control/move_to_pose/move_to_pose.py
index cc29666297..279ba0625b 100644
--- a/Control/move_to_pose/move_to_pose.py
+++ b/Control/move_to_pose/move_to_pose.py
@@ -74,7 +74,7 @@ def calc_control_command(self, x_diff, y_diff, theta, theta_goal):
         alpha = angle_mod(np.arctan2(y_diff, x_diff) - theta)
         beta = angle_mod(theta_goal - theta - alpha)
         v = self.Kp_rho * rho
-        w = self.Kp_alpha * alpha - controller.Kp_beta * beta
+        w = self.Kp_alpha * alpha - self.Kp_beta * beta
 
         if alpha > np.pi / 2 or alpha < -np.pi / 2:
             v = -v

From c76f74703f6b9f7b2ec3bf993eb9c9c5d2ccb4ac Mon Sep 17 00:00:00 2001
From: Yi-Chen Zhang <chris7462@gmail.com>
Date: Sun, 28 Apr 2024 07:55:25 -0400
Subject: [PATCH 0856/1080] Fix covariance dimension (#998)

---
 Localization/particle_filter/particle_filter.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Localization/particle_filter/particle_filter.py b/Localization/particle_filter/particle_filter.py
index 17051623ca..ba54a3d12b 100644
--- a/Localization/particle_filter/particle_filter.py
+++ b/Localization/particle_filter/particle_filter.py
@@ -96,10 +96,10 @@ def calc_covariance(x_est, px, pw):
     calculate covariance matrix
     see ipynb doc
     """
-    cov = np.zeros((3, 3))
+    cov = np.zeros((4, 4))
     n_particle = px.shape[1]
     for i in range(n_particle):
-        dx = (px[:, i:i + 1] - x_est)[0:3]
+        dx = (px[:, i:i + 1] - x_est)
         cov += pw[0, i] * dx @ dx.T
     cov *= 1.0 / (1.0 - pw @ pw.T)
 

From de2434361cd017851e10e4866bf6e6dca3b9cc95 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 28 Apr 2024 21:17:33 +0900
Subject: [PATCH 0857/1080] fix plot issue in random_inverse_kinematics.py
 (#1007)

---
 ArmNavigation/n_joint_arm_3d/NLinkArm3d.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py b/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py
index 0cea963168..0459e234b2 100644
--- a/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py
+++ b/ArmNavigation/n_joint_arm_3d/NLinkArm3d.py
@@ -127,7 +127,8 @@ def inverse_kinematics(self, ref_ee_pose, plot=False):
 
         if plot:
             self.fig = plt.figure()
-            self.ax = Axes3D(self.fig)
+            self.ax = Axes3D(self.fig, auto_add_to_figure=False)
+            self.fig.add_axes(self.ax)
 
             x_list = []
             y_list = []

From b93f170d32a4132762ad879ffca19befa8eb218f Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 Apr 2024 08:56:40 +0900
Subject: [PATCH 0858/1080] Bump ruff from 0.4.1 to 0.4.2 in /requirements
 (#1008)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.1 to 0.4.2.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.4.1...v0.4.2)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 8e4d166c4e..ced1457da6 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.3
 pytest == 8.1.1 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.9.0 # For unit test
-ruff == 0.4.1 # For unit test
+ruff == 0.4.2 # For unit test

From bbf7d1ed857dc79e306f9c7fa09323cf74d0c71d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 Apr 2024 12:49:55 +0900
Subject: [PATCH 0859/1080] Bump pytest from 8.1.1 to 8.2.0 in /requirements
 (#1010)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.1.1 to 8.2.0.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/8.1.1...8.2.0)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index ced1457da6..ad5ab50f47 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.26.4
 scipy == 1.13.0
 matplotlib == 3.8.4
 cvxpy == 1.4.3
-pytest == 8.1.1 # For unit test
+pytest == 8.2.0 # For unit test
 pytest-xdist == 3.5.0 # For unit test
 mypy == 1.9.0 # For unit test
 ruff == 0.4.2 # For unit test

From 1262b0bfd1823f4a0476eb5060bd8384dea34128 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 Apr 2024 16:00:24 +0900
Subject: [PATCH 0860/1080] Bump pytest-xdist from 3.5.0 to 3.6.1 in
 /requirements (#1009)

Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.5.0 to 3.6.1.
- [Release notes](https://github.com/pytest-dev/pytest-xdist/releases)
- [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.5.0...v3.6.1)

---
updated-dependencies:
- dependency-name: pytest-xdist
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index ad5ab50f47..b56c6e734f 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,6 +3,6 @@ scipy == 1.13.0
 matplotlib == 3.8.4
 cvxpy == 1.4.3
 pytest == 8.2.0 # For unit test
-pytest-xdist == 3.5.0 # For unit test
+pytest-xdist == 3.6.1 # For unit test
 mypy == 1.9.0 # For unit test
 ruff == 0.4.2 # For unit test

From e14134d1e705076e83ee4228fd463bbfa5fccd61 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 1 May 2024 07:54:14 +0900
Subject: [PATCH 0861/1080] Bump mypy from 1.9.0 to 1.10.0 in /requirements
 (#1011)

Bumps [mypy](https://github.com/python/mypy) from 1.9.0 to 1.10.0.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/1.9.0...v1.10.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index b56c6e734f..0575353a11 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.8.4
 cvxpy == 1.4.3
 pytest == 8.2.0 # For unit test
 pytest-xdist == 3.6.1 # For unit test
-mypy == 1.9.0 # For unit test
+mypy == 1.10.0 # For unit test
 ruff == 0.4.2 # For unit test

From 16c16b6ff56dbebf4fd0ae0f170c9acc6df080dd Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 4 May 2024 23:08:14 +0900
Subject: [PATCH 0862/1080] clean up fast slam codes (#1012)

* clean up fast slam codes

* clean up fast slam codes

* clean up fast slam codes
---
 SLAM/FastSLAM1/fast_slam1.py | 100 ++++++++++++++++---------------
 SLAM/FastSLAM2/fast_slam2.py | 110 +++++++++++++++++------------------
 2 files changed, 104 insertions(+), 106 deletions(-)

diff --git a/SLAM/FastSLAM1/fast_slam1.py b/SLAM/FastSLAM1/fast_slam1.py
index 17f6d5e572..98f8a66417 100644
--- a/SLAM/FastSLAM1/fast_slam1.py
+++ b/SLAM/FastSLAM1/fast_slam1.py
@@ -17,8 +17,8 @@
 R = np.diag([1.0, np.deg2rad(20.0)]) ** 2
 
 #  Simulation parameter
-Q_sim = np.diag([0.3, np.deg2rad(2.0)]) ** 2
-R_sim = np.diag([0.5, np.deg2rad(10.0)]) ** 2
+Q_SIM = np.diag([0.3, np.deg2rad(2.0)]) ** 2
+R_SIM = np.diag([0.5, np.deg2rad(10.0)]) ** 2
 OFFSET_YAW_RATE_NOISE = 0.01
 
 DT = 0.1  # time tick [s]
@@ -72,19 +72,18 @@ def normalize_weight(particles):
 
 
 def calc_final_state(particles):
-    xEst = np.zeros((STATE_SIZE, 1))
+    x_est = np.zeros((STATE_SIZE, 1))
 
     particles = normalize_weight(particles)
 
     for i in range(N_PARTICLE):
-        xEst[0, 0] += particles[i].w * particles[i].x
-        xEst[1, 0] += particles[i].w * particles[i].y
-        xEst[2, 0] += particles[i].w * particles[i].yaw
+        x_est[0, 0] += particles[i].w * particles[i].x
+        x_est[1, 0] += particles[i].w * particles[i].y
+        x_est[2, 0] += particles[i].w * particles[i].yaw
 
-    xEst[2, 0] = pi_2_pi(xEst[2, 0])
-    #  print(xEst)
+    x_est[2, 0] = pi_2_pi(x_est[2, 0])
 
-    return xEst
+    return x_est
 
 
 def predict_particles(particles, u):
@@ -235,28 +234,27 @@ def resampling(particles):
     pw = np.array(pw)
 
     n_eff = 1.0 / (pw @ pw.T)  # Effective particle number
-    # print(n_eff)
 
     if n_eff < NTH:  # resampling
         w_cum = np.cumsum(pw)
         base = np.cumsum(pw * 0.0 + 1 / N_PARTICLE) - 1 / N_PARTICLE
         resample_id = base + np.random.rand(base.shape[0]) / N_PARTICLE
 
-        inds = []
-        ind = 0
+        indexes = []
+        index = 0
         for ip in range(N_PARTICLE):
-            while (ind < w_cum.shape[0] - 1) \
-                    and (resample_id[ip] > w_cum[ind]):
-                ind += 1
-            inds.append(ind)
+            while (index < w_cum.shape[0] - 1) \
+                    and (resample_id[ip] > w_cum[index]):
+                index += 1
+            indexes.append(index)
 
         tmp_particles = particles[:]
-        for i in range(len(inds)):
-            particles[i].x = tmp_particles[inds[i]].x
-            particles[i].y = tmp_particles[inds[i]].y
-            particles[i].yaw = tmp_particles[inds[i]].yaw
-            particles[i].lm = tmp_particles[inds[i]].lm[:, :]
-            particles[i].lmP = tmp_particles[inds[i]].lmP[:, :]
+        for i in range(len(indexes)):
+            particles[i].x = tmp_particles[indexes[i]].x
+            particles[i].y = tmp_particles[indexes[i]].y
+            particles[i].yaw = tmp_particles[indexes[i]].yaw
+            particles[i].lm = tmp_particles[indexes[i]].lm[:, :]
+            particles[i].lmP = tmp_particles[indexes[i]].lmP[:, :]
             particles[i].w = 1.0 / N_PARTICLE
 
     return particles
@@ -275,34 +273,34 @@ def calc_input(time):
     return u
 
 
-def observation(xTrue, xd, u, rfid):
+def observation(x_true, xd, u, rfid):
     # calc true state
-    xTrue = motion_model(xTrue, u)
+    x_true = motion_model(x_true, u)
 
     # add noise to range observation
     z = np.zeros((3, 0))
     for i in range(len(rfid[:, 0])):
 
-        dx = rfid[i, 0] - xTrue[0, 0]
-        dy = rfid[i, 1] - xTrue[1, 0]
+        dx = rfid[i, 0] - x_true[0, 0]
+        dy = rfid[i, 1] - x_true[1, 0]
         d = math.hypot(dx, dy)
-        angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
+        angle = pi_2_pi(math.atan2(dy, dx) - x_true[2, 0])
         if d <= MAX_RANGE:
-            dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5  # add noise
-            angle_with_noize = angle + np.random.randn() * Q_sim[
+            dn = d + np.random.randn() * Q_SIM[0, 0] ** 0.5  # add noise
+            angle_with_noize = angle + np.random.randn() * Q_SIM[
                 1, 1] ** 0.5  # add noise
             zi = np.array([dn, pi_2_pi(angle_with_noize), i]).reshape(3, 1)
             z = np.hstack((z, zi))
 
     # add noise to input
-    ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5
-    ud2 = u[1, 0] + np.random.randn() * R_sim[
+    ud1 = u[0, 0] + np.random.randn() * R_SIM[0, 0] ** 0.5
+    ud2 = u[1, 0] + np.random.randn() * R_SIM[
         1, 1] ** 0.5 + OFFSET_YAW_RATE_NOISE
     ud = np.array([ud1, ud2]).reshape(2, 1)
 
     xd = motion_model(xd, ud)
 
-    return xTrue, z, xd, ud
+    return x_true, z, xd, ud
 
 
 def motion_model(x, u):
@@ -331,7 +329,7 @@ def main():
     time = 0.0
 
     # RFID positions [x, y]
-    RFID = np.array([[10.0, -2.0],
+    rfid = np.array([[10.0, -2.0],
                      [15.0, 10.0],
                      [15.0, 15.0],
                      [10.0, 20.0],
@@ -340,17 +338,17 @@ def main():
                      [-5.0, 5.0],
                      [-10.0, 15.0]
                      ])
-    n_landmark = RFID.shape[0]
+    n_landmark = rfid.shape[0]
 
     # State Vector [x y yaw v]'
-    xEst = np.zeros((STATE_SIZE, 1))  # SLAM estimation
-    xTrue = np.zeros((STATE_SIZE, 1))  # True state
-    xDR = np.zeros((STATE_SIZE, 1))  # Dead reckoning
+    x_est = np.zeros((STATE_SIZE, 1))  # SLAM estimation
+    x_true = np.zeros((STATE_SIZE, 1))  # True state
+    x_dr = np.zeros((STATE_SIZE, 1))  # Dead reckoning
 
     # history
-    hxEst = xEst
-    hxTrue = xTrue
-    hxDR = xTrue
+    hist_x_est = x_est
+    hist_x_true = x_true
+    hist_x_dr = x_dr
 
     particles = [Particle(n_landmark) for _ in range(N_PARTICLE)]
 
@@ -358,18 +356,18 @@ def main():
         time += DT
         u = calc_input(time)
 
-        xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID)
+        x_true, z, x_dr, ud = observation(x_true, x_dr, u, rfid)
 
         particles = fast_slam1(particles, ud, z)
 
-        xEst = calc_final_state(particles)
+        x_est = calc_final_state(particles)
 
-        x_state = xEst[0: STATE_SIZE]
+        x_state = x_est[0: STATE_SIZE]
 
         # store data history
-        hxEst = np.hstack((hxEst, x_state))
-        hxDR = np.hstack((hxDR, xDR))
-        hxTrue = np.hstack((hxTrue, xTrue))
+        hist_x_est = np.hstack((hist_x_est, x_state))
+        hist_x_dr = np.hstack((hist_x_dr, x_dr))
+        hist_x_true = np.hstack((hist_x_true, x_true))
 
         if show_animation:  # pragma: no cover
             plt.cla()
@@ -377,16 +375,16 @@ def main():
             plt.gcf().canvas.mpl_connect(
                 'key_release_event', lambda event:
                 [exit(0) if event.key == 'escape' else None])
-            plt.plot(RFID[:, 0], RFID[:, 1], "*k")
+            plt.plot(rfid[:, 0], rfid[:, 1], "*k")
 
             for i in range(N_PARTICLE):
                 plt.plot(particles[i].x, particles[i].y, ".r")
                 plt.plot(particles[i].lm[:, 0], particles[i].lm[:, 1], "xb")
 
-            plt.plot(hxTrue[0, :], hxTrue[1, :], "-b")
-            plt.plot(hxDR[0, :], hxDR[1, :], "-k")
-            plt.plot(hxEst[0, :], hxEst[1, :], "-r")
-            plt.plot(xEst[0], xEst[1], "xk")
+            plt.plot(hist_x_true[0, :], hist_x_true[1, :], "-b")
+            plt.plot(hist_x_dr[0, :], hist_x_dr[1, :], "-k")
+            plt.plot(hist_x_est[0, :], hist_x_est[1, :], "-r")
+            plt.plot(x_est[0], x_est[1], "xk")
             plt.axis("equal")
             plt.grid(True)
             plt.pause(0.001)
diff --git a/SLAM/FastSLAM2/fast_slam2.py b/SLAM/FastSLAM2/fast_slam2.py
index 2b57e3bcc3..d4cf0d84dd 100644
--- a/SLAM/FastSLAM2/fast_slam2.py
+++ b/SLAM/FastSLAM2/fast_slam2.py
@@ -17,8 +17,8 @@
 R = np.diag([1.0, np.deg2rad(20.0)]) ** 2
 
 #  Simulation parameter
-Q_sim = np.diag([0.3, np.deg2rad(2.0)]) ** 2
-R_sim = np.diag([0.5, np.deg2rad(10.0)]) ** 2
+Q_SIM = np.diag([0.3, np.deg2rad(2.0)]) ** 2
+R_SIM = np.diag([0.5, np.deg2rad(10.0)]) ** 2
 OFFSET_YAW_RATE_NOISE = 0.01
 
 DT = 0.1  # time tick [s]
@@ -35,16 +35,16 @@
 
 class Particle:
 
-    def __init__(self, N_LM):
+    def __init__(self, n_landmark):
         self.w = 1.0 / N_PARTICLE
         self.x = 0.0
         self.y = 0.0
         self.yaw = 0.0
         self.P = np.eye(3)
         # landmark x-y positions
-        self.lm = np.zeros((N_LM, LM_SIZE))
+        self.lm = np.zeros((n_landmark, LM_SIZE))
         # landmark position covariance
-        self.lmP = np.zeros((N_LM * LM_SIZE, LM_SIZE))
+        self.lmP = np.zeros((n_landmark * LM_SIZE, LM_SIZE))
 
 
 def fast_slam2(particles, u, z):
@@ -73,18 +73,18 @@ def normalize_weight(particles):
 
 
 def calc_final_state(particles):
-    xEst = np.zeros((STATE_SIZE, 1))
+    x_est = np.zeros((STATE_SIZE, 1))
 
     particles = normalize_weight(particles)
 
     for i in range(N_PARTICLE):
-        xEst[0, 0] += particles[i].w * particles[i].x
-        xEst[1, 0] += particles[i].w * particles[i].y
-        xEst[2, 0] += particles[i].w * particles[i].yaw
+        x_est[0, 0] += particles[i].w * particles[i].x
+        x_est[1, 0] += particles[i].w * particles[i].y
+        x_est[2, 0] += particles[i].w * particles[i].yaw
 
-    xEst[2, 0] = pi_2_pi(xEst[2, 0])
+    x_est[2, 0] = pi_2_pi(x_est[2, 0])
 
-    return xEst
+    return x_est
 
 
 def predict_particles(particles, u):
@@ -266,21 +266,21 @@ def resampling(particles):
         base = np.cumsum(pw * 0.0 + 1 / N_PARTICLE) - 1 / N_PARTICLE
         resample_id = base + np.random.rand(base.shape[0]) / N_PARTICLE
 
-        inds = []
-        ind = 0
+        indexes = []
+        index = 0
         for ip in range(N_PARTICLE):
-            while (ind < w_cum.shape[0] - 1) \
-                    and (resample_id[ip] > w_cum[ind]):
-                ind += 1
-            inds.append(ind)
+            while (index < w_cum.shape[0] - 1) \
+                    and (resample_id[ip] > w_cum[index]):
+                index += 1
+            indexes.append(index)
 
         tmp_particles = particles[:]
-        for i in range(len(inds)):
-            particles[i].x = tmp_particles[inds[i]].x
-            particles[i].y = tmp_particles[inds[i]].y
-            particles[i].yaw = tmp_particles[inds[i]].yaw
-            particles[i].lm = tmp_particles[inds[i]].lm[:, :]
-            particles[i].lmP = tmp_particles[inds[i]].lmP[:, :]
+        for i in range(len(indexes)):
+            particles[i].x = tmp_particles[indexes[i]].x
+            particles[i].y = tmp_particles[indexes[i]].y
+            particles[i].yaw = tmp_particles[indexes[i]].yaw
+            particles[i].lm = tmp_particles[indexes[i]].lm[:, :]
+            particles[i].lmP = tmp_particles[indexes[i]].lmP[:, :]
             particles[i].w = 1.0 / N_PARTICLE
 
     return particles
@@ -299,35 +299,35 @@ def calc_input(time):
     return u
 
 
-def observation(xTrue, xd, u, RFID):
+def observation(x_true, xd, u, rfid):
     # calc true state
-    xTrue = motion_model(xTrue, u)
+    x_true = motion_model(x_true, u)
 
     # add noise to range observation
     z = np.zeros((3, 0))
 
-    for i in range(len(RFID[:, 0])):
+    for i in range(len(rfid[:, 0])):
 
-        dx = RFID[i, 0] - xTrue[0, 0]
-        dy = RFID[i, 1] - xTrue[1, 0]
+        dx = rfid[i, 0] - x_true[0, 0]
+        dy = rfid[i, 1] - x_true[1, 0]
         d = math.hypot(dx, dy)
-        angle = pi_2_pi(math.atan2(dy, dx) - xTrue[2, 0])
+        angle = pi_2_pi(math.atan2(dy, dx) - x_true[2, 0])
         if d <= MAX_RANGE:
-            dn = d + np.random.randn() * Q_sim[0, 0] ** 0.5  # add noise
-            angle_noise = np.random.randn() * Q_sim[1, 1] ** 0.5
+            dn = d + np.random.randn() * Q_SIM[0, 0] ** 0.5  # add noise
+            angle_noise = np.random.randn() * Q_SIM[1, 1] ** 0.5
             angle_with_noise = angle + angle_noise  # add noise
             zi = np.array([dn, pi_2_pi(angle_with_noise), i]).reshape(3, 1)
             z = np.hstack((z, zi))
 
     # add noise to input
-    ud1 = u[0, 0] + np.random.randn() * R_sim[0, 0] ** 0.5
-    ud2 = u[1, 0] + np.random.randn() * R_sim[
+    ud1 = u[0, 0] + np.random.randn() * R_SIM[0, 0] ** 0.5
+    ud2 = u[1, 0] + np.random.randn() * R_SIM[
         1, 1] ** 0.5 + OFFSET_YAW_RATE_NOISE
     ud = np.array([ud1, ud2]).reshape(2, 1)
 
     xd = motion_model(xd, ud)
 
-    return xTrue, z, xd, ud
+    return x_true, z, xd, ud
 
 
 def motion_model(x, u):
@@ -356,7 +356,7 @@ def main():
     time = 0.0
 
     # RFID positions [x, y]
-    RFID = np.array([[10.0, -2.0],
+    rfid = np.array([[10.0, -2.0],
                      [15.0, 10.0],
                      [15.0, 15.0],
                      [10.0, 20.0],
@@ -365,17 +365,17 @@ def main():
                      [-5.0, 5.0],
                      [-10.0, 15.0]
                      ])
-    n_landmark = RFID.shape[0]
+    n_landmark = rfid.shape[0]
 
     # State Vector [x y yaw v]'
-    xEst = np.zeros((STATE_SIZE, 1))  # SLAM estimation
-    xTrue = np.zeros((STATE_SIZE, 1))  # True state
-    xDR = np.zeros((STATE_SIZE, 1))  # Dead reckoning
+    x_est = np.zeros((STATE_SIZE, 1))  # SLAM estimation
+    x_true = np.zeros((STATE_SIZE, 1))  # True state
+    x_dr = np.zeros((STATE_SIZE, 1))  # Dead reckoning
 
     # history
-    hxEst = xEst
-    hxTrue = xTrue
-    hxDR = xTrue
+    hist_x_est = x_est
+    hist_x_true = x_true
+    hist_x_dr = x_dr
 
     particles = [Particle(n_landmark) for _ in range(N_PARTICLE)]
 
@@ -383,18 +383,18 @@ def main():
         time += DT
         u = calc_input(time)
 
-        xTrue, z, xDR, ud = observation(xTrue, xDR, u, RFID)
+        x_true, z, x_dr, ud = observation(x_true, x_dr, u, rfid)
 
         particles = fast_slam2(particles, ud, z)
 
-        xEst = calc_final_state(particles)
+        x_est = calc_final_state(particles)
 
-        x_state = xEst[0: STATE_SIZE]
+        x_state = x_est[0: STATE_SIZE]
 
         # store data history
-        hxEst = np.hstack((hxEst, x_state))
-        hxDR = np.hstack((hxDR, xDR))
-        hxTrue = np.hstack((hxTrue, xTrue))
+        hist_x_est = np.hstack((hist_x_est, x_state))
+        hist_x_dr = np.hstack((hist_x_dr, x_dr))
+        hist_x_true = np.hstack((hist_x_true, x_true))
 
         if show_animation:  # pragma: no cover
             plt.cla()
@@ -402,21 +402,21 @@ def main():
             plt.gcf().canvas.mpl_connect(
                 'key_release_event',
                 lambda event: [exit(0) if event.key == 'escape' else None])
-            plt.plot(RFID[:, 0], RFID[:, 1], "*k")
+            plt.plot(rfid[:, 0], rfid[:, 1], "*k")
 
             for iz in range(len(z[:, 0])):
                 landmark_id = int(z[2, iz])
-                plt.plot([xEst[0][0], RFID[landmark_id, 0]], [
-                    xEst[1][0], RFID[landmark_id, 1]], "-k")
+                plt.plot([x_est[0][0], rfid[landmark_id, 0]], [
+                    x_est[1][0], rfid[landmark_id, 1]], "-k")
 
             for i in range(N_PARTICLE):
                 plt.plot(particles[i].x, particles[i].y, ".r")
                 plt.plot(particles[i].lm[:, 0], particles[i].lm[:, 1], "xb")
 
-            plt.plot(hxTrue[0, :], hxTrue[1, :], "-b")
-            plt.plot(hxDR[0, :], hxDR[1, :], "-k")
-            plt.plot(hxEst[0, :], hxEst[1, :], "-r")
-            plt.plot(xEst[0], xEst[1], "xk")
+            plt.plot(hist_x_true[0, :], hist_x_true[1, :], "-b")
+            plt.plot(hist_x_dr[0, :], hist_x_dr[1, :], "-k")
+            plt.plot(hist_x_est[0, :], hist_x_est[1, :], "-r")
+            plt.plot(x_est[0], x_est[1], "xk")
             plt.axis("equal")
             plt.grid(True)
             plt.pause(0.001)

From 20818bfe3c7185e065435ea4d204510db6dd37f5 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 7 May 2024 21:37:44 +0900
Subject: [PATCH 0863/1080] Bump ruff from 0.4.2 to 0.4.3 in /requirements
 (#1014)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.2 to 0.4.3.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.4.2...v0.4.3)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 0575353a11..4db551eb8c 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.3
 pytest == 8.2.0 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.10.0 # For unit test
-ruff == 0.4.2 # For unit test
+ruff == 0.4.3 # For unit test

From dfcc08e36723e7a371048147d2879f7b373e1902 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 14 May 2024 08:44:53 +0900
Subject: [PATCH 0864/1080] Bump ruff from 0.4.3 to 0.4.4 in /requirements
 (#1017)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.3 to 0.4.4.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.4.3...v0.4.4)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 4db551eb8c..909b39faa7 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.4.3
 pytest == 8.2.0 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.10.0 # For unit test
-ruff == 0.4.3 # For unit test
+ruff == 0.4.4 # For unit test

From ed5004b0f84081b93a2aceebe96defe013d99e06 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?YSF=E3=83=84?=
 <50619491+yousefbilal@users.noreply.github.com>
Date: Sat, 18 May 2024 13:56:04 +0400
Subject: [PATCH 0865/1080] fixed hard-coded plot limits (#1018)

---
 PathPlanning/RRT/rrt.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/RRT/rrt.py b/PathPlanning/RRT/rrt.py
index 55092e6e00..e6dd9b648b 100644
--- a/PathPlanning/RRT/rrt.py
+++ b/PathPlanning/RRT/rrt.py
@@ -199,7 +199,7 @@ def draw_graph(self, rnd=None):
         plt.plot(self.start.x, self.start.y, "xr")
         plt.plot(self.end.x, self.end.y, "xr")
         plt.axis("equal")
-        plt.axis([-2, 15, -2, 15])
+        plt.axis([self.min_rand, self.max_rand, self.min_rand, self.max_rand])
         plt.grid(True)
         plt.pause(0.01)
 

From 36b630040224c79bfa05974b8107b7a384e8271a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sun, 19 May 2024 20:54:57 +0900
Subject: [PATCH 0866/1080] Bump cvxpy from 1.4.3 to 1.5.1 in /requirements
 (#1016)

* Bump cvxpy from 1.4.3 to 1.5.1 in /requirements

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.4.3 to 1.5.1.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.4.3...v1.5.1)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

* fix cvxpy update issue

---------

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>
---
 .../model_predictive_speed_and_steer_control.py    | 14 ++++++++++++--
 requirements/requirements.txt                      |  2 +-
 runtests.sh                                        |  2 +-
 3 files changed, 14 insertions(+), 4 deletions(-)

diff --git a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
index c55018cf26..eb2d7b6a73 100644
--- a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
+++ b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
@@ -6,14 +6,16 @@
 
 """
 import matplotlib.pyplot as plt
+import time
 import cvxpy
 import math
 import numpy as np
 import sys
 import pathlib
-from utils.angle import angle_mod
 sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
+from utils.angle import angle_mod
+
 from PathPlanning.CubicSpline import cubic_spline_planner
 
 NX = 4  # x = x, y, v, yaw
@@ -281,7 +283,7 @@ def linear_mpc_control(xref, xbar, x0, dref):
     constraints += [cvxpy.abs(u[1, :]) <= MAX_STEER]
 
     prob = cvxpy.Problem(cvxpy.Minimize(cost), constraints)
-    prob.solve(solver=cvxpy.ECOS, verbose=False)
+    prob.solve(solver=cvxpy.CLARABEL, verbose=False)
 
     if prob.status == cvxpy.OPTIMAL or prob.status == cvxpy.OPTIMAL_INACCURATE:
         ox = get_nparray_from_matrix(x.value[0, :])
@@ -545,6 +547,7 @@ def get_switch_back_course(dl):
 
 def main():
     print(__file__ + " start!!")
+    start = time.time()
 
     dl = 1.0  # course tick
     # cx, cy, cyaw, ck = get_straight_course(dl)
@@ -560,6 +563,9 @@ def main():
     t, x, y, yaw, v, d, a = do_simulation(
         cx, cy, cyaw, ck, sp, dl, initial_state)
 
+    elapsed_time = time.time() - start
+    print(f"calc time:{elapsed_time:.6f} [sec]")
+
     if show_animation:  # pragma: no cover
         plt.close("all")
         plt.subplots()
@@ -582,6 +588,7 @@ def main():
 
 def main2():
     print(__file__ + " start!!")
+    start = time.time()
 
     dl = 1.0  # course tick
     cx, cy, cyaw, ck = get_straight_course3(dl)
@@ -593,6 +600,9 @@ def main2():
     t, x, y, yaw, v, d, a = do_simulation(
         cx, cy, cyaw, ck, sp, dl, initial_state)
 
+    elapsed_time = time.time() - start
+    print(f"calc time:{elapsed_time:.6f} [sec]")
+
     if show_animation:  # pragma: no cover
         plt.close("all")
         plt.subplots()
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 909b39faa7..76f203f423 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 1.26.4
 scipy == 1.13.0
 matplotlib == 3.8.4
-cvxpy == 1.4.3
+cvxpy == 1.5.1
 pytest == 8.2.0 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.10.0 # For unit test
diff --git a/runtests.sh b/runtests.sh
index a2a683e571..12d1b80453 100755
--- a/runtests.sh
+++ b/runtests.sh
@@ -5,4 +5,4 @@ echo "Run test suites! "
 # -Werror: warning as error
 # --durations=0: show ranking of test durations
 # -l (--showlocals); show local variables when test failed
-pytest -n auto tests -l -Werror --durations=0
+pytest tests -l -Werror --durations=0

From e752e50421e66f3982cfd84188499137c55d2925 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 21 May 2024 21:16:11 +0900
Subject: [PATCH 0867/1080] --- (#1020)

updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 76f203f423..65885f423b 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.26.4
 scipy == 1.13.0
 matplotlib == 3.8.4
 cvxpy == 1.5.1
-pytest == 8.2.0 # For unit test
+pytest == 8.2.1 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.10.0 # For unit test
 ruff == 0.4.4 # For unit test

From cfb736316aed6a6160e45e1c236beda0a5f01622 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 23 May 2024 21:48:35 +0900
Subject: [PATCH 0868/1080] --- (#1019)

updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 65885f423b..cbf18ad938 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 1.26.4
 scipy == 1.13.0
-matplotlib == 3.8.4
+matplotlib == 3.9.0
 cvxpy == 1.5.1
 pytest == 8.2.1 # For unit test
 pytest-xdist == 3.6.1 # For unit test

From 5751829bab294019bb78917d91f280552bb549d2 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 25 May 2024 23:31:54 +0900
Subject: [PATCH 0869/1080] Enhance lqr steering control docs (#1015)

* enhance lqr steering control docs

* enhance lqr steering control docs

* enhance lqr steering control docs

* enhance lqr steering control docs

* improve lqr steering control docs
---
 .../lqr_steer_control/lqr_steer_control.py    |  14 +--
 docs/README.md                                |   2 +-
 .../lqr_steering_control_main.rst             | 102 ++++++++++++++++++
 .../lqr_steering_control_model.png            | Bin 0 -> 17366 bytes
 4 files changed, 111 insertions(+), 7 deletions(-)
 create mode 100644 docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_model.png

diff --git a/PathTracking/lqr_steer_control/lqr_steer_control.py b/PathTracking/lqr_steer_control/lqr_steer_control.py
index 4078ea56db..461d6e3722 100644
--- a/PathTracking/lqr_steer_control/lqr_steer_control.py
+++ b/PathTracking/lqr_steer_control/lqr_steer_control.py
@@ -55,7 +55,7 @@ def update(state, a, delta):
     return state
 
 
-def PIDControl(target, current):
+def pid_control(target, current):
     a = Kp * (target - current)
 
     return a
@@ -70,10 +70,11 @@ def solve_DARE(A, B, Q, R):
     solve a discrete time_Algebraic Riccati equation (DARE)
     """
     X = Q
-    maxiter = 150
+    Xn = Q
+    max_iter = 150
     eps = 0.01
 
-    for i in range(maxiter):
+    for i in range(max_iter):
         Xn = A.T @ X @ A - A.T @ X @ B @ \
             la.inv(R + B.T @ X @ B) @ B.T @ X @ A + Q
         if (abs(Xn - X)).max() < eps:
@@ -178,7 +179,7 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
         dl, target_ind, e, e_th = lqr_steering_control(
             state, cx, cy, cyaw, ck, e, e_th)
 
-        ai = PIDControl(speed_profile[target_ind], state.v)
+        ai = pid_control(speed_profile[target_ind], state.v)
         state = update(state, ai, dl)
 
         if abs(state.v) <= stop_speed:
@@ -202,8 +203,9 @@ def closed_loop_prediction(cx, cy, cyaw, ck, speed_profile, goal):
         if target_ind % 1 == 0 and show_animation:
             plt.cla()
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(cx, cy, "-r", label="course")
             plt.plot(x, y, "ob", label="trajectory")
             plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
diff --git a/docs/README.md b/docs/README.md
index 5c4145e8e3..fb7d4cc3bc 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -8,7 +8,7 @@ This folder contains documentation for the Python Robotics project.
 #### Install Sphinx and Theme
 
 ```
-pip install sphinx sphinx-autobuild sphinx-rtd-theme
+pip install sphinx sphinx-autobuild sphinx-rtd-theme sphinx_rtd_dark_mode sphinx_copybutton sphinx_rtd_dark_mode
 ```
 
 #### Building the Docs
diff --git a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst
index bf6d6b5854..b18e4728cd 100644
--- a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst
+++ b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst
@@ -8,7 +8,109 @@ control.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_steer_control/animation.gif
 
+Overview
+~~~~~~~~
+
+The LQR (Linear Quadratic Regulator) steering control model implemented in lqr_steer_control.py provides a simulation
+for an autonomous vehicle to track a desired trajectory by adjusting steering angle based on feedback from the current state and the desired trajectory.
+This model utilizes a combination of PID speed control and LQR steering control to achieve smooth and accurate trajectory tracking.
+
+Vehicle motion Model
+~~~~~~~~~~~~~~~~~~~~~
+
+The below figure shows the geometric model of the vehicle used in this simulation:
+
+.. image:: lqr_steering_control_model.png
+   :width: 600px
+
+The `e` and `theta` represent the lateral error and orientation error, respectively, with respect to the desired trajectory.
+And :math:`\dot{e}` and :math:`\dot{\theta}` represent the rates of change of these errors.
+
+The :math:`e_t` and :math:`theta_t` are the updated values of `e` and `theta` at time `t`, respectively, and can be calculated using the following kinematic equations:
+
+.. math:: e_t = e_{t-1} + \dot{e}_{t-1} dt
+
+.. math:: \theta_t = \theta_{t-1} + \dot{\theta}_{t-1} dt
+
+Where `dt` is the time difference.
+
+The change rate of the `e` can be calculated as:
+
+.. math:: \dot{e}_t = V \sin(\theta_{t-1})
+
+Where `V` is the vehicle speed.
+
+If the :math:`theta` is small,
+
+.. math:: \theta \approx 0
+
+the :math:`\sin(\theta)` can be approximated as :math:`\theta`:
+
+.. math:: \sin(\theta) = \theta
+
+So, the change rate of the `e` can be approximated as:
+
+.. math:: \dot{e}_t = V \theta_{t-1}
+
+The change rate of the :math:`\theta` can be calculated as:
+
+.. math:: \dot{\theta}_t = \frac{V}{L} \tan(\delta)
+
+where `L` is the wheelbase of the vehicle and :math:`\delta` is the steering angle.
+
+If the :math:`\delta` is small,
+
+.. math:: \delta \approx 0
+
+the :math:`\tan(\delta)` can be approximated as :math:`\delta`:
+
+.. math:: \tan(\delta) = \delta
+
+So, the change rate of the :math:`\theta` can be approximated as:
+
+.. math:: \dot{\theta}_t = \frac{V}{L} \delta
+
+The above equations can be used to update the state of the vehicle at each time step.
+
+To formulate the state-space representation of the vehicle dynamics as a linear model,
+the state vector `x` and control input vector `u` are defined as follows:
+
+.. math:: x_t = [e_t, \dot{e}_t, \theta_t, \dot{\theta}_t]^T
+
+.. math:: u_t = \delta_t
+
+The state transition equation can be represented as:
+
+.. math:: x_{t+1} = A x_t + B u_t
+
+where:
+
+:math:`\begin{equation*} A = \begin{bmatrix} 1 & dt & 0 & 0\\ 0 & 0 & v & 0\\ 0 & 0 & 1 & dt\\ 0 & 0 & 0 & 0 \\ \end{bmatrix} \end{equation*}`
+
+:math:`\begin{equation*} B = \begin{bmatrix} 0\\ 0\\ 0\\ \frac{v}{L} \\ \end{bmatrix} \end{equation*}`
+
+LQR controller
+~~~~~~~~~~~~~~~
+
+The Linear Quadratic Regulator (LQR) controller is used to calculate the optimal control input `u` that minimizes the quadratic cost function:
+
+:math:`J = \sum_{t=0}^{N} (x_t^T Q x_t + u_t^T R u_t)`
+
+where `Q` and `R` are the weighting matrices for the state and control input, respectively.
+
+for the linear model:
+
+:math:`x_{t+1} = A x_t + B u_t`
+
+The optimal control input `u` can be calculated as:
+
+:math:`u_t = -K x_t`
+
+where `K` is the feedback gain matrix obtained by solving the Riccati equation.
+
 References:
 ~~~~~~~~~~~
 -  `ApolloAuto/apollo: An open autonomous driving platform <https://github.com/ApolloAuto/apollo>`_
 
+- `Linear Quadratic Regulator (LQR) <https://en.wikipedia.org/wiki/Linear%E2%80%93quadratic_regulator>`_
+
diff --git a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_model.png b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_model.png
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From b1fb7fe24e353852b3ab8cc97511463746eac0e9 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 25 May 2024 23:46:08 +0900
Subject: [PATCH 0870/1080] improve lqr steering control docs

---
 .../lqr_steering_control_main.rst               |   2 +-
 .../lqr_steering_control_model.jpg              | Bin 0 -> 11721 bytes
 .../lqr_steering_control_model.png              | Bin 17366 -> 0 bytes
 3 files changed, 1 insertion(+), 1 deletion(-)
 create mode 100644 docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_model.jpg
 delete mode 100644 docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_model.png

diff --git a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst
index b18e4728cd..fcc9b5278a 100644
--- a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst
+++ b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst
@@ -20,7 +20,7 @@ Vehicle motion Model
 
 The below figure shows the geometric model of the vehicle used in this simulation:
 
-.. image:: lqr_steering_control_model.png
+.. image:: lqr_steering_control_model.jpg
    :width: 600px
 
 The `e` and `theta` represent the lateral error and orientation error, respectively, with respect to the desired trajectory.
diff --git a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_model.jpg b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_model.jpg
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diff --git a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_model.png b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_model.png
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From 3504db00a666d84de0e4738fad1db78142f34c17 Mon Sep 17 00:00:00 2001
From: RyuYamamoto <ryu.yamamoto@tier4.jp>
Date: Sun, 26 May 2024 15:42:25 +0900
Subject: [PATCH 0871/1080] fix: typo (#1023)

---
 PathPlanning/DStarLite/d_star_lite.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/DStarLite/d_star_lite.py b/PathPlanning/DStarLite/d_star_lite.py
index 86e777d7b7..f099530173 100644
--- a/PathPlanning/DStarLite/d_star_lite.py
+++ b/PathPlanning/DStarLite/d_star_lite.py
@@ -2,7 +2,7 @@
 D* Lite grid planning
 author: vss2sn (28676655+vss2sn@users.noreply.github.com)
 Link to papers:
-D* Lite (Link: http://idm-lab.org/bib/abstracts/papers/aaai02b.pd)
+D* Lite (Link: http://idm-lab.org/bib/abstracts/papers/aaai02b.pdf)
 Improved Fast Replanning for Robot Navigation in Unknown Terrain
 (Link: http://www.cs.cmu.edu/~maxim/files/dlite_icra02.pdf)
 Implemented maintaining similarity with the pseudocode for understanding.

From 9fed26e503fb28fbb6169e8fc3f8b4c29975e16f Mon Sep 17 00:00:00 2001
From: Tim Huff <89954856+timmarkhuff@users.noreply.github.com>
Date: Sat, 25 May 2024 23:43:05 -0700
Subject: [PATCH 0872/1080] Fixing
 ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py
 (#1022)

* fixing script

* removing unnecessary code

* fixing CI issue

---------

Co-authored-by: Tim Huff <tim@groundlight.ai>
---
 .../rrt_star_seven_joint_arm_control.py                   | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py b/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py
index 0f449bbe51..3bc2a5ec1d 100755
--- a/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py
+++ b/ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py
@@ -265,7 +265,7 @@ def steer(self, from_node, to_node, extend_length=float("inf")):
 
     def draw_graph(self, rnd=None):
         plt.cla()
-        self.ax.axis([-1, 1, -1, 1])
+        self.ax.axis([-1, 1, -1, 1, -1, 1])
         self.ax.set_zlim(0, 1)
         self.ax.grid(True)
         for (ox, oy, oz, size) in self.obstacle_list:
@@ -375,9 +375,9 @@ def main():
                              search_until_max_iter=False)
 
     if path is None:
-        print("Cannot find path")
+        print("Cannot find path.")
     else:
-        print("found path!!")
+        print("Found path!")
 
         # Draw final path
         if show_animation:
@@ -396,7 +396,7 @@ def main():
                         [y for y in y_points],
                         [z for z in z_points],
                         "o-", color="grey",  ms=4, mew=0.5)
-                plt.pause(0.01)
+                plt.pause(0.1)
 
             plt.show()
 

From 1aa9a12873c49393dfa1b17a01f72e38b71ece6d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 28 May 2024 20:50:36 +0900
Subject: [PATCH 0873/1080] Bump ruff from 0.4.4 to 0.4.5 in /requirements
 (#1024)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.4 to 0.4.5.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.4.4...v0.4.5)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index cbf18ad938..6a07880f45 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.1
 pytest == 8.2.1 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.10.0 # For unit test
-ruff == 0.4.4 # For unit test
+ruff == 0.4.5 # For unit test

From 01949a6edcf79100527a24ccf7d07fad235df03e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sat, 1 Jun 2024 17:46:27 +0900
Subject: [PATCH 0874/1080] Bump scipy from 1.13.0 to 1.13.1 in /requirements
 (#1025)

Bumps [scipy](https://github.com/scipy/scipy) from 1.13.0 to 1.13.1.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.13.0...v1.13.1)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 6a07880f45..337f56781d 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 1.26.4
-scipy == 1.13.0
+scipy == 1.13.1
 matplotlib == 3.9.0
 cvxpy == 1.5.1
 pytest == 8.2.1 # For unit test

From a9be11d131e404df00d9b91c91289aec7bc895ea Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 5 Jun 2024 07:21:06 +0900
Subject: [PATCH 0875/1080] Bump ruff from 0.4.5 to 0.4.7 in /requirements
 (#1026)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.5 to 0.4.7.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.4.5...v0.4.7)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 337f56781d..a7cdea961e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.1
 pytest == 8.2.1 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.10.0 # For unit test
-ruff == 0.4.5 # For unit test
+ruff == 0.4.7 # For unit test

From 9d06b1e3c3179f9da85f9646bf2e25aea0e4b068 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 13 Jun 2024 12:50:05 +0900
Subject: [PATCH 0876/1080] Bump ruff from 0.4.7 to 0.4.8 in /requirements
 (#1027)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.7 to 0.4.8.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.4.7...v0.4.8)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index a7cdea961e..7a1b36454c 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.1
 pytest == 8.2.1 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.10.0 # For unit test
-ruff == 0.4.7 # For unit test
+ruff == 0.4.8 # For unit test

From f289bd22b9390ab7fafe20c88fe8dda6919a4b67 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 13 Jun 2024 19:45:30 +0900
Subject: [PATCH 0877/1080] Bump pytest from 8.2.1 to 8.2.2 in /requirements
 (#1028)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.2.1 to 8.2.2.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/8.2.1...8.2.2)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 7a1b36454c..3263de2c56 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 1.26.4
 scipy == 1.13.1
 matplotlib == 3.9.0
 cvxpy == 1.5.1
-pytest == 8.2.1 # For unit test
+pytest == 8.2.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.10.0 # For unit test
 ruff == 0.4.8 # For unit test

From 83cfed15b6430b01130240da53a6c4d745ef9378 Mon Sep 17 00:00:00 2001
From: Yukihiro Saito <yukky.saito@gmail.com>
Date: Sat, 15 Jun 2024 07:46:15 +0900
Subject: [PATCH 0878/1080] fix(README.md): typo in  reeds-shepp path (#1031)

Signed-off-by: Yukihito Saito <yukky.saito@gmail.com>
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 7a01ae361a..ee40dfc7eb 100644
--- a/README.md
+++ b/README.md
@@ -407,7 +407,7 @@ Ref:
 
 ### RRT\* with reeds-shepp path
 
-![Robotics/animation.gif at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarReedsShepp/animation.gif))
+![Robotics/animation.gif at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/RRTStarReedsShepp/animation.gif)
 
 Path planning for a car robot with RRT\* and reeds shepp path planner.
 

From 15e1ed5de1b2f3d53eedf896b0c9efb9650d25bc Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 18 Jun 2024 07:57:18 +0900
Subject: [PATCH 0879/1080] Bump ruff from 0.4.8 to 0.4.9 in /requirements
 (#1033)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.8 to 0.4.9.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.4.8...v0.4.9)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 3263de2c56..3b51f38858 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.1
 pytest == 8.2.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.10.0 # For unit test
-ruff == 0.4.8 # For unit test
+ruff == 0.4.9 # For unit test

From 75f3e4d56cb68ae0d6c1b2fc61e7f265cb2005d4 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 25 Jun 2024 20:27:21 +0900
Subject: [PATCH 0880/1080] Bump cvxpy from 1.5.1 to 1.5.2 in /requirements
 (#1035)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.5.1 to 1.5.2.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.5.1...v1.5.2)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 3b51f38858..7cf9cfb7c3 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 1.26.4
 scipy == 1.13.1
 matplotlib == 3.9.0
-cvxpy == 1.5.1
+cvxpy == 1.5.2
 pytest == 8.2.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.10.0 # For unit test

From d6075ca7d039b0ce98fd51518b9f48ca13763b3e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 25 Jun 2024 21:32:49 +0900
Subject: [PATCH 0881/1080] Bump ruff from 0.4.9 to 0.4.10 in /requirements
 (#1036)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.9 to 0.4.10.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.4.9...v0.4.10)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 7cf9cfb7c3..576bfcb86d 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.2
 pytest == 8.2.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.10.0 # For unit test
-ruff == 0.4.9 # For unit test
+ruff == 0.4.10 # For unit test

From df5d99248dae9967a9e90517900e3858b0ae8b5d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 25 Jun 2024 22:12:57 +0900
Subject: [PATCH 0882/1080] Bump numpy from 1.26.4 to 2.0.0 in /requirements
 (#1032)

Bumps [numpy](https://github.com/numpy/numpy) from 1.26.4 to 2.0.0.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v1.26.4...v2.0.0)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 576bfcb86d..12830626aa 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 1.26.4
+numpy == 2.0.0
 scipy == 1.13.1
 matplotlib == 3.9.0
 cvxpy == 1.5.2

From 71766f0a3eba78fcf6ba3820ed74464f32f751ee Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 2 Jul 2024 07:38:06 +0900
Subject: [PATCH 0883/1080] Bump scipy from 1.13.1 to 1.14.0 in /requirements
 (#1040)

Bumps [scipy](https://github.com/scipy/scipy) from 1.13.1 to 1.14.0.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.13.1...v1.14.0)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 12830626aa..7285355871 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 2.0.0
-scipy == 1.13.1
+scipy == 1.14.0
 matplotlib == 3.9.0
 cvxpy == 1.5.2
 pytest == 8.2.2 # For unit test

From cca764edf600f3a29456981691af8f414c4030ea Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 2 Jul 2024 21:19:23 +0900
Subject: [PATCH 0884/1080] Bump mypy from 1.10.0 to 1.10.1 in /requirements
 (#1039)

Bumps [mypy](https://github.com/python/mypy) from 1.10.0 to 1.10.1.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.10.0...v1.10.1)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 7285355871..1c4806ab75 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.9.0
 cvxpy == 1.5.2
 pytest == 8.2.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test
-mypy == 1.10.0 # For unit test
+mypy == 1.10.1 # For unit test
 ruff == 0.4.10 # For unit test

From 0f8ce35bf236ac7c97baa99e4189f6bc283be834 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 2 Jul 2024 21:57:57 +0900
Subject: [PATCH 0885/1080] Bump ruff from 0.4.10 to 0.5.0 in /requirements
 (#1041)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.4.10 to 0.5.0.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/v0.4.10...0.5.0)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 1c4806ab75..5eb478dfca 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.2
 pytest == 8.2.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.10.1 # For unit test
-ruff == 0.4.10 # For unit test
+ruff == 0.5.0 # For unit test

From af7a75fa837f98ab2980e4f637e4273ba4a78e50 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Jul 2024 07:16:58 +0900
Subject: [PATCH 0886/1080] Bump matplotlib from 3.9.0 to 3.9.1 in
 /requirements (#1045)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.9.0 to 3.9.1.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.9.0...v3.9.1)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 5eb478dfca..406786edbc 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 2.0.0
 scipy == 1.14.0
-matplotlib == 3.9.0
+matplotlib == 3.9.1
 cvxpy == 1.5.2
 pytest == 8.2.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test

From 4b69c7a859e7658483c16cd5b17820f1056ae21c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Jul 2024 21:21:56 +0900
Subject: [PATCH 0887/1080] Bump ruff from 0.5.0 to 0.5.1 in /requirements
 (#1044)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.5.0 to 0.5.1.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.5.0...0.5.1)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 406786edbc..e334ab227a 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.2
 pytest == 8.2.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.10.1 # For unit test
-ruff == 0.5.0 # For unit test
+ruff == 0.5.1 # For unit test

From 26deca19b63382e2fffecc804fceb6694c21d5aa Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 16 Jul 2024 08:20:28 +0900
Subject: [PATCH 0888/1080] Bump ruff from 0.5.1 to 0.5.2 in /requirements
 (#1048)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.5.1 to 0.5.2.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.5.1...0.5.2)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index e334ab227a..aa91cb258f 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.2
 pytest == 8.2.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.10.1 # For unit test
-ruff == 0.5.1 # For unit test
+ruff == 0.5.2 # For unit test

From 1e101d1f8adbbb6cdb9334807067b872ac62004a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 16 Jul 2024 23:02:31 +0900
Subject: [PATCH 0889/1080] improve LQT steering and speed control document
 (#1047)

* improve LQT steering and speed control

* improve LQT steering and speed control

* improve LQT steering and speed control doc

* improve LQT steering and speed control doc

* improve LQT steering and speed control doc

* improve LQT steering and speed control doc
---
 .../lqr_speed_steer_control.py                |  13 +-
 .../lqr_speed_and_steering_control_main.rst   | 127 ++++++++++++++++++
 .../lqr_steering_control_model.jpg            | Bin 0 -> 11721 bytes
 .../lqr_speed_and_steering_control/speed.png  | Bin 0 -> 22550 bytes
 .../lqr_speed_and_steering_control/x-y.png    | Bin 0 -> 31256 bytes
 .../lqr_speed_and_steering_control/yaw.png    | Bin 0 -> 23991 bytes
 .../lqr_steering_control_main.rst             |  15 ++-
 tests/test_codestyle.py                       |   2 +-
 8 files changed, 149 insertions(+), 8 deletions(-)
 create mode 100644 docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_steering_control_model.jpg
 create mode 100644 docs/modules/path_tracking/lqr_speed_and_steering_control/speed.png
 create mode 100644 docs/modules/path_tracking/lqr_speed_and_steering_control/x-y.png
 create mode 100644 docs/modules/path_tracking/lqr_speed_and_steering_control/yaw.png

diff --git a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
index a70e30d378..d85fa98a84 100644
--- a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
+++ b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
@@ -55,6 +55,7 @@ def update(state, a, delta):
 def pi_2_pi(angle):
     return angle_mod(angle)
 
+
 def solve_dare(A, B, Q, R):
     """
     solve a discrete time_Algebraic Riccati equation (DARE)
@@ -221,8 +222,9 @@ def do_simulation(cx, cy, cyaw, ck, speed_profile, goal):
         if target_ind % 1 == 0 and show_animation:
             plt.cla()
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect('key_release_event',
-                    lambda event: [exit(0) if event.key == 'escape' else None])
+            plt.gcf().canvas.mpl_connect(
+                'key_release_event',
+                lambda event: [exit(0) if event.key == 'escape' else None])
             plt.plot(cx, cy, "-r", label="course")
             plt.plot(x, y, "ob", label="trajectory")
             plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
@@ -290,6 +292,13 @@ def main():
         plt.xlabel("x[m]")
         plt.ylabel("y[m]")
         plt.legend()
+        plt.subplots(1)
+
+        plt.plot(t, np.array(v)*3.6, label="speed")
+        plt.grid(True)
+        plt.xlabel("Time [sec]")
+        plt.ylabel("Speed [m/s]")
+        plt.legend()
 
         plt.subplots(1)
         plt.plot(s, [np.rad2deg(iyaw) for iyaw in cyaw], "-r", label="yaw")
diff --git a/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst b/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst
index 68ea9c88b2..a0b145456c 100644
--- a/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst
+++ b/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst
@@ -7,6 +7,133 @@ Path tracking simulation with LQR speed and steering control.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif
 
+`[Code Link] <https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py>`_
+
+Overview
+~~~~~~~~
+
+The LQR (Linear Quadratic Regulator) speed and steering control model implemented in `lqr_speed_steer_control.py` provides a simulation
+for an autonomous vehicle to track:
+
+1. A desired speed by adjusting acceleration based on feedback from the current state and the desired speed.
+
+2. A desired trajectory by adjusting steering angle based on feedback from the current state and the desired trajectory.
+
+by only using one LQT controller.
+
+Vehicle motion Model
+~~~~~~~~~~~~~~~~~~~~~
+
+The below figure shows the geometric model of the vehicle used in this simulation:
+
+.. image:: lqr_steering_control_model.jpg
+   :width: 600px
+
+The `e`, :math:`{\theta}`, and :math:`\upsilon` represent the lateral error, orientation error, and velocity error, respectively, with respect to the desired trajectory and speed.
+And :math:`\dot{e}` and :math:`\dot{\theta}` represent the rates of change of these errors.
+
+The :math:`e_t` and :math:`\theta_t`, and :math:`\upsilon` are the updated values of `e`, :math:`\theta`, :math:`\upsilon` and at time `t`, respectively, and can be calculated using the following kinematic equations:
+
+.. math:: e_t = e_{t-1} + \dot{e}_{t-1} dt
+
+.. math:: \theta_t = \theta_{t-1} + \dot{\theta}_{t-1} dt
+
+.. math:: \upsilon_t = \upsilon_{t-1} + a_{t-1} dt
+
+Where `dt` is the time difference and :math:`a_t` is the acceleration at the time `t`.
+
+The change rate of the `e` can be calculated as:
+
+.. math:: \dot{e}_t = V \sin(\theta_{t-1})
+
+Where `V` is the current vehicle speed.
+
+If the :math:`\theta` is small,
+
+.. math:: \theta \approx 0
+
+the :math:`\sin(\theta)` can be approximated as :math:`\theta`:
+
+.. math:: \sin(\theta) = \theta
+
+So, the change rate of the `e` can be approximated as:
+
+.. math:: \dot{e}_t = V \theta_{t-1}
+
+The change rate of the :math:`\theta` can be calculated as:
+
+.. math:: \dot{\theta}_t = \frac{V}{L} \tan(\delta)
+
+where `L` is the wheelbase of the vehicle and :math:`\delta` is the steering angle.
+
+If the :math:`\delta` is small,
+
+.. math:: \delta \approx 0
+
+the :math:`\tan(\delta)` can be approximated as :math:`\delta`:
+
+.. math:: \tan(\delta) = \delta
+
+So, the change rate of the :math:`\theta` can be approximated as:
+
+.. math:: \dot{\theta}_t = \frac{V}{L} \delta
+
+The above equations can be used to update the state of the vehicle at each time step.
+
+Control Model
+~~~~~~~~~~~~~~
+
+To formulate the state-space representation of the vehicle dynamics as a linear model,
+the state vector `x` and control input vector `u` are defined as follows:
+
+.. math:: x_t = [e_t, \dot{e}_t, \theta_t, \dot{\theta}_t, \upsilon_t]^T
+
+.. math:: u_t = [\delta_t, a_t]^T
+
+The linear state transition equation can be represented as:
+
+.. math:: x_{t+1} = A x_t + B u_t
+
+where:
+
+:math:`\begin{equation*} A = \begin{bmatrix} 1 & dt & 0 & 0 & 0\\ 0 & 0 & v & 0 & 0\\ 0 & 0 & 1 & dt & 0\\ 0 & 0 & 0 & 0 & 0\\ 0 & 0 & 0 & 0 & 1\\\end{bmatrix} \end{equation*}`
+
+:math:`\begin{equation*} B = \begin{bmatrix} 0 & 0\\ 0 & 0\\ 0 & 0\\ \frac{v}{L} & 0\\ 0 & dt \\ \end{bmatrix} \end{equation*}`
+
+LQR controller
+~~~~~~~~~~~~~~~
+
+The Linear Quadratic Regulator (LQR) controller is used to calculate the optimal control input `u` that minimizes the quadratic cost function:
+
+:math:`J = \sum_{t=0}^{N} (x_t^T Q x_t + u_t^T R u_t)`
+
+where `Q` and `R` are the weighting matrices for the state and control input, respectively.
+
+for the linear model:
+
+:math:`x_{t+1} = A x_t + B u_t`
+
+The optimal control input `u` can be calculated as:
+
+:math:`u_t = -K x_t`
+
+where `K` is the feedback gain matrix obtained by solving the Riccati equation.
+
+Simulation results
+~~~~~~~~~~~~~~~~~~~
+
+
+.. image:: x-y.png
+   :width: 600px
+
+.. image:: yaw.png
+   :width: 600px
+
+.. image:: speed.png
+   :width: 600px
+
+
+
 References:
 ~~~~~~~~~~~
 
diff --git a/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_steering_control_model.jpg b/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_steering_control_model.jpg
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diff --git a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst
index fcc9b5278a..baca7a33fc 100644
--- a/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst
+++ b/docs/modules/path_tracking/lqr_steering_control/lqr_steering_control_main.rst
@@ -8,6 +8,8 @@ control.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_steer_control/animation.gif
 
+`[Code Link] <https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/lqr_steer_control/lqr_steer_control.py>`_
+
 Overview
 ~~~~~~~~
 
@@ -23,10 +25,10 @@ The below figure shows the geometric model of the vehicle used in this simulatio
 .. image:: lqr_steering_control_model.jpg
    :width: 600px
 
-The `e` and `theta` represent the lateral error and orientation error, respectively, with respect to the desired trajectory.
+The `e` and :math:`\theta` represent the lateral error and orientation error, respectively, with respect to the desired trajectory.
 And :math:`\dot{e}` and :math:`\dot{\theta}` represent the rates of change of these errors.
 
-The :math:`e_t` and :math:`theta_t` are the updated values of `e` and `theta` at time `t`, respectively, and can be calculated using the following kinematic equations:
+The :math:`e_t` and :math:`\theta_t` are the updated values of `e` and :math:`\theta` at time `t`, respectively, and can be calculated using the following kinematic equations:
 
 .. math:: e_t = e_{t-1} + \dot{e}_{t-1} dt
 
@@ -38,9 +40,9 @@ The change rate of the `e` can be calculated as:
 
 .. math:: \dot{e}_t = V \sin(\theta_{t-1})
 
-Where `V` is the vehicle speed.
+Where `V` is the current vehicle speed.
 
-If the :math:`theta` is small,
+If the :math:`\theta` is small,
 
 .. math:: \theta \approx 0
 
@@ -72,6 +74,9 @@ So, the change rate of the :math:`\theta` can be approximated as:
 
 The above equations can be used to update the state of the vehicle at each time step.
 
+Control Model
+~~~~~~~~~~~~~~
+
 To formulate the state-space representation of the vehicle dynamics as a linear model,
 the state vector `x` and control input vector `u` are defined as follows:
 
@@ -79,7 +84,7 @@ the state vector `x` and control input vector `u` are defined as follows:
 
 .. math:: u_t = \delta_t
 
-The state transition equation can be represented as:
+The linear state transition equation can be represented as:
 
 .. math:: x_{t+1} = A x_t + B u_t
 
diff --git a/tests/test_codestyle.py b/tests/test_codestyle.py
index a7d11d270f..55e558c184 100644
--- a/tests/test_codestyle.py
+++ b/tests/test_codestyle.py
@@ -54,7 +54,7 @@ def run_ruff(files, fix):
         return 0, ""
     args = ['--fix'] if fix else []
     res = subprocess.run(
-        ['ruff', f'--config={CONFIG}'] + args + files,
+        ['ruff', 'check', f'--config={CONFIG}'] + args + files,
         stdout=subprocess.PIPE,
         encoding='utf-8'
     )

From c862d496f415399f28ba131abe503353c7b6dfcb Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 23 Jul 2024 10:15:23 +0900
Subject: [PATCH 0890/1080] Bump numpy from 2.0.0 to 2.0.1 in /requirements
 (#1051)

Bumps [numpy](https://github.com/numpy/numpy) from 2.0.0 to 2.0.1.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v2.0.0...v2.0.1)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index aa91cb258f..a0af999bba 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 2.0.0
+numpy == 2.0.1
 scipy == 1.14.0
 matplotlib == 3.9.1
 cvxpy == 1.5.2

From f3ca769076b11f03f02508ed112c85901e4b79c5 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 23 Jul 2024 20:14:40 +0900
Subject: [PATCH 0891/1080] Bump pytest from 8.2.2 to 8.3.1 in /requirements
 (#1049)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.2.2 to 8.3.1.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/8.2.2...8.3.1)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index a0af999bba..9cbe4747a0 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 2.0.1
 scipy == 1.14.0
 matplotlib == 3.9.1
 cvxpy == 1.5.2
-pytest == 8.2.2 # For unit test
+pytest == 8.3.1 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.10.1 # For unit test
 ruff == 0.5.2 # For unit test

From 76ba4ba1913fc7153e51fef434b3e955a58b3c2c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 23 Jul 2024 21:44:01 +0900
Subject: [PATCH 0892/1080] Bump mypy from 1.10.1 to 1.11.0 in /requirements
 (#1052)

Bumps [mypy](https://github.com/python/mypy) from 1.10.1 to 1.11.0.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.10.1...v1.11)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 9cbe4747a0..1055194460 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.9.1
 cvxpy == 1.5.2
 pytest == 8.3.1 # For unit test
 pytest-xdist == 3.6.1 # For unit test
-mypy == 1.10.1 # For unit test
+mypy == 1.11.0 # For unit test
 ruff == 0.5.2 # For unit test

From 65cb7f5b41b75ef0d198949e5274546b9f015cc6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Fri, 26 Jul 2024 17:53:37 +0900
Subject: [PATCH 0893/1080] Bump ruff from 0.5.2 to 0.5.4 in /requirements
 (#1050)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.5.2 to 0.5.4.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.5.2...0.5.4)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 1055194460..2c2e156d62 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.2
 pytest == 8.3.1 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.11.0 # For unit test
-ruff == 0.5.2 # For unit test
+ruff == 0.5.4 # For unit test

From b1856d5ef3384d7ce1e8ecdd4937d1f76b28d99f Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 Jul 2024 22:09:58 +0900
Subject: [PATCH 0894/1080] Bump ruff from 0.5.4 to 0.5.5 in /requirements
 (#1053)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.5.4 to 0.5.5.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.5.4...0.5.5)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 2c2e156d62..7a89f692fb 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.2
 pytest == 8.3.1 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.11.0 # For unit test
-ruff == 0.5.4 # For unit test
+ruff == 0.5.5 # For unit test

From f9ae5e5f887e527b7333b83d0332d5d25f1e0564 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 Jul 2024 22:10:12 +0900
Subject: [PATCH 0895/1080] Bump pytest from 8.3.1 to 8.3.2 in /requirements
 (#1054)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.3.1 to 8.3.2.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/8.3.1...8.3.2)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 7a89f692fb..528c8f713c 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 2.0.1
 scipy == 1.14.0
 matplotlib == 3.9.1
 cvxpy == 1.5.2
-pytest == 8.3.1 # For unit test
+pytest == 8.3.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.11.0 # For unit test
 ruff == 0.5.5 # For unit test

From 171e3a8f60c984afd493c333fb97a8b8f5fb19df Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 13 Aug 2024 09:06:26 +0900
Subject: [PATCH 0896/1080] Bump matplotlib from 3.9.1 to 3.9.1.post1 in
 /requirements (#1059)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.9.1 to 3.9.1.post1.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.9.1...v3.9.1.post1)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 528c8f713c..d9e5930498 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 2.0.1
 scipy == 1.14.0
-matplotlib == 3.9.1
+matplotlib == 3.9.1.post1
 cvxpy == 1.5.2
 pytest == 8.3.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test

From e1e3d8f5d7266229d46cd5b09d0c034d9c389eda Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 13 Aug 2024 09:54:41 +0900
Subject: [PATCH 0897/1080] Bump ruff from 0.5.5 to 0.5.7 in /requirements
 (#1058)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.5.5 to 0.5.7.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.5.5...0.5.7)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index d9e5930498..1bcbf1ac34 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.2
 pytest == 8.3.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.11.0 # For unit test
-ruff == 0.5.5 # For unit test
+ruff == 0.5.7 # For unit test

From e6c6814f7bd8865940f31354a84b18ebd659db87 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 20 Aug 2024 22:24:41 +0900
Subject: [PATCH 0898/1080] Bump cvxpy from 1.5.2 to 1.5.3 in /requirements
 (#1062)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.5.2 to 1.5.3.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.5.2...v1.5.3)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 1bcbf1ac34..bc09cdb54e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 2.0.1
 scipy == 1.14.0
 matplotlib == 3.9.1.post1
-cvxpy == 1.5.2
+cvxpy == 1.5.3
 pytest == 8.3.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.11.0 # For unit test

From 12d6e694dc62d4ab4441cfafb429623e07803270 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 20 Aug 2024 22:33:02 +0900
Subject: [PATCH 0899/1080] Bump ruff from 0.5.7 to 0.6.1 in /requirements
 (#1063)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.5.7 to 0.6.1.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.5.7...0.6.1)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index bc09cdb54e..54a3c3eaf5 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.11.0 # For unit test
-ruff == 0.5.7 # For unit test
+ruff == 0.6.1 # For unit test

From 791d4e7f003e6e5e75df2b4223fce40e05f87eef Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 21 Aug 2024 21:21:17 +0900
Subject: [PATCH 0900/1080] Bump matplotlib from 3.9.1.post1 to 3.9.2 in
 /requirements (#1061)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.9.1.post1 to 3.9.2.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.9.1.post1...v3.9.2)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 54a3c3eaf5..dcba39a756 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 2.0.1
 scipy == 1.14.0
-matplotlib == 3.9.1.post1
+matplotlib == 3.9.2
 cvxpy == 1.5.3
 pytest == 8.3.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test

From 6eed25537f3c683047ff154bcd09d7dea2dd898f Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 Aug 2024 07:07:58 +0900
Subject: [PATCH 0901/1080] Bump ruff from 0.6.1 to 0.6.2 in /requirements
 (#1064)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.1 to 0.6.2.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.6.1...0.6.2)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index dcba39a756..f219e1d279 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.11.0 # For unit test
-ruff == 0.6.1 # For unit test
+ruff == 0.6.2 # For unit test

From 2b6c9b0888734b4caa4bf1a8f3c810def3dfa0ba Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 Aug 2024 20:14:07 +0900
Subject: [PATCH 0902/1080] Bump scipy from 1.14.0 to 1.14.1 in /requirements
 (#1066)

Bumps [scipy](https://github.com/scipy/scipy) from 1.14.0 to 1.14.1.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.14.0...v1.14.1)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index f219e1d279..500002ff0e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 2.0.1
-scipy == 1.14.0
+scipy == 1.14.1
 matplotlib == 3.9.2
 cvxpy == 1.5.3
 pytest == 8.3.2 # For unit test

From b139717b297f90daa063e107154fc1ce96c5066e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 Aug 2024 20:23:07 +0900
Subject: [PATCH 0903/1080] Bump mypy from 1.11.0 to 1.11.2 in /requirements
 (#1065)

Bumps [mypy](https://github.com/python/mypy) from 1.11.0 to 1.11.2.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.11...v1.11.2)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 500002ff0e..0b8a51fcac 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.9.2
 cvxpy == 1.5.3
 pytest == 8.3.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test
-mypy == 1.11.0 # For unit test
+mypy == 1.11.2 # For unit test
 ruff == 0.6.2 # For unit test

From dab1a9b2dca0b27bc4eef30d98c516ec655ac2aa Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 3 Sep 2024 21:34:35 +0900
Subject: [PATCH 0904/1080] Bump ruff from 0.6.2 to 0.6.3 in /requirements
 (#1069)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.2 to 0.6.3.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.6.2...0.6.3)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 0b8a51fcac..8a27c1e301 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.11.2 # For unit test
-ruff == 0.6.2 # For unit test
+ruff == 0.6.3 # For unit test

From 4ae9555a699b2823ea2e065e0ffe11b3622aa2b6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 10 Sep 2024 21:39:10 +0900
Subject: [PATCH 0905/1080] Bump numpy from 2.0.1 to 2.1.1 in /requirements
 (#1071)

Bumps [numpy](https://github.com/numpy/numpy) from 2.0.1 to 2.1.1.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v2.0.1...v2.1.1)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 8a27c1e301..87107816bb 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 2.0.1
+numpy == 2.1.1
 scipy == 1.14.1
 matplotlib == 3.9.2
 cvxpy == 1.5.3

From ad600cd9023e67cd8064b678213d6586d076dd15 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 10 Sep 2024 21:39:27 +0900
Subject: [PATCH 0906/1080] Bump ruff from 0.6.3 to 0.6.4 in /requirements
 (#1072)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.3 to 0.6.4.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.6.3...0.6.4)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 87107816bb..c614c4e96a 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.2 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.11.2 # For unit test
-ruff == 0.6.3 # For unit test
+ruff == 0.6.4 # For unit test

From bf69f444afcfffd8ebb44ec8f0988598a0b37ab6 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 16 Sep 2024 22:03:54 +0900
Subject: [PATCH 0907/1080] add turning radius calculation doc (#1068)

* add turning radius calculation doc

* add turning radius calculation doc

* add turning radius calculation doc

* add turning radius calculation doc

* add turning radius calculation doc

* add turning radius calculation doc

* add turning radius calculation doc
---
 docs/modules/appendix/appendix_main.rst       |   1 +
 .../steering_motion_model/steering_model.png  | Bin 0 -> 72443 bytes
 .../turning_radius_calc1.png                  | Bin 0 -> 14926 bytes
 .../turning_radius_calc2.png                  | Bin 0 -> 21792 bytes
 .../appendix/steering_motion_model_main.rst   |  97 ++++++++++++++++++
 5 files changed, 98 insertions(+)
 create mode 100644 docs/modules/appendix/steering_motion_model/steering_model.png
 create mode 100644 docs/modules/appendix/steering_motion_model/turning_radius_calc1.png
 create mode 100644 docs/modules/appendix/steering_motion_model/turning_radius_calc2.png
 create mode 100644 docs/modules/appendix/steering_motion_model_main.rst

diff --git a/docs/modules/appendix/appendix_main.rst b/docs/modules/appendix/appendix_main.rst
index 8a29d84676..cb1ac04066 100644
--- a/docs/modules/appendix/appendix_main.rst
+++ b/docs/modules/appendix/appendix_main.rst
@@ -7,6 +7,7 @@ Appendix
    :maxdepth: 2
    :caption: Contents
 
+   steering_motion_model
    Kalmanfilter_basics
    Kalmanfilter_basics_2
 
diff --git a/docs/modules/appendix/steering_motion_model/steering_model.png b/docs/modules/appendix/steering_motion_model/steering_model.png
new file mode 100644
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diff --git a/docs/modules/appendix/steering_motion_model_main.rst b/docs/modules/appendix/steering_motion_model_main.rst
new file mode 100644
index 0000000000..6e444b7909
--- /dev/null
+++ b/docs/modules/appendix/steering_motion_model_main.rst
@@ -0,0 +1,97 @@
+
+Steering Motion Model
+-----------------------
+
+Turning radius calculation by steering motion model
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The turning Radius represents the radius of the circle when the robot turns, as shown in the diagram below.
+
+.. image:: steering_motion_model/steering_model.png
+
+When the steering angle is tilted by :math:`\delta`,
+the turning radius :math:`R` can be calculated using the following equation,
+based on the geometric relationship between the wheelbase (WB),
+which is the distance between the rear wheel center and the front wheel center,
+and the assumption that the turning radius circle passes through the center of
+the rear wheels in the diagram above.
+
+:math:`R = \frac{WB}{tan\delta}`
+
+The curvature :math:`\kappa` is the reciprocal of the turning radius:
+
+:math:`\kappa = \frac{tan\delta}{WB}`
+
+In the diagram above, the angular difference :math:`\Delta \theta` in the vehicle’s heading between two points on the turning radius :math:`R`
+is the same as the angle of the vector connecting the two points from the center of the turn.
+
+From the formula for the length of an arc and the radius,
+
+:math:`\Delta \theta = \frac{s}{R}`
+
+Here, :math:`s` is the distance between two points on the turning radius.
+
+So, yaw rate :math:`\omega` can be calculated as follows.
+
+:math:`\omega = \frac{v}{R}`
+
+and
+
+:math:`\omega = v\kappa`
+
+here, :math:`v` is the velocity of the vehicle.
+
+
+Turning radius calculation by 2 consecutive positions of the robot trajectory
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In this section, we will derive the formula for the turning radius from 2 consecutive positions of the robot trajectory.
+
+.. image:: steering_motion_model/turning_radius_calc1.png
+
+As shown in the upper diagram above, the robot moves from a point at time :math:`t` to a point at time :math:`t+1`.
+Each point is represented by a 2D position :math:`(x_t, y_t)` and an orientation :math:`\theta_t`.
+
+The distance between the two points is :math:`d = \sqrt{(x_{t+1} - x_t)^2 + (y_{t+1} - y_t)^2}`.
+
+The angle between the two vectors from the turning center to the two points is :math:`\theta = \theta_{t+1} - \theta_t`.
+Here, by drawing a perpendicular line from the center of the turning radius
+to a straight line of length :math:`d` connecting two points,
+the following equation can be derived from the resulting right triangle.
+
+:math:`sin\frac{\theta}{2} = \frac{d}{2R}`
+
+So, the turning radius :math:`R` can be calculated as follows.
+
+:math:`R = \frac{d}{2sin\frac{\theta}{2}}`
+
+The curvature :math:`\kappa` is the reciprocal of the turning radius.
+So, the curvature can be calculated as follows.
+
+:math:`\kappa = \frac{2sin\frac{\theta}{2}}{d}`
+
+Target speed by maximum steering speed
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If the maximum steering speed is given as :math:`\dot{\delta}_{max}`,
+the maximum curvature change rate :math:`\dot{\kappa}_{max}` can be calculated as follows:
+
+:math:`\dot{\kappa}_{max} = \frac{tan\dot{\delta}_{max}}{WB}`
+
+From the curvature calculation by 2 consecutive positions of the robot trajectory,
+
+the maximum curvature change rate :math:`\dot{\kappa}_{max}` can be calculated as follows:
+
+:math:`\dot{\kappa}_{max} = \frac{\kappa_{t+1}-\kappa_{t}}{\Delta t}`
+
+If we can assume that the vehicle will not exceed the maximum curvature change rate,
+
+the target minimum velocity :math:`v_{min}` can be calculated as follows:
+
+:math:`v_{min} = \frac{d_{t+1}+d_{t}}{\Delta t} = \frac{d_{t+1}+d_{t}}{(\kappa_{t+1}-\kappa_{t})}\frac{tan\dot{\delta}_{max}}{WB}`
+
+
+References:
+~~~~~~~~~~~
+
+- `Vehicle Dynamics and Control <https://link.springer.com/book/10.1007/978-1-4614-1433-9>`_

From e26d5cb99aed3d282508cc18457feb7a7b8ca18f Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 17 Sep 2024 21:13:36 +0900
Subject: [PATCH 0908/1080] Bump pytest from 8.3.2 to 8.3.3 in /requirements
 (#1077)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.3.2 to 8.3.3.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/8.3.2...8.3.3)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index c614c4e96a..6bbe33e378 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 2.1.1
 scipy == 1.14.1
 matplotlib == 3.9.2
 cvxpy == 1.5.3
-pytest == 8.3.2 # For unit test
+pytest == 8.3.3 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.11.2 # For unit test
 ruff == 0.6.4 # For unit test

From 3e178598d467b65154fb1b409ac9fab8339957b3 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 18 Sep 2024 21:55:03 +0900
Subject: [PATCH 0909/1080] Bump ruff from 0.6.4 to 0.6.5 in /requirements
 (#1078)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.4 to 0.6.5.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.6.4...0.6.5)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 6bbe33e378..f149183e96 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.3 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.11.2 # For unit test
-ruff == 0.6.4 # For unit test
+ruff == 0.6.5 # For unit test

From 660646a361162b9c286d5182558775eda5207ebe Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 24 Sep 2024 06:19:38 -0700
Subject: [PATCH 0910/1080] Bump ruff from 0.6.5 to 0.6.7 in /requirements
 (#1080)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.5 to 0.6.7.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.6.5...0.6.7)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index f149183e96..453922eb8c 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.3 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.11.2 # For unit test
-ruff == 0.6.5 # For unit test
+ruff == 0.6.7 # For unit test

From fafae87236eaf95785f5566e35fe9d6ba2fa3701 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 1 Oct 2024 20:51:46 +0900
Subject: [PATCH 0911/1080] Bump ruff from 0.6.7 to 0.6.8 in /requirements
 (#1081)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.7 to 0.6.8.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.6.7...0.6.8)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 453922eb8c..83f9bc438f 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.3 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.11.2 # For unit test
-ruff == 0.6.7 # For unit test
+ruff == 0.6.8 # For unit test

From 857a80febf8aef959d7df441f8948fe073e790d5 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 8 Oct 2024 21:05:58 +0900
Subject: [PATCH 0912/1080] Bump numpy from 2.1.1 to 2.1.2 in /requirements
 (#1083)

Bumps [numpy](https://github.com/numpy/numpy) from 2.1.1 to 2.1.2.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v2.1.1...v2.1.2)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 83f9bc438f..e006f944ff 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 2.1.1
+numpy == 2.1.2
 scipy == 1.14.1
 matplotlib == 3.9.2
 cvxpy == 1.5.3

From a791c79da200435cb89d707320e106c10ca5b6db Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 8 Oct 2024 21:37:36 +0900
Subject: [PATCH 0913/1080] Bump ruff from 0.6.8 to 0.6.9 in /requirements
 (#1082)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.8 to 0.6.9.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.6.8...0.6.9)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index e006f944ff..62f9bc183a 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.3 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.11.2 # For unit test
-ruff == 0.6.8 # For unit test
+ruff == 0.6.9 # For unit test

From 4d009a7916bffa53d2747cc6100180a3521e376c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 15 Oct 2024 12:48:45 +0900
Subject: [PATCH 0914/1080] Bump mypy from 1.11.2 to 1.12.0 in /requirements
 (#1084)

Bumps [mypy](https://github.com/python/mypy) from 1.11.2 to 1.12.0.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.11.2...v1.12.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 62f9bc183a..cbab816c8a 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.9.2
 cvxpy == 1.5.3
 pytest == 8.3.3 # For unit test
 pytest-xdist == 3.6.1 # For unit test
-mypy == 1.11.2 # For unit test
+mypy == 1.12.0 # For unit test
 ruff == 0.6.9 # For unit test

From 74e9dcc0921f8128488b449901812b1b51e54bad Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 22 Oct 2024 22:01:11 +0900
Subject: [PATCH 0915/1080] Bump ruff from 0.6.9 to 0.7.0 in /requirements
 (#1088)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.6.9 to 0.7.0.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.6.9...0.7.0)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index cbab816c8a..f275d9d82e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.3 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.12.0 # For unit test
-ruff == 0.6.9 # For unit test
+ruff == 0.7.0 # For unit test

From 899f749f33637159be3515264705ca7fe2d8609e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 22 Oct 2024 22:24:33 +0900
Subject: [PATCH 0916/1080] Bump mypy from 1.12.0 to 1.12.1 in /requirements
 (#1089)

Bumps [mypy](https://github.com/python/mypy) from 1.12.0 to 1.12.1.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.12.0...v1.12.1)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index f275d9d82e..0b44092a15 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.9.2
 cvxpy == 1.5.3
 pytest == 8.3.3 # For unit test
 pytest-xdist == 3.6.1 # For unit test
-mypy == 1.12.0 # For unit test
+mypy == 1.12.1 # For unit test
 ruff == 0.7.0 # For unit test

From 9ecc98d3e6af70865d902a88b5d96dffd4764b7c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 30 Oct 2024 23:27:05 +0900
Subject: [PATCH 0917/1080] Bump mypy from 1.12.1 to 1.13.0 in /requirements
 (#1093)

Bumps [mypy](https://github.com/python/mypy) from 1.12.1 to 1.13.0.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.12.1...v1.13.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 0b44092a15..92ffcf3a74 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.9.2
 cvxpy == 1.5.3
 pytest == 8.3.3 # For unit test
 pytest-xdist == 3.6.1 # For unit test
-mypy == 1.12.1 # For unit test
+mypy == 1.13.0 # For unit test
 ruff == 0.7.0 # For unit test

From 41fea75c0ee0cfb6a1c24307f0e0c1f1bb0af133 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 31 Oct 2024 21:32:30 +0900
Subject: [PATCH 0918/1080] Bump ruff from 0.7.0 to 0.7.1 in /requirements
 (#1092)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.7.0 to 0.7.1.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.7.0...0.7.1)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 92ffcf3a74..9fd97ba106 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.3 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.13.0 # For unit test
-ruff == 0.7.0 # For unit test
+ruff == 0.7.1 # For unit test

From 7c5959c5f2d0dbde7889870fc0056491577a5653 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 5 Nov 2024 21:01:38 +0900
Subject: [PATCH 0919/1080] Bump numpy from 2.1.2 to 2.1.3 in /requirements
 (#1095)

Bumps [numpy](https://github.com/numpy/numpy) from 2.1.2 to 2.1.3.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v2.1.2...v2.1.3)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 9fd97ba106..38a0aed0f8 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 2.1.2
+numpy == 2.1.3
 scipy == 1.14.1
 matplotlib == 3.9.2
 cvxpy == 1.5.3

From 5d8326957bcb40d62076e2920f5d61290f658fe6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 13 Nov 2024 21:44:25 +0900
Subject: [PATCH 0920/1080] Bump ruff from 0.7.1 to 0.7.3 in /requirements
 (#1097)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.7.1 to 0.7.3.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.7.1...0.7.3)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 38a0aed0f8..21c9c64364 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.3 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.13.0 # For unit test
-ruff == 0.7.1 # For unit test
+ruff == 0.7.3 # For unit test

From 3f87bd4cfc9b8cc5a6e3c97dcfd113f020023801 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 19 Nov 2024 07:40:59 +0900
Subject: [PATCH 0921/1080] Bump ruff from 0.7.3 to 0.7.4 in /requirements
 (#1099)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.7.3 to 0.7.4.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.7.3...0.7.4)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 21c9c64364..d3d3627d5c 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.3 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.13.0 # For unit test
-ruff == 0.7.3 # For unit test
+ruff == 0.7.4 # For unit test

From 0f91e58ca3d3e67bdbc592105140791efa6b2dbc Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 4 Dec 2024 08:25:12 +0900
Subject: [PATCH 0922/1080] Bump pytest from 8.3.3 to 8.3.4 in /requirements
 (#1102)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.3.3 to 8.3.4.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/8.3.3...8.3.4)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index d3d3627d5c..44875f28d6 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 2.1.3
 scipy == 1.14.1
 matplotlib == 3.9.2
 cvxpy == 1.5.3
-pytest == 8.3.3 # For unit test
+pytest == 8.3.4 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.13.0 # For unit test
 ruff == 0.7.4 # For unit test

From 455542d7178565517ac751a025e103d5ff11ced0 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 4 Dec 2024 15:36:51 +0900
Subject: [PATCH 0923/1080] Bump ruff from 0.7.4 to 0.8.1 in /requirements
 (#1103)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.7.4 to 0.8.1.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.7.4...0.8.1)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 44875f28d6..a224af5d40 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.13.0 # For unit test
-ruff == 0.7.4 # For unit test
+ruff == 0.8.1 # For unit test

From 115b32d2feb03bd96b52efeb2403dfb272c5e703 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Fri, 6 Dec 2024 08:31:54 +0900
Subject: [PATCH 0924/1080] Bump matplotlib from 3.9.2 to 3.9.3 in
 /requirements (#1101)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.9.2 to 3.9.3.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.9.2...v3.9.3)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index a224af5d40..91566508d6 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 2.1.3
 scipy == 1.14.1
-matplotlib == 3.9.2
+matplotlib == 3.9.3
 cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test
 pytest-xdist == 3.6.1 # For unit test

From f86eecec7fa851fcc52aeb09a7c58b4c3c0110d0 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 10 Dec 2024 06:46:52 +0900
Subject: [PATCH 0925/1080] Bump numpy from 2.1.3 to 2.2.0 in /requirements
 (#1106)

Bumps [numpy](https://github.com/numpy/numpy) from 2.1.3 to 2.2.0.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v2.1.3...v2.2.0)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 91566508d6..a99ed51781 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 2.1.3
+numpy == 2.2.0
 scipy == 1.14.1
 matplotlib == 3.9.3
 cvxpy == 1.5.3

From 4ebe7543ffac5f4eb40bceedec14f11703cc6a1f Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 10 Dec 2024 13:17:18 +0900
Subject: [PATCH 0926/1080] Bump ruff from 0.8.1 to 0.8.2 in /requirements
 (#1105)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.8.1 to 0.8.2.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.8.1...0.8.2)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index a99ed51781..dbc555e0c9 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.13.0 # For unit test
-ruff == 0.8.1 # For unit test
+ruff == 0.8.2 # For unit test

From 0d8cd9bb6cca5697a3cd3f1b279e4cd8a27d3cb7 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 17 Dec 2024 09:32:02 +0900
Subject: [PATCH 0927/1080] Bump matplotlib from 3.9.3 to 3.10.0 in
 /requirements (#1107)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.9.3 to 3.10.0.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.9.3...v3.10.0)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index dbc555e0c9..650e7a4048 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 2.2.0
 scipy == 1.14.1
-matplotlib == 3.9.3
+matplotlib == 3.10.0
 cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test
 pytest-xdist == 3.6.1 # For unit test

From e5eea35ee4a03082cd5acf5efea208d677df0a0a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 17 Dec 2024 09:37:51 +0900
Subject: [PATCH 0928/1080] Bump ruff from 0.8.2 to 0.8.3 in /requirements
 (#1108)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.8.2 to 0.8.3.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.8.2...0.8.3)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 650e7a4048..cd4a01f88f 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.13.0 # For unit test
-ruff == 0.8.2 # For unit test
+ruff == 0.8.3 # For unit test

From 4bc70480448295205fceb28053d0ed9c5efca99d Mon Sep 17 00:00:00 2001
From: Weipu Shan <weipu.shan@gmail.com>
Date: Fri, 20 Dec 2024 20:32:57 +0800
Subject: [PATCH 0929/1080] fix: arm_obstacle_navigation calc_heuristic_map
 node through 4 corners error (#1034)

* fix: arm_obstacle_navigation calc_heuristic_map node through 4 corners error

* style: remove trailing whitespace
---
 .../arm_obstacle_navigation/arm_obstacle_navigation.py |  8 ++++----
 .../arm_obstacle_navigation_2.py                       | 10 +++++-----
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
index 75b1f9d2c4..e377897e54 100644
--- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
+++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
@@ -203,10 +203,10 @@ def calc_heuristic_map(M, goal_node):
     for i in range(heuristic_map.shape[0]):
         for j in range(heuristic_map.shape[1]):
             heuristic_map[i, j] = min(heuristic_map[i, j],
-                                      i + 1 + heuristic_map[M - 1, j],
-                                      M - i + heuristic_map[0, j],
-                                      j + 1 + heuristic_map[i, M - 1],
-                                      M - j + heuristic_map[i, 0]
+                                      M - i - 1 + heuristic_map[M - 1, j],
+                                      i + heuristic_map[0, j],
+                                      M - j - 1 + heuristic_map[i, M - 1],
+                                      j + heuristic_map[i, 0]
                                       )
 
     return heuristic_map
diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
index 591cd401eb..10f4615c34 100644
--- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
+++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
@@ -105,7 +105,7 @@ def get_occupancy_grid(arm, obstacles):
     Args:
         arm: An instance of NLinkArm
         obstacles: A list of obstacles, with each obstacle defined as a list
-                   of xy coordinates and a radius. 
+                   of xy coordinates and a radius.
 
     Returns:
         Occupancy grid in joint space
@@ -234,10 +234,10 @@ def calc_heuristic_map(M, goal_node):
     for i in range(heuristic_map.shape[0]):
         for j in range(heuristic_map.shape[1]):
             heuristic_map[i, j] = min(heuristic_map[i, j],
-                                      i + 1 + heuristic_map[M - 1, j],
-                                      M - i + heuristic_map[0, j],
-                                      j + 1 + heuristic_map[i, M - 1],
-                                      M - j + heuristic_map[i, 0]
+                                      M - i - 1 + heuristic_map[M - 1, j],
+                                      i + heuristic_map[0, j],
+                                      M - j - 1 + heuristic_map[i, M - 1],
+                                      j + heuristic_map[i, 0]
                                       )
 
     return heuristic_map

From 1f4214012a612f2163b01930444d83712d99b5f9 Mon Sep 17 00:00:00 2001
From: Aryaz Eghbali <64126826+AryazE@users.noreply.github.com>
Date: Fri, 20 Dec 2024 14:00:49 +0100
Subject: [PATCH 0930/1080] Performance improvement (#1075)

---
 PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py b/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py
index 8968c54dcc..7e93caa8fb 100644
--- a/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py
+++ b/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py
@@ -64,7 +64,7 @@ def transform(
     is_visited = np.zeros_like(transform_matrix, dtype=bool)
     is_visited[src[0], src[1]] = True
     traversal_queue = [src]
-    calculated = [(src[0] - 1) * n_cols + src[1]]
+    calculated = set([(src[0] - 1) * n_cols + src[1]])
 
     def is_valid_neighbor(g_i, g_j):
         return 0 <= g_i < n_rows and 0 <= g_j < n_cols \
@@ -86,7 +86,7 @@ def is_valid_neighbor(g_i, g_j):
                 if not is_visited[ni][nj] \
                         and ((ni - 1) * n_cols + nj) not in calculated:
                     traversal_queue.append((ni, nj))
-                    calculated.append((ni - 1) * n_cols + nj)
+                    calculated.add((ni - 1) * n_cols + nj)
 
     return transform_matrix
 

From af456c70b0ec336442b0b32c5aab1a220aefaf74 Mon Sep 17 00:00:00 2001
From: Surabhi Gupta <103124390+SurabhiGupta17@users.noreply.github.com>
Date: Fri, 20 Dec 2024 18:42:31 +0530
Subject: [PATCH 0931/1080] Implement Catmull-Rom Spline with test and
 documentation (#1085)

---
 .../blending_functions.py                     |  34 ++++++
 .../catmull_rom_spline_path.py                |  86 +++++++++++++++
 .../catmull_rom_spline/blending_functions.png | Bin 0 -> 41129 bytes
 .../catmull_rom_path_planning.png             | Bin 0 -> 33505 bytes
 .../catmull_rom_spline_main.rst               | 103 ++++++++++++++++++
 tests/test_catmull_rom_spline.py              |  16 +++
 6 files changed, 239 insertions(+)
 create mode 100644 PathPlanning/Catmull_RomSplinePath/blending_functions.py
 create mode 100644 PathPlanning/Catmull_RomSplinePath/catmull_rom_spline_path.py
 create mode 100644 docs/modules/path_planning/catmull_rom_spline/blending_functions.png
 create mode 100644 docs/modules/path_planning/catmull_rom_spline/catmull_rom_path_planning.png
 create mode 100644 docs/modules/path_planning/catmull_rom_spline/catmull_rom_spline_main.rst
 create mode 100644 tests/test_catmull_rom_spline.py

diff --git a/PathPlanning/Catmull_RomSplinePath/blending_functions.py b/PathPlanning/Catmull_RomSplinePath/blending_functions.py
new file mode 100644
index 0000000000..f3ef5dd323
--- /dev/null
+++ b/PathPlanning/Catmull_RomSplinePath/blending_functions.py
@@ -0,0 +1,34 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+def blending_function_1(t):
+    return -t + 2*t**2 - t**3
+
+def blending_function_2(t):
+    return 2 - 5*t**2 + 3*t**3
+
+def blending_function_3(t):
+    return t + 4*t**2 - 3*t**3
+
+def blending_function_4(t):
+    return -t**2 + t**3
+
+def plot_blending_functions():
+    t = np.linspace(0, 1, 100)
+    
+    plt.plot(t, blending_function_1(t), label='b1')
+    plt.plot(t, blending_function_2(t), label='b2')
+    plt.plot(t, blending_function_3(t), label='b3')
+    plt.plot(t, blending_function_4(t), label='b4')
+    
+    plt.title("Catmull-Rom Blending Functions")
+    plt.xlabel("t")
+    plt.ylabel("Value")
+    plt.legend()
+    plt.grid(True)
+    plt.axhline(y=0, color='k', linestyle='--')
+    plt.axvline(x=0, color='k', linestyle='--')
+    plt.show()
+
+if __name__ == "__main__":
+    plot_blending_functions()
\ No newline at end of file
diff --git a/PathPlanning/Catmull_RomSplinePath/catmull_rom_spline_path.py b/PathPlanning/Catmull_RomSplinePath/catmull_rom_spline_path.py
new file mode 100644
index 0000000000..79916330c9
--- /dev/null
+++ b/PathPlanning/Catmull_RomSplinePath/catmull_rom_spline_path.py
@@ -0,0 +1,86 @@
+"""
+Path Planner with Catmull-Rom Spline
+Author: Surabhi Gupta (@this_is_surabhi)
+Source: http://graphics.cs.cmu.edu/nsp/course/15-462/Fall04/assts/catmullRom.pdf
+"""
+
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+def catmull_rom_point(t, p0, p1, p2, p3):
+    """
+    Parameters
+    ----------
+    t : float
+        Parameter value (0 <= t <= 1) (0 <= t <= 1)
+    p0, p1, p2, p3 : np.ndarray
+        Control points for the spline segment
+
+    Returns
+    -------
+    np.ndarray
+        Calculated point on the spline
+    """
+    return 0.5 * ((2 * p1) +
+                  (-p0 + p2) * t +
+                  (2 * p0 - 5 * p1 + 4 * p2 - p3) * t**2 +
+                  (-p0 + 3 * p1 - 3 * p2 + p3) * t**3)
+
+
+def catmull_rom_spline(control_points, num_points):
+    """
+    Parameters
+    ----------
+    control_points : list
+        List of control points
+    num_points : int
+        Number of points to generate on the spline
+
+    Returns
+    -------
+    tuple
+        x and y coordinates of the spline points
+    """
+    t_vals = np.linspace(0, 1, num_points)
+    spline_points = []
+    
+    control_points = np.array(control_points)
+    
+    for i in range(len(control_points) - 1):
+        if i == 0:
+            p1, p2, p3 = control_points[i:i+3]
+            p0 = p1
+        elif i == len(control_points) - 2:
+            p0, p1, p2 = control_points[i-1:i+2]
+            p3 = p2
+        else:
+            p0, p1, p2, p3 = control_points[i-1:i+3]
+        
+        for t in t_vals:
+            point = catmull_rom_point(t, p0, p1, p2, p3)
+            spline_points.append(point)
+    
+    return np.array(spline_points).T
+
+
+def main():
+    print(__file__ + " start!!")
+
+    way_points = [[-1.0, -2.0], [1.0, -1.0], [3.0, -2.0], [4.0, -1.0], [3.0, 1.0], [1.0, 2.0], [0.0, 2.0]]
+    n_course_point = 100  
+    spline_x, spline_y = catmull_rom_spline(way_points, n_course_point)
+
+    plt.plot(spline_x,spline_y, '-r', label="Catmull-Rom Spline Path")
+    plt.plot(np.array(way_points).T[0], np.array(way_points).T[1], '-og', label="Way points")
+    plt.title("Catmull-Rom Spline Path")
+    plt.grid(True)
+    plt.legend()
+    plt.axis("equal")
+    plt.show()
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/docs/modules/path_planning/catmull_rom_spline/blending_functions.png b/docs/modules/path_planning/catmull_rom_spline/blending_functions.png
new file mode 100644
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diff --git a/docs/modules/path_planning/catmull_rom_spline/catmull_rom_path_planning.png b/docs/modules/path_planning/catmull_rom_spline/catmull_rom_path_planning.png
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diff --git a/docs/modules/path_planning/catmull_rom_spline/catmull_rom_spline_main.rst b/docs/modules/path_planning/catmull_rom_spline/catmull_rom_spline_main.rst
new file mode 100644
index 0000000000..4d8d3bdefe
--- /dev/null
+++ b/docs/modules/path_planning/catmull_rom_spline/catmull_rom_spline_main.rst
@@ -0,0 +1,103 @@
+Catmull-Rom Spline Planning
+-----------------
+
+.. image:: catmull_rom_path_planning.png
+
+This is a Catmull-Rom spline path planning routine.
+
+If you provide waypoints, the Catmull-Rom spline generates a smooth path that always passes through the control points, 
+exhibits local control, and maintains 𝐶1 continuity.
+
+
+Catmull-Rom Spline Fundamentals
+~~~~~~~~~~~~~~
+
+Catmull-Rom splines are a type of cubic spline that passes through a given set of points, known as control points. 
+
+They are defined by the following equation for calculating a point on the spline:
+
+:math:`P(t) = 0.5 \times \left( 2P_1 + (-P_0 + P_2)t + (2P_0 - 5P_1 + 4P_2 - P_3)t^2 + (-P_0 + 3P_1 - 3P_2 + P_3)t^3 \right)`
+
+Where:
+
+* :math:`P(t)` is the point on the spline at parameter :math:`t`.
+* :math:`P_0, P_1, P_2, P_3` are the control points surrounding the parameter :math:`t`.
+
+Types of Catmull-Rom Splines
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+There are different types of Catmull-Rom splines based on the choice of the **tau** parameter, which influences how the curve 
+behaves in relation to the control points:
+
+1. **Uniform Catmull-Rom Spline**: 
+   The standard implementation where the parameterization is uniform. Each segment of the spline is treated equally, 
+   regardless of the distances between control points.
+
+
+2. **Chordal Catmull-Rom Spline**: 
+   This spline type takes into account the distance between control points. The parameterization is based on the actual distance 
+   along the spline, ensuring smoother transitions. The equation can be modified to include the chord length :math:`L_i` between 
+   points :math:`P_i` and :math:`P_{i+1}`:
+   
+   .. math::
+       \tau_i = \sqrt{(x_{i+1} - x_i)^2 + (y_{i+1} - y_i)^2}
+
+3. **Centripetal Catmull-Rom Spline**: 
+   This variation improves upon the chordal spline by using the square root of the distance to determine the parameterization, 
+   which avoids oscillations and creates a more natural curve. The parameter :math:`t_i` is adjusted using the following relation:
+   
+   .. math::
+       t_i = \sqrt{(x_{i+1} - x_i)^2 + (y_{i+1} - y_i)^2}
+
+
+Blending Functions
+~~~~~~~~~~~~~~~~~~
+
+In Catmull-Rom spline interpolation, blending functions are used to calculate the influence of each control point on the spline at a 
+given parameter :math:`t`. The blending functions ensure that the spline is smooth and passes through the control points while 
+maintaining continuity. The four blending functions used in Catmull-Rom splines are defined as follows:
+
+1. **Blending Function 1**:
+   
+   .. math::
+       b_1(t) = -t + 2t^2 - t^3
+
+2. **Blending Function 2**:
+   
+   .. math::
+       b_2(t) = 2 - 5t^2 + 3t^3
+
+3. **Blending Function 3**:
+   
+   .. math::
+       b_3(t) = t + 4t^2 - 3t^3
+
+4. **Blending Function 4**:
+   
+   .. math::
+       b_4(t) = -t^2 + t^3
+
+The blending functions are combined in the spline equation to create a smooth curve that reflects the influence of each control point.
+
+The following figure illustrates the blending functions over the interval :math:`[0, 1]`:
+
+.. image:: blending_functions.png
+
+Catmull-Rom Spline API
+~~~~~~~~~~~~~~~~~~~~~~~
+
+This section provides an overview of the functions used for Catmull-Rom spline path planning.
+
+API
+++++
+
+.. autofunction:: PathPlanning.Catmull_RomSplinePath.catmull_rom_spline_path.catmull_rom_point
+
+.. autofunction:: PathPlanning.Catmull_RomSplinePath.catmull_rom_spline_path.catmull_rom_spline
+
+
+References
+~~~~~~~~~~
+
+-  `Catmull-Rom Spline - Wikipedia <https://en.wikipedia.org/wiki/Centripetal_Catmull%E2%80%93Rom_spline>`__
+-  `Catmull-Rom Splines <http://graphics.cs.cmu.edu/nsp/course/15-462/Fall04/assts/catmullRom.pdf>`__
\ No newline at end of file
diff --git a/tests/test_catmull_rom_spline.py b/tests/test_catmull_rom_spline.py
new file mode 100644
index 0000000000..41a73588c3
--- /dev/null
+++ b/tests/test_catmull_rom_spline.py
@@ -0,0 +1,16 @@
+import conftest
+from PathPlanning.Catmull_RomSplinePath.catmull_rom_spline_path import catmull_rom_spline
+
+def test_catmull_rom_spline():
+    way_points = [[0, 0], [1, 2], [2, 0], [3, 3]]
+    num_points = 100
+
+    spline_x, spline_y = catmull_rom_spline(way_points, num_points)
+    
+    assert spline_x.size > 0, "Spline X coordinates should not be empty"
+    assert spline_y.size > 0, "Spline Y coordinates should not be empty"
+    
+    assert spline_x.shape == spline_y.shape, "Spline X and Y coordinates should have the same shape"
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)

From 7eeb9d215cb5652a800db507f066047bfd5d023f Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 21 Dec 2024 15:04:02 +0900
Subject: [PATCH 0932/1080] Add catmull_rom_spline doc (#1109)

---
 docs/modules/path_planning/path_planning_main.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/docs/modules/path_planning/path_planning_main.rst b/docs/modules/path_planning/path_planning_main.rst
index a3237f16f1..30802fd7a6 100644
--- a/docs/modules/path_planning/path_planning_main.rst
+++ b/docs/modules/path_planning/path_planning_main.rst
@@ -18,6 +18,7 @@ Path Planning
    rrt/rrt
    cubic_spline/cubic_spline
    bspline_path/bspline_path
+   catmull_rom_spline/catmull_rom_spline
    clothoid_path/clothoid_path
    eta3_spline/eta3_spline
    bezier_path/bezier_path

From b5988dbcbc3c1d8cdbb3f9f3f9dec75cf63defb9 Mon Sep 17 00:00:00 2001
From: parmaski <89462537+parmaski@users.noreply.github.com>
Date: Sat, 21 Dec 2024 19:39:16 +0900
Subject: [PATCH 0933/1080] Fix: Hybrid A* direction is incorrect (#1086)

* fix to preseve direction history

* add path to Reeds Shepp and its users e.g. hybrid A*
---
 PathPlanning/HybridAStar/hybrid_a_star.py                | 5 +++--
 PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py | 4 ++++
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
index 03db0dc167..1962efe295 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -105,12 +105,13 @@ def calc_next_node(current, steer, direction, config, ox, oy, kd_tree):
     x, y, yaw = current.x_list[-1], current.y_list[-1], current.yaw_list[-1]
 
     arc_l = XY_GRID_RESOLUTION * 1.5
-    x_list, y_list, yaw_list = [], [], []
+    x_list, y_list, yaw_list, direction_list = [], [], [], []
     for _ in np.arange(0, arc_l, MOTION_RESOLUTION):
         x, y, yaw = move(x, y, yaw, MOTION_RESOLUTION * direction, steer)
         x_list.append(x)
         y_list.append(y)
         yaw_list.append(yaw)
+        direction_list.append(direction == 1)
 
     if not check_car_collision(x_list, y_list, yaw_list, ox, oy, kd_tree):
         return None
@@ -134,7 +135,7 @@ def calc_next_node(current, steer, direction, config, ox, oy, kd_tree):
     cost = current.cost + added_cost + arc_l
 
     node = Node(x_ind, y_ind, yaw_ind, d, x_list,
-                y_list, yaw_list, [d],
+                y_list, yaw_list, direction_list,
                 parent_index=calc_index(current, config),
                 cost=cost, steer=steer)
 
diff --git a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
index 8e134ff38b..618d1d99ba 100644
--- a/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
+++ b/PathPlanning/ReedsSheppPath/reeds_shepp_path_planning.py
@@ -6,6 +6,10 @@
 co-author Videh Patel(@videh25) : Added the missing RS paths
 
 """
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
+
 import math
 
 import matplotlib.pyplot as plt

From b137ba1817af11ed944a28f8c1c8ad1a95848726 Mon Sep 17 00:00:00 2001
From: parmaski <89462537+parmaski@users.noreply.github.com>
Date: Sat, 21 Dec 2024 19:40:30 +0900
Subject: [PATCH 0934/1080] fix behavior when path is not found (#1104)

---
 PathPlanning/HybridAStar/car.py           | 2 +-
 PathPlanning/HybridAStar/hybrid_a_star.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/PathPlanning/HybridAStar/car.py b/PathPlanning/HybridAStar/car.py
index d7631133c7..959db74078 100644
--- a/PathPlanning/HybridAStar/car.py
+++ b/PathPlanning/HybridAStar/car.py
@@ -97,7 +97,7 @@ def pi_2_pi(angle):
 def move(x, y, yaw, distance, steer, L=WB):
     x += distance * cos(yaw)
     y += distance * sin(yaw)
-    yaw += pi_2_pi(distance * tan(steer) / L)  # distance/2
+    yaw = pi_2_pi(yaw + distance * tan(steer) / L)  # distance/2
 
     return x, y, yaw
 
diff --git a/PathPlanning/HybridAStar/hybrid_a_star.py b/PathPlanning/HybridAStar/hybrid_a_star.py
index 1962efe295..0fa04189c6 100644
--- a/PathPlanning/HybridAStar/hybrid_a_star.py
+++ b/PathPlanning/HybridAStar/hybrid_a_star.py
@@ -282,7 +282,7 @@ def hybrid_a_star_planning(start, goal, ox, oy, xy_resolution, yaw_resolution):
     while True:
         if not openList:
             print("Error: Cannot find path, No open set")
-            return [], [], []
+            return Path([], [], [], [], 0)
 
         cost, c_id = heapq.heappop(pq)
         if c_id in openList:

From ca54f1b6880b9731b06fc33db338bc28bd63c864 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 24 Dec 2024 07:50:00 +0900
Subject: [PATCH 0935/1080] Bump ruff from 0.8.3 to 0.8.4 in /requirements
 (#1111)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.8.3 to 0.8.4.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.8.3...0.8.4)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index cd4a01f88f..d28cd5f956 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.13.0 # For unit test
-ruff == 0.8.3 # For unit test
+ruff == 0.8.4 # For unit test

From d1f1190106d624e555b0fdd0853b65ad844e3204 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 24 Dec 2024 07:59:16 +0900
Subject: [PATCH 0936/1080] Bump numpy from 2.2.0 to 2.2.1 in /requirements
 (#1113)

Bumps [numpy](https://github.com/numpy/numpy) from 2.2.0 to 2.2.1.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v2.2.0...v2.2.1)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index d28cd5f956..0992ab6cf0 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 2.2.0
+numpy == 2.2.1
 scipy == 1.14.1
 matplotlib == 3.10.0
 cvxpy == 1.5.3

From aacae0d79a9595356bd7f15ad8e565220a04de61 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 24 Dec 2024 09:13:35 +0900
Subject: [PATCH 0937/1080] Bump mypy from 1.13.0 to 1.14.0 in /requirements
 (#1112)

Bumps [mypy](https://github.com/python/mypy) from 1.13.0 to 1.14.0.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.13.0...v1.14.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 0992ab6cf0..47db4245fd 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.10.0
 cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test
 pytest-xdist == 3.6.1 # For unit test
-mypy == 1.13.0 # For unit test
+mypy == 1.14.0 # For unit test
 ruff == 0.8.4 # For unit test

From 7d33f5f7140513d14bad016f48d132a08bc5c8a3 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 31 Dec 2024 07:04:57 +0900
Subject: [PATCH 0938/1080] Bump mypy from 1.14.0 to 1.14.1 in /requirements
 (#1116)

Bumps [mypy](https://github.com/python/mypy) from 1.14.0 to 1.14.1.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.14.0...v1.14.1)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 47db4245fd..bcfbbf97ac 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.10.0
 cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test
 pytest-xdist == 3.6.1 # For unit test
-mypy == 1.14.0 # For unit test
+mypy == 1.14.1 # For unit test
 ruff == 0.8.4 # For unit test

From c27803f781c00384c9de29fe1fd82ace8279d788 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 7 Jan 2025 11:49:14 +0900
Subject: [PATCH 0939/1080] build(deps): bump ruff from 0.8.4 to 0.8.6 in
 /requirements (#1117)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.8.4 to 0.8.6.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.8.4...0.8.6)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index bcfbbf97ac..5291c45c58 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.14.1 # For unit test
-ruff == 0.8.4 # For unit test
+ruff == 0.8.6 # For unit test

From 01d398f4ab276bcbcac3a7ce8d62b2e2712f0a5a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 7 Jan 2025 13:01:34 +0900
Subject: [PATCH 0940/1080] build(deps): bump scipy from 1.14.1 to 1.15.0 in
 /requirements (#1118)

Bumps [scipy](https://github.com/scipy/scipy) from 1.14.1 to 1.15.0.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.14.1...v1.15.0)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 5291c45c58..5aeded446f 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 2.2.1
-scipy == 1.14.1
+scipy == 1.15.0
 matplotlib == 3.10.0
 cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test

From 647ce9ccfa1f581bff558a16803bfd7e2c343c3e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 14 Jan 2025 07:24:59 +0900
Subject: [PATCH 0941/1080] build(deps): bump scipy from 1.15.0 to 1.15.1 in
 /requirements (#1119)

Bumps [scipy](https://github.com/scipy/scipy) from 1.15.0 to 1.15.1.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.15.0...v1.15.1)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 5aeded446f..f5a7ff315c 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 2.2.1
-scipy == 1.15.0
+scipy == 1.15.1
 matplotlib == 3.10.0
 cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test

From 4cf7531839a12faa9b7daf6a70aab4f596aa1b0c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 14 Jan 2025 07:25:29 +0900
Subject: [PATCH 0942/1080] build(deps): bump ruff from 0.8.6 to 0.9.1 in
 /requirements (#1120)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.8.6 to 0.9.1.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.8.6...0.9.1)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index f5a7ff315c..d212202cdb 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.14.1 # For unit test
-ruff == 0.8.6 # For unit test
+ruff == 0.9.1 # For unit test

From 4c2e3e8f9b686baef5774dcfe0afd7dda6150af4 Mon Sep 17 00:00:00 2001
From: Mritunjai <101278178+Mritunjaii@users.noreply.github.com>
Date: Mon, 20 Jan 2025 09:59:15 +0530
Subject: [PATCH 0943/1080]  hashmap instead of a linear search (#1110)

---
 .../AStar/a_star_searching_from_two_side.py   | 70 +++++++++----------
 1 file changed, 35 insertions(+), 35 deletions(-)

diff --git a/PathPlanning/AStar/a_star_searching_from_two_side.py b/PathPlanning/AStar/a_star_searching_from_two_side.py
index 822f5ea500..f43cea71b4 100644
--- a/PathPlanning/AStar/a_star_searching_from_two_side.py
+++ b/PathPlanning/AStar/a_star_searching_from_two_side.py
@@ -78,43 +78,43 @@ def boundary_and_obstacles(start, goal, top_vertex, bottom_vertex, obs_number):
 
 
 def find_neighbor(node, ob, closed):
-    # generate neighbors in certain condition
-    ob_list = ob.tolist()
-    neighbor: list = []
+    # Convert obstacles to a set for faster lookup
+    ob_set = set(map(tuple, ob.tolist()))  
+    neighbor_set = set()
+
+    # Generate neighbors within the 3x3 grid around the node
     for x in range(node.coordinate[0] - 1, node.coordinate[0] + 2):
         for y in range(node.coordinate[1] - 1, node.coordinate[1] + 2):
-            if [x, y] not in ob_list:
-                # find all possible neighbor nodes
-                neighbor.append([x, y])
-    # remove node violate the motion rule
-    # 1. remove node.coordinate itself
-    neighbor.remove(node.coordinate)
-    # 2. remove neighbor nodes who cross through two diagonal
-    # positioned obstacles since there is no enough space for
-    # robot to go through two diagonal positioned obstacles
-
-    # top bottom left right neighbors of node
-    top_nei = [node.coordinate[0], node.coordinate[1] + 1]
-    bottom_nei = [node.coordinate[0], node.coordinate[1] - 1]
-    left_nei = [node.coordinate[0] - 1, node.coordinate[1]]
-    right_nei = [node.coordinate[0] + 1, node.coordinate[1]]
-    # neighbors in four vertex
-    lt_nei = [node.coordinate[0] - 1, node.coordinate[1] + 1]
-    rt_nei = [node.coordinate[0] + 1, node.coordinate[1] + 1]
-    lb_nei = [node.coordinate[0] - 1, node.coordinate[1] - 1]
-    rb_nei = [node.coordinate[0] + 1, node.coordinate[1] - 1]
-
-    # remove the unnecessary neighbors
-    if top_nei and left_nei in ob_list and lt_nei in neighbor:
-        neighbor.remove(lt_nei)
-    if top_nei and right_nei in ob_list and rt_nei in neighbor:
-        neighbor.remove(rt_nei)
-    if bottom_nei and left_nei in ob_list and lb_nei in neighbor:
-        neighbor.remove(lb_nei)
-    if bottom_nei and right_nei in ob_list and rb_nei in neighbor:
-        neighbor.remove(rb_nei)
-    neighbor = [x for x in neighbor if x not in closed]
-    return neighbor
+            coord = (x, y)
+            if coord not in ob_set and coord != tuple(node.coordinate):
+                neighbor_set.add(coord)
+
+    # Define neighbors in cardinal and diagonal directions
+    top_nei = (node.coordinate[0], node.coordinate[1] + 1)
+    bottom_nei = (node.coordinate[0], node.coordinate[1] - 1)
+    left_nei = (node.coordinate[0] - 1, node.coordinate[1])
+    right_nei = (node.coordinate[0] + 1, node.coordinate[1])
+    lt_nei = (node.coordinate[0] - 1, node.coordinate[1] + 1)
+    rt_nei = (node.coordinate[0] + 1, node.coordinate[1] + 1)
+    lb_nei = (node.coordinate[0] - 1, node.coordinate[1] - 1)
+    rb_nei = (node.coordinate[0] + 1, node.coordinate[1] - 1)
+
+    # Remove neighbors that violate diagonal motion rules
+    if top_nei in ob_set and left_nei in ob_set:
+        neighbor_set.discard(lt_nei)
+    if top_nei in ob_set and right_nei in ob_set:
+        neighbor_set.discard(rt_nei)
+    if bottom_nei in ob_set and left_nei in ob_set:
+        neighbor_set.discard(lb_nei)
+    if bottom_nei in ob_set and right_nei in ob_set:
+        neighbor_set.discard(rb_nei)
+
+    # Filter out neighbors that are in the closed set
+    closed_set = set(map(tuple, closed))
+    neighbor_set -= closed_set
+
+    return list(neighbor_set)
+
 
 
 def find_node_index(coordinate, node_list):

From 1cb45a5d3bdcb07b254c20ce7763d527435c355e Mon Sep 17 00:00:00 2001
From: Aryaz Eghbali <64126826+AryazE@users.noreply.github.com>
Date: Mon, 20 Jan 2025 09:16:20 +0100
Subject: [PATCH 0944/1080] Fixed multitype list (#1076)

* Fixed multitype list

* Cast to float

* Reverted to all floats

* Moved all remaining to float
---
 .../BreadthFirstSearch/breadth_first_search.py         | 10 +++++-----
 PathPlanning/Dijkstra/dijkstra.py                      | 10 +++++-----
 .../ProbabilisticRoadMap/probabilistic_road_map.py     | 10 +++++-----
 PathPlanning/VoronoiRoadMap/voronoi_road_map.py        | 10 +++++-----
 4 files changed, 20 insertions(+), 20 deletions(-)

diff --git a/PathPlanning/BreadthFirstSearch/breadth_first_search.py b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
index ff662170e7..ad994732a5 100644
--- a/PathPlanning/BreadthFirstSearch/breadth_first_search.py
+++ b/PathPlanning/BreadthFirstSearch/breadth_first_search.py
@@ -220,20 +220,20 @@ def main():
     # set obstacle positions
     ox, oy = [], []
     for i in range(-10, 60):
-        ox.append(i)
+        ox.append(float(i))
         oy.append(-10.0)
     for i in range(-10, 60):
         ox.append(60.0)
-        oy.append(i)
+        oy.append(float(i))
     for i in range(-10, 61):
-        ox.append(i)
+        ox.append(float(i))
         oy.append(60.0)
     for i in range(-10, 61):
         ox.append(-10.0)
-        oy.append(i)
+        oy.append(float(i))
     for i in range(-10, 40):
         ox.append(20.0)
-        oy.append(i)
+        oy.append(float(i))
     for i in range(0, 40):
         ox.append(40.0)
         oy.append(60.0 - i)
diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py
index 004e49f15a..8a585e4b18 100644
--- a/PathPlanning/Dijkstra/dijkstra.py
+++ b/PathPlanning/Dijkstra/dijkstra.py
@@ -221,20 +221,20 @@ def main():
     # set obstacle positions
     ox, oy = [], []
     for i in range(-10, 60):
-        ox.append(i)
+        ox.append(float(i))
         oy.append(-10.0)
     for i in range(-10, 60):
         ox.append(60.0)
-        oy.append(i)
+        oy.append(float(i))
     for i in range(-10, 61):
-        ox.append(i)
+        ox.append(float(i))
         oy.append(60.0)
     for i in range(-10, 61):
         ox.append(-10.0)
-        oy.append(i)
+        oy.append(float(i))
     for i in range(-10, 40):
         ox.append(20.0)
-        oy.append(i)
+        oy.append(float(i))
     for i in range(0, 40):
         ox.append(40.0)
         oy.append(60.0 - i)
diff --git a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
index 294c389023..8bacfd5d19 100644
--- a/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
+++ b/PathPlanning/ProbabilisticRoadMap/probabilistic_road_map.py
@@ -273,20 +273,20 @@ def main(rng=None):
     oy = []
 
     for i in range(60):
-        ox.append(i)
+        ox.append(float(i))
         oy.append(0.0)
     for i in range(60):
         ox.append(60.0)
-        oy.append(i)
+        oy.append(float(i))
     for i in range(61):
-        ox.append(i)
+        ox.append(float(i))
         oy.append(60.0)
     for i in range(61):
         ox.append(0.0)
-        oy.append(i)
+        oy.append(float(i))
     for i in range(40):
         ox.append(20.0)
-        oy.append(i)
+        oy.append(float(i))
     for i in range(40):
         ox.append(40.0)
         oy.append(60.0 - i)
diff --git a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
index 0a1f6f5526..a27e1b6928 100644
--- a/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
+++ b/PathPlanning/VoronoiRoadMap/voronoi_road_map.py
@@ -146,20 +146,20 @@ def main():
     oy = []
 
     for i in range(60):
-        ox.append(i)
+        ox.append(float(i))
         oy.append(0.0)
     for i in range(60):
         ox.append(60.0)
-        oy.append(i)
+        oy.append(float(i))
     for i in range(61):
-        ox.append(i)
+        ox.append(float(i))
         oy.append(60.0)
     for i in range(61):
         ox.append(0.0)
-        oy.append(i)
+        oy.append(float(i))
     for i in range(40):
         ox.append(20.0)
-        oy.append(i)
+        oy.append(float(i))
     for i in range(40):
         ox.append(40.0)
         oy.append(60.0 - i)

From 9672434483459a00b13ece407079fbb53dd8a6b0 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 21 Jan 2025 08:02:30 +0900
Subject: [PATCH 0945/1080] build(deps): bump numpy from 2.2.1 to 2.2.2 in
 /requirements (#1122)

Bumps [numpy](https://github.com/numpy/numpy) from 2.2.1 to 2.2.2.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v2.2.1...v2.2.2)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index d212202cdb..e1c117cb1e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 2.2.1
+numpy == 2.2.2
 scipy == 1.15.1
 matplotlib == 3.10.0
 cvxpy == 1.5.3

From 2bc59fe61247982eaed5b0b7eab842658a5c07d8 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 21 Jan 2025 08:15:53 +0900
Subject: [PATCH 0946/1080] build(deps): bump ruff from 0.9.1 to 0.9.2 in
 /requirements (#1123)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.9.1 to 0.9.2.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.9.1...0.9.2)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index e1c117cb1e..11b91abb1f 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.14.1 # For unit test
-ruff == 0.9.1 # For unit test
+ruff == 0.9.2 # For unit test

From 5a66105ff5dc9924fc6b8f8986afaf52f0fd870d Mon Sep 17 00:00:00 2001
From: Aglargil <34728006+Aglargil@users.noreply.github.com>
Date: Wed, 22 Jan 2025 17:57:12 +0800
Subject: [PATCH 0947/1080] Extend frenet_optimal_trajectory to support more
 scenarios (#1114)

* feat: Add third derivative and curvature rate calculations to CubicSpline classes

* feat: Extend frenet_optimal_trajectory to support more scenarios

* fix: frenet optimal trajectory type check

* fix: cubic spline planner code style check

* fix: frenet optimal trajectory review

* feat: frenet_optimal_trajectory update doc

* fix: frenet optimal trajectory review

* fix: frenet optimal trajectory

* fix: frenet optimal trajectory
---
 .../CubicSpline/cubic_spline_planner.py       |  67 +++
 .../cartesian_frenet_converter.py             | 144 +++++
 .../frenet_optimal_trajectory.py              | 550 +++++++++++++-----
 .../frenet_frame_path_main.rst                |  30 +-
 tests/test_frenet_optimal_trajectory.py       |  40 +-
 5 files changed, 674 insertions(+), 157 deletions(-)
 create mode 100644 PathPlanning/FrenetOptimalTrajectory/cartesian_frenet_converter.py

diff --git a/PathPlanning/CubicSpline/cubic_spline_planner.py b/PathPlanning/CubicSpline/cubic_spline_planner.py
index 7cc7bedc79..2391f67c39 100644
--- a/PathPlanning/CubicSpline/cubic_spline_planner.py
+++ b/PathPlanning/CubicSpline/cubic_spline_planner.py
@@ -76,6 +76,11 @@ def calc_position(self, x):
 
         if `x` is outside the data point's `x` range, return None.
 
+        Parameters
+        ----------
+        x : float
+            x position to calculate y.
+
         Returns
         -------
         y : float
@@ -99,6 +104,11 @@ def calc_first_derivative(self, x):
 
         if x is outside the input x, return None
 
+        Parameters
+        ----------
+        x : float
+            x position to calculate first derivative.
+
         Returns
         -------
         dy : float
@@ -121,6 +131,11 @@ def calc_second_derivative(self, x):
 
         if x is outside the input x, return None
 
+        Parameters
+        ----------
+        x : float
+            x position to calculate second derivative.
+
         Returns
         -------
         ddy : float
@@ -137,6 +152,31 @@ def calc_second_derivative(self, x):
         ddy = 2.0 * self.c[i] + 6.0 * self.d[i] * dx
         return ddy
 
+    def calc_third_derivative(self, x):
+        """
+        Calc third derivative at given x.
+
+        if x is outside the input x, return None
+
+        Parameters
+        ----------
+        x : float
+            x position to calculate third derivative.
+
+        Returns
+        -------
+        dddy : float
+            third derivative for given x.
+        """
+        if x < self.x[0]:
+            return None
+        elif x > self.x[-1]:
+            return None
+
+        i = self.__search_index(x)
+        dddy = 6.0 * self.d[i]
+        return dddy
+
     def __search_index(self, x):
         """
         search data segment index
@@ -287,6 +327,33 @@ def calc_curvature(self, s):
         k = (ddy * dx - ddx * dy) / ((dx ** 2 + dy ** 2)**(3 / 2))
         return k
 
+    def calc_curvature_rate(self, s):
+        """
+        calc curvature rate
+
+        Parameters
+        ----------
+        s : float
+            distance from the start point. if `s` is outside the data point's
+            range, return None.
+
+        Returns
+        -------
+        k : float
+            curvature rate for given s.
+        """
+        dx = self.sx.calc_first_derivative(s)
+        dy = self.sy.calc_first_derivative(s)
+        ddx = self.sx.calc_second_derivative(s)
+        ddy = self.sy.calc_second_derivative(s)
+        dddx = self.sx.calc_third_derivative(s)
+        dddy = self.sy.calc_third_derivative(s)
+        a = dx * ddy - dy * ddx
+        b = dx * dddy - dy * dddx
+        c = dx * ddx + dy * ddy
+        d = dx * dx + dy * dy
+        return (b * d - 3.0 * a * c) / (d * d * d)
+
     def calc_yaw(self, s):
         """
         calc yaw
diff --git a/PathPlanning/FrenetOptimalTrajectory/cartesian_frenet_converter.py b/PathPlanning/FrenetOptimalTrajectory/cartesian_frenet_converter.py
new file mode 100644
index 0000000000..4cc8650c89
--- /dev/null
+++ b/PathPlanning/FrenetOptimalTrajectory/cartesian_frenet_converter.py
@@ -0,0 +1,144 @@
+"""
+
+A converter between Cartesian and Frenet coordinate systems
+
+author: Wang Zheng (@Aglargil)
+
+Ref:
+
+- [Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame]
+(https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf)
+
+"""
+
+import math
+
+
+class CartesianFrenetConverter:
+    """
+    A class for converting states between Cartesian and Frenet coordinate systems
+    """
+
+    @ staticmethod
+    def cartesian_to_frenet(rs, rx, ry, rtheta, rkappa, rdkappa, x, y, v, a, theta, kappa):
+        """
+        Convert state from Cartesian coordinate to Frenet coordinate
+
+        Parameters
+        ----------
+            rs: reference line s-coordinate
+            rx, ry: reference point coordinates
+            rtheta: reference point heading
+            rkappa: reference point curvature
+            rdkappa: reference point curvature rate
+            x, y: current position
+            v: velocity
+            a: acceleration
+            theta: heading angle
+            kappa: curvature
+
+        Returns
+        -------
+            s_condition: [s(t), s'(t), s''(t)]
+            d_condition: [d(s), d'(s), d''(s)]
+        """
+        dx = x - rx
+        dy = y - ry
+
+        cos_theta_r = math.cos(rtheta)
+        sin_theta_r = math.sin(rtheta)
+
+        cross_rd_nd = cos_theta_r * dy - sin_theta_r * dx
+        d = math.copysign(math.hypot(dx, dy), cross_rd_nd)
+
+        delta_theta = theta - rtheta
+        tan_delta_theta = math.tan(delta_theta)
+        cos_delta_theta = math.cos(delta_theta)
+
+        one_minus_kappa_r_d = 1 - rkappa * d
+        d_dot = one_minus_kappa_r_d * tan_delta_theta
+
+        kappa_r_d_prime = rdkappa * d + rkappa * d_dot
+
+        d_ddot = (-kappa_r_d_prime * tan_delta_theta +
+                  one_minus_kappa_r_d / (cos_delta_theta * cos_delta_theta) *
+                  (kappa * one_minus_kappa_r_d / cos_delta_theta - rkappa))
+
+        s = rs
+        s_dot = v * cos_delta_theta / one_minus_kappa_r_d
+
+        delta_theta_prime = one_minus_kappa_r_d / cos_delta_theta * kappa - rkappa
+        s_ddot = (a * cos_delta_theta -
+                  s_dot * s_dot *
+                  (d_dot * delta_theta_prime - kappa_r_d_prime)) / one_minus_kappa_r_d
+
+        return [s, s_dot, s_ddot], [d, d_dot, d_ddot]
+
+    @ staticmethod
+    def frenet_to_cartesian(rs, rx, ry, rtheta, rkappa, rdkappa, s_condition, d_condition):
+        """
+        Convert state from Frenet coordinate to Cartesian coordinate
+
+        Parameters
+        ----------
+            rs: reference line s-coordinate
+            rx, ry: reference point coordinates
+            rtheta: reference point heading
+            rkappa: reference point curvature
+            rdkappa: reference point curvature rate
+            s_condition: [s(t), s'(t), s''(t)]
+            d_condition: [d(s), d'(s), d''(s)]
+
+        Returns
+        -------
+            x, y: position
+            theta: heading angle
+            kappa: curvature
+            v: velocity
+            a: acceleration
+        """
+        if abs(rs - s_condition[0]) >= 1.0e-6:
+            raise ValueError(
+                "The reference point s and s_condition[0] don't match")
+
+        cos_theta_r = math.cos(rtheta)
+        sin_theta_r = math.sin(rtheta)
+
+        x = rx - sin_theta_r * d_condition[0]
+        y = ry + cos_theta_r * d_condition[0]
+
+        one_minus_kappa_r_d = 1 - rkappa * d_condition[0]
+
+        tan_delta_theta = d_condition[1] / one_minus_kappa_r_d
+        delta_theta = math.atan2(d_condition[1], one_minus_kappa_r_d)
+        cos_delta_theta = math.cos(delta_theta)
+
+        theta = CartesianFrenetConverter.normalize_angle(delta_theta + rtheta)
+
+        kappa_r_d_prime = rdkappa * d_condition[0] + rkappa * d_condition[1]
+
+        kappa = (((d_condition[2] + kappa_r_d_prime * tan_delta_theta) *
+                  cos_delta_theta * cos_delta_theta) / one_minus_kappa_r_d + rkappa) * \
+            cos_delta_theta / one_minus_kappa_r_d
+
+        d_dot = d_condition[1] * s_condition[1]
+        v = math.sqrt(one_minus_kappa_r_d * one_minus_kappa_r_d *
+                      s_condition[1] * s_condition[1] + d_dot * d_dot)
+
+        delta_theta_prime = one_minus_kappa_r_d / cos_delta_theta * kappa - rkappa
+
+        a = (s_condition[2] * one_minus_kappa_r_d / cos_delta_theta +
+             s_condition[1] * s_condition[1] / cos_delta_theta *
+             (d_condition[1] * delta_theta_prime - kappa_r_d_prime))
+
+        return x, y, theta, kappa, v, a
+
+    @ staticmethod
+    def normalize_angle(angle):
+        """
+        Normalize angle to [-pi, pi]
+        """
+        a = math.fmod(angle + math.pi, 2.0 * math.pi)
+        if a < 0.0:
+            a += 2.0 * math.pi
+        return a - math.pi
diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
index 331df36309..248894c1c6 100644
--- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
+++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
@@ -17,43 +17,314 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import copy
-import math
 import sys
 import pathlib
+
 sys.path.append(str(pathlib.Path(__file__).parent.parent))
 
-from QuinticPolynomialsPlanner.quintic_polynomials_planner import \
-    QuinticPolynomial
+from QuinticPolynomialsPlanner.quintic_polynomials_planner import QuinticPolynomial
 from CubicSpline import cubic_spline_planner
 
-SIM_LOOP = 500
+from enum import Enum, auto
+from FrenetOptimalTrajectory.cartesian_frenet_converter import (
+    CartesianFrenetConverter,
+)
+
+
+class LateralMovement(Enum):
+    HIGH_SPEED = auto()
+    LOW_SPEED = auto()
+
+
+class LongitudinalMovement(Enum):
+    MERGING_AND_STOPPING = auto()
+    VELOCITY_KEEPING = auto()
+
+
+# Default Parameters
+
+LATERAL_MOVEMENT = LateralMovement.HIGH_SPEED
+LONGITUDINAL_MOVEMENT = LongitudinalMovement.VELOCITY_KEEPING
 
-# Parameter
 MAX_SPEED = 50.0 / 3.6  # maximum speed [m/s]
-MAX_ACCEL = 2.0  # maximum acceleration [m/ss]
+MAX_ACCEL = 5.0  # maximum acceleration [m/ss]
 MAX_CURVATURE = 1.0  # maximum curvature [1/m]
-MAX_ROAD_WIDTH = 7.0  # maximum road width [m]
-D_ROAD_W = 1.0  # road width sampling length [m]
 DT = 0.2  # time tick [s]
 MAX_T = 5.0  # max prediction time [m]
 MIN_T = 4.0  # min prediction time [m]
-TARGET_SPEED = 30.0 / 3.6  # target speed [m/s]
-D_T_S = 5.0 / 3.6  # target speed sampling length [m/s]
 N_S_SAMPLE = 1  # sampling number of target speed
-ROBOT_RADIUS = 2.0  # robot radius [m]
 
 # cost weights
 K_J = 0.1
 K_T = 0.1
+K_S_DOT = 1.0
 K_D = 1.0
+K_S = 1.0
 K_LAT = 1.0
 K_LON = 1.0
 
+SIM_LOOP = 500
 show_animation = True
 
 
-class QuarticPolynomial:
+if LATERAL_MOVEMENT == LateralMovement.LOW_SPEED:
+    MAX_ROAD_WIDTH = 1.0  # maximum road width [m]
+    D_ROAD_W = 0.2  # road width sampling length [m]
+    TARGET_SPEED = 3.0 / 3.6  # maximum speed [m/s]
+    D_T_S = 0.5 / 3.6  # target speed sampling length [m/s]
+    # Waypoints
+    WX = [0.0, 2.0, 4.0, 6.0, 8.0, 10.0]
+    WY = [0.0, 0.0, 1.0, 0.0, -1.0, -2.0]
+    OBSTACLES = np.array([[3.0, 1.0], [5.0, -0.0], [6.0, 0.5], [8.0, -1.5]])
+    ROBOT_RADIUS = 0.5  # robot radius [m]
+
+    # Initial state parameters
+    INITIAL_SPEED = 1.0 / 3.6  # current speed [m/s]
+    INITIAL_ACCEL = 0.0  # current acceleration [m/ss]
+    INITIAL_LAT_POSITION = 0.5  # current lateral position [m]
+    INITIAL_LAT_SPEED = 0.0  # current lateral speed [m/s]
+    INITIAL_LAT_ACCELERATION = 0.0  # current lateral acceleration [m/s]
+    INITIAL_COURSE_POSITION = 0.0  # current course position
+
+    ANIMATION_AREA = 5.0  # Animation area length [m]
+
+    STOP_S = 4.0  # Merge and stop distance [m]
+    D_S = 0.3  # Stop point sampling length [m]
+    N_STOP_S_SAMPLE = 3  # Stop point sampling number
+else:
+    MAX_ROAD_WIDTH = 7.0  # maximum road width [m]
+    D_ROAD_W = 1.0  # road width sampling length [m]
+    TARGET_SPEED = 30.0 / 3.6  # target speed [m/s]
+    D_T_S = 5.0 / 3.6  # target speed sampling length [m/s]
+    # Waypoints
+    WX = [0.0, 10.0, 20.5, 35.0, 70.5]
+    WY = [0.0, -6.0, 5.0, 6.5, 0.0]
+    # Obstacle list
+    OBSTACLES = np.array(
+        [[20.0, 10.0], [30.0, 6.0], [30.0, 8.0], [35.0, 8.0], [50.0, 3.0]]
+    )
+    ROBOT_RADIUS = 2.0  # robot radius [m]
+
+    # Initial state parameters
+    INITIAL_SPEED = 10.0 / 3.6  # current speed [m/s]
+    INITIAL_ACCEL = 0.0  # current acceleration [m/ss]
+    INITIAL_LAT_POSITION = 2.0  # current lateral position [m]
+    INITIAL_LAT_SPEED = 0.0  # current lateral speed [m/s]
+    INITIAL_LAT_ACCELERATION = 0.0  # current lateral acceleration [m/s]
+    INITIAL_COURSE_POSITION = 0.0  # current course position
+
+    ANIMATION_AREA = 20.0  # Animation area length [m]
+    STOP_S = 25.0  # Merge and stop distance [m]
+    D_S = 2  # Stop point sampling length [m]
+    N_STOP_S_SAMPLE = 4  # Stop point sampling number
+
+
+class LateralMovementStrategy:
+    def calc_lateral_trajectory(self, fp, di, c_d, c_d_d, c_d_dd, Ti):
+        """
+        Calculate the lateral trajectory
+        """
+        raise NotImplementedError("calc_lateral_trajectory not implemented")
+
+    def calc_cartesian_parameters(self, fp, csp):
+        """
+        Calculate the cartesian parameters (x, y, yaw, curvature, v, a)
+        """
+        raise NotImplementedError("calc_cartesian_parameters not implemented")
+
+
+class HighSpeedLateralMovementStrategy(LateralMovementStrategy):
+    def calc_lateral_trajectory(self, fp, di, c_d, c_d_d, c_d_dd, Ti):
+        tp = copy.deepcopy(fp)
+        s0_d = fp.s_d[0]
+        s0_dd = fp.s_dd[0]
+        # d'(t) = d'(s) * s'(t)
+        # d''(t) = d''(s) * s'(t)^2 + d'(s) * s''(t)
+        lat_qp = QuinticPolynomial(
+            c_d, c_d_d * s0_d, c_d_dd * s0_d**2 + c_d_d * s0_dd, di, 0.0, 0.0, Ti
+        )
+
+        tp.d = []
+        tp.d_d = []
+        tp.d_dd = []
+        tp.d_ddd = []
+
+        # Calculate all derivatives in a single loop to reduce iterations
+        for i in range(len(fp.t)):
+            t = fp.t[i]
+            s_d = fp.s_d[i]
+            s_dd = fp.s_dd[i]
+
+            s_d_inv = 1.0 / (s_d + 1e-6) + 1e-6  # Avoid division by zero
+            s_d_inv_sq = s_d_inv * s_d_inv  # Square of inverse
+
+            d = lat_qp.calc_point(t)
+            d_d = lat_qp.calc_first_derivative(t)
+            d_dd = lat_qp.calc_second_derivative(t)
+            d_ddd = lat_qp.calc_third_derivative(t)
+
+            tp.d.append(d)
+            # d'(s) = d'(t) / s'(t)
+            tp.d_d.append(d_d * s_d_inv)
+            # d''(s) = (d''(t) - d'(s) * s''(t)) / s'(t)^2
+            tp.d_dd.append((d_dd - tp.d_d[i] * s_dd) * s_d_inv_sq)
+            tp.d_ddd.append(d_ddd)
+
+        return tp
+
+    def calc_cartesian_parameters(self, fp, csp):
+        # calc global positions
+        for i in range(len(fp.s)):
+            ix, iy = csp.calc_position(fp.s[i])
+            if ix is None:
+                break
+            i_yaw = csp.calc_yaw(fp.s[i])
+            i_kappa = csp.calc_curvature(fp.s[i])
+            i_dkappa = csp.calc_curvature_rate(fp.s[i])
+            s_condition = [fp.s[i], fp.s_d[i], fp.s_dd[i]]
+            d_condition = [
+                fp.d[i],
+                fp.d_d[i],
+                fp.d_dd[i],
+            ]
+            x, y, theta, kappa, v, a = CartesianFrenetConverter.frenet_to_cartesian(
+                fp.s[i], ix, iy, i_yaw, i_kappa, i_dkappa, s_condition, d_condition
+            )
+            fp.x.append(x)
+            fp.y.append(y)
+            fp.yaw.append(theta)
+            fp.c.append(kappa)
+            fp.v.append(v)
+            fp.a.append(a)
+        return fp
+
+
+class LowSpeedLateralMovementStrategy(LateralMovementStrategy):
+    def calc_lateral_trajectory(self, fp, di, c_d, c_d_d, c_d_dd, Ti):
+        s0 = fp.s[0]
+        s1 = fp.s[-1]
+        tp = copy.deepcopy(fp)
+        # d = d(s), d_d = d'(s), d_dd = d''(s)
+        # * shift s range from [s0, s1] to [0, s1 - s0]
+        lat_qp = QuinticPolynomial(c_d, c_d_d, c_d_dd, di, 0.0, 0.0, s1 - s0)
+
+        tp.d = [lat_qp.calc_point(s - s0) for s in fp.s]
+        tp.d_d = [lat_qp.calc_first_derivative(s - s0) for s in fp.s]
+        tp.d_dd = [lat_qp.calc_second_derivative(s - s0) for s in fp.s]
+        tp.d_ddd = [lat_qp.calc_third_derivative(s - s0) for s in fp.s]
+        return tp
+
+    def calc_cartesian_parameters(self, fp, csp):
+        # calc global positions
+        for i in range(len(fp.s)):
+            ix, iy = csp.calc_position(fp.s[i])
+            if ix is None:
+                break
+            i_yaw = csp.calc_yaw(fp.s[i])
+            i_kappa = csp.calc_curvature(fp.s[i])
+            i_dkappa = csp.calc_curvature_rate(fp.s[i])
+            s_condition = [fp.s[i], fp.s_d[i], fp.s_dd[i]]
+            d_condition = [fp.d[i], fp.d_d[i], fp.d_dd[i]]
+            x, y, theta, kappa, v, a = CartesianFrenetConverter.frenet_to_cartesian(
+                fp.s[i], ix, iy, i_yaw, i_kappa, i_dkappa, s_condition, d_condition
+            )
+            fp.x.append(x)
+            fp.y.append(y)
+            fp.yaw.append(theta)
+            fp.c.append(kappa)
+            fp.v.append(v)
+            fp.a.append(a)
+        return fp
+
+
+class LongitudinalMovementStrategy:
+    def calc_longitudinal_trajectory(self, c_speed, c_accel, Ti, s0):
+        """
+        Calculate the longitudinal trajectory
+        """
+        raise NotImplementedError("calc_longitudinal_trajectory not implemented")
+
+    def get_d_arrange(self, s0):
+        """
+        Get the d sample range
+        """
+        raise NotImplementedError("get_d_arrange not implemented")
+
+    def calc_destination_cost(self, fp):
+        """
+        Calculate the destination cost
+        """
+        raise NotImplementedError("calc_destination_cost not implemented")
+
+
+class VelocityKeepingLongitudinalMovementStrategy(LongitudinalMovementStrategy):
+    def calc_longitudinal_trajectory(self, c_speed, c_accel, Ti, s0):
+        fplist = []
+        for tv in np.arange(
+            TARGET_SPEED - D_T_S * N_S_SAMPLE, TARGET_SPEED + D_T_S * N_S_SAMPLE, D_T_S
+        ):
+            fp = FrenetPath()
+            lon_qp = QuarticPolynomial(s0, c_speed, c_accel, tv, 0.0, Ti)
+            fp.t = [t for t in np.arange(0.0, Ti, DT)]
+            fp.s = [lon_qp.calc_point(t) for t in fp.t]
+            fp.s_d = [lon_qp.calc_first_derivative(t) for t in fp.t]
+            fp.s_dd = [lon_qp.calc_second_derivative(t) for t in fp.t]
+            fp.s_ddd = [lon_qp.calc_third_derivative(t) for t in fp.t]
+            fplist.append(fp)
+        return fplist
+
+    def get_d_arrange(self, s0):
+        return np.arange(-MAX_ROAD_WIDTH, MAX_ROAD_WIDTH, D_ROAD_W)
+
+    def calc_destination_cost(self, fp):
+        ds = (TARGET_SPEED - fp.s_d[-1]) ** 2
+        return K_S_DOT * ds
+
+
+class MergingAndStoppingLongitudinalMovementStrategy(LongitudinalMovementStrategy):
+    def calc_longitudinal_trajectory(self, c_speed, c_accel, Ti, s0):
+        if s0 >= STOP_S:
+            return []
+        fplist = []
+        for s in np.arange(
+            STOP_S - D_S * N_STOP_S_SAMPLE, STOP_S + D_S * N_STOP_S_SAMPLE, D_S
+        ):
+            fp = FrenetPath()
+            lon_qp = QuinticPolynomial(s0, c_speed, c_accel, s, 0.0, 0.0, Ti)
+            fp.t = [t for t in np.arange(0.0, Ti, DT)]
+            fp.s = [lon_qp.calc_point(t) for t in fp.t]
+            fp.s_d = [lon_qp.calc_first_derivative(t) for t in fp.t]
+            fp.s_dd = [lon_qp.calc_second_derivative(t) for t in fp.t]
+            fp.s_ddd = [lon_qp.calc_third_derivative(t) for t in fp.t]
+            fplist.append(fp)
+        return fplist
+
+    def get_d_arrange(self, s0):
+        # Only if s0 is less than STOP_S / 3, then we sample the road width
+        if s0 < STOP_S / 3:
+            return np.arange(-MAX_ROAD_WIDTH, MAX_ROAD_WIDTH, D_ROAD_W)
+        else:
+            return [0.0]
+
+    def calc_destination_cost(self, fp):
+        ds = (STOP_S - fp.s[-1]) ** 2
+        return K_S * ds
+
+LATERAL_MOVEMENT_STRATEGY: LateralMovementStrategy
+LONGITUDINAL_MOVEMENT_STRATEGY: LongitudinalMovementStrategy
+
+if LATERAL_MOVEMENT == LateralMovement.HIGH_SPEED:
+    LATERAL_MOVEMENT_STRATEGY = HighSpeedLateralMovementStrategy()
+else:
+    LATERAL_MOVEMENT_STRATEGY = LowSpeedLateralMovementStrategy()
+
+if LONGITUDINAL_MOVEMENT == LongitudinalMovement.VELOCITY_KEEPING:
+    LONGITUDINAL_MOVEMENT_STRATEGY = VelocityKeepingLongitudinalMovementStrategy()
+else:
+    LONGITUDINAL_MOVEMENT_STRATEGY = MergingAndStoppingLongitudinalMovementStrategy()
 
+
+class QuarticPolynomial:
     def __init__(self, xs, vxs, axs, vxe, axe, time):
         # calc coefficient of quartic polynomial
 
@@ -61,29 +332,25 @@ def __init__(self, xs, vxs, axs, vxe, axe, time):
         self.a1 = vxs
         self.a2 = axs / 2.0
 
-        A = np.array([[3 * time ** 2, 4 * time ** 3],
-                      [6 * time, 12 * time ** 2]])
-        b = np.array([vxe - self.a1 - 2 * self.a2 * time,
-                      axe - 2 * self.a2])
+        A = np.array([[3 * time**2, 4 * time**3], [6 * time, 12 * time**2]])
+        b = np.array([vxe - self.a1 - 2 * self.a2 * time, axe - 2 * self.a2])
         x = np.linalg.solve(A, b)
 
         self.a3 = x[0]
         self.a4 = x[1]
 
     def calc_point(self, t):
-        xt = self.a0 + self.a1 * t + self.a2 * t ** 2 + \
-             self.a3 * t ** 3 + self.a4 * t ** 4
+        xt = self.a0 + self.a1 * t + self.a2 * t**2 + self.a3 * t**3 + self.a4 * t**4
 
         return xt
 
     def calc_first_derivative(self, t):
-        xt = self.a1 + 2 * self.a2 * t + \
-             3 * self.a3 * t ** 2 + 4 * self.a4 * t ** 3
+        xt = self.a1 + 2 * self.a2 * t + 3 * self.a3 * t**2 + 4 * self.a4 * t**3
 
         return xt
 
     def calc_second_derivative(self, t):
-        xt = 2 * self.a2 + 6 * self.a3 * t + 12 * self.a4 * t ** 2
+        xt = 2 * self.a2 + 6 * self.a3 * t + 12 * self.a4 * t**2
 
         return xt
 
@@ -94,111 +361,85 @@ def calc_third_derivative(self, t):
 
 
 class FrenetPath:
-
     def __init__(self):
         self.t = []
         self.d = []
-        self.d_d = []
-        self.d_dd = []
-        self.d_ddd = []
+        self.d_d = []  # d'(s)
+        self.d_dd = []  # d''(s)
+        self.d_ddd = []  # d'''(t) in low speed / d'''(s) in high speed
         self.s = []
-        self.s_d = []
-        self.s_dd = []
-        self.s_ddd = []
-        self.cd = 0.0
-        self.cv = 0.0
+        self.s_d = []  # s'(t)
+        self.s_dd = []  # s''(t)
+        self.s_ddd = []  # s'''(t)
         self.cf = 0.0
 
         self.x = []
         self.y = []
         self.yaw = []
+        self.v = []
+        self.a = []
         self.ds = []
         self.c = []
 
-
-def calc_frenet_paths(c_speed, c_accel, c_d, c_d_d, c_d_dd, s0):
+    def pop_front(self):
+        self.x.pop(0)
+        self.y.pop(0)
+        self.yaw.pop(0)
+        self.v.pop(0)
+        self.a.pop(0)
+        self.s.pop(0)
+        self.s_d.pop(0)
+        self.s_dd.pop(0)
+        self.s_ddd.pop(0)
+        self.d.pop(0)
+        self.d_d.pop(0)
+        self.d_dd.pop(0)
+        self.d_ddd.pop(0)
+
+
+def calc_frenet_paths(c_s_d, c_s_dd, c_d, c_d_d, c_d_dd, s0):
     frenet_paths = []
 
-    # generate path to each offset goal
-    for di in np.arange(-MAX_ROAD_WIDTH, MAX_ROAD_WIDTH, D_ROAD_W):
-
-        # Lateral motion planning
-        for Ti in np.arange(MIN_T, MAX_T, DT):
-            fp = FrenetPath()
-
-            # lat_qp = quintic_polynomial(c_d, c_d_d, c_d_dd, di, 0.0, 0.0, Ti)
-            lat_qp = QuinticPolynomial(c_d, c_d_d, c_d_dd, di, 0.0, 0.0, Ti)
-
-            fp.t = [t for t in np.arange(0.0, Ti, DT)]
-            fp.d = [lat_qp.calc_point(t) for t in fp.t]
-            fp.d_d = [lat_qp.calc_first_derivative(t) for t in fp.t]
-            fp.d_dd = [lat_qp.calc_second_derivative(t) for t in fp.t]
-            fp.d_ddd = [lat_qp.calc_third_derivative(t) for t in fp.t]
-
-            # Longitudinal motion planning (Velocity keeping)
-            for tv in np.arange(TARGET_SPEED - D_T_S * N_S_SAMPLE,
-                                TARGET_SPEED + D_T_S * N_S_SAMPLE, D_T_S):
-                tfp = copy.deepcopy(fp)
-                lon_qp = QuarticPolynomial(s0, c_speed, c_accel, tv, 0.0, Ti)
-
-                tfp.s = [lon_qp.calc_point(t) for t in fp.t]
-                tfp.s_d = [lon_qp.calc_first_derivative(t) for t in fp.t]
-                tfp.s_dd = [lon_qp.calc_second_derivative(t) for t in fp.t]
-                tfp.s_ddd = [lon_qp.calc_third_derivative(t) for t in fp.t]
-
-                Jp = sum(np.power(tfp.d_ddd, 2))  # square of jerk
-                Js = sum(np.power(tfp.s_ddd, 2))  # square of jerk
-
-                # square of diff from target speed
-                ds = (TARGET_SPEED - tfp.s_d[-1]) ** 2
-
-                tfp.cd = K_J * Jp + K_T * Ti + K_D * tfp.d[-1] ** 2
-                tfp.cv = K_J * Js + K_T * Ti + K_D * ds
-                tfp.cf = K_LAT * tfp.cd + K_LON * tfp.cv
-
-                frenet_paths.append(tfp)
+    for Ti in np.arange(MIN_T, MAX_T, DT):
+        lon_paths = LONGITUDINAL_MOVEMENT_STRATEGY.calc_longitudinal_trajectory(
+            c_s_d, c_s_dd, Ti, s0
+        )
+
+        for fp in lon_paths:
+            for di in LONGITUDINAL_MOVEMENT_STRATEGY.get_d_arrange(s0):
+                tp = LATERAL_MOVEMENT_STRATEGY.calc_lateral_trajectory(
+                    fp, di, c_d, c_d_d, c_d_dd, Ti
+                )
+
+                Jp = sum(np.power(tp.d_ddd, 2))  # square of jerk
+                Js = sum(np.power(tp.s_ddd, 2))  # square of jerk
+
+                lat_cost = K_J * Jp + K_T * Ti + K_D * tp.d[-1] ** 2
+                lon_cost = (
+                    K_J * Js
+                    + K_T * Ti
+                    + LONGITUDINAL_MOVEMENT_STRATEGY.calc_destination_cost(tp)
+                )
+                tp.cf = K_LAT * lat_cost + K_LON * lon_cost
+                frenet_paths.append(tp)
 
     return frenet_paths
 
 
 def calc_global_paths(fplist, csp):
-    for fp in fplist:
-
-        # calc global positions
-        for i in range(len(fp.s)):
-            ix, iy = csp.calc_position(fp.s[i])
-            if ix is None:
-                break
-            i_yaw = csp.calc_yaw(fp.s[i])
-            di = fp.d[i]
-            fx = ix + di * math.cos(i_yaw + math.pi / 2.0)
-            fy = iy + di * math.sin(i_yaw + math.pi / 2.0)
-            fp.x.append(fx)
-            fp.y.append(fy)
-
-        # calc yaw and ds
-        for i in range(len(fp.x) - 1):
-            dx = fp.x[i + 1] - fp.x[i]
-            dy = fp.y[i + 1] - fp.y[i]
-            fp.yaw.append(math.atan2(dy, dx))
-            fp.ds.append(math.hypot(dx, dy))
-
-        fp.yaw.append(fp.yaw[-1])
-        fp.ds.append(fp.ds[-1])
-
-        # calc curvature
-        for i in range(len(fp.yaw) - 1):
-            fp.c.append((fp.yaw[i + 1] - fp.yaw[i]) / fp.ds[i])
-
-    return fplist
+    return [
+        LATERAL_MOVEMENT_STRATEGY.calc_cartesian_parameters(fp, csp) for fp in fplist
+    ]
 
 
 def check_collision(fp, ob):
     for i in range(len(ob[:, 0])):
-        d = [((ix - ob[i, 0]) ** 2 + (iy - ob[i, 1]) ** 2)
-             for (ix, iy) in zip(fp.x, fp.y)]
+        d = [
+            ((ix - ob[i, 0]) ** 2 + (iy - ob[i, 1]) ** 2)
+            for (ix, iy) in zip(fp.x, fp.y)
+        ]
 
-        collision = any([di <= ROBOT_RADIUS ** 2 for di in d])
+        collision = any([di <= ROBOT_RADIUS**2 for di in d])
 
         if collision:
             return False
@@ -207,38 +448,41 @@ def check_collision(fp, ob):
 
 
 def check_paths(fplist, ob):
-    ok_ind = []
+    path_dict = {
+        "max_speed_error": [],
+        "max_accel_error": [],
+        "max_curvature_error": [],
+        "collision_error": [],
+        "ok": [],
+    }
     for i, _ in enumerate(fplist):
-        if any([v > MAX_SPEED for v in fplist[i].s_d]):  # Max speed check
-            continue
-        elif any([abs(a) > MAX_ACCEL for a in
-                  fplist[i].s_dd]):  # Max accel check
-            continue
-        elif any([abs(c) > MAX_CURVATURE for c in
-                  fplist[i].c]):  # Max curvature check
-            continue
+        if any([v > MAX_SPEED for v in fplist[i].v]):  # Max speed check
+            path_dict["max_speed_error"].append(fplist[i])
+        elif any([abs(a) > MAX_ACCEL for a in fplist[i].a]):  # Max accel check
+            path_dict["max_accel_error"].append(fplist[i])
+        elif any([abs(c) > MAX_CURVATURE for c in fplist[i].c]):  # Max curvature check
+            path_dict["max_curvature_error"].append(fplist[i])
         elif not check_collision(fplist[i], ob):
-            continue
+            path_dict["collision_error"].append(fplist[i])
+        else:
+            path_dict["ok"].append(fplist[i])
+    return path_dict
 
-        ok_ind.append(i)
 
-    return [fplist[i] for i in ok_ind]
-
-
-def frenet_optimal_planning(csp, s0, c_speed, c_accel, c_d, c_d_d, c_d_dd, ob):
-    fplist = calc_frenet_paths(c_speed, c_accel, c_d, c_d_d, c_d_dd, s0)
+def frenet_optimal_planning(csp, s0, c_s_d, c_s_dd, c_d, c_d_d, c_d_dd, ob):
+    fplist = calc_frenet_paths(c_s_d, c_s_dd, c_d, c_d_d, c_d_dd, s0)
     fplist = calc_global_paths(fplist, csp)
-    fplist = check_paths(fplist, ob)
+    fpdict = check_paths(fplist, ob)
 
     # find minimum cost path
     min_cost = float("inf")
     best_path = None
-    for fp in fplist:
+    for fp in fpdict["ok"]:
         if min_cost >= fp.cf:
             min_cost = fp.cf
             best_path = fp
 
-    return best_path
+    return [best_path, fpdict]
 
 
 def generate_target_course(x, y):
@@ -259,40 +503,39 @@ def generate_target_course(x, y):
 def main():
     print(__file__ + " start!!")
 
-    # way points
-    wx = [0.0, 10.0, 20.5, 35.0, 70.5]
-    wy = [0.0, -6.0, 5.0, 6.5, 0.0]
-    # obstacle lists
-    ob = np.array([[20.0, 10.0],
-                   [30.0, 6.0],
-                   [30.0, 8.0],
-                   [35.0, 8.0],
-                   [50.0, 3.0]
-                   ])
-
-    tx, ty, tyaw, tc, csp = generate_target_course(wx, wy)
-
-    # initial state
-    c_speed = 10.0 / 3.6  # current speed [m/s]
-    c_accel = 0.0  # current acceleration [m/ss]
-    c_d = 2.0  # current lateral position [m]
-    c_d_d = 0.0  # current lateral speed [m/s]
-    c_d_dd = 0.0  # current lateral acceleration [m/s]
-    s0 = 0.0  # current course position
-
-    area = 20.0  # animation area length [m]
+    tx, ty, tyaw, tc, csp = generate_target_course(WX, WY)
+
+    # Initialize state using global parameters
+    c_s_d = INITIAL_SPEED
+    c_s_dd = INITIAL_ACCEL
+    c_d = INITIAL_LAT_POSITION
+    c_d_d = INITIAL_LAT_SPEED
+    c_d_dd = INITIAL_LAT_ACCELERATION
+    s0 = INITIAL_COURSE_POSITION
+
+    area = ANIMATION_AREA
+
+    last_path = None
 
     for i in range(SIM_LOOP):
-        path = frenet_optimal_planning(
-            csp, s0, c_speed, c_accel, c_d, c_d_d, c_d_dd, ob)
+        [path, fpdict] = frenet_optimal_planning(
+            csp, s0, c_s_d, c_s_dd, c_d, c_d_d, c_d_dd, OBSTACLES
+        )
+
+        if path is None:
+            path = copy.deepcopy(last_path)
+            path.pop_front()
+        if len(path.x) <= 1:
+            print("Finish")
+            break
 
+        last_path = path
         s0 = path.s[1]
         c_d = path.d[1]
         c_d_d = path.d_d[1]
         c_d_dd = path.d_dd[1]
-        c_speed = path.s_d[1]
-        c_accel = path.s_dd[1]
-
+        c_s_d = path.s_d[1]
+        c_s_dd = path.s_dd[1]
         if np.hypot(path.x[1] - tx[-1], path.y[1] - ty[-1]) <= 1.0:
             print("Goal")
             break
@@ -301,15 +544,16 @@ def main():
             plt.cla()
             # for stopping simulation with the esc key.
             plt.gcf().canvas.mpl_connect(
-                'key_release_event',
-                lambda event: [exit(0) if event.key == 'escape' else None])
+                "key_release_event",
+                lambda event: [exit(0) if event.key == "escape" else None],
+            )
             plt.plot(tx, ty)
-            plt.plot(ob[:, 0], ob[:, 1], "xk")
+            plt.plot(OBSTACLES[:, 0], OBSTACLES[:, 1], "xk")
             plt.plot(path.x[1:], path.y[1:], "-or")
             plt.plot(path.x[1], path.y[1], "vc")
             plt.xlim(path.x[1] - area, path.x[1] + area)
             plt.ylim(path.y[1] - area, path.y[1] + area)
-            plt.title("v[km/h]:" + str(c_speed * 3.6)[0:4])
+            plt.title("v[km/h]:" + str(path.v[1] * 3.6)[0:4])
             plt.grid(True)
             plt.pause(0.0001)
 
@@ -320,5 +564,5 @@ def main():
         plt.show()
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()
diff --git a/docs/modules/path_planning/frenet_frame_path/frenet_frame_path_main.rst b/docs/modules/path_planning/frenet_frame_path/frenet_frame_path_main.rst
index e9d9041e0e..38efaf2b53 100644
--- a/docs/modules/path_planning/frenet_frame_path/frenet_frame_path_main.rst
+++ b/docs/modules/path_planning/frenet_frame_path/frenet_frame_path_main.rst
@@ -1,14 +1,40 @@
 Optimal Trajectory in a Frenet Frame
 ------------------------------------
 
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/animation.gif
-
 This is optimal trajectory generation in a Frenet Frame.
 
 The cyan line is the target course and black crosses are obstacles.
 
 The red line is predicted path.
 
+High Speed and Velocity Keeping Scenario
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/high_speed_and_velocity_keeping_frenet_path.gif
+
+This scenario shows how the trajectory is maintained at high speeds while keeping a consistent velocity.
+
+High Speed and Merging and Stopping Scenario
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/high_speed_and_merging_and_stopping_frenet_path.gif
+
+This scenario demonstrates the trajectory planning at high speeds with merging and stopping behaviors.
+
+Low Speed and Velocity Keeping Scenario
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/low_speed_and_velocity_keeping_frenet_path.gif
+
+This scenario demonstrates how the trajectory is managed at low speeds while maintaining a steady velocity.
+
+Low Speed and Merging and Stopping Scenario
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/low_speed_and_merging_and_stopping_frenet_path.gif
+
+This scenario illustrates the trajectory planning at low speeds with merging and stopping behaviors.
+
 Ref:
 
 -  `Optimal Trajectory Generation for Dynamic Street Scenarios in a
diff --git a/tests/test_frenet_optimal_trajectory.py b/tests/test_frenet_optimal_trajectory.py
index 72bb7a8341..b8761ff4a6 100644
--- a/tests/test_frenet_optimal_trajectory.py
+++ b/tests/test_frenet_optimal_trajectory.py
@@ -1,12 +1,48 @@
 import conftest
 from PathPlanning.FrenetOptimalTrajectory import frenet_optimal_trajectory as m
+from PathPlanning.FrenetOptimalTrajectory.frenet_optimal_trajectory import (
+    LateralMovement,
+    LongitudinalMovement,
+)
 
 
-def test1():
+def default_scenario_test():
     m.show_animation = False
     m.SIM_LOOP = 5
     m.main()
 
 
-if __name__ == '__main__':
+def high_speed_and_merging_and_stopping_scenario_test():
+    m.show_animation = False
+    m.LATERAL_MOVEMENT = LateralMovement.HIGH_SPEED
+    m.LONGITUDINAL_MOVEMENT = LongitudinalMovement.MERGING_AND_STOPPING
+    m.SIM_LOOP = 5
+    m.main()
+
+
+def high_speed_and_velocity_keeping_scenario_test():
+    m.show_animation = False
+    m.LATERAL_MOVEMENT = LateralMovement.HIGH_SPEED
+    m.LONGITUDINAL_MOVEMENT = LongitudinalMovement.VELOCITY_KEEPING
+    m.SIM_LOOP = 5
+    m.main()
+
+
+def low_speed_and_velocity_keeping_scenario_test():
+    m.show_animation = False
+    m.LATERAL_MOVEMENT = LateralMovement.LOW_SPEED
+    m.LONGITUDINAL_MOVEMENT = LongitudinalMovement.VELOCITY_KEEPING
+    m.SIM_LOOP = 5
+    m.main()
+
+
+def low_speed_and_merging_and_stopping_scenario_test():
+    m.show_animation = False
+    m.LATERAL_MOVEMENT = LateralMovement.LOW_SPEED
+    m.LONGITUDINAL_MOVEMENT = LongitudinalMovement.MERGING_AND_STOPPING
+    m.SIM_LOOP = 5
+    m.main()
+
+
+if __name__ == "__main__":
     conftest.run_this_test(__file__)

From 95eedba44713883d805fb92ff74aabbe1fa4f735 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 24 Jan 2025 13:24:26 +0900
Subject: [PATCH 0948/1080] Fix lint error (#1124)

---
 .../arm_obstacle_navigation.py                      |  4 ++--
 .../arm_obstacle_navigation_2.py                    |  4 ++--
 .../dynamic_movement_primitives.py                  | 13 ++++++-------
 .../Eta3SplineTrajectory/eta3_spline_trajectory.py  |  7 +++----
 PathPlanning/RRT/sobol/sobol.py                     | 12 ++++++------
 5 files changed, 19 insertions(+), 21 deletions(-)

diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
index e377897e54..593db42b34 100644
--- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
+++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
@@ -158,7 +158,7 @@ def astar_torus(grid, start_node, goal_node):
         while parent_map[route[0][0]][route[0][1]] != ():
             route.insert(0, parent_map[route[0][0]][route[0][1]])
 
-        print("The route found covers %d grid cells." % len(route))
+        print(f"The route found covers {len(route)} grid cells.")
         for i in range(1, len(route)):
             grid[route[i]] = 6
             plt.cla()
@@ -212,7 +212,7 @@ def calc_heuristic_map(M, goal_node):
     return heuristic_map
 
 
-class NLinkArm(object):
+class NLinkArm:
     """
     Class for controlling and plotting a planar arm with an arbitrary number of links.
     """
diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
index 10f4615c34..5295b7ca3a 100644
--- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
+++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
@@ -189,7 +189,7 @@ def astar_torus(grid, start_node, goal_node):
         while parent_map[route[0][0]][route[0][1]] != ():
             route.insert(0, parent_map[route[0][0]][route[0][1]])
 
-        print("The route found covers %d grid cells." % len(route))
+        print(f"The route found covers {len(route)} grid cells.")
         for i in range(1, len(route)):
             grid[route[i]] = 6
             plt.cla()
@@ -243,7 +243,7 @@ def calc_heuristic_map(M, goal_node):
     return heuristic_map
 
 
-class NLinkArm(object):
+class NLinkArm:
     """
     Class for controlling and plotting a planar arm with an arbitrary number of links.
     """
diff --git a/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py b/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py
index 468d3b9f97..aa34934c0e 100644
--- a/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py
+++ b/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py
@@ -18,7 +18,7 @@
 import numpy as np
 
 
-class DMP(object):
+class DMP:
 
     def __init__(self, training_data, data_period, K=156.25, B=25):
         """
@@ -215,21 +215,21 @@ def show_DMP_purpose(self):
         T_vals = np.linspace(T_orig, 2*T_orig, 20)
 
         for new_q0_value in q0_vals:
-            plot_title = "Initial Position = [%s, %s]" % \
-                         (round(new_q0_value[0], 2), round(new_q0_value[1], 2))
+            plot_title = (f"Initial Position = [{round(new_q0_value[0], 2)},"
+                          f" {round(new_q0_value[1], 2)}]")
 
             _, path = self.recreate_trajectory(new_q0_value, g_orig, T_orig)
             self.view_trajectory(path, title=plot_title, demo=True)
 
         for new_g_value in g_vals:
-            plot_title = "Goal Position = [%s, %s]" % \
-                         (round(new_g_value[0], 2), round(new_g_value[1], 2))
+            plot_title = (f"Goal Position = [{round(new_g_value[0], 2)},"
+                          f" {round(new_g_value[1], 2)}]")
 
             _, path = self.recreate_trajectory(q0_orig, new_g_value, T_orig)
             self.view_trajectory(path, title=plot_title, demo=True)
 
         for new_T_value in T_vals:
-            plot_title = "Period = %s [sec]" % round(new_T_value, 2)
+            plot_title = f"Period = {round(new_T_value, 2)} [sec]"
 
             _, path = self.recreate_trajectory(q0_orig, g_orig, new_T_value)
             self.view_trajectory(path, title=plot_title, demo=True)
@@ -257,5 +257,4 @@ def example_DMP():
 
 
 if __name__ == '__main__':
-
     example_DMP()
diff --git a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
index d034cb8a32..2dab7a7add 100644
--- a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
+++ b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
@@ -26,8 +26,7 @@
 
 class MaxVelocityNotReached(Exception):
     def __init__(self, actual_vel, max_vel):
-        self.message = 'Actual velocity {} does not equal desired max velocity {}!'.format(
-            actual_vel, max_vel)
+        self.message = f'Actual velocity {actual_vel} does not equal desired max velocity {max_vel}!'
 
 
 class eta3_trajectory(Eta3Path):
@@ -42,7 +41,7 @@ def __init__(self, segments, max_vel, v0=0.0, a0=0.0, max_accel=2.0, max_jerk=5.
         # ensure that all inputs obey the assumptions of the model
         assert max_vel > 0 and v0 >= 0 and a0 >= 0 and max_accel > 0 and max_jerk > 0 \
             and a0 <= max_accel and v0 <= max_vel
-        super(eta3_trajectory, self).__init__(segments=segments)
+        super(__class__, self).__init__(segments=segments)
         self.total_length = sum([s.segment_length for s in self.segments])
         self.max_vel = float(max_vel)
         self.v0 = float(v0)
@@ -166,7 +165,7 @@ def velocity_profile(self):
         try:
             assert np.isclose(self.vels[index], 0)
         except AssertionError as e:
-            print('The final velocity {} is not zero'.format(self.vels[index]))
+            print(f'The final velocity {self.vels[index]} is not zero')
             raise e
 
         self.seg_lengths[index] = s_sf
diff --git a/PathPlanning/RRT/sobol/sobol.py b/PathPlanning/RRT/sobol/sobol.py
index 428ba58a98..cddc9d7c72 100644
--- a/PathPlanning/RRT/sobol/sobol.py
+++ b/PathPlanning/RRT/sobol/sobol.py
@@ -370,8 +370,8 @@ def i4_sobol(dim_num, seed):
         if (dim_num < 1 or dim_max < dim_num):
             print('I4_SOBOL - Fatal error!')
             print('    The spatial dimension DIM_NUM should satisfy:')
-            print('        1 <= DIM_NUM <= %d' % dim_max)
-            print('    But this input value is DIM_NUM = %d' % dim_num)
+            print(f'        1 <= DIM_NUM <= {dim_max:d}')
+            print(f'    But this input value is DIM_NUM = {dim_num:d}')
             return None
 
         dim_num_save = dim_num
@@ -443,7 +443,7 @@ def i4_sobol(dim_num, seed):
         #
         l_var = i4_bit_lo0(seed)
 
-    elif (seed <= seed_save):
+    elif seed <= seed_save:
 
         seed_save = 0
         lastq = np.zeros(dim_num)
@@ -471,8 +471,8 @@ def i4_sobol(dim_num, seed):
     if maxcol < l_var:
         print('I4_SOBOL - Fatal error!')
         print('    Too many calls!')
-        print('    MAXCOL = %d\n' % maxcol)
-        print('    L =            %d\n' % l_var)
+        print(f'    MAXCOL = {maxcol:d}\n')
+        print(f'    L =            {l_var:d}\n')
         return None
 
 
@@ -819,7 +819,7 @@ def r8mat_write(filename, m, n, a):
     with open(filename, 'w') as output:
         for i in range(0, m):
             for j in range(0, n):
-                s = '  %g' % (a[i, j])
+                s = f'  {a[i, j]:g}'
                 output.write(s)
             output.write('\n')
 

From 2a489b3b82d3d0a3152cf7641fa137deb116972d Mon Sep 17 00:00:00 2001
From: parmaski <89462537+parmaski@users.noreply.github.com>
Date: Fri, 24 Jan 2025 13:28:12 +0900
Subject: [PATCH 0949/1080] Fix: dead link URL in doc (#1087)

* fix dead url links

* change link to MPC course

* remove dead link
---
 .github/FUNDING.yml                           |  2 +-
 .../arm_obstacle_navigation.py                |  2 +-
 .../arm_obstacle_navigation_2.py              |  2 +-
 PathPlanning/BugPlanning/bug.py               |  2 +-
 .../dynamic_movement_primitives.py            |  2 +-
 .../eta3_spline_trajectory.py                 |  2 +-
 .../InformedRRTStar/informed_rrt_star.py      |  2 +-
 .../quintic_polynomials_planner.py            |  2 +-
 PathPlanning/RRT/sobol/sobol.py               |  2 +-
 .../state_lattice_planner.py                  |  4 ++--
 .../wavefront_coverage_path_planner.py        |  2 +-
 PathTracking/cgmres_nmpc/cgmres_nmpc.py       |  2 +-
 README.md                                     | 21 +++++++++----------
 SLAM/GraphBasedSLAM/data/README.rst           |  2 +-
 appveyor.yml                                  |  2 +-
 docs/conf.py                                  |  2 +-
 docs/getting_started_main.rst                 |  2 +-
 docs/how_to_contribute_main.rst               |  2 +-
 docs/make.bat                                 |  2 +-
 .../particle_filter_localization_main.rst     |  2 +-
 .../rectangle_fitting_main.rst                |  4 ++--
 .../bezier_path/bezier_path_main.rst          |  2 +-
 .../bugplanner/bugplanner_main.rst            |  2 +-
 .../coverage_path/coverage_path_main.rst      |  2 +-
 .../dubins_path/dubins_path_main.rst          |  2 +-
 ...l_predictive_trajectory_generator_main.rst |  2 +-
 .../quintic_polynomials_planner_main.rst      |  2 +-
 .../reeds_shepp_path_main.rst                 |  2 +-
 docs/modules/path_planning/rrt/rrt_main.rst   |  8 +++----
 docs/modules/path_planning/rrt/rrt_star.rst   |  2 +-
 .../state_lattice_planner_main.rst            |  4 ++--
 .../cgmres_nmpc/cgmres_nmpc_main.rst          |  2 +-
 .../lqr_speed_and_steering_control_main.rst   |  2 +-
 ...ictive_speed_and_steering_control_main.rst |  4 ++--
 users_comments.md                             | 16 +++++---------
 35 files changed, 55 insertions(+), 62 deletions(-)

diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml
index 24f0926299..0d943696cc 100644
--- a/.github/FUNDING.yml
+++ b/.github/FUNDING.yml
@@ -1,4 +1,4 @@
 # These are supported funding model platforms
 github:  AtsushiSakai
 patreon: myenigma
-custom: https://www.paypal.me/myenigmapay/
+custom: https://www.paypal.com/paypalme/myenigmapay/
diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
index 593db42b34..9047c13851 100644
--- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
+++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation.py
@@ -34,7 +34,7 @@ def detect_collision(line_seg, circle):
     """
     Determines whether a line segment (arm link) is in contact
     with a circle (obstacle).
-    Credit to: http://doswa.com/2009/07/13/circle-segment-intersectioncollision.html
+    Credit to: https://web.archive.org/web/20200130224918/http://doswa.com/2009/07/13/circle-segment-intersectioncollision.html
     Args:
         line_seg: List of coordinates of line segment endpoints e.g. [[1, 1], [2, 2]]
         circle: List of circle coordinates and radius e.g. [0, 0, 0.5] is a circle centered
diff --git a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
index 5295b7ca3a..f5d435082a 100644
--- a/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
+++ b/ArmNavigation/arm_obstacle_navigation/arm_obstacle_navigation_2.py
@@ -66,7 +66,7 @@ def detect_collision(line_seg, circle):
     """
     Determines whether a line segment (arm link) is in contact
     with a circle (obstacle).
-    Credit to: http://doswa.com/2009/07/13/circle-segment-intersectioncollision.html
+    Credit to: https://web.archive.org/web/20200130224918/http://doswa.com/2009/07/13/circle-segment-intersectioncollision.html
     Args:
         line_seg: List of coordinates of line segment endpoints e.g. [[1, 1], [2, 2]]
         circle: List of circle coordinates and radius e.g. [0, 0, 0.5] is a circle centered
diff --git a/PathPlanning/BugPlanning/bug.py b/PathPlanning/BugPlanning/bug.py
index 62fec7c109..34890cb55a 100644
--- a/PathPlanning/BugPlanning/bug.py
+++ b/PathPlanning/BugPlanning/bug.py
@@ -1,7 +1,7 @@
 """
 Bug Planning
 author: Sarim Mehdi(muhammadsarim.mehdi@studio.unibo.it)
-Source: https://sites.google.com/site/ece452bugalgorithms/
+Source: https://web.archive.org/web/20201103052224/https://sites.google.com/site/ece452bugalgorithms/
 """
 
 import numpy as np
diff --git a/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py b/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py
index aa34934c0e..9ccd18b7c2 100644
--- a/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py
+++ b/PathPlanning/DynamicMovementPrimitives/dynamic_movement_primitives.py
@@ -9,7 +9,7 @@
 
 More information on Dynamic Movement Primitives available at:
 https://arxiv.org/abs/2102.03861
-https://www.frontiersin.org/articles/10.3389/fncom.2013.00138/full
+https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2013.00138/full
 
 """
 
diff --git a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
index 2dab7a7add..a73797dacb 100644
--- a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
+++ b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
@@ -6,7 +6,7 @@
         Atsushi Sakai (@Atsushi_twi)
 
 Refs:
-- https://jwdinius.github.io/blog/2018/eta3traj
+- https://jwdinius.github.io/blog/2018/eta3traj/
 - [eta^3-Splines for the Smooth Path Generation of Wheeled Mobile Robots]
 (https://ieeexplore.ieee.org/document/4339545/)
 
diff --git a/PathPlanning/InformedRRTStar/informed_rrt_star.py b/PathPlanning/InformedRRTStar/informed_rrt_star.py
index c37326e490..0483949c99 100644
--- a/PathPlanning/InformedRRTStar/informed_rrt_star.py
+++ b/PathPlanning/InformedRRTStar/informed_rrt_star.py
@@ -6,7 +6,7 @@
 
 Reference: Informed RRT*: Optimal Sampling-based Path planning Focused via
 Direct Sampling of an Admissible Ellipsoidal Heuristic
-https://arxiv.org/pdf/1404.2334.pdf
+https://arxiv.org/pdf/1404.2334
 
 """
 import sys
diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
index 8ba11a23d2..fdc181afab 100644
--- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
+++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
@@ -6,7 +6,7 @@
 
 Ref:
 
-- [Local Path planning And Motion Control For Agv In Positioning](http://ieeexplore.ieee.org/document/637936/)
+- [Local Path planning And Motion Control For Agv In Positioning](https://ieeexplore.ieee.org/document/637936/)
 
 """
 
diff --git a/PathPlanning/RRT/sobol/sobol.py b/PathPlanning/RRT/sobol/sobol.py
index cddc9d7c72..520d686a1d 100644
--- a/PathPlanning/RRT/sobol/sobol.py
+++ b/PathPlanning/RRT/sobol/sobol.py
@@ -13,7 +13,7 @@
     PYTHON versions by Corrado Chisari
 
     Original code is available at
-    http://people.sc.fsu.edu/~jburkardt/py_src/sobol/sobol.html
+    https://people.sc.fsu.edu/~jburkardt/py_src/sobol/sobol.html
 
     Note: the i4 prefix means that the function takes a numeric argument or
           returns a number which is interpreted inside the function as a 4
diff --git a/PathPlanning/StateLatticePlanner/state_lattice_planner.py b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
index f4b1461b7a..7f8e725e0a 100644
--- a/PathPlanning/StateLatticePlanner/state_lattice_planner.py
+++ b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
@@ -12,8 +12,8 @@
 
 - State Space Sampling of Feasible Motions for High-Performance Mobile Robot
 Navigation in Complex Environments
-http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.8210&rep=rep1
-&type=pdf
+https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf
+&doi=e2256b5b24137f89e473f01df288cb3aa72e56a0
 
 """
 import sys
diff --git a/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py b/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py
index 7e93caa8fb..c5a139454b 100644
--- a/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py
+++ b/PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py
@@ -4,7 +4,7 @@
 author: Todd Tang
 paper: Planning paths of complete coverage of an unstructured environment
          by a mobile robot - Zelinsky et.al.
-link: http://pinkwink.kr/attachment/cfile3.uf@1354654A4E8945BD13FE77.pdf
+link: https://pinkwink.kr/attachment/cfile3.uf@1354654A4E8945BD13FE77.pdf
 """
 
 import os
diff --git a/PathTracking/cgmres_nmpc/cgmres_nmpc.py b/PathTracking/cgmres_nmpc/cgmres_nmpc.py
index bb5699b888..a582c9da81 100644
--- a/PathTracking/cgmres_nmpc/cgmres_nmpc.py
+++ b/PathTracking/cgmres_nmpc/cgmres_nmpc.py
@@ -6,7 +6,7 @@
 
 Ref:
 Shunichi09/nonlinear_control: Implementing the nonlinear model predictive
-control, sliding mode control https://github.com/Shunichi09/nonlinear_control
+control, sliding mode control https://github.com/Shunichi09/PythonLinearNonlinearControl
 
 """
 
diff --git a/README.md b/README.md
index ee40dfc7eb..ec65342430 100644
--- a/README.md
+++ b/README.md
@@ -5,7 +5,6 @@
 ![GitHub_Action_MacOS_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/MacOS_CI/badge.svg)
 ![GitHub_Action_Windows_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/Windows_CI/badge.svg)
 [![Build status](https://ci.appveyor.com/api/projects/status/sb279kxuv1be391g?svg=true)](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics)
-[![codecov](https://codecov.io/gh/AtsushiSakai/PythonRobotics/branch/master/graph/badge.svg)](https://codecov.io/gh/AtsushiSakai/PythonRobotics)
 
 Python codes for robotics algorithm.
 
@@ -111,7 +110,7 @@ For development:
   
 - [pytest-xdist](https://pypi.org/project/pytest-xdist/) (for parallel unit tests)
   
-- [mypy](http://mypy-lang.org/) (for type check)
+- [mypy](https://mypy-lang.org/) (for type check)
   
 - [sphinx](https://www.sphinx-doc.org/) (for document generation)
   
@@ -328,7 +327,7 @@ The animation shows a robot finding its path and rerouting to avoid obstacles as
 
 Refs:
 
-- [D* Lite](http://idm-lab.org/bib/abstracts/papers/aaai02b.pd)
+- [D* Lite](http://idm-lab.org/bib/abstracts/papers/aaai02b.pdf)
 - [Improved Fast Replanning for Robot Navigation in Unknown Terrain](http://www.cs.cmu.edu/~maxim/files/dlite_icra02.pdf)
 
 ### Potential Field algorithm
@@ -357,9 +356,9 @@ This code uses the model predictive trajectory generator to solve boundary probl
 
 Ref: 
 
-- [Optimal rough terrain trajectory generation for wheeled mobile robots](http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328)
+- [Optimal rough terrain trajectory generation for wheeled mobile robots](https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328)
 
-- [State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments](http://www.frc.ri.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf)
+- [State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments](https://www.frc.ri.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf)
 
 
 ### Biased polar sampling
@@ -403,7 +402,7 @@ Ref:
 
 - [Incremental Sampling-based Algorithms for Optimal Motion Planning](https://arxiv.org/abs/1005.0416)
 
-- [Sampling-based Algorithms for Optimal Motion Planning](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.419.5503&rep=rep1&type=pdf)
+- [Sampling-based Algorithms for Optimal Motion Planning](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=bddbc99f97173430aa49a0ada53ab5bade5902fa)
 
 ### RRT\* with reeds-shepp path
 
@@ -421,7 +420,7 @@ A double integrator motion model is used for LQR local planner.
 
 Ref:
 
-- [LQR\-RRT\*: Optimal Sampling\-Based Motion Planning with Automatically Derived Extension Heuristics](http://lis.csail.mit.edu/pubs/perez-icra12.pdf)
+- [LQR\-RRT\*: Optimal Sampling\-Based Motion Planning with Automatically Derived Extension Heuristics](https://lis.csail.mit.edu/pubs/perez-icra12.pdf)
 
 - [MahanFathi/LQR\-RRTstar: LQR\-RRT\* method is used for random motion planning of a simple pendulum in its phase plot](https://github.com/MahanFathi/LQR-RRTstar)
 
@@ -436,7 +435,7 @@ It can calculate a 2D path, velocity, and acceleration profile based on quintic
 
 Ref:
 
-- [Local Path Planning And Motion Control For Agv In Positioning](http://ieeexplore.ieee.org/document/637936/)
+- [Local Path Planning And Motion Control For Agv In Positioning](https://ieeexplore.ieee.org/document/637936/)
 
 ## Reeds Shepp planning
 
@@ -523,7 +522,7 @@ Path tracking simulation with LQR speed and steering control.
 
 Ref:
 
-- [Towards fully autonomous driving: Systems and algorithms \- IEEE Conference Publication](http://ieeexplore.ieee.org/document/5940562/)
+- [Towards fully autonomous driving: Systems and algorithms \- IEEE Conference Publication](https://ieeexplore.ieee.org/document/5940562/)
 
 
 ## Model predictive speed and steering control
@@ -630,7 +629,7 @@ If you or your company would like to support this project, please consider:
 
 - [Become a backer or sponsor on Patreon](https://www.patreon.com/myenigma)
 
-- [One-time donation via PayPal](https://www.paypal.me/myenigmapay/)
+- [One-time donation via PayPal](https://www.paypal.com/paypalme/myenigmapay/)
 
 If you would like to support us in some other way, please contact with creating an issue.
 
@@ -640,7 +639,7 @@ If you would like to support us in some other way, please contact with creating
 
 They are providing a free license of their IDEs for this OSS development.   
 
-### [1Password](https://github.com/1Password/1password-teams-open-source)
+### [1Password](https://github.com/1Password/for-open-source)
 
 They are providing a free license of their 1Password team license for this OSS project.   
 
diff --git a/SLAM/GraphBasedSLAM/data/README.rst b/SLAM/GraphBasedSLAM/data/README.rst
index c875cce73f..15bc5b6c03 100644
--- a/SLAM/GraphBasedSLAM/data/README.rst
+++ b/SLAM/GraphBasedSLAM/data/README.rst
@@ -3,4 +3,4 @@ Acknowledgments and References
 
 Thanks to Luca Larlone for allowing inclusion of the `Intel dataset <https://lucacarlone.mit.edu/datasets/>`_ in this repo.
 
-1. Carlone, L. and Censi, A., 2014. `From angular manifolds to the integer lattice: Guaranteed orientation estimation with application to pose graph optimization <https://arxiv.org/pdf/1211.3063.pdf>`_. IEEE Transactions on Robotics, 30(2), pp.475-492.
+1. Carlone, L. and Censi, A., 2014. `From angular manifolds to the integer lattice: Guaranteed orientation estimation with application to pose graph optimization <https://arxiv.org/pdf/1211.3063>`_. IEEE Transactions on Robotics, 30(2), pp.475-492.
diff --git a/appveyor.yml b/appveyor.yml
index 4964bab3da..05ad8a2820 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -4,7 +4,7 @@ environment:
   global:
     # SDK v7.0 MSVC Express 2008's SetEnv.cmd script will fail if the
     # /E:ON and /V:ON options are not enabled in the batch script intepreter
-    # See: http://stackoverflow.com/a/13751649/163740
+    # See: https://stackoverflow.com/a/13751649/163740
     CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd"
 
   matrix:
diff --git a/docs/conf.py b/docs/conf.py
index 46f69cf17b..4e06f3f988 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -3,7 +3,7 @@
 #
 # This file does only contain a selection of the most common options. For a
 # full list see the documentation:
-# http://www.sphinx-doc.org/en/master/config
+# https://www.sphinx-doc.org/en/master/config
 
 # -- Path setup --------------------------------------------------------------
 
diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst
index 88f218545e..004d68227b 100644
--- a/docs/getting_started_main.rst
+++ b/docs/getting_started_main.rst
@@ -49,7 +49,7 @@ For development:
 .. _`pytest-xdist`: https://github.com/pytest-dev/pytest-xdist
 .. _`mypy`: https://mypy-lang.org/
 .. _`sphinx`: https://www.sphinx-doc.org/en/master/index.html
-.. _`ruff`: https://github.com/charliermarsh/ruff
+.. _`ruff`: https://github.com/astral-sh/ruff
 
 
 How to use
diff --git a/docs/how_to_contribute_main.rst b/docs/how_to_contribute_main.rst
index 48895d6fda..915b2155e4 100644
--- a/docs/how_to_contribute_main.rst
+++ b/docs/how_to_contribute_main.rst
@@ -159,6 +159,6 @@ Sponsors
 .. _`JetBrains`: https://www.jetbrains.com/
 .. _`Sponsor @AtsushiSakai on GitHub Sponsors`: https://github.com/sponsors/AtsushiSakai
 .. _`Become a backer or sponsor on Patreon`: https://www.patreon.com/myenigma
-.. _`One-time donation via PayPal`: https://www.paypal.me/myenigmapay/
+.. _`One-time donation via PayPal`: https://www.paypal.com/paypalme/myenigmapay/
 
 
diff --git a/docs/make.bat b/docs/make.bat
index 6aab964dcc..07dcebea41 100644
--- a/docs/make.bat
+++ b/docs/make.bat
@@ -22,7 +22,7 @@ if errorlevel 9009 (
 	echo.may add the Sphinx directory to PATH.
 	echo.
 	echo.If you don't have Sphinx installed, grab it from
-	echo.http://sphinx-doc.org/
+	echo.https://sphinx-doc.org/
 	exit /b 1
 )
 
diff --git a/docs/modules/localization/particle_filter_localization/particle_filter_localization_main.rst b/docs/modules/localization/particle_filter_localization/particle_filter_localization_main.rst
index c8652ce8a7..20a9eb58fc 100644
--- a/docs/modules/localization/particle_filter_localization/particle_filter_localization_main.rst
+++ b/docs/modules/localization/particle_filter_localization/particle_filter_localization_main.rst
@@ -34,4 +34,4 @@ References:
 ~~~~~~~~~~~
 
 - `_PROBABILISTIC ROBOTICS: <http://www.probabilistic-robotics.org>`_
-- `Improving the particle filter in high dimensions using conjugate artificial process noise <https://arxiv.org/pdf/1801.07000.pdf>`_
+- `Improving the particle filter in high dimensions using conjugate artificial process noise <https://arxiv.org/pdf/1801.07000>`_
diff --git a/docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst b/docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst
index 50a50141b2..b6ced1dc1d 100644
--- a/docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst
+++ b/docs/modules/mapping/rectangle_fitting/rectangle_fitting_main.rst
@@ -7,7 +7,7 @@ This is an object shape recognition using rectangle fitting.
 
 This example code is based on this paper algorithm:
 
-- `Efficient L\-Shape Fitting for Vehicle Detection Using Laser Scanners \- The Robotics Institute Carnegie Mellon University <https://www.ri.cmu.edu/publications/efficient-l-shape-fitting-for-vehicle-detection-using-laser-scanners>`_
+- `Efficient L\-Shape Fitting for Vehicle Detection Using Laser Scanners \- The Robotics Institute Carnegie Mellon University <https://www.ri.cmu.edu/publications/efficient-l-shape-fitting-for-vehicle-detection-using-laser-scanners/>`_
 
 The algorithm consists of 2 steps as below.
 
@@ -66,4 +66,4 @@ API
 References
 ~~~~~~~~~~
 
-- `Efficient L\-Shape Fitting for Vehicle Detection Using Laser Scanners \- The Robotics Institute Carnegie Mellon University <https://www.ri.cmu.edu/publications/efficient-l-shape-fitting-for-vehicle-detection-using-laser-scanners>`_
+- `Efficient L\-Shape Fitting for Vehicle Detection Using Laser Scanners \- The Robotics Institute Carnegie Mellon University <https://www.ri.cmu.edu/publications/efficient-l-shape-fitting-for-vehicle-detection-using-laser-scanners/>`_
diff --git a/docs/modules/path_planning/bezier_path/bezier_path_main.rst b/docs/modules/path_planning/bezier_path/bezier_path_main.rst
index fbba6a4496..d306a85352 100644
--- a/docs/modules/path_planning/bezier_path/bezier_path_main.rst
+++ b/docs/modules/path_planning/bezier_path/bezier_path_main.rst
@@ -17,4 +17,4 @@ Ref:
 
 -  `Continuous Curvature Path Generation Based on Bezier Curves for
    Autonomous
-   Vehicles <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.294.6438&rep=rep1&type=pdf>`__
+   Vehicles <https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=b00b657c3e0e828c589132a14825e7119772003d>`
diff --git a/docs/modules/path_planning/bugplanner/bugplanner_main.rst b/docs/modules/path_planning/bugplanner/bugplanner_main.rst
index 72cc46709f..81c91c0313 100644
--- a/docs/modules/path_planning/bugplanner/bugplanner_main.rst
+++ b/docs/modules/path_planning/bugplanner/bugplanner_main.rst
@@ -5,4 +5,4 @@ This is a 2D planning with Bug algorithm.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BugPlanner/animation.gif
 
-- `ECE452 Bug Algorithms <https://sites.google.com/site/ece452bugalgorithms/>`_
+- `ECE452 Bug Algorithms <https://web.archive.org/web/20201103052224/https://sites.google.com/site/ece452bugalgorithms/>`_
diff --git a/docs/modules/path_planning/coverage_path/coverage_path_main.rst b/docs/modules/path_planning/coverage_path/coverage_path_main.rst
index 828342a294..0a688b5ed2 100644
--- a/docs/modules/path_planning/coverage_path/coverage_path_main.rst
+++ b/docs/modules/path_planning/coverage_path/coverage_path_main.rst
@@ -29,6 +29,6 @@ This is a 2D grid based wavefront coverage path planner simulation:
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation2.gif
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation3.gif
 
-- `Planning paths of complete coverage of an unstructured environment by a mobile robot <http://pinkwink.kr/attachment/cfile3.uf@1354654A4E8945BD13FE77.pdf>`_
+- `Planning paths of complete coverage of an unstructured environment by a mobile robot <https://pinkwink.kr/attachment/cfile3.uf@1354654A4E8945BD13FE77.pdf>`_
 
 
diff --git a/docs/modules/path_planning/dubins_path/dubins_path_main.rst b/docs/modules/path_planning/dubins_path/dubins_path_main.rst
index 516638d83d..12172fb51e 100644
--- a/docs/modules/path_planning/dubins_path/dubins_path_main.rst
+++ b/docs/modules/path_planning/dubins_path/dubins_path_main.rst
@@ -72,5 +72,5 @@ Reference
 ~~~~~~~~~~~~~~~~~~~~
 -  `On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents <https://www.jstor.org/stable/2372560?origin=crossref>`__
 -  `Dubins path - Wikipedia <https://en.wikipedia.org/wiki/Dubins_path>`__
--  `15.3.1 Dubins Curves <http://planning.cs.uiuc.edu/node821.html>`__
+-  `15.3.1 Dubins Curves <https://lavalle.pl/planning/node821.html>`__
 -  `A Comprehensive, Step-by-Step Tutorial to Computing Dubin’s Paths <https://gieseanw.wordpress.com/2012/10/21/a-comprehensive-step-by-step-tutorial-to-computing-dubins-paths/>`__
diff --git a/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst b/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst
index 0fc997898e..463363ddcf 100644
--- a/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst
+++ b/docs/modules/path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst
@@ -19,4 +19,4 @@ Lookup table generation sample
 Ref:
 
 -  `Optimal rough terrain trajectory generation for wheeled mobile
-   robots <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
+   robots <https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
diff --git a/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst b/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst
index feec345bae..0412b3c9b3 100644
--- a/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst
+++ b/docs/modules/path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst
@@ -101,6 +101,6 @@ References:
 ~~~~~~~~~~~
 
 -  `Local Path Planning And Motion Control For Agv In
-   Positioning <http://ieeexplore.ieee.org/document/637936/>`__
+   Positioning <https://ieeexplore.ieee.org/document/637936/>`__
 
 
diff --git a/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path_main.rst b/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path_main.rst
index 6d456417fe..ff377eb91b 100644
--- a/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path_main.rst
+++ b/docs/modules/path_planning/reeds_shepp_path/reeds_shepp_path_main.rst
@@ -383,7 +383,7 @@ Hence, we have:
 Ref:
 
 -  `15.3.2 Reeds-Shepp
-   Curves <http://planning.cs.uiuc.edu/node822.html>`__
+   Curves <https://lavalle.pl/planning/node822.html>`__
 
 -  `optimal paths for a car that goes both forwards and
    backwards <https://pdfs.semanticscholar.org/932e/c495b1d0018fd59dee12a0bf74434fac7af4.pdf>`__
diff --git a/docs/modules/path_planning/rrt/rrt_main.rst b/docs/modules/path_planning/rrt/rrt_main.rst
index 5a3a30dcff..e5f351a7ba 100644
--- a/docs/modules/path_planning/rrt/rrt_main.rst
+++ b/docs/modules/path_planning/rrt/rrt_main.rst
@@ -57,7 +57,7 @@ Ref:
 
 -  `Informed RRT\*: Optimal Sampling-based Path Planning Focused via
    Direct Sampling of an Admissible Ellipsoidal
-   Heuristic <https://arxiv.org/pdf/1404.2334.pdf>`__
+   Heuristic <https://arxiv.org/pdf/1404.2334>`__
 
 .. _batch-informed-rrt*:
 
@@ -90,10 +90,10 @@ PID is used for speed control.
 Ref:
 
 -  `Motion Planning in Complex Environments using Closed-loop
-   Prediction <http://acl.mit.edu/papers/KuwataGNC08.pdf>`__
+   Prediction <https://acl.mit.edu/papers/KuwataGNC08.pdf>`__
 
 -  `Real-time Motion Planning with Applications to Autonomous Urban
-   Driving <http://acl.mit.edu/papers/KuwataTCST09.pdf>`__
+   Driving <https://acl.mit.edu/papers/KuwataTCST09.pdf>`__
 
 -  `[1601.06326] Sampling-based Algorithms for Optimal Motion Planning
    Using Closed-loop Prediction <https://arxiv.org/abs/1601.06326>`__
@@ -113,6 +113,6 @@ Ref:
 
 -  `LQR-RRT\*: Optimal Sampling-Based Motion Planning with Automatically
    Derived Extension
-   Heuristics <http://lis.csail.mit.edu/pubs/perez-icra12.pdf>`__
+   Heuristics <https://lis.csail.mit.edu/pubs/perez-icra12.pdf>`__
 
 -  `MahanFathi/LQR-RRTstar: LQR-RRT\* method is used for random motion planning of a simple pendulum in its phase plot <https://github.com/MahanFathi/LQR-RRTstar>`__
\ No newline at end of file
diff --git a/docs/modules/path_planning/rrt/rrt_star.rst b/docs/modules/path_planning/rrt/rrt_star.rst
index d36eaac70e..6deb6b9172 100644
--- a/docs/modules/path_planning/rrt/rrt_star.rst
+++ b/docs/modules/path_planning/rrt/rrt_star.rst
@@ -16,6 +16,6 @@ Simulation
 
 Ref
 ^^^
--  `Sampling-based Algorithms for Optimal Motion Planning <https://arxiv.org/pdf/1105.1186.pdf>`__
+-  `Sampling-based Algorithms for Optimal Motion Planning <https://arxiv.org/pdf/1105.1186>`__
 -  `Incremental Sampling-based Algorithms for Optimal Motion Planning <https://arxiv.org/abs/1005.0416>`__
 
diff --git a/docs/modules/path_planning/state_lattice_planner/state_lattice_planner_main.rst b/docs/modules/path_planning/state_lattice_planner/state_lattice_planner_main.rst
index 3ef0c7eca2..d5e7ed17d1 100644
--- a/docs/modules/path_planning/state_lattice_planner/state_lattice_planner_main.rst
+++ b/docs/modules/path_planning/state_lattice_planner/state_lattice_planner_main.rst
@@ -25,9 +25,9 @@ Lane sampling
 Ref:
 
 -  `Optimal rough terrain trajectory generation for wheeled mobile
-   robots <http://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
+   robots <https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
 
 -  `State Space Sampling of Feasible Motions for High-Performance Mobile
    Robot Navigation in Complex
-   Environments <http://www.frc.ri.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf>`__
+   Environments <https://www.frc.ri.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf>`__
 
diff --git a/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst
index a1980ba17e..23f1218a94 100644
--- a/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst
+++ b/docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst
@@ -60,7 +60,7 @@ Ref
 
 -  `Shunichi09/nonlinear_control: Implementing the nonlinear model
    predictive control, sliding mode
-   control <https://github.com/Shunichi09/nonlinear_control>`__
+   control <https://github.com/Shunichi09/PythonLinearNonlinearControl>`__
 
 -  `非線形モデル予測制御におけるCGMRES法をpythonで実装する -
    Qiita <https://qiita.com/MENDY/items/4108190a579395053924>`__
diff --git a/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst b/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst
index a0b145456c..ded187e972 100644
--- a/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst
+++ b/docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst
@@ -137,4 +137,4 @@ Simulation results
 References:
 ~~~~~~~~~~~
 
--  `Towards fully autonomous driving: Systems and algorithms <http://ieeexplore.ieee.org/document/5940562/>`__
+-  `Towards fully autonomous driving: Systems and algorithms <https://ieeexplore.ieee.org/document/5940562/>`__
diff --git a/docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst b/docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst
index 59a7166674..de55b545ba 100644
--- a/docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst
+++ b/docs/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst
@@ -133,5 +133,5 @@ Reference
 -  `Vehicle Dynamics and Control \| Rajesh Rajamani \|
    Springer <http://www.springer.com/us/book/9781461414322>`__
 
--  `MPC Course Material - MPC Lab @
-   UC-Berkeley <http://www.mpc.berkeley.edu/mpc-course-material>`__
+-  `MPC Book - MPC Lab @
+   UC-Berkeley <https://sites.google.com/berkeley.edu/mpc-lab/mpc-course-material>`__
diff --git a/users_comments.md b/users_comments.md
index 66e8b4022e..a2f798eac4 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -17,13 +17,7 @@ Ref:
 
 # Educational users
 
-This is a list of users who are using PythonRobotics for education.
-
-If you found other users, please make an issue to let me know.
-
-- [CSCI/ARTI 4530/6530: Introduction to Robotics (Fall 2018),  University of Georgia ](http://cobweb.cs.uga.edu/~ramviyas/csci_x530.html)
-
-- [CIT Modules & Programmes \- COMP9073 \- Automation with Python](https://courses.cit.ie/index.cfm/page/module/moduleId/14416)
+If you found users who are using PythonRobotics for education, please make an issue to let me know.
 
 # Stargazers location map
 
@@ -386,14 +380,14 @@ Dear Atsushi Sakai, <br>Thank you so much for creating PythonRobotics and docume
 1. B. Blaga, M. Deac, R. W. Y. Al-doori, M. Negru and R. Dǎnescu, "Miniature Autonomous Vehicle Development on Raspberry Pi," 2018 IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, 2018, pp. 229-236.
 doi: 10.1109/ICCP.2018.8516589
 keywords: {Automobiles;Task analysis;Autonomous vehicles;Path planning;Global Positioning System;Cameras;miniature autonomous vehicle;path planning;navigation;parking assist;lane detection and tracking;traffic sign recognition},
-URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8516589&isnumber=8516425
+URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8516589&isnumber=8516425
 
 2. Peggy (Yuchun) Wang and Caitlin Hogan, "Path Planning with Dynamic Obstacle Avoidance for a Jumping-Enabled Robot", AA228/CS238 class report, Department of Computer Science, Stanford University, URL: https://web.stanford.edu/class/aa228/reports/2018/final113.pdf
 
-3. Welburn, E, Hakim Khalili, H, Gupta, A, Watson, S & Carrasco, J 2019, A Navigational System for Quadcopter Remote Inspection of Offshore Substations. in The Fifteenth International Conference on Autonomic and Autonomous Systems 2019. URL:https://www.research.manchester.ac.uk/portal/files/107169964/ICAS19_A_Navigational_System_for_Quadcopter_Remote_Inspection_of_Offshore_Substations.pdf
+3. Welburn, E, Hakim Khalili, H, Gupta, A, Watson, S & Carrasco, J 2019, A Navigational System for Quadcopter Remote Inspection of Offshore Substations. in The Fifteenth International Conference on Autonomic and Autonomous Systems 2019. URL:https://research.manchester.ac.uk/portal/files/107169964/ICAS19_A_Navigational_System_for_Quadcopter_Remote_Inspection_of_Offshore_Substations.pdf
 
 4. E. Horváth, C. Hajdu, C. Radu and Á. Ballagi, "Range Sensor-based Occupancy Grid Mapping with Signatures," 2019 20th International Carpathian Control Conference (ICCC), Krakow-Wieliczka, Poland, 2019, pp. 1-5.
-URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8765684&isnumber=8765679
+URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8765684&isnumber=8765679
 
 5. Josie Hughes, Masaru Shimizu, and Arnoud Visser, "A Review of Robot Rescue Simulation Platforms for Robotics Education"
 URL: https://2019.robocup.org/downloads/program/HughesEtAl2019.pdf
@@ -408,7 +402,7 @@ URL: https://arxiv.org/abs/1910.01557
 URL: https://pdfs.semanticscholar.org/5c06/f3cb9542a51e1bf1a32523c1bc7fea6cecc5.pdf
 
 9. Brijen Thananjeyan, et al. "ABC-LMPC: Safe Sample-Based Learning MPC for Stochastic Nonlinear Dynamical Systems with Adjustable Boundary Conditions"
-URL: https://arxiv.org/pdf/2003.01410.pdf
+URL: https://arxiv.org/pdf/2003.01410
 
 # Others
 

From 732db3d40d1dbdcfe60a3a5a02948d306c6a4c88 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 25 Jan 2025 21:06:23 +0900
Subject: [PATCH 0950/1080] Fix Doc generation warning (#1125)

---
 docs/getting_started_main.rst                          |  2 +-
 .../catmull_rom_spline/catmull_rom_spline_main.rst     | 10 +++++-----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst
index 004d68227b..256319ab8f 100644
--- a/docs/getting_started_main.rst
+++ b/docs/getting_started_main.rst
@@ -40,7 +40,7 @@ For development:
 -  `sphinx`_ (for document generation)
 -  `ruff`_ (for code style check)
 
-.. _`Python 3.11.x`: https://www.python.org/
+.. _`Python 3.12.x`: https://www.python.org/
 .. _`NumPy`: https://numpy.org/
 .. _`SciPy`: https://scipy.org/
 .. _`Matplotlib`: https://matplotlib.org/
diff --git a/docs/modules/path_planning/catmull_rom_spline/catmull_rom_spline_main.rst b/docs/modules/path_planning/catmull_rom_spline/catmull_rom_spline_main.rst
index 4d8d3bdefe..72e558c486 100644
--- a/docs/modules/path_planning/catmull_rom_spline/catmull_rom_spline_main.rst
+++ b/docs/modules/path_planning/catmull_rom_spline/catmull_rom_spline_main.rst
@@ -1,5 +1,5 @@
 Catmull-Rom Spline Planning
------------------
+----------------------------
 
 .. image:: catmull_rom_path_planning.png
 
@@ -10,7 +10,7 @@ exhibits local control, and maintains 𝐶1 continuity.
 
 
 Catmull-Rom Spline Fundamentals
-~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Catmull-Rom splines are a type of cubic spline that passes through a given set of points, known as control points. 
 
@@ -24,7 +24,7 @@ Where:
 * :math:`P_0, P_1, P_2, P_3` are the control points surrounding the parameter :math:`t`.
 
 Types of Catmull-Rom Splines
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 There are different types of Catmull-Rom splines based on the choice of the **tau** parameter, which influences how the curve 
 behaves in relation to the control points:
@@ -51,7 +51,7 @@ behaves in relation to the control points:
 
 
 Blending Functions
-~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~
 
 In Catmull-Rom spline interpolation, blending functions are used to calculate the influence of each control point on the spline at a 
 given parameter :math:`t`. The blending functions ensure that the spline is smooth and passes through the control points while 
@@ -97,7 +97,7 @@ API
 
 
 References
-~~~~~~~~~~
+~~~~~~~~~~~~~~
 
 -  `Catmull-Rom Spline - Wikipedia <https://en.wikipedia.org/wiki/Centripetal_Catmull%E2%80%93Rom_spline>`__
 -  `Catmull-Rom Splines <http://graphics.cs.cmu.edu/nsp/course/15-462/Fall04/assts/catmullRom.pdf>`__
\ No newline at end of file

From bf8f1774b753d3b0915960628386bb220de8f0af Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 25 Jan 2025 23:30:14 +0900
Subject: [PATCH 0951/1080] Update doc organization (#1126)

---
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diff --git a/docs/conf.py b/docs/conf.py
index 4e06f3f988..919fa9ac76 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -19,7 +19,7 @@
 # -- Project information -----------------------------------------------------
 
 project = 'PythonRobotics'
-copyright = '2018-2023, Atsushi Sakai'
+copyright = '2018-now, Atsushi Sakai'
 author = 'Atsushi Sakai'
 
 # The short X.Y version
diff --git a/docs/index_main.rst b/docs/index_main.rst
index 67bd9889f2..08c318ea83 100644
--- a/docs/index_main.rst
+++ b/docs/index_main.rst
@@ -34,18 +34,18 @@ See this paper for more details:
    :caption: Contents
 
    getting_started
-   modules/introduction
-   modules/localization/localization
-   modules/mapping/mapping
-   modules/slam/slam
-   modules/path_planning/path_planning
-   modules/path_tracking/path_tracking
-   modules/arm_navigation/arm_navigation
-   modules/aerial_navigation/aerial_navigation
-   modules/bipedal/bipedal
-   modules/control/control
-   modules/utils/utils
-   modules/appendix/appendix
+   modules/1_introduction/introduction
+   modules/2_localization/localization
+   modules/3_mapping/mapping
+   modules/4_slam/slam
+   modules/5_path_planning/path_planning
+   modules/6_path_tracking/path_tracking
+   modules/7_arm_navigation/arm_navigation
+   modules/8_aerial_navigation/aerial_navigation
+   modules/9_bipedal/bipedal
+   modules/10_control/control
+   modules/11_utils/utils
+   modules/12_appendix/appendix
    how_to_contribute
 
 
diff --git a/docs/modules/control/control_main.rst b/docs/modules/10_control/control_main.rst
similarity index 100%
rename from docs/modules/control/control_main.rst
rename to docs/modules/10_control/control_main.rst
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similarity index 100%
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diff --git a/docs/modules/appendix/steering_motion_model/steering_model.png b/docs/modules/12_appendix/steering_motion_model/steering_model.png
similarity index 100%
rename from docs/modules/appendix/steering_motion_model/steering_model.png
rename to docs/modules/12_appendix/steering_motion_model/steering_model.png
diff --git a/docs/modules/appendix/steering_motion_model/turning_radius_calc1.png b/docs/modules/12_appendix/steering_motion_model/turning_radius_calc1.png
similarity index 100%
rename from docs/modules/appendix/steering_motion_model/turning_radius_calc1.png
rename to docs/modules/12_appendix/steering_motion_model/turning_radius_calc1.png
diff --git a/docs/modules/appendix/steering_motion_model/turning_radius_calc2.png b/docs/modules/12_appendix/steering_motion_model/turning_radius_calc2.png
similarity index 100%
rename from docs/modules/appendix/steering_motion_model/turning_radius_calc2.png
rename to docs/modules/12_appendix/steering_motion_model/turning_radius_calc2.png
diff --git a/docs/modules/appendix/steering_motion_model_main.rst b/docs/modules/12_appendix/steering_motion_model_main.rst
similarity index 100%
rename from docs/modules/appendix/steering_motion_model_main.rst
rename to docs/modules/12_appendix/steering_motion_model_main.rst
diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
new file mode 100644
index 0000000000..bdc7dc53e9
--- /dev/null
+++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
@@ -0,0 +1,4 @@
+Definition of Robotics
+----------------------
+
+TBD
diff --git a/docs/modules/1_introduction/2_software_for_robotics/software_for_robotics_main.rst b/docs/modules/1_introduction/2_software_for_robotics/software_for_robotics_main.rst
new file mode 100644
index 0000000000..835441f85d
--- /dev/null
+++ b/docs/modules/1_introduction/2_software_for_robotics/software_for_robotics_main.rst
@@ -0,0 +1,7 @@
+Software for Robotics
+----------------------
+
+Python for Robotics
+~~~~~~~~~~~~~~~~~~~~~
+
+TBD
diff --git a/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst b/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst
new file mode 100644
index 0000000000..4c60ab8851
--- /dev/null
+++ b/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst
@@ -0,0 +1,4 @@
+Technology for Robotics
+-------------------------
+
+TBD
diff --git a/docs/modules/1_introduction/introduction_main.rst b/docs/modules/1_introduction/introduction_main.rst
new file mode 100644
index 0000000000..4561efd349
--- /dev/null
+++ b/docs/modules/1_introduction/introduction_main.rst
@@ -0,0 +1,13 @@
+.. _introduction:
+
+Introduction
+============
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Table of Contents
+
+   1_definition_of_robotics/definition_of_robotics
+   2_software_for_robotics/software_for_robotics
+   3_technology_for_robotics/technology_for_robotics
+
diff --git a/docs/modules/localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst b/docs/modules/2_localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst
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diff --git a/docs/modules/introduction_main.rst b/docs/modules/introduction_main.rst
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index f9fb487297..0000000000
--- a/docs/modules/introduction_main.rst
+++ /dev/null
@@ -1,42 +0,0 @@
-
-Introduction
-============
-
-TBD
-
-Definition Of Robotics
-----------------------
-
-TBD
-
-History Of Robotics
-----------------------
-
-TBD
-
-Application Of Robotics
-------------------------
-
-TBD
-
-Software for Robotics
-----------------------
-
-TBD
-
-Software for Robotics
-----------------------
-
-TBD
-
-Python for Robotics
-----------------------
-
-TBD
-
-Learning Robotics Algorithms
-----------------------------
-
-TBD
-
-
diff --git a/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif b/docs/modules/slam/graph_slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
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From f1b171d88b00d8579e8cd4b0b1ee7cc42e0c8407 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 26 Jan 2025 22:12:28 +0900
Subject: [PATCH 0952/1080] Update head doc (#1127)

* update head doc

* update head doc
---
 LICENSE                       |  2 +-
 docs/getting_started_main.rst | 19 +++++++++++++++++--
 docs/index_main.rst           | 32 ++++++++++++++++----------------
 3 files changed, 34 insertions(+), 19 deletions(-)

diff --git a/LICENSE b/LICENSE
index c0c52a4321..1c9b928b54 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 The MIT License (MIT)
 
-Copyright (c) 2016 - 2022 Atsushi Sakai and other contributors:
+Copyright (c) 2016 - now Atsushi Sakai and other contributors:
 https://github.com/AtsushiSakai/PythonRobotics/contributors
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst
index 256319ab8f..db71960dcf 100644
--- a/docs/getting_started_main.rst
+++ b/docs/getting_started_main.rst
@@ -3,11 +3,15 @@
 Getting Started
 ===============
 
-What is this?
--------------
+.. _`What is PythonRobotics?`:
+
+What is PythonRobotics?
+------------------------
 
 This is an Open Source Software (OSS) project: PythonRobotics, which is a Python code collection of robotics algorithms.
 
+This is developped under `MIT license`_ and on `GitHub`_.
+
 The focus of the project is on autonomous navigation, and the goal is for beginners in robotics to understand the basic ideas behind each algorithm.
 
 In this project, the algorithms which are practical and widely used in both academia and industry are selected.
@@ -16,6 +20,17 @@ Each sample code is written in Python3 and only depends on some standard modules
 
 It includes intuitive animations to understand the behavior of the simulation.
 
+.. _GitHub: https://github.com/AtsushiSakai/PythonRobotics
+.. _`MIT license`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/LICENSE
+
+Features:
+
+1. Easy to read for understanding each algorithm's basic idea.
+
+2. Widely used and practical algorithms are selected.
+
+3. Minimum dependency.
+
 
 See this paper for more details:
 
diff --git a/docs/index_main.rst b/docs/index_main.rst
index 08c318ea83..8c967ac59e 100644
--- a/docs/index_main.rst
+++ b/docs/index_main.rst
@@ -6,32 +6,32 @@
 Welcome to PythonRobotics's documentation!
 ==========================================
 
-Python codes for robotics algorithm. The project is on `GitHub`_.
+"PythonRobotics" is a Python code collections and html based text book
+(This document) for robotics algorithm, which is developed on `GitHub`_.
 
-This is a Python code collection of robotics algorithms.
+See this section (:ref:`What is PythonRobotics?`) for more details of this project.
 
-Features:
+This project  is `the one of the most popular open-source software (OSS) in
+the field of robotics on GitHub`_.
+This is `user comments about this project`_, and
+this graph shows GitHub star history of this project:
 
-1. Easy to read for understanding each algorithm's basic idea.
+.. image:: https://api.star-history.com/svg?repos=AtsushiSakai/PythonRobotics&type=Date
+   :alt: Star History
+   :width: 80%
+   :align: center
 
-2. Widely used and practical algorithms are selected.
 
-3. Minimum dependency.
-
-See this paper for more details:
-
--  `[1808.10703] PythonRobotics: a Python code collection of robotics
-   algorithms`_ (`BibTeX`_)
-
-
-.. _`[1808.10703] PythonRobotics: a Python code collection of robotics algorithms`: https://arxiv.org/abs/1808.10703
-.. _BibTeX: https://github.com/AtsushiSakai/PythonRoboticsPaper/blob/master/python_robotics.bib
 .. _GitHub: https://github.com/AtsushiSakai/PythonRobotics
+.. _`user comments about this project`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/users_comments.md
+.. _`MIT license`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/LICENSE
+.. _`the one of the most popular open-source software (OSS) in the field of robotics on GitHub`: https://github.com/topics/robotics
 
+----------------------------------
 
 .. toctree::
    :maxdepth: 2
-   :caption: Contents
+   :caption: Table of Contents
 
    getting_started
    modules/1_introduction/introduction

From e93ada6cb84ad6be0cb7dd70af22e4defe84b0af Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 27 Jan 2025 22:11:15 +0900
Subject: [PATCH 0953/1080] update README (#1128)

---
 README.md                     | 12 ++++++------
 docs/getting_started_main.rst |  2 +-
 docs/index_main.rst           |  2 +-
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index ec65342430..8621a40923 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@
 ![GitHub_Action_Windows_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/Windows_CI/badge.svg)
 [![Build status](https://ci.appveyor.com/api/projects/status/sb279kxuv1be391g?svg=true)](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics)
 
-Python codes for robotics algorithm.
+Python codes and textbook for robotics algorithm.
 
 
 # Table of Contents
@@ -73,9 +73,9 @@ Python codes for robotics algorithm.
       * [1Password](#1password)
    * [Authors](#authors)
 
-# What is this?
+# What is PythonRobotics?
 
-This is a Python code collection of robotics algorithms.
+PythonRobotics is a Python code collection and a [textbook](https://atsushisakai.github.io/PythonRobotics/index.html) of robotics algorithms.
 
 Features:
 
@@ -90,7 +90,7 @@ See this paper for more details:
 - [\[1808\.10703\] PythonRobotics: a Python code collection of robotics algorithms](https://arxiv.org/abs/1808.10703) ([BibTeX](https://github.com/AtsushiSakai/PythonRoboticsPaper/blob/master/python_robotics.bib))
 
 
-# Requirements
+# Requirements to run the code
 
 For running each sample code:
 
@@ -116,13 +116,13 @@ For development:
   
 - [pycodestyle](https://pypi.org/project/pycodestyle/) (for code style check)
 
-# Documentation
+# Documentation (Textbook)
 
 This README only shows some examples of this project. 
 
 If you are interested in other examples or mathematical backgrounds of each algorithm, 
 
-You can check the full documentation online: [Welcome to PythonRobotics’s documentation\! — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/index.html)
+You can check the full documentation (textbook) online: [Welcome to PythonRobotics’s documentation\! — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/index.html)
 
 All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation gifs of PythonRobotics](https://github.com/AtsushiSakai/PythonRoboticsGifs)
 
diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst
index db71960dcf..86049ac2bf 100644
--- a/docs/getting_started_main.rst
+++ b/docs/getting_started_main.rst
@@ -10,7 +10,7 @@ What is PythonRobotics?
 
 This is an Open Source Software (OSS) project: PythonRobotics, which is a Python code collection of robotics algorithms.
 
-This is developped under `MIT license`_ and on `GitHub`_.
+This is developed under `MIT license`_ and on `GitHub`_.
 
 The focus of the project is on autonomous navigation, and the goal is for beginners in robotics to understand the basic ideas behind each algorithm.
 
diff --git a/docs/index_main.rst b/docs/index_main.rst
index 8c967ac59e..b9f334dd9b 100644
--- a/docs/index_main.rst
+++ b/docs/index_main.rst
@@ -6,7 +6,7 @@
 Welcome to PythonRobotics's documentation!
 ==========================================
 
-"PythonRobotics" is a Python code collections and html based text book
+"PythonRobotics" is a Python code collections and textbook
 (This document) for robotics algorithm, which is developed on `GitHub`_.
 
 See this section (:ref:`What is PythonRobotics?`) for more details of this project.

From 6744d4b104d9281f0a6c3404860b996f9d53d0a8 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 27 Jan 2025 22:41:52 +0900
Subject: [PATCH 0954/1080] copy paper contents (#1129)

* copy paper contents

* copy paper contents
---
 README.md                                     |  8 +++-
 docs/getting_started_main.rst                 | 42 ++++++++++++++-----
 .../definition_of_robotics_main.rst           |  5 ++-
 .../technology_for_robotics_main.rst          | 10 ++++-
 .../2_localization/localization_main.rst      |  1 +
 docs/modules/3_mapping/mapping_main.rst       |  2 +
 docs/modules/4_slam/slam_main.rst             |  2 +
 .../5_path_planning/path_planning_main.rst    |  2 +
 .../6_path_tracking/path_tracking_main.rst    |  2 +
 9 files changed, 60 insertions(+), 14 deletions(-)

diff --git a/README.md b/README.md
index 8621a40923..cb1816c2b5 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@
 ![GitHub_Action_Windows_CI](https://github.com/AtsushiSakai/PythonRobotics/workflows/Windows_CI/badge.svg)
 [![Build status](https://ci.appveyor.com/api/projects/status/sb279kxuv1be391g?svg=true)](https://ci.appveyor.com/project/AtsushiSakai/pythonrobotics)
 
-Python codes and textbook for robotics algorithm.
+Python codes and [textbook](https://atsushisakai.github.io/PythonRobotics/index.html) for robotics algorithm.
 
 
 # Table of Contents
@@ -85,7 +85,11 @@ Features:
 
 3. Minimum dependency.
 
-See this paper for more details:
+See this documentation 
+
+- [Getting Started — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/getting_started.html#what-is-pythonrobotics)
+
+or this paper for more details:
 
 - [\[1808\.10703\] PythonRobotics: a Python code collection of robotics algorithms](https://arxiv.org/abs/1808.10703) ([BibTeX](https://github.com/AtsushiSakai/PythonRoboticsPaper/blob/master/python_robotics.bib))
 
diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst
index 86049ac2bf..aaf304514d 100644
--- a/docs/getting_started_main.rst
+++ b/docs/getting_started_main.rst
@@ -9,27 +9,49 @@ What is PythonRobotics?
 ------------------------
 
 This is an Open Source Software (OSS) project: PythonRobotics, which is a Python code collection of robotics algorithms.
+These codes are developed under `MIT license`_ and on `GitHub`_.
 
-This is developed under `MIT license`_ and on `GitHub`_.
+This project has three main philosophies below:
 
-The focus of the project is on autonomous navigation, and the goal is for beginners in robotics to understand the basic ideas behind each algorithm.
+1. Easy to understand each algorithm's basic idea.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-In this project, the algorithms which are practical and widely used in both academia and industry are selected.
+The goal is for beginners in robotics to understand the basic ideas behind each algorithm.
+If the code is not easy to read, it would be difficult to achieve our goal of
+allowing beginners to understand the algorithms.
 
-Each sample code is written in Python3 and only depends on some standard modules for readability and ease of use. 
+Python[12] programming language is adopted in this project.
+Python has great libraries for matrix operation, mathematical and scientific operation,
+and visualization, which makes code more readable because such operations
+don’t need to be re-implemented.
+Having the core Python packages allows the user to focus on the algorithms,
+rather than the implementations.
 
 It includes intuitive animations to understand the behavior of the simulation.
 
-.. _GitHub: https://github.com/AtsushiSakai/PythonRobotics
-.. _`MIT license`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/LICENSE
-
-Features:
-
-1. Easy to read for understanding each algorithm's basic idea.
+about documenttion
 
 2. Widely used and practical algorithms are selected.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The second philosophy is that implemented algorithms have to be practical
+and widely used in both academia and industry.
+We believe learning these algorithms will be useful in many applications.
+For example, Kalman filters and particle filter for localization,
+grid mapping for mapping,
+dynamic programming based approaches and sampling based approaches for path planning,
+and optimal control based approach for path tracking.
+These algorithms are implemented in this project.
 
 3. Minimum dependency.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Each sample code is written in Python3 and only depends on some standard
+modules for readability and ease of use.
+
+
+.. _GitHub: https://github.com/AtsushiSakai/PythonRobotics
+.. _`MIT license`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/LICENSE
 
 
 See this paper for more details:
diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
index bdc7dc53e9..2f31834017 100644
--- a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
+++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
@@ -1,4 +1,7 @@
 Definition of Robotics
 ----------------------
 
-TBD
+In recent years, autonomous navigation technologies have received huge
+attention in many fields.
+Such fields include, autonomous driving[22], drone flight navigation,
+and other transportation systems.
diff --git a/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst b/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst
index 4c60ab8851..4dd1d1842f 100644
--- a/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst
+++ b/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst
@@ -1,4 +1,12 @@
 Technology for Robotics
 -------------------------
 
-TBD
+An autonomous navigation system is a system that can move to a goal over long
+periods of time without any external control by an operator.
+The system requires a wide range of technologies:
+- It needs to know where it is (localization)
+- Where it is safe (mapping)
+- Where and how to move (path planning)
+- How to control its motion (path following).
+
+The autonomous system would not work correctly if any of these technologies is missing.
diff --git a/docs/modules/2_localization/localization_main.rst b/docs/modules/2_localization/localization_main.rst
index db6651f6b8..22cbd094da 100644
--- a/docs/modules/2_localization/localization_main.rst
+++ b/docs/modules/2_localization/localization_main.rst
@@ -2,6 +2,7 @@
 
 Localization
 ============
+Localization is the ability of a robot to know its position and orientation with sensors such as Global Navigation Satellite System:GNSS etc. In localization, Bayesian filters such as Kalman filters, histogram filter, and particle filter are widely used[31]. Fig.2 shows localization simulations using histogram filter and particle filter.
 
 .. toctree::
    :maxdepth: 2
diff --git a/docs/modules/3_mapping/mapping_main.rst b/docs/modules/3_mapping/mapping_main.rst
index a98acaaf50..28e18984d3 100644
--- a/docs/modules/3_mapping/mapping_main.rst
+++ b/docs/modules/3_mapping/mapping_main.rst
@@ -2,6 +2,8 @@
 
 Mapping
 =======
+Mapping is the ability of a robot to understand its surroundings with external sensors such as LIDAR and camera. Robots have to recognize the position and shape of obstacles to avoid them. In mapping, grid mapping and machine learning algorithms are widely used[31][18]. Fig.3 shows mapping simulation results using grid mapping with 2D ray casting and 2D object clustering with k-means algorithm.
+
 .. toctree::
    :maxdepth: 2
    :caption: Contents
diff --git a/docs/modules/4_slam/slam_main.rst b/docs/modules/4_slam/slam_main.rst
index 86befa6e35..dec04f253a 100644
--- a/docs/modules/4_slam/slam_main.rst
+++ b/docs/modules/4_slam/slam_main.rst
@@ -4,6 +4,8 @@ SLAM
 ====
 
 Simultaneous Localization and Mapping(SLAM) examples
+Simultaneous Localization and Mapping (SLAM) is an ability to estimate the pose of a robot and the map of the environment at the same time. The SLAM problem is hard to
+solve, because a map is needed for localization and localization is needed for mapping. In this way, SLAM is often said to be similar to a ‘chicken-and-egg’ problem. Popular SLAM solution methods include the extended Kalman filter, particle filter, and Fast SLAM algorithm[31]. Fig.4 shows SLAM simulation results using extended Kalman filter and results using FastSLAM2.0[31].
 
 .. toctree::
    :maxdepth: 2
diff --git a/docs/modules/5_path_planning/path_planning_main.rst b/docs/modules/5_path_planning/path_planning_main.rst
index 30802fd7a6..4960330b3e 100644
--- a/docs/modules/5_path_planning/path_planning_main.rst
+++ b/docs/modules/5_path_planning/path_planning_main.rst
@@ -3,6 +3,8 @@
 Path Planning
 =============
 
+Path planning is the ability of a robot to search feasible and efficient path to the goal. The path has to satisfy some constraints based on the robot’s motion model and obstacle positions, and optimize some objective functions such as time to goal and distance to obstacle. In path planning, dynamic programming based approaches and sampling based approaches are widely used[22]. Fig.5 shows simulation results of potential field path planning and LQRRRT* path planning[27].
+
 .. toctree::
    :maxdepth: 2
    :caption: Contents
diff --git a/docs/modules/6_path_tracking/path_tracking_main.rst b/docs/modules/6_path_tracking/path_tracking_main.rst
index 504ba08795..d7a895b562 100644
--- a/docs/modules/6_path_tracking/path_tracking_main.rst
+++ b/docs/modules/6_path_tracking/path_tracking_main.rst
@@ -3,6 +3,8 @@
 Path Tracking
 =============
 
+Path tracking is the ability of a robot to follow the reference path generated by a path planner while simultaneously stabilizing the robot. The path tracking controller may need to account for modeling error and other forms of uncertainty. In path tracking, feedback control techniques and optimization based control techniques are widely used[22]. Fig.6 shows simulations using rear wheel feedback steering control and PID speed control, and iterative linear model predictive path tracking control[27].
+
 .. toctree::
    :maxdepth: 2
    :caption: Contents

From 53f1ba35d5c1b981eca55a91b72c53fb1000263a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 28 Jan 2025 09:19:20 +0900
Subject: [PATCH 0955/1080] build(deps): bump ruff from 0.9.2 to 0.9.3 in
 /requirements (#1130)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.9.2 to 0.9.3.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.9.2...0.9.3)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 11b91abb1f..e8bed7b6d7 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.14.1 # For unit test
-ruff == 0.9.2 # For unit test
+ruff == 0.9.3 # For unit test

From fc3562733814e43134da8a1d7d73e370d44b9f4e Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 28 Jan 2025 22:25:53 +0900
Subject: [PATCH 0956/1080] update getting started (#1131)

---
 docs/getting_started_main.rst         | 122 ++------------------------
 docs/how_to_read_textbook_main.rst    |   4 +
 docs/how_to_use_main.rst              |  30 +++++++
 docs/what_is_python_robotics_main.rst | 100 +++++++++++++++++++++
 4 files changed, 140 insertions(+), 116 deletions(-)
 create mode 100644 docs/how_to_read_textbook_main.rst
 create mode 100644 docs/how_to_use_main.rst
 create mode 100644 docs/what_is_python_robotics_main.rst

diff --git a/docs/getting_started_main.rst b/docs/getting_started_main.rst
index aaf304514d..07abeec451 100644
--- a/docs/getting_started_main.rst
+++ b/docs/getting_started_main.rst
@@ -3,120 +3,10 @@
 Getting Started
 ===============
 
-.. _`What is PythonRobotics?`:
-
-What is PythonRobotics?
-------------------------
-
-This is an Open Source Software (OSS) project: PythonRobotics, which is a Python code collection of robotics algorithms.
-These codes are developed under `MIT license`_ and on `GitHub`_.
-
-This project has three main philosophies below:
-
-1. Easy to understand each algorithm's basic idea.
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The goal is for beginners in robotics to understand the basic ideas behind each algorithm.
-If the code is not easy to read, it would be difficult to achieve our goal of
-allowing beginners to understand the algorithms.
-
-Python[12] programming language is adopted in this project.
-Python has great libraries for matrix operation, mathematical and scientific operation,
-and visualization, which makes code more readable because such operations
-don’t need to be re-implemented.
-Having the core Python packages allows the user to focus on the algorithms,
-rather than the implementations.
-
-It includes intuitive animations to understand the behavior of the simulation.
-
-about documenttion
-
-2. Widely used and practical algorithms are selected.
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The second philosophy is that implemented algorithms have to be practical
-and widely used in both academia and industry.
-We believe learning these algorithms will be useful in many applications.
-For example, Kalman filters and particle filter for localization,
-grid mapping for mapping,
-dynamic programming based approaches and sampling based approaches for path planning,
-and optimal control based approach for path tracking.
-These algorithms are implemented in this project.
-
-3. Minimum dependency.
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-Each sample code is written in Python3 and only depends on some standard
-modules for readability and ease of use.
-
-
-.. _GitHub: https://github.com/AtsushiSakai/PythonRobotics
-.. _`MIT license`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/LICENSE
-
-
-See this paper for more details:
-
-- PythonRobotics: a Python code collection of robotics algorithms: https://arxiv.org/abs/1808.10703
-
-.. _`Requirements`:
-
-Requirements
--------------
-
--  `Python 3.12.x`_
--  `NumPy`_
--  `SciPy`_
--  `Matplotlib`_
--  `cvxpy`_
-
-For development:
-
--  `pytest`_ (for unit tests)
--  `pytest-xdist`_ (for parallel unit tests)
--  `mypy`_ (for type check)
--  `sphinx`_ (for document generation)
--  `ruff`_ (for code style check)
-
-.. _`Python 3.12.x`: https://www.python.org/
-.. _`NumPy`: https://numpy.org/
-.. _`SciPy`: https://scipy.org/
-.. _`Matplotlib`: https://matplotlib.org/
-.. _`cvxpy`: https://www.cvxpy.org/
-.. _`pytest`: https://docs.pytest.org/en/latest/
-.. _`pytest-xdist`: https://github.com/pytest-dev/pytest-xdist
-.. _`mypy`: https://mypy-lang.org/
-.. _`sphinx`: https://www.sphinx-doc.org/en/master/index.html
-.. _`ruff`: https://github.com/astral-sh/ruff
-
-
-How to use
-----------
-
-1. Clone this repo and go into dir.
-
-.. code-block::
-
-    >$ git clone https://github.com/AtsushiSakai/PythonRobotics.git
-
-    >$ cd PythonRobotics
-
-
-2. Install the required libraries.
-
-using conda :
-
-.. code-block::
-
-    >$ conda env create -f requirements/environment.yml
-
-using pip :
-
-.. code-block::
-
-    >$ pip install -r requirements/requirements.txt
-
-
-3. Execute python script in each directory.
-
-4. Add star to this repo if you like it 😃.
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
 
+   what_is_python_robotics
+   how_to_use
+   how_to_read_textbook
diff --git a/docs/how_to_read_textbook_main.rst b/docs/how_to_read_textbook_main.rst
new file mode 100644
index 0000000000..3b92970e26
--- /dev/null
+++ b/docs/how_to_read_textbook_main.rst
@@ -0,0 +1,4 @@
+How To read this textbook
+--------------------------
+
+TBD
\ No newline at end of file
diff --git a/docs/how_to_use_main.rst b/docs/how_to_use_main.rst
new file mode 100644
index 0000000000..d21b07c1a3
--- /dev/null
+++ b/docs/how_to_use_main.rst
@@ -0,0 +1,30 @@
+How to use
+----------
+
+1. Clone this repo and go into dir.
+
+.. code-block::
+
+    >$ git clone https://github.com/AtsushiSakai/PythonRobotics.git
+
+    >$ cd PythonRobotics
+
+
+2. Install the required libraries.
+
+using conda :
+
+.. code-block::
+
+    >$ conda env create -f requirements/environment.yml
+
+using pip :
+
+.. code-block::
+
+    >$ pip install -r requirements/requirements.txt
+
+
+3. Execute python script in each directory.
+
+4. Add star to this repo if you like it 😃.
diff --git a/docs/what_is_python_robotics_main.rst b/docs/what_is_python_robotics_main.rst
new file mode 100644
index 0000000000..ce6e059dba
--- /dev/null
+++ b/docs/what_is_python_robotics_main.rst
@@ -0,0 +1,100 @@
+.. _`What is PythonRobotics?`:
+
+What is PythonRobotics?
+------------------------
+
+This is an Open Source Software (OSS) project: PythonRobotics, which is a Python code collection of robotics algorithms.
+These codes are developed under `MIT license`_ and on `GitHub`_.
+
+This project has three main philosophies below:
+
+1. Easy to understand each algorithm's basic idea.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The goal is for beginners in robotics to understand the basic ideas behind each algorithm.
+
+`Python`_ programming language is adopted in this project to achieve the goal.
+Python is a high-level programming language that is easy to read and write.
+If the code is not easy to read, it would be difficult to achieve our goal of
+allowing beginners to understand the algorithms.
+Python has great libraries for matrix operation, mathematical and scientific operation,
+and visualization, which makes code more readable because such operations
+don’t need to be re-implemented.
+Having the core Python packages allows the user to focus on the algorithms,
+rather than the implementations.
+
+PythonRobotics provides not only the code but also intuitive animations that
+visually demonstrate the process and behavior of each algorithm over time.
+This is an example of an animation gif file that shows the process of the
+path planning in a highway scenario for autonomous vehicle.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/high_speed_and_velocity_keeping_frenet_path.gif
+
+This animation is a gif file and is created using the `Matplotlib`_ library.
+All animaion gif files are stored in the `PythonRoboticsGifs`_ repository and
+all animation movies are uploaded to this `YouTube channel`_.
+
+PythonRobotics also provides a textbook that explains the basic ideas of each algorithm.
+
+.. _`Python`: https://www.python.org/
+.. _`PythonRoboticsGifs`: https://github.com/AtsushiSakai/PythonRoboticsGifs
+.. _`YouTube channel`: https://youtube.com/playlist?list=PL12URV8HFpCozuz0SDxke6b2ae5UZvIwa&si=AH2fNPPYufPtK20S
+
+
+2. Widely used and practical algorithms are selected.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The second philosophy is that implemented algorithms have to be practical
+and widely used in both academia and industry.
+We believe learning these algorithms will be useful in many applications.
+For example, Kalman filters and particle filter for localization,
+grid mapping for mapping,
+dynamic programming based approaches and sampling based approaches for path planning,
+and optimal control based approach for path tracking.
+These algorithms are implemented in this project.
+
+3. Minimum dependency.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Each sample code is written in Python3 and only depends on some standard
+modules for readability and ease of use.
+
+
+.. _GitHub: https://github.com/AtsushiSakai/PythonRobotics
+.. _`MIT license`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/LICENSE
+
+
+See this paper for more details:
+
+- PythonRobotics: a Python code collection of robotics algorithms: https://arxiv.org/abs/1808.10703
+
+.. _`Requirements`:
+
+Requirements
+============
+
+-  `Python 3.12.x`_
+-  `NumPy`_
+-  `SciPy`_
+-  `Matplotlib`_
+-  `cvxpy`_
+
+For development:
+
+-  `pytest`_ (for unit tests)
+-  `pytest-xdist`_ (for parallel unit tests)
+-  `mypy`_ (for type check)
+-  `sphinx`_ (for document generation)
+-  `ruff`_ (for code style check)
+
+.. _`Python 3.12.x`: https://www.python.org/
+.. _`NumPy`: https://numpy.org/
+.. _`SciPy`: https://scipy.org/
+.. _`Matplotlib`: https://matplotlib.org/
+.. _`cvxpy`: https://www.cvxpy.org/
+.. _`pytest`: https://docs.pytest.org/en/latest/
+.. _`pytest-xdist`: https://github.com/pytest-dev/pytest-xdist
+.. _`mypy`: https://mypy-lang.org/
+.. _`sphinx`: https://www.sphinx-doc.org/en/master/index.html
+.. _`ruff`: https://github.com/astral-sh/ruff
+

From 44bad786829e713c44259bc18cc7ac24b6c1e24c Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 29 Jan 2025 21:54:18 +0900
Subject: [PATCH 0957/1080] update getting started (#1132)

---
 docs/how_to_read_textbook_main.rst            |  4 ----
 docs/index_main.rst                           |  3 +--
 .../0_getting_started_main.rst}               |  7 +++---
 .../1_what_is_python_robotics_main.rst}       | 23 +++++++++++++------
 .../0_getting_started/2_how_to_use_main.rst}  |  0
 .../3_how_to_contribute_main.rst}             |  0
 .../4_how_to_read_textbook_main.rst           |  6 +++++
 7 files changed, 27 insertions(+), 16 deletions(-)
 delete mode 100644 docs/how_to_read_textbook_main.rst
 rename docs/{getting_started_main.rst => modules/0_getting_started/0_getting_started_main.rst} (53%)
 rename docs/{what_is_python_robotics_main.rst => modules/0_getting_started/1_what_is_python_robotics_main.rst} (79%)
 rename docs/{how_to_use_main.rst => modules/0_getting_started/2_how_to_use_main.rst} (100%)
 rename docs/{how_to_contribute_main.rst => modules/0_getting_started/3_how_to_contribute_main.rst} (100%)
 create mode 100644 docs/modules/0_getting_started/4_how_to_read_textbook_main.rst

diff --git a/docs/how_to_read_textbook_main.rst b/docs/how_to_read_textbook_main.rst
deleted file mode 100644
index 3b92970e26..0000000000
--- a/docs/how_to_read_textbook_main.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-How To read this textbook
---------------------------
-
-TBD
\ No newline at end of file
diff --git a/docs/index_main.rst b/docs/index_main.rst
index b9f334dd9b..c1e8d22d32 100644
--- a/docs/index_main.rst
+++ b/docs/index_main.rst
@@ -33,7 +33,7 @@ this graph shows GitHub star history of this project:
    :maxdepth: 2
    :caption: Table of Contents
 
-   getting_started
+   modules/0_getting_started/0_getting_started
    modules/1_introduction/introduction
    modules/2_localization/localization
    modules/3_mapping/mapping
@@ -46,7 +46,6 @@ this graph shows GitHub star history of this project:
    modules/10_control/control
    modules/11_utils/utils
    modules/12_appendix/appendix
-   how_to_contribute
 
 
 Indices and tables
diff --git a/docs/getting_started_main.rst b/docs/modules/0_getting_started/0_getting_started_main.rst
similarity index 53%
rename from docs/getting_started_main.rst
rename to docs/modules/0_getting_started/0_getting_started_main.rst
index 07abeec451..cfec5b3c73 100644
--- a/docs/getting_started_main.rst
+++ b/docs/modules/0_getting_started/0_getting_started_main.rst
@@ -7,6 +7,7 @@ Getting Started
    :maxdepth: 2
    :caption: Contents
 
-   what_is_python_robotics
-   how_to_use
-   how_to_read_textbook
+   1_what_is_python_robotics
+   2_how_to_use
+   3_how_to_contribute
+   4_how_to_read_textbook
diff --git a/docs/what_is_python_robotics_main.rst b/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst
similarity index 79%
rename from docs/what_is_python_robotics_main.rst
rename to docs/modules/0_getting_started/1_what_is_python_robotics_main.rst
index ce6e059dba..32b1ad4e5d 100644
--- a/docs/what_is_python_robotics_main.rst
+++ b/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst
@@ -8,7 +8,7 @@ These codes are developed under `MIT license`_ and on `GitHub`_.
 
 This project has three main philosophies below:
 
-1. Easy to understand each algorithm's basic idea.
+Philosophy 1. Easy to understand each algorithm's basic idea.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The goal is for beginners in robotics to understand the basic ideas behind each algorithm.
@@ -31,17 +31,26 @@ path planning in a highway scenario for autonomous vehicle.
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/FrenetOptimalTrajectory/high_speed_and_velocity_keeping_frenet_path.gif
 
 This animation is a gif file and is created using the `Matplotlib`_ library.
-All animaion gif files are stored in the `PythonRoboticsGifs`_ repository and
-all animation movies are uploaded to this `YouTube channel`_.
+All animation gif files are stored in the `PythonRoboticsGifs`_ repository and
+all animation movies are also uploaded to this `YouTube channel`_.
 
 PythonRobotics also provides a textbook that explains the basic ideas of each algorithm.
+The PythonRobotics textbook allows you to learn fundamental algorithms used in
+robotics with minimal mathematical formulas and textual explanations,
+based on PythonRobotics’ sample codes and animations.
+The contents of this document, like the code, are managed in the PythonRobotics
+`GitHub`_ repository and converted into HTML-based online documents using `Sphinx`_,
+which is a Python-based documentation builder.
+Please refer to this section ":ref:`How to read this textbook`" for information on
+how to read this document for learning.
+
 
 .. _`Python`: https://www.python.org/
 .. _`PythonRoboticsGifs`: https://github.com/AtsushiSakai/PythonRoboticsGifs
 .. _`YouTube channel`: https://youtube.com/playlist?list=PL12URV8HFpCozuz0SDxke6b2ae5UZvIwa&si=AH2fNPPYufPtK20S
 
 
-2. Widely used and practical algorithms are selected.
+Philosophy 2. Widely used and practical algorithms are selected.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The second philosophy is that implemented algorithms have to be practical
@@ -53,7 +62,7 @@ dynamic programming based approaches and sampling based approaches for path plan
 and optimal control based approach for path tracking.
 These algorithms are implemented in this project.
 
-3. Minimum dependency.
+Philosophy 3. Minimum dependency.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Each sample code is written in Python3 and only depends on some standard
@@ -84,7 +93,7 @@ For development:
 -  `pytest`_ (for unit tests)
 -  `pytest-xdist`_ (for parallel unit tests)
 -  `mypy`_ (for type check)
--  `sphinx`_ (for document generation)
+-  `Sphinx`_ (for document generation)
 -  `ruff`_ (for code style check)
 
 .. _`Python 3.12.x`: https://www.python.org/
@@ -95,6 +104,6 @@ For development:
 .. _`pytest`: https://docs.pytest.org/en/latest/
 .. _`pytest-xdist`: https://github.com/pytest-dev/pytest-xdist
 .. _`mypy`: https://mypy-lang.org/
-.. _`sphinx`: https://www.sphinx-doc.org/en/master/index.html
+.. _`Sphinx`: https://www.sphinx-doc.org/en/master/index.html
 .. _`ruff`: https://github.com/astral-sh/ruff
 
diff --git a/docs/how_to_use_main.rst b/docs/modules/0_getting_started/2_how_to_use_main.rst
similarity index 100%
rename from docs/how_to_use_main.rst
rename to docs/modules/0_getting_started/2_how_to_use_main.rst
diff --git a/docs/how_to_contribute_main.rst b/docs/modules/0_getting_started/3_how_to_contribute_main.rst
similarity index 100%
rename from docs/how_to_contribute_main.rst
rename to docs/modules/0_getting_started/3_how_to_contribute_main.rst
diff --git a/docs/modules/0_getting_started/4_how_to_read_textbook_main.rst b/docs/modules/0_getting_started/4_how_to_read_textbook_main.rst
new file mode 100644
index 0000000000..fb8db348c6
--- /dev/null
+++ b/docs/modules/0_getting_started/4_how_to_read_textbook_main.rst
@@ -0,0 +1,6 @@
+.. _`How to read this textbook`:
+
+How to read this textbook
+--------------------------
+
+TBD
\ No newline at end of file

From 2b9cc06000a4ae57dce885240d04e38e30cff8e3 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 30 Jan 2025 22:25:45 +0900
Subject: [PATCH 0958/1080] update getting started (#1133)

---
 .../0_getting_started_main.rst                |  2 +-
 .../1_what_is_python_robotics_main.rst        | 49 ++++++++++++-------
 ...rst => 2_how_to_run_sample_codes_main.rst} |  6 ++-
 3 files changed, 37 insertions(+), 20 deletions(-)
 rename docs/modules/0_getting_started/{2_how_to_use_main.rst => 2_how_to_run_sample_codes_main.rst} (84%)

diff --git a/docs/modules/0_getting_started/0_getting_started_main.rst b/docs/modules/0_getting_started/0_getting_started_main.rst
index cfec5b3c73..cb2cba4784 100644
--- a/docs/modules/0_getting_started/0_getting_started_main.rst
+++ b/docs/modules/0_getting_started/0_getting_started_main.rst
@@ -8,6 +8,6 @@ Getting Started
    :caption: Contents
 
    1_what_is_python_robotics
-   2_how_to_use
+   2_how_to_run_sample_codes
    3_how_to_contribute
    4_how_to_read_textbook
diff --git a/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst b/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst
index 32b1ad4e5d..8c932b7263 100644
--- a/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst
+++ b/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst
@@ -6,6 +6,9 @@ What is PythonRobotics?
 This is an Open Source Software (OSS) project: PythonRobotics, which is a Python code collection of robotics algorithms.
 These codes are developed under `MIT license`_ and on `GitHub`_.
 
+.. _GitHub: https://github.com/AtsushiSakai/PythonRobotics
+.. _`MIT license`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/LICENSE
+
 This project has three main philosophies below:
 
 Philosophy 1. Easy to understand each algorithm's basic idea.
@@ -60,35 +63,32 @@ For example, Kalman filters and particle filter for localization,
 grid mapping for mapping,
 dynamic programming based approaches and sampling based approaches for path planning,
 and optimal control based approach for path tracking.
-These algorithms are implemented in this project.
+These algorithms are implemented and explained in the textbook in this project.
 
 Philosophy 3. Minimum dependency.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Each sample code is written in Python3 and only depends on some standard
-modules for readability and ease of use.
-
-
-.. _GitHub: https://github.com/AtsushiSakai/PythonRobotics
-.. _`MIT license`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/LICENSE
-
-
-See this paper for more details:
+Each sample code of PythonRobotics is written in Python3 and only depends on
+some standard modules for readability and ease to setup and use.
 
-- PythonRobotics: a Python code collection of robotics algorithms: https://arxiv.org/abs/1808.10703
 
 .. _`Requirements`:
 
 Requirements
 ============
 
--  `Python 3.12.x`_
--  `NumPy`_
--  `SciPy`_
--  `Matplotlib`_
--  `cvxpy`_
+PythonRobotics depends only on the following five libraries,
+including Python itself, to run each sample code.
 
-For development:
+-  `Python 3.12.x`_ (for Python runtime)
+-  `NumPy`_ (for matrix operation)
+-  `SciPy`_ (for scientific operation)
+-  `cvxpy`_ (for convex optimization)
+-  `Matplotlib`_ (for visualization)
+
+If you only need to run the code, the five libraries mentioned above are sufficient.
+However, for code development or creating documentation for the textbook,
+the following additional libraries are required.
 
 -  `pytest`_ (for unit tests)
 -  `pytest-xdist`_ (for parallel unit tests)
@@ -107,3 +107,18 @@ For development:
 .. _`Sphinx`: https://www.sphinx-doc.org/en/master/index.html
 .. _`ruff`: https://github.com/astral-sh/ruff
 
+For instructions on installing the above libraries, please refer to
+this section ":ref:`How to run sample codes`".
+
+Arxiv paper
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+We have published a paper on this project on Arxiv in 2018.
+
+See this paper for more details about this Project:
+
+- `PythonRobotics: a Python code collection of robotics algorithms`_ (`BibTeX`_)
+
+.. _`PythonRobotics: a Python code collection of robotics algorithms`: https://arxiv.org/abs/1808.10703
+.. _`BibTeX`: https://github.com/AtsushiSakai/PythonRoboticsPaper/blob/master/python_robotics.bib
+
diff --git a/docs/modules/0_getting_started/2_how_to_use_main.rst b/docs/modules/0_getting_started/2_how_to_run_sample_codes_main.rst
similarity index 84%
rename from docs/modules/0_getting_started/2_how_to_use_main.rst
rename to docs/modules/0_getting_started/2_how_to_run_sample_codes_main.rst
index d21b07c1a3..eff54d3426 100644
--- a/docs/modules/0_getting_started/2_how_to_use_main.rst
+++ b/docs/modules/0_getting_started/2_how_to_run_sample_codes_main.rst
@@ -1,5 +1,7 @@
-How to use
-----------
+.. _`How to run sample codes`:
+
+How to run sample codes
+-------------------------
 
 1. Clone this repo and go into dir.
 

From a5fc2d039db9cdf6182d9fc97073b0d6e7388830 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 31 Jan 2025 21:38:06 +0900
Subject: [PATCH 0959/1080] update getting started (#1134)

Co-authored-by: Atsushi Sakai <atsushi_sakai@AtsushiSakainonotobukkukonpyuta.local>
---
 .../2_how_to_run_sample_codes_main.rst        | 94 ++++++++++++++++++-
 1 file changed, 90 insertions(+), 4 deletions(-)

diff --git a/docs/modules/0_getting_started/2_how_to_run_sample_codes_main.rst b/docs/modules/0_getting_started/2_how_to_run_sample_codes_main.rst
index eff54d3426..b92fc9bde0 100644
--- a/docs/modules/0_getting_started/2_how_to_run_sample_codes_main.rst
+++ b/docs/modules/0_getting_started/2_how_to_run_sample_codes_main.rst
@@ -3,7 +3,21 @@
 How to run sample codes
 -------------------------
 
-1. Clone this repo and go into dir.
+In this chapter, we will explain the setup process for running each sample code
+in PythonRobotics and describe the contents of each directory.
+
+Steps to setup and run sample codes
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+1. Install `Python 3.12.x`_
+
+Note that older versions of Python3 might work, but we recommend using
+the specified version, because the sample codes are only tested with it.
+
+2. If you prefer to use conda for package management, install
+Anaconda or Miniconda to use `conda`_ command.
+
+3. Clone this repo and go into dir.
 
 .. code-block::
 
@@ -12,7 +26,10 @@ How to run sample codes
     >$ cd PythonRobotics
 
 
-2. Install the required libraries.
+4. Install the required libraries.
+
+We have prepared requirements management files for `conda`_ and `pip`_ under
+the requirements directory. Using these files makes it easy to install the necessary libraries.
 
 using conda :
 
@@ -27,6 +44,75 @@ using pip :
     >$ pip install -r requirements/requirements.txt
 
 
-3. Execute python script in each directory.
+5. Execute python script in each directory.
+
+For example, to run the sample code of `Extented Kalman Filter` in the
+`localization` directory, execute the following command:
+
+.. code-block::
+
+    >$ cd localization/extended_kalman_filter
+
+	>$ python extended_kalman_filter.py
+
+Then, you can see this animation of the EKF algorithm based localization:
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif
+
+Please refer to the `Directory structure`_ section for more details on the contents of each directory.
+
+6. Add star to this repo if you like it 😃.
+
+.. _`Python 3.12.x`: https://www.python.org/
+.. _`conda`: https://docs.conda.io/projects/conda/en/stable/user-guide/install/index.html
+.. _`pip`: https://pip.pypa.io/en/stable/
+.. _`the requirements directory`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/requirements
+
+.. _`Directory structure`:
+
+Directory structure
+~~~~~~~~~~~~~~~~~~~~
+
+The top-level directory structure of PythonRobotics is as follows:
+
+Sample codes directories:
+
+- `AerialNavigation`_ : the sample codes of aerial navigation algorithms for drones and rocket landing.
+- `ArmNavigation`_ : the sample codes of arm navigation algorithms for robotic arms.
+- `Localization`_ : the sample codes of localization algorithms.
+- `Bipedal`_ : the sample codes of bipedal walking algorithms for legged robots.
+- `Control`_ : the sample codes of control algorithms for robotic systems.
+- `Mapping`_ : the sample codes of mapping or obstacle shape recognition algorithms.
+- `PathPlanning`_ : the sample codes of path planning algorithms.
+- `PathTracking`_ : the sample codes of path tracking algorithms for car like robots.
+- `SLAM`_ : the sample codes of SLAM algorithms.
+
+Other directories:
+
+- `docs`_ : This directory contains the documentation of PythonRobotics.
+- `requirements`_ : This directory contains the requirements management files.
+- `tests`_ : This directory contains the unit test files.
+- `utils`_ : This directory contains utility functions used in some sample codes in common.
+- `.github`_ : This directory contains the GitHub Actions configuration files.
+- `.circleci`_ : This directory contains the CircleCI configuration files.
+
+The structure of this document is the same as that of the sample code
+directories mentioned above.
+For more details, please refer to the :ref:`How to read this textbook` section.
+
 
-4. Add star to this repo if you like it 😃.
+.. _`AerialNavigation`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/AerialNavigation
+.. _`ArmNavigation`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/ArmNavigation
+.. _`Localization`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/Localization
+.. _`Bipedal`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/Bipedal
+.. _`Control`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/Control
+.. _`Mapping`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/Mapping
+.. _`PathPlanning`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/PathPlanning
+.. _`PathTracking`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/PathTracking
+.. _`SLAM`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/SLAM
+.. _`docs`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/docs
+.. _`requirements`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/requirements
+.. _`tests`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/tests
+.. _`utils`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/utils
+.. _`.github`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/.github
+.. _`.circleci`: https://github.com/AtsushiSakai/PythonRobotics/tree/master/.circleci

From f225c1845775cf8e1d8c62d04164743c2e9e00fc Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 1 Feb 2025 22:47:52 +0900
Subject: [PATCH 0960/1080] update getting started (#1135)

Co-authored-by: Atsushi Sakai <atsushi_sakai@AtsushiSakainonotobukkukonpyuta.local>
---
 .../3_how_to_contribute_main.rst              | 45 +++++++++++++++----
 1 file changed, 37 insertions(+), 8 deletions(-)

diff --git a/docs/modules/0_getting_started/3_how_to_contribute_main.rst b/docs/modules/0_getting_started/3_how_to_contribute_main.rst
index 915b2155e4..296e4e8658 100644
--- a/docs/modules/0_getting_started/3_how_to_contribute_main.rst
+++ b/docs/modules/0_getting_started/3_how_to_contribute_main.rst
@@ -2,9 +2,18 @@ How To Contribute
 =================
 
 This document describes how to contribute this project.
+There are several ways to contribute to this project as below:
+
+#. `Adding a new algorithm example`_
+#. `Reporting and fixing a defect`_
+#. `Adding missed documentations for existing examples`_
+#. `Supporting this project`_
+
+
+.. _`Adding a new algorithm example`:
 
 Adding a new algorithm example
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 This is a step by step manual to add a new algorithm example.
 
@@ -67,9 +76,16 @@ Please check other documents for details.
 
 You can build the doc locally based on `doc README`_.
 
-Note that the `reStructuredText`_ based doc should only focus on the mathematics and the algorithm of the example.
+For creating a gif animation, you can use this tool: `matplotrecorder`_.
+
+The created gif file should be stored in the `PythonRoboticsGifs`_ repository,
+so please create a PR to add it and refer to it in the doc.
+
+Note that the `reStructuredText`_ based doc should only focus on the
+mathematics and the algorithm of the example.
 
-Documentations related codes should be in the python script as the header comments of the script or docstrings of each function.
+Documentations related codes should be in the python script as the header
+comments of the script or docstrings of each function.
 
 
 .. _`submit a pull request`:
@@ -92,6 +108,9 @@ After that, I will start the review.
 
 Note that this is my hobby project; I appreciate your patience during the review process.
 
+　
+
+.. _`Reporting and fixing a defect`:
 
 Reporting and fixing a defect
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -115,19 +134,24 @@ in the test code to show the issue was solved.
 This doc `submit a pull request`_ can be helpful to submit a pull request.
 
 
+.. _`Adding missed documentations for existing examples`:
+
 Adding missed documentations for existing examples
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Adding the missed documentations for existing examples is also great contribution.
 
 If you check the `Python Robotics Docs`_, you can notice that some of the examples
-only have a simulation gif or short overview descriptions,
+only have a simulation gif or short overview descriptions or just TBD.,
 but no detailed algorithm or mathematical description.
+These documents needs to be improved.
 
 This doc `how to write doc`_ can be helpful to write documents.
 
+.. _`Supporting this project`:
+
 Supporting this project
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^
 
 Supporting this project financially is also a great contribution!!.
 
@@ -141,10 +165,12 @@ If you or your company would like to support this project, please consider:
 
 If you would like to support us in some other way, please contact with creating an issue.
 
-Sponsors
----------
+Current Major Sponsors
+-----------------------
+
+#. `JetBrains`_ : They are providing a free license of their IDEs for this OSS development.
+#. `1Password`_ : They are providing a free license of their 1Password team license for this OSS project.
 
-1. `JetBrains`_ : They are providing a free license of their IDEs for this OSS development.
 
 
 .. _`Python Robotics Docs`: https://atsushisakai.github.io/PythonRobotics
@@ -160,5 +186,8 @@ Sponsors
 .. _`Sponsor @AtsushiSakai on GitHub Sponsors`: https://github.com/sponsors/AtsushiSakai
 .. _`Become a backer or sponsor on Patreon`: https://www.patreon.com/myenigma
 .. _`One-time donation via PayPal`: https://www.paypal.com/paypalme/myenigmapay/
+.. _`1Password`: https://github.com/1Password/for-open-source
+.. _`matplotrecorder`: https://github.com/AtsushiSakai/matplotrecorder
+.. _`PythonRoboticsGifs`: https://github.com/AtsushiSakai/PythonRoboticsGifs
 
 

From e6f5dfe537d97cf79d55ad2803911a135aeaa1fd Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 2 Feb 2025 21:34:26 +0900
Subject: [PATCH 0961/1080] update getting started (#1136)

Co-authored-by: Atsushi Sakai <atsushi_sakai@AtsushiSakainonotobukkukonpyuta.local>
---
 .../0_getting_started/3_how_to_contribute_main.rst     |  2 +-
 .../0_getting_started/4_how_to_read_textbook_main.rst  | 10 +++++++++-
 docs/modules/1_introduction/introduction_main.rst      |  2 +-
 3 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/docs/modules/0_getting_started/3_how_to_contribute_main.rst b/docs/modules/0_getting_started/3_how_to_contribute_main.rst
index 296e4e8658..6e5c1be8ee 100644
--- a/docs/modules/0_getting_started/3_how_to_contribute_main.rst
+++ b/docs/modules/0_getting_started/3_how_to_contribute_main.rst
@@ -1,4 +1,4 @@
-How To Contribute
+How to contribute
 =================
 
 This document describes how to contribute this project.
diff --git a/docs/modules/0_getting_started/4_how_to_read_textbook_main.rst b/docs/modules/0_getting_started/4_how_to_read_textbook_main.rst
index fb8db348c6..1625c838af 100644
--- a/docs/modules/0_getting_started/4_how_to_read_textbook_main.rst
+++ b/docs/modules/0_getting_started/4_how_to_read_textbook_main.rst
@@ -3,4 +3,12 @@
 How to read this textbook
 --------------------------
 
-TBD
\ No newline at end of file
+This document is structured to help you learn the fundamental concepts
+behind each sample code in PythonRobotics.
+
+If you already have some knowledge of robotics technologies, you can start
+by reading any document that interests you.
+
+However, if you have no prior knowledge of robotics technologies, it is
+recommended that you first read the :ref:`Introduction` section and then proceed
+to the documents related to the technical fields that interest you.
\ No newline at end of file
diff --git a/docs/modules/1_introduction/introduction_main.rst b/docs/modules/1_introduction/introduction_main.rst
index 4561efd349..ec1f237545 100644
--- a/docs/modules/1_introduction/introduction_main.rst
+++ b/docs/modules/1_introduction/introduction_main.rst
@@ -1,4 +1,4 @@
-.. _introduction:
+.. _Introduction:
 
 Introduction
 ============

From 70269fe9601b6189914a739c4c0d42b598c3425b Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 3 Feb 2025 22:19:10 +0900
Subject: [PATCH 0962/1080] update getting started (#1138)

---
 .../python_for_robotics_main.rst}                          | 5 +----
 docs/modules/1_introduction/introduction_main.rst          | 7 ++++++-
 2 files changed, 7 insertions(+), 5 deletions(-)
 rename docs/modules/1_introduction/{2_software_for_robotics/software_for_robotics_main.rst => 2_python_for_robotics/python_for_robotics_main.rst} (51%)

diff --git a/docs/modules/1_introduction/2_software_for_robotics/software_for_robotics_main.rst b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
similarity index 51%
rename from docs/modules/1_introduction/2_software_for_robotics/software_for_robotics_main.rst
rename to docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
index 835441f85d..23f31da779 100644
--- a/docs/modules/1_introduction/2_software_for_robotics/software_for_robotics_main.rst
+++ b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
@@ -1,7 +1,4 @@
-Software for Robotics
-----------------------
-
 Python for Robotics
-~~~~~~~~~~~~~~~~~~~~~
+----------------------
 
 TBD
diff --git a/docs/modules/1_introduction/introduction_main.rst b/docs/modules/1_introduction/introduction_main.rst
index ec1f237545..a7ce55f9bf 100644
--- a/docs/modules/1_introduction/introduction_main.rst
+++ b/docs/modules/1_introduction/introduction_main.rst
@@ -3,11 +3,16 @@
 Introduction
 ============
 
+PythonRobotics is composed of two words: "Python" and "Robotics".
+Therefore, I will first explain these two topics, Robotics and Python.
+After that, I will provide an overview of the robotics technologies
+covered in PythonRobotics.
+
 .. toctree::
    :maxdepth: 2
    :caption: Table of Contents
 
    1_definition_of_robotics/definition_of_robotics
-   2_software_for_robotics/software_for_robotics
+   2_python_for_robotics/python_for_robotics
    3_technology_for_robotics/technology_for_robotics
 

From 5b06435be9e24a73c8ee9d0f0acb1e409a118141 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 4 Feb 2025 07:37:17 +0900
Subject: [PATCH 0963/1080] build(deps): bump ruff from 0.9.3 to 0.9.4 in
 /requirements (#1139)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.9.3 to 0.9.4.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.9.3...0.9.4)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index e8bed7b6d7..9d4e7deb4d 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.14.1 # For unit test
-ruff == 0.9.3 # For unit test
+ruff == 0.9.4 # For unit test

From 7b7bd784093e4d46108b667c54261d054894e875 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 5 Feb 2025 13:41:28 +0900
Subject: [PATCH 0964/1080] update introduction (#1141)

---
 .../python_for_robotics_main.rst              | 46 +++++++++++++++++++
 1 file changed, 46 insertions(+)

diff --git a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
index 23f31da779..1ad5316f53 100644
--- a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
+++ b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
@@ -1,4 +1,50 @@
 Python for Robotics
 ----------------------
 
+Python for general-purpose programming
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+`Python <https://www.python.org/>`_ is an general-purpose programming language developed by
+`Guido van Rossum <https://en.wikipedia.org/wiki/Guido_van_Rossum>`_ in the late 1980s.
+
+It features as follows:
+
+#. High-level
+#. Interpreted
+#. Dynamic type system (also type annotation is supported)
+#. Emphasizes code readability
+#. Rapid prototyping
+#. Batteries included
+#. Interoperability for C and Fortran
+
+Due to these features, Python is the most popular programming language
+for educational purposes for programming beginners.
+
+Python for Scientific Computing
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Python itself was not designed for scientific computing.
+However, scientists quickly recognized its strengths.
+For example,
+
+#. High-level and interpreted features enable scientists to focus on their problems without dealing with low-level programming tasks like memory management.
+#. Code readability, rapid prototyping, and batteries included features enables scientists who are not professional programmers, to solve their problems easily.
+#. The interoperability to wrap C and Fortran libraries enables scientists to access already existed powerful scientific computing libraries.
+
+To address the more needs of scientific computing, many libraries have been developed.
+
+- `NumPy <https://numpy.org/>`_ is the fundamental package for scientific computing with Python.
+- `SciPy <https://www.scipy.org/>`_ is a library that builds on NumPy and provides a large number of functions that operate on NumPy arrays and are useful for different types of scientific and engineering applications.
+- `Matplotlib <https://matplotlib.org/>`_ is a plotting library for the Python programming language and its numerical mathematics extension NumPy.
+- `Pandas <https://pandas.pydata.org/>`_ is a fast, powerful, flexible, and easy-to-use open-source data analysis and data manipulation library built on top of NumPy.
+- `SymPy <https://www.sympy.org/>`_ is a Python library for symbolic mathematics.
+
+And more domain-specific libraries have been developed:
+- `Scikit-learn <https://scikit-learn.org/stable/>`_ is a free software machine learning library for the Python programming language.
+- `Scikit-image <https://scikit-image.org/>`_ is a collection of algorithms for image processing.
+
+Python for Robotics
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 TBD
+

From 322fead45a62417c5d0d7dbb07ef17067122da7b Mon Sep 17 00:00:00 2001
From: Aglargil <34728006+Aglargil@users.noreply.github.com>
Date: Wed, 5 Feb 2025 20:56:13 +0800
Subject: [PATCH 0965/1080] feat: add DistanceMap (#1142)

* feat: add DistanceMap

* feat: add DistanceMap test

* feat: add DistanceMap doc

* feat: DistanceMap doc update

* feat: DistanceMap update
---
 Mapping/DistanceMap/distance_map.py           | 151 ++++++++++++++++++
 .../3_mapping/distance_map/distance_map.png   | Bin 0 -> 32698 bytes
 .../distance_map/distance_map_main.rst        |  27 ++++
 docs/modules/3_mapping/mapping_main.rst       |   1 +
 tests/test_distance_map.py                    | 118 ++++++++++++++
 5 files changed, 297 insertions(+)
 create mode 100644 Mapping/DistanceMap/distance_map.py
 create mode 100644 docs/modules/3_mapping/distance_map/distance_map.png
 create mode 100644 docs/modules/3_mapping/distance_map/distance_map_main.rst
 create mode 100644 tests/test_distance_map.py

diff --git a/Mapping/DistanceMap/distance_map.py b/Mapping/DistanceMap/distance_map.py
new file mode 100644
index 0000000000..54c98c6a75
--- /dev/null
+++ b/Mapping/DistanceMap/distance_map.py
@@ -0,0 +1,151 @@
+"""
+Distance Map
+
+author: Wang Zheng (@Aglargil)
+
+Ref:
+
+- [Distance Map]
+(https://cs.brown.edu/people/pfelzens/papers/dt-final.pdf)
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+INF = 1e20
+ENABLE_PLOT = True
+
+
+def compute_sdf(obstacles):
+    """
+    Compute the signed distance field (SDF) from a boolean field.
+
+    Parameters
+    ----------
+    obstacles : array_like
+        A 2D boolean array where '1' represents obstacles and '0' represents free space.
+
+    Returns
+    -------
+    array_like
+        A 2D array representing the signed distance field, where positive values indicate distance
+        to the nearest obstacle, and negative values indicate distance to the nearest free space.
+    """
+    a = compute_udf(obstacles)
+    b = compute_udf(obstacles == 0)
+    return a - b
+
+
+def compute_udf(obstacles):
+    """
+    Compute the unsigned distance field (UDF) from a boolean field.
+
+    Parameters
+    ----------
+    obstacles : array_like
+        A 2D boolean array where '1' represents obstacles and '0' represents free space.
+
+    Returns
+    -------
+    array_like
+        A 2D array of distances from the nearest obstacle, with the same dimensions as `bool_field`.
+    """
+    edt = obstacles.copy()
+    if not np.all(np.isin(edt, [0, 1])):
+        raise ValueError("Input array should only contain 0 and 1")
+    edt = np.where(edt == 0, INF, edt)
+    edt = np.where(edt == 1, 0, edt)
+    for row in range(len(edt)):
+        dt(edt[row])
+    edt = edt.T
+    for row in range(len(edt)):
+        dt(edt[row])
+    edt = edt.T
+    return np.sqrt(edt)
+
+
+def dt(d):
+    """
+    Compute 1D distance transform under the squared Euclidean distance
+
+    Parameters
+    ----------
+    d : array_like
+        Input array containing the distances.
+
+    Returns:
+    --------
+    d : array_like
+        The transformed array with computed distances.
+    """
+    v = np.zeros(len(d) + 1)
+    z = np.zeros(len(d) + 1)
+    k = 0
+    v[0] = 0
+    z[0] = -INF
+    z[1] = INF
+    for q in range(1, len(d)):
+        s = ((d[q] + q * q) - (d[int(v[k])] + v[k] * v[k])) / (2 * q - 2 * v[k])
+        while s <= z[k]:
+            k = k - 1
+            s = ((d[q] + q * q) - (d[int(v[k])] + v[k] * v[k])) / (2 * q - 2 * v[k])
+        k = k + 1
+        v[k] = q
+        z[k] = s
+        z[k + 1] = INF
+    k = 0
+    for q in range(len(d)):
+        while z[k + 1] < q:
+            k = k + 1
+        dx = q - v[k]
+        d[q] = dx * dx + d[int(v[k])]
+
+
+def main():
+    obstacles = np.array(
+        [
+            [1, 0, 0, 0, 0],
+            [0, 1, 1, 1, 0],
+            [0, 1, 1, 1, 0],
+            [0, 0, 1, 1, 0],
+            [0, 0, 1, 0, 0],
+            [0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0],
+            [0, 0, 1, 0, 0],
+            [0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0],
+        ]
+    )
+
+    # Compute the signed distance field
+    sdf = compute_sdf(obstacles)
+    udf = compute_udf(obstacles)
+
+    if ENABLE_PLOT:
+        _, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15, 5))
+
+        obstacles_plot = ax1.imshow(obstacles, cmap="binary")
+        ax1.set_title("Obstacles")
+        ax1.set_xlabel("x")
+        ax1.set_ylabel("y")
+        plt.colorbar(obstacles_plot, ax=ax1)
+
+        udf_plot = ax2.imshow(udf, cmap="viridis")
+        ax2.set_title("Unsigned Distance Field")
+        ax2.set_xlabel("x")
+        ax2.set_ylabel("y")
+        plt.colorbar(udf_plot, ax=ax2)
+
+        sdf_plot = ax3.imshow(sdf, cmap="RdBu")
+        ax3.set_title("Signed Distance Field")
+        ax3.set_xlabel("x")
+        ax3.set_ylabel("y")
+        plt.colorbar(sdf_plot, ax=ax3)
+
+        plt.tight_layout()
+        plt.show()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/modules/3_mapping/distance_map/distance_map.png b/docs/modules/3_mapping/distance_map/distance_map.png
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diff --git a/docs/modules/3_mapping/distance_map/distance_map_main.rst b/docs/modules/3_mapping/distance_map/distance_map_main.rst
new file mode 100644
index 0000000000..45273cd3bb
--- /dev/null
+++ b/docs/modules/3_mapping/distance_map/distance_map_main.rst
@@ -0,0 +1,27 @@
+Distance Map
+------------
+
+This is an implementation of the Distance Map algorithm for path planning.
+
+The Distance Map algorithm computes the unsigned distance field (UDF) and signed distance field (SDF) from a boolean field representing obstacles. 
+
+The UDF gives the distance from each point to the nearest obstacle. The SDF gives positive distances for points outside obstacles and negative distances for points inside obstacles.
+
+Example
+~~~~~~~
+
+The algorithm is demonstrated on a simple 2D grid with obstacles:
+
+.. image:: distance_map.png
+
+API
+~~~
+
+.. autofunction:: PathPlanning.DistanceMap.distance_map.compute_sdf
+
+.. autofunction:: PathPlanning.DistanceMap.distance_map.compute_udf
+
+References
+~~~~~~~~~~
+
+- `Distance Transforms of Sampled Functions <https://cs.brown.edu/people/pfelzens/papers/dt-final.pdf>`_ paper by Pedro F. Felzenszwalb and Daniel P. Huttenlocher. 
\ No newline at end of file
diff --git a/docs/modules/3_mapping/mapping_main.rst b/docs/modules/3_mapping/mapping_main.rst
index 28e18984d3..825b08d3ec 100644
--- a/docs/modules/3_mapping/mapping_main.rst
+++ b/docs/modules/3_mapping/mapping_main.rst
@@ -17,3 +17,4 @@ Mapping is the ability of a robot to understand its surroundings with external s
    circle_fitting/circle_fitting
    rectangle_fitting/rectangle_fitting
    normal_vector_estimation/normal_vector_estimation
+   distance_map/distance_map
diff --git a/tests/test_distance_map.py b/tests/test_distance_map.py
new file mode 100644
index 0000000000..df6e394e2c
--- /dev/null
+++ b/tests/test_distance_map.py
@@ -0,0 +1,118 @@
+import conftest  # noqa
+import numpy as np
+from Mapping.DistanceMap import distance_map as m
+
+
+def test_compute_sdf():
+    """Test the computation of Signed Distance Field (SDF)"""
+    # Create a simple obstacle map for testing
+    obstacles = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]])
+
+    sdf = m.compute_sdf(obstacles)
+
+    # Verify basic properties of SDF
+    assert sdf.shape == obstacles.shape, "SDF should have the same shape as input map"
+    assert np.all(np.isfinite(sdf)), "SDF should not contain infinite values"
+
+    # Verify SDF value is negative at obstacle position
+    assert sdf[1, 1] < 0, "SDF value should be negative at obstacle position"
+
+    # Verify SDF value is positive in free space
+    assert sdf[0, 0] > 0, "SDF value should be positive in free space"
+
+
+def test_compute_udf():
+    """Test the computation of Unsigned Distance Field (UDF)"""
+    # Create obstacle map for testing
+    obstacles = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]])
+
+    udf = m.compute_udf(obstacles)
+
+    # Verify basic properties of UDF
+    assert udf.shape == obstacles.shape, "UDF should have the same shape as input map"
+    assert np.all(np.isfinite(udf)), "UDF should not contain infinite values"
+    assert np.all(udf >= 0), "All UDF values should be non-negative"
+
+    # Verify UDF value is 0 at obstacle position
+    assert np.abs(udf[1, 1]) < 1e-10, "UDF value should be 0 at obstacle position"
+
+    # Verify UDF value is 1 for adjacent cells
+    assert np.abs(udf[0, 1] - 1.0) < 1e-10, (
+        "UDF value should be 1 for cells adjacent to obstacle"
+    )
+    assert np.abs(udf[1, 0] - 1.0) < 1e-10, (
+        "UDF value should be 1 for cells adjacent to obstacle"
+    )
+    assert np.abs(udf[1, 2] - 1.0) < 1e-10, (
+        "UDF value should be 1 for cells adjacent to obstacle"
+    )
+    assert np.abs(udf[2, 1] - 1.0) < 1e-10, (
+        "UDF value should be 1 for cells adjacent to obstacle"
+    )
+
+
+def test_dt():
+    """Test the computation of 1D distance transform"""
+    # Create test data
+    d = np.array([m.INF, 0, m.INF])
+    m.dt(d)
+
+    # Verify distance transform results
+    assert np.all(np.isfinite(d)), (
+        "Distance transform result should not contain infinite values"
+    )
+    assert d[1] == 0, "Distance at obstacle position should be 0"
+    assert d[0] == 1, "Distance at adjacent position should be 1"
+    assert d[2] == 1, "Distance at adjacent position should be 1"
+
+
+def test_compute_sdf_empty():
+    """Test SDF computation with empty map"""
+    # Test with empty map (no obstacles)
+    empty_map = np.zeros((5, 5))
+    sdf = m.compute_sdf(empty_map)
+
+    assert np.all(sdf > 0), "All SDF values should be positive for empty map"
+    assert sdf.shape == empty_map.shape, "Output shape should match input shape"
+
+
+def test_compute_sdf_full():
+    """Test SDF computation with fully occupied map"""
+    # Test with fully occupied map
+    full_map = np.ones((5, 5))
+    sdf = m.compute_sdf(full_map)
+
+    assert np.all(sdf < 0), "All SDF values should be negative for fully occupied map"
+    assert sdf.shape == full_map.shape, "Output shape should match input shape"
+
+
+def test_compute_udf_invalid_input():
+    """Test UDF computation with invalid input values"""
+    # Test with invalid values (not 0 or 1)
+    invalid_map = np.array([[0, 2, 0], [0, -1, 0], [0, 0.5, 0]])
+
+    try:
+        m.compute_udf(invalid_map)
+        assert False, "Should raise ValueError for invalid input values"
+    except ValueError:
+        pass
+
+
+def test_compute_udf_empty():
+    """Test UDF computation with empty map"""
+    # Test with empty map
+    empty_map = np.zeros((5, 5))
+    udf = m.compute_udf(empty_map)
+
+    assert np.all(udf > 0), "All UDF values should be positive for empty map"
+    assert np.all(np.isfinite(udf)), "UDF should not contain infinite values"
+
+
+def test_main():
+    """Test the execution of main function"""
+    m.ENABLE_PLOT = False
+    m.main()
+
+
+if __name__ == "__main__":
+    conftest.run_this_test(__file__)

From 2234abf63d07e5496e37c1b57a1b927c303272a8 Mon Sep 17 00:00:00 2001
From: Aglargil <34728006+Aglargil@users.noreply.github.com>
Date: Thu, 6 Feb 2025 12:05:20 +0800
Subject: [PATCH 0966/1080] fix: DistanceMap doc autofunction (#1143)

---
 docs/modules/3_mapping/distance_map/distance_map_main.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/modules/3_mapping/distance_map/distance_map_main.rst b/docs/modules/3_mapping/distance_map/distance_map_main.rst
index 45273cd3bb..0ef9e3022f 100644
--- a/docs/modules/3_mapping/distance_map/distance_map_main.rst
+++ b/docs/modules/3_mapping/distance_map/distance_map_main.rst
@@ -17,9 +17,9 @@ The algorithm is demonstrated on a simple 2D grid with obstacles:
 API
 ~~~
 
-.. autofunction:: PathPlanning.DistanceMap.distance_map.compute_sdf
+.. autofunction:: Mapping.DistanceMap.distance_map.compute_sdf
 
-.. autofunction:: PathPlanning.DistanceMap.distance_map.compute_udf
+.. autofunction:: Mapping.DistanceMap.distance_map.compute_udf
 
 References
 ~~~~~~~~~~

From 0676dfd67e099198c34c485e176b077ad6fa7374 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 6 Feb 2025 15:16:17 +0900
Subject: [PATCH 0967/1080] update introduction (#1144)

---
 .../python_for_robotics_main.rst              | 57 +++++++++++++++++--
 1 file changed, 51 insertions(+), 6 deletions(-)

diff --git a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
index 1ad5316f53..2f89f0c7b5 100644
--- a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
+++ b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
@@ -1,11 +1,13 @@
 Python for Robotics
 ----------------------
 
+This section explains the Python itself and features for Robotics.
+
 Python for general-purpose programming
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 `Python <https://www.python.org/>`_ is an general-purpose programming language developed by
-`Guido van Rossum <https://en.wikipedia.org/wiki/Guido_van_Rossum>`_ in the late 1980s.
+`Guido van Rossum <https://en.wikipedia.org/wiki/Guido_van_Rossum>`_ from the late 1980s.
 
 It features as follows:
 
@@ -17,7 +19,7 @@ It features as follows:
 #. Batteries included
 #. Interoperability for C and Fortran
 
-Due to these features, Python is the most popular programming language
+Due to these features, Python is one of the most popular programming language
 for educational purposes for programming beginners.
 
 Python for Scientific Computing
@@ -29,9 +31,9 @@ For example,
 
 #. High-level and interpreted features enable scientists to focus on their problems without dealing with low-level programming tasks like memory management.
 #. Code readability, rapid prototyping, and batteries included features enables scientists who are not professional programmers, to solve their problems easily.
-#. The interoperability to wrap C and Fortran libraries enables scientists to access already existed powerful scientific computing libraries.
+#. The interoperability to wrap C and Fortran libraries enables scientists to access already existed powerful and optimized scientific computing libraries.
 
-To address the more needs of scientific computing, many libraries have been developed.
+To address the more needs of scientific computing, many fundamental scientific computation libraries have been developed based on the upper features.
 
 - `NumPy <https://numpy.org/>`_ is the fundamental package for scientific computing with Python.
 - `SciPy <https://www.scipy.org/>`_ is a library that builds on NumPy and provides a large number of functions that operate on NumPy arrays and are useful for different types of scientific and engineering applications.
@@ -39,12 +41,55 @@ To address the more needs of scientific computing, many libraries have been deve
 - `Pandas <https://pandas.pydata.org/>`_ is a fast, powerful, flexible, and easy-to-use open-source data analysis and data manipulation library built on top of NumPy.
 - `SymPy <https://www.sympy.org/>`_ is a Python library for symbolic mathematics.
 
-And more domain-specific libraries have been developed:
+Also, more domain-specific libraries have been developed based on these fundamental libraries:
+
 - `Scikit-learn <https://scikit-learn.org/stable/>`_ is a free software machine learning library for the Python programming language.
 - `Scikit-image <https://scikit-image.org/>`_ is a collection of algorithms for image processing.
+- `Networkx <https://networkx.org/>`_ is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
+- `SunPy <https://sunpy.org/>`_ is a community-developed free and open-source software package for solar physics.
+- `Astropy <https://www.astropy.org/>`_ is a community-developed free and open-source software package for astronomy.
+
+Currently, Python is one of the most popular programming languages for scientific computing.
 
 Python for Robotics
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-TBD
+Scientific computation routine are very important for robotics.
+For example, matrix operation, optimization, and visualization are fundamental for robotics.
+
+Python has become an increasingly popular language in robotics due to its versatility, readability, and extensive libraries. Here's a breakdown of why Python is a great choice for robotics development:
+
+Advantages of Python for Robotics:
+
+Simplicity and Readability: Python's syntax is clear and concise, making it easier to learn and write code. This is crucial in robotics where complex algorithms and control logic are involved.
+Extensive Libraries: Python boasts a rich collection of libraries specifically designed for robotics:
+ROS (Robot Operating System): ROS, a widely used framework for robotics development, has strong Python support (rospy). This allows developers to easily create nodes, manage communication between different parts of a robot system, and utilize various ROS tools.
+OpenCV: This powerful library provides tools for computer vision tasks like image processing, object detection, and motion tracking, essential for robots that perceive and interact with their environment.
+NumPy and SciPy: These libraries offer efficient numerical computation and scientific tools, enabling developers to implement complex mathematical models and control algorithms.
+Scikit-learn: This library provides machine learning algorithms, which are increasingly important in robotics for tasks like perception, planning, and control.
+Cross-Platform Compatibility: Python code can run on various operating systems (Windows, macOS, Linux), providing flexibility in choosing hardware platforms for robotics projects.
+Large Community and Support: Python has a vast and active community, offering ample resources, tutorials, and support for developers. This is invaluable when tackling challenges in robotics development.
+Use Cases of Python in Robotics:
+
+Robot Control: Python can be used to write control algorithms for robot manipulators, mobile robots, and other robotic systems.
+Perception: Python, combined with libraries like OpenCV, enables robots to process sensor data (camera images, lidar data) to understand their surroundings.
+Path Planning: Python algorithms can be used to plan collision-free paths for robots to navigate in complex environments.
+Machine Learning: Python libraries like Scikit-learn empower robots to learn from data and improve their performance in tasks like object recognition and manipulation.
+Simulation: Python can be used to create simulated environments for testing and developing robot algorithms before deploying them on real hardware.
+Examples of Python in Robotics:
+
+Autonomous Navigation: Python is used in self-driving cars and other autonomous vehicles for tasks like perception, localization, and path planning.
+Industrial Robotics: Python is employed in manufacturing for robot control, quality inspection, and automation.
+Service Robotics: Python powers robots that perform tasks like cleaning, delivery, and customer service in various environments.
+Research and Education: Python is a popular choice in robotics research and education due to its ease of use and versatility.
+Getting Started with Python in Robotics:
+
+Learn Python Basics: Familiarize yourself with Python syntax, data structures, and programming concepts.
+Explore Robotics Libraries: Dive into libraries like ROS, OpenCV, and others relevant to your robotics interests.
+Practice with Projects: Start with small projects to gain hands-on experience, gradually increasing complexity.
+Join the Community: Engage with the robotics community through online forums, workshops, and conferences to learn from others and share your knowledge.
+In conclusion, Python's simplicity, extensive libraries, and strong community support make it an ideal language for robotics development. Whether you're a beginner or an experienced programmer, Python offers the tools and resources you need to build innovative and capable robots.
+
+Python is used for this `PythonRobotics` project because of the above features
+to achieve the purpose of this project described in the :ref:`What is PythonRobotics?`.
 

From 9936f344635e146b9a2ee402ab1672b4e7216d6e Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 7 Feb 2025 13:32:11 +0900
Subject: [PATCH 0968/1080] update introduction (#1145)

---
 .../definition_of_robotics_main.rst           |  7 +++
 .../python_for_robotics_main.rst              | 63 +++++++++----------
 2 files changed, 37 insertions(+), 33 deletions(-)

diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
index 2f31834017..fd151e3f20 100644
--- a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
+++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
@@ -5,3 +5,10 @@ In recent years, autonomous navigation technologies have received huge
 attention in many fields.
 Such fields include, autonomous driving[22], drone flight navigation,
 and other transportation systems.
+
+Examples of Python in Robotics:
+
+Autonomous Navigation: Python is used in self-driving cars and other autonomous vehicles for tasks like perception, localization, and path planning.
+Industrial Robotics: Python is employed in manufacturing for robot control, quality inspection, and automation.
+Service Robotics: Python powers robots that perform tasks like cleaning, delivery, and customer service in various environments.
+Research and Education: Python is a popular choice in robotics research and education due to its ease of use and versatility.
\ No newline at end of file
diff --git a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
index 2f89f0c7b5..90edd5dc0c 100644
--- a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
+++ b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
@@ -40,55 +40,52 @@ To address the more needs of scientific computing, many fundamental scientific c
 - `Matplotlib <https://matplotlib.org/>`_ is a plotting library for the Python programming language and its numerical mathematics extension NumPy.
 - `Pandas <https://pandas.pydata.org/>`_ is a fast, powerful, flexible, and easy-to-use open-source data analysis and data manipulation library built on top of NumPy.
 - `SymPy <https://www.sympy.org/>`_ is a Python library for symbolic mathematics.
+- `CVXPy <https://www.cvxpy.org/>`_ is a Python-embedded modeling language for convex optimization problems.
 
 Also, more domain-specific libraries have been developed based on these fundamental libraries:
 
 - `Scikit-learn <https://scikit-learn.org/stable/>`_ is a free software machine learning library for the Python programming language.
 - `Scikit-image <https://scikit-image.org/>`_ is a collection of algorithms for image processing.
-- `Networkx <https://networkx.org/>`_ is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
+- `Networkx <https://networkx.org/>`_ is a package for the creation, manipulation for complex networks.
 - `SunPy <https://sunpy.org/>`_ is a community-developed free and open-source software package for solar physics.
 - `Astropy <https://www.astropy.org/>`_ is a community-developed free and open-source software package for astronomy.
 
 Currently, Python is one of the most popular programming languages for scientific computing.
 
 Python for Robotics
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^
 
-Scientific computation routine are very important for robotics.
-For example, matrix operation, optimization, and visualization are fundamental for robotics.
+Python has become an increasingly popular language in robotics.
 
-Python has become an increasingly popular language in robotics due to its versatility, readability, and extensive libraries. Here's a breakdown of why Python is a great choice for robotics development:
+These are advantages of Python for Robotics:
 
-Advantages of Python for Robotics:
+Simplicity and Readability
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Python's syntax is clear and concise, making it easier to learn and write code.
+This is crucial in robotics where complex algorithms and control logic are involved.
 
-Simplicity and Readability: Python's syntax is clear and concise, making it easier to learn and write code. This is crucial in robotics where complex algorithms and control logic are involved.
-Extensive Libraries: Python boasts a rich collection of libraries specifically designed for robotics:
+
+Extensive libraries for scientific computation.
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Scientific computation routine are fundamental for robotics.
+For example:
+
+- Matrix operation is needed for rigid body transformation, state estimation, and model based control.
+- Optimization is needed for optimization based SLAM, optimal path planning, and optimal control.
+- Visualization is needed for robot teleoperation, debugging, and simulation.
+
+ROS supports Python
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
 ROS (Robot Operating System): ROS, a widely used framework for robotics development, has strong Python support (rospy). This allows developers to easily create nodes, manage communication between different parts of a robot system, and utilize various ROS tools.
-OpenCV: This powerful library provides tools for computer vision tasks like image processing, object detection, and motion tracking, essential for robots that perceive and interact with their environment.
-NumPy and SciPy: These libraries offer efficient numerical computation and scientific tools, enabling developers to implement complex mathematical models and control algorithms.
-Scikit-learn: This library provides machine learning algorithms, which are increasingly important in robotics for tasks like perception, planning, and control.
-Cross-Platform Compatibility: Python code can run on various operating systems (Windows, macOS, Linux), providing flexibility in choosing hardware platforms for robotics projects.
-Large Community and Support: Python has a vast and active community, offering ample resources, tutorials, and support for developers. This is invaluable when tackling challenges in robotics development.
-Use Cases of Python in Robotics:
-
-Robot Control: Python can be used to write control algorithms for robot manipulators, mobile robots, and other robotic systems.
-Perception: Python, combined with libraries like OpenCV, enables robots to process sensor data (camera images, lidar data) to understand their surroundings.
-Path Planning: Python algorithms can be used to plan collision-free paths for robots to navigate in complex environments.
-Machine Learning: Python libraries like Scikit-learn empower robots to learn from data and improve their performance in tasks like object recognition and manipulation.
-Simulation: Python can be used to create simulated environments for testing and developing robot algorithms before deploying them on real hardware.
-Examples of Python in Robotics:
-
-Autonomous Navigation: Python is used in self-driving cars and other autonomous vehicles for tasks like perception, localization, and path planning.
-Industrial Robotics: Python is employed in manufacturing for robot control, quality inspection, and automation.
-Service Robotics: Python powers robots that perform tasks like cleaning, delivery, and customer service in various environments.
-Research and Education: Python is a popular choice in robotics research and education due to its ease of use and versatility.
-Getting Started with Python in Robotics:
-
-Learn Python Basics: Familiarize yourself with Python syntax, data structures, and programming concepts.
-Explore Robotics Libraries: Dive into libraries like ROS, OpenCV, and others relevant to your robotics interests.
-Practice with Projects: Start with small projects to gain hands-on experience, gradually increasing complexity.
-Join the Community: Engage with the robotics community through online forums, workshops, and conferences to learn from others and share your knowledge.
-In conclusion, Python's simplicity, extensive libraries, and strong community support make it an ideal language for robotics development. Whether you're a beginner or an experienced programmer, Python offers the tools and resources you need to build innovative and capable robots.
+
+Cross-Platform Compatibility
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Python code can run on various operating systems (Windows, macOS, Linux), providing flexibility in choosing hardware platforms for robotics projects.
+
+Large Community and Support
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Python has a vast and active community, offering ample resources, tutorials, and support for developers. This is invaluable when tackling challenges in robotics development.
+
 
 Python is used for this `PythonRobotics` project because of the above features
 to achieve the purpose of this project described in the :ref:`What is PythonRobotics?`.

From 15e106839285033e1fa68cbed6f14e219f3af88f Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 7 Feb 2025 13:42:56 +0900
Subject: [PATCH 0969/1080] Update CONTRIBUTING.md

---
 CONTRIBUTING.md | 24 +++---------------------
 1 file changed, 3 insertions(+), 21 deletions(-)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 91f6dfa822..3bcc499e6a 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,23 +1,5 @@
-# Contributing to Python
+# Contributing
 
-:+1::tada: First of off, thanks very much for taking the time to contribute! :tada::+1:
+:+1::tada: First of all, thank you very much for taking the time to contribute! :tada::+1:
 
-The following is a set of guidelines for contributing to PythonRobotics. 
-
-These are mostly guidelines, not rules. 
-
-Use your best judgment, and feel free to propose changes to this document in a pull request.
-
-# Before contributing
-
-## Taking a look at the paper.
-
-Please check this paper to understand the philosophy of this project.
-
-- [\[1808\.10703\] PythonRobotics: a Python code collection of robotics algorithms](https://arxiv.org/abs/1808.10703) ([BibTeX](https://github.com/AtsushiSakai/PythonRoboticsPaper/blob/master/python_robotics.bib))
-
-## Check your Python version.
-
-We only accept a PR for Python 3.8.x or higher.
-
-We will not accept a PR for Python 2.x.
+Please check this document for contribution: [How to contribute — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/modules/0_getting_started/3_how_to_contribute.html) 

From a8f3388bbe80e41655acedc44c2cbb86d011fb48 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 9 Feb 2025 21:00:02 +0900
Subject: [PATCH 0970/1080] update introduction (#1146)

---
 .../3_how_to_contribute_main.rst              | 37 ++++++++++++++-----
 .../python_for_robotics_main.rst              | 29 ++++++++++++---
 2 files changed, 51 insertions(+), 15 deletions(-)

diff --git a/docs/modules/0_getting_started/3_how_to_contribute_main.rst b/docs/modules/0_getting_started/3_how_to_contribute_main.rst
index 6e5c1be8ee..874564cbb8 100644
--- a/docs/modules/0_getting_started/3_how_to_contribute_main.rst
+++ b/docs/modules/0_getting_started/3_how_to_contribute_main.rst
@@ -9,10 +9,30 @@ There are several ways to contribute to this project as below:
 #. `Adding missed documentations for existing examples`_
 #. `Supporting this project`_
 
+Before contributing
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Please check following items before contributing:
+
+Understanding this project
+---------------------------
+
+Please check this :ref:`What is PythonRobotics?` section and this paper
+`PythonRobotics: a Python code collection of robotics algorithms`_
+to understand the philosophies of this project.
+
+.. _`PythonRobotics: a Python code collection of robotics algorithms`: https://arxiv.org/abs/1808.10703
+
+Check your Python version.
+---------------------------
+
+We only accept a PR for Python 3.12.x or higher.
+
+We will not accept a PR for Python 2.x.
 
 .. _`Adding a new algorithm example`:
 
-Adding a new algorithm example
+1. Adding a new algorithm example
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 This is a step by step manual to add a new algorithm example.
@@ -112,8 +132,8 @@ Note that this is my hobby project; I appreciate your patience during the review
 
 .. _`Reporting and fixing a defect`:
 
-Reporting and fixing a defect
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+2. Reporting and fixing a defect
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Reporting and fixing a defect is also great contribution.
 
@@ -136,8 +156,8 @@ This doc `submit a pull request`_ can be helpful to submit a pull request.
 
 .. _`Adding missed documentations for existing examples`:
 
-Adding missed documentations for existing examples
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+3. Adding missed documentations for existing examples
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Adding the missed documentations for existing examples is also great contribution.
 
@@ -150,8 +170,8 @@ This doc `how to write doc`_ can be helpful to write documents.
 
 .. _`Supporting this project`:
 
-Supporting this project
-^^^^^^^^^^^^^^^^^^^^^^^^
+4. Supporting this project
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Supporting this project financially is also a great contribution!!.
 
@@ -165,8 +185,7 @@ If you or your company would like to support this project, please consider:
 
 If you would like to support us in some other way, please contact with creating an issue.
 
-Current Major Sponsors
------------------------
+Current Major Sponsors:
 
 #. `JetBrains`_ : They are providing a free license of their IDEs for this OSS development.
 #. `1Password`_ : They are providing a free license of their 1Password team license for this OSS project.
diff --git a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
index 90edd5dc0c..65b1705150 100644
--- a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
+++ b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
@@ -1,6 +1,8 @@
 Python for Robotics
 ----------------------
 
+Python is used for this `PythonRobotics` project because of the above features
+to achieve the purpose of this project described in the :ref:`What is PythonRobotics?`.
 This section explains the Python itself and features for Robotics.
 
 Python for general-purpose programming
@@ -76,7 +78,27 @@ For example:
 
 ROS supports Python
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
-ROS (Robot Operating System): ROS, a widely used framework for robotics development, has strong Python support (rospy). This allows developers to easily create nodes, manage communication between different parts of a robot system, and utilize various ROS tools.
+`ROS`_ (Robot Operating System) is an open-source and widely used framework for robotics development.
+It is designed to help developping complicated robotic applications.
+ROS provides essential tools, libraries, and drivers to simplify robot programming and integration.
+
+Key Features of ROS:
+
+- Modular Architecture – Uses a node-based system where different components (nodes) communicate via messages.
+- Hardware Abstraction – Supports various robots, sensors, and actuators, making development more flexible.
+- Powerful Communication System – Uses topics, services, and actions for efficient data exchange between components.
+- Rich Ecosystem – Offers many pre-built packages for navigation, perception, and manipulation.
+- Multi-language Support – Primarily uses Python and C++, but also supports other languages.
+- Simulation & Visualization – Tools like Gazebo (for simulation) and RViz (for visualization) aid in development and testing.
+- Scalability & Community Support – Widely used in academia and industry, with a large open-source community.
+
+ROS has strong Python support (`rospy`_ for ROS1 and `rclpy`_ for ROS2).
+This allows developers to easily create nodes, manage communication between
+different parts of a robot system, and utilize various ROS tools.
+
+.. _`ROS`: https://www.ros.org/
+.. _`rospy`: http://wiki.ros.org/rospy
+.. _`rclpy`: https://docs.ros.org/en/jazzy/Tutorials/Beginner-Client-Libraries/Writing-A-Simple-Py-Publisher-And-Subscriber.html
 
 Cross-Platform Compatibility
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -85,8 +107,3 @@ Python code can run on various operating systems (Windows, macOS, Linux), provid
 Large Community and Support
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 Python has a vast and active community, offering ample resources, tutorials, and support for developers. This is invaluable when tackling challenges in robotics development.
-
-
-Python is used for this `PythonRobotics` project because of the above features
-to achieve the purpose of this project described in the :ref:`What is PythonRobotics?`.
-

From e304f07a997bf40977a004ac2a66f476aa2aa861 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 10 Feb 2025 11:21:19 +0900
Subject: [PATCH 0971/1080] update introduction (#1147)

---
 .../python_for_robotics_main.rst              | 32 +++++++++++++++++--
 1 file changed, 29 insertions(+), 3 deletions(-)

diff --git a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
index 65b1705150..b677d2c59b 100644
--- a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
+++ b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
@@ -1,9 +1,10 @@
 Python for Robotics
 ----------------------
 
-Python is used for this `PythonRobotics` project because of the above features
-to achieve the purpose of this project described in the :ref:`What is PythonRobotics?`.
-This section explains the Python itself and features for Robotics.
+A programing language, Python is used for this `PythonRobotics` project
+to achieve the purposes of this project described in the :ref:`What is PythonRobotics?`.
+
+This section explains the Python itself and features for science computing Robotics.
 
 Python for general-purpose programming
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -107,3 +108,28 @@ Python code can run on various operating systems (Windows, macOS, Linux), provid
 Large Community and Support
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 Python has a vast and active community, offering ample resources, tutorials, and support for developers. This is invaluable when tackling challenges in robotics development.
+
+Situations which Python is NOT suitable for Robotics
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+We explained the advantages of Python for robotics.
+However, Python is not always the best choice for robotics development.
+
+These are situations where Python is NOT suitable for robotics:
+
+High-speed real-time control
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Python is an interpreted language, which means it is slower than compiled languages like C++.
+This can be a disadvantage when real-time control is required,
+such as in high-speed motion control or safety-critical systems.
+
+So, for these applications, we recommend to understand the each algorithm you
+needed using this project and implement it in other suitable languages like C++.
+
+Resource-constrained systems
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Python is a high-level language that requires more memory and processing power
+compared to low-level languages.
+So, it is difficult to run Python on resource-constrained systems like
+microcontrollers or embedded devices.
+In such cases, C or C++ is more suitable for these applications.

From 610f35ff58e6535efa619f9ed73a113c6ca2c7f7 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 11 Feb 2025 10:42:10 +0900
Subject: [PATCH 0972/1080] build(deps): bump mypy from 1.14.1 to 1.15.0 in
 /requirements (#1148)

Bumps [mypy](https://github.com/python/mypy) from 1.14.1 to 1.15.0.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.14.1...v1.15.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 9d4e7deb4d..178046ac0d 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,5 +4,5 @@ matplotlib == 3.10.0
 cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test
 pytest-xdist == 3.6.1 # For unit test
-mypy == 1.14.1 # For unit test
+mypy == 1.15.0 # For unit test
 ruff == 0.9.4 # For unit test

From ba307673013376204ceb5a6def16da0e3a86a15d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 11 Feb 2025 11:35:28 +0900
Subject: [PATCH 0973/1080] build(deps): bump ruff from 0.9.4 to 0.9.6 in
 /requirements (#1149)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.9.4 to 0.9.6.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.9.4...0.9.6)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 178046ac0d..f5f674d7d2 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.15.0 # For unit test
-ruff == 0.9.4 # For unit test
+ruff == 0.9.6 # For unit test

From b298609b2832c8029baa98b2d3906abce0263ec4 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 11 Feb 2025 21:15:34 +0900
Subject: [PATCH 0974/1080] update introduction doc (#1151)

---
 .../definition_of_robotics_main.rst           | 81 ++++++++++++++++---
 .../python_for_robotics_main.rst              |  2 +-
 .../technology_for_robotics_main.rst          |  9 +++
 3 files changed, 82 insertions(+), 10 deletions(-)

diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
index fd151e3f20..f6fba646b4 100644
--- a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
+++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
@@ -1,14 +1,77 @@
 Definition of Robotics
 ----------------------
 
-In recent years, autonomous navigation technologies have received huge
-attention in many fields.
-Such fields include, autonomous driving[22], drone flight navigation,
-and other transportation systems.
+What is Robotics?
+^^^^^^^^^^^^^^^^^^
 
-Examples of Python in Robotics:
+Robot is a machine that can perform tasks automatically or semi-autonomously.
+Robotics is the study of robots.
+The field of robotics has wide areas of technologies such as mechanical engineering,
+electrical engineering, computer science, and artificial intelligence (AI),
+to create machines that can perform tasks autonomously or semi-autonomously.
+
+The History of Robots
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+This timeline highlights key milestones in the history of robotics:
+
+Ancient and Early Concepts (Before 1500s)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The idea of **automated machines** has existed for thousands of years. Ancient civilizations imagined mechanical beings:
+
+- **Ancient Greece (4th Century BC)** – Greek engineer **Hero of Alexandria** designed early **automata** (self-operating machines) powered by water or air.
+- **Chinese and Arabic Automata (9th–13th Century)** – Inventors like **Al-Jazari** created intricate mechanical devices, including water clocks and humanoid robots.
+
+The Birth of Modern Robotics (1500s–1800s)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+- **Leonardo da Vinci’s Robot (1495)** – Designed a humanoid knight with mechanical movement.
+- **Jacques de Vaucanson’s Automata (1738)** – Created robotic figures like a mechanical duck that could "eat" and "digest."
+- **Industrial Revolution (18th–19th Century)** – Machines began replacing human labor in factories, setting the foundation for automation.
+
+The Rise of Industrial Robots (1900s–1950s)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+- **The Term “Robot” (1921)** – Czech writer **Karel Čapek** introduced the word *“robot”* in his play *R.U.R. (Rossum’s Universal Robots)*.
+- **Early Cybernetics (1940s–1950s)** – Scientists like **Norbert Wiener** developed theories of self-regulating machines, influencing modern robotics.
+
+The Birth of Modern Robotics (1950s–1980s)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+- **First Industrial Robot (1961)** – *Unimate*, created by **George Devol and Joseph Engelberger**, was the first programmable robot used in a factory.
+- **Rise of AI & Autonomous Robots (1970s–1980s)** – Researchers developed mobile robots like **Shakey** (Stanford, 1966) and AI-based control systems.
+
+Advanced Robotics and AI Integration (1990s–Present)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+- **Autonomous Vehicles & Drones** – Self-driving cars and UAVs (unmanned aerial vehicles) became more advanced.
+- **Medical Robotics** – Robots like **da Vinci Surgical System** revolutionized healthcare.
+- **Personal Robots** – Devices like **Roomba** (vacuum robot) and **Sophia** (AI humanoid) became popular.
+- **Collaborative Robots (Cobots)** – Robots started working alongside humans in industries.
+
+Key Components of Robotics
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Robotics consists of several essential components:
+
+#. Sensors – Gather information from the environment (e.g., cameras, LiDAR, gyro, accelerometer, wheel encoders).
+#. Actuators – Enable movement and interaction with the world (e.g., motors, hydraulic systems).
+#. Computers – Process sensor data and make decisions (e.g., micro-controllers, CPUs, GPUs).
+#. Power Supply – Provides energy to run the robot (e.g., batteries, solar power).
+#. Software & Algorithms – Allow the robot to function and make intelligent decisions (e.g., ROS, machine learning models, localization, mapping, path planning, control).
+
+This project, PythonRobotics, focuses on the software and algorithms part of robotics.
+
+Applications of Robots
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Robots come in various forms depending on their purpose:
+
+#. 🤖 Industrial Robots – Used in manufacturing (e.g., robotic arms in manufacturing factories).
+#. 🏠 Service Robots – Assist in daily life (e.g., vacuum robots, delivery robots).
+#. 🚗 Autonomous Vehicles – Self-driving cars and drones.
+#. 👨‍⚕️ Medical Robots – Assist in surgeries and healthcare.
+#. 🚀 Space & Exploration Robots – Used for planetary exploration (e.g., NASA’s Mars rovers).
+#. 🐶 Humanoid & Social Robots – Designed to interact with humans (e.g., ASIMO, Sophia).
 
-Autonomous Navigation: Python is used in self-driving cars and other autonomous vehicles for tasks like perception, localization, and path planning.
-Industrial Robotics: Python is employed in manufacturing for robot control, quality inspection, and automation.
-Service Robotics: Python powers robots that perform tasks like cleaning, delivery, and customer service in various environments.
-Research and Education: Python is a popular choice in robotics research and education due to its ease of use and versatility.
\ No newline at end of file
diff --git a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
index b677d2c59b..c47c122853 100644
--- a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
+++ b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
@@ -4,7 +4,7 @@ Python for Robotics
 A programing language, Python is used for this `PythonRobotics` project
 to achieve the purposes of this project described in the :ref:`What is PythonRobotics?`.
 
-This section explains the Python itself and features for science computing Robotics.
+This section explains the Python itself and features for science computing and robotics.
 
 Python for general-purpose programming
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
diff --git a/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst b/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst
index 4dd1d1842f..93dc9e3466 100644
--- a/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst
+++ b/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst
@@ -1,6 +1,15 @@
 Technology for Robotics
 -------------------------
 
+
+Autonomous Navigation
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+In recent years, autonomous navigation technologies have received huge
+attention in many fields.
+Such fields include, autonomous driving[22], drone flight navigation,
+and other transportation systems.
+
 An autonomous navigation system is a system that can move to a goal over long
 periods of time without any external control by an operator.
 The system requires a wide range of technologies:

From be608f067cbd96f34955b81d3e5be9e22a46f588 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 12 Feb 2025 21:51:29 +0900
Subject: [PATCH 0975/1080] update introduction doc (#1152)

---
 .../definition_of_robotics_main.rst           | 38 ++++++++++++++-----
 .../technology_for_robotics_main.rst          |  3 ++
 2 files changed, 31 insertions(+), 10 deletions(-)

diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
index f6fba646b4..63525057fe 100644
--- a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
+++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
@@ -1,14 +1,24 @@
 Definition of Robotics
 ----------------------
 
+This section explains the definition, history, key components, and applications of robotics.
+
 What is Robotics?
 ^^^^^^^^^^^^^^^^^^
 
 Robot is a machine that can perform tasks automatically or semi-autonomously.
 Robotics is the study of robots.
-The field of robotics has wide areas of technologies such as mechanical engineering,
-electrical engineering, computer science, and artificial intelligence (AI),
-to create machines that can perform tasks autonomously or semi-autonomously.
+
+The word “robot” comes from the Czech word “robota,” which means “forced labor” or “drudgery.”
+It was first used in the 1920 science fiction play `R.U.R.`_ (Rossum’s Universal Robots)
+by the Czech writer `Karel Čapek`_.
+In the play, robots were artificial workers created to serve humans, but they eventually rebelled.
+
+Over time, “robot” came to refer to machines or automated systems that can perform tasks,
+often with some level of intelligence or autonomy.
+
+.. _`R.U.R.`: https://thereader.mitpress.mit.edu/origin-word-robot-rur/
+.. _`Karel Čapek`: https://en.wikipedia.org/wiki/Karel_%C4%8Capek
 
 The History of Robots
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -18,22 +28,30 @@ This timeline highlights key milestones in the history of robotics:
 Ancient and Early Concepts (Before 1500s)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The idea of **automated machines** has existed for thousands of years. Ancient civilizations imagined mechanical beings:
+The idea of **automated machines** has existed for thousands of years.
+Ancient civilizations imagined mechanical beings:
+
+- **Ancient Greece (4th Century BC)** – Greek engineer `Hero of Alexandria`_ designed early **automata** (self-operating machines) powered by water or air.
+- **Chinese and Arabic Automata (9th–13th Century)** – Inventors like `Ismail Al-Jazari`_ created intricate mechanical devices, including water clocks and automated moving peacocks driven by hydropower.
 
-- **Ancient Greece (4th Century BC)** – Greek engineer **Hero of Alexandria** designed early **automata** (self-operating machines) powered by water or air.
-- **Chinese and Arabic Automata (9th–13th Century)** – Inventors like **Al-Jazari** created intricate mechanical devices, including water clocks and humanoid robots.
+.. _`Hero of Alexandria`: https://en.wikipedia.org/wiki/Hero_of_Alexandria
+.. _`Ismail Al-Jazari`: https://en.wikipedia.org/wiki/Ismail_al-Jazari
 
 The Birth of Modern Robotics (1500s–1800s)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-- **Leonardo da Vinci’s Robot (1495)** – Designed a humanoid knight with mechanical movement.
-- **Jacques de Vaucanson’s Automata (1738)** – Created robotic figures like a mechanical duck that could "eat" and "digest."
-- **Industrial Revolution (18th–19th Century)** – Machines began replacing human labor in factories, setting the foundation for automation.
+- `Leonardo da Vinci’s Robot`_  (1495) – Designed a humanoid knight with mechanical movement.
+- `Jacques de Vaucanson’s Digesting Duck`_ (1738) – Created robotic figures like a mechanical duck that could "eat" and "digest."
+- `Industrial Revolution`_ (18th–19th Century) – Machines began replacing human labor in factories, setting the foundation for automation.
+
+.. _`Leonardo da Vinci’s Robot`: https://en.wikipedia.org/wiki/Leonardo%27s_robot
+.. _`Jacques de Vaucanson’s Digesting Duck`: https://en.wikipedia.org/wiki/Jacques_de_Vaucanson
+.. _`Industrial Revolution`: https://en.wikipedia.org/wiki/Industrial_Revolution
 
 The Rise of Industrial Robots (1900s–1950s)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-- **The Term “Robot” (1921)** – Czech writer **Karel Čapek** introduced the word *“robot”* in his play *R.U.R. (Rossum’s Universal Robots)*.
+- **The Term “Robot” (1921)** – Czech writer `Karel Čapek`_ introduced the word *“robot”* in his play `R.U.R.`_ (Rossum’s Universal Robots).
 - **Early Cybernetics (1940s–1950s)** – Scientists like **Norbert Wiener** developed theories of self-regulating machines, influencing modern robotics.
 
 The Birth of Modern Robotics (1950s–1980s)
diff --git a/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst b/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst
index 93dc9e3466..e460059e20 100644
--- a/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst
+++ b/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst
@@ -1,6 +1,9 @@
 Technology for Robotics
 -------------------------
 
+The field of robotics needs wide areas of technologies such as mechanical engineering,
+electrical engineering, computer science, and artificial intelligence (AI).
+
 
 Autonomous Navigation
 ^^^^^^^^^^^^^^^^^^^^^^^^

From 1ecc154fbaf9bc2342671fe93a305e8f0e3f510f Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 13 Feb 2025 17:19:03 +0900
Subject: [PATCH 0976/1080] update contribution link in README.md to fix
 invalid link (#1154)

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index cb1816c2b5..818d7b0d4e 100644
--- a/README.md
+++ b/README.md
@@ -617,7 +617,7 @@ This is a list of user's comment and references:[users\_comments](https://github
 
 Any contribution is welcome!! 
 
-Please check this document:[How To Contribute — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/how_to_contribute.html)
+Please check this document:[How To Contribute — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/modules/0_getting_started/3_how_to_contribute.html)
 
 # Citing
 

From 156483000524488d64687a853ac030e8d57c18a6 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 13 Feb 2025 17:56:17 +0900
Subject: [PATCH 0977/1080] update robotics definition document to enhance
 references and clarity (#1155)

---
 .../definition_of_robotics_main.rst           | 29 ++++++++++++++-----
 1 file changed, 22 insertions(+), 7 deletions(-)

diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
index 63525057fe..265814e068 100644
--- a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
+++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
@@ -52,21 +52,36 @@ The Rise of Industrial Robots (1900s–1950s)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 - **The Term “Robot” (1921)** – Czech writer `Karel Čapek`_ introduced the word *“robot”* in his play `R.U.R.`_ (Rossum’s Universal Robots).
-- **Early Cybernetics (1940s–1950s)** – Scientists like **Norbert Wiener** developed theories of self-regulating machines, influencing modern robotics.
+- **Early Cybernetics (1940s–1950s)** – Scientists like `Norbert Wiener`_ developed theories of self-regulating machines, influencing modern robotics (Cybernetics).
+
+.. _`Norbert Wiener`: https://en.wikipedia.org/wiki/Norbert_Wiener
 
 The Birth of Modern Robotics (1950s–1980s)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-- **First Industrial Robot (1961)** – *Unimate*, created by **George Devol and Joseph Engelberger**, was the first programmable robot used in a factory.
-- **Rise of AI & Autonomous Robots (1970s–1980s)** – Researchers developed mobile robots like **Shakey** (Stanford, 1966) and AI-based control systems.
+- **First Industrial Robot (1961)** – `Unimate`_, created by `George Devol`_ and `Joseph Engelberger`_, was the first programmable robot used in a factory.
+- **Rise of AI & Autonomous Robots (1970s–1980s)** – Researchers developed mobile robots like `Shakey`_ (Stanford, 1966) and AI-based control systems.
+
+.. _`Unimate`: https://en.wikipedia.org/wiki/Unimate
+.. _`George Devol`: https://en.wikipedia.org/wiki/George_Devol
+.. _`Joseph Engelberger`: https://en.wikipedia.org/wiki/Joseph_Engelberger
+.. _`Shakey`: https://en.wikipedia.org/wiki/Shakey_the_robot
 
 Advanced Robotics and AI Integration (1990s–Present)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-- **Autonomous Vehicles & Drones** – Self-driving cars and UAVs (unmanned aerial vehicles) became more advanced.
-- **Medical Robotics** – Robots like **da Vinci Surgical System** revolutionized healthcare.
-- **Personal Robots** – Devices like **Roomba** (vacuum robot) and **Sophia** (AI humanoid) became popular.
-- **Collaborative Robots (Cobots)** – Robots started working alongside humans in industries.
+- **Autonomous Vehicles** – Self-driving cars for robo taxi like `Waymo`_ and autonomous haulage system in mining like `AHS`_ became more advanced and bisiness-ready.
+- **Medical Robotics** – Robots like `da Vinci Surgical System`_ revolutionized healthcare.
+- **Personal Robots** – Devices like `Roomba`_ (vacuum robot) and `Sophia`_ (AI humanoid) became popular.
+- **Service Robots** - Assistive robots like serving robots in restaurants and hotels like `Bellabot`_.
+- **Collaborative Robots (Drones)** – Collaborative robots like UAV (Unmanned Aerial Vehicle) in drone shows and delivery services.
+
+.. _`Waymo`: https://waymo.com/
+.. _`AHS`: https://www.futurebridge.com/industry/perspectives-industrial-manufacturing/autonomous-haulage-systems-the-future-of-mining-operations/
+.. _`da Vinci Surgical System`: https://en.wikipedia.org/wiki/Da_Vinci_Surgical_System
+.. _`Roomba`: https://en.wikipedia.org/wiki/Roomba
+.. _`Sophia`: https://en.wikipedia.org/wiki/Sophia_(robot)
+.. _`Bellabot`: https://www.pudurobotics.com/en
 
 Key Components of Robotics
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^

From 77ad3344b5e9df26ca370725d3921dd354c755b1 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 14 Feb 2025 21:20:35 +0900
Subject: [PATCH 0978/1080] update robotics definition document to improve
 clarity and add references (#1157)

---
 .../definition_of_robotics_main.rst           | 28 +++++++------------
 1 file changed, 10 insertions(+), 18 deletions(-)

diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
index 265814e068..f54d4d41fa 100644
--- a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
+++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
@@ -70,12 +70,17 @@ The Birth of Modern Robotics (1950s–1980s)
 Advanced Robotics and AI Integration (1990s–Present)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
+- **Industrial Robots** – Advanced robots like `Baxter`_ and `Amazon Robotics`_ revolutionized manufacturing and logistics.
 - **Autonomous Vehicles** – Self-driving cars for robo taxi like `Waymo`_ and autonomous haulage system in mining like `AHS`_ became more advanced and bisiness-ready.
+- **Exploration Robots** – Used for planetary exploration (e.g., NASA’s `Mars rovers`_).
 - **Medical Robotics** – Robots like `da Vinci Surgical System`_ revolutionized healthcare.
 - **Personal Robots** – Devices like `Roomba`_ (vacuum robot) and `Sophia`_ (AI humanoid) became popular.
 - **Service Robots** - Assistive robots like serving robots in restaurants and hotels like `Bellabot`_.
 - **Collaborative Robots (Drones)** – Collaborative robots like UAV (Unmanned Aerial Vehicle) in drone shows and delivery services.
 
+.. _`Baxter`: https://en.wikipedia.org/wiki/Baxter_(robot)
+.. _`Amazon Robotics`: https://en.wikipedia.org/wiki/Amazon_Robotics
+.. _`Mars rovers`: https://en.wikipedia.org/wiki/Mars_rover
 .. _`Waymo`: https://waymo.com/
 .. _`AHS`: https://www.futurebridge.com/industry/perspectives-industrial-manufacturing/autonomous-haulage-systems-the-future-of-mining-operations/
 .. _`da Vinci Surgical System`: https://en.wikipedia.org/wiki/Da_Vinci_Surgical_System
@@ -88,23 +93,10 @@ Key Components of Robotics
 
 Robotics consists of several essential components:
 
-#. Sensors – Gather information from the environment (e.g., cameras, LiDAR, gyro, accelerometer, wheel encoders).
-#. Actuators – Enable movement and interaction with the world (e.g., motors, hydraulic systems).
-#. Computers – Process sensor data and make decisions (e.g., micro-controllers, CPUs, GPUs).
-#. Power Supply – Provides energy to run the robot (e.g., batteries, solar power).
-#. Software & Algorithms – Allow the robot to function and make intelligent decisions (e.g., ROS, machine learning models, localization, mapping, path planning, control).
+#. Sensors – Gather information from the environment (e.g., Cameras, LiDAR, GNSS, Gyro, Accelerometer, Wheel encoders).
+#. Actuators – Enable movement and interaction with the world (e.g., Motors, Hydraulic systems).
+#. Computers – Process sensor data and make decisions (e.g., Micro-controllers, CPUs, GPUs).
+#. Power Supply – Provides energy to run the robot (e.g., Batteries, Solar power).
+#. Software & Algorithms – Allow the robot to function and make intelligent decisions (e.g., ROS, Machine learning models, Localization, Mapping, Path planning, Control).
 
 This project, PythonRobotics, focuses on the software and algorithms part of robotics.
-
-Applications of Robots
-^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Robots come in various forms depending on their purpose:
-
-#. 🤖 Industrial Robots – Used in manufacturing (e.g., robotic arms in manufacturing factories).
-#. 🏠 Service Robots – Assist in daily life (e.g., vacuum robots, delivery robots).
-#. 🚗 Autonomous Vehicles – Self-driving cars and drones.
-#. 👨‍⚕️ Medical Robots – Assist in surgeries and healthcare.
-#. 🚀 Space & Exploration Robots – Used for planetary exploration (e.g., NASA’s Mars rovers).
-#. 🐶 Humanoid & Social Robots – Designed to interact with humans (e.g., ASIMO, Sophia).
-

From 35c08824d00bd9fb452d1ee951018b177e65fd00 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 15 Feb 2025 16:08:23 +0900
Subject: [PATCH 0979/1080] add external sensors documentation to appendix
 (#1159)

---
 docs/modules/12_appendix/appendix_main.rst    |  1 +
 .../12_appendix/external_sensors_main.rst     | 56 +++++++++++++++++++
 2 files changed, 57 insertions(+)
 create mode 100644 docs/modules/12_appendix/external_sensors_main.rst

diff --git a/docs/modules/12_appendix/appendix_main.rst b/docs/modules/12_appendix/appendix_main.rst
index cb1ac04066..89a7fa9303 100644
--- a/docs/modules/12_appendix/appendix_main.rst
+++ b/docs/modules/12_appendix/appendix_main.rst
@@ -10,4 +10,5 @@ Appendix
    steering_motion_model
    Kalmanfilter_basics
    Kalmanfilter_basics_2
+   external_sensors
 
diff --git a/docs/modules/12_appendix/external_sensors_main.rst b/docs/modules/12_appendix/external_sensors_main.rst
new file mode 100644
index 0000000000..a1dba3b214
--- /dev/null
+++ b/docs/modules/12_appendix/external_sensors_main.rst
@@ -0,0 +1,56 @@
+External Sensors for Robots
+============================
+
+Introduction
+------------
+
+In recent years, the application of robotic technology has advanced,
+particularly in areas such as autonomous vehicles and disaster response robots.
+A crucial element in these technologies is external recognition—the robot's ability to understand its surrounding environment, identify safe zones, and detect moving objects using onboard sensors. Achieving effective external recognition involves various techniques, but equally important is the selection of appropriate sensors. Robots, like the sensors they employ, come in many forms, but external recognition sensors can be broadly categorized into three types. Developing an advanced external recognition system requires a thorough understanding of each sensor's principles and characteristics to determine their optimal application. This article summarizes the principles and features of these sensors for personal study purposes.
+
+Laser Sensors
+-------------
+
+Laser sensors measure distances by utilizing light, commonly referred to as Light Detection and Ranging (LIDAR). They operate by emitting light towards an object and calculating the distance based on the time it takes for the reflected light to return, using the speed of light as a constant.
+
+Radar Sensors
+-------------
+
+TBD
+
+
+Monocular Cameras
+-----------------
+
+Monocular cameras utilize a single camera to recognize the external environment. Compared to other sensors, they can detect color and brightness information, making them primarily useful for object recognition. However, they face challenges in independently measuring distances to surrounding objects and may struggle in low-light or dark conditions.
+
+Requirements for Cameras and Image Processing in Robotics
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+While camera sensors are widely used in applications like surveillance, deploying them in robotics necessitates meeting specific requirements:
+
+1. High dynamic range to adapt to various lighting conditions
+2. Wide measurement range
+3. Capability for three-dimensional measurement through techniques like motion stereo
+4. Real-time processing with high frame rates
+5. Cost-effectiveness
+
+Stereo Cameras
+--------------
+
+Stereo cameras employ multiple cameras to measure distances to surrounding objects. By knowing the positions and orientations of each camera and analyzing the disparity in the images (parallax), the distance to a specific point (the object represented by a particular pixel) can be calculated.
+
+Characteristics of Stereo Cameras
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Advantages of stereo cameras include the ability to obtain high-precision and high-density distance information at close range, depending on factors like camera resolution and the distance between cameras (baseline). This makes them suitable for indoor robots that require precise shape recognition of nearby objects. Additionally, stereo cameras are relatively cost-effective compared to other sensors, leading to their use in consumer products like Subaru's EyeSight system. However, stereo cameras are less effective for long-distance measurements due to a decrease in accuracy proportional to the square of the distance. They are also susceptible to environmental factors such as lighting conditions.
+
+Ultrasonic Sensors
+------------------
+
+Ultrasonic sensors are commonly used in indoor robots and some automotive autonomous driving systems. Their features include affordability compared to laser or radar sensors, the ability to detect very close objects, and the capability to sense materials like glass, which may be challenging for lasers or cameras. However, they have limitations such as shorter maximum measurement distances and lower resolution and accuracy.
+
+References
+----------
+
+TBD

From e82a12319b5336beaff01ab9fd2ce7818bb08dfb Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 16 Feb 2025 21:41:22 +0900
Subject: [PATCH 0980/1080] add internal sensors documentation to appendix and
 create new internal sensors overview (#1161)

---
 docs/modules/12_appendix/appendix_main.rst    |  1 +
 .../12_appendix/external_sensors_main.rst     |  4 +++
 .../12_appendix/internal_sensors_main.rst     | 32 +++++++++++++++++++
 3 files changed, 37 insertions(+)
 create mode 100644 docs/modules/12_appendix/internal_sensors_main.rst

diff --git a/docs/modules/12_appendix/appendix_main.rst b/docs/modules/12_appendix/appendix_main.rst
index 89a7fa9303..a55389e1e6 100644
--- a/docs/modules/12_appendix/appendix_main.rst
+++ b/docs/modules/12_appendix/appendix_main.rst
@@ -10,5 +10,6 @@ Appendix
    steering_motion_model
    Kalmanfilter_basics
    Kalmanfilter_basics_2
+   internal_sensors
    external_sensors
 
diff --git a/docs/modules/12_appendix/external_sensors_main.rst b/docs/modules/12_appendix/external_sensors_main.rst
index a1dba3b214..d36b852d42 100644
--- a/docs/modules/12_appendix/external_sensors_main.rst
+++ b/docs/modules/12_appendix/external_sensors_main.rst
@@ -1,6 +1,10 @@
 External Sensors for Robots
 ============================
 
+This project, `PythonRobotics`, focuses on algorithms, so hardware is not included.
+However, having basic knowledge of hardware in robotics is also important for understanding algorithms.
+Therefore, we will provide an overview.
+
 Introduction
 ------------
 
diff --git a/docs/modules/12_appendix/internal_sensors_main.rst b/docs/modules/12_appendix/internal_sensors_main.rst
new file mode 100644
index 0000000000..13b8de4203
--- /dev/null
+++ b/docs/modules/12_appendix/internal_sensors_main.rst
@@ -0,0 +1,32 @@
+Internal Sensors for Robots
+============================
+
+This project, `PythonRobotics`, focuses on algorithms, so hardware is not included.
+However, having basic knowledge of hardware in robotics is also important for understanding algorithms.
+Therefore, we will provide an overview.
+
+Introduction
+------------
+
+Global Navigation Satellite System (GNSS)
+-------------------------------------------
+
+Gyroscope
+----------
+
+Accelerometer
+--------------
+
+Magnetometer
+--------------
+
+Inertial Measurement Unit (IMU)
+--------------------------------
+
+Pressure Sensor
+-----------------
+
+Temperature Sensor
+--------------------
+
+

From c92aaf36d8665a3b9ecd1a661f151cfd6ede4c66 Mon Sep 17 00:00:00 2001
From: Aglargil <34728006+Aglargil@users.noreply.github.com>
Date: Mon, 17 Feb 2025 18:47:04 +0800
Subject: [PATCH 0981/1080] feat: add ElasticBands (#1156)

* feat: add ElasticBands

* feat: Elastic Bands update

* feat: ElasticBands update

* feat: ElasticBands add test

* feat: ElasticBands reduce occupation

* fix: ElasticBands test

* feat: ElasticBands remove tangential component

* feat: Elastic Bands update

* feat: Elastic Bands doc

* feat: Elastic Bands update

* feat: ElasticBands update
---
 Mapping/DistanceMap/distance_map.py           |  51 +++
 PathPlanning/ElasticBands/elastic_bands.py    | 300 ++++++++++++++++++
 PathPlanning/ElasticBands/obstacles.npy       | Bin 0 -> 384 bytes
 PathPlanning/ElasticBands/path.npy            | Bin 0 -> 224 bytes
 .../elastic_bands/elastic_bands_main.rst      |  73 +++++
 .../5_path_planning/path_planning_main.rst    |   1 +
 tests/test_elastic_bands.py                   |  23 ++
 7 files changed, 448 insertions(+)
 create mode 100644 PathPlanning/ElasticBands/elastic_bands.py
 create mode 100644 PathPlanning/ElasticBands/obstacles.npy
 create mode 100644 PathPlanning/ElasticBands/path.npy
 create mode 100644 docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst
 create mode 100644 tests/test_elastic_bands.py

diff --git a/Mapping/DistanceMap/distance_map.py b/Mapping/DistanceMap/distance_map.py
index 54c98c6a75..0dcc7380c5 100644
--- a/Mapping/DistanceMap/distance_map.py
+++ b/Mapping/DistanceMap/distance_map.py
@@ -11,11 +11,62 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
+import scipy
 
 INF = 1e20
 ENABLE_PLOT = True
 
 
+def compute_sdf_scipy(obstacles):
+    """
+    Compute the signed distance field (SDF) from a boolean field using scipy.
+    This function has the same functionality as compute_sdf.
+    However, by using scipy.ndimage.distance_transform_edt, it can compute much faster.
+
+    Example: 500×500 map
+    • compute_sdf: 3 sec
+    • compute_sdf_scipy: 0.05 sec
+
+    Parameters
+    ----------
+    obstacles : array_like
+        A 2D boolean array where '1' represents obstacles and '0' represents free space.
+
+    Returns
+    -------
+    array_like
+        A 2D array representing the signed distance field, where positive values indicate distance
+        to the nearest obstacle, and negative values indicate distance to the nearest free space.
+    """
+    # distance_transform_edt use '0' as obstacles, so we need to convert the obstacles to '0'
+    a = scipy.ndimage.distance_transform_edt(obstacles == 0)
+    b = scipy.ndimage.distance_transform_edt(obstacles == 1)
+    return a - b
+
+
+def compute_udf_scipy(obstacles):
+    """
+    Compute the unsigned distance field (UDF) from a boolean field using scipy.
+    This function has the same functionality as compute_udf.
+    However, by using scipy.ndimage.distance_transform_edt, it can compute much faster.
+
+    Example: 500×500 map
+    • compute_udf: 1.5 sec
+    • compute_udf_scipy: 0.02 sec
+
+    Parameters
+    ----------
+    obstacles : array_like
+        A 2D boolean array where '1' represents obstacles and '0' represents free space.
+
+    Returns
+    -------
+    array_like
+        A 2D array of distances from the nearest obstacle, with the same dimensions as `bool_field`.
+    """
+    return scipy.ndimage.distance_transform_edt(obstacles == 0)
+
+
 def compute_sdf(obstacles):
     """
     Compute the signed distance field (SDF) from a boolean field.
diff --git a/PathPlanning/ElasticBands/elastic_bands.py b/PathPlanning/ElasticBands/elastic_bands.py
new file mode 100644
index 0000000000..785f822d14
--- /dev/null
+++ b/PathPlanning/ElasticBands/elastic_bands.py
@@ -0,0 +1,300 @@
+"""
+Elastic Bands
+
+author: Wang Zheng (@Aglargil)
+
+Ref:
+
+- [Elastic Bands: Connecting Path Planning and Control]
+(http://www8.cs.umu.se/research/ifor/dl/Control/elastic%20bands.pdf)
+"""
+
+import numpy as np
+import sys
+import pathlib
+import matplotlib.pyplot as plt
+from matplotlib.patches import Circle
+
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
+
+from Mapping.DistanceMap.distance_map import compute_sdf_scipy
+
+# Elastic Bands Params
+MAX_BUBBLE_RADIUS = 100
+MIN_BUBBLE_RADIUS = 10
+RHO0 = 20.0  # Maximum distance for applying repulsive force
+KC = 0.05  # Contraction force gain
+KR = -0.1  # Repulsive force gain
+LAMBDA = 0.7  # Overlap constraint factor
+STEP_SIZE = 3.0  # Step size for calculating gradient
+
+# Visualization Params
+ENABLE_PLOT = True
+# ENABLE_INTERACTIVE is True allows user to add obstacles by left clicking
+# and add path points by right clicking and start planning by middle clicking
+ENABLE_INTERACTIVE = False
+# ENABLE_SAVE_DATA is True allows saving the path and obstacles which added
+# by user in interactive mode to file
+ENABLE_SAVE_DATA = False
+MAX_ITER = 50
+
+
+class Bubble:
+    def __init__(self, position, radius):
+        self.pos = np.array(position)  # Bubble center coordinates [x, y]
+        self.radius = radius  # Safety distance radius ρ(b)
+        if self.radius > MAX_BUBBLE_RADIUS:
+            self.radius = MAX_BUBBLE_RADIUS
+        if self.radius < MIN_BUBBLE_RADIUS:
+            self.radius = MIN_BUBBLE_RADIUS
+
+
+class ElasticBands:
+    def __init__(
+        self,
+        initial_path,
+        obstacles,
+        rho0=RHO0,
+        kc=KC,
+        kr=KR,
+        lambda_=LAMBDA,
+        step_size=STEP_SIZE,
+    ):
+        self.distance_map = compute_sdf_scipy(obstacles)
+        self.bubbles = [
+            Bubble(p, self.compute_rho(p)) for p in initial_path
+        ]  # Initialize bubble chain
+        self.kc = kc  # Contraction force gain
+        self.kr = kr  # Repulsive force gain
+        self.rho0 = rho0  # Maximum distance for applying repulsive force
+        self.lambda_ = lambda_  # Overlap constraint factor
+        self.step_size = step_size  # Step size for calculating gradient
+        self._maintain_overlap()
+
+    def compute_rho(self, position):
+        """Compute the distance field value at the position"""
+        return self.distance_map[int(position[0]), int(position[1])]
+
+    def contraction_force(self, i):
+        """Calculate internal contraction force for the i-th bubble"""
+        if i == 0 or i == len(self.bubbles) - 1:
+            return np.zeros(2)
+
+        prev = self.bubbles[i - 1].pos
+        next_ = self.bubbles[i + 1].pos
+        current = self.bubbles[i].pos
+
+        # f_c = kc * ( (prev-current)/|prev-current| + (next-current)/|next-current| )
+        dir_prev = (prev - current) / (np.linalg.norm(prev - current) + 1e-6)
+        dir_next = (next_ - current) / (np.linalg.norm(next_ - current) + 1e-6)
+        return self.kc * (dir_prev + dir_next)
+
+    def repulsive_force(self, i):
+        """Calculate external repulsive force for the i-th bubble"""
+        h = self.step_size  # Step size
+        b = self.bubbles[i].pos
+        rho = self.bubbles[i].radius
+
+        if rho >= self.rho0:
+            return np.zeros(2)
+
+        # Finite difference approximation of the gradient ∂ρ/∂b
+        dx = np.array([h, 0])
+        dy = np.array([0, h])
+        grad_x = (self.compute_rho(b - dx) - self.compute_rho(b + dx)) / (2 * h)
+        grad_y = (self.compute_rho(b - dy) - self.compute_rho(b + dy)) / (2 * h)
+        grad = np.array([grad_x, grad_y])
+
+        return self.kr * (self.rho0 - rho) * grad
+
+    def update_bubbles(self):
+        """Update bubble positions"""
+        new_bubbles = []
+        for i in range(len(self.bubbles)):
+            if i == 0 or i == len(self.bubbles) - 1:
+                new_bubbles.append(self.bubbles[i])  # Fixed start and end points
+                continue
+
+            f_total = self.contraction_force(i) + self.repulsive_force(i)
+            v = self.bubbles[i - 1].pos - self.bubbles[i + 1].pos
+
+            # Remove tangential component
+            f_star = f_total - f_total * v * v / (np.linalg.norm(v) ** 2 + 1e-6)
+
+            alpha = self.bubbles[i].radius  # Adaptive step size
+            new_pos = self.bubbles[i].pos + alpha * f_star
+            new_pos = np.clip(new_pos, 0, 499)
+            new_radius = self.compute_rho(new_pos)
+
+            # Update bubble and maintain overlap constraint
+            new_bubble = Bubble(new_pos, new_radius)
+            new_bubbles.append(new_bubble)
+
+        self.bubbles = new_bubbles
+        self._maintain_overlap()
+
+    def _maintain_overlap(self):
+        """Maintain bubble chain continuity (simplified insertion/deletion mechanism)"""
+        # Insert bubbles
+        i = 0
+        while i < len(self.bubbles) - 1:
+            bi, bj = self.bubbles[i], self.bubbles[i + 1]
+            dist = np.linalg.norm(bi.pos - bj.pos)
+            if dist > self.lambda_ * (bi.radius + bj.radius):
+                new_pos = (bi.pos + bj.pos) / 2
+                rho = self.compute_rho(
+                    new_pos
+                )  # Calculate new radius using environment model
+                self.bubbles.insert(i + 1, Bubble(new_pos, rho))
+                i += 2  # Skip the processed region
+            else:
+                i += 1
+
+        # Delete redundant bubbles
+        i = 1
+        while i < len(self.bubbles) - 1:
+            prev = self.bubbles[i - 1]
+            next_ = self.bubbles[i + 1]
+            dist = np.linalg.norm(prev.pos - next_.pos)
+            if dist <= self.lambda_ * (prev.radius + next_.radius):
+                del self.bubbles[i]  # Delete if redundant
+            else:
+                i += 1
+
+
+class ElasticBandsVisualizer:
+    def __init__(self):
+        self.obstacles = np.zeros((500, 500))
+        self.obstacles_points = []
+        self.path_points = []
+        self.elastic_band = None
+        self.running = True
+
+        if ENABLE_PLOT:
+            self.fig, self.ax = plt.subplots(figsize=(8, 8))
+            self.fig.canvas.mpl_connect("close_event", self.on_close)
+            self.ax.set_xlim(0, 500)
+            self.ax.set_ylim(0, 500)
+
+        if ENABLE_INTERACTIVE:
+            self.path_points = []  # Add a list to store path points
+            # Connect mouse events
+            self.fig.canvas.mpl_connect("button_press_event", self.on_click)
+        else:
+            self.path_points = np.load(pathlib.Path(__file__).parent / "path.npy")
+            self.obstacles_points = np.load(
+                pathlib.Path(__file__).parent / "obstacles.npy"
+            )
+            for x, y in self.obstacles_points:
+                self.add_obstacle(x, y)
+            self.plan_path()
+
+        self.plot_background()
+
+    def on_close(self, event):
+        """Handle window close event"""
+        self.running = False
+        plt.close("all")  # Close all figure windows
+
+    def plot_background(self):
+        """Plot the background grid"""
+        if not ENABLE_PLOT or not self.running:
+            return
+
+        self.ax.cla()
+        self.ax.set_xlim(0, 500)
+        self.ax.set_ylim(0, 500)
+        self.ax.grid(True)
+
+        if ENABLE_INTERACTIVE:
+            self.ax.set_title(
+                "Elastic Bands Path Planning\n"
+                "Left click: Add obstacles\n"
+                "Right click: Add path points\n"
+                "Middle click: Start planning",
+                pad=20,
+            )
+        else:
+            self.ax.set_title("Elastic Bands Path Planning", pad=20)
+
+        if self.path_points:
+            self.ax.plot(
+                [p[0] for p in self.path_points],
+                [p[1] for p in self.path_points],
+                "yo",
+                markersize=8,
+            )
+
+        self.ax.imshow(self.obstacles.T, origin="lower", cmap="binary", alpha=0.8)
+        self.ax.plot([], [], color="black", label="obstacles")
+        if self.elastic_band is not None:
+            path = [b.pos.tolist() for b in self.elastic_band.bubbles]
+            path = np.array(path)
+            self.ax.plot(path[:, 0], path[:, 1], "b-", linewidth=2, label="path")
+
+            for bubble in self.elastic_band.bubbles:
+                circle = Circle(
+                    bubble.pos, bubble.radius, fill=False, color="g", alpha=0.3
+                )
+                self.ax.add_patch(circle)
+                self.ax.plot(bubble.pos[0], bubble.pos[1], "bo", markersize=10)
+            self.ax.plot([], [], color="green", label="bubbles")
+
+        self.ax.legend(loc="upper right")
+        plt.draw()
+        plt.pause(0.01)
+
+    def add_obstacle(self, x, y):
+        """Add an obstacle at the given coordinates"""
+        size = 30  # Side length of the square
+        half_size = size // 2
+        x_start = max(0, x - half_size)
+        x_end = min(self.obstacles.shape[0], x + half_size)
+        y_start = max(0, y - half_size)
+        y_end = min(self.obstacles.shape[1], y + half_size)
+        self.obstacles[x_start:x_end, y_start:y_end] = 1
+
+    def on_click(self, event):
+        """Handle mouse click events"""
+        if event.inaxes != self.ax:
+            return
+
+        x, y = int(event.xdata), int(event.ydata)
+
+        if event.button == 1:  # Left click to add obstacles
+            self.add_obstacle(x, y)
+            self.obstacles_points.append([x, y])
+
+        elif event.button == 3:  # Right click to add path points
+            self.path_points.append([x, y])
+
+        elif event.button == 2:  # Middle click to end path input and start planning
+            if len(self.path_points) >= 2:
+                if ENABLE_SAVE_DATA:
+                    np.save(
+                        pathlib.Path(__file__).parent / "path.npy", self.path_points
+                    )
+                    np.save(
+                        pathlib.Path(__file__).parent / "obstacles.npy",
+                        self.obstacles_points,
+                    )
+                self.plan_path()
+
+        self.plot_background()
+
+    def plan_path(self):
+        """Plan the path"""
+
+        initial_path = self.path_points
+        # Create an elastic band object and optimize
+        self.elastic_band = ElasticBands(initial_path, self.obstacles)
+        for _ in range(MAX_ITER):
+            self.elastic_band.update_bubbles()
+            self.path_points = [b.pos for b in self.elastic_band.bubbles]
+            self.plot_background()
+
+
+if __name__ == "__main__":
+    _ = ElasticBandsVisualizer()
+    if ENABLE_PLOT:
+        plt.show(block=True)
diff --git a/PathPlanning/ElasticBands/obstacles.npy b/PathPlanning/ElasticBands/obstacles.npy
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diff --git a/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst b/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst
new file mode 100644
index 0000000000..139996f291
--- /dev/null
+++ b/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst
@@ -0,0 +1,73 @@
+Elastic Bands
+-------------
+
+This is a path planning with Elastic Bands.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ElasticBands/animation.gif
+
+
+Core Concept
+~~~~~~~~~~~~
+- **Elastic Band**: A dynamically deformable collision-free path initialized by a global planner.
+- **Objective**:
+
+  * Shorten and smooth the path.
+  * Maximize obstacle clearance.
+  * Maintain global path connectivity.
+
+Bubble Representation
+~~~~~~~~~~~~~~~~~~~
+- **Definition**: A local free-space region around configuration :math:`b`:
+
+  .. math::
+     B(b) = \{ q: \|q - b\| < \rho(b) \},
+  
+  where :math:`\rho(b)` is the radius of the bubble.
+
+
+Force-Based Deformation
+~~~~~~~~~~~~~~~~~~~~~~~
+The elastic band deforms under artificial forces:
+
+Internal Contraction Force
+++++++++++++++++++++++++++
+- **Purpose**: Reduces path slack and length.
+- **Formula**: For node :math:`b_i`:
+
+  .. math::
+     f_c(b_i) = k_c \left( \frac{b_{i-1} - b_i}{\|b_{i-1} - b_i\|} + \frac{b_{i+1} - b_i}{\|b_{i+1} - b_i\|} \right)
+
+  where :math:`k_c` is the contraction gain.
+
+External Repulsion Force
++++++++++++++++++++++++++
+- **Purpose**: Pushes the path away from obstacles.
+- **Formula**: For node :math:`b_i`:
+
+  .. math::
+     f_r(b_i) = \begin{cases} 
+     k_r (\rho_0 - \rho(b_i)) \nabla \rho(b_i) & \text{if } \rho(b_i) < \rho_0, \\
+     0 & \text{otherwise}.
+     \end{cases}
+
+  where :math:`k_r` is the repulsion gain, :math:`\rho_0` is the maximum distance for applying repulsion force, and :math:`\nabla \rho(b_i)` is approximated via finite differences:
+
+  .. math::
+     \frac{\partial \rho}{\partial x} \approx \frac{\rho(b_i + h) - \rho(b_i - h)}{2h}.
+
+Dynamic Path Maintenance
+~~~~~~~~~~~~~~~~~~~~~~~
+1. **Node Update**:
+   
+   .. math::
+      b_i^{\text{new}} = b_i^{\text{old}} + \alpha (f_c + f_r),
+
+   where :math:`\alpha` is a step-size parameter, which often proportional to :math:`\rho(b_i^{\text{old}})`
+
+2. **Overlap Enforcement**:
+- Insert new nodes if adjacent nodes are too far apart
+- Remove redundant nodes if adjacent nodes are too close
+
+Ref:
+
+-  `Elastic Bands: Connecting Path Planning and Control <http://www8.cs.umu.se/research/ifor/dl/Control/elastic%20bands.pdf>`__
diff --git a/docs/modules/5_path_planning/path_planning_main.rst b/docs/modules/5_path_planning/path_planning_main.rst
index 4960330b3e..65fbfdbc3d 100644
--- a/docs/modules/5_path_planning/path_planning_main.rst
+++ b/docs/modules/5_path_planning/path_planning_main.rst
@@ -31,3 +31,4 @@ Path planning is the ability of a robot to search feasible and efficient path to
    hybridastar/hybridastar
    frenet_frame_path/frenet_frame_path
    coverage_path/coverage_path
+   elastic_bands/elastic_bands
\ No newline at end of file
diff --git a/tests/test_elastic_bands.py b/tests/test_elastic_bands.py
new file mode 100644
index 0000000000..ad4e13af1a
--- /dev/null
+++ b/tests/test_elastic_bands.py
@@ -0,0 +1,23 @@
+import conftest
+import numpy as np
+from PathPlanning.ElasticBands.elastic_bands import ElasticBands
+
+
+def test_1():
+    path = np.load("PathPlanning/ElasticBands/path.npy")
+    obstacles_points = np.load("PathPlanning/ElasticBands/obstacles.npy")
+    obstacles = np.zeros((500, 500))
+    for x, y in obstacles_points:
+        size = 30  # Side length of the square
+        half_size = size // 2
+        x_start = max(0, x - half_size)
+        x_end = min(obstacles.shape[0], x + half_size)
+        y_start = max(0, y - half_size)
+        y_end = min(obstacles.shape[1], y + half_size)
+        obstacles[x_start:x_end, y_start:y_end] = 1
+    elastic_bands = ElasticBands(path, obstacles)
+    elastic_bands.update_bubbles()
+
+
+if __name__ == "__main__":
+    conftest.run_this_test(__file__)

From cbe61f8ca62785a072652741d8b33832caee1942 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 18 Feb 2025 10:45:49 +0900
Subject: [PATCH 0982/1080] build(deps): bump scipy from 1.15.1 to 1.15.2 in
 /requirements (#1163)

Bumps [scipy](https://github.com/scipy/scipy) from 1.15.1 to 1.15.2.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.15.1...v1.15.2)

---
updated-dependencies:
- dependency-name: scipy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index f5f674d7d2..ea4550edb8 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 2.2.2
-scipy == 1.15.1
+scipy == 1.15.2
 matplotlib == 3.10.0
 cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test

From 395fca59cc1f9c44e9ea7f7c08ce41958e399481 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 18 Feb 2025 20:06:34 +0900
Subject: [PATCH 0983/1080] fix: update robotics documentation for clarity and
 correct terminology (#1165)

---
 docs/modules/12_appendix/external_sensors_main.rst           | 2 ++
 docs/modules/12_appendix/internal_sensors_main.rst           | 2 ++
 .../1_definition_of_robotics/definition_of_robotics_main.rst | 5 +++++
 .../technologies_for_robotics_main.rst}                      | 4 +++-
 docs/modules/1_introduction/introduction_main.rst            | 2 +-
 5 files changed, 13 insertions(+), 2 deletions(-)
 rename docs/modules/1_introduction/{3_technology_for_robotics/technology_for_robotics_main.rst => 3_technologies_for_robotics/technologies_for_robotics_main.rst} (88%)

diff --git a/docs/modules/12_appendix/external_sensors_main.rst b/docs/modules/12_appendix/external_sensors_main.rst
index d36b852d42..3597418150 100644
--- a/docs/modules/12_appendix/external_sensors_main.rst
+++ b/docs/modules/12_appendix/external_sensors_main.rst
@@ -1,3 +1,5 @@
+.. _`External Sensors for Robots`:
+
 External Sensors for Robots
 ============================
 
diff --git a/docs/modules/12_appendix/internal_sensors_main.rst b/docs/modules/12_appendix/internal_sensors_main.rst
index 13b8de4203..18f209098e 100644
--- a/docs/modules/12_appendix/internal_sensors_main.rst
+++ b/docs/modules/12_appendix/internal_sensors_main.rst
@@ -1,3 +1,5 @@
+.. _`Internal Sensors for Robots`:
+
 Internal Sensors for Robots
 ============================
 
diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
index f54d4d41fa..1e7a833b19 100644
--- a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
+++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
@@ -17,6 +17,10 @@ In the play, robots were artificial workers created to serve humans, but they ev
 Over time, “robot” came to refer to machines or automated systems that can perform tasks,
 often with some level of intelligence or autonomy.
 
+Currently, 2 millions robots are working in the world, and the number is increasing every year.
+In South Korea, where the adoption of robots is particularly rapid,
+50 robots are operating per 1,000 people.
+
 .. _`R.U.R.`: https://thereader.mitpress.mit.edu/origin-word-robot-rur/
 .. _`Karel Čapek`: https://en.wikipedia.org/wiki/Karel_%C4%8Capek
 
@@ -100,3 +104,4 @@ Robotics consists of several essential components:
 #. Software & Algorithms – Allow the robot to function and make intelligent decisions (e.g., ROS, Machine learning models, Localization, Mapping, Path planning, Control).
 
 This project, PythonRobotics, focuses on the software and algorithms part of robotics.
+If you are interested in `Sensors` hardware, you can check :ref:`Internal Sensors for Robotics`_ or :ref:`External Sensors for Robotics`_.
diff --git a/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst b/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst
similarity index 88%
rename from docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst
rename to docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst
index e460059e20..64c6945c32 100644
--- a/docs/modules/1_introduction/3_technology_for_robotics/technology_for_robotics_main.rst
+++ b/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst
@@ -1,8 +1,10 @@
-Technology for Robotics
+Technologies for Robotics
 -------------------------
 
 The field of robotics needs wide areas of technologies such as mechanical engineering,
 electrical engineering, computer science, and artificial intelligence (AI).
+This project, `PythonRobotics`, only focus on computer science and artificial intelligence.
+
 
 
 Autonomous Navigation
diff --git a/docs/modules/1_introduction/introduction_main.rst b/docs/modules/1_introduction/introduction_main.rst
index a7ce55f9bf..1871dfc3b1 100644
--- a/docs/modules/1_introduction/introduction_main.rst
+++ b/docs/modules/1_introduction/introduction_main.rst
@@ -14,5 +14,5 @@ covered in PythonRobotics.
 
    1_definition_of_robotics/definition_of_robotics
    2_python_for_robotics/python_for_robotics
-   3_technology_for_robotics/technology_for_robotics
+   3_technologies_for_robotics/technologies_for_robotics
 

From d53711998aeb1d0b3aab7a62862ef5d358f2aff9 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 18 Feb 2025 21:49:28 +0900
Subject: [PATCH 0984/1080] fix: update robotics documentation for clarity and
 correct terminology (#1166)

---
 README.md                                                  | 4 ++++
 .../0_getting_started/1_what_is_python_robotics_main.rst   | 6 ++++++
 .../definition_of_robotics_main.rst                        | 2 +-
 .../5_path_planning/elastic_bands/elastic_bands_main.rst   | 7 ++++---
 4 files changed, 15 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 818d7b0d4e..9e605435ce 100644
--- a/README.md
+++ b/README.md
@@ -89,6 +89,10 @@ See this documentation
 
 - [Getting Started — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/getting_started.html#what-is-pythonrobotics)
 
+or this Youtube video:
+
+- [PythonRobotics project audio overview](https://www.youtube.com/watch?v=uMeRnNoJAfU)
+
 or this paper for more details:
 
 - [\[1808\.10703\] PythonRobotics: a Python code collection of robotics algorithms](https://arxiv.org/abs/1808.10703) ([BibTeX](https://github.com/AtsushiSakai/PythonRoboticsPaper/blob/master/python_robotics.bib))
diff --git a/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst b/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst
index 8c932b7263..2a7bd574f0 100644
--- a/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst
+++ b/docs/modules/0_getting_started/1_what_is_python_robotics_main.rst
@@ -110,6 +110,12 @@ the following additional libraries are required.
 For instructions on installing the above libraries, please refer to
 this section ":ref:`How to run sample codes`".
 
+Audio overview of this project
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+For an audio overview of this project, please refer to this `YouTube video`_.
+
+.. _`YouTube video`: https://www.youtube.com/watch?v=uMeRnNoJAfU
+
 Arxiv paper
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
index 1e7a833b19..5d78bf858b 100644
--- a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
+++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
@@ -103,5 +103,5 @@ Robotics consists of several essential components:
 #. Power Supply – Provides energy to run the robot (e.g., Batteries, Solar power).
 #. Software & Algorithms – Allow the robot to function and make intelligent decisions (e.g., ROS, Machine learning models, Localization, Mapping, Path planning, Control).
 
-This project, PythonRobotics, focuses on the software and algorithms part of robotics.
+This project, `PythonRobotics`, focuses on the software and algorithms part of robotics.
 If you are interested in `Sensors` hardware, you can check :ref:`Internal Sensors for Robotics`_ or :ref:`External Sensors for Robotics`_.
diff --git a/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst b/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst
index 139996f291..8a3e517105 100644
--- a/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst
+++ b/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst
@@ -16,7 +16,7 @@ Core Concept
   * Maintain global path connectivity.
 
 Bubble Representation
-~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~
 - **Definition**: A local free-space region around configuration :math:`b`:
 
   .. math::
@@ -56,7 +56,7 @@ External Repulsion Force
      \frac{\partial \rho}{\partial x} \approx \frac{\rho(b_i + h) - \rho(b_i - h)}{2h}.
 
 Dynamic Path Maintenance
-~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~
 1. **Node Update**:
    
    .. math::
@@ -68,6 +68,7 @@ Dynamic Path Maintenance
 - Insert new nodes if adjacent nodes are too far apart
 - Remove redundant nodes if adjacent nodes are too close
 
-Ref:
+References
+~~~~~~~~~~~~~~~~~~~~~~~
 
 -  `Elastic Bands: Connecting Path Planning and Control <http://www8.cs.umu.se/research/ifor/dl/Control/elastic%20bands.pdf>`__

From 8064488a1d63a1579d8c9d786a376c8b583c02a8 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 18 Feb 2025 21:49:40 +0900
Subject: [PATCH 0985/1080] build(deps): bump numpy from 2.2.2 to 2.2.3 in
 /requirements (#1164)

Bumps [numpy](https://github.com/numpy/numpy) from 2.2.2 to 2.2.3.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v2.2.2...v2.2.3)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index ea4550edb8..8176364c29 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 2.2.2
+numpy == 2.2.3
 scipy == 1.15.2
 matplotlib == 3.10.0
 cvxpy == 1.5.3

From 2b7080991e5289c9524a505244fcd2a4995da06e Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Wed, 19 Feb 2025 13:36:07 +0900
Subject: [PATCH 0986/1080] Add GitHub copilot pro sponser (#1167)

* fix: correct terminology in documentation and update Sphinx options

* fix: correct terminology in documentation and update Sphinx options

* fix: correct terminology in documentation and update Sphinx options

* fix: correct terminology in documentation and update Sphinx options

* fix: correct terminology in documentation and update Sphinx options
---
 docs/Makefile                                                  | 3 ++-
 docs/modules/0_getting_started/3_how_to_contribute_main.rst    | 2 ++
 .../1_definition_of_robotics/definition_of_robotics_main.rst   | 2 +-
 docs/modules/4_slam/graph_slam/graphSLAM_SE2_example.rst       | 2 +-
 4 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/docs/Makefile b/docs/Makefile
index 9296811e02..ae495eb36d 100644
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -2,7 +2,8 @@
 #
 
 # You can set these variables from the command line.
-SPHINXOPTS    =
+# SPHINXOPTS with -W means turn warnings into errors to fail the build when warnings are present.
+SPHINXOPTS    = -W
 SPHINXBUILD   = sphinx-build
 SPHINXPROJ    = PythonRobotics
 SOURCEDIR     = .
diff --git a/docs/modules/0_getting_started/3_how_to_contribute_main.rst b/docs/modules/0_getting_started/3_how_to_contribute_main.rst
index 874564cbb8..1e61760649 100644
--- a/docs/modules/0_getting_started/3_how_to_contribute_main.rst
+++ b/docs/modules/0_getting_started/3_how_to_contribute_main.rst
@@ -187,6 +187,7 @@ If you would like to support us in some other way, please contact with creating
 
 Current Major Sponsors:
 
+#. `GitHub`_ : They are providing a GitHub Copilot Pro license for this OSS development.
 #. `JetBrains`_ : They are providing a free license of their IDEs for this OSS development.
 #. `1Password`_ : They are providing a free license of their 1Password team license for this OSS project.
 
@@ -202,6 +203,7 @@ Current Major Sponsors:
 .. _`doc README`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/docs/README.md
 .. _`test_codestyle.py`: https://github.com/AtsushiSakai/PythonRobotics/blob/master/tests/test_codestyle.py
 .. _`JetBrains`: https://www.jetbrains.com/
+.. _`GitHub`: https://www.github.com/
 .. _`Sponsor @AtsushiSakai on GitHub Sponsors`: https://github.com/sponsors/AtsushiSakai
 .. _`Become a backer or sponsor on Patreon`: https://www.patreon.com/myenigma
 .. _`One-time donation via PayPal`: https://www.paypal.com/paypalme/myenigmapay/
diff --git a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
index 5d78bf858b..ca595301a6 100644
--- a/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
+++ b/docs/modules/1_introduction/1_definition_of_robotics/definition_of_robotics_main.rst
@@ -104,4 +104,4 @@ Robotics consists of several essential components:
 #. Software & Algorithms – Allow the robot to function and make intelligent decisions (e.g., ROS, Machine learning models, Localization, Mapping, Path planning, Control).
 
 This project, `PythonRobotics`, focuses on the software and algorithms part of robotics.
-If you are interested in `Sensors` hardware, you can check :ref:`Internal Sensors for Robotics`_ or :ref:`External Sensors for Robotics`_.
+If you are interested in `Sensors` hardware, you can check :ref:`Internal Sensors for Robots` or :ref:`External Sensors for Robots`.
diff --git a/docs/modules/4_slam/graph_slam/graphSLAM_SE2_example.rst b/docs/modules/4_slam/graph_slam/graphSLAM_SE2_example.rst
index 491320512b..15963aff79 100644
--- a/docs/modules/4_slam/graph_slam/graphSLAM_SE2_example.rst
+++ b/docs/modules/4_slam/graph_slam/graphSLAM_SE2_example.rst
@@ -165,7 +165,7 @@ different data sources into a single optimization problem.
             6             215.8405          -0.000000
 
 
-.. figure:: graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/GraphBasedSLAM/Graph_SLAM_optimization.gif
 
 .. code:: ipython3
 

From c7fb228d24856d6c7a0cb28c715176bfb8b17281 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 20 Feb 2025 12:11:04 +0900
Subject: [PATCH 0987/1080] fix: update section references to use consistent
 formatting (#1169)

---
 .../technologies_for_robotics_main.rst        | 48 +++++++++++++++----
 .../2_localization/localization_main.rst      |  2 +-
 docs/modules/3_mapping/mapping_main.rst       |  2 +-
 docs/modules/4_slam/slam_main.rst             |  2 +-
 .../5_path_planning/path_planning_main.rst    |  2 +-
 .../6_path_tracking/path_tracking_main.rst    |  2 +-
 .../7_arm_navigation/arm_navigation_main.rst  |  2 +-
 .../aerial_navigation_main.rst                |  2 +-
 docs/modules/9_bipedal/bipedal_main.rst       |  2 +-
 9 files changed, 48 insertions(+), 16 deletions(-)

diff --git a/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst b/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst
index 64c6945c32..c77997a138 100644
--- a/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst
+++ b/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst
@@ -5,22 +5,54 @@ The field of robotics needs wide areas of technologies such as mechanical engine
 electrical engineering, computer science, and artificial intelligence (AI).
 This project, `PythonRobotics`, only focus on computer science and artificial intelligence.
 
+The technologies for robotics are categorized as following 3 categories:
 
+#. `Autonomous Navigation`_
+#. `Manipulation`_
+#. `Robot type specific technologies`_
+
+.. _`Autonomous Navigation`:
 
 Autonomous Navigation
 ^^^^^^^^^^^^^^^^^^^^^^^^
-
-In recent years, autonomous navigation technologies have received huge
-attention in many fields.
-Such fields include, autonomous driving[22], drone flight navigation,
-and other transportation systems.
-
-An autonomous navigation system is a system that can move to a goal over long
+Autonomous navigation is a capability that can move to a goal over long
 periods of time without any external control by an operator.
-The system requires a wide range of technologies:
+
+To achieve autonomous navigation, the robot needs to have the following technologies:
 - It needs to know where it is (localization)
 - Where it is safe (mapping)
+- Where is is safe and where the robot is in the map (Simultaneous Localization and Mapping (SLAM))
 - Where and how to move (path planning)
 - How to control its motion (path following).
 
 The autonomous system would not work correctly if any of these technologies is missing.
+
+In recent years, autonomous navigation technologies have received huge
+attention in many fields.
+For example, self-driving cars, drones, and autonomous mobile robots in indoor and outdoor environments.
+
+In this project, we provide many algorithms, sample codes,
+and documentations for autonomous navigation.
+
+#. :ref:`Localization`
+#. :ref:`Mapping`
+#. :ref:`SLAM`
+#. :ref:`Path planning`
+#. :ref:`Path tracking`
+
+
+
+.. _`Manipulation`:
+
+Manipulation
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+#. :ref:`Arm Navigation`
+
+.. _`Robot type specific technologies`:
+
+Robot type specific technologies
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+#. :ref:`Aerial Navigation`
+#. :ref:`Bipedal`
diff --git a/docs/modules/2_localization/localization_main.rst b/docs/modules/2_localization/localization_main.rst
index 22cbd094da..770a234b69 100644
--- a/docs/modules/2_localization/localization_main.rst
+++ b/docs/modules/2_localization/localization_main.rst
@@ -1,4 +1,4 @@
-.. _localization:
+.. _`Localization`:
 
 Localization
 ============
diff --git a/docs/modules/3_mapping/mapping_main.rst b/docs/modules/3_mapping/mapping_main.rst
index 825b08d3ec..34a3744893 100644
--- a/docs/modules/3_mapping/mapping_main.rst
+++ b/docs/modules/3_mapping/mapping_main.rst
@@ -1,4 +1,4 @@
-.. _mapping:
+.. _`Mapping`:
 
 Mapping
 =======
diff --git a/docs/modules/4_slam/slam_main.rst b/docs/modules/4_slam/slam_main.rst
index dec04f253a..98211986c2 100644
--- a/docs/modules/4_slam/slam_main.rst
+++ b/docs/modules/4_slam/slam_main.rst
@@ -1,4 +1,4 @@
-.. _slam:
+.. _`SLAM`:
 
 SLAM
 ====
diff --git a/docs/modules/5_path_planning/path_planning_main.rst b/docs/modules/5_path_planning/path_planning_main.rst
index 65fbfdbc3d..a0f9c30a3d 100644
--- a/docs/modules/5_path_planning/path_planning_main.rst
+++ b/docs/modules/5_path_planning/path_planning_main.rst
@@ -1,4 +1,4 @@
-.. _path_planning:
+.. _`Path Planning`:
 
 Path Planning
 =============
diff --git a/docs/modules/6_path_tracking/path_tracking_main.rst b/docs/modules/6_path_tracking/path_tracking_main.rst
index d7a895b562..130a2340c1 100644
--- a/docs/modules/6_path_tracking/path_tracking_main.rst
+++ b/docs/modules/6_path_tracking/path_tracking_main.rst
@@ -1,4 +1,4 @@
-.. _path_tracking:
+.. _`Path Tracking`:
 
 Path Tracking
 =============
diff --git a/docs/modules/7_arm_navigation/arm_navigation_main.rst b/docs/modules/7_arm_navigation/arm_navigation_main.rst
index bbbc872c58..7acd3ee7d3 100644
--- a/docs/modules/7_arm_navigation/arm_navigation_main.rst
+++ b/docs/modules/7_arm_navigation/arm_navigation_main.rst
@@ -1,4 +1,4 @@
-.. _arm_navigation:
+.. _`Arm Navigation`:
 
 Arm Navigation
 ==============
diff --git a/docs/modules/8_aerial_navigation/aerial_navigation_main.rst b/docs/modules/8_aerial_navigation/aerial_navigation_main.rst
index b2ccb071af..7f76689770 100644
--- a/docs/modules/8_aerial_navigation/aerial_navigation_main.rst
+++ b/docs/modules/8_aerial_navigation/aerial_navigation_main.rst
@@ -1,4 +1,4 @@
-.. _aerial_navigation:
+.. _`Aerial Navigation`:
 
 Aerial Navigation
 =================
diff --git a/docs/modules/9_bipedal/bipedal_main.rst b/docs/modules/9_bipedal/bipedal_main.rst
index fc5b933055..dc387dc4e8 100644
--- a/docs/modules/9_bipedal/bipedal_main.rst
+++ b/docs/modules/9_bipedal/bipedal_main.rst
@@ -1,4 +1,4 @@
-.. _bipedal:
+.. _`Bipedal`:
 
 Bipedal
 =================

From f343573a7bb9b99fd8fcfc72cef7336ccf859c28 Mon Sep 17 00:00:00 2001
From: Aglargil <34728006+Aglargil@users.noreply.github.com>
Date: Thu, 20 Feb 2025 18:09:30 +0800
Subject: [PATCH 0988/1080] Update move_to_pose for cases where alpha > pi/2 or
 alpha < -pi/2 (#1168)

* Update move_to_pose for cases where alpha > pi/2 or alpha < -pi/2

* Update move_to_pose

* Add move_to_pose test

* Update move_to_pose
---
 Control/move_to_pose/move_to_pose.py | 90 +++++++++++++++++++---------
 tests/test_move_to_pose.py           | 74 ++++++++++++++++++++++-
 2 files changed, 134 insertions(+), 30 deletions(-)

diff --git a/Control/move_to_pose/move_to_pose.py b/Control/move_to_pose/move_to_pose.py
index 279ba0625b..34736a2e21 100644
--- a/Control/move_to_pose/move_to_pose.py
+++ b/Control/move_to_pose/move_to_pose.py
@@ -5,6 +5,7 @@
 Author: Daniel Ingram (daniel-s-ingram)
         Atsushi Sakai (@Atsushi_twi)
         Seied Muhammad Yazdian (@Muhammad-Yazdian)
+        Wang Zheng (@Aglargil)
 
 P. I. Corke, "Robotics, Vision & Control", Springer 2017, ISBN 978-3-319-54413-7
 
@@ -12,8 +13,13 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
-from random import random
+import sys
+import pathlib
+
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 from utils.angle import angle_mod
+from random import random
+
 
 class PathFinderController:
     """
@@ -70,14 +76,20 @@ def calc_control_command(self, x_diff, y_diff, theta, theta_goal):
         # [-pi, pi] to prevent unstable behavior e.g. difference going
         # from 0 rad to 2*pi rad with slight turn
 
+        # Ref: The velocity v always has a constant sign which depends on the initial value of α.
         rho = np.hypot(x_diff, y_diff)
-        alpha = angle_mod(np.arctan2(y_diff, x_diff) - theta)
-        beta = angle_mod(theta_goal - theta - alpha)
         v = self.Kp_rho * rho
-        w = self.Kp_alpha * alpha - self.Kp_beta * beta
 
+        alpha = angle_mod(np.arctan2(y_diff, x_diff) - theta)
+        beta = angle_mod(theta_goal - theta - alpha)
         if alpha > np.pi / 2 or alpha < -np.pi / 2:
+            # recalculate alpha to make alpha in the range of [-pi/2, pi/2]
+            alpha = angle_mod(np.arctan2(-y_diff, -x_diff) - theta)
+            beta = angle_mod(theta_goal - theta - alpha)
+            w = self.Kp_alpha * alpha - self.Kp_beta * beta
             v = -v
+        else:
+            w = self.Kp_alpha * alpha - self.Kp_beta * beta
 
         return rho, v, w
 
@@ -85,6 +97,7 @@ def calc_control_command(self, x_diff, y_diff, theta, theta_goal):
 # simulation parameters
 controller = PathFinderController(9, 15, 3)
 dt = 0.01
+MAX_SIM_TIME = 5  # seconds, robot will stop moving when time exceeds this value
 
 # Robot specifications
 MAX_LINEAR_SPEED = 15
@@ -101,18 +114,19 @@ def move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal):
     x_diff = x_goal - x
     y_diff = y_goal - y
 
-    x_traj, y_traj = [], []
+    x_traj, y_traj, v_traj, w_traj = [x], [y], [0], [0]
 
     rho = np.hypot(x_diff, y_diff)
-    while rho > 0.001:
+    t = 0
+    while rho > 0.001 and t < MAX_SIM_TIME:
+        t += dt
         x_traj.append(x)
         y_traj.append(y)
 
         x_diff = x_goal - x
         y_diff = y_goal - y
 
-        rho, v, w = controller.calc_control_command(
-            x_diff, y_diff, theta, theta_goal)
+        rho, v, w = controller.calc_control_command(x_diff, y_diff, theta, theta_goal)
 
         if abs(v) > MAX_LINEAR_SPEED:
             v = np.sign(v) * MAX_LINEAR_SPEED
@@ -120,18 +134,35 @@ def move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal):
         if abs(w) > MAX_ANGULAR_SPEED:
             w = np.sign(w) * MAX_ANGULAR_SPEED
 
+        v_traj.append(v)
+        w_traj.append(w)
+
         theta = theta + w * dt
         x = x + v * np.cos(theta) * dt
         y = y + v * np.sin(theta) * dt
 
         if show_animation:  # pragma: no cover
             plt.cla()
-            plt.arrow(x_start, y_start, np.cos(theta_start),
-                      np.sin(theta_start), color='r', width=0.1)
-            plt.arrow(x_goal, y_goal, np.cos(theta_goal),
-                      np.sin(theta_goal), color='g', width=0.1)
+            plt.arrow(
+                x_start,
+                y_start,
+                np.cos(theta_start),
+                np.sin(theta_start),
+                color="r",
+                width=0.1,
+            )
+            plt.arrow(
+                x_goal,
+                y_goal,
+                np.cos(theta_goal),
+                np.sin(theta_goal),
+                color="g",
+                width=0.1,
+            )
             plot_vehicle(x, y, theta, x_traj, y_traj)
 
+    return x_traj, y_traj, v_traj, w_traj
+
 
 def plot_vehicle(x, y, theta, x_traj, y_traj):  # pragma: no cover
     # Corners of triangular vehicle when pointing to the right (0 radians)
@@ -144,16 +175,16 @@ def plot_vehicle(x, y, theta, x_traj, y_traj):  # pragma: no cover
     p2 = np.matmul(T, p2_i)
     p3 = np.matmul(T, p3_i)
 
-    plt.plot([p1[0], p2[0]], [p1[1], p2[1]], 'k-')
-    plt.plot([p2[0], p3[0]], [p2[1], p3[1]], 'k-')
-    plt.plot([p3[0], p1[0]], [p3[1], p1[1]], 'k-')
+    plt.plot([p1[0], p2[0]], [p1[1], p2[1]], "k-")
+    plt.plot([p2[0], p3[0]], [p2[1], p3[1]], "k-")
+    plt.plot([p3[0], p1[0]], [p3[1], p1[1]], "k-")
 
-    plt.plot(x_traj, y_traj, 'b--')
+    plt.plot(x_traj, y_traj, "b--")
 
     # for stopping simulation with the esc key.
     plt.gcf().canvas.mpl_connect(
-        'key_release_event',
-        lambda event: [exit(0) if event.key == 'escape' else None])
+        "key_release_event", lambda event: [exit(0) if event.key == "escape" else None]
+    )
 
     plt.xlim(0, 20)
     plt.ylim(0, 20)
@@ -162,15 +193,16 @@ def plot_vehicle(x, y, theta, x_traj, y_traj):  # pragma: no cover
 
 
 def transformation_matrix(x, y, theta):
-    return np.array([
-        [np.cos(theta), -np.sin(theta), x],
-        [np.sin(theta), np.cos(theta), y],
-        [0, 0, 1]
-    ])
+    return np.array(
+        [
+            [np.cos(theta), -np.sin(theta), x],
+            [np.sin(theta), np.cos(theta), y],
+            [0, 0, 1],
+        ]
+    )
 
 
 def main():
-
     for i in range(5):
         x_start = 20.0 * random()
         y_start = 20.0 * random()
@@ -178,10 +210,14 @@ def main():
         x_goal = 20 * random()
         y_goal = 20 * random()
         theta_goal = 2 * np.pi * random() - np.pi
-        print(f"Initial x: {round(x_start, 2)} m\nInitial y: {round(y_start, 2)} m\nInitial theta: {round(theta_start, 2)} rad\n")
-        print(f"Goal x: {round(x_goal, 2)} m\nGoal y: {round(y_goal, 2)} m\nGoal theta: {round(theta_goal, 2)} rad\n")
+        print(
+            f"Initial x: {round(x_start, 2)} m\nInitial y: {round(y_start, 2)} m\nInitial theta: {round(theta_start, 2)} rad\n"
+        )
+        print(
+            f"Goal x: {round(x_goal, 2)} m\nGoal y: {round(y_goal, 2)} m\nGoal theta: {round(theta_goal, 2)} rad\n"
+        )
         move_to_pose(x_start, y_start, theta_start, x_goal, y_goal, theta_goal)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()
diff --git a/tests/test_move_to_pose.py b/tests/test_move_to_pose.py
index 8bc11a8d24..94c3ec1102 100644
--- a/tests/test_move_to_pose.py
+++ b/tests/test_move_to_pose.py
@@ -1,13 +1,81 @@
+import itertools
+import numpy as np
 import conftest  # Add root path to sys.path
 from Control.move_to_pose import move_to_pose as m
 
 
-def test_1():
+def test_random():
     m.show_animation = False
     m.main()
 
 
-def test_2():
+def test_stability():
+    """
+    This unit test tests the move_to_pose.py program for stability
+    """
+    m.show_animation = False
+    x_start = 5
+    y_start = 5
+    theta_start = 0
+    x_goal = 1
+    y_goal = 4
+    theta_goal = 0
+    _, _, v_traj, w_traj = m.move_to_pose(
+        x_start, y_start, theta_start, x_goal, y_goal, theta_goal
+    )
+
+    def v_is_change(current, previous):
+        return abs(current - previous) > m.MAX_LINEAR_SPEED
+
+    def w_is_change(current, previous):
+        return abs(current - previous) > m.MAX_ANGULAR_SPEED
+
+    # Check if the speed is changing too much
+    window_size = 10
+    count_threshold = 4
+    v_change = [v_is_change(v_traj[i], v_traj[i - 1]) for i in range(1, len(v_traj))]
+    w_change = [w_is_change(w_traj[i], w_traj[i - 1]) for i in range(1, len(w_traj))]
+    for i in range(len(v_change) - window_size + 1):
+        v_window = v_change[i : i + window_size]
+        w_window = w_change[i : i + window_size]
+
+        v_unstable = sum(v_window) > count_threshold
+        w_unstable = sum(w_window) > count_threshold
+
+        assert not v_unstable, (
+            f"v_unstable in window [{i}, {i + window_size}], unstable count: {sum(v_window)}"
+        )
+        assert not w_unstable, (
+            f"w_unstable in window [{i}, {i + window_size}], unstable count: {sum(w_window)}"
+        )
+
+
+def test_reach_goal():
+    """
+    This unit test tests the move_to_pose.py program for reaching the goal
+    """
+    m.show_animation = False
+    x_start = 5
+    y_start = 5
+    theta_start_list = [0, np.pi / 2, np.pi, 3 * np.pi / 2]
+    x_goal_list = [0, 5, 10]
+    y_goal_list = [0, 5, 10]
+    theta_goal = 0
+    for theta_start, x_goal, y_goal in itertools.product(
+        theta_start_list, x_goal_list, y_goal_list
+    ):
+        x_traj, y_traj, _, _ = m.move_to_pose(
+            x_start, y_start, theta_start, x_goal, y_goal, theta_goal
+        )
+        x_diff = x_goal - x_traj[-1]
+        y_diff = y_goal - y_traj[-1]
+        rho = np.hypot(x_diff, y_diff)
+        assert rho < 0.001, (
+            f"start:[{x_start}, {y_start}, {theta_start}], goal:[{x_goal}, {y_goal}, {theta_goal}], rho: {rho} is too large"
+        )
+
+
+def test_max_speed():
     """
     This unit test tests the move_to_pose.py program for a MAX_LINEAR_SPEED and
     MAX_ANGULAR_SPEED
@@ -18,5 +86,5 @@ def test_2():
     m.main()
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     conftest.run_this_test(__file__)

From 64779298ffa77c918c63f9206b694f0d8d439c71 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 21 Feb 2025 21:40:21 +0900
Subject: [PATCH 0989/1080] =?UTF-8?q?refactor:=20rename=20files=20and=20up?=
 =?UTF-8?q?date=20references=20for=20inverted=20pendulum=20an=E2=80=A6=20(?=
 =?UTF-8?q?#1171)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* refactor: rename files and update references for inverted pendulum and path tracking modules

* refactor: rename inverted pendulum control files and update type check references

* refactor: update import statements to use consistent casing for InvertedPendulum module
---
 Control/move_to_pose/__init__.py                    |   0
 .../inverted_pendulum_lqr_control.py                |   0
 .../inverted_pendulum_mpc_control.py                |   0
 {Control => PathTracking/move_to_pose}/__init__.py  |   0
 .../move_to_pose/move_to_pose.py                    |   0
 .../move_to_pose/move_to_pose_robot.py              |   0
 docs/index_main.rst                                 |   2 +-
 docs/modules/10_control/control_main.rst            |  12 ------------
 .../inverted-pendulum.png                           | Bin
 .../inverted_pendulum_main.rst}                     |   6 ++++--
 docs/modules/11_utils/utils_main.rst                |   2 +-
 docs/modules/12_appendix/appendix_main.rst          |   2 +-
 .../technologies_for_robotics_main.rst              |  10 ++++++++++
 .../move_to_a_pose_control_main.rst                 |   0
 docs/modules/6_path_tracking/path_tracking_main.rst |   1 +
 tests/test_inverted_pendulum_lqr_control.py         |   2 +-
 tests/test_inverted_pendulum_mpc_control.py         |   2 +-
 tests/test_move_to_pose.py                          |   2 +-
 tests/test_move_to_pose_robot.py                    |   2 +-
 tests/test_mypy_type_check.py                       |   2 +-
 20 files changed, 23 insertions(+), 22 deletions(-)
 delete mode 100644 Control/move_to_pose/__init__.py
 rename {Control/inverted_pendulum => InvertedPendulum}/inverted_pendulum_lqr_control.py (100%)
 rename {Control/inverted_pendulum => InvertedPendulum}/inverted_pendulum_mpc_control.py (100%)
 rename {Control => PathTracking/move_to_pose}/__init__.py (100%)
 rename {Control => PathTracking}/move_to_pose/move_to_pose.py (100%)
 rename {Control => PathTracking}/move_to_pose/move_to_pose_robot.py (100%)
 delete mode 100644 docs/modules/10_control/control_main.rst
 rename docs/modules/{10_control/inverted_pendulum_control => 10_inverted_pendulum}/inverted-pendulum.png (100%)
 rename docs/modules/{10_control/inverted_pendulum_control/inverted_pendulum_control_main.rst => 10_inverted_pendulum/inverted_pendulum_main.rst} (97%)
 rename docs/modules/{10_control => 6_path_tracking}/move_to_a_pose_control/move_to_a_pose_control_main.rst (100%)

diff --git a/Control/move_to_pose/__init__.py b/Control/move_to_pose/__init__.py
deleted file mode 100644
index e69de29bb2..0000000000
diff --git a/Control/inverted_pendulum/inverted_pendulum_lqr_control.py b/InvertedPendulum/inverted_pendulum_lqr_control.py
similarity index 100%
rename from Control/inverted_pendulum/inverted_pendulum_lqr_control.py
rename to InvertedPendulum/inverted_pendulum_lqr_control.py
diff --git a/Control/inverted_pendulum/inverted_pendulum_mpc_control.py b/InvertedPendulum/inverted_pendulum_mpc_control.py
similarity index 100%
rename from Control/inverted_pendulum/inverted_pendulum_mpc_control.py
rename to InvertedPendulum/inverted_pendulum_mpc_control.py
diff --git a/Control/__init__.py b/PathTracking/move_to_pose/__init__.py
similarity index 100%
rename from Control/__init__.py
rename to PathTracking/move_to_pose/__init__.py
diff --git a/Control/move_to_pose/move_to_pose.py b/PathTracking/move_to_pose/move_to_pose.py
similarity index 100%
rename from Control/move_to_pose/move_to_pose.py
rename to PathTracking/move_to_pose/move_to_pose.py
diff --git a/Control/move_to_pose/move_to_pose_robot.py b/PathTracking/move_to_pose/move_to_pose_robot.py
similarity index 100%
rename from Control/move_to_pose/move_to_pose_robot.py
rename to PathTracking/move_to_pose/move_to_pose_robot.py
diff --git a/docs/index_main.rst b/docs/index_main.rst
index c1e8d22d32..75805d1184 100644
--- a/docs/index_main.rst
+++ b/docs/index_main.rst
@@ -43,7 +43,7 @@ this graph shows GitHub star history of this project:
    modules/7_arm_navigation/arm_navigation
    modules/8_aerial_navigation/aerial_navigation
    modules/9_bipedal/bipedal
-   modules/10_control/control
+   modules/10_inverted_pendulum/inverted_pendulum
    modules/11_utils/utils
    modules/12_appendix/appendix
 
diff --git a/docs/modules/10_control/control_main.rst b/docs/modules/10_control/control_main.rst
deleted file mode 100644
index cee2aa9e8e..0000000000
--- a/docs/modules/10_control/control_main.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-.. _control:
-
-Control
-=================
-
-.. toctree::
-   :maxdepth: 2
-   :caption: Contents
-
-   inverted_pendulum_control/inverted_pendulum_control
-   move_to_a_pose_control/move_to_a_pose_control
-
diff --git a/docs/modules/10_control/inverted_pendulum_control/inverted-pendulum.png b/docs/modules/10_inverted_pendulum/inverted-pendulum.png
similarity index 100%
rename from docs/modules/10_control/inverted_pendulum_control/inverted-pendulum.png
rename to docs/modules/10_inverted_pendulum/inverted-pendulum.png
diff --git a/docs/modules/10_control/inverted_pendulum_control/inverted_pendulum_control_main.rst b/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst
similarity index 97%
rename from docs/modules/10_control/inverted_pendulum_control/inverted_pendulum_control_main.rst
rename to docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst
index e41729fd61..048cbea9ac 100644
--- a/docs/modules/10_control/inverted_pendulum_control/inverted_pendulum_control_main.rst
+++ b/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst
@@ -1,5 +1,7 @@
-Inverted Pendulum Control
------------------------------
+.. _`Inverted Pendulum`:
+
+Inverted Pendulum
+------------------
 
 An inverted pendulum on a cart consists of a mass :math:`m` at the top of a pole of length :math:`l` pivoted on a
 horizontally moving base as shown in the adjacent.
diff --git a/docs/modules/11_utils/utils_main.rst b/docs/modules/11_utils/utils_main.rst
index ff79a26205..95c982b077 100644
--- a/docs/modules/11_utils/utils_main.rst
+++ b/docs/modules/11_utils/utils_main.rst
@@ -1,4 +1,4 @@
-.. _utils:
+.. _`utils`:
 
 Utilities
 ==========
diff --git a/docs/modules/12_appendix/appendix_main.rst b/docs/modules/12_appendix/appendix_main.rst
index a55389e1e6..d0b9eeea3a 100644
--- a/docs/modules/12_appendix/appendix_main.rst
+++ b/docs/modules/12_appendix/appendix_main.rst
@@ -1,4 +1,4 @@
-.. _appendix:
+.. _`Appendix`:
 
 Appendix
 ==============
diff --git a/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst b/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst
index c77997a138..0ed51e961b 100644
--- a/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst
+++ b/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst
@@ -56,3 +56,13 @@ Robot type specific technologies
 
 #. :ref:`Aerial Navigation`
 #. :ref:`Bipedal`
+#. :ref:`Inverted Pendulum`
+
+
+Additional Information
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+#. :ref:`utils`
+#. :ref:`Appendix`
+
+
diff --git a/docs/modules/10_control/move_to_a_pose_control/move_to_a_pose_control_main.rst b/docs/modules/6_path_tracking/move_to_a_pose_control/move_to_a_pose_control_main.rst
similarity index 100%
rename from docs/modules/10_control/move_to_a_pose_control/move_to_a_pose_control_main.rst
rename to docs/modules/6_path_tracking/move_to_a_pose_control/move_to_a_pose_control_main.rst
diff --git a/docs/modules/6_path_tracking/path_tracking_main.rst b/docs/modules/6_path_tracking/path_tracking_main.rst
index 130a2340c1..d98e324583 100644
--- a/docs/modules/6_path_tracking/path_tracking_main.rst
+++ b/docs/modules/6_path_tracking/path_tracking_main.rst
@@ -16,3 +16,4 @@ Path tracking is the ability of a robot to follow the reference path generated b
    lqr_speed_and_steering_control/lqr_speed_and_steering_control
    model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control
    cgmres_nmpc/cgmres_nmpc
+   move_to_a_pose_control/move_to_a_pose_control
diff --git a/tests/test_inverted_pendulum_lqr_control.py b/tests/test_inverted_pendulum_lqr_control.py
index cbbabb93b1..62afda71c3 100644
--- a/tests/test_inverted_pendulum_lqr_control.py
+++ b/tests/test_inverted_pendulum_lqr_control.py
@@ -1,5 +1,5 @@
 import conftest
-from Control.inverted_pendulum import inverted_pendulum_lqr_control as m
+from InvertedPendulum import inverted_pendulum_lqr_control as m
 
 
 def test_1():
diff --git a/tests/test_inverted_pendulum_mpc_control.py b/tests/test_inverted_pendulum_mpc_control.py
index 800aefd7d5..94859c2e0a 100644
--- a/tests/test_inverted_pendulum_mpc_control.py
+++ b/tests/test_inverted_pendulum_mpc_control.py
@@ -1,6 +1,6 @@
 import conftest
 
-from Control.inverted_pendulum import inverted_pendulum_mpc_control as m
+from InvertedPendulum import inverted_pendulum_mpc_control as m
 
 
 def test1():
diff --git a/tests/test_move_to_pose.py b/tests/test_move_to_pose.py
index 94c3ec1102..e06d801555 100644
--- a/tests/test_move_to_pose.py
+++ b/tests/test_move_to_pose.py
@@ -1,7 +1,7 @@
 import itertools
 import numpy as np
 import conftest  # Add root path to sys.path
-from Control.move_to_pose import move_to_pose as m
+from PathTracking.move_to_pose import move_to_pose as m
 
 
 def test_random():
diff --git a/tests/test_move_to_pose_robot.py b/tests/test_move_to_pose_robot.py
index a93b44d198..7a82f98556 100644
--- a/tests/test_move_to_pose_robot.py
+++ b/tests/test_move_to_pose_robot.py
@@ -1,5 +1,5 @@
 import conftest  # Add root path to sys.path
-from Control.move_to_pose import move_to_pose as m
+from PathTracking.move_to_pose import move_to_pose as m
 
 
 def test_1():
diff --git a/tests/test_mypy_type_check.py b/tests/test_mypy_type_check.py
index 07afb40afd..6b933c1011 100644
--- a/tests/test_mypy_type_check.py
+++ b/tests/test_mypy_type_check.py
@@ -7,12 +7,12 @@
     "AerialNavigation",
     "ArmNavigation",
     "Bipedal",
-    "Control",
     "Localization",
     "Mapping",
     "PathPlanning",
     "PathTracking",
     "SLAM",
+    "InvertedPendulum"
 ]
 
 

From 6e13e8292aad80661093603ed262c6f0bdcb4137 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 25 Feb 2025 08:07:42 +0900
Subject: [PATCH 0990/1080] build(deps): bump ruff from 0.9.6 to 0.9.7 in
 /requirements (#1173)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.9.6 to 0.9.7.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.9.6...0.9.7)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 8176364c29..b439ea4266 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.4 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.15.0 # For unit test
-ruff == 0.9.6 # For unit test
+ruff == 0.9.7 # For unit test

From 0c8ff11645e6804db18bec6bd918e03ed03454f7 Mon Sep 17 00:00:00 2001
From: Jonathan Schwartz <jonbs98@gmail.com>
Date: Tue, 25 Feb 2025 06:53:36 -0500
Subject: [PATCH 0991/1080] Space-Time AStar (#1170)

* wip - sketch out obstacles

* move to correct path

* better animation

* clean up

* use np to sample points

* implemented time-based A*

* cleaning up Grid + adding new obstacle arrangement

* added unit test

* formatting p1

* format STA* file

* remove newlines by docstrings

* linter

* working on typehints

* fix linter errors

* lint some more

* appease AppVeyor

* dataclasses are :fire:

* back to @total_ordering

* trailing whitespace

* add docs page on SpaceTimeA*

* docs lint

* remove trailing newlines in doc

* address comments

* Update docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst

---------

Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>
---
 .../GridWithDynamicObstacles.py               | 273 ++++++++++++++++++
 .../TimeBasedPathPlanning/SpaceTimeAStar.py   | 220 ++++++++++++++
 .../TimeBasedPathPlanning/__init__.py         |   0
 .../5_path_planning/path_planning_main.rst    |   1 +
 .../time_based_grid_search_main.rst           |  22 ++
 tests/test_space_time_astar.py                |  33 +++
 6 files changed, 549 insertions(+)
 create mode 100644 PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py
 create mode 100644 PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py
 create mode 100644 PathPlanning/TimeBasedPathPlanning/__init__.py
 create mode 100644 docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst
 create mode 100644 tests/test_space_time_astar.py

diff --git a/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py b/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py
new file mode 100644
index 0000000000..7b0190d023
--- /dev/null
+++ b/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py
@@ -0,0 +1,273 @@
+"""
+This file implements a grid with a 3d reservation matrix with dimensions for x, y, and time. There
+is also infrastructure to generate dynamic obstacles that move around the grid. The obstacles' paths
+are stored in the reservation matrix on creation.
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+from enum import Enum
+from dataclasses import dataclass
+
+@dataclass(order=True)
+class Position:
+    x: int
+    y: int
+
+    def as_ndarray(self) -> np.ndarray:
+        return np.array([self.x, self.y])
+
+    def __add__(self, other):
+        if isinstance(other, Position):
+            return Position(self.x + other.x, self.y + other.y)
+        raise NotImplementedError(
+            f"Addition not supported for Position and {type(other)}"
+        )
+
+    def __sub__(self, other):
+        if isinstance(other, Position):
+            return Position(self.x - other.x, self.y - other.y)
+        raise NotImplementedError(
+            f"Subtraction not supported for Position and {type(other)}"
+        )
+
+
+class ObstacleArrangement(Enum):
+    # Random obstacle positions and movements
+    RANDOM = 0
+    # Obstacles start in a line in y at center of grid and move side-to-side in x
+    ARRANGEMENT1 = 1
+
+
+class Grid:
+    # Set in constructor
+    grid_size: np.ndarray
+    reservation_matrix: np.ndarray
+    obstacle_paths: list[list[Position]] = []
+    # Obstacles will never occupy these points. Useful to avoid impossible scenarios
+    obstacle_avoid_points: list[Position] = []
+
+    # Number of time steps in the simulation
+    time_limit: int
+
+    # Logging control
+    verbose = False
+
+    def __init__(
+        self,
+        grid_size: np.ndarray,
+        num_obstacles: int = 40,
+        obstacle_avoid_points: list[Position] = [],
+        obstacle_arrangement: ObstacleArrangement = ObstacleArrangement.RANDOM,
+        time_limit: int = 100,
+    ):
+        self.obstacle_avoid_points = obstacle_avoid_points
+        self.time_limit = time_limit
+        self.grid_size = grid_size
+        self.reservation_matrix = np.zeros((grid_size[0], grid_size[1], self.time_limit))
+
+        if num_obstacles > self.grid_size[0] * self.grid_size[1]:
+            raise Exception("Number of obstacles is greater than grid size!")
+
+        if obstacle_arrangement == ObstacleArrangement.RANDOM:
+            self.obstacle_paths = self.generate_dynamic_obstacles(num_obstacles)
+        elif obstacle_arrangement == ObstacleArrangement.ARRANGEMENT1:
+            self.obstacle_paths = self.obstacle_arrangement_1(num_obstacles)
+
+        for i, path in enumerate(self.obstacle_paths):
+            obs_idx = i + 1  # avoid using 0 - that indicates free space in the grid
+            for t, position in enumerate(path):
+                # Reserve old & new position at this time step
+                if t > 0:
+                    self.reservation_matrix[path[t - 1].x, path[t - 1].y, t] = obs_idx
+                self.reservation_matrix[position.x, position.y, t] = obs_idx
+
+    """
+    Generate dynamic obstacles that move around the grid. Initial positions and movements are random
+    """
+    def generate_dynamic_obstacles(self, obs_count: int) -> list[list[Position]]:
+        obstacle_paths = []
+        for _ in (0, obs_count):
+            # Sample until a free starting space is found
+            initial_position = self.sample_random_position()
+            while not self.valid_obstacle_position(initial_position, 0):
+                initial_position = self.sample_random_position()
+
+            positions = [initial_position]
+            if self.verbose:
+                print("Obstacle initial position: ", initial_position)
+
+            # Encourage obstacles to mostly stay in place - too much movement leads to chaotic planning scenarios
+            # that are not fun to watch
+            weights = [0.05, 0.05, 0.05, 0.05, 0.8]
+            diffs = [
+                Position(0, 1),
+                Position(0, -1),
+                Position(1, 0),
+                Position(-1, 0),
+                Position(0, 0),
+            ]
+
+            for t in range(1, self.time_limit - 1):
+                sampled_indices = np.random.choice(
+                    len(diffs), size=5, replace=False, p=weights
+                )
+                rand_diffs = [diffs[i] for i in sampled_indices]
+
+                valid_position = None
+                for diff in rand_diffs:
+                    new_position = positions[-1] + diff
+
+                    if not self.valid_obstacle_position(new_position, t):
+                        continue
+
+                    valid_position = new_position
+                    break
+
+                # Impossible situation for obstacle - stay in place
+                #   -> this can happen if the oaths of other obstacles this one
+                if valid_position is None:
+                    valid_position = positions[-1]
+
+                positions.append(valid_position)
+
+            obstacle_paths.append(positions)
+
+        return obstacle_paths
+
+    """
+    Generate a line of obstacles in y at the center of the grid that move side-to-side in x
+    Bottom half start moving right, top half start moving left. If `obs_count` is less than the length of
+    the grid, only the first `obs_count` obstacles will be generated.
+    """
+    def obstacle_arrangement_1(self, obs_count: int) -> list[list[Position]]:
+        obstacle_paths = []
+        half_grid_x = self.grid_size[0] // 2
+        half_grid_y = self.grid_size[1] // 2
+
+        for y_idx in range(0, min(obs_count, self.grid_size[1])):
+            moving_right = y_idx < half_grid_y
+            position = Position(half_grid_x, y_idx)
+            path = [position]
+
+            for t in range(1, self.time_limit - 1):
+                # sit in place every other time step
+                if t % 2 == 0:
+                    path.append(position)
+                    continue
+
+                # first check if we should switch direction (at edge of grid)
+                if (moving_right and position.x == self.grid_size[0] - 1) or (
+                    not moving_right and position.x == 0
+                ):
+                    moving_right = not moving_right
+                # step in direction
+                position = Position(
+                    position.x + (1 if moving_right else -1), position.y
+                )
+                path.append(position)
+
+            obstacle_paths.append(path)
+
+        return obstacle_paths
+
+    """
+    Check if the given position is valid at time t
+
+    input:
+        position (Position): (x, y) position
+        t (int): time step
+
+    output:
+        bool: True if position/time combination is valid, False otherwise
+    """
+    def valid_position(self, position: Position, t: int) -> bool:
+        # Check if new position is in grid
+        if not self.inside_grid_bounds(position):
+            return False
+
+        # Check if new position is not occupied at time t
+        return self.reservation_matrix[position.x, position.y, t] == 0
+
+    """
+    Returns True if the given position is valid at time t and is not in the set of obstacle_avoid_points
+    """
+    def valid_obstacle_position(self, position: Position, t: int) -> bool:
+        return (
+            self.valid_position(position, t)
+            and position not in self.obstacle_avoid_points
+        )
+
+    """
+    Returns True if the given position is within the grid's boundaries
+    """
+    def inside_grid_bounds(self, position: Position) -> bool:
+        return (
+            position.x >= 0
+            and position.x < self.grid_size[0]
+            and position.y >= 0
+            and position.y < self.grid_size[1]
+        )
+
+    """
+    Sample a random position that is within the grid's boundaries
+
+    output:
+        Position: (x, y) position
+    """
+    def sample_random_position(self) -> Position:
+        return Position(
+            np.random.randint(0, self.grid_size[0]),
+            np.random.randint(0, self.grid_size[1]),
+        )
+
+    """
+    Returns a tuple of (x_positions, y_positions) of the obstacles at time t
+    """
+    def get_obstacle_positions_at_time(self, t: int) -> tuple[list[int], list[int]]:
+        x_positions = []
+        y_positions = []
+        for obs_path in self.obstacle_paths:
+            x_positions.append(obs_path[t].x)
+            y_positions.append(obs_path[t].y)
+        return (x_positions, y_positions)
+
+
+show_animation = True
+
+
+def main():
+    grid = Grid(
+        np.array([11, 11]),
+        num_obstacles=10,
+        obstacle_arrangement=ObstacleArrangement.ARRANGEMENT1,
+    )
+
+    if not show_animation:
+        return
+
+    fig = plt.figure(figsize=(8, 7))
+    ax = fig.add_subplot(
+        autoscale_on=False,
+        xlim=(0, grid.grid_size[0] - 1),
+        ylim=(0, grid.grid_size[1] - 1),
+    )
+    ax.set_aspect("equal")
+    ax.grid()
+    ax.set_xticks(np.arange(0, 11, 1))
+    ax.set_yticks(np.arange(0, 11, 1))
+    (obs_points,) = ax.plot([], [], "ro", ms=15)
+
+    # for stopping simulation with the esc key.
+    plt.gcf().canvas.mpl_connect(
+        "key_release_event", lambda event: [exit(0) if event.key == "escape" else None]
+    )
+
+    for i in range(0, grid.time_limit - 1):
+        obs_positions = grid.get_obstacle_positions_at_time(i)
+        obs_points.set_data(obs_positions[0], obs_positions[1])
+        plt.pause(0.2)
+    plt.show()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py b/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py
new file mode 100644
index 0000000000..3b3613d695
--- /dev/null
+++ b/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py
@@ -0,0 +1,220 @@
+"""
+Space-time A* Algorithm
+    This script demonstrates the Space-time A* algorithm for path planning in a grid world with moving obstacles.
+    This algorithm is different from normal 2D A* in one key way - the cost (often notated as g(n)) is
+    the number of time steps it took to get to a given node, instead of the number of cells it has
+    traversed. This ensures the path is time-optimal, while respescting any dynamic obstacles in the environment.
+
+    Reference: https://www.davidsilver.uk/wp-content/uploads/2020/03/coop-path-AIWisdom.pdf
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import (
+    Grid,
+    ObstacleArrangement,
+    Position,
+)
+import heapq
+from collections.abc import Generator
+import random
+from dataclasses import dataclass
+from functools import total_ordering
+
+
+# Seed randomness for reproducibility
+RANDOM_SEED = 50
+random.seed(RANDOM_SEED)
+np.random.seed(RANDOM_SEED)
+
+@dataclass()
+# Note: Total_ordering is used instead of adding `order=True` to the @dataclass decorator because
+#     this class needs to override the __lt__ and __eq__ methods to ignore parent_index. Parent
+#     index is just used to track the path found by the algorithm, and has no effect on the quality
+#     of a node.
+@total_ordering
+class Node:
+    position: Position
+    time: int
+    heuristic: int
+    parent_index: int
+
+    """
+    This is what is used to drive node expansion. The node with the lowest value is expanded next.
+    This comparison prioritizes the node with the lowest cost-to-come (self.time) + cost-to-go (self.heuristic)
+    """
+    def __lt__(self, other: object):
+        if not isinstance(other, Node):
+            return NotImplementedError(f"Cannot compare Node with object of type: {type(other)}")
+        return (self.time + self.heuristic) < (other.time + other.heuristic)
+
+    def __eq__(self, other: object):
+        if not isinstance(other, Node):
+            return NotImplementedError(f"Cannot compare Node with object of type: {type(other)}")
+        return self.position == other.position and self.time == other.time
+
+
+class NodePath:
+    path: list[Node]
+    positions_at_time: dict[int, Position] = {}
+
+    def __init__(self, path: list[Node]):
+        self.path = path
+        for node in path:
+            self.positions_at_time[node.time] = node.position
+
+    """
+    Get the position of the path at a given time
+    """
+    def get_position(self, time: int) -> Position | None:
+        return self.positions_at_time.get(time)
+
+    """
+    Time stamp of the last node in the path
+    """
+    def goal_reached_time(self) -> int:
+        return self.path[-1].time
+
+    def __repr__(self):
+        repr_string = ""
+        for i, node in enumerate(self.path):
+            repr_string += f"{i}: {node}\n"
+        return repr_string
+
+
+class SpaceTimeAStar:
+    grid: Grid
+    start: Position
+    goal: Position
+
+    def __init__(self, grid: Grid, start: Position, goal: Position):
+        self.grid = grid
+        self.start = start
+        self.goal = goal
+
+    def plan(self, verbose: bool = False) -> NodePath:
+        open_set: list[Node] = []
+        heapq.heappush(
+            open_set, Node(self.start, 0, self.calculate_heuristic(self.start), -1)
+        )
+
+        expanded_set: list[Node] = []
+        while open_set:
+            expanded_node: Node = heapq.heappop(open_set)
+            if verbose:
+                print("Expanded node:", expanded_node)
+
+            if expanded_node.time + 1 >= self.grid.time_limit:
+                if verbose:
+                    print(f"\tSkipping node that is past time limit: {expanded_node}")
+                continue
+
+            if expanded_node.position == self.goal:
+                print(f"Found path to goal after {len(expanded_set)} expansions")
+                path = []
+                path_walker: Node = expanded_node
+                while True:
+                    path.append(path_walker)
+                    if path_walker.parent_index == -1:
+                        break
+                    path_walker = expanded_set[path_walker.parent_index]
+
+                # reverse path so it goes start -> goal
+                path.reverse()
+                return NodePath(path)
+
+            expanded_idx = len(expanded_set)
+            expanded_set.append(expanded_node)
+
+            for child in self.generate_successors(expanded_node, expanded_idx, verbose):
+                heapq.heappush(open_set, child)
+
+        raise Exception("No path found")
+
+    """
+    Generate possible successors of the provided `parent_node`
+    """
+    def generate_successors(
+        self, parent_node: Node, parent_node_idx: int, verbose: bool
+    ) -> Generator[Node, None, None]:
+        diffs = [
+            Position(0, 0),
+            Position(1, 0),
+            Position(-1, 0),
+            Position(0, 1),
+            Position(0, -1),
+        ]
+        for diff in diffs:
+            new_pos = parent_node.position + diff
+            if self.grid.valid_position(new_pos, parent_node.time + 1):
+                new_node = Node(
+                    new_pos,
+                    parent_node.time + 1,
+                    self.calculate_heuristic(new_pos),
+                    parent_node_idx,
+                )
+                if verbose:
+                    print("\tNew successor node: ", new_node)
+                yield new_node
+
+    def calculate_heuristic(self, position) -> int:
+        diff = self.goal - position
+        return abs(diff.x) + abs(diff.y)
+
+
+show_animation = True
+verbose = False
+
+def main():
+    start = Position(1, 11)
+    goal = Position(19, 19)
+    grid_side_length = 21
+    grid = Grid(
+        np.array([grid_side_length, grid_side_length]),
+        num_obstacles=40,
+        obstacle_avoid_points=[start, goal],
+        obstacle_arrangement=ObstacleArrangement.ARRANGEMENT1,
+    )
+
+    planner = SpaceTimeAStar(grid, start, goal)
+    path = planner.plan(verbose)
+
+    if verbose:
+        print(f"Path: {path}")
+
+    if not show_animation:
+        return
+
+    fig = plt.figure(figsize=(10, 7))
+    ax = fig.add_subplot(
+        autoscale_on=False,
+        xlim=(0, grid.grid_size[0] - 1),
+        ylim=(0, grid.grid_size[1] - 1),
+    )
+    ax.set_aspect("equal")
+    ax.grid()
+    ax.set_xticks(np.arange(0, grid_side_length, 1))
+    ax.set_yticks(np.arange(0, grid_side_length, 1))
+
+    (start_and_goal,) = ax.plot([], [], "mD", ms=15, label="Start and Goal")
+    start_and_goal.set_data([start.x, goal.x], [start.y, goal.y])
+    (obs_points,) = ax.plot([], [], "ro", ms=15, label="Obstacles")
+    (path_points,) = ax.plot([], [], "bo", ms=10, label="Path Found")
+    ax.legend(bbox_to_anchor=(1.05, 1))
+
+    # for stopping simulation with the esc key.
+    plt.gcf().canvas.mpl_connect(
+        "key_release_event", lambda event: [exit(0) if event.key == "escape" else None]
+    )
+
+    for i in range(0, path.goal_reached_time()):
+        obs_positions = grid.get_obstacle_positions_at_time(i)
+        obs_points.set_data(obs_positions[0], obs_positions[1])
+        path_position = path.get_position(i)
+        path_points.set_data([path_position.x], [path_position.y])
+        plt.pause(0.2)
+    plt.show()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/PathPlanning/TimeBasedPathPlanning/__init__.py b/PathPlanning/TimeBasedPathPlanning/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/docs/modules/5_path_planning/path_planning_main.rst b/docs/modules/5_path_planning/path_planning_main.rst
index a0f9c30a3d..0c84a19c22 100644
--- a/docs/modules/5_path_planning/path_planning_main.rst
+++ b/docs/modules/5_path_planning/path_planning_main.rst
@@ -12,6 +12,7 @@ Path planning is the ability of a robot to search feasible and efficient path to
    dynamic_window_approach/dynamic_window_approach
    bugplanner/bugplanner
    grid_base_search/grid_base_search
+   time_based_grid_search/time_based_grid_search
    model_predictive_trajectory_generator/model_predictive_trajectory_generator
    state_lattice_planner/state_lattice_planner
    prm_planner/prm_planner
diff --git a/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst
new file mode 100644
index 0000000000..0c26badec7
--- /dev/null
+++ b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst
@@ -0,0 +1,22 @@
+Time based grid search
+----------------------
+
+Space-time astar
+~~~~~~~~~~~~~~~~~~~~~~
+
+This is an extension of A* algorithm that supports planning around dynamic obstacles.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar/path_animation.gif
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar/path_animation2.gif
+
+The key difference of this algorithm compared to vanilla A* is that the cost and heuristic are now time-based instead of distance-based.
+Using a time-based cost and heuristic ensures the path found is optimal in terms of time to reach the goal.
+
+The cost is the amount of time it takes to reach a given node, and the heuristic is the minimum amount of time it could take to reach the goal from that node, disregarding all obstacles.
+For a simple scenario where the robot can move 1 cell per time step and stop and go as it pleases, the heuristic for time is equivalent to the heuristic for distance.
+
+References:
+~~~~~~~~~~~
+
+-  `Cooperative Pathfinding <https://www.davidsilver.uk/wp-content/uploads/2020/03/coop-path-AIWisdom.pdf>`__
diff --git a/tests/test_space_time_astar.py b/tests/test_space_time_astar.py
new file mode 100644
index 0000000000..5290738eb4
--- /dev/null
+++ b/tests/test_space_time_astar.py
@@ -0,0 +1,33 @@
+from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import (
+    Grid,
+    ObstacleArrangement,
+    Position,
+)
+from PathPlanning.TimeBasedPathPlanning import SpaceTimeAStar as m
+import numpy as np
+import conftest
+
+
+def test_1():
+    start = Position(1, 11)
+    goal = Position(19, 19)
+    grid_side_length = 21
+    grid = Grid(
+        np.array([grid_side_length, grid_side_length]),
+        obstacle_arrangement=ObstacleArrangement.ARRANGEMENT1,
+    )
+
+    m.show_animation = False
+    planner = m.SpaceTimeAStar(grid, start, goal)
+
+    path = planner.plan(False)
+
+    # path should have 28 entries
+    assert len(path.path) == 31
+
+    # path should end at the goal
+    assert path.path[-1].position == goal
+
+
+if __name__ == "__main__":
+    conftest.run_this_test(__file__)

From 67a3ca7138384b4a013974dbf22383252f83686e Mon Sep 17 00:00:00 2001
From: Aglargil <34728006+Aglargil@users.noreply.github.com>
Date: Fri, 28 Feb 2025 19:30:24 +0800
Subject: [PATCH 0992/1080] add state machine (#1172)

* add state machine

state_machine test

state_machine_update

* add state machine test/doc

* state machine update

* state machine generate_plantuml() can show diagram by using https://www.plantuml.com/plantuml/
---
 .../StateMachine/robot_behavior_case.py       | 111 +++++++
 MissionPlanning/StateMachine/state_machine.py | 294 ++++++++++++++++++
 docs/index_main.rst                           |   1 +
 .../mission_planning_main.rst                 |  12 +
 .../state_machine/robot_behavior_case.png     | Bin 0 -> 28565 bytes
 .../state_machine/state_machine_main.rst      |  74 +++++
 tests/test_state_machine.py                   |  51 +++
 7 files changed, 543 insertions(+)
 create mode 100644 MissionPlanning/StateMachine/robot_behavior_case.py
 create mode 100644 MissionPlanning/StateMachine/state_machine.py
 create mode 100644 docs/modules/13_mission_planning/mission_planning_main.rst
 create mode 100644 docs/modules/13_mission_planning/state_machine/robot_behavior_case.png
 create mode 100644 docs/modules/13_mission_planning/state_machine/state_machine_main.rst
 create mode 100644 tests/test_state_machine.py

diff --git a/MissionPlanning/StateMachine/robot_behavior_case.py b/MissionPlanning/StateMachine/robot_behavior_case.py
new file mode 100644
index 0000000000..03ee60ae9f
--- /dev/null
+++ b/MissionPlanning/StateMachine/robot_behavior_case.py
@@ -0,0 +1,111 @@
+"""
+A case study of robot behavior using state machine
+
+author: Wang Zheng (@Aglargil)
+"""
+
+from state_machine import StateMachine
+
+
+class Robot:
+    def __init__(self):
+        self.battery = 100
+        self.task_progress = 0
+
+        # Initialize state machine
+        self.machine = StateMachine("robot_sm", self)
+
+        # Add state transition rules
+        self.machine.add_transition(
+            src_state="patrolling",
+            event="detect_task",
+            dst_state="executing_task",
+            guard=None,
+            action=None,
+        )
+
+        self.machine.add_transition(
+            src_state="executing_task",
+            event="task_complete",
+            dst_state="patrolling",
+            guard=None,
+            action="https://github.com/reset_task",
+        )
+
+        self.machine.add_transition(
+            src_state="executing_task",
+            event="low_battery",
+            dst_state="returning_to_base",
+            guard="is_battery_low",
+        )
+
+        self.machine.add_transition(
+            src_state="returning_to_base",
+            event="reach_base",
+            dst_state="charging",
+            guard=None,
+            action=None,
+        )
+
+        self.machine.add_transition(
+            src_state="charging",
+            event="charge_complete",
+            dst_state="patrolling",
+            guard=None,
+            action="https://github.com/battery_full",
+        )
+
+        # Set initial state
+        self.machine.set_current_state("patrolling")
+
+    def is_battery_low(self):
+        """Battery level check condition"""
+        return self.battery < 30
+
+    def reset_task(self):
+        """Reset task progress"""
+        self.task_progress = 0
+        print("[Action] Task progress has been reset")
+
+    # Modify state entry callback naming convention (add state_ prefix)
+    def on_enter_executing_task(self):
+        print("\n------ Start Executing Task ------")
+        print(f"Current battery: {self.battery}%")
+        while self.machine.get_current_state().name == "executing_task":
+            self.task_progress += 10
+            self.battery -= 25
+            print(
+                f"Task progress: {self.task_progress}%, Remaining battery: {self.battery}%"
+            )
+
+            if self.task_progress >= 100:
+                self.machine.process("task_complete")
+                break
+            elif self.is_battery_low():
+                self.machine.process("low_battery")
+                break
+
+    def on_enter_returning_to_base(self):
+        print("\nLow battery, returning to charging station...")
+        self.machine.process("reach_base")
+
+    def on_enter_charging(self):
+        print("\n------ Charging ------")
+        self.battery = 100
+        print("Charging complete!")
+        self.machine.process("charge_complete")
+
+
+# Keep the test section structure the same, only modify the trigger method
+if __name__ == "__main__":
+    robot = Robot()
+    print(robot.machine.generate_plantuml())
+
+    print(f"Initial state: {robot.machine.get_current_state().name}")
+    print("------------")
+
+    # Trigger task detection event
+    robot.machine.process("detect_task")
+
+    print("\n------------")
+    print(f"Final state: {robot.machine.get_current_state().name}")
diff --git a/MissionPlanning/StateMachine/state_machine.py b/MissionPlanning/StateMachine/state_machine.py
new file mode 100644
index 0000000000..de72f0f451
--- /dev/null
+++ b/MissionPlanning/StateMachine/state_machine.py
@@ -0,0 +1,294 @@
+"""
+State Machine
+
+author: Wang Zheng (@Aglargil)
+
+Ref:
+
+- [State Machine]
+(https://en.wikipedia.org/wiki/Finite-state_machine)
+"""
+
+import string
+from urllib.request import urlopen, Request
+from base64 import b64encode
+from zlib import compress
+from io import BytesIO
+from collections.abc import Callable
+from matplotlib.image import imread
+from matplotlib import pyplot as plt
+
+
+def deflate_and_encode(plantuml_text):
+    """
+    zlib compress the plantuml text and encode it for the plantuml server.
+
+    Ref: https://plantuml.com/en/text-encoding
+    """
+    plantuml_alphabet = (
+        string.digits + string.ascii_uppercase + string.ascii_lowercase + "-_"
+    )
+    base64_alphabet = (
+        string.ascii_uppercase + string.ascii_lowercase + string.digits + "+/"
+    )
+    b64_to_plantuml = bytes.maketrans(
+        base64_alphabet.encode("utf-8"), plantuml_alphabet.encode("utf-8")
+    )
+    zlibbed_str = compress(plantuml_text.encode("utf-8"))
+    compressed_string = zlibbed_str[2:-4]
+    return b64encode(compressed_string).translate(b64_to_plantuml).decode("utf-8")
+
+
+class State:
+    def __init__(self, name, on_enter=None, on_exit=None):
+        self.name = name
+        self.on_enter = on_enter
+        self.on_exit = on_exit
+
+    def enter(self):
+        print(f"entering <{self.name}>")
+        if self.on_enter:
+            self.on_enter()
+
+    def exit(self):
+        print(f"exiting <{self.name}>")
+        if self.on_exit:
+            self.on_exit()
+
+
+class StateMachine:
+    def __init__(self, name: str, model=object):
+        """Initialize the state machine.
+
+        Args:
+            name (str): Name of the state machine.
+            model (object, optional): Model object used to automatically look up callback functions
+                for states and transitions:
+                State callbacks: Automatically searches for 'on_enter_<state>' and 'on_exit_<state>' methods.
+                Transition callbacks: When action or guard parameters are strings, looks up corresponding methods in the model.
+
+        Example:
+            >>> class MyModel:
+            ...     def on_enter_idle(self):
+            ...         print("Entering idle state")
+            ...     def on_exit_idle(self):
+            ...         print("Exiting idle state")
+            ...     def can_start(self):
+            ...         return True
+            ...     def on_start(self):
+            ...         print("Starting operation")
+            >>> model = MyModel()
+            >>> machine = StateMachine("my_machine", model)
+        """
+        self._name = name
+        self._states = {}
+        self._events = {}
+        self._transition_table = {}
+        self._model = model
+        self._state: State = None
+
+    def _register_event(self, event: str):
+        self._events[event] = event
+
+    def _get_state(self, name):
+        return self._states[name]
+
+    def _get_event(self, name):
+        return self._events[name]
+
+    def _has_event(self, event: str):
+        return event in self._events
+
+    def add_transition(
+        self,
+        src_state: str | State,
+        event: str,
+        dst_state: str | State,
+        guard: str | Callable = None,
+        action: str | Callable = None,
+    ) -> None:
+        """Add a transition to the state machine.
+
+        Args:
+            src_state (str | State): The source state where the transition begins.
+                Can be either a state name or a State object.
+            event (str): The event that triggers this transition.
+            dst_state (str | State): The destination state where the transition ends.
+                Can be either a state name or a State object.
+            guard (str | Callable, optional): Guard condition for the transition.
+                If callable: Function that returns bool.
+                If str: Name of a method in the model class.
+                If returns True: Transition proceeds.
+                If returns False: Transition is skipped.
+            action (str | Callable, optional): Action to execute during transition.
+                If callable: Function to execute.
+                If str: Name of a method in the model class.
+                Executed after guard passes and before entering new state.
+
+        Example:
+            >>> machine.add_transition(
+            ...     src_state="idle",
+            ...     event="start",
+            ...     dst_state="running",
+            ...     guard="can_start",
+            ...     action="https://github.com/on_start"
+            ... )
+        """
+        # Convert string parameters to objects if necessary
+        self.register_state(src_state)
+        self._register_event(event)
+        self.register_state(dst_state)
+
+        def get_state_obj(state):
+            return state if isinstance(state, State) else self._get_state(state)
+
+        def get_callable(func):
+            return func if callable(func) else getattr(self._model, func, None)
+
+        src_state_obj = get_state_obj(src_state)
+        dst_state_obj = get_state_obj(dst_state)
+
+        guard_func = get_callable(guard) if guard else None
+        action_func = get_callable(action) if action else None
+        self._transition_table[(src_state_obj.name, event)] = (
+            dst_state_obj,
+            guard_func,
+            action_func,
+        )
+
+    def state_transition(self, src_state: State, event: str):
+        if (src_state.name, event) not in self._transition_table:
+            raise ValueError(
+                f"|{self._name}| invalid transition: <{src_state.name}> : [{event}]"
+            )
+
+        dst_state, guard, action = self._transition_table[(src_state.name, event)]
+
+        def call_guard(guard):
+            if callable(guard):
+                return guard()
+            else:
+                return True
+
+        def call_action(action):
+            if callable(action):
+                action()
+
+        if call_guard(guard):
+            call_action(action)
+            if src_state.name != dst_state.name:
+                print(
+                    f"|{self._name}| transitioning from <{src_state.name}> to <{dst_state.name}>"
+                )
+                src_state.exit()
+                self._state = dst_state
+                dst_state.enter()
+        else:
+            print(
+                f"|{self._name}| skipping transition from <{src_state.name}> to <{dst_state.name}> because guard failed"
+            )
+
+    def register_state(self, state: str | State, on_enter=None, on_exit=None):
+        """Register a state in the state machine.
+
+        Args:
+            state (str | State): The state to register. Can be either a string (state name)
+                                or a State object.
+            on_enter (Callable, optional): Callback function to be executed when entering the state.
+                                         If state is a string and on_enter is None, it will look for
+                                         a method named 'on_enter_<state>' in the model.
+            on_exit (Callable, optional): Callback function to be executed when exiting the state.
+                                        If state is a string and on_exit is None, it will look for
+                                        a method named 'on_exit_<state>' in the model.
+        Example:
+            >>> machine.register_state("idle", on_enter=on_enter_idle, on_exit=on_exit_idle)
+            >>> machine.register_state(State("running", on_enter=on_enter_running, on_exit=on_exit_running))
+        """
+        if isinstance(state, str):
+            if on_enter is None:
+                on_enter = getattr(self._model, "on_enter_" + state, None)
+            if on_exit is None:
+                on_exit = getattr(self._model, "on_exit_" + state, None)
+            self._states[state] = State(state, on_enter, on_exit)
+            return
+
+        self._states[state.name] = state
+
+    def set_current_state(self, state: State | str):
+        if isinstance(state, str):
+            self._state = self._get_state(state)
+        else:
+            self._state = state
+
+    def get_current_state(self):
+        return self._state
+
+    def process(self, event: str) -> None:
+        """Process an event in the state machine.
+
+        Args:
+            event: Event name.
+
+        Example:
+            >>> machine.process("start")
+        """
+        if self._state is None:
+            raise ValueError("State machine is not initialized")
+
+        if self._has_event(event):
+            self.state_transition(self._state, event)
+        else:
+            raise ValueError(f"Invalid event: {event}")
+
+    def generate_plantuml(self) -> str:
+        """Generate PlantUML state diagram representation of the state machine.
+
+        Returns:
+            str: PlantUML state diagram code.
+        """
+        if self._state is None:
+            raise ValueError("State machine is not initialized")
+
+        plant_uml = ["@startuml"]
+        plant_uml.append("[*] --> " + self._state.name)
+
+        # Generate transitions
+        for (src_state, event), (
+            dst_state,
+            guard,
+            action,
+        ) in self._transition_table.items():
+            transition = f"{src_state} --> {dst_state.name} : {event}"
+
+            # Add guard and action if present
+            conditions = []
+            if guard:
+                guard_name = guard.__name__ if callable(guard) else guard
+                conditions.append(f"[{guard_name}]")
+            if action:
+                action_name = action.__name__ if callable(action) else action
+                conditions.append(f"/ {action_name}")
+
+            if conditions:
+                transition += "\\n" + " ".join(conditions)
+
+            plant_uml.append(transition)
+
+        plant_uml.append("@enduml")
+        plant_uml_text = "\n".join(plant_uml)
+
+        try:
+            url = f"http://www.plantuml.com/plantuml/img/%7Bdeflate_and_encode(plant_uml_text)%7D"
+            headers = {"User-Agent": "Mozilla/5.0"}
+            request = Request(url, headers=headers)
+
+            with urlopen(request) as response:
+                content = response.read()
+
+            plt.imshow(imread(BytesIO(content), format="png"))
+            plt.axis("off")
+            plt.show()
+        except Exception as e:
+            print(f"Error showing PlantUML: {e}")
+
+        return plant_uml_text
diff --git a/docs/index_main.rst b/docs/index_main.rst
index 75805d1184..65634f32e8 100644
--- a/docs/index_main.rst
+++ b/docs/index_main.rst
@@ -44,6 +44,7 @@ this graph shows GitHub star history of this project:
    modules/8_aerial_navigation/aerial_navigation
    modules/9_bipedal/bipedal
    modules/10_inverted_pendulum/inverted_pendulum
+   modules/13_mission_planning/mission_planning
    modules/11_utils/utils
    modules/12_appendix/appendix
 
diff --git a/docs/modules/13_mission_planning/mission_planning_main.rst b/docs/modules/13_mission_planning/mission_planning_main.rst
new file mode 100644
index 0000000000..385e62f68e
--- /dev/null
+++ b/docs/modules/13_mission_planning/mission_planning_main.rst
@@ -0,0 +1,12 @@
+.. _`Mission Planning`:
+
+Mission Planning
+================
+
+Mission planning includes tools such as finite state machines and behavior trees used to describe robot behavior and high level task planning.  
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
+
+   state_machine/state_machine
diff --git a/docs/modules/13_mission_planning/state_machine/robot_behavior_case.png b/docs/modules/13_mission_planning/state_machine/robot_behavior_case.png
new file mode 100644
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diff --git a/docs/modules/13_mission_planning/state_machine/state_machine_main.rst b/docs/modules/13_mission_planning/state_machine/state_machine_main.rst
new file mode 100644
index 0000000000..abaece1b11
--- /dev/null
+++ b/docs/modules/13_mission_planning/state_machine/state_machine_main.rst
@@ -0,0 +1,74 @@
+State Machine
+-------------
+
+A state machine is a model used to describe the transitions of an object between different states. It clearly shows how an object changes state based on events and may trigger corresponding actions.
+
+Core Concepts
+~~~~~~~~~~~~~
+
+- **State**: A distinct mode or condition of the system (e.g. "Idle", "Running"). Managed by State class with optional on_enter/on_exit callbacks
+- **Event**: A trigger signal that may cause state transitions (e.g. "start", "stop")
+- **Transition**: A state change path from source to destination state triggered by an event
+- **Action**: An operation executed during transition (before entering new state)
+- **Guard**: A precondition that must be satisfied to allow transition
+
+API
+~~~
+
+.. autoclass:: MissionPlanning.StateMachine.state_machine.StateMachine
+   :members: add_transition, process, register_state
+   :special-members: __init__
+
+PlantUML Support
+~~~~~~~~~~~~~~~~
+
+The ``generate_plantuml()`` method creates diagrams showing:
+
+- Current state (marked with [*] arrow)
+- All possible transitions
+- Guard conditions in [brackets]
+- Actions prefixed with /
+
+Example
+~~~~~~~
+
+state machine diagram:
++++++++++++++++++++++++
+.. image:: robot_behavior_case.png
+
+state transition table:
++++++++++++++++++++++++
+.. list-table:: State Transitions
+   :header-rows: 1
+   :widths: 20 15 20 20 20
+
+   * - Source State
+     - Event
+     - Target State
+     - Guard
+     - Action
+   * - patrolling
+     - detect_task
+     - executing_task
+     - 
+     - 
+   * - executing_task
+     - task_complete
+     - patrolling
+     - 
+     - reset_task
+   * - executing_task
+     - low_battery
+     - returning_to_base
+     - is_battery_low
+     - 
+   * - returning_to_base
+     - reach_base
+     - charging
+     - 
+     - 
+   * - charging
+     - charge_complete
+     - patrolling
+     - 
+     - 
\ No newline at end of file
diff --git a/tests/test_state_machine.py b/tests/test_state_machine.py
new file mode 100644
index 0000000000..e36a8120fd
--- /dev/null
+++ b/tests/test_state_machine.py
@@ -0,0 +1,51 @@
+import conftest
+
+from MissionPlanning.StateMachine.state_machine import StateMachine
+
+
+def test_transition():
+    sm = StateMachine("state_machine")
+    sm.add_transition(src_state="idle", event="start", dst_state="running")
+    sm.set_current_state("idle")
+    sm.process("start")
+    assert sm.get_current_state().name == "running"
+
+
+def test_guard():
+    class Model:
+        def can_start(self):
+            return False
+
+    sm = StateMachine("state_machine", Model())
+    sm.add_transition(
+        src_state="idle", event="start", dst_state="running", guard="can_start"
+    )
+    sm.set_current_state("idle")
+    sm.process("start")
+    assert sm.get_current_state().name == "idle"
+
+
+def test_action():
+    class Model:
+        def on_start(self):
+            self.start_called = True
+
+    model = Model()
+    sm = StateMachine("state_machine", model)
+    sm.add_transition(
+        src_state="idle", event="start", dst_state="running", action="https://github.com/on_start"
+    )
+    sm.set_current_state("idle")
+    sm.process("start")
+    assert model.start_called
+
+
+def test_plantuml():
+    sm = StateMachine("state_machine")
+    sm.add_transition(src_state="idle", event="start", dst_state="running")
+    sm.set_current_state("idle")
+    assert sm.generate_plantuml()
+
+
+if __name__ == "__main__":
+    conftest.run_this_test(__file__)

From 346037a6e2362ed17870928ccf26992b60b0e54c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 4 Mar 2025 07:26:47 +0900
Subject: [PATCH 0993/1080] build(deps): bump pytest from 8.3.4 to 8.3.5 in
 /requirements (#1178)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.3.4 to 8.3.5.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/8.3.4...8.3.5)

---
updated-dependencies:
- dependency-name: pytest
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index b439ea4266..b46a0e41f1 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,7 +2,7 @@ numpy == 2.2.3
 scipy == 1.15.2
 matplotlib == 3.10.0
 cvxpy == 1.5.3
-pytest == 8.3.4 # For unit test
+pytest == 8.3.5 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.15.0 # For unit test
 ruff == 0.9.7 # For unit test

From cd09abd5e00ca29d15f42c539121cf62751ecd52 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 4 Mar 2025 12:21:33 +0900
Subject: [PATCH 0994/1080] build(deps): bump ruff from 0.9.7 to 0.9.9 in
 /requirements (#1179)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.9.7 to 0.9.9.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.9.7...0.9.9)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index b46a0e41f1..3e2a1e7c7c 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.5 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.15.0 # For unit test
-ruff == 0.9.7 # For unit test
+ruff == 0.9.9 # For unit test

From 5f3be9bccd6366493ea8d159ceeae26d55ed5250 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 4 Mar 2025 12:52:57 +0900
Subject: [PATCH 0995/1080] build(deps): bump matplotlib from 3.10.0 to 3.10.1
 in /requirements (#1181)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.10.0 to 3.10.1.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.10.0...v3.10.1)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 3e2a1e7c7c..552bf8482b 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 2.2.3
 scipy == 1.15.2
-matplotlib == 3.10.0
+matplotlib == 3.10.1
 cvxpy == 1.5.3
 pytest == 8.3.5 # For unit test
 pytest-xdist == 3.6.1 # For unit test

From 30a61add126e2c21c035d50a7a448ed8eb06afb0 Mon Sep 17 00:00:00 2001
From: Surya Singh <133056660+spnsingh@users.noreply.github.com>
Date: Fri, 7 Mar 2025 09:01:37 -0500
Subject: [PATCH 0996/1080] bug: fix typo on line 6 of SpaceTimeAStar.py
 (#1182)

* bug: fix typo on line 6 of SpaceTimeAStar.py

* bug: removed extra line return on line 11 of SpaceTimeAStar.py
---
 PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py b/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py
index 3b3613d695..a7aed41869 100644
--- a/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py
+++ b/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py
@@ -3,7 +3,7 @@
     This script demonstrates the Space-time A* algorithm for path planning in a grid world with moving obstacles.
     This algorithm is different from normal 2D A* in one key way - the cost (often notated as g(n)) is
     the number of time steps it took to get to a given node, instead of the number of cells it has
-    traversed. This ensures the path is time-optimal, while respescting any dynamic obstacles in the environment.
+    traversed. This ensures the path is time-optimal, while respecting any dynamic obstacles in the environment.
 
     Reference: https://www.davidsilver.uk/wp-content/uploads/2020/03/coop-path-AIWisdom.pdf
 """

From fc160179c06cb4b57f821d83a9bdcf192379bd72 Mon Sep 17 00:00:00 2001
From: Aglargil <34728006+Aglargil@users.noreply.github.com>
Date: Sat, 8 Mar 2025 18:26:23 +0800
Subject: [PATCH 0997/1080] feat: add behavior tree (#1177)

* feat: add behavior tree

* feat: add behavior tree test

* feat: add behavior tree doc

* feat: add behavior tree update
---
 MissionPlanning/BehaviorTree/behavior_tree.py | 690 ++++++++++++++++++
 .../BehaviorTree/robot_behavior_case.py       | 247 +++++++
 .../BehaviorTree/robot_behavior_tree.xml      |  57 ++
 .../behavior_tree/behavior_tree_main.rst      | 104 +++
 .../behavior_tree/robot_behavior_case.svg     |  22 +
 .../mission_planning_main.rst                 |   1 +
 tests/test_behavior_tree.py                   | 207 ++++++
 7 files changed, 1328 insertions(+)
 create mode 100644 MissionPlanning/BehaviorTree/behavior_tree.py
 create mode 100644 MissionPlanning/BehaviorTree/robot_behavior_case.py
 create mode 100644 MissionPlanning/BehaviorTree/robot_behavior_tree.xml
 create mode 100644 docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst
 create mode 100644 docs/modules/13_mission_planning/behavior_tree/robot_behavior_case.svg
 create mode 100644 tests/test_behavior_tree.py

diff --git a/MissionPlanning/BehaviorTree/behavior_tree.py b/MissionPlanning/BehaviorTree/behavior_tree.py
new file mode 100644
index 0000000000..59f4c713f1
--- /dev/null
+++ b/MissionPlanning/BehaviorTree/behavior_tree.py
@@ -0,0 +1,690 @@
+"""
+Behavior Tree
+
+author: Wang Zheng (@Aglargil)
+
+Ref:
+
+- [Behavior Tree](https://en.wikipedia.org/wiki/Behavior_tree_(artificial_intelligence,_robotics_and_control))
+"""
+
+import time
+import xml.etree.ElementTree as ET
+from enum import Enum
+
+
+class Status(Enum):
+    SUCCESS = "success"
+    FAILURE = "failure"
+    RUNNING = "running"
+
+
+class NodeType(Enum):
+    CONTROL_NODE = "ControlNode"
+    ACTION_NODE = "ActionNode"
+    DECORATOR_NODE = "DecoratorNode"
+
+
+class Node:
+    """
+    Base class for all nodes in a behavior tree.
+    """
+
+    def __init__(self, name):
+        self.name = name
+        self.status = None
+
+    def tick(self) -> Status:
+        """
+        Tick the node.
+
+        Returns:
+            Status: The status of the node.
+        """
+        raise ValueError("Node is not implemented")
+
+    def tick_and_set_status(self) -> Status:
+        """
+        Tick the node and set the status.
+
+        Returns:
+            Status: The status of the node.
+        """
+        self.status = self.tick()
+        return self.status
+
+    def reset(self):
+        """
+        Reset the node.
+        """
+        self.status = None
+
+    def reset_children(self):
+        """
+        Reset the children of the node.
+        """
+        pass
+
+
+class ControlNode(Node):
+    """
+    Base class for all control nodes in a behavior tree.
+
+    Control nodes manage the execution flow of their child nodes according to specific rules.
+    They typically have multiple children and determine which children to execute and in what order.
+    """
+
+    def __init__(self, name):
+        super().__init__(name)
+        self.children = []
+        self.type = NodeType.CONTROL_NODE
+
+    def not_set_children_raise_error(self):
+        if len(self.children) == 0:
+            raise ValueError("Children are not set")
+
+    def reset_children(self):
+        for child in self.children:
+            child.reset()
+
+
+class SequenceNode(ControlNode):
+    """
+    Executes child nodes in sequence until one fails or all succeed.
+
+    Returns:
+        - Returns FAILURE if any child returns FAILURE
+        - Returns SUCCESS when all children have succeeded
+        - Returns RUNNING when a child is still running or when moving to the next child
+
+    Example:
+        .. code-block:: xml
+
+            <Sequence>
+                <Action1 />
+                <Action2 />
+            </Sequence>
+    """
+
+    def __init__(self, name):
+        super().__init__(name)
+        self.current_child_index = 0
+
+    def tick(self) -> Status:
+        self.not_set_children_raise_error()
+
+        if self.current_child_index >= len(self.children):
+            self.reset_children()
+            return Status.SUCCESS
+        status = self.children[self.current_child_index].tick_and_set_status()
+        if status == Status.FAILURE:
+            self.reset_children()
+            return Status.FAILURE
+        elif status == Status.SUCCESS:
+            self.current_child_index += 1
+            return Status.RUNNING
+        elif status == Status.RUNNING:
+            return Status.RUNNING
+        else:
+            raise ValueError("Unknown status")
+
+
+class SelectorNode(ControlNode):
+    """
+    Executes child nodes in sequence until one succeeds or all fail.
+
+    Returns:
+        - Returns SUCCESS if any child returns SUCCESS
+        - Returns FAILURE when all children have failed
+        - Returns RUNNING when a child is still running or when moving to the next child
+
+    Examples:
+        .. code-block:: xml
+
+            <Selector>
+                <Action1 />
+                <Action2 />
+            </Selector>
+    """
+
+    def __init__(self, name):
+        super().__init__(name)
+        self.current_child_index = 0
+
+    def tick(self) -> Status:
+        self.not_set_children_raise_error()
+
+        if self.current_child_index >= len(self.children):
+            self.reset_children()
+            return Status.FAILURE
+        status = self.children[self.current_child_index].tick_and_set_status()
+        if status == Status.SUCCESS:
+            self.reset_children()
+            return Status.SUCCESS
+        elif status == Status.FAILURE:
+            self.current_child_index += 1
+            return Status.RUNNING
+        elif status == Status.RUNNING:
+            return Status.RUNNING
+        else:
+            raise ValueError("Unknown status")
+
+
+class WhileDoElseNode(ControlNode):
+    """
+    Conditional execution node with three parts: condition, do, and optional else.
+
+    Returns:
+        First executes the condition node (child[0])
+        If condition succeeds, executes do node (child[1]) and returns RUNNING
+        If condition fails, executes else node (child[2]) if present and returns result of else node
+        If condition fails and there is no else node, returns SUCCESS
+
+    Example:
+        .. code-block:: xml
+
+            <WhileDoElse>
+                <Condition />
+                <Do />
+                <Else />
+            </WhileDoElse>
+    """
+
+    def __init__(self, name):
+        super().__init__(name)
+
+    def tick(self) -> Status:
+        if len(self.children) != 3 and len(self.children) != 2:
+            raise ValueError("WhileDoElseNode must have exactly 3 or 2 children")
+
+        condition_node = self.children[0]
+        do_node = self.children[1]
+        else_node = self.children[2] if len(self.children) == 3 else None
+
+        condition_status = condition_node.tick_and_set_status()
+        if condition_status == Status.SUCCESS:
+            do_node.tick_and_set_status()
+            return Status.RUNNING
+        elif condition_status == Status.FAILURE:
+            if else_node is not None:
+                else_status = else_node.tick_and_set_status()
+                if else_status == Status.SUCCESS:
+                    self.reset_children()
+                    return Status.SUCCESS
+                elif else_status == Status.FAILURE:
+                    self.reset_children()
+                    return Status.FAILURE
+                elif else_status == Status.RUNNING:
+                    return Status.RUNNING
+                else:
+                    raise ValueError("Unknown status")
+            else:
+                self.reset_children()
+                return Status.SUCCESS
+        else:
+            raise ValueError("Unknown status")
+
+
+class ActionNode(Node):
+    """
+    Base class for all action nodes in a behavior tree.
+
+    Action nodes are responsible for performing specific tasks or actions.
+    They do not have children and are typically used to execute logic or operations.
+    """
+
+    def __init__(self, name):
+        super().__init__(name)
+        self.type = NodeType.ACTION_NODE
+
+
+class SleepNode(ActionNode):
+    """
+    Sleep node that sleeps for a specified duration.
+
+    Returns:
+        Returns SUCCESS after the specified duration has passed
+        Returns RUNNING if the duration has not yet passed
+
+    Example:
+        .. code-block:: xml
+
+            <Sleep sec="1.5" />
+    """
+
+    def __init__(self, name, duration):
+        super().__init__(name)
+        self.duration = duration
+        self.start_time = None
+
+    def tick(self) -> Status:
+        if self.start_time is None:
+            self.start_time = time.time()
+        if time.time() - self.start_time > self.duration:
+            return Status.SUCCESS
+        return Status.RUNNING
+
+
+class EchoNode(ActionNode):
+    """
+    Echo node that prints a message to the console.
+
+    Returns:
+        Returns SUCCESS after the message has been printed
+
+    Example:
+        .. code-block:: xml
+
+            <Echo message="Hello, World!" />
+    """
+
+    def __init__(self, name, message):
+        super().__init__(name)
+        self.message = message
+
+    def tick(self) -> Status:
+        print(self.name, self.message)
+        return Status.SUCCESS
+
+
+class DecoratorNode(Node):
+    """
+    Base class for all decorator nodes in a behavior tree.
+
+    Decorator nodes modify the behavior of their child node.
+    They must have a single child and can alter the status of the child node.
+    """
+
+    def __init__(self, name):
+        super().__init__(name)
+        self.type = NodeType.DECORATOR_NODE
+        self.child = None
+
+    def not_set_child_raise_error(self):
+        if self.child is None:
+            raise ValueError("Child is not set")
+
+    def reset_children(self):
+        self.child.reset()
+
+
+class InverterNode(DecoratorNode):
+    """
+    Inverter node that inverts the status of its child node.
+
+    Returns:
+        - Returns SUCCESS if the child returns FAILURE
+        - Returns FAILURE if the child returns SUCCESS
+        - Returns RUNNING if the child returns RUNNING
+
+    Examples:
+        .. code-block:: xml
+
+            <Inverter>
+                <Action />
+            </Inverter>
+    """
+
+    def __init__(self, name):
+        super().__init__(name)
+
+    def tick(self) -> Status:
+        self.not_set_child_raise_error()
+        status = self.child.tick_and_set_status()
+        return Status.SUCCESS if status == Status.FAILURE else Status.FAILURE
+
+
+class TimeoutNode(DecoratorNode):
+    """
+    Timeout node that fails if the child node takes too long to execute
+
+    Returns:
+        - FAILURE: If the timeout duration has been exceeded
+        - Child's status: Otherwise, passes through the status of the child node
+
+    Example:
+        .. code-block:: xml
+
+            <Timeout sec="1.5">
+                <Action />
+            </Timeout>
+    """
+
+    def __init__(self, name, timeout):
+        super().__init__(name)
+        self.timeout = timeout
+        self.start_time = None
+
+    def tick(self) -> Status:
+        self.not_set_child_raise_error()
+        if self.start_time is None:
+            self.start_time = time.time()
+        if time.time() - self.start_time > self.timeout:
+            return Status.FAILURE
+        print(f"{self.name} is running")
+        return self.child.tick_and_set_status()
+
+
+class DelayNode(DecoratorNode):
+    """
+    Delay node that delays the execution of its child node for a specified duration.
+
+    Returns:
+        - Returns RUNNING if the duration has not yet passed
+        - Returns child's status after the duration has passed
+
+    Example:
+        .. code-block:: xml
+
+            <Delay sec="1.5">
+                <Action />
+            </Delay>
+    """
+
+    def __init__(self, name, delay):
+        super().__init__(name)
+        self.delay = delay
+        self.start_time = None
+
+    def tick(self) -> Status:
+        self.not_set_child_raise_error()
+        if self.start_time is None:
+            self.start_time = time.time()
+        if time.time() - self.start_time > self.delay:
+            return self.child.tick_and_set_status()
+        return Status.RUNNING
+
+
+class ForceSuccessNode(DecoratorNode):
+    """
+    ForceSuccess node that always returns SUCCESS.
+
+    Returns:
+        - Returns RUNNING if the child returns RUNNING
+        - Returns SUCCESS if the child returns SUCCESS or FAILURE
+    """
+
+    def __init__(self, name):
+        super().__init__(name)
+
+    def tick(self) -> Status:
+        self.not_set_child_raise_error()
+        status = self.child.tick_and_set_status()
+        if status == Status.FAILURE:
+            return Status.SUCCESS
+        return status
+
+
+class ForceFailureNode(DecoratorNode):
+    """
+    ForceFailure node that always returns FAILURE.
+
+    Returns:
+        - Returns RUNNING if the child returns RUNNING
+        - Returns FAILURE if the child returns SUCCESS or FAILURE
+    """
+
+    def __init__(self, name):
+        super().__init__(name)
+
+    def tick(self) -> Status:
+        self.not_set_child_raise_error()
+        status = self.child.tick_and_set_status()
+        if status == Status.SUCCESS:
+            return Status.FAILURE
+        return status
+
+
+class BehaviorTree:
+    """
+    Behavior tree class that manages the execution of a behavior tree.
+    """
+
+    def __init__(self, root):
+        self.root = root
+
+    def tick(self):
+        """
+        Tick once on the behavior tree.
+        """
+        self.root.tick_and_set_status()
+
+    def reset(self):
+        """
+        Reset the behavior tree.
+        """
+        self.root.reset()
+
+    def tick_while_running(self, interval=None, enable_print=True):
+        """
+        Tick the behavior tree while it is running.
+
+        Args:
+            interval (float, optional): The interval between ticks. Defaults to None.
+            enable_print (bool, optional): Whether to print the behavior tree. Defaults to True.
+        """
+        while self.root.tick_and_set_status() == Status.RUNNING:
+            if enable_print:
+                self.print_tree()
+            if interval is not None:
+                time.sleep(interval)
+        if enable_print:
+            self.print_tree()
+
+    def to_text(self, root, indent=0):
+        """
+        Recursively convert the behavior tree to a text representation.
+        """
+        current_text = ""
+        if root.status == Status.RUNNING:
+            # yellow
+            current_text = "\033[93m" + root.name + "\033[0m"
+        elif root.status == Status.SUCCESS:
+            # green
+            current_text = "\033[92m" + root.name + "\033[0m"
+        elif root.status == Status.FAILURE:
+            # red
+            current_text = "\033[91m" + root.name + "\033[0m"
+        else:
+            current_text = root.name
+        if root.type == NodeType.CONTROL_NODE:
+            current_text = "  " * indent + "[" + current_text + "]\n"
+            for child in root.children:
+                current_text += self.to_text(child, indent + 2)
+        elif root.type == NodeType.DECORATOR_NODE:
+            current_text = "  " * indent + "(" + current_text + ")\n"
+            current_text += self.to_text(root.child, indent + 2)
+        elif root.type == NodeType.ACTION_NODE:
+            current_text = "  " * indent + "<" + current_text + ">\n"
+        return current_text
+
+    def print_tree(self):
+        """
+        Print the behavior tree.
+
+        Node print format:
+            Action: <Action>
+            Decorator: (Decorator)
+            Control: [Control]
+
+        Node status colors:
+            Yellow: RUNNING
+            Green: SUCCESS
+            Red: FAILURE
+        """
+        text = self.to_text(self.root)
+        text = text.strip()
+        print("\033[94m" + "Behavior Tree" + "\033[0m")
+        print(text)
+        print("\033[94m" + "Behavior Tree" + "\033[0m")
+
+
+class BehaviorTreeFactory:
+    """
+    Factory class for creating behavior trees from XML strings.
+    """
+
+    def __init__(self):
+        self.node_builders = {}
+        # Control nodes
+        self.register_node_builder(
+            "Sequence",
+            lambda node: SequenceNode(node.attrib.get("name", SequenceNode.__name__)),
+        )
+        self.register_node_builder(
+            "Selector",
+            lambda node: SelectorNode(node.attrib.get("name", SelectorNode.__name__)),
+        )
+        self.register_node_builder(
+            "WhileDoElse",
+            lambda node: WhileDoElseNode(
+                node.attrib.get("name", WhileDoElseNode.__name__)
+            ),
+        )
+        # Decorator nodes
+        self.register_node_builder(
+            "Inverter",
+            lambda node: InverterNode(node.attrib.get("name", InverterNode.__name__)),
+        )
+        self.register_node_builder(
+            "Timeout",
+            lambda node: TimeoutNode(
+                node.attrib.get("name", SelectorNode.__name__),
+                float(node.attrib["sec"]),
+            ),
+        )
+        self.register_node_builder(
+            "Delay",
+            lambda node: DelayNode(
+                node.attrib.get("name", DelayNode.__name__),
+                float(node.attrib["sec"]),
+            ),
+        )
+        self.register_node_builder(
+            "ForceSuccess",
+            lambda node: ForceSuccessNode(
+                node.attrib.get("name", ForceSuccessNode.__name__)
+            ),
+        )
+        self.register_node_builder(
+            "ForceFailure",
+            lambda node: ForceFailureNode(
+                node.attrib.get("name", ForceFailureNode.__name__)
+            ),
+        )
+        # Action nodes
+        self.register_node_builder(
+            "Sleep",
+            lambda node: SleepNode(
+                node.attrib.get("name", SleepNode.__name__),
+                float(node.attrib["sec"]),
+            ),
+        )
+        self.register_node_builder(
+            "Echo",
+            lambda node: EchoNode(
+                node.attrib.get("name", EchoNode.__name__),
+                node.attrib["message"],
+            ),
+        )
+
+    def register_node_builder(self, node_name, builder):
+        """
+        Register a builder for a node
+
+        Args:
+            node_name (str): The name of the node.
+            builder (function): The builder function.
+
+        Example:
+            .. code-block:: python
+
+                factory = BehaviorTreeFactory()
+                factory.register_node_builder(
+                    "MyNode",
+                    lambda node: MyNode(
+                        node.attrib.get("name", MyNode.__name__),
+                        node.attrib["my_param"],
+                    ),
+                )
+        """
+        self.node_builders[node_name] = builder
+
+    def build_node(self, node):
+        """
+        Build a node from an XML element.
+
+        Args:
+            node (Element): The XML element to build the node from.
+
+        Returns:
+            BehaviorTree Node: the built node
+        """
+        if node.tag in self.node_builders:
+            root = self.node_builders[node.tag](node)
+            if root.type == NodeType.CONTROL_NODE:
+                if len(node) <= 0:
+                    raise ValueError(f"{root.name} Control node must have children")
+                for child in node:
+                    root.children.append(self.build_node(child))
+            elif root.type == NodeType.DECORATOR_NODE:
+                if len(node) != 1:
+                    raise ValueError(
+                        f"{root.name} Decorator node must have exactly one child"
+                    )
+                root.child = self.build_node(node[0])
+            elif root.type == NodeType.ACTION_NODE:
+                if len(node) != 0:
+                    raise ValueError(f"{root.name} Action node must have no children")
+            return root
+        else:
+            raise ValueError(f"Unknown node type: {node.tag}")
+
+    def build_tree(self, xml_string):
+        """
+        Build a behavior tree from an XML string.
+
+        Args:
+            xml_string (str): The XML string containing the behavior tree.
+
+        Returns:
+            BehaviorTree: The behavior tree.
+        """
+        xml_tree = ET.fromstring(xml_string)
+        root = self.build_node(xml_tree)
+        return BehaviorTree(root)
+
+    def build_tree_from_file(self, file_path):
+        """
+        Build a behavior tree from a file.
+
+        Args:
+            file_path (str): The path to the file containing the behavior tree.
+
+        Returns:
+            BehaviorTree: The behavior tree.
+        """
+        with open(file_path) as file:
+            xml_string = file.read()
+        return self.build_tree(xml_string)
+
+
+xml_string = """
+    <Sequence name="Sequence">
+        <Echo name="Echo0" message="Hello, World0!" />
+        <Delay name="Delay" sec="1.5">
+            <Echo name="Echo1" message="Hello, World1!" />
+        </Delay>
+        <Echo name="Echo2" message="Hello, World2!" />
+    </Sequence>
+   """
+
+
+def main():
+    factory = BehaviorTreeFactory()
+    tree = factory.build_tree(xml_string)
+    tree.tick_while_running()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/MissionPlanning/BehaviorTree/robot_behavior_case.py b/MissionPlanning/BehaviorTree/robot_behavior_case.py
new file mode 100644
index 0000000000..6c39aa76b2
--- /dev/null
+++ b/MissionPlanning/BehaviorTree/robot_behavior_case.py
@@ -0,0 +1,247 @@
+"""
+Robot Behavior Tree Case
+
+This file demonstrates how to use a behavior tree to control robot behavior.
+"""
+
+from behavior_tree import (
+    BehaviorTreeFactory,
+    Status,
+    ActionNode,
+)
+import time
+import random
+import os
+
+
+class CheckBatteryNode(ActionNode):
+    """
+    Node to check robot battery level
+
+    If battery level is below threshold, returns FAILURE, otherwise returns SUCCESS
+    """
+
+    def __init__(self, name, threshold=20):
+        super().__init__(name)
+        self.threshold = threshold
+        self.battery_level = 100  # Initial battery level is 100%
+
+    def tick(self):
+        # Simulate battery level decreasing
+        self.battery_level -= random.randint(1, 5)
+        print(f"Current battery level: {self.battery_level}%")
+
+        if self.battery_level <= self.threshold:
+            return Status.FAILURE
+        return Status.SUCCESS
+
+
+class ChargeBatteryNode(ActionNode):
+    """
+    Node to charge the robot's battery
+    """
+
+    def __init__(self, name, charge_rate=10):
+        super().__init__(name)
+        self.charge_rate = charge_rate
+        self.charging_time = 0
+
+    def tick(self):
+        # Simulate charging process
+        if self.charging_time == 0:
+            print("Starting to charge...")
+
+        self.charging_time += 1
+        charge_amount = self.charge_rate * self.charging_time
+
+        if charge_amount >= 100:
+            print("Charging complete! Battery level: 100%")
+            self.charging_time = 0
+            return Status.SUCCESS
+        else:
+            print(f"Charging in progress... Battery level: {min(charge_amount, 100)}%")
+            return Status.RUNNING
+
+
+class MoveToPositionNode(ActionNode):
+    """
+    Node to move to a specified position
+    """
+
+    def __init__(self, name, position, move_duration=2):
+        super().__init__(name)
+        self.position = position
+        self.move_duration = move_duration
+        self.start_time = None
+
+    def tick(self):
+        if self.start_time is None:
+            self.start_time = time.time()
+            print(f"Starting movement to position {self.position}")
+
+        elapsed_time = time.time() - self.start_time
+
+        if elapsed_time >= self.move_duration:
+            print(f"Arrived at position {self.position}")
+            self.start_time = None
+            return Status.SUCCESS
+        else:
+            print(
+                f"Moving to position {self.position}... {int(elapsed_time / self.move_duration * 100)}% complete"
+            )
+            return Status.RUNNING
+
+
+class DetectObstacleNode(ActionNode):
+    """
+    Node to detect obstacles
+    """
+
+    def __init__(self, name, obstacle_probability=0.3):
+        super().__init__(name)
+        self.obstacle_probability = obstacle_probability
+
+    def tick(self):
+        # Use random probability to simulate obstacle detection
+        if random.random() < self.obstacle_probability:
+            print("Obstacle detected!")
+            return Status.SUCCESS
+        else:
+            print("No obstacle detected")
+            return Status.FAILURE
+
+
+class AvoidObstacleNode(ActionNode):
+    """
+    Node to avoid obstacles
+    """
+
+    def __init__(self, name, avoid_duration=1.5):
+        super().__init__(name)
+        self.avoid_duration = avoid_duration
+        self.start_time = None
+
+    def tick(self):
+        if self.start_time is None:
+            self.start_time = time.time()
+            print("Starting obstacle avoidance...")
+
+        elapsed_time = time.time() - self.start_time
+
+        if elapsed_time >= self.avoid_duration:
+            print("Obstacle avoidance complete")
+            self.start_time = None
+            return Status.SUCCESS
+        else:
+            print("Avoiding obstacle...")
+            return Status.RUNNING
+
+
+class PerformTaskNode(ActionNode):
+    """
+    Node to perform a specific task
+    """
+
+    def __init__(self, name, task_name, task_duration=3):
+        super().__init__(name)
+        self.task_name = task_name
+        self.task_duration = task_duration
+        self.start_time = None
+
+    def tick(self):
+        if self.start_time is None:
+            self.start_time = time.time()
+            print(f"Starting task: {self.task_name}")
+
+        elapsed_time = time.time() - self.start_time
+
+        if elapsed_time >= self.task_duration:
+            print(f"Task complete: {self.task_name}")
+            self.start_time = None
+            return Status.SUCCESS
+        else:
+            print(
+                f"Performing task: {self.task_name}... {int(elapsed_time / self.task_duration * 100)}% complete"
+            )
+            return Status.RUNNING
+
+
+def create_robot_behavior_tree():
+    """
+    Create robot behavior tree
+    """
+
+    factory = BehaviorTreeFactory()
+
+    # Register custom nodes
+    factory.register_node_builder(
+        "CheckBattery",
+        lambda node: CheckBatteryNode(
+            node.attrib.get("name", "CheckBattery"),
+            int(node.attrib.get("threshold", "20")),
+        ),
+    )
+
+    factory.register_node_builder(
+        "ChargeBattery",
+        lambda node: ChargeBatteryNode(
+            node.attrib.get("name", "ChargeBattery"),
+            int(node.attrib.get("charge_rate", "10")),
+        ),
+    )
+
+    factory.register_node_builder(
+        "MoveToPosition",
+        lambda node: MoveToPositionNode(
+            node.attrib.get("name", "MoveToPosition"),
+            node.attrib.get("position", "Unknown Position"),
+            float(node.attrib.get("move_duration", "2")),
+        ),
+    )
+
+    factory.register_node_builder(
+        "DetectObstacle",
+        lambda node: DetectObstacleNode(
+            node.attrib.get("name", "DetectObstacle"),
+            float(node.attrib.get("obstacle_probability", "0.3")),
+        ),
+    )
+
+    factory.register_node_builder(
+        "AvoidObstacle",
+        lambda node: AvoidObstacleNode(
+            node.attrib.get("name", "AvoidObstacle"),
+            float(node.attrib.get("avoid_duration", "1.5")),
+        ),
+    )
+
+    factory.register_node_builder(
+        "PerformTask",
+        lambda node: PerformTaskNode(
+            node.attrib.get("name", "PerformTask"),
+            node.attrib.get("task_name", "Unknown Task"),
+            float(node.attrib.get("task_duration", "3")),
+        ),
+    )
+    # Read XML from file
+    xml_path = os.path.join(os.path.dirname(__file__), "robot_behavior_tree.xml")
+    return factory.build_tree_from_file(xml_path)
+
+
+def main():
+    """
+    Main function: Create and run the robot behavior tree
+    """
+    print("Creating robot behavior tree...")
+    tree = create_robot_behavior_tree()
+
+    print("\nStarting robot behavior tree execution...\n")
+    # Run for a period of time or until completion
+
+    tree.tick_while_running(interval=0.01)
+
+    print("\nBehavior tree execution complete!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/MissionPlanning/BehaviorTree/robot_behavior_tree.xml b/MissionPlanning/BehaviorTree/robot_behavior_tree.xml
new file mode 100644
index 0000000000..0bca76a3ff
--- /dev/null
+++ b/MissionPlanning/BehaviorTree/robot_behavior_tree.xml
@@ -0,0 +1,57 @@
+<Selector name="Robot Main Controller">
+    <!-- Charge battery when power is low -->
+    <Sequence name="Battery Management">
+        <Inverter name="Low Battery Detection">
+            <CheckBattery name="Check Battery" threshold="30" />
+        </Inverter>
+        <Echo name="Low Battery Warning" message="Battery level low! Charging needed" />
+        <ChargeBattery name="Charge Battery" charge_rate="20" />
+    </Sequence>
+    <!-- Main task sequence -->
+    <Sequence name="Patrol Task">
+        <Echo name="Start Task" message="Starting patrol task" />
+        <!-- Move to position A -->
+        <Sequence name="Move to Position A">
+            <MoveToPosition name="Move to A" position="A" move_duration="2" />
+            <!-- Handle obstacles -->
+            <Selector name="Obstacle Handling A">
+                <Sequence name="Obstacle Present">
+                    <DetectObstacle name="Detect Obstacle" obstacle_probability="0.3" />
+                    <AvoidObstacle name="Avoid Obstacle" avoid_duration="1.5" />
+                </Sequence>
+                <Echo name="No Obstacle" message="Path clear" />
+            </Selector>
+            <PerformTask name="Position A Task" task_name="Check Device Status" task_duration="2" />
+        </Sequence>
+        <!-- Move to position B -->
+        <Sequence name="Move to Position B">
+            <Delay name="Short Wait" sec="1">
+                <Echo name="Prepare Movement" message="Preparing to move to next position" />
+            </Delay>
+            <MoveToPosition name="Move to B" position="B" move_duration="3" />
+            <!-- Handle obstacles with timeout -->
+            <Timeout name="Limited Time Obstacle Handling" sec="2">
+                <Sequence name="Obstacle Present">
+                    <DetectObstacle name="Detect Obstacle" obstacle_probability="0.4" />
+                    <AvoidObstacle name="Avoid Obstacle" avoid_duration="1.8" />
+                </Sequence>
+            </Timeout>
+            <PerformTask name="Position B Task" task_name="Data Collection" task_duration="2.5" />
+        </Sequence>
+        <!-- Move to position C -->
+        <WhileDoElse name="Conditional Move to C">
+            <CheckBattery name="Check Sufficient Battery" threshold="50" />
+            <Sequence name="Perform Position C Task">
+                <MoveToPosition name="Move to C" position="C" move_duration="2.5" />
+                <ForceSuccess name="Ensure Completion">
+                    <PerformTask name="Position C Task" task_name="Environment Monitoring"
+                        task_duration="2" />
+                </ForceSuccess>
+            </Sequence>
+            <Echo name="Skip Position C" message="Insufficient power, skipping position C task" />
+        </WhileDoElse>
+        <Echo name="Complete Patrol" message="Patrol task completed, returning to charging station" />
+        <MoveToPosition name="Return to Charging Station" position="Charging Station"
+            move_duration="4" />
+    </Sequence>
+</Selector> 
\ No newline at end of file
diff --git a/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst b/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst
new file mode 100644
index 0000000000..ae3e16da81
--- /dev/null
+++ b/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst
@@ -0,0 +1,104 @@
+Behavior Tree
+-------------
+
+Behavior Tree is a modular, hierarchical decision model that is widely used in robot control, and game development. 
+It present some similarities to hierarchical state machines with the key difference that the main building block of a behavior is a task rather than a state.
+Behavior Tree have been shown to generalize several other control architectures (https://ieeexplore.ieee.org/document/7790863)
+
+Core Concepts
+~~~~~~~~~~~~~
+
+Control Node
+++++++++++++
+
+.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.ControlNode
+
+.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.SequenceNode
+
+.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.SelectorNode
+
+.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.WhileDoElseNode
+
+Action Node
+++++++++++++
+
+.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.ActionNode
+
+.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.EchoNode
+
+.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.SleepNode
+
+Decorator Node
+++++++++++++++
+
+.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.DecoratorNode
+
+.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.InverterNode
+
+.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.TimeoutNode
+
+.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.DelayNode
+
+.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.ForceSuccessNode
+
+.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.ForceFailureNode
+
+Behavior Tree Factory
++++++++++++++++++++++
+
+.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.BehaviorTreeFactory
+  :members:
+
+Behavior Tree
++++++++++++++
+
+.. autoclass:: MissionPlanning.BehaviorTree.behavior_tree.BehaviorTree
+  :members:
+
+Example
+~~~~~~~
+
+Visualize the behavior tree by `xml-tree-visual <https://xml-tree-visual.vercel.app/>`_.
+
+.. image:: ./robot_behavior_case.svg
+
+Print the behavior tree
+
+.. code-block:: text
+
+    Behavior Tree
+    [Robot Main Controller]
+        [Battery Management]
+            (Low Battery Detection)
+                <Check Battery>
+            <Low Battery Warning>
+            <Charge Battery>
+        [Patrol Task]
+            <Start Task>
+            [Move to Position A]
+                <Move to A>
+                [Obstacle Handling A]
+                    [Obstacle Present]
+                        <Detect Obstacle>
+                        <Avoid Obstacle>
+                    <No Obstacle>
+                <Position A Task>
+            [Move to Position B]
+                (Short Wait)
+                    <Prepare Movement>
+                <Move to B>
+                (Limited Time Obstacle Handling)
+                    [Obstacle Present]
+                        <Detect Obstacle>
+                        <Avoid Obstacle>
+                <Position B Task>
+            [Conditional Move to C]
+                <Check Sufficient Battery>
+                [Perform Position C Task]
+                    <Move to C>
+                    (Ensure Completion)
+                        <Position C Task>
+                <Skip Position C>
+            <Complete Patrol>
+            <Return to Charging Station>
+    Behavior Tree
diff --git a/docs/modules/13_mission_planning/behavior_tree/robot_behavior_case.svg b/docs/modules/13_mission_planning/behavior_tree/robot_behavior_case.svg
new file mode 100644
index 0000000000..a3d43aed52
--- /dev/null
+++ b/docs/modules/13_mission_planning/behavior_tree/robot_behavior_case.svg
@@ -0,0 +1,22 @@
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+      .node rect { 
+        fill: #fff; 
+        stroke-width: 1px; 
+      }
+      .node text { 
+        font: 12px sans-serif; 
+      }
+      .node-attributes { 
+        font: 10px sans-serif; 
+        fill: #666; 
+      }
+      .link { 
+        fill: none; 
+        stroke: #999; 
+        stroke-width: 1px; 
+      }
+      line {
+        stroke: #444;
+        stroke-width: 2px;
+      }
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text-anchor="middle">PerformTask</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Position B Task</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">task_name: Data Collection</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="65" x="-105" text-anchor="start">task_duration: 2.5</text><line x1="-105" y1="70" x2="105" y2="70" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(1845,360)"><rect x="-115" y="-15" width="230" height="70" stroke="#90EE90" style="stroke: rgb(144, 238, 144);"/><text dy="5" text-anchor="middle">CheckBattery</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Check Sufficient 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dy="25" x="-105" text-anchor="start">name: Skip Position C</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">message: Insufficient power,</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="65" x="-105" text-anchor="start">skipping position C task</text><line x1="-105" y1="70" x2="105" y2="70" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(-270,480)"><rect x="-115" y="-15" width="230" height="50" stroke="#4A90E2" style="stroke: rgb(74, 144, 226);"/><text dy="5" text-anchor="middle">Sequence</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Obstacle Present</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(0,480)"><rect x="-115" y="-15" width="230" height="70" stroke="#D0021B" style="stroke: rgb(208, 2, 27);"/><text dy="5" text-anchor="middle">Echo</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: No Obstacle</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">message: Path clear</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(585,480)"><rect x="-115" y="-15" width="230" height="90" stroke="#D0021B" style="stroke: rgb(208, 2, 27);"/><text dy="5" text-anchor="middle">Echo</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Prepare Movement</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">message: Preparing to move to</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="65" x="-105" text-anchor="start">next position</text><line x1="-105" y1="70" x2="105" y2="70" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(1125,480)"><rect x="-115" y="-15" width="230" height="50" stroke="#4A90E2" style="stroke: rgb(74, 144, 226);"/><text dy="5" text-anchor="middle">Sequence</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Obstacle Present</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(1980,480)"><rect x="-115" y="-15" width="230" height="90" stroke="#90EE90" style="stroke: rgb(144, 238, 144);"/><text dy="5" text-anchor="middle">MoveToPosition</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Move to C</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">position: C</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="65" x="-105" text-anchor="start">move_duration: 2.5</text><line x1="-105" y1="70" x2="105" y2="70" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(2250,480)"><rect x="-115" y="-15" width="230" height="50" stroke="#F5A623" style="stroke: rgb(245, 166, 35);"/><text dy="5" text-anchor="middle">ForceSuccess</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Ensure Completion</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(-405,600)"><rect x="-115" y="-15" width="230" height="70" stroke="#90EE90" style="stroke: rgb(144, 238, 144);"/><text dy="5" text-anchor="middle">DetectObstacle</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Detect Obstacle</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">obstacle_probability: 0.3</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(-135,600)"><rect x="-115" y="-15" width="230" height="70" stroke="#90EE90" style="stroke: rgb(144, 238, 144);"/><text dy="5" text-anchor="middle">AvoidObstacle</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Avoid Obstacle</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">avoid_duration: 1.5</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(990,600)"><rect x="-115" y="-15" width="230" height="70" stroke="#90EE90" style="stroke: rgb(144, 238, 144);"/><text dy="5" text-anchor="middle">DetectObstacle</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Detect Obstacle</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">obstacle_probability: 0.4</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(1260,600)"><rect x="-115" y="-15" width="230" height="70" stroke="#90EE90" style="stroke: rgb(144, 238, 144);"/><text dy="5" text-anchor="middle">AvoidObstacle</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Avoid Obstacle</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">avoid_duration: 1.8</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/></g><g class="node" transform="translate(2250,600)"><rect x="-115" y="-15" width="230" height="110" stroke="#90EE90" style="stroke: rgb(144, 238, 144);"/><text dy="5" text-anchor="middle">PerformTask</text><line x1="-105" y1="10" x2="105" y2="10" stroke="#444" stroke-width="2"/><text class="node-attributes" dy="25" x="-105" text-anchor="start">name: Position C Task</text><line x1="-105" y1="30" x2="105" y2="30" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="45" x="-105" text-anchor="start">task_name: Environment</text><line x1="-105" y1="50" x2="105" y2="50" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="65" x="-105" text-anchor="start">Monitoring</text><line x1="-105" y1="70" x2="105" y2="70" stroke="#ddd" stroke-width="1"/><text class="node-attributes" dy="85" x="-105" text-anchor="start">task_duration: 2</text><line x1="-105" y1="90" x2="105" y2="90" stroke="#ddd" stroke-width="1"/></g></g></svg>
\ No newline at end of file
diff --git a/docs/modules/13_mission_planning/mission_planning_main.rst b/docs/modules/13_mission_planning/mission_planning_main.rst
index 385e62f68e..c35eacd8d5 100644
--- a/docs/modules/13_mission_planning/mission_planning_main.rst
+++ b/docs/modules/13_mission_planning/mission_planning_main.rst
@@ -10,3 +10,4 @@ Mission planning includes tools such as finite state machines and behavior trees
    :caption: Contents
 
    state_machine/state_machine
+   behavior_tree/behavior_tree
diff --git a/tests/test_behavior_tree.py b/tests/test_behavior_tree.py
new file mode 100644
index 0000000000..0898690448
--- /dev/null
+++ b/tests/test_behavior_tree.py
@@ -0,0 +1,207 @@
+import pytest
+import conftest
+
+from MissionPlanning.BehaviorTree.behavior_tree import (
+    BehaviorTreeFactory,
+    Status,
+    ActionNode,
+)
+
+
+def test_sequence_node1():
+    xml_string = """
+        <Sequence>
+            <Echo name="Echo 1" message="Hello, World1!" />
+            <Echo name="Echo 2" message="Hello, World2!" />
+            <ForceFailure name="Force Failure">
+                <Echo name="Echo 3" message="Hello, World3!" />
+            </ForceFailure>
+        </Sequence>
+    """
+    bt_factory = BehaviorTreeFactory()
+    bt = bt_factory.build_tree(xml_string)
+    bt.tick()
+    assert bt.root.status == Status.RUNNING
+    assert bt.root.children[0].status == Status.SUCCESS
+    assert bt.root.children[1].status is None
+    assert bt.root.children[2].status is None
+    bt.tick()
+    bt.tick()
+    assert bt.root.status == Status.FAILURE
+    assert bt.root.children[0].status is None
+    assert bt.root.children[1].status is None
+    assert bt.root.children[2].status is None
+
+
+def test_sequence_node2():
+    xml_string = """
+        <Sequence>
+            <Echo name="Echo 1" message="Hello, World1!" />
+            <Echo name="Echo 2" message="Hello, World2!" />
+            <ForceSuccess name="Force Success">
+                <Echo name="Echo 3" message="Hello, World3!" />
+            </ForceSuccess>
+        </Sequence>
+    """
+    bt_factory = BehaviorTreeFactory()
+    bt = bt_factory.build_tree(xml_string)
+    bt.tick_while_running()
+    assert bt.root.status == Status.SUCCESS
+    assert bt.root.children[0].status is None
+    assert bt.root.children[1].status is None
+    assert bt.root.children[2].status is None
+
+
+def test_selector_node1():
+    xml_string = """
+        <Selector>
+            <ForceFailure name="Force Failure">
+                <Echo name="Echo 1" message="Hello, World1!" />
+            </ForceFailure>
+            <Echo name="Echo 2" message="Hello, World2!" />
+            <Echo name="Echo 3" message="Hello, World3!" />
+        </Selector>
+    """
+    bt_factory = BehaviorTreeFactory()
+    bt = bt_factory.build_tree(xml_string)
+    bt.tick()
+    assert bt.root.status == Status.RUNNING
+    assert bt.root.children[0].status == Status.FAILURE
+    assert bt.root.children[1].status is None
+    assert bt.root.children[2].status is None
+    bt.tick()
+    assert bt.root.status == Status.SUCCESS
+    assert bt.root.children[0].status is None
+    assert bt.root.children[1].status is None
+    assert bt.root.children[2].status is None
+
+
+def test_selector_node2():
+    xml_string = """
+        <Selector>
+            <ForceFailure name="Force Success">
+                <Echo name="Echo 1" message="Hello, World1!" />
+            </ForceFailure>
+            <ForceFailure name="Force Failure">
+                <Echo name="Echo 2" message="Hello, World2!" />
+            </ForceFailure>
+        </Selector>
+    """
+    bt_factory = BehaviorTreeFactory()
+    bt = bt_factory.build_tree(xml_string)
+    bt.tick_while_running()
+    assert bt.root.status == Status.FAILURE
+    assert bt.root.children[0].status is None
+    assert bt.root.children[1].status is None
+
+
+def test_while_do_else_node():
+    xml_string = """
+        <WhileDoElse>
+            <Count name="Count" count_threshold="3" />
+            <Echo name="Echo 1" message="Hello, World1!" />
+            <Echo name="Echo 2" message="Hello, World2!" />
+        </WhileDoElse>
+    """
+
+    class CountNode(ActionNode):
+        def __init__(self, name, count_threshold):
+            super().__init__(name)
+            self.count = 0
+            self.count_threshold = count_threshold
+
+        def tick(self):
+            self.count += 1
+            if self.count >= self.count_threshold:
+                return Status.FAILURE
+            else:
+                return Status.SUCCESS
+
+    bt_factory = BehaviorTreeFactory()
+    bt_factory.register_node_builder(
+        "Count",
+        lambda node: CountNode(
+            node.attrib.get("name", CountNode.__name__),
+            int(node.attrib["count_threshold"]),
+        ),
+    )
+    bt = bt_factory.build_tree(xml_string)
+    bt.tick()
+    assert bt.root.status == Status.RUNNING
+    assert bt.root.children[0].status == Status.SUCCESS
+    assert bt.root.children[1].status is Status.SUCCESS
+    assert bt.root.children[2].status is None
+    bt.tick()
+    assert bt.root.status == Status.RUNNING
+    assert bt.root.children[0].status == Status.SUCCESS
+    assert bt.root.children[1].status is Status.SUCCESS
+    assert bt.root.children[2].status is None
+    bt.tick()
+    assert bt.root.status == Status.SUCCESS
+    assert bt.root.children[0].status is None
+    assert bt.root.children[1].status is None
+    assert bt.root.children[2].status is None
+
+
+def test_node_children():
+    # ControlNode Must have children
+    xml_string = """
+        <Sequence>
+        </Sequence>
+    """
+    bt_factory = BehaviorTreeFactory()
+    with pytest.raises(ValueError):
+        bt_factory.build_tree(xml_string)
+
+    # DecoratorNode Must have child
+    xml_string = """
+        <Inverter>
+        </Inverter>
+    """
+    with pytest.raises(ValueError):
+        bt_factory.build_tree(xml_string)
+
+    # DecoratorNode Must have only one child
+    xml_string = """
+        <Inverter>
+            <Echo name="Echo 1" message="Hello, World1!" />
+            <Echo name="Echo 2" message="Hello, World2!" />
+        </Inverter>
+    """
+    with pytest.raises(ValueError):
+        bt_factory.build_tree(xml_string)
+
+    # ActionNode Must have no children
+    xml_string = """
+        <Echo name="Echo 1" message="Hello, World1!">
+            <Echo name="Echo 2" message="Hello, World2!" />
+        </Echo>
+    """
+    with pytest.raises(ValueError):
+        bt_factory.build_tree(xml_string)
+
+    # WhileDoElse Must have exactly 2 or 3 children
+    xml_string = """
+        <WhileDoElse>
+            <Echo name="Echo 1" message="Hello, World1!" />
+        </WhileDoElse>
+    """
+    with pytest.raises(ValueError):
+        bt = bt_factory.build_tree(xml_string)
+        bt.tick()
+
+    xml_string = """
+        <WhileDoElse>
+            <Echo name="Echo 1" message="Hello, World1!" />
+            <Echo name="Echo 2" message="Hello, World2!" />
+            <Echo name="Echo 3" message="Hello, World3!" />
+            <Echo name="Echo 4" message="Hello, World4!" />
+        </WhileDoElse>
+    """
+    with pytest.raises(ValueError):
+        bt = bt_factory.build_tree(xml_string)
+        bt.tick()
+
+
+if __name__ == "__main__":
+    conftest.run_this_test(__file__)

From e7f893ef1aa503df04c83ec6355d18dd05d36394 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 9 Mar 2025 20:21:24 +0900
Subject: [PATCH 0998/1080] feat: add linkcode_resolve function for enhanced
 documentation linking (#1185)

* feat: add linkcode_resolve function for enhanced documentation linking

* feat: enhance documentation linking with linkcode and relative path resolution
---
 docs/conf.py | 47 +++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 45 insertions(+), 2 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index 919fa9ac76..eeabab11b1 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -13,6 +13,8 @@
 #
 import os
 import sys
+from pathlib import Path
+
 sys.path.insert(0, os.path.abspath('../'))
 
 
@@ -41,7 +43,7 @@
     'matplotlib.sphinxext.plot_directive',
     'sphinx.ext.autodoc',
     'sphinx.ext.mathjax',
-    'sphinx.ext.viewcode',
+    'sphinx.ext.linkcode',
     'sphinx.ext.napoleon',
     'sphinx.ext.imgconverter',
     'IPython.sphinxext.ipython_console_highlighting',
@@ -184,4 +186,45 @@
 ]
 
 
-# -- Extension configuration -------------------------------------------------
+# -- linkcode setting -------------------------------------------------
+
+import inspect
+import os
+import sys
+import functools
+
+GITHUB_REPO = "https://github.com/AtsushiSakai/PythonRobotics"
+GITHUB_BRANCH = "master"
+
+
+def linkcode_resolve(domain, info):
+    if domain != "py":
+        return None
+
+    modname = info["module"]
+    fullname = info["fullname"]
+
+    try:
+        module = __import__(modname, fromlist=[fullname])
+        obj = functools.reduce(getattr, fullname.split("."), module)
+    except (ImportError, AttributeError):
+        return None
+
+    try:
+        srcfile = inspect.getsourcefile(obj)
+        srcfile = get_relative_path_from_parent(srcfile, "PythonRobotics")
+        lineno = inspect.getsourcelines(obj)[1]
+    except Exception:
+        return None
+
+    return f"{GITHUB_REPO}/blob/{GITHUB_BRANCH}/{srcfile}#L{lineno}"
+
+
+def get_relative_path_from_parent(file_path: str, parent_dir: str):
+    path = Path(file_path).resolve()
+
+    try:
+        parent_path = next(p for p in path.parents if p.name == parent_dir)
+        return str(path.relative_to(parent_path))
+    except StopIteration:
+        raise ValueError(f"Parent directory '{parent_dir}' not found in {file_path}")

From 73ebcd85dc967dbb160e29ee4c529b7a64f11e2b Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 12 Mar 2025 21:26:09 +0900
Subject: [PATCH 0999/1080] build(deps): bump ruff from 0.9.9 to 0.9.10 in
 /requirements (#1186)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.9.9 to 0.9.10.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.9.9...0.9.10)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 552bf8482b..cf6dea3a46 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.5 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.15.0 # For unit test
-ruff == 0.9.9 # For unit test
+ruff == 0.9.10 # For unit test

From 1308e76424723eb68a4a864117e60c0bbd054df1 Mon Sep 17 00:00:00 2001
From: Jonathan Schwartz <jonbs98@gmail.com>
Date: Thu, 13 Mar 2025 10:24:53 -0400
Subject: [PATCH 1000/1080] Add expanded node set to SpaceTime AStar (#1183)

* speed up spacetime astar

* forgot to include hash impl on Position

* add condition to test on node expansions

* remove heuristic from Node __hash__ impl

* update rst with note about optimization
---
 .../GridWithDynamicObstacles.py               |  3 ++
 .../TimeBasedPathPlanning/SpaceTimeAStar.py   | 51 +++++++++++++------
 .../time_based_grid_search_main.rst           | 17 +++++++
 tests/test_space_time_astar.py                |  1 +
 4 files changed, 57 insertions(+), 15 deletions(-)

diff --git a/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py b/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py
index 7b0190d023..7416ff2f08 100644
--- a/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py
+++ b/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py
@@ -30,6 +30,9 @@ def __sub__(self, other):
             f"Subtraction not supported for Position and {type(other)}"
         )
 
+    def __hash__(self):
+        return hash((self.x, self.y))
+
 
 class ObstacleArrangement(Enum):
     # Random obstacle positions and movements
diff --git a/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py b/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py
index a7aed41869..c4e2802d37 100644
--- a/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py
+++ b/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py
@@ -20,7 +20,7 @@
 import random
 from dataclasses import dataclass
 from functools import total_ordering
-
+import time
 
 # Seed randomness for reproducibility
 RANDOM_SEED = 50
@@ -48,11 +48,17 @@ def __lt__(self, other: object):
             return NotImplementedError(f"Cannot compare Node with object of type: {type(other)}")
         return (self.time + self.heuristic) < (other.time + other.heuristic)
 
+    """
+    Note: cost and heuristic are not included in eq or hash, since they will always be the same
+          for a given (position, time) pair. Including either cost or heuristic would be redundant.
+    """
     def __eq__(self, other: object):
         if not isinstance(other, Node):
             return NotImplementedError(f"Cannot compare Node with object of type: {type(other)}")
         return self.position == other.position and self.time == other.time
 
+    def __hash__(self):
+        return hash((self.position, self.time))
 
 class NodePath:
     path: list[Node]
@@ -86,6 +92,8 @@ class SpaceTimeAStar:
     grid: Grid
     start: Position
     goal: Position
+    # Used to evaluate solutions
+    expanded_node_count: int = -1
 
     def __init__(self, grid: Grid, start: Position, goal: Position):
         self.grid = grid
@@ -98,7 +106,8 @@ def plan(self, verbose: bool = False) -> NodePath:
             open_set, Node(self.start, 0, self.calculate_heuristic(self.start), -1)
         )
 
-        expanded_set: list[Node] = []
+        expanded_list: list[Node] = []
+        expanded_set: set[Node] = set()
         while open_set:
             expanded_node: Node = heapq.heappop(open_set)
             if verbose:
@@ -110,23 +119,25 @@ def plan(self, verbose: bool = False) -> NodePath:
                 continue
 
             if expanded_node.position == self.goal:
-                print(f"Found path to goal after {len(expanded_set)} expansions")
+                print(f"Found path to goal after {len(expanded_list)} expansions")
                 path = []
                 path_walker: Node = expanded_node
                 while True:
                     path.append(path_walker)
                     if path_walker.parent_index == -1:
                         break
-                    path_walker = expanded_set[path_walker.parent_index]
+                    path_walker = expanded_list[path_walker.parent_index]
 
                 # reverse path so it goes start -> goal
                 path.reverse()
+                self.expanded_node_count = len(expanded_set)
                 return NodePath(path)
 
-            expanded_idx = len(expanded_set)
-            expanded_set.append(expanded_node)
+            expanded_idx = len(expanded_list)
+            expanded_list.append(expanded_node)
+            expanded_set.add(expanded_node)
 
-            for child in self.generate_successors(expanded_node, expanded_idx, verbose):
+            for child in self.generate_successors(expanded_node, expanded_idx, verbose, expanded_set):
                 heapq.heappush(open_set, child)
 
         raise Exception("No path found")
@@ -135,7 +146,7 @@ def plan(self, verbose: bool = False) -> NodePath:
     Generate possible successors of the provided `parent_node`
     """
     def generate_successors(
-        self, parent_node: Node, parent_node_idx: int, verbose: bool
+        self, parent_node: Node, parent_node_idx: int, verbose: bool, expanded_set: set[Node]
     ) -> Generator[Node, None, None]:
         diffs = [
             Position(0, 0),
@@ -146,13 +157,17 @@ def generate_successors(
         ]
         for diff in diffs:
             new_pos = parent_node.position + diff
+            new_node = Node(
+                new_pos,
+                parent_node.time + 1,
+                self.calculate_heuristic(new_pos),
+                parent_node_idx,
+            )
+
+            if new_node in expanded_set:
+                continue
+
             if self.grid.valid_position(new_pos, parent_node.time + 1):
-                new_node = Node(
-                    new_pos,
-                    parent_node.time + 1,
-                    self.calculate_heuristic(new_pos),
-                    parent_node_idx,
-                )
                 if verbose:
                     print("\tNew successor node: ", new_node)
                 yield new_node
@@ -166,9 +181,12 @@ def calculate_heuristic(self, position) -> int:
 verbose = False
 
 def main():
-    start = Position(1, 11)
+    start = Position(1, 5)
     goal = Position(19, 19)
     grid_side_length = 21
+
+    start_time = time.time()
+
     grid = Grid(
         np.array([grid_side_length, grid_side_length]),
         num_obstacles=40,
@@ -179,6 +197,9 @@ def main():
     planner = SpaceTimeAStar(grid, start, goal)
     path = planner.plan(verbose)
 
+    runtime = time.time() - start_time
+    print(f"Planning took: {runtime:.5f} seconds")
+
     if verbose:
         print(f"Path: {path}")
 
diff --git a/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst
index 0c26badec7..48dc1289c2 100644
--- a/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst
+++ b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst
@@ -16,6 +16,23 @@ Using a time-based cost and heuristic ensures the path found is optimal in terms
 The cost is the amount of time it takes to reach a given node, and the heuristic is the minimum amount of time it could take to reach the goal from that node, disregarding all obstacles.
 For a simple scenario where the robot can move 1 cell per time step and stop and go as it pleases, the heuristic for time is equivalent to the heuristic for distance.
 
+One optimization that was added in `this PR <https://github.com/AtsushiSakai/PythonRobotics/pull/1183>`__ was to add an expanded set to the algorithm. The algorithm will not expand nodes that are already in that set. This greatly reduces the number of node expansions needed to find a path, since no duplicates are expanded. It also helps to reduce the amount of memory the algorithm uses.
+
+Before::
+
+    Found path to goal after 204490 expansions
+    Planning took: 1.72464 seconds
+    Memory usage (RSS): 68.19 MB
+
+
+After::
+
+    Found path to goal after 2348 expansions
+    Planning took: 0.01550 seconds
+    Memory usage (RSS): 64.85 MB
+
+When starting at (1, 11) in the structured obstacle arrangement (second of the two gifs above).
+
 References:
 ~~~~~~~~~~~
 
diff --git a/tests/test_space_time_astar.py b/tests/test_space_time_astar.py
index 5290738eb4..390c7732dc 100644
--- a/tests/test_space_time_astar.py
+++ b/tests/test_space_time_astar.py
@@ -28,6 +28,7 @@ def test_1():
     # path should end at the goal
     assert path.path[-1].position == goal
 
+    assert planner.expanded_node_count < 1000
 
 if __name__ == "__main__":
     conftest.run_this_test(__file__)

From 41187d67b11f06265431b87dcf0b9da679345249 Mon Sep 17 00:00:00 2001
From: Zichao Yang <yangtzech@qq.com>
Date: Sun, 16 Mar 2025 21:40:11 +0800
Subject: [PATCH 1001/1080] fix "ModuleNotFoundError: No module named 'utils'"
 (#1188)

---
 .../lqr_speed_steer_control/lqr_speed_steer_control.py     | 4 ++--
 PathTracking/lqr_steer_control/lqr_steer_control.py        | 4 ++--
 PathTracking/rear_wheel_feedback/rear_wheel_feedback.py    | 7 +++++--
 PathTracking/stanley_controller/stanley_controller.py      | 4 ++--
 4 files changed, 11 insertions(+), 8 deletions(-)

diff --git a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
index d85fa98a84..5831d02d30 100644
--- a/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
+++ b/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py
@@ -11,9 +11,9 @@
 import numpy as np
 import scipy.linalg as la
 import pathlib
-from utils.angle import angle_mod
-sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
+from utils.angle import angle_mod
 from PathPlanning.CubicSpline import cubic_spline_planner
 
 # === Parameters =====
diff --git a/PathTracking/lqr_steer_control/lqr_steer_control.py b/PathTracking/lqr_steer_control/lqr_steer_control.py
index 461d6e3722..3c066917ff 100644
--- a/PathTracking/lqr_steer_control/lqr_steer_control.py
+++ b/PathTracking/lqr_steer_control/lqr_steer_control.py
@@ -11,9 +11,9 @@
 import numpy as np
 import sys
 import pathlib
-from utils.angle import angle_mod
-sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
+from utils.angle import angle_mod
 from PathPlanning.CubicSpline import cubic_spline_planner
 
 Kp = 1.0  # speed proportional gain
diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
index 1702bd47dd..fd04fb6d17 100644
--- a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
+++ b/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
@@ -8,11 +8,14 @@
 import matplotlib.pyplot as plt
 import math
 import numpy as np
-from utils.angle import angle_mod
-
+import sys
+import pathlib
 from scipy import interpolate
 from scipy import optimize
 
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
+from utils.angle import angle_mod
+
 Kp = 1.0  # speed proportional gain
 # steering control parameter
 KTH = 1.0
diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py
index 08aa049395..bc98175f17 100644
--- a/PathTracking/stanley_controller/stanley_controller.py
+++ b/PathTracking/stanley_controller/stanley_controller.py
@@ -13,9 +13,9 @@
 import matplotlib.pyplot as plt
 import sys
 import pathlib
-from utils.angle import angle_mod
-sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
+from utils.angle import angle_mod
 from PathPlanning.CubicSpline import cubic_spline_planner
 
 k = 0.5  # control gain

From aa61a6ea570e013d346c40874f8a33890ec66d28 Mon Sep 17 00:00:00 2001
From: Jonathan Schwartz <jonbs98@gmail.com>
Date: Mon, 17 Mar 2025 09:01:07 -0400
Subject: [PATCH 1002/1080] Safe Interval Path Planner (#1184)

* it works and is WAY faster than a*

* some bug fixes from testing different scenarios

* add some docs & address todos

* add sipp test

* spiff up comments

revert changes in speed-up

* explain what the removal is doing

* linting

* fix docs build

* docs formatting

* revert change to file (maybe linter did it?)

* point at gifs in gifs repo

* use raw githubusercontent gif links

* change formatting on planner results

* format output differently

* proper formatting final

* missing underline

* revert unintended change

* grammar + add descriptions for gifs

* missing ::

* add title to gifs section

* dont use sections for sub-sections

* constent a* spelling

* Update PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Update tests/test_safe_interval_path_planner.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Update docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst

Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>

* Update PathPlanning/TimeBasedPathPlanning/SafeInterval.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* addressing comments

* revert np.full change

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>
---
 .../GridWithDynamicObstacles.py               |  57 +++-
 .../TimeBasedPathPlanning/SafeInterval.py     | 303 ++++++++++++++++++
 .../time_based_grid_search_main.rst           |  43 ++-
 tests/test_safe_interval_path_planner.py      |  33 ++
 4 files changed, 434 insertions(+), 2 deletions(-)
 create mode 100644 PathPlanning/TimeBasedPathPlanning/SafeInterval.py
 create mode 100644 tests/test_safe_interval_path_planner.py

diff --git a/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py b/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py
index 7416ff2f08..2a47023f8c 100644
--- a/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py
+++ b/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py
@@ -33,6 +33,10 @@ def __sub__(self, other):
     def __hash__(self):
         return hash((self.x, self.y))
 
+@dataclass
+class Interval:
+    start_time: int
+    end_time: int
 
 class ObstacleArrangement(Enum):
     # Random obstacle positions and movements
@@ -40,6 +44,14 @@ class ObstacleArrangement(Enum):
     # Obstacles start in a line in y at center of grid and move side-to-side in x
     ARRANGEMENT1 = 1
 
+"""
+Generates a 2d numpy array with lists for elements.
+"""
+def empty_2d_array_of_lists(x: int, y: int) -> np.ndarray:
+    arr = np.empty((x, y), dtype=object)
+    # assign each element individually - np.full creates references to the same list
+    arr[:] = [[[] for _ in range(y)] for _ in range(x)]
+    return arr
 
 class Grid:
     # Set in constructor
@@ -89,7 +101,7 @@ def __init__(
     """
     def generate_dynamic_obstacles(self, obs_count: int) -> list[list[Position]]:
         obstacle_paths = []
-        for _ in (0, obs_count):
+        for _ in range(0, obs_count):
             # Sample until a free starting space is found
             initial_position = self.sample_random_position()
             while not self.valid_obstacle_position(initial_position, 0):
@@ -234,6 +246,49 @@ def get_obstacle_positions_at_time(self, t: int) -> tuple[list[int], list[int]]:
             y_positions.append(obs_path[t].y)
         return (x_positions, y_positions)
 
+    """
+    Returns safe intervals for each cell.
+    """
+    def get_safe_intervals(self) -> np.ndarray:
+        intervals = empty_2d_array_of_lists(self.grid_size[0], self.grid_size[1])
+        for x in range(intervals.shape[0]):
+            for y in range(intervals.shape[1]):
+                intervals[x, y] = self.get_safe_intervals_at_cell(Position(x, y))
+
+        return intervals
+
+    """
+    Generate the safe intervals for a given cell. The intervals will be in order of start time.
+    ex: Interval (2, 3) will be before Interval (4, 5)
+    """
+    def get_safe_intervals_at_cell(self, cell: Position) -> list[Interval]:
+        vals = self.reservation_matrix[cell.x, cell.y, :]
+        # Find where the array is zero
+        zero_mask = (vals == 0)
+
+        # Identify transitions between zero and nonzero elements
+        diff = np.diff(zero_mask.astype(int))
+
+        # Start indices: where zeros begin (1 after a nonzero)
+        start_indices = np.where(diff == 1)[0] + 1
+
+        # End indices: where zeros stop (just before a nonzero)
+        end_indices = np.where(diff == -1)[0]
+
+        # Handle edge cases if the array starts or ends with zeros
+        if zero_mask[0]:  # If the first element is zero, add index 0 to start_indices
+            start_indices = np.insert(start_indices, 0, 0)
+        if zero_mask[-1]:  # If the last element is zero, add the last index to end_indices
+            end_indices = np.append(end_indices, len(vals) - 1)
+
+        # Create pairs of (first zero, last zero)
+        intervals = [Interval(int(start), int(end)) for start, end in zip(start_indices, end_indices)]
+
+        # Remove intervals where a cell is only free for one time step. Those intervals not provide enough time to
+        # move into and out of the cell each take 1 time step, and the cell is considered occupied during
+        # both the time step when it is entering the cell,  and the time step when it is leaving the cell.
+        intervals = [interval for interval in intervals if interval.start_time != interval.end_time]
+        return intervals
 
 show_animation = True
 
diff --git a/PathPlanning/TimeBasedPathPlanning/SafeInterval.py b/PathPlanning/TimeBasedPathPlanning/SafeInterval.py
new file mode 100644
index 0000000000..7fea10c67f
--- /dev/null
+++ b/PathPlanning/TimeBasedPathPlanning/SafeInterval.py
@@ -0,0 +1,303 @@
+"""
+Safe interval path planner
+    This script implements a safe-interval path planner for a 2d grid with dynamic obstacles. It is faster than
+    SpaceTime A* because it reduces the number of redundant node expansions by pre-computing regions of adjacent
+    time steps that are safe ("safe intervals") at each position. This allows the algorithm to skip expanding nodes
+    that are in intervals that have already been visited earlier.
+
+    Reference: https://www.cs.cmu.edu/~maxim/files/sipp_icra11.pdf
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import (
+    Grid,
+    Interval,
+    ObstacleArrangement,
+    Position,
+    empty_2d_array_of_lists,
+)
+import heapq
+import random
+from dataclasses import dataclass
+from functools import total_ordering
+import time
+
+@dataclass()
+# Note: Total_ordering is used instead of adding `order=True` to the @dataclass decorator because
+#     this class needs to override the __lt__ and __eq__ methods to ignore parent_index. The Parent
+#     index and interval member variables are just used to track the path found by the algorithm,
+#     and has no effect on the quality of a node.
+@total_ordering
+class Node:
+    position: Position
+    time: int
+    heuristic: int
+    parent_index: int
+    interval: Interval
+
+    """
+    This is what is used to drive node expansion. The node with the lowest value is expanded next.
+    This comparison prioritizes the node with the lowest cost-to-come (self.time) + cost-to-go (self.heuristic)
+    """
+    def __lt__(self, other: object):
+        if not isinstance(other, Node):
+            return NotImplementedError(f"Cannot compare Node with object of type: {type(other)}")
+        return (self.time + self.heuristic) < (other.time + other.heuristic)
+
+    """
+    Equality only cares about position and time. Heuristic and interval will always be the same for a given
+    (position, time) pairing, so they are not considered in equality.
+    """
+    def __eq__(self, other: object):
+        if not isinstance(other, Node):
+            return NotImplemented
+        return self.position == other.position and self.time == other.time
+
+@dataclass
+class EntryTimeAndInterval:
+    entry_time: int
+    interval: Interval
+
+class NodePath:
+    path: list[Node]
+    positions_at_time: dict[int, Position] = {}
+
+    def __init__(self, path: list[Node]):
+        self.path = path
+        for (i, node) in enumerate(path):
+            if i > 0:
+                # account for waiting in interval at previous node
+                prev_node = path[i-1]
+                for t in range(prev_node.time, node.time):
+                    self.positions_at_time[t] = prev_node.position
+
+            self.positions_at_time[node.time] = node.position
+
+    """
+    Get the position of the path at a given time
+    """
+    def get_position(self, time: int) -> Position | None:
+        return self.positions_at_time.get(time)
+
+    """
+    Time stamp of the last node in the path
+    """
+    def goal_reached_time(self) -> int:
+        return self.path[-1].time
+
+    def __repr__(self):
+        repr_string = ""
+        for i, node in enumerate(self.path):
+            repr_string += f"{i}: {node}\n"
+        return repr_string
+
+
+class SafeIntervalPathPlanner:
+    grid: Grid
+    start: Position
+    goal: Position
+
+    def __init__(self, grid: Grid, start: Position, goal: Position):
+        self.grid = grid
+        self.start = start
+        self.goal = goal
+
+        # Seed randomness for reproducibility
+        RANDOM_SEED = 50
+        random.seed(RANDOM_SEED)
+        np.random.seed(RANDOM_SEED)
+
+    """
+    Generate a plan given the loaded problem statement. Raises an exception if it fails to find a path.
+    Arguments:
+        verbose (bool): set to True to print debug information
+    """
+    def plan(self, verbose: bool = False) -> NodePath:
+
+        safe_intervals = self.grid.get_safe_intervals()
+
+        open_set: list[Node] = []
+        first_node_interval = safe_intervals[self.start.x, self.start.y][0]
+        heapq.heappush(
+            open_set, Node(self.start, 0, self.calculate_heuristic(self.start), -1, first_node_interval)
+        )
+
+        expanded_list: list[Node] = []
+        visited_intervals = empty_2d_array_of_lists(self.grid.grid_size[0], self.grid.grid_size[1])
+        while open_set:
+            expanded_node: Node = heapq.heappop(open_set)
+            if verbose:
+                print("Expanded node:", expanded_node)
+
+            if expanded_node.time + 1 >= self.grid.time_limit:
+                if verbose:
+                    print(f"\tSkipping node that is past time limit: {expanded_node}")
+                continue
+
+            if expanded_node.position == self.goal:
+                print(f"Found path to goal after {len(expanded_list)} expansions")
+                path = []
+                path_walker: Node = expanded_node
+                while True:
+                    path.append(path_walker)
+                    if path_walker.parent_index == -1:
+                        break
+                    path_walker = expanded_list[path_walker.parent_index]
+
+                # reverse path so it goes start -> goal
+                path.reverse()
+                return NodePath(path)
+
+            expanded_idx = len(expanded_list)
+            expanded_list.append(expanded_node)
+            entry_time_and_node = EntryTimeAndInterval(expanded_node.time, expanded_node.interval)
+            add_entry_to_visited_intervals_array(entry_time_and_node, visited_intervals, expanded_node)
+
+            for child in self.generate_successors(expanded_node, expanded_idx, safe_intervals, visited_intervals):
+                heapq.heappush(open_set, child)
+
+        raise Exception("No path found")
+
+    """
+    Generate list of possible successors of the provided `parent_node` that are worth expanding
+    """
+    def generate_successors(
+        self, parent_node: Node, parent_node_idx: int, intervals: np.ndarray, visited_intervals: np.ndarray
+    ) -> list[Node]:
+        new_nodes = []
+        diffs = [
+            Position(0, 0),
+            Position(1, 0),
+            Position(-1, 0),
+            Position(0, 1),
+            Position(0, -1),
+        ]
+        for diff in diffs:
+            new_pos = parent_node.position + diff
+            if not self.grid.inside_grid_bounds(new_pos):
+                continue
+
+            current_interval = parent_node.interval
+
+            new_cell_intervals: list[Interval] = intervals[new_pos.x, new_pos.y]
+            for interval in new_cell_intervals:
+                # if interval starts after current ends, break
+                # assumption: intervals are sorted by start time, so all future intervals will hit this condition as well
+                if interval.start_time > current_interval.end_time:
+                    break
+
+                # if interval ends before current starts, skip
+                if interval.end_time < current_interval.start_time:
+                    continue
+
+                # if we have already expanded a node in this interval with a <= starting time, skip
+                better_node_expanded = False
+                for visited in visited_intervals[new_pos.x, new_pos.y]:
+                    if interval == visited.interval and visited.entry_time <= parent_node.time + 1:
+                        better_node_expanded = True
+                        break
+                if better_node_expanded:
+                    continue
+
+                # We know there is a node worth expanding. Generate successor at the earliest possible time the
+                # new interval can be entered
+                for possible_t in range(max(parent_node.time + 1, interval.start_time), min(current_interval.end_time, interval.end_time)):
+                    if self.grid.valid_position(new_pos, possible_t):
+                        new_nodes.append(Node(
+                            new_pos,
+                            # entry is max of interval start and parent node time + 1 (get there as soon as possible)
+                            max(interval.start_time, parent_node.time + 1),
+                            self.calculate_heuristic(new_pos),
+                            parent_node_idx,
+                            interval,
+                        ))
+                        # break because all t's after this will make nodes with a higher cost, the same heuristic, and are in the same interval
+                        break
+
+        return new_nodes
+
+    """
+    Calculate the heuristic for a given position - Manhattan distance to the goal
+    """
+    def calculate_heuristic(self, position) -> int:
+        diff = self.goal - position
+        return abs(diff.x) + abs(diff.y)
+
+
+"""
+Adds a new entry to the visited intervals array. If the entry is already present, the entry time is updated if the new
+entry time is better. Otherwise, the entry is added to `visited_intervals` at the position of `expanded_node`.
+"""
+def add_entry_to_visited_intervals_array(entry_time_and_interval: EntryTimeAndInterval, visited_intervals: np.ndarray, expanded_node: Node):
+    # if entry is present, update entry time if better
+    for existing_entry_and_interval in visited_intervals[expanded_node.position.x, expanded_node.position.y]:
+        if existing_entry_and_interval.interval == entry_time_and_interval.interval:
+            existing_entry_and_interval.entry_time = min(existing_entry_and_interval.entry_time, entry_time_and_interval.entry_time)
+
+    # Otherwise, append
+    visited_intervals[expanded_node.position.x, expanded_node.position.y].append(entry_time_and_interval)
+
+
+show_animation = True
+verbose = False
+
+def main():
+    start = Position(1, 18)
+    goal = Position(19, 19)
+    grid_side_length = 21
+
+    start_time = time.time()
+
+    grid = Grid(
+        np.array([grid_side_length, grid_side_length]),
+        num_obstacles=250,
+        obstacle_avoid_points=[start, goal],
+        obstacle_arrangement=ObstacleArrangement.ARRANGEMENT1,
+        # obstacle_arrangement=ObstacleArrangement.RANDOM,
+    )
+
+    planner = SafeIntervalPathPlanner(grid, start, goal)
+    path = planner.plan(verbose)
+    runtime = time.time() - start_time
+    print(f"Planning took: {runtime:.5f} seconds")
+
+    if verbose:
+        print(f"Path: {path}")
+
+    if not show_animation:
+        return
+
+    fig = plt.figure(figsize=(10, 7))
+    ax = fig.add_subplot(
+        autoscale_on=False,
+        xlim=(0, grid.grid_size[0] - 1),
+        ylim=(0, grid.grid_size[1] - 1),
+    )
+    ax.set_aspect("equal")
+    ax.grid()
+    ax.set_xticks(np.arange(0, grid_side_length, 1))
+    ax.set_yticks(np.arange(0, grid_side_length, 1))
+
+    (start_and_goal,) = ax.plot([], [], "mD", ms=15, label="Start and Goal")
+    start_and_goal.set_data([start.x, goal.x], [start.y, goal.y])
+    (obs_points,) = ax.plot([], [], "ro", ms=15, label="Obstacles")
+    (path_points,) = ax.plot([], [], "bo", ms=10, label="Path Found")
+    ax.legend(bbox_to_anchor=(1.05, 1))
+
+    # for stopping simulation with the esc key.
+    plt.gcf().canvas.mpl_connect(
+        "key_release_event", lambda event: [exit(0) if event.key == "escape" else None]
+    )
+
+    for i in range(0, path.goal_reached_time() + 1):
+        obs_positions = grid.get_obstacle_positions_at_time(i)
+        obs_points.set_data(obs_positions[0], obs_positions[1])
+        path_position = path.get_position(i)
+        path_points.set_data([path_position.x], [path_position.y])
+        plt.pause(0.2)
+    plt.show()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst
index 48dc1289c2..04001d4504 100644
--- a/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst
+++ b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst
@@ -33,7 +33,48 @@ After::
 
 When starting at (1, 11) in the structured obstacle arrangement (second of the two gifs above).
 
-References:
+
+Safe Interval Path Planning
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The safe interval path planning algorithm is described in this paper:
+
+`SIPP: Safe Interval Path Planning for Dynamic Environments <https://www.cs.cmu.edu/~maxim/files/sipp_icra11.pdf>`__
+
+It is faster than space-time A* because it pre-computes the intervals of time that are unoccupied in each cell. This allows it to reduce the number of successor node it generates by avoiding nodes within the same interval.
+
+**Comparison with SpaceTime A*:**
+
+Arrangement 1 starting at (1, 18)
+
+SafeInterval planner::
+
+    Found path to goal after 322 expansions
+    Planning took: 0.00730 seconds
+
+SpaceTime A*::
+
+    Found path to goal after 2717154 expansions
+    Planning took: 20.51330 seconds
+
+**Benchmarking the Safe Interval Path Planner:**
+
+250 random obstacles::
+
+    Found path to goal after 764 expansions
+    Planning took: 0.60596 seconds
+
+.. image:: https://raw.githubusercontent.com/AtsushiSakai/PythonRoboticsGifs/refs/heads/master/PathPlanning/TimeBasedPathPlanning/SafeIntervalPathPlanner/path_animation.gif
+
+Arrangement 1 starting at (1, 18)::
+
+    Found path to goal after 322 expansions
+    Planning took: 0.00730 seconds
+
+.. image:: https://raw.githubusercontent.com/AtsushiSakai/PythonRoboticsGifs/refs/heads/master/PathPlanning/TimeBasedPathPlanning/SafeIntervalPathPlanner/path_animation2.gif
+
+References
 ~~~~~~~~~~~
 
 -  `Cooperative Pathfinding <https://www.davidsilver.uk/wp-content/uploads/2020/03/coop-path-AIWisdom.pdf>`__
+-  `SIPP: Safe Interval Path Planning for Dynamic Environments <https://www.cs.cmu.edu/~maxim/files/sipp_icra11.pdf>`__
diff --git a/tests/test_safe_interval_path_planner.py b/tests/test_safe_interval_path_planner.py
new file mode 100644
index 0000000000..f1fbf90d2a
--- /dev/null
+++ b/tests/test_safe_interval_path_planner.py
@@ -0,0 +1,33 @@
+from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import (
+    Grid,
+    ObstacleArrangement,
+    Position,
+)
+from PathPlanning.TimeBasedPathPlanning import SafeInterval as m
+import numpy as np
+import conftest
+
+
+def test_1():
+    start = Position(1, 11)
+    goal = Position(19, 19)
+    grid_side_length = 21
+    grid = Grid(
+        np.array([grid_side_length, grid_side_length]),
+        obstacle_arrangement=ObstacleArrangement.ARRANGEMENT1,
+    )
+
+    m.show_animation = False
+    planner = m.SafeIntervalPathPlanner(grid, start, goal)
+
+    path = planner.plan(False)
+
+    # path should have 31 entries
+    assert len(path.path) == 31
+
+    # path should end at the goal
+    assert path.path[-1].position == goal
+
+
+if __name__ == "__main__":
+    conftest.run_this_test(__file__)

From 19fc48d60fea057f6c14365c502db4d18490c3a8 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 18 Mar 2025 20:22:31 +0900
Subject: [PATCH 1003/1080] build(deps): bump numpy from 2.2.3 to 2.2.4 in
 /requirements (#1189)

Bumps [numpy](https://github.com/numpy/numpy) from 2.2.3 to 2.2.4.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v2.2.3...v2.2.4)

---
updated-dependencies:
- dependency-name: numpy
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index cf6dea3a46..3ea4197736 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 2.2.3
+numpy == 2.2.4
 scipy == 1.15.2
 matplotlib == 3.10.1
 cvxpy == 1.5.3

From 764ba21f86fc0d5c2ae5b613449ca81ea6406036 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 18 Mar 2025 20:29:12 +0900
Subject: [PATCH 1004/1080] build(deps): bump ruff from 0.9.10 to 0.11.0 in
 /requirements (#1191)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.9.10 to 0.11.0.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.9.10...0.11.0)

---
updated-dependencies:
- dependency-name: ruff
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 3ea4197736..5438678f87 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.5 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.15.0 # For unit test
-ruff == 0.9.10 # For unit test
+ruff == 0.11.0 # For unit test

From 1c1596be21f015b3b2c3db6489b93cd94d6eca01 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 28 Apr 2025 08:51:09 +0900
Subject: [PATCH 1005/1080] docs: enhance contribution guide with instructions
 for linking code in GitHub (#1202)

---
 docs/_static/img/source_link_1.png             | Bin 0 -> 87987 bytes
 docs/_static/img/source_link_2.png             | Bin 0 -> 237071 bytes
 .../3_how_to_contribute_main.rst               |  13 +++++++++++++
 3 files changed, 13 insertions(+)
 create mode 100644 docs/_static/img/source_link_1.png
 create mode 100644 docs/_static/img/source_link_2.png

diff --git a/docs/_static/img/source_link_1.png b/docs/_static/img/source_link_1.png
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HcmV?d00001

diff --git a/docs/modules/0_getting_started/3_how_to_contribute_main.rst b/docs/modules/0_getting_started/3_how_to_contribute_main.rst
index 1e61760649..30e3001f36 100644
--- a/docs/modules/0_getting_started/3_how_to_contribute_main.rst
+++ b/docs/modules/0_getting_started/3_how_to_contribute_main.rst
@@ -107,6 +107,18 @@ mathematics and the algorithm of the example.
 Documentations related codes should be in the python script as the header
 comments of the script or docstrings of each function.
 
+Also, each document should have a link to the code in Github.
+You can easily add the link by using the `.. autoclass::`, `.. autofunction::`, and `.. automodule` by Sphinx's `autodoc`_ module.
+
+Using this `autodoc`_ module, the generated documentations have the link to the code in Github like:
+
+.. image:: /_static/img/source_link_1.png
+
+When you click the link, you will jump to the source code in Github like:
+
+.. image:: /_static/img/source_link_2.png
+
+
 
 .. _`submit a pull request`:
 
@@ -210,5 +222,6 @@ Current Major Sponsors:
 .. _`1Password`: https://github.com/1Password/for-open-source
 .. _`matplotrecorder`: https://github.com/AtsushiSakai/matplotrecorder
 .. _`PythonRoboticsGifs`: https://github.com/AtsushiSakai/PythonRoboticsGifs
+.. _`autodoc`: https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html
 
 

From f466f25f5328e019bd1e0ccc62b176ff2fc72187 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Mon, 28 Apr 2025 08:53:35 +0900
Subject: [PATCH 1006/1080] build(deps): bump ruff from 0.11.0 to 0.11.6 in
 /requirements (#1201)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.0 to 0.11.6.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.11.0...0.11.6)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.11.6
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 5438678f87..f3e6c3dc51 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,4 +5,4 @@ cvxpy == 1.5.3
 pytest == 8.3.5 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.15.0 # For unit test
-ruff == 0.11.0 # For unit test
+ruff == 0.11.6 # For unit test

From af0442d358b1deca5710c0c6812424b12b39f7e7 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 1 May 2025 13:08:29 +0900
Subject: [PATCH 1007/1080] build(deps): update cvxpy version from 1.5.3 to
 1.6.5 in requirements (#1203)

* build(deps): update cvxpy version from 1.5.3 to 1.6.5 in requirements

* Add ECOS solver and improve solver handling for stability

Added ECOS to requirements and enhanced compatibility with cvxpy solvers by specifying 'order' for matrix reshaping. Updated solver configurations in rocket landing and pendulum control for consistency and reliability. Improved test behavior by enforcing stricter warning handling in pytest.
---
 .../rocket_powered_landing/rocket_powered_landing.py     | 9 +++++----
 InvertedPendulum/inverted_pendulum_mpc_control.py        | 2 +-
 requirements/requirements.txt                            | 3 ++-
 tests/conftest.py                                        | 2 +-
 4 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
index 239f3629c1..1918dc1cee 100644
--- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
+++ b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
@@ -31,6 +31,7 @@
 W_DELTA_SIGMA = 1e-1  # difference in flight time
 W_NU = 1e5  # virtual control
 
+print(cvxpy.installed_solvers())
 solver = 'ECOS'
 verbose_solver = False
 
@@ -462,11 +463,11 @@ def __init__(self, m, K):
         # x_t+1 = A_*x_t+B_*U_t+C_*U_T+1*S_*sigma+zbar+nu
         constraints += [
             self.var['X'][:, k + 1] ==
-            cvxpy.reshape(self.par['A_bar'][:, k], (m.n_x, m.n_x)) @
+            cvxpy.reshape(self.par['A_bar'][:, k], (m.n_x, m.n_x), order='F') @
             self.var['X'][:, k] +
-            cvxpy.reshape(self.par['B_bar'][:, k], (m.n_x, m.n_u)) @
+            cvxpy.reshape(self.par['B_bar'][:, k], (m.n_x, m.n_u), order='F') @
             self.var['U'][:, k] +
-            cvxpy.reshape(self.par['C_bar'][:, k], (m.n_x, m.n_u)) @
+            cvxpy.reshape(self.par['C_bar'][:, k], (m.n_x, m.n_u), order='F') @
             self.var['U'][:, k + 1] +
             self.par['S_bar'][:, k] * self.var['sigma'] +
             self.par['z_bar'][:, k] +
@@ -536,7 +537,7 @@ def solve(self, **kwargs):
             with warnings.catch_warnings():  # For User warning from solver
                 warnings.simplefilter('ignore')
                 self.prob.solve(verbose=verbose_solver,
-                            solver=solver)
+                                solver=solver)
         except cvxpy.SolverError:
             error = True
 
diff --git a/InvertedPendulum/inverted_pendulum_mpc_control.py b/InvertedPendulum/inverted_pendulum_mpc_control.py
index 9a5fa2ab41..c45dde8acc 100644
--- a/InvertedPendulum/inverted_pendulum_mpc_control.py
+++ b/InvertedPendulum/inverted_pendulum_mpc_control.py
@@ -91,7 +91,7 @@ def mpc_control(x0):
     prob = cvxpy.Problem(cvxpy.Minimize(cost), constr)
 
     start = time.time()
-    prob.solve(verbose=False)
+    prob.solve(verbose=False, solver=cvxpy.CLARABEL)
     elapsed_time = time.time() - start
     print(f"calc time:{elapsed_time:.6f} [sec]")
 
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index f3e6c3dc51..e9c68be7e6 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,8 @@
 numpy == 2.2.4
 scipy == 1.15.2
 matplotlib == 3.10.1
-cvxpy == 1.5.3
+cvxpy == 1.6.5
+ecos == 2.0.14
 pytest == 8.3.5 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.15.0 # For unit test
diff --git a/tests/conftest.py b/tests/conftest.py
index 3485fe8150..b707b22d00 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -10,4 +10,4 @@
 
 
 def run_this_test(file):
-    pytest.main([os.path.abspath(file)])
+    pytest.main(args=["-W", "error", "-Werror", "--pythonwarnings=error", os.path.abspath(file)])

From 1f729cb8cbd5075871bb52c8fb31df2732ad6641 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 1 May 2025 13:59:15 +0900
Subject: [PATCH 1008/1080] build(deps): bump ruff from 0.11.6 to 0.11.7 in
 /requirements (#1205)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.6 to 0.11.7.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.11.6...0.11.7)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.11.7
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index e9c68be7e6..88cbb21f81 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.3.5 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.15.0 # For unit test
-ruff == 0.11.6 # For unit test
+ruff == 0.11.7 # For unit test

From a2c42c3d6837658edb734e9f16ab64c1bd2662e7 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Thu, 1 May 2025 21:53:12 +0900
Subject: [PATCH 1009/1080] Update Python version to 3.13 across the project
 (#1208)

* Update Python version to 3.13 across the project

Upgraded the required Python version to 3.13 in configurations, CI workflows, documentation, and environment files. This ensures compatibility with the latest Python release and maintains consistency across all project components.

* Update Python version to 3.13 across the project

Upgraded the required Python version to 3.13 in configurations, CI workflows, documentation, and environment files. This ensures compatibility with the latest Python release and maintains consistency across all project components.
---
 .circleci/config.yml                                        | 2 +-
 .github/workflows/Linux_CI.yml                              | 2 +-
 .github/workflows/MacOS_CI.yml                              | 2 +-
 .github/workflows/Windows_CI.yml                            | 2 +-
 README.md                                                   | 2 +-
 appveyor.yml                                                | 2 +-
 docs/modules/0_getting_started/3_how_to_contribute_main.rst | 2 +-
 requirements/environment.yml                                | 2 +-
 ruff.toml                                                   | 4 ++--
 9 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 9f803ece4a..f6eff674de 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -6,7 +6,7 @@ orbs:
 jobs:
   build_doc:
     docker:
-      - image: cimg/python:3.12
+      - image: cimg/python:3.13
     steps:
       - checkout
       - run:
diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index 7b3dc14751..907d36452d 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ '3.12' ]
+        python-version: [ '3.13' ]
 
     name: Python ${{ matrix.python-version }} CI
 
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index 5ea15ac72e..03db65f43d 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -16,7 +16,7 @@ jobs:
     runs-on: macos-latest
     strategy:
       matrix:
-        python-version: [ '3.12' ]
+        python-version: [ '3.13' ]
     name: Python ${{ matrix.python-version }} CI
     steps:
       - uses: actions/checkout@v4
diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml
index b9c8dea649..5cb19699b2 100644
--- a/.github/workflows/Windows_CI.yml
+++ b/.github/workflows/Windows_CI.yml
@@ -16,7 +16,7 @@ jobs:
     runs-on: windows-latest
     strategy:
       matrix:
-        python-version: [ '3.12' ]
+        python-version: [ '3.13' ]
     name: Python ${{ matrix.python-version }} CI
     steps:
       - uses: actions/checkout@v4
diff --git a/README.md b/README.md
index 9e605435ce..6d04c90bc8 100644
--- a/README.md
+++ b/README.md
@@ -102,7 +102,7 @@ or this paper for more details:
 
 For running each sample code:
 
-- [Python 3.12.x](https://www.python.org/)
+- [Python 3.13.x](https://www.python.org/)
  
 - [NumPy](https://numpy.org/)
  
diff --git a/appveyor.yml b/appveyor.yml
index 05ad8a2820..72d89acf11 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -8,7 +8,7 @@ environment:
     CMD_IN_ENV: "cmd /E:ON /V:ON /C .\\appveyor\\run_with_env.cmd"
 
   matrix:
-     - PYTHON_DIR: C:\Python310-x64
+     - PYTHON_DIR: C:\Python313-x64
 
 branches:
   only:
diff --git a/docs/modules/0_getting_started/3_how_to_contribute_main.rst b/docs/modules/0_getting_started/3_how_to_contribute_main.rst
index 30e3001f36..0325aaacae 100644
--- a/docs/modules/0_getting_started/3_how_to_contribute_main.rst
+++ b/docs/modules/0_getting_started/3_how_to_contribute_main.rst
@@ -26,7 +26,7 @@ to understand the philosophies of this project.
 Check your Python version.
 ---------------------------
 
-We only accept a PR for Python 3.12.x or higher.
+We only accept a PR for Python 3.13.x or higher.
 
 We will not accept a PR for Python 2.x.
 
diff --git a/requirements/environment.yml b/requirements/environment.yml
index afbb3fb8ce..023a3d75bf 100644
--- a/requirements/environment.yml
+++ b/requirements/environment.yml
@@ -2,7 +2,7 @@ name: python_robotics
 channels:
   - conda-forge
 dependencies:
-  - python=3.12
+  - python=3.13
   - pip
   - scipy
   - numpy
diff --git a/ruff.toml b/ruff.toml
index 5823ca3db7..d76b715a06 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -5,8 +5,8 @@ ignore = ["E501", "E741", "E402"]
 exclude = [
 ]
 
-# Assume Python 3.11
-target-version = "py312"
+# Assume Python 3.13
+target-version = "py313"
 
 [per-file-ignores]
 

From 22cbee49218a1f8acb07ad488416db3c952c19d6 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Fri, 2 May 2025 10:01:19 +0900
Subject: [PATCH 1010/1080] Standardize "Ref:" to "Reference" across files
 (#1210)

Updated all instances of "Ref:" to "Reference" for consistency in both code and documentation. This change improves clarity and aligns with standard terminology practices.
---
 .../rocket_powered_landing.py                 |   2 +-
 .../two_joint_arm_to_point_control.py         |   2 +-
 .../ensemble_kalman_filter.py                 |   2 +-
 Mapping/DistanceMap/distance_map.py           |   2 +-
 .../rectangle_fitting/rectangle_fitting.py    |   2 +-
 MissionPlanning/BehaviorTree/behavior_tree.py |   2 +-
 MissionPlanning/StateMachine/state_machine.py |   4 +-
 PathPlanning/ElasticBands/elastic_bands.py    |   2 +-
 .../Eta3SplinePath/eta3_spline_path.py        |   2 +-
 .../cartesian_frenet_converter.py             |   2 +-
 .../frenet_optimal_trajectory.py              |   2 +-
 .../potential_field_planning.py               |   2 +-
 .../quintic_polynomials_planner.py            |   2 +-
 .../state_lattice_planner.py                  |   2 +-
 PathTracking/cgmres_nmpc/cgmres_nmpc.py       |   2 +-
 PathTracking/move_to_pose/move_to_pose.py     |   2 +-
 .../stanley_controller/stanley_controller.py  |   2 +-
 README.md                                     |  42 ++---
 .../Kalmanfilter_basics_2_main.rst            |   2 +-
 .../12_appendix/Kalmanfilter_basics_main.rst  |   2 +-
 .../steering_motion_model_main.rst            |   2 +-
 ...samble_kalman_filter_localization_main.rst |   5 +
 ...tended_kalman_filter_localization_main.rst | 168 +++++++++---------
 .../histogram_filter_localization_main.rst    |   7 +-
 .../particle_filter_localization_main.rst     |   8 +-
 ...cented_kalman_filter_localization_main.rst |   8 +-
 .../modules/4_slam/ekf_slam/ekf_slam_main.rst |   2 +-
 .../4_slam/graph_slam/graph_slam_main.rst     |   2 +-
 .../bezier_path/bezier_path_main.rst          |   2 +-
 .../eta3_spline/eta3_spline_main.rst          |   2 +-
 .../frenet_frame_path_main.rst                |   2 +-
 .../grid_base_search_main.rst                 |   6 +-
 ...l_predictive_trajectory_generator_main.rst |   2 +-
 .../prm_planner/prm_planner_main.rst          |   2 +-
 .../quintic_polynomials_planner_main.rst      |   2 +-
 .../reeds_shepp_path_main.rst                 |   2 +-
 docs/modules/5_path_planning/rrt/rrt_main.rst |   8 +-
 .../state_lattice_planner_main.rst            |   2 +-
 .../vrm_planner/vrm_planner_main.rst          |   2 +-
 .../lqr_speed_and_steering_control_main.rst   |   2 +-
 .../lqr_steering_control_main.rst             |   2 +-
 .../pure_pursuit_tracking_main.rst            |   2 +-
 .../rear_wheel_feedback_control_main.rst      |   2 +-
 .../stanley_control/stanley_control_main.rst  |   2 +-
 users_comments.md                             |   2 +-
 45 files changed, 173 insertions(+), 155 deletions(-)

diff --git a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
index 1918dc1cee..e8ba8fa220 100644
--- a/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
+++ b/AerialNavigation/rocket_powered_landing/rocket_powered_landing.py
@@ -5,7 +5,7 @@
 author: Sven Niederberger
         Atsushi Sakai
 
-Ref:
+Reference:
 - Python implementation of 'Successive Convexification for 6-DoF Mars Rocket Powered Landing with Free-Final-Time' paper
 by Michael Szmuk and Behcet Acıkmese.
 
diff --git a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
index c2227f18e3..09969c30be 100644
--- a/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
+++ b/ArmNavigation/two_joint_arm_to_point_control/two_joint_arm_to_point_control.py
@@ -5,7 +5,7 @@
 Author: Daniel Ingram (daniel-s-ingram)
         Atsushi Sakai (@Atsushi_twi)
 
-Ref: P. I. Corke, "Robotics, Vision & Control", Springer 2017,
+Reference: P. I. Corke, "Robotics, Vision & Control", Springer 2017,
  ISBN 978-3-319-54413-7 p102
 - [Robotics, Vision and Control]
 (https://link.springer.com/book/10.1007/978-3-642-20144-8)
diff --git a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
index 2bab3b49c1..e8a988a270 100644
--- a/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
+++ b/Localization/ensemble_kalman_filter/ensemble_kalman_filter.py
@@ -4,7 +4,7 @@
 
 author: Ryohei Sasaki(rsasaki0109)
 
-Ref:
+Reference:
 Ensemble Kalman filtering
 (https://rmets.onlinelibrary.wiley.com/doi/10.1256/qj.05.135)
 
diff --git a/Mapping/DistanceMap/distance_map.py b/Mapping/DistanceMap/distance_map.py
index 0dcc7380c5..0f96c9e8c6 100644
--- a/Mapping/DistanceMap/distance_map.py
+++ b/Mapping/DistanceMap/distance_map.py
@@ -3,7 +3,7 @@
 
 author: Wang Zheng (@Aglargil)
 
-Ref:
+Reference:
 
 - [Distance Map]
 (https://cs.brown.edu/people/pfelzens/papers/dt-final.pdf)
diff --git a/Mapping/rectangle_fitting/rectangle_fitting.py b/Mapping/rectangle_fitting/rectangle_fitting.py
index 177f078871..7902619666 100644
--- a/Mapping/rectangle_fitting/rectangle_fitting.py
+++ b/Mapping/rectangle_fitting/rectangle_fitting.py
@@ -4,7 +4,7 @@
 
 author: Atsushi Sakai (@Atsushi_twi)
 
-Ref:
+Reference:
 - Efficient L-Shape Fitting for Vehicle Detection Using Laser Scanners -
 The Robotics Institute Carnegie Mellon University
 https://www.ri.cmu.edu/publications/
diff --git a/MissionPlanning/BehaviorTree/behavior_tree.py b/MissionPlanning/BehaviorTree/behavior_tree.py
index 59f4c713f1..9ad886aafb 100644
--- a/MissionPlanning/BehaviorTree/behavior_tree.py
+++ b/MissionPlanning/BehaviorTree/behavior_tree.py
@@ -3,7 +3,7 @@
 
 author: Wang Zheng (@Aglargil)
 
-Ref:
+Reference:
 
 - [Behavior Tree](https://en.wikipedia.org/wiki/Behavior_tree_(artificial_intelligence,_robotics_and_control))
 """
diff --git a/MissionPlanning/StateMachine/state_machine.py b/MissionPlanning/StateMachine/state_machine.py
index de72f0f451..454759236e 100644
--- a/MissionPlanning/StateMachine/state_machine.py
+++ b/MissionPlanning/StateMachine/state_machine.py
@@ -3,7 +3,7 @@
 
 author: Wang Zheng (@Aglargil)
 
-Ref:
+Reference:
 
 - [State Machine]
 (https://en.wikipedia.org/wiki/Finite-state_machine)
@@ -23,7 +23,7 @@ def deflate_and_encode(plantuml_text):
     """
     zlib compress the plantuml text and encode it for the plantuml server.
 
-    Ref: https://plantuml.com/en/text-encoding
+    Reference https://plantuml.com/en/text-encoding
     """
     plantuml_alphabet = (
         string.digits + string.ascii_uppercase + string.ascii_lowercase + "-_"
diff --git a/PathPlanning/ElasticBands/elastic_bands.py b/PathPlanning/ElasticBands/elastic_bands.py
index 785f822d14..77d4e6e399 100644
--- a/PathPlanning/ElasticBands/elastic_bands.py
+++ b/PathPlanning/ElasticBands/elastic_bands.py
@@ -3,7 +3,7 @@
 
 author: Wang Zheng (@Aglargil)
 
-Ref:
+Reference:
 
 - [Elastic Bands: Connecting Path Planning and Control]
 (http://www8.cs.umu.se/research/ifor/dl/Control/elastic%20bands.pdf)
diff --git a/PathPlanning/Eta3SplinePath/eta3_spline_path.py b/PathPlanning/Eta3SplinePath/eta3_spline_path.py
index dc07d3c84b..3f685e512f 100644
--- a/PathPlanning/Eta3SplinePath/eta3_spline_path.py
+++ b/PathPlanning/Eta3SplinePath/eta3_spline_path.py
@@ -5,7 +5,7 @@
 author: Joe Dinius, Ph.D (https://jwdinius.github.io)
         Atsushi Sakai (@Atsushi_twi)
 
-Ref:
+Reference:
 - [eta^3-Splines for the Smooth Path Generation of Wheeled Mobile Robots]
 (https://ieeexplore.ieee.org/document/4339545/)
 
diff --git a/PathPlanning/FrenetOptimalTrajectory/cartesian_frenet_converter.py b/PathPlanning/FrenetOptimalTrajectory/cartesian_frenet_converter.py
index 4cc8650c89..482712ceaf 100644
--- a/PathPlanning/FrenetOptimalTrajectory/cartesian_frenet_converter.py
+++ b/PathPlanning/FrenetOptimalTrajectory/cartesian_frenet_converter.py
@@ -4,7 +4,7 @@
 
 author: Wang Zheng (@Aglargil)
 
-Ref:
+Reference:
 
 - [Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame]
 (https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf)
diff --git a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
index 248894c1c6..4b82fb70fd 100644
--- a/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
+++ b/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py
@@ -4,7 +4,7 @@
 
 author: Atsushi Sakai (@Atsushi_twi)
 
-Ref:
+Reference:
 
 - [Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame]
 (https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf)
diff --git a/PathPlanning/PotentialFieldPlanning/potential_field_planning.py b/PathPlanning/PotentialFieldPlanning/potential_field_planning.py
index 8f136b5ee3..603a9d16cf 100644
--- a/PathPlanning/PotentialFieldPlanning/potential_field_planning.py
+++ b/PathPlanning/PotentialFieldPlanning/potential_field_planning.py
@@ -4,7 +4,7 @@
 
 author: Atsushi Sakai (@Atsushi_twi)
 
-Ref:
+Reference:
 https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf
 
 """
diff --git a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
index fdc181afab..86f9f662da 100644
--- a/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
+++ b/PathPlanning/QuinticPolynomialsPlanner/quintic_polynomials_planner.py
@@ -4,7 +4,7 @@
 
 author: Atsushi Sakai (@Atsushi_twi)
 
-Ref:
+Reference:
 
 - [Local Path planning And Motion Control For Agv In Positioning](https://ieeexplore.ieee.org/document/637936/)
 
diff --git a/PathPlanning/StateLatticePlanner/state_lattice_planner.py b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
index 7f8e725e0a..05f8df78f8 100644
--- a/PathPlanning/StateLatticePlanner/state_lattice_planner.py
+++ b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
@@ -8,7 +8,7 @@
 https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning
 /ModelPredictiveTrajectoryGenerator/lookup_table_generator.py
 
-Ref:
+Reference:
 
 - State Space Sampling of Feasible Motions for High-Performance Mobile Robot
 Navigation in Complex Environments
diff --git a/PathTracking/cgmres_nmpc/cgmres_nmpc.py b/PathTracking/cgmres_nmpc/cgmres_nmpc.py
index a582c9da81..ee40e73504 100644
--- a/PathTracking/cgmres_nmpc/cgmres_nmpc.py
+++ b/PathTracking/cgmres_nmpc/cgmres_nmpc.py
@@ -4,7 +4,7 @@
 
 author Atsushi Sakai (@Atsushi_twi)
 
-Ref:
+Reference:
 Shunichi09/nonlinear_control: Implementing the nonlinear model predictive
 control, sliding mode control https://github.com/Shunichi09/PythonLinearNonlinearControl
 
diff --git a/PathTracking/move_to_pose/move_to_pose.py b/PathTracking/move_to_pose/move_to_pose.py
index 34736a2e21..faf1264953 100644
--- a/PathTracking/move_to_pose/move_to_pose.py
+++ b/PathTracking/move_to_pose/move_to_pose.py
@@ -76,7 +76,7 @@ def calc_control_command(self, x_diff, y_diff, theta, theta_goal):
         # [-pi, pi] to prevent unstable behavior e.g. difference going
         # from 0 rad to 2*pi rad with slight turn
 
-        # Ref: The velocity v always has a constant sign which depends on the initial value of α.
+        # The velocity v always has a constant sign which depends on the initial value of α.
         rho = np.hypot(x_diff, y_diff)
         v = self.Kp_rho * rho
 
diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_controller/stanley_controller.py
index bc98175f17..01c2ec0229 100644
--- a/PathTracking/stanley_controller/stanley_controller.py
+++ b/PathTracking/stanley_controller/stanley_controller.py
@@ -4,7 +4,7 @@
 
 author: Atsushi Sakai (@Atsushi_twi)
 
-Ref:
+Reference:
     - [Stanley: The robot that won the DARPA grand challenge](http://isl.ecst.csuchico.edu/DOCS/darpa2005/DARPA%202005%20Stanley.pdf)
     - [Autonomous Automobile Path Tracking](https://www.ri.cmu.edu/pub_files/2009/2/Automatic_Steering_Methods_for_Autonomous_Automobile_Path_Tracking.pdf)
 
diff --git a/README.md b/README.md
index 6d04c90bc8..05fd0262df 100644
--- a/README.md
+++ b/README.md
@@ -168,7 +168,7 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation
 
 <img src="https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/extended_kalman_filter/animation.gif" width="640" alt="EKF pic">
 
-Ref:
+Reference
 
 - [documentation](https://atsushisakai.github.io/PythonRobotics/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.html)
 
@@ -186,7 +186,7 @@ It is assumed that the robot can measure a distance from landmarks (RFID).
 
 These measurements are used for PF localization.
 
-Ref:
+Reference
 
 - [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
 
@@ -207,7 +207,7 @@ The filter integrates speed input and range observations from RFID for localizat
 
 Initial position is not needed.
 
-Ref:
+Reference
 
 - [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
 
@@ -256,7 +256,7 @@ It can calculate a rotation matrix, and a translation vector between points and
 
 ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/iterative_closest_point/animation.gif)
 
-Ref:
+Reference
 
 - [Introduction to Mobile Robotics: Iterative Closest Point Algorithm](https://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf)
 
@@ -275,7 +275,7 @@ Black points are landmarks, blue crosses are estimated landmark positions by Fas
 ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif)
 
 
-Ref:
+Reference
 
 - [PROBABILISTIC ROBOTICS](http://www.probabilistic-robotics.org/)
 
@@ -321,7 +321,7 @@ This is a 2D grid based the shortest path planning with D star algorithm.
 
 The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm.
 
-Ref:
+Reference
 
 - [D* Algorithm Wikipedia](https://en.wikipedia.org/wiki/D*)
 
@@ -346,7 +346,7 @@ This is a 2D grid based path planning with Potential Field algorithm.
 
 In the animation, the blue heat map shows potential value on each grid.
 
-Ref:
+Reference
 
 - [Robotic Motion Planning:Potential Functions](https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf)
 
@@ -362,7 +362,7 @@ This script is a path planning code with state lattice planning.
 
 This code uses the model predictive trajectory generator to solve boundary problem.
 
-Ref: 
+Reference 
 
 - [Optimal rough terrain trajectory generation for wheeled mobile robots](https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328)
 
@@ -390,7 +390,7 @@ Cyan crosses means searched points with Dijkstra method,
 
 The red line is the final path of PRM.
 
-Ref:
+Reference
 
 - [Probabilistic roadmap \- Wikipedia](https://en.wikipedia.org/wiki/Probabilistic_roadmap)
 
@@ -406,7 +406,7 @@ This is a path planning code with RRT\*
 
 Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions.
 
-Ref:
+Reference
 
 - [Incremental Sampling-based Algorithms for Optimal Motion Planning](https://arxiv.org/abs/1005.0416)
 
@@ -426,7 +426,7 @@ A double integrator motion model is used for LQR local planner.
 
 ![LQR_RRT](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif)
 
-Ref:
+Reference
 
 - [LQR\-RRT\*: Optimal Sampling\-Based Motion Planning with Automatically Derived Extension Heuristics](https://lis.csail.mit.edu/pubs/perez-icra12.pdf)
 
@@ -441,7 +441,7 @@ Motion planning with quintic polynomials.
 
 It can calculate a 2D path, velocity, and acceleration profile based on quintic polynomials.
 
-Ref:
+Reference
 
 - [Local Path Planning And Motion Control For Agv In Positioning](https://ieeexplore.ieee.org/document/637936/)
 
@@ -451,7 +451,7 @@ A sample code with Reeds Shepp path planning.
 
 ![RSPlanning](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true)
 
-Ref:
+Reference
 
 - [15.3.2 Reeds\-Shepp Curves](http://planning.cs.uiuc.edu/node822.html) 
 
@@ -477,7 +477,7 @@ The cyan line is the target course and black crosses are obstacles.
 
 The red line is the predicted path.
 
-Ref:
+Reference
 
 - [Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame](https://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf)
 
@@ -492,7 +492,7 @@ This is a simulation of moving to a pose control
 
 ![2](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif)
 
-Ref:
+Reference
 
 - [P. I. Corke, "Robotics, Vision and Control" \| SpringerLink p102](https://link.springer.com/book/10.1007/978-3-642-20144-8)
 
@@ -503,7 +503,7 @@ Path tracking simulation with Stanley steering control and PID speed control.
 
 ![2](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif)
 
-Ref:
+Reference
 
 - [Stanley: The robot that won the DARPA grand challenge](http://robots.stanford.edu/papers/thrun.stanley05.pdf)
 
@@ -517,7 +517,7 @@ Path tracking simulation with rear wheel feedback steering control and PID speed
 
 ![PythonRobotics/figure_1.png at master · AtsushiSakai/PythonRobotics](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif)
 
-Ref:
+Reference
 
 - [A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles](https://arxiv.org/abs/1604.07446)
 
@@ -528,7 +528,7 @@ Path tracking simulation with LQR speed and steering control.
 
 ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif)
 
-Ref:
+Reference
 
 - [Towards fully autonomous driving: Systems and algorithms \- IEEE Conference Publication](https://ieeexplore.ieee.org/document/5940562/)
 
@@ -539,7 +539,7 @@ Path tracking simulation with iterative linear model predictive speed and steeri
 
 <img src="https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/model_predictive_speed_and_steer_control/animation.gif" width="640" alt="MPC pic">
 
-Ref:
+Reference
 
 - [documentation](https://atsushisakai.github.io/PythonRobotics/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.html)
 
@@ -551,7 +551,7 @@ A motion planning and path tracking simulation with NMPC of C-GMRES
 
 ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/cgmres_nmpc/animation.gif)
 
-Ref:
+Reference
 
 - [documentation](https://atsushisakai.github.io/PythonRobotics/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.html)
 
@@ -591,7 +591,7 @@ This is a 3d trajectory generation simulation for a rocket powered landing.
 
 ![3](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/rocket_powered_landing/animation.gif)
 
-Ref:
+Reference
 
 - [documentation](https://atsushisakai.github.io/PythonRobotics/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing.html)
 
diff --git a/docs/modules/12_appendix/Kalmanfilter_basics_2_main.rst b/docs/modules/12_appendix/Kalmanfilter_basics_2_main.rst
index 9ae6fc5bcb..b7ff84e6f6 100644
--- a/docs/modules/12_appendix/Kalmanfilter_basics_2_main.rst
+++ b/docs/modules/12_appendix/Kalmanfilter_basics_2_main.rst
@@ -331,7 +331,7 @@ are vectors and matrices, but the concepts are exactly the same:
 -  Use the process model to predict the next state (the prior)
 -  Form an estimate part way between the measurement and the prior
 
-References:
+Reference
 ~~~~~~~~~~~
 
 1. Roger Labbe’s
diff --git a/docs/modules/12_appendix/Kalmanfilter_basics_main.rst b/docs/modules/12_appendix/Kalmanfilter_basics_main.rst
index 6548377e07..a1d99d47ef 100644
--- a/docs/modules/12_appendix/Kalmanfilter_basics_main.rst
+++ b/docs/modules/12_appendix/Kalmanfilter_basics_main.rst
@@ -552,7 +552,7 @@ a given (X,Y) value.
 .. image:: Kalmanfilter_basics_files/Kalmanfilter_basics_28_1.png
 
 
-References:
+Reference
 ~~~~~~~~~~~
 
 1. Roger Labbe’s
diff --git a/docs/modules/12_appendix/steering_motion_model_main.rst b/docs/modules/12_appendix/steering_motion_model_main.rst
index 6e444b7909..c697123fa2 100644
--- a/docs/modules/12_appendix/steering_motion_model_main.rst
+++ b/docs/modules/12_appendix/steering_motion_model_main.rst
@@ -91,7 +91,7 @@ the target minimum velocity :math:`v_{min}` can be calculated as follows:
 :math:`v_{min} = \frac{d_{t+1}+d_{t}}{\Delta t} = \frac{d_{t+1}+d_{t}}{(\kappa_{t+1}-\kappa_{t})}\frac{tan\dot{\delta}_{max}}{WB}`
 
 
-References:
+Reference
 ~~~~~~~~~~~
 
 - `Vehicle Dynamics and Control <https://link.springer.com/book/10.1007/978-1-4614-1433-9>`_
diff --git a/docs/modules/2_localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst b/docs/modules/2_localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst
index 2543d1186a..214e645d10 100644
--- a/docs/modules/2_localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst
+++ b/docs/modules/2_localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst
@@ -5,3 +5,8 @@ Ensamble Kalman Filter Localization
 
 This is a sensor fusion localization with Ensamble Kalman Filter(EnKF).
 
+Code Link
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: Localization.ensemble_kalman_filter.ensemble_kalman_filter.enkf_localization
+
diff --git a/docs/modules/2_localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst b/docs/modules/2_localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst
index da214b6de5..adb41e5e40 100644
--- a/docs/modules/2_localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst
+++ b/docs/modules/2_localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization_main.rst
@@ -23,28 +23,40 @@ is estimated trajectory with EKF.
 
 The red ellipse is estimated covariance ellipse with EKF.
 
-Code: `PythonRobotics/extended_kalman_filter.py at master ·
-AtsushiSakai/PythonRobotics <https://github.com/AtsushiSakai/PythonRobotics/blob/master/Localization/extended_kalman_filter/extended_kalman_filter.py>`__
+Code Link
+~~~~~~~~~~~~~
 
-Kalman Filter with Speed Scale Factor Correction
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+.. autofunction:: Localization.extended_kalman_filter.extended_kalman_filter.ekf_estimation
 
+Extended Kalman Filter algorithm
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. image:: ekf_with_velocity_correction_1_0.png
-   :width: 600px
+Localization process using Extended Kalman Filter:EKF is
 
-This is a Extended kalman filter (EKF) localization with velocity correction.
+=== Predict ===
 
-This is for correcting the vehicle speed measured with scale factor errors due to factors such as wheel wear.
+:math:`x_{Pred} = Fx_t+Bu_t`
 
-Code: `PythonRobotics/extended_kalman_ekf_with_velocity_correctionfilter.py
-AtsushiSakai/PythonRobotics <https://github.com/AtsushiSakai/PythonRobotics/blob/master/Localization/extended_kalman_filter/extended_kalman_ekf_with_velocity_correctionfilter.py>`__
+:math:`P_{Pred} = J_f P_t J_f^T + Q`
 
-Filter design
-~~~~~~~~~~~~~
+=== Update ===
 
-Position Estimation Kalman Filter
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+:math:`z_{Pred} = Hx_{Pred}`
+
+:math:`y = z - z_{Pred}`
+
+:math:`S = J_g P_{Pred}.J_g^T + R`
+
+:math:`K = P_{Pred}.J_g^T S^{-1}`
+
+:math:`x_{t+1} = x_{Pred} + Ky`
+
+:math:`P_{t+1} = ( I - K J_g) P_{Pred}`
+
+
+
+Filter design
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 In this simulation, the robot has a state vector includes 4 states at
 time :math:`t`.
@@ -82,27 +94,9 @@ In the code, “observation” function generates the input and observation
 vector with noise
 `code <https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_filter.py#L34-L50>`__
 
-Kalman Filter with Speed Scale Factor Correction
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-In this simulation, the robot has a state vector includes 5 states at
-time :math:`t`.
-
-.. math:: \textbf{x}_t=[x_t, y_t, \phi_t, v_t, s_t]
-
-x, y are a 2D x-y position, :math:`\phi` is orientation, v is
-velocity, and s is a scale factor of velocity.
-
-In the code, “xEst” means the state vector.
-`code <https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_ekf_with_velocity_correctionfilter.py#L163>`__
-
-The rest is the same as the Position Estimation Kalman Filter.
 
 Motion Model
-~~~~~~~~~~~~
-
-Position Estimation Kalman Filter
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+~~~~~~~~~~~~~~~~~
 
 The robot model is
 
@@ -137,9 +131,61 @@ Its Jacobian matrix is
 
 :math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & -v \sin(\phi) \Delta t & \cos(\phi) \Delta t\\ 0 & 1 & v \cos(\phi) \Delta t & \sin(\phi) \Delta t\\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 1 \end{bmatrix} \end{equation*}`
 
+Observation Model
+~~~~~~~~~~~~~~~~~
+
+The robot can get x-y position information from GPS.
+
+So GPS Observation model is
+
+.. math:: \textbf{z}_{t} = g(\textbf{x}_t) = H \textbf{x}_t
+
+where
+
+:math:`\begin{equation*} H = \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ \end{bmatrix} \end{equation*}`
+
+The observation function states that
+
+:math:`\begin{equation*} \begin{bmatrix} x' \\ y' \end{bmatrix} = g(\textbf{x}) = \begin{bmatrix} x \\ y \end{bmatrix} \end{equation*}`
+
+Its Jacobian matrix is
+
+:math:`\begin{equation*} J_g = \begin{bmatrix} \frac{\partial x'}{\partial x} & \frac{\partial x'}{\partial y} & \frac{\partial x'}{\partial \phi} & \frac{\partial x'}{\partial v}\\ \frac{\partial y'}{\partial x}& \frac{\partial y'}{\partial y} & \frac{\partial y'}{\partial \phi} & \frac{\partial y'}{ \partial v}\\ \end{bmatrix} \end{equation*}`
+
+:math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ \end{bmatrix} \end{equation*}`
+
 
 Kalman Filter with Speed Scale Factor Correction
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+This is a Extended kalman filter (EKF) localization with velocity correction.
+
+This is for correcting the vehicle speed measured with scale factor errors due to factors such as wheel wear.
+
+
+In this simulation, the robot has a state vector includes 5 states at
+time :math:`t`.
+
+.. math:: \textbf{x}_t=[x_t, y_t, \phi_t, v_t, s_t]
+
+x, y are a 2D x-y position, :math:`\phi` is orientation, v is
+velocity, and s is a scale factor of velocity.
+
+In the code, “xEst” means the state vector.
+`code <https://github.com/AtsushiSakai/PythonRobotics/blob/916b4382de090de29f54538b356cef1c811aacce/Localization/extended_kalman_filter/extended_kalman_ekf_with_velocity_correctionfilter.py#L163>`__
+
+The rest is the same as the Position Estimation Kalman Filter.
+
+.. image:: ekf_with_velocity_correction_1_0.png
+   :width: 600px
+
+Code Link
+~~~~~~~~~~~~~
+
+.. autofunction:: Localization.extended_kalman_filter.ekf_with_velocity_correction.ekf_estimation
+
+
+Motion Model
+~~~~~~~~~~~~
 
 The robot model is
 
@@ -178,33 +224,7 @@ Its Jacobian matrix is
 Observation Model
 ~~~~~~~~~~~~~~~~~
 
-Position Estimation Kalman Filter
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-The robot can get x-y position infomation from GPS.
-
-So GPS Observation model is
-
-.. math:: \textbf{z}_{t} = g(\textbf{x}_t) = H \textbf{x}_t
-
-where
-
-:math:`\begin{equation*} H = \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ \end{bmatrix} \end{equation*}`
-
-The observation function states that
-
-:math:`\begin{equation*} \begin{bmatrix} x' \\ y' \end{bmatrix} = g(\textbf{x}) = \begin{bmatrix} x \\ y \end{bmatrix} \end{equation*}`
-
-Its Jacobian matrix is
-
-:math:`\begin{equation*} J_g = \begin{bmatrix} \frac{\partial x'}{\partial x} & \frac{\partial x'}{\partial y} & \frac{\partial x'}{\partial \phi} & \frac{\partial x'}{\partial v}\\ \frac{\partial y'}{\partial x}& \frac{\partial y'}{\partial y} & \frac{\partial y'}{\partial \phi} & \frac{\partial y'}{ \partial v}\\ \end{bmatrix} \end{equation*}`
-
-:math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ \end{bmatrix} \end{equation*}`
-
-Kalman Filter with Speed Scale Factor Correction
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-The robot can get x-y position infomation from GPS.
+The robot can get x-y position information from GPS.
 
 So GPS Observation model is
 
@@ -225,32 +245,8 @@ Its Jacobian matrix is
 :math:`\begin{equation*} 　= \begin{bmatrix} 1& 0 & 0 & 0 & 0\\ 0 & 1 & 0 & 0 & 0\\ \end{bmatrix} \end{equation*}`
 
 
-Extended Kalman Filter
-~~~~~~~~~~~~~~~~~~~~~~
-
-Localization process using Extended Kalman Filter:EKF is
-
-=== Predict ===
-
-:math:`x_{Pred} = Fx_t+Bu_t`
-
-:math:`P_{Pred} = J_f P_t J_f^T + Q`
-
-=== Update ===
-
-:math:`z_{Pred} = Hx_{Pred}`
-
-:math:`y = z - z_{Pred}`
-
-:math:`S = J_g P_{Pred}.J_g^T + R`
-
-:math:`K = P_{Pred}.J_g^T S^{-1}`
-
-:math:`x_{t+1} = x_{Pred} + Ky`
-
-:math:`P_{t+1} = ( I - K J_g) P_{Pred}`
 
-Ref:
-~~~~
+Reference
+^^^^^^^^^^^
 
 -  `PROBABILISTIC-ROBOTICS.ORG <http://www.probabilistic-robotics.org/>`__
diff --git a/docs/modules/2_localization/histogram_filter_localization/histogram_filter_localization_main.rst b/docs/modules/2_localization/histogram_filter_localization/histogram_filter_localization_main.rst
index fafd578333..3a175b1316 100644
--- a/docs/modules/2_localization/histogram_filter_localization/histogram_filter_localization_main.rst
+++ b/docs/modules/2_localization/histogram_filter_localization/histogram_filter_localization_main.rst
@@ -16,6 +16,11 @@ The filter uses speed input and range observations from RFID for localization.
 
 Initial position information is not needed.
 
+Code Link
+~~~~~~~~~~~~~
+
+.. autofunction:: Localization.histogram_filter.histogram_filter.histogram_filter_localization
+
 Filtering algorithm
 ~~~~~~~~~~~~~~~~~~~~
 
@@ -107,7 +112,7 @@ There are two ways to calculate the final positions:
 
 
 
-References:
+Reference
 ~~~~~~~~~~~
 
 - `_PROBABILISTIC ROBOTICS: <http://www.probabilistic-robotics.org>`_
diff --git a/docs/modules/2_localization/particle_filter_localization/particle_filter_localization_main.rst b/docs/modules/2_localization/particle_filter_localization/particle_filter_localization_main.rst
index 20a9eb58fc..d648d8e080 100644
--- a/docs/modules/2_localization/particle_filter_localization/particle_filter_localization_main.rst
+++ b/docs/modules/2_localization/particle_filter_localization/particle_filter_localization_main.rst
@@ -15,6 +15,12 @@ It is assumed that the robot can measure a distance from landmarks
 
 This measurements are used for PF localization.
 
+Code Link
+~~~~~~~~~~~~~
+
+.. autofunction:: Localization.particle_filter.particle_filter.pf_localization
+
+
 How to calculate covariance matrix from particles
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -30,7 +36,7 @@ The covariance matrix :math:`\Xi` from particle information is calculated by the
 
 - :math:`\mu_j` is the :math:`j` th mean state of particles.
 
-References:
+Reference
 ~~~~~~~~~~~
 
 - `_PROBABILISTIC ROBOTICS: <http://www.probabilistic-robotics.org>`_
diff --git a/docs/modules/2_localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst b/docs/modules/2_localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst
index bb6b5b664b..a7a5aab61b 100644
--- a/docs/modules/2_localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst
+++ b/docs/modules/2_localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst
@@ -7,7 +7,13 @@ This is a sensor fusion localization with Unscented Kalman Filter(UKF).
 
 The lines and points are same meaning of the EKF simulation.
 
-References:
+Code Link
+~~~~~~~~~~~~~
+
+.. autofunction:: Localization.unscented_kalman_filter.unscented_kalman_filter.ukf_estimation
+
+
+Reference
 ~~~~~~~~~~~
 
 - `Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization <https://www.researchgate.net/publication/267963417_Discriminatively_Trained_Unscented_Kalman_Filter_for_Mobile_Robot_Localization>`_
diff --git a/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst b/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst
index b27971225e..a1486ffe1e 100644
--- a/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst
+++ b/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst
@@ -578,7 +578,7 @@ reckoning and control functions are passed along here as well.
 
 .. image:: ekf_slam_1_0.png
 
-References:
+Reference
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 - `PROBABILISTIC ROBOTICS <http://www.probabilistic-robotics.org/>`_
diff --git a/docs/modules/4_slam/graph_slam/graph_slam_main.rst b/docs/modules/4_slam/graph_slam/graph_slam_main.rst
index 987eed385c..a2ef513527 100644
--- a/docs/modules/4_slam/graph_slam/graph_slam_main.rst
+++ b/docs/modules/4_slam/graph_slam/graph_slam_main.rst
@@ -17,7 +17,7 @@ The black stars are landmarks for graph edge generation.
 .. include:: graphSLAM_formulation.rst
 .. include:: graphSLAM_SE2_example.rst
 
-References:
+Reference
 ~~~~~~~~~~~
 
 - `A Tutorial on Graph-Based SLAM <http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf>`_
diff --git a/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst b/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst
index d306a85352..9d9b3de709 100644
--- a/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst
+++ b/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst
@@ -13,7 +13,7 @@ You can get different Beizer course:
 
 .. image:: Figure_2.png
 
-Ref:
+Reference
 
 -  `Continuous Curvature Path Generation Based on Bezier Curves for
    Autonomous
diff --git a/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst b/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst
index ffc3cc6451..82e0a71044 100644
--- a/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst
+++ b/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst
@@ -7,7 +7,7 @@ Eta^3 Spline path planning
 
 This is a path planning with Eta^3 spline.
 
-Ref:
+Reference
 
 -  `\\eta^3-Splines for the Smooth Path Generation of Wheeled Mobile
    Robots <https://ieeexplore.ieee.org/document/4339545/>`__
diff --git a/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst b/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst
index 38efaf2b53..371d536e3f 100644
--- a/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst
+++ b/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst
@@ -35,7 +35,7 @@ Low Speed and Merging and Stopping Scenario
 
 This scenario illustrates the trajectory planning at low speeds with merging and stopping behaviors.
 
-Ref:
+Reference
 
 -  `Optimal Trajectory Generation for Dynamic Street Scenarios in a
    Frenet
diff --git a/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst b/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst
index 1644ed00cc..bf82c9f391 100644
--- a/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst
+++ b/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst
@@ -63,7 +63,7 @@ This is a 2D grid based shortest path planning with D star algorithm.
 
 The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm.
 
-Ref:
+Reference
 
 -  `D* search Wikipedia <https://en.wikipedia.org/wiki/D*>`__
 
@@ -74,7 +74,7 @@ This is a 2D grid based path planning and replanning with D star lite algorithm.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStarLite/animation.gif
 
-Ref:
+Reference
 
 - `Improved Fast Replanning for Robot Navigation in Unknown Terrain <http://www.cs.cmu.edu/~maxim/files/dlite_icra02.pdf>`_
 
@@ -88,7 +88,7 @@ This is a 2D grid based path planning with Potential Field algorithm.
 
 In the animation, the blue heat map shows potential value on each grid.
 
-Ref:
+Reference
 
 -  `Robotic Motion Planning:Potential
    Functions <https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf>`__
diff --git a/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst b/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst
index 463363ddcf..1c5df1c9cc 100644
--- a/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst
+++ b/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst
@@ -16,7 +16,7 @@ Lookup table generation sample
 
 .. image:: lookup_table.png
 
-Ref:
+Reference
 
 -  `Optimal rough terrain trajectory generation for wheeled mobile
    robots <https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
diff --git a/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst b/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst
index 0628719176..f816c5c1b9 100644
--- a/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst
+++ b/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst
@@ -13,7 +13,7 @@ Cyan crosses means searched points with Dijkstra method,
 
 The red line is the final path of PRM.
 
-Ref:
+Reference
 
 -  `Probabilistic roadmap -
    Wikipedia <https://en.wikipedia.org/wiki/Probabilistic_roadmap>`__
diff --git a/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst b/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst
index 0412b3c9b3..66c3001c05 100644
--- a/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst
+++ b/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst
@@ -97,7 +97,7 @@ Each velocity and acceleration boundary condition can be calculated with each or
 
 :math:`v_{xe}=v_ecos(\theta_e), v_{ye}=v_esin(\theta_e)`
 
-References:
+Reference
 ~~~~~~~~~~~
 
 -  `Local Path Planning And Motion Control For Agv In
diff --git a/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst b/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst
index ff377eb91b..a4a5d28e01 100644
--- a/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst
+++ b/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst
@@ -380,7 +380,7 @@ Hence, we have:
 - :math:`v = (t - φ)`
 
 
-Ref:
+Reference
 
 -  `15.3.2 Reeds-Shepp
    Curves <https://lavalle.pl/planning/node822.html>`__
diff --git a/docs/modules/5_path_planning/rrt/rrt_main.rst b/docs/modules/5_path_planning/rrt/rrt_main.rst
index e5f351a7ba..da3a4957a5 100644
--- a/docs/modules/5_path_planning/rrt/rrt_main.rst
+++ b/docs/modules/5_path_planning/rrt/rrt_main.rst
@@ -53,7 +53,7 @@ This is a path planning code with Informed RRT*.
 
 The cyan ellipse is the heuristic sampling domain of Informed RRT*.
 
-Ref:
+Reference
 
 -  `Informed RRT\*: Optimal Sampling-based Path Planning Focused via
    Direct Sampling of an Admissible Ellipsoidal
@@ -68,7 +68,7 @@ Batch Informed RRT\*
 
 This is a path planning code with Batch Informed RRT*.
 
-Ref:
+Reference
 
 -  `Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the
    Heuristically Guided Search of Implicit Random Geometric
@@ -87,7 +87,7 @@ In this code, pure-pursuit algorithm is used for steering control,
 
 PID is used for speed control.
 
-Ref:
+Reference
 
 -  `Motion Planning in Complex Environments using Closed-loop
    Prediction <https://acl.mit.edu/papers/KuwataGNC08.pdf>`__
@@ -109,7 +109,7 @@ A double integrator motion model is used for LQR local planner.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif
 
-Ref:
+Reference
 
 -  `LQR-RRT\*: Optimal Sampling-Based Motion Planning with Automatically
    Derived Extension
diff --git a/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst b/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst
index d5e7ed17d1..bf89ac11ae 100644
--- a/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst
+++ b/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst
@@ -22,7 +22,7 @@ Lane sampling
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif
 
-Ref:
+Reference
 
 -  `Optimal rough terrain trajectory generation for wheeled mobile
    robots <https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
diff --git a/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst b/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst
index 92e729ab29..65e0e53465 100644
--- a/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst
+++ b/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst
@@ -11,7 +11,7 @@ Cyan crosses mean searched points with Dijkstra method,
 
 The red line is the final path of Vornoi Road-Map.
 
-Ref:
+Reference
 
 -  `Robotic Motion Planning <https://www.cs.cmu.edu/~motionplanning/lecture/Chap5-RoadMap-Methods_howie.pdf>`__
 
diff --git a/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst b/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst
index ded187e972..568ef9a0df 100644
--- a/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst
+++ b/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst
@@ -134,7 +134,7 @@ Simulation results
 
 
 
-References:
+Reference
 ~~~~~~~~~~~
 
 -  `Towards fully autonomous driving: Systems and algorithms <https://ieeexplore.ieee.org/document/5940562/>`__
diff --git a/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst b/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst
index baca7a33fc..831423f364 100644
--- a/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst
+++ b/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst
@@ -113,7 +113,7 @@ The optimal control input `u` can be calculated as:
 
 where `K` is the feedback gain matrix obtained by solving the Riccati equation.
 
-References:
+Reference
 ~~~~~~~~~~~
 -  `ApolloAuto/apollo: An open autonomous driving platform <https://github.com/ApolloAuto/apollo>`_
 
diff --git a/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst b/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst
index 5c7bcef85f..d7354cf8fb 100644
--- a/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst
+++ b/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst
@@ -9,7 +9,7 @@ speed control.
 The red line is a target course, the green cross means the target point
 for pure pursuit control, the blue line is the tracking.
 
-References:
+Reference
 ~~~~~~~~~~~
 
 -  `A Survey of Motion Planning and Control Techniques for Self-driving
diff --git a/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst b/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst
index 70875fdc6c..d18cd6fbf7 100644
--- a/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst
+++ b/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst
@@ -6,7 +6,7 @@ PID speed control.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif
 
-References:
+Reference
 ~~~~~~~~~~~
 -  `A Survey of Motion Planning and Control Techniques for Self-driving
    Urban Vehicles <https://arxiv.org/abs/1604.07446>`__
diff --git a/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst b/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst
index fe325b0102..69089ac33b 100644
--- a/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst
+++ b/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst
@@ -6,7 +6,7 @@ control.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif
 
-References:
+Reference
 ~~~~~~~~~~~
 
 -  `Stanley: The robot that won the DARPA grand
diff --git a/users_comments.md b/users_comments.md
index a2f798eac4..1674f21377 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -10,7 +10,7 @@ This is an electric wheelchair control demo by [Katsushun89](https://github.com/
 
 ![1](https://github.com/AtsushiSakai/PythonRoboticsGifs/blob/master/Users/WHILL_Model_CR_with_move_to_a_pose/WHLL_Model_CR_with_move_to_a_pose.gif)
 
-Ref:
+Reference:
 
 - [toioと同じように動くWHILL Model CR (in Japanese)](https://qiita.com/KatsuShun89/items/68fde7544ecfe36096d2)
 

From 5392fcff4d555421b4417827dc67c611ad14cc06 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sat, 3 May 2025 09:25:12 +0900
Subject: [PATCH 1011/1080] Refactor module links and improve code
 documentation. (#1211)

* Refactor module links and improve code documentation.

Updated documentation to rename "API" sections to "Code Link" for clarity and consistency. Enhanced docstrings for `circle_fitting` and `kmeans_clustering` functions, improving parameter descriptions and adding return value details. Fixed typos in function and file names in the ray casting grid map module.

* Fix import typo in ray casting grid map test module.

Corrected the import statement in the test file by updating the module's name to `ray_casting_grid_map` for consistency with the source file. This ensures proper functionality of the test suite.
---
 Mapping/circle_fitting/circle_fitting.py      | 33 +++++++++++++++----
 .../kmeans_clustering/kmeans_clustering.py    | 27 ++++++++++++++-
 .../ray_casting_grid_map.py}                  |  4 +--
 .../circle_fitting/circle_fitting_main.rst    |  4 +++
 .../distance_map/distance_map_main.rst        |  4 +--
 .../gaussian_grid_map_main.rst                |  6 ++++
 .../k_means_object_clustering_main.rst        |  6 ++++
 .../lidar_to_grid_map_tutorial_main.rst       |  6 ++++
 .../normal_vector_estimation_main.rst         |  8 ++---
 .../point_cloud_sampling_main.rst             |  8 ++---
 .../ray_casting_grid_map_main.rst             |  7 +++-
 .../rectangle_fitting_main.rst                |  4 +--
 ...id_map.py => test_ray_casting_grid_map.py} |  2 +-
 13 files changed, 96 insertions(+), 23 deletions(-)
 rename Mapping/{raycasting_grid_map/raycasting_grid_map.py => ray_casting_grid_map/ray_casting_grid_map.py} (96%)
 rename tests/{test_raycasting_grid_map.py => test_ray_casting_grid_map.py} (71%)

diff --git a/Mapping/circle_fitting/circle_fitting.py b/Mapping/circle_fitting/circle_fitting.py
index 2eba550127..b5714b507c 100644
--- a/Mapping/circle_fitting/circle_fitting.py
+++ b/Mapping/circle_fitting/circle_fitting.py
@@ -16,12 +16,33 @@
 
 def circle_fitting(x, y):
     """
-    Circle Fitting with least squared
-        input: point x-y positions
-        output  cxe x center position
-                cye y center position
-                re  radius of circle
-                error: prediction error
+    Fits a circle to a given set of points using a least-squares approach.
+
+    This function calculates the center (x, y) and radius of a circle that best fits
+    the given set of points in a two-dimensional plane. It minimizes the squared
+    errors between the circle and the provided points and returns the calculated
+    center coordinates, radius, and the fitting error.
+
+    Raises
+    ------
+    ValueError
+        If the input lists x and y do not contain the same number of elements.
+
+    Parameters
+    ----------
+    x : list[float]
+        The x-coordinates of the points.
+    y : list[float]
+        The y-coordinates of the points.
+
+    Returns
+    -------
+    tuple[float, float, float, float]
+        A tuple containing:
+        - The x-coordinate of the center of the fitted circle (float).
+        - The y-coordinate of the center of the fitted circle (float).
+        - The radius of the fitted circle (float).
+        - The fitting error as a deviation metric (float).
     """
 
     sumx = sum(x)
diff --git a/Mapping/kmeans_clustering/kmeans_clustering.py b/Mapping/kmeans_clustering/kmeans_clustering.py
index e18960e990..cee01e5ad5 100644
--- a/Mapping/kmeans_clustering/kmeans_clustering.py
+++ b/Mapping/kmeans_clustering/kmeans_clustering.py
@@ -17,12 +17,37 @@
 
 
 def kmeans_clustering(rx, ry, nc):
+    """
+    Performs k-means clustering on the given dataset, iteratively adjusting cluster centroids
+    until convergence within a defined threshold or reaching the maximum number of
+    iterations.
+
+    The implementation initializes clusters, calculates initial centroids, and refines the
+    clusters through iterative updates to optimize the cost function based on minimum
+    distance between datapoints and centroids.
+
+    Arguments:
+        rx: List[float]
+            The x-coordinates of the dataset points to be clustered.
+        ry: List[float]
+            The y-coordinates of the dataset points to be clustered.
+        nc: int
+            The number of clusters to group the data into.
+
+    Returns:
+        Clusters
+            An instance containing the final cluster assignments and centroids after
+            convergence.
+
+    Raises:
+        None
+
+    """
     clusters = Clusters(rx, ry, nc)
     clusters.calc_centroid()
 
     pre_cost = float("inf")
     for loop in range(MAX_LOOP):
-        print("loop:", loop)
         cost = clusters.update_clusters()
         clusters.calc_centroid()
 
diff --git a/Mapping/raycasting_grid_map/raycasting_grid_map.py b/Mapping/ray_casting_grid_map/ray_casting_grid_map.py
similarity index 96%
rename from Mapping/raycasting_grid_map/raycasting_grid_map.py
rename to Mapping/ray_casting_grid_map/ray_casting_grid_map.py
index 8ce37b925b..c7e73f0630 100644
--- a/Mapping/raycasting_grid_map/raycasting_grid_map.py
+++ b/Mapping/ray_casting_grid_map/ray_casting_grid_map.py
@@ -48,7 +48,7 @@ def atan_zero_to_twopi(y, x):
     return angle
 
 
-def precasting(minx, miny, xw, yw, xyreso, yawreso):
+def pre_casting(minx, miny, xw, yw, xyreso, yawreso):
 
     precast = [[] for i in range(int(round((math.pi * 2.0) / yawreso)) + 1)]
 
@@ -81,7 +81,7 @@ def generate_ray_casting_grid_map(ox, oy, xyreso, yawreso):
 
     pmap = [[0.0 for i in range(yw)] for i in range(xw)]
 
-    precast = precasting(minx, miny, xw, yw, xyreso, yawreso)
+    precast = pre_casting(minx, miny, xw, yw, xyreso, yawreso)
 
     for (x, y) in zip(ox, oy):
 
diff --git a/docs/modules/3_mapping/circle_fitting/circle_fitting_main.rst b/docs/modules/3_mapping/circle_fitting/circle_fitting_main.rst
index 1892d1f8f7..e243529a9c 100644
--- a/docs/modules/3_mapping/circle_fitting/circle_fitting_main.rst
+++ b/docs/modules/3_mapping/circle_fitting/circle_fitting_main.rst
@@ -11,3 +11,7 @@ The red crosses are observations from a ranging sensor.
 
 The red circle is the estimated object shape using circle fitting.
 
+Code Link
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: Mapping.circle_fitting.circle_fitting.circle_fitting
diff --git a/docs/modules/3_mapping/distance_map/distance_map_main.rst b/docs/modules/3_mapping/distance_map/distance_map_main.rst
index 0ef9e3022f..ec60e752c9 100644
--- a/docs/modules/3_mapping/distance_map/distance_map_main.rst
+++ b/docs/modules/3_mapping/distance_map/distance_map_main.rst
@@ -14,8 +14,8 @@ The algorithm is demonstrated on a simple 2D grid with obstacles:
 
 .. image:: distance_map.png
 
-API
-~~~
+Code Link
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. autofunction:: Mapping.DistanceMap.distance_map.compute_sdf
 
diff --git a/docs/modules/3_mapping/gaussian_grid_map/gaussian_grid_map_main.rst b/docs/modules/3_mapping/gaussian_grid_map/gaussian_grid_map_main.rst
index b0f112a871..50033d2a20 100644
--- a/docs/modules/3_mapping/gaussian_grid_map/gaussian_grid_map_main.rst
+++ b/docs/modules/3_mapping/gaussian_grid_map/gaussian_grid_map_main.rst
@@ -6,3 +6,9 @@ Gaussian grid map
 This is a 2D Gaussian grid mapping example.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/gaussian_grid_map/animation.gif
+
+Code Link
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: Mapping.gaussian_grid_map.gaussian_grid_map.generate_gaussian_grid_map
+
diff --git a/docs/modules/3_mapping/k_means_object_clustering/k_means_object_clustering_main.rst b/docs/modules/3_mapping/k_means_object_clustering/k_means_object_clustering_main.rst
index e098ca5409..0ece604009 100644
--- a/docs/modules/3_mapping/k_means_object_clustering/k_means_object_clustering_main.rst
+++ b/docs/modules/3_mapping/k_means_object_clustering/k_means_object_clustering_main.rst
@@ -4,3 +4,9 @@ k-means object clustering
 This is a 2D object clustering with k-means algorithm.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/kmeans_clustering/animation.gif
+
+Code Link
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: Mapping.kmeans_clustering.kmeans_clustering.kmeans_clustering
+
diff --git a/docs/modules/3_mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst b/docs/modules/3_mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst
index 1f62179efd..29f5878e48 100644
--- a/docs/modules/3_mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst
+++ b/docs/modules/3_mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst
@@ -196,3 +196,9 @@ Let’s use this flood fill on real data:
 
 .. image:: lidar_to_grid_map_tutorial_14_1.png
 
+Code Link
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: Mapping.lidar_to_grid_map.lidar_to_grid_map.main
+
+
diff --git a/docs/modules/3_mapping/normal_vector_estimation/normal_vector_estimation_main.rst b/docs/modules/3_mapping/normal_vector_estimation/normal_vector_estimation_main.rst
index a4d1bf0df2..68a19e1534 100644
--- a/docs/modules/3_mapping/normal_vector_estimation/normal_vector_estimation_main.rst
+++ b/docs/modules/3_mapping/normal_vector_estimation/normal_vector_estimation_main.rst
@@ -25,8 +25,8 @@ This is an example of normal vector calculation:
 
 .. figure:: normal_vector_calc.png
 
-API
-=====
+Code Link
+==========
 
 .. autofunction:: Mapping.normal_vector_estimation.normal_vector_estimation.calc_normal_vector
 
@@ -67,8 +67,8 @@ This is an example of RANSAC based normal vector estimation:
 
 .. figure:: ransac_normal_vector_estimation.png
 
-API
-=====
+Code Link
+==========
 
 .. autofunction:: Mapping.normal_vector_estimation.normal_vector_estimation.ransac_normal_vector_estimation
 
diff --git a/docs/modules/3_mapping/point_cloud_sampling/point_cloud_sampling_main.rst b/docs/modules/3_mapping/point_cloud_sampling/point_cloud_sampling_main.rst
index cbb5652f56..8cb08d4b78 100644
--- a/docs/modules/3_mapping/point_cloud_sampling/point_cloud_sampling_main.rst
+++ b/docs/modules/3_mapping/point_cloud_sampling/point_cloud_sampling_main.rst
@@ -27,8 +27,8 @@ This method determines which each point is in a grid, and replaces the point
 clouds that are in the same Voxel with their average to reduce the number of
 points.
 
-API
-=====
+Code Link
+==========
 
 .. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.voxel_point_sampling
 
@@ -61,8 +61,8 @@ Although this method does not have good performance comparing the Farthest
 distance sample where each point is distributed farther from each other,
 this is suitable for real-time processing because of its fast computation time.
 
-API
-=====
+Code Link
+==========
 
 .. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.poisson_disk_sampling
 
diff --git a/docs/modules/3_mapping/ray_casting_grid_map/ray_casting_grid_map_main.rst b/docs/modules/3_mapping/ray_casting_grid_map/ray_casting_grid_map_main.rst
index cc5a1a1c5b..bee2f64193 100644
--- a/docs/modules/3_mapping/ray_casting_grid_map/ray_casting_grid_map_main.rst
+++ b/docs/modules/3_mapping/ray_casting_grid_map/ray_casting_grid_map_main.rst
@@ -3,4 +3,9 @@ Ray casting grid map
 
 This is a 2D ray casting grid mapping example.
 
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/raycasting_grid_map/animation.gif
\ No newline at end of file
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Mapping/raycasting_grid_map/animation.gif
+
+Code Link
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: Mapping.ray_casting_grid_map.ray_casting_grid_map.generate_ray_casting_grid_map
diff --git a/docs/modules/3_mapping/rectangle_fitting/rectangle_fitting_main.rst b/docs/modules/3_mapping/rectangle_fitting/rectangle_fitting_main.rst
index b6ced1dc1d..06d85efe61 100644
--- a/docs/modules/3_mapping/rectangle_fitting/rectangle_fitting_main.rst
+++ b/docs/modules/3_mapping/rectangle_fitting/rectangle_fitting_main.rst
@@ -57,8 +57,8 @@ This evaluation function uses the squreed distances between the edges of the rec
 Calculating the squared error is the same as calculating the variance.
 The smaller this variance, the more it signifies that the points fit within the rectangle.
 
-API
-~~~~~~
+Code Link
+~~~~~~~~~~~
 
 .. autoclass:: Mapping.rectangle_fitting.rectangle_fitting.LShapeFitting
 	:members:
diff --git a/tests/test_raycasting_grid_map.py b/tests/test_ray_casting_grid_map.py
similarity index 71%
rename from tests/test_raycasting_grid_map.py
rename to tests/test_ray_casting_grid_map.py
index f08ae9277e..2d192c9310 100644
--- a/tests/test_raycasting_grid_map.py
+++ b/tests/test_ray_casting_grid_map.py
@@ -1,5 +1,5 @@
 import conftest  # Add root path to sys.path
-from Mapping.raycasting_grid_map import raycasting_grid_map as m
+from Mapping.ray_casting_grid_map import ray_casting_grid_map as m
 
 
 def test1():

From e1cdb24ecfd68ff4eaf51626e5a3f4854da588a4 Mon Sep 17 00:00:00 2001
From: Yuri Harada <55676955+NightzDev@users.noreply.github.com>
Date: Fri, 2 May 2025 21:51:20 -0300
Subject: [PATCH 1012/1080] docs: rewrite internal sensors section with
 detailed descriptions and references (#1197)

* docs: rewrite and enhance internal sensors documentation

* Update docs/modules/12_appendix/internal_sensors_main.rst

---------

Co-authored-by: Atsushi Sakai <asakai.amsl+github@gmail.com>
---
 .../12_appendix/internal_sensors_main.rst     | 41 +++++++++++++++----
 1 file changed, 34 insertions(+), 7 deletions(-)

diff --git a/docs/modules/12_appendix/internal_sensors_main.rst b/docs/modules/12_appendix/internal_sensors_main.rst
index 18f209098e..fa2594a2bf 100644
--- a/docs/modules/12_appendix/internal_sensors_main.rst
+++ b/docs/modules/12_appendix/internal_sensors_main.rst
@@ -3,32 +3,59 @@
 Internal Sensors for Robots
 ============================
 
-This project, `PythonRobotics`, focuses on algorithms, so hardware is not included.
-However, having basic knowledge of hardware in robotics is also important for understanding algorithms.
-Therefore, we will provide an overview.
+This project, `PythonRobotics`, focuses on algorithms, so hardware is not included. However, having basic knowledge of hardware in robotics is also important for understanding algorithms. Therefore, we will provide an overview.
 
 Introduction
-------------
+-------------
+
+In robotic systems, internal sensors play a crucial role in monitoring the robot’s internal state, such as orientation, acceleration, angular velocity, altitude, and temperature. These sensors provide essential feedback that supports control, localization, and safety mechanisms. While external sensors perceive the environment, internal sensors give the robot self-awareness of its own motion and condition. Understanding the principles and characteristics of these sensors is vital to fully utilize their information within algorithms and decision-making systems. This section outlines the main internal sensors used in robotics.
 
 Global Navigation Satellite System (GNSS)
--------------------------------------------
+-----------------------------------------
+
+GNSS is a satellite-based navigation system that provides global positioning and time information by analyzing signals from multiple satellites. It is commonly used in outdoor environments for absolute positioning. Although GNSS offers global coverage without infrastructure dependency, its performance is limited indoors or in obstructed areas, and it suffers from low update rates and susceptibility to signal noise. It is widely used in outdoor navigation for drones, vehicles, and delivery robots.
 
 Gyroscope
 ----------
 
+A gyroscope measures angular velocity around the robot’s axes, enabling orientation and attitude estimation through detection of the Coriolis effect. Gyroscopes are compact, cost-effective, and provide high update rates, but they are prone to drift and require calibration or sensor fusion for long-term accuracy. These sensors are essential in drones, balancing robots, and IMU-based systems for motion tracking.
+
 Accelerometer
---------------
+---------------
+
+An accelerometer measures linear acceleration along one or more axes, typically by detecting mass displacement due to motion. It is small, affordable, and useful for detecting movement, tilt, or vibration. However, accelerometers are limited by noisy output and cannot independently determine position without integration and fusion. They are commonly applied in wearable robotics, step counters, and vibration sensing.
 
 Magnetometer
 --------------
 
+A magnetometer measures the direction and intensity of magnetic fields, enabling heading estimation based on Earth’s magnetic field. It is lightweight and useful for orientation, especially in GPS-denied environments, though it is sensitive to interference from electronics and requires calibration. Magnetometers are often used in conjunction with IMUs for navigation and directional awareness.
+
 Inertial Measurement Unit (IMU)
 --------------------------------
 
+An IMU integrates a gyroscope, accelerometer, and sometimes a magnetometer to estimate a robot's motion and orientation through sensor fusion techniques such as Kalman filters. IMUs are compact and provide high-frequency data, which is essential for localization and navigation in GPS-denied areas. Nonetheless, they accumulate drift over time and require complex filtering to maintain accuracy. IMUs are found in drones, mobile robots, and motion tracking systems.
+
 Pressure Sensor
------------------
+----------------
+
+Pressure sensors detect atmospheric or fluid pressure by measuring the force exerted on a diaphragm. They are effective for estimating altitude and monitoring environmental conditions, especially in drones and underwater robots. Although cost-effective and efficient, their accuracy may degrade due to temperature variation and limitations in low-altitude resolution.
 
 Temperature Sensor
 --------------------
 
+Temperature sensors monitor environmental or internal component temperatures, using changes in resistance or voltage depending on sensor type (e.g., RTD or thermocouple). They are simple and reliable for safety and thermal regulation, though they may respond slowly and be affected by nearby electronics. Common applications include battery and motor monitoring, safety systems, and ambient sensing.
+
+References
+----------
 
+- *Introduction to Autonomous Mobile Robots*, Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza
+- *Probabilistic Robotics*, Sebastian Thrun, Wolfram Burgard, Dieter Fox
+- Wikipedia articles:
+
+  - `Inertial Measurement Unit (IMU) <https://en.wikipedia.org/wiki/Inertial_measurement_unit>`_
+  - `Accelerometer <https://en.wikipedia.org/wiki/Accelerometer>`_
+  - `Gyroscope <https://en.wikipedia.org/wiki/Gyroscope>`_
+  - `Magnetometer <https://en.wikipedia.org/wiki/Magnetometer>`_
+  - `Pressure sensor <https://en.wikipedia.org/wiki/Pressure_sensor>`_
+  - `Temperature sensor <https://en.wikipedia.org/wiki/Thermometer>`_
+- `Adafruit Sensor Guides <https://learn.adafruit.com/>`_
\ No newline at end of file

From d2fe5ae8f0e414f394c00570771c07807d167e68 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 4 May 2025 09:50:49 +0900
Subject: [PATCH 1013/1080] Add code links to SLAM module documentation (#1212)

This commit updates SLAM module documentation files to include direct code links for better navigation. It also adjusts imports in `test_iterative_closest_point.py` to reflect updated module structure and adds a safety check for the directory in `runtests.sh`.
---
 .../icp_matching.py}                                      | 0
 docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst          | 5 +++++
 docs/modules/4_slam/FastSLAM2/FastSLAM2_main.rst          | 6 ++++++
 docs/modules/4_slam/ekf_slam/ekf_slam_main.rst            | 6 ++++++
 docs/modules/4_slam/graph_slam/graph_slam_main.rst        | 6 ++++++
 .../iterative_closest_point_matching_main.rst             | 8 +++++++-
 runtests.sh                                               | 1 +
 tests/test_iterative_closest_point.py                     | 2 +-
 8 files changed, 32 insertions(+), 2 deletions(-)
 rename SLAM/{iterative_closest_point/iterative_closest_point.py => ICPMatching/icp_matching.py} (100%)

diff --git a/SLAM/iterative_closest_point/iterative_closest_point.py b/SLAM/ICPMatching/icp_matching.py
similarity index 100%
rename from SLAM/iterative_closest_point/iterative_closest_point.py
rename to SLAM/ICPMatching/icp_matching.py
diff --git a/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst b/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst
index a2aa521216..b6bafa0982 100644
--- a/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst
+++ b/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst
@@ -22,6 +22,11 @@ The red points are particles of FastSLAM.
 Black points are landmarks, blue crosses are estimated landmark
 positions by FastSLAM.
 
+Code Link
+~~~~~~~~~~~~
+
+.. autofunction:: SLAM.FastSLAM1.fast_slam1.fast_slam1
+
 
 Introduction
 ~~~~~~~~~~~~
diff --git a/docs/modules/4_slam/FastSLAM2/FastSLAM2_main.rst b/docs/modules/4_slam/FastSLAM2/FastSLAM2_main.rst
index 9e79b496a3..59ed3b9f75 100644
--- a/docs/modules/4_slam/FastSLAM2/FastSLAM2_main.rst
+++ b/docs/modules/4_slam/FastSLAM2/FastSLAM2_main.rst
@@ -7,6 +7,12 @@ The animation has the same meanings as one of FastSLAM 1.0.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM2/animation.gif
 
+Code Link
+~~~~~~~~~~~
+
+.. autofunction:: SLAM.FastSLAM2.fast_slam2.fast_slam2
+
+
 References
 ~~~~~~~~~~
 
diff --git a/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst b/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst
index a1486ffe1e..3967a7ae4d 100644
--- a/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst
+++ b/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst
@@ -21,6 +21,12 @@ This is a simulation of EKF SLAM.
 -  Blue line: ground truth
 -  Red line: EKF SLAM position estimation
 
+Code Link
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autofunction:: SLAM.EKFSLAM.ekf_slam.ekf_slam
+
+
 Introduction
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
diff --git a/docs/modules/4_slam/graph_slam/graph_slam_main.rst b/docs/modules/4_slam/graph_slam/graph_slam_main.rst
index a2ef513527..2ef17e4179 100644
--- a/docs/modules/4_slam/graph_slam/graph_slam_main.rst
+++ b/docs/modules/4_slam/graph_slam/graph_slam_main.rst
@@ -13,6 +13,12 @@ The black stars are landmarks for graph edge generation.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/GraphBasedSLAM/animation.gif
 
+Code Link
+~~~~~~~~~~~~
+
+.. autofunction:: SLAM.GraphBasedSLAM.graph_based_slam.graph_based_slam
+
+
 .. include:: graphSLAM_doc.rst
 .. include:: graphSLAM_formulation.rst
 .. include:: graphSLAM_SE2_example.rst
diff --git a/docs/modules/4_slam/iterative_closest_point_matching/iterative_closest_point_matching_main.rst b/docs/modules/4_slam/iterative_closest_point_matching/iterative_closest_point_matching_main.rst
index a30b1fc99b..772fe62889 100644
--- a/docs/modules/4_slam/iterative_closest_point_matching/iterative_closest_point_matching_main.rst
+++ b/docs/modules/4_slam/iterative_closest_point_matching/iterative_closest_point_matching_main.rst
@@ -10,7 +10,13 @@ points to points.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/iterative_closest_point/animation.gif
 
+Code Link
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: SLAM.ICPMatching.icp_matching.icp_matching
+
+
 References
-~~~~~~~~~~
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 - `Introduction to Mobile Robotics: Iterative Closest Point Algorithm <https://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf>`_
diff --git a/runtests.sh b/runtests.sh
index 12d1b80453..c4460c8aa7 100755
--- a/runtests.sh
+++ b/runtests.sh
@@ -1,4 +1,5 @@
 #!/usr/bin/env bash
+cd "$(dirname "$0")" || exit 1
 echo "Run test suites! "
 
 # === pytest based test runner ===
diff --git a/tests/test_iterative_closest_point.py b/tests/test_iterative_closest_point.py
index 3e726b5649..cdfa89cc55 100644
--- a/tests/test_iterative_closest_point.py
+++ b/tests/test_iterative_closest_point.py
@@ -1,5 +1,5 @@
 import conftest
-from SLAM.iterative_closest_point import iterative_closest_point as m
+from SLAM.ICPMatching import icp_matching as m
 
 
 def test_1():

From a38da41bafaf82bb56d4491d94a0095c1019ac4a Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Sun, 4 May 2025 20:32:11 +0900
Subject: [PATCH 1014/1080] Add code links to documentation and fix naming
 inconsistencies (#1213)

Added references to related Python functions in documentation for better navigation and usability. Corrected inconsistencies in module and test names to align with their respective directories and improve clarity.
---
 .../rear_wheel_feedback_control.py}                 |  0
 .../stanley_control.py}                             |  0
 .../10_inverted_pendulum/inverted_pendulum_main.rst | 12 ++++++++++++
 .../behavior_tree/behavior_tree_main.rst            |  2 +-
 .../state_machine/state_machine_main.rst            |  4 ++--
 .../cgmres_nmpc/cgmres_nmpc_main.rst                |  5 +++++
 .../lqr_speed_and_steering_control_main.rst         |  7 ++++++-
 .../lqr_steering_control_main.rst                   |  6 +++++-
 ...l_predictive_speed_and_steering_control_main.rst | 13 ++++++-------
 .../move_to_a_pose_main.rst}                        |  8 +++++++-
 docs/modules/6_path_tracking/path_tracking_main.rst |  2 +-
 .../pure_pursuit_tracking_main.rst                  |  6 ++++++
 .../rear_wheel_feedback_control_main.rst            |  6 ++++++
 .../stanley_control/stanley_control_main.rst        |  6 ++++++
 .../n_joint_arm_to_point_control_main.rst           |  7 +++++++
 .../obstacle_avoidance_arm_navigation_main.rst      |  7 ++++++-
 .../7_arm_navigation/planar_two_link_ik_main.rst    |  6 ++++++
 .../drone_3d_trajectory_following_main.rst          |  5 +++++
 .../rocket_powered_landing_main.rst                 |  8 ++++++++
 .../bipedal_planner/bipedal_planner_main.rst        |  5 +++++
 tests/test_rear_wheel_feedback.py                   |  2 +-
 tests/test_stanley_controller.py                    |  2 +-
 22 files changed, 102 insertions(+), 17 deletions(-)
 rename PathTracking/{rear_wheel_feedback/rear_wheel_feedback.py => rear_wheel_feedback_control/rear_wheel_feedback_control.py} (100%)
 rename PathTracking/{stanley_controller/stanley_controller.py => stanley_control/stanley_control.py} (100%)
 rename docs/modules/6_path_tracking/{move_to_a_pose_control/move_to_a_pose_control_main.rst => move_to_a_pose/move_to_a_pose_main.rst} (97%)

diff --git a/PathTracking/rear_wheel_feedback/rear_wheel_feedback.py b/PathTracking/rear_wheel_feedback_control/rear_wheel_feedback_control.py
similarity index 100%
rename from PathTracking/rear_wheel_feedback/rear_wheel_feedback.py
rename to PathTracking/rear_wheel_feedback_control/rear_wheel_feedback_control.py
diff --git a/PathTracking/stanley_controller/stanley_controller.py b/PathTracking/stanley_control/stanley_control.py
similarity index 100%
rename from PathTracking/stanley_controller/stanley_controller.py
rename to PathTracking/stanley_control/stanley_control.py
diff --git a/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst b/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst
index 048cbea9ac..58dc0f2e57 100644
--- a/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst
+++ b/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst
@@ -89,6 +89,12 @@ and :math:`P` is the unique positive definite solution to the discrete time
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/InvertedPendulumCart/animation_lqr.gif
 
+Code Link
+^^^^^^^^^^^
+
+.. autofunction:: InvertedPendulum.inverted_pendulum_lqr_control.main
+
+
 MPC control
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 The MPC controller minimize this cost function defined as:
@@ -101,3 +107,9 @@ subject to:
 - Initial state
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/InvertedPendulumCart/animation.gif
+
+Code Link
+^^^^^^^^^^^
+
+.. autofunction:: InvertedPendulum.inverted_pendulum_mpc_control.main
+
diff --git a/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst b/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst
index ae3e16da81..22849f7c54 100644
--- a/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst
+++ b/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst
@@ -5,7 +5,7 @@ Behavior Tree is a modular, hierarchical decision model that is widely used in r
 It present some similarities to hierarchical state machines with the key difference that the main building block of a behavior is a task rather than a state.
 Behavior Tree have been shown to generalize several other control architectures (https://ieeexplore.ieee.org/document/7790863)
 
-Core Concepts
+Code Link
 ~~~~~~~~~~~~~
 
 Control Node
diff --git a/docs/modules/13_mission_planning/state_machine/state_machine_main.rst b/docs/modules/13_mission_planning/state_machine/state_machine_main.rst
index abaece1b11..3f516d46a9 100644
--- a/docs/modules/13_mission_planning/state_machine/state_machine_main.rst
+++ b/docs/modules/13_mission_planning/state_machine/state_machine_main.rst
@@ -12,8 +12,8 @@ Core Concepts
 - **Action**: An operation executed during transition (before entering new state)
 - **Guard**: A precondition that must be satisfied to allow transition
 
-API
-~~~
+Code Link
+~~~~~~~~~~~
 
 .. autoclass:: MissionPlanning.StateMachine.state_machine.StateMachine
    :members: add_transition, process, register_state
diff --git a/docs/modules/6_path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst b/docs/modules/6_path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst
index 23f1218a94..914a4555ff 100644
--- a/docs/modules/6_path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst
+++ b/docs/modules/6_path_tracking/cgmres_nmpc/cgmres_nmpc_main.rst
@@ -17,6 +17,11 @@ Nonlinear Model Predictive Control with C-GMRES
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/cgmres_nmpc/animation.gif
    :alt: gif
 
+Code Link
+~~~~~~~~~~~~
+
+.. autofunction:: PathTracking.cgmres_nmpc.cgmres_nmpc.NMPCControllerCGMRES
+
 Mathematical Formulation
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
diff --git a/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst b/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst
index 568ef9a0df..b0ba9e0d45 100644
--- a/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst
+++ b/docs/modules/6_path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control_main.rst
@@ -7,7 +7,12 @@ Path tracking simulation with LQR speed and steering control.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif
 
-`[Code Link] <https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/lqr_speed_steer_control/lqr_speed_steer_control.py>`_
+
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: PathTracking.lqr_speed_steer_control.lqr_speed_steer_control.lqr_speed_steering_control
+
 
 Overview
 ~~~~~~~~
diff --git a/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst b/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst
index 831423f364..fc8f2a33aa 100644
--- a/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst
+++ b/docs/modules/6_path_tracking/lqr_steering_control/lqr_steering_control_main.rst
@@ -8,7 +8,11 @@ control.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_steer_control/animation.gif
 
-`[Code Link] <https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/lqr_steer_control/lqr_steer_control.py>`_
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: PathTracking.lqr_steer_control.lqr_steer_control.lqr_steering_control
+
 
 Overview
 ~~~~~~~~
diff --git a/docs/modules/6_path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst b/docs/modules/6_path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst
index de55b545ba..76a6819a46 100644
--- a/docs/modules/6_path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst
+++ b/docs/modules/6_path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst
@@ -5,13 +5,6 @@ Model predictive speed and steering control
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/model_predictive_speed_and_steer_control/animation.gif?raw=true
    :alt: Model predictive speed and steering control
 
-   Model predictive speed and steering control
-
-code:
-
-`PythonRobotics/model_predictive_speed_and_steer_control.py at master ·
-AtsushiSakai/PythonRobotics <https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py>`__
-
 This is a path tracking simulation using model predictive control (MPC).
 
 The MPC controller controls vehicle speed and steering base on
@@ -22,6 +15,12 @@ This code uses cvxpy as an optimization modeling tool.
 -  `Welcome to CVXPY 1.0 — CVXPY 1.0.6
    documentation <http://www.cvxpy.org/>`__
 
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: PathTracking.model_predictive_speed_and_steer_control.model_predictive_speed_and_steer_control.iterative_linear_mpc_control
+
+
 MPC modeling
 ~~~~~~~~~~~~
 
diff --git a/docs/modules/6_path_tracking/move_to_a_pose_control/move_to_a_pose_control_main.rst b/docs/modules/6_path_tracking/move_to_a_pose/move_to_a_pose_main.rst
similarity index 97%
rename from docs/modules/6_path_tracking/move_to_a_pose_control/move_to_a_pose_control_main.rst
rename to docs/modules/6_path_tracking/move_to_a_pose/move_to_a_pose_main.rst
index 77ec682a30..19bb0e4c14 100644
--- a/docs/modules/6_path_tracking/move_to_a_pose_control/move_to_a_pose_control_main.rst
+++ b/docs/modules/6_path_tracking/move_to_a_pose/move_to_a_pose_main.rst
@@ -3,7 +3,13 @@ Move to a Pose Control
 
 In this section, we present the logic of PathFinderController that drives a car from a start pose (x, y, theta) to a goal pose. A simulation of moving to a pose control is presented below.
 
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/move_to_pose/animation.gif
+
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: PathTracking.move_to_pose.move_to_pose.move_to_pose
+
 
 Position Control of non-Holonomic Systems
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/docs/modules/6_path_tracking/path_tracking_main.rst b/docs/modules/6_path_tracking/path_tracking_main.rst
index d98e324583..742cc807e6 100644
--- a/docs/modules/6_path_tracking/path_tracking_main.rst
+++ b/docs/modules/6_path_tracking/path_tracking_main.rst
@@ -16,4 +16,4 @@ Path tracking is the ability of a robot to follow the reference path generated b
    lqr_speed_and_steering_control/lqr_speed_and_steering_control
    model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control
    cgmres_nmpc/cgmres_nmpc
-   move_to_a_pose_control/move_to_a_pose_control
+   move_to_a_pose/move_to_a_pose
diff --git a/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst b/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst
index d7354cf8fb..ff6749bbbe 100644
--- a/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst
+++ b/docs/modules/6_path_tracking/pure_pursuit_tracking/pure_pursuit_tracking_main.rst
@@ -9,6 +9,12 @@ speed control.
 The red line is a target course, the green cross means the target point
 for pure pursuit control, the blue line is the tracking.
 
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: PathTracking.pure_pursuit.pure_pursuit.pure_pursuit_steer_control
+
+
 Reference
 ~~~~~~~~~~~
 
diff --git a/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst b/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst
index d18cd6fbf7..56d0db63ad 100644
--- a/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst
+++ b/docs/modules/6_path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control_main.rst
@@ -6,6 +6,12 @@ PID speed control.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif
 
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: PathTracking.rear_wheel_feedback_control.rear_wheel_feedback_control.rear_wheel_feedback_control
+
+
 Reference
 ~~~~~~~~~~~
 -  `A Survey of Motion Planning and Control Techniques for Self-driving
diff --git a/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst b/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst
index 69089ac33b..3c491804f6 100644
--- a/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst
+++ b/docs/modules/6_path_tracking/stanley_control/stanley_control_main.rst
@@ -6,6 +6,12 @@ control.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif
 
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: PathTracking.stanley_control.stanley_control.stanley_control
+
+
 Reference
 ~~~~~~~~~~~
 
diff --git a/docs/modules/7_arm_navigation/n_joint_arm_to_point_control_main.rst b/docs/modules/7_arm_navigation/n_joint_arm_to_point_control_main.rst
index cc6279f681..56900acde1 100644
--- a/docs/modules/7_arm_navigation/n_joint_arm_to_point_control_main.rst
+++ b/docs/modules/7_arm_navigation/n_joint_arm_to_point_control_main.rst
@@ -11,3 +11,10 @@ plotting area.
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/n_joint_arm_to_point_control/animation.gif
 
 In this simulation N = 10, however, you can change it.
+
+
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: ArmNavigation.n_joint_arm_to_point_control.n_joint_arm_to_point_control.main
+
diff --git a/docs/modules/7_arm_navigation/obstacle_avoidance_arm_navigation_main.rst b/docs/modules/7_arm_navigation/obstacle_avoidance_arm_navigation_main.rst
index 899a64a5cd..4433ab531b 100644
--- a/docs/modules/7_arm_navigation/obstacle_avoidance_arm_navigation_main.rst
+++ b/docs/modules/7_arm_navigation/obstacle_avoidance_arm_navigation_main.rst
@@ -3,4 +3,9 @@ Arm navigation with obstacle avoidance
 
 Arm navigation with obstacle avoidance simulation.
 
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/arm_obstacle_navigation/animation.gif
\ No newline at end of file
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/ArmNavigation/arm_obstacle_navigation/animation.gif
+
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: ArmNavigation.arm_obstacle_navigation.arm_obstacle_navigation.main
diff --git a/docs/modules/7_arm_navigation/planar_two_link_ik_main.rst b/docs/modules/7_arm_navigation/planar_two_link_ik_main.rst
index 2da2b0dc30..5b2712eb48 100644
--- a/docs/modules/7_arm_navigation/planar_two_link_ik_main.rst
+++ b/docs/modules/7_arm_navigation/planar_two_link_ik_main.rst
@@ -11,6 +11,12 @@ This is a interactive simulation.
 
 You can set the goal position of the end effector with left-click on the plotting area.
 
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: ArmNavigation.two_joint_arm_to_point_control.two_joint_arm_to_point_control.main
+
+
 Inverse Kinematics for a Planar Two-Link Robotic Arm
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
diff --git a/docs/modules/8_aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following_main.rst b/docs/modules/8_aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following_main.rst
index c3bdc33cdc..1805bb3f6d 100644
--- a/docs/modules/8_aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following_main.rst
+++ b/docs/modules/8_aerial_navigation/drone_3d_trajectory_following/drone_3d_trajectory_following_main.rst
@@ -4,3 +4,8 @@ Drone 3d trajectory following
 This is a 3d trajectory following simulation for a quadrotor.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/drone_3d_trajectory_following/animation.gif
+
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: AerialNavigation.drone_3d_trajectory_following.drone_3d_trajectory_following.quad_sim
diff --git a/docs/modules/8_aerial_navigation/rocket_powered_landing/rocket_powered_landing_main.rst b/docs/modules/8_aerial_navigation/rocket_powered_landing/rocket_powered_landing_main.rst
index 6c90d2b44e..4bf5117500 100644
--- a/docs/modules/8_aerial_navigation/rocket_powered_landing/rocket_powered_landing_main.rst
+++ b/docs/modules/8_aerial_navigation/rocket_powered_landing/rocket_powered_landing_main.rst
@@ -1,6 +1,14 @@
 Rocket powered landing
 -----------------------------
 
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/AerialNavigation/rocket_powered_landing/animation.gif
+
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: AerialNavigation.rocket_powered_landing.rocket_powered_landing.main
+
+
 Simulation
 ~~~~~~~~~~
 
diff --git a/docs/modules/9_bipedal/bipedal_planner/bipedal_planner_main.rst b/docs/modules/9_bipedal/bipedal_planner/bipedal_planner_main.rst
index 2ee5971e7a..6253715cc1 100644
--- a/docs/modules/9_bipedal/bipedal_planner/bipedal_planner_main.rst
+++ b/docs/modules/9_bipedal/bipedal_planner/bipedal_planner_main.rst
@@ -4,3 +4,8 @@ Bipedal Planner
 Bipedal Walking with modifying designated footsteps
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Bipedal/bipedal_planner/animation.gif
+
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: Bipedal.bipedal_planner.bipedal_planner.BipedalPlanner
diff --git a/tests/test_rear_wheel_feedback.py b/tests/test_rear_wheel_feedback.py
index 895eb188b3..eccde52358 100644
--- a/tests/test_rear_wheel_feedback.py
+++ b/tests/test_rear_wheel_feedback.py
@@ -1,5 +1,5 @@
 import conftest  # Add root path to sys.path
-from PathTracking.rear_wheel_feedback import rear_wheel_feedback as m
+from PathTracking.rear_wheel_feedback_control import rear_wheel_feedback_control as m
 
 
 def test1():
diff --git a/tests/test_stanley_controller.py b/tests/test_stanley_controller.py
index a1d8073764..bd590cb51a 100644
--- a/tests/test_stanley_controller.py
+++ b/tests/test_stanley_controller.py
@@ -1,5 +1,5 @@
 import conftest  # Add root path to sys.path
-from PathTracking.stanley_controller import stanley_controller as m
+from PathTracking.stanley_control import stanley_control as m
 
 
 def test1():

From 73e1c0bebccf4f4fc67ae59ada53b783a8533096 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 5 May 2025 17:29:45 +0900
Subject: [PATCH 1015/1080] Add "Code Link" sections and rename classes for
 consistency (#1214)

This commit adds "Code Link" sections to documentation across various path planning modules, linking to relevant class and function APIs. Additionally, several class renaming changes were made, such as `Dijkstra` to `DijkstraPlanner` and `eta3_trajectory` to `Eta3SplineTrajectory`, to enhance naming consistency. Minor fixes include file restructuring and image renaming for the RRT module.
---
 ..._rrtstar.py => batch_informed_rrt_star.py} |   0
 PathPlanning/Dijkstra/dijkstra.py             |   4 +-
 .../eta3_spline_trajectory.py                 |  14 ++---
 .../bezier_path/bezier_path_main.rst          |   9 ++-
 .../bspline_path/bspline_path_main.rst        |   8 +--
 .../bugplanner/bugplanner_main.rst            |   8 +++
 .../catmull_rom_spline_main.rst               |   4 +-
 .../clothoid_path/clothoid_path_main.rst      |   5 ++
 .../coverage_path/coverage_path_main.rst      |  21 +++++++
 .../cubic_spline/cubic_spline_main.rst        |   8 +--
 .../dubins_path/dubins_path_main.rst          |   2 +-
 .../dynamic_window_approach_main.rst          |  12 +++-
 .../elastic_bands/elastic_bands_main.rst      |   7 ++-
 .../eta3_spline/eta3_spline_main.rst          |   7 +++
 .../frenet_frame_path_main.rst                |   6 ++
 .../grid_base_search_main.rst                 |  50 ++++++++++++++++
 .../hybridastar/hybridastar_main.rst          |   5 ++
 .../lqr_path/lqr_path_main.rst                |   5 ++
 ...l_predictive_trajectory_generator_main.rst |   7 +++
 .../prm_planner/prm_planner_main.rst          |   7 +++
 .../quintic_polynomials_planner_main.rst      |   5 ++
 .../reeds_shepp_path_main.rst                 |   7 +++
 .../closed_loop_rrt_star_car/Figure_1.png     | Bin
 .../closed_loop_rrt_star_car/Figure_3.png     | Bin
 .../closed_loop_rrt_star_car/Figure_4.png     | Bin
 .../closed_loop_rrt_star_car/Figure_5.png     | Bin
 docs/modules/5_path_planning/rrt/rrt_main.rst |  54 ++++++++++++++++++
 docs/modules/5_path_planning/rrt/rrt_star.rst |   6 ++
 .../state_lattice_planner_main.rst            |  17 ++++++
 .../time_based_grid_search_main.rst           |   9 +++
 .../visibility_road_map_planner_main.rst      |   7 ++-
 .../vrm_planner/vrm_planner_main.rst          |   6 ++
 tests/test_batch_informed_rrt_star.py         |   2 +-
 33 files changed, 277 insertions(+), 25 deletions(-)
 rename PathPlanning/BatchInformedRRTStar/{batch_informed_rrtstar.py => batch_informed_rrt_star.py} (100%)
 rename docs/modules/5_path_planning/{ => rrt}/closed_loop_rrt_star_car/Figure_1.png (100%)
 rename docs/modules/5_path_planning/{ => rrt}/closed_loop_rrt_star_car/Figure_3.png (100%)
 rename docs/modules/5_path_planning/{ => rrt}/closed_loop_rrt_star_car/Figure_4.png (100%)
 rename docs/modules/5_path_planning/{ => rrt}/closed_loop_rrt_star_car/Figure_5.png (100%)

diff --git a/PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py b/PathPlanning/BatchInformedRRTStar/batch_informed_rrt_star.py
similarity index 100%
rename from PathPlanning/BatchInformedRRTStar/batch_informed_rrtstar.py
rename to PathPlanning/BatchInformedRRTStar/batch_informed_rrt_star.py
diff --git a/PathPlanning/Dijkstra/dijkstra.py b/PathPlanning/Dijkstra/dijkstra.py
index 8a585e4b18..f5a4703910 100644
--- a/PathPlanning/Dijkstra/dijkstra.py
+++ b/PathPlanning/Dijkstra/dijkstra.py
@@ -12,7 +12,7 @@
 show_animation = True
 
 
-class Dijkstra:
+class DijkstraPlanner:
 
     def __init__(self, ox, oy, resolution, robot_radius):
         """
@@ -246,7 +246,7 @@ def main():
         plt.grid(True)
         plt.axis("equal")
 
-    dijkstra = Dijkstra(ox, oy, grid_size, robot_radius)
+    dijkstra = DijkstraPlanner(ox, oy, grid_size, robot_radius)
     rx, ry = dijkstra.planning(sx, sy, gx, gy)
 
     if show_animation:  # pragma: no cover
diff --git a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
index a73797dacb..e72d33261e 100644
--- a/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
+++ b/PathPlanning/Eta3SplineTrajectory/eta3_spline_trajectory.py
@@ -29,7 +29,7 @@ def __init__(self, actual_vel, max_vel):
         self.message = f'Actual velocity {actual_vel} does not equal desired max velocity {max_vel}!'
 
 
-class eta3_trajectory(Eta3Path):
+class Eta3SplineTrajectory(Eta3Path):
     """
     eta3_trajectory
 
@@ -300,8 +300,8 @@ def test1(max_vel=0.5):
         trajectory_segments.append(Eta3PathSegment(
             start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
-        traj = eta3_trajectory(trajectory_segments,
-                               max_vel=max_vel, max_accel=0.5)
+        traj = Eta3SplineTrajectory(trajectory_segments,
+                                    max_vel=max_vel, max_accel=0.5)
 
         # interpolate at several points along the path
         times = np.linspace(0, traj.total_time, 101)
@@ -334,8 +334,8 @@ def test2(max_vel=0.5):
         trajectory_segments.append(Eta3PathSegment(
             start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
-        traj = eta3_trajectory(trajectory_segments,
-                               max_vel=max_vel, max_accel=0.5)
+        traj = Eta3SplineTrajectory(trajectory_segments,
+                                    max_vel=max_vel, max_accel=0.5)
 
         # interpolate at several points along the path
         times = np.linspace(0, traj.total_time, 101)
@@ -400,8 +400,8 @@ def test3(max_vel=2.0):
         start_pose=start_pose, end_pose=end_pose, eta=eta, kappa=kappa))
 
     # construct the whole path
-    traj = eta3_trajectory(trajectory_segments,
-                           max_vel=max_vel, max_accel=0.5, max_jerk=1)
+    traj = Eta3SplineTrajectory(trajectory_segments,
+                                max_vel=max_vel, max_accel=0.5, max_jerk=1)
 
     # interpolate at several points along the path
     times = np.linspace(0, traj.total_time, 1001)
diff --git a/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst b/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst
index 9d9b3de709..19fb89a1b1 100644
--- a/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst
+++ b/docs/modules/5_path_planning/bezier_path/bezier_path_main.rst
@@ -13,8 +13,15 @@ You can get different Beizer course:
 
 .. image:: Figure_2.png
 
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: PathPlanning.BezierPath.bezier_path.calc_4points_bezier_path
+
+
 Reference
+~~~~~~~~~~~~~~~
 
 -  `Continuous Curvature Path Generation Based on Bezier Curves for
    Autonomous
-   Vehicles <https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=b00b657c3e0e828c589132a14825e7119772003d>`
+   Vehicles <https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=b00b657c3e0e828c589132a14825e7119772003d>`__
diff --git a/docs/modules/5_path_planning/bspline_path/bspline_path_main.rst b/docs/modules/5_path_planning/bspline_path/bspline_path_main.rst
index e734352e34..00e5ef2fdb 100644
--- a/docs/modules/5_path_planning/bspline_path/bspline_path_main.rst
+++ b/docs/modules/5_path_planning/bspline_path/bspline_path_main.rst
@@ -105,8 +105,8 @@ The default spline degree is 3, so curvature changes smoothly.
 
 .. image:: interp_and_curvature.png
 
-API
-++++
+Code link
+++++++++++
 
 .. autofunction:: PathPlanning.BSplinePath.bspline_path.interpolate_b_spline_path
 
@@ -133,8 +133,8 @@ The default spline degree is 3, so curvature changes smoothly.
 
 .. image:: approx_and_curvature.png
 
-API
-++++
+Code Link
+++++++++++
 
 .. autofunction:: PathPlanning.BSplinePath.bspline_path.approximate_b_spline_path
 
diff --git a/docs/modules/5_path_planning/bugplanner/bugplanner_main.rst b/docs/modules/5_path_planning/bugplanner/bugplanner_main.rst
index 81c91c0313..e1cd2fe353 100644
--- a/docs/modules/5_path_planning/bugplanner/bugplanner_main.rst
+++ b/docs/modules/5_path_planning/bugplanner/bugplanner_main.rst
@@ -5,4 +5,12 @@ This is a 2D planning with Bug algorithm.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/BugPlanner/animation.gif
 
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: PathPlanning.BugPlanning.bug.main
+
+Reference
+~~~~~~~~~~~~
+
 - `ECE452 Bug Algorithms <https://web.archive.org/web/20201103052224/https://sites.google.com/site/ece452bugalgorithms/>`_
diff --git a/docs/modules/5_path_planning/catmull_rom_spline/catmull_rom_spline_main.rst b/docs/modules/5_path_planning/catmull_rom_spline/catmull_rom_spline_main.rst
index 72e558c486..fa2a2ff72b 100644
--- a/docs/modules/5_path_planning/catmull_rom_spline/catmull_rom_spline_main.rst
+++ b/docs/modules/5_path_planning/catmull_rom_spline/catmull_rom_spline_main.rst
@@ -88,8 +88,8 @@ Catmull-Rom Spline API
 
 This section provides an overview of the functions used for Catmull-Rom spline path planning.
 
-API
-++++
+Code Link
+++++++++++
 
 .. autofunction:: PathPlanning.Catmull_RomSplinePath.catmull_rom_spline_path.catmull_rom_point
 
diff --git a/docs/modules/5_path_planning/clothoid_path/clothoid_path_main.rst b/docs/modules/5_path_planning/clothoid_path/clothoid_path_main.rst
index 1e8cee694a..16d0ec03c1 100644
--- a/docs/modules/5_path_planning/clothoid_path/clothoid_path_main.rst
+++ b/docs/modules/5_path_planning/clothoid_path/clothoid_path_main.rst
@@ -73,6 +73,11 @@ The final clothoid path can be calculated with the path parameters and Fresnel i
         &y(s)=y_{0}+\int_{0}^{s} \sin \left(\frac{1}{2} \kappa^{\prime} \tau^{2}+\kappa \tau+\vartheta_{0}\right) \mathrm{d} \tau
         \end{aligned}
 
+Code Link
+~~~~~~~~~~~~~
+
+.. autofunction:: PathPlanning.ClothoidPath.clothoid_path_planner.generate_clothoid_path
+
 
 References
 ~~~~~~~~~~
diff --git a/docs/modules/5_path_planning/coverage_path/coverage_path_main.rst b/docs/modules/5_path_planning/coverage_path/coverage_path_main.rst
index 0a688b5ed2..eaa876c80b 100644
--- a/docs/modules/5_path_planning/coverage_path/coverage_path_main.rst
+++ b/docs/modules/5_path_planning/coverage_path/coverage_path_main.rst
@@ -8,6 +8,11 @@ This is a 2D grid based sweep coverage path planner simulation:
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/GridBasedSweepCPP/animation.gif
 
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.GridBasedSweepCPP.grid_based_sweep_coverage_path_planner.planning
+
 Spiral Spanning Tree
 ~~~~~~~~~~~~~~~~~~~~
 
@@ -17,6 +22,14 @@ This is a 2D grid based spiral spanning tree coverage path planner simulation:
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/SpiralSpanningTreeCPP/animation2.gif
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/SpiralSpanningTreeCPP/animation3.gif
 
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.SpiralSpanningTreeCPP.spiral_spanning_tree_coverage_path_planner.main
+
+Reference
++++++++++++++
+
 - `Spiral-STC: An On-Line Coverage Algorithm of Grid Environments by a Mobile Robot <https://ieeexplore.ieee.org/abstract/document/1013479>`_
 
 
@@ -29,6 +42,14 @@ This is a 2D grid based wavefront coverage path planner simulation:
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation2.gif
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/WavefrontCPP/animation3.gif
 
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.WavefrontCPP.wavefront_coverage_path_planner.wavefront
+
+Reference
++++++++++++++
+
 - `Planning paths of complete coverage of an unstructured environment by a mobile robot <https://pinkwink.kr/attachment/cfile3.uf@1354654A4E8945BD13FE77.pdf>`_
 
 
diff --git a/docs/modules/5_path_planning/cubic_spline/cubic_spline_main.rst b/docs/modules/5_path_planning/cubic_spline/cubic_spline_main.rst
index 33471f7c85..a110217a2e 100644
--- a/docs/modules/5_path_planning/cubic_spline/cubic_spline_main.rst
+++ b/docs/modules/5_path_planning/cubic_spline/cubic_spline_main.rst
@@ -171,8 +171,8 @@ the second derivative by:
 
 These equations can be calculated by differentiating the cubic polynomial.
 
-API
-===
+Code Link
+==========
 
 This is the 1D cubic spline class API:
 
@@ -199,8 +199,8 @@ Curvature of each point can be also calculated analytically by:
 
 :math:`\kappa=\frac{y^{\prime \prime} x^{\prime}-x^{\prime \prime} y^{\prime}}{\left(x^{\prime2}+y^{\prime2}\right)^{\frac{2}{3}}}`
 
-API
-===
+Code Link
+==========
 
 .. autoclass:: PathPlanning.CubicSpline.cubic_spline_planner.CubicSpline2D
 	:members:
diff --git a/docs/modules/5_path_planning/dubins_path/dubins_path_main.rst b/docs/modules/5_path_planning/dubins_path/dubins_path_main.rst
index 12172fb51e..5a3d14464b 100644
--- a/docs/modules/5_path_planning/dubins_path/dubins_path_main.rst
+++ b/docs/modules/5_path_planning/dubins_path/dubins_path_main.rst
@@ -62,7 +62,7 @@ You can generate a path from these information and the maximum curvature informa
 A path type which has minimum course length among 6 types is selected,
 and then a path is constructed based on the selected type and its distances.
 
-API
+Code Link
 ~~~~~~~~~~~~~~~~~~~~
 
 .. autofunction:: PathPlanning.DubinsPath.dubins_path_planner.plan_dubins_path
diff --git a/docs/modules/5_path_planning/dynamic_window_approach/dynamic_window_approach_main.rst b/docs/modules/5_path_planning/dynamic_window_approach/dynamic_window_approach_main.rst
index d645838597..ac5322df94 100644
--- a/docs/modules/5_path_planning/dynamic_window_approach/dynamic_window_approach_main.rst
+++ b/docs/modules/5_path_planning/dynamic_window_approach/dynamic_window_approach_main.rst
@@ -5,7 +5,17 @@ Dynamic Window Approach
 
 This is a 2D navigation sample code with Dynamic Window Approach.
 
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif
+
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.DynamicWindowApproach.dynamic_window_approach.dwa_control
+
+
+Reference
+~~~~~~~~~~~~
+
 -  `The Dynamic Window Approach to Collision
    Avoidance <https://www.ri.cmu.edu/pub_files/pub1/fox_dieter_1997_1/fox_dieter_1997_1.pdf>`__
 
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DynamicWindowApproach/animation.gif
diff --git a/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst b/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst
index 8a3e517105..d0109d4ec3 100644
--- a/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst
+++ b/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst
@@ -5,6 +5,11 @@ This is a path planning with Elastic Bands.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ElasticBands/animation.gif
 
+Code Link
++++++++++++++
+
+.. autoclass:: PathPlanning.ElasticBands.elastic_bands.ElasticBands
+
 
 Core Concept
 ~~~~~~~~~~~~
@@ -69,6 +74,6 @@ Dynamic Path Maintenance
 - Remove redundant nodes if adjacent nodes are too close
 
 References
-~~~~~~~~~~~~~~~~~~~~~~~
++++++++++++++
 
 -  `Elastic Bands: Connecting Path Planning and Control <http://www8.cs.umu.se/research/ifor/dl/Control/elastic%20bands.pdf>`__
diff --git a/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst b/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst
index 82e0a71044..9ee343e8a7 100644
--- a/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst
+++ b/docs/modules/5_path_planning/eta3_spline/eta3_spline_main.rst
@@ -7,7 +7,14 @@ Eta^3 Spline path planning
 
 This is a path planning with Eta^3 spline.
 
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autoclass:: PathPlanning.Eta3SplineTrajectory.eta3_spline_trajectory.Eta3SplineTrajectory
+
+
 Reference
+~~~~~~~~~~~~~~~
 
 -  `\\eta^3-Splines for the Smooth Path Generation of Wheeled Mobile
    Robots <https://ieeexplore.ieee.org/document/4339545/>`__
diff --git a/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst b/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst
index 371d536e3f..0f84d381ea 100644
--- a/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst
+++ b/docs/modules/5_path_planning/frenet_frame_path/frenet_frame_path_main.rst
@@ -7,6 +7,12 @@ The cyan line is the target course and black crosses are obstacles.
 
 The red line is predicted path.
 
+Code Link
+~~~~~~~~~~~~~~
+
+.. autofunction:: PathPlanning.FrenetOptimalTrajectory.frenet_optimal_trajectory.main
+
+
 High Speed and Velocity Keeping Scenario
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
diff --git a/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst b/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst
index bf82c9f391..c4aa6882aa 100644
--- a/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst
+++ b/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst
@@ -10,6 +10,12 @@ This is a 2D grid based path planning with Breadth first search algorithm.
 
 In the animation, cyan points are searched nodes.
 
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.BreadthFirstSearch.breadth_first_search.BreadthFirstSearchPlanner
+
+
 Depth First Search
 ~~~~~~~~~~~~~~~~~~~~
 
@@ -19,6 +25,12 @@ This is a 2D grid based path planning with Depth first search algorithm.
 
 In the animation, cyan points are searched nodes.
 
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.DepthFirstSearch.depth_first_search.DepthFirstSearchPlanner
+
+
 .. _dijkstra:
 
 Dijkstra algorithm
@@ -30,6 +42,12 @@ This is a 2D grid based shortest path planning with Dijkstra's algorithm.
 
 In the animation, cyan points are searched nodes.
 
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.Dijkstra.dijkstra.DijkstraPlanner
+
+
 .. _a*-algorithm:
 
 A\* algorithm
@@ -43,6 +61,12 @@ In the animation, cyan points are searched nodes.
 
 Its heuristic is 2D Euclid distance.
 
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.AStar.a_star.AStarPlanner
+
+
 Bidirectional A\* algorithm
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -52,6 +76,12 @@ This is a 2D grid based shortest path planning with bidirectional A star algorit
 
 In the animation, cyan points are searched nodes.
 
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.BidirectionalAStar.bidirectional_a_star.BidirectionalAStarPlanner
+
+
 .. _D*-algorithm:
 
 D\* algorithm
@@ -63,7 +93,14 @@ This is a 2D grid based shortest path planning with D star algorithm.
 
 The animation shows a robot finding its path avoiding an obstacle using the D* search algorithm.
 
+Code Link
++++++++++++++
+
+.. autoclass:: PathPlanning.DStar.dstar.Dstar
+
+
 Reference
+++++++++++++
 
 -  `D* search Wikipedia <https://en.wikipedia.org/wiki/D*>`__
 
@@ -74,7 +111,13 @@ This is a 2D grid based path planning and replanning with D star lite algorithm.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/DStarLite/animation.gif
 
+Code Link
++++++++++++++
+
+.. autoclass:: PathPlanning.DStarLite.d_star_lite.DStarLite
+
 Reference
+++++++++++++
 
 - `Improved Fast Replanning for Robot Navigation in Unknown Terrain <http://www.cs.cmu.edu/~maxim/files/dlite_icra02.pdf>`_
 
@@ -88,7 +131,14 @@ This is a 2D grid based path planning with Potential Field algorithm.
 
 In the animation, the blue heat map shows potential value on each grid.
 
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.PotentialFieldPlanning.potential_field_planning.potential_field_planning
+
+
 Reference
+++++++++++++
 
 -  `Robotic Motion Planning:Potential
    Functions <https://www.cs.cmu.edu/~motionplanning/lecture/Chap4-Potential-Field_howie.pdf>`__
diff --git a/docs/modules/5_path_planning/hybridastar/hybridastar_main.rst b/docs/modules/5_path_planning/hybridastar/hybridastar_main.rst
index a9876fa3d4..36f340e0c2 100644
--- a/docs/modules/5_path_planning/hybridastar/hybridastar_main.rst
+++ b/docs/modules/5_path_planning/hybridastar/hybridastar_main.rst
@@ -4,3 +4,8 @@ Hybrid a star
 This is a simple vehicle model based hybrid A\* path planner.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/HybridAStar/animation.gif
+
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.HybridAStar.hybrid_a_star.hybrid_a_star_planning
diff --git a/docs/modules/5_path_planning/lqr_path/lqr_path_main.rst b/docs/modules/5_path_planning/lqr_path/lqr_path_main.rst
index 788442ff63..1eb1e4d840 100644
--- a/docs/modules/5_path_planning/lqr_path/lqr_path_main.rst
+++ b/docs/modules/5_path_planning/lqr_path/lqr_path_main.rst
@@ -4,3 +4,8 @@ LQR based path planning
 A sample code using LQR based path planning for double integrator model.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRPlanner/animation.gif?raw=true
+
+Code Link
++++++++++++++
+
+.. autoclass:: PathPlanning.LQRPlanner.lqr_planner.LQRPlanner
diff --git a/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst b/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst
index 1c5df1c9cc..76472a6792 100644
--- a/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst
+++ b/docs/modules/5_path_planning/model_predictive_trajectory_generator/model_predictive_trajectory_generator_main.rst
@@ -6,6 +6,12 @@ generator.
 
 This algorithm is used for state lattice planner.
 
+Code Link
+~~~~~~~~~~~~~
+
+.. autofunction:: PathPlanning.ModelPredictiveTrajectoryGenerator.trajectory_generator.optimize_trajectory
+
+
 Path optimization sample
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -17,6 +23,7 @@ Lookup table generation sample
 .. image:: lookup_table.png
 
 Reference
+~~~~~~~~~~~~
 
 -  `Optimal rough terrain trajectory generation for wheeled mobile
    robots <https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
diff --git a/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst b/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst
index f816c5c1b9..d58d1e2633 100644
--- a/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst
+++ b/docs/modules/5_path_planning/prm_planner/prm_planner_main.rst
@@ -13,7 +13,14 @@ Cyan crosses means searched points with Dijkstra method,
 
 The red line is the final path of PRM.
 
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: PathPlanning.ProbabilisticRoadMap.probabilistic_road_map.prm_planning
+
+
 Reference
+~~~~~~~~~~~
 
 -  `Probabilistic roadmap -
    Wikipedia <https://en.wikipedia.org/wiki/Probabilistic_roadmap>`__
diff --git a/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst b/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst
index 66c3001c05..c7bc3fb55c 100644
--- a/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst
+++ b/docs/modules/5_path_planning/quintic_polynomials_planner/quintic_polynomials_planner_main.rst
@@ -9,6 +9,11 @@ Motion planning with quintic polynomials.
 It can calculate 2D path, velocity, and acceleration profile based on
 quintic polynomials.
 
+Code Link
+~~~~~~~~~~~~~~~
+
+.. autofunction:: PathPlanning.QuinticPolynomialsPlanner.quintic_polynomials_planner.quintic_polynomials_planner
+
 
 
 Quintic polynomials for one dimensional robot motion
diff --git a/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst b/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst
index a4a5d28e01..4dd54d7c97 100644
--- a/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst
+++ b/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst
@@ -5,6 +5,12 @@ A sample code with Reeds Shepp path planning.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ReedsSheppPath/animation.gif?raw=true
 
+Code Link
+==============
+
+.. autofunction:: PathPlanning.ReedsSheppPath.reeds_shepp_path_planning.reeds_shepp_path_planning
+
+
 Mathematical Description of Individual Path Types
 =================================================
 Here is an overview of mathematical derivations of formulae for individual path types.
@@ -381,6 +387,7 @@ Hence, we have:
 
 
 Reference
+=============
 
 -  `15.3.2 Reeds-Shepp
    Curves <https://lavalle.pl/planning/node822.html>`__
diff --git a/docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_1.png b/docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_1.png
similarity index 100%
rename from docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_1.png
rename to docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_1.png
diff --git a/docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_3.png b/docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_3.png
similarity index 100%
rename from docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_3.png
rename to docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_3.png
diff --git a/docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_4.png b/docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_4.png
similarity index 100%
rename from docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_4.png
rename to docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_4.png
diff --git a/docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_5.png b/docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_5.png
similarity index 100%
rename from docs/modules/5_path_planning/closed_loop_rrt_star_car/Figure_5.png
rename to docs/modules/5_path_planning/rrt/closed_loop_rrt_star_car/Figure_5.png
diff --git a/docs/modules/5_path_planning/rrt/rrt_main.rst b/docs/modules/5_path_planning/rrt/rrt_main.rst
index da3a4957a5..1bd5497f4c 100644
--- a/docs/modules/5_path_planning/rrt/rrt_main.rst
+++ b/docs/modules/5_path_planning/rrt/rrt_main.rst
@@ -14,6 +14,12 @@ This is a simple path planning code with Rapidly-Exploring Random Trees
 Black circles are obstacles, green line is a searched tree, red crosses
 are start and goal positions.
 
+Code Link
+^^^^^^^^^^
+
+.. autoclass:: PathPlanning.RRT.rrt.RRT
+
+
 .. include:: rrt_star.rst
 
 
@@ -24,6 +30,12 @@ RRT with dubins path
 
 Path planning for a car robot with RRT and dubins path planner.
 
+Code Link
+^^^^^^^^^^
+
+.. autoclass:: PathPlanning.RRTDubins.rrt_dubins.RRTDubins
+
+
 .. _rrt*-with-dubins-path:
 
 RRT\* with dubins path
@@ -33,6 +45,12 @@ RRT\* with dubins path
 
 Path planning for a car robot with RRT\* and dubins path planner.
 
+Code Link
+^^^^^^^^^^
+
+.. autoclass:: PathPlanning.RRTStarDubins.rrt_star_dubins.RRTStarDubins
+
+
 .. _rrt*-with-reeds-sheep-path:
 
 RRT\* with reeds-sheep path
@@ -42,6 +60,12 @@ RRT\* with reeds-sheep path
 
 Path planning for a car robot with RRT\* and reeds sheep path planner.
 
+Code Link
+^^^^^^^^^^
+
+.. autoclass:: PathPlanning.RRTStarReedsShepp.rrt_star_reeds_shepp.RRTStarReedsShepp
+
+
 .. _informed-rrt*:
 
 Informed RRT\*
@@ -53,7 +77,14 @@ This is a path planning code with Informed RRT*.
 
 The cyan ellipse is the heuristic sampling domain of Informed RRT*.
 
+Code Link
+^^^^^^^^^^
+
+.. autoclass:: PathPlanning.InformedRRTStar.informed_rrt_star.InformedRRTStar
+
+
 Reference
+^^^^^^^^^^
 
 -  `Informed RRT\*: Optimal Sampling-based Path Planning Focused via
    Direct Sampling of an Admissible Ellipsoidal
@@ -68,12 +99,20 @@ Batch Informed RRT\*
 
 This is a path planning code with Batch Informed RRT*.
 
+Code Link
+^^^^^^^^^^
+
+.. autoclass:: PathPlanning.BatchInformedRRTStar.batch_informed_rrt_star.BITStar
+
+
 Reference
+^^^^^^^^^^^
 
 -  `Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the
    Heuristically Guided Search of Implicit Random Geometric
    Graphs <https://arxiv.org/abs/1405.5848>`__
 
+
 .. _closed-loop-rrt*:
 
 Closed Loop RRT\*
@@ -87,7 +126,14 @@ In this code, pure-pursuit algorithm is used for steering control,
 
 PID is used for speed control.
 
+Code Link
+^^^^^^^^^^
+
+.. autoclass:: PathPlanning.ClosedLoopRRTStar.closed_loop_rrt_star_car.ClosedLoopRRTStar
+
+
 Reference
+^^^^^^^^^^^^
 
 -  `Motion Planning in Complex Environments using Closed-loop
    Prediction <https://acl.mit.edu/papers/KuwataGNC08.pdf>`__
@@ -98,6 +144,7 @@ Reference
 -  `[1601.06326] Sampling-based Algorithms for Optimal Motion Planning
    Using Closed-loop Prediction <https://arxiv.org/abs/1601.06326>`__
 
+
 .. _lqr-rrt*:
 
 LQR-RRT\*
@@ -109,7 +156,14 @@ A double integrator motion model is used for LQR local planner.
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/LQRRRTStar/animation.gif
 
+Code Link
+^^^^^^^^^^
+
+.. autoclass:: PathPlanning.LQRRRTStar.lqr_rrt_star.LQRRRTStar
+
+
 Reference
+~~~~~~~~~~~~~
 
 -  `LQR-RRT\*: Optimal Sampling-Based Motion Planning with Automatically
    Derived Extension
diff --git a/docs/modules/5_path_planning/rrt/rrt_star.rst b/docs/modules/5_path_planning/rrt/rrt_star.rst
index 6deb6b9172..960b9976d5 100644
--- a/docs/modules/5_path_planning/rrt/rrt_star.rst
+++ b/docs/modules/5_path_planning/rrt/rrt_star.rst
@@ -7,6 +7,12 @@ This is a path planning code with RRT\*
 
 Black circles are obstacles, green line is a searched tree, red crosses are start and goal positions.
 
+Code Link
+^^^^^^^^^^
+
+.. autoclass:: PathPlanning.RRTStar.rrt_star.RRTStar
+
+
 Simulation
 ^^^^^^^^^^
 
diff --git a/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst b/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst
index bf89ac11ae..9f8cc0c50f 100644
--- a/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst
+++ b/docs/modules/5_path_planning/state_lattice_planner/state_lattice_planner_main.rst
@@ -12,17 +12,34 @@ Uniform polar sampling
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/UniformPolarSampling.gif
 
+Code Link
+^^^^^^^^^^^^^
+
+.. autofunction:: PathPlanning.StateLatticePlanner.state_lattice_planner.calc_uniform_polar_states
+
+
 Biased polar sampling
 ~~~~~~~~~~~~~~~~~~~~~
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/BiasedPolarSampling.gif
 
+Code Link
+^^^^^^^^^^^^^
+.. autofunction:: PathPlanning.StateLatticePlanner.state_lattice_planner.calc_biased_polar_states
+
+
 Lane sampling
 ~~~~~~~~~~~~~
 
 .. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/StateLatticePlanner/LaneSampling.gif
 
+Code Link
+^^^^^^^^^^^^^
+.. autofunction:: PathPlanning.StateLatticePlanner.state_lattice_planner.calc_lane_states
+
+
 Reference
+~~~~~~~~~~~~~~~
 
 -  `Optimal rough terrain trajectory generation for wheeled mobile
    robots <https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328>`__
diff --git a/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst
index 04001d4504..9517e95b31 100644
--- a/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst
+++ b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst
@@ -33,6 +33,10 @@ After::
 
 When starting at (1, 11) in the structured obstacle arrangement (second of the two gifs above).
 
+Code Link
+^^^^^^^^^^^^^
+.. autoclass:: PathPlanning.TimeBasedPathPlanning.SpaceTimeAStar.SpaceTimeAStar
+
 
 Safe Interval Path Planning
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -73,6 +77,11 @@ Arrangement 1 starting at (1, 18)::
 
 .. image:: https://raw.githubusercontent.com/AtsushiSakai/PythonRoboticsGifs/refs/heads/master/PathPlanning/TimeBasedPathPlanning/SafeIntervalPathPlanner/path_animation2.gif
 
+Code Link
+^^^^^^^^^^^^^
+.. autoclass:: PathPlanning.TimeBasedPathPlanning.SafeInterval.SafeIntervalPathPlanner
+
+
 References
 ~~~~~~~~~~~
 
diff --git a/docs/modules/5_path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst b/docs/modules/5_path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst
index 3c9b7c008c..aac96d6e19 100644
--- a/docs/modules/5_path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst
+++ b/docs/modules/5_path_planning/visibility_road_map_planner/visibility_road_map_planner_main.rst
@@ -13,6 +13,11 @@ red crosses are visibility nodes, and blue lines area collision free visibility
 
 The red line is the final path searched by dijkstra algorithm frm the visibility graphs.
 
+Code Link
+~~~~~~~~~~~~
+.. autoclass:: PathPlanning.VisibilityRoadMap.visibility_road_map.VisibilityRoadMap
+
+
 Algorithms
 ~~~~~~~~~~
 
@@ -64,7 +69,7 @@ The red line is searched path in the figure:
 You can find the details of Dijkstra algorithm in :ref:`dijkstra`.
 
 References
-^^^^^^^^^^
+~~~~~~~~~~~~
 
 - `Visibility graph - Wikipedia <https://en.wikipedia.org/wiki/Visibility_graph>`_
 
diff --git a/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst b/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst
index 65e0e53465..a9a41aab74 100644
--- a/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst
+++ b/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst
@@ -11,7 +11,13 @@ Cyan crosses mean searched points with Dijkstra method,
 
 The red line is the final path of Vornoi Road-Map.
 
+Code Link
+~~~~~~~~~~~~~~~
+.. autoclass:: PathPlanning.VoronoiRoadMap.voronoi_road_map.VoronoiRoadMapPlanner
+
+
 Reference
+~~~~~~~~~~~~
 
 -  `Robotic Motion Planning <https://www.cs.cmu.edu/~motionplanning/lecture/Chap5-RoadMap-Methods_howie.pdf>`__
 
diff --git a/tests/test_batch_informed_rrt_star.py b/tests/test_batch_informed_rrt_star.py
index 3ad78bdb23..cf0a9827a8 100644
--- a/tests/test_batch_informed_rrt_star.py
+++ b/tests/test_batch_informed_rrt_star.py
@@ -1,7 +1,7 @@
 import random
 
 import conftest
-from PathPlanning.BatchInformedRRTStar import batch_informed_rrtstar as m
+from PathPlanning.BatchInformedRRTStar import batch_informed_rrt_star as m
 
 
 def test_1():

From 0c82dde2be24e5f3955018caa061750d7404978d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 6 May 2025 07:22:51 +0900
Subject: [PATCH 1016/1080] build(deps): bump ruff from 0.11.7 to 0.11.8 in
 /requirements (#1215)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.7 to 0.11.8.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.11.7...0.11.8)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.11.8
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 88cbb21f81..e78b9b8d51 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.3.5 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.15.0 # For unit test
-ruff == 0.11.7 # For unit test
+ruff == 0.11.8 # For unit test

From 292c9dc613d1b0ace3d25ca8a0295e19549516f2 Mon Sep 17 00:00:00 2001
From: atm <72185056+sutatoruta@users.noreply.github.com>
Date: Sat, 10 May 2025 09:34:19 +0900
Subject: [PATCH 1017/1080] fix broken links (#1216)

---
 .github/pull_request_template.md |  2 +-
 README.md                        | 12 ++++++------
 users_comments.md                |  2 +-
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
index b6ac52efa2..c0d9f7eab2 100644
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -1,7 +1,7 @@
 <!-- 
 Thanks for contributing a pull request! 
 Please check this document before submitting:
-- [How to contribute](https://atsushisakai.github.io/PythonRobotics/how_to_contribute.html#adding-a-new-algorithm-example)
+- [How to contribute](https://atsushisakai.github.io/PythonRobotics/modules/0_getting_started/3_how_to_contribute.html#adding-a-new-algorithm-example)
 
 Note that this is my hobby project; I appreciate your
 patience during the review process.
diff --git a/README.md b/README.md
index 05fd0262df..85be274adb 100644
--- a/README.md
+++ b/README.md
@@ -87,7 +87,7 @@ Features:
 
 See this documentation 
 
-- [Getting Started — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/getting_started.html#what-is-pythonrobotics)
+- [Getting Started — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/modules/0_getting_started/1_what_is_python_robotics.html)
 
 or this Youtube video:
 
@@ -170,7 +170,7 @@ All animation gifs are stored here: [AtsushiSakai/PythonRoboticsGifs: Animation
 
 Reference
 
-- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.html)
+- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/2_localization/extended_kalman_filter_localization_files/extended_kalman_filter_localization.html)
 
 ## Particle filter localization
 
@@ -490,7 +490,7 @@ Reference
 
 This is a simulation of moving to a pose control
 
-![2](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif)
+![2](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Control/move_to_pose/animation.gif)
 
 Reference
 
@@ -541,7 +541,7 @@ Path tracking simulation with iterative linear model predictive speed and steeri
 
 Reference
 
-- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.html)
+- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/6_path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.html)
 
 - [Real\-time Model Predictive Control \(MPC\), ACADO, Python \| Work\-is\-Playing](http://grauonline.de/wordpress/?page_id=3244)
 
@@ -553,7 +553,7 @@ A motion planning and path tracking simulation with NMPC of C-GMRES
 
 Reference
 
-- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.html)
+- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/6_path_tracking/cgmres_nmpc/cgmres_nmpc.html)
 
 
 # Arm Navigation
@@ -593,7 +593,7 @@ This is a 3d trajectory generation simulation for a rocket powered landing.
 
 Reference
 
-- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/aerial_navigation/rocket_powered_landing/rocket_powered_landing.html)
+- [documentation](https://atsushisakai.github.io/PythonRobotics/modules/8_aerial_navigation/rocket_powered_landing/rocket_powered_landing.html)
 
 # Bipedal
 
diff --git a/users_comments.md b/users_comments.md
index 1674f21377..30dc2d2d85 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -6,7 +6,7 @@ This is an electric wheelchair control demo by [Katsushun89](https://github.com/
 
 [WHILL Model CR](https://whill.jp/model-cr) is the control target, [M5Stack](https://m5stack.com/) is used for the controller, and [toio](https://toio.io/) is used for the control input device.
 
-[Move to a Pose Control — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/modules/control/move_to_a_pose_control/move_to_a_pose_control.html) is used for its control algorithm ([code link](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/move_to_pose/move_to_pose.py)).
+[Move to a Pose Control — PythonRobotics documentation](https://atsushisakai.github.io/PythonRobotics/modules/6_path_tracking/move_to_a_pose/move_to_a_pose.html) is used for its control algorithm ([code link](https://github.com/AtsushiSakai/PythonRobotics/blob/master/PathTracking/move_to_pose/move_to_pose.py)).
 
 ![1](https://github.com/AtsushiSakai/PythonRoboticsGifs/blob/master/Users/WHILL_Model_CR_with_move_to_a_pose/WHLL_Model_CR_with_move_to_a_pose.gif)
 

From cfaab296a4212948333f6316e98575ef904a0932 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 13 May 2025 21:55:01 +0900
Subject: [PATCH 1018/1080] build(deps): bump ruff from 0.11.8 to 0.11.9 in
 /requirements (#1218)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.8 to 0.11.9.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.11.8...0.11.9)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.11.9
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index e78b9b8d51..32a9b31938 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.3.5 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.15.0 # For unit test
-ruff == 0.11.8 # For unit test
+ruff == 0.11.9 # For unit test

From c9051758e74896d35c633d506c1df59d49220463 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 20 May 2025 22:32:30 +0900
Subject: [PATCH 1019/1080] build(deps): bump ruff from 0.11.9 to 0.11.10 in
 /requirements (#1220)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.9 to 0.11.10.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.11.9...0.11.10)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.11.10
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 32a9b31938..4463aacb36 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.3.5 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.15.0 # For unit test
-ruff == 0.11.9 # For unit test
+ruff == 0.11.10 # For unit test

From 453c25e461a0cda5c8a572c388307b41c92759a2 Mon Sep 17 00:00:00 2001
From: Yutharsan <yutharsan.22@cse.mrt.ac.lk>
Date: Sun, 25 May 2025 10:27:02 +0530
Subject: [PATCH 1020/1080] Add root directory to system path for lattice
 planner (#1221)

---
 PathPlanning/StateLatticePlanner/state_lattice_planner.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/PathPlanning/StateLatticePlanner/state_lattice_planner.py b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
index 05f8df78f8..554cd0f3b7 100644
--- a/PathPlanning/StateLatticePlanner/state_lattice_planner.py
+++ b/PathPlanning/StateLatticePlanner/state_lattice_planner.py
@@ -22,7 +22,9 @@
 import numpy as np
 import math
 import pathlib
+
 sys.path.append(str(pathlib.Path(__file__).parent.parent))
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
 
 import ModelPredictiveTrajectoryGenerator.trajectory_generator as planner
 import ModelPredictiveTrajectoryGenerator.motion_model as motion_model

From bdf50dffcdc72d923b5e3d2e25be5e237d0849f6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 May 2025 21:36:36 +0900
Subject: [PATCH 1021/1080] build(deps): bump
 larsoner/circleci-artifacts-redirector-action (#1224)

Bumps [larsoner/circleci-artifacts-redirector-action](https://github.com/larsoner/circleci-artifacts-redirector-action) from 1.0.0 to 1.1.0.
- [Release notes](https://github.com/larsoner/circleci-artifacts-redirector-action/releases)
- [Commits](https://github.com/larsoner/circleci-artifacts-redirector-action/compare/v1.0.0...v1.1.0)

---
updated-dependencies:
- dependency-name: larsoner/circleci-artifacts-redirector-action
  dependency-version: 1.1.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/circleci-artifacts-redirector.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/circleci-artifacts-redirector.yml b/.github/workflows/circleci-artifacts-redirector.yml
index 78eb479aa1..692942cddd 100644
--- a/.github/workflows/circleci-artifacts-redirector.yml
+++ b/.github/workflows/circleci-artifacts-redirector.yml
@@ -6,7 +6,7 @@ jobs:
     name: Run CircleCI artifacts redirector!!
     steps:
       - name: run-circleci-artifacts-redirector
-        uses: larsoner/circleci-artifacts-redirector-action@v1.0.0
+        uses: larsoner/circleci-artifacts-redirector-action@v1.1.0
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           api-token: ${{ secrets.CIRCLECI_TOKEN }}

From 74610fd56bfe7eee02054c48afaa7328bec0d6b7 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 27 May 2025 21:36:59 +0900
Subject: [PATCH 1022/1080] build(deps): bump ruff from 0.11.10 to 0.11.11 in
 /requirements (#1225)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.10 to 0.11.11.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.11.10...0.11.11)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.11.11
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 4463aacb36..cc15d4e335 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.3.5 # For unit test
 pytest-xdist == 3.6.1 # For unit test
 mypy == 1.15.0 # For unit test
-ruff == 0.11.10 # For unit test
+ruff == 0.11.11 # For unit test

From 26264a343269cbde9f4d3c6d9016684ed4f32d5c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 3 Jun 2025 07:32:07 +0900
Subject: [PATCH 1023/1080] build(deps): bump pytest-xdist from 3.6.1 to 3.7.0
 in /requirements (#1226)

Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.6.1 to 3.7.0.
- [Release notes](https://github.com/pytest-dev/pytest-xdist/releases)
- [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.6.1...v3.7.0)

---
updated-dependencies:
- dependency-name: pytest-xdist
  dependency-version: 3.7.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index cc15d4e335..78ca18f42d 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,6 +4,6 @@ matplotlib == 3.10.1
 cvxpy == 1.6.5
 ecos == 2.0.14
 pytest == 8.3.5 # For unit test
-pytest-xdist == 3.6.1 # For unit test
+pytest-xdist == 3.7.0 # For unit test
 mypy == 1.15.0 # For unit test
 ruff == 0.11.11 # For unit test

From 4b4d377a541656f6f063c05cbc5e8f0883ad3fd3 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 3 Jun 2025 09:26:11 +0900
Subject: [PATCH 1024/1080] build(deps): bump pytest from 8.3.5 to 8.4.0 in
 /requirements (#1228)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.3.5 to 8.4.0.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/8.3.5...8.4.0)

---
updated-dependencies:
- dependency-name: pytest
  dependency-version: 8.4.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 78ca18f42d..5a21fd2b27 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,7 +3,7 @@ scipy == 1.15.2
 matplotlib == 3.10.1
 cvxpy == 1.6.5
 ecos == 2.0.14
-pytest == 8.3.5 # For unit test
+pytest == 8.4.0 # For unit test
 pytest-xdist == 3.7.0 # For unit test
 mypy == 1.15.0 # For unit test
 ruff == 0.11.11 # For unit test

From eee0031eb862e22f5d108d32a023327d87ededec Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 3 Jun 2025 23:11:13 +0900
Subject: [PATCH 1025/1080] build(deps): bump mypy from 1.15.0 to 1.16.0 in
 /requirements (#1227)

Bumps [mypy](https://github.com/python/mypy) from 1.15.0 to 1.16.0.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.15.0...v1.16.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-version: 1.16.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 5a21fd2b27..f154142331 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ cvxpy == 1.6.5
 ecos == 2.0.14
 pytest == 8.4.0 # For unit test
 pytest-xdist == 3.7.0 # For unit test
-mypy == 1.15.0 # For unit test
+mypy == 1.16.0 # For unit test
 ruff == 0.11.11 # For unit test

From ef8c154c73a562f9a90857559e4f380df6968893 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 4 Jun 2025 12:59:20 +0900
Subject: [PATCH 1026/1080] build(deps): bump ruff from 0.11.11 to 0.11.12 in
 /requirements (#1229)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.11 to 0.11.12.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.11.11...0.11.12)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.11.12
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index f154142331..0c0719cca5 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.0 # For unit test
 pytest-xdist == 3.7.0 # For unit test
 mypy == 1.16.0 # For unit test
-ruff == 0.11.11 # For unit test
+ruff == 0.11.12 # For unit test

From a8a45033c6daffcb6e5ccc8b02727d407314ee6a Mon Sep 17 00:00:00 2001
From: Zichao Yang <yangtzech@qq.com>
Date: Sat, 7 Jun 2025 19:05:32 +0800
Subject: [PATCH 1027/1080] feat: Add reverse mode in pure pursuit (#1194)

* feat: Add vehicle model rendering function and pause simulation function

* feat: Add support for reverse mode and related function adjustments

* feat: Add reverse mode test case

* feat: Limit the maximum steering angle to improve path tracking control

* feat: Add a newline for improved readability in plot_arrow function

* fix: Replace max/min with np.clip for steering angle limitation in pure pursuit control
---
 PathTracking/pure_pursuit/pure_pursuit.py | 168 +++++++++++++++++++---
 tests/test_pure_pursuit.py                |   4 +
 2 files changed, 155 insertions(+), 17 deletions(-)

diff --git a/PathTracking/pure_pursuit/pure_pursuit.py b/PathTracking/pure_pursuit/pure_pursuit.py
index ff995033a9..48453927ab 100644
--- a/PathTracking/pure_pursuit/pure_pursuit.py
+++ b/PathTracking/pure_pursuit/pure_pursuit.py
@@ -10,6 +10,11 @@
 import math
 import matplotlib.pyplot as plt
 
+import sys
+import pathlib
+sys.path.append(str(pathlib.Path(__file__).parent.parent.parent))
+from utils.angle import angle_mod
+
 # Parameters
 k = 0.1  # look forward gain
 Lfc = 2.0  # [m] look-ahead distance
@@ -17,26 +22,37 @@
 dt = 0.1  # [s] time tick
 WB = 2.9  # [m] wheel base of vehicle
 
-show_animation = True
 
+# Vehicle parameters
+LENGTH = WB + 1.0  # Vehicle length
+WIDTH = 2.0  # Vehicle width
+WHEEL_LEN = 0.6  # Wheel length
+WHEEL_WIDTH = 0.2  # Wheel width
+MAX_STEER = math.pi / 4  # Maximum steering angle [rad]
 
-class State:
 
-    def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0):
+show_animation = True
+pause_simulation = False  # Flag for pause simulation
+is_reverse_mode = False   # Flag for reverse driving mode
+
+class State:
+    def __init__(self, x=0.0, y=0.0, yaw=0.0, v=0.0, is_reverse=False):
         self.x = x
         self.y = y
         self.yaw = yaw
         self.v = v
-        self.rear_x = self.x - ((WB / 2) * math.cos(self.yaw))
-        self.rear_y = self.y - ((WB / 2) * math.sin(self.yaw))
+        self.direction = -1 if is_reverse else 1  # Direction based on reverse flag
+        self.rear_x = self.x - self.direction * ((WB / 2) * math.cos(self.yaw))
+        self.rear_y = self.y - self.direction * ((WB / 2) * math.sin(self.yaw))
 
     def update(self, a, delta):
         self.x += self.v * math.cos(self.yaw) * dt
         self.y += self.v * math.sin(self.yaw) * dt
-        self.yaw += self.v / WB * math.tan(delta) * dt
+        self.yaw += self.direction * self.v / WB * math.tan(delta) * dt
+        self.yaw = angle_mod(self.yaw)
         self.v += a * dt
-        self.rear_x = self.x - ((WB / 2) * math.cos(self.yaw))
-        self.rear_y = self.y - ((WB / 2) * math.sin(self.yaw))
+        self.rear_x = self.x - self.direction * ((WB / 2) * math.cos(self.yaw))
+        self.rear_y = self.y - self.direction * ((WB / 2) * math.sin(self.yaw))
 
     def calc_distance(self, point_x, point_y):
         dx = self.rear_x - point_x
@@ -51,6 +67,7 @@ def __init__(self):
         self.y = []
         self.yaw = []
         self.v = []
+        self.direction = []
         self.t = []
 
     def append(self, t, state):
@@ -58,6 +75,7 @@ def append(self, t, state):
         self.y.append(state.y)
         self.yaw.append(state.yaw)
         self.v.append(state.v)
+        self.direction.append(state.direction)
         self.t.append(t)
 
 
@@ -117,14 +135,18 @@ def pure_pursuit_steer_control(state, trajectory, pind):
     if ind < len(trajectory.cx):
         tx = trajectory.cx[ind]
         ty = trajectory.cy[ind]
-    else:  # toward goal
+    else:
         tx = trajectory.cx[-1]
         ty = trajectory.cy[-1]
         ind = len(trajectory.cx) - 1
 
     alpha = math.atan2(ty - state.rear_y, tx - state.rear_x) - state.yaw
 
-    delta = math.atan2(2.0 * WB * math.sin(alpha) / Lf, 1.0)
+    # Reverse steering angle when reversing
+    delta = state.direction * math.atan2(2.0 * WB * math.sin(alpha) / Lf, 1.0)
+
+    # Limit steering angle to max value
+    delta = np.clip(delta, -MAX_STEER, MAX_STEER)
 
     return delta, ind
 
@@ -142,10 +164,111 @@ def plot_arrow(x, y, yaw, length=1.0, width=0.5, fc="r", ec="k"):
                   fc=fc, ec=ec, head_width=width, head_length=width)
         plt.plot(x, y)
 
+def plot_vehicle(x, y, yaw, steer=0.0, color='blue', is_reverse=False):
+    """
+    Plot vehicle model with four wheels
+    Args:
+        x, y: Vehicle center position
+        yaw: Vehicle heading angle
+        steer: Steering angle
+        color: Vehicle color
+        is_reverse: Flag for reverse mode
+    """
+    # Adjust heading angle in reverse mode
+    if is_reverse:
+        yaw = angle_mod(yaw + math.pi)  # Rotate heading by 180 degrees
+        steer = -steer  # Reverse steering direction
+
+    def plot_wheel(x, y, yaw, steer=0.0, color=color):
+        """Plot single wheel"""
+        wheel = np.array([
+            [-WHEEL_LEN/2, WHEEL_WIDTH/2],
+            [WHEEL_LEN/2, WHEEL_WIDTH/2],
+            [WHEEL_LEN/2, -WHEEL_WIDTH/2],
+            [-WHEEL_LEN/2, -WHEEL_WIDTH/2],
+            [-WHEEL_LEN/2, WHEEL_WIDTH/2]
+        ])
+
+        # Rotate wheel if steering
+        if steer != 0:
+            c, s = np.cos(steer), np.sin(steer)
+            rot_steer = np.array([[c, -s], [s, c]])
+            wheel = wheel @ rot_steer.T
+
+        # Apply vehicle heading rotation
+        c, s = np.cos(yaw), np.sin(yaw)
+        rot_yaw = np.array([[c, -s], [s, c]])
+        wheel = wheel @ rot_yaw.T
+
+        # Translate to position
+        wheel[:, 0] += x
+        wheel[:, 1] += y
+
+        # Plot wheel with color
+        plt.plot(wheel[:, 0], wheel[:, 1], color=color)
+
+    # Calculate vehicle body corners
+    corners = np.array([
+        [-LENGTH/2, WIDTH/2],
+        [LENGTH/2, WIDTH/2],
+        [LENGTH/2, -WIDTH/2],
+        [-LENGTH/2, -WIDTH/2],
+        [-LENGTH/2, WIDTH/2]
+    ])
+
+    # Rotation matrix
+    c, s = np.cos(yaw), np.sin(yaw)
+    Rot = np.array([[c, -s], [s, c]])
+
+    # Rotate and translate vehicle body
+    rotated = corners @ Rot.T
+    rotated[:, 0] += x
+    rotated[:, 1] += y
+
+    # Plot vehicle body
+    plt.plot(rotated[:, 0], rotated[:, 1], color=color)
+
+    # Plot wheels (darker color for front wheels)
+    front_color = 'darkblue'
+    rear_color = color
+    
+    # Plot four wheels
+    # Front left
+    plot_wheel(x + LENGTH/4 * c - WIDTH/2 * s,
+              y + LENGTH/4 * s + WIDTH/2 * c,
+              yaw, steer, front_color)
+    # Front right  
+    plot_wheel(x + LENGTH/4 * c + WIDTH/2 * s,
+              y + LENGTH/4 * s - WIDTH/2 * c,
+              yaw, steer, front_color)
+    # Rear left
+    plot_wheel(x - LENGTH/4 * c - WIDTH/2 * s,
+              y - LENGTH/4 * s + WIDTH/2 * c,
+              yaw, color=rear_color)
+    # Rear right
+    plot_wheel(x - LENGTH/4 * c + WIDTH/2 * s,
+              y - LENGTH/4 * s - WIDTH/2 * c,
+              yaw, color=rear_color)
+
+    # Add direction arrow
+    arrow_length = LENGTH/3
+    plt.arrow(x, y,
+             -arrow_length * math.cos(yaw) if is_reverse else arrow_length * math.cos(yaw),
+             -arrow_length * math.sin(yaw) if is_reverse else arrow_length * math.sin(yaw),
+             head_width=WIDTH/4, head_length=WIDTH/4,
+             fc='r', ec='r', alpha=0.5)
+
+# Keyboard event handler
+def on_key(event):
+    global pause_simulation
+    if event.key == ' ':  # Space key
+        pause_simulation = not pause_simulation
+    elif event.key == 'escape':
+        exit(0)
 
 def main():
     #  target course
-    cx = np.arange(0, 50, 0.5)
+    cx = -1 * np.arange(0, 50, 0.5) if is_reverse_mode else np.arange(0, 50, 0.5)
     cy = [math.sin(ix / 5.0) * ix / 2.0 for ix in cx]
 
     target_speed = 10.0 / 3.6  # [m/s]
@@ -153,7 +276,7 @@ def main():
     T = 100.0  # max simulation time
 
     # initial state
-    state = State(x=-0.0, y=-3.0, yaw=0.0, v=0.0)
+    state = State(x=-0.0, y=-3.0, yaw=math.pi if is_reverse_mode else 0.0, v=0.0, is_reverse=is_reverse_mode)
 
     lastIndex = len(cx) - 1
     time = 0.0
@@ -173,22 +296,33 @@ def main():
 
         time += dt
         states.append(time, state)
-
         if show_animation:  # pragma: no cover
             plt.cla()
             # for stopping simulation with the esc key.
-            plt.gcf().canvas.mpl_connect(
-                'key_release_event',
-                lambda event: [exit(0) if event.key == 'escape' else None])
-            plot_arrow(state.x, state.y, state.yaw)
+            plt.gcf().canvas.mpl_connect('key_release_event', on_key)
+            # Pass is_reverse parameter
+            plot_vehicle(state.x, state.y, state.yaw, di, is_reverse=is_reverse_mode)
             plt.plot(cx, cy, "-r", label="course")
             plt.plot(states.x, states.y, "-b", label="trajectory")
             plt.plot(cx[target_ind], cy[target_ind], "xg", label="target")
             plt.axis("equal")
             plt.grid(True)
             plt.title("Speed[km/h]:" + str(state.v * 3.6)[:4])
+            plt.legend()  # Add legend display
+
+            # Add pause state display
+            if pause_simulation:
+                plt.text(0.02, 0.95, 'PAUSED', transform=plt.gca().transAxes,
+                        bbox=dict(facecolor='red', alpha=0.5))
+
             plt.pause(0.001)
 
+            # Handle pause state
+            while pause_simulation:
+                plt.pause(0.1)  # Reduce CPU usage
+                if not plt.get_fignums():  # Check if window is closed
+                    exit(0)
+
     # Test
     assert lastIndex >= target_ind, "Cannot goal"
 
diff --git a/tests/test_pure_pursuit.py b/tests/test_pure_pursuit.py
index 0e0b83bf6c..89c1829514 100644
--- a/tests/test_pure_pursuit.py
+++ b/tests/test_pure_pursuit.py
@@ -6,6 +6,10 @@ def test1():
     m.show_animation = False
     m.main()
 
+def test_backward():
+    m.show_animation = False
+    m.is_reverse_mode = True
+    m.main()
 
 if __name__ == '__main__':
     conftest.run_this_test(__file__)

From 4ffd8e76e3429daaeb85a2db0764e7af71604af3 Mon Sep 17 00:00:00 2001
From: atm <72185056+sutatoruta@users.noreply.github.com>
Date: Sat, 7 Jun 2025 20:07:06 +0900
Subject: [PATCH 1028/1080] Optimize is_obstacle using set (#1223)

---
 PathPlanning/DStarLite/d_star_lite.py | 36 ++++++---------------------
 1 file changed, 7 insertions(+), 29 deletions(-)

diff --git a/PathPlanning/DStarLite/d_star_lite.py b/PathPlanning/DStarLite/d_star_lite.py
index f099530173..1a44d84fa5 100644
--- a/PathPlanning/DStarLite/d_star_lite.py
+++ b/PathPlanning/DStarLite/d_star_lite.py
@@ -63,9 +63,7 @@ def __init__(self, ox: list, oy: list):
         self.y_max = int(abs(max(oy) - self.y_min_world))
         self.obstacles = [Node(x - self.x_min_world, y - self.y_min_world)
                           for x, y in zip(ox, oy)]
-        self.obstacles_xy = np.array(
-            [[obstacle.x, obstacle.y] for obstacle in self.obstacles]
-        )
+        self.obstacles_xy = {(obstacle.x, obstacle.y) for obstacle in self.obstacles}
         self.start = Node(0, 0)
         self.goal = Node(0, 0)
         self.U = list()  # type: ignore
@@ -73,7 +71,7 @@ def __init__(self, ox: list, oy: list):
         self.kold = 0.0
         self.rhs = self.create_grid(float("inf"))
         self.g = self.create_grid(float("inf"))
-        self.detected_obstacles_xy = np.empty((0, 2))
+        self.detected_obstacles_xy: set[tuple[int, int]] = set()
         self.xy = np.empty((0, 2))
         if show_animation:
             self.detected_obstacles_for_plotting_x = list()  # type: ignore
@@ -84,18 +82,8 @@ def create_grid(self, val: float):
         return np.full((self.x_max, self.y_max), val)
 
     def is_obstacle(self, node: Node):
-        x = np.array([node.x])
-        y = np.array([node.y])
-        obstacle_x_equal = self.obstacles_xy[:, 0] == x
-        obstacle_y_equal = self.obstacles_xy[:, 1] == y
-        is_in_obstacles = (obstacle_x_equal & obstacle_y_equal).any()
-
-        is_in_detected_obstacles = False
-        if self.detected_obstacles_xy.shape[0] > 0:
-            is_x_equal = self.detected_obstacles_xy[:, 0] == x
-            is_y_equal = self.detected_obstacles_xy[:, 1] == y
-            is_in_detected_obstacles = (is_x_equal & is_y_equal).any()
-
+        is_in_obstacles = (node.x, node.y) in self.obstacles_xy
+        is_in_detected_obstacles = (node.x, node.y) in self.detected_obstacles_xy
         return is_in_obstacles or is_in_detected_obstacles
 
     def c(self, node1: Node, node2: Node):
@@ -157,7 +145,7 @@ def initialize(self, start: Node, goal: Node):
             self.g = self.create_grid(math.inf)
             self.rhs[self.goal.x][self.goal.y] = 0
             self.U.append((self.goal, self.calculate_key(self.goal)))
-            self.detected_obstacles_xy = np.empty((0, 2))
+            self.detected_obstacles_xy = set()
 
     def update_vertex(self, u: Node):
         if not compare_coordinates(u, self.goal):
@@ -215,12 +203,7 @@ def detect_changes(self):
                    compare_coordinates(spoofed_obstacle, self.goal):
                     continue
                 changed_vertices.append(spoofed_obstacle)
-                self.detected_obstacles_xy = np.concatenate(
-                    (
-                        self.detected_obstacles_xy,
-                        [[spoofed_obstacle.x, spoofed_obstacle.y]]
-                    )
-                )
+                self.detected_obstacles_xy.add((spoofed_obstacle.x, spoofed_obstacle.y))
                 if show_animation:
                     self.detected_obstacles_for_plotting_x.append(
                         spoofed_obstacle.x + self.x_min_world)
@@ -241,12 +224,7 @@ def detect_changes(self):
                compare_coordinates(new_obs, self.goal):
                 return changed_vertices
             changed_vertices.append(Node(x, y))
-            self.detected_obstacles_xy = np.concatenate(
-                (
-                    self.detected_obstacles_xy,
-                    [[x, y]]
-                )
-            )
+            self.detected_obstacles_xy.add((x, y))
             if show_animation:
                 self.detected_obstacles_for_plotting_x.append(x +
                                                               self.x_min_world)

From ed02c96abd15f39fbad2bc092c6b46292c0610b4 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 11 Jun 2025 12:18:34 +0900
Subject: [PATCH 1029/1080] build(deps): bump ruff from 0.11.12 to 0.11.13 in
 /requirements (#1233)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.12 to 0.11.13.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.11.12...0.11.13)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.11.13
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 0c0719cca5..5f8bf52415 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.0 # For unit test
 pytest-xdist == 3.7.0 # For unit test
 mypy == 1.16.0 # For unit test
-ruff == 0.11.12 # For unit test
+ruff == 0.11.13 # For unit test

From 194be251e1e62faf94b47f6500444f6f5c698dd8 Mon Sep 17 00:00:00 2001
From: Rg Zhong <35895359+phonixer@users.noreply.github.com>
Date: Sun, 15 Jun 2025 21:22:10 +0800
Subject: [PATCH 1030/1080] Update README.md (#1235)

The link is invalid and needs to be updated.
---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 85be274adb..d1b801f219 100644
--- a/README.md
+++ b/README.md
@@ -366,7 +366,7 @@ Reference
 
 - [Optimal rough terrain trajectory generation for wheeled mobile robots](https://journals.sagepub.com/doi/pdf/10.1177/0278364906075328)
 
-- [State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments](https://www.frc.ri.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf)
+- [State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environments](https://www.cs.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf)
 
 
 ### Biased polar sampling

From 0e93ecb66957c5ff6642252c6ca3290b0955d63e Mon Sep 17 00:00:00 2001
From: Shibo Liu <78792689+ShiboWork@users.noreply.github.com>
Date: Sat, 21 Jun 2025 07:04:57 +0800
Subject: [PATCH 1031/1080] Fix/path smoothing robot radius (#1231)

* Fix: Include robot_radius in path_smoothing collision check

* Test: Add unit test to verify smoothed path respects robot_radius
---
 PathPlanning/RRT/rrt_with_pathsmoothing.py  | 121 +++++++++++++++-----
 tests/test_rrt_with_pathsmoothing_radius.py |  48 ++++++++
 2 files changed, 141 insertions(+), 28 deletions(-)
 create mode 100644 tests/test_rrt_with_pathsmoothing_radius.py

diff --git a/PathPlanning/RRT/rrt_with_pathsmoothing.py b/PathPlanning/RRT/rrt_with_pathsmoothing.py
index 2ed6a366d1..ac68efe23f 100644
--- a/PathPlanning/RRT/rrt_with_pathsmoothing.py
+++ b/PathPlanning/RRT/rrt_with_pathsmoothing.py
@@ -51,30 +51,93 @@ def get_target_point(path, targetL):
     return [x, y, ti]
 
 
-def line_collision_check(first, second, obstacleList):
-    # Line Equation
-
-    x1 = first[0]
-    y1 = first[1]
-    x2 = second[0]
-    y2 = second[1]
-
-    try:
-        a = y2 - y1
-        b = -(x2 - x1)
-        c = y2 * (x2 - x1) - x2 * (y2 - y1)
-    except ZeroDivisionError:
-        return False
-
-    for (ox, oy, size) in obstacleList:
-        d = abs(a * ox + b * oy + c) / (math.hypot(a, b))
-        if d <= size:
-            return False
-
-    return True  # OK
-
-
-def path_smoothing(path, max_iter, obstacle_list):
+def is_point_collision(x, y, obstacle_list, robot_radius):
+    """
+    Check whether a single point collides with any obstacle.
+
+    This function calculates the Euclidean distance between the given point (x, y)
+    and each obstacle center. If the distance is less than or equal to the sum of
+    the obstacle's radius and the robot's radius, a collision is detected.
+
+    Args:
+        x (float): X-coordinate of the point to check.
+        y (float): Y-coordinate of the point to check.
+        obstacle_list (List[Tuple[float, float, float]]): List of obstacles defined as (ox, oy, radius).
+        robot_radius (float): Radius of the robot, used to inflate the obstacles.
+
+    Returns:
+        bool: True if the point is in collision with any obstacle, False otherwise.
+    """
+    for (ox, oy, obstacle_radius) in obstacle_list:
+        d = math.hypot(ox - x, oy - y)
+        if d <= obstacle_radius + robot_radius:
+            return True  # Collided
+    return False
+
+
+def line_collision_check(first, second, obstacle_list, robot_radius=0.0, sample_step=0.2):
+    """
+    Check if the line segment between `first` and `second` collides with any obstacle.
+    Considers the robot_radius by inflating the obstacle size.
+
+    Args:
+        first (List[float]): Start point of the line [x, y]
+        second (List[float]): End point of the line [x, y]
+        obstacle_list (List[Tuple[float, float, float]]): Obstacles as (x, y, radius)
+        robot_radius (float): Radius of robot
+        sample_step (float): Distance between sampling points along the segment
+
+    Returns:
+        bool: True if collision-free, False otherwise
+    """
+    x1, y1 = first[0], first[1]
+    x2, y2 = second[0], second[1]
+
+    dx = x2 - x1
+    dy = y2 - y1
+    length = math.hypot(dx, dy)
+
+    if length == 0:
+        # Degenerate case: point collision check
+        return not is_point_collision(x1, y1, obstacle_list, robot_radius)
+
+    steps = int(length / sample_step) + 1  # Sampling every sample_step along the segment
+
+    for i in range(steps + 1):
+        t = i / steps
+        x = x1 + t * dx
+        y = y1 + t * dy
+
+        if is_point_collision(x, y, obstacle_list, robot_radius):
+            return False  # Collision found
+
+    return True  # Safe
+
+
+def path_smoothing(path, max_iter, obstacle_list, robot_radius=0.0):
+    """
+    Smooths a given path by iteratively replacing segments with shortcut connections,
+    while ensuring the new segments are collision-free.
+
+    The algorithm randomly picks two points along the original path and attempts to
+    connect them with a straight line. If the line does not collide with any obstacles
+    (considering the robot's radius), the intermediate path points between them are
+    replaced with the direct connection.
+
+    Args:
+        path (List[List[float]]): The original path as a list of [x, y] coordinates.
+        max_iter (int): Number of iterations for smoothing attempts.
+        obstacle_list (List[Tuple[float, float, float]]): List of obstacles represented as
+            (x, y, radius).
+        robot_radius (float, optional): Radius of the robot, used to inflate obstacle size
+            during collision checking. Defaults to 0.0.
+
+    Returns:
+        List[List[float]]: The smoothed path as a list of [x, y] coordinates.
+
+    Example:
+        >>> smoothed = path_smoothing(path, 1000, obstacle_list, robot_radius=0.5)
+    """
     le = get_path_length(path)
 
     for i in range(max_iter):
@@ -94,7 +157,7 @@ def path_smoothing(path, max_iter, obstacle_list):
             continue
 
         # collision check
-        if not line_collision_check(first, second, obstacle_list):
+        if not line_collision_check(first, second, obstacle_list, robot_radius):
             continue
 
         # Create New path
@@ -119,14 +182,16 @@ def main():
         (3, 10, 2),
         (7, 5, 2),
         (9, 5, 2)
-    ]  # [x,y,size]
+    ]  # [x,y,radius]
     rrt = RRT(start=[0, 0], goal=[6, 10],
-              rand_area=[-2, 15], obstacle_list=obstacleList)
+              rand_area=[-2, 15], obstacle_list=obstacleList,
+              robot_radius=0.3)
     path = rrt.planning(animation=show_animation)
 
     # Path smoothing
     maxIter = 1000
-    smoothedPath = path_smoothing(path, maxIter, obstacleList)
+    smoothedPath = path_smoothing(path, maxIter, obstacleList,
+                                  robot_radius=rrt.robot_radius)
 
     # Draw final path
     if show_animation:
diff --git a/tests/test_rrt_with_pathsmoothing_radius.py b/tests/test_rrt_with_pathsmoothing_radius.py
new file mode 100644
index 0000000000..a1159255b5
--- /dev/null
+++ b/tests/test_rrt_with_pathsmoothing_radius.py
@@ -0,0 +1,48 @@
+import conftest
+import math
+
+from PathPlanning.RRT import rrt_with_pathsmoothing as rrt_module
+
+def test_smoothed_path_safety():
+    # Define test environment
+    obstacle_list = [
+        (5, 5, 1.0),
+        (3, 6, 2.0),
+        (3, 8, 2.0),
+        (3, 10, 2.0),
+        (7, 5, 2.0),
+        (9, 5, 2.0)
+    ]
+    robot_radius = 0.5
+
+    # Disable animation for testing
+    rrt_module.show_animation = False
+
+    # Create RRT planner
+    rrt = rrt_module.RRT(
+        start=[0, 0],
+        goal=[6, 10],
+        rand_area=[-2, 15],
+        obstacle_list=obstacle_list,
+        robot_radius=robot_radius
+    )
+
+    # Run RRT
+    path = rrt.planning(animation=False)
+
+    # Smooth the path
+    smoothed = rrt_module.path_smoothing(path, max_iter=1000,
+                                         obstacle_list=obstacle_list,
+                                         robot_radius=robot_radius)
+
+    # Check if all points on the smoothed path are safely distant from obstacles
+    for x, y in smoothed:
+        for ox, oy, obs_radius in obstacle_list:
+            d = math.hypot(x - ox, y - oy)
+            min_safe_dist = obs_radius + robot_radius
+            assert d > min_safe_dist, \
+                f"Point ({x:.2f}, {y:.2f}) too close to obstacle at ({ox}, {oy})"
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)

From fb667d3d7e635c1b6c6ca13a1b40e8ae623e15a0 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sat, 21 Jun 2025 19:01:21 +0900
Subject: [PATCH 1032/1080] build(deps): bump cvxpy from 1.6.5 to 1.6.6 in
 /requirements (#1238)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.6.5 to 1.6.6.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.6.5...v1.6.6)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-version: 1.6.6
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 5f8bf52415..98838b4f68 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 2.2.4
 scipy == 1.15.2
 matplotlib == 3.10.1
-cvxpy == 1.6.5
+cvxpy == 1.6.6
 ecos == 2.0.14
 pytest == 8.4.0 # For unit test
 pytest-xdist == 3.7.0 # For unit test

From e34b90081f05bd92041415593939e819633e7f6d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sat, 21 Jun 2025 19:01:52 +0900
Subject: [PATCH 1033/1080] build(deps): bump mypy from 1.16.0 to 1.16.1 in
 /requirements (#1237)

Bumps [mypy](https://github.com/python/mypy) from 1.16.0 to 1.16.1.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.16.0...v1.16.1)

---
updated-dependencies:
- dependency-name: mypy
  dependency-version: 1.16.1
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 98838b4f68..b61c51850e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ cvxpy == 1.6.6
 ecos == 2.0.14
 pytest == 8.4.0 # For unit test
 pytest-xdist == 3.7.0 # For unit test
-mypy == 1.16.0 # For unit test
+mypy == 1.16.1 # For unit test
 ruff == 0.11.13 # For unit test

From b537376c7619b8ab487e6d1169b4f304e600b358 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 24 Jun 2025 19:46:10 +0900
Subject: [PATCH 1034/1080] build(deps): bump ruff from 0.11.13 to 0.12.0 in
 /requirements (#1239)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.11.13 to 0.12.0.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.11.13...0.12.0)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index b61c51850e..ce89e2f2db 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.0 # For unit test
 pytest-xdist == 3.7.0 # For unit test
 mypy == 1.16.1 # For unit test
-ruff == 0.11.13 # For unit test
+ruff == 0.12.0 # For unit test

From c50abb38c89451f07ced8687a488105b85064b31 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Fri, 27 Jun 2025 22:10:29 +0900
Subject: [PATCH 1035/1080] build(deps): bump pytest from 8.4.0 to 8.4.1 in
 /requirements (#1240)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.4.0 to 8.4.1.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/8.4.0...8.4.1)

---
updated-dependencies:
- dependency-name: pytest
  dependency-version: 8.4.1
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index ce89e2f2db..30458e03d1 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,7 +3,7 @@ scipy == 1.15.2
 matplotlib == 3.10.1
 cvxpy == 1.6.6
 ecos == 2.0.14
-pytest == 8.4.0 # For unit test
+pytest == 8.4.1 # For unit test
 pytest-xdist == 3.7.0 # For unit test
 mypy == 1.16.1 # For unit test
 ruff == 0.12.0 # For unit test

From e8c6a0f2d81d96e503a7924beadc174b7e73f470 Mon Sep 17 00:00:00 2001
From: Viral Panchal <53462443+Viralpanchal7899@users.noreply.github.com>
Date: Fri, 27 Jun 2025 09:12:20 -0400
Subject: [PATCH 1036/1080] Enhance external sensors documentation.  (#1236)

* add: external sensors documentation

* add a line
---
 docs/modules/12_appendix/external_sensors_main.rst | 13 ++++++++-----
 1 file changed, 8 insertions(+), 5 deletions(-)

diff --git a/docs/modules/12_appendix/external_sensors_main.rst b/docs/modules/12_appendix/external_sensors_main.rst
index 3597418150..b7caaf2c3a 100644
--- a/docs/modules/12_appendix/external_sensors_main.rst
+++ b/docs/modules/12_appendix/external_sensors_main.rst
@@ -10,9 +10,7 @@ Therefore, we will provide an overview.
 Introduction
 ------------
 
-In recent years, the application of robotic technology has advanced,
-particularly in areas such as autonomous vehicles and disaster response robots.
-A crucial element in these technologies is external recognition—the robot's ability to understand its surrounding environment, identify safe zones, and detect moving objects using onboard sensors. Achieving effective external recognition involves various techniques, but equally important is the selection of appropriate sensors. Robots, like the sensors they employ, come in many forms, but external recognition sensors can be broadly categorized into three types. Developing an advanced external recognition system requires a thorough understanding of each sensor's principles and characteristics to determine their optimal application. This article summarizes the principles and features of these sensors for personal study purposes.
+In recent years, the application of robotic technology has advanced, particularly in areas such as autonomous vehicles and disaster response robots. A crucial element in these technologies is external recognition—the robot's ability to understand its surrounding environment, identify safe zones, and detect moving objects using onboard sensors. Achieving effective external recognition involves various techniques, but equally important is the selection of appropriate sensors. Robots, like the sensors they employ, come in many forms, but external recognition sensors can be broadly categorized into three types. Developing an advanced external recognition system requires a thorough understanding of each sensor's principles and characteristics to determine their optimal application. This article summarizes the principles and features of these sensors for personal study purposes.
 
 Laser Sensors
 -------------
@@ -22,7 +20,7 @@ Laser sensors measure distances by utilizing light, commonly referred to as Ligh
 Radar Sensors
 -------------
 
-TBD
+Radar measures distances using radio waves, commonly referred to as Radio Detection and Ranging (RADAR). It operates by transmitting radio signals towards an object and calculating the distance based on the time it takes for the reflected waves to return, using the speed of radio waves as a constant. 
 
 
 Monocular Cameras
@@ -59,4 +57,9 @@ Ultrasonic sensors are commonly used in indoor robots and some automotive autono
 References
 ----------
 
-TBD
+- Wikipedia articles:
+
+  - `Lidar Sensors <https://en.wikipedia.org/wiki/Lidar>`_
+  - `Radar Sensors <https://en.wikipedia.org/wiki/Radar>`_
+  - `Stereo Cameras <https://en.wikipedia.org/wiki/Stereo_camera>`_
+  - `Ultrasonic Sensors <https://en.wikipedia.org/wiki/Ultrasonic_transducer>`_
\ No newline at end of file

From 93228bda8c5a8aaa129a90ca1294e3bf2741e154 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 1 Jul 2025 23:19:42 +0900
Subject: [PATCH 1037/1080] build(deps): bump ruff from 0.12.0 to 0.12.1 in
 /requirements (#1244)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.0 to 0.12.1.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.12.0...0.12.1)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.1
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 30458e03d1..410bf3f39d 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.7.0 # For unit test
 mypy == 1.16.1 # For unit test
-ruff == 0.12.0 # For unit test
+ruff == 0.12.1 # For unit test

From 3a1ffdeb11dab931ff70bbcc3cabb21d879c0eaf Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 8 Jul 2025 22:22:52 +0900
Subject: [PATCH 1038/1080] build(deps): bump pytest-xdist from 3.7.0 to 3.8.0
 in /requirements (#1245)

Bumps [pytest-xdist](https://github.com/pytest-dev/pytest-xdist) from 3.7.0 to 3.8.0.
- [Release notes](https://github.com/pytest-dev/pytest-xdist/releases)
- [Changelog](https://github.com/pytest-dev/pytest-xdist/blob/master/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest-xdist/compare/v3.7.0...v3.8.0)

---
updated-dependencies:
- dependency-name: pytest-xdist
  dependency-version: 3.8.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 410bf3f39d..23c7393dce 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -4,6 +4,6 @@ matplotlib == 3.10.1
 cvxpy == 1.6.6
 ecos == 2.0.14
 pytest == 8.4.1 # For unit test
-pytest-xdist == 3.7.0 # For unit test
+pytest-xdist == 3.8.0 # For unit test
 mypy == 1.16.1 # For unit test
 ruff == 0.12.1 # For unit test

From 19bff8c04b91abb58c024fceb6e10ffab4e7d723 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 8 Jul 2025 22:51:17 +0900
Subject: [PATCH 1039/1080] build(deps): bump ruff from 0.12.1 to 0.12.2 in
 /requirements (#1246)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.1 to 0.12.2.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.12.1...0.12.2)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.2
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 23c7393dce..96734f3c2a 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.16.1 # For unit test
-ruff == 0.12.1 # For unit test
+ruff == 0.12.2 # For unit test

From e9c86ab707ace8fa1407222b99c8946dcb20ec80 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 15 Jul 2025 22:21:03 +0900
Subject: [PATCH 1040/1080] build(deps): bump ruff from 0.12.2 to 0.12.3 in
 /requirements (#1248)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.2 to 0.12.3.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.12.2...0.12.3)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.3
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 96734f3c2a..21bf7b9dec 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.16.1 # For unit test
-ruff == 0.12.2 # For unit test
+ruff == 0.12.3 # For unit test

From d918947360f7e2393c2f1cef110dab77bbbe50db Mon Sep 17 00:00:00 2001
From: Jonathan Schwartz <jonbs98@gmail.com>
Date: Wed, 16 Jul 2025 08:56:00 -0400
Subject: [PATCH 1041/1080] Collaborative astar (#1247)

* consolidate Node definition

* add base class for single agent planner

* add base class for single agent planner

* its working

* use single agent plotting util

* cleanup, bug fix, add some results to docs

* remove seeding from sta* - it happens in Node

* remove stale todo

* rename CA* and speed up plotting

* paper

* proper paper (ofc its csail)

* some cleanup

* update docs

* add unit test

* add logic for saving animation as gif

* address github bot

* Revert "add logic for saving animation as gif"

This reverts commit 639167795c80a76a1dbd40a6fbf1f1ba70c14d54.

* fix tests

* docs lint

* add gifs

* copilot review

* appease mypy
---
 .../TimeBasedPathPlanning/BaseClasses.py      |  49 ++++++
 .../GridWithDynamicObstacles.py               |  95 +++++++---
 PathPlanning/TimeBasedPathPlanning/Node.py    |  99 +++++++++++
 .../TimeBasedPathPlanning/Plotting.py         | 135 +++++++++++++++
 .../PriorityBasedPlanner.py                   |  95 ++++++++++
 .../TimeBasedPathPlanning/SafeInterval.py     | 163 ++++--------------
 .../TimeBasedPathPlanning/SpaceTimeAStar.py   | 150 +++-------------
 .../time_based_grid_search_main.rst           |  19 ++
 tests/test_priority_based_planner.py          |  39 +++++
 tests/test_safe_interval_path_planner.py      |   4 +-
 tests/test_space_time_astar.py                |   6 +-
 11 files changed, 566 insertions(+), 288 deletions(-)
 create mode 100644 PathPlanning/TimeBasedPathPlanning/BaseClasses.py
 create mode 100644 PathPlanning/TimeBasedPathPlanning/Node.py
 create mode 100644 PathPlanning/TimeBasedPathPlanning/Plotting.py
 create mode 100644 PathPlanning/TimeBasedPathPlanning/PriorityBasedPlanner.py
 create mode 100644 tests/test_priority_based_planner.py

diff --git a/PathPlanning/TimeBasedPathPlanning/BaseClasses.py b/PathPlanning/TimeBasedPathPlanning/BaseClasses.py
new file mode 100644
index 0000000000..745cde45fb
--- /dev/null
+++ b/PathPlanning/TimeBasedPathPlanning/BaseClasses.py
@@ -0,0 +1,49 @@
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import (
+    Grid,
+    Position,
+)
+from PathPlanning.TimeBasedPathPlanning.Node import NodePath
+import random
+import numpy.random as numpy_random
+
+# Seed randomness for reproducibility
+RANDOM_SEED = 50
+random.seed(RANDOM_SEED)
+numpy_random.seed(RANDOM_SEED)
+
+class SingleAgentPlanner(ABC):
+    """
+    Base class for single agent planners
+    """
+    
+    @staticmethod
+    @abstractmethod
+    def plan(grid: Grid, start: Position, goal: Position, verbose: bool = False) -> NodePath:
+        pass
+
+@dataclass
+class StartAndGoal:
+    # Index of this agent
+    index: int
+    # Start position of the robot
+    start: Position
+    # Goal position of the robot
+    goal: Position
+
+    def distance_start_to_goal(self) -> float:
+        return pow(self.goal.x - self.start.x, 2) + pow(self.goal.y - self.start.y, 2)
+
+class MultiAgentPlanner(ABC):
+    """
+    Base class for multi-agent planners
+    """       
+    
+    @staticmethod
+    @abstractmethod
+    def plan(grid: Grid, start_and_goal_positions: list[StartAndGoal], single_agent_planner_class: SingleAgentPlanner, verbose: bool = False) -> tuple[list[StartAndGoal], list[NodePath]]:
+        """
+        Plan for all agents. Returned paths are in order corresponding to the returned list of `StartAndGoal` objects
+        """
+        pass
\ No newline at end of file
diff --git a/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py b/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py
index 2a47023f8c..ccc2989001 100644
--- a/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py
+++ b/PathPlanning/TimeBasedPathPlanning/GridWithDynamicObstacles.py
@@ -7,31 +7,7 @@
 import matplotlib.pyplot as plt
 from enum import Enum
 from dataclasses import dataclass
-
-@dataclass(order=True)
-class Position:
-    x: int
-    y: int
-
-    def as_ndarray(self) -> np.ndarray:
-        return np.array([self.x, self.y])
-
-    def __add__(self, other):
-        if isinstance(other, Position):
-            return Position(self.x + other.x, self.y + other.y)
-        raise NotImplementedError(
-            f"Addition not supported for Position and {type(other)}"
-        )
-
-    def __sub__(self, other):
-        if isinstance(other, Position):
-            return Position(self.x - other.x, self.y - other.y)
-        raise NotImplementedError(
-            f"Subtraction not supported for Position and {type(other)}"
-        )
-
-    def __hash__(self):
-        return hash((self.x, self.y))
+from PathPlanning.TimeBasedPathPlanning.Node import NodePath, Position
 
 @dataclass
 class Interval:
@@ -43,6 +19,8 @@ class ObstacleArrangement(Enum):
     RANDOM = 0
     # Obstacles start in a line in y at center of grid and move side-to-side in x
     ARRANGEMENT1 = 1
+    # Static obstacle arrangement
+    NARROW_CORRIDOR = 2
 
 """
 Generates a 2d numpy array with lists for elements.
@@ -87,6 +65,8 @@ def __init__(
             self.obstacle_paths = self.generate_dynamic_obstacles(num_obstacles)
         elif obstacle_arrangement == ObstacleArrangement.ARRANGEMENT1:
             self.obstacle_paths = self.obstacle_arrangement_1(num_obstacles)
+        elif obstacle_arrangement == ObstacleArrangement.NARROW_CORRIDOR:
+            self.obstacle_paths = self.generate_narrow_corridor_obstacles(num_obstacles)
 
         for i, path in enumerate(self.obstacle_paths):
             obs_idx = i + 1  # avoid using 0 - that indicates free space in the grid
@@ -184,6 +164,26 @@ def obstacle_arrangement_1(self, obs_count: int) -> list[list[Position]]:
             obstacle_paths.append(path)
 
         return obstacle_paths
+    
+    def generate_narrow_corridor_obstacles(self, obs_count: int) -> list[list[Position]]:
+        obstacle_paths = []
+
+        for y in range(0, self.grid_size[1]):
+            if y > obs_count:
+                break
+
+            if y == self.grid_size[1] // 2:
+                # Skip the middle row
+                continue
+
+            obstacle_path = []
+            x = self.grid_size[0] // 2 # middle of the grid
+            for t in range(0, self.time_limit - 1):
+                obstacle_path.append(Position(x, y))
+
+            obstacle_paths.append(obstacle_path)
+
+        return obstacle_paths
 
     """
     Check if the given position is valid at time t
@@ -196,11 +196,11 @@ def obstacle_arrangement_1(self, obs_count: int) -> list[list[Position]]:
         bool: True if position/time combination is valid, False otherwise
     """
     def valid_position(self, position: Position, t: int) -> bool:
-        # Check if new position is in grid
+        # Check if position is in grid
         if not self.inside_grid_bounds(position):
             return False
 
-        # Check if new position is not occupied at time t
+        # Check if position is not occupied at time t
         return self.reservation_matrix[position.x, position.y, t] == 0
 
     """
@@ -289,9 +289,48 @@ def get_safe_intervals_at_cell(self, cell: Position) -> list[Interval]:
         # both the time step when it is entering the cell,  and the time step when it is leaving the cell.
         intervals = [interval for interval in intervals if interval.start_time != interval.end_time]
         return intervals
+    
+    """
+    Reserve an agent's path in the grid. Raises an exception if the agent's index is 0, or if a position is
+    already reserved by a different agent.
+    """
+    def reserve_path(self, node_path: NodePath, agent_index: int):
+        if agent_index == 0:
+            raise Exception("Agent index cannot be 0")
+        
+        for i, node in enumerate(node_path.path):
+            reservation_finish_time = node.time + 1
+            if i < len(node_path.path) - 1:
+                reservation_finish_time = node_path.path[i + 1].time
 
-show_animation = True
+            self.reserve_position(node.position, agent_index, Interval(node.time, reservation_finish_time))
+
+    """
+    Reserve a position for the provided agent during the provided time interval.
+    Raises an exception if the agent's index is 0, or if the position is already reserved by a different agent during the interval.
+    """
+    def reserve_position(self, position: Position, agent_index: int, interval: Interval):
+        if agent_index == 0:
+            raise Exception("Agent index cannot be 0")
+
+        for t in range(interval.start_time, interval.end_time + 1):
+            current_reserver = self.reservation_matrix[position.x, position.y, t]
+            if current_reserver not in [0, agent_index]:
+                raise Exception(
+                    f"Agent {agent_index} tried to reserve a position already reserved by another agent: {position} at time {t}, reserved by {current_reserver}"
+                )
+            self.reservation_matrix[position.x, position.y, t] = agent_index
+
+    """
+    Clears the initial reservation for an agent by clearing reservations at its start position with its index for
+    from time 0 to the time limit.
+    """
+    def clear_initial_reservation(self, position: Position, agent_index: int):
+        for t in range(self.time_limit):
+            if self.reservation_matrix[position.x, position.y, t] == agent_index:
+                self.reservation_matrix[position.x, position.y, t] = 0
 
+show_animation = True
 
 def main():
     grid = Grid(
diff --git a/PathPlanning/TimeBasedPathPlanning/Node.py b/PathPlanning/TimeBasedPathPlanning/Node.py
new file mode 100644
index 0000000000..728eebb676
--- /dev/null
+++ b/PathPlanning/TimeBasedPathPlanning/Node.py
@@ -0,0 +1,99 @@
+from dataclasses import dataclass
+from functools import total_ordering
+import numpy as np
+from typing import Sequence
+
+@dataclass(order=True)
+class Position:
+    x: int
+    y: int
+
+    def as_ndarray(self) -> np.ndarray:
+        return np.array([self.x, self.y])
+
+    def __add__(self, other):
+        if isinstance(other, Position):
+            return Position(self.x + other.x, self.y + other.y)
+        raise NotImplementedError(
+            f"Addition not supported for Position and {type(other)}"
+        )
+
+    def __sub__(self, other):
+        if isinstance(other, Position):
+            return Position(self.x - other.x, self.y - other.y)
+        raise NotImplementedError(
+            f"Subtraction not supported for Position and {type(other)}"
+        )
+
+    def __hash__(self):
+        return hash((self.x, self.y))
+
+@dataclass()
+# Note: Total_ordering is used instead of adding `order=True` to the @dataclass decorator because
+#     this class needs to override the __lt__ and __eq__ methods to ignore parent_index. Parent
+#     index is just used to track the path found by the algorithm, and has no effect on the quality
+#     of a node.
+@total_ordering
+class Node:
+    position: Position
+    time: int
+    heuristic: int
+    parent_index: int
+
+    """
+    This is what is used to drive node expansion. The node with the lowest value is expanded next.
+    This comparison prioritizes the node with the lowest cost-to-come (self.time) + cost-to-go (self.heuristic)
+    """
+    def __lt__(self, other: object):
+        if not isinstance(other, Node):
+            return NotImplementedError(f"Cannot compare Node with object of type: {type(other)}")
+        return (self.time + self.heuristic) < (other.time + other.heuristic)
+
+    """
+    Note: cost and heuristic are not included in eq or hash, since they will always be the same
+          for a given (position, time) pair. Including either cost or heuristic would be redundant.
+    """
+    def __eq__(self, other: object):
+        if not isinstance(other, Node):
+            return NotImplementedError(f"Cannot compare Node with object of type: {type(other)}")
+        return self.position == other.position and self.time == other.time
+
+    def __hash__(self):
+        return hash((self.position, self.time))
+
+class NodePath:
+    path: Sequence[Node]
+    positions_at_time: dict[int, Position]
+    # Number of nodes expanded while finding this path
+    expanded_node_count: int
+
+    def __init__(self, path: Sequence[Node], expanded_node_count: int):
+        self.path = path
+        self.expanded_node_count = expanded_node_count
+
+        self.positions_at_time = {}
+        for i, node in enumerate(path):
+            reservation_finish_time = node.time + 1
+            if i < len(path) - 1:
+                reservation_finish_time = path[i + 1].time
+
+            for t in range(node.time, reservation_finish_time):
+                self.positions_at_time[t] = node.position
+
+    """
+    Get the position of the path at a given time
+    """
+    def get_position(self, time: int) -> Position | None:
+        return self.positions_at_time.get(time)
+
+    """
+    Time stamp of the last node in the path
+    """
+    def goal_reached_time(self) -> int:
+        return self.path[-1].time
+
+    def __repr__(self):
+        repr_string = ""
+        for i, node in enumerate(self.path):
+            repr_string += f"{i}: {node}\n"
+        return repr_string
\ No newline at end of file
diff --git a/PathPlanning/TimeBasedPathPlanning/Plotting.py b/PathPlanning/TimeBasedPathPlanning/Plotting.py
new file mode 100644
index 0000000000..7cd1f615d8
--- /dev/null
+++ b/PathPlanning/TimeBasedPathPlanning/Plotting.py
@@ -0,0 +1,135 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.backend_bases import KeyEvent
+from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import (
+    Grid,
+    Position,
+)
+from PathPlanning.TimeBasedPathPlanning.BaseClasses import StartAndGoal
+from PathPlanning.TimeBasedPathPlanning.Node import NodePath
+
+'''
+Plot a single agent path.
+'''
+def PlotNodePath(grid: Grid, start: Position, goal: Position, path: NodePath):
+    fig = plt.figure(figsize=(10, 7))
+    ax = fig.add_subplot(
+        autoscale_on=False,
+        xlim=(0, grid.grid_size[0] - 1),
+        ylim=(0, grid.grid_size[1] - 1),
+    )
+    ax.set_aspect("equal")
+    ax.grid()
+    ax.set_xticks(np.arange(0, grid.grid_size[0], 1))
+    ax.set_yticks(np.arange(0, grid.grid_size[1], 1))
+
+    (start_and_goal,) = ax.plot([], [], "mD", ms=15, label="Start and Goal")
+    start_and_goal.set_data([start.x, goal.x], [start.y, goal.y])
+    (obs_points,) = ax.plot([], [], "ro", ms=15, label="Obstacles")
+    (path_points,) = ax.plot([], [], "bo", ms=10, label="Path Found")
+    ax.legend(bbox_to_anchor=(1.05, 1))
+
+    # for stopping simulation with the esc key.
+    plt.gcf().canvas.mpl_connect(
+        "key_release_event",
+        lambda event: [exit(0) if event.key == "escape" else None]
+        if isinstance(event, KeyEvent) else None
+    )
+
+    for i in range(0, path.goal_reached_time()):
+        obs_positions = grid.get_obstacle_positions_at_time(i)
+        obs_points.set_data(obs_positions[0], obs_positions[1])
+        path_position = path.get_position(i)
+        if not path_position:
+            raise Exception(f"Path position not found for time {i}.")
+
+        path_points.set_data([path_position.x], [path_position.y])
+        plt.pause(0.2)
+    plt.show()
+
+'''
+Plot a series of agent paths.
+'''
+def PlotNodePaths(grid: Grid, start_and_goals: list[StartAndGoal], paths: list[NodePath]):
+    fig = plt.figure(figsize=(10, 7))
+
+    ax = fig.add_subplot(
+        autoscale_on=False,
+        xlim=(0, grid.grid_size[0] - 1),
+        ylim=(0, grid.grid_size[1] - 1),
+    )
+    ax.set_aspect("equal")
+    ax.grid()
+    ax.set_xticks(np.arange(0, grid.grid_size[0], 1))
+    ax.set_yticks(np.arange(0, grid.grid_size[1], 1))
+
+    # Plot start and goal positions for each agent
+    colors = [] # generated randomly in loop
+    markers = ['D', 's', '^', 'o', 'p']  # Different markers for visual distinction
+
+    # Create plots for start and goal positions
+    start_and_goal_plots = []
+    for i, path in enumerate(paths):
+        marker_idx = i % len(markers)
+        agent_id = start_and_goals[i].index
+        start = start_and_goals[i].start
+        goal = start_and_goals[i].goal
+        
+        color = np.random.rand(3,)
+        colors.append(color)
+        sg_plot, = ax.plot([], [], markers[marker_idx], c=color, ms=15, 
+                            label=f"Agent {agent_id} Start/Goal")
+        sg_plot.set_data([start.x, goal.x], [start.y, goal.y])
+        start_and_goal_plots.append(sg_plot)
+
+    # Plot for obstacles
+    (obs_points,) = ax.plot([], [], "ro", ms=15, label="Obstacles")
+
+    # Create plots for each agent's path
+    path_plots = []
+    for i, path in enumerate(paths):
+        agent_id = start_and_goals[i].index
+        path_plot, = ax.plot([], [], "o", c=colors[i], ms=10, 
+                            label=f"Agent {agent_id} Path")
+        path_plots.append(path_plot)
+
+    ax.legend(bbox_to_anchor=(1.05, 1))
+
+    # For stopping simulation with the esc key
+    plt.gcf().canvas.mpl_connect(
+        "key_release_event",
+        lambda event: [exit(0) if event.key == "escape" else None]
+        if isinstance(event, KeyEvent) else None
+    )
+
+    # Find the maximum time across all paths
+    max_time = max(path.goal_reached_time() for path in paths)
+
+    # Animation loop
+    for i in range(0, max_time + 1):
+        # Update obstacle positions
+        obs_positions = grid.get_obstacle_positions_at_time(i)
+        obs_points.set_data(obs_positions[0], obs_positions[1])
+        
+        # Update each agent's position
+        for (j, path) in enumerate(paths):
+            path_positions = []
+            if i <= path.goal_reached_time():
+                res = path.get_position(i)
+                if not res:
+                    print(path)
+                    print(i)
+                path_position = path.get_position(i)
+                if not path_position:
+                    raise Exception(f"Path position not found for time {i}.")
+
+                # Verify position is valid
+                assert not path_position in obs_positions
+                assert not path_position in path_positions
+                path_positions.append(path_position)
+
+                path_plots[j].set_data([path_position.x], [path_position.y])
+        
+        plt.pause(0.2)
+
+    plt.show()
\ No newline at end of file
diff --git a/PathPlanning/TimeBasedPathPlanning/PriorityBasedPlanner.py b/PathPlanning/TimeBasedPathPlanning/PriorityBasedPlanner.py
new file mode 100644
index 0000000000..2573965cf6
--- /dev/null
+++ b/PathPlanning/TimeBasedPathPlanning/PriorityBasedPlanner.py
@@ -0,0 +1,95 @@
+"""
+Priority Based Planner for multi agent path planning.
+The planner generates an order to plan in, and generates plans for the robots in that order. Each planned
+path is reserved in the grid, and all future plans must avoid that path.
+
+Algorithm outlined in section III of this paper: https://pure.tudelft.nl/ws/portalfiles/portal/67074672/07138650.pdf
+"""
+
+import numpy as np
+from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import (
+    Grid,
+    Interval,
+    ObstacleArrangement,
+    Position,
+)
+from PathPlanning.TimeBasedPathPlanning.BaseClasses import MultiAgentPlanner, StartAndGoal
+from PathPlanning.TimeBasedPathPlanning.Node import NodePath
+from PathPlanning.TimeBasedPathPlanning.BaseClasses import SingleAgentPlanner
+from PathPlanning.TimeBasedPathPlanning.SafeInterval import SafeIntervalPathPlanner
+from PathPlanning.TimeBasedPathPlanning.Plotting import PlotNodePaths
+import time
+
+class PriorityBasedPlanner(MultiAgentPlanner):
+
+    @staticmethod
+    def plan(grid: Grid, start_and_goals: list[StartAndGoal], single_agent_planner_class: SingleAgentPlanner, verbose: bool = False) -> tuple[list[StartAndGoal], list[NodePath]]:
+        """
+        Generate a path from the start to the goal for each agent in the `start_and_goals` list.
+        Returns the re-ordered StartAndGoal combinations, and a list of path plans. The order of the plans
+        corresponds to the order of the `start_and_goals` list.
+        """
+        print(f"Using single-agent planner: {single_agent_planner_class}")
+
+        # Reserve initial positions
+        for start_and_goal in start_and_goals:
+            grid.reserve_position(start_and_goal.start, start_and_goal.index, Interval(0, 10))
+
+        # Plan in descending order of distance from start to goal
+        start_and_goals = sorted(start_and_goals,
+                    key=lambda item: item.distance_start_to_goal(),
+                    reverse=True)
+
+        paths = []
+        for start_and_goal in start_and_goals:
+            if verbose:
+                print(f"\nPlanning for agent:  {start_and_goal}" )
+
+            grid.clear_initial_reservation(start_and_goal.start, start_and_goal.index)
+            path = single_agent_planner_class.plan(grid, start_and_goal.start, start_and_goal.goal, verbose)
+
+            if path is None:
+                print(f"Failed to find path for {start_and_goal}")
+                return []
+
+            agent_index = start_and_goal.index
+            grid.reserve_path(path, agent_index)
+            paths.append(path)
+
+        return (start_and_goals, paths)
+
+verbose = False
+show_animation = True
+def main():
+    grid_side_length = 21
+
+    start_and_goals = [StartAndGoal(i, Position(1, i), Position(19, 19-i)) for i in range(1, 16)]
+    obstacle_avoid_points = [pos for item in start_and_goals for pos in (item.start, item.goal)]
+
+    grid = Grid(
+        np.array([grid_side_length, grid_side_length]),
+        num_obstacles=250,
+        obstacle_avoid_points=obstacle_avoid_points,
+        # obstacle_arrangement=ObstacleArrangement.NARROW_CORRIDOR,
+        obstacle_arrangement=ObstacleArrangement.ARRANGEMENT1,
+        # obstacle_arrangement=ObstacleArrangement.RANDOM,
+    )
+
+    start_time = time.time()
+    start_and_goals, paths = PriorityBasedPlanner.plan(grid, start_and_goals, SafeIntervalPathPlanner, verbose)
+
+    runtime = time.time() - start_time
+    print(f"\nPlanning took: {runtime:.5f} seconds")
+
+    if verbose:
+        print(f"Paths:")
+        for path in paths:
+            print(f"{path}\n")
+
+    if not show_animation:
+        return
+
+    PlotNodePaths(grid, start_and_goals, paths)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/PathPlanning/TimeBasedPathPlanning/SafeInterval.py b/PathPlanning/TimeBasedPathPlanning/SafeInterval.py
index 7fea10c67f..446847ac6d 100644
--- a/PathPlanning/TimeBasedPathPlanning/SafeInterval.py
+++ b/PathPlanning/TimeBasedPathPlanning/SafeInterval.py
@@ -9,7 +9,6 @@
 """
 
 import numpy as np
-import matplotlib.pyplot as plt
 from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import (
     Grid,
     Interval,
@@ -17,8 +16,11 @@
     Position,
     empty_2d_array_of_lists,
 )
+from PathPlanning.TimeBasedPathPlanning.BaseClasses import SingleAgentPlanner
+from PathPlanning.TimeBasedPathPlanning.Node import Node, NodePath
+from PathPlanning.TimeBasedPathPlanning.Plotting import PlotNodePath
+
 import heapq
-import random
 from dataclasses import dataclass
 from functools import total_ordering
 import time
@@ -29,116 +31,50 @@
 #     index and interval member variables are just used to track the path found by the algorithm,
 #     and has no effect on the quality of a node.
 @total_ordering
-class Node:
-    position: Position
-    time: int
-    heuristic: int
-    parent_index: int
+class SIPPNode(Node):
     interval: Interval
 
-    """
-    This is what is used to drive node expansion. The node with the lowest value is expanded next.
-    This comparison prioritizes the node with the lowest cost-to-come (self.time) + cost-to-go (self.heuristic)
-    """
-    def __lt__(self, other: object):
-        if not isinstance(other, Node):
-            return NotImplementedError(f"Cannot compare Node with object of type: {type(other)}")
-        return (self.time + self.heuristic) < (other.time + other.heuristic)
-
-    """
-    Equality only cares about position and time. Heuristic and interval will always be the same for a given
-    (position, time) pairing, so they are not considered in equality.
-    """
-    def __eq__(self, other: object):
-        if not isinstance(other, Node):
-            return NotImplemented
-        return self.position == other.position and self.time == other.time
-
 @dataclass
 class EntryTimeAndInterval:
     entry_time: int
     interval: Interval
 
-class NodePath:
-    path: list[Node]
-    positions_at_time: dict[int, Position] = {}
-
-    def __init__(self, path: list[Node]):
-        self.path = path
-        for (i, node) in enumerate(path):
-            if i > 0:
-                # account for waiting in interval at previous node
-                prev_node = path[i-1]
-                for t in range(prev_node.time, node.time):
-                    self.positions_at_time[t] = prev_node.position
-
-            self.positions_at_time[node.time] = node.position
-
-    """
-    Get the position of the path at a given time
-    """
-    def get_position(self, time: int) -> Position | None:
-        return self.positions_at_time.get(time)
-
-    """
-    Time stamp of the last node in the path
-    """
-    def goal_reached_time(self) -> int:
-        return self.path[-1].time
-
-    def __repr__(self):
-        repr_string = ""
-        for i, node in enumerate(self.path):
-            repr_string += f"{i}: {node}\n"
-        return repr_string
-
-
-class SafeIntervalPathPlanner:
-    grid: Grid
-    start: Position
-    goal: Position
-
-    def __init__(self, grid: Grid, start: Position, goal: Position):
-        self.grid = grid
-        self.start = start
-        self.goal = goal
-
-        # Seed randomness for reproducibility
-        RANDOM_SEED = 50
-        random.seed(RANDOM_SEED)
-        np.random.seed(RANDOM_SEED)
+class SafeIntervalPathPlanner(SingleAgentPlanner):
 
     """
     Generate a plan given the loaded problem statement. Raises an exception if it fails to find a path.
     Arguments:
         verbose (bool): set to True to print debug information
     """
-    def plan(self, verbose: bool = False) -> NodePath:
+    @staticmethod
+    def plan(grid: Grid, start: Position, goal: Position, verbose: bool = False) -> NodePath:
 
-        safe_intervals = self.grid.get_safe_intervals()
+        safe_intervals = grid.get_safe_intervals()
 
-        open_set: list[Node] = []
-        first_node_interval = safe_intervals[self.start.x, self.start.y][0]
+        open_set: list[SIPPNode] = []
+        first_node_interval = safe_intervals[start.x, start.y][0]
         heapq.heappush(
-            open_set, Node(self.start, 0, self.calculate_heuristic(self.start), -1, first_node_interval)
+            open_set, SIPPNode(start, 0, SafeIntervalPathPlanner.calculate_heuristic(start, goal), -1, first_node_interval)
         )
 
-        expanded_list: list[Node] = []
-        visited_intervals = empty_2d_array_of_lists(self.grid.grid_size[0], self.grid.grid_size[1])
+        expanded_list: list[SIPPNode] = []
+        visited_intervals = empty_2d_array_of_lists(grid.grid_size[0], grid.grid_size[1])
         while open_set:
-            expanded_node: Node = heapq.heappop(open_set)
+            expanded_node: SIPPNode = heapq.heappop(open_set)
             if verbose:
                 print("Expanded node:", expanded_node)
 
-            if expanded_node.time + 1 >= self.grid.time_limit:
+            if expanded_node.time + 1 >= grid.time_limit:
                 if verbose:
                     print(f"\tSkipping node that is past time limit: {expanded_node}")
                 continue
 
-            if expanded_node.position == self.goal:
-                print(f"Found path to goal after {len(expanded_list)} expansions")
+            if expanded_node.position == goal:
+                if verbose:
+                    print(f"Found path to goal after {len(expanded_list)} expansions")
+
                 path = []
-                path_walker: Node = expanded_node
+                path_walker: SIPPNode = expanded_node
                 while True:
                     path.append(path_walker)
                     if path_walker.parent_index == -1:
@@ -147,14 +83,14 @@ def plan(self, verbose: bool = False) -> NodePath:
 
                 # reverse path so it goes start -> goal
                 path.reverse()
-                return NodePath(path)
+                return NodePath(path, len(expanded_list))
 
             expanded_idx = len(expanded_list)
             expanded_list.append(expanded_node)
             entry_time_and_node = EntryTimeAndInterval(expanded_node.time, expanded_node.interval)
             add_entry_to_visited_intervals_array(entry_time_and_node, visited_intervals, expanded_node)
 
-            for child in self.generate_successors(expanded_node, expanded_idx, safe_intervals, visited_intervals):
+            for child in SafeIntervalPathPlanner.generate_successors(grid, goal, expanded_node, expanded_idx, safe_intervals, visited_intervals):
                 heapq.heappush(open_set, child)
 
         raise Exception("No path found")
@@ -162,9 +98,10 @@ def plan(self, verbose: bool = False) -> NodePath:
     """
     Generate list of possible successors of the provided `parent_node` that are worth expanding
     """
+    @staticmethod
     def generate_successors(
-        self, parent_node: Node, parent_node_idx: int, intervals: np.ndarray, visited_intervals: np.ndarray
-    ) -> list[Node]:
+        grid: Grid, goal: Position, parent_node: SIPPNode, parent_node_idx: int, intervals: np.ndarray, visited_intervals: np.ndarray
+    ) -> list[SIPPNode]:
         new_nodes = []
         diffs = [
             Position(0, 0),
@@ -175,7 +112,7 @@ def generate_successors(
         ]
         for diff in diffs:
             new_pos = parent_node.position + diff
-            if not self.grid.inside_grid_bounds(new_pos):
+            if not grid.inside_grid_bounds(new_pos):
                 continue
 
             current_interval = parent_node.interval
@@ -203,12 +140,12 @@ def generate_successors(
                 # We know there is a node worth expanding. Generate successor at the earliest possible time the
                 # new interval can be entered
                 for possible_t in range(max(parent_node.time + 1, interval.start_time), min(current_interval.end_time, interval.end_time)):
-                    if self.grid.valid_position(new_pos, possible_t):
-                        new_nodes.append(Node(
+                    if grid.valid_position(new_pos, possible_t):
+                        new_nodes.append(SIPPNode(
                             new_pos,
                             # entry is max of interval start and parent node time + 1 (get there as soon as possible)
                             max(interval.start_time, parent_node.time + 1),
-                            self.calculate_heuristic(new_pos),
+                            SafeIntervalPathPlanner.calculate_heuristic(new_pos, goal),
                             parent_node_idx,
                             interval,
                         ))
@@ -220,8 +157,9 @@ def generate_successors(
     """
     Calculate the heuristic for a given position - Manhattan distance to the goal
     """
-    def calculate_heuristic(self, position) -> int:
-        diff = self.goal - position
+    @staticmethod
+    def calculate_heuristic(position: Position, goal: Position) -> int:
+        diff = goal - position
         return abs(diff.x) + abs(diff.y)
 
 
@@ -229,7 +167,7 @@ def calculate_heuristic(self, position) -> int:
 Adds a new entry to the visited intervals array. If the entry is already present, the entry time is updated if the new
 entry time is better. Otherwise, the entry is added to `visited_intervals` at the position of `expanded_node`.
 """
-def add_entry_to_visited_intervals_array(entry_time_and_interval: EntryTimeAndInterval, visited_intervals: np.ndarray, expanded_node: Node):
+def add_entry_to_visited_intervals_array(entry_time_and_interval: EntryTimeAndInterval, visited_intervals: np.ndarray, expanded_node: SIPPNode):
     # if entry is present, update entry time if better
     for existing_entry_and_interval in visited_intervals[expanded_node.position.x, expanded_node.position.y]:
         if existing_entry_and_interval.interval == entry_time_and_interval.interval:
@@ -257,8 +195,7 @@ def main():
         # obstacle_arrangement=ObstacleArrangement.RANDOM,
     )
 
-    planner = SafeIntervalPathPlanner(grid, start, goal)
-    path = planner.plan(verbose)
+    path = SafeIntervalPathPlanner.plan(grid, start, goal, verbose)
     runtime = time.time() - start_time
     print(f"Planning took: {runtime:.5f} seconds")
 
@@ -268,35 +205,7 @@ def main():
     if not show_animation:
         return
 
-    fig = plt.figure(figsize=(10, 7))
-    ax = fig.add_subplot(
-        autoscale_on=False,
-        xlim=(0, grid.grid_size[0] - 1),
-        ylim=(0, grid.grid_size[1] - 1),
-    )
-    ax.set_aspect("equal")
-    ax.grid()
-    ax.set_xticks(np.arange(0, grid_side_length, 1))
-    ax.set_yticks(np.arange(0, grid_side_length, 1))
-
-    (start_and_goal,) = ax.plot([], [], "mD", ms=15, label="Start and Goal")
-    start_and_goal.set_data([start.x, goal.x], [start.y, goal.y])
-    (obs_points,) = ax.plot([], [], "ro", ms=15, label="Obstacles")
-    (path_points,) = ax.plot([], [], "bo", ms=10, label="Path Found")
-    ax.legend(bbox_to_anchor=(1.05, 1))
-
-    # for stopping simulation with the esc key.
-    plt.gcf().canvas.mpl_connect(
-        "key_release_event", lambda event: [exit(0) if event.key == "escape" else None]
-    )
-
-    for i in range(0, path.goal_reached_time() + 1):
-        obs_positions = grid.get_obstacle_positions_at_time(i)
-        obs_points.set_data(obs_positions[0], obs_positions[1])
-        path_position = path.get_position(i)
-        path_points.set_data([path_position.x], [path_position.y])
-        plt.pause(0.2)
-    plt.show()
+    PlotNodePath(grid, start, goal, path)
 
 
 if __name__ == "__main__":
diff --git a/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py b/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py
index c4e2802d37..b85569f5dc 100644
--- a/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py
+++ b/PathPlanning/TimeBasedPathPlanning/SpaceTimeAStar.py
@@ -9,101 +9,25 @@
 """
 
 import numpy as np
-import matplotlib.pyplot as plt
 from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import (
     Grid,
     ObstacleArrangement,
     Position,
 )
+from PathPlanning.TimeBasedPathPlanning.Node import Node, NodePath
 import heapq
 from collections.abc import Generator
-import random
-from dataclasses import dataclass
-from functools import total_ordering
 import time
+from PathPlanning.TimeBasedPathPlanning.BaseClasses import SingleAgentPlanner
+from PathPlanning.TimeBasedPathPlanning.Plotting import PlotNodePath
 
-# Seed randomness for reproducibility
-RANDOM_SEED = 50
-random.seed(RANDOM_SEED)
-np.random.seed(RANDOM_SEED)
-
-@dataclass()
-# Note: Total_ordering is used instead of adding `order=True` to the @dataclass decorator because
-#     this class needs to override the __lt__ and __eq__ methods to ignore parent_index. Parent
-#     index is just used to track the path found by the algorithm, and has no effect on the quality
-#     of a node.
-@total_ordering
-class Node:
-    position: Position
-    time: int
-    heuristic: int
-    parent_index: int
+class SpaceTimeAStar(SingleAgentPlanner):
 
-    """
-    This is what is used to drive node expansion. The node with the lowest value is expanded next.
-    This comparison prioritizes the node with the lowest cost-to-come (self.time) + cost-to-go (self.heuristic)
-    """
-    def __lt__(self, other: object):
-        if not isinstance(other, Node):
-            return NotImplementedError(f"Cannot compare Node with object of type: {type(other)}")
-        return (self.time + self.heuristic) < (other.time + other.heuristic)
-
-    """
-    Note: cost and heuristic are not included in eq or hash, since they will always be the same
-          for a given (position, time) pair. Including either cost or heuristic would be redundant.
-    """
-    def __eq__(self, other: object):
-        if not isinstance(other, Node):
-            return NotImplementedError(f"Cannot compare Node with object of type: {type(other)}")
-        return self.position == other.position and self.time == other.time
-
-    def __hash__(self):
-        return hash((self.position, self.time))
-
-class NodePath:
-    path: list[Node]
-    positions_at_time: dict[int, Position] = {}
-
-    def __init__(self, path: list[Node]):
-        self.path = path
-        for node in path:
-            self.positions_at_time[node.time] = node.position
-
-    """
-    Get the position of the path at a given time
-    """
-    def get_position(self, time: int) -> Position | None:
-        return self.positions_at_time.get(time)
-
-    """
-    Time stamp of the last node in the path
-    """
-    def goal_reached_time(self) -> int:
-        return self.path[-1].time
-
-    def __repr__(self):
-        repr_string = ""
-        for i, node in enumerate(self.path):
-            repr_string += f"{i}: {node}\n"
-        return repr_string
-
-
-class SpaceTimeAStar:
-    grid: Grid
-    start: Position
-    goal: Position
-    # Used to evaluate solutions
-    expanded_node_count: int = -1
-
-    def __init__(self, grid: Grid, start: Position, goal: Position):
-        self.grid = grid
-        self.start = start
-        self.goal = goal
-
-    def plan(self, verbose: bool = False) -> NodePath:
+    @staticmethod
+    def plan(grid: Grid, start: Position, goal: Position, verbose: bool = False) -> NodePath:
         open_set: list[Node] = []
         heapq.heappush(
-            open_set, Node(self.start, 0, self.calculate_heuristic(self.start), -1)
+            open_set, Node(start, 0, SpaceTimeAStar.calculate_heuristic(start, goal), -1)
         )
 
         expanded_list: list[Node] = []
@@ -113,13 +37,15 @@ def plan(self, verbose: bool = False) -> NodePath:
             if verbose:
                 print("Expanded node:", expanded_node)
 
-            if expanded_node.time + 1 >= self.grid.time_limit:
+            if expanded_node.time + 1 >= grid.time_limit:
                 if verbose:
                     print(f"\tSkipping node that is past time limit: {expanded_node}")
                 continue
 
-            if expanded_node.position == self.goal:
-                print(f"Found path to goal after {len(expanded_list)} expansions")
+            if expanded_node.position == goal:
+                if verbose:
+                    print(f"Found path to goal after {len(expanded_list)} expansions")
+
                 path = []
                 path_walker: Node = expanded_node
                 while True:
@@ -130,14 +56,13 @@ def plan(self, verbose: bool = False) -> NodePath:
 
                 # reverse path so it goes start -> goal
                 path.reverse()
-                self.expanded_node_count = len(expanded_set)
-                return NodePath(path)
+                return NodePath(path, len(expanded_set))
 
             expanded_idx = len(expanded_list)
             expanded_list.append(expanded_node)
             expanded_set.add(expanded_node)
 
-            for child in self.generate_successors(expanded_node, expanded_idx, verbose, expanded_set):
+            for child in SpaceTimeAStar.generate_successors(grid, goal, expanded_node, expanded_idx, verbose, expanded_set):
                 heapq.heappush(open_set, child)
 
         raise Exception("No path found")
@@ -145,8 +70,9 @@ def plan(self, verbose: bool = False) -> NodePath:
     """
     Generate possible successors of the provided `parent_node`
     """
+    @staticmethod
     def generate_successors(
-        self, parent_node: Node, parent_node_idx: int, verbose: bool, expanded_set: set[Node]
+        grid: Grid, goal: Position, parent_node: Node, parent_node_idx: int, verbose: bool, expanded_set: set[Node]
     ) -> Generator[Node, None, None]:
         diffs = [
             Position(0, 0),
@@ -160,20 +86,22 @@ def generate_successors(
             new_node = Node(
                 new_pos,
                 parent_node.time + 1,
-                self.calculate_heuristic(new_pos),
+                SpaceTimeAStar.calculate_heuristic(new_pos, goal),
                 parent_node_idx,
             )
 
             if new_node in expanded_set:
                 continue
 
-            if self.grid.valid_position(new_pos, parent_node.time + 1):
+            # Check if the new node is valid for the next 2 time steps - one step to enter, and another to leave
+            if all([grid.valid_position(new_pos, parent_node.time + dt) for dt in [1, 2]]):
                 if verbose:
                     print("\tNew successor node: ", new_node)
                 yield new_node
 
-    def calculate_heuristic(self, position) -> int:
-        diff = self.goal - position
+    @staticmethod
+    def calculate_heuristic(position: Position, goal: Position) -> int:
+        diff = goal - position
         return abs(diff.x) + abs(diff.y)
 
 
@@ -194,8 +122,7 @@ def main():
         obstacle_arrangement=ObstacleArrangement.ARRANGEMENT1,
     )
 
-    planner = SpaceTimeAStar(grid, start, goal)
-    path = planner.plan(verbose)
+    path = SpaceTimeAStar.plan(grid, start, goal, verbose)
 
     runtime = time.time() - start_time
     print(f"Planning took: {runtime:.5f} seconds")
@@ -206,36 +133,7 @@ def main():
     if not show_animation:
         return
 
-    fig = plt.figure(figsize=(10, 7))
-    ax = fig.add_subplot(
-        autoscale_on=False,
-        xlim=(0, grid.grid_size[0] - 1),
-        ylim=(0, grid.grid_size[1] - 1),
-    )
-    ax.set_aspect("equal")
-    ax.grid()
-    ax.set_xticks(np.arange(0, grid_side_length, 1))
-    ax.set_yticks(np.arange(0, grid_side_length, 1))
-
-    (start_and_goal,) = ax.plot([], [], "mD", ms=15, label="Start and Goal")
-    start_and_goal.set_data([start.x, goal.x], [start.y, goal.y])
-    (obs_points,) = ax.plot([], [], "ro", ms=15, label="Obstacles")
-    (path_points,) = ax.plot([], [], "bo", ms=10, label="Path Found")
-    ax.legend(bbox_to_anchor=(1.05, 1))
-
-    # for stopping simulation with the esc key.
-    plt.gcf().canvas.mpl_connect(
-        "key_release_event", lambda event: [exit(0) if event.key == "escape" else None]
-    )
-
-    for i in range(0, path.goal_reached_time()):
-        obs_positions = grid.get_obstacle_positions_at_time(i)
-        obs_points.set_data(obs_positions[0], obs_positions[1])
-        path_position = path.get_position(i)
-        path_points.set_data([path_position.x], [path_position.y])
-        plt.pause(0.2)
-    plt.show()
-
+    PlotNodePath(grid, start, goal, path)
 
 if __name__ == "__main__":
     main()
diff --git a/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst
index 9517e95b31..a44dd20a97 100644
--- a/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst
+++ b/docs/modules/5_path_planning/time_based_grid_search/time_based_grid_search_main.rst
@@ -81,9 +81,28 @@ Code Link
 ^^^^^^^^^^^^^
 .. autoclass:: PathPlanning.TimeBasedPathPlanning.SafeInterval.SafeIntervalPathPlanner
 
+Multi-Agent Path Planning
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Priority Based Planning
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The planner generates an order to plan in, and generates plans for the robots in that order. Each planned path is reserved in the grid, and all future plans must avoid that path. The robots are planned for in descending order of distance from start to goal.
+
+This is a sub-optimal algorithm because no replanning happens once paths are found. It does not guarantee the shortest path is found for any particular robot, nor that the final set of paths found contains the lowest possible amount of time or movement.
+
+Static Obstacles:
+
+.. image:: https://raw.githubusercontent.com/AtsushiSakai/PythonRoboticsGifs/refs/heads/master/PathPlanning/TimeBasedPathPlanning/PriorityBasedPlanner/priority_planner2.gif
+
+Dynamic Obstacles:
+
+.. image:: https://raw.githubusercontent.com/AtsushiSakai/PythonRoboticsGifs/refs/heads/master/PathPlanning/TimeBasedPathPlanning/PriorityBasedPlanner/priority_planner.gif
+
 
 References
 ~~~~~~~~~~~
 
 -  `Cooperative Pathfinding <https://www.davidsilver.uk/wp-content/uploads/2020/03/coop-path-AIWisdom.pdf>`__
 -  `SIPP: Safe Interval Path Planning for Dynamic Environments <https://www.cs.cmu.edu/~maxim/files/sipp_icra11.pdf>`__
+- `Prioritized Planning Algorithms for Trajectory Coordination of Multiple Mobile Robots <https://pure.tudelft.nl/ws/portalfiles/portal/67074672/07138650.pdf>`__
\ No newline at end of file
diff --git a/tests/test_priority_based_planner.py b/tests/test_priority_based_planner.py
new file mode 100644
index 0000000000..e2ff602d88
--- /dev/null
+++ b/tests/test_priority_based_planner.py
@@ -0,0 +1,39 @@
+from PathPlanning.TimeBasedPathPlanning.GridWithDynamicObstacles import (
+    Grid,
+    NodePath,
+    ObstacleArrangement,
+    Position,
+)
+from PathPlanning.TimeBasedPathPlanning.BaseClasses import StartAndGoal
+from PathPlanning.TimeBasedPathPlanning import PriorityBasedPlanner as m
+from PathPlanning.TimeBasedPathPlanning.SafeInterval import SafeIntervalPathPlanner
+import numpy as np
+import conftest
+
+
+def test_1():
+    grid_side_length = 21
+
+    start_and_goals = [StartAndGoal(i, Position(1, i), Position(19, 19-i)) for i in range(1, 16)]
+    obstacle_avoid_points = [pos for item in start_and_goals for pos in (item.start, item.goal)]
+
+    grid = Grid(
+        np.array([grid_side_length, grid_side_length]),
+        num_obstacles=250,
+        obstacle_avoid_points=obstacle_avoid_points,
+        obstacle_arrangement=ObstacleArrangement.ARRANGEMENT1,
+    )
+
+    m.show_animation = False
+
+    start_and_goals: list[StartAndGoal]
+    paths: list[NodePath]
+    start_and_goals, paths = m.PriorityBasedPlanner.plan(grid, start_and_goals, SafeIntervalPathPlanner, False)
+
+    # All paths should start at the specified position and reach the goal
+    for i, start_and_goal in enumerate(start_and_goals):
+        assert paths[i].path[0].position == start_and_goal.start
+        assert paths[i].path[-1].position == start_and_goal.goal
+
+if __name__ == "__main__":
+    conftest.run_this_test(__file__)
diff --git a/tests/test_safe_interval_path_planner.py b/tests/test_safe_interval_path_planner.py
index f1fbf90d2a..bbcd4e427c 100644
--- a/tests/test_safe_interval_path_planner.py
+++ b/tests/test_safe_interval_path_planner.py
@@ -18,9 +18,7 @@ def test_1():
     )
 
     m.show_animation = False
-    planner = m.SafeIntervalPathPlanner(grid, start, goal)
-
-    path = planner.plan(False)
+    path = m.SafeIntervalPathPlanner.plan(grid, start, goal)
 
     # path should have 31 entries
     assert len(path.path) == 31
diff --git a/tests/test_space_time_astar.py b/tests/test_space_time_astar.py
index 390c7732dc..1194c61d2e 100644
--- a/tests/test_space_time_astar.py
+++ b/tests/test_space_time_astar.py
@@ -18,9 +18,7 @@ def test_1():
     )
 
     m.show_animation = False
-    planner = m.SpaceTimeAStar(grid, start, goal)
-
-    path = planner.plan(False)
+    path = m.SpaceTimeAStar.plan(grid, start, goal)
 
     # path should have 28 entries
     assert len(path.path) == 31
@@ -28,7 +26,7 @@ def test_1():
     # path should end at the goal
     assert path.path[-1].position == goal
 
-    assert planner.expanded_node_count < 1000
+    assert path.expanded_node_count < 1000
 
 if __name__ == "__main__":
     conftest.run_this_test(__file__)

From 0a8766929d83dfa90b262f0dba03bef59c84dca7 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 29 Jul 2025 23:08:47 +0900
Subject: [PATCH 1042/1080] build(deps): bump ruff from 0.12.3 to 0.12.5 in
 /requirements (#1251)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.3 to 0.12.5.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.12.3...0.12.5)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.5
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 21bf7b9dec..39519f93c0 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.16.1 # For unit test
-ruff == 0.12.3 # For unit test
+ruff == 0.12.5 # For unit test

From 7e8fbfb635fd7fd8beabbd7fe8f6c876af471201 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 30 Jul 2025 12:57:43 +0900
Subject: [PATCH 1043/1080] build(deps): bump cvxpy from 1.6.6 to 1.7.1 in
 /requirements (#1253)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.6.6 to 1.7.1.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.6.6...v1.7.1)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-version: 1.7.1
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 39519f93c0..6ca0bf9606 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 2.2.4
 scipy == 1.15.2
 matplotlib == 3.10.1
-cvxpy == 1.6.6
+cvxpy == 1.7.1
 ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test

From f9db30bff1df50b078dd3696ee8ce05f2bc8ebe5 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 30 Jul 2025 18:37:08 +0900
Subject: [PATCH 1044/1080] build(deps): bump mypy from 1.16.1 to 1.17.0 in
 /requirements (#1252)

Bumps [mypy](https://github.com/python/mypy) from 1.16.1 to 1.17.0.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.16.1...v1.17.0)

---
updated-dependencies:
- dependency-name: mypy
  dependency-version: 1.17.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 6ca0bf9606..66b5202aca 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ cvxpy == 1.7.1
 ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test
-mypy == 1.16.1 # For unit test
+mypy == 1.17.0 # For unit test
 ruff == 0.12.5 # For unit test

From 458c52f39024e4970bef420d3cac8cbe0776628e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 7 Aug 2025 07:33:33 +0900
Subject: [PATCH 1045/1080] build(deps): bump
 larsoner/circleci-artifacts-redirector-action (#1254)

Bumps [larsoner/circleci-artifacts-redirector-action](https://github.com/larsoner/circleci-artifacts-redirector-action) from 1.1.0 to 1.2.0.
- [Release notes](https://github.com/larsoner/circleci-artifacts-redirector-action/releases)
- [Commits](https://github.com/larsoner/circleci-artifacts-redirector-action/compare/v1.1.0...v1.2.0)

---
updated-dependencies:
- dependency-name: larsoner/circleci-artifacts-redirector-action
  dependency-version: 1.2.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/circleci-artifacts-redirector.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/circleci-artifacts-redirector.yml b/.github/workflows/circleci-artifacts-redirector.yml
index 692942cddd..f6dd3f2e7c 100644
--- a/.github/workflows/circleci-artifacts-redirector.yml
+++ b/.github/workflows/circleci-artifacts-redirector.yml
@@ -6,7 +6,7 @@ jobs:
     name: Run CircleCI artifacts redirector!!
     steps:
       - name: run-circleci-artifacts-redirector
-        uses: larsoner/circleci-artifacts-redirector-action@v1.1.0
+        uses: larsoner/circleci-artifacts-redirector-action@v1.2.0
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           api-token: ${{ secrets.CIRCLECI_TOKEN }}

From db06b9f9669b216b4a78269c8acfa07abc6d45e7 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 7 Aug 2025 21:54:40 +0900
Subject: [PATCH 1046/1080] build(deps): bump mypy from 1.17.0 to 1.17.1 in
 /requirements (#1255)

Bumps [mypy](https://github.com/python/mypy) from 1.17.0 to 1.17.1.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.17.0...v1.17.1)

---
updated-dependencies:
- dependency-name: mypy
  dependency-version: 1.17.1
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 66b5202aca..f8f16dc34d 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ cvxpy == 1.7.1
 ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test
-mypy == 1.17.0 # For unit test
+mypy == 1.17.1 # For unit test
 ruff == 0.12.5 # For unit test

From 6455a4db8f7e3d995af0b08087f0c9543f2b7ca3 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Sat, 9 Aug 2025 11:36:45 +0900
Subject: [PATCH 1047/1080] build(deps): bump ruff from 0.12.5 to 0.12.7 in
 /requirements (#1256)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.5 to 0.12.7.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.12.5...0.12.7)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.7
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index f8f16dc34d..38001d34a9 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.17.1 # For unit test
-ruff == 0.12.5 # For unit test
+ruff == 0.12.7 # For unit test

From b85b5d2ee4420b6f3f3d7f54a3d33cf86fbc90d4 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 12 Aug 2025 16:57:02 +0900
Subject: [PATCH 1048/1080] build(deps): bump actions/checkout from 4 to 5
 (#1257)

Bumps [actions/checkout](https://github.com/actions/checkout) from 4 to 5.
- [Release notes](https://github.com/actions/checkout/releases)
- [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md)
- [Commits](https://github.com/actions/checkout/compare/v4...v5)

---
updated-dependencies:
- dependency-name: actions/checkout
  dependency-version: '5'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/Linux_CI.yml   | 2 +-
 .github/workflows/MacOS_CI.yml   | 2 +-
 .github/workflows/Windows_CI.yml | 2 +-
 .github/workflows/codeql.yml     | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index 907d36452d..22a8baa4e7 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -17,7 +17,7 @@ jobs:
     name: Python ${{ matrix.python-version }} CI
 
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v5
     - run: git fetch --prune --unshallow
 
     - name: Setup python
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index 03db65f43d..aa162cde2e 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -19,7 +19,7 @@ jobs:
         python-version: [ '3.13' ]
     name: Python ${{ matrix.python-version }} CI
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
       - run: git fetch --prune --unshallow
 
       - name: Update bash
diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml
index 5cb19699b2..4d83a3a696 100644
--- a/.github/workflows/Windows_CI.yml
+++ b/.github/workflows/Windows_CI.yml
@@ -19,7 +19,7 @@ jobs:
         python-version: [ '3.13' ]
     name: Python ${{ matrix.python-version }} CI
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
       - run: git fetch --prune --unshallow
 
       - name: Setup python
diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index 878a4a4435..0ed1d7e90d 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -16,7 +16,7 @@ jobs:
 
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v4
+      uses: actions/checkout@v5
       with:
         # We must fetch at least the immediate parents so that if this is
         # a pull request then we can checkout the head.

From cec3a844a5fce464bb5c402720168b5cd2a5ba96 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 12 Aug 2025 22:07:38 +0900
Subject: [PATCH 1049/1080] build(deps): bump ruff from 0.12.7 to 0.12.8 in
 /requirements (#1258)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.7 to 0.12.8.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.12.7...0.12.8)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.8
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 38001d34a9..1a7b43c438 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.17.1 # For unit test
-ruff == 0.12.7 # For unit test
+ruff == 0.12.8 # For unit test

From 721dd180772f34dd3f07183af8e67ff239c229c8 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 20 Aug 2025 01:09:55 +0900
Subject: [PATCH 1050/1080] build(deps): bump ruff from 0.12.8 to 0.12.9 in
 /requirements (#1259)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.8 to 0.12.9.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.12.8...0.12.9)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.9
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 1a7b43c438..21098a13f9 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.17.1 # For unit test
-ruff == 0.12.8 # For unit test
+ruff == 0.12.9 # For unit test

From ddafd936e705ae83d64b942eae8a1194326bdb5b Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 26 Aug 2025 21:19:40 +0900
Subject: [PATCH 1051/1080] build(deps): bump ruff from 0.12.9 to 0.12.10 in
 /requirements (#1261)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.9 to 0.12.10.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.12.9...0.12.10)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.10
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 21098a13f9..18b2a04d04 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.17.1 # For unit test
-ruff == 0.12.9 # For unit test
+ruff == 0.12.10 # For unit test

From e1685187fb5004b6c177cc65f7e4caf93aec5d5a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 26 Aug 2025 21:20:00 +0900
Subject: [PATCH 1052/1080] build(deps): bump cvxpy from 1.7.1 to 1.7.2 in
 /requirements (#1262)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.7.1 to 1.7.2.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.7.1...v1.7.2)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-version: 1.7.2
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 18b2a04d04..348b54fc76 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 2.2.4
 scipy == 1.15.2
 matplotlib == 3.10.1
-cvxpy == 1.7.1
+cvxpy == 1.7.2
 ecos == 2.0.14
 pytest == 8.4.1 # For unit test
 pytest-xdist == 3.8.0 # For unit test

From 1461ac25535274f95ec40aff2ea40dc2638ff1eb Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Sep 2025 20:14:43 +0900
Subject: [PATCH 1053/1080] build(deps): bump actions/setup-python from 5 to 6
 (#1264)

Bumps [actions/setup-python](https://github.com/actions/setup-python) from 5 to 6.
- [Release notes](https://github.com/actions/setup-python/releases)
- [Commits](https://github.com/actions/setup-python/compare/v5...v6)

---
updated-dependencies:
- dependency-name: actions/setup-python
  dependency-version: '6'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/Linux_CI.yml   | 2 +-
 .github/workflows/MacOS_CI.yml   | 2 +-
 .github/workflows/Windows_CI.yml | 2 +-
 .github/workflows/gh-pages.yml   | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml
index 22a8baa4e7..152a3b0682 100644
--- a/.github/workflows/Linux_CI.yml
+++ b/.github/workflows/Linux_CI.yml
@@ -21,7 +21,7 @@ jobs:
     - run: git fetch --prune --unshallow
 
     - name: Setup python
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v6
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml
index aa162cde2e..ab04dc01dc 100644
--- a/.github/workflows/MacOS_CI.yml
+++ b/.github/workflows/MacOS_CI.yml
@@ -26,7 +26,7 @@ jobs:
         run: brew install bash
 
       - name: Setup python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
 
diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml
index 4d83a3a696..a4385e595b 100644
--- a/.github/workflows/Windows_CI.yml
+++ b/.github/workflows/Windows_CI.yml
@@ -23,7 +23,7 @@ jobs:
       - run: git fetch --prune --unshallow
 
       - name: Setup python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
 
diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml
index e08c6106c0..84165b9cd2 100644
--- a/.github/workflows/gh-pages.yml
+++ b/.github/workflows/gh-pages.yml
@@ -14,7 +14,7 @@ jobs:
       pages: write
     steps:
     - name: Setup python
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v6
     - name: Checkout
       uses: actions/checkout@master
       with:

From 1ee1788114b47830eaed7ef028b35ccff73086d6 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Sep 2025 20:52:09 +0900
Subject: [PATCH 1054/1080] build(deps): bump pytest from 8.4.1 to 8.4.2 in
 /requirements (#1265)

Bumps [pytest](https://github.com/pytest-dev/pytest) from 8.4.1 to 8.4.2.
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](https://github.com/pytest-dev/pytest/compare/8.4.1...8.4.2)

---
updated-dependencies:
- dependency-name: pytest
  dependency-version: 8.4.2
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 348b54fc76..fc11c01101 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -3,7 +3,7 @@ scipy == 1.15.2
 matplotlib == 3.10.1
 cvxpy == 1.7.2
 ecos == 2.0.14
-pytest == 8.4.1 # For unit test
+pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.17.1 # For unit test
 ruff == 0.12.10 # For unit test

From d241f4a7bb64e8683308202c12165e7f04415f38 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 9 Sep 2025 21:18:59 +0900
Subject: [PATCH 1055/1080] build(deps): bump ruff from 0.12.10 to 0.12.12 in
 /requirements (#1266)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.10 to 0.12.12.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.12.10...0.12.12)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.12.12
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index fc11c01101..9690665821 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.17.1 # For unit test
-ruff == 0.12.10 # For unit test
+ruff == 0.12.12 # For unit test

From db3b1ab8738888a9e086ae7c1e35cbb4677ad6fa Mon Sep 17 00:00:00 2001
From: Copilot <198982749+Copilot@users.noreply.github.com>
Date: Sat, 13 Sep 2025 22:41:25 +0900
Subject: [PATCH 1056/1080] Fix typos in comments and documentation (#1260)

* Initial plan

* Fix typos in comments and documentation

Co-authored-by: AtsushiSakai <3813847+AtsushiSakai@users.noreply.github.com>

* Revert filename change in users_comments.md as requested

Co-authored-by: AtsushiSakai <3813847+AtsushiSakai@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: AtsushiSakai <3813847+AtsushiSakai@users.noreply.github.com>
---
 .../0_getting_started/3_how_to_contribute_main.rst        | 2 +-
 users_comments.md                                         | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/modules/0_getting_started/3_how_to_contribute_main.rst b/docs/modules/0_getting_started/3_how_to_contribute_main.rst
index 0325aaacae..9e773e930c 100644
--- a/docs/modules/0_getting_started/3_how_to_contribute_main.rst
+++ b/docs/modules/0_getting_started/3_how_to_contribute_main.rst
@@ -176,7 +176,7 @@ Adding the missed documentations for existing examples is also great contributio
 If you check the `Python Robotics Docs`_, you can notice that some of the examples
 only have a simulation gif or short overview descriptions or just TBD.,
 but no detailed algorithm or mathematical description.
-These documents needs to be improved.
+These documents need to be improved.
 
 This doc `how to write doc`_ can be helpful to write documents.
 
diff --git a/users_comments.md b/users_comments.md
index 30dc2d2d85..c814e74c4b 100644
--- a/users_comments.md
+++ b/users_comments.md
@@ -101,7 +101,7 @@ Thank you!
 
 ---
 
-Dear AtsushiSakai, <br>Thanks a lot for the wonderful collection of projects.It was really useful. I really appreciate your time in maintaing and building this repository.It will be really great if I get a chance to meet you in person sometime.I got really inspired looking at your work. <br>All the very best for all your future endeavors! <br> Thanks once again, <br>
+Dear AtsushiSakai, <br>Thanks a lot for the wonderful collection of projects.It was really useful. I really appreciate your time in maintaining and building this repository.It will be really great if I get a chance to meet you in person sometime.I got really inspired looking at your work. <br>All the very best for all your future endeavors! <br> Thanks once again, <br>
 
 ---Ezhil Bharathi 
 
@@ -163,7 +163,7 @@ so kind of you can you make videos of it.
 
 ---
 
-Dear AtshshiSakai, <br>You are very wise and smart that you created this library for students wanting to learn Probabilistic Robotics and actually perform robot control. <br>Hats off to you and your work. <br>I am also reading your book titled : Python Robotics
+Dear AtsushiSakai, <br>You are very wise and smart that you created this library for students wanting to learn Probabilistic Robotics and actually perform robot control. <br>Hats off to you and your work. <br>I am also reading your book titled : Python Robotics
 
 --Himanshu
 
@@ -263,7 +263,7 @@ Hey Atsushi <br>We are working on a multiagent simulation game and this project
 
 ---
 
-Thanks a lot for this amazing set of very useful librarires!
+Thanks a lot for this amazing set of very useful libraries!
 
 --Razvan Viorel Mihai
 
@@ -275,7 +275,7 @@ Dear Atsushi Sakai, <br> <br>This is probably one of the best open-source roboti
 
 ---
 
-hanks frnd, for ur contibusion
+Thanks friend, for your contribution
 
 —--
 

From 0b7f8aa22d4be6128dcd1d1db2f69a6479f50494 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 17 Sep 2025 10:44:06 +0900
Subject: [PATCH 1057/1080] build(deps): bump mypy from 1.17.1 to 1.18.1 in
 /requirements (#1267)

Bumps [mypy](https://github.com/python/mypy) from 1.17.1 to 1.18.1.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.17.1...v1.18.1)

---
updated-dependencies:
- dependency-name: mypy
  dependency-version: 1.18.1
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 9690665821..45e86f3cb2 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ cvxpy == 1.7.2
 ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
-mypy == 1.17.1 # For unit test
+mypy == 1.18.1 # For unit test
 ruff == 0.12.12 # For unit test

From c5871248be46388abdd7cb5e0a2e9d52c4492c7e Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 17 Sep 2025 21:55:53 +0900
Subject: [PATCH 1058/1080] build(deps): bump ruff from 0.12.12 to 0.13.0 in
 /requirements (#1268)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.12.12 to 0.13.0.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.12.12...0.13.0)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.13.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 45e86f3cb2..673f1ec179 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.18.1 # For unit test
-ruff == 0.12.12 # For unit test
+ruff == 0.13.0 # For unit test

From 4a8dbf2199c4bce998ff10f49579d30a5462c82c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 24 Sep 2025 00:09:21 +0900
Subject: [PATCH 1059/1080] build(deps): bump
 larsoner/circleci-artifacts-redirector-action (#1271)

Bumps [larsoner/circleci-artifacts-redirector-action](https://github.com/larsoner/circleci-artifacts-redirector-action) from 1.2.0 to 1.3.1.
- [Release notes](https://github.com/larsoner/circleci-artifacts-redirector-action/releases)
- [Commits](https://github.com/larsoner/circleci-artifacts-redirector-action/compare/v1.2.0...v1.3.1)

---
updated-dependencies:
- dependency-name: larsoner/circleci-artifacts-redirector-action
  dependency-version: 1.3.1
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/circleci-artifacts-redirector.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/circleci-artifacts-redirector.yml b/.github/workflows/circleci-artifacts-redirector.yml
index f6dd3f2e7c..0e64bab96c 100644
--- a/.github/workflows/circleci-artifacts-redirector.yml
+++ b/.github/workflows/circleci-artifacts-redirector.yml
@@ -6,7 +6,7 @@ jobs:
     name: Run CircleCI artifacts redirector!!
     steps:
       - name: run-circleci-artifacts-redirector
-        uses: larsoner/circleci-artifacts-redirector-action@v1.2.0
+        uses: larsoner/circleci-artifacts-redirector-action@v1.3.1
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           api-token: ${{ secrets.CIRCLECI_TOKEN }}

From d4dd8bbc9bbbe4cbe4a3711992dceaf76f5a2a7d Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 24 Sep 2025 12:42:30 +0900
Subject: [PATCH 1060/1080] build(deps): bump cvxpy from 1.7.2 to 1.7.3 in
 /requirements (#1274)

Bumps [cvxpy](https://github.com/cvxpy/cvxpy) from 1.7.2 to 1.7.3.
- [Release notes](https://github.com/cvxpy/cvxpy/releases)
- [Commits](https://github.com/cvxpy/cvxpy/compare/v1.7.2...v1.7.3)

---
updated-dependencies:
- dependency-name: cvxpy
  dependency-version: 1.7.3
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 673f1ec179..0a326d3b6e 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,7 +1,7 @@
 numpy == 2.2.4
 scipy == 1.15.2
 matplotlib == 3.10.1
-cvxpy == 1.7.2
+cvxpy == 1.7.3
 ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test

From 7a37f2e403f0f2c2a7d29849e4e804ee5ef45012 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 24 Sep 2025 19:32:16 +0900
Subject: [PATCH 1061/1080] build(deps): bump mypy from 1.18.1 to 1.18.2 in
 /requirements (#1272)

Bumps [mypy](https://github.com/python/mypy) from 1.18.1 to 1.18.2.
- [Changelog](https://github.com/python/mypy/blob/master/CHANGELOG.md)
- [Commits](https://github.com/python/mypy/compare/v1.18.1...v1.18.2)

---
updated-dependencies:
- dependency-name: mypy
  dependency-version: 1.18.2
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 0a326d3b6e..8703fb1f18 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,5 +5,5 @@ cvxpy == 1.7.3
 ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
-mypy == 1.18.1 # For unit test
+mypy == 1.18.2 # For unit test
 ruff == 0.13.0 # For unit test

From ffaa02fb9377efb4649eacb39e76865e2ca920e8 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 24 Sep 2025 21:21:05 +0900
Subject: [PATCH 1062/1080] build(deps): bump ruff from 0.13.0 to 0.13.1 in
 /requirements (#1273)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.13.0 to 0.13.1.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.13.0...0.13.1)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.13.1
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 8703fb1f18..a7e9eb25f9 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.18.2 # For unit test
-ruff == 0.13.0 # For unit test
+ruff == 0.13.1 # For unit test

From 722efb8dbb5ca9a01fcedfd6b0fe36623d013d39 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 30 Sep 2025 21:49:29 +0900
Subject: [PATCH 1063/1080] build(deps): bump ruff from 0.13.1 to 0.13.2 in
 /requirements (#1275)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.13.1 to 0.13.2.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.13.1...0.13.2)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.13.2
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index a7e9eb25f9..050bd1e57c 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.18.2 # For unit test
-ruff == 0.13.1 # For unit test
+ruff == 0.13.2 # For unit test

From c4c7d5957fe88eaf3f3624588cd2123a7de44ce8 Mon Sep 17 00:00:00 2001
From: Sanskar Modi <sansyprog8@gmail.com>
Date: Mon, 6 Oct 2025 19:28:05 +0530
Subject: [PATCH 1064/1080] fixed the scope of range function (#1276)

---
 .../model_predictive_speed_and_steer_control.py                 | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
index eb2d7b6a73..205f37e62f 100644
--- a/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
+++ b/PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py
@@ -318,7 +318,7 @@ def calc_ref_trajectory(state, cx, cy, cyaw, ck, sp, dl, pind):
 
     travel = 0.0
 
-    for i in range(T + 1):
+    for i in range(1, T + 1):
         travel += abs(state.v) * DT
         dind = int(round(travel / dl))
 

From 194dc52083268dec76e51ae0807583437dae753f Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 7 Oct 2025 21:59:44 +0900
Subject: [PATCH 1065/1080] build(deps): bump scipy from 1.15.2 to 1.16.2 in
 /requirements (#1280)

Bumps [scipy](https://github.com/scipy/scipy) from 1.15.2 to 1.16.2.
- [Release notes](https://github.com/scipy/scipy/releases)
- [Commits](https://github.com/scipy/scipy/compare/v1.15.2...v1.16.2)

---
updated-dependencies:
- dependency-name: scipy
  dependency-version: 1.16.2
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 050bd1e57c..a8601c982f 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,5 +1,5 @@
 numpy == 2.2.4
-scipy == 1.15.2
+scipy == 1.16.2
 matplotlib == 3.10.1
 cvxpy == 1.7.3
 ecos == 2.0.14

From 1ff2b862652188756b0351fca6163598f0d5b95a Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 8 Oct 2025 18:53:10 +0900
Subject: [PATCH 1066/1080] build(deps): bump numpy from 2.2.4 to 2.3.3 in
 /requirements (#1282)

Bumps [numpy](https://github.com/numpy/numpy) from 2.2.4 to 2.3.3.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v2.2.4...v2.3.3)

---
updated-dependencies:
- dependency-name: numpy
  dependency-version: 2.3.3
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index a8601c982f..8b2a8dcbf1 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 2.2.4
+numpy == 2.3.3
 scipy == 1.16.2
 matplotlib == 3.10.1
 cvxpy == 1.7.3

From 52c9c07114ab67879e462cd23b39c578c9cafa1b Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 8 Oct 2025 19:11:28 +0900
Subject: [PATCH 1067/1080] build(deps): bump matplotlib from 3.10.1 to 3.10.6
 in /requirements (#1283)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.10.1 to 3.10.6.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.10.1...v3.10.6)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-version: 3.10.6
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 8b2a8dcbf1..43a16ef1e2 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 2.3.3
 scipy == 1.16.2
-matplotlib == 3.10.1
+matplotlib == 3.10.6
 cvxpy == 1.7.3
 ecos == 2.0.14
 pytest == 8.4.2 # For unit test

From 6ec297fbb3055abf02030ad5b81bbb149371d161 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Wed, 8 Oct 2025 19:22:26 +0900
Subject: [PATCH 1068/1080] build(deps): bump ruff from 0.13.2 to 0.13.3 in
 /requirements (#1281)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.13.2 to 0.13.3.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.13.2...0.13.3)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.13.3
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 43a16ef1e2..153add3786 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.18.2 # For unit test
-ruff == 0.13.2 # For unit test
+ruff == 0.13.3 # For unit test

From 2895d880b296b42470d95906d55cbbc0f6073383 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 14 Oct 2025 07:22:12 +0900
Subject: [PATCH 1069/1080] build(deps): bump github/codeql-action from 3 to 4
 (#1287)

Bumps [github/codeql-action](https://github.com/github/codeql-action) from 3 to 4.
- [Release notes](https://github.com/github/codeql-action/releases)
- [Changelog](https://github.com/github/codeql-action/blob/main/CHANGELOG.md)
- [Commits](https://github.com/github/codeql-action/compare/v3...v4)

---
updated-dependencies:
- dependency-name: github/codeql-action
  dependency-version: '4'
  dependency-type: direct:production
  update-type: version-update:semver-major
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 .github/workflows/codeql.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index 0ed1d7e90d..dcbcdbaa88 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -24,7 +24,7 @@ jobs:
 
     # Initializes the CodeQL tools for scanning.
     - name: Initialize CodeQL
-      uses: github/codeql-action/init@v3
+      uses: github/codeql-action/init@v4
       with:
         config-file: ./.github/codeql/codeql-config.yml
       # Override language selection by uncommenting this and choosing your languages
@@ -34,7 +34,7 @@ jobs:
     # Autobuild attempts to build any compiled languages  (C/C++, C#, or Java).
     # If this step fails, then you should remove it and run the build manually (see below)
     - name: Autobuild
-      uses: github/codeql-action/autobuild@v3
+      uses: github/codeql-action/autobuild@v4
 
     # ℹ️ Command-line programs to run using the OS shell.
     # 📚 https://git.io/JvXDl
@@ -48,4 +48,4 @@ jobs:
     #   make release
 
     - name: Perform CodeQL Analysis
-      uses: github/codeql-action/analyze@v3
+      uses: github/codeql-action/analyze@v4

From 01a85c3c848975b5efc9b11b6a6a3be1e0a8b8dd Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 14 Oct 2025 07:23:37 +0900
Subject: [PATCH 1070/1080] build(deps): bump matplotlib from 3.10.6 to 3.10.7
 in /requirements (#1289)

Bumps [matplotlib](https://github.com/matplotlib/matplotlib) from 3.10.6 to 3.10.7.
- [Release notes](https://github.com/matplotlib/matplotlib/releases)
- [Commits](https://github.com/matplotlib/matplotlib/compare/v3.10.6...v3.10.7)

---
updated-dependencies:
- dependency-name: matplotlib
  dependency-version: 3.10.7
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 153add3786..166f4e2ddc 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,6 +1,6 @@
 numpy == 2.3.3
 scipy == 1.16.2
-matplotlib == 3.10.6
+matplotlib == 3.10.7
 cvxpy == 1.7.3
 ecos == 2.0.14
 pytest == 8.4.2 # For unit test

From b0c81aedbcfefe43fac7b298dd3b7664dcabd8a9 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 14 Oct 2025 19:30:32 +0900
Subject: [PATCH 1071/1080] build(deps): bump ruff from 0.13.3 to 0.14.0 in
 /requirements (#1288)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.13.3 to 0.14.0.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.13.3...0.14.0)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.14.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 166f4e2ddc..c6223c01df 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.18.2 # For unit test
-ruff == 0.13.3 # For unit test
+ruff == 0.14.0 # For unit test

From 39e29bbc15cddb86830b391e44f2edf819aa49b8 Mon Sep 17 00:00:00 2001
From: Copilot <198982749+Copilot@users.noreply.github.com>
Date: Tue, 14 Oct 2025 20:23:16 +0900
Subject: [PATCH 1072/1080] Add comprehensive documentation for Ensemble Kalman
 Filter localization (#1278)

* Initial plan

* Add comprehensive documentation for Ensemble Kalman Filter localization

Co-authored-by: AtsushiSakai <3813847+AtsushiSakai@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: AtsushiSakai <3813847+AtsushiSakai@users.noreply.github.com>
---
 ...samble_kalman_filter_localization_main.rst | 196 +++++++++++++++++-
 1 file changed, 194 insertions(+), 2 deletions(-)

diff --git a/docs/modules/2_localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst b/docs/modules/2_localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst
index 214e645d10..6274859002 100644
--- a/docs/modules/2_localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst
+++ b/docs/modules/2_localization/ensamble_kalman_filter_localization_files/ensamble_kalman_filter_localization_main.rst
@@ -1,12 +1,204 @@
-Ensamble Kalman Filter Localization
+Ensemble Kalman Filter Localization
 -----------------------------------
 
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/Localization/ensamble_kalman_filter/animation.gif
 
-This is a sensor fusion localization with Ensamble Kalman Filter(EnKF).
+This is a sensor fusion localization with Ensemble Kalman Filter(EnKF).
+
+The blue line is true trajectory, the black line is dead reckoning trajectory,
+and the red line is estimated trajectory with EnKF.
+
+The red ellipse is estimated covariance ellipse with EnKF.
+
+It is assumed that the robot can measure distance and bearing angle from landmarks (RFID).
+
+These measurements are used for EnKF localization.
 
 Code Link
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. autofunction:: Localization.ensemble_kalman_filter.ensemble_kalman_filter.enkf_localization
 
+
+Ensemble Kalman Filter Algorithm
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The Ensemble Kalman Filter (EnKF) is a Monte Carlo approach to the Kalman filter that
+uses an ensemble of state estimates to represent the probability distribution. Unlike
+the traditional Kalman filter that propagates the mean and covariance analytically,
+EnKF uses a collection of samples (ensemble members) to estimate the state and its uncertainty.
+
+The EnKF algorithm consists of two main steps:
+
+=== Predict ===
+
+For each ensemble member :math:`i = 1, ..., N`:
+
+1. Add random noise to the control input:
+
+   :math:`\mathbf{u}^i = \mathbf{u} + \epsilon_u`, where :math:`\epsilon_u \sim \mathcal{N}(0, \mathbf{R})`
+
+2. Predict the state:
+
+   :math:`\mathbf{x}^i_{pred} = f(\mathbf{x}^i_t, \mathbf{u}^i)`
+
+3. Predict the observation:
+
+   :math:`\mathbf{z}^i_{pred} = h(\mathbf{x}^i_{pred}) + \epsilon_z`, where :math:`\epsilon_z \sim \mathcal{N}(0, \mathbf{Q})`
+
+=== Update ===
+
+1. Calculate ensemble mean for states:
+
+   :math:`\bar{\mathbf{x}} = \frac{1}{N}\sum_{i=1}^N \mathbf{x}^i_{pred}`
+
+2. Calculate ensemble mean for observations:
+
+   :math:`\bar{\mathbf{z}} = \frac{1}{N}\sum_{i=1}^N \mathbf{z}^i_{pred}`
+
+3. Calculate state deviation:
+
+   :math:`\mathbf{X}' = \mathbf{x}^i_{pred} - \bar{\mathbf{x}}`
+
+4. Calculate observation deviation:
+
+   :math:`\mathbf{Z}' = \mathbf{z}^i_{pred} - \bar{\mathbf{z}}`
+
+5. Calculate covariance matrices:
+
+   :math:`\mathbf{U} = \frac{1}{N-1}\mathbf{X}'\mathbf{Z}'^T`
+
+   :math:`\mathbf{V} = \frac{1}{N-1}\mathbf{Z}'\mathbf{Z}'^T`
+
+6. Calculate Kalman gain:
+
+   :math:`\mathbf{K} = \mathbf{U}\mathbf{V}^{-1}`
+
+7. Update each ensemble member:
+
+   :math:`\mathbf{x}^i_{t+1} = \mathbf{x}^i_{pred} + \mathbf{K}(\mathbf{z}_{obs} - \mathbf{z}^i_{pred})`
+
+8. Calculate final state estimate:
+
+   :math:`\mathbf{x}_{est} = \frac{1}{N}\sum_{i=1}^N \mathbf{x}^i_{t+1}`
+
+9. Calculate covariance estimate:
+
+   :math:`\mathbf{P}_{est} = \frac{1}{N}\sum_{i=1}^N (\mathbf{x}^i_{t+1} - \mathbf{x}_{est})(\mathbf{x}^i_{t+1} - \mathbf{x}_{est})^T`
+
+
+Filter Design
+~~~~~~~~~~~~~
+
+In this simulation, the robot has a state vector with 4 states at time :math:`t`:
+
+.. math:: \mathbf{x}_t = [x_t, y_t, \phi_t, v_t]^T
+
+where:
+
+- :math:`x, y` are 2D position coordinates
+- :math:`\phi` is orientation (yaw angle)
+- :math:`v` is velocity
+
+The filter uses an ensemble of :math:`N=20` particles to represent the state distribution.
+
+Input Vector
+^^^^^^^^^^^^
+
+The robot has a velocity sensor and a gyro sensor, so the control input vector at each time step is:
+
+.. math:: \mathbf{u}_t = [v_t, \omega_t]^T
+
+where:
+
+- :math:`v_t` is linear velocity
+- :math:`\omega_t` is angular velocity (yaw rate)
+
+The input vector includes sensor noise modeled by covariance matrix :math:`\mathbf{R}`:
+
+.. math:: \mathbf{R} = \begin{bmatrix} \sigma_v^2 & 0 \\ 0 & \sigma_\omega^2 \end{bmatrix}
+
+
+Observation Vector
+^^^^^^^^^^^^^^^^^^
+
+The robot can observe landmarks (RFID tags) with distance and bearing measurements:
+
+.. math:: \mathbf{z}_t = [d_t, \theta_t, x_{lm}, y_{lm}]
+
+where:
+
+- :math:`d_t` is the distance to the landmark
+- :math:`\theta_t` is the bearing angle to the landmark
+- :math:`x_{lm}, y_{lm}` are the known landmark positions
+
+The observation noise is modeled by covariance matrix :math:`\mathbf{Q}`:
+
+.. math:: \mathbf{Q} = \begin{bmatrix} \sigma_d^2 & 0 \\ 0 & \sigma_\theta^2 \end{bmatrix}
+
+
+Motion Model
+~~~~~~~~~~~~
+
+The robot motion model is:
+
+.. math::  \dot{x} = v \cos(\phi)
+
+.. math::  \dot{y} = v \sin(\phi)
+
+.. math::  \dot{\phi} = \omega
+
+.. math::  \dot{v} = 0
+
+Discretized with time step :math:`\Delta t`, the motion model becomes:
+
+.. math:: \mathbf{x}_{t+1} = f(\mathbf{x}_t, \mathbf{u}_t) = \mathbf{F}\mathbf{x}_t + \mathbf{B}\mathbf{u}_t
+
+where:
+
+:math:`\begin{equation*} \mathbf{F} = \begin{bmatrix} 1 & 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 0 \\ \end{bmatrix} \end{equation*}`
+
+:math:`\begin{equation*} \mathbf{B} = \begin{bmatrix} \cos(\phi) \Delta t & 0\\ \sin(\phi) \Delta t & 0\\ 0 & \Delta t\\ 1 & 0\\ \end{bmatrix} \end{equation*}`
+
+The motion function expands to:
+
+:math:`\begin{equation*} \begin{bmatrix} x_{t+1} \\ y_{t+1} \\ \phi_{t+1} \\ v_{t+1} \end{bmatrix} = \begin{bmatrix} x_t + v_t\cos(\phi_t)\Delta t \\ y_t + v_t\sin(\phi_t)\Delta t \\ \phi_t + \omega_t \Delta t \\ v_t \end{bmatrix} \end{equation*}`
+
+
+Observation Model
+~~~~~~~~~~~~~~~~~
+
+For each landmark at position :math:`(x_{lm}, y_{lm})`, the observation model calculates
+the expected landmark position in the global frame:
+
+.. math:: 
+   \begin{bmatrix} 
+   x_{lm,obs} \\ 
+   y_{lm,obs} 
+   \end{bmatrix} = 
+   \begin{bmatrix} 
+   x + d \cos(\phi + \theta) \\ 
+   y + d \sin(\phi + \theta) 
+   \end{bmatrix}
+
+where :math:`d` and :math:`\theta` are the observed distance and bearing to the landmark.
+
+The observation function projects the robot state to expected landmark positions,
+which are then compared with actual landmark positions for the update step.
+
+
+Advantages of EnKF
+~~~~~~~~~~~~~~~~~~
+
+- **No Jacobian required**: Unlike EKF, EnKF does not need to compute Jacobian matrices, making it easier to implement for nonlinear systems
+- **Handles non-Gaussian distributions**: The ensemble representation can capture non-Gaussian features of the state distribution
+- **Computationally efficient**: For high-dimensional systems, EnKF can be more efficient than maintaining full covariance matrices
+- **Easy to parallelize**: Each ensemble member can be propagated independently
+
+
+Reference
+~~~~~~~~~
+
+- `Ensemble Kalman filtering <https://rmets.onlinelibrary.wiley.com/doi/10.1256/qj.05.135>`_
+- `PROBABILISTIC ROBOTICS <http://www.probabilistic-robotics.org/>`_
+

From fc6b36e3092f0fefde02ad06a6a95406fd83579d Mon Sep 17 00:00:00 2001
From: Copilot <198982749+Copilot@users.noreply.github.com>
Date: Sun, 19 Oct 2025 18:48:21 +0900
Subject: [PATCH 1073/1080] Add comprehensive documentation for Unscented
 Kalman Filter localization (#1290)

* Initial plan

* Add comprehensive UKF documentation with algorithm details

Co-authored-by: AtsushiSakai <3813847+AtsushiSakai@users.noreply.github.com>

* Add comprehensive docstrings to UKF core functions

Co-authored-by: AtsushiSakai <3813847+AtsushiSakai@users.noreply.github.com>

* Fix codestyle issues - remove trailing whitespace from docstrings

Co-authored-by: AtsushiSakai <3813847+AtsushiSakai@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: AtsushiSakai <3813847+AtsushiSakai@users.noreply.github.com>
---
 .../unscented_kalman_filter.py                |  77 ++++++
 ...cented_kalman_filter_localization_main.rst | 240 +++++++++++++++++-
 2 files changed, 316 insertions(+), 1 deletion(-)

diff --git a/Localization/unscented_kalman_filter/unscented_kalman_filter.py b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
index 4af748ec71..2119e1eacc 100644
--- a/Localization/unscented_kalman_filter/unscented_kalman_filter.py
+++ b/Localization/unscented_kalman_filter/unscented_kalman_filter.py
@@ -91,6 +91,29 @@ def observation_model(x):
 
 
 def generate_sigma_points(xEst, PEst, gamma):
+    """
+    Generate sigma points for UKF.
+
+    Sigma points are deterministically sampled points used to capture
+    the mean and covariance of the state distribution.
+
+    Parameters
+    ----------
+    xEst : numpy.ndarray
+        Current state estimate (n x 1)
+    PEst : numpy.ndarray
+        Current state covariance estimate (n x n)
+    gamma : float
+        Scaling parameter sqrt(n + lambda)
+
+    Returns
+    -------
+    sigma : numpy.ndarray
+        Sigma points (n x 2n+1)
+        sigma[:, 0] = xEst
+        sigma[:, 1:n+1] = xEst + gamma * sqrt(P)
+        sigma[:, n+1:2n+1] = xEst - gamma * sqrt(P)
+    """
     sigma = xEst
     Psqrt = scipy.linalg.sqrtm(PEst)
     n = len(xEst[:, 0])
@@ -149,6 +172,35 @@ def calc_pxz(sigma, x, z_sigma, zb, wc):
 
 
 def ukf_estimation(xEst, PEst, z, u, wm, wc, gamma):
+    """
+    Unscented Kalman Filter estimation.
+
+    Performs one iteration of UKF state estimation using the unscented transform.
+
+    Parameters
+    ----------
+    xEst : numpy.ndarray
+        Current state estimate [x, y, yaw, v]^T (4 x 1)
+    PEst : numpy.ndarray
+        Current state covariance estimate (4 x 4)
+    z : numpy.ndarray
+        Observation vector [x_obs, y_obs]^T (2 x 1)
+    u : numpy.ndarray
+        Control input [velocity, yaw_rate]^T (2 x 1)
+    wm : numpy.ndarray
+        Weights for calculating mean (1 x 2n+1)
+    wc : numpy.ndarray
+        Weights for calculating covariance (1 x 2n+1)
+    gamma : float
+        Sigma point scaling parameter sqrt(n + lambda)
+
+    Returns
+    -------
+    xEst : numpy.ndarray
+        Updated state estimate (4 x 1)
+    PEst : numpy.ndarray
+        Updated state covariance estimate (4 x 4)
+    """
     #  Predict
     sigma = generate_sigma_points(xEst, PEst, gamma)
     sigma = predict_sigma_motion(sigma, u)
@@ -194,6 +246,31 @@ def plot_covariance_ellipse(xEst, PEst):  # pragma: no cover
 
 
 def setup_ukf(nx):
+    """
+    Setup UKF parameters and weights.
+
+    Calculates the weights for mean and covariance computation,
+    and the scaling parameter gamma for sigma point generation.
+
+    Parameters
+    ----------
+    nx : int
+        Dimension of the state vector
+
+    Returns
+    -------
+    wm : numpy.ndarray
+        Weights for calculating mean (1 x 2n+1)
+        wm[0] = lambda / (n + lambda)
+        wm[i] = 1 / (2(n + lambda)) for i > 0
+    wc : numpy.ndarray
+        Weights for calculating covariance (1 x 2n+1)
+        wc[0] = lambda / (n + lambda) + (1 - alpha^2 + beta)
+        wc[i] = 1 / (2(n + lambda)) for i > 0
+    gamma : float
+        Sigma point scaling parameter sqrt(n + lambda)
+        where lambda = alpha^2 * (n + kappa) - n
+    """
     lamb = ALPHA ** 2 * (nx + KAPPA) - nx
     # calculate weights
     wm = [lamb / (lamb + nx)]
diff --git a/docs/modules/2_localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst b/docs/modules/2_localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst
index a7a5aab61b..01d161b17e 100644
--- a/docs/modules/2_localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst
+++ b/docs/modules/2_localization/unscented_kalman_filter_localization/unscented_kalman_filter_localization_main.rst
@@ -5,7 +5,10 @@ Unscented Kalman Filter localization
 
 This is a sensor fusion localization with Unscented Kalman Filter(UKF).
 
-The lines and points are same meaning of the EKF simulation.
+The blue line is true trajectory, the black line is dead reckoning trajectory,
+the green points are GPS observations, and the red line is estimated trajectory with UKF.
+
+The red ellipse is estimated covariance ellipse with UKF.
 
 Code Link
 ~~~~~~~~~~~~~
@@ -13,7 +16,242 @@ Code Link
 .. autofunction:: Localization.unscented_kalman_filter.unscented_kalman_filter.ukf_estimation
 
 
+Unscented Kalman Filter Algorithm
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The Unscented Kalman Filter (UKF) is a nonlinear state estimation technique that uses
+the unscented transform to handle nonlinearities. Unlike the Extended Kalman Filter (EKF)
+that linearizes the nonlinear functions using Jacobians, UKF uses a deterministic sampling
+approach called sigma points to capture the mean and covariance of the state distribution.
+
+The UKF algorithm consists of two main steps:
+
+=== Predict ===
+
+1. Generate sigma points around the current state estimate:
+
+   :math:`\chi_0 = \mathbf{x}_{t}`
+
+   :math:`\chi_i = \mathbf{x}_{t} + \gamma\sqrt{\mathbf{P}_t}_i` for :math:`i = 1, ..., n`
+
+   :math:`\chi_i = \mathbf{x}_{t} - \gamma\sqrt{\mathbf{P}_t}_{i-n}` for :math:`i = n+1, ..., 2n`
+
+   where :math:`\gamma = \sqrt{n + \lambda}` and :math:`\lambda = \alpha^2(n + \kappa) - n`
+
+2. Propagate sigma points through the motion model:
+
+   :math:`\chi^-_i = f(\chi_i, \mathbf{u}_t)`
+
+3. Calculate predicted state mean:
+
+   :math:`\mathbf{x}^-_{t+1} = \sum_{i=0}^{2n} w^m_i \chi^-_i`
+
+4. Calculate predicted state covariance:
+
+   :math:`\mathbf{P}^-_{t+1} = \sum_{i=0}^{2n} w^c_i (\chi^-_i - \mathbf{x}^-_{t+1})(\chi^-_i - \mathbf{x}^-_{t+1})^T + \mathbf{Q}`
+
+=== Update ===
+
+1. Generate sigma points around the predicted state:
+
+   :math:`\chi_i = \text{generate\_sigma\_points}(\mathbf{x}^-_{t+1}, \mathbf{P}^-_{t+1})`
+
+2. Propagate sigma points through the observation model:
+
+   :math:`\mathcal{Z}_i = h(\chi_i)`
+
+3. Calculate predicted observation mean:
+
+   :math:`\mathbf{z}^-_{t+1} = \sum_{i=0}^{2n} w^m_i \mathcal{Z}_i`
+
+4. Calculate innovation covariance:
+
+   :math:`\mathbf{S}_t = \sum_{i=0}^{2n} w^c_i (\mathcal{Z}_i - \mathbf{z}^-_{t+1})(\mathcal{Z}_i - \mathbf{z}^-_{t+1})^T + \mathbf{R}`
+
+5. Calculate cross-correlation matrix:
+
+   :math:`\mathbf{P}_{xz} = \sum_{i=0}^{2n} w^c_i (\chi_i - \mathbf{x}^-_{t+1})(\mathcal{Z}_i - \mathbf{z}^-_{t+1})^T`
+
+6. Calculate Kalman gain:
+
+   :math:`\mathbf{K} = \mathbf{P}_{xz}\mathbf{S}_t^{-1}`
+
+7. Update state estimate:
+
+   :math:`\mathbf{x}_{t+1} = \mathbf{x}^-_{t+1} + \mathbf{K}(\mathbf{z}_t - \mathbf{z}^-_{t+1})`
+
+8. Update covariance estimate:
+
+   :math:`\mathbf{P}_{t+1} = \mathbf{P}^-_{t+1} - \mathbf{K}\mathbf{S}_t\mathbf{K}^T`
+
+
+Sigma Points and Weights
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The UKF uses deterministic sigma points to represent the state distribution. The weights
+for combining sigma points are calculated as:
+
+**Mean weights:**
+
+:math:`w^m_0 = \frac{\lambda}{n + \lambda}`
+
+:math:`w^m_i = \frac{1}{2(n + \lambda)}` for :math:`i = 1, ..., 2n`
+
+**Covariance weights:**
+
+:math:`w^c_0 = \frac{\lambda}{n + \lambda} + (1 - \alpha^2 + \beta)`
+
+:math:`w^c_i = \frac{1}{2(n + \lambda)}` for :math:`i = 1, ..., 2n`
+
+where:
+
+- :math:`\alpha` controls the spread of sigma points around the mean (typically :math:`0.001 \leq \alpha \leq 1`)
+- :math:`\beta` incorporates prior knowledge of the distribution (:math:`\beta = 2` is optimal for Gaussian distributions)
+- :math:`\kappa` is a secondary scaling parameter (typically :math:`\kappa = 0`)
+- :math:`n` is the dimension of the state vector
+
+
+Filter Design
+~~~~~~~~~~~~~
+
+In this simulation, the robot has a state vector with 4 states at time :math:`t`:
+
+.. math:: \mathbf{x}_t = [x_t, y_t, \phi_t, v_t]^T
+
+where:
+
+- :math:`x, y` are 2D position coordinates
+- :math:`\phi` is orientation (yaw angle)
+- :math:`v` is velocity
+
+In the code, "xEst" means the state vector.
+
+The covariance matrices are:
+
+- :math:`\mathbf{P}_t` is the state covariance matrix
+- :math:`\mathbf{Q}` is the process noise covariance matrix
+- :math:`\mathbf{R}` is the observation noise covariance matrix
+
+**Process Noise Covariance** :math:`\mathbf{Q}`:
+
+.. math:: 
+   \mathbf{Q} = \begin{bmatrix} 
+   0.1^2 & 0 & 0 & 0 \\
+   0 & 0.1^2 & 0 & 0 \\
+   0 & 0 & (0.017)^2 & 0 \\
+   0 & 0 & 0 & 1.0^2
+   \end{bmatrix}
+
+**Observation Noise Covariance** :math:`\mathbf{R}`:
+
+.. math:: 
+   \mathbf{R} = \begin{bmatrix} 
+   1.0^2 & 0 \\
+   0 & 1.0^2
+   \end{bmatrix}
+
+
+Input Vector
+^^^^^^^^^^^^
+
+The robot has a velocity sensor and a gyro sensor, so the control input vector at each time step is:
+
+.. math:: \mathbf{u}_t = [v_t, \omega_t]^T
+
+where:
+
+- :math:`v_t` is linear velocity [m/s]
+- :math:`\omega_t` is angular velocity (yaw rate) [rad/s]
+
+The input vector includes sensor noise.
+
+
+Observation Vector
+^^^^^^^^^^^^^^^^^^
+
+The robot has a GNSS (GPS) sensor that can measure x-y position:
+
+.. math:: \mathbf{z}_t = [x_t, y_t]^T
+
+The observation includes GPS noise with covariance :math:`\mathbf{R}`.
+
+
+Motion Model
+~~~~~~~~~~~~
+
+The robot motion model is:
+
+.. math::  \dot{x} = v \cos(\phi)
+
+.. math::  \dot{y} = v \sin(\phi)
+
+.. math::  \dot{\phi} = \omega
+
+.. math::  \dot{v} = 0
+
+Discretized with time step :math:`\Delta t`, the motion model becomes:
+
+.. math:: \mathbf{x}_{t+1} = f(\mathbf{x}_t, \mathbf{u}_t) = \mathbf{F}\mathbf{x}_t + \mathbf{B}\mathbf{u}_t
+
+where:
+
+:math:`\begin{equation*} \mathbf{F} = \begin{bmatrix} 1 & 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ 0 & 0 & 1 & 0 \\ 0 & 0 & 0 & 0 \\ \end{bmatrix} \end{equation*}`
+
+:math:`\begin{equation*} \mathbf{B} = \begin{bmatrix} \cos(\phi) \Delta t & 0\\ \sin(\phi) \Delta t & 0\\ 0 & \Delta t\\ 1 & 0\\ \end{bmatrix} \end{equation*}`
+
+The motion function expands to:
+
+:math:`\begin{equation*} \begin{bmatrix} x_{t+1} \\ y_{t+1} \\ \phi_{t+1} \\ v_{t+1} \end{bmatrix} = \begin{bmatrix} x_t + v_t\cos(\phi_t)\Delta t \\ y_t + v_t\sin(\phi_t)\Delta t \\ \phi_t + \omega_t \Delta t \\ v_t \end{bmatrix} \end{equation*}`
+
+:math:`\Delta t = 0.1` [s] is the time interval.
+
+
+Observation Model
+~~~~~~~~~~~~~~~~~
+
+The robot can get x-y position information from GPS.
+
+The GPS observation model is:
+
+.. math:: \mathbf{z}_{t} = h(\mathbf{x}_t) = \mathbf{H} \mathbf{x}_t
+
+where:
+
+:math:`\begin{equation*} \mathbf{H} = \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ \end{bmatrix} \end{equation*}`
+
+The observation function states that:
+
+:math:`\begin{equation*} \begin{bmatrix} x_{obs} \\ y_{obs} \end{bmatrix} = h(\mathbf{x}) = \begin{bmatrix} x \\ y \end{bmatrix} \end{equation*}`
+
+
+UKF Parameters
+~~~~~~~~~~~~~~
+
+The UKF uses three tuning parameters:
+
+- **ALPHA = 0.001**: Controls the spread of sigma points around the mean. Smaller values result in sigma points closer to the mean.
+- **BETA = 2**: Incorporates prior knowledge about the distribution. For Gaussian distributions, the optimal value is 2.
+- **KAPPA = 0**: Secondary scaling parameter. Usually set to 0 or 3-n where n is the state dimension.
+
+These parameters affect the calculation of :math:`\lambda = \alpha^2(n + \kappa) - n`, which determines
+the sigma point spread and weights.
+
+
+Advantages of UKF over EKF
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The Unscented Kalman Filter offers several advantages over the Extended Kalman Filter:
+
+- **No Jacobian required**: UKF does not need to compute Jacobian matrices, which can be error-prone and computationally expensive for complex nonlinear systems
+- **Higher accuracy**: UKF captures the mean and covariance to the second order (third order for Gaussian distributions) while EKF only achieves first-order accuracy
+- **Better handling of nonlinearities**: The unscented transform provides a better approximation of the probability distribution after nonlinear transformation
+- **Easier implementation**: For highly nonlinear systems, UKF is often easier to implement since it doesn't require analytical derivatives
+- **Numerical stability**: UKF can be more numerically stable than EKF for strongly nonlinear systems
+
+
 Reference
 ~~~~~~~~~~~
 
 - `Discriminatively Trained Unscented Kalman Filter for Mobile Robot Localization <https://www.researchgate.net/publication/267963417_Discriminatively_Trained_Unscented_Kalman_Filter_for_Mobile_Robot_Localization>`_
+- `The Unscented Kalman Filter for Nonlinear Estimation <https://www.seas.harvard.edu/courses/cs281/papers/unscented.pdf>`_
+- `PROBABILISTIC ROBOTICS <http://www.probabilistic-robotics.org/>`_

From 1311b94d9b651da8bec85a47f0db2771d99e0c91 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Mon, 20 Oct 2025 21:44:33 +0900
Subject: [PATCH 1074/1080] Potential fix for code scanning alert no. 328:
 Workflow does not contain permissions (#1294)

Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
---
 .github/workflows/codeql.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index dcbcdbaa88..b402f989e8 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -8,6 +8,8 @@ on:
   schedule:
     - cron: '0 19 * * 0'
 
+permissions:
+  contents: read
 jobs:
   CodeQL-Build:
 

From 59d0cc7b4244a9e7fbe7b63ad14a70ff5db8c8c5 Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 21 Oct 2025 07:56:16 +0900
Subject: [PATCH 1075/1080] build(deps): bump ruff from 0.14.0 to 0.14.1 in
 /requirements (#1296)

Bumps [ruff](https://github.com/astral-sh/ruff) from 0.14.0 to 0.14.1.
- [Release notes](https://github.com/astral-sh/ruff/releases)
- [Changelog](https://github.com/astral-sh/ruff/blob/main/CHANGELOG.md)
- [Commits](https://github.com/astral-sh/ruff/compare/0.14.0...0.14.1)

---
updated-dependencies:
- dependency-name: ruff
  dependency-version: 0.14.1
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index c6223c01df..a2d843d7b7 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -6,4 +6,4 @@ ecos == 2.0.14
 pytest == 8.4.2 # For unit test
 pytest-xdist == 3.8.0 # For unit test
 mypy == 1.18.2 # For unit test
-ruff == 0.14.0 # For unit test
+ruff == 0.14.1 # For unit test

From 10764c2657ef36e9bcec6108020fd9c22563e93c Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 21 Oct 2025 20:05:29 +0900
Subject: [PATCH 1076/1080] build(deps): bump numpy from 2.3.3 to 2.3.4 in
 /requirements (#1297)

Bumps [numpy](https://github.com/numpy/numpy) from 2.3.3 to 2.3.4.
- [Release notes](https://github.com/numpy/numpy/releases)
- [Changelog](https://github.com/numpy/numpy/blob/main/doc/RELEASE_WALKTHROUGH.rst)
- [Commits](https://github.com/numpy/numpy/compare/v2.3.3...v2.3.4)

---
updated-dependencies:
- dependency-name: numpy
  dependency-version: 2.3.4
  dependency-type: direct:production
  update-type: version-update:semver-patch
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 requirements/requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index a2d843d7b7..306d93b736 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-numpy == 2.3.3
+numpy == 2.3.4
 scipy == 1.16.2
 matplotlib == 3.10.7
 cvxpy == 1.7.3

From 2d3cb831c94230017ca5467a6ce2e25ec107f8c5 Mon Sep 17 00:00:00 2001
From: Atsushi Sakai <asakai.amsl+github@gmail.com>
Date: Tue, 21 Oct 2025 21:20:50 +0900
Subject: [PATCH 1077/1080] =?UTF-8?q?Revert=20"Potential=20fix=20for=20cod?=
 =?UTF-8?q?e=20scanning=20alert=20no.=20328:=20Workflow=20does=20not=20con?=
 =?UTF-8?q?t=E2=80=A6"=20(#1298)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This reverts commit 1311b94d9b651da8bec85a47f0db2771d99e0c91.
---
 .github/workflows/codeql.yml | 2 --
 1 file changed, 2 deletions(-)

diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index b402f989e8..dcbcdbaa88 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -8,8 +8,6 @@ on:
   schedule:
     - cron: '0 19 * * 0'
 
-permissions:
-  contents: read
 jobs:
   CodeQL-Build:
 

From 69d07fcc00dc0b8e7b673019f43bcec48fd4d7fb Mon Sep 17 00:00:00 2001
From: Anish Kumar <B24331@STUDENTS.IITMANDI.AC.IN>
Date: Tue, 21 Oct 2025 17:52:18 +0530
Subject: [PATCH 1078/1080] added pso algorithm  (#1279)

* added pso algorithm also known as bird flocking algorithm

* fix: Resolve linting issues in PSO implementation

* improved code formattings

* added docstring and added pso in main rst file

* fixed paths

* fixed changes requested

* fixed typo

* improved readilbility
---
 .gitignore                                    |   1 +
 .../particle_swarm_optimization.py            | 335 ++++++++++++++++++
 README.md                                     |  19 +
 .../particleSwarmOptimization_main.rst        | 153 ++++++++
 .../5_path_planning/path_planning_main.rst    |   1 +
 tests/test_particle_swarm_optimization.py     |  11 +
 6 files changed, 520 insertions(+)
 create mode 100755 PathPlanning/ParticleSwarmOptimization/particle_swarm_optimization.py
 create mode 100644 docs/modules/5_path_planning/particleSwarmOptimization/particleSwarmOptimization_main.rst
 create mode 100755 tests/test_particle_swarm_optimization.py

diff --git a/.gitignore b/.gitignore
index c971b8f9c5..8a0b1257b5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -15,6 +15,7 @@ _build/
 # Distribution / packaging
 .Python
 env/
+venv/
 build/
 develop-eggs/
 dist/
diff --git a/PathPlanning/ParticleSwarmOptimization/particle_swarm_optimization.py b/PathPlanning/ParticleSwarmOptimization/particle_swarm_optimization.py
new file mode 100755
index 0000000000..4d1df5e197
--- /dev/null
+++ b/PathPlanning/ParticleSwarmOptimization/particle_swarm_optimization.py
@@ -0,0 +1,335 @@
+"""
+Particle Swarm Optimization (PSO) Path Planning
+
+author: Anish (@anishk85)
+
+See Wikipedia article (https://en.wikipedia.org/wiki/Particle_swarm_optimization)
+
+References:
+    - Kennedy, J.; Eberhart, R. (1995). "Particle Swarm Optimization"
+    - Shi, Y.; Eberhart, R. (1998). "A Modified Particle Swarm Optimizer"
+    - https://machinelearningmastery.com/a-gentle-introduction-to-particle-swarm-optimization/
+
+This implementation uses PSO to find collision-free paths by treating
+path planning as an optimization problem where particles explore the
+search space to minimize distance to target while avoiding obstacles.
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+import matplotlib.patches as patches
+import signal
+import sys
+# Add show_animation flag for consistency with other planners
+show_animation = True
+
+def signal_handler(sig, frame):
+    print("\nExiting...")
+    plt.close("all")
+    sys.exit(0)
+
+signal.signal(signal.SIGINT, signal_handler)
+
+class Particle:
+    """Represents a single particle in the PSO swarm.
+    Each particle maintains its current position, velocity, and personal best
+    position discovered during the search. Particles explore the search space
+    by updating their velocity based on personal experience (cognitive component)
+    and swarm knowledge (social component).
+    Attributes:
+        search_bounds: List of tuples [(x_min, x_max), (y_min, y_max)] defining search space
+        max_velocity: Maximum velocity allowed in each dimension (5% of search space range)
+        position: Current 2D position [x, y] in search space
+        velocity: Current velocity vector [vx, vy]
+        personal_best_position: Personal best position found so far
+        personal_best_value: Fitness value at personal best position
+        path: List of all positions visited by this particle
+    """
+
+    def __init__(self, search_bounds, spawn_bounds):
+        self.search_bounds = search_bounds
+        self.max_velocity = np.array([(b[1] - b[0]) * 0.05 for b in search_bounds])
+        self.position = np.array([np.random.uniform(b[0], b[1]) for b in spawn_bounds])
+        self.velocity = np.random.randn(2) * 0.1
+        self.personal_best_position = self.position.copy()
+        self.personal_best_value = np.inf
+        self.path = [self.position.copy()]
+
+    def update_velocity(self, gbest_pos, w, c1, c2):
+        """Update particle velocity using PSO equation:
+        v = w*v + c1*r1*(personal_best - x) + c2*r2*(gbest - x)
+        """
+        r1 = np.random.rand(2)
+        r2 = np.random.rand(2)
+        cognitive = c1 * r1 * (self.personal_best_position - self.position)
+        social = c2 * r2 * (gbest_pos - self.position)
+        self.velocity = w * self.velocity + cognitive + social
+        self.velocity = np.clip(self.velocity, -self.max_velocity, self.max_velocity)
+
+    def update_position(self):
+        self.position = self.position + self.velocity
+        # Keep in bounds
+        for i in range(2):
+            self.position[i] = np.clip(
+                self.position[i], self.search_bounds[i][0], self.search_bounds[i][1]
+            )
+        self.path.append(self.position.copy())
+
+class PSOSwarm:
+
+    def __init__(
+        self, n_particles, max_iter, target, search_bounds, spawn_bounds, obstacles
+    ):
+        self.n_particles = n_particles
+        self.max_iter = max_iter
+        self.target = np.array(target)
+        self.obstacles = obstacles
+        self.search_bounds = search_bounds
+        # PSO parameters
+        self.w_start = 0.9  # Initial inertia weight
+        self.w_end = 0.4  # Final inertia weight
+        self.c1 = 1.5  # Cognitive coefficient
+        self.c2 = 1.5  # Social coefficient
+        # Initialize particles
+        self.particles = [
+            Particle(search_bounds, spawn_bounds) for _ in range(n_particles)
+        ]
+        self.gbest_position = None
+        self.gbest_value = np.inf
+        self.gbest_path = []
+        self.iteration = 0
+
+    def fitness(self, pos):
+        """Calculate fitness - distance to target + obstacle penalty"""
+        dist = np.linalg.norm(pos - self.target)
+        # Obstacle penalty
+        penalty = 0
+        for ox, oy, r in self.obstacles:
+            obs_dist = np.linalg.norm(pos - np.array([ox, oy]))
+            if obs_dist < r:
+                penalty += 1000  # Inside obstacle
+            elif obs_dist < r + 5:
+                penalty += 50 / (obs_dist - r + 0.1)  # Too close
+        return dist + penalty
+
+    def check_collision(self, start, end, obstacle):
+        """Check if path from start to end hits obstacle using line-circle intersection
+            Args:
+            start: Starting position (numpy array)
+            end: Ending position (numpy array)
+            obstacle: Tuple (ox, oy, r) representing obstacle center and radius
+        Returns:
+            bool: True if collision detected, False otherwise
+        """
+        ox, oy, r = obstacle
+        center = np.array([ox, oy])
+        # Vector math for line-circle intersection
+        d = end - start
+        f = start - center
+        a = np.dot(d, d)
+        # Guard against zero-length steps to prevent ZeroDivisionError
+        if a < 1e-10:  # Near-zero length step
+            # Check if start point is inside obstacle
+            return np.linalg.norm(f) <= r
+        b = 2 * np.dot(f, d)
+        c = np.dot(f, f) - r * r
+        discriminant = b * b - 4 * a * c
+        if discriminant < 0:
+            return False
+        # Check if intersection on segment
+        sqrt_discriminant = np.sqrt(discriminant)
+        t1 = (-b - sqrt_discriminant) / (2 * a)
+        t2 = (-b + sqrt_discriminant) / (2 * a)
+        return (0 <= t1 <= 1) or (0 <= t2 <= 1)
+
+    def step(self):
+        """Run one PSO iteration
+        Returns:
+            bool: True if algorithm should continue, False if completed
+        """
+        if self.iteration >= self.max_iter:
+            return False
+        # Update inertia weight (linear decay)
+        w = self.w_start - (self.w_start - self.w_end) * (
+            self.iteration / self.max_iter
+        )
+        # Evaluate all particles
+        for particle in self.particles:
+            value = self.fitness(particle.position)
+            # Update personal best
+            if value < particle.personal_best_value:
+                particle.personal_best_value = value
+                particle.personal_best_position = particle.position.copy()
+            # Update global best
+            if value < self.gbest_value:
+                self.gbest_value = value
+                self.gbest_position = particle.position.copy()
+        if self.gbest_position is not None:
+            self.gbest_path.append(self.gbest_position.copy())
+        # Update particles
+        for particle in self.particles:
+            particle.update_velocity(self.gbest_position, w, self.c1, self.c2)
+            # Predict next position
+            next_pos = particle.position + particle.velocity
+            # Check collision
+            collision = False
+            for obs in self.obstacles:
+                if self.check_collision(particle.position, next_pos, obs):
+                    collision = True
+                    break
+            if collision:
+                # Reduce velocity if collision detected
+                particle.velocity *= 0.2
+            particle.update_position()
+        self.iteration += 1
+        if show_animation and self.iteration % 20 == 0:
+            print(
+                f"Iteration {self.iteration}/{self.max_iter}, Best: {self.gbest_value:.2f}"
+            )
+        return True
+
+def main():
+    """Run PSO path planning algorithm demonstration.
+    This function demonstrates PSO-based path planning with the following setup:
+    - 15 particles exploring a (-50,50) x (-50,50) search space
+    - Start zone: (-45,-45) to (-35,-35)
+    - Target: (40, 35)
+    - 4 circular obstacles with collision avoidance
+    - Real-time visualization showing particle convergence (if show_animation=True)
+    - Headless mode support for testing (if show_animation=False)
+    The algorithm runs for up to 150 iterations, displaying particle movement,
+    personal/global best positions, and the evolving optimal path.
+    """
+    print(__file__ + " start!!")
+    # Set matplotlib backend for headless environments
+    if not show_animation:
+        plt.switch_backend("Agg")  # Use non-GUI backend for testing
+    # Setup parameters
+    N_PARTICLES = 15
+    MAX_ITER = 150
+    SEARCH_BOUNDS = [(-50, 50), (-50, 50)]
+    TARGET = [40, 35]
+    SPAWN_AREA = [(-45, -35), (-45, -35)]
+    OBSTACLES = [(10, 15, 8), (-20, 0, 12), (20, -25, 10), (-5, -30, 7)]
+    swarm = PSOSwarm(
+        n_particles=N_PARTICLES,
+        max_iter=MAX_ITER,
+        target=TARGET,
+        search_bounds=SEARCH_BOUNDS,
+        spawn_bounds=SPAWN_AREA,
+        obstacles=OBSTACLES,
+    )
+    # pragma: no cover
+    if show_animation:
+        # Visualization setup
+        signal.signal(signal.SIGINT, signal_handler)
+        fig, ax = plt.subplots(figsize=(10, 10))
+        ax.set_xlim(SEARCH_BOUNDS[0])
+        ax.set_ylim(SEARCH_BOUNDS[1])
+        ax.set_title("PSO Path Planning with Collision Avoidance", fontsize=14)
+        ax.grid(True, alpha=0.3)
+        # Draw obstacles
+        for ox, oy, r in OBSTACLES:
+            circle = patches.Circle((ox, oy), r, color="gray", alpha=0.7)
+            ax.add_patch(circle)
+        # Draw spawn area
+        spawn_rect = patches.Rectangle(
+            (SPAWN_AREA[0][0], SPAWN_AREA[1][0]),
+            SPAWN_AREA[0][1] - SPAWN_AREA[0][0],
+            SPAWN_AREA[1][1] - SPAWN_AREA[1][0],
+            linewidth=2,
+            edgecolor="green",
+            facecolor="green",
+            alpha=0.2,
+            label="Start Zone",
+        )
+        ax.add_patch(spawn_rect)
+        # Draw target
+        ax.plot(TARGET[0], TARGET[1], "r*", markersize=20, label="Target")
+        # Initialize plot elements
+        particles_scatter = ax.scatter(
+            [], [], c="blue", s=50, alpha=0.6, label="Particles"
+        )
+        gbest_scatter = ax.plot([], [], "yo", markersize=12, label="Best Position")[0]
+        particle_paths = [
+            ax.plot([], [], "b-", lw=0.5, alpha=0.2)[0] for _ in range(N_PARTICLES)
+        ]
+        gbest_path_line = ax.plot([], [], "y--", lw=2, alpha=0.8, label="Best Path")[0]
+        iteration_text = ax.text(
+            0.02,
+            0.95,
+            "",
+            transform=ax.transAxes,
+            fontsize=12,
+            verticalalignment="top",
+            bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.5),
+        )
+        ax.legend(loc="upper right")
+        def animate(frame):
+            """Animation function for matplotlib FuncAnimation"""
+            if not swarm.step():
+                return (
+                    particles_scatter,
+                    gbest_scatter,
+                    gbest_path_line,
+                    iteration_text,
+                    *particle_paths,
+                )
+            # Update particle positions
+            positions = np.array([p.position for p in swarm.particles])
+            particles_scatter.set_offsets(positions)
+            # Update particle paths
+            for i, particle in enumerate(swarm.particles):
+                if len(particle.path) > 1:
+                    path = np.array(particle.path)
+                    particle_paths[i].set_data(path[:, 0], path[:, 1])
+            # Update global best
+            if swarm.gbest_position is not None:
+                gbest_scatter.set_data(
+                    [swarm.gbest_position[0]], [swarm.gbest_position[1]]
+                )
+                if len(swarm.gbest_path) > 1:
+                    gbest = np.array(swarm.gbest_path)
+                    gbest_path_line.set_data(gbest[:, 0], gbest[:, 1])
+            # Update text
+            iteration_text.set_text(
+                f"Iteration: {swarm.iteration}/{MAX_ITER}\n"
+                f"Best Fitness: {swarm.gbest_value:.2f}"
+            )
+            return (
+                particles_scatter,
+                gbest_scatter,
+                gbest_path_line,
+                iteration_text,
+                *particle_paths,
+            )
+        # Create animation and store reference to prevent garbage collection
+        animation_ref = animation.FuncAnimation(
+            fig, animate, frames=MAX_ITER, interval=100, blit=True, repeat=False
+        )
+        plt.tight_layout()
+        plt.show()
+        # Keep reference to prevent garbage collection
+        return animation_ref
+    else:
+        # Run without animation for testing
+        print("Running PSO algorithm without animation...")
+        iteration_count = 0
+        while swarm.step():
+            iteration_count += 1
+            if iteration_count >= MAX_ITER:
+                break
+        print(f"PSO completed after {iteration_count} iterations")
+        print(f"Best fitness: {swarm.gbest_value:.2f}")
+        if swarm.gbest_position is not None:
+            print(
+                f"Best position: [{swarm.gbest_position[0]:.2f}, {swarm.gbest_position[1]:.2f}]"
+            )
+        return None
+if __name__ == "__main__":
+    try:
+        main()
+    except KeyboardInterrupt:
+        print("\nProgram interrupted by user")
+        plt.close("all")
+        sys.exit(0)
\ No newline at end of file
diff --git a/README.md b/README.md
index d1b801f219..65445fa4ce 100644
--- a/README.md
+++ b/README.md
@@ -36,6 +36,7 @@ Python codes and [textbook](https://atsushisakai.github.io/PythonRobotics/index.
          * [D* Lite algorithm](#d-lite-algorithm)
          * [Potential Field algorithm](#potential-field-algorithm)
          * [Grid based coverage path planning](#grid-based-coverage-path-planning)
+         * [Particle Swarm Optimization (PSO)](#particle-swarm-optimization-pso)  
       * [State Lattice Planning](#state-lattice-planning)
          * [Biased polar sampling](#biased-polar-sampling)
          * [Lane sampling](#lane-sampling)
@@ -356,6 +357,24 @@ This is a 2D grid based coverage path planning simulation.
 
 ![PotentialField](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/GridBasedSweepCPP/animation.gif)
 
+### Particle Swarm Optimization (PSO)
+
+This is a 2D path planning simulation using the Particle Swarm Optimization algorithm.
+
+![PSO](https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ParticleSwarmOptimization/animation.gif)
+
+PSO is a metaheuristic optimization algorithm inspired by bird flocking behavior. In path planning, particles explore the search space to find collision-free paths while avoiding obstacles.
+
+The animation shows particles (blue dots) converging towards the optimal path (yellow line) from start (green area) to goal (red star).
+
+References
+
+- [Particle swarm optimization - Wikipedia](https://en.wikipedia.org/wiki/Particle_swarm_optimization)
+
+- [Kennedy, J.; Eberhart, R. (1995). "Particle Swarm Optimization"](https://ieeexplore.ieee.org/document/488968)
+
+
+
 ## State Lattice Planning
 
 This script is a path planning code with state lattice planning.
diff --git a/docs/modules/5_path_planning/particleSwarmOptimization/particleSwarmOptimization_main.rst b/docs/modules/5_path_planning/particleSwarmOptimization/particleSwarmOptimization_main.rst
new file mode 100644
index 0000000000..b430faec1e
--- /dev/null
+++ b/docs/modules/5_path_planning/particleSwarmOptimization/particleSwarmOptimization_main.rst
@@ -0,0 +1,153 @@
+.. _particle_swarm_optimization:
+
+Particle Swarm Optimization Path Planning
+------------------------------------------
+
+This is a 2D path planning simulation using the Particle Swarm Optimization algorithm.
+
+PSO is a metaheuristic optimization algorithm inspired by the social behavior of bird flocking or fish schooling. In path planning, particles represent potential solutions that explore the search space to find collision-free paths from start to goal.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ParticleSwarmOptimization/animation.gif
+
+Algorithm Overview
+++++++++++++++++++
+
+The PSO algorithm maintains a swarm of particles that move through the search space according to simple mathematical rules:
+
+1. **Initialization**: Particles are randomly distributed near the start position
+2. **Evaluation**: Each particle's fitness is calculated based on distance to goal and obstacle penalties
+3. **Update**: Particles adjust their velocities based on:
+   - Personal best position (cognitive component)
+   - Global best position (social component)  
+   - Current velocity (inertia component)
+4. **Movement**: Particles move to new positions and check for collisions
+5. **Convergence**: Process repeats until maximum iterations or goal is reached
+
+Mathematical Foundation
++++++++++++++++++++++++
+
+The core PSO velocity update equation is:
+
+.. math::
+
+   v_{i}(t+1) = w \cdot v_{i}(t) + c_1 \cdot r_1 \cdot (p_{i} - x_{i}(t)) + c_2 \cdot r_2 \cdot (g - x_{i}(t))
+
+Where:
+- :math:`v_{i}(t)` = velocity of particle i at time t
+- :math:`x_{i}(t)` = position of particle i at time t  
+- :math:`w` = inertia weight (controls exploration vs exploitation)
+- :math:`c_1` = cognitive coefficient (attraction to personal best)
+- :math:`c_2` = social coefficient (attraction to global best)
+- :math:`r_1, r_2` = random numbers in [0,1]
+- :math:`p_{i}` = personal best position of particle i
+- :math:`g` = global best position
+
+Position update:
+
+.. math::
+
+   x_{i}(t+1) = x_{i}(t) + v_{i}(t+1)
+
+Fitness Function
+++++++++++++++++
+
+The fitness function combines distance to target with obstacle penalties:
+
+.. math::
+
+   f(x) = ||x - x_{goal}|| + \sum_{j} P_{obs}(x, O_j)
+
+Where:
+- :math:`||x - x_{goal}||` = Euclidean distance to goal
+- :math:`P_{obs}(x, O_j)` = penalty for obstacle j
+- :math:`O_j` = obstacle j with position and radius
+
+The obstacle penalty function is defined as:
+
+.. math::
+
+   P_{obs}(x, O_j) = \begin{cases}
+   1000 & \text{if } ||x - O_j|| < r_j \text{ (inside obstacle)} \\
+   \frac{50}{||x - O_j|| - r_j + 0.1} & \text{if } r_j \leq ||x - O_j|| < r_j + R_{influence} \text{ (near obstacle)} \\
+   0 & \text{if } ||x - O_j|| \geq r_j + R_{influence} \text{ (safe distance)}
+   \end{cases}
+
+Where:
+- :math:`r_j` = radius of obstacle j
+- :math:`R_{influence}` = influence radius (typically 5 units)
+
+Collision Detection
++++++++++++++++++++
+
+Line-circle intersection is used to detect collisions between particle paths and circular obstacles:
+
+.. math::
+
+   ||P_0 + t \cdot \vec{d} - C|| = r
+
+Where:
+- :math:`P_0` = start point of path segment
+- :math:`\vec{d}` = direction vector of path
+- :math:`C` = obstacle center
+- :math:`r` = obstacle radius
+- :math:`t \in [0,1]` = parameter along line segment
+
+Algorithm Parameters
+++++++++++++++++++++
+
+Key parameters affecting performance:
+
+- **Number of particles** (n_particles): More particles = better exploration but slower
+- **Maximum iterations** (max_iter): More iterations = better convergence but slower  
+- **Inertia weight** (w): High = exploration, Low = exploitation
+- **Cognitive coefficient** (c1): Attraction to personal best
+- **Social coefficient** (c2): Attraction to global best
+
+Typical values:
+- n_particles: 20-50
+- max_iter: 100-300
+- w: 0.9 → 0.4 (linearly decreasing)
+- c1, c2: 1.5-2.0
+
+Advantages
+++++++++++
+
+- **Global optimization**: Can escape local minima unlike gradient-based methods
+- **No derivatives needed**: Works with non-differentiable fitness landscapes
+- **Parallel exploration**: Multiple particles search simultaneously
+- **Simple implementation**: Few parameters and straightforward logic
+- **Flexible**: Easily adaptable to different environments and constraints
+
+Disadvantages
++++++++++++++
+
+- **Stochastic**: Results may vary between runs
+- **Parameter sensitive**: Performance heavily depends on parameter tuning
+- **No optimality guarantee**: Metaheuristic without convergence proof
+- **Computational cost**: Requires many fitness evaluations
+- **Prone to stagnation**: Premature convergence where the entire swarm can get trapped in a local minimum if exploration is insufficient
+
+Code Link
++++++++++
+
+.. autofunction:: PathPlanning.ParticleSwarmOptimization.particle_swarm_optimization.main
+
+Usage Example
++++++++++++++
+
+.. code-block:: python
+
+   import matplotlib.pyplot as plt
+   from PathPlanning.ParticleSwarmOptimization.particle_swarm_optimization import main
+   
+   # Run PSO path planning with visualization
+   main()
+
+References
+++++++++++
+
+- `Particle swarm optimization - Wikipedia <https://en.wikipedia.org/wiki/Particle_swarm_optimization>`__
+- Kennedy, J.; Eberhart, R. (1995). "Particle Swarm Optimization". Proceedings of IEEE International Conference on Neural Networks. IV. pp. 1942–1948.
+- Shi, Y.; Eberhart, R. (1998). "A Modified Particle Swarm Optimizer". IEEE International Conference on Evolutionary Computation.
+- `A Gentle Introduction to Particle Swarm Optimization <https://machinelearningmastery.com/a-gentle-introduction-to-particle-swarm-optimization/>`__
+- Clerc, M.; Kennedy, J. (2002). "The particle swarm - explosion, stability, and convergence in a multidimensional complex space". IEEE Transactions on Evolutionary Computation. 6 (1): 58–73.
\ No newline at end of file
diff --git a/docs/modules/5_path_planning/path_planning_main.rst b/docs/modules/5_path_planning/path_planning_main.rst
index 0c84a19c22..5132760dc5 100644
--- a/docs/modules/5_path_planning/path_planning_main.rst
+++ b/docs/modules/5_path_planning/path_planning_main.rst
@@ -19,6 +19,7 @@ Path planning is the ability of a robot to search feasible and efficient path to
    visibility_road_map_planner/visibility_road_map_planner
    vrm_planner/vrm_planner
    rrt/rrt
+   particleSwarmOptimization/particleSwarmOptimization
    cubic_spline/cubic_spline
    bspline_path/bspline_path
    catmull_rom_spline/catmull_rom_spline
diff --git a/tests/test_particle_swarm_optimization.py b/tests/test_particle_swarm_optimization.py
new file mode 100755
index 0000000000..95720022f6
--- /dev/null
+++ b/tests/test_particle_swarm_optimization.py
@@ -0,0 +1,11 @@
+import conftest  # Add root path to sys.path
+from PathPlanning.ParticleSwarmOptimization import particle_swarm_optimization as m
+
+
+def test_1():
+    m.show_animation = False
+    m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
\ No newline at end of file

From 99af1f391686cc7fa10b14bc5ed667cbe7b3f5eb Mon Sep 17 00:00:00 2001
From: Musab Kasbati <43878981+Musab1Blaser@users.noreply.github.com>
Date: Wed, 22 Oct 2025 15:03:13 +0500
Subject: [PATCH 1079/1080] Add theta star planner (#1293)

* Theta Star path compression

* add tests and documentation for theta star algorithm

* remove erraneous nested loop

* improve documentation for theta star planner

* add reference for theta star algorithm

* use built-in function for norm

* Add Bresenham line algorithm description to docs
---
 PathPlanning/ThetaStar/theta_star.py          | 345 ++++++++++++++++++
 .../grid_base_search_main.rst                 |  29 ++
 tests/test_theta_star.py                      |  11 +
 3 files changed, 385 insertions(+)
 create mode 100644 PathPlanning/ThetaStar/theta_star.py
 create mode 100644 tests/test_theta_star.py

diff --git a/PathPlanning/ThetaStar/theta_star.py b/PathPlanning/ThetaStar/theta_star.py
new file mode 100644
index 0000000000..73a1448b28
--- /dev/null
+++ b/PathPlanning/ThetaStar/theta_star.py
@@ -0,0 +1,345 @@
+"""
+
+Theta* grid planning
+
+author: Musab Kasbati (@Musab1Blaser)
+
+See Wikipedia article (https://cdn.aaai.org/AAAI/2007/AAAI07-187.pdf)
+
+"""
+
+import math
+
+import matplotlib.pyplot as plt
+
+show_animation = True
+use_theta_star = True
+
+
+class ThetaStarPlanner:
+
+    def __init__(self, ox, oy, resolution, rr):
+        """
+        Initialize grid map for theta star planning
+
+        ox: x position list of Obstacles [m]
+        oy: y position list of Obstacles [m]
+        resolution: grid resolution [m]
+        rr: robot radius[m]
+        """
+
+        self.resolution = resolution
+        self.rr = rr
+        self.min_x, self.min_y = 0, 0
+        self.max_x, self.max_y = 0, 0
+        self.obstacle_map = None
+        self.x_width, self.y_width = 0, 0
+        self.motion = self.get_motion_model()
+        self.calc_obstacle_map(ox, oy)
+
+    class Node:
+        def __init__(self, x, y, cost, parent_index):
+            self.x = x  # index of grid
+            self.y = y  # index of grid
+            self.cost = cost
+            self.parent_index = parent_index
+
+        def __str__(self):
+            return str(self.x) + "," + str(self.y) + "," + str(
+                self.cost) + "," + str(self.parent_index)
+
+    def planning(self, sx, sy, gx, gy): 
+        """
+        Theta star path search
+
+        input:
+            s_x: start x position [m]
+            s_y: start y position [m]
+            gx: goal x position [m]
+            gy: goal y position [m]
+
+        output:
+            rx: x position list of the final path
+            ry: y position list of the final path
+        """
+
+        start_node = self.Node(self.calc_xy_index(sx, self.min_x),
+                               self.calc_xy_index(sy, self.min_y), 0.0, -1)
+        goal_node = self.Node(self.calc_xy_index(gx, self.min_x),
+                              self.calc_xy_index(gy, self.min_y), 0.0, -1)
+
+        open_set, closed_set = dict(), dict()
+        open_set[self.calc_grid_index(start_node)] = start_node
+
+        while True:
+            if len(open_set) == 0:
+                print("Open set is empty..")
+                break
+
+            c_id = min(
+                open_set,
+                key=lambda o: open_set[o].cost + self.calc_heuristic(goal_node,
+                                                                     open_set[
+                                                                         o]))
+            current = open_set[c_id]
+
+            # show graph
+            if show_animation:  # pragma: no cover
+                x = self.calc_grid_position(current.x, self.min_x)
+                y = self.calc_grid_position(current.y, self.min_y)
+
+                # Draw an arrow toward the parent
+                if current.parent_index != -1 and current.parent_index in closed_set:
+                    parent = closed_set[current.parent_index]
+                    px = self.calc_grid_position(parent.x, self.min_x)
+                    py = self.calc_grid_position(parent.y, self.min_y)
+
+                    # Vector from current to parent
+                    dx = px - x
+                    dy = py - y
+
+                    # scale down vector for visibility
+                    norm = math.hypot(dx, dy)
+                    dx /= norm
+                    dy /= norm
+
+                    # Draw a small arrow (scale it down for visibility)
+                    plt.arrow(x, y, dx, dy,
+                            head_width=0.5, head_length=0.5,
+                            fc='c', ec='c', alpha=0.7)
+
+                # For stopping simulation with the esc key
+                plt.gcf().canvas.mpl_connect(
+                    'key_release_event',
+                    lambda event: [exit(0) if event.key == 'escape' else None]
+                )
+
+                if len(closed_set.keys()) % 10 == 0:
+                    plt.pause(0.001)
+
+            if current.x == goal_node.x and current.y == goal_node.y:
+                print("Find goal")
+                goal_node.parent_index = current.parent_index
+                goal_node.cost = current.cost
+                break
+
+            # Remove the item from the open set
+            del open_set[c_id]
+
+            # Add it to the closed set
+            closed_set[c_id] = current
+
+            # expand_grid search grid based on motion model
+            for i, _ in enumerate(self.motion):
+                node = self.Node(current.x + self.motion[i][0],
+                                    current.y + self.motion[i][1],
+                                    current.cost + self.motion[i][2], c_id)  # cost may later be updated by theta star path compression
+                n_id = self.calc_grid_index(node)
+
+                if not self.verify_node(node):
+                    continue
+
+                if n_id in closed_set:
+                    continue
+
+                # Theta* modification:
+                if use_theta_star and current.parent_index != -1 and current.parent_index in closed_set:
+                    grandparent = closed_set[current.parent_index]
+                    if self.line_of_sight(grandparent, node):
+                        # If parent(current) has line of sight to neighbor
+                        node.cost = grandparent.cost + math.hypot(node.x - grandparent.x, node.y - grandparent.y)
+                        node.parent_index = current.parent_index # compress path directly to grandparent
+
+                if n_id not in open_set:
+                    open_set[n_id] = node
+                else:
+                    if open_set[n_id].cost > node.cost:
+                        # This path is the best until now. record it
+                        open_set[n_id] = node 
+
+
+        rx, ry = self.calc_final_path(goal_node, closed_set)
+
+        return rx, ry
+
+    def calc_final_path(self, goal_node, closed_set):
+        # generate final course
+        rx, ry = [self.calc_grid_position(goal_node.x, self.min_x)], [
+            self.calc_grid_position(goal_node.y, self.min_y)]
+        parent_index = goal_node.parent_index
+        while parent_index != -1:
+            n = closed_set[parent_index]
+            rx.append(self.calc_grid_position(n.x, self.min_x))
+            ry.append(self.calc_grid_position(n.y, self.min_y))
+            parent_index = n.parent_index
+
+        return rx, ry
+
+    @staticmethod
+    def calc_heuristic(n1, n2):
+        w = 1.0  # weight of heuristic
+        d = w * math.hypot(n1.x - n2.x, n1.y - n2.y)
+        return d
+
+    def calc_grid_position(self, index, min_position):
+        """
+        calc grid position
+
+        :param index:
+        :param min_position:
+        :return:
+        """
+        pos = index * self.resolution + min_position
+        return pos
+
+    def calc_xy_index(self, position, min_pos):
+        return round((position - min_pos) / self.resolution)
+
+    def calc_grid_index(self, node):
+        return (node.y - self.min_y) * self.x_width + (node.x - self.min_x)
+
+    def line_of_sight(self, node1, node2):
+        """
+        Check if there is a direct line of sight between two nodes.
+        Uses Bresenham’s line algorithm for grid traversal.
+        """
+        x0 = node1.x
+        y0 = node1.y
+        x1 = node2.x
+        y1 = node2.y
+
+        dx = abs(x1 - x0)
+        dy = abs(y1 - y0)
+        sx = 1 if x0 < x1 else -1
+        sy = 1 if y0 < y1 else -1
+
+        err = dx - dy
+
+        while True:
+            if not self.verify_node(self.Node(x0, y0, 0, -1)):
+                return False
+            if x0 == x1 and y0 == y1:
+                break
+            e2 = 2 * err
+            if e2 > -dy:
+                err -= dy
+                x0 += sx
+            if e2 < dx:
+                err += dx
+                y0 += sy
+        return True
+
+
+    def verify_node(self, node):
+        px = self.calc_grid_position(node.x, self.min_x)
+        py = self.calc_grid_position(node.y, self.min_y)
+
+        if px < self.min_x:
+            return False
+        elif py < self.min_y:
+            return False
+        elif px >= self.max_x:
+            return False
+        elif py >= self.max_y:
+            return False
+
+        # collision check
+        if self.obstacle_map[node.x][node.y]:
+            return False
+
+        return True
+
+    def calc_obstacle_map(self, ox, oy):
+
+        self.min_x = round(min(ox))
+        self.min_y = round(min(oy))
+        self.max_x = round(max(ox))
+        self.max_y = round(max(oy))
+        print("min_x:", self.min_x)
+        print("min_y:", self.min_y)
+        print("max_x:", self.max_x)
+        print("max_y:", self.max_y)
+
+        self.x_width = round((self.max_x - self.min_x) / self.resolution)
+        self.y_width = round((self.max_y - self.min_y) / self.resolution)
+        print("x_width:", self.x_width)
+        print("y_width:", self.y_width)
+
+        # obstacle map generation
+        self.obstacle_map = [[False for _ in range(self.y_width)]
+                             for _ in range(self.x_width)]
+        for ix in range(self.x_width):
+            x = self.calc_grid_position(ix, self.min_x)
+            for iy in range(self.y_width):
+                y = self.calc_grid_position(iy, self.min_y)
+                for iox, ioy in zip(ox, oy):
+                    d = math.hypot(iox - x, ioy - y)
+                    if d <= self.rr:
+                        self.obstacle_map[ix][iy] = True
+                        break
+
+    @staticmethod
+    def get_motion_model():
+        # dx, dy, cost
+        motion = [[1, 0, 1],
+                  [0, 1, 1],
+                  [-1, 0, 1],
+                  [0, -1, 1],
+                  [-1, -1, math.sqrt(2)],
+                  [-1, 1, math.sqrt(2)],
+                  [1, -1, math.sqrt(2)],
+                  [1, 1, math.sqrt(2)]]
+
+        return motion
+
+
+def main():
+    print(__file__ + " start!!")
+
+    # start and goal position
+    sx = 10.0  # [m]
+    sy = 10.0  # [m]
+    gx = 50.0  # [m]
+    gy = 50.0  # [m]
+    grid_size = 2.0  # [m]
+    robot_radius = 1.0  # [m]
+
+    # set obstacle positions
+    ox, oy = [], []
+    for i in range(-10, 60):
+        ox.append(i)
+        oy.append(-10.0)
+    for i in range(-10, 60):
+        ox.append(60.0)
+        oy.append(i)
+    for i in range(-10, 61):
+        ox.append(i)
+        oy.append(60.0)
+    for i in range(-10, 61):
+        ox.append(-10.0)
+        oy.append(i)
+    for i in range(-10, 40):
+        ox.append(20.0)
+        oy.append(i)
+    for i in range(0, 40):
+        ox.append(40.0)
+        oy.append(60.0 - i)
+
+    if show_animation:  # pragma: no cover
+        plt.plot(ox, oy, ".k")
+        plt.plot(sx, sy, "og")
+        plt.plot(gx, gy, "xb")
+        plt.grid(True)
+        plt.axis("equal")
+
+    theta_star = ThetaStarPlanner(ox, oy, grid_size, robot_radius)
+    rx, ry = theta_star.planning(sx, sy, gx, gy)
+
+    if show_animation:  # pragma: no cover
+        plt.plot(rx, ry, "-r")
+        plt.pause(0.001)
+        plt.show()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst b/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst
index c4aa6882aa..e2f4d8832d 100644
--- a/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst
+++ b/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst
@@ -82,6 +82,35 @@ Code Link
 .. autofunction:: PathPlanning.BidirectionalAStar.bidirectional_a_star.BidirectionalAStarPlanner
 
 
+.. _Theta*-algorithm:
+
+Theta\* algorithm
+~~~~~~~~~~~~~~~~~
+
+This is a 2D grid based shortest path planning with Theta star algorithm.
+
+It offers an optimization over the A star algorithm to generate any-angle paths. Unlike A star, which always assigns the current node as the parent of the successor, Theta star checks for a line-of-sight (unblocked path) from an earlier node (typically the parent of the current node) to the successor, and directly assigns it as a parent if visible. This reduces cost by replacing staggered segments with straight lines. 
+
+Checking for line of sight involves verifying that no obstacles block the straight line between two nodes. On a grid, this means identifying all the discrete cells that the line passes through to determine if they are empty. Bresenham’s line algorithm is commonly used for this purpose. Starting from one endpoint, it incrementally steps along one axis, while considering the gradient to determine the position on the other axis. Because it relies only on integer addition and subtraction, it is both efficient and precise for grid-based visibility checks in Theta*.
+
+As a result, Theta star produces shorter, smoother paths than A star, ideal for ground or aerial robots operating in continuous environments where smoother motion enables higher acceleration and reduced travel time.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathPlanning/ThetaStar/animation.gif
+
+In the animation, each cyan arrow represents a node pointing to its parent.
+
+Reference
+++++++++++++
+
+-  `Theta*: Any-Angle Path Planning on Grids <https://cdn.aaai.org/AAAI/2007/AAAI07-187.pdf>`__
+-  `Bresenham's line algorithm <https://en.wikipedia.org/wiki/Bresenham%27s_line_algorithm>`__
+
+Code Link
++++++++++++++
+
+.. autofunction:: PathPlanning.ThetaStar.theta_star.ThetaStarPlanner
+
+
 .. _D*-algorithm:
 
 D\* algorithm
diff --git a/tests/test_theta_star.py b/tests/test_theta_star.py
new file mode 100644
index 0000000000..10ceae44e3
--- /dev/null
+++ b/tests/test_theta_star.py
@@ -0,0 +1,11 @@
+import conftest
+from PathPlanning.ThetaStar import theta_star as m
+
+
+def test_1():
+    m.show_animation = False
+    m.main()
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)
\ No newline at end of file

From 2e6baeb2d326b152a28d185338935746468de151 Mon Sep 17 00:00:00 2001
From: Musab Kasbati <43878981+Musab1Blaser@users.noreply.github.com>
Date: Sat, 25 Oct 2025 20:49:06 +0500
Subject: [PATCH 1080/1080] corrected obvious typos in text and mathematical
 notations as well as grammatical corrections (#1304)

---
 .../0_getting_started/2_how_to_run_sample_codes_main.rst  | 2 +-
 .../0_getting_started/3_how_to_contribute_main.rst        | 2 +-
 .../10_inverted_pendulum/inverted_pendulum_main.rst       | 4 ++--
 docs/modules/12_appendix/Kalmanfilter_basics_main.rst     | 2 +-
 .../behavior_tree/behavior_tree_main.rst                  | 4 ++--
 .../2_python_for_robotics/python_for_robotics_main.rst    | 2 +-
 .../technologies_for_robotics_main.rst                    | 2 +-
 .../histogram_filter_localization_main.rst                | 2 +-
 .../particle_filter_localization_main.rst                 | 2 +-
 .../lidar_to_grid_map_tutorial_main.rst                   | 2 +-
 docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst          | 4 ++--
 docs/modules/4_slam/ekf_slam/ekf_slam_main.rst            | 2 +-
 docs/modules/4_slam/graph_slam/graphSLAM_doc.rst          | 4 ++--
 .../5_path_planning/dubins_path/dubins_path_main.rst      | 2 +-
 .../5_path_planning/elastic_bands/elastic_bands_main.rst  | 2 +-
 .../grid_base_search/grid_base_search_main.rst            | 2 +-
 .../reeds_shepp_path/reeds_shepp_path_main.rst            | 2 +-
 .../5_path_planning/vrm_planner/vrm_planner_main.rst      | 2 +-
 .../model_predictive_speed_and_steering_control_main.rst  | 8 ++++----
 .../move_to_a_pose/move_to_a_pose_main.rst                | 2 +-
 .../n_joint_arm_to_point_control_main.rst                 | 2 +-
 docs/modules/7_arm_navigation/planar_two_link_ik_main.rst | 2 +-
 22 files changed, 29 insertions(+), 29 deletions(-)

diff --git a/docs/modules/0_getting_started/2_how_to_run_sample_codes_main.rst b/docs/modules/0_getting_started/2_how_to_run_sample_codes_main.rst
index b92fc9bde0..33440bb137 100644
--- a/docs/modules/0_getting_started/2_how_to_run_sample_codes_main.rst
+++ b/docs/modules/0_getting_started/2_how_to_run_sample_codes_main.rst
@@ -46,7 +46,7 @@ using pip :
 
 5. Execute python script in each directory.
 
-For example, to run the sample code of `Extented Kalman Filter` in the
+For example, to run the sample code of `Extended Kalman Filter` in the
 `localization` directory, execute the following command:
 
 .. code-block::
diff --git a/docs/modules/0_getting_started/3_how_to_contribute_main.rst b/docs/modules/0_getting_started/3_how_to_contribute_main.rst
index 9e773e930c..f975127107 100644
--- a/docs/modules/0_getting_started/3_how_to_contribute_main.rst
+++ b/docs/modules/0_getting_started/3_how_to_contribute_main.rst
@@ -132,7 +132,7 @@ At first, please fix all CI errors before code review.
 You can check your PR doc from the CI panel.
 
 After the "ci/circleci: build_doc" CI is succeeded,
-you can access you PR doc with clicking the [Details] of the "ci/circleci: build_doc artifact" CI.
+you can access your PR doc with clicking the [Details] of the "ci/circleci: build_doc artifact" CI.
 
 .. image:: /_static/img/doc_ci.png
 
diff --git a/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst b/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst
index 58dc0f2e57..47ae034195 100644
--- a/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst
+++ b/docs/modules/10_inverted_pendulum/inverted_pendulum_main.rst
@@ -4,7 +4,7 @@ Inverted Pendulum
 ------------------
 
 An inverted pendulum on a cart consists of a mass :math:`m` at the top of a pole of length :math:`l` pivoted on a
-horizontally moving base as shown in the adjacent.
+horizontally moving base as shown in the adjacent figure.
 
 The objective of the control system is to balance the inverted pendulum by applying a force to the cart that the pendulum is attached to.
 
@@ -97,7 +97,7 @@ Code Link
 
 MPC control
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
-The MPC controller minimize this cost function defined as:
+The MPC controller minimizes this cost function defined as:
 
 .. math:: J = x^T Q x + u^T R u
 
diff --git a/docs/modules/12_appendix/Kalmanfilter_basics_main.rst b/docs/modules/12_appendix/Kalmanfilter_basics_main.rst
index a1d99d47ef..1ed5790d52 100644
--- a/docs/modules/12_appendix/Kalmanfilter_basics_main.rst
+++ b/docs/modules/12_appendix/Kalmanfilter_basics_main.rst
@@ -55,7 +55,7 @@ Variance, Covariance and Correlation
 Variance
 ^^^^^^^^
 
-Variance is the spread of the data. The mean does’nt tell much **about**
+Variance is the spread of the data. The mean doesn’t tell much **about**
 the data. Therefore the variance tells us about the **story** about the
 data meaning the spread of the data.
 
diff --git a/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst b/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst
index 22849f7c54..4b0ef82c12 100644
--- a/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst
+++ b/docs/modules/13_mission_planning/behavior_tree/behavior_tree_main.rst
@@ -2,8 +2,8 @@ Behavior Tree
 -------------
 
 Behavior Tree is a modular, hierarchical decision model that is widely used in robot control, and game development. 
-It present some similarities to hierarchical state machines with the key difference that the main building block of a behavior is a task rather than a state.
-Behavior Tree have been shown to generalize several other control architectures (https://ieeexplore.ieee.org/document/7790863)
+It presents some similarities to hierarchical state machines with the key difference that the main building block of a behavior is a task rather than a state.
+Behavior Trees have been shown to generalize several other control architectures (https://ieeexplore.ieee.org/document/7790863)
 
 Code Link
 ~~~~~~~~~~~~~
diff --git a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
index c47c122853..e3f38a55fd 100644
--- a/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
+++ b/docs/modules/1_introduction/2_python_for_robotics/python_for_robotics_main.rst
@@ -1,7 +1,7 @@
 Python for Robotics
 ----------------------
 
-A programing language, Python is used for this `PythonRobotics` project
+A programming language, Python is used for this `PythonRobotics` project
 to achieve the purposes of this project described in the :ref:`What is PythonRobotics?`.
 
 This section explains the Python itself and features for science computing and robotics.
diff --git a/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst b/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst
index 0ed51e961b..dd3cd1d86c 100644
--- a/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst
+++ b/docs/modules/1_introduction/3_technologies_for_robotics/technologies_for_robotics_main.rst
@@ -3,7 +3,7 @@ Technologies for Robotics
 
 The field of robotics needs wide areas of technologies such as mechanical engineering,
 electrical engineering, computer science, and artificial intelligence (AI).
-This project, `PythonRobotics`, only focus on computer science and artificial intelligence.
+This project, `PythonRobotics`, only focuses on computer science and artificial intelligence.
 
 The technologies for robotics are categorized as following 3 categories:
 
diff --git a/docs/modules/2_localization/histogram_filter_localization/histogram_filter_localization_main.rst b/docs/modules/2_localization/histogram_filter_localization/histogram_filter_localization_main.rst
index 3a175b1316..b5697d2dd9 100644
--- a/docs/modules/2_localization/histogram_filter_localization/histogram_filter_localization_main.rst
+++ b/docs/modules/2_localization/histogram_filter_localization/histogram_filter_localization_main.rst
@@ -68,7 +68,7 @@ But, the probability is getting uncertain without observations:
 
 
 The `gaussian filter <https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter.html>`_
-is used in the simulation for adding noize.
+is used in the simulation for adding noise.
 
 Step3: Update probability by observation
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
diff --git a/docs/modules/2_localization/particle_filter_localization/particle_filter_localization_main.rst b/docs/modules/2_localization/particle_filter_localization/particle_filter_localization_main.rst
index d648d8e080..8bf22a246b 100644
--- a/docs/modules/2_localization/particle_filter_localization/particle_filter_localization_main.rst
+++ b/docs/modules/2_localization/particle_filter_localization/particle_filter_localization_main.rst
@@ -13,7 +13,7 @@ and the red line is estimated trajectory with PF.
 It is assumed that the robot can measure a distance from landmarks
 (RFID).
 
-This measurements are used for PF localization.
+These measurements are used for PF localization.
 
 Code Link
 ~~~~~~~~~~~~~
diff --git a/docs/modules/3_mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst b/docs/modules/3_mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst
index 29f5878e48..c97d204a82 100644
--- a/docs/modules/3_mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst
+++ b/docs/modules/3_mapping/lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial_main.rst
@@ -19,7 +19,7 @@ unknown (unobserved) areas, which are close to 0.5.
 .. figure:: grid_map_example.png
 
 In order to construct the grid map from the measurement we need to
-discretise the values. But, first let’s need to ``import`` some
+discretise the values. But, first we need to ``import`` some
 necessary packages.
 
 .. code:: ipython3
diff --git a/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst b/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst
index b6bafa0982..296f1766de 100644
--- a/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst
+++ b/docs/modules/4_slam/FastSLAM1/FastSLAM1_main.rst
@@ -112,7 +112,7 @@ will converge to the correct estimate.
     MAX_RANGE = 20.0  # maximum observation range
     M_DIST_TH = 2.0  # Threshold of Mahalanobis distance for data association.
     STATE_SIZE = 3  # State size [x,y,yaw]
-    LM_SIZE = 2  # LM srate size [x,y]
+    LM_SIZE = 2  # LM state size [x,y]
     N_PARTICLE = 100  # number of particle
     NTH = N_PARTICLE / 1.5  # Number of particle for re-sampling
     
@@ -409,7 +409,7 @@ probably will die out.
 3- Resampling
 ~~~~~~~~~~~~~
 
-In the reseampling steps a new set of particles are chosen from the old
+In the resampling steps a new set of particles are chosen from the old
 set. This is done according to the weight of each particle.
 
 The figure shows 100 particles distributed uniformly between [-0.5, 0.5]
diff --git a/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst b/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst
index 3967a7ae4d..05bd636ef5 100644
--- a/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst
+++ b/docs/modules/4_slam/ekf_slam/ekf_slam_main.rst
@@ -32,7 +32,7 @@ Introduction
 
 EKF SLAM models the SLAM problem in a single EKF where the modeled state
 is both the pose :math:`(x, y, \theta)` and an array of landmarks
-:math:`[(x_1, y_1), (x_2, x_y), ... , (x_n, y_n)]` for :math:`n`
+:math:`[(x_1, y_1), (x_2, y_2), ... , (x_n, y_n)]` for :math:`n`
 landmarks. The covariance between each of the positions and landmarks
 are also tracked.
 
diff --git a/docs/modules/4_slam/graph_slam/graphSLAM_doc.rst b/docs/modules/4_slam/graph_slam/graphSLAM_doc.rst
index 5297604809..3d329ae131 100644
--- a/docs/modules/4_slam/graph_slam/graphSLAM_doc.rst
+++ b/docs/modules/4_slam/graph_slam/graphSLAM_doc.rst
@@ -28,7 +28,7 @@ available that uses graph-based approaches to perform online estimation
 or to solve for a subset of the poses.
 
 GraphSLAM uses the motion information as well as the observations of the
-environment to create least square problem that can be solved using
+environment to create a least squares problem that can be solved using
 standard optimization techniques.
 
 Minimal Example
@@ -331,7 +331,7 @@ between node 0 and 1 will be created. The equations for the error is as follows:
 
 :math:`e_{ij}^y = y_j + d_j sin(\psi_j + \theta_j) - y_i - d_i sin(\psi_i + \theta_i)`
 
-:math:`e_{ij}^x = \psi_j + \theta_j - \psi_i - \theta_i`
+:math:`e_{ij}^{\psi} = \psi_j + \theta_j - \psi_i - \theta_i`
 
 Where :math:`[x_i, y_i, \psi_i]` is the pose for node :math:`i` and
 similarly for node :math:`j`, :math:`d` is the measured distance at
diff --git a/docs/modules/5_path_planning/dubins_path/dubins_path_main.rst b/docs/modules/5_path_planning/dubins_path/dubins_path_main.rst
index 5a3d14464b..74cb757886 100644
--- a/docs/modules/5_path_planning/dubins_path/dubins_path_main.rst
+++ b/docs/modules/5_path_planning/dubins_path/dubins_path_main.rst
@@ -13,7 +13,7 @@ It can generates a shortest path between two 2D poses (x, y, yaw) with maximum c
 
 Generated paths consist of 3 segments of maximum curvature curves or a straight line segment.
 
-Each segment type can is categorized by 3 type: 'Right turn (R)' , 'Left turn (L)', and 'Straight (S).' 
+Each segment type can be categorized by 3 types: 'Right turn (R)' , 'Left turn (L)', and 'Straight (S).' 
 
 Possible path will be at least one of these six types: RSR, RSL, LSR, LSL, RLR, LRL.
 
diff --git a/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst b/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst
index d0109d4ec3..e37cbd4e77 100644
--- a/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst
+++ b/docs/modules/5_path_planning/elastic_bands/elastic_bands_main.rst
@@ -67,7 +67,7 @@ Dynamic Path Maintenance
    .. math::
       b_i^{\text{new}} = b_i^{\text{old}} + \alpha (f_c + f_r),
 
-   where :math:`\alpha` is a step-size parameter, which often proportional to :math:`\rho(b_i^{\text{old}})`
+   where :math:`\alpha` is a step-size parameter, which is often proportional to :math:`\rho(b_i^{\text{old}})`
 
 2. **Overlap Enforcement**:
 - Insert new nodes if adjacent nodes are too far apart
diff --git a/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst b/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst
index e2f4d8832d..b7450107b6 100644
--- a/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst
+++ b/docs/modules/5_path_planning/grid_base_search/grid_base_search_main.rst
@@ -91,7 +91,7 @@ This is a 2D grid based shortest path planning with Theta star algorithm.
 
 It offers an optimization over the A star algorithm to generate any-angle paths. Unlike A star, which always assigns the current node as the parent of the successor, Theta star checks for a line-of-sight (unblocked path) from an earlier node (typically the parent of the current node) to the successor, and directly assigns it as a parent if visible. This reduces cost by replacing staggered segments with straight lines. 
 
-Checking for line of sight involves verifying that no obstacles block the straight line between two nodes. On a grid, this means identifying all the discrete cells that the line passes through to determine if they are empty. Bresenham’s line algorithm is commonly used for this purpose. Starting from one endpoint, it incrementally steps along one axis, while considering the gradient to determine the position on the other axis. Because it relies only on integer addition and subtraction, it is both efficient and precise for grid-based visibility checks in Theta*.
+Checking for line of sight involves verifying that no obstacles block the straight line between two nodes. On a grid, this means identifying all the discrete cells that the line passes through to determine if they are empty. Bresenham’s line algorithm is commonly used for this purpose. Starting from one endpoint, it incrementally steps along one axis, while considering the gradient to determine the position on the other axis. Because it relies only on integer addition and subtraction, it is both efficient and precise for grid-based visibility checks in Theta star.
 
 As a result, Theta star produces shorter, smoother paths than A star, ideal for ground or aerial robots operating in continuous environments where smoother motion enables higher acceleration and reduced travel time.
 
diff --git a/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst b/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst
index 4dd54d7c97..bedcfc33d8 100644
--- a/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst
+++ b/docs/modules/5_path_planning/reeds_shepp_path/reeds_shepp_path_main.rst
@@ -17,7 +17,7 @@ Here is an overview of mathematical derivations of formulae for individual path
 
 In all the derivations below, radius of curvature of the vehicle is assumed to be of unit length and start pose is considered to be at origin.  (*In code we are removing the offset due to start position and normalising the lengths before passing the values to these functions.*)
 
-Also, (t, u, v) respresent the measure of each motion requried. Thus, in case of a turning maneuver, they represent the angle inscribed at the centre of turning circle and in case of straight maneuver, they represent the distance to be travelled. 
+Also, (t, u, v) represent the measure of each motion required. Thus, in case of a turning maneuver, they represent the angle inscribed at the centre of turning circle and in case of straight maneuver, they represent the distance to be travelled. 
 
 1. **Left-Straight-Left**
 
diff --git a/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst b/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst
index a9a41aab74..46b86123f3 100644
--- a/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst
+++ b/docs/modules/5_path_planning/vrm_planner/vrm_planner_main.rst
@@ -9,7 +9,7 @@ In the animation, blue points are Voronoi points,
 
 Cyan crosses mean searched points with Dijkstra method,
 
-The red line is the final path of Vornoi Road-Map.
+The red line is the final path of Voronoi Road-Map.
 
 Code Link
 ~~~~~~~~~~~~~~~
diff --git a/docs/modules/6_path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst b/docs/modules/6_path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst
index 76a6819a46..cc5a6812ca 100644
--- a/docs/modules/6_path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst
+++ b/docs/modules/6_path_tracking/model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control_main.rst
@@ -34,17 +34,17 @@ Input vector is:
 
 .. math::  u = [a, \delta]
 
-a: accellation, δ: steering angle
+a: acceleration, δ: steering angle
 
-The MPC cotroller minimize this cost function for path tracking:
+The MPC cotroller minimizes this cost function for path tracking:
 
 .. math:: min\ Q_f(z_{T,ref}-z_{T})^2+Q\Sigma({z_{t,ref}-z_{t}})^2+R\Sigma{u_t}^2+R_d\Sigma({u_{t+1}-u_{t}})^2
 
-z_ref come from target path and speed.
+z_ref comes from target path and speed.
 
 subject to:
 
--  Linearlied vehicle model
+-  Linearized vehicle model
 
 .. math:: z_{t+1}=Az_t+Bu+C
 
diff --git a/docs/modules/6_path_tracking/move_to_a_pose/move_to_a_pose_main.rst b/docs/modules/6_path_tracking/move_to_a_pose/move_to_a_pose_main.rst
index 19bb0e4c14..b3d23ab2be 100644
--- a/docs/modules/6_path_tracking/move_to_a_pose/move_to_a_pose_main.rst
+++ b/docs/modules/6_path_tracking/move_to_a_pose/move_to_a_pose_main.rst
@@ -32,7 +32,7 @@ Constructor
 
    PathFinderController(Kp_rho, Kp_alpha, Kp_beta)
 
-Constructs an instantiate of the PathFinderController for navigating a 3-DOF wheeled robot on a 2D plane.
+Constructs an instance of the PathFinderController for navigating a 3-DOF wheeled robot on a 2D plane.
 
 Parameters:
 
diff --git a/docs/modules/7_arm_navigation/n_joint_arm_to_point_control_main.rst b/docs/modules/7_arm_navigation/n_joint_arm_to_point_control_main.rst
index 56900acde1..6c1db7d2a5 100644
--- a/docs/modules/7_arm_navigation/n_joint_arm_to_point_control_main.rst
+++ b/docs/modules/7_arm_navigation/n_joint_arm_to_point_control_main.rst
@@ -3,7 +3,7 @@ N joint arm to point control
 
 N joint arm to a point control simulation.
 
-This is a interactive simulation.
+This is an interactive simulation.
 
 You can set the goal position of the end effector with left-click on the
 plotting area.
diff --git a/docs/modules/7_arm_navigation/planar_two_link_ik_main.rst b/docs/modules/7_arm_navigation/planar_two_link_ik_main.rst
index 5b2712eb48..8d3b709e47 100644
--- a/docs/modules/7_arm_navigation/planar_two_link_ik_main.rst
+++ b/docs/modules/7_arm_navigation/planar_two_link_ik_main.rst
@@ -7,7 +7,7 @@ Two joint arm to point control
 
 This is two joint arm to a point control simulation.
 
-This is a interactive simulation.
+This is an interactive simulation.
 
 You can set the goal position of the end effector with left-click on the plotting area.